diff --git a/cirkit/templates/circuit_templates/_factories.py b/cirkit/templates/circuit_templates/_factories.py
index 86325d5b..72ef1703 100644
--- a/cirkit/templates/circuit_templates/_factories.py
+++ b/cirkit/templates/circuit_templates/_factories.py
@@ -17,7 +17,7 @@
SoftmaxParameter,
TensorParameter,
)
-from cirkit.templates.region_graph import PoonDomingos, QuadGraph, QuadTree, RegionGraph
+from cirkit.templates.region_graph import PoonDomingos, QuadGraph, QuadTree, RandomBinaryTree, RegionGraph
from cirkit.utils.scope import Scope
@@ -44,6 +44,8 @@ def build_image_region_graph(
return QuadTree(image_shape, num_patch_splits=4)
if name == "quad-graph":
return QuadGraph(image_shape)
+ if name == "random-binary-tree":
+ return RandomBinaryTree(np.prod(image_shape))
if name == "poon-domingos":
delta = max(np.ceil(image_shape[0] / 8), np.ceil(image_shape[1] / 8))
return PoonDomingos(image_shape, delta=delta)
diff --git a/cirkit/templates/circuit_templates/data.py b/cirkit/templates/circuit_templates/data.py
index 4a147854..6ede543e 100644
--- a/cirkit/templates/circuit_templates/data.py
+++ b/cirkit/templates/circuit_templates/data.py
@@ -29,6 +29,7 @@ def image_data(
'quad-tree-2' (the Quad-Tree with two splits per region node),
'quad-tree-4' (the Quad-Tree with four splits per region node),
'quad-graph' (the Quad-Graph region graph),
+ 'random-binary-tree' (the random binary tree on "flat" pixels),
'poon-domingos' (the Poon-Domingos architecture).
input_layer: The name of the input layer. It can be one of the following:
'categorical' (encoding a Categorical distribution over pixel channel values),
@@ -52,7 +53,7 @@ def image_data(
Raises:
ValueError: If one of the arguments is not one of the specified allowed ones.
"""
- if region_graph not in ["quad-tree-2", "quad-tree-4", "quad-graph", "poon-domingos"]:
+ if region_graph not in ["quad-tree-2", "quad-tree-4", "quad-graph", "random-binary-tree", "poon-domingos"]:
raise ValueError(f"Unknown region graph called {region_graph}")
if input_layer not in ["categorical", "binomial"]:
raise ValueError(f"Unknown input layer called {input_layer}")
diff --git a/notebooks/README.md b/notebooks/README.md
new file mode 100644
index 00000000..ba6a26c2
--- /dev/null
+++ b/notebooks/README.md
@@ -0,0 +1,19 @@
+# Running Notebooks
+
+
+```bash
+pip install ".[notebooks]"
+jupyter notebook
+```
+
+
+# Suggested Path through Notebooks
+
+
+While you can explore the notebooks in any order, we recommend you start from [learning a circuit](learning-a-circuit.ipynb) and proceed as in the graph below.
+
+```mermaid
+graph TD;
+ A[Learning a circuit]-->B[Compilation Options];
+ A-->C[Region Graphs and Parametrisation];
+ A-->D[Probabilistic Integral Circuits];
diff --git a/notebooks/region-graphs-and-parametrisation.ipynb b/notebooks/region-graphs-and-parametrisation.ipynb
new file mode 100644
index 00000000..57a5f1ad
--- /dev/null
+++ b/notebooks/region-graphs-and-parametrisation.ipynb
@@ -0,0 +1,1081 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f401baa9-1e28-4c51-925e-9a0ca7448973",
+ "metadata": {},
+ "source": [
+ "# Notebook on Region Graphs and Sum Product Layers\n",
+ "\n",
+ "\n",
+ "# Goals\n",
+ "By the end of this tutorial you will:\n",
+ "\n",
+ "* [know what a region graph is](#What-is-a-Region-Graph?)\n",
+ "* know how to [choose between region graphs](#Choosing-Region-Graphs) for your circuit\n",
+ "* understand how to parametrize a circuit by [choosing a sum product layer](#Choosing-a-Sum-Product-Layer)\n",
+ "* build circuits to **tractably** estimate a [probability distribution over images](#Experiments)[1](#fn1)\n",
+ "\n",
+ "We say that such circuits are tractable in the sense that the circuits allow us to compute exact likelihoods and marginal/conditional likelihoods.\n",
+ "\n",
+ "\n",
+ "\n",
+ "# How to build your circuit\n",
+ "\n",
+ "In order to build a circuit, we need two main ingredients:\n",
+ "\n",
+ "1. A **region graph**\n",
+ "2. A **parametrization**\n",
+ "\n",
+ "\n",
+ "## Why do we need these ingredients?\n",
+ "\n",
+ "We need these ingredients because we want to build **tractable/efficient** models that are **expressive**.\n",
+ "\n",
+ "### 1. [Region Graph](#Regions-Graphs:-High-Level)\n",
+ "\n",
+ "The structure of the region graph is what determines if inference is tractable or not. Cirkit makes it easy for you to use the following region graphs:\n",
+ "\n",
+ "* random-binary-tree\n",
+ "* quad-graph\n",
+ "* quad-tree-2\n",
+ "* quad-tree-4\n",
+ "* poon-domingos\n",
+ "\n",
+ "Depending on your data, some of these region graphs will be a better choice than others. E.g. for images, quad-tree and quad-graph are more appropriate, as we will see.\n",
+ "\n",
+ "### 2. [Parametrization](#Parametrization)\n",
+ "\n",
+ " \n",
+ "#### Choice of sum product layers\n",
+ "Given the **region graph**, we have specified the hierarchical relationship between the random variables, but we have not yet defined the computational graph needed to compute output distribution distribution. To do so, we need to define the structure of products and sum layers. These come in three flavours in cirkit:\n",
+ "* $CP$\n",
+ "* $CP^T$\n",
+ "* Tucker\n",
+ "\n",
+ "#### Choice of Overparametrization\n",
+ "\n",
+ "The goal of overparametrisation is to make the circuit more expressive, i.e. to improve how well it can fit a given distribution, while keeping the circuit tractable.\n",
+ "\n",
+ "Given a region graph and a choice of sum product layer, the simplest way to build a circuit is to associate a single input distribution unit per leaf\n",
+ "region, a single sum per inner region, and a single product unit per partition, and then connect them\n",
+ "following the RG structure. We can adapt this strategy to the *deep learning recipe*, and output instead an **overparameterized**\n",
+ "circuit. With overparameterization we refer to the process of “populating”\n",
+ "a RG with not one but many sum, product and input units of the same scope.\n",
+ "\n",
+ "The learnable parameters of a circuit exist in two places:\n",
+ "* Input layers (e.g. two parameters ($\\mu$, $\\sigma$) for a univariate Gaussian and a parameter per class for a Categorical input)\n",
+ "* Sum layers (one parameter per input to the sum layer)\n",
+ "\n",
+ "Given the region graph and choice of sum product layer, the simplest circuit we can define parametrizes a single input distribution per leaf node of the region graph and has a single sum unit per input to the sum layer.\n",
+ "However, we can follow the deep learning recipe increase the expressivity of the circuit by overparametrising it.\n",
+ "We do so by:\n",
+ "\n",
+ "* Parametrising multiple copies of the input distribution per leaf node\n",
+ "* Parametrising multiple sum units per input to the sum layer\n",
+ "\n",
+ "For example, in the notebook we will use:\n",
+ "\n",
+ "```\n",
+ "NUM_INPUT_UNITS = 64 # we use 64 input distributions per leaf node\n",
+ "NUM_SUM_UNITS = 64 # we use 64 sum units per input to the sum layer\n",
+ "```\n",
+ "\n",
+ "[1](#fn1-back) This is intractable for general neural network models.\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Data Setup\n",
+ "\n",
+ "As with the [previous tutorial](https://github.com/april-tools/cirkit/blob/main/notebooks/learning-a-circuit.ipynb), we will be working on the MNIST Dataset, so we begin by loading that:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ca0fb84d-5931-4ac8-89d9-68509dd0aac6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from torch.utils.data import DataLoader\n",
+ "from torchvision import transforms, datasets\n",
+ "\n",
+ "PIXEL_RANGE=255\n",
+ "# Store an example image\n",
+ "example_image = None\n",
+ "\n",
+ "# Load the MNIST data set and data loaders\n",
+ "transform = transforms.Compose([\n",
+ " transforms.ToTensor(),\n",
+ " # Set pixel values in the [0-255] range\n",
+ " transforms.Lambda(lambda x: (PIXEL_RANGE * x).long())\n",
+ "])\n",
+ "\n",
+ "# Function to plot the first 9 images from the dataloader\n",
+ "def plot_sample_images(dataloader, gridsize=4):\n",
+ " global example_image\n",
+ " # Get the first batch of images and labels\n",
+ " images, labels = next(iter(dataloader))\n",
+ " \n",
+ " example_image = images[0]\n",
+ " images = images[:gridsize**2]\n",
+ " \n",
+ " # Create a 3x3 subplot\n",
+ " fig, axes = plt.subplots(gridsize, gridsize, figsize=(8, 8))\n",
+ " \n",
+ " # Plot each image\n",
+ " for i, ax in enumerate(axes.flat):\n",
+ " # Convert the image to a 2D array (since MNIST images are 28x28 pixels)\n",
+ " img = images[i].numpy().squeeze()\n",
+ "\n",
+ " # Display the image\n",
+ " ax.imshow(img, cmap='gray')\n",
+ " ax.axis('off') # Turn off axis\n",
+ " ax.set_title(f\"Label: {labels[i].item()}\", fontsize=20)\n",
+ " \n",
+ " # Adjust spacing between subplots\n",
+ " fig.suptitle('Example MNIST Images')\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "data_train = datasets.MNIST('datasets', train=True, download=True, transform=transform)\n",
+ "data_test = datasets.MNIST('datasets', train=False, download=True, transform=transform)\n",
+ "\n",
+ "# Instantiate the training and testing data loaders\n",
+ "train_dataloader = DataLoader(data_train, shuffle=True, batch_size=256)\n",
+ "test_dataloader = DataLoader(data_test, shuffle=False, batch_size=256)\n",
+ "\n",
+ "# Call the function to plot images\n",
+ "plot_sample_images(train_dataloader, gridsize=4)"
+ ]
+ },
+ {
+ "attachments": {
+ "188eb52b-518f-4aa4-98e4-c3abf192ce7a.png": {
+ "image/png": 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"
+ },
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"
+ },
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fGHab24YCNEXoBQ4eOcLR88GkiqJ9tuQx7Xt9IYN8/sAvS/CEyYihKE9DNQax+PNJ+Oob8vGkfngU0CJZeHjk2M1jZV06n1l4DQ51uvPFiqP8+30b7JQMok4vYkiHLny9N+q5Pvw1/Zovh4Lv7dwQpF7cz8EIeZytpPCoytWgWtmSueLEGLSYzyf5om/4MZP6eRRwXYyG0i7Za+SbYW7zOD/FHNumtWi1qB9v/1gLh1JGko6dYeeAHZy/li6F5HGrek6EknR/vDYQfTKU1JJqjoxxBO6NwohCqWqOlH5o8Zi6oi2lI+JJMBb3iypYdmxMnVoPpN4Uk0iS/sl+q1SjenjVU6M416Tj9OZUcHv+yhboNm5j4ahrSI0sLscjlkuj/ubfw2Voumogfee0o+l7tfB8yROv/s3ptLgffX6phb0FGEKCODlsDTvWxpKvKqXe5fTQ9ezZkEy5T3vxxvcNqVzFLNN+m1nh3LMVry3uRBVtCCc+XMM/mxLy9d0y/AI4t+gkx3/xZfewNSxrs5QNE89x+0kDh2fxzQDY0GH0aLpojjL1y2XcLqy9tOnA6NFd0BydypfLbj+//qz+OssHtuHVCXsJNyg4tJ7Atr2L+Kipw+PHe/MqvDV3D+veiWfGZ3O5qCtYoCn9Z0nREceyN8vz9sqk5/P1TRGsnbaUgAzQNOrH+40s7kXfdP1oBM3KWWBm5817w3vjITf9lDxbU6o61Ss8i45R4VC9GuVe6D5XYengli0nXYFWYxfyU1fHR8YmkR7A0q9m42flhrubC3YPbSJUWZejYpWa1G/dlaZmN7gSZQRVObpN28yyMf14rWML6tesQgUPD9zdnbE1ex5b6zmmdM3XmbD+JIfnv0UVs3vpAxNJFxfw7suDWR1UfBGh4dJyFvjG33fylNId+Gh44zxqPRoIvR1KRg5fyERyVCTJJcDHMfOuj5eZ8kiAal6rPo/feWsi/npArpMDz/7ZknxJOMy3/T5j+93i05Okf3/ix53xOL85mg9rFyLBoVI9gfGyouEXi/i2rTUKAlPMUX7sN5glgcbntgvNaralZXlNVvJUwaJWK1qUk/UsJE+AYo6FeUk8qjaJf3/6kZ3xzrw5+kMKYyZQPen3mOP4eke6/+iFrRZMEYEc9NnG2WtyNjtv1Ng3ccVa/eC/HRq6YllSzVF6DNFBmQ6WtrR5yT6kWWNH9VddHpQoi7uN378pT3SWCKmRRAaCU9+GVLR9LjMcpEUmk55mQkilKwaMRCzYxeHL5Wj7x8vU8y6Viy+mxa5bR7p95o6ZAhiSufnddo6eymsqKJ2Q6bs5diINdYPGtH/fCW1u5ty9Bp1+9MZWJBEwaTfnbuQVIAkyrgdyZb0/t87GY3Bwo874N3l3fTPKaUvKN2f7Lo/+fOlTjfjtE/l2S0whJwdUePT/Ep9q8Wyf+C1bYp7DxLgpnM0jezJ05XVShYJVw89Zv/ErWhTGPqkc6TxlJRPa2BQ4NpD+s6TozGYk4aGJpGY8pxNXuoPsOZyMQI1H42ZUVD+wPw6df+Lo3TT0sRdY8LaHLFFdEm2NygILs2fj5arMLfh/dzSXuipDfp9Bbzf1Q7GDIP36PD74ZCN381H11FM/8N6YHUQKLVUGzuMPn6ovzPmfL8AIakktn6Ws/64ttqpsHXvjTz4cMJVzumKywZcucD1bDkLjUZe6ea4G1VK7TfOcNd8VM7yaNcGhBPSQptYwpoxuTlnNPfVR0Di15sufhlLjsSNIKseOnHuwE6NIny3J25EPZs3wAfx8Nrn4gnOjPwt+XEmQUp0BI7phXySDQSXe7tOKUlniZIrYzoQf/iHpee1H21eZsXM13w0byKBPprF183gayXOzJC9SPOa/gB9XBqFUH8CIbvZF+GQVZbp2oNun5TFTQMTc4ehnh7mTKPskL8zbtaPntOZ496pF/XE9eG14uRLrZIpUHfqsVf/C+GgEkHElknj3sjiUCHuqoswrNXC/f4RYOsFb/LPtDik4+kPXCExwoUY3++c0ADARF5SIXFdcTHoTdZ1TKxJwG9me2lU0+cqsXZ/m1K6c5Wylx3DxtwvE5CKzppuXOLY2FhPmeLzlhV0+vr22oTdetVSQEsqZGddJyVUQFCx7dKH/zv70XdWLV75rRcPubpSxUErMN+fEgqaD3qWhJoS/JkznbGG34lg0ZdC7DdGE/MWE6Wefs508RgKXfsSHi/zQCVAsGvDZvIm0s38C62TuzcifPqJWQRNo0n+WFBUZAfgHPb8LU0yRwYSnCkDB3slJTh7IWP3/PSr3t5n5+/tU0T403ggDQX9+zLDlQbnuWDTF7mHsez9wIhEs63/G0uk9S8Z5uk8tWnkhsKDepzP5rKFFtpkjEwlHvmfE9EsUhylPS9Nhyp6xtS5NaSXvbnB841dWTuxFXdcyWNlXps2Q+Swb07CEzGDZ0W6SL2f2LeOX8eP49rcVHDi3l/GtbB5/a+xO1uyM/A9B4H94tiQPdJz/5V0++ivooV0yRUv6qUUsPJqKxrs37zQoqvluFfZ1amfbqmkkbPsGDuqe3960qvEGX81ZwqLf/kcnd+mpSF4k0jm1aCFHUzV4936HBkW+LEaDQ//W1Kue6SoZb15k/5wQMmTH5IEW+y5Naf9TZ9q850HpEmyOFO29XW8CXWxazgl2UzIBm0KwfbkaNiUkglbKVaV6qwfbNA1nr3M9qJCelSmVwC1BGJrWoJqr8pyahCjCzqdJVSsWBKkHAriTqCbt6EmOb7iLLj8RNHPG86UHpWwMF/3xD3j4BgPhay4RngZoHSnfuFT+u4fUNrg3tUUB0vZfxC/I9Bx+cy6fVb0fA1pbknF5Cb/vSijkO6qp3m8ArS0zuLzkdwp9ezFiCl3F6HFbydw4rcb17a8Zdb/MyhNkA5qM4rNXCr5rQfrPkiIZti4c4nj08zsdLlJTSctykszMzGSHylhdggqn135hwce1eWTNgjGczZ8NYc7DJXRNwawZ7sPvfnoU+/ZMXjaBVqVftFZ5UdDUYvAHHbHO3rkiheO/fcfayJJhzPN1llXOdPhqA+dDE0iOucmB+QPxtipJDWyGR+sBfDbxeyZ80o8WrgUxiumcmzmFjVGmYni2JA83nqidn/HO+IPoa7SggV1xmYBU9i3+C3+Dhnqvv0WtIuxStY1tjkk+U2wwwYly/aNEUuJI3cfiv/wxaOrx+lu1imei3cyJ2m+XzyrNYSJu7XEu3ZL24rnHvBQW1pn/MyM8McdZEPoTpzgbWJnGb9iWICe5FJW7V8by3tiVEYn/lsKdESQiAvA7pqJi96pYPafev+FiADdvyyJIxYOR2KsxGIwp3N1xiZPjNrFnY367XlXY13bkfoU9Qwyhx5IfChPC8f83AQEojvaUdXzchJca20pZieP0CG7siX/Gu1eewTfnelt5unVrgrkpnE3LthNb2GC+fDe6NTHHFL6JZdtjnxN50nHk18lsvheja6rRd+gr2P2nrEY5uvd5CXtZ0UhSYtBzcu0mAmQlTYnkBcOGdt8tZExj60dyvKbYvXw9aAqnU+87O1yd48PHa4Mxqsvz5swljKz94tV0f6E2Xzh17kTDh7ZAmqK3MW9FIEYp/UWMidh94xj081l0MgYsOUG5/yJ8Bs3neulO/LRyDA0si2nVYoov63eEYVRXpX2nqkW6rVKkppF9g4LK2oly1jIKkUhKGim+69kRZkRdtT2dqhbX0nEF645VcL63SCs1lEtr7iJjxOccdWnKZNW4NQVGE3XvMOSEOxz7KRjX0c1wL2Ericxa16SK64PzxGJ2+hFe4LonJuJ3XCfMqjI12lo8n31mTODq/KvES4e+2EhPzniQVBd6Is5G5RtfqcpaYpGtTG1KZM4kuykwhPCIzF9UHKywKoCZ17hYU0qVKdPRp8PQPWffnFcMW75zR+poTMTvXc+2qMKGwOXp3LEOGlM8e9dvI+p5EKbYjcxafgNDVuNqqnXnjYb/fTW03Uuv0spKkcoqKRnDVuASflh2Q/qMEsmLiGUTxi2aQDvbR45yJunED/hMOEgCkHxsMgPH/UuMMKfGhwuZ2/fFPI/khVr6rXJrSfOqGvZfyVaoQOg4uWEDN0eNxrOAPWiIv8XlK4GERkQSmwKlbOxwqlCLBnXceer5x6Q7XPILIzEtjbTUVFLT0kjL+tOpq9F1QHvyO8jdqE8mMSGJpOREkpKSSU5R4Vy3PhUe2u1giLvN9ZuhxKRBKVtnKntWxqEQE2Umg46UxESSkpJITE4mOSkFrVsD6rrnEqCmR3B8yZcMHb2Mi6n/fVahUM/OOzQg5sYlrgWFExEVR6pihb2jE66ValGnsl3hFMGoJzkxgaSkZBKTkkhOTkHlXJf6jzY6t6/fJDSz0XGu7Ellh2KcnUw6woR+n7I9phID1yxluNd5hhbTq+hP/sO+u0bU5VrQtm7RbqtMCfDjjuFBMGffuRttLZ/AWUyJJDg0EbWDK64Ols9mgDDE4X/iCGcDkyhTtxNdvB2f/nPSEwgPiSDN0oXyzqX/Xx3CZIjz58SRswQmlaFupy54Oz7DYd6YQlRoODE6DXblXHC0MUdOZ+VrJTj5zz7uGtWUa9GWusUomIqDCy6VVARfNwGChN1+hH/qinsB38mUmkpyRDqKrRVWdlrZ7w+3T5qOtJg0dDoFc+cyWN93tAykRhuwKGvx9NtMY0vZKlrw00NSJBE3TVSpmYzfBF8iOnSmV3vLknegs7kbNV+25fKiOAQgggO4dqwFbm0LcCKtIYbr2yKw6tIi21kNBbFbGaSGJ5OutsDKqRRadbEJCSGz/+HoId2LrQzGDFIjUtDpFczLWmFZWlOC5FCNUzNXSm25mVkWQymFc+PH+CQGU7YdBQoa85zedvrVKOLv+WPWFg9W+ucX61lbYK6CZBMY/SOISq9NBbPn55vzfFLVtrSqpOHUjUPsOpDEu2+WLtR7Vm3bikqaU9w4tIsDSe/yZokusWAicssadsea7vviZZu1pt7T6McyDWlQXcPO51H9iyKuKNoPKna/15gaR1RkFLGpKmzdK+J6v0ajjqiINOzKFTAHYEwhMjiURLUDrq4OWBagc0wx+5n47jh2lYBD1dMTwgmJSMPSpTzOpf9/lTMypkQRGh6DTmNHORdHbMxVUodKxseREhlMaKIaB1dXHCzVhVVu4qIiiYpNRWXrTkXX0vd1WRcVQZpdOeyKINNtVnsUC6YeoMWHW8lRJEfouDR9MF/U/RbVpCmcTlYo0/wrlk3pUiLO0H0WvFg1ZTQVqeCmQrlCtm2qgvQLBzgY8zmeTvn0YnoYh5f+xvRlW/A9fYPY9IcT4gpau6q07Pk+Iz8bRo9aD9dwNBH0Wzs8PzuUa/3ljONfUl3zZeHcWbfBbO2by8SC4QwTWnZiyrkk9AYjIvurahsy8exxxtfWgCmGU8t/Zurvq9h1NoSke8tCUFCZl8WrQx9GjP+WIc0c8jBSOja+X5UBf0WSlp6R88wItLSceo0DX1S57/gYb69n3Kfz2HvyOOdCUx66HjLOfEsd7bePKmTHOdzePeyhw0sK9+y8HNeEa1v4/bf5rN99lAvBifdXxjzoVi22FevTumtfhn06hJer5BVxGzgzoSWdppwjSW/AmLPRaTjxLMfH10aDiZhTy/l56u+s2nWWkCTDfVlUVOaU9epAnxHj+XZIs6I1KqZQ1g7rz9SzKpqMX8mMXi6o9OeLSVENXPM9RIhRQVunCY2K1L+JYcemAyRldYrKti1fjn+zwAdHm2JOsXL6LJau3cHpOHs8q9hjiAggMNWNFt368NHoUfTwfPwshTHmHOvmL2Dd7mNcDookMcMCx8retHy1L0M/eJN6tomcXjSO0dP2klSpEV7G06z/4Fv6rTnGvO4OELaLGdO3cS0yhpiYaGKiY4mJjcdxwBp2f/34s1lMcRdYN2cOyzfs4FiIFlcPR8yTwwmKVFO1XXfe9hnJh69WJfcvSeXUksn8eTKCmJgYomNiiImJITa5Ed+dW07f0oAuiL0LZ/HHhn2cvRFBskFLGddq1G/Tg8Ejh9Cp4rNcMWsk5tw65i9Yx+5jlwmKTCTDwpHK3i15te9QPnizHraJp1k0bjTT9iZRqZEXxtPr+eDbfqw5No/utiHsmDaLHdcjs74vs30Tq45kz4YRVFLruLl1Gj/MXs9BvxhUjtVpO+ArJo5og4v6UVm/e2QRP/+6mI3Hw9CWc8HGGEmAfyTmVZrxUu+hfDbyTera5zd7fIcd02axMyA6s51js9pb14qfLyzkdQs9wYdW8sfSjew9eY07MXrMbJ3xqNGYTj3e4d23W+PxH5pbd9uXJbMX8Pe+M9y4m0i6pgyu1erTpsdgRg7pxDPrSsM1fA+FYFS01GnSqHgnvDQ22Lqr4HqmxyjuBhN0xYh7/bz7TcTdxW/Zea7uDCIiwQI7DwtMMXEkpFnj0r463oPrU6XSwwnhDO7+fZLrF1PRxaeRFq9DF6dDl+pE85lGMtwAACAASURBVE0vU90a0CdwZ90FLu8OJvJ2ChlGNWZOtjg2rkLtAbXxKJ+79mdcvMLJrXdJjdehi0tDF68jLT6DcqP60aWbGWAg8ehlzq+9QfCVeFJTTaitrbH1cqNy73rUbmGTh10RJB86xznf2Kz3TSMtQYcusRQ1p79Ok9p5DHQpsQT+eYYLu+6SqpTC2tkSM60R/d1EUrCj8rveOF46zN5dTryys0OBJ3EKjpZy3o6otodgMsQRfCSa8IMHOKmrT7fhrnnLW0oEfqtDSNWoUWtVqDUqFI0CBiMGvRGj3oCxjDNeb7o/g3JDasp196TsshNEGQBTMoGbb6Fv68njliwYL/nhH2BLtYmuj3f8TWnc3XKOCxv8Cbqsx6KiDeaGVOJDDFjXr4Rn30bU72SHtghkLCM8kvCzIQSuO8+VY4n3V32KO7c4MyvxId9VhWWT2tRpanU/GZ9x8Qont0VkymZ8Grp4PboEHar2nXhrrEeO54mEUC4vDCAqVocu/sGfvkx1Xl7WHJfsQmFM5NbS8wTdSsuU+SxdTa9Qj16z62GjNhC/7yynVgYQGqhDsbfDrUcTmvcvn8uqfBMpZy5zZvEVbl5IQeVghbkplbhbqWg8XPDoWocGA6rhaFvckahC6ddfopfZOa4cT8OyeW3qdrXOd+IjIySRVOOD/rF2tc4xVsfdiLvfp4qlFm1BPtFae7/fREwcsTGCCi7Kc/LN+WYnqO9VCsU/jqP7T6F/swOFWYpkVrs+XqUU/OOOsv+Unjc7lOQyC2kc2Xv0vi8OGmrWr/90xnl1VVq2qIxrau4H1JsK7D8bCdw5jw1+RkqVKoWFmQa1YiIjXY9ep0en12NwaInP+61xzPYgw9W/mb7lJopFqaz7FEwZ6aQmpVOx23C6V9c8hbiiJPvhT8Hv/a8k+7Flzq/MXX+CcFVZ3Ms7UUabTlzILcKoTs//DaPuqdF8sL4ha6/NIU9VMcVwauV0Zi1dy47Tcdh7VsHeEEFAYCpuLbrR56PRjOrh+VCslELwuWMc3b+ZBTMXsy8oNSsPYOLGtqlMuP3QyKl2pt2QD2j31E9wNRF3YR1z5ixnw45jhGhd8XA0Jzk8iEh1Vdp1fxufkR/yatXcIz3DpTVM2XCNDAEi7ijhpkx/L2TfHCallHlgA1WOtPL5iE5uhX9/Y+BO5m3ww1iqFKUszNCoM3VFr9eh1+nRGxxo6fM+rXMqGH9P38JNxYJSWfcppgzSU5NIr9iN4d2rZ+qw4S5HFv3Mr4s3cjxMSzkXG4yRAfhHmlOl2Uv0HvoZI9+sS95iaOTOjmnM2nGdyJgYYqIz48HYxKqM3LOBEZXU6G5uZdoPs1l/0I8YlSPV2w7gq4kjaJMVDJqeOFYvCh1K5dbeZcxftpl9p/wJT8rAws6FCl4tefn1vvR7owkuzyRLbCLm1Eqmz1rK2h2nibP3pIq9gYiAQFLdWtCtz0eMHtWDvFMpyfhtmcOvc9dzIlxFWffyOJXRkh4Xwq0wqN7zfwyre4rRH6yn4dprzCmScVBNFZ/fmeF7ngGrg3PkGkXGDRYOGAAIlHLd+HnplzS25IXlBStWXxo3N1sUInLUvxS6K5y7lAEdzXM3vCdmMfTdr/k7IBmTAMXak+6jRjGoR2tqOqmJv3mSXStmMn31OfYvGceBtcvp8+tfzB9aj+wLQlSWdjg5OZEBCH0C0Qn6B++hmGNT1ibP1TjCkEZCXDLpogAr/E1G9KmppBtM5HW5KeogP/Z/h0m+Omp2fZOPv29A3RqOpJ5ZyqQpW7mlj+Lyzll8tH8vF9buY3a3crk4YCYMuhR06YZHJgly/YaEAE4ev0SwwYqyjuakxMWRku10YEVrjb3doyswzGxzO6ytcM9+BP0N1o1+jxG/HyMyQ6Co7anT63MG9+lKk5rlsSOB4CuH2f7nHyzafoqtc0+ybdEvtP90PksmvYqH5tH3MepTSU03YMq70Tn4Y3/emeSLrmZX3vz4exrUrYFj6hmWTprC1lt6oi7vZNZH+9l7YS37ZnejXJHEiTou/DaAD1cF49hjAX9+3RTr4lRTUyynTvtjQE3FmjUpuljZRNSOr5mwMQYToCpTnxEr/mSUV0HMoIHbm8bS/6OZHE3zYuBPW1k1uDnOmqxBbv0khg4fz+trVzFw3mZ+f6dKHgFSOoEbxtD/w9mc0Nekzzffsap3Cyoq4ZzaOotvvunL4tmz6FE/jQDzt5m25yLt3Iysf8eNlbpE1q3ez8zub6CJ8+fIvv1cuhvMrbCErEO4NXglGx5Tbzidm+vHMmjUXE6o2/P51M0s7tWQclkvqws+zMqpYxjXqx5zXx7LHwvG0eERIU0n+MxefA9EcffOHaJSjJl2zrwKOhMYw7bw2ZvjuFD3fYaOW8IvTb1wSDzH2snDGTVjJOuWrGTM3zuZ3MH+6a+6SA9kw5j+fDj7BPqaffjmu1X0blERJfwUW2d9wzd9FzN7Vg/qpwVg/vY09lxsh5txPe+4rUSXuI7V+2fSvUcs1w7v48C1SEKCQklIFwhAa5uKUcRz5LteDNpRjZGTVvC/0Ml0H7qaBZ8c5XLqEQ5+6f1gUDXFcHByb/ov1/L219M4tKoFbhb3VOA0C0cPZezkvqxeuJCvlq/kmw7lcm8PEYffEV8OXIsm7E4wsWmmzHrUVrXQJZxj/pB3mXDInq4+7zPuvbpULmsk7MxOlkyfwYT3/2Dqjz0YPWs2X3Z2LeTqt3RurB5J/8n+1H//A8YumkJTLwcSz61l8vBRzBi5jiUrx/D3zsl0eAYFjU2xpzjtbwB1RWrWLO5a9xrKuFihkFkLHGMCkZdSEfVL5zp2Je45zK4J5wnT2eP1eXde7u2KlSazTWP/OcHeicfYttMPr4nd6fCabbZ+MZJ85Q4hJ9NICU8i7d5peVobDAJMETc5NPIIUTVqUefDzrT2dsAiJZKAub4cWLafgL+v0WhWL1o0t3i03ufdCIJPhJIWlUhS7L0JbzW2egHGZAJ/2s4Rv7J49WnCS2OcsbdNJ2rnCXy/P8eBnVfxG9KNnp+4Zyvx8WBiQR8URsjJaFLCE0lJzrJAanvSjbmPlSI8AN+h/xLoUI8O0/pQ2UOb4/fSb93g5Ffb2XEmDZwcnlEpSwXrJq7Ya0KINpiIXLaZXe5edF7gjX0+Q4LQxxG8/iw3glNIf2QViQqtozVl6plR5Q13nsUxWapqNajufZqos5mtknbQj5vR1fAqq+Q7foVuDiCxRi1q1Mpfk0yhNzjyxT7OnzVg/3oruv9aG5esoD79ZgAnxu/j+Ah/rr/ekdcm1MDW7FnKmJHozUc5ujsVgQX2nibiApLJEGCKiiTIN/EhOVdhW7YatbNNLJjuRhB8PAR9bBIJUQ9K2lgl5SJVukTuHg0mPC6FxDAd98RXqZD+6LgqdMSeCSb0ZhpJocn3ZUFVxoAQOsLmbGPPAVvq/e9l6kWcZOv461z5IZwY3Vv0Hur4wJ6Z0gidu51/NqnwHNaW3r+5Yp0Vqoj4CC7//C9H5u7Ef+1lGk99mabNrYp5B4M5jt2a0a5bQa7NIPx0trJxZuUo38wih66nRuseLL5Rqwr2bVr1g/YTaaRGCXBRnpNvznfGBG/vqmg2nSP03GmCjR0oVAVAa2+8q2rYdC6Uc6eDMXaoWnJXvBuucfp8tvMxVHZUrOjwlMZ5czrM8CMwLxtXYP/ZSOTZbaz66yYRwUGEJ2YriaWosCxbiWqdHXhrYGscs98VfJTlv8zmckx6tuvNsHWvz0cNPsw2sfBf4ooS6oc/Lb/3v7xC8AZGvDaYLU4jmbf6GN1yLBg0key/ie983uStI1HgWgd9XiJ6exNj+3/EzKNpeA38ia2rBtM8s3NI9lvPpKHDGf/6WlYNnMfm39+hyr3OSb/Eiq/HsSHCBA41qW24zpWQZEwYuXv+H7bdfshWaTxxfmsI7ZyfZix0k/VjBzFq7gnU7T9n6ubF9GpYLkt+dAQfXsnUMePoVW8uL4/9gwXjOjySjzCEnOGfbfsyd2rpI4jPdH65e24n26Oytam6Eva9hj7ZxELkWbat+oubEcEEhSfyIGWkoLIsS6VqnXF4ayCtcyoYR5f/wuzLMaRnu97M1p36HzXgw+7VUcUcZHLv/izXvs3X0w6xqoUbFln9H3t6IaOHjmVy39UsXPgVy1d+k0ucmzk+xV47zL4D14gMCSI0IT0z56a1JdUoiD/yHb0G7aDayEms+F8ok7sPZfWCTzh6OZUjB7/EW1MYW1O0OiTijzP9vUH8FtiAIZ+MYfm0ltQonUbwlQOsm/sTU/vP4vsfevP1rBl80sbp6emo4Tabxvbno5lHSfMayE9bVzG4uXNm3Jzsx/pJQxk+/nXWrhrIvM2/804Vs4eVmw0jXmPwFidGzlvNsW5VcvjZpmR/Nn3nw5tvHSEKV+roi3BMU7nx1ozf8T3diwUBGTkWuAsBiqYiA+YuYLDnC35OrCgujHfF3M5mgszNBff/tA2+FRcznvRHdcJ3RCWhfug3USzEqwtjcnsJcXfbx8LbWrl/rapsRzH1VGIu1yaLs790Ek6qzOsUjZt4c/ktYcjjTSLndxHm2d/B/FWxKC6/d08WO4a453h3tdtgsUOXzy0Z8SLgz/6igjrbc7QNxYSTx8QPbeyFlefbYtbxGGF8+DmDy2d7jiI0nv8T+1Py66sUEbJ7pKitzd6uWtFy6o08v19knBLjamtz9m3DieJSYfv2SZ6ddFz82N5JaJSsvjKrLgb+FSDScr04RVxb/JaooM2SAUUr3F6bLS6m5flhIj7gT9G/gjrH+zSccFIc+6GNsLfyFG/POi5ijA/17o7Bony2flI0nuJ/+Tb6U1M0EblzuPAyV4lStf8ndmd/Md0OMdhNnaOPzDrPFXeNz/iVdPvE8EpqgWIuusyPfLKvuj1NtDMroDwIIYQhWpxaMEjULa0SKFrh2MhHLDgbX8CnZYgby/uKKuaKUCxqi4//iRTGXNo5ZvdwUUOrCMWiphi5Jy7XvgjfNERUN1eEoqkifDZHPPQ7RhHx97vCQ41QtJ5i8ObwrH83iNtrR4h2TV4SX2wKeUSfz05oJkopCNAIrzHHhT7P70gR52Z2E+W1ijCvMVRsCTfkaUf9FvQUrmpFWNYYKP66mU/Lxh0Wo+ubCQUE5t3Eott7xei27cSn20Mf7Y+MK+LH5hZCQRGaKsPEv8lPW9TDxaYh1YW5oghNFR+xOeKhXjJGiL/f9RBqFKH1HCw2h2f9u+G2WDuinWjy0hdiU0jOe/Q3F4qe5VSZ9qvFT+LEBh/h3XqCOJ4ohBDxYlkPy8xvB2HWbrq4Y8w2tqzrJ8qXqib6zDku7uZq9+KE76d1hLmCUCzriS/2P14eDWFrRL/y6iy7Vku0ae4mqr0xS5zK7daUK2LhmxWEVkEoFlXF24uuivyGE6E/KEZV02R+a/3xYu/GoaJRi0/E9tBH+z/jyo+iuYUiUDSiyrB/RfIzMRPDRSU1QjHvIp7MTJhEwpJ1YqbnNDHdc5qY7rVanL5tfGI7Gj37zwe/5TldLJ54R2Tkcl3cxh1iSa1pYnrt5cL3YIow5fJeaYf3imVe08T02suE75E8BpuEUHGox4zM59XaJC6H3hGH+q0TB/YnPWp/MqLFybdmiume08SMjnvF7XyHljQROnVl1rfMFJvXJYg7U9eKdT8GiiRDLt89b7WY5TlNTK+5WOw7kp5/M6VEiGO9M99jes1l4tD5XGyHIUFcHDxXzGi3WwQm5NN7MTfFng7TxYxWu8Ut/TMai/Th4lDX6Vnvu1QcPGMouEREh4rTQ/4QMzyniek1fhd/fX1O3A7SiWc9fAphEol//i3m3JfFWeLvFQm5yFl2nyhA7Go5W6xfGp/v+xlvXxU7Os4U0z1nihWTbomUXC42xQWJfS/PENM9Z4pl393Ow6Y8Gxkz3jgh/vLK/O5ZH1zO357l8k7Bk5dl9pfnNLFgXKDIyEffU/bvEUtrZl47o/N+EZKPDBruXBJbmmdeO/PtkyJ8926xsu8xEZ6UOZ5e/WhWVl9NEzP7nxWJxgd9mbxzh1hYZ4nYsTJMJGfk8d4/Ls9sH++V4uAJnXhuSLwhdrWafv/b/xjl95BMpYlLg2fe//e5I/1EekE0IOSMWFsrS/6rzxd7fAsWWOS4r+VuEagrjm9+zO3LewpLBaGy7yc2FDpESBTLe1oKBZWw77dBpJRk2UhZI96yeRB7o/ESY47ri/glCuE/J54Sk1vbCBUIUAvXt1eKkPzELvGsmNLBTqhQiTJ1fcTCkxEP2ZunFVeUJD/82fi9hcIQJOa/Yic0FXzEtnxyLsbIrcKnsibPPEvGjeWibxVzoSgWovbH/4jIXHTYGLNbDK+hFYpiIWqO3CNyf5xBXP2+idCCAHPRbXF8EejWOTGzW3mhVcxFjaFbRN6hnp9Y0NNVqBVLUWPgXyK/UM9w5TvRWJsp+xWH7xVP33QmilOTWwubrByb2vVtsTJ/BRNnp3QQdiqEqkxd4bPwpIjIeJBfXNevvChVrY+Yc/xu7uN8nK/4tI65UFCEZb0vxGPFUH9TLOxZLlP/tS3ETyc2CB/v1mJCZjAo4pf1EJZZOSfM2onpd4xPaGuelQ5FivldzLNix5qiWSM3UW/45jxsmE5cX9FPVNUqQrGsKd5bGSDys8wZJ8aKWpoC2NCMG2J53yrCXFGERe2PxT+5K5XYPbyG0CqKsKg5UuQ0eQYRNP8VYaepIHzyV26x1aey0KjdxOAdRe8vxe//XHhbKI/kt1E5iJdmXRMZ4sXmBavwpGBmbvboihdhJCEu/tGJM795DHx/DheT79VFcaD7L8v5rFFuhSmtqD9qHl+3t0EBhCGUDWPG8Xfk06qbZ055N8fCzQpqbKjcvilVcixJ0XNh6hC+D+rM/H9WMqLpw6sRrGjzUitKZztYzHBzM+tO5DOtp7LErWVzalgUwzqpwj7bFMb64X0Zvz8yayuShurDFzCrT1VyXzNkSY33/2D2u26ZK3tEBqHbPuPtz3cSZcp95apN5fY0zdno6C9MZcj3QXSe/w8rRzTl4QW8Vm1eotWDRkcYbrJ53Qme9WSq4cZihgyah59VOyavmkJn+xKg8okB3Aw3gWKLq8vTKAIriLm8k7WrV7M629+qFUuYP30yYz54nZbVKtNs2AaSGwxg4qoTXDu+kMH1bQq23+Pczwz8eA039SoqDvyF77vkpqcq7Dt+xdhu9ig6P+Z9MoVjD3du/C7Gf7IEfz1oGg3mi1edHinj4NT9EwbW0SIy/Fky8mt2xmeu+PToPQvfE7uY2sPtEX32atsYl8d2q4moHZ/T5/PthCr1+HzZb7zmrM7TFlUftJDf3/NA77ecof2/52RKHpfaNqJ9w3vtoefib5Px77uQqa/ksjpeU52e3eqiQWAI2szqg0+3Vnb8rvF8ssQfPRoaDf6CVx8ufadyovsnA6mjFWT4L2Hk1zvJbF4Pes/y5cSuqfR4aMWNWcWOtL63uizjKnN/8OON2V/StHTWmNCsHtYKoFjh3bbFgxU/pnD+/mMjoWkBrB7RmhYjdpLwaOPRZuxYujuoEKkXmPnFLC495nQ3dbmWNKmW2bIi/RpXSo9h8+oRNMpNlC298Fm8klF1zEF3g7Uj+jDON75gGpVykMkTQ/FZ/jOvuD4qJ5rqPelWVwPCQNDm1RzUPQszcZNMM+GKSwmoFa2xyFE0BV1cGg+vxzdcPc2e766TkKFQ5vXWtGidW51+BYvmTWnUzgLSY7n0w+ncD+At40T52vd28xmJXnKCuG6daNXW+lH7o7GjSvtMPRShN/E/lZ8gWeDU1Cnr8FMwXj/LyaDqdPqiEtbqR+2aXadKlNUAxkRu7gjJ/wBCS3vK18l/RbXp1jWuHNWjqe1O+TL5eHP2FWnQ2+nZ7lQxc6Rym0yfDmM8gbsLfii3ysGVugMrY602x3V4d3p9Vw+PCkVR+1bBukuNbAdLGwjb5p/vgcZp+/y4pXOjetcyeb+fPpIzY33xDzailPei9ScVsczlYsXWgyYfVMJCMRK3+gCnzxmKVsaeGAucW7pQqkCupArLJu6ULeBCc7WbO26Vsj7WEMvFebFUHd8YZ2sALY51HbPKRmko28TlQbuaUrix5ibJ+nj8J61j7aRbufiEFrh90JjKdoAuigtTzxP9XJwCaiJh03luRGZZSbuKNPukWk6ZMhkxZC87qyqgn69SssmxEYP+Ofrmx5kkV1ccVWBKukVAWGH3apnh6uqIChNJtwIIK8mHnKfEkaDL1vdKKSytijo+Kaj/DJRuxKhv+uChzuznxNgEDPndV7ouA15vgJl9V37eNB+fxk45SkQ8tbiiJPnhz8jvLdQK+OsrWLgnnlKNOtLONh8T4tiVz3wa5F62Q3eOnwd+zJqbelQVB/LL911ylLq6/xv2HflqbDfsFR1+8z5hyrESYIhMUez4vA+fbw9Fqfc5y357jbxDveoMWvg773no8Vs+lP7fnySl2F68NI1GfUMfj6wSQomxJOSvYNQd8DoNzOzp+vMm5vs0xklzTwz/5o+NoaQFrGZE6xaM2JmQi860YezY7jioBKkXZvLFrEv5+x1mFenYOqvEEhlcnfsDfm/M5svMYBCr+s2olxkMYuXdlhaP7IAooK0pAh0SGQHc9fyFTTO646bJPQfg2X8eyz6ri1naNZYP7cO3hxL+Y//qOPfzQD5ecxO9qiIDf/meLrkrFR2/Gks3ewWd3zw+mXLsgU9kvM6KhXuIL9WIjvkrN10/86FBMW0MsGk7kYVfNqX0I9vGY/j3q8FMPfdinxP2wh0dodbkbkHT0x+K3E2RrPt2EruzZY/VLj0Z0ts170ZRV6H/4Jewy7rAdHcTc1beemrb9BWl8Il7lVqTc2A0XGXzxki6TP6NvpVy1yqtk1PO+v7GMK5ejn5MCRU1mmLbU1vwZyfv+56xq2492E6nrckb7zR/TOkfGzr365HlMAJCj98fo/ntpD6vRkeTs9G5unkjkV0m81vfSrk7KlonnHI2OmFXLxP9LM9zSjrCxHc+YWu0B/3mLc9MMJYADKEhhBsFqMpS7qnUghIkBBxl39697M32t2/fbnZsXMXiZZs4eltQ482xTJ83g/F96uNQUFk2BrFk7BSOJJhAW5f3hnUkzxynyoXub3XAViVIv7acP3Yl5fjn2B2LWX/bgECFc92GVMztHTQ1adYw82BAY/DfLN4W+3i7YWn1+GRJ8j4mjFyAfzpYtfFhSCPLx2XNeGXke3hrTSQe/5GPp13Mw+lSsLLOSn6mH2TR4aaMHZjX2Sdqyru7oFEAYyR+V8KeYomTWHYsXs9tgwCVM3UbViT35m1GQ0c1YCT478U8tnlVVlhlNa7x6ib2uPkwtPa9rZka6oz9h9P7NrHj1A0OT2j8YJu6KZywu1lbIUUGwUcO4W/Izf/pQtdmpVAQ6M/vZGfQY1pEZYaFWVZna+sx4uePqJmf81S6FWMn9sFVDSLtErOGfcfh1ALoqP8RIrt8zftV8lAUdXncXTIPFTVG+nHlqWcwDISGhJNpJsoVUcm4/JO5Kk3OlzCmP2S8jQlc/eU0YcmApiw13/HIu160yorKr7hjroDp5jUuHUrP9Zlay3uTWqFcOeNM49fzKgmlwto5K6FvSiPuRkq+47liaZaphxgJXR+G8wde2ObR1Srn0lk14QVpgbGkGPNvJ41l/t688UYscQYwhieQZMzfPS1T3xnrZ9j36RfOcOzfdDLPVhXEbzpLQGRBay9mEHk4DNGhLS8Pc6UoR1fFoTI12pV60KaXruPnn0ePm5K5sfUOtKxBVSclz4Ro4t+HOX0mHVBRtmdd3K3z7mOrDp64lwEyYrm25jbpRSpj/6HdClrDH0ClQVvQTlVp0WYtfjHduMmdcrWp7am+L8dlh/ai7/LX6PH3+/T+2PnB2GRKIeW+E2gi+WwYcbmME4p9BSrUzdQr47UggkKL/yDQx3plsYGcWBiSeeac1haviZ2o7fFw4xsxZfteRaUUrBRS9pJJwoTRIJ6jb84fjatr5nhnuktISGEzrhpcXTPLYpjuhhBSgiegTEYjOSvlqVCpin7xWoH853upwfaDecdLi4Ig5cAyVl7Lz1ClcPLUddz7fc67Dzv7TzGuKFF++LPyewuB/upVAgwC3Z1A7hjzzytUbtEU90fU00jQkrFMOZKACS113xtGx7w7B5fub9HBVoVIv8byP3aRVMx6lbxvAiMX+JOOFW18hvD4UO8VRr7njdaUyPEfP2baxWI0GlbtGfyOF1oFRMoBlq28lq9sppw8xXX3fnz+bs54zxQext2sJJDICObIIf9c4lcVjl260qyUAkLP+Z07CXqMP2pllaVjxqts2uOGz9Da9318TZ2x/HN6H5t2nOLG4Qk0NntCW1MUOqSpzbtfvPno+a05sKbZJ5/zmoMKkXKWXz/6gWP/IR9uDFrC2ClHyDR57zEsb6VC5dKdtzrYohLpXFv+B/dNnv4qVwMMCN0dAvNXbtSVW9DUvbgCR0safz6Jfh6PNrAp8Sjf+0zkcDIvLC/cxIJel3uAXsoyp3U1Bixh9ubIbMG3glnd5jR9jBG2aduW+tosyyB0nNq1J4+V7cXl2ZpQynXj/dfzniBRlSmDlZIzMZsYF4fpee98Uyh//rqCwOyBilVNvGs+ftrS3Ls+Nc0eNIrIuMofP68rYN8KTEo5ur3/Oq55NzplcjY6IjGOONOza4t1wwcw5YxCw7ErmdPbrcQouzEmOvO7FVvsHZ7GbJWKyr2+Y96CBSzI9rdwyV9sPnCV4OvbmPCSHTdWj6V7g/q8NmkXwQX0ndJPzWWWbyIC0FTuQOfq+cuSTYsWeGsUMEZwYPfpbKsP0wm4cIUkU6atKWNnk6fT71gua5eRKZnL5/1yTdoUzogbCVzyEysCDQg0eLXvSPkCzOUQvQAAIABJREFUCIOmRhuau6pB6Dg9ZwobY/NONGUKtBH3jj2on08yRnPf9gjiYqJ5aumA9AAuXEnKtGFKGexs8kqIO1Iua9eOKfky5/3SH/vT92t1pxrwfqXrQyuXSuPZrgddGzrnTCxqatDpJU8sFEApRfUuL+UxAWCNh3vZrP4O5lZQYZz6UlhZPT4itu/qQ+/KGkCQ4b+YKatCH2/rNVV5tWf9fJKlGsqUyUpkizhiop92YsdITHTmmKTY2uNQ7IWiBRlpOftGU0qdIwlmvHSR8ycy5Ulxd8ejUv5KZl7fJXOVtimV0CMReU7c3Us6WjevgmM+J1uqrM3QZumWLj6t4HLt7E7l/MZIjRlmWX6RSEgjTRTkV/NL5GQmd02Xz+E7N5iUfEReXdEJlxo2hVrxW1AM18/zz1d3cJ/6Nh1ezZLluFucWhxGRkGG2BsXOLKjNI3H1Mi2A7SosKBi9yoPJl0M0fhvzn23hSnUn+untFTqXpk8F+CnR3BhRUjmWKO2wb2lXf7jSmkXXKqrAEHqqdtEpBeljP0XV6EIEpdpJsq2fXi3hxl2TatQsbZVzoUnGjs8Wt47Y0WNXSuPPM730FLaJSupYUoisaRPLJhSufXrQa7fFaC2ouLYbrTvktuBx0/Y0cKEeG6/+TH20cYmcxekKZbo6MImXBVsbDKfaYqNJroE71hQWZfGWp29ddJISy36Xi1clYD6vPduUywUEOlnWbn0RJ7+uSliA8t2lqOvT6tH7O7TiytKmB9eJH5v/qitrbFSQcaZ6Qz/zpe7+fy0tloDmtetnHNFf/op5s7yJTGzc+jQuXr+h5HatKCFtwYFIxEHdnO6ODctGANZ8tMKAg0CNF6071i+APKtoUab5pmLj3SnmTNlI7HF9gEa6r/3Lk0zFYyzK5dyIm8FY8Oynf/H3nlHR1VtDfx3p6WTRgoJvaRA6L13EBCxoSKiNB8qCPrw8SwoPkRFREQFBJEuTemhIyT03ksSSIH0Qnqdds/3x4SQXiCE4MdeaxZrkXtnztnt7Hb2xmXEOLqZF2bDfgzwMM0Zkyw8GTjAu3gaWtelTs1cfzAijDLZ8L4ziKHFYAYVqri38ejFsEFtcTV7CF1TJTJkhbVN2SeTwvlZXuxVAwmBPmAps9dFP2CcUMfZRb/gZxIqGvbpT+kqz5YuXVpgUnmH2X9PqJTWWJuEm/kTv8KvdOGmTeeWNHR9HM5jFufmfcm6CGOxvmTWpXmM/3gfSU980LUSztTqD4Ls7Jyih6RkgbOrY36NRPSB/VzMfwUXBQ7u7mUOtVU4NKJhTUXe7+kCL3NNX51wIKFq2ZkuVuXTj3kYMRqffPInH2TviYwC9Fc6uuKiLo+edca5hlSAR5IPbefvcpYfSKqWdK440h/RUMocrvw4infWhuP47A+s+6ILNtVJSrOyyRGApMG8CtprmdcfzAzfQ/z6vDuKnDB2fjmMfhM2E12mUjdwfdc+gvXCJFeNvPDWlHUQN6ahgwQYibpxlQKd0vJuJEkolCWr3gKzweVKOHnkSHy3HCdDAIoaeHjXL99AP3UzmuW23ZHj97L5QBlXISUNnj4+lIoiSZF36Oj1hkpNZt5Hr4KS0St4YPSqGtC6dc1yHprW9JpzjEuHtrH90EWOzulVwtkiYWFplhugz3k0jrVZBwb2cjHRXE7Ff+ueMhOmksYTH5/SmV1S5FESvaGyLSRBVu5ZLmnMMZcev97SZ+kL0M3SJX/QSCbR/w6puXauoo4DDmXoCsnRDltb014zgu+SXSoKldg3cSxdbqX71b6yvvyBN2XDmjhqyilbBvmhVZKqVT3qOJj4PXLBFlYP3sS+eZe4dfoumYX4X3JpxsAlHfKuuFea/33nBvs/uIHdp0No38aWRm+3xMUs99zfeIxLgWWczHIaAfOuwMju+Dymqih1J2+a1JXy5CV1bwBRRfJJMkk7Aol1bIR3t5KJLAeHER5+b+i2LfaNyjghFJbY1jF58yIuicREUa14rCJmWOXHRmrg5G1Rzt/RUHvaK4xcPZShq0cyfFqdEs5PCZW5Mi9waMimGoNMyuaDHNqShqy0ov7Hwxj8Rs0SgnMqFPn+IORyphoM4r7tIClQqKQnaM9liJaFRe55pyW7whk2BRYWpoAa2keYoKsM0NSlbv42i3IaSSkV8YgM5OSUbUPKBi1ZGelk5FSGt6Wk8cgx9LNVAAZubVjOgWIrT43c2bCKo81GMdpH9Wj9implhz9+u9es0zP0cVKAfBe/mQPwbtqHNz9bwF/+V4nJLLhThftoVvt+Qut8JDJc38W+YFPFuKRqhFfZxKFxQwfT7d2oG1yNf3zRQjnSly3HTTEQRQ0PvOuXL6iqbtaMJrktvuL3buZA6uNTC8rGIxnTzxYFYLi1geXFCxjGOxtYdbQZo0b7FNWz1r2Yc+wSh7Zt59DFo8zpVUJkT7LA0sx0doicbMrPhioatG5NzUdi+lUj3xFrOnRoZsKvnILflj0PVkhtuM6ufcGmTiKSikZe3qXrJxQ4N26ISeVFceNq/D3h5pk+plZ/d/1mMsC7KX3e/IwFf/lzNabQLW2FO6NX+/JJ66ruhySTdOBj3pp1kjRhQ9shvXEvbJ8IHTeXTODDrTH8E3ML/7DR1EbSUjOKEkpVD88m+VOIeq5eCqRAXgEJM8NdLp8/X7rzbryLwSxfOCEhhhg9YFZdcCBRw9kZS/7/gfbccc5lFgpMWFiVr9pRss5reZIn+xlXuXBDx4jOmrJfr+GMc7VAukzivmm8Pt2fTM93+eP3cTSuZlIuZ2flJRbMNFUUlFE14q0fP2H9gfc5kKHl5spJfNivK+tHuJYcKJbjOX4iKLcKVMJKkcyFw4dLV5rGSLJybzSJlESSZXKv2mpo0twLa8UNUmSZpIREjMUqYCNpKfcq763wauHx8Eo6xZ9D57Qmh11hj2N5rSFFDWo6mCOhQ8ipHDt4El59pjQhwN6+/KsVlRk90jShuZc1ihspyHISCYnFYxdjGimmayNIVl608KgAdiV36tarAL8q7PDsNQzPAr+fTviVs1y4EUp4ZCRxd5O45Xfv5px4RAE1NW3aNEMlRWEUgqxTfhzLGc9LlqWR0p7yk/JRrFsmO+teYsEMjeLx61VtqjZf0EuFfRP7fC1Nsoi+eP/Wn1qhJf5MZOlJKGMGBpUECEjLIUem5Mp3SYO5bQWQIMrvZGhqVGAugBAPX91YoxGdPmpM5PRgMgwC3Z1IApdEErgEUJtj16wWbu1qU3+IN42aWlZ69Ysccwu/dw6TNvhFBnQxBYClJi3o8Nw1fP9KQ2TFcO7bKzRa3hoHZfH8nvX3Sc7FeDLwR8fHV52jqYXnM45cXmxqYyliQwg82o16A/IZo7p4gnYnUuOZ3riZlSy/Wflb8FhKaK9HEqkqXeYzsk3taITQkp0qoJZUfXjssZrg1ti4VyDQrTDHvmMj7AvoBh1pQXEkBKeSHptOVnIOKafvzXQRiGrsjWrPn2LvtyFkKmvQePpzDHitJiXW9iiUqPLfFDaWb2NCFvkyEEpUmidoz2WBmQVmSgmETHZWFmBVwdfNML2ezQO8XoXRB29aeJkhhRhMpJTjCb+dUX5nOnMdrzqOxlevQKVSoVQqUeS10hIY9Xr0BgNGWQBqWn1xhov/a/XQy1bUepExQ6eza00MxuhtrNj+LYNGFpqPYLjOqjU36D5lQ9F2I5XqV1QzO7w62L12w/jf7Bfxe3szkXoDKbf8WPONH2u+kZA0DjRu24Vu3Xsx+LVRvNDaqVC8Ryb++AmCcs9CyUpB8oXDHC6dOERmqXMDvCkklkqcRwsp/oc4pzUpRoW9I+V39WriYC6BTiCnHuPgSS2vPvOYglqKWrw4ZijTd60hxhjNthXb+XbQyEI3xQ1cX7WGG92nsKGEfj4KO096DfMsSKn0cK6cvcCN0HAiI+O4m3QLv3uJICFXIMgr4V633qOz/aqN76jEtY5pLpVeCDJPHuJ4zjherGCsS44/zon7QoUi+QKHD5fRMjUyK+8GdkpiMlAHsGPY/2bzot/bbI7UY0i5hd+ab/Bb8w2SpMGhcVu6dOtOr8GvMeqF1jg9hssKcvgG3hv3KwFaCafB37F2yyuETO7C87/dvN+iHRCGO6ydNJFebf9iTP3HfiW/co/2f9RujJGEhmcWcUoUNdvTqVn+req4e7dw6x8DoStG0XFFBX0IS9O1u+oEapUKif9/oIuNKTqzQKEon/KXFCgURQ3A6Gi5vEhHVQ2QbghZwfixiwgw78G3634oclWvWoBCaaKJEMhCpqouTilqD6RvcxUHTupBjmP7gtXcemUaniXpdDmSqDhjnj4xhu5n8cIzZctWpxcZ3lnCwqczrvm25jBkNC+6b2d5hJH486e4aRhI88Ia2BjFxaumLLbSbRijh9Z8aOwYwkO4o723Cw3m5XbCFZiZa0zBI4wk3A4r8/kK9cgVlRk+cmDI6Bdx376cCGM850/dxDCwOUXRe5GrMTKgxG3YaIZWoOREUlqZrmFWmABxnN+2hlV/bGS732XiNI3o1KMr7Vs2pZFPE2xCtrLlQsqj5HxqODthJYFWgJwewe04IzQoxZhRKHhspMxTE4rc75apQjVRgi7IISks675toXbCrU0+QZIz8vVKBzniDlfXxpatK1o1pokEyia1yrAjJKSK7L8C9JCqvK+1AtsXnuEF9VEOfH2V2Pz3gfU5pFwKI+VSGDeWn6Hmiz0Y+GkzalZScEzcDePoO/u5kdaQQW+65NMP5tSf3IXG/vu4lSDQnT7N0U0NGfqqbVG2S73NyR9iqfvFayVeea8qPDo954HTsrvE6QGRRdj2ULL6eecVUxguBHAzwpGmw1xKKZgRZMRl3beHjamEb7hMXJlsUYvGzwBmNanlJFUzHnucZFGjtniA/Royifs7kIAdQYSeukuWxhbXdm64eDlg62GPOjyE4Bvaap10MYZcZf8HZ4kTNfH5fii9B9mWobZVWDpqANMVDNlQzqpyQ74gkMIcCwfpCdpzOZS3uHcGqh7idQXK6uzhKxzp3r0l6t0nTEV+wsCt6zfQ0Z1ymagWQ/j+6Ek+MRjQa7NIiQ3h3OZf+GHzDe7VlykcujLlu//wrIcTzg08KmnhdgwaN5yG638m2JDEvpV/EjFiIvlrTrQnVrAhYQhzX3AuyguV7FdULzu8Oti9Shq8tYYDGlfGfrCEU/H6+wlZXSK3Tvpy66QvK3/4ipajf2TN/NH45JWEy0RGxd2f/WEMZf/ihZwpmzi8OLwzkoUPnV0fl6FqIDzkDvddPXPK7+qZ5c5uE2BM4HZYCuDy2FSD3aBxDG+4np+DDSTtW8mfESOYWFDAWLEhgSFzX8BZURYbnmfbmlX8sXE7fpfj0DTqRI+u7WnZtBE+TWwI2bqFCrOhpMTK2urRuySP3XcEpY0tVhKk5fqOYWX5jsWGUqKIuy9UhO5fzMKyhYpOLw6ns2SBT2fX++tp8BZrDmhwHfsBS07F5wXrhdCReOskvrdO4rvyB75qOZof18xntI911TGu7jo/j32fvyKMKOuNZOHSCXhqFHh+t5QPTg5k7pWCHXWMsdv56O2FdNo1ucxba08S/LMSC9ogAsOMRQ5Z5/7P0qNQDzYhiyKo8Jiwjg3veVJukZEUmDs1xOP/4/WA6ghS0cFvQq+nXF06hB69oZhgzpPkD2vP8s3rH7I9xpJ2H75D+9TTHD5c2qEVQELBazuIlFBOHz2Mbe7+lTW96NbMuXL9CQtLLCRIQYe2KvtRKmtR180MCT0Cge7SMU6mTcPTvqTTMH8VgALXITNZO6cTD6z/7YYwc94oDo9cRcilVfy8732WDHHIZ5zIJP09l6Wn9aBuwIgfvmKY48ObLnJ2zn1j86H4K6das7/dkJnMG3WYkatCuLTqZ/a9v4QhDvnwJyfx99ylmNA7gh++GkaF0KuQKtii20DE3m+Z8uEP7AhKw6zhICb/cpTJr3akVl5AUseJqzMffSJYY5aPb3XodNVdmSmwsLRAIgV0WrSPezn6BO6G3RciZfOGNHRXFNAV96uIJax6deaZabX4Z9WhVK7p6fBsb17p1YrwnQHcOhpF3LUEkmJ19wOGspa7m/5ma7yBl35tWULv+YoY/Ylc/s8+LgXqMRvciHq2hU57Z0+6Tb1F1CchZIkcbv98hMBuQ2ian85oCf/5KHead+bVzo//mqqividebc4Qd9qQ628HEhznRYtaEqAjfHswWc3b4NlIUXokMp+NJDk1oNPc7tTSPOXSByNKRe1GmfQjZzn87XlCQ3Uo69Sn1RfDaTW4FlZ5NDASHXSqWm9bxAXjN9GfMIM7HRcPpmNHy/JMXMHSyQIJ020Mka1HJ1P2gO0cw/15IgoLLB0VT9Cey9JTWnSyAEmJhaXZA7yuw/S6BZZm1ZljlDR6YRjtZpzkhFYABm6fOk6YsXvJxT4F5MwRj/b5Wxz3Z+gQS67sHs223LYg6vZv8vn4YThW8srNu45jZMtfmXleT+aRVawJmMD0vOLFdPat2Ixi+AYGWFeBX1HtoDrYveZ4jfiFY8++z4F1f7B53xFOn7tEQGTq/UCkMYVLy8fTLyYb/+3v4mXq91LgBofCdQgz186h0xNBHJnsnMpJPGtzHnOvPfOujBvZkl9nnkefeYRVawKYML1ZXsAyfd8KNiuGs2FAKUFjQwR7v53Chz/sICjNjIaDJvPL0cm82rHW/TtRuhNcnfkgXKh4xMUS1cd3lJT5/d4H8x3l/LdBFK4MmbmWOQ8hVOZeI/jl2LO8f2Adf2zex5HT57gUEEnqfeEm5dJyxveLIdt/O+96VUWoO51jX77JZ4eSEObNmLTsZ4bfG7pq24Mvl33K0T4zOJVeoBkzSQenM25OVw5Nb1vyDLQnzrv7B4H25B78CjcAUzVk+JgBhXrMa3Bzc0JBRIEe98LClWYtWvDUn6o60MUHcT0iC7NanjR1e7gMjaamE/YSBfuK6ssZlBI56AoF2VE44uT0BI0hSbvIicvpCAFn542gz7wHiJ+dncuwXnPvB8he/IOMza9Xrt9tndueSmSRmVGV9XfKfBX4IHQhBN7UQccSJF7lgouTAgJMwZfMtPSHNNoUuD/zFkO89vFneAQrxjyH5ZzZTHmuDa6GcE78+T3//XwlwZYtGPvLen5+tW6lVEQoHeyxy2cY5JQ7SiujzblX7SOhsrOv5gEdd555awhe+/4kPGIFY56zZM7sKTzXxhVD+An+/P6/fL4yGMsWY/ll/c+8WvdRynYWl355laFTdxGpl3Dq+w07/ppGJ/vHo0+MKcmmYXSApHTAqWZ1D3krsLYytcERWZlkPOYyXcPF20TcG1AtmVH3JS/s8qNQZYmlgwShpmf0Gbonu51LFYFkbU+917pQ7zWTjtUnJBF/LpyQndcJ9L9LtkGQdfQER7c05LlXbB7CiZJJXuvHiRM5gALbRg7FGL8SNYb1ort/LPv3ZiLuhnDsy2u4LW6RR+uco8fx/9ueTusaV4+bqsoaNHmuDidPh5mGiGZFErQ7FZ9xdihSwgj00+P+gSe2ytJlzdLRIu9cJEuH7kllKDmT2IPxqDrWp2aNJ6EqRE/Cmt3smB1GhgEsOndl6E/tqGX7hN3wSIvi1PsHCNQ3pPfKATT3LL4RkKzVojeqMcvrT6rEtm4NlCSZEgWZOnSi7O49Ij0HXa6rJ9nb4eAsPUF7LoOFs7JMPoxkgVWFp9bLZGWZkjTlbgP7ONVX4zcYN3A2p3aYOggYLu3CN+w/eDZ+QPtEXQMbC4m8ZunmFlg8kshJM956sxtzL/iRqbvIHyvOMG1uFzSYhjav3OvGyL87Fh9LqHS/ojpB9bJ7FTYeDJwwk4ETcmUjNpBzRw+wbd0K/th1hQS9kbi9nzJ1xbP4vl0HBSpcXJxQmIiDyEwjvQqII8eeYcdFC3oNbI7dA6NKiYO93X0bSZdT/oIcWUvOvaCspMLO3u4x85GKZm+9Sbe5F/DL1HHxjxWcmTaXLiYBY8vKvbiN/LtE152sS/zy6lCm7opELznR95sd/DWtE/aKpzJUURDZOabW1Q/hO6pcXLiv8jJJqwyhUtjgMXACM03CjZwVS+C5oxzYto4Vf+ziSoIeY9xePp26gmd9337E3clkYrd9yJi5F8mSbOkyfRVf9y0YL7Fs91+WzfSn29RDJMsFjBlOfTOOGT2P8l13G/4J8M8Z3iwnsnv1DiKNBbdn3/fffNC9cMBaTcsOLSl4W9lIQnQMep5CFYa7iFw9no7tOvDKL9cwPOS3qZu3wstMKuRnZpBRnhsLchYZhSadSZZNadX0CUozmdej05DneO658n2GtC1aUat0ac0z+Z4Z3L525TsTzi44SoBIIuFu1Q4NVyrz7dh4h8CgrFK0Yy1a+dzDkUxSbAzZD8nvNxbN4HDv9dwMOMLvE5oQtGA0XRq6UMu7L++siKDpO4vxv36G30d5V9qcFGUdH7zvVfTJySQmlrO9l5xOYtK9vs4qPFq1rubq5AaLZhym9/qbBBz5nQlNglgwugsNXWrh3fcdVkQ05Z3F/lw/8zujvB/tNbP0g5/w2ke7iNSDquEYlq9/fIYhGAi+dTuvUkvZoDnNq739osTZxdEU7ExKoIrVRCHQEeEbQu5oDhSNfWg31LpgkFthhZOHVV5/55y7mRh5CsVy46mjbPrsZjHDqiXUTo64D2pNj4UjeWN1F+o4SCC0RGy7SerDINSQzE3f6Dz7Tm2tKT5JobDBc3ovPN1Mf80+epwDyxIwgCnRMCscl0/74O1eXUxnCat+XtSzv3/GxO4KJNEgyDwQyB1RF68B1mUkZCQsvR3zEiUiNZOsnCeVuRK5MfskwTGVEQ2SkR+xEOtOHGf3d6akglS7Gf3mPYFJBV0ilz/axYXUhvRb8UyJAXYwEPH1WratLti6wczHBTvVPd7LJrscOJcTssjJ08cuOKmerD2XvrcEkgSgqImLc8UTCwkJSabhrTVdKNfrukSCTuzB9+9z3Emv4uEdCjdem/Y2TXPnbAjtaf5YdenhEptVIj5K6r02loH2piHOwRvvDXE2DW0+3uItRnkrq8ivqD5QHexerd80eo//k6JujgJL16b0GD6FeVsvcN1vFv2clUhyKgdXb8TU7EJBrVY+1MolnZwUS0wVEEd/bQVTv9hMhPxwPFnHx5v7rl4i5Xf1Ekm6F/9QedCqtfVj5yVlvdcYO9DeNMQ5eGPeEGfjnQ2sOt6Ct0Z5l3AjOJ2Dn7zGR7si0aOi4ZjlrH9ikgrVzXeEtLj4PN/nQX1HRa1W+NwXKmIfVKi0fkzrPZ4/i2FshaUrTXsMZ8q8rVy47sesfs4oJZnUg6vZGPZoDTlD8HL+9e5KgvUSzoO+Y8V/2xYTv9HQ9P3fmTvUtQjfiuwr/Dh+GnsS/xmjnP8xiYWME7P5YkN0AWdeYdeDz+a+TUNl0W3XHPwifQoMQxRkXLvAtXJbNDqCdszn+xUniP8njvV+EpnZ7RkGtTUrYFeKxNuEJZdNIGNECHeyREGHvcsg+ts/QQiwGciXm7ezfXv5Pmsnti1Stals8TYrt957Zht/ftyj8g2G2vVwN5dATiS+ioVHpcqnDISW4ICbpSS0zOj47ADclCb9oL1+gSvl1Q/a25w5c6dgxYgxmO1br9CwXTtsXDsz+qsV7D0XTGxKOqmJUQSf38+ab9+mu3slJ7MsuzOod+6QUTmN4MA75Qt66gO4EZL7pKoxAwZ7V2v2NwZvZ+uVhrRrZ4Nr59F8tWIv54JjSUlPJTEqmPP71/Dt291xf9S5Qjmc1bOXcVMnQDKj66TPSpl1ItDpDEUr1uRwfhvehf/6V0ITIDme4ycDc/lcRYOBz9Kq2udLldSu545JTcQ/1jPWcOMCp30zTDRS2eHz3/bFDMJV4dqrXl5w1ngrnrvl1RW6VGKvpD10Yv1JARGfQtzBECKzS48OWbZtz4D/NMBMAmN40v3hwg+kHJJJyTcvqbQbJZJTY3rOaomDxnRGRC/Yx4kT8Vyf4U9Mt970HGhVvWZY2TXAq8/9NcmBNwm8ksJN30hUPb1o4FCOc7FlA+req/rWJRIfVF5HzEDalVjSnpQrDhXpUZSZQ3bWozwn0gj47RopepO+c3ujA/VLnBUgkPVysd9xdfJGjp1+TNrDmMatz305EdWAAcv741W3lGpGYyZ3QwzYNiwYmVA0qkPt3ESeSEgjvczqLpmMqIxcfanAsZ07Foona8+lfn18nGlWnMKNuvUqmjExEh9nGuaucKtLWa/rgtbydqfGNOs2mOf6d6CJV1+m742hKo9by64fM2dsY9OgTqHn6m/fsDH6QVcgHtnIgCL+pvPzjH3BDSVgjN7Kiu0JyIbrrFoTRK8xr1DyhdhK9iuqC1QTu9cYHczZ7Tvwzyo99OXU9WNWffcs9gqBPiSQoFy9Y9bxWQaYiIPQXudC+YnD7TNnuPMYiWPZfRC9czMLclowgXfKd47rA25w39UbwGDvatDMROHM82NfMMmJMZqtK7aTIJuGNgf1GsMrJQiYHL6a2ctuYmLDrkz6bBAls6EOnUEU8x2/MbzLf/HX/v+Uofw8ffVqUG684CF8R7OOPDvApCsRWq5fuEL5Vd4ZztwTKmM0wWe3s6N04Ubh1JWPV33Hs/YKhD6EwKBHWDKedY7Zb33ErlgZdf03WLT0bTxKEh9lA0b/+jMj6haegyvQ3/qdd6ZsJvofEE/+RyQWcoJWMm7UT1zP18pGUjfgtYUr+cCneAorXF9h6liP3KnjuS7STV+2XCifMMp3VjF19FQ+/mgRx3J4CkUcOGusC99nNhoLBU8EWVnZgISFZSU468qGjPlweK7BlvsLhgsc9k8t01GJP3yU6/kXp2rAG/9+HTfFU1JWOpg1wbOuEuQM7oRGVWFlr6m9ipTPCbsTFERmkefSCbseSjoNJ2D3AAAgAElEQVRg1X8K73UwvWO4c5iDN8rjwMvErPuAYdM2E51/c4YIwqOzuX0ruIqDiHYMnTIeH40EGAg4dbJclSyGgEMci5QBiRrdJzCuXfWORhsiwonOvs2t4Mccos0+yeGzuYN+VR5071GnlF77OmKi7xZ15IWBtPgYkrJLD+oZymEvGUM3sPGYaWiUZNuT9yd1weyJUBOemNTEHUKjHlP9f1Ys57+6QJwWkDS4/GsAXbsX3wlT3bU1LVqY7A0RHUV4SLmG+5DpexjfObfI/P90xSE1ktuny3ItJKw618FJBZJGiephDATJDI1lXuid1JCkUnSwhHnXbjwzpTZmEqC9y8UJf3Is1pt+U+uV0QdVJm7BX/z1S2wVBuc01H2uMTb3bBVjMrd+PULAJQsaPV+vfLJuWY/Wr7uaCg2M6USeTCzX+kV8EIffPUpwXNXsVrLW3LfZ8w/wzac3DTolyhIKyCUrdd77ZQUgDdfjSHqUMpkTQ9TVXC5U2ePW3qYUh0wmMz6naBBBCHSJmeQU44PIKSGcPHSEa/GPKDoi5xDx4078A+sycHlfmpR1iycrlrhQGxwKV3ppatGkX24LD20yd8vUm0YSb5pa56B2pvEz9lXnyFbWnkuBjPAIEmVQOjXBo2ZFd5ZBeEQiMkqcmnhQ6uvGUJZMfJdlF1Nyh9UK9NH+zB73X7YnV+VhYM+g2Uv5sJXJB5Tjt/PJhxuIeBDZy04hJauqGgtZ03/cCJqoADmZfSs2EnxsORuShzJ2aOlTHSrVr6guUGV2bznOpWR/dvlllOkPuvbtQ0sVSGbmmEt5xGHKex2wNBGHwwdvlMtfk2PW8cGwaWwuQhyJGjb34xv6osMcEdk5aDXmaB42CGI3lCnjfUzfYwjgVLnOcQMBh45hcvVq0H3COKqLq2fdfxwjTAJG8r4VbAw+xvINyQwdO7TEuSnZJw9zNlcHqDy606NOKbpXF0P03aIYEoY04mOSqv4mUTXzHTFcw++YqWD74XxHK/pPeY8OJqHizuGDlE/lxbDug2FM2xydX7jx3+VHmdLt2pc+JuHG3PwRlQLJSRz45C1mnUxFmPvw/vKfeKlW6We2otZLzF/8Nk3UUhG6hW+YzHsrwyoQE5NJCTnJoSPXiK9G2eYnO2yqi+H40vfp3/Nt/rqtzzO6JQsPXluyi2Wv1ytFKK3o8fliprbJ17fXEMhv05cQVBbDawP5fcqX7E1R0njUOAY8Hd5cFJTuNKpvXYDB5IRoYgwFjeAbN25jVFjTyLNuJQy6VOA47H9883zt+0EIOYU9v/5euhLTXmDxb35k5jGQmkYjv+PzvjZP6fhI4iDetGpmjYSRsKCgBw6yV9x9UFGvfu18fCbICbpOUOH4VvJmPuj5EdszAVUzJn79Dk3NJdBfZfmPO0goy1LTXmTxwlO0GfES9fIztdIFVyeZK0uns/R65WUj5YJxhmLBvP1UZo1qgFoSpB1czuqbZWE9Ff/f1nNNL5AsWzFp1tt4KMuihCgHVR6d06d0ccVJvsLS6UupRPRWfMWyNt+AKzWq0oz0DH8OnMrMvb4vChiNOr0Stbr0wMC5cwllOA5J7J2zkBM5AiRr2k75mrcbKcve7OMlZa6aaEUzawmMYQQFVZ2WyHszNYbzU3w5fUEHkhkuYwbx7CS3kmcwqRxp+WELU6W74S43VoSQVZau0MVzZW0szs82oYayLIJU7l7FIyFjOb9NziRk062y2xQaZWQBykbOOD5MEZ3KEVfvfOPLz98hKqv0s8Jp7CAGvu5oCrbrDMhGA7ocUea+Yi8koXa2qdJbDao23jRpIOXRIP1IGHfdGuPVTl1+22lkF5o3VgIyiZsuEppUFi0NxG+4TGxTTxqXEGCtbB6T7KyxtrznX2aSWUgtiLR0MvVW2JTQA0ZRyxabXDYwZOtLWZeOiL13yMrHh5Wu7mQjRv19/CtKI1VmJOGXiosEGDEapCLvGsNW8VqLpnTt25MWjTszdV98JSe69CSs3s2+fbZ0/KIV9lmpJIclF/NJIjEontgL4dz87SrhRlvsi1TrKHEd0QJ3K8CQTPix5NLXmh1DeG4BmGXfVjRtUAE3Nr+BJCrKl5W555L5LvBGMHohoW7WmpbqCpJFF8iNYD1CUtOsdUtKfT39CAdOZBTBgTHOj33nqjhKYduTrzctYUQjDRJGov6awHP/3kVsBZk2xd+P89qKUbU89nOJNkqH0bzZ1gwJQebRJbwzYxNmr46jb1lDQirTr6guxltV2b3lWksM25dtKrv612jAICTMm7bCR33/7G828WveaWqOhJ6ry39kR9nE4eLihZxqM4KXihBHgWNtd6xNV8a5GxtTyOeVSYmMQlerboGiyAcDc9pPncWoBmokkcbB5asp29Xz57f119ALCctWk5j1tkeJsRhRRbZ/PgFj9JttMZNAZB5lyTsz2GT2KuNKETBZm29GlFpF6Wx4gOLZUIdeqS6qPx9y32XqmirzHUM4ezahTJsgZf9i1l7Xl+k7lkeHqppN5Ot3mmJSecv5cUfZv6+9uJiFp9ow4qV6BX4tZvsyNpUt3BgMAsm8Ka181I+AOWUiNk5k3KIAtJItXT9fxde9yzObRIHjoO/4/cMWhdrxA8ZYfD8azy/Xy3Ofw0jYqtdo0bQrfXu2oHHnqeyrJu1znrjEgjEzhmt+f7Lwywk809yTHhMWcCzOkDdg1NrzJebsPcIfY7zLnrBt15NZW5Yz3udepZBMyqFPeHH8Cq6kFk/IpKsb+WRwPybtiMW6w8f8/mVvrHkKRcGGASOey+tVaJKZA2z++34pjC7gdxbtSgLnQbzcr5KC+MoGvLlsIzN6OucmFwQZx79m9H93El6crOaE8NfksfxwIbcaTFLjPvA7Ni58+elthUcGlrTv4INakkkIuPZAlUlyWlqBga6ZGeUZgKagdr8+NM9XFmIMOcGxQtXQuhtXueVYhzq5Z5FN729YP7s/zkqZqLWTmbAyuORrfHIS/p9PYJnNVL4dU6+gglV589yQ5qhidzGpowftn3mRl4e/wiuvvMKrr73Om2PGM+G9yXz4n0+Z+eMKth0NKlfFpEhLyR0wJtBmZxePB4UjQ+ev54uudkiZx5k1bjZns0o+MBP2fcbkZSEYVbV4dt46ZnSxLPHZ1NR0k4EgMkhNK33B+rS03ASewKA3VKp9qvJ+jiHNVcTumkRHj/Y88+LLDH/lFV555VVee/1NxoyfwHuTP+Q/n87kxxXbOBqUVHZlgDGV1NwGk8KQSUZmedi7Ez075lYoGQLw/7uk1lNZnP9xHkG9h9NCBYgs4mJTTLg0RBIR70KdOqVEU0U6+2dMZFWIoUTaRG2eygcrwzCgpvYLP7P+844ln4tyvr1mpFI6KfWkpWWa6CcM6A2PwNOwbE8HHzWSnEDAtYgHuNkk0GXoC+DDWJ4qHVlL4t4T+L60mWNHMhFmNWg47Xle+G8DrMtw/DSdujDoo7pYKAQZvv4c2pJS8rrlHCLnH+S6VRu6vFijmEC0QJueG/wUerRlTLCWM3R5czRkQ+mBUJGu5V5nFX16GYOm9Tp096qfjMYynAGBLi0XycKAoZRAvPbwaU4dyirlt2VSD4aRIFvS6KVGD9fuRGFJo5Fe93u5xwdzeUdqqXsRaWmkJssoLFQoAX3ABfa8c4zbpRjv+kuXuRbgQqPullXbLknjjNcQp3znjYKag71xrUjloXUduszrRl0HCREXiP/n10nRlUznnNMnOfSXhjZTm1FDUUU8ZuZOvY6mmjk5NJroxILfmn0igkSv2riVdFxZu1Mn91aRISqVjBKEU3/lPBci6tKksSKXF7LJLi1AY9ShvVeZYtCjL0+po0Ut3O9Fjg1JRJ5IK2HfeuJWXiC5UxNqqky6IOuu1oRPYwYZiZbYuCoK+Ck31y1iW4QJ5yL9Egs+/42ASrvIJ5Pm+zc7vw8nM/wWfq+vZvUzq0r4rOaP59axccQW9vwWib62A/bF8KSirg9dRzmhRCZh8wVuJ4sS+S59z2VuRQuoUZt2kz0qNERdpGnJu9ienYNO//j2XEKUjxsBERhR4dmxExW+sJB6g4AII6g86dipZukOvtoa6+IqOSVrbKyrvtmbquFIVh34gwmtbFGIDC4tGMGgf60qwRcvhkJxe/lixmYMPq2pq5YqwBPlsJ9L9De9eGNMb2wkELpr+J+pzxtvtaE8OfBK8yuqix1eVXZvOeU1efdXfOFbWkLVSNj2nVyUXRk25oWCsmbTm2/Wz6a/sxI5ai2TJ6wkuGTikOT/OROW2TD12zHUK0bozHs8Q29b0zyOwONHKXjBL5G/D96gRc+uZQ6tL5ep4ziU+eu/oKudRObxWYybfZaSXb0E9n02mWUhRlS1nmXeuhmU5OoZU1Pz5CQzM6OKcgtKvN4YQ2+TgHHN/wz133iLNqrS2LAnHa1M8m8I8OfvktpBZZ3nx3lB9B7eAhMbxhGbYiKMITKCeJc6FGRDYz4ZM5BZLmewgrqmSmRIwrKBI5dmTOKPUmYPyNFb+PeUVdyRy/Yd8+8rIzWtBN6wofc365nd3xmlHMXayRNYWbJQISf58/mEZdhM/ZYxhYRKTt7NV1/4ltoi1xi2nZ0XZVyHjeGFmpUZzJPRpsZwfftnjJj4JxEGUDcYylu9bcgsdz7ehu7/W8annYoWIMnJfnwxvrT4zL0N3mTdom1E6EyJ5PRLC/j8t4DqEeITVQTG2wvFUHdn4ex87+Mkamike6n1vI+kthaOzvmfu/dxFHaWaqGQKPIOkkrYeQ8V/15yWITnPMDiUi6KpePaCSf1vfVIQuPSSgwZ+5H43/c/iZ/nfSOm//tt8VLX+sJaIQkkC9Hw+e/FsbvGAl+jv/iV6OFmWq+TrZmQCqzRTNg6mf7m3neOuG4wvZO9+z3h6Wr6f0drdYF3JKWFsMvdf+1XVoq7eT90Tszs5mbCi5OtMCuAE0koLexycVZbDF8RL4xCCJG9S7zr6Wr6fwdroS70jtrawfQ3V28xaU8uEg1hYsGz7rnfZSfMFVLBd2xqCmdnZ+HabLLYXxzejdFiy7+8hZWUb22ObcXIT74Tc778l+hVWy0klZt4flmwMBR+92F/OydYbJraW9Q2z31PUgrbJn3EGx/MEHN+WiAWzJ8tvnj/NdGjgXUuT0lCYdVAPDN9l7ijL45J9OLczG7CLZd3bc0K8m4BWg1fIeJNSBe73vUUrs7OwtnZQVirpSK87uDsLJydXYX3pD2PVgC1/uI/rWsVkKkiPApC0tQQTnnPuIj6Yzc/kuXoL0wXLdUIyeZ5sSqxHPrj7jXhv3e32OW7VWxYPldM6VsnHw9LQuXeW0yeu0xs2Oordu3eK/yu5fJ9kS9KFHsmeQvze+9KVqLfgjv5nk0SO8c3Fk0mHRLZBV7MEFeWvi48LSUhqd3FwC98xa2MQnuKOS4WjWkl3FtPFNsjDSXomsPi8452QkExeqyIXlMIq7rdxKivd4rQnMKidVEc2LNLbN+4RHwy0F0oc3lYVW+ImL5kg9i6c684F1XMGpLPiJ9eaCjMJaWo2fkDse5aSkE8aWPEycWjhY+1JBQ2PmLs6oBCeDDJQuS5/WL3zq1i/aKPRG9XZe6aFcKxy0Tx89qtYufu/eJ0mOnN7LDTYv/unWLr2p/Eu50ccvcuCaVbHzF1wVqxdedusf9clDBUAl+lHP5cdLRTlI1bEJLCStTtNkp8vTNUFECvMVkEHN0rdvtuEitnvy6aWtzTIZbC84XpYsGaTWLHrt1ir/81cddY/DoyzswWvWsqhQRCUbO3mHEoWhRQK5nBwvfLwaJF71nidMp1Mae7tZCQhEXLyWJnWJy4suh50aT3XBFQBCnxYslAM9N61J3ElK9fEd7NR4hFZ5ML8XuKuPT7W8LHRiEkhZ1o8+5GEVycntRHinP7d4udW9eLRR/1Fq7KXPwoHEWXiT+LtVt3it37TwsTKbNF2GkT3df+9K7o5KDI061ufaaKBbl0L5bvHkxLiAvTWwo1krB5fpUoW03IIutmhLjtHypC9geKa8v8xOYeP4n5Hj/mfn4Raz+9JG4eDBVhh8MKfEIP3hSBf10QJ2fuEn/1XyR+yn1++fhjIuiWtoLr1omEjbvFyhY/ivlNfxNbfgoRyZmF5Dc+Slz++A+xdNhBERKbn3JGkX71tgjzuyUC1x4Wm7rMz1v/4lcPios7bolQ/9siJsLETfqIGHHbP1QE77ggDg5flPfsT93+EkfWBIhgv1Bx+2q6MAohjLGxpmd3XhJHRi3J3eOPYn6HDeLQ6txnL6eY+DQnRcQcCxWh+wPExRnrxa+euc96/SY2fXtJBOwPFWHHYkW6wYT3jBt3RJh/sAjacExs6XZvzfPFsrePiys7b4nQI/eeFUK3fYdY4PGjmN96ifit23pxeH+y0BXBoUGkHjws1rf9Raz89KZIN1YGP2WLyJ823N9Lx7/EmUvFCIU2Q0RvOSq29flFLH7ZXwSHZ4joVTtN9PT4UfzSf4c4cyy1oDwLo8g4e07s6L1IrPupkKxXlY1955z4q3nu3nw2iHOhD4Y0XeBVsWfgL2K+x09i6dgTIuR2IeroM0TU2n3ij25/iEN/pxfQO4+Ox/L9/PVTYkPrH8V8j5/Eyi9CRabx3m8HiwODl4m9e7OEXMr+ck4dFn80/1HM914u9u9LL6Q3ZZF96YLwfWGzOHc5RpwYdk9/LBCrPzghrvjeEiGHc9dkzBaJZ8NE2KGb4vqS3WJ18/t6ZuV7x8WlbTdFiH+ouH36rsgqgRS6y2fEpo7z8/jx5MmMguvJShYhP28Vf4w6JWLS7opzIxaYeHConwiNyBQJa3eIlaPOiURDQV4MX9g/n92NQN1ZzL5VGXpZFtknDos/Wv6YT6+W/7NoSmAxsn5P7hLEpbeXiJ885ovf3r0qkosRIv3NS2J71/lifsuV4sCuVFEqh6cni6hjYSIs9zy4vuqE2PfSr/d50uNnsfLtw+LcxgBx8+9QEeofJm4fjxMZxircc2HI3CFGuyoEygZikl/FndnMHaOFqwKhbDBJlP16ijgwyaOIL2jT5Rtx6XEosHuQdlmsmtRTuJtJAiRh0eAZ8Z/lh8XtjBIlWkT6zRdvNLcVNVq9L3yjk8Xx/3UV9gqE2bDVIrNY1/QB7efiIHGjeM1ZIUAStkOWiqgKqd2H8Suqnx3+6OzeCsjAHy8ISwmhsHERru6dxIdbgovhAa24vWOq6GhnKTzGbS6RZhlXlorXPS2FJKmF+8AvhG9R4ojji8aIVu6txcTtkaXgLkdcmNVZ2EgISe0p3tkVl6u7DCJq29uiWYNRYtNdY6WKUfKZn8QLDc2FpKwpOn+wTlxLKfj92piTYvFoH2EtKYSNz1ixOqCop2eMvSwO7tkltv+5VHwxtH6erlDYtxWjZy0R67bsELt27xUngtMfoUJIFBtfcxYKEJLtELG0TAHLEGdm9xY1lZIAhajZe4Y4FF1QoWUG+4ovB7cQvWedFinX54ju1qa4XsvJO0VY3BWx6PkmovfcAGEQRpEccFTs3e0rNq2cLV5vapEbM5GEpecLYvqCNWLTjl1i915/ca0E+lVU1zxq31FSNxP/OXpXXJg3UDRqNlIsvlDYd9SL2BM/i1c9LEv1HUval9J9gPh48QaxbdceceBidHFCJZa+7iksJUmo3QeKL3xviYxCvx9zfJEY08pdtJ64XRRQeZl/iBcsJYHCRri4uotOH24RwcUoeO3tHWJqRzth6TFObI56eLnK9p0gmrg4CQc7K2GmUhSJm907OyW1pbBzrCfGbM4uwUiPFyuG174fg6uhKf67JJWwcsgX/3brJL44VcgPNYaLhf2tCryv7jxbVAeQhKiaUUfG0Ln08voPxx5qhoaEpDLDxs4BBwdHnOs2pX23nvTuN5B+nRti+5BJqYybu/l90Sp2HDrGmRsxZBoLDvNVWbvRoudzjJgwmQlDvShcY2848ylteszlpsIMMzM1arUGjUaFQjag0+nQ63RodTqEz2ecOTWD5irI2fYW9V7ZQJpag0aT+1ErkWQDWq0WvV6PTqtD6v0LYXsn4AygO80nbXvywy0FGo0GtUaDmUaDSiEw6nXodHp0Oi06vUSvn8LY864LipytjKr7Kn+mq03vqE3/qhQyBp0OnV6PXqdDp7dlxF+3WTnMHIwhfN/Lh8/OSmg0Zmg0Bd/R6nTotVr09m+wOXQZz5oXX2lxbvW3fP3Leg5cjr6PU0mJVb0ejJv1C9+ObFZ0gnpl/DaQc8ePlYuWseXAEU5fjSStUGWtpLajXotO9Bj4Mm+/9wbd3EvqIKfj9Cdt6fnDLRQaDRq1Bo2ZBo1KgTDq89FXj9TrJ8L2vIuLIoeto+ry6p/pqDUaNGq16V+VAtlgopM+l162I/4iduWwRyeA2r+Z2Ox5Vt/VYKZRo1apUCokkKR8GVPT4DMhGzDoTTwkDVxK0p8jKn89ulNMa9md72+5MN43mKWDSr9fpN07Aa/hG0jJlRG1suDahRAgjCYe1umwffVPgn4bVHw/QMNtdv5vAhO/P0C4FlTuzzJ7/UL+5aPn9LJ/895iC770W8vrdYoqlIzAbcz7chaLd1wk0dqLzp1a0NDZjPTwK5y6lEKT12Ywb+YoWtkVp4wyCd65kO/mLmTt6WzqtvTB3Uoy4V02mnCuzSYtKY7oyFjSdCL3Fo0Kt8Hz2bd5Ij65G8pa+xKuY3ZhMDPDTKPBzMwMlcKIPkeLVqdFmwP9f4tmx2jbYqvirv35A19+9xu7rmTj1r477Zo4oU6PJPDcSa6kONLhxYl89sUkBjUsrnwlgd8G1WeSn8DMzAyNRoOZmdqk57RadDodWq2g/bfX8Jtajzs/9MbnkzNIec+ameRYq0Wr06HVapH6LybKdyx2D8NTmcHsXPgdcxeu5XR2XVr6uGNCr0A2GtDrdWiz00iKiyYyNg1d7tEnqdwYPH8fmyf6mPhFd4ypLfqx4I4SMzMNGo0ZZho1CkkghEA26MjJziLHfhRbgxYzsASVkROyg7kzf2T1juMEZ9jQrN9gerSojeZuEOeOXSSn68f8Om8C7exAF7yB94e/w++XU5Exw73rBOat/J5XGmmK4v6ZOkzYpwV1V+YE/M3wgK95/z+rCavbk96t6mKji+aK304OXNfi3nU4kz//nHf7ltBrPeE3BtWfhJ8wu79XtUk/abU6dDotWtGeb6/5MbXeHX7o7cMnZ6T7z5qZzjetVmt6VivRf3EUvmPtKklNTKNl9++55TIe3+CllK4mDNyevoIdf2U+UDWVpNZg4WSFlasdzp0b0nhQE+o2MX/ga526kGAuLjjDlYPx5Fg6UKtVTWwdleii7xIboMVuSCd6TPHGqYZUoBLp6vjl+J8SKDUqFBoFSrUCSQhknRGj3ohRJ3CZ+iYvjbUhffkm1vwQh6RRolArUGmUSJJA6I0YdDJGnRGpax/GL/HBuHErK2ZGIjRKlGoFSo3SxNN6Iwa9jKwzIpp3ZdSatthEn2fzkOPESaZnFRolSqWEMBox6kzPGqW69Dk6jGY19ARO+o0D/nLumpUo1RKSbPp9o86Ikbr0PTaMpjag37mTpZ8m4fXTS3RxCuHol+eIpCa1WztgbaPAmJpO0uUIouKsaDimO91G165QVXLpxUY5xG48wqF5ASSkCTCvQe3BntRtaoNGn01aSBxRR8JJ0NrRaHRXuo1vQA1NboX+leucmneG66fSMKDBrlMD6rV1poY6m+RLYdw6mk6Nl/sx5HMPbB/H7EM5g6v/WsWho3rUXXsz6veW9+cuVFiXJhGy4hRnNoQQn6LBoZUrNetYosxM5+6lBLT1Pej4SWe8vc0LVFxlPTIeK1i1nnniDH7fXiTsph6L5vWoXUcm+XwCymEDePbDeliWum8jqX+fwm/OZcKjFDh0aUBtLxvM0JEeGE1ssgMtZ/ShpXcqJ19ez5mAQmVx6vr0O/E8zcyjOPLcFi5HSSg1SpRq0z4kKdeOMhox5hgw2HozdF9f6pVQtW4ID+HCgosEHIomJUuNY5cGuHtao0hOJv58AoY27ejzSQtcaoDxThD+kw9yLVAHKLFq25wes7vjUXiAcMZZfhoznm/33Ubl1Z1GSTdp8ftVfun1sNN1DAR/tJRdvg/SKkdBzYkjGDHZqWSdqksldMEBDi2PRvg0pf27LWnYqgbKxESi91/i9O+3SLFtQOfv+9OmjUWpt4LkayfYMOoiqapcuiglJKQC87tN7q9AGGSMehmjVI9+R4bgaVGFe86//RMf0bznD4S4TWBP0GL6m1foxOHER83p+UMIbhP2ELS4f9m39nNC2P7Vv/ly9RFCsh1oNug9vvv+A3q4KnnckH5jE99//jVLdl0mXgtq+yZ06tWN9i2b0tDVBmVOEpHBVzlz5CBHbqpoP/pL5n8zlrb2CpCTODZnNB+EjefYkueK4OHh7Oei1cOHJjVnwOJsRv51g1UvVNz2eTC/onra4Y/G7i0/ZK1/GddxAbzx50G+rrWdj977Dn/RnJ5dmlLHVo02OZwbpw5yNMqN56bOYc4HPXFRlEocts37klmLd3Ax0Rqvzp1o0dAZs/Rwrpy6REqT15gxbyajWtmVLuNyLAe/nciH8325lu5Mu4E9aWQM4tglM0at2Mqs/s6V30Ik9Rp//vAl3/22iyvZbrTv3o4mTmrSIwM5d/IKKY4deHHiZ3wxaRDFuXo5m0dSe8RmsnJjTBozM9QqBbmBAgzabLKydHhMO8GpL1vyqMyerEOTaD5gMdkj/+LGqhfKwZ85hOyYy8wfV7PjeDAZNs3oN7gHLWpruBt0jmMXc+j68a/Mm9AOO3QEb3if4e/8zuVUGczc6TphHiu/f4VGGh3Hprag34I7KPPkRoNaISGEQMgGdDnZZOXYM2prEIuLcQYfRNc8Ot+xHrNqrePisudxVGgJ3gFoqJQAACAASURBVPolkz9ZR2T9nvRoWRebnFhuXT7MgRPxOHYp3XcsvC+NmRnqQvpEMXg5GVveKE6oCNw2jy9nLWbHxUSsvTrTqUVDnM3SCb9yikspTXhtxjxmjmpFAZWXtZ6XXccR8MafHPy6Fts/eo/v/AXNe3ahaR1b1Npkwm+c4uDRKNyem8qcOR/Q0+XhpSpn8xvUeWMrOnNLLCw0qArEznLjAkaDKf6klRi4NJI/R1gWd62OxUOaMPW4EjONCrVajUqpKGKXIARCyBiNBvQ6PTq9G//ac5G53TSFzLyfGDP+W/bdVuHVvRFJN1sQfPOXx352V1li4UkDXUoUEVGxxN9NIUs2o4ZLXZo0roud5iluHji2nRJNREQUMYlZqBwb0rxZndy+g1UDhtQYwqPjSEhIJkuywsHJCdfadXCxVj0lTpWDgbOftqLrt4G4v7ePgIV9Ma/S35dJv3mQDWs2suf4JQLvJJCldKBhx+eZMuNjhjUu3QE3pgZz0v80AeExpMg1cK3rRcc+3fAoKbspR7F9ylDe+jUI15dns3Lhe3RyVJYS/4rjut9WVv38PYv2hZKNFV1nn+HwtKZUnstnJCX4BIdPBxEem4zBsiZu9ZrRpWcH6lg9WdwkR21nytC3+DXIlZdnr2The50oGb0yOXHX8du6ip+/X8S+0Gyw6srsM4eZ1vQRONQ50Zz3O8yFW1Ekac1waeCFT4cutKtbCMlyJtHXLxMq16NNS3eKv5FcOLFwmP80UoIxicAjf3P0yh1SZGuc3Orj060n7dyf8AFAhrN82qor3wa6896+ABb2NX/itiCnpxBzOpbkmEy0sgZLN3tcO7ljbyPx/xHkyHCCI+1p3OleC0o96VfCibicTGaWhNrWApvGrri3cMD8EdlbIvUuodsCCD0ZQ8KdTHKyBEobC2waOFGrayM8B9fHoYZU7LmVfj6YmwfCCL+QSFqiDsnBjpqeLtQe7EPTLrY8TmtCe+Io22ZE4PLJi/TqUwmyYtSScjGS2NB0MtMEmpo2OLSsg1sDMx4/9+pJPRvKnatp6JQWOHRsSH0vy/IHZ2QdaVejiAlKISPZANZW2HrUom5bezQKQM4iasdNEg1mWNhZYOFggYWjpeljraz8/WsziDsVaeJHnRLL2vY4tnDDxa1Qf2BZT8atBFJFDZy9rCmze7DxBrO6vULy3Av80PVJcGAEOcG3ubnjJqFn75IWr0VYWGBVpyZ1BnjjPbA2Nf6Rc+0MXPqiHR2+ukbtifu5saBPxWxiwyW+aNeBr67VZuL+GyzoY/7PwEpSIP47NrPj0HmCI2OJjY0jSavBrqYzrvWa0aXfIIY825+2bo9vv3LMYVbu1NN3bL8S5h+UU91W1K+ozlCpdm8FcHj7IJvCPHipd53csziLiDN/c+h0INGZSqztnajTtCM9Onqa5mGVnzgEn/TndEA4MSkyNVzr4tWxD908bCuWEMgM48jOfZy7nYbSyZteQwfR0ukRWw3GFIJPHOZ0UDixyQYsa7pRr1kXenaowxPh6skxHF65E33fsfSroIDlRJ/H7/AFbkUloTVzoYGXDx26tKMoG0Zz/XIocr02tKwuflNVyJAxicAjBzhyOZwUYY1zlfqORlKDT+J/OoDwmBTkGq7U9epIn24exReKG29zcFMYHi/1zmtTlRVxhr8PnSYwOhOltT1OdZrSsUdHPB3+fwVrjTdm0e2VZE5e++Gxr+VpYuEpPIWn8HgUYdD39Gr9X046/esBqrOeqJ1ye+kw2r2zG32PuZw+8G+8ymtHyvHsmdiDYUuCEF7TOHbpOzo+TW4WMTaWDmvHO7v19Jh7mgP/9qL86N3DxB7DWBIk8Jp2jEvfdaR6o7eExMI/l7gEfd+L1v89idO/ylmF+RSewlN4Ck/BBNp9TPD+mkYH/Jj2jz4rnnDQnebj1t2Yc8uH6afPMrO1qoKvf0zrbnO45TOd02dn0vppvdRTeApP4Sk8hafw/8DMm4D3140IPTLtsa/l6Xjap/AUnsJjAWWT0Uwa5oKI8mXNnuR/7kaNd9i0+gBJwpIeo94qf1IBQOHMgClv0kYFxtCrXM14yjdF0buJ1QeSEJY9GPWWFxVD7wCmvNkGFUZCr17lKXqrnZagyehJDHMRRPmu4Z+sJp7CU3gKT6HSz8fQ81xVtaBd7adJheoM/8feeUdFdW1//HvvVHpREUURBSmCKKCoiIi9PrHG9kti7DHWFGNJrIkmJsZuYowaewV7iQZRUEQx9oqIqChFeh+m7N8fMwrGGRh0EDTns9Zbb73ncO+5e++zzy7n3JsfuRXBMSpYdxuPkU3K2xXIR+TWYMSorNFt/Eg0YU0FBoPBYDD+C1Ee4v65DqFnsyoxGtZYYDAYleR9aqD/3OkINE1CyNINiFG+p89J6cjIIhBEMDYt/35rzsQERhwAKoJMpmJ284p4M5BFBIiMUX7xcjAxUb+rmYpkYOKtim6iP+ZOD4RpUgiWboiBkomEwWAw9Eo47+07jOSAbvCVMGlUWVRPsOOXLYiTtMAX330Me768f74Dv2yJg6TFF/juY3uW2DMYDAaD8Z8I8+5h3+FkBHTzrRo5O9MIg8GoLATOY7BosjdUkaux9GTO+/mQwkboFOgAIXJwZv9RPCtX8VqJB3sP4JKcg7hJIAJqMJf9qng7IdBBCOScwf6jz1A+8T7A3gOXIOfEaBIYgHdBvMUvLyT8N95jKIDzmEWY7K1C5OqleF/dBIPBYBiUrL+weH0O+o/oCFMmjSqL7MIK/HIsF86jFmJi4/IeN5DhwopfcCzXGaMWTkRjdlqBwWAwGIz/SJi3GOtz+mNEx6oR5bEqFYPBqEQk8PlyEUY7PcaGrxYgMv99fEZjBMxYjJFuYiTunIiPl1xApl7VbyWeHpuGIbNOIdeiJaYtmcCSRq3iDcCMxSPhJk7EzokfY8mFTP2aC8qnODZtCGadyoVFy2lYMqExqrx4lc/wLEPzdJSBtGf/kf37Eh98uWg0nB5vwFcLIpHPrJ7BYDBKIQ/nF89FWLMZmNKCHVeoshRdw5KvViOm1lAsmNGm3B9TLbq2BF+tjkGtoQswo40JkyeDwWAwGP+JMO88Fs8NQ7MZU1BVwjzBnDlz5jDNMBiMSkNcH218gdCVv+CwsjM+DKyN961+zpm6oGvvZhDc+Qtbfl2N7RcyYGznhIb2VpBw//61Auk3j2DNtyMxbNpuPK0/CD/u3Iyv/KzAMWvRJl2YunRF72YC3PlrC35dvR0XMoxh59QQ9laSV2WmSMfNI2vw7chhmLb7KeoP+hE7N38FP6sqKl1VIs5s3YT9J8Nw+M9lWHvsAXJUACgD9248QmZqHG5evYP8mp6ob8m/x26iDXwRipW/HIay84cIrM26bAwGg6Fl0UDywQnot9IWP23/Gj5mLHKomihwc8kgDN9phvG7t2FCI6Ny/vlNLBk0HDvNxmP3tgko758zGAwGg8F4F8O8ZByc0A8rbX/C9q99UFXCPI6IiGmHwWBULko82vER2o6+hqDg0/ilk/V7epyqEA9PbcTq33fiwIkoPBbaw6OxG+rZWMJEWITc9ETE3bqGW0kSuPh3QdDQMRg7sAVsxcxC9BPvQ5zauBq/7zyAE1GPIbT3QGO3erCxNIGwKBfpiXG4de0WkiQu8O8ShKFjxmJgC1tUafEWRWN+16HY/EQIiVQKiUQCqUQIKGQolBVBVlgImdwCfX4Px4/t3vOdqcpH2PFRW4y+FoTg07+gkzU7dMlgMBgv+8l4bJgwDwWjVmCcF9vFXkWrAsg4NQOdgjbC9scwhIx1LV8cosrAqRmdELTRFj+GhWCsKwsSGQwGg8H4b4R5GzBhXgFGrRiHqhTmscYCg8GoIuTi4uK+CPpZhSmH9uNLn/c8IVYV4lnsdVyPeYxnmTkoUIlgbGaFWo5u8HB1gBXLE99QvM8Qe/06Yh4/Q2ZOAVQiY5hZ1YKjmwdcHazAxPuuuomLWNw3CD+rpuDQ/i/hw+pmDAaDwXiHKLr9G/p1/BYZI4NxYHYAytcjL8Lt3/qh47cZGBl8ALMDrNl7jRkMBoPBYFQqrLHAYDCqEHm4sW4chv4ixvdRa9HTjEmEwWD8203cwLpxQ/GL+HtEre0J5iYYDAaD8U5QdAZf+43B/Y/+xLqJzWFR7j//Gn5j7uOjP9dhYnMLJk8Gg8FgMBiVDmssMBiMKocsKxNKC0sYM1EwGAztXgJZmUpYWDIvwWAwGIx3hSJkZSlgYfGaa1dRFrIUFrBgSx+DwWAwGIwqAmssMBgMBoPBYDAYDAaDwWAwGAwGg8HQG/ZaRgaDwWAwGAwGg8FgMBgMBoPBYDAYesMaCwwGg8FgMBgMBoPBYDAYDAaDwWAw9IY1FhgMBoPBYDAYDAaDwWAwGAwGg8Fg6A1rLDAYDAaDwWAwGAwGg8FgMBgMBoPB0BvWWGAwGAwGg8FgMBgMBoPBYDAYDAaDoTesscBgMBgMBoPBYDAYDAaDwah4sm7gWFgMipgkKgklHkYcxsVnKiYKBoPxxrDGAoPBYDAYDAaDwWAwGAwGo0KR3d2C4f498dOlfLy7ZW0VVO90TZ5QGLMOg/0/wOorucwoGQzGG8ERETExMBiMV8IlWToSU4EadtYQM3Ew3tygkPksBTKTuqhpysTBYFQcCuSlP0MWWaF2NSkTB9N7JQwlD+nPskBWtcFM8D20tLx0PMsiWNWuBqbeKqMUpD/LAqxrw1rCxMHMIQ0pWRyq1bZGVTOHorsbMLTrFNzttQOhS7qixju5zTUdez9pisHHWmLd5R0YalvBD6FIw62IMETfeYSEp4lITpdBbGED+0Y+COjQHk1radFy4Q1sW34NTT4fAnehrgvn4eKPPdF9mQQzjoRgclPj9z9OSktBFlcNtZmjZLDY1qCwxgKDwQAKz2PZqG+x63YSUtPSkZ6RhsxsGZQmQdiYsBcfWlTSuFT5SLrzD85fuIirdxOQkpaOzMws5MkUUKhUIHAQCEQQCoUQioQQCcWQmlqimk0N2NZ1RuOm3vD2dIQ164y89cDt5poRGL/hBp6lpyE9PR3pmbkoUhmjz5YUBA8xZiJiMAyE7PR89P3mIJ6kpSMtPQ0Z6dnIl/NwnBSKm0sDWGOY6b2iR4LT8/vim4NPkJaehvS0DKRn50POO2JS6E0sDWAW+I4bGub3/QYHn6QhPS0daRnpyM6Xg3echNCbS8HUWylKQdic3vj2SKJmzqUjI7sAct4NX5+5jB9aMKX8p8g6jOkDFuDvZI3/zchAdoEcgqazEH1hLpoIq9BYM0/h6/a9sLnuYoTtHgWXl0y1CBeXj8Tcv9Je4xQDB4HUEjXt6qCOvTN8u/VGJ3drVNSjq5LXo7fzSBzMMUKH5bdxfLx9BbwGRIlnF7Zg+arNCDkagdvPigCRBeq4NELDOpbg857h6YMYxKRI0LjXp5g57yv0c3u+cysP52a2QYclDvg9NQT/Z1zqw+Dg2AAMOd0WW0//hl62/Hs0NaZjwIK/kaxZuzIyslEgF6DprGhcmNsEQuY9GCy2NRhsPr23qJB5/SC2HrwOuWsQPunbGBZMKAydsQsH45pO8JDY4tmtUzjysBAKAkSu3vA2rQTbvX0M2zb8iU3bDiH6aQFUr93+5CCydETroE8wefokBLmYVMiIC2MPYMWqg4gVe2HI5DFoW0vwHzcoDlLrenBupITVg4s49SAXMiUBnMEFjwMrVuFgrBheQyZjTNtaELDZzOz5vzbbTGrC0aURzJJjcP7xA+TLVSD2pkum97fo701qOsKlkRmSY87j8YN8yFWk38tWmQ9/FwwNNR1d0MgsGTHnH+NBvhz6qpdRgXPO1hGujSyQdOccHsflQ07QQymFiD2wAqsOxkLsNQSTx7QFW97fAwTmqOXkhkaWT3Ez8hQe5MtBQNXzpcp4bB79IZYkdcH6gyP+1VQAAB5G1exQt64URYU5ePbgEiIi7yFDQS/+3cq9M7q1tIeZiH8ppVAUZCA16RHOB+/F+sR80NfjUcurK4ZOmo0ZQ5vA0qAOS4XkA8E4lUMAFSAyZC8ejZsEBwPeQ/bwBFZ8Ox0/7riEVIUItX0HYObCERj2QVs4mpW8UT6eRO3H7z/Nx8iWe7Dv5234dVQTcJHfY9wvV1BA9igzheZr4n8//YoRrXpgxIeNcPLQZDSWvC9Toxac3BrB8ulNRJ56gHx5lZwZjKq6zr5ubPtfhRjvIUpK3j+WXI05AkDgbeiD7SmkZIJh6IH82mzyFoEAATWYEEaFb/HehQ+O0oJBTchayBEvrUYNvNpQ1z6D6eM+PlSNh8aea1CLIZ/R+PHj6bNxY2n0yOH04aB+9L9O/uTjYkcWIo44aH5b4j+8hTdNOZxk+HmQcYRG1Rdq7smRcbO5dFnO7KiYXDoxrgEJAAJnQn235hlK8HRkVH0Scmr9csbNaC4TPLPn//rav6kPWfMgQEhOk06TjAmF6f2tDiWZNvWxJh4gCJ1o0mkZ8+Hvk6Ulb6I+1jwBIKHTJDrNHEwVUMpTWtvDXL1mCxvR11GyUpb3UVRfqMkNOWNqNvcysRn3npnDwxXUQZP/i7xm0ZUqo2AF3VnWgSyFttTnz4d65mJ5dHqSMwmf53KixjQzuowHUmTSvbD1NLWLA0k5EDgp2XecSsH3DJjNKhPo1y6mL3JNThpAi+8rDLeeh82lDrWExIEj3sqbhq+OotQyBVZIsTvHUBOLGtRm2i80zlOqHp8kiDbpmXZlnviMGorMqM2im++hX1DSwxUdyJgDASLymnWF+T5GBcW2/11Yz+V9RHEdq+asw918TY9alYH7MclQMckw9CDr4iXEKADwFmjRptlbejdnPu7smIi2zXth4eUGGLc+HHGpKbh/KRxHQ7ZhzRgfGD3fmiL0xIc/L8eKFSuwctWvWLN2HTZt34MDxyNw8U4C0jMe4sL+VZjarwmqCYv3s6iyLmH52Bk4nGnYkRfdDkPEY4VmRwgh/9ophCez2VaMEVxc6hl+f0jRbYRFPMbzjUyUfw2nwpmfK0nGxv6oM3ALct6CPb/OvRiGhoe1qyvbgVqJ8+c/r3feGq6uep46YD78nfOdvLUrXJmDqWJKqQaXhjX1mHNFuB0WgcfFEw7XToWDhavvmTnUdEXD6lWvvKOMX48v54VB5jMB84bq+9ogKeztbUv81hwWVmUcfRZYwCnwE/x47ArOrx+OxsYyPPr7Jwxq9wFWGOgDxaqEfQg+k/fiJADJzmNv8AMo3/jKMtzdNAwBPeYgNFEBrlobzDkUinWftkC1MgUmgeMHq3Fi9xBkLvsCq68VoryH/S3aT8XULkJELpqOjQ/fN8fAo6ZrQ1RnlU9GRce2/+kslPEewoHjS86FQAwb6Mbee8XQg3xEhkcjnwBO4oM2/m/hXfiqdITP7oLWQ1fhttM0HDm3B/M/9Ec9k2IjLkxORrrqeQ7lAAcr3a6LN6mLZr3G4cc9/+Dagc/R3Lz4t8onR7H/bKFBhy92a4vWdYSaI7kcpO7+8KvJXGvJZUYqFRv8LUgQu6Ft6zp43jvipO7w96vJFrVia0dK4hNk58vLVah7PXt+vXsxKgCpGOxzdJU3f5jeAalYz5EwH/4O+k4pxMzBVLkYSyLV513PYri1bY06xRMO7v5+YOHq+1YCkEAq4arYoHLw9w8LcTTTFv2njoVHOQoSPP+6BmoBz2G/4+CGj+EsAeQJBzGla18sufKmOaAKj/YF45yxIxyfv1+JinBh7x7cU77ZdRP3T0TvMVtwN5/Amfjgyz17MdPPsly+oEanH7FlTgAsXkdsvD3+b/oINMw8jLmzDyBN9b5NDSmq3NRgvEPpFQt+yvZAjPcPoQcmLv8Zw7u1Q8f+n2Pt8V0Y58J6bAw9KLqK8HOpUAEQNvJHmxoV7SKUuL/+Ewz8/gzSeScMXzQD/q80DZR48uip+v2xAAR16qO+SJ9rC1C72wKs+8Ib4ueBBGUhPUNp2Eew7IFlR3dg/rhhGD5lCQ7un4VmrIv3FrBEj2VHsWP+OAwbPgVLDu7HLCb4EshxLyYeirdiz695LwbjfZs/DObDmZ0xquqM67EMR3fMx7hhwzFlyUHsn9WMbTpjVDjKmLVYuCUenMuHGN/T+i3eWYB6A35DyIJAWPIEZfLfmD50GkLf5OS6Mh57Qy7AKmghvutloymkEeT/7EXw3dfPL5Vxf+LTsetwp5AATgrvL37D3EDr1yjUSeA58Qd86v56G7qkLYbjIx8hErbPwdJLRcx4GQyG3rB44r2Eh7XfRPxxZCITBaOc8VI4zsYpAQhQp1UAGlawh1ClBOPbWYeQpORh2X4cRvtJtfxKgUcPn2p283GQ1q0Pe737ZGK4desE5+8u4oYcAF8NNW0N/1Amrv0wc1U/ZkBvGxNX9Ju5CkzyWii6ioioVKgc34I9v8G9GIz3bf4wmA9ndsaoohMOrv1mgoWrjLfo5BC97g9E5gvhOWAIvMVv+/4SuI//CRM2++G7K0WQ3V6NCfP6IPqXtjB5nTz5QTBCoquhx/Tu6Ck/ilpb1+OJEiD5Zezdcxtfz/Iof3FN9QTbps7AwSR1Y0JQeyC+mdwM0td9ZKkvJn/RHb8N34dy91AELhj6YRt8fyEMG379C1+u+x8smBEzGAw9YCcWGAzGC9LPnMF1Bam/rxDgU8GvVlDh2cFNOJRhi1YjV+NE8AS4aWsYqNJx/8EzzbsrBajb0Anliksl4he/582aw9+bHWVjvP/ILuzCvnuK9+5eDAazaQazMwaDwXgHyD+J9dtjoBA2Rd8P3CtnR6vYC6NGB8KEA0By3Fk7B7/HvM7pAiXu7dmLf2x6YkBbY5i274/uz785Qwpc27cbN1/DnReeXYzv9qdoNtAJ0XDwGHS3epMH5lGz1yB0tn6t9yGhTs+e8JWokLhvIw6nMxNmMBj6eg8Gg8FQR384V/L7Cq1NKvh+PCz/txxXEx4gcu0YNLPU4Y4Usbj3/CNSnCk8vNzK1VgouHsX8UpNoNVjMLpZM00z3nOUcdiwYCNiFe/ZvRgMMJtmMDtjMBiMd4G8sD048lQJgVM7dHSqrNcy87Dr1QstNC/Yp9wzWLMmCrJy++y7CN57GbX+NwD+UgDG7fDB/+poPuhKkN/Yjz3Xy+vQ07F3xSbEPv+ourAhevXzwRsf7LDqjB7+Jq/1OiS+Tid0aCyEKjMUew49Y0bMYDD09LSVHVfnPcOj2DuIiU9Glox99pHBqDSKriD8XNqL7ysE2FS8e5DYNED9aqWHT6qMW7idoNlZInSFV9PyfFA6E6GHwpGlAjiTVpg0vTeqTF9BmY+MpHjE3LqF2Kc5L71jufBZMjIqotigzMOzR7G4ExOP5CwZ+9Au3kMZq9Jwau5HmPFXWsXfuyLupcxDSnwMYh+nIV/5H9HZf3vhQVZiHGLikpBjwNf5FmUlIi4mDknlvej7YtMMfRSDvJR4xMQ+RtqbKqYoC0lxsYhPyYOS2VnVzg/Lq6u37Q+T4hAbn4K8KqrDoqwkxMXGI6UKDVCREYOzh3Zg8/YjuPZMWdGGyeIFvZHhwrGTSFIKYOPXFk3ElTcSvqYf/Fyen5dQIDZkG86Ws7OguL0He6/VRa8BrTQn+qVo80Ev1HveL5Hfwr7dl8v1zRxVygHsPJ7+wo746i3RpqkhBGUOH2+X1zshInBCW//6EKqyEfHXaeQwQ36tdSYxLgZxSTl4/dBWifyMJMTH3MKt2KfIeblQgORyFAqUeSmIj4nF47R8KJmM3714szJqRq9B5XxjQZGEs+t+wuL1exH1VISatSygTLmHmBQJHFt2xoAxX2Bi/yawruDGdlHcfsz78nvs/ucZ+Lot8MEXczG9j4v6nXaqZ4jevBRLNx7BhdgUFHBmqOPZBt0Gj8PEQV6w0lpzzcOD8BBs37UPf5+/i0dJmSgytoV7m/4YP2MyejQo4215qgzc2LcGKzcdQ/TteKRxNeHZdTS+nfUJmlvzUOZlIl9kCbNX1hsFEk+vxOyFW3H6TjokDQIxbNZ3mBRYC+USoSoTt49uwbpth3H+dhwepshgVNMRzToOxNhJw9GmtrA0YWL/vC/x/e5LSBPbw7ffl/huWi84Pn/kokSc2/Yr1u4+jvN3niBLaQpbpxboPW4qJvRt9Hbe3/ea8jWsnSiRfi0Eq1dsxol/7uBBGo86zfti8rxv8EEjY0CRg4xCI1iZCnUmHXH75+HL73fjYjJQt+VAfDF/Jvo6a17vUxiP0LWLsXJXOG48kcG6oRda9xqLL8e0Re0yZrsyPhxnHygACFCnZRs0FObjwYn1WPFHCE5djkVKvhAWtV3g98F4fP1pDziZvB13Ib95DXc1X24W2DRGUwf9rVpxZx2WhiRCyVuj/fzfMNndcC5P9fQvLFt6CLdT0pCWloq01HSkpWeixoc7cfwbHx3ONRd3DqzC4tV7cD6RR/W6dWBjLkJRRgIePAVcek/CuCbRmDp6D3x23caq9oZ4bZMCSWfX4afF67E36ilENWvBQpmCezEpkDi2ROcBY/DFxP5oYv12dxLlPwjFxjUbsf9kNGIScyCXWqFWvUZo3bUvBg/tB99aQl2Cx1/LluLQ7RSkpaUhNS0V6WnpyKzxIXYe/wY+Jf9M+QhHlqzA0XupSEtLQ3p6mvq/C/3x09U/0Fcqw+OILfj9z70IvXAbj9JkEFvawt61OToGDcFHA9vAXvp2ZKx8dARLVhzB3Rf2lIbU9Gw4TTyBkPH1ISi8j4NLFmDlnnDcSeNRw6UtPpw5F+MD1H4+7/FlnIs8hf1rl2P9yXjkazY/qWIPYdGch3j5e+cC2AaOwuhA2xe7C8pjz296L21FtugtS7Hiz104cjED1s6OsFYk415cPuz8emLQp1MxOcgZxga1QEPMi3xEb/gOWy8ka2xRY1+5zTD/8iYMNtP45D9W4PeQk7gUm4xcPerHmQAAIABJREFUhQjmtRvCKyAIIyeOQkcHabnW6FvHdmBHyBGcuhyLJynZIDNbOHgEIOij0Rje0xVGBo+F1XPoyN3i+ZaWmo5sp4k4ETIe9QWFuH9wCRas3IPwO2nga7ig7YczMXd8AGoJdK/FV3evwqpNIThyLgGi2vaoIclFYnwKBE6B6DVwBCaO7QGncilchYyru7Fq1SaEHDmHBFFt2NeQIDcxHikCJwT2GogRE8eih46LVmmbrgy96xzKLRzbsQMhR07hcuwTpGQTzGwd4BEQhI9GD0dPVyN9Fs9y+3DD2Z8KadFbsHTFn9h15CIyrJ3haK1A8r045Nv5oeegTzF1chCc9VFM7m3sW70CG3YdQOiVRCjMa8HWuAhZ+ULU8myHvsMnY+LQ5rARvCu+U4XMW8ewY0cIjpy6jNgnKcgmM9g6eCAg6COMHt4TrhVtaBWVH5ZbV0rEHf0NIXeUMDIyglQshIBTQV4kg6xQhkKZDIpqrTHikzaoUUI5ilvBWHrgPjipkebvOKjkRcjPKYJDz8/Qy0WoNS68vW81VmzYhQOhV5CoMEctW2MUZeVDWMsT7foOx+SJQ9Hc5nVjNBWe/rUMSw/dRkpamia2SEN6Zg18uPM4vvER6iG+fVi9YgN2HQjFlUQFzGvZwrgoC/nCWvBs1xfDJ0/E0OY2MGwUqUTa5d1Ys3Y3jp+7gfiUbMilNdDAszV6DB6D0f2bwjL7ItbNmIoloTmo36wRlBf3YPTsodh57jf0skwwoO8wQLzw1uJR7RQ+DMOGlWsRfPIfxCZlo0hojtoNvRAQNBITR3WEg7Qi5vNthEUkQMmJ0Ni3GSqxrwAIG8CpvhjcVTkIgDLhJI5dKkL7VvqOSoGbu/fhpn0vLG9RnJtJ/AagV4NfsfSeAoAcd/btwT9zmqOFnpctOBuKyBwqHqabF7wMIigBnFr7oUHtfJT/jUhieHg1ghEXg4zIU4iW9cebpqPKmANYHnIHKokUUiMjSMUCkFwGmUztT4tEjTBgwv9Q/cJG/HY6BWIjtQ+VCAlyWaHG78pRq92n+LCFumqkfHAUv+68hkKJEYyMpJBKRICiCIV5uZA0GYzh7WrrsYO6EA/DNmDl2mCc/CcWSdlFEJrXRkOvAASNnIhRHR30/taFKuMqdq9ahU0hR3AuQYTa9jUgyU1EfIoAToG9MHDERIzt4VTmepx75wBWLV6NPecTwVevizo25hAVZSBBXSjApHFNED11NPb47MLtVe11vrZalRaNLUtX4M9dR3AxwxrOjtZQJN9DXL4d/HoOwqdTJyPI+c0zq/zoDfhu6wUka9aXtLQ0pKXnotn8y9ikToQQH/oHVvwegpOXYpGcq4CokmVcIbGtwePNyqgZvSH0llGmnqa57eqSY+cvacPZBCoo/hdKi15DI7ytSMCJyK7jHApNUlbcQDL/psnultR4zE66k3qXVna1JF7kSMP2PCFF9gX65X/1qGaTofTDvsuUkKMgRcZ1+nO4G5nw5tTqu4ske/mpKP3ieprYvh4ZiaqTz/99RzvOxVOOvJBSbx+mhT3tSWLbg367I9c5nMLYPTS5VQ0SiWpRu682U+SDTMpNjKK1H7tTrVazKfTYN+RrKSCR/Qg6kF3yL+V0f/NgaujQheYeukPp6RfoGx8Rcaat6Purcn21QqlRq2hYUysSmjhS1y/+oLDbyZSbk0CX9i2g3vXFJKzmR9OOJ5J2jWTS35PdybLxGNp5J5XuruxKlryIGo46QMlKorybm2iUT21y6DCRVh25Rk9z5SR7eprmtrMmnjcn31mRlFvBdvfa8jWknRTcpZ3jfamaUEx1u35LO88/pPSk63Rgbheys+tDf0QfoM8aGRMvbURTI2Q6zHYyuVs2pjE771Dq3ZXU1ZInUf2PaPdTBeXd3kKjvWqSU685tOdSIuUVJlHkLz2ptkBE9n1+p1uy0m0g+Y8eZMKBwFvTwI1RtGGEF9k596Cpf4TSrZQCUhQ8omNTfcmM46lau8V0VfZWPAY9XBJIYoAAjqSdf6VEfd1C4TX6uZ0l8ZwJeYzdRwkGdifyG8upn48HOdtZkIgDASBASI2+jiKtolE+ouBPm5CVXSeafTD2FZtX5tyl4Kn+VEPAEQR2NPJIoSEcLp2e247qOnamLzecpYSCEv+UFk1rRniTlYAjkV1HmhOaRBXlcVPWdCEJQOBMqO+WJ3RuSX9yq+tNQ+dtppO3U0lGMsp8dIn2Lx5JrWoKSWDpSYN+Pk3JSq2Cp+X9fMjD2Y4sRJxG7iBho68p6t+Cl1+hxb19qLFLPapmxBOn+S1n0pe2Jl2i3/7Pg2zrBdAn8zbQwdOX6ObNaDqxaR4N8a5GAo4nM5c+NP/4E1K8BRnLL/9MQd4e5FzHksTc8+cSkd+P90iecYbmBTpTy1Er6fjNq7RhcD0ScyDOtBUtuConIhmd+747NffxIR8fH/JpXIdMefWz8pYNyPv5///iPy1o8K93Xnou/e35ze/10nPH76Uv/G1JxFtQk+G/UmRi8bqVc3s3fRVgQwLejNyHb6VYmcECEQPNiwwK/syXPN3qk42J4IV9QdKT1mcSKZ7sp0mt3Clw7M+0/cRlepQto7yEKNow1ocseBBv6UvTQ9P0mHdKSju3mka0tCWRsDp5D5lDW8Nv0ZNsOclzEujygeX0WTtncu+7hM6cmUXeIrXunCadpjcWmfwy/RzkTR7OdchSzL14RpHfj3RPnkFn5gWSc8tRtPL4Tbq6YTDVE3MEzpRaLbhK2iIQWexumhJgRxKJPXWduYMuJpUYYcEjilg7nvxqiMjE8X80X1+fJIul3VMCyE4iIfuuM2nHxaQSz11AjyLW0ni/GiQycaT/zQ+lV0PLqmrTlaj3V4aSRudWj6CWtiISVvemIXO2UvitJ5Qtl1NOwmU6sPwzaufsTn2XnKEzs7xJBBCETjTptOwNfbgB7U8eT3u/8CdbEU8WTYbTr5GJxb/JuU27vwogGwFPZu7DaWupilFS4vE51K2+MXGC6uQ7agUdu5n8Ip9RZN6jsI2zaIC7Ndl1XkhnM6q+71SmnaPVI1qSrUhI1b2H0Jyt4XTrSTbJ5TmUcPkALf+sHTm796UlZ87QLG+RWl9Ok+i0AQ2tYvLDN9FVN/Jp7Ey1zUXFvh0gcDwZ13CkJkN+p/v/Uk7h0Ynkavnv34vI0sGPZoa9GtMpE4/TnG71yZgTUHXfUbTi2E1KLh4c3QvbSLMGuJO1XWdaWDy4f1sCnZ/mTkKAIGxEX786iejG8n7k4+FMdhYlxqb1t68MkI7P6Ub1jTkSVPelUSuO0c3iAVLmvTDaOGsAuVvbUeeFZynDUMYgu0/Bk1tRDSFP5o2H0E/7LlJ8Sio9vHqUVo5sRla8mOz8B1BPn6bUb1EYJRQSUd5uGmzJE3hLGrynwHC+w1DxwtuIR4mIZOE0uaFQ/axes+iKXEb3to+hFu7taOzP2+nE5UeULcujhKgNNNbHgnjwZOk7nULTDJ8BKJN/p25GHEHYkCaHv46zKJkDgiBqTYtiFa9rVHR6oqN6ngAETkztlj3SP++RX6SZnlJym3rulZg48kuX4usKnemLs/o+q5yiZ3io10uAAJ5qfnKghO+rPORXZpGXCASRH/14T/HG1ys8PYvaN3UjB2sJcVwJ/wiOJNYO5N5+DkXIlJSwaTg182hItcyEL/lRTmROtZ29acTW4lqULGoutallQoJ/XU9s6UDdf7nxSgwgC59MDYXq3Mpr1hWSy+7R9jEtyL3dWPp5+wm6/CibZHkJFLVhLPlYqH2J7/RQKntqyCh29xQKsJOQxL4rzdxxkV4ObSNo7Xg/qiEyIcf/zS9l/VLSo+BPqYmVHXWafZBiXy0U0N3gqeRfQ0AcBGQ38ggV6rCr+L1fkL+tiHiLJjT810gqDg9y6PburyjARkC8mTsN3xr7xvFiRvBn5OvpRvVtSupCQj3ViRDtn9SK3AOriowrMLY1ZLxZGTUjA/B2GwvKJNo9tA4ZNRxEq6KStCadlBFGnzeWEAeOjJt+RacyK8Rd0o3vW5CJyxQKz1P/72uzvUkEjkRNR9H03nXItsMPFFXi3hmHR1MDTQIkajafbr7wsbl0Y8MwcjfliDNpTKN2xr4yyZUp2+kDGyHZjTxCeVpGkx21kNrZCIkT1aNBm++/LJfCaPqmqSlZWpoQDxBn2Y+2lhiX/ObPFFjDk748nf2i0LG+p5Q4iKjxzItUdmshl66s6kMOEo6ENdvT/PDUVxbZvMivqbGII966Ey2/LdeSI35PLUxcaIpamCS/Npu8RSBO7EuzQ5ZTT7ua1HZW2CtFwtx9H5END+LMutPaxIprIr2+fA1oJ5lnaX7b6iTgRNTwkz306KVBZFPoBFeyqVlDXdDT5SDkN+j7FibkMiVcbUfyazTHW0TgROQzcjr1qmdH7WaHvlx4V9ynxQES4jgzarPoRin2kEv7Pq5JPEAQ1KGmTWuRXddFdC79X3pJ20i9zTkCX50G7cp8C04jl4KHVlePCyLymnWV9GqX5VylNQMakFhgRc2nHKQniood46U5LcmIK62xoKD4Nd3JSliPRhwqJfVSptDBEQ1IaJBFQklJu4dSHaOGNGhVFCVpd7gU9nljknAgzrgpfVUxDrdEY0FMbi2bkV3Tz2h/gnZNFt7dTEOdRMRxxuT28Ra6V8ranXtpDrU04nQXpUpq4OlOGlpHoA5Qxe4U0MqOGvZbQdHaHjnvJv3Rvx6JOBAndaKB627pCN4qQsYyuv9Hb6rJaxoLP5ynkBGe1GZOFGUTEWVupCDj58U4MQUuffTqs976nnxF6uBO0nM9ZRrcng1zL3nsJhrsKCGOk5LHhGOUotQW8x2nz1xFxHFScpt4wgCFiwqaFxlnaKqXWJ0ISXrSuoehNLVtIH1+WEshQH6TFraSEgeOhI7j6O/c0u3h7pZP1DGGkSsN23pHe9KpSKSjU3zI3sWFagoqpsAsu/8H9a7Ja4ozP9D5kBHk2WYORakNkzYGGb9IBMWBS+nRv/SZd3k59awjIk7iSmMOJOoskBTeWUu9awuIM3alYdvvl15IybtMy3vWIREnIdcxByhR90Vpbe/aJOCMyXXY9lcKglXPpquO3kl2l7Z84k6mHEdGrsNo6x3tZQ9F4lGa4mNPLi41SaBn8lUeH/5G9iePpU2DHUnCcST1mEDHtCuGjn/mSiKOI6nbRDqhVTEKit81gtyMOQJvQ51/uax9Y0zuDdoxrQs5SETkMOqwVhuqKr5TdncLfeJuShxnRK7DtpJ29Soo8egU8rF3IZeaAsM3FiokPzSQrjIv0aKO1po4lKcafdbTw9KeO+M8zfU3Jw48mTUaSivOJmmdj4r4XTTCzZg48GTT+Re6rH1wdGPHNOriICGRwyg6nPk6jYWXJhzNaWmknidl/VYRT7tGuJExB+JtOtMv2gdIuTd20LQuDiQROdCowwaIIZWJtG+UC0k4joSOI2j/v5NHZTIFf2RPAnAkch5J+58nPYqHtGt8IPl2/or2/Wsn0ev7joqJFyomHtXeWAjdO4aa+U2hw1oSIfnNhdRKyhE4ITmO+9vgm/wKT35G9QUgTtKF1qS8XrxmuMaCgm7Ma1aiiC+g+uPDSN9MS35+GrlL3Wn6Bbn2fxMWb8hpODlczzU4j3Z+YFGigF52zP3WyN5EvY05Am9NQ0PyDGgUd2hNL1t1jACerLqvonta1hxl+mn6sqlELRuRJ30enq69CaRIpXMLO1ENHsSJ7KjHwlB6WKBrapRsLITS3jHNyG/KYS01AjndXNiKpByIEzrSuFKD9Dy6vLwn1RFxJHEdQwd0B6F0Z21vqi3gyNh1GG3XEoQq4tdQdysh1RtxqJRcR0kpB0dQA6GuxoKcYjcNJkcJR5zUgyYcS9EiNyWlHf+MXEUccVI3mnjCUC3hDDoz1YvEnKaxsO4hhU5tS4GfVx0Zv5XY9o3jzcqoGb2DjQVlwirqpCmIcKIGNOZIpvYJs3UAVedB4CTUfP41kht6ILIomtrIhFosuKVJWGUUMcVZ023mSFh/GIW8VJ2V0elJTpp/56nGwB2Uqvn/76wNIjsBR5zAjvqsu6d9rIobNK+ZiMRtl1D8v2xLdus3+p+dkDhOSl7Tz1KOloUwdlFrzSTlSBK4tPgaymTa2r8m2f3fHkp7ccHTNNFRSICA6ow+VsaCmUvRP3ciWyFHnFFT+jo8W8dafI9+9BMRwJPt0N3F99LIIGpqIzJpsYBuaeavLGIKOQtB4IRkZGROjSed0NqJlF/6hjxFIPA16ZODFdOffyP5GspOCm/Qqh61SMBxZNJ8Fp3T4jvlV2drdhuCOPM+tCldm9lOpUYmLWjBC0EXB4/gjMl76mktC5GcLs9qSiKABA3GU5gug5Cdoc+dhS+6/Wa+39LZbK1RIo2vLyhn4PQmCjxHU9004xI40GehZThOZQbdCJlH/RuZk9DKh8ZuuUV5b8G3FYZNoAaCUoJCxU2a31xEnFk/2lZG5K649T35SgywSCgTaFUnTbLEiajBmCNaCxfKlK00oDqvtv/m8+ma3PDyedFYAEdChyG0Pb70xT7n7DRqKuEInAn5TAvXXXApDKMJDQR6FaVImUDL2olf7Aqq1nk53SrtWbMj6CtPdVDLGXnS5ycz3pqMlQ8WU4BYHfw2/+hjatZ6bvEJIfk1Wuhnph6XSTOafUFm4MaCHvZsiHsVXKIF/hbEAySoP5aOZesOop/+2Yeq8SBO7EHTIqvqvCikoyPt1EGnpBNNmtSOev8Wq6MorqDb37dQJ7gCOxp5pEB3AnF0AnkYcQTektotvlG6z1XE0R9BmgZxRRSYlQ9ocYB6Domaf0QfN2tNc4sNk64t9CMzTn0yqdnsCy/dW5lymD51ERPHiclr5vky/LKSUvcPp3oCEG/eiuaez9UZVB/+1IXEHEdir5l0vgxnr0zdT8PrCQi8ObWae15nEaXybboK6V2ZQkcneJARB+It29HiG6VfWRH3BwVpCnh67eoqlw9/XfsroEsL/MmCB0FQn8bqVgwpn/5JfarxBE5MHtMiX4mhC68uokBLngCebHqvp4dK7U2h89PcX5z84q2H0J68quk7lSlHaYKHEXHgybLdYipdvQqK+yNI0/Q2bGOhIvJDQ+pKFj2TPEUcARwZt1tCpdcrFHTvRz8SW3WiFXcVugZHiwItiQeIt+lN67UPjmTnp5H781M9vDUN0Ta48jQWqJDCJjTQFEdK+20hXV0USJY8CLwN9V7/UHtRT3aeprk/PwXBk/WQPW8cc2ccGUX1hRyBE1HLhdpP7Mgvz6KmInVxuPQi3Bv6joqKFyoiHtXSWBA6B1K7pj3pV13FeMVt+r6F+gSSwG4kHTFwKp6ypitJORBf8xM68FrXNmxj4f5P/iUaCxxZDNylp73K6NxXriT1/IYuadOT/BJ94ykqPn3nOEF3vv2yhOi3LpISu+1F5DP3OlVAGvYaie3fNK6e4MXJaUPuz5NfmE4eIk29ofvv9FSpY/1a24PMORCEbvTVuVL8WsF++thGRA3Hh2qp82hrLAjJObAdNe35K+meGt9TC42PsRt5RMcpEiWlHP6UXMQccWIvmll2EEr7h9cjAXgybzWXXg5tFXRzfnMScWbUr+xCAX3vK9HaWCi4tID8LXh142zsMdIdHjylP/tUIx4ciT2mUaSB6tKFR0eSnUDdWOg0aRK16/1bFZLx24htDRBvVkbN6F1sLMijZ1JjUbHz9Jh+QbvzTF1PPU244iOLCkOPYwZ5GLWgBbcVLxz72u6awIG3pQFbn74SQBVe+4NGdw2kzh8tpL81HjAj9AvyNObUwdT//tARtBJRzl76sKaQqg3Z87LDK7xCC1qbqXctOn1KxzN1TdJRmkkqIs8SpxAU936iNqYNadLpYmN6UdSHiFosuF3KQqCk1CPqiQrOhFrOu1TK0btCOjyitmZHf1/akvGSMGmGh9FL90pZ252MNYV6qdcMOqfDB8hOTSRHIQicGfXfXgHl3zeUr2HspICiZzcnYw7ECZ3os791LHl5u2ighTppEQcspjgtnd3oGR5k1GIBPR+OMuk36iJV/43U+1u6UKBdz4mrOqiDMlET+uaS9nBFcWcBtdTMTd68Lf2ky8EW7KePbdQLVu0Rh6mi3Zjy4TJqJ+E0Cd8g2lXSPysLKOnWGTq040/6bck8+mJ4ELV0MCeB0Jq8/u9nCn309vZ8yF7sVtFRiM3bTYMtOYKoOX13S1F2ocWp7psvEvJomtm4ONAVeUynC9odLq3vaaLZFWKYI6+6GwsiajrrctlBszKZtvRXn1ThRO40NVLX9pPzNM1dqF9RilJozfMAXuRNs/V4VVza3o81voEjkevnFJH3lmSc8ht1kagTThPz2hS0/l/H67PvUti+I3QxsdCwhVF97fmN76WgB6s7kwWntgnvOWVsIEjfTgOsNX5n2H7dgXKlzgsZhU/WNJY5CZn6TKOoUqZwzqbemlMhIvL/Sceu/PQDNNxeU3R1+Zwi9EjM83YPIkuuol6JU5wE8ybmVDto/b9eK5RNd8P20ZGLif9aG3LoxDhHEnIgzrQjrYzX44Si/ArN8hKp1zff+aRtuuacGEeOQvXrKzqujNfjlQZyujLLi0QAcVJfmq/DB1S6TVchvacfGE72miajy+cReryiIY92D7LU7IjWo7FQTh/+OvaneLCaOltocgrvOWUU/dJp+wD17nRB7WG0v6RiFLG0rL2Z5tmcaUp4gc7EMnjo8x3uIM60D23OrIq+M50ODLfXFJld6HP9DI0GWXIGbywYPD80sK5IcZ+WBKrjf07UlL69VJrUU2nbADvynavrhK2CYpe1VxeyISTnKeE651VB8FCy5p+/usWU+mgdXHkaCzK9fquIXUbtzTR6dp5CusUXTEOt+ReFWtM+m+nNziyk0db+VprT03VpzHEdi2jhERpRW6B5jVhf2pxWQWtXhcULFRCPamksACJy/zqqlFwthzb11pxgEfnTT/cNWj6my982IRFAQvdpdP61fIUhGwtKerS0xLXAkVGfLfo1FmQRNMVZqn59jo5nvTrbu/g1ogIHGve3Hv5UmUirOopfaiw0n3+TKvSQvd59mFv0na+IAAE1mBBm2HxfE9+pN0h1pJU6CmjK5DXU1YhT+8nPz+iMa5QPV1LH6u1p2YPSJVfcWABB5E5flx6kU2/NaUqR/0/am8k5J2ico5A4cGTacSXpF9pqXjHFScl3fsk1Io92D7YkDiJq/t2tMmygkMImOFHdfzcWFA9odWfNCRiRN80po8OZvn2Aen0R1KZh+7MNolpZ+GRyEmo265r60LQqJeOKj20NEm9WRs3IQPBv9bs5rh3R2VkKDgBn5IIund20f+DU1B51q6uHpnr8APEG/dK1Evf/Pon7DTuis5PmI0v5UTh7qRAEDqImozH9g1qvfOxF0ngE1hwNw18bp6FDLR7Ij8B3E1bgej6BE7lj7Lcfwp7X/uGN8z/9gL3p9TDwk84wLTGOWysm4ofIHBAnhu+oyWhvoePDH3EPkKYCILBBC393jcyUuB+yFxdtuqD3i48IyRC56wDiFAAn8kCX7k46P6SlSgrGpNG/I6aIIKjdH19P8Cr1wykCAQcOAOVeQ/T14m+vK+//jZP3G6Jj5+f3ykfU2UsoJACC+vh44VS01PFRkpx7sXiqBMBXR82ahv5wrAHkawA7KbqyGON/voh84mAcMBFfBprq+KZXLOLzCYAQzq3boO6/xaG8j79P3kfDjp3xfDiFUedwpYgAzhTtPx0HH60KJGRl5YE0Hw6Li1Nol0HEGVxXAOBEaPTpDxjvrv3LUcrEe7ifoQLAo3qtmqjoz/3mRJ7BFTkB4CDxCYC/iXq8KeE/YYBnLdRx90fPQcMwdsos/LLtIuD/ObZE3cGFzV+gfd2395mwMh2pwBSmJjwg/wdLP5uPsKRSnJqoIbxbNUEDW8GbOlx07OwMqdrhwqVLZ7hpd7iwr1td/Qyqx3hgWIf7CiamZuDKFKgNevYNhDkHkPw21v6wDU9VryN4XRjBxKTMUcC62wgMaCAEQJDHrMeP255A9VZkzL2Ym/kKT3TvVuPlRzVzRmBQN/jYSirHnt+UomisXhGGbFJ/UK99JxeU+vlICz/4eQrBQYnk08dxUVZV54VGb6RE3Q5B8CpFPUJzc6hNkJCRlgrS8qHAK8vnYttjJQAhGvToC189vm7GS40g4SpOdS8sM18Bz+7dXvpwKWAG58AgdPOxfeljcsq4DfhhcxwUBAgbtUOHOrxeegpoVRsCEAovrsKPe9P/tRjFYcMPmxGnvijadaijh90K4RrQCrUFABVexKof9yK9ytl0FdK74gqWz90G9VAaoEdfXz0+sMdDaiQBV0HOpvz2V4To1SsQplYMGrTvBJfSFQM/P08IOUCZfBrHSzibwohlWB6eA7WKe6BvC13SkKLTp5MRYCsCJ6wO3/Hj0N2s6vlOxZXlmLvtMZTPn0c/Q4NRBTgYQ+eHBteVwAFDhneBFQ+Q/Dq2rD2FfF3ZR/w2rD/TBCNGeGh/hsIILFsejhx6Pq9a6JxX0k6fYnKALUScENV9x2PcGxuSPhOuEBHLliM8h174IN3i64RPJwfAVsRBWN0X48d1xxuNsOgert7MUcdZnDmsLHTEwYIaqKn5Iq0q9wau3CmqkLXr7cTRBopHtU4sJ/To7QVJKWuiubmJWjaUgbRUMmgenpaaARUAztIa1So6YSz7q6LIyysoEWtxMDI20mtGyKJ24+Ajd/Qe4K7D3wrhPqAPmgo1elQ+xqHdESgsczqawsxU8NJ6WVCQD0IVgLOAhSkHQIX01FQoDerwPTCwnxdEHED5Z7En5KHW6xdcvIDrRQRAgbj9OxEp025nD0OCcc13EAbY629kQqce6F16kA5zzbykjDS8OjWUiNvwAzaVQryPAAAgAElEQVTHKUAQolG7DtAvtA1AK3UQiourfkRxaCuAqakJeMjxz9LPMD8sCbq9iAgNvVuhSQPbl+owRdGrsSIsW7PutUen0oMdWPj5wVMd7OD08YuQGcp0NPNNWbcDgqqUjCs6tjVQvFkZNaN3pX7w8robiEVnruDkvv04eTkCi3QUWcEZwVgTuFJhAfIN6mGVyJE6ot/wPmiiUXbR1XBEpaoATohmA4fCU1jWNVR4vOUHrLtTBAIHYZMPMMT71SJmXvxJLPvYH10X3ofXjA34vmOJcCvnLyxZcRbZBHBSP3zwgaOOIm0ezkZcgrp+7IuAF9GdEtni+ug34SO0ej5n809hx/54KMBB7PMBBnsIdQaNZxd9i11PlCBOBPdh49HdsvSJkpKiDg6gSkfqs+JQRpkjhWO/4ehTLEyER6VCBUDU9CN81sFC5xiioq6on0viiibuIgNXpN9cvm9uJ2nYv2glovMI4MwQOGQAtK95KqSdOYMbCmiaG41fDVyUOZA69sPwPk00/1aEKxHnkKYCOJEnAtvV0DGZ5Yi9r1mwqQC5udqcUwEiwy+igABO6o9Rn+p2sDnnotTj5E3RyLOMhPaNKURkaCSyVQA4EZp26AAbHkDWAXw9ZDqCb2ZCQcXLmKh+H8xb9i0G+dSo4HG9BpKW6Nq+BniokBo2D53dGqH9RzOxcvcpXE/Mezk54O0wbNNBTPd606cwReCiM7hych/2n7yMiEWBMNURAhgZaxZLKkRBfpUIaWHq6wt3oXp+ZIaF4OgzVSXozRddAjUNNFUWTu09ipeHUfEyFtb3gld1Hu8TipuH8VesHASAEzrC1a2MJiBvA6cG1uAAKJ/cwvUUVdWeF5wYLh4eKPWpOP6Fz5bLFa8WCIrOY/O2q+omPW+OZn5NIa5KShTWh5dXdT2CSBUSDobgbC4B4GHu7AYHveJfEdzdG2rmXgqOBZ9AVsmrJhxEyNlcqMXjDDf9LgqRuzsaqi+KlGPBOJFVxWy6Cum96PxmbLtaqJFxM/g1Fb979qe4icN/xUK9P0EIR1e3MuTJw8apAazVisGt6ykv4uAL+47ioUL9G9OmLdCklAuZtf4WYU+ykZOThKiFHWHNVzXfWYTzm7fhaqFmXjbzQ6Wq16D5YUXoiodNnxHoU1ugLmTt+QMH0rRqCDf/3IS7HYZjgJ12pRdd2Iej6sGBN22KFqUPDt+GPUF2Tg6Sohaio/VbiAWKLmDf0YfqohZviqYtmpQyZ8zQ+tswPMnOQU5SFBZ2tH7jwgLHFa+RAl53kbik6lWqCvAdVS2OLjMe1RaKuMDDo/SJzfEvIhHIFYaMswn5Ber1gxNL1c2ZSoWQm5f/0pyuaWenhx3IELn7IBI8+6C/m+68TODaH319RJrCoxJPDu9CWH5Z1xbD3r52ifqECtnpmeUq4isKC1Gm2lQKyPJzkZNbqP+1eSMYaZQmKygwcLNDAJcB/dFczAFUiHN7duP+KwPLwd+7/oJZi5aw4QFF/AHsDNciUOV97A6+Bb8PglBTb+fDQezigdKnBofiqSF/VcaqBBwMOYtcTazm7Oag32ZLkTvc1UEoVCnHEPwiCJWgZdf2qMEDqtQwzOvshkbtP8LMlbtx6noi8l4uFMBu2CYcnO5Vot6hwM3DfyFWsxlT6OiKssMDJzRQBzt4cus6UgyaZldFGVdwbGuoeLNSakYGCs3f9g15SxcEBrn8q2b6CNeiL+FW3CMkJCQjNf0ewp5bN6lg2HKSGM0mb8Hm4rvjUUSk2qEJnODfVg+jVT7Ari0nkaVSO8e6rtXx8MQRxBXkICPlKR7G3sTl8xE4HZ0EM+8gfBUSjMk9HWFcYuF4smMVdj9Wqgui3t3Qs66Ou8r+QcSFLKjAQdy0DfwtdD0HkHtyFw49UQKcMfwGD9Ikz1rWl2fBWLbxntrwRY0Q1L+MxFVxB7c1iQwggqhED0DcbDK2lBiE8lEEIu+rd9o5tu+su1NXdBmnzqRACQ4izwAEVDNksFwR8i2/nShj/sTK/cnq3Rom/ujd00ZHAFOAyAhNYd+kOQJaainri5th8kuCjkdE5AMoAAgd/dBG1/Mp7+PS1VTN7h8pTEyFWnURfj5d3Qzy7ITOOtvBhYgKO4ccAjhpc7T1N67g5OYyTkYkq4MgoQe6dG+glrlFb6y7ew/9fxyJUT+EIVGuPtVYdGcNJs7vj+glbXUE/pWJJYLm/oC+YaMQnCCHIvMewjYvQNjmBeA4MaydfODn3waB3Qfhwz5eqCEwmMOFS2AQXF52uHh0LRqXbsXhUUICklPTcS8sReNnqXwJWgUisK2LWkYcICdQ3jmcPFuIEX2N3/IoRPD2doeQewIlEfKjwnCmcCT6Gb89GXN29qj3XvUVVEg5G4m7mh4nZ8Ij49JpnC41GlEiIV+TsFEm0jJUQF2+6s4LzhxWVvqHV6Tl4sq4cJx9oBGSwA4O9SRVS42cHez1MsxMnDp5ETJSB9FW1fQt6PAwr24NKQcUkQpZZ0JxTjYQXTViyDx1EhfVFwVvVQ369t548+qwVl8UqqwzCD0nw8Cukipj01VH70rEhZ9F8VAcUKVMUE/7U6WcRWSxYsBnXMLp0hUDZUI+RJrTRJlpGQDqAooYnDqjKbaCRy0HB5QpDl4KE2kV9Z3KOISffaB5HgHsHOqhstVrsPywonRl2gmjhrpg44+3oEg9jHU7HuKDz+q97M8Kz+CP7dno83sPWGlPqBBz6oym6QHwtRzgUPbgIDWsIZVeG4k5hTPFA4RD2QMELzWBQUYobojGrqbgb2VCpUrHszSl9lKFMhuZOSrNPHCFp7PQ4L6j6sXResSjr4QiVtA/FDH0uFUoyH/eWJBAXOlxrBKZ6ZnFPoMzgou7c9mN+8Jw7DqYAJNasdj4zfRS4hcVknkLcHgGAqBMPIo9J/PQradJqWU4N09XSLj7mo1yKqQ8ikcuoKc/zsO2gdUw7KAcvFAIoUAAAc+/aM6RUg65XAGFUgUCIGo6Cxei56KpXjYhgZFEAA4EVUE+8gGYGDLHc+yP/q1mI/JUAYqig7H77heY2ahE8pt5DDv/tsWgkJl40q831iYk4ODOMPzcqcdL41De3Y2Q2DaY+D+bcjQ1OZhbWelfBNW21mSewsmLMnVtjLdCNf2DUFS3loJDEUiVhTOh54CBXdWVgqC5+KFvGEYFJ0CuyMS9sM1YELYZCzgOYmsn+Pj5o01gdwz6sA+8/l0oUKXgbORdzbrHwYTPwKXTp0t/RmUC8kWaRn1mGt40tXoXZFyRsa3B4s3KqhkZgEprbyiS/8G+zRuxZed+hF1NhtixJQJaN0eTRo7waGiG+3tDcCnzbYwkHRER16AggK/eAm08y+5aqZ79jRP/aAxdUA1W2Sfx53oOvEgKU8tqsLH1QI+JgzG7dWs0qaUl1FIl41DIafVRZgjh1r4T6umqC8dG4NxjJQARnP38SzkClIW/dhxCohLgzNthyAB7HYVvFVIP7cSJDPX0FTbsip6NSjcDVfoVXLn/fAbWhr297t+nR0TgmlqYaBWge5eLMjYUp+MUAIRwCmiH+oacFBUi3/LaiRIxe3bhXIG6cyz26oAOuhxi0RVERKWrmxtN/NHGQh+zPYMz1xUAeFRr0QY6h5N9CZefOzmBPRo4CLUWsCIfqHVR368NGujSRdFl/D975x0V5dXE4d+7hbKA9CaIwQKKKKioiCBgrLFi18SuUWNNsyTRFI0xfrbEEo29RbEhomhsoKIC9oKAgiIqCEpRacuW+f7YpShtwQVWc59zODnH7L7v3Zm5M3PnttNhikK/sIU3fCyqN0ssshFA6NgNnxRbKcLTs0fPXw5hT64nui67pShakQSxG3/CximnMLORBnnZgiTKfhR2nNSC1diZWB+eqpjYA0CUj7T7lxB0/xKCti7DApfRWLFjJUY766vT4eLqoR3YttMfgSE3kaLVEO4dO6CNixMaOjeGQXwADtaMw62EwAxgqMcBrwiQv8bjhykA7Gu61IE6FubQ4wAxAfLXj5GQIkOpDqtaZMyBr6cPvQ9sYuHJ0xTIqNAB4cS6NYiscDWbO/oPag9O1xntrXga3i944PEqsTyPSjkI6UE8Hhdm2YYwNuE0SoscX0+xVbdCGSci/pG4cLWblo7qK4N42jrQ4gAQIHuegIeZciiWpEmRGP9IOVkBQEtH9VX9PG3oFD0UCQ8zAVhqjE1rjt6leBD/uGigZ2gMTTJBVe1P/uQpUooUgwcn1mFNxYqBe/9BaM/pwrm9lVIcj/AoSVY0YDYyQs1nGWr0ndIHiC8yNBgam0BT1PvO48Nq05UW3MaMQrs/5uBCXjbObdmKOxN/fGP3csaRjQgw/BTBHXTK7FePHiUVrhrm6hjBSMPSVemjRygSXx0Y1WgDTdBzdH/YBG7GY1kqrobfg7RbyV3csqfXcTtZDoCPun1Ho3cldnWqHLs0Lo+uRD5a+BUe3jEVebcWK7ecEMlBilN0azH1fIG7sSmFMY3TckGHDnUq/FpO6D4cTTaGpc19XDh/v4JPW8Bc+AIpEgJkyTi2/xSyevUtZ7EbD6ZeXnARBuNivmKRnPR+FO7mA14qJTW66Pm/87g0VwqpRIyczGeIv3IAq5YdwN1spTJ5Jugw43d828sB5hb2qMwcXOG+IB5f/QVDfn0MGOSJH86eRLbkOg7ujcLsn1oUvif92D6E1h+A+a07I6mvHTaveYjko3tx8nVP9DMo8qdRewPw2Hc2PjGppG3yeJWIeVRix4Y0MR6PipJQqJ7a8qCto6U4XhwyPE94WLxQgFE7TkLLaixmrg9HalGhAPlp93Ep6D4uBW3FsgUuGL1iB1aOdi6yLfkTPE2RoSg9OIF1ayIr/o3u/TGoPQdd5/awUnP/1EgZV2Nuq7Z8s7ZrRu9AzU8sSB/j+G8z8OWyw4h9pY0GPaZj1fnpGNLOumh2Nv8ibv9SQyluTjjOXc4BgYOeW8cy7wMojuTODUQXrJSz6Imf/TehZ2WWauSFI+yKclsZvy46+JRxlijkeH7+AqIKjsjxal6mwuTPg7Ar+AXk4MGk63D4WZe96vxiSLhiWxH4sPL6uNwtwgCQe+kCrhX8XrOWaNO4rFbkIPzcZeQoV957tS9LmDLEHQ7GTSkAwUfo2lPNW/2rQb6VthN5KkJCbylXIAjQ1LcTytxU8PB8YWG/cWn3K5TWnEvncFkhaLTxcoeozM+dRUROwapOV7QpsY1Tjhfnlccw8YzQxrNlmbqQ3jiMY3FSAEI4d+uJ6q3dy5AQ/C9uSRXys+/ySeGRVEUYoOMPSzEm4BOsj5cqbmPKCsPqlWfx+epOEEHz0GkyDKvCemHayX+w88C/OBdxBTein+BlYcSQIfPGZozvnIzc0EBMbvKuLlqKx8d/w4wvl+Fw7CtoN+iB6avOY/qQdrAucri4ePsXcJomLI4PrtiIKD8/v3baoaVdrE/ko2QzqlfGPI6nebp5x+JY8RX6PKue+GXXErjX6DEcmt8vSCwuKpxzfAj4GmYFPE61goU8F3lidVQrxMjLLbKh3DyxWrbmi4seqhE2rTl6J4jFRTLm+AJolAmqaH/y4qvgeFbo+csuLKmSs5FAUsz3CwS1sS5Ljb6TxBAXGRr4An7txxm1jQ+rT1f8xiMwpvNvuHgkE/k3d2Jj2Gz86aMcAMqfYu/mEDiNXqQ8xrGM1r3ZOM07ulMiQX6xMkFNm7pRz1+wfMRZfLotHje2/Yl/p61Hz+JHQMnTcWrpBkRIAKH9MCxb0BeV2vSuauzSxHyhwnxUo5IE6Ip0wSETyBer7fz2KpN/C7fvFRReOWi59UafCo9QzEbI/mCke8zF2VMzK14EKX+EVV2cMONMDghypBzfjxMv+6J/OQsG+Q390NftR1y6qIi30oRwXHgog5cjXyUZmzq0gWmxf+nSuydEt4Ix+pDyrgZhG4ycNx59TSstMIjz5SBw4OuKqmFHGw82foPhM/cUjr6S4PZBf9z4oQXcBIoaStDe83AY8Bsa83XQYGg/NFy/AvdSj2Hv8Uz0G2RUUJiA/6EUdPm5B4xqOhrn5kE9qW3e24UCDFsVhl7TTuKfnQfw77kIXLkRjScvC04OIcgyb2Dz+M5Izg1F4OQmihgiL77jiAernr9g1xJ3zTpCVSNkXH25rfryzdqqGamjV9ckOTewys8DfX4MQMxrE3RadBqXg37DyOJJY03HmZvncClNcW5+Cy8vqDLhKUl+pjzXkINW8zZwq6TNyBJjcP+lcgunrivaupb163Nw4bziCAFOr02x8/9LDjaeBezCqUy54tLT4T1hWvbLEX1PuRWQ00aLNq4VyD4PF4+dwQu5wlzMP+4JL1GZwsS5S2mQg4OghSe8yvLysocIPHINEgIE9T+BXzv1al/98q2CnUju4FaMMgjwTNGqTZMyJzfSwpQXJ/Mt4O7VQoVBRj5unA9XXDgtcEaHDiZlfu7qyXNIlSl0Z+zzCbxL6K7YMUw6reHloVtmYn3z8FHckwIQNkNvv2bVOxiSJeLwkSvIL7zcrm3pwdHwY8z5tgsMeUXtfPjPn/BP1pDzfEr1ugZw6DYRv/x9EKE3E5GemYSos3ux8st+cDEXggNBlnIc3329pdjK1So5XNxY5QePPj8iIOY1TDotwunLQfhtZPHBkAZDucjLo8ICm4m5Wa00Q5aZodz9BHB8E5ib8d9/GcufIfLQMdzOlNfCuwSwtCy6E4ayXykusay5ROS90BnPxARGvKIiYGFfeN/gm8DYqNgEYZ7q5QW5OK9wlQ4nMIJxoUD4MDE2Kiri5OepXrSQi5FX9FAYGRtplE1rjt55MDExKvpN4jy8jyYosLQsuqSVsvGq6oqBiXHRkOllZkb1X7BZnb6TZwKTIkODOC+vdi8MVef4sDp1xbPGwHF9YMUHIH2AvRuOIKMgV4jZhm23vTB2qF05g2seTEyMi/7/y0xkaFi/4pmYoEh8L5FZ0w3k2aD7qJ5oYmUFg8dbMKbPl9ga9gDpOTlIjTmF1RO6YOj6OIhajMWmo+sxxK46SxmalS+Un49q3GAH+noi8ABQTrZyQWPtkXcpGGeeKbfi8AzRdexnFS+QyzqN/cdewn1A/zJPPnjzJ9dDv4GeECmTE/nzf7H/RAW7WPiN8Nm4bkVxX3oDR4MevsNlyULUMdAtNsmlA13dqsSfHOQoT13QVepR7RZi2QeDOxmCB0ASfRB7LytmyuQph7Ev3BkDB9qDD0Cr7VD4OQgAeRr+9Q9GwV28+Zf34lBmdwzpXPOrtfkmxihKbfOhemorhzhPUjjBJTAyLrXvGDh0w8Rf/sbB0JtITM9EUtRZ7F35Jfq5mCsuvZal4Ph3X2NLQaFAYAnLomQH2a9e4z0dNVSzjKsvt1VbvlkrNSN1ef0a4zVOzx2Kb44+gQQCNBizGbtnuRdLXmqlZITE8xfxQAaA3wAennYqbZeV5OdDrjwv2NrBsSgYqPrWjHRkKm1N4NgSrcraiZd/rfD8f6GLZ7Hz/99+YAL27j6LLAL4tn0xvJthYcfKiL+FB8WLR7IMpL8seHkjtHQ1rigS4/DJZOU59w0weGx3lNmMxPO4qBAmGnboWMZFxYAs7gAOXckHQYCPeg2Ae0FyJs9A/K14ZLxjx1C7fKtiJ5JnePZCObmh1RxurcuafcrDpYLCvqgNvArvV8jHs5i7eJpTqqARdvEBZAAE9h7wKmsJhfQWjp9KUJy3x6+PwRN6lZzRz7+Gc+GKi7kFTp7wMi/DmKXXcSAwBhJwEDr3Qf/Ci8FleH7vNh69VvN4+kkQgiIVM8qChr0xsK1WmS6s/ohZGNGgaJpDnnkc6zbfhVSToqM4BLN8x2NvWknj5oms4NRxEGYsD8C1qBAs7GwBPifHy9Pb4f+w6qnl69NzMfSbo3giAQQNxmDz7llwN36PztR5lYJU5Rm64NujeXODWmiEFHH3EwqLm3z75ijejPdWxpI72PL1fByoiSykxLt4sHZ1hrXSbcnTnyE5t+Z++vuiMy2nlmhWMDqlNKSkyvBewq8H56amymRTjoy0NBXvzZLjdVo6cgtiuYMrWhaOHfmo59y0cJWqPCMNaSqasvx1GtKLHgrXlvoaZdOao3ctOLVshqKmpOB9NEGetSucixSDZ1VWTDO0ctYrvJjzeVJy9ecY1ek7tZzQsplI+XsIaSmpqD31qnl8WM26MuwxHkMbCgDIkXpkE/Y+lgMQI2LzLrzoMwG9TcrvV81aOUNP2a9kz5OQLNWsPqPVrBWcixqIpJpuoOwu1v54Fr677yH63EZMbByL1aM90MDSGk0/noQtj50waV0ooiI3YkTT6t2brFn5Qvn5qAYGf1hYmiqOI0l/jhe1Gj9e4/SuQ3gkUxQahU6fY87wuhUWwV6f3IdjrztgoJ+tyndD2fQdgI76BTMLaTix/3jh5GNZ36k7dBYmOGkp78IRI2LnNtx4p90oaliCLX+O5+kEgAczS4vqKRjyLNBrcBeFr5fGIcA/HGLI8TRgH662GoT+BedTa7XC0IHOEHJyZJzei6BUhc8N9w+EuOdQ+OrVgnXXc0bToiQUaaonoUhLL7gMWwAH15YFhQKEzPLF+L2l5Mg8EaycOmLQjOUIuBaFkIWdYcHnIH95Gtv9lZNQPGu4Olsr61NypD9LRi7eb9Qv4+rNbdWWb9ZCzUhtXbqmXiRP3I7Fm+4hnwBOuwOmft8DZdUwQfnIl1Ipz/gbgzxmI1Rt++nSca7g3HxTd3ipeCu4tqEhdDmF+EwtLSu9cpunowMdTuH4RQ0c0aCMB8jun8WFR1IAfDTy8IJtWYX62D3YfTEPBAEa9P8UPgV5lvg8furWETMPZb7hnESKxoPj2cK+QfmtTz+8AXsTFGf5G3f+Gl92LDuJSz9XdL+Cu1eLMrZfSRG9dz+u5BMgaIz+w9wLVyPlX/gFPbxm4GDaOxq1muVbJTsheeFZlXxbJzQzLed+BeUuD6GLV+H9CvKUnZjQvg+WXC4ls0g/j/O3pCDwYNzOC2U1Rxy+G/tjJAA46LabiGmlRF5Z/DlcTFAcw2Tn4VXm6o38SH8cjJEAnBCtBg5F4bxC1hF849MZ807nqdNb4EnQYUTkEQABGvcdWP6uIJEXpk7yKAwSIDGubvoLZ7Mrfk9WwmWEx76sgYFSEuIuB+JwaE75tmveAXO2/Y5exjyQJB4xsZKqOlxsX7wJ9xQOFx2mfo8eZTtc5OdLS65skCfi70EemB1aOxuYxbdvI1YZIwX23dDLtRY2dMpTceFSjLIoIYB9t15F/e0DkHFtod2uF7rW5Su7axSu3VJ1BCVGQmQkHlVVXO+Tzky7oHs75aozWRLuxWZC/l5qWwSvHr7KSQA5XsXFKAf3FVZVEX03XlnsFKBR109Q/CQ/kVcP+CrjqvxVHGJUeygk0XcRX+BXGnV94+4ejbBpDdK7aZfuaKfMF2VJ9xCbKX8fnQ16da2rGGyTGFHXbkFlzSREIrJQMcbo0r29siAsR/rVCMURkqqUA1NiEZsq1Sw7gym6dG+nHMvIkHQvFrWlXvWPD6tZV9rtMXZES2hxAL0Owebt0ZC+/hcb9+tg6HjvCo/hNO7SHe2VhXt5+lVEqN44xMamVv+ElnEXdG+vnJiRp+NqxB0V3ylFSmws3tXUZXGBCLjVAG5uBrBqPxoLthzHlbhnyHz9EmlP43D1xA78NsELNlrVbpialS+Ul49qZlkQtvVtoMMB8rRUpNZi+BBfX46f/3kKGQBO2BgTls6BR4VHWGfi+L4TyPMaiL7WqpfLeFZ9MMDbQFnalyPj5H4Ep1eUJnXAnCVj0UioOBVecvtvLPJPqnLsJ3VcliFLRcoLxcUYde3qV9spBSafDEF3M8XdWQ8D/RGWnYiD+2+j3aB+xc78F6D54AFwFXCglyHYezgF8rww+B8Beg/xhE5tGJXICz18lYtm5K8QF/NItcl5STTuFiWh6PpJ0wKBIynuMgIPhyKn/EIBOszZht97GYNHEsTHxEJRKdBGu15doUgPCOKoa1A9PUhAZOQjaNxIVO0yrubcVl35Zk3XjNRIjU0s5F46qzgTHoDAwQsdyztIPj8ZSS9KKpmkr5CanK6+GbicS4Xn5uu6ecFdRc+k3awZGlfSw768+Cv691+G61JAYNcA9bQ4ADwYlHljehbOb9ipuIeAZ15uoT5q7wFclxAgaIi+g9oVFeqvHMbx1Fbo7FNsnbrAFh/ZaisCnlAXovK2cspisHnNYTyXAzzTzvh5+fhyzhfMwaWC+xVEbdDRo4zUWnoHew/chIQAYbOBGFq4kj8flw8FI7lVZ3QyeTe1qle+VbQTLXOYK2+D4SzqwqYMueVE7seRB8rJjQ6ehZMbr8+ewCVea3iUkjXmhJ/DlRwCOC206tAeumX8vjPb9yNeCnDazpi6eAqalmhD8fsVDNHOq3UZMhDjwu6DiJMCnFY7DBziULhjIzskEKfFHdDVS41hXZ6IwIBLil0cwuYYNNytgjMC+XAYMw1+xW4dkiXswm+b48oOQC+vYN0oN9g5dMX8ky9qxglSBkKPhiCrwqT0Y3RyEQCcNnR0uKo6XJy9rDxfU+AAr471ytllk4/kpBclE1iS4lVqMtLVvORBKlUl8ElxJyRMcYEgZwjvaVPhodZt51Ko0gzZgz3wD1McEcEZemPaVI+iYxk0WMZcHYPCFZGQSEoWBSgXeWIt5SW2tfAuvS6Y8UVbxWSg9BHOnlZth5E8+R/M7DsLB5JkH1y/KOkIrDF4Yn/FIIFycfVsGLJV+V5+Pmo/tXwTo94zMN5ZsRpPGh2uOFawwi4ajTNhTyAHwNXxwsRxb8UBo96YMd5ZcbmzNBrhl1TZCSFF9JkwPFE8FF4Tx5U6aV2rNq1BeudZD8bE/opBEuVexdkwlVqC/HxNskA9dJnxBdoqFINHZ0/jrmqKwT8z+2LWgamVmwoAACAASURBVKTC4ZL14MkYpEzmpLHHcVSVB8mTsHV8R0z654mG+U4erAdPRH+FoSH36lmopt58qFu96h8fql9Xb40y4DRyDHz0OYDEuLZ9I4J2b8aJhiMxpoVApX41eZCNIvZIY3H8qCo6lCNp63h0nPSPwn9Vd+yZPEg5bpEi9vhRqCa+rRjfcRL+eccGSh8nIik3AffjankrR43lC2rIRzUU7caOsOMD8qxHePC0llbTZl/GkmnLcTWXAF4dtJmzGYu7GVf8vYzj2H8yH96D+sCyUnd4WKD3AF8YFmxaeHkaB44+r/Brxj0WY8OXroqYIE9F4NwvsedxVWSWi8zMnHc/BicrEY/T5ADfHI0dzKqvYGjYDYM/sQIfgOzxYezesRMHYjtgcJ83d0nwmwzGwLZa4Og1zu4NQNzpPQjW6Ysh7rXVC4zQe8Z4OCuSUESHX4Jqqe0ZhCmSUNTxmohxbyShhIzQowipuFCAjzu5QAAO2jo6hftT9LrMwBdtFTsRpY/O4rRqyQ6S/5mJvrMOIEnjdqVWh4yrM7dVV75ZwzUjdaYPNfUiuTi/aNZGKCi3UJgVehLh2cqNXPTWwIkvhFBNbcq/cR7hioPq0dzTU+XLn/iNu6FrEyE4yJGqwhbbvKi1+GzQr4iybAI7HgAjb3Ry0wEHObJevirl+3KknfweU9fdh6TgIuSyqtnyJIScvQMpAYJGvdG/sPNk49yuQ3jh/SkGvnH+pBG8fVpDhwMoPx3Py+yhMsStn4nfwrIBYT0MXrURU8pb2Zd/A+fDlfcruHihYxnHFsufX8DFaAkAIZr26ovmBY/MPoudAS/g8+kg1c4xLNcPqVG+VbUTYSu0bVGw6lBWuo3kXceKWRsV9xbwDNGybXNlv8jG+X/DIGnfBT4GJZPXm+fDFXdekBziMg6ck95Zi0V7HkPG6aLFjNWY76VfavJx4fwV5BHAabeGl2cZk0GyeFyIeAKZ8rKrfoWXXaXj6D/HgJ4j0MtUfb5CFrcP/hdyQeCg1WYohjurMItn0gfTRztBWLhr4SVCl/yCwOel2Lf4DlYP74OpO6JgOmQt/p7c8N39W/G0gMoJ3oGbsD+poqgog1RK4HSc4OosrKrDLXapmxCC8h0uTpbucJEv4UMoVGckkCH+8mU8r0gEmSewblcUJOCg33oGfp3QsPQBnWqCL22EhitXnldQiEzH8SVrcDGPAE4frWf8igkN+TUk43cbEvBMbWGjr1zN/eJZiaMW5JlP8DTfGnZ1+XhXsVbtXQI0m/IrJjnpgIMEtzevwOEKjUKM6+vWILzVMAyoapCo9n5Bxf5bkQ4r+v88mPb7HrN8jcGDHKlBG7ArsQIZydNwMvgCFFcMEWQymdrPWK3S83Ta4OuFI2Av5ECvTmPz9nsV5k0vQ//G7jsSECeC69SFmODwts510ObrhRhhLwRHr3B683ZFLC3/ofh79x1IiIPIdSoWTnAo1a/Urk1rkN55puj3/Sz4GvMUlylu2IWKm3ISwRdeKnwIySCTkTqC5zvZn6DZFPw6yQk6HCC5vRkrDj+vcBJKfH0d1oS3wrAB9YtVfnriu++7wowHUP4NbF5ZdNZzmQPcqC3YGOGCgf3qaZzv5Jn2w/ezfKFQbxA27EqsQC5ypJ0MxgXlHWYkk0GmBkOrlvGhmnVVQnd2QzCup+K+C2ncVkz8IRLuY4erOH4xRs/vvkdXReNwY/PKilc0S6OwZWMEXAb2Qz1euZ2ogj6i2meNe36H77uagQdC/o3NWFlxAxG1ZSMiXAaiX713Ky3wLa1gLr+FDT9sQJQaN0NT5Q2zZvJodeSj1ZmKvANaTV3RTJ8DZA8RGyutKc0VqTAjHEsH+eGXi68g5xmh7Ve7EfBjB1R8gpQMMZv/wrFXTeHhaVrJYhkPFr0Hwrfg8j/5S5zZf1SFHRuG8P51P9YPawgtDpA93YeJfb7C0WeVnKjLDEXIVfE7qzU/5i7iJARO2AwtXYTVZyTQR+chPRWLKWRJ2Dd3Ge77DMEnby8y5TfEoIHtocMRcsK245s/j8Gw3+BK33Oqzq6h0+ZrLBxhDyFHeHV6M7ZXnIQi9O/duCMhcCJXTF04AW+ntvLkQGzaX/FuFZlUCuJ04OTqXBT3BM0w5ddJcFIkO9i84nDF423xdaxbE45Wwwa8e/3tPZFxdea2ass3a7JmpNZBYg0hjV1OPvocASBO5EPL46SlfzD7Cv3i40GfDXUhIUCcblf6K1lGRER5JydRQ49FFCNVS4sodnF7EgIEgQN9FSau1Hfj13YjYx5I2OJ7uiIp63NienJ8Hvla6VPjYZspOq/g32WUsnc42fBBgobTKCS3+Hdk9Oz0fPKxEBCP4wjgSKvDEipLXCS9SwvbCAngkfXYI1TwKGn8Gupm7kgzQrJKfEWWspuGWPIIPGPy255CspKfoNSTs6iNIY84oR35rb1F2RXqdzG1F4IAATX99hKVJU1pzG/kLgSBb0sTT+QVyjNuVRcyazKDSmluFVCjfKtsJzJK3tqPjHkgnsUIOvj67ccm0sHxTcnEwoIMeCAInWlupNKQMvbRcEtj6rvlWUndSO/T7x5C4gACODLsuYGS3v5QViT94lGHeJyQ6g/eSvfLsk/xWZrRSEAASOj2C90pSwbiCJrtJCBAQE6zwwt1K765gNqZtKGfr4vV6CkkdPOn1iTkQOAMqOvaR6XYZxkSf/Q39TTmUWEs4/hk1WMV3c4r/lviyX9sE9Lh6VOzMTspVk1Nzzs5iez4IIBPDaaFUF4Jv7aT/EQcgRNSg3GHKKWcHyWNW0m++nyyHr6PXsiq7HBpuY++wk44Efksj6PS1ZtNV37xIY/PhpKLEAROl7r+layQed5JmtTQgxapweGmru9G2gBxoobkaG9PA7Y9oDKfKntKB8Y0IiHHkdC2P20u04CVbbTjEwDiN5hGIXnltoLWd9NW2oYO6TfoT5vjJGX23yf7R1MjoUJntv03l+xH1Shj6f3fyUOosGPtbusptdISf0E7BxgTDyBOryutffKmIaXuGEA2H/9JCbIq2rOa3pV3ayV1teATx/HJxm8T3ReX7U/TQr6l1jY+tOTmO3Taau0XuRQ0xpJ4UPiugbvLD2avdviRHqeQsf2UM2XKWJa4hz6zFypltIUelBOr4jf7ka2OkAScIj4YDd5Dr9WayCniDwCCdjdaXynDfE0RCzzJiAfiGXrQgsiyswpZ6nGa0lSLOE5A1r3XUXQ5Kn8dsYA8jXgEniF5LIgsO1eRpdLxKU1Ji+NIYN2b1pX3UA2wac3Ru4wS93xG9kKOOL4N+W0px3dL42mzny3pCAXKPmZEg/e8rih4qu7D38X+8m7Ryq4WxOc44tv40aayFUOytBD6trUN+Sy5WTKXlSXRgbGNSYsDccKP6DP/xLLlkRFOC7zrktu88DLsUgN8pyyR9nxmT0KlXLaUbWgkjd9MfrY6JBQox3JGg2mPGgyt2saHatVVKc059QXZ8xVxmm/3OR3Lqly/SjowlhprKXKMjz7zp8SyG0fhC7yprts8Ci+1cXl0cpId8QECv16xsVVFn21A08rpcLKkAzS2sRZx4Ej40WfkX3YDKSN8AXnXdaN54dnvbhCSazS/lTZxHI/06rlRN78BNHDQIBo0aDANGTaCRo/7nCZPm0nfzP2Zlm8OoHMxaSStjthVrfmCmvPRN1KRIBpjqRgLcQYDqfxU5BXt8NNT/Ea+PU05k6fm4k88LfXUIg5C8vj9PlV+NCGh2z+3VozBAYKwLf16V4WnyDLpzr551KuhiDiAOF176rssnDJU7Jsvzv1CvuY8As+SBmx/SpUeikljaFE7YeF4lNP3pRVxKv56yQPaO8mVDHkggEcGruNo681MFd3KMzo2rTmJzFtQSztlrUC7L22vQrdM/bsH6XIgocs8ui6p5iJh7mmaYq/IA8CvS6MDX5eRD6+lrgWxQtiafrxZuYblBo0hS54iTzIYuJvK7xo7yE9P8S6+/RQqs2u8jqAFnkbEA48MPRZQ2amtjFKPT6GmWhxxAmvqvS76rfwim3b6iRT+tsE4OlR+oYBW+uoT33o47StRKMijWyu7kgVfmbNtuk9lpwdpFPJta7LxWUI31VQP0WwZ11Bu+675Zk3XjNQIau5VWRS52JfM+BwBPDLz/ZHOJL3pELLjguinT1qQ78IIyoxaQl76HIHTJZfpR+hhyi1a268x+S6NJrXMK1AqbeqlSBh4FiMpoLIFbfFd+qunNfF55vTJmugSRYHM23tpnl9TMqrjQP0Xh9AzWcni2aHPm5Iuz5A8vjtDz6REslexdGRhX2po6kTD5owg54JC/axL5XSMXDo5uT7xISCn2RGKz2XfpBVdbanJpCNlGJmMnviPpAZaHGk5jqP9CeI3nMyVjeOppSGPhFYdaMbeWFIlzUjd1Iv0ORB4VjQmqJwIlnuKJtfnE4Qu9MM1hf6zry+jzjZNafLRF6S2PqE2+b6DnYhv0hJvI+LxrGjQrqTC3yZLv0TL+9qTictUOhwTSGNt+QRBY5p5TkxEuRQ5343qNJ5Cp16VNg7dTL31FX3I3LcXtTdpSlNOFMlN9uIcLexsTQJeHWo+ZivdKUcV0qiF1EYpg4bTQ8uRQSYd+MyCeBCS51Kl480Io/nt65LbDxcoS51uIi+MvnZQTHbwLIaSf1olE9DFnmTAodjkgpAsWw+gLxf/Sct/nk5+LUxJaNCEBi0Pe2cHLEu6TiePHaVA//U0t5sN8ZVFHUH9nvTD+j0UcOQ4XXkqfTNI8AzI0sqG3L88SHGl6EaccJi+bmdEIodxdODpuzUwK3Ix+ZrxFbZr5ks/nkkiyZsOl4J++oRa+C6kiMwoWuKlTxw40nWZTkceptCttf2ose9SilaDw01d3420OSE1+/Y8vbi2nLo1bEafrrtGGW/9RMmzi/TnEAcScTwyajWZ/OPyShM8XT95jI4G+tP6ud3Ihq9MMgX1qecP62lPwBE6fuVp+RMLQnea8etgatp8GK29nPGW38mkGxtHkbMBjzieEbWa7E9xedUvY1lGNJ0/HkxB+7fS4uFOpKu0Y07kSH4/rKYd+w/T0eDjFHpHNT+Zd20htTdQJCaOk44WJibSp4doQjN7GrH/zedUyp7f8V1vyPDWBhruKCKOE5JNt/kUdP8tjyJJpgtrx5CrTUuaEvjkneO/evuFhJ5cOUHBRwJo99pvyNdKOTACj0w9ptCfuwLoSPAJiniomN3OfRhBJ4KPUMCuP2iyu4liEgIc8et2oq9X76KAI8F04srTEr9RHO9PE13qEI8TkePwdXTt7Y4jfkqnF/Umh0Z9aNn/hiuTehCn70SD5/9Fuw4G0bHIRKrSuEGWQdHnj1Nw0H7aung4OelySt8qIke/H2j1jv10+GgwHQ+9o4JPzaDIP/yogQ5HfLP2NPOfO5T5xnfElHxpHY121ieOZ0DOY7dTdG7FTcyI/IP8GugQxzej9jP/oTtvPpTEyZdo3Whn0ud4ZOA8lrar8FBNsOla1fubL6J4/4nkUodHnMiRhpfiu8VPT9Oi3g7UqM8y+t9wy0Lb1ncaTPP/2kUHg45RZKK48j5cnfaXdYs2DHckEceR0KYbzQ+6/1b+IqHkC2tpjKsNtZwSSE/KUowkgQKmu5ERjyNOtzENXBZKT8Vv5tiZ0fvoK3dLajBoM8WINdvOSBxP/hNdqA6PI5HjcFp37e2YKKanpxdRb4dG1GfZ/2i4smgJTp+cBs+nv3YdpKBjkZRYZUOrxvGhGnVV0g6u03xXIXGckJznRlahn0koIWA6uRnxiON0qfHAZRT6ZuNIlhlN+75yJ8sGg2jzG42TUdL1k3TsaCD5r59L3Wz4yoIHn2y6zqF1ew7R0WMn6XqSrJzPCqh+zx9o/Z4AOnL8CpUW3iUJATTdzYh4HEe6jQfSstCnb/5OWSZF7/uK3C0b0KDNMaSuZUaZZ+dRO6NiC4XK+eN4emTnOYJ+PfLgzfGqGnxH9eXRas5HJU/oyolgOhKwm9Z+40tWygkv8EzJY8qftCvgCAWfiCBFKpJLDyMUecuuPyaTuwmvcDFW3U5f02pl3lJWvldZG7/2gwsJwZFBv21U8ZBORi/uhNLx4KMUFLCHNi+dQR/XK1hMBwKnRy3Hraa9h4Pp2LFjb/wdPbyf/tm0kn6cNpR8GhspJto5ETXoMZf8o15V2M5HF/bT9nWL6avB7mRbYCsAcXqNqPv0JbTlwHmKL69An5tA4cGBtHfbKvp5vCdZC7hidsojA6eB9MPqHbT/8FEKPhFBCbnlT/jc3DaVvG20CydGun+7mc4mlD3iznsSQis/a06GdVxpWlASZVz4mToY86o4sZBNh0dbEQ98sp8aQmqebio1xzg3szEJAOLXm0DHymqvLJk29qpDHDjSavcrqTLHJHlyRZF3715L3/haKSZVAeKZetCUP5V5d8RDxcLc3IcUcSKYjgTsoj8mu5NJQU7Fr0udvl6t7Eel+MqMSPrDrwHpcHwyaz+T/rmT+WY/FifTpXWjyVmfI56BM43dHk25pchcMbHAIwNLK7Jx/5IOll4ooMNftyMjkQONO1DWpFcW3downBxFHHFCG+o2P4hKpgcXaO0YV7JpOYUCn7xbX39/ZFzNua268s1aqBm9hxMLimAWF7iARvo0JiMhRwITZ+r+2Rc0a85MGuvnSQ4fudGYdZeVM8piur/7c3I15CmcqrYNeU73pzh1ZS2SSJrrLFQkYaMCKL0qz3h1gzaOa01mOmbk6jeJZs+fT3OmjaLe7vZkat6Euk5eQcfiyim7ylLp4top1MPFlszN65KdQ0vqMu53Cop9RU/XdFEUlnhWNOZw+RFB+mgPjWqsS1oNetDUbyZRnxb1yWXUFrqbV34QfRj4HXVvoEdCy9bU//Ov6dspn1J3F0vSNXKkrpP+pNAkVR2NhCLnOpMQIH7dEXSgXGFKKWH3SGqsq0ONe02nbyf1pub1XWnstmj1By51yPcd7USWfILmf2xD2oZNqc+UOTR7yiByr29DbmPX0+V0RcL/4tQcamcsJPP2o2n6CA+ytWhLc06nlRossoPGkBVPkVz13JhMD3aPpWZWjtRjwjf05dge5GolIiOHHvTV9htUURqVd2Ii1eODOGED+uJk+dMDkpiN1N9OiwycB9JX34ynbk0+IvcZBylBql6VvT4yjmz5IE5Yl7r/cbPyNpF3g/7oYaPY8fD24ENgTM7959G+KPWs5cze2Z8MhNqkq1+HjEzMyNLahmxsrMjC1JgMDUSkIxRR7y3KVSbZ/9AAA11ymhxEyelXaN3YtmTfoA31GTOD5syfT/NmT6NRvduQrVFdajtyGYU+U0+AyI0LpAUjfaixkZA4gQk5d/+Mvpg1h2aO9SNPh4/Ibcw6uqxcwiO+v5s+dzUkHgcCp002ntPJX00ON3V9N9KtN5oClKO3vPsHaU4PR7Jr3o1GTJlF38+ZTuMGeFOjOkKqY+9N41acooS8MgVP/Q2EpK2rT3WMTMjM0ppsbGzIysKUjA0NSKQjJFHvLRVMLHSgJXG59DDoB+rVxI6adR1BU2d9T3NnjqGeLuakJahD9t7jaMWphAptUF0yFp//ihy1tUhHZECGRqZkblmXbGxtydbGhmzqWpOlmTHVEelS/YnHVewXMko+tZD6NzcjIadNNm360PChPam1rQ15zD1RYgVEpez5Hd9VsuNHU8DPQ6i1tS7xtMzJybs3DR0zjkYN7EItbc3J3ncabb2eobaJZ/X1i1Ra311EQm1d0q9jRCZmFmRtY0M21pZkbmJEhvoi0hHqktfSeJKSlOKXepGuUJtEb8jYmizNTMiojj7pagtJ1GtT6avqsuMoaOEwcrPWJaGpM3UfOZVmfz+HZozzI/dG9uQ2bDGdTpIWWy305p9W178opUrjvfP0laM2aemIyMDQiEzNLamujS3Z2tqQjU1dsrY0I+M6ItKtP5GOq+iwM2/707wBrchSR0B1GrSnnoNH0uhP/ahz63qkJzQguw6j6PfgeKrUWDjzNvnPG0CtLHVIUKcBte85mEaO/pT8OremenpCMrDrQKN+Dy6/MKCJNl1bei+1KUG0cJgbWesKydS5O42cOpu+nzODxvm5UyN7Nxq2+DQlSYvt3HnjT4u6/pVSeR+udvt7TdEBP9OQ1taky9Micydv6j10DI0bNZC6tLQlc3tfmrb1eonBZSnJBiWcXEqjPeqRHp9HeratqfuQMTR+3Ajq59mITC1cadj/QilZ+p7YGWVTXNBCGuZmTbpCU3LuPpKmzv6e5swYR37ujcjebRgtPp1E0mKrod/40+pKf72ToVXn+FBduio5nnmw0pf0RB1p2f2qJ8N5CSdp6WgPqqfHJ56eLbXuPoTGjB9HI/p5UiNTC3Id9j8KLdG4bNrZ36DU2GNmYkR19HVJW6hHfjuyS/mspeKzVhZkamxIBiIdEop6U5nhPS+BTi4dTR719IjP0yPb1t1pyJjxNG5EP/JsZEoWrsPof6HJpLbhQNZ9Cvp9PHnb6ZKOuSO18+1EnTp1ok6+vuTT0Ys6tG9Hbq5NqYG1IWlxxQrAgrrUc/XtovxITb6jevJoNeejqeupu6iYT7VQ+FRrS3MyMTIkfZEOCXW9aGm8VLGLwEuXhNqiN+3hDdsRUa9NGWpRp/jSt9REAOLbjKfg3Ir9avB4m8LCZOX+OOJrG5JlfUdq2aEXjZv/NwXfSSPV1rQrczkdPTIwMi6Sn4UZmRjVIX2RNgl1PWlJObsOZAkrqFMdfTI0NiVzS2uqa2OjyOFtbRU5oakxGdXRJ5GOFgl1fWh5ggoZ7aso2jevP7laKCcYhMbk4OVHY7/6iZauXkfr/1pJv30/lYZ3aUGW2jpk4zmJNlxJV/p7GaWd/416t/6cAnMrq7QLikV+fDuaeCKXagJJxBxqJhRQw2kh5RSEZZS6oz+Z8HSp4zLVdsCkru9OomJjG4uCvNvchIwM9UmkIyRdr6Wk6BpLyUtXSNqiN3MTa0ulHehqk1DUi0rvGpl0238eDWhlSTqCOtSgfU8aPHI0ferXmVrX0yOhgR11GPU7BZeZhGbTPwMMSNdpMgUlp9OVdWOprX0DatNnDM2YM5/mz5tN00b1pja2RlS37UhaFvqMKk4PAujnIa3JWpdHWuZO5N17KI0ZN4oGdmlJtub25DttK13PePeR1fsj42rObdWVb9ZSzUgdcEREtXEEU17SVYScvYb7T9Mh1raEfRNntPVwg53eW2daZSch6uYDyOu3gouNSI0tkON5xB7436yDTsN6wcmg6k/KTriAf0Nu4OELMXRNrGDj0Bpe7R1hIqjqE19j79CPMMw/HaTfG5seHMYY84qOFIvFv4eO485LQzRy74LubW1Uu9RJmoboc6cQducxXvFMYdfQCW5ebWCvX7kTBeXPI7Bn3x0YdxqMHk0qFubLe2dxIvQmUrQ+Qrsu3dDGpiYv36mMfNVhJ9lIOB+ME1cTkatnh1adP4GX/ZuGLk29iqNBYXggtkCbnv3hWb80eeQjfJYrvP4XDanQHYtuh2GuIx95jy4gMDgcT+TmaOjcFh09m8BElXPyZEk4v/MAYq16YHi3Rqiod8nToxFy4izupOmhsWdXdHWxhECtehEjZJYvvrntgtFz5mOyt3XVni++h30/zsayQzeRCkNY2dqjudcn6D94ADo3NQa/NhyeLAGn9z+EwwBf1FP+qJzHkTh1JgIxSdng6xvDvJ4T2nVsB0cTrepwuLgachbX7j9FulgblvZN4NzWA24lHS6Som7igbw+WrnYQFS9QkF6zDmcPHcTiZkEfYu6+MjZE95u1fXe5/i7ez1M/FcMCDtgSfRZfNuQD8jSEXPuFM7feoRMuT7M634EZ09vuFU23mikjBX+5+G5I/j3SgJe8c3R1Kc3eriYq7nvquldspeIuxSKiOhEJGfKUcfKDk3adYKng2H1XAilsTorT0aZiL0QgsjoRKRkCWBm1wguHbzRsq7iniBZ/DFsOJYIbRNTmJmaw8zCDBZmFrCwNIWBFg+ahCwzDhfPRiA28RkypCKY1a2PZh7eaFtP753kE3fxLCJiE/EsQwqRWV3Ub+YB77b1oPc+27QG6V2WGYsLIZGITkxBlsAMdo1c0MG7JRRNkSH+2AYcS9SGiakZTM0V7TCzsIClqQE0xwRleBl3CaER0UhMzoS8jhXsmrRDJ08HGFaqjXJkPYxAyKUoPEpKQw7fEDaObeDj0xqVCyGa4jtlyIy9gJDIaCSmZEFgZodGLh3g3bIuFOqNx7ENx5CobQJTM1OYm1kobM3CEqYGWmrx09U3PlSXropkFb+iC7zPjcLVA6Mqd8Fraa3LeoiIkEuIepSEtBw+DG0c0cbHB61tRJrRZeRZeBgRgktRj5CUlgO+oQ0c2/jAp7X6YqL8aSBm9B6Fv2KtMHDxVqz5wh2m/LL1mZcShZCAbfjzf2vx74NcQK8DFkeexSynasj01ZovVHM+qklIL+M71w74LcYGX/wbjTUf64BRWRmmIyb0MA4cPoOrcU/w7NkzpKSLoWVkBgur+mjm0Rk9evZCl9ZKP/2ur7sxH25tF+CO7RScuLsanWpCZfIUhO04gvxOo9GpXjn9VxyLw1tvwm74YLgaaKKyZMiMu4izEbFIfJYBqcgMdes3g4d3W5Sf2sqQcHo/HjoMgG9RoQCRp84gIiYJ2Xx9GJvXg1O7jmjnaILKVApkL+NwKTQC0YnJyJTXgZVdE7Tr5AkHQ9572iGqKuPaym0rmW/Wds3oHai1iQVGeYXRM5japCvWJMgh9FiMu+dmoRGfieU/L19ZPP7n7YTZF/LBb/wlQu4sh6cWUyeDoRplDOQYDAaDwWAwKkP+ZXzXdjBSF97Cxl4GTB7vPMZJwIa+bpgULEHHpRE4+VUTlSfW5KnHMKVjX6yPJTSZFYYbv7eDwEJHnAAAIABJREFUZg+P/kv5qAyx//NBy9mXYP75McSu6wI2taDRjg0Rc1rCc8l9OP8Qgcu/tKymhUgMBuNDg8dEoHlIo8/iwlMZAD4aeXrDjtW+mHwBIPM8wm5JQODBqK0nWrJJBQaDwWAwGAwGo0Z5fWI9dksHYVxXNqmgDmSP9mP7yXSQqCNGjGpSqWImz6IrZoxsBQFkeHD7NrKYODUIPhqPnoq+loSnQTtwLINJRKPJuYhdB+5BbtIDU8e7sEkFBoOheixmIqhh8tPwICYBGdKyPiBFVEAQoqQAJ2wKv4GtwerHTL4AkHfpPC5nE8DpoLWXB3SZthkMBoPBYDAYDPWSeRl/Tx+MfkO/wfZb2W/+P3ky9m8Ihu3IsWjLBmlqgdIz8JIIEIqgX+kl7Rz09HTBAaB8McRyJk9Ngmc+ED/P9YH+s4NYueUeZEwkGoocT/csx84H2mj39UKMsmNlQgaDUQlfz0RQg+768V6Md/kIjZwaw2noDjwtJfGRJ+/DbxtvQUJ8WPX/DtNas7liJl8AyMfN85fwXA5A0AyeXuas8zIYlR24Fh78R2BnADIYDAaDwSiJDFGrp2Da6n0I9F+OSTM341GxMUVe5Cqsut0FX41vArapXD0InLrA5yMB8DoMgceeo1JzA7KHCDh8DRJOCy4+HWH+HgyQ/lv5KB8OE5dgZis5Lq5diTOvmb1rJOJIrFp+HFkOE/Db9OZstwKDwagUrDZZY0hxd8sSbI/JApEMaQkPkfF21pRzB39N+Ar7k+TQbTYJG/4cDCumISZfAJDew4kzcZAB4Nu2RQd2NjyDUck6wXM8L3AKlIG052zNFIPBYDAYjBIJA7KysiFXVnw5HlcsH7+LNd/9A7Mv56KPCZOU2hB1xHfLxqOpVjL8p4/CishM1SYXZEk4Pmc45odmwdB9DlZMew8Kov/FfFS7Nb5Z8jkaPd6CbxddRA6zeA0jH7dWfIu196zx6aLv4KXHJMJgMCoH/6effvqJiaEmILy6vAt/nXoMMnLFFyuWYYSTAQpS1byE41j46UB8/28aLH1mY9ehJfjEihWP/9vylSHpoj92HTiK/X8uwOrTScgjgIMcsvyXeHTnBu680Iejgzk7LovBKA15MsJ2bUfgmRAc3foHNhx/iNdyxUDu/p1EZL54gKibMcixbAF7IzaLy2AwGAwGgw8Lo1Qc3RmG/NZT8Pffs9HBXACI4+A/ZSiWac/HvsW+MOaYpNQHB33H7ujnxkfMvzvx19rdiMwQwaZRY9gZa6OEqKXpiApej3njR2POviTYD/0d/ju+hYemKoXlo9Cy90JbnMbq5Uch6zoCPnXZmnhNQRq1AkPH+sNg6j78M82JHbfMYDAqH8WJiJ0IUVO8jsSqSZOx8FA8tBq3gluzj2CqS3j95BYuXLgPzqk7RnzxDb4c0fa92MbJ5FvdpGHvOG98d14GHZEIIl1daPNlEOflIS83F7l5Euh0+AnBW0egHrMXBqMk+ZexoPun2PFUAG0dHWhra0NHWwBIxcgT50OclwexxBB+f5/D777aTF4MBoPBYDAA5CPO/yuM+OoAsl06oaXRK9y7cg/6A5dh40+9UJ+t6Kk+8h4hdNta/O1/GCfDH0Ng54zmTevDwkgPgvwspCc/wN1bd/FM2xGe3fri04mTMKSdlWYvsmL5qAJZIvaM9Mbnt/riwNnl6GLCBrC1jTwjFN916YttVr8j5OAkNGG+jcFgVAE2sVAbMfXlI9y5fQ+JqS+RJxdC39QaDZxd4WjOPDmTL4PBYDAYDAaDwah18lIRc/06HuRaonnbFqinzwqhNYk87znibt/GvcfPkfk6F3KhCAbG1mjY1BlNPjJmO7bfR7KuYFn/vlgq/xJHAr9Ba3bsTu2RH411AzpjXsZ4HDj8IzqyiR4Gg1FF2MQCg8FgMBgMBoPBYDAYDAajesm+g01ffIrlWr8ifEMvGDCJ1AL5CJvtgYnxI7F103S0MWQSYTAYVYdNLDAYDAaDwWAwGAwGg8FgMGoAMV5mymBoJGKiqCXyX76E1NAQTAMMBuNdYRMLDAaDwWAwGAwGg8FgMBgMBoPBYDBUhh2kxmAwGAwGg8FgMBgMBoPBYDAYDAZDZdjEAoPBYDAYDAaDwWAwGAwGg8FgMBgMlWETCwwGg8FgMBgMBoPBYDAYDAaDwWAwVIZNLDAYDAaDwWAwGAwGg8FgMBgMBoPBUBk2scBgMBgMBoPBYDAYDAaDwWAwGAwGQ2XYxAKDwWAwGAwGg8FgMBgMBoPBYDAYDJVhEwsMBoPBYDAYDAaDwWAwGAwGg8FgMFSGTSwwGAwGg8FgMBgMBoPBYDAYDAaDwVAZARMBQ31IkZ2WipecKeqaaDNxMBjvTdfNRvrzlyDjujDVYeJgMAAAcjEyn6dCrFcPlvpMHP9NspAQHoqwKzdwO/4ldOs1Q4deA9DFwYCJhsFgMBjvUThLQHhoGK7cuI34l7qo16wDeg3oAhbONGMclpb6EpxpXbASCoPBeB/hiIiYGBhV4eXRuRi06BRS0tKRlpGOjIxXyJXw4Tr/MiJ/dmGzVgyGRiLG2QX98UPQU6SlpyE9LQPpr3Ig4TXEjNNRWNlRi4mI8V8c1SFq/ThM3XIHz9PTkJ6ejvTMLOTLRfDbmYoDw0VMRP+1HOf6Bnw9cR523NJG+08/Q9/29ZERuAi/nxag3+qj2DXWUQ15ThYCZ3bH71cF0NESQCAQgM/ngcdxb3yKSA65TAaZTAqpRAJxvj56LAvEPI8Cfy1H2t4p6LPyDoQ6WhAKhRC88RwCyQlyuQT5ubnIFpui/+r9mN2G+XsGg8H4wKMZrm/4GhPn7cAt7fb49LO+aF8/A4GLfsdpQT+sProLYx3ZqL0m9XF07iAsOpWCtPQ0ZKRnIONVLiR8V8y/HImfXZguGAzG+wfzXB8weXGHsWpNEOK0WmL4zInwtuar9fn8OtZo1NQJRklRuBj6EDkSAsBngv/PI0fm7SDsCroNSZO+GNO/OQyZUDQIDnqWDeHoZICUexF4/DAHEjmxg/Gqxwsj7vAqrAmKg1bL4Zg50RvWzEVqbL/QMakPBycZjB9eQejDLIhlBHBMMv9FMsN+xCe9FyJc2hyT/Q/jj752wOX5aDMtEfl5hP3fzkM/v70YZvyub5IhPf4KLoWJK5mA2cD51ZvrgiQv7uPKpTDkq/T9BmiXw/TM0ORUMhO3g3Yh6LYETfqOQf/mhu/3exiMWskpMxH24yfovTAc0uaT4X/4DyjCWRtMS8xHHu3Ht/P6wW/vMBhrTJs/dPioY90ITZ2MkBR1EaEPc8BKKIwPylNVcw2S1QY0FGJ8mGQE0wR7AXEAARyJ3H6m65LqepmMHq36mEQcCBBSy/k3SMI08B9FRimBk6iJiCMABJ4FDd6dSjImGA1VVwpt9zMhHkAQNKIZZ8VMJmp1wxPIXqDsC5yI3H6+znzje0EWnfyiAfEBAqdH/XdlM5H8l3h1mqY5CInjhNR8TjjlFvzzDj/S46Doz0IvWvpAqpaYmf3iCSUmJir/Euj8Qm8y4infU+yP03Kgzw/EKD73JInS89560utn9DDuLl07d5i2/zGDfC35iu/pOdHwxTvp8KkLdO1uHCU8ekSJicmUwdw9oyYQP6GLB/bRqZhXlcpNAic1UY4rQDyLwbQ7VaYZ72Ew3qOc8tXpaeQg5IgTNqc54YXRjHb46SlrBCCh11JSSzhjeXAVUoBHtOpjkUIXwpY0/waTDuO9d1Q1WINktQFNgq1R/UDJjw7B+cdSKNazEXJuheJcirzC72VsGwjbITvxulJv48GySWOYMWtiSG9jzU+bEJujXEkpz0D8vRTImWQ0E54JmjSxZotkKvaM2DbQFkN2VsYz5iM65DweS5V9gXJwK/QcUlhneA/QhaNj/f9Ev6hazP+QkSNl7wpsvS8BBG74dIwbCq6d0esxGVPdLaGjZYwWo6ZgkJ06LIQHkakN6tWrp/yrD885G7G4h3mJTWSUfx9bv1+NGD0b1LOxhvFb5zDz9C3xUcOmaOnVA+204hD1XAbwLNFrRSC2zf4UvT/2QMumDVHfzg716lnBiJ2CxKh2snDySx94DxyEHsOW4pZU1VRyDX7aFIuiVDIe98oNnjX1Hgaj1kb2lc8p5SnYu2IrFOHsU4xxK4xm6DF5KtwtdaBl3AKjpgyCHV9D2vyfG4dZokljM7ZpnPHheKoq1iCZH/8A3BlT8IeJVlNvdLAVKE9x4KDTzBMelhWpW4bU5Kd4lSOpdCGY09aBNjsyggEOXDEz45n4YPSQpuzMNQ1GR4vdElYhslQkP32FHEllPKMWmnp3gK1A6Rg5HTTz9IAli7rvRWqko6P1HzgFqeox/8MlD+dOhiGLAL5dG7h/VFRt4Zl2weKLz5ArTsfNDUOqqRADgN8IE/76A4Ns+G/ZICE/dh0+/zIAz8pRWM7lRRg1OxipJETD0evw97hGLAYzaqk7hWJPwANIiIPQ0kb1+MdxxQaoPJj4jMaQpoLafw+DUXsj+8rnlHnncDIsCwQ+7Nq4oyic8WDaZTEuPsuFOP0mNgyxq6aFFCwPVmXcrK2jzU7dZHw4nqpKNUjmxz8EWPb0oWLUE38c2wP7VUfwQNsVw76cArcKtS3B/XsJkDLpMarsUZwx/c+leLLwEB7ptcSwOfMw2pGth2e850ju415C5T2jUc8/cGyPPVYdeQBt12H4coobC7oMTTJsFvPfRp6Kx8k5IACciQUsail88eoNwZ9/ncHVAZsQJyl2lwJJkbBrGr7wbY19oz8qUQySp5/EnFGLEPEKELX6GltX9oMVG88xaon8WxcQ8VwOQIhmnl4q72wWOE/Hn0ufYOGhR9BrOQxz5o1GealkTb2HwajdoX3lckp56mMk5xAADiYWFrWyC5PlwQzGf85RVaEGyfz4hwBT8weMXpMB+H7NgEqMAG7ifPgLyBsy2TGqXA6Bicd0bAyezkTB+GDIv3ke4S/kqLxr1EOTAd+jMm6YwahBw2Yx/20oBzm5ykK+lhZq77QgHix6L8WGaRHoseI28orf0yxLRuDXE7Cm3VFMb1qshfLH8J8yDn/FiMGZdMLCbT/B04CplFFbyJF04RLuywAI6sPDq6HqhU2eCTymb4RqqWRNvYfBqPWRfaVySsrJQVE403ov2sxgMD4AT1XZGiTz4x8EbB0ToxBx5F4cus/WLjIYDEYxz4jIvYfAXCODxXxGzWIInwUbMbuNfoljEuTpp/HD2N9xJafgX/Jxd804TNv7GDK+LQb+uQXTndkxd4za5BUuXrgJCQE8w7bwaqX1nr+HwWAwGAwGg1EabGKBoUD2AFsWbUMcqzEwGAxGMde4BYu2xbHjYhgs5jNqHlFbfLfpJ/gYlbjKGa8jFmHcT+fwEkDWpYUY/d0ppJE2mkzaiLXD7MBOdGHUKuIrOH/5NQgctFt7wUP0nr+HwWAwGAwGg1EqtT6xIMt+jsS4GNxLSMFLsaZcHyhDdmoC7sU9RlqOTAOak43UhHuIe5yGammOPA2hP4/Ed/+mvRcXOMqyU5FwL+7/7F1nVFVX037OuY0uoEizUmwgRRAEhYC9kGAv8UtixJbY3yRGY15r1MTEFEuiscUWFUXFgkZFpCgoBBULqICgKIJcQPrllvl+nEsT7gWUZvLutbKyhMM5e8/smXmm7D14Ii6C/H8y/L/xv/HP1U0NMkrxMj0ZD5KfI7+0vqrxMlZ++BX+Ev+7WtvKch7gyulD2HcwCHEvmoCx8iLkPE/Bg3v3kPgsv0oSp+RFBnL+icHvBlvza+CVhrT5LZB3pS/TkfwgGc/rK/AtdAhtF2D7+hFoWy23UILbP0/DFwcO4PMp3yGmgIFen6XY890QtP5HlQ29vg7/32hGO3I/AtfT5QD46NG3H4zYt/s7/xv/wwEN64OW4uXzZCSmZKLwf85sSwTCeHDlNA7tO4iguBf/nHhD6UukJz9A8vN8/DvNqRyFLx4jMeEBUjJeQvIPc++aJEbWBLLxT8PxDWMic/A85QHu3UvEs/wqFhIvMnJaRAFk8/RYkD3HlZ3fY8Ou44h6JoCxaSvIMx/iQaYIln0GY9zMzzBvrD0M1ZRbyR8H4adNQbifKYZYnAVxlhhZ2XmwmncBx+Z0Bq8kCad+WovNR8OQIGZh1PUdfLB0JeZ4mqqo4lJAHL0fP2/6A/5BMcgx7AJLQxkyHiajyNwdPhM/waIFvujSVJUwCjGi9/+MTX/4IygmB4ZdLGEoy8DD5CKYu/tg4ieLsMC3C16djuLZX/jl59OIL6dLNsTZuTD64DDOf+1UheGFT24g8uplBG7fiF2XUlCkvIdRkXga61ekQlDlzTyYeE3HDC+TemSjSpAashubtwfg0t+JeJ5XCr6eGawdPeE7bR6mD+wEjTqTIxr7f96EP/yDEJNjiC6WhpBlPERykTncfSbik0UL4Psqc+SPEfTTJgTdz4RYLEaWkh55VvNw4dgcdOaVIOnUT1i7+SjCEsRgjbrinQ+WYuUcT5g2cqlfaXIgVn2+Bkf+fgG2vSvGf7YSS0Z15eiheIHofT/j5z1BuJ6YiWJGF+3sPDBs0qeYN9ERBjUyoBCPwo7hoP8JXLx2H4+f56JUywQ2HmMx56sFGGFRC6UVObhzYhs27z2H6PgUiBlj2A2dgf8u+xi9DVnIC3NRJNCHbrUT5jKkh27G8nUHEJqQDZGFF6Ys+wbzvUzrVy2pyEX82f3Y+ecZXItPRmqmBJrGlnAeOAGz5k+FhxlfHTERuOpzrDkSC7GwA1zGfI5vFr8Hy7Ill6Yj8s/fsP3IeVxLeIqXch2YWLli5KeLMHd0D7SqBxjJjD6M7Tv98df1+0jLzINU2xTd3UZg2mf/wfierVqcEVLk3sO5Q4dwLOgybiQ+RWYeQdekE2w9ffHhjKnw6abZZLqpKHo3vjlwHRliMcRZYojFYoizC+C8+gb2TtIFUIKU4B3Y9PsxXIpNREaBDILX1BdQ5ODWkS3YsvcYgiLTIDDrACNRAdJTMsGz8sJ7E/wwb9YIWGnVLEtPbkTi6uVAbN+4C5dSuEaugAKJp9djRWpVzQieCbymz4BXeYdUBZ799Qt+Ph2PTOVas7LFyM41wgeHz+Nrp8p7uQjRu7/BgesZSh3F0SW7wBmrb+wFR5YUBO/YhN+PXUJsYgYKZALomVnD0dMX0+ZNx8BOGnXev+IbR7Bt+xGcj7yDlMw8SDWMYGHXFyMmzcSMsQ7Qz4vBzq8W4afgfHR27gF5zFHMWD4ZhyO34r0Gj1QWIOHkFmz49SiupbNo074d2uoJUJqThkfPgK4j5+NT+2gsmnEUTv7x2NJfVLaRsPubPxGdKUZ2djbE2WLkiLORI/TB71c3YICo6j6I2L4eR2++gDhbjGxxNsQ5YuTk6mPigUtY2ad+V2YUPQrGnm17EHgpGg/S8yHVMIBpxx7oO3Q0Jk0eAxdTfuOsuYHwSsPZ/IZaR4NpOuTcOoItW/biWFAk0gRm6GAkQkF6CjJ5VvB6bwL85s3CCCsVAE52G4e/O4Z4KQGUg6vpnKdJaZewZVUh9MrvIWJh1M8Pnww0b+KqHB4s/X7DLyE38cGhJ5BV7uUsTcSODz4AQGCMffD9H0vQ+59Qsf0mOvxtwH4xGUD7Ppjw2WosHd0FIiVmTgnejg2b/RF25ykkhtZw7PseZn0+E++Y1cFdk2cj7tiv2LTvAv5OeAQx2w69Ry/Aqq/Ho4cWIMvPQYmmAXT4tWGpv/GCbQ/X8Z9h5ZJR6MoBU7yI3oeff96DoOuJyCxmoNvODh7DJuHTeRPh2CoLV/fuwsXUEiiU+1N27yjuyAAwQige+OObFUcBAAyrhW6+szHBXruSDKYjdPNyrDsQioRsESy8pmDZN/Ph9SpDFJlN851a9E1u/Fns3/knzlyLR3JqJiSaxrB0HogJs+ZjqoeZGue6FMmBq/D5miOIFQvRwWUMPv9mMd6rAKxIj/wTv20/gvPXEvD0pRw6JlZwHfkpFs0djR71gZnyTEQf3o6d/n/h+v00ZOZJoW3aHW4jpuGz/4xHy4Csr2dLuBjAWTzMEkMszka2mPt/Sb/vcWvHaGhIniB8/+/443gwrsc/hlgihL5JB3TrPRC+73+ICR4d6o4nX9cHVbHe+BO/YtNuf5wMvol0mR5MTbRQ+rIIfFM7eI+eigXzJqN3W16V/VZ3TAnIbh/Gd8fiwZmzq+DMGSHt0hasKtSruFaPNUI/vxmwiPsdxxLk0NTUhIaQDx6jgLRUAkmJBCUSCWSt+8LvY48qCTvZvQD8fDIJjIam8u8YKKSlKMovRSef2XivK1uvOTelfyAX38CRbdtx5Hwk7qRkIk+qASMLO/QdMQkzZ4yFg34eYnZ+hUU/BSO/szN6yGNwdMZyTD4cia3vtW4621+SipDdm7E94BL+TnyOvFL+a2J/QJFzC0e2bMHeY0GITBPArIMRRAXpSMnkwcrrPUzwm4dZI6xe8dvkeHByI44lKCDS0ICmpgaEPIJUIoFEUgKJpBSCHuMw9902uL5nK0IzhdDU1ISmpgh8kir3TwkkUlN4f/IBXFtx73x09jccjiuBSFMTmhoaEAkAWWkJCgtEsJ80Fd5mjUNh2fMr2Pn9Buw6HoVnAmOYtpIj8+EDZIos0WfwOMz8bB7G2hs2/knPRsAnDaWf3lQ2UI8YZIPj+KJo7P7mAK5nKL+ttAkFzqtxY+8kcO50MHZs+h3HLsUiMaMAMoEezKwd4ek7DfOmD0QnjcZkfP30eIXJSMDJLRvw69FrSGfboH27ttATlCIn7RGeoStGzv8U9tGLMOOoE/zjt6B/c9+ASk085FmhtNK7PVkO/px2X0mj4orfkDh6G/n1MiAeIyDzgSso+Llc5XukN34g31621KWdPgkZhgAQICD37x6SNCeCVnl1oT7TN9P5u7do96SOJGRAjI4brb0lreFlKXT8s35kImCplf1U+u1qOpU/lR9PR77wpLY8lnRtptKBREmj00iacpw+62dCArYV2U/9ja6mV8w5P/4IfeHZlnisLtlMPUCvTkd6ZyONcbKlLuatSMBASRc+9fgyiqo+KqHINcOpt5MTOTk5kVPPdqTDcs+z+hbUq+zn5f+50qTfEkimYs6SsAVkzed44LjsJkklD+ngTFey8Z5FPxy8QDce55GkMI2ids8ip1YsgdUnlyXBJJbXSg1KOf4Z9TMRENvKnqb+dpUqyJFP8Ue+IM+2PGJ1bWjqgcSqa5TeoB98e5Ftl3akL2SIAbc+gft39FCaQxGrvKhLn+m0+fxdurV7EnUUMgRGh9zW3iJpYzI49yItsNGnnjMPU0LWfdo8VJ9YgSVNOfqUZHnX6cd3O5Kx/WT69sQNSsuXkSznNv0xtTtps3rk9k3MK3yUU3bMLprXvyNpCtqQ0/99Q4ciUyhfWkJZ8WdonU8HEpmMoK0JqldUkniUFrgZkUBgSt5f7KOrj3KpID2Ktn9kQ6Zuyyn43Nfkos8jQQc/OplXlTdJ+yaRdachtPJ0AmVnX6evnQTE6LjRmlt1paCcsqK20BQHA+JrW9LQz3ZQSHwGFeSnUeyJtTSys5D4rd1p8fl0qnmr5NLFBTak33MmHU7Iovubh5I+KyDr6ScpQ05UeHcvTXcyo04D5tGWoDh6ViAlybNQWultSCyrRy7LrlJBnaaZTmeXeJKpTnsa9MUuunw/i4qleZQac5Z2fO5N5vq96IvgbGoxQy6myF/9qI+JgPhtetH7Kw5Q2L2nlCeVUn7aDTq5cTZ5d7Gh0T9FUMSyXiQACHwrmh8qaTTdlBMwm1zsulPnttrEK9dNIvLZlUske0qB893IxutN9QWRJPEILfQ0J5GoAw1deohinleaSPFjCt8+h9yNBKRt+S6tDn5efV9JImnN8N7luq9nOx1iAQJY0rfo9YpedCIn10n0W4Ksilzc2TiGnGy7kHkrQbneAb8HfRn1Kn1zKGC2C9l170xttXkVz4p8iCNLIM13syGvWT/QwQs36HGehArTomj3LCdqxYJYfRdaEiymWskiSaKABW5kxGdJr+f79P2JGErJzKLUW2dp8zRnMmCFZN5vHPk4OdCY9SGUVkJEhUdokj5H+0lHixt4fz6mgE/sycB8EC0/lVhNBuX59ylgUT8y4jEEnjlNCyqpvJFotos92Vibki6/zPaDWDM/OlPy6ovSaO9UZ+rZrRO10WQr8cKaFoSp3+uZ24aQCCAw2jR6/1OK/GksdW/fiyav2keX4rNIQhLKfRxLgRumkZsxn3j6djTxh1DKkDfCmhsErzSQzW+odTTUkCTSkYWeZC4SUYehS+lQzPNKNrKYHodvpznuRiTQtqR3VwdTjbCyOIi+6OesXLcttdNhCQAxImPqVoUmLjT2l7jGxQfqxCbzNM2wrqRTKv3H8DvRRwHpJKe3f7yxDn+rsF9n+vDIM5IVxtP+GY5kbPUerTgaS+mFJfT86o/kY8YjQYdR9Ps99fqq+P5hmuPSmvjC9jT0v4fpWmo2Pb99klYOMSfzUTso+uRs6qHFkkaPRRQuqTuWspxylJ7K8uj6j+9SR2N7mvztCbqRlk8yWQ7d/mMqdddmSc/tG4rJj6R1Pq7kXC4r9tRRTylHGqbUvfznzuTsOpC+PC2uxK8k2jfJmjoNWUmnE7Ip+/rX5CRgSMdtDVWDkpIm+o5KHzaKtkxxIAO+NlkO/Yx2hMRTRkE+pcWeoLUjO5OQ35rcF5+ndLmqLbCAbPR70szDCZR1fzMN1WdJYD2dTnKAlfZOdyKzTgNo3pYgintWQFLJMwpd6U2GLEt6LsvoakEd55l+lpZ4mpJO+0H0xa7LdD+rmKR5qRRzdgd97m1O+r2+oGaHrG9gS6Q3N9BIp57UtWNr0mTLcABD2qMP0PPYrfR/tibU0fNjWrX7FIXG3qW70Rdo76r3qVdrHjGsLnXC7wi8AAAgAElEQVQdtZrOP5XVxSN/fR+0Gk/O04phnUmL4VEbl+m06dxdyiiDVrJcehiyh5aNsyFD88G07krOa2JKouKgL6ifs1IObMvsPEMi425VbbzLWPolrpAi1wwjp55dyEzvFdvCsKRlZEn27/9OSa+QquTsPOqm/+rzAtLv5E5LQ0rqPeem8Q8klBSwgNyM+MTq9aT3vz9BMSmZlJV6i85unkbOBiwJzfvROB8nchiznkI4IExHJukTC5b0Jx2l4saxeBS2wJr4AEHgSMtuSkny8CDNdLUh7zfF/iShxCMLydNcRKIOQ2npoRiqak7DafscdzISaJPlu6tfibuVUOiy/uTQvRMZiphXeC0iw0421H9FOEmUONv2FUwOMCTQM6MuvfzoQLlClFDUSg8yrezvAARGSPqdhtOPdxrDAsspK3Qlebe3pMGf76YracVVfOXobX7Uy4BHjMCcBq5QgREbNMjXkPikofRTw8hG3WOQjYDjcwJotosdde/clrR5FftQ5LOLcklGTwPnk5uNF8364SBduPGY8iSFlBa1m2Y5teLW4LKEgsWNyfz66UTORAbQJ/YGZD5oOZ1KrGYhKf9+AC3qZ0Q8BsQzn0aN7W7VZTRtYkH+nI5Mbkea1hNpS9TzmgF8Tgj9p6eIGDCk5fAFXc6tg6HYMZKMWWVi4dtrdMzPjjxWRFEeEVHuHvLVKttgQvL6+fErfE6kvZMsScQwpGE7l85lymsM0p2f3Y0EDEMa3efRhZxG3HaJe2mSpYgYRoNs556jmqdznmZ3ExDDaFD3eReo5ukUUOyKPqTJ1E2oZffWkIugshDW0yxWSSwE0/GZzuS+8AxVx21SurvOjTQYEMO3pE8vFqgVwsS9k8hSxBCjYUtzz2XWYETlJD4/m7oJGGI0utM8FcyRJO2gkcasUnl/S9eO+ZGdxwqK4jYJ7fHVKhdyodfP9FjeiIpljStpd11IYYXcv+OW9yIBGBI4TKclI9uRyYBvKaoSA3LOzCALAbeHBc6r6a6sgsd3dk8hGx2GGO2eNP1wIlWLq2UepPFt+WQ+LYgKa5hNXtQ68m7LJ0bQkSbuS6oqkyXR9LWDDunraxMLEKM/hg5Umpf07g/kZWRHn4fmlQdJd/loEAMB9VwaUwcHvYBubhlFnUQM8Y370+qwrGr8Lbz6JfUUMMQaDqKN8TWY1DtryFW7Ky3kiEnSuOXUSwBihC60/NhG8jE3pneWhVQL9hWc+JDasiBGdzhtT6+N2cV0fWUf0uMZk8/W+zXIUR5d+NSKtFzXVOJNc0Zl7tP+j21Ih2FIs9sUOpBQMxSWpZ+lhU4dqGtXY+LVIbHQcLophyIWOZKQUToOO1MpeNE75PWfN9UXRIU3NpJPOwExom4082S6ykRoScJ2GmnGI0arG005mESq2Saje2tcuMRLmZNTn1EQSyv6aHK6RQ144MgSQYschdyzIh/amRpMi97xov+ceVp9ftK7tM5NgxgwxLf8lNSSRZ5OJ6Z3JRHDEN/SjwJfFQZ5BgV82IF4YEjQZRoFlsmDLJX853iRy+Av6ERaQypEGaVsG04G/I7kdzpHXRSVTvlZEF9NcDo7ZD714KtJLFT+asYZmtGF/xqJBSF17+NM5g6zKTCtZq1Wcn8fTbYSEMNoUfeP9tNDSSOtuQHxyuvZ/IbjXYOMwhu00acdCRgRdZt5ktJVCzxtH2lGPEaLuk05WC1IUnWJd2l1bwEBIF6n2RTcAoB6lcDk5c/JToOpllgA25oGb4pvtqRHw7G0YXV4i8Z+K5TYz2kaLXmvI5l7L6fgKnhERkkbPEnEMKTrsZ5UxVxyr6ymd9rwiBFY08dHH1fdA3nBNLdbWzI24hxZVc6nSiwlcKDpS0ZSO5MB9G1VYEozLJTOscCZVr8Kfkou0KwOPAIY0hqylVTDLCnd/cGLjOw+pwoouYt8NBiCoCctjallRzfVd4io4OYWGtVJRAzfmPqvDqOs6oCVvuwpIIY1pEEba5BF6R1a46pNXReGcXhcGkfLewkIjJBclh+jjT7mZPzOMgqpDljpw7YsgdGl4dvrkDwsvk4r++gRz9iHtt6vwc7lXaBPrbTIdc3dZpT0hrIlMnp2eDK143GBTKGNJ7mZW9OYTdE12rTCuztobEcBMWBIw2oC7bxX0iQ+qCzFn/y6axEDltoO/pFuFNTsE905tJiGdBKRoNN0OpP7hpiSiGR3V1NvAQjgUafZwaTWnOXG0vqBhuWFNEajdlGqutfnXKOV/fSIAUu6PSbTpivPa44z1GvOjeUfyCn9xHTqKmKI4VuSX2DGK7yUU0bAh9SBB2IEXWhaYJmcySjVfw55uQymL06kNVLi/pXEQvBxmunsTgvfFPtTId3Y6EPtBAyJus2kk6oBEiVsH0lmPIa0uk2hgzUApJKEbfSeCU+JNQxo+JaHNSRZ5JQd+jk5iJTxCrv/UFh2zRSTZUXSukFGxIIhgfkIWhec2khJGyL58yM0uZ0mWU/cQlHPa9bzOSH/oZ4ihsBokcMXl+sdA3ttzr8RPmko/dQYslGPGGQj4PiciEXkKGSU/s1OSg1eRO94/YfO1JBMlt5dR24aDIHhk+WnF6mgKRhfF50oS6Ftww2I39GP1JvIU+Rnwf93JhbkaVtokDLIzwgsaGZQbo0bPPPAOGrDchnR3qtrrw6TP9pAnkIuqN37w4/Iue9KulXGI2kcrXPXJQYgRtuZll+XVAkYxq7tR61YEHidada5PNXfePYHjWrNEhgh2S6+So3Cu+JYWtuvFbEA8TrPItXTkdOzP0ZRaxbECG1p8dWaZ1MSMpcseE2dWOBTFy9vcvD5jRJVCL0sfg25KoGO+bQglcakOHYt9WvFEsCjzrPOkWpyPKM/RrUmFgwJbRdTjeSQP6INnkJOeff+kD5y7ksrKzYJxa1zJ12Gq051Xn6dGu1ciiSKFvXQJte195SAQULhC7twgAIM8TtPoWNVPCMJhc63Uv6eJaMJhyhL+fOE7b5kzmOI4ZnTqJ0Pa5YT2R1a5Swg4Ts/UcorFkdybyu9a84nhtEgxyVXKL8GwJ64vq8S4DEk8vq54h3yDDow1pjM/+8oldeDSUJpniWfAB61m3GuFhkpoOgfBpEJnyFG04G+DFPBXdlD+s5dQABLJpOPkPgVMBa1qAdpu66le8q9JglfSF34IDB80tTUo57zL9RYxSKN/ZrsBCCwxvTxKfVwRnZ/A72jzZDAaSXdllZPOsT+NII6aDLE6E+iI4XNrNHlmXR2ri1pMiBW35s23KnF6UjeQb5KUKM2sdDQuunsNDLncY7DoPnzyXvk1jfWF/LMM/RJVyExjJAcl14j9ayQU1bgVOrIA7F6brTyWkHjJBaohELmWigTN7U5VCV0dpo596xoEM2f700jtyaqCJjJKH6NKzcvnjlNC1K9h3OCplNnPkNgBNRnXc2nzqQ3lpGDksYd/U5TTmPuUWXgltEdQ3/WguBk99aQi0hNcLr4NE01YeuUWCAqpINjdZVArh6JBTDE7/Q+HUxRnzXMv7KYc6gYbXJaHFbVhjbImhsWr7yWzW9I3jWArjvzSVcSMgwJHZfStVp0rzwrkKZ25BFYPXJbeU2189DCEwtEhXR9ZR8Or7ySXGD1+tKa2GJ6W0ej6PAWjP3Kg0lgSKvXIgrNUaObeRY0J6T6Ziy5s4VGmPKIYbSp97LIGva1lG4tV55KBEN6o/ZSdg2BLZVYCiCG35mmHKsa0JaEziersqSu0QQ6lKXKpgio9+q7KhM/8owDNNbYnP7vqLjSu+eRJR8EXjuacU69ADbVdwqif6BBJnxiGE1y+DJMhT8io4ffuZMAINZkMh0Rv7oFFlEPbVdaW0FkWtiFz9kYTU3S6zmfLtQMWOlrOw4HG398qpYAnIzub3iHtBkBOa28Xd0vKI6ln0Z0IE2GIf1JR5oxr9BwtkSe9gt5CyslWDfeUxs3yAv/guxEDAEMadr9hy7lNLIPWnKL1nvpEwsQ23Yk7UqtOdgqubaYbJRFZGAN6f2jhW+IKeuZWCAiSfRSshNwtNHy/kl9El6534UGg2jTfVkD4eDG8Q8oJ4imd+YTA4YEfdZRQs1AmJY5KG1/LcG8Rkss8LuQl7cD+fz2pthfTplnPqGuQoYYoSMtrR0gUeDUjsQDS3puK6m6OZXS9SW23HcZXRr++7OakyzyZ7R9hB4xAPG7f0GRalhdHPgRtRVY05zg/MZEFZS2ZRBpKeMYAouZVHPoMZMOjGvDFVGKetPquCYq0XgDfNJg+qmRZKNOMcjGwvElZ2maOZcIEw2aT/O9R9JW1UqE1rgq12Y+jYKaBELXrhM53c2Q7pg/a0l2cHEK0b8xsSCNXko9BSi/tsh2yfWajX/WLvLRZiqOBtVWBZy5lYaIuMCrtp4Z+e565Wh03n0KORFEMelVKS579CsNbqX8Tq8VpF6PZNPBcVwmn2c2hQLzGhxl0aNfB1MrhqNNrxW1JFSyD9I4Q06hmE0JrFGhSK4tJht+UycWQBDY0JdRanZ3/l4aqamke7/vawYuskf06+BWymqoXrSiFiWffXAcGbIg8MxoSo3MyaStQ0ScE6StR2a+u145SpVH90NOUFBMOpU0qgx8RbaarrQ2XlY+r+3DlRlp1oTGHahurEvidtCMoV40+MN1dPEZ99uc4M/IToshgCXDd3dQqqoyivzj9IExn1q/f7Rq4qDkJq3tq8tVPVh9QudzVenm6UqAJyC7SqcQZA+/Jw8da5ofWkGtCkdUQK5r49VUgcspK4gDPGC0qc+qWDWOUgmd8TNTnpgYTfurnA6Opq9sNat8K3P78HIAoeH4FUWqMFKSy0qHktGlsQfVWTIp3VjmQALwqPPsS9X3RvEZ8jNluUoph2UU28zlotknp1IHpSHv+p/wOlSAFNKRifrKYKuqxEIj6KawBcqgBEMiHSda/Kb6gvLpwqeWxGdAjM5A2pxSh7oi6U1a5ijg9orLahVXIbxpYkFC1xbbKB2G2hwqCYUtUCYRGRHpOC0m9WQZqawGEVC/71VV7IrpwFgDrgKN155mnlfxwpIg8jPjKa/FGU37xI0ZFz1Ck/QZgqA3fXNPVhsypblW7VUHpyWXlcnMuiQWiinwo7YcLeqVWBCQw7IbtVeCyzNo/1ilYyKwoUVXixt0zQ2NV17L5jck795w5F/4lCz53BHxgZtT6lBJKKWbyxxJABCj4UKrVd190uITC0RUeJ5mdeRVP7UAhrQcF1N4Pr2Fo7F0eAvFfvLntHWIBlf0pNGL/nu9ZmstT99CA5SFU/Zfx1bVQ8XRtLy3lhLHzaaLKvhe6D+Bs9+MkDw3JNdQBasOS7FkMu4APasOTGnHjKHkNfhDWnfxWbUKx9RfvEmDqe0kpIwefu9BOtbzqQJKViq2EVTGyjVjyab4jjwriEt4gSHtPqtIXe6u5IwfmbEgMPo0uipgpeivbEnTdS1VwP/tNLys2E7Dkb5SDViVdo4h3bEH1SfcygJAvM40+1L1HV18xo9MWW4vOCyLbUYd1oC2JHObMgYAEvRaXodrrcR0/CNlEQcjoG7/Ca9O0wbzQWWU+Et/ZSKYT10WhqkupguYzL0DIDA6NGpf7htiyvonFkiWRD95cT4pI3Cg/6p1arLoz3Hm5LKytivk6jfnhvcPiMQHxpIBy9Gh/czzKuhQQkF+Zty+YPVpdKMCYRWJBYAENl++OfbPv0CfWnLBYp2Bm6lu5nQZOQpAYDTIZXV1nnK/55JO2gM3q4g7yClj21Bufvwu9J8IiWrdvXkgten/Cz1q1JP+Uope2lPpw4EgsKUl12verVm7fEibqbhWvWkuIHhNfNKAMbLGko26xCAbDcdLwmiBFV95pakOOS2OUqP78mnvSOXpAUE/+j5J1iQyX5tOLDwyifQZkKD3N1S7iZxLVu1bRmKhSfvP8bsNxOAuGmAAMJpdMWRw95obeeh0QPs23NQUTx4hpdY210x5Y4wimR2GDzOq2lxHtwu8fIfByURUpYFW9K+bEJJHAPiw6D8IXdV2FWkFd3c78BlAnhGK8zGSBu7oFo1fN4WAm44F+g/qqr7JSSt3uNvxwUCOjNDzqGk6bDP17eBbjcBIRzXdQ/h60NPmeEY5YmRRTeT4FZtC8sCRoz8GqWcOWrm7w45jDkLPx0CibpcUyWA3fFiVRlSALrp4+WKYkwkar++JHEkXLyHJeiAGWyk78BRF4UpsCQgMBPYzsGS8aTW+iXr6YdvZEPy1ZzEGmLJAUTi+mbsJt4sIjMAGs/77ATqwNTcJu/b9tzie3RETPh4MnUrzuLdpHr69mg9ihHCZvgD9W6loEJr8CGIFAF5buPazUe5JOZKOHUdM2yEY6VpGLQmu+p9EsgxgBLYYMtxKZQMkxfMAzJ/xOx6UEnhmY/HlXEe1Tbd4PAYMACqIQ/Tt0opVJF3EpSRrDBxc9q0iRF2JRQkB4HXGR+sWoY+KHj/5DxPxTA6AbQNjY3Wtml4iJjoBMiiQnZqMbMUrvxb2xqTZ4zH03Wn4cetCOPLRfEN2ExtX/oknck6HjBjtUodmZiw0NEUVzdyaSDdVyCNB3n4AfN9QX8iTd+PbfcmQEcDv4Y0B7di6GCR4upmBB0JJzBZ8dzy7EZhSXy2s5ATJ0X6AL9STRQ8cWQg54ixQjbx7iFt386EAAEYPBq1U7HWeEYwNlTa34A5uJpQ2Yh9aHehos4D0b/w8ezVCnqsx8AJr9HKzh4UJTyV9Wab+5K3v0NbRrf1P2bbwGe0FPQYgaTy2f/snnikaas0tBK80KO/exJQmY/e3+5DMCTy8B7Srg6Tx0c3TDWY8gEpisOW748jG2ziKEPPjCvz5RF5T1zQU3fwR0xb/Vd1WtfDRmDq8RWK/kihE3iwFgYFO/0/wqVPN1ppevkQhce94nJwMWSWdcHPDHPwQUwRitOA573N46dT87ceJykbt/C7o69G+GjZTh6UYgT1mLBkP0+rAFH7bziLkrz1YPOBV3JqHK+E3ICGAbeUCj15CFWRJwrHjMWg7ZCQqoORV+J9MhgwMBLZDMNxKnf5ogu8oniNg/gz8/qAUxDPD2C/nwlE9YAXDAVbERd9GaaU5XLyUBOuBg1EB/68glgOs6PzROixSDViRyAFWtDE2Vt9c9GUMohNkgCIbqcnZqA5ZJ2H2+KF4d9qP2LrQsfkEvrFsiaa2EhepG4YY5jcOFnwAJMWDXd/hz6eKxvFBS8Lxy8Yw5FMZLndVics1Bn2CBZ4mEDB8tHGZg0+H6zYApqwvXzrh/alDYMACJL2N/dsvo0iVVkv5E7si7OHnZ1tLU9b6z7kh/QOgFA9v3UU+B4ShZ9BKhQzxYGRsyM1WUYA7NxNQ2uSCwYfViJFviP3lSN79LfYly0Dgo4f3ANTNnHrCjQNIiNnyHV41p3zbCRjjKAADQtGVoziWWhMGKUbM9dsoJQCyZAQevlpjPAbyVBwLiIPLxHHowGtcenYbOBhdNBgADDS7DsHg7jXvVp0O7cGFHhV48igFsibi+Ovgk4aLkTWebNS65Robx5e70+0xwNdRDb7jQ09Pm3ucciCuWYk0eWyAp6MDzkT+jNmrQ6DeRPaCm70FTHho9tG0sWcdL6yPuIlLJwJx6UY41teMggFGE1oipaEoKeYAcV1VSGdHOLapw7Jkd3Hmr0RICQDDh2W37hDWQqq2VhYwZADIn+Le7cyGjQnePYO/EqXgpmOJbt2FtQYxrCwMwU3nHm5nthQvkoGwqy1sheqfYctYJJVCVm3qMtw98xcSOeaAb9kNtZPDChYcc/D03m2oJQe/Mxwd2zRD4kWOfA1LjJk6CvZKG1B6KwxRWQqA4cN5wmTY1RqYVuDJ/m+xM4FzSPn24/F+Dc5UYcol/PJRPwxdlwTHr3ZjzcBKADX/L/y06QryCGA03DF+vKUKQ1KIK+GxKCWA0XGBp6tG+TryhJ0xZu6HcCvT1EWXcSgwBTIwEDqNxyRbVQspwZX1/4X/UzmIEcBmyhwM11cb1UZmZg7nHCmykfWigrHyfA1YjpmKURXERFhUFhQABA4fYvaAVirnEBV1k1uXqBvsbQRq6S2XEwiEl0FfwHfWFpy796LCoLJGGLD0IM6e/B2zXfWbVfJKr+3Dn7dKQABYPWe4OwjfAt3UEPpCgbRTx3ClgACw0OvSHZ3qZFwFsLGx5va+IhPnAi7gZUuJsjFCdLW1VW+TGBYVZJFBFZUZpuJ5nkqlR1WcE0VjmhNRHwztbwQWCmSFrMLg7j3Q/8Ol2HzkMm6nF1ZdB2uOKXtPYYm6jB2DFjN0XFxgw+f2ZG7IMZwt01dvuuaWglcamnevORRpp3DsSoFS13VB97oJPAQ2NrDmBB6Z5wJw4SXesqFA9oXF+OibSOSRLpxGeMOc/4oAUCkebJuJhcfToXiL1tUkOrwlYb+b4YgUKwBGADsv71eCCRVDmpgELn5DKC4oqAh2iAOxfnM0CglgdL3w/rgONeM4hRgREXcgA8Br64p+PavLo2osxYDvPAGT7eopw5IYhF3LB4GByNkDfbVUkSUPws5jMPdDN1RAyUMITJEBjBBO4yfBlt+83ym5sh7/9X8KOTEQ2EzBHPWAFaWZmchRKGU160WFDMrzoWE5BlNH2SuDsKW4FRaFLA6w4sPZA6ASsUZF4SYHWNHN3gYCtaIkh5wIoJcI+sIXs7acw70XFSEg1mgAlh48i5O/z0azQtZmtiUilyHwUhYVKV5exvGzLxrFBy29fgJnUzmpZXUc4Gqv5kW6ffHfkKfIy8/H86h1GGjYHOWBLNqO8sMoMx4AOVKP7sBJcY2eAe7+sRf3B0zFOPPGmmdD+AfVgDBY1UAY1GRAuBGxvyINp45dAWdO9dCleyfUzZzawMZaKROZ5xDwKkDidcW4sb0hZAAqicTRI0mollrIvwj/v3Th2qctWMiQcvIwwmrITMmTjiDgnjvG+xo3uj3W8VqPiJuXcCLwEm6Er4fq0KMWRMqETUlxEaipeV9nfNLAMbJmko2mwvGMsCts1SsRMBVKBFJZy0DOoj5D0d+IBRRZCFk1GN179MeHSzfjyOXbSC+sOkfWfAr2nlrSvMWtZdu4yU2Wfld4+XZ9BdQ+Rlx0LO4lP0ZaWgaysh8ipGzXk6JezhFj3gEd66ClFJlXcPW+EqIz2mBzYhEaqp4c8rQiCJRKJ1ecA6B9gzlVmVeuomI6LHJiQ6F+OnKkFQmUGbZciHMUQHu2BYgCAz0Dg7pvrJr4q8jElav3lQ4UA202B7GhoerfKU9DkUCZjMoVQy05GHN06NgctBLCecF+7KvEw8fhV5EkB8CzQr936mD85Y/gv/8SXioAgIf23dog9UIQkovzkZP5DKmJd3HjWjhCo59Dt5cvvjgWgAU+ltCqtNeeHtqCI0/kABgIeg2DT3sVX5X8jfDrL6EAA6GDB/q1UrUOoOCSP04/lQOMFtwnTVQq/Rp2+osA/LLnIRcgE/SA71gH9QBKloB4ZVAbEEBQyaMSOi/A/kqTkD8Ox9UkOQA+LPsPVl3RW3oDlyMyIQcDgZ0nPFur2wsGcOxlBf75u5ApchC9fQ6G7ZgPHTMb9Bk4ClPnzsZ4JyM0f5JYjuSwK3ik1CE8807oKHobdFMD6Avk4vKlGEiIc4oMWtc1cMRCr40hNBiglBR4GRGMSMkEDBW1BDWqBwODuptnUgXyhNbo2U0H7L1cKBTZeCGW12z25XnI5UpWwGh3g12XxoQG+vBd+S1Gh0xHQJoUstyHCNm3FiH71oJhhDC0coJ7Pw94DZ+ID0Y5wqgFVGDUdfBM2sNUkwGkBCqMxKUrJfAbrfXGa245eKVl8C738iXEcAIP1qA12tTRnLN6bWDICTwULyMQHCnBhBYh8HXUxo8P4VO/3xAvYWA0/DscODYeSfPcMfL3B5xNLdMHslQcmDMbXk5H8HGnt0GAmkiHtyDslxJ+lbPXfEu413CKoOy5pNhbXPAZDDS0dcpPjT74YzMCMxQcRu43Ej5tVayr+CrCY4pBYKDd2xN9aiiXVo2leLDq9w7qu4Vk98IQ+VwOQACbvh4qkyYQOmPB/ipIEpf8T4ODku6YNNFaLbZq9O8oXiDglz14yAFW9PAdC/X1GjIkxCeWy6KgKmCtOgf5Y4Rf5QJzfMv+GKwasOLG5QhkygFGYAdPz9bqZcPAEb2s+Dh/VwZFTjS2zxmGHfN1YGbTBwNHTcXc2ePh1CKMajPbEkEv9LLhg3kqB1ERokIigGljGtgHleHB5Qgo8wpgTTuhk6h2naahrdG8rNEZhOmTu2LPd/cgyzqDnYdSMX52x6r7riQCOw7mYdTvI2DQkuMJSv1r3bMbdNh7yFUokP1CjJqRsBx5uWUnfLXRza5L0wfJGgL7517GpRgJ5zezBmhdd4CENoYaYFAKUrxERHAkMGFoZYQLy7Fj4bb8Ki4XlyI64Ajuf7YUPSrJZu65w7hoMhHHlj7FmJHbkZZ2CodDfsCgEdpV6Hz/yDEkeszDu22bwh6z0O/qhaqhRznyH8chOvYekh+nIS0jC9kPQ1ARemyOpFId8UmDxsiaTzaaCsczegaou0hRs+QTazaRvlj57WiETA9AmlSG3Ich2Lc2BPvWMmCEhrByckc/Dy8Mn/gBRjm2hDgUN5ottyHL+Bsn9u3B/sOBCLmVAaFlH3j27Q37HpawtdZF0vFjiM2tvxHiaXNHR2qVy7SnyJBTRVDu/FZsuV576WOf0ePgxmjC1s2kId1FpD3NQMV0knF+6xbUPp0+GD3ODYymLdxMWLSUwbJsPYpIqXpWWJGGpxlyVJDjPLZuuV77O/uMxjg3Bpq2blBHDoanzR3BbfaRjfDwOMgIYNu4wsOu9gpzxYuLuPC3EjDwWsMg7xL+2MWAFWhAR7812prYYsS8SVjety/sTWsAp4oMnD4Wyl1rAz669x+Ejiq0kTwxHM3krLIAACAASURBVJFPOKeti3s/NUcpX+KvQ6eRLgcYPW/VlXNQIOv0YVzgyrnAtx4Knx7qVZAi+yZuJpVFy83QoYPq57PDwxHHERNunvYqExbyxGCEJssA8GHl6Y3OPPUq0nneSnxwdDL+SFTSneQoeBqHi3viELz/F2yYtRMBv4xWSccm0qhITnpSAapbGXDVym+BbnpjfSF7jKRUSfnPhRp1P6nBijQgZAAQIH+Rgke5CsC4JegGFmx97vghVbU1hhgxZTTMA3fhiTwTf0c9gGxIz2qGX/70Bm6nKwDwYOY7Be+2aVwa8Dp/hH0XhDCZugDbojLLAzFEpRA/jMSph5E49ccGrLafgp/2/YwptjpvR+SXp4tW2gyQR4AiH08eZQDo/MZrbkl4pfl5J8PjpFRlEBqAUAPCugs8NCoEHimPcgEYvx17q/QuNk6diyNP5OB1nIwt22eiq5BF1++2Y0HkEPwQV1JFN8qfB+Lz6VvQ58y8WqvZmn00kQ5vSdgvIuI2ZADY1uqwXx5ib5QFEHjoYNGJ093yBzjqH4liAsAI4ThggEqnvPRmOKKyFQAjhH0/D5VV8aqwlKuHHeq3fRR4Fh6BBBkAfge4e1rX3eF9+RcOnU6HHAz0vN/HOLV3ZDT+dxRZp3H4gvLELN8aQ316qHeaFdm4eTOpnF9mHTqofj47HOFxMhBYtHHzhL1qwIrgUO4KLL6VJ7w717JKvjPmrfwARyf/gUSlkiR5AZ7GXcSeuGDs/2UDZu0MwC+jOzZ7IKJZbQmrh7ZG2mAgAUGB/CcpjeCDypCa+qy8qpvR04f+W1EoIYTzxx/B9ZfFuFJSiLDdf+DOzOVVTtTnnN6B460mI6hv4yZB3tg/KEPCI6ZgtHkgdj2RI/PvKDyQDUHP6kAYN25zJ/14Zr6Y8m5znG57c+wve5yE1AqAhLqbUxYiDSF39TDkeJHyqLrMdhyDcf2+RuiFQkhvHIP/3S+xonxjZOPskcvoOGYZnAY+g2+HXdjyKB1n/C8gf8RIlN+bILsL/+NP4P3lcBg2sZ+c8fcJ7NuzH4cDQ3ArQwjLPp7o29sePSxtYa2bhOPHYpHbTFJXZ3zSwDGy5pGNJsTxbP2uzCVqKXqYh84f7cMFoQmmLtiGqMyyIlsClYrxMPIUHkaewh8bVsN+yk/Y9/MUtARXuekTC7InOLduPhZuOIn7eSJYDJuHTeHzMMHVtOL+q9KruL3q9aJiLFM3I6SonNlmTTBi1QGs79Nc3peiSnaUNRmBVQfWo09LdwYbjRyVM4YsTEaswoH1fdBg5GCZ+t3L3VijKAph0UVcJZmzp8p+AJWH9M5NxJdleNuOwMrDOzGiPriuJAoRMcXKxIQZ+nqp6HMCBV6EX8Fd7vw8XD16qlQWihencCAoCwqwMBz8PkaZqjI3JbgaEsUdzwQPJh4DYF8LU4sjryC2bL1tHNHbWtUsihAVFo0iAhjt3vBwU3kmHokng3BLBoDfCYNHONS6r1jTMdgebgb377/Fr4eCEZdeWB5oJ3ku/v7NDzO62yNotmUzOmoEiaQiMMPw+OAx/xLdpChGiaQhkIAEJcX/PHWqP2IVfvwgFJP3JOHmno34a+42jKh8xF6RjYs/bMc1KSDoPAkbVvuidRN4UxrdJmFThA/mXvgT+wP+Qti1GNyMT8PL8uiCHLk3d2HawHQUXw7EJ934LZ/YDA9MJeNSWlraIGtuWXiluXmnQHGJpEGOqUveGoHPR8SKD7H0UjZIwwZzdm7EODOlkLbyxIqdXyG8/3JE5Vc5q47s4K/ht74vLn3tBI2WvLym0uEtBvtFVmC/3h6qsV9RJEKvFZVXnjr05vCaIjMEl+Nk3M/53eHdX/WJh0flJyOsa+yvoB5LOcOzLsC0yshDRNgNlJb1PXAS1lmuX5w6gKAsBcAaYvD7o2DKNu93Sq6GIIoDrOCZeGBA7YAVV2LLKoXbwLG3tUrcXBQVhmiOyOjt4QaViDXxJII4wIpOg0eg9hsuWZiO2Y5wM3d8/+2vOBQch/RCeXlAQp77N37zm4Hu9kGYbdn8Ue7mtCVCUSViVrbVDeiDSqWV3svn4y1AMdx+t/4AHw9ch6unc1F6az92RHyJjV5KK6J4Cv9dIegxZa3y6se3Aghj1Y8fIHTyHiTd3IONf83FthGGlYKjCmRf/AHbOSCMSRtWw7c1i7dxKIpL0DDmtKQG9WKOUeO9sOTiGeRJb+PY4Zv42s5ZaZdOwT+8C8asswZPwwITR1pi208PkHnWH+dyR2Kc8uo12c3DOJExCCuHNd1dbLIn57Bu/kJsOHkfeSILDJu3CeHzJsDVtKLSvfTqbaxqTnxQV3zS0DGyZpGNfyOOfy0LiW6TNiHCZy4u/LkfAX+F4VrMTcSnvaxIxMtzcXPXNAxML8blwE/Q3K5y02rNopvYNMod7y0/joR8Q/RfG4zoU+vwYeWkQhMNvrFxxbFZKkRefnOmqPgwNq5oOE2FecinFra3Fc9x/cRZ3M5VNAVzYFzBHBTm5YP+geqi9FaY8o5dPuw8POqUuZemP8cL5bF4Yc/ecK6nJZE/TsDDl8orTzQd4OIgUulcXgnnriZgtHtX6q9Q3Tg8P34AF3MVXPPS90egteqPI/5BrvIonQh2vR1qkfsSXD17SXkNAAujASPgoaWSmAiLFHN3Atv1g4cqvCJ/hMDTsZASwO84HKNc66Z5eCZumLYhELFPxXh25xIOrJuB/hbKZj+KlwjZ54+H8uZV5YaG+hU6RFLCNbH+N+gmniEM9CsFc0vq3qhWISkpN84MXx8G+uxrqMbrOHH2NnJb6oXmrDmGfjQC3UxMoPtkNz5+byH+iEhGdlERMhMuYvP0QZi4LRFadlOx88w2TOjQhLCA1UWXITOx6vdjuHzrMbJzn+FuqD9+XjgS9kZckzh5xjl89dluPHkbLoynYpSUCR7Dg6FRmwZZc7PgldpsfrPxjgdDA/2KApLSEkjqLvAoqRB46BvovwWbSoHnJxbi4x9uoIhpBbev92DNgKqXT2g5f4mdq7xh8KroUj6i1vpheXh+C49iNa8Ob3LsdzMcUWIFAD5s+/ZVif1K/76AsEwOWLAGXhj+DgeApHfikKBcNNu6F3qr8iQV4vKTEby2feBRl14JVbCUBzzqW1IquYbQsr4HTp6q+x7UoG+OH7gIDkr64P0RrZv5O3I8jn+gtOsMRHa94VALXCy5ehaXOMAK1mgARqgGrLgVFgkO/tuhn2rAikeBpxHLAVYMH+VaR1+ZBxO3adgQGIun4me4c+kA1s3oDwtlk1vFyxDs838IeUsRiGaxJXLk5uQpfUsGPEOjRvBBWRgaGlQEWl7mIudtcWZZU4z1e49rBipLhv/208gpo1zCHuy57YGpEzvg7Qm9szAf+hFGdDOBie4T7P74PSz8IwLJ2UUoykzAxc3TMWjiNiRq2WHqzjPYNuFtWtur5tQAFea0FHU3pwpISqTlMsHXN6iRjsbvjUf/ViwAKeKP+SO6lPvbjJNHEGU7FmM78wAI4TJxFLrwAYX4LxwOyi6fT7T/CeQOnYCBTVRdXXRzE0a5v4flxxOQb9gfa4OjcWrdh1WSCm/VaPAYWXPIxr8Nx7+pieyCITNX4fdjl3HrcTZyn91FqP/PWDjSHkYCBiA5Ms59hc92P2n23mpNqDfzEbxkIj4/kwYp+LD4eBcOLupT3RFqMpvpAFtTZbWGIhvP04ublQ2mDraomM5zpLe0BJz0DnZ/tgwBTRHdYU3hYGuqrKxSIPt5Ov55+Uiuv0KyHADPAu79OtSp2l1aWgqF8h5i0y5dUV8fWp6TjVylPuZ3dUQvPVV+T2x5fwWBfb9K/RVefWEK/A+GooAAXjtfvD+kVTlAyUmKQ3LloJQ8B9kvyz5uBUeHWm7mLInEyQvpnPPDt8D4qUNVHuOXPw7HVY6YsOzriQ4qr3cKwImYUhD46OQzBn3KcIUiB0lxScqme+qGCG17eOP9xdsQHHMaC3sKwYCgSHuEFFlz7ichejjaQEtppUmcgUz5v0Q38drDtnvZvcMK5IjFdTSsCuSLs7nrJADwuzjAUed1VONufLYsoOUGvuX38OvyUHgffID4sB2YaX0fm6e4w8LYFN0HzMLuJz0wa+tl3L2+Ax9012r8+UhCsMh7GvzF1QnGapmgh+c4zP/xOGLvhuCbgW3BYxR4GbwXhx+9+YaWSRtZSPMykKnsVQFeZ/Tsqdsga24WvFKTzW9G3lV2SNrbdi8/VaPIEUNcR9lT5IuRXSHwcHBs+VdsyRJ3YcYnfyBRyqDtsO+w+0unGqqbhegxdwd+eNekGo6g4jj8NG0RzopbcGaumXV4k2O/CCX243eGu0dnFdhPhrhzF5W4goeO46fDR+k/S58/L++7IOzpDJXF+iWR5f0VtHp7VPRXKH2OhHtPa8VSFu790KG+/RXuRCAqg7uluUe/6n0PFLnJiEvOqcZfeYo/DoYWgMBDO9/3UQElc5AUl1wtcd/435EjJ/ulMmDDh5WjQy13yZcg8uQFpHOAFRbjp2KoasCK8KvJkAPgWfaFp2rAioATMSglgN/JB2MqACtykuKQlFMHKRG1RQ/v97F4WzBiTi9ETyEDkAJpj1LQrJC1uW2JLBEPU8qCqDx07tmzEXxQIWx62UKZz4H8xTOky/DWjFbDpmGiJR+AApmnd8L/iQKABNd2HUDWe9PxriHeoiHHvV+XI9T7IB7Eh2HHTGvc3zwF7hbGMO0+ALN2P0GPWVtx+e517PigO7Tw9g5ee1t0rwBIENcdIEGcXVyu87o4ONbsFbb1wfhBXMJMlngch6MkgOIpjh/5G73GjS6/NlnYayLG2grAKHIQ7H+K618gicLhQAlGTPSGdpOEHoOxZOLnOJMmBfgW+HjXQSzqY/DWJo0aJ0bWHLLx78Lxr2ciF8F7mn8NdGGhZdIDnuPm48fjsbgb8g0GtuWBUbxE8N7DeNTMFQNNJluKx3vx7c4HKCWAEfXFnKXDVDfaolKUyqiGd/yOce5f4rKkASYkcoXPYDNOMEmCu7FxKK0rs1Ou43qqpEHpI3L1wWAzTk2Q5C5i4+o8G6Rcv44Gnk4zDxFcfQaDIwdBcjcWdSdHCq5fT0XLJ0c2wsr6K7TuAw+Huh09ELVqBU2GE93Wxsb1PlbLamhAg+EcUi2LrrBQ8QL5w1BcSZUB4MHK3QPtVPk99w/h4NUSEPiwGD0ZXmUWRxKOFUM8seBEbmVvAVrc5MGw7dDZQv3ss09uh3+KDAALg4GfYaGnanOWHVZxJ3AflXcCyxDvfxQxpQTwrTF6Up/y6q/SK6swzGM+jom5fxdc/wmje7SGnrELvryo4sZFAw9Mn+gIPgBGvzUMm/lUeetBQ+GqpK/82QPcb6AS+pavm7TgMcxbCVAUyEtMQGqdDKsU8feSlFV7fFgNHo7ufPzjhjwxEMfjLODsrAsTtylYvfscYhKfIzf/JcRPE/H3+X1YN90D5k11s478GRKjA3HycpF6XWXUF4v3fAcfAxYkTULCfWmNEKbuV5vkIyursFFPv0lu38Z95d7jdx4CnzK9/qZrbil4pUF59wYS7zEM3kqPRJGXiIS6CTyk8feQVMYfq8EY3tIFvigG3370Oc48V0DQ6f/w6/bpUNlXndcZU37biEkd+K9cB0qQPtyBWfMD8KzF5hb+TTq8Um8tA1fV2E8ShYNHEyAFwGi6YubcikAMKag8INquh43Kq+tKb4YrT8UKYO9R1l9BgYz90+H23vpasFRr9PFwqHd/hacREbgvA8BrBzcPq1cwag4C53vC6/PTr9xjLcf9QwdxtYQAvgVGT/ZCBZRcgSGeC3Ait6m/w0JLS5OTJYZFu84W6vF29kls9+eC9azBQHy20FN1ACY7rKK/Qh8PqGqxIYv3x1FlIYz16EkVhTClV7BqmAfmlwFWFOD6T6PRo7UejF2+hGrIOh0THfkAGOi3NmzeHgvNbEsUmVcQmSArM9YY4uPQKD6owaChcCs7KZL9N67dqWNmQZaB+/czmzf5I3LD1A8cIWQAyg/Brr3xkOX/hR1HNTBx2jtvV/BdnojA43GwcHaGrokbpqzejXMxiXiem4+X4qdI/Ps89q2bDg/zf8Ad1FoeGOatTNQr8pCYkFq300nSeNyrAEgYPLy7igcNMXzCUK6vj+wRAg9HoPDxMRy97YpxI00qAov8nhg/xgF8hvAyxB8nMxQoiTiM03gXE/o1xeWMCjze+y12PijlTrb1nYOlw4ygOvRYiuqhRwUe/z4O7l9ebkFxpQaOkTWTbPxrcPxrm8hERAeehHoTycKo72Ls+c4HBixBmpSABna36j2aLLFQHBnK3ScJgN/FA57t1UCa0nQ8y6ruAZEsD5np2Q1Uva6NQfM/hYsWA0CG1NBg3KuLBVek488FvlgU8KxhCaQ9CPM/deEqjmWpCA2+h7pN508s8F2EgGdvlqJi9HTLqyoglVb/NhWjRCJUNkxp/KE9aD4+ddECR45QBNeNOUj/cwF8FwXgmbyFa4xKd+xqOleqJKvNnNjYwLqeOvTl1TUYPXoDbsgAfgcLtBcyAFjoGhiocJQKEL59P9eHgDVSG6i/6x+AG1IC+JbwHVdxTLs05iTOZfbCQK9KR9T47dCpnYhz1ASa0FJ3ClGegF1bTuKFAmBbD8TKH6epabJchMiyO4G1esPTXQXcld2Bf8AtSAkQ2IzFxPIyv1JEnwhCeq+B6G/IGdnfv/gaJ+KzkZ95ExfCU1XKItdkiwcTd0/Y8qvvx9ykSFwKu4PMJkAkrOl4zBzNBR+p+G+ERhTW6e9KS6UtWjfVZei/Ox/TbLmmY7L4KC6YUqvjFo9LEWlQAGD0PDDTz7mGfc5AT1cbFaqx+sqpuAQSobKBaAscsieP8aw4BQ8TW1CpHOXg8pkQFNS2p00GoL89H2BE0NCogcCsLnR1lD9XkPqmWyWx+Pue7LUSCzJZXZCaDHdCIjjbw7TCO3PnwF3UUGtueLzy2ja/oXj3ZgKP+dNsOZmTxSMqsi4V7jLEX4pAGifw8JjpV++rBJt0KLJxYclH+CbyJUjDFnN3/YIx6i+dB2s6Bj9vnQ5rwas8k+HxoXn49I9H9bj+pGntV+Pp8JaG/aIQFsNhP2GvvnDTrPmxgkt7cTRJBjAi2M75FrO7VwAgoZERDJUFIm3NzFUEiItw/ehpJMsA8KzQt1875XP5CD0fCdbJXT2W0nSGR5/6Bn/ycSW8rO+BKzydqoI8edIebDrBwHfKu1Wvf5LdhX/ADe6KSktfjCu/orIUMSfPIbPXQHjpN/V3+GjXqR1EHGCFpnrAioRdW/6fvfMOr/l8//j7c0ZO9pCdEyFClpgxIyLRWkXt2Z8ZitodSrXKlyptldpKUa3am1jVWEVQeyeIlRCSkIgkzjl5//44QZApJxE8r+s6l+uScz7jfu7xfp77M7BRL1jx/rif0SeXlyw/Ovjk/QqmqBkUkMMCrRZnVq7BSb1gRfvO/k99+/GR9QiLrY73G5bKXBf6FV98vR7nE5IRd2In9l3LUbHqG/FyJwQE+b2s/zPu4/LBf7D3TFzxLKIVRS3RapF3tdbhyvIV2J9GABKsGgzGoABVkcxBZc4dMaBDZoxqL2Lblvzo5wzELO6DoP5/6evVa0MB3+69EGwuAUzHsSULsGnZQuzw6I5eld+wxTztDVyPSUV0ZBS0eNuxRsuhfeCnF0g4f+gg8ldO/8F+vUCCZf1+CM1FIFk16YgPnOQAdLixcRn++HMNLtbriA8dsmoUObw7tkctIwlM3oOV66Kwa3kYjFt1etYkLdqVRxzco69ngAKe9YOQ+9JjDF5eeiS0SXGITShZt+obdI3sdcXGu6DjC10id2NLeJ4VEk7vNYS+RD65ePj1UXx3LKQ/fnaFnVKRq/h/uHsnDqVAv5jD51bAoJEroXzZ9K9WMisOxHf9fWEsAZrTCzF14908nTr9+FzMOlQdXdqVMXgBrzjwO/T3NYYEDU4vnKoXqbkfDY7PnYVD1bugXRl5Nikki4XyMJHM1hVq88zO4b3bL92umXH/Jm49doabS85ZmVn+zWtEmPfgYOB3/eGrHxwsnLoReZvjOObOOoTqXdohG3OUqPc0ZH3GbqXAwHy/LFVeoQkaeyshIQNxMbF5FoG0s7Pxfx2+w1lHb7jJAFg3QMMaxpCQgYcPkrL5fQbid47GoLmR0Dx5EXJOk8uMGITvOQMtAUX5lmj7NLunYO/S9bjX4CO0f+557dZoEOwPYwng4wTczVHp6BA1bxi+358CKEuj44wFGJhbR/rxCew7lPlM4Cr1EZTD4/Yy7v6LA+c1AJTwadEKlZ5sMmUP/lx3D8EfddD7jfYqLl1NByFBqW6Kj7v4Zd+AybiJnbvOQGvki+593nthcqjD1d87o7JvPbzXoDLK1/0M2+OKeIYgs0Xr0SMQYiMDMuKwaf5SXM9jlxnxOxH27wN9rqAOOh2LJTcZPF8Y18RnE7rBXSmBSbuwcMmlPGPjwe5fseyMBpRMUXXQBPT1lGdbIm1d1TDPvJL23u0XYy4D92/ewmNnN2SfGp/LwvnIQQa1ij5nODrBPuMU5n89H2fTSsyqKWI3/IbVeV5CrYNWS0jGvqjqp8zGNUvDvbS+WcnUFDzKZXPJu1Zh++0nuiInX8/+GC4fOZJ3/bm/A3OXnoUGEsz9h+K7vi++zL1w52xovfLqNd9AY1cojFHzswno5q6ExCTsWrgEl/IOePy67Aw0lGBadRAm9PXM+Ypdvm7VkIEbKwYidPZ5pEtWqPfN7/guJD/PkZXBttlkLBheOfPOxqzDcRubPu+DGWcf58vni71+FVkOL2Ha7+Q+HLqXAYDISM/hucLaM5g9cTlu6CSYVB6KmWPqI+vN/srqtVD5yd2JuuytlHZ8KkYs0NtQZlUNtSpl6rOUfdi+X4O6jYJz0VKAolIgAgv6gkbtNVy8on/ZtKJyHdR57lkXidg+eSaOVx6CL5pbvyAlw7HnjBaEAuVbtn22UJCyF0vX30ODj9rjOSlZTPuxbhAMf71gRcLdnBc9dFHzMOz7/UiBEqU7zsCCgT653N3wGCf2Hcp8v0IV1M9ZsOLfA+ehAaD0aYFWzwQr9vy5DveCP0KHTG2lvXoJV9MJSEqom36MLn7Z7z3j5k7sOqOFkW939HnvhXaG7ip+71wZvvXeQ4PK5VH3s+2Iyyj6PGfoWqK7chRH8yrWCdvww6wDSCMgmftj6Hd98dJ7rA02B7VB869Go7GdDOBjnFg4DU8fN59jHJ3FogURqNK+NUrLCqspC1fOZG6dENrcPvOxN4vR7+vDqNO7K8rICzbOr3rMBlPCckc42Wfg1PyvMb/kCOEiO2Pjmp9hQjd3KCUiaddCLMlbIGH3r8twRkNIplUxaEJfeOY2xubvo1Nz/YVsuphVGDUlEsGdPnjpfUFyjw5oX9cYEh9h/5LPMX2rFVp3LK4LADKQnuWl7EpFriuP2L3zEFIyLyN7fulRA7lSWeQjX8AFTMOtkRVRbOS9BllMOt5w02mD+ma+cmJGLDb8tjrvu411Wmgpwdi3KvyUrzuVFRPaiz8z2FwiAEqmwfw5Spv9F1OO8n/BAfy/zlWoBCiZNOacWB1JMm1nf3oETOSFF36qjZzMAKXedVRN5jGuIAeWdorTGjtQLkmUq9vwt8j0HL+qiw/nF/5qBv9wkulFZKe0U9PY2EFOSZJT3eY35nw4OsaHf0F/dTB/OJn9l9J29qebHATkLDc4nGm57vke/2xnQxlAyawxZ9/UPffXuD/aUf3edEbrsv916qZedJSBgESL9sv4MLddJf3BNmZ6X5C7D+Q/OR5YGk9Na0wHuURJrmab3yJztrsunuFf+FMd/AOzNYc2kpMDlPr0omrCeXF8jWh5cVJdKgFC4clP96cX6LeXZzehjQxUVh7No5qcvpfOm9u+YYiTOSt0Wcjzac/85s7KrlTLQYXHYIanPu9Tt3eNYbCDgjJJIiDRqN4PzClUqT3HCTWVBGR07r2ZTzalvTyLTey9ODT8ZS/Q3VnGTo4yQmbDNkvuUJeNX8ftHMGaVjJKSje2mX2KKXnmlkmsqwQBBX2+OJijj2gvfM86ShByV/bbkfbUnlEzGtHOeyifHq42kj8GGtO68RSevq/LMf5ur+3Jskojeg3axsTsbFNLyaclTTJizfFnisG3dLy+/P/orsyMmUVXqM3RcJe5sI0rjZUKSgAhWbPj8uRiyU1Fky+SGTE+kNYyUGYVwPGHc/YcXdw2DvQxoiQp6NxyLs/nFoL3/mQ7GxkBiWaNZ/P51BjHP9qp+d70aOpyyGE7+7tRDhDychwcnlsWTuWmXo6UAYRkwfbLcrUKk/5oQzNJn9/dB/6Tc37XHOOY6ipKkoxmpWuwSZt2bN+hAzt06MhOXbqxZ+jHHDB4GD8fNY4/L1zHvRfic/YZQ5DyJ9uYSoSkZLnQ9byjyyW2o6YxxFxO566reE+XfT68Or2h3g7KOvz+Qk66IoJjQkLYu2slfd6V2bDzytztGzevCVUAJVMPerm7s93vucSS7hbX9CpPpSRR6dqWCyM1RXPOBtUrr1DzDTp2hSc5YjwDrWWEzIoB4w/nXCt0cdw20IdGkkSFc0vOzS3g0w/wcy8FAVBm34MbUotTG+iYdj+GZ9aPZL3MnKP0+D/OPxDJe2kF2EzqUY6va6HP6899JFrUGcfDeRbV11W/iiCHlzDtFzk5gEpJb1fJqjnnx7wYHA95+H8BtJRJVJbpyMWR2Yg8XSwXt7ahDDI6dFvLF6u29vpa9vEpRQcHC8oAKv1G8XDmZhJXdaWjTSsuuq3LVUt5frq/4PMcbVSmrWW0676eWUMnYecgelvX5LcRqdm42wTWVIKQObP35tRnWndWE9p7ZdFmxb0f3R0u6+RIGWS0abMk23yni9vJETWtKJOUdGszZlSJWwAAIABJREFUm6fyFqycVFfvjwqfL3gwZ8HK7+soCcjp2m/H0/qujZrBRnbez+lrbeSPDDS2ZuMpp5mzZL3NtT3LUmnkxUHbErO1TS3ls1whGdXk+DOaN0MHxM1jE1XmcRubs1zbhYzK6dB1N7m6Z3kqJVBSurLtwkjmfJaGmoPqGLOmNysY6c+37P+t4PUcxUQiD41vQJca3/BQSmE1JZl+4HN6KUBARvseG/gq5Szl70/oLtfbV+72Mbc+LPDKRoGOuWjmBxoeG1OdKkmizKw0azRpw3btO7BDhw7s2KkLu/UM5ccDBnPY56M47ueFXLf3AuO1xVUXikj7J0dwfKA1ZZDRKmB8LnVfx7htA+ljJFFSOLPl3PP5yv2puwY+8wuXntyQnMOcdHZjmkv68VT6f8uTmuKyq5YXfw5+um/T4J+Z89Lj/xgc8H/sXEVJQKJJ4znULz2mcWd/DwZMvFA086JC6RND5aeiiY38rkEWiY5P3cRejjJ9TbBoz9xDKol/tDHT62W5Owf+k1YMvpl3Tkz5sw1NJVBSlmPo+jvU5eLnUdNCaC53ZtdV96jj6wXFt6uHPDwphHZySS8GQ77lPzHPZ5eUqE0c+0FlhkyI4P2zP7C+uURIJqwyZDOv3jnF2a0rMOSn89SS1CWe575tYdy0ejEndfWlyROhburFNl/P5B+rN3JL2DbuPpMPIz88xfldvWgqSVSqm3DMpsgXCpmGsf/OZq+qalYbuIE3i7jYPDw1n129TClJSqqbjOGmyBciQhPLf2f3YlV1NQ7ccPO5ZKeLOc6dW7dww4p5HNVETXlmQlWUac6v5y3nus3bePRW9ieQdmwC61rohY9X/y1PhZ721nr2rejObquft6Xm5lHuCNvMdctm8/MQJ32AAJTZBnDg9KVctzmMOyKu6oVM6lVG7Ajj5nVL+cuAOiwlyxwvuQsbfjaTS9dtZtiOo3z50B7y1Pyu9DKVKCnVbDJmE182x7+c3asq1dUGckPWwdEl8vy+bQzbtJqLJ3Wlr4mUOUk2pVebrznzj9XcuCWM23afKbKFjxyWrPhbC3NKAGUO3bmuoCIt/RznNHemXGbPD2adfylZ3z+9kt+08aG1pSfbTgrnS/NH3S2u/9iHJjIrBnz1D29rSV3SRW6e0Ioetr7sMrIb/Z4s1I84mIvASOXOAWUoh4K+X0bov5dyklMbu9K7/+YcbKrjzRXdWc5IopFXKFdHpz9X+I4u6MNqVjIqnepx6MqLzE96j/uthV44yJzYa1Mus7rUvzmgjJxQVuHXx/S5J+X4FL6v9uGALVl9W8fba3vR3cKHPRafYlI2RejUH/3ob6Vi2XbzeS67g9Rd56xGZs8t7CjrTiom/0rn5RX9WMVSRsnUi13nHmPiC2ORfmsXJ7b0ZPkPp/DHrpmCFhLNfTtyzJylXLtpKw9fTzdobiqefJHIw7+0YTljiXK7uhz215kXJtrpjD04lz39zCnJLOjXewnP5znTSuOxCXVpIYGS0ov9tzwp8FreWt+XFd27cfVzzq5jzPGd3LplA1fMG8Umanlm40bBMs2/5rzl67h525Nj1/Dm0R0M27yOy2Z/zhAneaa/yGgbMJDTl67j5rAdjLiqP8jUqxF6Gy79hQPqlHo6bnKXhvxsZqYNj956SQDf3/MNa1vLXlhkzP4jyczoFtiN322+wiKRV08WFGQWdHRSs87wtYzKJmzTozfys9rWNPUM5ZpbuSToxHB+VtmEkqSgR681L9VnXfwhTmvjz8Y/RDBiTDV9YwESzSt24rdz/+LaTVt5JJuiHjevCVWSkhW/2Md7x35mE4+K/CibWNLcPsDpnTxpKsloXX0AV0SlFe05G1CvFLTmG3zsDEDi4V/YppwxJbkd6w77i2deWFlLjz3IuT39aC7JaOHXm0uyC3jdbZ7ctZVbNqzk/DEtWVaZqRVkNvTvOYHz/lqr1woHophs6CWFTf1YwdGepazNqFLIsmkG6P1VUprS2rYMe61JzXHStaiDKx0cHOjg4EB7S6PstyUpaFbK4en3HFzqcMyh9BJUvwyQw0us9rvHhS0ztZ99CFvULUWfgTueHYPuHvdOeJ/OChktK/Xi4jM565n0kz+wgbWMMqcOXPq0OaFjwsGf2cq9FKsM2sgLG3rTVQ4qKgzj3nSSqYc5poYlKwz8Oxtdk1VLObB7gYWpPvecHOdPlSTRutOKzLyk473wMazvWJqtfz2f/SJu6k69NlP48ssIvS+mnJzKxq7e7L85uzlcce2H1N1cwe7ljCgZeTF0dXQWPaxj/NEF7FPNijKlE+sNXcmL+ROsbGGunwc79dqUy4Uzqfx7QBnKoWSVr4/pzyflOKe8r6bPgC3P+63uNtf2cqeFTw8uPpXNyCad4h/9/GmlKst2888xe8k6i40yF2j1HyXrToos+saCIWpJlsaCss5QftfRh5W6zOaRF4v1/RNc0MOPFjKJMuvqHLAiKh8apxBz0Bd8NnrdENawllGSTFih/RTuvvV83tXdP89Vn9ahY7kOXHgh/RU1Jam7fZK7tm7hhpXzOaZl2aeNTJmNP3tOmMe/1urXRw5E5bOaaY5zTFUlJUlJv1GH89lwLNgxF8v84P4eflPbOlM35/GRZDRzC2S37zbzSpEI4eLR/kw8zF/alKOxJKdd3WH868z95/NceiwPzu1JP3OJMgs/9l5yPv/Np/S9HFZBQUDO0n235pjLdLEL2MJSIiQj1v7uHLXFWXIfHuakEDv9WpjMjiHf/sPnlx5TGLVpLD+oHMIJEfd59of6NJdAyaQKh2y+yjunZrN1hRD+dN6AR21QfWKg/GSg2HjVNUiD6HjNTR7dEcbN65Zx9uchdMpsekFmy4CB0zPzQgT1IZXKqxH6+Fv6ywDWKZU5P5bkdGn4GWdmxl9O66WvOPAFyolPGgsyC0c6qetw+NqobGIsndEbP2Nta1N6hq7hrdfdVSjexoJ+IKM2jGf34Aq0VkpUlPJj0//7hCNGDmPvNoH0LFuDveYeybz6N52Ryz5mVSv9REtSqRk4ZAWjMita+r5P6aUyorGpBa2sbWnv6EK1qytd1WqqXZzpaGdDS1MTlum3LZ+LI8k8v24cO/k700RmRHvfBmzZuRdDe7Rno2qutHcP4eDFx19aVCgyks9z3bhO9Hc2oczInr4NWrJzr1D2aN+I1Vzt6R4ymIuPJ74khFP+bEsLpYom5pa0LmVHR2c11WonOtja0MrClMZKU7ZcdD9Hp4/9ewLbVrKjUlJRXfNDdu3cnP6uagaM2vHSFSVx85rSNMu+HJzVVKud6WhfitZW5jQ1VtKk/k+8rCW1l39ifRMlVabmtLQuRTtHZ6rVajo72rGUtSXNTVRUmrbgb4k5mWMdx3Xyp7OJjEb2vmzQsjN7hfZg+0bV6GrvzpDBi3n8pdXTffzUS0UjY1NaWFnT1t6RLmpXurqqqVa70NnRjjaWpjQp04/b0ooxDDSHOcpPSUhyqnusY8KrbCPpBBeE+tPO2I5V2/Tnl2PGcOTgHmxZx5229t5sPGAqt0blMjHUxfHA7IFsVsWV9vYudPOsxkahk7npYhJvzWqkb9TJnNhrY+6XX2mvLWePCiY0KteMgz7vzw8rl2GVHouyX2zPIqiubviKTcuZUenoz7Yff8YvBn7EplUcaWLtxcb9p3N3TH6TuYaHR/lRCVDu0o1rcjWmltHLurOCiTErtBjCL/q3ZKUyVdn79/PZ5Ig0Xlz5KRuWsaHavwV7Df+a344ZxeH9OrGhjx2t3eozdNoe5naYyYensX1lR1pYqlmzeRA9Kgwq1mybErWJE7rUoLOJkrZ+Tdl90JccPXIoQ9vUYXn3GuwyaRdjtFmulHnuY8TGc+4YNDcVZ764f3oFv2lXnY7GClqWq8vmHbuz50dt+L5/aZopLehWrwcnh13O826YLMqYf09oy0p2SkoqNWt+2JWdm/vTVR3AUTtevJIghX+2taBSZUJzS2uWsnOks1pNtZMDbW2saGFqTKVpS+rTcBznNTXN8l0H/XedHWlfyppW5qY0Vpqw/k+XqaWWl3+qTxOliqbP5XdnOtqVorWlOU1USpq2+O35O2geRnLT5D5s4GZCY3sv1g5pyIYNG7JhSAiDg+qzXt3arFHVh+WcrWgkPVtYkBQubD7ztOGbCyl/sZ2FCX0HbGJswlHO7V2L7uVq8sNeQzlyzBh+8+Vg9mhZk67WLqzVfQp338676KZfWc9RzSrQUq6kXaVm7DFkJL8ZM4pDe35Af59a7DbzCJOo4YmnjYWsHxWbZ+NIcfOa0KR0T67LVPZpkWs5spkX3So1YbeBIzh65BCGtmvA8pZKWro3YOjUvxmdVlznbCi9UrCaXxRjZxDun+aKb9qxuqMxFZblWLd5R3bv+RHbvO/P0mZKWrjVY4/JYbycU8CnrmZXWyVVJma0sLahrYOTXiuon2gFW1pbmNDU/1ueMPDVdqmrP6KdsSktre3o6OxCtVqt17KuWbSKkwPtSlnT0syGHf7K4SR0tzmnqQVNLaxpY2tHBydnuqjVVKufbCvzk6mRnRztaWtjTQtzX362L73E1a9C5fCSqv1SNrGXU+bdb80XMPbKMvau6ESvZn35+fDebFbViabWnmz26RKeSMpH7O4Yw/fUKlr5fMiBI7/kwA51WEZdg73nHWGCTt+o+Htkbdoo7Vm35xB2C3ClQ62R3BWvy0VLSZSre3Bdwiue48MI/tDIhUpzP7b/fBSHdq1PD/cADlx6IZdaouW15T1YwcSI5ZoN4uf9P2TlMlXYY9G5nH9TXPshqbm6gV81LUczpSP9237Mz74YyI+aVqGjiTW9Gvfn9N0x+V4s0xwepb9wR+7CbrkLVmqjl7F7BRMaV2jBIV/0Z8tKZVi19+9Z7kDOKlkvcuWnDVnGRk3/Fr04/OtvOWbUcPbr1JA+dtZ0qx/KaXtyO85kHp7WnpUdLWiprsnmQR6sMCi8CGPBgLUka2Oh3g+MSr3KTV+3oLdbRTbuNogjRo/isF7NWcXeiApLdzYIncq/owsW+K80B81umKJ38qeeASxtJqfMzJX+TTuxV59QdmsdyPK2Dqza5Ufujn1xlAqiKcnU1V1pq1TRxMyC1ja2dHDSr4+on6yP2FrTwsSU/t+eYP7KmZZXpoXQzDSIUyLz6+kFO+ainx88ZOSmyezTwI0mxvb0qh2i18ENQxgSHMT69eqydo2q9CnnTCsj6VlTXVLQpflMnjZ4nSgG7f+smvL0im/YrrojjRWWLFe3OTt278mP2rxP/9JmVFq4sV6PyQy7nFLAc9AwYmRFKhUeHByemut6wx9tS1FmUhD/MaTIiuKG8d0ZXMGaSknBUn5N+X+fjODIYb3ZJtCTZWv04twjmZZLj+Syj6vSSqZ/aoNKHcghK6IM+5SSItAnhctPhouNQq1BFlbHx81jU1MlVSaZecHhSV6wZylrK5qbGlNpUp8/6ZMIf6pvQqXK9Pn85Oyo19rmJlQpTdkip0WGVyt6BcqJKX+1o4WJLwdsimXC0bnsXcud5Wp+yF5DR3LMmG/45eAebFnTldYutdh9ym7eLgFNBZKUSL6Wx4+mxfyH8D3HEHkrAekqR7h7+6FWQA24mb3weKmUGJw9eQUZZaqjitq0GI5MhwdRB7E74jyux95HhqUT3Lxro2GgJ6xkr8FQugeIOrgbEeevI/Z+Biyd3OBduyECPa2K8AUZKbi6dzO2H41GktwePsEt0ayKPUrCq5p0D6JwcHcEzl+Pxf0MSzi5eaN2w0B4vpbBefVnq92NWI4VJy3RsEsL+FoUYqSi/8X28BO4ei8dJqWcoPb0R/26Xij1yoOVjJWdy6LLigTQvCV+u7IRvezzeubdRWxfvw1nHlihfJ1GaFpLjXy9l0kbj/N7/8b+MzeQJLOFm4cvatSvCXfzgo1lxt0ILF91BjYNO6KZd97GfHBpD3bsPok7RmVRu1ET1FSrcj3GC4cO4VTkVVyPS4XC2hkelQMQVKscrArwjFHduQkI7JiIg2emFH/M3L+If8MP4/z1O3iosINb+Sqo16AaXIwznx+/dT62XlehlK0dbO3t4GDnADsHBzjaWsBIVtJyU0HPPQoH9kTg4vXbSNSaws6lDCoGNECt0mavGHBXsXfzdhyNToLc3gfBLZuhin3JfYldxq0NGNqyB+ZcdEL7SYsx65M6sJXnnJfS7pxF+LrfMf3H2dh+JRUwq4dJh/dghK/cgIMSjV2rr8KzXQhKZ5ru0Y3D+PufCFyISYHc3Ab2pX1RO6g2vEoZFSivJkcfwb6DpxB1Mx6PZJZwcfdDnUaB8LSQAchA3P4/seK0FjZ2trCzt4e9vQMcHBzz9vUs+iDhwl7s3HsS1+8T5g4uKOsXiAY11DB9LedsKL2Sz5pfZOdhsIBH1IE9iLh4HbcTtTC1c0GZigFoUKs0zCB4ZbO+1vpl4Bz+Gnl8aASq1v8R57VK1Jl4GvtHeUGedg3/bgjDoZsZsPfwQ62gQHiXKkC+TYnGvrAd+O96KszcquP9D+rD/TnTaBH33xZs2n8F6Q410bxtIMqoctFSK07CsmEXtCiMMM1IxIW9u7DvVCzk6soIfK8+PK3zTkgPLm7H+m1n8MCqPOo0aopaalXJ2E+mHePP78Xf+8/gRpIMtm4e8K1RHzXdzQumdzLuImL5KpyxaYiOzbyRp5UfXMKeHbtx8o4RytZuhCY1c9fX2vgLOHToFCKvXkdcqgLWzh6oHBCEWuWskH+v0uHchEB0TPwJZ6bUK6LANmAtufsrmpbuh+3pgLLeDzi/5wt4yAFdwgXs/XsfTl27jwxze7iU9UNggxoozFKCoeagGQ+vIiL8IM5ei0H8Izms1F6oGRwM/2JZ5yj4usjlqY3QYG8P/LemBxxlb1jizbiFDUNboseci3BqPwmLZ32COjkLYWSk3cHZ8HX4ffqPmL39ClJhhnqTDmPPCF/I8Sajw/2oA9gTcRHXbydCa2oHlzIVEdCgFl61nGbc2Y8/Nj9Gw54Nc30xcvrFjVh80g1dO1aFxWs7/zTE/BeOPccicSshHSpHd3j71UJADbcX9GEGUmLO4uSVDJSpXgUlMiQNlZ9KYmwIHZ9ZIndh9VVPtAspnTkXe4Qbh//GPxEXEJMih7mNPUr71kZQbS+UKkEvsH5tjQWBQFACSf8Hg7wbY1Z0BpQBk3Bu7wiUlwuzFNqs2/vB5zsPXNk7QhhDUFyqBPNb1UD/MA2CforAzk+9890czojbioFBrTDvIuE9Yj9OTK4NI2FRgUDUL8GrJmRc/rEBfL/8F4/lFTA8/Ax+DhRZVVAiIx7b+/ngO4+d2DvCo+Qfbg6NBYGBeHwEX9XqiLgJp7CghcUbdvA6RM9vhRr9w6AJ+gkROz+Fd/6FMLYODEKreRdB7xHYf2IyaouULXiLNImIDYGhkQkTCASCJ2jP78G/t3QA5Cgf2ABuQpwbpHhf+e80FJVrCFMIis/rrq3Gkp0JoGkQuvXwLtAdZzKHxhjavToU0OHK6dN4KMwpEIj6JSgE97Fv/yloCMisayGwmpiFC0pqyF/Bf6cVqFzDVdhCgOQd87BM2wGhjS3eQF++htVLdiKBpgjq1iP/C6d6IYzGQ7ujugLQXTmN00IIC96uSaKIDYHBEY0FgeBd4XE8rlyIRqI2py9ocXbdJpzVApLSB23a+4urlA1SvCOxfssdBDWrJWwhKDaYkIgHJKA0hblxQX8twczMBBIAPk5Heoawp0Ag6pfglUk7iH1HUkBIMPavjwATYRJBSQ359dhyJwjNaqmEMd4F7h/Br0M6onXnz7HkVMrzf8uIxer5YXDt3hu13sQJIROQ+IAglDAtuBCGZGYGE70QRroQwoK3a5IoYkNgcERjQSB4B8i4sRJ9qpRFed8K8O38B25lUwMyYlfh+wWnoKEcTm2/wmB/hTCcAXiwfQoWJrdH6PvmwhiCYkPh2wjBZRVA8n5s2HoXBZJ9uqtYt/EYNJIRqgQHwV4oBYFA1C/BK/P45D4cvJsBQIGKgfVFThWU1IjH9ikLkdw+FG9OyBNPn+ksnu5cQHQ4O3MgBs9chQ0rfkb/YQtxLYtYTDs8AzNON8KnfbzfzPcLKHzRKLgsFEjG/g1bcbdgQhhX123EMY0EoyrBCBJJW/B2TRJFbAgMjvAEgeCtR4tzi37AkgsPQeoQH30ViS8WkEdnMKfvp1gdkwGTiv0xf3pHOInsUHhSIjBlXDhqfDUctcXFX4LixDQIX03pAx+jWKwY0gNTD9/PX3NBF4NtI7tizO6HsKozElMHV4JoMQoEon4JXl2DXdrxD6J0AOSuqFXPA+Ipk4KSGfJTMC68Br4aXhtvSsjr7t59OqdhYjzu6sQ4FsB6ePgwBRmZ/RhJJmVJW+cw66u/YDd8FD4s9cYKYQR9NQV9fIwQu2IIekw9jPv5E8KI2TYSXcfsxkOrOhg5dTAqCSEseLsmiSI2BAZHPnbs2LHCDALB2wyRdGQp5vx9A7Suik+mTkE3Xws8kY9p0dsw4aP2GL09Ho7BX2Lp+h/wgZOY9haajDvYNLgdZjr9iGVf+sNCEiYRFCcSzL2aonUNOS5s/xNzZi/D4URTqMtXgJuNCi+5ozYBZ8Pm4Zs+PTFyVQzcO0/Gij++QICNcFyBQNQvYZKCoos5gBVL12DL6ukYP3MXYtIISECG7jEeXDuDE2fuwdzLE/bimZOCEhHymzC43Uw4/bgMX/pboCSHfEbsfixdsgH/hG/B4l/mY9vVZGQAYGIkzly/j3tXzuLkhUdwrOwOa3GRVC7I4WAdhy1/7sdj/4H49dcvUc9eAaRHYcXAzpiiGoNVk0LwJstAydwLTVvXgPzCdvw5ZzaWHU6Eqbo8KrjZQPWyEEbC2TDM+6YPeo5chRj3zpi84g98EWADUQIFb90sUcSGwNA+RYr7BgWCt57kw5jRfwAmrL8MowrVUaNiWdiaEMk3T+HffyMh+TZFt08+x/ButcQt+oZaVIhehMH/S0XfGZ+gmpmwh+A1knYNu3+fjV9XbMTOQzegcPNDJZ8ycLA2g+LxQyTEXsG5U+dwW+WFwCat8FG//uhU20m8Y0UgEPVL1K9XJH5lKBp8tQ86Y1OYmprARCWHLj0NaWmpSE1Ng8a4HsaGLUa30kJ0CV57xCN60WD8L7UvZnxSDSU95B8fGY+mH/2BWwoVjI1VUKmMoVIA2vQ0pD9OR1paOjRWbfDr3skIEXdb5WVNRK34FN0+XYOUKg1RzToJl45egnn7KVgwtgXKvDVCMA3Xdv+O2b+uwMadh3BD4Qa/Sj4o42ANM8VjPEyIxZVzp3DutgpegU3Q6qN+6N+pNpyEEBa8/ZNEERsCgyAaCwLBuzR1eHANZ05fwvW4B0jLUMLc1hnl/KrCS1wyJxC8E2Sk3UXU6dO4dOMu7ienIkNpCgsbZ3j4+MG7rI1oJggEAoFAIBC8S6TF4cLx47iS6ohKtSqjtPlb3PDMSMPdqNM4fekG7t5PRmqGEqYWNnD28IGfd1nYCCEseHcniSI2BK+MaCwIBAKBQCAQCAQCgUAgEAgEAoFAIMg34v5bgUAgEAgEAoFAIBAIBAKBQCAQCAT5RjQWBAKBQCAQCAQCgUAgEAgEAoFAIBDkG9FYEAgEAoFAIBAIBAKBQCAQCAQCgUCQb0RjQSAQCAQCgUAgEAgEAoFAIBAIBAJBvhGNBYFAIBAIBAKBQCAQCAQCgUAgEAgE+UY0FgQCgUAgEAgEAoFAIBAIBAKBQCAQ5BvRWBAIBAKBQCAQCAQCgUAgEAgEAoFAkG9EY0EgEAgEAoFAIBAIBAKBQCAQCAQCQb4RjQWBQCAQCAQCgUAgEAgEAoFAIBAIBPlGNBYEAoFAIBAIBAKBQCAQCAQCgUAgEOQb0VgQCIoIbUoCYmPikSZMISi48yA+JgYJ6cIUAsE7EPBIiY9BjAh4gSBLGYxHTEwCRFQIBCWsXiXEIiZezG4EAoFAIBDoUQgTCAQGIH0Pxrf9GptuxSMhPgHxiQlIeqSBzGModp2dhiAjYSJBTjzAllEdMPHvO4hPiEdiQiISk1KhkVfFmCOHMa6KSNMCQZGREY9/F/+K8BhdESqtCmg5vBOqqDIjfssodJj4N+5k1orExCSkauSoOuYIDo+rIoSZ4B0sg1swqsNE/H0nHgnxiUhITERSqgbyqmNw5PA4iDIoELyu6c14tP16E27FJ2Rq1CQ80sjgMXQXzk4LQsmb3jzEhmFNMfk/BYyNFFAoFJDLZZBJ0nPfIjOQodNBp9NCq9Eg/bE5mk3ZgG8CnpxRBuJXDsSH085AaWwEpVIJxXPbIZhBZGRo8Dg1FSnptmg7czW+rCkmfAKBQCB49xBSvSSTFoWNM2ZhU5QRqnUdhn4NnCF/G/b1NiKZwdHDC74Wd3Ap4gauPtIgg0V4S5AYr7cIOSydy8PH1xoxZw9g99VH0BAQA/pOJHlEbZyBWZuiYFStK4b1awBnMe7Fi/YS1v84Bj9d0D5L55ISZrYOsLM2h4mRAhJ10KbfR8z1O3ioZZa8bwxbt9KwN1VCLgN0mnQ8Sk7A3bv3kZr1e0ZBMO3cAVXK6iuC3NIZ5X18YR1zFgd2X8UjEfCCApX/jZgxaxOijKqh67B+aPA2JA25JZzL+8DXOgZnD+zG1UcaiKgQdUxQEqY3jvDw8oXFnUuIuHEVjzQZYIl+4IEOCZeP4uD+At7rJFfDL4nP/ZfmXiSOHtyPx/n6fTnUfiT8RSAQCATvKBSUUBIZ1tedCgkEQMm0Bscd17wF+3r70d1ZwjalZARARfmh3JMuxkuQX+e5xhnvmVICCGU1jjkhxvWtzvJhfemukAiAkExZY9xxihEvZlI3sIeDjIBEY48WHL04nOfj0rILTt7+oy1tZfq8CyhYftAuPszumykxPBk2h0MauFApgVBU5MgITbbbvDZVLAuPAAAgAElEQVTjPZpKIKBktTEnxPgL8koa7Ouu0NcISDStMY5vW/nXXZvB90z1eVFZbQxFGRR1TFAiIpN3lrRhKVlm/Ru6h+kl9DhT7t3k9evXMz/R3DehAa2f1u5nH8nIkx+vuaD/3s0YJrxQ+nXJt3k16hyP7d3IJb8MZYijXP87M192nfQnN/79L4+di2L0tWu8fj2WienCSwQCgUDwbiLesVAcJP6O9q6d8GdyAX7z+DzC993Ak4se+egUdu+9g4yStC9BtshKecO7KC/XKlHjlYjf27uiU4Ec7p0I+lezi8wR3hXsxMtv3gke43z4Ptx4Fsg4tXsv7ojEW7yk3sO9JEJRthv+3LcBE3oEw9telV1wwsa9LGyfBqcExzJlYZzdN02dUblZf/yyIxy/NLODLCMBd+9qs92mo3cF2ImAf/c03iuX/3Dsu6GFPmsQj07txt6SmjRe0S4yR29UEEEh6pigpM1uUMrb+w24G0UGU1s1Spcunfkpg8CRCzCpmf1L2pqPI7F49ExcMFOjtNoZNi+Ufpm5I8p6+KBa/WaobRSFs3d1gMwRLaZuwO9ffoSW7wWgmo8Hyri5oXRpJ1iLpyAJBAKB4J1VCYIiRxcXi1tJj6ApiNA28kGDeq5QZD7KUTKuiMAAxzwHrDj3JcgJYxipinDzJWm8dHGIvZWERxoxizSMXSSojFWQhAXfAYzg06AeXJ8FMioGBsBRJN5iDtU4xGeYoeGICWjjnIfxX3hGc55xauSJvhM/QRX5fdy9p8l+kypjqETAv3sa75XLfwPUc1Vk+p4E44qBCCihSeOV7SKpYCyCQtQxQQmc3hhB9SYet7w8+s75BR3U8hfqNvH44lx8PHwdbueSpx4dmYgeX4Yhjkp49JyLX0PLi2dJCwQCgUCQBVEXiwFN5CVEawv6K2s0/2UrlrvPwOYrKlTtMhwDayhK1L4Er4sSNF6aSFwquMO9C0Ev7CLIO5Kb/4Kty90xY/MVqKp2wfCBNURRLmZ09+4iQVkLXVuoi6Q5q6jYEs28f8Leu3ehg4V4ZrzQeIVNGvhl63K4z9iMK6qq6DJ8IEqqXCtWuwhEHRMIckFWuhOmz/kH/7X7DVGaLO9SoBbRSwfjkxB/rOpZ9qUanZGwEyN7TEREEmBa/TMsntYaTqJxJhAIBALB83NeYYKi5jFO7juEexkeBf+pmTfajZ6FdiVyX4LXSgkZr8cn9+HQvQx4iBERdhG8SiDDu91ozBKJ97WRcfcuHrhWRfWiusRW4YvqfmZYfzcOOpQTjQWh8QxQ/tthdIlPGsVvF4GoYwJBzsjg0PInzB8cgWZTTyMt63uadbHY8FlfzKq9BUN8sjzPKOMGVgwMxZwL6ZBKNcSE38ci0EJYUiAQCASCl6usoGhJP4yV6yOhfdv2JRAgHYdXrkekcDhhF4HgDeXR3XtIc/eCZ5E9G9kInl5ueHD3rnhvkdB4wi4CgUDw2rBC8PgF+LKm+UuPMsxI2IWve0/G0UdP/ucxzs0KxeCVN6CTu6L99EUY4qcSJhQIBAKBIBtEY6FI0eHKoon4PUr7lu1LIAB0VxZh4u9RYuFA2EUgeGNr9J27ibBwUaPoLkJUwNXVESl37yFNGFxoPGEXgUAgeH2Y1sJXv41FsPVLr3JGcsREhI7diwcAHh6cgJ5f/Y14quDdfwFmd3ETdxwKBAKBQJADb3FjQYeUu9cRdeESou88QHqxXyqYgfjd49D9q+2Iz3ib9pUTj/Eg9gouXbmN5Mcl3C/ionEp6gbiH+lEBnhVj4vfjXHdv8J2gzpcSfGhVz+OorFLUYVCCuKiLyHqRjzevVAonjygTbyEfzcvxx/LwnDqrq5E+Wpx2flR4m1EXzqHc1ExSM66zph2F3cSX//Co+TsjzZBFaAswn2Y+TVC60o2r0GHvBjycYi+FIUb8Y/wJob84wexuHLpCm4b0tkfP0DslUu4cjsZBdtqSdBdBRj7R4m4HX0J585FIeb5QMTdO4kGbIS/WXZ5s+ugqGOCN8l2JWf+ZeQ3DPN/aA6Hl3oLaTg9rQ++WLoUn/ecjKMPJVjWGY3fJzeBrbgUUyAQCASCHCmmdyzocD1sKmZsjcS9+HjEJyQgPj4e8QlpCPzxJBa0NUb6jX3489fFWLfrMM5fj0e6kTWc3LxR8/1W6Nq9E+q7GedPAN/+F7/9OAUL1x1CjNIRzlY6xEVeQpzKA3Uad0C/z4agfZVSOV91oLuOsKkzEHYxDvHx8bgXfw/x9xKQVH4Idq4dBHd5Gi5vmoqJM1dj74V4yOy90KDbaIwbFARnOYCUGzh+8AB2b5iP6Qv/QfSjzIc4ZkRh8w9jce2F1Qu5UzD6fhz87EVQGTHY/ss0bD7/bP8J8Qm4b98NK3Z8Df+sI1ac+8p2/piIk6tmYdaStQg7eBNKFzfYqx4iNjoO8vLB+LBTKIb0b47yptn//NGRRZiw9DDuxMcj/l58pk88RI3xx7GkiwWANETvWoAZv67FP8eicOehFkpLF1SoFoRWfYag7/tlkT+vyED8kT8xbcZirAw7isRSnvAopcWdyCt4pA5Ai84DMGJYK3ia5m9b989tw/LlaxG2+ziibsUhiRZwKuuHoFbd8XHvFvA2KaIwKtB4PcKRRROw9PCdzO/q7ZvwsAbGH18CvXmjsWvBDPy69h8ci7qDh1olLF0qoFpQK/QZ0hfvl33Ruim4cfwgDuzegPnTF+Kf6Edgpk2iNv+AsS87HIL7fozg3N5yVkgfMpxtC3McRWCXF0m7hvBFMzF/zT/4L+o2kh4r8hirnM4zHkf+nIYZi1ci7GgiSnl6oJT2DiKvPII6oAU6DxiBYa08YZpd/g67iLjMWL0Xfw8JSeUxZOdaDHKXI+3yJkydOBOr915AvMweXg26YfS4QQhyfvXru3TXwzB1RhguxsUjPv6efr8JSSg/ZCfWDnKHPO0yNk2diJmr9+JCvAz2Xg3QbfQ4DApyziG/GyoP6BB/fBXmzV+FHQfPIDouCRpje5SrXA/Nu/TDx+2rwjrpKH77agSm7kqGew1f6I6uxsfffoQVB+fiQ1sgZvsvmLb5/DN7JsQj4b49uq3Yga/zSrxvQN59eGEjZk2ZjdURsZDZlYargyWUjxNx82oM4NUaQz+pgiMjPsZq/5U4P6shXt/DBeTw7jUT04t6EaP2MCyqXZBfpOFa+CLMnL8G//wXhdtJj6F4pdoHZMQfwZ/TZmDxyjAcTSwFT49S0N6JxJVHagS06IwBI4ahlachEmxR5IkMJJ5chVmzlmBt2EHcVLrAzV6Fh7HRiJOXR/CHnRA6pD+aF7BAZCSexKpZs7BkbRgO3lTCxc0eqoexiI6To3zwh+gUOgT9m5dH9um+kLrrlcv/dvwybTPOP82HCYhPuA/7biuw42v/7MX8wwvYOGsKZq+OQKzMDqVdHWCpfIzEm1cRAy+0HvoJqhwZgY9X+2Pl+VloWJhALAa7pF0Lx6KZ87Hmn/8QdTsJjxWWcKlQDUGt+mBI3/dRNv9B8Qp1UNSxd62OFYRiPZ6i0s0Z93Fu23IsXxuG3cejcCsuCbRwQlm/ILTq/jF6t/BGwac3hpx/Gbb2e4TOwS/hJ9Bt+Q1os77LWROFBd26ASAkxxb4cfEo1Cz24xMIBAKB4A2DxYKGJ6a0pn8lL5axNaFMAvUV24xtl97msbn/Rz+nMgzq9T8u2rSHx86e5ZGdS/i/rtVpK5cos/Bim/E7eEub2z50vLdnHENKe7Dx54v4783ULH+K55F5oaxuI6ekVPP9sbt4W5fToR7nT62q08/TldZGEiXoj1UZMJmRmkTu/18wPev05cwdZ3lyUReWMZIIyZx1J56khmT6we/4QU1/+vv709+/El3NZfpzlVmzXPUn///sU7vLHF7Iel6aM5zezp9+nmpaKSX9bwEqfL/kofTnD7U49/Ui6VGrODxITZXKjU1HL+fR21l+kHqd++YPYoC9kmYeLTl+121mZ+7ENQNZq7IP3R3MKH/iE1CxxcL7pPYWNwyty4rB/fnTsp08fj2J6Sk3eWhRf/pbyQiZNWuN2sV4XV6uF811nwXSSSmjVZXenHMglponf0s+z1VfBNFBLqNFxd5cGpX7SeviD3J2aB06KRW0q96VY5fu5blbSdRoknnz+EZOHxhCz4ptOXX/fo6prtTbsvxQ7kk3VBgVZLwSuWZgLVb2caeDmfypH0PVgnrzbuDQuhUZ3P8nLtt5nNeT0ply8xAX9fenlQyUWdfiqF3xz49b+kF+90HNp/5UydWcMoCAjNblqr/kb/61u3DOBW2R+pAhKPRxGNQu6dw7rAIVAKGsxjEnNEyPXMZ+tSsypCBjlW0orONngU5UyqxYpfccHoh9GglMPr+KXwQ5UC6zYMXeS/l8KGh4/KdWrO7nSVdrI0pPYlUZwMmRGibu/x+DPeuw78wdPHtyEbuUMaIEieZ1J/KkphDufvwntqruR09XaxpJT/xdyYDJkdQk7uf/gj1Zp+9M7jh7kou6lKGRBErmdTkxu50aKg+kX+aaYXVpr5DRslJX/rj+KKPj7vHaya2c2acGbWRGVAd2YAv/qmz3QzhvppFMWcUu1vqc1WV1KkkNz0xvR38/T6qtlM9iU+HLL/NIvCU/7+p4fc0AVrFRs9G3mxj18MU/J/PimhEMtJdTgpzqPmFM45tD+r+f0VOBp74Y+ONlagu7zb3DWEGh3161MSeoSY/ksn61WTGkkLWPGkav+4yBTkrKrKqw95wDfBbyyTy/6gsGOcgps6jI3kujmG4AnWfQPJEexVXDg6hWqejWdDSXH72d5RhTeX3ffA4KsKfSzIMtx+ei6V7Ir1GrhjNIraLKrSlHLz/K50NoH+cPCqC90oweLcdzVzYbLbTueuXyP53t/P3oqbai8mncKuj75aFsx053fQ0HVLGhutG33PRyIDL54hqOCLSnXALl6j4MK2QgGtQu6Xs5rIJCr72rjeEJTTojl/Vj7Yoh7P/TMu48fp1J6Sm8eWgR+/tbUQYZrWuN4q68g6IQdVDUsXenjhWc4jqeotHNOsYfnM3QOk5UKuxYvetYLt17jreSNNQk3+TxjdM5MMSTFdtO5f79Y1hdqc895Yfuyb1uGHD+VVTo4jbz4wpZ/DfLR1KUZY81sUU29xAIBAKB4G0Cxb5HbQxXfORKOUBIRqwYVJfqCu0448j9bL6cwrML2rOMUiIkY5bv9BvP5TD50d1exY9cTVih8yweup39alZi+KespJIIyZRVv9jN+3kJuMsL2NpRltlYmMSItaGsXH8sDyWR5H3+3sr0qRgxCp7G6y+qD+05fldL+dyCbkF4eGws65hI+VvsL8Z9pRyfzhauSkoqb/bbGJvjwkrahfls7SKnZOrNnstyW4BJ5P4R1WgkZQrx365x14gGDP50SzbNJA3Pfl+XxhIoKTz4yd8PcxG1UVzSxYMqSaKx32Bui9Nl1y3gjoHeVEoSjX2GcGdiDr5w8U/2qmhOSTKhd8+lvJCa7SAwdutw+rt50ctRbvjGwqv6RuJ+jqhmpPdVVQv+dm0XRzQI5qdbbr08Jpqz/L6uMSVIVHh8wpzNq+W572pRmXXyVAAM70OvhuGPo3B2eamxsGsd+9UI4PBCjRWpiVrCLh4qSpIx/QZvY/ahsIMDvZWUJGP6DNnJxBwWJBa0dtQ3TpQBnBSxlqGV63OsPiny/u+taPpkUm0UzGnXDTElS+flBa3pKMtckJkUwbWhlVl/7CHqU/HvbGX6ZMHGiMHTrhdNHtDFcn1fL6okiQqPUG6488J2dHe4prsb5ZCo9OzDDbGZf9de48pBwazV+Auuv6l7MZA5to6JPjbzWJB5E/KuNnoeP7BRsEzoZibmsogRtymU5RSisaBfQ83aWNjFdf1qMGB4IWsfNYxa0oUeKomSsR8Hb4vLZnFEx/gdA+mtlCgZ+3DIzkQDGqqQeSLlOKe3cKVSUtG730bG5uzsnN/ahXLJlN49l/FyroORwuPTW9BVKVHl3Y8bc94oL8xvTRe5RFPvnlyW20YLqbtesfrz2Ng6NJFyaSxooznvAxsqyoRyc2Jui2qbGFpOYZDGgkHt8kJjYde6fqwRMJxbsrm6SHP2e9Y1lghJQY9P/mauUWGoOijq2FtdxwpH0R1P0ejmdF78sxcrmkuUTLzZc+kFZj+9ieXW4f508/KiozwfjQUDzr+Kmvu7P2dlY+mlxgJktmw84zw1FAgEAoFAkBfF31igjjd/CaFRZuGW2Tbm9HO5le0k7vuiMlUSCMmElT/9Jxuhr+PNWY0yJ6oSleX6Mex+trMoLu1gRxlASVWT40/lIRd0VzklyEg/uanZnT1q1OO4k+lPBeGp7wNoIenvvKjx7eFsJneFnFylhXNwOXmxNBbyuy9d3BYO8DKiJBmx2ugIpuQx1vc29GYZOSizrMtxETmL5rStfaiW64V4o6FDGdJ6LqNyUMPa89+xthJE5hWv2YpgpvLYxEBayUDI3dl/W1LORxmzmG1sZYRkRL+RB15a6NLFbeVgPxNKkNE6ZArP5Noo0PLKglaZk8eiaywUyDeYxq191PpmnqoRhw4NYeu5UTlMNrQ8/11t/cK4XM0+Yak5nuerLqAXlQ8VOBMVyXEYsLGg8GRwSFW2mFPIsUo9xomBVpQBlLv3Z86hoGPM4ja0lYGSkR9HHkjL9jtXpwTp87eyJrv3qMF6404+zX2aU98zwEIiINGsxrc8bCDf112dwiAj/YJMze49WKPeOD5Lxaf4fYAFJYCSWQ1++9xODZcHEsP60l0hEZKSdb6/kO2YaI6PYdXM3JT74vqz2AwfXE4fm7ksyLwZeVfLs+NrUilZsN1fD/Ox8KgSjQVmbSwo6Bkcwqot5hSy9pGpxyYy0EpGQE73/tuYc8jHcHEbW8og0chvJA8YbDAKkSd0cdwywItGkkSjaqMZkZLHnu5tYO8yckJmybrjInJYWNYxbssAehlJlIyqcXTeG+WG3mUoh4yWdccx53T/OhoLZFr4YJaT59xY0J4dz5pKiRbt/mLukaivV6oS3FhQeAYzpGoLzsk5KPhdbf2+5Oo+DMs5KAxYB0Ude3vrmAHiswiOp2hsp2Pc1sH0M5EImTVDppzJ/Q4E7RUuaJXZMM61sWA4fy0eUnh4XB39fP6F5oLMsh6/O5ZKgUAgEAgEuYPXsdO4eU2oypyYV//2ZN5XA8SvYw+1fhFVUnrz030vSioNj4yulLmYB0Lpx1GHs9/qvYUtaCY9uxU590WBOM5totKLCzNLurRa+MLt9km8GL6eYUdjsxdDhZ5cRXBkRUXxNBbyta9k7vzEgwoJlMzf58zofFyNrDnBMdWUBCQa1xqf46NR0vcOY3mFvjGkMvfnyEO5yMvkJWydebW+MvDHbK9S1F6dzcZWmd+pPpa595ASuKxDKf2E06UnNyQ9/7eNvd0yJ0xe/HRfPgRmyip2tpaKtrFQEN9gOvcOK69frJZUNPcfydzN2zrzasjcFs5edQG96HyoYBTVcRiwsQBQWfHLQo6VlldnN6ZV5neqjz2Ve75NWMYOpfQLki49N2S7IBk3NzN/y8xo6dKKC194XEjSxXCuDzvKWEPOEOPmsolK/3gpM0sXtlr4wi3+SRcZvj6MR1/YqeHyQDyXtrfRT6jlpdlvRw4nlxbGUBd9rZJZt+Uf8XmPd8TIipmNpJwWZN6UvJvCVV2sKUHJmhPO5VFb0xg+uDxLi8ZClsYCCGVFflnI2kftVc5ubKW/elhZnWPzuIAiYVkHlpKBkLuw54YkA4bsq+WJ5J2f0EOhf8Tk+zOj8/EYCg1PjKlGJUDJuBbHZ+fsyTv5iYdC/+il92cyfyE0htWUICRj1hqfg059TY2F9IiRrKjIubGQsqoLrSVQWXMCz+XhoGnhg1m+dMltLABKVvzyUC55IplLWmdeLa8M5I/ZB4XB66CoY29rHTNAfBr8eIrIdgkb2dst8wIlr0+Zv+lNZ1pLuTcWDOevxUjKDvYvI3/5rgVINK02kvuSxYKRQCAQCAS5IXvd73gwMTODlNeXSjVDaIdyUACg5hIWTv4LtzKyfkEB7/cbw9NYAiDBxKsJGvtk/+Iwc7fSsJPpXyh142o0tHns+smxZTzSovIHzWD/nMUs4BncCs38nYrm5ZPFOTr52JfuyiJM+uMKtAQUviF4zzUfP1J4I6iuC+Qg0o7OwuR1CXnYmtCVfg+tquViUYUlLM0yv50Yj3t88QuPcWT2DIQnEYAC5Ro2gleu75GzQkBAZSgkQHdnD3YcTX/6F+2J6Rj31w3oACjKNUfbWvl4vZrMGCYq6bWPV7aeTB1Kv9cKuZvXEnrzEonx90ADHnZR+9Cbdhx57BDlm7cu3Fg9PoLZM8KhD4VyaNjIC7mHQgACKisgQYc7e3YgSyhklxShrfwBmj2fFGHhGYxWzfzhZNCk+HSneKStjA+a2T8fAhaeCG7VDP7P7dRweQCPI3HybDIyAECyhI1VDi+bldvDsZT+yDIensGJC48LHchvTt6Vw9zcDDJo8N+0gRgffjuX+qpEhep1UaWcE+QQPDVx+eZoXagxAB4fmY0Z4UnQh3xDNMrd6WEVEIDKeqfHnh1HkW7wkC1AntBdwaJJf+CK3tkR8p5rPiJEAe+gunCRA0w7ilmT1+F5b9fhyqJJ+OOKFoQCviHvIX8hFIS6+o3i6KzJ+H/27jusyvr9A/j7OYPDXrLBgVvEHJB7VzbUXFlpw52aOfpWtk3Thn3rW2maZmlpVpYbVxrurT/NiQMVEUXQAygyDmfcvz8Oigs4wAFB36/r4uq68jzjfJ7PfX/W8zyn1NO9HZt/tasrXFSA8f++wYiJG3CxgI6utlYTtGhYHQHqchsU6Ny9cQF9bA3c3XPHEZIK/d2Dwv7tINux+7Qds+eVts/5lE7ZmfDvlAn47ZzZWq8694RtwxsnFDy8sWN9LTOZ2Pu/8bllcccvUSLz3/9h8Dt/I8XCPgoREVHxe4Plgg5NH28Pf7W1Q35l4xKsvnRrC+/a/gts/Xc9li5bj/1bvkB713w6e07OuZ0iQXZWpu2TpppQNG7sU1EKrBRYkBC1GNuuCQAV3GvXQzWbBqJa1K9fyzp5ZEnGmkXrcKWQ7rhDnXCEOxT8GdX1C2E0wnR7Z890BCv/joVRACga1KhbDw6FhIFfzerwVgCYz+PooeQbHeRd837Dgezc7xzZEo0cKvhlVBxQJzy84PJQVMgrXhMsFa4OVZTzKP1rZTqyEn/HGmENhRqoW6+QCqzyQ83q3rCGwlEcSi7o6msQ2rhx7kJtGc4zhTZGY1sOarc8kFvUSl6Zq/M9vNzSplgsFSVm7JB3oUPzJzrCVwVYLm/Ax53qIazjy3j/u7+w8VAiMiy3lnVw/7mIerdxwRN8DxR7XAMTjqz8G7HWSg9NjbooPORrorq10uP80UNItvvkie15wpIQhcXbrkEAqNxro55tlR3a+vVRK7d/mLxmEdZduWWniFq8DdYQckftetVsW8zS1kd9605hSV6DReuuVJiapGv+BDpaAxEbPu6EemEd8fL73+GvjYeQeGsgQhXcH3Oj3kXjchqIikMdhBccFFDyggLGO4OilNtBtmP3VztW3nJ6KZVdzi7M++0AsnPzYmTLRrDL8MbO9bUsxrYp695Bv0k7cFXcENG5A4I1t62cSA5OzByK15ckgmsLRERE+Y34Kghtkyaor1Fw3iyQzJ3YsDUbg3s539I58azTHt3q3LyVGenxB7Fn31Gcjk9AQtJlpJzccGPgLEXpLSvBqFJV9QBXlTRsXL8XBrGWtVclWxdZVHD38YajAuSIBVe2RmOH4Tk8ocu/k+3u5WV7xRTLHR09S/I2bD+ee4ue4gJV6j5s2lTwHs0JmdDmLjil6VMBVAbMp7F525ncu27VCK5WtXSeTCnTUbo7vLxsD3uxWCpgHaoo51Ha18qC5G3bkRcKKqTu24SCQ8GMhExt7t2fadCnWoDKqnxjNbhK1TJfbFWCq8CWVGy3PAAADrXQoK4rVEfTYLGk4JLefPfm03wVaemW3EPWxUO1NRUkZkqedwHAs9sEfN5zA4YsSoDRlIaTG+bh0w3z8KmiwMG7JiJatkab9k/h+Zd6oLEvn1Ww+zWwJGPb9uO5bZYCF1Uq9m3aVPA+zQnI1ObeMZumR4EhX8zvZWueSNu4HnutlR0qr0o2L1qq3H3gba3ssFzZiugdBjx3vbKnbcT6vQbrRKnKC5Vs3yl8vB2hIAdiuYKt0TuA556oGFXJsxsmfN4TG4YsQoLRhLSTGzDv0w2Y96kCxcEbNSNaonWb9njq+ZfQo7FvuX5qSHH3gu3NoNxlEry020G2Y/dbO1aucnoplZ359GZsO5Nbr9TBqFbVPh1Zu9bXMmCJ/wOvDvoeMQYFvk9NxvzFz+LUqJbo/sMJ6+LI9UtjOov5r41A+4i/MKAa+y5ERES3qzALCyp3P/i6KIBBAEs6zsUlAQi9yydNSPq/pZj3y69YsGwDDiQ5oEbztmj1cEOE1QhHLbdTWLJ4H9KK2j1Uu8DV5QFeWDDF49RZw427mBwcbb+3RaVzhIMCQADzpTicSbMA/vmXpUqlgu0vEpI7njqxJJxHklluDBBPr52BabsL32Pznr3RQnFCeIuA3O98GqfO5XXzPby8oVT4C6mCSlWEbyFSIetQhTiPUr9WFiScT0JeKJzG2hnTUHgoNEfP3i2gOIWjRYCqoKQIF1eXMl5YUKB2sb7mo9ABo73yAADAG53790Twstk4Z07G/+08AdPjDe5oQM3n9+NQogWAGkHd+qNrSR/nqEB51zpBEYp+89bBIWAgxszcieTrI3MR5OhPYkfUSeyI+hlfTWyI/l/Pwzf9w+HKfpj9roElAeeTzMgL+bWYMW134fts3hO9WyhwCm+BAHsHtM15woT4U2dzJ9AAODjafs0XvHQAACAASURBVAetSgfHvMqOuDNpAPxzQ+gUzubtFLaHkAo6RwdY92rGpbgzFagmqRHabx7WOQRg4JiZ2JlszK0TAsnR4+SOKJzcEYWfv5qIhv2/xrxv+iPctdwGBUrWZSnldpDt2P3XjpWnnF5KZWc6fQp5wxsPeHnbZ3Rj3/paynKOYMrAkfjrnBnqqi9g2qyhqOOgQp3JszBmx+P48mD2LdfDfHEZ3hwyDc1Xjir0SUAiIqIHTQV6C4EDdDc15Dk5d77z03RuDT4b/Tq+Wn4cV3XV8eSoqdgy6jk0C8y7EyNn+yF8XJz+k0op0uDmvmPJQrbBHl1wA7KzSvlUb77rRxWAzh/PxxfNi9ELFAMMN76zArVGDYU5o+LXoQpUl0tY4Lc8xaAK6IyP53+B5nYbEKmg3IOkqFJsG6jbLQ/k8uz8Mf730ia88Msp/PvLFPw9ciY6e980wWFJwT9fzsIuI6AN7YOvJnZDJdV9EjNF4VgXfaZuRZeR6/Dbr4vw9+Zd2PtvDBKu5E1umtP+xezBjyIxayOWDa/L1yHZr/G76Y5tFQI6f4z5XzTHvZ0DsTVPWJCVbbDLRJ/hpspuycqGfUIou4JVBkfU7TMVW7uMxLrffsWivzdj195/EZNw5caduGJOw7+zB+PRxCxsXDYcde/LQCztdpDtGPtcFa/fLAZDXl5U1NCo7bSwYOf6WnrSsXX8y3h/fQrEsT5e+2kKegflVnSPthj/03vY0vEj7Ey/5aVgSIn+AIO+aIX1H0TAEURERJQ34qsozGlIvSo3OkHevj63/HPmv1PRo+XT+GjJMaR7d8Sn0XsQ9dnLtywqlMcBz8XdS7H6UFoZvLexhMdSe8PLM6/jmZNt+w9sWQzZNwayisYTXp6lW+00/v55P7ItGbiaXsxOucob3jfOVWDIzi71u5vuq/HQxd1YuvoQ0izlrA7d4/O4o1xKLxLg75/345CScRXpD1AFtlseuJEPgvFEv86oGxAAt3NzMODp1/Hz1tNIycxE8rF/8N2Qx/D8zFg4PzQQP62cieeq2CHPVaC8e3vXwq324xj68Q9YvPEA4lPScOHIJvz5zevo3tAXWgUQcxLWvPcG5pzjW4vtWOnhn1fpkXE1vQK1WWp4e3nmTbbmZNv+Q9IWA7LzKjs8vTxvCiEv5IVQDmwPIQsM2dcXwxRoPL3KeR8vn0h0q43Hh36MHxZvxIH4FKRdOIJNf36D17s3hK81EJG05j28MedcGZ5jWZZLxW4H2Y7dy3asPKTF0ik7lbc38oY3BmRnS/msr6WVf5a+jgFf7kem4oEWH/yCTx65Nb87R76Nnz7uAK/bq5ukY+eng/DRlnT2N4iIiG4Z/VcQptiTiLveQ1KHokEDt7x/TI/Gu8+/iZUJRkBTHQNm/46xzb0qwJcz4vCcNzBuUVkM6Ep4LHVlhNerlFumFqTq9Tbux4J0fQqyci+dpnYjNC7lR+5VgY0QHpj7DkxLCi4mFvMWJ4cwNK7vnDvRIdAnJcPMnGF7jTs8B2+MW5T3uHV5qUP3+DzuKJdSTO+BjcKRFwoXkfgA3e1ntzxwnfkopn+0CR1+P4GYzT9iaK3j+K5/S1T3D0S9R4ZhzrkwDJuxEUd2/4iX6jnbaVKh4uRdwIANYztg8J93OUeVMwLC2qL36P9hyb4j2DDpUfipFViuRGPugjPMq/ar9GgUHpj7znwLUi4mouKEvBqVw+vduDvakqqH3sYcaUnXIyWvsqPRTZVdXTkc9fJ2Cr3tO4U+JSt3YUGD2o0al/M+3k2RuGEsOgz+8y7lp4JzQBja9h6N/y3ZhyMbJuFRPzUUyxVEz12AM2UWiGVZLhW7HWQ7VtbtWHlLi6VTdg5hjVHfOXfBQvRISjaXz/paKvMJs/HK8J8Ra1Tg9+RkzHk7AnfWdAeEjfwRX3YNuOM3aCTrIL4ePBar9bwpgoiIKK/HXSFYkLxtB47l/h6UJvRxdGnkcOPf4ud+jp9O5ECgQNfqNbz/pG++X0xycmC6yztY43/ojZZvb7T9DrkHjjPaPNkhd9BvwdXYYzhrUz/UiJijp3InjjSo2ekp1Cvtx+11zdClU5C1MygGHNl3EDm2DsjjdmP32eu1oBIee6IZnBQAMOPCieNlcJc561Dp16EKVJdLHApd0CnIOiwSwxHsO2hzJCBu926crcgJ0W55IHc+JnYZlhysjshINwS06I+Jc9Zgb+xFpKVfgf58LP5v7Tx8NqQNgh3ux5ixhRkXYvdg2fKNyCyw1+GLVu/8gsldvKASI04dOw4jk6O9Kj2adekEa8gLDEf2wfaQj8Pu3WfvaR/Iuc2T6JC7CGC5GotjtlV2GGOO4lTuRzU1O+Gpmyu7cxs82SF3Ys5yFbHHztq2kGWMwdG8naLTU/UqTC0wX4jFnmXLsbHgQIRvq3fwy+Qu8FIJjKeO4fh9GogVuh1kO1ah+lwVpt9c6TE80czJeuOU+QJOHLfT00N2rq92l7kXn/d7EysvWqCt9iKmzxqCfH+bXB2K/t9PQZ8qmtteWyYwnvwRw0YvwgWOCYmIiHJHFveYyWTDSMZ8Gn8s2IpsAaB4oN3I19DyxhuOsrBj0x5kirXzVLtNW1RW57+rnMQLuHxHR0BgupqMxBQ731mhuMPNRbnRyTOa7jxuVrYBDrk/EFjej+XZdTQGh1u3N8XsxA5b7tYwxWD91gRYACjubTB0UGQZvO/ZBY+NfhVNnRUAJpzdFI2jJhs2syTitzHdMHbRhRvhEfjsUPQMUlvL7/82YWuGLcfPQc59P1OmwN3N5UZdMt5Z4SBZ2TA45P54XDmrQ6V3HsUrl9ILhccw+tWmsIbCWWyKPgrbQuE3jOk2FosuVOR7ye2VB3Iv/7l4XMiKw8lYU5l+i4qTd63tTOrGldhwrbCeRwAe6dgQGijQOTrevU1Kj8Ou6PXYey4DVJSQH41Xm1qftDOd3YRo2yo9En8bg25jF+GehrxnV4weHG7NjaYY7Nxhy925JsSs34oEa2VHm6GDEHlLZfdE19GDEW7dKWJ27oBtIbQeW607hXuboRgU6XBv+3hFjcTUjVhZeCAi4JGOaKgBFJ0jHO11kuWtXCp0O8h2rOzbsfKlVMpOFYhnh/a0LkJLFv5v01bYNrzJKeRGAPvWV7uypGDdu/0waccViGM4Rs7+Fr0CC54GUQX2wjczhqCW9rZMJSbE/zEKr/5ctCcu0+N2IXr9XrBbQ0RE95t7vLBgxum9e3GpkD5SypovMG17NgQKXCNG45MhNW56NNECw00/5KzVFNTtvIaN63Yi48bLbW7uKxmh1mrvMk1SktKthJBg19w75S7jYuJtvStLGhLO5yCwShDUdx/r33Qicu+P5fgw3pj0EkK1CuRqNGbPPVHo4OzKxh/w+2EjRHFGo9cmYUhtdQFTUnn/LazcC/t3Tf0R+GRYGBwVwHhoNr5efqnQCQrD/hmYtrMJ+vSqelOxdsf7Y63v2bQkR2HW/PhC9mOBft0qbLti/ZSYzTCXxitGi1I37F661tRRKSQYrrl3UV2+mHhbXbAgLeE8cgKr5N49WzZ1yGaldh7FLJfSiwTUH/EJhoU5QoERh2Z/jeWXCo0E7J8xDTub9EGvqmrYNykWe5qsWFvZKw8AgNo/AL6Wg5j1wSwcyS6VQL77t6xAedc6P7AMPy28UGg5m00miOKIsEbhuL3ltZz7A/2a1EOLRx9B07DmGLXyEsrFjYG35Vqxc+22yzXQ1MeIT4YhzFrpMfvr5Sg85PdjxrSdaNKnF6qq72XIOuLhNybhpVAtFLmK6NlzcaLwyo4ffj8MoyhwbvQaJg2pfUcfx/HhNzDppVBoFcHV6NmYW/hOsfGH33HYKFCcG+G1SUNw93Rfwn5XaTb/lkQs+2lh4XfUmk0wiQLHsEYI19prZGGHcrFvUNi/HWQ7dl+3Y+Uqp5dK2alQqfv7GNvBCypYkBw1C/PjC6lVFj3WrdoG6/BGYDab73ru9qyv9sya5xaMwKDpMTAoHmj14S/4pIOnbWOdJyfjx9cfyn16/ebceRFRbw7G1CM5th3/j35oUq8FHn2kKcKaj8LKS3zcgYiI7iNyDyTPfFx0gACKOLpWl56zY8WYz2fNCQulf02tKFBEG9JTZp+8/ZMmOf6/9uKqWPfn3P5/Emu6+74y9n4s7Vu+KM831AqgiFOn7yXRLCKSLeuG1ZCWnx6TWzY1nZTJLbXWfqHucZmZXPTvevnXXuKlgkBxkU7TE8R8S0HMk17Bj8iUOPPdN85eJ8OqqAWAqKuPlA3Z5eFY6bJrYmvxVEFUHi1l4u6MfM/HnLxGRtRzEEXRSGDXGRJjyP/cs6IGiL/Keg3dnvldrhX0Ra/Okx4uivVcQ0fI+vzONfugfNPJT9SKIurgHvLTyfxPwKzfIG9FBEv7Lw7IHZ8yx8sfL4aKNnc/c06b8t2P6dRs6RHiKFqN9fwUz2flj/RSCKIi1Y0siRrgLypAoLjJM78XWLpydV4PcVEggFpCR6yX7PwrnPTyUgmgiEun6ZJwa4WTeb2C5ZEpcWIuozpUdKV0HsUul9K7VtkHv5FOfmpRFLUE9/hJ8g8Fs+g3vCURwe3liwN3+5BJTk5uKVpAAJ08XpykWAymk5OlpdY6Rtc9PlOKdFR75QHjPhnXRCeKohKXypHyeI9e8kzv3tK797PyXJ+XpP+gV2T4yDHy5rsT5H+zl8jmY3oxFX5ysm5YFVEDAnV1GZlvIFeEvJshv/ZwtrbV1QfJ0iRzARc0Vr7p4CrqwL7y12XzHXXs8MeRuXXMem66jlNvi6N7I2P5AAlQXT8vrYS/tyffvoutSqXtk2w5+E0n8VMroqiDpcdPJyX/kNfLhrciJLj9F3LAbvm1ZHkifddEae2pEqg8pOXE3ZKRf2WXNSPqiYOiiCawq8woqLKn75KJrT1FBZV4tJwo+YeQWZLXjJB6DooomkDpOiNGDKXV7yp28z9Mqqiteb/6yA135P2MX3uIswJRtNVl0NIkMRdwnWK/6SCu6kDp+9dlsedZlqhcsqJkgL/K2odye0YKbgavyrweLqIAAnWojMg/KOzYDrIdu3/bsZIrnfMpnbIzx/8hL4Zqc2NijuQ/vDHJqdk9JMRRK5rc8bbns39IemnX1xIzS3baBTm89B1pldsv19Z4UWZtPymXi3Lts/bKxBZu1jxzy58ibs0nFNCeXC++w/JxpDZvO0UnHacmCBER0f3iHi8saKX56E/k2XoNpM/0PZJ62xgj7d8fpV+4m6gUlXg2GS4LYvPpBVzbLZ938BG1AoHKRzp8tF4uGG+d8IiNGi9PPdRBJu1KkyNftBFXBaI4NZRRK85I0sHp0r1WB/kyxiRiTpWYLWtkVdRC+fnzvhLmpOR2ApylTo8P5Lt5C2X5ylWyZuNhuWzLKCx7n0xq4WadbKkzTFZen2wxnZelQ+pL6EsLb92P+YLsX7daVi5bIDPffVyC1bkT1Jqq0vmDmfLHkhWyZu/5e3ysVNn9bQ+p7qiI2qeFjPntsKTdUhYGSdwxQ/qHu4qicpPwgXMlJuvOvRgT9sraVStkye/T5c0OAdZBCSCqSi1lxJT5smTFKlm764xkiYhknZFda1fJiiXz5dvhzcU7d3JHUQdJxze+k/lLVsiqtXvl/O2d4msHZVbfOuKsKKINflzGRZ28rZNvlMRt02VAo2BpPGKZJOTXqTackgVDG4q7ShHnOn1lxr7U2wbhBjkf/al0rV1Tnv7qv9I3d1AMxVXCnh0n389fLFGrd0t8SXrNRbpeRknYu1ZWrVgiv09/UzoEqHM7tCqp1HKETJm/RFasWiu7zlgvTNaZXdZrMf9bGd7c2zqxDUXUQR3lje9yr8Xe87cNMLNl36QW4qZAFG0dGbby+gSGSc4vHSL1Q1+ShfkGiX3qUMmVxnkUtVzK4lqJXDs4S/rWcRZF0Urw4+Mk6uRtw11jomybPkAaBTeWEcsSbtreLKkxW2TNqihZ+PPn0jfMKXdwpYhznR7ywXfzZOHylbJqzUY5fNl+U1Pm1BjZsmaVRC38WT7vGyZOSm7MO9eRHh98J/MWLpeVq9bIxsM2TIjZKQ+kbfpQmnmqbhtY3v1PUblIldYvyScrTt828WeWC/vXyeqVy2TBzHfl8WC1tTwVjVTt/IHM/GOJrFhzl1xW7vPu9YUFlbj5B0hw89dlcexdRtuGOFn+RjPxdK4tgxadv8u1M8vZ6Z1ybxawxoF3798kpSw7KdlnZffa1bJ69WpZtXK5LPpttkyZ8Ko8XsP5lokFlWcD6fWfz2T6LwtkSdRKWb16taxeEy0HLhZcI8uk7ZNrcnBWX6njrIiiDZbHx0XJnSG/TaYPaCTBjUfIsgRTSSPWrnkidfe30qO6oyhqH2kx5jc5nHZbi5u4Q2b0DxdXRSVu4QNlri0NROpu+bZHdXFU1OLTYoz8djjt1vpnSJQdM/pLuKsiKrdwGTg3RrLs3ccrdvO/X9atXinLFsyUdx8PtvZ3oYimamf5YOYfsmTFGtmbWwmuLyyo3PwlILi5vL449i6LMwaJW/6GNPN0ltqDFsl5ey/cFbVcjAmyd+0qWbHkd5n+ZgcJUOfGmaqStBwxJbee7xJrM5glZ3ZZ28z53w6X5t7X+1tqCer4hnyX22buvTMoStAOsh17MNqx4imb8ymdfrPh1AIZ2tBdVIqz1Ok7Q/bdPhg3nJfoT7tK7ZpPy1f/7Zu7aAJRXMPk2XHfy/zFUbJ6d/ydiwL2Gn8VeWFnqNTy9xVvTxfRaVR3WQyw5k5F6yyelarKgEVZ+a3QyJzeIeLn5yd+fn7i6+5w930pGnHx9rvxOb+g5jJu522lYT4r0zu55m2v8pbev5Vpr4aIiOj+Xlho9UWsZJ2Jkg+61JUq9TvJS6+NlfffHSMDOjcUXweNuIe2k0Ff/yNxhd1ZkBUryya+LO1reYpW0Yh3+BPy4qtj5Z0xA6VH69pSLXKAzNiTmttJOim/v9JIPFSK9W7I4NYyakGstVNk2CL/qaMTB0dncfPwlEq+/hIUHCIhIcESHBwkgf4+4uXuLE5Vh8oaG+92MCf+I5N6NhAfrSK64Ifl6b7PS+eIEAlu+a6svf2uzoxfpaebVnROruLu6S0+/oESHBwsAX6VxMvDTZwdteLcdU65OFbaoQXyYa8m4u+oEffqLaTzsy9L/xd6yKMRlcVF6yZVWvWTyatO5Xu3YfLMJ8RZqxMnV3fx9PYRv8BgCQ4OFH9fb/H0cBVnR604tflSTplETKe+lDZOWtE533qugf4+4u3pLq5OOtE6d5GfUu92pHSJWTJBnosIFCeVg/iGtZOuzw+QQf2ekccah4hvaAcZ+fP+Oxa27pQhsVGTpE9koDhpK0n4Ey/La2+/L++MHiQ9mteU0Mg+8nn0BTHddLfdLX8OneT7pBIETpGuV7LMfMJZtDoncXX3FG8fPwkMDpbgQH/x9fYUD1dncdQ6SZsvT4lJTHLqyzbipNWJc+618L9+LXy8xdPdVZx0WnHu8pOk3lnh5J9JPaWBj1YUXbA8/HRfeb5zhIQEt5R31yYVOlAuaR2yF7ufR5HKpYyulYhIeowsmfCcRAQ6icrBV8LadZXnBwySfs88Jo1DfCW0w0j5ef+di2Zb/lNHdA6O4uzmIZ6VfMU/KFhCQkIkODhYggL9xcfLXZydqsrQNfa7/c+w5T9SR+cgjs5u4uFZSXz9gyQ4JERCgoMlOChQ/H28xN3ZSaoOXSO2HbWEeeDaSYmaPFjaVXESR9860qxDR+nYsaN07NBB2rdtI61aNJPIRvWkeqCHOChK3sSMJkg6f3fopnPMkF97ut10vf2t1zvATyp5eYibs6NonbvKnLSKlncz5LdebuIUNlyiElNk74yB0jS0ujz89AAZ/c44Gffh2zKyX1d5OMRTgpq+LF9tvJh/fsg+JD/2i5RAd3cJaTlcfo81lmkfxZwwVR51cxY3Dy+p5Osn/gGBEhQcLMHBIRIScvNfcG4MBIi/n494e3qIq7Ov9P0ro5A+UFm1fSLpMUtkwnMREuikEgffMGnX9XkZMKifPPNYYwnxDZUOI3+W/an2mFUuhTyRdkgWfNhLmvg7isa9urTo/Ky83P8F6fFohFR20YpblVbSb/IqOVWkBiJNDi34UHo18RdHjbtUb9FZnn25v7zQ41GJqOwiWrcq0qrfZFlVhJ0Wqd9V7Oa/p7jdVGeseT9A/Cp5iYebszhqnaVrbtLI+K2XuDmFyfCoREnZO0MGNg2V6g8/LQNGvyPjxn0ob4/sJ10fDhHPoKby8lcb5WIpPQ1UpHJJnilPON/Uv/G7Xs99c+PKUbRObeRLa1DIl22cRKtzvjWHBvqLj7enuLs6iU7rLF3yD4pitINsxx6Mdqy449qyO59S6TdnxErUpD4SGegk2krh8sTLr8nb778jowf1kOY1QyWyz+cSfcF009MYt/45dPpekkqjvhZnYWHhC+Lj6Czunj7iHxgkwcHB1pgLuWksH+BnzRUuXtL7t3xKynxRvn/CTZzdPMWrko/45dcPyI3lAH9fqeTlKW6uYfLGFsNdujU/Sr/IQHF3D5GWw3+XMu7WEBERlSpFRMr8jdmXfngClYf+DQO0aPVFDDa9VQNqmJFybDP+2XIQZ9MscPUNQrXw1mgXGQznIu09Gxf+bwM27TuJ8ykG6PxDUTe8KVpGVoHLbe87zLhwBAdOW1C1SUMEO5f+9844sxkr/t6LuKtq+NZrj65PNoSvpuIfy5wWi+2bduF4/EWkmpzhE1QV9Vu2Q9PKLuXsxV9mXIndgY27YhCfmAaLewCq1G2Gjq1rw0NVtP2kHd+GDbtjEJ90DRqfKqjZsBXaNQ6CIwCYT2H1rNWI13mjkk8l+Pr4wcfPB35+/qjk5nDvfzG9dCocNq/4G3vjrkLtWw/tuz6JhkWocOWlDtn9PEpYLqX3Ra8gdsdG7IqJR2KaBe4BVVC3WUe0ru1xf9bPEuYBy/llGN21H74/HoBnPv8Z015tjkrq/N+lm510BBuW/IIp/52Ov09nAS6t8PnuTRgbpi6/ddUO5RoXvRBnavdCh8q5dTzzHHb/sx67jl1AhtoVXr6VEdasLZrV8X4gf4Tz3oZ8LHZs3IWY+ESkWdwRUKUumnVsjdoeFSDizWmI3b4Ju47H42KqCc4+QahavyXaNa0MlxLU17TY7di06zjiL6bC5OyDoKr10bJdUxQ/3Zddv6vAbxYXjYVnaqNXh8qwHj4T53b/g/W7juFChhquXr6oHNYMbZvVgXcZBGJ5KZf7qx1kO/bA5/TSKDtzGo5v24DdMfFIuqaBT5WaaNiqHRoHOVr/+dRqzFodD513JfhU8rWObXz84OdfCW4OqjIYfxEREVF5VI4WFoiIiMrb6D0Os7pFYtgqI9p+uQvr/lMXts6JWZJXY0Tbbph5XFB37Fb8O7kZJ9SJiIjtGBERERHdF3ifABERUT7MZxdi7roUiHNbvNTP9skYAFD5dcLol5tAAzNOHzqEayxOIiJiO0ZERERE9wkuLBAREeVDUlJxRQTQOsPVsahbK3BxcYICQHIMMFhYnkRExHaMiIiIiO4P92Zh4aa3L5X9i5iIiIhsowl7DO2raYD0rVi2+hKKNKdiPoMly/fBqDigYfu28OVSPhERsR0jIiIiovvEPegemnHpUmpup1aQqr8EM68DERGVR85t8d5Xg1HPIRELRvXD17vTbJuUMV/Amnf6YtzGa/Bo/g6+HtkAGpYmERGxHSMiIiKi+4R6/Pjx40v/MBYkbp2PucvWY8PKn/HtrDU4k24BIEg9eRjxaZdx+sgBHMv0x0Ohnnw/ExERlRMKXOs8ge6Rahz7+1d8P/137E51RnDNWqjipYNy+8dNKTiyaiY+HNwf7/x1AaHPT8aCeW+hpZfCoiQiIrZjRERERHT/9DRFyuJlRDnYM/EJvDDvPDQ6RzjqdNA56qCBCYZsA3IM2cg2GOHR4wdsntwBOl4XIiIqb7LPYuMv0/HDguVYt/McNFXC0aBeVfh5ukCTcw0piadx9OBRXNTVQevHu+GFocPwXLMAOLDkiIiI7RgRERER3WfKaGGBiIjo/mHJvoTYQ4dw4twlpKVnwaJ1hptXIGrUC0fdal6chCEiIrZjRERERHRf48ICERERERERERERERHZjD9nQERERERERERERERENuPCAhERERERERERERER2YwLC0REREREREREREREZDMuLBARERERERERERERkc24sEBERERERERERERERDbjwgIREREREREREREREdmMCwtERERERERERERERGQzLiwQEREREREREREREZHNuLBAREREREREREREREQ248ICERERERERERERERHZjAsLRERERERERERERERkMy4sEBERERERERERERGRzbiwQERERERERERERERENuPCAhERERERERERERER2YwLC0REREREREREREREZDMuLBARERERERERERERkc24sEBERERERERERERERDbjwgIREREREREREREREdmMCwtERERERERERERERGQzLiwQEREREREREREREZHNuLBAREREREREREREREQ248ICERERERERERERERHZjAsLRERERERERERERERkMy4sEBERERERERERERGRzbiwQERERERERERERERENuPCAhERERERERERERER2YwLC0REREREREREREREZDMuLBARERERERERERERkc24sEBERERERERERERERDbjwgIREREREREREREREdmMCwtERERERERERERERGQzLiwQEREREREREREREZHNuLBAREREREREREREREQ248ICERERERERERERERHZjAsLRERERERERERERERkMy4sEBERERERERERERGRzbiwQERERERERERERERENuPCAhERERERERERERER2YwLC0REAKO6KwAAIABJREFUREREREREREREZDMuLBARERERERERERERkc24sEBERERERERERERERDbjwgIREREREREREREREdmMCwtERERERERERERERGQzLiwQEREREREREREREZHNuLBAREREREREREREREQ248ICERERERERERERERHZjAsLRERERERERERERERkMy4sEBERERERERERERGRzbiwQERERERERERERERENuPCAhERERERERERERER2YwLC0REREREREREREREZDMNi6AcMWVAn3wFSqUgeOtYHGVU6MhIuYQr8EYQC/2euha3Exu37sW/h07hilNl1G/VBb0eqw03Fg3dSJEpuHQF8A7yBqOViIrNYkDapWQYXCrD35XFQfeqUdPj6JYN2HMsHgkXEpGUYoCDhx+qhEWg7SMd0SjwLi1d9mH8NuUgGv6nL+pzFEccAxMRUYUaywu8girBkcVxX1FEREr9KDm78M3gydicqYGDTgedgwO0Wi20agWAwGI2w2w2wWjIRk7lZ/DVJ70QpLrLtg466HRF2Pb6LnZ/i8Gfb0Km1npsnc4BWo0K5pxsZGdlI9vghLbv/oBRDzuUYdFfwcp3e+PTf5KgT9EjNSUVqVezYFQ3wrg9uzGhIUcLpcGwYTy6f7gKifoU6FP0SEm9iiyjCvXe3or9nzeDA4uo7F3Zj1lvDMWH8w5C1+IFvNitBaqmLsOnk6Oh6f4dVs4fiDoMhwcxWDG++4dYlahHij4F+pRUXM0yQlXvbWzd/zmaMViJyLZhDI7MHITX5hzGpRQ9UlJSkJJ2DTkWZ/T4NRmL+jqziO5TOXunYPCEv6G32DwsgqJ2gIuHF7x9glGr4cNo3ro1mtb0suOdWGZc2v0rpkybh8WrtyDmUg6g9UBInTDUCvGEKuMSLpw5gRPJOjR4ejje//gt9Kp3ffUrAzveb4NHvq6GHy4vxousulQxO/4cAxMR3fdj+U2Y2PMDRJ3PHcunpuBqphGqGqMRfeQbtOVY/r5SNi23ygmefj7wunQVybHbsWrnGaRb8tYzFJULQhq3QGSdyvD3dIX6tm29/P1QSX8VSUc3Yfnuc8i6aSmkwG2v78LRDR4O2Ti5ZwP2nkmD6fr2igae1ZuhTdMmcHO8/a1Q2YhdPhXTomLh0Lgvxgxth0C1PQtFDffAmqgX5okLR7Zj45lMGAWAmpWyNCkuAahRNwweF49hx7nTyDQK+EaweyhtKz56qism7TShwfAFWP5tN1TBHox7eCTic7IhC9/Ch9174M8+XiyrBy9YEVCjLsI8LuLYjnM4nWmEbdFa2rmbiCpYMoGjd1XUDjPD68xebDxzDQazAIq9j8PcU96onCohuHJlOOZkI/3SGezbsh0nU024PgxQedRGu0ebIdTLERo1YDZkISMjA5nJJ7Bj6xLM+TYNOYoTgpt2xYuvvI7XX24GvxJcU8PZdZj64buY/Mc+XDZpEdS0N97/bBD6P9sONdxubt0ycX7nMvzw34kY3Hwhln75G74f0hDK9k/w6v/+RZZUgVTwa5MduxxTp0Uh1qEx+o4ZinYMlgcIx8BlHG1sm1gOvI7Fa6iwfOo0RMU6oHHfMRjaLpBpqohjef8adRDmloQTu87hTKYRFimlmTdeq3tPylyqrHolVNSAABBAKw3f3SUZNm2rl5VDaohGydv2IZu3FRHzZdn55VMSpFZEcagm3b/dLan5neWqIRKqUazHUZwlcsJ+MZZWkZjPytRHnEUBBNrGMu5fo1BpM8uFWZ3FXYEAGgl7e6cYWChl7KpEj6wtWkURbYN3ZGfW9f89T3q4KDdivM2Xp8XEwnqwo/XCLOnsbq0TmrC3ZWcBwVqmuZuIKphrsu7V6tY+qOIiPedn2G3PzD3lX8am0VJbkzeGaDSuoGtklMsHl8kXLz4knipFoGjFN3Kw/HQgvVh9zqQNE+SRQI0oUETl1UQGTt8pl82FbZctsQuGSkMPX2nzzv/k1YccrWMFXTeZm1GBL0TqKhkSqrF+FyjiHDlB9jNYHtAOHsfApR9ubJtYDryOxTiSrBoSemPeUXGOlAlsqIqf6pPmSg9vlXUsX3O0bDLwWt1v7sGt2p54ZOCzN73aRGAwmW18dMIbnUa+hIY3bZuZabD9sQtVJTQb/SkGP6RDraFzMG/Uw/C86wdzELNhC85df7RBMnFw42YkFfoodSp+eSYEz/2aXsRbqvxRt5YP75svSRkW/T42VKpTC/5cyrxnLEl/4uufT8IIDSJfGIDI6y/ac3kSw19rDn9HB3g91A8jelfhivMDTlWpDmrZFKzFzN2pv+CZkOdQ6mmHiO4xJ9SpU7UU2pTi9hupLDlWqYKAmzrbLq5uBTy4okGlBk/jrXnb8c8nbeGlGHFp708Y0vFxfBB9CbZfWgOOz+2Ptp3HIzrRBKVSG4xfEY2fhjdDpUI7/jrUeHY61v3VF2nfvoHpB7Mr/JMKAJATswFbzl1/ckSQeXAjNjNYHtAOHsfApRxtbJtYDuVTscZeZTjOy4nBhi3nkHeog9i4OQmsMsVM9d51Ube0Hi0pT9fqAZ5TuCftuEPj7uhc6/pygAmnVkfhgMm2bTX1nsaTYdob28atWYa9OUU4uOEs4i8/hBdfaY38f6/PAfXatUKIJne4oTiifuuW8C+stMzJSDx/FZnGolZjBTpHnf2fyq+Iil2Gxaj8Okf+psI9lL15HbZeE0BdBQ83r5Y30aOqhMc+346LWQakHJiF56pwWYG9ER0cbQrW4uVuc3Iizl/NhJG9RaL7vtvr6OhQCv2tYvYbqYwvv6oYAx8XRLz1Ez5q5woFAot+Oz57YTDmnDbbsK0FictGofvQX3E8U6C4RODNhUvwfkvPItVZ38cm49fxbeFxn9Qnh3rt0CpEkxuHChzrt0ZLBssDimPgUo42tk0sh3KpeGOvMhznOdRDu1YhyDtUfbRu6c9F0GJzhIOu1DoV5eZaPchzCvfm15EcmqB75xr4OuY4TACMx1dg6b8f4+FIG05HUx+dH6+Jzw/EwATAdGoVlu77FC2b2zZFnLlpOTYEdMZ/6hZ8LM/O32L1H6GYuuI0dI364PURkYUXlvEkTsSZmDdKgmX4gLAg+VwiMgWA4g0/Py4ekH0UJ3cbT54A0w4RlXXuoQpCHYrnnm+N9zauQaYAlqSVGP/pGjz7Y2e4FTTAPP0zhg/7CceyBVAc0eSNGZjQ3rsYg10dHhr1OYb/2g6TD+VU/KcWPDvj29V/IHTqCpzWNUKf10cgksFCxLaJ5fDAKO7Yq+zGeZ7o/O1q/BE6FStO69Coz+sYwYaqvEZ3ublWD/Kcwj2KDgdEdu+M6t8cxwkTAOMxrFh2CBMiG9twQkbo9VfzOtWmk1i5dD8mNW9mw93nmdi0NBo+Ty5DvUIP5IK6vd7HtF62f6ucA1uw87IFNRjdxcYyfFAIMjOzcuPYAQ58dITspqi5OwcHtuzEZQuzDhGVZe6hikMF7wbhqKJeg2MmADDjwsrF2JzdGZ0d89nEch6/jX0PURetTzaog57DB2Mi4VjcU3BsijFvPIUZA5ci7X6Ilrq98D6DhYhtE8vhAVSSsVcZjvNc6qLX+9PAKlMhOhXl4Fo92HMK9+xpHoeHu+Op0Ouz+0bERC3BYVtWdzI3Y+k6A7y8r5+6CSdXLrPtVUrZ27As2hude4SVwoqKAbv/XIqTvOuVZUhEFSjt7MafS0+CaYeIiPKj9vCE203va7GknMO5q5YChhxfYdKy5Nx3/GpQq89QPOVVsiGb/9PPo5M3X8RAREQce3GcRw9svS6H7l3v1KEpejxZ7cYEv/HoCiw5UvhlyNyyFOv9hmJSn9C8bY+vwDIbVhayty7BPx5PoXu4/ZcVzKfn4NNfYpm0WIZEVHGyDk7P+RS/xDLrEBFR/iQzC9k3D6Bc/eDvmt8wKgVLps5F7PVfEtTUwtO9Ikr+u15endC5tQvfR09ERBx7cZxHD2S9Lp/u4W0vOjTr8QRu/C6r6TBWLDlayKRyNrYvjYbXU8/ghV5P5m2b+yqlgrc1YPvSf+D6ZDc0sPO6gkW/ERNefg9/6yvGr3TkXEnE6ROncTE9p9ycU0UrQ7uVozkTqRfjcOLoUcReSL+lDmdfSkKqqZS3L1kJ4EriaZw4fRHlqCoVmyn1BLat+APzfl+Fg5fMtl2+jGTEnYjFOX0mzOXxS5kzkBx3ArHn9Mg036tTuIT42GM4EZeEK4byFN8W6DdOwMvv/Q09f7T5/qqD5gxcio/FsRNxSLpiQMkurxmZqRcRd+IojsZeQPqtSRZJhSfpEm7PukLlJRYebBknjyHelDd88n6sC9o559O6JC/HgrUpN8pb5dMcbRrZ452P7ohoUqeIT10zB5X7vto9+U5mZFyKR+yxE4hLugL7dc9ycOXiacTGJSPjgc/jZmQkx+FE7DnoS7FRK8745UEe7xV9qJ6Ki3EncPRoLC7cmkBxKSm1gt0QWVpxXx7HXuVhnFdRYqdsctX9q7TqWsXKvff0F0h0LXrgycrfY1qcGRATDq1YimMfPIR8HygwbMfSaHc8uSAcrmE98ETl7zE9zgzAiKNRS3FkXGM0zHfbHVi6zglPzmtUwJe24MLf3+KbFTFI1uuhv6zH5RQ9UtJ88dKCtfgg4uYtM3Bu/w5s37gMs6bMxvq4zNz3xVsQu+ILjD+rvXXX6gC0H/IK2gcUYS0n+yw2zPkOsxatx//FXsTVHA3cg2qhcdtuGDxqCB6t5mhzZU898BemTZuLxat2IEEbhCq+OlxLjEOyuibaP/0cBo0ahs418xkhZe7BnEm/YU+yHikpKdCn6JGqT0GqQxf8sP0rPHLzL7xbUrF11hdY+O8l6FP0SNGnQJ+qR2qaJ56fvx4TbvmR7TIoQzsnjRKV4w3XcGz5NHw1fSF2JargUzkEfu5a5KQm4MwFoE730Xi14R6MfWUhIv6MwbSOOjtvX5IiSMWBv6Zh2tzFWLUjAdqgKvDVXUNiXDLUNdvj6ecGYdSwzrh7EZhwaMFkLI4xQiBI3Z5oHXRLAtZP+xgZ7nn34Kl8W2PQ8EcRXOJLbYZ+/1+YOesvrN1xGHHJV2F09EX1h1qhc5+heOWZRvC8uhc/vTcWX0enIzQyDOa9C/HKRy9gwY4ZeLrSnSdg0e/Br99Mxc9/rsLeVG/UruENU9JJnM4MRssuz2P42DHoVvv2AsjEnjmTMH93EvR6PS7r9dDr9Ui5FomJ++eijxuA7DhE/zgVPyxej32xSbhm0hYv3i167Pn1G0z9+U+s2psK79o14G1KwsnTmQhu2QXPDx+LMd1qw/m2copf9TWmrjqel/v0l5FytSZGrVuM10LVyD4Vha8//Q4LNx+DXuWLOu1ewvsTXkPbwLv88LbpIrb99F98NXsJdl7Qwj/QA+bkkziRrEON5p3Qe+gbGPVMQ3ir7RebNufujHPYv2M7Ni6bhSmz1yMuM/eOUkssVnwxHnemnfYY8kp7BKjMOL16BhYfM8PJyQmODhqoFQuMOQYYsg3INhhgqtQKgwa0ge9N1cZ0dBG+WX4KiqNT7nYKLMYcZKbnoFqXEXi6jp2bYfMJLJ+yGMcsOjg6OsHJ0QFqMcJgsJ6nIUeLsN4j0dVnN36ZsQnJDtbzctJpIEYDsg3ZMGQbYAzsgOEvNYMHAJjPYPX3C3AwW2f9Do46aGFCTnYGrukaos/ADghSlbQOlnhojYvbfsJ/v5qNJTsvQOsfCA9zMk6eSIauRnN06j0Ub4x6Bg1trHTXji3HtK+mY+GuRKh8KiPEzx3anFQkWJMsRr/aEHvGvoKFEX8iZlpH6OyyvTVP/LYnGfqUlBvtZ0qqA7r8sB1f3drYInXrLHyx8F9c0qfktrep0KemwfP5+Vg/ofktd0dn7pmDSfN3Iyk3PvR6PfQp1xA5cT/mWhMQ4qJ/xNQfFmP9vlgkZwJOnoGo0+IJ9HhxAF5qX+2W75gRuwo/TJ2DZZsO4MyldJgcvBBSJwIdeg3FqH6tEWTrHOo9qSt36eacicYvM3/BsvV7cCIxHUZHLwRWDUOrJ3qizwu90DRQY4d+Y1m1A3aIBXM8Vn09FauOJ+ee62XoL6fgas1RWLf4NYSqs3Eq6mt8+t1CbD6mh8q3Dtq99D4mvNYWgeoKkiuLRI9VSzchPbe5UHm2w7vjnoF3Pp/O2haN7el5P7GsqdcYje3yW1Jq1GzVEtWDMlHYG5FKmsNKpRd94W98+80KxCTroc+tU/qUNPi+tABrP4iw66C0eH21cj51YYfvZLq4DT/99yvMXrITF7T+CPQwI/nkCSTraqB5p94Y+sYoPNPQG0Xrnl1DzNLpmDrnTyyP/heJJncEBjgj50omNIEPoUPPgRgz6gU87FeEvdptDFxIlyl+Fb6eugrHb9RJPS6nXEXNUeuw+LVQqLNPIerrT/Hdws04plfBt047vPT+BLzWNjCfMrJAv+dXfDP1Z/y5ai9SvWujhrcJSSdPIzO4Jbo8Pxxjx3RD4VWvpOMXFKFtsvd4r+h9jqRrJmjdg1CrcVt0GzwKQx6tBvtcYUvxyuHaMSyf9hWmL9yFRJUPKof4wV2bg9SEM7iAOug++lU03DMWryyMwJ8x01Dcoba1/q3Gyct66PUpSNFb/5vd+r848GNPOBrOYcuvP+DnJdHYHRMPvcEBngFVUPfhR9Gt78t4rk0Vm8qp5HFfwnFiscdeZTnOu9FQ4e9vv8GKmLz+T4o+BWm+L2HB2g9QWOhUnNixV66y7ivt6Br88cdirNq4H7Hnk3FV3BBQLRxtu72MVwZ2QV2nUulU2H6tMvdgzqT52J2Um+tzY+1a5ETsn9sH1i54NH6c+gMWr9+H2KRrMGndEVSrMdp2G4xRQx7FHc1Oieu1/evPPSX3VJb8M7yqqAEBIIo2UiYeNuX76ewNI6V2w/dkj9G67bqbtoW2iXx0wJj/tptGS53wsbLDUND5GOXwlF4SEV5bgj20olzftyZM3t5524aGHfLJUw9LRESERERESIMQV1EBAqjEs3qTG///xl+zPvL9sfy+m0E2j6klGkCgbSzj/jWK4eTvMrRZfekw7Ev5fd1+ib9qkIyEnTJnWIR4qCAqz6bybrRezIUVsSFW/nq9rQTrdFLlifflj70XxXBT+cdvmSWvtfQVrUsN6ToxWi7ebYepi2RE04ZSv1aguGkUa5kAogoaJCuzb/usOUHmDoyUBnWriY+T6qYyrCVjNpdmGRadcdc7Ul8DATQS9vZOMZR2OYqImONl0fCG4hX8mHwUFSvXbv/n9OOyaGxr8VUrAnWwDF6Vbd/tS8AQ+5e83jZYdLoq8sT7f8jeizeVWFa8bJn1mrT01YpLja4yMfriXepmlqx6q7VE5l7P8OvXW9GJf91br3XTZ76Vg8aSnvApWTSmhfhqVOLeoK/8d+leiUu+LGcPrJbvBkeKl8pBglv3li4RjaTXFxskIVtEMv6SPp4qgcpT+izMuiM/xC15Q1oHaEXl0VAGfr9dEm+cY7rE/PWWtPVTi8qtvgycH3tbfUqVRSOaykP1QsXPRZ0XF7ouMjtNxHR+mYxuUV/alzDejXFL5I3WAaJVeUjDgd/L9rwTlPSYv+Sttn6iVrlJ/YHzJdZw63fb/2U3aRJeW0I8HURRrufVljL5pFFSt34s7Ws3lyHfrZUjB+ZIn6oOokAR1xafyu1p13x5k0zoUFlqdHpT5mxLkLxSNIt+z0wZ1MRL1IpWgh8dL9EXzbYGq7xTXyMARBP2tuw0FD93G3Z8Ik89fL2uNZAQV5X1sypPqd4k4o6806zP92JNOwbZ8cmTEtGgtgS533QMQKCoxNm3hjTs+4Ocui1FZa8eJXU9b/+8VjyrtZT3N2Tbv1nN3iTjOjaSetW8Rafk5WsAoui8pVr9jjJ+i0HMCXNlYGS41Ap0E41y67m5B9WWJoPmS+L1y2PYKRPaBIqL+rb9OXhKtaf+J4eN9qiDJWC+LJsmdJDKNTrJm3O2ScJNoWvW75GZg5qIl1oRbfCjMv6uuemWnUn8ouHS0CtYHvsoSmLvTLJyfNFYae2rFgVqCR68SrLttr01TzSsX0sC3TR5dUYVJIPubGwlYe5AiWxQV6r5OInqxjXUSK0xm+9oz1IXjZCmD9WTUD8XUd/4rE66WBOQLBvdQuq3HyZf/r5O9sdfFUNGguycM0wiPFSiqL2kxQcbRG8WEcmQQz++KBENHpcxU/6SDYcuyDVDusRtmS4vhzmLoqjFr8NnsjPdhrC+F3VFRJJnPi46QKC4SM9fz8uOr5+RepWbyAsfz5P1MZfFIAZJi98ny74aLC38NaL2fEie/3KTJJlL2G8si3bAXrFg3C9fdmsi4bVDxNNBuXGu2paT5aQxVbZ+3F5qNx8i3609Igfm9JGqDopAcZUWnx4QYznOleazX0t7h+v710qrL2Kl8F6lWZJXDpPaWmv+U7k3llFRFwq4DkbZ8164aG98D5X4D1guWWU2tippDis9xsNTpFdEuNQO9hDtTTmr0D540Y5Sgr5aeWWP72SWy5smSIfKNaTTm3Nk263JQfbMHCRNvNSiaIPl0fEFjGNu32viWhn/ZKg4K2rxaTpEpq45IknXd21Kk5MbfpFxveuLd3An+WxbatmOgW0p2f1fSrcm4VI7xFMcbvSZtNJy8kkxpm6Vj9vXluZDvpO1Rw7InD5VxUGBKK4t5NO7zTkY42TJG60lQKsSj4YD5fvtiZJ3mWLkr7faip9aJW71B8r8gho1u4xfitI22Xu8V7I+B1Se0vTd6Nw+R8ljp6jlYI5fJMMbeknwYx9J1J0JVNKPL5KxrX1FrUDUwYOlJENt479fSfeIBlKnaiVxUl2vf4q49JwvF/fNkBfDA6Rq2wHy8Zwo2bTviBzZs07mftxXmlRSi6Jykzo9Jsra86YC2wP7xH3JxonFH3uV5TjvRkMlU3pFSHjtYPHQ5o177j7+rKCxY69cJSJm/Q6ZPqi5BGg14tOkr4yfv1mOnr8qRmO6JOxfLlNGdJDa9XvK11u3yrgmWmtZ1hwtmwx26VTYfq1SF8mIpg9JvVC/W8azui6zJU1Mcn7ZaGlRv70M+/J3Wbc/Xq4aMiRh5xwZFuEhKqjEs+m7En1bwZa8Xtu//txLuNcnkLV2qFRR5w0gHp50JJ9OfrZsGl1HHnpn140O0+3bRkw4JMZ8Ju63vF5Xwt7cZnsH8to+Gd/cyZq4Cm2ETXL0k6a5g4jcwC/aDOitnaroJTI0sqW8vvL8nWVhPCKftXAUBYpoarwq/1wrYLcZ+2VKlxDRKjqpO3S5JOZXkbOPyazuQaJWnKVu/9/vGOzdLGXDaAnTFLCwcHOpJK2UV2pr8l9YsGsZluLCgt3K0SRxM58SL01VGbQitYAMnSxRg6qL5o6FgZJuX3wZ+6dIlxCtKLq6MnR5ouRfBLOke5BaFOe60v/3UwUM2E1yZOLD1uutriYjou08rDUnytIhdUSnKKKpMUiW3T4jZE6SRS9XETUU0dYeLMuuz6Cazsqfr7WXpp3ekqUJ5ls6NLFz+0gNnSKKY7iMXJN8l2RuFv3aEVJXq4jiWE9GrcvnGqVulbGNHay5RddFfjobLWPbtZf/lDDejbFzpU8NnSiKo4SPXCPJ5rs1/mtlRF2tKIqj1Bu1TlLzGdD82N3fuuijbSmf71osgx5qI+N3XhURkbRfuonz9Y6OQ3v5Jv6mA5kvyl8vhIhTredl2s6Ld8/HqRvkPw10okAR50ZvycY0m4K1kIWFYuZu01H5pKn2lsk9m6Ttky8e9b6xEOrbY7acLeicUnfJhNbuokAlbmEvyNRtF8tgMiNbjs18WgJy20iV11My7eRdprbMKbLpzUaiU6yD6Yf+s1lSzHeP2cs7PpPHfFUCRSvBnT+T6LNZpVMHiziJdvGvFyTEqZY8P22nXLx7pZMN/2kgOgWiODeStwqodKa4mfKUl0aqDlpRwLmZJTlqkFTX3Dkp9//snXdUlMf6x7/vFpYuvYiooKIg9gJiBXsF7DG/2L0msUVNDN4kmkSjJlejNxqvvZdgBRHsgooFsUUFVFBUEAQVkCIsW57fH7sK6C4ssEsx8zmHc8+NO7vzPvPUmXlnKtu+WLClWW6CUhYWSgRbCv2XiyKHULOwUFwWkfPakB6njLWbn9CZed2px5xQ+rA2lVDM0k6kz4E4QROafiaLnuz5lNp6L6QIFYNbEBVALYQcgdOjFv8ufaKwenTl/YUFPXL1bE8OradRcLLq7LHg/k76tLGQOM6QXMftonht+R6dxAHt2kJRSNhEfrY85cLCMoo6NIladv2RFCEhi7b7Gr4r8vV6rKLiIaGm+cpyLyxIX1L0xonUykTh96zbT6KNN8qSWR4FjqxTbIFE2xPnpaM1H6RTcunGj55kwGlbPlrM1WrQooI2nkn2fD99Ws+Amoz+k66odg6UGT6HWog4AmdIrb+JoKwydW0fTXI1JA48sunzO93MVT3Wd/8KoL4NRSRsOIVCs6qoBi43Ynq4yY9secqFhWVRdGhSS+r64xVSuLrt5Gv4djJKj3qsevp+UKMdnzQiEceRvvsMOq46qNHJac1IyHGk7zqTVKqe1uuX8sUm7dd7Fc05GtGX2h1gzeQgfUzrB5iToMEkKr3UDqFJzoJKLywUz7FTAj+lenzFwoJe827UyaEJDVsdrdIO82I20fAGQuLAkX7jUbQ5VnUndGH3laoTK1N7VWWd9+6nfiRPA06j+rPW2I62fBURie/vognNjYnjDKjZ+N10L1+1bqcem03t6jelprZ87S4sVHCsMiPnURs9TrmwsJmenJlH3XvMoVAVi3SSmKXUSZ8jcAJq9OVpytWRrmlff/6BCwuUd4z+VY//boVWz2MJxaqSkDiS5ri60zfFXznIO0ZTirUVdlhEKl94EF+kuW6uNCeyPBpcQOEznBVvRFTlwoJD1oydAAAgAElEQVTAhXp4t6ZB/1NX7Egp7hcP5YSsA00Oy1c7uRz6RVPS4zjSa/MdReWVUWy9DKaJDfgEnil1+ilKvdHkH6WJdjyNFhaI8mjvcBOl46+lCwvalKM0hhZ1EBJnMoz2lJErSWN/oY6i9xYGKtu+ooV4eih90VSPOE6P2nwXRXllTPK9DJ5IDfggnmkn+ikqt1oWFjLDppCTgCNwQvJcek+lLUluLqDWQhDAL6MAJ8q/sYS61OERwCenz48rigyVj59C2/wtiQeO9NwD6FKBat9ybLKDwreIetOsWd7kt66S9p5/g5Z0qUM8gPhOn9Nx9R2klG3+ZMkDcXruFKCygzJKXNGN9ACCsAONHdeeOv/09zv7kNxeSl4mnGJHTfuFdLWY4ciS/6TeysKLEzrT1LAs1RMau0eQFU/xtkqHRbdJUraxar6wUB7fXYkkQBz9HbUUKuRg6L2y1AVZIinF/+pFeua9afX9KkwBJFdpvrvi+TiTAbQhRaZGbTfSQFOOAAG5fnO5lAmefAoeZ0PCJtPpTI4udbA8DiqZ/uytnNjkhOQ8NUxlUSRL300jrHgEcCTqsEjNG1FKv8SZ0LCynSz90lH03qRcZduXlPXRiXaKwq2shQUiyts7nEw4TRYWiAqOTSYHviLW9p41i7z91lGCGrWUxv1CHkLFpLCd32Qa2nY07U5Ws0dGfJm+cVXaqcscUptyVZeuvL+wAI4EDcfQ3sel22TOxQBqLeIInBG1CzhfStFdnrxRB3FAq7ZQvEEireimp1hY6DCWxrXvTD/9/S4i0O2lXgrd44yo/cKrH+heTfKVJRcWBNRs7Gras3cv7S32t3vHFlq3chHNm+JPXk6mxBeaUaPu4+invTfopUZdSqd1fUXF3u4qbeOT1pcVtOiDdEtB+Axy5mt3YUG7uVrNQDvPJKPkP3srJ/s4EjpPJdXpWTrtHmFFPIA4UQdaVJpzKPibfuthRjyAeDZ+tOWJ6rggjgqg5m93kvIsaMyBPN3XwBX1D4krqJuewmY7jB1H7Tv/REWu7jYt9VLUtJxRe1pYPPmlfLqxpAvV4YHAd6LP1Qc1kqVsI39LHoHTI/eASx/Ynrbrl/LEJt3UexXNORSLntod4bLlII1ZRB2EHJkM20O5Zfja2F86kkhrCwtEsuT/kvfb+MSzpD5/xJYaN7IvfEMtRYrYatByDp3N/HCMtG73lawTtbKwUEV1njJQ0Qxnfpn1Z+2xHe35Kln6MZrhbkAceGTmvYLuiku3l0ebfJULt7pZWNB0rJSCpckOis+Kes+iWd5+tE69YOkXD4UO8R0mk9qwUwld05X+VDW8aj+LybAbfPvaKc90I0huHkFw/IeXhhReC8IJri/82uqVaOtXrK30VgiCH6hoeyMIJ2R94dehPAeccjA0MgBX1fKQPsDF9L74fkIjNefc8VHP0R4CDoAsHfdiUlRe2pV79kfM3PgAhTBCt0lT0L6Mc7h4lgMwc1xLCOXZuLJ0BlbeVnMVEc8Yxkaaqg2vemSoRbQqR3EsYuOloIKnePS09Itx+M5e8HB8T86VbV8xCeDsjzOx8UEhYNQNk6a0L+Osax4sB8zEuJZCyLOvYOmMlbhd5bdaZSBsywE8kRLAs0Ordg1V2pLA1RPtrPkAZEg6uAVHM9QdK/kYWwN+xcXXckDYCuO+7AkTtY9vjyEjfWDGIxTG7cCGEzkqfYuRsdIuCs9jc6QHAsZXxt5leLw1AL9efA05hGg17kv0VN9B2A8ZCR8zHqgwDjs2nECOis8YvbVbWSyCTjlg0lT3d+e1C1oE4Pi1swgKi0ZC5I8o7lblqSl4LlGcL0iSJFy88EDFpWY8WPftD08DDiAxbh07hsdavSeqany3XpuJmNDZABwI+ZHbsa1URc9C9LUnaP3Vcnzuwq86UxC0waih7hByAOWew75DySovb+XZDsEQL31wkCI+eD+i1V0QJU9H0jM5uk6fi+7GutTB8pyvmYqU5xLF/TwkQdLFC3igYih41n3R31MxXuJbx3BMpdKJERsbDykV4Omjp6VfiMl3hpeHI3habV9STsbGRtA42hoawUBDpeeMjJWfLcT5zZHwCBiPRmrUkl/PEfYCDoAcz4N3Inno9xil7vIbgSMclQeHSp/EIOa16nN6q01XPuww3Md+g+ENSrdJY8/Z+HqwJXiUhxsrvsCSywVa8D3ajgPatoXiDYxgpFQuWWwQTjlMwlT3dxEBLQKO49rZIIRFJyDyxw7QqzW+kvA6/hLOnjmDM8X+zp49ibDDe7BlexAuPSE0Gx6AVev+iwWj28BSky7JZZDJqKQIebwqyoW16YN0C1cOn6XZYeXaztVqAFp7JjlSU55DkZ4RJEkXcUG1c0Df/p5QpGe3cOzYYzU6JMPD9bOx6FwW5BCg8adz8El91ZokT05C6lt7oELk50t0XgNXFJ7RW52UITboFBwmTUWRq2uBgOPXcDYoDNEJkfixWPIre7wVAb9ehGKYxuFL9UENPPshGOljBh4VIm7HBpRUPS3XL+WKTbqr9yqWc8iQfi8GKVVcH4hjYxEvJRQ8fYTSS20+nL084KhFB8rT04eesnPC1tPxny9cS713xqRLAH4aXRd8EPLvrMaXiyLx5r1z77Vr95WvE2tTnacMVO/yn4/BdrTnqzIRGjAVa+/mgwRNMPHnL9Fcr3R7cfrk/+BtylXzWL0TLIyVny08vxmRHgEYr16wcLQXQBF27iEmRduXW9fGuTZ1Pat2DNHdvx/slGNJkhs4EvzwPYdWiJvBJyDr64f2eqW0ld5CSFD8B21vBR1HYV8/dNSrDaIRoPFAP7Qp5RIggakpjDhFkMh89RL0gdd4hK3LduKRlACBG7x71tPgaQRo1q0T6vIBKriGP389jAw1cuGVxyfU5lUFbcuRr1yUkVzHqmmLEP68FC8gbIK2nVrB2a6Yk6ts+wqJYCuW7XwEhQi80bOeBnYhaIZunRSJTsG1P/Hr4YyqHbfCePwdk6OYQOVMYV5HXaCwhq3y9kN57l3cuqd6NrUwei1Wh2eDAAicfdC7jAsk63h5oaWAA2RpOHfyGsSlGQbJ4NjTt3L2XhiNtavDka3oIHx6Ny39AsQ6XvBqKQAHGdLOncQ1cSl2K38DacsB6G9dctxNXHrAt3872IneH/pe6OOiDw4AZ9AUffuoSYqN68PRSin7pEQ81mpArCLfzW+IMRP7wpwHkOQOdm2MeC+hL57I7cGWyFaYNMkdVXv9qADuo4ahjZAD6A0uHjiEJ6ryofxruHqnEARA+igYgZdUa63sySEcvN0Ro0fUL1ns6kIHNX7EZujVxwX6CqVD07594Kpa6VDf0UqhHfIkJKpUOr5yMl+C66umYVH4c6hXTSGatO2EVs52xWRR2fbvabIOgy2nnFiVOfaEb+kOCKZG3LuJlcH+zdRf7serA1MT5WflmXj5SqbKoVafrqjAyNikbMnxbDBoaA+YcgBJ4rBx2R6kyLXhe7QYB7RuC+pCghQtB/RHyZBgApcevujfzk71BcA11lfy4Oy/COs2bsTGYn+btu5F8LlYJN0/ih/7mCPhrwAMadsGg38+gSRNYhXPGCbG/BJ6lZ//RvWYaT8wadUH1aYCVDe5WvWivWcSoFmvPnBROAcYNO2LPqqdA4zrO0KRnsmRlPhYtf4UXMB//zivuMxc4IyBQz3UXhaq3/sLfNXNDkJOAKuO0/HlABPd1sCVnLB8OyH7RtoSA/pbl9RTExf08O2PdiWS30JEr12NcEVQg7NPb5Q+THXg5dUSimE6h5PFg5qW65fyWJuu672K5ByU+QovqWq9Dt/YGIpSexWmLQpH6aV2W3Rq5Qw7XThQAyMYlZmcWKD/pBFwFgAgCR5s+RV7nslLzklo0+61UCfWuulLDX6q9tiO9nyV9NYf+GlPEmQABM4DMbSjBtdF8/RhIOKqdaxUp+CO6OnbBqJS4pKpqZHi45SJV9p1SrVzrq36LbOUwq6HL/rYKLtCEtwICUZi8XpUegtBxwvR1+/DnVCq2iaUaHsbwcfF6OPnCRFqAZwemrq7o9Q1EI73buAkEukHO1DlySE4dDEXBIBn6gLXhppFPGHz5mjCVwSV9OMHceq1VuYvai1al6PIE/18rMGDHC/Df0YfVzf4jP0Oa/ZH4E5qXslx5Dlg/I4QzG9TzONXtn35JYDkkEO4mEsAeDB1cYVmIhCiefMmikJVno7jB0/hdVWbEVdkK3y1Xo5KFCRylRNFUsSEnkCChABwEDRqBle9suagGsPZQrFj4FnsHaTLdWvv0phQnEhQ7FLlBI3QrOwOorGzhWLl/Vks7pTaQQGc2rRRJpsaYNwDv0XewtmgYJy9eQG/9TBW80wGMFQmF1SQjzdUGz0EDzb+k+BfV7Fr7MmBTTjySo0ObduB+z0nYoRD1YdcftMRGN5BDxwIBZcPYP/DDyd7c07vwwkTD3ja8ADpYxwJPK9i4k+Gh/sPItZrJHxteVWog2UqHXr8FolbZ4MQfPYmLvzWA8ZqApeBoUiZGBYgX6XSieDZzwfWPED+Mhw/93GFm89YfLdmPyLupCKvpJOFw/gdCJnfptgEaGXbV3Ww5aDX1L1oR6aaz/CUw80zbYbmzvxSbaJoLUQKieRDGVevrlRCyzp2RHOBIi5mhR/CsRda7IcW4oD2bUFdSHBCmzZW5Sweaoev/GBCtOEALAw5i//5OYBXkIijP/qi19SDahaViqOH+vXrFpuslyM7I6tcO6ulBQWQlvU7cinEb3KRk1tQ7Lu17YNqCzrO1T6CZzLu8Rsib51FUPBZ3LzwG9SnZ4YQKSfuC9QsiBVeDcKxJ4qpR55xa3i0KqVjJp3xQ/gzZOfk4PmVpehlwasCX1h5BE5t0EaT5Fcag9ATCYpd4ZwAjZq5lt5/8GDT2BmKYXqG2DvpOqpfamK9V76cAxJJ2X5Qy4g8+8FH4UAR/nMfuLr5YOx3a7A/4g5SSzpQ8BzGY0fIfLSpRgcq6tgXPWwVgyV/HYHDx168V5Zpz+61Uid+fLNFtcd2tOarChG1cw/+LlA+c3svtNarvSPI6TWFe+mCBVckWEi06pRq71ybam9QEzDyhm8fG2zf/hxyEAqvhSDo8Rx8rXwlRXrrCI4X9MFKD1GZbSU3QnAk8Wt801jZ9k4wjr3pheVeolqi3aYwN9d8WEhFNpEVcRbXxIqQwDO31NjZ80ytYKHPAYUE+etInLksxqh+IvxT0b4czeD70zIMDZ+Cg8kSSLPiEb5zCcJ3LgHH6cGicTt4demKHgNG4zP/NrD+wLFUtn25JYCIs9egEAEP5paaTibwYGplAYUI5HgdeQaXxaNQZaqk1wQtmhmDF5sFuTwDL17JVLs6WTaychT2wxk1Q0sXFZ+Rp+PipfvKnRscjHiZuHHuXOmOU5aMN0LlpHnWK2TKof5V2UrbuxzpFy/hvnJrCWfEQ+aNczhXegeR/EaonFTKwqvSOwiH+g3KNYnEM2uKHr5NS/5izlPcjr6B2EdPkZychpcZ8Qh/W/GSHPLa6iSMe2PKp02x/ddYSF+GYvNfTzBy2nvyKojEpr3Z8N8wEObV0Ud+Iwwf3gkLL0UgvzAaB/ffx9zv3IpNemXheOBp2I0+hO+eDYPfxmQkhwQifHlvDDQqPoj3sf9QArrOHAwbXlXqoEZKh6Y9fNG0pNLh6e1o3Ih9hKfJyUh7mYH48HSlrpHaQtzM9ycsGxqOKQeTIZFmIT58J5aE78QSjoOeRWO08+qCrj0GYPRn/mijwslWtn0VJxwwNTfXOBHkmVnAQuNhkquQcQ3QlYqakZ0j7A04QEKgvMs4e7EAk4Ya1pi8Txe2oLqvDqjfgPdx+kqVVVIjjFs5H3tPzcCpXDEebJuO2b06Y+8ndqXERQFcWzaDiHsIKSn1/ulj5AIabnDKw55RlhgfIgFPIICAzwefx3s34UgyCSQSKaQyOQiAsPUCXI3+Ca0FtdEHaas213Gu9lE8Ew9mTXugZHomQ87T24i+EYtHT5ORnPYSGfHhKErPVDkHKR5EREK5rgCefUM0LFOxedA30q9aX1hZt+xQH5q4Onn6RVwqCmrgZd7AudKDGmTJbyBUTuJmvcoE4Kj9+qVG1nvlyzmqpT4w88VPy4YifMpBJEukyIoPx84l4di5hAOnZ4HG7bzQpWsPDBj9GfzbWKPaPaiwLdo2F4B7JgPRG1wJjwQmDysxRtqxe2itTvzIZotqje1ozVfJHuH8xURlbOLDoWED1ObZQs7UHJqHHYJ2w04tnWtTm/3WjNkZ+Pj1hvXOnUiTA1QYjZDgJ5g9xxl8SHH3yDHk9foPOok0aXsNIcGPMWduI0XboDBk9/wVXrVG43nlO/6A6IOE7+nDJ0oFBaCnD40XEXki6OtxAAiQvcDjxCwAtv/QQKEbOfKdxmHnKT3YTfwK66+k4+2GTqJCvIq/jJD4ywjZtgKLWo3Hyp2rMN695NaCyrYvnwie4uETMYpEoPlyNE+kPCeSANmLx0jMkgO2VZV2WGDg+KFwCN6CJFk6rl95AGnfFh84O9mzm7iTKgfAR13f8RisauVInoxnabJ3MpA9Ool1f14tex+x51CM6MTBwL0T7Hi6tHc5kp+lQVbUQZxc9yeult1BDB3RCZyBOzqV1kGOD6NynPNeQn3SriNo53bsCgxG+N9p0GvkiW6dO6CVWyO4NzHBw8OHcCOrtvsJPbSfMA4e/w3AxYI8nN+6DXenLkTLYsqWeXQTDtf5FGGd9aupj3w0GDYCXb4/h1N5Etw8tA8x3/5Y1MeMY9gf0QDDFrRDrxRf1N/yJxJTQ7HvVA4G+hUdWSCN2YfDSd74doBF1epg+ZQO14N2YvuuQASH/400vUbw7NYZHVq5oZF7E5g8PIxDZSkd3wnjdp6Cnt1EfLX+CtKLnCwKX8Xjckg8Lodsw4pFrTB+5U6sGu9ecld4ZdtXdcZRnrPfebzy+YIPtrzVIF0ptxmZoI4RB2QTIM9BUmIaAKcakvfpyBZUhgSjctyzVdt8pZrRqdcXPVsIcOqyBJCnIXjNDsSPnIem6s8Eg2XXrmglDMOlQsU9ftL4GMQWAl01SqMMMPA/F3B5vhRSiRhvsp7j4bWDWL3iIGLzlGPPs0DnWb/im0EusLZxQol5xVrmg7SCznO1j+mZpEi7HoSd23chMDgcf6fpoZFnN3Tu0ApujdzRxOQhDh+6gaxS6qMnT4ruNuBMzWCmtdlVHfjCik01gW+kOBKn7GF6hrSioIZHJ9fhz7KDGjyHjkAnzgDunex0U7/U0HqvfPfNEKr+hWY+nMbtxCk9O0z8aj2upCvvLQKBCl8h/nII4i+HYNuKRWg1fiV2rhoP92pN4kxhY20EDmIQ5MhJeqzWbitn99qvEz+O6aLaYzta81XSR3iYVDS7XsfconYfZsIr3zHvWg07tXauTTU15u1Xk55+6G21G7vS5QCJcfVIMJ7Omg0nisXhsNfoucxL7WqYSU8/9LLajd3pcoAKEX0kGE++mgNnikVQaBZ6/uIF/X+Mh5Mjv0CslUAsLsjHPxfdyVG/2SdYHTkIM07twa6DJ3A+6hpuxSXj9bviT4asW1swuVcq8iOC8UUzgVbbay6CfBSItSIBVLUqmQ38Gb9/dg6fbn+IW9v/wIkZ6zGw+FZbeQZOL9+IKAkgdPoEKxb5wlKVL5YXX5nmwW7gz9j9myf0apCeFt9ZwrMbiJ93/wZPrXWQB45XznRBmoTjS2dh9oojuJ8tgnP/mVh9YSZGedgX+fDCS7jz88dxphq/yWeY0GspLh3NQuHfu7Ap8lv80UMZceTPsG9LONzGL1Eeo1JNOZODP0b2mI/TodmQ3DmEwFvfo2V7AQA50kP24YLLMCxtwoe+82j4NVqPlQ/ScWzfcWT5jYCZsiC5FRiEtN4/ob9ZVeugRkqHpONLMWv2Chy5nw2Rc3/MXH0BM0d5wL5I6XDpzs+aJb/6zfDJ6kgMmnEKe3YdxInzUbh2Kw7Jr4sKTFnWLWyZ3Aup+REI/qJZyWSqsu0/4rha/bpS0XkmfglfWFhYWEM7qmVb+MCZcOBxH6+vVN1xe9SvKwIHCQiEwluRuJw9D01Lea2C38gfvu0X4vIlRR4pfXwFFxNl6NpUkxlYHixdOsCy2H/pPXggDG+HYXyQ8ogKYQeM/WEyfC115MNqnWup6blazXgmadJxLJ01GyuO3Ee2yBn9Z67GhZmj4FHkHFB46Q7KSs8kkmL+TyD4KOMXj9NsEk9efGcwzw4Df96N3yoR1LRWv/xD6j3doI9mn6xG5KAZOLVnFw6eOI+oa7cQl/y6aEOfLAu3tkxGr9R8RAR/gWbVaAR6omL6piI30Zbda61O/KjS2tpjO1rzVSSG+N0zc+AL+PgHawDzvSW8QU3BpCd8e1kqO0QQXw3BkSQ5pHFBOJbZE35e+qW29fugrQzSe0EIy+gJv66G1a83z68i6NgdZOn8nT4+LMzNigy8sEDzC8nkYhS8jZicAGbmZjXL9qpMhlUgR54JXPpOxc8bDiHi76fIyEpBzLl9WDXbD62sheBAkKUdx7/nbkWSquetbHuNRGABc7NiEykFml9tJxcXoEgEZjA3q2JXw3NAv3ED0czODiZJWzFhyGxsi3yEjDdvkH7vNNZM6Y3R6xNg2HIiNoeux6j6avonsIXtuwupCHnZOahZ1wEIYGtbdLkc5WUrLtOrLt7cwmp/LwxZeBj3cizgs+QMokOWYmzxRYXaFfHx/GoQjt3JUv86Ns8ewycNUVzeJn2EfRuPIlP5T7J727H9TldMHF2/eoMtzxZDRvqgDg+AJA6H9kWjEADkaTiy/wrchw+HEx+AXkeM9neBAHK8OhGIsLd3QRVGY19QFvqN6qViZ2t16+Ab3FrtD68hC3H4Xg4sfJbgTHQIlo4tPpFasfTIxKUvpv68AYci/sbTjCykxJzDvlWz4dfKGkIOIFkajv97LraqdtKVbP8xUsP8VbkKqXwUFLwNanxYWFvVwE7qyha05Ydqga9Ukw+K9PXe5YNU+BD3HpSxsMRvjP+b1BfvUh/pLYSGJJbrnoWSCGFqYlCseNeHgYGufVhtci01PVer/md6c2s1/L2GYOHhe8ix8MGSM9EIWTq2xOSiprHRwsK8yE5fZyGz1gu7MsNkW3SZPeUhu7JBTVv1yz+l3tNlyDJxQd+pP2PDoQj8/TQDWSkxOLdvFWb7tYK1woEi7fi/MXdrUjUe6SpDVma20jdw4FtY68jua1jtVVN+qxbZjtZ8Fc8CFmZFsUlcUIB/cAionK59ZL63Bnn/Oujl2/Pd+b1UcAXBIY8RFxyGlz5+KH1t4L224igcOfIE94LC8MLHD92rf10BkrtbMXfBQSRVwcKCo7vru90L8sxXeKXhb8pzXiEjX6mhAhe0blP5d/ukEmktlKGO5CgOxzzvydin4ot4hnZw6zYCs34/jBsx4VjcywZ8To7XZ3Yg8O1N5pVtX24ROMLd9e2CnRyZr15pGHTlyHmVgSIRtEabqn5NVBaLtQvPwXvvA8Sd34SpTe5jzXgvONvaw7Xn59ia5IbP10Ug5uomfOZqWOpESGt3e+UZmnJkPE9FzdqMw4N9a3fYKzdByjOeI7XaOpiDM/NH4+vQZEgggPOELdg7zxPmtbrGkODu1rlYcLD0oqFO/8kY3Uj5BsDRzdiXJAcgRtSW3Xg5ZAoGW1S/ntgMGone5jwAUiQcDsQVMSB/dhj7r7fFiKH1lHauh7ajh8NdyEGeeQb7QhTnsIuvBCJYPBCjvY1qnA7mnJmP0V+HIlkCCJwnYMveefCssNKJET7PG5P3qfB1PEPYuXXDiFm/4/CNGIQv7gUbPgf56zPYEfh2wrCy7SsVbCGt8fZUk/xVOclOQ7ryTGvwndCihUmN66J2bUFHmX6N95Vq0iF+sTcNZE9w7/6bMnW97uh5mOKmXJAgMaJ2bcetSr3owlWBD6utM4A1PVer5mfKOYP5o79GqMI5YMKWvZjnaV7BSQA9NG/rDiOlOspepCBVin8sPPvWcC8Kanhe2aCmrfrln1LvaRlx+Dx4T96noubnwdDODd1GzMLvh28gJnwxetnwwclf48yOQCRWlwOVJiD+8ds30fhwatFCR3Zf82qvGvFbtch2tOar9NzQprmhMiMhvEpLr935Q3Xq9Ufme2uUbzHr4wcf83crC4g6/Cc2HU2Dj183GJa3bdAabDj6HD5+3WH4D1Nbw6794a2cEZdnJ+DeE83MXRIXi4fKjwoa98EAV4FKldH8jbccvHyZV2tXMbUuR1kKEqKDcSSi9IKUZ90ZAdt/xSBzHkjyEPfuS7TTvvwSQNf+3srFFTmyE+5BMxFIEBf7UBlkBGjcZwBcq/gVUVlCMA7fdkb79iaw6zQei7Yex7WE58jKeY1XzxJw/eROLJ3SFQ5lvgEogsegPqjLVwRPccwN3NZ0YkD8GFevPtH8TZcKIvIYhD6KDoLEMbiheQfx+OpVPNFSB+VPd2DZ5gcoJIATdcb07/oX7Yx4HypEoZRUfMcGjPD6FhHiWuYsRJ0w8bM20OMAygnHlh1xkOacwKYD+hg9uYbEIIsBGNVPcSmUNDEYgZF5eHroAO54jIBfsYOYBS1GYlhrATh6jfB9R5AmL0Bk4FFg8Ch00a9hOih/ih3LNuOBQunQefp36K9e6VBYKFVx7P9TbBjhhW8jxABkSEmIRvCRCLwp3cmic8B2/DrIHDyS4OG9+1B42cq2f+9j5Xi9POflS+TVgmBbU/xVuScb7tzB/bdx3akvBrWuYYesaN0W/sG+UgUCQbGFBRIjIe5B2Qt5hp0R8NtENBYqDsGV3NmAJYEpFZ4oIY0O9tWuD6pFQbjG52rV90xyPN2xDJsfFILAQdR5Or7rbw316VkhpCrux3m6YQS8vo2AGIB5737opFxZkGdcR9RdDZ9psdIAACAASURBVFcWpGm4fz8dH9U6hMgDg/rUVSwAkRgxN25D82G6iqvvBTXt1S//jHpP6/VjSgKig4+g9FKbB+vOAdj+6yCY8wiSh/dwv5ocqDz9Ii7fk75NTtB3UOsim9Wy3TNque1ozVdZonc/Dxhwypzjwf0qOk2E6U9N9701a9HSrA/8fMzeHWmUH7ka65O94dvDqAJt12BDkoZtKw0HUxOjd3uJJCp26VN+AcR6yks2dC7HwZg12V3xW9I4XLmsyeqXFHFnI5EsB8CZouvUSWivKmnhmcDEWPkQcir9ApOCG7geK9VwYaGGyVBXcqRMRISGI7csw7TrCZ9WAoATQV+f0177cotgFia7K3bcSeOu4LImr21I43A2MhkKEXTF1Entq/ycW2nSU6TkP0Z8QuXLF6Pes/BlR8XKvPTJOZyJ1eQ75Ujd8xV85x1Eiq6X8Y16Y9aXHWGo6CDOnYnVqGiTp+7BV77zcFBLHcy/fA7RbxTWLnDpim6OpZwlXZiKlJcf6hJJs5GemlF1Ow05U5gYvfM6+NDtEPILxNArdgyGmmknuI2dgB7GHEBi3NixCSF7t+Bko7GY0FJQpq5kPbyMs+fvIl2nmXsd9B05QHEMiSwJR/buxK6D99F55BDYFM8E+M0wcnhH6HGEnHP7cDjhDP4K04fvKE/1x1lVlw7mX8a5aOWZ4wIXdO3mCPVaV4jUlJcf+nCSIjs9FRn5RWOeGRGK8LKdLHr6tIIAHET6+sX0o7Lti9IzExNj5X+XlzGpWIAb12NVFIU1kBrir97FCqkmswFS3A2PVPhyrg66z5gOr5p2ioBObEEXVMZXVl+pZGxkWMxGZXhy/z7yPvhcDhJjHiGn2H8x778MG2e3VuzulqcjeP5s/JVUER3OR1bWGw3zaW35oKqKTZqR8zgKZ85eQ1JeFeZqOY8RdeYsrqn70RJflYWHl8/i/N10rU3CaeeZ8nH5XDQU6ZkALl27ofT0LAUfpmcEaXY6UpXOgWc/El+McFD4GOl9HA/VxI/LkbJtMrp9vkdRI300GKH3rC/RURHU8OTcGWg2TKnY85Uv5h1M0Vn98k+o93QBZUYgtGwHCruePlCU2vrQ1/bchFSqwWKvDI/+CkRkAQHgUKf7DEx/l5xo3+5rXu1VM36r9tiOtnwVD/Yjp2JoXb5CftfPITJPk98vRKEEHy8V1LWPyffWsLehzNHXz1txDjQAkshg3tMX3kYatvXtAdPibXv5wqfCr4XISygClSFGy3oOMFauNr18nvpegiVHVvIzFNrXV+48UV0IFP1vWaVDWf+ujw5zF+MzJyE4ysaZLTvwoCzH8ToCG/behYQ4GLaejsVTXFQXpQJHODmKwAGg/Dy8KUX3c87sx4nnype3SQaZjHQsw0qMMFWVHOVIDd6MAyllOQ0ZpFICp++G1u5CLbYvJ/odMHfxZ3AScqDsM9iyo+xdeq8jNmDvXQmIM0Tr6YsxxYVfph5rez6Mb2sHa/ltbPx+I2IKKjsP0hzTfvkcbvocILmDLSuP4EVZ4hffxLo/r6DtJ8PQgK9rexeg+bRf8LmbPjhIcGfLShwpu4O4ue5PXGn7CYap6mAFBkQuLiza+SAUlBrgciNO4UreW99QPOcohIQvhLD8xlox382zRD0HY0UglL/E8/ff55dnIflZIezr10VZbodXfxQmDVTsBpImbMPU76/Cc+IYNeNfZKeJ20ejpVtn9OzeEo07zcWJdN1V3Ma9RmFgXT4AGVL2z8eK+B4YNeD9s0f4aDRiODrpc6A3kdjx9R84VscPI9vrVa0OaqZ0xe6pE0JQutLhlGqlQ6GED6GweA4djM0Hyt5ZLJNKQZw+3Fq7l9DZyrZ/K1NHJ0eIFMEWeaUHW+wvCraQyWQae5VyeSCtOOpq0hU19vcwOrpsf551Eut2x0ACDsbtZuGXKY3U+IPy5I1ajgM6sgVdxOiK+UotThpVQGcbNKxXbMwJBfdjcP/9rX6ZB/FV968RXKLAroPuvxzA+k8aQY8DZM/2Y+qQOQh9Xk4/nxWB8OtijfteeR+k+9ikeViXI+mvcWjr2gm9enaEm+dMhKoyWi3navKkvzCurSs69eqJjm6emBn6Qr08ZYnYProl3Dr3RPeWjdFp7gloRVxaeSY5xMUudBWW7hwQceoK8t4dclE8PZOA/845mGPgv79DHyseQIW4tWVV0Z1MaidCYrB1UxRaDfeDI0+XOXHVegfFME3DL5+7QTFMW7DyyAuUPUzr8OeVtvhkWAPd1S/liU06rfdqyghrGKPlqQjefABll9pSSImDvltrVKbUVvnVj67hWlnGnnEcv/15CQUEcMbtMOuXKWjEL3pW7du9lgap0rVXFdZ5mgSqWmQ72vJVPEs/fDfPG+Y8QJ4ego27n5bxPXK8OhWGi68VnyKZDDLSpXlr8OXadkoV1TUd609VUuOOWbPo64ceb1cHeNbo7ecDTdcGLPr5w/tdWyv08u2Jih83RcjOUl6URWLk55euUfrd+sG7juL86nsXLyCthHW9wukzsWjZvTOM1FjC69c5CnugXLzOLn03kyQ7W3nkAUEqUf1GAM9yMFbtXYDOZhzyLi7GpGXR6l+Llr/Aie9mYvNDGQT2g/D7noXwUvs+uim691TsNqQ3T/BQ3c6rN1exfMUj9BzhBgEAUCZevJDqUIYVGOHsLOXFkYTc19lVJkd5ZhgWLQgpteiQJQbj6E057HwnwN+Kp9X25XURloNXYe+CzjDj8nBx8SQsi1b/fqj8xQl8N3MzHsoEsB/0O/Ys9FJztIEMr1+/ta885OVqN7oIXIdgYAsBnodOh4dLB/QbOhwjRo7EyJGjMHrMWEyYPBVfzpyNb/79M1ZuDcKF+xmlng9o4r0Ee5f1hg1fjme7Z2LqtgT1rw/KMxDxw1RsNpmLpRMaqHCy2rd3mHhjyd5l6G3Dh/zZbsycug0J6juIjIgfMHWzCeYunYAGPNVjo+ifFHm5Gm1DgKFnd3goV+qlcRE4re5dvjfXsfL3+/Ae0RICAPQmDc+V71BKk5OQbusIR8EHxoos5SVTlPsa2aQt362Pbv2Ui9nSe7h4Ia1kYvTqNM7EtkT3zpp4HQsMmjxcUcjLs5BhMBiT/axLD7KyB9izNghJhYqsJufWGvywIU53AdawB0YPqQ8+AHl2Dgz7jUI/FXfL8xsMw8iuRuAoH1FHw2HnNxKty9pMrFUd1PR5PNHdQ/mmmzQOEaefqLHjN7i+8nfc9x6BlgqlQ9pz5eVZ0mQkpdvCsbjSyTMRtmgBQkp3sgg+ehNyO19M8LcqOc6Vbf822nbvqdhZRG/w5GGS2me7unwFHvUcATdFsEXmixelJqXy16+huC5AEftkpTsgZCvPWJLnvsbr0ioHeSay3hmnFBJJDdIVFXCGTrC8tRDTd5Vyvrw8BYfmzML2J3II6/njj70/wENfG75Hy3FAV7YAALLXeK28X4KkedAwJGjXV2qr7szORvF0Iy9XkwtxeajXywctir0uK3t4CZHPSkq4MPYO4i0d4fj+HIzAGZ9uP4VdU1ujDo+Qe2sNPun/L2y//VrDTqfh+IKFOCh1R5v6Qs12VVbWB1VBbCqeg4vz89WPgywO21bsRUKBoi+5MRvwe2CqjnM1GeK2rcDeBMWllJQbgw2/ByJVri6U78HaoCQoxJWDW2t+wIY47ew4r/wzGcKzu4fyTgQp4iJOQ316thK/3/fGCIVzwJu058ojLqRITkqHraMj3noHfuN/YfP68Wiix0GWtAuzZu2D+pdxshC1bAbWyj7HogkNP9hwpfWcuKKTucViozQvF5q7OhN4L9mLZb1twJc/w+6ZU7FNfVCDPCMCP0zdDJO5SzHhvaCm7fpF89ikq3qv4jkHSSVafhNT0xgtR2bYIixQ3jOmRluQGHwUN+V28J3gDystBzDKOYmF07bjoTo3In+Gg3O/wrZEKSCsB/8/9uKHEsmJbuy+MnWi9mqvKqzzitef4nyo/qnaZDva8lV8NJu+Bf8b4wQhsnFq8VfYUcpFI7JH2zHly914xinfw85MR7q2317QaKzeCbYor819jdLDjgTZ2cqj3UkKiVqnVFFd053+VDlU40in7X5mxAHEsx5D+7Oqqi0RkYxSbp6iY6HBFLh+PvV14BMHEDgBNRj4Pa3/6zAdPX6NnklVtS2gG4s7kQkH4oRN6fPQNJIREZGUngVNoeZOn9GBl7Jin5dQ8rWTFHb0MO1d+zV52/GV62U8svSaRn/sPkxHw05SVGI+ERHlJ0bRybCjdHj3f+kLTwviAQRwxK/rQ3PX7KbDR8Po5LVn9H7XMq/+l/yd9YnjW1Gnr/bQ3SxZiX8Xp16mdePdyZjjkYn7RNoRl1+2mDLDaW5LA+I4ATWacJCS3/tR2asrtMq/HfX5LYqiFrQhobKvxs1H0cJ1e+hQyDGKTpZqQYblR5Zyk04dC6XgwPU0v68D8TmlHB36UMC6vygo9Bidupny4SNrQ455u8jfkCPwTMjWzoE8Zx+ihLwPPyZ+fITmepiRocskOvhMpr32lSKTrv7Xn5z1OeJbdaKv9tylkiIQU+rldTTe3Zg4ngm5T9xBH4pARs//PkPHQoNp38YFNLihUGFf4JF5u/G0eP0eOnQklMKOX6KEnMr3OOvcD+RhxqNia9Fq/zieEdXv8hn9cvQRFaj9xly6vXEMNTXkiBM6UN8FIRSfW/ITktSLtHZCa3JoM42CS+h41dh77u2NNKapIXGckBz6LqCQDztIF9dOoNYObWhacHKx9jLKjLtAx8NC6MC2ZTTGzUA5NhwZNvWn79fspANHQinseATdVWuDuXR1mTdZ8TkCeGTlvZDOpkhKmkBCCP04oCV5L46irJjfqKsxR+AMqNXMo5SYdpvW+jUh7+Vxin7JUujmqWMUGhxI6+f3JQc+pxgrvgP1CVhHfwWF0rFTNyvvuwtu0OJOJsSBI2HTzyk0Tfl80mcUNKU5OX12gDR2O5KbtKC1kDhOSO7zr5K4TIf0lP7sbaSUteJP2GmZTiOs+PxX1EQAAt+RphzLUxsLUzcNIlMOxOl50C+xUo2/v+I6WDFyry4jbyvFePOsvGnh2RSSlFQ6CvlxALX0XkxRWTH0W1dj4sCRQauZdDQxjW6v9aMm3sspTkpElEe7/A2JA49MbO3IwXM2HVLtZOnIXA8yM3ShSQefUZF6VLb9h343fG5LMuA4EjSaQAc/DLZ0ZZU/tevzG0VFLaA2QqU/M25Ooxauoz2HQuhYtELGkuRrCp+ydy197W1HfKW+8Sy9aNofSp8SlUj5CgdEUSfD6Ojh3fTfLzzJgqfUT749+Xz9P/or6CiFnXxrT9kUf/E4hYUcpB3Lx1NrE4WdghNSw4Hf07q/gujosdN0K1VW7bryLlNc35dEnJCaf3OBXt74nfo2ak6frrtBme91UfL8Ev0xyoUMOR6Ztf2CAhMKKpk36jYOaNUWZJkUd+E4hYUcoG3LxpCbwdtxNaSm/t/Tmp0H6EhoGB2PuEsVSsvK6ysrmvO9vEsRx8MoNOQw/bVlOc3q6UhC7q2/5Ujg4E0zl2+mvw6HUGjYcQq/m67aHmWv6Nh0V9J/25Yzol5rnhT7bAYdndyYmkw/S+qz6Gz6e/t06u4gIg4gzsCJ+n2zhc49zlXboiA5nFb9XwuqY9qaZoSkUObFn6izOY8g8qUdKt23lnyQjmKTuhxc0GAgfb/+Lzp89Dhdez9Qy57Q2j7GRX3hWdCIPRmlRYVK5GpFdv1kbR8yfqcrPLIYsYcy1IbyP6m3EVcsrxRSp2XxWtTkSj5T7lVa5m2lkDfPirwXnqWS6VkeJYT8SANaetPiqCyK+a0rGXMgzqAVzTyaSGm315JfE29aHve+rCT0+PBMam/GI44zoCbDV1DEs5LWLMuKo/1zPMnWeQRtuSeu0pxYI53MjKMLx8Mo5MA2WjbGjQyUY84ZNiX/79fQzgNHKDTsOEXcfUmyshMg2jimKRlyHAkd+tKCkHjKfU9eqRfX0oTWDtRmWjAlS3VZv1Q0L9ZGvaednIPj1yWfuWto9+HiOYdu53bydvmTIQfimdiSnYMnzT6UQB96UDE9PjKXPMwMyWXSQdJaqZ2+nvqKlP7Wcxb9MtKVWnyylqLfT06ybtGmce5kwuOIZ9aWvghMUF27atXutVEnVqb2qsI6T139KWhAA79fT38dPkrHrz3T0VxJFdmOlnwViR9S4NRWZMrjyLDpGFp3I/M9PymmZ2eW0GCXxjRkxX9ojK3Sr3HG5DZyAf1v9yEKOXaVnoornFRoPlaSZLp2MoyOHt5La7/2Jjv+25zCkrym/aGUVRQpwk4+JUYpYtTu/35BnhZv+82nuj5zaY0yRn2Qr1RqTkE7+lOd1MCFBRmlbfWlOhyPrEbvo6wqa6tMyIeakFBkQMamZmRhZUv2Dg7kYGdDluZ1yMRQn4SGg2mrui+WpdLpxUOphZWQOJEDdRgyhkYPbEf1HLxo/sm09wwtndb3Myz2WzaK37K3JWsLM6pjbEj6QgPquvwhSUlKD5d3JQOhiAyNTcnMwops7R3IwcGebK0syMzUmAxEQjIctJkyVWYodyjwh2HU1lafBKbO1GngSBo7/lPy79WOHI2EZFK/M437NYwe5pVjcupREM3v34RM+UKyatGfxs0MoB8WzKdZ4wdQO9eO9NmaaMomCd16t7BQ/E9EAzdnakGG5Sdv11AyEYrIQClHGxVyNPLfqSbTq6Qc8/bQMBMDcvsihFIzrtG6iR3JybkDDZkwiwIWLKAfvp1B4wZ3oHpmdanj2BUU8Vym3fZaIOtOIP0wrC3Z6gvI1LkTDRw5lsZ/6k+92jmSkdCE6nceR7+GPSTVIsinA2MsSSgyICMTMzK3tCG7ug5Ur54DOTg4UF17W7I0MyEDw3a08JakkjVYPIX8Opm61zcgfeum5OHtQz4+PuTj7U09unWlzp08qH1rV3K2r0N6XFExyAnq0sA1d0pZXCDKiTtMP41qR/YGPNKzdqPug0fThEnjaHjvNlTP2om8Z2yjm+8ngVVp7zlxdPinUdTO3oB4etbk1n0wjZ4wicYN701t6lmTk/cM2nbzw8B/YU5TEunpk6FJHTKztCbbug5Ur169d2NjZW5KhgYNaOrx0qSTTwnBi2hsjyZkJuRIYOFO/f7vS5oX8BVN9O9CLg3b04R10cp+iyl+77+odR2eYpJF5EBdZgZSgrhoIW2oiZBEBsZkamZBVjb25ODgQPa2VmRhZkrGBiISGvlrxXfLUk/T4qEtyErIkcihAw0ZM5oGtqtHDl7z6WRaeexISo9WeZORYTdaEa9ZBZRzdRUNb2lLJqYO1GFgN2rUZLpuQ6wkigKaC0nQaAaFl5KQyNJ30lALHhl0W0Hx5S3mKqSDFSc/IZgWje1BTcyExAksyL3f/9GX8wLoq4n+1MWlIbWfsI6ilcYijt9L/2pdh3gcCJyIHLrMpMAipaM9w0zIwO0LCknNoGvrJlJHJ2fqMGQCzQpYQAt++JZmjBtMHeqZUd2OY2lFxPP3nqGy7VUGWwqa35+amPJJaNWC+o+bSQE/LKD5s8bTgHau1PGzNRSdTSS5VbSwUPxPNFDhJ9LX9yNDVbHP2oLM6hiTob6QDLoup4dSIunD5dTVQEgiQ6Xt2aqwPcNBtDmTiCTX6PtW+qSnb0gmdczI0tqW6iptz8rcjEyNDEgkNKNRgXk1QlfeLiwYOI6nw8rsviD+EAX0b0r1W/Slz6bNo+8CZtKkYd2psamQTJ2606SVp+lxgTbyRt3HAa3ZgvgCzWkqem9c6ynjdV2yt7Uic1NDMmgwlUoNCVr0lRWh4Ni/qOFbnberSw4ODuRQrx7VU/45ODiQQ107slHKtMGUMPXxX5JIId/3ofoiTrkoMZiWn39K2RkP6dR/fKlJo9G0+6kGmpodQ/t/GEqtbZQLDEJzcunqTxPn/EjL16yj9f9bRUu/m05jerckW5E+OXT5nDZey1DagIxeXVhKg9v9i4LzVeujtnyQLmLT+zm4QqftyMbSnOqYGJK+0JAGqwjUBXc20bj29mRqWo+8vthLCRJNwlBFcrUSP0qbxrUne1NTquf1Be0t9Udz6Oqq4dTS1oRMHTrQwG6NqMn0cK3rc6WeKT+BgheNpR5NzEjICcjCvR/935fzKOCrieTfxYUatp9A64qcA+39V2uqw1PousihC80MTFC7AFjw+BQtH+9FjkZ84hnVo3b9RtGEyZPoM78u1NjShlp/8h+KSJVWT05cVoi9MIeaivRI39CE6phZkrVtXYWPcHAgh7r2ZGtlTqaGBtRg6nHSzNXlUNzhn2hUO3sy4OmRtVt3Gjx6Ak0aN5x6t6lH1k7eNGPbTVKrelqrXyqXF1eu3tNhzqHjuZ28PcPIxMCNvghJpYxr62hiRydy7jCEJswKoAULfqBvZ4yjwR3qkVndjjR2RQQ9125yUrSw0Pk3SshPpJDvB1Gz+s2pz2fTad538+mrCQOplbUeCUydqPuklXT6cUFZCbKW7F5bdWJFa68qrPPerz+V+mhnY0nmdUzIUF9IhoO36miupCptp5K+qsQC1WL6pL09GQgtyb3fWJr+7XcUMGsS+Xs2Jqf2n9CyMykkzQ+hCbYqFkz1+tD/0iqcVGg+VunrqZ+hqnkFa7Iwq0PGhvokNOhKyxWCpeVdDUgoMiypb/a2ZGVhRqbGBiQSGtIgFYKt7JxCZfWnOuGIqOZd95dxBNP6/ALR4tP4vZ9JudtO7/sL9BZVoK22yEvE+aMncO1xNvjWrugxuD9aWdeAS+lkWUi4dA5R95/ieaYUhlZ10aC5F7p3dKzg8UJy5DyOxoXLt5GQ/ApveKao6+QOz95d4GLCAyBHeuQuBN6RwtzKElbW1rC2toGNjS1sLU2gx6uFMqyMHGWPceZAIlyGeb876uVN0lWcPhuFeyl54Bubw9rRDR7dPNDUQk/77bUqggRcOheF+0+fI1NqCKu6DdDcqzs6OhpV+/DInwVj1uBx+N99Owxftg1/fukJS756HS5Ii0H44e344z9rceJRPmDUGcuunsM8N34Zr0wn4HJEFOKepiJLbgq7+s3g4dMFLnVqyAlzstdIuByBqLinSM2Sw9SuPpp5+KCLS50qOXKiIOU6ws/dQPyzDIhFtnBq5o6OXu1R/z0VkeelIObvR5A3aItWDtX7Ml9e4nkcPXENj7P5sHbtgcH9W6F8bkeGhyt7o/v5cbh+cBxsyyloWexidBmZict3V+jSQpAWuRNHC30w3qe0C17FuH9kG/6uPwYjW5vUDh0sSMH18HO4Ef8MGWIRbJ2awb2jF9p/qHRIifkbj+QN0LaVQ7FXSGV4fOYAEl2GwbvIyeLq6bOIupeCPL4xzK0d4ebRDR5NLVTcI1LZ9qWMWs5jRF+4jNsJyXj1hgfTuk5w9+yNLi4m4AGQp0diV+AdSM2tYGllDWtra9jY2MDW1hImejXu1Msa568AGTLuncep83/jaRbB2KYuGrp3Qff2DjXnFeMqtQXdy7syvrIaMwzkPDiDv3YG4tjFW7j35AXe8C3g7OGHWQsD4Nu4HLd6SzNwL+IIDh45i+sJyXj+/DnSMsTQM7OCjV0DNPfqhf4DB6F3u7rQL4dcdeGDqiY26cq1VHWuJkPs4i4Ymbkcd1d0roHPVICU6+E4dyMezzLEENk6oZl7R3i1r/9eDSNHXkoM/n4kR4O2raBJeibPTURU+GXEPEnBqzd81HFoig49eqCdQ630opXWg9cJlxERFYenqVmQm9qhfjMP+HRxgbphqqr65WOp93Qyao/P4ECiC4Z5vz3+5w2Srp7G2ah7SMnjw9jcGo5uHujm0RRaL7VfbEA/x6k4IQaEnX9D3Llv0IgPyDLu4fzpC7j9JAtyY2vUbeiOLt3bo3xmpTu7r/7aq2b+Vu2xnfL7KnXfk3X/IsKvxuFpWi4EVvXRuFVndG+jzGFkD3Fs4zE8FVnA0soS1lY2sLKxgo2NLSxN9MD7yHxJZXWtNvremrmwwGAwGBXLCLHRtz0+D5Og2/IonJrTDJr6cHn6MUzr5ov19wnN5kXi1q8e0GMSZZSHwmj8u+NIpC++jU2Dyj8ZLz4xFa6/NMKj8/OYLBkMBvOVjBoBi03lkhZOTHXFL41O4fy8RkwcDFa/MDRDzcICg8Fg1AZ4TAQMBuOjycufHMCOUxkgw274bJzmSTkA8Gz6YNbYthBAhkd37iCXiZNRTnJOrsde6QhM6lORiTIZHl2/A0HL9kyQDAaD+UpGTcmsWGwql7ge4fodAVq2r8dkwWD1C4PBYDD+EbCFBQaD8dFAGZl4TQQIDWGsX97WHIyMDMABoEIxxHImT8Z7ZEVjw8yR8Bv9NXbcziv5b/JUHNgYhnpjJ6JjRbaKyeIRFJqGbv07MjkzGAzmKxk1AxabyimuIISmdUP/jiImDAarXxgMBoPxj4AtLDAYjI8GgVtv9GgoAHIiEXzsBcqVW8sScfjIDUg4PbTq0Q3WzDsySioIYtZMw4w1+xEc+Ds+/2oLnhRTsIKrq7H6Tm/MmdwMFXlz+fWJFdiSMxyTehkzUTMYDOYrGTUCFpvKJS2cWLEFOcMngYmLweoXRvkgvDufnJ1UzmAwahks9DAYjI8Hw27494rJcNVLReDMcVh5NUuz5FyWguMBY7AgIhd1PAOwckYLCJg0GSWVBLm5eZArc32OxxX9kzQWf/57D6xmz8cQiwp8dV4UVvwUjvb/ng0PtsmRwWAwX8moCbDYVE5xrcBP4e3x79keYOJisPqFUa7I+eIFMpWDTpmv8ELGZMJgMGoP/B9//PFHJgYGg/FxwMG4aT/4tefj3old+N/avbiaaQiHxk1Q31wE7v2PSzMQE7YeP0wej4D9KXAa/SsCd34DL3OOiZLxfriEjVk6QndForDdNGzY8C06WwsAcQICnpAQIwAAIABJREFUp43GCtEC7F/mjXKrjjwNITOGYY3df7D323YwYarHYDCYr2RUNyw2lVNcIZgxbA3s/rMX37YzARMXg9UvjDL9Rmokdu8IxtnwUGz770YcT8yBHABlxuPu0yy8fBSDv++9gW1LJ5ix7cAMBqMmRzEi9q4Vg8H4CCl4gojta7Eh8AhOXUmCoL47Wrg2gI2ZEQSFuchIfYTY27F4LmqKLn198enUzzHKww7syGeGegqREDgHn805iLxWPmhjlo0H1x7AePgKbPpxEBpUQHlkj7dixs/5mLL6S7QxYhJmMBjMVzKqHxabyiUtPN46Az/nT8HqL9uAiYvB6heGRpEyehH6fboTzwQi6OuLIBLpQyQApOICiAvFKCgQQ1LHHxvO/wpv9hoUg8GowbCFBQaD8dEjL3iBhDt38CDpBbJy8iEXGsLE3B6NXN3RrKE5S8YZ5Sz60nHv5k08yrdFi44t4WjMthExGAwG85UMBoPB6hcGg8Fg/LNgCwsMBoPBYDAYDAaDwWAwGAwGg8FgMDSGbR1iMBgMBoPBYDAYDAaDwWAwGAwGg6ExbGGBwWAwGAwGg8FgMBgMBoPBYDAYDIbGsIUFBoPBYDAYDAaDwWAwGAwGg8FgMBgawxYWGAwGg8FgMBgMBoPBYDAYDAaDwWBoDFtYYDAYDAaDwWAwGAwGg8FgMBgMBoOhMWxhgcFgMBgMBoPBYDAYDAaDwWAwGAyGxrCFBQaDwWAwGAwGg8FgMBgMBoPBYDAYGsMWFhgMBoPBYDAYDAaDwWAwGAwGg8FgaAxbWGAwGAwGg8FgMBgMBoPBYDAYDAaDoTFsYYHBYDAYDAaDwWAwGAwGg8FgMBgMhsawhQVGxZHm4VVKCjLETBSM6lbFV0hJyQBTRQaDwWAwGAwGg8FgMBgMBkP3CJgIGJrxGqHzR2DJ6TS8yniFzIxMZGbnQ8JvjQXRV/FTK6ZKjKpSxVDMH7EEp9NeIeNVJjIyM5GdLwG/9QJEX/0JTBUZ2qEQrw4uweMHEi1/Lwe+62i09HNnK/tqkSH/2gE8K/SCs5cjkxODwWAwGAwGg8FgMBg1EDYFx9AQPkztG8PVzQwpMZcQkfgGEgLAZ5JhVLUqmsK+sSvczFIQcykCiW8kYKpYCyhIwJHVfyIkQQ9txnyFqd3ta/iYEWSZqchLyYe8MAfi5Hhkp+WCin2CZ9YY5k0cINTjfdAWskLI8rJRkP4UeenZkBdryGvfBE2HuMNAxYy5LOkU4vedRq6wOeqP+RQ2Vv88zZY/3oGor3/Gy5ZL4Oj1CUTMehgMBoPBYDAYDAaDwahxsIUFhoYYo+vMtegKAPKnWNPHFTPPvCkxycZgVI0qdsXMtV2hUMU16OM6E2feME2s2WTh2Mw+GLYpEVICuF1n8fLiZSxoXZNDkAg2k9fD5u3/laUh8du+iI7IUvx/Xj04Lw1F2w5lTXvLIUm9gWdB6xC7+zRyCwB6kYw8GT5cWMgJx81pn+NRigwA8CQ6Ez5bZ8H8nxSpZY/wcPlKpOcSeDIpSP7/7d13eFRV+sDx752SSaEkgVASei+RGqp0QYo0BQJus6A/VBALLAvrrouKYgNdBQuiCOgiCb0jhNAJTZAaQoCQQgqkt+nn90dCT0IgCYHd9/M88/jI3Lmn3HPOTM577znIoo1CCCGEEEIIIcQDqBz/XE9l4chajP4pU67Cw3YNdNVp1riqzPWI8h/AqjejcVVpiQ8862lCd8Vgz4//qJxjbN+ZiPNhKoO+CpXrVUO71vg8MXkV52kCHcaaAdR7eR59Zr2ApyuoK7Hk2G8/0nFhH5cTHdf//2wYl1Oc/0MNxUHWync5uT9D+owQQgghhBBCCPGAK78ZOUcS8XEZ5NicchUeumugYXI1XZ9gE6K8aCZcTdISH3guzen5aC0M+ZdKc21Jt67VH7rgpKbX3zQOot1N29Ph2mky7cc0Q7Mkkp3kuO0Iff3OVK12PQ1dwwCqev/vBM6ccUEc+WYHVnkASQghhBBCCCGEeOCV3wILtrNERNnlCpQnuQZCiPvCkyf+vZFf6n/JuvMm2jz9BuMD/hdX4nPFa/gIqv6ygJwEO9S95YmHin1o++UcPH4JIdulBXX+8Be8/1eqyRFD1GdzMQf0o0Lor2Q5pNcIIYQQQgghhBAPsnKbsrD+vouwK04ayjVAroEQ4r+eRzNGvDWXEf/j1aCr2ZNq9T8nJcEMBWxLbKg3kBZTB/6P1YqT7I0zOJ30FJ3etHNo+6/SX4QQQgghhBBCiAdcOa2xYOFA0CrOys3y5UiugRBC3Hf6ung3rUxufCyyEGAelbSa37+7RJ0pL+PlIkubCSGEEEIIIYQQD4NyCSw4zi/gg4WRyJx2+ZFrIIQQZch2mYzIWKy3RQ8MVKzjiyXhErKVAOBMJPrzz8jqN53m/m6yd48QQgghhBBCCPGQuO+BBWfydt75y9/ZnPyw3qtpJT3+PBHnE8i0Ppz5eKiugSObpKgIImOSySmHNbcdOakkREVw6lQklzJvDMOYuZyY+oAEZhxkX44mMjyCqMR0LM6HsMyOHFIToog4dYrIS5k3pWG+nEjqAxIBs6bHcz7iPAnl2/nFQ8AZF8TB177giu3Wd/S4BYyirr/3fQ8sODPOc2XXGqI2bSMt9UHYxMBJbsgHnIgaSNux7TFIsxFCCCGEEEIIIR4a9+nv+Gxijuxj7/bVfPfFD2yLysmfUHESue5jpl803ny4vga9Xvw/etXQ4YhYwxcrwnGaXHF1c8PVRY+yWbBYLJgtFqzGFox6dQhVDyzkmx1JuLi54ebmhsmgsFnMWMwWzBYbNXu/zJ87VQbAcWEjXy89htnkhpubK64mI9itmLOzMLV+mud7+94ccXGm8nvwXOYuWsGGfbEYfevgY8oiPioJfaNeDB09lokvPUEj9zKuxhLl496vQbGZLxK6YA7fLd/G4cgEMqwGKvk2pm2PYbww8UX61nMtZjmTOfjT53z5YxAbDqXi3aQh3vZEzp7Pwa/rYMa8PIXXhzXh5mI6iN7wGV9uOENScjLJV5K5knyFlIxGTNyyggn19ZjPreWzD+awbGc4yTofmvb8M2+9M4EeNW/ZQDUrnDVzZ/HVsv3E66pSu1Y1KhmtpMZe4BJNGf7aK7Q+OIX/W9aeoNNz6WMqn85rT9jD95/M4oeVYVwyVqdmZQdJZyNIMjWk8+OjGDdpIiNbe6MvzsnKpcxZhK+Zy6yvlrE/XkfV2rWoVsmINTWWC5eg6fDXeKX1Qab83zLaB51m7l0kar4YyoI537F822EiEzKwGirh27gtPYa9wMQX+1LcpghOUn8PZu7cRazYsI9Yoy91fExkxUeRpG9Er6GjGTvxJZ64tdM5IljzxQrCnSZcXd1wc3VBr2xYLBYsZgsWq5EWo15lSNUDLPxmB0kueeOWm8mAslkw549dtpq9efnPnaicN3Cx8eulHDObcHNzw9XVhBE7VnM2WabWPP18b3zNB1kw42cOJCaTnHyF5ORkkpNTyAp4jyOLnqYiYI4KYf6X81ix7TciE7OwG++1bopTfZfY/O/PWXc6ieTk/D6ZnEKaz59Z+us/aH/TN1AOBxfM4OcDifnHJpOcnExKVgDvHVnE03mZJ2T+l8xbsY3fIhPJshvvbYy5D1RWJrZCgnz6FqNp1eKGYy/vIOI/IWSmpmFJS8GaloYlIwPToLn0fOGRO9wB4MASvp5zqzaQeOwM2SlZOE3eVGgcQM3+f6DBYy1xyT7G+bkziTiQhUeLxqhTGzj07XC6LPgAP09d+dVR8maOzY3A7+0P8HGTH2RCCCGEEEIIIcRDRd0Pln3q/UEdVPv27VX79u3VI7UqKB0o0CnPBu2u/fu1V6en1dfhdqWUUuYdb6s+bZqret4mpWkouPrSlMm7nmrZZ7raZXGo2EXPqwD/xqpmRYPSuH6cZqykfJu0U2N/jleOq9kJe0d1r+mh9Lecz8Wznho0+4Sy3Zj1yGD1Rg8/ZTLVUQPe+kUdSrBcfzM3Wu36boLq6mNUHg2HqPdCEq6lUepVWNJ8lOAaFJAbtfP1xsoACmNb9fZRm7KcXaLGdWqper/0qVqy5YiKzrCo7NgwteCl9qqyDqXz7KimhSTfsX5sUSvVpG41lFFXWbV+/mu1N/761cg8Haz+2qOa0usqqpbP/6wiLTd9Uh35dJhq599E1fJ0ud5WjF3VR2dtKnX3u6pXk87qxTm/qpO/L1BP13VRGpqq0OUD9fsNF9wRvVy93NpL+fX7l1obmXVL7hwq88xyNaWbj9JrKL3fC2qDWZUDh7qy4x3Vu3ZD9fjkBWpPbO4NbyWrg9+OVe289Eoz+qm+00NUwh0qvVzK7IhWy19urbz8+ql/rY1Ut6WaeUYtn9JN+eg1hd5PvVBYopad6vXGBgUoY9u31VGbRZ1dMk51atlbvfTpErXlSLTKsGSr2LAF6qX2lZUOnfLsOE2FJDuK0+lU8Bs9lJ/JpOoMeEv9cihBXW9yuSp613dqQlcfZfRoqIa8d0s9m3eot/u0Uc3reSuTpt0wzqA0k7eq17KPmr7Lohyxi9TzAf6qcc2KynDjeKQZVSXfJqrd2J9V/PWBS73Tvaby0N9yPhdPVW/QbHXCppRKXa7Gd2ylmtevdtNxpsE/qDRlV3GrX1NdWvYqed0Ul+2E+mJEe+XfxE9VNl7Pj6HF31SY5daDU9Xy8R1Vq+b1VTUP/fVx3DRY/ZCmlD1utXqtS0vVq4RjTAkKo1K+GqCC2tdVS9vXVUs7DVYnIu2F9tHcVc+p5f3/quKK0V8ckQvU7j/2U+sH+F8/f/sGauMXvyl7UR+0XlQxnz6pVnWsp5aPnqhOhx5TWSkpKisiVEW8N1itCGik1ox9We3840C1e+FelW1RSuWuV/t61lNLAx5R+7bmqnLjuKxi/9VLbZq1V1lv/OeTH6uNnfLqIPiVRSrXoYQQQgghhBBCCPEA4v4naVen3u+ojKDApAb/kFa8j5nD1bdDayh9/mS416C56mwBcyKOlB1qchtT3qSUsZV6c2dKwRNN9itq38x+ykeH0ox+6omZIeriLefLPvKFGlzLqDRTMzVuTXyhEzzm8O/UcF+90tybqWeXnCt6IugelH4+7vEaXHNLYCFkpRoX0FW9sT7u9jRtJ9XMLq5KQ1OGhq+orVmFn9UWuUg93dCkNM1V+b+6SSUVcOEcyb+q8c2MStNcVfOJW1Rqgdk7p+YPr54XODF2VR/uX6HGtuqupodlKKWUSls4TLlfncR16aU+j85PyB6lvh3kpQx1x6p1qYXn05G0Vo1tYCi3wIIjIVj9sZabajxmrgpLsBV4TGrom+oRk6bQ3FWbv25XhV7hcimzXUV9O0h5GeqqsUUnqtaObaAMdxFYCFk5TgV0fUOtj7u99dtOzlRdXDWFZlANX9mqsorudOqLwbWUUTOpZuPWqPjCO536briv0mvuqtmzS9S5244zq/Bvh6oa+rz2pvMapOYWPHCpHZPbKJOGAqNq9eZOlVLwwKWu7Jup+vnoFJpR+T0xU4VcLHhyOHX3FNXWRcsPLHyvLoZMUT17vVnyurlHWb9NV53dtCICCzdlXk1p65I3jpsGq+8vhqgpPXupN0s4xpRM8QMLzuRd6rfRTVRQMQMLNzQ8lfLtcLWsOIEFR6KKfa+3Cm5fVwUN+6uKvTUg5LisYt7uooLa11VBT05RsZevjnOxKvqj0WrL+PdVbGJ5zdo7lHnHX9X6Uf9SCZm3vCOBBSGEEEIIIYQQ4qGge2gerTA15fl/PEdzI4DCjhEPl9sP03l1483xfamoAcqG3uhR8DIS+ip0fn0Cg6oaaTRuEb9M7UOdG1bRcF7ewOQxk1kfp9Fm8kJmD6lR6JIypqbPM//rZ6hjCWfRuD/x/oHsUiv2g5KPwh95yWbnjHeIG7uITwb53p43Q1OGD26NAYU9ajW/7DQXfB7zET559lWWnrOgq/csn77fH58CLpzO+zHemjoYb81M+Ddv8NE+y+0HudTjse5N89f5snHqqw8IHzGHaZ0qAuDRtjNtKmiAhkernnStnpeQ48xi5m9Jwy3gMXp5Fl5knc9AJo1tV07rgTuJXz6PlXG5nP1lAt27TmBj+u1HefaYytShVdCpHH7/4q98ebzgTQrKpcyOMyyev4U0twAeKzpRBk4aSztDcZviTma8E8fYRZ8wyPf2XmJoOpzBrQ2g7ESt/oXCmiLOy2yYPIbJ6+PQ2kxm4ewh1NAXMS7N/5pn6lgIXzSOP71/gJt7nYmmz/+D5/IGLpQdjAUPXHR7czx98wYubHojHgUPXFTp/DoTBlXF2Ggci36ZSp86BS//4xnQm/ZXO5HlGLNnRPD0/I9LVjcl4NGiJx1qFvMrxzOA3u198sduC8dmzyDi6fl8XJIxptS74hWS137D6R+/yn/N4eQ3Mzn8z7/wa+CznI203MMeCu5UatcK12JUk3XfbI6uPY8TA17Dx1HT+5YP6ari+4eRVDaAig7myNfbsQLo/ag95Rf6zvk7ftXK5yeASt/O8X//Ro0336B6BXlyVAghhBBCCCGEeBjpHqbMGtqO5il/IxqKrB1BrIh1Flik6kOH0tVVA/tZVgcfpLBtVp1JMcQ5uzNhUk9untvIYtv0iXwXYQWPHox9MQD3O1RjlUETeaaVEWdGGDNf/YxjpbLZ7IOSjyLYI9iT1J9/PNewkICHnlq1a2LQAEcS4ScvcfuWoQ6iFkzloz3pODHS+plXeKxi4WWsOTSQPp46lPU0i+ZtJrOAYzw83NAAHKdYtcWPseP8uTqda3hkKpsObWPVhoNE7p5Oh/w3LKdOcdauMEefJ7rIfU31NOjaidrl0nucxF9KwKYAFLaYPeyKKOAi63zoP7Azbhooy1E2boyioCKVS5ktpzh11o4yR3O+6ETRN+hKp2Imao/YQ1L/f/Bcw0KiAPpa1K5pIK8phnPyUsFpZ22bzsTvIrDiQY+xLxJwh71TdFUGMfGZVhidGYTNfJXPbu10hraMfsofowYqawdBK2IpcOSqPpShXV3RsHN2dTAHCx+4iIlz0n3CJHoWNSmreVDBTQPAuvN7dneayrMlrJsS0dzxyM9PMQ7Go0J+H7bu5PvdnZj6bEnGmLLoiimkbl9B1Lrlea+1y4hau5yLW3aSnlaCgdfVHf0dqymN+DUbyHbk9XXPZrUK/DLX1W+Ll6cOcJITEsSl9AfhmzydhK8/4HLAP/DvXFl+hQkhhBBCCCGEEA+phyqwgMGf0SPa5k3Q5exh2YqLBU4g5R46wHGrAuycX72UvZaCTubg4orlHOs4hlF1bp6ucpxfwIeLz2NXYGjRm8dqFaOaDM3o0cUXPQrzobl8tDKlxMV9UPJxhwRp9MRw2haxr66hUiU8NABFavKV2+/itR7kqy9DyVCAoQF9+jUt+s74yl3p2sqAhoPEHb9yqKDre3VizpmDvdUgBt7y+EPFJr0YNrA9NW7It75CBTx0YDv8OePfCyWhiLlBY+N2dGndoPA72cuwvpv1fZwmrnlPXLg17c/jzQuurQp1alNVB+Ak5kIUBRWnXMqsr0CFvET5fPx7hBadKO26tKZBcRI1NOKJ4W0xFVF3lSp55DUNlUryFVVQp2PBh4s5n9fp6P1YrWIMkgaa9eiCrx6U+RBzP1pJyi3v+48eQdu8gYs9y1ZwseCBiwPHrSjAfn41SwseuHBcXMHyYx0ZM6rOnTfmzu8HylGbx4aVsG7u+7fNtcxT+7FhJRtjyqQrNqHRrF8ZuCwk77V8O0+sP8ST2/fQe1ogld21ezptsT5lu0DaufxnY7QKuFQopCXoquBaOb/ic8+QFmUt969x6/5Z/H4wgNav9MBFfoMJIYQQQgghhBAPrYcrsICepqNG0sFFA2Vm37Jgzt02QZfJ1qDNVOzUmWo6sEetYenOnNtP5ThH8PJTdA0cRvWbasFJ7NoV7MlSgI5KTZpTr1gTqUZatmycN9HnTGLT8i2U7ObQByUfd6C50NTfv+gJIk13raHZbPbb7ta2n1zP5kgbCtAMDWnW/A7TTbpqNGrgnXd3ddwpjic5izjYQP22bfMn2Itm6jyAPj46cF4h9N3Had6iD395aw7B248Tn31zGjq/Z1m0dhpty2E9pAq9Pmb30W2sWr2NI7s+plchd61rbu6Y8idbzbk5BU62lkuZTZ0Z0McHHU6uhL7L481b0OcvbzEneDvH47Nvbh86P55dtJZpxUhUc2mKv3/RbUfTXWuJ2Oy3txtn7FpW7MlCAbpKTWhevE6HsWVLGud1OpI2LWfLLZ1O33QUIzu4oKEw71tG8O0DF5lbg9hcsROd8wYu1izdye0jl4Nzwcs51TWQYdV1d9FNS1435aYUxpj7ml1XP3xGzKDDmKZl+gWradfLrhWakLq536tyvpZZezk5exdVXvsbNSs/ZD8/hBBCCCGEEEIIcZOH7i97fcORjOziiobCenA5wWdumaBL28TSrTUY89lbDPPVgyOWtUtDuXW3AceZYFZEdmfUkGq3VEIa27cdwqLyqserStViVpKOSlW9cdUAnKTvDmGfpSQlfVDycQdaJby8ij/TrJy3Tvk5SdqzlzP5N61rHjpSf9vBjh1FvfYSm2PMv7s6jeTUoqYRNfzq1C1e3XkO450Pn6KWUQNlJ+1sKIs/eJXA3q3x865Gk65Def5vs1l25PL9WWqliGvs2bQXw4b2oqnn1ZI5yIw+wrZVPzP/i4+Y/tZfmfx5KFdjLrfXe3mW2ZNh73zIU7XyljWzp50ldPEHvBrYm9Z+3lRr0pWhz/+N2cuOcNlxN03Ri+I3RUVBVZK2fRuH8jodOq8qxQpIAegqVcU7r9PhTN9NyK2dTt+QkSO74KqBsh5kefCZW+ozjU1Lt1JjzGe8NcwXPQ5i1y4l9PaBi+AVkXQfNYS7WR6/NOqm3JR4jCkPLlTu0AE3rYxOb6xP5Xoe+UNoGpb0QjqKMwvb1QChW0Mq1zGUY51kkfTdu8S3mMoj3bzQEEIIIYQQQgghxMPs4btlUF+XEaO64a6Bsh1hRdDJm5Z4SdkYzPa6Ixjdvi9jhtVBj4P49UFsuWkhfjsng1YS0zuQQd63nN8ezbmL1zfddHEt/mINOpMrLvmzJY7LUVxIK8EE14OSj2I0IZ3uLqaI1K23zDqJjUvEcfWfHef59Zu5zJ1b1Osbtjk689SoQAKffpIuNYpoxpoejwoexWzoeuo/s5gtC8fTpZrxhokvhbImc3bfWhZ8PInADk0IeOFHTmSVd2ewk3h4GZ9OHE6Hut5UafI44z9fQViMhUq1/enUri4VtAezzPr6z7B4y0LGd6mGUbuxeVhJPruPtQs+ZlJgB5oEvMCPxU1Up6NETRE70ecu5gfzABfX4i/VojPher3TEXUh7bZ6rjtiFN3yBi6OrAji5M0DF8Hb6zJidHv6jhlGHT044tcTdPPAhf1kECtjehN428BV1nVTvl9Tuocw85q3D6Yy+4b1pOaQAbjpAGcyqccvFPiUhjPpJOlXnIAOt54j8fUsv69825EvOLqzBa0m9CvW5tRCCCGEEEIIIYR4sBkevizr8HsykF7TtrI+w8bxFUs5+o9WBBgAZxJrg3bRZMRMGutdaTBmOA2//YyIpI0EbUpj+CjPvFPYj7J0VSL93hmI562nd+ZitpTGxJQFc24JPv6g5KPMOW+6w1hX4wne/fljOruUXnvR7mZSEleaPf0luwe/ypb//MTyzTvZf+gop2PT8zdMBuVI4+gPL9A3Ppftq1+mWTn0InvMJma+9gaz1pwhw9SAgRO/ZNfE0XSqeX0heuve47yrPbhldm32NF/uHsyrW/7DT8s3s3P/IY6ejiX9eqKkHf2BF/rGk7t9NS+XeUU7yTVbSmW1GEsBnU7n9ySBvaaxdX0GtuMrWHr0H7TKG7hIWhvEriYjmNlYj2uDMQxv+C2fRSSxMWgTacNH5Y9Tdo4uXUViv3cY6ClfXg86zasttQbVwqOM9mFx6fYmbQaFsX9dNGnrFpAweia+Ny4v5Ewj6aclJNtB8x1K65cfL8NAxx3kHub0rM1UGh+MXxWJKgghhBBCCCGEEP8NHsi/8J0JB1i18TiF3Wivqz6UwD6V0QG20ysIOpi3IaUzcQ3BYf6MHFkfPeDScQxPNjGAM5nNSzdc21DVejCIVWkDGN23gMXp9d54eV6fjbWai7+OkNNivjYRqxk88SrJ3aHlnI87XYPSY6B6dZ9rDVFlZ5D5ANxwrKvYhP7j3mXeiu38Hp1C2qWT7Aj6nDeGt8bHqIFykLjp70xaEHPf13PPOfolT3Ydyr9WhpPp3YcPQg6yduZfbgoqPDRl1lWkSf9xvDtvBdt/jyYl7RIndwTx+RvDae2Tt1SSI3ETf5+0gJgyr2g93l6e15/asJqxFL/TYb7e6fD08ixo4GJoYB8q5w1crAg6iDVv4GJNcBj+I0dSP2/gYsyTTTDgJHnzUjZcH7gIWpXGgNF9qSDfXQ88zbs7zcY/ReWyiofpalBjcB8qVvXBkBjMwTff5cLRaCzmXMxRuzk744/sWx6FoXEgHf49kzo1yu/r3nFmA7GRccRO78mKbs1ZXsRr5Yvfkpn/NI/z0Hus73HDey9/Lw1LCCGEEEIIIYR4QDyQgQXbiQVMent54ROJumoMDuyHlw6wR7JyaRgWnMStDOZwu1E8VSu/WC7tGDPSH6PmJDUkiLVJTsBC2NLVWJ4YQ2+PAs6tr41/8yr5FeMkNTm5mJOoTjKTU8jNn1s0NGlD25LM/pVzPu54DUqxCdZs40/N/Lt6nSkJxJfTExaW0Cn0fiGIZOfteXSv0YIeo15j9srfOBk6g77V9GjOdEIWLeXC/dxwITOEaWMmsz7WBoYGPPfDEqZ09rrnjlwuZbaEMqX3CwTdnig69xq06DGK12av5LeToczoWw295iQ9ZBGbiVB0AAATQUlEQVRLy7yi9dT2b87VG6qdqckF1EshvS4zmZTrnY42BXY6HdUGB9Ivb+AicuVSwizgjFtJ8OF2jHqqVv51dKHdmJH4GzWcqSEErU3CCVjClrLa8gRjChy4xP8cx1nOfbufau+HMuC7j2hQ+zyR059iQ78ObHrp71xIbEzDvy+l/6KPqVffrXx/aNR9ijbvzabjPz+iQ5GvD2n9eL1rn9MajKDNDe+3f66nXHchhBBCCCGEEOIB8dCuSeA9aDQDquZN0F1YvZTd2dGsWHacTqOGc/3GTAOPBI6gjUFDpYcStCYRp3k3S9fBkNHdcC3wzO50H9g7f3LRSUZkOBeLNZ9p4/Spc/kbshpo9PggmpfoTtUHJR9lz9RpMI/75kUWlOUkvx2zFvOTFqIOHOBiKW1O7bgUycHVa9ieU3SX8Xl0Kgs/GoyXTmE7F84Z2/2qKSfRiz7k+wgrCg3ToxN4a6BPoZ1YWa3YVQHnmDeKrn/bjqW8yuy4ROTB1awpOlF0Po8ydeFHDPbSoWznCL8PFe3efSC98yMLzoxIwovX6bCdPsW5/EMNjR5nUGGdznsQowfkbcRuv7CapbuziV6xjOOdRjH8hjvKDY8EMqKNAU2lExq0hkSnmd15AxfdXOWLS4Az5ldiz9bBq0UF3FqN4pHpi+i36hBP7jrB8E07eXzu57R6siPuxvLPq+bVEt9+Q6nT/06vIdRs5H3tqSHNuwV+N36uYyO58EIIIYQQQgghxAOiHAILGpUqelybOLDZ7LcdoXLNWFyub0BcoMr9CRxUAz3giFnDksU/sfzMowQOrXZTofTNAhnZ0QVNZbIjaCWRIb+wwXUYozsXvmyM55DXeMHfBQ2wnw5jX3FuW7afZtvuWJyAVqk748YGUNJtAsouH6V0DUqLRz9ee6Uj7hpgv8iOkFPYi/ExZ/x/eH3YFJZfKr072VXqdtaHZt2x29R4rA+tDaCZXHEtqI6caZzbt42dJ5KwlFructm34yA5CsBAk+49qF3E+u3W+Etcua3JKOwZScSn5JZ+me+uotm+PpQ7plrjMfrkJYqr631ojJ5DeO0F/7x2bz9N2L7iPClk5/S23cTmdTq6jxtLQKGdvzL9AwdRI2/gYs2Sxfy0/AyPBg6l2s0DF4EjO+KiKTJ3BLEyMoRfNrgybHRnTAgBKvESZkssWTH20j1xdgzJB/aSkpDz8FRGZhT7Q7ZxKCZbGoYQQgghhBBCCHGf6MojySq1/KiQfyf+lYT4WyaRnaTFxmGtWQffIje9rEDf0U/kHeO4RPC0WZztNZpB3rccpm/IqJFdcNUUObsXMfmLjVQeHljExB/g2oFJM/5MfaOGygjhh0URd5zoTt8+jyUnbCjNnTYTZvBik1LYsbPM8lEa10Dd8N87bYpwp/cNtBz/Pi+1cEXDxvEfPmPN5TtN51o48s1cwto9zYi6+rtPsjDOeFZ/v4xLd0reYceuNFxbtMH/1juCHRdYOKYVLR59jJ6tGtFl0maSSmVJKScW6/WnOYyGohpxFtu3hJGdHz66sTqsVht6o7F0y3wPZYlf/T3L7pwodrtCc21Bm8ISLc2miCsdJs3gz/WNaCqDkB8WEXHnTse8JSewKQ33NhOY8WITiur9FfqO5om8gYtLwdOYdbYXo28fuGg4aiRdXDVUzm4WTf6CjZWHExhwl+HKUq2bEl/yG9JSD1nmC0pHFbMcJUihiNNrVXwwqdOc/2oJ6aUUvVQJa9j/x76EvPIHto4azm+7kynXLW+Kkbgz5heeadecLn0fo2OLzkxcf/m+73sjhBBCCCGEEEL8LyqXpZBcewygd+W8ZYzC9+wi8aZZgGS2hpyiVc9HudNK4u69xjC0jh5wkpHpzoDRA7h9y1Q9dUcE0t1DQ+XuZ11oDYYHtsFwh2qpMuRzlrz9KJ5aNntmjOXDg4Xfvem8vJm3Jn7POYeBmoNn859/dcW9lC5PWeWjZNfASXp6Zt7kjcoiPaPoJwZsGRlkKwCF3WYveK6oYm8+WPIh/arpccb9zMRxPxJZ6IpITlK2/5Nx31dk0sznqHtbK3bckD872Vl3cxerk9QN7/F2/rr2BXNwYfU6jjhrMOy5J6l6S/qOiP/w1aoYrApQmRyd80/mnS6Nu4rd6dyzEx4agJ3T27cWujxWzuHPmH2mN6NaGQBFTmJC/kbcdmJjkqheu/YNfaDkZb4XztQNvPf22iKDLo4Lq1l3xEmNYc/xZEGJOtNJz8w7gcpKp+imaCMjIzuv/Sk7NnvBs5a6KkP4fMnbPOqpkb1nBmM/PEhO4Z2OzW9N5PtzDgw1BzP7P/+iq/sdBy7GDK2DHnBmZOI+YDQDCtjrWV93BIHdPdBULvvXhVJjeCBt7mZZszKomxJRGaTl78yuLLnX9oHhfo0xJW+x2LJvaAkqG1t2GUxhZ2fkL2GmcFrMhX871OtLzUYGzLvfZutTQ9gx+SX2Tn2FvVPHs++tNzjw3jQOf/Iuv8/5N2fWbOZyVNodJtwdZKz7jujY/ChFbgTnf1pH7n2dpc+r42shJXM2NlV0nk//OIslkea88FPWSebNXkq8RBaEEEIIIYQQQogyp58+ffr0+56qe1Ma27fyn9AYsqMjyQkIZFBjDzQcXFr9JuN+rM1fv3yGlu53WPrE4EfFqCUsDktBqzWGD2aPoFEBNzVrFWtjDZvH2ggrxg7jmTO1Jz53nBh1wa/HUzzmFcHWTVtZHbyN1Nqd6dKy2g3LwFhJCJvPxJEvMD/cQMvn5rH+x+dpVqprlZRRPu76GtiJOxzCniPHORyykDnfb+NClgLsJMan4eZm5UpsDGlGP/w8DZijDhC67yjHD27i2zkLCYvNRQGZcTHkuBrJuRxLTKYH9XwrXotuGap35qkBvkRt28K+PWtYfsBOk84daeJ9w13a9gT2fvsao9/YS9evlvFuD69rG1ynhe9hx8Fj/L53NV9/9QvHLtsBJ/FnIzFrNlITY7kYdQmrVx2qFdC2bMeX8snKcBzuLlwK2UR8/d50b+7NzU3KysW103j6tVUY/zCfn6e0p9KtpzKfYNk3Gzl/dUsAx2XMdf7I2G7eJWwLOqq0aol+30pCL+ZgjT7McTrweLf6VLzWnnM4t+4Dnn9tOz3nLmJcpV0s2nqR3EuxWNsOoL0lmLc/jKP/u6/zaFVd6ZX5btiOs/STlYQ73HG5FMKm+Pr07t4c71v6rvXiWqY9/RqrjH9g/s9TaH81UXsch0P2cOT4YUIWzuH7bRfIa4qJxKe54Wa9QmxMGkY/PzwNZqIOhLLv6HEObvqWOQvDiM1VQCZxMTm4GnO4HBtDpkc9fK9XIi5+PXjqMS8itm5i6+pgtqXWpnOXllS7YTkma0IY8yeO5IX54RhaPse89T/yfLE6vwG/ilEsWRxGilaLMR/MZkTBAxe1rWHMWxuB1diB8XOm0vNOA9d9qJu74oznaMgefjvxG6E/z+W7TZFkKiAzkctOd/QZCcRlV6CRb8X7MsbcZebJPbOHK2ciyTx3jKTti4lYuZecq3P9Kp2MiGjsuRmYrySQGx9NVmwcDrdauLrfXQdRV06SdPQE6eH7uBi0hISYbEBhT05BueqxJydgd6uNm8cNJdFVw7OelaTQA+SkJpIdFUnG+bN5r3PhpJ05QerJIyQfDSNxxzqighcSdeAcdu+WeNepXGCdOM5v4vzu6PwAhIaxxXCa9muGvgx/DjgvnyDx95NknDvFlT3BRCwLIetqwCY5iqxccGZdITc+hpxcdypUvTncbT61gm83nMOaP0ZWDvgTkwIfoXy3qxZCCCGEEEIIIf77aUqp8lnpwJlAyMzxvPH5Wk5kViOgf08aOs6w+6iJPy9YyYx+1Yo1GWQ/MI023T7F/NIWTnzRq5ANmZ1c/mkUzZ7ZiP8nx9j2ZqO7mihJPxHErOkfMW/9MXJ9O9A9oDE+xkxiww+x71gaVTo+xfi33mbCwAal9KTCfcrHXV2Dy8wbWI8JoQqTyYSLiwsmkxGd047VYsFqtWKxKDrMPEHopLpcnNUb/2kH0K4da8Kgc2K3WLBYrVgsFrR+3xC39vnbnzLJCmfV7OnM+GYNR5Ir0KxLZ1o1qIYpM5pjYUdJazyGf81+lz+38bwhf1Z2T2pF3zkX0V9L0wWjTkMphXLasZpzyTF78eeVZ/im/+0TwDlLRlJj7Gn+FBTC+zVXM/mVj9iuHqFn1xbUrmzEkhrNqbAQdsX5MnTSx3z8ek+qF9hIszj47+d4YeZmogzN6N4whYhW84n4slfpNATzOdZ8+i6fLVrDnsgsKrbsy6AerajlcoUzh3ZzxPwoU7+ezbgAT7BG8suro3hp/u+kO8Hk9yjjZv/IJ4ENcSnVMt+FnCWMrDGW038KIuT9mqye/AofbVc80rMrLWpXxmhJJfpUGCG74vAdOomPP36dnjcmenkeA+tNIFSZMJlccHExYTLqcNqtWCxWrFYLFtWBmSdCmVT3IrN6+zPtgHb9WJMBndOOxWLJO9ai0e+bONY+71lQpyNo1nQ+mreeY7m+dOgeQGMfI5mx4Rzad4y0Kh15avxbvD1hIA3upvPbDzCtTTc+Nb/ElhNf0KuQDZmdl39iVLNn2Oj/Cce2vUmjOw1c97NuinWtf2ZEjedYb8/Pj8mEyaDDYTNjsVixWMzQbx7Za569v2NMseRy8W+dOBQGeqMRnUGPpmmg3RA0UAqUE+V04LTZcdqg5j/306X/3U1r2ze+xJp3tqGMLuiMRnQuLug0J06rBafNisMK1d86QPchlW7IXhSXgr7mTPAqks1+eDaqfu0pJOWw47TbcFqysKYkknsl8/rTCvpq1Jy0mK6BTW//HrSe4fwHkzkRegGt4XBavzudOrUMZfpTwLr2RdZ9tA/NZEJv1KNpOq5tAJRfv067DafNDF0+4skPh918AssJvh/3HP9cGYHe/498sugLxjQ0IIQQQgghhBBCiLJVfoGFq7IvsHPdZg5FZaD3aU6vIQNp7XMXkwLORHYvXoe1z7P0KWonW8sZ1vz4O3X+EEibiveWVUdaJHt37OdMdAKpdneq+talZdeedKztcV+rrNTzUdJrUGYFTSdy33b2n44mPs1JpRp1aNapD92aVC6TNbwcUSEsu9CEEb2vLhOUQ8yBrWzbH86lbD0VvHyo3aITPTo1xbvYS907ODWjG4Gpn3Ji1qOlnGMzlw6HsuO3s8SlWDBVr08z/450DahzyxJWTrIvneT3807qtmuNn3tZl/mOFU3Isgs0GdGb2vnNLCfmAFu37Sf8Ujb6Cl741G5Bpx6daFpqiZa40xG5dwf7z0STkGrHvaovdVt2pWfH2txbr3OSuHsx66x9eLZP7SICnRbOrPmR3+v8gcB7HbjEfx2V9CtH3pjEuYs+1JrwKe1GtcOkL7ytOZIjSApdztnFi0iIs4Bre1otCqJZA71UphBCCCGEEEIIIe5J+QcWhPivZmHzuOa833ALO6c0lOoQQpSMI4Zzk4ZweLcdn9dX0fNPjYodaFUpofz24oucu6io+JdlPD6xLRJaEEIIIYQQQgghxL3QSRUIUYYc5zl83ECrgFpSF0KIEnPGb+RiWBq4dqTu4EZ39SWuefeg8RP+aDjIjjyDXapTCCGEEEIIIYQQ90gCC0KUIcfZVaxP7MHAjiapDCFEyWWkYwMwuGG462FFw+DmmreFgc2K0ynVKYQQQgghhBBCiHsjgQUhykw6m2f9QObIsfStILUhhCiFL+0G3fCpqYecg1zak8xdrWXoiCF2xwmcuFC5fUdM8gtACCGEEEIIIYQQ90imFYQoI9n7Z/FOaAB/f6MT8ryCEKJUuHai+RtjqGRMIubTSUScSC9ecMGRSPyXEzl5OBvjIy/Tdkwz+QEghBBCCCGEEEKIeyabNwtRBpyJa3npsWkwM4RvhlSXCTwhRCmyk7NvLodnfkV8SnVqjHyFZiMH41OrQt4yRzcdmkb6vuWc/fFboo7lUKH/JAImP0NVTxmVhBBCCCGEEEIIce8ksCBEqXMQteBV3s19kS9faYuHVIgQoixYYklat5jzW7aScPwSuhpNqVzfD1MFd3T2bCxXosk4exazsSFVu/Sj7og/Utu/GnqpOSGEEEIIIYQQQpSQBBaEEEKIh5yyJJMVeYbMxGSsOea8zZ0rVqNC/aZU9K0swQQhhBBCCCGEEEKUKgksCCGEEEIIIYQQQgghhBCi2GSRZSGEEEIIIYQQQgghhBBCFJsEFoQQQgghhBBCCCGEEEIIUWwSWBBCCCGEEEIIIYQQQgghRLFJYEEIIYQQQgghhBBCCCGEEMUmgQUhhBBCCCGEEEIIIYQQQhSbBBaEEEIIIYQQQgghhBBCCFFsElgQQgghhBBCCCGEEEIIIUSxSWBBCCGEEEIIIYQQQgghhBDFJoEFIYQQQgghhBBCCCGEEEIUmwQWhBBCCCGEEEIIIYQQQghRbBJYEEIIIYQQQgghhBBCCCFEsUlgQQghhBBCCCGEEEIIIYQQxSaBBSGEEEIIIYQQQgghhBBCFJsEFoQQQgghhBBCCCGEEEIIUWwSWBBCCCGEEEIIIYQQQgghRLH9P++kR1LVJ9QOAAAAAElFTkSuQmCC"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "0c1354c3-64a7-4934-9ef8-c829ed635ef9",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true
+ },
+ "source": [
+ "# Regions Graphs: High Level\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "## What is a Region Graph?\n",
+ "\n",
+ "\n",
+ "
\n",
+ "\n",
+ "E.g. for the example on the right we have the root node (region) $\\mathbf{X} = X_1, X_2, X_3$.\n",
+ "Below the root is a partition node, that splits the join region into two regions: $X_1, X_3$ and $X_2$.\n",
+ "\n",
+ "## Images\n",
+ "In our example, each random variable, $X_i$, represents a single pixel in the image.\n",
+ "If we think of the region graph in a top down fashion, the region graph is splitting the image into subsets of pixels.\n",
+ "If those subsets of pixels are contiguous in the image, we can think of the region graph as splitting the image into patches, as we illustrate on the right.\n",
+ "\n",
+ "
\n",
+ "\n",
+ "## Modelling Pixels with Random Variables\n",
+ "More specifically, we assign each pixel a **categorical random variable** with 256 output classes (i.e. one for each possible grayscale pixel value).\n",
+ "\n",
+ "\n",
+ "## Specifying Region Graphs in Cirkit\n",
+ "We begin where we left off in the [previous tutorial](https://github.com/april-tools/cirkit/blob/main/notebooks/learning-a-circuit.ipynb).\n",
+ "Recall we used:\n",
+ "\n",
+ "```python\n",
+ "from cirkit.templates import circuit_templates\n",
+ "\n",
+ "symbolic_circuit = circuit_templates.image_data(\n",
+ " (1, 28, 28), # The shape of MNIST image, i.e., (num_channels, image_height, image_width)\n",
+ " # ----------------------------------------------------------------------------------------------------\n",
+ " region_graph='quad-graph', # Select the structure of the circuit to follow the QuadGraph region graph \n",
+ " # -----------------------------------------------------------------------------------------------------\n",
+ " input_layer='categorical', # Use Categorical distributions for the pixel values (0-255) as input layers\n",
+ " num_input_units=64, # Each input layer consists of 64 Categorical input units\n",
+ " sum_product_layer='cp', # Use CP sum-product layers, i.e., alternate dense sum layers and hadamard product layers\n",
+ " num_sum_units=64, # Each dense sum layer consists of 64 sum units\n",
+ " sum_weight_param='softmax' # Parameterize the weights of dense sum layers with 'softmax'\n",
+ ")\n",
+ "```\n",
+ "\n",
+ "The easiest way to specify which region graph to use is by changing the `region_graph` line in the above `image_data` function. For the time being, we will treat the region graph as a hyperparameter - we will explain the details of some region graphs and some considerations for choosing one over another in the [following section](#Region-Graphs:-Deep-Dive)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d330c91a-16a6-42bc-8e36-09a2e0b12b9e",
+ "metadata": {},
+ "source": [
+ "# Example using the `image_data` API\n",
+ "\n",
+ "In our example we used a **Quad Graph** region graph.\n",
+ "The `image_data` API conveniently allows us to specify the region graph as a string.\n",
+ "It can be one of:\n",
+ "\n",
+ "* random-binary-tree\n",
+ "* quad-graph\n",
+ "* quad-tree-2\n",
+ "* quad-tree-4\n",
+ "* poon-domingos `we avoid this for this task as it can be very computationally expensive`\n",
+ "\n",
+ "Each such region graph can be used by simply replacing the `region_graph` option via the API:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "56c8b0ce-ab82-44c7-ac70-2c6e8dd54ba8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from cirkit.templates import circuit_templates\n",
+ "\n",
+ "\n",
+ "def define_circuit(region_graph):\n",
+ " circuit = circuit_templates.image_data(\n",
+ " (1, 28, 28), # The shape of MNIST image, i.e., (num_channels, image_height, image_width)\n",
+ " # ----------------------------------------------------------------------------------------------------\n",
+ " region_graph=region_graph, # Select the structure of the circuit to follow the QuadGraph region graph \n",
+ " # -----------------------------------------------------------------------------------------------------\n",
+ " input_layer='categorical', # Use Categorical distributions for the pixel values (0-255) as input layers\n",
+ " num_input_units=64, # Each input layer consists of 64 Categorical input units\n",
+ " sum_product_layer='cp', # Use CP sum-product layers, i.e., alternate dense sum layers and hadamard product layers\n",
+ " num_sum_units=64, # Each dense sum layer consists of 64 sum units\n",
+ " sum_weight_param='softmax' # Parameterize the weights of dense sum layers with 'softmax'\n",
+ " )\n",
+ " return circuit\n",
+ "\n",
+ "circuits = dict()\n",
+ "circuits['quad-graph + cp'] = define_circuit('quad-graph')\n",
+ "# We can change the region graph to Random Binary Tree\n",
+ "circuits['random-binary-tree + cp'] = define_circuit('random-binary-tree')\n",
+ "# Or Quad Tree\n",
+ "circuits['quad-tree-2 + cp'] = define_circuit('quad-tree-2')\n",
+ "circuits['quad-tree-4 + cp'] = define_circuit('quad-tree-4')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d1695ae-aa42-436f-a42d-001477e5c08b",
+ "metadata": {},
+ "source": [
+ "Next we take a deeper dive into region graphs.\n",
+ "If you would rather work with circuits using the `image_data` api, you can skip the deep dive and go run the experiments [here](#Experiments)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8344716b-5771-49a1-8056-d50e79e67bdb",
+ "metadata": {},
+ "source": [
+ "# Region Graphs: Deep Dive\n",
+ "\n",
+ "
\n",
+ " \n",
+ "> RQ1: Consider the task of distribution estimation on image data. What region graph should you use?\n",
+ "\n",
+ "
\n",
+ "\n",
+ "The random binary tree is a general and simple choice - we simply hierarchically split the pixels into subsets without taking into account the spatial structure of the image. We illustrate such a binary tree on the right.\n",
+ "Note that the partitions happen to be diagonals. In general, in a large image the subsets of pixels are unlikely to be contiguous.\n",
+ "\n",
+ "### Options\n",
+ "For the random binary tree we need to select the `depth`, which we set to `None` to make the depth of the tree as large as possible.\n",
+ "For the random binary tree we can also specify to have more than a single region graph, buy specifying `num_repetitions`, as popularized by the [RAT-SPN paper](https://proceedings.mlr.press/v115/peharz20a). Here we choose a single repetition for simplicity.\n",
+ "\n",
+ "The code is as follows.\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "2225f363-bbfb-45d3-bd12-4094a9423aac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from cirkit.templates.region_graph import RandomBinaryTree\n",
+ "# Note that the random binary tree works on flat inputs (i.e. vectors)\n",
+ "# We therefore compute the number of random variables needed (one per pixel value)\n",
+ "img_shape = example_image.shape[1:]\n",
+ "n = np.prod(img_shape)\n",
+ "\n",
+ "# We can also specify depth and number of repetitions\n",
+ "# depth=None means maximum possible\n",
+ "rnd = RandomBinaryTree(n, depth=None, num_repetitions=1)\n",
+ "\n",
+ "circuits = dict()\n",
+ "circuits['random-binary-tree + cp'] = define_circuit_from_rg(rnd) # cp is the default second argument"
+ ]
+ },
+ {
+ "attachments": {
+ "adc5cbc0-bb00-4836-850e-bb4f3ff53ddf.png": {
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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "2b488844-7216-4053-a3db-f653ccaafee3",
+ "metadata": {},
+ "source": [
+ "# Quad Tree\n",
+ "\n",
+ "
\n",
+ "\n",
+ "While the random binary tree is general and straightforward to create, it ignores the spatial structure present in images: pixels that are close together are likely to be correlated.\n",
+ "What if we partioned the pixels into subsets in a way that creates actual patches of contiguous pixels?\n",
+ "\n",
+ "This is what the quad tree and quad graph region graphs do, we illustrate a `quad-tree-2` on the right.\n",
+ "Note the difference with the random binary tree: the partitions are image patches instead of random subsets of pixels.\n",
+ "\n",
+ "\n",
+ "### Options\n",
+ "Here we use `num_patch_splits`=$2$ to specify that the image is folded into two subsets when split, i.e. the region graph is a binary tree.\n",
+ "It is also possible to choose `num_patch_splits`=$4$ to break each patch into 4 quadrants, i.e. each region in the region graph has $4$ children. For more details see the `num_patch_splits` argument.\n",
+ "\n",
+ "
\n",
+ "\n",
+ "In contrast to the quad tree, a quad graph is a directed acyclic graph.\n",
+ "This means that it can model more partitions of the random variables than the quad tree (e.g. see plot on the right compared to above).\n",
+ "While this can make the resulting circuit more expressive, it also increases the number of learnable parameters in the circuit, since the quad graph region graph has more regions that the quad tree.\n",
+ "\n",
+ "
\n",
+ "\n",
+ "> RQ2: What is the best sum product layer parametrisation for our image distribution estimation task?\n",
+ "\n",
+ "
\n",
+ "\n",
+ "## Choosing a Sum Product Layer\n",
+ "\n",
+ "In the previous examples we used `cp` as the sum product layer by default. But what is `cp`? To understand this we first need to introduce the layers that comprise sum product layers.\n",
+ "\n",
+ "## Layers\n",
+ "\n",
+ "![layer-names.png](attachment:2ddb3e9f-28f4-4dfd-9370-c0f66702ee70.png)\n",
+ "\n",
+ "The layers above are fundamental building blocks of circuits.\n",
+ "\n",
+ "### Input Layer\n",
+ "Input layers (left) are random variables. In our case, our input layers are Categorical random variables with 256 classes, one per possible pixel value.\n",
+ "\n",
+ "### Product Layer\n",
+ "There are two types of product layer (center):\n",
+ "1. Hadamard\n",
+ "2. Kronecker\n",
+ "\n",
+ "The inputs to a Hadamard layer must have the same dimension and the computed output is the element-wise product of those inputs.\n",
+ "On the other hand, kronecker takes an outer product. So if the first input has $a$ dimensions and the second has $b$ dimensions, the output has $ab$ dimensions.\n",
+ "\n",
+ "### Sum Layer\n",
+ "For sum layer (right), each sum unit weighs an input and computes the sum.\n",
+ "\n",
+ "\n",
+ "### Learnable Parameters\n",
+ "From the layers above, only the input layers and sum layers admit learnable parameters, which we commonly represent as matrices ($\\mathbf{W}, \\mathbf{Q}$).\n",
+ "However, note that the choice of product layer affects the number of inputs to sum layers, so can therefore affect the number of learnable parameters in a circuit.\n",
+ "\n",
+ "## Layer Representations\n",
+ "\n",
+ "Sum product layers are made by combining a product layer with a sum layer from above.\n",
+ "To easily see the combinations, we represent each layer with the following representations.\n",
+ "![layer-representations.png](attachment:a5241df3-a151-4c3f-a34b-98294baaef74.png)\n",
+ "\n",
+ "## Sum Product Layers\n",
+ "\n",
+ "Below, we show a diagram of each sum product layer by using the representations introduced above.\n",
+ "\n",
+ "![layers-with-eq.png](attachment:62b9388e-3445-4a33-97e4-3d4eaa7735e1.png)\n",
+ "\n",
+ "We can now show how the sum product layers are constructed from their parts. At the top of the figure we show an equation describing the layer, where $l_i$ are the sub-circuits that feed into our sum product layer, $\\mathbf{Z}$ are inputs to the sub-circuits, and $\\mathbf{Q}$ and $\\mathbf{W}$ are matrices of parameters for the sum layers.\n",
+ "\n",
+ "Now that we have introduced `cp.T` and `tucker`, we construct versions of our circuits with these sum product layers, in order to compare the effect of the parametrisation on the results in the experiments we will run next."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "c809c46f-7189-47d2-b12e-7225865c7f5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "circuits['random-binary-tree + cp.T'] = define_circuit_from_rg(rnd, 'cp-t')\n",
+ "circuits['random-binary-tree + Tucker'] = define_circuit_from_rg(rnd, 'tucker')\n",
+ "\n",
+ "circuits['quad-tree-2 + cp.T'] = define_circuit_from_rg(qt, 'cp-t')\n",
+ "circuits['quad-tree-2 + Tucker'] = define_circuit_from_rg(qt, 'tucker')\n",
+ "\n",
+ "circuits['quad-graph + cp.T'] = define_circuit_from_rg(qg, 'cp-t')\n",
+ "circuits['quad-graph + Tucker'] = define_circuit_from_rg(qg, 'tucker')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0ae0d982-2059-4f4c-8e38-4367753dbd04",
+ "metadata": {},
+ "source": [
+ "# Experiments\n",
+ "We now compare results on distribution estimation for the region graphs and sum product layers we have introduced."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "24e281cd-fb26-4780-96e1-664d9f4cf9c0",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Training circuit with region graph \"random-binary-tree + cp\"\n",
+ "Step 100: Average NLL: 3422.423\n",
+ "Step 200: Average NLL: 1614.733\n",
+ "Step 300: Average NLL: 1013.035\n",
+ "Step 400: Average NLL: 954.595\n",
+ "Step 500: Average NLL: 930.759\n",
+ "Step 600: Average NLL: 928.207\n",
+ "Step 700: Average NLL: 924.093\n",
+ "Step 800: Average NLL: 916.476\n",
+ "Step 900: Average NLL: 919.580\n",
+ "Step 1000: Average NLL: 911.700\n",
+ "Step 1100: Average NLL: 916.981\n",
+ "Average test LL: 914.781\n",
+ "Bits per dimension: 1.683\n",
+ "\n",
+ "Training circuit with region graph \"quad-tree-2 + cp\"\n",
+ "Step 100: Average NLL: 3423.503\n",
+ "Step 200: Average NLL: 1599.988\n",
+ "Step 300: Average NLL: 951.543\n",
+ "Step 400: Average NLL: 851.868\n",
+ "Step 500: Average NLL: 802.758\n",
+ "Step 600: Average NLL: 779.314\n",
+ "Step 700: Average NLL: 761.668\n",
+ "Step 800: Average NLL: 743.466\n",
+ "Step 900: Average NLL: 737.404\n",
+ "Step 1000: Average NLL: 723.257\n",
+ "Step 1100: Average NLL: 722.298\n",
+ "Average test LL: 714.139\n",
+ "Bits per dimension: 1.314\n",
+ "\n",
+ "Training circuit with region graph \"quad-graph + cp\"\n",
+ "Step 100: Average NLL: 3413.503\n",
+ "Step 200: Average NLL: 1570.938\n",
+ "Step 300: Average NLL: 943.364\n",
+ "Step 400: Average NLL: 844.029\n",
+ "Step 500: Average NLL: 792.305\n",
+ "Step 600: Average NLL: 771.743\n",
+ "Step 700: Average NLL: 757.542\n",
+ "Step 800: Average NLL: 737.154\n",
+ "Step 900: Average NLL: 734.906\n",
+ "Step 1000: Average NLL: 719.278\n",
+ "Step 1100: Average NLL: 718.698\n",
+ "Average test LL: 711.308\n",
+ "Bits per dimension: 1.309\n",
+ "\n",
+ "Training circuit with region graph \"random-binary-tree + cp.T\"\n",
+ "Step 100: Average NLL: 3503.262\n",
+ "Step 200: Average NLL: 1745.239\n",
+ "Step 300: Average NLL: 1055.843\n",
+ "Step 400: Average NLL: 970.174\n",
+ "Step 500: Average NLL: 937.951\n",
+ "Step 600: Average NLL: 934.095\n",
+ "Step 700: Average NLL: 929.031\n",
+ "Step 800: Average NLL: 919.245\n",
+ "Step 900: Average NLL: 920.922\n",
+ "Step 1000: Average NLL: 912.927\n",
+ "Step 1100: Average NLL: 917.076\n",
+ "Average test LL: 916.002\n",
+ "Bits per dimension: 1.686\n",
+ "\n",
+ "Training circuit with region graph \"random-binary-tree + Tucker\"\n",
+ "Step 100: Average NLL: 3533.276\n",
+ "Step 200: Average NLL: 2006.147\n",
+ "Step 300: Average NLL: 1183.784\n",
+ "Step 400: Average NLL: 981.932\n",
+ "Step 500: Average NLL: 932.100\n",
+ "Step 600: Average NLL: 924.787\n",
+ "Step 700: Average NLL: 918.032\n",
+ "Step 800: Average NLL: 907.464\n",
+ "Step 900: Average NLL: 911.107\n",
+ "Step 1000: Average NLL: 904.106\n",
+ "Step 1100: Average NLL: 907.956\n",
+ "Average test LL: 902.902\n",
+ "Bits per dimension: 1.661\n",
+ "\n",
+ "Training circuit with region graph \"quad-tree-2 + cp.T\"\n",
+ "Step 100: Average NLL: 3511.839\n",
+ "Step 200: Average NLL: 1719.128\n",
+ "Step 300: Average NLL: 984.015\n",
+ "Step 400: Average NLL: 861.948\n",
+ "Step 500: Average NLL: 810.703\n",
+ "Step 600: Average NLL: 790.831\n",
+ "Step 700: Average NLL: 773.647\n",
+ "Step 800: Average NLL: 753.012\n",
+ "Step 900: Average NLL: 746.483\n",
+ "Step 1000: Average NLL: 730.322\n",
+ "Step 1100: Average NLL: 726.819\n",
+ "Average test LL: 724.648\n",
+ "Bits per dimension: 1.333\n",
+ "\n",
+ "Training circuit with region graph \"quad-tree-2 + Tucker\"\n",
+ "Step 100: Average NLL: 3557.726\n",
+ "Step 200: Average NLL: 2031.747\n",
+ "Step 300: Average NLL: 1125.442\n",
+ "Step 400: Average NLL: 888.607\n",
+ "Step 500: Average NLL: 816.974\n",
+ "Step 600: Average NLL: 792.850\n",
+ "Step 700: Average NLL: 772.091\n",
+ "Step 800: Average NLL: 750.501\n",
+ "Step 900: Average NLL: 744.235\n",
+ "Step 1000: Average NLL: 731.285\n",
+ "Step 1100: Average NLL: 728.470\n",
+ "Average test LL: 720.184\n",
+ "Bits per dimension: 1.325\n",
+ "\n",
+ "Training circuit with region graph \"quad-graph + cp.T\"\n",
+ "Step 100: Average NLL: 3474.797\n",
+ "Step 200: Average NLL: 1632.745\n",
+ "Step 300: Average NLL: 965.570\n",
+ "Step 400: Average NLL: 854.478\n",
+ "Step 500: Average NLL: 803.621\n",
+ "Step 600: Average NLL: 780.305\n",
+ "Step 700: Average NLL: 764.711\n",
+ "Step 800: Average NLL: 743.587\n",
+ "Step 900: Average NLL: 736.669\n",
+ "Step 1000: Average NLL: 722.102\n",
+ "Step 1100: Average NLL: 719.495\n",
+ "Average test LL: 717.598\n",
+ "Bits per dimension: 1.321\n",
+ "\n",
+ "Training circuit with region graph \"quad-graph + Tucker\"\n",
+ "Step 100: Average NLL: 3550.641\n",
+ "Step 200: Average NLL: 1989.352\n",
+ "Step 300: Average NLL: 1108.914\n",
+ "Step 400: Average NLL: 885.228\n",
+ "Step 500: Average NLL: 808.847\n",
+ "Step 600: Average NLL: 778.896\n",
+ "Step 700: Average NLL: 760.512\n",
+ "Step 800: Average NLL: 741.714\n",
+ "Step 900: Average NLL: 737.141\n",
+ "Step 1000: Average NLL: 721.132\n",
+ "Step 1100: Average NLL: 721.241\n",
+ "Average test LL: 713.684\n",
+ "Bits per dimension: 1.313\n"
+ ]
+ }
+ ],
+ "source": [
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "from cirkit.pipeline import compile\n",
+ "\n",
+ "\n",
+ "def train_and_eval_circuit(cc):\n",
+ "\n",
+ " # Set some seeds\n",
+ " random.seed(42)\n",
+ " np.random.seed(42)\n",
+ " torch.manual_seed(42)\n",
+ " # torch.cuda.manual_seed(42)\n",
+ " \n",
+ " # Set the torch device to use\n",
+ " device = torch.device('cuda')\n",
+ "\n",
+ " # Compile the circuit\n",
+ " circuit = compile(cc)\n",
+ " \n",
+ " # Move the circuit to chosen device\n",
+ " circuit = circuit.to(device)\n",
+ " \n",
+ " num_epochs = 5\n",
+ " step_idx = 0\n",
+ " running_loss = 0.0\n",
+ " stats = dict()\n",
+ "\n",
+ " stats['# trainable parameters'] = sum(p.numel() for p in circuit.parameters())\n",
+ " stats['train loss'] = []\n",
+ " \n",
+ " # Initialize a torch optimizer of your choice,\n",
+ " # e.g., Adam, by passing the parameters of the circuit\n",
+ " optimizer = torch.optim.Adam(circuit.parameters(), lr=0.01)\n",
+ " \n",
+ " for epoch_idx in range(num_epochs):\n",
+ " for i, (batch, _) in enumerate(train_dataloader):\n",
+ " # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
+ " # so we unsqueeze a dimension for the channel.\n",
+ " BS = batch.shape[0]\n",
+ " batch = batch.view(BS, 1, -1).to(device)\n",
+ " \n",
+ " # Compute the log-likelihoods of the batch, by evaluating the circuit\n",
+ " log_likelihoods = circuit(batch)\n",
+ " \n",
+ " # We take the negated average log-likelihood as loss\n",
+ " loss = -torch.mean(log_likelihoods)\n",
+ " loss.backward()\n",
+ " # Update the parameters of the circuits, as any other model in PyTorch\n",
+ " optimizer.step()\n",
+ " optimizer.zero_grad()\n",
+ " running_loss += loss.detach() * len(batch)\n",
+ " step_idx += 1\n",
+ " if step_idx % 100 == 0:\n",
+ " average_nll = running_loss / (100 * len(batch))\n",
+ " print(f\"Step {step_idx}: Average NLL: {average_nll:.3f}\")\n",
+ " running_loss = 0.0\n",
+ " \n",
+ " stats['train loss'].append(average_nll.cpu().item())\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " test_lls = 0.0\n",
+ " \n",
+ " for batch, _ in test_dataloader:\n",
+ " # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
+ " # so we unsqueeze a dimension for the channel.\n",
+ " BS = batch.shape[0]\n",
+ " batch = batch.view(BS, 1, -1).to(device)\n",
+ " \n",
+ " # Compute the log-likelihoods of the batch\n",
+ " log_likelihoods = circuit(batch)\n",
+ " \n",
+ " # Accumulate the log-likelihoods\n",
+ " test_lls += log_likelihoods.sum().item()\n",
+ " \n",
+ " # Compute average test log-likelihood and bits per dimension\n",
+ " average_nll = - test_lls / len(data_test)\n",
+ " bpd = average_nll / (28 * 28 * np.log(2.0))\n",
+ " print(f\"Average test LL: {average_nll:.3f}\")\n",
+ " print(f\"Bits per dimension: {bpd:.3f}\")\n",
+ " \n",
+ " stats['test loss'] = average_nll\n",
+ " stats['test bits per dimension'] = bpd\n",
+ "\n",
+ " # Free GPU memory\n",
+ " circuit = circuit.to('cpu')\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
+ " stats['train loss (min)'] = min(stats['train loss'])\n",
+ " return stats\n",
+ "\n",
+ "\n",
+ "results = dict()\n",
+ "for k, cc in circuits.items():\n",
+ " print('\\nTraining circuit with region graph \"%s\"' % k)\n",
+ " \n",
+ " results[k] = train_and_eval_circuit(cc)\n",
+ " results[k]['sum product layer'] = k.split('+')[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "92d01fa7-c45e-47d2-a2e1-8e368bba30ab",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
# trainable parameters
\n",
+ "
test loss
\n",
+ "
test bits per dimension
\n",
+ "
train loss (min)
\n",
+ "
sum product layer
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
quad-graph
\n",
+ "
25,641,474
\n",
+ "
711.308
\n",
+ "
1.309
\n",
+ "
718.698
\n",
+ "
cp
\n",
+ "
\n",
+ "
\n",
+ "
quad-graph
\n",
+ "
421,306,626
\n",
+ "
713.684
\n",
+ "
1.313
\n",
+ "
721.132
\n",
+ "
Tucker
\n",
+ "
\n",
+ "
\n",
+ "
quad-tree-2
\n",
+ "
19,251,328
\n",
+ "
714.139
\n",
+ "
1.314
\n",
+ "
722.298
\n",
+ "
cp
\n",
+ "
\n",
+ "
\n",
+ "
quad-graph
\n",
+ "
19,259,778
\n",
+ "
717.598
\n",
+ "
1.321
\n",
+ "
719.495
\n",
+ "
cp.T
\n",
+ "
\n",
+ "
\n",
+ "
quad-tree-2
\n",
+ "
217,845,760
\n",
+ "
720.184
\n",
+ "
1.325
\n",
+ "
728.470
\n",
+ "
Tucker
\n",
+ "
\n",
+ "
\n",
+ "
quad-tree-2
\n",
+ "
16,048,192
\n",
+ "
724.648
\n",
+ "
1.333
\n",
+ "
726.819
\n",
+ "
cp.T
\n",
+ "
\n",
+ "
\n",
+ "
random-binary-tree
\n",
+ "
217,845,760
\n",
+ "
902.902
\n",
+ "
1.661
\n",
+ "
904.106
\n",
+ "
Tucker
\n",
+ "
\n",
+ "
\n",
+ "
random-binary-tree
\n",
+ "
19,251,328
\n",
+ "
914.781
\n",
+ "
1.683
\n",
+ "
911.700
\n",
+ "
cp
\n",
+ "
\n",
+ "
\n",
+ "
random-binary-tree
\n",
+ "
16,048,192
\n",
+ "
916.002
\n",
+ "
1.686
\n",
+ "
912.927
\n",
+ "
cp.T
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " # trainable parameters test loss \\\n",
+ "quad-graph 25,641,474 711.308 \n",
+ "quad-graph 421,306,626 713.684 \n",
+ "quad-tree-2 19,251,328 714.139 \n",
+ "quad-graph 19,259,778 717.598 \n",
+ "quad-tree-2 217,845,760 720.184 \n",
+ "quad-tree-2 16,048,192 724.648 \n",
+ "random-binary-tree 217,845,760 902.902 \n",
+ "random-binary-tree 19,251,328 914.781 \n",
+ "random-binary-tree 16,048,192 916.002 \n",
+ "\n",
+ " test bits per dimension train loss (min) \\\n",
+ "quad-graph 1.309 718.698 \n",
+ "quad-graph 1.313 721.132 \n",
+ "quad-tree-2 1.314 722.298 \n",
+ "quad-graph 1.321 719.495 \n",
+ "quad-tree-2 1.325 728.470 \n",
+ "quad-tree-2 1.333 726.819 \n",
+ "random-binary-tree 1.661 904.106 \n",
+ "random-binary-tree 1.683 911.700 \n",
+ "random-binary-tree 1.686 912.927 \n",
+ "\n",
+ " sum product layer \n",
+ "quad-graph cp \n",
+ "quad-graph Tucker \n",
+ "quad-tree-2 cp \n",
+ "quad-graph cp.T \n",
+ "quad-tree-2 Tucker \n",
+ "quad-tree-2 cp.T \n",
+ "random-binary-tree Tucker \n",
+ "random-binary-tree cp \n",
+ "random-binary-tree cp.T "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Print results\n",
+ "df = pd.DataFrame.from_dict(results, orient='index')\n",
+ "df = df.drop(columns='train loss')\n",
+ "\n",
+ "df.index = df.index.map(lambda x: x.split('+')[0])\n",
+ "df[\"# trainable parameters\"] = df[\"# trainable parameters\"].map('{:,d}'.format)\n",
+ "pd.options.display.float_format = \"{:,.3f}\".format\n",
+ "\n",
+ "df.sort_values('test bits per dimension')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e308f69-fea6-408e-bbb6-2180257413f2",
+ "metadata": {},
+ "source": [
+ "# Results\n",
+ "\n",
+ "In the results above we have ordered the models based on the resulting `test bits per dimension` where the lower the number is the better our model is at distribution estimation.\n",
+ "\n",
+ "**[RQ1](#Region-Graphs:-Deep-Dive)**: As can be seen, quad-graph and quad-tree are better suited for modelling image data; they have superior generalisation as measured in `bits per dimension` when compared to random-binary-tree for all sum product layers.\n",
+ "\n",
+ "**[RQ2](#Parametrization)**: We see that for quad-tree and quad-graph, CP and CP.T give us the best results for our task, especially when taking into account how many more trainable parameters `tucker` requires. However, `tucker` does give us better results when we use a random-binary-tree region graph."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53f13ecb-1672-4b7e-a95f-4fbaebeeb7f4",
+ "metadata": {},
+ "source": [
+ "# Further reading\n",
+ "\n",
+ "For more details on region graphs, parametrisations and folding, see Chapters 4 and 5 of [What is the Relationship between Tensor Factorizations\n",
+ "and Circuits ](https://arxiv.org/abs/2409.07953).\n",
+ "For the implementations, see the code for the supported [Region Graphs](https://github.com/april-tools/cirkit/blob/main/cirkit/templates/region_graph/algorithms.py). More specifically, if you are interested in building region-graphs that capture the structure of your dataset, you may want to check out [Chow-Liu Trees](https://github.com/april-tools/cirkit/blob/main/cirkit/templates/region_graph/algorithms/chow_liu.py).\n",
+ "\n",
+ "Another important choice has to do with optimising the circuit via folding, learn more in the notebook on [compilation](https://github.com/april-tools/cirkit/blob/main/notebooks/compilation-options.ipynb)."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
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