diff --git a/notebooks/5. lottery_tickets.ipynb b/notebooks/5. lottery_tickets.ipynb
index 61ad2e9..c02c1fa 100644
--- a/notebooks/5. lottery_tickets.ipynb	
+++ b/notebooks/5. lottery_tickets.ipynb	
@@ -1,2234 +1,2249 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "dKUcDM76bHx3"
-   },
-   "source": [
-    "# **MNIST-1D**: Finding & analyzing lottery tickets\n",
-    "Sam Greydanus\n",
-    "\n",
-    "In this notebook we'll show how to find a sparsely-masked MLP which can train and generalize better than its fully-connected counterpart. This phenomenon was first described by [Frankle & Carbin (2018)](https://arxiv.org/abs/1803.03635), who referred to these sparse masks as \"winning lottery tickets.\" We'll also analyze these lottery tickets and show that they have learned spatial priors such as local connectivity.\n",
-    "\n",
-    "This case study is meant to demonstrate the convenience and computational savings of working with the MNIST-1D dataset. You can find more details at https://github.com/greydanus/mnist1d."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dKUcDM76bHx3"
+      },
+      "source": [
+        "# **MNIST-1D**: Finding & analyzing lottery tickets\n",
+        "Sam Greydanus\n",
+        "\n",
+        "In this notebook we'll show how to find a sparsely-masked MLP which can train and generalize better than its fully-connected counterpart. This phenomenon was first described by [Frankle & Carbin (2018)](https://arxiv.org/abs/1803.03635), who referred to these sparse masks as \"winning lottery tickets.\" We'll also analyze these lottery tickets and show that they have learned spatial priors such as local connectivity.\n",
+        "\n",
+        "This case study is meant to demonstrate the convenience and computational savings of working with the MNIST-1D dataset. You can find more details at https://github.com/greydanus/mnist1d."
+      ]
     },
-    "id": "Sg2i1QmhKW5d",
-    "outputId": "162cdd26-1fd5-4ef4-c988-d9d9b8cbc605"
-   },
-   "outputs": [],
-   "source": [
-    "!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\n",
-    "    \n",
-    "# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\n",
-    "!git clone https://github.com/greydanus/mnist1d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "Sg2i1QmhKW5d"
+      },
+      "outputs": [],
+      "source": [
+        "!python -m pip install git+https://github.com/greydanus/mnist1d.git@master\n",
+        "\n",
+        "# Download repo directly (gives access to notebooks/models.py and notebooks/train.py)\n",
+        "!git clone https://github.com/greydanus/mnist1d"
+      ]
     },
-    "id": "KaQo7QhvXvid",
-    "outputId": "359f4e92-04c2-4abe-a618-d93169dee754"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using: cpu\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import copy\n",
-    "\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import torch.optim as optim\n",
-    "\n",
-    "# Try attaching to GPU\n",
-    "DEVICE = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\n",
-    "print('Using:', DEVICE)\n",
-    "\n",
-    "plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Only run this if you're in Google Colab"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if False:\n",
-    "    # Only run this in Colab\n",
-    "    from google.colab import drive\n",
-    "    drive.mount('/content/gdrive')\n",
-    "    project_dir = \"/content/gdrive/My Drive/Research/mnist1d/\"\n",
-    "else:\n",
-    "    project_dir = './'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nre26wEOfZsM"
-   },
-   "source": [
-    "## Get the MNIST1D dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mnist1d.data import get_dataset, get_dataset_args\n",
-    "from mnist1d.utils import set_seed, to_pickle\n",
-    "\n",
-    "import sys ; sys.path.append('./mnist1d/notebooks')\n",
-    "from train import get_model_args, train_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KaQo7QhvXvid",
+        "outputId": "bd69f1a3-680f-4638-c07a-0cd64df8ed24"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using: cuda\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import copy\n",
+        "\n",
+        "import torch, os\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import torch.optim as optim\n",
+        "\n",
+        "# Try attaching to GPU\n",
+        "DEVICE = str(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))\n",
+        "print('Using:', DEVICE)\n",
+        "\n",
+        "plt.style.use('https://github.com/greydanus/mnist1d/raw/master/notebooks/mpl_style.txt')"
+      ]
     },
-    "id": "I-vm_gh5xTJs",
-    "outputId": "c4d3e5b7-43ff-46b6-ad4b-cb518a214d66"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading MNIST1D dataset from https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl\n",
-      "Saving to ./mnist1d_data.pkl\n",
-      "Successfully loaded data from ./mnist1d_data.pkl\n",
-      "Examples in training set: 4000\n",
-      "Examples in test set: 1000\n",
-      "Length of each input: 40\n",
-      "Number of classes: 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "args = get_dataset_args()\n",
-    "data = get_dataset(args=args)  # by default, this will download a pre-made dataset from the GitHub repo\n",
-    "\n",
-    "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
-    "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
-    "print(\"Length of each input: {}\".format(data['x'].shape[-1]))\n",
-    "print(\"Number of classes: {}\".format(len(data['templates']['y'])))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "O2vy0FKjfDwr"
-   },
-   "source": [
-    "## Make an MLP that can be masked\n",
-    "These parameter-wise binary masks are how we will represent sparsity in this project. There's not a great PyTorch API for this yet, so here's a temporary solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "id": "uBx5gNW-mqH_"
-   },
-   "outputs": [],
-   "source": [
-    "class SparseLinear(torch.nn.Module):\n",
-    "  def __init__(self, x_size, y_size):\n",
-    "    super(SparseLinear, self).__init__()\n",
-    "    self.linear = torch.nn.Linear(x_size, y_size)\n",
-    "    param_vec = torch.cat([p.flatten() for p in self.parameters()])\n",
-    "    self.mask = torch.ones_like(param_vec).to(DEVICE)\n",
-    "\n",
-    "  def forward(self, x, apply_mask=True):\n",
-    "    if apply_mask:\n",
-    "      self.apply_mask()\n",
-    "    return self.linear(x)\n",
-    "\n",
-    "  def update_mask(self, new_mask):\n",
-    "    self.mask = new_mask\n",
-    "    self.apply_mask()\n",
-    "\n",
-    "  def apply_mask(self):\n",
-    "    self.vec2param(self.param2vec())\n",
-    "\n",
-    "  def param2vec(self):\n",
-    "    vec = torch.cat([p.flatten() for p in self.parameters()])\n",
-    "    return self.mask * vec\n",
-    "\n",
-    "  def vec2param(self, vec):\n",
-    "    pointer = 0\n",
-    "    for param in self.parameters():\n",
-    "      param_len = np.cumprod(param.shape)[-1]\n",
-    "      new_param = vec[pointer:pointer+param_len].reshape(param.shape)\n",
-    "      param.data = new_param.data\n",
-    "      pointer += param_len\n",
-    "\n",
-    "class SparseMLP(torch.nn.Module):\n",
-    "  def __init__(self, input_size, output_size, hidden_size=100):\n",
-    "    super(SparseMLP, self).__init__()\n",
-    "    self.linear1 = SparseLinear(input_size, hidden_size)\n",
-    "    self.linear2 = SparseLinear(hidden_size, hidden_size)\n",
-    "    self.linear3 = SparseLinear(hidden_size, output_size)\n",
-    "    self.layers = [self.linear1, self.linear2, self.linear3]\n",
-    "\n",
-    "  def forward(self, x):\n",
-    "    h = torch.relu(self.linear1(x))\n",
-    "    h = h + torch.relu(self.linear2(h))\n",
-    "    h = self.linear3(h)\n",
-    "    return h\n",
-    "\n",
-    "  def get_layer_masks(self):\n",
-    "    return [l.mask for l in self.layers]\n",
-    "\n",
-    "  def set_layer_masks(self, new_masks):\n",
-    "    for i, l in enumerate(self.layers):\n",
-    "      l.update_mask(new_masks[i])\n",
-    "\n",
-    "  def get_layer_vecs(self):\n",
-    "    return [l.param2vec() for l in self.layers]\n",
-    "\n",
-    "  def set_layer_vecs(self, vecs):\n",
-    "    for i, l in enumerate(self.layers):\n",
-    "      l.vec2param(vecs[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2hwmH2vIbHin"
-   },
-   "source": [
-    "## Updating masks\n",
-    "Here's a helper function for updating masks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "Md2F9WDgYSqT"
-   },
-   "outputs": [],
-   "source": [
-    "# find a mask, given some heuristic and desired sparsity\n",
-    "def get_mask(scores, percent_sparse):\n",
-    "  # scores: per-weight scores for determining which weights to drop\n",
-    "  # percent_sparse: how much to sparsify the model\n",
-    "  num_to_drop = int(percent_sparse * len(scores))\n",
-    "  ixs_to_drop = torch.sort(scores)[1][:num_to_drop] # sort from low score to high, select k with lowest score\n",
-    "  mask = torch.ones_like(scores)\n",
-    "  mask[ixs_to_drop] = 0\n",
-    "  return mask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z0McGMV-a3Xo"
-   },
-   "source": [
-    "## The prune-and-retrain cycle\n",
-    "This is the core method for finding a lottery ticket. We train a model for a fixed number of epochs, prune it, and then re-train and re-prune. We repeat this cycle until we achieve the desired level of sparsity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "id": "5idcbyA3Ylz_"
-   },
-   "outputs": [],
-   "source": [
-    "def find_lottery_ticket(model, dataset, args, sparsity_schedule, criteria_fn=None,\n",
-    "                        prune_print_every=None, **kwargs):\n",
-    "  if prune_print_every is None:\n",
-    "    prune_print_every = np.inf\n",
-    "\n",
-    "  if criteria_fn is None:\n",
-    "    print(\"Using default magnitude-based pruning\")\n",
-    "    criteria_fn = lambda init_params, final_params: final_params.abs()\n",
-    "\n",
-    "  init_params = model.get_layer_vecs()\n",
-    "  stats = {'train_losses':[], 'test_losses':[], 'train_accs':[], 'test_accs':[]}\n",
-    "  models = []\n",
-    "  for i, percent_sparse in enumerate(sparsity_schedule):\n",
-    "\n",
-    "    # layer-wise pruning, where pruning heuristic is determined by criteria_fn\n",
-    "    final_params = model.get_layer_vecs()\n",
-    "    scores = [criteria_fn(ip, fp) for ip, fp in zip(init_params, final_params)]\n",
-    "    masks = [get_mask(s, percent_sparse) for s in scores]\n",
-    "\n",
-    "    # update model with mask and init parameters\n",
-    "    model.set_layer_vecs(init_params)\n",
-    "    model.set_layer_masks(masks)\n",
-    "\n",
-    "    # training process\n",
-    "    results = train_model(dataset, model, args)\n",
-    "    model = results['checkpoints'][-1]\n",
-    "\n",
-    "    # store stats\n",
-    "    stats['train_losses'].append(results['train_losses'])\n",
-    "    stats['test_losses'].append(results['test_losses'])\n",
-    "    stats['train_accs'].append(results['train_acc'])\n",
-    "    stats['test_accs'].append(results['test_acc'])\n",
-    "\n",
-    "    # print progress\n",
-    "    if (i+1) % prune_print_every == 0:\n",
-    "      print('\\tretrain #{}, sparsity {:.2f}, final_train_loss {:.3e}, max_acc {:.1f}, last_acc {:.1f}, mean_acc {:.1f}'\n",
-    "            .format(i+1, percent_sparse, results['train_losses'][-1], np.max(results['test_acc']),\n",
-    "            results['test_acc'][-1], np.mean(results['test_acc']) ))\n",
-    "      models.append(copy.deepcopy(model))\n",
-    "\n",
-    "  stats = {k: np.stack(v) for k, v in stats.items()}\n",
-    "  return models, stats"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "m4lokvdD4DKI"
-   },
-   "source": [
-    "## Choose hyperparameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "OUe7-b-7Yl2c"
-   },
-   "outputs": [],
-   "source": [
-    "# train settings\n",
-    "model_args = get_model_args()\n",
-    "model_args.total_steps = 1501\n",
-    "model_args.hidden_size = 500\n",
-    "model_args.print_every = 5000 # print never\n",
-    "model_args.eval_every = 100\n",
-    "model_args.learning_rate = 2e-2\n",
-    "model_args.device = DEVICE\n",
-    "\n",
-    "# sparsity settings\n",
-    "num_retrains = 100\n",
-    "sparsity_schedule = np.linspace(0,1.,num_retrains) #1-np.cumprod(np.ones(num_retrains)*tau)/tau # tau = .97"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hVgDM5rI4J65"
-   },
-   "source": [
-    "## Find a lottery ticket and a random ticket (takes ~1 hour)\n",
-    "You can also cache results as shown in subsequent cells. Then you can load them anytime to perform analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P2LCuLJjrqf6"
+      },
+      "source": [
+        "## Only run this if you're in Google Colab"
+      ]
     },
-    "id": "AgKoAhiluzTt",
-    "outputId": "5910dfd9-cd70-45a3-e28a-cf5dcf309290"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "############  Trial 0  ############\n",
-      "   Random pruning\n",
-      "\tretrain #1, sparsity 0.00, final_train_loss 4.496e-01, max_acc 64.3, last_acc 63.7, mean_acc 57.3\n",
-      "\tretrain #2, sparsity 0.01, final_train_loss 3.567e-01, max_acc 64.9, last_acc 63.4, mean_acc 57.7\n",
-      "\tretrain #3, sparsity 0.02, final_train_loss 1.404e-01, max_acc 65.4, last_acc 65.4, mean_acc 58.2\n",
-      "\tretrain #4, sparsity 0.03, final_train_loss 2.194e-01, max_acc 64.1, last_acc 61.8, mean_acc 56.4\n",
-      "\tretrain #5, sparsity 0.04, final_train_loss 1.615e-01, max_acc 64.4, last_acc 64.0, mean_acc 57.3\n",
-      "\tretrain #6, sparsity 0.05, final_train_loss 1.287e-01, max_acc 64.1, last_acc 64.1, mean_acc 57.5\n",
-      "\tretrain #7, sparsity 0.06, final_train_loss 1.039e-01, max_acc 66.5, last_acc 66.5, mean_acc 58.2\n",
-      "\tretrain #8, sparsity 0.07, final_train_loss 1.810e-01, max_acc 64.8, last_acc 64.8, mean_acc 57.8\n",
-      "\tretrain #9, sparsity 0.08, final_train_loss 3.885e-01, max_acc 66.0, last_acc 63.4, mean_acc 58.0\n",
-      "\tretrain #10, sparsity 0.09, final_train_loss 1.092e-01, max_acc 65.1, last_acc 65.1, mean_acc 57.4\n",
-      "\tretrain #11, sparsity 0.10, final_train_loss 2.019e-01, max_acc 68.4, last_acc 68.4, mean_acc 58.1\n",
-      "\tretrain #12, sparsity 0.11, final_train_loss 6.389e-02, max_acc 65.7, last_acc 65.7, mean_acc 57.8\n",
-      "\tretrain #13, sparsity 0.12, final_train_loss 2.589e-01, max_acc 64.7, last_acc 63.4, mean_acc 56.7\n",
-      "\tretrain #14, sparsity 0.13, final_train_loss 1.034e-01, max_acc 65.7, last_acc 65.7, mean_acc 57.8\n",
-      "\tretrain #15, sparsity 0.14, final_train_loss 1.135e-01, max_acc 65.9, last_acc 64.7, mean_acc 57.7\n",
-      "\tretrain #16, sparsity 0.15, final_train_loss 3.345e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.8\n",
-      "\tretrain #17, sparsity 0.16, final_train_loss 2.576e-01, max_acc 65.6, last_acc 65.6, mean_acc 57.5\n",
-      "\tretrain #18, sparsity 0.17, final_train_loss 2.727e-01, max_acc 65.0, last_acc 60.7, mean_acc 57.5\n",
-      "\tretrain #19, sparsity 0.18, final_train_loss 1.460e-01, max_acc 64.9, last_acc 63.1, mean_acc 57.1\n",
-      "\tretrain #20, sparsity 0.19, final_train_loss 4.037e-01, max_acc 64.4, last_acc 61.8, mean_acc 57.4\n",
-      "\tretrain #21, sparsity 0.20, final_train_loss 1.930e-01, max_acc 63.9, last_acc 63.9, mean_acc 57.3\n",
-      "\tretrain #22, sparsity 0.21, final_train_loss 1.880e-01, max_acc 64.5, last_acc 64.5, mean_acc 57.6\n",
-      "\tretrain #23, sparsity 0.22, final_train_loss 1.951e-01, max_acc 68.9, last_acc 64.6, mean_acc 58.4\n",
-      "\tretrain #24, sparsity 0.23, final_train_loss 6.497e-02, max_acc 64.6, last_acc 63.6, mean_acc 57.5\n",
-      "\tretrain #25, sparsity 0.24, final_train_loss 2.159e-01, max_acc 66.3, last_acc 65.4, mean_acc 58.1\n",
-      "\tretrain #26, sparsity 0.25, final_train_loss 1.731e-01, max_acc 65.2, last_acc 64.1, mean_acc 58.0\n",
-      "\tretrain #27, sparsity 0.26, final_train_loss 2.841e-01, max_acc 64.9, last_acc 62.6, mean_acc 57.3\n",
-      "\tretrain #28, sparsity 0.27, final_train_loss 1.989e-01, max_acc 65.0, last_acc 63.9, mean_acc 57.6\n",
-      "\tretrain #29, sparsity 0.28, final_train_loss 2.725e-01, max_acc 66.9, last_acc 65.7, mean_acc 58.6\n",
-      "\tretrain #30, sparsity 0.29, final_train_loss 1.993e-01, max_acc 65.1, last_acc 65.1, mean_acc 57.4\n",
-      "\tretrain #31, sparsity 0.30, final_train_loss 8.761e-02, max_acc 66.3, last_acc 66.3, mean_acc 58.6\n",
-      "\tretrain #32, sparsity 0.31, final_train_loss 1.284e-01, max_acc 65.6, last_acc 65.1, mean_acc 58.2\n",
-      "\tretrain #33, sparsity 0.32, final_train_loss 1.716e-01, max_acc 65.7, last_acc 61.9, mean_acc 57.4\n",
-      "\tretrain #34, sparsity 0.33, final_train_loss 1.007e-01, max_acc 65.0, last_acc 63.0, mean_acc 57.5\n",
-      "\tretrain #35, sparsity 0.34, final_train_loss 3.126e-01, max_acc 62.8, last_acc 62.8, mean_acc 56.7\n",
-      "\tretrain #36, sparsity 0.35, final_train_loss 1.274e-01, max_acc 66.5, last_acc 63.0, mean_acc 57.6\n",
-      "\tretrain #37, sparsity 0.36, final_train_loss 7.045e-02, max_acc 67.5, last_acc 64.6, mean_acc 58.6\n",
-      "\tretrain #38, sparsity 0.37, final_train_loss 1.877e-01, max_acc 63.9, last_acc 63.3, mean_acc 57.1\n",
-      "\tretrain #39, sparsity 0.38, final_train_loss 9.114e-02, max_acc 63.5, last_acc 62.8, mean_acc 56.7\n",
-      "\tretrain #40, sparsity 0.39, final_train_loss 2.272e-01, max_acc 63.5, last_acc 62.5, mean_acc 56.9\n",
-      "\tretrain #41, sparsity 0.40, final_train_loss 2.468e-01, max_acc 65.9, last_acc 65.9, mean_acc 58.1\n",
-      "\tretrain #42, sparsity 0.41, final_train_loss 1.708e-01, max_acc 65.3, last_acc 64.9, mean_acc 57.1\n",
-      "\tretrain #43, sparsity 0.42, final_train_loss 1.165e-01, max_acc 66.7, last_acc 66.0, mean_acc 58.3\n",
-      "\tretrain #44, sparsity 0.43, final_train_loss 1.619e-01, max_acc 63.3, last_acc 61.5, mean_acc 57.4\n",
-      "\tretrain #45, sparsity 0.44, final_train_loss 1.828e-01, max_acc 64.8, last_acc 64.1, mean_acc 57.2\n",
-      "\tretrain #46, sparsity 0.45, final_train_loss 2.150e-01, max_acc 63.6, last_acc 63.0, mean_acc 56.9\n",
-      "\tretrain #47, sparsity 0.46, final_train_loss 1.775e-01, max_acc 64.5, last_acc 64.5, mean_acc 57.3\n",
-      "\tretrain #48, sparsity 0.47, final_train_loss 1.486e-01, max_acc 64.1, last_acc 62.9, mean_acc 57.2\n",
-      "\tretrain #49, sparsity 0.48, final_train_loss 1.346e-01, max_acc 64.0, last_acc 63.4, mean_acc 57.5\n",
-      "\tretrain #50, sparsity 0.49, final_train_loss 3.854e-01, max_acc 67.4, last_acc 64.1, mean_acc 58.9\n",
-      "\tretrain #51, sparsity 0.51, final_train_loss 3.776e-01, max_acc 64.5, last_acc 62.7, mean_acc 57.2\n",
-      "\tretrain #52, sparsity 0.52, final_train_loss 3.287e-01, max_acc 66.6, last_acc 63.8, mean_acc 59.5\n",
-      "\tretrain #53, sparsity 0.53, final_train_loss 1.328e-01, max_acc 64.3, last_acc 62.0, mean_acc 57.3\n",
-      "\tretrain #54, sparsity 0.54, final_train_loss 1.222e-01, max_acc 65.1, last_acc 63.8, mean_acc 58.5\n",
-      "\tretrain #55, sparsity 0.55, final_train_loss 3.083e-01, max_acc 65.0, last_acc 64.9, mean_acc 58.2\n",
-      "\tretrain #56, sparsity 0.56, final_train_loss 2.905e-01, max_acc 64.8, last_acc 64.8, mean_acc 58.5\n",
-      "\tretrain #57, sparsity 0.57, final_train_loss 4.715e-01, max_acc 66.0, last_acc 63.8, mean_acc 58.3\n",
-      "\tretrain #58, sparsity 0.58, final_train_loss 3.301e-01, max_acc 65.9, last_acc 62.7, mean_acc 58.4\n",
-      "\tretrain #59, sparsity 0.59, final_train_loss 2.384e-01, max_acc 66.5, last_acc 65.9, mean_acc 58.8\n",
-      "\tretrain #60, sparsity 0.60, final_train_loss 1.855e-01, max_acc 66.9, last_acc 66.0, mean_acc 59.1\n",
-      "\tretrain #61, sparsity 0.61, final_train_loss 1.508e-01, max_acc 67.4, last_acc 67.4, mean_acc 59.6\n",
-      "\tretrain #62, sparsity 0.62, final_train_loss 2.587e-01, max_acc 68.7, last_acc 65.0, mean_acc 59.5\n",
-      "\tretrain #63, sparsity 0.63, final_train_loss 1.655e-01, max_acc 67.8, last_acc 67.8, mean_acc 59.4\n",
-      "\tretrain #64, sparsity 0.64, final_train_loss 2.137e-01, max_acc 68.5, last_acc 68.5, mean_acc 60.1\n",
-      "\tretrain #65, sparsity 0.65, final_train_loss 1.147e-01, max_acc 66.6, last_acc 65.4, mean_acc 59.0\n",
-      "\tretrain #66, sparsity 0.66, final_train_loss 2.592e-02, max_acc 69.2, last_acc 69.2, mean_acc 60.5\n",
-      "\tretrain #67, sparsity 0.67, final_train_loss 6.086e-02, max_acc 66.8, last_acc 66.8, mean_acc 58.5\n",
-      "\tretrain #68, sparsity 0.68, final_train_loss 1.198e-01, max_acc 69.5, last_acc 67.6, mean_acc 61.2\n",
-      "\tretrain #69, sparsity 0.69, final_train_loss 1.240e-01, max_acc 69.3, last_acc 67.7, mean_acc 60.3\n",
-      "\tretrain #70, sparsity 0.70, final_train_loss 3.103e-01, max_acc 70.4, last_acc 67.8, mean_acc 61.3\n",
-      "\tretrain #71, sparsity 0.71, final_train_loss 1.987e-01, max_acc 66.8, last_acc 63.7, mean_acc 59.7\n",
-      "\tretrain #72, sparsity 0.72, final_train_loss 8.070e-02, max_acc 67.9, last_acc 67.7, mean_acc 59.9\n",
-      "\tretrain #73, sparsity 0.73, final_train_loss 6.432e-02, max_acc 68.8, last_acc 68.0, mean_acc 60.9\n",
-      "\tretrain #74, sparsity 0.74, final_train_loss 2.594e-01, max_acc 65.9, last_acc 63.6, mean_acc 59.7\n",
-      "\tretrain #75, sparsity 0.75, final_train_loss 1.392e-01, max_acc 70.7, last_acc 70.7, mean_acc 61.6\n",
-      "\tretrain #76, sparsity 0.76, final_train_loss 9.738e-02, max_acc 68.3, last_acc 65.2, mean_acc 59.9\n",
-      "\tretrain #77, sparsity 0.77, final_train_loss 3.490e-02, max_acc 68.2, last_acc 67.8, mean_acc 61.0\n",
-      "\tretrain #78, sparsity 0.78, final_train_loss 3.542e-01, max_acc 68.2, last_acc 65.4, mean_acc 60.9\n",
-      "\tretrain #79, sparsity 0.79, final_train_loss 3.038e-01, max_acc 68.3, last_acc 66.4, mean_acc 60.8\n",
-      "\tretrain #80, sparsity 0.80, final_train_loss 4.198e-03, max_acc 69.8, last_acc 69.8, mean_acc 60.7\n",
-      "\tretrain #81, sparsity 0.81, final_train_loss 1.928e-01, max_acc 68.6, last_acc 64.4, mean_acc 60.3\n",
-      "\tretrain #82, sparsity 0.82, final_train_loss 8.036e-02, max_acc 66.4, last_acc 66.4, mean_acc 58.7\n",
-      "\tretrain #83, sparsity 0.83, final_train_loss 1.277e-01, max_acc 68.4, last_acc 68.4, mean_acc 60.4\n",
-      "\tretrain #84, sparsity 0.84, final_train_loss 7.811e-02, max_acc 71.0, last_acc 71.0, mean_acc 60.2\n",
-      "\tretrain #85, sparsity 0.85, final_train_loss 1.570e-02, max_acc 68.3, last_acc 66.8, mean_acc 59.9\n",
-      "\tretrain #86, sparsity 0.86, final_train_loss 7.442e-02, max_acc 68.7, last_acc 68.7, mean_acc 60.2\n",
-      "\tretrain #87, sparsity 0.87, final_train_loss 3.673e-02, max_acc 67.4, last_acc 67.4, mean_acc 59.7\n",
-      "\tretrain #88, sparsity 0.88, final_train_loss 3.136e-01, max_acc 65.8, last_acc 60.4, mean_acc 57.6\n",
-      "\tretrain #89, sparsity 0.89, final_train_loss 2.045e-01, max_acc 62.4, last_acc 61.2, mean_acc 55.4\n",
-      "\tretrain #90, sparsity 0.90, final_train_loss 1.460e-01, max_acc 64.6, last_acc 62.5, mean_acc 57.4\n",
-      "\tretrain #91, sparsity 0.91, final_train_loss 1.540e-01, max_acc 64.6, last_acc 62.2, mean_acc 55.2\n",
-      "\tretrain #92, sparsity 0.92, final_train_loss 1.667e-01, max_acc 60.8, last_acc 60.5, mean_acc 53.5\n",
-      "\tretrain #93, sparsity 0.93, final_train_loss 3.222e-01, max_acc 62.2, last_acc 60.1, mean_acc 54.2\n",
-      "\tretrain #94, sparsity 0.94, final_train_loss 4.063e-01, max_acc 56.1, last_acc 55.5, mean_acc 50.3\n",
-      "\tretrain #95, sparsity 0.95, final_train_loss 4.640e-01, max_acc 56.6, last_acc 53.4, mean_acc 49.7\n",
-      "\tretrain #96, sparsity 0.96, final_train_loss 7.417e-01, max_acc 51.8, last_acc 51.7, mean_acc 44.9\n",
-      "\tretrain #97, sparsity 0.97, final_train_loss 1.105e+00, max_acc 42.9, last_acc 42.9, mean_acc 37.4\n",
-      "\tretrain #98, sparsity 0.98, final_train_loss 1.820e+00, max_acc 26.8, last_acc 26.8, mean_acc 25.2\n",
-      "\tretrain #99, sparsity 0.99, final_train_loss 2.151e+00, max_acc 20.5, last_acc 20.0, mean_acc 19.4\n",
-      "\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\n",
-      "   Magnitude pruning\n",
-      "\tretrain #1, sparsity 0.00, final_train_loss 4.496e-01, max_acc 64.3, last_acc 63.7, mean_acc 57.3\n",
-      "\tretrain #2, sparsity 0.01, final_train_loss 2.104e-01, max_acc 65.1, last_acc 65.1, mean_acc 57.5\n",
-      "\tretrain #3, sparsity 0.02, final_train_loss 1.320e-01, max_acc 65.4, last_acc 63.8, mean_acc 57.9\n",
-      "\tretrain #4, sparsity 0.03, final_train_loss 4.203e-01, max_acc 64.8, last_acc 63.8, mean_acc 57.5\n",
-      "\tretrain #5, sparsity 0.04, final_train_loss 3.162e-01, max_acc 64.5, last_acc 62.8, mean_acc 57.0\n",
-      "\tretrain #6, sparsity 0.05, final_train_loss 1.317e-01, max_acc 66.5, last_acc 65.7, mean_acc 58.3\n",
-      "\tretrain #7, sparsity 0.06, final_train_loss 2.786e-01, max_acc 65.6, last_acc 64.8, mean_acc 57.8\n",
-      "\tretrain #8, sparsity 0.07, final_train_loss 8.870e-02, max_acc 66.0, last_acc 65.5, mean_acc 58.3\n",
-      "\tretrain #9, sparsity 0.08, final_train_loss 4.527e-02, max_acc 66.2, last_acc 65.7, mean_acc 58.3\n",
-      "\tretrain #10, sparsity 0.09, final_train_loss 4.450e-02, max_acc 65.7, last_acc 65.6, mean_acc 58.1\n",
-      "\tretrain #11, sparsity 0.10, final_train_loss 2.659e-01, max_acc 65.7, last_acc 64.1, mean_acc 58.5\n",
-      "\tretrain #12, sparsity 0.11, final_train_loss 6.871e-02, max_acc 67.0, last_acc 63.9, mean_acc 58.8\n",
-      "\tretrain #13, sparsity 0.12, final_train_loss 8.924e-02, max_acc 64.4, last_acc 64.1, mean_acc 57.9\n",
-      "\tretrain #14, sparsity 0.13, final_train_loss 1.170e-01, max_acc 66.5, last_acc 64.0, mean_acc 58.5\n",
-      "\tretrain #15, sparsity 0.14, final_train_loss 2.108e-01, max_acc 65.0, last_acc 64.8, mean_acc 58.2\n",
-      "\tretrain #16, sparsity 0.15, final_train_loss 1.403e-01, max_acc 64.4, last_acc 63.4, mean_acc 57.5\n",
-      "\tretrain #17, sparsity 0.16, final_train_loss 1.429e-01, max_acc 65.9, last_acc 63.7, mean_acc 58.2\n",
-      "\tretrain #18, sparsity 0.17, final_train_loss 1.667e-01, max_acc 66.0, last_acc 65.2, mean_acc 58.3\n",
-      "\tretrain #19, sparsity 0.18, final_train_loss 6.673e-02, max_acc 65.8, last_acc 65.8, mean_acc 58.5\n",
-      "\tretrain #20, sparsity 0.19, final_train_loss 4.793e-02, max_acc 65.6, last_acc 64.9, mean_acc 59.0\n",
-      "\tretrain #21, sparsity 0.20, final_train_loss 2.338e-01, max_acc 67.0, last_acc 63.6, mean_acc 58.8\n",
-      "\tretrain #22, sparsity 0.21, final_train_loss 2.666e-01, max_acc 65.2, last_acc 63.7, mean_acc 58.8\n",
-      "\tretrain #23, sparsity 0.22, final_train_loss 2.908e-01, max_acc 64.8, last_acc 63.1, mean_acc 58.6\n",
-      "\tretrain #24, sparsity 0.23, final_train_loss 1.844e-01, max_acc 66.4, last_acc 63.2, mean_acc 59.1\n",
-      "\tretrain #25, sparsity 0.24, final_train_loss 9.827e-02, max_acc 67.7, last_acc 65.8, mean_acc 59.7\n",
-      "\tretrain #26, sparsity 0.25, final_train_loss 1.239e-01, max_acc 65.9, last_acc 64.3, mean_acc 59.4\n",
-      "\tretrain #27, sparsity 0.26, final_train_loss 3.136e-01, max_acc 66.9, last_acc 64.1, mean_acc 59.0\n",
-      "\tretrain #28, sparsity 0.27, final_train_loss 1.373e-01, max_acc 64.9, last_acc 64.8, mean_acc 58.6\n",
-      "\tretrain #29, sparsity 0.28, final_train_loss 1.592e-01, max_acc 65.1, last_acc 63.9, mean_acc 59.5\n",
-      "\tretrain #30, sparsity 0.29, final_train_loss 2.779e-01, max_acc 65.7, last_acc 63.7, mean_acc 59.0\n",
-      "\tretrain #31, sparsity 0.30, final_train_loss 3.154e-01, max_acc 65.6, last_acc 65.6, mean_acc 59.4\n",
-      "\tretrain #32, sparsity 0.31, final_train_loss 2.630e-01, max_acc 64.8, last_acc 63.5, mean_acc 58.9\n",
-      "\tretrain #33, sparsity 0.32, final_train_loss 3.002e-01, max_acc 66.0, last_acc 63.3, mean_acc 59.3\n",
-      "\tretrain #34, sparsity 0.33, final_train_loss 1.036e-01, max_acc 66.8, last_acc 66.8, mean_acc 60.0\n",
-      "\tretrain #35, sparsity 0.34, final_train_loss 1.759e-01, max_acc 68.6, last_acc 68.6, mean_acc 60.1\n",
-      "\tretrain #36, sparsity 0.35, final_train_loss 9.921e-02, max_acc 66.8, last_acc 61.9, mean_acc 60.0\n",
-      "\tretrain #37, sparsity 0.36, final_train_loss 1.315e-01, max_acc 66.1, last_acc 63.9, mean_acc 60.1\n",
-      "\tretrain #38, sparsity 0.37, final_train_loss 6.281e-02, max_acc 67.6, last_acc 67.6, mean_acc 59.9\n",
-      "\tretrain #39, sparsity 0.38, final_train_loss 4.987e-02, max_acc 67.9, last_acc 66.3, mean_acc 60.7\n",
-      "\tretrain #40, sparsity 0.39, final_train_loss 7.680e-02, max_acc 67.7, last_acc 67.7, mean_acc 60.0\n",
-      "\tretrain #41, sparsity 0.40, final_train_loss 2.369e-01, max_acc 67.0, last_acc 64.4, mean_acc 60.6\n",
-      "\tretrain #42, sparsity 0.41, final_train_loss 2.180e-01, max_acc 66.9, last_acc 66.7, mean_acc 60.2\n",
-      "\tretrain #43, sparsity 0.42, final_train_loss 1.257e-01, max_acc 66.9, last_acc 64.0, mean_acc 60.2\n",
-      "\tretrain #44, sparsity 0.43, final_train_loss 1.477e-01, max_acc 66.6, last_acc 66.0, mean_acc 61.5\n",
-      "\tretrain #45, sparsity 0.44, final_train_loss 2.908e-01, max_acc 65.4, last_acc 63.9, mean_acc 59.2\n",
-      "\tretrain #46, sparsity 0.45, final_train_loss 1.143e-01, max_acc 67.2, last_acc 65.1, mean_acc 61.2\n",
-      "\tretrain #47, sparsity 0.46, final_train_loss 6.441e-02, max_acc 69.8, last_acc 69.8, mean_acc 62.2\n",
-      "\tretrain #48, sparsity 0.47, final_train_loss 7.669e-02, max_acc 68.4, last_acc 66.8, mean_acc 61.0\n",
-      "\tretrain #49, sparsity 0.48, final_train_loss 3.343e-01, max_acc 69.4, last_acc 66.5, mean_acc 61.9\n",
-      "\tretrain #50, sparsity 0.49, final_train_loss 1.707e-01, max_acc 67.9, last_acc 63.7, mean_acc 61.0\n",
-      "\tretrain #51, sparsity 0.51, final_train_loss 1.092e-01, max_acc 68.1, last_acc 65.2, mean_acc 61.1\n",
-      "\tretrain #52, sparsity 0.52, final_train_loss 5.731e-03, max_acc 68.7, last_acc 68.7, mean_acc 61.8\n",
-      "\tretrain #53, sparsity 0.53, final_train_loss 1.828e-01, max_acc 68.8, last_acc 68.5, mean_acc 62.9\n",
-      "\tretrain #54, sparsity 0.54, final_train_loss 4.926e-01, max_acc 70.1, last_acc 65.7, mean_acc 62.3\n",
-      "\tretrain #55, sparsity 0.55, final_train_loss 2.720e-01, max_acc 70.1, last_acc 66.1, mean_acc 61.7\n",
-      "\tretrain #56, sparsity 0.56, final_train_loss 1.102e-01, max_acc 69.7, last_acc 68.1, mean_acc 62.2\n",
-      "\tretrain #57, sparsity 0.57, final_train_loss 6.770e-02, max_acc 68.9, last_acc 68.5, mean_acc 63.2\n",
-      "\tretrain #58, sparsity 0.58, final_train_loss 5.719e-04, max_acc 70.6, last_acc 70.6, mean_acc 62.6\n",
-      "\tretrain #59, sparsity 0.59, final_train_loss 1.383e-01, max_acc 70.3, last_acc 69.7, mean_acc 63.9\n",
-      "\tretrain #60, sparsity 0.60, final_train_loss 6.819e-03, max_acc 72.0, last_acc 71.0, mean_acc 64.2\n",
-      "\tretrain #61, sparsity 0.61, final_train_loss 4.517e-02, max_acc 68.9, last_acc 66.8, mean_acc 62.3\n",
-      "\tretrain #62, sparsity 0.62, final_train_loss 9.992e-02, max_acc 69.9, last_acc 68.8, mean_acc 63.2\n",
-      "\tretrain #63, sparsity 0.63, final_train_loss 4.193e-02, max_acc 71.3, last_acc 70.0, mean_acc 63.8\n",
-      "\tretrain #64, sparsity 0.64, final_train_loss 3.520e-01, max_acc 69.4, last_acc 68.9, mean_acc 63.2\n",
-      "\tretrain #65, sparsity 0.65, final_train_loss 6.583e-02, max_acc 70.0, last_acc 67.8, mean_acc 62.9\n",
-      "\tretrain #66, sparsity 0.66, final_train_loss 3.471e-01, max_acc 69.1, last_acc 68.3, mean_acc 63.6\n",
-      "\tretrain #67, sparsity 0.67, final_train_loss 2.180e-01, max_acc 70.1, last_acc 66.1, mean_acc 63.3\n",
-      "\tretrain #68, sparsity 0.68, final_train_loss 2.270e-01, max_acc 70.0, last_acc 70.0, mean_acc 63.8\n",
-      "\tretrain #69, sparsity 0.69, final_train_loss 8.205e-02, max_acc 70.0, last_acc 69.6, mean_acc 64.0\n",
-      "\tretrain #70, sparsity 0.70, final_train_loss 3.572e-01, max_acc 71.7, last_acc 67.3, mean_acc 63.9\n",
-      "\tretrain #71, sparsity 0.71, final_train_loss 2.629e-01, max_acc 71.6, last_acc 67.3, mean_acc 64.4\n",
-      "\tretrain #72, sparsity 0.72, final_train_loss 3.175e-02, max_acc 72.1, last_acc 67.8, mean_acc 64.5\n",
-      "\tretrain #73, sparsity 0.73, final_train_loss 7.677e-02, max_acc 71.2, last_acc 68.7, mean_acc 65.1\n",
-      "\tretrain #74, sparsity 0.74, final_train_loss 3.483e-02, max_acc 72.0, last_acc 70.9, mean_acc 65.8\n",
-      "\tretrain #75, sparsity 0.75, final_train_loss 7.723e-02, max_acc 71.2, last_acc 68.2, mean_acc 65.0\n",
-      "\tretrain #76, sparsity 0.76, final_train_loss 7.176e-03, max_acc 73.8, last_acc 73.8, mean_acc 66.5\n",
-      "\tretrain #77, sparsity 0.77, final_train_loss 2.003e-02, max_acc 73.0, last_acc 73.0, mean_acc 65.8\n",
-      "\tretrain #78, sparsity 0.78, final_train_loss 3.270e-01, max_acc 72.8, last_acc 70.3, mean_acc 66.2\n",
-      "\tretrain #79, sparsity 0.79, final_train_loss 9.662e-02, max_acc 73.5, last_acc 73.5, mean_acc 67.0\n",
-      "\tretrain #80, sparsity 0.80, final_train_loss 6.117e-04, max_acc 76.7, last_acc 76.6, mean_acc 68.2\n",
-      "\tretrain #81, sparsity 0.81, final_train_loss 1.342e-01, max_acc 74.7, last_acc 71.4, mean_acc 67.0\n",
-      "\tretrain #82, sparsity 0.82, final_train_loss 7.379e-04, max_acc 75.7, last_acc 75.7, mean_acc 67.8\n",
-      "\tretrain #83, sparsity 0.83, final_train_loss 3.335e-03, max_acc 73.9, last_acc 72.7, mean_acc 67.8\n",
-      "\tretrain #84, sparsity 0.84, final_train_loss 2.655e-02, max_acc 74.7, last_acc 71.8, mean_acc 67.1\n",
-      "\tretrain #85, sparsity 0.85, final_train_loss 1.307e-01, max_acc 74.1, last_acc 72.8, mean_acc 67.1\n",
-      "\tretrain #86, sparsity 0.86, final_train_loss 6.702e-02, max_acc 73.1, last_acc 71.1, mean_acc 67.8\n",
-      "\tretrain #87, sparsity 0.87, final_train_loss 1.255e-03, max_acc 75.6, last_acc 75.6, mean_acc 67.9\n",
-      "\tretrain #88, sparsity 0.88, final_train_loss 7.730e-03, max_acc 74.8, last_acc 73.5, mean_acc 67.8\n",
-      "\tretrain #89, sparsity 0.89, final_train_loss 1.411e-01, max_acc 74.6, last_acc 73.4, mean_acc 68.7\n",
-      "\tretrain #90, sparsity 0.90, final_train_loss 1.797e-01, max_acc 74.7, last_acc 73.4, mean_acc 67.9\n",
-      "\tretrain #91, sparsity 0.91, final_train_loss 1.561e-02, max_acc 74.6, last_acc 72.8, mean_acc 68.0\n",
-      "\tretrain #92, sparsity 0.92, final_train_loss 2.485e-02, max_acc 73.6, last_acc 73.0, mean_acc 67.9\n",
-      "\tretrain #93, sparsity 0.93, final_train_loss 1.381e-01, max_acc 72.5, last_acc 71.3, mean_acc 66.7\n",
-      "\tretrain #94, sparsity 0.94, final_train_loss 1.125e-01, max_acc 74.0, last_acc 72.9, mean_acc 67.4\n",
-      "\tretrain #95, sparsity 0.95, final_train_loss 3.116e-01, max_acc 72.8, last_acc 70.5, mean_acc 66.8\n",
-      "\tretrain #96, sparsity 0.96, final_train_loss 1.614e-01, max_acc 70.7, last_acc 69.0, mean_acc 63.7\n",
-      "\tretrain #97, sparsity 0.97, final_train_loss 2.883e-01, max_acc 66.9, last_acc 66.9, mean_acc 60.5\n",
-      "\tretrain #98, sparsity 0.98, final_train_loss 6.175e-01, max_acc 62.2, last_acc 61.9, mean_acc 56.2\n",
-      "\tretrain #99, sparsity 0.99, final_train_loss 1.315e+00, max_acc 44.5, last_acc 44.5, mean_acc 39.7\n",
-      "\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\n",
-      "############  Trial 1  ############\n",
-      "   Random pruning\n",
-      "\tretrain #1, sparsity 0.00, final_train_loss 3.748e-01, max_acc 63.5, last_acc 63.3, mean_acc 57.1\n",
-      "\tretrain #2, sparsity 0.01, final_train_loss 2.095e-01, max_acc 66.4, last_acc 66.0, mean_acc 57.7\n",
-      "\tretrain #3, sparsity 0.02, final_train_loss 1.885e-01, max_acc 64.4, last_acc 63.5, mean_acc 57.0\n",
-      "\tretrain #4, sparsity 0.03, final_train_loss 3.798e-01, max_acc 65.0, last_acc 62.2, mean_acc 57.4\n",
-      "\tretrain #5, sparsity 0.04, final_train_loss 2.249e-01, max_acc 64.9, last_acc 64.7, mean_acc 57.7\n",
-      "\tretrain #6, sparsity 0.05, final_train_loss 4.421e-01, max_acc 62.8, last_acc 60.3, mean_acc 57.0\n",
-      "\tretrain #7, sparsity 0.06, final_train_loss 5.141e-02, max_acc 65.3, last_acc 64.3, mean_acc 57.5\n",
-      "\tretrain #8, sparsity 0.07, final_train_loss 2.880e-01, max_acc 64.2, last_acc 63.9, mean_acc 56.7\n",
-      "\tretrain #9, sparsity 0.08, final_train_loss 3.537e-01, max_acc 67.0, last_acc 64.4, mean_acc 58.1\n",
-      "\tretrain #10, sparsity 0.09, final_train_loss 4.277e-01, max_acc 62.8, last_acc 61.9, mean_acc 56.4\n",
-      "\tretrain #11, sparsity 0.10, final_train_loss 2.345e-01, max_acc 65.8, last_acc 64.5, mean_acc 58.1\n",
-      "\tretrain #12, sparsity 0.11, final_train_loss 2.818e-01, max_acc 64.0, last_acc 64.0, mean_acc 56.7\n",
-      "\tretrain #13, sparsity 0.12, final_train_loss 1.740e-01, max_acc 65.9, last_acc 62.9, mean_acc 57.9\n",
-      "\tretrain #14, sparsity 0.13, final_train_loss 2.762e-01, max_acc 65.4, last_acc 65.4, mean_acc 57.3\n",
-      "\tretrain #15, sparsity 0.14, final_train_loss 3.607e-01, max_acc 65.1, last_acc 64.0, mean_acc 57.9\n",
-      "\tretrain #16, sparsity 0.15, final_train_loss 1.370e-01, max_acc 66.7, last_acc 65.6, mean_acc 58.1\n",
-      "\tretrain #17, sparsity 0.16, final_train_loss 2.150e-01, max_acc 65.6, last_acc 62.8, mean_acc 57.8\n",
-      "\tretrain #18, sparsity 0.17, final_train_loss 2.446e-01, max_acc 65.0, last_acc 64.5, mean_acc 57.3\n",
-      "\tretrain #19, sparsity 0.18, final_train_loss 1.258e-01, max_acc 64.0, last_acc 63.1, mean_acc 57.4\n",
-      "\tretrain #20, sparsity 0.19, final_train_loss 5.143e-01, max_acc 65.6, last_acc 64.4, mean_acc 58.5\n",
-      "\tretrain #21, sparsity 0.20, final_train_loss 2.398e-01, max_acc 65.3, last_acc 62.3, mean_acc 57.8\n",
-      "\tretrain #22, sparsity 0.21, final_train_loss 3.182e-01, max_acc 65.3, last_acc 61.2, mean_acc 57.8\n",
-      "\tretrain #23, sparsity 0.22, final_train_loss 1.577e-01, max_acc 64.8, last_acc 64.8, mean_acc 57.6\n",
-      "\tretrain #24, sparsity 0.23, final_train_loss 2.066e-01, max_acc 63.2, last_acc 61.9, mean_acc 57.6\n",
-      "\tretrain #25, sparsity 0.24, final_train_loss 1.326e-01, max_acc 65.7, last_acc 62.8, mean_acc 57.0\n",
-      "\tretrain #26, sparsity 0.25, final_train_loss 2.607e-01, max_acc 64.4, last_acc 62.4, mean_acc 57.5\n",
-      "\tretrain #27, sparsity 0.26, final_train_loss 3.491e-02, max_acc 66.4, last_acc 66.4, mean_acc 57.7\n",
-      "\tretrain #28, sparsity 0.27, final_train_loss 1.908e-01, max_acc 66.6, last_acc 64.2, mean_acc 58.8\n",
-      "\tretrain #29, sparsity 0.28, final_train_loss 1.184e-01, max_acc 64.3, last_acc 62.5, mean_acc 57.0\n",
-      "\tretrain #30, sparsity 0.29, final_train_loss 1.829e-01, max_acc 63.6, last_acc 62.5, mean_acc 56.9\n",
-      "\tretrain #31, sparsity 0.30, final_train_loss 2.469e-01, max_acc 65.7, last_acc 64.8, mean_acc 57.9\n",
-      "\tretrain #32, sparsity 0.31, final_train_loss 1.199e-01, max_acc 65.7, last_acc 64.5, mean_acc 57.8\n",
-      "\tretrain #33, sparsity 0.32, final_train_loss 1.736e-01, max_acc 65.4, last_acc 65.4, mean_acc 58.3\n",
-      "\tretrain #34, sparsity 0.33, final_train_loss 1.833e-01, max_acc 63.3, last_acc 61.8, mean_acc 55.9\n",
-      "\tretrain #35, sparsity 0.34, final_train_loss 1.056e-01, max_acc 66.5, last_acc 64.7, mean_acc 57.9\n",
-      "\tretrain #36, sparsity 0.35, final_train_loss 1.864e-01, max_acc 65.3, last_acc 64.1, mean_acc 57.1\n",
-      "\tretrain #37, sparsity 0.36, final_train_loss 1.561e-01, max_acc 62.8, last_acc 61.9, mean_acc 56.6\n",
-      "\tretrain #38, sparsity 0.37, final_train_loss 1.313e-01, max_acc 64.4, last_acc 63.3, mean_acc 57.3\n",
-      "\tretrain #39, sparsity 0.38, final_train_loss 1.765e-01, max_acc 65.9, last_acc 65.3, mean_acc 58.6\n",
-      "\tretrain #40, sparsity 0.39, final_train_loss 2.346e-01, max_acc 62.7, last_acc 62.2, mean_acc 56.7\n",
-      "\tretrain #41, sparsity 0.40, final_train_loss 7.222e-02, max_acc 66.0, last_acc 65.1, mean_acc 58.0\n",
-      "\tretrain #42, sparsity 0.41, final_train_loss 2.007e-01, max_acc 67.2, last_acc 63.9, mean_acc 58.4\n",
-      "\tretrain #43, sparsity 0.42, final_train_loss 1.291e-01, max_acc 65.8, last_acc 64.4, mean_acc 58.3\n",
-      "\tretrain #44, sparsity 0.43, final_train_loss 7.897e-02, max_acc 64.5, last_acc 64.5, mean_acc 57.2\n",
-      "\tretrain #45, sparsity 0.44, final_train_loss 3.494e-01, max_acc 64.4, last_acc 63.9, mean_acc 57.3\n",
-      "\tretrain #46, sparsity 0.45, final_train_loss 2.083e-01, max_acc 66.4, last_acc 63.3, mean_acc 57.8\n",
-      "\tretrain #47, sparsity 0.46, final_train_loss 9.681e-02, max_acc 66.9, last_acc 65.7, mean_acc 59.4\n",
-      "\tretrain #48, sparsity 0.47, final_train_loss 1.541e-01, max_acc 64.6, last_acc 64.6, mean_acc 56.6\n",
-      "\tretrain #49, sparsity 0.48, final_train_loss 1.747e-01, max_acc 63.4, last_acc 63.2, mean_acc 57.2\n",
-      "\tretrain #50, sparsity 0.49, final_train_loss 3.309e-01, max_acc 65.4, last_acc 63.9, mean_acc 57.9\n",
-      "\tretrain #51, sparsity 0.51, final_train_loss 3.990e-01, max_acc 67.6, last_acc 67.6, mean_acc 58.5\n",
-      "\tretrain #52, sparsity 0.52, final_train_loss 1.626e-01, max_acc 63.0, last_acc 60.8, mean_acc 56.6\n",
-      "\tretrain #53, sparsity 0.53, final_train_loss 2.141e-01, max_acc 65.2, last_acc 65.0, mean_acc 58.3\n",
-      "\tretrain #54, sparsity 0.54, final_train_loss 1.124e-01, max_acc 66.7, last_acc 64.5, mean_acc 57.9\n",
-      "\tretrain #55, sparsity 0.55, final_train_loss 1.119e-01, max_acc 65.9, last_acc 65.9, mean_acc 58.5\n",
-      "\tretrain #56, sparsity 0.56, final_train_loss 2.519e-01, max_acc 67.1, last_acc 67.1, mean_acc 58.7\n",
-      "\tretrain #57, sparsity 0.57, final_train_loss 6.872e-02, max_acc 69.2, last_acc 67.0, mean_acc 60.8\n",
-      "\tretrain #58, sparsity 0.58, final_train_loss 6.790e-01, max_acc 66.6, last_acc 66.6, mean_acc 58.0\n",
-      "\tretrain #59, sparsity 0.59, final_train_loss 3.295e-01, max_acc 66.1, last_acc 65.3, mean_acc 58.0\n",
-      "\tretrain #60, sparsity 0.60, final_train_loss 1.242e-01, max_acc 65.6, last_acc 65.6, mean_acc 58.5\n",
-      "\tretrain #61, sparsity 0.61, final_train_loss 1.274e-01, max_acc 67.5, last_acc 67.5, mean_acc 58.8\n",
-      "\tretrain #62, sparsity 0.62, final_train_loss 7.421e-02, max_acc 66.1, last_acc 66.1, mean_acc 58.7\n",
-      "\tretrain #63, sparsity 0.63, final_train_loss 2.761e-01, max_acc 67.0, last_acc 64.2, mean_acc 59.1\n",
-      "\tretrain #64, sparsity 0.64, final_train_loss 6.194e-02, max_acc 65.7, last_acc 65.1, mean_acc 58.8\n",
-      "\tretrain #65, sparsity 0.65, final_train_loss 5.419e-01, max_acc 65.7, last_acc 63.4, mean_acc 58.7\n",
-      "\tretrain #66, sparsity 0.66, final_train_loss 4.241e-01, max_acc 66.8, last_acc 63.9, mean_acc 59.4\n",
-      "\tretrain #67, sparsity 0.67, final_train_loss 2.410e-01, max_acc 68.6, last_acc 66.5, mean_acc 60.8\n",
-      "\tretrain #68, sparsity 0.68, final_train_loss 9.067e-02, max_acc 68.5, last_acc 68.0, mean_acc 60.9\n",
-      "\tretrain #69, sparsity 0.69, final_train_loss 1.409e-01, max_acc 69.1, last_acc 69.1, mean_acc 59.9\n",
-      "\tretrain #70, sparsity 0.70, final_train_loss 1.219e-01, max_acc 69.5, last_acc 69.5, mean_acc 61.0\n",
-      "\tretrain #71, sparsity 0.71, final_train_loss 7.578e-02, max_acc 69.0, last_acc 66.7, mean_acc 60.2\n",
-      "\tretrain #72, sparsity 0.72, final_train_loss 1.416e-01, max_acc 67.7, last_acc 67.2, mean_acc 60.3\n",
-      "\tretrain #73, sparsity 0.73, final_train_loss 1.388e-01, max_acc 69.6, last_acc 66.1, mean_acc 60.6\n",
-      "\tretrain #74, sparsity 0.74, final_train_loss 3.017e-01, max_acc 68.7, last_acc 68.7, mean_acc 60.9\n",
-      "\tretrain #75, sparsity 0.75, final_train_loss 2.607e-01, max_acc 66.1, last_acc 65.3, mean_acc 59.4\n",
-      "\tretrain #76, sparsity 0.76, final_train_loss 2.065e-01, max_acc 67.5, last_acc 64.3, mean_acc 60.3\n",
-      "\tretrain #77, sparsity 0.77, final_train_loss 1.545e-01, max_acc 66.8, last_acc 66.8, mean_acc 59.4\n",
-      "\tretrain #78, sparsity 0.78, final_train_loss 1.002e-01, max_acc 67.1, last_acc 67.1, mean_acc 59.6\n",
-      "\tretrain #79, sparsity 0.79, final_train_loss 1.747e-01, max_acc 66.7, last_acc 66.3, mean_acc 59.2\n",
-      "\tretrain #80, sparsity 0.80, final_train_loss 6.890e-02, max_acc 69.7, last_acc 69.7, mean_acc 59.6\n",
-      "\tretrain #81, sparsity 0.81, final_train_loss 1.326e-01, max_acc 68.7, last_acc 68.7, mean_acc 60.0\n",
-      "\tretrain #82, sparsity 0.82, final_train_loss 1.869e-01, max_acc 65.8, last_acc 65.4, mean_acc 59.7\n",
-      "\tretrain #83, sparsity 0.83, final_train_loss 5.936e-02, max_acc 68.2, last_acc 66.0, mean_acc 60.7\n",
-      "\tretrain #84, sparsity 0.84, final_train_loss 4.791e-02, max_acc 69.4, last_acc 67.8, mean_acc 60.3\n",
-      "\tretrain #85, sparsity 0.85, final_train_loss 1.012e-01, max_acc 67.1, last_acc 67.1, mean_acc 59.7\n",
-      "\tretrain #86, sparsity 0.86, final_train_loss 1.663e-01, max_acc 66.1, last_acc 65.2, mean_acc 59.1\n",
-      "\tretrain #87, sparsity 0.87, final_train_loss 1.706e-02, max_acc 67.8, last_acc 66.9, mean_acc 59.8\n",
-      "\tretrain #88, sparsity 0.88, final_train_loss 4.770e-02, max_acc 68.6, last_acc 68.6, mean_acc 59.1\n",
-      "\tretrain #89, sparsity 0.89, final_train_loss 1.111e-01, max_acc 67.4, last_acc 67.4, mean_acc 58.4\n",
-      "\tretrain #90, sparsity 0.90, final_train_loss 1.310e-01, max_acc 64.4, last_acc 63.6, mean_acc 57.8\n",
-      "\tretrain #91, sparsity 0.91, final_train_loss 1.776e-01, max_acc 64.3, last_acc 62.0, mean_acc 56.5\n",
-      "\tretrain #92, sparsity 0.92, final_train_loss 2.040e-01, max_acc 63.6, last_acc 63.6, mean_acc 54.6\n",
-      "\tretrain #93, sparsity 0.93, final_train_loss 2.549e-01, max_acc 59.1, last_acc 58.5, mean_acc 51.8\n",
-      "\tretrain #94, sparsity 0.94, final_train_loss 2.377e-01, max_acc 58.8, last_acc 56.0, mean_acc 51.7\n",
-      "\tretrain #95, sparsity 0.95, final_train_loss 4.286e-01, max_acc 53.7, last_acc 51.0, mean_acc 47.1\n",
-      "\tretrain #96, sparsity 0.96, final_train_loss 7.995e-01, max_acc 49.8, last_acc 47.6, mean_acc 45.2\n",
-      "\tretrain #97, sparsity 0.97, final_train_loss 1.125e+00, max_acc 45.4, last_acc 43.4, mean_acc 40.2\n",
-      "\tretrain #98, sparsity 0.98, final_train_loss 1.835e+00, max_acc 29.4, last_acc 27.6, mean_acc 27.1\n",
-      "\tretrain #99, sparsity 0.99, final_train_loss 2.124e+00, max_acc 17.9, last_acc 17.6, mean_acc 16.8\n",
-      "\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\n",
-      "   Magnitude pruning\n",
-      "\tretrain #1, sparsity 0.00, final_train_loss 3.748e-01, max_acc 63.5, last_acc 63.3, mean_acc 57.1\n",
-      "\tretrain #2, sparsity 0.01, final_train_loss 2.640e-01, max_acc 66.7, last_acc 63.8, mean_acc 57.5\n",
-      "\tretrain #3, sparsity 0.02, final_train_loss 1.012e-01, max_acc 67.3, last_acc 67.3, mean_acc 58.6\n",
-      "\tretrain #4, sparsity 0.03, final_train_loss 2.345e-01, max_acc 64.8, last_acc 62.6, mean_acc 57.9\n",
-      "\tretrain #5, sparsity 0.04, final_train_loss 4.655e-01, max_acc 66.5, last_acc 62.6, mean_acc 58.1\n",
-      "\tretrain #6, sparsity 0.05, final_train_loss 2.892e-01, max_acc 65.1, last_acc 64.8, mean_acc 57.9\n",
-      "\tretrain #7, sparsity 0.06, final_train_loss 3.364e-02, max_acc 65.0, last_acc 64.1, mean_acc 58.2\n",
-      "\tretrain #8, sparsity 0.07, final_train_loss 7.816e-02, max_acc 66.4, last_acc 63.4, mean_acc 58.5\n",
-      "\tretrain #9, sparsity 0.08, final_train_loss 1.411e-01, max_acc 64.9, last_acc 62.3, mean_acc 57.6\n",
-      "\tretrain #10, sparsity 0.09, final_train_loss 1.119e-01, max_acc 66.0, last_acc 64.9, mean_acc 58.9\n",
-      "\tretrain #11, sparsity 0.10, final_train_loss 1.085e-01, max_acc 66.1, last_acc 63.5, mean_acc 58.5\n",
-      "\tretrain #12, sparsity 0.11, final_train_loss 4.023e-01, max_acc 66.7, last_acc 64.4, mean_acc 58.5\n",
-      "\tretrain #13, sparsity 0.12, final_train_loss 2.557e-01, max_acc 66.7, last_acc 62.9, mean_acc 58.5\n",
-      "\tretrain #14, sparsity 0.13, final_train_loss 1.438e-01, max_acc 65.1, last_acc 65.0, mean_acc 58.8\n",
-      "\tretrain #15, sparsity 0.14, final_train_loss 1.493e-01, max_acc 65.7, last_acc 64.3, mean_acc 58.6\n",
-      "\tretrain #16, sparsity 0.15, final_train_loss 5.650e-02, max_acc 66.2, last_acc 65.3, mean_acc 59.1\n",
-      "\tretrain #17, sparsity 0.16, final_train_loss 6.147e-02, max_acc 66.7, last_acc 65.0, mean_acc 59.3\n",
-      "\tretrain #18, sparsity 0.17, final_train_loss 7.218e-02, max_acc 66.5, last_acc 65.4, mean_acc 59.1\n",
-      "\tretrain #19, sparsity 0.18, final_train_loss 1.860e-01, max_acc 66.1, last_acc 65.3, mean_acc 58.7\n",
-      "\tretrain #20, sparsity 0.19, final_train_loss 1.812e-01, max_acc 68.1, last_acc 68.1, mean_acc 60.0\n",
-      "\tretrain #21, sparsity 0.20, final_train_loss 1.521e-01, max_acc 65.2, last_acc 63.5, mean_acc 58.5\n",
-      "\tretrain #22, sparsity 0.21, final_train_loss 3.860e-01, max_acc 65.6, last_acc 65.5, mean_acc 59.2\n",
-      "\tretrain #23, sparsity 0.22, final_train_loss 1.002e-01, max_acc 65.8, last_acc 63.6, mean_acc 59.1\n",
-      "\tretrain #24, sparsity 0.23, final_train_loss 1.657e-01, max_acc 66.9, last_acc 66.9, mean_acc 60.2\n",
-      "\tretrain #25, sparsity 0.24, final_train_loss 2.576e-01, max_acc 65.4, last_acc 64.8, mean_acc 59.3\n",
-      "\tretrain #26, sparsity 0.25, final_train_loss 9.064e-02, max_acc 66.2, last_acc 66.2, mean_acc 59.3\n",
-      "\tretrain #27, sparsity 0.26, final_train_loss 1.891e-01, max_acc 66.4, last_acc 65.4, mean_acc 59.5\n",
-      "\tretrain #28, sparsity 0.27, final_train_loss 2.190e-01, max_acc 67.1, last_acc 66.2, mean_acc 60.0\n",
-      "\tretrain #29, sparsity 0.28, final_train_loss 1.191e-01, max_acc 66.1, last_acc 64.6, mean_acc 59.2\n",
-      "\tretrain #30, sparsity 0.29, final_train_loss 3.686e-01, max_acc 68.9, last_acc 66.9, mean_acc 61.2\n",
-      "\tretrain #31, sparsity 0.30, final_train_loss 3.809e-01, max_acc 67.5, last_acc 66.8, mean_acc 60.5\n",
-      "\tretrain #32, sparsity 0.31, final_train_loss 1.262e-01, max_acc 67.2, last_acc 63.6, mean_acc 59.9\n",
-      "\tretrain #33, sparsity 0.32, final_train_loss 1.303e-01, max_acc 65.7, last_acc 64.6, mean_acc 59.2\n",
-      "\tretrain #34, sparsity 0.33, final_train_loss 2.253e-01, max_acc 66.2, last_acc 64.3, mean_acc 59.6\n",
-      "\tretrain #35, sparsity 0.34, final_train_loss 1.906e-01, max_acc 68.1, last_acc 67.2, mean_acc 60.8\n",
-      "\tretrain #36, sparsity 0.35, final_train_loss 2.238e-01, max_acc 66.5, last_acc 64.3, mean_acc 60.3\n",
-      "\tretrain #37, sparsity 0.36, final_train_loss 1.373e-01, max_acc 66.9, last_acc 65.3, mean_acc 60.4\n",
-      "\tretrain #38, sparsity 0.37, final_train_loss 2.481e-01, max_acc 67.3, last_acc 63.3, mean_acc 60.7\n",
-      "\tretrain #39, sparsity 0.38, final_train_loss 2.046e-01, max_acc 68.3, last_acc 68.3, mean_acc 61.6\n",
-      "\tretrain #40, sparsity 0.39, final_train_loss 1.087e-01, max_acc 68.4, last_acc 65.8, mean_acc 61.1\n",
-      "\tretrain #41, sparsity 0.40, final_train_loss 1.406e-01, max_acc 69.1, last_acc 66.0, mean_acc 61.7\n",
-      "\tretrain #42, sparsity 0.41, final_train_loss 1.018e-01, max_acc 68.0, last_acc 65.2, mean_acc 61.3\n",
-      "\tretrain #43, sparsity 0.42, final_train_loss 9.331e-02, max_acc 68.3, last_acc 66.6, mean_acc 61.4\n",
-      "\tretrain #44, sparsity 0.43, final_train_loss 3.600e-01, max_acc 68.1, last_acc 64.3, mean_acc 61.1\n",
-      "\tretrain #45, sparsity 0.44, final_train_loss 6.415e-02, max_acc 66.8, last_acc 66.8, mean_acc 61.2\n",
-      "\tretrain #46, sparsity 0.45, final_train_loss 1.313e-01, max_acc 67.7, last_acc 66.2, mean_acc 61.6\n",
-      "\tretrain #47, sparsity 0.46, final_train_loss 1.707e-01, max_acc 68.5, last_acc 64.2, mean_acc 60.9\n",
-      "\tretrain #48, sparsity 0.47, final_train_loss 7.259e-02, max_acc 69.8, last_acc 69.8, mean_acc 62.4\n",
-      "\tretrain #49, sparsity 0.48, final_train_loss 1.364e-01, max_acc 69.3, last_acc 66.2, mean_acc 61.9\n",
-      "\tretrain #50, sparsity 0.49, final_train_loss 4.850e-01, max_acc 70.8, last_acc 67.4, mean_acc 62.7\n",
-      "\tretrain #51, sparsity 0.51, final_train_loss 1.773e-01, max_acc 69.3, last_acc 66.5, mean_acc 61.7\n",
-      "\tretrain #52, sparsity 0.52, final_train_loss 2.575e-01, max_acc 67.8, last_acc 65.1, mean_acc 61.0\n",
-      "\tretrain #53, sparsity 0.53, final_train_loss 1.245e-01, max_acc 69.2, last_acc 67.2, mean_acc 62.6\n",
-      "\tretrain #54, sparsity 0.54, final_train_loss 8.073e-02, max_acc 69.0, last_acc 67.0, mean_acc 62.3\n",
-      "\tretrain #55, sparsity 0.55, final_train_loss 5.844e-02, max_acc 69.2, last_acc 68.0, mean_acc 62.6\n",
-      "\tretrain #56, sparsity 0.56, final_train_loss 1.582e-01, max_acc 69.6, last_acc 66.2, mean_acc 62.9\n",
-      "\tretrain #57, sparsity 0.57, final_train_loss 2.221e-01, max_acc 69.1, last_acc 66.7, mean_acc 63.0\n",
-      "\tretrain #58, sparsity 0.58, final_train_loss 1.825e-01, max_acc 69.1, last_acc 67.1, mean_acc 62.3\n",
-      "\tretrain #59, sparsity 0.59, final_train_loss 6.024e-02, max_acc 67.7, last_acc 67.7, mean_acc 61.6\n",
-      "\tretrain #60, sparsity 0.60, final_train_loss 3.228e-01, max_acc 67.2, last_acc 66.2, mean_acc 61.4\n",
-      "\tretrain #61, sparsity 0.61, final_train_loss 3.757e-02, max_acc 69.9, last_acc 66.7, mean_acc 63.0\n",
-      "\tretrain #62, sparsity 0.62, final_train_loss 1.044e-01, max_acc 70.6, last_acc 65.5, mean_acc 62.4\n",
-      "\tretrain #63, sparsity 0.63, final_train_loss 1.952e-01, max_acc 68.7, last_acc 68.1, mean_acc 62.9\n",
-      "\tretrain #64, sparsity 0.64, final_train_loss 1.646e-01, max_acc 70.8, last_acc 66.3, mean_acc 63.6\n",
-      "\tretrain #65, sparsity 0.65, final_train_loss 2.328e-01, max_acc 71.5, last_acc 67.1, mean_acc 64.2\n",
-      "\tretrain #66, sparsity 0.66, final_train_loss 1.231e-01, max_acc 69.7, last_acc 69.7, mean_acc 63.1\n",
-      "\tretrain #67, sparsity 0.67, final_train_loss 2.448e-01, max_acc 72.3, last_acc 68.6, mean_acc 64.7\n",
-      "\tretrain #68, sparsity 0.68, final_train_loss 1.005e-01, max_acc 70.8, last_acc 69.1, mean_acc 64.0\n",
-      "\tretrain #69, sparsity 0.69, final_train_loss 8.157e-02, max_acc 70.5, last_acc 68.6, mean_acc 63.5\n",
-      "\tretrain #70, sparsity 0.70, final_train_loss 2.428e-02, max_acc 70.3, last_acc 70.3, mean_acc 63.6\n",
-      "\tretrain #71, sparsity 0.71, final_train_loss 2.935e-02, max_acc 69.2, last_acc 69.2, mean_acc 63.3\n",
-      "\tretrain #72, sparsity 0.72, final_train_loss 4.624e-02, max_acc 73.7, last_acc 71.0, mean_acc 65.6\n",
-      "\tretrain #73, sparsity 0.73, final_train_loss 1.170e-01, max_acc 70.7, last_acc 69.6, mean_acc 64.2\n",
-      "\tretrain #74, sparsity 0.74, final_train_loss 3.694e-02, max_acc 73.3, last_acc 71.1, mean_acc 65.9\n",
-      "\tretrain #75, sparsity 0.75, final_train_loss 8.128e-02, max_acc 71.2, last_acc 69.8, mean_acc 64.6\n",
-      "\tretrain #76, sparsity 0.76, final_train_loss 2.670e-01, max_acc 71.5, last_acc 68.6, mean_acc 65.5\n",
-      "\tretrain #77, sparsity 0.77, final_train_loss 8.681e-02, max_acc 73.6, last_acc 71.7, mean_acc 66.6\n",
-      "\tretrain #78, sparsity 0.78, final_train_loss 2.218e-01, max_acc 71.7, last_acc 71.7, mean_acc 65.1\n",
-      "\tretrain #79, sparsity 0.79, final_train_loss 5.045e-02, max_acc 72.2, last_acc 69.9, mean_acc 65.9\n",
-      "\tretrain #80, sparsity 0.80, final_train_loss 5.363e-02, max_acc 72.6, last_acc 71.4, mean_acc 65.9\n",
-      "\tretrain #81, sparsity 0.81, final_train_loss 6.788e-02, max_acc 71.7, last_acc 71.0, mean_acc 65.8\n",
-      "\tretrain #82, sparsity 0.82, final_train_loss 5.439e-02, max_acc 71.5, last_acc 69.6, mean_acc 66.3\n",
-      "\tretrain #83, sparsity 0.83, final_train_loss 1.045e-01, max_acc 74.1, last_acc 72.1, mean_acc 67.0\n",
-      "\tretrain #84, sparsity 0.84, final_train_loss 6.884e-02, max_acc 73.2, last_acc 69.7, mean_acc 67.1\n",
-      "\tretrain #85, sparsity 0.85, final_train_loss 1.757e-02, max_acc 72.9, last_acc 72.6, mean_acc 67.2\n",
-      "\tretrain #86, sparsity 0.86, final_train_loss 3.807e-02, max_acc 72.4, last_acc 72.3, mean_acc 67.0\n",
-      "\tretrain #87, sparsity 0.87, final_train_loss 1.191e-01, max_acc 73.9, last_acc 71.4, mean_acc 67.6\n",
-      "\tretrain #88, sparsity 0.88, final_train_loss 2.096e-01, max_acc 73.6, last_acc 70.3, mean_acc 66.9\n",
-      "\tretrain #89, sparsity 0.89, final_train_loss 7.122e-03, max_acc 72.9, last_acc 72.1, mean_acc 66.1\n",
-      "\tretrain #90, sparsity 0.90, final_train_loss 3.823e-02, max_acc 72.8, last_acc 72.8, mean_acc 67.1\n",
-      "\tretrain #91, sparsity 0.91, final_train_loss 2.173e-01, max_acc 72.7, last_acc 70.1, mean_acc 66.3\n",
-      "\tretrain #92, sparsity 0.92, final_train_loss 5.690e-02, max_acc 74.4, last_acc 73.9, mean_acc 67.8\n",
-      "\tretrain #93, sparsity 0.93, final_train_loss 1.798e-01, max_acc 73.3, last_acc 71.2, mean_acc 66.8\n",
-      "\tretrain #94, sparsity 0.94, final_train_loss 7.649e-02, max_acc 73.3, last_acc 72.1, mean_acc 66.9\n",
-      "\tretrain #95, sparsity 0.95, final_train_loss 8.435e-02, max_acc 71.5, last_acc 69.7, mean_acc 64.8\n",
-      "\tretrain #96, sparsity 0.96, final_train_loss 1.016e-01, max_acc 71.6, last_acc 71.6, mean_acc 64.2\n",
-      "\tretrain #97, sparsity 0.97, final_train_loss 2.693e-01, max_acc 68.7, last_acc 67.5, mean_acc 61.8\n",
-      "\tretrain #98, sparsity 0.98, final_train_loss 5.981e-01, max_acc 64.1, last_acc 62.4, mean_acc 57.4\n",
-      "\tretrain #99, sparsity 0.99, final_train_loss 1.099e+00, max_acc 46.8, last_acc 46.6, mean_acc 42.2\n",
-      "\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "num_trials = 2\n",
-    "trials = {'rand_models': [], 'rand_stats': [], 'lott_models': [], 'lott_stats': []}\n",
-    "for t in range(num_trials):\n",
-    "  print(\"############  Trial {}  ############\".format(t))\n",
-    "  print(\"   Random pruning\")\n",
-    "  set_seed(model_args.seed+t)\n",
-    "  model = SparseMLP(model_args.input_size, model_args.output_size, hidden_size=model_args.hidden_size).to(DEVICE)\n",
-    "\n",
-    "  def criteria_fn(init_params, final_params):\n",
-    "    mask = (final_params == 0).int()  # if params are already set to zero, keep them set to zero\n",
-    "    return torch.rand(final_params.shape).to(DEVICE) #* mask\n",
-    "  models, stats = find_lottery_ticket(model, data, model_args, sparsity_schedule,\n",
-    "                                      criteria_fn=criteria_fn, prune_print_every=1)\n",
-    "  trials['rand_models'].append(models)\n",
-    "  trials['rand_stats'].append(stats)\n",
-    "\n",
-    "  print(\"   Magnitude pruning\")\n",
-    "  set_seed(model_args.seed+t)\n",
-    "  model = SparseMLP(model_args.input_size, model_args.output_size, hidden_size=model_args.hidden_size).to(DEVICE)\n",
-    "\n",
-    "  criteria_fn = lambda init_params, final_params: final_params.abs()\n",
-    "  models, stats = find_lottery_ticket(model, data, model_args, sparsity_schedule,\n",
-    "                  criteria_fn=criteria_fn, prune_print_every=1)\n",
-    "  trials['lott_models'].append(models)\n",
-    "  trials['lott_stats'].append(stats)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uVy2yEsA4UMT"
-   },
-   "source": [
-    "## Plot results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Wy6JFxutxVfQ"
-   },
-   "outputs": [],
-   "source": [
-    "to_pickle(trials, path=project_dir + 'lottery.pkl')  # cache results because they take awhile (~1hr) to compute"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ImA4Y9wnyx8k"
-   },
-   "outputs": [],
-   "source": [
-    "# trials = mnist1d.from_pickle(path=project_dir + 'lottery.pkl')   # optionally load precomputed results from your Drive"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 501
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "k28zaUnjrqf6",
+        "outputId": "577c1519-5b0f-4dd6-dd21-9b9bed441890",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "if True:\n",
+        "    # Only run this in Colab\n",
+        "    from google.colab import drive\n",
+        "    drive.mount('/content/gdrive')\n",
+        "    project_dir = \"/content/gdrive/My Drive/Research/mnist1d/\"\n",
+        "else:\n",
+        "    project_dir = './'"
+      ]
     },
-    "id": "LrqTjYn9NySW",
-    "outputId": "9a30c772-86f5-4b2b-895b-d578ff63537c"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABiwAAAJECAYAAACFCQxcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAewgAAHsIBbtB1PgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZwU1bn/8e/DzDAzgICiKIuCCl7FBaKCGjdQolFjMJIoLghIXHLVGONNbozeQPwlGo1mEb1JjCIar0jEhLhEjVFwReIeN0RlEUFk32dhZp7fH6d6pmame7pnaXqG+bxfr3p1LadOna6umT7VT51zzN0FAAAAAAAAAACQSx1yXQAAAAAAAAAAAAACFgAAAAAAAAAAIOcIWAAAAAAAAAAAgJwjYAEAAAAAAAAAAHKOgAUAAAAAAAAAAMg5AhYAAAAAAAAAACDnCFgAAAAAAAAAAICcI2ABAAAAAAAAAAByjoAFAAAAAAAAAADIOQIWAAAAAAAAAAAg5whYAAAAAAAAAACAnCNgAQAAAAAAAAAAco6ABQAAAAAAAAAAyDkCFgAAAAAAAAAAIOcIWAAAAAAAAAAAgJwjYAEAAAAAAAAAAHKOgAUAAAAAAAAAAMg5AhYAAAAAAAAAACDnCFgAAAAAAAAAAICcI2ABADswM/PElOuyNIeZDY+9lzm5Lk97YmaLY+e+f67LAwAA0BzUjwEAaN0IWABAK2Vmc+I3VI2Yhue67AAAAEBLo34MAMCOj4AFAKDdMbPxsRvYabkuT3OY2eTYe5mc6/IAAACg7aF+DABoLfJzXQAAQEZelfSvDNMuy2ZBAAAAgFaA+jEAADsgAhYA0Db83d0nN3Ynd7cslAUAAADINerHAADsgOgSCgAAAAAAAAAA5BwBCwAAAAAAAAAAkHMELABgBxYbbM4bSLM4lq5/tK6vmf0/M3vbzNab2RYzm29mU8ysX4bH7mZm55jZH8xsnpmtNrNyM9toZp+Y2XQzO8vMttt3kZlNi87FPbHV4+LnKTbNSZPXiWb2ezN7z8zWmlmZmS03s6fM7HIzK86wTHua2SQze97MvojyKTezNdH5f8DMvmNme9TZb070XibFVk9K8V6mZXaGms/M+pnZ9Wb2SvR+yqPXV8zsp2a2ZyPyGmpmt5vZG2a2zswqzKzEzD6P8vtddA11biCPXc3sv8zsn9HnU2pm26Lr+j0zm2lm3zezvVvmDAAAgNaM+nG9MlE/boKozvud6DN718w2RHXMNWb2TlRPPbKJeZ8SXSPvRvkl6q5vROu/bmZpu3g3s30sDED+vJkti+rBW81soZnNMrMrzKxnin3T/p3UST8nts/wTNOYWS8z+7GZ/cvMVphZpZmtT7JvqznfZtYzuh49Km+fDI9jZrYodg6+3pTyAu2CuzMxMTExtcJJ0hxJHk2Tm5hHYn9vIM3iWLr+ks6QtD6+b51pq6TT0hz3TEmlDeQRn96StHea/IbH0s9pxjmdlmGZUh5H0p6SZmew/zJJx6Ypz8XR+cykPC82cH2km6Y145zVuj7SpL1WUkmaspRI+u80+eRL+kMj3t/PUuQzStLaDPP4LNd/80xMTExMTEwNT6J+HM9veCz9nGac02mNqHMlPY7aX/34l5KqMjzOdEmdMsz3QIXB5DPJ98EG8imUdLukbRnkUy5pp6b8nTRw7odnkkap6+rrW/v5ljQztu3aDI83MrbPckl5Tb0GmZh29IlBtwEAcSMl/V5SnqRPJc2VtFHS3gqVynxJxZL+bGYHufuiFPn0VKgoS9Jnkt6XtELh5qOLpAMkHSrJJA2W9LyZDXH3NVl4T3H/lLRZ0v6STozWzZf0TJK0H9VdYWYHRGl7Ratc0hsK769EUh9Jx0naSVJvSU+b2SnuPjtJXmco/CifsFHhfH8mqUJSN0n7STpIUsck5furpHclDZM0NFr3qqR/JUn7SpJ1LcrMbpd0WWzVZoUb1xWS9pA0QuGzL5L0CzPbw92vSpHdLxVuVhOWKbyvVQqtQ3tIGiTpPxooz+EKNxKJuk6JwnlYLKlMUldJ+0o6WFKnDN8mAABof6gf16B+HOyp8Dm5pA+jaY1CgKCHpC8p1DMlaYykrmb2NXf3VBlGLQ4eUThPCZ9GZV8rqbNC3XewpAKFOnWyfLpI+oeko2Krt0p6SdLSqNx9JB0WlbVA4dre3r4saXJ0/DWSnpe0WuHv5Et10rbG832npNHR/IVmdkNDx4tMjM1Pc/fKNOmB9ivXERMmJiYmpuSTcvMEWanCDcv5kqxOugMVbhYSaac2kOfpkn4kaUADafaW9GQsv7saSDs8lm5OC5zb8bH8pmW4T2eFG6/Efn+XtG+SdF0l/a9qPz3TLUm6N2NppijFk0AKN7DfkvSLFNsnN/c6SfO+49dH/xRpzopfawpdCnRNcl7+VCfdmUny6qGap8EqJI2rey3G0vaSdIWkiUm2/TV2nJmSdk6RR5GkUyX9vqXPHRMTExMTE1PLTtSPa6Wlfpy7+vEPovO1awNpjlUI8CTKcX4DafdUeDAnkXahpK+mSLuzpEsk/TLF9gdj+VRI+omkzknSdVB4oGhWis8i7d9JnfTxv83hGaTZptBq4jpJBXXSFbb2860QQFkYy2NEmvOzi2paWFUl+zthYmKqmWhhAQBtw6lmtmsG6W5092XNOE5HSWe4+5N1N7j7e2Z2iaTHolXfMrOL3b0iSdpHJT3a0IHcfZGZnS7pNUmHSDrPzH7g7uuaUf5s+r7Ck29S+DH8m+5eVTeRu2+U9J9m1knhx/Zeki6VdFMiTfTk05Bocamk77p70idy3H2zpIeiqdWx0MfyL2KrHpJ0Yd334+4bzewChaeYRkWrbzazWXXO41GqaRXxoLvfm+rY7v65ws1sMsdGr2WSxkfnMVkepQo3139PdRwAANAqUT/OvXZZP3b3X2aQ5gUz+4qkDxQekLlC0v0pkt8oKXEtL5F0lLt/kSLfdardCqWamY2UdHZs1fnu/mCKfKoUWkPPTvNWsiVf0nXu/vO6G9y9rM5yqzvf7u5mdpekRPknquFzeZ5qWljNcfdPGkgLtHsELACgbRiqmmbNDblLofucpnos2c1YzN9V08VPoun6O009mLtvM7P/U7ghK5J0jNLcyOWCmRVIujxaLJN0abKbsTp+LOkChadvzlPshkzhKbOENaluxtqIkxSeBpRCH7gN3Vy6mV2m0KKhQKHp9lckPRVLFj83q5pRrkQ+W1MFKwAAQJtG/TiHqB+n5+6LzWy2pFMkDTWzrlHwplo0YHM8yHBpqh/PM3B1bH5GqmBFK7FctT//ZsvB+b5H0k8VflsdbWaXu3u9AcMj8e6g7mri8YB2g4AFACCuwaeUoh+c31a4IZPCIIQN3pCZWXdJRyo0me+hcCPXIZZk/9j8ELXCGzJJhyv0pypJz7j7ynQ7uPtyM5uvcNN6kJl1c/cN0ebVCk2Ci6JtR7v7S9ko+HZwQmz+7+6+oqHE7r7MzJ5U6BZBCk3R4wGLpbH5M83sxkzOdxJLJe0jaWczO9vdZzQhDwAAAOrHyVE/lmRmeymMmbGfpO4K45lYLEniwZ7E2CQv1MlipGp+m/soTXCsoXIUKnQTlpCqFXJrMTNZS6R0Wsv5lkJrbzN7TNIZCtftuQpdn9Ut82FRWSRpnaS/NPWYQHtBwAIA2oafuvvk7XCcTJ4Giw/81zVVIjPrq9BV0DdV0/w1nUya9edCfNC6vtEA05noHr2apL6SNkiSu5eb2SyFQeHyJT1rZjMUxlp4voEnc1qj+KB4L2e4z0uqCVgcWmfbKwrBhj0l7SXpPTO7R+FGfZ67l2d4jD8r9BMtSdPN7GxJMyTNbmIABAAAtC7Uj3OrXdePzewohc/yWNX+wbwhyT7LI2Pzc5pRpCGqGRh6q6R5zchre3i9MYlb4flOuFMhYCGFVhT1Ahaq3bri/qhLWgANIGABAIjbkD6JtsXmC5IlMLMvSXpGYZCyxtipkem3l96x+UOiqbHqnourJB0maaBC38hjo6nKzN5TeBroaUlP1O3HtZXZLTa/JMN9Fsfma91IRN0gjFXoC7pLtP0H0VRqZq9Jel6h+4WXG+gu4GcKT5kdqXBT841okpl9pHB+n5H0qLtvyrDcAACg/aF+nFy7rR+b2YUK3fpk+sN5QrLPcvfY/MImF6p2Pkub0nphO8u469dWer4TnlK4B+on6VAzG+LubyU2mlmxQsuLBLqDAjLQIX0SAEA70uy+YqPmyA+r5gZklcKPxyMUnprvLKmDu5u7m6QJsd1b6/dStxbIo9ZDAlHXSYcrnJt4v6kdJB0s6T8VBi/83Mx+ZGZ5LVCGbOgSm9+S4T7xdPVuJNz9OYVm0/dJKoltSvTj/GNJL0qab2Zn1N0/ymOLpOMVAh2L62weKOlCSf8naYWZ3RzdTAAAANRF/Ti5dlk/NrNBCoMwJ348f0/SlQrdFO0uqTjxOUaf5b2x3ZN9lvG6cHPGXWupfLaXkvRJWvX5llQ9ePndsVUT6yQZrZq/lVfd/d/NPSbQHrTWLz4AQNs1WjV9hy6TNNjd/8fd57j7Z+6+tc5T8a31qbG4+A/st8UrxY2Y5tTN1N03uvv/SOqj0BLgB5JmKfThm7CzpBslPWxmjX2qaHuIV/Q7Z7hPPF3S1g3uvtDdxym04Piqwo3rbNW+udlP0l/N7Psp8ih391sUxrIYIum7kh5U7YE3Oymc99kELQAAQJZQP95x6sffU02g5SlJh7r7be7+qruvTNLdT7rPMl4X7pIyVXotlU9TZev3xdZ6vuOmSqqM5s8zs6LYNgbbBpqAgAUAoKWdGJv/jbt/niZ9v2wWpoXEn/DaI2WqJnL3Snef5+63uPs3FJ4WOlbSI7FkoxRudlubeHPuvTLcp39sfnWqRFJoKeHuT0U39ScoDEz5LdXuT/pGM+vTQB7u7m+7+xR3P8fd+yqMnXFPLNkRki7LsPwAAACNQf24kVpx/Tj+WV6Xwfhq6T7L+HncO2Wq9OL57Glmze0CvrpLqQzzaokWN8m01vNdzd2XKXRXK4VgWqIb2n0VWnxLIcA3vSWOB7QHBCwAAC0t3p9tJoMUHpetgjSgsU3744PWfTnbT3K5e5W7v6gwgNvTsU1fT5Y8m2XJwJux+S9nuE883RuNOZi7l7j7TIXxKRI3HB0lndzIfN5090R/uAnJzi8AAEBzUT9uplZUP874szSzbko/tscrsfkRTS2UpLckJVobdFJ4GKc5NsbmezSU0Mw6KrR8zobWer7rujM2n2hVcaFqurJ6iHHzgMwRsAAAtLSq2HynhhKa2WGShma3OEnFmw4nHRixjpckrY/m+0o6vcVLlETUNcCjsVW7J0nW2PfS0p6NzZ9qZj0bSmxmvSWdkmL/jLn7WoXPJSHZuclE/Cm9puYBAADQEOrHLaQV1I8z/iwlfTuD4z+tmtYMA82sUQ/hJESDkM+Orbq8KfnELI7ND0mT9usKY81lQ6s830k8IWlpNH+CmQ2QND62ne6ggEYgYAEAaGkLY/Mpn1g3s06q/STK9rQmNp+yK6GE6AbgN7FV/9tQF0R1mdnudZZ3ip5EysSesfmVSbY36r1kwT8kLYrmC1X7PNUSPXk3RTU3Ep9I+medNA0+wVVH0nNjZoVmlmmftOnOLwAAQHNRP66jDdePM/0sB0qalC4zd18uaUZs1R/qnptG+FVsfoyZjWliPlLtFjTjUyUys66SftGM46TTms93PN9KhbEspNCq4n7VtA75wN1fSrojgKQIWAAAWlr8iadxZna1meXFE0RPnPxDYRyB+IB928u7sfkjzCyTsRdulfReNN9H0mtm9i0zS/pdama7mtnFZvaGwmCBcYdJWmxmk81sUIr988zsbElXxFY/kea9nBQ1hd5u3L1K0o9iq84xsz/WDRiY2U4KY0acGVv9w2j/uCvM7C0z+46ZJe0P2cy6mNnPVfP0YaXC9ZTQS9JSM7vFzA5PVXYz+4qkn8ZWJTu/AAAAzUX9WDtM/Tj+Wf4q2RP6ZnaipDkKA0Bn8lleI2ltNN9P0txUT/6bWffoHN5cd5u7/1PSQ7FV95vZT6JAWN18OpjZCDP7a4rz80BsfoyZ1WuxYWb7K7SW3ldSWao310yt9nwncbdqWoQcUWc9gEZo7iA8AADU4u7/MLPnFfreNUm3SLosujHZIGmgwhgGeZKWSfqtpEwqgC1ZxhVm9nJUjiJJb5vZk5I+V00l8xN3/11sn81m9nWFFgF7Kwwu+GdJq83sFUkrFN7vLpIGKbzPxM1asm6Peik8BTTJzFYo9Du7QqGJ8u4KN23xPltfkPRgknz+pdD8eM8oz/lm9g+FwawT/fe+6u4zkuzbItz9z2Z2nGoGrf62pLPNbLbCOBM9FQbMiwcxfuPuf0mR5WBJ/yvpDjP7ROGmc7VCy4xeCp9bPK9fuPvSOnl0l3S1pKvNbK3CWBvLFLoI6KnQv+0+sfQLFK5FAACAFkX9eIeqH/9Goa67W/S+now+x/ejvA+VdGCU9imFFiBjG8rQ3Zea2VmSZinUcfeO8l0SvZe10fr9FLpnKpD0txTZfVvhR/hhCtfTTyX90MxeUjgnphBcOlw1Y1PUG3/E3V80s8clnRatmmJmlymMAeGS/kPSkQqf57SozMfXzacFtPbzXTffJyWdGltdLum+jN4pgGoELAAA2XCWpL8rVCClUAncu06a9yV9S6EynQtXKtwo7aTw43bdJtPPSfpdfIW7L4ye2P+9pG8qVO53lfS1Bo6zXvUHiCtRuPFKfA/vIemrDeQxU9KFSVojyN2rzOw/JT2sMPj0HpIuqJPsXtVu+tzi3P3y6MbyOoWuoXZS8mbbpZKud/cbU2QVH4zOJA2IpmTKJf3c3a+vs36bwlNehdHyLgoBk1TmSDrH3XPxNCMAAGgfqB/XaLP1Y3dfaWajFMZB2zVafahqPteEWQpdKWX0QIy7P2Nmx0TlGhyt7hdNyWxOkc9GMxseHfdChaBFZ0knpcinVKG1cjIXKAQBEi2W94+muLsVHlp6KkUezdLaz3cSd6p2wOIRd1+V4b4AIgQsAAAtzt2/MLMvKzwNM0bSQQqDpK2U9KHCzcH/uftWM8vJDZm7v2Zmhyg0KR+h8LR9F4VKfUP7rZV0lpkdJOkcScMVbjZ7KDx9tl7Sx5LeUHja7Gl3L62TxzwLg1OPlHSMpC8pNKXuER1/o8L4Dq9Iut/d/5WmTI9FN4qXRfntFb2Xek9LZZO7/8zM/qTwuZ+scF66K5yThQo3Mne5+6cN5HGrmT0s6SsKT/gdLKm/pK6qOb8fKNxM3+fuS5LksSwaC+MESccqPI03QOHJrI4KQZElkl6VNCNqPg8AAJA11I93nPqxu881swMlfU9hsPFEq93PJb0ele9RSTLL/HDu/raZfUnSGdF0lELLks4K73+hQguAR9VAgMDdSyRdbGa/Ugg6nKhQn95F4YGfzyX9W2EA6hnuvilFPmtj1+w5Ci0ZukT7vyrpTnd/urHvs7Fa+/mu4++q/eAUg20DTWDunj4VAAAAAAAAACApMzteofW2FB6Q2idZKyAADWPQbQAAAAAAAABonomx+akEK4CmoYUFAAAAAAAAADSRme0haZHCoPUVkvq5+/Lclgpom2hhAQAAAAAAAABNYGZ5CgN+F0Wr/kywAmg6WlgAAAAAAAAAQIbM7FxJwxQGIj9W0n7RpjJJB7v7R7kqG9DW5ee6AAAAAAAAAADQhpwkaVyS9d8nWAE0DwELAAAAAAAAAGiaTZJel3Sruz+W68IAbR1dQgEAAABAxMw6SrpA0rckHSJpF0nbJC2T9LKkP7r7yxnkc4qkiyUNlbSbpFWSXpV0p7s/kZ3SAwAAAG0bAQsAAAAAkGRm/SQ9LunANEmnSLrSk9xMmVkHSXdKmtjA/ndJusTdq5paVgAAAGBH1CHXBQAAAACAXDOzAtUOVvxb0nhJRyn0U329pC3Rtisk/XeKrH6ummDFm5LOURiU85xoWZK+LelnLVd6AAAAYMdACwsAAAAA7Z6ZfVPSQ9HiXEnHuntlnTSHRdsKJK2XtJu7V8S27yfpPYWxAl+TdJy7l8S2d5L0nKTDJVVIOsDdP87amwIAAADaGFpYAAAAAID05dj8jXWDFZLk7q9LSgym2V3SAXWSfE8hWCFJV8SDFdH+WxVaZyhKd1VzCw0AAADsSAhYAAAAAIDUMTa/sIF0nyTbx8xM0qhocb67v5Js52j9h9HiqGg/AAAAACJgAQAAAABSTRBBkvZpIN2+0atL+ii2fm9JvaP559IcK7G9j6T+GZYPAAAA2OERsAAAAAAAabqkjdH8f5tZXt0EZvYlSadFiw+4+8bY5kGx+flpjhXfXrdbKQAAAKDdyk+fBO2NmRVKOjhaXCWpXv+9AAAAQAvIk7RbNP+Ou5flqiDuvtrMxioELo6W9KqZ/UbSAkldonVXK3QD9UY0H9c3Nv9ZmsMtjc3v2ZhymlnfNEk6Stpf0kpRlwcAAED2ZKUuT8ACMrP36qwqUsPN4AEAAICWNlTSa7ksgLs/YmaHKQQjJkq6t06SLyT9j6Q/RgNox+0Um9+c5lBbYvNdGlnMpemTAAAAANtVi9Xl6RIKAAAAACSZWUdJFygMnp1sMOzdJZ0vaWSSbUWx+fI0h4o/fVbcmDICAAAAOzJaWEDufmB82cz6SVosSf/617/Uq1evXBQLAAAAO7jPP/9cw4YNSyyuymVZzKyzpCckHavQjdLNku6RtFAhGHGEpJ9IOkbSLDP7L3f/VSyL0th8xzSHK4zNlzSyqOm6kOoj6RWJujwAAACyJ1t1eQIWSKa6n9tevXqpb9903eQCAAAAzZbrsRYmKwQrJGmiu8e7gyqX9LSZzZb0D0kjJP3SzJ5x97ejNJti6dN189Q5Np+u+6ha3L3B8THMahqGUJcHAADAdtJidXm6hAIAAADQrln4lf/CaHFBnWBFNXevUBjDQgr3UuNjm+OBhHRRgngrCcakAAAAACIELAAAAAC0d7tL2iWafzNN2tdj8/vH5t9PsT6Z+PYP0qQFAAAA2g0CFgAAAADau4rYfLpucwtS7LdI0vJo/vg0eRwXvS5TNHYcAAAAAAIWAAAAALBW0sZo/igzayhoEQ9GLErMuLtL+lu0uL+ZHZls52h9ooXF36L9AAAAAIiABQAAAIB2zt2rJD0eLfaWdG2ydGa2s6SbYqseq5PkN6oZcHCKmRXX2b9Y0pRosSJKDwAAACCSrrkzkJGSkhJt3LhRW7ZsUWVliw0KDwBAm5GXl6fOnTura9euKi4uTr8DgNbmekmjJHWSNNnMDpN0r6SFkookHSnpe5L2itI/4+7/iGfg7gvM7JeSfiTpcEkvmdlNkj6RtK+k/5b0pSj5L939o+y+pcxQlwcAtHfU5YHWg4AFmm3Dhg1avnx5+oQAAOzAKioqVFZWprVr16p3797q1q1brosEoBHcfb6ZjZI0XdKukk6PpmSelfStFNuuldRT0oUKwYkHk6S5W9J1zSpwC6EuDwAAdXmgNSFggWYpKSmpd4OTn89lBQBofyoqasbeXb58uQoLC1VUVJTDEgFoLHf/p5ntL2mipFMkHSipu0L3TSskvSrpAUmPpBp7IupeaqKZPSzpYklDFQIgq6P9/+DuT2T7vWSCujwAAAF1eaD1oDaKZtm4cWP1fNeuXbXHHnsoLy8vhyUCACA3KisrtWLFiurvxg0bNnCTA7RB7r5G0s3R1Jx8/i7p7y1SqCyhLg8AQEBdHmg9GHQbzbJly5bqeW5wAADtWV5envbYY4/q5fh3JAC0RtTlAQAIqMsDrQcBCzRLYlC+/Px8bnAAAO1eXl5edXcqDFwLoLWjLg8AQA3q8kDrQMACAAAAAAAAAADkHAELAAAAAAAAAACQcwQsAAAAAAAAAABAzhGwAHZg/fv3V//+/XNdDOTY4sWLZWYaP358rosCcD0CAJAh6vKQqDuhdeF6BLA9ELAAAKQ1fPhwmVnSbVRaAQAAgNaLujwAoC0hYAEAAAAAAAAAAHKOgAUAAAAAAAAAAMg5AhZAM82ZM0dmpsmTJ+vll1/WSSedpO7du1c3uZ06dapGjRql/v37q6ioSLvssotOPvlkzZ49u8G8XnvtNX3lK1/RTjvtpG7duukb3/iGFi9enLQMf/vb3zR06FAVFxdr991310UXXaR169alLPPq1av1ve99T3vvvbcKCwvVs2dPnXXWWXr33XfrpR0/frzMTAsXLtQtt9yi/fbbT8XFxRo0aJAefPBBSVJ5ebmuvfba6vd4yCGH6IknnmjC2cT2tGTJEk2cOFF9+vRRx44d1bdvX02cOFGffvpprXRmpueee656PjGNHz9e06ZN09577y1Juvfee2ttnzNnTnUe7q6pU6fq6KOPVteuXdWpUycdfvjhmjp1ar1yTZ48uXr/adOm6dBDD1WnTp00fPhw3XXXXTIz3XzzzUnf07PPPisz0yWXXNJCZwlNVVlZqZtuukkDBgxQUVGRBgwYoBtvvFFVVVVJ069cuVJXXXWVBgwYoMLCQu26664aPXp00v9LiT69N2/erCuvvFK9e/dWYWGhDjnkEM2cObNe+g0bNugnP/mJBg0apC5duqhr164aMGCAxo0bpyVLltRK25hrFQDQ9lGXpy7fVlGXRzZRlweQS/m5LgB2UFVV0po1uS5F5nr0kDo0L3738ssv64YbbtCIESN08cUXV1cUL7vsMg0ePFgjR47UbrvtpmXLlmnWrFkaOXKk/vKXv2jUqFH18nr11Vd18803a8SIEbrkkkv05ptvatasWXrnnXf07rvvqqioqDrtfffdp3Hjxqlr164aO3asunfvrscee0wjR45UeXm5OnbsWCvvVatW6aijjtInn3yi4cOHa8yYMVq0aJFmzpypxx9/XE899ZSOOeaYemX6/ve/r3nz5un0009XXl6eHnzwQZ177rnaeeedNWXKFL3//vs67bTTVFpaqgceeECjRo3SBx98oH333bdZ5zVb2uElWsuCBQt0zDHHaNWqVTr99NN14IEH6t1339XUqVP16KOP6sUXX9R+++0nSZo0aZKmTZumJUuWaNKkSR7ct8IAACAASURBVNV5DBkyRP3799eVV16p3/72txo8eLDOOOOM6u2JQSLdXeedd56mT5+ugQMH6txzz1XHjh319NNPa+LEiXr//fd1yy231CvjL3/5S82ePVujRo3SSSedpLy8PJ1zzjm6+uqrdffdd+uHP/xhvX3++Mc/SpIuuuiiljtZWbBq1aom79ulSxcVFxcn3bZ69Wq5e5Py7dSpkzp37tzkctV18cUXa+rUqdp777112WWXqbS0VL/61a/08ssv10ub+H/02Wef6aSTTtIZZ5yhlStX6uGHH9ZTTz2lZ555RkcccUStfbZt26aTTjpJ69at0+jRo7V161Y9+OCDOuuss/Tkk0/qpJNOkhSuv5NPPlnz5s3T0Ucfra9+9avq0KGDlixZokceeURjx45Vv379qtM25VoFgB1OW6ootVAlibp826nLt6XLU2r5erxEXT7XqMvXRl0eQItzdyamWpOkvpJcki9dutQbsmDBAn///fd9wYIFtTesXOkutZ1p5coG32dDZs+e7YnzNXXq1HrbFy5cWG/d8uXLvXfv3j5w4MCUeT344IO1to0dO9Yl+fTp06vXbdiwwbt27eqdO3f2Dz/8sHp9eXm5H3fccS7J+/XrVyufCRMmuCS/5ppraq1//PHHXZIPGDDAKysrq9ePGzfOJfl+++3nK2Pnad68eS7Ju3fv7sccc4xv3ry5etuMGTNckl9xxRXJTlmr0I4uUV+0aJFL8nHjxlWvGzFihEvyP/zhD7XS3nHHHS7JTzjhhFrrjz/+eA9fGZnlH3fnnXe6JJ8wYYKXl5dXry8rK/PTTz/dJflrr71WvX7SpEkuyTt37uz//ve/6+X3ne98xyX5nDlzaq1fs2aNFxYW+pAhQ5KWozVJ/J03Zbr99ttT5rvrrrs2Od9Jkya12PtL/C8bPHhwrf8Nn332WXUZ49fLl7/8Zc/Ly/Mnn3yyVj4ffvih77TTTn7wwQfXWt+vXz+X5KNGjfKysrLq9f/85z9dkp988snV6/7973+7JD/jjDPqlbO0tNQ3bdpUvdzYazWVlN+NAOpZunRp/H9RX89xPXhHmNpdXb45lSSnLt8W6/Jt6fJsgUuUunwrRF2eujyAIFt1ebqEAlrIoYceqgkTJtRbn2hiG9erVy+NHj1aH330Ub0mjJJ03HHH6eyzz6617sILL5QUnthKmDVrljZu3KgLL7yw+gkaSSooKNDPf/7zevmWl5dr+vTp6tGjh6677rpa20499VR95Stf0ccff6yXXnqp3r7XXnutdtttt+rlYcOGaZ999tH69ev185//vNbTHKNHj1ZBQYHefvvtevkg9z799FPNnj1bgwYNqvf00qWXXqr9999fzz77rJYuXdoix7v99tvVuXNn3XHHHSooKKhe37Fjx+rrdPr06fX2u/jii3XwwQfXW3/ppZdKku66665a6//0pz+prKys1T+R1R7cd999kqSf/OQntf439OnTR1deeWWttG+++aZefvlljRs3TieffHKtbfvtt58uuuii6idS6/r1r39d68nTE088Uf369av1fzIh2ZNshYWF6tKlS/VyU69VAEDbR12eunxbQV0e2UZdHkCu0SUU0EKGDh2adP3ChQt144036tlnn9WyZctUVlZWa/vy5curmzAmHHbYYfXy6du3ryRp/fr11esSNxHHHntsvfRHHXWU8vNr/4nPnz9fpaWlGjFihDp16lRvnxEjRujpp5/WW2+9VS/PIUOG1Evfq1cvLVy4sN62vLw89ezZU8uXL6+3D3LvrbfekiQdf/zx1f0zJ3To0EHHHXec5s+fr7feekt77rlns461detWvfPOO+rdu7duuummetu3bdsmKVybdQ0bNixpnocccoiOPPJIzZw5U1OmTFH37t0lSXfffbc6deqk8847r1llRvM19L+p7rpXXnlFkvTFF19o8uTJ9dInro358+froIMOql7fvXv3pD8i9e3bV3Pnzq1ePuCAA3TIIYdo+vTp+uyzz3TGGWdo+PDhGjJkiDrE+mdozrUKAGj7qMvXoC7fulGXR7ZRlweQawQsgBay++6711v38ccfa9iwYdq4caNGjBih008/XV27dlWHDh00Z84cPffcc/VueiSpa9eu9dYlblgqKyur123YsEGS1LNnz3rp8/Ly1KNHj1rrNm7cmLKsUrhpiafLtEyptiUqBWhdmnMdNNa6devk7lq2bJl++tOfpky3ZcuWeutSlU+SLrnkEk2YMEH333+/Lr/8cs2bN0/vvPOOxo0bp27dujW73GieDRs2qEOHDtp1113rbav7ua5du1aS9Pjjj+vxxx9PmWfdayTV55yfn19rMMD8/Hw9++yzmjx5sh5++GFdffXVkqTddttNl19+ua699lrl5eU161oFALR91OXrb6Mu3zpRl0e2UZcHkGsELJAdPXpIK1fmuhSZq3Mz0BR1n26RQhPHdevW6U9/+pPOP//8WtsuvfRSPffcc806ZuJLfmWSc11ZWak1a9aoT58+1esSNyNffPFF0vxWrFhRK92OrB1eotW253WQyOOwww7Ta6+91qh9k/1NJZx99tm66qqrdNddd+nyyy+vblLeVpqQJ/ubzVS82XNdH3zwQaL/8kZL9qRmU3Xr1k1VVVVavXp1re4npPrXXeIamTJlii6//PIWK0Ncjx49NGXKFN12222aP3++nn32WU2ZMkWTJk1SQUGBrrnmmmZdqwCww2lLFaUWqiRRl2872tLlKbVsPV6iLt8aUJevQV0eQDYQsEB2dOgg1flia48++eQTSdKoUaNqrXf3pH3LNtbgwYMlSS+88IK+9a1v1do2d+5cVVRU1Fq3//77q6ioSK+++qq2bt1ar1IzZ84cScmbjO9o2vMlmvh8n3/+ebl7rZsJd9fzzz9fK50UnvKTws1zYj7Ztrp22mknHXDAAfrggw+0fv366ibfzVVcXKwLLrhAt912m2bPnq0ZM2bogAMO0NFHH90i+Wdb3Yp/S0n2FFQuDB48WG+88YZeeOEFnXnmmbW2vfDCC7WWjzjiCEnhf1a2bnISzEwHHHCADjjgAH3961/XXnvtpUceeUTXXHNN1q5VAGiT2nNFKYa6fOvU3i9P6vK5R12+BnV5ANnAoNtAFiX6s33xxRdrrf/FL36RdNCpxho1apS6du2qqVOnasGCBdXrt23bVm8gPikMNnXOOedo9erVuvHGG2tte/LJJ/XUU09pwIABbaaiiKbZa6+9NGLECL333nuaOnVqrW133nmnPvjgA51wwgm1+rzdZZddJCnp4H0777yzzCzlwH7f/e53tXXrVl100UVJm+AuWrRIixcvbvT7uOSSSyRJ559/vjZt2tRmnshqD8aOHStJuv7662t95suWLdNvf/vbWmmHDRumI444QtOnT9eMGTPq5VVVVdWsJ1gXL16c9PpKPB1WVFRUvS5b1yoAoG2iLo/WiLo8so26PIBco4UFkEWXXnqp7rnnHo0ePVpnnXWWevTooVdeeUVvvPGGTjvttAb7eMxEt27ddNttt2n8+PEaOnSoxowZo27duumxxx5TcXFxdf+lcTfddJOee+45/exnP9PLL7+sI444QosXL9ZDDz2kTp066Z577qk1eBV2TL/73e90zDHH6KKLLtKjjz6qQYMG6b333tMjjzyi3XbbTb/73e9qpT/hhBM0c+ZMjR49WqeccoqKioo0ePBgnX766erSpYuGDh2q559/XmPHjtXAgQPVoUMHjR07Vv369dMll1yiV155Rffee69eeukljRw5Ur1799YXX3yh+fPna968eXrggQfUv3//Rr2HQYMG6dhjj9ULL7ygwsJCXXDBBS14htAcI0aM0IQJE3TPPffo4IMP1je+8Q2VlZVpxowZOvLII/XYY4/VSj99+nSNGDFCY8aM0W9+8xsdeuihKi4u1qeffqq5c+dq1apVKi0tbVJZ3nrrLZ155pkaNmyYBg0apD322EPLli3TrFmz1KFDB1111VXVabN1rQIA2ibq8mitqMsjm6jLA8g5d2diqjVJ6ivJJfnSpUu9IQsWLPD333/fFyxY0GC6Hdns2bNdkk+aNCnl9qOPPtp32mkn7969u5966qn++uuv+6RJk1ySz549O6O8Fi1a5JJ83Lhx9bb99a9/9cMOO8wLCwu9Z8+e/u1vf9vXrl3r/fr18379+tVLv2rVKv/ud7/r/fr184KCAt911139m9/8pr/zzjv10o4bN84l+aJFi+ptO/744z38G6kv1bGx/aW6dhYvXuwTJkzwXr16eX5+vvfq1csnTJjgixcvrpfHtm3b/Ic//KHvtddenp+fXy+/Dz/80E899VTv3r27m1m9a9vdfcaMGT5y5EjfeeedvaCgwPv06ePDhw/3W2+91VetWlWdLtnfRip33XWXS/IxY8Y05pRgO6ioqPAbb7zR99lnH+/YsaPvs88+fsMNN/jHH3+c9Hpcu3atX3fddX7QQQd5cXGxd+nSxQcOHOjnnnuu/+Uvf6mVtqH/L3X/Ly1dutR/9KMf+ZFHHuk9e/b0jh07+l577eVnnnmmz507N2kemV6rqfDdCGRu6dKlnqh3SurrraAu3NYn6vKNQ12eunxrR10euUBdvn1/NwKZylZd3tybNqAPdlxm1lfSUik0Ge3bt2/KtB999JEqKiqUn5+vgQMHbq8iAoAk6fLLL9cdd9yhZ555RieccEKuiwNI4rsRaIzPPvss3m3Jnu7+WS7LsyOgLg+graAuj9aI70Ygc9mqy9NWFADQJq1atUr33nuv/uM//kMjRozIdXEAAAAAZIi6PAAgFcawAAC0KY8//rjeeOMNzZw5U5s3b9bkyZNlZrkuFgAAAIA0qMsDANIhYAEAaFMeeugh3Xvvverdu7duuOEGjRkzJtdFAgAAAJAB6vIAgHQIWAAA2pRp06Zp2rRpuS4GAAAAgEaiLg8ASIcxLAAAAAAAAAAAQM4RsAAAAAAAAAAAADlHwAIAAAAAAAAAAOQcAQsAAAAAAAAAAJBzBCzQLHl5eZKkiooKVVZW5rg0AADkVmVlpSoqKiTVfEcCQGtFXR4AgBrU5YHWgYAFmqVz587V8ytWrOBGBwDQblVWVmrFihXVy/HvSABojajLAwAQUJcHWo/8XBcAbVvXrl21du1aSdLGjRu1ceNG5edzWQEA2p/E01gJ3bp1y1FJACAz1OUBAAioywOtB7VRNEtxcbF69+6t5cuXV6+r+08eAID2pnfv3ioqKsp1MQCgQdTlAQCoj7o8kFsELNBs3bp1U2FhoTZs2KAtW7bQlBwA0C7l5eWpc+fO6tatGzc4ANoM6vIAAFCXB1oTAhZtmJkNlfRTSV+WVCDpHUm/cvc/b++yFBUV8Q8dAAAAaIOoywMAAKC1IGDRRpnZCElPSSqV9KCkTZJGS5phZnu6+625LB8AAAAAAAAAAI3RIdcFQOOZWb6kP0qqknScu1/s7ldLGixpgaQbzKxfLssIAAAAAAAAAEBjELBom06QtK+kB9z9rcRKd98g6QZJHSWNy1HZAAAAAAAAAABotJwFLMzsJjPz2DQ8V2XJlJn1NLOvmdn1ZvaEma2OlX9aE/LrZ2a3mtl8M9tiZmvN7FUz+4GZdWpg1+HR6z+SbHsqej2+seUBAAAAAAAAACBXcjKGhZkNkfT9XBy7mb5oqYzM7HRJ90vqGlvdSdLh0fRtMzvN3T9OsvvA6PWjuhvcfYWZbY6lAQAAAAAAAACg1dvuLSzMrIOkOxWCJSu39/Fb0KdK3sIhLTP7kqQZCsGKzZKulfRlSScqjE0hSftJetzMdkqSRbfodUOKQ2yMpQEAAAAAAAAAoNXLRZdQ35U0VNJ8SXfn4PjNcb2k0yXt4e79JF3SxHx+K6lYUoWkk9z9Bnef6+7PuvvFkn4YpdtP0tXNLTQAAAAAAAAAAK3ddg1YmNlekv5ftHippPIWyPNAM3vezHbLMH2emT1gZuc19ljuPsndH3P3JncNZWbDJB0bLd7t7nOTJLtV0gfR/JVmVlBne6JlRapWFF2VuvUFAAAAAAAAAACtzvZuYXGHpC6S7nX355qbmZkVKwwyfaykf5rZLmnSd5B0j6RzJE0zs8ObW4YmOCM2f0+yBO5eJem+aLG7pBF1kiTGrqg3ToWZ7aFwjuuNbwEAAAAAAAAAQGu13QIWZnaWpK9JWivpv1oiT3cvkfQTSS7pEEn/MLOkrQ7MzCT9QdLYaNUDkl5viXI00jHR65Y0x48HdI5Ose2kJPudnGR/AAAAAAAAAABate0SsDCz7grjNkjSf7v76pbK292nSro8WjxM0pMpBqq+XdK3o/kZki50d2+pcjTCAdHrx+5e0UC6+Un2SXhG0kJJ55rZkMTKKFjzY4Wutu4TAAAAAAAAAABtxPZqYXGzpD0kvaQsDLTt7v+rmlYbR0p63Mw6Jbab2a8k/We0OEvS+e5e2dLlSMfMiiTtGi1+1lBad1+n0ApDkvass61CIfjSQdLzZnanmd0q6W2Fgbp/7O6LW7DoAAAAAAAAAABkVX62D2Bmxyr8uF4h6dJstWpw91ujMS3+n8KYFo+a2WkKXUZdFSV7QtLZaVo2ZFO85cfmDNJvkdRZYUyKWtx9tpkdI+mnks6WVCDpHYUWLDMaUygze6/OqqxfFwAAAAAAAAAAxGX1h2kz6yjpTkkm6dfu/m42j+fuP4taVlwj6QRJ70raN9r8rKQz3b08m2VIoyg2n0k5yqLX4mQb3f1fkk5pbqEAAAAAAAAAAMi1bD9J/2NJ+0v6VKElQNa5+4+jlhbfU02w4kVJX3f30u1RhgbEj98xg/SF0WtJFspSzd0PjC+bWV9JS7N5TAAAAAAAAAAA4rI2hoWZ7a/Q0kGSrnD3LQ2lb2Gf1lleqdrBglzZFJuv181TEp2j10y6jwIAAAAAAAAAoM3KZguLqxRaESyU1MnMxiRJc1Bs/gQz2yOaf7SpAQ4z+46kX0WLayT1kHSmpPvMbKy7VzUl35bg7qVmlihT34bSmtnOqglY0NoBAAAAAAAAALBDy2bAItGd0T6SpmeQ/n9i83srDDjdKGZ2oaQ7osU3JI2UdIukCyWdK6nMzCZma+DvDL2vMCj4ADPLb2AA8P1j8x9kv1gAAAAAAAAAAORO1rqE2t7M7FxJf1QY4PsdSSe5+zpJF0n6vyjZBEm/y00Jq70YvXaWdFgD6Y6Pzb+UveIAAAAAAAAAAJB7WQtYuPt4d7eGJtUeiHtEbNvixhzLzEZLuk/h/XwgaaS7r4nKUSVpnKSHouSXmNlvm/v+mmFWbH5CsgRm1kHSBdHiekmzs10oAAAAAAAAADuGqipp/frwCrQlbb6FhZmdrtDlVJ6kjySd6O4r42ncvVKhS6i/Rau+a2Y3b9eC1pTlX5JeiBYnmtlRSZJdLemAaP637r5tuxQOAAAAAAAAQJv3wQfSW29J77+f65IAjZPNMSyyzsyKFbqBKpC0SCFY8XmytO5eYWZnKbRwOEXSD8xsZhRAyPR4x0gaEFu1a2x+gJmNr3PMaSmyulKhm6diSf8wsxsUWlEUSxoj6eIo3QJJt2ZaPgAAAAAAAABYu1bati20sgDakjYdsHD3EjM7U9Ldkk5x96Vp0pdH6R+V9HhjghWRbyt0L5XM0dEUNy1FOd40s7Ml3S+pq6QbkiRbIOk0d9/UyDICAAAAAAAAaKfKy6XKSmnrVqmgIHQL1aHN97OD9qJNBywkyd1fNrODom6fMklfamZfzTR9trj7o2Z2iEJri9Mk9ZVULuljhfE2bnf3rTksIgAAAAAAAIA2prQ0vG7ZInXrFlpaFBbmtkxApnIasHD3yZImt0A+jQo+NDVY4e7jJY1vyr4p8lsi6fvRBAAAAAAAAADNkghYbI0ehSZggbaExkAAAAAA2j0zm2Nm3shpeJ08xjdi3/G5eacAAGBHV1IiVVSErqGkELAA2goCFgAAAADQeFWSPsp1IQAAAOoqLQ3BioqKsJwIXABtQZsfwwIAAAAAWsAESZ3TpBkkaUY0/4y7L2sg7cmSljew/bNGlA0AACBjpaVSWVkYbLuykhYWaFsIWAAAAABo99x9Ubo0ZjY2tnhfmuQL3H1xswoFAADQBCUlNa0qCFigraFLKAAAAABIw8w6SDovWtws6S85LA4AAEBS7qF1RSJgER/LAmgLCFgAAAAAQHonSuoTzc909625LAwAAEAyZWU1QQsptK6ghQXaEgIWAAAAAJDeBbH5dN1BAQAA5ERJSXilSyi0VQQsAAAAAKABZtZF0jeixSWS5mSw2z1mttzMys1stZm9YmY/M7M+6XcFAABomtLS0MKCLqHQVjHoNgAAAAA0bLSkztH8/e7uGewzPDbfI5qOkHS1mX3P3f/QlIKYWd80SfZoSr4AAGDHUFJSu0VFW+gSyl3auFHq0kXKy8t1aZBrBCwAAAAAoGGN6Q5qocKA3HMlLY3W7aMQ9PimpCJJvzczd/c7m1CWpemTAACA9qq0tKZFRV5e6BKqokKqqpI6tNK+dj76SFq+XOreXRoyJNelQa4RsAAAAACAFKIWDcOjxVfcfUEDyf8q6d4kLTBelTTDzL6mEMwokPRrM3vE3Ve0dJkBAED7VVpaM+B2p04hWCGFVhaFhbkrV0PWrAllXr8+lLOgINclQi610rgaAAAAALQK56vmvunehhK6+4aGuoty98ckXR8tdpI0sQnl2TPNNLQJeQIAgB1ESUloYVFQEKZEd1DpuoVyl9atqxm0e3upqqoJVkjS5s3b9/hofQhYAAAAAEBqY6PXMkkzWiC/OyUlghrHN3Znd/+soUkSLTYAAGinKitDYKK8XOrYMQQsKivDtnQBixUrpLffll5/ffuOeVFaGl43bQplJWABAhYAAAAAkISZHS5pULT4mLuva26e7r5S0pposU9z8wMAAEhI/PhfVha6f8rPr+kSKjGuRSrr14f9KiqkDRuyW864RJnLy8M8AQsQsAAAAACA5OKDbTfYHVQjpew2CgAAoKkS3TklWljk5YUulxItLxpSViZt3RrSbt2a/bImlJaG7qjKy8NxN23afsdG60TAAgAAAADqMLMCSWOixVWSnmihfHeTtGu0uLwl8gQAAJDCj/9VVaGVRKJLKCnzgMW2bSHosWVL9suakBhzIzFfUlLTjRXaJwIWAAAAAFDfKZJ2i+YfcPeKFsr3YkkWzT/XQnkCAACotDQEHqQQsMjPD/MVFem7hEoELEpLt2/AIl7mkpLQ2mJ7Hh+tDwELAAAAAKgv3h3UfekSm1l/M/tSmjRfk/STaLFE0j1NLx4AAEBt8dYKiS6hpBCIaKiFxbZtoWVGYhyJrVtD4GB7iJc50T0U41i0b/m5LgAAAAAAtCZmtrOkr0WL77r7Gxns1l/SbDObK+lRSW9LWhlt20fSN6Mp0briv9x9WYsVGgAAtHulpeHHf7PQHVQi6JCuS6hEC4dt20LaqqoQSOjUafuVWQrlLS1lHIv2joAFAAAAANR2tqTCaD5t64o6joqmVLZKusrd72xKwQAAAFJJ/PjfsWMIWpiFVhbpuoSKByyqqsL8li3ZD1hUVISprCwca+vWECihhUX7RsACAAAAAGobG71WSvq/DPd5XdL5CsGKwyX1UhhcO1/SOknvSXpG0l3uvjJVJgAAAE1RXh5aRyQCFgl5eem7hCorC60bEmm2bQsBi912S71PSygpCa/l5VJxcSh/YtBv9xBwQftDwAIAAAAAYtz96Cbss0khuJFpgAMAAKDFlJaG17IyqUuXmvUFBSEQUFERWk90SDKicWLA7Xhe22Pg60SZy8ulbt1C+bZuDa9bttR+H2g/GHQbAAAAAAAAANqweGuFeAuL/PwQrJBSt7LIZcCisjJMhYWhlUXifdAtVPtFwAIAAAAAAAAA2rDS0ppWFMm6hJJSByzKy2u2FRSEoEFJSc14FtlSUlIztkbHjmEci6qqEEAhYNF+0SUUAAAAAAAAALRhpaU1g2fn50v33y+99po0cKB03HFhfboWFmahG6bS0jCGxNat2e2WKTFIuFQzULjEwNvtHQELAAAAAAAAAGjD4q0V5s6V7r47zL/9tlRUJB18cM32usrKwraCgpB248awPtvjSJSUhGN36BCOLYXXrVtDwIKBt9snuoQCAAAAAAAAgDYs0VohL0+aN6/2tvffD+NEJGthkRiQe9u20MqhqCisKy/P7jgW7jWBkngXVolxLCoqagblRvtCwAIAAAAAAAAA2qjEj/9lZeHH/yVLam9fsyZ1wCLRjVRFRWjdUFwclktLQ0uHbCkvrxmvorCwZn2nTgy83d4RsAAAAAAAAACANiox5kR5eRi/4tNPa29fsyYEJJJ1CZUIWCS6hCooCF00lZZmt4VFIiiRrIVFosXH9ghYbNsmbdgQzh9aB8awAAAAAAAAAIA2KtF1Unl5TTdLcZs3h3EpGmphsW1bCFaYhW6hSktDUKGyMnQzlc0y1w1YSKF1x6ZNLX/cOHfpzTfDsfr1k/beO7vHQ2ZoYQEAAAAAAAAAbVRJSU0Li5Urk6f5/PPUAYuKirB/InBQVFTTAiJb3UIlxtyIH1cK83l54fjZbmGxcWN4fyUloRUKWgcCFgAAAAAAAADQRpWW1gQjPv88eZrly1N3CZXYt6AgvBYX17S8yFa3UCUlNeWJBywSx09sT1bmlrJ2bQjWrFtXE6BB7hGwAAAAAAAAAIA2qrS0JsCwfHnyNMuXp25hkQgK5EeDBxQW1gyIna2ARaKFhSR9+KH0+99LL7wQWlwkAhZSdltZrFsXup0qLQ1dXyXOIXKLMSwAAACao6pKWrEi1Op79Mh1aQAAAAC0M/HWCkuXJk/zxRehNUFVVRhUOyFZC4uiovCazYG3E2X+/HPp5ptr1k+aJB1yiLRqVQgibNok7bJLyx9/27bQfdZdd0lLlkhnnikNYrSkCwAAIABJREFUGRJu65BbtLAAAAD4/+y9d5hcZ3n3/31mts9qq6Qt6pYsuWAbG1eaaS8tYJqBAMGhOoFfXngdWkJJfi8hTkhiOoTeDAZT3ABTDDFYLmqWZHVppV1t77Nt+s7M8/7x3YdTZ7bNFq3uz3XNNXPKnPOcM+eceZ77e5f50NcHnDoFHDmycAleBUEQBEEQBEEQcmCiFYqKgI4O/3VMbQt3lEUq5Sy4DTjrSCyEYJHNWume9u1zLnvwQavw9kLWsRgdBe66i/s7ehT4l3/xtkVYGkSwEARBEARBmA+RCHvS2Sx7vYIgCIIgCIIgCItEOk3BIZmk/1QuHyo/wcIIB0awsFNWZqWaSqcL2+ZEgu/JpFdgOXrUEk8WUrAIh4FHHnHOe/DBhdmXMDskJZQgCIIgCMJ8sFeCW6h4aUEQBEEQBEEQBB+M8T+VYhqlXAwPM8WSXbAww5hUyl+wMMObaBSori5cm019inic6ZjsxGLA2bOMsojFuE46bdXX8ENrChsjI/QhKy4Gtm9nlEguenu96bOOH5/T4QgFRgQLQRAEQRCE+ZBKWclgRbAQBEEQBEEQBGERsQsWJorCj3SaooURKQCryPTkpJWGyVBWxigErQsvWCQSHD51dPgXAj98GLj6amt4FYkANTXOdVIpti8cplAxOUlBJhIBKivZ/i1b/Pcfi7HQdzbrnN/SMv9jE+aPCBaCIAiCIAjzwVSpy2YXLl5ZEARBEARBEATBh3ichvp0mlED+ejqcgoEdsHCHWFRXk6xIpksvF+WqbmRq97GkSPAc55DgcUMs4xgEY0yKmNwkMtiMRbmnpiw2rluHdu/ebNVl8NOOMwyhG7a23m8Unh7aRHBQhAEQRAEYT6YCAvzLj1cQRAEQRAEQRAWCWP8B4Du7vzrdnd7BYtMhoZ/v5RQZvuFFizicbbZnZLJcOiQFfGRSFCwGB+noGCiRAYGGFmRyQCBALBqFdDWBnzve5x3223AhRcCa9Z4tz8yApw44Z2fTrOGxlVXFe5YhdkjgoUgCIIgCMJcmZyk29HkpBWLHYmIYCEIgiAIgiAIQl4iEdZYcKdimi1GsNDaKwCYwtmG7m5vSigjYLgFi6IivhZCsJguwmJoiAIFwAiK/n6gr4/f6++n4BAMAvX1QFUVEAoBP/kJ8NWvWtu44w7gJS/xChbZLOtcnD7tv+8DB0SwWGoCS90AQRAEQRCEcxbT25+c5OdMRupYCIIgCIIgCIKQl3AY2LcP2L2bqYn86jjMFBOtEI0yLZIdt+G9p8cbYWGmS0q82y4r4/bNcKdQxOMUJPr6cq9z5Ii1/2iU0RMnTlDoWbcOuOQSoLmZYsW3vuUUKwAKHbt28d3O+DiPx13s275fYWkRwUIQBEEQBGGumF57Os33eFzqWAiCIAiCIAiCkJeREYoFXV2MitizJ7/xPhdaW9EKw8POZSUlwJVXOuf19eUWLIp88vCUl1sRGm7D/1wxxbFPn/YWvbZz5AiLZ5t6E/E4sGEDcPHFjJoIBvn9z38e+OEP/bdx6JA3TVY4zMgOU7/DzbFjczsuoXBISihBEARBEIS5Yo+wACz3H0EQBEEQBEEQhBwkkxxKDA0xPVFzM4cUvb3A9u2MGpgJsRiN9vE4t2Vn40ZGItjp6/OmhEqlKFYEfNzaS0u5TjbLYY4pfD0fjACSKyWT4fBh4H3vY6qq0lKgutpZQDudBj79aeD3v8+9jYMHmULqggsocAAUi44ezf0dv2LcwuIiERaCIAiCIAhzxVSp05rTiYQ1ahAEQRAEQRAEQfDBHtkQCtHj//RpGtf37QNaW2e2nZERS0xwCxabNnkFi4kJCiQAhzCpFNvhrl9hMIW3k8nC+WXF43x3H+OmTc7ptjYOrRoaKJTYxYpkEvjnf84vVgAUH5JJFugGeLwTE8DJk7m/09nprPvhhwmwFxYGESwEQRAEQRDmiunhA4y5jsfZ8y9UvPR0ZLOM/S5kQllBEARBEARBEBYUI1gEg8CWLYwASKVoSO/uZn2F/v7ptzM6yqGH1ozOsLNpE9DY6P1OZyeHEZOT/F4+wcIUBJ9tIPk99wCf/CRw5ox3WSJBny93DYlXvtJbR8OvnoTW3PbjjzvnFxUBr3mNd19Hj1ppoUZG+N7WlrvtmUz+CIzubuDRR/OvI8wPESwEQRAEQRDmSipludesWmW54ixWWqjTpxkrfeCAFeUhCIIgCIIgCMKyRWtLsDAG+qoq4KKLgLVrGQ0QjQKDg9NvZ2yMJfSCQQoRdjZt4vZXr3bO7+3lvk0NByNYdHSwcPXPf26V5QsGuSyRmPkQ52tfA173OkZAvOAFlkhgSCQYDeKOCLnsMp4DO36Cxd69XrGitBT4278FXvpSr0izbx+PZ3ycbYnHWTskHwcP5l7W1cVh4ODg/IqlC7kRwUIQBEEQBGGupFJWdMOqVXRVSiYXr/D2yAhjmuNxb5U9QRAEQRAEQRCWHfbIBnuh60CAxvaiIsu4ni/TbDTKbUQiTJcUDjuXmxRLzc3O+T09TsHCBI1/4APA3XcDX/oS8I53sBA4wLRQiQT9tHIVqrbz2c9anzs6gDvucC6Px4Hjx53ziosZZXLZZc75hw97t//znzunQyHgH/4B2LqVRbkvvdS5/Phxtru7m+eou9tKi2VoanJOP/WU/7Elk2y/OdeLFVh/viGChSAIgiAIwlwxERaBAFBRwXmzcT+aD9ks9zUywp5yX9/C71MQBEEQBEEQhHlhFwrcqZiUoh/UxARTE42N5d7O6KhVv8JtgA8GrfoV+QSLbJb7eeopZ8TD4CDwkY8A//mf1rADmH6YE4sBLS3OeXfd5awJkUh4a0hs20ahxi1YnDjhzH7b3m4JKYZ3vhOorWV0SkUF8PSnO5e3t/PY+vu5LbdYUlICPPvZznnudQzmPLe1cRgogsXCIIKFIAiCIAjCXDHuSEVF7OkGg3S5WYwIi0TCqpQ3PCy1LARBEARBEAThHMB02e0poexUVXFIkUp5oybsjIxQQNDamz5q/XoresMdPdDby23bC3/nqsfw4IPAP/4jcOiQNezIx5NPeqNC2tqAX/6Sn7XmMObUKec6JhXUpZc6i2tPTjrX/dnPnN+rqgJ27KDw09DA437Ws5zrDA5StMhm+XLX1diyxRuVceKE//ENDQEf/CDw0Y8Ct9ySW9gQ5ocIFoIgCIIgCHMhk6FbTTptjTTKy63RxUInNDXuPIkEXX0yGYmyEARBEARBEIRlTjJJw3067V/setUqvk9M5BYs3PUr/ApuG9yChT3CwqSn8ku9ZBgeZl2Kf/93Gv7zlc5z15Yw3HUX01yZY29tdS7fsYPvlZUUEOyYto2NAb/7nXPZi17Eodf69UwNtXkzIyzKypzrnTnDIVMk4q1fsW0bcM01znnd3f7RJHfeSVEG4Dn/whf8j1eYHyJYCIIgCIIgzAW7a5RxXyovt+KdFzrKIhazRJNMhj1wESwEQRAEQRAEYVljj2xwCxbDw8A3vwncfz9TK0Wj/nUjIhEOAyIRGvnb253L7YKFOyVUfz+HLKYdw8Ms9G3HL/Lj4YeBT3wif5qqffv85//+94ziiMcZpeBOYXXxxdbnpz3NucwU3v7FL5wB5UVFwJVXAjU1jLS48EKKN1VVzu0BFCCGhigC9fQ4l23bBlx7rTOyQ2vg2DHneskk8Ic/OOflqnUhzA8RLARBEARBEOaC6S0nk+xxa01XHpMMdqHrWMRiljhSVET3q1gs/whCEARBEARBEIQlJZm0hhJ2wUJr4JOfZOHrP/6R3vtGUHBjAqyjUX/BYvNm67NbsEinWQzbCBbuFEm1tcD3vgc84xne/e7a5TXk29tvxAU3ExPAQw+xne50S6EQIyQM7joWR47wfN13n3P+tdcC1dWs1bFmDVBfz/kVFV7Ro6ODQ6X+fr7sbNvGY7a3AQAOHnROd3V5BYqurpkVIhdmhwgW5zBKqWuUUg8qpUaVUlGl1C6l1BuWul2CIAiCcF6QSrHn+/GPA7fdBrz//VakxWIU3t69G7j1Vro5HT5M96pk0hsPLgiCIAiCIAjCssEeYWGPZDhzhrUiDL29jFjwSws1OmoNN4JBrxF+40brc02NN0XS2bNWO06fdi678kqgsZEFt2+7zRsF8qc/+aeFGhryih92fvELttsteGzfDgRsFmq3YDE+TgHFLdxcfz3TXZWVUXQwlJV5t3H6NM/DyIizxoZSrJ8RDFp1NAz23wJgPQ931t90WupYLAQiWJyjKKWeD+AxAM8G8BMAXwXQCOBupdQHlrJtgiAIgnBekEoxGauJZz582EqquhiFt2+/nfHMkQjw7W+zJx8Os6pcJrOw+xYEQRAEQRAEYU4YoSAQoKHc8Mgj3nV37fIa2bW26jEUFXkLbgcCwIYN1rRS3iiL1lZuM5n0FsC+8krrezfdBFx+uXP5U0/5B3Xv2ZO/jN/+/RQ13AZ+U7/C0NAArF3rnPfjH3u/s20bsHo1cMEFQGmptUwpb3TI5KQzQN3Q2MhtAN7C2+5okd/+1v+48tX/EOaGCBbnIEqpIgDfAJAF8Fyt9a1a6w8AuALAKQC3K6U25duGIAiCIAjzxK93v3Mne8smwiJfRbr5MDHh3Hc6zfjqcJif3UloBUEQBEEQBEFYFhjBwi9ywc2BA/RFGh+35k1McJ6pX3H2rPM7TU0ckoTDFAeGh72Ft813Ojqc2wYswcJw4YXO6cOHvSIJQMHCTl2dM7Ijm2VaJ3dEhzuyAfCmdLILNgBw4408dqUoOrhpaHBGmQAUINwRIFu3sgwhwGLddk6etD7HYsBjj3n3A+ROkSXMHREszk1eAGArgLu01n/OqKa1HgNwO4ASAH+9RG0TBEEQhPODZNJb5PrIEc6Px9mrjscXZt8tLd55Z85w5DMxIWmhBEEQBEEQBGEZkk5TbHALFmfPUjxw09lJXyR7OiRTvyIWo9He/T1TcLu7m/vr7vYa9U3h6aNHnfMbGrzRGJtcLtGnTnGbdt+sRMK7rSuvpLBg54EHvIHofoKFO6WTnaYmRlhUVgKrVjmjVAwVFd6IiaNHvWLJ9u2WYOEWanp7rUiSP/7RWyjcYBc2hMKwoIKFUqpKKfWXSqk7lFJ/UkqdVkqNKaVSSqkBpdQflVIfVkrVL2Q7CoVSaq1S6hVKqU8qpX6tlBpSSump13fnsL1NU+fmxFQNirBSaq9S6kNKqYo8X33e1PvvfJaZAKUbfZYJgiAIglAoBga8goSpNGfmL1QdC3dkB8Akq6WlHM2Mj3MEIwiCIAiCIAjCssEUaJ6cdNav8EsHZXjsMWcdC3v9Cr8Ii02buJ9MhkJFNstoBzu9vRy6uI3tV17JqAU7TU3Oeek0jf/2tFDDw16fqqc/HXjta53zJiac07W1TP+UyTBqw5yffILFy17GtFeVlaxL4Uco5E1l5RdhsW2bJViYWhZ2TPTEAw/kbk++uh3C3FjoCItrAfwIwN8DeC4YFVAFoBjAGtCo/mkAJ5RSL1ngthSCfgC/APAJAC8FMGehRSn1SgCHwHOzA0AFgFoAVwP4DwAHlFLbcnzdBGN53Cu11n0AIrZ1BEEQBEFYCNwjA8P+/Zbb1ELVsWht9c4bGaFr0/g4RxESZSEIgiAIgiAIywq7YFFUZM33Swdl2LuXAkUySfFhbIyG/+Jiplxqb3euv2mTJWjU1dEgHwo51+np4dDBHXFw1VXO6WyWwoa9JgbAOhb2LLSDg/7b+l//i1Ebudixg2LImTOM2mht5f42b/a2GeC866+nn1ZxcW7Bwi/CIhz2+nTZBYuSEu7Xjim8/fvf5z6GtraFywR8vrIYKaE6AXwfwPsBvBbADQCeBeCNAH4KIANgNYAHlFJXLEJ7CkUH/CMcpkUpdSWAu0HxJgLgYwCeCeCFYG0KANgO4FdKqVU+m6ieevcpcQMAGLetIwiCIAjnJvF4/qptS417ZGA4dIhtj8f9IyyyWboyHTgw95RRufZtevgjI0B/v/ScBUEQBEEQBGEZkUpZ7yYllDHU5+LIEfpDhcPe+hWplNdPadMmGuZLSxkxUFNjGeUN4+P+wxF3HQdj4Hcb/596igW0tWZ7jh3zigHXXAPU1wMvfnHuY7voIgowsRijQZJJfg4GvXUsAODlL+f+QiEKHVVV/tstLwfWr+c5ysWqVSy4bT83l1ziXOfQIQa354uimJgAurpyLxdmz0ILFg9rrTdqrf9aa/0FrfW9WutdWuvHtdY/0Vq/AcDNU+uWAPjnBW7PfPkkgFcCaNRabwLwN3PczucBlANIA3ix1vp2rfUTWuv/0VrfCuDDU+ttB/CB+TZaEARBEKYlkVhe4sDAALB7N92JTK9+OaF17l6p6bEnEv4RFmfPclQyODj3+OHOTv/5Tz0FVFczJjuVcia7FQRBEARBEARhSTEFtwErJZQ7HVRpqXN6YoKixfCwVb8iHqcxvrPTW5B640Ya/Sumks3X1jLSwp3q6Te/cU5v2ACsWeOcF4sx/dIVLhfzEyd4LGNjbJM7Y21NDQtar1oFvOIV/ucCYIRFXx+PpaGBQoUpAu4WLAIB4KabOMyqrOTLHqViJxikELFjR+59b9rECBV7Gih3GqnDh4Gf/9w5r6rKWzDdRGIIhWFBBQutdWYG69wHwGRMe85s96GUulQp9YhSas30awNKqaBS6i6l1Ftmuy+t9T9rrX+pte6f7Xdt+78W1nF+S2v9hM9qdwA4PvX5/Uop123w58iKXFEUVcgdfSEIgiAITsJhYNcuvpaLODAwwN5xPO5ffW6pSaWsSnV+mDoWiQRHFIbRUR5PXx9fQ0Ozj7LQOve+n3qKI5JEgudP0kIJgiAIgiAIwrLBLlgYo7c7HdQznuEVDnbtYhD1yIjlE1VZ6Q28XruWRngjWAQCFEaqqjhMcG/TjrvoNMCA8YoKr3gwPu4sCH7ihHP5jh3ct1L8rjtCw9DczOFQY6MVMWEEi+c+1ykmvPCFVsREvvoVhooKb8SEna1bvZEn7giT48eBBx90zrvhBopCdo4cyd8WYXYsRkqomWBKrpTN5ktKqXKwyPRzAPxeKVU3zfoBAN8B8CYA31VKXT2Hts6XV9s+f8dvBa11FkyjBQA1AJ7vWsXUrvDUqVBKNQKohE99C0EQBEHwpauLPdp02lnNbSmJROhKNDhI47xJ9rpcSKUoOOTi2DGrCp1JC5VOsycfibBnPzLCed3ds9t3PJ573+PjPGfFxRw5hMPLR4QSBEEQBEEQhPMct2DR1+ctfH3NNd6IBlMmb3SUw4niYkZi+BXcNv5QoRCFj5ISihVuwcIdYO+uXwFYwkdzs1cg2LuX/lfDw95jsAsFdXVM5eSmsZHbt0dLVFWx/akURYGPfYwiwiteAdx2G4dWJSV8zUSwyFe8+8ILvYKFe/2hIeAJl6v5X/wFxQ47bsFGmB9LLlgopXYAMPrVrH5erXUcwD8B0AAuB/A7pZRv1IFSSgH4GoC3Ts26C8CTc2nzPHn21Ht0mv3b9dVn5VjmlwXuJa51BEEQBCE3k5M0nA8Ps/c8MTH9dxajTYkEe6qDg+ytLrcoC79kse7lBw8yGsK4QLW0sIfd0WHFZw8NcTvp9Mz3bUSJXBw8yNHI6CjjwyUtlCAIgiAIgiAsC5JJy5+oqAjYudO5PBRiNML11zvnnzljRR5EIky1BHg9+039CqVojK+upmhRXc16EvlwRxekUhyahUKM2nBHSRw6xHWSSW8NDrvgUlsL3HijlQLLsHUrh31NTRQqNmywalIY36/nPx/47GeBD3yAx2NqdwA8pnyYyJBADuv3RRd5BYutW73pnuwB88EgcPPNwPbtznVaxG28oCyJYKGUqlBKXaiU+nvQsG4yjn1uttvSWn8bwN9NTT4DwG9yFKr+EoB3TX2+G8A7tF6SSpQXT72f1lrns07YxZuLXcv+AKAVwJuVUn9+nEyJNR8FkIIVoSEIwjlAJsOcj6ZeriAsGgMDNGqPjrJn61dzYbExbYjHeXMMDDDKIpFY2nbZicdZ1NqOu8d86BDbHI3yGPr7Gc2SyQCbN9PVaGiIYkW+aA03bW3eRLV2DhxgWzIZ7lsEC0EQBEEQBEFYFpgIi5ISigrudFCXXUaD/HXXOQ3tmQw9/e31Kw4c4MvO9u1WGieTYmntWu6vqSl3u7Zu9Q5nTBHtigrWl3CnhTp9moLFwIAlphjs0RqlpWzDM5/pXGfTJgovoRCHR/X1FAQqK73bM+fAHHu++hWGigoKEu70TQC/u3mzV7AoKmLkRS6uuorn8WKXpbatLX9bhNmxaIKFUuptSimtlNJgdMEpsFZDw9Qq/w5GPcwarfVXAHxwavJ6AL9SSlXY9v0ZAO+dmrwPwF/NpL5GoVFKlQFYPTWZt3681noEPE8AsMG1LA2KLwEAjyilvq6UugPAU2Ch7o9qrc8WsOmCICwwfX20Y7a350+LLwgFx/QuTe8vEll61SwSYULUQ4fYyx4aYk/YnaB1KWlvd7raAMBrXuOcPnqU53ZkhIqkSTq7bh1HDKtXU6wYGWFaqJme9+kKdT/1FEcFpmLdyEh+gUMQBEEQBEEQhAUnm6VYkU7Ti39oiEMGO097GrvylZXAtm3OZXv2WL5doRDw9a87l9fXA89+trN+RShE0aK0lAJBLnLVrygpYVurq1lbw05vL4cxhw8755eXew36dXXAe9/LoRBAw/9VVzEtVHU1l1dWsp3V1TxO93DLXrtjunRQgBXU7idArFtnFeZ2k6veBsB0UIBXvOnttTIBC/NnyVNCATgI4Fqt9T/OJ+JBa30HgE9MTT4HwC+UUmVKqdsB3DY1/9cA3jhNZMNCYo/8mIkLq7nUK90LtNYPg+mlHgPwRgDvAdAP4C+nzsWMUUodtb/ACA5BEBaRSISO2CMjtNMutb1YOE9IJBhrOzrKaRPRMNsi0IXmV78C3vEO4KtfBW6/nSrewACVvVxt05qplk6f9vZsFwK3C01ZmdV7NSQSTDYbi/HV1cWedd1Uya3ycroUDQ7yuGYaCeHet4mbNsRijEmurrbEKPMbC4IgCIIgCIKwJJiyfKkURQB3OqiKCharLi3ltFsgOHyYGXxLSlgw21034ZZbuN1UittatYr+X0oxLZS77oIdv/oVJlID4JDjGc9wRjVoTV+pY8ec39u2jUKJndpatuHjHwe+9jXgXe/iPBNdYaiv577smXUNkcjM61cAXK+oyCueAJZ44ydYuFNj2XnjG/l++eXO+Vp7hRth7iymYHEfgMumXteCha/vBetX/Egp9Yr57kBr/SkA/zY1+QIARwD849T0/wB4rdZ6KStP2ouKz6QdpsKoz+0DaK33aK1fprWu1lpXaK2v01rfPe9WCoKw6ESjtG0ODPDdnWlGEBaEgQEas8fG6F5ixIClTgv1rW9ZNR0GBoDPf56x0smkt6ocwGM4fJhiRXv74tS7cIsGTU2MmHC74+zezfeODro4bdjgXL5mjRXZ0pU3+NLCfXxbt9I1yc6BA+zpJxIcsUhaKEEQBEEQBEFYUoxgMTlJYeGRR5zLr76aBvbSUooMN9zgXD44yMDt8nIOmeysX8/C1sbL30RWGNau9U+NBHCY4jbAZ7McpoRC3F9xMcUEd7TCyZPeAPALL6Q/l52aGu5n1Sr6VyWTVnSFvRh4fT2Pv7TUqmNhiEQsIWS6+hUGU8fCzdatFDSCQe8yv/UB4IILWPfCHE9Dg3O5CBaFY9EEC631qNb6yNRrr9b6x1rr1wK4BcAFAO5XSr2tAPv5KKxaGEY7fBTATVrrpU5+bd9/Sc61LKY0VSyoq6vW+lL7C8ALF3J/giB4iUbZGTAO7+3tEmUhLAImHVQ2y95iJkPj9lIW3s5kKDzYSSSAb34T+MEP2GaTTBVgew8cYO+9tZVhSn19C38DudNTmYSwz362c/7+/WxPJEI3HnePeNUq9sYHBxkFMZ1YNDnpLfbd0OAVSg4csCrxjY2JYCEIgiAIgiAIS4xdsIjHmQHXztVXc7hQVGT5QlW6cq6cPcuohs5O5/x3vIPfi8X4XlLiFCyqqjhscAsJAKM63BER8TiHVKGQJQ6EQl5jfnc3X3YuuYSCi51AgEZ+M0SprqaYsGWLc73aWp4DEyxuhnX2+hWhkLcwdi4qKoDmZp5POxdf7B9dAeQWLNwB9e62Hz8+szYJ07PkKaG01ncC+OlUW76klKorwGbdrpUDcIoFS4XdAuRJ8+SDeVwsgwqogiAsFIkE/3xN58VkvRkcXNp2CSucaJTG8ZER9uJMTK3x9l8qurr80xdpDdx5J/CZz1iCRixGQSAcZgqkSISfk8mFT4HkHiEYweI5z3HOn5gAHn+ckRSVlTy/99wD/NM/Ad/+NiNJVq+mqJBKTR9l4Vfsu7kZuOIK57zDhylEmYp1pvi3IAiCIAiCIAhLQirFsX82y2GMvcxcWRmN6CYdVHMzjfyXXebcxvHjwA9/6Jy3fTtw4438HI1a4oM7c+zatdyuG790ULEYRYfycms7oZDXT6qlhbU47LjbbDB1KgIB+svV1HhTOwUCFC2qqjhUMr5qs61fYaio4Ll95Ssp4gBMbXXJJbkFC79i3ABw883OaXe0yalTM2+XkJ8lFyymuH/qPQTgpfPZkFLqPQA+MzVp3AlfC+D7SqklPd6pCA/TpvX51lVK1cISLDrzrSsIwrmN+QNOJPhnGo/TvihRFsKCMjDAHuD4OHuExcWWS85SChZHjuRf/sADwK23MvZ4/34KLi0tVpRILMabqa9vYdvpdiMyKZnWrWOssJ0zZ3h+v/514A1vAL74RSasvfNORo7U1dGNaGiIv0sqT9bIWMx7bE1N3gS3ySRHM/aKdRJlIQiCIAiCIAhLRjJpdfV37XIuu+46DmlMBERVFaMRrr7aud6+fV6B4NZbaejXmvaEigoa5434YVi71ip6bSeTni4oAAAgAElEQVRXwe2KCooWRrAoK/Omjkq43MOLinJHKNTWsp3NzRQE7LUr7NTXUxwJBjlcNe0pLuYxzVawCAYpynzxi8BXvgK85S3cTi7BIhCwUj8ZqquBZz7TOW/HDue0OzWWnXBYygrOhuUiWNj9iPPUrM+PUuodAL48NbkfwIUAvj01/WYA31TKHZS06JhSNNuUUkV51rPfGhJUJAgrmGjUysSzejX/UPv7OV/si8KC0d9vJQU1Pb7ycvZwJye9Pc/Fwh1H6xfru3s343G7uxltUVRE95a1a9kbDYcZomTqYBSadNqblsnuqvTc5zqX/c//AG9+M/CjH3nFoPvuYy+8ro43vF/KJzuRiDfCorGR8d3u0YepY2Eq1skDRRAEQRAEQRCWjGQydzqo5z6Xy43QEAzSwO/OOOvmGc+wfJdM9oaKCm90BUABxF1Sr7jYX2CIxSxjv4nYUIp1MNauzd2eLVty15cIhdiG1av5yiU81NdbQokRLOZSvwKwioaXlfHc1tVx+FhUZC3zw114+6UvdRYcB7zRJmfP8vy76e/n733w4NKXizxXWC6ChX2EPaefTin1ZgDfAKAAHAbwYq31CIB3AzDBUm8H8N/zaGcheHTqPQTgGXnWu9H2+bGFa44gCEuNKbgN8E+0ocHK1rMYtYOXI5EIcOyYpMVaMEyKoJERxtQ++CBw++2MWnDH3C427jjaq64CPvYxr3Bx5gzwjW+wl7ltG5eb+OGREfYUF+oCGhmhKGLHpIQCvGmhzMjBj1QKuPdepozKZLjt7m5nfLidri6Ocuw0NHAk4XbxOXDAWbFufNz7XUEQBEEQBEEQFgUjWHR1OYcHxcWMpMhkaBMwhvTaWooD6/PkaHn3u63P8akKuLkEC8AbIfG0p3kjMVIpvozAYHf9DoWYTikXF16YO3IBYLqoyy7Lvw1Tf6OqiscUj3OYWllpRY/MlPJytr+0lOc/mbSON1873/c+67hLSjgk9TsWO4kEyyq66ejgcEzrpS0XeS6xXASL19s+z7qmulLqdQC+Dx7PcQAv0loPA4DWOgvgr8E6GQDwN0qpz8+vufPiPtvnt/utMJW66papyVEADy90o1Yi4TDwxBOSQ05Y/kSjVv2K0lL+KZeXU4UfH6f98nzjzBk6mR87ZnW6VgyTk8CTTzKd0UJFAExHfz97oJEI6yt87nPAQw8Bd9wBtLWxjUvRk8pmvT28piaKFh/6kLcS3GOPWVEVhtpatj8SWbi0UO6i4ICVEgqgW5FfrHUu7ruPo5PqaoosqVRusaWtzTkdDNIFCfC6Rh07xl6zcU3S2iu0CIIgCIIgCIIwb3buBN77XuDHP869jhEsBgac8zdvtoY09lRF1dX0yXJ7+xue9zynz1I0SsEjGMwtWNxyiyWAFBUBb32rdx3jwxYKebcTCuWuUQHQlyyfEFBSwuFLYBqLdH29tW8zrJtt/QrAqsNRVsah0UwFi6c/nRERn/oUA/z9jnnLFu823JEz4TB/F5MAIF/2X8FiQQULpdTblFI+9ecd69wG4OVTk20Ads5yH68E8CMAQQAtAF6otXbc+lrrDJgSytTKeJ9S6j9ms59CobXeA+sY36mUusFntQ8AuHjq8+e11uIOOQc6O2lv6+k5Pw2+wrmB1uwMxOP84w4G+Ye6di2v31iMtSzONyIR5uVMJr322XMek4ppdNTbU10MtGZvaXSURvL777eWZbPAnj1LV3g7FvMWnQ4G6ZKyaRMLVdvdexIJ4Je/dK4fCrEHGg7zPC+E4uUWLGprnT1Vpayqd3ZKSoCbbmKv187EBPCrXzHKIpmkuNDR4S1io7X3gVBXx95+cTELb9t7/pOTwNGjHOmk05JnThAEQRAEQRAWgNOngRe8APjv/wbe9Cbgnnu862hNY/XkpDfD6+bNlhNjSYkVYREI0EB/3XXe7QUCwDvf6Zxn0jgpxcgIP5qagB/8APi//xf46EeBrVu960SjbEdx8ewFi+3brToc86G+nkPBykoO64qKuN3ZChYAz0lpqWV/KS217C/5uPxyRlbkEowCAW8djqNHndOdnVaZyHTa+p2F/Cx0hMX/D6BbKfV1pdQtSqlnKaWuUEo9Wyn1HqXUo7AKZKcA3DolLswIpVQ5mAaqGBQ7Xqi19k38rLVOA3gDgF9PzfqQUura2RzMVLvfZl4A7PXht9mXTS3PxfsBxAEUAfidUuoflVLXK6Wer5T6GgAjppwCcMds2ihYRKOWkrlsDL4mN/lS5YYXlh3JJG3GiYTT3llTwz/R/n7alU2pgfOBdNrK69nXR5v+igqbNDnAksmlObCREfaUR0bYs3Z73Le0LF3h7fFxr2Bx6aWMHNi+nVXOXvQi5/Kf/tTrplJXZwky7tFAIXCraPboCsOb3mT1/mtrgbe/HfjJT4DbbgOe9Sxvxbaf/cyK/85VxMavmPiaNdZIor7em5T2wAFnxbpwOHe6KUEQBEEQBEEQZs03v+kMnv/qV73rpFI0mPuVrNuyhcPD4mJ22+22gdpa4JprvGmQ/uIvnKmiMhmOoUMhq/ufi+3bWTNjwwZveirAEj4Af8Higgu8aaQACiWXXOL0MZsrlZXch6lXMZf6FYaKCktE0ZrnMl90xWzYts05ffKk9TkS4bDb+CmaVFvC9OQr+lwo6sA6Eu/Os04XgHdorX8/mw1rreNKqdcC+BaAl2mtO6dZPzW1/i8A/Goq2mE2vAtML+XHs6Zedr6box0HlFJvBPADAFUAbvdZ7RSAv9BaryQz3aIxOcmHgMl1FwrR4DuXB1tBaWnhk6qiArh2VnrZ+UU6TcNaTc30cYLnONEo3xMJdkQMSjEtfUcHr+H2dm+uyZWKcYhPJvl5zRpmCbriiqVtV8GwFy1ZClFgYID7j8eBRx7xLu/vZ2haYyMfpLNJEDpf2tutm8Jw8cXsEZuY4H/9V6avMoyMAL/5DSMXDLW1HAWMjtLAv2lTYXrNhrNnndMNDbxIlWI7V61iD/sb32CoUF2dd8Twl3/JdFyG/n7g4Yf539DWxmujvZ3V6AyxmFeAWbuWv1FlJXvd27Y5VfoDB5wV69Jp/iHaHziCIAiCIAiCIMyZ3bud0+60QIDlWZ9Kcbhlx0RYGBHAXgy6tpbzb7zRGgbV1gJ/7bJO2utX5IquMDQ1cVi4cSNw4gTbY/yeslkOO5qbObxwDwdNibxt27zRBOvWWdlqC0F9PYcw3d3WcMdPKJkOU/dCKQoW9rRb82X7dud0S4v1uauLv/foKKclJdTMWWhL4EvA9Eb3ADgEoB9AGsAEgDMAfg7WcdihtX4o10byobV+HMDTtNZnZ7h+AsBLtdafm8v+CoXW+hcALgfwWVCciIH1KvYB+AiAK7XWPkmyhZlgNwKbjCBLHmVhcoebakFLlbt+IYlErGSH8+HQIb7OgwIk0Si9GSYnvWGTtbX8Ux0Y4KWzoqIM8mA6WuaPvLfXv8bxOYnW/NGNmhqNetP+LCTZrJUOqr+fNQ78MD3sxRZU3O0pLqYYsGULFWel6F7kjom++26nW1BJCXvp4bD1R1BI3H8o1dX8LVMpChfHj/P8ptNU3Pzcmy67jNEjdn78Y6uITV8fb3p7lEUs5nXHamy0BIuSEm/h7RMn+D1TsS6VkrRQgiAIgiAIglAgtGatAzv9/d6SdEawGBz0mk1MhEVpKYc8dtuA6ea/9a3A618PvOUtwOc/7xUGYjH6e5aV5a5fYWfHDg47mps5PDD2hkSCx5SvcHcoRL8yN1u3Fk4IAOi7VVrKY5pL/QqDEYDMeS2kYOEuHm6C8ZNJ6zoIBvkyztXC9CyoYKG1Pqm1/ozW+nVa6yu01o1a62KtdZXWepvW+mat9Xe11vOycM4mjdRc1rd9721aazXT1wy21661/nut9Q6tdUhrXau1vkZr/R/zPSfnO9Eo7XLmD6G/fxkYfONxGq+MtLrS0kKNjgL79gF7987vRJvoikRihVio8xOLWZeC+w9TKdo6R0b4x5arBu9Kw9wqpgbx+Djt5q2ti2vbXxDsOcDicct9ZbEYHub+R0a8bkB2jh/neospWGjtdEcBKFaYJKx23vMe53RPjzdapK7Oqmhf6OLbna6Azupq9qB37AAuvJCf+/vpcmSiJfx405uc062trCHS2MjvuHMamjxpdhoa2OOurOT0JZc4BZJs1hJCAN5QIlgIgiAIgiAIQkE4e9Yy89jZv985nUyya97d7ZxfXs4uvREsysq8w5/aWgoUz3wmA7XdWWABDh3M0GkmgkVZGQPZV6/mUKKzk0PAaNQqVJ1PsPDLALFjhzM6ZL7U1HBoU1fH9sxXsCgt5faKigonWLjreQwNcbjV00O7RjjM366kRGpYzIaVnWtFOG8xNiqAD/bRUdoHlzTKwhjxjafvSntK9fdbefnnYxw07vVjY1ZFqunIZq2c7+cY9uxAfqGNJo1ZNEo74/lAPG7dHo2N7Ej09NB+uxQ1qguKPfzLXOuLKQp0dbENo6PAE0/kXs/UsVhMlTce9woBzc3+N8aLXuSNJPjRj5yKVnU1e6PhMNU+d2LWuaK1d5SxahV77SUlHD1s3MjoiXXreDGfPk0xwv3cv+EGpquyY8SFsjI+S8fHKTAB/O3cN0FzM/dbVsbjranxJlK9805g1y6ORMbHrUg/QRAEQRAEQRDmxZ4cyeZ37XJOG/OG21yyeTPnZ7Mc+vgZ/GtrOT8YzD1EM3UngsGZiwbNzRw+bNxIY3pvL7dTXs5ojXyChV/K5u3bCxthEQhQrFi9miLKXAWLoiJryGRSXBWqnZde6s1kfvAgh4zhMIehq1ezDZOTVh0TIT8iWAgrErvXenMzs4oMDFDpXDKb9sQEjVUmvm6lCRbhMA1ho6M0Ds7VFd4Y0YywM5MfrL2dHuEHDxbOKLkIaG1dqyUl/uU6Skp4/Rrb8TkfYTAD7IJFSQnv4ViMl1Zb2zleL9ieAyyTYa91sQSLoSHeV319rGuQL8prZCR/ZMBsyWSAe+7hK9c9OjHhFSw2bPDmSgMoELijE1paGOVlCAQoWoyMcJ+FClEaHvYa+80IoqaGrkbXX8+47nXrgIsu4igkkWB6pt5e6xwEAsAb3+jc1sGDXK+hgeckFrPU9v5+S+gyGMFCKSux64tf7H2g/Nu/8fecmOBNtFBRFqOjfB7v2eMVdgRBEARBEARhhWEvS2fHL8IiV8FtkyYoV6oiU35u1Sp/wcKIIaEQRYaZlu9TykoN1dRkDRlDIQ4nTBC3G7Mfe4bb6mrgaU8rrGABMM1UUxPbOZf6FYaKCqv+BlC4dlZUcEhm5/HHrSwZJtV3cbElVEhaqOkRwUJYkRiv9aIiPhTWrqU9PZVawigLY3gC+JRaSYJFJMLjMZ7AqdTcc8abHOuxmJXvfzrCYa4/Obk0RYx9MPbRfD9zImFlB8r3Z1lRwcPLZM4Pp2hzCRQV0Ttk1Sq+enu57Jy2gdpDagD+oItxzWoNnDnDe3R83Btd8ZzneIswHzhg5eeaL7fcArzudXzdfLP/OpGIv2Dh1ysNhYBnP5tGfTs/+pFzuq7OEoXcxarnypkzzulAgEJFeblV3c7EV19/PQWLhgbrfWCAgoSJmnjhC52FtQFGWdTU8Nj7+ykCDA8zQsaOUlZKKMASLC64AHj7253rRqPAZz7D39RdG2O+TE7yt9uzh4JLRwdv2NOnxX1IEARBEARBOCfRemZDoSef9J9/5IhzOpnk0MQvwsKedcEvOqK0lEOgVass24AdYyeYScFtN+XlFE3WrOFwIpu19pVL+AiF+P7udwMvfIHG9ddl8eEPcxjk5282H8rKWC+jsXF+26mo4LZKS2knLCoqTPsADr/snDrFIVwqRXskwH2a62klmQMXChEshBWHUZYTCetBWVdHw2d/P21Fi2701dopWKRSK6uGhYlzM8WEU6m55+6JxRAZy6A9vArJidT0P5YRNUZG+M+6TASL06eZvv7gwdxREebQ7NeqH6GQpd+s9LRQ6TQvH5O/09DczHnDw7SFnrM16+2CRTDI+2UxrtmeHl5EPT00ersN369+NXDllc55hw/zfb7tGxpyCgn33eetVQHw4u7pcc7btMn/5ggG2Zt+9aud8w8coHe/wVSoC4f5jCjEc/fUKed0fT33Ewx6Y6YDAfasr72WI5HmZva2Kyqonp8+zXXcIs7OnfyNGhoo/sbj3K97dFNdzZ6viWuurLSS3r74xRRD7HR2skD52Bhf8/1to1EWSn/iCf6m3d08puPHeaNmMoUTigRhgVFK/VEppWf5el6e7b1MKXWvUqpLKZWcer9XKfWyRTwsQRAEQRDmgCmk/eij+TNeaw089ZT/srNnndEQyaR/Bm1TcNsETedyZqyttcSInh5u2wgX0SiHBcXFM6tf4WbdOg4tNmzg8KSiwkpP7YcZgjSszuBVTz+LN13Xim3r4r71N5YL9giLQtbZAFjG0E5HBx1YjT8ZYKWEAiTCYiaIYCGsOOwp4s2DIRh0Rll0dCxBo+zFdVdahMXwMHD//cyTfuAApeShoTnlL9LRGA6fKkXrcDWOtRRPH2FhChebPELLoI5FNku9ZnSUzfIrwAU4swPlEywqKrjNRGLlCxbGnpxIOAWL8nJ20Pr6uGzR7+FCYM8BZnpJ8fjCPw/SafaWw2Hu251MdeNGihVXXeWcf/JkYQpvt7R4nwX33eddr7XV23PbuDF33G9lJfCSl3h75H5RFqOjhTOeuyMs6ut5gZqUTH4Eg3S7ueYaxjNv2cLY5kSCRv5XvMJyUwJ4vn7yEyt+uK+P14i7/fX1fLcLFkrxgZJIAB/8IPdj5+BB4Gc/4/b27aPr11xqlZjRWWcnX0ePWiGMzc083+PjhS94LgjLhywAj/qqlAoopb4J4EEArwawDkDJ1PurATyolPqGUkrGYYIgCIKwTIlGLb+hfFk6Tp/OPVzKZoHdu63pZJJByO4hz+bNXGZsArmM6XV17PZXV3N4c+YMfcyOHaNvlvneXAQLpRgQXl7OIVhJyfTbCYWAsnQEmWQG2VQGpfHRgqeDKiQVFVaaq0K38+KLndNnz3Lov2aNNa+42IraEcFieqSjLKw4jDZgf+ADzLgRCFDlNPWhFw1TfMDkHl9JgkU6TbHii19kLOT3vw88/DCfwLks9XkYH0ohGctgIFJOB+DBeP4vmN5BPM7XMhAs7LbRVIrajR8mIAXIL1iYP9PFroG8FJjzkUpZNlhDU5OVaquri/b3c4pEwhLXysr4wy5G4e2ODu67r4/7dwsWr3oVe6juCItIhKmL5nvRuQ38ANtg/wETCfbq7JSW0mCf6+aorGQv+nnPc85/9FGnolVXh1N9VdizV2G8pX/+hWBaW53TNTXsrYdCFCbyUV4OXHYZX7W1jL4YGeH3brrJue7DD/OCN1EWiYQ3AsX0gI2oEwpZblnmOvvkJ71x4b/8JfDzn/M8dXXx2f3UU7N7Zo+PW3kWw2GOoC66iAW/167liCActupmLDVDQxzRzeF/SThveDuAy6Z52YvO/EFr7Zek8F8BvHPq8wEAbwJw7dT7gan57wLwqQK3XxAEQRCEAjE2xqGTSUucqzv76KP5t2MKcpui2m7xY9Uq+iCZDAPBoHccbKip4fBnyxYOJy66iOJCVRW/Y4Ktc31/OsrL2ZU3mWmnK3AdCgFlairPdTqNEjW5rAWL6moeV0mJU0goBJdc4pzu6WFEhV30MYLFSjIHLiQFzNglCMuDWMy6+e12rmCQD6WBAdp/OjqA7dsXqVETE5axErBy3qwERkaABx5wznvgAaZqMRWGZkoyieGRACYTGfSOh1AbTaCnL4Ttk5N8uvsRjfJ8mmIQyyAl1PAwmxSPs6MzNMQ/fndoZDRqXQYDA8yqYkpxpNPWeyDAEM26OisqYzq7qB+Tk2xbTU3h80oWiliMx5zJeNtoOhb9/exMHTnCIl/GyXwhiUb5zBgZYZjshg1z3AjA63T1avaWBgZ4wJHIwhxIMkmD9OAg9/Pkk86q5WVlTB0E0Cu+sdHpEb9vH9MZzQc/weLQIbojXX01L/BIxJumqrGRN02+CItgEHjuc4Hf/96KYNMa+Kd/Aj77WaC2FrFsGXrSa5Hpj6KrqxKXDA3Nr4fqFlbq6micn40rU309RxepFC/o/n7gNa9xRodEozSwX3GFtY67Qt/atewJmwLbJn67vJxiQTbL3/UTnwD+4R+s315r4Ic/5Po7drD3Ho/zAq+tZbW86R4yw8O8pmIxHktdnfe8dHVZiXpnm0y3kGjNiKFkksc532taWJFordumW0cp9Vbb5Pd9lm8H8MGpyX0Anqu1Np4Xe5VSDwD4E4CrAXxIKfVtrfXp+bVcEARBEGaHyaScr0bB+c7YGLu54+McR/f3+3dn3b5gbg4e5LsZ97tL9m3ZYi03Bbdz/SaBAH3M4nG2a2KCr0jE6uabeglzpbmZ3fiioulrPIRCQGk2AZVJQ2fSKFWpgqdaKiTBIHDddfw951O8248rrnBOmxTWSvFauusu+qNls8D738/hk5AfESyEFUc+r/XVq2kfHBig8XPz5rmrz7NiYsJqVChkyet+buTnGh0dVq57Q08P8Kc/0aB44YUz7wXF4/jjwRp85acXQGuNW4pOoXxTCbaORxGszyHvRyLWuY3HLeFiCS3yw8NWzfHRUdpGJyac9kx7dqDeXuDv/356x+9sln9sExPTezv4cfIkxZNAgNvZuNGycy4XTGYvwL8T0djI5W1TZiUjWrhrFheK8XF6wRgRKpFg52Pdujmcu2iUX06nndXI8rnszJe2Np6wgQFeNL/+tXP5i19spTHKZNgDtq/z1FNWVbe5qGSAv2DR2sqLsbub6s/EhNfdqKmJQmWu/Zp219UxyuLBB61l7e3ABz4AfOYziGINUF2NSOswEqNTKZjmI1i485GtWcPfcrYG+XXrOGJZu5Ztamykim6vkbFrF3+TNWu4jju9UkOD9z/EHuOcTPLzNdcA73wn8I1vWOtFo8BXv8q0Wi99KR9aq1bxj7Gjwxo95SIctnLU+R17bS3bbOLTt25dugeOUYLDYbZhJfz3CovOVAqnt0xNRgDc47Pa/4E1vvrfNrECAKC1jiml/jeAJ6bWuw3A/7cwLRYEQRAEf44cYbeosZFe+oKXsTHL32x0lMOprVu9po0DB5zTlZVOH8rjxzn8M2Nct4/W5s1WeiAjWORDKXatKyqsItTZrJVppBA+QjM1pYRCgEolUaaSyCiNQDq1rCMsAA4FCi1WABR6qqstOxBAkevgQeDHP3YmAvnKV4CXv7zwbVhpLDNTlSDMH1PT1s/OVVREx1bjeGp/mCwYxn0hFuOTsazM+kdaCVEWDz3kXwH5nntoIJpF+o2B9jg+9NWteLJzLfZ3NeC/Hno6UkmNgfY8aaEiEWfhA2BJ00KZ6298nJ2JaJSnwZ0Wyp4d6A9/mFmWmoceot14rrbtsTHaO3t6aC/eu3f5pVUygkV7O3UwU0TMEAiwU1dTQ1t8OMzU+YODhW1HOMzOxf79tLl2dDA3aG8vf7c51RKxF9w21b4CgYUrvB2J8Afv6+PF2NJC47EdU7Q6EuGJbGhwLjc9bJ97anJyhrean2CRzTI++uxZPg8jEa+70bp1+XuTpaV80JeVUbBYt865vK0N+MAHkBicQLo0hDjKkRya4DNprs+ITIYXhJ3mZp7f2SaLDQaB9ev5p1RczB7t9dc71zEuW2adgQHn8oYG7zmyCxZx27PzTW8CbrzR247f/hb43Oesh4sZkeUjmeRvNj7OfflFwAWDvFFNJEau3HiLwfg4H7LmQbEof/7CCuSFYB0KAPiZ1jpmX6iUUgBeNTV5Qmvt63M5Nf/k1OSrpr4nCIIgCIvC+DjHOtHo8hsLLhcSCWd5TFML1W3ayGY5hLJz883O6TNnOARLJtkddWd43bzZOUScS4RCIEChorp6cf2DQiEASQoWJUgBk8s7JdRCs3mzc/r224Fvfcs79OzrK0xpxZWOCBbCiiKZpF1kYoLhVv/5n8DXv876og8/zCwkExPUDlKpRUqrHY1a7vQVFTTsTE5aDT6XiUZzJ208eZIW51lYku/+eRDhCcvw1TW2CruOhNDbnqMikUn+Z68NUogiwfNgaMhqgrH9jo15T4P504rH/e25fphU8XMxlpvi3skk/yBPnqRN8tAhdrKWy6UYj7PW8Oc+B3zkI8CHP2zdLgalgE2b6MBtoh+OHZvexjpTjh3jeenpoc3blHEwWXPSaa/df0a4w7/stQZM6EYhOXOG2x0epsH7hz90Lr/8cnrRa00jfFkZC0PbSSR4sbjuqXSaesPevTxf+ZqePZ3jAn/ySV54ra3+KaE2bJje/cUY54uLgU9/mlEZdlpbsfpT70dmLIJUeRWSo3HoZMorOsyUri7vwW7YQOP8XEYX69bR03/tWt7c7sLnHR28EAMBCiPu3m5zs3+EhUl+axcslOJN9axnedvR3s6UUY89xnZMJ6KFw7xuzI2Ri/p6S5BayuLbpgZIMsn2iGAhzI1bbJ896aAAbAHQPPX5T9NsyyxfB2Dz/JolCIIgCDOnu5vdIRNBbs8WKxDTVYxGreGaCVq3c+SIlZXW8Ld/65yOxzm2TCbZHXUbqrds4e8QCLD7fi4Z/IOZFMqCkyhTCZQGUlA6i7Lg5PRfXKFs2+aczndvuYP2BS8iWAgrCmPLufNO1hN98EGmBP/yl1l39P3vB97zHuBjHwO+8AUfw+/wcOHdDIxnZzxOg1ZJCQ1e2ewiV/5eAIaGgN27cy+/6y5a6mdY5Pbe33gNfr/eU4/x4Ul/25mtJsBYoBapdGDJC28PD1s11uvqaDscG/M2y9Si6O/3emo873kMEbzpJmeUUDZLG+9cBAsjSExOsk3FxbRnnz1Le+iePXRwn289YsRicy5qm8nwPN1/vzVv/37g29/2rquUlSmgsWMAACAASURBVDa/o4Pn/fhxRkDM5xhSKXZEu7uZmSeRoD364ostB/5IZA6HmM1aOcAAhm/Ye8Bmw7lIJCwj8UwIh6mq9PTwmfOb33hrL5joitFRtqG5mW4hJrbYsGePR901uVy7u3nOn3zSv/mDbREEBnK4j7S1sX19fTwX7hHAxo3TxyPbowlWrQI+8xlPlEh5xylc+61boZAFlEKyf5Q33lwEojZXivuSEp6vuSYALiriBVZfz8/V1d58bybKwu/GzyVYAM5ry1BeDvzLvwB/93deMSidprr/X//lr7LaGR62HmLuuPPOTuDjH6cA0tnJ9oXDfC3Vf974uHWBmsgQQZgFSqlKAK+ZmmwH8Eef1ezlFk9Ms0n78ovn3jJBEARBoP/P4cPTO6FNTrKLNzhoOYWlcvgGns+Y8Xsmw65+IMCh1eCg0wjt9t1cs4al0tzd+Sef5Hnu7PQOQUyEhemaL+caEB7icYTKMihDAqVIoawkC5VaJp6QS8COHTNft7e38P6KKw0RLIQVhUm/s29f/vXSaeBXv2Kd1j/b/86c4b/8oUNzNrj6YupXaG1FWACWu/u5zO7d+dN87N5NI+kMzufICPDoAe+/896WWowMpjyhkwD+XF2qpbMMB8KbsLezEdGR1JIJFqkU7WDj47SzlpTQ/jgxwc6O3f4Xi/GycNuQa2pYL/hDHwJuu431GewcOMDLZraXTiJBe7ApWL1tG+3BkQgjCPr6WAP54EGvjXPGxOPsjR08OCdv6nic9mC3TfPuuylcuFHKsvV2dPBSPHmSl11b29yOw9gxR0cphlx0EbdvPF5KS/l7jo9701VNe3Ba00P/Yx9j0ZK3vtVKaWZSx/mRTvMEHDpEN57pRItEwgoLGR/nxXLXXc51LryQxaq1Zm+pqoqG7tWrva4hBw542mZs1YODPOfhMJtorwk9MAC0/zFPDduzZ/mjxWK8XtwuKJs3zyzCwp5aq7GRxbZdokVt73G8+Gd/g6KSAAWLdHpuHv+trc7p+nrGQs8nWayJsmho4IV39dXO5UawcLtjhULW8dspLrYS4PrdBH19TMB7223+dSpOnQK+973cgkU2ywf2xIQ3siQeBz74QUZq7N7Na72igsdlFNrFxhQGN+5v0aj1UBaEmfM6AKGpzz/Q2vdBvN72uctnuR17DrwNs22MUmp9vheAxmk3IgiCIKwIxsY4Furv5xAgH8ZIGg6vnKQPC4EpuA2wu11Tw+5vOk2/HcOePc7vXX45x6juuiCHD7ML6h5K1NZy28mk5YN0LkVYIBZDqDSNciRQqlIoL82e1xfU5Zf7z7/2Wm+6qIEBEQunQwQLYUURi9EGNtNn5O7dU/ac1lbK3V1dfGoUMte2yUEF8N/H/BOd64JFOs3iC3bq650e0VqzwtAMcvX87CdZTKa9j6RUJohfP16N/q5Jr30pGsVAXxbdY5XojlZjPBPCoZZyJEbiSxLbahzg+/t5ar7wBSucdGzMeVmZcgZuh+1LL3U6arszxBw5wvfZpjP78peZwv7jH2f0EUCD/MUXW7VxT5/mT7Vvnze35ozo7ORBRSJzys8UjzNKwo3WwL/9m38WFyNarF7N3be08FhaW3l/HzgwO++F8XE+AiYnacN3O82vWmVF0MxE10wkpgYDRkR74AHreTAyAvz0p9aKuQSLwUE+Kzo6+PnEidyixeSklfuuvZ3PnK9+1WmcDQaZaysYtBKyNjVZMchPe5pzm8ePe6I7olHn4+vUKf7kJ09akS7HjwOTJ/LkO4tG+aN2d3sLbodCvDCni7AITdkP7cb5piZGWrgKazf0H8blP/kYkuEoz3139+zDcdyjjNWred5mW7/CTlGRVcuiqIiFt+0YFdEtsNTVWamf3JjIk0zG2RM2CVObmvg7v/e9wKte5b3Qd+3ideon/hq1bnzce5P88IfOe39khDdhNssbpq+vAGFcs8SokOZYYjErnZUgzJzp0kEBgF25nC43pf3mqpxDezqnee2dwzYFQRCEcxDTNevsZNcrV8IIkwV2ZMRKFwyI0dSNKd9nguFNWbZkkt1Ie1f3qaec333GM/judjo8fZq/k3vsb4zYySSHPcXF/qXhli3xOEJFSZQEMygPJlFekjm3bVzz5GUvc/r+XXwxfek+/WmviNXff16fqhkhgoWwoohGvV4F5cVpXH5BBOsaJlFa6jSUnDoFTBxppyGwu5sW5UikcPmtMxnLs7O8nF7A5h8olTq3n1Cjo5bnr+Gii7xFY3/7W1qRpzFS/eynuZf//sk6pCMJjw08NhTFydZihGOlGIxU4MxYPcbHsjh0JoTJsZj/xhYQ4yz+wx8ys8q991IgCAR4SUUilqO9yQ7kttO6Ozem02MwGXRmk9EklWI9F6Ph3HuvVRzM1P3dto2d1pMn+ed56pSVa3PGO+nr40kYG+P1MUvRKB5nPQQ/hoaAO+7IfRmtX88OXyDAzvqRIxQvOzv5TNi1a2aBN+Pjlp7gDscdGmIbUym+pqtjMTTE/e7bB6RGpiz87qpsO3fywojFcgsWvb3oOZvCkaMKkZPdPM8tLd71Mhn+aGNjjBgLBllk4tQp53pvfjN/8GyW26qp4fPp0kv5fsMNTiP05CTbbTuB9uxW27fT1t7VZaUYO3mSx59XsAB4EqNR7w9vIiSmi7CoqOCP7o4maG4GPvtZZOpWO1av6jqOmu99ng2Nx2dfjMRdcMa0cz6CBcAL2NSyWL/eWS1vcpLhK+7ohPp6vvudI7/C26YAe1MT271uHV/Pex7wf/6P8/u9vXz5CY8m4XE87jzunh4WoHHzy19SWDK1MRa7fsT4OEeeqRQVRxPfL3UshBkyFbHwvKnJXVrrUzlWtSus05l/7P+u55IvpSAIgrDMMAkdzHjzzBn/MdPwMIcjQ0Ps6mcyfJ3LJon4LP0Us9npfWdMFzESsTKtrlpFv6KREZ7HdNoKardz7bV8v+IK5/zTLRo4eRLtJ53Rz1u2WOKRCZA+p4jFUFc8gaJAFiXBLNaEYuf2BTVPKiuBr3wFeN/7WDrwy18Gnv50Llu71rluf7+IhdMhgoWwoohGaSizc2Pmf/C1F/0EP/jwYfzzrb2OZS2nshg92UexYnCQRqJIhK9CpIuIRKz6FebfJxCgITGVOrdrWLS1We7+hiuvBF7zGq+x7ac/zWsYjESAPz2a+3HU1luOo4czjnQzmbTG0SNAdDyDrokaVFdpBEuLcKanHGPRIA7tTS5qxg+TJeX4caenRX8/O40TE1zHGL2zWdrv3JEMbuf2iy7yGs737JmdYNHR4XUm/s53nNOVlcy5WFtLw/OZM7Rt7t07w2CJnh7+1kb0y2ZnnSc+V4SFYedOpnLLRU0NM91ccgkzAyUSdIg/dowG9t7e3N8FLKfraJS2Y7vj+t13A69/PXDrrSwHEYlMb+tub7cCrLpaUzw49w9hjNGmyIm7xx2NYjI8gZaTGfRFV+Gp4yWIne6xKoIbslmKCqOjPGiteQDfdzkCb9oE/NVf8fPQEPff1ETjd3U1P69fz5edJ590hJSY+uElJVaAwKZN/MlPneK109kJNESnESxaWhgp4FbuTB2N6SIsAgHeIOXl7Bzbz9+6dej7+JeQrHKKFiW97azT0N09++Lb7ggLk85pOmFlOuxRFqtWeaMsdu3yRliYCJJcERbmxzHRGXaxYts2jpCamijurF9vjcgMO3f6p4UKh63r2J4K67//23LVs9PayoeICctZ7OLbY2OW2GZGCia6Jx9as635assI5wt/BWvM9L0869k7dT43pgP7Q2MuiRg3TPO6Zg7bFARBEM5BjGABcIgQjfpn4ezuthzoTDfyXE760NXFiPq9e2d2DGNjwBNP8JXPDGNq9aVSVjC3UhxrmiynQ0Mcwrm388xn8t3tdDg0rDDWn0BXtzOqefNmy2hdWnqO1a8AgFgMxZkErt/Uixs296KmNH7uXlAFIBCgP9eznsUhlh1XxmL09YlgMR0iWAgrhkSCfx6tZ5yS+dXZ3bj8J59AUesp7Kh1Gl/iiQCO7E/RKLNuHa210SgNFYUoymms1KbgtqGkxPoXXOz0GD7MKXvS737n/GJREXOvX3YZcN11znXvvddrkLRxzz1AMmn9eQeRxmo4f6tf/bbIUTf19JEExiMBnO0pQUl5EBsbEti6PgWtgdbOEoz2JXDkyByObWCA3teuL2anScdoOi+/+Y132dGj/P7EhBWFAVBcs//8xcXeQk1FRV4Pjf37rbREM8FtYwVof3aHsAaDTK90wQW8n06e5Ok4fnwam10mwx6wSYYaj9PtZJbe60NDFFfsuENiv/xl7zpuTDmAiy5iqYbJyZkVyjZ1GWIx5+06NgZ84xvW9O9+x8s5Gs3dyYjFrN97aAjo6Ugj8/gu/5V37rSeOzFXZFBfH+KRDHQkhrPpdZioXofDx4JItfeyEaZSuglpaGtjozZtYvyp3YAcCDAVVEmJVU+gro69Y1PLoKnJ/6Lbu5f3xeTkn5+1JnTZUFtLO3sgwA7Y2rXAmjGnYBGrcIoHOHqUF537Amtu5sjAzxjvxh5N4Bo1jNdtwePv+jYmKl3p3Ht7WSjm1KnZFTtxP8c2bJh/dIXBiB9r1/LCtbN7t3fkaYzvuQQLgD/Q4KBXrDAK07Zt3M7Gjd7aJfv2edNCJRKcHh/nCK6oiPOffNJbddDOH/7AB8zwMB8qi1Vhzq5CFhVZ7nHmGPI9RNva6DZnCgcJ5zNvnXpPArg7z3p2RXq6NE8h2+dZq2Ja6658LwCLrAwKgiAIS0E6za5sLMauVizGIVhbm9P/0swfGmLX0QTqptPnbjent5fjtLExBpnn615GIqwjYYK7Oztzr2v3dbGPCWtrObQygtDjjzu/19DA7jZAk4i7i37iTBH6wk4npy1brPO/IBEWsRhw8838wV/72sI64hjH3GQSRWVFKC3KWDau85iKCg7BslnnMMotWEiExfSIYCGsGIy9r+Os09B8FfajYrQHT7v3X1A/chq1lc6nwsGjxdBNzXQzCIX4j5FOF6bwtj2/TMg2NjWChdZL/pRqawMeeYR5FWdMNOo1Tm3bxnOoFPC2tzmXTUwAd96Z0zh0t2v4/3w8jLfDGQLw2JPliMWslEi9bQl0D5UiFc9gU3MKwQBQEirGBfVjSMXSaDuTxfAwje0z1oTGxuiO39rqqIatNdPIP/GENyOMwQQWPPGEd9mTT/InHx3lLkzBLvc5377d3/bormNx+LBVR3YmuHNlGr79bf9zU1VFY39lJcWBZHKa6IS+Pq705S8DH/0oi3d0dMz6Hjp40NmeYJCbs5NIAJ/6lL8jtx+hEDt+JnAq3/eM/TIWc96uO3c6O/xa034M5D7Evj4rzf/QQBap2CTSe3wqhwO8eA4c4IbtnciplE2JQdrA4kWr0DqxFuOVTTh8SCPTMxW+c+gQd2hUlC1bmIrNnX7qda9j+AlAI3Y2y0iGhgbLwF1czPvYndqtpYWCVGvrnzteiYQ3AKKsjKLbZZdRcyjvcd4wHU+/yfmFzk6eJLfX/bp17LW7ayv4UVlpNcQlPkQTQYSrtuCnr/4Bxitdbi69vcD7388bdCbEYl7RYNOm+RXctlNcbEVZuEOtTO0SO2vX0vgeDHq3VVbG+aaOhfmdjVhhWL+eP9iaNUwFZufoUf4/2aMsTC2TSMQ67nQa+OIX8x/bzp38LU3S5FwFvf3Qeu7CfixmPSzNTR0K8T7J9xA1xeiHhngO5lTUR1gJKKWuBjD14MQvtdb5lHh7oe31Odci9kLbecwmgiAIgpAbE/Qai9GgXlXFLkw8zggEQ3c3x0FjY+z2FSpLdTrNsWm+EnsLgakzMTbGofv4OHI6K8bjHC6Nj3Pd4WF26f2yMZgkAX4R96EQp8NhjgH3uqpFXXaZ9bmykk6Adv54sAaZrDfCIplkt72oaAEiLL73PRawDIfpROpOszAfEgn+6IkEG67UuR2yUyBqazlMqqykHcacjkaX/1w0WtjSuSsRESyEFYMxZkcTTuPNVaCRsKbnGHb89gtYt8qZBuLEeDNiVVNPD2O0m0m6iJlg4jOVclr2iostoWIJH+jZLO2FkQg7NDN2iB8eZl4iO5deyj+qLVtoFL36aufyH/3I10gViwEPP+ycdzN+5hEsJmJBPPywVV8h3JdCeERhfdUo1vXsxbqdP0ZJbBTlpRoX1I0gNp5Gezudef3S/fvS1cU/XON+MsXQEDsug4M8X35ZZIaHgYce8rd/jY6yj2AM4r293I1NEwHgrV9hcEddhMNs6kyDgPwiLAB23A4c8F8WDNLp2tgYBwZyRKtozcY88ADwpz/xem5rY2fIVICbAab8gp0LLmB6/ZtcNu6WFuBb35rRZgHwtjad+Xwayvi4VWPE3ln805+86x46lLsEgim8PjLCz5OxSUR7J1DSnkcV3LmT586eMmp4GJicRHJwHJnySmRQhFRaoTXaiNHyRhw7mIIeGLQKSI+Nsdc7MQF885vO7Tc3A+94Bz+n0/xBV6+mKGCqvdnXveoqpyE8m6Wy2duLWN/4n2s558rYFAwCKpNGaT8jErJQ+DVeigfWvgvJoKsn7lefYePGmadZqqxkWEdpqUOw0BqIJQJIpAJIrt6A+17/A0RqXDbE7m7gDW/IfZPYcd+wAIWVQkVYABQQioqoGBrXN4P7XmpszB2BohTPS0UF12ts9IoVhqYmVoR7/vOd82MxPiDsz+3hYSsUyRz3ffd5I0/+5m+cbUunqfJNTvJG6+zMH2mhNR90J04Ajz1GF7aentmPhP1UyFDIKryd63/eRIsND/Nzb+8cQxGFFYC92Ha+dFAAYC/Gc1HOtbzL8yRDFARBEITcmEzWySS7fc3N7KMPD9N/LJVid6uvj/OUYoC1yVI9X/tyVxe7dH19M0wjXCDMODgSsdIAh8NeZ8VUyhIrzpyx0jKn0/7+MyazcSTidGAz1Nay+5jJeH3DrrzS+qyUdwz/yHFnEYM1a9hdTyatYU/BIyzcYSA7dxZu28bwYa8Yfq7XaS0A69bxHtuyhcO61lZebyYNm508SUgEiGAhrCD86rbWYwgbYeWPaTz1CG6YdD6kTw7UYSI+ldbCyOiRCP/V5mOgsMdnlpc7PYWLiy1X70Tiz3aTxQ62MBmrTD7LlpYZHvLjj/Of3o6pMNXcTOOXyZNv6OsDfvADz6Z+8QunU3QAGbwG9+JinMANcP7BPvTb7J+9KTrPplFXGsUVJ3+Gy+/8MC6859O48kvvRBAZhFQcm1dHMBZOo7OTdq5pjfuJBJWJwUErMmaqYV1dPD/d3eyInT7NDp8hEuH/sl86KMPx4+zYmE5QNOr9g3I7VZttp9NeJ+7du73lEHKRKyoEyB1lAbATW1PjtN15MEU57r/fOX///llFKvnVr7j4Yr6/5z10ZLdz99305pkWrVEZ7cNk9wCS8ey0gkU0ylvVdBbHxngoblpaeOgjI+CNa7soRkd5PYTDtOlWlyZQfng3FPIYW0+coNBjj7Do6wOiUSSjaaRCNQCA9WuSSKUDOJtsxlBxI04djPF7Q0NWeqLPftabUPVDH7LUBSMOmLhld8+4uppihruX/eijQDSK6PEOJOI8lnwlJkr7OxDIpJFCMV6F+/Fy/Bofue8GvKXEVZj5d7/zfnm2ggXgKbydSAWQ1QqJVABlJVkk65rx6zd9H7E1G53f7+0F3vnO6ffjFjWqqrjPQkVYAPxvCIX4MjdALpqa8qfMqqnhb9nYyOIufmKFYe1aqoPuG81Uq4/FrEI9Y2PsfZeX82L/7ned37nwQhZ8eeELnfN/8xu2d3CQ1+uxY/wvOXSID+lkkttuaWGo2qFDFIm6uiylev/+2aVrHB/nvZDNWipkKGSlaswlWPT3c3k8bkVZiAvUeYdSqhjAX05NDgL49TRfaQNgwnFunGbd5069dwM4O5f2CYIgCIK9fkV5Ofvm9fVWfvz2dnZr0mkOVWprLZ8kY5KYjw1iZMTKvrmYXaWxMUY4fOc7HBdHIuw22p0V02l2J00URnExjcipFLuIfhkEjBgRj/sLFjU1XD466s2WcI2repTbGTE26cx1bDLyLqhg4c6l7OeANVficWfFcJNFJJ0uTD3YcxSl+NtXVdEBM53maS8q8vqj5UtNJohgIawgolFvxoyrsB/uhCKvGnK6Zrd0l2NkwuZJbNJFGGl9DgwOAkf3RDEWCXoT4gNWDvkpd4iu/8fedYe3VZ3vV8uWLNmyJe8VZyeQkBBCgDDLpuwNpexNaSl7tOzxY7QJI+xRyt4hhL0SRoDs5SQ43tuWZe29zu+PT1dX517JcWKnpXDf5/GT6Orq3HXuOd/5xvt2USLrqlUZxvZwePj8N9uJ9Am5q4sue5satPE48OWX/LbiYuIzKiggS8BqBY4+Wu5we+cdWXOvv85/PhDfoDSpXyGtsti4SYW2Nhrw9SyEGqMdtWsWpr432DtR1vg9EA6jwBhHlTWcyqDI6GxPR3e3qLsgRHIcDni9dJ8GBmjy6ekhA2XzZrF7DA6Sz3jr1uzNp9NCAWTgSJMPMlVYDAxQ95EGM1atGnmFBUCZIdJy1nRYLHSefn8WrdzOTuJykho/gQBFIIZZthMMyt9fofvo9cDf/y7Xs/jnP4d+NRIJ4PUXArjlPhMWfazFYONg1tOJRumUhfiioBv//feZg3hCwngoBITW/UzGYH09EAigv5/umVCaXWIKoLpJUqYxaxYvcswYKYr7fPR/IeIxOIiQyoColsaQgrw4xlaE4Atp0JmoRq+qEu0dKkrlsFrpYUof6HHHATNn0v8Fx2tpKXVIqYNaQGWlXItm5Upg9Wr4B4MI99ALNVRMwdDTDAbgYjyLD3Fsavu7waPRmc6WIq3WMptpLNmW4LYArZb2FQIWyQhcIEQPUQhY5OgScBsrse5PzyFRVcO3sXTptgcJKbdaWRm9nIKOw2ghP5/aFei7MiE3l1ZMQz0AQZdi+nQKZm0LxcXyldaGDSKFk8slivEUFNCA+PzzPDkrAPz5z7QSPuEEfrvdTuNFMEiD5ebN9N60t5MGy48/0kTY2koTUkMDDQo2G+23dSutuNesoe3DWV1nIiEWEgiyVVIKSorCYBEOixFrBb81HAVAyId7jTE2pPgKY4wBEKL3U1Qq1d6Z9ktuFyosFiV/p0CBAgUKFGw3vF5ac6jVoulcXk7mcF8frV07OsjkiUbJ3BMgBCwSiR1zN8RitB4V1ssOx8gLUofr566vB266idbEb7xBrEcuF1LJiq2tlNzmcolr4XHjaJmh15PZn24mCnC7MzN6CzAY6G/TJvlaXsquKpUElEIocg+FyKTPzc3M9DoiSD3io+khDwR4xfD0pNzfeJWFVktLsPx86neCbopUx0IJWAwNJWCh4H8HHo8oKCuBwPjQ0sB/Nwtr0Lr/OWAqsavvAZ6vPBTRoHGFU6wNFOgi4vEdooXq7qYJrKc1hJ/b9GJ9psNBHtY77xQj3ckazN5emugjEUnhgttNTpzly0dHBFwClwscH73dTn7nIecXp1Mk8BcwdSqNxukh4/HjgbPO4vdbu5bLuA+FiEYpHadADGqcjjdhgMixlEiosGgREA3HMabYB2vjCujCvJVRuuWblOVlzqHfer3b8JvH4yJfeSIhlkAMDqKzU0z8ra4mI6e9ndrcuFH0/y5cyDeployugsC22w3A58Pmjbw1VllJwYF0RCK0fyY/Yn094OnyIN7Svk0LUxpLkJ7bv/6VvcrCZBK5Oh0OSd9wu6lfSi9ewE8/DbvCoq1NXpabHu+aMEGeBN/eTgZqNrz4IvDMK0Zsbjdh6XoLPvxIxdlV6RCqVfx+3jhdujR7+6tWASwYhLMzyakWDiPe3JZKIF+2jJiZmhriqOuUlN8eeCBw5JH8tqVLaewJhWiFkaxQCecVIRJXA2DQaRlMhjhqS8NweHXoVVeh1TwT/ZpK6sdPPcW3WVwMXHIJ/Z8xGqDUaqpJFXQiMqGsDDj2WD5KlEiAvf8+Av0+BLvsyFFHhzSqDT3NuB134iWcK/tuCdLoh6QrG4FYd7gVFoAovJ1IpB6wP6ShpJ+4Grk5Ceg0DBFvGBFfBJ5z/iSPgElLpqWQlipVVo4uHZSA/HxaSU2YkL2CwmKh8x+qwkLgdZOm8mSDSkX9Mh1tbTQ22mw0AEQi1D8LCih97aOP+P0POUQk8J00SR60/uwzir6OH09tuN10X+vr6YVuaqJARm8vXVtdHe0/YQI9261bybLv6qJA11BBBEGjwu/no5BqNb3kAoeAdMKz2+m3n35K76TLRYNTplWtgl870umgXhrmbx4GIEzwj6lUKi5XMvlZEH2JJfdXoECBAgUKthvRaGZCB52OcpPsdjJzwmH6v6DtJ2CkLNVuNy0vhIBFPL4dFNMZsGULJYttK0eEMeCTT/glxLJl5KIQmDzb21Pye4hGyWksmM1WqxjAkVZZCOaeRkPmeChErpv0tXJhoZwZoKJCXsycThGVCWPHisUIO0VwmzG5DpvNNnrJsMK6FaBOkKQzBvCbD1gA1H+mTaOlXW0tvRtmM7+PoHupIDOUgIWC/w1Eo8D69eQ8kfI+gcbJWAxobeG9rjPVG9BxzBVo+N2lqW3l6EcVp4sIuJ94lQRpjzoKePJJGjUCge0OWHR2kg/HZgM6mmMIemPwh5PZv7feCnz4ITlA7r2XTjgSQcAVQSBARkQoJAlY9PfTRpdLFEAdJQjOc59PLE/r6xM5IDPC66Usa+kzEPju0z3uKhVw7rk8FVYoxFVnfPopr/mgQgIn4b3U5wJ4cSre5g61ahUwoTIIfQ5DyRa5uIC5YwNy/MRhlBMLIjeXTtvrzU6Xjr4+6mN2u+gI9HgQtrkx0BfHwABdXlGRyFTT2kptbthAc/PXX/NNnnSSvLCmsRGI2V3wbWxBwzpe7CJTdYXdLtIyzZ3LsiFbbQAAIABJREFUf+f3A4G7/4HIgw/TTcliFHg8csNRSlX/888UW8gElYqu2+Wi14KTGujspJPM9uO1a6mDDdVvw2GgqwsrPuZriPP0cdR4NnFlHaecQmwz6Xj55cyZCT/8QN9xp7OZNA4yxVA8ntQrmXpu2eigBGzaBHhb7Yh9uZS4zbZuhb3RibgviDfeoIKijz8GbpxfgfawJJ1ir70oIJAOv58EXXw+um63G0gkEDIUIRpTQ6dlqdepKD+GSmsY/c4cODwaNPcYkPj0M3kVwIUXihGYvj4knG50GyfBE8ujzpwNWi1l+Euy5FVr18KwZQ1Cvjj07qGJahd+V4y7cVvG777Gwdl/WFYm1/3ZFtJXYMm6+EBYg1CEzBxDxAOdrQvRfgcQjyM4YTc55ZVUTEcKaQlVdfXo0kEJyM+n6zebs9NCFRVtO2CxIzj4YFmQCitWUN/s7xcD5yYTsGABv3rT60m7Ih3SKou1a2nVl59P92/XXSmwUVxML59aTf1y2jRaxRUW0jaTiZ5XVRUNRlu20DuydSuvKJkO4VylUUiAXnIh+CBNBujvp8Dfyy8T1d28eWR7RCJKlcVvCCqVqgjAMcmP9YyxIWYDEYyxrQAeSn6cDWCZSqU6XaVSzVapVKcDWJbcDgAPMcaGq7KlQIECBQoUcBCq/TMROpSU0DpSEOD2+fjqCoDMSGF9vCP+ZSGXRZAtEBIgdwSRCFJV6lIWIyl8vsz+irfeEimwBgbIfAuFKE8mfVlhsZCp7XCQOSkEPgIBcgkIpuOWLcCJJ5JL48ILxSBFUZH8HDOt5Sk/LHsRpSC4DdD5jbrgtizbEKKI6WggGKT2V64Ezj4buPZa4LXX6DslYAGA8rOmTqU+U14uD0oJ1G0KMkMJWCj430B3t+gscLl40QOInInuIJ+RO7nSB5aTi/q9L8LKiWemtkurLFZjD/pPLEaeZ4G3W0gbGAY6OihRVCi99DkiiPtDsAeTs126k39ggH4QjWKwP5Zi2fB4JAELocazrY3+X18/asKfgkCXz0e+oIoK2t7bS5O8LFbjcBD1z9Kl/DnodMAee5DFk05xA5BDSsi2FZBWUvHqq/xX++M7lIMX370AL3Cfu7uB9uYoVJEwipvkjnIVYyhp+jFlNeXniyw7GZP9BdFol4sslJISmlk8HnTbdIi5fHA4aLMgTjZuHBk5ra10n779Vp54e/zxct3xtWsZtJ5BOGxxtPTwzlgp5VMiQYEQi4V8x+Xl4jMSUL8mAsPTDwO33UaOQKluAeT+a5UKOOIIai8d6VUWHg9ltzzxBPnqOjtFrs5U/ECIsn3wQdZZlm3dSs8hW5UFYxSIbGjA6h/4c59c7oHaOSgGk0D3/tpr+QqRaJQkG9Jf0+5u4L775Iez9ycQ7HdnzPwR9CsA0be5bBnf1aXMPz4f4H/yJdQ89TeKTtx1F2xtfvRv7OcqhyJxDV7EeeLvKibAY6okx+wee/CNvv8+9cdgEBgcRCLPBF3rz4gs+gglqz+BKiZmxJQWRWE2xtDvzEHMHwZ7gadQw7hxwGGH0f9dLqC/H42qSWj0lGF9bFfEVJIKAymqqqhKSmJZjf3xNYTtXujD7qxCKsuXA39bdWLWpr9UHZ5d0UPo6NtbYaHT0UNKzg/+oBqhiApwu5Ez2IOcHIAVlyBaWYtwUbm8bjtb4E2AtMKiomLnVFgYjdTJ8/KyByysVnohtuceDQcVFfLBaM0akZ/W46F7vWIFRWvTcdZZopqcy0WR0OnT5alEUr2bvDw67sSJ1Gctlsz18CoVtT9lCrXZ1UWDZHt75tSk9CikNGBhNNJ3QvmcgEiEIpHviYFzeL1UGeJwiCTQCn4LOB2A8IINt7pCwN+AlPGyO4A3AKxM/ivkWz4P4O8jPEcFChQoUCCB3U65XL8FqhWvl0ygSETuCNVoyLxyOmltpNVSHkg6BAYfgY12eyEwKQNkprndZJrtCNGh0E5fn8jGmQ1ut3yNK+CJJ6gtQaNz3Dh5IEBIRBwc5MW3BddPIECm4mOPicvr1lbgyiuBZ58VzzMdAvtuOvLygAl12dPnx4wR73tOzk6osMj2EjQ0jLxtQek9FKKkXME+XraM/FxKwCKFkhIKmpWXy6twbDblVg0FJWCh4JePeJxmnCSfO+Jx2QwRCMj5781wwTLRikRFFWyBfGw69C+wTaA09dlYxe2bClgI+PlnmuEEYvttoK2Novx9ffRXXBgFi8XhcSVgjxdmpsxJlswNDiTQ309BDrdb1AsAY6LYqaDUk0ndaQchZMwHAuR/0mrJqHE46LCNjWnGRn8/8R85HPIs5AkTKF1DSFWQ4ogj+M/LlgHxeEaR6nQ6KIHG5AB8i/Hgr/me+SZcN68Sh4Q/xn74DnOxDIfjM3yQ5MkvbU4LWJhYKusjY4mqw0EOzoEBkVamoADxYAQ9vSoMdgbAGM+qIgh2CRkg77/PN7n77jQZSSUAVq8CTDlhtDRG4fTyzmKpj1B4PlYrTXKFhfKy0i9xaPI/XwJvv01BC0l/lWafWK30uH7/e357YyOxlV10ESVF33orNbl4MXDLLdQ/HQ5q3uMBGUCRiJzTKw2qWAyR5Wuy1wbbbNRgUxM2bObvx1RLP604UjxahMmTqXolHWvXkj8RoEd5222ZmVvsUTP6W/yy+AljYsBCqxWT1r+RFPDss4/INSpgQ33aNDowgMLXn8L7n+plhsfHEG+4bdJ+6LQl/WDSDPSGBnIGh0LApk2IL3gCNzxWi/M2Xo8zv78SH/xfPReYKi2KIBzVoOSL16BxSFKaLr2ULPJgEOjoQMhoRZ+qAgP6asSNBdsWbTeZSPtAcsOt7WtQ3LYS+kSAnMaSIOrWrcAddwBxZOeL6maVaMb4zF8KAYvtqbBIF95O3p9AUIVQtwM5QTc0JVbk1JQDBQWIVI5FeOpMuSj05s3Za3LdbvnYW1ubmdx2pBAoi/LyqPogE4QBabQrLAoKgDlz+G0NDfSCCLpORiNpV6SjvBw47TT6P2NilLO/Xz4HfP65LOlgu6DT0b0vKhKt/EyVD5mikAKE/iLVsbDZgBdekPeDFSsokBGLScrMFPyKcXby3ziAV4faUQrGWIIxdiGAo0GaFj0AIsl/FwH4PWPsIsbY6GSgKFCgQIECADSNb9pEy+yWll8/1YqQ2whkzs63WMi08/mIyVS6VBeSsZIs1dsFQa/P66VjC0yfgqD19sLjofNwUDH0kNJyLld27ehIBHj0UTITp0yR51OCMYAxWCy0r9crsia53bSMiMcz66MmElRAcMkl8goLaQ4aQGb6pDGZb2xlJS1bfD7aT6PZCRUW2aqQG0ehuFPoeL298gfe0KB44SWoqaElrrTAv79fqbAYCkrAQsEvH7299BbbbDTBOJ30ZqeF7v1+YMsmft23O9bCM3Y3OFUWRMzFKBtjQP+ZVwHHHovdx/OD6hrMQjz9dWhro0YlztJMaG2l3Xt6KFhRUQFUFwVgyI0TN6LDC5aJG31gANFAFEtXGnHuOQwPPECZ4pFIcoIWhL+DQVFMtquLDiTlItwBuFxyQSmrlQ7V1ZU2eXd2UoXI4CBlGEtn7unT6UfZeNKFDG8BW7YAHR34+GN55sTJeJf+U1kJ7LsvAEAFufh2z4AOq/pr8B0OwDLshx8xF1/gcByPD3AXbkVBbwNyB8iRasqhGSCrjoWgNh4IiNnBJhP6PHmIeoKwd4UQDgPPPEMVIYKz22Ag5/XWrWQYp0Ng+pHqTvgDKgzaEnB6eOe80Sh3hNvtZGDp9ZToXlYG7C2R7/we+yGIpFP3scco43ndOu7GSrNPhKTwXXeV6/B+8w09YmlWTDxOJbaCEdrXkazZ/f57WfAwrOMdg/Yv1yPQl8FqZYwyo91uRL0hbLHx/WfqLiqx/0vewQsuIF7WdDz5JD2bf/5zaJHxxiYg6AhyxShCTFDIpgHI7lrNF2Jhzz3J8E3HYvC0TmWrPsQXy+VZ9xswA12oAgBsrfodBtw5CIbVFAUR+p2Af/2LVOQeeQTLNhbgGxwEAEhAg3mO8/HQvWGEfFRpYdQnYInbMP47SXXF7NnkeI7FqBPk5qIjZwIiJgt6I8UpmZZtorKSMucl1Ef7bnkOBneSQy5tRdHXB9x8s7zY50bcj2Ij76TOSgtVU0NWu1aLWIzOU5AREIQDm5slfmO9XiSbDQYR8sUQ7+hGyBuBvrwQKCqCrrYCqKlBNM+MUFgld8y73fKqgdTJfi0vt5k9Wy4IM1rIz6dBpqhIPjgA4gsw2gELjQY44AB+m91OA53PR/dg3Tr5S3bBBeK5+P00aNTWUnv77MOvkP3+IQOdw0ZZmSj8JARIBAhRyECAj0KmX6deT+cilBsCFFD/6iv5sRij6K3brdBC/UbAGNuXMaZijGkZYztkdDHGPmaMncAYq2KM5Sb/PYEx9slon68CBQoU/NYxMCAuWQXNgQzF578qeL20VFKrMxfdqlSUZDdjhlwrERDNo2h0+52mwrra6yWz1WwWKZV2hBYqPfgiyCFkw5YtQ5+v3Q783/9lqPTwemnhvnkzTLlR6PVJlo6kboXHQ2ahSkVVOtnQnkFCUlq4LWDXuswLrro6kcFAYFSQVsCMGNkCFkMtlocLIfkoE39Xe7vihc+A0lI5Y4bDsVOkan81UAIWCn7ZEDj2nE6aFfR6eqsFXYck/H6gdSOfLjwLa+CePAf9dg3MYwphKDZhzKxi4LDDsOeVvOc3ACMakBbu9PnE7O8hAhYtLTQed3fT7oJjGcEgzPoIvH41Ktd+DFWmukibDQ6XGo+8V41ojJw5W7aQH8fhAE2ojNFkUFRETjyHg6yxxsYdEgQXkK5fodGI5YcqFV1DMAg4Bhlav+9GtKGFPIONjXSh0rTsOXNEoYNM2G8/3oJKJOB+54tUOaWAffE9KpFUvZoyhXQxkjgHL0GF4SUi3o67cA5egrn+ewCAJhqC0SgaQVyw3++nvjUwQBZbkt6FqTXoipTCZY+ir1+Fe+5meOcdElA+6yzgpZfoHhUUyIMVhYV0yQMDdLvGjOG/b/poKyJr6rltu+7K+z0DATF+YjKRAVhcTLdEpRL7Uhh6/ICkuEU0SiUSfX3kUEw+J6k9UlFBRqVaDZx66rBuKQDq54KQmW1DHxLhKAk0pMFjrsaGsXzFgLFhNdZu0MA7IFkxDAzQRfb1Yb2zBoEIz7c0dUKULDe/X6x3TsJgAK66im/O4wH+8pfMvsZ09Dd6wZwursrC4xHLf4XMlmQhUAo6Hb0bUoaeTZiGFoxNfX4PJ2EwkDk95hMchUhuPhoLZiMaA1VZaDRyLYvly4kqC0A9psna+dh3IK67pwj9yfLl/dY8ipxImjGsUlF1BWMUTU0kEK4ah76IBQOGGiQSNIQOK2BRWkodUNJZquwbUNn+Az2bgQGAMQSDFKzgaO0A/AGv4j7cgt0n89U/GQMWKhWVJ+n1cDhIi2TlSurSmzbR39tvU7n30qVpixmVSqyQikQQ2NRK122phN6SB4wbB21FCdTqtCyyujo5mW82lfU07R0ANEZJA02jCUF4W62mVaYUQsBitCmhAArESFdMq1bRnKtWy7n8xo4l7QsBbje9MPn5dH/Vanm0ddGi4fEFLF1KUchvv5Xvr9fTedpstChKD+QLUchM+hUCjEYxMcHjoUH90Uezn9f69RTFDASy09wpUKBAgQIFCv7jsNupUFYIVgjrvZEUdP7SEY2SPS8V3JZCpcr+nSBbtiMVFk4nHTseJ5NPYEl1u3c8YCE8L4+H/jL5vEMhuaxcXp6craChAXjwwTSzzu2mhbGQgedyceLb7e10PX4/3c9vvx3+uVdWyql+BMwYk9lmrKtjcDjI3WW1kg8pEyPqiJAtYLEtkZDhQIhOZUqk7epSKiwyIC9PnnQJUN9TkBlKwELBLxuC6HR/PznNyspocAyFUpndgqOxuY0f4acbW2HTj0EkApSXq2CZUYOCklygthZl6EeZmXeg/qQ/iD92e7ucLiINPh+N9T095K+rqkrzXwUCMGt9UEdDqFjzUeZr6+vDyqZCtPXxZIVLltAhY04vXWciQaObxUIHSAYNIus2I+TesYnA7xd9OdIySZMJKDV4MebRa7HLrSchcdYfgfPOo4zvefP4nUtLiRLKbJYT/AswGGTcSIOLl8mMAI4OaupUjgiyBl24GvOHfX2v4GxctPIyuD0AgkFOx4KrsujqShksKClJWXMOjxbBHDP6bGq8/lUpvD7RygsEKAH+rLOI6UtKG3TkkXRvu7vJHyYt+/vWNhmr/fxGqUiX3S7GTyoraZtWC9SZ7JhawluAKVoogKqR/vEPup516wCPRxawqKwUOTKnTSNez0zIyZHrCX/yCdDVHELM5oB9TTsdIw3rxhwP/1S+HrbA1gxfnwdv/DuEjRuTG4XqiqST8Ec7r6RdUhiB1RyjGyhUi0jew7lzgQMP5M9PStNZADfmYDm3zdEdQMjmkQUshFdN8G1Kn+ucOeR3nT0bMBl5h6ZQZcEA/BPXIhs+wVFwTt4bTKvDgCsHfY4cRKIq4Oijs1qoTZiQcfum8ARc+cAYNK5woG7VO9x3sUOOoPeyu5te8ro6dAasiFSPxc8NajQ1iYnl24RaTRb4qaeCSVKzxq94A3C76B3y+fDww/Ly7IOwBC/gAkCjwYzZfGXR1zhErmNhsdB4l5ub6iI//UR8sVdRgRyuu46GonPPpYBGCkLAAoCf5SFeWY2Izgj95DGpVZROJwoDQpWhymLFisz3Ydky/vPMmTtHcFuAILxtMJD4uRTl5dRnRn1lAwo+S8vDGhooErVhg7zC4IIL+PNwu8VVa3Exve/Sqo2WFvk9leLTTykI++GHwO23iyJ+6SgvF8sSOzrEKGOmKKQURqNY9+/xAB99JOlQGfD227SaVqosFChQoECBgl8EBgcpocXhIFNAEJIWEnR+rRByCIcydbYFrZbMzR0NWHi9dK9//JESjPR6MgODwWEmRiURColi14CoJSFNggLIZJMyCNTUAGeeKc9eX7IEePhhIGZ30Y+8XlovJ0tK0sW3bTb6jd8vSpql49JLSW4tE3bZJYtJzhjGFDhhNsgjL3W1CQwOkhtFpxPX/KOKbAGLnp6RBxSETNDe3szHVQIWMuTm0hJJ+r5mozdToAQsFPySIfBgC8IOQqavRkMzpN0OxOMIBum/AwHe8z6+Loo+vwlmM/l8xkzMIe+x2QxYLNit3Mbt/5NRwmfe00MzViiUccAVfN02G/lMUsGKJNeKgQUxredz6MKZvYJsYAAfrauSbV+/nhyJrt6gmGYglEBUVgImEwL1zdj8ziasen4dGrYkZCWJ24LLlTpNOa8jgIPfuASzv5sPS+MK5Nq6slt7U6dSA9nooARIOMxXrdPKjJgUHRRAs77ZTE7XJB7EDXh80sM4+4h+3DPmGTyOK/AkLsPTuATXmp+FWsVXYPwY2xNXzhuPjpYYTCbySQWDaQGLaJQCYYOD5JRNc8Z2DujhV5mwaON4NPVktgCdTkrGlZbwHX009Y3cXEo8lmbkb8auMs2U9IyQeJzatljIeCkrE8+3zNeM3xl4BzwXsADIYhT4m37+GS0tvEu4ooJeIYHP9J576PjFxeSMv/BCuq7Fi8lXmA6fD/jgvRh87jhiH33OlSDENDloH/s7YMYMxHWi9kAzxuHKx6bgkusLMWMG8NRToHvu99P9z8vDilY+U31KbQCRmAp9USv8QVVGWiiAhM+GkhB4GWfjKPDMG32uPHidMTi7xWz/dKp7g4FsWSkdlFDmW1QEHFC6hftuMY5FQqPD99gPqyBx9KbhCxyG/ikHwGqOYtCjQzyhwqBHRw9b6tBNolGbRXQZgD1ehD+9vh9eTJyT2hbX5qLzmMvJ8rbbgepqRAxm9ORPxpvv5eDee4HHHyddEqGIa5tIjjvRk8/kNpsGWlFavwQIh/Hp+yF8/jn/s11Rj4U4EbmIIGSpxO6z+O8HUIJNkETrSkoAnQ7uiB5PPAFcdhlVbbz2GvnK04ficBhYsCCtGwocahYLAhXjETYUAjU10BeJQWEhYCFoSMsCFvX18hWW2y1ftey2284R3BaQLrxdUcHTQtXW0n0abTooAWYzDQbpaGqi7VLB7KlTU/R9AOhdjURoX6ORztFioT5UJZnvnngie7m4y0Xfp+O55+SRxGxVFpmikFII230+Ot699/LfFxYCZ5/Nb2toICo8u11ZiClQoECBAgX/ZTgcZKI5nZQLVVREZhJAZsGvPWCRTXB7eyAIb2+PWSM49T0e4MUXgfvuo7XF4sUi0+b2VFkIeSZLl5Kp2dBAx8hEC+V2EzVsOiZPJnfERRfJncEffABce5MWzp6gSGUciwGRCCe+DdA1RaMQk+ySMBpJ0u+JJyhPR5qnmWUZBwQCMObGMKFUvo4tL44hFKI1uGA2jzqype7bbCPnIQoGqdNI1ccBCmKEwzKdQwWZqyyyaaMrUAIWCn7JsNspctvfT44owYFTWEjWSSwGDAwgEJDrBhnhQ15dKSIqPcrLaSIym0EzQkUFUFWFWWP4WXRNYibfSFubmIIsoX8Ih+m0BgbI+csxgwhqyR4PZrZkENtOQpVIYFmjvCYsFgOWLmEY7I3QRCCoMAGUAuDxIOfeOzBzwcWY/fej4Pj4R6xYQfPCkM7HcJhOOJFI6VeEQpTkumhRWjUfYyhekaUqhLsAFbD//nRumUgx0zAwg3eqv+/lg0N740fUIJkBoNOJgYo0lWkNEjjF92/sPWEAN9iuSwtXPIvLDmrAvec3wwQ+ONTlzsef7qnA1q3UdTgdi54eutl2O1k4yXvsC2rg8mnx/dZifL5Vwue0DcyaRZOQ309J6VptMqZj5CfrRJoYsVrNuKCGYDBZrWISNQCguRmWHB+O8KcFdkCC8QM6STrJW28Ba9eC+QNob+O/GjNGFGCLx+nfxx6jxOGHHgL++EeSJcnJodsvNcCWrTViw7oYitYv5bY3jjkUpZUawFwI13gKyLyLk7AHVqPJWw6A+uc11wCdy5PUYn4/UFaGNVv5TPWptQF09OvRHzChcdACR3+U9pcYPcXFwMUXIyP+hntwHBZjV/CO5tZQOXx+IGxzIxgUK42EcmqNht6JWEz8jU4nsvLkxTw4p/d+rs1vcCB+OugmzMM13PYKDR8U9SEfS/N+j4K8OOIJFSJRFQIhNVnHwrskwGgELr4Ym637c20YwNMqRZGDC/EC/o67wQA0zPkjulCNWFcfDXxWK7pMU7BsnRHPPy/ewqYmUdt7m9BqgcpKeA87CX4LX+9ct/xNtG2N4OGX+aClSRfC+zgBhSADPWitRkWNTgzAJSGjhUo64m96uBzPPbftRPalS9Noz4qK6B7W1sJvKkOoYhyg0XCsSTk5IudsOAxZ9RcaG+WG95df8h1CoGkayepwW0inuIrHgbvvBo47Djj+eIoqarU7hw4KoJu03378tmCQSsukq8+LLuJ5Btxuegb5+RSkKCkhqzyRAE4+mf9tby+NVZnw9NNy+kGACIkbGvhtmbQs0qOQ2dIOc3NF2rnPPpNVjOGPfwTOOEM+v732GnWiTBllChQoUKBAgYL/CJxOyjNxOmnZLgQrhHyOcPjXH7AYSnB7uBBs41hs+CLlTieZdk1NfE7Pp5+KbL5DaVBI4fVSoesrr5Bt/9RTRJUtUCalw+2WV1iMHUu5PSUlVAkhlZjb0GzCZU/OQIM/mTyTJtqRLr4tuH6W8/mB2Gcfuk9aLeWyPP00rZP1euDww6m6IyN8PuTpE5hg4YU01SoGvS6WYjTYKdUVQHZPeH//yAMWAoc1JyqY9p3TqehYZEBeHmTr4WyFMAqUgIWCXzI6OkQyw/QwpMVCk4zPB/T1we8HGtfxjuqZWId26x4wWXOouiLd75ykMJIKI23y1vLC2x0ddOxQSJbd3dVFE+qllxIDDxeFTwZailrXwOySRLXTZs9elKPeXp7x0r/+MgGHWyOv8fzgA7AbboDWRwEUfdCJgpcfR38/+XAkessiGEuRwLOmZrjddGtfegl45BEqlTzrLOCSS4A3XgyhPTAEN7vRSPWQZ55JWcZ6fdaUAMaShox+D0QMlI3shQnvg9c5OAtpnOjjx4uW5iw+Jbu4rx61zUuhC/KOrIEZh2Lv6X68N/MuVIOfmH1BDW6+mcHlomuORAC/j1HAwukky6y4mKzaxx6D4dRjUDv/ajyzqByMiY44tZrhppvk/s10/P735GAtKhJ1IswFDFNqstfEjq+Lp/yejFH3KSyUlIa6XEBfH9RtLTh08C3Oac2gxhv7PMo3Gg4DTz+NvmY/iQunH2+8yLqj0WzbVrn8cpHfFAASTIW3lpRC7+GNk55dDkFuEQUV+yfOxV8xH6fgXXhg5vYLBoGrH6wg48ZgQFBfhK1dvOO3rjwIX1CDuvIwLFY1OrrU6B/UZHRgHnusnFLrcHyGO3E7AKC6kH8h+lg5BrsCYE5nqpQZ4KnuM9FBqVTULeteuxdHh95DDsQ0pBh0eIJdjkU4nvvdjfF7MRsruW3L2qqRqyPLOxRRI2Dz0ctbWgrcdRc5pa+4ArjtNsQP+B267Hru9w8ctwxHqj+T3Yd78Xfcon0I3027HFFPAL2DOUBJCaIFVnzfUIK775YHNNvatqNcu6oKfhjRfNBF3GbmduPeN8YjHOXNiX9OehoTIKY/hYproMrNSY9BAsgQsCgthSukx7/fHR7dUiRC4xgAGotmzwZ23x2BsrEIRdRcvBegZyjYzuEw5NRH4TDVtKdjyRL+8/jxcktzZ0AQ3gYo4n711fRCFhTQheysCguAuOLGjuW3ffcd/3nWLNkYnaKDUqloXK2pocCA2QxMmiQnGH71VQqkp2PjRlrxZkI4TCl8trRgoMFAg2Z/P33f0cFHIdVq+vzWW8A77/ABKYF2TiqqVFZGq9PGRlqJpqOV7m50AAAgAElEQVStjYJYPT2/bnJsBQoUKFCg4BeMpiYyO9rayAyorSXzQ1i3RCK/7mlaCFgIgttpsnLbBa1WTOYZrn/Z6STTSiqFIMiPut20xhxu1YbXyxfxxuNkasXjvBtG+CxNaJoxg0y62lr6u/JKQKfjb4TNl4c/v7IXPttal6qwAMQC7cFBup+RiLzCQkpDPG4ccMMNwAMPUCW4mV/uivD5YEAQE4r5gEV1SRgBH4PVSvd/p8jiMZZZXwKgCx0JvalQrt7Tk73TdHYq1cgZIBTPp0Nhms0OJWCh4JcJwZOYdGpy1BtGI83KDgfgcsHvCKNlLe91nanegO7iGSgwq5GbK9EP1WiAadOwz+wY95tQTIctSEt1j8cpMiHRsYjHKVjx3HPiRH3ddZQVkIo0u92oqucdLoGysWDTd0t9XoxjwbK8guvr1RhwauF3RWlUi8UoqjB/PlSS1IeqzuXoaI2jqYn8MKtXU5kkl43gcqXIJAP9XkSjwJo1JE6WjsZG4OmXDJiAZszCatyPG+FEIZafcB823/4miSx/+CGl4u+5Jz2XLHRQiQRlRnR2Ap09GnSMPQgAiRIHIQZhtIjhdLwp/nDqVHr+zc1kDaR5G9WJGPb/8UHuOO663RAuosBP8X5TsBx7YRZ4Pp9IRIVFi+h5JeIMzpVNFIgaGCALw++n1P/33oPa78FNHZfD4ecdxeef5MYRRwD33w/Mny+neiovF/17lZXkf7ZaAVPCg12qM2QKJzFtvJj6IwRUrFYKeuTlJW9kQwM51VavhoEFsD94x+GXOATxQw7jG+7uRsvTX3CbdDoK3lmtZNAXFGw7YFFeTknG6VjvHpvSbQCAQetEqGtqAJMJ/TELTl99Ex7BX7O2+d53JfhmXQFQXo61jSbE4uJ7oFYxFJpiyNXFYTbFUVsLlBk86O1To6vBLzPC1Wrgb38Tg5KH4gu8hj9AgwTi2ly0HHMVtOA504JNPQi4Y3B1++Hx0DsdDtP99vlIXzgdBx5Ir0+xvx017z4CE/w4BLy69+tLyrn32QwXLsAL+D14YfLlWwqg0zKoEjFEugcQ7BigBzFlCnDQQcCTTwKnnw7U1qJza5C7NwBQPbsCd1/ei6vVD8vu6/2x67Bw7Vi4+iPoDlnBDHnY4KzGjTdmXrB1dw9TxwIAcnPhN5aic+Yx6CsSO/+f8RiaAnxa0HHHAafHeGHmYHENmFojCy4txUF8oLiiAgsWViIY4gNt+fmU9H/OOfJ37+230wog8vIQ1psRi9ErnptLffzrrykLT6ejfRlL2tBWq9wxv3KluHJjjGjW0rHbbjtXv0JAuvC28ACF89Jqd27AwmyW02VJceGF/GfBM2A2U5/OzaVrKCqiAEA0Cpx/Pl+REQoleeKSiMdprhsKDge99OmdOr3KQlg9+3xkLwwMUDT+ySeJD+2ss4Bbb6XJ0mikkqqmJv4YJ55IQRGLheY66UrylVeo/eXLaVXrcGy/h0CBAgUKFChQsENIJMQk7pwcWgcI5oWQZPRrpoQS9NiE3MZly4DTTqO/88+n/IzhJtALdKnA8PzLjCGVCJjJ0drWJlI8DafKgjFy+Uh1FxsbRZkyAR4PHTPGu3EwezatD8xmMtnGFnvwwMVNKCngO0A0psb9X8zGgk/GIxaJp0pKrFa6Jo+H/BfpfhS9Xp7fBJAZKuSVZqVz8vmgCQdwyMROWPJFx/6he1DSpMCoIK0IGRUI/p9saG7ecdtVKO0ZSnxBCVhkhNEoD1j09irsWdmgBCwU/DLR0UEDoc+XOZPVYgHcbrBYHJ42B7Z28NQYUyw2xPUmGI2SYIUAvR6l+01CWREfEf7B/Ht+v95eOge/P+Uo2rBBrj0dj1OlxVMLYoiHo9D3NMPawns9u/Y/A+EKsdRDWmXAt6fCt2tNcPiSNZrXXy/nDk+i0NGCyfldiEbJr93dTXSFHLfj4CDN+G43XPYYGKPYw1BYi1m4Gfdjb9UKrM7dBzaHFoMDyZHU7SZnVG5uRjqoWIzuU18fzWODg4B3LmWpvoI/cvservkKJUijGZk8Waxu6e+XqVab3XwFxcAM0VHvHjsTVnMc3+IAHAf+fq1ZA7Q2J+Df3A5nS7J2OBSifnbFFanozb9wPt7GadxvZ45x4swDRIts5kzye915JyXhzp1LSb8eD01ABgMV8hQVAcbAAA7SfAs1MtfY7lYlWmF2O/3WZEqje3c46F50daXqXw/Fl1wb61sL4Lz4Blk9aetqXqmsvFyklc/PJ5+iQDk/FM48JYpiM7/TNZiHMMhh2jvjSHQFirBwdQ0uubMa9T3yPqGGOAszpsI1i3+HmNGM5Vt4HYDakgDy+lsxbfAbWDrXo64OqCgMotowCHt3CG2tTDahV1QAb92wEg4U4XMcDivoutv3Pg0DRZMwQdPG7e/t88PnB1xtLo45RvBdSumg9tmHusqc92+BOkqG17FYzLWZYLyD/RI8g3z4ZAGL9n49+nvi0A/2IOSPkyO/dgw9uz33pH9raoDSUvzs4J2keblxFJpi8E2YibMvN+EpzRVQgb8Zn6204MUlYxAyFKHdYcL5VxdmpBYFxHjscBEwlyOUyMWPMy4DALyEs/EvXMDtM2FsHH/6E6Dv46vLAuVj0dws18xxoxBrkSy7MJkQm7QLnvqA1zrYay9iAjr5ZGLOOvVUvo2GBj75X7ChBT3lv/yFGJX+/GcKXAA0rKYWsdJVyJYtInec3S7Xr9h9952rXyEgXXhbuCghYKHT7TxKKIBWfJlWZwL23VcuBu520/nm51N1hYCaGlrRCdSOxxzD/+7rr2nCAICFC+Ur1ksvhaw0p6mJBHiEAH56lUUiIWbORSLAtdfyGWaJBOlQXHcd/X3wAd92VRVVEVZU0LnX1cmrLLq7KWjR0UEv0oYNFOjq7h4+n4ICBQoUKFCgYIcQDpOvNRIhh7KKN8NTAYuUZtmvDEKFeDBI5uBjj4l2bXs7rVNPPZW0JTZuHNovLaNL3QaEZC+vN3MC/5Yt9L3PNzwdi0CAzlG6vuvsJB9Cehsej1y/wmIh081qpeqKykrAFHZAFw1gwYlfYrdavroBAN5dXYd7XxkDFhZpoQQWVsEkFbDXXnKTmzG690Ihb1aW2CRbiCU/ivlXNuHUud24/+jvMHuiC+a8mMC6u3OwLZ6h/v7tyFyTIBAQ9WazoaNDCVhkQCZKqP5+hT0rG5SAhYJfHjwechb199MMmqnGrqgISCTg7PKjv9GD7iCf5V9do4LakIu8vCFK9AoLsds0fmZckcvzxaOzU/ToeTxIJIC//jV7xsKbnxTgtufrULj+G6ggWgbhnHy073Ys3NZx1BTy8RV4HQcp9+SXayzwt9vJoSLh1l6HGXgAN2AZ5gIAKpuXYfJkckgPDFCcpacnbQ6y2+lavF643cDmjXFZdUU2bGUTsahpGnzqArSuc4O1J6m6CgpETZE0hMPA2rVkYDQ3065jxwKRAw5DDypk131O/AX+gELIWdCxGD9+yPPj9DHUathmHgYjAngVZ6EEvIbAx4si8PT64GocQMLloZO8/voU92IDJuHPeIz7TYE+jFuOXANNPMplKahUpO9w333kDNXp6BkWF9P15uQARTofVH4f9ux6H3PxQ8bzn20gZ6ggXFZcTEZRqnDF4xFThJJiLQeC5yzq6NKgV1sL3H47x9/UyngNDiGYApBhJiSJZ6KKFxCLAXpnLy45mk+hacYEXIjncYb6TRz00/0459+H4tF3q+Hx8dNKLkJ4BhfjGuu/uO1rOkrwwpt5+GkRTy11iO11nPv6Udj/5Uux27zzUPfkjZgyNowSjQNjS/zw9AfR0iL3CU544RYUwQVhvRLV52PjvpcB5WWYUMhby3ZPDryDEUQG3HA4yObSaOi+S+mg9tyT7pm1eQXqfngttV0asEiHFtFUP5qNVSgGT3mzfKUKuXkahIurwMyFCI6fRmlBwrOrrQUMBmwN8c+vqjicWpC5J8zGrKsPxD9r5dnoizePx/PfT8Zlj+yCjRtVsu8F9PYOP2DBGOCPGxDKNaNv6kFYb9gLl+NJbh9DThy3X9IDfdyPXDf/7nXkToTPR/FHqdDYuzVXI3rUscB11+H9hqnotvOrgjlz6NlMmEB/Rx8tT3h/7jnx/34/LXoiEfJFp+vNCb5pITMNAEUc07F5s5jO9c03cmN7+vT/TIVFXp4ovC0ELGIx2qbR7NwKi7w8ChJkOoZKRYqDUrjddF80Gj5gYbFQsKK0lMbQ00+XR64efZQqGl6QzAfjx9OK+847KXiQjh9+4Kmc0qss/H76u+eeoRdTnZ0yjSoccQS9g2VldM4WC3DoofJ0qM8+I1uloYHG5u5uYOtW4Kefti8SqECBAgUKFCjYLgjO+Ugks6mSmys6AH+NVRZer5ib0dfHM2UKiESAL76gxJ3zzycpsg0b5NUJOh3Z+ULF+bYgMCr7fJlNrPZ2kVHb6ZQfL9O1rF8v3x6LUXW0kF8IUJtbt/L71dVR0AqgNbgl148xhS7AH4AnZsI//tyOE/eX0I8CWLquCJ99Sv4ajYbWKLW18nOR0kEB5FqJRMjPJOQXyRAK0UUEgzDka1FijmHuLi7UWT0Ih4BicxSFhSPTHxkS21JyHomOhZDxOJSeW3u7ErDIAL2efHbpGBgQ+7gCHkrAQsEvDs6NXejsBBJONzkM3G7KhnzpJWDxYhr4kyni3a0RbNrCzxB6BKGvq0CeJRcq1RABCwCz5vK0P6vDkozRlhYxS9PtxlNPAd9+O/T5/9BYgtPrb0UnRHHazVNPhiNaAHs+BSw+xZGIQHTKadQMZ57BB082thfA/OrjMgHYJZpDsDd+wk14APthGS7FU8jZuBpqNQ1+QtAiJUbu9xPFxsUXA1ddBdUP3+Ott/lzLiggDsZjjwWsufJI+9frrNjgGwdfYTVsnWFKwygooGBFGmVTIECVDE4nGROhEPmbzGYgWD0RL+VdxtHmmFQ+3vFbUEDtFRbSaF5VRZZDFtiqZqbooFLbdj+C2oYft+A+7rvGDj1WrmCI+4IIr91M0YZkECKEXJyJ1xEAX9P50LinUKJ1ikKuGTAwQNdaU8MLZxkc3dAjhLr2pbJMewCoRifG+TYA8TgGB8VLr6xMM3wEglKfL1VhMQProYMYhmcMWNE/hiytQ8SAUAvGcccrLxeNIoEz02jMbqs4ncCmNSHUrw5jz+I2TCvjjb1X8Ue8mTgNg4HMltZ4NOEn7I2L8Rxu9d2MsiLeaHnwhWIs7+az6edgBd/IDz+gfPkiTLd0w2IIYrzViWCQEqyFTBzLik9hWc1XnXQcegEGzBOQX2ZEXQ0f3WgPliDgjCARioD5Ayn9imx0UKqtW3DEy2dx28tzXagryZyVcsSYn1Mi8hokcAR4zYnl3TXIrSpGKNcMTJmCQF4x34BGA0yYgK2DfCC2spi/f/6qydj96oNxzalyg/SVryrw2Xf8c9Hzwx1sNrrm4ZSghkK0X8hUDG1eDk5nb8relWuP3IRq/SAMnQ2y37dqJ6Kqiny+U6bw332lPhSOi24Eiosx73Ne46C2FjjsMApUVFYSG9OcOUQNlY4PPxQDtIEA2cc2m1wKobubvuMCFlLqo44OMvIZI+W/dNTUiFHFnQ2hWsFgoBOOxWjs1Wrp+50ZsADIWS/VnABojBnHjy2IxegBmM00yEhXX7W1NL4bDNSZpAGP5mYqgZGWrv/1r6KI9333yQNFb74J3HQTDQgGAx2/v58G5WeekasybgtjxogRsSlTqIqkvJye+3HH8fvabMBXX4k8FO3tFOzy+xUyWgUKFChQoGAnIhQSKywymUPpmmW/Rh2LdMHtTM5+KdrbyZVy1VVkztx8M9FGtbaKZiVnGw8BgbnbZsueFd7VRS4cxiiPZCh4PLLczBQ2b6Yl+OAgteXxZBbcFtY4KhUwtaAbJm0YYww2+LSFCIQ1+MtJ3bjxjHbkqPnoyYLn9Jxm9I8/8klxOTnA3nvzxwuHKTG0pITyb6QmcQrCwiQYRJ5ZhxxtAlCpYPPmIVcTgyknsvOqK4BtV1h0d+94wCIQoJcwWxm/cHylbEAGtVqirwvqc9uKL/1Wof1vn4ACBemIeMO4944IWjZ78UDhmyh0tMkHwq++Am69FcGYFs1dbmxc1QWgKPX1NGxGR9keKCjKSWW9C7Db7WBpNZFSx9kWrwF9UEEjVEcMDNCMVFgId9CMm27iZ6TCQuAPfwCeemoQiTSv3wZWgT3wCV7HmZiOeqyZfhRcAx4U5FlRCuANHAyksq71mFitxtGHavDKq/qUoZBgarweOxRn48VUu05TDU6LP4tw0AuA0uKfwUlY+lUr/nxoF6rH5UKno4m0sTEHBoMZ9sU/ovjNpEZEOAzdu2/ie9cFQBqv/5FHkh7qpEnA3a4/o/67QZyDl5BIDhGxOPDqt3pUn6pC1DgGh+R1QG00cvoVK1cCCxd6UVERgt9PBkNdHfnXhATWF1XHpF03cAx7DX4E4QegAWCZOJF+YLVSuofFAuy6K/xaLQIZ0jN+rDoQrn43zEbRsnBZqlBSWAaDqx8n40k8iPPRixmp7z//IR+3dMyHofEHBAH4ADAAV2Ae1qKGO79z8S8c13Ar1ux6NyL5uYAqBuSKAZfCwhKEw9RFS0qor02aBEQiYXgGB4GGBuga6hH09WMfLAJwDXf+s/ApAl0/w9nfhfaOPBQUCCxoVgBq0TILBICWFkQAkJpKBLvgG6zHzFRb368EjttDT9zrn38OJBJoQBF3PRXFJuTkUIlFfj7d4oICoKcnBofDyfFnBoMUrytwdyAR9qK+04PLSh7Hlf1/kj0HwAyAXy0cNG0A79XPQgxeOoPwAG45aiGuel8MqDQnBOejeI4T8EPqUwFAYb133oGltBQz9yzHhmAe1OOrsGbNAHJygPJwG4rv/gNXwxA2WbF+n5PQFwcqMADzhASwTtzj51gVdN6fEfAbYHI6EQjkoaSEjOVo1A5AyLZh2GdgESbPuwbxsI87Rv8RZ2JmwIa2L9NXQXkAjDj4OBN63pqJyn6yvn+Pj/Eqfg8kacFWtyXgNTC4EjoMup3o6spcqr25O5y8N3oA+agqkRt9vqAX++/mgD80iKcXV8m+F5CbS8VEd98tbovHczAwYIbfL/cDu91uRNKMzMFBeo8HfMDPWxkaQnlIf25/xEvYX+2Hy7MvcrZu4c/RWApTuQklJUAo5MW0aSEu8LuhK4HmThc2NajxYyPj2j3ySPJXV1YCxcUaWJIUdJdfDvzzn2IbHo8fTz4ZwHnnkX3c1we8/768EocxikcIpd9VVYCquhrFWq2YAsYYrZBmzEBwxQpwYalJk2iMkgpFZ0FJFhW9cDgMz3AWCULA3Oeji3I6gWgU1kQC6gxBk0gkAne6OuF2oqioCFph5SroWKxZgxgAJ0BW9kknyasSHA4x+qVSye6P2WxGjl5PA2VHB0WhFi9GorUVKVI8aXrgwQcD1dXisUwmUji8447Ugy0AkLt8OWlJHHwwTcheL/DMMxiQKkGWlQF//zut7D/5JHNQ4cwzAY0GprFjYRDSnyZOpHH42GOBTz+Fvb1drJ9cvJgG0VNOofNraSG6wXCY7p+ElDgvLw/GLETHDocD8R2kk9Lr9cjPUvXjcrkQ3U4+jGz9VoECBQoUKPglIBQSaYwy5ZDk5JCpEI//eisshArxFZJcr8pKMqmyVTYEg1QM+tNP9HnmTNIrjEa3HbCIxcgk8nqH9lU3NlKyUSBA1QjS6up02O1UrJoJra1kXg4Oku0ei8mFvidOTDO34nHkuGyYmGdD2MCgV+fC6QVK4v24rv6v2DtRiBPxfuq3/qAaDz0EPPggtSFNTBUq7QUI6widjpKwqquHFtxGNApEozBYdNA4AF2OClGmRrkxCJ3KyBUkjzq2FbDo7R1ZwCIQABftkaKvb8cpp37lqKrig6oA9fUZM7L/5jcLxpjyp/xxfwCqQR471tnZyf5TaLrin+wAzfcs6brb4b+52nPY/Ou62AcfMLZxI3+M4uLiHW53fOUNyXMT/665hrHnn2estHjKCM75CnbG73rZkg887MAD+fbNmLHD7c6YcQpbuJCx1tOu5xq9Hg8w4JQdb3finqzrrWWMffMNY6EQ8/sZ+8c/GNPpGAOu2OF2dwEYO/54xp58krElSxiz2Rhbt46xTz9lt1ssO9yuCXrZc3sfxzEGsAUj6GcA2KOPMjZ/PmMLFjD25ZeMNTZSP3vrrbdG1K7NZqOGfD66F88+y9gBB7AlIzzfq89Zyr0Pmzcz9vHHjN1wQ/2I2gWWMIAxq5WxQw5h7K67GPv6tV7WUlQ9onbfSn9oRUWMzZ/PnIu/Y0s+8o/wfBewV/Z6hL3+lx/YJ/M3s/nzGfvgA8ZOPCHOgB0fI4Db2W67MfbkvAB79dKlzF05hcW0OWz5xLMYsMsI2r2CAYyd+btu9sotm9jnD61jS+avZUvmr2Uzxu8xgnZPYfPmMdbbKx+PTzllx8eIOdZq7oXrrpzNvnxrkG3axNi5545gjNhlF+4c585NP8ztO9xucXExY9Om8YPERRcx9u23bIFON6K+lg0jHiMWLmQsGk215/czFo8ztmTJkhG1W19fL56k283Yu+8ylpfH6kfQJgC2ZMkSxrq6GPv6a8Yef5yxF19kbN48Zhthu9wYATCm0TBWWcnYCNtdsGAB/8C8Xprz7ruPFY+g3dtvvz1rn9hllx0fI6644oqs7R544IGj1m93Fjo7O9OPX81+Abbw//of/ku2vAIFChT8J7B5M2OLF9M67JNPaLmU/vfhh/Tdxx8z1tDw3z3X0UYoRNf4wguMPfggbwYBjM2bx9jChYxddhlj1dXy7zP9HXccY++8w9jq1UMf226nYz/+OGOHH569vcpKxh59lLHXXmPs22/JRs2EeJz2y9aO2Uwm49KljLW3M/bpp4yp1fw+L76Y1mBvL2NLlrDEgsfZ93/7mL1560b2yWH/YBFDQeoH5+JfsuNcdRX1GfJniH+33ML3q7feon61eDFjP/3EWCw2xM3asIGxRYsYmz+fBRZ9zpbMX8uevXYze/iEJeyL275lTYvqh/jxKOC88/iL0Wr5z5Mm0UVFItvXbjxOD+T++7fdsV5/fadc2v86mpvl7+b8+f/tsxoZdpYtr1RYKPhFIBwG9n/pIvTGRy5kWmwMAvpt6FfsAJp7eDWlPfcklotQCNCoRiayud90D9QxHQ45hOfQdyOTYvjwoNUCPe1R7Ld+WWqbB/l4GpcCWLnD7Ua1eWjPm4LS6Tno6cvFm28Cf/vbtvkph4WyMqquyMkh2hWTiepJKyq2XU+aBXqEUI5GNGFiatvfcQ+OwYcAhsGFMwRKSuhUjUaqrqirS34xHI6d4cDrpTksEAB+/nnEzVUbXdRhk3WzVislRqTJXuwQTj2gD8edMICqmSUilZXPhO6qPQHnNrI7hgunE3juORTO3xX5gR3rC+kIOwJIOKPIscYBbzfy1C6s/SlbTe/wceSRQEhlQO2cyWjc/SXou5thsxdA17IG0RFq8Y4xDMDhzYPdo0NxQRSlRSNXEezoGH26fUGYXECiohL5Fh0mTx5dntbLLiMZg1HB7NlElCugvp5UA3+pSo1qdaqGv7mZyogLd3y6yAyTicbhW28lMWxpCt/2orycqg9KSqi6Yfp0UrT/8cdROV0AlEqZSQFypBBq/hMJGj9/jemaChQoUKBAwf8IBAp9ILuGBUA+hl/blC1oDwYC8soEo5HMK62WJMNOO40KS3/6CVi9mhg0M6GlZXgVFk6nWPw7FIVNT0+KURsVFVQsmyyS5uDzDU1p5XYTndWYMXS8dEpgAbvumvYhWTWgikZQWRjAxBf/gtJNS7j9H8Zf8RUOQRdEbbSnn6ZjpZv9Wi2ZqQKkVFCTJ3PM2JkvLhgENBroTVqowKDPZdAZo9CCobJwJ4sWSB/Q9OkkNCpAUDP3eDjWjG1C4GNra+O3Wyy03Zkmct7URNsyinz8dpGXR8ui9CIYaeWQAoKiYaHgF4HcXOCuC0fnLc23aJBXmAuNZnQDFtwx8kkHdOJEoLwoDER33FtvURMXvtUYwhFHAIbcUXJ2A4gPOlHYIk5Mz+ASeDCym2IbAEIGC5ZvMmHhQmLYGJVgBUAef4uFLBuVimow6+rkRH/biTtxO/e5HtPxJk5Hh3pk7Y4ZQz6sujrROASww8EVGTweMgpstsxqatuJMfl2znixWABVIg4jRlauOXe6D9W7mjlbJKw1oq1s7+w/Gg6kpKCbNgFvvonqnJHfC7tLDb83Aa8rBr13AO42B9r6DNv+4RAYO5bKsC0WIG9sGSZO0aJgUgUMY8uQm8NGfM4zrF3YpdiGsqIoHF4dNrfnIRwd2TTe1TX6AQtNlF8ZxsqqsesMLTSa0bVXTzmF2HhGBekrEoBEeDZsGKXGdwKSq3O/n56hz0eLwR1kFMoMtZomuylTgIMOGnl7Gg1Z50JkxeMBzjtvZG0efDCtzv8TqK6mBd1/Qr9EgQIFChQoUJAVoRA5xLVaGfsiAHF7JPLr07BIYxqSOfvnzElbj4Ls7pkzKcnn2WeB996jPJS5c/nfdXSIGm9siCWLoF+RSGxbJkwQ3w4Gs+eSeL3bNrcbGyk4E43KBbdLStLopgIBijoMDgJNTai76wJZsAIACuHGC+C11EIh4MUX+f322IMCEwDdE4EKqrycKH2GTBQSoj/BIGAwQKVWwZCbQK6OwZofQZE+CINmJ+s7SD3gUs0+VzKRcXtpoQTxlPZ2fvvYsaT5lo7Ozl9u8td/EXl5lKebDkXDIjOUgIWCXwwuvKMGh0vEaXcEaksRjNbclE7nzsCFZ/gxaxZNYiX2LSjw7LjA5hhNJ4zaCIqNQYwby7DfdNe2fzQMrFsHOJY3QhslKy0CHR7GX0fc7uAgGSgrVpDW6ajNQRoNzUJy+n0AACAASURBVP4aDQUsBNTUkGUwApyBNzAdvDV0Y94CPJkzsvuxzz5kBE6eLBo0iEbFjIWRQtCvkFpnO4jqFQsp88TvB3p7od2yEebWdTC5R5iRbDDI0psGHSr0j5+b5QfDxAknyK3Bf/8bpS8+OLJ2AXS6C8DUGrhaXTD5+rB288idkDNn0uKoogIoK1ehYM4U5NdZoSkvhT5vZNOtTptAWZkKxb42VBoc2GWMH2VFUcTiI4sAdHePPr2oKsKnZxVOKoXRMvpOXoOBJAdGBfvuy392OICvvx6lxncCku9bSwutNXp7afOoa9uZzaMbECguRkpcyu0emtR4ONh/f+C114h8eWeLkAMUvFGyxBQoUKBAwU6E3Q58/312Xv/fOhIJMcs/29TvcpHjXhCSHsoJ/78GQb8iEpE7+6X5N1IUFVGux2WX8ds9Hup3jGW3JcNhWkJ6vbR2kFauTJ7Mf/75ZzL5Bgao7UCGggKnky9wBoBcHZ+82d4u+tRbWvh9x40TBbfR20tZlA0NwNNPQ+WVO+ITWuowh+FLXIHHM19oEgccIP7fbqdrr63dhtC2AGFxFQikRDDy9AkU5MVgMsRRme8ln8HO6piMybXapFGqRGLHhLeDwcwVzXV18oCFEAlTwMFgkAcsMknrKVBEtxX8gqAqNOOxRxLY/foYAhExg9qSH8FH/7cB46vCwMsvU2pAEk/hEtyGe1Kfp2Ejps0pg6koBwUFcr/Cli1bBG7fFD7+mE/y1KnjeDhxJf6Nc7EC6RnixGVy0K42HL9LD8rDBaiu1EF178n4IRFBemLrvwyX48bgndxxDprpxKTqAJ75kBfGfUR7LFg8CqshAI0hgMNnO/DFKqFm8n0AUaiQAEuLL06sDuDlvR/HuHceAgC8hxNwLebDD9GxFI/n4I7PzYjgHtyF2/A6zkQ3qpPfPgfgCdx/P29cmJrWYcb1h6c+J1Rq3H1pFx5/ShwqGNNh3jxRV5TH/QDuSH3SaoGHHgI+/JC00gVMxSZ8g99xv9RMn07OLKs1zfIAoFLh+nvuwZ8qKoDHHiMr6uSTicLF70eb3YROXxFa/KWIQYPKwiAKDDGAJVCy+RsU9DWhxVyB88NbcM3y3VLNdgUsAC4FcFZq215T3Th670EY9QkU5wex/78vgqk/LX3EYADmz6cJeaYoeM2hsxPHzZ4N2333Ac89x1lWW2sOxrojbsDJ/z4emjTanK9PfBS5Z5+OWXuoYDAAVquVDAG/nwyd5mYAwFwANoDKOW6/HUfftRdWbhAdwWecATz6KADG8O97OnD9Y2IFyXSsx+4rTgA8l5FCOmOA34+K9etx0icf4aDCKdg05ijYdjsUtWO1GFcbRYU1GY269VbOmoyWVWHF6f/AoB3o1Y9FSd1Y7hYkEuTvLd1lCpqrpiC/W05ndY/6NjyauJLb9vQ1DThwhhOWgqSgrpB+cOON4k6xGFRvvYn0Gou4SoPNly+A5+ATAI0G7e1kr44fD0ybRt3ppj+049XvhPthQkPkOxxZG8SgqgRlpfl4Y1Fd8rstABgOxed4DX9EVKvHl3vegpxD98PYOWUYt5coRssYsGoV2ce9vYBOl4eyMjreuHEAcg0w7T0NiAG33/Edbr45zgUHTjkF2GsvEqXbbTc+wPrKK8A1KY12PUotcagqyjEpbwAa/1p0GqdApy3GPy77BxyDEeQNdiC3shiaEgvMe06CxUKLEulCbsEC4K67hE856OujVyoa5anBnnvuOTzxxBMAyC5ds4aE6ObP59t7dtrDOL7+vtTnsLEIBj8v+ly4S2Uq/e3+++/HHXfcgfp64JBDGBjL7PydNw84/nj+nmgy1F1ffDGVcQPXA8gkCE/juzRr6uabKXNqwgSgqkpFN8to5MtNtmzBBQBOEz5fcglw1lnA1KkZj7M9OO6442AbbtWUIOTc2Eif6+pgLSmBy0UB5N5ecSyeOXPu8NvNgKKiIn6D2QzodJhcXQ3bE0/wwWSAXoItW2jcrqyk7K0MqY5modyxoIA6mtkM9PfDWlMD23vvkWemrY0WU+PH077xOK12y8upIzT+P3vvHR7XWaaN32d6LxppRtJYzbIVyS22E9tJnASSQEIKLYSSJSGw1CVhW2C5gCWwSxY2u7ALy7IFPtj9PsoSwtIWfglhQ0gjISSOHce9qliyehuNps/vj1uvzjnvOTOW5FHiMvd1ybKmnPKe97zneZ77ee7nEJ8RGgs/4PFwkn/wg2wG/u1vk00PhzH03vfyApeDKGdvbQU2bZrXLPPNM9AS7Hbse/ZZFPfs4Xe/+U2jpx2PA3fcwXnS2Mj1eg6eMppoTzzxxGk13S6Fn/zkJ4tuul1FFVVUUcUrBxHjGxigPXm60q3nGkSg3IywyOdZ/f/MM8y+v/tuFkhmMstfIJnLsSA+maQp4D69wu2SmJ6mbX7kiJ5csFjoVywE8bjR7D1yhLZxOm0+VkLpZ3raWPgfCNDc0ZJsL7wAvPe97L3c0MAMcpnUeP55YwXM6zYP4ae/U5MVu7uZ41Jfb0zqb22dG+dCgTsaGQEeeMAg/5B012DHDX+JVccfQf3zvwAA3IeP4xeuW9CdkiLH4FiKXCYhBSXUqk8pBSUGKZ/nBZqbCA01aYxN2RDw51HrmWNvcrnlucFF0qMWF13EfWltwt5eoKNjcbJNySQHRe663tZmLNvv7eVnlyuL+CyF3c57UAuRfFaFHlXCooozCu0ffC3ufLoff//95vnXxqaBv/muEz+99yVY7rwTU0eGEdj1BADgIC4HoAYQL/RNw+HvgtdrLgdVW1treO366/V/ZwvAnfih6fFFfCl87LKn0GXNIpYAcP1fAIPd0OafztY04ooP34SLH3DiuQOqXslvdtahd2gWgGq9dGIfLp99FnusE7DlnEBiGjdv7cPf/FsEEwgDcz0stBSL3VrAJ/8oC7tyDermCIsP4id4teUAbmp4DodP6AMin8ensAOb0YNmzatBbFhfxLZt+gdTTTKhGU0gFYrixjc04Jnf06AQ2LvXODYXXgjcfLMfn/mM+kDK5RjgHB3Vf/ZdykOokxMKOjsZLJIDYgC8DQ3w3nEHUx0OHKBR4vUC8TgimwOw9ETgm3agZ8yLsaQVOVce8boMsqvWYLRQAHI5bJycQufxMewf1ApouiGuR3vjLN5x9ThaYh6E/Xl4XQ4E7rwbkXs0QfXZWeC//gv4zGfMBVHTaaCvD87JSdTl84Za2ZdWXgHF3QxfXQt8/WrVRFdhGIV6P5qbNZbt5CSNh0RivsLCgbnZvmEDEIlg+6UR/P5F9TuHDzPuarMBI0UPADXo1oEJOJIzPP7rruNF/NnPENu3DwAQB7B+Yj96AoOYueoudLXNRUAPHDAG5G57B7aGkjjob4bXGsDJVASuUVX+cmqK1762zQ//pktRJxMWdjs+e08U9/9DEIPj9DQ6m2ewcZUbHU05vb20dSsFWH/wA90mtPP0V6/5PCw3/SFCbse8JE5TE5O3Rexz9UU24Ak1GLsHa9Ey9QN4118FwIoXDom7mGvEDXgO3mA9Hn/d5zFeux0bLvChaXMj6rQ7Bo1Wh4N2qdNJ56ilRTX0bTZR9lmDNWv0bQAOHQIuv5xFJB4PdNuWk1bqm4qw1obhWtEMpacb7RN70NTQgt7aBkwp0/D6bIhsDiJ8UQsszdJBaiDzbMLhmJnRF7MENQvoyAjf08qeAkB9NI/1r9uIOu30mBmHAZo0JL/fD7/fj+3bga7OIvbuMxrHG9YVcO21llNnLwHYvJm3w4svegEYKwEuvpgcw3e/q7fPp6a4hHi9dEAAkN363e9031dXCACvehUvrjwJlgCn04m6hW6nUODaMzRE5ycSQdHlxpEjvG4TE+rHCgXHwre7EMzNA1sggLpUyljxND3NydvczLGR04VkKAoHfHoaGBiAZWYGdeEw8PrXGz87Ps4L1NTEmyyX42QUJIWMujoty4dTjkKxSI80FuNisUDpwdrVq3k9XC7gXe8C/uM/SNoInDjBvhwXXMDtBwILipLUmIk7VwChijc3qaKKKqqoYrkwl0+ERIKPYDmhpAo9YSHnAfzylyQrAGb2/+hHDOKnUstDWBSLTNI6eZL+bj7Pn0SidG7b6UDINpm1N1y3buFSqRYLpa137lRfEzl2pRLiJybU5Ho5G7ylxZjPMzLCcVcUXgunkzFtYcLlcgazGw2RNK5s79MRFn193PecQIAO84TFyAhvlp//3FCGMbLuVfj1NfdiGFE0je+ef92HGXx1xX1445EvGRKoNm1SY0m9vbwHGxoWIAUlIPpXAPOERSSYw+XrJ2HpnQIyc4TKct3gfSY9JIWdrn2vv58XNJlceEV1Mkk7fnhY/7rdbiQ9BGFRhQGy23HyJH05M4m78xnV4ajijILVacOf/0kBF3foS9N+/nQtvvu/MSQzNux6xxcwXrcaO7AJv8bVus81R2fhCjhgsy28f0U0euoYCwD43Dl8/gPduOoqBbFUN/Cnf2qg+ROhFdh51/9BOtKIj769Fx6nPltSbtz9JvwECoqITR3iYj49jebB5/Bm/LjkcdzxupPwN/jwLPQ6hBcU9uE/b/4ZXnu1saHEQ7gee7FW99rbb9F/rlgEkvv14nnWWB08HuA97ymvttHZCdxzD/mEm27Svyc39VUU4JqGfTBg40ZaMqWaPq1cSXb+ggtoka1aBaxYAUt7G9a9vg11r16LVde2o/XVrUjF27Ff6cJAeA3ynWuBpiYokRq8702jppuuCWTxp7f0Yl1bEtFQFl3NM7j4gmlErljLIKUWTzzBdPPubmNKyPHjtCKHh/m+ppon73AhsXozMgU7EvX6rN/wxFE0BKUsiKkpGhBHjhg1e+YydrdfqV/C9+9XG7EdHdQbHW2YI09+8Qvga18D/u7v9EG2OTQ/8T10fv1uNSvj+9/Xf6CmBti2DY2+KbSttqF+pQe1MSt6e9XA6cgIbR530AnnVVJtssUCfPrTCF++Fg985iXcdMkIXnXhOD57x3E0RdPmyR3ve58xJWcOx698F/Zc9RGMJThBRZ/yYJBxUUXhT0enPhWmH3EU5ipXTo450D+qz1C+Gr/GwdfcieHWLXDWeKE0NpjacbEYSYlAgEasx8NMLi38fr4uG/J796oavHISjEimF4jHFXjXtkJpqKeFU1MDx0A32p192BQ8io5OCyI1RVgayi9mnZ36v2dmONXK9bGYmeHy9Nxz+tdf+1pgNr4aM7VlAr0ej6nsj8MBXHGleSbPW99uQVub6VsGKAqnhxksFuCP/ojXp7lZ/97AgElzwc2bS+8oHGa2fBlPsFiscA8JAZGVJFL1HA4MT7tEzH/+nsnllsEnsNlUZkd4qQKFAkkFh4PHZpIQYApRRed0kpgthYkJVW4uEODNpijGjC4ZQ0OqVlY5zMzwHPx+Xt/FQGgQxGKsppAn2FNP0UkrFIDBwfLbWpZJU0UVVVRRxdkI8agVKi0Vl3o8ByB6/ppVTTz1lP5vYU9Xuo/F7Cy3/dvfArt309/t7WW7vYEBmjfL8XgXJlAyyf1qcSo5KBmya9XTo8ptmWFigi6pohj7V6xcSTJCDubv2kWzb3SUdreW6EgkjJJWG1YmsCY0AItF9aELBR7b8ePGJMhVq+Z6dgwMMBD/YymG0tiIkx/+HLyxALI5C6Y8epnpK3O/xttuMk4OIQclzrmpiabwQpKp5k9udpaDpamCtVjAA85pCIvlgExYRCIsD5F9ssFB3kyLkYWanWWcQ+5+HggYO6tPTZUuHZiepr1+nhIa8lzSSvxWoaJKWFRxxqH+4hX48p8eh8ehD6jf/S/t2HPci2NTYdzgfRwXYQf6oa+lqmnywhtxQlEW14x1w4bS78VwEh/zfg0//OwevO/GAbgbQtQ2l1aUqVATfnTzd5AOs0IgFs7ij95YXozuTfgJACA8fpRWzcQEbHt24e243/Tzq+uncPmF0+gd98G7Ioypunbd+3V9L+AT7x/FR+4qwmYpbSV1KgdwyYX6YM6JE4DtpJ6wsDfWoamJgdbXvc58W+3tLLetqWGQ+EMfMgZstVi3Dsh1SQNus7FEQzTbNoPVygvV0sKUkC1baJl1dMDZGMGmi61Ys4bxo85O/h4aAvYfUHAyG8HJcBca14SwplUfmXXZcvjr2w5iXVsSK+oy2No1jVhNVj2MD31Iz9YUCsC3vsWnijaqnEzSihTBKcmKTK1eD0+EAcfxxjW697xDx1HnlkgJUe8r96+IRjnAFgu2Xa5nkRIJtRji2DH9OK7EXLbJ5CTT/MtoZirPPA388R/Tynz8cf2bt9wyn1Xd0gKsWBfCihU0ULu7aUgmErSL3G7Af8OVFEsF6FV8/OPUnQdwydpp/MWtvfj07d1oiqYRC5ewkO12ylJJtdW5rZfi+J1/j1CDG8PDPCUR43Q69fHTVV122BT9mtJ/lAbSjoN6JqIWw2iJzmJi67VIWX1wtdYDimJKWPh8/InHuea0txszI/x+HpNIuBbIZlkVk0oZCYs5LmUezc2Az69wcsfjfKG2lgZpLscbUPQHKIOVK423WE9P+T4WySQdMvkz177OCng8GNrwmtJfbmgoWRP/hjcYX6uvyeDmm40Zc+XwzneaZ83dcINqDMpG4cCAqms8j3J19ILpKSEVNDVFVaMnngAee4zj9eyz5DZ376Zjt2SZWreb65/HAygKCooVRwfcmJzkNWls5McM51MpaPtYDAxwwhw4QC9zbIzvWyylyWYZ4bBaeTA1ZT4w+TzXmVCIc3rdOl7k+nrus1Tkob+fP7Oz5tllWiQSHFe3e4Hpchr4fHzI1NfTEf2zP9PrA2QywG9+w/W2FGFRKHAMn3yydDfKKqqooooqziuIxCPxu6roZ4RI9gH0LlomQ7tLi/Fx+ianymFYDPJ5Kg8cO0b/ef9+mkXj4zQFEgmaNuVyMpaCwUHu8+RJ/h6XiprlFgWnQkeH/u/e3tK2ZCrFn0SCZtPhw/r3V6+maSQrce7YwQLYfJ7mm0joB+izyaTL5lXTcClpNNfp7bz+fsa2tSajMucWIZXiYPzrvxodqj/5E8QaLPC6C3Da8xh26JUcnBOD+MM3j6O1VX3N5yNhUSxyv4EAfbnVqxcgBQWoFQuiGld2vKzWl5+wEPpDspLF2NjiGm/ncpwkcjMRQYisXGkcJLNmPIUCL353t3ESnCdoazPGDOT7qooqYVHFmQibDdtfX4OP/0GP7uXhSQdu/dwavPfvOvHMcWMT5loMw97eBF+tG37/Ah8oc3jjG42vXY1H8AO8Fb1own3Jj+DqC05wUXnqKeChh3SfTYdiePatX8SYPYbErHpb3XjJmKFaRKARJ7AFv+cp988RBTMzwN69uBq/Ri30ZXY2SwF/cPUgxgtBrGi2oKkJSK65SPcZ/9FdUKancPN1M/ivDV9ADOaZqB8r3gfHhBpEGRxkVnwsrScsEI+jtZXPn3e/2xiPam1lBnNbG4Nmra2Ma73//fo2FFpccgnQvfUWFBXN8rNhA60fEzkoHbxeRn2bm03LFqNRKgi1tnJTXV2M8Y2MAKMTVozb6vDmdzjhdTMjwGnP454bnsVFzj24sKYXnc1J2G1S8Ky+npJEWuzdy5rjsTG1HPLYMVp4IyMcMMliVtathdXrgidox3CdnrDwjRyDJWVSYZFMGlPtV61i4M7jwYomxaBO8/TT/C3bEfMVFiYohkIo2iSFwCNHWEWkzZ5wuyknNTXFALnLhfZNAdTPJf37fDR2rVbG/xoaACVSQ7Lhs58FvvIV4Fq1R4rdVsTa1hnU12Swrm2mdAlkocBzvuMONTK9cSNs//Y1hC+Ioq6OttPkJA8tGCQHpk2a9kWcaAroPYfjAy6gUMCe5/VezFV4FH3XvBvwepGqWwGXxwqncy6DxwSrV3M4BIcgw++nQVJTw89qceAAp43Wvi4WVWl9AXFvsVykg6k+K1Zw0rvdC7t/wOGTK8q6u09dYfGrX+lf6+qaIyYDAQxtuaH0l+PxkjX4r341YLfr77ebXzM1L+O1UNTUAG9+s/41p5OVYQJyxcbJk/QRdA6s3Hhbi/XrOcYlCKHDhxlcOH6c90B3N/9//DjvxWPH+PqSoChqmY7djv5RJ2bzDvT3854TyUyZTGUd8nkEgyppMjpKB8zt5rVdtYrzLhJZ+EPXalUZ7lzOvAtjIsH7PhTiTeVwqDeYw2GegnTyJFnqeJzrdiJRPjU1keAAKsriKywA3pRCB85iAa65Rv/+k08ykiG0PbQoFhnhGB7motWjt3eqqKKKKqo4PyEeXZlM+QbI5zNmZ80JixdfNLeD9u+vrH0kpG+PHKE54nLRzly7luZHOk0bs5KExdgYz2N0lOaO3MshHjf2Oz4V5AqLRELfF00LcS6JBM022W8Qys4yYbFzp+qTDQ/zuokqkV27jHHyC1smYFGK6GzQD96JE8aKlVhszgYeGGCW0G9/q//A1VcDW7ciEsjCZi2ixp/DkLVR9xHH1DAchRS+9CVWjm/bBnzuczQ/R0Z4vA0N/HuheTnzNp+wlwWKRdp72orl5SIsZKdD6A/JvuL4uFpuvxAIm122Wxsb1QQgeaDMovDj4xzc/n6Ol8y+nQcIBIwqw3LlUhVVwqKKMxWNjfjU+4dxSceY7uUj/W7k8sZpuwJ9uPeSX6AYCMMbsC5YDkrg9tuZSL9mDfC67VP40m078LDyOrwVP4QdOSjFIuxH9tNa+OIX9V92uTD1J5+GzWWDY6Qf48cm5p/0igJ89B1GaSgAeCN+CovoTiGCL8UicPAg7MjhNnxH9/lru3oQiRSxap0bdXU0Mtyv0mcEu4/tRX5qBhgbw3VD38bzuAiX4GndZxrQj3fiuygcPjqvXDQwQAMrMCk9fFpaYLFwX+Ewk+4FT7B2LaVYOjrUEkmRaNvezgbQMhwOJsqmOi7EoVs/jWI0xg3cdRcfbhUQF7VaeSxbtvCc2tq4z7VreX1fc50N3/wPCz77iTS+/ZkjeNstwJYNaYTHjnAwzOp3/+APjCWO3/se58Phw0wRGR6mBWa18mmjNUCsVrg3dMDmc8Ff58JAWK8NZB85qbdqReRxYsKYat/RQcvY64WiGFVsXniBNseY/tYxJyy8XuDWW6F84hNQ7rzz1NqVr389z0sE+GIxKBYFF1zAB25rK43VcJjDUF8PGi6KwgthkkldE8hhbVsSAa/JuBcKtBb372emyLZt7J78rW+xK/XGjVixgoft8agKLFo5KAGXx4KWFfp9HMithGu4F88f0xtWl7t3YGTTa5ENRpCHDW53+aEJBjnfSpUJi5io222UZBKVsLOz6vAMDxvjm/G4lNzf3s4Bb2zk2DqdCw66yhVQfX20Vc0S3YtFxoBlOah53ikQwGz9SkxHS7AMZQgLjwe44xY1WF0fTuEDt04vaRn46Ef1hNKNN+pvWZmwEA5ZJqPh5NrbS8sabdpUsmHc6Cjvuf5+khYzM7S7T56kPX/sGD/T02PoAbhwzBEWOYsD3SedGJ22I53m5bda+bNsFRahEOYfBBs28HdLC+dfezvXpBKSbSURifCmslrNPfqJCVU2SljzK1bwtYYGDrj2JhHrb309Py/WHaFTJ6NQ4IXy+bjNMk2rS8Lt5gUQUlXXXKNfdFIpJjeILpxaHD/O13p6+NuszKqKKqqooorzDlrp+1xu+eKZIyPGps1nC7QVFto8ErkfgsCBA5UlLEQ/5XSaJEFrK30BRVFt9USitAmylP3t2cPt9fbSVJXbC156aWmRgkyGdqms4NPYaCwc3r/f3JbU9q+Qi0J9PjVeLVdtTE4CR58fQ7S2gExGPYdi0SjfVRfOYWXdFPzODNbW67Wf9u41KoK2tgIuZ5H++z/+o/Gg7rwTAE3YqHcG4dwQppL67DNLIQ/H6ABqaoBPfhL427+lSnU+z4TOmhqe14KloAA16UZLWBQKwCc+weS7D3+Yk3I5b3C5wkKwWXKn58lJlYFaiIaZWJzk7UejdOz8fqPslBzLAGi3p1L8PTtrbIpyHoA9LvWvyQmLVVSbbldxpsJigbWjHd/9zEGsf89FSGbMs1q9rjze+eoTuLnlOYz4LkTe44DDsfD+FQI+HwPs27cDo4eyKA55MVnThppRTXb73r3AD39otD7uugs1V6yF5UU/QkoWowMZrOjpheJxAzU1iIWBD72hH//wgD7tQchBAeATuFjkYj0XhPlr3INutOA3nhuwvmEEN2wZxuoWF7zNfqzbMHeON2wH/lrdTGiqF91HMmhLHIW//yD8AH6DV+MT+AL+HR+EH9P4Ad4GJzKYfqkH/bWM09TX88c7JrHxc2kSwSCfb9u3A/fey0P0+/kQF20lRLAwGKRRdOGFDGo+/LC6ucsv52+3Gxh9ywfR8YdXMGIrSjQqCI+HsbVEgoaX6GUgfl73OifcjnY4ugvAyQww7uPD98ABnmyxyAdpOs3f113HhtXaa/bAA2RtXnyRD9uxMZ7H976nP5iuLijhEEIuYMLlQU9KX2GhFAuspNi2jQcn6sD37tUbMorCa+JyzWvmXHYZiz0Edu822gWKUkRsTS2wZ05eym4H3vQm9ufIZnkhL7qIF+/rXzfXiLdaKQclOjDPMxI8rDVr1GBsLqcmQgN2ThK/n+NTqrmYEOAXUeR0mhcvm+X+hGZ8JEILdS54XFOjNqzu7mZ80+02Ziy4XEBzmw3Yo762B2uR+uVDGMzrS6xWb/ICdjtSVu/8dxcjUWQ2dB6P2ptYixMnOKQrVnCamZVZ22w8HwNp0trKgxscZAC5lKcioblZT0CI8ux02hi3nZ0FHn1UPw1tNuCqq+b+mMv6H1p/DfyPmBikjY1lg8FfuS+Feusw+g7N4sbXpLF23dIaBF90EWPD//zP3GVHh75xmZlO6MmTHLZMZu4QFYXM5mOP6T8satxLEBbHjnGqJhJcyrTPHHupmQAAIABJREFUH5FMNTjIqdvXB13Z+YLh9wMOB3pmIkjBgpMjdoTD6rx0ONSeHMXigqfCwuB0kvUVems+Hyfj6XSFi0R4kMEgyQft+l8o8LW6Ok42IddksXCA02k6OCdO8EKPjvL/0SjXpHicfwcCfBiZ9FBBMsmB8vkWLwelRUsLJ1Isxhtp+3ZWVgg89hiNC6+Xx64onAzd3fz85CRfKxS4Pp7OQlNFFVVUUcVZj0SCarC/+pVqp1QaqRQD4Pk8H0ObNlXYblhmCMLCbtebIqUIiyNHKtvDQqj2AkbVU7udZlMiobYjXIzqg4xkkm7m5CSDmcEgty+r7JTqX5FI0NUuFOibaZOWRNG2VhTg0KHShIXIE5GT60U+iaLQBKur0/djfuGpJFa1A35/DYaGmF/V309ZLS02rE4i4koi78ziwtgAgHXz783MGFWK29sBd2qciWyy7/r+99O+m9MEqx/JoH86Bks0hoLFBktBzSByDvdB5u2Gh3ntRB7MYqTGMTmpDqKw6557Tp2gqRRv8De9afkIC7OLBBgJi/FxNWFGSLGWgygBkyudRcW/zcZ9aBk1uWxAJCSKmNrwML8rV6Sc4zBTPVhyNf45jGqFRRVnLmprsbLLifvee9Dwls1awC1XDuG7n9qLWy85BqetgETeBW+YqbmLJSwsFsYt3G6gaHciEs4jGZdqGh94QNXbEdi8GbjxRlgv3YradfUIN/uRr2vAVCDOp9yJE8DAAG66aACXrVWzSNsCo7gKj6rbGRri4r9z5/xLfiTw/cAH8I07d+HdW/ais6OAmmYfLtpiUc9v0yZDN+y63p2YfUbtYOVEBvdZP4WX6q/BABpwOZjOsAqH55NlGxqAlngOtkEpZUKTlrxyJWMu7e0cK7udMabGRn1idzisZmvcfruqstLWRlmpQmGOgG/w0YITVs5SJDkWAJ+PsbGaGu4iFOL8CAYBh9vKlPc1azBftmK388F6/Lhau+p2s7pAjjb//Of8bKFAQ8lmA/7jP4zzZN06wOdD2J+Dp86DnCeAGa8UUe/uVo2bqSkaMHL6jIiGika4oMSWFocOscRWi0i4AMs9nwbe+lbKW33zm8wGzud5Ts3N3GZXF/CRj5BtkvGa1/DCpdMczFBIZ1RYLDzNWIzjrctoD4fVYK/Qyzl6lB7Zvn1kWV56iQff00MjJpulddjZyQhvPM6o9Pr1usCxMI5DId4KgkuRp5PTCbS16UsI9mAt9rygt8rjygm4L9sEAEgpHigKt3uq4pNTQSj6NDcbCw727uVvYS/KhEU0yv2bSlLV1/N6LSLoKlcbCIl9sz4WExN60hHgnNOtsX4/hi++3nxnjY1lK6c8ETf+6j3Hcd/bnsdNV0zC5l16ldU11wBf+AJv1WJR5f0Ac8JHlNPrsu42bTJuuKODg29CWIyMcNxOnuTtID97FIWXKJfjZ/v6luib+P1IZSzoS9dheNKBPKy6qm6HY5nlI2pq+BBoaFA1zk4HIrMgGFSJYQGRuhgMcjHR7isWU1P5Zme5XvT18QILXcLVq9VG2rOz5mmV09Nq/4rTefY4HMxaE/1jZFmoRILPiXSaDuzkJKMMo6N87kcivGiJhLEsrooqqqiiivMK6TQDuZ/8JOOZ990HfPvbld/P4CDtkmPH1ArRswX5PO2oTEbvAp84UTrYJ1R75QqDpWJ6mja7xaLPySkUmLP2jW+w3eViexnLyGRIVkxN8Ry8XhJYMjHj9Zr345yZobvl8dCXMKuykAtkDx82EhaZDM2pmRnuS1Yr1uZMeb1G+dsXDniB2VlEo9zO9DRJJLnh9sb2BGqcM/A5MljhnUTYpzeY5Rh5ayvgffG3+oRCgMlHl15KVq67GygWEeioh6ezGTWtQSQ8+kQW59iArrogm6WJVltLF2bB1RWzs3TCh4bU6mExQWSf/uTJ5a2wkG8GQVjIsYyZGTp7onfcqSBkHOTu5w0NKjkj70MuMZ+Y4N+Tk7yJxsf593lWZaEoRhm3KmFhRLXCooozG6tW4c63Po/fvnAM//UMo2ybVk/jfTf2Y03LLJ+6g0nkbQ7MZm2orXXB6z1l31lT+HxcZ321TqyYzcDWuQrQPkhli8PvZ3A3EAA8HsQ2ezBkqYcrM4XxpIJgzD2/qCu9Pfirm6bwYNdqDE44sSVyFPb7NQt3Os0Hl/TkznWuR41lApH6FOrqQ+i80g+rNlnZ4WCQWROhXjP5NAoJvbDkVLwLroZ6KJrkg/rkEQQ61fO29g8YrRjNE9pqpVGTSjHGEg6rBIYWQg7I6+Uz+N57eXoOh9r4bJ6wGJtLVxCZp68UolFex337OHlmZ3nA2gixzcbS0o9/XH0tkaCR9OEP88R+9CMjWeF2kxDx+RCOumDJWuH1AolYO7xHNeknvb20fl0u1RLet0+/ra4u1fCZMwouvlj/kUwG+J//0b/WGLfAXR9kpm9PDy+E3a52SOvo4LkI6ZK77+Z5PfggN+D3k30aG+O4+Hzz1RVa2GwkLQwIh9WAbybD/1utPBerlX87HPzb4VBTkRSFgdKWlrKpLfX1NORra7kLOcYJcNOtbfo51o84foSbda9trT0G+AOA243ZjHU+Y6gShIVogdDRoe8vduCA2p8tEjESFgY5qNOEfM+KLKKZGb0iUj4PPPOM0aF47WulDQaDmKlrxUDNWjSM7dG/F48bSFUdXC5YrAqijTbAWTxtWbi6Oi6lLhenuSAQhCqZ1l8QRqHOMduyxbjRNXMVUdJFEL1GpqdJRthsvIVkYsnp5HUVVRa9vYssLQcAtxvHR3xI2xQMJTyovUA/rHa7SjilUhVR11t+RCJcUxSFTouohJicLF0qpSgcvERCLeerqVEb0YvylWiU27Fa6RjJ65XoXwGcXoUFQG/jxAnuI5slsapNG/zlL4HbbuPaOz1Nu6C3l+ff1MS/p6dVh/F0UjGrqKKKVwbFoprA0t5+dqWrV3HGYHoa+M1v9LG9hx6ipn4lMTjIR6SwX6xWNTh7pkPkIMiExbPPlv7OyZN07dLp00/gFv3PkkluS3ur338/yQqAEr0bNtCFWWxeRC5Hk+D4cZoIR4/yXEWTXtnV3LLFaHvOzJAU8Hj4PRGEn57WJ9fIhMXRozRFtPasSIRPJHguZu0VAY6Hx8MlUNtOYtcxP/LJcfjj/Iw4jpER/XY2rZxE0J6E1VuEogDt9Qk8d7j04LU1ZeH7/Kf0N4xQBDhxgvZhLDZvU9bXN2B6KICErx6BhMrSOScG1QRFcL6IhCMhdVUWhQJtPNGfoq+PJ1hbq9p0cknMzAx/louwkIP/pQgLQNX7OhVhIRJsDh82dj9vbOQkFdrgWvT381zFxBNSqLOztIP7+jgZnE7a8aWaRp6DkC+HrLRVRZWwqOJMh98PpT6G7/7VYdz5//0eg8UYEp4oJkbzmO0bhTszCRQKmHHWAjkF3lrXoqsrBAKBuaZZq+2InABi29qAH5T5wtvfziDJnBVSUwPYXVaEV4ZxciCEfOMYrLYBBkUmJmCbmMDrG57HgVATnKEA8g4XrBlN5md3tyFA7d54AVZ7TsDeGkZsVRqok/ooAKzy0BAWtQMvwjE+qPuIfeMaeAJuQJORERzvRlDbd0mmdB0Og0xTOEwCXUiOdHYaYys+nxqbHh7mZ4UBLHgAqxXwBxSgZROtojPhweRyUbRyYIAPbKED5PHQUslkeKybN+trZx9/nKzBz3+upsoLWCwkOfx+wOuFpyEI5+Bc3L5pFXD0GfWzPT20fsNhNaAlpzwJwkI0RADnXVubvtpS6gmP+gYF9pVNQH5OB8fjoYHg9zMLxetl0K6vTw0i/vmfMzJ98CCzVOrreX61tapG0UIRDKoWrAyrldFWMd6igbT4WUAmt9XKqZpO8/9mh+ZyAfEWO6xKAfmius1fQ58R3blGmZe9Sc2o/FAlCAtF4fZWr9YTFkJDWJSXy46A6NNRKcgN8SYm1N4LWvT1cVpr4fcbq3rg8yGRtuFg27V6wkJUUJUL3FgsXCBE8HgpvQQ0ENxYKER7WCsLJRMWAwOqEtk8LrmEX9CStxs2cG5K65Sorvjd79jaKJ2mnf3VrxoJpliMt9XwMNfAFSvK8zgyBgeBbz55ASZPJtGxwW2IvwtJKGCZ+lgsB2pr6Rn7/SphUSzy/6KJt9w7CFDL5VIpfqapic9iLQsUjfLGCgY58NoBE+zcihW8rqcbobFaVW2xoSE2edQSFhMTvJHe8Q4e87Fj3O+OHSwJcjjYNT4e52cX3NWxiiqqOGMgGhUJqTmTpI4qqjgVEgmjeoqcyHy6EDlRoqhvYIA+8MGDLGI+0yEIi3RaX/haSg4K4G159ChNvNMlLERMN5nUB/5zOSpHa/Hgg8CrX33qbRYK3O7YGPMwpqd5zOk054NIurFa+ZospXTZZfq/k0mer9vN7/l8qtqONpkHMBIWMzO0OdNp1eydnOS453LcthzXXr2an62t5WdXryoCUG3/ZNqGA4ctWLOaPlpPD4sftAgFi7hw5TSUXBbesANKnxWdsfGShIXFAmw8/lNYXpKyqq66ijtpalK7GsfjQDCIWEbB0SdyyISigCaJU0tYpFK850T8/ZSSbOPjdNwSCdqAg4P0a7UascWikbAA+HnZKasEpqeNF0mk8gcCPC5t/7iFEhaiz8XRo/rX6+rUc43FjCU2uRwXmC1bOBZCDspioT0/M8NBj8UYp5CbLZ7DkFUPzqZqt5cLZ0CUsIoqToGVK6EMD2P7JQUcePE4+mYmkRz34vikFR2rgrAG/ZiZ9sHq9sHltiw5WTIWU9fplWELlJ42BhXMGmFedRWD1Hb7PGGhKFyvZ2aAgQEFk7YIarpCjFDZbEAwiMzQBGZ7k4g6p5COtcDTq3l47dtnYMOVlW1Y4ZwCWhpoBZgFb7dtowzRHILduw1sfeCy9cYolojWCWtE1jqsqzPdn1DbcLvNE1NFP+aJCTWjRVQIJpPq//3+uQ+fCWSFgMgQMIPLxfc+8hHgve9VA5q5HBt9yRkSdjvw2c+SOp+ampdACWc4DunWDkArlS8Ii9lZblNOFfL76QA7nRxETRB461a9gyNP2ZYWkGnq6aFh5HBwwnd0qIyT08kgn9CHHx+nPI6QyBkbo5FSU8O5sZgsYNEdPJHgnLLZOD42W8WyEFtbeUnsdnPCwmIBPCEHGmpS6BstrRO/ceU0YK8HvF6kx2h/iUKQ04FovO3xGHsY9PfTdhMSPzJh0dRU2QoLuSFeoUByQitrmsmQQ5XJr6uuMgm0W61IwI+xNa/HZS99Hfb0HPOxZcvCNPm9LBWfJy9OAxYLl0oh06TNJDNrvC0y5eYRiwFvfCPw4x/z7zVrSBRKclCiumJyEvi//1ddXo8fZ3HS+9+v35fDweMaHladtYX6KMUi1dyeeMILwIutJ4CLX63/jN3O21P0Ilkqjh7ltV+16mVYmgUhHAySoM3l1I6OoRDXmlKEZXs7B7+5meuiPJiiCb2IAAgdA0Bl5k63f4UWjY28iQRz2tWlT0D48Y9JShw9Smf2v/+bnS0FxscZSRkbqxIWVVRxNmJmhmtYLmeur1hFFQtAIqHKVQqMj1d2H4ODtH0SCT6yhobofjoc/L9Z26czCakU7dZcTrVH02lWNJTDoUM0MU5XgTiRoJmiScgHwIpkWdlx9+5T97EYG2M+mFg+RKxZtAqw22nyiHPdsUNv51ks9AMFkknmawgpo9pa5qb97nc0eQYH9ccTizGGrRWSOHCAfSiF2TQxoZpOcga4x6PmfwQCPM4a2xRW1DnRN6wmIb2w14k113P8Bwb0vfQAYMO6PCLBHDCcgcXngztgx7roEADzkuT6eqD5KalnZE0NE0rb5mI4HR06m8rpBMK1VuRr9ZPcMTE0r6c6MKDa7E1NJj6PaPwyMcGfqSlerN5eXpi6Oh6c9oKLkiYZQhaq0jBL0xdOnt2uViELiGqP2dnSvSYBlUmTF6lYjDeDyCAcGODk0WbC7d1Lv1DIP01OcsJYLByzsTGO54kTPNbzpEpRdl8mJ/UuSxXVHhZVnA1wOvnEiEaxOp5EMGxB6zovsnWN6CuuAGIxzNS2wLeSXWuWWmFhsfC51tEB2MJ+VShSxooVwE03MdhhteqkamIxVTFnfBxqY+LOThQidRixN0CpCSFQGEcxLrHHjz+uL68TkVeHw1waQ0B0shaQA+ceD3V6ZLZ7cFAf2ZYrLEpYrBaLsW+FDCEXZbGoJFCxqJIXLtfSZLtecTQ30/q79lr96/KYu90Unr3sMlV6ZK7BbGsrh9a/pUv/nb4+PqHEgGn6mQBgwF9orktB4FKN1gRWrgS/u2YNo+KdnQyoydazsMxCIbU8RmBsjOfhdEInnr9QiLT3OQk12O0VNUYsFj70y2XCuAIONNWXLr1dEZyan/YZuxf5POdqJYwG0SfH4+GSoDWAi0XKLonbUW6aHo9X1nBpaDBuTyiSCR7u2DEWbsl6sQY5qDlMKwFYG2PYc+PHUdh8EYnUD31oYQREW5vaQ6YCcjjRKK+bkIUSkGWYxBKoC/C73ayKuvtu4I47+OPxGAiL4WHero88YvQLfvpTc547GuX4Dg2RpFoosfDMM8ATT6h/P/usMQNTzKdMxrxlw0IwNkYiZWDA2Ltw2VBbqz5Dp6borDgcHPNyVVw+H9fE9es5b8zWkmhULfkTegYA11hR1VWp3kmiIXgwyLnyqlfp3x8aAr77XcoGfuELerIC4MDv3FntY1FFFWcr0mkuwOm0+QPgLMfsrEryV7F8EHFPLUTAuxIoFmn7jI/zsRmJ0K0V8dfDh8/8azw7q/bqErbPzp36/l0Wi7Ea+MCBpdtHWmgbbmvdMbkiGaAtNTxcvo/F4cMc+wMHWAXc3a1Wb7S10WXTFh8/84z++2vXqrGP2VmVrGhv5/Vdt04tjA+HaYdqj0c03tZi/37VRs1mae8mEjSRZR9FtKIUhAUKBXiSw+io15/0c/t9KGRyUBSaZ7Lq8YWdGdT4s/MBc1/YhgvCQ3DYzBuPtDbnUfPcr/Qv3nILncEVKxgcN0kAia9QUKzTdzrOnxzG0EAeU1O8F+rreU7z/QVmZ2l479gBPPkknbYjR5ildPw4L6LNRns0Hjf6MgeN/VgBcHKUu+EKBQb6dxuTUctCdkzCYZVds9uNnZ5F2ZX4fylMT/Mmkp3DWGxOV92n2t1yc29RYTI8zMk1O6smDc0pG2B4mK+fR7awWfLa8eMv+2Gc0agSFlWcHWhuBtxuWNZ2Yc0lAXibImi6qA7jkVUY9rRgBl74/Apcrgrpb4qu0nJKrsXCQJbFwqBEMKjLABXZ2CKxM5Phg6/npAN7ppsxpMQQithgtQD2uBSMkWvAOjrUeldRtmCGzs7yQZeODj5EhA67QDarZ8hlC1l+mC0C4bCq+y+ee6LZmUns7+yB08kH8Ac+UDpzPBAAvvQlVibkcnywax7gLhdjbLFrpGYPU1O0bKem+COXjm7cqB6DFG02k93XYj5QGwpx56WkCnw+Ve8zk1GDfJkMLdWaGvPOwmcJXG4FLY2lM1k2rRie76ORypNRM+GHlgzReNtuN5Zg796tKspoY6sA7e7TLWHXwkTtDSdO0JFNJumYnDxprK5obKRjJCOTAVI2H/zePArrL4TlXbdT/kZY/KeCz6d2bK8AQiGVH5uaUkkYeTnP5xkn1hEHYm3fto33nFjjNcS0qK6YmDD2iwE4fmaOq+Cgh4fVCpaF4DvfMb726KPGbQNqvGwpEFmDQlb2ZUEkwoP3eNSm1GLMT1Vp4PPxM6WIT1EJFg4zOiMI2Er2r9AiFuPxNDfzeSt30vv2t8lwler6+fDDHPiXbfCrqKKKiiGV0ovbn0MQSRUHDhh7bFVROWSzamWoFoUC7YZKYGyM+xkfp1ljtfIxGAwyxikC3mcyUikjYSHLQa1dqxaICxw6VDnCQjTcFjGHoSHg9783//yOHUa7XmBigts6eZLXWZgPnZ3zKka62PfkpD6BBVCT1rJZ3p8OB8mKmhq6fOL70aiaDyIfj+yTHDig2pIiEV+YTnLsXUtYOJ2AMzEKryWFNr++QcXOw368690W/Nd/cR7K/Su2bUjCac3RELXb4QvZ4Xdk0FxnbhOtsR2EdVaqZrvxRuDCC3lCJcqEa2uB2q5a3Wv+mZMY6C/Oy2iFw6yGt1rB49mxgzdGdzcdh337qGnV08NJ1dTEyHMpZ81MDgrgxMlm9QmCWoyO8jNDQ4uLYsuEhZY8EBUWWkxMqNLX5QiLqSl1wmpRX89z18as5OYMou+FVg5KGxCqq1Od0POokUMkYnRFzvQ1+OVGlbCo4uyA1cpsyq4ueF69FRfctBrhtjAiUet8oM3rrWAcVSygr3qV/oH3vvepZWo+nylREItx4VEUkuLHjnHtrasDLrjQiZbGLGrCRTiip8js7OxUCQthVZrBai3R6XgOa9fyYNrbjcFDrfaMTFichn6g281d+f0890JB9d/E8+ysRXMzrZ23vc34XiQCfPnLrF4AykuPtLcbr+mxY7Tinn9enyqkKLRgRb8HKYK+aVP5xHQ5c6YsmprUTIehIb42NkbDIhg8q3WZXS5gZWvpNLXNK4bU/hUpnrLDUbnqBr+fx2CxGJ0DkWkka7qK3mWVroyVb29he4omfamUscjn6qvNjyORAGC3w1/jQDjmUA1vu/0V6eAoZKFCIVUXGOAyWqv3UXDsmIkD6/Wqa6XoqKiZBENDXM8ef7y0//DDH5onQwkfYXCQCUqncp4zGWogy3jkEWNBHmAicbUIJJNqIKASTv2CEAjw4IWebi7H/9fUnH61jc2m9rsQMi35PCMyPh+vaaVL/S64gNttbeUNsxg88wwH/jzKLKuiinMG2gqLVKpyKfFnAASPuhCJ8yqWDjM5KIFKVT0ODqrXUtsiKh6nTXHiBPdVaRmqSkJLWNjtPG6ZsNi2zZi53N19+vO3VMPtBx8snYvw3HOlCYv+fm4vkWD8oKamdH+zbBa45x7jtRH9K0TSe3s7zZ4NG/RmVDDIbYtWidolSvZJDh4sIn/gMJBMzseys1maN7JsbUsL9+PxACgUEEycgCc3iZWhcSjQB+JPDFjw9a+zAFsLrxfY2pVQDWeHA96QHVYr0F5nXp5y8aiUVbV+PQmLBVTOhjr1SgHemUGsaZqab7Lt9WrEBCYmaJ8+/TSNfsHctLQwxtLZWT55BihdYSEa1JSShRJNuYeHF1fiJgf8teSBWYWFmFRmDUoECgW114Q8CRsb6TeJZKBw2JglduQIxzKb5W85tiX0xIQstdxY8RyF1WoUrpCr6M93VAmLKs4eOJ1cYF0uRKM0ruJxtS9vqZ4KS4LQLGpuBj72MeD224HPfQ649VYu5ELvqARhYbUyMBaL0Qjo6mKAsG2tF+tXzWJ9Z1aXsWsKkRbv95/64Suy782weTMDN2YSV1oKVyYs5AfNIhEK8dCLRVUWUTTcrqQe/8sOu51B/Vtv1WfQNjQAX/mKftwSCZ60kFky244WPT0cKNny7uzk/C/RAdrtNs98F7tZ1KUMh3mBolEeSyLBAFoopEqcnaVwOoH2Mn0DNjaNzmu6pVJqrL2ShIXoly7LE/X2cqjlJBxBgFYacuKLyPo/cYKXe98+Y9GXViNXC1Embgv7Ea6fGzSbjWvkaTbRXipKyULJ/UOOH6efoIstaRsxezycAHNGtbZ3xS9+UXr/w8PAr39tfN1m47GNjHC8T5Uw9cQT5oZrf79+rigKp+7pVFjMzPC72ezL2LhbUfiwFNkGdjvXn3JyUIuBKFN3ODgRhLb8Qp6rS4HDofY8ec1rSp+HzQbcfLP+tWQSeOyxKmFRRRVnI2S29xyqlBLL5tSUPpemisoikShtEwwOnv7283naHuPjNGm0yWMOB92YsTEex8GDZy7nJm41h4MmRG+v0V7dts28768sQ7RYaBtui9yxfN48sURA28dCi0yGtuLoqFrpUgrFIl3MF6X+0ps20Z4XTdRFYfO6dcacDyHFFAxye9oWBsbG2wrwuuuAlSuR2rF3fg2YnTWSL4IY8ngA9PcjYEvCMzMMT9CKKy8swdRIWL8eiPr1Wl8+bxFwudAZM7eJLj3+ff0Lb3zjwhNdJP/bmstgpfPEfFXL6tUa/uGRR+jz33MPm9Y1NfEnHF5Y0kuphtuAWmZSioiYnVV7S2azhl6nJSHHdGTCQq6wGB7m2JUjLBIJnouc/m+xqPElsaiEQkZH8/hxZnzJclACohmsYMjOoyoLWT2rSljoUSUsqjhr0d7Oh0prqxoIrKhSjc/HKFw0Ctx2G3tFFItcsP1+tVmFBI+H8Zd4nCRFaysNh8suAzq7FERafFACfn1qixkEGyMkLcqhlCZQKMSBCgQ4QHLkWmsZyw83OaK6SAi5RJtNLZ/VNdw+m9HUxCDm5z7HbI477gD+/d+NTxyt9IjZ5DSLWgPGznHbtmE+gi4i3jB+xAz19UsIuDc1cc643SRRMhnO15qaVyRjvlJwuYCmFhssFmPp7cq6KYRCynw2fSqlDnOlJKEEz+nx0DHU2rnFIisazEqtl6Pxlhy4Hx2l/Sh6xz3/vP59j0ctHJIxPT3X0iccQCCoqLpXwCs2X0rJQsm3XE8Pf+sC9F6vyq56vfML1swMdX1nZ4Hf/tZ4rWRu5v77zau86+o4DwYH+VMqiSibZW/mUpAJEbud38lmlxZoSCbVfnsvG2EBMDNN6DkGg6qwdqW2LSotRHPEUiRypSBK4eNx4Prrje9v2AB885vARz5C6QItfvlLHmepVM0qqqjizINgvbWM8TkkCyUkbtNpnmp1eVoeJBKlA1WVICyGh3ntxsf5mLJIUaBIhLZcTw/tEmEfnUkQjakFYQGwr5cWtbV0ff1+Y47V/v2nR8RMTxsbbj//fPnrU6qPxcAAz2VsjGMvXw8t/vu/jUkydXXApz5Fk+loYbg/AAAgAElEQVTECZpRtbW070tVaQhZqPlem5ptyaGG53AxMDiI2q//Tcn+FW43/RRFAVx26qwG0sOw5HNwR7y447qT+JM3HENrjUnDaQ02bgQC9lk1cG+zwekowu51YE2NcXBtlgK6ZnfoX3znO8vuQweTps7tyjFsvySP7dulsfjyl9WLt2sXB30xZcgDA6VJAJGgUo6wEFJNY2O80AuZwOVUMywWY0r/8DBtcNHYz8wJEM6UXAZWV6dKuQqH2ePhTajF2BgXODM5KAFRXT0ywptKjEs2qzbaO3Cgchp5ZwhkbmehksHnC6qERRVnLSwWZpV7vbzRnc7KBRYBcCEV1ojIlBL6RqfIzlyzhtnI27czyFdbqzFEREdqp7N09mV9vZr+IjX2NsWll5qXInZ00CoRwXK5PlZYo6mUKv8jUAHCAuDuRYOys7rhthZWKyddczNwww2UDpMfvFrpEdGASoac/iO0MeVrccklmI+gi5IiCaUIi4aGkjKepSHS0+vqVK/A5zurqyuAubnndSAWMqYIbo4PzjNsRYcTqRQ/LzjDSkAoyYllpbNT/76QSNUiHl+eiiT59haEBcBMNVmneuNG83kkAtx+PxBs9MLitKtlvcArRliUkoWSOVuRrKTzPcSDxO1G0enCcCaInTupT9zXx+/IPSoiEeDjH9e/duyY0ZEGOJ+iUXXMd+1SM1jlY3v88dLn+Oij+sCRw6Ha9oslHNJpLllC1SSbfRmDUiJyEgjwgoXDS1i0SkCeCKOjvKEUZfkIC0BtwH3rrVycRSXJxz5G51d4J9ddp//erl10xCbLO/dVVFHFGQTxABFscTZ7ThEWiQRPRzxfqlUWy4NyFRaViM8NDnIf2Szt0P/8T+Bv/kaV/1QUxjWzWT4qKyVDVUmIWy2dLt2/YutW1SWW3azT7WOh7U0sTEXZHozHjQksch+LYpFx7IkJLhmyXKkWv/sd8K//qn/N6QTuvZe2p1DPiccZXpB71GkRCPDYQiGei1AiMmu8/RwuBgAEdz81n38ny0GJHntuN2AZOAGkUvBNnoDF74U3YEUqY8WbrhzDt97xK/zT+3bh2kunDGSKojC3Q0nP9QESFdoAfGE7ot4EoiG9Udvu6oMdGhmlri6jU1UOXq8xjjM8DEsmpff5ZmeNWr0vvgh89rMLl2cqJQcFqP3VSm2ruxv4+tfJWO3Zw8VXbnhtBpltlHWA5b9zOf1zy4xgEcEcs4bbQkpaG4tau9YYrzh6lJNexLdkWK0kLUZHeUy7dlGK66mnOO6HDnEbe/eeU1WMchKhzDed76gSFlWc1XC5GEhrajL2lD5tiCoKm01dFBMJLqaiI1MJCJLZNFtCBGe8XmNJnkBnJxdqv19tiFEODQ1GehZQ+x4IwkOO1okV0azsTmbGFwmh/e/3c/jO+obbMoRFWl/P4NL4uNp0sVBQI5Bm/SsE5Brc3l4+lLUIh1lqWSyWZeVKyfXISRQLgvBaRLlrTQ3vg3IW9VkAp5P/xOuM0dzN8bn+FV7v/CUUTeQqCdF4GzBWLOzebXRWhQNSaci3dyqlSgXMzBjt664u82qBRELT0qdG4VwRQrmi58orBDNZKHkJHB1VE4rm4fEgnbXgeDaOZ/YFsac3gL4+Xps9e2g/792r385b3sKlQL7fvi9VrAvU1nKIDh+m7f7CC/psN2Gny/vRYmSEc0bAblcDSYslLESVh1jClrKNJUM4KDU1nOyVkoMSmJOSnGcKy5HIlYLoO+Tzkcn6/OeBf/s3Etza5/kVV+gjG4UCm2+/UrJQ1UhkFVUsHmKx1C7A55D+tohTieBmdZmoPPJ52iKlVFBOl7BIp2ljjI3R9njgAarb/O//AnffrdoSLhcfw4lE6UTrVxLCHRe5VCLpQwuRwFUsGvP0Dh8+PcJCkHei4fbYGCtutbj0UqN79/zzesJibEy1u7U5PjK6u1nMLyeQfOITJBjyeSYZBYP0L9rby1dqALSNhVuqPaZShIV7qBvKxDi8XqNvEI/PuaaOHH3Y0VFYchn44kF4nHmksxbkYIVitWB94xg+cVsfHngAuOsuHmskAvzxHwNbLy7womYyOr/BG7LDbcuhLapfTy/MSB3OX//68ictw+Ew2prDw8bJcfCgsTQGIIv0hS8srNqhlBwUwPOdmjInLLJZMoq//CXw5JPAv/wLWcfe3tJNugVk6SiZoIjFjDaw6JeRzZYmLMwabjc0qISFvA+ZPTt2zFwOSovaWlW/rr+f5MuxY3SIdu/mNclmjTpwZzFkn3yhyl/nC6qERRVnPXw+PmQrKgclNgxwERaM89SUyiAvVf9ayI34/aVlL1auVBu9LmQ/bre5XsvGjdxOKcKiv58PPZnK9XorIskRDusJirO+4bYWQrMxHKaF393NWuM9e5gFcOzYqaVHZJZtYICZBFps26Z6DMKTKLEps+D6kgtlhGZRNMpAYjR6aiv4DIfdDlhddjTF9N62RSliQ+NcOazPN+8QlRnuJUM03rZajZlfR48aKyxaW5cn5t/WZrycvb2cgjMzxnhpNMrjk21zQ0ufSIQ3urYPxCsEM1mo1lb9eQs5ViF1MTAA7NhpwT/8uA3/8dsOHO5340C3C4cP08aOx43ZfIEAdYRzORZbabFzp7lmsrj+grQYG+OyIWQF+vvZzkDrk7hcRl5aKwslelgUi4t3yJNJjo/wF4GXOVCxYgXnjMdTecIiFOLgCBnGciRyJeFy0TgJh+m09fXxGSGcrp07efHXrdN/76GHXhHC4tAh4D2tj+KPGx7AJ1/7e3zpM+aNLquoogoJqRQXUBHRF115zwGkUjwtbYXFQhOLq1g4ZmY4xqIHrwwhdb9UDA1xik5O8lH46KPqe4UC8Ld/q5IBgrAAzrxiP9HPPp/nY33HDv18tFqBiy7iI3T3buOj/vDhpXOJouG2UAxQFMaStXaxw8HWkRddpP+u3Meiv1+V4Szlbk9OAp/8pPF43/Me1dYcHub92djI67oQ1z0apW3s9+sJC5lk2YHNKIAJFnUnXihZYeFyAZ6ZYRqNQ0NAOIxA2AqviyzLTMrKHWazQCaDgL+It7yFBadf+hLwpjcBNb45wzOb1bE3voAFTrcFl7bpg+Tvzv0f/YHcdtupT1wLM8JiaMhoPMv6uFo8+igrZk9FHpQjLIDSzbT7+vT7Hx7mPtPp8hpkMzPGG1cmLJxO42QRxIxZHwvxEEgmjftuaOAkkAM8oZCxV+fhw/q4lBnmfHH8/ve0mYeGeOMEg+r2xsY4bueIPqEcDxgaqj5ntTi7o09VVLGcEA0YPB5aFCL9RaRIn04wLhQq31hUCNeXaOxtgMXCiJkWsRgXdp9PLbuTCYuRET6gZMKiQgGjcFiN2TudxiZvZz0aGnidVq+mpbdqFcdY6Jc3NpZvrrJhg/7vbFafMg2ohIXFwoEskfJvtdJIliFnzCwYVivnYW0tL96SSjXOPLhcwAVteitgbXwc3oBtvvIpleLpiyqhSkLMf6H7qk1wKRSMjsmSr98p4PUaFb4GBrjMyctBOExVsmSStqYwokRLH59PM1Y1NRzHUKjy5SmLhJkslNNpbDVz+DCdx9/+luTCRz8KfPJfm/DX32nHh/7hAvziQQtqa8kJJ5NshK3Fm99MH6e+npVO8pJ9//3mx2ezqTrLx45xOd63j5Ucvb3Ab36j//zmzcC11+pfe+wxNUYmiK2l9KDQVpmI63s6WYiLRihEDcWtWytf+SA6TYZCXAAcjuVpuG2GaJRrZzzOe0PISUajfK25GbjmGv13uruZMvqyXgCgZ9c4/nPgOnz15Fvxhf/dgvv+OqWPalVRRRXmSKf10QXRVPRUgayzACJuJSosisVqhcVyIJEo3zOiFJGxUAwOMoYpAuay8mx/P4sAAdpyQiLSLLn8lYRouA3wUS7Lbm7YwOMfHqYpIcdFk8nyCj3loG247XbzXpD7SmzdSlNDrnofGFBd7lSK13NkhOcgjrFQoD16//0M5r/97cYk8quvBm6/nf/PZHhd6+po2sjVJKXg89E8D4c578R4yoTFFII4DG403v97jI8b52FTE21PT3pc1RuLRhHw5OB0FGGzFpBMWdUmawCQzc6TNX7/nKBFUVOlpiUs3HnA5UJX3TA+8QfH8dY3Z/HPHf+E6/GQehCrVrFr92KwEMKiWDR2OZfx859TsqkUCoVTT7iTJ82j0y+9ZHzt6ad5I5ersjAr05IJC7PG22NjdH5nZ42EhfhbLCRatLaqpfZaOJ3GuFN3Nyd8Ka3ldJo3wN13A1/8IpnUUIjXOB4nyRIO8+bJ5YwL2VkK+d4tFM6rnuOnRJWwqKKKchB9LLJZVa8jEDj9YEc4rHbelSHEJYUk1UIjpm9+s54tv/Za7kNrrckPjmKR6RJyhLKUVNUiIdSstDI4y6HH/4pBUWgdr1pF0mLVKkYgV67kWDc28u9SKfL19eUZHIsFuPhizDdUAMrOB7M+FjJrvyi0tFCebP36c4ZpcjqBLZtz6FxB48vnyuH9l7zE8bVYALd7vr85UHnCwuNRVeUKBaODoIWinOb1KwMzDspi4eWWy+vXrOEysmoVl8JDh1TFC9HSZ165zmbjFxoaTrsPTiVgJgsla4UeO6ba///+7/oYbTJlwf/8D/C+9wH/9E/AN76h9xG8Xpb/i77OoZAx/vz442p5b7FIQuL++4G//3vgxz9mzDoS4f5PnuT74+NG7vLKK4GrrtK/NjnJDENA9fFEFuBiMDOjZlcWi/QDXnYpCJvt1PKHS0UsxnW4vr48ibwcWLWKF7ipiT8tLZyEbW10Iq+5xmhTPPjgy15lkfiVvrrPryTIVFZRRRXlodXSc7lUHdSXmXRcDoiG24IYFw2Pq6gsEgljha0Wp/M4SCT4Mz5OG7RUn4yf/YxJzcLuNUvUfqWhJSzsdvP+FcUi7ZdIhD8yaSF6diwWcsPtXbuM0i2XXMLxW7VKVaEUEH0s+vu5nYkJ2o3791P26S1vAd7/fhJHzz1ntME6O4G/+AvVTOrvpz8RizGWu5gcoWiUZpCiqNe4thaIuvQXfL6PRfcufO5z+m24XDRplGKBhMX0NC+K242Al8yY11XATMpC+25uEcnPZtDdrapr19VB3wdI4zN7nHkobifcxVk016XwzhsmcPvAffoDecMbFn7iAg6HUeZYloSanqaDoEVjozGp5vvfB773PfP99PcbM9HkGIu2ubQWZpUZ+/bxmMxK4QXkSHcwaHRmzQiLwUF+TkhDaXtEiMkvOyYOB50YoQoiQ1b/GBgwr3DO58n+3X47bwDBlI6PAz/8of6zkYgqpXWOaCeFQsY86HJFNOcbllHAt4oqzgGINASALK4oFxDSEktFOMzFXSYQAKZUiyj/YoiRaJSL/AMPMCAi6jS1gZlgkKuitgb08GFjWk8strjzKQHRPsPv57PlnGi4LcPl4ngvBYrC75plUQAkCnw+GjyCsJAtYA3M+liUC4gv6PjOkcoKAZcLcPvt+MCN/QhMdCPW4oJvZghw1c/XeIv+5opS+SIBkYTi8dA2Xr/e2M9NIBqtiDKbKRwO2t3aauOhITp6MmEhlOVCIS5PR44wYUgUb4lMrXnU1p4x/U60slBCEmHlSn2VxMGD9AsGBoAf/ch8O+k0yQUZb3wj/ZtYTG1NdPHFjDcLH6VYBL72NTplv/ud0Qjt6QH+/M95TQYGuFbu3KkGiACO8/btHPOVKynPJfDII7z3BWGRyZxehQWwtCqNMxoijQ/g87tUdtdywGrlTVQsGgmZbJaT4qqr9JPvf/+XE6Vc98wKY/qx5wHcMP+3y6WUfd5UUUUVc0in1SiqVmdFpGKfxUgk9HGrKmGxPEgk9M91GdoeV4vF0BCv29QUA9uy8qwWf//3wLe+RVt5ZobHlc+/vI/MchCEhaIwVinbU9u28f1CgbdeTQ0fo9pKkVIu16kgN9yWm203N3NfbjfHq6tLTSgBSELcfDPPYWxMrar+sz87tb1VW0tSQwQ1EwkuM83NvFZyIs6pEI2SuAoEOLdEscFmyy48hCvVY8bFuAU/xJ/u+SB2S/f92rVzy1siAY93boLN2VkuRwEOWwEeZx6D4w4UPXYoc0H5Ez05ZIv0JwKBueT/vjQnWqGgCxRYLIAn5ITbNoPRmTw8LzyFwLRUdvLOdy7u5MWG5ViHTFiMjxsJi8sv50F/61t6OaJvfIMOt6wFJldXhMOcGNrKgFL6P7L+FsAb+ZlnyIj19Jg7iTJhIZeVA2oCjxZDQ6pzCnDCi+fX1BQnv9xYb/VqjodQBZEhK0mMjOiJjWKR/Tm++U1WX5hBZnK9Xh6XaAAzPX3WJ1UKpXlte5DTrao7l1CtsKiiinIQVQ5WKy0gv1+N3J0ORAPQFSuMlOrKlQtq7G2Ax0OL5ooraDGJUgY5tUTOej5yxFhhYdbAe4kQfSzOqYbblUS5LHRRMiEqLETzgxK4/HI9IRSPn3N8w2nD5QLsPicUmxURfwa+7FxgYa5hmEiKdLv5sxxtO7QVR+UqKBobl6fhNsDzkqVFBwZoi2rtdUWhFJHHw9/hMI/Z5WJW1um29FlumMlCyTzx4CDt9u99T08SnAouF6seFIXOniC4nE7g+uv1n336aWYvmmXM/M//0F6Pxbj0jo0Z5aC6utQl/dJL9e89+SQfT1Yrz3exFRaix562kGsppMfpYtmlaNevZ/WPWb+nlwNm1SN2O0nrN71J//rUFFmvl0uf98QJJA7ogwCO8Csr6VZFFWcNRBRV2O7ZrCoje5ZDNNwWmJOhr6KCEPKa5SShlkpYFIu0O8T3w+HyCjXDw8BXv0pbZmaG3zfrv/tKQdxqZnJQsRgD98L+cblIWMjxWrO+YguBICwsFtpHjz+uf//aa/WJ5rJS8+7d3EY2y1hrKAT853+Wt7UUhUkwX/6ymgdULJKsEYRMW9vilTQ9HrWdVzLJMbXMzmDbrF4G8nfYhtvwHfwqo2/QVlMD3HTTnMpmagq2/FyHdk3MIejNwesuoFBUkCoyo2ZivIix4QLicY5TV9ecn6UlfaXMRl/QCreTTdaiz0osUWurcaAXCnlijI7qjefRUSNh0dVFpubDHzZuz0z/Va6SuOACI1FSirAoxWC+8AK/MzlpXgIlExZmMR0zwmJwkJNKlBFpb/zpaS5S8vl0dZVvUCoTOLkccN111DYTP/fcU5qsAMyrKGpraSdnMudM8205F7pKWKioEhZVVFEO2sbbABfkctp7i0E4zG3JD5KVK5cWBRQWklY6yOlU/9ZuXwshmK6FWeXHElFby+Hy+c7coOYrinJNCi65RE0VcjpPGb1ubAQ+/3ka8sEgtfjPlKyoMwVOJ2DzOmFRisjAoXo+Vivg9WJ6ms6Az7d8BFs4zOMIBPhTihRZbIn3YiEXBvX3MwNMPoZolOPhdNIvqK2l0lkwqFaLyMvMmQQhC+V2kwyQl7eJCeArXzHa+Nu3A1u2lL6HXv96Tp9IhM6i6BsNAK997eIcyC9+kcdWU8Mlev9+/fuXXKKew/bt+th3MsmEK4BTebHVEaIS5OGHgX/+Z8bJ5YqL5cbUFPDUU5x/yxajdzjUbpNnEuJxsoAykfLggy+fgPgPf4hp6PUa7Q1nRpVUFVWc0RBNHYQtIR6G6fRZT1iI1hzJpOoOVSssKo9k8tSa5ZOTS2uJMjXF6zg+TpvNZjMSFrJU78MPs/JXNLg+U/pYCB6wFGGxdSttI20fOrfbaPMdOrTIOVwoIJPMzd/SHg/wq1/pY8x2O+00Za4wMRAg0aDFwACDkIkEr8nICBNOZDQ20r787GeBn/yEVS/a2PrQEEnEeJzXbqmJadGo6oOMjQH+A89hS1E/qL/FdjyAt+le8/vV1gJOJ+DJTKiTROM4Bbx5uB15AEUk8y5k8hb0nrQj6JxFJMIigXkfR0tYaHpYAIDPU4DLZwXSabQcfFh/EjfdtHQpUZmwmJ1VG73kcgyii+7zAqtXc9DXr6dWrBa//73xJjYjLOTeGcPD5oRFKe22gwd5bLOz5iynfAxydhpgLgklKiwAfePt9BwZtWePUd5qzRo+80rpfTc2mjvTxeLCF7SJCeN1CIV4k4+MkGhZTLbZGQq5WGZk5JU5jjMRVcKiiirKQdR1igV8sTJN5RAOc5F/wxtU2abrrmPUaymNvUU6uJawkKsrAPMGSDJhUUHhfPFc7+ioZvubolS2r0gVEsaB272g6PXddzM7+yc/MTborWLu9nA64bAXkLW4NC8C8HgwOcnbzu2uWO95A4SubnMz91NKUUz0MVsuyEvB8LBRKqCjg8couDKbjRW+9fU8vpqa01fIW26Ew/R/IhH6InV1Rmk6EfAXaG4GPvlJ4CMfAf7yLyn9pCVlQiGSEvk8txeN8vdcGxQ4nXy/FOQivclJ4L77aL/v26fPaFUU4FWv4nLudHLs29v13//1r/nbblc5TjP/xwzJJB3/73+f/sjDDzNW/nLKr/f3c39C5uC8gsVCluqGG/SvP/20uSTAcuD++5GQCAu3t8p2V1HFKZFOc+HOZvmgEXZ7KnXWExbahtui/1a1wqLySCQYbyunWS5XuiwUU1NqsU8gwCCYHAj7zGeM+VBf/zqP60zqYyHOP5PhLSdLOwlZXG21qMVi7Mc8Pm6MBWeztL0OHZKSJrJZ4JlnMP3Is0AyidlZ2ngPPaT//hVX0D4WcrJNTXSlzfpYDA9zmfj2t/XvBQLAd74DfPe7lAkVdp8WiQSJj1iM/vWqVUv3E6JR3tPBIKVo8k88iYvwfNnvuFwkK4RapcuSgac4wwnq9eoyfAKeHGzIYt3Rn6HxuZ/ixJAdlkIOzZEk6uqkmIBgRwFjhYU7D6vHhYaxlxCakuIVS5GDEpAbUQO8OVIpGqJydYXTSVutro431A03GIPxP/2p+n+zhttmFRYjI0aDPZ0uz2Du2kWCYXTUSCLIJIbZeTocxuMQZI3drhIW2hKr56W5EY9zIlgspTP8FGVxDdFXrwb+6q+Mk1qusrBa6XyOjXHx1GopnaWQCQuhzFVFlbCooory0HaMFuRFpQgLEbHasAG4917qhfzRHy2tf4U4Vm2qs8dj3lhUjlLu32+0RivcLDcS4TNtOYOvZy3WrDF/fds2DtjQkCpNtoDIsKJQLr2jY/kaNp/NcLkAWK2wO63IKE71RY8HRYsVk5Nq4sZyBuI7O2nbt7SULmgqV3xTCci3ebForEBeu3a++GQeFgt5to4OtYfwmQxF4foTDvNcxsbK6/263cBdd3EJbWykTX/99ZSMuvNO4NZbWZ6fzapkSHMznUeLhb9nZoD3vletuBZtDD7wAUrf/uhHdHC1ePZZEo1yxl1LC48jGJwrv3cYK+Cffpr+hcOhBpMWSjgkk2yZoMXu3fQB8vmFbeN0MT2tD6ycd4hGmVKpzSzM580lBiqNnh7g6acxDb3DWSphrooqKo1cjgHEs9JBF6Vo2mpNu/2cqLBIJFTJQI9H7ZtbJSwqi0SC8/9U1YVLacKaSKi2gMdjjJ8Kuc+77tK/PjHBXrczM2dOhYU4j0yGxII2xmu1qnZRKsXYsrD9urqM+X/aauJcDnjxRcaHe3slO3h8HMhkkJgqIDcygUyG65Qsq3/DDRwrIeUfiaiSR1o8+yxd7p4e7lOL224zaTeQycwbYtks54nPR9uypeX0FKpdLpoezc205Wv3PYl6DGIFek0/71CyuPdeuq1i2XOkp+Bx5jnRpKC1z53HBT/4HF7z4Edx6cN/jdc+9pdoCU7AY0kZ+yumUmrDbSlY4HPnAacTnSce0X8nHlelk5cC0WRTC9HHYnycPT61aGnhTyDA45yeNuq/PvSQOlH7+owsY0eHsbJhfFwlvgWOHi1vxO/cqX7vwAH94iETFgutsADUKotkkvNudlbVMZMZwjVrVIaunALEX/7lqQ3KeBz49KfZi3XNGmMMzEwWKhLhzTs5yfeXUoJ2BkFu/VitsFBRJSyqqOJU8PnU+k7RRboSsNv5oPT5uOC63XzgiwytpRAjXq+qG2KxLKzCQq6uMPtMFcuHdevMmZxt2/gQnp1lxHQRJJbbTSP9nGtwXgE4HBxuh9dOSShgvoRgepr2WShEO2g5+lcI2O1qXwK5bFygs3P59g/QOCq3nDkcKp8m26KKQvuyo+PsmGfifhAJOeUIi7e9jdcmGORtJzSRk0nglltIOgQC9COjUW5TOKlCIiuT4ff/3/9jL7mf/AT4x38k2dHWxvG7+25jRs2//Rvw2GP61y66iNsWhIXdDlx4oV6qKpOhpJKosAAWLuk0M2N0nAWH/XLIQgmSYmaGx36Wx/iWjo0b2YhIi5/9bPn3+4MfAIChwqJKWFTxcqG3l8mscg+lswLigEWwDaANLwJwCy11OwOhzer3eHh6Qpbn5SKzzwdMTxsD4GYJy0tJIhZ9FxSF01JWqFm9mvbFddcBl132/7P33fGRXeXZz5k+o9Gol111ba/uFRvb68UGGwz+CMWmBGPTA3wJISSEBD5TEkgIgUAcDLaxMdgUG2wTmlljL7YX1931Fu9q1bXq0mh6n7nfH+8c3XvPvVMkzWhXq/v8fvpJmrkzt51z7jnv877Po37v4EHguedkWbBTDS5RlU5rY6fbttE8TJLUPnR87icm1vBk8UyGvsvno3swPk5x5vlKz2gUSKUQ9KURDZHsjFiJXF9PSeSJxLwdHkwmWkuIySV8rpV97M6jqYkqeVXw+2lQPHIE0uQUBgckMCZXNy/UaFsPmzdTG1jTLKFlhEqN9aoszEjh3uqPz1sSxGJZfjYchCudXTwJCwpz73E0P//Y/P+ts6+g0XccW9b6YTEpAuy8JFg5hipgtUiwVdrReVLI5lmKHBRAJyCW0k9Oyq7oImGxfj3dKKuVJu9zc8D116u3CYWAPVliRexs9fX0OZEoyGRof8pnhWhuLWJoiMiVwUFqrMbtsVkAACAASURBVMpqXDG4r1dhYbWqy+c5OGHBB/5AgH7GxrQVH7yEiJff5cK119Ii6GtfA/7+78mg/K675J/776dyo127qOMEAtrqDz2fCi5FNTNDx7vCy7MNwiI3DMLCgIFCqKykWV51NQWMS1kmwB2pAZpVBoOyf8Vi0iYqKtSzJb2IQ6Hqiaqq8jn9GtDC7dY+mK1WmuVOTtK9cLtP/zT2FQK+aLNVWIiw4NmQbjd8PlnztlxyUEpUV9Ntvfxy/WGl3N7Adrtc1q2HdetoeDKbT2+PimLA7QsaGmhdJXY5jquuIlNrt5vm+Fu3Uhdcu5bWBjzTcGpK5rKVNkTK4TMcpqbFbYlEeDzApz+tfk30uQMoiMCrXOx2Ohe3m4rzlHjiCXovnca8eXwx6O/XZm4uJ2ERClGQIRo9I1RUFo+qKuAd71C/duJE+dPOs1UcImFRLg8fAwZE+Hyywa+obnHaIx6npKNMRq6QcjjkwXMFD2g82M39AHiFBWBUWZQSoZBWfaazUz+WuBDwZIBolJqkyaSvUAPISRRiEssLL1C/PB2qLLjhNkBkihIXXEC/uVyUw0HXz+WicxIrlg8dou2OHCFVnb4++v7pabofx45l23p2p8EgEAllwBjJ7ipx9dXyfEupxlxdrU1Impqi5Hixmvl979NYN9DBRKOA14uxV6YRPjaCjvoQKipoblqKcITJRHPdi2p6YA1RwPdCPK/Z7h7cgnf47oQpTBPUeByw2zJAMEgeFmazVv/qgQc039MReRXV7pR6gipWqSkRCABPPYXNP/8SqgJCwPzmmxd2siJyERZzc3TdRSPojRvpPJua5Gwjh0PWIuN45BFqXGJn442wulpLzIjG26IzfFeXVjXjxAm6jiMjxLSNjtJxz82pt9MjLCwWakBi1tTkJHWadJq+m8emRN1cbg7D3dvzwWSiRdT27XTtGhtpYcR/WlrkxpxO0/7EY9arsAAoyh8O03nn2maFQGyKhum2DIOwMGCgELiOiNMp63uU8rutVopCzc3Rw6Gykn4WYwzK9SN5arBeinhHR/5Zjl6JoIHyQkyTOftsmilHIvRwr6jQTioMLBoOB2Bz25BmFqTtNMGWXBXLJgelRGen7LmrRH19+btiIcJC9K9Y6WhtlQ3p9dTyurupuru+nq5NfT3Nw7lkbWUlVVp7verCJyW37PHIgZ18cap0mpKvHA5SAsoF7lfh8dCwzYkjq1VbCf/CC/JiPpEojmxIpehzIrh07XIQFsEgxfqi0TNCRWVpeOc7ta/pZZaVCn1989oYoiSUQVgYWA5IEgUIA4HlG3NKCl5JAagrLLjExwod0LiPONfsB+j0DMKCMl/376cs/aVeh1iMrqmo4tLRoZ2nLJSw4MkP3ChakrRJ321tspJKba32EdTbS8d4OvhYcMLC69XGJjlhwePgWaVXADSPEtV3jxwhUoInqcdisp/y8DDFQPv6AESjSIQSiEcziIQyGB3V3ofdu2Viz26Xn53V1fo+FqJ3RVeXjudZIkEDo98P30gA0z0+tNhnUDl+AttsJ2BFaSu3zM/+af7vW3APPIwYKoYMvo2P4T24H0yS4D5CgetYDHBkojBJadijPnmSynHyJLB3r2Y/jYFsKVEuwsJqJaLge98DPvxh4C1vAb7wBdQ+ra42TdU3aStSFwo9woL7QmQy2ooCnkXW0kLHWV1Ng8ENN6i3O3GCCAc9w22ArpO4wJucVBMWYnVHeztlUynx1FO0sJmbo4bc26slOgB9wgKgcxDT+kXj7elpGqCUGmoAZUxx05ZiJosNDbLBX75KCJ7BJCZp5iIjqqroOGZm6L6tuAmEDPFWiLzTaoZBWBgwUAg2G0WGLrhAO5osFVVVslkRT19ZirE3l61yu3Mfa6EopUFYLD/ESciVV9LkhUuRFSKZDCwIdjtgraDqioSDnIxDMcu8HFRtbf7q1lKCMZoDn3uu+vW1a3WyrUoMu10wvRNwphEWbjcNudzsT6lp7HAAH/0o/a6pUXvutLbKJukALWb5HF2UhuWZdRUV+TOFOT/tclF2Xq7S/h076Lt44IITFjYbrReUSVrptJylmUwWV2ERiVC2n4hkkh5JyyHPopQd4QEJHhRbdfB4tNlqC41SLQQ/+9n8n2KFRanULw0YyIdoVM4EL3bcOq0Qj8tRa2WFhSStaI07peG200kyOA89JCcdr0bCwuslkuLwYVk2aDG+EkqEQvRbjMd1dmoJi4VKhIRCcrWly0WfF4Ngdrs6kVyUMJqaIs78dKmw4JL9SlRVyUk/0ahcOM3nro2N0HgmjI1RXHloiK5Tdzdt395O84/RUUpan51MIRiQgFQK0VBGE7ft7KSkEk4KAfKzk1d5iNXSolfJbbfprDm8XiCdRswXw3C8CdWuOBqC/Vhv6ofHN0xmGEttfEootEjXYAKHrv4kPv1p4Im6t+Fj+O/59yqPPj9PLNuTQbgsCbBYVBu0fvBBXU8BNj4m63ZxKGX1jh8nA7gf/5j+zuFLkLzuzUvX7bXbtYTF9DTtc3RUO3Zv306/XS5aKNTX00C4dau2bPvhh9UyTYC6EYqxlulpNWEhluC0ttJiQYmpKRqIGhroeAMB0oZVgifB6sFq1a8w4SV10ajsoXLkiHq7bdvksq1i9EP5wrq6mtjPXH4TgQDtW1wU5TIgZ4zuw9wcddzx8cLHcppCzEv1ek/NcZyOMAgLAwaKgc1Wnqgd95ngDxOnk5jixRIWXEpo69bcjDqQ36MiXwTTQHlw2200EamuphTvyy6jGXRT0/LpE60iOByAzUMpTwmbG6isnJeDcrmWn7NzOMgXQclJXXtt+febj7usrpaD+mcKYQHQsMj53He9ixa6zc3Al74k+9pbLNphcNMmeTELUBtxOLRd026Xr1kkkntOPjdHw35LC23z8Y/re4Gcdx4dDw9ccJKFe7uKCn886JFMFpdoFIlopRU4pqeXr8KCrwvPABWVpUNMNiinJJTC1FussNCrQjJgoNRQBsaLrQw7raBM+/7a14AvflEOJMZiK1DjihAKyQbbL70EfPazwC9+Qac4Pr6irTkWDJ+PKipeeYXOvbeXsu95BcpSEArR94ix544O7VJwoY8CZTKA06kN9DsctFz0+WT5kQ0btBKgBw5QM86VSJBKUZVCOSVMeIw7kdAq7XCfLyCb+Z89fj53tVhImkkULti3j2KnnZ1yzNVmo3mZ1wv4fRkc7zVTLDSRRiQKvPC8elK3ezfN3cNh2edFWVGh52OhxPbt2pw1AJBmvQjNxjA4WwlrTSXadtagcZ0bLRilLPqpKfpdqgCtIPnT/vrtuOUWYMMW9UWr7Hlpfox2JIJwpRTJlhwzM8Dvf6+/n6kpbcfhsnqSRKbVBQxyAm1bgS98oZizyg+bLbdxgNhZbDbysOBoaZFluOfmtFUWe/Zo2XelLplehYWyg4lyVG1tlMEkKn08/jgt5Nxu6oSiL6nGxV0BPePt556jQYkvYgAaAPTMw3lDL4aw4E701dWy7JMeAgGafIrHPTeXe2FQW0ss4NwcMZEr1HxbJCyMCgsZBmFhwMCpRk2NPNhz74mlpDa63fQAypeRn4+wMLwSlh8NDcDnPgfcfjvw3vfShMnhoAd7e7tRXVFiOBzZCovGBiQ9dZAam+DzyQVPyyUHpcRVV1EF9OteR5n+H/hA+fdpNufmNZXJM2cSYVFfLxMNO3ZQvPaBB2hITKfp/YYGbXWLzUbmhB4PzaOrq2n9oNc1q6po4ZrLRyIep8VtTQ1tx820b71VvV1dHe2TMfmRYDLRsdhstOZT+mcAtFaxWIoP/PX25lYcmpkpf/CQK9/xtZBBWEC7aimX815Pj6q8RqywMAgLA8uBYJD6fTpN49aKrLCIx4G77wb+8AcyE7r9dlnfaoUOZjzYLUkqXhOZDPC7362OCotkkgj9AwcoLtzXR89MpSzWUttrMEjJw+Kztr1dG0tdKCGgNNx2OLSB/rY2in/W1lKyQyxG80KeSM7BDa5zVVkcP04J4YcOkVdwOSokEwlqe5GINpbM5aAAmbBgTE0ctLVpl76vvkrXWXzW1dVR/H1kIIVIjOHklA2RKMOrQxUIhdWTvquvlpPQXS5tMnt1NeaNqvWgTFaKRomU6j8cwaFjVvT2mZC0OtHZmkSlK4NNOx00KayooID2zAxl8S+VFNUzmL78cnR0ANHN6vJv9+BhxGNUcWJPh+FK+ulCKzNufv7z3Izm1BTdJFESim8/OKj9jN0O6fzz0X/pu/H0rXdj/2d/DntHCZIrLRZtZQTvZH196tc7OtSLobo6WTs2ECDmSi/riKOxUc1AikSB0sMiHNZOzHfupP1dfLH69aeeos91dlLnFas6xDJwJaxWkqBWwucjE2yXS866evZZ9Tbr1smaazzRthhwWSibTV8WKhqlc/F4aHEqLrByLVa41u/sLHXG00G/bhEQp/7c18+AQVgYMHDqwT0y+EynunrpZY6FkI+wyPeegfKAz6rtdnrQBgI0mbHbc7sDG1g0+GLGVl+FRH0LwgnrvBxUXd3yyUGJuOUW4FvfogXMcvGGuaSINm2Ss9SKSZ5ZKWCMkpG4VwnPWJyepjmy3Z47Iam2lu5LQwOtW3JZGnk8sjSA3jpybo6GeE6QNTfTHPvqq4HrrqNtXC7gxhvpkcCtiTgcDpqfp1JawmJwUCYz4nGt9ICIp5/O/d5yEBZcDiMSoePOZFa0ikppIDKm5aqwUEYhAYSYOlFisYWeBgwsBMoKqxVXYZFK0c/0tFrTZ2REHZgrNBCfhuD3pb9fGycaGys9YSFJ9L2nk6LH+Dg9BwcGKNjP44KbNtFcohSERSikjY06HDQPEQmLhUiE6Blui4H+9naaW3BJ/qEhaqpnnaXe7sQJauZ6hEUwSM3/5Emaf4yPky/WYrKDJYn2deCANgGbX+eeHm2yNycsOEfIY6nKpXR1NcX6lfB6cycotbUBUjyJk9N2IJlENAq8fEI9Gd65k+ZvfJ6nNNxW7nfjRm3VCgBceiklzoRCRPQcP059IOMLoMkTwcaqCWzflILHlcb2rjDNA202WqfX19OYEw6TXE+BqoS8EAPSdjtw9tlUMfIataF0xWQ/kpNemGNhWM0SGW4rTzoUAh57LPe+EglgYkJLWPCqC3G+87nPAY8+Cvb5zyP8mmuQ2rAVnmZX6cIkYpl5KEQdRzSV6exU68jyxQSP4SSTJOecC6ImWT4Pi54eLet34YW0H3EfoRBVRQwNURnYvn3q9wsRFlu2aBnKhx6iDszLmkQdtPPOo/eK9a/gKCQL5ffLsuatrdoS9nym2m633KaWWvZ2iqBnVWoYbxMMwsKAgVMNbrDt8dBsZzmiBKKOiBKi+6+B8kNJWESjNCGtqaHVRLnJq1UIpaxOIoF5OaiKilOrvmUy0YLqrLPK71/B0dqqnxzT3S0nwuRLGlqJWLOGzrm+nhasgQB1u/p6Go7zFbh1ddG64dxzcxNbHg/dS6dTn7DwemXCZNMm2q6qiuJbn/oUrRXuuouqXHgFhhKcsAC0FTLDw3S/lObb+SCoAGiOs9zBw2BQNv7+4x/ldaxBWChQLg8LgbAImtUNzaiwMFBucMNt3t+5lN2KUXTgA6RelH1oSA6grLABjd+HaJSk8kWMj5eesPB6KU537NjpQ1oEAnTr/H561m7aRM9uxuT541IIC36dxaTylhZ69i+FsFAmA+Qy3F63jn47HBSPjcXo2u/cqd5ubIweQ3qJywMD1E68XppLHztGJM/Bg5S0vxCubmxM9o44cEB9vvw6Hzqk/kx3txzo42OH0yknjXAwRrJQShw/njvOb7MBrQ0x+IIWzAVMmPFZcHhAXW7MLQV4woXFoo3f5vKxYIzUgBMJuoY2G53Lju0ZrK+aRpt9Cl31IWzZynDhliBcDuFCrl1LXz44SBMpMbN+IVD4VwCgrPvsJLPp+vMhKTLdWSYN16svwZ4OA/E4XOa4+qQfeUQ93plM2kWEcmwEZK0vseObzcBrXzvvp7CleQ5bNySx/ZwSLkr0MpSGhrQabV1d2sXSmjV0jDU11Dne8pbc+1HKQQH5PSxE4+zKSooHVFdTpxVjOF/4AjWm739fe9z5CAuuLXvDDepzS6WAH/yA/h4Z0ZI3nOBwOheW0WY2q2Wh+CDFEQjQ9zFGTKB4b/IRFjab7Bu1orIeZOjlKxuEBcGIhBkwcKrBGI1SDQ3yg6/cMCosTi9wgyseSefVFYafSFmgNC7mhAXPdtfLcDiT4XJpi3g6O2mBxRdaZxqsVpoL19fTYnpoiNoCl3sqBJcrfwU0V/Zzu7WERThMba6mhob9pib6m1dZ+HxyvNpspnugR1jw9Z9Y5eHzqavr8wVTMhnSJs8Fr1dOIC4XgkHg0UdJTeXnPwe+/nV9r8NVBXEQKseK5cgRlYmiBCCUUUd4jAoLA+UGN9zmCZGJhJwlvSLAB1g989u+vhWrccez26emgJdf1r7PZehLvc9kkhJ7BweXljBeKoRC1Db53JDHbUMhksX60Y/o1i/2WvD5gSg7395OsUCRsFhI1UIwqDbcnpzUEg48P23HDprrrV1LcdM1a9QxZkkiAiEQUJOJPh/NEyYmaA61ZQstXfr66Dk+PExzDDEuqYdkkgL3s7MUe/d6iZxQ2sGkUkSIKCHKQQG5566ve536/6kpSpTIhRpHDFX2GE763Hh2cC1SaTlsZrHIye56httKVFdTNYUS11xDFbsDAzTX6+qi+eeGWi/OX+/DpfUnsGWzhOaGNKwWHQbXZKLJejJJ5S0TE/SzGIhGza95zfyfjoZKJFrUcYHKEy/CkSSm2emQ5Isdj1PGjRJXXKENsA8P03HzySWftIrVFe3tckOMRGBxO9BYm4K1dgFZ/YVQW6stfxke1l5LkXAAqNFXVtLNTyRom1xJn4UqLPx+eeAVG3lrK12Hujq61spGD+RnBS+6KPd7Vis15IYG4I1vVL+3bx+V1z33nPr1igq6Zh7P4mJWDQ10vURZqGSSOlJVFS2e+ICkRCHCAlihupIEXnmvhEFYEAzCwoCB0wGdnTTot7Utj/5KLlKCT4AMLD+U6ey1tTRBMaorygLuA2C10vwolaIFBa9WXU3Qk0DicgBnKmEByBIIPNGnoUHff24x4GQFX8MoA/5eL+3H7ZbJhs5OucpiYkLOOuaPAnEBbLfLa7jaWm3ymjJRK1/gr7+f1rm5wNcS5QweBoPAr38t/59KUbIf105flRArLBaSVlssfvpT1b/RujZkMmq9YKPCwkC5oTTcBuTAb1nGnGCQooOlDGbwlG492TYu68EDMeVEKERlaseOlWTgDIXoubhvnz5hPTNT+lOKxehy8iq7fLGp5UAyKVf7OZ0yWSFJlND8yCPAb34D/OM/Lr5J8essJpZ3ddE+xYpfv7/4ioVChtsuF82DeLLG+vW0Pz4PEeOrhw7RsSqTMAYG5AqU5maam6xbR987M0NdYGaGSK9c/hcc/f10HcfG6FoPDNBnX32VCJ1YjLqZSO6IhIXZTDFYvbnrxRdr1W8eeCBPl0kk0FYTgtOaxksj6syeiy6i6yZJMmEhWjlwVFdT8v3559M1uugi4OMfl31DurpovnfWWUCbeQzutJ8Gw0KGenY7xQ3m5mSmZ6F+FqkUSQkpccUVqn8tO7ep/q8d3A+7KQlHMgizp0LuHL/9rZZVu+kmrXYp79yxGDUqPk6KJIEyVsEvcqmN9Ww2bUc7dkx7HUU9MQ5uvA3Q+bz5zfrbFaqwAOTrIvqJ8OtQW0vX+jWvKbxYZQz4y78Err029zZWK11Pk4kaqEg+PPSQtsTu7LPpXjU20mCxEEkofg4mE3UKn0/ufHwy4PEQMWOxaEvI8y1YOGHBB+4VCvEWGIQFwYiGGTBwOsDtJo0RXp9bbqxdqz+r0ot+GVgeuFwUIW5spAdvManeBhYNu12e31itp14O6lTBbldnnXGJVMbovTOVsODqew0NstH6mjWl4wirquRrx9c9mQzNz7nkLc+erKqi/fMqi9lZWptVVNA9EJO/uB611UprPXGoGB+nfaVS+efte/bkPweejVkuwiKVolj8wID69cFBWsOsUBnapUOssNAzJ1wKRBddAKFLr9FsdiZ51xg4PaE03K6sLK4ybNE4fJgGFzFyuxTEYnTQeqSizycbAZWbsBgepv1NTCzOPEAA968Q5dA5MpnSEwrRKD3/Egm6bMPD5a3uKwReFRCNqs2bDx9WVyaeOKGN9y5kH7GYtkCns5Oe83qERS4vYxGFDLdbW2l+wc9t7Vqak7S10fxCjDFzLowTD7OzdDxjY/T9PNDGGB33xo30d08PfebIkdyVKIEAzVs4cbNpEz0GR0aoSff10TV68UU1ueBwUHUIh/Je6c1dGQM+8Qn1a/39+rJnAIBEApZMAlXOOHqm1ZHE3bvpdyxGx6RnuM1RXU3x1498BPiv/wL+9V9pWJidpftQUUESoJZ0nPqv1yvr1BZCTY3sms7NMBZSnnTwoHbAFcpBzOepjZkbpo7CYUnBlQ7KGTXptGZegfPPp4oDUZaIm+JwlhKghq3H3PHvjsfpIldUlDaZz2bTljKJhIHNljs+43bL6gixGOmEiZOnNWvUGSCplL6cEg/I9/erX+cVKjyLbO1arb4ZQPGDiy8G/u7vqATsnnvyXyse77FY6O9bb1W/PzZGA54SGzbQMbjd2kGiGOSShfL75fJ1Xs4mfn8u023+vWbziq6wALQcpUFYEAzCwoCB1QizWd/VtxTpxQYWB5eLJj28umK1pfovM5Q+AFw3crXJQQHU5HbvJqPvG28E7rhDrpBm7MwlLAB5odjSQnNksfp4KfB4ZE1jHqsKBml+XlND6yOlrBSvsqiuzpouZvT9KwCZwLBaaY0nDuV8Ts/1sXNBlC0WwT3xykVYBIMUxBAzRnkG5QpTUSkdxBVLodTUheKVVzRB2+CbbtZsttDEOQMGFgqlf0VVlUy0lnzM4brWwSAF9kulN8TNYnNFFXhFR7kHM7+fgpzR6OJlYRQIhSiOqad0xTE2VlpCgcvYA7TfeFwrnb6c4NUPPE7K8ctfarcVyYCF7GNmRmsw3dFB8wFxSRYOF5dAr2e4LR5je7uasACIKKiooP2LMebhYWpifF4wMEDHHwpRPJapC/TgdFJs0+UinjAU0n/ec6PtSIS6UXMzzZ3a2ujviQmaE6TTWglL0e+NG25zDzE93HijNg764x/rb8sZtCf6OiFBPkGXC7jkEvqb3w+XK7f/mcNBP243nWckQrHp2lpad2zcmI1dT03Rifr9NFEUL2ou8FKZoSGaL4gB93wQNbG4obcSQnC8znsCjtgcXNakPFF48kkt4XBzdl4hXvDJSVkLkA/28bg2g54TFspSoVJPTOx2bUcT/UDa23MvhrgGrNMpd7jXv169zdat8t9zc0QC9PRo9zs2Rh1iaEj9urLcicsx3XgjVa9cfz3w138N/PCHwOc/T4u5N7yBjLELtR9OWJjNNJi//vWFfUy7u+m4uTTUYiDKQmUyNAh6PHRM/B6Li5uZmfyZTFzneUUZYakhxiFmZk7NcZxuMAgLAwZWK/SMt1djivnpgqYmevg3NuY3yTJQEih9AKqqaG2Qz5fgTAVf7J17LlUEb9hAgQMeFD+TCQs+76+ro+4nVjIsBXzhWlEhL2i9XlrTOJ1a7wmPh46nqYnm7ozlJixE03hxTs8zXwt5z4mG22LFeyhUmPRYCoJBCoqJWPXG2+IiUIxmLRWPP67+v6kJoXMuV72UL+BjwEApIEmybI3VKgeFy6LowIMcs7PqNPGlgkfZc8m2DQ3JFRblCqDEYhToe/hh6tsTE0tiElIpuly//33+7SYmSudjkcnI3I/NRrHM6Wk6rVJ6ZSQSskdxIXD/CkAeC71eYO9e7bYLiQ9zZDI0N+jrU79uNlPyhNOp9RcDiuOjijHcbmvTEhZWK/lQVFbSnFCZnJ3JENcdCNC9CYUoPu100t/330+xb2Uz5yrDXOJpbk57vuPjdD9OntT6djQ303HOztLnxfmCUg6K+3U4HGoJLxEeD/DOd6pfe+UVbSI5Uql5uaI/9KjXZK99rTwP45JhJlP+WHp1NcW2JYmugd1OSTNr1ijmgxMTRFak0zQPCIXI3Ot3v8uvBWY204VOJGTX8uPHi+s8f/qT+n89zwPhNXMmidqxw3BVmuWJ6I9+pP7M5s0kHwRoCQu/nwLVsZg82M/Oatk4HqeIRuVSoVITFjabljgQJ72dnfINF8GrJBwOecB497vl0menE3jXu+TtZ2fnTcQ1c73xcbo2IlOsdGyvq5P1bK+9FvjbvyXTbKuV7sPatfS9xZAJygqLdJra0V/9Ve7tOzpk3bi2tuIJNRF1dbJhg99P7TyTof+VZkF6ibUiKaYEb4sryghLDZGw0FObXI0wCAsDBlYr9HwsDBmiUwebDTjnHKpvNqoryg4uCbWa5aAAdfBbKcfhdNL8+0xuiowBO3dStxP1mpcKu12W1OI+KYEArSFsNn2fOmWVBZfq1cvYs1joJxdhMTxM55avMnpiQivFxA0kObgJarmqq3MRFpmMnHG5KiGuWEpdYSFGrs49F6G4WgoyX8DHgIFSgBtu86Aqj52URdGBB5ICARrUSiWzFo/Td+f6Pi7MzyPy5cD4OPB//y/50nzjGyTLsoQoRzBIcUPRa1UcD8bHS0cm8Psdj9Mzs76eToETDLkQjdJ2xfo6HDpEz71DhwrzR5xM43FSgPyW9Ligvr6Fk1GcwxKTqXnyBPfUEq/71FTh71YabjudMimgREcHzSNEYrq6mn6amrQS8gcO0DXp66M4YzhM85y/+ivgrruA228H7r1X/RmrleY20SiREjyeDlBX7O+XZTBbWrTnW1dHy9Xjx7XdTElY8O5VyHvNZgOuu04bz33gAWHDbOMenLCjaxWl/gAAIABJREFUd0qdObL7arnB8fGLscKEhdMpz6k7O2l+N5/Qzjue10snEI8DH/oQ8J3vkIbUV76Sv9E6HHTDvF4qSRkZIa2rkZHcHSQW0+phXX65drvGRk1j8IwcgavWQcf0jW9oJ5Q33STfzJYWrTTR4KAsCZVMauV+lIyd0kim1FqVepJQIrhGmx7MZjkTieuDVVUB3/8+8K1v0bjM4y3c8ETp46DE9DSxZ+J93qbwEOFm1x4PPc8kia7f5CQtZB2O4uXFlYQFH9h27gSuukp/+02bqC3Y7fpsarHgslA1NbTf8XG6D06nev7LM7mUKGS8zR9KK1QWSoxFGIQFwSAsDBhYrdAjLPTYbAMGzkDwsnGe6FFovnqmwmqlNYDVSvPGRILWNmey4bYSFgutLcoRnK2qooVsJkPzcUmi9YleEAKQqz2am+nam82512Zc0iyR0CavcVWBfBUWohyUy6UlbWIxWj+XK87m9eaWkz9xYhV7WIgLtFistBdDlDvYvFkTzDKqKwyUG0rDbW5Wy4nWko853CBBkmhQKwVhwbM4p6ZyBwQnJmRNh3IxsA8/rA72/fSn+bNQCyAUokINJRlhMlFWuRKlrLBQmq7bbBQLkySKwY2Pa4c/SaLg9wsvkMyQboWDIPvl9VKb48edr8pClFRijF579FH97Qf7Fi4xxpPJRRWctjY5Nmq3a4PgxRIW/Jq5XNrnbGWlHG/UG+tramjuIRbiHztGv7k5utsN/OIX6u50//1aY+yKCjovr5e6w4kTFGtVGm3X1uae71RVaYuYmprUxeg8PlnM3LWmhiqKlXj2WSHmnh2EHj+iTuSr8yRx9jYK7qbTtF+XS1YGyoXqallmta1N9q2Y/8zEBAWeg0G6GN/8prpf79mjrY4UUVtL5IDPR27l4+PUOV58UZat49JLL78MPPaYtmRHMNwGQAeuzPIH4JnqhbvRRePNb3+r3r6jg4yhOWw2bVmxSFiIY1Znp3xxymW4zY+tUMZaPsICkI23lcQ0N1hRNmpOXrvd+iXUk5M0oCnBlRc4uOleZaU8UE1M0D1qbqaSnWKvkckku9QrmdgPfUittcaxYwd1nqVUV3AoZaGiUTofxtTZXBYLnY8ShQgLnnm3QissxKZoeFgQDMLCgIHVCj3CYv365T8OAwZOAfjcs75+9cpBAbK5Nq8m5otcrrdrYPHweGSZk9lZmo9brfkTk/i6qKkpP5GilDRratJu5/XSvD2V0s8IfeIJ9f/bt+sHn7hnbKmRSFCWa66AV39/cVrdC93n1NSpNXItCnpmOsVEqYqFSFhs3z4vIcJhEBYGyo1QSDbc5uMkfw6VPDkyEpEHslCIoqVL9bHgBEg+I1CAtMozmfIRFqIzts9H+1wkyRkMAr/6lfq1Sy9VJ/kCpSUseBwvmaT5iMVCMTr+/FEGkvmzo7eX4ns8Y191eU+cAJ5+WlW+MDxMzxQun5+Ps9Iz3N63L3e2qxigLwa8/Yvx4vZ2eZ+MaWOaxRIW/HroGW53dMhz4FyEhdmslt4H6D6EA2nMzdG1MZup6kSJdJq80ETU1tJ8e3SUut+hQ2qjbTEuKUI0Nr/gAvW8JxaTq08LxWvdblLRUXqTAMCDD8p/S/EEHn6qFj/Zr9b033XuHMxpavhKUqiQUhH3sWhtJfJi82bFtc9k6MbOzdFJHTyonaQBlLFfKOW6oYG+vKqKLvbx49ToDx2iaop9+6hBDA8T2aBEZaWGmJjHzp2qf5uCfbAcfQX47ne1J/q5z2nLs8XMmpERWRIqkdASFpwtK6fhNlAcYdHVJU+49eB2yzcz38OLd0qnk85FbKjT0zIryNHerj3n2lo5q2lqSm3+0tmZ/1xEcLM95cS4qQl4xzvU21VUUKezWgt31mKglIUCaMHE3ek5rFat8ofI8Cphs8lGWCu0wkJMnjQIC4JBWBgwsFphEBYGVjHcbloQu936XWE1gRMWPFnHnJWlXQ0VFuWEx6P2AqipoWuab2FbWUkGjI2N+au6uaQZQOtbcf0wPZ0/0UissOjultuBEuUiLEIhqnzPhcFBWnPwc1gqJIn2d/Sodj142kGUCQDyu98uBOGwNsC6bZtRYWFg2aHMAuftjVeNZaXjSweluWsoVBofCx4QKRRBHhiQfSxKjVRKG8kFKEt3kebbL76o9Rm4/nptgnQpJaF4AQwgP9caGuj5OTFBlzgUouDNCy/QcNjXR/FYDamRydAYpzDx5ZL5/FYVKrLhkurRqBzU1jPb5piatSDgK1KXSrGPaFQ7tHd1qcdfUT6ykAmrsjqEeyuIFRatrfLzXi9Zx+2m1886S/16Mgm88tggxvdPwOOha6LXT/ftyyoNpVKUfTAyAkgSWlpkE25uPs1jrfniwbGYdr6glIPi2xTrveZ2U5xUrBras4fuRyIBfPU7FfivX7YjnVGHyl533tz8xCQcpuvrcOQ23FZiwwbZZFsVmOQZJryM5Jvf1P+CcBj42tcK65lZrRTo3riRJvR9fdRBxsaIqPjFL4BPfpL8MZQQjUvE9xQwjZ0EvvQl7bF89rP6sQSRsDh5khorN0vTq7AAymu4DehPoJXgmmb5Kgp4h7Fa8xPFkYistVtRoW00fGBTQm+BWlsra5D5/dSZ6+uJidSrjMgHPgik0+p7edNN1H44rrmG9tHSUhqtYLOZzqO6WjaAEZN1LBatLl2+BAF+7mXJelgeiJcglz3WasMqzSk1YMAA1q+nhwQvZWes9ELuBgycxhCzx1YrlIHqYHB1GG4vB7hEgNtNsROuC10Ia9fSTz5wSShA9rFQzuOV2a9cF5xjakobwGhupmOrrlYn8M3OysHDUlYhBQL6/hUcPGM1EtE3Hl8oZmZoXez10qMukyl9ol7JYLHQjVBG1EolZKunnbJ1K0KCjLVBWBgoJ7jhdiRCzx7+/LHZ5DhDLFbCKj8lYRGL0YDm8+mbCRUL/n2FyESue1MOwmJoSKsdD9DgOjlZONAmIBwGHnpI/VpjIwWHxd3oeeQuFjzJGpCfa2YzPZdGR+kYDh2iS+73yz5NXV30rJiYoM8Fg0Aly5pDeL30YiSC4WEXolH6LEDPAr+fNtO7PKGQ3A6dTtrfSy/lP4ee5304/5oijG4V+/D7tQGp7u6lERbK6hAuSSlWWKxdqzXcVoIxegStXUuxXGUs+bkjblxpD8LdHsevf53DiBjAf/83cO4XJmCZm6ObWVUF5vGgs5OOh7cn0WhbDwcPqpMXTCZN/ByxmJwkkk+9B5DHlWuuoUIG3vbSaeB736NY+vHj2sHn+q392LAmBKToPhfrX8FRV6dfQImpKZllevDB/GTqiy+SlNMNNxTeoctFLMncHE0QDx8GfvMbkovSw65dub9LNONOJrUZJbfequ+BAWgJC6WRSSymHUeXw3Cbo6GBHkJ6GTKtrcUxYIDaeFsPvMEA9J1ickospr03euSP3U779HjoObZ2LXUkMbhfDJSsZTot/+10kjfJ88/TQHXhhbTtYvaRCw0NNKBxT49iCIt8klBlNcJaHoiKsHNzp+Y4Tjecrss1AwYMlBs1NcDNN8v/X3XV0kyUDBgwsCLBzccBOQGIMSNouVTwpCGXS04iKtUQy5O0zGZaY4lrQS6HLEnaefvever/7XYK/DQ2atdPfLJc6rm/309r51wIBGQjzlJgZIQCOdPTpUmuLjvEVUu5CIuGBqCyUlNhIUplGDBQEqTTwPQ0ov6EynCbQ+mZWbLKLl6ykUjIkcxQaOk+FlzPStRs0MskDgQWFt0fGgL+9KfCWkNPPqmfbf3qqzRo8wh9EQiFKDP+D39Qv/6GN8jkgRKSRIH8UoBL2QPqTPu6OmoT3Mfi5EkKdFdUyKo3TU0U9I/FskFwXkGTvS+h8SBmZykmbLPRZ0IhunW5fCzE6h/Ru6LCnoTbri4vefWl4h9WXLZ/aEh7+9rb1QF38VFQSCKEG27zCouxMW3Ta2uTPW5zobqa+qZY6dk3YkdLTQQP/SSVtwpqaAh49FcmaoOp1PxBWK0034jH6Z61tubn1Px+8sVQYutWNZnJrQMcDplAyAdeedLYCFxyifq9PXv0vbXec9VJ/M2VL8tjCeTxy2xewjOTk2uBALFiogn2xRdrGZ077sgfuBVhs1FVxX/8R26y4uyzyT09Fzh7lQu7dwPvelfu95WGI4CszQbQuYhkAa8sKKfhNofdru1oHB0d9H4+OByyg30uwiKdpvcqKuhczGZ9Pdf+fvX/yioHJWpribDg5ULd3YvLwrFa5YoJ8R64XDTQdnbSXHHNmvylUAsFl4Vas4b2JQ5IVqu23UxN5Z4cWCz0fSuYsBA5G78/t0XWaoJBWKxgMMYuYIz9mjHmY4yFGWN/Zoy9/VQfl4EVhHvvBe68E/jyl4G77jrVR2PAgIFTAFEKiC/6TtsM9BUEj4fWJzU19FNo3VMseECDB/g6OtTv87VsMqmd24tyUF1dFDRwOLRrNh7TK7Us1KFD0PgmiFXmPT2lISz8fooFTE3RejGdXlAc79RgoVGqYiH6V2Sz18R7YRAWBsqCgQHgyBGEnj4AJBLzQdXHHqNY1xe/SGNXJrP4eIPXK0vKAZADSPE4BXdsNmrwPLK7WPCyADFFftcu9cMznaYgoV5Gsh7iceAjHyFX4I99LL/u0jPP6L8+NEQB4iJloQIB4MAB8u9WjrmMEWEB0KUT44UKi4glgUtC2Wzq+J3JRESJ309SfrOzpEjS3S0nAitJDa8X8I1FZEYgGsXw8SjicSLfGxrolnPCQo+zymTo0vHkjXhc6yn8usZXsD2tLhHsOVa8jhkfb8XrV1tL/UEZtxMfBYUkQgoZbldXU3wzX4UFQPMVk0nHx2LKhXQwjN88qf7wDTdQMr8SP/hNI/zTWWZCwZq4XPS927blT17v7QU+/GFtcoMoB8XnJ3Z7cZXBPPbtdJIsVL65rsOawv+7ZQDvf/0YTAxAKoVEJIW+PmpiXNln0R7EwSC1m8FB6oBKVFcDn/kM8OlPq1+PxUgaqlgfnjvvJCZGD42NwN//PW2TK2gPUGfIFTzfsoWOUe8iJJOkxSmyZum0PFEdHFS/xyfMQHkNtzm4pJIeuroKl+wAsvE2N48ToeyUDQ10rTyewlpiuaQA6upoEOzooO9QGnMvBGKFhRLZBAPU1emTB0uFxULKHvX1+m3LYtEvNxflw5Tgi6J4vLB02mkIkbDIZJaeW3EmwAhHrFAwxq4C8AyAywD8FMD/AGgG8BPG2KdO5bEZWEEwmYAPfIA0Jxdq1GTAgIEzAna7nK0PyH5wBpaO2lqaP1dWlsanjoMTH7kIi/FxWjPpJRo9+aT6/82b6Ti7uvSzeySptIRFPA68/LL6taYmbbCjVITF8DCtFXlVRSmSq8sO8UYU0gEpFkUSFkb/l8EYa2eM/T/G2IuMsWnGWIwxNsIY+xNj7HbG2Hadz7yPMSYV+fO+U3BapwYzM8DsLIIzcSRODCGdzGByEvjP/6RM8KNHgUcekVVCFopkkoKbAwOKpq4kLLiUBjcpWESpFU/eD3qT+oRFd7c2LZ1nNRczoH372yTb4vUSk/Nv/6a/XSaTW6comSTR/+npgkHNuTmqrPiXf9GaJV94oboqUKyyKEWFRTIpJ63rEfo1NRSLczoppiX64ypJjUgEGOhJzt/zyFwcUyNxTE3R5fqXfwFuu41+Dw7qPwfCYbrHnEzbs0cda2VMwuen/wqbU+ooet9JR9HjdDhM5yz6x7a0UBxTeR3ER0EhiZBChtvd3TTXs1rzExYVFTS/EH0swgkb/us7JiRTcgjJagXe/W5tgn4wYsEPntkgS6IpAojKOace9uyh7xM5t8pK8lSZx9wcood7gVBoQXNXTljU1gJXXKG/TUt1GP/9nn147U4/YLFAkoCpWTOOnTAhFqM5U1XVEud2Xi9N1u65R5ud/6lPEWlx4YXAm96kfu+VV7T6bXoIhYDf/U77uscDvOc9wA9+QN9dSBKaMWC75lFHHfJLX8rtnTAzQ429slLLePJqArGSjFdXlNtwm8Nmyx3w7+4ujrAoZLwdich6ZdzQTk8WSgnGchMWVVVUjtXUlNsovRgoCQuRaOGasI2NdJ+LuQ4LRVMTDTJ618FqpXsvkjr5qov4oiiTKZ0J3jJCTzLOMN42CIsVCcaYBcD3AGQAvFaSpA9KkvQpAGcB6AHwFcZYR77vMGDAgAEDBgB5ccyrLBwOI2BZKtTU0Fpi8+bFJ0DpwWajtQ+fm4sqJJkMxap4ohHHzIw2W/E1r6G1VnOzNsksGNSv0lgKgkGtgeaWLdr1Sn//0gmLcJgm+9PT8rosHKY45WmdfCVmOpaLsMgGKURplHJJRa80MMY+DuAogH8GcB6AegB2AK2ghKF/AnDbKTvAlYRUigI5oRCCfVOIzEaB2Vk884y60OHECbXlxEIQidB3eb0K5Qievj8yAvz+9xSlVvpYLACcBzh0CNh/0ISoT0d2qbkZ2LFD/VpPDw04xQxojz+u/v8nP9HfbmYmt7wLQAfKM2RzYHaWCq3f+17g17/Wvv/GN6r/FwOzo6NLH0d5jJZXWIhgjJJsRTNqJXj14vg44J9OYHaKSJqRESARjME7k8bTT8v+5DMzFB/mhLwSXFGKExaPPKJ+/4KWMZwd+zPWQy2vNzBqLexnothHNKolfLgclDJRXXwm+3y5C4OKMdxub5fnfIVkP6urKRlCPIZnU2o/g+uvp0fWjh2kLqzEo4e7MXDSWnTZVDoN/M//UAxcHAPq6oB//VchqDc6ivhcFFbvJMwmaUGEBb/WKgIki4u2BHDHO55E1xo6iEjCgp7ZWozNWFHnis3P6bZvX+Lcbm6OzK9FZul1rwMuu0z+/8Mf1nbA739fW50gYs8e9YU0meiEv/1t4P3vJ5bs/POLcw2/+GL1/w4HKTTkqszIZGSPgooKLeM5OEidTWTuOGFRbsNtjnyERVdXcaXRbjdtx5i+LFQ4LOuVeTxEOBQiLJqa8pu4dXcDO3cuTb83F2ERjdKA2tBA10dcZCwH+HGJ96YYwgJYkbJQTqeWFzIIC4OwWKnYBWAdgB9LknSAvyhJkh/AVwDYAPzlKTo2AwYMGDCwgsDn4haLPHc1CIvSoalJu04rBRwO2SfQ7dYGFaantZnKoj65zUbSxevX0zpKj7AQSY+lQo+waG/XVpsPDtKxL0W1ZWREToJubJSTq9NpbVXBaQUxzapcklDZ7D3xWhiEBcAY+xyAbwGoACUDfRrAlQDOAbA7+/+zoOShfLgWwI48P78s/dGfhsg2MikSRcifRmQyAEtoDnufVF++VIqqLRYTa+BxIl5hNj6effGFF4Cvf50CfJ/9LA2O4fCCCItgkAoapqeJTE3FkvD1ebUR7zVrtITF4CAdRzGEhRi4PHRIX3vp6afzZ5D299M1zxFEHxigaoO//msid0RcfjkRyV4vxVR9Pm1ccmysaBWknOD3mRfALAaM0WUPziYQCgEDJ62IJc2YmGSYnrMiOhfXJJm//DLtUyRrueG2JNFlF21/3l5FXyQSFsPjVrpI+SS8FPsIh7VG5tu2aWOPYkVJIJB7F0rDbaeTDufoUfU2ra0yMVQozllTo+9joYTVKuG666iZ9vZS0b7NKveJjGTCd57YDClTmLCbmiJ1Ij2Obts24LvfFRLOUykglULMG4HDnARCoaLlDLmVgMNB1/iDH6RYvt0OvOetEXz51j5UWqKA1YoJrw09J52AyYyNDT601ETQ3k7SVIUMw/MilaJGcM896tfr64FPfEL9mstF8lBKNiuZBL761fys4f/+r/r/nTuBj36UJl3r1tFYlas6QsTll5NTOUCTqS98QVsaq4TPR5Ot+np9d/WREToHsYxGSVjwqoRyExZiRwNogt3cXHyFBW9QeoSFaHji8ejrsCrR0bEErbEiwTPVLBZ5ME+n6ZnlcNDA2tZWPv+QYo5NNP8rRFjw5+IKJCwAWQ2NwyAsAEu5d8AYOx/AdaBMpK0AGgAkAYyBJI3ukiTp6XIfRynAGGsEcGH254LsD19V3itJ0vsW+H0dAD4B4HoAbQDiAPpAEk/fkSQp15P9yuzv3+u8x6dkOQocDRgwYMCAARl8raLUjzYIi9Mfdjvds0yG1hnt7epE/JkZuTriyBHKJhUDAdu20XqMJ3mJa7bFVlhwn4jKSq1H3/Hj2qTflhbtdmNj8zLki2qP8TjF6qanaY3I14U8Q9XnO40D8wsVLi8GwaA2MJCN/ohBu2KSLc9kMMauBvDF7L/3AbhNkiQxOrwHwL8zxgpFe3okSRos8SGuPGRlmKLBFFJWJyKTUUxkMpic1ubODQ4C55yz8F1w8+ZkkrqM0wm0W6MwKcsHwmEy8tm6laK/mUxBqZGJCeIRQqEskRpOI5QAYiPCQFZRQYEdUTolFiOyMCvBlhPxuL7O0gMPUCRXiT/9Kf93DQ/TRXC7af+KoNuLL5LngJ4UuM1GCdiXXaZVahEv0+QkBc+X4sMajdLzK5PRj5sePQrcfTc9zxobKX62dq3sAbx2LcX/qqsBpzmOsUkb3KkMDiXWIBlNYTZhxnMnJE2sPBoFDh4kmSnleKf0gBBJjjW1cVwTpZKLDVCTvxMBF+LRDOzT0/RAywFeBdHTo32u6iVLi89kv5/at14MVWm4XVdHZuFKTovL0NvtcoJKPlRXy6o0zz2nv811VycQj9vnkwEaG4F3XjOL+/5XDk6/NNKEBx+P4y03RuDU0Tzx+4Ef/5h8ofU4uDe9Cfj4x3WONxuQjPrjqKpOwBz0weEo7qHO46/cJ/mmm4A3v5mun9s/B4zSgQSSTkz4bGiujaPJGYLbmcGm9VZ4OpOAZYkGxHNzVFElMlB/93f6AeKzzgL+4i+An/1Mfu3YMfKyUVZjcPT0aJMU3v52Ig62bFn4BMjtBv7hH4Arr6SJIycWcmF6mjoXN0wRM+VPnqRGI04IlYbbvAzmVBAWa9bQ5LEYJpUzYHrG24kENWyXi86DMblyQgzGK7EcUt1KwkI0QeeDY6H7XC7wCouFEBZWK51HOr1iCYvaWvWz2SAsykxYMMb2Arhc5y0bgA3Zn/cxxu4D8AFJkgqnJZxaFFfrWQQYY28CcD8A5bLQBeD87M9tjLHrJUnq1fk4p7NPiG9IkjTBGAsptjFgwIABAwZygksLWa2yrnA5pEoNlBYOhxzg4T4WSm+IqSl6PZ2mJD6/H3jiCfV3nHuuOntSXBeEQgvXk49GKdsyEqG21NFBcToe7Nq3T719ZSWtFUVvvVSKAmZnnbU4wmJ0lL7D66XgjdksxwAiEboei/EQ5AoGPPmuLNAzE1kqxFRhxua1j8UKi9VMWDDGTAC4mv9BALdKkpQzj3wFrF1OD2RT10NxK9DQgOiEhJf36weChocpmFsEl6DC4cPAJz9JZOcVV1BC8vRMCk1iZH7/fgoWtrQQaZFDliOTAfr6aCzxemk8ev554JFfmuBxbMR/bqiAKpTDJVvq62lAGxuT3zt4UOsWLKKvT99z4tFH1YSFJBHroMSGDerg5OQkMS0tLfR31ugomaSYpR5ZsXEj8Dd/I/ufSpI6eVskMMbH6RmTa3xOpeSYUy5wkglQxwUTCUo8/+lP5So7vUITkwl4/evpuNd4wujvBfwJM1BVhZlwDGEpjd/v1S8l2LuXlHe42okkyXJNiQS9r8Qbzx9H43Mkw7UOfar3MjDjxHOz2N44mZew4H4YYuVDfT01H5GwEJ/JsZicDCBCSbZYrcAvhdqtq66iOUMhw20OLk9y7rnaIgAAsLEE3nDBDKKpFrS1EZnnHY/hnZeP4ddPuTETkieSd/62HT/8Yxq7ryECYsMGOtaHHgIefFDryQxQ2/nEJ2j7SIT6YE2NgtiKxZBOS0jEJDiSIbhiEhhaARTOSjebZYPzuTm69/PVGdOJ+WxzX9wJuzWN5tok2lNxdLmnwSoqqdEuhakDaMdPCzm7u3blHyduvZUmUUoZpXvvJW1PMRtfrK5oaACuvZYmVfkMRHKBX6BiNLB4R+Jjoh5hMTZGk1OxQkRJWFRWUicvtnRmMchFWLS1yfqrhWAyaRsUvx9Kw20+ueIT+HwGKPmqV0oF3pl4hYXPRx2trY0G9q1by+cdUggmE7VTcXFQqMIC0DfwWyEQ85UMwqL8klC8hY0B+CaAvwBVJ1wC4G8A8Bb3XgA/KPOxlBrD0K9wKAjG2DkAfgIiK0IA/hHApQCuBnlTAMBGAP/LGNOjlLmgXa4VbECxjQEDBgwYMJAXnLDgpoXlrkI2sHToERZKTExQAINLNTz7rNZj9p3vVAcuxOBIOKwOKBWCz0eyKV4vVVJMTFAM7vnniUCJxWQdcY4NG6jtNTVp4/QnTizOx4LLyszMULCLr0UdDlr/hEL6+uXFYGKCruexY4vy7C0O4oqlFDsSMy0bG+cXzyJhkU82eRXgGshJP1/NR1YYWABCISASQTBuQ8LkQLKyFs8P6zN+PBZXhLqOCt/4BikhxWKUHf/4b1M4OW7WesBEIsTuplJIzfgwPk7B+OFhCrr299O4deAAvc7fS6WoSi0cYRj32vGPB98O1RCiZDDFKoujR+VM21w4dkz/9RdeUEs7hUJaXb23vEXNDnBNI79fVVl1//1aKSKbjVRi7riD/H03bCB7G/6zcSPFDUUZc683N5c6MUGJ3wcP5h9nOTnAjwOgS/XBD1Igu5AkYCZD/hv33gt4zBG4zVFMBFxIuyoxHanAc8c8iMT0wx3PP599DngpcBx57hAyyTSiUSK/VNUJlgyuau9DfYCMgqsQQAPUWlq9P9tPY7WeJEwW3CND9JZYt05Wv1FCLwE7l1VGtosBoOe9aND95jfT72IJC4AIgu4uCS3QBglvZXeDzU6j2p2C3U7bBkaDsLEkPnTxAc320bgZjz1G9/ZDHyKj7rvu0icramupP7/pTcTh9fVQqtq/AAAgAElEQVRRmzp2jBLyJQlALIZ4mIZnRyKACouOxlcecJ9kSRJim9l+KkmAP2ZFt38/6o89jc7mGFg6+zgohU7mwICWudq1K/9n7HbgLwXV795e6mxKRKNaDdDrrqMGtRiyAqDGyRtpoWDw9DQdq8cj+yCIgWe9cYyzdstluA3kJiw6OhaWvZWrQYXDNMm12dTZIB5P/qq7QkbopQAn3cxmajMjI0Tg19XRg6CcRFExsFi0pA7PxtLDGUBYiOugPDZUqwblJiyOAXgHgHZJkv6vJEkPSZL0giRJf5Yk6RsAzgbpwgLATYyx15b5eJaK2wG8CUCzJEkdAD60yO/5JgAngBSAayRJ+ookSfskSXpCkqQPAvi77HYbAXxqqQdtwIABAwYM5IPdTvNWw3B75cDhoLm8yUSBFZGw4H55a9ZQwKlPnRCKTZuASy5Rv6ZXMTA7S0GhfHE2vr+DBylIcuIEBfdGR8kXdnKS1uX792vXp52d8hquu1v9Xk/P4ggLnvk7M0NBlPFxCn58+9u0BuaVI4v5br+f1nWZzIJiIwvDchAWiixg8TxWOWHxtuxvCcCv+IuMsVrG2AbGWB7RaQO6yGQoaBONIii5EUmYMTBVgdmwftSUFyYsNN6grDADgAd/whDypfQ76ksvIRMM4+DzcRw7RmMN/+ntpe4yPk5/84RTsQCiN9aGPihK1PIRFtybQi86y3HkiP7rqRSVGihPVBwTrrhCazYwPk4HH40CgQD8fgrsK7F2LfDDH1KFwsUXkxRXayu93tJCf7e1URBFT6FEJD84RkdprJ2byz/OckLcbKZr+93vkvyPXjVFPtx/P/DiyyasqfAjmrahf7oSgYwLj7+SuwxuYoKOP3h4CAgEEDrpA0ZHEY1qJZAu2+FHw/ghmCW5AWiMt49mjWr1TEGyyHYD9PerX+dVLSKRUFenjdXqfb3ScNvhAB5+WP3+OefIQ77NVjxhUV0NeDI+vBZPqV63IY5/yHwZrSf+iCaHL7utBCkQgm8yjre6foPb5nMwtejp0Vc6NJuJe7vrLrkLeb10fps20fN8dJT6Zcwfh6tnPy5+9W40nXwRLnN8QdE9Hl8G9AmLUNqJc1+4E9fecxO2f/fjMH3zGzRxkKSlExaRCMm6KRk5mw047zz6W5KosUxPazMKrrpKWx56331qZvDJJ9UdjzEiLJYywWeMPu9wyCVwekgkaKJUX0/nxCsmWlu1JVfPPqv+f7kNtwE6Jr2O1tm5MGMdZYNSkpbcvwJQn0tVVX4z623bit/3YmGx0H21WKgTmM3Uthoby1hCvABYrdqKtUxGK2+q3J6xFU1Y6PkRrnaUlbCQJOmNkiT9VJIknfpWQJKkGagD8n+x0H0wxrYxxvYyxnSoUd3tzYyxHzPG3rXQfUmS9HlJkn4lSdKipaEYYxdClsm6S5KkfTqbfR3Aq9m/P8kYE2sOeT5LriWlB7mrLwwYMGDAgAEVuB+CQVisHPB1lNVKc3Nx3cMTkBobaQ0iSp7v3q1Nnmps1K7ZeDlyrvW5JFEgj3tT9PXR927eTAEGu52CQr29FIQSA1Ht7TJhIVbA9/UtnFTIZChD2+ej2EJNDfDP/0yqKg8/TNIW4TAd92KUliIROci2GMKjKIgpVoHA4spBlBAJCwXDJcZDcijkrBZcnP09KElSkDF2M2PsEIBZUJLVLGPsOGPsbxljxUQz7mGMjTHGEoyxGcbYnxljX2KM5daNOdMQiQCSRIbbcCMSM+OVvtwmnhMTOlnPBTAzo/XQHhgy4+DLuktQ4OhRDBwJwz8VQ8+xDA4doqz6o0fp59gxmXjdsIG6pBhbA4AnoMiIrqmRO5NovD03R8HsxRAWAPDYY/LfT6mDx2hspIi0ypEYskZ8Mon05AyefVabiP3RjwJvextxHfmSaSsrKb4mzg/0CIt0Wpaml6TcfGsmQ+MoJ5dzVVVYrcDNNwPvfz/JP+3cqU2IliTgy/e2IBrKoKEmhXDcjJdPNiOSkAOkjGmtAZ7Zm4JvPApMTSE0MI34xBymBsMa7/PdZ8/APaqugOm2qasO+qRuYoTyEBahEFXriM8efutEIsFu1xLIegEsXrkRjeqbhb/1rXSduQToQggL++Qw/g/UDMgHcSfacBLbeh+FM+LFunWALRaE2xbH3GQCHa/8Cnfig/gVrseb8ChMyNEPFbj6aoq7f/KT6mfQ7CxdA4eDYqnr11O/9O89gCt/9CFcfPw+nPfI51H16p+11VR54HbT3MhmU8SXUylqwMkkwiEJF7/wbfkDL75I2RfJ5MLLv0TMzWkHlJ07aRDr7aXS2BMniL3t7VVPNsxm4L3vVX/2xAn194lyUDt20DixVPPkigq5DFspS6XEzIxsHMZNZiwW2rfYccWGyrNWIpHlMdzmcLnUFQ1WK0lnLbTCQmxQkiQTFna7mgDxeOga6enmmc3LU2EB0LnyY2hvp/vLGdRTDYuF7r/YbgvJQiUSpamCOgUQu8gChrQzFqdIlEyFPyr+XpdzKx0wxpwgk+nLAfyhUNZTVpf2HgA3AfhB1hB8ufEWxd86ipCAJEkZkMkfAFQDuErYhK86NeJ2jLFmAG7o+FsYMGDAgAEDeuBJUybT6tavX0ng6yg+N6+p0a7rODmwb5822e7667XSX3rBES4roTf3T6cpxjY8TOuHkydpst3dLWtEr1tH/6dS5C8p7q+1lY67rk6bIDw0tHBSYGqKjnVqis7l+HE1SXL0KAVAuI/FQsEzZMtKWIgVFonE0qssxMCAYkEqJqDX1CxtVysV2XXC5uy/M4yxbwL4EQAhXR4bAfwbgCcYY4XonSsBrAFgBVAH4CKQFGwvY2yxldpgjLXm+wFwGqRHZpGNpsaCSaQsDgSjJhzozR04i0blflwscsX6f7g3RwZrKoXk3n0YnbIi5o+hrY2Se7u7aRxav566yJYtFGvi8noi9uBq+Z9MhvqZ369mYjn279eyg0qIOkFKPPOMzB7/+c+qt9I7zsKAvxa+VqGZ9vVRwCwYxMChEH75y3lpfgD07PjIR4qTgOTPFjGYolcJEQzO7xaxWO6hKx6Xiak77tD/rk2bgO9+PYgPvHkK73lXBp/5DPDNb1LByYc/rN7WF7Liy49uR3NjGt1rYvjtK2opkV27SOpfiWf/lIEvYAICAQSnooh6I3j5WXXD81SkcH7LBGqn1SxGR6Na+qkX60mGZ2BA96S5R4ZYZVhRQfyx3a5NGDCbiyMs/H56HuspAbW0UDVlPC4rphRLWNjtQNVsH96Kh/BZfBkb0INbcDf+HX8LAGgdfgY1sTG01Ufhisyg1haGc/QEagdfBgNwPX6NR/FmDKEDHz7rWTTUaBX2LrwQ+N73gM99TqsaxGUp6+qoO519NrXBTetSuPDV++a3Y1IG7t/+nC5Avj6mgGi8DWCeiJASSdQNvghzRigtnZoiwmKpwdDJSdIkU6Kzk26uyUTZ7Rs2ENFgs2lvul6Vxb33UiMbGNAOiLt3y27rS0FrKw2IbW1U+iJmumcyNE7xQDy/oZWV9Dk96SUllBUWTmf5Dbc5bDYaUNavp7H75psp1X2hhAWgblCxGF2Tigrt86Cykq6RON8DZKJnOcAJi+ZmOqatW5feTkoFfmxi2UExhEWWeFxpELuI4WFxehAWyuykwvS7ApIkRQH8M6hseyeA3zPGdKsOGGMMwHcBvCf70o8BvLTgo106Lsv+DhfYvzJ95jU53rtG53PX6nzegAEDBgwYyInmZlorbdhgEBYrBXY7reX43JwxrSzU8DD93rNH/frOnfryHlarNljt8+lnO0sSBfAmJ2l9PD1NgZGWFm0AzOOhwJOYKdTdTe9xc26xwmJujtbEerGBZJJiB8PDlFx46BAlQPb0UPAmFqOJv2jyDZBMbziszcguhFiMpDyeeoqSpZetwgJYel24WGGxmeLyPMilxCqusKiCvDbaAeATAMYBvBtALQAXgCsA8IjxpQDuzvFd/QD+HcBbQf59FwJ4J4CfgdYtDgD/wxj74CKPdaTAzwuL/N7SIxgE4nEEoxbAbseh/goEIuqAiImp0+q5F0WxEKXgOV6caMN+nK37Xt2hpzA7Z0ZrVQh1dRQY9njkhE6XSx7Lnn9eXwHlj7gKGW7y29BAH5yYoKCjKAv1yiu5KywkSdtHlYhEgEceocHw4EHVW0Ntl6F/1I5j7vPUn8kOnv7xCEaGMnj0EXWV1utfrx8r04PTSXEj0VOBP2OUCARoXInF6HRzERZ83Bkc1A5vFgv5C3/n36NYE+1HqHdCU7nwtrdpZQ0PnGzAffs24udPNSASV1ZXSNi9WysZf7TXipNDaUjpDEImD8Jjfrx0XF1GcuXZPqTDMTTNCRUWjWqmtxfr6cR/+EOS7dq/n1j87AMsFqO3xbba1UVx0VyxUXE81su4nZ6mbjY9TT5SSrz1rdQc43E5wdvpBLW5IgJ61d4BMABfxufQg024G7fCDgrsm9NJrOt7HBgdRZNpGlXw4YLeBzTf0YpRfKrhPjzw+Vfxla+Q2fmuXcB//Afw1a9SnFgPs7Oy9P/atXQtLrgA6GyKoHFaHZQ3v3pYLtcpAlwK1elUPMuz9yocYegeelL/gJZKWGQyZLYtjgU33EAERXc3EQLd3XTy9fU0GVLeK7MZeM971J/nVRZidUVlJXDppaUpn3a7icWtraWFw8SE2ixlbo4aeX09VXRwhqyyki60nimLEkrDbaez/IbbHDYbTYo/9jHgi18Ezj+f9r0QSSilGSAf3HjDcjq1iyuTSb/qBNCfoJcLNhvdn6YmtUbr6QCLhdr6YggLYEXKQonTfz3pvNWG04E+u0Lx96s5t8oBSZLuZow5AHwHwHkAfssYu0aSJFGw9NsAbsv+/RMA75ekpdbXLwpbsr97Cxj5KWdFW4T39oAWQjczxr4lSdIBAMiSNZ8FkIBcoWHAgAEDBgzkhdksrxMMrAwwJi+4+dy8o4NkTTgGBymIIepx796trwxgsWgJC+73IK7Px8cp4M8Di5x8yHe8YjyOJyHz9dKmTeq1BiDLQinXjYEAxezSafpJJOSfZJIIC+7VKCQjA6AYUjhM5xSLFZ9E9/jjFERLp+n7f/AD+nuxHpY5UVVFF0w5TZ2czB3VKYRAQCtTkiUs9JJRT6f16jJDGdFxAIgAuEqSJGXq+17G2C4A+wCcBeBGxthFkiQpe9kvANyrs854AcBPGGNvBPAwqOriG4yxRyVJyiHKfAYgFCL/irgNCbcDL/WoG9iGNSFIySR6Z+TBZ3BwYTFBUcJHia/iM3gQN2ler584jJZ4P+qsGwA05v1+PTkoAJhGI45gG3bgMAXwGhtpUAyFiLBQfvDoUVmPTmR19eSi1qyh1zkee4wCadzkI4uRdVfg1WMM1vQOnOuohC0mL4HTE1M4NtWF5wIVmJhU7/OWW/KesgaVlVr/Uz1VGL9fPpVIhP5OpbRJu7EYXQqx+KumBvj612lOkhgNoWfYgUwkjtpEBG1N8qUzmYDPfIakpJTD2w/3tsNmUXe9K3b64LB6cPHFZtx3nxz7zWQYnj3gwvlX1iLVuBbHD6UxOacOUu4+dw7J8WlURcZVr2+8pA5Q+KYMohNJWGD9zW8oA37dOgrg9vYCHg9C5mYg04yeHnW+KI9L56p6EJ/JYsZtLEbPeZ+Pmpty1HG7iZgCqD9VVWVVa6wSESrBID0L8gSS3bODOd8DAPfTvwVufB0aPQwTJ49j/ah+zmTFRB/MUhqXnBPDJZcUfuim03T5mpqo7fC4rskEdLunIU0JxxWJ0DnV1hYd8OW2AzwZ25rNzA75kugY18l28HqXTlgEAkRYKNHVRSVdHR3EyvBJSX8/7Wtigm680ldg1y4ix0ZG5NfuuUfL/l10kb6szmLBy2gBmnQND9NE1O0mssjjkctnOdxuauT5CAuLhYgabrjd1FR+w20OTjYA1I440bKQCgtA26DCYfoOs1m/UqSqigZVsSJGNHQrJ1pb6XlVX5/fU+NUwGJZeIWFclEUiy1PhU4JYRAWWpzSCots6fXfK176aa5t80GSpP8GsrWJpD37v4yxeTqWMfYfAD6a/feXAN6dy1ejnMgSK7zH5RD+I0iSNAeqwgCANuG9FIh8MYEWTncyxr4O4CCoTP2zkiQNlvDQDRgwYMCAAQOnGbj3CA/ci2uN4WFg7151Yp7FAlx5pX6yndWqzeYMBrVysMkkraNnZ2k91tVVOMgdjWqDivxztbUUhKqt1VaJiD4W4TAlKvv9VFVx6BApqQwM0BrG56Nr0tZG63ghtgeApEd4oH4hslB33CGb7kYiwO9+V6YqC7O5uLTaYiEyRSZTXsKiVHGNFQgxHe/7AlkBYL7C+x8VL71DeN+fLylKkqRfAbg9+68LwK2LONa2Aj8XLOI7Sw+ugxONIpByIRC3avwrrjrLi/YadZ7Z8PDCkiPzFSf8DG9DH7QBIAYJZw/+ggaVXAayoGC7qN6ixBPYRZ3G46Efh4Mi6KKPxcmTNODoBTuPqbP303Ynpq8TdOr37gV+9SvVSwl7JQ5YzqeKO6cdY3XqfU6/MoFwzIyH96qjIO3twBvekPuc9FBZqa1Q0IsbBQLyuMiJCz3f81iMni2iFNQ558gJFL6JGJBIotUxA+9MGiO9cVVAvqoK+Kd/Akwm+UVJYogn5RAHg4Rd587BweJYt04rDf9sTy1G0msAsxlPj6rbSXNdAtvXzMI1JjQwux1bd6nZmzQsGEQnPSQeeUSW5hkaAkZGEDo8CO+RURUHBdC52u25CQuxCkYkLKanZR9asaLw+uvpeyWJrvX8foJBulHxOJHheeCYUN+gUKXAWh08CASDcCb8WHfgIZgk/b5UwQmGIh+aXi8dd10dxa5ViQEHDoBldEI5f/7z/HhTDDQ+yfE4pEQSNYP7YUnr+FSUgrDwerU36rLLKHDd3KwOkq9dK8sGzcyoxym9Kou+Pm1J0yWX0KSolAZ17e10vG1t9L0DA9Qwo1EiNHipGgf/O19AvL2dznU5Dbc5lIRFLCb/vVjCAqDziEblUj29c/F49M2txXLjcqKujrTyNm0qTh9wOWG1Lq7Cgi+KzoAKC2UB02rFqZaE+mtQiTQAPCxJ0qIlmiRJ+jqAf8r+ezmAxxhjDsbYV7L7AYDfAHhHgcqGckI5UhUjsMgJC83SUZKkP4LkpZ4BLZQ+AmASwDuz16JoMMaOKH9AFRwGDBgwYMCAgdMYDoecCJZIaIP9Q0NaPeuzzqI1VbEVFnqERV8frQPGxmj7YgLcr74qB/sBiplv2EBrOz5B93i0RQQnTsjxjViMyIpgkNbHTqcsJbV1K53b1q2U2Op06stBAXL2djS6MFkoUXu8v38ZZaGWIgklRnMbG+eF0Q3CQgUxrPr7PNvuAcDXE4shB+4ESUMB6mrzoiBJ0sl8PwBOj4qNrA5OMhhDAJX48xEPInF1SdKV5/g1hIVCSaco6Jk/c2Rgntfcz5itqvfWHP6DbIyaA4cP55fFfwK7INXUymVgjY0UNGxvlwNfAO3nyBH9LxN0gkLN63F859vV23i9yPzwR6qXRpvPRX2rK+u5wRDr3qZ63z54DL1zdTjQryZAb7xxYWongD5hMTamzuiPRCieGwpR7Csep3FfFUOdngYCAUSj9GwZHFR/5zbFKfhnkqg0R1Bv8qK9IQrvyTCGh9X73L41gw9cr8NMZ3H5ulFU2pNodofAGHkmKPH8UDPmUItoHHipV32ddp/rBYtGUDUllIF0dqKuVoKnQh1S6EX2AbZvH7WDhgZ60AwMIDwewIGX1V9jtdIzzGIpvsJCDGBxOah9+9R9xmSi+wzICcfzhIXfT8Fvn6/gQ8w0ps6xPLj1JqSV/SidBp58EhgYQN2RvTm/p8I7Qtcin/G8Atxs22rVelvgwAH9Dx08SOdVJMHPE//N5mycPJFAOJRB18mn9T/ACQtuzr0YcKkwJS69VBuUBWiSV19PP6mUdsKya5fWy0KJrVvl90tJWABUEVJXR4ybxUIZItwkWxwoHA7aJl8pN6+KWW7DbYAagbIEjDeKhXo5KBsUNzzjRuV6pbgeD40TIpbLcPt0Ry5JqIkJtSGTEspF0RlAWMRiRfOvZyxOGWHBGLsCwL9m/50CBdyXBEmSvgTgX7L/7gJwGMA/ZP9/AsD/kSRJhy5fNihp2mKOg087dKcwkiQ9L0nSGyRJqpIkySVJ0kWSJP1kyUdpwIABAwYMGDjtoSQskkktYREOa9f1l19OAQD+OSXMZu1kWZSE8vtprcCzRDWBhBwQDWvb2mgNwpgckPF4tJXwvMIimSTCIBCg12w2WvvW1NBa0GbTJoflIiySSUrQCoeLr7Dw+bRJXYODy2i8vRTnPZGwaG2dv1hi5nOutrEaIElSHICSGRrJs20MAI+KFXAS1f38FAB+U1sW+vkVg2xw3juTQfeTd2PqoT+p3t7cHsaaxjQ6atVB/NHR4j0zJUkb9O5sV2d534NbMIEmDJ19g+p19/QAXKM9eRkJcRwxMXXxzJO4EjFPo8wA1NRQR/L5tIGnI0f0A7ZKLT8AkzWb8WLqbMzVrlPv+5ia2IhsuwAtrYy6s92OxCa1b4Z75Bh+d6ITybS85LdYgHe9K8fJ5kFlpTYZ2O9XB9ADAbof0ag8rqt8LG6/nVjpnTth2vc0Zma0anVbt9LvZDiBcDCDKnMIVlMadWY/OjxzmJuDmrSIxfD2K6dxcZe2UoAxCbs2DMPJoqi2hOBwUIxXiXDChsMjVXjhmEfjrbL7rBkkgnE0zgnlgZs2ARUV6GhRB63nCYtkkhpOczNVs7lcCPnTeKVHnbHd3S1L9OdK5hafyT6fHCuPx+nazs5qVYZe+1pZgYc/v+cJC14GE4vJxsC5IATXA+3b4N186f9n77vD46qu7ded3jQjjUbF6t22XDE2NjbEoYReklAeJC+NvJRHEhLykl9IpYTkpVOTFwJpBELNC48WCARCMGC6ce+WZMmyukYaaTT1/P7Yc3TvOfeONCoGl7u+bz5pZm6fe0/Za++1xGWeeQZ47DEoKTWImFbEAK01GYOrqzWnTlM229bF2uUBBUdrKw1QciT4dT7J8TgivTHUdhloSQJEhPCLGZ9GSCke15uK5ecDy5Zl92qoqFCD9/J5GVVZaLFqFa2rKLPvBaEo9LD6/ZQlwqV7nE69L4Oi0MUOhfQu8hx88PduG24D6qCHE8wOx9SrKwD1hnK5VC0fjyd7CbIsncUh+x8dq7Db6T6QOx5eUmYELWExk0qo9whGFnbHuvH2e0JYKIqyAKTvagOVX1+SGbjPGIyxbwG4OfOWj/LWAbggM7l4L6Hdfy7TQZ77ckh5NcbYAu0LwGmHcn8mTJgwYcKEiZmDe1gANDYvLp54juVykTrARBn0ciKTlrDgvrAjI6qcst1uvB0ZcnyhtlY1ueVJbC6XPr63f78q/xQOE1lhtdL8eCLviOHh7DENgAiXSIRiJ7nEHeTqCn5s2QxlZwx51jKbklAaWQY5Tpsty/cYglZMejJ3Ev79dCu33wsvvXcXkQgQjyP+xkYUv/oYHk2fJ3x92mIKwFUXiUHMgQE1aDkZurr0MdCrPh2B1aIGYWNw4RZ8GW9WfxijPjGjteS1x6ZEWJyxQhSVHkIA6y0nUoNcWEiNIjfKzUivjWPzZmPCQpKE6iuajznlFrQtOCvrcQFAfLkmcOx0YrhaDHTZklG8LVVXnHTS9BRHXC7jZO49e9T/w2H6zUZHqQItHKb/h4YyC95wA71pbUXFj78knzYcDmrbAWCoKwqAwY8hlAVGsCDQjqB1CDXlcQwMUGyaMQDRKCwKwzWnvooiv3jDnLQojIA7iTneIXjTw5g/HygvB6rmiEzYK1v8eO5tsZShsWgQ1WhFZDCO0n7JKbu5GfB6UTtPnM6PExYASXhlkHR4MDaSwtY2MfJeX6/G17K1vXKfHA6rRB5XCXrxRb3W+SWXqP/HYhT3457ACIfpnucBvWwpvLGYLkjuWNCIgROk+3LLFh1jsmX+RYi7xECtt2Mn7WuS6gTZbFtAMkk6kEZIp+mB5XJXk8DjoWR+txuIjpJuVmDHq7CnsjQ8iYTaF08nGDowoG9QTjzROMueIxBQg/1GFSrZqiz8fjLIdrnUE51t2GwkfefzEeFQUEA/mJG0EDfeznaunLAYHVWPd7arQrKBP4R8MDpdwkJ7QyUSuVWKyOREXp7aCB7r4L+HkQdLNlko7aToCKywyM/XP6omYfEuQ1GUWlCJdQGAFEjCKHv94PTQJr3vhl6X9r2ANo8tl4J73krnIh9lwoQJEyZMmDiGwBPnuFG1xTKxOsDixVmyFTWQE+OGh2nelU6T7MrwMCVcut3GCgZGiMd1CcSoq6P5hxyXP+448X0iQfsdGKAAGGNqMt9EeP31iZNG29rUeX8uVRZvvKH/LJmcWDt/RpArLGbivCcfpIYVkissTMIC2jlJVudLRVH8UH3pJhBUzrp+kWb97Ho2RzoiEbDRKNybX8PTOBNhiMHzL79yOfwtG1FeFNdVLrS1TRATPHCAyirSaZ1fqd0OLGkYwfvniRmYv8KV6FcK0Xv8mcLnJRv+TgFOg2ds/37R0xYALlzVjblWUSLomehJ1BD7fBSw4+Vj5VLxzI4dxiyn9IyOVDejpASInH4htmEensA5uA1fxPX4Hh7F+UjBgrTVhuHFJ6krOZ1I+IKIBtRs1NdwAvYNiIH4C85nk3oOZUMopLfX0ZpmDw1RPPemm4Drrweuu478PxIJIP7QI0KgOm/3BuzcJE7Pm5rUeFO4eww+exw2JFEYUhBCLxZWDyNoCaO2ltrtlhYgPRIFEgkEXHFc99FdyPMQf1iQl8BZK/rg81vgxzBqiyIIOKLwuBlWzhcb/Rc3BfDqNvGinL58EIjFYB3ohTMhTcWXLAG8XjQ2ioFZgbDYtGn8nlqBFsgAACAASURBVIooeYhHk9jXKQZBq6pUtZxs5L/czw4NqSQ7l4P65z/FZZqb1UoVgJbnVYhuZLS4eIUFkL3qwcBV3bGkGb7TVoqaYum08LAyRcGmRR/BQJ7oWeDtzNwsE2iccLPtYFA02x5HOEyDgWx46y0aKOQQ5VMUGg+53UAskkAkakXtvucnXomXBE2HsNi3T5/9sHq1wUlKqKgg4sLhyL3K4tRTVa2xQxn4d7locBkK0Y8mt3kcPh/dM9mMt2tr6ZrGYhT49/nePU8Ffi9rKyymqpkHqJUsfCDF/SsmanCbm4HTNLnCn/pU7plARzv4dbBa9URXNsKCT4oSiSOSsLBY9EVIJmHxLkJRlDIAzwIoA2UVXcEY+79Z3sd/AvhF5i3/eT8M4O6Myfd7hkyFBz8mg/ovFYqiFEAlLLKWpJswYcKECRMmjk3wBDBOWAB6WSgtjj+e5mBTJSz4mL+tjYJR0aigKjQpNm4U5/aKQvNbq1Ufl6+q0gdodu2ieX48TkRHLpJF6yVFB7kaY+/eTBAtnhth8WYWl7UdO0Q99VnDoayw0KRXmxUWOvxF8/+HJljuQwD4E/DiBMtlw2c1678wjfWPDEQiGOoaRUHrO3hA9CbHSXgRjX3rcdytn8K8l+7CHL94M7a2Zok3DA8DO3dSo7B/v2z/gMpKoDwwgs8ufgUKVNZyCAG80V6K7iUfEJZ3DXUjsPklauAkMkFOhvZ7k2gKHMQpeE74/IXwUjC7gx6g8nIKFBYW6p/j0VFqNLRs6sgIETCgyfEd+Cz+6/lzccklwEnXfgDN2Ibz8ASuwm24DtfjQjyKpdiAB4OfRyqgaUAzBqVD5WpVx534jLD7koI4TlsZmXaydV4eee1qwf1Dkkk6lSeeEH2cH3oIQCqF5Dq9zE7LVjFQzoPsqRQw3BtHwBqB05ZCXl0RkEqh0DKABYUHUVBAkvdDQ8De3SkkItTBNNeO4e/v/288XPf/8Nf33wKPM405xSnkYRihQAIYGkKpJ4y1C8UIUGefE2NxtZNQFIZTV0aA6mrkRaW2NxCg8kKfT1dAs0tpUt8wNl51EGFe7OgqQFIjzaUo1N/l5WVXyQH0cbpwmPqteJzkofbs0UuiXXyx+D4WU+Ov7kTmHh8ZoQudTGYnLCRH9KTLC1txECULQsCK7NY9fYtPgcXlRFdALOXx9LQgPATs3RbDnj3GxUYTmm0DNKCYqCxy2zZqI6YgC+V2A0gm0d0N1B54aeIVenroumUjLHbtouqazZtFzwnGgKefFp99u510OicjFIqLadmiItqmfP6nnioaWlutwCmn0P9u96E3pvL5yBzmuOOyZ5PwKgMjCSS3m37s/n469kAg94yY2QAPjM+0wgIQjbc9HjqfieS4/H7g298GrrkGuPVWIixMEPjvMRXCAlAnRclkdq+Lwxiyb5FJWLxLUBQlBOAZqNlKX2KM3T3L+7gCwC8zb98C0Ajgd5n3HwFwl6K8W1RtVvBhdYOiKBPlB2qHQNsO4fGYMGHChAkTJo5AaJPCuERENsIiEKDKfS4lnA3ynID7cgK0j4MHiWSYSsLe66+L76uqKOjldOqPhcsha7FhAwU2amtzC6inUsCrr4qfnXOO+L6ri2IakUhuhIWRJBRAwbpDksQlMzmy02quGBjQz3bmzx//V66wmG2Z6yMNjLGNAP6WeXu5oig6mVRFUUoB3Jh5Gwfwe813NYqiHCevI61/HoDvZd5GtesfVchoSI+u34h4AngUon/Ev4Fs9xSWRtWrD2PJqMgO7NuXJSY4PEzBv4EB4OBB7JTsBWprgeqCITRjGz6M/xW++/3ri/CBOy9FkdKLYnShGF0oRzv++69zMdoXpcivJooqExZLq/phP9iOD6SeEj5/s78WfWEbBbl4FnFREQXpZNJiyxYxQKw5gXvw7/g87sDGPb4JOcrNWITLe27DVVdbRek7pxPDVSQxMgwf7sdlwnrnrexGyDL9ai2fLzthwbmel18Wv29vB/Zv7IOyQ9R/SkPB7g6xweGExXA4DTYWQ8AyjML8FJS8THZ2OIxCpR8L5yZQUADU1zPEhhPY0epEJOVG6VtPYtWT38NFe3+KtY99Hau3/AbefDvqCgaooQ6HUYJuzJ8TRsCVveE+riGCUCCJFKwoPCBpC3LpGq9XqGIAgH2oRVKrJPcCcZER5sXbHWI2QGUl3R5e78QJ9vJ3iQT1Wb299Bi8JRl5BwLkX6EFJywsFsA5FhZlmWKx7BUPkps9m1OO45YpsJaEgNNPz3rMkZPPRtAzht5SUe7G2n0A+9psSEbGxisU29rEmOKEZtuAPntAzkYfGCCJtcHBnExweOI/EgkEd74CZ1LDoigKea5o0d+fXR8/FqNAam8vlWZt2EDlmQcPUrv1osRtH3ecSDRkg8VCFyMYpOCt3KdbrcCNN5JjfVkZ8LWvUcA/Q2K+a9JKE8HtpmMxGqRyM+6BASrhslqzV2IcCvCsfLudrrXVOjPCgq/r9dKgdqLwo89HgfmFC+leO9YHYVpoiSSZwHr8ceA3v9EPYgExi+sIrLKQhwwmYfEuQFGUAICnAfBu/RrG2C8nWGU6+/gIgDtB2UqbAJzBGBsA8BkA92YW+xSA/5nN/U4DXODRC+D4CZZbq/l/EqrfhAkTJkyYMHGswWpVTZInq7A4/nh1zjrRfMhojhgOE3GhNdpuaQF+8Qvgrrsm93GQ5ZTmzqU5nByTByiAIxtv79tH2bSc3EinJ5ao3rpVf0yXXaaffx48SIRFJDJxElY8nl0yuzU3D9GpQ744ubqDy5CrKywWQRLKrLAwxFcADILmSY8rivLfiqKcrCjKckVRrgTwOtRK6e8yxrSpfjUA3lIU5WVFUb6pKMo5mfWWK4pyqaIoDwJ4FKqX3dek9Y8eZG4u21uv43GchwhUHW8LUrgYDwuLL0y8LbzPWmExOkrBwC1bgGgUO7eJD29DA+BKj6Ik2opv4MfCd8m0BYNRJ3pZIXpQjB4U4wDK8dDgGfj+3VVIDY9Q+dXYGCIRPVG5srQN+c5RrMULQvVGLGXHPzfkqw9QZSU1zAUF+oDk1q1iarmmROQmXG1wwtmxeTNw1VXAt75F5MobuwL4u/UsPIPT8SNcgxGNArFFSeOs5v0oSEzfNtLIeLstI8QcDpM8lCyhBQAvPReHs0VklrZjHiJJsVHmBEC4Kwq3IwVHchSh4ky4IhAY74yC6V4sXQoU+uJoKo/AhTHsHgwhtO4RYXsr/vVzVHW8jAJPjK55OAxnuBuFSj9WVutNujlOP54I4pExK0q73hG/bGwcD2g2NYlfJZkNbdD83hs2AOEwInEHNh0UswFqauiULBYDo9XRUbovuroM+2TuKx2J6Jv4pUvFqgTGqK90ODT+FdpOKxbL3olJ0kv2mnLqR0Mh8l4wCuouWYL8Mi8cpUGMlIkVFgVDbWjI70ZT0QCamqgYKRymoojeXjqfCc22AeAd6fdYsUKvU/baazRQyCHS5/NlbAescTTu/pv45YIFeo+Bvj61PFNGXx9d8I4OGjTs3k3/b99OmRuvvSYuv2bN5HJQHGVlFLgNBlXzEi0qK4HbbwfuvRc46yy6kLw9OhwIC54tIw/wACIsMn5HCAbpNR1JppnA4aDry0t4p7t/fkNxh/vJjMO5ubjPp3lITQCghkxRjAmseBy47z7gIx+hv9rBwhFOWMjD/5kUWB8NOOSEhaIoHgBPAFiW+egHjLEfT7DKdPZxEYC7QeezDcDpjLE+AGCMpQF8AsBDmcU/pyjKLbO5/ylCO5IyrPnKSFd9PPN2EMAkYoomTJgwYcKEiWMRPKE3kaCEyWyExdKlNA/iFerZwKWDtRgepmTF/n6aM6fTFCB77DGaG//oR9m319url5tetIiOWxekAR3bggXiZz09FNgZGgLuv5/mJxdeSJXzRkSCLAdVV0fHLRt6t7dTDIuxiUmX7duzJ2q2tEwoxz19yBdnuu7eu0WtfZSWChNok7DQgzG2E8D5ALoAuABcA/K2eB1UyV0BUu+5kTH2kyybORHAD0FzoNczrwcAXAJKrhoF8DnG2K8O3Zm8x4hEMBZJIrDrDdyF/xC+WlI/gtFzL0XaqmZHz5cKyvfvzxJruPtuevi/9z3gpz/Fvj1i4G5uA8lAFIzsxwq8gdPwbE6Hu76tHHc8HBp3jH7t5aQQE7RZ0lixcBR+ywgK0Y+l2CCs/9wGTZDN66WoQ3ExWKVEWGzZIj54GYOfd7AYb49Pl0UUoxur8AqaBU94Fa+8Qm3y139Wgi88cS7OwDP4Ib4tLLOsqheV7l640yPTbrRcLr1EPScohoZI8cYIb26wwpIUA7zrsUp4X1REL8aAoZ44/O44bMkx5IcyggT5+dTJjYwAPT3w+4HlTUMozk+gPtCHKm8vCtvFYLbCGGp//z0KJI+M0CuZRKnShZMXGAez7bY0Tl5MZYVjwwmE+iRn8IzhNkDNtByP3G1TK9iQTiP14suIjFqwvUvU+aiupn4tP99Asn7PHsoQ2LYNwVSPTmWns5P6ZC4JpcWSJeJ73nc5nYDbnlSvg9tNgT3ukm4ESRJqfIBht1M55qpV+nXe9z4EQna4Qj6w6hrhK1s6gbqxbVhYGUZj5RhKS6ngLxCg/njPngnMtjlkDbjGRv1Jc1Iwh0jfeCIHi6C+VZR6w/vepy8p4oSFUcYEZ11SKVqPm39t3UryYHI2+Pvfn7tck9NJD0goRNvXyk0ZIRpVDVKmWy0w2/D5SBJK1vWsraXr6nLRDyKzou8GHA71BUz/mvEbyu9Xb+bJwI3VD5dqmMMJdjvdw83NxhlVkQhVWnzsY1R1kU7TdU8m6f8jkLCQi0lyVLc7anFICQtFURwA/gpgTeajWxhj35nlfZwP4D4AVgC7AJzGGBNSRxhjKZAkFPfLuEpRlGwTjEMKxthrULVuP60oyokGi/0XAD7auYUxNnk9owkTJkyYMGHimIPfr853h4Zoki8TEuXlNEfkc8GJYLfr9VO50bbHQzG4d95Rqy0ACpYZZdUC+uoKt1uVppL3wyEbb/f0AD/+MXDppcAdd6j66J2dwHe/qy8+kAmLVasoC1ieBHDJGS6xkQ0bNmT/rq3NuCJ9xpBTrLgMzlQhp99WVAga0/Kxm3NlAmNsHYAFAK4H8A6AIQBjAPaBJJyOZ4x912DVNwH8O4jYeBVAG4iciIMIkOcAfBtALWPsN4f4NN5bRCIYXr8ZB6L5eAaib8SZKwfQdvoVePsqVQ1LJix4BZQAxiiLOMMkpJ/8G9raxQavuZ4CibZ+muXfji+iELmlKD60oRFPPGMHRkbw8jOiwH5j6TAClX54w1QQc6rkY/Hy1oD4iFZVYTCVh03+1eJOWlvp5DKIb6Tz/iM+ISwWClEF2333pLCu6Qq8gtXYhEW4Bx9FjaUtp/PR4vwVXQiiP+fM82yQJfs6OuhnGRwcV0DSYeOBEEYhsqEyYcGrK0ZGgNTIGAK2UQQ9Y7DkaUoD7Xba0cAAkEzCHotgceUAagIDmD/wMhTo20glGgV+/Wu144hGEbINYuXiKOzWtG751QvC8Lnpc9f+nbCmNRU8ikIZ95mGUlH0SQK7Kk8R3sf/8SL2droQiYmsREMDkR06qX5+Mbu6gIEB2PfuQMAvnlfGbx579+p/yqVLxfc8ru50Au5U5oGKROgcHA5aIJk0ZuXbpPtMe7JFRcDZZ4vf19YCdXVQqipRUu2CtzwfMa/Y0TePvY1QIIEK/xBWrqTxSVUV8Q4u1wRm2wAdo6wB19Cg99PYs4fIg/7+STXsuS911b5/wRWTEgOMCIv+fmPCIpmk+zIcputaUkIu8o2NdO/KD0d9vb56YzJUVNAP6fdPHMVMpegYcxn0vZvIy6NrUVMjfr5gAV23YJCecaNslkMNXmFht6sSUdOB3U6/EX+wcyEsKirohi8tzT4wPlZhs9GkxmYDfvUr4PzzYWjC1NsL/Pzn1Nbz3y6bdNthDrlPMCssDi3uA3BG5v/nAPxWUZSFE7yaJtiWDoqiuEEyUHbQBOI0xlin0bKMsSSAS6Hq0n5dUZQTpri/kxRF+SR/AdBaWjVov8t8nw1fBmnW2gD8PVMyvkpRlFMURbkDACdTdgL4+VSO0YQJEyZMmDBx7CAUUqvIh4ZoTC97Gp50Es2/cvFeNCIsrFaac9XU0HbkBEcAeDZLErPsX9HYSBmlXKbYCIsX6zNOn3rKeN7R00MVHjwT+uBBfUXHCSdQjEEOtO3eTeuNjEycrChrhGsRj+szXGcFRhUWOehx6yATFlKgQA4IH2pvziMJjLE+xth1jLGljLEAY8zNGKtjjF3BGHs7yzrDjLF7GWNfZIytYoxVM8a8jDEnY6yUMXYaY+yHcnLVUYlIBGzdy/gdrgDTTDm9rhTWLqEHbm/eEsTtFFCbB8njIJOcLCiftLUJfi7tqEAsKTYki+oy2eKZSO487EA7KnDL0t/hx98dwi++tA+/+NQm/OG4m3EPPgo7xMz/m59fgrdfi+O1LWKgb2FTDA474Ok3Jiy27PUIRO7BsXy8c6AI+8tXIW3RpMgzJujwp7fvRAI23IN/F7Z32mnU3h7ssWLP8ksBABYwfBR/xv0n/AJXXZV7bKva3oE1x40i6I7SQz8DwqJRVPnB0BB5hm/cmD2GGk/b8SxEz4NXIObsjctBhQF7chQeNoKQPy5mOufn0wKM0TlEIlDGoqgtHEL9gXXIit5e4H/+h7JtBwZgtVtQVWPFcY0yI6bKQaXTQKBN8q/gAUVNQyn3KzuD4nk5N72ONzeKwc9gIIXqavp9dYQFz9CPRID9+2GPRRBwi53fwAD1W3KFYSDAdLFg3m86HBnD7WSSOi6vl64tX8CoyqK9XXyvlfMJhahzPfNMGnhUVgKXX04BfrcbVe+rQVW1BelyqcLowAHa59AQHA6qsFi6lAo2mprUuK3h+GDbNn2nNX8+yVNpfQLiceq4Uymge/Km1ucDarY8IX44bx6RDjJhEQ7TxY/HxSQC7hYeDosu6l4v9btyX7x6de5yUBx+P72KiqiCQsfoZsArqNzuw4+wACj7pLiYAs8f+QjdS4zRs1VSYhyQPtTgHhYOBxEOM7G9LS6mbRQU5EZ82O008J03b2b7PRrBKyzSabqeX/0q8Mc/UnWSER55RB00xONHZIWF3CyYhMWhxYc1/58KYCPIXyLb6+9T2ThjLJrZx3YApzLGsuT3jS8fzyz/LICrM9UOU8F/gLKq+Ounmu/WSN9lNdDLTHL+DZSt5QOVjL8CInU+m1lsJ4BzGWOHIm/PhAkTJkyYMHEUIBBQCYWhIZrznXee+n1+PiUJ2mz0mmzuarPpg2CJBM23+bxr2zbdanj2WX0BQDqtr7CYN4/mrEb+FRyFhfoEvImwfj3w0EPq/1pwogXQV25Eo+MxLwwPqx6kMt42DE2rMLoeM4Z8gSYrA8kGOUgiGYjKFRaTyS2bMJETUimkhkbgfmc9focrhK8+sLwfTjtDR48DHX0uDBfWAAACGMIcHBCWbW2ViEpJv34nxFw3jweY4x+h56VfNZd2IYbaiiRKy+047tQCHLc4hZrTGnB25Rbcgc8J20imrfjGX1dhOCrq8BzfSA+Lo49YiZPxImxQScRkSsE//kHt4J49JCX3Uks5rvz9CajFXvxWex3eeQdIJJAYS8G+fy+ewlnogehxUFdHbVRNDeA6/wx0XPJljBTVoPX4D+P1NV/GKacA99xD6li1tdRuFhYyFPoTKHKGMQcHMAcHcAaexoO2j8LusSM/kNG/GxycNPM8G+bO1cfUtm4Fnnlm4vUeh9oxDSEPWyBq/3EpwHBvAgFXDEpsDMFChX5LHkwPBOg9l/uJROgiMQbHRrGzSXkk9nXfPsrAzRj7lhYmsGah2KbmeZJYOZ9+Z0P/ipoaCqZqOlJZanAHaxSq2CypJHZuEoNmNWVxBALUP+mk8rlpVDQKpNOwtO5DwB3XLTI4qCfnl9SEocTEfXH/CkXJEBbcP8XrpZ3HYnTTyoQFY0QuaKElLHim/+c/T1kD11xDpRLFxUB1Nax5HjTOtcA9VyIsOjvpGDQyh/n5wPLlND6oq1M9mHWQPSD8furYg0F9qcvmzbQPTTVTNvjsMczZImVdcOdymbBgjKpfGBN9LHp76RomEiJhARDxI5M/a9dOr8MtL6f1HA6hjRPACQuX6/DKQvB46PmZNw+49lrgH/8APvMZ0Wn9vZCDAuh+5h4WM5XQqq+nqp/Fi2fn2I5l8AoLQO2zKiro/vn1r6nh0CKRUM2nEokjkrCQSexsj/mxgveAvpxdMMZeBrCQMdaS4/JjAM5ijN18SA9s8uN4DMBiADeByIlRkF/FGwC+AeA4xtju7FswYcKECRMmTBzrsFhoru73q/LeH/oQSSV9/ONkjO125+69aLfrY+WahGak08YVFgcO6AP3u3bprReWLVOPORvcbl1cfRxWK3DqqXpS5Te/IWl4mbBYuZLiMRYLBULk/XZ20jGm0+J5cjCmN96Vk/9275527C87jCQRuKTJVDAJYSEnaJqEhYlZwcgIhrZ34IXBxeiAWPJ19so+7Ot0oSdsR0XRGJLlaqBRloVqa5PiDZvEjHeZsKipAZSxqMpCauCuCqF/2I7+dD4QCIAVl2DrWV/FvwWfxX/hZ8KyiZSY3l1RNIayUAwKGKzdRFjkIYITIAZQn3uO2qG2NuDJJ4HrfuZFW7cLbelK/Ad+ixdxEm1/I2ns977VBmsihj/gk7rzWLiQ4nrFxUDzCT6UX/lBjH3nRuz76HfgLguis5Payo9/HPjd74CHHwYefljBwz/cicev+CsOoBwHUI6ncRZOiL6AUKwd1gINsz3NKEgopG+eNm4E/vlP8bOSkMgAP47zkAYxHa9jhVB1Y7NR5UY0CsTDUQS8KRQog7Dt2wVcfDHJgNx5J3VgVitF7Pv6qHGPRkmTUAq4W2+5iS6eFs8+S9m5f/oTAt+9Ct989hRUQfVp+Ny8FxBPKmg56MSeAy5jw21A6EjnzRMX2dPl0xkx7egWO5660hFjOShANcVmjILwiQQK7BHdIuEwsHePmCXQUBJBqkssc4nHM6QIY3DHBunZsNvp99+5U5WDkn1Nenr0ZY0yKRAKUadhs9GOKispQF5ZSd+73foSlK4uOr/hYaF8SlGIG6itncBnS84eaGig4KXVqtfC2rmTznFoSDS5N0DZtn/AMSINADhh4fXqZX04CcIJCy6zFg4b+xDI5i7BoLr9qaKoSDXfHhw0zrQYG6Ogu6IcXhUW3Hjb7aZrl0rRfReN0vn4fO8dweJw0OCOG8PNFF6vWS0xG9ASFvK9Pncu8NOf6p/9V16hNu4IrbCQ+1eTsDiEYIwpU3zVTHM/WXLiZmd5zXqfnMr55LC9VsbYVxljczPl4gWMsRWMsZ8wxrK4X5kwYcKECRMmTKgIhWhuZLOp8+VTT1Uzb7n3otU6eeKYUYWFNpDf3p7ds+Ef/xDfy3JQJSXksaEolGg3ES65RHxfUECBufvvJzLmW98S54LpNHDDDfp4xqpVFBvh/qJy5cb+/TSniUaNy67b2/VyUccfL75vbc3uWTpt+P16ZmSqznt9fXoWRoquyYSFnBhqwsS0EIkg+c91uBOfET5uqhgFYwoiUSvq5oyhKJCAp0F115UJC12FhcSW7oKoT1RXB3qYO/UKwc6KIgSLrGhvB8YKywGnE8myKmw6++u40XkjzsXjWU+nuXoELkcaHlcaSpeasS3LQq1fT/HYBx8ksjiREKeDX8YtSMECbN2G4YMjGHp1G3pRiMdwvrDcmWdSInV1NanuFNd6AbsdhQ0FCPhSKKtzIRbLouzkdGJ0Tj0SLpF9LGl/i9oV3uBNVRYqw8o6nXpD5Hvv1fcLX71E9D/oRBneypiKy3JQDQ0MDkem/0qMwWeNIuSKAL//vRrY/vOfibAKBKhR5sHuaJRYIi3mzaNg9g9+oO/0Xn+dykE2bEDR4B68hDX4Ba7G/+EC/OSt01F6/y2IjqZR6+5EYEjKim9uVqVjMmiSBK3bulxIrVR9SzpQhv1JMUt/UfUwrEhlJywiEWr/8/OBqiodYdHbC3TtHUFPr3h/hRxhbH0rhs721DiJHovRb2aJReG0Jqmz2rUL+Pd/B77zHeCmm2h/cicm+1dYrfofPhRSSzyLiqhjnztX7buMCIvublXW69VXKbj40kvEeP3wh8DXvpZdZ1EiLMd/j0CAHhQtOjroHJLJSass7I/+Rfygvl50l5erLPj2eOPEiQMuB6UdnBw8SA2CFieeaGwgnAssFiLigkF6BowqL8fGcs9SebfBCQuAnt3+fhp4+v366/xugg9KD7eqlGMddnt2woLjRMkSeP16lURNJLKvd5jCJCxEHPEVFiZMmDBhwoQJE8cygkGaH3NZKC1SKRqzcynjyRK+7HaKkWihjXkbVVdwPP+8OC+QCYu5c9Vq+8mO49RTKXHqtNOAr3yFiIpPfUrNSF2+nOItWnR3iwoNFgtV5Y+OqnNRObi0axctNzSkSlBrIRtuezz6uMghISwsFj1zNFUhW7m6wmrVCdDLQcZc/CFNmJgUkQgGX9mGJ3Cu8PGypmEkkwoaK6II+pNYXD+CvLlqEFT2sWhtlRIkpXtarrBobAQFwTo6xONxu1FU40N5hQKHA2g54ECqZA6Qn49oaR12nHM17lU+hgXYbHg6zTWjcDsZ8tJh4aGRCYudO8kq4Y47jGMkb2MZ/oBPwj4axo4ndsO6fQvuw+VIQNU5t9vJy7imhmLuNhvU9HOPB/VzbXDnO1FQQLFQ3X6cTsDhQLhU1Cry732bOgGLhYKcRg1eNmzeDKxbRyfIGKoklR+ZKF7YFMPS6kGU54uBdk7MyIbbC+roRw6HAb9tBEpsDKGhvXr/gYcfpg6Kky6pFAWN5YZ6dYYwaGggdnsCVKADV+NmgbqV+AAAIABJREFUXIDHoAA4/p3f4bKnrkDzwefFBR0OusGkYKZcDRhPWtA+/wNgFgqyvYQ1wvcuexLzq0fgtUT1xH00Suc2MqJ22Pn5KCwUO8y+9ij2bRIbb58zgVMqdyHoi6Nn7zC2bCHCnRMWrmQECktTcP3uu9Ubp6MD+Ne/9J1YS4v43shYwuOh4wyF6PvycrETcbv1WQKpFBEJ3d30t7WVXrfeCnz72yTbdfrpxv4Mchknz6wOBKh8Uyandu6kAQyXcDJCMgk8LpGVa9eK7+VAem8vkQWcsOjtpf/HxvSs/x13iAMTRQEuu2xm5YylpXQ/+nzG0UxeYeFyCfJkhwXy8tRB4MgIHX9BAd1bckXUuwmvlwapNTV6Ys7EewcjSSgZq1eL7/v7qeybP3dHWJWFTFiEw0cc5zKrMAkLEyZMmDBhwoSJIxh2O82RAwF1zszB/8/FcBuYvMJiIr+GgQHVS3ZkRJ/02tREx5pLtb3fT6TERRdRRYORb+EnPjGxRPCiRTQvjsdVwoIbu3K0tdFcaGiIlpMD+HIcrK5Op/aBlpbsihPr1gGf/jTws59NQzZKnrXMlLCYM0cXJJFjQiZhYWI2MLJpLx48cBJSUINlLmsCK+eH0VgxioK8JI5rGEZBXlKVj4G+wkKn9CNlXssVFs0NmYxKWT4tFEJjfRp+P1UtxONAx1iIGobiYgxULELX6R/Bo7gAhRCfswJPDJXFMbgcKfiHxYz7E/EK7FY1kpBOA3/5y8Q8wLfwQwwhD/YNr8Gxd5tODmrZMmrLdbG72lpg+XL4T1qMUIge51TKoPDK5QLsdgzOEaup7Ns2EVmRl0cNXiKhZ7iNMDREbU9fHwW3t21Dbc3ERMfyun5s3ZTC/GKxiuNxnIf+xhN0hMXSvN3EQURSCFhHkIdhOFp26je8bh0dD5eFikbpt5aJDW3G7cknUyM8BRTseg0N914vflhZSUFNKWO9uFifxL4xXIWB8oVIwYK78XHhu9pgGMG8JEJug06DVx6MjgobDZbahcUGBxhaW8VV51WOwGVJosLdiwXBTpSUUJ/MWMZwOzlM233lFX3l3ZYt414g49i3T1ymQpR2G0dVFd1TgYDocQGoAw95UDE4SGxbdzfdV52dImnQ0gL88pfiOgcO6PvAFSvob34+DSx0+lx7KHgZj2evKLrjDv39I8s1yQHsvj7q0Ln/R28v/XZc8ojDSCvtvPOo9HMmckF+P7VdwSB14toyNC615HIdftUVAN0rikL3Rk8PHWswSKSX3T75+ocSk2qSmXjXwU23gexR+4oKYRwBgKqxOGEhS9sd5pCH/ozpK72PJZiEhQkTJkyYMGHCxBGOwkJ1HqiNQXFZaqczt7mr1aofLPOgBzBxhQVAEuEAZdxq5xY2G2Wi5upnyOPqXm92MsBqJXkouSKE48QT1WAnP/f588W5KGMUqxgZofiDHA956y3xfWOjvsJibExvfgpQvOess0hf/utfB773PePjzArZcGOqEi4yYVFermN+ZIIm27U0YSJnMIbRJ57DbyEGiZc2RtBUMYaCvBSWNQ7D685I+mgCoTJhEYtBDcz29wvZxAnYsBdigHRBMCMFJQcgi4thdTuwYAHFEysqgP4BBf151RQ4Ky7GgaZT4Fy+GP+LD8MBNcDxWfZr+BO9sFkB76BYueEMuLCoykCSRYOFC8X33SjBjfgOLO+8jT3bE3gLosbcWWdRPJKrpoyDB0MtFtTVUZseClHMTyBDnU4wxYJ9wWXi+vv20QMfCFDDGI/nZEg8nqm6fz8Fkg8eRINXL7nFYbUw1HsOokjpQVONaBb9Fo7H30o/hT6IWkirki9RvDceQ547iZB1ANixQ79xxoBHHqELxAkLmRkvLtbLEF1+OXlh1NdTFvW55wKf+xxw3XWI3nYnWj/6TaRtYrBUkVmn6mrDjlRR9CbRb+4L4kDNibgSv8ITGrNxAJhb1A9rMoYipwFZFA5Th5JKCYHvUEBku4eHGXYcEMnnxY1RuP12rCrcjeqCIZT7h9HcTM2+250x3B4c1Gs3AtRXMCZmO8iEhRwQ5CgpAdasIaZNDvTyYKO8bjRK2QaLFlEGwMGDRKBpcdtt4iBCNqhyuchwG6ABg8WivufYto0699FR43s9GgW+/33xs7o6vVeHbATd10fPRCxGz1Q8nikP8ovyNbffLq7n8RB5VlSkP5aporSUOmyrVSSg+KCPl9UebvB4VF3QZFI1WXuvzLZNHN6w2ejZVpSJywxkWagNG6hNkdu1IwBGFnZTHf4fTTAJCxMmTJgwYcKEiSMchYVq8qxW0pgrA1gsucvyynPpeJzm+9GoPjAvJyKuW0f7lOWg5s8fVyrJqcLCZqO5tsdD+9V4cwoIhYBvftP4u1WraF2rNZNh6qZ4gpwoygOiQ0P6ScE7Bp6rBqoghkTO3XeLZMutt04x0UuetcyUsKip0WV1yhUWciKsCRNTxugoXv5HFPskMuHMEwbhUaKYW9gLRzyjmc/NhTONzhx0wg+RABiPW2/cKHzeghqhggMA5nozFRAy81hSAjgc8Hio0isYpNf+bieixVX0QAcC2L38Miyqj2I9VuHLuBm/xJW4Ifp1XPjoZ2Adi8Ddf0DYrBIMYvV8KVtdg/POA265BThhiRgwuRlfQbSjH48cFIMsQU8Ua9ZMrozi8VB8j8vgawtKwjEntrd5sN21DEmrVJq2eTMRFlZrxgihS5SrkZFIEPnT26tq5+3di6ZAV9ZV5pYOosbdhfIGD5bNH0UAYmroz/ZeJLwvRSeael9GuDcBnzVK3g7pLpLzMcKTT1IHEY1SZyeX/Rllr/f3U1D9ttuAK68kPcHLLgPWroV7YQOq/+MMWG652ThSxMG1nwyCwJLSHra1evDtnq/gN/ic8LnbGsc581vgYqPwMQMzKO5fwTvRTIdblC8G83sjbnRHxOM4rnEYeaVeOGNDaAj2Y1VZG+rrqTjAY4ujxBuha2dUVbN/P+1TW84kl3DIQXwt7Ha95xKHy6VfV5abkk2pAarmeegh9f0bb4jfNzSomQ0WC3Xuq8TKHQwNEVHR16eSDFr89Kf6aqwPfEDfMRpVWMTj9OrtpcD7yIgoB/XUU/o++NJLqa2bjVLGkhJ6jgMBUd5tbEwd9ByOXgx8MMoZ2cJCGhSagw8TRuBVNzbb1AiLffuIoE0kjjjCwuXSJyyYhIUJEyZMmDBhwoSJIxYeD70CAbVaAFANt4Hck+2MgmUDmYRXLXFgsQCf/7wYp4hGgZdf1scWuHST3Z5bhQVAc3p+zC0txvEGgCoePvIR8bOKClKq4FLgAMUcHA59NuyOHXTtwmFans9thob0SaacrJB1yzPS7gJkWeyREeCxxyY8ZRFyhYUs4zEZ5GCJlHXMmFlhYWL2Ed/XgXvbThY+q/H1Yk3JbpQOboe/Yxvdmzt30mvHjvFglQK9j8Xu3ZlnSzLclf0r8j0xBPMSFKCQdd1LSsaZ0pISagsqKqgtaukPIBUqBkIhMF8etpx2FeYWDeBmXI0r8T9wIIGirs1YdNdXYD/QIm43GMTpxxtrNVxyCXD11dSOffHKNGwWNdiSgAM39H8JDyQ/LKzz/hNG4XDkJuVeU0PtWVGRqkizZw+wr90Bu52hoTaFSKkUSd+8mYKZwSA1qIkEVVBkQ1cXdSb9/RRYrK8HRkcxFwbVDxmcVLUf5XN9sBb4MS+5FWfjb8L3GzpERnwV1sPXsR2RnlEErBG42Si87Tuza+hFo8SM2+0UjJZZdDlwxRiVoRQUUBQoP9/Yv6O5meSBZM0/jnnziAgx6Ehlb6QnXy3Eo9tFDxEHYrit9EbUlERR5BiiDkF7DIkEEQY9PcBPfgLccANl43/jGygd3S1sK5kWQzheVwpzK0aRV+ymwF5fH+zhXtTMiWH1amB18yDyrKPAE08Yn1s6DWzfrmbnA3rTbVnuKVe43fp1tR1rR4feTJvj5z9X/5eNUuTfKT+f9iNnXOzerZpia8mJgQFx+wBVaBx/PJE12vtP9rAYG6NtxmLqwweoRMTICPDb34rrlJQAH/wgHedM5KA4eJA/GFR9T4DpDfrebfh8aqVFQQGxr7NxTUwcfeByUFbrxLqqixbpfWG2bKFn4wgjLAA9f2cSFiZMmDBhwoQJEyaOaBQWqvNlnkQ5NkbxAqczd+9Fv19PKgwM6BNZuSyJrMJw7736GNiCBRRcU5TcKiwAIl9cLprLplKUBLp1K8VVOjrE6oUrriCjboDmwV/8Iu2LG27b7WocQw4ubd1K5zw8THEbnpwtJXTDaqU5kcWil8puaRFjPZ2detIG0McwJsRMJKEYo0CNFhLLEo/r53+yX6gJE1NF259ewCP4oPDZ6Ut74B4bQN1iH0nyaF9NTcK9KctCtbZm1GIk6R+ZsKgNhlUTZpncmzNHkENraKBnvqaGtt2Ryvi7lJQg6fLhnQ9dh7ECUaIkf8+bUGQWMhjE2mXDCAVVMkJRgI9/nMjcoiJSymlc5Ma5J4hVH0/gXHRB3McHLvIjP9/Ys0eG00mkS1ERtU379tGp19YpaKhjKC1Kwd0klZPxwHBREV2r/n5qTLOVsB04QMHYZJI6GJ8PqK9HhbsPFkXvY+G0JXHB6aMIlTuxoGYERV2bcR4eN9iwihPxCrztO4CRUQSswwjZw8ZyUFo88gh1QL29YtDf5aILrsXAADV2nAWSA7xaFBYCN90EXHCB+Dk3lHa7DSsJ5D4lEhU7WwtSuA+X42M9N8HhAJ1jKiUG0njQ+9lnxfv3tdcw79YvGF2FcTRWjMJuB/J8jM5hYIB+s85OKApgGQ7TNZvIs2TTJrHCQjaunwlhITP8Bw6o5YZG1RUcb7yhSkHJ0l/y75yfT4Mcmch47DEiRsNh6pg5rrtOfz2uvpqqQdJpkbApLtb/7h0d1OmPjtK2fT51kPWnP+nboA9/mBqd2awkKC2l/TocKknLB31cdulwBDfe5mbbphyUiWzItcLCatXrtR7BhMVMFWGPJpiEhQkTJkyYMGHCxFEA7lnodtP8Weu9OBVlAJtNH7geGNDLHpWV0ZxejhvICa88gY7PO3KtsCguphhRZSVVNixaRLEEj4eOZ9cuNSZgtQLf+Q7w4IPAAw8AK1dSPCSVouU5CWMUzxjKJLum06QEwScGsuF2VZWqGCJvo7VVjPU8+aTxOT333BTM84zMRHKFNuuTQ2JZZNULwDTdNjFz3P8AVRBwOJUYzm/aiZp5LjjmFFLgs7GRgpgNDRR1n4CwaGvLxDalILZsuN0U6KaHORbTP2SaCguAYo8LFlDbUFZGfhax0upxLfWY3Yd3/u2/EfNJUQMZRUXIzweuvyaKujpqr77wBSIsQiHah8UClJUruOJjcRQq2aMOy21vY+4Ce07VFRxVVXRaZWW07/nz6VSb5zMcNy8KZ6MkxbN9O3UMDgcFeHt66L0siwPQNRwdpQbR5xOytu1z6zDHrw/4n7xgAMXldsyvHkXQn0SwcwvOxt9gRfbM2FVYD+fYEGqiW+BAAiW2Pr0clByIOngQeO01fSN9/PEi28MYnRs3BSkvVwO82SJAdjsFrq+5hhitefOohG8CTwA5Hi/jt/g0Poy/whGPoPjgRviRCZRrSRPeacuBeQAhyQhexsIa2o7PnaJ+I5Wi/qKzU60weeCBiQ9y61a1ExsZ0fc3E0lCTQSjCgtOCKTTExMWAPDjH9O12b9f/HzlSvG9308P29q14ufJJPCHP9C9PzpKbUR7O1XTaHHWWSTZ5PXSgzU0pDra22z6sqeDB+naplKU7cA7z/Z24C9/EZddvBhYvpxIhNksYwyF6NiCQXpek0lVB5RXMByO4IZrpaU02NQZ9pgwkUGuFRaAvrpu1y56jo9AwmKmirBHE3LMtTNhwoQJEyZMmDBxOMPvp1hLIECy49P1XrTbiWSQ1RNkwqKigoJlc+dS/CebFPry5TSn53GkXCssLBbKXG1spHgA99zlSZF79tAgnicsKoqoBsFjQZywAGieXFVFf7VySHv3UixraEid98uxsMZGdTtLlojftbTQ/kIZL1k5EZsjHgf++Efgy1/O4QLIKVYTZcfKkOWg7HadJJQsBwUcnpLXJo4csLEY7mnjxjYU7Ds5+CZcoSgcc1zo4RImWtjtwmdl2AAgCoCCWK2tRK7lZe7pXgAMwGYUj+8DACocm9HTlpEwkhsjlwsYGoLH5YI30xi63dQWJBIU1209MIzifA8w0AW4rBgcSKH9/O9j5cP/D86EQTY+QA3C4CBOWDmC2xb40N5Oj1koRGbbFgswODgImy0B+MbwmbKH8KOOjxlu6ozQUxgepooIHid1OBwIZCl7CofDiMfjyM8nftJup1hoWRmgtEXQ0zWmNkgciQTwxhuwL16M/KIiNaDT3j4uezM8PIyxsTEiiA4epFdFhY4EKivpREdYDL6uXdaChbVR2KxU9ZDfsQU2DGANXsK/sBzAqLC8BUlU43X0AMjveRnu+SsQ3bcFUW0mPADl4osRCkuVFw88ALS1IQpgnHtdskQ8Tm5MVFdHwXG/nwLXdjuds8+Hoiy+FbFTTsHQypVqsHxsjF49PbpljZPmCwFY8N/5P8InB/+ofrrvJfTOX0M/WmurWiHS0kJlMnv2GGwrChdaMQYPgALIIZylDcNw2GIYGM6QDIpC27PZiAD5/e/1BMTpp1M1RwaBbdvg4ISFhhxIA+gD6IExOPfJ4LdY4PR46F7Uesu0tKDnwAEarGixZg3w0kvq+0cfpbJNLSwW+BYtglv6DHl5wNKl6F29Guzll9XvIhHg5pvp4XC5SApKayhlsQDf+ha1EZWV8CYSNJg4cED1W5gzB/0HD2I8x7u1le61cJg609JSen/rrWJg1WoFTjkFLqsVeUbi9KA2IiGbjucIh9uNQDBIzykftGWyVHgbMR3Y7XbkZyFXxtuIacBqtSIYDNL1DYcFMmtkZASjo6MTrJ0diqIgJLd3GUSjUUSMMjRyRFEWk/RYLIahqYzLJBQWFsJiULEVj8cRlhNOpoCCggLYDEqqk8kkBqYqLapBIBCAw6D8L51Oo28GEXW/3w9nlolBz8AAPVcjI8bJCFrMnUttX6ZN9SWTcG/YQMRzOi1USfX29oLJsoA5wuPxjI8jZPT39yM1USXIBHC5XMjLyFrJ3dL+/YPo6ZlaG5Htvj3iwBgzX+ZLeAGoAM0F2P79+5kJEyZMmDBh4sjA1q2MPfkkYzfdxNiddzJ2662MPf88Y11duW9j927GVq9mjEb99DrrLPE9wNi3vsXYn/5E+3r/+/Xfa5f75S8Zu/dextatm/k5xuOMrV/P2F/+Qvv++9/pHOXXPffQfp9/nrG+Plp3zx7Gnn6asYULxWP84AfF5bu6GFu2TFzmyisZi0ZpO62t+vN84QX6LhplzOvNfj1WrWIsnc7hRO+7T1yxujrHFRljf/iDuG5VFWMjI8Iimzbpjy2Vym3zs4n9+/czPu4EUMEOg7Hwkf56r8by//rJK5r7CTN43S7cl88+NsqYojAGsNAMtnvttdcKxxuLMfbPf9KzX1raPO3tfvIT/8mee46xZ59l7K23GEsm1X2sXbt22tu9+OKLs17riy++eNrbXbtkCTV0v/kNdRTPP89Yfz9jjLErr7xy2tudW1mjNsBPPcXSFgtjAPsJvsaAa6e93VAgwNi3v23YoN4+o/sMxp3H88+zB6+d/vHSq5t9cNFu9uaJVwrH+w+ff4bb3SxcAq8rwR698R32fz+4b0bbfR5g7P776eZ9+unxHXTP8Po+eO+9dE2XLBF/u8svn9F2b7/9dv1DsXcvY3//Owt5vdPe7rXXXkuDoH/8g7Ff/5oGBc8+y9g557DmGRzvleeey9i2bYbP8ozaiAsvpOt75500ILrpJjre1taZtRFr12Zte2bSRjQ3N2fd7rUzeOZCoVDW7d5+++0zutey4cEHH5zRdru7uw23+/zzz89ou5s3bzbc7ubNm2e03eeff95wu93d3TPa7oMPPpj1Gs9ku7cDjJ18Mj0fo6PCdkOh0LS3K48jtGhunv444sorrxzfzmc/KzaXpaVTbyPebRyqsbwpCWXChAkTJkyYMHGUIBSiBD6Hg5IKp+O9yCsstNAmKwKUdBgKqRn5sgqDFsuWqSokuVZXTHZ8xcWqbFW2hKuREVXCmXvx5eXRcdTUiMtu20bJt/E4VaZ0demVOebNU69nZaVeHpov/8ILxvLoHK+9Rkm+k8KowmKykngOucKCOwxrIFdYuFyG8uwmTOSMu+5ks7IdWUJoz7/207x9lsFNqwsLZ7Z5t0dBTQ21K4sXU1L1EYGiIuooRkf1kjvTgNWiuYh79kDJeGOcj8dmvG28//36ipHDGGsXD+CcJe3YVbRG+NwSmX5GthHehxeRn+yF1zW9rF4BXBZKa4o9U9hs1LFUVYmfG0hfzRj5+TRAmKkUUm0tDTBqaqjP7ejQG29PFTbb7MpBcdjtNBjh4wWHgxqgw9Vw24SJdxMbN1J1xREmC2VUHHmswpyWmDBhwoQJEyZMHCUoKKC5Opcumo73IpdE1kKuem9spOX4dhcuNPY/aGhQyQK7PXf/islQXExz8owiiw6MEfHg9dI14P4ZnGCRTbN376a4hNVKKgUbNoiKEYDo1aEoOoWlcbWSJ54QP6+vF1Ug0mmShZoUck340FDusxbZcLuyUsdGyAoJpoy0iZkgPJDGQ7uWTL5gDiiCKBPT/vahE3DmKjEzIesUhWKc9fVHEFkBUFDW4SCpn/5+YlpnixjSyDfNxQ7kI1fzniyw2YAPfWiGB2UArp04y7jqonY0VsZQUudFNFR5SPYBAGeP/S9W3PxR5HXtnnzhybB1K12PlpaZb4tDUahzqa0VP9+4cfb2weH30/5mSlhYLEBzMw1wKiro2ZgNsmE2Dbe1KC2l47Na1UGWqe9owgQN6HfuFE3mjgDIak655iodjTAJCxMmTJgwYcKEiaMEPImPkwcuFwXtpzJ/N6qwkFFbS3Euh0OVjDWqslixQo2xz1aFBUDn5PXSuY6M6CXro1E6Jq1/BUBxE5uNzGm11ySVIm9Pr5fmN3IFRFERxfy1mD9ffL93Lx3HY1Iy8VlnAeefL372xBM5mG/LrBE3Us0FMmEhm55CX2ExFVLLhAkZfl8az1z/Ci4tfBZuzCw4UIqDwvt9uw/dbD0/n557A8nvoxc825Qb/wwMUOPV3q5naqcLjXG2AqDO05l92Vxx3nmz14lw9PfP7vYyWLU8hZULhnF8STuUVSsnX2GaWIsX4Bo8CM8vbpz5xrZsmf0KC8CYsDgUsFpVQ+eZwu2mjItgkO65mRIANtvs37scJSV07tzA+lDuy4SJIw0vvJD72PkwgZyvdCxXWBxLQzMTJkyYMGHChImjHqEQ+YxaLFM33AZ0HriGqK4WTbTjcfLwlIP1K1aoZILDMXsVFgDF2YaGKDYRDosZSTyZyu1WKzw48vJoMrBwIbBpk/r5q69ShvSKFUKsDYBouM2xYIH4vrUVePttfXLq2WdTVcWDD6qfbd5M+zvzzAlO0OhH6O4md/CJwJiesGho0C1mVliYmE0odhvWfO80eOftxMd2bMf2Vx/Ax361GhZ3DoGzSAS45prxh+QXGettjtYelU3bBuCmxb/FDzeqLOBqrMMj+DDw618Dd98tatidfTbwjW8Azc3wZGHlysqA2257Edu2pZCXR49YdFc79mwcQR32oGZhHhrnavL8BgcpuD9/PlBXB5fB88XxyCOPjBvqptNAxye+hcqn7xKWieUVYvSNbTqi2MjglOOuu+7Cr371q6zfY+NGoK2N5GxuuUUwPLafdpq6HDfs7e0FXC786FOfwnWnn07mz1VVxqVzWWDVlqloDbIBXLl4Ff5j/e3j7/N8aewYq4YtqSFIKirounJUVQG33AKFB6D9fmo0H310fJErAFxaWAjceacaqG5tpY6nsZHavpIS8UAz5uPo6CDCYs4cXYnNBatXo/vPf6brUFdH8kCNjTlfi8KuLljSMaAnAaxeBjz+MABgNUD1Q//v/wGrVlGn09pKJuJac2mfD/jDH8ZLdj79kyY89koRyHSbEEQfFmIzAGBeMinVJRlg7lzg2mvVxn7DBuD66wEAAYCkBAcHBXmwQgDd3/kOcNVVOZ+7Fn6/n7ZXV0fXOCMTBkA83i9+kSpoFi2i908+CXzyk8Yb3bsXvtJS4+/y87HtrrvAduygLINAAHjmGcDoWfmv/6K2QQOhjQiF6D52uYBAAC8CEIS3vvpVoKmJ7q9bbgH++U/1u4ULadstLUBDA1wT3DvaNmKqcPBMkIICykJIJMYHfZO2ERPAzstSDfCjH/0I11133bS2a52gBO3rX/86vvCFL0xru8oEJNUVV1yBSy+9dFrbnQgXXHABumXT+CmgUI5KZ7B69eoZbbcgS8bR3LlzZ7TdANdhlVBYWDij7fon6GO6u7vpGWxpoVdTkzr5yIbf/x549FGMU4zr1xNhoTHe3rZtG/f7mjKyjSMA4MUXX5yR6TaHfGvk5T2ClpZjk7UwCQsTJkyYMGHChImjCIWFNCb3+2mePdXEwMmklhWF4kp8zuBwUFLuwoUUz+H2CZWV9NnAAMVcrNbZTforLqb5i99P+5AJC7dbvQ5a+Hz03UUXUTwvHFa/u/9+OneZsKiv1xMfSyT1m5YWIY4GgI5p5Uo6hkBA3Nf//i9w0kkTEEp+P1007eSnpyfLwhr09Yk7AmiSJ8EkLEwcCjSfU4OihgGc8dVFcHizB70EFBYK9+gi7BG+3hedAwbK1A8B6HCcAEB94Beim94Z3fvV1SSZImssaFBSAgSDQdTUECeYlweki5zwBbpRYu1DpSOBonwNgTg2Rg90YSE1hnLjoEG+1JimTns/iiTCIlw1F+WNRVNKDM8WPBpHWRkdZzhMDbE2mKr1uLFaibRxJ3tjAAAgAElEQVTo6wNKSpBntSIvkaAAaGXl9LLVo1EKwmtwzpkenJTyY93rTigK8PlPx5D/eCPcezarC2nJCoAC+m63qIdx0UVCQ+sG4F6zRi0L5OV19fX0wzY3G+t9NTQQO79jBzHf0u/kdDhQxDPrS0roWkxwD+kwOqoy9PX11NCPjMCBzJ3b0kK+HKkU3T/btonrL18uRK3mFAagvecB4ATnRlhiFHSz6b41wFlnkUETD3AvXqyWSAJ0nTdsoI4xAwuAoiVLpnbuMnjmQChED1gG41v0eoEzzhCv8cc/DtxwA5UualFdPXG1RiCAUFER9ZWKQr/9ySfTPf/MM+pyxcXA978/ccdnt9MAx+0GhocRdLlELXy/nwY9jAFvvimue8optO/8fGp/5BJNDeQ2YlooLSXyLZkcH/RN2kZME3l5eciboM2bLrxeL7yHwHvD7XbDfQgGOE6nE0UzeS6ywOFwHJLt2my2Q7Jdi8VySLYLgLY7MEBtdG8v3duTlQOfcoo4GG9pIVJ+cHA8ESh0iPyQgpNle+UImbAIh/Nn1AQfyTAJCxMmTJgwYcKEiaMInKQIhabnvTiZJFR1NW1XW2ExNERz8xtuAO67jwiMyy6jbcXjqofEbFZYeDx0boEAxVe4sTdAsSIuhSUTNtx4OxgEvvQl4Ic/VJM+02ngT38SOQKA4n1yYiBPBOUYGgL+/GfxsxNPpHiFzQZ88IOid8Uzz1DiqeynMQ5FoYPUkhS5EBZydYXNZkpCmXjX4PA5UL6sZPIFtbBYKPs7g/kQg7ft6TL8Bp/F5/AbAMCOkQrh+0ZkAvB791JAVovS0kkzMm02iklHoxTX6O8HElYvHHYGq8cDb7wFYIVq8F7b2EwxEFZwwUlg31CgaLM7m5pmRcVGgMejNrhNTSJhsXmzuGxREZEFt91Gf4eHc9fws9upLO2889Rrsnu3kEkPRUHRihr8YHkML21yoqQEmN9kg3NvPbBns/F2AWDpUiIU0mmqgigpoaqL1avFKppTTlH/7+qi4+CESzZzktJSCp57PNk9CqLR6ZsYc50xm406lOXLSZqEg/tFhMOUFS8TFitW0N94HBgZQaEyAECsrvMsn4/9+DdUvvTAxMdisVCHfOGFxFQPDNC5p9NUTqQlil5/nR4CLWTD7KmCm2mVlQmExTiOP546am1nrSjAlVcCX/uauOzixRPvKxBQO/7+fnpZrcAll9DnzzxDg4B7783t2fV66Tni96BWLmtkhK7t22/rO9Q1a+i35ed0KAy3tSgqIhImGp28CtOEiSMJdruq2ZhL9cLChfTcabNy1q2j/mQyQqGzk4iR2tr31AdGJizGxujRPhYTi0zCwoQJEyZMmDBh4ihDaSmN1e32KSl6ADA23dZi3jxKKJQrLBij/V59tbi8NrY327LKxcWqLNTgIL1PpWhwX1xM8w05XsUTAz0empNccQVwlybhWY47AKLhNkd1NZ2PVvJdloM69VR1nvWJT4iExb59VKleWzvBdSksFEmKvhzMh2XCorTUMAPcrLAwcVhBYwqzEJtRjnZ0QCUmvoxbsBKvYinewZ5usVFrQqYkatcu/TNSVpZTw1NWBhw4QPHOvj7A4bDCVeAG7D54rWMUnOQBjHhc3eYUWVh3YyWi5fVwt6vPqWPJ/AnWmCZ8PgrU2u36jPTOTmpXeMqmzQbcc4+eyMgV69cDjz9Osl5NTTo5KJSVweZ1orLYjlUZInlusxWW5nnAY/9nvE2nkwKvXO+vs5PaMY+H5JR+8AOq4rjwQgpEAdQJDA5SgN3hoABzNhQU0G8XDFLAXttRcWgjRFMNXnGCg2flr14tEha8siMYBN55R2/+XVpKv0emuqTQVqzbRXPtGPYcfw0KVzbCc//viKkvLaWbmZdaLllCzPlJJ9Fv3tVF17Gigq5fVZVIWLz4ot7htbp6aucug1/Digqq4JBx8snG2QWf/zxw442i4dPy5RPvy2aj7RQW0l+vV31Wv/c94Lrr6HnIJiklgxMWAN2HWsKiM+PLsm6duE5jI2WMdHbS7+v1Ti5jM1MoClUTmTBxtMFmUzOGcnGfttmAE04AnntO/WzdOuDyyyeW9Rsbo3Y5laLnaeHCmR33DGCkFtbXR03osQaTsDBhwoQJEyZMmDjKUF5Oc2TuvzgVcN9Km814bsDH+zIJkUgYz8kTCYoxKcrsz9l5/MDvVwkLHvcx8q8AKPZgs9H3AwM0h9m8mWJuRvB69QbbAF2n+npKlDWC00m+Hhzvex8lCGsTwJ95hmI19fVZTlBmjnIxiN0jyumgvNyQjZCJmfcwmcyECQouh0JAby/sSOJ2fBEfwiPjX8fgwqV4EH/3fAg9fSILOV5h8eabqrwNR0lJTg2Pz0dkRShEj1AsBhQV5cGWdMFpsYjZ0rEYNQzazM9cYbEg/aGLgdt+BABI2+xwffKyqW0jF/Bj9Xjo/DOSROPYvFmtTPjzn6dPVnDs2wf8538CH/2oXtopU+FVM9cJZz41RwUF0JepabFkCbGqgQAF4CMRCrA3NdFnP/mJuHwsRiVr+fnUbtbV6cvitFAUClqPjBBTNTCg97oYG6PIEZcGmgpcLtVIKhwmbUCth0MiQZn5NTX6zqesjNbnjvBeLyr684G/qot4HQnUl49CAYPznNOA808Tt9HaSgTG/PlkuGSz0TM2Zw5dK17hIUsVaY2dADrvmWqRuFx0vWtq9N+Vl9Nvqij6KhavF/jKV4hkAKhTveKKyfdXV0fXmQ9m/H56TYeV54SHolDjoEVnJ7U3L70kfn7SSWpGQF7eoa+uMGHiaIbdrrblufpDnHiiSFhs2kTj59HR7OXEBw/S9gcHp57pNcsIBHSWP8csYZGlRtKECRMmTJgwYcLEkQpFURNIpwO7nQbMRuDqQg4HDag5YaGtNNCCJ67yOf9swuOhuFx+Ps1DMuoZsFjo3I3mHDyR0+OhmFEqBXzzm/pYFUd9ffZ4Q1Y5JwDHHSdu02olWSgtnn2WYh7aSYkAOc1qOhUWlZWGQVW5wsIkLEy8p/D5hODpB/F/uLRCDATuQhMuTP1F+MyCFOqQ0bnXBuQBanTy8nIu7SorUxdnDHCFfPC40tSQcG+MdJoaDqdz2mVJnq9/Afs/fR3a1lyOgZvvhlI9Q8kdIzid1JC73RR4l7NFeWD6nXfIpHQ2wDX1nn9e/LyhAXA4oFgtKCvTSA4uXpxdsmnxYmrQg0FqtKur6bofOGC835YWamQrK4m5nqi6gqO0lNYJBKj6oLOTqh5SKWLrk0nqSKajq88D8C4XnUdenj4D/q236O/bb4ufcyPnigoK8tfXY8UHy9FQr5JxpzW2wYcReFxpPS/Dg27BILX9cj/idNJ18noFKTZDlJdn/41yhaLQdTDynli+nDpjj8d4P9dcQ0bhl19OxFouEbuCApLUWraMMixKSqZfQsi1JXk1jhadncRuyjJ0nLCw2+lam4SFCRPTBx+/Wq25VVgAKkHMkUhQhlC2MTRj9DwPDFB/qfWqeQ/A7W+0yGX4fzTCrLAwYcKECRMmTJgwIYATFvIA2eOhuX9vrxrn4QlPsZi+oiGVopfdPrv+FVoUF1Ms0WKhuQZPoFKU7F64XFkEoOUDAeD668nTIpEQl62vz56QtXAhmWcbgftXaPGJTwB33KG+7+4GNm6k+FSxXvFDHyAZGDDemRYyYZHFoFQmLA6Bh6YJE7nD5yN5Gk3w9jv5t2P7QCk2jqglSBtjIktYgxY4ETfeJg9255gdX1REj08oBHR0AC6vFV6HF7DkU2Oole3hZrzTgFJchIqPrgVLngRLqdGDP0vw+egYUynKtH/1VfW7zZspqH3jjSJjarEAZ59NjeJEZkYABXmeflpf1SWjqcm4A8jPp4C8bKwMUMPrcNA5VFdT1UZZGf31+0VGvbOTAkyNjbS8xsB9QrhcdI7RKF2D3l41+Kz1KJmuEbBWTojLQmkrWTZvpvtKKzMEEIkQCFBwPpMh4LADN98CPHR3FJWOLhSOHYQnGkGex+A3Ghyk36aggDpsIyKgsJCC6QsWqLJVRpitlF63m37HQEAl/+x28q9wu7Mz5k4ncNVVdJ0mMK4+ZOCdv8ulz+Lo7NTLQZWVUZ+7c6faqZqEhQkT0wc3weN+QLkgLw9YtUr0OnriCRqEG7UjAwM0ienvp7afk9ZTraCcRRQUiEXVJmFhwoQJEyZMmDBhwgRofmA0x543j8bwPJZTUEDjfO5jISOeiSPm6t86HRQVUbwrL4/iNMkkHZfVmp1o4MbbVivFqgIBihF94QvAzTeLyy5alL0yZCL/T61/BceqVRSTbWtTP3vqKfLnPGSERRa9KVkS6j2ugDdxrMNi0d2rpYPbcWfe1Thz5I8YhHHwvMHWAmRLuuQSLjkSFhYLJeaPjVEs0uUCvMX5wHDGD2JwUG1UZlBhAacTSlMjlN5eY5mc2UJennqMsnb3nj3ADf+fvXuPj+Ms77//vSStVtJKsiRLliX5FB9jh5wTiJMA4VQKJCFQaKHlEAqFQtsH+gAP5QX0BxRKKbQPLYXSlEKg8NADlHJo+ZUUKC2nNskTTgFik9iOfLYT2/FJtmzdvz/umezM7Ekr7Xk/79drX9qZnZ17tCPN3jPXXPf1Tn+RPurWW6WnPc1HUy+6qPiwSpJPG/v0p30NjHwXkzo6fNv5AhZ9ff61ZMBi2TL/2tCQb3/VKv95nz/vMyAefNB/GaVS/uL3oUPZcRC3bCnvItPatX6Hh5/TTFCv5ORJH81OpysXsNi6VbrttuzrR4/6zy46jFkq5YfD6uqKfSmkUn51l2/tUfeeOZ19KKOe2X0a6Mzzxfrww9kvuUKpg+GQWKdO+c9327b8y1Xq77O31+/TX/ol6Z//2QeIbr3Vb2dPT/EUv5GR0sVyq6Wry29fvpTNI0ekr30tPu+66/zf6enTvnPS35+94AqgfNEMi/kGLCTpxhvjAYvt2/2wkY95TO53xP79/n/21KnsvDNn6hqwWLo0fi9AuwYsGBIKAAAAMV1d+W+u3bIlXps0XCadzgYnosJshe7u6mVYhLUqwhtlw5oZAwOFAw3htZHe3vhN0zff7K/Vhfr6/LxCCtXkW7s2/3BRZr5GbNTXv+5vGk7WXJWUO5RHqYDF0aO5FyALDPlBhgUaTuKfZujw/br0yDf1cb2s4Fs2Dh0svL7RUX/wKWMsuokJf10kvDE9szLI0giHhTpzxr8QDre0UFNT/sJ0Nf/x+vuzkdkVK+IXX+bmcociuuQS6Vd/1R935ubmVzMnlfIXnj/ykex4gVETE9mL0kl9fX64qKRLL/Xtj4z4fdjZmS0kvXKl3x/T0/5L58EH/b4ZG/MBr3I/z4EBP3zI1q3+C27dOp8JsGqV/3vs6lr4xfJMxm97d7c/wK9enTtU1Ze+FJ9ety47hFHkIv6jNxmnTGd6h9Qz1KOOrg4NnEoMR3TmjA+2DA/7z7dYJHpiwv9++Yo0hSoZsOju9ncAfOQjPjXxyit956Gzs7HHJAwDT/nu4kjWa7nuuuzQdOF4lQAWbiEZFpIvvJ2sO/PlL+d+r83O+n5zOD88canzsFDJr51k175dELAAAABATCpVOmARXmMI61gUy7AIh3KulrExf90pHPmi1HWa3t5sBkb0hioz6Y1vlF70IukpT5E+/GF/jamQdevy34B17bWFa4C8+MXx6ZMnpc9+1t/RnSN5xvLII4U3RsodmiW8uzmPZIZFoe0Fauaii2KTqZnjSp95RLfoC3qtPpD3LRsnTuSdL8nfoV5mseTeXn9dfGwsqHcz0u0PJkND/gLGI4/EhwtqZNHI7PnzxesVDA76egHDw4XHBCxm/Xp/IfolL4lnZTzhCYXHBOzpyT9804YNfpt7euIZAps2+YvHK1f6/bB9uz/GrVrld9pihi9Kp/3fy4YNvq7C9ddLl13mayEsNNoeZmaEQy6Z+cBIVHJM9gsv9J99IuUuepOxlixRX69kQ4PqP3kgfhHvyBG/0NCQz6AoJqzLcMUVhZcpMKRg2Xp6/HaFFx3NfBAn/B9q9IBFGHApNkzakiX+Lobjx/2ypZYHUNpCaliEyz/jGfF5d9yRG2Q8eNAfkx5+2Lc1O+uz3goV5quRZKyFgAUAAACgwjeVbt6cDVj09GRrUYYBi+jIFpLv96dS2eWqZdkyf24yOOjb6+4ufqNtWN8iLLwd3lB19qwfneTqq6Xf/m1/w3CxC/ldXflHXLrmmsI3Vl55pfT4x8fn/eM/+pGckp9fTobFI48UP2FLDgc1Pl7wF0hmWHAjKOouHOYnjz/W/6OrdFfO/I3rC1Wsl79gW2bAQvLX0KemIpsTjYieOJGNvlbzoFYJYWQ2TCUrlBJm5iOpy5f7x5Il/mrJzEzugaKYVEp62ct8Ee/XvEZ685v9Hedm+YM7Zv6O++jnmE77wMPISO4F3+7ubH2HpUv9gXv1an9BOV9K22J0dfmD4mL2cTqdHVIovFv32muLv+fKK/0+SwQsojcZq7NTfeMDyizrV4cimTDO+edLlmTThEqZnPQZLYUUi9iXI9z/0c5CGLDo6WnsYZMymexdGmNjhZfbutUvc+KED8CYcScAsFgdHfFg53ydP++PbdEMy5MnfXZXtLO9b5/Pnjx/PnvcnZ2te8Aieag5dKg+21FvBCwAAAAQk0rl1lQIhwh3Lj7EU3jjoXO5Baujw0dVM8MiHF56aCg7xHypmgxhPVopO3Tt9u0+HrBhg78etmVL6esoyetkAwP+mlOh93V0SK99bXzeyZPSZz6T54bmZNTo+PHcDzkqGbCYmip4F3gyw4KABeouLDScR7dm9TeTb4nVpelJz+mqxxWpsTA+vqADTzrtr4s/er13dNT/44ZR0PDi5QKCITVllj3QnT2bk8HyqFtu8cNBhZHcyUn/M51e2MDZK1dKz3++j/yUCu6MjfkhpcJI80tf6rd3aMjvgORwXkuX+u2bnPSZFQMD8ztQ10s4nNDsrL8gdsklhYsrDQ35uwIymZy6GeGvFyav9K0Y0cCg+fccOuS/gE+e9Pt5ZMQHeubztz8y4j/nycn8r69aNc9ftIRkwOLsWT/sV7GC240i3BfpdO5NBFHXX+/388yM/50GBuo6Bj7QMrq6yq9hsWePP3AmMwu/+MVstvKJE/7x8MPZsWQlf3yq85BQyUMNNSwAAAAA+XODa66JX0e45ZZ4Ee1owCK8LpKsYxFmWEjVvxl5bMwHKfr7/faUulYTXo/r7PQZ4du3+23duNFf67n88uLXJkLJET6uuab0kOdbt/qRUqL+8R+l++5LLJhvSKh8xUJCyYDFihUFi+aSYYGG09lZdMz8C7Zk9Kd/6m/6Hh2V3vvmo1p6yVR2LLikiYnKBBV6e/1Fy/Bu6cUU3K61/v7sBfJ8NSauvNKPf9fXFxTtyPiDaSrlD4BHjxbO6jp+XNq5s/BQdWfOlI5Y9/b6L5d3vEP627/1wzGFqXKFMgTWrfMH6ZER/7yR72IPhxOSfGQ8lfIpfPlceKE/EOe5i7+z08duwp89w70amOj3y5496/fBww/7z7u/v/RwUCEz/39SKJi1cuX81lNKOHZkd7f/uwiLNjVDwKKvL5smWmiIp54e/7cbfrEODPClClRKGLCY75BQx4754+HUlPTMZ8Zf+9GPpP/+b/98377s8TPM6pMaIsMiOSQUAQsAAABA/ppKJiO96U1+SPI//EPpl36pcMAi7OMn+/dhhkV4rlFNY2O+jaVL53f9KryRqrfXX2MYGvLDsI+O+iG953sN5TWvyQ4LNTQkvepVpa9TDA/7m4qTmeq33Zb4DJMRk1KFcJM1LPJdoAxWE9YFDXFtBQ2hQM0VSRp47Gb9xm9Id90l3Xuv9Du/ec7/AxcapqVSAQvJt9GsAYt0Olso/Lrrsq+Njvq6FbOz/qJsJuOX6+jwF7zDgGm+KyUPP+yPNydOSLt25b/zNfwCCNvPp6/PLxOOzxcWjM5kCh+EOzt9RPn66xdXt6IWMplssCa8YzcZ5Q5dfnne4aAk/13R1eUfvb1BMsyG5dmAyMGDPrg0MuIXSl7tKmZiIv9wYWNjlU2NDO9uOH/e/910dfm/yUYPWHR0+G3v7S3cubj6av+7hUPGpVJ8qQKVkkr5Y6NzvgNbzLlz0vS0D3wvXSo9/em5Y8R+8pN+PQcO+Lo/Zv57p7PTPxogYJHs/hfr+rcyAhYAAACICbMiRkZ8X3/rVt+fP3s225+PBiw6OrI3Toac88sXqrdaaT09/gbVycn51QmNFt6emPB3bYfDeZdzjXNgQPr7v5c++EHpT/7En2SUuk7R25u/lsVnPyv99KeRGflSNQ4eLLziZIZFvgIb8sNfJetlNPJNymgjmzcXfu3ii9XR4a/FLlsmWW9wYJmayl12cHB+qVbzNTrqDxhhalYzBSzCu8NPn/YBipe9THrhC32R7OiBPHpRZ3LSX1AeHvYBi+gB4/Bh6cEH/fFp48ZswdKo8+f9Ixrdzie8ez2d9uvt7PQHo1L1F8Ir+I0uDAKl09mAxeMelzvUVViQO5oRk5BKxQMWmZUj2YDdyZP+AtzIiP/nKBQgyqe7Ox7IClVqOKhQNB3z2LHmKLgdCof2KpQ+GX5+x49TvwKotOhdT6WyLPbs8d9XYS2k4WHpaU+LL/PlL/uA+7lz/vttaCi7/lTKn7wQsGgIBCwAAAAQE14HSmZgR2tSRK9zSbkBi7DUQqnrVZU0Pu6DFvO5lmjB8N/Ll/v3rVvn31vOdZ5Qf79/f1jIez4Bj02bpFe8In7d6tQp6QMfiFwb7O/PHZu9UOW9kyd9envUxo15F81XR7cZrhmhDRQqDJ3vtfDiZ74Lq+GFxUplWPT3+wPZ0JD/n2yWgEUm4w8yfX0+YNHf79PmXvlKf0Xk1Cl/0OvpiQcsenv9hZ6lS/2BPyx6c/CgtHu3D+CsWpUtjB3WUQjlS8fLJ1rbwDl/kbejY34Fo5tBWP8gk/F38p454/+GtmyJLxdGzPNkV4TCa3Z9fX43dnSaD9YND/sX+/v9572Qz+4JT8jdT9UMWMzO+umwKHmjCwMW+caJ7OjwY0HOzPi/+8FB/6h2WinQLsJorVS8jsWxY/44OzXlD5SbNvnU6VtuyV3u05/2neGzZ+P/16mUPz7NzRUfgrXKkoeasC54uyFgAQAAgJjwGnlXV7yDnC9g0dOTvUE22rePBiyqWXB7MTZs8NkYl1++uKG6w1E/Bgbmf1NlOi394i9KT3pSfP4//EMkUSJMU48qNJBtcjgoKbfYYCBZcFvKzZgH6uKyy/LP7+jIHS6qu9v/j+S7sBqe7Vfy4DM25v8fzXKKIjessC5Fb6+/oJq84nHqVGSMocRBYHIy+96HHvIB0b17/QXxFSv8QXN4OF5HITTfgEU4ZmC4n8opGN0MUikf3Jma8r9neFdv8o7fa6/1F7gLDW+m7DW7MGAhyUfcwyGgRkaKD1tUzNhYbhClSD2ZBQnTGsML+c1QvyKUyfgdEA65FXXJJf4zf+SRbKH7cobkAlBcNMOi0FX7c+d85t+SJf7/dMMG/z0yPOzHeb3kkvjyX/5yvO5PqLs7+/1VxyyLZMDCOT/qX7shYAEAAICY8Hy8UMCiszMb1AhHG0mn4337+V6vqqeeHh+wWOzIDUuXZofJKufm1rEx6fWvj2d1nD4tvfe9iZVHFQpYJIeDGhsrOHxFMsMiHLEEqLvwwmvSqlW5f6RhpDTfGHDhhd9khtJihHfBr1nj76BuFv392UyGcFii0KlT8YLbUaOj/vNdutTf3nnggB8/b2LCf+br1vmgRVj/Ipr9dfas3z/d3aWzUfr6/MEzrGfQKtkVoQsv9H8va9f6L9QdO6RnPUt63vP839N110nPfa4PGBX5rFKp7BCLj8aWurp80CIs3jTfYtv5PO958embblr4uvKJpmOG080UsAh/JoMR4XBQx45lMysIWACVM58Mi+lp/3PFCv//F/0eWbdOuvnm+PJ33+37zcn+RjgklNRQAQupPQtvE7AAAABATGenv37V2Zk9NwhrUuQLQIQjPczNZYeQmp3N3kzZ6hfDe3ulxz5Wuuqq8utsPv3p0i/8Qnzepz/th+GVlHsyVWgg22TAYmqq4MWvZIZFOIw80BDyBSAKDG+mdNpfoEgGJsbG/LyFjPFWSFeXv/hc6TvPq21gIHvQPn06O//8eX9QDy8c56urMDGRLUa6YoW/CLR+vQ/eSP61vj7/eZ84kV1/vnS8Qnp7s0NLdXQUzTJoSl1d0sUX+/2wdq0PEu3ZI/3Wb/nCR7/8y/53LzIclJSt+5xTHmH1an+BLszkWKjXvU56zWt8MfO3v1264YaFryuf8O8gLMKeTjdPwCIs1tXT47/oQ0uW+GyZsGD8kiXZrCQAlVGqhsWRIz5gGAbQk/2Fnh7pRS/KzSL8r//Kndfd7b8b5+ZyA/w11NOTexhpx4BFE1SqAgAAQK11dflHNADhXOGARXht6swZ/76w4LbUuBkWlbTQ6xOdndK73y199av+/Ejy50h/8Ae+Ju6CMyympgperE1mWHBtBQ1l40Z/92PUhRfmX7anxz9WrYoPi7ZmTetHSuerv98fC3p74wGLU6f8z76+wmPCTUxIu3b54TV6e/0wc9G7+M18IOPkSf8lcPCgv4BeTsAizLAI70xvxbH/e3p80OKee/zf6q5d/u/z6NFs3Y4SgZrJSf99HCa0PKq7249ruFi9vdL/+l/S/v1+Gysdxe7q8p2CsGh9OHxSMwjrwPT0SE9+sq+ns3+/9NSn+v0Xfi8PDpJdAVRaKuX/B6N3UYVmZ31dpaEh/9i4MX/tqg0bpGc8w4+7GvqP/5C++U0fLA6zBycmfGB5drbuhbeHh+Nf2e0YsCDDAgAAADlSqfi5QbEhnpIBC8n39cN5XDcs7oorcmsCfvKTwXi1yYBFoUFsk4mEas0AACAASURBVAGL9esLtkfAAg0tX3AiOf50KJ32B5pf/MXsVdwtW3wtjEoV3G524UXh3t5skELKFtwOC/Dkk077Cz3j4/4ibb4hh8bH/Wc9OurvdJ2dzQYswmG7igmLRvf1LS5DoNENDPi/zeFh/znu3++j00ND/kJ3icBOKuVHNpmYqOI2Llvm/9fKTRWcrzAdMwxY9PVVp51qCAtvm/ki5S99afbv9dgx/38W1hMBUDnhcFCdnfEMC+d88DcMnC9bVjjw29Eh/fZv5853zg93+P3vS1/5ivSxj0nveY//LqtzwCL5tRz9+m4XZFgAAAAgRxiwmJvzj1IBi87ObGaF5H+GQw1x3bC0P/oj6fOf9+dOkr+r6gtfkF4yPKLYfa5HjuRfQTJgsW5dwbbyDQkFNIyLL86d95jH5F82nfYHq/XrpQ9/2EdYjx7NBjLgD87heEJHjviDjFm2fkW+gttRk5P+UWz9ExP+4vv+/dLhw/4LYGjI74dSd+oPDmaH2WmWO+4XanQ0e2w+e9Zf6B4YKDkcVMsI725wzgcAKjlkW7VFh3o6fTr7/Px5/6U6Odlcw1wBzSJM105mWBw44O/AWbfOf5dt2FB8PY9/vHT11dKddxZf7uRJn31RaCjKGkl2YcLzq3bSRN8QAAAAqJXkkLFnz2ZrUiQDFtGhqcMbksIbbOdzvQr+PGvz5vi8u++WDs3NI8NiZsanxEdt2lSwLTIs0NAuvTQ+bVZ8SCgzf8Dq7vYZSWbZoWfghQWdncuOyx1edO3sXHzUcmrK74OlS33A4vz5/NHtQvr72+dC78qV/uJ2+LMV63YUEs2waLb9ncn4/5VUKj62/fHj/v9qyRIfkKLDA1RWvgyLEyd8gHx83Ae9t2zJrWWVz6c+5QMXsXH18piernuGBQELMiwAAACQRyqVPUcIa7MWGpI8vOCdTvvz+PPn/SOVao/6FZVy1VXST36Snb7nHunI1SOK3Xv7yCPxuzslaceObGpGqEjAIplhQcACDWXtWumaa6Tvfc9P33hjospwRBiU6O6OH6TCAAa8/v743eGpVDYNLl/B7XKl0z5LYGZGOnTIzysnYNFuNmzwn7+Zv+DWLsG1TCabvlksq6cRhRc4e3riAYtjx7KZIwwHBVReGIgIC+udO+eHgurv98PrrV49/2HsNm6U/vzPpQcflO6916/j4Yf9cFD//d/Z5fbt88foMCOxDpJfCwQsAAAAAMUzLEoFLMJh0Lu7/bn77KyfH2ZYYH6uusrXrgjdf790+KqlioUeHnkkW9g2lBwOani46B27yQyLUjeaATX38Y9LH/2ov1Dx6lcXXi48GIUX4Gdn/YGro4OARVR/v/9curt9wCKMRhcruF2uFSv8EB1Llvgvgu5uoqGFmPlhzmZm2utLcnTUDx82N5e/HkojS6ezKabHjvl5zvnv5LEx/z9VKLAKYOE6O7NFt8+c8dkPc3PSqlW+vxvtD8/HsmV+eMT+fmlkxA9DefJkPGBx4ID//z5zpm6B92QXJjy3aicMCQUAAIAchTIswhERksKRHs6f99fDJG6wLVc4jHto/35px8nEHZvHjvkTqahkwGJqqugHnwxYNNvIHGgDq1f7SvQ33VT8wmYYWe3u9mfzs7PZA1Q7XQguJVp4+/RpX7+is7N4we1yDQz4C7ZjYz5gRIpdaeGQZu3CzGf/bd6cvSOiWZhl61icPes7OydO+J9LlvgLn81UkwNoJuF3/enTvh+8apX/f9y8ufxjaPgdNTzsh1l1LrdO06FD2YBFnTAkFBkWAAAAyCNfDYtiAYgwYCFlL4gzjHx5Lr3Un0PNzflp56Q95+MnUe7kSdmJE35cp/BCY76ARZELJ8khoQhYoOH09krXXuufl7oIGKZ3hRkWYcCCDIusMN2tt9dfiAkLcUuVHZpnxQp/MSm805yABVpJJpP9m56ZiWcSMRwUUD3Ru6jGxvx3zIUXLuwkI5XywYrjx33NpZMnfb85anbWDxVFwKKuCAEDAAAgRyqVzcAOh2ueb8Di+PHs+7leNX99fbm1hfediw/tZM75O8IOHszOTAYs1q0r2k4yw6LZhhJHm+jomN8dyz09/oATFpQmYJFff78/yJw/7w/SfX3xwEUljI76/TEx4af5AkAryRewGBz0x6mRkfpuG9DKwruoenv998vKldLSpQtf3/i4/05MpfzwUEuX5qaP17nwNgELAhYAAADII+y3d3b60UOk0gGLzk7/iNa7IMOiPJdfHp/+6e7c9Iezu/Zkx9eVcgMW69cXbSOZYTE4WO5WAg0kzLCQ/MWFri5/AGN4lrho4e25uWz9ikoOSWTmC0r39fmhvPgCQCvJZLL1cY4c8XdhL1niC/52MXgJUDVhyvaaNf7/7YILFre+0VH/vzw05G8CMssdFmp6OnvHVh0QsCBgAQAAgDzCc+9wyFipdMBCyl6fYhj5hbn66vj0ffd3aS4V/xDPbp/2Zy5Hj/qfO3fG37Rpk4pJZlgQsEBTCzMsQqkU2RX5DAzEh9WoZMHtqKVLpeuvz00XA5pdJuN/9vb6L9LOTh8IZDgooLqGhvz/Wn+/tGXL4m9I6Oz031UjI9msw2TAYvfuumZYJM+fKLoNAAAAKHv9r6vL34wbZk8UGj0kGbDo7vbv5abD8jzucfHpBx80HR6NByA6v/5Vf9fXgQPSrl3ZohehEhcKkxkWQ0ML3VqgAaTT2THoJIrnFBIWqwmHguruZjw4oBzd3fFi8oOD/rhDwAKorhUrpMsukx772MoNNTg+nh3P9ujR7FCGob17GRKqzghYAAAAIEc0w0LKdpwLnSeE17+iGRYMX16+Sy/NfuaSH/XpG5e8LrZM730/kO6+2xfP3bYtvoLBwdyTroRkhkVYHxdoSsm0rq4uMizy6enxn01vrw9aSAQsgHJF61gsWeK/czneANVl5u+uSdaZWIyREf+dODycP2Cxf39dh4RK/qoELAAAAAD5i+YdHdnARamAheSvg4XLRYMXmL/e3tya2d+a+mWd7UtEFT7zGZ/G/sMfxudPTZWMFCUDFmRYoKmFf+/Rgw8XEPML61j09WWDFwDmLwxYmPmAH9kVQHPq6PD/v8PDPlM52Rk+cMCPw5TMYq4RMiwIWAAAAKCA6JBO8xniKcysDpcnw2JhLr44Pv2zXX2afvJL4zPvvVe66y7/M2pqqmQRXYaEQkuJHnQkf5AiWprfwEA2YEF2BVC+MGAxOOjv7CBgATSvZct8fyGaeRg6flw6dapuw0IlAxbUsAAAAAACqVR8SKhSAYhkwIJrhgtzySXx6W3bTXue8EKd7hmOv3D77dL998fnrV1bdN2zs7nnXgwJhabW0eEPVuEBq6ODDItC+vv9gTmTIWABLEQm428KGB/3FziTFzkBNI/hYd9fGB72343JG3727q3bsFDJbkwdy2nUDQELAAAA5NXVVX7Aoqsre92QDIuFueKK+PT0tHTclujeK18cf+Hee6Xvfz8+b8OGoutODgclEbBAC+jpyQYtJAIWhYyO+kBFf3/JWjcA8hgY8Bc2+/qkycl6bw2AxTCTxsZ8qnFnp7R0afz1PXvqFimghgUBCwAAABSwkAwLKXvjLhkWC5Ov8Pb2vX3adcUtOtGTOJlK3vm1cWPRdecLWPT3L3BDgUaRTmfHrQunkauzU7rySumaa6hfASxER4d01VX+zoKpqXpvDYDFWrbM9x/6+3MDFrt3N8yQUGRYAAAAAIFUKl7DYr4Bi/ACOBkWC7NkSe7ITtt2Z9SdSevOjb9W/M2bNxd9OVm/QiJggRYQZlhEC28DQDWkUr6GRYl6UQCawJIlvg8xNJRb1G337oYZEooMCwAAACAQDgkVPkoFIMLhoPr7/Xk81wwXJp3OTZS4b09GPcO9+vGG5+p0pkCRz0xGWrGi6LqTGRbRm9KBphXNsEil/F3QAAAApSxb5oMVyQyLvXsbJsOCotsAAABAIMywCEdXmU/GxMhItn4dNx8uTHe3tGlTfN62XT1KjQxIy8d172W/mv+Nk5Mld1IyYMGoMGgJ6XQ2qkqkFAAAzNeyZf6EJ3nTz/79DROwIMMCAAAACIQZFmGneT4Bi02bpMsuky66qLrb1srMpIsvjs/bvcd0YuwC9Szp0U8f+1KdHlyW+8apqZJRouSQUH19i9xYoBGEB6e+PgIWAABg/vr7/UnPqlXx+YcPSydP1mWTkkW3qWEBAAAABFIpf/27t9f34+czdFBHh8+qjhaNRvk2b879vLc/0Kn0uimd6h7Srl/4jdw3rV9fcr1kWKAl9fX5g09PD0VZAABAeTIZaeXK+Ly5OWnPHuncuZpvDhkWBCwAAABQQHjBvLeXAtq1NjAgrVkTn7dtm9TTn9KZsRU6cP3zdG58Mr5AMi0jj2SGBQELtISuLv/3f8EFuXdIAgAAFJPJSGNjuanHe/bUJb2BGhYELAAAAFBAmI7c10fAotbyFt6+z++Huc5unVmzSQde847sjtm0SbrllpLrJcMCLWt4WFq9OnccBQAAgGIyGd/5bpDC22RYSPNI7AcAAEA7CgMVMzPS6Gi9t6a9pNM+BvGv/5qdFwYsJOm0evXwU35ZU5ePS/ffL11yiTQ4WHK9yYAFNSwAAADQ1sKhJcfGpOnp7Py9e/2JUI0l770gYAEAAAAEOjqkq6/2nWTuxK+tdNrXsYgKb/Lq6PA/T3UNStde6+8G6+uTliwpud7kkFCZTAU3usWY2SpJL5f0LEmrJQ1IOiRpp6RvSPoH59yPi7z/GZJeKelqSWPBe++UdJtz7itV3XgAAADMT9ghnpiIz9+9uyEyLNpxSCgCFgAAACios5NgRT2k076GRVdXvNbftm2+pvDMjHT6tDQ3vFQdW7f66ujzkMywoD5xfmb2O5LeIykZ0lkRPK6XNCjpdXne2yHpNvlgR9RU8LjFzD4q6VXOubkKbzoAAADK0d3t0xomE/Xh9u1riIBFO2ZYUMMCAAAAaDDptD9vWrkyPn/bNh9ACrPTT53SvIMVUm6GxcDA4razFZnZWyX9uXywYpukN0q6QdLlkp4aTH9HUqFgw7uVDVbcI+mFkh4b/LwnmP8KSe+q/NYDAACgbJlMbsd7/35/h1CNJQMW0ZuX2gUZFgAAAECD6e72cYgLLpB27MjOv+8+6clPlo4d89OnTpWXJZHMsJhH2Yu2YmZPkfQHweQnJb3COZdMxP+apPebWeJ0UjKzjZLeEEzeJekJzrnwTPdOM/uipG9KukrSG83sY865n1f69wAAAEAZMhlp9er4vJkZn2Vx+eU13RQyLMiwAAAAABqOmT9ZWb8+Pn/bNl94+/x5f/Jy8mR5601mWBCwyAqGcvrLYPIHkl6eJ1jxKOdcvtPH1yl7U9jvRIIV4XtOSfqdYLJL0u8uaqMBAACweH190tSUH481KnrnUI0ki27PzkrO1Xwz6oqABQAAANCA0mlp06b4vH37soX3zpwJhoQqAxkWRf2CpA3B8/c658pKwDczk/TsYPJnzrnv5VsumH9fMPns4H0AAACol0zGF+8bHY3P37275ikOyQwL5/zNSu2EgAUAAADQgNJpPyRU8i6rn/7UZ2DMzJSfYZEMWAwNLW4bW8zzg59O0pfDmWY2YmYbzGykxPsvkBRWa/xmiWXD16ckrSlzOwEAAFBJmYz/uWxZfP7evTUvvJ0MWEjZG5baBQELAAAAoAGl077AdnI43e98xw8LNTPj6wDOFSr9nEdySKglSxa/nS3kmuDnTufccTP7VTP7kaSH5ItvP2Rm95nZG8wsnef9WyLPf1airejrmxe+yQAAAFi0VMpHCiYm4vMbJGDRbnUsCFgAAAAADSid9udOj3lMfP73vufnnznjU8RPn87//nzIsMgvqF9xYTB52Mz+TNKnJSU+fW2U9D5JXzez5Ke3IvJ8d4kmpyPPV5a5rSuKPSQtL2d9AAAAkM+ymJqKz6tDwCKZXS0RsAAAAADQANJpP5TuJZfE5x896ofTDQMV861j4RwZFkUsUfbc6GJJ/5ekfZJeJGlEUp+kJ0oK61JcK+ljiXUMRJ4nQkM5ooN59Ze5rdMlHneWuT4AAAD09UmrVsXn7d3r05priAwLAhYAAABAQ+rp8T8nJqQVK+Kvff/7vvjeuXPzD1jMzOQOH0XA4lGZyPMeSackPck592nn3BHn3Gnn3H9KerKkHwTLPcfMHpd4X6jUaWX0Vr3ehW40AAAAKiSTyR2L9ehR6eGHa7oZBCwIWAAAAAANKR1USUilpEsvjb92113+ZzmFt5PZFRIBi4jkrXMfdc7dl1zIOXda0lsis36lwDrynGrGRGtglDGolyQ/hFSxx9Vlrg8AAACZjDQ5KZnF5z/wQE03g6LbUle9NwAAAABAru5uf74U1rH4l3/JvjY9LR086DMv5pthkaxfIUn95Q5G1LqS4ZyvFln2a5LOyZ9LRYMD0XWU+mSjGR2lho+Kcc4VrY9hyZNsAAAAlJbJ+A740JB05Eh2/o4dNd2Mzs7ceWRYAAAAAKg7M3/O1N3tb/YaGYm/ft99vgbgqVO+PkUpyYCFmR+qF5Jz7oykQ5FZ00WWnZF0OJgci7wUDSQkBvHKES20XbAtAAAA1EhXl09xHh+Pz9+1q6abEd6wFEXAAgAAAEBDSKf9CcvcnLR1a/y1H//YF96em5tfLcDkkFC9vbkZ723u3sjzPPe2xYSvn4vM+0nk+YUl3h99/acllgUAAEAt9PVJU1PxeXv21HxMpuSwUAQsAAAAADSEMGAhSY99bPy17dulhx7yz+dTxyKZYdFLqeek/4w8X1toITMblDQaTO6JvLRD0t7g+RNLtPWEyPt3zn8TAQAAUDWZjB9zNWrfvppHDJIZFu1Ww4KABQAAANCgogGLiy7KFuKW/DBQP/yhdP78/OpYJDMsGA4qx+ciz59TZLnnSApzU/4rnOmcc5K+EExeaGbX5HtzMD/MsPhC8D4AAADUWyYjrVwZn7d3b80DFmRYAAAAAGhI6XT2hKWjQ7rqqvjrP/6xHw5qPgGLZIZFT09ltrFVOOd+KOkrweQLzewpyWXMbLmkdwWTZyV9PLHIBySdD55/0MxieSzB9AeDyXPB8gAAAGgEmUzukFD7988vnbmCurri0wQsAAAAADSEdFrq7PTBitlZ6brr4q//7GfSI48sbEgoMizyep2ko/LnSV82s/eY2ePN7Coze42kO5UtqP0251x0SCg557ZJel8weZWkb5vZrwTv/xVJ3w7mS9L7nHPbq/0LAQAAYJ76+qTJyfi8uTlpx46abka7Z1h0lV4EAAAAQD2EQ0B1d/uAxTXX+ELZ4SBCZ89K99zjh9p1rngR7cOH49OZTHW2uZk557aZ2U2SPitpXNLvBY/YYpLe7Zz74wKreYukZZJ+XdLlkv4uzzJ/I+mtFdloAAAAVEZXlzQ25jvK0TuC7r9fevzja7YZ7R6wIMMCAAAAaFBhwCKV8icqw8PSli3xZe6809exOHOm+Lp27YpPJ7Pd4TnnviXpIknvkPQDSY9ImpEvqv1xSVc6595W5P1zzrmXS3qWfE2LvfLDR+0Npp/pnHuFc26uqr8IAAAAypfJSOPj8Xl1zrBot6LbZFgAAAAADSoasJiZ8c+vu066997sMvfc47MrTp0qXpciGbBYs6aim9pSnHMPSXp78FjoOv5V0r9WaJMAAABQC2EdiwceyM7bubOmm5BKxafJsAAAAADQEMz8HVbhkFBSbh2LY8ek++4rXXg7eZ61fn3FNhMAAABoDZlMbh2L/ftrugnhTUshAhYAAAAAGkY67e+ymp31mRQrV/qaFVH/+Z/FAxbnz0vT0/F5a9dWflsBAACAppbJSKOj8XkHDtR0E6hhAQAAAKBhhQELyQctzKRrr40v893vSqdPF17H3r3SuXPxeevWVXY7AQAAgKbX15cbsDh0SJqrXfmxdq9hQcACAAAAaGDRgEV4d1VyWKidO6X77y+8juRwUN3duVkaAAAAQNvr7MwdEurQoZqmOZBhAQAAAKBh9fRkT1rCu6suukgaHIwv973v+aGf8kkW3B4fzy3mBwAAAEDS6tXx6dnZmtaxoOg2AAAAgIaVTvsbvTo6sgGLzk7p0kvjy01PF65jkcywWL684psJAAAAtIZkwELKvQOoiii6DQAAAKBhhScsYeHt0NRUfLk9ewoHLJLnVwwHBQAAABQwPOwfUdPTNWueIaEAAAAANKxowCJ6spIMOkxPFy68ncywWLOmUlsHAAAAtJh8hbd3765Z8xTdBgAAANCwwhOW7u74yUoyYLF3r3TiRP51JDMs8mW5AwAAAFD+gMXevTVrngwLAAAAAA2ro8OftIQZFqdPS2fO5NahOHs2N5NCkubmcgMWZFgAAAAABXR2SuPj8Xl1DFicOVOzphtCV703AAAAAEBx6bQPWJw7J913n5/nnJ8fPYHZvt3PN8vO278/966sdeuqv80AAABA05qYiE/v31+zpts9w4KABQAAANDgBgelkRFpYEA6f94/5uakyUlpx47sctPTPoDR05Odl8yuSKWklStrs90AAABAU5qaik8fPFizpglYAAAAAGhoF1zgh9KdmfHBinPn/M+1a+MBi927pVOn4gGL5DBRy5ZJvb012WwAAACgOSULxtUwYJFKxacJWAAAAABoKF1duTd5SdIll0hf+1p2evduX+MiKplhsXx57kkQAAAAgIhVq+LTx45JJ09KmUzVm273DAuKbgMAAABNatOm+HSYYRGVzLCYnIzXuAAAAACQsHp17rzknUBV0u5FtwlYAAAAAE1qy5b49L590vHj8XnJgAX1KwAAAIASxseldDo+r04BCzIsAAAAADSFZIbFuXPxmhYSAQsAAACgbD090uhofN6DD9ak6WTAYna2Js02DAIWAAAAQJMaG5P6++PzHnjAF+SWJOdyz6vWrKnJpgEAAADNq7PTd7aj9uypSdPtXnSbgAUAAADQpMxyh9eNFt4+dCi3CDcBCwAAAGAeli+PT9coYMGQUAAAAACa1tq18ek9e7KFt5PD7HZ2SqtW1Wa7AAAAgKY2MRGf3revJs0SsAAAAADQtDZsiE9PT2cDFsn6FWNjUiZTk80CAAAAmtvkZHx6//6aNEvAAgAAAEDTShbe3r27cMBi+XIpna7JZgEAAADNbcWK+PSBAzVpNlnDgqLbAAAAAJrGli3x6QMHpEce8c/zBSySd2wBAAAAyCM5lurhw5JzVW822V8nYAEAAACgaWzeHJ8+f1564AF/LvXAA/HXJielDs4AAAAAgNJWr45Pnz0rHTpU9WYZEgoAAABA01q6VBocjM978EF/YpMsur1yZe22CwAAAGhqa9ZIZvF5yQ52FZBhAQAAAKCprVkTn969Wzp50gcuoghYAAAAAPPU1ycND8fnEbCoOgIWAAAAQJNbuzY+vXu3tHevD1pEJQMbAAAAAAowk8bG4vOmp6veLEW3AQAAADS19evj09PT0s9/Hp/X0ZE7DC8AAACAIpYvj0/v3l31JpMZFufP+0e7IGABAAAANLlNm+LTe/ZIO3bE542OSv39tdsmAAAAoOklAxZ79lS9yWTAQmqvLAsCFgAAAECT27w5Pn3woLR9e3ze8uVSOl27bQIAAACa3tRUfHrfvqo3mS9gcfZs1ZttGAQsAAAAgCZ30UXxaeeku+6KzxsfJ2ABAAAAlGXlyvj0/v1VbzJZw0IiwwIAAABAExkakoaH4/N++MP4NBkWAAAAQJmSAYsDB6reJBkWAAAAAJpesqD2mTPx6YkJqbOzdtsDAAAANL01a+LTx45JMzNVbZKABQAAAICmd8EFxV9P3hwGAAAAoIR8ney9e6vaJAELAAAAAE1v/frir69aVZvtAAAAAFrG8LDU0xOf9+CDVW2SGhYAAAAAmt6mTcVfT2azAwAAACjBTFq2LD5v166qN9nVFZ9HhgUAAACAprJ5c+HXRkakgYHabQsAAADQMsbH49PT01VvMpllQcACAAAAQFO56KLCry1fLqXTtdsWAAAAoGVMTMSnd++uepMELAAAAAA0tSVLpKVL879GwAIAAABYoKmp+PSePVVvMll4m4AFAAAAgKZTqLA2AQsAAABggVasiE/v21f1JpMZFhTdBgAAANB01q7NP398nIAFAAAAsCDJu4L27696kwwJBQAAAKDprV+ff/7ERO5JDwAAAIB5WLMmPn3woORcVZtkSCgAAAAATW/TpvzzV66s7XYAAAAALWP16vj07Kx0+HBVmyRgAQAAAKDpbd6cf36h2hYAAAAASli+XDKLz6ty4W1qWAAAAABoelu25M4bGvIPAAAAAAuQSklLl8bn7d5d1SbJsAAAAADQ9AYHpWXL4vMouA0AAAAs0vh4fHrXrqo2R8ACAAAAQEtIDv9EwAIAAABYpImJ+PT0dFWbI2ABAAAAoCVceGF8+oILCFgAAAAAizI1FZ9mSKiqIWABAAAAtJBbb5WWLPHPh4akZz4z94QHAAAAQBlWrIhP791b1ebaueh2V703AAAAAEDlbNkifeIT0te/Lq1f72takGEBAAAALMLq1fHpffuq2lyy/95OGRYELAAAAIAW0tvrMywuvFDKZPw8AhYAAADAIiQLxe3fX9XmGBIKAAAAQEvo7fU/02l/otPdLZnVd5sAAACAprZyZXz66FHp9OmqNUfAAgAAAEBL6O6WOjr8z1SK7AoAAABg0ZJFt6Wq1rEgYAEAAACgJZhJPT0+UNHVRcACAAAAWLTBwWwqc2jPnqo1R8ACAAAAQMvo7c0OB0XAAgAAAFgkM2n58vi8KgYskn34M2eq1lTDIWABAAAAtJjeXn+Sw5BQAAAAQIVMTMSnH3ywak21c4ZFV703AAAAAEBl9fVla1n09NR7awAAAIAWkKxjUcWARSoVn26nDAsCFgAAAECLGR+XjhzxAYvR0XpvDQAAlRTndAAAH81JREFUANACVqyIT+/eXbWmkhkWs7NVa6rhELAAAAAAWkxnp3TRRfXeCgAAAKCFrFoVn6bodlVQwwIAAAAAAAAAgGKSAYv9+6vWFAELAAAAAGhjZubm+fiPAu+/tYx13Frb3w4AAACLlqxhceCANDdXlaYIWAAAAAAAAAAAgPySAYtz56TDh6vSVLLoNjUsAAAAAKA9/aWkDxd5/eQ81vF0SXuLvF69Co0AAACojuXLpY6OeFbF7t3SsmUVb6qdMywIWAAAAABA1kHn3I8XuY5tzrmdldgYAAAANIiuLml0VDp4MDtv1y7piisq3lQ7BywYEgoAAAAAAAAAgFKWL49P79xZlWYIWAAAAAAAAAAAgMImJ+PT09NVaSZZw4KABQAAAAAAAAAAyEoGLPbsqUozyQyLdiq6TcACAAAAALKeb2Y/MbNTZnbczLab2SfM7EllrOPjZrbXzM6a2WEz+56ZvcvMpqq21QAAAKi+FSvi03v3VqWZdg5YUHQb+XSGT/bt21fP7QAAAEALS/Q1OwstV2NbEtPrg8dLzOyfJd3qnDtWYh03RJ4vDR6Pk/R6M3udc+6vFrpxZraixCKPBkXoywMAAFRYf398evdu/6iwo0fj0zMzVWlmUarVlzfnXKXWhRZhZldJurPe2wEAAIC2crVz7q56NW5mJyV9UdLXJP1M0glJY5KeKOk35YMOkvRNSU9zzs0m3n+rpLdJ+idJ35UUDmi8VtIvSXqeJAvmvco5d9sCt5MTOAAAADSaivXlCVggBwELAAAA1EG9AxZDzrmjBV4bl/QVSZcHs17rnPvzxDJLJD3iCpxgmdmN8sGMlKRTktY55/YvYDs5gQMAAECjIWCB6jGztKSLg8lDks7XsPnlygZLrpZU9kkcmgL7ufWxj9sD+7k9sJ/bQ732c6d8FoMk/cg5d6ZG7ZbNzNbKZ16kJP3cObdhAet4q6Q/CCbf6px79wLWUWpIqG5JF0o6KPryqDz2cXtgP7cH9nN7YD+3h5bqy1PDAjmCP6663N1mZtHJ/c65BhudDZXAfm597OP2wH5uD+zn9lDn/byrhm0tmHPuATO7Q9IzJa03s0nnXLlVFm+T9E75oaGeKKnsgMU8980D5a63EjhetD72cXtgP7cH9nN7YD+3h1bry3dUeoUAAAAA0KJ+Enk+VXCpApxzByU9tND3AwAAAK2OgAUAAAAAzE8lxtNlTF4AAACgAAIWAAAAADA/WyLPyx0OSmY2Jml0oe8HAAAAWh0BCwAAAAAowcwukPS0YPJ+59yeBazmlfL1KyTpmxXZMAAAAKCFELAAAAAA0NbM7CYz6yry+rikz0nqDmZ9OPH6GjO7vEQbN0r6/WDytKSPL3yLAQAAgNZUsFMOAAAAAG3ig5JSZvY5Sd+VtFM+qDAq6QZJr1J2KKdvSfpQ4v1rJH3DzL4r6UuSfiDpYPDaWknPCx5hdsUbFpihAQAAALQ0AhYAAAAAIE1K+p3gUcjnJL3COXemwOtbg0chpyT9rnPutoVtIgAAANDazDlX720AAAAAgLoxsydKeqJ8sGGtfDbFoKQTkqYlfUfSJ5xz3y3w/gFJNwfvv0rSRLCOLklHJN0r6WuSPuqcO5hvHQAAAAAIWAAAAAAAAAAAgAZA0W0AAAAAAAAAAFB3BCwAAAAAAAAAAEDdEbAAAAAAAAAAAAB1R8ACAAAAAAAAAADUHQELAAAAAAAAAABQdwQsAAAAAAAAAABA3RGwAAAAAAAAAAAAdUfAAgAAAAAAAAAA1B0BCwAAAAAAAAAAUHcELFBxZrbazP7EzH5mZifN7GEzu9PM3mhmfRVs5xlm9nkz221mZ4KfnzezZ1SqDRRWzf1sZn1m9lwz+8tgnUfMbNbMHjKz75rZ281seaV+FxRWq//nRJt9ZvaAmbngsbMa7SCrlvvZzJ5qZreb2c+Dto6Z2TYz+6yZvdrM+ivZHrJqsZ/NbI2ZvdfM7jazo8Gx+2Ez+46Z/b6ZLatEO4gzs2VmdqOZvdPMvmJmhyPH0Nur1OYLzeyrZrbfzGbMbJeZfcrMtlajPdQOffn2QF++PdCXbw/05Vsf/fjWRT8+P3PO1Xsb0ELM7CZJn5I0WGCRbZKe5Zz7+SLa6JB0m6SXF1nso5Je5ZybW2g7KKya+9nMLpH0bUmlOjqPSHqlc+7vy20D81OL/+cC7b5f0usjs3Y559ZUsg1k1Wo/m9mwpI9LenaJRS93zn1/MW0hV42+n18s6a8k9RZZ7GFJL3DO3bHQdpDLzIp16D/hnLu1gm31SvqspGcWWGRO0judc++oVJuoHfry7YG+fHugL98e6Mu3PvrxrY1+fH5kWKBizOxySX8vfxA9Iektkq6V9BRJfx0stlHSv5jZwCKaereyJzj3SHqhpMcGP+8J5r9C0rsW0QYKqMF+HlT2BOfbkt4s6WmSrpD0dPkv0blguU9zF1511PD/OV+7r5M0I+l4pdaL/Gq1n81siaQ7lD3B+bykX5N0jaSrJT1X0p9J2r3QNlBYLfazmV0n6Xb5k5w5+RPaW+S/n58n6UvBoiOSvmBmaxfSDublQUlfreL6P6bsSc43lN3PL5d0v/z5xdvN7JVV3AZUAX359kBfvj3Ql28P9OVbH/34tkM/PuSc48GjIg9J/ynJSZqVtDXP628MXneS3r7ANjYG63eS7pTUm3i9L5gfbsf6en8urfao9n6W//L9e0lbiizzbPkvUifp5wqyxXg0z34u0GanpLuCdb5N0s7g+c56fx6t+qjVfpb0yWAdM5JuLrKcSeqq9+fSao8afT9/ObKO1xRY5k8iy/xFvT+XVnpIeoekGyWNB9NrIp/17RVs58mR9X5RUmfi9VFJu4LXj0garvdnw6Os/Utfvg0e9OXb40Ffvj0e9OVb/0E/vvUf9OMLbG+9dwyP1njIR+TCP/yPFFimQ9JPIn/8qQW08+FIO9cUWOaayDIfqvdn00qPWu3neW7LZyPbckW9P5tWetRrP0v6v4P1/UxSNyc5rbGfJV0faecN9f692+1Rw/38cPD+w0WWWRLZlrvr/dm08qOKJzr/quxJ84oCy7wg0vYb6/1Z8Jj3vqUv3wYP+vLt8aAv3x4P+vKt/6Af354P+vH+wZBQqJRbIs8/nm8B58eg/WQwOSTpSeU0YGambAriz5xz3yvQzvck3RdMPjt4Hyqj6vu5DN+IPF9XpTbaVc33s5mtlvTOYPI3nXNnF7M+zEut9vNvBz+PSfqLBbwfi1Or/dwd/NxRaAHn3DFJhxPLo0kEwww8JZj8d+dcoWEf/kl+bHpJek7VNwyVQl++PdCXbw/05dsDffnWRz8eFdGM/XgCFqiU64OfJyXdXWS5b0aeX1dmGxdImsyznmLtTMlHJ1EZtdjP85WOPD9fpTbaVT3284clZST9rXPuPxa5LsxP1fezmXUre3HqDufcTDC/08xWmtkaM+spZ50oW63+n8OLixcUWsDMBuVTjaPLo3lcrewJasF+WHCRKrwQfbWZpaq9YagI+vLtgb58e6Av3x7oy7c++vGolKbrxxOwQKVsDn7+3Dl3rshyP8vznvnaUmA9lW4HhdViP8/XEyPPf1qlNtpVTfezmb1AvvDTEUmvX+h6ULZa7OdLJYUnMT8ys0Ez+4D83TkPyt/Fc8zM7jCzG8pcN+anVv/PHwl+LjWz3yywzNvyLI/msZB+WJekDdXZHFQYffn2QF++PdCXbw/05Vsf/XhUStP14wlYYNGCiHoYaS2UViRJcs4dkY8OS9LKMptaEXletB1J05Hn5baDPGq4n+ezLZdKelYw+SPnHCc5FVLr/Wxmw5I+EEz+nnPu0ELWg/LUcD9HO0Yd8oUYXyufrhzqlvRUSV83szeVuX4UUeP/548pm47+ITP7azO7ycyuMrPnmtnnJb0heP3dzrl/X0AbqC/6YS2Kvnx7oC/fHujLtwf68q2PfjwqrOn6YAQsUAkDkecn5rF8eCDtr2I7JyPPy20H+dVqPxdlZmlJH5XUGcx6SyXXj5rv5/dJGpf0XUl/vcB1oHy12s8jkedvkr9D43/LF5DrkbRM0qvlx8Q1SX9kZs9OrgQLVrP/Z+fceefcSyU9X9IPJL1C0hcl3Snpc/Jj8H5D0tOcc28td/1oCPTDWhd9+fZAX7490JdvD/TlWx/9eFRS0/XBCFigEqJjFs6nuNaZ4GdvFds5E3lebjvIr1b7uZS/kHRV8PwTzrkvVXj97a5m+9nMniDp1yWdky/O58pdBxasVvs5k2jzDkk3OufudM6dcc4dcs59RNKNkuaC5d5DgdWKqelx28w2S3qJpIsLLLJV0svNbGoh60fd0Q9rXfTl2wN9+fZAX7490JdvffTjUUlN1wcjYIFKmIk87y64VFZYYO10FduJFnErtx3kV6v9XJCZvVk+2i/5aP9vVWrdeFRN9nNwd91t8nfi/Jlz7oflvB+LVo/jtiS9yTmXU1jTOfctSf8UTG5W4Y4yylOz47aZPV7+7sqbJO2R9GJJy4N2V8ofr09JeoGk/zGzi8ptA3VHP6x10ZdvD/Tl2wN9+fZAX7710Y9HJTVdH4yABSrheOT5fNKFwij9fNLaFtpO9E6ActtBfrXaz3mZ2ask/WEw+TNJz3TOnSzyFixMrfbzWyRtkh8f8X+V+V4sXj2O24ecc/cUWfbfIs+vLrMd5FeT/RxctPiMpCWS9ku6xjn3KefcAefcrHNut3Puw5KeIN9ZnpT0iXLaQEOgH9a66Mu3B/ry7YG+fHugL9/66MejkpquD9ZVr4bROpxzM2b2kKSlihdyyREU5Qr/+KeLLZtHtDBM0XYULwxTbjvIo4b7Od/6Xijpw8HkLvmxEw8vdr3IVcP9HBZk+3dJNxXIGg7XnTGzFwTPDzrnvl5mW0io4X6OLl9Oca+xMttBHjXcz78oKUwP/6Bzbn+B7bnXzD4lf3ftlWZ2qXPuB2W2hfpJ9sPuKrIs/bAmQl++PdCXbw/05dsDffnWRz8eFdZ0/XgCFqiUn0h6vKT1ZtblnDtXYLkLI89/uoA28q2n0u2gsFrs5xgzu1nSJ+UzwvZJeopzrlRnCYtTi/0cpiG+LHgUMyp/14ckfVMSJzmVUYv9fG/keWfBpXJfL7QtKF8t9vPmyPP/v8Sydys7HMiF8oX90BwW0g87J2l7dTYHFUZfvj3Ql28P9OXbA3351kc/HpXSdP14hoRCpXwr+JmRdGWR5Z4Yef7tMtvYIWlvnvXk84Tg5x5JO8tsB4XVYj8/ysyeIukf5IOrD8nfjXX/QteHeavpfkbdVH0/O+d2SXowmFxTogDfusjzPeW0g6Jq8f8cPXkqdTNMqsD70PjuVLZIX8F+mJl1S7omfI9zbrbaG4aKoC/fHujLtwf68u2Bvnzrox+PSmm6fjwBC1TKP0ee573Dwsw6JL0kmDwq6RvlNOCcc5K+EExeaGbX5FsumB9GBL8QvA+VUfX9HFnPtfL7Oy3pmKSnO+fuLf4uVEgt/p+t1EN+yABJ2hWZf0OZvwsKq9X/8+eCn4OSnlJkuedGnn+r4FIoVy32847I88eXWDbaQd5RcCk0HOfccUlfCyafamaFhid4rvz/uyR9vuobhkqhL98e6Mu3B/ry7YG+fOujH4+KaMZ+PAELVIRz7n8k/Vcw+XIz25pnsdcrm272Z8lInZndYGYueNxeoKkPSDofPP+gmfUm1tEr6YPB5LlgeVRIrfazmV0m6V/k7yQ4KelZzrm7K/E7oLQa/j+jjmp83J4Jnv+pmQ0mFzCzF0m6IZj8F+cc45VXSI3289cknQqev9rMLs63LWb2DEnPCSb3SPr+/H8TVJuZ3RrZz28vsNj7g59dkj5kZrHhIcxsVNJ7g8mjkj5alY1FxdGXbw/05dsDffn2QF++9dGPx3y1Yj+eGhaopNfKp5/1Svqqmf2hfHS3V9ILJL0yWG6bpD9ZSAPOuW1m9j5JvyfpKknfNrP3SrpfPgXxTZIuDxZ/n3OOcZMrr6r72czWSfo3SUPBrLdKOmZmjynytoPOuYPltoWiqv7/jIZQi+P2g2b2+5L+WNLFkv4nOG7/UP7ujedKenWw+COSfndhvwqKqOp+ds4dNbM/kvROSQOSvmNmH5R0h6QjksYlPVvSbyh7s8zvOefmFvwbIcbMrpe0PjJrNPJ8vZndGl3eOXf7Qtpxzn3dzP5O/u/mZkl3mNkH5If5uVjSWyStChZ/k3PuyELaQd3Ql28P9OXbA3359kBfvvXRj29x9OMLcM7x4FGxh6Sb5FN+XYHHfZLWF3jvDZHlbi/SRoekvynShpOPBHbU+/No1Uc197OkW0vs23yPt9f7M2nFRy3+n0u0vzN4/856fxat/KjVfpb0HklzRdo5IGlrvT+PVn1Uez9LMkn/b4l97OTHTn1DvT+PVntIur2c780C64h+/769SFu98ndOF1r/eb6Xm/dRi+8E0Zev+6Oa+1n05RvmUYv/5xLt7xR9+ZbZz6Iv37L7WPTj671/by/nO7PAOqLfvW8v0lbT9OMZEgoV5Zz7kqRL5A922+RTy45KukvBHVPOuZ8vso0559zLJT1LflzUvfIHzr3B9DOdc69wRHyrphb7GfXHfm4PtdrPzrk3S7pO0t/Kn8Ceke943ynpbZI2Oue+u9h2kF+197PzflfS1ZI+IunHko7Ld3qPSbpb0p9Keoxz7v0FV4SG55w77Zx7lqRfk7/77qB8P2xa0v8n6Xrn3Nvrt4VYDPry7YE+XntgP7cH+vKtj348KqWZ+vEWRFgAAAAAAAAAAADqhgwLAAAAAAAAAABQdwQsAAAAAAAAAABA3RGwAAAAAAAAAAAAdUfAAgAAAAAAAAAA1B0BCwAAAAAAAAAAUHcELAAAAAAAAAAAQN0RsAAAAAAAAAAAAHVHwAIAAAAAAAAAANQdAQsAAAAAAAAAAFB3BCwAAAAAAAAAAEDdEbAAAAAAAAAAAAB1R8ACAAAAAAAAAADUHQELAAAAAAAAAABQdwQsAAAAAAAAAABA3RGwAAAAAAAAAAAAdUfAAgAAAAAAAAAA1B0BCwAAmoiZrTEzFzxurff2AAAAACiNfjwAzE9XvTcAAAAzy0h6saSbJV0qaakkk/SIpJ2SfiTpu5L+t3Nuuk6bCQAAACCCfjwAoNIIWAAA6srMtkr6O0mr8rw8GjyukvQySQckLa/d1jWf4G6tjweTFzjndtZvawAAANCq6MdXFv14APAIWAAA6sbMNkr6N0kDwawvSvqspG2Szsqf5Fwq6WmSnlSPbWw0wYmL1Xs7AAAA0L7ox5ePfjwAzA8BCwBAPb1b2ZOclznnbs+zzB2S3m9mY5J+uVYbBgAAAKAg+vEAgKogYAEAqAsz65T0rGDyrgInOY9yzh2S9KFqbxcAAACAwujHAwCqqaPeGwAAaFtjknqD5z9f6ErMbKeZOTO7PZi+2sw+Y2bTZjYT/Py4mV1YYj0TZvYaM/usmW03s5NmdsbM9pjZF8zsV8ys4Pemmd0QbIcLnneY2a+b2TfM7ICZzYXbGHnPlWb2N2a2LWgv3N67zexDZnazmVniPWsi7dyabF/ZcW8laUdk2ei2jQa/mzOzj8zjM74p8n7ujgMAAGhv9OPpxwNA1ZBhAQCol7OR55srsUIz+3VJf6X499sKSbdKeqGZvdg594953tcpabfyB/InJd0cPF5uZs91zp0osSk98mP6PrXItv6upPfnaXNF8LhC0mvkU+1LtVcW59xhM/uCpOdL+hUze51zbqbIW14W/HxY0hcquS0AAABoOvTj6ccDQNUQsAAA1IVz7mEz2yVptaRLzexNkt7nnJtb4Covk/Srkg5Keo+k/5E/4XimpNdJSkv6tJntcM7dlXhvePfT1yV9RdKPJB2SP8lYK+k3JG2VLxr4IUkvLbEt75V0iXzxwdsl7ZI0LmlQkszsEmVPcnZI+gtJ35c/kRiQtEm+OOGzy/j975R0cfCedwXzni5pb2K5HcHPj8qf6AxJeo6kz+Rbqfkxh28MJj/tnDtTxjYBAACgxdCPpx8PANVkzrl6bwMAoE2Z2evlO/yhnfInB9+R9D/OuR353pdYx075kyXJn1Bc45zbn1jmSZK+Kh+ov9M599jE6yZpnXOuYEq7mb1D0u9LcpI2Oee2J16/QdI3IrPe5Zx7W4F1vVPS2ySdDNo9UGC5JZKOR0/+zGyNsicrOQUOg/TyMJ38AufczgLr7pD+T3v3GmpZWcYB/P9kpKWRWpRlYWFldNVSC7rZTTBMpNAYkEqzqxRdIAoJwz5UX4wsUEw0E80wwxLpftNCMkITKTEyyyvZbbTwNs3Th7VOszuevc+cmTnumTO/Hxz2Wut917vW3l/O8/K8l9yU4bf7QXcfPqXeh5OcNp4e1N3XLlUPAICdhzheHA+wWuxhAcA8fT7JORPnT0/ywSQXJbmpqu6sqovGtVdrqQYW+ejiTk6SdPdPknx5PD2kqg5eVN6zOjmjU5P8NcMorqOWqXtjkk/NKN9nod60Ts74Xuu3YqTaTGO7C7/966rqaVOqLkwjv0YnBwCAkTheHA+wKiQsAJib7t7Y3e9McniS7ybZsKjKk5K8NcNoraurav8Zzf0js9dlnexQTV2TNhlGLVXVU6rqgKp6flU9P8P6vLeOVV406/4kX+/u/8wov2P8fG5VHTqj3mo7J8nGDPHAQ6bHV9VLMkxPX6gLAADieHE8wKqRsABg7rr7B919RJLHZ1ir9pQklyVZP1Ht4CRXVtWTpzRzTXcv7ihNujabNgh8weLCGhxXVT/JsDnebUluyLAO7sLfgWP1Jyzzla5bpvxrSR7MsB7vL6rqsqp679ip2pwRaNtEd9+aYVPBZNjQcLGFUVn3J7ng4XgnAAB2HOJ4cTzAtiZhAcB2o7vv7u7vdPep3X1UhpFZJ2QYdZUkT07y6Sm3/2WZtjdk2AwvSfaeLKuq3ZJcnuT8JIclefQyr7pc+T9mFXb3DUnWjfUemWEzvDMydKb+UlXnV9Url3nGtnL2+Ll/Vb1q4WJV7Zph88MkubS7Z34nAAB2XuJ4cTzAtiJhAcB2q7vv7+5zM3QKFrx53GjuIdW34lEnJzliPP5ZkmOTPDPJHkl26e7q7kpy5VhnudFTs6aRJ0m6+5Ikz0jyniTfTHLXWPSEJMcluaKqvjLlu25LlyVZWH/3+InrRyfZazw2jRwAgM0mjhfHA2wpCQsAtnvd/b0kt4yne2WYcr7Yk2a1UVWPzKYRWX+fuF5JThxPr0zy2u6+uLv/0N3/XrRZ3v+N6Npa42Z8Z3X3W7r7iUmem+QTSW4fq7w9yQe25TOXeIcHk3x1PD2mqvYYjxc6PX9O8sPVfAcAANYmcfzqEccDa5WEBQA7itsnjpcahXXg2JmZ5kVJHjUeXz9xfe8k+4zHFy/q2PzP2AE4YDPfdYt09++6+7NJXpbk3+PlY1fazBY8emE6+e4ZOjtPTfKG8dp5034TAADYDOL4zWxmCx4tjgfWHAkLALZ7VfWYDKOWkuTuJH9botreSd40o5kTJo4nRxpNdo52n3H/iYvqrpruviXJjePpchsDLnbfxPGum/m8G7NpmvzxGUaEPSJDp+ncFT4fAACSiONXeLs4HiASFgDMSVXtUVW/rKojZ63vOpZ9Mcljx0vf7u5po49Oq6qHTCmvqlcnefd4+uvu/tVE8V1J/jkerxs3qVt8/yGZvkngilXV0VW154zypyV5znj6xxU2f8fE8f4ruG9hdNYrs2n6+k+7e6XPBwBgDRPHi+MBVtPDkmEGgCkOzbBZ3G1VdWmSq5L8Kck9SfZMclCGEVUvGOuvT/LJKW39JsPorV9X1WeSXJ1hZNIbk3w4w/+8DUlOmrypuzdW1QXj9Rcm+XlVnZbk90keN97//iT/yjCd/dlb/a2TDyW5oKouT/LjJL8bv9teSQ7O0NF49Fj3zBW2fU2G0Vm7Jfl0VT2Y4TddmA5+W3ffu8R9Fyc5PcN3Xugs2qQPAICliOPF8QCrQsICgHnZkOTODOvO7puho3HSjPq/T7Kuu2+eUn5tki8lOWP8XOyBJG/v7l8uUXZykpcnOTBDR+PCReV/T/KWJKdm23R0kuQxSY4Z/5ayMckp3X3pShrt7nuq6vQkH0vy4iTfX1TlNUl+usR991bVhUneN15an+SSlTwbAICdgjheHA+waiQsAJiL7r6vqvbNsDHd68fPAzKMCtotw2Z1t2cYcfWtJJd09wPLtHl2VV2fYSTWKzKsG3tXkh8l+Vx3/3bKfeur6uVJPpJhc7xnZeiI3ZLk8iRf6O5bq2rrvvQm65IcmeSwDKPJ9hnf9b4Mo6iuSHJmd1+3he1/PEPH8G1JnpdhtNUum3Hf+dnU0bloygguAAB2YuJ4cTzAaqrpywcCwPavqm5Osl+S87r7HfN9mx1bVb0ryVnj6Uu7++p5vg8AAGuXOH7bEccDa4lNtwGABSeMn9fr5AAAwA5DHA+sGRIWAECq6lUZpvMnK98gEAAAmANxPLDW2MMCAHZSVbVfkl0zrI/7+fHynUnOmdtLAQAAM4njgbVMwgIAdl4/y7Bu8KQP2KQPAAC2a+J4YM2yJBQAcE+Sq5Ic2d3fmPfLAAAAm0UcD6w51d3zfgcAAAAAAGAnZ4YFAAAAAAAwdxIWAAAAAADA3ElYAAAAAAAAcydhAQAAAAAAzJ2EBQAAAAAAMHcSFgAAAAAAwNxJWAAAAAAAAHMnYQEAAAAAAMydhAUAAAAAADB3EhYAAAAAAMDcSVgAAAAAAABzJ2EBAAAAAADMnYQFAAAAAAAwdxIWAAAAAADA3ElYAAAAAAAAcydhAQAAAAAAzJ2EBQAAAAAAMHcSFgAAAAAAwNxJWAAAAAAAAHP3X3dha1NyK1zqAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1600x600 with 2 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nre26wEOfZsM"
+      },
+      "source": [
+        "## Get the MNIST1D dataset"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def average_over(trials, trial_name, key):\n",
-    "  ys = [trials[trial_name][i][key] for i in range(len(trials[trial_name]))]\n",
-    "  return np.stack(ys).mean(0), np.stack(ys).std(0) / np.sqrt(len(ys))\n",
-    "\n",
-    "x = sparsity_schedule\n",
-    "rand_color, lott_color = 'r', 'b'\n",
-    "fig = plt.figure(figsize=[8,3], dpi=200)\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "y, y_err = average_over(trials, 'rand_stats', 'test_losses')\n",
-    "y, y_err = y[:,-1], y_err[:,-1]\n",
-    "plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\n",
-    "y, y_err = average_over(trials, 'lott_stats', 'test_losses')\n",
-    "y, y_err = y[:,-1], y_err[:,-1]\n",
-    "plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\n",
-    "plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\n",
-    "plt.xlabel('Sparsity') ; plt.title('Final test loss')\n",
-    "plt.yscale('log')\n",
-    "plt.ylim(None, 5e0)\n",
-    "plt.legend(fontsize=7, ncol=3, loc='upper left')\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "y, y_err = average_over(trials, 'rand_stats', 'test_accs')\n",
-    "y, y_err = y[:,-1], y_err[:,-1]\n",
-    "plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\n",
-    "y, y_err = average_over(trials, 'lott_stats', 'test_accs')\n",
-    "y, y_err = y[:,-1], y_err[:,-1]\n",
-    "plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\n",
-    "plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\n",
-    "plt.xlabel('Sparsity') ; plt.title('Final test accuracy')\n",
-    "plt.ylim(55, 80) #plt.ylim(70, 85)\n",
-    "plt.legend(fontsize=7, ncol=3, loc='upper left')\n",
-    "\n",
-    "# plt.subplot(1,3,3)\n",
-    "# y, y_err = average_over(trials, 'rand_stats', 'test_accs')\n",
-    "# y, y_err = y.max(-1), y_err[range(y.shape[0]),y.argmax(-1)]\n",
-    "# plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\n",
-    "# y, y_err = average_over(trials, 'lott_stats', 'test_accs')\n",
-    "# y, y_err = y.max(-1), y_err[range(y.shape[0]),y.argmax(-1)]\n",
-    "# plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\n",
-    "# plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\n",
-    "# plt.xlabel('Sparsity') ; plt.title('Max test accuracy')\n",
-    "# plt.ylim(55, 80) #plt.ylim(55, 75)\n",
-    "# plt.legend(fontsize=7, ncol=3, loc='upper left')\n",
-    "\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + 'lottery.png')\n",
-    "fig.savefig(project_dir + 'lottery.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XvVxUbWNRirk"
-   },
-   "source": [
-    "## Qualitative analysis of masks\n",
-    "What do the masks look like? Looking at the first layer in particular is interesting because we'd like to see whether the lottery ticket has learned any biases towards local connectivity, which would indicate a spatial prior."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 879
     },
-    "id": "CA8k18eGRhqy",
-    "outputId": "3db315e0-eb65-4a41-b8b5-e9fcd08a28fd"
-   },
-   "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "NOn2afSRrqf7"
+      },
+      "outputs": [],
+      "source": [
+        "from mnist1d.data import get_dataset, get_dataset_args\n",
+        "from mnist1d.utils import set_seed, to_pickle, from_pickle\n",
+        "\n",
+        "import sys ; sys.path.append('./mnist1d/notebooks')\n",
+        "from train import get_model_args, train_model"
+      ]
     },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 3000x1800 with 4 Axes>"
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "I-vm_gh5xTJs",
+        "outputId": "73b73308-0052-424f-be4e-628e75eef117"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading MNIST1D dataset from https://github.com/greydanus/mnist1d/raw/master/mnist1d_data.pkl\n",
+            "Saving to ./mnist1d_data.pkl\n",
+            "Successfully loaded data from ./mnist1d_data.pkl\n",
+            "Examples in training set: 4000\n",
+            "Examples in test set: 1000\n",
+            "Length of each input: 40\n",
+            "Number of classes: 10\n"
+          ]
+        }
+      ],
+      "source": [
+        "args = get_dataset_args()\n",
+        "data = get_dataset(args=args)  # by default, this will download a pre-made dataset from the GitHub repo\n",
+        "\n",
+        "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
+        "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
+        "print(\"Length of each input: {}\".format(data['x'].shape[-1]))\n",
+        "print(\"Number of classes: {}\".format(len(data['templates']['y'])))"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Get the masks for the weights of the first layer weights\n",
-    "retrain_step = 90\n",
-    "lott_model = trials['lott_models'][0][retrain_step]\n",
-    "lott_mask1 = (lott_model.linear1.linear.weight == 0).cpu().detach().numpy().T\n",
-    "lott_model = trials['lott_models'][1][retrain_step]\n",
-    "lott_mask2 = (lott_model.linear1.linear.weight == 0).cpu().detach().numpy().T\n",
-    "\n",
-    "retrain_step = 91\n",
-    "rand_model = trials['rand_models'][0][retrain_step]\n",
-    "rand_mask1 = (rand_model.linear1.linear.weight == 0).cpu().detach().numpy().T\n",
-    "rand_model = trials['rand_models'][1][retrain_step]\n",
-    "rand_mask2 = (rand_model.linear1.linear.weight == 0).cpu().detach().numpy().T\n",
-    "\n",
-    "# Sort masks along hidden layer axis\n",
-    "def sort_by_adjacency(mask):\n",
-    "  mask = 1. * mask\n",
-    "  def score_fn(m):\n",
-    "    adjacency = np.sum(m[:-1]*m[1:])\n",
-    "    first_nonzero = np.where(m==1)[0][0] if np.sum(m) > 0 else 41\n",
-    "    num_nonzero = np.sum(m)\n",
-    "    center_of_mass = np.mean(np.where(m==1)[0])\n",
-    "    return adjacency + 0.001 * num_nonzero #- 0.001 * center_of_mass # + 0.001 * num_nonzero #- 0.001 * first_nonzero\n",
-    "  scores = [-score_fn(1-mask[:,i]) for i in range(mask.shape[1])]\n",
-    "  sorted_ixs = np.argsort(scores)\n",
-    "  return mask[:,sorted_ixs].copy()\n",
-    "\n",
-    "sort_fn = sort_by_adjacency\n",
-    "\n",
-    "# Plot masks\n",
-    "fig = plt.figure(figsize=[10,6], dpi=300)\n",
-    "plt.subplot(4,1,1)\n",
-    "plt.imshow(sort_fn(rand_mask1), cmap='gray')\n",
-    "plt.title('Random ticket #1 ({:.0f} % sparse)'.format(rand_mask1.sum()/(40*5)), loc='left')\n",
-    "plt.xlabel('Hidden layer axis') ; plt.ylabel(\"Input axis\")\n",
-    "plt.subplot(4,1,2)\n",
-    "plt.imshow(sort_fn(rand_mask2), cmap='gray')\n",
-    "plt.title('Random ticket #2 ({:.0f} % sparse)'.format(rand_mask2.sum()/(40*5)), loc='left')\n",
-    "plt.xlabel('Hidden layer axis') ; plt.ylabel(\"Input axis\")\n",
-    "plt.subplot(4,1,3)\n",
-    "plt.imshow(sort_fn(lott_mask1), cmap='gray')\n",
-    "plt.title('Lottery ticket #1 ({:.0f} % sparse)'.format(lott_mask1.sum()/(40*5)), loc='left')\n",
-    "plt.xlabel('Hidden layer axis') ; plt.ylabel(\"Input axis\")\n",
-    "plt.subplot(4,1,4)\n",
-    "plt.imshow(sort_fn(lott_mask2), cmap='gray')\n",
-    "plt.title('Lottery ticket #2 ({:.0f} % sparse)'.format(lott_mask2.sum()/(40*5)), loc='left')\n",
-    "plt.xlabel('Hidden layer axis') ; plt.ylabel(\"Input axis\")\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + \"lottery_mask_vis.png\")\n",
-    "fig.savefig(project_dir + \"lottery_mask_vis.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-7ZaXZ9zv3UL"
-   },
-   "source": [
-    "## Let's make a new dataset (from the same distribution) to see how the ticket transfers\n",
-    "Nuance: in order to properly evaluate a lottery ticket, we need to train it on a new dataset from the same distribution, and evaluate it on a new test set from the same distribution. This isn't done in other papers because it's hard to duplicate, eg CIFAR-10. But we can do it here.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "HugIsM2_iMqm",
-    "outputId": "1870ae22-3517-4d1f-a226-8f77adccab89"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Did or could not load data from ./mnist1d_dat.pkl. Rebuilding dataset...\n",
-      "Examples in training set: 4000\n",
-      "Examples in test set: 1000\n",
-      "Length of each input: 40\n",
-      "Number of classes: 10\n"
-     ]
-    }
-   ],
-   "source": [
-    "args = get_dataset_args()\n",
-    "args.seed = args.seed + 1  # new manual seed -> new dataset from same dataset\n",
-    "data = get_dataset(args=args, download=False, regenerate=True)\n",
-    "\n",
-    "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
-    "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
-    "print(\"Length of each input: {}\".format(data['x'].shape[-1]))\n",
-    "print(\"Number of classes: {}\".format(len(data['templates']['y'])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O2vy0FKjfDwr"
+      },
+      "source": [
+        "## Make an MLP that can be masked\n",
+        "These parameter-wise binary masks are how we will represent sparsity in this project. There's not a great PyTorch API for this yet, so here's a temporary solution."
+      ]
     },
-    "id": "Hr39T9Dmwzh4",
-    "outputId": "9a17b3a1-09fc-4ba9-d44e-cc6f2ca27dca"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "############  Trial 0  ############\n",
-      "step 1000, dt 3.10s, train_loss 1.862e-03, test_loss 1.858e+00, train_acc 100.0, test_acc 62.9\n",
-      "step 2000, dt 2.95s, train_loss 4.772e-04, test_loss 2.139e+00, train_acc 100.0, test_acc 63.1\n",
-      "step 3000, dt 2.94s, train_loss 1.921e-04, test_loss 2.333e+00, train_acc 100.0, test_acc 63.1\n",
-      "step 4000, dt 2.87s, train_loss 9.151e-05, test_loss 2.496e+00, train_acc 100.0, test_acc 63.1\n",
-      "step 5000, dt 2.87s, train_loss 4.699e-05, test_loss 2.641e+00, train_acc 100.0, test_acc 63.3\n",
-      "step 6000, dt 2.87s, train_loss 2.539e-05, test_loss 2.779e+00, train_acc 100.0, test_acc 63.1\n",
-      "step 1000, dt 2.87s, train_loss 2.087e-03, test_loss 1.794e+00, train_acc 100.0, test_acc 72.5\n",
-      "step 2000, dt 2.86s, train_loss 4.190e-04, test_loss 2.179e+00, train_acc 100.0, test_acc 71.8\n",
-      "step 3000, dt 2.88s, train_loss 1.530e-04, test_loss 2.420e+00, train_acc 100.0, test_acc 71.5\n",
-      "step 4000, dt 2.89s, train_loss 6.824e-05, test_loss 2.615e+00, train_acc 100.0, test_acc 71.2\n",
-      "step 5000, dt 2.87s, train_loss 3.355e-05, test_loss 2.789e+00, train_acc 100.0, test_acc 71.4\n",
-      "step 6000, dt 2.87s, train_loss 1.755e-05, test_loss 2.950e+00, train_acc 100.0, test_acc 71.5\n",
-      "step 1000, dt 2.87s, train_loss 4.055e-03, test_loss 2.410e+00, train_acc 100.0, test_acc 57.9\n",
-      "step 2000, dt 2.87s, train_loss 8.096e-04, test_loss 2.935e+00, train_acc 100.0, test_acc 58.6\n",
-      "step 3000, dt 2.87s, train_loss 2.922e-04, test_loss 3.273e+00, train_acc 100.0, test_acc 58.2\n",
-      "step 4000, dt 2.87s, train_loss 1.294e-04, test_loss 3.543e+00, train_acc 100.0, test_acc 58.3\n",
-      "step 5000, dt 2.87s, train_loss 6.341e-05, test_loss 3.783e+00, train_acc 100.0, test_acc 58.5\n",
-      "step 6000, dt 2.90s, train_loss 3.286e-05, test_loss 4.002e+00, train_acc 100.0, test_acc 58.8\n",
-      "\n",
-      "############  Trial 1  ############\n",
-      "step 1000, dt 2.90s, train_loss 1.061e-03, test_loss 1.999e+00, train_acc 100.0, test_acc 64.3\n",
-      "step 2000, dt 2.91s, train_loss 2.865e-04, test_loss 2.285e+00, train_acc 100.0, test_acc 64.8\n",
-      "step 3000, dt 2.89s, train_loss 1.189e-04, test_loss 2.495e+00, train_acc 100.0, test_acc 64.9\n",
-      "step 4000, dt 2.88s, train_loss 5.793e-05, test_loss 2.669e+00, train_acc 100.0, test_acc 64.7\n",
-      "step 5000, dt 2.88s, train_loss 3.040e-05, test_loss 2.827e+00, train_acc 100.0, test_acc 64.0\n",
-      "step 6000, dt 2.86s, train_loss 1.658e-05, test_loss 2.981e+00, train_acc 100.0, test_acc 64.0\n",
-      "step 1000, dt 2.90s, train_loss 2.208e-03, test_loss 1.916e+00, train_acc 100.0, test_acc 70.0\n",
-      "step 2000, dt 2.87s, train_loss 4.253e-04, test_loss 2.348e+00, train_acc 100.0, test_acc 70.4\n",
-      "step 3000, dt 2.87s, train_loss 1.520e-04, test_loss 2.619e+00, train_acc 100.0, test_acc 69.8\n",
-      "step 4000, dt 2.89s, train_loss 6.764e-05, test_loss 2.837e+00, train_acc 100.0, test_acc 69.9\n",
-      "step 5000, dt 2.87s, train_loss 3.337e-05, test_loss 3.028e+00, train_acc 100.0, test_acc 69.8\n",
-      "step 6000, dt 2.87s, train_loss 1.740e-05, test_loss 3.202e+00, train_acc 100.0, test_acc 69.7\n",
-      "step 1000, dt 2.89s, train_loss 5.743e-03, test_loss 2.414e+00, train_acc 100.0, test_acc 60.3\n",
-      "step 2000, dt 2.88s, train_loss 1.036e-03, test_loss 2.982e+00, train_acc 100.0, test_acc 60.2\n",
-      "step 3000, dt 2.87s, train_loss 3.590e-04, test_loss 3.329e+00, train_acc 100.0, test_acc 60.1\n",
-      "step 4000, dt 2.85s, train_loss 1.567e-04, test_loss 3.604e+00, train_acc 100.0, test_acc 59.9\n",
-      "step 5000, dt 2.87s, train_loss 7.610e-05, test_loss 3.844e+00, train_acc 100.0, test_acc 59.6\n",
-      "step 6000, dt 2.87s, train_loss 3.933e-05, test_loss 4.062e+00, train_acc 100.0, test_acc 59.6\n"
-     ]
-    }
-   ],
-   "source": [
-    "retrain_step = 91\n",
-    "\n",
-    "model_args = get_model_args()\n",
-    "model_args.hidden_size = 500\n",
-    "model_args.eval_every = 100\n",
-    "model_args.learning_rate = 2e-2\n",
-    "model_args.device = DEVICE\n",
-    "model_args.total_steps = 6000\n",
-    "model_args.print_every = 1000\n",
-    "model_args.batch_size = 500 # higher batch size since we want to show overfitting\n",
-    "\n",
-    "results = {'dense': [], 'lott': [], 'rand': []}\n",
-    "for t in range(len(trials['rand_stats'])):\n",
-    "  print(\"\\n############  Trial {}  ############\".format(t))\n",
-    "  set_seed(model_args.seed + t)\n",
-    "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
-    "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
-    "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
-    "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
-    "\n",
-    "  rand_model = copy.deepcopy(dense_model)\n",
-    "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
-    "\n",
-    "  lott_model = copy.deepcopy(dense_model)\n",
-    "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
-    "\n",
-    "  dense = train_model(data, dense_model, model_args) ; results['dense'].append(dense)\n",
-    "  lott = train_model(data, lott_model, model_args)   ; results['lott'].append(lott)\n",
-    "  rand = train_model(data, rand_model, model_args)   ; results['rand'].append(rand)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 394
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "uBx5gNW-mqH_"
+      },
+      "outputs": [],
+      "source": [
+        "class SparseLinear(torch.nn.Module):\n",
+        "  def __init__(self, x_size, y_size):\n",
+        "    super(SparseLinear, self).__init__()\n",
+        "    self.linear = torch.nn.Linear(x_size, y_size)\n",
+        "    param_vec = torch.cat([p.flatten() for p in self.parameters()])\n",
+        "    self.mask = torch.ones_like(param_vec).to(DEVICE)\n",
+        "\n",
+        "  def forward(self, x, apply_mask=True):\n",
+        "    if apply_mask:\n",
+        "      self.apply_mask()\n",
+        "    return self.linear(x)\n",
+        "\n",
+        "  def update_mask(self, new_mask):\n",
+        "    self.mask = new_mask\n",
+        "    self.apply_mask()\n",
+        "\n",
+        "  def apply_mask(self):\n",
+        "    self.vec2param(self.param2vec())\n",
+        "\n",
+        "  def param2vec(self):\n",
+        "    vec = torch.cat([p.flatten() for p in self.parameters()])\n",
+        "    return self.mask * vec\n",
+        "\n",
+        "  def vec2param(self, vec):\n",
+        "    pointer = 0\n",
+        "    for param in self.parameters():\n",
+        "      param_len = np.cumprod(param.shape)[-1]\n",
+        "      new_param = vec[pointer:pointer+param_len].reshape(param.shape)\n",
+        "      param.data = new_param.data\n",
+        "      pointer += param_len\n",
+        "\n",
+        "class SparseMLP(torch.nn.Module):\n",
+        "  def __init__(self, input_size, output_size, hidden_size=100):\n",
+        "    super(SparseMLP, self).__init__()\n",
+        "    self.linear1 = SparseLinear(input_size, hidden_size)\n",
+        "    self.linear2 = SparseLinear(hidden_size, hidden_size)\n",
+        "    self.linear3 = SparseLinear(hidden_size, output_size)\n",
+        "    self.layers = [self.linear1, self.linear2, self.linear3]\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    h = torch.relu(self.linear1(x))\n",
+        "    h = h + torch.relu(self.linear2(h))\n",
+        "    h = self.linear3(h)\n",
+        "    return h\n",
+        "\n",
+        "  def get_layer_masks(self):\n",
+        "    return [l.mask for l in self.layers]\n",
+        "\n",
+        "  def set_layer_masks(self, new_masks):\n",
+        "    for i, l in enumerate(self.layers):\n",
+        "      l.update_mask(new_masks[i])\n",
+        "\n",
+        "  def get_layer_vecs(self):\n",
+        "    return [l.param2vec() for l in self.layers]\n",
+        "\n",
+        "  def set_layer_vecs(self, vecs):\n",
+        "    for i, l in enumerate(self.layers):\n",
+        "      l.vec2param(vecs[i])"
+      ]
     },
-    "id": "mV4WcbmKLdDu",
-    "outputId": "bd740ae4-4b86-4788-c2d5-b4d68aa8218c"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 520x390 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2hwmH2vIbHin"
+      },
+      "source": [
+        "## Updating masks\n",
+        "Here's a helper function for updating masks."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def average_over_results(results, name):\n",
-    "  ys = [results[name][t]['test_acc'] for t in range(len(results[name]))]\n",
-    "  return np.stack(ys).mean(0), np.stack(ys).std(0) / np.sqrt(len(ys))\n",
-    "\n",
-    "fig = plt.figure(figsize=(4, 3), dpi=130)\n",
-    "plt.subplot(1,1,1)\n",
-    "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
-    "\n",
-    "y, y_err = average_over_results(results, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "\n",
-    "plt.title('Lottery ticket ({:.0f}% sparse)'.format(100*sparsity_schedule[retrain_step]))\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=7, ncol=3)\n",
-    "plt.ylim(50,76)\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + 'lottery_asymptotes.png')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DuzZ6YNzcOrN"
-   },
-   "source": [
-    "## Remove spatial priors and see how lottery ticket fares\n",
-    "First, we do this by shuffling the entire sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "LYyFs2PMcXxM"
-   },
-   "outputs": [],
-   "source": [
-    "data_shuff = {}\n",
-    "np.random.seed(0)\n",
-    "shuffle_ixs = np.random.permutation(40) #np.array(range(32)) #\n",
-    "for k in data.keys():\n",
-    "  if k in ['x', 'x_test', 'steps']:\n",
-    "    data_shuff[k] = data[k][...,shuffle_ixs].copy() # shuffle sequence\n",
-    "  else:\n",
-    "    data_shuff[k] = data[k].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "zi1BcrH61OjV",
-    "outputId": "3a1ccdbe-b10f-469f-b789-052f33c58a14"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "############  Trial 0  ############\n",
-      "step 1000, dt 2.91s, train_loss 1.480e-03, test_loss 2.025e+00, train_acc 100.0, test_acc 65.3\n",
-      "step 2000, dt 2.90s, train_loss 3.825e-04, test_loss 2.330e+00, train_acc 100.0, test_acc 65.2\n",
-      "step 3000, dt 2.89s, train_loss 1.552e-04, test_loss 2.547e+00, train_acc 100.0, test_acc 65.2\n",
-      "step 4000, dt 2.89s, train_loss 7.286e-05, test_loss 2.731e+00, train_acc 100.0, test_acc 65.3\n",
-      "step 5000, dt 2.88s, train_loss 3.738e-05, test_loss 2.895e+00, train_acc 100.0, test_acc 65.0\n",
-      "step 6000, dt 2.89s, train_loss 2.023e-05, test_loss 3.048e+00, train_acc 100.0, test_acc 64.9\n",
-      "step 1000, dt 2.92s, train_loss 3.568e-03, test_loss 2.357e+00, train_acc 100.0, test_acc 63.1\n",
-      "step 2000, dt 2.88s, train_loss 6.780e-04, test_loss 2.935e+00, train_acc 100.0, test_acc 62.9\n",
-      "step 3000, dt 2.90s, train_loss 2.415e-04, test_loss 3.287e+00, train_acc 100.0, test_acc 63.1\n",
-      "step 4000, dt 2.90s, train_loss 1.076e-04, test_loss 3.561e+00, train_acc 100.0, test_acc 63.5\n",
-      "step 5000, dt 2.91s, train_loss 5.290e-05, test_loss 3.808e+00, train_acc 100.0, test_acc 63.8\n",
-      "step 6000, dt 2.88s, train_loss 2.752e-05, test_loss 4.036e+00, train_acc 100.0, test_acc 63.7\n",
-      "step 1000, dt 2.91s, train_loss 4.650e-03, test_loss 2.658e+00, train_acc 100.0, test_acc 59.9\n",
-      "step 2000, dt 2.88s, train_loss 8.427e-04, test_loss 3.248e+00, train_acc 100.0, test_acc 59.7\n",
-      "step 3000, dt 2.90s, train_loss 2.929e-04, test_loss 3.615e+00, train_acc 100.0, test_acc 59.6\n",
-      "step 4000, dt 2.88s, train_loss 1.282e-04, test_loss 3.906e+00, train_acc 100.0, test_acc 59.4\n",
-      "step 5000, dt 2.90s, train_loss 6.238e-05, test_loss 4.160e+00, train_acc 100.0, test_acc 59.3\n",
-      "step 6000, dt 2.91s, train_loss 3.221e-05, test_loss 4.392e+00, train_acc 100.0, test_acc 59.7\n",
-      "\n",
-      "############  Trial 1  ############\n",
-      "step 1000, dt 2.91s, train_loss 1.592e-03, test_loss 1.903e+00, train_acc 100.0, test_acc 62.9\n",
-      "step 2000, dt 2.90s, train_loss 4.174e-04, test_loss 2.208e+00, train_acc 100.0, test_acc 63.8\n",
-      "step 3000, dt 2.91s, train_loss 1.700e-04, test_loss 2.420e+00, train_acc 100.0, test_acc 64.6\n",
-      "step 4000, dt 2.87s, train_loss 8.144e-05, test_loss 2.597e+00, train_acc 100.0, test_acc 64.4\n",
-      "step 5000, dt 2.89s, train_loss 4.188e-05, test_loss 2.757e+00, train_acc 100.0, test_acc 64.4\n",
-      "step 6000, dt 2.91s, train_loss 2.266e-05, test_loss 2.907e+00, train_acc 100.0, test_acc 64.2\n",
-      "step 1000, dt 2.92s, train_loss 4.011e-03, test_loss 2.255e+00, train_acc 100.0, test_acc 63.9\n",
-      "step 2000, dt 2.89s, train_loss 6.794e-04, test_loss 2.810e+00, train_acc 100.0, test_acc 63.2\n",
-      "step 3000, dt 2.88s, train_loss 2.342e-04, test_loss 3.140e+00, train_acc 100.0, test_acc 63.0\n",
-      "step 4000, dt 2.91s, train_loss 1.016e-04, test_loss 3.401e+00, train_acc 100.0, test_acc 62.6\n",
-      "step 5000, dt 2.90s, train_loss 4.950e-05, test_loss 3.629e+00, train_acc 100.0, test_acc 62.3\n",
-      "step 6000, dt 2.89s, train_loss 2.569e-05, test_loss 3.840e+00, train_acc 100.0, test_acc 61.9\n",
-      "step 1000, dt 2.93s, train_loss 5.337e-03, test_loss 2.558e+00, train_acc 100.0, test_acc 57.4\n",
-      "step 2000, dt 2.91s, train_loss 9.791e-04, test_loss 3.175e+00, train_acc 100.0, test_acc 57.2\n",
-      "step 3000, dt 2.93s, train_loss 3.429e-04, test_loss 3.552e+00, train_acc 100.0, test_acc 57.6\n",
-      "step 4000, dt 2.89s, train_loss 1.512e-04, test_loss 3.851e+00, train_acc 100.0, test_acc 57.5\n",
-      "step 5000, dt 2.90s, train_loss 7.450e-05, test_loss 4.114e+00, train_acc 100.0, test_acc 56.9\n",
-      "step 6000, dt 2.89s, train_loss 3.849e-05, test_loss 4.355e+00, train_acc 100.0, test_acc 56.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "results_shuff = {'dense': [], 'lott': [], 'rand': []}\n",
-    "for t in range(len(trials['rand_stats'])):\n",
-    "  print(\"\\n############  Trial {}  ############\".format(t))\n",
-    "  set_seed(model_args.seed + t)\n",
-    "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
-    "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
-    "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
-    "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
-    "\n",
-    "  rand_model = copy.deepcopy(dense_model)\n",
-    "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
-    "\n",
-    "  lott_model = copy.deepcopy(dense_model)\n",
-    "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
-    "\n",
-    "  dense = train_model(data_shuff, dense_model, model_args) ; results_shuff['dense'].append(dense)\n",
-    "  lott = train_model(data_shuff, lott_model, model_args)   ; results_shuff['lott'].append(lott)\n",
-    "  rand = train_model(data_shuff, rand_model, model_args)   ; results_shuff['rand'].append(rand)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 394
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "Md2F9WDgYSqT"
+      },
+      "outputs": [],
+      "source": [
+        "# find a mask, given some heuristic and desired sparsity\n",
+        "def get_mask(scores, percent_sparse):\n",
+        "  # scores: per-weight scores for determining which weights to drop\n",
+        "  # percent_sparse: how much to sparsify the model\n",
+        "  num_to_drop = int(percent_sparse * len(scores))\n",
+        "  ixs_to_drop = torch.sort(scores)[1][:num_to_drop] # sort from low score to high, select k with lowest score\n",
+        "  mask = torch.ones_like(scores)\n",
+        "  mask[ixs_to_drop] = 0\n",
+        "  return mask"
+      ]
     },
-    "id": "fuZli6M8YorY",
-    "outputId": "c48058ed-3ac7-4a51-f5c8-e3728fe8c8e9"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 975x390 with 2 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z0McGMV-a3Xo"
+      },
+      "source": [
+        "## The prune-and-retrain cycle\n",
+        "This is the core method for finding a lottery ticket. We train a model for a fixed number of epochs, prune it, and then re-train and re-prune. We repeat this cycle until we achieve the desired level of sparsity."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(7.5, 3), dpi=130)\n",
-    "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "plt.title('LT on baseline examples')\n",
-    "y, y_err = average_over_results(results, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "plt.title('LT on spatially shuffled examples')\n",
-    "y, y_err = average_over_results(results_shuff, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_shuff, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_shuff, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
-    "\n",
-    "\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + 'lottery_asymptotes_shuffled.png')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "WEcny3zHkHBU"
-   },
-   "source": [
-    "## Looks like the lottery ticket has some spatial priors, but we need to run more tests to be sure:\n",
-    "There may be a correlation between the spatial priors of the data and how important particular input indices are. If that is the case, then the lottery ticket procedure may have disproportionately pruned weights corresponding to inputs that didn't contribute much to classification. If this is the case, then the \"spatial priors\" of the lottery ticket are not particularly significant and won't transfer well to other datasets.\n",
-    "\n",
-    "But maybe the lottery ticket does have some spatial priors that will transfer well. In order to test whether that is the case, we can simply flip the non-shuffled dataset upside down. This will break the correlation between particular indices that matter more than others while still leaving spatial priors intact in the data. Think about it this way: a CNN will still do well if you flip all your images upside down. So our model's performance on flipped data is a good hint at how well its spatial priors will transfer. If this doesn't hurt performance much, then the lottery ticket represents some sort of reasonably good spatial prior."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "l1D-huHK55gF",
-    "outputId": "2d55bc4d-70c1-4124-b518-48fd101889c1"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([ 0.79027827,  0.81387517,  0.96495485,  0.72122595, -0.03240748,\n",
-       "       -0.28947403,  0.38705837,  0.73723783,  0.38279205, -0.16746251,\n",
-       "       -0.21399577,  0.025955  ,  0.00291882,  0.28282577, -1.25675551,\n",
-       "       -2.06009933, -1.71629861, -0.30305327,  0.02960669, -1.49053473,\n",
-       "       -2.16724905, -1.83906903, -0.26118889,  0.36413035, -0.17891161,\n",
-       "       -0.27629091, -0.13228634,  0.01394688, -0.47465012, -0.57515785,\n",
-       "       -0.37445353,  0.05180054,  0.32990678,  0.05195449, -0.91084646,\n",
-       "       -0.94110542, -0.34693864, -0.3011403 , -0.95394325, -1.39942009])"
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "5idcbyA3Ylz_"
+      },
+      "outputs": [],
+      "source": [
+        "def find_lottery_ticket(model, dataset, args, sparsity_schedule, criteria_fn=None,\n",
+        "                        prune_print_every=None, **kwargs):\n",
+        "  if prune_print_every is None:\n",
+        "    prune_print_every = np.inf\n",
+        "\n",
+        "  if criteria_fn is None:\n",
+        "    print(\"Using default magnitude-based pruning\")\n",
+        "    criteria_fn = lambda init_params, final_params: final_params.abs()\n",
+        "\n",
+        "  init_params = model.get_layer_vecs()\n",
+        "  stats = {'train_losses':[], 'test_losses':[], 'train_accs':[], 'test_accs':[]}\n",
+        "  models = []\n",
+        "  for i, percent_sparse in enumerate(sparsity_schedule):\n",
+        "\n",
+        "    # layer-wise pruning, where pruning heuristic is determined by criteria_fn\n",
+        "    final_params = model.get_layer_vecs()\n",
+        "    scores = [criteria_fn(ip, fp) for ip, fp in zip(init_params, final_params)]\n",
+        "    masks = [get_mask(s, percent_sparse) for s in scores]\n",
+        "\n",
+        "    # update model with mask and init parameters\n",
+        "    model.set_layer_vecs(init_params)\n",
+        "    model.set_layer_masks(masks)\n",
+        "\n",
+        "    # training process\n",
+        "    results = train_model(dataset, model, args)\n",
+        "    model = results['checkpoints'][-1]\n",
+        "\n",
+        "    # store stats\n",
+        "    stats['train_losses'].append(results['train_losses'])\n",
+        "    stats['test_losses'].append(results['test_losses'])\n",
+        "    stats['train_accs'].append(results['train_acc'])\n",
+        "    stats['test_accs'].append(results['test_acc'])\n",
+        "\n",
+        "    # print progress\n",
+        "    if (i+1) % prune_print_every == 0:\n",
+        "      print('\\tretrain #{}, sparsity {:.2f}, final_train_loss {:.3e}, max_acc {:.1f}, last_acc {:.1f}, mean_acc {:.1f}'\n",
+        "            .format(i+1, percent_sparse, results['train_losses'][-1], np.max(results['test_acc']),\n",
+        "            results['test_acc'][-1], np.mean(results['test_acc']) ))\n",
+        "      models.append(copy.deepcopy(model))\n",
+        "\n",
+        "  stats = {k: np.stack(v) for k, v in stats.items()}\n",
+        "  return models, stats"
       ]
-     },
-     "execution_count": 49,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data['x_test'][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KQLbj83smpxo"
-   },
-   "outputs": [],
-   "source": [
-    "data_flip = {}\n",
-    "for k in data.keys():\n",
-    "  if k in ['x', 'x_test', 'steps']:\n",
-    "    data_flip[k] = data[k][...,::-1].copy() # flip sequence upside-down\n",
-    "  else:\n",
-    "    data_flip[k] = data[k].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "4WZzYfTk57Y0",
-    "outputId": "8271a1b4-fce9-4b2d-bafe-348a01cca39e"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([-1.39942009, -0.95394325, -0.3011403 , -0.34693864, -0.94110542,\n",
-       "       -0.91084646,  0.05195449,  0.32990678,  0.05180054, -0.37445353,\n",
-       "       -0.57515785, -0.47465012,  0.01394688, -0.13228634, -0.27629091,\n",
-       "       -0.17891161,  0.36413035, -0.26118889, -1.83906903, -2.16724905,\n",
-       "       -1.49053473,  0.02960669, -0.30305327, -1.71629861, -2.06009933,\n",
-       "       -1.25675551,  0.28282577,  0.00291882,  0.025955  , -0.21399577,\n",
-       "       -0.16746251,  0.38279205,  0.73723783,  0.38705837, -0.28947403,\n",
-       "       -0.03240748,  0.72122595,  0.96495485,  0.81387517,  0.79027827])"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m4lokvdD4DKI"
+      },
+      "source": [
+        "## Choose hyperparameters"
       ]
-     },
-     "execution_count": 51,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_flip['x_test'][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "XhioZOATWY_D",
-    "outputId": "9636b1ee-426b-488c-e29f-4f55b1936691"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "############  Trial 0  ############\n",
-      "step 1000, dt 2.72s, train_loss 1.263e-03, test_loss 1.969e+00, train_acc 100.0, test_acc 65.2\n",
-      "step 2000, dt 2.68s, train_loss 3.488e-04, test_loss 2.243e+00, train_acc 100.0, test_acc 64.9\n",
-      "step 3000, dt 2.80s, train_loss 1.448e-04, test_loss 2.438e+00, train_acc 100.0, test_acc 65.2\n",
-      "step 4000, dt 2.61s, train_loss 6.933e-05, test_loss 2.601e+00, train_acc 100.0, test_acc 65.2\n",
-      "step 5000, dt 2.70s, train_loss 3.618e-05, test_loss 2.750e+00, train_acc 100.0, test_acc 65.1\n",
-      "step 6000, dt 2.67s, train_loss 1.973e-05, test_loss 2.893e+00, train_acc 100.0, test_acc 65.0\n",
-      "step 1000, dt 2.70s, train_loss 2.454e-03, test_loss 2.073e+00, train_acc 100.0, test_acc 68.4\n",
-      "step 2000, dt 2.71s, train_loss 4.561e-04, test_loss 2.554e+00, train_acc 100.0, test_acc 68.8\n",
-      "step 3000, dt 2.66s, train_loss 1.610e-04, test_loss 2.852e+00, train_acc 100.0, test_acc 69.0\n",
-      "step 4000, dt 2.83s, train_loss 7.099e-05, test_loss 3.088e+00, train_acc 100.0, test_acc 68.9\n",
-      "step 5000, dt 2.88s, train_loss 3.444e-05, test_loss 3.295e+00, train_acc 100.0, test_acc 68.6\n",
-      "step 6000, dt 2.91s, train_loss 1.790e-05, test_loss 3.479e+00, train_acc 100.0, test_acc 68.4\n",
-      "step 1000, dt 2.80s, train_loss 3.854e-03, test_loss 2.559e+00, train_acc 100.0, test_acc 59.7\n",
-      "step 2000, dt 2.64s, train_loss 7.258e-04, test_loss 3.120e+00, train_acc 100.0, test_acc 59.6\n",
-      "step 3000, dt 2.69s, train_loss 2.584e-04, test_loss 3.466e+00, train_acc 100.0, test_acc 59.4\n",
-      "step 4000, dt 2.74s, train_loss 1.151e-04, test_loss 3.745e+00, train_acc 100.0, test_acc 59.6\n",
-      "step 5000, dt 2.73s, train_loss 5.670e-05, test_loss 3.989e+00, train_acc 100.0, test_acc 59.7\n",
-      "step 6000, dt 2.70s, train_loss 2.960e-05, test_loss 4.216e+00, train_acc 100.0, test_acc 59.3\n",
-      "\n",
-      "############  Trial 1  ############\n",
-      "step 1000, dt 2.64s, train_loss 1.341e-03, test_loss 1.957e+00, train_acc 100.0, test_acc 65.2\n",
-      "step 2000, dt 2.68s, train_loss 3.917e-04, test_loss 2.201e+00, train_acc 100.0, test_acc 64.6\n",
-      "step 3000, dt 2.69s, train_loss 1.677e-04, test_loss 2.385e+00, train_acc 100.0, test_acc 64.4\n",
-      "step 4000, dt 2.70s, train_loss 8.160e-05, test_loss 2.550e+00, train_acc 100.0, test_acc 64.3\n",
-      "step 5000, dt 2.66s, train_loss 4.305e-05, test_loss 2.701e+00, train_acc 100.0, test_acc 64.2\n",
-      "step 6000, dt 2.60s, train_loss 2.358e-05, test_loss 2.843e+00, train_acc 100.0, test_acc 64.2\n",
-      "step 1000, dt 2.69s, train_loss 2.735e-03, test_loss 2.098e+00, train_acc 100.0, test_acc 68.7\n",
-      "step 2000, dt 2.61s, train_loss 5.162e-04, test_loss 2.567e+00, train_acc 100.0, test_acc 68.4\n",
-      "step 3000, dt 2.65s, train_loss 1.841e-04, test_loss 2.854e+00, train_acc 100.0, test_acc 68.8\n",
-      "step 4000, dt 2.61s, train_loss 8.233e-05, test_loss 3.085e+00, train_acc 100.0, test_acc 68.8\n",
-      "step 5000, dt 2.76s, train_loss 4.066e-05, test_loss 3.283e+00, train_acc 100.0, test_acc 68.9\n",
-      "step 6000, dt 2.60s, train_loss 2.121e-05, test_loss 3.465e+00, train_acc 100.0, test_acc 69.1\n",
-      "step 1000, dt 2.70s, train_loss 4.806e-03, test_loss 2.381e+00, train_acc 100.0, test_acc 61.7\n",
-      "step 2000, dt 2.63s, train_loss 1.049e-03, test_loss 2.833e+00, train_acc 100.0, test_acc 61.9\n",
-      "step 3000, dt 2.58s, train_loss 3.966e-04, test_loss 3.140e+00, train_acc 100.0, test_acc 62.3\n",
-      "step 4000, dt 2.68s, train_loss 1.802e-04, test_loss 3.391e+00, train_acc 100.0, test_acc 62.6\n",
-      "step 5000, dt 2.83s, train_loss 8.962e-05, test_loss 3.615e+00, train_acc 100.0, test_acc 62.5\n",
-      "step 6000, dt 2.64s, train_loss 4.692e-05, test_loss 3.825e+00, train_acc 100.0, test_acc 62.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "results_flip = {'dense': [], 'lott': [], 'rand': []}\n",
-    "for t in range(len(trials['rand_stats'])):\n",
-    "  print(\"\\n############  Trial {}  ############\".format(t))\n",
-    "  set_seed(model_args.seed + t)\n",
-    "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
-    "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
-    "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
-    "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
-    "\n",
-    "  rand_model = copy.deepcopy(dense_model)\n",
-    "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
-    "\n",
-    "  lott_model = copy.deepcopy(dense_model)\n",
-    "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
-    "\n",
-    "  dense = train_model(data_flip, dense_model, model_args) ; results_flip['dense'].append(dense)\n",
-    "  lott = train_model(data_flip, lott_model, model_args)   ; results_flip['lott'].append(lott)\n",
-    "  rand = train_model(data_flip, rand_model, model_args)   ; results_flip['rand'].append(rand)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 394
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "OUe7-b-7Yl2c"
+      },
+      "outputs": [],
+      "source": [
+        "# train settings\n",
+        "model_args = get_model_args()\n",
+        "model_args.total_steps = 1501\n",
+        "model_args.hidden_size = 500\n",
+        "model_args.print_every = 5000 # print never\n",
+        "model_args.eval_every = 100\n",
+        "model_args.learning_rate = 2e-2\n",
+        "model_args.device = DEVICE\n",
+        "\n",
+        "# sparsity settings\n",
+        "num_retrains = 100\n",
+        "sparsity_schedule = np.linspace(0,1.,num_retrains) #1-np.cumprod(np.ones(num_retrains)*tau)/tau # tau = .97"
+      ]
     },
-    "id": "oHJP9rKCYsPO",
-    "outputId": "18a961f6-e9df-43db-f74d-005edb134b46"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 975x390 with 2 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hVgDM5rI4J65"
+      },
+      "source": [
+        "## Find a lottery ticket and a random ticket (takes ~1 hour)\n",
+        "You can also cache results as shown in subsequent cells. Then you can load them anytime to perform analysis."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(7.5, 3), dpi=130)\n",
-    "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "plt.title('LT on spatially shuffled examples')\n",
-    "y, y_err = average_over_results(results_shuff, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_shuff, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_shuff, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "plt.title('LT on spatially flipped examples')\n",
-    "y, y_err = average_over_results(results_flip, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_flip, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_flip, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
-    "\n",
-    "\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + 'lottery_asymptotes_flipped.png')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZNYf4pgm63YM"
-   },
-   "source": [
-    "## This has become a monster notebook.\n",
-    "Instead of responsibly dividing it into two notebooks, let's save a checkpoint and blunder on. Now we can start from here without having to recompute everything from scratch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "flw5BuLq28hk"
-   },
-   "outputs": [],
-   "source": [
-    "things = [trials, model_args, retrain_step, results, results_shuff, results_flip]\n",
-    "to_pickle(things, path=project_dir + 'lottery_analysis.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "m6z7ja7w22eA"
-   },
-   "outputs": [],
-   "source": [
-    "things = from_pickle(path=project_dir + 'lottery_analysis.pkl')\n",
-    "[trials, model_args, retrain_step, results, results_shuff, results_flip] = things"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HeKNrtfX8uZ6"
-   },
-   "source": [
-    "## Our lottery ticket generalizes surprisingly well to a flipped version of the dataset. What happens if we shuffle _chunks_ of the sequence\n",
-    "If the lottery ticket has learned good spatial priors, then we would expect its performance to decrease only partially when we shuffle chunks of the sequence, since some of the spatial information is still available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "dkW-FFb68wRs"
-   },
-   "outputs": [],
-   "source": [
-    "data_chunks = {}\n",
-    "for k in data.keys():\n",
-    "  if k in ['x', 'x_test', 't']:\n",
-    "    data_chunks[k] = np.concatenate([data[k][...,20:30],\n",
-    "                                     data[k][...,30:40],\n",
-    "                                     data[k][...,:10],\n",
-    "                                     data[k][...,10:20]],\n",
-    "                                     axis=-1).copy() # exhange first and second halves\n",
-    "  else:\n",
-    "    data_chunks[k] = data[k].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "1Z3H-k-fXTty",
-    "outputId": "2eb953a8-eb4b-461d-89c3-bbb4d22d235a"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "############  Trial 0  ############\n",
-      "step 1000, dt 2.61s, train_loss 1.261e-03, test_loss 1.848e+00, train_acc 100.0, test_acc 65.8\n",
-      "step 2000, dt 2.60s, train_loss 3.699e-04, test_loss 2.082e+00, train_acc 100.0, test_acc 65.9\n",
-      "step 3000, dt 2.80s, train_loss 1.589e-04, test_loss 2.252e+00, train_acc 100.0, test_acc 65.7\n",
-      "step 4000, dt 2.87s, train_loss 7.863e-05, test_loss 2.399e+00, train_acc 100.0, test_acc 65.4\n",
-      "step 5000, dt 2.83s, train_loss 4.175e-05, test_loss 2.532e+00, train_acc 100.0, test_acc 65.2\n",
-      "step 6000, dt 2.86s, train_loss 2.304e-05, test_loss 2.660e+00, train_acc 100.0, test_acc 64.9\n",
-      "step 1000, dt 2.80s, train_loss 3.025e-03, test_loss 2.054e+00, train_acc 100.0, test_acc 67.3\n",
-      "step 2000, dt 2.83s, train_loss 5.786e-04, test_loss 2.509e+00, train_acc 100.0, test_acc 66.7\n",
-      "step 3000, dt 2.89s, train_loss 2.071e-04, test_loss 2.788e+00, train_acc 100.0, test_acc 67.5\n",
-      "step 4000, dt 2.89s, train_loss 9.164e-05, test_loss 3.013e+00, train_acc 100.0, test_acc 67.8\n",
-      "step 5000, dt 2.85s, train_loss 4.495e-05, test_loss 3.216e+00, train_acc 100.0, test_acc 67.6\n",
-      "step 6000, dt 2.85s, train_loss 2.347e-05, test_loss 3.401e+00, train_acc 100.0, test_acc 67.7\n",
-      "step 1000, dt 2.61s, train_loss 4.517e-03, test_loss 2.477e+00, train_acc 100.0, test_acc 59.9\n",
-      "step 2000, dt 2.66s, train_loss 8.364e-04, test_loss 3.054e+00, train_acc 100.0, test_acc 59.6\n",
-      "step 3000, dt 2.64s, train_loss 2.911e-04, test_loss 3.406e+00, train_acc 100.0, test_acc 60.0\n",
-      "step 4000, dt 2.65s, train_loss 1.281e-04, test_loss 3.687e+00, train_acc 100.0, test_acc 60.4\n",
-      "step 5000, dt 2.65s, train_loss 6.303e-05, test_loss 3.933e+00, train_acc 100.0, test_acc 60.2\n",
-      "step 6000, dt 2.61s, train_loss 3.278e-05, test_loss 4.156e+00, train_acc 100.0, test_acc 60.3\n",
-      "\n",
-      "############  Trial 1  ############\n",
-      "step 1000, dt 2.62s, train_loss 1.461e-03, test_loss 2.002e+00, train_acc 100.0, test_acc 63.8\n",
-      "step 2000, dt 2.62s, train_loss 3.779e-04, test_loss 2.311e+00, train_acc 100.0, test_acc 63.7\n",
-      "step 3000, dt 2.64s, train_loss 1.509e-04, test_loss 2.529e+00, train_acc 100.0, test_acc 63.9\n",
-      "step 4000, dt 2.59s, train_loss 7.129e-05, test_loss 2.710e+00, train_acc 100.0, test_acc 64.1\n",
-      "step 5000, dt 2.60s, train_loss 3.662e-05, test_loss 2.871e+00, train_acc 100.0, test_acc 64.0\n",
-      "step 6000, dt 2.64s, train_loss 1.968e-05, test_loss 3.025e+00, train_acc 100.0, test_acc 64.0\n",
-      "step 1000, dt 2.71s, train_loss 5.053e-03, test_loss 2.030e+00, train_acc 100.0, test_acc 63.8\n",
-      "step 2000, dt 2.81s, train_loss 9.375e-04, test_loss 2.577e+00, train_acc 100.0, test_acc 63.3\n",
-      "step 3000, dt 2.64s, train_loss 3.287e-04, test_loss 2.921e+00, train_acc 100.0, test_acc 63.7\n",
-      "step 4000, dt 2.63s, train_loss 1.436e-04, test_loss 3.187e+00, train_acc 100.0, test_acc 64.1\n",
-      "step 5000, dt 2.66s, train_loss 7.020e-05, test_loss 3.420e+00, train_acc 100.0, test_acc 64.6\n",
-      "step 6000, dt 2.60s, train_loss 3.624e-05, test_loss 3.633e+00, train_acc 100.0, test_acc 64.7\n",
-      "step 1000, dt 2.79s, train_loss 4.729e-03, test_loss 2.389e+00, train_acc 100.0, test_acc 62.2\n",
-      "step 2000, dt 2.60s, train_loss 8.541e-04, test_loss 2.965e+00, train_acc 100.0, test_acc 62.2\n",
-      "step 3000, dt 2.61s, train_loss 3.015e-04, test_loss 3.333e+00, train_acc 100.0, test_acc 62.3\n",
-      "step 4000, dt 2.58s, train_loss 1.339e-04, test_loss 3.622e+00, train_acc 100.0, test_acc 62.2\n",
-      "step 5000, dt 2.67s, train_loss 6.573e-05, test_loss 3.875e+00, train_acc 100.0, test_acc 62.9\n",
-      "step 6000, dt 2.63s, train_loss 3.438e-05, test_loss 4.117e+00, train_acc 100.0, test_acc 62.8\n"
-     ]
-    }
-   ],
-   "source": [
-    "results_chunks = {'dense': [], 'lott': [], 'rand': []}\n",
-    "for t in range(len(trials['rand_stats'])):\n",
-    "  print(\"\\n############  Trial {}  ############\".format(t))\n",
-    "  set_seed(model_args.seed + t)\n",
-    "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
-    "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
-    "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
-    "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
-    "\n",
-    "  rand_model = copy.deepcopy(dense_model)\n",
-    "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
-    "\n",
-    "  lott_model = copy.deepcopy(dense_model)\n",
-    "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
-    "\n",
-    "  dense = train_model(data_chunks, dense_model, model_args) ; results_chunks['dense'].append(dense)\n",
-    "  lott = train_model(data_chunks, lott_model, model_args)   ; results_chunks['lott'].append(lott)\n",
-    "  rand = train_model(data_chunks, rand_model, model_args)   ; results_chunks['rand'].append(rand)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 394
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "7S98UZVwrqf-"
+      },
+      "outputs": [],
+      "source": []
     },
-    "id": "XCyIMdQaYxPF",
-    "outputId": "60aff5b3-97da-44e6-fcc3-951e8e2f24eb"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 975x390 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "OZG3nMVarqf-",
+        "outputId": "872f067b-5add-4deb-a71f-afe0b7c964bd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "############  Trial 0  ############\n",
+            "   Random pruning\n",
+            "\tretrain #1, sparsity 0.00, final_train_loss 3.049e-01, max_acc 63.8, last_acc 60.6, mean_acc 57.5\n",
+            "\tretrain #2, sparsity 0.01, final_train_loss 1.643e-01, max_acc 63.0, last_acc 62.7, mean_acc 56.5\n",
+            "\tretrain #3, sparsity 0.02, final_train_loss 3.007e-01, max_acc 64.6, last_acc 61.9, mean_acc 56.6\n",
+            "\tretrain #4, sparsity 0.03, final_train_loss 3.816e-01, max_acc 66.3, last_acc 65.1, mean_acc 57.8\n",
+            "\tretrain #5, sparsity 0.04, final_train_loss 1.357e-01, max_acc 65.2, last_acc 63.4, mean_acc 57.7\n",
+            "\tretrain #6, sparsity 0.05, final_train_loss 1.819e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.2\n",
+            "\tretrain #7, sparsity 0.06, final_train_loss 2.058e-01, max_acc 64.7, last_acc 63.5, mean_acc 56.7\n",
+            "\tretrain #8, sparsity 0.07, final_train_loss 1.882e-01, max_acc 63.5, last_acc 62.6, mean_acc 57.3\n",
+            "\tretrain #9, sparsity 0.08, final_train_loss 1.787e-01, max_acc 66.2, last_acc 66.2, mean_acc 58.1\n",
+            "\tretrain #10, sparsity 0.09, final_train_loss 2.929e-01, max_acc 65.2, last_acc 61.5, mean_acc 57.2\n",
+            "\tretrain #11, sparsity 0.10, final_train_loss 1.403e-01, max_acc 64.4, last_acc 64.1, mean_acc 57.6\n",
+            "\tretrain #12, sparsity 0.11, final_train_loss 4.808e-01, max_acc 66.5, last_acc 66.5, mean_acc 58.4\n",
+            "\tretrain #13, sparsity 0.12, final_train_loss 1.503e-02, max_acc 65.6, last_acc 65.6, mean_acc 58.2\n",
+            "\tretrain #14, sparsity 0.13, final_train_loss 3.335e-01, max_acc 65.3, last_acc 64.5, mean_acc 57.8\n",
+            "\tretrain #15, sparsity 0.14, final_train_loss 3.036e-01, max_acc 64.8, last_acc 63.3, mean_acc 57.7\n",
+            "\tretrain #16, sparsity 0.15, final_train_loss 1.258e-01, max_acc 64.1, last_acc 64.0, mean_acc 57.7\n",
+            "\tretrain #17, sparsity 0.16, final_train_loss 1.454e-01, max_acc 66.7, last_acc 66.7, mean_acc 58.4\n",
+            "\tretrain #18, sparsity 0.17, final_train_loss 1.902e-01, max_acc 65.9, last_acc 65.9, mean_acc 57.6\n",
+            "\tretrain #19, sparsity 0.18, final_train_loss 1.574e-01, max_acc 65.2, last_acc 65.2, mean_acc 57.5\n",
+            "\tretrain #20, sparsity 0.19, final_train_loss 1.899e-01, max_acc 66.4, last_acc 64.4, mean_acc 57.5\n",
+            "\tretrain #21, sparsity 0.20, final_train_loss 1.964e-01, max_acc 64.0, last_acc 61.9, mean_acc 56.5\n",
+            "\tretrain #22, sparsity 0.21, final_train_loss 2.644e-01, max_acc 65.3, last_acc 64.1, mean_acc 58.1\n",
+            "\tretrain #23, sparsity 0.22, final_train_loss 1.974e-01, max_acc 64.9, last_acc 63.3, mean_acc 57.9\n",
+            "\tretrain #24, sparsity 0.23, final_train_loss 2.788e-01, max_acc 67.4, last_acc 66.7, mean_acc 58.3\n",
+            "\tretrain #25, sparsity 0.24, final_train_loss 4.549e-01, max_acc 66.0, last_acc 64.8, mean_acc 58.8\n",
+            "\tretrain #26, sparsity 0.25, final_train_loss 4.360e-01, max_acc 66.6, last_acc 66.6, mean_acc 58.6\n",
+            "\tretrain #27, sparsity 0.26, final_train_loss 2.478e-01, max_acc 63.0, last_acc 63.0, mean_acc 56.6\n",
+            "\tretrain #28, sparsity 0.27, final_train_loss 1.934e-01, max_acc 64.9, last_acc 61.3, mean_acc 56.8\n",
+            "\tretrain #29, sparsity 0.28, final_train_loss 3.084e-01, max_acc 64.2, last_acc 62.8, mean_acc 57.0\n",
+            "\tretrain #30, sparsity 0.29, final_train_loss 1.207e-01, max_acc 65.8, last_acc 65.8, mean_acc 57.9\n",
+            "\tretrain #31, sparsity 0.30, final_train_loss 2.014e-01, max_acc 66.7, last_acc 63.7, mean_acc 57.9\n",
+            "\tretrain #32, sparsity 0.31, final_train_loss 8.057e-02, max_acc 65.6, last_acc 61.3, mean_acc 57.3\n",
+            "\tretrain #33, sparsity 0.32, final_train_loss 8.852e-02, max_acc 68.1, last_acc 68.1, mean_acc 58.7\n",
+            "\tretrain #34, sparsity 0.33, final_train_loss 1.510e-01, max_acc 65.0, last_acc 63.2, mean_acc 57.4\n",
+            "\tretrain #35, sparsity 0.34, final_train_loss 2.996e-01, max_acc 64.2, last_acc 64.2, mean_acc 57.9\n",
+            "\tretrain #36, sparsity 0.35, final_train_loss 8.030e-02, max_acc 64.9, last_acc 62.2, mean_acc 57.5\n",
+            "\tretrain #37, sparsity 0.36, final_train_loss 1.635e-01, max_acc 66.3, last_acc 65.2, mean_acc 58.8\n",
+            "\tretrain #38, sparsity 0.37, final_train_loss 6.735e-02, max_acc 63.7, last_acc 63.7, mean_acc 57.8\n",
+            "\tretrain #39, sparsity 0.38, final_train_loss 3.698e-01, max_acc 65.3, last_acc 65.3, mean_acc 57.4\n",
+            "\tretrain #40, sparsity 0.39, final_train_loss 2.767e-01, max_acc 64.8, last_acc 63.3, mean_acc 56.8\n",
+            "\tretrain #41, sparsity 0.40, final_train_loss 1.180e-01, max_acc 65.5, last_acc 62.3, mean_acc 58.1\n",
+            "\tretrain #42, sparsity 0.41, final_train_loss 3.294e-01, max_acc 64.4, last_acc 64.0, mean_acc 57.6\n",
+            "\tretrain #43, sparsity 0.42, final_train_loss 2.655e-01, max_acc 65.3, last_acc 65.3, mean_acc 57.9\n",
+            "\tretrain #44, sparsity 0.43, final_train_loss 4.925e-02, max_acc 66.4, last_acc 66.4, mean_acc 57.6\n",
+            "\tretrain #45, sparsity 0.44, final_train_loss 1.300e-01, max_acc 65.7, last_acc 65.7, mean_acc 58.1\n",
+            "\tretrain #46, sparsity 0.45, final_train_loss 1.975e-01, max_acc 64.3, last_acc 64.0, mean_acc 57.9\n",
+            "\tretrain #47, sparsity 0.46, final_train_loss 1.274e-01, max_acc 66.8, last_acc 66.8, mean_acc 58.3\n",
+            "\tretrain #48, sparsity 0.47, final_train_loss 1.977e-01, max_acc 65.8, last_acc 64.7, mean_acc 58.0\n",
+            "\tretrain #49, sparsity 0.48, final_train_loss 2.444e-01, max_acc 65.1, last_acc 61.1, mean_acc 57.4\n",
+            "\tretrain #50, sparsity 0.49, final_train_loss 2.323e-01, max_acc 65.5, last_acc 63.6, mean_acc 57.9\n",
+            "\tretrain #51, sparsity 0.51, final_train_loss 4.207e-01, max_acc 64.5, last_acc 63.8, mean_acc 57.7\n",
+            "\tretrain #52, sparsity 0.52, final_train_loss 6.677e-02, max_acc 66.4, last_acc 66.4, mean_acc 58.3\n",
+            "\tretrain #53, sparsity 0.53, final_train_loss 1.237e-01, max_acc 64.5, last_acc 64.5, mean_acc 57.7\n",
+            "\tretrain #54, sparsity 0.54, final_train_loss 2.749e-01, max_acc 65.1, last_acc 65.1, mean_acc 57.2\n",
+            "\tretrain #55, sparsity 0.55, final_train_loss 1.711e-01, max_acc 65.3, last_acc 65.3, mean_acc 58.4\n",
+            "\tretrain #56, sparsity 0.56, final_train_loss 3.145e-01, max_acc 65.4, last_acc 62.8, mean_acc 57.9\n",
+            "\tretrain #57, sparsity 0.57, final_train_loss 3.244e-01, max_acc 64.5, last_acc 62.8, mean_acc 58.0\n",
+            "\tretrain #58, sparsity 0.58, final_train_loss 2.251e-01, max_acc 65.3, last_acc 65.3, mean_acc 58.2\n",
+            "\tretrain #59, sparsity 0.59, final_train_loss 3.221e-01, max_acc 65.6, last_acc 61.4, mean_acc 57.4\n",
+            "\tretrain #60, sparsity 0.60, final_train_loss 2.820e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.9\n",
+            "\tretrain #61, sparsity 0.61, final_train_loss 1.327e-01, max_acc 66.5, last_acc 65.8, mean_acc 58.7\n",
+            "\tretrain #62, sparsity 0.62, final_train_loss 2.563e-01, max_acc 66.8, last_acc 64.2, mean_acc 58.7\n",
+            "\tretrain #63, sparsity 0.63, final_train_loss 2.175e-01, max_acc 66.8, last_acc 62.6, mean_acc 58.7\n",
+            "\tretrain #64, sparsity 0.64, final_train_loss 1.689e-01, max_acc 67.0, last_acc 63.4, mean_acc 59.1\n",
+            "\tretrain #65, sparsity 0.65, final_train_loss 1.267e-01, max_acc 66.8, last_acc 66.8, mean_acc 59.2\n",
+            "\tretrain #66, sparsity 0.66, final_train_loss 4.512e-02, max_acc 66.6, last_acc 66.0, mean_acc 58.9\n",
+            "\tretrain #67, sparsity 0.67, final_train_loss 4.977e-01, max_acc 68.1, last_acc 65.3, mean_acc 60.7\n",
+            "\tretrain #68, sparsity 0.68, final_train_loss 2.331e-01, max_acc 66.4, last_acc 63.7, mean_acc 60.2\n",
+            "\tretrain #69, sparsity 0.69, final_train_loss 6.705e-01, max_acc 66.0, last_acc 65.8, mean_acc 58.9\n",
+            "\tretrain #70, sparsity 0.70, final_train_loss 3.464e-01, max_acc 67.6, last_acc 65.2, mean_acc 59.6\n",
+            "\tretrain #71, sparsity 0.71, final_train_loss 5.212e-02, max_acc 66.7, last_acc 66.7, mean_acc 59.6\n",
+            "\tretrain #72, sparsity 0.72, final_train_loss 8.926e-02, max_acc 69.6, last_acc 69.6, mean_acc 60.5\n",
+            "\tretrain #73, sparsity 0.73, final_train_loss 2.125e-01, max_acc 67.4, last_acc 65.4, mean_acc 59.9\n",
+            "\tretrain #74, sparsity 0.74, final_train_loss 1.469e-01, max_acc 67.7, last_acc 66.2, mean_acc 60.1\n",
+            "\tretrain #75, sparsity 0.75, final_train_loss 3.047e-01, max_acc 68.9, last_acc 68.9, mean_acc 61.0\n",
+            "\tretrain #76, sparsity 0.76, final_train_loss 8.189e-02, max_acc 70.8, last_acc 68.8, mean_acc 61.0\n",
+            "\tretrain #77, sparsity 0.77, final_train_loss 1.042e-01, max_acc 67.8, last_acc 67.8, mean_acc 60.0\n",
+            "\tretrain #78, sparsity 0.78, final_train_loss 2.325e-01, max_acc 68.2, last_acc 67.5, mean_acc 61.2\n",
+            "\tretrain #79, sparsity 0.79, final_train_loss 4.025e-02, max_acc 69.2, last_acc 69.0, mean_acc 60.0\n",
+            "\tretrain #80, sparsity 0.80, final_train_loss 2.265e-02, max_acc 70.4, last_acc 68.6, mean_acc 61.6\n",
+            "\tretrain #81, sparsity 0.81, final_train_loss 3.038e-01, max_acc 67.5, last_acc 64.3, mean_acc 59.6\n",
+            "\tretrain #82, sparsity 0.82, final_train_loss 1.315e-01, max_acc 66.6, last_acc 64.3, mean_acc 59.0\n",
+            "\tretrain #83, sparsity 0.83, final_train_loss 1.024e-01, max_acc 66.5, last_acc 64.5, mean_acc 58.6\n",
+            "\tretrain #84, sparsity 0.84, final_train_loss 1.428e-01, max_acc 68.0, last_acc 67.7, mean_acc 59.9\n",
+            "\tretrain #85, sparsity 0.85, final_train_loss 1.841e-01, max_acc 66.4, last_acc 63.6, mean_acc 59.4\n",
+            "\tretrain #86, sparsity 0.86, final_train_loss 1.311e-01, max_acc 69.2, last_acc 64.9, mean_acc 60.4\n",
+            "\tretrain #87, sparsity 0.87, final_train_loss 9.295e-02, max_acc 65.4, last_acc 65.4, mean_acc 57.0\n",
+            "\tretrain #88, sparsity 0.88, final_train_loss 2.912e-02, max_acc 66.3, last_acc 65.1, mean_acc 58.8\n",
+            "\tretrain #89, sparsity 0.89, final_train_loss 1.244e-01, max_acc 66.1, last_acc 63.3, mean_acc 58.2\n",
+            "\tretrain #90, sparsity 0.90, final_train_loss 9.934e-02, max_acc 64.6, last_acc 60.5, mean_acc 57.5\n",
+            "\tretrain #91, sparsity 0.91, final_train_loss 8.553e-02, max_acc 62.3, last_acc 62.3, mean_acc 55.5\n",
+            "\tretrain #92, sparsity 0.92, final_train_loss 1.895e-01, max_acc 61.1, last_acc 60.6, mean_acc 55.2\n",
+            "\tretrain #93, sparsity 0.93, final_train_loss 2.010e-01, max_acc 62.1, last_acc 59.4, mean_acc 55.1\n",
+            "\tretrain #94, sparsity 0.94, final_train_loss 4.363e-01, max_acc 58.0, last_acc 55.5, mean_acc 51.5\n",
+            "\tretrain #95, sparsity 0.95, final_train_loss 4.079e-01, max_acc 58.0, last_acc 55.9, mean_acc 50.4\n",
+            "\tretrain #96, sparsity 0.96, final_train_loss 6.772e-01, max_acc 51.5, last_acc 51.5, mean_acc 45.7\n",
+            "\tretrain #97, sparsity 0.97, final_train_loss 1.144e+00, max_acc 41.2, last_acc 41.2, mean_acc 37.1\n",
+            "\tretrain #98, sparsity 0.98, final_train_loss 1.639e+00, max_acc 35.0, last_acc 34.6, mean_acc 32.0\n",
+            "\tretrain #99, sparsity 0.99, final_train_loss 2.161e+00, max_acc 16.9, last_acc 16.7, mean_acc 16.1\n",
+            "\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\n",
+            "   Magnitude pruning\n",
+            "\tretrain #1, sparsity 0.00, final_train_loss 2.098e-01, max_acc 66.4, last_acc 66.4, mean_acc 57.5\n",
+            "\tretrain #2, sparsity 0.01, final_train_loss 2.680e-01, max_acc 65.6, last_acc 62.0, mean_acc 57.4\n",
+            "\tretrain #3, sparsity 0.02, final_train_loss 1.899e-01, max_acc 66.6, last_acc 66.6, mean_acc 57.9\n",
+            "\tretrain #4, sparsity 0.03, final_train_loss 1.663e-01, max_acc 65.4, last_acc 65.0, mean_acc 57.6\n",
+            "\tretrain #5, sparsity 0.04, final_train_loss 8.916e-02, max_acc 67.0, last_acc 64.8, mean_acc 57.9\n",
+            "\tretrain #6, sparsity 0.05, final_train_loss 1.819e-01, max_acc 65.2, last_acc 64.5, mean_acc 57.4\n",
+            "\tretrain #7, sparsity 0.06, final_train_loss 6.880e-02, max_acc 65.2, last_acc 63.7, mean_acc 58.7\n",
+            "\tretrain #8, sparsity 0.07, final_train_loss 3.691e-01, max_acc 64.6, last_acc 62.2, mean_acc 57.5\n",
+            "\tretrain #9, sparsity 0.08, final_train_loss 1.075e-01, max_acc 65.1, last_acc 65.1, mean_acc 58.1\n",
+            "\tretrain #10, sparsity 0.09, final_train_loss 3.350e-01, max_acc 64.8, last_acc 64.4, mean_acc 58.0\n",
+            "\tretrain #11, sparsity 0.10, final_train_loss 3.674e-01, max_acc 67.1, last_acc 66.0, mean_acc 58.6\n",
+            "\tretrain #12, sparsity 0.11, final_train_loss 5.202e-02, max_acc 66.4, last_acc 66.4, mean_acc 57.7\n",
+            "\tretrain #13, sparsity 0.12, final_train_loss 2.351e-01, max_acc 65.2, last_acc 62.5, mean_acc 57.9\n",
+            "\tretrain #14, sparsity 0.13, final_train_loss 1.356e-01, max_acc 65.2, last_acc 64.9, mean_acc 58.4\n",
+            "\tretrain #15, sparsity 0.14, final_train_loss 1.443e-01, max_acc 65.5, last_acc 62.9, mean_acc 58.1\n",
+            "\tretrain #16, sparsity 0.15, final_train_loss 1.327e-01, max_acc 67.1, last_acc 64.6, mean_acc 59.1\n",
+            "\tretrain #17, sparsity 0.16, final_train_loss 1.914e-02, max_acc 66.0, last_acc 65.8, mean_acc 58.4\n",
+            "\tretrain #18, sparsity 0.17, final_train_loss 8.779e-02, max_acc 67.6, last_acc 65.7, mean_acc 59.2\n",
+            "\tretrain #19, sparsity 0.18, final_train_loss 2.390e-01, max_acc 66.9, last_acc 65.8, mean_acc 59.8\n",
+            "\tretrain #20, sparsity 0.19, final_train_loss 1.094e-01, max_acc 64.5, last_acc 64.4, mean_acc 58.5\n",
+            "\tretrain #21, sparsity 0.20, final_train_loss 2.105e-01, max_acc 65.8, last_acc 64.9, mean_acc 59.0\n",
+            "\tretrain #22, sparsity 0.21, final_train_loss 1.279e-01, max_acc 65.6, last_acc 65.2, mean_acc 58.7\n",
+            "\tretrain #23, sparsity 0.22, final_train_loss 1.527e-01, max_acc 66.7, last_acc 63.9, mean_acc 59.5\n",
+            "\tretrain #24, sparsity 0.23, final_train_loss 2.863e-01, max_acc 66.0, last_acc 65.5, mean_acc 59.3\n",
+            "\tretrain #25, sparsity 0.24, final_train_loss 2.645e-01, max_acc 65.9, last_acc 65.0, mean_acc 59.5\n",
+            "\tretrain #26, sparsity 0.25, final_train_loss 2.483e-02, max_acc 66.5, last_acc 66.5, mean_acc 59.8\n",
+            "\tretrain #27, sparsity 0.26, final_train_loss 8.681e-02, max_acc 67.8, last_acc 67.8, mean_acc 60.2\n",
+            "\tretrain #28, sparsity 0.27, final_train_loss 6.083e-02, max_acc 67.5, last_acc 67.5, mean_acc 59.6\n",
+            "\tretrain #29, sparsity 0.28, final_train_loss 8.720e-02, max_acc 65.9, last_acc 65.2, mean_acc 59.0\n",
+            "\tretrain #30, sparsity 0.29, final_train_loss 1.702e-01, max_acc 65.7, last_acc 65.7, mean_acc 58.8\n",
+            "\tretrain #31, sparsity 0.30, final_train_loss 4.470e-02, max_acc 66.1, last_acc 64.4, mean_acc 59.4\n",
+            "\tretrain #32, sparsity 0.31, final_train_loss 1.289e-01, max_acc 67.2, last_acc 63.9, mean_acc 59.3\n",
+            "\tretrain #33, sparsity 0.32, final_train_loss 4.072e-01, max_acc 67.8, last_acc 66.1, mean_acc 60.2\n",
+            "\tretrain #34, sparsity 0.33, final_train_loss 5.773e-02, max_acc 67.5, last_acc 67.5, mean_acc 59.7\n",
+            "\tretrain #35, sparsity 0.34, final_train_loss 1.195e-01, max_acc 67.7, last_acc 65.9, mean_acc 60.4\n",
+            "\tretrain #36, sparsity 0.35, final_train_loss 4.007e-02, max_acc 66.1, last_acc 65.7, mean_acc 59.9\n",
+            "\tretrain #37, sparsity 0.36, final_train_loss 1.940e-01, max_acc 67.5, last_acc 66.5, mean_acc 60.3\n",
+            "\tretrain #38, sparsity 0.37, final_train_loss 8.476e-02, max_acc 66.8, last_acc 66.8, mean_acc 60.2\n",
+            "\tretrain #39, sparsity 0.38, final_train_loss 1.641e-01, max_acc 66.9, last_acc 66.9, mean_acc 60.3\n",
+            "\tretrain #40, sparsity 0.39, final_train_loss 7.798e-03, max_acc 67.6, last_acc 67.6, mean_acc 60.8\n",
+            "\tretrain #41, sparsity 0.40, final_train_loss 5.746e-02, max_acc 66.0, last_acc 66.0, mean_acc 60.1\n",
+            "\tretrain #42, sparsity 0.41, final_train_loss 4.911e-01, max_acc 66.2, last_acc 63.8, mean_acc 60.2\n",
+            "\tretrain #43, sparsity 0.42, final_train_loss 9.151e-02, max_acc 67.5, last_acc 66.3, mean_acc 60.8\n",
+            "\tretrain #44, sparsity 0.43, final_train_loss 3.245e-02, max_acc 68.2, last_acc 67.1, mean_acc 61.7\n",
+            "\tretrain #45, sparsity 0.44, final_train_loss 2.915e-01, max_acc 66.8, last_acc 65.7, mean_acc 60.9\n",
+            "\tretrain #46, sparsity 0.45, final_train_loss 1.463e-01, max_acc 68.2, last_acc 66.5, mean_acc 61.4\n",
+            "\tretrain #47, sparsity 0.46, final_train_loss 4.686e-02, max_acc 69.1, last_acc 69.1, mean_acc 62.1\n",
+            "\tretrain #48, sparsity 0.47, final_train_loss 2.505e-02, max_acc 67.9, last_acc 65.9, mean_acc 61.0\n",
+            "\tretrain #49, sparsity 0.48, final_train_loss 1.867e-02, max_acc 68.0, last_acc 68.0, mean_acc 61.7\n",
+            "\tretrain #50, sparsity 0.49, final_train_loss 1.702e-01, max_acc 68.6, last_acc 68.6, mean_acc 62.0\n",
+            "\tretrain #51, sparsity 0.51, final_train_loss 4.023e-01, max_acc 67.8, last_acc 67.4, mean_acc 62.1\n",
+            "\tretrain #52, sparsity 0.52, final_train_loss 5.574e-01, max_acc 68.5, last_acc 63.8, mean_acc 61.8\n",
+            "\tretrain #53, sparsity 0.53, final_train_loss 1.979e-02, max_acc 68.5, last_acc 68.5, mean_acc 62.0\n",
+            "\tretrain #54, sparsity 0.54, final_train_loss 1.828e-01, max_acc 68.1, last_acc 66.5, mean_acc 61.8\n",
+            "\tretrain #55, sparsity 0.55, final_train_loss 2.017e-01, max_acc 69.3, last_acc 66.6, mean_acc 62.4\n",
+            "\tretrain #56, sparsity 0.56, final_train_loss 1.099e-01, max_acc 69.7, last_acc 67.0, mean_acc 63.3\n",
+            "\tretrain #57, sparsity 0.57, final_train_loss 2.292e-01, max_acc 68.5, last_acc 65.0, mean_acc 62.1\n",
+            "\tretrain #58, sparsity 0.58, final_train_loss 7.869e-02, max_acc 68.3, last_acc 66.7, mean_acc 62.2\n",
+            "\tretrain #59, sparsity 0.59, final_train_loss 3.369e-01, max_acc 71.8, last_acc 63.7, mean_acc 63.1\n",
+            "\tretrain #60, sparsity 0.60, final_train_loss 2.457e-01, max_acc 69.3, last_acc 68.0, mean_acc 63.6\n",
+            "\tretrain #61, sparsity 0.61, final_train_loss 1.809e-01, max_acc 71.4, last_acc 71.4, mean_acc 63.9\n",
+            "\tretrain #62, sparsity 0.62, final_train_loss 5.555e-02, max_acc 68.4, last_acc 67.5, mean_acc 62.1\n",
+            "\tretrain #63, sparsity 0.63, final_train_loss 6.513e-02, max_acc 70.7, last_acc 67.8, mean_acc 64.2\n",
+            "\tretrain #64, sparsity 0.64, final_train_loss 1.546e-01, max_acc 69.9, last_acc 67.0, mean_acc 63.8\n",
+            "\tretrain #65, sparsity 0.65, final_train_loss 2.609e-02, max_acc 69.7, last_acc 67.9, mean_acc 63.7\n",
+            "\tretrain #66, sparsity 0.66, final_train_loss 7.977e-02, max_acc 70.8, last_acc 69.3, mean_acc 64.3\n",
+            "\tretrain #67, sparsity 0.67, final_train_loss 6.476e-02, max_acc 71.4, last_acc 68.6, mean_acc 64.3\n",
+            "\tretrain #68, sparsity 0.68, final_train_loss 2.149e-02, max_acc 69.8, last_acc 69.8, mean_acc 64.4\n",
+            "\tretrain #69, sparsity 0.69, final_train_loss 2.586e-01, max_acc 72.6, last_acc 69.8, mean_acc 65.4\n",
+            "\tretrain #70, sparsity 0.70, final_train_loss 4.413e-03, max_acc 70.7, last_acc 70.2, mean_acc 65.1\n",
+            "\tretrain #71, sparsity 0.71, final_train_loss 8.177e-02, max_acc 71.6, last_acc 69.2, mean_acc 65.0\n",
+            "\tretrain #72, sparsity 0.72, final_train_loss 1.059e-01, max_acc 71.6, last_acc 70.6, mean_acc 65.1\n",
+            "\tretrain #73, sparsity 0.73, final_train_loss 1.178e-01, max_acc 72.8, last_acc 70.5, mean_acc 65.1\n",
+            "\tretrain #74, sparsity 0.74, final_train_loss 2.022e-01, max_acc 70.9, last_acc 68.0, mean_acc 64.8\n",
+            "\tretrain #75, sparsity 0.75, final_train_loss 2.201e-01, max_acc 71.5, last_acc 71.5, mean_acc 66.1\n",
+            "\tretrain #76, sparsity 0.76, final_train_loss 1.076e-03, max_acc 72.9, last_acc 72.8, mean_acc 66.3\n",
+            "\tretrain #77, sparsity 0.77, final_train_loss 2.370e-02, max_acc 72.5, last_acc 71.2, mean_acc 66.2\n",
+            "\tretrain #78, sparsity 0.78, final_train_loss 2.687e-01, max_acc 72.8, last_acc 71.5, mean_acc 66.1\n",
+            "\tretrain #79, sparsity 0.79, final_train_loss 3.283e-01, max_acc 74.7, last_acc 70.4, mean_acc 67.0\n",
+            "\tretrain #80, sparsity 0.80, final_train_loss 1.361e-01, max_acc 72.9, last_acc 72.9, mean_acc 66.7\n",
+            "\tretrain #81, sparsity 0.81, final_train_loss 8.892e-02, max_acc 72.1, last_acc 71.2, mean_acc 66.1\n",
+            "\tretrain #82, sparsity 0.82, final_train_loss 1.099e-02, max_acc 74.1, last_acc 72.2, mean_acc 67.6\n",
+            "\tretrain #83, sparsity 0.83, final_train_loss 7.655e-02, max_acc 73.2, last_acc 70.6, mean_acc 67.0\n",
+            "\tretrain #84, sparsity 0.84, final_train_loss 2.787e-02, max_acc 73.2, last_acc 73.2, mean_acc 66.8\n",
+            "\tretrain #85, sparsity 0.85, final_train_loss 1.591e-01, max_acc 74.2, last_acc 70.4, mean_acc 66.5\n",
+            "\tretrain #86, sparsity 0.86, final_train_loss 3.671e-02, max_acc 72.5, last_acc 72.0, mean_acc 66.5\n",
+            "\tretrain #87, sparsity 0.87, final_train_loss 1.610e-02, max_acc 73.2, last_acc 72.6, mean_acc 67.3\n",
+            "\tretrain #88, sparsity 0.88, final_train_loss 5.061e-02, max_acc 73.7, last_acc 73.7, mean_acc 67.4\n",
+            "\tretrain #89, sparsity 0.89, final_train_loss 8.562e-03, max_acc 73.5, last_acc 73.1, mean_acc 67.8\n",
+            "\tretrain #90, sparsity 0.90, final_train_loss 1.127e-03, max_acc 74.3, last_acc 74.3, mean_acc 67.7\n",
+            "\tretrain #91, sparsity 0.91, final_train_loss 1.294e-03, max_acc 75.0, last_acc 75.0, mean_acc 67.4\n",
+            "\tretrain #92, sparsity 0.92, final_train_loss 2.673e-03, max_acc 76.1, last_acc 75.6, mean_acc 68.4\n",
+            "\tretrain #93, sparsity 0.93, final_train_loss 4.614e-02, max_acc 73.2, last_acc 71.7, mean_acc 67.0\n",
+            "\tretrain #94, sparsity 0.94, final_train_loss 4.515e-02, max_acc 72.1, last_acc 70.4, mean_acc 66.2\n",
+            "\tretrain #95, sparsity 0.95, final_train_loss 1.088e-01, max_acc 71.8, last_acc 69.2, mean_acc 65.4\n",
+            "\tretrain #96, sparsity 0.96, final_train_loss 1.432e-01, max_acc 70.4, last_acc 69.3, mean_acc 64.4\n",
+            "\tretrain #97, sparsity 0.97, final_train_loss 1.624e-01, max_acc 69.0, last_acc 68.1, mean_acc 62.5\n",
+            "\tretrain #98, sparsity 0.98, final_train_loss 5.247e-01, max_acc 63.9, last_acc 63.0, mean_acc 58.0\n",
+            "\tretrain #99, sparsity 0.99, final_train_loss 1.332e+00, max_acc 41.9, last_acc 41.9, mean_acc 37.1\n",
+            "\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\n",
+            "############  Trial 1  ############\n",
+            "   Random pruning\n",
+            "\tretrain #1, sparsity 0.00, final_train_loss 2.207e-01, max_acc 65.2, last_acc 61.4, mean_acc 57.7\n",
+            "\tretrain #2, sparsity 0.01, final_train_loss 3.362e-01, max_acc 63.6, last_acc 63.3, mean_acc 57.4\n",
+            "\tretrain #3, sparsity 0.02, final_train_loss 8.536e-02, max_acc 66.5, last_acc 64.5, mean_acc 58.2\n",
+            "\tretrain #4, sparsity 0.03, final_train_loss 2.506e-01, max_acc 65.8, last_acc 64.1, mean_acc 57.8\n",
+            "\tretrain #5, sparsity 0.04, final_train_loss 2.158e-01, max_acc 66.9, last_acc 66.9, mean_acc 58.9\n",
+            "\tretrain #6, sparsity 0.05, final_train_loss 1.427e-01, max_acc 65.0, last_acc 64.1, mean_acc 58.0\n",
+            "\tretrain #7, sparsity 0.06, final_train_loss 3.646e-01, max_acc 66.4, last_acc 62.8, mean_acc 57.4\n",
+            "\tretrain #8, sparsity 0.07, final_train_loss 3.427e-01, max_acc 63.9, last_acc 61.7, mean_acc 57.2\n",
+            "\tretrain #9, sparsity 0.08, final_train_loss 4.462e-01, max_acc 66.0, last_acc 66.0, mean_acc 58.3\n",
+            "\tretrain #10, sparsity 0.09, final_train_loss 3.153e-01, max_acc 65.7, last_acc 62.0, mean_acc 57.5\n",
+            "\tretrain #11, sparsity 0.10, final_train_loss 1.032e-01, max_acc 65.3, last_acc 65.3, mean_acc 57.3\n",
+            "\tretrain #12, sparsity 0.11, final_train_loss 3.200e-01, max_acc 66.3, last_acc 64.0, mean_acc 58.0\n",
+            "\tretrain #13, sparsity 0.12, final_train_loss 3.028e-01, max_acc 66.8, last_acc 62.8, mean_acc 57.2\n",
+            "\tretrain #14, sparsity 0.13, final_train_loss 2.299e-01, max_acc 64.5, last_acc 64.5, mean_acc 57.6\n",
+            "\tretrain #15, sparsity 0.14, final_train_loss 1.602e-01, max_acc 64.0, last_acc 64.0, mean_acc 57.1\n",
+            "\tretrain #16, sparsity 0.15, final_train_loss 1.575e-01, max_acc 66.4, last_acc 65.0, mean_acc 58.4\n",
+            "\tretrain #17, sparsity 0.16, final_train_loss 2.326e-01, max_acc 65.5, last_acc 64.1, mean_acc 57.8\n",
+            "\tretrain #18, sparsity 0.17, final_train_loss 1.836e-01, max_acc 64.3, last_acc 64.3, mean_acc 57.3\n",
+            "\tretrain #19, sparsity 0.18, final_train_loss 3.852e-01, max_acc 64.9, last_acc 61.8, mean_acc 57.9\n",
+            "\tretrain #20, sparsity 0.19, final_train_loss 1.805e-01, max_acc 66.1, last_acc 64.8, mean_acc 58.6\n",
+            "\tretrain #21, sparsity 0.20, final_train_loss 1.983e-01, max_acc 64.8, last_acc 64.0, mean_acc 58.2\n",
+            "\tretrain #22, sparsity 0.21, final_train_loss 3.935e-01, max_acc 65.9, last_acc 63.5, mean_acc 57.9\n",
+            "\tretrain #23, sparsity 0.22, final_train_loss 1.159e-01, max_acc 66.6, last_acc 65.4, mean_acc 59.0\n",
+            "\tretrain #24, sparsity 0.23, final_train_loss 3.676e-02, max_acc 65.5, last_acc 63.8, mean_acc 58.1\n",
+            "\tretrain #25, sparsity 0.24, final_train_loss 4.002e-01, max_acc 64.6, last_acc 64.4, mean_acc 58.0\n",
+            "\tretrain #26, sparsity 0.25, final_train_loss 1.545e-01, max_acc 64.1, last_acc 64.1, mean_acc 56.9\n",
+            "\tretrain #27, sparsity 0.26, final_train_loss 2.444e-01, max_acc 65.8, last_acc 63.8, mean_acc 57.9\n",
+            "\tretrain #28, sparsity 0.27, final_train_loss 4.631e-02, max_acc 65.6, last_acc 65.6, mean_acc 58.0\n",
+            "\tretrain #29, sparsity 0.28, final_train_loss 1.937e-01, max_acc 66.5, last_acc 65.8, mean_acc 58.2\n",
+            "\tretrain #30, sparsity 0.29, final_train_loss 1.904e-01, max_acc 63.3, last_acc 61.5, mean_acc 57.0\n",
+            "\tretrain #31, sparsity 0.30, final_train_loss 1.601e-01, max_acc 65.9, last_acc 64.0, mean_acc 58.6\n",
+            "\tretrain #32, sparsity 0.31, final_train_loss 6.696e-02, max_acc 65.2, last_acc 64.3, mean_acc 57.9\n",
+            "\tretrain #33, sparsity 0.32, final_train_loss 4.213e-01, max_acc 65.1, last_acc 61.9, mean_acc 57.6\n",
+            "\tretrain #34, sparsity 0.33, final_train_loss 2.514e-01, max_acc 65.3, last_acc 64.2, mean_acc 57.1\n",
+            "\tretrain #35, sparsity 0.34, final_train_loss 3.237e-01, max_acc 66.1, last_acc 64.1, mean_acc 58.4\n",
+            "\tretrain #36, sparsity 0.35, final_train_loss 1.561e-01, max_acc 66.3, last_acc 65.8, mean_acc 58.4\n",
+            "\tretrain #37, sparsity 0.36, final_train_loss 1.816e-01, max_acc 64.6, last_acc 63.3, mean_acc 57.8\n",
+            "\tretrain #38, sparsity 0.37, final_train_loss 4.493e-01, max_acc 64.9, last_acc 64.3, mean_acc 58.2\n",
+            "\tretrain #39, sparsity 0.38, final_train_loss 1.117e-01, max_acc 65.4, last_acc 64.5, mean_acc 58.4\n",
+            "\tretrain #40, sparsity 0.39, final_train_loss 1.525e-01, max_acc 66.4, last_acc 64.3, mean_acc 58.3\n",
+            "\tretrain #41, sparsity 0.40, final_train_loss 1.725e-01, max_acc 66.4, last_acc 64.8, mean_acc 58.3\n",
+            "\tretrain #42, sparsity 0.41, final_train_loss 1.022e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.9\n",
+            "\tretrain #43, sparsity 0.42, final_train_loss 2.723e-01, max_acc 64.8, last_acc 61.5, mean_acc 56.7\n",
+            "\tretrain #44, sparsity 0.43, final_train_loss 2.613e-01, max_acc 63.4, last_acc 63.4, mean_acc 56.6\n",
+            "\tretrain #45, sparsity 0.44, final_train_loss 2.547e-01, max_acc 65.0, last_acc 65.0, mean_acc 57.1\n",
+            "\tretrain #46, sparsity 0.45, final_train_loss 6.731e-02, max_acc 65.4, last_acc 65.4, mean_acc 57.1\n",
+            "\tretrain #47, sparsity 0.46, final_train_loss 1.824e-01, max_acc 66.2, last_acc 64.4, mean_acc 57.9\n",
+            "\tretrain #48, sparsity 0.47, final_train_loss 2.740e-01, max_acc 64.7, last_acc 64.7, mean_acc 57.4\n",
+            "\tretrain #49, sparsity 0.48, final_train_loss 1.234e-01, max_acc 66.3, last_acc 66.3, mean_acc 57.8\n",
+            "\tretrain #50, sparsity 0.49, final_train_loss 1.978e-01, max_acc 64.2, last_acc 62.1, mean_acc 56.9\n",
+            "\tretrain #51, sparsity 0.51, final_train_loss 3.184e-01, max_acc 64.6, last_acc 61.7, mean_acc 57.5\n",
+            "\tretrain #52, sparsity 0.52, final_train_loss 3.193e-01, max_acc 65.3, last_acc 61.4, mean_acc 57.5\n",
+            "\tretrain #53, sparsity 0.53, final_train_loss 2.327e-01, max_acc 64.6, last_acc 63.9, mean_acc 57.4\n",
+            "\tretrain #54, sparsity 0.54, final_train_loss 1.270e-01, max_acc 66.8, last_acc 64.4, mean_acc 58.4\n",
+            "\tretrain #55, sparsity 0.55, final_train_loss 2.003e-01, max_acc 66.0, last_acc 65.6, mean_acc 58.9\n",
+            "\tretrain #56, sparsity 0.56, final_train_loss 2.780e-01, max_acc 65.1, last_acc 65.1, mean_acc 58.7\n",
+            "\tretrain #57, sparsity 0.57, final_train_loss 2.825e-01, max_acc 63.9, last_acc 61.5, mean_acc 56.7\n",
+            "\tretrain #58, sparsity 0.58, final_train_loss 2.648e-01, max_acc 64.6, last_acc 64.6, mean_acc 58.3\n",
+            "\tretrain #59, sparsity 0.59, final_train_loss 7.645e-02, max_acc 67.3, last_acc 66.5, mean_acc 59.0\n",
+            "\tretrain #60, sparsity 0.60, final_train_loss 7.587e-02, max_acc 65.2, last_acc 64.5, mean_acc 58.9\n",
+            "\tretrain #61, sparsity 0.61, final_train_loss 2.544e-01, max_acc 65.9, last_acc 65.5, mean_acc 58.9\n",
+            "\tretrain #62, sparsity 0.62, final_train_loss 6.965e-02, max_acc 65.0, last_acc 65.0, mean_acc 57.9\n",
+            "\tretrain #63, sparsity 0.63, final_train_loss 1.750e-01, max_acc 66.9, last_acc 63.1, mean_acc 59.4\n",
+            "\tretrain #64, sparsity 0.64, final_train_loss 1.237e-01, max_acc 66.5, last_acc 64.4, mean_acc 58.0\n",
+            "\tretrain #65, sparsity 0.65, final_train_loss 5.669e-01, max_acc 65.7, last_acc 64.6, mean_acc 59.4\n",
+            "\tretrain #66, sparsity 0.66, final_train_loss 5.150e-01, max_acc 66.4, last_acc 63.9, mean_acc 59.1\n",
+            "\tretrain #67, sparsity 0.67, final_train_loss 3.314e-02, max_acc 66.1, last_acc 65.8, mean_acc 59.3\n",
+            "\tretrain #68, sparsity 0.68, final_train_loss 1.040e-01, max_acc 70.0, last_acc 70.0, mean_acc 60.8\n",
+            "\tretrain #69, sparsity 0.69, final_train_loss 2.082e-01, max_acc 67.3, last_acc 67.2, mean_acc 59.9\n",
+            "\tretrain #70, sparsity 0.70, final_train_loss 2.170e-01, max_acc 67.3, last_acc 66.3, mean_acc 59.1\n",
+            "\tretrain #71, sparsity 0.71, final_train_loss 1.172e-02, max_acc 67.2, last_acc 67.2, mean_acc 60.0\n",
+            "\tretrain #72, sparsity 0.72, final_train_loss 1.133e-01, max_acc 67.3, last_acc 67.3, mean_acc 59.8\n",
+            "\tretrain #73, sparsity 0.73, final_train_loss 3.219e-01, max_acc 68.5, last_acc 64.4, mean_acc 59.6\n",
+            "\tretrain #74, sparsity 0.74, final_train_loss 7.892e-02, max_acc 69.0, last_acc 69.0, mean_acc 60.9\n",
+            "\tretrain #75, sparsity 0.75, final_train_loss 1.364e-01, max_acc 67.8, last_acc 67.5, mean_acc 60.0\n",
+            "\tretrain #76, sparsity 0.76, final_train_loss 1.902e-01, max_acc 67.6, last_acc 64.9, mean_acc 60.7\n",
+            "\tretrain #77, sparsity 0.77, final_train_loss 5.066e-02, max_acc 66.1, last_acc 66.1, mean_acc 58.8\n",
+            "\tretrain #78, sparsity 0.78, final_train_loss 2.923e-02, max_acc 68.0, last_acc 67.5, mean_acc 60.3\n",
+            "\tretrain #79, sparsity 0.79, final_train_loss 1.753e-02, max_acc 67.4, last_acc 67.4, mean_acc 58.9\n",
+            "\tretrain #80, sparsity 0.80, final_train_loss 2.361e-01, max_acc 69.7, last_acc 66.3, mean_acc 61.0\n",
+            "\tretrain #81, sparsity 0.81, final_train_loss 2.193e-01, max_acc 69.0, last_acc 65.4, mean_acc 60.6\n",
+            "\tretrain #82, sparsity 0.82, final_train_loss 2.304e-01, max_acc 69.6, last_acc 67.2, mean_acc 61.8\n",
+            "\tretrain #83, sparsity 0.83, final_train_loss 4.335e-02, max_acc 69.4, last_acc 69.4, mean_acc 60.7\n",
+            "\tretrain #84, sparsity 0.84, final_train_loss 1.548e-01, max_acc 70.0, last_acc 68.0, mean_acc 61.2\n",
+            "\tretrain #85, sparsity 0.85, final_train_loss 9.804e-03, max_acc 68.1, last_acc 67.4, mean_acc 61.0\n",
+            "\tretrain #86, sparsity 0.86, final_train_loss 7.915e-02, max_acc 66.7, last_acc 66.0, mean_acc 59.5\n",
+            "\tretrain #87, sparsity 0.87, final_train_loss 6.169e-02, max_acc 66.3, last_acc 66.1, mean_acc 59.8\n",
+            "\tretrain #88, sparsity 0.88, final_train_loss 1.341e-01, max_acc 65.2, last_acc 62.6, mean_acc 57.7\n",
+            "\tretrain #89, sparsity 0.89, final_train_loss 1.145e-01, max_acc 66.2, last_acc 63.9, mean_acc 58.2\n",
+            "\tretrain #90, sparsity 0.90, final_train_loss 1.098e-01, max_acc 65.7, last_acc 65.7, mean_acc 57.1\n",
+            "\tretrain #91, sparsity 0.91, final_train_loss 1.535e-01, max_acc 62.1, last_acc 60.6, mean_acc 54.9\n",
+            "\tretrain #92, sparsity 0.92, final_train_loss 1.370e-01, max_acc 62.4, last_acc 61.8, mean_acc 56.1\n",
+            "\tretrain #93, sparsity 0.93, final_train_loss 1.365e-01, max_acc 61.9, last_acc 61.9, mean_acc 53.8\n",
+            "\tretrain #94, sparsity 0.94, final_train_loss 2.537e-01, max_acc 60.4, last_acc 60.4, mean_acc 52.8\n",
+            "\tretrain #95, sparsity 0.95, final_train_loss 5.880e-01, max_acc 56.1, last_acc 54.6, mean_acc 49.3\n",
+            "\tretrain #96, sparsity 0.96, final_train_loss 6.913e-01, max_acc 50.3, last_acc 49.9, mean_acc 43.7\n",
+            "\tretrain #97, sparsity 0.97, final_train_loss 1.221e+00, max_acc 43.9, last_acc 43.7, mean_acc 38.7\n",
+            "\tretrain #98, sparsity 0.98, final_train_loss 1.651e+00, max_acc 27.4, last_acc 27.2, mean_acc 25.4\n",
+            "\tretrain #99, sparsity 0.99, final_train_loss 2.140e+00, max_acc 17.1, last_acc 15.4, mean_acc 15.9\n",
+            "\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\n",
+            "   Magnitude pruning\n",
+            "\tretrain #1, sparsity 0.00, final_train_loss 2.163e-01, max_acc 65.4, last_acc 64.8, mean_acc 58.4\n",
+            "\tretrain #2, sparsity 0.01, final_train_loss 2.496e-01, max_acc 66.3, last_acc 64.5, mean_acc 57.7\n",
+            "\tretrain #3, sparsity 0.02, final_train_loss 2.256e-01, max_acc 64.8, last_acc 64.3, mean_acc 58.1\n",
+            "\tretrain #4, sparsity 0.03, final_train_loss 1.605e-01, max_acc 66.1, last_acc 63.6, mean_acc 58.8\n",
+            "\tretrain #5, sparsity 0.04, final_train_loss 1.577e-01, max_acc 65.9, last_acc 65.9, mean_acc 58.1\n",
+            "\tretrain #6, sparsity 0.05, final_train_loss 2.365e-01, max_acc 64.5, last_acc 61.9, mean_acc 57.5\n",
+            "\tretrain #7, sparsity 0.06, final_train_loss 4.548e-01, max_acc 64.6, last_acc 64.5, mean_acc 58.3\n",
+            "\tretrain #8, sparsity 0.07, final_train_loss 2.571e-01, max_acc 66.1, last_acc 65.1, mean_acc 58.2\n",
+            "\tretrain #9, sparsity 0.08, final_train_loss 2.494e-01, max_acc 65.7, last_acc 63.3, mean_acc 58.8\n",
+            "\tretrain #10, sparsity 0.09, final_train_loss 2.295e-01, max_acc 67.3, last_acc 65.9, mean_acc 58.5\n",
+            "\tretrain #11, sparsity 0.10, final_train_loss 2.025e-01, max_acc 65.7, last_acc 65.7, mean_acc 58.4\n",
+            "\tretrain #12, sparsity 0.11, final_train_loss 1.314e-01, max_acc 68.3, last_acc 63.1, mean_acc 58.4\n",
+            "\tretrain #13, sparsity 0.12, final_train_loss 1.235e-01, max_acc 66.7, last_acc 66.7, mean_acc 58.8\n",
+            "\tretrain #14, sparsity 0.13, final_train_loss 2.829e-01, max_acc 65.3, last_acc 65.2, mean_acc 59.0\n",
+            "\tretrain #15, sparsity 0.14, final_train_loss 3.520e-01, max_acc 66.4, last_acc 63.1, mean_acc 59.0\n",
+            "\tretrain #16, sparsity 0.15, final_train_loss 9.407e-02, max_acc 66.0, last_acc 64.9, mean_acc 59.1\n",
+            "\tretrain #17, sparsity 0.16, final_train_loss 5.955e-01, max_acc 64.8, last_acc 63.8, mean_acc 59.0\n",
+            "\tretrain #18, sparsity 0.17, final_train_loss 9.409e-02, max_acc 67.5, last_acc 65.0, mean_acc 59.5\n",
+            "\tretrain #19, sparsity 0.18, final_train_loss 8.431e-02, max_acc 65.9, last_acc 63.1, mean_acc 59.5\n",
+            "\tretrain #20, sparsity 0.19, final_train_loss 1.912e-01, max_acc 68.5, last_acc 67.3, mean_acc 59.4\n",
+            "\tretrain #21, sparsity 0.20, final_train_loss 1.168e-01, max_acc 67.8, last_acc 64.9, mean_acc 60.2\n",
+            "\tretrain #22, sparsity 0.21, final_train_loss 1.616e-01, max_acc 68.0, last_acc 66.8, mean_acc 59.9\n",
+            "\tretrain #23, sparsity 0.22, final_train_loss 2.757e-01, max_acc 65.8, last_acc 64.4, mean_acc 59.5\n",
+            "\tretrain #24, sparsity 0.23, final_train_loss 1.645e-01, max_acc 66.3, last_acc 65.5, mean_acc 59.1\n",
+            "\tretrain #25, sparsity 0.24, final_train_loss 2.350e-01, max_acc 65.8, last_acc 63.4, mean_acc 58.3\n",
+            "\tretrain #26, sparsity 0.25, final_train_loss 1.111e-01, max_acc 65.6, last_acc 65.6, mean_acc 59.0\n",
+            "\tretrain #27, sparsity 0.26, final_train_loss 7.742e-02, max_acc 67.4, last_acc 65.2, mean_acc 59.7\n",
+            "\tretrain #28, sparsity 0.27, final_train_loss 1.275e-01, max_acc 67.4, last_acc 64.7, mean_acc 60.3\n",
+            "\tretrain #29, sparsity 0.28, final_train_loss 3.118e-02, max_acc 66.6, last_acc 65.2, mean_acc 60.4\n",
+            "\tretrain #30, sparsity 0.29, final_train_loss 1.879e-01, max_acc 67.1, last_acc 66.0, mean_acc 59.4\n",
+            "\tretrain #31, sparsity 0.30, final_train_loss 2.051e-01, max_acc 67.1, last_acc 67.1, mean_acc 59.9\n",
+            "\tretrain #32, sparsity 0.31, final_train_loss 1.074e-01, max_acc 67.2, last_acc 67.2, mean_acc 59.4\n",
+            "\tretrain #33, sparsity 0.32, final_train_loss 1.168e-01, max_acc 68.9, last_acc 68.7, mean_acc 60.8\n",
+            "\tretrain #34, sparsity 0.33, final_train_loss 1.290e-01, max_acc 67.2, last_acc 62.6, mean_acc 60.2\n",
+            "\tretrain #35, sparsity 0.34, final_train_loss 8.810e-02, max_acc 66.4, last_acc 65.2, mean_acc 59.5\n",
+            "\tretrain #36, sparsity 0.35, final_train_loss 2.669e-01, max_acc 67.5, last_acc 67.5, mean_acc 59.9\n",
+            "\tretrain #37, sparsity 0.36, final_train_loss 1.822e-01, max_acc 67.1, last_acc 65.3, mean_acc 61.1\n",
+            "\tretrain #38, sparsity 0.37, final_train_loss 1.708e-01, max_acc 67.9, last_acc 67.7, mean_acc 60.5\n",
+            "\tretrain #39, sparsity 0.38, final_train_loss 6.793e-02, max_acc 69.7, last_acc 67.6, mean_acc 61.8\n",
+            "\tretrain #40, sparsity 0.39, final_train_loss 7.624e-02, max_acc 67.0, last_acc 66.5, mean_acc 60.1\n",
+            "\tretrain #41, sparsity 0.40, final_train_loss 1.223e-01, max_acc 68.4, last_acc 65.4, mean_acc 60.4\n",
+            "\tretrain #42, sparsity 0.41, final_train_loss 2.133e-01, max_acc 66.3, last_acc 65.7, mean_acc 60.2\n",
+            "\tretrain #43, sparsity 0.42, final_train_loss 1.590e-01, max_acc 68.1, last_acc 66.3, mean_acc 61.5\n",
+            "\tretrain #44, sparsity 0.43, final_train_loss 2.161e-01, max_acc 69.1, last_acc 68.3, mean_acc 61.7\n",
+            "\tretrain #45, sparsity 0.44, final_train_loss 1.438e-01, max_acc 69.2, last_acc 69.2, mean_acc 61.7\n",
+            "\tretrain #46, sparsity 0.45, final_train_loss 3.218e-01, max_acc 68.6, last_acc 66.4, mean_acc 61.0\n",
+            "\tretrain #47, sparsity 0.46, final_train_loss 1.055e-01, max_acc 69.7, last_acc 68.3, mean_acc 62.4\n",
+            "\tretrain #48, sparsity 0.47, final_train_loss 6.646e-03, max_acc 70.3, last_acc 69.6, mean_acc 62.4\n",
+            "\tretrain #49, sparsity 0.48, final_train_loss 3.413e-01, max_acc 69.2, last_acc 68.8, mean_acc 62.1\n",
+            "\tretrain #50, sparsity 0.49, final_train_loss 2.401e-01, max_acc 70.4, last_acc 69.1, mean_acc 62.2\n",
+            "\tretrain #51, sparsity 0.51, final_train_loss 2.569e-01, max_acc 68.0, last_acc 65.0, mean_acc 61.3\n",
+            "\tretrain #52, sparsity 0.52, final_train_loss 2.675e-01, max_acc 70.0, last_acc 65.5, mean_acc 61.8\n",
+            "\tretrain #53, sparsity 0.53, final_train_loss 1.676e-01, max_acc 68.4, last_acc 65.4, mean_acc 61.8\n",
+            "\tretrain #54, sparsity 0.54, final_train_loss 2.891e-01, max_acc 68.7, last_acc 67.1, mean_acc 62.5\n",
+            "\tretrain #55, sparsity 0.55, final_train_loss 5.487e-01, max_acc 70.8, last_acc 65.2, mean_acc 63.2\n",
+            "\tretrain #56, sparsity 0.56, final_train_loss 1.458e-01, max_acc 67.3, last_acc 66.9, mean_acc 62.1\n",
+            "\tretrain #57, sparsity 0.57, final_train_loss 8.602e-02, max_acc 69.9, last_acc 68.7, mean_acc 63.1\n",
+            "\tretrain #58, sparsity 0.58, final_train_loss 2.292e-01, max_acc 69.8, last_acc 68.2, mean_acc 62.6\n",
+            "\tretrain #59, sparsity 0.59, final_train_loss 8.172e-04, max_acc 70.9, last_acc 70.7, mean_acc 63.2\n",
+            "\tretrain #60, sparsity 0.60, final_train_loss 3.560e-01, max_acc 69.7, last_acc 64.6, mean_acc 63.1\n",
+            "\tretrain #61, sparsity 0.61, final_train_loss 1.309e-01, max_acc 69.2, last_acc 67.9, mean_acc 63.0\n",
+            "\tretrain #62, sparsity 0.62, final_train_loss 8.264e-02, max_acc 70.5, last_acc 69.7, mean_acc 64.0\n",
+            "\tretrain #63, sparsity 0.63, final_train_loss 1.658e-01, max_acc 71.0, last_acc 68.3, mean_acc 63.9\n",
+            "\tretrain #64, sparsity 0.64, final_train_loss 3.615e-02, max_acc 71.1, last_acc 67.1, mean_acc 63.4\n",
+            "\tretrain #65, sparsity 0.65, final_train_loss 2.848e-01, max_acc 69.2, last_acc 66.0, mean_acc 63.1\n",
+            "\tretrain #66, sparsity 0.66, final_train_loss 4.801e-02, max_acc 70.6, last_acc 67.9, mean_acc 64.3\n",
+            "\tretrain #67, sparsity 0.67, final_train_loss 1.565e-01, max_acc 70.5, last_acc 70.1, mean_acc 63.8\n",
+            "\tretrain #68, sparsity 0.68, final_train_loss 2.377e-02, max_acc 72.1, last_acc 71.6, mean_acc 65.7\n",
+            "\tretrain #69, sparsity 0.69, final_train_loss 9.818e-02, max_acc 70.6, last_acc 67.4, mean_acc 64.1\n",
+            "\tretrain #70, sparsity 0.70, final_train_loss 1.072e-01, max_acc 71.9, last_acc 68.4, mean_acc 63.8\n",
+            "\tretrain #71, sparsity 0.71, final_train_loss 1.351e-01, max_acc 70.9, last_acc 70.4, mean_acc 64.8\n",
+            "\tretrain #72, sparsity 0.72, final_train_loss 9.054e-02, max_acc 70.9, last_acc 70.9, mean_acc 64.7\n",
+            "\tretrain #73, sparsity 0.73, final_train_loss 3.488e-02, max_acc 71.1, last_acc 69.1, mean_acc 65.0\n",
+            "\tretrain #74, sparsity 0.74, final_train_loss 2.987e-02, max_acc 71.5, last_acc 70.5, mean_acc 64.4\n",
+            "\tretrain #75, sparsity 0.75, final_train_loss 1.610e-01, max_acc 71.8, last_acc 68.6, mean_acc 64.8\n",
+            "\tretrain #76, sparsity 0.76, final_train_loss 1.373e-01, max_acc 72.1, last_acc 69.9, mean_acc 66.2\n",
+            "\tretrain #77, sparsity 0.77, final_train_loss 8.389e-02, max_acc 71.7, last_acc 70.8, mean_acc 64.8\n",
+            "\tretrain #78, sparsity 0.78, final_train_loss 1.139e-01, max_acc 71.3, last_acc 71.3, mean_acc 65.3\n",
+            "\tretrain #79, sparsity 0.79, final_train_loss 1.330e-01, max_acc 72.8, last_acc 71.5, mean_acc 67.1\n",
+            "\tretrain #80, sparsity 0.80, final_train_loss 2.172e-02, max_acc 72.1, last_acc 72.1, mean_acc 66.4\n",
+            "\tretrain #81, sparsity 0.81, final_train_loss 1.449e-01, max_acc 73.4, last_acc 70.5, mean_acc 65.9\n",
+            "\tretrain #82, sparsity 0.82, final_train_loss 3.688e-03, max_acc 73.1, last_acc 72.4, mean_acc 66.7\n",
+            "\tretrain #83, sparsity 0.83, final_train_loss 3.161e-01, max_acc 72.6, last_acc 68.6, mean_acc 65.6\n",
+            "\tretrain #84, sparsity 0.84, final_train_loss 9.952e-02, max_acc 72.1, last_acc 72.0, mean_acc 66.1\n",
+            "\tretrain #85, sparsity 0.85, final_train_loss 2.680e-01, max_acc 73.4, last_acc 71.0, mean_acc 66.8\n",
+            "\tretrain #86, sparsity 0.86, final_train_loss 5.618e-03, max_acc 74.8, last_acc 74.1, mean_acc 67.1\n",
+            "\tretrain #87, sparsity 0.87, final_train_loss 1.564e-01, max_acc 72.9, last_acc 68.7, mean_acc 66.0\n",
+            "\tretrain #88, sparsity 0.88, final_train_loss 1.238e-02, max_acc 74.5, last_acc 74.5, mean_acc 67.0\n",
+            "\tretrain #89, sparsity 0.89, final_train_loss 1.012e-01, max_acc 74.7, last_acc 71.9, mean_acc 68.0\n",
+            "\tretrain #90, sparsity 0.90, final_train_loss 2.759e-02, max_acc 74.1, last_acc 74.1, mean_acc 67.1\n",
+            "\tretrain #91, sparsity 0.91, final_train_loss 4.520e-02, max_acc 72.9, last_acc 72.4, mean_acc 66.4\n",
+            "\tretrain #92, sparsity 0.92, final_train_loss 1.914e-02, max_acc 74.4, last_acc 74.4, mean_acc 66.3\n",
+            "\tretrain #93, sparsity 0.93, final_train_loss 9.094e-02, max_acc 73.7, last_acc 70.0, mean_acc 66.6\n",
+            "\tretrain #94, sparsity 0.94, final_train_loss 1.071e-01, max_acc 74.3, last_acc 70.3, mean_acc 67.1\n",
+            "\tretrain #95, sparsity 0.95, final_train_loss 7.500e-02, max_acc 70.8, last_acc 70.5, mean_acc 64.9\n",
+            "\tretrain #96, sparsity 0.96, final_train_loss 7.664e-02, max_acc 71.5, last_acc 71.5, mean_acc 65.6\n",
+            "\tretrain #97, sparsity 0.97, final_train_loss 2.434e-01, max_acc 68.3, last_acc 67.3, mean_acc 61.7\n",
+            "\tretrain #98, sparsity 0.98, final_train_loss 4.330e-01, max_acc 65.0, last_acc 63.8, mean_acc 59.3\n",
+            "\tretrain #99, sparsity 0.99, final_train_loss 1.397e+00, max_acc 42.4, last_acc 41.5, mean_acc 37.7\n",
+            "\tretrain #100, sparsity 1.00, final_train_loss 2.303e+00, max_acc 10.2, last_acc 10.2, mean_acc 10.2\n"
+          ]
+        }
+      ],
+      "source": [
+        "num_trials = 2\n",
+        "trials = {'rand_models': [], 'rand_stats': [], 'lott_models': [], 'lott_stats': []}\n",
+        "for t in range(num_trials):\n",
+        "  print(\"############  Trial {}  ############\".format(t))\n",
+        "  print(\"   Random pruning\")\n",
+        "  set_seed(model_args.seed+t)\n",
+        "  model = SparseMLP(model_args.input_size, model_args.output_size, hidden_size=model_args.hidden_size).to(DEVICE)\n",
+        "\n",
+        "  def criteria_fn(init_params, final_params):\n",
+        "    mask = (final_params == 0).int()  # if params are already set to zero, keep them set to zero\n",
+        "    return torch.rand(final_params.shape).to(DEVICE) #* mask\n",
+        "  models, stats = find_lottery_ticket(model, data, model_args, sparsity_schedule,\n",
+        "                                      criteria_fn=criteria_fn, prune_print_every=1)\n",
+        "  trials['rand_models'].append(models)\n",
+        "  trials['rand_stats'].append(stats)\n",
+        "\n",
+        "  print(\"   Magnitude pruning\")\n",
+        "  set_seed(model_args.seed+t)\n",
+        "  model = SparseMLP(model_args.input_size, model_args.output_size, hidden_size=model_args.hidden_size).to(DEVICE)\n",
+        "\n",
+        "  criteria_fn = lambda init_params, final_params: final_params.abs()\n",
+        "  models, stats = find_lottery_ticket(model, data, model_args, sparsity_schedule,\n",
+        "                  criteria_fn=criteria_fn, prune_print_every=1)\n",
+        "  trials['lott_models'].append(models)\n",
+        "  trials['lott_stats'].append(stats)"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(7.5, 3), dpi=130)\n",
-    "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "plt.title('LT on spatially shuffled examples')\n",
-    "y, y_err = average_over_results(results_shuff, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_shuff, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_shuff, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "plt.title('LT on spatially chunked examples')\n",
-    "y, y_err = average_over_results(results_chunks, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_chunks, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_chunks, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
-    "\n",
-    "\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + 'lottery_asymptotes_chunks.png')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IxL3o0QpY1aB"
-   },
-   "source": [
-    "## Rebuild models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "T0ndBUSlYGSX"
-   },
-   "outputs": [],
-   "source": [
-    "set_seed(model_args.seed)\n",
-    "dense_model = SparseMLP(model_args.input_size, model_args.output_size,\\\n",
-    "                        hidden_size=model_args.hidden_size).to(DEVICE)\n",
-    "_rand_model = copy.deepcopy(trials['rand_models'][0][retrain_step])\n",
-    "_lott_model = copy.deepcopy(trials['lott_models'][0][retrain_step])\n",
-    "\n",
-    "rand_model = copy.deepcopy(dense_model)\n",
-    "rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
-    "\n",
-    "lott_model = copy.deepcopy(dense_model)\n",
-    "lott_model.set_layer_masks(_lott_model.get_layer_masks())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "prwCrfGMmhqG"
-   },
-   "source": [
-    "## Looks like our lottery ticket has some decent spatial priors\n",
-    "One thing this implies is that the lottery ticket may have more local connectivity\n",
-    "## We can test for this by measuring the probability of nonzero parameters being adjacent to one another\n",
-    "This will tell us whether local connections are more frequent in lottery tickets compared to random tickets. Let's do this.\n",
-    "\n",
-    "### 1. Get masks indicating nonzero weights of first fully-connected layer\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "e_KnW4D-ldpo"
-   },
-   "outputs": [],
-   "source": [
-    "rand_model = copy.deepcopy(dense_model)\n",
-    "rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
-    "\n",
-    "lott_model = copy.deepcopy(dense_model)\n",
-    "lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
-    "\n",
-    "w1_rand = rand_model.linear1.linear.weight.cpu().detach().numpy()\n",
-    "w1_lott = lott_model.linear1.linear.weight.cpu().detach().numpy()\n",
-    "\n",
-    "w1_rand_nonzero = (w1_rand!=0).astype(np.float32)\n",
-    "w1_lott_nonzero = (w1_lott!=0).astype(np.float32)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wimJAObnPgmh"
-   },
-   "source": [
-    "### 2. Count the number of times that nonzero parameters are adjacent to one another"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "qFySsiEPHxgl"
-   },
-   "outputs": [],
-   "source": [
-    "w1_rand_2adjacent, w1_lott_2adjacent = [], []\n",
-    "w1_rand_3adjacent, w1_lott_3adjacent = [], []\n",
-    "w1_rand_4adjacent, w1_lott_4adjacent = [], []\n",
-    "input_dim = w1_rand.shape[1] * 1.\n",
-    "for k in range(w1_rand.shape[0]):\n",
-    "  w1_rand_2adjacent += [np.sum(w1_rand_nonzero[k,:-1] * w1_rand_nonzero[k,1:]) / input_dim]\n",
-    "  w1_lott_2adjacent += [np.sum(w1_lott_nonzero[k,:-1] * w1_lott_nonzero[k,1:]) / input_dim]\n",
-    "\n",
-    "  w1_rand_3adjacent += [np.sum(w1_rand_nonzero[k,:-2] * w1_rand_nonzero[k,1:-1] * w1_rand_nonzero[k,2:]) / input_dim]\n",
-    "  w1_lott_3adjacent += [np.sum(w1_lott_nonzero[k,:-2] * w1_lott_nonzero[k,1:-1] * w1_lott_nonzero[k,2:]) / input_dim]\n",
-    "\n",
-    "  w1_rand_4adjacent += [np.sum(w1_rand_nonzero[k,:-3] * w1_rand_nonzero[k,1:-2] * \\\n",
-    "                               w1_rand_nonzero[k,2:-1] * w1_rand_nonzero[k,3:]) / input_dim]\n",
-    "  w1_lott_4adjacent += [np.sum(w1_lott_nonzero[k,:-3] * w1_lott_nonzero[k,1:-2] * \\\n",
-    "                              w1_lott_nonzero[k,2:-1] * w1_rand_nonzero[k,3:]) / input_dim]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6v3g661oPmXk"
-   },
-   "source": [
-    "### 3. Calculate basic statistics for these adjacencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "C2Q4VavTJ2vY"
-   },
-   "outputs": [],
-   "source": [
-    "ticket_names = ['random', 'lottery']\n",
-    "sqrtN = np.sqrt(len(w1_rand_2adjacent))\n",
-    "\n",
-    "p_2adjacent = [np.mean(w1_rand_2adjacent), np.mean(w1_lott_2adjacent)]\n",
-    "p_2adjacent_err = [np.std(w1_rand_2adjacent)/sqrtN, np.std(w1_lott_2adjacent)/sqrtN]\n",
-    "\n",
-    "p_3adjacent = [np.mean(w1_rand_3adjacent), np.mean(w1_lott_3adjacent)]\n",
-    "p_3adjacent_err = [np.std(w1_rand_3adjacent)/sqrtN, np.std(w1_lott_3adjacent)/sqrtN]\n",
-    "\n",
-    "p_4adjacent = [np.mean(w1_rand_4adjacent), np.mean(w1_lott_4adjacent)]\n",
-    "p_4adjacent_err = [np.std(w1_rand_4adjacent)/sqrtN, np.std(w1_lott_4adjacent)/sqrtN]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3tjV5fJVPrLF"
-   },
-   "source": [
-    "### 4. Plot the empirical statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 307
     },
-    "id": "QAsv0pzCdG7L",
-    "outputId": "b5fa16f1-bc3a-4005-fa30-aa765b9c2bb0"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 850x300 with 3 Axes>"
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "AgKoAhiluzTt"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uVy2yEsA4UMT"
+      },
+      "source": [
+        "## Plot results"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=[8.5,3], dpi=100)\n",
-    "\n",
-    "ax = plt.subplot(1,3,1)\n",
-    "x = np.arange(len(ticket_names))\n",
-    "ax.set_xticks(x)\n",
-    "ax.set_xticklabels(ticket_names)\n",
-    "barlist = plt.bar(x, p_2adjacent, yerr=p_2adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\n",
-    "barlist[0].set_color('red')\n",
-    "plt.title(\"p(two adjacent weights)\")\n",
-    "\n",
-    "ax = plt.subplot(1,3,2)\n",
-    "x = np.arange(len(ticket_names))\n",
-    "ax.set_xticks(x)\n",
-    "ax.set_xticklabels(ticket_names)\n",
-    "barlist = plt.bar(x, p_3adjacent, yerr=p_3adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\n",
-    "barlist[0].set_color('red')\n",
-    "plt.title(\"p(three adjacent weights)\")\n",
-    "\n",
-    "ax = plt.subplot(1,3,3)\n",
-    "x = np.arange(len(ticket_names))\n",
-    "ax.set_xticks(x)\n",
-    "ax.set_xticklabels(ticket_names)\n",
-    "barlist = plt.bar(x, p_4adjacent, yerr=p_4adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\n",
-    "barlist[0].set_color('red')\n",
-    "plt.title(\"p(four adjacent weights)\")\n",
-    "\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + 'lottery_adjacency.png')\n",
-    "fig.savefig(project_dir + 'lottery_adjacency.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pypkWV9Zkv26"
-   },
-   "source": [
-    "## One more sanity check: keep sparsity pattern but use different initialization\n",
-    "Based on what we're seeing above, the sparsity pattern itself represents some sort of inductive bias for local connectivity. If this is true, then we should be able to start from a different (scratch) initialization and still see a jump in performance. Let's try it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "uKVimjBFaXXs",
-    "outputId": "570cef5f-8e11-4120-f866-d9cc8b80a7cd"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "############  Trial 0  ############\n",
-      "step 1000, dt 2.90s, train_loss 1.061e-03, test_loss 1.999e+00, train_acc 100.0, test_acc 64.3\n",
-      "step 2000, dt 2.88s, train_loss 2.865e-04, test_loss 2.285e+00, train_acc 100.0, test_acc 64.8\n",
-      "step 3000, dt 2.79s, train_loss 1.189e-04, test_loss 2.495e+00, train_acc 100.0, test_acc 64.9\n",
-      "step 4000, dt 2.69s, train_loss 5.793e-05, test_loss 2.669e+00, train_acc 100.0, test_acc 64.7\n",
-      "step 5000, dt 2.70s, train_loss 3.040e-05, test_loss 2.827e+00, train_acc 100.0, test_acc 64.0\n",
-      "step 6000, dt 2.76s, train_loss 1.658e-05, test_loss 2.981e+00, train_acc 100.0, test_acc 64.0\n",
-      "step 1000, dt 2.73s, train_loss 2.962e-03, test_loss 1.963e+00, train_acc 100.0, test_acc 68.7\n",
-      "step 2000, dt 2.75s, train_loss 6.231e-04, test_loss 2.388e+00, train_acc 100.0, test_acc 68.3\n",
-      "step 3000, dt 2.69s, train_loss 2.351e-04, test_loss 2.656e+00, train_acc 100.0, test_acc 68.4\n",
-      "step 4000, dt 2.68s, train_loss 1.071e-04, test_loss 2.870e+00, train_acc 100.0, test_acc 68.0\n",
-      "step 5000, dt 2.65s, train_loss 5.368e-05, test_loss 3.058e+00, train_acc 100.0, test_acc 67.8\n",
-      "step 6000, dt 2.73s, train_loss 2.810e-05, test_loss 3.240e+00, train_acc 100.0, test_acc 67.5\n",
-      "step 1000, dt 2.75s, train_loss 3.762e-03, test_loss 2.555e+00, train_acc 100.0, test_acc 59.3\n",
-      "step 2000, dt 2.70s, train_loss 7.200e-04, test_loss 3.133e+00, train_acc 100.0, test_acc 58.8\n",
-      "step 3000, dt 2.76s, train_loss 2.603e-04, test_loss 3.492e+00, train_acc 100.0, test_acc 59.3\n",
-      "step 4000, dt 2.71s, train_loss 1.162e-04, test_loss 3.777e+00, train_acc 100.0, test_acc 59.4\n",
-      "step 5000, dt 2.77s, train_loss 5.740e-05, test_loss 4.024e+00, train_acc 100.0, test_acc 59.3\n",
-      "step 6000, dt 2.81s, train_loss 2.998e-05, test_loss 4.250e+00, train_acc 100.0, test_acc 59.3\n",
-      "\n",
-      "############  Trial 1  ############\n",
-      "step 1000, dt 2.71s, train_loss 2.096e-03, test_loss 1.859e+00, train_acc 100.0, test_acc 64.1\n",
-      "step 2000, dt 2.71s, train_loss 5.229e-04, test_loss 2.147e+00, train_acc 100.0, test_acc 64.3\n",
-      "step 3000, dt 2.70s, train_loss 2.083e-04, test_loss 2.343e+00, train_acc 100.0, test_acc 64.2\n",
-      "step 4000, dt 2.69s, train_loss 9.910e-05, test_loss 2.497e+00, train_acc 100.0, test_acc 63.9\n",
-      "step 5000, dt 2.68s, train_loss 5.129e-05, test_loss 2.633e+00, train_acc 100.0, test_acc 64.0\n",
-      "step 6000, dt 2.72s, train_loss 2.780e-05, test_loss 2.758e+00, train_acc 100.0, test_acc 63.9\n",
-      "step 1000, dt 2.79s, train_loss 3.603e-03, test_loss 1.900e+00, train_acc 100.0, test_acc 68.1\n",
-      "step 2000, dt 2.84s, train_loss 6.549e-04, test_loss 2.331e+00, train_acc 100.0, test_acc 67.2\n",
-      "step 3000, dt 2.73s, train_loss 2.277e-04, test_loss 2.596e+00, train_acc 100.0, test_acc 67.0\n",
-      "step 4000, dt 2.82s, train_loss 9.915e-05, test_loss 2.800e+00, train_acc 100.0, test_acc 67.1\n",
-      "step 5000, dt 2.82s, train_loss 4.878e-05, test_loss 2.976e+00, train_acc 100.0, test_acc 67.0\n",
-      "step 6000, dt 2.79s, train_loss 2.515e-05, test_loss 3.142e+00, train_acc 100.0, test_acc 67.0\n",
-      "step 1000, dt 2.75s, train_loss 5.970e-03, test_loss 2.593e+00, train_acc 100.0, test_acc 58.0\n",
-      "step 2000, dt 2.71s, train_loss 1.149e-03, test_loss 3.157e+00, train_acc 100.0, test_acc 57.6\n",
-      "step 3000, dt 2.70s, train_loss 4.081e-04, test_loss 3.532e+00, train_acc 100.0, test_acc 57.4\n",
-      "step 4000, dt 2.71s, train_loss 1.785e-04, test_loss 3.832e+00, train_acc 100.0, test_acc 57.3\n",
-      "step 5000, dt 2.70s, train_loss 8.812e-05, test_loss 4.095e+00, train_acc 100.0, test_acc 57.0\n",
-      "step 6000, dt 2.76s, train_loss 4.600e-05, test_loss 4.337e+00, train_acc 100.0, test_acc 57.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "results_seed = {'dense': [], 'lott': [], 'rand': []}\n",
-    "for t in range(len(trials['rand_stats'])):\n",
-    "  print(\"\\n############  Trial {}  ############\".format(t))\n",
-    "  set_seed(model_args.seed + t + 1)\n",
-    "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
-    "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
-    "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
-    "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
-    "\n",
-    "  rand_model = copy.deepcopy(dense_model)\n",
-    "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
-    "\n",
-    "  lott_model = copy.deepcopy(dense_model)\n",
-    "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
-    "\n",
-    "  dense = train_model(data, dense_model, model_args) ; results_seed['dense'].append(dense)\n",
-    "  lott = train_model(data, lott_model, model_args)   ; results_seed['lott'].append(lott)\n",
-    "  rand = train_model(data, rand_model, model_args)   ; results_seed['rand'].append(rand)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 394
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "Wy6JFxutxVfQ"
+      },
+      "outputs": [],
+      "source": [
+        "to_pickle(trials, path=project_dir + 'lottery.pkl')  # cache results because they take awhile (~1hr) to compute"
+      ]
     },
-    "id": "Q6aftRykasYD",
-    "outputId": "42c24a21-ec7b-4d07-de36-07721d81e931"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 975x390 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "ImA4Y9wnyx8k"
+      },
+      "outputs": [],
+      "source": [
+        "# trials = from_pickle(path=project_dir + 'lottery.pkl')   # optionally load precomputed results from your Drive"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "LrqTjYn9NySW",
+        "outputId": "3dc00e06-30bb-4a1c-b290-da0c3edb29c3"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x600 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def average_over(trials, trial_name, key):\n",
+        "  ys = [trials[trial_name][i][key] for i in range(len(trials[trial_name]))]\n",
+        "  return np.stack(ys).mean(0), np.stack(ys).std(0) / np.sqrt(len(ys))\n",
+        "\n",
+        "x = sparsity_schedule\n",
+        "rand_color, lott_color = 'r', 'b'\n",
+        "fig = plt.figure(figsize=[8,3], dpi=200)\n",
+        "\n",
+        "plt.subplot(1,2,1)\n",
+        "y, y_err = average_over(trials, 'rand_stats', 'test_losses')\n",
+        "y, y_err = y[:,-1], y_err[:,-1]\n",
+        "plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\n",
+        "y, y_err = average_over(trials, 'lott_stats', 'test_losses')\n",
+        "y, y_err = y[:,-1], y_err[:,-1]\n",
+        "plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\n",
+        "plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\n",
+        "plt.xlabel('Sparsity') ; plt.title('Final test loss')\n",
+        "plt.yscale('log')\n",
+        "plt.ylim(None, 5e0)\n",
+        "plt.legend(fontsize=7, ncol=3, loc='upper left')\n",
+        "\n",
+        "plt.subplot(1,2,2)\n",
+        "y, y_err = average_over(trials, 'rand_stats', 'test_accs')\n",
+        "y, y_err = y[:,-1], y_err[:,-1]\n",
+        "plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\n",
+        "y, y_err = average_over(trials, 'lott_stats', 'test_accs')\n",
+        "y, y_err = y[:,-1], y_err[:,-1]\n",
+        "plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\n",
+        "plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\n",
+        "plt.xlabel('Sparsity') ; plt.title('Final test accuracy')\n",
+        "plt.ylim(55, 80) #plt.ylim(70, 85)\n",
+        "plt.legend(fontsize=7, ncol=3, loc='upper left')\n",
+        "\n",
+        "# plt.subplot(1,3,3)\n",
+        "# y, y_err = average_over(trials, 'rand_stats', 'test_accs')\n",
+        "# y, y_err = y.max(-1), y_err[range(y.shape[0]),y.argmax(-1)]\n",
+        "# plt.plot(x, y, '-', color=rand_color, label='random') ; plt.fill_between(x, y-y_err, y+y_err, color=rand_color, alpha=0.25)\n",
+        "# y, y_err = average_over(trials, 'lott_stats', 'test_accs')\n",
+        "# y, y_err = y.max(-1), y_err[range(y.shape[0]),y.argmax(-1)]\n",
+        "# plt.plot(x, y, '-', color=lott_color, label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color=lott_color, alpha=0.25)\n",
+        "# plt.plot(x, np.ones_like(x) * y[0], 'k--', label='dense')\n",
+        "# plt.xlabel('Sparsity') ; plt.title('Max test accuracy')\n",
+        "# plt.ylim(55, 80) #plt.ylim(55, 75)\n",
+        "# plt.legend(fontsize=7, ncol=3, loc='upper left')\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "os.makedirs(project_dir + 'figures/', exist_ok=True)\n",
+        "fig.savefig(project_dir + 'figures/lottery.png')\n",
+        "fig.savefig(project_dir + 'figures/lottery.pdf')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XvVxUbWNRirk"
+      },
+      "source": [
+        "## Qualitative analysis of masks\n",
+        "What do the masks look like? Looking at the first layer in particular is interesting because we'd like to see whether the lottery ticket has learned any biases towards local connectivity, which would indicate a spatial prior."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 905
+        },
+        "id": "CA8k18eGRhqy",
+        "outputId": "234d2f65-a55b-4383-e10b-c8e76da431fb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+            "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+            "  ret = ret.dtype.type(ret / rcount)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 3000x1800 with 4 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Get the masks for the weights of the first layer weights\n",
+        "retrain_step = 90\n",
+        "lott_model = trials['lott_models'][0][retrain_step]\n",
+        "lott_mask1 = (lott_model.linear1.linear.weight == 0).cpu().detach().numpy().T\n",
+        "lott_model = trials['lott_models'][1][retrain_step]\n",
+        "lott_mask2 = (lott_model.linear1.linear.weight == 0).cpu().detach().numpy().T\n",
+        "\n",
+        "retrain_step = 91\n",
+        "rand_model = trials['rand_models'][0][retrain_step]\n",
+        "rand_mask1 = (rand_model.linear1.linear.weight == 0).cpu().detach().numpy().T\n",
+        "rand_model = trials['rand_models'][1][retrain_step]\n",
+        "rand_mask2 = (rand_model.linear1.linear.weight == 0).cpu().detach().numpy().T\n",
+        "\n",
+        "# Sort masks along hidden layer axis\n",
+        "def sort_by_adjacency(mask):\n",
+        "  mask = 1. * mask\n",
+        "  def score_fn(m):\n",
+        "    adjacency = np.sum(m[:-1]*m[1:])\n",
+        "    first_nonzero = np.where(m==1)[0][0] if np.sum(m) > 0 else 41\n",
+        "    num_nonzero = np.sum(m)\n",
+        "    center_of_mass = np.mean(np.where(m==1)[0])\n",
+        "    return adjacency + 0.001 * num_nonzero #- 0.001 * center_of_mass # + 0.001 * num_nonzero #- 0.001 * first_nonzero\n",
+        "  scores = [-score_fn(1-mask[:,i]) for i in range(mask.shape[1])]\n",
+        "  sorted_ixs = np.argsort(scores)\n",
+        "  return mask[:,sorted_ixs].copy()\n",
+        "\n",
+        "sort_fn = sort_by_adjacency\n",
+        "\n",
+        "# Plot masks\n",
+        "fig = plt.figure(figsize=[10,6], dpi=300)\n",
+        "plt.subplot(4,1,1)\n",
+        "plt.imshow(sort_fn(rand_mask1), cmap='gray')\n",
+        "plt.title('Random ticket #1 ({:.0f} % sparse)'.format(rand_mask1.sum()/(40*5)), loc='left')\n",
+        "plt.xlabel('Hidden layer axis') ; plt.ylabel(\"Input axis\")\n",
+        "plt.subplot(4,1,2)\n",
+        "plt.imshow(sort_fn(rand_mask2), cmap='gray')\n",
+        "plt.title('Random ticket #2 ({:.0f} % sparse)'.format(rand_mask2.sum()/(40*5)), loc='left')\n",
+        "plt.xlabel('Hidden layer axis') ; plt.ylabel(\"Input axis\")\n",
+        "plt.subplot(4,1,3)\n",
+        "plt.imshow(sort_fn(lott_mask1), cmap='gray')\n",
+        "plt.title('Lottery ticket #1 ({:.0f} % sparse)'.format(lott_mask1.sum()/(40*5)), loc='left')\n",
+        "plt.xlabel('Hidden layer axis') ; plt.ylabel(\"Input axis\")\n",
+        "plt.subplot(4,1,4)\n",
+        "plt.imshow(sort_fn(lott_mask2), cmap='gray')\n",
+        "plt.title('Lottery ticket #2 ({:.0f} % sparse)'.format(lott_mask2.sum()/(40*5)), loc='left')\n",
+        "plt.xlabel('Hidden layer axis') ; plt.ylabel(\"Input axis\")\n",
+        "\n",
+        "plt.show()\n",
+        "fig.savefig(project_dir + \"figures/lottery_mask_vis.png\")\n",
+        "fig.savefig(project_dir + \"figures/lottery_mask_vis.pdf\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-7ZaXZ9zv3UL"
+      },
+      "source": [
+        "## Let's make a new dataset (from the same distribution) to see how the ticket transfers\n",
+        "Nuance: in order to properly evaluate a lottery ticket, we need to train it on a new dataset from the same distribution, and evaluate it on a new test set from the same distribution. This isn't done in other papers because it's hard to duplicate, eg CIFAR-10. But we can do it here.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HugIsM2_iMqm",
+        "outputId": "b794237a-491c-49b0-81ca-910ce231153a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Did or could not load data from ./mnist1d_data.pkl. Rebuilding dataset...\n",
+            "Examples in training set: 4000\n",
+            "Examples in test set: 1000\n",
+            "Length of each input: 40\n",
+            "Number of classes: 10\n"
+          ]
+        }
+      ],
+      "source": [
+        "args = get_dataset_args()\n",
+        "args.seed = args.seed + 1  # new manual seed -> new dataset from same dataset\n",
+        "data = get_dataset(args=args, download=False, regenerate=True)\n",
+        "\n",
+        "print(\"Examples in training set: {}\".format(len(data['y'])))\n",
+        "print(\"Examples in test set: {}\".format(len(data['y_test'])))\n",
+        "print(\"Length of each input: {}\".format(data['x'].shape[-1]))\n",
+        "print(\"Number of classes: {}\".format(len(data['templates']['y'])))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Hr39T9Dmwzh4",
+        "outputId": "013104f1-71e6-4a15-8059-9eccb2fb3667"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "############  Trial 0  ############\n",
+            "step 1000, dt 2.13s, train_loss 1.240e-03, test_loss 2.061e+00, train_acc 100.0, test_acc 64.2\n",
+            "step 2000, dt 2.11s, train_loss 3.471e-04, test_loss 2.366e+00, train_acc 100.0, test_acc 63.9\n",
+            "step 3000, dt 2.11s, train_loss 1.445e-04, test_loss 2.573e+00, train_acc 100.0, test_acc 64.1\n",
+            "step 4000, dt 2.30s, train_loss 6.962e-05, test_loss 2.752e+00, train_acc 100.0, test_acc 64.2\n",
+            "step 5000, dt 2.59s, train_loss 3.619e-05, test_loss 2.913e+00, train_acc 100.0, test_acc 64.4\n",
+            "step 6000, dt 2.45s, train_loss 1.959e-05, test_loss 3.065e+00, train_acc 100.0, test_acc 64.2\n",
+            "step 1000, dt 2.49s, train_loss 4.270e-03, test_loss 2.168e+00, train_acc 100.0, test_acc 65.2\n",
+            "step 2000, dt 2.10s, train_loss 7.413e-04, test_loss 2.697e+00, train_acc 100.0, test_acc 66.0\n",
+            "step 3000, dt 2.10s, train_loss 2.565e-04, test_loss 3.024e+00, train_acc 100.0, test_acc 65.7\n",
+            "step 4000, dt 2.56s, train_loss 1.127e-04, test_loss 3.285e+00, train_acc 100.0, test_acc 65.3\n",
+            "step 5000, dt 2.42s, train_loss 5.497e-05, test_loss 3.514e+00, train_acc 100.0, test_acc 65.0\n",
+            "step 6000, dt 2.19s, train_loss 2.843e-05, test_loss 3.723e+00, train_acc 100.0, test_acc 64.9\n",
+            "step 1000, dt 2.20s, train_loss 4.512e-03, test_loss 2.093e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 2000, dt 3.68s, train_loss 8.575e-04, test_loss 2.572e+00, train_acc 100.0, test_acc 62.7\n",
+            "step 3000, dt 5.50s, train_loss 3.075e-04, test_loss 2.885e+00, train_acc 100.0, test_acc 62.3\n",
+            "step 4000, dt 2.55s, train_loss 1.374e-04, test_loss 3.135e+00, train_acc 100.0, test_acc 62.1\n",
+            "step 5000, dt 2.16s, train_loss 6.770e-05, test_loss 3.355e+00, train_acc 100.0, test_acc 61.8\n",
+            "step 6000, dt 2.11s, train_loss 3.529e-05, test_loss 3.560e+00, train_acc 100.0, test_acc 61.5\n",
+            "\n",
+            "############  Trial 1  ############\n",
+            "step 1000, dt 2.12s, train_loss 1.915e-03, test_loss 1.847e+00, train_acc 100.0, test_acc 64.9\n",
+            "step 2000, dt 2.43s, train_loss 4.859e-04, test_loss 2.125e+00, train_acc 100.0, test_acc 64.9\n",
+            "step 3000, dt 2.60s, train_loss 1.959e-04, test_loss 2.317e+00, train_acc 100.0, test_acc 64.9\n",
+            "step 4000, dt 2.08s, train_loss 9.348e-05, test_loss 2.475e+00, train_acc 100.0, test_acc 64.4\n",
+            "step 5000, dt 2.09s, train_loss 4.838e-05, test_loss 2.616e+00, train_acc 100.0, test_acc 64.3\n",
+            "step 6000, dt 2.11s, train_loss 2.624e-05, test_loss 2.749e+00, train_acc 100.0, test_acc 64.2\n",
+            "step 1000, dt 2.07s, train_loss 2.831e-03, test_loss 1.931e+00, train_acc 100.0, test_acc 67.4\n",
+            "step 2000, dt 2.39s, train_loss 5.438e-04, test_loss 2.335e+00, train_acc 100.0, test_acc 67.4\n",
+            "step 3000, dt 2.49s, train_loss 1.919e-04, test_loss 2.599e+00, train_acc 100.0, test_acc 66.7\n",
+            "step 4000, dt 2.11s, train_loss 8.503e-05, test_loss 2.804e+00, train_acc 100.0, test_acc 66.9\n",
+            "step 5000, dt 2.09s, train_loss 4.208e-05, test_loss 2.982e+00, train_acc 100.0, test_acc 66.7\n",
+            "step 6000, dt 2.10s, train_loss 2.203e-05, test_loss 3.147e+00, train_acc 100.0, test_acc 66.8\n",
+            "step 1000, dt 2.10s, train_loss 5.974e-03, test_loss 2.531e+00, train_acc 100.0, test_acc 58.5\n",
+            "step 2000, dt 2.42s, train_loss 1.162e-03, test_loss 3.096e+00, train_acc 100.0, test_acc 58.3\n",
+            "step 3000, dt 2.78s, train_loss 4.151e-04, test_loss 3.466e+00, train_acc 100.0, test_acc 58.9\n",
+            "step 4000, dt 2.10s, train_loss 1.835e-04, test_loss 3.758e+00, train_acc 100.0, test_acc 58.9\n",
+            "step 5000, dt 2.11s, train_loss 9.036e-05, test_loss 4.015e+00, train_acc 100.0, test_acc 58.7\n",
+            "step 6000, dt 2.13s, train_loss 4.693e-05, test_loss 4.252e+00, train_acc 100.0, test_acc 58.4\n"
+          ]
+        }
+      ],
+      "source": [
+        "retrain_step = 91\n",
+        "\n",
+        "model_args = get_model_args()\n",
+        "model_args.hidden_size = 500\n",
+        "model_args.eval_every = 100\n",
+        "model_args.learning_rate = 2e-2\n",
+        "model_args.device = DEVICE\n",
+        "model_args.total_steps = 6000\n",
+        "model_args.print_every = 1000\n",
+        "model_args.batch_size = 500 # higher batch size since we want to show overfitting\n",
+        "\n",
+        "results = {'dense': [], 'lott': [], 'rand': []}\n",
+        "for t in range(len(trials['rand_stats'])):\n",
+        "  print(\"\\n############  Trial {}  ############\".format(t))\n",
+        "  set_seed(model_args.seed + t)\n",
+        "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
+        "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
+        "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
+        "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
+        "\n",
+        "  rand_model = copy.deepcopy(dense_model)\n",
+        "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
+        "\n",
+        "  lott_model = copy.deepcopy(dense_model)\n",
+        "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
+        "\n",
+        "  dense = train_model(data, dense_model, model_args) ; results['dense'].append(dense)\n",
+        "  lott = train_model(data, lott_model, model_args)   ; results['lott'].append(lott)\n",
+        "  rand = train_model(data, rand_model, model_args)   ; results['rand'].append(rand)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "mV4WcbmKLdDu",
+        "outputId": "387173db-226d-4ceb-c53c-412b25dda0a6"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 520x390 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def average_over_results(results, name):\n",
+        "  ys = [results[name][t]['test_acc'] for t in range(len(results[name]))]\n",
+        "  return np.stack(ys).mean(0), np.stack(ys).std(0) / np.sqrt(len(ys))\n",
+        "\n",
+        "fig = plt.figure(figsize=(4, 3), dpi=130)\n",
+        "plt.subplot(1,1,1)\n",
+        "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
+        "\n",
+        "y, y_err = average_over_results(results, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "\n",
+        "plt.title('Lottery ticket ({:.0f}% sparse)'.format(100*sparsity_schedule[retrain_step]))\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=7, ncol=3)\n",
+        "plt.ylim(50,76)\n",
+        "\n",
+        "plt.show()\n",
+        "fig.savefig(project_dir + 'figures/lottery_asymptotes.png')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DuzZ6YNzcOrN"
+      },
+      "source": [
+        "## Remove spatial priors and see how lottery ticket fares\n",
+        "First, we do this by shuffling the entire sequence."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "LYyFs2PMcXxM"
+      },
+      "outputs": [],
+      "source": [
+        "data_shuff = {}\n",
+        "np.random.seed(0)\n",
+        "shuffle_ixs = np.random.permutation(40) #np.array(range(32)) #\n",
+        "for k in data.keys():\n",
+        "  if k in ['x', 'x_test', 'steps']:\n",
+        "    data_shuff[k] = data[k][...,shuffle_ixs].copy() # shuffle sequence\n",
+        "  else:\n",
+        "    data_shuff[k] = data[k].copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zi1BcrH61OjV",
+        "outputId": "53c07096-0a97-4012-f0f3-a9f23c589a3e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "############  Trial 0  ############\n",
+            "step 1000, dt 2.38s, train_loss 1.026e-03, test_loss 2.220e+00, train_acc 100.0, test_acc 62.0\n",
+            "step 2000, dt 4.73s, train_loss 2.889e-04, test_loss 2.539e+00, train_acc 100.0, test_acc 62.5\n",
+            "step 3000, dt 3.96s, train_loss 1.204e-04, test_loss 2.755e+00, train_acc 100.0, test_acc 62.8\n",
+            "step 4000, dt 2.64s, train_loss 5.798e-05, test_loss 2.935e+00, train_acc 100.0, test_acc 63.1\n",
+            "step 5000, dt 2.13s, train_loss 3.012e-05, test_loss 3.100e+00, train_acc 100.0, test_acc 62.9\n",
+            "step 6000, dt 2.09s, train_loss 1.635e-05, test_loss 3.258e+00, train_acc 100.0, test_acc 63.1\n",
+            "step 1000, dt 4.69s, train_loss 6.246e-03, test_loss 2.304e+00, train_acc 100.0, test_acc 63.9\n",
+            "step 2000, dt 3.86s, train_loss 1.050e-03, test_loss 2.967e+00, train_acc 100.0, test_acc 64.3\n",
+            "step 3000, dt 2.36s, train_loss 3.544e-04, test_loss 3.381e+00, train_acc 100.0, test_acc 63.5\n",
+            "step 4000, dt 2.44s, train_loss 1.532e-04, test_loss 3.698e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 5000, dt 2.10s, train_loss 7.416e-05, test_loss 3.975e+00, train_acc 100.0, test_acc 62.9\n",
+            "step 6000, dt 2.24s, train_loss 3.817e-05, test_loss 4.232e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 1000, dt 2.93s, train_loss 4.180e-03, test_loss 2.455e+00, train_acc 100.0, test_acc 61.8\n",
+            "step 2000, dt 3.27s, train_loss 8.040e-04, test_loss 3.021e+00, train_acc 100.0, test_acc 62.5\n",
+            "step 3000, dt 2.11s, train_loss 2.879e-04, test_loss 3.369e+00, train_acc 100.0, test_acc 62.1\n",
+            "step 4000, dt 2.12s, train_loss 1.281e-04, test_loss 3.643e+00, train_acc 100.0, test_acc 61.9\n",
+            "step 5000, dt 2.12s, train_loss 6.355e-05, test_loss 3.881e+00, train_acc 100.0, test_acc 62.0\n",
+            "step 6000, dt 2.54s, train_loss 3.321e-05, test_loss 4.102e+00, train_acc 100.0, test_acc 61.9\n",
+            "\n",
+            "############  Trial 1  ############\n",
+            "step 1000, dt 2.48s, train_loss 1.180e-03, test_loss 2.192e+00, train_acc 100.0, test_acc 64.8\n",
+            "step 2000, dt 2.14s, train_loss 3.177e-04, test_loss 2.537e+00, train_acc 100.0, test_acc 64.6\n",
+            "step 3000, dt 2.09s, train_loss 1.286e-04, test_loss 2.784e+00, train_acc 100.0, test_acc 64.7\n",
+            "step 4000, dt 2.12s, train_loss 6.140e-05, test_loss 2.988e+00, train_acc 100.0, test_acc 64.5\n",
+            "step 5000, dt 2.10s, train_loss 3.186e-05, test_loss 3.166e+00, train_acc 100.0, test_acc 64.4\n",
+            "step 6000, dt 2.51s, train_loss 1.718e-05, test_loss 3.335e+00, train_acc 100.0, test_acc 64.4\n",
+            "step 1000, dt 2.41s, train_loss 5.973e-03, test_loss 2.463e+00, train_acc 100.0, test_acc 63.3\n",
+            "step 2000, dt 2.10s, train_loss 9.788e-04, test_loss 3.133e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 3000, dt 2.08s, train_loss 3.328e-04, test_loss 3.546e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 4000, dt 2.10s, train_loss 1.454e-04, test_loss 3.860e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 5000, dt 2.17s, train_loss 7.071e-05, test_loss 4.131e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 6000, dt 2.57s, train_loss 3.626e-05, test_loss 4.380e+00, train_acc 100.0, test_acc 62.6\n",
+            "step 1000, dt 2.41s, train_loss 4.246e-03, test_loss 2.450e+00, train_acc 100.0, test_acc 58.9\n",
+            "step 2000, dt 2.16s, train_loss 8.269e-04, test_loss 2.975e+00, train_acc 100.0, test_acc 58.9\n",
+            "step 3000, dt 2.14s, train_loss 2.962e-04, test_loss 3.309e+00, train_acc 100.0, test_acc 59.2\n",
+            "step 4000, dt 2.07s, train_loss 1.311e-04, test_loss 3.572e+00, train_acc 100.0, test_acc 59.7\n",
+            "step 5000, dt 2.08s, train_loss 6.450e-05, test_loss 3.803e+00, train_acc 100.0, test_acc 59.6\n",
+            "step 6000, dt 2.56s, train_loss 3.344e-05, test_loss 4.020e+00, train_acc 100.0, test_acc 59.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "results_shuff = {'dense': [], 'lott': [], 'rand': []}\n",
+        "for t in range(len(trials['rand_stats'])):\n",
+        "  print(\"\\n############  Trial {}  ############\".format(t))\n",
+        "  set_seed(model_args.seed + t)\n",
+        "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
+        "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
+        "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
+        "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
+        "\n",
+        "  rand_model = copy.deepcopy(dense_model)\n",
+        "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
+        "\n",
+        "  lott_model = copy.deepcopy(dense_model)\n",
+        "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
+        "\n",
+        "  dense = train_model(data_shuff, dense_model, model_args) ; results_shuff['dense'].append(dense)\n",
+        "  lott = train_model(data_shuff, lott_model, model_args)   ; results_shuff['lott'].append(lott)\n",
+        "  rand = train_model(data_shuff, rand_model, model_args)   ; results_shuff['rand'].append(rand)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "fuZli6M8YorY",
+        "outputId": "8204f7a7-2da1-4a3f-bef7-f9d7f0695b62"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 975x390 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure(figsize=(7.5, 3), dpi=130)\n",
+        "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
+        "\n",
+        "plt.subplot(1,2,1)\n",
+        "plt.title('LT on baseline examples')\n",
+        "y, y_err = average_over_results(results, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
+        "\n",
+        "plt.subplot(1,2,2)\n",
+        "plt.title('LT on spatially shuffled examples')\n",
+        "y, y_err = average_over_results(results_shuff, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_shuff, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_shuff, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
+        "\n",
+        "\n",
+        "plt.show()\n",
+        "fig.savefig(project_dir + 'figures/lottery_asymptotes_shuffled.png')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WEcny3zHkHBU"
+      },
+      "source": [
+        "## Looks like the lottery ticket has some spatial priors, but we need to run more tests to be sure:\n",
+        "There may be a correlation between the spatial priors of the data and how important particular input indices are. If that is the case, then the lottery ticket procedure may have disproportionately pruned weights corresponding to inputs that didn't contribute much to classification. If this is the case, then the \"spatial priors\" of the lottery ticket are not particularly significant and won't transfer well to other datasets.\n",
+        "\n",
+        "But maybe the lottery ticket does have some spatial priors that will transfer well. In order to test whether that is the case, we can simply flip the non-shuffled dataset upside down. This will break the correlation between particular indices that matter more than others while still leaving spatial priors intact in the data. Think about it this way: a CNN will still do well if you flip all your images upside down. So our model's performance on flipped data is a good hint at how well its spatial priors will transfer. If this doesn't hurt performance much, then the lottery ticket represents some sort of reasonably good spatial prior."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "l1D-huHK55gF",
+        "outputId": "f556c5bc-e56c-47db-9a1d-1a68e3abd4f3"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 0.79027827,  0.81387517,  0.96495485,  0.72122595, -0.03240748,\n",
+              "       -0.28947403,  0.38705837,  0.73723783,  0.38279205, -0.16746251,\n",
+              "       -0.21399577,  0.025955  ,  0.00291882,  0.28282577, -1.25675551,\n",
+              "       -2.06009933, -1.71629861, -0.30305327,  0.02960669, -1.49053473,\n",
+              "       -2.16724905, -1.83906903, -0.26118889,  0.36413035, -0.17891161,\n",
+              "       -0.27629091, -0.13228634,  0.01394688, -0.47465012, -0.57515785,\n",
+              "       -0.37445353,  0.05180054,  0.32990678,  0.05195449, -0.91084646,\n",
+              "       -0.94110542, -0.34693864, -0.3011403 , -0.95394325, -1.39942009])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ],
+      "source": [
+        "data['x_test'][0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "KQLbj83smpxo"
+      },
+      "outputs": [],
+      "source": [
+        "data_flip = {}\n",
+        "for k in data.keys():\n",
+        "  if k in ['x', 'x_test', 'steps']:\n",
+        "    data_flip[k] = data[k][...,::-1].copy() # flip sequence upside-down\n",
+        "  else:\n",
+        "    data_flip[k] = data[k].copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4WZzYfTk57Y0",
+        "outputId": "5883b071-5b02-403b-eea1-6d96d097d340"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([-1.39942009, -0.95394325, -0.3011403 , -0.34693864, -0.94110542,\n",
+              "       -0.91084646,  0.05195449,  0.32990678,  0.05180054, -0.37445353,\n",
+              "       -0.57515785, -0.47465012,  0.01394688, -0.13228634, -0.27629091,\n",
+              "       -0.17891161,  0.36413035, -0.26118889, -1.83906903, -2.16724905,\n",
+              "       -1.49053473,  0.02960669, -0.30305327, -1.71629861, -2.06009933,\n",
+              "       -1.25675551,  0.28282577,  0.00291882,  0.025955  , -0.21399577,\n",
+              "       -0.16746251,  0.38279205,  0.73723783,  0.38705837, -0.28947403,\n",
+              "       -0.03240748,  0.72122595,  0.96495485,  0.81387517,  0.79027827])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "data_flip['x_test'][0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XhioZOATWY_D",
+        "outputId": "f1fb330a-bf11-4674-e246-e057cdd1b22d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "############  Trial 0  ############\n",
+            "step 1000, dt 2.12s, train_loss 2.058e-03, test_loss 1.848e+00, train_acc 100.0, test_acc 63.8\n",
+            "step 2000, dt 2.12s, train_loss 5.547e-04, test_loss 2.112e+00, train_acc 100.0, test_acc 63.6\n",
+            "step 3000, dt 2.12s, train_loss 2.285e-04, test_loss 2.296e+00, train_acc 100.0, test_acc 63.1\n",
+            "step 4000, dt 2.09s, train_loss 1.108e-04, test_loss 2.446e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 5000, dt 2.28s, train_loss 5.790e-05, test_loss 2.581e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 6000, dt 2.69s, train_loss 3.170e-05, test_loss 2.708e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 1000, dt 2.12s, train_loss 4.957e-03, test_loss 2.464e+00, train_acc 100.0, test_acc 62.3\n",
+            "step 2000, dt 2.49s, train_loss 8.296e-04, test_loss 3.146e+00, train_acc 100.0, test_acc 62.1\n",
+            "step 3000, dt 2.90s, train_loss 2.855e-04, test_loss 3.552e+00, train_acc 100.0, test_acc 62.2\n",
+            "step 4000, dt 2.11s, train_loss 1.243e-04, test_loss 3.868e+00, train_acc 100.0, test_acc 62.3\n",
+            "step 5000, dt 2.59s, train_loss 6.059e-05, test_loss 4.137e+00, train_acc 100.0, test_acc 62.2\n",
+            "step 6000, dt 2.36s, train_loss 3.133e-05, test_loss 4.387e+00, train_acc 100.0, test_acc 61.8\n",
+            "step 1000, dt 2.10s, train_loss 5.640e-03, test_loss 2.355e+00, train_acc 100.0, test_acc 61.2\n",
+            "step 2000, dt 2.05s, train_loss 1.047e-03, test_loss 2.922e+00, train_acc 100.0, test_acc 61.5\n",
+            "step 3000, dt 2.06s, train_loss 3.650e-04, test_loss 3.274e+00, train_acc 100.0, test_acc 60.9\n",
+            "step 4000, dt 2.09s, train_loss 1.605e-04, test_loss 3.552e+00, train_acc 100.0, test_acc 61.1\n",
+            "step 5000, dt 2.54s, train_loss 7.831e-05, test_loss 3.796e+00, train_acc 100.0, test_acc 61.0\n",
+            "step 6000, dt 2.46s, train_loss 4.053e-05, test_loss 4.017e+00, train_acc 100.0, test_acc 60.9\n",
+            "\n",
+            "############  Trial 1  ############\n",
+            "step 1000, dt 2.10s, train_loss 1.874e-03, test_loss 1.812e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 2000, dt 2.10s, train_loss 5.455e-04, test_loss 2.040e+00, train_acc 100.0, test_acc 62.5\n",
+            "step 3000, dt 2.08s, train_loss 2.332e-04, test_loss 2.199e+00, train_acc 100.0, test_acc 62.7\n",
+            "step 4000, dt 2.08s, train_loss 1.145e-04, test_loss 2.336e+00, train_acc 100.0, test_acc 62.8\n",
+            "step 5000, dt 2.62s, train_loss 6.067e-05, test_loss 2.461e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 6000, dt 2.45s, train_loss 3.349e-05, test_loss 2.580e+00, train_acc 100.0, test_acc 63.1\n",
+            "step 1000, dt 2.45s, train_loss 3.344e-03, test_loss 2.244e+00, train_acc 100.0, test_acc 64.3\n",
+            "step 2000, dt 2.10s, train_loss 6.178e-04, test_loss 2.775e+00, train_acc 100.0, test_acc 63.5\n",
+            "step 3000, dt 2.10s, train_loss 2.206e-04, test_loss 3.099e+00, train_acc 100.0, test_acc 63.8\n",
+            "step 4000, dt 2.75s, train_loss 9.788e-05, test_loss 3.353e+00, train_acc 100.0, test_acc 63.9\n",
+            "step 5000, dt 2.68s, train_loss 4.829e-05, test_loss 3.577e+00, train_acc 100.0, test_acc 64.1\n",
+            "step 6000, dt 2.09s, train_loss 2.518e-05, test_loss 3.785e+00, train_acc 100.0, test_acc 63.8\n",
+            "step 1000, dt 2.13s, train_loss 4.186e-03, test_loss 2.534e+00, train_acc 100.0, test_acc 59.2\n",
+            "step 2000, dt 2.08s, train_loss 8.259e-04, test_loss 3.073e+00, train_acc 100.0, test_acc 59.4\n",
+            "step 3000, dt 2.08s, train_loss 2.980e-04, test_loss 3.413e+00, train_acc 100.0, test_acc 58.4\n",
+            "step 4000, dt 2.20s, train_loss 1.329e-04, test_loss 3.683e+00, train_acc 100.0, test_acc 58.7\n",
+            "step 5000, dt 2.66s, train_loss 6.548e-05, test_loss 3.921e+00, train_acc 100.0, test_acc 59.3\n",
+            "step 6000, dt 2.13s, train_loss 3.394e-05, test_loss 4.143e+00, train_acc 100.0, test_acc 59.6\n"
+          ]
+        }
+      ],
+      "source": [
+        "results_flip = {'dense': [], 'lott': [], 'rand': []}\n",
+        "for t in range(len(trials['rand_stats'])):\n",
+        "  print(\"\\n############  Trial {}  ############\".format(t))\n",
+        "  set_seed(model_args.seed + t)\n",
+        "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
+        "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
+        "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
+        "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
+        "\n",
+        "  rand_model = copy.deepcopy(dense_model)\n",
+        "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
+        "\n",
+        "  lott_model = copy.deepcopy(dense_model)\n",
+        "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
+        "\n",
+        "  dense = train_model(data_flip, dense_model, model_args) ; results_flip['dense'].append(dense)\n",
+        "  lott = train_model(data_flip, lott_model, model_args)   ; results_flip['lott'].append(lott)\n",
+        "  rand = train_model(data_flip, rand_model, model_args)   ; results_flip['rand'].append(rand)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "oHJP9rKCYsPO",
+        "outputId": "25924188-39c4-41bd-daeb-2659628df5e4"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 975x390 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure(figsize=(7.5, 3), dpi=130)\n",
+        "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
+        "\n",
+        "plt.subplot(1,2,1)\n",
+        "plt.title('LT on spatially shuffled examples')\n",
+        "y, y_err = average_over_results(results_shuff, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_shuff, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_shuff, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
+        "\n",
+        "plt.subplot(1,2,2)\n",
+        "plt.title('LT on spatially flipped examples')\n",
+        "y, y_err = average_over_results(results_flip, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_flip, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_flip, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
+        "\n",
+        "\n",
+        "plt.show()\n",
+        "fig.savefig(project_dir + 'figures/lottery_asymptotes_flipped.png')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZNYf4pgm63YM"
+      },
+      "source": [
+        "## This has become a monster notebook.\n",
+        "Instead of responsibly dividing it into two notebooks, let's save a checkpoint and continue on. Now we can start from here without having to recompute everything from scratch."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "flw5BuLq28hk"
+      },
+      "outputs": [],
+      "source": [
+        "things = [trials, model_args, retrain_step, results, results_shuff, results_flip]\n",
+        "to_pickle(things, path=project_dir + 'lottery_analysis.pkl')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "m6z7ja7w22eA"
+      },
+      "outputs": [],
+      "source": [
+        "things = from_pickle(path=project_dir + 'lottery_analysis.pkl')\n",
+        "[trials, model_args, retrain_step, results, results_shuff, results_flip] = things"
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(7.5, 3), dpi=130)\n",
-    "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "plt.title('LT on baseline examples')\n",
-    "y, y_err = average_over_results(results, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "plt.title('LT with different initializations')\n",
-    "y, y_err = average_over_results(results_seed, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_seed, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_seed, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
-    "\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + 'lottery_asymptotes_reinit.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 596
     },
-    "id": "R1mZVHMdbG9w",
-    "outputId": "be0351ce-b074-4f3a-8e2b-d01f1f1fa279"
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 750x600 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HeKNrtfX8uZ6"
+      },
+      "source": [
+        "## Our lottery ticket generalizes surprisingly well to a flipped version of the dataset. What happens if we shuffle _chunks_ of the sequence\n",
+        "If the lottery ticket has learned good spatial priors, then we would expect its performance to decrease only partially when we shuffle chunks of the sequence, since some of the spatial information is still available."
       ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "id": "dkW-FFb68wRs"
+      },
+      "outputs": [],
+      "source": [
+        "data_chunks = {}\n",
+        "for k in data.keys():\n",
+        "  if k in ['x', 'x_test', 't']:\n",
+        "    data_chunks[k] = np.concatenate([data[k][...,20:30],\n",
+        "                                     data[k][...,30:40],\n",
+        "                                     data[k][...,:10],\n",
+        "                                     data[k][...,10:20]],\n",
+        "                                     axis=-1).copy() # exhange first and second halves\n",
+        "  else:\n",
+        "    data_chunks[k] = data[k].copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1Z3H-k-fXTty",
+        "outputId": "b4badfca-8a69-4e2d-ea38-82307fa295c0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "############  Trial 0  ############\n",
+            "step 1000, dt 2.11s, train_loss 7.306e-04, test_loss 1.987e+00, train_acc 100.0, test_acc 65.8\n",
+            "step 2000, dt 3.06s, train_loss 1.904e-04, test_loss 2.261e+00, train_acc 100.0, test_acc 65.6\n",
+            "step 3000, dt 6.64s, train_loss 7.772e-05, test_loss 2.442e+00, train_acc 100.0, test_acc 65.3\n",
+            "step 4000, dt 3.71s, train_loss 3.719e-05, test_loss 2.596e+00, train_acc 100.0, test_acc 65.3\n",
+            "step 5000, dt 2.28s, train_loss 1.899e-05, test_loss 2.733e+00, train_acc 100.0, test_acc 65.4\n",
+            "step 6000, dt 2.50s, train_loss 1.017e-05, test_loss 2.859e+00, train_acc 100.0, test_acc 65.3\n",
+            "step 1000, dt 2.51s, train_loss 6.653e-03, test_loss 2.594e+00, train_acc 100.0, test_acc 60.8\n",
+            "step 2000, dt 2.19s, train_loss 1.126e-03, test_loss 3.315e+00, train_acc 100.0, test_acc 60.9\n",
+            "step 3000, dt 2.89s, train_loss 3.901e-04, test_loss 3.751e+00, train_acc 100.0, test_acc 61.4\n",
+            "step 4000, dt 4.32s, train_loss 1.694e-04, test_loss 4.089e+00, train_acc 100.0, test_acc 61.4\n",
+            "step 5000, dt 4.50s, train_loss 8.211e-05, test_loss 4.376e+00, train_acc 100.0, test_acc 61.4\n",
+            "step 6000, dt 2.28s, train_loss 4.241e-05, test_loss 4.637e+00, train_acc 100.0, test_acc 61.3\n",
+            "step 1000, dt 2.77s, train_loss 4.013e-03, test_loss 2.463e+00, train_acc 100.0, test_acc 59.6\n",
+            "step 2000, dt 3.43s, train_loss 8.205e-04, test_loss 3.009e+00, train_acc 100.0, test_acc 59.4\n",
+            "step 3000, dt 5.78s, train_loss 2.991e-04, test_loss 3.371e+00, train_acc 100.0, test_acc 59.5\n",
+            "step 4000, dt 4.58s, train_loss 1.345e-04, test_loss 3.661e+00, train_acc 100.0, test_acc 59.7\n",
+            "step 5000, dt 4.22s, train_loss 6.639e-05, test_loss 3.917e+00, train_acc 100.0, test_acc 59.6\n",
+            "step 6000, dt 2.51s, train_loss 3.482e-05, test_loss 4.155e+00, train_acc 100.0, test_acc 59.4\n",
+            "\n",
+            "############  Trial 1  ############\n",
+            "step 1000, dt 2.51s, train_loss 2.273e-03, test_loss 1.851e+00, train_acc 100.0, test_acc 63.4\n",
+            "step 2000, dt 2.13s, train_loss 6.219e-04, test_loss 2.101e+00, train_acc 100.0, test_acc 63.3\n",
+            "step 3000, dt 2.10s, train_loss 2.555e-04, test_loss 2.273e+00, train_acc 100.0, test_acc 62.9\n",
+            "step 4000, dt 2.10s, train_loss 1.230e-04, test_loss 2.419e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 5000, dt 2.15s, train_loss 6.432e-05, test_loss 2.552e+00, train_acc 100.0, test_acc 63.3\n",
+            "step 6000, dt 2.52s, train_loss 3.497e-05, test_loss 2.675e+00, train_acc 100.0, test_acc 62.9\n",
+            "step 1000, dt 2.50s, train_loss 5.340e-03, test_loss 2.263e+00, train_acc 100.0, test_acc 64.0\n",
+            "step 2000, dt 2.08s, train_loss 9.568e-04, test_loss 2.852e+00, train_acc 100.0, test_acc 64.1\n",
+            "step 3000, dt 2.06s, train_loss 3.413e-04, test_loss 3.234e+00, train_acc 100.0, test_acc 63.1\n",
+            "step 4000, dt 2.06s, train_loss 1.518e-04, test_loss 3.538e+00, train_acc 100.0, test_acc 63.2\n",
+            "step 5000, dt 2.14s, train_loss 7.474e-05, test_loss 3.811e+00, train_acc 100.0, test_acc 62.8\n",
+            "step 6000, dt 2.57s, train_loss 3.874e-05, test_loss 4.062e+00, train_acc 100.0, test_acc 62.4\n",
+            "step 1000, dt 2.42s, train_loss 4.071e-03, test_loss 2.451e+00, train_acc 100.0, test_acc 58.9\n",
+            "step 2000, dt 2.11s, train_loss 8.358e-04, test_loss 2.953e+00, train_acc 100.0, test_acc 59.0\n",
+            "step 3000, dt 2.09s, train_loss 3.024e-04, test_loss 3.289e+00, train_acc 100.0, test_acc 58.4\n",
+            "step 4000, dt 2.11s, train_loss 1.355e-04, test_loss 3.558e+00, train_acc 100.0, test_acc 58.5\n",
+            "step 5000, dt 2.10s, train_loss 6.709e-05, test_loss 3.789e+00, train_acc 100.0, test_acc 58.6\n",
+            "step 6000, dt 2.62s, train_loss 3.518e-05, test_loss 4.005e+00, train_acc 100.0, test_acc 59.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "results_chunks = {'dense': [], 'lott': [], 'rand': []}\n",
+        "for t in range(len(trials['rand_stats'])):\n",
+        "  print(\"\\n############  Trial {}  ############\".format(t))\n",
+        "  set_seed(model_args.seed + t)\n",
+        "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
+        "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
+        "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
+        "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
+        "\n",
+        "  rand_model = copy.deepcopy(dense_model)\n",
+        "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
+        "\n",
+        "  lott_model = copy.deepcopy(dense_model)\n",
+        "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
+        "\n",
+        "  dense = train_model(data_chunks, dense_model, model_args) ; results_chunks['dense'].append(dense)\n",
+        "  lott = train_model(data_chunks, lott_model, model_args)   ; results_chunks['lott'].append(lott)\n",
+        "  rand = train_model(data_chunks, rand_model, model_args)   ; results_chunks['rand'].append(rand)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "XCyIMdQaYxPF",
+        "outputId": "a851f3fa-3238-4b5f-84dd-9eafc2896fb3"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 975x390 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure(figsize=(7.5, 3), dpi=130)\n",
+        "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
+        "\n",
+        "plt.subplot(1,2,1)\n",
+        "plt.title('LT on spatially shuffled examples')\n",
+        "y, y_err = average_over_results(results_shuff, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_shuff, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_shuff, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
+        "\n",
+        "plt.subplot(1,2,2)\n",
+        "plt.title('LT on spatially chunked examples')\n",
+        "y, y_err = average_over_results(results_chunks, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_chunks, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_chunks, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
+        "\n",
+        "\n",
+        "plt.show()\n",
+        "fig.savefig(project_dir + 'figures/lottery_asymptotes_chunks.png')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IxL3o0QpY1aB"
+      },
+      "source": [
+        "## Rebuild models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "id": "T0ndBUSlYGSX"
+      },
+      "outputs": [],
+      "source": [
+        "set_seed(model_args.seed)\n",
+        "dense_model = SparseMLP(model_args.input_size, model_args.output_size,\\\n",
+        "                        hidden_size=model_args.hidden_size).to(DEVICE)\n",
+        "_rand_model = copy.deepcopy(trials['rand_models'][0][retrain_step])\n",
+        "_lott_model = copy.deepcopy(trials['lott_models'][0][retrain_step])\n",
+        "\n",
+        "rand_model = copy.deepcopy(dense_model)\n",
+        "rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
+        "\n",
+        "lott_model = copy.deepcopy(dense_model)\n",
+        "lott_model.set_layer_masks(_lott_model.get_layer_masks())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "prwCrfGMmhqG"
+      },
+      "source": [
+        "## Looks like our lottery ticket has some decent spatial priors\n",
+        "One thing this implies is that the lottery ticket may have more local connectivity\n",
+        "## We can test for this by measuring the probability of nonzero parameters being adjacent to one another\n",
+        "This will tell us whether local connections are more frequent in lottery tickets compared to random tickets. Let's do this.\n",
+        "\n",
+        "### 1. Get masks indicating nonzero weights of first fully-connected layer\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "id": "e_KnW4D-ldpo"
+      },
+      "outputs": [],
+      "source": [
+        "rand_model = copy.deepcopy(dense_model)\n",
+        "rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
+        "\n",
+        "lott_model = copy.deepcopy(dense_model)\n",
+        "lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
+        "\n",
+        "w1_rand = rand_model.linear1.linear.weight.cpu().detach().numpy()\n",
+        "w1_lott = lott_model.linear1.linear.weight.cpu().detach().numpy()\n",
+        "\n",
+        "w1_rand_nonzero = (w1_rand!=0).astype(np.float32)\n",
+        "w1_lott_nonzero = (w1_lott!=0).astype(np.float32)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wimJAObnPgmh"
+      },
+      "source": [
+        "### 2. Count the number of times that nonzero parameters are adjacent to one another"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "qFySsiEPHxgl"
+      },
+      "outputs": [],
+      "source": [
+        "w1_rand_2adjacent, w1_lott_2adjacent = [], []\n",
+        "w1_rand_3adjacent, w1_lott_3adjacent = [], []\n",
+        "w1_rand_4adjacent, w1_lott_4adjacent = [], []\n",
+        "input_dim = w1_rand.shape[1] * 1.\n",
+        "for k in range(w1_rand.shape[0]):\n",
+        "  w1_rand_2adjacent += [np.sum(w1_rand_nonzero[k,:-1] * w1_rand_nonzero[k,1:]) / input_dim]\n",
+        "  w1_lott_2adjacent += [np.sum(w1_lott_nonzero[k,:-1] * w1_lott_nonzero[k,1:]) / input_dim]\n",
+        "\n",
+        "  w1_rand_3adjacent += [np.sum(w1_rand_nonzero[k,:-2] * w1_rand_nonzero[k,1:-1] * w1_rand_nonzero[k,2:]) / input_dim]\n",
+        "  w1_lott_3adjacent += [np.sum(w1_lott_nonzero[k,:-2] * w1_lott_nonzero[k,1:-1] * w1_lott_nonzero[k,2:]) / input_dim]\n",
+        "\n",
+        "  w1_rand_4adjacent += [np.sum(w1_rand_nonzero[k,:-3] * w1_rand_nonzero[k,1:-2] * \\\n",
+        "                               w1_rand_nonzero[k,2:-1] * w1_rand_nonzero[k,3:]) / input_dim]\n",
+        "  w1_lott_4adjacent += [np.sum(w1_lott_nonzero[k,:-3] * w1_lott_nonzero[k,1:-2] * \\\n",
+        "                              w1_lott_nonzero[k,2:-1] * w1_rand_nonzero[k,3:]) / input_dim]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6v3g661oPmXk"
+      },
+      "source": [
+        "### 3. Calculate basic statistics for these adjacencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "id": "C2Q4VavTJ2vY"
+      },
+      "outputs": [],
+      "source": [
+        "ticket_names = ['random', 'lottery']\n",
+        "sqrtN = np.sqrt(len(w1_rand_2adjacent))\n",
+        "\n",
+        "p_2adjacent = [np.mean(w1_rand_2adjacent), np.mean(w1_lott_2adjacent)]\n",
+        "p_2adjacent_err = [np.std(w1_rand_2adjacent)/sqrtN, np.std(w1_lott_2adjacent)/sqrtN]\n",
+        "\n",
+        "p_3adjacent = [np.mean(w1_rand_3adjacent), np.mean(w1_lott_3adjacent)]\n",
+        "p_3adjacent_err = [np.std(w1_rand_3adjacent)/sqrtN, np.std(w1_lott_3adjacent)/sqrtN]\n",
+        "\n",
+        "p_4adjacent = [np.mean(w1_rand_4adjacent), np.mean(w1_lott_4adjacent)]\n",
+        "p_4adjacent_err = [np.std(w1_rand_4adjacent)/sqrtN, np.std(w1_lott_4adjacent)/sqrtN]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3tjV5fJVPrLF"
+      },
+      "source": [
+        "### 4. Plot the empirical statistics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 328
+        },
+        "id": "QAsv0pzCdG7L",
+        "outputId": "88f425f0-07ed-466b-9232-12c1765a9797"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 850x300 with 3 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure(figsize=[8.5,3], dpi=100)\n",
+        "\n",
+        "ax = plt.subplot(1,3,1)\n",
+        "x = np.arange(len(ticket_names))\n",
+        "ax.set_xticks(x)\n",
+        "ax.set_xticklabels(ticket_names)\n",
+        "barlist = plt.bar(x, p_2adjacent, yerr=p_2adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\n",
+        "barlist[0].set_color('red')\n",
+        "plt.title(\"p(two adjacent weights)\")\n",
+        "\n",
+        "ax = plt.subplot(1,3,2)\n",
+        "x = np.arange(len(ticket_names))\n",
+        "ax.set_xticks(x)\n",
+        "ax.set_xticklabels(ticket_names)\n",
+        "barlist = plt.bar(x, p_3adjacent, yerr=p_3adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\n",
+        "barlist[0].set_color('red')\n",
+        "plt.title(\"p(three adjacent weights)\")\n",
+        "\n",
+        "ax = plt.subplot(1,3,3)\n",
+        "x = np.arange(len(ticket_names))\n",
+        "ax.set_xticks(x)\n",
+        "ax.set_xticklabels(ticket_names)\n",
+        "barlist = plt.bar(x, p_4adjacent, yerr=p_4adjacent_err, width=0.8, alpha=0.5, ecolor='black', capsize=10)\n",
+        "barlist[0].set_color('red')\n",
+        "plt.title(\"p(four adjacent weights)\")\n",
+        "\n",
+        "plt.show()\n",
+        "fig.savefig(project_dir + 'figures/lottery_adjacency.png')\n",
+        "fig.savefig(project_dir + 'figures/lottery_adjacency.pdf')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pypkWV9Zkv26"
+      },
+      "source": [
+        "## One more sanity check: keep sparsity pattern but use different initialization\n",
+        "Based on what we're seeing above, the sparsity pattern itself represents some sort of inductive bias for local connectivity. If this is true, then we should be able to start from a different (scratch) initialization and still see a jump in performance. Let's try it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uKVimjBFaXXs",
+        "outputId": "1e1284bf-e110-4c3d-c690-7820245cebc4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "############  Trial 0  ############\n",
+            "step 1000, dt 2.16s, train_loss 1.894e-03, test_loss 1.885e+00, train_acc 100.0, test_acc 63.7\n",
+            "step 2000, dt 2.14s, train_loss 5.030e-04, test_loss 2.173e+00, train_acc 100.0, test_acc 63.7\n",
+            "step 3000, dt 2.11s, train_loss 2.060e-04, test_loss 2.362e+00, train_acc 100.0, test_acc 63.2\n",
+            "step 4000, dt 2.12s, train_loss 9.894e-05, test_loss 2.518e+00, train_acc 100.0, test_acc 63.1\n",
+            "step 5000, dt 2.67s, train_loss 5.124e-05, test_loss 2.658e+00, train_acc 100.0, test_acc 63.0\n",
+            "step 6000, dt 2.30s, train_loss 2.772e-05, test_loss 2.792e+00, train_acc 100.0, test_acc 63.3\n",
+            "step 1000, dt 2.10s, train_loss 3.732e-03, test_loss 2.108e+00, train_acc 100.0, test_acc 64.4\n",
+            "step 2000, dt 2.12s, train_loss 6.494e-04, test_loss 2.648e+00, train_acc 100.0, test_acc 64.1\n",
+            "step 3000, dt 2.15s, train_loss 2.236e-04, test_loss 2.985e+00, train_acc 100.0, test_acc 64.0\n",
+            "step 4000, dt 2.20s, train_loss 9.737e-05, test_loss 3.251e+00, train_acc 100.0, test_acc 63.8\n",
+            "step 5000, dt 2.68s, train_loss 4.741e-05, test_loss 3.478e+00, train_acc 100.0, test_acc 64.2\n",
+            "step 6000, dt 2.26s, train_loss 2.450e-05, test_loss 3.686e+00, train_acc 100.0, test_acc 64.7\n",
+            "step 1000, dt 2.11s, train_loss 4.827e-03, test_loss 2.364e+00, train_acc 100.0, test_acc 61.4\n",
+            "step 2000, dt 2.09s, train_loss 8.880e-04, test_loss 2.919e+00, train_acc 100.0, test_acc 61.2\n",
+            "step 3000, dt 2.10s, train_loss 3.137e-04, test_loss 3.274e+00, train_acc 100.0, test_acc 61.1\n",
+            "step 4000, dt 2.20s, train_loss 1.377e-04, test_loss 3.553e+00, train_acc 100.0, test_acc 61.0\n",
+            "step 5000, dt 2.70s, train_loss 6.715e-05, test_loss 3.797e+00, train_acc 100.0, test_acc 60.8\n",
+            "step 6000, dt 2.19s, train_loss 3.493e-05, test_loss 4.020e+00, train_acc 100.0, test_acc 60.8\n",
+            "\n",
+            "############  Trial 1  ############\n",
+            "step 1000, dt 2.18s, train_loss 6.785e-04, test_loss 2.296e+00, train_acc 100.0, test_acc 63.3\n",
+            "step 2000, dt 2.10s, train_loss 1.693e-04, test_loss 2.644e+00, train_acc 100.0, test_acc 63.7\n",
+            "step 3000, dt 2.11s, train_loss 6.550e-05, test_loss 2.879e+00, train_acc 100.0, test_acc 64.3\n",
+            "step 4000, dt 2.48s, train_loss 3.064e-05, test_loss 3.076e+00, train_acc 100.0, test_acc 64.3\n",
+            "step 5000, dt 4.33s, train_loss 1.563e-05, test_loss 3.256e+00, train_acc 100.0, test_acc 64.3\n",
+            "step 6000, dt 2.43s, train_loss 8.339e-06, test_loss 3.427e+00, train_acc 100.0, test_acc 64.2\n",
+            "step 1000, dt 2.14s, train_loss 3.437e-03, test_loss 1.917e+00, train_acc 100.0, test_acc 65.9\n",
+            "step 2000, dt 2.09s, train_loss 6.429e-04, test_loss 2.340e+00, train_acc 100.0, test_acc 66.4\n",
+            "step 3000, dt 2.12s, train_loss 2.280e-04, test_loss 2.604e+00, train_acc 100.0, test_acc 66.2\n",
+            "step 4000, dt 2.08s, train_loss 1.008e-04, test_loss 2.818e+00, train_acc 100.0, test_acc 66.2\n",
+            "step 5000, dt 2.57s, train_loss 4.977e-05, test_loss 3.008e+00, train_acc 100.0, test_acc 66.5\n",
+            "step 6000, dt 2.38s, train_loss 2.579e-05, test_loss 3.185e+00, train_acc 100.0, test_acc 66.3\n",
+            "step 1000, dt 2.11s, train_loss 4.176e-03, test_loss 2.460e+00, train_acc 100.0, test_acc 60.0\n",
+            "step 2000, dt 2.08s, train_loss 8.480e-04, test_loss 2.974e+00, train_acc 100.0, test_acc 60.2\n",
+            "step 3000, dt 2.08s, train_loss 3.081e-04, test_loss 3.310e+00, train_acc 100.0, test_acc 60.5\n",
+            "step 4000, dt 2.11s, train_loss 1.384e-04, test_loss 3.581e+00, train_acc 100.0, test_acc 60.5\n",
+            "step 5000, dt 2.61s, train_loss 6.869e-05, test_loss 3.818e+00, train_acc 100.0, test_acc 60.2\n",
+            "step 6000, dt 2.33s, train_loss 3.587e-05, test_loss 4.039e+00, train_acc 100.0, test_acc 60.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "results_seed = {'dense': [], 'lott': [], 'rand': []}\n",
+        "for t in range(len(trials['rand_stats'])):\n",
+        "  print(\"\\n############  Trial {}  ############\".format(t))\n",
+        "  set_seed(model_args.seed + t + 1)\n",
+        "  dense_model = SparseMLP(model_args.input_size, model_args.output_size, \\\n",
+        "                          hidden_size=model_args.hidden_size).to(DEVICE)\n",
+        "  _rand_model = copy.deepcopy(trials['rand_models'][t][retrain_step])\n",
+        "  _lott_model = copy.deepcopy(trials['lott_models'][t][retrain_step])\n",
+        "\n",
+        "  rand_model = copy.deepcopy(dense_model)\n",
+        "  rand_model.set_layer_masks(_rand_model.get_layer_masks())\n",
+        "\n",
+        "  lott_model = copy.deepcopy(dense_model)\n",
+        "  lott_model.set_layer_masks(_lott_model.get_layer_masks())\n",
+        "\n",
+        "  dense = train_model(data, dense_model, model_args) ; results_seed['dense'].append(dense)\n",
+        "  lott = train_model(data, lott_model, model_args)   ; results_seed['lott'].append(lott)\n",
+        "  rand = train_model(data, rand_model, model_args)   ; results_seed['rand'].append(rand)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "Q6aftRykasYD",
+        "outputId": "0008766a-bfdc-47cf-a587-3bf77e01ee17"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-56-fba28d50d039>:28: UserWarning: The figure layout has changed to tight\n",
+            "  plt.tight_layout() ; plt.show()\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 975x390 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure(figsize=(7.5, 3), dpi=130)\n",
+        "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
+        "\n",
+        "plt.subplot(1,2,1)\n",
+        "plt.title('LT on baseline examples')\n",
+        "y, y_err = average_over_results(results, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
+        "\n",
+        "plt.subplot(1,2,2)\n",
+        "plt.title('LT with different initializations')\n",
+        "y, y_err = average_over_results(results_seed, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_seed, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_seed, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=3)\n",
+        "\n",
+        "plt.show()\n",
+        "fig.savefig(project_dir + 'figures/lottery_asymptotes_reinit.png')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 640
+        },
+        "id": "R1mZVHMdbG9w",
+        "outputId": "3039b71f-06bf-489d-939d-35782c382d91"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 750x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig = plt.figure(figsize=(7.5/2, 3), dpi=200)\n",
+        "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
+        "\n",
+        "plt.subplot(1,1,1)\n",
+        "plt.title('Lottery ticket analysis ({:.0f}% sparse)'.format(100*sparsity_schedule[retrain_step]))\n",
+        "y, y_err = average_over_results(results_seed, 'dense')\n",
+        "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_seed, 'rand')\n",
+        "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
+        "y, y_err = average_over_results(results, 'lott')\n",
+        "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_shuff, 'lott')\n",
+        "plt.plot(x, y, 'g-', label='lottery (shuffle spatial dim. of data)') ; plt.fill_between(x, y-y_err, y+y_err, color='g', alpha=0.15)\n",
+        "y, y_err = average_over_results(results_seed, 'lott')\n",
+        "plt.plot(x, y, 'c-', label='lottery (reinit weights, keep mask)') ; plt.fill_between(x, y-y_err, y+y_err, color='c', alpha=0.15)\n",
+        "plt.ylim(50,76)\n",
+        "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
+        "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=2, loc='lower right')\n",
+        "\n",
+        "plt.show()\n",
+        "fig.savefig(project_dir + 'figures/lottery_summary.png')\n",
+        "fig.savefig(project_dir + 'figures/lottery_summary.pdf')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "B7QpF0J213tI"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "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",
+      "version": "3.9.18"
     }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(7.5/2, 3), dpi=200)\n",
-    "x = range(0, model_args.total_steps+1, model_args.eval_every)\n",
-    "\n",
-    "plt.subplot(1,1,1)\n",
-    "plt.title('Lottery ticket analysis ({:.0f}% sparse)'.format(100*sparsity_schedule[retrain_step]))\n",
-    "y, y_err = average_over_results(results_seed, 'dense')\n",
-    "plt.plot(x, y, 'k-', label='dense') ; plt.fill_between(x, y-y_err, y+y_err, color='k', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_seed, 'rand')\n",
-    "plt.plot(x, y, 'r-', label='random') ; plt.fill_between(x, y-y_err, y+y_err, color='r', alpha=0.15)\n",
-    "y, y_err = average_over_results(results, 'lott')\n",
-    "plt.plot(x, y, 'b-', label='lottery') ; plt.fill_between(x, y-y_err, y+y_err, color='b', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_shuff, 'lott')\n",
-    "plt.plot(x, y, 'g-', label='lottery (shuffle spatial dim. of data)') ; plt.fill_between(x, y-y_err, y+y_err, color='g', alpha=0.15)\n",
-    "y, y_err = average_over_results(results_seed, 'lott')\n",
-    "plt.plot(x, y, 'c-', label='lottery (reinit weights, keep mask)') ; plt.fill_between(x, y-y_err, y+y_err, color='c', alpha=0.15)\n",
-    "plt.ylim(50,76)\n",
-    "plt.ylabel('Test accuracy') ; plt.xlabel(\"Train step\")\n",
-    "plt.xticks(fontsize=9) ; plt.yticks(fontsize=9) ; plt.legend(fontsize=6, ncol=2, loc='lower right')\n",
-    "\n",
-    "plt.tight_layout() ; plt.show()\n",
-    "fig.savefig(project_dir + 'lottery_summary.png')\n",
-    "fig.savefig(project_dir + 'lottery_summary.pdf')"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "machine_shape": "hm",
-   "provenance": []
-  },
-  "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",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file