diff --git a/notebooks/TacotronPlayGround.ipynb b/notebooks/TacotronPlayGround.ipynb index 31aa7ff..2f495c9 100644 --- a/notebooks/TacotronPlayGround.ipynb +++ b/notebooks/TacotronPlayGround.ipynb @@ -31,6 +31,8 @@ "import librosa\n", "import librosa.display\n", "\n", + "from torchviz import make_dot, make_dot_from_trace\n", + "\n", "from TTS.models.tacotron import Tacotron \n", "from TTS.layers import *\n", "from TTS.utils.data import *\n", @@ -80,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -91,16 +93,262 @@ ] }, { - "ename": "KeyError", - "evalue": "'unexpected key \"module.embedding.weight\" in state_dict'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;31m# load the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'model'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_cuda\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[0;34m(self, state_dict, strict)\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 489\u001b[0m raise KeyError('unexpected key \"{}\" in state_dict'\n\u001b[0;32m--> 490\u001b[0;31m .format(name))\n\u001b[0m\u001b[1;32m 491\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[0mmissing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mown_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'unexpected key \"module.embedding.weight\" in state_dict'" - ] + "data": { + "text/plain": [ + "Tacotron(\n", + " (embedding): Embedding(149, 256)\n", + " (encoder): Encoder(\n", + " (prenet): Prenet(\n", + " (layers): ModuleList(\n", + " (0): Linear(in_features=256, out_features=256)\n", + " (1): Linear(in_features=256, out_features=128)\n", + " )\n", + " (relu): ReLU()\n", + " (dropout): Dropout(p=0.5)\n", + " )\n", + " (cbhg): CBHG(\n", + " (relu): ReLU()\n", + " (conv1d_banks): ModuleList(\n", + " (0): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(1,), stride=(1,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (1): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(2,), stride=(1,), padding=(1,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (2): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (3): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (4): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (5): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (6): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (7): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (8): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (9): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(10,), stride=(1,), padding=(5,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (10): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(11,), stride=(1,), padding=(5,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (11): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(12,), stride=(1,), padding=(6,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (12): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(13,), stride=(1,), padding=(6,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (13): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(14,), stride=(1,), padding=(7,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (14): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(15,), stride=(1,), padding=(7,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (15): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(16,), stride=(1,), padding=(8,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " )\n", + " (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n", + " (conv1d_projections): ModuleList(\n", + " (0): BatchNormConv1d(\n", + " (conv1d): Conv1d (2048, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (1): BatchNormConv1d(\n", + " (conv1d): Conv1d (128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n", + " (bn): BatchNorm1d(128, eps=0.001, momentum=0.99, affine=True)\n", + " )\n", + " )\n", + " (pre_highway): Linear(in_features=128, out_features=128)\n", + " (highways): ModuleList(\n", + " (0): Highway(\n", + " (H): Linear(in_features=128, out_features=128)\n", + " (T): Linear(in_features=128, out_features=128)\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " (1): Highway(\n", + " (H): Linear(in_features=128, out_features=128)\n", + " (T): Linear(in_features=128, out_features=128)\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " (2): Highway(\n", + " (H): Linear(in_features=128, out_features=128)\n", + " (T): Linear(in_features=128, out_features=128)\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " (3): Highway(\n", + " (H): Linear(in_features=128, out_features=128)\n", + " (T): Linear(in_features=128, out_features=128)\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " )\n", + " (gru): GRU(128, 128, batch_first=True, bidirectional=True)\n", + " )\n", + " )\n", + " (decoder): Decoder(\n", + " (input_layer): Linear(in_features=256, out_features=256)\n", + " (prenet): Prenet(\n", + " (layers): ModuleList(\n", + " (0): Linear(in_features=400, out_features=256)\n", + " (1): Linear(in_features=256, out_features=128)\n", + " )\n", + " (relu): ReLU()\n", + " (dropout): Dropout(p=0.5)\n", + " )\n", + " (attention_rnn): AttentionWrapper(\n", + " (rnn_cell): GRUCell(384, 256)\n", + " (alignment_model): BahdanauAttention(\n", + " (query_layer): Linear(in_features=256, out_features=256)\n", + " (tanh): Tanh()\n", + " (v): Linear(in_features=256, out_features=1)\n", + " )\n", + " )\n", + " (project_to_decoder_in): Linear(in_features=512, out_features=256)\n", + " (decoder_rnns): ModuleList(\n", + " (0): GRUCell(256, 256)\n", + " (1): GRUCell(256, 256)\n", + " )\n", + " (proj_to_mel): Linear(in_features=256, out_features=400)\n", + " )\n", + " (postnet): CBHG(\n", + " (relu): ReLU()\n", + " (conv1d_banks): ModuleList(\n", + " (0): BatchNormConv1d(\n", + " (conv1d): Conv1d (80, 80, kernel_size=(1,), stride=(1,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (1): BatchNormConv1d(\n", + " (conv1d): Conv1d (80, 80, kernel_size=(2,), stride=(1,), padding=(1,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (2): BatchNormConv1d(\n", + " (conv1d): Conv1d (80, 80, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (3): BatchNormConv1d(\n", + " (conv1d): Conv1d (80, 80, kernel_size=(4,), stride=(1,), padding=(2,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (4): BatchNormConv1d(\n", + " (conv1d): Conv1d (80, 80, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (5): BatchNormConv1d(\n", + " (conv1d): Conv1d (80, 80, kernel_size=(6,), stride=(1,), padding=(3,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (6): BatchNormConv1d(\n", + " (conv1d): Conv1d (80, 80, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (7): BatchNormConv1d(\n", + " (conv1d): Conv1d (80, 80, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " )\n", + " (max_pool1d): MaxPool1d(kernel_size=2, stride=1, padding=1, dilation=1, ceil_mode=False)\n", + " (conv1d_projections): ModuleList(\n", + " (0): BatchNormConv1d(\n", + " (conv1d): Conv1d (640, 256, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n", + " (bn): BatchNorm1d(256, eps=0.001, momentum=0.99, affine=True)\n", + " (activation): ReLU()\n", + " )\n", + " (1): BatchNormConv1d(\n", + " (conv1d): Conv1d (256, 80, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n", + " (bn): BatchNorm1d(80, eps=0.001, momentum=0.99, affine=True)\n", + " )\n", + " )\n", + " (pre_highway): Linear(in_features=80, out_features=80)\n", + " (highways): ModuleList(\n", + " (0): Highway(\n", + " (H): Linear(in_features=80, out_features=80)\n", + " (T): Linear(in_features=80, out_features=80)\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " (1): Highway(\n", + " (H): Linear(in_features=80, out_features=80)\n", + " (T): Linear(in_features=80, out_features=80)\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " (2): Highway(\n", + " (H): Linear(in_features=80, out_features=80)\n", + " (T): Linear(in_features=80, out_features=80)\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " (3): Highway(\n", + " (H): Linear(in_features=80, out_features=80)\n", + " (T): Linear(in_features=80, out_features=80)\n", + " (relu): ReLU()\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " )\n", + " (gru): GRU(80, 80, batch_first=True, bidirectional=True)\n", + " )\n", + " (last_linear): Linear(in_features=160, out_features=1025)\n", + ")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -134,25671 +382,6 @@ "model.eval()" ] }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('module.embedding.weight', \n", - " -3.5297e-02 -3.2110e-02 1.5772e-02 ... 1.5752e-03 8.5511e-02 -2.4540e-03\n", - " 2.2812e-02 4.3733e-02 -8.5045e-02 ... 3.9608e-02 5.9179e-02 2.2359e-02\n", - " -5.6533e-02 2.8566e-01 -5.8419e-01 ... -1.9973e-01 3.0205e-01 9.3615e-02\n", - " ... ⋱ ... \n", - " -2.3212e-01 1.7337e-01 -1.8613e-01 ... -2.9493e-02 -2.2340e-03 8.0515e-03\n", - " -3.9615e-01 1.3994e-01 -4.2236e-02 ... 2.7774e-01 -2.1261e-02 4.8095e-01\n", - " 1.0893e-01 3.4349e-01 6.2014e-01 ... 4.3346e-01 -2.2796e-01 -2.4084e-01\n", - " [torch.FloatTensor of size 149x256]),\n", - " ('module.encoder.prenet.layers.0.weight', \n", - " -1.0014e-01 2.9802e-02 -2.4292e-01 ... -1.8605e-01 -7.1386e-02 -2.3602e-02\n", - " 3.2112e-01 -1.7793e-02 -4.7806e-02 ... -6.9432e-02 -2.0528e-02 -9.1605e-02\n", - " 1.8654e-01 6.4952e-02 9.3887e-02 ... -1.0719e-02 3.4214e-02 6.8752e-02\n", - " ... ⋱ ... \n", - " 6.9887e-03 1.6763e-01 1.2888e-03 ... 7.4507e-03 -6.6574e-02 -6.5742e-04\n", - " 2.4360e-01 3.5927e-02 3.5399e-02 ... 1.2186e-01 -1.2406e-02 -1.6318e-01\n", - " -1.1759e-02 -8.7780e-03 -1.9734e-01 ... -5.8825e-02 6.7379e-02 -1.0530e-02\n", - " [torch.FloatTensor of size 256x256]),\n", - " ('module.encoder.prenet.layers.0.bias', \n", - " -0.0704\n", - " -0.2325\n", - " -0.1103\n", - " -0.0531\n", - " -0.1821\n", - " -0.0734\n", - " -0.0968\n", - " -0.1854\n", - " -0.0928\n", - " -0.2251\n", - " -0.1243\n", - " -0.0883\n", - " -0.0765\n", - " -0.0324\n", - " -0.1123\n", - " -0.2250\n", - " -0.0198\n", - " -0.2890\n", - " -0.0232\n", - " -0.0220\n", - " 0.0422\n", - " -0.0454\n", - " 0.0288\n", - " -0.1473\n", - " -0.0470\n", - " -0.1018\n", - " -0.0934\n", - " -0.1430\n", - " -0.0440\n", - " -0.1701\n", - " -0.1629\n", - " -0.1001\n", - " 0.0394\n", - " -0.0823\n", - " -0.0032\n", - " -0.0437\n", - " -0.1353\n", - " -0.3906\n", - " -0.1264\n", - " -0.0330\n", - " 0.0433\n", - " -0.1669\n", - " -0.1263\n", - " -0.0900\n", - " -0.1593\n", - " -0.1618\n", - " -0.1133\n", - " -0.0787\n", - " -0.1686\n", - " -0.0694\n", - " -0.0861\n", - " 0.0319\n", - " -0.0489\n", - " -0.0673\n", - " -0.2221\n", - " -0.1320\n", - " -0.0541\n", - " -0.4774\n", - " -0.0294\n", - " -0.0766\n", - " 0.0717\n", - " -0.1841\n", - " -0.0333\n", - " -0.1523\n", - " -0.1103\n", - " -0.0361\n", - " -0.0648\n", - " -0.0957\n", - " -0.1330\n", - " -0.1408\n", - " 0.0165\n", - " -0.1503\n", - " -0.1603\n", - " -0.0686\n", - " -0.0476\n", - " -0.2059\n", - " -0.1780\n", - " -0.0734\n", - " -0.1016\n", - " -0.0711\n", - " -0.1130\n", - " -0.2282\n", - " -0.0068\n", - " -0.1549\n", - " -0.1033\n", - " 0.0493\n", - " -0.2593\n", - " -0.0633\n", - " -0.2569\n", - " -0.1936\n", - " -0.3189\n", - " -0.0126\n", - " -0.2992\n", - " -0.0018\n", - " -0.0711\n", - " -0.0621\n", - " -0.2238\n", - " -0.1572\n", - " -0.0731\n", - " -0.0806\n", - " -0.0980\n", - " 0.0449\n", - " -0.1195\n", - " -0.1380\n", - " -0.1631\n", - " -0.0250\n", - " -0.1572\n", - " -0.0357\n", - " -0.1325\n", - " -0.0770\n", - " -0.2003\n", - " 0.0232\n", - " -0.0048\n", - " -0.1999\n", - " 0.0309\n", - " -0.1343\n", - " -0.2083\n", - " -0.1762\n", - " -0.1273\n", - " -0.1509\n", - " -0.1643\n", - " 0.0228\n", - " -0.1819\n", - " -0.0917\n", - " 0.0122\n", - " -0.0810\n", - " -0.2499\n", - " -0.1043\n", - " -0.0875\n", - " -0.0225\n", - " 0.0012\n", - " -0.1237\n", - " -0.1153\n", - " -0.0151\n", - " 0.0029\n", - " -0.0437\n", - " -0.3011\n", - " 0.0030\n", - " -0.0078\n", - " -0.0594\n", - " -0.0669\n", - " -0.2825\n", - " -0.0541\n", - " -0.0008\n", - " -0.0352\n", - " -0.1139\n", - " -0.0350\n", - " -0.2285\n", - " -0.2661\n", - " -0.0469\n", - " -0.0023\n", - " -0.1536\n", - " -0.1614\n", - " 0.0145\n", - " -0.1819\n", - " -0.0677\n", - " -0.0682\n", - " -0.0521\n", - " -0.0962\n", - " -0.0995\n", - " -0.0487\n", - " -0.0144\n", - " -0.1920\n", - " -0.1974\n", - " -0.0702\n", - " -0.0893\n", - " -0.0509\n", - " -0.0741\n", - " -0.1373\n", - " 0.0637\n", - " -0.2082\n", - " -0.1559\n", - " -0.2094\n", - " -0.2431\n", - " -0.1071\n", - " -0.0244\n", - " -0.1300\n", - " -0.1789\n", - " 0.0219\n", - " -0.2220\n", - " -0.0408\n", - " -0.2379\n", - " -0.2404\n", - " -0.0639\n", - " -0.0447\n", - " -0.1562\n", - " -0.0362\n", - " -0.2018\n", - " -0.0858\n", - " -0.0118\n", - " -0.0631\n", - " -0.0660\n", - " -0.0260\n", - " -0.1357\n", - " -0.3616\n", - " -0.4833\n", - " -0.0934\n", - " -0.0108\n", - " -0.0121\n", - " -0.0484\n", - " -0.2504\n", - " -0.1337\n", - " -0.1002\n", - " -0.1239\n", - " -0.0047\n", - " 0.0031\n", - " -0.1129\n", - " 0.0301\n", - " 0.0399\n", - " -0.0143\n", - " -0.1699\n", - " -0.0369\n", - " -0.0570\n", - " -0.1132\n", - " -0.0772\n", - " -0.0208\n", - " -0.0780\n", - " -0.0719\n", - " -0.0142\n", - " 0.0278\n", - " -0.0418\n", - " -0.0729\n", - " -0.0724\n", - " -0.0749\n", - " -0.0849\n", - " -0.0984\n", - " -0.1697\n", - " -0.0529\n", - " -0.3286\n", - " -0.0006\n", - " 0.0464\n", - " -0.0439\n", - " -0.0135\n", - " -0.1863\n", - " -0.0453\n", - " -0.1910\n", - " -0.1649\n", - " -0.1927\n", - " -0.1597\n", - " -0.0844\n", - " -0.1204\n", - " -0.0122\n", - " -0.2126\n", - " -0.0206\n", - " -0.2664\n", - " -0.0634\n", - " -0.3220\n", - " -0.0365\n", - " -0.0187\n", - " -0.1900\n", - " -0.2600\n", - " -0.0692\n", - " -0.1204\n", - " -0.3588\n", - " -0.0812\n", - " -0.0753\n", - " [torch.FloatTensor of size 256]),\n", - " ('module.encoder.prenet.layers.1.weight', \n", - " -1.6126e-01 2.8914e-02 -1.0028e-01 ... -3.5420e-02 -1.3256e-01 -3.0317e-03\n", - " -8.8005e-01 -5.2990e-01 -1.5287e-01 ... 2.2524e-02 1.0137e-01 -1.0721e-01\n", - " -1.4194e+00 -4.4239e-01 -1.8868e-02 ... -2.4959e-01 -8.1351e-01 -1.4342e+00\n", - " ... ⋱ ... \n", - " -9.7612e-01 -5.1730e-01 -7.7711e-01 ... -5.1752e-02 -6.2894e-01 -8.6156e-01\n", - " -2.0189e-01 -5.1400e-01 -4.7799e-01 ... -2.3133e-01 1.4875e-01 -3.8852e-01\n", - " -3.5884e-02 -1.0765e-01 -2.2468e-01 ... -1.0134e-01 -8.1144e-02 2.8081e-02\n", - " [torch.FloatTensor of size 128x256]),\n", - " ('module.encoder.prenet.layers.1.bias', \n", - " -0.3585\n", - " 1.6995\n", - " 2.3166\n", - " 1.7364\n", - " 0.4701\n", - " 1.5738\n", - " 2.1307\n", - " 1.2531\n", - " 1.6245\n", - " 2.1560\n", - " 0.9820\n", - " 1.8875\n", - " 2.4729\n", - " 1.0907\n", - " 2.2598\n", - " 1.9542\n", - " 1.9548\n", - " 1.5077\n", - " 1.0076\n", - " 2.6810\n", - " 2.4430\n", - " 1.9737\n", - " -0.0700\n", - " 1.8026\n", - " -0.4326\n", - " 1.5797\n", - " 1.2291\n", - " 1.4399\n", - " 1.8057\n", - " 2.7138\n", - " 0.3383\n", - " 2.0052\n", - " 1.9785\n", - " 2.7670\n", - " 2.1501\n", - " 0.8639\n", - " 2.3999\n", - " 2.3451\n", - " 1.7723\n", - " 1.1212\n", - " -0.2053\n", - " 1.8817\n", - " 2.6431\n", - " 1.9419\n", - " -0.2995\n", - " 1.9662\n", - " 1.1049\n", - " 1.8972\n", - " 1.4069\n", - " 1.3095\n", - " 2.8848\n", - " 2.2875\n", - " 2.4240\n", - " 1.6281\n", - " 2.4198\n", - " 1.2517\n", - " 1.8351\n", - " 2.3133\n", - " 1.7785\n", - " -0.3808\n", - " 2.4419\n", - " 2.2181\n", - " 2.6447\n", - " 2.2217\n", - " 1.2486\n", - " 0.3046\n", - " 0.6007\n", - " 0.9984\n", - " 2.0237\n", - " 2.5613\n", - " 0.9227\n", - " 0.3079\n", - " 2.2933\n", - " 1.0479\n", - " 1.7179\n", - " 1.8413\n", - " 2.2759\n", - " 1.6229\n", - " 0.4718\n", - " 1.7324\n", - " 2.0497\n", - " 3.0045\n", - " 0.8048\n", - " 1.7378\n", - " 2.7005\n", - " 1.3603\n", - " 1.8880\n", - " 0.8904\n", - " 0.4747\n", - " 2.2508\n", - " 2.0709\n", - " 1.5177\n", - " -0.3586\n", - " 0.9661\n", - " 1.7081\n", - " 2.5825\n", - " 1.9696\n", - " 2.3685\n", - " 2.0387\n", - " 1.4500\n", - " 2.4367\n", - " 0.4283\n", - " 1.3150\n", - " 2.2226\n", - " 2.4524\n", - " 2.1837\n", - " 0.7118\n", - " 2.1082\n", - " 2.2519\n", - " 1.7399\n", - " 1.9642\n", - " 1.2885\n", - " 2.7799\n", - " -0.4589\n", - " 1.0244\n", - " 0.5252\n", - " 1.9328\n", - " 1.9372\n", - " 1.6940\n", - " 0.9164\n", - " 2.0596\n", - " 0.9265\n", - " 1.7577\n", - " 2.0141\n", - " 1.5980\n", - " 2.5939\n", - " 0.3658\n", - " -0.3199\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.0.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -6.6251e-02\n", - " -6.8183e-01\n", - " -8.8998e-01\n", - " ⋮ \n", - " -7.3685e-02\n", - " -1.7196e-01\n", - " -6.8184e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " -3.6669e-02\n", - " 1.0216e-01\n", - " 2.9180e-01\n", - " ⋮ \n", - " -4.3819e-01\n", - " 1.1236e-02\n", - " -8.6161e-02\n", - " \n", - " ( 2 ,.,.) = \n", - " 3.1580e-02\n", - " -4.6436e-02\n", - " -3.1578e-02\n", - " ⋮ \n", - " -2.9636e-01\n", - " -3.1398e-01\n", - " -9.8746e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -4.8537e-02\n", - " -9.7628e-01\n", - " -4.7115e-02\n", - " ⋮ \n", - " 3.7837e-02\n", - " -2.5917e-01\n", - " -4.0414e-01\n", - " \n", - " (126,.,.) = \n", - " -1.0193e-01\n", - " 4.4068e-01\n", - " -5.4034e-01\n", - " ⋮ \n", - " -1.7104e-01\n", - " -2.3850e-01\n", - " -1.6505e-01\n", - " \n", - " (127,.,.) = \n", - " 2.5536e-02\n", - " -9.0031e-01\n", - " -7.3607e-01\n", - " ⋮ \n", - " -3.3190e-01\n", - " -7.5025e-03\n", - " -1.0425e-01\n", - " [torch.FloatTensor of size 128x128x1]),\n", - " ('module.encoder.cbhg.conv1d_banks.0.bn.weight', \n", - " 0.6591\n", - " -1.2572\n", - " 0.8739\n", - " 0.0423\n", - " -0.8999\n", - " 0.4206\n", - " 1.2460\n", - " -1.7693\n", - " 1.1016\n", - " 0.3619\n", - " -1.5488\n", - " -0.4151\n", - " 0.0202\n", - " -1.1553\n", - " 0.6241\n", - " -0.7603\n", - " 0.1831\n", - " -1.3233\n", - " -1.1399\n", - " 0.2576\n", - " 0.3289\n", - " 0.1837\n", - " -0.3407\n", - " 0.3372\n", - " -0.7382\n", - " 0.3482\n", - " 0.3916\n", - " 0.6138\n", - " -0.0488\n", - " -1.7011\n", - " 0.5796\n", - " 0.2722\n", - " -0.4631\n", - " 0.0869\n", - " -1.8734\n", - " 0.7504\n", - " -0.4008\n", - " -0.0150\n", - " -1.9485\n", - " -1.5207\n", - " 0.1789\n", - " -1.8307\n", - " 0.4566\n", - " 0.4261\n", - " 0.8417\n", - " -0.2912\n", - " 0.0864\n", - " 0.0459\n", - " 0.3181\n", - " -0.5764\n", - " -0.1530\n", - " 0.0720\n", - " 0.4791\n", - " 0.1626\n", - " -1.7365\n", - " 0.9922\n", - " 0.2440\n", - " 0.3228\n", - " 0.2166\n", - " 0.2625\n", - " -1.8546\n", - " -2.8205\n", - " 0.4102\n", - " 0.2564\n", - " 0.8064\n", - " -1.9707\n", - " -1.5620\n", - " 0.2139\n", - " 1.7856\n", - " 0.1005\n", - " 0.6677\n", - " 1.7832\n", - " 0.2558\n", - " 0.4171\n", - " -1.5547\n", - " -0.3117\n", - " 0.2358\n", - " -0.7742\n", - " 0.1305\n", - " -0.0683\n", - " -1.2802\n", - " -1.3206\n", - " -0.0826\n", - " 0.5054\n", - " -1.3429\n", - " -0.8753\n", - " -1.3754\n", - " -0.0851\n", - " 0.4566\n", - " 1.2336\n", - " 0.3783\n", - " -2.4549\n", - " -1.1048\n", - " 1.9755\n", - " 0.7881\n", - " -0.1720\n", - " 0.5107\n", - " -1.2934\n", - " -1.0566\n", - " -1.4817\n", - " -0.9210\n", - " -0.1149\n", - " 0.8603\n", - " 1.7239\n", - " -0.0900\n", - " -0.0847\n", - " 0.0394\n", - " 0.5084\n", - " 0.2656\n", - " 0.4883\n", - " 0.6596\n", - " -1.0905\n", - " -0.5039\n", - " -2.9462\n", - " 0.0330\n", - " -0.0026\n", - " -0.0639\n", - " 0.7629\n", - " 0.7044\n", - " 0.5001\n", - " 0.0064\n", - " -0.9646\n", - " 0.0032\n", - " 0.0372\n", - " 1.2075\n", - " 0.9911\n", - " 0.2357\n", - " 0.8908\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.0.bn.bias', \n", - " 0.0496\n", - " -0.3761\n", - " 0.1720\n", - " 0.0088\n", - " -0.1417\n", - " 0.0287\n", - " 0.1284\n", - " -0.6863\n", - " 0.1531\n", - " 0.1071\n", - " -0.3297\n", - " -0.1519\n", - " -0.0036\n", - " -0.3632\n", - " 0.1521\n", - " -0.3017\n", - " 0.0584\n", - " -0.2840\n", - " -0.2838\n", - " 0.0796\n", - " 0.1045\n", - " 0.0586\n", - " -0.1395\n", - " 0.0719\n", - " -0.2271\n", - " 0.0499\n", - " 0.0884\n", - " 0.2548\n", - " -0.0211\n", - " -0.2821\n", - " 0.1719\n", - " 0.0869\n", - " -0.1280\n", - " 0.0238\n", - " -0.3729\n", - " 0.2631\n", - " -0.1296\n", - " -0.0357\n", - " -0.2797\n", - " -0.4018\n", - " 0.0218\n", - " -0.4129\n", - " 0.1654\n", - " 0.0324\n", - " 0.1249\n", - " -0.0757\n", - " -0.0278\n", - " 0.0114\n", - " 0.1644\n", - " -0.0654\n", - " -0.0445\n", - " 0.0206\n", - " 0.2573\n", - " -0.0784\n", - " -0.4035\n", - " 0.2922\n", - " -0.0144\n", - " 0.0615\n", - " 0.0280\n", - " 0.0292\n", - " -0.2697\n", - " -0.3457\n", - " 0.2012\n", - " -0.0295\n", - " 0.2810\n", - " -0.3361\n", - " -0.2685\n", - " 0.0690\n", - " 0.2289\n", - " 0.0216\n", - " 0.1789\n", - " 0.1590\n", - " 0.0330\n", - " 0.1722\n", - " -0.3000\n", - " -0.0665\n", - " 0.0471\n", - " -0.2774\n", - " 0.0282\n", - " -0.0082\n", - " -0.4068\n", - " -0.3256\n", - " -0.0221\n", - " 0.1838\n", - " -0.3682\n", - " -0.2745\n", - " -0.3362\n", - " -0.0176\n", - " 0.0966\n", - " 0.0550\n", - " 0.0761\n", - " -0.2648\n", - " -0.4274\n", - " 0.2190\n", - " 0.3803\n", - " -0.0387\n", - " 0.0363\n", - " -0.3328\n", - " -0.2526\n", - " -0.3576\n", - " -0.4342\n", - " -0.0747\n", - " 0.1348\n", - " 0.2771\n", - " 0.0020\n", - " -0.0246\n", - " -0.0045\n", - " 0.1549\n", - " 0.0506\n", - " -0.0160\n", - " 0.2828\n", - " -0.3593\n", - " -0.2290\n", - " -0.4338\n", - " -0.0021\n", - " -0.0034\n", - " -0.0237\n", - " 0.1105\n", - " 0.0896\n", - " 0.0697\n", - " -0.0307\n", - " -0.1974\n", - " -0.0047\n", - " 0.0069\n", - " 0.1067\n", - " 0.2567\n", - " -0.0471\n", - " 0.2444\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.0.bn.running_mean', \n", - " 0.0870\n", - " 0.8101\n", - " 0.5247\n", - " 2.2122\n", - " 0.4873\n", - " 0.0197\n", - " 0.0576\n", - " 3.7668\n", - " 0.3194\n", - " 0.2192\n", - " 1.2198\n", - " 1.0392\n", - " 1.1233\n", - " 6.1512\n", - " 0.6100\n", - " 4.5341\n", - " 0.1639\n", - " 2.1069\n", - " 0.4447\n", - " 0.6375\n", - " 0.0551\n", - " 0.7121\n", - " 1.6102\n", - " 0.1971\n", - " 2.0502\n", - " 0.1030\n", - " 0.0089\n", - " 0.0028\n", - " 2.7830\n", - " 0.3154\n", - " 0.5923\n", - " 0.0087\n", - " 1.6420\n", - " 0.1864\n", - " 0.3373\n", - " 0.4181\n", - " 1.1021\n", - " 1.2134\n", - " 0.4320\n", - " 1.1481\n", - " 0.0204\n", - " 0.7017\n", - " 0.2296\n", - " 0.0967\n", - " 0.0505\n", - " 0.6132\n", - " 0.1166\n", - " 0.9885\n", - " 3.3698\n", - " 0.0310\n", - " 0.7492\n", - " 0.5738\n", - " 0.2248\n", - " 2.9998\n", - " 0.6050\n", - " 0.0560\n", - " 0.6973\n", - " 0.1761\n", - " 0.2494\n", - " 0.1141\n", - " 0.7000\n", - " 2.7446\n", - " 0.0505\n", - " 0.1667\n", - " 0.3415\n", - " 1.4325\n", - " 0.5639\n", - " 0.0893\n", - " 0.1094\n", - " 4.7658\n", - " 0.3892\n", - " 0.2030\n", - " 0.0042\n", - " 0.0783\n", - " 0.4031\n", - " 0.6467\n", - " 0.1158\n", - " 2.0079\n", - " 0.1498\n", - " 1.2992\n", - " 1.4152\n", - " 1.1754\n", - " 1.8695\n", - " 0.0708\n", - " 2.6983\n", - " 0.7383\n", - " 0.8094\n", - " 0.4446\n", - " 0.5453\n", - " 0.9943\n", - " 0.4924\n", - " 0.1364\n", - " 1.9822\n", - " 0.1972\n", - " 1.9096\n", - " 0.8834\n", - " 0.0014\n", - " 2.7301\n", - " 0.6040\n", - " 1.6772\n", - " 1.4184\n", - " 1.2977\n", - " 0.0487\n", - " 0.6774\n", - " 1.9628\n", - " 1.7026\n", - " 1.2595\n", - " 0.0462\n", - " 1.2077\n", - " 0.0032\n", - " 1.5108\n", - " 2.9752\n", - " 2.2626\n", - " 0.1426\n", - " 0.5018\n", - " 1.4696\n", - " 1.5673\n", - " 1.3662\n", - " 2.2242\n", - " 0.0077\n", - " 2.6727\n", - " 3.0063\n", - " 2.0216\n", - " 0.5201\n", - " 0.1247\n", - " 0.2195\n", - " 0.3367\n", - " 0.2375\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.0.bn.running_var', \n", - " 0.3437\n", - " 3.1590\n", - " 3.5308\n", - " 16.8469\n", - " 3.0804\n", - " 0.0273\n", - " 0.2468\n", - " 31.7647\n", - " 1.4386\n", - " 0.9426\n", - " 6.6400\n", - " 3.8405\n", - " 9.8438\n", - " 50.8472\n", - " 4.1808\n", - " 21.3124\n", - " 0.5102\n", - " 9.6467\n", - " 1.7094\n", - " 2.8162\n", - " 0.1607\n", - " 2.5650\n", - " 5.6620\n", - " 1.3423\n", - " 10.7854\n", - " 0.3705\n", - " 0.0262\n", - " 0.0040\n", - " 16.0253\n", - " 1.5826\n", - " 2.8785\n", - " 0.0123\n", - " 7.1599\n", - " 0.4248\n", - " 1.3036\n", - " 3.5653\n", - " 5.6428\n", - " 11.3821\n", - " 1.8151\n", - " 6.7774\n", - " 0.0608\n", - " 3.9251\n", - " 0.9103\n", - " 0.3961\n", - " 0.1327\n", - " 2.1445\n", - " 0.2094\n", - " 5.4538\n", - " 25.3652\n", - " 0.0586\n", - " 4.3396\n", - " 2.1219\n", - " 0.9367\n", - " 6.9949\n", - " 3.4362\n", - " 0.3150\n", - " 3.5132\n", - " 0.9837\n", - " 0.8066\n", - " 0.2909\n", - " 3.5503\n", - " 22.5505\n", - " 0.1241\n", - " 0.4674\n", - " 1.4275\n", - " 8.2066\n", - " 2.8071\n", - " 0.3559\n", - " 0.2892\n", - " 18.5240\n", - " 3.1646\n", - " 1.0415\n", - " 0.0045\n", - " 0.3100\n", - " 2.6372\n", - " 3.3976\n", - " 0.2980\n", - " 8.8771\n", - " 0.4361\n", - " 3.1446\n", - " 12.0329\n", - " 5.2685\n", - " 8.6919\n", - " 0.3742\n", - " 15.9113\n", - " 3.2323\n", - " 3.6449\n", - " 2.1727\n", - " 2.5035\n", - " 5.6634\n", - " 5.1186\n", - " 0.7378\n", - " 10.0247\n", - " 1.0395\n", - " 33.3002\n", - " 3.8079\n", - " 0.0013\n", - " 14.0447\n", - " 3.7312\n", - " 10.0017\n", - " 6.9413\n", - " 6.1180\n", - " 0.1488\n", - " 3.0287\n", - " 11.6265\n", - " 6.9702\n", - " 3.3300\n", - " 0.1468\n", - " 7.1564\n", - " 0.0031\n", - " 14.6520\n", - " 18.4089\n", - " 12.1343\n", - " 0.4487\n", - " 2.2629\n", - " 9.2180\n", - " 5.7901\n", - " 7.2934\n", - " 18.6814\n", - " 0.0180\n", - " 14.8378\n", - " 14.6351\n", - " 8.0705\n", - " 2.4461\n", - " 0.8510\n", - " 1.0535\n", - " 1.1406\n", - " 0.7860\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.1.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -1.7469e-03 -5.8269e-02\n", - " 6.7772e-02 -8.3827e-01\n", - " -1.9679e+00 3.3696e-01\n", - " ⋮ \n", - " -6.3320e-01 -4.1194e-01\n", - " -6.1837e-01 -4.3298e-01\n", - " 1.2093e-01 -2.6532e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " -7.0265e-02 -4.4727e-02\n", - " -1.3089e-02 1.0594e-01\n", - " -1.4405e-01 3.7635e-01\n", - " ⋮ \n", - " 3.0504e-01 5.5851e-02\n", - " 3.3968e-01 -7.2954e-03\n", - " 5.3568e-02 1.9476e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " 1.2149e-02 -6.5031e-02\n", - " -1.1192e-02 2.0588e-02\n", - " -9.9884e-02 2.7532e-01\n", - " ⋮ \n", - " 2.2661e-01 -1.4156e-01\n", - " 1.0049e-01 3.1395e-03\n", - " 7.0527e-02 -2.9458e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -4.5840e-02 -3.6793e-02\n", - " 1.6884e-01 -4.0931e-01\n", - " 2.5989e-01 2.7923e-01\n", - " ⋮ \n", - " -2.6109e-01 -3.3987e-01\n", - " 1.5648e-01 -6.1283e-02\n", - " -5.4954e-01 -1.1350e-01\n", - " \n", - " (126,.,.) = \n", - " -6.2937e-02 4.4589e-02\n", - " -6.2535e-02 -5.3324e-01\n", - " 2.1476e-01 -3.5696e-01\n", - " ⋮ \n", - " 2.1135e-01 -5.8720e-01\n", - " -1.0717e-01 -4.9246e-02\n", - " 2.3508e-01 -8.5545e-03\n", - " \n", - " (127,.,.) = \n", - " 1.0879e-01 -3.1740e-02\n", - " 4.4000e-03 -1.4642e+00\n", - " 3.1502e-01 2.8231e-01\n", - " ⋮ \n", - " -7.7851e-01 5.1266e-02\n", - " -1.2681e-03 -2.2417e-01\n", - " -2.1902e-01 -3.5927e-01\n", - " [torch.FloatTensor of size 128x128x2]),\n", - " ('module.encoder.cbhg.conv1d_banks.1.bn.weight', \n", - " 0.4733\n", - " 0.0235\n", - " -0.0062\n", - " 0.4201\n", - " 1.3014\n", - " 0.1988\n", - " -0.0900\n", - " 1.3110\n", - " 0.3646\n", - " 0.4270\n", - " -1.1778\n", - " 0.7919\n", - " 0.1093\n", - " -0.0262\n", - " 0.7808\n", - " 0.4058\n", - " 1.0810\n", - " -0.0285\n", - " 0.4852\n", - " 0.3591\n", - " 0.5424\n", - " 0.8443\n", - " 0.5654\n", - " -0.3290\n", - " 1.2563\n", - " 0.2957\n", - " 0.6080\n", - " -1.0073\n", - " -0.6875\n", - " 0.6321\n", - " 0.6867\n", - " -1.3513\n", - " -0.3735\n", - " 1.0324\n", - " 0.4403\n", - " -1.3225\n", - " 1.0055\n", - " -1.0831\n", - " 0.3380\n", - " -0.7721\n", - " -0.0921\n", - " 0.6735\n", - " 0.7410\n", - " 0.0396\n", - " 1.0561\n", - " 0.0040\n", - " 1.0135\n", - " 0.8185\n", - " 0.7132\n", - " 1.1297\n", - " -0.0641\n", - " 0.5488\n", - " 0.6189\n", - " 0.6287\n", - " 1.6989\n", - " 0.2293\n", - " 0.5385\n", - " 0.7866\n", - " -0.0101\n", - " 0.7307\n", - " -0.8609\n", - " 0.1057\n", - " 1.3026\n", - " 0.3684\n", - " 1.4587\n", - " -0.1244\n", - " 0.6186\n", - " -1.1850\n", - " 1.2956\n", - " -1.2645\n", - " 0.4718\n", - " 0.0087\n", - " 0.2734\n", - " -0.3027\n", - " -0.9086\n", - " -0.7481\n", - " -0.0305\n", - " 0.1738\n", - " 0.6157\n", - " 0.2185\n", - " -0.5635\n", - " 0.4902\n", - " 0.4909\n", - " -0.7121\n", - " -1.4196\n", - " 0.0211\n", - " 0.2764\n", - " 0.8459\n", - " -1.0611\n", - " -1.9599\n", - " 0.6789\n", - " -0.0087\n", - " 0.8961\n", - " 0.4669\n", - " 0.3157\n", - " 0.9896\n", - " 0.5475\n", - " 0.4048\n", - " -0.7899\n", - " 0.9126\n", - " -0.2827\n", - " 0.0139\n", - " -1.1461\n", - " 1.1425\n", - " 0.2756\n", - " 1.0780\n", - " -1.2292\n", - " 0.5387\n", - " 0.2735\n", - " 1.2863\n", - " -1.5314\n", - " -0.0675\n", - " 0.3419\n", - " 0.9257\n", - " 0.5979\n", - " -0.1600\n", - " 0.6024\n", - " 0.3802\n", - " -0.0378\n", - " 0.4436\n", - " 0.4443\n", - " 1.3742\n", - " 0.3733\n", - " 0.0133\n", - " -1.9551\n", - " -1.2258\n", - " -0.9844\n", - " 0.5876\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.1.bn.bias', \n", - " 0.1701\n", - " -0.0081\n", - " -0.0007\n", - " 0.2301\n", - " 0.2036\n", - " 0.0267\n", - " -0.0508\n", - " 0.1225\n", - " 0.1918\n", - " 0.2228\n", - " -0.2664\n", - " 0.3788\n", - " 0.0042\n", - " -0.0011\n", - " 0.0979\n", - " 0.0099\n", - " 0.0533\n", - " -0.0044\n", - " 0.0644\n", - " 0.1301\n", - " 0.1707\n", - " 0.0083\n", - " -0.1809\n", - " -0.0810\n", - " 0.1219\n", - " 0.0308\n", - " -0.0266\n", - " -0.3285\n", - " -0.1071\n", - " 0.0112\n", - " 0.0175\n", - " -0.3918\n", - " -0.0652\n", - " 0.2522\n", - " 0.1066\n", - " -0.1811\n", - " 0.0816\n", - " -0.2488\n", - " 0.1812\n", - " -0.3955\n", - " -0.0276\n", - " -0.0875\n", - " 0.0278\n", - " 0.0080\n", - " 0.1422\n", - " 0.0212\n", - " 0.1189\n", - " 0.0657\n", - " 0.1570\n", - " 0.4617\n", - " -0.0123\n", - " 0.2006\n", - " 0.1181\n", - " 0.2937\n", - " 0.2162\n", - " 0.0653\n", - " -0.0357\n", - " 0.1045\n", - " -0.0054\n", - " 0.0629\n", - " -0.2165\n", - " 0.0177\n", - " 0.1745\n", - " 0.1962\n", - " 0.1149\n", - " -0.0463\n", - " 0.0938\n", - " -0.2009\n", - " 0.1857\n", - " -0.3686\n", - " 0.1085\n", - " 0.0068\n", - " 0.0247\n", - " -0.0742\n", - " -0.3292\n", - " -0.1746\n", - " -0.0137\n", - " -0.0106\n", - " 0.1596\n", - " 0.0604\n", - " -0.1541\n", - " -0.1548\n", - " 0.0034\n", - " -0.1204\n", - " -0.1631\n", - " -0.0175\n", - " 0.1083\n", - " 0.0942\n", - " -0.1659\n", - " -0.3056\n", - " 0.5220\n", - " 0.0009\n", - " 0.1898\n", - " 0.0333\n", - " 0.1392\n", - " 0.1659\n", - " -0.1028\n", - " 0.0858\n", - " -0.2728\n", - " 0.3501\n", - " -0.0931\n", - " -0.0023\n", - " -0.0371\n", - " 0.0988\n", - " -0.0181\n", - " 0.0447\n", - " -0.3148\n", - " 0.0571\n", - " 0.0850\n", - " 0.1255\n", - " -0.2664\n", - " -0.0209\n", - " 0.0853\n", - " 0.1626\n", - " 0.0210\n", - " -0.0426\n", - " 0.1644\n", - " 0.0993\n", - " -0.0196\n", - " 0.0656\n", - " 0.0561\n", - " 0.3851\n", - " -0.0397\n", - " -0.0042\n", - " -0.1487\n", - " -0.2297\n", - " -0.2560\n", - " -0.1302\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.1.bn.running_mean', \n", - " 0.2278\n", - " 1.3284\n", - " 5.4859\n", - " 2.7325\n", - " 0.3709\n", - " 5.2223\n", - " 1.6543\n", - " 0.0211\n", - " 1.6813\n", - " 0.9461\n", - " 4.6698\n", - " 0.6030\n", - " 1.4935\n", - " 4.3162\n", - " 0.2561\n", - " 2.1722\n", - " 0.7438\n", - " 6.9892\n", - " 0.5223\n", - " 7.1926\n", - " 1.5904\n", - " 1.4662\n", - " 0.9416\n", - " 1.1059\n", - " 0.3037\n", - " 5.7653\n", - " 0.0300\n", - " 3.2579\n", - " 0.8365\n", - " 0.2451\n", - " 0.2678\n", - " 0.8860\n", - " 0.8639\n", - " 0.2315\n", - " 2.2403\n", - " 3.4847\n", - " 0.1228\n", - " 0.8269\n", - " 1.9055\n", - " 4.1844\n", - " 1.5615\n", - " 0.3953\n", - " 0.8498\n", - " 2.7287\n", - " 1.2589\n", - " 4.2431\n", - " 0.3563\n", - " 0.3371\n", - " 1.4904\n", - " 1.7422\n", - " 0.9660\n", - " 3.5193\n", - " 0.1304\n", - " 3.3561\n", - " 0.1678\n", - " 5.5767\n", - " 0.2415\n", - " 0.0174\n", - " 0.2963\n", - " 0.2006\n", - " 2.3182\n", - " 6.5804\n", - " 0.2712\n", - " 0.9532\n", - " 0.2055\n", - " 2.6334\n", - " 0.2016\n", - " 0.6562\n", - " 0.1193\n", - " 1.0522\n", - " 7.8449\n", - " 1.7169\n", - " 0.6664\n", - " 1.3208\n", - " 1.4695\n", - " 2.0873\n", - " 2.7356\n", - " 0.6997\n", - " 0.2519\n", - " 5.0028\n", - " 1.6218\n", - " 0.1138\n", - " 0.2069\n", - " 0.7647\n", - " 0.1726\n", - " 4.4857\n", - " 0.3926\n", - " 0.2857\n", - " 0.9989\n", - " 0.3531\n", - " 4.2766\n", - " 1.1147\n", - " 0.1493\n", - " 0.5459\n", - " 6.1042\n", - " 0.0601\n", - " 0.2063\n", - " 0.1297\n", - " 2.2081\n", - " 1.3151\n", - " 2.6887\n", - " 7.2990\n", - " 0.0021\n", - " 0.3346\n", - " 1.2581\n", - " 0.3534\n", - " 2.4283\n", - " 0.1082\n", - " 5.4669\n", - " 0.1340\n", - " 0.2159\n", - " 2.2148\n", - " 0.6639\n", - " 0.2035\n", - " 1.0627\n", - " 1.4622\n", - " 1.6266\n", - " 1.6433\n", - " 1.1739\n", - " 0.0596\n", - " 9.3307\n", - " 0.6544\n", - " 1.2687\n", - " 2.7657\n", - " 0.4171\n", - " 1.5379\n", - " 1.2445\n", - " 0.3488\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.1.bn.running_var', \n", - " 1.3367\n", - " 7.6086\n", - " 29.6188\n", - " 19.9479\n", - " 1.9745\n", - " 22.7413\n", - " 5.9792\n", - " 0.0637\n", - " 10.5301\n", - " 6.9245\n", - " 45.1371\n", - " 4.3097\n", - " 7.0463\n", - " 30.0321\n", - " 1.6828\n", - " 10.8739\n", - " 3.6743\n", - " 33.7168\n", - " 3.1064\n", - " 33.4510\n", - " 12.2032\n", - " 11.5044\n", - " 4.9928\n", - " 4.6164\n", - " 2.3413\n", - " 29.8270\n", - " 0.0870\n", - " 22.6399\n", - " 4.5027\n", - " 1.0404\n", - " 2.8947\n", - " 5.9069\n", - " 4.3338\n", - " 1.4424\n", - " 15.1429\n", - " 31.3466\n", - " 0.4048\n", - " 4.3270\n", - " 11.8973\n", - " 27.4739\n", - " 5.2958\n", - " 1.8844\n", - " 5.1403\n", - " 11.1396\n", - " 10.1982\n", - " 25.9107\n", - " 2.5645\n", - " 1.8474\n", - " 7.7697\n", - " 23.2299\n", - " 3.5681\n", - " 23.7102\n", - " 0.5725\n", - " 42.8053\n", - " 0.8816\n", - " 44.7615\n", - " 1.0349\n", - " 0.0376\n", - " 0.9509\n", - " 1.1513\n", - " 18.1707\n", - " 42.8582\n", - " 1.3485\n", - " 5.3684\n", - " 0.9533\n", - " 13.0618\n", - " 1.1537\n", - " 3.8212\n", - " 0.4204\n", - " 6.5487\n", - " 41.9320\n", - " 16.4796\n", - " 4.3733\n", - " 6.8044\n", - " 10.9295\n", - " 13.2641\n", - " 11.7745\n", - " 3.6665\n", - " 1.6709\n", - " 25.2989\n", - " 11.1395\n", - " 0.4067\n", - " 0.7982\n", - " 4.9462\n", - " 1.2926\n", - " 12.2255\n", - " 1.8526\n", - " 1.4234\n", - " 6.5699\n", - " 2.1196\n", - " 46.5058\n", - " 5.0214\n", - " 0.5336\n", - " 4.7017\n", - " 35.4098\n", - " 0.2543\n", - " 1.0787\n", - " 0.7361\n", - " 15.2158\n", - " 9.3967\n", - " 14.4744\n", - " 47.6725\n", - " 0.0015\n", - " 1.4966\n", - " 8.1529\n", - " 2.0170\n", - " 24.5650\n", - " 0.4699\n", - " 24.9536\n", - " 0.6458\n", - " 1.1774\n", - " 8.2636\n", - " 5.0634\n", - " 0.8180\n", - " 10.3734\n", - " 7.5653\n", - " 9.4897\n", - " 11.5371\n", - " 4.4367\n", - " 0.2274\n", - " 46.4561\n", - " 6.3732\n", - " 8.3160\n", - " 14.7590\n", - " 2.9065\n", - " 15.0195\n", - " 8.1915\n", - " 1.9411\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.2.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -5.4286e-02 -9.4237e-02 4.7910e-02\n", - " -2.0761e-01 1.6850e-01 -1.9503e+00\n", - " -4.2229e-01 -1.3163e+00 -2.3051e-01\n", - " ⋮ \n", - " 6.4546e-02 -1.0657e+00 -1.1922e+00\n", - " 5.5012e-02 -5.6582e-01 -2.0700e-01\n", - " 2.8215e-02 -1.4465e-01 -1.5023e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " -2.9313e-02 -4.5640e-03 -2.5330e-02\n", - " -1.8320e+00 9.9294e-02 2.6152e-01\n", - " 1.1664e-01 9.9127e-02 -2.0662e+00\n", - " ⋮ \n", - " -8.1441e-01 -8.7617e-01 1.7442e-02\n", - " -1.5637e-01 1.0771e-01 -6.5480e-01\n", - " 1.2788e-01 1.0396e-01 -5.3307e-02\n", - " \n", - " ( 2 ,.,.) = \n", - " 1.3774e-02 -2.2764e-02 1.7160e-02\n", - " -5.4339e-01 8.6952e-02 8.0073e-03\n", - " -2.6484e-01 -1.3497e+00 8.9788e-02\n", - " ⋮ \n", - " 1.1648e-01 -7.7175e-01 1.5116e-01\n", - " -2.1602e-01 2.5640e-01 1.0208e-01\n", - " 1.2165e-01 -2.6676e-02 -4.5937e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -4.6806e-02 -3.6873e-02 3.9662e-02\n", - " -7.0332e-02 -8.7892e-01 -1.4400e+00\n", - " 1.8573e-01 9.4182e-02 1.1713e-01\n", - " ⋮ \n", - " 1.5029e-01 -3.6476e-02 6.7077e-02\n", - " 2.8011e-01 -4.7094e-02 2.2105e-01\n", - " -2.4804e-01 -1.3658e-02 -3.4306e-03\n", - " \n", - " (126,.,.) = \n", - " -1.9260e-02 -1.2697e-02 -1.4019e-02\n", - " -1.3391e-01 -3.4813e-01 -4.7117e-01\n", - " 1.4140e-01 1.4029e-01 -1.3311e-01\n", - " ⋮ \n", - " 1.8775e-01 5.7309e-02 2.6160e-01\n", - " 2.2137e-01 -2.6558e-02 -3.1574e-02\n", - " 1.0015e-02 -1.2476e-01 -3.1886e-02\n", - " \n", - " (127,.,.) = \n", - " 2.4374e-02 1.2599e-02 1.4980e-02\n", - " -1.8930e-01 -3.1155e-02 -2.2507e-01\n", - " 3.4954e-01 -6.2865e-02 4.2429e-01\n", - " ⋮ \n", - " 2.4214e-01 -4.6694e-02 -1.8781e-01\n", - " -2.5548e-01 2.9772e-01 4.9350e-01\n", - " 1.5860e-01 -2.1079e-01 1.1477e-01\n", - " [torch.FloatTensor of size 128x128x3]),\n", - " ('module.encoder.cbhg.conv1d_banks.2.bn.weight', \n", - " 0.7882\n", - " 1.1679\n", - " 0.6868\n", - " -0.5602\n", - " 0.6926\n", - " 0.3480\n", - " 1.3040\n", - " -0.5898\n", - " 0.8252\n", - " -0.1509\n", - " 0.6994\n", - " 0.5404\n", - " 0.7474\n", - " 0.9570\n", - " 0.1598\n", - " 0.5288\n", - " 0.8474\n", - " -0.4721\n", - " 0.7928\n", - " 0.6296\n", - " 0.9907\n", - " -0.6676\n", - " -0.1030\n", - " -1.0869\n", - " 0.4828\n", - " 1.1944\n", - " -0.3796\n", - " 0.7430\n", - " 0.5693\n", - " 0.4382\n", - " 0.3220\n", - " 0.3703\n", - " 0.8995\n", - " 0.7451\n", - " -1.3021\n", - " -0.8754\n", - " -0.8579\n", - " 0.5799\n", - " 0.4983\n", - " 0.5480\n", - " -2.1142\n", - " 0.9737\n", - " 1.2022\n", - " 0.3887\n", - " 0.5268\n", - " 1.2057\n", - " 0.8936\n", - " 0.3334\n", - " 0.7513\n", - " 0.6445\n", - " -0.7795\n", - " 1.0365\n", - " 0.4544\n", - " 0.5647\n", - " 0.7380\n", - " 1.1126\n", - " 0.6847\n", - " 0.2264\n", - " 0.3797\n", - " -1.0073\n", - " 0.9932\n", - " 0.5080\n", - " -1.0126\n", - " 0.8422\n", - " -0.9466\n", - " 0.4633\n", - " 1.2529\n", - " 0.7878\n", - " 0.3980\n", - " 0.9587\n", - " 0.5316\n", - " -0.5880\n", - " 0.6710\n", - " 0.7551\n", - " 0.5722\n", - " 0.5651\n", - " -0.8144\n", - " 0.8886\n", - " 0.4788\n", - " 0.6518\n", - " 0.9727\n", - " -0.7357\n", - " 1.2086\n", - " 0.5821\n", - " 0.2523\n", - " 1.3351\n", - " 1.0008\n", - " 1.0258\n", - " -0.8218\n", - " 0.2004\n", - " 0.4271\n", - " 0.9437\n", - " -0.3267\n", - " 1.2607\n", - " 0.6416\n", - " 0.6931\n", - " -0.6647\n", - " -1.7838\n", - " 0.9953\n", - " -0.5783\n", - " -0.8165\n", - " 0.8011\n", - " 0.8279\n", - " 1.0071\n", - " -0.9063\n", - " 0.7007\n", - " -1.4719\n", - " -1.4968\n", - " -1.0625\n", - " 0.8999\n", - " 0.7239\n", - " 0.0646\n", - " -0.9613\n", - " 0.6627\n", - " 0.7891\n", - " 1.0621\n", - " 0.6897\n", - " 0.7706\n", - " 0.5824\n", - " 0.7817\n", - " -1.3201\n", - " -0.9172\n", - " 0.3756\n", - " -1.4005\n", - " 0.8096\n", - " 0.6342\n", - " -0.7933\n", - " 0.1299\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.2.bn.bias', \n", - " 0.0638\n", - " 0.0563\n", - " 0.2731\n", - " -0.1831\n", - " 0.0058\n", - " -0.1194\n", - " 0.1583\n", - " -0.1720\n", - " 0.0106\n", - " -0.0349\n", - " 0.0823\n", - " 0.0839\n", - " 0.2265\n", - " 0.3051\n", - " 0.0007\n", - " -0.1692\n", - " 0.1714\n", - " -0.0963\n", - " 0.0330\n", - " 0.0207\n", - " -0.1119\n", - " -0.1277\n", - " -0.0389\n", - " -0.3312\n", - " -0.0054\n", - " 0.0919\n", - " -0.0990\n", - " 0.2415\n", - " 0.1045\n", - " 0.0839\n", - " -0.0179\n", - " 0.1326\n", - " 0.1313\n", - " 0.3185\n", - " -0.1222\n", - " -0.2094\n", - " -0.2618\n", - " -0.1596\n", - " 0.2535\n", - " -0.0093\n", - " -0.0895\n", - " 0.1695\n", - " 0.1285\n", - " 0.1045\n", - " 0.0112\n", - " 0.0559\n", - " 0.2153\n", - " -0.0550\n", - " 0.1243\n", - " 0.0311\n", - " -0.0646\n", - " 0.3177\n", - " 0.0043\n", - " 0.0961\n", - " 0.0593\n", - " 0.0338\n", - " 0.0701\n", - " 0.0092\n", - " -0.0261\n", - " -0.3211\n", - " 0.1917\n", - " 0.0501\n", - " -0.1208\n", - " 0.2013\n", - " 0.0326\n", - " 0.0782\n", - " 0.3299\n", - " 0.0598\n", - " 0.0763\n", - " -0.0259\n", - " 0.2968\n", - " -0.1930\n", - " 0.2456\n", - " 0.0162\n", - " 0.0692\n", - " 0.1265\n", - " -0.1297\n", - " 0.4153\n", - " -0.0290\n", - " 0.0303\n", - " 0.2357\n", - " -0.2576\n", - " 0.2377\n", - " 0.0680\n", - " 0.1226\n", - " 0.3216\n", - " 0.1055\n", - " 0.1506\n", - " -0.1748\n", - " 0.1498\n", - " -0.1754\n", - " -0.0923\n", - " -0.0671\n", - " 0.0901\n", - " -0.0195\n", - " 0.0387\n", - " -0.1568\n", - " -0.2594\n", - " -0.0551\n", - " -0.0781\n", - " -0.1634\n", - " 0.0727\n", - " -0.0663\n", - " 0.1575\n", - " -0.2397\n", - " 0.1048\n", - " -0.1084\n", - " -0.3413\n", - " -0.1369\n", - " 0.1626\n", - " 0.0728\n", - " -0.0186\n", - " -0.1495\n", - " -0.0135\n", - " 0.1119\n", - " 0.1673\n", - " 0.0699\n", - " 0.1361\n", - " 0.3015\n", - " 0.2214\n", - " -0.1587\n", - " -0.2259\n", - " 0.2413\n", - " -0.1080\n", - " 0.0616\n", - " 0.3122\n", - " -0.2073\n", - " 0.0341\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.2.bn.running_mean', \n", - " 1.1630\n", - " 0.2190\n", - " 0.6604\n", - " 6.8502\n", - " 0.5800\n", - " 0.7456\n", - " 0.8671\n", - " 4.6402\n", - " 0.1669\n", - " 3.2986\n", - " 0.3361\n", - " 1.2620\n", - " 0.4217\n", - " 0.3617\n", - " 1.4444\n", - " 0.6314\n", - " 0.9157\n", - " 1.0370\n", - " 1.7103\n", - " 0.1520\n", - " 0.2035\n", - " 3.8058\n", - " 1.4098\n", - " 3.0435\n", - " 1.8632\n", - " 0.2145\n", - " 1.3484\n", - " 0.0739\n", - " 3.0820\n", - " 4.6308\n", - " 0.8663\n", - " 5.6400\n", - " 1.1108\n", - " 1.6447\n", - " 0.2646\n", - " 2.1980\n", - " 3.5989\n", - " 0.5466\n", - " 0.4014\n", - " 0.5370\n", - " 0.1492\n", - " 0.4578\n", - " 0.0107\n", - " 0.9942\n", - " 1.0825\n", - " 0.5560\n", - " 0.4456\n", - " 1.5005\n", - " 2.0964\n", - " 1.6159\n", - " 0.0207\n", - " 1.3731\n", - " 1.0164\n", - " 0.6175\n", - " 0.9700\n", - " 0.0986\n", - " 2.0744\n", - " 1.9988\n", - " 1.1954\n", - " 2.7618\n", - " 0.1706\n", - " 0.0942\n", - " 1.4422\n", - " 0.9258\n", - " 1.6989\n", - " 12.8986\n", - " 0.7410\n", - " 0.3696\n", - " 0.9520\n", - " 0.4256\n", - " 0.8205\n", - " 4.3940\n", - " 1.5047\n", - " 0.6868\n", - " 3.1306\n", - " 2.5420\n", - " 1.0325\n", - " 0.3960\n", - " 0.7334\n", - " 1.4882\n", - " 0.5329\n", - " 2.3058\n", - " 0.0159\n", - " 0.7815\n", - " 10.4380\n", - " 0.1061\n", - " 0.1331\n", - " 0.6696\n", - " 2.0808\n", - " 3.2479\n", - " 4.0375\n", - " 0.5901\n", - " 1.7219\n", - " 0.2626\n", - " 1.2888\n", - " 2.9755\n", - " 2.2197\n", - " 0.2895\n", - " 0.9920\n", - " 1.0100\n", - " 1.7728\n", - " 0.3082\n", - " 0.1416\n", - " 0.2235\n", - " 2.5931\n", - " 0.0869\n", - " 0.4106\n", - " 0.7252\n", - " 1.9628\n", - " 0.0089\n", - " 1.1316\n", - " 4.9027\n", - " 1.3078\n", - " 0.1001\n", - " 0.2413\n", - " 0.2598\n", - " 0.3739\n", - " 0.1684\n", - " 1.3397\n", - " 0.2249\n", - " 1.6900\n", - " 1.6697\n", - " 1.3694\n", - " 0.4396\n", - " 0.8980\n", - " 0.2888\n", - " 1.2549\n", - " 4.6069\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.2.bn.running_var', \n", - " 9.7994\n", - " 1.2514\n", - " 4.1081\n", - " 46.8629\n", - " 3.6430\n", - " 4.7418\n", - " 6.4415\n", - " 33.7882\n", - " 0.9173\n", - " 15.8640\n", - " 3.5358\n", - " 11.0178\n", - " 3.2181\n", - " 2.7233\n", - " 6.4556\n", - " 5.5252\n", - " 7.7024\n", - " 5.3057\n", - " 14.0739\n", - " 1.0280\n", - " 1.1828\n", - " 28.1004\n", - " 7.1049\n", - " 23.1384\n", - " 13.8020\n", - " 1.1702\n", - " 8.0575\n", - " 0.3680\n", - " 29.6075\n", - " 42.2086\n", - " 5.7056\n", - " 61.3984\n", - " 8.8590\n", - " 14.3460\n", - " 1.8237\n", - " 17.0321\n", - " 38.5890\n", - " 4.8810\n", - " 3.5166\n", - " 3.8442\n", - " 1.1839\n", - " 2.8182\n", - " 0.0253\n", - " 13.5511\n", - " 8.0220\n", - " 3.5922\n", - " 3.4204\n", - " 13.1982\n", - " 14.3898\n", - " 15.3038\n", - " 0.0906\n", - " 13.9396\n", - " 7.8078\n", - " 5.1677\n", - " 7.0526\n", - " 0.5769\n", - " 16.6135\n", - " 13.2465\n", - " 9.8636\n", - " 23.3770\n", - " 0.9680\n", - " 0.6132\n", - " 11.0925\n", - " 7.6960\n", - " 15.5853\n", - " 49.7492\n", - " 5.7911\n", - " 1.9542\n", - " 6.6985\n", - " 2.6736\n", - " 6.1363\n", - " 38.2642\n", - " 14.4988\n", - " 4.6359\n", - " 34.5189\n", - " 19.9102\n", - " 7.4275\n", - " 2.7323\n", - " 7.2089\n", - " 12.1778\n", - " 2.7965\n", - " 21.4060\n", - " 0.0439\n", - " 5.3728\n", - " 58.0339\n", - " 0.5599\n", - " 0.6702\n", - " 4.5155\n", - " 19.7068\n", - " 24.7468\n", - " 32.4359\n", - " 3.7611\n", - " 8.8014\n", - " 1.4880\n", - " 9.7919\n", - " 22.7541\n", - " 17.9358\n", - " 1.9441\n", - " 7.9773\n", - " 5.0693\n", - " 15.6299\n", - " 2.0215\n", - " 0.7720\n", - " 1.1532\n", - " 23.9688\n", - " 0.3376\n", - " 2.5808\n", - " 6.6397\n", - " 15.5070\n", - " 0.0205\n", - " 9.0777\n", - " 22.0513\n", - " 11.2502\n", - " 0.5872\n", - " 1.3869\n", - " 1.4350\n", - " 2.5002\n", - " 0.9065\n", - " 11.0231\n", - " 1.2137\n", - " 12.7303\n", - " 15.6336\n", - " 9.6978\n", - " 3.0546\n", - " 6.9087\n", - " 2.1190\n", - " 8.6749\n", - " 36.0892\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.3.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -3.0199e-02 -4.5101e-02 4.1657e-02 -1.3989e-01\n", - " 5.9655e-02 1.3077e-01 -1.0970e+00 9.7011e-03\n", - " -1.3781e+00 2.7077e-01 -1.9742e-02 2.1801e-02\n", - " ⋮ \n", - " -9.1431e-02 1.5480e-01 2.4212e-02 7.3165e-02\n", - " 1.4958e-01 -4.2327e-01 1.9488e-01 -6.6253e-01\n", - " -1.3541e-01 -2.3292e-01 -3.2272e-01 -1.7024e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " 3.2595e-02 -3.2977e-03 4.1706e-02 -1.3661e-03\n", - " -1.0077e+00 -2.9712e-01 -4.7499e-02 -5.7860e-01\n", - " 2.7742e-01 5.4497e-02 2.4373e-01 -1.1944e+00\n", - " ⋮ \n", - " -7.5219e-01 5.6820e-02 -4.2962e-01 -2.7082e-01\n", - " 6.5423e-02 2.2855e-01 -7.5106e-02 2.1938e-01\n", - " -1.2268e-01 -2.7853e-02 8.2643e-02 -2.9314e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " -3.8005e-02 -1.8723e-02 9.9610e-03 -1.8004e-02\n", - " 2.8541e-01 -4.0136e-01 -6.4734e-01 -9.4345e-01\n", - " -4.9899e-03 1.0887e-01 -5.8836e-02 -5.9108e-01\n", - " ⋮ \n", - " -5.1846e-01 4.7479e-03 -4.8246e-01 -1.0629e-01\n", - " 8.4392e-02 9.2139e-02 -5.7263e-02 -2.2137e-01\n", - " -2.2947e-01 -8.1368e-02 -1.8130e-01 1.4157e-01\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -1.9266e-02 -5.8194e-03 -5.9193e-02 1.2790e-02\n", - " 7.3518e-02 1.8488e-01 1.2557e-01 1.2247e-01\n", - " -1.3630e-01 5.6454e-02 1.3659e-01 -6.8306e-02\n", - " ⋮ \n", - " 4.1640e-01 -1.0616e-01 -2.5067e-01 1.5842e-01\n", - " -7.1185e-02 1.9634e-01 -1.7411e-01 2.7764e-01\n", - " 3.3945e-03 -3.4820e-03 8.2969e-02 -1.0172e-01\n", - " \n", - " (126,.,.) = \n", - " -1.9898e-02 1.1437e-02 -6.1109e-02 -2.0294e-02\n", - " -1.9157e-01 1.5399e-01 -2.3774e+00 -1.2851e+00\n", - " 4.7223e-02 -8.3199e-01 1.5482e-01 -1.4128e+00\n", - " ⋮ \n", - " 9.8303e-02 8.7049e-02 -4.9641e-01 1.8631e-02\n", - " -1.5819e-01 -7.8419e-01 -4.9958e-01 -4.4801e-01\n", - " -2.3128e-01 -8.0637e-02 -1.0088e-01 3.4297e-02\n", - " \n", - " (127,.,.) = \n", - " -4.6688e-02 2.6377e-02 -1.1830e-01 4.1776e-02\n", - " 1.7381e-01 7.2495e-02 -4.0078e-02 1.2306e-01\n", - " -1.6091e-01 -1.0786e-01 -1.6649e-01 3.2966e-02\n", - " ⋮ \n", - " 2.8699e-01 2.1908e-01 -4.5487e-01 -2.5770e-02\n", - " 2.0351e-01 -4.6262e-01 1.0559e-01 -3.1717e-01\n", - " -4.0802e-02 6.2488e-02 1.3862e-01 -1.4880e-02\n", - " [torch.FloatTensor of size 128x128x4]),\n", - " ('module.encoder.cbhg.conv1d_banks.3.bn.weight', \n", - " 0.6762\n", - " 1.2839\n", - " 0.5925\n", - " 0.4043\n", - " 0.5573\n", - " 0.4524\n", - " 0.4971\n", - " -0.5960\n", - " 0.5021\n", - " -0.9494\n", - " 0.5080\n", - " -0.8624\n", - " 0.6140\n", - " 0.5730\n", - " -1.2350\n", - " 0.7618\n", - " 0.4530\n", - " 1.6662\n", - " -0.3794\n", - " 0.5542\n", - " 0.4506\n", - " 0.5946\n", - " 0.5797\n", - " 0.4881\n", - " 0.4952\n", - " -0.5356\n", - " 0.6488\n", - " -0.8908\n", - " 0.4057\n", - " 0.6898\n", - " 0.6750\n", - " -0.1251\n", - " 0.6325\n", - " 0.4851\n", - " 0.4389\n", - " 0.4041\n", - " 0.4299\n", - " 0.7708\n", - " 0.7325\n", - " -0.0463\n", - " 0.6394\n", - " 0.5451\n", - " 0.3378\n", - " 0.7166\n", - " 0.6030\n", - " 1.1028\n", - " 0.3994\n", - " -0.9556\n", - " 0.3748\n", - " 0.5475\n", - " 0.4361\n", - " 0.3910\n", - " 0.7346\n", - " 0.6367\n", - " 0.5006\n", - " 0.6014\n", - " 0.6725\n", - " 0.4923\n", - " 0.6960\n", - " 0.3339\n", - " 0.3371\n", - " 0.3961\n", - " 0.4107\n", - " 0.5951\n", - " 0.4860\n", - " 0.5769\n", - " -0.7742\n", - " 0.4339\n", - " 0.7209\n", - " 0.4315\n", - " 0.5832\n", - " 0.5863\n", - " 0.5736\n", - " 0.4677\n", - " -1.0682\n", - " 0.9264\n", - " 0.7246\n", - " 0.7324\n", - " 0.4137\n", - " -0.5774\n", - " 0.4973\n", - " 0.7447\n", - " 0.3545\n", - " 0.7746\n", - " 0.6656\n", - " 0.5835\n", - " 0.5291\n", - " 1.1132\n", - " 0.4102\n", - " 0.5999\n", - " 0.3807\n", - " 0.3736\n", - " 0.6172\n", - " 1.1931\n", - " -0.0256\n", - " 0.4723\n", - " 0.6797\n", - " 0.8751\n", - " 0.4438\n", - " 0.4281\n", - " 0.3294\n", - " 0.9565\n", - " 0.7108\n", - " 0.8660\n", - " 0.7950\n", - " 1.0954\n", - " -0.0163\n", - " 0.0693\n", - " 0.4338\n", - " -0.0255\n", - " 0.0793\n", - " -0.7395\n", - " 0.0218\n", - " 0.3456\n", - " 0.6162\n", - " -0.6018\n", - " 1.5660\n", - " 0.7036\n", - " 0.8461\n", - " 0.6650\n", - " 0.3238\n", - " -0.1641\n", - " 0.3654\n", - " 0.6098\n", - " 0.6175\n", - " -0.5964\n", - " 0.6494\n", - " 0.3895\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.3.bn.bias', \n", - " 0.1157\n", - " 0.4014\n", - " 0.1449\n", - " 0.0870\n", - " 0.0422\n", - " -0.1932\n", - " 0.2791\n", - " -0.1141\n", - " 0.0616\n", - " -0.1564\n", - " 0.1885\n", - " -0.1000\n", - " 0.0900\n", - " 0.1455\n", - " -0.3226\n", - " 0.0664\n", - " 0.1438\n", - " 0.0250\n", - " -0.0582\n", - " 0.0415\n", - " 0.0589\n", - " 0.1084\n", - " 0.0381\n", - " -0.0546\n", - " 0.0147\n", - " -0.1381\n", - " -0.0526\n", - " -0.1121\n", - " 0.0751\n", - " 0.0238\n", - " 0.0121\n", - " -0.0361\n", - " 0.1103\n", - " 0.1307\n", - " -0.0235\n", - " -0.0487\n", - " 0.0064\n", - " 0.1435\n", - " -0.2283\n", - " -0.0059\n", - " 0.0107\n", - " -0.0105\n", - " 0.0806\n", - " 0.0485\n", - " 0.0593\n", - " 0.2291\n", - " -0.0547\n", - " -0.2096\n", - " -0.1142\n", - " 0.0520\n", - " 0.0096\n", - " -0.0157\n", - " 0.0705\n", - " -0.1203\n", - " 0.2084\n", - " 0.0232\n", - " 0.1335\n", - " 0.0985\n", - " -0.0865\n", - " 0.0218\n", - " -0.0995\n", - " 0.1750\n", - " 0.0665\n", - " 0.1305\n", - " 0.1409\n", - " -0.0519\n", - " -0.2571\n", - " -0.2469\n", - " 0.1067\n", - " -0.1175\n", - " -0.0143\n", - " 0.0273\n", - " 0.1013\n", - " -0.1832\n", - " -0.0928\n", - " 0.1175\n", - " 0.0343\n", - " 0.1175\n", - " 0.1041\n", - " 0.0484\n", - " 0.2421\n", - " 0.1170\n", - " -0.0565\n", - " 0.1435\n", - " 0.0914\n", - " 0.1470\n", - " -0.1090\n", - " 0.2005\n", - " 0.0871\n", - " 0.0101\n", - " 0.0458\n", - " -0.0110\n", - " 0.1671\n", - " 0.0892\n", - " 0.0073\n", - " 0.0335\n", - " 0.0925\n", - " 0.1804\n", - " -0.0319\n", - " -0.0401\n", - " 0.1655\n", - " 0.1278\n", - " 0.1544\n", - " 0.2714\n", - " -0.1484\n", - " 0.0515\n", - " -0.0145\n", - " 0.0018\n", - " 0.0577\n", - " 0.0064\n", - " -0.0056\n", - " -0.0825\n", - " -0.0013\n", - " 0.0528\n", - " -0.0137\n", - " -0.1068\n", - " 0.0190\n", - " 0.0481\n", - " 0.0621\n", - " 0.1866\n", - " 0.0732\n", - " -0.0291\n", - " -0.0383\n", - " 0.2391\n", - " 0.1083\n", - " -0.1501\n", - " -0.0074\n", - " -0.0611\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.3.bn.running_mean', \n", - " 0.2907\n", - " 0.9724\n", - " 0.0591\n", - " 7.0921\n", - " 0.3564\n", - " 1.5779\n", - " 13.5156\n", - " 1.0280\n", - " 0.5696\n", - " 2.6036\n", - " 0.2261\n", - " 2.1813\n", - " 0.6724\n", - " 0.6929\n", - " 0.7458\n", - " 0.3476\n", - " 1.9931\n", - " 0.1909\n", - " 7.2323\n", - " 0.5527\n", - " 0.3362\n", - " 2.3720\n", - " 1.5167\n", - " 3.6392\n", - " 1.3306\n", - " 2.6000\n", - " 0.5801\n", - " 1.3793\n", - " 1.9162\n", - " 0.3574\n", - " 0.1882\n", - " 1.2014\n", - " 1.2526\n", - " 0.7860\n", - " 0.5745\n", - " 0.3189\n", - " 0.7388\n", - " 0.7234\n", - " 0.0937\n", - " 9.9037\n", - " 1.0526\n", - " 0.1291\n", - " 1.8733\n", - " 0.9669\n", - " 1.0198\n", - " 0.0990\n", - " 10.1295\n", - " 1.1176\n", - " 1.2340\n", - " 1.5836\n", - " 2.7698\n", - " 1.7052\n", - " 1.6756\n", - " 0.2054\n", - " 1.8708\n", - " 0.8747\n", - " 0.3102\n", - " 4.3427\n", - " 0.2556\n", - " 1.4933\n", - " 1.1341\n", - " 10.7140\n", - " 1.2381\n", - " 0.5267\n", - " 0.3254\n", - " 0.1514\n", - " 2.8627\n", - " 0.3161\n", - " 0.6128\n", - " 3.2774\n", - " 1.0922\n", - " 1.4499\n", - " 0.1820\n", - " 0.3361\n", - " 4.7489\n", - " 0.9300\n", - " 0.3193\n", - " 0.1965\n", - " 0.1582\n", - " 4.3853\n", - " 0.7887\n", - " 0.7031\n", - " 2.5427\n", - " 0.1071\n", - " 0.4976\n", - " 0.3115\n", - " 0.3476\n", - " 0.3716\n", - " 0.8462\n", - " 0.5684\n", - " 0.4245\n", - " 0.4826\n", - " 0.9536\n", - " 0.1709\n", - " 5.0987\n", - " 2.5484\n", - " 0.1011\n", - " 0.0435\n", - " 1.7191\n", - " 0.7203\n", - " 0.7345\n", - " 1.1218\n", - " 0.2452\n", - " 5.0673\n", - " 0.8436\n", - " 0.0573\n", - " 8.1118\n", - " 3.5657\n", - " 1.1770\n", - " 3.5329\n", - " 1.3464\n", - " 1.5698\n", - " 5.1944\n", - " 3.2881\n", - " 0.3376\n", - " 4.2355\n", - " 1.4871\n", - " 2.0011\n", - " 0.0229\n", - " 1.0462\n", - " 2.2058\n", - " 3.6935\n", - " 0.6207\n", - " 2.2306\n", - " 0.3112\n", - " 2.4165\n", - " 0.1631\n", - " 2.1980\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.3.bn.running_var', \n", - " 1.8418\n", - " 12.1676\n", - " 0.4626\n", - " 43.6007\n", - " 4.3930\n", - " 15.3208\n", - " 87.6767\n", - " 7.0887\n", - " 5.4963\n", - " 25.9627\n", - " 2.0877\n", - " 20.8844\n", - " 5.1665\n", - " 5.3227\n", - " 6.8652\n", - " 2.6800\n", - " 16.6571\n", - " 4.4653\n", - " 43.1472\n", - " 3.8001\n", - " 2.2434\n", - " 23.9898\n", - " 15.3317\n", - " 31.4329\n", - " 9.5696\n", - " 18.6807\n", - " 6.8543\n", - " 12.6780\n", - " 21.7198\n", - " 2.8985\n", - " 1.4760\n", - " 5.6311\n", - " 10.7562\n", - " 7.2283\n", - " 5.4736\n", - " 3.2340\n", - " 7.0783\n", - " 6.1088\n", - " 0.5139\n", - " 48.0629\n", - " 13.1178\n", - " 0.7780\n", - " 15.6500\n", - " 8.2420\n", - " 9.1927\n", - " 0.6737\n", - " 64.0188\n", - " 7.9275\n", - " 12.5226\n", - " 18.7606\n", - " 29.9964\n", - " 13.7883\n", - " 15.7356\n", - " 1.4478\n", - " 15.4805\n", - " 7.1590\n", - " 2.7078\n", - " 36.3994\n", - " 1.7871\n", - " 9.7304\n", - " 10.0635\n", - " 72.9783\n", - " 9.4999\n", - " 5.3499\n", - " 2.3363\n", - " 0.9517\n", - " 26.1254\n", - " 2.6923\n", - " 4.6281\n", - " 26.8849\n", - " 10.3987\n", - " 13.1628\n", - " 1.4532\n", - " 2.4368\n", - " 52.3030\n", - " 13.3247\n", - " 2.5919\n", - " 1.7007\n", - " 1.1596\n", - " 42.2475\n", - " 6.4425\n", - " 5.6024\n", - " 19.3698\n", - " 0.5629\n", - " 3.6024\n", - " 2.4057\n", - " 2.8786\n", - " 2.9411\n", - " 7.8058\n", - " 4.0184\n", - " 4.0513\n", - " 4.5486\n", - " 10.3213\n", - " 1.0285\n", - " 26.9182\n", - " 23.5436\n", - " 0.7894\n", - " 0.2378\n", - " 17.0778\n", - " 5.9807\n", - " 6.3857\n", - " 8.9704\n", - " 1.8861\n", - " 65.6700\n", - " 7.0327\n", - " 0.2992\n", - " 74.7671\n", - " 15.9068\n", - " 12.2373\n", - " 14.9016\n", - " 6.7227\n", - " 13.5921\n", - " 39.4942\n", - " 22.5308\n", - " 2.6461\n", - " 29.9793\n", - " 11.1880\n", - " 17.1316\n", - " 0.0988\n", - " 9.8629\n", - " 14.2450\n", - " 27.3753\n", - " 5.3518\n", - " 21.6821\n", - " 2.3591\n", - " 19.7895\n", - " 1.0592\n", - " 17.0844\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.4.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 1.8676e-02 4.3095e-02 -1.5726e-02 -4.3661e-02 -9.8139e-03\n", - " -2.0828e-02 3.0502e-01 -2.4855e-01 -1.1689e-01 -1.2934e-02\n", - " -2.4981e-01 6.2893e-02 -1.9024e-01 -5.0738e-02 3.3915e-01\n", - " ⋮ \n", - " 1.6444e-01 -6.0511e-01 -5.3369e-01 -2.9930e-01 3.1245e-01\n", - " -2.8105e-01 2.4210e-01 -5.1733e-01 -5.1014e-01 -3.4072e-01\n", - " -2.4825e-01 -7.7791e-02 -2.0725e-02 -4.8233e-02 -9.1717e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " 6.9531e-02 -4.4087e-02 9.2566e-02 -4.6828e-02 3.0438e-02\n", - " 1.0145e-01 1.8946e-01 -2.9237e+00 -2.4120e+00 1.6530e-01\n", - " -4.3283e-02 -7.5086e-02 -6.1910e-01 -1.1024e+00 3.5328e-01\n", - " ⋮ \n", - " -1.2166e-01 1.3522e-01 2.8608e-04 2.4502e-01 -7.5205e-02\n", - " -4.6553e-02 1.3329e-02 -8.7335e-01 -3.2406e-02 2.4081e-01\n", - " 1.9314e-01 3.8841e-02 -6.1874e-01 4.3118e-02 -2.3558e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " 1.9084e-02 -6.8872e-03 -4.7294e-02 -1.1103e-03 5.2093e-02\n", - " 3.9098e-01 -4.9400e-02 -5.5011e-02 -1.4766e-01 3.7494e-01\n", - " -3.2525e-01 1.6407e-01 1.2210e-01 2.1276e-01 3.4819e-02\n", - " ⋮ \n", - " -1.2377e-01 3.4028e-02 2.1447e-01 -4.6228e-01 8.3448e-02\n", - " -4.6388e-01 2.2220e-01 -3.5371e-01 2.5234e-01 3.4024e-01\n", - " 3.3419e-02 1.7029e-01 3.2783e-02 -5.3499e-02 9.5706e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -4.0932e-02 -2.5763e-02 7.7512e-03 3.6522e-02 3.7960e-02\n", - " -3.4469e-02 -1.0437e+00 -7.4566e-01 -1.6032e-02 1.0350e-01\n", - " -8.7426e-01 1.6048e-01 -8.3122e-02 -1.8413e-03 -9.7011e-01\n", - " ⋮ \n", - " -1.6335e-01 7.2348e-02 -1.1172e+00 -4.5718e-01 -1.0383e+00\n", - " 2.5089e-01 1.4073e-01 -5.9574e-01 -9.8118e-01 1.5447e-01\n", - " -1.8739e-01 -4.8575e-01 -1.2125e-01 -3.5486e-01 -4.8543e-01\n", - " \n", - " (126,.,.) = \n", - " 4.5631e-02 1.0822e-02 -2.5906e-02 8.9613e-03 -4.3499e-02\n", - " -6.0878e-01 -9.8588e-01 -5.2404e-01 -8.0881e-01 -3.5317e-01\n", - " -3.4792e-02 -1.0723e+00 -7.0286e-01 -5.1342e-01 8.7097e-03\n", - " ⋮ \n", - " 1.7486e-01 5.7678e-02 -5.5733e-01 -1.9898e-01 -3.2039e-01\n", - " 1.6729e-01 -2.5874e-01 -3.0634e-01 -4.0217e-01 7.8019e-02\n", - " -3.8354e-02 5.5831e-02 -1.5912e-01 -1.3364e-01 -1.0953e-01\n", - " \n", - " (127,.,.) = \n", - " -3.2366e-02 2.3642e-02 3.2589e-02 -1.1044e-02 8.8390e-03\n", - " -7.1364e-01 -3.9804e-01 2.8420e-02 -1.2269e+00 -4.1309e-01\n", - " -1.2522e-01 5.2512e-02 1.4330e-02 9.9259e-02 3.5246e-02\n", - " ⋮ \n", - " -1.6750e-02 -4.6182e-01 -8.4984e-01 -7.7151e-01 6.1423e-02\n", - " 8.0939e-02 -4.9918e-01 -3.9325e-02 -3.2784e-01 -4.4412e-01\n", - " 7.7830e-02 -6.5344e-02 1.7677e-02 -5.8119e-02 2.2737e-02\n", - " [torch.FloatTensor of size 128x128x5]),\n", - " ('module.encoder.cbhg.conv1d_banks.4.bn.weight', \n", - " 0.3426\n", - " 0.6003\n", - " 0.2337\n", - " 0.2767\n", - " -1.5664\n", - " 0.6790\n", - " 0.2967\n", - " 0.6112\n", - " 0.5981\n", - " 1.2043\n", - " 0.8861\n", - " 0.4381\n", - " 0.5049\n", - " 0.4052\n", - " 1.1499\n", - " 0.0001\n", - " 0.5920\n", - " -0.6619\n", - " 0.5119\n", - " 0.8957\n", - " 0.4586\n", - " 0.8248\n", - " 0.5741\n", - " 0.3737\n", - " -0.7913\n", - " 0.3334\n", - " 0.4213\n", - " 0.7619\n", - " 0.9248\n", - " 0.8743\n", - " 0.8350\n", - " 1.1013\n", - " 1.1371\n", - " 0.4845\n", - " 0.5254\n", - " 0.3206\n", - " 0.4344\n", - " 0.5647\n", - " 0.5539\n", - " 0.8183\n", - " 0.6006\n", - " 0.4469\n", - " 1.2965\n", - " -0.6258\n", - " 0.4940\n", - " 0.6888\n", - " 0.5895\n", - " 0.5103\n", - " 0.1940\n", - " -0.0739\n", - " 0.4408\n", - " -1.0388\n", - " 0.4637\n", - " 0.5204\n", - " 0.5693\n", - " 0.7005\n", - " 0.3624\n", - " 0.5829\n", - " 0.5393\n", - " 0.5945\n", - " 0.2330\n", - " 0.2835\n", - " 0.7863\n", - " 0.4587\n", - " 0.5319\n", - " -0.6582\n", - " -1.5175\n", - " 0.5959\n", - " 0.3762\n", - " 0.4533\n", - " 0.8087\n", - " 0.7461\n", - " 0.7765\n", - " 0.4178\n", - " 0.5040\n", - " 0.5407\n", - " 0.7416\n", - " -0.9143\n", - " -1.0565\n", - " 1.1500\n", - " -1.2856\n", - " 0.4806\n", - " 0.5750\n", - " 1.2181\n", - " 0.7721\n", - " 0.6361\n", - " 0.4319\n", - " 0.5130\n", - " 0.0221\n", - " -0.9896\n", - " -1.1924\n", - " 0.4069\n", - " 0.6089\n", - " 0.3713\n", - " -0.1563\n", - " 0.4905\n", - " 0.3348\n", - " 0.5328\n", - " 0.5235\n", - " 0.5093\n", - " 0.4743\n", - " 0.5250\n", - " 0.6474\n", - " 0.3690\n", - " 0.4880\n", - " 0.5428\n", - " 0.5635\n", - " 0.4048\n", - " 0.4607\n", - " -0.8978\n", - " 0.6659\n", - " 0.6544\n", - " -1.4788\n", - " -1.0401\n", - " 0.9904\n", - " 0.5127\n", - " 0.4872\n", - " 0.4587\n", - " 0.7145\n", - " 0.7750\n", - " 0.3730\n", - " -1.0018\n", - " 0.5827\n", - " 0.3664\n", - " 0.6188\n", - " 0.6160\n", - " -1.4819\n", - " 0.7840\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.4.bn.bias', \n", - " 0.1269\n", - " 0.1285\n", - " 0.0361\n", - " -0.0386\n", - " -0.1509\n", - " 0.0291\n", - " -0.0117\n", - " 0.0266\n", - " 0.1041\n", - " 0.1785\n", - " 0.2022\n", - " -0.1472\n", - " 0.0636\n", - " -0.0567\n", - " 0.0901\n", - " -0.0037\n", - " 0.0677\n", - " -0.0685\n", - " 0.0430\n", - " 0.0297\n", - " 0.0887\n", - " -0.1836\n", - " 0.1372\n", - " -0.1900\n", - " -0.0034\n", - " -0.1570\n", - " 0.0834\n", - " 0.1421\n", - " 0.1412\n", - " -0.0480\n", - " 0.0061\n", - " -0.0238\n", - " 0.0758\n", - " -0.0220\n", - " 0.0793\n", - " 0.0252\n", - " -0.0169\n", - " 0.1349\n", - " -0.0997\n", - " -0.0222\n", - " 0.0424\n", - " 0.2772\n", - " 0.2115\n", - " -0.1162\n", - " -0.0630\n", - " -0.1354\n", - " 0.0988\n", - " 0.0382\n", - " -0.0212\n", - " -0.0052\n", - " -0.0553\n", - " -0.2416\n", - " 0.0796\n", - " 0.1696\n", - " 0.0128\n", - " 0.2235\n", - " 0.0418\n", - " 0.0549\n", - " 0.0119\n", - " -0.2704\n", - " -0.0125\n", - " 0.0839\n", - " 0.0265\n", - " 0.0725\n", - " 0.1788\n", - " -0.0850\n", - " -0.0673\n", - " 0.2108\n", - " -0.0181\n", - " -0.0814\n", - " 0.0149\n", - " 0.0042\n", - " 0.1059\n", - " -0.0182\n", - " 0.0980\n", - " 0.1088\n", - " 0.1629\n", - " -0.1967\n", - " -0.1704\n", - " 0.0361\n", - " -0.2944\n", - " -0.0876\n", - " 0.0523\n", - " 0.0819\n", - " 0.1366\n", - " -0.2225\n", - " -0.1965\n", - " 0.0689\n", - " -0.0139\n", - " -0.2385\n", - " -0.2867\n", - " 0.0257\n", - " -0.1125\n", - " 0.0647\n", - " -0.0456\n", - " 0.0824\n", - " 0.1223\n", - " 0.0441\n", - " -0.0074\n", - " 0.1459\n", - " 0.0766\n", - " 0.2161\n", - " 0.0482\n", - " -0.0085\n", - " 0.1937\n", - " 0.1123\n", - " 0.1412\n", - " -0.0042\n", - " 0.0901\n", - " -0.1947\n", - " 0.0449\n", - " -0.0446\n", - " -0.0334\n", - " -0.2068\n", - " 0.1845\n", - " -0.0796\n", - " 0.0236\n", - " 0.0239\n", - " 0.0896\n", - " 0.0036\n", - " 0.0559\n", - " 0.0695\n", - " 0.0764\n", - " -0.0007\n", - " -0.1054\n", - " 0.0849\n", - " -0.1300\n", - " 0.0266\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.4.bn.running_mean', \n", - " 1.1662e+00\n", - " 5.7806e-01\n", - " 8.6421e+00\n", - " 1.5111e+00\n", - " 1.4286e-01\n", - " 4.7887e-01\n", - " 2.5149e+00\n", - " 4.1017e-01\n", - " 1.7029e+00\n", - " 6.8182e-01\n", - " 5.2371e-01\n", - " 4.1583e+00\n", - " 9.4160e-02\n", - " 1.1704e+00\n", - " 2.1705e-02\n", - " 1.2901e+01\n", - " 5.2419e+00\n", - " 4.7075e-01\n", - " 7.1928e-02\n", - " 8.2839e-02\n", - " 3.8725e-01\n", - " 1.5272e-01\n", - " 2.7374e+00\n", - " 1.2776e+00\n", - " 4.3609e-09\n", - " 2.0726e+00\n", - " 1.1753e+00\n", - " 5.5592e-01\n", - " 3.3004e-01\n", - " 1.3981e+00\n", - " 4.9237e-02\n", - " 2.0195e-01\n", - " 3.9373e-01\n", - " 9.7453e-01\n", - " 1.2037e+01\n", - " 1.4995e+00\n", - " 6.6274e-01\n", - " 3.8213e-01\n", - " 6.1267e-01\n", - " 4.6233e-02\n", - " 1.9922e-01\n", - " 4.2108e-01\n", - " 2.4134e+00\n", - " 3.7927e+00\n", - " 8.8383e-01\n", - " 2.1092e+00\n", - " 3.5873e-02\n", - " 1.0479e+00\n", - " 2.5503e+00\n", - " 4.3555e+00\n", - " 9.6033e-01\n", - " 1.9151e+00\n", - " 8.3043e-01\n", - " 9.1108e-01\n", - " 6.2211e-01\n", - " 6.0335e-02\n", - " 1.8175e+00\n", - " 2.3355e-01\n", - " 6.3597e-01\n", - " 3.3824e-01\n", - " 4.1144e+00\n", - " 1.0162e+01\n", - " 2.9306e-01\n", - " 1.6782e-01\n", - " 3.0191e+00\n", - " 1.3903e+00\n", - " 7.1522e-02\n", - " 2.3149e-01\n", - " 7.9709e-01\n", - " 4.1425e-01\n", - " 5.9430e-02\n", - " 6.7193e-02\n", - " 1.7077e-01\n", - " 8.2555e+00\n", - " 1.0324e+00\n", - " 2.9795e-01\n", - " 2.9554e+00\n", - " 3.0318e+00\n", - " 1.3888e+00\n", - " 4.4584e-02\n", - " 7.3407e-01\n", - " 3.9918e+00\n", - " 4.1640e-01\n", - " 4.0112e-01\n", - " 1.1698e-01\n", - " 1.3736e+00\n", - " 8.5450e-01\n", - " 3.1089e+00\n", - " 9.4334e+00\n", - " 2.0476e+00\n", - " 2.7410e+00\n", - " 2.9759e-01\n", - " 3.9749e-01\n", - " 3.2181e-01\n", - " 7.5011e+00\n", - " 4.1735e-01\n", - " 1.6323e+01\n", - " 3.7291e-01\n", - " 5.9362e-01\n", - " 7.4685e-01\n", - " 5.1302e-01\n", - " 4.3762e-01\n", - " 1.5844e+00\n", - " 4.9481e-01\n", - " 1.2357e+00\n", - " 6.6337e-01\n", - " 3.1602e-01\n", - " 7.9581e+00\n", - " 5.7080e+00\n", - " 1.7927e+00\n", - " 1.4669e+00\n", - " 1.4931e+00\n", - " 6.7405e-03\n", - " 1.7772e+00\n", - " 5.2709e-01\n", - " 3.7220e-01\n", - " 8.1531e-01\n", - " 1.0348e+00\n", - " 8.1878e-02\n", - " 3.5830e-01\n", - " 1.9062e+00\n", - " 1.2071e+00\n", - " 2.6719e-01\n", - " 8.8440e-01\n", - " 7.4879e-01\n", - " 4.4662e-01\n", - " 8.5571e-02\n", - " 8.4275e-02\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.4.bn.running_var', \n", - " 1.1161e+01\n", - " 5.8489e+00\n", - " 6.2559e+01\n", - " 1.3760e+01\n", - " 1.1316e+00\n", - " 4.6863e+00\n", - " 2.8584e+01\n", - " 4.4339e+00\n", - " 2.0113e+01\n", - " 7.2040e+00\n", - " 4.8571e+00\n", - " 3.6512e+01\n", - " 6.5280e-01\n", - " 1.0856e+01\n", - " 1.2327e-01\n", - " 9.9910e+01\n", - " 6.3146e+01\n", - " 3.9984e+00\n", - " 5.3184e-01\n", - " 5.8083e-01\n", - " 2.9553e+00\n", - " 1.1699e+00\n", - " 3.3601e+01\n", - " 1.3705e+01\n", - " 3.9209e-09\n", - " 1.9752e+01\n", - " 1.3838e+01\n", - " 4.7755e+00\n", - " 3.3254e+00\n", - " 1.4998e+01\n", - " 2.8412e-01\n", - " 1.9239e+00\n", - " 3.3981e+00\n", - " 9.1401e+00\n", - " 8.0982e+01\n", - " 1.4672e+01\n", - " 7.0427e+00\n", - " 3.5584e+00\n", - " 5.6003e+00\n", - " 3.3115e-01\n", - " 1.5336e+00\n", - " 4.0793e+00\n", - " 2.8563e+01\n", - " 3.5319e+01\n", - " 7.8510e+00\n", - " 2.2402e+01\n", - " 2.3532e-01\n", - " 1.1328e+01\n", - " 1.2928e+01\n", - " 2.3886e+01\n", - " 8.7510e+00\n", - " 1.8744e+01\n", - " 7.6653e+00\n", - " 9.4153e+00\n", - " 5.6654e+00\n", - " 4.7516e-01\n", - " 1.8806e+01\n", - " 2.1668e+00\n", - " 6.3653e+00\n", - " 2.6073e+00\n", - " 3.9408e+01\n", - " 6.9438e+01\n", - " 2.9417e+00\n", - " 1.2135e+00\n", - " 2.8289e+01\n", - " 1.4237e+01\n", - " 4.5265e-01\n", - " 2.0719e+00\n", - " 7.5292e+00\n", - " 3.9462e+00\n", - " 3.8858e-01\n", - " 4.1534e-01\n", - " 1.1462e+00\n", - " 6.7391e+01\n", - " 1.1868e+01\n", - " 2.6739e+00\n", - " 3.1718e+01\n", - " 3.5087e+01\n", - " 1.3465e+01\n", - " 3.2188e-01\n", - " 6.7510e+00\n", - " 4.8879e+01\n", - " 4.4594e+00\n", - " 3.8057e+00\n", - " 8.5733e-01\n", - " 1.1967e+01\n", - " 8.7259e+00\n", - " 3.7869e+01\n", - " 8.3978e+01\n", - " 2.3187e+01\n", - " 3.3431e+01\n", - " 2.8585e+00\n", - " 3.4761e+00\n", - " 3.1597e+00\n", - " 3.5961e+01\n", - " 3.7471e+00\n", - " 9.6401e+01\n", - " 3.2155e+00\n", - " 6.9874e+00\n", - " 7.7710e+00\n", - " 4.5784e+00\n", - " 4.4322e+00\n", - " 1.7687e+01\n", - " 4.4191e+00\n", - " 1.1004e+01\n", - " 6.7891e+00\n", - " 2.8761e+00\n", - " 4.6781e+01\n", - " 4.7436e+01\n", - " 1.9463e+01\n", - " 1.4606e+01\n", - " 1.7952e+01\n", - " 4.9074e-02\n", - " 2.2192e+01\n", - " 5.1112e+00\n", - " 2.9833e+00\n", - " 9.0143e+00\n", - " 1.0664e+01\n", - " 6.4345e-01\n", - " 2.8626e+00\n", - " 2.0771e+01\n", - " 1.2641e+01\n", - " 2.3985e+00\n", - " 7.9465e+00\n", - " 6.2094e+00\n", - " 4.9007e+00\n", - " 6.8326e-01\n", - " 5.1287e-01\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.5.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -1.6707e-02 1.5062e-02 -2.4290e-02 -2.0553e-02 7.6927e-02 2.5556e-02\n", - " -1.5825e-01 1.8375e-01 -1.3715e+00 -1.0272e-01 -1.0912e+00 5.3088e-03\n", - " -2.9068e-01 -4.3543e-01 -4.9879e-02 1.9555e-02 1.5983e-01 7.6618e-02\n", - " ⋮ \n", - " -3.8250e-03 -8.1282e-02 7.3171e-02 1.9127e-02 1.2154e-01 -1.3264e+00\n", - " -5.9122e-02 -4.1025e-01 -3.0630e-01 -8.2133e-02 -5.3492e-01 1.9383e-01\n", - " 1.1465e-01 -8.7856e-02 1.9600e-01 -2.7086e-01 -3.4039e-02 -6.0773e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " -5.3984e-04 9.0873e-04 9.3033e-03 -9.9289e-03 -2.5024e-02 6.0367e-02\n", - " -4.0239e-01 1.9475e-02 -5.3144e-01 -2.0597e+00 -9.3734e-02 1.1011e-01\n", - " 1.1027e-01 -8.8092e-02 -6.0984e-01 2.7714e-01 -2.5632e+00 -1.7113e-02\n", - " ⋮ \n", - " -7.9546e-01 -1.1268e+00 5.8257e-02 1.1209e-01 1.2857e-01 -1.9039e-01\n", - " 1.4975e-01 1.0255e-01 -5.6228e-01 -4.2333e-01 -4.6771e-01 5.2113e-02\n", - " 9.4120e-02 -2.0300e-01 -1.4289e-01 -3.4866e-01 -3.5331e-01 6.1327e-03\n", - " \n", - " ( 2 ,.,.) = \n", - " 8.2903e-02 -2.8668e-02 -4.9796e-03 8.3328e-02 7.2743e-03 -1.2039e-03\n", - " 1.2976e-01 3.7283e-02 5.2159e-02 2.3635e-01 7.3198e-02 -1.5133e+00\n", - " 1.3264e-01 -1.4702e-01 1.6758e-01 1.0320e-01 -8.4526e-02 6.9735e-02\n", - " ⋮ \n", - " -1.4193e-01 -1.2630e-02 1.8733e-01 1.9073e-01 -2.8929e-01 -3.8090e-02\n", - " -3.0786e-01 -6.9524e-01 -2.1240e-01 -8.6229e-01 -2.4218e-02 9.6994e-02\n", - " -1.9311e-01 9.7328e-03 -2.3853e-01 -6.1829e-02 2.2485e-02 2.0454e-01\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -1.9382e-02 -5.8581e-02 2.0791e-02 -3.7576e-02 -2.6406e-02 -1.5199e-02\n", - " 2.0389e-02 -1.8575e-01 -1.1484e-01 1.1992e-01 -1.4563e-02 -6.6121e-02\n", - " 3.8035e-02 -1.8355e-02 -3.8509e-01 1.3890e-01 -3.7742e-01 3.6402e-02\n", - " ⋮ \n", - " 3.2539e-01 1.4324e-01 1.2824e-01 7.3527e-02 -1.1932e-01 -2.4204e-01\n", - " -1.6890e-01 7.2598e-02 -7.4014e-03 -8.6582e-02 -2.3952e-01 9.7968e-02\n", - " 5.8235e-02 -7.7000e-02 5.9546e-02 4.4890e-02 3.4878e-01 -2.8980e-01\n", - " \n", - " (126,.,.) = \n", - " 6.8186e-02 2.5241e-02 8.6074e-03 -5.2280e-02 -2.3363e-02 3.2551e-02\n", - " 1.8539e-01 -5.2975e-02 -4.4095e-01 -1.0673e+00 2.9257e-01 1.3955e-01\n", - " 4.7721e-03 1.0114e-01 -4.3054e-01 -1.8220e-01 -3.3834e-01 -3.7529e-01\n", - " ⋮ \n", - " -1.3286e-01 6.4792e-03 1.3652e-01 -1.6517e+00 -3.3156e-01 2.8734e-01\n", - " -9.4012e-01 3.8226e-01 1.5905e-01 -3.5121e-01 6.5831e-02 -1.9648e-01\n", - " 6.5724e-02 -1.5490e-01 6.5657e-02 -1.0527e-01 -1.5488e-01 -9.4005e-03\n", - " \n", - " (127,.,.) = \n", - " -2.3498e-02 6.9453e-03 4.4837e-02 2.5762e-02 4.6459e-02 2.5738e-02\n", - " 1.3858e-01 -5.1303e-01 -2.2565e-02 9.0544e-02 1.9153e-01 1.7328e-01\n", - " -8.9238e-02 -5.8354e-02 5.8674e-02 1.4331e-02 -1.2725e-01 -9.0258e-02\n", - " ⋮ \n", - " 5.6267e-02 7.4358e-02 -2.7942e-01 -1.4918e+00 -1.2619e+00 -7.9139e-03\n", - " -1.6702e-01 2.0627e-01 -7.0767e-02 6.5989e-03 6.8970e-02 1.4741e-01\n", - " 8.9828e-02 -1.9443e-01 -1.1682e-01 1.7129e-01 5.7646e-02 1.2306e-02\n", - " [torch.FloatTensor of size 128x128x6]),\n", - " ('module.encoder.cbhg.conv1d_banks.5.bn.weight', \n", - " 0.6030\n", - " 0.4659\n", - " 0.6145\n", - " 0.5868\n", - " 0.5053\n", - " 0.4299\n", - " 0.3208\n", - " 0.3690\n", - " 0.5187\n", - " 0.3643\n", - " 0.7580\n", - " 0.5170\n", - " -0.9788\n", - " 0.2888\n", - " 0.6778\n", - " 0.5030\n", - " 0.3522\n", - " 0.5321\n", - " 0.6273\n", - " -1.3146\n", - " 0.4277\n", - " 0.6141\n", - " 0.4835\n", - " 0.4332\n", - " 0.5083\n", - " -0.9616\n", - " -0.8252\n", - " 0.4086\n", - " -0.6273\n", - " 0.5303\n", - " 0.5253\n", - " 0.4767\n", - " 0.4248\n", - " 0.6602\n", - " 0.6277\n", - " 0.6253\n", - " 0.4188\n", - " 0.6715\n", - " 0.6525\n", - " 0.4297\n", - " 0.4657\n", - " 0.3448\n", - " 0.6934\n", - " -0.0343\n", - " 0.6080\n", - " 0.4010\n", - " 0.4177\n", - " 0.4304\n", - " 0.5196\n", - " -0.9563\n", - " 0.5729\n", - " 0.4634\n", - " 0.5252\n", - " 0.4574\n", - " 0.4260\n", - " 0.5545\n", - " 0.7378\n", - " 0.5902\n", - " -0.9966\n", - " 0.6326\n", - " 0.5971\n", - " -0.9012\n", - " -0.6216\n", - " 0.2051\n", - " 0.5022\n", - " 0.4865\n", - " -0.9215\n", - " 0.4763\n", - " 0.3403\n", - " 0.3322\n", - " -0.7515\n", - " 1.1560\n", - " 0.4566\n", - " 0.5261\n", - " 0.3933\n", - " 0.4992\n", - " 0.3358\n", - " 0.4127\n", - " 0.4077\n", - " 0.4432\n", - " 1.0589\n", - " 0.6539\n", - " 0.2347\n", - " -0.8491\n", - " 0.6595\n", - " 0.5311\n", - " 0.4118\n", - " 0.4477\n", - " 0.1726\n", - " 0.5200\n", - " 0.4053\n", - " 0.4654\n", - " 0.9625\n", - " 0.5017\n", - " 0.5881\n", - " 0.5774\n", - " 0.4825\n", - " 0.3396\n", - " -0.7531\n", - " 0.3112\n", - " 0.5963\n", - " 1.0316\n", - " 0.3974\n", - " 0.5853\n", - " 0.7602\n", - " 0.4758\n", - " 0.3991\n", - " 0.4226\n", - " 0.5176\n", - " 0.7534\n", - " 0.6088\n", - " 0.5584\n", - " -0.8560\n", - " 0.7328\n", - " 0.4891\n", - " -0.8217\n", - " 0.4753\n", - " 0.6604\n", - " 0.6666\n", - " 0.4886\n", - " 0.5125\n", - " 0.4264\n", - " 0.5448\n", - " -1.0820\n", - " 0.3960\n", - " 0.2083\n", - " 0.5247\n", - " 0.9695\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.5.bn.bias', \n", - " 0.0947\n", - " 0.0718\n", - " 0.0124\n", - " 0.2336\n", - " 0.0085\n", - " 0.1004\n", - " -0.0893\n", - " -0.0778\n", - " -0.0524\n", - " 0.0065\n", - " 0.1136\n", - " -0.0418\n", - " -0.1065\n", - " -0.0254\n", - " 0.1232\n", - " 0.0879\n", - " 0.0633\n", - " -0.0840\n", - " 0.1193\n", - " -0.2054\n", - " 0.1677\n", - " 0.2151\n", - " 0.0697\n", - " 0.0597\n", - " 0.0166\n", - " -0.1709\n", - " -0.1288\n", - " 0.0489\n", - " -0.1630\n", - " -0.0754\n", - " 0.0570\n", - " 0.0086\n", - " 0.1426\n", - " 0.0505\n", - " 0.0564\n", - " 0.2278\n", - " 0.0128\n", - " -0.0277\n", - " 0.0535\n", - " 0.0196\n", - " 0.0594\n", - " 0.0252\n", - " -0.0084\n", - " 0.0057\n", - " 0.0969\n", - " 0.0142\n", - " -0.1417\n", - " 0.1198\n", - " -0.1133\n", - " -0.2663\n", - " 0.2193\n", - " 0.0708\n", - " 0.0075\n", - " 0.0174\n", - " 0.0811\n", - " -0.0021\n", - " 0.0313\n", - " 0.1300\n", - " -0.1708\n", - " 0.0537\n", - " 0.1554\n", - " -0.1699\n", - " -0.1159\n", - " -0.0202\n", - " -0.0004\n", - " 0.0829\n", - " -0.2699\n", - " -0.0136\n", - " 0.0054\n", - " -0.0462\n", - " -0.1225\n", - " 0.0095\n", - " 0.0144\n", - " 0.1135\n", - " -0.0139\n", - " 0.0421\n", - " -0.0032\n", - " 0.0377\n", - " 0.0843\n", - " 0.0332\n", - " 0.1215\n", - " 0.3692\n", - " -0.0251\n", - " -0.1014\n", - " -0.1097\n", - " 0.0472\n", - " 0.1536\n", - " 0.0918\n", - " -0.0179\n", - " 0.1474\n", - " -0.0726\n", - " 0.0957\n", - " 0.1166\n", - " 0.0688\n", - " 0.2160\n", - " 0.0116\n", - " -0.0253\n", - " 0.1411\n", - " -0.0984\n", - " -0.0216\n", - " 0.1054\n", - " -0.0392\n", - " -0.1219\n", - " 0.1568\n", - " -0.0006\n", - " -0.0553\n", - " -0.0160\n", - " 0.0574\n", - " -0.0736\n", - " 0.0534\n", - " -0.0771\n", - " 0.0323\n", - " -0.1174\n", - " -0.0647\n", - " 0.0409\n", - " -0.0897\n", - " 0.1087\n", - " 0.0810\n", - " -0.0016\n", - " -0.0134\n", - " 0.0187\n", - " 0.0645\n", - " -0.0278\n", - " -0.1699\n", - " 0.0223\n", - " 0.0254\n", - " -0.1488\n", - " 0.0309\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.5.bn.running_mean', \n", - " 0.1942\n", - " 0.2407\n", - " 2.3295\n", - " 13.7009\n", - " 0.5646\n", - " 1.5938\n", - " 1.6236\n", - " 0.1412\n", - " 3.5567\n", - " 0.3877\n", - " 0.1246\n", - " 1.3773\n", - " 2.0049\n", - " 5.1180\n", - " 0.4043\n", - " 0.2196\n", - " 2.6233\n", - " 0.2074\n", - " 0.4685\n", - " 0.4912\n", - " 1.4852\n", - " 0.0517\n", - " 2.0695\n", - " 0.3175\n", - " 0.1819\n", - " 2.0328\n", - " 2.0154\n", - " 0.4518\n", - " 7.0997\n", - " 0.6767\n", - " 0.0989\n", - " 0.8559\n", - " 0.5069\n", - " 0.0173\n", - " 0.0892\n", - " 0.3716\n", - " 2.1047\n", - " 0.4246\n", - " 0.1018\n", - " 4.0117\n", - " 0.4722\n", - " 1.4483\n", - " 0.3713\n", - " 9.8769\n", - " 1.0078\n", - " 2.7833\n", - " 0.2142\n", - " 2.5666\n", - " 2.5710\n", - " 3.1552\n", - " 1.3968\n", - " 0.2538\n", - " 0.8166\n", - " 1.4437\n", - " 0.2308\n", - " 6.6636\n", - " 0.3067\n", - " 0.1399\n", - " 0.7262\n", - " 0.1478\n", - " 0.1166\n", - " 3.7425\n", - " 6.9665\n", - " 1.8447\n", - " 1.2830\n", - " 0.4066\n", - " 3.4474\n", - " 0.5367\n", - " 0.3763\n", - " 0.4006\n", - " 2.5741\n", - " 0.1998\n", - " 0.4160\n", - " 0.3257\n", - " 1.5232\n", - " 1.1630\n", - " 2.7245\n", - " 0.2250\n", - " 0.8890\n", - " 2.0377\n", - " 0.0878\n", - " 2.4357\n", - " 0.8960\n", - " 2.0837\n", - " 0.5346\n", - " 0.0699\n", - " 0.7732\n", - " 0.5608\n", - " 1.8463\n", - " 0.0790\n", - " 1.3423\n", - " 0.4863\n", - " 0.1751\n", - " 2.8209\n", - " 2.3684\n", - " 0.3946\n", - " 0.8917\n", - " 14.5403\n", - " 1.9912\n", - " 6.0808\n", - " 0.5597\n", - " 0.0064\n", - " 1.8138\n", - " 0.5429\n", - " 0.1226\n", - " 0.2695\n", - " 0.4319\n", - " 0.6293\n", - " 0.2789\n", - " 0.0554\n", - " 0.9388\n", - " 0.0294\n", - " 2.7917\n", - " 0.2053\n", - " 0.1704\n", - " 4.8849\n", - " 0.4043\n", - " 0.2905\n", - " 0.2785\n", - " 0.2442\n", - " 3.3915\n", - " 6.8654\n", - " 0.8866\n", - " 0.8732\n", - " 2.7530\n", - " 2.3496\n", - " 2.6061\n", - " 0.6980\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.5.bn.running_var', \n", - " 2.0262\n", - " 1.9987\n", - " 25.1144\n", - " 218.8765\n", - " 6.4417\n", - " 17.2455\n", - " 17.5415\n", - " 1.4495\n", - " 46.8464\n", - " 4.0630\n", - " 1.1017\n", - " 16.3569\n", - " 23.2124\n", - " 31.4073\n", - " 3.9895\n", - " 2.2963\n", - " 22.1562\n", - " 2.0646\n", - " 5.9942\n", - " 4.7428\n", - " 17.5209\n", - " 0.3373\n", - " 26.3400\n", - " 3.9097\n", - " 1.7637\n", - " 25.6737\n", - " 22.4007\n", - " 4.7676\n", - " 54.8449\n", - " 8.0765\n", - " 0.8915\n", - " 8.6571\n", - " 4.9478\n", - " 0.1151\n", - " 0.8361\n", - " 3.9047\n", - " 19.3890\n", - " 5.0134\n", - " 0.8813\n", - " 32.9457\n", - " 4.8735\n", - " 15.3827\n", - " 3.2114\n", - " 72.7250\n", - " 10.3798\n", - " 27.6761\n", - " 2.1794\n", - " 24.0510\n", - " 35.4656\n", - " 40.1592\n", - " 17.1983\n", - " 2.5397\n", - " 8.5297\n", - " 13.3604\n", - " 2.1418\n", - " 61.0993\n", - " 2.9829\n", - " 1.3570\n", - " 7.2358\n", - " 1.4686\n", - " 1.0055\n", - " 53.2624\n", - " 58.9634\n", - " 11.5873\n", - " 13.8819\n", - " 4.3137\n", - " 48.9352\n", - " 5.7693\n", - " 3.6650\n", - " 4.0847\n", - " 30.2679\n", - " 1.4583\n", - " 3.7447\n", - " 3.1868\n", - " 17.5012\n", - " 11.1999\n", - " 36.6523\n", - " 2.1900\n", - " 12.0475\n", - " 17.7696\n", - " 0.7518\n", - " 28.8415\n", - " 7.9658\n", - " 24.2708\n", - " 5.0903\n", - " 0.5693\n", - " 8.9742\n", - " 5.9398\n", - " 12.8828\n", - " 0.5220\n", - " 17.0810\n", - " 5.1503\n", - " 1.5296\n", - " 26.6620\n", - " 25.7122\n", - " 4.1311\n", - " 11.0452\n", - " 105.3546\n", - " 16.2668\n", - " 40.0044\n", - " 5.6596\n", - " 0.0321\n", - " 25.1323\n", - " 5.5465\n", - " 1.2754\n", - " 5.0395\n", - " 4.8464\n", - " 7.7836\n", - " 2.8487\n", - " 0.3995\n", - " 12.0627\n", - " 0.2450\n", - " 34.3101\n", - " 2.2691\n", - " 1.5937\n", - " 65.4531\n", - " 3.9816\n", - " 3.0409\n", - " 2.7536\n", - " 2.6018\n", - " 47.5258\n", - " 63.4379\n", - " 11.8568\n", - " 9.6248\n", - " 23.6316\n", - " 16.6635\n", - " 31.3495\n", - " 8.4266\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.6.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -4.5353e-03 4.5222e-03 -3.9971e-02 ... -3.5038e-02 4.8943e-02 -5.0395e-03\n", - " 8.4028e-02 2.2407e-02 -6.3263e-01 ... -2.4554e-01 -4.2537e-03 -8.9171e-01\n", - " -4.7564e-02 6.2109e-02 -9.9553e-01 ... 1.4646e-01 -9.9596e-01 1.6758e-01\n", - " ... ⋱ ... \n", - " 2.2037e-01 1.9275e-01 1.5566e-01 ... -4.8230e-01 -9.3068e-01 -1.2051e-01\n", - " -2.5592e-02 -6.7962e-01 -2.8601e-01 ... 1.0041e-01 -3.2003e-01 -2.3566e-02\n", - " 9.6592e-02 -1.9106e-01 8.4323e-02 ... -7.2522e-01 -4.6632e-02 1.1757e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " 3.8156e-02 3.8708e-02 9.7287e-03 ... 4.9475e-02 4.8088e-02 3.4232e-02\n", - " -1.2674e-01 1.4788e-01 -2.9398e-01 ... 1.5944e-01 -1.7241e-02 7.4101e-02\n", - " 3.7165e-01 -1.2560e-01 1.2577e-01 ... -1.2738e-01 5.0040e-01 -6.6209e-01\n", - " ... ⋱ ... \n", - " 1.1140e-01 2.7386e-01 -4.2877e-01 ... 2.7582e-01 9.3554e-02 -5.7552e-01\n", - " -5.0430e-01 1.2536e-01 -3.1027e-01 ... -4.4512e-01 -4.1321e-01 7.5062e-02\n", - " -2.2296e-01 9.1203e-02 -1.4282e-01 ... -1.0473e-01 2.4301e-01 -1.2898e-03\n", - " \n", - " ( 2 ,.,.) = \n", - " -3.0079e-02 1.5203e-02 -2.8322e-02 ... -4.0184e-03 -1.2454e-02 8.4558e-04\n", - " 6.6261e-02 -7.0814e-02 -6.4725e-02 ... 7.4598e-02 -5.3767e-01 -4.6577e-02\n", - " -1.0299e-01 -6.4324e-02 -9.7807e-02 ... -6.6077e-01 -6.5349e-02 -4.2513e-02\n", - " ... ⋱ ... \n", - " -2.4065e-01 2.6608e-01 1.7404e-01 ... -6.9059e-02 -4.1446e-01 9.7021e-02\n", - " -9.1595e-02 -2.3584e-01 1.2416e-01 ... -9.2408e-01 4.9623e-02 -5.6548e-01\n", - " -7.9593e-02 -9.2951e-02 -1.1617e-01 ... -4.8386e-02 -9.6230e-02 -1.4643e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " 1.0336e-02 -2.2211e-03 3.0974e-02 ... 5.5323e-02 7.0989e-03 1.1988e-02\n", - " -3.3972e-02 -2.6190e-01 3.3510e-02 ... -2.2103e+00 -1.2093e+00 -8.1943e-02\n", - " 1.9794e-02 -1.1747e-01 2.1005e-01 ... -3.2863e-02 6.6548e-02 1.8791e-01\n", - " ... ⋱ ... \n", - " 1.8541e-01 2.1995e-01 -9.6276e-01 ... -1.6338e-01 -8.7571e-01 -1.0884e+00\n", - " 2.3457e-01 2.4633e-01 1.5244e-01 ... -2.2887e-01 -2.0436e-01 9.2099e-02\n", - " 7.3668e-02 -1.5229e-01 -2.5827e-01 ... -1.0859e-01 -4.0845e-01 -2.8507e-02\n", - " \n", - " (126,.,.) = \n", - " -1.6866e-02 2.1091e-02 -1.3386e-02 ... -4.4216e-03 6.1371e-02 -1.1978e-02\n", - " 5.1298e-02 -1.8507e-01 1.7350e-01 ... -6.8860e-01 1.3121e-01 1.1693e-01\n", - " -2.1713e-02 9.1121e-02 -8.1626e-01 ... -8.0668e-02 7.9635e-02 -1.6500e-01\n", - " ... ⋱ ... \n", - " 5.4700e-02 -1.4175e-01 7.1425e-02 ... -9.5701e-01 3.1120e-01 -2.1914e-02\n", - " -3.2772e-01 4.5213e-02 -4.7685e-01 ... -3.1668e-01 -3.9196e-01 2.9794e-02\n", - " 2.3587e-01 6.2199e-02 9.8133e-02 ... -2.6222e-01 -2.2871e-02 8.5439e-02\n", - " \n", - " (127,.,.) = \n", - " 3.3882e-02 -5.7220e-03 -2.2094e-02 ... 9.1597e-03 4.2177e-03 6.7870e-02\n", - " -1.4162e+00 -7.0018e-02 -7.7413e-01 ... -9.2951e-01 -1.7080e+00 -1.0842e-01\n", - " -2.1010e-03 1.1678e-01 -3.1630e-01 ... -3.0953e-01 2.2892e-01 -1.1174e+00\n", - " ... ⋱ ... \n", - " -6.8819e-02 -2.1103e-03 2.1246e-02 ... 1.1823e-01 -8.3776e-01 -1.3537e+00\n", - " -1.6399e-01 2.0398e-01 -8.5368e-01 ... -5.6074e-01 -5.9672e-01 1.0280e-01\n", - " 1.9300e-01 1.8914e-02 -8.1840e-02 ... 1.1959e-01 -3.3886e-01 -3.0645e-01\n", - " [torch.FloatTensor of size 128x128x7]),\n", - " ('module.encoder.cbhg.conv1d_banks.6.bn.weight', \n", - " 0.7607\n", - " 0.5658\n", - " 0.4069\n", - " 0.5596\n", - " 0.4923\n", - " 0.3722\n", - " 0.5383\n", - " 0.5383\n", - " 0.5162\n", - " 0.4805\n", - " 0.3922\n", - " 0.6085\n", - " 0.3954\n", - " 0.3648\n", - " 0.3961\n", - " 0.7022\n", - " 0.4645\n", - " 0.4231\n", - " 0.6034\n", - " 0.4023\n", - " 0.4413\n", - " 0.3966\n", - " 0.7327\n", - " -1.1406\n", - " 0.4644\n", - " 0.4746\n", - " 0.4408\n", - " -0.9712\n", - " 0.4288\n", - " 0.6129\n", - " 0.5061\n", - " 0.5056\n", - " 0.4656\n", - " -0.9311\n", - " 0.4196\n", - " 0.4411\n", - " 0.4886\n", - " 0.6136\n", - " -0.6578\n", - " 0.4390\n", - " -1.1062\n", - " 0.4580\n", - " 0.4731\n", - " 0.4692\n", - " 0.5310\n", - " -0.8401\n", - " 0.5045\n", - " 0.4854\n", - " 0.6072\n", - " 0.4684\n", - " 0.5032\n", - " 0.5790\n", - " -0.8204\n", - " 0.4661\n", - " 0.4229\n", - " 0.5374\n", - " 0.3683\n", - " 0.4203\n", - " 0.3933\n", - " 0.4200\n", - " 1.0160\n", - " 0.5978\n", - " 0.4463\n", - " 0.5107\n", - " 0.5004\n", - " 0.5872\n", - " 0.6598\n", - " -1.0738\n", - " 0.5930\n", - " 0.5918\n", - " 0.6508\n", - " 0.5747\n", - " 0.5351\n", - " 0.4417\n", - " 0.5006\n", - " 0.4125\n", - " 0.8759\n", - " 0.4766\n", - " 0.6038\n", - " 0.5418\n", - " -1.2765\n", - " 0.6014\n", - " 0.5849\n", - " 0.4119\n", - " 0.4250\n", - " 0.5348\n", - " 0.5735\n", - " 0.4446\n", - " 0.8250\n", - " 0.3214\n", - " 0.5479\n", - " 0.2924\n", - " 0.3977\n", - " 0.4694\n", - " 0.5606\n", - " 0.5359\n", - " 0.5207\n", - " 0.5898\n", - " 0.5368\n", - " -0.6191\n", - " 0.5788\n", - " 0.7520\n", - " 0.4601\n", - " 0.5408\n", - " 0.4477\n", - " 0.7225\n", - " 0.4985\n", - " -0.5981\n", - " 0.3489\n", - " 0.4543\n", - " 0.4469\n", - " 0.5317\n", - " 0.4642\n", - " 0.5542\n", - " 0.3984\n", - " 0.7196\n", - " 0.7181\n", - " 0.5273\n", - " 0.4640\n", - " 0.0085\n", - " 0.5395\n", - " 0.5949\n", - " 0.6260\n", - " 0.8270\n", - " 0.4650\n", - " 0.5774\n", - " 0.5891\n", - " 0.6750\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.6.bn.bias', \n", - " 0.0465\n", - " 0.2987\n", - " 0.0744\n", - " 0.1089\n", - " 0.0278\n", - " -0.0894\n", - " 0.1110\n", - " -0.0822\n", - " 0.1167\n", - " 0.0487\n", - " 0.0298\n", - " 0.1312\n", - " -0.1109\n", - " 0.1366\n", - " 0.0715\n", - " 0.1303\n", - " 0.0864\n", - " 0.0792\n", - " -0.0341\n", - " -0.0299\n", - " 0.0299\n", - " 0.0357\n", - " 0.1634\n", - " -0.1350\n", - " 0.0503\n", - " -0.0015\n", - " -0.0014\n", - " -0.1345\n", - " 0.0592\n", - " 0.1044\n", - " 0.1948\n", - " -0.0091\n", - " 0.0063\n", - " -0.0762\n", - " 0.0760\n", - " 0.0956\n", - " 0.0144\n", - " 0.0784\n", - " -0.1574\n", - " -0.1276\n", - " -0.1545\n", - " -0.0514\n", - " 0.0524\n", - " 0.0895\n", - " -0.0015\n", - " -0.2377\n", - " 0.0847\n", - " -0.0527\n", - " 0.1217\n", - " 0.0508\n", - " -0.0818\n", - " 0.1196\n", - " -0.1154\n", - " 0.0045\n", - " -0.1345\n", - " 0.0840\n", - " 0.0765\n", - " 0.1920\n", - " 0.1128\n", - " 0.0415\n", - " 0.0997\n", - " -0.1596\n", - " -0.0704\n", - " 0.0274\n", - " 0.0970\n", - " -0.0021\n", - " 0.0329\n", - " -0.1977\n", - " -0.0295\n", - " 0.1885\n", - " 0.0173\n", - " 0.0856\n", - " 0.1171\n", - " -0.0773\n", - " -0.0033\n", - " 0.0388\n", - " 0.1215\n", - " -0.1076\n", - " -0.0509\n", - " 0.0968\n", - " -0.3588\n", - " 0.1007\n", - " -0.0009\n", - " -0.0015\n", - " 0.0628\n", - " 0.1344\n", - " 0.1188\n", - " -0.1710\n", - " 0.1497\n", - " -0.0175\n", - " 0.1135\n", - " 0.1049\n", - " 0.0318\n", - " -0.0166\n", - " 0.0242\n", - " 0.0569\n", - " 0.1420\n", - " -0.1035\n", - " 0.0536\n", - " -0.1027\n", - " -0.1302\n", - " 0.0295\n", - " 0.0140\n", - " 0.1080\n", - " 0.0770\n", - " 0.1285\n", - " -0.0579\n", - " -0.0593\n", - " 0.0450\n", - " -0.2370\n", - " 0.0294\n", - " 0.2751\n", - " -0.0870\n", - " 0.0337\n", - " 0.0056\n", - " 0.0325\n", - " -0.0473\n", - " 0.0454\n", - " -0.0045\n", - " -0.0056\n", - " 0.1151\n", - " 0.0345\n", - " 0.0490\n", - " 0.2114\n", - " -0.0237\n", - " 0.0176\n", - " 0.0554\n", - " 0.0154\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.6.bn.running_mean', \n", - " 0.1225\n", - " 5.6629\n", - " 0.3841\n", - " 0.4293\n", - " 0.3980\n", - " 0.9333\n", - " 0.3633\n", - " 0.5193\n", - " 0.8983\n", - " 0.3812\n", - " 1.3739\n", - " 0.1601\n", - " 0.3842\n", - " 3.3985\n", - " 0.5929\n", - " 0.0909\n", - " 0.8515\n", - " 0.3350\n", - " 0.0835\n", - " 0.5757\n", - " 1.8449\n", - " 1.4684\n", - " 0.2797\n", - " 0.8511\n", - " 5.5845\n", - " 0.9328\n", - " 0.2805\n", - " 0.1486\n", - " 0.2183\n", - " 0.4159\n", - " 0.2561\n", - " 1.0573\n", - " 0.6213\n", - " 2.2505\n", - " 1.3800\n", - " 2.7031\n", - " 11.2693\n", - " 1.5261\n", - " 5.5516\n", - " 1.0681\n", - " 2.6043\n", - " 0.8857\n", - " 0.4740\n", - " 0.1695\n", - " 7.5865\n", - " 3.3979\n", - " 0.3413\n", - " 1.0511\n", - " 0.4375\n", - " 1.3408\n", - " 0.9484\n", - " 1.4140\n", - " 1.3673\n", - " 1.3293\n", - " 0.9539\n", - " 0.2955\n", - " 1.1923\n", - " 0.7360\n", - " 0.4612\n", - " 0.2043\n", - " 0.0889\n", - " 0.5241\n", - " 1.0609\n", - " 0.3513\n", - " 0.7633\n", - " 0.6991\n", - " 0.3560\n", - " 1.9585\n", - " 0.5934\n", - " 0.5133\n", - " 2.8330\n", - " 0.4533\n", - " 0.0918\n", - " 0.5697\n", - " 0.3809\n", - " 3.3425\n", - " 0.1446\n", - " 0.1284\n", - " 0.1832\n", - " 1.7694\n", - " 1.3482\n", - " 5.0727\n", - " 0.4330\n", - " 0.9860\n", - " 0.6478\n", - " 11.7975\n", - " 1.7110\n", - " 0.7188\n", - " 0.0323\n", - " 5.5869\n", - " 0.2135\n", - " 0.7491\n", - " 0.2602\n", - " 0.3105\n", - " 1.0022\n", - " 1.0937\n", - " 0.0551\n", - " 0.4785\n", - " 0.1808\n", - " 7.3116\n", - " 1.1992\n", - " 0.0383\n", - " 0.5896\n", - " 0.3206\n", - " 6.3004\n", - " 0.6682\n", - " 0.2665\n", - " 0.4015\n", - " 0.9495\n", - " 1.3414\n", - " 1.7563\n", - " 2.1910\n", - " 0.1055\n", - " 0.1848\n", - " 0.6613\n", - " 0.0694\n", - " 1.1714\n", - " 1.0957\n", - " 0.2106\n", - " 14.4284\n", - " 1.2125\n", - " 0.2225\n", - " 0.1841\n", - " 0.0471\n", - " 0.5587\n", - " 0.3019\n", - " 3.0310\n", - " 0.1366\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.6.bn.running_var', \n", - " 1.2599\n", - " 73.2295\n", - " 3.8698\n", - " 5.0350\n", - " 4.1386\n", - " 11.7909\n", - " 4.4939\n", - " 6.1335\n", - " 11.6138\n", - " 4.7495\n", - " 17.9012\n", - " 1.8395\n", - " 3.5470\n", - " 35.2414\n", - " 7.7309\n", - " 0.8977\n", - " 10.8892\n", - " 3.6262\n", - " 0.9323\n", - " 6.7581\n", - " 20.3248\n", - " 18.4403\n", - " 2.6988\n", - " 11.7070\n", - " 46.7139\n", - " 12.6321\n", - " 3.6993\n", - " 1.2615\n", - " 2.0364\n", - " 5.4538\n", - " 2.9888\n", - " 11.9071\n", - " 7.0494\n", - " 28.3594\n", - " 15.1422\n", - " 27.9737\n", - " 82.0041\n", - " 20.0873\n", - " 57.2968\n", - " 12.5148\n", - " 35.7018\n", - " 10.4392\n", - " 6.3748\n", - " 2.0117\n", - " 100.2919\n", - " 41.9469\n", - " 3.8628\n", - " 11.7238\n", - " 4.9764\n", - " 17.7406\n", - " 12.6431\n", - " 14.7179\n", - " 12.8097\n", - " 14.9661\n", - " 10.4469\n", - " 3.3374\n", - " 12.9786\n", - " 9.2808\n", - " 5.3461\n", - " 1.6822\n", - " 0.9324\n", - " 7.0337\n", - " 11.8969\n", - " 3.9069\n", - " 9.7212\n", - " 7.7170\n", - " 4.3436\n", - " 22.5543\n", - " 6.9971\n", - " 5.5269\n", - " 46.4625\n", - " 7.0761\n", - " 0.9344\n", - " 6.4191\n", - " 4.1967\n", - " 31.3372\n", - " 1.3874\n", - " 1.0796\n", - " 1.6380\n", - " 24.6118\n", - " 19.2409\n", - " 54.6652\n", - " 5.4432\n", - " 10.5934\n", - " 8.4867\n", - " 127.3044\n", - " 25.4032\n", - " 8.7970\n", - " 0.4156\n", - " 36.1659\n", - " 2.5299\n", - " 8.9533\n", - " 3.0954\n", - " 3.1635\n", - " 11.6492\n", - " 9.1341\n", - " 0.7056\n", - " 5.0821\n", - " 1.6385\n", - " 72.9483\n", - " 14.3235\n", - " 0.3451\n", - " 7.7560\n", - " 3.5609\n", - " 73.2377\n", - " 8.5205\n", - " 3.3550\n", - " 4.4147\n", - " 12.5989\n", - " 14.3726\n", - " 20.5676\n", - " 25.9159\n", - " 1.0745\n", - " 2.0458\n", - " 7.9261\n", - " 0.6185\n", - " 16.3564\n", - " 11.1497\n", - " 2.3524\n", - " 212.2715\n", - " 13.2993\n", - " 2.2799\n", - " 2.1828\n", - " 0.4154\n", - " 6.3970\n", - " 3.1219\n", - " 35.8959\n", - " 1.4659\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.7.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 5.8847e-03 2.1921e-03 -1.8377e-02 ... -1.3964e-02 2.6978e-02 5.5054e-02\n", - " 7.2680e-02 -3.6535e-01 -5.2221e-03 ... -2.3408e-01 1.2410e-01 3.1972e-03\n", - " 1.3844e-02 -1.1377e-01 1.9315e-02 ... 3.8549e-02 -1.6374e+00 4.1895e-01\n", - " ... ⋱ ... \n", - " -4.8199e-01 3.3490e-01 -3.2895e-01 ... 8.4339e-02 6.1372e-02 -1.5737e+00\n", - " -1.0041e+00 -1.5568e-01 5.7800e-02 ... -5.0289e-01 9.4830e-02 1.0988e-02\n", - " -1.4340e-01 -3.7784e-01 1.9994e-01 ... -1.7842e-01 -5.8743e-02 -7.0364e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " -3.3945e-02 -8.2994e-04 1.3010e-02 ... 1.9219e-02 -2.1708e-02 3.9321e-02\n", - " 3.4651e-02 -1.7901e-01 -5.5429e-01 ... -7.5145e-01 -4.6915e-01 6.6311e-02\n", - " 2.7764e-01 -9.3591e-02 1.3791e-01 ... -2.7145e-01 2.8122e-02 1.0651e-01\n", - " ... ⋱ ... \n", - " 8.7166e-02 1.5260e-01 -7.1763e-02 ... -1.3844e+00 -1.0487e+00 -1.2397e+00\n", - " -1.2803e-01 -3.3113e-02 -3.9552e-01 ... -3.1659e-01 1.0684e-01 8.3546e-02\n", - " -8.9448e-02 2.4670e-02 2.8182e-02 ... 1.6902e-01 -5.5595e-02 -2.6387e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " 2.4514e-02 2.0854e-02 -3.0092e-02 ... 4.2989e-02 3.4699e-02 3.5149e-02\n", - " -8.7910e-02 -3.7414e-02 -1.6182e-02 ... 1.3356e-01 -3.4510e-01 2.9737e-01\n", - " -5.0063e-02 -2.8385e-01 2.0934e-01 ... 2.8240e-02 8.9445e-02 -7.5023e-01\n", - " ... ⋱ ... \n", - " -1.0467e-01 -2.0252e-01 -9.7439e-02 ... -1.9067e-03 7.2500e-02 -3.6290e-02\n", - " 5.2983e-01 3.4188e-02 -1.1855e-01 ... -2.9443e-02 -2.1767e-01 -1.4374e-01\n", - " 2.0587e-01 -5.5229e-02 1.2016e-01 ... 1.0737e-01 2.8937e-03 9.0145e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " 2.3339e-02 -2.2911e-02 -2.5257e-02 ... 3.4837e-02 -3.6842e-03 -2.4066e-02\n", - " 1.7219e-01 2.6568e-01 3.3388e-02 ... -8.4869e-02 1.0888e-01 -1.6184e-01\n", - " 1.4101e-01 1.0440e-01 -1.5647e-01 ... 1.7188e-01 -1.0538e+00 -1.3552e+00\n", - " ... ⋱ ... \n", - " -3.3658e-01 1.9421e-01 7.1169e-04 ... -4.4340e-01 -1.4363e+00 1.1073e-01\n", - " -2.8200e-01 -2.7627e-01 -3.5717e-01 ... -2.6617e-01 1.8424e-01 -5.1155e-02\n", - " -2.2967e-01 -9.3827e-02 1.8522e-01 ... -3.8467e-01 -8.9247e-02 -5.5430e-02\n", - " \n", - " (126,.,.) = \n", - " -1.6671e-02 -4.0164e-02 -3.8903e-02 ... -6.3660e-02 -3.1307e-02 -2.3072e-02\n", - " -2.9091e-01 1.1981e-01 8.3795e-02 ... -1.5997e+00 -1.1918e+00 -9.8389e-02\n", - " 1.3939e-01 1.5886e-01 1.7142e-01 ... 2.3185e-01 -5.8920e-01 2.3799e-01\n", - " ... ⋱ ... \n", - " 3.5581e-02 2.9580e-02 1.4395e-01 ... -6.6605e-01 -1.4438e+00 -7.2258e-01\n", - " 1.3443e-02 -1.0530e-01 -6.6303e-01 ... -5.2410e-01 -1.6476e-01 9.5399e-02\n", - " 2.5848e-02 -8.6919e-02 -1.2343e-01 ... 7.9862e-02 -1.0104e-02 -3.2548e-01\n", - " \n", - " (127,.,.) = \n", - " 1.3345e-02 1.6564e-02 -3.3550e-02 ... 2.5938e-02 2.8583e-02 -4.1905e-02\n", - " 1.3643e-01 -1.3233e-01 1.6668e-01 ... 1.4648e-01 -1.8051e-01 -9.3971e-02\n", - " 7.5661e-02 1.2190e-01 -1.9221e-01 ... 6.9154e-03 -1.6507e-02 -5.3025e-02\n", - " ... ⋱ ... \n", - " 3.2565e-01 1.2659e-01 1.3699e-01 ... -1.9743e-01 2.6545e-01 -3.4281e-02\n", - " -1.7613e-02 -8.1578e-02 4.1549e-01 ... -1.9517e-01 4.2245e-01 2.1490e-02\n", - " 9.5571e-02 1.0512e-01 -6.3192e-02 ... 1.4061e-01 2.6262e-01 1.6268e-01\n", - " [torch.FloatTensor of size 128x128x8]),\n", - " ('module.encoder.cbhg.conv1d_banks.7.bn.weight', \n", - " 0.5201\n", - " 0.6178\n", - " 0.3503\n", - " 0.1448\n", - " 0.5527\n", - " 0.4999\n", - " 0.7059\n", - " 0.4651\n", - " 0.4145\n", - " 0.4550\n", - " 0.5447\n", - " 0.4034\n", - " 0.4155\n", - " 0.3661\n", - " 0.5640\n", - " 0.5221\n", - " 0.3899\n", - " 0.4972\n", - " 0.5214\n", - " 0.4538\n", - " 0.5328\n", - " -1.0370\n", - " 0.6178\n", - " 0.5419\n", - " 0.4244\n", - " 0.3987\n", - " -1.6089\n", - " 0.4248\n", - " 0.3864\n", - " 0.4433\n", - " 0.4740\n", - " 0.5513\n", - " 0.4550\n", - " -1.1776\n", - " 0.5307\n", - " 0.5215\n", - " 0.4541\n", - " 0.5152\n", - " 0.4265\n", - " -0.4415\n", - " 0.4885\n", - " 0.6703\n", - " 0.4037\n", - " 0.5493\n", - " 0.3952\n", - " 0.3893\n", - " 0.4978\n", - " 0.5512\n", - " 0.4581\n", - " 0.0155\n", - " 0.6011\n", - " 0.4262\n", - " 0.4914\n", - " 0.4226\n", - " 0.4978\n", - " 0.5835\n", - " 0.4875\n", - " 0.3945\n", - " 0.3939\n", - " 0.4874\n", - " 0.5940\n", - " 0.5797\n", - " -0.9385\n", - " 0.6759\n", - " 0.5902\n", - " 0.5815\n", - " 0.5256\n", - " 0.4608\n", - " -1.2700\n", - " 0.4835\n", - " 0.5862\n", - " 0.8845\n", - " 0.5304\n", - " 0.3843\n", - " 0.4911\n", - " 0.4653\n", - " -0.8797\n", - " 0.4928\n", - " -1.0461\n", - " 0.4720\n", - " 0.5692\n", - " -0.8213\n", - " 0.4002\n", - " 0.4373\n", - " 0.5114\n", - " 0.4222\n", - " 0.3675\n", - " -1.3332\n", - " 0.4836\n", - " 0.5117\n", - " 0.4860\n", - " 0.4630\n", - " 0.4095\n", - " -0.6982\n", - " -0.8360\n", - " 0.5048\n", - " 0.4621\n", - " 0.4235\n", - " -0.8588\n", - " 0.5175\n", - " 0.3910\n", - " 0.5467\n", - " 0.5042\n", - " 0.5353\n", - " 0.4100\n", - " -1.0143\n", - " 0.4884\n", - " 0.4789\n", - " 0.4557\n", - " 0.6047\n", - " 0.4890\n", - " 1.0135\n", - " 0.6491\n", - " -0.9885\n", - " 0.4902\n", - " 0.4262\n", - " 0.5985\n", - " 0.3811\n", - " 0.4982\n", - " 0.4380\n", - " -1.0503\n", - " 0.5028\n", - " 0.3959\n", - " -0.9950\n", - " 0.5104\n", - " 0.4080\n", - " 0.5989\n", - " -0.7140\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.7.bn.bias', \n", - " 0.0531\n", - " 0.1352\n", - " -0.1326\n", - " -0.0226\n", - " 0.1226\n", - " -0.0110\n", - " -0.0515\n", - " -0.1070\n", - " -0.0722\n", - " 0.0605\n", - " 0.0245\n", - " 0.1540\n", - " 0.0093\n", - " 0.0824\n", - " -0.1027\n", - " 0.0786\n", - " 0.0775\n", - " 0.0218\n", - " 0.0305\n", - " -0.0334\n", - " -0.0583\n", - " -0.2360\n", - " 0.1077\n", - " 0.0735\n", - " 0.0465\n", - " 0.0191\n", - " -0.1012\n", - " -0.0647\n", - " -0.0001\n", - " 0.1090\n", - " -0.0216\n", - " 0.0867\n", - " -0.1156\n", - " -0.0776\n", - " 0.0726\n", - " -0.0987\n", - " 0.2782\n", - " 0.0555\n", - " -0.0561\n", - " -0.0393\n", - " 0.1253\n", - " 0.0206\n", - " -0.1254\n", - " 0.0507\n", - " 0.0083\n", - " 0.0365\n", - " 0.0373\n", - " 0.0833\n", - " 0.1624\n", - " -0.0043\n", - " 0.1495\n", - " 0.0487\n", - " 0.0595\n", - " -0.0549\n", - " 0.1385\n", - " 0.0319\n", - " 0.0761\n", - " 0.1448\n", - " -0.0136\n", - " 0.0397\n", - " 0.2314\n", - " 0.2268\n", - " -0.0985\n", - " 0.1825\n", - " 0.1466\n", - " -0.0436\n", - " 0.0372\n", - " 0.0725\n", - " -0.0878\n", - " -0.0063\n", - " 0.1393\n", - " 0.1552\n", - " -0.0325\n", - " 0.0941\n", - " 0.0756\n", - " 0.1570\n", - " -0.1996\n", - " -0.0028\n", - " -0.2038\n", - " 0.0497\n", - " 0.0060\n", - " -0.1240\n", - " -0.0317\n", - " 0.0253\n", - " 0.0478\n", - " -0.0950\n", - " 0.0721\n", - " -0.2091\n", - " 0.0940\n", - " -0.1020\n", - " 0.0115\n", - " 0.0147\n", - " 0.1373\n", - " -0.1032\n", - " 0.0048\n", - " -0.1266\n", - " -0.1190\n", - " 0.1090\n", - " -0.0406\n", - " 0.1024\n", - " -0.1009\n", - " 0.0821\n", - " 0.0140\n", - " 0.2145\n", - " 0.0478\n", - " -0.2095\n", - " -0.1442\n", - " 0.0544\n", - " 0.0345\n", - " 0.0340\n", - " 0.0834\n", - " 0.2172\n", - " 0.1414\n", - " -0.1626\n", - " -0.0807\n", - " 0.0232\n", - " -0.0692\n", - " -0.0451\n", - " 0.0634\n", - " 0.0790\n", - " -0.1703\n", - " 0.0544\n", - " 0.0259\n", - " -0.2925\n", - " -0.1293\n", - " 0.0527\n", - " 0.0511\n", - " -0.0644\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.7.bn.running_mean', \n", - " 0.2812\n", - " 0.1372\n", - " 3.9745\n", - " 7.4301\n", - " 1.2966\n", - " 2.3002\n", - " 0.3036\n", - " 1.0330\n", - " 0.1696\n", - " 0.1970\n", - " 0.0372\n", - " 0.8095\n", - " 3.5538\n", - " 0.6015\n", - " 1.1094\n", - " 0.5394\n", - " 6.0354\n", - " 0.2902\n", - " 1.2992\n", - " 1.0459\n", - " 1.4696\n", - " 3.5347\n", - " 1.2122\n", - " 0.8080\n", - " 0.5515\n", - " 0.7807\n", - " 0.0896\n", - " 0.6803\n", - " 1.1137\n", - " 0.1049\n", - " 2.4879\n", - " 0.1218\n", - " 0.5865\n", - " 1.5523\n", - " 4.8728\n", - " 0.9130\n", - " 0.9491\n", - " 1.5586\n", - " 2.0178\n", - " 12.4710\n", - " 0.8012\n", - " 0.3510\n", - " 0.3879\n", - " 8.8085\n", - " 1.0802\n", - " 0.4705\n", - " 0.9743\n", - " 0.4803\n", - " 0.5677\n", - " 9.9005\n", - " 1.3111\n", - " 0.3241\n", - " 1.5443\n", - " 0.9569\n", - " 1.3462\n", - " 1.8252\n", - " 0.9787\n", - " 1.1744\n", - " 0.8573\n", - " 0.9252\n", - " 4.9456\n", - " 4.2871\n", - " 0.0392\n", - " 0.4906\n", - " 3.7855\n", - " 0.1991\n", - " 0.8360\n", - " 0.8939\n", - " 0.3714\n", - " 0.2258\n", - " 2.8575\n", - " 0.2458\n", - " 0.1251\n", - " 0.9596\n", - " 0.2072\n", - " 0.5053\n", - " 3.7698\n", - " 9.7333\n", - " 1.5940\n", - " 1.2681\n", - " 0.1128\n", - " 3.9079\n", - " 4.3071\n", - " 0.1252\n", - " 0.5050\n", - " 1.4095\n", - " 1.2890\n", - " 0.6643\n", - " 0.2945\n", - " 0.3837\n", - " 1.9824\n", - " 0.4402\n", - " 0.7092\n", - " 2.5530\n", - " 6.0500\n", - " 0.6629\n", - " 0.4099\n", - " 1.3019\n", - " 5.8735\n", - " 0.8943\n", - " 0.2518\n", - " 0.9939\n", - " 0.1958\n", - " 2.5477\n", - " 0.3094\n", - " 2.3750\n", - " 0.7993\n", - " 1.1749\n", - " 0.4016\n", - " 8.2615\n", - " 0.7854\n", - " 0.1583\n", - " 1.5999\n", - " 5.3653\n", - " 0.5013\n", - " 0.9868\n", - " 0.3480\n", - " 0.2088\n", - " 0.2505\n", - " 0.3646\n", - " 1.0942\n", - " 0.3739\n", - " 0.5132\n", - " 3.1107\n", - " 0.9551\n", - " 0.9754\n", - " 0.0634\n", - " 5.9613\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.7.bn.running_var', \n", - " 3.4528\n", - " 1.4335\n", - " 56.6181\n", - " 43.0498\n", - " 19.2615\n", - " 33.8639\n", - " 3.2992\n", - " 13.0646\n", - " 1.7917\n", - " 2.1548\n", - " 0.5802\n", - " 10.7251\n", - " 53.0717\n", - " 7.4029\n", - " 16.0876\n", - " 7.8325\n", - " 64.2025\n", - " 3.4285\n", - " 17.8234\n", - " 14.2309\n", - " 20.2142\n", - " 46.8463\n", - " 13.8736\n", - " 11.6845\n", - " 6.8415\n", - " 10.1249\n", - " 0.5939\n", - " 7.8891\n", - " 14.9711\n", - " 1.2082\n", - " 26.2391\n", - " 1.4065\n", - " 7.8775\n", - " 21.0462\n", - " 59.2802\n", - " 12.7272\n", - " 15.2322\n", - " 23.1035\n", - " 29.2466\n", - " 100.4376\n", - " 11.8395\n", - " 4.3766\n", - " 4.0224\n", - " 99.0680\n", - " 14.3279\n", - " 4.6216\n", - " 10.4203\n", - " 7.0414\n", - " 7.9117\n", - " 88.7863\n", - " 14.2661\n", - " 3.8882\n", - " 23.2832\n", - " 12.0185\n", - " 20.1529\n", - " 22.3754\n", - " 13.8920\n", - " 15.9952\n", - " 10.1813\n", - " 13.3677\n", - " 67.4268\n", - " 49.7770\n", - " 0.2859\n", - " 7.1800\n", - " 45.6419\n", - " 2.0598\n", - " 11.4943\n", - " 10.0566\n", - " 3.6953\n", - " 2.3665\n", - " 36.3690\n", - " 2.9168\n", - " 1.4601\n", - " 12.4855\n", - " 2.7109\n", - " 6.1154\n", - " 47.0909\n", - " 84.9703\n", - " 28.9722\n", - " 17.3121\n", - " 1.7708\n", - " 46.1098\n", - " 40.3095\n", - " 1.3421\n", - " 6.2969\n", - " 19.3591\n", - " 18.4596\n", - " 8.4604\n", - " 3.7825\n", - " 4.2503\n", - " 28.9321\n", - " 4.7093\n", - " 9.0993\n", - " 26.3167\n", - " 79.0112\n", - " 8.9691\n", - " 4.0326\n", - " 19.4620\n", - " 78.8674\n", - " 12.4432\n", - " 3.5051\n", - " 12.4378\n", - " 2.4673\n", - " 29.6752\n", - " 3.7716\n", - " 32.0126\n", - " 10.5445\n", - " 13.3852\n", - " 5.1200\n", - " 84.1416\n", - " 12.3769\n", - " 1.6237\n", - " 19.6032\n", - " 71.8491\n", - " 5.8113\n", - " 10.7161\n", - " 4.0810\n", - " 2.1904\n", - " 2.5760\n", - " 5.0541\n", - " 12.6978\n", - " 4.9397\n", - " 5.0230\n", - " 37.5503\n", - " 12.6321\n", - " 13.6513\n", - " 0.5706\n", - " 66.9323\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.8.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -2.7547e-02 4.2572e-02 4.9609e-02 ... 3.9734e-02 4.3074e-02 3.7359e-02\n", - " 7.7272e-02 1.5924e-01 1.1967e-01 ... -1.0723e+00 -1.3182e+00 8.4199e-02\n", - " 1.2160e-01 -2.6351e-01 -9.3865e-02 ... 1.9552e-01 3.4170e-01 -3.8920e-01\n", - " ... ⋱ ... \n", - " 8.6547e-02 1.8861e-02 1.8739e-01 ... -1.3392e+00 1.6523e-01 -7.8392e-01\n", - " 6.9175e-02 5.4181e-02 2.3608e-01 ... -6.6663e-01 -8.3389e-02 -2.4729e-01\n", - " -1.6154e-01 -5.3170e-02 7.6704e-02 ... -8.9796e-02 -4.5192e-02 -1.7497e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " 2.0992e-02 -6.3350e-03 5.8229e-03 ... -7.8143e-03 1.5739e-02 9.7577e-04\n", - " -6.3255e-02 1.6842e-01 2.9652e-01 ... -1.1351e+00 2.6696e-02 -2.3831e-01\n", - " -7.5772e-03 4.0876e-01 2.8722e-02 ... -3.6409e-01 1.6402e-02 6.1327e-02\n", - " ... ⋱ ... \n", - " 4.7118e-03 4.9662e-02 -1.0712e-01 ... 2.1898e-01 2.2844e-01 4.2636e-01\n", - " -1.5443e-01 -4.4623e-02 -4.2821e-02 ... 3.0447e-01 -1.1925e+00 -1.1519e-01\n", - " -6.3182e-02 8.5808e-02 -1.1307e-01 ... 5.1514e-02 -1.5927e-01 -2.3402e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " -8.6872e-03 9.8962e-03 -1.3881e-02 ... 6.4804e-03 1.7940e-02 -1.6702e-02\n", - " -5.5718e-02 -3.8603e-02 3.3173e-01 ... -5.4399e-02 -2.6046e-02 -1.8899e-01\n", - " 1.7230e-01 -3.0073e-01 1.2899e-01 ... 1.5112e-01 -2.3045e-01 -2.2903e-01\n", - " ... ⋱ ... \n", - " -6.5743e-02 -4.2672e-03 1.5176e-01 ... 6.4832e-03 1.4361e-01 3.0495e-02\n", - " 1.7581e-01 -1.3613e-01 -1.2332e-01 ... -1.2568e-01 -4.6832e-03 -2.4509e-01\n", - " -4.4992e-02 -1.2710e-01 1.3147e-02 ... -3.4621e-02 2.4574e-02 -4.5390e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -1.7292e-02 -7.6999e-03 1.0731e-02 ... 6.0297e-03 3.7521e-03 2.6006e-02\n", - " -7.9919e-02 -4.9393e-02 -5.8662e-01 ... -3.1168e-01 -1.3692e+00 -9.1817e-01\n", - " -3.7842e-01 5.7602e-02 -7.9871e-02 ... 9.0100e-03 -9.7410e-02 5.3491e-02\n", - " ... ⋱ ... \n", - " -2.0529e-01 6.3498e-02 2.7442e-02 ... -7.3926e-01 1.5999e-01 -1.1082e-01\n", - " -2.1393e-01 1.3996e-01 -7.4514e-02 ... -1.9744e-01 -4.8600e-01 -4.3896e-01\n", - " 1.2696e-02 -1.9454e-01 5.5097e-02 ... -9.6668e-03 -7.0573e-01 -4.2450e-01\n", - " \n", - " (126,.,.) = \n", - " 9.5369e-03 -1.4266e-02 -9.3971e-03 ... 1.1653e-02 -4.1660e-03 1.2811e-02\n", - " 1.8217e-01 2.1676e-02 -3.0760e-01 ... 1.6222e-01 1.0926e-01 -4.7690e-02\n", - " 5.3789e-02 -2.5205e-01 2.3654e-01 ... -5.4497e-03 7.4248e-02 -2.2669e-01\n", - " ... ⋱ ... \n", - " -1.6270e-01 -8.9960e-02 -6.6290e-02 ... 5.3901e-02 2.5797e-04 7.1787e-01\n", - " -1.0061e-01 1.4840e-01 -1.0621e-01 ... -5.7225e-01 3.4818e-02 4.1044e-01\n", - " -3.5203e-02 -7.5935e-03 8.3569e-02 ... 1.1512e-01 1.9343e-02 -8.4515e-02\n", - " \n", - " (127,.,.) = \n", - " 1.7825e-02 5.7461e-03 -5.2420e-02 ... -8.4316e-03 3.7530e-02 7.4160e-03\n", - " -7.5303e-01 2.5693e-01 -5.4925e-01 ... -1.6473e-01 9.0183e-03 -1.2218e-01\n", - " -3.1759e-01 5.5704e-02 -2.5702e-01 ... 1.2007e-01 -5.1170e-01 -5.0482e-01\n", - " ... ⋱ ... \n", - " 7.2999e-02 2.4884e-01 -6.3399e-01 ... -1.0819e-01 -8.2761e-01 -3.9823e-02\n", - " -6.4678e-02 2.1777e-01 1.4814e-01 ... -2.4322e-01 2.4405e-01 -5.4170e-01\n", - " 1.5825e-01 -1.0545e-01 8.2131e-02 ... 3.3050e-01 -3.3931e-01 -6.4139e-02\n", - " [torch.FloatTensor of size 128x128x9]),\n", - " ('module.encoder.cbhg.conv1d_banks.8.bn.weight', \n", - " 0.5771\n", - " 0.4581\n", - " -0.7716\n", - " 0.6157\n", - " -0.0321\n", - " 0.5181\n", - " -0.9333\n", - " 0.9233\n", - " 0.4347\n", - " 0.4704\n", - " -1.3071\n", - " 0.4834\n", - " 0.4864\n", - " 0.3935\n", - " 0.4802\n", - " 0.4552\n", - " 0.4248\n", - " 0.5687\n", - " 0.4133\n", - " 0.5554\n", - " 0.5055\n", - " 0.5408\n", - " 0.4969\n", - " 0.4613\n", - " -0.9117\n", - " 0.6503\n", - " 0.3440\n", - " 0.4934\n", - " 0.4743\n", - " 0.7277\n", - " 0.5781\n", - " -0.9944\n", - " 0.4250\n", - " 0.4817\n", - " 0.4396\n", - " 0.6737\n", - " 0.4569\n", - " 0.4752\n", - " 0.4585\n", - " 0.4791\n", - " 0.7359\n", - " 0.5473\n", - " 0.5542\n", - " 0.8879\n", - " 0.4969\n", - " 0.4156\n", - " 0.4636\n", - " 0.5663\n", - " 0.6065\n", - " 0.4312\n", - " 0.4343\n", - " -1.1273\n", - " -1.2112\n", - " 0.4511\n", - " -1.0567\n", - " 0.4800\n", - " 0.7169\n", - " 0.6837\n", - " 0.4633\n", - " 0.4376\n", - " 0.4631\n", - " 0.3726\n", - " 0.4705\n", - " 0.4251\n", - " -0.7982\n", - " -1.0721\n", - " 0.6287\n", - " 0.3680\n", - " 0.4368\n", - " 0.4333\n", - " -0.9332\n", - " 0.3998\n", - " 0.4077\n", - " 0.4922\n", - " 0.4723\n", - " 0.5908\n", - " 0.5140\n", - " -0.8896\n", - " -0.7219\n", - " 0.4918\n", - " 0.5012\n", - " 0.4491\n", - " 0.3801\n", - " 0.3578\n", - " 0.3361\n", - " -0.8209\n", - " 0.5648\n", - " 0.5712\n", - " 0.4660\n", - " 0.5767\n", - " -0.9550\n", - " 0.5229\n", - " 1.1241\n", - " 0.4727\n", - " 0.4580\n", - " 0.4395\n", - " 0.4749\n", - " 0.4501\n", - " 0.4727\n", - " 0.4406\n", - " -0.0748\n", - " 0.5233\n", - " 0.3423\n", - " 0.5639\n", - " 0.5692\n", - " 0.6152\n", - " 0.4194\n", - " -1.0430\n", - " -0.9918\n", - " 0.5580\n", - " 0.4808\n", - " 0.4698\n", - " 0.5428\n", - " 0.4862\n", - " 0.4849\n", - " 0.3935\n", - " 0.4148\n", - " 0.6059\n", - " 0.3756\n", - " 0.4541\n", - " 0.4012\n", - " 0.4700\n", - " -1.0728\n", - " 0.4493\n", - " 0.5653\n", - " 0.5266\n", - " -0.7840\n", - " 0.5118\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.8.bn.bias', \n", - " 0.0501\n", - " -0.0455\n", - " -0.1282\n", - " 0.0573\n", - " -0.0026\n", - " 0.2111\n", - " -0.2480\n", - " 0.1674\n", - " -0.0672\n", - " 0.1419\n", - " -0.1950\n", - " -0.0008\n", - " 0.0860\n", - " 0.0740\n", - " -0.0304\n", - " 0.0451\n", - " 0.0265\n", - " 0.0886\n", - " -0.0701\n", - " 0.0380\n", - " 0.0024\n", - " 0.1123\n", - " 0.0816\n", - " -0.0170\n", - " -0.0699\n", - " 0.2331\n", - " 0.0394\n", - " 0.0186\n", - " -0.0887\n", - " 0.1286\n", - " 0.0954\n", - " -0.3693\n", - " 0.2549\n", - " 0.0197\n", - " 0.0338\n", - " -0.0266\n", - " 0.0174\n", - " 0.1036\n", - " -0.0043\n", - " 0.1029\n", - " 0.1415\n", - " 0.0642\n", - " 0.1490\n", - " 0.1656\n", - " 0.0606\n", - " 0.1683\n", - " 0.0088\n", - " 0.0462\n", - " -0.0647\n", - " -0.0888\n", - " -0.1728\n", - " -0.2408\n", - " -0.0847\n", - " -0.0096\n", - " -0.0791\n", - " -0.2313\n", - " -0.0319\n", - " 0.1064\n", - " 0.0187\n", - " 0.0005\n", - " -0.0566\n", - " 0.1294\n", - " 0.0037\n", - " -0.0533\n", - " -0.0811\n", - " -0.1528\n", - " 0.1238\n", - " -0.0294\n", - " 0.1289\n", - " -0.1212\n", - " -0.2811\n", - " 0.1584\n", - " 0.0270\n", - " 0.0790\n", - " 0.1521\n", - " -0.0276\n", - " 0.0304\n", - " -0.2490\n", - " -0.0301\n", - " -0.0343\n", - " 0.0026\n", - " -0.0490\n", - " -0.0082\n", - " 0.0151\n", - " 0.0540\n", - " -0.0843\n", - " 0.2147\n", - " 0.1075\n", - " 0.0691\n", - " 0.0812\n", - " -0.1074\n", - " 0.0371\n", - " 0.0939\n", - " 0.0553\n", - " 0.0268\n", - " -0.0289\n", - " 0.0969\n", - " 0.0376\n", - " 0.0850\n", - " 0.0560\n", - " -0.0032\n", - " 0.1189\n", - " -0.1336\n", - " 0.1118\n", - " 0.0979\n", - " -0.1019\n", - " 0.0585\n", - " -0.1189\n", - " -0.2122\n", - " 0.0355\n", - " 0.0324\n", - " 0.1024\n", - " -0.0053\n", - " 0.0846\n", - " -0.0164\n", - " 0.0347\n", - " -0.0575\n", - " 0.1198\n", - " 0.0437\n", - " 0.0006\n", - " 0.0076\n", - " 0.0700\n", - " -0.3651\n", - " 0.1086\n", - " -0.0448\n", - " 0.1511\n", - " -0.1061\n", - " 0.0879\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.8.bn.running_mean', \n", - " 0.2172\n", - " 1.0771\n", - " 4.1651\n", - " 0.9412\n", - " 9.1795\n", - " 0.7024\n", - " 4.9453\n", - " 0.0088\n", - " 0.4241\n", - " 0.5401\n", - " 0.4936\n", - " 0.9792\n", - " 0.2949\n", - " 0.4522\n", - " 1.2292\n", - " 0.4527\n", - " 0.2819\n", - " 0.3859\n", - " 0.1360\n", - " 5.6269\n", - " 1.4467\n", - " 0.9929\n", - " 0.6515\n", - " 0.1727\n", - " 1.0151\n", - " 0.8062\n", - " 0.8452\n", - " 0.2774\n", - " 1.3303\n", - " 1.9343\n", - " 0.2595\n", - " 7.0958\n", - " 1.3832\n", - " 0.1969\n", - " 1.2018\n", - " 0.2215\n", - " 0.3508\n", - " 0.1674\n", - " 0.1257\n", - " 0.6598\n", - " 0.2392\n", - " 0.0863\n", - " 1.0187\n", - " 0.2623\n", - " 2.1649\n", - " 0.8956\n", - " 1.5223\n", - " 0.0435\n", - " 0.5574\n", - " 0.6954\n", - " 0.8634\n", - " 2.5703\n", - " 1.4503\n", - " 1.2198\n", - " 2.5609\n", - " 0.5472\n", - " 0.1622\n", - " 0.7101\n", - " 0.7225\n", - " 0.1451\n", - " 0.8528\n", - " 1.9435\n", - " 0.5099\n", - " 0.2758\n", - " 7.3982\n", - " 1.0463\n", - " 1.3533\n", - " 0.1834\n", - " 0.3139\n", - " 1.3081\n", - " 9.1900\n", - " 1.7530\n", - " 0.3186\n", - " 0.0864\n", - " 0.3088\n", - " 3.5589\n", - " 3.8022\n", - " 4.5939\n", - " 0.1067\n", - " 2.4051\n", - " 1.6988\n", - " 3.5461\n", - " 1.2979\n", - " 0.2601\n", - " 0.2093\n", - " 0.6377\n", - " 0.9761\n", - " 0.7301\n", - " 0.8001\n", - " 0.1085\n", - " 2.1115\n", - " 0.2482\n", - " 0.0415\n", - " 0.0286\n", - " 0.4192\n", - " 0.9545\n", - " 0.8663\n", - " 0.3971\n", - " 1.4581\n", - " 0.2442\n", - " 8.4836\n", - " 0.1308\n", - " 1.3314\n", - " 0.4853\n", - " 0.2627\n", - " 20.8991\n", - " 0.4634\n", - " 6.1021\n", - " 3.4489\n", - " 1.1361\n", - " 1.6796\n", - " 0.4814\n", - " 0.1891\n", - " 0.5700\n", - " 0.7645\n", - " 0.5785\n", - " 0.9796\n", - " 0.2983\n", - " 2.0324\n", - " 0.8786\n", - " 0.2386\n", - " 1.3299\n", - " 3.0389\n", - " 0.8985\n", - " 0.3829\n", - " 0.1680\n", - " 6.3584\n", - " 0.9019\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.8.bn.running_var', \n", - " 2.6072\n", - " 15.8986\n", - " 41.4874\n", - " 12.6203\n", - " 120.1542\n", - " 9.2696\n", - " 69.7438\n", - " 0.1051\n", - " 5.7360\n", - " 7.4304\n", - " 5.7254\n", - " 13.7302\n", - " 3.6712\n", - " 5.7503\n", - " 19.8554\n", - " 5.4608\n", - " 3.6343\n", - " 4.9982\n", - " 1.3920\n", - " 56.8145\n", - " 23.1102\n", - " 13.2925\n", - " 8.4560\n", - " 2.5918\n", - " 14.3851\n", - " 12.5008\n", - " 11.4232\n", - " 3.0210\n", - " 22.5177\n", - " 24.3255\n", - " 3.4395\n", - " 105.0245\n", - " 20.6933\n", - " 2.8084\n", - " 18.2451\n", - " 3.1466\n", - " 4.5639\n", - " 2.3411\n", - " 1.3012\n", - " 9.6010\n", - " 3.1274\n", - " 1.0201\n", - " 16.0106\n", - " 4.2138\n", - " 34.5435\n", - " 12.2854\n", - " 23.6870\n", - " 0.5350\n", - " 7.4291\n", - " 8.9021\n", - " 11.7848\n", - " 36.8306\n", - " 20.2813\n", - " 17.7479\n", - " 36.3394\n", - " 7.2768\n", - " 2.1176\n", - " 10.1342\n", - " 11.8166\n", - " 2.0295\n", - " 10.7391\n", - " 28.6486\n", - " 6.8892\n", - " 4.2384\n", - " 101.8086\n", - " 10.5774\n", - " 15.8857\n", - " 2.3810\n", - " 3.1255\n", - " 19.9336\n", - " 109.9822\n", - " 27.0445\n", - " 3.5758\n", - " 1.2688\n", - " 4.0835\n", - " 55.7266\n", - " 59.2590\n", - " 59.2517\n", - " 1.0412\n", - " 31.4357\n", - " 22.5261\n", - " 39.3778\n", - " 23.9771\n", - " 1.8874\n", - " 1.7850\n", - " 6.1222\n", - " 13.9891\n", - " 8.8358\n", - " 11.9521\n", - " 0.9764\n", - " 29.9719\n", - " 3.0948\n", - " 0.4055\n", - " 0.3364\n", - " 4.8352\n", - " 13.7660\n", - " 12.5649\n", - " 5.3636\n", - " 24.2606\n", - " 3.5150\n", - " 38.5196\n", - " 1.5219\n", - " 13.0963\n", - " 6.5039\n", - " 3.6606\n", - " 184.7602\n", - " 7.0112\n", - " 75.9883\n", - " 56.8520\n", - " 17.2093\n", - " 27.7894\n", - " 6.7595\n", - " 2.5488\n", - " 8.0841\n", - " 10.2645\n", - " 7.5262\n", - " 12.5878\n", - " 3.9603\n", - " 28.3046\n", - " 11.7080\n", - " 2.7740\n", - " 20.5979\n", - " 48.2758\n", - " 11.8036\n", - " 5.0028\n", - " 1.9099\n", - " 58.3826\n", - " 12.9609\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.9.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -1.2490e-03 -2.1708e-02 -1.3717e-02 ... -3.0470e-02 2.3697e-02 -2.0284e-02\n", - " 5.8066e-04 -1.5938e-01 1.0033e-01 ... -1.2398e-01 7.3970e-02 -2.7781e-01\n", - " 5.9249e-03 4.6126e-02 1.9371e-02 ... 3.9461e-01 -4.0300e-01 -8.3866e-02\n", - " ... ⋱ ... \n", - " 2.8160e-01 3.5017e-01 4.8683e-03 ... -4.4023e-01 -1.8014e+00 1.0488e-01\n", - " -2.9543e-01 9.8797e-02 -4.6013e-02 ... -1.5038e-01 1.5641e-01 -1.1150e+00\n", - " -1.8047e-01 4.8216e-02 -8.1360e-02 ... -7.3457e-02 -1.7689e-01 1.3239e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " -1.8107e-02 -1.6579e-02 -2.2401e-02 ... -3.5960e-03 2.1527e-02 1.0929e-02\n", - " -1.8054e-01 -2.7563e-01 4.2408e-02 ... 3.0541e-02 -4.6706e-02 -1.8934e-01\n", - " -1.1431e-01 2.2401e-01 -2.3183e-01 ... 2.9956e-02 -2.6556e-02 4.7747e-02\n", - " ... ⋱ ... \n", - " -1.4706e-01 -3.5813e-02 3.4498e-01 ... -5.3065e-02 3.5055e-03 -4.3279e-02\n", - " 6.3806e-02 -1.0685e-01 1.2551e-01 ... 1.1024e-01 1.9199e-01 2.5452e-01\n", - " -2.1525e-02 -5.1525e-02 1.2442e-02 ... 2.5958e-02 -6.9614e-02 1.0687e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " 2.2546e-02 3.1870e-02 6.1550e-03 ... 2.5899e-02 2.4231e-03 -2.4090e-02\n", - " 1.6648e-01 -2.2620e-01 1.8013e-03 ... 2.6781e-01 -2.2226e-01 -2.3612e-01\n", - " -2.5657e-01 1.0353e-02 6.2530e-02 ... 9.9296e-02 2.0786e-01 3.0163e-01\n", - " ... ⋱ ... \n", - " -5.6254e-01 -1.4620e-01 -1.5079e-01 ... -1.5648e-02 1.4819e-01 -2.9422e-01\n", - " 7.9525e-02 4.4902e-01 -1.9844e-01 ... 2.4591e-01 -2.6186e-02 9.1251e-02\n", - " -2.2625e-02 -3.3239e-01 5.0177e-02 ... -1.7797e-01 1.0420e-01 -4.0915e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " 1.6619e-02 -1.2193e-02 1.8705e-02 ... -3.8572e-02 1.2710e-02 -4.4064e-03\n", - " -9.2771e-03 -2.2271e-01 1.7887e-01 ... 1.1817e-01 -1.3845e-01 1.2891e-01\n", - " -3.6453e-02 -2.4411e-01 3.2742e-01 ... 2.4662e-01 -1.0074e-01 -7.5640e-01\n", - " ... ⋱ ... \n", - " 1.5101e-01 4.6638e-02 -4.4681e-01 ... -2.7759e-01 -2.9360e-01 -4.4486e-03\n", - " 5.1798e-01 -5.7905e-01 -5.9879e-01 ... 1.4691e-01 3.9102e-01 -2.4114e-02\n", - " -4.9070e-02 7.3786e-02 1.1952e-01 ... 6.9462e-02 -4.3941e-02 -1.3187e-01\n", - " \n", - " (126,.,.) = \n", - " -2.0730e-02 -2.2372e-02 -1.4065e-02 ... -6.3372e-02 3.2751e-02 5.6936e-02\n", - " -1.6303e-01 -3.9686e-01 4.6270e-02 ... -8.9372e-02 -1.7251e-01 1.2557e-01\n", - " 3.6537e-02 1.4654e-01 5.6433e-01 ... 3.5981e-01 4.9119e-02 -1.3890e-01\n", - " ... ⋱ ... \n", - " -1.7771e-01 -1.9284e-01 3.4713e-01 ... 1.7397e-01 -3.4972e-02 4.2198e-01\n", - " -3.6182e-01 -2.9011e-01 -8.5047e-01 ... 2.9102e-02 -8.1940e-01 6.1122e-02\n", - " 4.3997e-02 1.5453e-01 -7.1149e-02 ... -5.7751e-02 1.6488e-01 -2.2338e-01\n", - " \n", - " (127,.,.) = \n", - " -2.2192e-02 -2.3334e-02 -1.8660e-02 ... -1.6811e-02 1.4472e-02 -2.1724e-02\n", - " -3.5289e-01 7.6216e-02 1.3014e-01 ... 3.1498e-02 1.8133e-01 -8.8622e-02\n", - " 2.3091e-01 -7.8763e-02 1.1455e-01 ... -4.8957e-02 -2.1180e-02 9.7200e-02\n", - " ... ⋱ ... \n", - " -1.1430e-01 -9.1477e-02 5.9393e-02 ... -7.1157e-02 -3.4201e-02 -7.4520e-02\n", - " 2.3236e-01 -9.3504e-02 8.0771e-02 ... -1.7873e-01 -3.8356e-02 -7.7936e-02\n", - " -3.5841e-02 -4.4784e-02 -3.8030e-02 ... 3.6888e-02 1.9318e-02 -3.7652e-03\n", - " [torch.FloatTensor of size 128x128x10]),\n", - " ('module.encoder.cbhg.conv1d_banks.9.bn.weight', \n", - " 0.4393\n", - " -0.9549\n", - " -1.0875\n", - " -1.0638\n", - " 0.4982\n", - " -0.2653\n", - " -1.0011\n", - " 0.5027\n", - " 0.4402\n", - " 0.6328\n", - " -1.1375\n", - " 0.5881\n", - " -1.0941\n", - " 0.4750\n", - " 0.5537\n", - " -1.1034\n", - " 0.4594\n", - " 0.4753\n", - " 0.4670\n", - " 0.4599\n", - " 0.3778\n", - " 0.5000\n", - " 0.4827\n", - " 0.2698\n", - " 0.4689\n", - " 0.5821\n", - " 0.4883\n", - " 0.5259\n", - " 0.6125\n", - " 0.4771\n", - " 0.4144\n", - " 0.4943\n", - " 0.9913\n", - " 0.3862\n", - " 0.4912\n", - " 0.6556\n", - " 0.5625\n", - " -1.0870\n", - " 0.3494\n", - " 0.4605\n", - " 0.5620\n", - " 0.5033\n", - " -1.0619\n", - " 0.5407\n", - " 0.4440\n", - " 0.5110\n", - " 0.4817\n", - " 0.4087\n", - " 0.3723\n", - " 0.4755\n", - " 0.5537\n", - " 0.5215\n", - " 0.4659\n", - " 0.4154\n", - " 0.4723\n", - " 0.5282\n", - " 0.4582\n", - " 0.4558\n", - " 0.4564\n", - " -1.0588\n", - " 0.4236\n", - " -0.9117\n", - " 0.4967\n", - " 0.4320\n", - " -0.9488\n", - " 0.4758\n", - " 0.5198\n", - " 0.4111\n", - " 0.5109\n", - " 0.4726\n", - " 0.5565\n", - " 0.5091\n", - " -1.0525\n", - " 0.4263\n", - " 0.4744\n", - " 0.4414\n", - " 0.6061\n", - " 0.4788\n", - " 0.4085\n", - " 0.3768\n", - " 0.5847\n", - " 0.4593\n", - " -0.5649\n", - " 0.4581\n", - " 0.3943\n", - " -1.1525\n", - " 0.4698\n", - " 0.5119\n", - " 0.7235\n", - " 0.4772\n", - " -1.0129\n", - " 0.4497\n", - " 0.4374\n", - " 0.4186\n", - " 0.5272\n", - " -1.0513\n", - " 0.4193\n", - " 0.5946\n", - " 0.5846\n", - " 0.6834\n", - " 0.5183\n", - " -1.0543\n", - " 0.5302\n", - " -0.9866\n", - " 0.5402\n", - " 0.4783\n", - " 0.4794\n", - " -1.0009\n", - " -1.0717\n", - " 0.4186\n", - " 0.5195\n", - " 0.4428\n", - " 0.4568\n", - " 0.4693\n", - " 0.4228\n", - " 0.5647\n", - " 0.3728\n", - " 0.5470\n", - " 0.3970\n", - " 0.6895\n", - " 0.3913\n", - " 0.4568\n", - " 0.4436\n", - " 0.5078\n", - " 0.5220\n", - " 0.4835\n", - " 0.4882\n", - " -0.4537\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.9.bn.bias', \n", - " 0.0586\n", - " -0.1105\n", - " -0.1877\n", - " -0.2257\n", - " 0.1193\n", - " -0.0651\n", - " -0.0004\n", - " 0.0020\n", - " 0.0956\n", - " 0.1345\n", - " -0.1910\n", - " 0.1584\n", - " -0.2718\n", - " 0.0781\n", - " 0.0980\n", - " -0.2071\n", - " 0.0967\n", - " 0.0668\n", - " 0.0064\n", - " -0.0072\n", - " 0.0819\n", - " 0.0101\n", - " -0.0022\n", - " 0.0207\n", - " -0.0344\n", - " -0.0508\n", - " 0.0962\n", - " -0.1183\n", - " 0.0321\n", - " 0.0021\n", - " -0.1047\n", - " 0.1579\n", - " 0.0669\n", - " 0.0278\n", - " 0.1390\n", - " 0.0801\n", - " 0.0536\n", - " -0.1815\n", - " 0.0936\n", - " 0.0856\n", - " 0.0218\n", - " 0.1254\n", - " -0.1436\n", - " 0.0402\n", - " -0.0151\n", - " 0.1040\n", - " 0.0806\n", - " 0.0061\n", - " 0.0105\n", - " -0.0671\n", - " -0.0062\n", - " 0.1902\n", - " 0.0505\n", - " 0.0838\n", - " 0.0965\n", - " 0.2213\n", - " 0.0852\n", - " 0.0050\n", - " 0.0234\n", - " -0.1593\n", - " 0.0914\n", - " -0.2165\n", - " -0.0612\n", - " 0.0422\n", - " -0.2179\n", - " -0.1791\n", - " -0.0908\n", - " 0.0078\n", - " 0.0684\n", - " -0.0234\n", - " -0.0024\n", - " 0.0164\n", - " -0.2556\n", - " -0.0098\n", - " 0.0991\n", - " 0.0982\n", - " -0.0484\n", - " -0.1098\n", - " 0.0130\n", - " 0.0209\n", - " 0.0349\n", - " 0.0068\n", - " -0.0038\n", - " 0.2075\n", - " 0.1310\n", - " -0.0343\n", - " 0.0645\n", - " 0.1235\n", - " 0.0625\n", - " 0.0277\n", - " -0.2461\n", - " -0.1395\n", - " 0.1059\n", - " -0.0418\n", - " 0.0936\n", - " -0.2501\n", - " 0.1003\n", - " 0.0725\n", - " 0.0844\n", - " 0.1106\n", - " 0.0130\n", - " -0.2846\n", - " 0.1275\n", - " -0.2598\n", - " 0.0714\n", - " 0.0708\n", - " -0.0137\n", - " -0.0779\n", - " -0.0494\n", - " -0.1282\n", - " 0.0250\n", - " -0.0276\n", - " 0.0298\n", - " -0.0035\n", - " 0.1645\n", - " 0.0869\n", - " 0.0116\n", - " 0.0805\n", - " 0.0296\n", - " 0.1166\n", - " 0.0299\n", - " 0.0568\n", - " -0.0186\n", - " 0.1003\n", - " 0.1489\n", - " 0.1541\n", - " -0.0144\n", - " -0.0573\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.9.bn.running_mean', \n", - " 6.5877e-01\n", - " 1.1888e+00\n", - " 4.0905e+00\n", - " 3.5394e+00\n", - " 4.6724e-01\n", - " 5.0541e+00\n", - " 1.5191e-02\n", - " 6.1297e-01\n", - " 9.2574e-01\n", - " 8.4650e-02\n", - " 4.4347e+00\n", - " 9.4158e+00\n", - " 1.8806e+00\n", - " 2.5577e+00\n", - " 3.9566e-01\n", - " 2.4452e+00\n", - " 2.2579e-01\n", - " 2.1187e+00\n", - " 4.6313e-01\n", - " 7.4665e-01\n", - " 4.7345e-01\n", - " 8.1957e-01\n", - " 4.6520e-01\n", - " 3.9985e+00\n", - " 1.0356e+00\n", - " 4.9184e-01\n", - " 3.4647e-01\n", - " 2.6489e-01\n", - " 7.6358e-01\n", - " 1.5014e+00\n", - " 7.5902e-01\n", - " 6.3828e-01\n", - " 4.4555e-02\n", - " 7.4685e-01\n", - " 1.3885e-01\n", - " 2.2320e-01\n", - " 4.3190e-01\n", - " 2.1437e+00\n", - " 3.7883e-01\n", - " 1.0564e+00\n", - " 8.6116e-01\n", - " 5.1337e-01\n", - " 3.4238e+00\n", - " 5.4124e-02\n", - " 2.4536e-01\n", - " 5.7007e-01\n", - " 5.2306e-01\n", - " 2.8920e+00\n", - " 1.4835e+00\n", - " 8.9156e-01\n", - " 2.7911e+00\n", - " 2.8729e-01\n", - " 1.4278e+00\n", - " 6.7166e-01\n", - " 3.4051e-01\n", - " 1.7970e+00\n", - " 4.4385e-01\n", - " 2.7944e-01\n", - " 2.3780e+00\n", - " 2.4005e+00\n", - " 2.5483e-01\n", - " 3.9349e+00\n", - " 1.3342e+00\n", - " 5.1655e-01\n", - " 4.9710e+00\n", - " 1.4501e+00\n", - " 1.9810e+00\n", - " 4.1460e-01\n", - " 1.2548e+00\n", - " 1.2262e+00\n", - " 4.2413e-26\n", - " 7.5281e-01\n", - " 2.9965e+00\n", - " 3.6136e-01\n", - " 1.4276e+00\n", - " 7.1920e-02\n", - " 7.4376e+00\n", - " 1.5449e+00\n", - " 7.0433e-01\n", - " 1.1369e+00\n", - " 8.1408e+00\n", - " 1.8017e+00\n", - " 5.4147e+00\n", - " 2.7499e-01\n", - " 2.8041e-01\n", - " 2.9431e+00\n", - " 3.9210e-01\n", - " 1.4347e+00\n", - " 1.0004e-01\n", - " 1.0050e+01\n", - " 2.4921e+00\n", - " 1.3566e+00\n", - " 3.3763e-01\n", - " 5.7150e-01\n", - " 3.5560e-01\n", - " 5.6552e+00\n", - " 3.4802e-01\n", - " 7.8090e+00\n", - " 1.4393e+00\n", - " 2.0824e-01\n", - " 1.2301e-01\n", - " 2.5660e+00\n", - " 2.9211e-01\n", - " 2.7372e+00\n", - " 4.3169e-02\n", - " 1.8924e-01\n", - " 7.7597e-01\n", - " 3.7526e+00\n", - " 3.0975e+00\n", - " 4.3442e-01\n", - " 3.6992e-01\n", - " 1.2951e+00\n", - " 9.7277e-01\n", - " 5.0649e-01\n", - " 8.0088e-01\n", - " 2.7125e-01\n", - " 4.7215e-01\n", - " 4.0176e-02\n", - " 1.7603e+00\n", - " 8.2303e-02\n", - " 2.1448e-01\n", - " 8.7293e-01\n", - " 1.0862e+00\n", - " 6.3130e-01\n", - " 1.8604e+01\n", - " 9.2569e-01\n", - " 3.1713e+00\n", - " 1.8322e+00\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.9.bn.running_var', \n", - " 1.1393e+01\n", - " 1.2072e+01\n", - " 6.1747e+01\n", - " 5.2921e+01\n", - " 7.0069e+00\n", - " 3.7881e+01\n", - " 2.4796e-01\n", - " 1.0894e+01\n", - " 1.3262e+01\n", - " 1.5698e+00\n", - " 7.2534e+01\n", - " 1.1644e+02\n", - " 2.9410e+01\n", - " 3.6548e+01\n", - " 5.2717e+00\n", - " 3.9736e+01\n", - " 2.7101e+00\n", - " 3.0697e+01\n", - " 6.0937e+00\n", - " 9.7516e+00\n", - " 6.7279e+00\n", - " 1.3124e+01\n", - " 5.1916e+00\n", - " 4.5021e+01\n", - " 1.5338e+01\n", - " 7.6580e+00\n", - " 4.9303e+00\n", - " 4.1603e+00\n", - " 1.1069e+01\n", - " 2.3312e+01\n", - " 1.0472e+01\n", - " 9.4337e+00\n", - " 6.0192e-01\n", - " 1.1085e+01\n", - " 1.5251e+00\n", - " 2.9384e+00\n", - " 8.5122e+00\n", - " 2.9296e+01\n", - " 5.6508e+00\n", - " 1.0549e+01\n", - " 1.1747e+01\n", - " 7.0393e+00\n", - " 5.2226e+01\n", - " 8.6393e-01\n", - " 2.9830e+00\n", - " 7.4713e+00\n", - " 8.0775e+00\n", - " 4.7513e+01\n", - " 2.4230e+01\n", - " 1.3114e+01\n", - " 4.0316e+01\n", - " 3.8614e+00\n", - " 2.1979e+01\n", - " 9.1312e+00\n", - " 6.1349e+00\n", - " 2.4939e+01\n", - " 6.3974e+00\n", - " 4.5273e+00\n", - " 2.5589e+01\n", - " 3.2393e+01\n", - " 3.5256e+00\n", - " 6.0869e+01\n", - " 2.2742e+01\n", - " 6.9768e+00\n", - " 7.8588e+01\n", - " 2.3854e+01\n", - " 2.8853e+01\n", - " 4.8536e+00\n", - " 1.8289e+01\n", - " 1.7872e+01\n", - " 1.3410e-25\n", - " 1.1533e+01\n", - " 4.3636e+01\n", - " 5.7138e+00\n", - " 2.2906e+01\n", - " 7.8805e-01\n", - " 8.7948e+01\n", - " 2.0688e+01\n", - " 9.1638e+00\n", - " 1.7218e+01\n", - " 9.7784e+01\n", - " 2.0290e+01\n", - " 6.1067e+01\n", - " 4.0045e+00\n", - " 3.5471e+00\n", - " 4.0518e+01\n", - " 5.4415e+00\n", - " 1.6939e+01\n", - " 1.4608e+00\n", - " 8.3743e+01\n", - " 2.9854e+01\n", - " 2.1994e+01\n", - " 4.8434e+00\n", - " 7.4962e+00\n", - " 5.7864e+00\n", - " 8.8313e+01\n", - " 4.5509e+00\n", - " 1.0137e+02\n", - " 2.2057e+01\n", - " 3.6852e+00\n", - " 1.6732e+00\n", - " 4.1326e+01\n", - " 3.7805e+00\n", - " 4.0370e+01\n", - " 5.2707e-01\n", - " 2.1986e+00\n", - " 1.0860e+01\n", - " 4.0454e+01\n", - " 4.4730e+01\n", - " 4.9725e+00\n", - " 4.6923e+00\n", - " 1.9501e+01\n", - " 1.6277e+01\n", - " 7.7052e+00\n", - " 1.2423e+01\n", - " 4.5366e+00\n", - " 7.3450e+00\n", - " 4.4356e-01\n", - " 2.6865e+01\n", - " 1.0230e+00\n", - " 3.0405e+00\n", - " 1.2984e+01\n", - " 1.7935e+01\n", - " 9.7486e+00\n", - " 1.5390e+02\n", - " 1.1770e+01\n", - " 5.4263e+01\n", - " 1.4682e+01\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.10.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 3.1965e-02 -2.1600e-02 -1.0129e-02 ... -3.9500e-02 2.4110e-02 -5.2788e-02\n", - " -3.4690e-01 -2.2403e-01 -1.4392e-01 ... 1.9043e-01 -4.2190e-02 9.6729e-02\n", - " -5.6671e-02 8.6796e-02 4.9753e-02 ... 4.1030e-01 -5.7082e-02 -2.4434e-01\n", - " ... ⋱ ... \n", - " -3.9421e-01 -3.9091e-01 1.9921e-01 ... -5.9211e-04 7.9745e-02 2.5677e-01\n", - " 5.6460e-02 -4.6051e-01 -7.2606e-01 ... 1.4965e-01 2.2783e-02 2.5805e-01\n", - " -6.5163e-02 -6.0904e-02 -1.0758e-01 ... -1.2326e-01 1.9627e-01 5.3745e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " -1.1012e-02 -2.4141e-02 7.4641e-03 ... -3.5029e-02 2.1609e-02 2.7473e-02\n", - " 6.4160e-02 -4.5087e-02 2.4176e-02 ... -2.1199e+00 1.6511e-01 7.0646e-02\n", - " -2.0581e-02 -3.0219e-01 2.2424e-01 ... -1.9432e+00 -2.2556e-02 2.3356e-01\n", - " ... ⋱ ... \n", - " 3.6412e-02 8.1937e-02 -3.6133e-01 ... -1.4206e-01 -1.0086e-01 3.7693e-03\n", - " -1.2741e-01 2.0047e-01 -3.4011e-01 ... -2.2786e-01 9.9893e-02 -9.1123e-02\n", - " 4.0323e-02 -6.2912e-02 -1.3398e-01 ... 1.2671e-01 -3.4339e-01 5.6047e-03\n", - " \n", - " ( 2 ,.,.) = \n", - " 2.4882e-02 6.8116e-03 -2.3403e-03 ... 1.7273e-02 -3.0395e-02 1.5216e-02\n", - " -7.1228e-02 1.7549e-02 1.7411e-01 ... -3.0658e-01 -3.1027e-01 -1.8680e-02\n", - " 2.0702e-02 3.8690e-01 3.5982e-01 ... -1.4068e-02 4.9060e-01 -6.0749e-01\n", - " ... ⋱ ... \n", - " -3.4607e-01 1.7243e-01 -2.9192e-01 ... -1.2063e-01 -6.2334e-01 1.5975e-01\n", - " 4.4745e-02 -6.6337e-01 9.9555e-02 ... 1.0091e-01 -5.8471e-01 -6.7909e-01\n", - " -3.9671e-01 3.0501e-02 -2.3217e-01 ... 5.8709e-02 -1.5712e-01 -1.7326e-01\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " 8.0183e-04 4.9156e-02 3.7375e-02 ... 3.8611e-02 -2.6888e-02 2.1561e-02\n", - " 2.7643e-01 -4.8574e-01 3.0004e-01 ... -5.0056e-01 1.2600e-01 6.9725e-02\n", - " -1.2696e-01 -7.3011e-01 -2.5322e-01 ... 1.9872e-01 -4.8887e-01 3.7973e-01\n", - " ... ⋱ ... \n", - " -5.3998e-01 9.9488e-02 5.8411e-02 ... -1.0019e+00 -3.1663e-01 -9.8524e-02\n", - " -2.3971e-01 6.2732e-02 -5.4200e-01 ... 1.1759e-01 -1.0818e+00 -4.4208e-01\n", - " -5.9369e-03 -2.3910e-02 -3.4175e-02 ... -4.2960e-01 1.6119e-01 -1.5830e-01\n", - " \n", - " (126,.,.) = \n", - " 3.0219e-02 -1.9569e-02 2.2828e-02 ... -4.9221e-03 -1.5746e-04 1.6835e-03\n", - " -5.2897e-02 -1.0896e+00 -2.5633e-02 ... -1.3852e+00 8.2560e-02 -3.2648e-01\n", - " 1.7529e-01 3.1022e-01 7.6865e-02 ... -6.4377e-01 -1.1810e+00 -5.8760e-03\n", - " ... ⋱ ... \n", - " -1.1371e-01 2.4317e-01 -2.2551e-01 ... 1.5635e-01 3.2738e-02 -2.9651e-01\n", - " -3.6801e-01 1.8591e-01 -8.9321e-01 ... -2.3886e-01 -2.5043e-02 -3.4334e-01\n", - " 9.4663e-02 -1.5956e-02 4.3235e-03 ... 6.6401e-02 7.0955e-02 -9.5309e-02\n", - " \n", - " (127,.,.) = \n", - " 7.3776e-03 -1.5134e-02 -2.4655e-02 ... 3.2836e-02 2.8032e-03 8.1508e-03\n", - " -1.9972e-01 8.0481e-02 -3.8044e-02 ... 5.5677e-02 -1.1366e-01 -2.4903e-01\n", - " -9.5893e-02 2.1776e-02 3.0244e-01 ... -6.6698e-01 6.8781e-02 -3.9274e-01\n", - " ... ⋱ ... \n", - " -7.4045e-01 -7.1610e-02 8.1012e-02 ... -9.6516e-01 -4.3659e-01 5.7861e-02\n", - " 4.5081e-02 -8.4197e-03 -2.9812e-01 ... 9.3342e-02 -2.5426e-01 6.9766e-02\n", - " -2.3646e-01 -1.3777e-01 -2.1241e-01 ... -2.0303e-01 -3.3080e-01 -3.8868e-02\n", - " [torch.FloatTensor of size 128x128x11]),\n", - " ('module.encoder.cbhg.conv1d_banks.10.bn.weight', \n", - " 0.4344\n", - " 0.6601\n", - " 0.5257\n", - " 0.4728\n", - " 0.5926\n", - " 0.6298\n", - " 0.4142\n", - " 0.4840\n", - " 0.5788\n", - " 0.5373\n", - " 0.4757\n", - " 0.4920\n", - " -1.0094\n", - " 0.4214\n", - " -0.4071\n", - " 0.4764\n", - " 0.5595\n", - " 0.4574\n", - " 0.4613\n", - " 0.4398\n", - " -0.1176\n", - " 0.4166\n", - " -1.0335\n", - " 0.4902\n", - " 0.4966\n", - " 0.5661\n", - " 0.4466\n", - " 0.4938\n", - " 0.5169\n", - " 0.4404\n", - " 0.4754\n", - " 0.3960\n", - " 0.5418\n", - " 0.4070\n", - " 0.6619\n", - " 0.4451\n", - " 0.5086\n", - " 0.5592\n", - " 0.4527\n", - " 0.4330\n", - " 0.5056\n", - " -1.0021\n", - " 0.5319\n", - " 0.4430\n", - " 0.5225\n", - " 0.4582\n", - " -1.1048\n", - " 0.5603\n", - " 0.4973\n", - " 0.5415\n", - " 0.3753\n", - " 0.4543\n", - " 0.5833\n", - " -1.1240\n", - " 0.5791\n", - " 0.5694\n", - " 0.4648\n", - " -1.1631\n", - " 0.4888\n", - " 0.5441\n", - " 0.4827\n", - " 0.5610\n", - " 0.4867\n", - " 0.5662\n", - " 0.6429\n", - " -0.8323\n", - " -1.2634\n", - " 0.4140\n", - " 0.4953\n", - " -1.0699\n", - " 0.4622\n", - " 0.5307\n", - " 0.5351\n", - " 0.3556\n", - " 0.5135\n", - " 0.4880\n", - " 0.9097\n", - " 0.6635\n", - " 0.6228\n", - " 0.6124\n", - " 0.5160\n", - " 0.5250\n", - " 0.5747\n", - " -0.9779\n", - " -1.1890\n", - " 0.3801\n", - " -0.8307\n", - " 0.4638\n", - " 0.5340\n", - " 0.4631\n", - " -1.1818\n", - " -0.0781\n", - " 0.4186\n", - " 0.4079\n", - " 0.4136\n", - " 0.5133\n", - " 0.6921\n", - " 0.7630\n", - " 0.5433\n", - " -1.1873\n", - " 0.4674\n", - " 0.6153\n", - " 0.4806\n", - " -1.1369\n", - " 0.4613\n", - " 0.5009\n", - " 0.4665\n", - " 0.4379\n", - " 0.5447\n", - " 0.4623\n", - " 0.5299\n", - " 0.5023\n", - " 0.5153\n", - " -1.1337\n", - " 0.5276\n", - " 0.5530\n", - " 0.2472\n", - " 0.9015\n", - " 0.8050\n", - " 0.4522\n", - " 0.4985\n", - " 0.4078\n", - " 0.5552\n", - " 0.5015\n", - " 0.4041\n", - " 0.5260\n", - " 0.5415\n", - " 0.5411\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.10.bn.bias', \n", - " 0.0919\n", - " 0.1599\n", - " -0.1932\n", - " -0.0641\n", - " -0.0096\n", - " -0.0893\n", - " 0.1003\n", - " -0.0897\n", - " -0.0102\n", - " -0.0688\n", - " 0.0816\n", - " 0.1825\n", - " 0.0328\n", - " 0.0858\n", - " -0.1020\n", - " -0.0027\n", - " 0.1956\n", - " 0.0956\n", - " 0.1314\n", - " 0.0746\n", - " -0.0201\n", - " 0.0738\n", - " -0.0781\n", - " -0.0908\n", - " 0.0215\n", - " 0.0816\n", - " 0.0249\n", - " -0.2307\n", - " 0.0006\n", - " 0.0656\n", - " -0.0424\n", - " -0.1228\n", - " 0.0058\n", - " -0.1347\n", - " 0.1288\n", - " 0.1105\n", - " -0.1432\n", - " 0.1302\n", - " 0.0002\n", - " -0.0217\n", - " 0.0076\n", - " -0.2547\n", - " -0.0165\n", - " -0.0844\n", - " -0.0056\n", - " 0.0757\n", - " -0.1091\n", - " -0.1264\n", - " 0.0169\n", - " 0.0392\n", - " -0.0662\n", - " -0.0453\n", - " 0.2432\n", - " -0.1353\n", - " 0.0020\n", - " 0.1203\n", - " 0.1139\n", - " -0.2315\n", - " -0.0388\n", - " 0.1765\n", - " 0.0539\n", - " 0.0279\n", - " 0.0893\n", - " -0.0326\n", - " 0.0203\n", - " -0.0750\n", - " -0.2031\n", - " 0.1531\n", - " -0.0247\n", - " -0.1084\n", - " 0.0231\n", - " 0.1744\n", - " 0.0488\n", - " 0.0008\n", - " 0.0818\n", - " 0.1781\n", - " 0.0758\n", - " 0.0973\n", - " 0.0312\n", - " -0.0496\n", - " 0.0482\n", - " 0.0648\n", - " -0.0318\n", - " -0.2598\n", - " -0.1971\n", - " -0.1048\n", - " 0.0023\n", - " 0.0289\n", - " -0.0186\n", - " 0.1050\n", - " -0.1225\n", - " -0.0108\n", - " -0.0859\n", - " 0.0186\n", - " 0.0906\n", - " 0.1087\n", - " 0.1233\n", - " 0.0986\n", - " -0.0136\n", - " -0.2576\n", - " 0.0323\n", - " 0.2639\n", - " -0.0318\n", - " -0.1497\n", - " 0.1004\n", - " 0.0851\n", - " -0.0893\n", - " 0.0045\n", - " 0.0408\n", - " -0.0955\n", - " 0.0782\n", - " 0.0695\n", - " -0.1207\n", - " -0.3635\n", - " 0.1621\n", - " 0.1723\n", - " -0.0080\n", - " 0.1664\n", - " 0.1579\n", - " 0.1677\n", - " -0.0714\n", - " 0.0826\n", - " 0.0476\n", - " 0.0803\n", - " 0.0645\n", - " 0.1041\n", - " 0.0326\n", - " 0.0649\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.10.bn.running_mean', \n", - " 0.5715\n", - " 0.4787\n", - " 0.5886\n", - " 0.8980\n", - " 3.5874\n", - " 0.8712\n", - " 0.4066\n", - " 0.3407\n", - " 0.2876\n", - " 0.2127\n", - " 0.7389\n", - " 3.5558\n", - " 1.7456\n", - " 0.2775\n", - " 10.9636\n", - " 0.4779\n", - " 1.1393\n", - " 0.6408\n", - " 1.8196\n", - " 1.6055\n", - " 4.1998\n", - " 1.6512\n", - " 5.2940\n", - " 1.6774\n", - " 0.4384\n", - " 0.6230\n", - " 0.7512\n", - " 3.3535\n", - " 2.6237\n", - " 1.3885\n", - " 0.3585\n", - " 0.4600\n", - " 2.8188\n", - " 0.5538\n", - " 1.7370\n", - " 0.5905\n", - " 1.5130\n", - " 3.0031\n", - " 0.8443\n", - " 1.7084\n", - " 0.1227\n", - " 4.0383\n", - " 0.4113\n", - " 0.1819\n", - " 2.9395\n", - " 0.2186\n", - " 0.2448\n", - " 1.1199\n", - " 1.2371\n", - " 1.2992\n", - " 0.3041\n", - " 0.1607\n", - " 2.4229\n", - " 3.3267\n", - " 0.1393\n", - " 1.7397\n", - " 0.4127\n", - " 1.6051\n", - " 0.1115\n", - " 0.7717\n", - " 0.9341\n", - " 0.1516\n", - " 0.5486\n", - " 1.1999\n", - " 4.9575\n", - " 11.2588\n", - " 1.5768\n", - " 0.4544\n", - " 0.6733\n", - " 1.8999\n", - " 0.4666\n", - " 1.3727\n", - " 0.1095\n", - " 0.3338\n", - " 1.1352\n", - " 0.4972\n", - " 0.0371\n", - " 1.5335\n", - " 0.0357\n", - " 0.5844\n", - " 0.1059\n", - " 1.0272\n", - " 2.3088\n", - " 4.1160\n", - " 0.6063\n", - " 0.2265\n", - " 7.8183\n", - " 0.6386\n", - " 0.2476\n", - " 0.3767\n", - " 1.3118\n", - " 8.1675\n", - " 0.7651\n", - " 0.0587\n", - " 0.9415\n", - " 0.7294\n", - " 12.8802\n", - " 1.8015\n", - " 3.8814\n", - " 2.8758\n", - " 0.6577\n", - " 3.3869\n", - " 0.5129\n", - " 2.2430\n", - " 1.4347\n", - " 1.2082\n", - " 0.2646\n", - " 0.8686\n", - " 0.6948\n", - " 0.9758\n", - " 0.3923\n", - " 0.1918\n", - " 0.5296\n", - " 5.1353\n", - " 0.9857\n", - " 2.3894\n", - " 2.8527\n", - " 0.0900\n", - " 2.0014\n", - " 0.7127\n", - " 1.4246\n", - " 0.3800\n", - " 0.1628\n", - " 0.5197\n", - " 0.3114\n", - " 0.9503\n", - " 0.6195\n", - " 0.0770\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.10.bn.running_var', \n", - " 8.2030\n", - " 6.9862\n", - " 9.9357\n", - " 15.2206\n", - " 63.2403\n", - " 13.4121\n", - " 7.2176\n", - " 5.0432\n", - " 4.0066\n", - " 2.1296\n", - " 12.5899\n", - " 53.5196\n", - " 28.3303\n", - " 3.4958\n", - " 74.1906\n", - " 7.4082\n", - " 16.8727\n", - " 10.0798\n", - " 28.3960\n", - " 28.3827\n", - " 34.1655\n", - " 25.4091\n", - " 98.1128\n", - " 28.2085\n", - " 6.3762\n", - " 10.2647\n", - " 10.9746\n", - " 48.4148\n", - " 42.2198\n", - " 15.5746\n", - " 4.7592\n", - " 5.8319\n", - " 39.3070\n", - " 8.7624\n", - " 32.9965\n", - " 9.7541\n", - " 21.1747\n", - " 38.0584\n", - " 13.8016\n", - " 14.1439\n", - " 1.4819\n", - " 63.9721\n", - " 5.7939\n", - " 2.3634\n", - " 42.8073\n", - " 3.4405\n", - " 2.6276\n", - " 18.2133\n", - " 19.6721\n", - " 22.8966\n", - " 3.9301\n", - " 2.2315\n", - " 33.0830\n", - " 50.7738\n", - " 1.7183\n", - " 22.4970\n", - " 5.4776\n", - " 24.4470\n", - " 1.9185\n", - " 12.9313\n", - " 14.1143\n", - " 2.5983\n", - " 8.1474\n", - " 18.5348\n", - " 71.7504\n", - " 126.3550\n", - " 23.6516\n", - " 7.1524\n", - " 9.9798\n", - " 25.4920\n", - " 7.4487\n", - " 20.8167\n", - " 1.6687\n", - " 4.8173\n", - " 15.6865\n", - " 5.9931\n", - " 0.5863\n", - " 21.7323\n", - " 0.3726\n", - " 8.5008\n", - " 1.6352\n", - " 14.4115\n", - " 32.1514\n", - " 70.6772\n", - " 8.6440\n", - " 3.3432\n", - " 124.2477\n", - " 8.7533\n", - " 3.7762\n", - " 6.4966\n", - " 17.0977\n", - " 49.6824\n", - " 11.3608\n", - " 0.8637\n", - " 12.2889\n", - " 11.0555\n", - " 175.4644\n", - " 26.1177\n", - " 70.9077\n", - " 44.7209\n", - " 10.0761\n", - " 47.5161\n", - " 7.7064\n", - " 32.5647\n", - " 23.2061\n", - " 20.2472\n", - " 3.1556\n", - " 13.3795\n", - " 11.0379\n", - " 17.0404\n", - " 6.6057\n", - " 2.9541\n", - " 8.3151\n", - " 84.5365\n", - " 17.6665\n", - " 32.6644\n", - " 18.0309\n", - " 0.8674\n", - " 32.1254\n", - " 10.9736\n", - " 21.6199\n", - " 5.6371\n", - " 2.2261\n", - " 7.9350\n", - " 4.4895\n", - " 15.9966\n", - " 8.4143\n", - " 1.1489\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.11.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 1.4652e-02 5.3001e-03 1.0720e-03 ... 1.5744e-02 1.2328e-02 1.4525e-02\n", - " 7.5329e-02 -1.8212e-01 -5.1388e-01 ... -1.1485e+00 -8.6555e-02 9.2271e-02\n", - " -1.5347e-01 -1.8767e-01 3.9370e-02 ... -7.1084e-02 6.2531e-02 -1.3709e-01\n", - " ... ⋱ ... \n", - " -7.7978e-02 -2.1314e-02 4.5352e-02 ... -1.2343e-02 -3.4504e-01 -8.0914e-03\n", - " -6.4026e-01 6.0411e-02 -9.7488e-01 ... -2.4730e-01 -3.6128e-01 1.5796e-02\n", - " -1.0371e-01 -1.2962e-01 3.2543e-01 ... 4.1171e-03 -5.8695e-01 -6.4797e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " -1.0019e-02 -2.5829e-02 4.0702e-03 ... -3.8123e-02 8.5839e-04 -1.2530e-02\n", - " 9.8703e-02 2.6438e-01 -2.0426e-02 ... -1.3528e+00 3.5872e-02 6.7084e-02\n", - " -8.0820e-03 -7.2237e-02 -2.2399e-01 ... -5.5380e-02 -2.0954e-01 -3.6943e-01\n", - " ... ⋱ ... \n", - " -3.1969e-02 2.7417e-01 1.2250e-01 ... -1.4495e-01 -8.4950e-01 -2.2803e-01\n", - " 3.2143e-01 1.2398e-01 -4.5293e-01 ... -1.4413e-01 2.1823e-01 -3.8627e-01\n", - " 1.7834e-02 2.2267e-02 -7.0417e-02 ... 4.2893e-02 -2.8627e-01 3.4247e-02\n", - " \n", - " ( 2 ,.,.) = \n", - " 1.9375e-02 2.3812e-02 1.1276e-02 ... 5.1828e-03 -2.3985e-02 2.3789e-02\n", - " -4.3739e-01 2.0894e-01 -3.8575e-02 ... 3.2274e-01 1.7369e-01 1.0408e-01\n", - " 1.8813e-01 2.6524e-01 5.7974e-01 ... -8.7242e-03 -1.0690e-01 -7.8983e-01\n", - " ... ⋱ ... \n", - " 2.3529e-01 -6.2904e-02 -1.4557e-01 ... -4.5544e-02 1.7732e-01 8.4309e-03\n", - " 3.3294e-01 -8.4541e-02 8.7829e-02 ... -1.1917e-01 -4.7819e-02 -7.6159e-01\n", - " 1.3981e-02 -1.6293e-02 -3.2810e-03 ... -2.1877e-01 1.0173e-01 -1.8190e-01\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " 3.4189e-03 -2.0456e-02 1.4243e-02 ... -2.3227e-03 9.3239e-04 -1.0247e-03\n", - " -3.3739e-02 -1.0891e-01 4.8999e-02 ... 1.4597e-02 1.6591e-01 5.4610e-01\n", - " 2.9653e-01 -1.0343e-01 1.2764e-01 ... -2.8191e-01 1.4097e-01 -1.2534e-01\n", - " ... ⋱ ... \n", - " -2.1604e-01 4.4984e-02 -2.8327e-03 ... -2.6177e-02 2.6116e-01 -3.5064e-01\n", - " -3.2880e-03 -4.0734e-02 -1.9771e-02 ... -1.2193e-01 -2.4205e-01 3.3782e-02\n", - " -5.5796e-02 3.7811e-02 2.0099e-02 ... 7.9827e-02 1.1639e-01 -3.2006e-01\n", - " \n", - " (126,.,.) = \n", - " -1.4516e-02 1.7064e-02 -9.8402e-03 ... -2.3872e-02 -3.4032e-02 1.4728e-02\n", - " 1.5041e-01 -9.3227e-02 6.9412e-03 ... 1.4876e-01 1.2624e-01 -8.2570e-03\n", - " 1.4423e-01 5.5396e-02 3.7413e-01 ... -3.2377e-02 9.6140e-03 -1.9368e-01\n", - " ... ⋱ ... \n", - " -1.3670e-01 1.9511e-01 1.7143e-02 ... 2.1694e-01 2.6442e-01 1.4836e-01\n", - " 1.3229e-01 -1.1568e-01 -2.9643e-01 ... 2.0208e-02 -8.4069e-01 -3.5946e-01\n", - " -5.8840e-02 3.1408e-02 4.1788e-02 ... 1.1578e-01 8.8983e-02 -9.6329e-02\n", - " \n", - " (127,.,.) = \n", - " 2.7546e-02 -5.6759e-03 1.0084e-02 ... 2.6578e-02 1.1931e-02 -1.4826e-02\n", - " -1.6599e-01 -2.7726e-02 1.0764e-01 ... -2.8034e-02 1.1052e-01 1.5459e-01\n", - " 4.3881e-01 -1.1269e-01 -5.1397e-01 ... 1.1887e-01 -5.0752e-03 1.7655e-02\n", - " ... ⋱ ... \n", - " -1.3443e-01 -3.9785e-01 -6.7975e-01 ... 2.2963e-01 -2.1617e-01 -5.5946e-01\n", - " -1.4329e-01 -7.1816e-02 8.6829e-02 ... -2.5498e-01 1.2733e-01 -5.0500e-01\n", - " -2.2701e-02 -3.5109e-02 8.2566e-02 ... 6.8880e-02 -2.8114e-02 -2.3018e-02\n", - " [torch.FloatTensor of size 128x128x12]),\n", - " ('module.encoder.cbhg.conv1d_banks.11.bn.weight', \n", - " 0.4696\n", - " 0.5719\n", - " -1.1061\n", - " 0.5807\n", - " -0.7647\n", - " 0.4872\n", - " 0.4623\n", - " 0.4274\n", - " -1.3260\n", - " 0.4248\n", - " 0.4323\n", - " 0.4575\n", - " 0.5025\n", - " 0.4771\n", - " 0.4064\n", - " 0.5374\n", - " 0.6726\n", - " 0.4568\n", - " 0.4295\n", - " 0.7191\n", - " 0.4623\n", - " 0.5528\n", - " -1.2894\n", - " 0.4423\n", - " 0.5022\n", - " -1.1094\n", - " 0.4809\n", - " 0.5221\n", - " 0.4350\n", - " 0.5107\n", - " 0.4495\n", - " 0.5038\n", - " -0.9673\n", - " -1.2126\n", - " 0.7197\n", - " 0.5469\n", - " 0.7150\n", - " 0.6002\n", - " 0.4375\n", - " 0.4025\n", - " 0.5752\n", - " 0.4551\n", - " -1.1043\n", - " 0.4402\n", - " 0.5183\n", - " -1.0622\n", - " 0.7087\n", - " 0.5022\n", - " 0.4157\n", - " 0.4661\n", - " 0.4746\n", - " -1.0687\n", - " 0.4714\n", - " 0.4893\n", - " -1.1576\n", - " 0.4664\n", - " 0.4239\n", - " -1.0739\n", - " 0.5324\n", - " 0.5097\n", - " 0.3901\n", - " 0.5238\n", - " 0.5563\n", - " 0.4259\n", - " 0.4894\n", - " 0.4330\n", - " -1.1346\n", - " 0.4826\n", - " 0.4866\n", - " -1.1332\n", - " 0.5377\n", - " -1.0953\n", - " 0.4420\n", - " -0.9456\n", - " 0.4166\n", - " 0.5060\n", - " -1.2205\n", - " 0.4793\n", - " -1.0951\n", - " 0.4486\n", - " 0.5078\n", - " 0.5554\n", - " 0.8730\n", - " 0.6453\n", - " 0.4621\n", - " -0.6677\n", - " 0.4828\n", - " 0.5072\n", - " 0.4975\n", - " 0.6089\n", - " -0.9835\n", - " -0.0207\n", - " 0.5271\n", - " 0.6392\n", - " -0.8556\n", - " -1.2751\n", - " -1.1145\n", - " 0.4019\n", - " 0.4133\n", - " 0.5971\n", - " 0.5816\n", - " 0.4599\n", - " 0.4032\n", - " 0.9104\n", - " 0.5933\n", - " 0.4664\n", - " 0.4314\n", - " 0.4786\n", - " 0.4945\n", - " -1.2253\n", - " 0.6391\n", - " 0.5243\n", - " 0.5276\n", - " 0.5435\n", - " 0.4402\n", - " 0.4819\n", - " -1.0076\n", - " 0.4569\n", - " 0.4705\n", - " 0.4374\n", - " 0.4443\n", - " 0.6577\n", - " 0.4741\n", - " 0.5412\n", - " 0.4809\n", - " -0.2688\n", - " 0.4772\n", - " -1.3877\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.11.bn.bias', \n", - " -0.1058\n", - " 0.1084\n", - " -0.3531\n", - " 0.1187\n", - " -0.0526\n", - " -0.0250\n", - " 0.0292\n", - " 0.0647\n", - " -0.2039\n", - " -0.0704\n", - " 0.0664\n", - " 0.0294\n", - " -0.0517\n", - " 0.0872\n", - " 0.1567\n", - " 0.0910\n", - " -0.1163\n", - " 0.0101\n", - " 0.0445\n", - " -0.0107\n", - " 0.1458\n", - " -0.0375\n", - " -0.2707\n", - " 0.0549\n", - " 0.0212\n", - " -0.1040\n", - " 0.0820\n", - " 0.1413\n", - " -0.1988\n", - " 0.0427\n", - " 0.1023\n", - " 0.0553\n", - " -0.2724\n", - " -0.2291\n", - " 0.1637\n", - " -0.0511\n", - " 0.0080\n", - " -0.0833\n", - " -0.0119\n", - " 0.0671\n", - " 0.0635\n", - " -0.0437\n", - " -0.1125\n", - " 0.0252\n", - " -0.1392\n", - " -0.2180\n", - " 0.2414\n", - " 0.0596\n", - " -0.0395\n", - " 0.0883\n", - " -0.3027\n", - " -0.1139\n", - " -0.0174\n", - " -0.0760\n", - " -0.3178\n", - " -0.0409\n", - " -0.0582\n", - " -0.1743\n", - " 0.0023\n", - " -0.1353\n", - " 0.0581\n", - " 0.0447\n", - " -0.0721\n", - " 0.0643\n", - " 0.1069\n", - " 0.0617\n", - " -0.1114\n", - " 0.0584\n", - " 0.0626\n", - " -0.2247\n", - " 0.0815\n", - " -0.0843\n", - " 0.0049\n", - " -0.1601\n", - " 0.0536\n", - " -0.0974\n", - " -0.1126\n", - " -0.0245\n", - " -0.2789\n", - " 0.0085\n", - " -0.1055\n", - " 0.1799\n", - " 0.0619\n", - " 0.0099\n", - " -0.0358\n", - " 0.0111\n", - " 0.0501\n", - " -0.0037\n", - " 0.0825\n", - " 0.0898\n", - " 0.0390\n", - " -0.0124\n", - " 0.0855\n", - " 0.0772\n", - " -0.0457\n", - " -0.1813\n", - " -0.1343\n", - " -0.1044\n", - " -0.1445\n", - " 0.1514\n", - " 0.2075\n", - " -0.1159\n", - " -0.0705\n", - " -0.0395\n", - " -0.0904\n", - " 0.1480\n", - " -0.0233\n", - " 0.0901\n", - " 0.0267\n", - " -0.1453\n", - " 0.1292\n", - " -0.0030\n", - " -0.0621\n", - " 0.0499\n", - " -0.1750\n", - " 0.1231\n", - " -0.1312\n", - " -0.0642\n", - " 0.0314\n", - " -0.0623\n", - " -0.0681\n", - " -0.2011\n", - " 0.1394\n", - " 0.0520\n", - " 0.1993\n", - " -0.0699\n", - " -0.0923\n", - " -0.2286\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.11.bn.running_mean', \n", - " 0.8973\n", - " 1.0403\n", - " 4.8064\n", - " 2.1079\n", - " 0.1551\n", - " 0.8337\n", - " 1.7971\n", - " 0.6739\n", - " 4.2610\n", - " 1.1109\n", - " 0.9439\n", - " 0.5355\n", - " 1.3070\n", - " 0.2914\n", - " 1.5545\n", - " 1.5114\n", - " 6.3411\n", - " 0.3893\n", - " 1.6662\n", - " 3.2795\n", - " 1.6572\n", - " 6.7529\n", - " 1.8553\n", - " 1.4979\n", - " 0.5757\n", - " 4.7291\n", - " 0.9452\n", - " 0.8243\n", - " 0.7778\n", - " 1.3144\n", - " 0.3643\n", - " 0.7754\n", - " 4.2364\n", - " 3.2026\n", - " 18.7853\n", - " 1.7417\n", - " 0.1808\n", - " 2.4082\n", - " 2.0200\n", - " 0.6738\n", - " 2.0175\n", - " 1.1166\n", - " 3.8472\n", - " 0.6636\n", - " 2.0998\n", - " 4.3731\n", - " 9.3946\n", - " 0.4795\n", - " 0.2454\n", - " 0.5545\n", - " 1.1134\n", - " 2.0495\n", - " 1.7427\n", - " 0.7735\n", - " 3.7053\n", - " 0.3383\n", - " 0.1242\n", - " 3.1761\n", - " 0.4819\n", - " 1.1880\n", - " 0.0979\n", - " 2.6670\n", - " 2.3404\n", - " 1.3646\n", - " 1.2772\n", - " 1.5946\n", - " 7.1215\n", - " 0.9997\n", - " 2.6096\n", - " 3.1457\n", - " 0.7870\n", - " 1.3130\n", - " 1.3897\n", - " 7.1944\n", - " 1.5634\n", - " 0.5821\n", - " 1.0111\n", - " 0.8214\n", - " 2.0198\n", - " 0.2335\n", - " 1.0773\n", - " 1.2245\n", - " 0.1167\n", - " 0.3181\n", - " 1.7782\n", - " 0.1862\n", - " 0.8499\n", - " 1.5571\n", - " 1.3499\n", - " 7.5830\n", - " 5.2754\n", - " 0.0000\n", - " 0.2575\n", - " 0.3283\n", - " 0.0546\n", - " 2.8495\n", - " 1.7159\n", - " 1.3518\n", - " 0.4363\n", - " 1.7090\n", - " 2.2579\n", - " 1.9332\n", - " 0.6331\n", - " 0.5330\n", - " 8.2135\n", - " 0.7789\n", - " 0.8577\n", - " 0.9780\n", - " 0.3793\n", - " 1.0137\n", - " 3.6428\n", - " 1.2983\n", - " 1.0218\n", - " 0.4764\n", - " 1.1906\n", - " 0.9962\n", - " 4.0012\n", - " 0.7476\n", - " 1.5306\n", - " 0.6303\n", - " 1.0833\n", - " 1.0098\n", - " 0.7644\n", - " 0.3430\n", - " 1.3532\n", - " 13.6173\n", - " 1.5928\n", - " 1.0167\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.11.bn.running_var', \n", - " 15.9795\n", - " 17.4517\n", - " 94.5124\n", - " 26.5261\n", - " 1.7434\n", - " 13.4592\n", - " 29.4983\n", - " 10.8412\n", - " 70.0869\n", - " 19.7031\n", - " 13.3471\n", - " 6.9818\n", - " 23.0768\n", - " 3.9078\n", - " 29.2266\n", - " 27.8719\n", - " 112.1345\n", - " 5.1913\n", - " 29.6777\n", - " 46.6807\n", - " 27.0041\n", - " 89.0012\n", - " 33.1502\n", - " 22.0520\n", - " 8.1558\n", - " 79.3337\n", - " 18.6742\n", - " 14.1399\n", - " 11.9580\n", - " 20.0961\n", - " 5.2169\n", - " 12.5176\n", - " 70.1408\n", - " 57.7935\n", - " 184.6222\n", - " 21.1908\n", - " 2.6207\n", - " 39.1001\n", - " 33.0373\n", - " 9.4337\n", - " 30.4736\n", - " 16.5897\n", - " 63.7404\n", - " 8.9600\n", - " 30.5162\n", - " 75.9287\n", - " 151.3965\n", - " 7.5285\n", - " 4.1469\n", - " 9.7788\n", - " 16.7911\n", - " 35.3818\n", - " 33.8144\n", - " 12.2592\n", - " 70.2199\n", - " 6.0559\n", - " 1.6863\n", - " 39.9687\n", - " 7.8825\n", - " 15.8410\n", - " 1.6622\n", - " 49.5056\n", - " 45.0633\n", - " 20.5786\n", - " 20.9901\n", - " 27.1553\n", - " 117.9534\n", - " 15.9916\n", - " 50.4664\n", - " 55.2017\n", - " 15.1110\n", - " 21.9056\n", - " 22.0118\n", - " 85.8351\n", - " 25.7032\n", - " 9.7736\n", - " 16.3477\n", - " 12.8946\n", - " 33.3585\n", - " 3.1145\n", - " 17.9771\n", - " 16.4898\n", - " 1.7553\n", - " 5.7009\n", - " 26.2157\n", - " 2.5881\n", - " 13.6531\n", - " 29.6845\n", - " 16.6336\n", - " 118.7652\n", - " 68.9620\n", - " 0.0000\n", - " 3.7575\n", - " 5.1797\n", - " 0.4994\n", - " 55.4025\n", - " 25.7519\n", - " 18.1042\n", - " 6.0330\n", - " 25.7486\n", - " 36.9491\n", - " 38.7223\n", - " 8.7716\n", - " 10.5446\n", - " 108.5823\n", - " 13.5954\n", - " 15.2862\n", - " 12.7029\n", - " 4.9249\n", - " 14.5424\n", - " 53.4624\n", - " 24.4559\n", - " 15.0345\n", - " 6.6778\n", - " 20.6577\n", - " 19.7080\n", - " 63.5889\n", - " 13.8471\n", - " 27.6995\n", - " 9.9054\n", - " 18.7738\n", - " 17.2054\n", - " 12.9331\n", - " 5.1603\n", - " 25.3986\n", - " 81.0888\n", - " 24.8573\n", - " 14.9030\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.12.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 2.5124e-03 -2.2784e-02 1.0984e-02 ... 4.9846e-04 2.8635e-02 1.0756e-03\n", - " -1.3106e-01 1.4341e-01 7.9323e-02 ... 6.3507e-02 -4.5839e-02 2.1501e-01\n", - " 9.0208e-02 -1.7771e-01 -4.5272e-01 ... -6.9815e-02 -1.9479e-01 -1.8784e-03\n", - " ... ⋱ ... \n", - " -1.3932e-01 -3.7012e-01 -4.7639e-01 ... -1.0966e+00 1.5456e-01 -9.4229e-01\n", - " 5.7549e-02 -3.5620e-02 -2.3836e-01 ... 2.2370e-01 -3.1791e-01 -4.1511e-01\n", - " 7.0249e-02 1.9633e-01 1.1341e-01 ... -3.2817e-01 2.5300e-01 -1.3422e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " -1.7731e-02 4.5285e-02 -1.2814e-02 ... -2.7173e-03 -1.4069e-02 -3.6759e-03\n", - " -7.2258e-02 -4.0775e-01 -1.8876e-01 ... 4.4626e-02 -1.0953e-01 -4.6202e-01\n", - " -1.1532e-02 2.4754e-01 -1.5029e-01 ... 3.5624e-01 -4.6946e-02 -1.4037e-01\n", - " ... ⋱ ... \n", - " -3.6031e-01 1.9696e-01 1.2519e-01 ... -1.9825e-01 -4.8447e-02 -1.3655e+00\n", - " 3.2029e-01 -6.1391e-02 1.5598e-01 ... -1.5652e-01 1.6171e-01 1.2432e-01\n", - " 6.3132e-02 -1.3603e-01 1.7496e-01 ... 5.9061e-02 9.7532e-02 -1.7508e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " -6.3073e-03 -7.1700e-03 -9.2430e-03 ... 1.5414e-02 -1.2460e-02 -1.5091e-03\n", - " -1.9551e-01 1.3026e-01 1.2604e-01 ... 9.2214e-02 -1.9176e-01 2.9766e-01\n", - " 1.9804e-01 -3.1485e-01 2.3391e-01 ... -1.3164e-01 4.3471e-01 -1.9872e-02\n", - " ... ⋱ ... \n", - " 2.8241e-01 1.6263e-01 -1.1973e-01 ... -1.9607e-01 2.0565e-01 -4.4889e-01\n", - " 1.6790e-01 -3.0801e-01 3.3417e-01 ... 4.0863e-02 -6.6297e-02 1.1063e-01\n", - " 7.7762e-02 1.0632e-01 6.3908e-02 ... 1.2705e-01 -4.4205e-02 9.7256e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -2.7683e-02 1.0624e-02 -5.0701e-03 ... -2.5705e-02 -3.8035e-02 -1.6509e-02\n", - " -3.8700e-01 -1.5979e-02 -2.6519e-01 ... 3.7613e-02 -3.7213e-02 1.0377e-01\n", - " -6.5393e-02 -1.7880e-01 -4.2023e-02 ... 1.0308e-02 -6.8235e-02 1.3338e-02\n", - " ... ⋱ ... \n", - " -6.8673e-01 1.2962e-01 -1.2936e-01 ... -1.4112e+00 3.2245e-01 3.7807e-02\n", - " 1.8645e-02 -7.1574e-02 -3.7431e-01 ... 5.2542e-02 -5.8785e-02 3.6606e-02\n", - " -2.0383e-01 8.8988e-02 4.6355e-02 ... -2.3510e-01 -2.2312e-01 -3.7548e-01\n", - " \n", - " (126,.,.) = \n", - " 1.6585e-02 1.8607e-02 -3.3634e-02 ... 6.2257e-03 1.5424e-03 -2.4602e-02\n", - " -3.4146e-01 2.2077e-02 -4.2619e-01 ... 1.9176e-01 -1.5382e-02 1.8814e-01\n", - " 2.1378e-01 -2.0700e-01 1.5258e-01 ... -2.4342e-01 -1.1730e-01 1.1733e-01\n", - " ... ⋱ ... \n", - " 9.8764e-02 3.2841e-01 3.0355e-01 ... 5.4214e-02 -9.0069e-02 -3.2505e-01\n", - " -3.9483e-01 2.0615e-01 2.1718e-01 ... -4.8824e-01 -8.4747e-01 1.1944e-01\n", - " -3.3625e-02 -4.8884e-03 4.1738e-02 ... -1.3514e-01 -2.0050e-01 -1.4370e-01\n", - " \n", - " (127,.,.) = \n", - " 2.5590e-02 1.8387e-02 2.0806e-02 ... 8.1549e-03 -1.6850e-03 7.8334e-03\n", - " -2.8174e-01 -3.6004e-01 -2.3312e-01 ... 4.5390e-02 4.9092e-02 -4.5602e-02\n", - " 6.9535e-01 1.4977e-01 -2.8423e-02 ... -1.3624e-01 1.0768e-01 -2.6318e-01\n", - " ... ⋱ ... \n", - " 5.7602e-02 1.3463e-01 5.8125e-03 ... -2.4428e-02 -5.6623e-01 -2.7310e-01\n", - " 5.5486e-02 2.0697e-01 -2.1729e-01 ... 2.6449e-01 -7.7781e-02 -2.6877e-01\n", - " 8.7469e-02 -4.8216e-02 4.2947e-02 ... 2.2505e-01 -2.1960e-01 -1.7593e-01\n", - " [torch.FloatTensor of size 128x128x13]),\n", - " ('module.encoder.cbhg.conv1d_banks.12.bn.weight', \n", - " 0.5730\n", - " 0.4872\n", - " -0.8539\n", - " 0.4749\n", - " 0.5131\n", - " -1.1585\n", - " -1.4734\n", - " 0.7568\n", - " 0.5510\n", - " 0.5189\n", - " 0.5052\n", - " 0.6131\n", - " 0.5743\n", - " 0.5302\n", - " 0.4877\n", - " 0.5639\n", - " 0.5930\n", - " -1.1419\n", - " 0.4849\n", - " 0.5213\n", - " -1.2542\n", - " 0.5455\n", - " 0.4479\n", - " -0.9798\n", - " 0.4703\n", - " 0.5385\n", - " -0.5705\n", - " -1.1007\n", - " 0.5222\n", - " -1.0825\n", - " 0.6221\n", - " 0.4761\n", - " 0.4963\n", - " 0.5569\n", - " 0.5680\n", - " 0.5440\n", - " -1.0525\n", - " 0.5683\n", - " 0.5189\n", - " 0.6212\n", - " 0.4043\n", - " -0.9544\n", - " -1.3453\n", - " 0.5191\n", - " 0.6303\n", - " 0.5956\n", - " 0.5559\n", - " 0.5394\n", - " 1.3447\n", - " 0.7187\n", - " 0.5206\n", - " 0.5399\n", - " 0.4557\n", - " -1.2270\n", - " 0.5964\n", - " 0.4943\n", - " 0.5270\n", - " 0.7588\n", - " 0.5271\n", - " 0.6135\n", - " 0.5529\n", - " 0.4835\n", - " 0.5829\n", - " 0.0193\n", - " 0.4772\n", - " 0.4560\n", - " 0.5157\n", - " 0.4611\n", - " 0.5422\n", - " 0.5020\n", - " 0.4983\n", - " 0.5436\n", - " 0.5950\n", - " 0.4876\n", - " 0.4689\n", - " -0.3098\n", - " 0.5467\n", - " 0.0583\n", - " 0.4947\n", - " 0.4951\n", - " 0.4450\n", - " 0.5582\n", - " 0.5557\n", - " -0.9568\n", - " 0.4925\n", - " 0.5055\n", - " 0.5056\n", - " 0.5599\n", - " -1.2361\n", - " 0.4219\n", - " 0.4890\n", - " 0.4836\n", - " 0.6297\n", - " 0.5976\n", - " 0.6654\n", - " 0.4469\n", - " -1.2179\n", - " 0.6102\n", - " 0.2783\n", - " 0.4592\n", - " 0.4686\n", - " 0.4252\n", - " -1.3238\n", - " 0.4724\n", - " 0.3981\n", - " 0.5550\n", - " 0.6266\n", - " 0.4612\n", - " 0.5325\n", - " 0.5209\n", - " 0.6053\n", - " 0.5557\n", - " 0.4515\n", - " 0.4776\n", - " 0.4911\n", - " 0.4925\n", - " 0.5737\n", - " 0.6716\n", - " 0.4675\n", - " -1.0168\n", - " 0.5330\n", - " 0.5361\n", - " 0.5203\n", - " 0.5810\n", - " 0.5359\n", - " 0.4884\n", - " 0.7344\n", - " 0.4919\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.12.bn.bias', \n", - " 0.0372\n", - " -0.0224\n", - " -0.2256\n", - " 0.1013\n", - " 0.0967\n", - " -0.1798\n", - " -0.1600\n", - " 0.1434\n", - " 0.0034\n", - " 0.0257\n", - " 0.0465\n", - " -0.1792\n", - " -0.0230\n", - " -0.0695\n", - " -0.0222\n", - " -0.0355\n", - " 0.0586\n", - " -0.0271\n", - " 0.0935\n", - " 0.1585\n", - " -0.1269\n", - " -0.0820\n", - " 0.1005\n", - " -0.2136\n", - " -0.0031\n", - " 0.0654\n", - " -0.0345\n", - " -0.1961\n", - " -0.0700\n", - " -0.2523\n", - " 0.1570\n", - " 0.0983\n", - " 0.0166\n", - " -0.0398\n", - " -0.1316\n", - " -0.0009\n", - " -0.2186\n", - " 0.0827\n", - " -0.0064\n", - " 0.1099\n", - " -0.1261\n", - " -0.0138\n", - " -0.1116\n", - " 0.0792\n", - " -0.1398\n", - " 0.1277\n", - " 0.0497\n", - " -0.1172\n", - " 0.1723\n", - " 0.1022\n", - " 0.0088\n", - " 0.0905\n", - " 0.0054\n", - " -0.1959\n", - " 0.0717\n", - " -0.0222\n", - " -0.2334\n", - " 0.1637\n", - " -0.0481\n", - " 0.1706\n", - " -0.0529\n", - " -0.0409\n", - " 0.0606\n", - " 0.0078\n", - " -0.0984\n", - " 0.1355\n", - " 0.0632\n", - " -0.0456\n", - " 0.1148\n", - " -0.0248\n", - " 0.0301\n", - " 0.0840\n", - " 0.0625\n", - " -0.0165\n", - " -0.0693\n", - " -0.0636\n", - " -0.1416\n", - " -0.0125\n", - " 0.0959\n", - " 0.0557\n", - " 0.0163\n", - " 0.1104\n", - " -0.1205\n", - " -0.2128\n", - " -0.0318\n", - " 0.0453\n", - " -0.0566\n", - " 0.0477\n", - " -0.1948\n", - " 0.0128\n", - " 0.0273\n", - " 0.0031\n", - " -0.0068\n", - " 0.0112\n", - " 0.1647\n", - " 0.0586\n", - " -0.2183\n", - " -0.1291\n", - " -0.0029\n", - " 0.1077\n", - " -0.0951\n", - " 0.1773\n", - " -0.1475\n", - " 0.0573\n", - " 0.0275\n", - " -0.1403\n", - " 0.2314\n", - " -0.1141\n", - " 0.2082\n", - " 0.1744\n", - " -0.1538\n", - " 0.0801\n", - " 0.0501\n", - " 0.0025\n", - " -0.0200\n", - " 0.0973\n", - " 0.1787\n", - " 0.2107\n", - " 0.0047\n", - " -0.0693\n", - " 0.0694\n", - " 0.0482\n", - " 0.0716\n", - " -0.0190\n", - " -0.1061\n", - " 0.0990\n", - " 0.1247\n", - " 0.0677\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.12.bn.running_mean', \n", - " 1.6643\n", - " 0.7914\n", - " 18.2355\n", - " 0.6188\n", - " 0.6880\n", - " 3.9849\n", - " 0.0993\n", - " 2.8688\n", - " 5.5944\n", - " 0.4411\n", - " 2.5454\n", - " 0.9192\n", - " 1.0979\n", - " 0.9350\n", - " 0.4640\n", - " 6.2717\n", - " 2.3771\n", - " 7.7375\n", - " 0.3193\n", - " 0.4917\n", - " 2.0095\n", - " 1.0701\n", - " 1.5729\n", - " 14.6311\n", - " 0.3919\n", - " 0.6385\n", - " 0.0995\n", - " 7.7049\n", - " 0.2759\n", - " 4.8997\n", - " 0.6901\n", - " 0.4588\n", - " 0.4694\n", - " 1.7162\n", - " 1.2722\n", - " 0.4624\n", - " 7.4349\n", - " 0.2309\n", - " 1.6574\n", - " 6.2443\n", - " 0.7438\n", - " 9.8981\n", - " 0.1851\n", - " 1.1118\n", - " 4.9262\n", - " 2.5397\n", - " 0.3932\n", - " 1.3472\n", - " 0.1245\n", - " 4.5497\n", - " 1.3292\n", - " 1.7824\n", - " 0.0314\n", - " 3.1071\n", - " 2.4115\n", - " 0.7718\n", - " 1.1463\n", - " 3.7740\n", - " 1.0823\n", - " 0.2714\n", - " 1.8357\n", - " 1.1610\n", - " 0.5519\n", - " 6.8656\n", - " 0.5687\n", - " 0.3749\n", - " 0.1267\n", - " 2.3691\n", - " 0.4179\n", - " 1.6421\n", - " 0.5482\n", - " 0.3998\n", - " 0.2772\n", - " 0.9582\n", - " 2.1013\n", - " 7.3207\n", - " 0.8778\n", - " 6.0328\n", - " 1.1068\n", - " 0.3546\n", - " 0.7101\n", - " 2.3588\n", - " 1.4954\n", - " 16.8315\n", - " 1.0917\n", - " 0.7447\n", - " 1.3966\n", - " 1.6128\n", - " 3.0740\n", - " 0.8368\n", - " 1.9746\n", - " 0.7762\n", - " 2.1567\n", - " 0.5987\n", - " 4.5678\n", - " 0.4576\n", - " 3.7072\n", - " 4.7982\n", - " 0.3503\n", - " 1.0569\n", - " 6.0528\n", - " 1.2477\n", - " 0.3078\n", - " 0.0929\n", - " 0.4153\n", - " 0.6627\n", - " 0.3931\n", - " 0.6685\n", - " 0.7672\n", - " 0.5199\n", - " 4.8562\n", - " 0.0687\n", - " 1.2443\n", - " 3.8482\n", - " 0.0919\n", - " 0.3363\n", - " 2.0254\n", - " 2.4370\n", - " 0.4994\n", - " 2.8464\n", - " 0.3332\n", - " 0.7634\n", - " 0.2546\n", - " 3.4189\n", - " 2.1983\n", - " 0.4134\n", - " 0.7395\n", - " 1.0887\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.12.bn.running_var', \n", - " 24.7021\n", - " 12.1259\n", - " 200.8881\n", - " 9.5477\n", - " 11.7550\n", - " 64.6297\n", - " 1.1414\n", - " 52.8568\n", - " 57.6167\n", - " 6.9328\n", - " 49.5094\n", - " 15.8128\n", - " 19.0611\n", - " 15.9187\n", - " 7.2106\n", - " 59.7456\n", - " 38.3615\n", - " 116.4506\n", - " 5.2705\n", - " 8.0799\n", - " 38.5880\n", - " 16.4950\n", - " 31.4097\n", - " 191.6912\n", - " 6.2062\n", - " 10.4048\n", - " 0.9764\n", - " 117.2513\n", - " 5.2514\n", - " 73.4023\n", - " 14.5581\n", - " 7.2274\n", - " 8.0633\n", - " 33.8416\n", - " 20.4415\n", - " 7.9939\n", - " 133.4245\n", - " 2.7753\n", - " 28.9187\n", - " 87.0000\n", - " 13.3420\n", - " 139.6145\n", - " 2.5720\n", - " 20.2775\n", - " 92.7000\n", - " 48.3540\n", - " 6.4112\n", - " 22.0264\n", - " 1.3546\n", - " 68.5772\n", - " 24.9688\n", - " 34.0636\n", - " 0.4005\n", - " 51.7549\n", - " 42.1333\n", - " 12.8884\n", - " 21.4131\n", - " 65.8662\n", - " 14.6326\n", - " 5.1442\n", - " 35.9827\n", - " 19.4669\n", - " 9.0503\n", - " 72.6889\n", - " 8.8729\n", - " 5.6320\n", - " 1.7500\n", - " 43.4587\n", - " 7.7485\n", - " 26.1731\n", - " 7.0052\n", - " 5.8300\n", - " 4.1561\n", - " 17.6958\n", - " 39.7497\n", - " 50.7230\n", - " 13.5891\n", - " 33.3082\n", - " 19.6486\n", - " 6.0075\n", - " 12.3842\n", - " 41.6913\n", - " 27.7635\n", - " 232.8391\n", - " 17.8972\n", - " 13.6521\n", - " 22.5059\n", - " 26.8656\n", - " 52.6071\n", - " 15.8357\n", - " 32.6477\n", - " 12.5625\n", - " 36.2878\n", - " 9.6681\n", - " 69.5989\n", - " 6.4244\n", - " 69.6563\n", - " 53.4896\n", - " 2.5188\n", - " 17.5069\n", - " 55.6254\n", - " 23.0790\n", - " 4.2944\n", - " 1.2833\n", - " 7.3206\n", - " 11.1468\n", - " 7.2908\n", - " 10.5342\n", - " 14.3333\n", - " 6.6158\n", - " 59.1373\n", - " 0.8878\n", - " 22.2354\n", - " 72.2668\n", - " 1.1781\n", - " 5.2295\n", - " 32.1831\n", - " 35.1829\n", - " 7.5751\n", - " 34.5029\n", - " 5.4838\n", - " 12.5656\n", - " 3.7906\n", - " 41.8978\n", - " 45.2942\n", - " 7.5254\n", - " 13.7072\n", - " 20.0205\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.13.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 1.7745e-02 -4.4126e-03 -4.8155e-03 ... -1.1811e-02 -1.2982e-02 4.8544e-03\n", - " 1.3211e-01 6.4812e-03 3.9755e-02 ... 1.3613e-01 -1.4129e-01 2.5989e-01\n", - " 1.3647e-01 -2.4757e-02 8.7259e-02 ... -3.0748e-01 -1.5349e-01 4.7045e-02\n", - " ... ⋱ ... \n", - " 2.7293e-01 -6.1637e-02 2.6514e-01 ... -9.1236e-02 1.3647e-01 -4.1393e-01\n", - " 1.8076e-01 1.4856e-01 -9.5415e-02 ... 2.6375e-02 9.1741e-02 -6.9988e-02\n", - " 1.8714e-02 2.2266e-01 -8.0913e-03 ... -1.3792e-01 2.0871e-03 6.6364e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " 1.4681e-02 -2.8576e-02 -3.3572e-03 ... -3.5963e-02 -2.6190e-03 -1.6106e-02\n", - " -3.0001e-01 -1.0300e-01 -2.9653e-01 ... -2.3369e-01 1.2638e-01 -8.7112e-02\n", - " -1.7102e-02 5.5029e-02 -2.2974e-01 ... -2.6807e-01 2.8310e-01 -2.0486e-01\n", - " ... ⋱ ... \n", - " -7.0041e-01 -9.4564e-01 1.6966e-02 ... 2.6940e-01 -2.8557e-01 1.2184e-01\n", - " -3.2194e-01 1.2850e-01 1.9510e-01 ... 2.4279e-01 2.1059e-01 -5.5531e-01\n", - " 6.1039e-02 -7.2539e-02 7.3692e-02 ... 1.1038e-01 1.2474e-02 -1.0941e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " -2.4671e-02 3.1477e-02 -1.8527e-02 ... 7.6964e-03 1.1642e-02 -3.8481e-03\n", - " -2.4675e-01 -1.0154e-01 -1.0051e-01 ... 2.4323e-01 1.1612e-01 -3.4125e-01\n", - " 1.4512e-03 3.5649e-01 -2.7982e-01 ... -9.7022e-01 -1.5814e-01 -4.0816e-01\n", - " ... ⋱ ... \n", - " -3.7522e-02 2.2590e-02 1.0087e-01 ... 2.1287e-01 -3.6022e-01 -5.4785e-01\n", - " -5.4449e-01 -5.5681e-02 -2.9264e-01 ... 1.3152e-01 4.3553e-02 5.6911e-02\n", - " 1.3604e-01 2.1225e-01 -1.1628e-01 ... 1.2776e-01 -5.8087e-02 9.8960e-02\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -2.4497e-02 -3.7381e-03 -1.4529e-02 ... -8.6942e-03 -2.6910e-02 3.1877e-02\n", - " -6.6612e-02 1.3753e-01 -9.7248e-02 ... -1.5097e-01 1.2860e-01 -7.2238e-01\n", - " 1.7896e-01 2.9136e-01 1.9650e-01 ... -2.0288e-01 -2.9753e-01 -1.3484e-01\n", - " ... ⋱ ... \n", - " 9.3054e-02 1.7660e-01 1.1617e-01 ... -1.8692e-01 -2.5153e-01 -1.1928e+00\n", - " 2.1528e-01 2.8482e-01 7.7727e-03 ... -3.3763e-02 -7.1726e-01 -2.4033e-01\n", - " -5.5266e-02 2.0036e-01 5.1280e-02 ... 6.3100e-03 -1.4751e-01 -8.7516e-02\n", - " \n", - " (126,.,.) = \n", - " -1.8795e-02 -1.4157e-02 1.8972e-02 ... 5.5805e-02 4.5948e-02 -5.0825e-03\n", - " 1.0776e-01 -7.5393e-01 -4.2556e-02 ... 5.1656e-02 1.6126e-01 -4.3502e-02\n", - " -3.9349e-01 2.7921e-01 -2.4415e-01 ... 7.9829e-02 8.0948e-02 -2.1672e-01\n", - " ... ⋱ ... \n", - " -4.9517e-02 -2.4103e-01 2.3441e-01 ... 6.0706e-02 -2.1137e-01 4.1497e-01\n", - " 1.5899e-01 -2.3921e-01 1.2935e-01 ... 1.4634e-01 1.8446e-01 -1.5869e-01\n", - " 1.4117e-01 9.5894e-03 1.1945e-02 ... -2.3359e-02 1.0747e-01 2.3452e-01\n", - " \n", - " (127,.,.) = \n", - " -2.3473e-02 -9.4031e-03 -1.5690e-02 ... -2.3350e-02 1.3103e-02 2.7526e-03\n", - " -3.6479e-01 -4.8598e-01 9.1681e-02 ... 1.8447e-01 -1.1075e-01 -5.3281e-02\n", - " 1.7906e-02 2.0140e-01 2.0678e-02 ... 7.1253e-02 3.2277e-01 -5.8525e-01\n", - " ... ⋱ ... \n", - " 2.8675e-01 -4.1663e-01 -1.8265e-01 ... 1.1046e-01 2.5976e-01 5.3310e-01\n", - " -1.2504e+00 -7.7143e-01 -8.7459e-01 ... -1.3034e+00 4.1625e-01 -4.3742e-01\n", - " -4.1942e-02 2.4496e-02 -2.3016e-01 ... -2.1655e-01 -2.3814e-01 2.8768e-01\n", - " [torch.FloatTensor of size 128x128x14]),\n", - " ('module.encoder.cbhg.conv1d_banks.13.bn.weight', \n", - " -0.6298\n", - " 0.5734\n", - " 0.6999\n", - " 0.5770\n", - " 0.5808\n", - " -1.0896\n", - " 0.6252\n", - " 0.4294\n", - " 0.5675\n", - " 0.4857\n", - " -0.9622\n", - " 0.5191\n", - " -1.2440\n", - " 0.5543\n", - " 0.6442\n", - " 0.4937\n", - " 0.4889\n", - " 0.5038\n", - " 0.4638\n", - " 0.6041\n", - " -1.0582\n", - " 0.5609\n", - " 0.5107\n", - " 0.5738\n", - " 1.2248\n", - " 0.5928\n", - " 0.6933\n", - " 0.4571\n", - " 0.4580\n", - " 0.5306\n", - " -1.3751\n", - " 0.5884\n", - " 0.5455\n", - " 0.5871\n", - " 0.3757\n", - " 0.4592\n", - " 0.6180\n", - " 0.5713\n", - " 0.4721\n", - " 0.4965\n", - " -1.3051\n", - " 0.6142\n", - " 0.5013\n", - " 0.5209\n", - " -1.0818\n", - " 0.5345\n", - " 0.5207\n", - " 0.5435\n", - " 0.6100\n", - " 0.4462\n", - " -1.2715\n", - " -1.1698\n", - " 0.4006\n", - " 0.5464\n", - " 0.4761\n", - " 0.4700\n", - " 0.5575\n", - " 0.5582\n", - " 0.5443\n", - " 0.6845\n", - " 0.5603\n", - " 0.4991\n", - " 0.5492\n", - " 0.5362\n", - " -0.9964\n", - " -1.1201\n", - " 0.5724\n", - " 0.5301\n", - " 0.6036\n", - " -1.1646\n", - " 0.4824\n", - " -1.1610\n", - " -1.1281\n", - " 0.8662\n", - " 0.5791\n", - " 0.4489\n", - " 0.4560\n", - " 0.5783\n", - " 0.5846\n", - " 0.6263\n", - " 0.4736\n", - " 0.4545\n", - " 0.5589\n", - " 0.5207\n", - " 0.5753\n", - " 0.5958\n", - " 0.4252\n", - " -1.1730\n", - " 0.4535\n", - " -1.0682\n", - " 0.5419\n", - " 0.5654\n", - " 0.4253\n", - " 0.4681\n", - " 0.4466\n", - " 0.6438\n", - " -0.9214\n", - " 0.6425\n", - " -0.8597\n", - " 0.5556\n", - " 0.7262\n", - " 0.5962\n", - " 0.5239\n", - " -1.0640\n", - " -1.1503\n", - " 0.5666\n", - " 0.4708\n", - " 0.5036\n", - " 0.7284\n", - " -1.0950\n", - " 0.4702\n", - " 0.5276\n", - " 0.4963\n", - " 0.5367\n", - " -1.0664\n", - " -1.8053\n", - " 0.5561\n", - " 0.7688\n", - " 0.6790\n", - " 0.4250\n", - " -0.9473\n", - " 0.5733\n", - " 0.5620\n", - " 0.5780\n", - " -1.2931\n", - " 0.5389\n", - " -1.0939\n", - " 0.5530\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.13.bn.bias', \n", - " -0.0561\n", - " 0.1463\n", - " -0.0223\n", - " -0.1471\n", - " 0.0307\n", - " -0.2083\n", - " 0.3170\n", - " -0.0127\n", - " -0.0884\n", - " 0.0868\n", - " -0.0308\n", - " 0.0781\n", - " -0.2038\n", - " 0.0971\n", - " 0.2286\n", - " -0.0160\n", - " -0.0069\n", - " 0.0550\n", - " -0.0146\n", - " -0.1066\n", - " -0.3287\n", - " 0.0944\n", - " 0.1207\n", - " -0.1343\n", - " 0.1753\n", - " 0.1776\n", - " -0.0046\n", - " 0.1129\n", - " 0.0106\n", - " -0.0064\n", - " -0.1826\n", - " -0.0928\n", - " 0.1313\n", - " 0.0583\n", - " 0.0808\n", - " -0.0809\n", - " -0.0025\n", - " 0.1123\n", - " 0.0335\n", - " 0.1228\n", - " -0.1225\n", - " 0.0506\n", - " -0.2536\n", - " 0.0916\n", - " -0.2610\n", - " -0.1203\n", - " -0.0946\n", - " 0.1251\n", - " 0.0648\n", - " -0.0789\n", - " -0.1172\n", - " -0.2161\n", - " -0.0128\n", - " 0.0003\n", - " -0.0881\n", - " 0.0959\n", - " -0.0816\n", - " 0.1423\n", - " -0.0136\n", - " 0.0030\n", - " 0.1279\n", - " 0.0361\n", - " 0.0590\n", - " -0.1067\n", - " 0.0429\n", - " -0.1987\n", - " 0.0586\n", - " -0.2063\n", - " 0.1145\n", - " -0.1234\n", - " 0.0314\n", - " -0.0583\n", - " -0.0012\n", - " 0.4081\n", - " 0.0521\n", - " -0.0389\n", - " -0.0896\n", - " 0.2290\n", - " 0.0357\n", - " -0.2267\n", - " -0.0293\n", - " -0.0579\n", - " -0.0582\n", - " 0.0758\n", - " -0.0022\n", - " 0.1472\n", - " 0.0484\n", - " -0.2202\n", - " 0.0362\n", - " -0.1892\n", - " -0.0270\n", - " -0.0069\n", - " -0.1290\n", - " 0.1452\n", - " 0.2094\n", - " 0.0482\n", - " -0.0642\n", - " 0.0597\n", - " -0.1137\n", - " -0.1909\n", - " 0.0971\n", - " -0.0526\n", - " 0.0954\n", - " -0.0354\n", - " -0.2829\n", - " -0.0599\n", - " -0.1084\n", - " 0.0489\n", - " 0.1386\n", - " -0.2417\n", - " 0.0975\n", - " 0.1261\n", - " 0.0397\n", - " 0.1821\n", - " -0.2596\n", - " -0.1610\n", - " -0.1578\n", - " 0.1419\n", - " 0.1052\n", - " -0.1480\n", - " -0.0142\n", - " 0.0103\n", - " -0.1412\n", - " -0.0728\n", - " -0.1254\n", - " -0.0122\n", - " -0.1145\n", - " -0.0517\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.13.bn.running_mean', \n", - " 17.4920\n", - " 1.0646\n", - " 3.4161\n", - " 2.5212\n", - " 0.2399\n", - " 3.9630\n", - " 3.9160\n", - " 1.2984\n", - " 0.7471\n", - " 0.1815\n", - " 4.5067\n", - " 1.1112\n", - " 6.2854\n", - " 1.6546\n", - " 5.1655\n", - " 0.5798\n", - " 0.4880\n", - " 2.4264\n", - " 0.4774\n", - " 2.8909\n", - " 8.2921\n", - " 0.5392\n", - " 0.2935\n", - " 1.0429\n", - " 0.0977\n", - " 1.4176\n", - " 13.4034\n", - " 1.8930\n", - " 2.9164\n", - " 1.0526\n", - " 6.6331\n", - " 1.1044\n", - " 0.5818\n", - " 3.0707\n", - " 1.0960\n", - " 0.6549\n", - " 2.7949\n", - " 0.4472\n", - " 0.2479\n", - " 0.8273\n", - " 4.1181\n", - " 1.4051\n", - " 0.6379\n", - " 4.2596\n", - " 4.9504\n", - " 2.7049\n", - " 1.4155\n", - " 0.6877\n", - " 1.9380\n", - " 1.3744\n", - " 4.1370\n", - " 8.1351\n", - " 0.5726\n", - " 0.7805\n", - " 0.9762\n", - " 0.7450\n", - " 4.0150\n", - " 2.4144\n", - " 0.6979\n", - " 7.2928\n", - " 2.1864\n", - " 0.4118\n", - " 1.7665\n", - " 1.2200\n", - " 15.8744\n", - " 1.8149\n", - " 6.4295\n", - " 0.7391\n", - " 4.1496\n", - " 6.8999\n", - " 0.1356\n", - " 6.1834\n", - " 6.0212\n", - " 3.2767\n", - " 2.8325\n", - " 0.4972\n", - " 0.6639\n", - " 4.4855\n", - " 0.5347\n", - " 4.7216\n", - " 0.7470\n", - " 0.7470\n", - " 0.3932\n", - " 0.7181\n", - " 1.0118\n", - " 0.3567\n", - " 0.8297\n", - " 3.6417\n", - " 0.4934\n", - " 8.8014\n", - " 1.3052\n", - " 0.7521\n", - " 0.3537\n", - " 1.0392\n", - " 0.6150\n", - " 1.0066\n", - " 7.1790\n", - " 3.1220\n", - " 9.6088\n", - " 1.3720\n", - " 0.1756\n", - " 3.4566\n", - " 0.8404\n", - " 4.0510\n", - " 2.2497\n", - " 0.9266\n", - " 0.1584\n", - " 0.6906\n", - " 2.6445\n", - " 1.3801\n", - " 0.8279\n", - " 1.8389\n", - " 0.6896\n", - " 0.6774\n", - " 2.4618\n", - " 0.1562\n", - " 1.0760\n", - " 3.2444\n", - " 3.0993\n", - " 0.6256\n", - " 9.9324\n", - " 2.0248\n", - " 0.8545\n", - " 2.7719\n", - " 0.7054\n", - " 0.5972\n", - " 2.9989\n", - " 1.9024\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.13.bn.running_var', \n", - " 130.6758\n", - " 16.3050\n", - " 50.5998\n", - " 34.0163\n", - " 3.6374\n", - " 64.6052\n", - " 66.3693\n", - " 23.5074\n", - " 12.8490\n", - " 2.1329\n", - " 65.6858\n", - " 21.4228\n", - " 108.2539\n", - " 30.2416\n", - " 75.6314\n", - " 10.1012\n", - " 8.1167\n", - " 46.0568\n", - " 7.9003\n", - " 53.8135\n", - " 162.3741\n", - " 9.2731\n", - " 4.8722\n", - " 15.9702\n", - " 1.3483\n", - " 24.6443\n", - " 188.5522\n", - " 37.6928\n", - " 33.8167\n", - " 18.8139\n", - " 117.1080\n", - " 15.8111\n", - " 9.6284\n", - " 57.6474\n", - " 20.5505\n", - " 9.7821\n", - " 38.9682\n", - " 7.1397\n", - " 3.9510\n", - " 14.9775\n", - " 75.2078\n", - " 25.4755\n", - " 11.5768\n", - " 74.0677\n", - " 93.5028\n", - " 47.9340\n", - " 17.0871\n", - " 11.7656\n", - " 31.3784\n", - " 27.1431\n", - " 75.9914\n", - " 132.0832\n", - " 10.5657\n", - " 14.9817\n", - " 15.1234\n", - " 13.5866\n", - " 68.0268\n", - " 39.2316\n", - " 9.1437\n", - " 110.1679\n", - " 39.0589\n", - " 5.5239\n", - " 30.1566\n", - " 22.4781\n", - " 210.9006\n", - " 28.0044\n", - " 101.8984\n", - " 12.3503\n", - " 70.4904\n", - " 124.0370\n", - " 2.0574\n", - " 117.9289\n", - " 103.3480\n", - " 53.4346\n", - " 46.7365\n", - " 7.8339\n", - " 12.1007\n", - " 79.9240\n", - " 8.7048\n", - " 64.1523\n", - " 15.3030\n", - " 14.1568\n", - " 6.2242\n", - " 12.8202\n", - " 17.7651\n", - " 6.3197\n", - " 15.5565\n", - " 70.9639\n", - " 7.4195\n", - " 156.1693\n", - " 26.9980\n", - " 11.9868\n", - " 5.0034\n", - " 19.4145\n", - " 9.7820\n", - " 18.3046\n", - " 142.1911\n", - " 47.1392\n", - " 147.3607\n", - " 21.9535\n", - " 2.4825\n", - " 64.3683\n", - " 14.1823\n", - " 64.5069\n", - " 40.4032\n", - " 16.1720\n", - " 2.4418\n", - " 15.4943\n", - " 47.0530\n", - " 25.9800\n", - " 14.1696\n", - " 33.9273\n", - " 7.4451\n", - " 12.6679\n", - " 43.7350\n", - " 2.3258\n", - " 18.4054\n", - " 65.9993\n", - " 41.0610\n", - " 10.8307\n", - " 178.3945\n", - " 29.6639\n", - " 14.5938\n", - " 47.5990\n", - " 11.8685\n", - " 9.8107\n", - " 48.8423\n", - " 35.8358\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.14.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -9.6386e-03 3.0975e-03 -2.5502e-03 ... 1.5233e-02 2.2425e-02 -3.1626e-02\n", - " 9.2219e-03 -9.3129e-02 -1.1708e-03 ... -6.1531e-01 4.4129e-02 -6.2089e-01\n", - " -3.7936e-01 -5.3634e-02 4.1889e-01 ... -2.1778e-01 5.3546e-01 -1.3575e-01\n", - " ... ⋱ ... \n", - " 1.9159e-01 -3.5651e-01 -1.5055e-01 ... -2.5301e-01 -1.4246e-01 -7.7590e-02\n", - " -5.7804e-02 2.4481e-01 -5.1024e-01 ... 3.3023e-01 -1.1456e-01 -4.6667e-01\n", - " 8.0287e-02 -6.9695e-02 -1.5106e-01 ... -2.3315e-01 -3.4729e-02 5.8015e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " -1.7977e-02 -1.1838e-02 -2.9291e-02 ... 1.1945e-02 6.4109e-03 -2.0085e-02\n", - " 2.2693e-01 1.1070e-01 -1.2156e-01 ... 3.9183e-02 1.7745e-01 -9.5893e-02\n", - " -4.8388e-02 -2.2365e-01 1.0250e-01 ... 1.1775e-01 5.5102e-02 -2.7197e-01\n", - " ... ⋱ ... \n", - " 3.2907e-01 -3.3454e-01 1.9553e-01 ... -6.8944e-02 2.0750e-01 -5.4171e-01\n", - " 1.0093e-01 -2.0825e-01 -2.1511e-01 ... -1.2759e-01 -5.0020e-01 -2.9427e-01\n", - " 8.3563e-02 8.9445e-02 -1.6162e-01 ... -1.0580e-01 -3.8315e-02 1.1123e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " 7.2138e-04 -3.1993e-02 9.4911e-03 ... -8.7489e-03 1.7652e-02 4.0780e-03\n", - " -1.3638e-02 -1.3663e-01 3.2960e-02 ... 4.7582e-02 -2.4319e-01 2.2738e-01\n", - " -7.7567e-01 -9.2321e-02 -1.1529e-01 ... -6.4581e-01 3.7072e-01 2.2190e-01\n", - " ... ⋱ ... \n", - " 1.3387e-01 -1.0459e-02 -2.2410e-01 ... -7.1280e-01 9.4690e-02 -1.0131e+00\n", - " 2.6751e-02 -2.9849e-01 -7.0005e-01 ... 1.3902e-01 -3.7694e-01 -1.2595e-01\n", - " -2.6215e-01 1.2256e-01 -2.8348e-01 ... 1.1236e-01 -2.6863e-01 -2.8805e-01\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " 1.3993e-02 -7.3525e-03 -2.6726e-02 ... 2.4495e-02 -4.0242e-03 3.1814e-02\n", - " 1.4469e-01 1.2966e-01 2.0149e-01 ... -2.7853e-02 -6.0647e-01 -1.7823e-01\n", - " -1.1353e-01 -8.8484e-02 -5.0216e-01 ... -1.3258e-01 1.6498e-02 -3.3027e-01\n", - " ... ⋱ ... \n", - " -9.3859e-02 -3.1071e-01 -1.3097e-01 ... -9.2499e-03 8.2636e-02 7.8388e-02\n", - " -4.2766e-01 -1.2047e+00 -1.3363e-01 ... -2.5266e-02 1.3353e-01 -1.5399e-03\n", - " -6.4824e-02 -9.3140e-02 -7.5200e-02 ... -4.9299e-02 -2.9521e-01 2.1217e-01\n", - " \n", - " (126,.,.) = \n", - " 1.1726e-02 -9.5735e-03 2.0397e-02 ... 1.7887e-02 1.1456e-02 -1.8427e-02\n", - " -4.9714e-02 1.2782e-02 7.9058e-02 ... 8.2599e-03 -1.2522e-01 -3.1907e-01\n", - " 1.7756e-01 -1.2267e+00 2.0300e-01 ... 1.5960e-01 1.7278e-01 1.4348e-01\n", - " ... ⋱ ... \n", - " 2.3941e-01 -1.3360e-01 -2.8874e-01 ... -1.0559e+00 2.5745e-01 8.1122e-03\n", - " -3.4058e-01 3.1620e-01 -1.1758e+00 ... 2.4224e-02 -5.0012e-02 5.8442e-02\n", - " 3.5690e-02 8.2707e-02 3.0048e-02 ... -1.3737e-01 -4.6989e-02 3.6012e-02\n", - " \n", - " (127,.,.) = \n", - " -1.9347e-02 -1.3504e-02 8.1035e-03 ... 1.6393e-02 -2.4877e-02 -2.7250e-02\n", - " 2.1160e-01 2.5268e-01 4.1301e-01 ... 4.0031e-02 8.8184e-03 -2.1723e-01\n", - " 4.2787e-01 -3.9118e-01 -3.0853e-01 ... 1.4695e-01 -2.9853e-03 1.2570e-01\n", - " ... ⋱ ... \n", - " -7.8121e-01 -1.0999e-01 7.8042e-02 ... 2.2160e-01 1.1807e-01 -7.1094e-01\n", - " -3.6032e-02 8.9521e-02 1.7480e-02 ... -3.0489e-01 -1.5970e-01 -6.4072e-01\n", - " 1.6602e-01 1.1174e-02 -5.0506e-02 ... 2.8903e-01 1.0849e-01 -2.9205e-01\n", - " [torch.FloatTensor of size 128x128x15]),\n", - " ('module.encoder.cbhg.conv1d_banks.14.bn.weight', \n", - " 0.5219\n", - " 0.5235\n", - " 0.5185\n", - " 0.4848\n", - " 0.6538\n", - " 0.6652\n", - " 0.5997\n", - " 0.4674\n", - " 0.5417\n", - " 0.5630\n", - " 0.4184\n", - " 0.5542\n", - " 0.5512\n", - " 0.8797\n", - " 0.5544\n", - " 0.4707\n", - " 0.7109\n", - " -1.5327\n", - " 0.4725\n", - " 0.4867\n", - " 0.4607\n", - " 0.5565\n", - " 0.4141\n", - " 0.5092\n", - " 0.6183\n", - " 0.6033\n", - " 0.6924\n", - " 0.4890\n", - " -1.0302\n", - " 0.4388\n", - " 0.5975\n", - " 0.5200\n", - " -1.1502\n", - " 0.4562\n", - " 0.5402\n", - " 0.7230\n", - " 0.4460\n", - " 0.5195\n", - " 0.4628\n", - " 0.5026\n", - " 0.5357\n", - " 0.5895\n", - " 0.5508\n", - " -1.0624\n", - " 0.5107\n", - " 0.7285\n", - " 0.6568\n", - " 0.4984\n", - " -1.0707\n", - " -1.3855\n", - " 0.7256\n", - " 0.4902\n", - " 0.5819\n", - " 0.1455\n", - " 0.5163\n", - " -1.0136\n", - " 0.4848\n", - " 0.5624\n", - " -0.9739\n", - " 0.5099\n", - " 0.6078\n", - " 0.5977\n", - " 0.5011\n", - " 0.6138\n", - " 0.4872\n", - " 0.4663\n", - " 0.6338\n", - " -1.1063\n", - " 0.4789\n", - " 0.5408\n", - " 0.5189\n", - " 0.5583\n", - " 0.0727\n", - " 0.4613\n", - " 0.7490\n", - " 0.4590\n", - " -1.2506\n", - " 0.5050\n", - " 0.4902\n", - " 0.4852\n", - " 0.4187\n", - " 0.5186\n", - " 0.5131\n", - " 0.5070\n", - " 0.5693\n", - " -1.4681\n", - " 0.6262\n", - " 0.5092\n", - " 0.5781\n", - " 0.5648\n", - " 0.5477\n", - " 0.5868\n", - " 0.5475\n", - " -1.1519\n", - " 0.6011\n", - " 0.5648\n", - " 0.5236\n", - " 0.4696\n", - " 0.5879\n", - " 0.5043\n", - " 0.5454\n", - " -1.2099\n", - " 0.4637\n", - " 0.4895\n", - " 0.5363\n", - " 0.5922\n", - " 0.5929\n", - " -1.2131\n", - " 0.5626\n", - " -1.0432\n", - " 0.5450\n", - " 0.5449\n", - " 0.5089\n", - " 0.4572\n", - " 0.6318\n", - " -1.2045\n", - " 0.7876\n", - " 0.5422\n", - " 0.5030\n", - " 0.6382\n", - " 0.4502\n", - " 0.6880\n", - " 0.4789\n", - " -1.1577\n", - " -1.0733\n", - " 0.7786\n", - " 0.5372\n", - " 0.5880\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.14.bn.bias', \n", - " -0.0542\n", - " 0.1266\n", - " 0.1778\n", - " -0.0647\n", - " 0.1476\n", - " 0.0771\n", - " -0.1118\n", - " 0.0865\n", - " 0.0586\n", - " 0.0690\n", - " -0.0246\n", - " -0.0222\n", - " -0.0052\n", - " 0.0949\n", - " -0.0511\n", - " -0.0623\n", - " 0.0722\n", - " -0.2330\n", - " 0.0828\n", - " -0.0208\n", - " -0.0656\n", - " -0.1528\n", - " 0.0490\n", - " 0.0603\n", - " 0.1099\n", - " 0.0511\n", - " 0.0996\n", - " -0.1498\n", - " -0.3277\n", - " -0.0455\n", - " 0.0740\n", - " 0.1005\n", - " 0.0992\n", - " 0.0310\n", - " -0.0707\n", - " -0.0016\n", - " 0.0342\n", - " -0.0672\n", - " -0.1504\n", - " -0.0168\n", - " 0.0355\n", - " -0.0045\n", - " -0.0201\n", - " -0.0226\n", - " 0.0032\n", - " -0.1744\n", - " -0.0375\n", - " -0.0445\n", - " -0.1340\n", - " -0.2778\n", - " 0.2175\n", - " 0.0942\n", - " 0.1344\n", - " -0.0153\n", - " 0.1511\n", - " -0.1204\n", - " -0.0295\n", - " 0.1024\n", - " -0.1679\n", - " 0.0331\n", - " 0.0851\n", - " 0.0104\n", - " 0.0666\n", - " -0.0171\n", - " 0.1244\n", - " -0.0577\n", - " -0.1414\n", - " -0.0597\n", - " -0.0504\n", - " 0.0238\n", - " 0.0037\n", - " 0.0110\n", - " 0.0093\n", - " 0.0258\n", - " 0.0402\n", - " 0.0357\n", - " -0.2347\n", - " -0.0108\n", - " 0.0670\n", - " -0.2170\n", - " -0.0681\n", - " 0.0122\n", - " -0.0017\n", - " -0.0375\n", - " -0.0331\n", - " -0.1268\n", - " 0.0359\n", - " 0.0069\n", - " 0.0363\n", - " 0.0017\n", - " 0.0716\n", - " 0.0311\n", - " 0.0447\n", - " -0.2538\n", - " -0.0221\n", - " 0.0460\n", - " -0.0071\n", - " -0.0262\n", - " 0.1562\n", - " -0.0758\n", - " 0.1113\n", - " -0.0886\n", - " 0.0382\n", - " -0.0714\n", - " -0.0329\n", - " 0.1428\n", - " -0.0073\n", - " -0.1931\n", - " 0.1464\n", - " -0.1238\n", - " -0.0247\n", - " -0.0744\n", - " -0.0178\n", - " 0.0669\n", - " 0.1170\n", - " -0.1239\n", - " 0.1023\n", - " -0.0063\n", - " -0.1049\n", - " 0.1635\n", - " -0.1547\n", - " 0.1345\n", - " -0.1381\n", - " -0.0946\n", - " 0.0067\n", - " 0.0341\n", - " -0.0042\n", - " 0.0046\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.14.bn.running_mean', \n", - " 1.6630\n", - " 0.6570\n", - " 1.0669\n", - " 1.2803\n", - " 2.3932\n", - " 0.7346\n", - " 2.2573\n", - " 0.3021\n", - " 0.6393\n", - " 0.7293\n", - " 0.2556\n", - " 3.0511\n", - " 1.7600\n", - " 0.1173\n", - " 1.2432\n", - " 0.3600\n", - " 1.1406\n", - " 0.8445\n", - " 0.6656\n", - " 0.8301\n", - " 1.0421\n", - " 2.6189\n", - " 0.3224\n", - " 1.0295\n", - " 0.8101\n", - " 1.3762\n", - " 0.0762\n", - " 0.6325\n", - " 5.1418\n", - " 0.8932\n", - " 0.9764\n", - " 0.2143\n", - " 7.0799\n", - " 0.4281\n", - " 0.5141\n", - " 2.6536\n", - " 1.4388\n", - " 1.8036\n", - " 1.0811\n", - " 2.5022\n", - " 0.9273\n", - " 1.4977\n", - " 1.5585\n", - " 15.1198\n", - " 0.6438\n", - " 12.4013\n", - " 2.5994\n", - " 1.4340\n", - " 0.5407\n", - " 3.4046\n", - " 4.0653\n", - " 0.5159\n", - " 2.6401\n", - " 7.2569\n", - " 0.2240\n", - " 8.0407\n", - " 0.7292\n", - " 0.8736\n", - " 10.5882\n", - " 0.9707\n", - " 2.2749\n", - " 8.4105\n", - " 1.3705\n", - " 0.7365\n", - " 0.4197\n", - " 2.5428\n", - " 2.4664\n", - " 0.0913\n", - " 0.7941\n", - " 2.3891\n", - " 0.6959\n", - " 3.5539\n", - " 15.5003\n", - " 0.9059\n", - " 2.1630\n", - " 0.6838\n", - " 0.6097\n", - " 0.3797\n", - " 0.5167\n", - " 2.0228\n", - " 1.0823\n", - " 0.1929\n", - " 0.4074\n", - " 0.7406\n", - " 0.5121\n", - " 0.4157\n", - " 8.3492\n", - " 0.8532\n", - " 0.5392\n", - " 1.6911\n", - " 0.3076\n", - " 0.5448\n", - " 0.4696\n", - " 3.3314\n", - " 0.4010\n", - " 0.5486\n", - " 2.4953\n", - " 1.9634\n", - " 0.4421\n", - " 0.6672\n", - " 7.0037\n", - " 5.8962\n", - " 0.4794\n", - " 1.3660\n", - " 0.8045\n", - " 1.1081\n", - " 0.6598\n", - " 1.7219\n", - " 4.9092\n", - " 7.6263\n", - " 2.3817\n", - " 1.0561\n", - " 0.1707\n", - " 0.0651\n", - " 9.3424\n", - " 7.5609\n", - " 0.5790\n", - " 1.7707\n", - " 1.0401\n", - " 1.9168\n", - " 1.4665\n", - " 8.6957\n", - " 0.7398\n", - " 5.2830\n", - " 6.6171\n", - " 0.4233\n", - " 1.1271\n", - " 2.6168\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.14.bn.running_var', \n", - " 32.5494\n", - " 10.8456\n", - " 22.1475\n", - " 21.1634\n", - " 45.5213\n", - " 16.8906\n", - " 33.3678\n", - " 4.3462\n", - " 12.7202\n", - " 12.3183\n", - " 4.3537\n", - " 56.0020\n", - " 34.1852\n", - " 1.7884\n", - " 18.7564\n", - " 5.2967\n", - " 18.5741\n", - " 13.8993\n", - " 12.8565\n", - " 14.8653\n", - " 19.3688\n", - " 53.3120\n", - " 5.2661\n", - " 18.4368\n", - " 15.0291\n", - " 25.6777\n", - " 1.2540\n", - " 11.3215\n", - " 92.9001\n", - " 17.1117\n", - " 19.6549\n", - " 3.8317\n", - " 118.1074\n", - " 7.4878\n", - " 9.8521\n", - " 50.2246\n", - " 27.7167\n", - " 33.7030\n", - " 19.7347\n", - " 48.3847\n", - " 15.2201\n", - " 27.6255\n", - " 28.1951\n", - " 228.9194\n", - " 12.7087\n", - " 193.8901\n", - " 49.8693\n", - " 28.7549\n", - " 8.2892\n", - " 60.6948\n", - " 58.8147\n", - " 9.6232\n", - " 51.1853\n", - " 35.7705\n", - " 4.3653\n", - " 144.7705\n", - " 11.7567\n", - " 16.4082\n", - " 171.8419\n", - " 17.9198\n", - " 42.9024\n", - " 142.2819\n", - " 16.0703\n", - " 12.2012\n", - " 7.6679\n", - " 47.4451\n", - " 50.2608\n", - " 1.2489\n", - " 15.4597\n", - " 45.2365\n", - " 12.3541\n", - " 55.4427\n", - " 74.4852\n", - " 19.1685\n", - " 39.5750\n", - " 11.1432\n", - " 10.0188\n", - " 7.5313\n", - " 10.2896\n", - " 38.4401\n", - " 19.4796\n", - " 3.1335\n", - " 6.7418\n", - " 14.2166\n", - " 8.8405\n", - " 6.7862\n", - " 106.6172\n", - " 16.2704\n", - " 7.6732\n", - " 34.2658\n", - " 5.4813\n", - " 11.2076\n", - " 8.8754\n", - " 56.3180\n", - " 5.0904\n", - " 8.6993\n", - " 48.9169\n", - " 35.8389\n", - " 7.9664\n", - " 10.8085\n", - " 121.9062\n", - " 108.6408\n", - " 9.0403\n", - " 27.3738\n", - " 11.2957\n", - " 20.1465\n", - " 13.1769\n", - " 30.6585\n", - " 90.7982\n", - " 147.7374\n", - " 43.3435\n", - " 20.0636\n", - " 3.1555\n", - " 0.8837\n", - " 141.9837\n", - " 114.1726\n", - " 10.1553\n", - " 29.6741\n", - " 20.3391\n", - " 26.6045\n", - " 31.1960\n", - " 130.3760\n", - " 13.1262\n", - " 90.5731\n", - " 98.7055\n", - " 6.0907\n", - " 16.6492\n", - " 49.3750\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.15.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 2.5623e-02 8.7796e-03 1.2732e-02 ... -2.4308e-02 -3.4531e-02 2.3106e-02\n", - " 3.2059e-01 3.7947e-02 -1.5134e-01 ... 4.1423e-02 1.4764e-02 -7.1850e-01\n", - " -3.0223e-01 -2.4837e-02 -3.8476e-01 ... 2.5859e-01 -2.4867e-01 -2.0122e-01\n", - " ... ⋱ ... \n", - " -3.2559e-01 -2.0264e-01 -2.0674e-01 ... -4.4623e-01 6.1603e-02 -1.4264e-01\n", - " 2.1641e-02 3.0297e-01 -1.5960e-02 ... -6.5608e-01 1.6242e-01 1.1450e-01\n", - " 7.3044e-02 1.4630e-01 8.5614e-02 ... -9.3979e-02 -3.5798e-02 -1.3267e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " 1.8823e-02 -3.0998e-02 -8.2396e-03 ... -2.8736e-02 9.6009e-04 2.1219e-02\n", - " -2.3189e+00 -7.2369e-02 2.4549e-01 ... -9.9233e-03 -5.9065e-01 6.0773e-02\n", - " 1.1587e-01 1.7568e-01 1.6188e-01 ... -7.3982e-01 -1.7959e-01 -9.6831e-01\n", - " ... ⋱ ... \n", - " 1.1053e-01 -3.7228e-01 5.2072e-02 ... -6.5126e-01 -2.0501e-01 -6.8167e-02\n", - " -2.3758e-01 -5.0526e-01 -2.4295e-01 ... 3.8033e-02 -5.6007e-03 1.5756e-02\n", - " 1.6876e-01 -1.5944e-01 -3.1784e-02 ... -4.3685e-01 -1.4064e-01 8.8937e-02\n", - " \n", - " ( 2 ,.,.) = \n", - " -3.0412e-02 -9.0673e-03 1.5075e-02 ... -1.4950e-02 3.6065e-03 -1.4769e-03\n", - " -3.9896e-01 1.4230e-01 -6.3218e-02 ... 4.4698e-01 -7.1939e-02 -3.8281e-02\n", - " -5.2769e-01 -3.1808e-01 3.9282e-03 ... -2.3788e-01 9.1319e-02 -6.3756e-01\n", - " ... ⋱ ... \n", - " 2.7757e-01 3.0037e-01 1.9336e-01 ... 8.2704e-02 3.3772e-01 1.7109e-01\n", - " -5.2782e-01 -5.2583e-02 -4.1151e-01 ... 2.9238e-01 -1.5851e-02 1.7705e-01\n", - " -3.1717e-02 -8.4575e-02 7.6925e-02 ... -4.8261e-02 -1.1192e-02 -1.3210e-01\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -2.4106e-02 6.6282e-03 -1.8465e-02 ... -2.3786e-02 2.1597e-03 -1.3986e-02\n", - " -1.4231e-01 3.4451e-02 1.0366e-01 ... -4.9271e-01 -1.2217e-01 -1.0208e-01\n", - " -2.5530e-01 1.1695e-01 3.4305e-02 ... 5.0204e-03 -3.1896e-01 -9.6129e-02\n", - " ... ⋱ ... \n", - " 1.1271e-01 1.5310e-01 1.8242e-01 ... -1.4086e-01 4.2609e-02 -6.2007e-01\n", - " -1.0876e-01 -6.2901e-01 -3.5915e-01 ... 1.3079e-01 -6.1319e-01 5.1483e-02\n", - " -3.8345e-02 -5.3109e-02 -5.6655e-02 ... 1.9094e-02 -7.8271e-03 5.1170e-03\n", - " \n", - " (126,.,.) = \n", - " -5.0714e-03 -1.3371e-02 -5.9716e-03 ... -4.7321e-04 -1.5764e-02 2.2393e-03\n", - " -2.1972e-01 -1.0131e-02 -7.3959e-01 ... -1.0185e-01 -8.5600e-01 2.1385e-02\n", - " 2.9147e-01 1.6190e-01 1.7895e-01 ... -1.2549e+00 1.2088e-01 -3.7305e-01\n", - " ... ⋱ ... \n", - " 3.9247e-01 -5.5293e-01 1.1685e-01 ... 2.6583e-01 -2.7808e-01 1.0660e-02\n", - " 1.9686e-01 -2.6261e-01 -3.2170e-01 ... -2.5084e-01 -3.7867e-01 -8.2786e-01\n", - " 1.1004e-01 -1.3190e-01 -3.9576e-02 ... 9.9157e-03 -1.3757e-01 -4.7838e-02\n", - " \n", - " (127,.,.) = \n", - " -2.9438e-03 -6.5579e-03 1.0269e-02 ... -2.6457e-02 -1.8135e-02 8.6984e-03\n", - " -1.8795e-01 -2.1250e-02 -1.5791e-01 ... 1.1983e-01 1.2248e-01 -1.7003e-01\n", - " -4.6693e-03 -2.2383e-01 2.8204e-02 ... 2.2932e-02 -1.6864e-01 -4.6507e-01\n", - " ... ⋱ ... \n", - " -1.7754e-02 1.6717e-01 -2.0567e-01 ... 1.1366e-01 -1.0704e-01 5.5078e-02\n", - " 3.0380e-01 -1.5191e-01 2.2612e-01 ... 6.7546e-01 6.6147e-02 -1.0390e-01\n", - " 1.0645e-01 7.5489e-02 1.0369e-01 ... 5.6466e-02 1.0624e-01 3.4812e-02\n", - " [torch.FloatTensor of size 128x128x16]),\n", - " ('module.encoder.cbhg.conv1d_banks.15.bn.weight', \n", - " 0.5354\n", - " 0.4807\n", - " 0.4978\n", - " 0.5211\n", - " 1.1324\n", - " 0.4205\n", - " 0.4623\n", - " 0.4773\n", - " 0.5666\n", - " 0.5067\n", - " -1.3785\n", - " 0.4616\n", - " 0.4947\n", - " 0.5664\n", - " 0.5703\n", - " 0.6450\n", - " 0.5456\n", - " 0.5743\n", - " 0.6894\n", - " 0.5498\n", - " 0.5564\n", - " 0.4933\n", - " 0.6864\n", - " -1.2691\n", - " 0.5701\n", - " 0.4165\n", - " 0.6027\n", - " 0.5206\n", - " 0.7312\n", - " 0.4842\n", - " 0.6203\n", - " 0.5053\n", - " 0.5330\n", - " 0.5511\n", - " 0.4457\n", - " 0.5000\n", - " 0.5081\n", - " 0.6040\n", - " 0.5334\n", - " 0.4590\n", - " 0.5156\n", - " 0.4433\n", - " 0.5143\n", - " 0.6415\n", - " 0.5571\n", - " 0.5321\n", - " 0.6534\n", - " 0.8133\n", - " 0.4513\n", - " 0.7035\n", - " 0.7359\n", - " 0.4414\n", - " 0.0663\n", - " 0.5566\n", - " 0.6337\n", - " 0.4520\n", - " 0.4172\n", - " -1.3365\n", - " -1.2600\n", - " 0.4947\n", - " 0.5563\n", - " 0.5269\n", - " -1.1807\n", - " 0.5702\n", - " 0.5892\n", - " -0.7873\n", - " 0.7370\n", - " 0.4751\n", - " 0.4577\n", - " 0.6010\n", - " 0.5396\n", - " -1.1463\n", - " 0.5493\n", - " 0.5495\n", - " 0.6661\n", - " 0.4422\n", - " 0.5413\n", - " 0.5884\n", - " -1.1723\n", - " 0.5093\n", - " 0.5242\n", - " -1.0168\n", - " 0.5333\n", - " 0.5542\n", - " 0.7148\n", - " 0.6392\n", - " 0.9323\n", - " 0.4502\n", - " 0.5671\n", - " 0.5374\n", - " 0.5238\n", - " 0.5455\n", - " 0.5135\n", - " 0.5911\n", - " 0.4962\n", - " 0.4329\n", - " 0.6753\n", - " 0.4411\n", - " 1.1292\n", - " 0.5388\n", - " 0.5069\n", - " 0.7309\n", - " 0.7884\n", - " -1.3002\n", - " 0.8160\n", - " 0.4713\n", - " -1.1157\n", - " 0.5198\n", - " 0.4954\n", - " 0.6287\n", - " 0.5222\n", - " 0.5438\n", - " 0.4195\n", - " 0.5281\n", - " 0.5035\n", - " -1.0355\n", - " -1.0846\n", - " 0.4906\n", - " 0.5894\n", - " 0.6320\n", - " 0.5293\n", - " 0.5036\n", - " 0.6611\n", - " 0.4767\n", - " 0.5030\n", - " -1.1010\n", - " 0.4676\n", - " -1.1475\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.15.bn.bias', \n", - " 0.0524\n", - " -0.0104\n", - " -0.0355\n", - " -0.0305\n", - " 0.1230\n", - " 0.0507\n", - " 0.0776\n", - " -0.1148\n", - " -0.0288\n", - " -0.0372\n", - " -0.1988\n", - " 0.1076\n", - " 0.0862\n", - " -0.0735\n", - " -0.0246\n", - " 0.1221\n", - " -0.1754\n", - " 0.0359\n", - " 0.1625\n", - " 0.0628\n", - " 0.1405\n", - " -0.0864\n", - " 0.1173\n", - " -0.1877\n", - " 0.0042\n", - " 0.1162\n", - " -0.0527\n", - " 0.0511\n", - " 0.0336\n", - " 0.0657\n", - " 0.0162\n", - " -0.0186\n", - " 0.1740\n", - " -0.0569\n", - " -0.0111\n", - " 0.0413\n", - " -0.1015\n", - " 0.0365\n", - " 0.0929\n", - " -0.0713\n", - " 0.1852\n", - " -0.0286\n", - " 0.0135\n", - " 0.1054\n", - " 0.0419\n", - " 0.2542\n", - " 0.0080\n", - " 0.0018\n", - " -0.1296\n", - " 0.0003\n", - " -0.0428\n", - " 0.2202\n", - " 0.0091\n", - " -0.0100\n", - " 0.2106\n", - " 0.2011\n", - " -0.0366\n", - " -0.2762\n", - " -0.2564\n", - " 0.0600\n", - " 0.1685\n", - " -0.0183\n", - " -0.2047\n", - " 0.0162\n", - " 0.0286\n", - " 0.0164\n", - " 0.2825\n", - " 0.0731\n", - " 0.1029\n", - " 0.0540\n", - " 0.0565\n", - " -0.2636\n", - " 0.1703\n", - " 0.0017\n", - " -0.0392\n", - " -0.0126\n", - " -0.0149\n", - " 0.0537\n", - " -0.1298\n", - " 0.0554\n", - " 0.0028\n", - " -0.0728\n", - " 0.0218\n", - " -0.1667\n", - " 0.0594\n", - " 0.1006\n", - " -0.0431\n", - " -0.0145\n", - " -0.0626\n", - " -0.0886\n", - " -0.0074\n", - " 0.0046\n", - " 0.0371\n", - " 0.0730\n", - " 0.0990\n", - " 0.0714\n", - " 0.0552\n", - " 0.0027\n", - " 0.0155\n", - " 0.0420\n", - " -0.0998\n", - " -0.0555\n", - " 0.0023\n", - " -0.2370\n", - " 0.1692\n", - " 0.1662\n", - " -0.2809\n", - " 0.1173\n", - " 0.0028\n", - " 0.0015\n", - " 0.0263\n", - " 0.0441\n", - " 0.0612\n", - " 0.0263\n", - " 0.1237\n", - " -0.0759\n", - " -0.2447\n", - " 0.0603\n", - " 0.0570\n", - " 0.0240\n", - " -0.0808\n", - " 0.0285\n", - " -0.1290\n", - " -0.1056\n", - " 0.0802\n", - " -0.0803\n", - " 0.0163\n", - " -0.0485\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.15.bn.running_mean', \n", - " 0.9682\n", - " 0.4254\n", - " 2.4118\n", - " 0.5825\n", - " 0.0772\n", - " 1.0123\n", - " 0.1869\n", - " 0.3811\n", - " 2.3747\n", - " 0.4519\n", - " 3.6932\n", - " 0.4964\n", - " 0.1543\n", - " 0.5014\n", - " 3.8483\n", - " 8.6164\n", - " 0.7243\n", - " 2.0867\n", - " 5.3134\n", - " 0.4526\n", - " 0.7189\n", - " 0.3508\n", - " 9.6724\n", - " 13.2361\n", - " 0.7571\n", - " 0.3200\n", - " 1.0713\n", - " 0.3110\n", - " 5.7738\n", - " 1.0436\n", - " 2.2685\n", - " 0.6763\n", - " 0.9107\n", - " 1.6475\n", - " 0.7067\n", - " 0.1283\n", - " 1.1515\n", - " 0.4261\n", - " 3.3797\n", - " 0.8306\n", - " 0.3278\n", - " 0.4212\n", - " 1.1886\n", - " 0.3921\n", - " 1.7379\n", - " 7.5866\n", - " 1.2422\n", - " 5.0648\n", - " 0.9494\n", - " 4.7285\n", - " 3.9109\n", - " 1.1449\n", - " 0.0000\n", - " 0.3054\n", - " 7.2068\n", - " 0.8918\n", - " 1.2764\n", - " 6.3968\n", - " 3.8331\n", - " 0.4384\n", - " 1.5619\n", - " 1.4865\n", - " 1.6991\n", - " 2.0914\n", - " 1.7627\n", - " 0.0007\n", - " 3.1607\n", - " 0.6808\n", - " 0.3569\n", - " 1.5402\n", - " 0.3563\n", - " 11.9602\n", - " 1.8894\n", - " 1.0778\n", - " 3.6062\n", - " 0.5507\n", - " 2.2300\n", - " 3.4993\n", - " 3.8718\n", - " 0.4806\n", - " 0.9608\n", - " 0.1307\n", - " 1.4573\n", - " 1.5545\n", - " 0.1142\n", - " 0.8029\n", - " 0.5071\n", - " 0.8431\n", - " 0.9313\n", - " 1.3237\n", - " 1.2521\n", - " 1.7532\n", - " 0.3991\n", - " 1.4072\n", - " 1.0194\n", - " 0.3311\n", - " 2.3480\n", - " 0.5486\n", - " 0.0001\n", - " 0.5424\n", - " 1.0699\n", - " 0.4954\n", - " 8.2006\n", - " 2.5720\n", - " 3.0251\n", - " 0.6186\n", - " 11.2387\n", - " 1.3537\n", - " 1.1902\n", - " 1.2435\n", - " 1.0801\n", - " 1.3664\n", - " 0.3012\n", - " 0.1557\n", - " 1.1768\n", - " 0.0840\n", - " 0.5783\n", - " 1.1622\n", - " 0.5601\n", - " 4.6154\n", - " 0.5846\n", - " 0.7167\n", - " 5.6987\n", - " 0.9826\n", - " 1.2777\n", - " 6.8550\n", - " 0.3461\n", - " 7.7890\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_banks.15.bn.running_var', \n", - " 19.3441\n", - " 7.2210\n", - " 52.1670\n", - " 10.0785\n", - " 0.8694\n", - " 19.9234\n", - " 3.0400\n", - " 7.1023\n", - " 48.8550\n", - " 6.8233\n", - " 79.8547\n", - " 9.1791\n", - " 2.2923\n", - " 8.2448\n", - " 73.9919\n", - " 141.0537\n", - " 14.1035\n", - " 40.1774\n", - " 91.0158\n", - " 9.2547\n", - " 12.4517\n", - " 5.1783\n", - " 157.9901\n", - " 231.3361\n", - " 13.6326\n", - " 5.5508\n", - " 26.2938\n", - " 6.0275\n", - " 89.2656\n", - " 19.1362\n", - " 46.1800\n", - " 10.6488\n", - " 18.0966\n", - " 33.1807\n", - " 12.2791\n", - " 1.9163\n", - " 23.7852\n", - " 8.4300\n", - " 72.0607\n", - " 14.9116\n", - " 5.9400\n", - " 6.2749\n", - " 21.8647\n", - " 6.2331\n", - " 34.8539\n", - " 116.9517\n", - " 23.9962\n", - " 107.2270\n", - " 19.6034\n", - " 88.5838\n", - " 85.4811\n", - " 23.7413\n", - " 0.0000\n", - " 5.1239\n", - " 124.2238\n", - " 15.7109\n", - " 24.0241\n", - " 124.8482\n", - " 79.0445\n", - " 7.0774\n", - " 30.6366\n", - " 26.3645\n", - " 31.4898\n", - " 44.0007\n", - " 36.0663\n", - " 0.0021\n", - " 48.7602\n", - " 11.8073\n", - " 6.2930\n", - " 23.4775\n", - " 7.3132\n", - " 242.1393\n", - " 39.2080\n", - " 16.3218\n", - " 48.5017\n", - " 9.5221\n", - " 43.5962\n", - " 52.8122\n", - " 74.0767\n", - " 7.5589\n", - " 18.0335\n", - " 1.5151\n", - " 29.8387\n", - " 33.0634\n", - " 1.5266\n", - " 11.5707\n", - " 10.7495\n", - " 16.7786\n", - " 20.2605\n", - " 22.8422\n", - " 25.2861\n", - " 35.6485\n", - " 8.2998\n", - " 25.4361\n", - " 20.3728\n", - " 6.9308\n", - " 45.0786\n", - " 10.2328\n", - " 0.0008\n", - " 8.8821\n", - " 20.8823\n", - " 10.3538\n", - " 142.8971\n", - " 43.0934\n", - " 52.4043\n", - " 10.1972\n", - " 201.6355\n", - " 27.1906\n", - " 23.0948\n", - " 23.9127\n", - " 19.4774\n", - " 23.5042\n", - " 5.9305\n", - " 2.9476\n", - " 22.1587\n", - " 1.1825\n", - " 9.4386\n", - " 23.9990\n", - " 10.7355\n", - " 100.3000\n", - " 9.3527\n", - " 13.9741\n", - " 128.4238\n", - " 18.1951\n", - " 23.8309\n", - " 136.7777\n", - " 6.1809\n", - " 149.2479\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_projections.0.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 3.0814e-01 4.3928e-02 5.9386e-02\n", - " 1.1993e-01 5.8452e-02 4.1407e-01\n", - " -3.3929e-01 -2.6133e-01 -3.4512e-01\n", - " ⋮ \n", - " 2.6667e-01 6.0643e-01 -3.8510e-01\n", - " -3.8135e-01 -1.7756e-01 1.8003e-01\n", - " -3.7152e-02 -2.8518e-03 -5.0003e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " 6.7310e-03 -1.9074e-01 -4.0766e-01\n", - " -3.7384e-01 3.8173e-02 -8.6705e-02\n", - " -1.4769e-01 -4.1923e-02 -1.0642e-01\n", - " ⋮ \n", - " -6.6837e-02 6.2199e-01 2.5974e-02\n", - " -1.1596e-01 -2.3071e-01 7.0891e-02\n", - " -6.6795e-01 2.1777e-01 1.5540e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " -2.3889e-02 1.2623e-02 -1.6646e+00\n", - " 3.5820e-01 -2.0916e-01 1.3814e+00\n", - " -1.3201e-01 -1.2885e-01 2.0583e-01\n", - " ⋮ \n", - " -1.4197e-01 1.5728e-01 -4.6813e-02\n", - " 4.1027e-02 1.1533e-01 -2.0033e-01\n", - " 1.0978e-01 -1.6570e-01 -8.2350e-02\n", - " ... \n", - " \n", - " (125 ,.,.) = \n", - " -9.2253e-03 -4.7290e-01 -6.3990e-01\n", - " 1.8074e-01 5.8933e-01 3.6671e-01\n", - " -2.7379e-01 -1.4911e-01 -1.0068e-02\n", - " ⋮ \n", - " 4.6931e-01 3.3843e-02 -9.3615e-02\n", - " 1.8630e-01 2.2686e-01 -5.0762e-02\n", - " 4.6377e-01 5.5982e-01 2.5739e-01\n", - " \n", - " (126 ,.,.) = \n", - " -3.0432e-01 2.8374e-01 -2.8736e-02\n", - " -6.6618e-02 -3.0130e-01 1.1153e-01\n", - " 1.0508e-01 -2.2993e-02 6.0722e-02\n", - " ⋮ \n", - " -1.5460e-01 3.0680e-01 -2.1939e-01\n", - " 1.0348e-02 1.1076e-01 -2.4765e-01\n", - " -1.2953e-01 2.0722e-01 -2.8834e-01\n", - " \n", - " (127 ,.,.) = \n", - " -3.5357e-01 5.5979e-03 -1.1791e-01\n", - " -1.3253e-01 -1.0162e-01 -4.8239e-01\n", - " -5.9012e-02 1.8944e-01 -2.2635e-01\n", - " ⋮ \n", - " 1.0666e-01 3.3147e-01 -3.4622e-02\n", - " -2.3130e-02 3.4870e-01 2.0421e-01\n", - " -3.4532e-01 4.7640e-01 -1.8542e-01\n", - " [torch.FloatTensor of size 128x2048x3]),\n", - " ('module.encoder.cbhg.conv1d_projections.0.bn.weight', \n", - " 0.6646\n", - " 0.8037\n", - " 0.5687\n", - " -0.3298\n", - " 0.3331\n", - " 0.5846\n", - " 0.7305\n", - " 0.5224\n", - " 0.5024\n", - " 0.3957\n", - " 0.4767\n", - " 0.6206\n", - " 0.7052\n", - " -0.4729\n", - " 0.4754\n", - " 0.4523\n", - " 0.3607\n", - " 0.5262\n", - " 0.5655\n", - " 0.2685\n", - " 0.5239\n", - " 0.4226\n", - " 0.7581\n", - " 0.4334\n", - " 0.9309\n", - " 0.3395\n", - " 0.3421\n", - " 0.4946\n", - " 0.6343\n", - " 0.3663\n", - " 0.5080\n", - " 0.5530\n", - " 0.3504\n", - " 0.3564\n", - " 0.5745\n", - " 0.6107\n", - " 0.4151\n", - " 0.6471\n", - " 0.5921\n", - " 0.4796\n", - " 0.7057\n", - " 0.4181\n", - " 0.4424\n", - " 0.8080\n", - " 0.5520\n", - " 0.5493\n", - " 0.7327\n", - " 0.4723\n", - " 0.5089\n", - " 0.4511\n", - " 1.0728\n", - " 0.7614\n", - " 0.4652\n", - " 0.5035\n", - " 0.5966\n", - " 0.5513\n", - " 0.6273\n", - " 0.4190\n", - " 0.6361\n", - " 0.6292\n", - " 0.4825\n", - " 0.5430\n", - " 0.4997\n", - " 0.6707\n", - " 0.3601\n", - " 0.4622\n", - " 0.6502\n", - " -0.3914\n", - " 0.6328\n", - " 0.7951\n", - " 0.5793\n", - " 0.5665\n", - " 0.8058\n", - " 0.4266\n", - " 0.6564\n", - " 0.6774\n", - " 1.7108\n", - " 0.5781\n", - " 0.4265\n", - " 0.4686\n", - " 1.0867\n", - " 0.4749\n", - " -0.0049\n", - " 0.6564\n", - " -0.5044\n", - " 0.5283\n", - " 1.3802\n", - " 0.4614\n", - " 0.5294\n", - " 0.5872\n", - " 0.4923\n", - " 0.5253\n", - " 0.6028\n", - " 0.5324\n", - " 0.5360\n", - " -0.4594\n", - " 0.6569\n", - " 0.5223\n", - " 0.5158\n", - " 0.6301\n", - " 0.2296\n", - " 0.4971\n", - " 0.5606\n", - " 0.3990\n", - " 0.1675\n", - " 0.6395\n", - " 0.8900\n", - " 0.7790\n", - " 0.6523\n", - " 0.4834\n", - " 0.4640\n", - " 0.7101\n", - " 0.4878\n", - " 1.0137\n", - " 0.2852\n", - " 0.3972\n", - " 0.5455\n", - " 0.4692\n", - " 0.2003\n", - " 0.5367\n", - " 0.4520\n", - " 0.5018\n", - " 0.5938\n", - " 0.4570\n", - " 0.5801\n", - " 0.5462\n", - " 0.4063\n", - " 0.5967\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_projections.0.bn.bias', \n", - " 0.4170\n", - " -0.0814\n", - " 0.5861\n", - " -0.9265\n", - " 0.1705\n", - " 0.4536\n", - " -0.2573\n", - " -0.1538\n", - " 1.0144\n", - " 0.1783\n", - " -0.6816\n", - " 0.4903\n", - " 0.0312\n", - " 1.1306\n", - " 0.2297\n", - " -0.2553\n", - " -0.0439\n", - " 0.6143\n", - " -0.0467\n", - " -0.5743\n", - " -1.1164\n", - " 0.5284\n", - " 0.6900\n", - " -0.6266\n", - " -0.3952\n", - " 0.1032\n", - " 0.4301\n", - " 0.5007\n", - " -0.7266\n", - " 0.0655\n", - " -0.5523\n", - " -0.0068\n", - " 0.4664\n", - " -0.9305\n", - " 0.7540\n", - " 0.0923\n", - " 0.0029\n", - " 0.1134\n", - " -0.3432\n", - " 0.3094\n", - " 0.8301\n", - " 0.0221\n", - " 0.7642\n", - " -0.2436\n", - " 0.4244\n", - " 0.2470\n", - " 0.2969\n", - " -1.0682\n", - " 1.0367\n", - " 0.3307\n", - " 0.8700\n", - " -0.4417\n", - " 0.4280\n", - " 0.7529\n", - " 0.9893\n", - " -0.3281\n", - " 0.2944\n", - " 0.2873\n", - " 0.6717\n", - " 0.1098\n", - " -0.8139\n", - " 0.2548\n", - " -1.1177\n", - " 0.4356\n", - " 0.1965\n", - " 0.2115\n", - " 0.1635\n", - " 0.5615\n", - " 0.7445\n", - " 0.3350\n", - " 0.3104\n", - " 0.1556\n", - " 0.2709\n", - " 0.1253\n", - " 0.6787\n", - " -0.4149\n", - " -0.0092\n", - " -0.0661\n", - " 0.5637\n", - " 0.7340\n", - " 0.5761\n", - " 0.8845\n", - " -1.7518\n", - " 0.9397\n", - " -0.5018\n", - " 0.5829\n", - " -0.4377\n", - " 0.7131\n", - " 0.1205\n", - " 0.4713\n", - " 0.5456\n", - " 0.7895\n", - " -0.1775\n", - " 0.7853\n", - " 0.5514\n", - " 0.2082\n", - " -0.0249\n", - " -0.0716\n", - " 0.6822\n", - " 0.2103\n", - " 0.7948\n", - " 0.3662\n", - " 0.0700\n", - " 0.0494\n", - " 0.3584\n", - " 0.3580\n", - " 0.9084\n", - " 0.8465\n", - " 0.0509\n", - " 0.0737\n", - " 0.6599\n", - " 0.3823\n", - " 0.1007\n", - " -0.0749\n", - " -0.0053\n", - " -0.0542\n", - " 0.0226\n", - " 1.2841\n", - " -0.1019\n", - " -0.1142\n", - " -0.7577\n", - " -0.0020\n", - " -0.1057\n", - " 0.4090\n", - " -0.8755\n", - " 0.6086\n", - " -0.6107\n", - " 0.3299\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_projections.0.bn.running_mean', \n", - " 0.2210\n", - " 0.0529\n", - " 1.3052\n", - " 0.0680\n", - " 0.2829\n", - " 4.6885\n", - " 0.0422\n", - " 0.3684\n", - " 0.4773\n", - " 0.2964\n", - " 0.0397\n", - " 0.1658\n", - " 5.2520\n", - " 1.7325\n", - " 10.4315\n", - " 0.4226\n", - " 0.0934\n", - " 4.8479\n", - " 0.8025\n", - " 0.1040\n", - " 0.2436\n", - " 0.0433\n", - " 0.0000\n", - " 20.9054\n", - " 0.0000\n", - " 0.0833\n", - " 0.0000\n", - " 4.5476\n", - " 3.2742\n", - " 1.0132\n", - " 0.2117\n", - " 9.1506\n", - " 0.0000\n", - " 0.0000\n", - " 1.2540\n", - " 18.8371\n", - " 0.0970\n", - " 0.5599\n", - " 0.0559\n", - " 6.6174\n", - " 6.5105\n", - " 0.1380\n", - " 0.0998\n", - " 1.5197\n", - " 21.1912\n", - " 4.4543\n", - " 5.1361\n", - " 0.0000\n", - " 0.0000\n", - " 10.7140\n", - " 0.3029\n", - " 12.9685\n", - " 6.7112\n", - " 0.1514\n", - " 5.7137\n", - " 0.2961\n", - " 0.1161\n", - " 5.8437\n", - " 20.4616\n", - " 0.4551\n", - " 0.0253\n", - " 0.3599\n", - " 23.7951\n", - " 0.0565\n", - " 1.5301\n", - " 1.6234\n", - " 0.0763\n", - " 14.0220\n", - " 19.9584\n", - " 28.1291\n", - " 4.8282\n", - " 0.0908\n", - " 2.7046\n", - " 1.5839\n", - " 0.0581\n", - " 15.3864\n", - " 0.0195\n", - " 0.1280\n", - " 22.8007\n", - " 0.0701\n", - " 0.0049\n", - " 0.0750\n", - " 0.0000\n", - " 11.3023\n", - " 0.7947\n", - " 5.9371\n", - " 0.0394\n", - " 0.0097\n", - " 2.5397\n", - " 4.2118\n", - " 44.7946\n", - " 5.3529\n", - " 6.1608\n", - " 33.0016\n", - " 0.1493\n", - " 1.0825\n", - " 23.0581\n", - " 0.0943\n", - " 6.2559\n", - " 0.2262\n", - " 0.0000\n", - " 5.2692\n", - " 0.6173\n", - " 0.0426\n", - " 0.0000\n", - " 15.9105\n", - " 0.0000\n", - " 2.2465\n", - " 0.0751\n", - " 7.9508\n", - " 0.0000\n", - " 0.0000\n", - " 0.1523\n", - " 0.0193\n", - " 0.0817\n", - " 0.1041\n", - " 1.2988\n", - " 1.0068\n", - " 0.0189\n", - " 22.4981\n", - " 5.0923\n", - " 0.2965\n", - " 5.1567\n", - " 5.4683\n", - " 0.0821\n", - " 2.8410\n", - " 0.2114\n", - " 0.1182\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_projections.0.bn.running_var', \n", - " 10.1925\n", - " 2.7325\n", - " 40.7506\n", - " 2.2552\n", - " 5.7887\n", - " 125.8415\n", - " 1.2202\n", - " 11.9626\n", - " 13.4820\n", - " 8.2248\n", - " 1.1643\n", - " 1.7009\n", - " 137.7545\n", - " 42.2349\n", - " 215.7925\n", - " 10.8992\n", - " 4.0058\n", - " 130.2910\n", - " 21.7233\n", - " 1.8677\n", - " 6.0895\n", - " 1.6989\n", - " 0.0000\n", - " 275.7753\n", - " 0.0000\n", - " 2.8889\n", - " 0.0000\n", - " 106.1226\n", - " 101.9748\n", - " 22.6417\n", - " 6.4413\n", - " 208.0539\n", - " 0.0000\n", - " 0.0000\n", - " 33.9741\n", - " 362.8148\n", - " 3.0024\n", - " 18.1610\n", - " 2.1819\n", - " 145.9947\n", - " 149.0669\n", - " 3.7496\n", - " 4.9660\n", - " 36.9479\n", - " 307.1859\n", - " 99.5560\n", - " 129.2765\n", - " 0.0005\n", - " 0.0000\n", - " 262.8981\n", - " 16.3489\n", - " 288.6512\n", - " 158.7979\n", - " 7.0622\n", - " 123.5744\n", - " 5.8243\n", - " 6.8162\n", - " 113.7955\n", - " 358.1396\n", - " 17.4511\n", - " 0.7085\n", - " 12.1286\n", - " 283.4341\n", - " 2.8528\n", - " 38.1696\n", - " 37.2666\n", - " 3.2165\n", - " 239.5753\n", - " 336.7714\n", - " 544.2413\n", - " 116.3443\n", - " 4.9104\n", - " 81.7024\n", - " 37.3843\n", - " 3.3905\n", - " 341.0682\n", - " 0.5235\n", - " 6.1596\n", - " 301.3521\n", - " 2.4722\n", - " 0.0616\n", - " 2.5850\n", - " 0.0000\n", - " 276.1491\n", - " 18.7097\n", - " 161.9815\n", - " 1.8596\n", - " 0.1399\n", - " 57.3707\n", - " 119.9483\n", - " 365.9788\n", - " 149.1642\n", - " 156.6599\n", - " 396.5631\n", - " 4.4044\n", - " 23.1890\n", - " 556.5134\n", - " 3.8096\n", - " 158.6554\n", - " 10.4037\n", - " 0.0000\n", - " 128.4091\n", - " 16.4895\n", - " 1.0318\n", - " 0.0000\n", - " 304.3759\n", - " 0.0005\n", - " 69.4994\n", - " 3.5712\n", - " 144.1262\n", - " 0.0000\n", - " 0.0000\n", - " 5.1930\n", - " 0.7164\n", - " 1.2742\n", - " 3.1561\n", - " 40.9657\n", - " 25.8127\n", - " 0.4160\n", - " 376.6815\n", - " 91.2860\n", - " 8.4013\n", - " 129.7561\n", - " 116.1720\n", - " 3.4888\n", - " 56.6032\n", - " 7.4175\n", - " 6.8700\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_projections.1.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -7.9624e-02 9.5472e-02 -6.5400e-02\n", - " -9.0511e-02 -1.2361e-01 -2.9827e-01\n", - " -1.0070e-01 -4.9918e-01 1.5549e-01\n", - " ⋮ \n", - " -2.0914e-01 8.3182e-01 3.5420e-01\n", - " -9.0347e-02 -1.9814e-01 1.5357e-01\n", - " 2.0842e-01 -1.6191e-01 -1.3249e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " -2.6338e-01 2.0028e-01 -5.5048e-01\n", - " -6.3165e-02 2.0704e-01 4.7220e-01\n", - " 1.5427e-01 -2.4553e-01 -2.8350e-01\n", - " ⋮ \n", - " 2.4556e-02 -2.8573e-01 6.1484e-02\n", - " 2.4085e-01 5.7162e-03 3.0859e-01\n", - " 3.4030e-01 1.2176e-01 1.1497e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " 1.2434e-02 4.0604e-01 2.5618e-01\n", - " 8.7541e-02 -5.6916e-02 -5.7893e-01\n", - " -1.9563e-01 -7.3344e-02 -6.0057e-02\n", - " ⋮ \n", - " 1.0950e-01 3.1177e-02 -1.4969e-03\n", - " -8.3942e-02 -1.2569e-01 -2.7982e-01\n", - " 1.3968e-01 -2.1056e-01 -3.8614e-01\n", - " ... \n", - " \n", - " (125,.,.) = \n", - " -2.0339e-01 -9.2574e-02 1.9148e-01\n", - " 1.9614e-01 1.8801e-01 5.5208e-01\n", - " -5.3128e-02 4.0566e-01 -3.9731e-01\n", - " ⋮ \n", - " 2.4068e-01 3.5523e-02 -1.7478e-01\n", - " 1.3921e-01 -3.7821e-01 -7.7398e-02\n", - " 3.1290e-01 1.0250e-01 1.6110e-01\n", - " \n", - " (126,.,.) = \n", - " 1.8149e-01 1.4161e-01 -2.1990e-01\n", - " 9.2477e-02 1.8051e-01 3.1933e-01\n", - " 4.1670e-02 6.6928e-02 2.5147e-01\n", - " ⋮ \n", - " -2.6391e-01 -7.2917e-02 -5.0047e-02\n", - " 1.1001e-01 3.4186e-01 2.3474e-01\n", - " 1.2854e-01 1.6606e-01 -1.7315e-02\n", - " \n", - " (127,.,.) = \n", - " -2.3090e-01 1.5042e-01 6.3597e-01\n", - " -3.4157e-02 3.5767e-03 3.4091e-01\n", - " 4.9821e-02 -5.0298e-02 5.0703e-02\n", - " ⋮ \n", - " 2.7414e-02 1.3014e-01 2.9182e-01\n", - " 1.6557e-01 4.1488e-01 1.0505e-01\n", - " 8.3979e-02 -3.8755e-01 -1.7537e-02\n", - " [torch.FloatTensor of size 128x128x3]),\n", - " ('module.encoder.cbhg.conv1d_projections.1.bn.weight', \n", - " 0.4536\n", - " 0.3744\n", - " 0.3930\n", - " 0.4028\n", - " 0.1871\n", - " 0.3296\n", - " 0.5046\n", - " 0.3456\n", - " 0.5806\n", - " 0.6941\n", - " 0.1379\n", - " 0.4948\n", - " 0.3155\n", - " 0.2385\n", - " 0.2987\n", - " 0.4990\n", - " 0.3377\n", - " 0.4929\n", - " 0.3430\n", - " 0.6483\n", - " 0.4081\n", - " 0.5141\n", - " 0.8667\n", - " 0.7486\n", - " 0.3455\n", - " 0.8252\n", - " 0.4224\n", - " 0.3750\n", - " 0.6520\n", - " 0.3503\n", - " 0.7377\n", - " 0.6230\n", - " 0.4638\n", - " 0.7627\n", - " 0.4598\n", - " 0.4961\n", - " 0.6081\n", - " 0.4609\n", - " -0.2415\n", - " 0.3845\n", - " 0.6408\n", - " 0.6914\n", - " 0.7386\n", - " 0.5714\n", - " 0.2910\n", - " 0.6573\n", - " 0.5436\n", - " 0.2170\n", - " 0.4840\n", - " 0.7204\n", - " 0.2262\n", - " 0.1596\n", - " 0.5102\n", - " 0.3687\n", - " 0.4028\n", - " -0.2457\n", - " 0.4936\n", - " 0.4966\n", - " 0.7913\n", - " 0.4287\n", - " 0.5383\n", - " 0.5517\n", - " 0.6433\n", - " 0.6154\n", - " 0.1992\n", - " -0.2114\n", - " 0.3606\n", - " 0.1503\n", - " 0.6788\n", - " 0.5619\n", - " 0.5162\n", - " 0.5477\n", - " 0.5638\n", - " 0.2326\n", - " 0.5656\n", - " 0.6506\n", - " 0.4850\n", - " 0.3377\n", - " 0.3538\n", - " 0.3885\n", - " 0.2085\n", - " 0.3807\n", - " 0.1975\n", - " 0.5298\n", - " 0.8238\n", - " 0.8133\n", - " 0.3542\n", - " 0.2515\n", - " 0.8385\n", - " 0.6650\n", - " 0.6492\n", - " 0.4470\n", - " 0.2840\n", - " 0.1060\n", - " 0.4425\n", - " 0.8025\n", - " 0.4587\n", - " 0.5400\n", - " 0.7236\n", - " 0.6267\n", - " 0.6552\n", - " 0.8278\n", - " 0.2184\n", - " 0.2870\n", - " 0.2500\n", - " -0.2185\n", - " 0.5200\n", - " 0.5064\n", - " 0.2679\n", - " 0.6613\n", - " 0.5352\n", - " 0.4196\n", - " 0.5948\n", - " 0.4679\n", - " 0.4783\n", - " 0.4823\n", - " 0.6754\n", - " 0.2985\n", - " 0.1426\n", - " 0.1292\n", - " 0.4998\n", - " -0.1704\n", - " 0.5583\n", - " 0.1927\n", - " 0.4749\n", - " 0.2017\n", - " 0.5920\n", - " 0.3369\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_projections.1.bn.bias', \n", - " 0.0224\n", - " -0.2522\n", - " 0.2020\n", - " 0.2377\n", - " 0.0741\n", - " -0.0521\n", - " -0.1118\n", - " -0.1838\n", - " 0.1101\n", - " -0.0721\n", - " 0.0287\n", - " 0.2076\n", - " -0.0525\n", - " 0.0451\n", - " -0.0014\n", - " 0.0122\n", - " 0.1394\n", - " -0.0573\n", - " -0.1542\n", - " 0.0116\n", - " -0.0492\n", - " 0.0087\n", - " -0.0062\n", - " -0.1424\n", - " 0.1519\n", - " -0.0766\n", - " -0.1431\n", - " 0.1400\n", - " -0.2043\n", - " -0.0008\n", - " -0.0737\n", - " -0.0599\n", - " -0.0529\n", - " -0.0173\n", - " -0.1263\n", - " -0.0410\n", - " 0.0255\n", - " -0.1789\n", - " -0.1656\n", - " -0.0316\n", - " -0.0390\n", - " 0.0087\n", - " -0.1299\n", - " -0.0694\n", - " -0.1189\n", - " -0.0169\n", - " -0.0213\n", - " -0.0727\n", - " -0.0509\n", - " 0.0268\n", - " 0.0202\n", - " 0.1228\n", - " -0.0614\n", - " 0.0028\n", - " -0.2373\n", - " 0.1186\n", - " -0.0383\n", - " -0.2088\n", - " 0.0736\n", - " 0.1062\n", - " -0.0432\n", - " 0.0197\n", - " -0.0035\n", - " -0.1340\n", - " -0.0258\n", - " -0.5595\n", - " 0.0046\n", - " -0.0175\n", - " -0.0872\n", - " -0.1930\n", - " 0.2642\n", - " -0.0697\n", - " 0.0933\n", - " 0.0899\n", - " -0.0901\n", - " 0.0514\n", - " -0.1184\n", - " -0.0806\n", - " -0.0036\n", - " 0.0551\n", - " -0.1512\n", - " -0.3144\n", - " 0.0791\n", - " 0.0281\n", - " -0.0380\n", - " -0.1759\n", - " -0.0889\n", - " -0.0217\n", - " -0.1630\n", - " 0.1199\n", - " 0.1335\n", - " 0.0501\n", - " -0.0056\n", - " -0.0496\n", - " -0.2410\n", - " -0.1760\n", - " -0.1461\n", - " 0.1274\n", - " -0.0090\n", - " -0.0659\n", - " 0.0040\n", - " -0.0251\n", - " -0.1979\n", - " -0.0803\n", - " -0.0057\n", - " -0.0306\n", - " 0.0850\n", - " 0.1497\n", - " -0.0224\n", - " -0.0528\n", - " -0.1483\n", - " -0.0866\n", - " -0.0954\n", - " -0.3011\n", - " 0.0900\n", - " 0.0274\n", - " -0.1553\n", - " -0.0255\n", - " 0.0925\n", - " 0.0752\n", - " -0.0417\n", - " -0.0615\n", - " -0.1534\n", - " -0.1725\n", - " 0.0274\n", - " -0.0475\n", - " 0.0416\n", - " 0.1518\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_projections.1.bn.running_mean', \n", - " -3.5261\n", - " 1.9191\n", - " -0.1514\n", - " 5.6740\n", - " 2.1472\n", - " 2.9415\n", - " 3.8381\n", - " 0.5452\n", - " -2.8366\n", - " -0.8421\n", - " -4.5869\n", - " -9.6256\n", - " 8.2001\n", - " 6.1331\n", - " 6.1160\n", - " 4.5427\n", - " 6.6272\n", - " 5.4206\n", - " -5.9134\n", - " 9.2229\n", - " -0.2210\n", - " -8.7986\n", - " 4.6308\n", - " 10.5411\n", - " -0.4133\n", - " 1.3050\n", - " 1.7876\n", - " -0.1916\n", - " -13.8851\n", - " 6.0357\n", - " -3.5755\n", - " -4.1219\n", - " -5.5356\n", - " 1.2398\n", - " -11.9075\n", - " -5.1448\n", - " 2.4730\n", - " 6.0906\n", - " -0.5620\n", - " 5.0333\n", - " 17.5819\n", - " 7.1774\n", - " -8.2541\n", - " 2.5919\n", - " 11.7343\n", - " 3.0234\n", - " -8.7040\n", - " 9.8797\n", - " 0.1937\n", - " -7.9667\n", - " 0.2270\n", - " 1.2282\n", - " -1.4745\n", - " -1.1372\n", - " 1.0151\n", - " -15.1402\n", - " 11.5094\n", - " 2.8092\n", - " 10.8497\n", - " 6.5561\n", - " -2.9229\n", - " 5.3241\n", - " 1.9052\n", - " 6.8665\n", - " -2.0176\n", - " -1.5431\n", - " -2.9267\n", - " 6.5498\n", - " -8.8167\n", - " -0.1278\n", - " 18.3676\n", - " -3.3496\n", - " 4.1914\n", - " 7.4549\n", - " -2.3751\n", - " 2.1590\n", - " 6.0434\n", - " -3.1926\n", - " 11.5383\n", - " -2.7574\n", - " 7.1369\n", - " -6.9124\n", - " -4.5749\n", - " 2.3652\n", - " 4.7353\n", - " 3.2585\n", - " 9.8200\n", - " -0.7865\n", - " -9.5018\n", - " 7.3156\n", - " 1.1741\n", - " -2.2538\n", - " 5.6080\n", - " -1.7893\n", - " -12.8355\n", - " -6.6571\n", - " 3.1501\n", - " 2.1819\n", - " 8.3854\n", - " -9.3287\n", - " -0.4803\n", - " -12.0142\n", - " -0.7332\n", - " 0.8964\n", - " 7.8064\n", - " 0.6350\n", - " -5.6905\n", - " 5.0025\n", - " 0.1746\n", - " -0.8536\n", - " 5.9803\n", - " 12.3864\n", - " 4.7458\n", - " 1.7846\n", - " 3.8921\n", - " 4.2181\n", - " -9.9728\n", - " 0.8573\n", - " 1.1001\n", - " 6.4840\n", - " 2.7009\n", - " -10.3976\n", - " -2.7385\n", - " -2.1108\n", - " 12.0861\n", - " 3.0430\n", - " 0.1592\n", - " 6.1069\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.conv1d_projections.1.bn.running_var', \n", - " 15.6029\n", - " 16.0301\n", - " 18.0898\n", - " 9.2120\n", - " 35.9527\n", - " 16.2714\n", - " 38.2591\n", - " 22.0204\n", - " 21.8823\n", - " 18.5069\n", - " 18.2544\n", - " 12.6123\n", - " 11.2886\n", - " 13.9873\n", - " 15.9373\n", - " 11.0099\n", - " 10.1090\n", - " 40.7009\n", - " 22.7567\n", - " 11.3390\n", - " 19.0217\n", - " 27.5536\n", - " 27.9724\n", - " 29.1658\n", - " 9.1891\n", - " 34.6404\n", - " 13.5092\n", - " 11.2521\n", - " 57.5699\n", - " 9.4996\n", - " 21.5500\n", - " 36.2159\n", - " 17.4185\n", - " 32.5253\n", - " 16.5135\n", - " 12.8911\n", - " 19.9967\n", - " 21.7646\n", - " 19.1525\n", - " 22.9861\n", - " 16.1516\n", - " 30.4740\n", - " 44.5169\n", - " 17.5279\n", - " 15.5373\n", - " 14.6567\n", - " 31.9899\n", - " 13.7391\n", - " 12.5149\n", - " 20.9873\n", - " 9.4325\n", - " 25.0610\n", - " 11.6550\n", - " 13.4542\n", - " 13.1076\n", - " 17.1637\n", - " 56.5875\n", - " 27.5515\n", - " 19.5355\n", - " 10.7034\n", - " 34.9299\n", - " 33.0139\n", - " 20.3878\n", - " 31.6955\n", - " 14.9919\n", - " 16.4654\n", - " 14.5991\n", - " 24.7710\n", - " 14.9264\n", - " 24.2895\n", - " 18.3864\n", - " 18.8397\n", - " 23.3574\n", - " 18.7483\n", - " 13.5217\n", - " 41.0459\n", - " 24.1666\n", - " 24.7859\n", - " 23.9255\n", - " 25.7402\n", - " 14.9454\n", - " 10.2282\n", - " 30.3052\n", - " 16.6936\n", - " 32.2529\n", - " 38.8537\n", - " 20.7137\n", - " 29.7605\n", - " 19.3282\n", - " 17.5068\n", - " 18.4878\n", - " 46.9227\n", - " 14.5460\n", - " 49.6237\n", - " 9.6516\n", - " 36.9525\n", - " 16.7046\n", - " 12.6520\n", - " 20.6808\n", - " 39.4360\n", - " 23.3886\n", - " 29.7674\n", - " 16.2744\n", - " 13.4464\n", - " 11.3885\n", - " 8.5819\n", - " 19.9894\n", - " 10.8315\n", - " 13.5345\n", - " 22.2764\n", - " 16.8990\n", - " 25.8441\n", - " 21.5513\n", - " 9.1293\n", - " 24.4879\n", - " 19.5241\n", - " 32.1755\n", - " 13.3793\n", - " 18.0319\n", - " 35.8084\n", - " 18.4339\n", - " 13.5934\n", - " 22.9312\n", - " 23.8098\n", - " 21.8152\n", - " 9.1292\n", - " 21.1067\n", - " 13.2162\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.pre_highway.weight', \n", - " 7.6424e-03 2.5910e-02 5.8759e-02 ... 2.5778e-02 5.1379e-02 -4.2433e-03\n", - " -2.4070e-02 7.6139e-02 4.9189e-02 ... 6.4792e-02 4.8108e-02 -7.3651e-02\n", - " -3.4366e-02 -7.9627e-02 -4.4232e-02 ... 3.5146e-02 -8.0961e-02 -5.9398e-02\n", - " ... ⋱ ... \n", - " 7.6387e-02 1.1982e-02 -7.0450e-04 ... 5.7644e-02 5.5235e-02 -7.8248e-02\n", - " 6.9834e-02 -7.8794e-02 6.8049e-02 ... 4.9438e-02 2.3717e-02 5.5143e-03\n", - " -2.9588e-02 -8.4442e-02 2.6452e-02 ... -4.1797e-02 4.1798e-02 2.6897e-02\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.0.H.weight', \n", - " -3.7753e-01 -1.1873e-01 -7.2885e-02 ... -1.3706e-01 -5.0812e-01 -3.1839e-01\n", - " -3.1247e-01 -1.9993e-01 -8.0211e-01 ... -3.2072e-01 2.9533e-01 -2.5461e-02\n", - " 6.9581e-02 -6.6954e-02 -1.6043e-01 ... -1.3403e-01 -3.1564e-01 -3.1844e-01\n", - " ... ⋱ ... \n", - " -3.6133e-01 1.4214e-02 1.2277e-01 ... -3.4546e-01 1.7992e-01 -1.3199e-01\n", - " -9.9197e-02 -3.4521e-02 -1.2004e-01 ... -3.2145e-01 -1.7860e-01 -1.6176e-01\n", - " 1.3408e-01 3.0038e-03 -1.0454e-01 ... -1.3727e-04 3.0389e-02 -8.2818e-02\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.0.H.bias', \n", - " -0.5865\n", - " -0.3727\n", - " -0.0313\n", - " 0.0137\n", - " -0.1726\n", - " -0.1130\n", - " 0.0186\n", - " -0.2501\n", - " 0.0633\n", - " -0.1866\n", - " 0.3170\n", - " 0.0386\n", - " -0.3819\n", - " -0.1964\n", - " -0.3243\n", - " -0.3649\n", - " -0.1672\n", - " 0.0802\n", - " -0.4135\n", - " -0.5153\n", - " -0.3261\n", - " -1.1598\n", - " -0.0941\n", - " -0.1548\n", - " -0.3674\n", - " -0.3337\n", - " -0.1484\n", - " -0.3088\n", - " 0.2971\n", - " -0.0667\n", - " -0.2780\n", - " 0.0620\n", - " -0.7120\n", - " -0.4120\n", - " 0.1090\n", - " 0.1015\n", - " -0.3369\n", - " 0.2702\n", - " -0.2184\n", - " -0.2087\n", - " -0.7956\n", - " -0.1349\n", - " -0.2185\n", - " -0.4237\n", - " -0.2828\n", - " -0.3616\n", - " 0.0301\n", - " 0.2710\n", - " -0.3773\n", - " -0.4989\n", - " 0.5445\n", - " 0.0151\n", - " -0.3162\n", - " -0.1979\n", - " -0.4540\n", - " -0.3024\n", - " -0.2572\n", - " -0.6145\n", - " -0.2004\n", - " -0.2538\n", - " 0.0036\n", - " -0.1976\n", - " -0.1802\n", - " -0.4072\n", - " -0.3396\n", - " 0.2006\n", - " 0.0554\n", - " 0.3043\n", - " -0.2079\n", - " 0.0379\n", - " 0.0255\n", - " -0.1156\n", - " -0.4118\n", - " 0.0619\n", - " -0.2979\n", - " -0.0777\n", - " -0.4252\n", - " 0.2074\n", - " 0.1137\n", - " 0.2852\n", - " -0.1483\n", - " -0.0824\n", - " -0.1568\n", - " -0.2427\n", - " -0.2911\n", - " -0.3581\n", - " -0.0607\n", - " 0.0008\n", - " -0.1334\n", - " -0.1024\n", - " 0.4847\n", - " -0.5820\n", - " 0.4444\n", - " -0.1540\n", - " -0.3775\n", - " 0.0230\n", - " -0.2560\n", - " -0.0936\n", - " -0.3532\n", - " 0.0069\n", - " 0.1352\n", - " -0.0705\n", - " 0.0704\n", - " -0.1387\n", - " 0.2529\n", - " -0.2255\n", - " -0.0730\n", - " -0.1451\n", - " -0.3517\n", - " 0.0273\n", - " -0.3147\n", - " -0.3470\n", - " -0.5478\n", - " -0.5797\n", - " -0.1703\n", - " -0.3998\n", - " -0.3479\n", - " -0.4414\n", - " -0.1925\n", - " -0.1970\n", - " -0.2915\n", - " 0.5914\n", - " -0.0482\n", - " 0.4137\n", - " -0.1621\n", - " 0.1581\n", - " -0.6951\n", - " 0.1022\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.highways.0.T.weight', \n", - " 4.2534e-01 -1.5347e-01 -2.6262e-01 ... 5.3239e-01 5.2091e-02 -9.6632e-02\n", - " 2.0936e-01 3.7558e-01 2.5726e-01 ... 4.1763e-02 3.9232e-02 -1.2335e-01\n", - " -1.4236e-02 1.9510e-01 6.7196e-01 ... -1.5553e-01 -9.3686e-02 -1.0255e+00\n", - " ... ⋱ ... \n", - " -4.6444e-02 1.9064e-01 -1.6661e-01 ... 1.2270e+00 -1.3526e-01 -1.0569e-01\n", - " -4.0731e-01 -2.1208e-02 -3.2148e-01 ... -7.6064e-02 5.6086e-01 2.5529e-01\n", - " 5.8205e-03 5.0058e-01 1.7055e-02 ... -4.7348e-01 1.8220e-01 -5.3471e-02\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.0.T.bias', \n", - " -1.6184\n", - " -1.0429\n", - " -1.3170\n", - " -1.3835\n", - " -0.4131\n", - " -0.7471\n", - " -1.3204\n", - " -1.1247\n", - " -1.5811\n", - " -1.2261\n", - " -1.1172\n", - " -0.8929\n", - " -1.2016\n", - " -1.7639\n", - " -1.4354\n", - " -1.2490\n", - " -0.9711\n", - " -1.1048\n", - " -1.1083\n", - " -1.2327\n", - " -1.0377\n", - " -1.7015\n", - " -0.7842\n", - " -1.0672\n", - " -0.6804\n", - " -1.0488\n", - " -1.0506\n", - " -1.0861\n", - " -0.8274\n", - " -1.4053\n", - " -0.8013\n", - " -0.8329\n", - " -1.5080\n", - " -1.0560\n", - " -1.0205\n", - " -0.9094\n", - " -1.3420\n", - " -1.3321\n", - " -0.9121\n", - " -1.4326\n", - " -1.2343\n", - " -0.8188\n", - " -1.0797\n", - " -1.0230\n", - " -0.8973\n", - " -0.5101\n", - " -0.9391\n", - " -1.6211\n", - " -1.0409\n", - " -1.3219\n", - " -1.6896\n", - " -0.4855\n", - " -1.0583\n", - " -0.9602\n", - " -1.4328\n", - " -0.9121\n", - " -1.3012\n", - " -1.2760\n", - " -1.0642\n", - " -1.4541\n", - " -0.5764\n", - " -1.4959\n", - " -1.1411\n", - " -0.8906\n", - " -0.9969\n", - " -0.8866\n", - " -1.4349\n", - " -0.9254\n", - " -0.8737\n", - " -1.0402\n", - " -0.8703\n", - " -1.2408\n", - " -0.9886\n", - " -1.3280\n", - " -1.1675\n", - " -1.4920\n", - " -1.1507\n", - " -1.2237\n", - " -1.3544\n", - " -0.9596\n", - " -1.1802\n", - " -0.9479\n", - " -0.1265\n", - " -0.5966\n", - " -1.0964\n", - " -0.8681\n", - " -1.1972\n", - " -0.7485\n", - " -1.0767\n", - " -0.9577\n", - " -1.4237\n", - " -1.4708\n", - " -1.5494\n", - " -0.9201\n", - " -1.5097\n", - " -0.9607\n", - " -0.8944\n", - " -1.1210\n", - " -1.0278\n", - " -0.7736\n", - " -0.8854\n", - " -1.1615\n", - " -1.6448\n", - " -0.9676\n", - " -1.5240\n", - " -0.7192\n", - " -1.2818\n", - " -0.3621\n", - " -1.5124\n", - " -1.5226\n", - " -1.3278\n", - " -1.3497\n", - " -1.2536\n", - " -1.1199\n", - " -1.0604\n", - " -1.5927\n", - " -0.8126\n", - " -1.0362\n", - " -0.4343\n", - " -0.8960\n", - " -1.2733\n", - " -1.5969\n", - " -1.3012\n", - " -1.1393\n", - " -0.6058\n", - " -1.0270\n", - " -1.4415\n", - " -1.2745\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.highways.1.H.weight', \n", - " -1.2903e-01 -3.1060e-01 1.0214e-01 ... -3.9553e-01 -1.0350e-01 -9.7856e-02\n", - " 1.8118e-01 1.3375e-02 1.9977e-01 ... -1.5329e-02 -9.9447e-02 -3.4168e-01\n", - " 3.8714e-02 1.3862e-01 -6.2023e-02 ... -6.8721e-01 5.3937e-02 1.0958e-01\n", - " ... ⋱ ... \n", - " 9.2582e-02 7.8591e-02 -1.9832e-02 ... -1.0931e-01 2.1502e-01 -2.8412e-01\n", - " 2.1670e-01 -2.8787e-01 1.7272e-02 ... -5.8934e-01 -4.1751e-01 1.1831e-01\n", - " -8.7897e-02 -7.7806e-02 -3.2964e-01 ... -4.8497e-01 8.8042e-01 5.9294e-02\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.1.H.bias', \n", - " 0.0852\n", - " 0.1422\n", - " -0.2038\n", - " -0.4496\n", - " -0.1156\n", - " -0.3202\n", - " -0.0841\n", - " 0.3041\n", - " -0.4135\n", - " -0.3150\n", - " -0.2499\n", - " -0.1967\n", - " -0.1445\n", - " 0.1640\n", - " -0.0770\n", - " -0.3320\n", - " -0.1990\n", - " -0.2207\n", - " -0.1029\n", - " -0.0388\n", - " -0.1529\n", - " -0.3993\n", - " -0.2705\n", - " -0.4385\n", - " -0.4369\n", - " 0.4232\n", - " -0.0673\n", - " -0.1282\n", - " -0.0281\n", - " -0.3006\n", - " -0.3058\n", - " -0.1669\n", - " -0.3118\n", - " -0.2587\n", - " -0.4164\n", - " 0.3357\n", - " 0.0765\n", - " -0.2905\n", - " -0.0282\n", - " -0.3647\n", - " -0.1817\n", - " 0.0875\n", - " -0.3000\n", - " -0.3787\n", - " -0.2694\n", - " -0.0468\n", - " -0.1556\n", - " -0.1751\n", - " -0.1883\n", - " -0.2814\n", - " 0.3436\n", - " -0.2187\n", - " -0.1210\n", - " -0.3293\n", - " -0.1639\n", - " 0.3319\n", - " 0.5747\n", - " 0.0551\n", - " -0.2188\n", - " 0.1805\n", - " -0.0447\n", - " 0.0290\n", - " -0.3679\n", - " -0.2118\n", - " 0.0838\n", - " -0.5260\n", - " -0.3697\n", - " -0.1344\n", - " -0.2490\n", - " -0.3448\n", - " -0.0436\n", - " -0.2243\n", - " -0.1787\n", - " 0.0374\n", - " -0.1492\n", - " -0.3518\n", - " -0.2739\n", - " -0.2630\n", - " -0.0347\n", - " -0.4719\n", - " 0.0626\n", - " -0.5353\n", - " 0.0813\n", - " -0.5074\n", - " -0.2277\n", - " -0.0963\n", - " -0.2280\n", - " -0.2200\n", - " -0.2598\n", - " -0.3971\n", - " 0.0129\n", - " 0.0856\n", - " -0.3171\n", - " 0.0893\n", - " -0.3148\n", - " -0.1448\n", - " -0.3684\n", - " -0.1098\n", - " 0.1464\n", - " -0.1974\n", - " -0.2048\n", - " -0.2799\n", - " -0.3617\n", - " -0.3129\n", - " 0.1010\n", - " -0.2835\n", - " -0.1181\n", - " -0.2271\n", - " 0.3809\n", - " -0.2949\n", - " -0.3094\n", - " 0.1898\n", - " -0.0179\n", - " -0.3059\n", - " -0.2801\n", - " -0.2374\n", - " -0.3814\n", - " -0.1881\n", - " -0.3585\n", - " -0.2194\n", - " -0.0044\n", - " 0.1414\n", - " -0.2586\n", - " -0.1702\n", - " 0.1336\n", - " -0.0514\n", - " -0.2933\n", - " -0.1440\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.highways.1.T.weight', \n", - " -2.7291e-01 -8.4563e-02 -5.1859e-01 ... 6.6748e-01 -3.1786e-01 1.7333e-01\n", - " -4.4926e-03 1.5435e-01 -2.6212e-01 ... 2.8715e-01 -4.1305e-01 2.7199e-01\n", - " -1.1743e-01 1.0964e-01 2.5068e-01 ... -3.6285e-02 -4.6603e-01 -3.1373e-01\n", - " ... ⋱ ... \n", - " 1.6104e-01 3.8883e-01 2.7417e-01 ... 5.4226e-01 -1.6939e-01 -5.0346e-02\n", - " -3.9585e-01 1.5602e-01 -2.7457e-01 ... -3.7775e-02 2.2263e-01 4.5654e-01\n", - " 3.1321e-01 -8.3668e-02 3.3402e-01 ... -1.5699e-01 -7.2676e-02 -1.5020e-02\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.1.T.bias', \n", - " -0.8735\n", - " -0.9374\n", - " -0.5376\n", - " -0.7254\n", - " -0.6993\n", - " -1.1141\n", - " -0.6093\n", - " -0.5540\n", - " -1.1648\n", - " -1.1545\n", - " -0.5369\n", - " -0.9158\n", - " -0.7628\n", - " -1.0001\n", - " -1.0033\n", - " -0.6922\n", - " -0.6518\n", - " -0.4973\n", - " -0.9699\n", - " -0.7759\n", - " -0.8246\n", - " -0.8996\n", - " -0.9112\n", - " -0.9865\n", - " -0.8448\n", - " -0.7220\n", - " -1.1545\n", - " -1.0397\n", - " -0.7810\n", - " -1.0055\n", - " -0.6162\n", - " -0.8090\n", - " -0.9596\n", - " -0.9937\n", - " -0.6643\n", - " -0.7168\n", - " -0.8435\n", - " -0.8227\n", - " -1.2901\n", - " -0.9706\n", - " -0.6679\n", - " -0.9203\n", - " -0.9916\n", - " -0.9663\n", - " -0.4326\n", - " -0.8739\n", - " -0.8837\n", - " -1.0407\n", - " -0.6802\n", - " -1.2371\n", - " -1.1714\n", - " -0.8032\n", - " -0.9634\n", - " -1.2142\n", - " -0.7706\n", - " -0.6415\n", - " -0.8907\n", - " -1.0116\n", - " -0.7812\n", - " -0.5034\n", - " -0.8496\n", - " -1.4659\n", - " -0.7487\n", - " -1.1192\n", - " -0.5994\n", - " -0.9254\n", - " -1.5166\n", - " -1.0100\n", - " -0.8003\n", - " -0.8005\n", - " -0.3146\n", - " -0.9065\n", - " -1.0724\n", - " -0.5451\n", - " -0.8384\n", - " -0.7926\n", - " -0.9792\n", - " -1.1625\n", - " -0.8456\n", - " -0.8491\n", - " -0.8859\n", - " -0.9054\n", - " -0.3732\n", - " -0.8253\n", - " -0.9892\n", - " -1.1470\n", - " -0.7574\n", - " -0.8059\n", - " -0.8198\n", - " -0.7491\n", - " -1.0176\n", - " -0.4271\n", - " -0.7732\n", - " -0.5335\n", - " -1.0020\n", - " -0.7807\n", - " -0.7100\n", - " -0.8340\n", - " -0.7395\n", - " -1.0114\n", - " -0.7979\n", - " -1.0000\n", - " -1.0414\n", - " -0.9079\n", - " -1.1180\n", - " -0.6561\n", - " -0.9785\n", - " -0.7766\n", - " -0.7542\n", - " -0.8809\n", - " -1.0164\n", - " -0.8534\n", - " -0.8284\n", - " -1.0077\n", - " -0.9135\n", - " -1.2533\n", - " -0.7508\n", - " -0.6234\n", - " -0.4095\n", - " -0.8056\n", - " -0.8089\n", - " -0.9309\n", - " -1.1503\n", - " -1.0148\n", - " -0.9564\n", - " -0.9559\n", - " -1.1823\n", - " -0.9343\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.highways.2.H.weight', \n", - " -4.1199e-01 -2.5735e-01 -1.1230e-01 ... -3.3251e-01 2.1625e-01 -1.0272e-01\n", - " -5.3729e-01 -1.6226e-01 -1.8906e-01 ... -2.6635e-01 -1.7354e-01 -1.5279e-01\n", - " -6.4851e-02 -3.5364e-01 -3.8214e-01 ... -2.6754e-01 -2.2258e-01 2.1006e-01\n", - " ... ⋱ ... \n", - " -1.7147e-01 -5.9854e-03 -9.5970e-02 ... -6.7819e-02 6.9713e-02 -8.3691e-02\n", - " -3.1862e-01 -7.5826e-02 -1.8070e-01 ... -3.9192e-01 -2.1869e-01 -1.9810e-01\n", - " 4.5282e-02 -3.1387e-02 -1.6171e-01 ... -1.7033e-01 -2.2421e-01 -1.2614e-01\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.2.H.bias', \n", - " -0.2122\n", - " -0.2343\n", - " -0.1348\n", - " -0.1589\n", - " -0.1955\n", - " -0.2032\n", - " 0.2329\n", - " -0.2732\n", - " -0.0481\n", - " -0.2075\n", - " 0.2106\n", - " 0.0758\n", - " -0.0937\n", - " 0.1371\n", - " -0.3273\n", - " -0.3655\n", - " -0.2403\n", - " -0.2024\n", - " -0.2262\n", - " -0.0624\n", - " -0.2996\n", - " -0.0521\n", - " 0.0110\n", - " -0.2283\n", - " -0.3445\n", - " -0.1740\n", - " -0.2238\n", - " -0.2046\n", - " -0.3556\n", - " -0.1143\n", - " 0.2028\n", - " -0.2763\n", - " -0.3627\n", - " 0.2728\n", - " -0.0679\n", - " 0.0887\n", - " -0.3453\n", - " -0.3231\n", - " -0.0891\n", - " -0.1272\n", - " -0.0018\n", - " -0.1163\n", - " -0.2272\n", - " -0.2614\n", - " -0.2545\n", - " -0.1889\n", - " -0.1265\n", - " -0.0453\n", - " -0.3391\n", - " -0.0437\n", - " -0.2636\n", - " -0.3149\n", - " -0.2720\n", - " -0.3746\n", - " -0.2083\n", - " 0.0788\n", - " -0.1900\n", - " 0.0926\n", - " -0.2319\n", - " -0.2448\n", - " -0.2503\n", - " -0.2315\n", - " -0.1846\n", - " -0.0152\n", - " -0.1811\n", - " -0.2365\n", - " -0.0769\n", - " -0.0788\n", - " -0.2445\n", - " -0.2496\n", - " 0.3307\n", - " -0.1891\n", - " -0.2120\n", - " -0.4236\n", - " -0.3208\n", - " -0.0614\n", - " -0.3653\n", - " -0.2695\n", - " 0.0829\n", - " 0.0220\n", - " -0.2381\n", - " -0.1541\n", - " -0.0260\n", - " -0.1162\n", - " -0.3177\n", - " -0.3546\n", - " -0.2281\n", - " -0.2564\n", - " -0.2692\n", - " -0.0759\n", - " -0.1668\n", - " -0.3211\n", - " -0.2489\n", - " -0.2288\n", - " -0.3650\n", - " -0.1224\n", - " -0.1687\n", - " -0.0150\n", - " 0.0104\n", - " -0.3318\n", - " -0.2807\n", - " -0.0672\n", - " -0.2096\n", - " -0.3078\n", - " -0.1677\n", - " 0.0028\n", - " -0.1035\n", - " -0.1555\n", - " -0.3375\n", - " -0.3359\n", - " -0.2737\n", - " -0.3322\n", - " -0.2961\n", - " -0.3377\n", - " -0.0618\n", - " -0.4349\n", - " 0.2850\n", - " -0.1604\n", - " -0.2247\n", - " -0.2893\n", - " -0.1868\n", - " 0.3563\n", - " -0.1133\n", - " -0.2874\n", - " -0.0059\n", - " 0.0826\n", - " -0.2108\n", - " -0.2225\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.highways.2.T.weight', \n", - " 7.8885e-02 3.3253e-01 1.8680e-01 ... 4.2248e-01 2.0193e-01 2.0562e-01\n", - " 1.3543e-01 4.2545e-01 3.1608e-01 ... -1.3264e-01 -4.2416e-02 2.7504e-01\n", - " -8.7588e-01 1.8678e-01 1.1842e-01 ... -5.6120e-02 1.2951e-01 7.0980e-02\n", - " ... ⋱ ... \n", - " 1.6965e-01 -1.4437e-01 -3.9676e-01 ... 5.1236e-01 5.9884e-04 -1.4009e-01\n", - " -4.0851e-01 -1.3036e-01 -3.5849e-01 ... 1.6514e-01 4.4848e-01 1.5563e-02\n", - " -3.3969e-01 8.3092e-02 1.9582e-02 ... 2.3415e-01 -4.9319e-01 4.0139e-01\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.2.T.bias', \n", - " -0.8153\n", - " -0.7770\n", - " -0.7813\n", - " -1.1026\n", - " -0.8793\n", - " -0.9383\n", - " -0.5527\n", - " -0.7065\n", - " -0.8250\n", - " -0.9231\n", - " -0.5422\n", - " -0.5655\n", - " -0.6027\n", - " -0.5251\n", - " -1.0486\n", - " -1.0117\n", - " -0.6038\n", - " -0.4492\n", - " -0.9319\n", - " -0.7762\n", - " -0.8418\n", - " -0.8602\n", - " -0.5735\n", - " -1.0869\n", - " -0.7593\n", - " -0.9178\n", - " -0.9908\n", - " -1.0369\n", - " -0.7088\n", - " -0.8628\n", - " -0.6217\n", - " -0.8813\n", - " -0.8341\n", - " -0.6698\n", - " -0.4780\n", - " -0.2960\n", - " -1.1446\n", - " -1.1255\n", - " -0.8254\n", - " -0.9693\n", - " -0.6734\n", - " -0.7371\n", - " -0.9512\n", - " -0.6976\n", - " -0.5678\n", - " -0.5780\n", - " -0.7421\n", - " -0.9606\n", - " -0.7212\n", - " -0.9026\n", - " -0.9789\n", - " -0.7526\n", - " -1.0406\n", - " -0.8483\n", - " -0.8528\n", - " -0.7178\n", - " -1.1469\n", - " -0.9338\n", - " -0.8029\n", - " -0.9971\n", - " -0.8680\n", - " -1.0543\n", - " -0.7668\n", - " -0.5666\n", - " -1.1054\n", - " -0.8139\n", - " -0.8125\n", - " -1.1192\n", - " -0.7838\n", - " -0.6822\n", - " -0.9070\n", - " -0.9213\n", - " -1.0835\n", - " -1.0484\n", - " -0.8505\n", - " -0.8808\n", - " -0.7637\n", - " -1.0705\n", - " -0.4922\n", - " -1.0723\n", - " -0.7494\n", - " -0.7859\n", - " -0.8495\n", - " -0.6520\n", - " -0.8290\n", - " -1.0147\n", - " -0.9081\n", - " -0.9225\n", - " -0.8036\n", - " -0.3723\n", - " -0.9247\n", - " -0.7306\n", - " -0.9188\n", - " -0.7129\n", - " -0.8161\n", - " -0.8964\n", - " -0.9712\n", - " -0.6160\n", - " -0.6626\n", - " -1.1156\n", - " -0.8525\n", - " -0.8618\n", - " -0.7801\n", - " -1.0254\n", - " -1.0769\n", - " -0.7159\n", - " -0.9362\n", - " -0.4980\n", - " -0.8991\n", - " -0.8185\n", - " -0.8928\n", - " -0.9891\n", - " -1.0699\n", - " -0.8665\n", - " -0.6343\n", - " -0.9490\n", - " -0.4492\n", - " -1.1470\n", - " -0.4669\n", - " -0.6436\n", - " -0.8893\n", - " -1.0752\n", - " -0.8446\n", - " -0.9765\n", - " -0.6132\n", - " -0.7831\n", - " -1.0923\n", - " -1.0290\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.highways.3.H.weight', \n", - " -2.0468e-01 -3.7653e-01 -2.5355e-01 ... -2.8372e-01 3.3695e-01 -2.2480e-01\n", - " -4.6384e-01 -2.0348e-01 1.8848e-02 ... -7.7341e-02 1.1872e-01 -3.9130e-01\n", - " -7.8048e-01 1.7562e-02 2.8630e-02 ... -2.9237e-01 5.8745e-02 -4.5702e-01\n", - " ... ⋱ ... \n", - " 3.7976e-02 -9.6244e-02 -1.5971e-01 ... -2.0324e-01 2.6845e-01 -8.0729e-02\n", - " -1.0789e-01 3.6107e-02 1.4457e-01 ... -5.0796e-02 -5.0617e-01 -2.9675e-01\n", - " 3.7937e-03 -3.9479e-01 1.3545e-01 ... -3.1718e-01 9.4908e-02 -2.5654e-01\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.3.H.bias', \n", - " -0.2521\n", - " -0.1256\n", - " -0.1813\n", - " -0.1982\n", - " -0.2478\n", - " -0.1292\n", - " -0.2231\n", - " -0.2275\n", - " -0.2880\n", - " -0.2083\n", - " -0.1963\n", - " -0.2633\n", - " -0.0872\n", - " -0.2966\n", - " -0.1935\n", - " -0.2297\n", - " 0.0641\n", - " -0.1298\n", - " -0.2047\n", - " -0.1377\n", - " -0.0130\n", - " -0.2552\n", - " -0.1561\n", - " -0.3377\n", - " -0.2934\n", - " -0.1902\n", - " -0.2115\n", - " -0.2594\n", - " 0.0602\n", - " -0.2319\n", - " -0.0750\n", - " 0.0117\n", - " -0.0910\n", - " -0.1090\n", - " -0.2593\n", - " -0.2097\n", - " -0.3011\n", - " -0.1155\n", - " -0.0650\n", - " -0.2091\n", - " -0.0570\n", - " -0.2633\n", - " -0.1900\n", - " -0.1681\n", - " -0.2742\n", - " -0.1280\n", - " -0.1124\n", - " -0.0569\n", - " -0.2899\n", - " 0.0240\n", - " -0.3327\n", - " -0.1901\n", - " -0.1869\n", - " -0.1819\n", - " -0.2468\n", - " -0.2594\n", - " -0.1714\n", - " -0.1905\n", - " -0.2057\n", - " -0.2568\n", - " -0.2461\n", - " -0.2513\n", - " -0.1808\n", - " -0.1319\n", - " -0.3379\n", - " -0.1989\n", - " -0.1165\n", - " -0.2927\n", - " -0.1664\n", - " -0.2408\n", - " -0.1338\n", - " -0.0668\n", - " -0.1319\n", - " -0.1546\n", - " -0.1039\n", - " -0.2541\n", - " -0.1639\n", - " -0.2998\n", - " -0.0444\n", - " -0.1570\n", - " -0.2315\n", - " -0.2158\n", - " -0.1707\n", - " -0.2178\n", - " -0.1815\n", - " -0.1527\n", - " -0.2077\n", - " 0.0212\n", - " -0.2341\n", - " -0.2250\n", - " -0.3521\n", - " -0.2077\n", - " 0.0823\n", - " -0.1912\n", - " -0.2247\n", - " -0.2378\n", - " -0.1769\n", - " -0.1661\n", - " -0.2944\n", - " -0.0687\n", - " -0.2118\n", - " 0.0014\n", - " -0.1662\n", - " -0.2974\n", - " -0.2595\n", - " -0.2134\n", - " -0.2725\n", - " -0.2799\n", - " -0.2954\n", - " -0.2916\n", - " -0.1599\n", - " -0.1841\n", - " -0.1736\n", - " -0.1969\n", - " -0.0531\n", - " -0.3190\n", - " -0.1531\n", - " -0.1976\n", - " 0.0470\n", - " 0.1481\n", - " -0.2712\n", - " -0.2474\n", - " -0.2162\n", - " -0.1807\n", - " -0.1803\n", - " -0.2616\n", - " -0.1937\n", - " -0.1448\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.highways.3.T.weight', \n", - " 2.5584e-01 6.0501e-01 -1.2028e-02 ... 1.4831e-01 -6.3848e-01 5.3987e-01\n", - " -2.5646e-01 9.3154e-01 5.8705e-02 ... -9.9718e-02 6.1999e-01 -6.4101e-02\n", - " 3.0758e-01 4.8709e-01 5.0105e-01 ... 2.6345e-01 -8.4133e-04 -2.3307e-02\n", - " ... ⋱ ... \n", - " 2.2438e-01 6.7495e-02 1.4857e-01 ... 5.7131e-01 3.8120e-01 1.6802e-01\n", - " -1.2598e-01 1.9640e-01 8.7319e-02 ... 4.7567e-02 -1.6886e-01 2.6160e-01\n", - " 1.5070e-01 -5.0804e-01 -6.2564e-02 ... 1.5024e-01 -3.4810e-01 6.8077e-01\n", - " [torch.FloatTensor of size 128x128]),\n", - " ('module.encoder.cbhg.highways.3.T.bias', \n", - " -0.8211\n", - " -0.9055\n", - " -0.8022\n", - " -0.8859\n", - " -0.8126\n", - " -0.8288\n", - " -0.2889\n", - " -0.6799\n", - " -1.0391\n", - " -0.8127\n", - " -0.3466\n", - " -0.7359\n", - " -0.6630\n", - " -0.8266\n", - " -1.1506\n", - " -0.8722\n", - " -0.7322\n", - " -0.6060\n", - " -0.4909\n", - " -0.5537\n", - " -1.0603\n", - " -0.7157\n", - " -0.6458\n", - " -0.5851\n", - " -1.0656\n", - " -0.7572\n", - " -1.0238\n", - " -0.9974\n", - " -0.8249\n", - " -0.7353\n", - " -0.5338\n", - " -0.8046\n", - " -0.8106\n", - " -0.5974\n", - " -0.5874\n", - " -0.5483\n", - " -0.8189\n", - " -0.8038\n", - " -1.0415\n", - " -0.8823\n", - " -0.6835\n", - " -0.7159\n", - " -0.9431\n", - " -0.7083\n", - " -0.5089\n", - " -0.6600\n", - " -0.8509\n", - " -0.9963\n", - " -0.8149\n", - " -0.8327\n", - " -0.9570\n", - " -0.8497\n", - " -1.0057\n", - " -0.8482\n", - " -0.9211\n", - " -0.7666\n", - " -0.7587\n", - " -0.7446\n", - " -0.7783\n", - " -0.7760\n", - " -0.6613\n", - " -0.9420\n", - " -0.8696\n", - " -0.6928\n", - " -0.7344\n", - " -0.6867\n", - " -0.9212\n", - " -0.9734\n", - " -0.9513\n", - " -0.7135\n", - " -0.7699\n", - " -0.6956\n", - " -0.6958\n", - " -1.1282\n", - " -0.7229\n", - " -0.7191\n", - " -0.6430\n", - " -0.6834\n", - " -0.6007\n", - " -0.9842\n", - " -0.6797\n", - " -0.8361\n", - " -0.7900\n", - " -0.7384\n", - " -0.8635\n", - " -1.0334\n", - " -0.8858\n", - " -0.9168\n", - " -0.6682\n", - " -0.7500\n", - " -0.7028\n", - " -0.8536\n", - " -0.6623\n", - " -0.8275\n", - " -0.7837\n", - " -0.6650\n", - " -0.8374\n", - " -0.8792\n", - " -0.6498\n", - " -1.0726\n", - " -0.6899\n", - " -0.6971\n", - " -0.7316\n", - " -0.9693\n", - " -1.1316\n", - " -0.7718\n", - " -0.9469\n", - " -0.6285\n", - " -0.3868\n", - " -0.7878\n", - " -0.7114\n", - " -0.7186\n", - " -0.8038\n", - " -0.7342\n", - " -0.6545\n", - " -0.9427\n", - " -0.5628\n", - " -0.8622\n", - " -0.6102\n", - " -0.4838\n", - " -0.6442\n", - " -0.6065\n", - " -0.8593\n", - " -0.9375\n", - " -0.5052\n", - " -0.6902\n", - " -0.7971\n", - " -0.9253\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.encoder.cbhg.gru.weight_ih_l0', \n", - " 3.2007e-02 5.4455e-01 -7.1443e-01 ... -9.4162e-02 -4.3151e-01 -6.8749e-01\n", - " 2.8130e-01 -5.0280e-01 -2.2537e-01 ... 1.4932e-01 7.0647e-01 -4.4751e-01\n", - " 4.5330e-01 -1.2628e+00 2.0161e-01 ... 1.6025e-01 6.3556e-01 9.8590e-01\n", - " ... ⋱ ... \n", - " 2.9572e-02 -1.3180e-01 4.3215e-01 ... 1.5172e-01 1.8325e-01 1.8813e-01\n", - " -1.6686e-01 -5.6037e-01 1.7777e-01 ... 1.3757e-01 -5.7515e-02 -2.3497e-02\n", - " 1.6238e-01 1.8440e-01 3.2782e-01 ... -3.0081e-01 1.9484e-03 2.0204e-01\n", - " [torch.FloatTensor of size 384x128]),\n", - " ('module.encoder.cbhg.gru.weight_hh_l0', \n", - " 2.8526e-01 -3.1090e-01 3.6447e-01 ... -5.1902e-02 3.2500e-02 -1.9262e-01\n", - " 1.1010e-01 -2.3967e-01 4.5641e-02 ... 2.8706e-01 5.7539e-01 1.9892e-01\n", - " 2.4201e-01 4.6614e-01 -1.0062e+00 ... 2.0535e-01 -8.0792e-02 8.0792e-02\n", - " ... ⋱ ... \n", - " 4.1073e-01 -3.2760e-01 2.7843e-01 ... -1.4044e+00 -5.8386e-01 2.3167e-01\n", - " -2.3053e-02 -4.4727e-01 1.9749e-01 ... 2.8223e-02 -1.8616e+00 4.7450e-02\n", - " 5.1008e-01 1.4143e-02 1.1105e-01 ... -2.9560e-01 4.1204e-01 -1.6465e+00\n", - " [torch.FloatTensor of size 384x128]),\n", - " ('module.encoder.cbhg.gru.bias_ih_l0', \n", - " -0.4155\n", - " -0.3321\n", - " -0.3048\n", - " -0.6956\n", - " -0.3557\n", - " -0.3485\n", - " -0.3999\n", - " -0.6418\n", - " -0.3979\n", - " 0.0681\n", - " -0.5136\n", - " -0.3784\n", - " -0.2780\n", - " -0.5601\n", - " 0.2084\n", - " -0.2112\n", - " -0.2885\n", - " -0.1304\n", - " -0.2071\n", - " -0.2958\n", - " -0.3458\n", - " -0.3430\n", - " -0.3756\n", - " -0.2285\n", - " -0.2183\n", - " -0.4922\n", - " -0.0754\n", - " -0.4418\n", - " -0.4054\n", - " -0.3207\n", - " -0.3050\n", - " -0.2280\n", - " -0.1868\n", - " -0.4659\n", - " -0.3845\n", - " -0.1640\n", - " -0.3571\n", - " -0.2205\n", - " -0.3029\n", - " -0.4748\n", - " -0.3206\n", - " -0.3986\n", - " -0.3350\n", - " -0.2090\n", - " -0.5457\n", - " -0.1707\n", - " -0.1325\n", - " -0.2547\n", - " -0.4407\n", - " -0.4723\n", - " -0.2774\n", - " -0.5082\n", - " -0.3236\n", - " -0.3112\n", - " -0.4210\n", - " -0.3686\n", - " -0.1771\n", - " -0.3533\n", - " -0.1693\n", - " -0.3630\n", - " -0.5839\n", - " 0.0258\n", - " -0.3645\n", - " -0.4769\n", - " -0.4490\n", - " -0.2534\n", - " -0.2482\n", - " -0.4709\n", - " -0.4346\n", - " -0.4537\n", - " -0.3565\n", - " -0.2979\n", - " -0.2655\n", - " -0.3364\n", - " -0.1936\n", - " -0.2150\n", - " -0.3746\n", - " 0.0178\n", - " -0.3488\n", - " -0.3854\n", - " -0.4342\n", - " -0.3697\n", - " -0.1661\n", - " -0.2310\n", - " -0.2932\n", - " -0.4776\n", - " -0.4042\n", - " -0.3637\n", - " -0.5312\n", - " -0.2954\n", - " -0.2021\n", - " -0.4496\n", - " -0.1322\n", - " -0.4744\n", - " -0.1750\n", - " -0.1595\n", - " -0.1616\n", - " -0.4852\n", - " -0.3841\n", - " -0.0904\n", - " -0.2831\n", - " -0.3742\n", - " -0.2667\n", - " -0.2912\n", - " -0.2815\n", - " -0.4033\n", - " -0.2178\n", - " -0.3491\n", - " -0.3175\n", - " -0.4574\n", - " -0.4432\n", - " -0.3113\n", - " -0.3502\n", - " -0.3915\n", - " -0.2927\n", - " -0.5635\n", - " -0.0827\n", - " -0.3517\n", - " -0.2997\n", - " -0.2913\n", - " -0.3453\n", - " -0.1541\n", - " -0.2960\n", - " -0.3400\n", - " -0.3662\n", - " -0.3617\n", - " -0.3401\n", - " -0.2206\n", - " 0.0005\n", - " 0.2320\n", - " 0.0617\n", - " -0.2193\n", - " 0.1220\n", - " 0.1716\n", - " 0.0387\n", - " 0.1160\n", - " 0.4116\n", - " 0.8825\n", - " 0.2311\n", - " 0.1303\n", - " 0.6885\n", - " 0.1943\n", - " 1.0475\n", - " -0.2056\n", - " 0.7786\n", - " 0.2430\n", - " 0.8656\n", - " -0.0933\n", - " 0.5916\n", - " -0.1442\n", - " 0.2344\n", - " 0.2769\n", - " 0.1473\n", - " 0.3291\n", - " 0.1934\n", - " 0.4735\n", - " -0.0404\n", - " -0.0812\n", - " -0.4299\n", - " -0.0574\n", - " 0.1115\n", - " 0.2498\n", - " 0.2578\n", - " 0.0039\n", - " 0.5349\n", - " 0.2167\n", - " -0.1984\n", - " 0.3020\n", - " 0.4183\n", - " 0.0827\n", - " 0.5940\n", - " 0.4513\n", - " -0.0776\n", - " 0.0700\n", - " -0.3964\n", - " 0.0625\n", - " 0.0910\n", - " 0.6217\n", - " 0.4383\n", - " 0.2796\n", - " 0.1113\n", - " 0.0774\n", - " 0.2578\n", - " 0.1069\n", - " -0.0121\n", - " -0.0714\n", - " -0.0098\n", - " 0.1720\n", - " 0.2910\n", - " 0.6613\n", - " 0.3495\n", - " 0.2671\n", - " 0.0405\n", - " -0.2306\n", - " 0.0031\n", - " 0.2956\n", - " 0.0708\n", - " 0.0342\n", - " 0.1232\n", - " -0.1602\n", - " -0.0502\n", - " 0.1984\n", - " 0.1321\n", - " 0.4394\n", - " -0.0117\n", - " 0.9800\n", - " 0.3483\n", - " 0.0724\n", - " -0.0905\n", - " 0.3448\n", - " 0.1114\n", - " 0.3323\n", - " -0.2112\n", - " 0.6743\n", - " 0.2827\n", - " 0.1825\n", - " 0.1283\n", - " 0.0693\n", - " -0.0141\n", - " 0.1136\n", - " 0.0255\n", - " 0.2815\n", - " 0.0372\n", - " -0.1901\n", - " 1.0761\n", - " 0.4441\n", - " 0.5777\n", - " 0.6788\n", - " 0.1622\n", - " -0.3291\n", - " -0.1040\n", - " 0.2530\n", - " 0.2226\n", - " 0.0436\n", - " -0.0288\n", - " -0.0954\n", - " 0.3503\n", - " 0.1399\n", - " 0.2990\n", - " 0.2709\n", - " 0.1577\n", - " 0.5494\n", - " 0.0870\n", - " -0.3541\n", - " 0.5600\n", - " -0.0591\n", - " 0.0816\n", - " 0.6198\n", - " 0.3193\n", - " -0.2191\n", - " 0.1602\n", - " -0.0296\n", - " -0.1147\n", - " -0.0194\n", - " -0.0492\n", - " -0.2893\n", - " 0.0315\n", - " -0.0166\n", - " -0.0184\n", - " 0.0121\n", - " -0.0088\n", - " -0.0027\n", - " 0.0265\n", - " 0.0132\n", - " 0.0018\n", - " -0.0272\n", - " -0.0002\n", - " -0.0125\n", - " 0.0045\n", - " -0.0020\n", - " -0.0041\n", - " 0.0166\n", - " -0.0004\n", - " -0.0344\n", - " 0.0080\n", - " -0.0104\n", - " -0.0004\n", - " 0.0003\n", - " -0.0094\n", - " -0.0328\n", - " 0.0091\n", - " 0.0158\n", - " 0.0027\n", - " 0.0115\n", - " 0.0098\n", - " 0.0391\n", - " -0.0165\n", - " 0.0250\n", - " 0.0223\n", - " -0.0112\n", - " -0.0005\n", - " -0.0163\n", - " 0.0075\n", - " -0.0054\n", - " -0.0146\n", - " 0.0169\n", - " -0.0056\n", - " -0.0101\n", - " -0.0117\n", - " 0.0372\n", - " 0.0263\n", - " -0.0326\n", - " 0.0485\n", - " -0.0069\n", - " -0.0269\n", - " -0.0044\n", - " -0.0074\n", - " -0.0025\n", - " -0.0048\n", - " 0.0194\n", - " 0.0300\n", - " -0.0119\n", - " 0.0037\n", - " -0.0352\n", - " -0.0390\n", - " 0.0608\n", - " -0.0027\n", - " 0.0059\n", - " -0.0266\n", - " -0.0211\n", - " -0.0342\n", - " 0.0032\n", - " -0.0065\n", - " -0.0209\n", - " -0.0110\n", - " 0.0007\n", - " -0.0052\n", - " 0.0088\n", - " 0.0066\n", - " -0.0215\n", - " -0.0257\n", - " -0.0106\n", - " 0.0212\n", - " -0.0171\n", - " -0.0154\n", - " -0.0073\n", - " -0.0020\n", - " -0.0270\n", - " 0.0151\n", - " -0.0326\n", - " 0.0075\n", - " 0.0117\n", - " 0.0249\n", - " 0.0320\n", - " 0.0157\n", - " 0.0160\n", - " 0.0388\n", - " 0.0345\n", - " 0.0266\n", - " -0.0044\n", - " 0.0264\n", - " -0.0309\n", - " -0.0014\n", - " -0.0206\n", - " 0.0185\n", - " -0.0013\n", - " 0.0004\n", - " 0.0215\n", - " -0.0258\n", - " 0.0112\n", - " -0.0389\n", - " -0.0145\n", - " 0.0122\n", - " 0.0118\n", - " 0.0131\n", - " -0.0098\n", - " 0.0018\n", - " -0.0337\n", - " 0.0028\n", - " 0.0133\n", - " 0.0160\n", - " -0.0130\n", - " 0.0274\n", - " 0.0097\n", - " 0.0143\n", - " -0.0441\n", - " -0.0135\n", - " -0.0019\n", - " 0.0555\n", - " -0.0274\n", - " -0.0183\n", - " 0.0123\n", - " -0.0122\n", - " -0.0438\n", - " [torch.FloatTensor of size 384]),\n", - " ('module.encoder.cbhg.gru.bias_hh_l0', \n", - " -0.3324\n", - " -0.2636\n", - " -0.3443\n", - " -0.7007\n", - " -0.3168\n", - " -0.2623\n", - " -0.4678\n", - " -0.5638\n", - " -0.4448\n", - " 0.2232\n", - " -0.3805\n", - " -0.2953\n", - " -0.3160\n", - " -0.5969\n", - " 0.2410\n", - " -0.2382\n", - " -0.3254\n", - " -0.1246\n", - " -0.1753\n", - " -0.3835\n", - " -0.4032\n", - " -0.4362\n", - " -0.4141\n", - " -0.2424\n", - " -0.3258\n", - " -0.3726\n", - " -0.1523\n", - " -0.3338\n", - " -0.4174\n", - " -0.2525\n", - " -0.3905\n", - " -0.2104\n", - " -0.2400\n", - " -0.3530\n", - " -0.3890\n", - " -0.2062\n", - " -0.3817\n", - " -0.2957\n", - " -0.4194\n", - " -0.5868\n", - " -0.3884\n", - " -0.3095\n", - " -0.4296\n", - " -0.1893\n", - " -0.5139\n", - " -0.2669\n", - " -0.1194\n", - " -0.3773\n", - " -0.4873\n", - " -0.4685\n", - " -0.1368\n", - " -0.4118\n", - " -0.3678\n", - " -0.2931\n", - " -0.4648\n", - " -0.3296\n", - " -0.2718\n", - " -0.3486\n", - " -0.0516\n", - " -0.2209\n", - " -0.6266\n", - " 0.1345\n", - " -0.3854\n", - " -0.4379\n", - " -0.4165\n", - " -0.2786\n", - " -0.2046\n", - " -0.4067\n", - " -0.3728\n", - " -0.4412\n", - " -0.4051\n", - " -0.2663\n", - " -0.3059\n", - " -0.4357\n", - " -0.2192\n", - " -0.1052\n", - " -0.2918\n", - " -0.0670\n", - " -0.4026\n", - " -0.4681\n", - " -0.3601\n", - " -0.2093\n", - " -0.2464\n", - " -0.2066\n", - " -0.3683\n", - " -0.4958\n", - " -0.4797\n", - " -0.3821\n", - " -0.5263\n", - " -0.2097\n", - " -0.1749\n", - " -0.3779\n", - " -0.2407\n", - " -0.4482\n", - " -0.1921\n", - " -0.2409\n", - " -0.2942\n", - " -0.5252\n", - " -0.4661\n", - " -0.1872\n", - " -0.2110\n", - " -0.3630\n", - " -0.3668\n", - " -0.2823\n", - " -0.2967\n", - " -0.3671\n", - " -0.2426\n", - " -0.2324\n", - " -0.2456\n", - " -0.3978\n", - " -0.3852\n", - " -0.3906\n", - " -0.3389\n", - " -0.4650\n", - " -0.3562\n", - " -0.6517\n", - " -0.0692\n", - " -0.3172\n", - " -0.3680\n", - " -0.3382\n", - " -0.2662\n", - " -0.2351\n", - " -0.3653\n", - " -0.2909\n", - " -0.2948\n", - " -0.2182\n", - " -0.3879\n", - " -0.2211\n", - " -0.0638\n", - " 0.2472\n", - " 0.0489\n", - " -0.0662\n", - " 0.0359\n", - " 0.1067\n", - " 0.0882\n", - " 0.0249\n", - " 0.4015\n", - " 0.8504\n", - " 0.2799\n", - " 0.0461\n", - " 0.6607\n", - " 0.2023\n", - " 1.0062\n", - " -0.2213\n", - " 0.8321\n", - " 0.3461\n", - " 0.9642\n", - " -0.1767\n", - " 0.4647\n", - " -0.1128\n", - " 0.2089\n", - " 0.3121\n", - " 0.0332\n", - " 0.2687\n", - " 0.2742\n", - " 0.4417\n", - " 0.0058\n", - " -0.1918\n", - " -0.3485\n", - " -0.0085\n", - " 0.0888\n", - " 0.1906\n", - " 0.2361\n", - " -0.1330\n", - " 0.6340\n", - " 0.1948\n", - " -0.2205\n", - " 0.3279\n", - " 0.3521\n", - " 0.1421\n", - " 0.5140\n", - " 0.4459\n", - " -0.0698\n", - " 0.0853\n", - " -0.3250\n", - " 0.1003\n", - " 0.0428\n", - " 0.4710\n", - " 0.3304\n", - " 0.2012\n", - " 0.0356\n", - " 0.0750\n", - " 0.2146\n", - " 0.0363\n", - " 0.0198\n", - " -0.0927\n", - " -0.0490\n", - " 0.2173\n", - " 0.4293\n", - " 0.7105\n", - " 0.4576\n", - " 0.2871\n", - " 0.0533\n", - " -0.2388\n", - " 0.0702\n", - " 0.3513\n", - " 0.0898\n", - " -0.0335\n", - " 0.0856\n", - " -0.1336\n", - " -0.0065\n", - " 0.1672\n", - " 0.1079\n", - " 0.3741\n", - " 0.0139\n", - " 1.0074\n", - " 0.3532\n", - " 0.0353\n", - " -0.0585\n", - " 0.3025\n", - " 0.1956\n", - " 0.1709\n", - " -0.2493\n", - " 0.6448\n", - " 0.2334\n", - " 0.1509\n", - " 0.1396\n", - " 0.1179\n", - " 0.0650\n", - " 0.1516\n", - " 0.0633\n", - " 0.3230\n", - " -0.0687\n", - " -0.2229\n", - " 0.9816\n", - " 0.3944\n", - " 0.4913\n", - " 0.7222\n", - " 0.2434\n", - " -0.3003\n", - " -0.0925\n", - " 0.2632\n", - " 0.3519\n", - " 0.1211\n", - " -0.0183\n", - " -0.0195\n", - " 0.4433\n", - " 0.1283\n", - " 0.3443\n", - " 0.3609\n", - " 0.0334\n", - " 0.5134\n", - " -0.0298\n", - " -0.2188\n", - " 0.5276\n", - " 0.0114\n", - " 0.0276\n", - " 0.5599\n", - " 0.3557\n", - " -0.2125\n", - " 0.1562\n", - " -0.0786\n", - " -0.0632\n", - " -0.0968\n", - " -0.0286\n", - " -0.4307\n", - " -0.0858\n", - " 0.0490\n", - " 0.0511\n", - " -0.0643\n", - " 0.0182\n", - " 0.0145\n", - " -0.0713\n", - " -0.0547\n", - " 0.0017\n", - " 0.0403\n", - " 0.0069\n", - " 0.0232\n", - " -0.0281\n", - " 0.0315\n", - " 0.0454\n", - " -0.0308\n", - " 0.0274\n", - " 0.0587\n", - " -0.0261\n", - " 0.0316\n", - " 0.0094\n", - " -0.0150\n", - " 0.0226\n", - " 0.0829\n", - " -0.0262\n", - " -0.0449\n", - " -0.0152\n", - " -0.0277\n", - " -0.0229\n", - " -0.1034\n", - " 0.0613\n", - " -0.0601\n", - " -0.0383\n", - " 0.0514\n", - " -0.0016\n", - " 0.0361\n", - " -0.0196\n", - " 0.0211\n", - " 0.0380\n", - " -0.0685\n", - " 0.0311\n", - " 0.0322\n", - " 0.0471\n", - " -0.0937\n", - " -0.0973\n", - " 0.0930\n", - " -0.1017\n", - " 0.0296\n", - " 0.0931\n", - " 0.0100\n", - " 0.0059\n", - " 0.0206\n", - " 0.0215\n", - " -0.0494\n", - " -0.0895\n", - " 0.0149\n", - " -0.0236\n", - " 0.0978\n", - " 0.0867\n", - " -0.1546\n", - " 0.0023\n", - " -0.0112\n", - " 0.0661\n", - " 0.0772\n", - " 0.0852\n", - " -0.0093\n", - " 0.0098\n", - " 0.0716\n", - " 0.0540\n", - " -0.0106\n", - " 0.0131\n", - " -0.0172\n", - " -0.0238\n", - " 0.0653\n", - " 0.0811\n", - " 0.0017\n", - " -0.0489\n", - " 0.0220\n", - " 0.0361\n", - " 0.0340\n", - " 0.0156\n", - " 0.0678\n", - " -0.0318\n", - " 0.0601\n", - " -0.0316\n", - " -0.0399\n", - " -0.0763\n", - " -0.1014\n", - " -0.0504\n", - " -0.0512\n", - " -0.1088\n", - " -0.0949\n", - " -0.0490\n", - " 0.0054\n", - " -0.0582\n", - " 0.0834\n", - " -0.0200\n", - " 0.0834\n", - " -0.0350\n", - " 0.0003\n", - " -0.0014\n", - " -0.0655\n", - " 0.0722\n", - " -0.0105\n", - " 0.1002\n", - " 0.0321\n", - " -0.0231\n", - " -0.0414\n", - " -0.0302\n", - " 0.0381\n", - " -0.0080\n", - " 0.0859\n", - " 0.0093\n", - " -0.0398\n", - " -0.0581\n", - " 0.0648\n", - " -0.0445\n", - " -0.0229\n", - " -0.0461\n", - " 0.1085\n", - " 0.0413\n", - " -0.0018\n", - " -0.1469\n", - " 0.0784\n", - " 0.0475\n", - " -0.0404\n", - " 0.0248\n", - " 0.0912\n", - " [torch.FloatTensor of size 384]),\n", - " ('module.encoder.cbhg.gru.weight_ih_l0_reverse', \n", - " 3.9114e-01 2.9058e-01 2.1761e-01 ... 6.4113e-02 2.4866e-02 2.0332e-01\n", - " -1.0174e-01 -3.9031e-01 -3.4186e-01 ... -1.7112e-01 -3.6747e-01 1.9842e-01\n", - " -6.4938e-01 7.2838e-01 1.8530e-01 ... -3.3848e-01 -5.1010e-01 2.4907e-02\n", - " ... ⋱ ... \n", - " -2.7396e-02 -5.2042e-02 1.7369e-01 ... -1.7471e-01 1.4356e-01 -1.5332e-01\n", - " -1.5901e-01 -1.2751e-01 -1.3193e-01 ... -1.4338e-01 1.6812e-01 1.0909e-02\n", - " -1.3883e-01 -9.4185e-02 -7.3227e-02 ... -1.6537e-02 -8.9718e-02 1.9451e-01\n", - " [torch.FloatTensor of size 384x128]),\n", - " ('module.encoder.cbhg.gru.weight_hh_l0_reverse', \n", - " -9.0639e-02 -1.0697e-01 5.7864e-01 ... 7.3469e-02 -1.2463e-02 -2.9977e-01\n", - " 7.5671e-02 -3.0402e-01 -3.2234e-02 ... -4.2422e-01 -4.2969e-01 4.5207e-01\n", - " 8.1829e-03 -3.2530e-01 -1.0873e-01 ... -3.3056e-01 -2.7219e-01 4.2230e-01\n", - " ... ⋱ ... \n", - " -1.5967e-01 -2.3172e-01 -3.3635e-01 ... -9.2274e-01 -2.5547e-01 -2.0461e-01\n", - " -4.4421e-02 4.6869e-02 -1.8232e-01 ... -1.9337e-01 -1.5851e+00 2.4948e-01\n", - " 4.0944e-02 -3.1717e-01 1.9521e-01 ... 2.7803e-01 1.8609e-01 -1.1735e+00\n", - " [torch.FloatTensor of size 384x128]),\n", - " ('module.encoder.cbhg.gru.bias_ih_l0_reverse', \n", - " -3.9950e-01\n", - " -3.2197e-01\n", - " -1.3405e-01\n", - " -3.2604e-01\n", - " -5.6099e-01\n", - " -2.8250e-01\n", - " -1.9845e-01\n", - " -3.1501e-01\n", - " -1.3463e-01\n", - " -5.6306e-02\n", - " -3.3890e-01\n", - " -4.3012e-01\n", - " -2.8359e-01\n", - " -2.2534e-01\n", - " -2.9135e-01\n", - " -4.1709e-01\n", - " -4.8963e-01\n", - " -2.6772e-01\n", - " -3.2265e-01\n", - " -5.5409e-01\n", - " -3.2530e-01\n", - " -2.0639e-01\n", - " -4.3631e-01\n", - " -4.2849e-01\n", - " -2.5627e-01\n", - " -2.3475e-01\n", - " -1.8201e-01\n", - " -2.5451e-01\n", - " -5.4825e-01\n", - " -2.9104e-01\n", - " -5.2274e-01\n", - " -4.0654e-01\n", - " -3.4696e-01\n", - " -2.7961e-01\n", - " -2.9184e-01\n", - " -4.3392e-01\n", - " -1.2083e-01\n", - " -2.7390e-01\n", - " -2.5458e-01\n", - " -3.6923e-01\n", - " -3.2601e-01\n", - " -4.3903e-01\n", - " -4.0077e-01\n", - " -4.9911e-01\n", - " -7.1123e-01\n", - " -2.9732e-01\n", - " -2.0078e-01\n", - " -4.2338e-01\n", - " -1.3699e-01\n", - " -1.6686e-01\n", - " -2.2572e-01\n", - " -1.8250e-01\n", - " -2.4997e-01\n", - " -2.6874e-01\n", - " -1.9583e-01\n", - " -2.4155e-01\n", - " -2.2515e-01\n", - " -2.9014e-01\n", - " -2.9633e-01\n", - " -5.5905e-01\n", - " -3.7270e-01\n", - " -2.5255e-01\n", - " -2.9211e-01\n", - " -2.9391e-01\n", - " -2.3087e-01\n", - " -4.1957e-01\n", - " -4.2438e-01\n", - " -3.2394e-01\n", - " -3.4436e-01\n", - " -3.5398e-01\n", - " -3.5293e-01\n", - " -2.9384e-01\n", - " -3.7704e-01\n", - " -5.6181e-01\n", - " -3.2076e-01\n", - " -3.3572e-01\n", - " -4.2303e-01\n", - " -3.5085e-01\n", - " -1.1059e-01\n", - " -3.9819e-01\n", - " -2.7747e-01\n", - " -2.2572e-01\n", - " -3.1374e-01\n", - " -6.2688e-01\n", - " -3.4165e-01\n", - " -2.8978e-01\n", - " -1.8716e-01\n", - " -7.6714e-01\n", - " -4.0007e-02\n", - " -4.8165e-01\n", - " -1.8806e-01\n", - " -4.2463e-01\n", - " -3.6502e-01\n", - " -3.3924e-01\n", - " -2.8496e-01\n", - " -3.3000e-01\n", - " -3.8442e-01\n", - " -3.8950e-01\n", - " -3.5248e-01\n", - " -4.5389e-01\n", - " -1.9361e-01\n", - " -3.3409e-01\n", - " -6.1777e-01\n", - " -1.5660e-01\n", - " -4.0810e-01\n", - " -2.7564e-01\n", - " -3.7781e-01\n", - " -3.9068e-01\n", - " -3.2880e-01\n", - " -3.6881e-01\n", - " -5.4130e-01\n", - " -3.6642e-01\n", - " -1.5730e-01\n", - " -2.2717e-01\n", - " -3.7211e-01\n", - " -1.1571e-01\n", - " -4.5240e-02\n", - " -2.6568e-01\n", - " -2.9602e-01\n", - " -4.1572e-01\n", - " -2.0671e-01\n", - " -5.1318e-01\n", - " -3.3514e-01\n", - " -5.7200e-01\n", - " -3.1647e-01\n", - " -3.4052e-01\n", - " -4.6007e-01\n", - " -4.2392e-01\n", - " -9.5999e-02\n", - " 1.9549e-01\n", - " 6.2338e-02\n", - " 3.6352e-01\n", - " 6.6420e-02\n", - " 2.2219e-03\n", - " 2.7094e-01\n", - " 2.1709e-02\n", - " -1.6166e-01\n", - " 1.3865e+00\n", - " 3.2875e-02\n", - " 3.7118e-01\n", - " 5.3729e-01\n", - " -1.1936e-01\n", - " 3.8217e-03\n", - " 1.2877e-01\n", - " 1.6440e-01\n", - " -9.4198e-02\n", - " 1.4893e-01\n", - " 4.6403e-02\n", - " 5.4027e-01\n", - " 4.7590e-01\n", - " 6.0755e-02\n", - " -2.3104e-01\n", - " 4.8152e-01\n", - " 2.9563e-01\n", - " -6.8155e-02\n", - " -1.4239e-01\n", - " 1.0185e-01\n", - " 7.0267e-02\n", - " -1.1993e-01\n", - " 6.3600e-01\n", - " 1.6313e-01\n", - " -9.9922e-02\n", - " 1.7793e-01\n", - " 1.9935e-01\n", - " 1.2773e-01\n", - " 2.9412e-01\n", - " 1.2534e-01\n", - " 4.4731e-01\n", - " 7.1293e-02\n", - " 1.1292e-01\n", - " -8.9293e-02\n", - " 3.4067e-01\n", - " 1.7642e-01\n", - " -1.6595e-01\n", - " 5.9712e-01\n", - " -1.0002e-01\n", - " 1.9485e-01\n", - " 1.9248e-01\n", - " 1.0541e+00\n", - " 7.2503e-01\n", - " 9.6133e-01\n", - " 1.9803e-01\n", - " 6.9215e-03\n", - " 5.8363e-03\n", - " 1.5684e-01\n", - " 6.0974e-01\n", - " 3.1397e-01\n", - " -7.1457e-02\n", - " 1.7027e-01\n", - " 7.2058e-01\n", - " 1.8164e-01\n", - " 3.4612e-01\n", - " 4.1798e-01\n", - " 1.1724e-01\n", - " 6.3091e-02\n", - " 4.3745e-02\n", - " 1.1416e-01\n", - " -6.2468e-02\n", - " -1.9612e-01\n", - " 8.5380e-01\n", - " 1.2593e-01\n", - " 3.4382e-01\n", - " 5.9211e-01\n", - " -2.1804e-01\n", - " 1.3949e-01\n", - " 4.4558e-02\n", - " 2.8600e-01\n", - " 5.1862e-02\n", - " 2.0543e-01\n", - " -2.3415e-01\n", - " -1.3609e-01\n", - " 2.2412e-01\n", - " -1.0314e-01\n", - " -2.8997e-01\n", - " 4.8292e-02\n", - " -4.4030e-03\n", - " 1.4104e+00\n", - " 3.2007e-01\n", - " 1.2052e-01\n", - " 9.1789e-02\n", - " 2.0554e-01\n", - " 7.6838e-02\n", - " 3.4112e-01\n", - " 1.3785e-01\n", - " -2.2267e-02\n", - " -1.2027e-01\n", - " 4.9677e-01\n", - " 1.8642e-02\n", - " 4.0587e-01\n", - " -1.0680e-01\n", - " 3.3480e-02\n", - " 1.0233e+00\n", - " 1.4441e-01\n", - " -1.1909e-01\n", - " -3.0416e-01\n", - " -2.4717e-01\n", - " -5.9753e-02\n", - " -7.1970e-02\n", - " -2.8127e-02\n", - " -6.7975e-02\n", - " 1.8998e-01\n", - " -5.9482e-02\n", - " -2.2827e-01\n", - " 1.3982e+00\n", - " 1.0563e+00\n", - " -9.7214e-03\n", - " -6.2421e-02\n", - " 4.8036e-02\n", - " -3.0814e-02\n", - " -2.7699e-01\n", - " 3.1028e-01\n", - " 5.1062e-01\n", - " -1.1891e-01\n", - " -9.7181e-02\n", - " 6.2741e-02\n", - " -1.1922e-01\n", - " -1.6630e-02\n", - " -1.6036e-02\n", - " 2.1753e-02\n", - " -1.7682e-02\n", - " 5.4466e-03\n", - " -1.1839e-02\n", - " 2.4157e-02\n", - " -2.7820e-03\n", - " -1.2229e-02\n", - " -2.3654e-02\n", - " -4.3722e-03\n", - " -1.3692e-02\n", - " 1.3510e-02\n", - " -1.5224e-03\n", - " 3.9796e-02\n", - " -1.5609e-02\n", - " -7.2699e-03\n", - " 1.8364e-02\n", - " 5.2180e-02\n", - " -7.0756e-03\n", - " -2.6243e-02\n", - " -1.8955e-02\n", - " -7.5550e-03\n", - " -3.2503e-02\n", - " -3.0747e-02\n", - " 3.7648e-03\n", - " -6.3179e-03\n", - " -9.0970e-03\n", - " 5.7825e-03\n", - " -1.6808e-02\n", - " 1.1765e-02\n", - " -1.9976e-02\n", - " -8.9799e-03\n", - " 1.6962e-03\n", - " 3.2469e-02\n", - " 4.8755e-02\n", - " -2.5837e-02\n", - " 2.7148e-02\n", - " -9.4387e-03\n", - " 4.4295e-03\n", - " -4.7579e-02\n", - " 1.4396e-02\n", - " -1.5253e-02\n", - " 1.3900e-02\n", - " 9.5453e-05\n", - " -3.3910e-02\n", - " -2.8879e-03\n", - " -1.6263e-02\n", - " -4.2506e-03\n", - " -8.4950e-03\n", - " 9.2556e-03\n", - " 2.9998e-02\n", - " 4.6513e-03\n", - " -5.2122e-02\n", - " -4.4481e-02\n", - " 1.5333e-02\n", - " 2.2972e-02\n", - " -1.2462e-02\n", - " -3.4175e-03\n", - " 4.6433e-03\n", - " -1.8690e-03\n", - " 1.7714e-02\n", - " -1.9650e-03\n", - " -2.6035e-02\n", - " 1.6315e-02\n", - " -1.1207e-02\n", - " 1.3503e-02\n", - " -1.1432e-02\n", - " 7.8864e-03\n", - " -1.4371e-02\n", - " 5.3492e-02\n", - " -2.3405e-02\n", - " -4.6617e-03\n", - " 1.4812e-02\n", - " 9.7913e-03\n", - " 2.2707e-02\n", - " -3.8388e-02\n", - " -1.3629e-03\n", - " 4.2772e-02\n", - " 2.2527e-02\n", - " 3.8240e-02\n", - " 9.8582e-03\n", - " 1.6111e-02\n", - " -2.1650e-03\n", - " -1.9264e-02\n", - " 2.3081e-02\n", - " -3.5951e-02\n", - " 1.0299e-02\n", - " -1.5311e-02\n", - " 2.5592e-03\n", - " -3.0966e-02\n", - " -1.5051e-02\n", - " -1.7087e-02\n", - " 1.3155e-02\n", - " -1.0265e-02\n", - " 1.2869e-02\n", - " -8.2125e-03\n", - " -2.9881e-02\n", - " -1.3611e-02\n", - " -1.8783e-03\n", - " -1.5054e-02\n", - " -2.1433e-02\n", - " -6.1975e-03\n", - " 3.2115e-02\n", - " -6.9664e-03\n", - " 4.0004e-03\n", - " -8.5284e-03\n", - " -1.5563e-02\n", - " -1.0801e-02\n", - " 8.9668e-03\n", - " 1.5517e-03\n", - " 1.4980e-03\n", - " -1.9663e-02\n", - " 9.3043e-03\n", - " -1.8283e-02\n", - " -1.7655e-02\n", - " -1.7385e-02\n", - " -1.5408e-02\n", - " -3.1511e-02\n", - " -1.6622e-02\n", - " 4.2455e-02\n", - " 6.7878e-02\n", - " 3.2178e-02\n", - " 8.9143e-03\n", - " 9.9809e-03\n", - " -2.0120e-02\n", - " -3.9052e-03\n", - " -6.8284e-03\n", - " [torch.FloatTensor of size 384]),\n", - " ('module.encoder.cbhg.gru.bias_hh_l0_reverse', \n", - " -0.4021\n", - " -0.3769\n", - " -0.2140\n", - " -0.4048\n", - " -0.4409\n", - " -0.2962\n", - " -0.3530\n", - " -0.3547\n", - " -0.2006\n", - " -0.0939\n", - " -0.2148\n", - " -0.3912\n", - " -0.2509\n", - " -0.3723\n", - " -0.2090\n", - " -0.3679\n", - " -0.6192\n", - " -0.2744\n", - " -0.2821\n", - " -0.6300\n", - " -0.2462\n", - " -0.3459\n", - " -0.4790\n", - " -0.3206\n", - " -0.3240\n", - " -0.2225\n", - " -0.2448\n", - " -0.3012\n", - " -0.5411\n", - " -0.1877\n", - " -0.6550\n", - " -0.3096\n", - " -0.3862\n", - " -0.3820\n", - " -0.2281\n", - " -0.4488\n", - " -0.2567\n", - " -0.2342\n", - " -0.3323\n", - " -0.3611\n", - " -0.1804\n", - " -0.5137\n", - " -0.3178\n", - " -0.4012\n", - " -0.6883\n", - " -0.3295\n", - " -0.2554\n", - " -0.4798\n", - " -0.2267\n", - " -0.2884\n", - " -0.1101\n", - " -0.3275\n", - " -0.2861\n", - " -0.3019\n", - " -0.2099\n", - " -0.2571\n", - " -0.3100\n", - " -0.3889\n", - " -0.2770\n", - " -0.5019\n", - " -0.3791\n", - " -0.2051\n", - " -0.3247\n", - " -0.3686\n", - " -0.2871\n", - " -0.3502\n", - " -0.4598\n", - " -0.2021\n", - " -0.3041\n", - " -0.4478\n", - " -0.3348\n", - " -0.3264\n", - " -0.3935\n", - " -0.5275\n", - " -0.3446\n", - " -0.3875\n", - " -0.3014\n", - " -0.3336\n", - " -0.1255\n", - " -0.3976\n", - " -0.2708\n", - " -0.1827\n", - " -0.2783\n", - " -0.6199\n", - " -0.4063\n", - " -0.3431\n", - " -0.2897\n", - " -0.6350\n", - " -0.1377\n", - " -0.3942\n", - " -0.0478\n", - " -0.3938\n", - " -0.4251\n", - " -0.2682\n", - " -0.2178\n", - " -0.2168\n", - " -0.3732\n", - " -0.4737\n", - " -0.3745\n", - " -0.3642\n", - " -0.1977\n", - " -0.2043\n", - " -0.5977\n", - " -0.1634\n", - " -0.5290\n", - " -0.3496\n", - " -0.3478\n", - " -0.4321\n", - " -0.2788\n", - " -0.2496\n", - " -0.4516\n", - " -0.3969\n", - " -0.1678\n", - " -0.2187\n", - " -0.3149\n", - " -0.0863\n", - " -0.0248\n", - " -0.2742\n", - " -0.2219\n", - " -0.4662\n", - " -0.1856\n", - " -0.3597\n", - " -0.3117\n", - " -0.4578\n", - " -0.2414\n", - " -0.3564\n", - " -0.4715\n", - " -0.4142\n", - " -0.1192\n", - " 0.2282\n", - " 0.0998\n", - " 0.3681\n", - " -0.0937\n", - " -0.0333\n", - " 0.2070\n", - " 0.0235\n", - " -0.1256\n", - " 1.3631\n", - " 0.0545\n", - " 0.3700\n", - " 0.6947\n", - " -0.1998\n", - " 0.0415\n", - " 0.1162\n", - " 0.1360\n", - " -0.0844\n", - " 0.2483\n", - " 0.0018\n", - " 0.5331\n", - " 0.5480\n", - " 0.0129\n", - " -0.0840\n", - " 0.5069\n", - " 0.3349\n", - " -0.0571\n", - " -0.0355\n", - " 0.0312\n", - " 0.1560\n", - " -0.1759\n", - " 0.5901\n", - " 0.1501\n", - " 0.0128\n", - " 0.2224\n", - " 0.0474\n", - " 0.0988\n", - " 0.2010\n", - " 0.1655\n", - " 0.4528\n", - " 0.0771\n", - " 0.1318\n", - " -0.0493\n", - " 0.2723\n", - " 0.0769\n", - " -0.2386\n", - " 0.6518\n", - " -0.0598\n", - " 0.3625\n", - " 0.1767\n", - " 0.9518\n", - " 0.6138\n", - " 1.0032\n", - " 0.1404\n", - " 0.0313\n", - " 0.0960\n", - " 0.2134\n", - " 0.4506\n", - " 0.2143\n", - " -0.0808\n", - " 0.2908\n", - " 0.6168\n", - " 0.1607\n", - " 0.3082\n", - " 0.2554\n", - " 0.1755\n", - " 0.0763\n", - " 0.1613\n", - " 0.2722\n", - " -0.1114\n", - " -0.2297\n", - " 0.8030\n", - " 0.0292\n", - " 0.3153\n", - " 0.5183\n", - " -0.1982\n", - " 0.1279\n", - " 0.0764\n", - " 0.1635\n", - " -0.0785\n", - " 0.1573\n", - " -0.2740\n", - " -0.2416\n", - " 0.2376\n", - " -0.1834\n", - " -0.3681\n", - " 0.0012\n", - " 0.0962\n", - " 1.2691\n", - " 0.2833\n", - " 0.2742\n", - " 0.0638\n", - " 0.2562\n", - " 0.1418\n", - " 0.3912\n", - " 0.1948\n", - " -0.0925\n", - " -0.0010\n", - " 0.6521\n", - " 0.0980\n", - " 0.3893\n", - " -0.1378\n", - " -0.0100\n", - " 1.0812\n", - " 0.1997\n", - " -0.1443\n", - " -0.3224\n", - " -0.3206\n", - " -0.0624\n", - " -0.1788\n", - " 0.1056\n", - " 0.0937\n", - " 0.2355\n", - " -0.0579\n", - " -0.2225\n", - " 1.5624\n", - " 0.9694\n", - " -0.1254\n", - " -0.0760\n", - " 0.1729\n", - " 0.0904\n", - " -0.2842\n", - " 0.1807\n", - " 0.4844\n", - " -0.1173\n", - " -0.1267\n", - " 0.1129\n", - " -0.2652\n", - " 0.0412\n", - " 0.0245\n", - " -0.0584\n", - " 0.0137\n", - " -0.0256\n", - " 0.0435\n", - " -0.0638\n", - " -0.0002\n", - " 0.0219\n", - " 0.0493\n", - " -0.0016\n", - " 0.0335\n", - " -0.0372\n", - " -0.0163\n", - " -0.1106\n", - " 0.0539\n", - " 0.0132\n", - " -0.0349\n", - " -0.1317\n", - " 0.0202\n", - " 0.0458\n", - " 0.0272\n", - " -0.0015\n", - " 0.1109\n", - " 0.0943\n", - " -0.0222\n", - " 0.0050\n", - " 0.0363\n", - " -0.0365\n", - " 0.0504\n", - " -0.0554\n", - " 0.0515\n", - " 0.0207\n", - " 0.0082\n", - " -0.0852\n", - " -0.1534\n", - " 0.0594\n", - " -0.0727\n", - " 0.0335\n", - " -0.0088\n", - " 0.1092\n", - " -0.0471\n", - " 0.0300\n", - " -0.0515\n", - " -0.0115\n", - " 0.0812\n", - " 0.0010\n", - " 0.0595\n", - " 0.0341\n", - " 0.0243\n", - " -0.0234\n", - " -0.0822\n", - " -0.0227\n", - " 0.1416\n", - " 0.1211\n", - " -0.0128\n", - " -0.0706\n", - " 0.0334\n", - " 0.0051\n", - " -0.0237\n", - " -0.0058\n", - " -0.0463\n", - " 0.0036\n", - " 0.0705\n", - " -0.0559\n", - " 0.0278\n", - " -0.0305\n", - " 0.0348\n", - " -0.0263\n", - " 0.0674\n", - " -0.1551\n", - " 0.0591\n", - " 0.0149\n", - " -0.0758\n", - " -0.0294\n", - " -0.0772\n", - " 0.1146\n", - " 0.0091\n", - " -0.1018\n", - " -0.0859\n", - " -0.1003\n", - " -0.0120\n", - " -0.0486\n", - " 0.0101\n", - " 0.0587\n", - " -0.0660\n", - " 0.0979\n", - " -0.0533\n", - " 0.0480\n", - " -0.0106\n", - " 0.0422\n", - " 0.0438\n", - " 0.0387\n", - " -0.0297\n", - " 0.0189\n", - " -0.0110\n", - " 0.0288\n", - " 0.0946\n", - " 0.0560\n", - " 0.0084\n", - " 0.0286\n", - " 0.0514\n", - " 0.0132\n", - " -0.0559\n", - " 0.0266\n", - " -0.0069\n", - " 0.0305\n", - " 0.0457\n", - " 0.0135\n", - " -0.0472\n", - " 0.0024\n", - " -0.0213\n", - " 0.0408\n", - " -0.0099\n", - " 0.0599\n", - " 0.0540\n", - " 0.0526\n", - " 0.0459\n", - " 0.0755\n", - " 0.0624\n", - " -0.1091\n", - " -0.2088\n", - " -0.0742\n", - " -0.0100\n", - " -0.0230\n", - " 0.0552\n", - " 0.0098\n", - " 0.0287\n", - " [torch.FloatTensor of size 384]),\n", - " ('module.decoder.input_layer.weight', \n", - " 5.8912e-02 -1.6154e-01 3.1346e-01 ... -8.0450e-02 -1.7943e-01 -4.4032e-01\n", - " 2.2493e-02 -5.4650e-01 4.6403e-01 ... 1.4908e-02 -2.3215e-01 2.7284e-02\n", - " 9.7050e-02 4.0065e-01 -2.3527e-02 ... -3.0692e-01 7.2489e-02 2.4635e-01\n", - " ... ⋱ ... \n", - " -3.5142e-03 6.1435e-02 -6.3044e-03 ... 4.7249e-02 1.6870e-02 -2.7558e-01\n", - " -9.6333e-01 2.4835e-01 -1.5142e-01 ... 4.1154e-01 -8.1726e-02 -4.9164e-02\n", - " 3.7292e-01 2.6657e-01 -3.5470e-01 ... 6.5989e-02 7.4646e-02 2.0836e-01\n", - " [torch.FloatTensor of size 256x256]),\n", - " ('module.decoder.prenet.layers.0.weight', \n", - " 1.7273e-02 3.9850e-02 3.8051e-02 ... 2.3819e-01 2.4384e-01 3.2467e-01\n", - " 2.7207e-02 -2.0696e-02 4.7891e-02 ... -5.0082e-02 2.9328e-02 -4.2173e-02\n", - " -4.6377e-02 -5.8427e-02 5.9433e-02 ... 9.2617e-03 2.8656e-02 2.6222e-01\n", - " ... ⋱ ... \n", - " -6.4205e-02 -1.9410e-02 2.5475e-02 ... 9.8911e-02 5.9283e-02 1.9599e-01\n", - " -1.0034e-02 -1.4641e-02 -5.9885e-02 ... 3.0947e-01 1.8075e-01 3.6630e-01\n", - " -4.7642e-02 4.9048e-01 -8.2729e-02 ... -7.3440e-01 -2.3007e-01 1.2729e-01\n", - " [torch.FloatTensor of size 256x400]),\n", - " ('module.decoder.prenet.layers.0.bias', \n", - " 1.2574\n", - " -0.0297\n", - " 0.8272\n", - " 0.1237\n", - " -0.6448\n", - " -0.3559\n", - " -0.6406\n", - " -1.0272\n", - " -0.9062\n", - " -0.3825\n", - " -1.1827\n", - " -0.4707\n", - " -0.3198\n", - " -0.5765\n", - " -0.2746\n", - " 0.2238\n", - " -0.5342\n", - " -0.2980\n", - " -0.3369\n", - " -0.3599\n", - " -0.6481\n", - " -0.2758\n", - " -0.5455\n", - " -0.1720\n", - " -0.9845\n", - " -0.0390\n", - " -0.5530\n", - " -0.1325\n", - " -1.4598\n", - " 1.6848\n", - " -0.2840\n", - " -0.4153\n", - " -0.9280\n", - " 0.3325\n", - " -0.0751\n", - " -1.5679\n", - " -0.6500\n", - " -0.4325\n", - " -0.8774\n", - " -0.0603\n", - " -0.0973\n", - " 0.9125\n", - " -0.6883\n", - " -0.1912\n", - " -0.4294\n", - " -1.2876\n", - " 1.7439\n", - " -0.0499\n", - " -0.7083\n", - " -1.3910\n", - " 1.2655\n", - " -1.7360\n", - " -0.0509\n", - " -0.4689\n", - " -0.4943\n", - " -0.9908\n", - " -0.2800\n", - " -0.4613\n", - " -0.0472\n", - " -0.7536\n", - " 0.2111\n", - " -0.5066\n", - " -0.8105\n", - " -0.6232\n", - " -0.2159\n", - " -0.4624\n", - " 1.1024\n", - " 0.3514\n", - " 0.8865\n", - " -0.0117\n", - " -0.3849\n", - " -0.5286\n", - " -0.7260\n", - " 0.2438\n", - " -0.2764\n", - " -1.1041\n", - " -0.7391\n", - " -0.4548\n", - " 0.9607\n", - " 0.2563\n", - " 1.0791\n", - " -1.1938\n", - " -1.4059\n", - " -0.0218\n", - " -0.5807\n", - " -0.0211\n", - " -1.1994\n", - " -0.3751\n", - " -0.4187\n", - " -0.6417\n", - " -0.5826\n", - " -1.8560\n", - " -0.3148\n", - " -0.2558\n", - " 0.1604\n", - " -0.1702\n", - " -0.0172\n", - " 0.3932\n", - " -0.7518\n", - " 0.4139\n", - " -0.0991\n", - " 0.2794\n", - " -0.3773\n", - " -0.0568\n", - " -0.4718\n", - " 1.8045\n", - " -0.3920\n", - " 0.2627\n", - " 0.2605\n", - " 0.4709\n", - " -0.2575\n", - " 0.1251\n", - " -0.0557\n", - " 1.7317\n", - " -0.4669\n", - " -0.3428\n", - " -0.0377\n", - " 0.1490\n", - " -0.3555\n", - " -1.0507\n", - " -0.4364\n", - " -0.7193\n", - " 0.3214\n", - " -0.3130\n", - " -1.2584\n", - " 0.2316\n", - " -0.2101\n", - " 1.6875\n", - " -0.5976\n", - " -0.1807\n", - " -1.1705\n", - " 0.9523\n", - " -0.4907\n", - " -0.6100\n", - " 0.7783\n", - " -1.3257\n", - " 0.2497\n", - " -0.5033\n", - " -0.3916\n", - " -1.2852\n", - " 1.0912\n", - " -1.1056\n", - " -0.0825\n", - " 0.3236\n", - " -0.6353\n", - " -0.6368\n", - " -0.6667\n", - " -0.1264\n", - " -0.8609\n", - " 0.4090\n", - " -0.2521\n", - " -0.1683\n", - " -0.0963\n", - " 0.2585\n", - " -1.3112\n", - " -0.2364\n", - " -0.4060\n", - " -0.3156\n", - " -0.2951\n", - " -0.0159\n", - " -0.5303\n", - " -0.5743\n", - " -0.3195\n", - " -1.0213\n", - " 1.1266\n", - " -0.3234\n", - " 1.2449\n", - " -0.7429\n", - " -0.6232\n", - " -0.5871\n", - " -0.9705\n", - " -0.6896\n", - " 2.2192\n", - " -1.2712\n", - " 0.9313\n", - " -0.3202\n", - " 1.3223\n", - " -0.2517\n", - " -0.0472\n", - " -0.5210\n", - " -0.2545\n", - " 1.4358\n", - " -0.2809\n", - " -0.6639\n", - " -0.7321\n", - " 1.2307\n", - " -1.3464\n", - " -0.2210\n", - " 1.6999\n", - " -0.4472\n", - " -1.1767\n", - " 2.2247\n", - " -0.5072\n", - " 1.6496\n", - " -0.4081\n", - " -0.0079\n", - " 1.4584\n", - " -1.0027\n", - " 1.2522\n", - " 1.2128\n", - " -0.9032\n", - " 1.2793\n", - " -0.6196\n", - " -0.3898\n", - " -0.9331\n", - " 0.3800\n", - " 1.7871\n", - " 0.1263\n", - " 0.9310\n", - " -0.4832\n", - " 1.0980\n", - " 0.4972\n", - " -0.0218\n", - " 0.1663\n", - " -0.1926\n", - " -0.4412\n", - " -0.4890\n", - " 0.3012\n", - " -0.4918\n", - " -0.5552\n", - " -0.7084\n", - " 1.7566\n", - " 1.2132\n", - " -0.6182\n", - " -0.6995\n", - " -0.5213\n", - " 0.2395\n", - " -0.4751\n", - " 1.3885\n", - " 0.0127\n", - " -0.7025\n", - " -1.3511\n", - " -0.0942\n", - " -0.0687\n", - " 0.1353\n", - " -0.5863\n", - " -0.8277\n", - " -0.2539\n", - " -0.4305\n", - " -1.5516\n", - " -0.3325\n", - " -0.3001\n", - " -0.0283\n", - " 0.9374\n", - " -0.8915\n", - " 0.3668\n", - " -0.2711\n", - " -1.1822\n", - " -0.4988\n", - " -1.7255\n", - " 0.2521\n", - " 0.3801\n", - " -0.9418\n", - " -1.5530\n", - " -0.3145\n", - " 0.2827\n", - " [torch.FloatTensor of size 256]),\n", - " ('module.decoder.prenet.layers.1.weight', \n", - " -1.3509e-03 -4.9811e-02 -3.0601e+00 ... 1.2224e-02 6.3256e-04 -5.1213e-01\n", - " -7.1447e-04 -2.4918e-02 -2.3079e-04 ... -4.0298e-01 8.0245e-02 5.5424e-02\n", - " 1.8214e-03 -3.6424e-02 -2.4487e-03 ... 9.2734e-03 3.9090e-05 -1.6429e-01\n", - " ... ⋱ ... \n", - " 4.1529e-03 4.6767e-02 3.3784e-03 ... -4.9681e-04 -4.5640e-03 -1.7410e+00\n", - " 2.1006e-03 6.3024e-04 1.9130e-03 ... -2.2718e-02 -8.2343e-03 -9.1887e-02\n", - " 7.8437e-04 -2.0041e-02 -3.0045e-01 ... 4.5585e-03 -2.2033e-03 -2.6096e-01\n", - " [torch.FloatTensor of size 128x256]),\n", - " ('module.decoder.prenet.layers.1.bias', \n", - " 0.0052\n", - " -0.0042\n", - " -0.0056\n", - " -0.1226\n", - " -0.3664\n", - " -0.0042\n", - " -0.9969\n", - " 0.3418\n", - " 0.2695\n", - " 0.3532\n", - " -0.0236\n", - " -0.7367\n", - " -0.0029\n", - " 0.2559\n", - " 0.0523\n", - " -1.1205\n", - " -0.0039\n", - " -0.1988\n", - " 0.2844\n", - " 0.0206\n", - " -0.8661\n", - " 0.5405\n", - " -0.1517\n", - " -0.0011\n", - " -0.1248\n", - " 0.0041\n", - " -0.0378\n", - " -0.0906\n", - " 0.0570\n", - " 0.4385\n", - " 0.5438\n", - " 0.0329\n", - " 0.0029\n", - " -0.9980\n", - " 0.3965\n", - " -0.0089\n", - " -1.1389\n", - " -0.1909\n", - " 0.2859\n", - " -0.0189\n", - " -0.0014\n", - " 0.1089\n", - " 0.0194\n", - " -0.0660\n", - " -0.6653\n", - " -0.1948\n", - " -0.0060\n", - " 0.3791\n", - " -0.0064\n", - " 0.3199\n", - " 0.0274\n", - " 0.0885\n", - " -0.0027\n", - " 0.2967\n", - " 0.4002\n", - " 0.0870\n", - " -0.0071\n", - " 0.0584\n", - " -0.0073\n", - " -0.8491\n", - " 0.1680\n", - " -0.0017\n", - " 0.3167\n", - " -0.0034\n", - " 0.0167\n", - " 0.1565\n", - " 0.0116\n", - " 0.3706\n", - " -0.0044\n", - " -0.0025\n", - " 0.2208\n", - " -0.0007\n", - " 0.1015\n", - " 0.1852\n", - " -0.0199\n", - " -0.0067\n", - " 0.2664\n", - " 0.0052\n", - " 0.0027\n", - " 0.0724\n", - " -0.6335\n", - " 0.3221\n", - " -0.0028\n", - " -0.0143\n", - " -0.0027\n", - " -0.1618\n", - " 0.3082\n", - " -0.5741\n", - " 0.1174\n", - " 0.0087\n", - " 0.0087\n", - " -0.0076\n", - " 0.0071\n", - " -1.0007\n", - " -0.4847\n", - " -0.0075\n", - " -0.0015\n", - " 0.0655\n", - " 0.5898\n", - " 0.1552\n", - " 0.0606\n", - " -0.0090\n", - " -0.0814\n", - " -0.0840\n", - " 0.0140\n", - " -0.0111\n", - " -0.2604\n", - " -0.0040\n", - " 0.0634\n", - " 0.0555\n", - " 0.4157\n", - " -0.0055\n", - " 0.2500\n", - " 0.3200\n", - " -0.1563\n", - " -0.0049\n", - " -0.7962\n", - " 0.0093\n", - " -0.6326\n", - " -0.0270\n", - " 0.1141\n", - " -0.3724\n", - " -0.0036\n", - " 0.4631\n", - " -0.0074\n", - " 0.0300\n", - " -0.5286\n", - " 0.2050\n", - " [torch.FloatTensor of size 128]),\n", - " ('module.decoder.attention_rnn.rnn_cell.weight_ih', \n", - " 4.7004e-03 -3.1616e-03 5.6298e-03 ... 4.1007e-01 -1.1769e-01 1.5694e-02\n", - " -2.1963e-02 -4.6315e-02 -3.6587e-02 ... 2.2953e-02 4.3843e-01 -2.3121e-02\n", - " 1.5446e-02 -1.2748e-02 1.3458e-02 ... 3.1746e-01 -1.2326e-01 -3.6464e-01\n", - " ... ⋱ ... \n", - " 1.1824e-02 1.0912e-01 3.9496e-02 ... -3.3261e-01 -8.9657e-02 -2.0552e-01\n", - " -4.6938e-02 4.5614e-02 -6.5803e-02 ... -1.6535e-01 1.5010e-01 1.0953e-01\n", - " 9.1592e-04 1.6509e-03 1.6584e-03 ... 6.4068e-04 -2.5964e-02 -5.1105e-02\n", - " [torch.FloatTensor of size 768x384]),\n", - " ('module.decoder.attention_rnn.rnn_cell.weight_hh', \n", - " 2.3133e-01 -1.3324e-01 6.8962e-02 ... -1.3204e-01 -1.4500e-01 -8.0639e-02\n", - " 3.8879e-01 -4.8046e-01 5.5766e-01 ... 8.2341e-01 -7.1176e-02 -1.7622e-01\n", - " -1.3903e-01 -2.3238e-01 1.5178e+00 ... 5.0531e-01 -2.3371e-01 1.5687e-01\n", - " ... ⋱ ... \n", - " 4.3659e-02 -1.8181e-02 1.4424e-01 ... -2.3447e+00 1.1663e-01 2.8783e-02\n", - " 1.3827e-01 -1.5128e-01 -2.8599e-02 ... 1.6510e-01 -7.6042e-01 6.6707e-02\n", - " 1.8982e-01 6.8345e-04 1.3821e-01 ... -2.7056e-01 4.6980e-03 -1.2982e+00\n", - " [torch.FloatTensor of size 768x256]),\n", - " ('module.decoder.attention_rnn.rnn_cell.bias_ih', \n", - " -0.1681\n", - " -0.4455\n", - " -0.0855\n", - " -0.2734\n", - " -0.2902\n", - " -0.3903\n", - " -0.0983\n", - " -0.3286\n", - " 0.0890\n", - " 0.1034\n", - " -0.3072\n", - " -0.1984\n", - " -0.3123\n", - " -0.3316\n", - " -0.1818\n", - " -0.2477\n", - " -0.3372\n", - " -0.2289\n", - " -0.2351\n", - " -0.1868\n", - " -0.0510\n", - " -0.1700\n", - " -0.6182\n", - " -0.4167\n", - " -0.3103\n", - " -0.3025\n", - " -0.1819\n", - " -0.1224\n", - " -0.1600\n", - " -0.1490\n", - " -0.3483\n", - " -0.3005\n", - " 0.0135\n", - " 0.2279\n", - " -0.3633\n", - " -0.5285\n", - " -0.0272\n", - " -0.0846\n", - " -0.3191\n", - " -0.2163\n", - " -0.3472\n", - " 0.4668\n", - " -0.2450\n", - " -0.1129\n", - " -0.2643\n", - " -0.1567\n", - " -0.2560\n", - " -0.4324\n", - " -0.0101\n", - " -0.0095\n", - " -0.4006\n", - " -0.2120\n", - " -0.2007\n", - " 0.0248\n", - " -0.3071\n", - " -0.2836\n", - " -0.1074\n", - " -0.2105\n", - " -0.2390\n", - " 0.0074\n", - " -0.1894\n", - " -0.2280\n", - " -0.0391\n", - " -0.3245\n", - " -0.1079\n", - " -0.2870\n", - " -0.2642\n", - " -0.1295\n", - " -0.1607\n", - " -0.6550\n", - " 0.2058\n", - " -0.1507\n", - " -0.3115\n", - " -0.3720\n", - " -0.5016\n", - " -0.0754\n", - " -0.1310\n", - " -0.0842\n", - " -0.4348\n", - " -0.1354\n", - " 0.0201\n", - " -0.4004\n", - " -0.3730\n", - " -0.0806\n", - " -0.1812\n", - " -0.3134\n", - " -0.4739\n", - " -0.1196\n", - " -0.1110\n", - " 0.0135\n", - " -0.2281\n", - " -0.2914\n", - " -0.1191\n", - " -0.1852\n", - " -0.1134\n", - " -0.1330\n", - " -0.0775\n", - " -0.1139\n", - " -0.0373\n", - " -0.1966\n", - " -0.1528\n", - " -0.3453\n", - " -0.3157\n", - " -0.2631\n", - " -0.2700\n", - " -0.1754\n", - " 0.0399\n", - " -0.3159\n", - " 0.3600\n", - " -0.1543\n", - " -0.0227\n", - " -0.4256\n", - " -0.0859\n", - " -0.4042\n", - " -0.2984\n", - " -0.1030\n", - " -0.5381\n", - " -0.3036\n", - " 0.0867\n", - " -0.1804\n", - " -0.0901\n", - " -0.0799\n", - " 0.2442\n", - " -0.2991\n", - " -0.0975\n", - " -0.2165\n", - " -0.1760\n", - " -0.2790\n", - " -0.2457\n", - " -0.2866\n", - " -0.1029\n", - " -0.2726\n", - " -0.1853\n", - " -0.3342\n", - " -0.4926\n", - " -0.3865\n", - " -0.0976\n", - " -0.1514\n", - " -0.3091\n", - " -0.3345\n", - " -0.2481\n", - " -0.2871\n", - " -0.1821\n", - " -0.0286\n", - " 0.0421\n", - " -0.3300\n", - " -0.5272\n", - " 0.0578\n", - " -0.4468\n", - " -0.2035\n", - " -0.2977\n", - " -0.3573\n", - " -0.2553\n", - " -0.0288\n", - " -0.2515\n", - " -0.3434\n", - " -0.1521\n", - " -0.0886\n", - " -0.0664\n", - " -0.3043\n", - " -0.3734\n", - " -0.3612\n", - " -0.3121\n", - " -0.2435\n", - " -0.3681\n", - " 0.3880\n", - " -0.1687\n", - " -0.1656\n", - " -0.2758\n", - " -0.1889\n", - " -0.3179\n", - " -0.6930\n", - " -0.2761\n", - " -0.1971\n", - " -0.2255\n", - " 0.0814\n", - " -0.0797\n", - " -0.1322\n", - " -0.2608\n", - " -0.0973\n", - " 0.1345\n", - " -0.2937\n", - " -0.2879\n", - " -0.2330\n", - " -0.3231\n", - " -0.3696\n", - " -0.2892\n", - " 0.1844\n", - " -0.3503\n", - " -0.4406\n", - " -0.0191\n", - " 0.1077\n", - " -0.0890\n", - " -0.4048\n", - " -0.2865\n", - " -0.3503\n", - " 0.0461\n", - " -0.1139\n", - " -0.2748\n", - " -0.0668\n", - " -0.5985\n", - " -0.4329\n", - " -0.3664\n", - " -0.0504\n", - " -0.0366\n", - " -0.2466\n", - " -0.4081\n", - " -0.3905\n", - " -0.4826\n", - " -0.1981\n", - " -0.1180\n", - " -0.0637\n", - " -0.3156\n", - " -0.2758\n", - " -0.1164\n", - " -0.4320\n", - " -0.1839\n", - " -0.3343\n", - " -0.1842\n", - " -0.2677\n", - " -0.1974\n", - " -0.3704\n", - " 0.1662\n", - " -0.0225\n", - " -0.0275\n", - " -0.2811\n", - " -0.0940\n", - " -0.0287\n", - " -0.2577\n", - " -0.4922\n", - " -0.3372\n", - " -0.2350\n", - " -0.2281\n", - " 0.0020\n", - " -0.1796\n", - " -0.1772\n", - " -0.2580\n", - " -0.2861\n", - " -0.2207\n", - " -0.2044\n", - " -0.0210\n", - " -0.4599\n", - " -0.1910\n", - " -0.3374\n", - " -0.4828\n", - " -0.3753\n", - " -0.2594\n", - " -0.1496\n", - " 0.1280\n", - " -0.2443\n", - " -0.1205\n", - " -0.1102\n", - " -0.2538\n", - " -0.2303\n", - " -0.2475\n", - " -0.0475\n", - " -0.3693\n", - " 0.5033\n", - " -0.2319\n", - " 0.4061\n", - " -0.2932\n", - " 0.2920\n", - " 0.3194\n", - " 0.2956\n", - " 0.4079\n", - " -0.1020\n", - " -0.3090\n", - " 0.3526\n", - " -0.1023\n", - " 0.6887\n", - " 0.4414\n", - " 0.0618\n", - " 0.5093\n", - " -0.0824\n", - " 0.2660\n", - " -0.1215\n", - " 0.5445\n", - " 0.5103\n", - " -0.5923\n", - " 0.5078\n", - " 0.8807\n", - " 0.3953\n", - " -0.3786\n", - " -0.0840\n", - " 0.2337\n", - " -0.3545\n", - " -0.2247\n", - " 0.7402\n", - " 0.0166\n", - " -0.5177\n", - " 0.5897\n", - " 1.0498\n", - " -0.3881\n", - " -0.0463\n", - " 0.0124\n", - " 0.2867\n", - " 0.6141\n", - " -0.3570\n", - " -0.8429\n", - " 0.1788\n", - " -0.3630\n", - " -0.8819\n", - " 0.4702\n", - " 0.4737\n", - " -1.2320\n", - " -0.9242\n", - " 0.3525\n", - " 0.5829\n", - " -0.5321\n", - " 0.6006\n", - " 0.8337\n", - " 0.3379\n", - " -0.1771\n", - " -0.8779\n", - " 0.6235\n", - " -0.1372\n", - " -0.4700\n", - " 0.6897\n", - " 0.4867\n", - " 0.3006\n", - " -0.2144\n", - " 0.2536\n", - " -0.1740\n", - " -0.4235\n", - " -0.5595\n", - " 0.9610\n", - " 0.0160\n", - " 0.6872\n", - " -0.4486\n", - " -0.3360\n", - " 0.3887\n", - " -0.1723\n", - " 0.3747\n", - " -0.2266\n", - " 0.3598\n", - " -0.5144\n", - " -0.0569\n", - " 0.7886\n", - " 0.6107\n", - " -0.3781\n", - " 0.0526\n", - " -0.1562\n", - " 0.2887\n", - " -0.4112\n", - " 0.0853\n", - " 0.1482\n", - " 0.6354\n", - " -0.4740\n", - " 0.0292\n", - " 1.0279\n", - " 0.4255\n", - " 0.1468\n", - " -0.7064\n", - " -0.0603\n", - " -0.0846\n", - " -0.2341\n", - " 0.2208\n", - " 0.8614\n", - " 0.1438\n", - " -0.0266\n", - " 0.5498\n", - " 0.5653\n", - " -0.1237\n", - " 0.4341\n", - " -0.6534\n", - " 0.0016\n", - " 0.1957\n", - " 0.4631\n", - " 0.0945\n", - " 0.5586\n", - " 0.2923\n", - " 0.2120\n", - " 0.6231\n", - " 0.3097\n", - " -0.0693\n", - " 0.1286\n", - " -0.3333\n", - " 0.3914\n", - " -0.2655\n", - " -0.7806\n", - " 0.0748\n", - " 0.4576\n", - " 0.4348\n", - " 0.9007\n", - " -0.3443\n", - " 0.8456\n", - " 0.4984\n", - " 0.3020\n", - " -0.0093\n", - " 0.4828\n", - " 0.4315\n", - " -0.1974\n", - " 0.2839\n", - " 0.2093\n", - " 0.4478\n", - " -0.2094\n", - " 0.5609\n", - " 0.6765\n", - " -0.2177\n", - " 0.6834\n", - " 0.0592\n", - " 0.4823\n", - " 0.7880\n", - " -0.0486\n", - " 0.0854\n", - " -0.1783\n", - " 0.3725\n", - " -0.0166\n", - " 0.7774\n", - " 0.2578\n", - " 0.2012\n", - " -0.6923\n", - " 0.4831\n", - " 0.2770\n", - " 0.2333\n", - " 0.9920\n", - " 0.9016\n", - " 1.0211\n", - " 0.6877\n", - " 0.8542\n", - " 0.4827\n", - " -0.3787\n", - " 0.7731\n", - " -0.7951\n", - " 0.2204\n", - " 0.3461\n", - " 0.4762\n", - " 0.8330\n", - " 0.6178\n", - " 0.1453\n", - " 0.2667\n", - " -0.1538\n", - " -0.5029\n", - " 0.2894\n", - " 0.7792\n", - " -0.2938\n", - " 0.0511\n", - " -0.2496\n", - " -0.2892\n", - " -0.3114\n", - " 0.5537\n", - " -0.3806\n", - " -0.3456\n", - " -0.0560\n", - " 0.2599\n", - " 0.6866\n", - " 0.2795\n", - " -0.3941\n", - " -0.2718\n", - " 0.5049\n", - " 0.6830\n", - " 0.6329\n", - " 0.5453\n", - " -0.3399\n", - " 0.3233\n", - " 0.3098\n", - " 0.9976\n", - " 0.4178\n", - " 0.1519\n", - " 0.1064\n", - " -0.5418\n", - " -0.2228\n", - " 0.6781\n", - " 0.3510\n", - " 0.9180\n", - " 0.0556\n", - " -0.1268\n", - " 0.1080\n", - " -0.0807\n", - " 0.1642\n", - " 0.2019\n", - " -0.2052\n", - " 0.0701\n", - " 0.5781\n", - " 0.3520\n", - " 0.2864\n", - " -0.0198\n", - " 0.4802\n", - " 0.0686\n", - " -0.1297\n", - " 0.3799\n", - " -0.5801\n", - " 0.2267\n", - " -0.3331\n", - " 0.1789\n", - " 0.6574\n", - " -0.2033\n", - " 0.4127\n", - " 0.3014\n", - " -0.4878\n", - " -0.1851\n", - " 0.2485\n", - " 0.0927\n", - " 0.2120\n", - " 0.9425\n", - " 0.1457\n", - " 0.2978\n", - " 0.4554\n", - " 0.4970\n", - " -0.5606\n", - " -0.2625\n", - " 0.5333\n", - " -0.3710\n", - " 0.2715\n", - " -0.2672\n", - " 0.5287\n", - " 0.1724\n", - " -0.1804\n", - " 0.4135\n", - " 0.2100\n", - " 0.5956\n", - " 0.1735\n", - " -0.1697\n", - " 0.0212\n", - " 0.0500\n", - " 0.0162\n", - " 0.0217\n", - " -0.0038\n", - " -0.0292\n", - " -0.0375\n", - " -0.0807\n", - " 0.0075\n", - " -0.0146\n", - " 0.0072\n", - " -0.0216\n", - " -0.0081\n", - " -0.0122\n", - " -0.0221\n", - " 0.0021\n", - " -0.1028\n", - " 0.0248\n", - " 0.0013\n", - " -0.0282\n", - " 0.0260\n", - " -0.0007\n", - " 0.0086\n", - " -0.0044\n", - " -0.0003\n", - " -0.0160\n", - " -0.0177\n", - " 0.0968\n", - " 0.0884\n", - " -0.0166\n", - " 0.0074\n", - " -0.0274\n", - " 0.0712\n", - " 0.0031\n", - " 0.0033\n", - " -0.0614\n", - " -0.0058\n", - " -0.0979\n", - " -0.0347\n", - " 0.0047\n", - " -0.5094\n", - " -0.0331\n", - " 0.0248\n", - " 0.0739\n", - " -0.1455\n", - " -0.0031\n", - " -0.0054\n", - " -0.0301\n", - " 0.0418\n", - " -0.0298\n", - " -0.0051\n", - " -0.0114\n", - " 0.0393\n", - " 0.0115\n", - " -0.0170\n", - " 0.0274\n", - " -0.0185\n", - " 0.0036\n", - " 0.0992\n", - " -0.0060\n", - " -0.0107\n", - " -0.0020\n", - " -0.0102\n", - " -0.0238\n", - " 0.0093\n", - " -0.0593\n", - " 0.0540\n", - " 0.0948\n", - " 0.0009\n", - " 0.0047\n", - " -0.0669\n", - " -0.0652\n", - " -0.0174\n", - " -0.0050\n", - " -0.0460\n", - " -0.0368\n", - " 0.0073\n", - " -0.0226\n", - " 0.0407\n", - " -0.1166\n", - " -0.0127\n", - " 0.0295\n", - " -0.0485\n", - " -0.0491\n", - " 0.0840\n", - " -0.0081\n", - " -0.0437\n", - " 0.0251\n", - " -0.0226\n", - " 0.0027\n", - " -0.0135\n", - " 0.0099\n", - " 0.0272\n", - " 0.0331\n", - " -0.0159\n", - " 0.0967\n", - " 0.0151\n", - " -0.2298\n", - " 0.0420\n", - " 0.0340\n", - " 0.0214\n", - " -0.0144\n", - " -0.0021\n", - " 0.0025\n", - " -0.0134\n", - " 0.0341\n", - " -0.0051\n", - " 0.5071\n", - " 0.0183\n", - " 0.0527\n", - " -0.0089\n", - " 0.0089\n", - " 0.0229\n", - " -0.0395\n", - " -0.0221\n", - " -0.0034\n", - " 0.0094\n", - " 0.3358\n", - " 0.0210\n", - " 0.0231\n", - " -0.0355\n", - " 0.0187\n", - " 0.1024\n", - " 0.1728\n", - " -0.0074\n", - " -0.0199\n", - " 0.0116\n", - " 0.0285\n", - " 0.0012\n", - " -0.0164\n", - " 0.0300\n", - " 0.0188\n", - " 0.0261\n", - " 0.0009\n", - " 0.0303\n", - " 0.0122\n", - " -0.0124\n", - " -0.0008\n", - " -0.0001\n", - " -0.0164\n", - " -0.0088\n", - " -0.1008\n", - " -0.0192\n", - " 0.0444\n", - " 0.0021\n", - " -0.0120\n", - " 0.1039\n", - " 0.0013\n", - " 0.0005\n", - " -0.0113\n", - " -0.0187\n", - " 0.0402\n", - " 0.0104\n", - " 0.0056\n", - " -0.0675\n", - " 0.0169\n", - " -0.0246\n", - " -0.0080\n", - " -0.0207\n", - " -0.0091\n", - " -0.0100\n", - " 0.0288\n", - " -0.0229\n", - " 0.0028\n", - " -0.5419\n", - " 0.0176\n", - " -0.0364\n", - " -0.0238\n", - " -0.0585\n", - " 0.0434\n", - " -0.0093\n", - " 0.0032\n", - " -0.0320\n", - " 0.0736\n", - " 0.0288\n", - " 0.0025\n", - " 0.0717\n", - " 0.0175\n", - " 0.1066\n", - " -0.3397\n", - " -0.0192\n", - " -0.0216\n", - " -0.0105\n", - " 0.0057\n", - " -0.1191\n", - " 0.0371\n", - " 0.1501\n", - " 0.0231\n", - " -0.0081\n", - " 0.0215\n", - " 0.2007\n", - " 0.0494\n", - " 0.0154\n", - " -0.0065\n", - " 0.0078\n", - " -0.0508\n", - " -0.0921\n", - " -0.0307\n", - " 0.0148\n", - " 0.0052\n", - " -0.0226\n", - " -0.0015\n", - " -0.0533\n", - " 0.0225\n", - " 0.0718\n", - " -0.0091\n", - " -0.0112\n", - " 0.0106\n", - " 0.0108\n", - " -0.0658\n", - " -0.0417\n", - " 0.0511\n", - " 0.0098\n", - " -0.0226\n", - " 0.0397\n", - " -0.0010\n", - " 0.0005\n", - " -0.0055\n", - " 0.0009\n", - " -0.0248\n", - " 0.0215\n", - " 0.0667\n", - " -0.0166\n", - " 0.0769\n", - " -0.0188\n", - " -0.0282\n", - " -0.2376\n", - " 0.0055\n", - " 0.0075\n", - " -0.0412\n", - " 0.0070\n", - " 0.0015\n", - " 0.0111\n", - " 0.0065\n", - " -0.0147\n", - " 0.0011\n", - " -0.0076\n", - " 0.0141\n", - " -0.0171\n", - " -0.0178\n", - " -0.0050\n", - " -0.0329\n", - " 0.0181\n", - " 0.0225\n", - " -0.0003\n", - " -0.0145\n", - " 0.0534\n", - " -0.1561\n", - " 0.0157\n", - " 0.0602\n", - " -0.0238\n", - " 0.0468\n", - " 0.0414\n", - " 0.0061\n", - " 0.2552\n", - " [torch.FloatTensor of size 768]),\n", - " ('module.decoder.attention_rnn.rnn_cell.bias_hh', \n", - " -0.1619\n", - " -0.3476\n", - " -0.1480\n", - " -0.3070\n", - " -0.3330\n", - " -0.4126\n", - " -0.1426\n", - " -0.3965\n", - " 0.0937\n", - " 0.0854\n", - " -0.2954\n", - " -0.2285\n", - " -0.3341\n", - " -0.3151\n", - " -0.1267\n", - " -0.2881\n", - " -0.3631\n", - " -0.1389\n", - " -0.2565\n", - " -0.1751\n", - " -0.0839\n", - " -0.1953\n", - " -0.5660\n", - " -0.3454\n", - " -0.2550\n", - " -0.2794\n", - " -0.2598\n", - " -0.1146\n", - " -0.2088\n", - " -0.1589\n", - " -0.2954\n", - " -0.2815\n", - " 0.0091\n", - " 0.2411\n", - " -0.2810\n", - " -0.5252\n", - " -0.0954\n", - " -0.1224\n", - " -0.2838\n", - " -0.1160\n", - " -0.2590\n", - " 0.4528\n", - " -0.2510\n", - " -0.1378\n", - " -0.2298\n", - " -0.1317\n", - " -0.2359\n", - " -0.3929\n", - " -0.0677\n", - " 0.0196\n", - " -0.3766\n", - " -0.2093\n", - " -0.1193\n", - " 0.0242\n", - " -0.3060\n", - " -0.3011\n", - " -0.1356\n", - " -0.3085\n", - " -0.2883\n", - " 0.0095\n", - " -0.0880\n", - " -0.2309\n", - " -0.0859\n", - " -0.3217\n", - " -0.0338\n", - " -0.3068\n", - " -0.2076\n", - " -0.0829\n", - " -0.1129\n", - " -0.6464\n", - " 0.1541\n", - " -0.1448\n", - " -0.3018\n", - " -0.3953\n", - " -0.4760\n", - " -0.1088\n", - " -0.0596\n", - " -0.0990\n", - " -0.3875\n", - " -0.0677\n", - " 0.0721\n", - " -0.3655\n", - " -0.3776\n", - " -0.0781\n", - " -0.1582\n", - " -0.3194\n", - " -0.4237\n", - " -0.1050\n", - " -0.0755\n", - " 0.0387\n", - " -0.2771\n", - " -0.3062\n", - " -0.1700\n", - " -0.1959\n", - " -0.1171\n", - " -0.1218\n", - " -0.0978\n", - " -0.0586\n", - " -0.1273\n", - " -0.2727\n", - " -0.2560\n", - " -0.3704\n", - " -0.3867\n", - " -0.3156\n", - " -0.3319\n", - " -0.1371\n", - " 0.0549\n", - " -0.3129\n", - " 0.4112\n", - " -0.1779\n", - " -0.0990\n", - " -0.4332\n", - " -0.0537\n", - " -0.3977\n", - " -0.2975\n", - " -0.1883\n", - " -0.5482\n", - " -0.1984\n", - " -0.0025\n", - " -0.1368\n", - " -0.1487\n", - " -0.0403\n", - " 0.3522\n", - " -0.2982\n", - " -0.0614\n", - " -0.1865\n", - " -0.2529\n", - " -0.2171\n", - " -0.1985\n", - " -0.2645\n", - " -0.0844\n", - " -0.3852\n", - " -0.3014\n", - " -0.2764\n", - " -0.4070\n", - " -0.3464\n", - " -0.1338\n", - " -0.2450\n", - " -0.3135\n", - " -0.3405\n", - " -0.3546\n", - " -0.2439\n", - " -0.2328\n", - " -0.0099\n", - " -0.0225\n", - " -0.3018\n", - " -0.4255\n", - " 0.0968\n", - " -0.3711\n", - " -0.2472\n", - " -0.2916\n", - " -0.3173\n", - " -0.3281\n", - " -0.0802\n", - " -0.2683\n", - " -0.3201\n", - " -0.1384\n", - " -0.1097\n", - " -0.0033\n", - " -0.2716\n", - " -0.4538\n", - " -0.3782\n", - " -0.3252\n", - " -0.1798\n", - " -0.2970\n", - " 0.3557\n", - " -0.0889\n", - " -0.1667\n", - " -0.3569\n", - " -0.1980\n", - " -0.2802\n", - " -0.6866\n", - " -0.2729\n", - " -0.1824\n", - " -0.2946\n", - " 0.0190\n", - " -0.1397\n", - " -0.1238\n", - " -0.2137\n", - " -0.0725\n", - " 0.1644\n", - " -0.2956\n", - " -0.2130\n", - " -0.2431\n", - " -0.4066\n", - " -0.3374\n", - " -0.2426\n", - " 0.0936\n", - " -0.2441\n", - " -0.4642\n", - " -0.0439\n", - " 0.1466\n", - " -0.1685\n", - " -0.3575\n", - " -0.3338\n", - " -0.2467\n", - " -0.0382\n", - " -0.1084\n", - " -0.2676\n", - " -0.0279\n", - " -0.5669\n", - " -0.3248\n", - " -0.2988\n", - " -0.1384\n", - " -0.0428\n", - " -0.1908\n", - " -0.4249\n", - " -0.3513\n", - " -0.4417\n", - " -0.2214\n", - " -0.1841\n", - " -0.1598\n", - " -0.2495\n", - " -0.3004\n", - " -0.0978\n", - " -0.4285\n", - " -0.2434\n", - " -0.3418\n", - " -0.2656\n", - " -0.3367\n", - " -0.2058\n", - " -0.3585\n", - " 0.2135\n", - " -0.0383\n", - " -0.0590\n", - " -0.3584\n", - " -0.1003\n", - " -0.0823\n", - " -0.2290\n", - " -0.4151\n", - " -0.3287\n", - " -0.2609\n", - " -0.2074\n", - " -0.0302\n", - " -0.2000\n", - " -0.1967\n", - " -0.2884\n", - " -0.2419\n", - " -0.2180\n", - " -0.1499\n", - " 0.0607\n", - " -0.4445\n", - " -0.2275\n", - " -0.3392\n", - " -0.5443\n", - " -0.4030\n", - " -0.1525\n", - " -0.1266\n", - " 0.1708\n", - " -0.2400\n", - " -0.1670\n", - " -0.2054\n", - " -0.2090\n", - " -0.2149\n", - " -0.2566\n", - " -0.1421\n", - " -0.3399\n", - " 0.5824\n", - " -0.2527\n", - " 0.3676\n", - " -0.3132\n", - " 0.3163\n", - " 0.2471\n", - " 0.2204\n", - " 0.4109\n", - " -0.1702\n", - " -0.3129\n", - " 0.3991\n", - " -0.1923\n", - " 0.6788\n", - " 0.3854\n", - " 0.0071\n", - " 0.5050\n", - " -0.1384\n", - " 0.2599\n", - " -0.1039\n", - " 0.5401\n", - " 0.4116\n", - " -0.5869\n", - " 0.5164\n", - " 0.8553\n", - " 0.3429\n", - " -0.3181\n", - " -0.1754\n", - " 0.3036\n", - " -0.3375\n", - " -0.1901\n", - " 0.7914\n", - " -0.0785\n", - " -0.6208\n", - " 0.5135\n", - " 1.0519\n", - " -0.3579\n", - " 0.0026\n", - " -0.0774\n", - " 0.3235\n", - " 0.6447\n", - " -0.2960\n", - " -0.7971\n", - " 0.2223\n", - " -0.4455\n", - " -0.7894\n", - " 0.4417\n", - " 0.4320\n", - " -1.2065\n", - " -0.8539\n", - " 0.2963\n", - " 0.5805\n", - " -0.6462\n", - " 0.6600\n", - " 0.8335\n", - " 0.3425\n", - " -0.1960\n", - " -0.8870\n", - " 0.6678\n", - " -0.1122\n", - " -0.4294\n", - " 0.6735\n", - " 0.4285\n", - " 0.3091\n", - " -0.1661\n", - " 0.2526\n", - " -0.1500\n", - " -0.3594\n", - " -0.5651\n", - " 0.9179\n", - " 0.0467\n", - " 0.6760\n", - " -0.3944\n", - " -0.3406\n", - " 0.4742\n", - " -0.1682\n", - " 0.4339\n", - " -0.3198\n", - " 0.3731\n", - " -0.5405\n", - " -0.0980\n", - " 0.8088\n", - " 0.6149\n", - " -0.4575\n", - " 0.0069\n", - " -0.0509\n", - " 0.2913\n", - " -0.4123\n", - " 0.1005\n", - " 0.1729\n", - " 0.6298\n", - " -0.4936\n", - " 0.0730\n", - " 1.0227\n", - " 0.4433\n", - " 0.0877\n", - " -0.6209\n", - " -0.0473\n", - " -0.0910\n", - " -0.2771\n", - " 0.2363\n", - " 0.7982\n", - " 0.2361\n", - " -0.0301\n", - " 0.5295\n", - " 0.5625\n", - " -0.0675\n", - " 0.4057\n", - " -0.6459\n", - " -0.0210\n", - " 0.1949\n", - " 0.3733\n", - " 0.1092\n", - " 0.5491\n", - " 0.3457\n", - " 0.1826\n", - " 0.6330\n", - " 0.2792\n", - " -0.1493\n", - " 0.1782\n", - " -0.3537\n", - " 0.3738\n", - " -0.3377\n", - " -0.7687\n", - " 0.0234\n", - " 0.4615\n", - " 0.3424\n", - " 0.8100\n", - " -0.2883\n", - " 0.7503\n", - " 0.4715\n", - " 0.2516\n", - " 0.0903\n", - " 0.3937\n", - " 0.4879\n", - " -0.1865\n", - " 0.1914\n", - " 0.1662\n", - " 0.5099\n", - " -0.2456\n", - " 0.5576\n", - " 0.6465\n", - " -0.1639\n", - " 0.7299\n", - " -0.0028\n", - " 0.5129\n", - " 0.8677\n", - " -0.0218\n", - " -0.0252\n", - " -0.0817\n", - " 0.3497\n", - " -0.0177\n", - " 0.8177\n", - " 0.2856\n", - " 0.1286\n", - " -0.6258\n", - " 0.4768\n", - " 0.2549\n", - " 0.2557\n", - " 0.9397\n", - " 0.9453\n", - " 0.9781\n", - " 0.6264\n", - " 0.7413\n", - " 0.4938\n", - " -0.3236\n", - " 0.7286\n", - " -0.7392\n", - " 0.2140\n", - " 0.2990\n", - " 0.5049\n", - " 0.8075\n", - " 0.6013\n", - " 0.1991\n", - " 0.3121\n", - " -0.2264\n", - " -0.5586\n", - " 0.1848\n", - " 0.7634\n", - " -0.2268\n", - " 0.0361\n", - " -0.2531\n", - " -0.3944\n", - " -0.2888\n", - " 0.5843\n", - " -0.3288\n", - " -0.2433\n", - " -0.0277\n", - " 0.2993\n", - " 0.6837\n", - " 0.3319\n", - " -0.3886\n", - " -0.3487\n", - " 0.4654\n", - " 0.6222\n", - " 0.6338\n", - " 0.4947\n", - " -0.3158\n", - " 0.4046\n", - " 0.2970\n", - " 1.0081\n", - " 0.4049\n", - " 0.1201\n", - " 0.0893\n", - " -0.5924\n", - " -0.2084\n", - " 0.6545\n", - " 0.3832\n", - " 0.9156\n", - " 0.1390\n", - " -0.1476\n", - " 0.0552\n", - " -0.0764\n", - " 0.1637\n", - " 0.1733\n", - " -0.2394\n", - " 0.1264\n", - " 0.5378\n", - " 0.3810\n", - " 0.2349\n", - " 0.0309\n", - " 0.4756\n", - " 0.0135\n", - " -0.1033\n", - " 0.3523\n", - " -0.6723\n", - " 0.2053\n", - " -0.3769\n", - " 0.0946\n", - " 0.6838\n", - " -0.2735\n", - " 0.3993\n", - " 0.2562\n", - " -0.4144\n", - " -0.2369\n", - " 0.2371\n", - " 0.0963\n", - " 0.2089\n", - " 0.9221\n", - " 0.2022\n", - " 0.2522\n", - " 0.4877\n", - " 0.4430\n", - " -0.4568\n", - " -0.2242\n", - " 0.5223\n", - " -0.3433\n", - " 0.2435\n", - " -0.3096\n", - " 0.4233\n", - " 0.1803\n", - " -0.2181\n", - " 0.3297\n", - " 0.1887\n", - " 0.4948\n", - " 0.0645\n", - " 0.1853\n", - " 0.0461\n", - " -0.5405\n", - " 0.0505\n", - " -0.2320\n", - " 0.0252\n", - " -0.0698\n", - " -0.1343\n", - " -0.1055\n", - " -0.1967\n", - " 0.0221\n", - " -0.0856\n", - " 0.4344\n", - " 0.0555\n", - " 0.0140\n", - " -0.0541\n", - " -0.0627\n", - " 0.3555\n", - " -0.0124\n", - " 0.0921\n", - " -0.0967\n", - " 0.4111\n", - " 0.0825\n", - " 0.1014\n", - " 0.0064\n", - " 0.0683\n", - " -0.3561\n", - " 0.0492\n", - " 0.0461\n", - " -0.2335\n", - " -0.1464\n", - " -0.2263\n", - " -0.1506\n", - " 0.3772\n", - " 0.1911\n", - " 0.0898\n", - " -0.1831\n", - " 0.2928\n", - " 0.0019\n", - " -0.0671\n", - " 0.0978\n", - " -0.4679\n", - " 0.2038\n", - " 0.1759\n", - " -0.1874\n", - " -0.4478\n", - " -0.2019\n", - " 0.0195\n", - " -0.6356\n", - " 0.5889\n", - " 0.0839\n", - " 0.0249\n", - " -0.0093\n", - " 0.1125\n", - " -0.1698\n", - " -0.1128\n", - " 0.0459\n", - " -0.4563\n", - " 0.0387\n", - " 0.0413\n", - " 0.1062\n", - " -0.0449\n", - " 0.3089\n", - " 0.1521\n", - " -0.1559\n", - " -0.1409\n", - " -0.0080\n", - " -0.1416\n", - " -0.0880\n", - " 0.0510\n", - " 0.4468\n", - " -0.0293\n", - " -0.1358\n", - " -0.0869\n", - " -0.0722\n", - " -0.1814\n", - " -0.0355\n", - " -0.1569\n", - " -0.0244\n", - " -0.1034\n", - " -0.1868\n", - " -0.1137\n", - " 0.0116\n", - " 0.2406\n", - " -0.1332\n", - " 0.1920\n", - " 0.1413\n", - " 0.0873\n", - " -0.1358\n", - " -0.3472\n", - " -0.1419\n", - " 0.2209\n", - " -0.1519\n", - " -0.0581\n", - " -0.1016\n", - " -0.2099\n", - " 0.2941\n", - " 0.0614\n", - " -0.3624\n", - " -0.1279\n", - " 0.0294\n", - " -0.0761\n", - " 0.0743\n", - " -0.2552\n", - " 0.0505\n", - " 0.0670\n", - " 0.2179\n", - " -0.0527\n", - " 1.1843\n", - " -0.1803\n", - " 0.0802\n", - " 0.0763\n", - " 0.0695\n", - " 0.2161\n", - " -0.0324\n", - " 0.1221\n", - " -0.1342\n", - " -0.0348\n", - " 0.2924\n", - " -0.0477\n", - " -0.5574\n", - " -0.2621\n", - " 0.5131\n", - " 0.0358\n", - " 0.2213\n", - " 0.1481\n", - " 0.0540\n", - " -0.1209\n", - " 0.3502\n", - " -0.0047\n", - " 0.3568\n", - " 0.2608\n", - " 0.3326\n", - " -0.0042\n", - " 0.0541\n", - " -0.1348\n", - " 0.1228\n", - " -0.1018\n", - " 0.1038\n", - " -0.0196\n", - " 0.0890\n", - " 0.2493\n", - " 0.3795\n", - " 0.0309\n", - " 0.0020\n", - " 0.0691\n", - " 0.0798\n", - " 0.3880\n", - " -0.1273\n", - " 0.1769\n", - " 0.2971\n", - " 0.1089\n", - " 0.0605\n", - " 0.0870\n", - " -0.1426\n", - " -0.1277\n", - " -0.2745\n", - " -0.3569\n", - " -0.3517\n", - " -0.0392\n", - " 0.0041\n", - " -0.0905\n", - " 0.1713\n", - " 0.0787\n", - " -0.0161\n", - " -0.8084\n", - " 0.0701\n", - " -0.1106\n", - " -0.1163\n", - " -0.1231\n", - " 0.2874\n", - " -0.1083\n", - " -0.0241\n", - " 0.1892\n", - " -0.0507\n", - " -0.1197\n", - " -0.0090\n", - " 0.2675\n", - " 0.1027\n", - " 0.0621\n", - " -0.2644\n", - " 0.1653\n", - " 0.3257\n", - " -0.1145\n", - " 0.2373\n", - " -0.1805\n", - " 0.2422\n", - " 0.2630\n", - " 0.0240\n", - " 0.0365\n", - " -0.0354\n", - " 0.5086\n", - " -0.2078\n", - " 0.1933\n", - " -0.0428\n", - " -0.0236\n", - " 0.0174\n", - " 0.0381\n", - " -0.1284\n", - " 0.3418\n", - " -0.0086\n", - " 0.0068\n", - " -0.0063\n", - " -0.1209\n", - " -0.2977\n", - " 0.0815\n", - " 0.0506\n", - " -0.2095\n", - " 0.0531\n", - " -0.0646\n", - " -0.0838\n", - " -0.2099\n", - " 0.0826\n", - " -0.0646\n", - " 0.2612\n", - " -0.0063\n", - " -0.0467\n", - " -0.0608\n", - " -0.0934\n", - " -0.1225\n", - " -0.0386\n", - " 0.0333\n", - " 0.5756\n", - " -0.1831\n", - " 0.2527\n", - " -0.0004\n", - " -0.1814\n", - " -0.1829\n", - " 0.0491\n", - " -0.0370\n", - " -0.0921\n", - " 0.0687\n", - " -0.0148\n", - " 0.3860\n", - " 0.0291\n", - " 0.1489\n", - " 0.0311\n", - " -0.1610\n", - " -0.0939\n", - " 0.0767\n", - " 0.0855\n", - " 0.0008\n", - " -0.2122\n", - " 0.2774\n", - " -0.1368\n", - " 0.0284\n", - " -0.1148\n", - " -0.1983\n", - " -0.0610\n", - " -0.0545\n", - " 0.1757\n", - " -0.0837\n", - " 0.0626\n", - " -0.0300\n", - " 0.0745\n", - " -0.4166\n", - " [torch.FloatTensor of size 768]),\n", - " ('module.decoder.attention_rnn.alignment_model.query_layer.weight',\n", - " \n", - " 5.8217e-01 -3.4247e-02 -4.2030e-01 ... -4.0968e-01 8.5390e-02 4.6055e-01\n", - " -6.2852e-02 7.3241e-01 1.9948e-01 ... 7.4486e-01 9.9272e-02 1.8832e-01\n", - " 2.8498e-03 6.8163e-02 -1.8771e-01 ... -2.8722e-02 1.2512e-01 -2.2392e-02\n", - " ... ⋱ ... \n", - " 4.7293e-02 2.6991e-01 -3.1210e-01 ... 6.0747e-01 -1.4412e-02 -2.3233e-01\n", - " 4.3386e-03 3.1905e-01 -2.3568e-01 ... 6.9467e-01 7.6569e-02 5.6162e-01\n", - " -7.3181e-02 -2.0433e-01 -2.2061e-01 ... 2.2420e-01 1.8482e-01 -1.5150e-01\n", - " [torch.FloatTensor of size 256x256]),\n", - " ('module.decoder.attention_rnn.alignment_model.v.weight', \n", - " \n", - " Columns 0 to 9 \n", - " 0.5073 -0.8066 -0.5430 -0.0781 0.5228 -0.6178 -0.7605 0.5892 0.3176 0.4537\n", - " \n", - " Columns 10 to 19 \n", - " -0.4171 0.6084 0.1839 -0.7084 -0.4441 -1.6197 -0.4959 -0.9911 -0.6512 -0.2274\n", - " \n", - " Columns 20 to 29 \n", - " -0.5381 0.6234 -0.4004 -0.8469 0.4973 -0.8723 -0.4026 0.2807 0.5562 -0.2542\n", - " \n", - " Columns 30 to 39 \n", - " -0.5353 -0.1086 -0.3278 0.7822 0.8560 1.0251 0.3340 0.2907 0.8487 -0.9684\n", - " \n", - " Columns 40 to 49 \n", - " 0.2930 -0.5106 0.7091 0.6632 -0.7062 -0.5953 0.6418 -0.7575 0.2727 -0.9261\n", - " \n", - " Columns 50 to 59 \n", - " 0.6242 -0.7467 1.1074 -1.0174 -0.2931 0.8765 -1.4872 -0.5117 1.3068 -0.8304\n", - " \n", - " Columns 60 to 69 \n", - " 0.2666 -0.8220 -0.6618 0.2560 -0.3534 -0.1411 -1.1381 -0.4390 0.9555 -0.3471\n", - " \n", - " Columns 70 to 79 \n", - " -0.8656 -0.4469 -0.8662 -0.3345 0.7019 0.6659 0.5447 -1.0600 0.8054 0.5610\n", - " \n", - " Columns 80 to 89 \n", - " 0.6442 -0.7685 -0.8629 -0.7881 0.7093 0.9787 0.3471 -0.5890 -0.5512 -0.4742\n", - " \n", - " Columns 90 to 99 \n", - " -0.4012 -0.4171 -0.4594 -0.5549 -0.5748 -0.7700 -0.7150 0.6140 0.5824 -0.1414\n", - " \n", - " Columns 100 to 109 \n", - " 0.3770 0.5924 -0.4207 -0.7606 0.4449 -0.1035 0.6338 0.8180 1.0246 -0.5367\n", - " \n", - " Columns 110 to 119 \n", - " 0.4984 0.5632 0.5072 0.4643 -0.4524 -0.7255 0.5640 0.6078 1.0864 0.2769\n", - " \n", - " Columns 120 to 129 \n", - " -0.6761 -0.3424 -0.7378 0.4411 -0.3803 0.4045 -0.7586 0.7523 0.2877 -0.5737\n", - " \n", - " Columns 130 to 139 \n", - " -0.6083 -0.6420 0.8977 0.9262 0.5735 -0.8141 0.6196 0.7017 -0.6651 0.9567\n", - " \n", - " Columns 140 to 149 \n", - " 0.7958 -0.6955 0.2351 -0.7377 -0.4900 -0.0508 0.5433 -0.7096 -1.1429 -0.3475\n", - " \n", - " Columns 150 to 159 \n", - " -0.7877 0.9206 -0.5850 -1.1290 0.7658 0.5059 0.9300 0.9337 0.7968 0.5796\n", - " \n", - " Columns 160 to 169 \n", - " 0.7807 0.4674 -0.8088 -0.9657 -0.5101 0.7808 -0.3687 0.4910 -0.4080 1.1659\n", - " \n", - " Columns 170 to 179 \n", - " 0.7607 0.1435 0.9547 0.3607 -0.5578 -0.7379 1.2265 -0.4966 -0.2176 -0.6519\n", - " \n", - " Columns 180 to 189 \n", - " -0.6896 -0.3904 -0.8627 0.3932 0.7155 0.4569 0.5685 0.6334 0.8212 -0.7214\n", - " \n", - " Columns 190 to 199 \n", - " -0.7570 0.6596 0.4377 0.7303 -0.5479 0.5378 1.0405 -0.5907 -0.2744 -0.7873\n", - " \n", - " Columns 200 to 209 \n", - " 0.3606 -0.3971 0.0997 -0.6636 -0.4120 -0.5314 0.2740 0.6491 0.8219 -0.6500\n", - " \n", - " Columns 210 to 219 \n", - " 0.3358 1.0261 -0.5197 -1.4257 0.7639 0.5901 1.0980 0.3868 0.3822 0.4242\n", - " \n", - " Columns 220 to 229 \n", - " 0.9219 -0.8746 -0.8677 -0.9909 0.4973 -0.8149 -0.5387 0.6924 -1.3391 0.4169\n", - " \n", - " Columns 230 to 239 \n", - " 0.5728 0.6056 -1.0567 -0.5872 0.7191 -0.3696 0.2235 -0.4116 -0.5580 0.5378\n", - " \n", - " Columns 240 to 249 \n", - " -0.4537 0.4198 -0.6692 -0.8861 -0.2353 -0.9916 0.5921 -0.6078 -0.9091 -0.6674\n", - " \n", - " Columns 250 to 255 \n", - " -0.5588 0.5099 0.8359 -0.4494 -0.7441 0.5094\n", - " [torch.FloatTensor of size 1x256]),\n", - " ('module.decoder.project_to_decoder_in.weight', \n", - " 1.7090e-02 -1.5314e-01 2.3427e-02 ... -4.0826e-02 7.2217e-02 -7.9281e-02\n", - " 6.5305e-02 -1.3720e-01 6.3315e-02 ... -3.4179e-02 6.6730e-03 -1.4187e-01\n", - " 1.3014e-01 1.4892e-02 -6.4547e-02 ... 9.2366e-02 1.0338e-01 1.1845e-01\n", - " ... ⋱ ... \n", - " 6.2698e-02 -4.4816e-02 -2.8500e-02 ... -2.0856e-01 5.4064e-02 -7.1827e-02\n", - " 5.5420e-03 -5.5788e-04 4.9956e-02 ... -8.5185e-02 -3.2172e-02 -1.4255e-01\n", - " 4.1809e-02 -1.2650e-01 5.5656e-02 ... -4.2012e-02 -1.4137e-02 -1.6233e-01\n", - " [torch.FloatTensor of size 256x512]),\n", - " ('module.decoder.project_to_decoder_in.bias', \n", - " -0.0729\n", - " -0.2827\n", - " 0.1118\n", - " -0.0552\n", - " 0.0032\n", - " 0.0943\n", - " -0.1231\n", - " 0.1936\n", - " 0.0312\n", - " 0.0930\n", - " -0.1576\n", - " -0.0244\n", - " -0.2076\n", - " -0.0441\n", - " -0.0181\n", - " 0.1134\n", - " 0.1269\n", - " -0.0643\n", - " 0.0213\n", - " -0.2247\n", - " -0.0852\n", - " -0.0004\n", - " -0.0464\n", - " 0.1204\n", - " -0.0111\n", - " -0.0043\n", - " -0.0793\n", - " -0.1642\n", - " 0.0791\n", - " -0.1492\n", - " 0.0745\n", - " -0.0026\n", - " 0.0297\n", - " -0.0307\n", - " -0.0568\n", - " 0.0283\n", - " 0.1270\n", - " -0.1008\n", - " -0.0651\n", - " 0.0315\n", - " 0.1378\n", - " 0.0780\n", - " 0.1301\n", - " 0.0409\n", - " -0.1453\n", - " 0.0380\n", - " -0.2262\n", - " -0.0416\n", - " 0.0032\n", - " -0.0030\n", - " -0.0308\n", - " -0.0902\n", - " -0.1086\n", - " -0.0271\n", - " 0.0075\n", - " 0.1064\n", - " -0.1719\n", - " -0.1063\n", - " -0.1929\n", - " -0.0272\n", - " 0.0355\n", - " 0.1189\n", - " 0.0705\n", - " -0.1847\n", - " -0.1368\n", - " -0.1176\n", - " -0.1104\n", - " 0.1135\n", - " 0.1158\n", - " -0.0149\n", - " -0.0117\n", - " 0.1930\n", - " -0.0138\n", - " -0.0000\n", - " -0.0603\n", - " -0.0073\n", - " 0.0229\n", - " -0.0834\n", - " -0.1326\n", - " -0.0476\n", - " 0.1620\n", - " 0.1176\n", - " 0.1045\n", - " -0.1281\n", - " -0.1108\n", - " 0.1548\n", - " 0.0974\n", - " 0.0707\n", - " 0.1988\n", - " -0.0117\n", - " 0.2109\n", - " -0.0471\n", - " -0.0105\n", - " -0.0242\n", - " 0.0535\n", - " 0.2667\n", - " -0.2243\n", - " -0.2015\n", - " 0.2367\n", - " 0.1542\n", - " 0.0132\n", - " 0.0792\n", - " -0.0275\n", - " -0.0020\n", - " 0.1622\n", - " -0.0105\n", - " 0.0358\n", - " 0.0155\n", - " 0.0508\n", - " -0.2329\n", - " -0.1213\n", - " -0.0849\n", - " 0.1247\n", - " -0.0858\n", - " 0.0492\n", - " 0.0653\n", - " -0.1860\n", - " -0.1709\n", - " -0.0788\n", - " 0.0936\n", - " 0.1256\n", - " -0.1903\n", - " 0.1031\n", - " 0.1291\n", - " 0.0779\n", - " -0.1129\n", - " -0.1542\n", - " -0.2169\n", - " -0.0414\n", - " -0.0035\n", - " 0.1739\n", - " -0.2442\n", - " 0.0305\n", - " 0.0882\n", - " -0.0153\n", - " -0.1542\n", - " -0.0818\n", - " -0.0500\n", - " 0.0210\n", - " -0.0720\n", - " 0.0030\n", - " 0.0696\n", - " 0.0871\n", - " -0.0157\n", - " -0.0520\n", - " 0.0367\n", - " -0.1358\n", - " -0.0309\n", - " 0.1577\n", - " -0.1377\n", - " 0.0137\n", - " -0.0637\n", - " 0.0874\n", - " -0.1855\n", - " 0.0585\n", - " 0.1164\n", - " 0.0031\n", - " -0.0132\n", - " 0.0757\n", - " -0.1253\n", - " 0.2182\n", - " -0.0690\n", - " 0.1712\n", - " -0.1668\n", - " 0.1482\n", - " -0.0694\n", - " 0.0394\n", - " 0.1385\n", - " -0.0414\n", - " 0.0532\n", - " -0.0451\n", - " 0.0992\n", - " 0.0341\n", - " -0.1527\n", - " 0.0802\n", - " 0.2008\n", - " -0.0263\n", - " 0.0494\n", - " -0.0201\n", - " 0.0747\n", - " 0.1764\n", - " -0.2041\n", - " 0.1243\n", - " -0.0636\n", - " 0.0933\n", - " 0.1667\n", - " 0.1320\n", - " -0.1841\n", - " 0.0046\n", - " 0.0358\n", - " 0.0354\n", - " 0.0346\n", - " 0.1220\n", - " 0.1459\n", - " -0.0471\n", - " -0.0443\n", - " 0.1796\n", - " 0.0054\n", - " 0.1263\n", - " -0.1085\n", - " 0.2157\n", - " 0.1334\n", - " 0.0768\n", - " 0.0626\n", - " -0.1337\n", - " 0.2519\n", - " -0.0244\n", - " 0.2387\n", - " -0.0890\n", - " 0.1807\n", - " -0.0319\n", - " -0.1225\n", - " 0.0283\n", - " -0.0626\n", - " -0.0355\n", - " 0.1421\n", - " -0.0180\n", - " 0.0384\n", - " 0.0579\n", - " -0.1816\n", - " -0.0709\n", - " 0.0547\n", - " -0.0697\n", - " -0.1428\n", - " 0.0438\n", - " -0.1040\n", - " 0.0245\n", - " -0.0847\n", - " 0.0092\n", - " -0.1438\n", - " 0.1096\n", - " 0.1755\n", - " 0.1201\n", - " -0.0789\n", - " 0.0149\n", - " -0.1176\n", - " 0.1574\n", - " 0.0123\n", - " -0.0054\n", - " 0.0103\n", - " -0.0059\n", - " -0.1272\n", - " 0.0023\n", - " -0.0200\n", - " 0.0168\n", - " 0.0094\n", - " 0.0279\n", - " -0.0089\n", - " -0.0046\n", - " 0.1179\n", - " 0.0226\n", - " -0.0539\n", - " 0.0648\n", - " 0.0334\n", - " 0.0096\n", - " -0.0831\n", - " [torch.FloatTensor of size 256]),\n", - " ('module.decoder.decoder_rnns.0.weight_ih', \n", - " 3.6559e-01 1.3628e-01 -2.1633e-01 ... 2.7516e-01 -7.1986e-02 4.4005e-02\n", - " 3.6500e-02 -1.9909e-01 1.4216e-01 ... -4.3430e-01 -8.3087e-02 2.8016e-02\n", - " 2.8174e-01 -5.7776e-02 6.6599e-02 ... -2.8246e-01 3.5993e-02 -2.9273e-01\n", - " ... ⋱ ... \n", - " 3.3959e-02 -1.1470e-01 -1.0531e-01 ... -8.3257e-01 -1.3246e-01 -4.4173e-02\n", - " -1.0553e-01 1.2328e-01 1.7012e-01 ... 7.6643e-02 -1.1219e+00 -1.8551e-01\n", - " 3.1992e-02 3.2217e-02 -4.4496e-02 ... 8.5311e-02 5.9092e-02 -3.5393e-01\n", - " [torch.FloatTensor of size 768x256]),\n", - " ('module.decoder.decoder_rnns.0.weight_hh', \n", - " -1.2001e-01 1.3434e-01 2.1710e-01 ... 2.0419e-01 1.1873e-01 -4.3647e-02\n", - " -1.8234e-01 1.6046e-01 4.4518e-02 ... -4.1734e-01 -1.2173e-01 5.9824e-02\n", - " 2.9158e-01 -2.7247e-02 1.5671e-02 ... -2.5096e-01 1.0294e-01 -3.6500e-01\n", - " ... ⋱ ... \n", - " -1.2932e-01 1.9027e-01 8.0898e-02 ... -4.4098e-01 -6.3198e-02 1.6503e-01\n", - " 7.6857e-02 1.4576e-01 -5.6706e-02 ... -5.8169e-02 -1.4532e+00 -8.6998e-02\n", - " 1.1599e-02 -5.3002e-02 1.2298e-01 ... -2.4869e-01 -1.9728e-01 -7.1696e-01\n", - " [torch.FloatTensor of size 768x256]),\n", - " ('module.decoder.decoder_rnns.0.bias_ih', \n", - " 0.0116\n", - " 0.1026\n", - " -0.3138\n", - " -0.0812\n", - " -0.1848\n", - " -0.0472\n", - " -0.0573\n", - " -0.1596\n", - " 0.1095\n", - " -0.1096\n", - " 0.0399\n", - " -0.0648\n", - " -0.0006\n", - " -0.0839\n", - " -0.1251\n", - " 0.0715\n", - " -0.1008\n", - " -0.0078\n", - " -0.3360\n", - " -0.0493\n", - " -0.1719\n", - " -0.0271\n", - " 0.0416\n", - " -0.0603\n", - " 0.0246\n", - " 0.0474\n", - " -0.0994\n", - " -0.1429\n", - " -0.0604\n", - " 0.0297\n", - " 0.0072\n", - " -0.0515\n", - " -0.0197\n", - " 0.1027\n", - " 0.0355\n", - " -0.1087\n", - " -0.2336\n", - " -0.1757\n", - " -0.0605\n", - " 0.0160\n", - " 0.0194\n", - " -0.0887\n", - " -0.0401\n", - " -0.0388\n", - " -0.0008\n", - " -0.2236\n", - " -0.0077\n", - " -0.0266\n", - " -0.1645\n", - " -0.2540\n", - " -0.0875\n", - " -0.1967\n", - " -0.0359\n", - " -0.2148\n", - " -0.2301\n", - " -0.2453\n", - " 0.0215\n", - " 0.0686\n", - " -0.0301\n", - " -0.0570\n", - " -0.0487\n", - " -0.2362\n", - " 0.1002\n", - " -0.1362\n", - " 0.0661\n", - " 0.0404\n", - " -0.0586\n", - " 0.0189\n", - " -0.0559\n", - " -0.2214\n", - " -0.0091\n", - " -0.2396\n", - " -0.1825\n", - " -0.1755\n", - " -0.0987\n", - " -0.0925\n", - " 0.0073\n", - " -0.2031\n", - " 0.0795\n", - " -0.1654\n", - " -0.2210\n", - " -0.0647\n", - " 0.0640\n", - " -0.2269\n", - " -0.3008\n", - " 0.0154\n", - " -0.0520\n", - " -0.1203\n", - " -0.0603\n", - " -0.0096\n", - " -0.2002\n", - " -0.0891\n", - " -0.0554\n", - " 0.0654\n", - " 0.1383\n", - " -0.1248\n", - " -0.2894\n", - " 0.0546\n", - " -0.1538\n", - " 0.0979\n", - " 0.0249\n", - " -0.2352\n", - " -0.3390\n", - " -0.1669\n", - " -0.0283\n", - " -0.0669\n", - " 0.0010\n", - " -0.0169\n", - " -0.1507\n", - " 0.0345\n", - " -0.3200\n", - " -0.0612\n", - " -0.1117\n", - " -0.0534\n", - " -0.1404\n", - " 0.2807\n", - " -0.1804\n", - " -0.1148\n", - " -0.1073\n", - " 0.0365\n", - " 0.0027\n", - " 0.0137\n", - " -0.0650\n", - " -0.0113\n", - " -0.1049\n", - " -0.2029\n", - " -0.1193\n", - " -0.0268\n", - " -0.0879\n", - " -0.1249\n", - " -0.1956\n", - " 0.0903\n", - " -0.0405\n", - " 0.0081\n", - " -0.0355\n", - " -0.0648\n", - " 0.0218\n", - " 0.0174\n", - " -0.0131\n", - " -0.1615\n", - " 0.1092\n", - " -0.1362\n", - " -0.1437\n", - " 0.0170\n", - " -0.1591\n", - " -0.2226\n", - " 0.0392\n", - " 0.0823\n", - " 0.0571\n", - " -0.0379\n", - " -0.1287\n", - " -0.1448\n", - " -0.1040\n", - " -0.0879\n", - " -0.1232\n", - " -0.1087\n", - " -0.0433\n", - " -0.0447\n", - " -0.1160\n", - " 0.0422\n", - " -0.3190\n", - " 0.1619\n", - " -0.0090\n", - " -0.0351\n", - " -0.1185\n", - " -0.0622\n", - " -0.2895\n", - " -0.1695\n", - " -0.1118\n", - " -0.1018\n", - " 0.1545\n", - " -0.0051\n", - " -0.0879\n", - " -0.0131\n", - " -0.0221\n", - " -0.1833\n", - " -0.0629\n", - " -0.0490\n", - " -0.0730\n", - " -0.1051\n", - " 0.0227\n", - " 0.0742\n", - " -0.0764\n", - " 0.0373\n", - " -0.0566\n", - " 0.0861\n", - " -0.1826\n", - " -0.0210\n", - " 0.0308\n", - " -0.1552\n", - " -0.1281\n", - " -0.2854\n", - " -0.4552\n", - " -0.1615\n", - " -0.2446\n", - " -0.1199\n", - " -0.0503\n", - " -0.0199\n", - " -0.0964\n", - " -0.0792\n", - " -0.1881\n", - " 0.0043\n", - " -0.1212\n", - " 0.1179\n", - " 0.0685\n", - " -0.0345\n", - " -0.1844\n", - " -0.1544\n", - " 0.0468\n", - " -0.1170\n", - " -0.0268\n", - " -0.0597\n", - " -0.0840\n", - " -0.1276\n", - " -0.0280\n", - " -0.1454\n", - " -0.1130\n", - " -0.2788\n", - " -0.1131\n", - " -0.1978\n", - " -0.2831\n", - " -0.1843\n", - " 0.0336\n", - " 0.0062\n", - " -0.0806\n", - " -0.1110\n", - " -0.0737\n", - " -0.2455\n", - " -0.0519\n", - " -0.0734\n", - " -0.1923\n", - " 0.0887\n", - " -0.0954\n", - " 0.1921\n", - " -0.2211\n", - " -0.0959\n", - " -0.2155\n", - " -0.1280\n", - " -0.0314\n", - " -0.0361\n", - " -0.0616\n", - " -0.1519\n", - " -0.0341\n", - " -0.0482\n", - " -0.1468\n", - " 0.1076\n", - " 0.1481\n", - " -0.1121\n", - " -0.0883\n", - " -0.1053\n", - " -0.2161\n", - " -0.1013\n", - " 0.1392\n", - " -0.0382\n", - " -0.1268\n", - " 0.0306\n", - " 0.2110\n", - " -0.0911\n", - " -0.1882\n", - " -0.0585\n", - " -0.3644\n", - " -0.3121\n", - " -0.3706\n", - " -0.0836\n", - " -0.0487\n", - " -0.3909\n", - " 0.0021\n", - " -0.2100\n", - " 0.1115\n", - " -0.1886\n", - " 0.2046\n", - " -0.3390\n", - " -0.3742\n", - " -0.1119\n", - " -0.1970\n", - " -0.3390\n", - " -0.2637\n", - " 0.2894\n", - " 0.0295\n", - " 0.1343\n", - " -0.0575\n", - " -0.3028\n", - " -0.3809\n", - " 0.0002\n", - " -0.2692\n", - " -0.0959\n", - " -0.3593\n", - " 0.1367\n", - " -0.0041\n", - " 0.2088\n", - " 0.0392\n", - " -0.2355\n", - " -0.1882\n", - " -0.1888\n", - " 0.0519\n", - " 0.1439\n", - " -0.1117\n", - " 0.3682\n", - " -0.1050\n", - " -0.0296\n", - " -0.1464\n", - " -0.1588\n", - " -0.5404\n", - " 0.0030\n", - " -0.0316\n", - " -0.1582\n", - " -0.2943\n", - " -0.5640\n", - " -0.1246\n", - " -0.1133\n", - " 0.0050\n", - " 0.0116\n", - " -0.2623\n", - " -0.3293\n", - " 0.0076\n", - " -0.1639\n", - " -0.2843\n", - " -0.5337\n", - " 0.0355\n", - " -0.5059\n", - " 0.0644\n", - " -0.2754\n", - " -0.4274\n", - " 0.1347\n", - " -0.1783\n", - " 0.4087\n", - " -0.0890\n", - " -0.1513\n", - " -0.1492\n", - " -0.2107\n", - " -0.4024\n", - " -0.0959\n", - " -0.1784\n", - " 0.1751\n", - " -0.2568\n", - " -0.1581\n", - " -0.1029\n", - " 0.2524\n", - " -0.1973\n", - " -0.2729\n", - " -0.2149\n", - " -0.0277\n", - " -0.0726\n", - " -0.0636\n", - " -0.0894\n", - " -0.5654\n", - " -0.1124\n", - " -0.2568\n", - " -0.2910\n", - " -0.3897\n", - " -0.1918\n", - " -0.1756\n", - " -0.4428\n", - " -0.0418\n", - " 0.0086\n", - " -0.3306\n", - " -0.2856\n", - " -0.1898\n", - " -0.3579\n", - " -0.2198\n", - " 0.0391\n", - " -0.0010\n", - " -0.1312\n", - " 0.0009\n", - " 0.1106\n", - " -0.0939\n", - " -0.4470\n", - " -0.1426\n", - " -0.1748\n", - " 0.3235\n", - " 0.3637\n", - " -0.3854\n", - " -0.1076\n", - " -0.2241\n", - " 0.0947\n", - " 0.3205\n", - " -0.0960\n", - " 0.1857\n", - " -0.2068\n", - " -0.4152\n", - " -0.3777\n", - " -0.4928\n", - " 0.1879\n", - " -0.1609\n", - " -0.1060\n", - " 0.0344\n", - " -0.2410\n", - " -0.2384\n", - " 0.1273\n", - " -0.1050\n", - " -0.1658\n", - " 0.0253\n", - " -0.2137\n", - " -0.3622\n", - " 0.1716\n", - " 0.0826\n", - " -0.3177\n", - " 0.0944\n", - " -0.0560\n", - " -0.1088\n", - " -0.1462\n", - " -0.2455\n", - " -0.0555\n", - " 0.1439\n", - " -0.1452\n", - " -0.0877\n", - " -0.2945\n", - " -0.4925\n", - " 0.0943\n", - " -0.3228\n", - " 0.1097\n", - " -0.5276\n", - " -0.2068\n", - " -0.2778\n", - " 0.1640\n", - " -0.2064\n", - " -0.2133\n", - " -0.2543\n", - " 0.1313\n", - " -0.0169\n", - " -0.0107\n", - " 0.0558\n", - " -0.2382\n", - " -0.3065\n", - " -0.0992\n", - " -0.3163\n", - " 0.0072\n", - " -0.1490\n", - " 0.0076\n", - " 0.0391\n", - " -0.1969\n", - " 0.0529\n", - " 0.0410\n", - " -0.0718\n", - " -0.2115\n", - " 0.1903\n", - " -0.1566\n", - " -0.3415\n", - " 0.1298\n", - " -0.0619\n", - " -0.0652\n", - " -0.1414\n", - " -0.6575\n", - " -0.1621\n", - " 0.0498\n", - " -0.1954\n", - " -0.2742\n", - " -0.2291\n", - " -0.2887\n", - " -0.0133\n", - " 0.2165\n", - " -0.0861\n", - " -0.1793\n", - " 0.0527\n", - " -0.2240\n", - " 0.0093\n", - " -0.1294\n", - " -0.1094\n", - " -0.0843\n", - " -0.1329\n", - " 0.3152\n", - " 0.2734\n", - " -0.0271\n", - " -0.3386\n", - " -0.1904\n", - " -0.3857\n", - " 0.1276\n", - " 0.0057\n", - " -0.1233\n", - " -0.2079\n", - " -0.1157\n", - " -0.0624\n", - " -0.1648\n", - " 0.1341\n", - " -0.3484\n", - " -0.1133\n", - " -0.1405\n", - " 0.0717\n", - " -0.1241\n", - " -0.0331\n", - " -0.2593\n", - " -0.0928\n", - " -0.5085\n", - " -0.2845\n", - " -0.2354\n", - " 0.0968\n", - " -0.2742\n", - " -0.0812\n", - " -0.0152\n", - " -0.2941\n", - " -0.0230\n", - " -0.2153\n", - " -0.0577\n", - " -0.5114\n", - " -0.1473\n", - " 0.0519\n", - " -0.2951\n", - " -0.4320\n", - " 0.3635\n", - " -0.3880\n", - " 0.1546\n", - " 0.1765\n", - " -0.2611\n", - " 0.5834\n", - " -0.2497\n", - " 0.0361\n", - " -0.1838\n", - " -0.0717\n", - " 0.2678\n", - " -0.0296\n", - " 0.0205\n", - " 0.1644\n", - " 0.1198\n", - " -0.0023\n", - " 0.0031\n", - " -0.0260\n", - " -0.0159\n", - " 0.1111\n", - " 0.0499\n", - " -0.0536\n", - " -0.0138\n", - " 0.0354\n", - " -0.0211\n", - " 0.0925\n", - " 0.0875\n", - " 0.0469\n", - " -0.0875\n", - " 0.1935\n", - " 0.0199\n", - " -0.0382\n", - " 0.0812\n", - " 0.0034\n", - " -0.2168\n", - " -0.0978\n", - " 0.0372\n", - " -0.0333\n", - " 0.0374\n", - " 0.0015\n", - " 0.0494\n", - " 0.0922\n", - " -0.0918\n", - " -0.0536\n", - " 0.1023\n", - " -0.0560\n", - " -0.0291\n", - " -0.0145\n", - " -0.0448\n", - " -0.0223\n", - " 0.1015\n", - " -0.0350\n", - " 0.0976\n", - " -0.0192\n", - " -0.0650\n", - " -0.0362\n", - " 0.0225\n", - " 0.0827\n", - " -0.1371\n", - " 0.0351\n", - " -0.0181\n", - " 0.1006\n", - " 0.1167\n", - " 0.0896\n", - " 0.0491\n", - " -0.0217\n", - " -0.1128\n", - " -0.0359\n", - " -0.0438\n", - " -0.0624\n", - " -0.0189\n", - " -0.0766\n", - " 0.0596\n", - " -0.1193\n", - " 0.0042\n", - " 0.0136\n", - " -0.1350\n", - " -0.0131\n", - " 0.0784\n", - " -0.0061\n", - " 0.0569\n", - " -0.1080\n", - " 0.0013\n", - " 0.0588\n", - " -0.0402\n", - " -0.0803\n", - " -0.0275\n", - " 0.0468\n", - " -0.0059\n", - " -0.0754\n", - " -0.1380\n", - " 0.0538\n", - " 0.0695\n", - " -0.0841\n", - " -0.0465\n", - " 0.0494\n", - " -0.1125\n", - " -0.0412\n", - " 0.0260\n", - " 0.1146\n", - " 0.0210\n", - " -0.0319\n", - " 0.0130\n", - " -0.1552\n", - " 0.0356\n", - " -0.0929\n", - " -0.0504\n", - " -0.0012\n", - " -0.1067\n", - " -0.0106\n", - " 0.1740\n", - " 0.0110\n", - " 0.0401\n", - " -0.0967\n", - " -0.1308\n", - " 0.0004\n", - " 0.0047\n", - " -0.0413\n", - " -0.0357\n", - " -0.0455\n", - " -0.0414\n", - " -0.0731\n", - " 0.0042\n", - " -0.0046\n", - " 0.0821\n", - " 0.1060\n", - " -0.0106\n", - " -0.0450\n", - " 0.0643\n", - " -0.0281\n", - " -0.0262\n", - " 0.0426\n", - " -0.0245\n", - " -0.0389\n", - " -0.0209\n", - " -0.0336\n", - " 0.0942\n", - " 0.0998\n", - " -0.0571\n", - " -0.0123\n", - " 0.0568\n", - " 0.0984\n", - " 0.1121\n", - " -0.0788\n", - " 0.0284\n", - " -0.0105\n", - " -0.0030\n", - " -0.0153\n", - " -0.0423\n", - " 0.0468\n", - " -0.1107\n", - " 0.0714\n", - " -0.0133\n", - " 0.0367\n", - " -0.1167\n", - " -0.0888\n", - " 0.1007\n", - " 0.0662\n", - " -0.0684\n", - " -0.0047\n", - " 0.0017\n", - " -0.0323\n", - " -0.0148\n", - " 0.0574\n", - " 0.0354\n", - " -0.0143\n", - " 0.0548\n", - " 0.0104\n", - " 0.0476\n", - " -0.1408\n", - " 0.0038\n", - " -0.0288\n", - " -0.0104\n", - " 0.1055\n", - " 0.0429\n", - " 0.1401\n", - " -0.0483\n", - " -0.0150\n", - " 0.0790\n", - " -0.0687\n", - " -0.1195\n", - " -0.0189\n", - " -0.0479\n", - " 0.0160\n", - " 0.0425\n", - " -0.0302\n", - " -0.0481\n", - " -0.0722\n", - " 0.0308\n", - " 0.1236\n", - " 0.0740\n", - " -0.1174\n", - " -0.0542\n", - " -0.0076\n", - " 0.1416\n", - " 0.0775\n", - " 0.1884\n", - " -0.1214\n", - " -0.0534\n", - " -0.0561\n", - " 0.0081\n", - " -0.0081\n", - " 0.0216\n", - " 0.0133\n", - " -0.0611\n", - " 0.0391\n", - " 0.0067\n", - " 0.0640\n", - " -0.0384\n", - " -0.0196\n", - " 0.0738\n", - " -0.0014\n", - " -0.0181\n", - " -0.0618\n", - " 0.0182\n", - " 0.0695\n", - " -0.1315\n", - " -0.1069\n", - " 0.0079\n", - " 0.1305\n", - " 0.0698\n", - " -0.0203\n", - " 0.0490\n", - " -0.1055\n", - " 0.0355\n", - " 0.1844\n", - " 0.0102\n", - " -0.1427\n", - " -0.0112\n", - " 0.0253\n", - " 0.0110\n", - " -0.0273\n", - " -0.0241\n", - " 0.0149\n", - " -0.0387\n", - " -0.0220\n", - " -0.0458\n", - " 0.0472\n", - " -0.0510\n", - " 0.1099\n", - " 0.0484\n", - " 0.0937\n", - " 0.0841\n", - " 0.0037\n", - " -0.0002\n", - " 0.0521\n", - " 0.1503\n", - " -0.0738\n", - " 0.0980\n", - " 0.0208\n", - " 0.0268\n", - " 0.0838\n", - " 0.0424\n", - " 0.0680\n", - " -0.0440\n", - " -0.0758\n", - " -0.0018\n", - " -0.1226\n", - " 0.0783\n", - " 0.0248\n", - " -0.1410\n", - " -0.1026\n", - " -0.0708\n", - " 0.0066\n", - " -0.0500\n", - " 0.0091\n", - " 0.0687\n", - " 0.0846\n", - " -0.0361\n", - " -0.0387\n", - " [torch.FloatTensor of size 768]),\n", - " ('module.decoder.decoder_rnns.0.bias_hh', \n", - " 0.0042\n", - " 0.1111\n", - " -0.3103\n", - " -0.1271\n", - " -0.1130\n", - " -0.0656\n", - " -0.0049\n", - " -0.0918\n", - " 0.1188\n", - " -0.1934\n", - " -0.0494\n", - " -0.1164\n", - " 0.0008\n", - " 0.0061\n", - " -0.0870\n", - " 0.0299\n", - " -0.1979\n", - " -0.0144\n", - " -0.3335\n", - " -0.1234\n", - " -0.1466\n", - " -0.1000\n", - " -0.0188\n", - " -0.0645\n", - " 0.0232\n", - " 0.0021\n", - " -0.0752\n", - " -0.1038\n", - " -0.0668\n", - " 0.0727\n", - " 0.0252\n", - " -0.0399\n", - " 0.0318\n", - " 0.0879\n", - " 0.0672\n", - " -0.1222\n", - " -0.2979\n", - " -0.2387\n", - " -0.0111\n", - " 0.0526\n", - " -0.0813\n", - " -0.1491\n", - " -0.0286\n", - " -0.0322\n", - " 0.0231\n", - " -0.1679\n", - " -0.0139\n", - " -0.0160\n", - " -0.0515\n", - " -0.1932\n", - " -0.1135\n", - " -0.2193\n", - " -0.0541\n", - " -0.1310\n", - " -0.2596\n", - " -0.2688\n", - " 0.0214\n", - " 0.0701\n", - " 0.0096\n", - " -0.1421\n", - " -0.1155\n", - " -0.2064\n", - " 0.0991\n", - " -0.1400\n", - " -0.0249\n", - " 0.0176\n", - " 0.0394\n", - " -0.0096\n", - " -0.1243\n", - " -0.1579\n", - " -0.0926\n", - " -0.2307\n", - " -0.1207\n", - " -0.1375\n", - " -0.0572\n", - " -0.0335\n", - " 0.0136\n", - " -0.2447\n", - " -0.0035\n", - " -0.1106\n", - " -0.2063\n", - " -0.1745\n", - " 0.0050\n", - " -0.2409\n", - " -0.2786\n", - " -0.0014\n", - " 0.0102\n", - " -0.2031\n", - " -0.1204\n", - " -0.0191\n", - " -0.1959\n", - " -0.1792\n", - " -0.0660\n", - " 0.0503\n", - " 0.1396\n", - " -0.0541\n", - " -0.2321\n", - " 0.0733\n", - " -0.1332\n", - " 0.1345\n", - " 0.1285\n", - " -0.2291\n", - " -0.3353\n", - " -0.1185\n", - " -0.0281\n", - " -0.0944\n", - " 0.0002\n", - " -0.0867\n", - " -0.1763\n", - " 0.0847\n", - " -0.3148\n", - " -0.0184\n", - " -0.0744\n", - " 0.0178\n", - " -0.2249\n", - " 0.2073\n", - " -0.0990\n", - " -0.0539\n", - " -0.1523\n", - " -0.0237\n", - " 0.0022\n", - " -0.0947\n", - " -0.0867\n", - " 0.0824\n", - " -0.0821\n", - " -0.1758\n", - " -0.1056\n", - " -0.0506\n", - " -0.0606\n", - " -0.0911\n", - " -0.1624\n", - " 0.0703\n", - " -0.0820\n", - " 0.0506\n", - " -0.1328\n", - " -0.0933\n", - " 0.0788\n", - " 0.0181\n", - " -0.0469\n", - " -0.1765\n", - " 0.0647\n", - " -0.2340\n", - " -0.0692\n", - " -0.0387\n", - " -0.1666\n", - " -0.2138\n", - " 0.0242\n", - " 0.0346\n", - " 0.0815\n", - " -0.1328\n", - " -0.0997\n", - " -0.2159\n", - " -0.1835\n", - " -0.0537\n", - " -0.0824\n", - " -0.1758\n", - " -0.0042\n", - " -0.0286\n", - " -0.0872\n", - " 0.0690\n", - " -0.3654\n", - " 0.0983\n", - " -0.0576\n", - " -0.0149\n", - " -0.1035\n", - " -0.1025\n", - " -0.2634\n", - " -0.1861\n", - " -0.1264\n", - " -0.0306\n", - " 0.1751\n", - " 0.0535\n", - " -0.1058\n", - " -0.0394\n", - " 0.0141\n", - " -0.1536\n", - " -0.1000\n", - " -0.0354\n", - " -0.0880\n", - " -0.0140\n", - " 0.0217\n", - " 0.0326\n", - " -0.0266\n", - " 0.0421\n", - " -0.0979\n", - " 0.0594\n", - " -0.2280\n", - " -0.0128\n", - " -0.0354\n", - " -0.1245\n", - " -0.1304\n", - " -0.3170\n", - " -0.4053\n", - " -0.1311\n", - " -0.2920\n", - " -0.1561\n", - " -0.0170\n", - " -0.1150\n", - " -0.1055\n", - " -0.0096\n", - " -0.2044\n", - " -0.0082\n", - " -0.1748\n", - " 0.0607\n", - " 0.1315\n", - " -0.0368\n", - " -0.1580\n", - " -0.0707\n", - " 0.0110\n", - " -0.0662\n", - " -0.0059\n", - " -0.0127\n", - " -0.0637\n", - " -0.0410\n", - " -0.0695\n", - " -0.0809\n", - " -0.0061\n", - " -0.2834\n", - " -0.1259\n", - " -0.1546\n", - " -0.2083\n", - " -0.1456\n", - " 0.0344\n", - " 0.0436\n", - " -0.0591\n", - " -0.1168\n", - " -0.0742\n", - " -0.1912\n", - " 0.0213\n", - " -0.1858\n", - " -0.2408\n", - " 0.0388\n", - " -0.2000\n", - " 0.0976\n", - " -0.2520\n", - " -0.1265\n", - " -0.1548\n", - " -0.0887\n", - " -0.0697\n", - " -0.1213\n", - " -0.0624\n", - " -0.2007\n", - " -0.1163\n", - " 0.0224\n", - " -0.1230\n", - " 0.0376\n", - " 0.1997\n", - " -0.1378\n", - " -0.0530\n", - " -0.1542\n", - " -0.2331\n", - " -0.1263\n", - " 0.1647\n", - " -0.0709\n", - " -0.1749\n", - " 0.0214\n", - " 0.1705\n", - " -0.1382\n", - " -0.2513\n", - " -0.0267\n", - " -0.3754\n", - " -0.3117\n", - " -0.3513\n", - " -0.1398\n", - " -0.0325\n", - " -0.3871\n", - " -0.0803\n", - " -0.2171\n", - " 0.0714\n", - " -0.2038\n", - " 0.2390\n", - " -0.3954\n", - " -0.3988\n", - " -0.0583\n", - " -0.2001\n", - " -0.2779\n", - " -0.2794\n", - " 0.3728\n", - " 0.0373\n", - " 0.0427\n", - " -0.0305\n", - " -0.3127\n", - " -0.4093\n", - " -0.0199\n", - " -0.2454\n", - " -0.0989\n", - " -0.4193\n", - " 0.0441\n", - " -0.0247\n", - " 0.1405\n", - " -0.0003\n", - " -0.1877\n", - " -0.2137\n", - " -0.1100\n", - " -0.0063\n", - " 0.1514\n", - " -0.1429\n", - " 0.4215\n", - " -0.1083\n", - " -0.0198\n", - " -0.1503\n", - " -0.1437\n", - " -0.4363\n", - " 0.0391\n", - " -0.1130\n", - " -0.2549\n", - " -0.3494\n", - " -0.5362\n", - " -0.1038\n", - " -0.0823\n", - " -0.0448\n", - " 0.0560\n", - " -0.2175\n", - " -0.3661\n", - " -0.0451\n", - " -0.1249\n", - " -0.2658\n", - " -0.5777\n", - " 0.0566\n", - " -0.5527\n", - " 0.1006\n", - " -0.2796\n", - " -0.3901\n", - " 0.1461\n", - " -0.2739\n", - " 0.4560\n", - " -0.1693\n", - " -0.2087\n", - " -0.2006\n", - " -0.2097\n", - " -0.4541\n", - " 0.0022\n", - " -0.1919\n", - " 0.1777\n", - " -0.2549\n", - " -0.1393\n", - " -0.1658\n", - " 0.2293\n", - " -0.2756\n", - " -0.2081\n", - " -0.1765\n", - " 0.0023\n", - " -0.0410\n", - " 0.0412\n", - " -0.0355\n", - " -0.6104\n", - " -0.1107\n", - " -0.2472\n", - " -0.3081\n", - " -0.3861\n", - " -0.1937\n", - " -0.2567\n", - " -0.3899\n", - " 0.0115\n", - " 0.0708\n", - " -0.3805\n", - " -0.2083\n", - " -0.2268\n", - " -0.3510\n", - " -0.2104\n", - " -0.0575\n", - " 0.0838\n", - " -0.1000\n", - " -0.0301\n", - " 0.1804\n", - " -0.0747\n", - " -0.3950\n", - " -0.1388\n", - " -0.1143\n", - " 0.3405\n", - " 0.3727\n", - " -0.3108\n", - " -0.1403\n", - " -0.1836\n", - " 0.0327\n", - " 0.3649\n", - " -0.1736\n", - " 0.2483\n", - " -0.2233\n", - " -0.3806\n", - " -0.3548\n", - " -0.5686\n", - " 0.1409\n", - " -0.1579\n", - " -0.1564\n", - " 0.0969\n", - " -0.1982\n", - " -0.2221\n", - " 0.1137\n", - " -0.0958\n", - " -0.2466\n", - " 0.0216\n", - " -0.2015\n", - " -0.2721\n", - " 0.2536\n", - " 0.1372\n", - " -0.2389\n", - " -0.0116\n", - " -0.0861\n", - " -0.0796\n", - " -0.1176\n", - " -0.2535\n", - " -0.0471\n", - " 0.1410\n", - " -0.1576\n", - " -0.0605\n", - " -0.2079\n", - " -0.4816\n", - " 0.0724\n", - " -0.3252\n", - " 0.1362\n", - " -0.4509\n", - " -0.1791\n", - " -0.2064\n", - " 0.1147\n", - " -0.1740\n", - " -0.1169\n", - " -0.1542\n", - " 0.2077\n", - " 0.0161\n", - " 0.0644\n", - " 0.1317\n", - " -0.2694\n", - " -0.2627\n", - " -0.0779\n", - " -0.2949\n", - " 0.0189\n", - " -0.0914\n", - " -0.0288\n", - " -0.0287\n", - " -0.1364\n", - " -0.0282\n", - " 0.1261\n", - " -0.1099\n", - " -0.2123\n", - " 0.1912\n", - " -0.2073\n", - " -0.3168\n", - " 0.1360\n", - " -0.0579\n", - " -0.1098\n", - " -0.0925\n", - " -0.6834\n", - " -0.0782\n", - " 0.0324\n", - " -0.2223\n", - " -0.3597\n", - " -0.2253\n", - " -0.2625\n", - " -0.0561\n", - " 0.2256\n", - " -0.1943\n", - " -0.1527\n", - " -0.0046\n", - " -0.2793\n", - " -0.0383\n", - " -0.0812\n", - " -0.1225\n", - " -0.0634\n", - " -0.1258\n", - " 0.3138\n", - " 0.1624\n", - " 0.0012\n", - " -0.2708\n", - " -0.1231\n", - " -0.3206\n", - " 0.0504\n", - " -0.0592\n", - " -0.0843\n", - " -0.2638\n", - " -0.1014\n", - " -0.1095\n", - " -0.1662\n", - " 0.1683\n", - " -0.4395\n", - " -0.1191\n", - " -0.1543\n", - " 0.0179\n", - " -0.1179\n", - " -0.0284\n", - " -0.2038\n", - " -0.1136\n", - " -0.4138\n", - " -0.2548\n", - " -0.3018\n", - " 0.1471\n", - " -0.2990\n", - " -0.0374\n", - " 0.0073\n", - " -0.4089\n", - " 0.0363\n", - " -0.2930\n", - " -0.1435\n", - " -0.4855\n", - " -0.1557\n", - " 0.0815\n", - " -0.1920\n", - " -0.3947\n", - " 0.3894\n", - " -0.3425\n", - " 0.0469\n", - " 0.1552\n", - " -0.3287\n", - " 0.5439\n", - " -0.3570\n", - " -0.0456\n", - " -0.2012\n", - " -0.0602\n", - " 0.2460\n", - " -0.0841\n", - " 0.0327\n", - " 0.1146\n", - " 0.0026\n", - " 0.0211\n", - " -0.0018\n", - " -0.0020\n", - " -0.0151\n", - " 0.0170\n", - " 0.0712\n", - " -0.9146\n", - " 0.0990\n", - " -0.0730\n", - " -0.0422\n", - " 0.0213\n", - " 0.0218\n", - " 0.3115\n", - " -0.0326\n", - " 0.2234\n", - " -0.0092\n", - " 0.0164\n", - " 0.2102\n", - " 0.2056\n", - " -0.4255\n", - " -0.3124\n", - " 0.0089\n", - " 0.0482\n", - " -0.8111\n", - " 0.9576\n", - " 0.1418\n", - " 0.2175\n", - " -0.1597\n", - " -0.0254\n", - " 0.1397\n", - " -0.0067\n", - " -0.0704\n", - " -0.2137\n", - " -0.0762\n", - " 0.0501\n", - " 0.0640\n", - " -0.1101\n", - " 0.0945\n", - " -1.4477\n", - " -0.0689\n", - " 0.5754\n", - " 0.1268\n", - " 0.6138\n", - " -0.1677\n", - " 0.0381\n", - " 0.6246\n", - " -0.8393\n", - " 0.4178\n", - " 0.0601\n", - " 0.0632\n", - " -0.1121\n", - " -0.0552\n", - " 0.0443\n", - " 0.0054\n", - " -0.0325\n", - " 0.1100\n", - " 0.8461\n", - " -0.1402\n", - " -0.5887\n", - " 0.0491\n", - " 0.2759\n", - " -0.0163\n", - " 0.1433\n", - " 0.1522\n", - " 0.0863\n", - " -0.0041\n", - " -0.0554\n", - " -0.0603\n", - " 0.0465\n", - " -0.1433\n", - " 0.0240\n", - " 0.0014\n", - " -0.2563\n", - " -0.7774\n", - " -0.0132\n", - " -1.0181\n", - " 0.0069\n", - " 0.9774\n", - " -0.3694\n", - " 0.1139\n", - " -0.0422\n", - " -0.1354\n", - " -0.0217\n", - " 0.2823\n", - " 0.1224\n", - " 0.0714\n", - " 0.0818\n", - " 0.1184\n", - " -0.0363\n", - " -0.1562\n", - " 0.2061\n", - " -0.0570\n", - " 0.3028\n", - " -0.1641\n", - " -0.0463\n", - " 0.0938\n", - " 0.1599\n", - " -0.5191\n", - " -0.8698\n", - " -0.1021\n", - " 0.0880\n", - " 0.3685\n", - " 0.6563\n", - " -0.1343\n", - " -0.0177\n", - " 0.1332\n", - " -0.0198\n", - " 0.0474\n", - " -0.0175\n", - " -0.0540\n", - " 0.8769\n", - " -0.0693\n", - " -0.0076\n", - " 0.0311\n", - " -0.1253\n", - " -0.0898\n", - " 0.2434\n", - " -0.0222\n", - " -0.0348\n", - " -0.7679\n", - " 0.0044\n", - " 0.6514\n", - " -0.5531\n", - " -0.0947\n", - " -0.1544\n", - " 0.0394\n", - " 0.5309\n", - " -0.7383\n", - " 0.1689\n", - " 0.0608\n", - " -0.0255\n", - " 0.0057\n", - " -1.6258\n", - " 0.0028\n", - " -0.0664\n", - " 0.0916\n", - " -0.1107\n", - " -0.0384\n", - " -0.0042\n", - " -0.0933\n", - " -0.0590\n", - " -0.2738\n", - " -0.0114\n", - " -0.0263\n", - " 0.0572\n", - " 0.0355\n", - " -0.2665\n", - " 0.0462\n", - " 0.0197\n", - " 0.0074\n", - " 0.1245\n", - " 0.0309\n", - " 0.2234\n", - " 0.1179\n", - " -0.1558\n", - " 0.6181\n", - " -0.5217\n", - " -0.0059\n", - " 0.3696\n", - " -0.0557\n", - " 0.0426\n", - " 0.2030\n", - " -0.3931\n", - " 0.0701\n", - " -0.0442\n", - " -0.2549\n", - " 0.3216\n", - " -0.0748\n", - " 0.0191\n", - " 0.1211\n", - " -0.0072\n", - " 0.5377\n", - " -0.6181\n", - " -0.0973\n", - " 0.1911\n", - " 0.0109\n", - " -0.1307\n", - " -0.4863\n", - " 0.0556\n", - " 0.0896\n", - " 0.9876\n", - " -0.5548\n", - " -0.2935\n", - " -0.0198\n", - " -0.0405\n", - " -0.0869\n", - " -0.0429\n", - " -0.0023\n", - " 0.2128\n", - " -0.0045\n", - " 0.0467\n", - " -0.0113\n", - " -0.0384\n", - " 0.0080\n", - " -0.0161\n", - " 0.6916\n", - " -0.0166\n", - " 0.0240\n", - " -0.0277\n", - " 0.2721\n", - " 0.0821\n", - " -0.1025\n", - " -0.0044\n", - " -0.2611\n", - " 0.1824\n", - " 0.0853\n", - " -0.1366\n", - " 0.3852\n", - " -0.1902\n", - " 0.0962\n", - " 0.1037\n", - " 0.0067\n", - " -0.3831\n", - " 0.0098\n", - " -0.1219\n", - " 0.2122\n", - " -0.0188\n", - " -0.1904\n", - " 0.0031\n", - " -0.1028\n", - " -0.0183\n", - " -0.1007\n", - " -0.8012\n", - " -0.0116\n", - " -1.4384\n", - " 0.0189\n", - " 0.0253\n", - " -0.0159\n", - " -0.0389\n", - " -0.0802\n", - " 0.3610\n", - " 0.1274\n", - " 0.7281\n", - " -0.0443\n", - " 0.0524\n", - " -0.0328\n", - " 0.6525\n", - " 0.8515\n", - " 0.0932\n", - " -0.6413\n", - " 0.0486\n", - " 0.0461\n", - " -0.1145\n", - " 0.1472\n", - " 0.0492\n", - " -0.8275\n", - " -0.0682\n", - " -0.2112\n", - " -0.0395\n", - " -0.0588\n", - " 0.0417\n", - " 0.8943\n", - " -0.0396\n", - " -0.2103\n", - " -0.0018\n", - " [torch.FloatTensor of size 768]),\n", - " ('module.decoder.decoder_rnns.1.weight_ih', \n", - " -3.6364e-01 -2.6506e-01 7.8591e-01 ... -1.3263e-01 -5.3440e-03 -4.3392e-01\n", - " -2.1735e-01 -1.8720e-01 4.5403e-01 ... -9.5811e-01 4.1448e-01 -3.0742e-01\n", - " 2.8933e-02 -4.2304e-02 -1.1554e-01 ... -3.4010e-02 1.6157e-01 -9.3898e-02\n", - " ... ⋱ ... \n", - " 3.0325e-02 1.1506e-01 9.0589e-02 ... -8.4466e-01 2.5002e-01 -6.8798e-02\n", - " 1.7801e-02 -2.8568e-01 4.4764e-01 ... 7.1204e-02 -8.3421e-01 1.2687e-01\n", - " 1.2704e-01 4.8787e-02 -4.5239e-02 ... 2.0003e-02 5.0038e-03 -9.1381e-01\n", - " [torch.FloatTensor of size 768x256]),\n", - " ('module.decoder.decoder_rnns.1.weight_hh', \n", - " -3.1674e-01 1.5555e-01 1.0488e-01 ... 3.6669e-02 1.9682e-02 -4.6027e-03\n", - " -1.2635e-01 1.2078e-01 2.6343e-02 ... -1.6014e-01 5.4211e-02 -1.3663e-01\n", - " -3.4995e-01 4.0837e-01 1.4931e-01 ... 8.0041e-02 -2.0933e-01 4.1624e-02\n", - " ... ⋱ ... \n", - " -4.4772e-02 -6.1291e-02 -7.8127e-02 ... 6.5423e-02 -2.3841e-01 3.9626e-02\n", - " 7.4623e-02 -1.1501e-01 -5.4775e-02 ... 1.4159e-01 -7.3516e-01 3.5701e-02\n", - " -5.0545e-02 1.0573e-01 9.4021e-02 ... -5.1631e-02 -5.5289e-02 -2.7928e-01\n", - " [torch.FloatTensor of size 768x256]),\n", - " ('module.decoder.decoder_rnns.1.bias_ih', \n", - " 0.0161\n", - " -0.2106\n", - " -0.1567\n", - " -0.0901\n", - " -0.0065\n", - " -0.1537\n", - " 0.0150\n", - " -0.0654\n", - " 0.0950\n", - " 0.0379\n", - " -0.2259\n", - " 0.0302\n", - " 0.0128\n", - " -0.0933\n", - " -0.1179\n", - " -0.1413\n", - " -0.1486\n", - " 0.0902\n", - " -0.0666\n", - " -0.1984\n", - " -0.0714\n", - " -0.1478\n", - " -0.1638\n", - " -0.2794\n", - " -0.2149\n", - " -0.0315\n", - " -0.2278\n", - " -0.0413\n", - " -0.2394\n", - " -0.2310\n", - " -0.0373\n", - " -0.1389\n", - " -0.3094\n", - " -0.1109\n", - " -0.1711\n", - " 0.0158\n", - " -0.2210\n", - " -0.2303\n", - " -0.0105\n", - " -0.0883\n", - " -0.0134\n", - " -0.1149\n", - " -0.0812\n", - " 0.0202\n", - " -0.1412\n", - " -0.1242\n", - " -0.0948\n", - " 0.0356\n", - " -0.1055\n", - " -0.0592\n", - " 0.1496\n", - " 0.0591\n", - " -0.3036\n", - " -0.2505\n", - " -0.0464\n", - " 0.1181\n", - " -0.0155\n", - " -0.1671\n", - " 0.0203\n", - " -0.1516\n", - " 0.0739\n", - " -0.2100\n", - " 0.0965\n", - " -0.0370\n", - " -0.1614\n", - " -0.0380\n", - " 0.0549\n", - " -0.0357\n", - " -0.0849\n", - " -0.0465\n", - " -0.0845\n", - " 0.0470\n", - " 0.0219\n", - " -0.0216\n", - " -0.0565\n", - " -0.2434\n", - " -0.0487\n", - " -0.1201\n", - " -0.1402\n", - " -0.0420\n", - " -0.2527\n", - " -0.0892\n", - " -0.1734\n", - " -0.2498\n", - " -0.0974\n", - " 0.0880\n", - " -0.2854\n", - " 0.0772\n", - " -0.0818\n", - " -0.1870\n", - " -0.0949\n", - " -0.1218\n", - " -0.1674\n", - " -0.1651\n", - " 0.0324\n", - " -0.1301\n", - " -0.3811\n", - " 0.0123\n", - " -0.0809\n", - " -0.1178\n", - " 0.3110\n", - " -0.0853\n", - " -0.1555\n", - " 0.0774\n", - " -0.0949\n", - " -0.0849\n", - " -0.0677\n", - " -0.1184\n", - " -0.0709\n", - " -0.0861\n", - " -0.0828\n", - " -0.2025\n", - " -0.1450\n", - " -0.2319\n", - " -0.3776\n", - " -0.1650\n", - " -0.2081\n", - " -0.1478\n", - " -0.1336\n", - " -0.1770\n", - " -0.0088\n", - " -0.3239\n", - " 0.0586\n", - " -0.1410\n", - " -0.2162\n", - " -0.0972\n", - " 0.0047\n", - " -0.1156\n", - " -0.1884\n", - " -0.1813\n", - " -0.0480\n", - " 0.0568\n", - " -0.1547\n", - " 0.0292\n", - " -0.2348\n", - " -0.1502\n", - " -0.1792\n", - " -0.0679\n", - " 0.0075\n", - " -0.0511\n", - " -0.0995\n", - " -0.0050\n", - " -0.1246\n", - " 0.0481\n", - " 0.0052\n", - " -0.1969\n", - " 0.0770\n", - " 0.0025\n", - " -0.1349\n", - " -0.1334\n", - " -0.0663\n", - " -0.1144\n", - " -0.2174\n", - " -0.1507\n", - " 0.0506\n", - " -0.3121\n", - " -0.0684\n", - " -0.0428\n", - " 0.0144\n", - " 0.1166\n", - " -0.1358\n", - " -0.0253\n", - " -0.2588\n", - " -0.1596\n", - " -0.2702\n", - " -0.1665\n", - " -0.1578\n", - " 0.1028\n", - " -0.2309\n", - " -0.1845\n", - " -0.0786\n", - " 0.0341\n", - " -0.1347\n", - " -0.0432\n", - " -0.0907\n", - " -0.1125\n", - " 0.0401\n", - " -0.1313\n", - " -0.1104\n", - " 0.0108\n", - " -0.0418\n", - " -0.1488\n", - " -0.1767\n", - " 0.0035\n", - " -0.1041\n", - " -0.1411\n", - " -0.1741\n", - " -0.1440\n", - " -0.2116\n", - " -0.0333\n", - " 0.1046\n", - " -0.1199\n", - " 0.0020\n", - " 0.1167\n", - " -0.1293\n", - " -0.1000\n", - " -0.0590\n", - " -0.1810\n", - " -0.0605\n", - " -0.0159\n", - " -0.1939\n", - " 0.0644\n", - " 0.3138\n", - " -0.2611\n", - " -0.0374\n", - " -0.0624\n", - " 0.0077\n", - " -0.0710\n", - " -0.0554\n", - " -0.0316\n", - " -0.2957\n", - " -0.2357\n", - " -0.0607\n", - " -0.2450\n", - " 0.0108\n", - " 0.0031\n", - " -0.0522\n", - " -0.0200\n", - " -0.0565\n", - " -0.0321\n", - " -0.0489\n", - " -0.3216\n", - " 0.0163\n", - " -0.2290\n", - " -0.1914\n", - " -0.2923\n", - " -0.1887\n", - " -0.0709\n", - " -0.3365\n", - " 0.0693\n", - " -0.2024\n", - " 0.0565\n", - " 0.0519\n", - " -0.1501\n", - " 0.0095\n", - " -0.1000\n", - " 0.0121\n", - " 0.0034\n", - " -0.1042\n", - " 0.0343\n", - " -0.2012\n", - " -0.0273\n", - " -0.1544\n", - " -0.1012\n", - " 0.0563\n", - " -0.0843\n", - " -0.3579\n", - " -0.0363\n", - " -0.1358\n", - " -0.0825\n", - " -0.0118\n", - " 0.0642\n", - " -0.3474\n", - " -0.1091\n", - " -0.3064\n", - " -0.2555\n", - " -0.2866\n", - " 0.1826\n", - " -0.1787\n", - " -0.1699\n", - " -0.2186\n", - " -0.1440\n", - " -0.1974\n", - " -0.1488\n", - " 0.0242\n", - " -0.0983\n", - " -0.0321\n", - " 0.1103\n", - " -0.1252\n", - " -0.3090\n", - " -0.1676\n", - " -0.2535\n", - " 0.1397\n", - " -0.4286\n", - " -0.1312\n", - " -0.1856\n", - " -0.0058\n", - " 0.0640\n", - " 0.0455\n", - " -0.0702\n", - " -0.2094\n", - " -0.2680\n", - " -0.3292\n", - " -0.3329\n", - " -0.1218\n", - " -0.4625\n", - " -0.4592\n", - " -0.0138\n", - " -0.2065\n", - " -0.4887\n", - " -0.7757\n", - " -0.3677\n", - " -0.4162\n", - " -0.4135\n", - " -0.0597\n", - " -0.0408\n", - " -0.2657\n", - " -0.0848\n", - " -0.3341\n", - " 0.0131\n", - " -0.0718\n", - " -0.2522\n", - " -0.1699\n", - " 0.0643\n", - " -0.1576\n", - " -0.3075\n", - " -0.2884\n", - " 0.2457\n", - " -0.0677\n", - " 0.0426\n", - " -0.0335\n", - " 0.1525\n", - " 0.0226\n", - " 0.1067\n", - " -0.1645\n", - " -0.1681\n", - " -0.3755\n", - " -0.0258\n", - " -0.2320\n", - " 0.0103\n", - " 0.2279\n", - " -0.1818\n", - " -0.4434\n", - " -0.2847\n", - " -0.1631\n", - " -0.1206\n", - " -0.2189\n", - " -0.1171\n", - " -0.1472\n", - " -0.1508\n", - " -0.0977\n", - " -0.2185\n", - " -0.1634\n", - " -0.2713\n", - " 0.0024\n", - " -0.1060\n", - " -0.3060\n", - " -0.2532\n", - " 0.0156\n", - " -0.4579\n", - " -0.2077\n", - " -0.2801\n", - " -0.2113\n", - " -0.1879\n", - " -0.2466\n", - " -0.3033\n", - " -0.0793\n", - " 0.1780\n", - " 0.0601\n", - " -0.5718\n", - " -0.3377\n", - " -0.2801\n", - " -0.4055\n", - " 0.1178\n", - " 0.0063\n", - " -0.0825\n", - " -1.0610\n", - " -0.1387\n", - " -0.3935\n", - " -0.2567\n", - " -0.2262\n", - " -0.0690\n", - " -0.0822\n", - " -0.0991\n", - " 0.0844\n", - " -0.0199\n", - " 0.1050\n", - " -0.0393\n", - " -0.1375\n", - " -0.1090\n", - " 0.0774\n", - " -0.1014\n", - " -0.2368\n", - " -0.0461\n", - " 0.1315\n", - " -0.1751\n", - " -0.1407\n", - " -0.3758\n", - " 0.0634\n", - " -0.0199\n", - " -0.0257\n", - " -0.0853\n", - " 0.0903\n", - " -0.1848\n", - " 0.0822\n", - " -0.2187\n", - " -0.3271\n", - " -0.0986\n", - " -0.3356\n", - " 0.0497\n", - " -0.0158\n", - " -0.3689\n", - " -0.2620\n", - " -0.2978\n", - " -0.1648\n", - " -0.1171\n", - " -0.1028\n", - " -0.0078\n", - " 0.0092\n", - " 0.1768\n", - " -0.2913\n", - " -0.1046\n", - " 0.2401\n", - " -0.1752\n", - " -0.2658\n", - " -0.2075\n", - " -0.0342\n", - " -0.2617\n", - " -0.4422\n", - " -0.0732\n", - " -0.2689\n", - " -0.3803\n", - " -0.3199\n", - " -0.0777\n", - " -0.1500\n", - " 0.1703\n", - " 0.1294\n", - " -0.1190\n", - " 0.0926\n", - " -0.1979\n", - " -0.1918\n", - " -0.0786\n", - " -0.0187\n", - " 0.1721\n", - " -0.1686\n", - " -0.1133\n", - " -0.5045\n", - " -0.2371\n", - " -0.1655\n", - " -0.2561\n", - " -0.3836\n", - " -0.3589\n", - " -0.1271\n", - " -0.2759\n", - " -0.0886\n", - " -0.3338\n", - " 0.0147\n", - " -0.2431\n", - " -0.0960\n", - " -0.1151\n", - " -0.0702\n", - " 0.0363\n", - " -0.0362\n", - " -0.1142\n", - " -0.0480\n", - " -0.2897\n", - " -0.0726\n", - " -0.1873\n", - " -0.1961\n", - " 0.3917\n", - " 0.0731\n", - " -0.1447\n", - " 0.0337\n", - " 0.0892\n", - " 0.0501\n", - " -0.1371\n", - " -0.0466\n", - " -0.3429\n", - " 0.1212\n", - " -0.0822\n", - " -0.1891\n", - " -0.4294\n", - " -0.1515\n", - " -0.2784\n", - " 0.0438\n", - " -0.0686\n", - " -0.1327\n", - " -0.6258\n", - " -0.1193\n", - " -0.1879\n", - " -0.1034\n", - " -0.1172\n", - " -0.1994\n", - " 0.2450\n", - " -0.0538\n", - " -0.0365\n", - " 0.1052\n", - " -0.0631\n", - " -0.1641\n", - " -0.2942\n", - " -0.1621\n", - " -0.2282\n", - " -0.1330\n", - " -0.2956\n", - " -0.1734\n", - " -0.3522\n", - " 0.0242\n", - " 0.0362\n", - " -0.0066\n", - " -0.1580\n", - " -0.1177\n", - " 0.1182\n", - " -0.1358\n", - " -0.3128\n", - " -0.0754\n", - " -0.1553\n", - " 0.3873\n", - " -0.1631\n", - " -0.1126\n", - " -0.0638\n", - " 0.1006\n", - " -0.1420\n", - " -0.3438\n", - " 0.1019\n", - " -0.2894\n", - " -0.3294\n", - " -0.2455\n", - " -0.0723\n", - " -0.5689\n", - " -0.3298\n", - " 0.3349\n", - " -0.2919\n", - " -0.0271\n", - " 0.0006\n", - " 0.0410\n", - " 0.0224\n", - " -0.0159\n", - " -0.0622\n", - " -0.0453\n", - " 0.0096\n", - " 0.0816\n", - " 0.0147\n", - " 0.0579\n", - " -0.0014\n", - " 0.0989\n", - " 0.0776\n", - " -0.0299\n", - " -0.0200\n", - " 0.0143\n", - " -0.0280\n", - " 0.0763\n", - " 0.0570\n", - " -0.0043\n", - " 0.0898\n", - " 0.0591\n", - " -0.0897\n", - " 0.0078\n", - " -0.0049\n", - " -0.0673\n", - " 0.1265\n", - " 0.0189\n", - " -0.0323\n", - " -0.0259\n", - " 0.0235\n", - " -0.0521\n", - " -0.1112\n", - " -0.0040\n", - " 0.0540\n", - " -0.0421\n", - " 0.1141\n", - " 0.0293\n", - " -0.0700\n", - " -0.0463\n", - " -0.0511\n", - " 0.0708\n", - " 0.0453\n", - " 0.0397\n", - " -0.0245\n", - " 0.0004\n", - " 0.0540\n", - " -0.0515\n", - " 0.0936\n", - " 0.0049\n", - " 0.0658\n", - " 0.0072\n", - " -0.0174\n", - " 0.0268\n", - " 0.0221\n", - " -0.0423\n", - " -0.0472\n", - " 0.0065\n", - " 0.0357\n", - " -0.0367\n", - " 0.0257\n", - " -0.0182\n", - " 0.0242\n", - " 0.0223\n", - " -0.0066\n", - " -0.0580\n", - " -0.1144\n", - " -0.0070\n", - " -0.0524\n", - " -0.0113\n", - " 0.0243\n", - " 0.0029\n", - " -0.0222\n", - " -0.0513\n", - " -0.0407\n", - " 0.0707\n", - " 0.0641\n", - " 0.0241\n", - " 0.0237\n", - " -0.0146\n", - " 0.0505\n", - " -0.0242\n", - " 0.0908\n", - " 0.0525\n", - " -0.0110\n", - " -0.0093\n", - " 0.0529\n", - " -0.0539\n", - " -0.0636\n", - " -0.0440\n", - " -0.0540\n", - " 0.0253\n", - " -0.0503\n", - " -0.0127\n", - " -0.0450\n", - " 0.0331\n", - " -0.0559\n", - " 0.0619\n", - " -0.0694\n", - " 0.0036\n", - " -0.0033\n", - " 0.0851\n", - " 0.0391\n", - " 0.0945\n", - " -0.0290\n", - " 0.0497\n", - " 0.0378\n", - " 0.0257\n", - " 0.1128\n", - " -0.0048\n", - " -0.0476\n", - " 0.0217\n", - " 0.0472\n", - " -0.0109\n", - " -0.0200\n", - " 0.0862\n", - " -0.0244\n", - " 0.0131\n", - " 0.0291\n", - " 0.0182\n", - " 0.0783\n", - " 0.0798\n", - " -0.0112\n", - " -0.0029\n", - " 0.0435\n", - " 0.0223\n", - " -0.0374\n", - " 0.0301\n", - " -0.0166\n", - " 0.0427\n", - " 0.0372\n", - " 0.0344\n", - " -0.0577\n", - " -0.0557\n", - " -0.0718\n", - " -0.0424\n", - " -0.0053\n", - " 0.0446\n", - " 0.0384\n", - " 0.0382\n", - " 0.0618\n", - " 0.0631\n", - " -0.0040\n", - " -0.0489\n", - " -0.0479\n", - " 0.0454\n", - " 0.0713\n", - " -0.0754\n", - " 0.0602\n", - " -0.0309\n", - " 0.0088\n", - " 0.0690\n", - " 0.0244\n", - " 0.0634\n", - " -0.0526\n", - " -0.0353\n", - " -0.0173\n", - " -0.0457\n", - " -0.0557\n", - " 0.0128\n", - " 0.0224\n", - " 0.0060\n", - " -0.0155\n", - " -0.0410\n", - " -0.0239\n", - " -0.0538\n", - " -0.0239\n", - " -0.0003\n", - " -0.0371\n", - " 0.0510\n", - " -0.0597\n", - " -0.0236\n", - " -0.0809\n", - " 0.0212\n", - " 0.0308\n", - " 0.0259\n", - " 0.0005\n", - " 0.0601\n", - " 0.0140\n", - " 0.0893\n", - " 0.0021\n", - " 0.0550\n", - " 0.0050\n", - " 0.0266\n", - " 0.0980\n", - " -0.0430\n", - " 0.1279\n", - " 0.0411\n", - " 0.0152\n", - " -0.0121\n", - " 0.0384\n", - " -0.0195\n", - " 0.0058\n", - " 0.0138\n", - " 0.0329\n", - " 0.0565\n", - " 0.0095\n", - " -0.0037\n", - " -0.0056\n", - " -0.0489\n", - " 0.0723\n", - " 0.0207\n", - " -0.0042\n", - " -0.0027\n", - " 0.0249\n", - " 0.0578\n", - " -0.0596\n", - " -0.0084\n", - " -0.0575\n", - " 0.0052\n", - " 0.0358\n", - " 0.0892\n", - " -0.0271\n", - " -0.0473\n", - " -0.0053\n", - " 0.0653\n", - " -0.0098\n", - " 0.0424\n", - " -0.0312\n", - " -0.0554\n", - " -0.0118\n", - " 0.0423\n", - " -0.0367\n", - " 0.0336\n", - " 0.0107\n", - " 0.0195\n", - " 0.0705\n", - " -0.0218\n", - " 0.0099\n", - " -0.1557\n", - " 0.0597\n", - " 0.0458\n", - " -0.0155\n", - " -0.0662\n", - " 0.0109\n", - " 0.0228\n", - " -0.0491\n", - " 0.0640\n", - " -0.0082\n", - " 0.0067\n", - " 0.0677\n", - " 0.0180\n", - " -0.1119\n", - " -0.0287\n", - " -0.0505\n", - " -0.0164\n", - " -0.0862\n", - " 0.0353\n", - " 0.0347\n", - " -0.0385\n", - " -0.0876\n", - " -0.0662\n", - " 0.0427\n", - " -0.0347\n", - " -0.0592\n", - " [torch.FloatTensor of size 768]),\n", - " ('module.decoder.decoder_rnns.1.bias_hh', \n", - " 0.0343\n", - " -0.2205\n", - " -0.1759\n", - " -0.0543\n", - " -0.1046\n", - " -0.1600\n", - " -0.0251\n", - " -0.0729\n", - " 0.0555\n", - " 0.0757\n", - " -0.1488\n", - " 0.0289\n", - " 0.0294\n", - " -0.0351\n", - " -0.1385\n", - " -0.1604\n", - " -0.1146\n", - " 0.0331\n", - " -0.1539\n", - " -0.1825\n", - " -0.0130\n", - " -0.0408\n", - " -0.0533\n", - " -0.2980\n", - " -0.2090\n", - " 0.0510\n", - " -0.2458\n", - " -0.0469\n", - " -0.1718\n", - " -0.2487\n", - " 0.0683\n", - " -0.1317\n", - " -0.2785\n", - " -0.1509\n", - " -0.2421\n", - " 0.0064\n", - " -0.2040\n", - " -0.1809\n", - " 0.0043\n", - " -0.0727\n", - " -0.0423\n", - " -0.1522\n", - " -0.1706\n", - " -0.0559\n", - " -0.0913\n", - " -0.0576\n", - " -0.0368\n", - " 0.0890\n", - " -0.1199\n", - " -0.0260\n", - " 0.1055\n", - " -0.0416\n", - " -0.3200\n", - " -0.1624\n", - " -0.0024\n", - " 0.1192\n", - " -0.0467\n", - " -0.1662\n", - " -0.0506\n", - " -0.1071\n", - " 0.0309\n", - " -0.1860\n", - " 0.1392\n", - " 0.0104\n", - " -0.1818\n", - " -0.1027\n", - " 0.0228\n", - " -0.0738\n", - " -0.1512\n", - " -0.0057\n", - " -0.1338\n", - " 0.1350\n", - " -0.0071\n", - " -0.0731\n", - " 0.0125\n", - " -0.2226\n", - " 0.0066\n", - " -0.2240\n", - " -0.0826\n", - " 0.0208\n", - " -0.2509\n", - " -0.1016\n", - " -0.0789\n", - " -0.2964\n", - " -0.0714\n", - " -0.0189\n", - " -0.2788\n", - " 0.0617\n", - " -0.0546\n", - " -0.2184\n", - " -0.0392\n", - " -0.0767\n", - " -0.1837\n", - " -0.1876\n", - " 0.0068\n", - " -0.1204\n", - " -0.3699\n", - " 0.0203\n", - " -0.0665\n", - " -0.0626\n", - " 0.2156\n", - " -0.0202\n", - " -0.1446\n", - " 0.0053\n", - " -0.1995\n", - " 0.0019\n", - " -0.1375\n", - " -0.1398\n", - " -0.0581\n", - " -0.1123\n", - " -0.0599\n", - " -0.2106\n", - " -0.2039\n", - " -0.2349\n", - " -0.4244\n", - " -0.1016\n", - " -0.2071\n", - " -0.1010\n", - " -0.0527\n", - " -0.2783\n", - " -0.0990\n", - " -0.3034\n", - " 0.0274\n", - " -0.0464\n", - " -0.2196\n", - " -0.1248\n", - " 0.0454\n", - " -0.1037\n", - " -0.2293\n", - " -0.1677\n", - " -0.0734\n", - " 0.0381\n", - " -0.1746\n", - " -0.0282\n", - " -0.3104\n", - " -0.0508\n", - " -0.2080\n", - " -0.1150\n", - " 0.0742\n", - " -0.0503\n", - " -0.1552\n", - " -0.1185\n", - " -0.1432\n", - " 0.0289\n", - " 0.0230\n", - " -0.2688\n", - " 0.0384\n", - " 0.0051\n", - " -0.2207\n", - " -0.1441\n", - " -0.1035\n", - " -0.0416\n", - " -0.2482\n", - " -0.1536\n", - " 0.0599\n", - " -0.3514\n", - " -0.0670\n", - " -0.0691\n", - " 0.0697\n", - " 0.0022\n", - " -0.0792\n", - " -0.0655\n", - " -0.1497\n", - " -0.1727\n", - " -0.1993\n", - " -0.1741\n", - " -0.1507\n", - " 0.0256\n", - " -0.2359\n", - " -0.1115\n", - " -0.0119\n", - " -0.0390\n", - " -0.2418\n", - " -0.0209\n", - " -0.0632\n", - " -0.1474\n", - " -0.0096\n", - " -0.0870\n", - " -0.0659\n", - " -0.0452\n", - " -0.1415\n", - " -0.1103\n", - " -0.1115\n", - " -0.0588\n", - " -0.1483\n", - " -0.1410\n", - " -0.1995\n", - " -0.1629\n", - " -0.2313\n", - " -0.1055\n", - " 0.0205\n", - " -0.0544\n", - " -0.0922\n", - " 0.1010\n", - " -0.0542\n", - " -0.0307\n", - " -0.0544\n", - " -0.1246\n", - " -0.0428\n", - " 0.0680\n", - " -0.1804\n", - " 0.0182\n", - " 0.2025\n", - " -0.2346\n", - " -0.1066\n", - " -0.1023\n", - " 0.0508\n", - " -0.0058\n", - " -0.0669\n", - " -0.0128\n", - " -0.2045\n", - " -0.2560\n", - " -0.0448\n", - " -0.2328\n", - " -0.0610\n", - " -0.0236\n", - " -0.0091\n", - " -0.0614\n", - " -0.0297\n", - " -0.1093\n", - " -0.0025\n", - " -0.3452\n", - " 0.0339\n", - " -0.1429\n", - " -0.1356\n", - " -0.2519\n", - " -0.1821\n", - " -0.0945\n", - " -0.3060\n", - " -0.0134\n", - " -0.2135\n", - " 0.1063\n", - " 0.0048\n", - " -0.0873\n", - " 0.0461\n", - " -0.0530\n", - " 0.0275\n", - " 0.0654\n", - " -0.1037\n", - " 0.0794\n", - " -0.1844\n", - " -0.1053\n", - " -0.1306\n", - " -0.1436\n", - " 0.0886\n", - " -0.0442\n", - " -0.2772\n", - " -0.0387\n", - " -0.2094\n", - " -0.0317\n", - " -0.0487\n", - " 0.0835\n", - " -0.2631\n", - " -0.0988\n", - " -0.2111\n", - " -0.1708\n", - " -0.2594\n", - " 0.1489\n", - " -0.2586\n", - " -0.1723\n", - " -0.1183\n", - " -0.1991\n", - " -0.2226\n", - " -0.1170\n", - " 0.1049\n", - " -0.1086\n", - " -0.0038\n", - " 0.0941\n", - " -0.0149\n", - " -0.2777\n", - " -0.1084\n", - " -0.1581\n", - " 0.1168\n", - " -0.4400\n", - " -0.1210\n", - " -0.1577\n", - " 0.0378\n", - " 0.1086\n", - " 0.0963\n", - " -0.0075\n", - " -0.1803\n", - " -0.2887\n", - " -0.2981\n", - " -0.3096\n", - " -0.0898\n", - " -0.5208\n", - " -0.4997\n", - " -0.1008\n", - " -0.2818\n", - " -0.4848\n", - " -0.8144\n", - " -0.3606\n", - " -0.3231\n", - " -0.3712\n", - " -0.0826\n", - " 0.0635\n", - " -0.3109\n", - " -0.1405\n", - " -0.3643\n", - " -0.0223\n", - " -0.0984\n", - " -0.2161\n", - " -0.0859\n", - " 0.0105\n", - " -0.2006\n", - " -0.3644\n", - " -0.3318\n", - " 0.2235\n", - " -0.0831\n", - " 0.0963\n", - " -0.0246\n", - " 0.0886\n", - " 0.0322\n", - " 0.0677\n", - " -0.0947\n", - " -0.2206\n", - " -0.3130\n", - " -0.0638\n", - " -0.2915\n", - " 0.0181\n", - " 0.2332\n", - " -0.1881\n", - " -0.4241\n", - " -0.3320\n", - " -0.1552\n", - " -0.2024\n", - " -0.2510\n", - " -0.0858\n", - " -0.1571\n", - " -0.1714\n", - " -0.1187\n", - " -0.2142\n", - " -0.1673\n", - " -0.3569\n", - " 0.0169\n", - " -0.1147\n", - " -0.2604\n", - " -0.1895\n", - " 0.0421\n", - " -0.4577\n", - " -0.2308\n", - " -0.2758\n", - " -0.2051\n", - " -0.2527\n", - " -0.2452\n", - " -0.2647\n", - " -0.0610\n", - " 0.2031\n", - " 0.0937\n", - " -0.5974\n", - " -0.2579\n", - " -0.2465\n", - " -0.3053\n", - " 0.0942\n", - " 0.0322\n", - " -0.1968\n", - " -1.1592\n", - " -0.1901\n", - " -0.4790\n", - " -0.2278\n", - " -0.2477\n", - " -0.1650\n", - " -0.0122\n", - " -0.0584\n", - " 0.0147\n", - " 0.0447\n", - " 0.0728\n", - " -0.0359\n", - " -0.1528\n", - " -0.0141\n", - " 0.0775\n", - " -0.1813\n", - " -0.2707\n", - " -0.0423\n", - " 0.1164\n", - " -0.1781\n", - " -0.1497\n", - " -0.4498\n", - " -0.0401\n", - " -0.1371\n", - " 0.0586\n", - " -0.0882\n", - " 0.1225\n", - " -0.0984\n", - " 0.1207\n", - " -0.1802\n", - " -0.2274\n", - " -0.1551\n", - " -0.2592\n", - " -0.0032\n", - " -0.0738\n", - " -0.4396\n", - " -0.3603\n", - " -0.3281\n", - " -0.2016\n", - " -0.1089\n", - " -0.1903\n", - " 0.0015\n", - " 0.0555\n", - " 0.2191\n", - " -0.2732\n", - " -0.1100\n", - " 0.1814\n", - " -0.0939\n", - " -0.2828\n", - " -0.1997\n", - " -0.0086\n", - " -0.1791\n", - " -0.4105\n", - " -0.1703\n", - " -0.2484\n", - " -0.3473\n", - " -0.2770\n", - " -0.0827\n", - " -0.2055\n", - " 0.2116\n", - " 0.0806\n", - " -0.0951\n", - " 0.1163\n", - " -0.1722\n", - " -0.2641\n", - " 0.0257\n", - " -0.0635\n", - " 0.1146\n", - " -0.1596\n", - " -0.0988\n", - " -0.5993\n", - " -0.2583\n", - " -0.2489\n", - " -0.3199\n", - " -0.3233\n", - " -0.3933\n", - " -0.1140\n", - " -0.3902\n", - " -0.0218\n", - " -0.3324\n", - " 0.0244\n", - " -0.2429\n", - " -0.1285\n", - " -0.1399\n", - " -0.0639\n", - " 0.0139\n", - " 0.0145\n", - " -0.0901\n", - " -0.0889\n", - " -0.2751\n", - " -0.1627\n", - " -0.2586\n", - " -0.1496\n", - " 0.3225\n", - " 0.1047\n", - " -0.1462\n", - " 0.1028\n", - " 0.0736\n", - " 0.0093\n", - " -0.1218\n", - " -0.0591\n", - " -0.2482\n", - " 0.0617\n", - " -0.0374\n", - " -0.1418\n", - " -0.3741\n", - " -0.1243\n", - " -0.2819\n", - " 0.0454\n", - " -0.0003\n", - " -0.1040\n", - " -0.6222\n", - " -0.0681\n", - " -0.1977\n", - " -0.0129\n", - " -0.1055\n", - " -0.1850\n", - " 0.1884\n", - " -0.0491\n", - " -0.0594\n", - " 0.0235\n", - " -0.1445\n", - " -0.2277\n", - " -0.2087\n", - " -0.1417\n", - " -0.2355\n", - " -0.2547\n", - " -0.2894\n", - " -0.1692\n", - " -0.2899\n", - " -0.0690\n", - " -0.0026\n", - " -0.0061\n", - " -0.1618\n", - " -0.2000\n", - " 0.0268\n", - " -0.1220\n", - " -0.2953\n", - " -0.0996\n", - " -0.1603\n", - " 0.3013\n", - " -0.1819\n", - " 0.0007\n", - " -0.1308\n", - " 0.0423\n", - " -0.1589\n", - " -0.3795\n", - " 0.1657\n", - " -0.2544\n", - " -0.2691\n", - " -0.2338\n", - " -0.0087\n", - " -0.5695\n", - " -0.2870\n", - " 0.2887\n", - " -0.3195\n", - " 0.6581\n", - " -0.1343\n", - " 0.0633\n", - " -0.0689\n", - " 0.0666\n", - " -0.0068\n", - " -0.2195\n", - " -0.1215\n", - " 0.5227\n", - " 0.0606\n", - " 0.0053\n", - " -0.0376\n", - " 0.0531\n", - " -0.0131\n", - " -0.0115\n", - " 0.0475\n", - " -0.0162\n", - " 0.0722\n", - " -0.0447\n", - " 0.0016\n", - " -0.0067\n", - " 0.0684\n", - " 0.0160\n", - " -0.0604\n", - " 0.1673\n", - " -0.0504\n", - " -0.0696\n", - " 0.0741\n", - " 0.0518\n", - " 0.2055\n", - " 0.0799\n", - " 0.0108\n", - " -0.1300\n", - " -0.2944\n", - " -0.0724\n", - " 0.0093\n", - " 0.0684\n", - " 0.1222\n", - " 0.5372\n", - " -0.0267\n", - " -0.4729\n", - " -0.0197\n", - " -0.2383\n", - " -0.0014\n", - " 0.0198\n", - " 0.1358\n", - " 0.6865\n", - " 0.0791\n", - " 0.0291\n", - " -0.0594\n", - " 0.0305\n", - " 0.0072\n", - " 0.0318\n", - " 0.0038\n", - " 0.1854\n", - " -0.1952\n", - " 0.0003\n", - " -0.0036\n", - " 0.1036\n", - " 0.1039\n", - " 0.0105\n", - " 0.0012\n", - " -0.0858\n", - " -0.0073\n", - " 0.0589\n", - " 0.0402\n", - " -0.0344\n", - " -0.0479\n", - " 0.0019\n", - " 0.0362\n", - " 0.0719\n", - " -1.6178\n", - " 0.1029\n", - " -1.5088\n", - " 0.0293\n", - " -0.0899\n", - " 0.9377\n", - " 0.0752\n", - " 0.0553\n", - " 0.0616\n", - " 0.0100\n", - " -0.0749\n", - " -0.0305\n", - " -0.2133\n", - " -0.0395\n", - " 1.6026\n", - " -0.0588\n", - " -0.0054\n", - " 0.0482\n", - " -0.2315\n", - " -0.0295\n", - " -0.2107\n", - " 0.1377\n", - " -0.1784\n", - " 0.0777\n", - " -0.4296\n", - " -0.0197\n", - " 0.0128\n", - " 0.0665\n", - " -0.0302\n", - " 0.0309\n", - " 0.2284\n", - " 1.3962\n", - " 0.0285\n", - " 0.0996\n", - " -0.0420\n", - " 0.0581\n", - " -0.0585\n", - " -0.0662\n", - " -0.0241\n", - " -0.0395\n", - " -0.0429\n", - " 0.1045\n", - " 0.1187\n", - " 0.0248\n", - " -0.0720\n", - " 0.0664\n", - " -0.0156\n", - " 0.0216\n", - " -0.3421\n", - " 0.0187\n", - " 0.1154\n", - " -0.0078\n", - " 0.0833\n", - " 0.0151\n", - " -0.0610\n", - " -0.0475\n", - " -0.1498\n", - " -0.0867\n", - " 0.1174\n", - " -0.0104\n", - " 0.1037\n", - " -0.3127\n", - " -0.0403\n", - " -0.0522\n", - " 0.0026\n", - " -0.0765\n", - " 0.4049\n", - " -0.1799\n", - " 0.1748\n", - " -0.0341\n", - " 0.1855\n", - " 0.0352\n", - " 0.0172\n", - " 0.0335\n", - " -0.0136\n", - " 0.1373\n", - " 0.1667\n", - " -0.7157\n", - " 0.0535\n", - " 0.1069\n", - " -0.2175\n", - " -0.2596\n", - " 0.0062\n", - " -0.0110\n", - " -0.0409\n", - " -0.9490\n", - " -0.1014\n", - " -0.1753\n", - " 0.1308\n", - " -0.0018\n", - " 0.1232\n", - " 0.0663\n", - " -0.0458\n", - " -0.1241\n", - " -0.0379\n", - " 0.0233\n", - " -0.0178\n", - " -0.0743\n", - " -0.0754\n", - " 0.0997\n", - " -0.0881\n", - " -0.0222\n", - " 0.2767\n", - " 0.1961\n", - " -0.2217\n", - " -0.0473\n", - " -0.1927\n", - " 0.2394\n", - " 0.0497\n", - " 0.1041\n", - " 0.0031\n", - " -0.0140\n", - " -0.1149\n", - " -0.0349\n", - " -0.0570\n", - " -0.0878\n", - " 0.0808\n", - " 0.0906\n", - " -0.0186\n", - " -0.0714\n", - " -0.4216\n", - " 0.0257\n", - " 0.0696\n", - " -0.0053\n", - " -0.0602\n", - " 0.0716\n", - " 0.0157\n", - " 0.5212\n", - " 0.0040\n", - " 0.1328\n", - " 0.0821\n", - " -0.0231\n", - " -0.0143\n", - " -0.0797\n", - " -0.1586\n", - " -0.0039\n", - " -0.1710\n", - " -0.0004\n", - " -0.1199\n", - " 0.0152\n", - " 0.1131\n", - " -0.0531\n", - " 0.1532\n", - " -0.0085\n", - " -0.1425\n", - " -0.0158\n", - " -0.1678\n", - " 0.0270\n", - " -0.0502\n", - " -0.0820\n", - " 0.0701\n", - " 0.2335\n", - " -0.0213\n", - " 0.2500\n", - " 0.0314\n", - " -0.1142\n", - " 0.1697\n", - " 0.2488\n", - " 0.1649\n", - " 0.0025\n", - " -0.0153\n", - " -0.1582\n", - " -0.0162\n", - " -0.0760\n", - " 0.0152\n", - " -0.1606\n", - " 0.0137\n", - " -0.0789\n", - " 0.0946\n", - " 0.1557\n", - " -1.0529\n", - " 0.0398\n", - " 0.0007\n", - " -0.6346\n", - " -0.0021\n", - " -0.0241\n", - " -0.1064\n", - " -0.1453\n", - " 0.1590\n", - " 0.0571\n", - " -0.0148\n", - " 0.0049\n", - " 0.1183\n", - " 0.0900\n", - " 0.0004\n", - " [torch.FloatTensor of size 768]),\n", - " ('module.decoder.proj_to_mel.weight', \n", - " -1.3074e-03 -2.4511e-03 4.0127e-03 ... -1.8974e-04 -3.5570e-03 -1.0731e-02\n", - " 1.6458e-04 -3.4461e-03 -1.7653e-02 ... -1.2636e-03 -2.7327e-04 1.3112e-02\n", - " -4.7382e-03 2.9522e-03 -3.0874e-02 ... 2.8109e-04 -1.5848e-03 -6.4812e-03\n", - " ... ⋱ ... \n", - " -4.6943e-03 4.4080e-03 -1.1201e-02 ... 1.1059e-01 -9.0196e-04 1.6526e-02\n", - " -6.3213e-03 5.4862e-03 -4.3771e-03 ... 1.0128e-01 -4.8409e-03 1.2473e-02\n", - " -2.1717e-03 4.6354e-03 -1.0125e-02 ... 9.4980e-02 -1.4286e-03 2.9772e-02\n", - " [torch.FloatTensor of size 400x256]),\n", - " ('module.decoder.proj_to_mel.bias', \n", - " 1.00000e-02 *\n", - " 0.0876\n", - " 0.2869\n", - " 0.7675\n", - " 1.1309\n", - " 1.3614\n", - " 1.3701\n", - " 1.3839\n", - " 1.4532\n", - " 1.4647\n", - " 1.5598\n", - " 1.5797\n", - " 1.6161\n", - " 1.6791\n", - " 1.6373\n", - " 1.5957\n", - " 1.5270\n", - " 1.5398\n", - " 1.4873\n", - " 1.4737\n", - " 1.4344\n", - " 1.3897\n", - " 1.3177\n", - " 1.2835\n", - " 1.2909\n", - " 1.2791\n", - " 1.2945\n", - " 1.2757\n", - " 1.2166\n", - " 1.2300\n", - " 1.1754\n", - " 1.1505\n", - " 1.1620\n", - " 1.2075\n", - " 1.2446\n", - " 1.2896\n", - " 1.2816\n", - " 1.3150\n", - " 1.3853\n", - " 1.4304\n", - " 1.4351\n", - " 1.3969\n", - " 1.3827\n", - " 1.3353\n", - " 1.3309\n", - " 1.3441\n", - " 1.3446\n", - " 1.3858\n", - " 1.4163\n", - " 1.4484\n", - " 1.5118\n", - " 1.5367\n", - " 1.5440\n", - " 1.4973\n", - " 1.4682\n", - " 1.4973\n", - " 1.5031\n", - " 1.5215\n", - " 1.5343\n", - " 1.5256\n", - " 1.5085\n", - " 1.4796\n", - " 1.4687\n", - " 1.4534\n", - " 1.4743\n", - " 1.4257\n", - " 1.3159\n", - " 1.1628\n", - " 1.0977\n", - " 1.0931\n", - " 1.0937\n", - " 1.2023\n", - " 1.2768\n", - " 1.3716\n", - " 1.4418\n", - " 1.4656\n", - " 1.4682\n", - " 1.3487\n", - " 1.2776\n", - " 1.1769\n", - " 0.6768\n", - " -0.0025\n", - " 0.3184\n", - " 0.7693\n", - " 1.1664\n", - " 1.2635\n", - " 1.3745\n", - " 1.4168\n", - " 1.4754\n", - " 1.4790\n", - " 1.5083\n", - " 1.6034\n", - " 1.5930\n", - " 1.6553\n", - " 1.6128\n", - " 1.5650\n", - " 1.5256\n", - " 1.5177\n", - " 1.4417\n", - " 1.4139\n", - " 1.3828\n", - " 1.3538\n", - " 1.3233\n", - " 1.2828\n", - " 1.2898\n", - " 1.3020\n", - " 1.2633\n", - " 1.2407\n", - " 1.2393\n", - " 1.2278\n", - " 1.1765\n", - " 1.1779\n", - " 1.1244\n", - " 1.1394\n", - " 1.1836\n", - " 1.2727\n", - " 1.3140\n", - " 1.3260\n", - " 1.3680\n", - " 1.3976\n", - " 1.3810\n", - " 1.3369\n", - " 1.3492\n", - " 1.3322\n", - " 1.3065\n", - " 1.2844\n", - " 1.3270\n", - " 1.3645\n", - " 1.4034\n", - " 1.4259\n", - " 1.4383\n", - " 1.4636\n", - " 1.4869\n", - " 1.4434\n", - " 1.4048\n", - " 1.4612\n", - " 1.4850\n", - " 1.4780\n", - " 1.5197\n", - " 1.4922\n", - " 1.4267\n", - " 1.4171\n", - " 1.4324\n", - " 1.4412\n", - " 1.4399\n", - " 1.3997\n", - " 1.2464\n", - " 1.1530\n", - " 1.0524\n", - " 1.0748\n", - " 1.0952\n", - " 1.1387\n", - " 1.2539\n", - " 1.3213\n", - " 1.3723\n", - " 1.4633\n", - " 1.4234\n", - " 1.2991\n", - " 1.2339\n", - " 1.0897\n", - " 0.6178\n", - " -0.0179\n", - " 0.3282\n", - " 0.7514\n", - " 1.1336\n", - " 1.3210\n", - " 1.3531\n", - " 1.3454\n", - " 1.4746\n", - " 1.4648\n", - " 1.5131\n", - " 1.6053\n", - " 1.6047\n", - " 1.6204\n", - " 1.6006\n", - " 1.5564\n", - " 1.5380\n", - " 1.5117\n", - " 1.4441\n", - " 1.4121\n", - " 1.3964\n", - " 1.3132\n", - " 1.2740\n", - " 1.2346\n", - " 1.2087\n", - " 1.2822\n", - " 1.2630\n", - " 1.2516\n", - " 1.2121\n", - " 1.1918\n", - " 1.1358\n", - " 1.1214\n", - " 1.1183\n", - " 1.1306\n", - " 1.1800\n", - " 1.2391\n", - " 1.2644\n", - " 1.3117\n", - " 1.3605\n", - " 1.4171\n", - " 1.3564\n", - " 1.3415\n", - " 1.3094\n", - " 1.2988\n", - " 1.2651\n", - " 1.2902\n", - " 1.3181\n", - " 1.3491\n", - " 1.3242\n", - " 1.4091\n", - " 1.4292\n", - " 1.4441\n", - " 1.4335\n", - " 1.4005\n", - " 1.3585\n", - " 1.3968\n", - " 1.4410\n", - " 1.4538\n", - " 1.4958\n", - " 1.4630\n", - " 1.4457\n", - " 1.3883\n", - " 1.3910\n", - " 1.3679\n", - " 1.3587\n", - " 1.3276\n", - " 1.2073\n", - " 1.0925\n", - " 1.0449\n", - " 1.0399\n", - " 1.0732\n", - " 1.1572\n", - " 1.2606\n", - " 1.3236\n", - " 1.3726\n", - " 1.4031\n", - " 1.3764\n", - " 1.3142\n", - " 1.2088\n", - " 1.0817\n", - " 0.5831\n", - " 0.0094\n", - " 0.2855\n", - " 0.7391\n", - " 1.1503\n", - " 1.2671\n", - " 1.3340\n", - " 1.3187\n", - " 1.3782\n", - " 1.4229\n", - " 1.4405\n", - " 1.5457\n", - " 1.5796\n", - " 1.5900\n", - " 1.5767\n", - " 1.5167\n", - " 1.4924\n", - " 1.4926\n", - " 1.4209\n", - " 1.3982\n", - " 1.3704\n", - " 1.3177\n", - " 1.2525\n", - " 1.2445\n", - " 1.2593\n", - " 1.2587\n", - " 1.2303\n", - " 1.2157\n", - " 1.1921\n", - " 1.1971\n", - " 1.1388\n", - " 1.1351\n", - " 1.1308\n", - " 1.1380\n", - " 1.1683\n", - " 1.2366\n", - " 1.2392\n", - " 1.2875\n", - " 1.3089\n", - " 1.3943\n", - " 1.3405\n", - " 1.3329\n", - " 1.3344\n", - " 1.3163\n", - " 1.2668\n", - " 1.3138\n", - " 1.3248\n", - " 1.3536\n", - " 1.3607\n", - " 1.4112\n", - " 1.4539\n", - " 1.4694\n", - " 1.4653\n", - " 1.4381\n", - " 1.4191\n", - " 1.4100\n", - " 1.4421\n", - " 1.4614\n", - " 1.4395\n", - " 1.4460\n", - " 1.3918\n", - " 1.3694\n", - " 1.3848\n", - " 1.4002\n", - " 1.3500\n", - " 1.3336\n", - " 1.1924\n", - " 1.0975\n", - " 1.0437\n", - " 1.0253\n", - " 1.1050\n", - " 1.1091\n", - " 1.2645\n", - " 1.3191\n", - " 1.3368\n", - " 1.4045\n", - " 1.3772\n", - " 1.2685\n", - " 1.1781\n", - " 1.0324\n", - " 0.6481\n", - " -0.0258\n", - " 0.3058\n", - " 0.7237\n", - " 1.1226\n", - " 1.2792\n", - " 1.2757\n", - " 1.3443\n", - " 1.3900\n", - " 1.4495\n", - " 1.4699\n", - " 1.5437\n", - " 1.5629\n", - " 1.5834\n", - " 1.5482\n", - " 1.4947\n", - " 1.4375\n", - " 1.4624\n", - " 1.4047\n", - " 1.3745\n", - " 1.3185\n", - " 1.2726\n", - " 1.2390\n", - " 1.2192\n", - " 1.1896\n", - " 1.2406\n", - " 1.2000\n", - " 1.1671\n", - " 1.1556\n", - " 1.1712\n", - " 1.1227\n", - " 1.1275\n", - " 1.1069\n", - " 1.1242\n", - " 1.1525\n", - " 1.2164\n", - " 1.2421\n", - " 1.2874\n", - " 1.3315\n", - " 1.3654\n", - " 1.3286\n", - " 1.3372\n", - " 1.3077\n", - " 1.3018\n", - " 1.2429\n", - " 1.2884\n", - " 1.3012\n", - " 1.3073\n", - " 1.3178\n", - " 1.4200\n", - " 1.3940\n", - " 1.4591\n", - " 1.4395\n", - " 1.3646\n", - " 1.3977\n", - " 1.3908\n", - " 1.3935\n", - " 1.4500\n", - " 1.4397\n", - " 1.4495\n", - " 1.3929\n", - " 1.3619\n", - " 1.3456\n", - " 1.3574\n", - " 1.3470\n", - " 1.3032\n", - " 1.1834\n", - " 1.0622\n", - " 1.0472\n", - " 1.0244\n", - " 1.0552\n", - " 1.1053\n", - " 1.2159\n", - " 1.3469\n", - " 1.3601\n", - " 1.3859\n", - " 1.3516\n", - " 1.2573\n", - " 1.1643\n", - " 1.0473\n", - " 0.6013\n", - " [torch.FloatTensor of size 400]),\n", - " ('module.postnet.conv1d_banks.0.conv1d.weight', \n", - " (0 ,.,.) = \n", - " 5.1769e-01\n", - " -1.9350e-01\n", - " -5.1565e-02\n", - " ⋮ \n", - " -1.4517e-02\n", - " 7.1059e-02\n", - " 6.6071e-02\n", - " \n", - " (1 ,.,.) = \n", - " -1.0924e-02\n", - " -8.0526e-02\n", - " 3.5597e-02\n", - " ⋮ \n", - " -2.0654e-01\n", - " -1.3508e-01\n", - " 3.8456e-01\n", - " \n", - " (2 ,.,.) = \n", - " 2.9784e+00\n", - " -6.2878e-01\n", - " -1.7459e-01\n", - " ⋮ \n", - " 7.2299e-02\n", - " 2.2709e-01\n", - " 6.0340e-01\n", - " ...\n", - " \n", - " (77,.,.) = \n", - " -9.9861e-01\n", - " 9.4519e-02\n", - " 1.9491e-01\n", - " ⋮ \n", - " 1.0430e-01\n", - " -1.9140e-02\n", - " 2.6940e-01\n", - " \n", - " (78,.,.) = \n", - " 2.1744e-01\n", - " -8.0680e-02\n", - " 2.1582e-01\n", - " ⋮ \n", - " -4.2295e-02\n", - " 1.6425e-02\n", - " -2.3594e-03\n", - " \n", - " (79,.,.) = \n", - " 1.7220e+00\n", - " -1.5493e+00\n", - " 2.8362e-01\n", - " ⋮ \n", - " 1.4140e-01\n", - " 1.6998e-02\n", - " 4.2408e-01\n", - " [torch.FloatTensor of size 80x80x1]),\n", - " ('module.postnet.conv1d_banks.0.bn.weight', \n", - " -11.9678\n", - " -12.2330\n", - " 0.5874\n", - " -12.7266\n", - " -14.8812\n", - " -2.5761\n", - " -11.9427\n", - " -4.2313\n", - " 0.6492\n", - " -14.3888\n", - " 0.5280\n", - " 0.5165\n", - " -3.7921\n", - " 0.6091\n", - " -1.2100\n", - " -5.2468\n", - " 0.4883\n", - " -9.6767\n", - " 0.2881\n", - " 0.4058\n", - " -11.3032\n", - " -13.2620\n", - " -14.9764\n", - " 0.3754\n", - " 0.3768\n", - " -11.5367\n", - " -1.2537\n", - " -4.8199\n", - " 0.4803\n", - " 0.3263\n", - " -5.1572\n", - " -11.5061\n", - " -12.7830\n", - " -11.6226\n", - " -4.4590\n", - " 0.3148\n", - " 0.6764\n", - " -11.8550\n", - " -12.6242\n", - " 0.0109\n", - " -14.1101\n", - " 0.6742\n", - " -13.7539\n", - " -11.9381\n", - " 0.4237\n", - " -12.1203\n", - " 0.2748\n", - " 0.2623\n", - " -13.3269\n", - " 0.2197\n", - " -14.4030\n", - " -13.9489\n", - " 1.0069\n", - " -13.2212\n", - " -13.3118\n", - " -11.0843\n", - " 0.6281\n", - " 0.3118\n", - " 0.5690\n", - " -15.3363\n", - " -9.2287\n", - " 0.3996\n", - " -12.4018\n", - " 0.2682\n", - " 0.4161\n", - " 0.3363\n", - " -2.4713\n", - " -11.9829\n", - " 0.2552\n", - " -12.6530\n", - " -4.9388\n", - " 0.6500\n", - " -2.2789\n", - " -12.4364\n", - " 0.4697\n", - " 0.3311\n", - " -12.2295\n", - " -3.9541\n", - " -11.6577\n", - " 0.3231\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.0.bn.bias', \n", - " -0.1770\n", - " -0.5260\n", - " -0.3186\n", - " -0.4653\n", - " -0.3628\n", - " 0.0852\n", - " -0.3752\n", - " 0.1456\n", - " -0.6313\n", - " -0.3645\n", - " -0.1815\n", - " -0.1057\n", - " 0.0914\n", - " -0.3637\n", - " 0.3904\n", - " -0.0688\n", - " 0.0782\n", - " -0.0764\n", - " -0.2970\n", - " -0.2733\n", - " -0.7308\n", - " -0.3784\n", - " -0.7094\n", - " 0.0536\n", - " -0.2227\n", - " -0.0357\n", - " 0.2188\n", - " -4.1813\n", - " -0.4352\n", - " -0.3667\n", - " -0.5476\n", - " 0.1492\n", - " -0.1594\n", - " -0.5768\n", - " -0.3024\n", - " -0.1974\n", - " -0.2981\n", - " -0.3037\n", - " 2.2149\n", - " -0.5135\n", - " -0.6731\n", - " -0.3076\n", - " -1.0072\n", - " -0.2102\n", - " -0.0984\n", - " -0.4764\n", - " -0.3976\n", - " -0.0539\n", - " -0.0830\n", - " -0.1599\n", - " -0.2148\n", - " -0.4588\n", - " -0.6995\n", - " -0.2935\n", - " -0.4384\n", - " -0.3426\n", - " -0.2200\n", - " -0.1131\n", - " -0.4610\n", - " -0.0497\n", - " -0.7868\n", - " -0.2811\n", - " -0.3139\n", - " -0.1979\n", - " -0.2312\n", - " -0.2283\n", - " -0.3468\n", - " -0.4847\n", - " -0.3325\n", - " -0.4417\n", - " -4.7423\n", - " -0.1914\n", - " -0.1727\n", - " -0.1415\n", - " -0.6530\n", - " -0.3162\n", - " -0.5499\n", - " -0.6805\n", - " -0.5549\n", - " -0.2024\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.0.bn.running_mean', \n", - " 2.1287e-05\n", - " 1.3011e-05\n", - " 3.0102e-01\n", - " 7.9208e-06\n", - " 2.9520e-04\n", - " 3.8135e-02\n", - " 1.0149e-05\n", - " 4.4172e-05\n", - " 9.3081e-01\n", - " 1.1545e-04\n", - " 1.1260e+00\n", - " 1.9549e+00\n", - " 1.2658e-04\n", - " 2.7999e-01\n", - " 6.6247e-02\n", - " 3.7660e-05\n", - " 1.8550e-01\n", - " 1.7950e-05\n", - " 2.6923e+00\n", - " 1.3024e-01\n", - " 6.4194e-06\n", - " 1.9988e-05\n", - " 3.6879e-04\n", - " 1.2148e-01\n", - " 3.0188e-02\n", - " 1.2351e-05\n", - " 1.2680e-02\n", - " 1.2651e-06\n", - " 1.6360e-01\n", - " 8.9487e-02\n", - " 4.3512e-05\n", - " 2.1093e-05\n", - " 2.2089e-05\n", - " 1.8914e-05\n", - " 1.8063e-04\n", - " 3.8698e+00\n", - " 1.5270e-01\n", - " 1.3995e-05\n", - " 5.7557e-06\n", - " 2.8174e+00\n", - " 1.2101e-05\n", - " 2.5533e+00\n", - " 2.9588e-06\n", - " 3.3828e-06\n", - " 1.7701e-01\n", - " 6.5255e-05\n", - " 1.2166e-01\n", - " 1.9112e+00\n", - " 1.5575e-05\n", - " 3.8261e+00\n", - " 2.3136e-05\n", - " 2.2370e-05\n", - " 2.5962e-01\n", - " 2.6652e-05\n", - " 4.7766e-05\n", - " 1.0692e-05\n", - " 4.4529e-01\n", - " 1.2559e-01\n", - " 2.7961e-01\n", - " 1.3356e-05\n", - " 1.8169e-04\n", - " 1.9655e-01\n", - " 8.2612e-06\n", - " 2.1740e+00\n", - " 6.1940e-02\n", - " 3.7653e-01\n", - " 1.4797e-04\n", - " 2.1150e-05\n", - " 5.7097e-02\n", - " 4.5371e-05\n", - " 6.2037e-06\n", - " 1.6608e-01\n", - " 1.8344e-04\n", - " 5.5461e-05\n", - " 1.8992e-01\n", - " 3.4101e-02\n", - " 4.6944e-05\n", - " 5.0050e-05\n", - " 2.6779e-05\n", - " 5.0445e-02\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.0.bn.running_var', \n", - " 3.4790e-07\n", - " 1.3100e-07\n", - " 9.3850e-02\n", - " 9.8571e-08\n", - " 4.2421e-06\n", - " 7.1133e-04\n", - " 9.6986e-08\n", - " 2.3501e-07\n", - " 1.9607e-01\n", - " 1.1185e-06\n", - " 7.5229e-01\n", - " 8.2943e-01\n", - " 1.5405e-06\n", - " 3.7033e-02\n", - " 3.0053e-03\n", - " 3.7897e-07\n", - " 4.2127e-02\n", - " 1.3493e-07\n", - " 1.8891e+00\n", - " 9.1971e-02\n", - " 5.4875e-08\n", - " 1.6678e-07\n", - " 1.0535e-05\n", - " 5.2598e-02\n", - " 1.0646e-02\n", - " 1.3590e-07\n", - " 4.9546e-04\n", - " 3.7495e-09\n", - " 3.6607e-02\n", - " 3.8562e-02\n", - " 4.6355e-07\n", - " 1.0277e-07\n", - " 3.1599e-07\n", - " 1.6569e-07\n", - " 1.9677e-06\n", - " 3.2907e+00\n", - " 2.8827e-02\n", - " 2.4286e-07\n", - " 7.4433e-08\n", - " 1.6466e+00\n", - " 7.0617e-08\n", - " 1.3520e+00\n", - " 1.6808e-08\n", - " 2.3399e-08\n", - " 4.7179e-02\n", - " 8.3522e-07\n", - " 2.4160e-02\n", - " 1.0465e+00\n", - " 2.0186e-07\n", - " 3.3099e+00\n", - " 3.4907e-07\n", - " 1.5072e-07\n", - " 1.6966e-02\n", - " 4.8098e-07\n", - " 3.1123e-07\n", - " 1.3480e-07\n", - " 1.4162e-01\n", - " 2.3337e-02\n", - " 7.2239e-02\n", - " 1.5036e-07\n", - " 2.4603e-06\n", - " 4.7200e-02\n", - " 7.7703e-08\n", - " 1.1384e+00\n", - " 9.7417e-03\n", - " 1.0520e-01\n", - " 1.4845e-06\n", - " 2.0648e-07\n", - " 1.6840e-02\n", - " 9.0840e-07\n", - " 2.7039e-08\n", - " 3.1538e-02\n", - " 4.6559e-06\n", - " 5.6221e-07\n", - " 2.9305e-02\n", - " 4.8931e-03\n", - " 7.6341e-07\n", - " 5.7731e-07\n", - " 2.6599e-07\n", - " 4.4374e-02\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.1.conv1d.weight', \n", - " (0 ,.,.) = \n", - " 2.2803e-01 3.5122e-02\n", - " 9.0641e-04 5.3618e-02\n", - " -7.8795e-02 1.4725e-02\n", - " ⋮ \n", - " -6.7398e-02 3.0243e-02\n", - " -1.4825e-01 -3.2397e-02\n", - " 4.5505e-02 6.4718e-02\n", - " \n", - " (1 ,.,.) = \n", - " 4.4825e-01 2.4676e-01\n", - " -1.0999e-01 -1.5665e-01\n", - " 2.9031e-02 -4.8570e-03\n", - " ⋮ \n", - " -8.9446e-02 1.8276e-01\n", - " 2.4644e-02 6.5687e-02\n", - " -1.9755e-02 -2.3221e-02\n", - " \n", - " (2 ,.,.) = \n", - " -4.8638e-01 1.2996e+00\n", - " -3.9909e-01 -1.7756e-01\n", - " -5.2663e-02 5.4679e-01\n", - " ⋮ \n", - " 1.1216e-01 8.3459e-02\n", - " -1.1575e-01 -8.2108e-02\n", - " -3.9344e-01 2.8520e-02\n", - " ...\n", - " \n", - " (77,.,.) = \n", - " 1.3718e-01 4.9113e-01\n", - " -3.1217e-03 1.6377e-02\n", - " 1.8865e-01 -6.7507e-02\n", - " ⋮ \n", - " 7.4536e-02 -4.8456e-02\n", - " 4.2454e-03 -1.0714e-01\n", - " 2.5783e-01 6.0486e-02\n", - " \n", - " (78,.,.) = \n", - " 1.4156e-01 8.3407e-02\n", - " -1.1070e-01 2.2991e-01\n", - " 1.2974e-01 8.5432e-02\n", - " ⋮ \n", - " 1.3325e-02 -7.5131e-02\n", - " 1.7926e-01 -8.9462e-03\n", - " -4.1264e-04 2.6007e-02\n", - " \n", - " (79,.,.) = \n", - " 3.1951e-01 -8.2399e-01\n", - " -2.8575e-01 3.6496e-02\n", - " -1.9432e-01 -3.7100e-01\n", - " ⋮ \n", - " 8.6748e-02 -1.2156e-01\n", - " -2.9631e-02 -1.3142e-01\n", - " 1.2614e-02 -1.6323e-01\n", - " [torch.FloatTensor of size 80x80x2]),\n", - " ('module.postnet.conv1d_banks.1.bn.weight', \n", - " -4.6559\n", - " -4.0374\n", - " 0.5697\n", - " 0.2481\n", - " -2.0727\n", - " 0.1974\n", - " -4.4233\n", - " -12.7494\n", - " -11.2140\n", - " -10.3780\n", - " -12.1605\n", - " -13.5825\n", - " 0.2552\n", - " -7.1670\n", - " -4.3186\n", - " -13.1333\n", - " 0.4902\n", - " -14.6622\n", - " -4.2768\n", - " -13.1279\n", - " -4.3025\n", - " 0.5430\n", - " 0.2588\n", - " -2.9486\n", - " -13.0833\n", - " -4.4657\n", - " 0.2853\n", - " -4.3336\n", - " -11.0073\n", - " 0.4570\n", - " -10.7826\n", - " 0.4264\n", - " 0.3180\n", - " 0.7737\n", - " -15.5740\n", - " -0.0243\n", - " -12.4222\n", - " -15.6479\n", - " 0.4490\n", - " 0.2373\n", - " -11.2645\n", - " 0.2832\n", - " -13.6475\n", - " 0.2258\n", - " -13.5477\n", - " -11.1852\n", - " -12.1963\n", - " -24.0367\n", - " -4.1066\n", - " -4.9623\n", - " 0.4799\n", - " 0.7183\n", - " -11.3978\n", - " 0.0855\n", - " -13.4413\n", - " 0.2855\n", - " 0.0430\n", - " 0.3015\n", - " -15.7421\n", - " -14.6467\n", - " 0.3438\n", - " 0.7918\n", - " -13.1479\n", - " -3.8025\n", - " 0.6624\n", - " -4.3095\n", - " -10.4288\n", - " 0.0540\n", - " -14.9388\n", - " -2.1997\n", - " -10.7721\n", - " -0.8526\n", - " -11.0237\n", - " -13.7308\n", - " -13.1054\n", - " -12.2283\n", - " 0.2761\n", - " -11.2452\n", - " -10.0655\n", - " 0.1193\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.1.bn.bias', \n", - " -3.5955\n", - " -2.4005\n", - " -0.4212\n", - " -0.3180\n", - " -0.1121\n", - " -0.2129\n", - " -0.1396\n", - " -0.2378\n", - " 0.1549\n", - " -0.2506\n", - " 0.6684\n", - " -0.4975\n", - " -0.2724\n", - " -0.3186\n", - " -1.2307\n", - " -0.4911\n", - " -0.3951\n", - " 0.0476\n", - " -2.0947\n", - " -0.3600\n", - " -0.1530\n", - " -0.2154\n", - " -0.1930\n", - " -0.1384\n", - " -0.5023\n", - " -2.6071\n", - " -0.4027\n", - " -3.1746\n", - " -0.1386\n", - " -0.4783\n", - " -0.4405\n", - " -0.5392\n", - " -0.1531\n", - " -0.5572\n", - " -0.2009\n", - " -0.2193\n", - " -0.4634\n", - " -0.5115\n", - " 0.2173\n", - " -0.4267\n", - " -0.5161\n", - " -0.1203\n", - " -0.3652\n", - " -0.4581\n", - " -0.5642\n", - " -0.1202\n", - " -0.2451\n", - " -0.5672\n", - " -4.2068\n", - " -4.4568\n", - " -0.0325\n", - " -0.4939\n", - " 0.4986\n", - " 0.2563\n", - " -0.4145\n", - " -0.2797\n", - " -0.3181\n", - " -0.2340\n", - " -0.3363\n", - " -0.4155\n", - " -0.2964\n", - " 0.0162\n", - " -0.5236\n", - " -0.4651\n", - " -0.6325\n", - " -3.2684\n", - " -0.3006\n", - " 0.3432\n", - " -0.5174\n", - " -0.2651\n", - " 0.3860\n", - " 0.5317\n", - " -0.3071\n", - " -0.5193\n", - " -0.5852\n", - " -2.6626\n", - " -0.3978\n", - " -0.6819\n", - " -0.3028\n", - " -0.1223\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.1.bn.running_mean', \n", - " 2.2363e-04\n", - " 2.9094e-04\n", - " 2.3534e-01\n", - " 2.4942e-02\n", - " 1.3132e-04\n", - " 1.7823e-02\n", - " 1.7010e-05\n", - " 2.1813e-05\n", - " 1.3471e-05\n", - " 1.5838e-05\n", - " 4.8694e-06\n", - " 3.0307e-05\n", - " 1.1366e-01\n", - " 4.4294e-05\n", - " 4.5411e-05\n", - " 1.9422e-05\n", - " 2.8304e-01\n", - " 4.2274e-05\n", - " 3.1824e-05\n", - " 4.1937e-05\n", - " 2.1202e-05\n", - " 1.4217e+00\n", - " 5.6688e+00\n", - " 6.3756e-05\n", - " 2.3630e-05\n", - " 2.1025e-06\n", - " 9.9421e-02\n", - " 6.7157e-06\n", - " 1.1267e-05\n", - " 1.7153e-01\n", - " 4.9026e-06\n", - " 1.7071e-01\n", - " 2.8186e-02\n", - " 5.9749e-01\n", - " 2.0170e-05\n", - " 3.7440e+00\n", - " 1.7805e-05\n", - " 3.5869e-06\n", - " 2.8605e-01\n", - " 8.7782e-02\n", - " 1.0984e-05\n", - " 4.1899e+00\n", - " 2.3876e-05\n", - " 1.3776e-01\n", - " 7.9306e-05\n", - " 1.1640e-05\n", - " 3.5394e-05\n", - " 2.7546e-04\n", - " 1.5698e-06\n", - " 1.9729e-06\n", - " 2.5174e-01\n", - " 6.5254e-01\n", - " 6.0419e-06\n", - " 6.2503e+00\n", - " 3.1011e-05\n", - " 5.1328e-02\n", - " 6.1270e+00\n", - " 2.8697e+00\n", - " 1.7399e-05\n", - " 4.1985e-05\n", - " 2.5586e-02\n", - " 3.5167e-01\n", - " 4.7537e-06\n", - " 6.9266e-06\n", - " 3.3323e-01\n", - " 2.5710e-04\n", - " 3.6380e-05\n", - " 6.8674e+00\n", - " 9.0483e-06\n", - " 1.6014e-05\n", - " 1.0817e-05\n", - " 3.3237e-05\n", - " 2.1958e-06\n", - " 1.7984e-05\n", - " 1.2633e-05\n", - " 1.6877e-07\n", - " 1.6081e-01\n", - " 4.6447e-06\n", - " 7.3561e-06\n", - " 5.9757e+00\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.1.bn.running_var', \n", - " 3.6303e-05\n", - " 6.7024e-05\n", - " 5.4235e-02\n", - " 5.1356e-03\n", - " 3.3565e-06\n", - " 4.4920e-03\n", - " 3.1855e-07\n", - " 2.6009e-07\n", - " 6.1145e-07\n", - " 3.7904e-07\n", - " 7.6811e-08\n", - " 8.5661e-07\n", - " 6.2907e-02\n", - " 9.7532e-07\n", - " 6.8136e-07\n", - " 2.7929e-07\n", - " 1.1577e-01\n", - " 3.2672e-06\n", - " 9.4797e-08\n", - " 2.8717e-06\n", - " 2.0015e-07\n", - " 9.1117e-01\n", - " 8.3589e+00\n", - " 1.0567e-06\n", - " 1.7558e-06\n", - " 1.3394e-08\n", - " 2.9529e-02\n", - " 5.6320e-08\n", - " 1.6233e-07\n", - " 7.0599e-02\n", - " 2.8805e-08\n", - " 7.6354e-02\n", - " 4.6784e-03\n", - " 1.2298e-01\n", - " 2.5616e-07\n", - " 2.8811e+00\n", - " 3.1228e-07\n", - " 2.9966e-08\n", - " 5.1873e-02\n", - " 2.1567e-02\n", - " 6.6883e-07\n", - " 3.7025e+00\n", - " 4.6636e-07\n", - " 2.7070e-02\n", - " 6.0551e-06\n", - " 2.2597e-07\n", - " 1.8449e-07\n", - " 5.8589e-05\n", - " 9.0792e-09\n", - " 1.6147e-08\n", - " 2.2007e-01\n", - " 8.9547e-02\n", - " 7.5446e-08\n", - " 7.9119e+00\n", - " 5.5627e-07\n", - " 4.0713e-02\n", - " 7.4552e+00\n", - " 2.0425e+00\n", - " 1.5050e-07\n", - " 2.4783e-06\n", - " 4.8531e-03\n", - " 1.8566e-01\n", - " 3.7803e-08\n", - " 4.1020e-08\n", - " 4.3489e-02\n", - " 4.7513e-05\n", - " 1.8622e-07\n", - " 1.0191e+01\n", - " 1.8340e-07\n", - " 3.2625e-07\n", - " 1.0982e-07\n", - " 5.2538e-07\n", - " 9.5139e-09\n", - " 1.8233e-06\n", - " 6.4853e-07\n", - " 3.3042e-10\n", - " 5.1054e-02\n", - " 2.2896e-07\n", - " 1.1318e-07\n", - " 7.8123e+00\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.2.conv1d.weight', \n", - " (0 ,.,.) = \n", - " -1.4133e+00 1.9358e+00 -2.2761e+00\n", - " -2.6115e-01 -6.4256e-01 -9.4213e-01\n", - " -5.8635e-02 2.6985e-01 -3.4424e-01\n", - " ⋮ \n", - " -3.3533e-01 -2.6463e-01 -6.2940e-01\n", - " -4.1993e-01 -4.0857e-01 -5.8928e-01\n", - " 2.4180e-01 8.2423e-01 3.4042e-01\n", - " \n", - " (1 ,.,.) = \n", - " -6.3384e-01 5.0494e-01 6.8654e-01\n", - " -6.1688e-02 2.3843e-01 8.5413e-02\n", - " 1.7572e-01 7.3334e-02 -3.5420e-01\n", - " ⋮ \n", - " 1.0617e-02 3.4396e-02 1.1085e-01\n", - " 1.0521e-01 1.0961e-01 1.7268e-01\n", - " -9.4826e-02 2.1547e-01 3.2199e-01\n", - " \n", - " (2 ,.,.) = \n", - " 5.9874e-01 1.4641e+00 -2.4349e-01\n", - " -1.1605e-01 7.6056e-02 -4.8551e-01\n", - " -4.4155e-02 9.2776e-02 -2.0977e-01\n", - " ⋮ \n", - " -5.5105e-02 1.1099e-01 3.2921e-01\n", - " -3.0498e-03 1.0972e-01 3.7460e-01\n", - " 2.8244e-01 5.9271e-01 1.1813e+00\n", - " ...\n", - " \n", - " (77,.,.) = \n", - " -3.3863e-01 3.4396e-02 -9.0110e-01\n", - " 2.6420e-01 -1.7286e-01 -2.4213e-02\n", - " 4.7889e-02 3.2799e-01 -6.6698e-02\n", - " ⋮ \n", - " -1.3399e-01 -9.5224e-02 -7.3040e-02\n", - " -1.1115e-02 2.4881e-02 -3.5365e-02\n", - " 2.4877e-01 1.2823e-01 1.5913e-01\n", - " \n", - " (78,.,.) = \n", - " 2.5783e-01 3.3355e-01 8.9789e-02\n", - " -7.6974e-03 -5.6598e-02 -2.3089e-02\n", - " 2.2232e-02 5.1629e-03 -3.7251e-02\n", - " ⋮ \n", - " -9.2541e-02 1.7696e-02 3.5425e-02\n", - " -6.2737e-02 -5.6951e-02 -3.3937e-02\n", - " 1.1730e-01 3.1792e-01 3.2876e-01\n", - " \n", - " (79,.,.) = \n", - " 7.1183e-01 -4.5185e-02 4.4113e-01\n", - " -3.0985e-01 1.8060e-01 -7.5757e-02\n", - " -9.0891e-02 8.3236e-02 -4.6741e-02\n", - " ⋮ \n", - " -2.8031e-02 3.6725e-02 -1.4995e-01\n", - " -2.8456e-01 -4.5680e-02 -8.1578e-02\n", - " 3.2646e-01 4.2007e-01 4.3912e-01\n", - " [torch.FloatTensor of size 80x80x3]),\n", - " ('module.postnet.conv1d_banks.2.bn.weight', \n", - " 1.1050\n", - " -12.6208\n", - " -0.6726\n", - " -4.5285\n", - " 0.1909\n", - " 0.4239\n", - " -0.0046\n", - " -4.7432\n", - " -7.8804\n", - " -4.4433\n", - " -14.6568\n", - " 0.5739\n", - " 1.0171\n", - " -5.0379\n", - " -11.2187\n", - " -4.2271\n", - " -7.7085\n", - " -8.0594\n", - " -1.3077\n", - " 0.9681\n", - " -3.9361\n", - " -17.2516\n", - " -5.4521\n", - " 0.0564\n", - " 0.2879\n", - " 0.4618\n", - " -13.9111\n", - " -3.3999\n", - " 0.1262\n", - " -14.1449\n", - " -15.6368\n", - " -3.6486\n", - " 0.4093\n", - " 0.1321\n", - " 0.0248\n", - " -4.5585\n", - " -20.8631\n", - " -3.5994\n", - " -11.6284\n", - " -14.0767\n", - " -4.4640\n", - " -13.8108\n", - " -12.9264\n", - " 0.2905\n", - " -15.1739\n", - " -1.8180\n", - " -11.9922\n", - " -8.4710\n", - " -0.0151\n", - " -0.5992\n", - " 0.2398\n", - " -12.1313\n", - " -3.7233\n", - " 0.7356\n", - " -14.2762\n", - " -18.0932\n", - " -2.5095\n", - " 0.6036\n", - " -12.8876\n", - " 1.1553\n", - " -11.8942\n", - " 0.9971\n", - " -4.0731\n", - " 0.5014\n", - " -4.7933\n", - " 0.9338\n", - " 0.3654\n", - " -12.9173\n", - " 0.1026\n", - " -14.0002\n", - " -8.5936\n", - " 0.5326\n", - " -2.1148\n", - " -3.5482\n", - " -13.0111\n", - " -18.3692\n", - " 0.5072\n", - " -0.8550\n", - " -4.5312\n", - " -8.0246\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.2.bn.bias', \n", - " -0.5262\n", - " -0.2552\n", - " -0.3539\n", - " -2.0449\n", - " -0.1213\n", - " -0.2403\n", - " -4.1364\n", - " -3.9105\n", - " -0.7265\n", - " -4.1398\n", - " -0.4853\n", - " -0.2733\n", - " -0.7255\n", - " -4.6119\n", - " -0.0156\n", - " -3.2841\n", - " 0.1398\n", - " -0.5341\n", - " -0.3197\n", - " -0.6035\n", - " -3.9686\n", - " -0.2954\n", - " -0.1068\n", - " -0.2325\n", - " -0.3792\n", - " -0.0235\n", - " -0.2452\n", - " -0.1333\n", - " 1.4335\n", - " -0.5091\n", - " -0.3646\n", - " -3.0977\n", - " -0.4734\n", - " 0.4052\n", - " -1.2922\n", - " -3.6614\n", - " -0.4880\n", - " -0.1273\n", - " -0.5268\n", - " -0.4922\n", - " -0.1440\n", - " -0.4297\n", - " -0.4102\n", - " -0.3040\n", - " -0.3347\n", - " -0.2140\n", - " -0.3307\n", - " -0.1347\n", - " 0.5467\n", - " 0.2248\n", - " -0.1635\n", - " -0.4661\n", - " -1.5422\n", - " -0.0478\n", - " -0.3919\n", - " -0.2397\n", - " 0.9143\n", - " -0.2806\n", - " -0.3381\n", - " -0.7008\n", - " -0.1280\n", - " -0.6332\n", - " -1.0078\n", - " -0.1564\n", - " -4.5764\n", - " -0.5432\n", - " -0.1128\n", - " -0.3690\n", - " 1.3157\n", - " -0.4662\n", - " -0.4705\n", - " -0.2594\n", - " -0.1613\n", - " -0.0501\n", - " -0.3376\n", - " -0.3958\n", - " -0.0910\n", - " 0.0476\n", - " -3.5011\n", - " -0.9231\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.2.bn.running_mean', \n", - " 5.6327e-01\n", - " 1.1281e-05\n", - " 3.5915e-01\n", - " 1.2443e-06\n", - " 3.6771e-02\n", - " 1.0874e+00\n", - " 4.7118e+00\n", - " 1.7777e-06\n", - " 4.6231e-06\n", - " 5.6352e-07\n", - " 3.9949e-05\n", - " 1.0184e-01\n", - " 2.5457e-01\n", - " 1.4301e-06\n", - " 5.0850e-06\n", - " 5.9391e-06\n", - " 5.5783e-06\n", - " 1.1063e-06\n", - " 2.1314e-04\n", - " 5.6636e-01\n", - " 1.9147e-07\n", - " 3.6496e-05\n", - " 3.3147e-05\n", - " 5.0854e+00\n", - " 2.1090e-02\n", - " 1.9207e-01\n", - " 3.2702e-05\n", - " 3.6508e-05\n", - " 8.4609e+00\n", - " 2.1203e-05\n", - " 1.6400e-05\n", - " 1.8826e-06\n", - " 1.6477e-01\n", - " 7.0851e+00\n", - " 8.7817e+00\n", - " 1.2372e-07\n", - " 5.8524e-05\n", - " 2.7282e-05\n", - " 7.8823e-06\n", - " 1.8508e-05\n", - " 2.0674e-05\n", - " 1.2403e-06\n", - " 7.4226e-06\n", - " 8.3879e-02\n", - " 5.3513e-05\n", - " 1.0964e-04\n", - " 2.9298e-06\n", - " 2.0372e-05\n", - " 5.7011e+00\n", - " 3.3937e-01\n", - " 4.5090e-02\n", - " 2.0355e-05\n", - " 7.5500e-06\n", - " 7.9768e-02\n", - " 1.0328e-05\n", - " 3.2163e-05\n", - " 3.6317e-05\n", - " 2.8576e-01\n", - " 7.1058e-06\n", - " 8.7182e-01\n", - " 6.5551e-06\n", - " 5.9124e-01\n", - " 3.8902e-07\n", - " 1.0481e+00\n", - " 3.0005e-06\n", - " 3.4302e-01\n", - " 1.0958e-01\n", - " 1.8918e-05\n", - " 7.7344e+00\n", - " 4.7039e-05\n", - " 1.6219e-05\n", - " 6.2909e-01\n", - " 2.4814e-05\n", - " 6.8329e-06\n", - " 4.5856e-05\n", - " 8.4643e-05\n", - " 5.9838e-01\n", - " 8.4416e-01\n", - " 1.6249e-07\n", - " 4.5940e-07\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.2.bn.running_var', \n", - " 8.5514e-02\n", - " 1.8193e-07\n", - " 3.7855e-01\n", - " 1.5033e-08\n", - " 6.3139e-03\n", - " 8.5694e-01\n", - " 4.1488e+00\n", - " 3.2303e-08\n", - " 3.3413e-08\n", - " 4.2499e-10\n", - " 9.4977e-07\n", - " 3.7310e-02\n", - " 2.6780e-02\n", - " 4.3901e-09\n", - " 5.3359e-08\n", - " 1.7452e-08\n", - " 7.7886e-08\n", - " 8.0926e-09\n", - " 4.5635e-06\n", - " 8.2369e-02\n", - " 3.3076e-10\n", - " 1.0183e-06\n", - " 8.9923e-07\n", - " 5.0726e+00\n", - " 5.1358e-03\n", - " 1.1375e-01\n", - " 8.6792e-07\n", - " 5.6404e-07\n", - " 1.4767e+01\n", - " 2.7675e-07\n", - " 2.7659e-07\n", - " 1.7719e-08\n", - " 5.3666e-02\n", - " 1.0820e+01\n", - " 1.5185e+01\n", - " 1.0127e-10\n", - " 1.6437e-06\n", - " 2.5839e-07\n", - " 1.6270e-07\n", - " 6.3398e-07\n", - " 5.0490e-07\n", - " 3.4103e-09\n", - " 6.3605e-08\n", - " 4.0738e-02\n", - " 1.3719e-06\n", - " 3.3032e-06\n", - " 1.4438e-08\n", - " 1.9050e-07\n", - " 6.0625e+00\n", - " 4.8638e-02\n", - " 1.6690e-02\n", - " 2.1606e-07\n", - " 4.0492e-08\n", - " 1.8964e-02\n", - " 1.5395e-07\n", - " 1.0584e-06\n", - " 1.5166e-06\n", - " 1.5925e-01\n", - " 1.2890e-07\n", - " 1.4933e-01\n", - " 5.4070e-08\n", - " 1.1468e-01\n", - " 2.1891e-09\n", - " 9.0264e-01\n", - " 1.4358e-07\n", - " 4.5188e-02\n", - " 1.1012e-01\n", - " 4.2025e-07\n", - " 1.2277e+01\n", - " 1.3405e-06\n", - " 4.2785e-07\n", - " 1.9064e-01\n", - " 4.0780e-07\n", - " 1.0703e-07\n", - " 1.2103e-07\n", - " 2.4075e-06\n", - " 4.2777e-01\n", - " 2.8713e-01\n", - " 8.7225e-10\n", - " 4.4934e-09\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.3.conv1d.weight', \n", - " (0 ,.,.) = \n", - " -1.3261e+00 -2.0517e-01 -4.4074e-01 7.2879e-02\n", - " 2.4019e-01 -1.9528e-01 -1.5823e-02 -2.8905e-01\n", - " 2.7706e-01 -4.2438e-01 -6.4276e-02 6.8291e-02\n", - " ⋮ \n", - " -5.1208e-02 -1.0592e-01 1.4846e-02 -3.9983e-03\n", - " 8.5784e-02 1.0458e-01 1.7470e-01 8.6012e-02\n", - " -2.6250e-02 -1.8079e-02 1.5872e-02 1.1640e-01\n", - " \n", - " (1 ,.,.) = \n", - " -3.0618e-01 1.9623e-01 -4.7689e-01 3.7074e-02\n", - " -5.9695e-01 -5.6618e-01 2.5753e-02 -5.4921e-02\n", - " 5.4218e-02 2.6022e-02 1.5518e-02 1.4428e-01\n", - " ⋮ \n", - " 1.2883e-01 9.6835e-02 6.7881e-02 -1.6458e-01\n", - " 1.4528e-01 4.6192e-02 1.0268e-01 -1.5206e-01\n", - " -1.3590e-02 -5.6363e-02 3.4911e-03 -3.2345e-01\n", - " \n", - " (2 ,.,.) = \n", - " -4.5412e-02 2.2586e-01 3.5024e-01 5.4553e-01\n", - " 2.3056e-02 1.1484e-02 6.0658e-02 -6.6450e-02\n", - " -9.8695e-02 7.8628e-02 6.5969e-03 3.2407e-02\n", - " ⋮ \n", - " 4.2043e-02 -1.1394e-02 -1.5054e-01 -2.1846e-02\n", - " -5.7694e-02 7.4430e-02 4.4309e-02 -1.0059e-01\n", - " 3.6066e-02 2.2695e-01 1.2893e-01 1.8071e-01\n", - " ...\n", - " \n", - " (77,.,.) = \n", - " 9.2358e-01 -3.5119e-02 -5.9368e-01 -1.1777e-01\n", - " -4.0169e-01 2.6219e-01 3.1201e-01 -3.7066e-01\n", - " -7.5183e-02 4.6646e-02 -3.9222e-02 2.0479e-02\n", - " ⋮ \n", - " 2.2368e-02 3.6631e-02 -1.6998e-01 4.6614e-02\n", - " 2.6064e-02 5.3832e-02 9.7212e-03 1.9178e-01\n", - " 6.9384e-02 3.0382e-03 6.0725e-02 4.4700e-01\n", - " \n", - " (78,.,.) = \n", - " -1.0538e-01 -1.1898e-01 -1.0235e-01 -1.4249e-01\n", - " -8.1546e-02 -1.5799e-02 -8.2799e-02 -8.0971e-02\n", - " 5.4072e-03 3.2377e-02 5.7772e-03 4.8725e-02\n", - " ⋮ \n", - " -1.5251e-02 -5.8531e-02 2.1212e-02 -6.8767e-02\n", - " 7.3147e-03 3.5619e-03 1.1491e-01 1.1213e-01\n", - " -1.4844e-01 -1.1175e-01 -5.2830e-02 -1.0134e-01\n", - " \n", - " (79,.,.) = \n", - " 1.3038e-01 2.6935e-01 1.2239e-01 -1.0109e-01\n", - " -1.2836e-02 -2.7897e-02 6.5750e-02 1.0875e-01\n", - " -8.8257e-04 2.9252e-02 -3.2304e-02 2.3341e-02\n", - " ⋮ \n", - " -5.9310e-02 -1.7101e-02 -9.5936e-02 -2.6483e-02\n", - " 3.8124e-02 -5.4581e-02 -2.2384e-02 1.0250e-01\n", - " 1.1930e-01 1.7073e-01 5.8444e-02 2.3007e-01\n", - " [torch.FloatTensor of size 80x80x4]),\n", - " ('module.postnet.conv1d_banks.3.bn.weight', \n", - " -2.5268\n", - " 0.4578\n", - " -12.8922\n", - " 0.0399\n", - " 0.0035\n", - " 0.5769\n", - " -4.0805\n", - " -4.1822\n", - " 0.0547\n", - " -3.5851\n", - " -9.6606\n", - " -3.9226\n", - " -4.4454\n", - " -13.0319\n", - " 0.4712\n", - " -4.4964\n", - " -11.3242\n", - " 0.3891\n", - " -3.9971\n", - " -14.5917\n", - " -3.2207\n", - " -1.1421\n", - " -4.2382\n", - " -12.9617\n", - " -4.2000\n", - " 0.3799\n", - " -4.8247\n", - " -2.3899\n", - " -3.1779\n", - " -4.3621\n", - " 0.1742\n", - " -4.8606\n", - " -3.2211\n", - " 0.4293\n", - " 0.4671\n", - " 0.6593\n", - " -0.0235\n", - " 0.0619\n", - " 0.2185\n", - " -4.0747\n", - " -4.9047\n", - " 0.1288\n", - " -4.2326\n", - " 0.4182\n", - " 0.6918\n", - " 0.5098\n", - " -8.0619\n", - " 0.4858\n", - " -11.1034\n", - " -4.1302\n", - " -3.1056\n", - " 0.0811\n", - " 0.3427\n", - " -2.8402\n", - " -11.3985\n", - " -11.4768\n", - " 0.0237\n", - " -2.0880\n", - " -0.0084\n", - " -4.7499\n", - " 0.4351\n", - " -0.3997\n", - " 0.4541\n", - " -4.9137\n", - " -11.7080\n", - " 0.9272\n", - " 0.0016\n", - " -4.0587\n", - " -14.6625\n", - " -0.1130\n", - " -4.9753\n", - " 0.2320\n", - " -11.6048\n", - " -1.7832\n", - " -2.4467\n", - " -12.0570\n", - " -4.6870\n", - " -15.0827\n", - " -1.8075\n", - " -4.0728\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.3.bn.bias', \n", - " -0.1262\n", - " -0.3013\n", - " -0.8146\n", - " 0.6035\n", - " -0.3680\n", - " -0.2605\n", - " -0.3404\n", - " -3.5481\n", - " 0.0310\n", - " -0.1748\n", - " -0.4248\n", - " -0.3888\n", - " -1.1308\n", - " -0.5568\n", - " -0.5755\n", - " -3.9179\n", - " -0.4536\n", - " -0.4253\n", - " -1.2827\n", - " -0.4353\n", - " -0.3216\n", - " -3.5588\n", - " -3.9900\n", - " -0.1729\n", - " -0.7320\n", - " -0.1958\n", - " -3.7417\n", - " 0.1820\n", - " -0.3782\n", - " -3.8825\n", - " -0.0597\n", - " -4.7667\n", - " -0.2451\n", - " -0.2762\n", - " 0.2594\n", - " -0.5799\n", - " 0.0799\n", - " 0.0899\n", - " -0.0234\n", - " -0.8848\n", - " -3.8244\n", - " -0.1071\n", - " -3.9127\n", - " -0.4297\n", - " -0.1180\n", - " -0.2737\n", - " -0.5355\n", - " -0.0077\n", - " -0.1744\n", - " -4.1097\n", - " -0.0084\n", - " -0.2154\n", - " -0.4333\n", - " -0.5057\n", - " -1.2638\n", - " -0.5293\n", - " -0.3836\n", - " 0.0018\n", - " -2.3551\n", - " -4.5227\n", - " -0.0428\n", - " 0.4557\n", - " -0.3260\n", - " -3.9979\n", - " -0.1996\n", - " -0.0859\n", - " -1.3325\n", - " -4.4337\n", - " -0.5358\n", - " 1.0591\n", - " -4.1141\n", - " -0.3156\n", - " -0.1987\n", - " -0.3252\n", - " -0.3384\n", - " -0.6654\n", - " -4.1062\n", - " -0.3360\n", - " -2.9950\n", - " -3.3073\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.3.bn.running_mean', \n", - " 9.0527e-05\n", - " 6.7213e-01\n", - " 2.2939e-06\n", - " 9.4556e+00\n", - " 7.7267e+00\n", - " 2.5540e-01\n", - " 7.7247e-06\n", - " 1.1427e-08\n", - " 1.0398e+01\n", - " 9.6055e-05\n", - " 3.0045e-06\n", - " 2.4925e-06\n", - " 8.6642e-06\n", - " 3.0603e-06\n", - " 3.1670e-01\n", - " 4.4633e-06\n", - " 1.0813e-06\n", - " 3.3733e-01\n", - " 5.9336e-06\n", - " 1.1803e-05\n", - " 5.7429e-06\n", - " 1.4443e-05\n", - " 1.2036e-06\n", - " 7.2304e-06\n", - " 5.8735e-06\n", - " 3.4058e-01\n", - " 7.3811e-06\n", - " 1.1589e-04\n", - " 2.6694e-05\n", - " 1.2107e-10\n", - " 1.3585e+01\n", - " 2.1797e-06\n", - " 1.2628e-05\n", - " 2.9931e+00\n", - " 2.0679e-01\n", - " 9.1944e-01\n", - " 4.9761e+00\n", - " 1.2069e+01\n", - " 3.6748e+00\n", - " 5.5600e-06\n", - " 1.0438e-08\n", - " 7.5946e+00\n", - " 6.3475e-06\n", - " 2.8479e-01\n", - " 3.9223e-01\n", - " 1.1487e+00\n", - " 3.7495e-06\n", - " 4.1301e-01\n", - " 4.5427e-07\n", - " 5.0823e-07\n", - " 1.7342e-05\n", - " 9.3364e+00\n", - " 2.5754e-01\n", - " 1.1765e-05\n", - " 1.9680e-06\n", - " 1.3843e-06\n", - " 8.1019e+00\n", - " 1.7430e-05\n", - " 1.4960e+00\n", - " 1.1329e-06\n", - " 3.1432e-02\n", - " 8.6991e-01\n", - " 6.7299e-02\n", - " 3.7045e-06\n", - " 2.1253e-06\n", - " 8.8729e-01\n", - " 9.0992e+00\n", - " 3.5421e-07\n", - " 2.4848e-05\n", - " 2.8979e-05\n", - " 4.1043e-07\n", - " 2.8082e+00\n", - " 2.6206e-06\n", - " 1.7932e-04\n", - " 7.1267e-05\n", - " 4.8661e-07\n", - " 1.7040e-07\n", - " 4.4248e-06\n", - " 1.5325e-05\n", - " 2.1064e-06\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.3.bn.running_var', \n", - " 1.2437e-06\n", - " 3.6682e-01\n", - " 1.5979e-08\n", - " 1.6722e+01\n", - " 1.1510e+01\n", - " 3.1597e-01\n", - " 7.3698e-08\n", - " 2.7022e-12\n", - " 2.3216e+01\n", - " 2.7142e-05\n", - " 5.0977e-08\n", - " 3.0948e-08\n", - " 6.7951e-08\n", - " 3.4256e-08\n", - " 9.0787e-02\n", - " 1.9166e-07\n", - " 9.8555e-09\n", - " 1.4556e-01\n", - " 3.7993e-08\n", - " 2.0463e-07\n", - " 9.7002e-08\n", - " 6.5270e-08\n", - " 1.2219e-08\n", - " 1.5361e-07\n", - " 7.6163e-08\n", - " 1.8414e-01\n", - " 4.5620e-08\n", - " 2.9798e-06\n", - " 7.6708e-07\n", - " 3.0188e-13\n", - " 3.2307e+01\n", - " 7.0671e-08\n", - " 4.0008e-07\n", - " 1.7935e+00\n", - " 3.5820e-01\n", - " 2.9136e-01\n", - " 5.2004e+00\n", - " 3.2772e+01\n", - " 2.9079e+00\n", - " 7.3876e-08\n", - " 8.5879e-12\n", - " 1.1208e+01\n", - " 6.4916e-08\n", - " 6.4894e-02\n", - " 2.0577e-01\n", - " 5.4269e-01\n", - " 4.1126e-08\n", - " 1.7521e-01\n", - " 9.4886e-10\n", - " 2.7684e-09\n", - " 9.6865e-07\n", - " 1.6125e+01\n", - " 1.5095e-01\n", - " 1.8722e-07\n", - " 3.4655e-08\n", - " 2.8854e-08\n", - " 1.2195e+01\n", - " 2.7359e-07\n", - " 9.6612e-01\n", - " 6.5774e-09\n", - " 9.0307e-03\n", - " 3.8151e-01\n", - " 2.9937e-02\n", - " 1.8729e-07\n", - " 2.4018e-08\n", - " 1.7031e-01\n", - " 1.5647e+01\n", - " 2.8821e-09\n", - " 1.3710e-06\n", - " 6.7044e-07\n", - " 3.3338e-09\n", - " 4.7245e+00\n", - " 1.7651e-08\n", - " 4.5999e-06\n", - " 3.1728e-06\n", - " 2.7243e-09\n", - " 4.4030e-10\n", - " 4.1070e-08\n", - " 1.1915e-07\n", - " 5.6752e-08\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.4.conv1d.weight', \n", - " (0 ,.,.) = \n", - " 1.5686e+00 -6.4739e-01 1.4181e-01 4.0395e-01 -7.0657e-01\n", - " -4.1751e-01 -2.0063e-02 1.7128e-01 -4.3060e-01 9.8187e-02\n", - " -4.4967e-02 1.4617e-01 1.0732e-01 1.3728e-01 1.6912e-01\n", - " ⋮ \n", - " 1.5863e-01 1.4459e-02 -3.8073e-03 -1.2433e-01 -3.4891e-02\n", - " 1.5925e-01 6.6438e-02 -9.0607e-03 -6.1685e-02 3.7638e-03\n", - " 1.9384e-01 1.6836e-01 -5.1740e-02 -1.5689e-01 1.0534e-03\n", - " \n", - " (1 ,.,.) = \n", - " -1.7182e-01 -1.8121e-01 -1.7164e-01 -1.9797e-01 -3.2164e-01\n", - " -2.1415e-02 3.4693e-02 -2.7767e-02 1.8646e-01 9.8169e-02\n", - " -6.0786e-02 -4.2094e-02 -4.6286e-02 6.9078e-02 9.2483e-02\n", - " ⋮ \n", - " 5.5376e-02 6.4676e-02 -3.8600e-02 5.1214e-02 -8.2247e-02\n", - " 7.9675e-02 6.6527e-02 -3.0137e-02 4.3228e-02 -6.6019e-02\n", - " 2.2593e-01 1.6549e-01 1.3740e-01 1.9141e-01 7.4694e-02\n", - " \n", - " (2 ,.,.) = \n", - " 5.8797e-01 1.1582e-01 2.7823e-01 1.8149e-01 4.8132e-02\n", - " 1.1961e-01 2.0705e-01 -5.0408e-02 3.7282e-02 -1.9043e-01\n", - " -1.5806e-01 4.0247e-02 8.6887e-02 1.2589e-01 -1.8701e-01\n", - " ⋮ \n", - " -8.8763e-02 1.3013e-02 -1.0821e-02 1.5986e-02 3.8196e-02\n", - " 1.5440e-04 -5.4442e-02 8.4634e-03 -1.2476e-01 5.0944e-02\n", - " 3.9365e-01 1.0105e-01 1.2978e-01 2.1548e-02 6.2940e-02\n", - " ...\n", - " \n", - " (77,.,.) = \n", - " -1.5847e-01 2.6119e-01 -5.9377e-02 -1.8277e+00 -1.5449e+00\n", - " 2.7968e-01 2.7494e-01 -2.2583e-01 1.4109e-01 -7.7504e-01\n", - " -6.5495e-02 -6.3089e-02 1.9451e-01 2.7907e-01 -4.3442e-02\n", - " ⋮ \n", - " 4.2854e-02 5.0023e-02 -6.5699e-02 -7.9344e-02 -1.7509e-01\n", - " 6.1825e-02 1.6443e-01 -1.5240e-02 1.2528e-01 1.4501e-01\n", - " -1.0310e-01 3.6294e-01 -8.2844e-04 1.3113e-01 1.6783e-01\n", - " \n", - " (78,.,.) = \n", - " -9.2892e-02 -2.0476e-01 -1.9328e-01 -1.6904e-01 -3.7297e-01\n", - " 6.4856e-02 -2.9276e-02 6.3521e-02 -1.6848e-02 -3.5620e-02\n", - " 1.7215e-02 6.9829e-02 -1.4160e-01 3.9499e-02 4.0538e-02\n", - " ⋮ \n", - " 3.3965e-02 6.5948e-02 2.1840e-02 5.3068e-02 -3.8973e-02\n", - " 6.9934e-02 -3.0774e-02 3.1385e-03 3.4650e-02 -5.8947e-02\n", - " 7.1828e-02 4.0206e-02 1.0805e-01 2.2138e-01 1.2469e-01\n", - " \n", - " (79,.,.) = \n", - " -1.5203e-01 -3.8435e-02 1.3500e-02 1.6194e-02 -1.2227e-01\n", - " 6.4620e-02 2.8093e-02 1.2704e-02 4.0136e-02 -2.4529e-01\n", - " -3.5442e-02 2.9202e-02 2.3826e-02 4.1322e-03 -1.2359e-01\n", - " ⋮ \n", - " -5.1499e-02 3.8150e-02 4.7023e-03 4.8181e-02 -3.8601e-02\n", - " -4.0502e-02 2.0468e-03 -5.0560e-02 5.2808e-02 8.2607e-03\n", - " 3.4581e-02 -1.8643e-03 7.2634e-02 1.5115e-01 5.9412e-02\n", - " [torch.FloatTensor of size 80x80x5]),\n", - " ('module.postnet.conv1d_banks.4.bn.weight', \n", - " -3.1114\n", - " -3.6436\n", - " -11.5585\n", - " -10.4227\n", - " -0.4435\n", - " -4.2407\n", - " -2.6719\n", - " 0.5268\n", - " -21.6844\n", - " 1.4152\n", - " 0.8414\n", - " -7.4078\n", - " 0.0875\n", - " -0.8201\n", - " -2.0815\n", - " -4.2865\n", - " 0.9424\n", - " 1.1091\n", - " -4.4419\n", - " -11.9075\n", - " -8.4086\n", - " -2.7601\n", - " 0.4206\n", - " 0.4259\n", - " 0.3753\n", - " -4.8195\n", - " 0.7709\n", - " -11.3462\n", - " 0.5276\n", - " 0.9512\n", - " -11.5833\n", - " -1.1161\n", - " 0.4165\n", - " 0.9368\n", - " 0.7865\n", - " -4.8721\n", - " -2.7109\n", - " -3.8780\n", - " -4.2648\n", - " -12.0094\n", - " 0.0308\n", - " 0.6082\n", - " -10.4139\n", - " 0.0122\n", - " 0.6591\n", - " 0.6041\n", - " -1.3099\n", - " -0.8088\n", - " 0.0389\n", - " 0.8472\n", - " -13.2373\n", - " -3.2597\n", - " -12.4455\n", - " 0.1006\n", - " -2.6336\n", - " -11.6239\n", - " -4.3382\n", - " -0.7267\n", - " 0.3470\n", - " -10.8323\n", - " 0.5100\n", - " -0.0568\n", - " 0.6117\n", - " -3.5144\n", - " 0.3754\n", - " 0.0783\n", - " -1.6908\n", - " 0.7457\n", - " -13.5645\n", - " -3.8569\n", - " -0.6424\n", - " -4.6047\n", - " -1.3007\n", - " 0.6398\n", - " 0.5641\n", - " -13.1959\n", - " -1.5564\n", - " -15.4920\n", - " -1.1279\n", - " -0.0059\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.4.bn.bias', \n", - " -0.0907\n", - " 0.1247\n", - " -0.6848\n", - " -0.6421\n", - " -0.1463\n", - " -1.9022\n", - " -0.4610\n", - " -0.3081\n", - " -0.4921\n", - " -0.6449\n", - " -0.4662\n", - " -0.1597\n", - " -0.0268\n", - " -0.0688\n", - " -0.1862\n", - " -3.4868\n", - " -0.3786\n", - " -0.2628\n", - " -1.9394\n", - " -0.4781\n", - " -0.5795\n", - " -3.9740\n", - " -0.2431\n", - " -0.3317\n", - " -0.3059\n", - " -4.1944\n", - " -0.4192\n", - " -0.4415\n", - " -0.2498\n", - " -0.1404\n", - " -0.5222\n", - " -0.0211\n", - " -0.2708\n", - " -0.6674\n", - " -0.6530\n", - " -0.5205\n", - " -0.1410\n", - " -0.2570\n", - " -0.2570\n", - " -0.4062\n", - " 0.8527\n", - " -0.5576\n", - " -0.1164\n", - " -0.2757\n", - " -0.1256\n", - " -0.6343\n", - " -0.2397\n", - " 0.1046\n", - " 1.1796\n", - " -0.1107\n", - " -0.2626\n", - " -0.7130\n", - " -0.5751\n", - " -0.1367\n", - " -0.2514\n", - " -0.5131\n", - " -2.3786\n", - " 0.1182\n", - " 0.0462\n", - " -0.6482\n", - " -0.3057\n", - " -1.7059\n", - " 0.0097\n", - " -0.2467\n", - " -0.1131\n", - " -0.2379\n", - " -0.2998\n", - " -0.0255\n", - " -0.4650\n", - " -2.1786\n", - " -0.1979\n", - " -4.2684\n", - " -0.0443\n", - " -0.1549\n", - " -0.4398\n", - " -0.5989\n", - " -0.1807\n", - " -0.5320\n", - " 0.3008\n", - " -4.1098\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.4.bn.running_mean', \n", - " 1.4791e-05\n", - " 7.1580e-06\n", - " 5.4625e-07\n", - " 1.5165e-06\n", - " 2.9518e-01\n", - " 1.1891e-05\n", - " 4.3821e-02\n", - " 1.6258e-01\n", - " 9.1277e-05\n", - " 4.3748e-01\n", - " 4.9826e-01\n", - " 4.8062e-06\n", - " 1.0348e+01\n", - " 1.1714e-01\n", - " 1.5927e-05\n", - " 1.1033e-06\n", - " 6.1062e-01\n", - " 3.8079e-01\n", - " 5.2303e-06\n", - " 8.5180e-06\n", - " 1.2921e-08\n", - " 8.4226e-07\n", - " 8.1688e-02\n", - " 1.3962e+00\n", - " 6.3438e-02\n", - " 2.4138e-06\n", - " 1.9679e+00\n", - " 1.0141e-06\n", - " 2.7648e-01\n", - " 3.3054e-01\n", - " 1.9053e-06\n", - " 2.1194e-01\n", - " 7.0494e-02\n", - " 8.1467e-01\n", - " 2.8048e-01\n", - " 7.4746e-06\n", - " 3.4446e-05\n", - " 4.9036e-05\n", - " 4.3190e-04\n", - " 3.0069e-06\n", - " 8.9921e+00\n", - " 3.8910e-01\n", - " 7.9356e-06\n", - " 1.2102e+01\n", - " 1.1947e+00\n", - " 9.2566e-01\n", - " 1.3713e-05\n", - " 4.0264e-01\n", - " 1.3412e+01\n", - " 8.5061e-01\n", - " 1.3104e-06\n", - " 3.8468e-06\n", - " 2.3956e-06\n", - " 1.9958e+01\n", - " 1.2555e-04\n", - " 1.4633e-06\n", - " 2.1924e-07\n", - " 3.1256e-01\n", - " 3.9039e+00\n", - " 3.6868e-06\n", - " 4.6208e-01\n", - " 4.9890e-01\n", - " 8.7584e-02\n", - " 2.2797e-05\n", - " 6.0309e-02\n", - " 1.0651e+01\n", - " 2.3329e-01\n", - " 3.6643e-01\n", - " 6.9491e-06\n", - " 7.5810e-06\n", - " 4.1290e-01\n", - " 3.0629e-06\n", - " 2.8954e-01\n", - " 4.0581e-01\n", - " 7.8708e-02\n", - " 8.6479e-06\n", - " 1.9115e-05\n", - " 1.5491e-05\n", - " 1.8777e-05\n", - " 3.3491e+00\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.4.bn.running_var', \n", - " 1.5262e-06\n", - " 1.3771e-07\n", - " 5.3700e-09\n", - " 2.2306e-08\n", - " 8.0874e-02\n", - " 6.9410e-08\n", - " 3.0631e-03\n", - " 1.6535e-01\n", - " 2.8116e-06\n", - " 6.4422e-02\n", - " 1.4913e-01\n", - " 5.5257e-08\n", - " 2.0834e+01\n", - " 1.7277e-02\n", - " 2.9805e-07\n", - " 1.5038e-08\n", - " 1.0679e-01\n", - " 4.8342e-02\n", - " 8.1840e-09\n", - " 6.3791e-07\n", - " 2.6678e-11\n", - " 2.2232e-09\n", - " 5.4488e-02\n", - " 6.7553e-01\n", - " 1.8869e-02\n", - " 1.2107e-07\n", - " 2.3370e+00\n", - " 3.0896e-09\n", - " 7.6846e-02\n", - " 7.0930e-02\n", - " 2.5471e-08\n", - " 2.4525e-02\n", - " 2.3188e-02\n", - " 1.3394e-01\n", - " 7.9304e-02\n", - " 2.3824e-07\n", - " 2.9613e-06\n", - " 9.3945e-07\n", - " 4.6336e-05\n", - " 4.3658e-08\n", - " 1.5431e+01\n", - " 9.4522e-02\n", - " 1.2893e-07\n", - " 2.6148e+01\n", - " 4.4857e-01\n", - " 5.1562e-01\n", - " 6.7592e-07\n", - " 9.9170e-02\n", - " 3.2691e+01\n", - " 4.5267e-01\n", - " 3.0760e-09\n", - " 3.3099e-08\n", - " 2.6102e-08\n", - " 7.2174e+01\n", - " 5.0823e-06\n", - " 1.1059e-08\n", - " 9.1769e-10\n", - " 6.5382e-02\n", - " 5.2364e+00\n", - " 6.6680e-08\n", - " 1.0776e-01\n", - " 1.1825e-01\n", - " 3.3046e-02\n", - " 4.7272e-07\n", - " 3.1214e-02\n", - " 2.3556e+01\n", - " 1.5954e-02\n", - " 2.6601e-01\n", - " 1.4740e-07\n", - " 7.1719e-08\n", - " 9.2206e-02\n", - " 9.0131e-08\n", - " 4.1669e-02\n", - " 2.6487e-01\n", - " 3.4801e-02\n", - " 1.5547e-07\n", - " 7.8035e-07\n", - " 4.5604e-07\n", - " 3.1844e-07\n", - " 2.0638e+00\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.5.conv1d.weight', \n", - " (0 ,.,.) = \n", - " -5.4860e-01 2.0713e-01 1.6055e+00 -5.2223e-01 1.8030e+00 2.6348e-01\n", - " -2.0093e-01 4.5769e-02 6.3496e-01 2.9092e-01 1.0631e-01 1.8241e-01\n", - " -1.3228e-01 7.9001e-02 4.3514e-02 1.4466e-01 -8.5870e-02 -1.6509e-01\n", - " ⋮ \n", - " -1.5503e-02 4.7154e-02 5.2289e-02 1.6896e-02 2.2193e-02 1.3157e-01\n", - " -1.2542e-01 -3.9076e-02 -4.4819e-02 2.0163e-01 2.1832e-01 3.3319e-01\n", - " -2.2883e-01 -2.0393e-01 -7.0349e-02 -2.6324e-02 -5.9681e-02 3.6948e-01\n", - " \n", - " (1 ,.,.) = \n", - " -1.7623e-01 -1.3750e-01 -3.1823e-02 -1.9775e-01 5.8317e-02 1.9174e-01\n", - " -3.5452e-02 -2.3127e-02 -2.1820e-02 2.3226e-02 1.4172e-01 2.6835e-01\n", - " -1.3102e-01 -2.6183e-02 -6.7891e-02 -1.7501e-02 -8.8783e-02 -5.0563e-02\n", - " ⋮ \n", - " -4.0606e-02 2.6082e-02 6.4175e-02 4.1513e-02 9.5760e-02 5.5027e-02\n", - " -1.0897e-02 9.1545e-02 4.0820e-02 5.1575e-02 4.0873e-02 -2.8130e-02\n", - " 3.7406e-02 -4.9818e-03 1.2074e-02 -1.0079e-02 1.5794e-02 3.5829e-03\n", - " \n", - " (2 ,.,.) = \n", - " 8.6653e-01 7.1018e-01 1.1930e-01 6.7257e-01 3.2135e-01 2.2231e-01\n", - " -5.5292e-01 -1.8671e-01 1.7102e-01 1.4232e-01 2.1184e-01 -8.6735e-02\n", - " 6.7562e-02 1.0823e-02 1.7989e-01 1.9343e-01 2.2654e-01 -5.1714e-02\n", - " ⋮ \n", - " -9.9573e-02 -6.6490e-02 -2.6533e-02 6.5583e-02 2.2089e-02 3.8572e-02\n", - " 5.8930e-02 -1.1670e-02 1.4769e-02 7.4453e-02 5.9849e-03 7.0727e-02\n", - " 3.0224e-01 5.2385e-03 2.7315e-01 2.0238e-01 1.1285e-01 3.0515e-01\n", - " ...\n", - " \n", - " (77,.,.) = \n", - " -1.4380e-01 -1.2970e-01 -7.8037e-02 -1.1121e-01 -9.8119e-02 4.0675e-01\n", - " -2.5799e-01 -3.3438e-01 -2.3150e-01 -2.2831e-01 -1.6482e-01 1.7353e-01\n", - " -4.2796e-02 -4.1783e-02 1.7217e-02 3.3401e-02 4.2506e-02 -6.9167e-02\n", - " ⋮ \n", - " 2.7723e-02 3.0782e-02 9.9269e-03 3.5218e-02 -4.7910e-02 -3.2218e-02\n", - " -5.2994e-03 2.0554e-02 2.8580e-02 3.0602e-02 -1.3117e-02 -5.0382e-02\n", - " -6.8279e-02 -1.0015e-02 -1.8131e-02 -7.6639e-02 -7.6285e-02 -2.6653e-01\n", - " \n", - " (78,.,.) = \n", - " 1.0527e-01 9.4512e-02 1.0673e-01 1.6742e-01 5.7785e-02 -2.8438e-02\n", - " 7.4955e-02 3.0069e-02 6.6862e-02 3.0888e-02 3.1180e-02 1.3977e-01\n", - " 9.2024e-03 2.2835e-02 -4.1930e-02 1.0184e-01 7.6296e-02 6.0169e-03\n", - " ⋮ \n", - " -9.7722e-02 -1.0904e-03 -6.9749e-02 -6.9405e-03 -1.2642e-01 -9.5243e-02\n", - " 5.8125e-03 -6.7751e-02 1.1047e-01 8.2299e-03 -4.2008e-02 5.1742e-02\n", - " 7.3912e-02 8.3876e-02 6.2410e-02 7.7047e-02 6.0862e-02 7.4805e-02\n", - " \n", - " (79,.,.) = \n", - " -1.0010e+00 -9.8334e-02 -1.6020e+00 7.5413e-01 -4.1561e-01 1.0840e+00\n", - " 2.4568e-01 3.1564e-01 -3.4452e-01 -4.6273e-02 1.0520e+00 -9.7148e-01\n", - " -7.6527e-02 -2.2401e-01 7.9422e-02 -2.6896e-01 3.6150e-01 6.4700e-02\n", - " ⋮ \n", - " 1.4473e-01 3.2172e-01 2.0931e-01 1.8520e-01 -2.2031e-01 -1.4759e-01\n", - " -6.1705e-02 -6.6544e-03 -1.3486e-01 -9.8805e-02 -1.5604e-01 -6.9360e-02\n", - " -3.2565e-01 -4.4507e-01 -4.6296e-01 2.4712e-01 3.9803e-01 7.2480e-01\n", - " [torch.FloatTensor of size 80x80x6]),\n", - " ('module.postnet.conv1d_banks.5.bn.weight', \n", - " -1.9655\n", - " -0.9887\n", - " -10.7031\n", - " 0.2270\n", - " -2.5000\n", - " -10.3362\n", - " -5.2901\n", - " -10.6125\n", - " -4.5590\n", - " -11.7356\n", - " 0.3789\n", - " -12.5152\n", - " -2.9383\n", - " -4.9282\n", - " -1.7910\n", - " -10.5731\n", - " 0.5355\n", - " -12.9075\n", - " 0.5282\n", - " 0.5553\n", - " 0.3319\n", - " 0.1124\n", - " -4.7897\n", - " 0.2692\n", - " 0.8829\n", - " 0.3214\n", - " -9.8628\n", - " -1.3829\n", - " -0.0948\n", - " 0.1667\n", - " 0.0405\n", - " -3.3630\n", - " -9.5162\n", - " 0.5304\n", - " -11.7365\n", - " -2.2216\n", - " 0.1461\n", - " 0.5525\n", - " 0.3589\n", - " -6.6794\n", - " -0.0055\n", - " -4.9119\n", - " -10.5283\n", - " 0.9588\n", - " 0.4601\n", - " 0.3697\n", - " 0.5197\n", - " -1.0671\n", - " 0.0264\n", - " 0.3258\n", - " -11.7318\n", - " -2.6943\n", - " -12.6896\n", - " -0.7154\n", - " -11.4747\n", - " -10.0193\n", - " 0.6377\n", - " 1.0143\n", - " 0.4361\n", - " -4.1749\n", - " 0.5932\n", - " 0.4845\n", - " -8.0335\n", - " -11.2354\n", - " -8.8210\n", - " -3.1390\n", - " -11.2160\n", - " 0.0663\n", - " 0.6004\n", - " -12.1574\n", - " -2.4649\n", - " -9.8693\n", - " -10.7270\n", - " 0.5790\n", - " -0.7557\n", - " 0.9959\n", - " -3.4245\n", - " -0.0401\n", - " -4.1284\n", - " -4.7673\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.5.bn.bias', \n", - " 0.0029\n", - " -4.3787\n", - " -0.2540\n", - " -0.2102\n", - " -0.3568\n", - " -0.0817\n", - " 0.2838\n", - " -0.2897\n", - " -1.2939\n", - " -0.1694\n", - " -0.0895\n", - " 0.3677\n", - " -0.2739\n", - " -4.3863\n", - " 0.1692\n", - " -0.5726\n", - " -0.4461\n", - " -0.2443\n", - " -0.2804\n", - " 0.1098\n", - " -0.1973\n", - " 1.5201\n", - " -4.3825\n", - " -1.7465\n", - " -0.3371\n", - " -0.3231\n", - " -0.3295\n", - " -0.1735\n", - " 0.5555\n", - " -0.2971\n", - " -1.6905\n", - " -4.2038\n", - " -0.4203\n", - " -0.2420\n", - " -0.5524\n", - " -0.0997\n", - " -0.2813\n", - " -0.4033\n", - " -0.4262\n", - " -0.2482\n", - " 0.7620\n", - " -4.1580\n", - " -0.7885\n", - " -0.6467\n", - " -0.1021\n", - " -0.0127\n", - " -0.3365\n", - " -0.4070\n", - " -1.0600\n", - " 0.1982\n", - " -0.3990\n", - " -0.2539\n", - " -0.3529\n", - " 0.3436\n", - " -0.3795\n", - " -0.3374\n", - " -0.3083\n", - " -0.6688\n", - " -0.2550\n", - " -0.1713\n", - " 0.0910\n", - " -0.3509\n", - " -0.3530\n", - " -0.6210\n", - " -0.4778\n", - " -4.2850\n", - " -0.3199\n", - " 1.1630\n", - " -0.3874\n", - " -0.4239\n", - " -0.2612\n", - " -0.4558\n", - " -0.3260\n", - " -0.5247\n", - " 0.0129\n", - " -0.1892\n", - " -4.2348\n", - " -4.2263\n", - " -4.3344\n", - " -0.0833\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.5.bn.running_mean', \n", - " 6.6881e-05\n", - " 1.9716e-05\n", - " 4.1062e-06\n", - " 1.0871e+01\n", - " 3.4829e-05\n", - " 5.4487e-06\n", - " 1.9180e-05\n", - " 7.2671e-06\n", - " 9.0508e-06\n", - " 3.4231e-06\n", - " 3.3783e-02\n", - " 3.4214e-06\n", - " 4.0916e-05\n", - " 1.5526e-06\n", - " 1.4476e-04\n", - " 2.3460e-06\n", - " 1.0616e+00\n", - " 1.0300e-06\n", - " 1.0699e+00\n", - " 9.0276e-01\n", - " 1.0792e-01\n", - " 1.4327e+01\n", - " 2.2017e-07\n", - " 4.1265e+00\n", - " 6.1647e-01\n", - " 4.5485e-02\n", - " 1.0817e-05\n", - " 3.3503e-01\n", - " 1.6846e-05\n", - " 1.7599e+01\n", - " 1.5968e+01\n", - " 2.5268e-07\n", - " 2.2920e-06\n", - " 2.3139e-01\n", - " 6.2033e-07\n", - " 4.5507e-05\n", - " 7.7429e+00\n", - " 1.5459e-01\n", - " 7.0055e+00\n", - " 9.1036e-06\n", - " 9.9461e+00\n", - " 4.3763e-06\n", - " 1.2773e-08\n", - " 7.7017e-01\n", - " 5.2742e+00\n", - " 7.2511e-02\n", - " 2.0556e-01\n", - " 3.8826e-01\n", - " 1.4060e+01\n", - " 6.8772e+00\n", - " 2.5492e-06\n", - " 1.2919e-05\n", - " 6.1022e-06\n", - " 9.8789e-01\n", - " 1.6830e-06\n", - " 3.6800e-06\n", - " 8.8781e-01\n", - " 4.8203e-01\n", - " 1.0681e-01\n", - " 4.1911e-07\n", - " 1.6110e-01\n", - " 9.8968e-01\n", - " 4.5397e-06\n", - " 2.4744e-06\n", - " 4.6496e-06\n", - " 6.7548e-08\n", - " 5.9375e-07\n", - " 1.5855e+01\n", - " 1.8195e-01\n", - " 1.4893e-06\n", - " 2.1666e-05\n", - " 3.2978e-06\n", - " 4.4232e-05\n", - " 4.8400e-01\n", - " 6.3990e-01\n", - " 4.5483e-01\n", - " 1.0750e-07\n", - " 3.2715e-01\n", - " 2.9006e-07\n", - " 4.9801e-05\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.5.bn.running_var', \n", - " 1.2244e-06\n", - " 5.9879e-08\n", - " 4.1235e-08\n", - " 2.1726e+01\n", - " 7.6592e-07\n", - " 7.7188e-08\n", - " 1.0208e-07\n", - " 1.2389e-07\n", - " 6.8075e-08\n", - " 2.7681e-08\n", - " 3.5808e-02\n", - " 2.7561e-08\n", - " 5.8326e-07\n", - " 4.8485e-08\n", - " 3.2186e-06\n", - " 2.3307e-08\n", - " 5.5778e-01\n", - " 9.4557e-09\n", - " 6.2819e-01\n", - " 7.9096e-01\n", - " 1.0496e-01\n", - " 3.8622e+01\n", - " 1.9758e-09\n", - " 3.9146e+00\n", - " 3.4936e-01\n", - " 2.1316e-02\n", - " 2.4903e-07\n", - " 5.6055e-02\n", - " 2.3548e-07\n", - " 5.9798e+01\n", - " 4.5718e+01\n", - " 1.8836e-09\n", - " 4.9038e-08\n", - " 6.8371e-02\n", - " 5.3013e-09\n", - " 6.8085e-07\n", - " 1.2107e+01\n", - " 7.2701e-02\n", - " 8.8856e+00\n", - " 2.6411e-07\n", - " 1.7349e+01\n", - " 2.9812e-07\n", - " 1.6223e-10\n", - " 2.3136e-01\n", - " 4.9386e+00\n", - " 2.3262e-02\n", - " 1.1678e-01\n", - " 1.1364e-01\n", - " 3.5626e+01\n", - " 2.2471e+01\n", - " 3.4680e-08\n", - " 3.9239e-07\n", - " 4.5764e-08\n", - " 4.5338e-01\n", - " 2.8175e-08\n", - " 6.0937e-08\n", - " 5.9102e-01\n", - " 1.1198e-01\n", - " 4.9348e-02\n", - " 9.3823e-10\n", - " 1.3880e-01\n", - " 1.2235e+00\n", - " 5.1027e-08\n", - " 5.1064e-08\n", - " 3.4925e-08\n", - " 2.1195e-11\n", - " 7.2309e-09\n", - " 4.6023e+01\n", - " 4.7265e-02\n", - " 1.1407e-08\n", - " 7.4242e-07\n", - " 1.9322e-07\n", - " 5.6772e-07\n", - " 1.6085e-01\n", - " 4.4126e-01\n", - " 7.5727e-02\n", - " 3.8255e-10\n", - " 3.3904e-02\n", - " 3.7415e-10\n", - " 1.2396e-06\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.6.conv1d.weight', \n", - " (0 ,.,.) = \n", - " -1.0649e+00 1.3727e+00 1.9003e-01 ... -1.9994e-01 3.3153e+00 -5.1286e+00\n", - " 6.0406e-02 -2.1307e-01 1.3011e-01 ... -7.6565e-01 1.6152e-01 5.9789e-01\n", - " 7.5477e-01 -1.1030e-01 3.3991e-01 ... -9.8794e-02 1.3341e-01 -1.6148e+00\n", - " ... ⋱ ... \n", - " -2.2227e-02 1.5231e-02 1.0512e-01 ... 2.2787e-01 2.4669e-01 4.8626e-01\n", - " -6.6963e-02 -2.3941e-02 -4.3805e-03 ... 4.8652e-02 5.6056e-04 3.2899e-01\n", - " -1.3463e-01 1.1059e-01 -2.1093e-01 ... -3.8216e-01 -2.2131e-01 -4.6672e-03\n", - " \n", - " (1 ,.,.) = \n", - " 1.9496e+00 -1.3853e+00 2.4915e+00 ... -2.8040e+00 2.5646e+00 -3.2165e+00\n", - " -4.1844e-01 -4.0175e-01 1.6977e-01 ... 2.6866e-01 6.1458e-01 1.1135e-01\n", - " -5.1263e-01 -3.4525e-01 -3.1196e-02 ... -2.3048e-02 3.7472e-01 4.6155e-01\n", - " ... ⋱ ... \n", - " 1.7881e-01 1.0789e-01 2.4740e-01 ... 2.3902e-02 8.6342e-02 1.2528e-02\n", - " 1.9066e-02 -2.1986e-03 7.4409e-02 ... 1.1230e-02 6.7261e-02 1.5289e-01\n", - " -2.4646e-01 -3.2945e-01 -6.5856e-02 ... 1.2225e-01 -8.9026e-02 -9.9658e-02\n", - " \n", - " (2 ,.,.) = \n", - " 3.6556e-01 1.9130e-01 4.4784e-01 ... -1.6449e-01 6.8066e-02 -1.1009e-02\n", - " 4.2291e-02 1.3204e-01 1.5855e-01 ... 3.1037e-01 1.4537e-01 2.1174e-01\n", - " 1.3034e-01 -7.7696e-02 4.2838e-02 ... -8.1076e-02 6.7180e-02 -8.8425e-02\n", - " ... ⋱ ... \n", - " 1.0065e-03 9.5219e-02 -1.1813e-02 ... 6.7723e-03 3.1524e-02 2.6009e-02\n", - " 1.0982e-01 -1.3729e-01 4.4807e-02 ... 7.5306e-02 5.6696e-02 -1.8068e-01\n", - " 1.8904e-01 1.3797e-01 2.5836e-01 ... 2.0835e-01 1.6837e-01 1.1935e-02\n", - " ...\n", - " \n", - " (77,.,.) = \n", - " 1.0173e+00 7.1137e-01 -7.3726e-02 ... -5.4410e-02 -4.9887e-02 -3.7306e-01\n", - " -6.0327e-01 -3.2560e-01 1.6308e-02 ... -2.2698e-01 -2.2651e-01 -4.7624e-01\n", - " -1.3541e-02 -1.5023e-02 1.1122e-01 ... 2.3900e-01 2.5451e-02 -1.3337e-01\n", - " ... ⋱ ... \n", - " -1.1223e-01 -1.4351e-03 -5.6440e-03 ... 2.6192e-02 1.3253e-02 -8.7034e-02\n", - " 3.0891e-03 1.4962e-01 5.6578e-02 ... 6.6190e-02 9.9097e-02 1.1227e-01\n", - " 1.1993e-01 1.4432e-01 6.9030e-02 ... 5.5823e-02 3.9141e-02 3.2510e-02\n", - " \n", - " (78,.,.) = \n", - " 1.6438e+00 -1.2697e-01 -4.9258e-01 ... -7.0856e-01 -3.7720e+00 -6.7071e+00\n", - " -4.9945e-01 -1.7624e-01 2.3253e-01 ... -2.7442e-01 2.8344e-01 5.4155e-01\n", - " -7.6488e-03 9.0009e-02 -7.3368e-02 ... -1.9346e-02 -5.6370e-02 -1.0089e-01\n", - " ... ⋱ ... \n", - " 3.8947e-02 8.0353e-02 7.6696e-02 ... 6.1145e-02 -1.6484e-01 -2.6345e-01\n", - " 7.9000e-02 7.5109e-02 6.8631e-02 ... 3.8451e-02 -9.6734e-02 -2.5716e-01\n", - " 2.6664e-01 1.3880e-01 2.1747e-01 ... 1.8959e-01 -2.9664e-02 -4.0854e-01\n", - " \n", - " (79,.,.) = \n", - " 6.4669e-01 9.4086e-02 -5.0195e-01 ... -2.7949e-01 -3.9004e-01 -4.9352e-01\n", - " -2.8771e-01 -1.7709e-01 -3.3198e-01 ... -6.6746e-02 1.3041e-01 -1.3144e-01\n", - " 1.8937e-01 2.3438e-01 8.7372e-02 ... -7.0633e-02 -2.0086e-01 -3.7153e-02\n", - " ... ⋱ ... \n", - " 4.0741e-02 2.6941e-02 1.8963e-01 ... 1.4338e-01 1.1682e-01 2.5949e-01\n", - " 1.1112e-01 -7.1629e-03 9.1774e-02 ... 1.5254e-01 9.0325e-03 1.7725e-01\n", - " 1.6518e-01 3.6820e-02 1.0384e-01 ... -3.3557e-02 -4.4994e-02 2.1228e-02\n", - " [torch.FloatTensor of size 80x80x7]),\n", - " ('module.postnet.conv1d_banks.6.bn.weight', \n", - " 1.1234\n", - " -1.2926\n", - " -10.5510\n", - " 0.7360\n", - " 0.8886\n", - " -8.5146\n", - " -6.4528\n", - " -0.6868\n", - " 0.4879\n", - " 0.9858\n", - " 0.6172\n", - " 0.5446\n", - " -11.2630\n", - " -10.3431\n", - " -11.7081\n", - " 0.5923\n", - " 0.6918\n", - " -10.9081\n", - " -11.1192\n", - " 0.0431\n", - " -3.6628\n", - " 0.5876\n", - " -12.2840\n", - " -7.0464\n", - " 0.5582\n", - " -10.5683\n", - " -10.1524\n", - " 0.7902\n", - " 1.2558\n", - " 1.0967\n", - " -10.5113\n", - " 0.6058\n", - " 1.2726\n", - " 1.3562\n", - " -10.5654\n", - " 1.4586\n", - " -10.0198\n", - " 0.6231\n", - " -8.3551\n", - " 0.0070\n", - " 0.7449\n", - " 1.0341\n", - " 0.3115\n", - " -0.7384\n", - " 1.6992\n", - " -8.6107\n", - " 0.7889\n", - " -2.5240\n", - " -10.2320\n", - " 1.3290\n", - " 0.6859\n", - " -11.5253\n", - " 1.4087\n", - " -14.0153\n", - " -12.3844\n", - " -2.2739\n", - " 0.7766\n", - " 1.3140\n", - " -9.7110\n", - " -1.0587\n", - " -9.7685\n", - " -1.7783\n", - " 0.5942\n", - " -1.4126\n", - " 0.7143\n", - " -10.1922\n", - " -11.2625\n", - " 1.3250\n", - " 1.0208\n", - " -2.5101\n", - " -4.1294\n", - " -9.9524\n", - " -4.3481\n", - " -1.6137\n", - " 0.7320\n", - " -1.8037\n", - " 1.4938\n", - " -10.5113\n", - " 0.6225\n", - " -0.1926\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.6.bn.bias', \n", - " -0.4338\n", - " -0.0459\n", - " -0.2383\n", - " -0.2096\n", - " -0.2667\n", - " -0.5104\n", - " -0.0578\n", - " 0.0849\n", - " -0.1379\n", - " -0.4549\n", - " -0.1499\n", - " -0.4967\n", - " -0.7976\n", - " -0.6348\n", - " -0.2504\n", - " -0.2461\n", - " -0.3132\n", - " -0.3489\n", - " -0.4976\n", - " 0.8112\n", - " -0.2946\n", - " -0.4577\n", - " -0.3724\n", - " -0.2919\n", - " -0.5411\n", - " -0.5325\n", - " -0.5417\n", - " -0.5636\n", - " -0.3449\n", - " -0.6064\n", - " -0.0808\n", - " -0.1803\n", - " -0.2893\n", - " -0.2053\n", - " -0.2342\n", - " -0.1587\n", - " -0.6827\n", - " -0.6597\n", - " -0.3597\n", - " 0.4662\n", - " 0.0072\n", - " -0.5884\n", - " -0.1537\n", - " -0.3313\n", - " -0.7711\n", - " -0.1141\n", - " -0.0630\n", - " -0.4278\n", - " -0.2871\n", - " -0.5986\n", - " -0.5690\n", - " -0.6288\n", - " -0.6113\n", - " 0.3876\n", - " -0.3945\n", - " 0.0004\n", - " -0.0573\n", - " -0.7955\n", - " -0.2263\n", - " -0.1895\n", - " -0.4541\n", - " 0.0696\n", - " -0.3114\n", - " 0.1292\n", - " -0.0607\n", - " 1.0270\n", - " -0.3494\n", - " -0.4729\n", - " -0.2606\n", - " 0.0859\n", - " -4.3991\n", - " -0.4413\n", - " -3.2549\n", - " -0.3289\n", - " -0.3401\n", - " 0.3182\n", - " -0.5562\n", - " -0.3727\n", - " 0.0064\n", - " -0.7360\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.6.bn.running_mean', \n", - " 4.3804e-01\n", - " 3.4535e-01\n", - " 1.3789e-06\n", - " 7.8018e-01\n", - " 2.0125e+00\n", - " 6.3429e-07\n", - " 3.9701e-07\n", - " 4.5727e-01\n", - " 1.0556e+00\n", - " 3.9728e-01\n", - " 4.3544e-01\n", - " 3.6977e-01\n", - " 1.2993e-06\n", - " 5.0158e-07\n", - " 1.5312e-05\n", - " 2.9213e-01\n", - " 3.4550e-01\n", - " 6.0703e-06\n", - " 7.2830e-07\n", - " 1.1630e+01\n", - " 1.4107e-05\n", - " 5.9418e-01\n", - " 1.8864e-05\n", - " 4.7275e-06\n", - " 4.3268e-01\n", - " 1.0982e-06\n", - " 1.4959e-05\n", - " 4.6972e-01\n", - " 3.5489e-01\n", - " 3.9912e-01\n", - " 3.6602e-06\n", - " 7.9997e-01\n", - " 2.9541e-01\n", - " 4.3830e-01\n", - " 4.7996e-06\n", - " 4.9214e-01\n", - " 5.5694e-07\n", - " 3.3097e-01\n", - " 6.1603e-07\n", - " 1.5817e+01\n", - " 2.2433e-01\n", - " 4.0282e-01\n", - " 1.4865e+00\n", - " 2.0431e-01\n", - " 3.2347e-01\n", - " 8.5819e-08\n", - " 8.5859e-01\n", - " 1.9079e-05\n", - " 9.9729e-06\n", - " 3.3272e-01\n", - " 4.5262e-01\n", - " 4.7821e-07\n", - " 5.1499e-01\n", - " 1.6921e-06\n", - " 7.5501e-06\n", - " 6.7233e-05\n", - " 4.4490e-01\n", - " 4.1153e-01\n", - " 5.2981e-06\n", - " 2.7974e-01\n", - " 1.8517e-06\n", - " 3.5282e-01\n", - " 3.6116e-01\n", - " 3.1202e-01\n", - " 7.7057e+00\n", - " 6.4928e-06\n", - " 1.9840e-06\n", - " 3.5685e-01\n", - " 5.5001e-01\n", - " 3.1763e-05\n", - " 1.5337e-05\n", - " 1.3852e-06\n", - " 3.7034e-06\n", - " 4.6679e-01\n", - " 5.9364e-01\n", - " 2.2923e-05\n", - " 4.2884e-01\n", - " 2.9967e-06\n", - " 2.1568e+00\n", - " 8.6376e-01\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.6.bn.running_var', \n", - " 6.9108e-02\n", - " 4.8026e-02\n", - " 1.3288e-08\n", - " 6.0707e-01\n", - " 8.9584e-01\n", - " 1.2293e-08\n", - " 4.6750e-09\n", - " 1.4253e-01\n", - " 5.9941e-01\n", - " 7.3726e-02\n", - " 3.2148e-01\n", - " 1.9845e-01\n", - " 5.7209e-08\n", - " 3.4721e-09\n", - " 3.2750e-07\n", - " 4.5857e-01\n", - " 8.8278e-02\n", - " 6.7437e-08\n", - " 1.2995e-09\n", - " 2.4064e+01\n", - " 2.4312e-07\n", - " 1.4615e-01\n", - " 1.0230e-06\n", - " 9.2319e-08\n", - " 5.6174e-02\n", - " 3.6997e-08\n", - " 2.0017e-07\n", - " 8.7901e-02\n", - " 4.2786e-02\n", - " 5.6097e-02\n", - " 7.0912e-08\n", - " 3.2873e-01\n", - " 5.6683e-02\n", - " 9.3703e-02\n", - " 5.3004e-08\n", - " 1.1316e-01\n", - " 3.4978e-09\n", - " 8.3531e-02\n", - " 1.1983e-08\n", - " 4.4049e+01\n", - " 2.3214e-01\n", - " 9.6722e-02\n", - " 6.0587e-01\n", - " 1.5013e-01\n", - " 3.9874e-02\n", - " 6.7535e-11\n", - " 6.0061e-01\n", - " 2.5476e-07\n", - " 2.0624e-07\n", - " 5.3841e-02\n", - " 3.0385e-01\n", - " 2.6532e-09\n", - " 9.7681e-02\n", - " 3.1858e-08\n", - " 4.2094e-08\n", - " 1.6999e-06\n", - " 2.4220e-01\n", - " 6.5469e-02\n", - " 7.7248e-08\n", - " 1.0687e-01\n", - " 9.6119e-08\n", - " 4.3373e-02\n", - " 3.4932e-01\n", - " 5.6417e-02\n", - " 1.1625e+01\n", - " 9.4767e-08\n", - " 1.7245e-08\n", - " 1.0143e-01\n", - " 1.5751e-01\n", - " 1.5589e-07\n", - " 3.1991e-08\n", - " 1.5279e-09\n", - " 1.0600e-07\n", - " 1.0521e-01\n", - " 5.8041e-01\n", - " 5.7862e-07\n", - " 6.8317e-02\n", - " 2.4081e-08\n", - " 1.9565e+00\n", - " 7.4052e-01\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.7.conv1d.weight', \n", - " (0 ,.,.) = \n", - " 1.0529e+00 3.8757e+00 3.7754e+00 ... 4.4254e-01 -3.4523e-01 -1.0291e-01\n", - " 1.5095e+00 2.4575e+00 1.6089e+00 ... 1.7952e-01 -1.2080e+00 -6.9600e-01\n", - " 6.5311e-01 1.1343e+00 1.4454e+00 ... 8.3564e-01 7.0717e-01 -1.5440e-01\n", - " ... ⋱ ... \n", - " -5.8892e-01 -6.6297e-01 -3.7407e-01 ... -2.4059e-03 4.1757e-01 3.0214e-01\n", - " -2.3222e-01 -4.4655e-01 -4.5594e-01 ... 1.1238e-02 4.7837e-01 1.5670e-01\n", - " 1.1653e+00 3.5081e-01 -2.5347e-01 ... -2.3556e-01 2.3423e-01 -1.9039e-01\n", - " \n", - " (1 ,.,.) = \n", - " -1.6885e+00 1.9148e+00 -8.8712e-01 ... -5.7404e-01 9.3855e-01 -1.8792e+00\n", - " 6.1432e-02 -4.1108e-01 4.7087e-02 ... -8.2081e-01 -6.1591e-01 -9.2098e-02\n", - " 1.9864e-02 2.4329e-02 -9.4784e-03 ... 3.0884e-01 1.7533e-01 1.2645e-01\n", - " ... ⋱ ... \n", - " 8.2526e-02 8.2653e-02 -5.7011e-02 ... 3.6185e-01 3.3817e-01 4.4439e-01\n", - " 1.6853e-01 7.1762e-02 -3.8038e-02 ... 2.2377e-01 1.5718e-01 1.7180e-01\n", - " 1.4553e-01 -1.6342e-01 -1.1863e-01 ... 1.0773e-01 -2.1940e-01 -9.9362e-02\n", - " \n", - " (2 ,.,.) = \n", - " -5.3311e-02 2.3856e-01 3.6569e-01 ... 9.0417e-02 3.6500e-01 6.7456e-01\n", - " 1.5800e-01 1.5400e-01 -1.4746e-01 ... -1.0636e-02 9.1024e-02 -7.5079e-03\n", - " -1.4288e-01 4.5058e-02 7.4069e-02 ... 7.3295e-02 7.5404e-02 2.2525e-01\n", - " ... ⋱ ... \n", - " 8.2440e-02 6.3924e-02 -1.6160e-01 ... 5.9316e-02 2.6346e-02 -2.0503e-02\n", - " -1.0922e-03 1.1213e-01 -1.8133e-01 ... 4.4410e-02 1.0853e-01 -4.5268e-02\n", - " 3.2731e-01 3.3002e-01 -1.4442e-01 ... 7.0902e-02 1.3694e-01 2.7840e-01\n", - " ...\n", - " \n", - " (77,.,.) = \n", - " 4.3486e-01 3.6720e-01 1.1861e-01 ... -1.2157e-01 3.8807e-01 1.3101e+00\n", - " -4.6372e-02 5.2028e-02 2.9571e-01 ... 4.1118e-01 1.7782e-01 2.5223e-01\n", - " -1.6780e-01 -3.2026e-01 7.4724e-02 ... -2.3654e-01 -3.5995e-01 5.3968e-02\n", - " ... ⋱ ... \n", - " -1.0499e-01 -2.9694e-02 -3.1468e-02 ... -1.5553e-01 -1.7116e-01 -1.2240e-01\n", - " -2.0299e-01 -7.8974e-02 -2.1433e-02 ... -1.7333e-01 -1.5325e-01 -1.1611e-01\n", - " -1.4810e-01 -8.7836e-02 4.9869e-01 ... 2.8257e-01 2.5560e-01 3.3153e-01\n", - " \n", - " (78,.,.) = \n", - " 3.1916e-01 -5.1179e-01 -5.4707e-01 ... 5.5400e-01 -5.0809e-01 -2.0861e+00\n", - " -1.9279e+00 -7.0329e-01 -8.3487e-01 ... -4.5820e-02 1.2540e+00 1.3178e+00\n", - " 2.2729e-01 3.3453e-01 9.6079e-02 ... -3.1711e-01 2.8326e-01 2.2800e-01\n", - " ... ⋱ ... \n", - " 7.6057e-03 -1.9277e-01 5.0295e-02 ... 8.5391e-02 1.8244e-01 3.4017e-01\n", - " 3.2960e-02 -1.0860e-01 5.6157e-02 ... 1.4160e-01 3.2270e-01 3.4672e-01\n", - " 3.1290e-01 7.7094e-02 1.4394e-01 ... 2.8697e-01 1.1527e-01 1.4380e-01\n", - " \n", - " (79,.,.) = \n", - " 4.3437e-02 -1.3139e-01 -2.8921e-01 ... -3.6401e-01 -3.9041e-01 -3.4898e-01\n", - " -6.4615e-02 -1.1073e-01 -9.7782e-02 ... -1.9688e-02 -5.5769e-02 2.3761e-02\n", - " -1.0203e-01 -7.6795e-02 -6.0822e-02 ... -8.2102e-02 1.5033e-01 2.0831e-01\n", - " ... ⋱ ... \n", - " 2.2731e-01 1.8507e-01 1.3185e-01 ... 8.3878e-02 -2.1591e-02 -6.5322e-02\n", - " 1.1341e-01 4.7051e-02 5.4270e-02 ... 4.8335e-04 -4.4908e-02 -1.3254e-01\n", - " -1.3947e-03 6.5450e-02 6.8548e-02 ... -5.2640e-02 -6.0732e-02 -1.5454e-01\n", - " [torch.FloatTensor of size 80x80x8]),\n", - " ('module.postnet.conv1d_banks.7.bn.weight', \n", - " 0.2272\n", - " -1.2905\n", - " -11.5955\n", - " -10.1562\n", - " -0.6380\n", - " 0.9985\n", - " -1.8432\n", - " -3.3960\n", - " -9.9084\n", - " -6.8331\n", - " 0.9846\n", - " 1.0592\n", - " -9.2445\n", - " -1.8706\n", - " -0.0822\n", - " 1.0948\n", - " 0.7832\n", - " -12.2910\n", - " -9.9979\n", - " 0.6151\n", - " -7.9451\n", - " -1.4832\n", - " 0.9173\n", - " -9.6940\n", - " -0.0553\n", - " 0.4439\n", - " 0.2380\n", - " 0.1145\n", - " -0.0336\n", - " 0.6230\n", - " -9.9262\n", - " 0.7364\n", - " 0.8043\n", - " 0.6777\n", - " 1.0693\n", - " 1.0780\n", - " -6.0967\n", - " -10.6311\n", - " 0.2970\n", - " 0.8357\n", - " -25.8087\n", - " 1.6300\n", - " -10.5496\n", - " 1.4628\n", - " -2.3917\n", - " 0.3279\n", - " -9.6571\n", - " 0.4302\n", - " 1.1084\n", - " 1.2249\n", - " -0.7977\n", - " -9.5146\n", - " -0.8734\n", - " 0.4514\n", - " 1.0442\n", - " 0.7329\n", - " 0.8109\n", - " 0.8417\n", - " 1.6141\n", - " -10.7616\n", - " -11.1164\n", - " -2.1931\n", - " 0.6783\n", - " -11.2959\n", - " 1.7023\n", - " -10.7230\n", - " 0.5006\n", - " -29.0832\n", - " -1.0327\n", - " -10.6104\n", - " 0.4630\n", - " 0.8340\n", - " 0.6053\n", - " 0.5325\n", - " -9.7004\n", - " 0.5927\n", - " -0.0174\n", - " -14.3934\n", - " -2.5815\n", - " -0.0260\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.7.bn.bias', \n", - " -0.1861\n", - " -0.3294\n", - " -0.4716\n", - " -0.3899\n", - " 0.1369\n", - " -0.5080\n", - " -0.2138\n", - " -0.2117\n", - " -0.2370\n", - " -0.6222\n", - " -0.8410\n", - " -0.4249\n", - " -0.2393\n", - " 0.2996\n", - " -1.3290\n", - " -0.2597\n", - " -0.7508\n", - " -0.3970\n", - " -0.3295\n", - " -0.4606\n", - " -0.4718\n", - " -1.7030\n", - " -0.0700\n", - " -0.0053\n", - " -4.2104\n", - " -0.1452\n", - " -0.3915\n", - " -0.1965\n", - " -4.5503\n", - " 0.0065\n", - " -0.3280\n", - " -0.0803\n", - " -0.1242\n", - " -0.2823\n", - " -0.3452\n", - " -0.1443\n", - " 0.6449\n", - " -0.3834\n", - " -0.1812\n", - " -0.4946\n", - " -0.4245\n", - " -0.3413\n", - " -0.4955\n", - " -0.3791\n", - " -0.3720\n", - " -0.2363\n", - " -0.6808\n", - " -0.1327\n", - " -0.5609\n", - " -0.4934\n", - " 0.1324\n", - " -0.3644\n", - " 0.0195\n", - " -0.0893\n", - " -0.2136\n", - " -0.2123\n", - " -0.3184\n", - " 0.1364\n", - " -0.5017\n", - " -0.4111\n", - " -0.2907\n", - " -0.1674\n", - " -0.5139\n", - " -0.4316\n", - " -0.3985\n", - " -0.3544\n", - " -0.5609\n", - " -0.1713\n", - " -0.0734\n", - " -0.2134\n", - " -0.4465\n", - " -0.6532\n", - " 0.1022\n", - " -0.2596\n", - " -0.2295\n", - " -0.3866\n", - " -4.4528\n", - " -0.6484\n", - " -0.2331\n", - " 0.4115\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.7.bn.running_mean', \n", - " 1.6550e-02\n", - " 6.5145e-02\n", - " 1.9891e-06\n", - " 5.4034e-06\n", - " 7.5269e-01\n", - " 5.0953e-01\n", - " 4.9518e-05\n", - " 1.9469e-05\n", - " 7.6647e-06\n", - " 2.7089e-06\n", - " 5.3703e-01\n", - " 4.1142e-01\n", - " 4.1399e-06\n", - " 1.7659e-04\n", - " 6.9984e-01\n", - " 3.8809e-01\n", - " 5.6094e-01\n", - " 1.3415e-05\n", - " 3.6147e-06\n", - " 2.3200e-01\n", - " 1.3136e-06\n", - " 1.9973e-05\n", - " 9.6954e-01\n", - " 1.4150e-05\n", - " 2.4474e-01\n", - " 1.2881e-02\n", - " 2.8005e-03\n", - " 1.8118e-03\n", - " 4.0178e-01\n", - " 3.7376e+00\n", - " 2.5784e-07\n", - " 2.0043e+01\n", - " 3.2837e-01\n", - " 2.6965e-01\n", - " 4.4999e-01\n", - " 5.4624e-01\n", - " 1.0382e-05\n", - " 2.4678e-10\n", - " 9.0943e-02\n", - " 3.8726e-01\n", - " 1.4177e-05\n", - " 3.8693e-01\n", - " 2.6754e-06\n", - " 5.6915e-01\n", - " 7.8282e-05\n", - " 3.4047e-02\n", - " 2.5442e-07\n", - " 1.1157e-02\n", - " 4.1637e-01\n", - " 4.8645e-01\n", - " 6.3600e-01\n", - " 3.6457e-06\n", - " 6.9767e-01\n", - " 2.0002e+01\n", - " 7.8001e-01\n", - " 3.4457e-01\n", - " 1.1104e-01\n", - " 2.3491e+01\n", - " 3.6410e-01\n", - " 4.6522e-06\n", - " 1.3357e-08\n", - " 5.2513e-06\n", - " 2.6707e-01\n", - " 3.3389e-08\n", - " 4.1382e-01\n", - " 2.0373e-06\n", - " 7.9752e-05\n", - " 2.6121e-05\n", - " 7.2409e-02\n", - " 3.5990e-09\n", - " 2.0751e-01\n", - " 6.8377e-01\n", - " 2.1786e-01\n", - " 8.5901e-02\n", - " 1.4765e-05\n", - " 5.3136e-01\n", - " 1.0216e+01\n", - " 1.6747e-06\n", - " 2.0335e-05\n", - " 5.0371e+00\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_banks.7.bn.running_var', \n", - " 3.5441e-02\n", - " 2.7839e-02\n", - " 8.9386e-09\n", - " 9.2294e-08\n", - " 4.3831e-01\n", - " 1.0638e-01\n", - " 1.3888e-06\n", - " 4.8367e-07\n", - " 6.0299e-07\n", - " 6.9106e-08\n", - " 1.0926e-01\n", - " 8.5390e-02\n", - " 2.9100e-07\n", - " 1.8051e-06\n", - " 3.1060e-01\n", - " 1.2558e-01\n", - " 2.1669e-01\n", - " 1.2312e-07\n", - " 5.0283e-08\n", - " 1.1093e-01\n", - " 3.4354e-09\n", - " 2.2049e-07\n", - " 1.4796e+00\n", - " 4.3755e-07\n", - " 3.0358e-02\n", - " 2.0056e-02\n", - " 1.2561e-03\n", - " 7.3848e-04\n", - " 5.6786e-02\n", - " 4.6312e+00\n", - " 2.0893e-10\n", - " 7.2274e+01\n", - " 2.2853e-01\n", - " 3.6890e-01\n", - " 1.0857e-01\n", - " 1.6555e-01\n", - " 1.4318e-07\n", - " 7.3806e-13\n", - " 4.6212e-02\n", - " 1.4513e-01\n", - " 2.5276e-07\n", - " 6.5982e-02\n", - " 2.9542e-08\n", - " 8.2092e-02\n", - " 8.8085e-07\n", - " 2.4429e-02\n", - " 5.6086e-10\n", - " 1.5524e-02\n", - " 8.6545e-02\n", - " 7.6177e-02\n", - " 2.7218e-01\n", - " 1.9632e-07\n", - " 2.0211e-01\n", - " 6.7753e+01\n", - " 1.6525e-01\n", - " 1.1700e-01\n", - " 3.5328e-02\n", - " 9.9202e+01\n", - " 4.7470e-02\n", - " 6.9109e-08\n", - " 1.7118e-11\n", - " 9.1332e-08\n", - " 1.7900e-01\n", - " 5.3306e-11\n", - " 6.9717e-02\n", - " 1.6193e-08\n", - " 3.3868e-06\n", - " 5.5110e-07\n", - " 3.8218e-02\n", - " 1.4721e-11\n", - " 8.3060e-02\n", - " 2.7641e-01\n", - " 1.0380e-01\n", - " 2.5674e-02\n", - " 1.8893e-06\n", - " 3.2152e-01\n", - " 1.8757e+01\n", - " 9.2221e-09\n", - " 7.9450e-07\n", - " 4.9587e+00\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_projections.0.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " -1.3957e-01 -1.2377e-01 -7.3275e-02\n", - " -1.4006e-02 1.5525e-01 2.0347e-01\n", - " 3.1622e-02 -1.2623e-02 2.2276e-01\n", - " ⋮ \n", - " 5.6694e-03 6.4154e-02 1.2554e-01\n", - " -1.9284e-01 -2.1595e-01 -3.1800e-01\n", - " -5.1342e-02 -7.2566e-02 -8.4694e-02\n", - " \n", - " ( 1 ,.,.) = \n", - " 1.2366e-01 4.6452e-02 4.3532e-02\n", - " -3.3146e-02 1.1825e-01 4.0882e-02\n", - " 1.7882e-01 8.2681e-02 -2.3802e-01\n", - " ⋮ \n", - " -2.5566e-02 6.5342e-02 2.3106e-02\n", - " 6.9835e-02 1.1464e-01 9.0261e-02\n", - " -1.3521e-01 -1.4328e-01 -2.3939e-01\n", - " \n", - " ( 2 ,.,.) = \n", - " 4.3039e-02 1.7098e-01 2.8486e-01\n", - " -1.0624e-01 -4.0530e-02 -2.6259e-02\n", - " 3.6910e-01 2.5975e-01 3.2112e-01\n", - " ⋮ \n", - " 5.4088e-03 -2.3620e-02 -1.2758e-02\n", - " -2.0870e-02 -7.2901e-02 8.4686e-02\n", - " -4.1611e-02 -5.8244e-02 -1.3187e-01\n", - " ... \n", - " \n", - " (253,.,.) = \n", - " 6.7897e-02 3.1555e-01 -1.0163e-01\n", - " 8.5174e-02 9.9121e-03 -2.6476e-02\n", - " -1.3393e-01 -4.0839e-01 -1.3982e-01\n", - " ⋮ \n", - " 1.8264e-01 3.2260e-02 2.5612e-02\n", - " 2.6153e-02 -5.8576e-02 -7.5391e-02\n", - " -8.1473e-02 -6.3125e-02 -1.1814e-01\n", - " \n", - " (254,.,.) = \n", - " 3.1201e-02 1.7619e-01 5.9133e-02\n", - " 8.9054e-02 -8.6981e-03 -2.5639e-02\n", - " 1.9719e-02 4.4718e-01 1.6546e-01\n", - " ⋮ \n", - " -1.0220e-02 -8.4788e-02 1.4365e-01\n", - " -3.9882e-01 7.7564e-03 2.0031e-02\n", - " -1.8677e-01 -2.0325e-01 -2.1621e-01\n", - " \n", - " (255,.,.) = \n", - " 1.5317e-01 3.0041e-02 4.7304e-01\n", - " 1.6127e-02 -9.4994e-02 1.3071e-01\n", - " -8.1051e-02 -1.4608e-01 -1.1953e-01\n", - " ⋮ \n", - " -1.0212e-01 6.9462e-02 1.4669e-01\n", - " 1.3095e-01 2.4410e-01 1.6540e-01\n", - " -7.0448e-02 -2.9191e-03 -5.3479e-02\n", - " [torch.FloatTensor of size 256x640x3]),\n", - " ('module.postnet.conv1d_projections.0.bn.weight', \n", - " 1.0064\n", - " 1.1528\n", - " 0.5419\n", - " 0.6797\n", - " 0.1785\n", - " 1.0887\n", - " 0.8183\n", - " 0.8454\n", - " 0.6532\n", - " 1.1385\n", - " 1.0083\n", - " 0.5864\n", - " 0.7713\n", - " 0.7591\n", - " 0.8494\n", - " 1.1892\n", - " 0.9368\n", - " 0.6308\n", - " 0.5809\n", - " 0.4602\n", - " 1.4810\n", - " 0.7448\n", - " 0.6929\n", - " 0.7922\n", - " 0.7155\n", - " 1.1933\n", - " 0.6255\n", - " 0.8659\n", - " 1.0119\n", - " 0.5903\n", - " 0.4898\n", - " 0.4952\n", - " 1.3038\n", - " 0.5721\n", - " 0.5777\n", - " 1.1893\n", - " 1.2678\n", - " 0.2680\n", - " 1.0207\n", - " 0.5819\n", - " 1.0601\n", - " 0.6649\n", - " -0.5641\n", - " 0.7843\n", - " 1.0840\n", - " 0.7581\n", - " 0.5106\n", - " 1.0091\n", - " 1.5557\n", - " 0.9729\n", - " 1.1628\n", - " 1.0890\n", - " 0.6408\n", - " 1.2018\n", - " 0.5435\n", - " 1.1324\n", - " 0.8478\n", - " 0.8005\n", - " 0.3795\n", - " 0.6147\n", - " 0.5328\n", - " 0.8774\n", - " 0.8778\n", - " 0.4229\n", - " 1.0529\n", - " 0.9138\n", - " 0.6883\n", - " 0.5685\n", - " -0.5754\n", - " 0.6103\n", - " 0.6324\n", - " -0.5165\n", - " 0.8187\n", - " 0.8786\n", - " 0.7707\n", - " 0.7669\n", - " 1.0023\n", - " 0.4995\n", - " 0.7858\n", - " 0.7484\n", - " 0.6385\n", - " 0.7918\n", - " 0.4057\n", - " 0.8278\n", - " 1.0741\n", - " 0.9531\n", - " 0.8309\n", - " 0.7774\n", - " 0.6918\n", - " 0.4724\n", - " 0.4893\n", - " 0.8787\n", - " 0.3343\n", - " 0.6209\n", - " 1.0224\n", - " 1.1883\n", - " 1.1650\n", - " 0.8231\n", - " 0.7204\n", - " 0.6680\n", - " 1.1056\n", - " 0.4447\n", - " 0.8551\n", - " 1.0782\n", - " 1.0202\n", - " 1.0038\n", - " 0.8246\n", - " 0.5232\n", - " 0.5444\n", - " 0.9478\n", - " -0.7929\n", - " 1.2236\n", - " 0.4436\n", - " 0.5050\n", - " 0.6681\n", - " 0.4004\n", - " 0.8633\n", - " 1.0582\n", - " 0.5568\n", - " 1.6561\n", - " 1.1483\n", - " 0.6300\n", - " 1.5092\n", - " 0.9100\n", - " -0.0984\n", - " 1.0042\n", - " 0.5777\n", - " 0.7218\n", - " 0.7370\n", - " 1.0029\n", - " 0.7365\n", - " 1.0735\n", - " 1.0629\n", - " 0.9720\n", - " 0.6372\n", - " 1.0330\n", - " 0.5227\n", - " 0.8334\n", - " 0.7459\n", - " 0.5253\n", - " 1.0443\n", - " 0.4467\n", - " 1.4061\n", - " 1.5157\n", - " 1.1923\n", - " 0.7804\n", - " 1.1574\n", - " 0.7108\n", - " 0.8723\n", - " 0.9795\n", - " 0.9159\n", - " 1.4306\n", - " 0.6564\n", - " 0.6755\n", - " 0.8446\n", - " 0.8883\n", - " 0.7525\n", - " 0.8763\n", - " 1.0265\n", - " 0.3895\n", - " 1.4116\n", - " 0.4657\n", - " 1.5146\n", - " 0.6319\n", - " 0.3985\n", - " 0.3801\n", - " 1.3908\n", - " 0.7146\n", - " 1.2559\n", - " 1.3587\n", - " 0.7255\n", - " 0.7276\n", - " 0.8790\n", - " 1.3085\n", - " 0.9402\n", - " 0.9422\n", - " 1.1547\n", - " 1.0940\n", - " 0.8019\n", - " 0.5170\n", - " 0.9326\n", - " 0.8033\n", - " -0.6534\n", - " 1.1897\n", - " 0.7316\n", - " 0.8126\n", - " 0.7897\n", - " 0.7867\n", - " 1.2840\n", - " 1.3320\n", - " 1.1048\n", - " 0.7991\n", - " 0.4574\n", - " 0.4702\n", - " 1.0722\n", - " 1.2828\n", - " 0.6300\n", - " 0.6376\n", - " 0.9071\n", - " 0.8140\n", - " 0.8752\n", - " 1.0837\n", - " 0.4878\n", - " 0.9065\n", - " 1.1854\n", - " 0.8245\n", - " 1.1576\n", - " 0.5712\n", - " 1.0625\n", - " 0.5408\n", - " 0.5943\n", - " 0.6183\n", - " 1.1269\n", - " 0.8454\n", - " 1.0975\n", - " 0.7584\n", - " 0.5840\n", - " 1.1025\n", - " 0.6991\n", - " 0.9294\n", - " 0.5743\n", - " 0.3285\n", - " 1.2038\n", - " 0.4224\n", - " 1.5905\n", - " 0.8091\n", - " 0.7251\n", - " 1.1017\n", - " 0.6961\n", - " 0.9184\n", - " 0.5409\n", - " 0.6847\n", - " 0.4472\n", - " 0.6680\n", - " 0.4501\n", - " 1.2013\n", - " 0.4849\n", - " 1.2200\n", - " 0.4478\n", - " 0.5941\n", - " 0.8964\n", - " 0.8002\n", - " 0.6811\n", - " 0.6297\n", - " 1.0296\n", - " 0.6396\n", - " 0.5490\n", - " 0.9496\n", - " 0.2422\n", - " 0.6818\n", - " 0.7932\n", - " 0.5752\n", - " 0.8121\n", - " 0.8411\n", - " 0.2931\n", - " 0.8513\n", - " [torch.FloatTensor of size 256]),\n", - " ('module.postnet.conv1d_projections.0.bn.bias', \n", - " 1.2812\n", - " 17.1439\n", - " 14.9504\n", - " 10.2376\n", - " 13.5551\n", - " 16.2688\n", - " 18.4524\n", - " 13.8033\n", - " 18.5095\n", - " 7.7307\n", - " 15.3203\n", - " 13.8901\n", - " 14.3580\n", - " 17.4806\n", - " 17.8106\n", - " 4.2802\n", - " 10.4670\n", - " 17.6544\n", - " 16.7808\n", - " 16.5132\n", - " 16.6126\n", - " 5.4833\n", - " 15.3519\n", - " 2.8125\n", - " 15.0179\n", - " 1.9103\n", - " 15.6746\n", - " 16.0496\n", - " 15.5229\n", - " 6.0329\n", - " 10.9956\n", - " 17.0978\n", - " 8.8174\n", - " 17.2174\n", - " 2.4152\n", - " 15.8846\n", - " 7.8960\n", - " 15.6841\n", - " 13.3945\n", - " 15.9559\n", - " 13.0596\n", - " 13.7260\n", - " 9.3993\n", - " 14.2777\n", - " 19.0674\n", - " 18.1998\n", - " 16.3006\n", - " 11.1128\n", - " 2.8543\n", - " 3.8619\n", - " 13.3833\n", - " 16.3539\n", - " 14.2264\n", - " 12.3962\n", - " 19.2930\n", - " 5.4506\n", - " 17.7114\n", - " 10.9192\n", - " 17.4551\n", - " 13.6520\n", - " 14.5601\n", - " 8.8583\n", - " 8.4442\n", - " 12.7413\n", - " 12.1143\n", - " 8.1509\n", - " 14.9320\n", - " 9.1842\n", - " 7.5563\n", - " 9.0867\n", - " 17.3073\n", - " 13.9380\n", - " 17.2866\n", - " 17.2508\n", - " 16.3253\n", - " 12.1834\n", - " 10.2829\n", - " 8.4941\n", - " 17.8155\n", - " 6.2684\n", - " 12.0686\n", - " 17.6839\n", - " 15.2739\n", - " 13.4139\n", - " 14.0117\n", - " 4.3912\n", - " 11.4854\n", - " 3.1571\n", - " 7.0127\n", - " 15.3345\n", - " 17.3543\n", - " 11.8236\n", - " 15.9150\n", - " 14.1339\n", - " 17.2696\n", - " 12.2230\n", - " 8.5823\n", - " 7.0506\n", - " 3.5910\n", - " 16.2675\n", - " 11.4610\n", - " 13.9098\n", - " 8.7838\n", - " 11.5545\n", - " 9.6257\n", - " 13.1291\n", - " 14.2880\n", - " 14.6919\n", - " 14.4129\n", - " 9.2726\n", - " 13.0777\n", - " 12.5134\n", - " 19.5631\n", - " 12.8410\n", - " 16.0184\n", - " 17.7974\n", - " 11.6444\n", - " 6.0009\n", - " 16.6255\n", - " 16.1276\n", - " 13.1532\n", - " 13.8993\n", - " 16.2597\n", - " 12.3058\n", - " 14.6869\n", - " 9.1101\n", - " 1.5676\n", - " 17.7581\n", - " 11.2021\n", - " 14.0878\n", - " 16.6297\n", - " 18.8601\n", - " 13.8957\n", - " 13.6028\n", - " 10.4222\n", - " 4.1116\n", - " 16.1921\n", - " 13.8891\n", - " 16.2134\n", - " 18.0202\n", - " 16.0847\n", - " 5.8780\n", - " 12.3433\n", - " 4.3988\n", - " 10.7676\n", - " 6.0629\n", - " 15.0563\n", - " 9.2647\n", - " 13.2574\n", - " 14.3415\n", - " 13.1033\n", - " 2.2080\n", - " 16.3508\n", - " 11.3074\n", - " 18.9634\n", - " -0.1173\n", - " 15.5784\n", - " 8.7789\n", - " 11.0170\n", - " 16.4970\n", - " 5.0056\n", - " 10.0968\n", - " 18.0503\n", - " 10.1258\n", - " 15.0371\n", - " 18.7282\n", - " 13.3921\n", - " 14.1134\n", - " 10.7251\n", - " 8.1674\n", - " 9.5328\n", - " 3.7456\n", - " 14.1296\n", - " 6.9803\n", - " 14.3006\n", - " 13.5137\n", - " 10.7654\n", - " 9.8910\n", - " 10.0740\n", - " 12.0639\n", - " 10.0878\n", - " 10.4553\n", - " 10.3515\n", - " 12.2953\n", - " 12.6187\n", - " 16.2756\n", - " 10.9309\n", - " 15.9904\n", - " 9.5199\n", - " 6.4851\n", - " 6.5385\n", - " 16.8993\n", - " 17.7989\n", - " 10.0278\n", - " 14.5653\n", - " 6.5099\n", - " 12.6526\n", - " 4.6957\n", - " 13.1451\n", - " 15.9175\n", - " 3.8352\n", - " 15.5789\n", - " 13.7593\n", - " 16.6251\n", - " 13.9896\n", - " 9.4880\n", - " 7.2424\n", - " 9.1136\n", - " 9.5095\n", - " 18.4899\n", - " 19.1777\n", - " 17.1391\n", - " 8.6616\n", - " 5.6155\n", - " 15.3622\n", - " 11.7768\n", - " 12.6844\n", - " 12.8171\n", - " 3.6978\n", - " 6.9844\n", - " 16.1621\n", - " 10.3522\n", - " 6.2863\n", - " 17.1446\n", - " 6.6472\n", - " 16.9068\n", - " 16.2942\n", - " 11.2442\n", - " 11.6082\n", - " 5.0071\n", - " 16.3506\n", - " 14.0592\n", - " 12.3528\n", - " 19.9916\n", - " 10.3705\n", - " 13.1318\n", - " 16.8948\n", - " 10.8688\n", - " 15.7174\n", - " 13.2476\n", - " 12.0157\n", - " 19.4167\n", - " 14.4521\n", - " 16.9671\n", - " 12.6741\n", - " 6.8380\n", - " 14.8143\n", - " 8.1581\n", - " 13.5745\n", - " 10.2358\n", - " 14.0739\n", - " 13.2618\n", - " 14.1687\n", - " 5.1842\n", - " 21.4828\n", - " 17.2211\n", - " [torch.FloatTensor of size 256]),\n", - " ('module.postnet.conv1d_projections.0.bn.running_mean', \n", - " 2.7753\n", - " 0.3818\n", - " 0.6334\n", - " 173.1261\n", - " 0.1075\n", - " 0.5092\n", - " 1.5174\n", - " 1318.5923\n", - " 2.3557\n", - " 1.5238\n", - " 1486.0178\n", - " 0.3207\n", - " 5.9736\n", - " 0.6928\n", - " 1.2868\n", - " 1.5721\n", - " 0.5284\n", - " 2.3495\n", - " 0.6995\n", - " 0.1308\n", - " 0.8089\n", - " 14.1699\n", - " 0.8847\n", - " 0.7157\n", - " 994.1609\n", - " 4.1544\n", - " 90.2139\n", - " 3.0803\n", - " 0.7165\n", - " 4.6952\n", - " 1.1713\n", - " 0.4256\n", - " 2.0226\n", - " 0.7897\n", - " 2.3223\n", - " 0.6126\n", - " 1660.7887\n", - " 0.6905\n", - " 1649.1803\n", - " 0.7538\n", - " 0.3325\n", - " 1.1968\n", - " 7.3061\n", - " 6.8361\n", - " 0.1586\n", - " 1.1100\n", - " 1.7231\n", - " 1535.1931\n", - " 0.3486\n", - " 4.3748\n", - " 1712.8698\n", - " 1585.2599\n", - " 176.4229\n", - " 0.3289\n", - " 0.3252\n", - " 1.3155\n", - " 938.7871\n", - " 0.7834\n", - " 148.8547\n", - " 0.6936\n", - " 1.0115\n", - " 1.1828\n", - " 2.8153\n", - " 0.5586\n", - " 2.7201\n", - " 1.1228\n", - " 1.8763\n", - " 1.7236\n", - " 19.2655\n", - " 239.5662\n", - " 2.7836\n", - " 1.3761\n", - " 296.3056\n", - " 913.4092\n", - " 2.0334\n", - " 0.9690\n", - " 1702.1686\n", - " 4.1709\n", - " 0.9101\n", - " 1.6110\n", - " 1.4030\n", - " 0.9680\n", - " 0.9362\n", - " 2.2457\n", - " 1490.8156\n", - " 2.9453\n", - " 1298.7178\n", - " 0.2146\n", - " 4.1666\n", - " 1.1035\n", - " 1.5424\n", - " 3.6634\n", - " 0.7759\n", - " 0.5930\n", - " 2.0226\n", - " 4.2671\n", - " 0.5106\n", - " 1.2427\n", - " 11.0188\n", - " 1307.5266\n", - " 1.7876\n", - " 1.1753\n", - " 5.3495\n", - " 1.4040\n", - " 1.1873\n", - " 1492.0095\n", - " 4.7756\n", - " 0.4510\n", - " 2.1644\n", - " 0.5204\n", - " 0.9193\n", - " 1754.6244\n", - " 83.8922\n", - " 1.0006\n", - " 3.4189\n", - " 251.2662\n", - " 3.6058\n", - " 1024.0649\n", - " 1.6469\n", - " 0.2748\n", - " 1425.5830\n", - " 2.7002\n", - " 0.1112\n", - " 3.5934\n", - " 0.0000\n", - " 268.0367\n", - " 3.7619\n", - " 491.7171\n", - " 0.5491\n", - " 0.9947\n", - " 0.7634\n", - " 0.7405\n", - " 1.0291\n", - " 259.1695\n", - " 1.1268\n", - " 1.1428\n", - " 1.5604\n", - " 6.6672\n", - " 9.2801\n", - " 1.2068\n", - " 1544.9845\n", - " 6.3380\n", - " 0.0696\n", - " 1.5572\n", - " 1551.6989\n", - " 4.1661\n", - " 224.0713\n", - " 8.1500\n", - " 0.7414\n", - " 1424.6758\n", - " 1.0982\n", - " 0.5337\n", - " 7.0338\n", - " 2.8724\n", - " 0.1823\n", - " 1.1163\n", - " 0.6475\n", - " 21.1496\n", - " 2.8433\n", - " 0.6622\n", - " 0.7595\n", - " 0.6273\n", - " 0.0477\n", - " 292.5533\n", - " 0.2493\n", - " 2.5269\n", - " 1678.3840\n", - " 4.8717\n", - " 1770.2681\n", - " 1800.8817\n", - " 1180.3121\n", - " 1.7565\n", - " 0.1644\n", - " 3.3804\n", - " 4.1438\n", - " 136.4131\n", - " 1667.0657\n", - " 1652.9028\n", - " 8.4203\n", - " 0.8433\n", - " 3.1129\n", - " 4.2489\n", - " 15.3234\n", - " 0.3517\n", - " 1.0394\n", - " 1227.4275\n", - " 1261.4265\n", - " 1.9013\n", - " 2.3953\n", - " 1.4071\n", - " 2.4481\n", - " 4.8915\n", - " 1.4859\n", - " 0.7486\n", - " 1612.6017\n", - " 0.6507\n", - " 0.6986\n", - " 2.5622\n", - " 0.3399\n", - " 6.5700\n", - " 2.0957\n", - " 0.0565\n", - " 2.1218\n", - " 0.9189\n", - " 691.7375\n", - " 1432.2279\n", - " 2.2276\n", - " 2.7685\n", - " 0.9708\n", - " 3.7125\n", - " 1.5601\n", - " 2.5054\n", - " 0.0597\n", - " 5.2947\n", - " 0.3169\n", - " 210.7291\n", - " 4.7917\n", - " 4.0418\n", - " 7.4134\n", - " 2.0436\n", - " 0.2562\n", - " 1.0144\n", - " 2.8074\n", - " 1.9981\n", - " 0.6851\n", - " 162.5275\n", - " 0.6670\n", - " 6.1842\n", - " 3.5061\n", - " 0.9552\n", - " 1.5708\n", - " 1.4534\n", - " 0.4530\n", - " 0.5706\n", - " 0.7868\n", - " 284.6758\n", - " 0.2575\n", - " 2.8801\n", - " 0.3087\n", - " 1.1671\n", - " 232.4743\n", - " 85.9347\n", - " 10.7732\n", - " 1.2698\n", - " 0.9208\n", - " 0.6425\n", - " 0.5448\n", - " 3.0128\n", - " 0.3295\n", - " 1.6000\n", - " 1.0468\n", - " 8.7842\n", - " 1.4011\n", - " 0.9018\n", - " 1.6300\n", - " 0.8632\n", - " [torch.FloatTensor of size 256]),\n", - " ('module.postnet.conv1d_projections.0.bn.running_var', \n", - " 87.6942\n", - " 7.1318\n", - " 3.9194\n", - " 20.9474\n", - " 0.3606\n", - " 20.5983\n", - " 13.0474\n", - " 344.4141\n", - " 9.4391\n", - " 6.7778\n", - " 323.8777\n", - " 2.4856\n", - " 14.0690\n", - " 23.9334\n", - " 8.4666\n", - " 8.3945\n", - " 3.4187\n", - " 9.3119\n", - " 3.6598\n", - " 0.6194\n", - " 25.1080\n", - " 54.8546\n", - " 4.9288\n", - " 3.8281\n", - " 183.1740\n", - " 152.2856\n", - " 27.4477\n", - " 16.0468\n", - " 4.5722\n", - " 21.0806\n", - " 5.6672\n", - " 14.7286\n", - " 12.1208\n", - " 2.9059\n", - " 59.0178\n", - " 4.9073\n", - " 411.7721\n", - " 2.8066\n", - " 444.3587\n", - " 2.9010\n", - " 7.0539\n", - " 7.6301\n", - " 42.0356\n", - " 26.9875\n", - " 20.4478\n", - " 10.1745\n", - " 9.6833\n", - " 427.8193\n", - " 3.5250\n", - " 41.9636\n", - " 516.1542\n", - " 393.0963\n", - " 21.4590\n", - " 4.3005\n", - " 6.8636\n", - " 12.1839\n", - " 111.5021\n", - " 5.9088\n", - " 71.1304\n", - " 4.0510\n", - " 4.5807\n", - " 6.5794\n", - " 13.2356\n", - " 2.5152\n", - " 16.9195\n", - " 4.1334\n", - " 9.8563\n", - " 8.3859\n", - " 97.8492\n", - " 23.7796\n", - " 16.0726\n", - " 7.0877\n", - " 26.4761\n", - " 126.4229\n", - " 19.0890\n", - " 5.9119\n", - " 444.1166\n", - " 47.8581\n", - " 8.0636\n", - " 7.6867\n", - " 9.7661\n", - " 9.9720\n", - " 3.9200\n", - " 9.8997\n", - " 443.1576\n", - " 23.4630\n", - " 249.8578\n", - " 1.3360\n", - " 23.5595\n", - " 7.3501\n", - " 6.8262\n", - " 17.0668\n", - " 3.8783\n", - " 4.9632\n", - " 29.8222\n", - " 28.6368\n", - " 4.3469\n", - " 7.3488\n", - " 47.0580\n", - " 283.7618\n", - " 12.8363\n", - " 6.3048\n", - " 32.6211\n", - " 12.6005\n", - " 5.2659\n", - " 418.9775\n", - " 20.6403\n", - " 5.1937\n", - " 13.2954\n", - " 4.2208\n", - " 4.4080\n", - " 502.2701\n", - " 47.2290\n", - " 7.4357\n", - " 13.4018\n", - " 295.2262\n", - " 13.6607\n", - " 334.0442\n", - " 4.8749\n", - " 6.5240\n", - " 366.2472\n", - " 16.7205\n", - " 5.6659\n", - " 12.5702\n", - " 0.0000\n", - " 27.1990\n", - " 104.4047\n", - " 71.3939\n", - " 4.5043\n", - " 8.3918\n", - " 6.6005\n", - " 9.5063\n", - " 7.0208\n", - " 35.3314\n", - " 4.8568\n", - " 5.2463\n", - " 6.5439\n", - " 37.8944\n", - " 24.7531\n", - " 7.8764\n", - " 429.4724\n", - " 52.3462\n", - " 3.5580\n", - " 28.2907\n", - " 499.4714\n", - " 14.6136\n", - " 19.3546\n", - " 68.2662\n", - " 10.0461\n", - " 454.9553\n", - " 8.8021\n", - " 4.4240\n", - " 33.5397\n", - " 14.6746\n", - " 5.6596\n", - " 4.7589\n", - " 4.2637\n", - " 558.6557\n", - " 19.8264\n", - " 2.9783\n", - " 7.4728\n", - " 2.8951\n", - " 1.7417\n", - " 23.0010\n", - " 1.3080\n", - " 14.2484\n", - " 512.3925\n", - " 31.2630\n", - " 631.1724\n", - " 514.1356\n", - " 293.9054\n", - " 15.0251\n", - " 4.8618\n", - " 26.9479\n", - " 19.3160\n", - " 50.9048\n", - " 424.8301\n", - " 453.1019\n", - " 41.4095\n", - " 4.3910\n", - " 12.6802\n", - " 15.6503\n", - " 83.9994\n", - " 2.0973\n", - " 4.7766\n", - " 251.0884\n", - " 397.8469\n", - " 24.1732\n", - " 24.4222\n", - " 9.3091\n", - " 69.4376\n", - " 17.9582\n", - " 10.6635\n", - " 3.7720\n", - " 435.2577\n", - " 7.4856\n", - " 5.7441\n", - " 13.6646\n", - " 6.8971\n", - " 18.4054\n", - " 16.5748\n", - " 1.4229\n", - " 15.4817\n", - " 5.4005\n", - " 95.4689\n", - " 368.6776\n", - " 12.5717\n", - " 9.3346\n", - " 3.1895\n", - " 12.7112\n", - " 5.1520\n", - " 19.3879\n", - " 2.3535\n", - " 20.2337\n", - " 2.8619\n", - " 31.6802\n", - " 17.9289\n", - " 32.6600\n", - " 37.4601\n", - " 10.6008\n", - " 2.5121\n", - " 3.9772\n", - " 15.4718\n", - " 9.1469\n", - " 4.6861\n", - " 31.5696\n", - " 10.4808\n", - " 26.3760\n", - " 20.5761\n", - " 6.5184\n", - " 8.2492\n", - " 6.2479\n", - " 1.9784\n", - " 5.1187\n", - " 5.4346\n", - " 22.5699\n", - " 2.8018\n", - " 15.6621\n", - " 2.4941\n", - " 8.5163\n", - " 48.1840\n", - " 31.3503\n", - " 21.0698\n", - " 11.0356\n", - " 7.7330\n", - " 2.9362\n", - " 5.3684\n", - " 28.6198\n", - " 0.8928\n", - " 7.6283\n", - " 6.7332\n", - " 37.0229\n", - " 9.1180\n", - " 5.0407\n", - " 9.2884\n", - " 7.9118\n", - " [torch.FloatTensor of size 256]),\n", - " ('module.postnet.conv1d_projections.1.conv1d.weight', \n", - " ( 0 ,.,.) = \n", - " 5.2195e-02 1.9907e-02 -7.0854e-02\n", - " 2.7770e-01 1.4073e+00 1.1054e+00\n", - " 5.4670e-01 4.2194e-01 -1.3198e-01\n", - " ⋮ \n", - " 1.9469e-01 1.1500e-01 -1.7242e-01\n", - " 6.7335e-01 1.2255e+00 3.9269e-01\n", - " 1.7183e-03 4.5895e-01 5.6313e-01\n", - " \n", - " ( 1 ,.,.) = \n", - " -1.0461e-01 -1.1721e-01 -2.7959e-01\n", - " 2.5528e-01 7.7451e-01 4.1646e-01\n", - " 9.0024e-02 4.9471e-01 6.7004e-01\n", - " ⋮ \n", - " -1.1452e-01 3.7357e-01 -5.2436e-01\n", - " 8.3869e-01 1.3527e+00 6.5560e-01\n", - " 3.7751e-01 6.1575e-01 5.9562e-02\n", - " \n", - " ( 2 ,.,.) = \n", - " 9.4125e-02 8.5159e-02 1.2492e-01\n", - " 8.1844e-01 1.6799e+00 1.2010e+00\n", - " 6.1985e-01 8.4654e-01 5.3842e-01\n", - " ⋮ \n", - " 1.4908e-02 1.7344e-01 -2.5035e-03\n", - " 1.3253e+00 1.9885e+00 1.3415e+00\n", - " 6.8385e-01 7.1794e-01 3.1948e-01\n", - " ... \n", - " \n", - " (77 ,.,.) = \n", - " -1.0669e-01 -8.0844e-02 -2.5232e-01\n", - " 1.1422e-02 1.0861e+00 5.3154e-01\n", - " 5.2617e-01 6.5394e-01 -6.2136e-02\n", - " ⋮ \n", - " 4.6888e-01 4.8954e-01 2.3976e-01\n", - " 9.7884e-01 1.8881e+00 5.1218e-01\n", - " 1.9927e-01 7.5671e-01 6.0120e-03\n", - " \n", - " (78 ,.,.) = \n", - " -1.1719e-01 4.8443e-02 9.3944e-02\n", - " 5.3119e-01 1.1578e+00 4.9808e-01\n", - " 8.0624e-01 1.2984e+00 4.4143e-01\n", - " ⋮ \n", - " 3.4782e-01 4.7327e-01 1.8600e-01\n", - " 8.2889e-01 1.0548e+00 4.3066e-01\n", - " 2.2504e-01 2.0290e-01 2.1498e-01\n", - " \n", - " (79 ,.,.) = \n", - " 1.1439e-01 -1.7348e-02 -3.0525e-01\n", - " 2.0294e-01 7.2653e-01 4.0443e-01\n", - " 4.9828e-01 4.0238e-01 1.2768e-01\n", - " ⋮ \n", - " 2.4098e-01 1.2117e-01 4.9355e-01\n", - " 4.3716e-01 8.7794e-01 4.6880e-01\n", - " 3.7854e-01 4.1184e-01 3.0943e-01\n", - " [torch.FloatTensor of size 80x256x3]),\n", - " ('module.postnet.conv1d_projections.1.bn.weight', \n", - " 1.1131\n", - " 0.9724\n", - " 1.0952\n", - " 2.0732\n", - " 0.5533\n", - " 1.1140\n", - " 0.6586\n", - " 0.9462\n", - " 0.5307\n", - " 0.8061\n", - " 1.5967\n", - " 0.8221\n", - " 0.8902\n", - " 0.9065\n", - " 0.4332\n", - " 1.3648\n", - " 1.5116\n", - " 0.8725\n", - " 0.8722\n", - " 1.0873\n", - " 1.2751\n", - " 0.9191\n", - " 0.9641\n", - " 0.8794\n", - " 0.4741\n", - " 0.7738\n", - " 0.7530\n", - " 0.6498\n", - " 0.9927\n", - " 1.0602\n", - " 1.1159\n", - " 1.1088\n", - " 0.7379\n", - " 0.8107\n", - " 1.4319\n", - " 0.9653\n", - " 0.6382\n", - " 0.4784\n", - " 0.4891\n", - " 1.1939\n", - " 0.7339\n", - " 0.6188\n", - " 0.8833\n", - " 2.4050\n", - " -0.3177\n", - " 0.9896\n", - " 1.1539\n", - " 0.9532\n", - " 1.2655\n", - " 2.0908\n", - " 0.8568\n", - " 0.6974\n", - " 1.0724\n", - " 1.1096\n", - " 0.8678\n", - " 0.8552\n", - " 1.0003\n", - " 0.7046\n", - " 1.0005\n", - " 1.0480\n", - " 0.5479\n", - " 1.1870\n", - " 0.2342\n", - " 1.0157\n", - " 1.1361\n", - " 1.0313\n", - " 0.9024\n", - " 1.1572\n", - " 0.1972\n", - " 0.7557\n", - " 1.2411\n", - " 1.2459\n", - " 1.3304\n", - " 0.9391\n", - " 0.6778\n", - " 0.8988\n", - " 1.1606\n", - " 1.3418\n", - " 1.2340\n", - " 0.6855\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_projections.1.bn.bias', \n", - " -0.3643\n", - " -0.1151\n", - " -0.4266\n", - " -0.2956\n", - " 0.0577\n", - " -0.4398\n", - " -0.3374\n", - " -0.4977\n", - " -0.3623\n", - " -0.4534\n", - " -0.2369\n", - " -0.1629\n", - " -0.4315\n", - " -0.2628\n", - " -0.1724\n", - " -0.5756\n", - " -0.1301\n", - " -0.4351\n", - " -0.3917\n", - " -0.2696\n", - " -0.1161\n", - " -0.4591\n", - " -0.4481\n", - " -0.4318\n", - " 0.0008\n", - " -0.2230\n", - " -0.2198\n", - " -0.4657\n", - " -0.4189\n", - " -0.2279\n", - " -0.5386\n", - " -0.5261\n", - " -0.4154\n", - " -0.3129\n", - " -0.5103\n", - " -0.2308\n", - " -0.1940\n", - " -0.2578\n", - " -0.3725\n", - " -0.5918\n", - " 0.2227\n", - " -0.5157\n", - " -0.2997\n", - " -0.6149\n", - " -0.2551\n", - " -0.6179\n", - " -0.1135\n", - " -0.2885\n", - " -0.2444\n", - " -0.5553\n", - " 0.0099\n", - " -0.3510\n", - " -0.4529\n", - " -0.2292\n", - " 0.0338\n", - " -0.4650\n", - " -0.5090\n", - " -0.1376\n", - " -0.2306\n", - " -0.4613\n", - " -0.1367\n", - " -0.1225\n", - " -0.1226\n", - " -0.3641\n", - " -0.1382\n", - " -0.4473\n", - " -0.4051\n", - " -0.2548\n", - " -0.0901\n", - " 0.0033\n", - " -0.5289\n", - " -0.6576\n", - " -0.3296\n", - " -0.3922\n", - " -0.2572\n", - " -0.3455\n", - " -0.3026\n", - " -0.3475\n", - " -0.1392\n", - " -0.1041\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_projections.1.bn.running_mean', \n", - " 4794.6543\n", - " 3585.3281\n", - " 4854.7217\n", - " 5939.0063\n", - " 2431.0156\n", - " 4948.5771\n", - " 3388.5972\n", - " 4649.7622\n", - " 2335.0654\n", - " 2515.7034\n", - " 5599.3979\n", - " 2067.6697\n", - " 3672.5522\n", - " 2640.3167\n", - " 1735.0277\n", - " 5550.9165\n", - " 6470.2280\n", - " 4514.9443\n", - " 3035.8079\n", - " 3553.8733\n", - " 6698.5781\n", - " 3057.2085\n", - " 3212.4167\n", - " 3854.0215\n", - " 832.7523\n", - " 3202.8611\n", - " 1762.6179\n", - " 2960.1370\n", - " 4398.3604\n", - " 3143.0359\n", - " 4999.7422\n", - " 5101.7256\n", - " 3999.0593\n", - " 3128.9534\n", - " 4951.9438\n", - " 5651.3735\n", - " 2015.0485\n", - " 1073.7839\n", - " 3401.7195\n", - " 3991.4004\n", - " 3251.1260\n", - " 2128.0889\n", - " 3225.2791\n", - " 8126.6465\n", - " -1240.8994\n", - " 4030.9817\n", - " 5127.5361\n", - " 4162.8989\n", - " 5225.8721\n", - " 7719.8164\n", - " 3666.3826\n", - " 3584.5942\n", - " 4275.7173\n", - " 3859.2456\n", - " 3013.1509\n", - " 3769.0432\n", - " 3723.0259\n", - " 2336.5325\n", - " 4272.7793\n", - " 4369.0854\n", - " 1019.1653\n", - " 4665.3867\n", - " 1117.1483\n", - " 4695.1147\n", - " 3874.8955\n", - " 3881.8813\n", - " 4239.8242\n", - " 4921.7319\n", - " 888.8702\n", - " 4234.1348\n", - " 3747.1763\n", - " 3375.3000\n", - " 3834.5476\n", - " 3834.1763\n", - " 3178.7554\n", - " 3425.8835\n", - " 4922.0522\n", - " 5076.1162\n", - " 3843.5701\n", - " 2470.2336\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.conv1d_projections.1.bn.running_var', \n", - " 2238.4553\n", - " 780.7103\n", - " 2429.7249\n", - " 2660.2173\n", - " 233.1463\n", - " 1949.3705\n", - " 633.8268\n", - " 1360.0896\n", - " 263.0512\n", - " 484.6798\n", - " 2081.5149\n", - " 235.7928\n", - " 674.4207\n", - " 409.4161\n", - " 297.9118\n", - " 2178.4915\n", - " 5070.1694\n", - " 1261.4553\n", - " 740.6363\n", - " 978.0815\n", - " 5075.9692\n", - " 579.2091\n", - " 738.3559\n", - " 1188.6294\n", - " 91.6367\n", - " 754.7532\n", - " 339.0374\n", - " 574.3027\n", - " 1214.8538\n", - " 713.2219\n", - " 2442.1743\n", - " 2224.1912\n", - " 1043.5409\n", - " 392.1762\n", - " 1358.2344\n", - " 3114.9062\n", - " 257.3273\n", - " 276.4440\n", - " 549.5962\n", - " 817.6767\n", - " 609.9324\n", - " 393.8593\n", - " 394.2612\n", - " 4004.6206\n", - " 97.1118\n", - " 744.3381\n", - " 2400.0366\n", - " 1287.6052\n", - " 1797.8269\n", - " 5475.1440\n", - " 891.5662\n", - " 755.5529\n", - " 1328.1738\n", - " 1087.8168\n", - " 711.0801\n", - " 859.1395\n", - " 937.4152\n", - " 276.7247\n", - " 1025.1550\n", - " 963.1095\n", - " 172.1337\n", - " 1515.5322\n", - " 94.4020\n", - " 2225.9226\n", - " 1343.9473\n", - " 659.2188\n", - " 1427.4015\n", - " 2310.4495\n", - " 50.0436\n", - " 1735.0659\n", - " 795.8002\n", - " 596.3701\n", - " 951.0836\n", - " 914.1016\n", - " 672.8875\n", - " 560.1312\n", - " 2155.1333\n", - " 2150.8623\n", - " 832.1367\n", - " 319.0779\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.pre_highway.weight', \n", - " 6.2643e-02 2.4435e-03 -1.2216e-02 ... -5.7193e-02 -9.9890e-02 1.6946e-02\n", - " -1.0881e-01 7.2113e-02 9.5153e-02 ... -1.0533e-01 -8.7101e-02 4.5390e-02\n", - " -3.6340e-04 -6.7453e-02 -8.3466e-02 ... -7.3356e-02 5.7696e-02 -4.9411e-02\n", - " ... ⋱ ... \n", - " 9.3740e-02 -3.9298e-02 6.4824e-02 ... 1.1028e-01 5.7303e-02 -4.1076e-02\n", - " 2.2935e-02 9.1074e-02 -8.9565e-02 ... 9.3405e-02 -5.5724e-02 -7.9542e-02\n", - " 6.1049e-02 2.0629e-02 2.0692e-02 ... -3.5433e-02 -8.5093e-02 -2.7144e-02\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.0.H.weight', \n", - " 2.4867e-01 4.1168e-02 -5.3582e-02 ... -9.9787e-02 3.2798e-01 -5.0461e-02\n", - " -6.6196e-03 -1.9700e-01 -1.6881e-01 ... 4.9598e-01 -7.2049e-02 2.4578e-01\n", - " 2.2826e-01 -2.1728e-01 2.0021e-01 ... 3.1759e-01 2.9525e-02 1.7780e-01\n", - " ... ⋱ ... \n", - " 6.7725e-02 6.6767e-01 -1.8588e-01 ... 9.5651e-01 3.6561e-02 5.3880e-02\n", - " 8.1281e-02 -1.3331e-01 2.2614e-01 ... 1.0429e-01 -1.0845e-01 1.0289e-01\n", - " -8.0866e-02 7.2808e-02 2.8952e-01 ... -3.7574e-02 -1.4812e-01 2.7187e-01\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.0.H.bias', \n", - " -0.1444\n", - " -0.2888\n", - " -0.3170\n", - " 0.0658\n", - " -0.0633\n", - " -0.2048\n", - " -0.0974\n", - " 0.2035\n", - " 0.0461\n", - " 0.1384\n", - " -0.1181\n", - " 0.1240\n", - " -0.0482\n", - " 0.0639\n", - " -0.1455\n", - " -0.3379\n", - " -0.1332\n", - " -0.2531\n", - " 0.1723\n", - " 0.3509\n", - " 0.0866\n", - " 0.0905\n", - " 0.1788\n", - " 0.1534\n", - " -0.0045\n", - " -0.2354\n", - " -0.1100\n", - " 0.0690\n", - " 0.2795\n", - " -0.1951\n", - " -0.0301\n", - " 0.0274\n", - " 0.8453\n", - " 0.1355\n", - " -0.1199\n", - " 0.3028\n", - " 0.0114\n", - " -0.3806\n", - " -0.1682\n", - " 0.2034\n", - " 0.4429\n", - " -0.4412\n", - " 0.0460\n", - " 0.0364\n", - " -0.0529\n", - " -0.1125\n", - " -0.1021\n", - " 0.1971\n", - " 0.0005\n", - " -0.0324\n", - " 0.1708\n", - " 0.3175\n", - " -0.6596\n", - " 0.0186\n", - " 0.2776\n", - " -0.1286\n", - " 0.0511\n", - " -0.1114\n", - " 0.1317\n", - " -0.0814\n", - " 0.1630\n", - " -0.0739\n", - " -0.0087\n", - " -0.3484\n", - " -0.3247\n", - " 0.1713\n", - " 0.1003\n", - " 0.1114\n", - " 0.1164\n", - " 0.2701\n", - " 0.2484\n", - " 0.0330\n", - " -0.0751\n", - " -0.0210\n", - " 0.0601\n", - " -0.1773\n", - " 0.1507\n", - " 0.0427\n", - " -0.1083\n", - " -0.0843\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.highways.0.T.weight', \n", - " 1.0957e-01 2.7731e-01 -1.2678e-01 ... -8.3220e-02 -6.2637e-02 -2.5856e-01\n", - " 1.7775e-02 2.9560e-01 6.6594e-02 ... -1.6999e-01 -1.0914e-01 9.8891e-02\n", - " 1.2318e-01 7.0119e-02 1.5634e-01 ... -1.2690e-01 8.3795e-02 -3.7894e-02\n", - " ... ⋱ ... \n", - " -6.3693e-02 -6.7203e-01 -2.5071e-01 ... 7.2427e-02 4.0980e-01 3.5086e-01\n", - " 1.4521e-01 -2.2465e-01 -1.8351e-01 ... -3.6090e-01 -1.9848e-01 7.7147e-02\n", - " 1.6308e-01 -6.5072e-01 -1.4871e-02 ... -1.8386e-01 -1.3974e-01 3.3131e-01\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.0.T.bias', \n", - " -0.9706\n", - " -0.9539\n", - " -1.0019\n", - " -0.6401\n", - " -0.6802\n", - " -0.7004\n", - " -1.1880\n", - " -1.0708\n", - " -0.8670\n", - " -0.8222\n", - " -1.0117\n", - " -1.0952\n", - " -0.9540\n", - " -1.0298\n", - " -1.0624\n", - " -0.6120\n", - " -0.3936\n", - " -0.8775\n", - " -1.0260\n", - " -0.9652\n", - " -0.4894\n", - " -0.9425\n", - " -0.9753\n", - " -0.8730\n", - " -1.3253\n", - " -1.0251\n", - " -1.0140\n", - " -0.8511\n", - " -0.7494\n", - " -1.0202\n", - " -1.0308\n", - " -0.9396\n", - " -1.0584\n", - " -0.7414\n", - " -0.9854\n", - " -0.8145\n", - " -1.1791\n", - " -1.1111\n", - " -0.7695\n", - " -0.7219\n", - " -0.7493\n", - " -1.2727\n", - " -1.0238\n", - " -0.5376\n", - " -1.0984\n", - " -0.8949\n", - " -0.7888\n", - " -0.9382\n", - " -0.7201\n", - " -0.5765\n", - " -0.8168\n", - " -1.0600\n", - " -1.0613\n", - " -0.8644\n", - " -0.9836\n", - " -1.3913\n", - " -1.2095\n", - " -1.0889\n", - " -0.6595\n", - " -0.4375\n", - " -0.9259\n", - " -1.0337\n", - " -1.2776\n", - " -0.7168\n", - " -1.1136\n", - " -1.0901\n", - " -0.8697\n", - " -0.9424\n", - " -1.0496\n", - " -0.7632\n", - " -0.7484\n", - " -1.1693\n", - " -0.8174\n", - " -0.9601\n", - " -0.8869\n", - " -0.8564\n", - " -0.6329\n", - " -0.7576\n", - " -0.8810\n", - " -1.0124\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.highways.1.H.weight', \n", - " 9.1230e-01 -1.3596e-01 -1.4093e-01 ... 1.5030e-01 7.6379e-01 -3.3012e-01\n", - " 6.1809e-01 1.2302e+00 -4.1767e-01 ... -2.9080e-01 -6.2494e-02 2.1230e-01\n", - " -1.0950e-01 2.2074e-01 6.3138e-01 ... 1.8428e-01 -7.8434e-02 -3.1081e-01\n", - " ... ⋱ ... \n", - " 1.4937e-01 4.2980e-01 2.6760e-02 ... 6.7943e-01 4.4142e-01 -2.7297e-01\n", - " -3.7320e-02 -1.9871e-01 1.0014e-01 ... -1.4039e-01 8.2422e-02 -4.0768e-01\n", - " -1.1515e-01 -2.6997e-01 1.8682e-01 ... -7.9322e-02 1.3985e-01 1.2204e-01\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.1.H.bias', \n", - " -0.2227\n", - " 0.0567\n", - " 0.0236\n", - " 0.2375\n", - " -0.0518\n", - " 0.0564\n", - " 0.0382\n", - " -0.2974\n", - " -0.1225\n", - " -0.0670\n", - " -0.3467\n", - " 0.0550\n", - " -0.1570\n", - " -0.1601\n", - " -0.0278\n", - " -0.1593\n", - " -0.0291\n", - " -0.0163\n", - " -0.0050\n", - " 0.0043\n", - " 0.0402\n", - " 0.1176\n", - " 0.3112\n", - " 0.1470\n", - " -0.1887\n", - " -0.1433\n", - " 0.0668\n", - " -0.0419\n", - " -0.0184\n", - " -0.1379\n", - " -0.1999\n", - " 0.2844\n", - " -0.2743\n", - " -0.2320\n", - " 0.1254\n", - " -0.1168\n", - " 0.0154\n", - " -0.2265\n", - " -0.0329\n", - " 0.0959\n", - " -0.0949\n", - " -0.1417\n", - " 0.2770\n", - " -0.3467\n", - " -0.1311\n", - " 0.0278\n", - " 0.0717\n", - " 0.0237\n", - " -0.0419\n", - " 0.1390\n", - " -0.2300\n", - " -0.0031\n", - " -0.1261\n", - " -0.1790\n", - " 0.1752\n", - " -0.1531\n", - " -0.1270\n", - " 0.0876\n", - " 0.3785\n", - " 0.1306\n", - " -0.0067\n", - " -0.0225\n", - " -0.1686\n", - " -0.1789\n", - " 0.3102\n", - " 0.5037\n", - " 0.1552\n", - " -0.2649\n", - " 0.0821\n", - " 0.0415\n", - " -0.0635\n", - " 0.0556\n", - " -0.0018\n", - " 0.0486\n", - " -0.2480\n", - " 0.1004\n", - " -0.1115\n", - " 0.0045\n", - " -0.0126\n", - " -0.1510\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.highways.1.T.weight', \n", - " -4.9927e-01 -2.6072e-01 -1.0327e+00 ... -3.0688e-01 9.2184e-01 -8.2108e-01\n", - " -2.4358e-01 -2.5978e-01 -1.2319e-01 ... -1.5505e-02 4.4448e-01 2.7695e-01\n", - " 2.6090e-01 -3.5126e-01 2.7459e-02 ... 5.0993e-02 -7.9095e-02 1.7979e-01\n", - " ... ⋱ ... \n", - " 2.3238e-03 -1.8175e-01 -2.5332e-01 ... -2.2763e-01 -4.7735e-02 1.1920e-01\n", - " -1.3132e-01 -3.8461e-01 -3.0022e-01 ... -2.1166e-02 -1.6448e-01 -2.1012e-01\n", - " -1.8645e-01 -3.8559e-01 2.9771e-02 ... 1.2365e-01 6.4848e-02 -2.0881e-01\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.1.T.bias', \n", - " -0.9412\n", - " -0.7066\n", - " -0.6889\n", - " -0.9583\n", - " -0.9480\n", - " -0.7388\n", - " -0.5661\n", - " -0.9317\n", - " -1.0198\n", - " -0.9792\n", - " -0.1181\n", - " -0.8724\n", - " -1.0719\n", - " -0.8781\n", - " -0.9275\n", - " -0.5863\n", - " -0.5480\n", - " -0.6873\n", - " -0.7334\n", - " -0.9447\n", - " -0.5334\n", - " -0.8045\n", - " -0.7259\n", - " -0.4503\n", - " -1.1942\n", - " -0.7621\n", - " -0.8239\n", - " -0.7301\n", - " -0.7982\n", - " -0.9111\n", - " -0.9003\n", - " -0.7060\n", - " -0.2966\n", - " -1.0202\n", - " -0.8996\n", - " -1.1389\n", - " -1.0155\n", - " -1.0354\n", - " -0.5346\n", - " -0.5635\n", - " -0.5768\n", - " -0.9595\n", - " -0.6603\n", - " -0.6183\n", - " -1.1203\n", - " -0.8846\n", - " -0.4604\n", - " -0.8136\n", - " -0.5419\n", - " -0.5879\n", - " -0.8771\n", - " -0.9569\n", - " -0.8667\n", - " -0.8589\n", - " -0.8157\n", - " -0.9056\n", - " -1.0193\n", - " -0.8903\n", - " -0.1502\n", - " -1.0415\n", - " -1.0744\n", - " -0.6627\n", - " -1.0806\n", - " -0.6878\n", - " -0.6414\n", - " -0.6909\n", - " -0.9609\n", - " -0.6565\n", - " -0.8993\n", - " -0.8040\n", - " -1.0628\n", - " -0.7982\n", - " -0.5010\n", - " -0.6472\n", - " -0.8983\n", - " -0.9107\n", - " -0.4975\n", - " -0.5973\n", - " -0.9040\n", - " -0.9348\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.highways.2.H.weight', \n", - " -2.4274e-01 -2.7250e-01 4.9373e-02 ... 1.1653e-01 4.0595e-01 -1.3510e-01\n", - " 3.1051e-02 -2.2746e-01 8.5297e-02 ... -9.7518e-02 7.8655e-02 -7.2070e-02\n", - " -1.0259e-01 -1.5005e-01 7.2323e-01 ... 2.5662e-02 2.6174e-01 3.0316e-02\n", - " ... ⋱ ... \n", - " -2.1651e-02 -2.5406e-01 1.0460e-01 ... -2.5282e-01 2.2565e-01 2.3399e-01\n", - " -1.0750e-01 1.9786e-01 3.0018e-01 ... -2.9912e-01 6.4900e-02 5.8003e-02\n", - " 1.6612e-02 2.2036e-03 3.4828e-01 ... -3.8414e-03 2.4652e-01 4.8551e-01\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.2.H.bias', \n", - " -0.0003\n", - " -0.0291\n", - " 0.0044\n", - " 0.0063\n", - " 0.0281\n", - " 0.0094\n", - " 0.0038\n", - " -0.0918\n", - " -0.0292\n", - " -0.0110\n", - " 0.0265\n", - " -0.0524\n", - " -0.0170\n", - " -0.1395\n", - " -0.0965\n", - " -0.0057\n", - " -0.1812\n", - " -0.0947\n", - " -0.2224\n", - " 0.0169\n", - " 0.0092\n", - " 0.0035\n", - " -0.0374\n", - " -0.0112\n", - " -0.1434\n", - " -0.0074\n", - " -0.1889\n", - " -0.0438\n", - " -0.1702\n", - " -0.0233\n", - " 0.0092\n", - " -0.0700\n", - " -0.0268\n", - " 0.0029\n", - " 0.0047\n", - " -0.0607\n", - " -0.0209\n", - " 0.0061\n", - " 0.0031\n", - " -0.1285\n", - " 0.0135\n", - " -0.0079\n", - " -0.1550\n", - " -0.1114\n", - " -0.0640\n", - " -0.0217\n", - " -0.0174\n", - " -0.1433\n", - " -0.0250\n", - " -0.0081\n", - " -0.0363\n", - " -0.0123\n", - " 0.0056\n", - " -0.0015\n", - " 0.0048\n", - " -0.0157\n", - " 0.0011\n", - " -0.1992\n", - " -0.0086\n", - " -0.0597\n", - " -0.0104\n", - " -0.1039\n", - " -0.0304\n", - " -0.1452\n", - " 0.0157\n", - " -0.1376\n", - " 0.0116\n", - " -0.2570\n", - " 0.0149\n", - " 0.0108\n", - " -0.3532\n", - " -0.0075\n", - " -0.0160\n", - " -0.0134\n", - " -0.0906\n", - " -0.1336\n", - " 0.0165\n", - " -0.0841\n", - " -0.0459\n", - " -0.0090\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.highways.2.T.weight', \n", - " 3.6572e-01 5.9279e-02 -1.4287e-01 ... -2.6212e-01 -1.1769e-01 2.0280e-01\n", - " 1.6128e-01 -5.0609e-02 1.7673e-01 ... -1.5705e-01 7.6402e-02 9.5267e-02\n", - " -1.4162e-01 1.2445e-01 2.3100e-01 ... -9.0434e-02 1.4724e-02 2.4342e-02\n", - " ... ⋱ ... \n", - " -1.0872e-01 2.2763e-01 3.7120e-01 ... 2.9659e-01 5.8435e-02 -3.6852e-01\n", - " 2.0416e-01 -6.6940e-03 4.8543e-02 ... -5.4834e-02 2.4496e-01 -3.3553e-02\n", - " 1.0520e-01 2.6947e-01 -3.5717e-01 ... -1.0301e+00 5.2067e-01 2.1574e-01\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.2.T.bias', \n", - " -0.7631\n", - " -1.1226\n", - " -0.8584\n", - " -0.7720\n", - " -0.7048\n", - " -0.7164\n", - " -0.9709\n", - " -0.9457\n", - " -0.8494\n", - " -0.4088\n", - " -0.6414\n", - " -1.0601\n", - " -1.0043\n", - " -0.9389\n", - " -0.8011\n", - " -0.6059\n", - " -0.6583\n", - " -0.7376\n", - " -0.4929\n", - " -1.0052\n", - " -0.7678\n", - " -1.0918\n", - " -0.9072\n", - " -0.7922\n", - " -1.0403\n", - " -1.1020\n", - " -1.0353\n", - " -0.7563\n", - " -0.8397\n", - " -0.7072\n", - " -0.9279\n", - " -0.6711\n", - " -0.3463\n", - " -0.8225\n", - " -0.7371\n", - " -1.0051\n", - " -1.1878\n", - " -0.9544\n", - " -1.0134\n", - " -0.8527\n", - " -0.9266\n", - " -1.0247\n", - " -1.0826\n", - " -0.7876\n", - " -1.2755\n", - " -0.7468\n", - " -0.5426\n", - " -1.0819\n", - " -0.4936\n", - " -1.2068\n", - " -0.6783\n", - " -0.6852\n", - " -0.9803\n", - " -0.7238\n", - " -0.9590\n", - " -0.6381\n", - " -0.9939\n", - " -1.0709\n", - " -0.8101\n", - " -0.6400\n", - " -0.4283\n", - " -0.9627\n", - " -0.9541\n", - " -0.7106\n", - " -0.7013\n", - " -1.0249\n", - " -0.9598\n", - " -0.3898\n", - " -1.1135\n", - " -0.7361\n", - " -1.0572\n", - " -1.1059\n", - " -0.5316\n", - " -0.9943\n", - " -0.6925\n", - " -0.8180\n", - " -0.9290\n", - " -0.9704\n", - " -0.9643\n", - " -0.6030\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.highways.3.H.weight', \n", - " -2.6135e-01 1.6507e-01 8.8397e-02 ... 5.1202e-04 -2.0750e-01 1.0729e-01\n", - " -1.2196e-01 -2.7860e-01 -5.0616e-02 ... 5.1362e-01 -1.6859e-01 -2.2015e-01\n", - " -1.5429e-01 1.3958e-01 2.1380e-01 ... -2.3058e-01 -3.3214e-03 6.4230e-02\n", - " ... ⋱ ... \n", - " 1.1218e-01 1.5627e-01 3.7896e-02 ... 1.4440e-01 4.5716e-02 5.7027e-02\n", - " -9.7311e-02 -4.6075e-02 9.4683e-02 ... -4.8174e-02 2.6431e-01 -4.8024e-02\n", - " -5.8064e-02 -2.0418e-01 -3.5700e-02 ... 3.8692e-01 1.0238e-01 -1.2282e-01\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.3.H.bias', \n", - " -0.0055\n", - " -0.0279\n", - " 0.0057\n", - " 0.0040\n", - " -0.1291\n", - " 0.0077\n", - " 0.0094\n", - " -0.0461\n", - " -0.0703\n", - " 0.0151\n", - " -0.0295\n", - " -0.0323\n", - " -0.0245\n", - " 0.0045\n", - " 0.0158\n", - " 0.0180\n", - " 0.0138\n", - " 0.0105\n", - " -0.0382\n", - " -0.0117\n", - " -0.0201\n", - " 0.0082\n", - " 0.0087\n", - " -0.0220\n", - " -0.0206\n", - " -0.0449\n", - " -0.0226\n", - " 0.0170\n", - " -0.0161\n", - " -0.1995\n", - " -0.0356\n", - " -0.0145\n", - " -0.1746\n", - " -0.0214\n", - " 0.0035\n", - " 0.0142\n", - " -0.0630\n", - " 0.0146\n", - " 0.0069\n", - " -0.0204\n", - " -0.1873\n", - " -0.0125\n", - " -0.0455\n", - " -0.2047\n", - " 0.0027\n", - " 0.0093\n", - " 0.0152\n", - " 0.0221\n", - " -0.1992\n", - " 0.0091\n", - " -0.0254\n", - " 0.0187\n", - " -0.0254\n", - " 0.0112\n", - " -0.0168\n", - " 0.0057\n", - " 0.0044\n", - " -0.0189\n", - " -0.1177\n", - " -0.0120\n", - " -0.0111\n", - " -0.1336\n", - " -0.0134\n", - " -0.0094\n", - " -0.0523\n", - " -0.0551\n", - " -0.0800\n", - " 0.0031\n", - " -0.0382\n", - " 0.0032\n", - " 0.0198\n", - " -0.1366\n", - " -0.0048\n", - " -0.0050\n", - " -0.0746\n", - " -0.0078\n", - " 0.0085\n", - " -0.1046\n", - " -0.0479\n", - " -0.1155\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.highways.3.T.weight', \n", - " 3.7147e-01 -9.9447e-02 3.6920e-02 ... -7.1776e-02 -6.0466e-03 2.2426e-01\n", - " -1.7026e-02 9.7215e-02 2.8594e-02 ... -2.9590e-01 -6.8981e-01 2.1689e-01\n", - " -9.5328e-02 7.0704e-02 5.8119e-01 ... -1.4032e-01 4.2478e-02 -1.0148e-01\n", - " ... ⋱ ... \n", - " -4.4207e-03 2.0096e-01 3.9831e-01 ... -2.6489e-01 6.0947e-01 -1.2633e-01\n", - " -3.0431e-01 1.5210e-01 -6.5500e-02 ... 2.9948e-01 -4.6027e-01 4.6829e-02\n", - " 2.5476e-01 1.2621e-02 6.4398e-02 ... 6.0937e-02 -1.7206e-01 1.1278e-01\n", - " [torch.FloatTensor of size 80x80]),\n", - " ('module.postnet.highways.3.T.bias', \n", - " -0.7148\n", - " -0.7708\n", - " -0.9425\n", - " -0.6730\n", - " -0.1652\n", - " -0.4560\n", - " -0.7011\n", - " -0.7110\n", - " -1.1224\n", - " -0.9510\n", - " -0.8533\n", - " -1.1357\n", - " -0.8920\n", - " -1.2216\n", - " -1.0353\n", - " -0.8836\n", - " -0.6786\n", - " -0.4523\n", - " -0.9874\n", - " -0.8611\n", - " -0.5129\n", - " -1.0361\n", - " -0.8023\n", - " -0.9454\n", - " -0.9796\n", - " -0.9083\n", - " -0.9954\n", - " -0.9217\n", - " -0.9016\n", - " -0.8483\n", - " -0.7846\n", - " -0.7049\n", - " -0.7861\n", - " -0.6688\n", - " -0.7724\n", - " -0.8507\n", - " -0.9932\n", - " -1.0269\n", - " -1.1675\n", - " -0.7789\n", - " -0.5356\n", - " -0.7966\n", - " -0.9293\n", - " -0.8827\n", - " -0.9904\n", - " -0.6809\n", - " -0.3062\n", - " -1.1263\n", - " -0.7787\n", - " -0.8213\n", - " -0.9363\n", - " -0.9193\n", - " -1.0666\n", - " -0.9542\n", - " -0.8626\n", - " -1.1382\n", - " -1.1062\n", - " -0.9254\n", - " -0.7698\n", - " -0.9057\n", - " -0.8849\n", - " -0.7466\n", - " -0.6831\n", - " -0.6838\n", - " -0.8847\n", - " -0.5496\n", - " -0.4942\n", - " -0.5930\n", - " -0.8724\n", - " -0.8466\n", - " -0.7382\n", - " -0.7323\n", - " -0.8553\n", - " -0.7705\n", - " -0.5874\n", - " -0.8952\n", - " -0.8524\n", - " -0.9613\n", - " -0.6429\n", - " -0.8777\n", - " [torch.FloatTensor of size 80]),\n", - " ('module.postnet.gru.weight_ih_l0', \n", - " 6.4939e-01 -7.9124e-01 4.0934e-02 ... -3.9241e-01 -1.3943e+00 -4.7296e-01\n", - " 3.7583e-01 5.4636e-01 6.5514e-02 ... -1.0450e+00 2.3660e-01 7.3014e-01\n", - " 3.7677e-01 -3.4706e-01 -5.9777e-01 ... -1.3496e-02 -1.6330e-01 -5.6673e-02\n", - " ... ⋱ ... \n", - " 3.3034e-02 5.7420e-02 2.5007e-01 ... -5.5500e-02 -3.0111e-03 -2.8446e-02\n", - " 2.9808e-02 -7.2426e-02 -2.0248e-01 ... -8.7287e-02 7.4915e-02 5.7272e-02\n", - " -3.2471e-02 1.0483e-03 -7.3799e-02 ... 7.3655e-03 -5.5721e-03 5.8514e-02\n", - " [torch.FloatTensor of size 240x80]),\n", - " ('module.postnet.gru.weight_hh_l0', \n", - " 4.3076e-01 6.5784e-01 -6.0003e-02 ... -4.1792e-01 3.4497e-01 -1.4939e-01\n", - " 4.5665e-02 -2.8360e-01 -9.2253e-01 ... 1.7441e-01 3.9119e-01 1.5422e-01\n", - " -1.8379e-01 5.7175e-01 2.3410e-01 ... 4.4199e-01 -2.0712e-01 6.1023e-01\n", - " ... ⋱ ... \n", - " 5.0059e-02 1.3538e-01 -4.8315e-01 ... -6.3149e+00 -9.0348e-02 -1.4320e-01\n", - " -8.1590e-02 3.6711e-01 -9.3420e-02 ... 1.8250e-01 -4.6494e+00 2.9142e-01\n", - " 9.1866e-02 1.0972e-01 9.2470e-01 ... -1.2952e+00 -1.1462e-01 -2.7750e+00\n", - " [torch.FloatTensor of size 240x80]),\n", - " ('module.postnet.gru.bias_ih_l0', \n", - " -0.2505\n", - " -0.2566\n", - " -0.0974\n", - " 0.0358\n", - " -0.6026\n", - " -0.7274\n", - " -0.4809\n", - " -0.1709\n", - " -0.1794\n", - " 0.1124\n", - " -0.0431\n", - " -0.1600\n", - " -0.3489\n", - " -0.7068\n", - " -0.0657\n", - " -0.4445\n", - " -0.1368\n", - " -0.4630\n", - " -0.8422\n", - " -0.3410\n", - " -0.1546\n", - " -0.3175\n", - " -0.1967\n", - " -0.5859\n", - " -0.1293\n", - " -0.1607\n", - " -0.1061\n", - " -1.0092\n", - " -0.9594\n", - " -0.3489\n", - " -0.3379\n", - " -0.3372\n", - " -0.1905\n", - " -0.2381\n", - " -0.0517\n", - " -0.3846\n", - " -0.6198\n", - " -0.0969\n", - " -0.2238\n", - " -0.5174\n", - " -0.6875\n", - " 0.0439\n", - " -0.3773\n", - " -0.0792\n", - " -0.2570\n", - " -0.2687\n", - " -0.0525\n", - " -0.4541\n", - " 0.1255\n", - " -0.1408\n", - " -0.5055\n", - " -0.0363\n", - " -0.3748\n", - " 0.0178\n", - " -0.1843\n", - " -0.1789\n", - " -0.1925\n", - " -0.6474\n", - " -0.3790\n", - " -0.1726\n", - " -0.4518\n", - " -0.2185\n", - " -0.1005\n", - " -0.0675\n", - " -0.6689\n", - " -0.4840\n", - " -0.2047\n", - " 0.0146\n", - " -0.7786\n", - " 0.0093\n", - " -0.1294\n", - " -0.7795\n", - " -0.0116\n", - " -0.3725\n", - " -0.5102\n", - " -0.0309\n", - " -0.1630\n", - " -0.2327\n", - " -0.1237\n", - " -0.1725\n", - " -0.0096\n", - " 0.2268\n", - " 0.3844\n", - " 0.0508\n", - " -0.3199\n", - " -0.6231\n", - " 0.4347\n", - " -0.4611\n", - " 0.3184\n", - " 0.3416\n", - " -0.1471\n", - " -0.0379\n", - " 0.2543\n", - " 0.0177\n", - " 0.1520\n", - " 0.2060\n", - " -0.2037\n", - " -0.4328\n", - " -0.1686\n", - " -0.3331\n", - " 0.1283\n", - " -0.0728\n", - " -0.1500\n", - " -0.3873\n", - " -0.5087\n", - " 0.0413\n", - " -0.3006\n", - " -0.5866\n", - " -0.0884\n", - " 0.2979\n", - " -0.3157\n", - " -0.0681\n", - " 0.2709\n", - " 0.2048\n", - " 0.2323\n", - " -0.1317\n", - " -0.5729\n", - " 0.5457\n", - " -0.2612\n", - " -0.1786\n", - " -0.0329\n", - " 0.6196\n", - " -0.0559\n", - " 0.1978\n", - " -0.5102\n", - " 0.2469\n", - " 0.3289\n", - " 0.1784\n", - " 0.6776\n", - " -0.0149\n", - " -0.1520\n", - " 0.6013\n", - " -0.0514\n", - " 0.3295\n", - " -0.3994\n", - " -0.3703\n", - " -0.1482\n", - " -0.1113\n", - " 0.2019\n", - " -0.1002\n", - " -0.2029\n", - " -0.2571\n", - " -0.2910\n", - " 0.5520\n", - " -0.1560\n", - " -0.1629\n", - " -0.1631\n", - " -0.0962\n", - " -0.2216\n", - " 0.6470\n", - " -0.0803\n", - " -0.0350\n", - " 0.0309\n", - " -0.4257\n", - " -0.0802\n", - " 0.6969\n", - " 0.2613\n", - " 0.4401\n", - " 0.4948\n", - " 0.0739\n", - " -0.0251\n", - " 0.0023\n", - " 0.0191\n", - " 0.0396\n", - " 0.0194\n", - " 0.0033\n", - " -0.0041\n", - " 0.0449\n", - " 0.0369\n", - " -0.0515\n", - " 0.0297\n", - " -0.0687\n", - " 0.0342\n", - " -0.0017\n", - " 0.0375\n", - " 0.0114\n", - " -0.0395\n", - " -0.0169\n", - " -0.0059\n", - " 0.0648\n", - " 0.0229\n", - " 0.0080\n", - " 0.0081\n", - " 0.0039\n", - " -0.0746\n", - " -0.0197\n", - " 0.0403\n", - " 0.0102\n", - " -0.0143\n", - " 0.0011\n", - " 0.0460\n", - " -0.0270\n", - " -0.0287\n", - " 0.0037\n", - " -0.0048\n", - " 0.0057\n", - " -0.0010\n", - " -0.0220\n", - " -0.0365\n", - " 0.0062\n", - " -0.0006\n", - " -0.0752\n", - " -0.0103\n", - " 0.0092\n", - " 0.0432\n", - " -0.0323\n", - " -0.0246\n", - " -0.0276\n", - " -0.0161\n", - " 0.0710\n", - " -0.0051\n", - " -0.0090\n", - " -0.0139\n", - " 0.0007\n", - " 0.0440\n", - " 0.0234\n", - " -0.0163\n", - " 0.0096\n", - " 0.0006\n", - " -0.0080\n", - " -0.0034\n", - " 0.0223\n", - " 0.0298\n", - " -0.0372\n", - " -0.0089\n", - " -0.0040\n", - " 0.0252\n", - " 0.0349\n", - " 0.0002\n", - " -0.0280\n", - " 0.0382\n", - " 0.0054\n", - " 0.0439\n", - " 0.0573\n", - " -0.0127\n", - " -0.0217\n", - " 0.0431\n", - " -0.0227\n", - " 0.0479\n", - " -0.0526\n", - " [torch.FloatTensor of size 240]),\n", - " ('module.postnet.gru.bias_hh_l0', \n", - " -0.1257\n", - " -0.2917\n", - " -0.2099\n", - " -0.1458\n", - " -0.5051\n", - " -0.7141\n", - " -0.4886\n", - " -0.1806\n", - " -0.0801\n", - " -0.0011\n", - " -0.1355\n", - " -0.0332\n", - " -0.2401\n", - " -0.6729\n", - " 0.0369\n", - " -0.5681\n", - " -0.2924\n", - " -0.4986\n", - " -0.6292\n", - " -0.1636\n", - " 0.0330\n", - " -0.2659\n", - " -0.1499\n", - " -0.6166\n", - " -0.1754\n", - " -0.2659\n", - " -0.0580\n", - " -0.9808\n", - " -0.9100\n", - " -0.2288\n", - " -0.3440\n", - " -0.2564\n", - " -0.0311\n", - " -0.3258\n", - " -0.0530\n", - " -0.4248\n", - " -0.6790\n", - " -0.1839\n", - " -0.0955\n", - " -0.4905\n", - " -0.5771\n", - " 0.0053\n", - " -0.2151\n", - " -0.0441\n", - " -0.1503\n", - " -0.2841\n", - " -0.0549\n", - " -0.4694\n", - " 0.0984\n", - " 0.0305\n", - " -0.6117\n", - " -0.1276\n", - " -0.5375\n", - " -0.0053\n", - " -0.0537\n", - " -0.3159\n", - " -0.3062\n", - " -0.6065\n", - " -0.3066\n", - " -0.1555\n", - " -0.5569\n", - " -0.3278\n", - " -0.2265\n", - " -0.0701\n", - " -0.6201\n", - " -0.5877\n", - " -0.2278\n", - " -0.0455\n", - " -0.7931\n", - " -0.0257\n", - " -0.0752\n", - " -0.8125\n", - " -0.0217\n", - " -0.2383\n", - " -0.7227\n", - " -0.0374\n", - " -0.0956\n", - " -0.0429\n", - " -0.0079\n", - " -0.0540\n", - " -0.0398\n", - " 0.0893\n", - " 0.3214\n", - " -0.0547\n", - " -0.1813\n", - " -0.6316\n", - " 0.3147\n", - " -0.4584\n", - " 0.2949\n", - " 0.4137\n", - " -0.1904\n", - " -0.1737\n", - " 0.3177\n", - " 0.0534\n", - " 0.0006\n", - " 0.1048\n", - " -0.3409\n", - " -0.3505\n", - " -0.0794\n", - " -0.2770\n", - " 0.0726\n", - " -0.0269\n", - " -0.2318\n", - " -0.2172\n", - " -0.4086\n", - " 0.0503\n", - " -0.2107\n", - " -0.4169\n", - " -0.2070\n", - " 0.3670\n", - " -0.3112\n", - " -0.2121\n", - " 0.2480\n", - " 0.2252\n", - " 0.2346\n", - " 0.0665\n", - " -0.5720\n", - " 0.5851\n", - " -0.1444\n", - " -0.3231\n", - " -0.0742\n", - " 0.4876\n", - " -0.1952\n", - " 0.0997\n", - " -0.5438\n", - " 0.1961\n", - " 0.4218\n", - " 0.2565\n", - " 0.6775\n", - " -0.0210\n", - " -0.2373\n", - " 0.4852\n", - " 0.0347\n", - " 0.3227\n", - " -0.5669\n", - " -0.2215\n", - " -0.0340\n", - " 0.0668\n", - " 0.2541\n", - " -0.1506\n", - " -0.1952\n", - " -0.1168\n", - " -0.3041\n", - " 0.5843\n", - " -0.1984\n", - " -0.0784\n", - " -0.1815\n", - " 0.0982\n", - " -0.1089\n", - " 0.6586\n", - " -0.1627\n", - " 0.1438\n", - " -0.0038\n", - " -0.5779\n", - " -0.1350\n", - " 0.5497\n", - " 0.3276\n", - " 0.4120\n", - " 0.4888\n", - " 0.1240\n", - " 0.0674\n", - " -0.0034\n", - " -0.0403\n", - " -0.0864\n", - " -0.0746\n", - " -0.0197\n", - " 0.0273\n", - " -0.1097\n", - " -0.0878\n", - " 0.0931\n", - " -0.0665\n", - " 0.1534\n", - " -0.0978\n", - " 0.0156\n", - " -0.0760\n", - " -0.0529\n", - " 0.0921\n", - " 0.0650\n", - " 0.0317\n", - " -0.1756\n", - " -0.0500\n", - " -0.0205\n", - " -0.0125\n", - " -0.0099\n", - " 0.1807\n", - " 0.0561\n", - " -0.0880\n", - " -0.0888\n", - " 0.1137\n", - " -0.0067\n", - " -0.1369\n", - " 0.0769\n", - " 0.0662\n", - " -0.0109\n", - " 0.0135\n", - " -0.0188\n", - " 0.0013\n", - " 0.0450\n", - " 0.0826\n", - " -0.0246\n", - " 0.0009\n", - " 0.1502\n", - " 0.0246\n", - " -0.0208\n", - " -0.1099\n", - " 0.0851\n", - " 0.0495\n", - " 0.0956\n", - " 0.0220\n", - " -0.1547\n", - " 0.0215\n", - " 0.0153\n", - " 0.0452\n", - " 0.0081\n", - " -0.0984\n", - " -0.0656\n", - " 0.0387\n", - " -0.0298\n", - " 0.0016\n", - " 0.0163\n", - " 0.0176\n", - " -0.0551\n", - " -0.0687\n", - " 0.0780\n", - " 0.0383\n", - " 0.0157\n", - " -0.0634\n", - " -0.0696\n", - " -0.0070\n", - " 0.0538\n", - " -0.0851\n", - " -0.0383\n", - " -0.0924\n", - " -0.1641\n", - " 0.0580\n", - " 0.0465\n", - " -0.0984\n", - " 0.0515\n", - " -0.1018\n", - " 0.1188\n", - " [torch.FloatTensor of size 240]),\n", - " ('module.postnet.gru.weight_ih_l0_reverse', \n", - " -1.9571e-01 -7.1658e-02 -1.3314e-01 ... -8.6115e-02 9.6457e-02 3.1947e-01\n", - " 2.7792e-01 -6.2562e-01 -2.6053e-01 ... 1.8648e-01 -2.5338e-01 -4.5606e-01\n", - " 3.9198e-01 3.2769e-01 1.4105e-01 ... -2.6209e-01 6.2235e-01 -3.0175e-01\n", - " ... ⋱ ... \n", - " 3.2538e-02 -5.2638e-02 -2.9837e-02 ... -1.7957e-01 3.9589e-02 1.6884e-01\n", - " 1.6656e-01 2.3316e-01 7.8167e-02 ... 6.7045e-03 1.5034e-02 2.1484e-01\n", - " 6.0119e-02 4.4352e-02 2.4869e-02 ... -1.8634e-01 1.0526e-01 -5.2547e-01\n", - " [torch.FloatTensor of size 240x80]),\n", - " ('module.postnet.gru.weight_hh_l0_reverse', \n", - " 1.7044e+00 -4.6533e-01 5.0316e-02 ... 6.1649e-02 -8.8314e-02 -2.6902e-01\n", - " -5.2162e-01 -5.4064e-01 2.2873e-01 ... -7.5252e-01 -5.2345e-02 -2.6505e-02\n", - " 1.8254e-01 -2.3672e-01 2.1709e-01 ... 1.1011e+00 -2.7084e-01 -3.3749e-01\n", - " ... ⋱ ... \n", - " -9.3583e-02 -4.7568e-02 3.3401e-01 ... -1.2074e+00 4.2794e-02 1.9333e+00\n", - " -2.1337e-01 -3.5673e-01 -7.3989e-02 ... -2.4008e-01 -9.2097e-01 1.1843e-01\n", - " 7.3042e-03 3.4483e-01 6.1662e-02 ... -3.6307e-01 1.1115e-01 1.4985e+00\n", - " [torch.FloatTensor of size 240x80]),\n", - " ('module.postnet.gru.bias_ih_l0_reverse', \n", - " -0.2834\n", - " -0.1341\n", - " -0.1891\n", - " -0.1270\n", - " -0.0979\n", - " 0.0250\n", - " -0.3082\n", - " -0.1290\n", - " -0.2570\n", - " -0.2486\n", - " 0.0449\n", - " -0.0050\n", - " -0.2715\n", - " -0.1192\n", - " -0.1564\n", - " -0.1569\n", - " -0.2715\n", - " -0.1705\n", - " -0.0844\n", - " -0.4546\n", - " -0.2333\n", - " -0.1900\n", - " -0.4071\n", - " 0.1633\n", - " -0.4212\n", - " -0.0644\n", - " -0.0426\n", - " -0.0172\n", - " -0.0573\n", - " -0.1484\n", - " -0.0129\n", - " -0.0973\n", - " -0.0774\n", - " -0.0528\n", - " -0.0528\n", - " -0.2679\n", - " -0.0995\n", - " -0.2083\n", - " -0.3124\n", - " 0.0962\n", - " -0.2425\n", - " -0.0225\n", - " -0.2383\n", - " -0.0144\n", - " -0.0467\n", - " -0.0518\n", - " -0.0031\n", - " 0.1910\n", - " -0.0607\n", - " -0.1113\n", - " -0.3463\n", - " 0.1373\n", - " -0.2563\n", - " -0.2841\n", - " -0.2629\n", - " -0.1472\n", - " -0.1935\n", - " -0.1878\n", - " -0.0706\n", - " -0.5098\n", - " -0.1341\n", - " -0.0148\n", - " -0.2255\n", - " -0.0354\n", - " 0.0876\n", - " -0.0750\n", - " -0.1063\n", - " -0.1258\n", - " -0.3909\n", - " -0.0564\n", - " -0.2521\n", - " -0.1769\n", - " -0.2716\n", - " -0.1493\n", - " -0.0496\n", - " -0.1065\n", - " -0.3822\n", - " -0.1321\n", - " 0.0315\n", - " -0.1653\n", - " -0.0777\n", - " 0.3395\n", - " -0.2367\n", - " -0.2367\n", - " -0.2159\n", - " -0.2686\n", - " 0.7723\n", - " 0.0111\n", - " 0.1415\n", - " 0.0081\n", - " -0.2737\n", - " -0.0718\n", - " 0.2837\n", - " 0.1893\n", - " -0.3025\n", - " 0.0827\n", - " -0.1914\n", - " 0.2433\n", - " 0.1645\n", - " 0.1867\n", - " -0.1336\n", - " 0.0415\n", - " -0.0685\n", - " 0.1470\n", - " 0.0657\n", - " -0.3073\n", - " -0.1858\n", - " 0.1482\n", - " 0.1246\n", - " -0.2530\n", - " 0.1587\n", - " 0.0094\n", - " -0.1678\n", - " 0.0576\n", - " -0.1429\n", - " -0.0371\n", - " 0.3333\n", - " 0.1869\n", - " 0.2635\n", - " -0.1213\n", - " -0.0520\n", - " -0.3667\n", - " -0.0626\n", - " 0.3608\n", - " 0.5578\n", - " -0.0925\n", - " 0.1044\n", - " -0.0112\n", - " -0.3684\n", - " 0.0313\n", - " -0.0474\n", - " 0.2086\n", - " 0.0270\n", - " 0.1090\n", - " 0.8672\n", - " 0.1158\n", - " -0.2498\n", - " 0.1239\n", - " 0.0900\n", - " -0.4163\n", - " -0.2231\n", - " 0.1602\n", - " 0.1050\n", - " 0.0660\n", - " 0.4067\n", - " 0.1662\n", - " -0.3401\n", - " 0.8094\n", - " 0.1300\n", - " -0.0209\n", - " -0.0355\n", - " 1.0411\n", - " 0.1142\n", - " 0.2019\n", - " 0.0569\n", - " -0.1053\n", - " 1.9178\n", - " 0.3461\n", - " 0.0406\n", - " -0.0130\n", - " -0.0287\n", - " 0.0203\n", - " -0.0028\n", - " 0.0256\n", - " -0.0373\n", - " 0.0146\n", - " -0.0237\n", - " -0.0067\n", - " -0.0089\n", - " -0.0216\n", - " 0.0407\n", - " 0.0337\n", - " -0.0112\n", - " 0.0036\n", - " 0.0085\n", - " 0.0190\n", - " -0.0212\n", - " 0.0172\n", - " -0.0325\n", - " 0.0043\n", - " 0.0076\n", - " -0.0122\n", - " -0.0075\n", - " -0.0015\n", - " -0.0089\n", - " 0.0133\n", - " 0.0669\n", - " 0.0499\n", - " -0.0491\n", - " 0.0011\n", - " -0.0022\n", - " -0.0078\n", - " -0.0192\n", - " 0.0348\n", - " 0.0217\n", - " 0.0040\n", - " 0.0124\n", - " -0.0149\n", - " -0.0068\n", - " 0.0506\n", - " -0.0112\n", - " -0.0273\n", - " -0.0091\n", - " -0.0442\n", - " -0.0102\n", - " 0.0340\n", - " 0.0467\n", - " -0.0276\n", - " 0.0458\n", - " 0.0079\n", - " -0.0068\n", - " 0.0548\n", - " 0.0014\n", - " 0.0232\n", - " 0.0111\n", - " 0.0248\n", - " 0.0341\n", - " -0.0333\n", - " 0.0020\n", - " -0.0115\n", - " -0.0184\n", - " 0.0211\n", - " -0.0325\n", - " -0.0263\n", - " -0.0312\n", - " -0.0194\n", - " 0.0092\n", - " 0.0252\n", - " 0.0259\n", - " 0.0115\n", - " 0.0070\n", - " 0.0371\n", - " -0.0159\n", - " -0.0056\n", - " 0.0381\n", - " 0.0253\n", - " -0.0043\n", - " 0.0014\n", - " -0.0099\n", - " 0.0038\n", - " [torch.FloatTensor of size 240]),\n", - " ('module.postnet.gru.bias_hh_l0_reverse', \n", - " -0.2487\n", - " -0.1271\n", - " -0.1493\n", - " 0.0263\n", - " 0.0129\n", - " 0.0412\n", - " -0.1970\n", - " -0.3277\n", - " -0.3353\n", - " -0.2026\n", - " -0.0747\n", - " -0.1377\n", - " -0.2107\n", - " -0.1382\n", - " -0.2139\n", - " -0.1606\n", - " -0.4580\n", - " -0.2586\n", - " -0.0390\n", - " -0.4157\n", - " -0.1222\n", - " -0.2138\n", - " -0.3036\n", - " 0.0978\n", - " -0.3649\n", - " -0.0272\n", - " -0.0919\n", - " -0.0896\n", - " 0.0116\n", - " -0.0178\n", - " -0.1166\n", - " 0.0206\n", - " -0.0256\n", - " -0.0908\n", - " -0.0805\n", - " -0.2593\n", - " -0.1308\n", - " -0.3071\n", - " -0.2022\n", - " 0.0909\n", - " -0.1760\n", - " -0.1884\n", - " -0.1719\n", - " -0.1515\n", - " -0.0075\n", - " 0.0053\n", - " -0.0405\n", - " 0.2614\n", - " -0.1736\n", - " 0.0894\n", - " -0.3122\n", - " 0.0602\n", - " -0.3385\n", - " -0.3736\n", - " -0.2544\n", - " 0.0354\n", - " -0.1137\n", - " -0.3462\n", - " -0.1956\n", - " -0.4153\n", - " -0.1730\n", - " 0.0075\n", - " -0.1659\n", - " 0.0991\n", - " -0.0186\n", - " -0.1592\n", - " -0.0166\n", - " -0.2992\n", - " -0.2731\n", - " -0.0349\n", - " -0.2382\n", - " -0.0450\n", - " -0.1722\n", - " -0.2531\n", - " -0.1807\n", - " -0.1135\n", - " -0.2719\n", - " -0.2557\n", - " -0.0144\n", - " -0.2027\n", - " 0.0466\n", - " 0.1740\n", - " -0.3893\n", - " -0.1778\n", - " -0.2417\n", - " -0.1082\n", - " 0.6921\n", - " 0.0596\n", - " 0.1283\n", - " -0.0760\n", - " -0.2241\n", - " -0.0975\n", - " 0.3336\n", - " 0.2793\n", - " -0.2995\n", - " 0.0315\n", - " -0.1422\n", - " 0.2703\n", - " 0.1530\n", - " 0.2506\n", - " -0.1593\n", - " 0.0975\n", - " -0.2239\n", - " 0.2017\n", - " 0.1103\n", - " -0.2734\n", - " -0.0930\n", - " -0.0133\n", - " 0.0313\n", - " -0.2820\n", - " 0.0176\n", - " 0.1565\n", - " -0.2222\n", - " 0.0820\n", - " -0.1499\n", - " 0.1038\n", - " 0.2361\n", - " 0.1533\n", - " 0.2672\n", - " -0.1057\n", - " -0.0180\n", - " -0.2015\n", - " -0.0127\n", - " 0.4115\n", - " 0.5030\n", - " -0.0607\n", - " 0.2297\n", - " -0.0195\n", - " -0.1869\n", - " -0.1665\n", - " -0.0110\n", - " 0.1128\n", - " -0.0069\n", - " 0.1217\n", - " 0.8779\n", - " 0.2806\n", - " -0.2943\n", - " 0.1384\n", - " -0.0913\n", - " -0.3071\n", - " -0.2291\n", - " 0.0558\n", - " -0.0693\n", - " -0.0422\n", - " 0.3369\n", - " -0.0317\n", - " -0.1905\n", - " 0.7677\n", - " 0.2580\n", - " 0.0240\n", - " 0.0153\n", - " 0.9563\n", - " 0.1226\n", - " 0.2429\n", - " 0.1906\n", - " -0.0149\n", - " 1.8935\n", - " 0.3781\n", - " -0.0177\n", - " 0.0269\n", - " 0.0797\n", - " -0.0511\n", - " 0.0123\n", - " -0.0557\n", - " 0.0786\n", - " -0.0151\n", - " 0.0576\n", - " 0.0093\n", - " 0.0177\n", - " 0.0563\n", - " -0.0821\n", - " -0.0707\n", - " 0.0199\n", - " -0.0187\n", - " -0.0318\n", - " -0.0384\n", - " 0.0585\n", - " -0.0411\n", - " 0.0692\n", - " -0.0140\n", - " -0.0212\n", - " 0.0343\n", - " 0.0252\n", - " 0.0025\n", - " 0.0241\n", - " -0.0278\n", - " -0.1441\n", - " -0.1080\n", - " 0.1002\n", - " 0.0010\n", - " 0.0017\n", - " 0.0162\n", - " 0.0458\n", - " -0.0687\n", - " -0.0423\n", - " -0.0112\n", - " -0.0268\n", - " 0.0391\n", - " 0.0182\n", - " -0.0957\n", - " 0.0330\n", - " 0.0602\n", - " 0.0215\n", - " 0.1023\n", - " 0.0070\n", - " -0.0670\n", - " -0.1032\n", - " 0.0485\n", - " -0.1075\n", - " -0.0313\n", - " 0.0147\n", - " -0.0862\n", - " -0.0018\n", - " -0.0198\n", - " -0.0436\n", - " -0.0486\n", - " -0.0878\n", - " 0.0881\n", - " -0.0095\n", - " 0.0378\n", - " 0.0495\n", - " -0.0407\n", - " 0.0855\n", - " 0.0551\n", - " 0.0526\n", - " 0.0357\n", - " -0.0338\n", - " -0.0639\n", - " -0.0750\n", - " -0.0179\n", - " -0.0145\n", - " -0.0488\n", - " 0.0365\n", - " 0.0254\n", - " -0.0829\n", - " -0.0473\n", - " -0.0232\n", - " -0.0070\n", - " 0.0135\n", - " -0.0169\n", - " [torch.FloatTensor of size 240]),\n", - " ('module.last_linear.weight', \n", - " 9.0400e-03 -7.2088e-03 -1.4630e-02 ... 6.1971e-03 -1.5822e-03 -2.7374e-03\n", - " 1.1868e-02 -4.7611e-03 -1.6505e-02 ... 6.2229e-03 -1.4371e-03 -2.4970e-03\n", - " 1.3643e-02 -4.6501e-03 -2.1297e-02 ... 1.0202e-02 -2.7155e-03 -2.3471e-03\n", - " ... ⋱ ... \n", - " 1.9957e-03 -6.6300e-03 1.2878e-02 ... -4.6978e-03 -3.2197e-02 -1.5346e-03\n", - " 2.5341e-03 -6.8375e-03 1.1034e-02 ... -4.8485e-03 -3.2630e-02 -2.1417e-03\n", - " 2.9717e-03 -7.6311e-03 9.8761e-03 ... -4.5076e-03 -3.3754e-02 -2.9296e-03\n", - " [torch.FloatTensor of size 1025x160]),\n", - " ('module.last_linear.bias', \n", - " 1.00000e-04 *\n", - " -1.3252\n", - " -1.4030\n", - " -1.1655\n", - " ⋮ \n", - " -0.1640\n", - " 0.0590\n", - " 1.3231\n", - " [torch.FloatTensor of size 1025])])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cp['model']" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -25808,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -25820,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -25844,7 +427,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " > Run-time: 8.474236488342285\n" + " > Run-time: 8.921625852584839\n" ] }, { @@ -25860,7 +443,7 @@ "text/html": [ "\n", " \n", " " @@ -25876,7 +459,7 @@ "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABBEAAAR4CAYAAABQAE75AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDVKj/AAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -25898,9 +481,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -25915,7 +498,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " > Run-time: 1.5912322998046875\n" + " > Run-time: 12.710890054702759\n" ] }, { @@ -25931,7 +514,7 @@ "text/html": [ "\n", " \n", " " @@ -25945,9 +528,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABBEAAAR4CAYAAABQAE75AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkUgkEolEIpFIJBKJhIX/H3l9ReFMtzw7AAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABBEAAAR4CAYAAABQAE75AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3XmYpWdZJ/7vXdVJdzokIYsESAIJ\nEDAYFDGyzQwgoIAoODPogBsCiguICtcMiAuIOm7jgoo4UZCMMgIyLhl/OIgsgzqyhEUkYUlMgHRW\nspCVXqrq+f1R1VA0z1td1X3eqjrVn891navr3O973uc5p6pruc/93E+11gIAAABwMDMbPQEAAABg\nOkgiAAAAAKsiiQAAAACsiiQCAAAAsCqSCAAAAMCqSCIAAAAAqyKJAAAAAKyKJAIAAACwKpIIAAAA\nwKps2+gJbAZH1/a2I8du9DQAAGDVqqobb60NPWDgSgPnD4SZcgNfB3On7Bx8yF1OubMb732J3Hb1\nHdn9ud1DX2xT6wnfcGy78ab50cf5wEf2vLW19sTRBzoMkghJduTYPKwet9HTAABgPQz9MV0DRboL\n4//hMGjwD/9kZvv2bnxhz57+pY4+un+h+f7za3NzK8+tO8ga/3YcSngwmhr4urn+aQ8ZfMwjn/PB\nbnwhX/75/l/f85ZDm9gmd+NN83nfW+81+jiz97j0lNEHOUySCAAAALCClmQhCxs9jU1BEgEAgCPL\n0LvfbQMrDg7Bwu7da3vAUMXBQPyQqCwYz1CVxxqXr1z+c/2Kg3avzw8Offn3ndk/cNW1Xxbae6u2\ne1udJAIAAACsqGW+qURI7M4AAAAArJJKBAAAAFjBYk8Ey3USSQQAANi8Jthj4JB2W2DT2/2tD+3G\nf+WVr+7Gn/u7j+jGT3vFJwfHmL+zv8VjT5uy3iKsneUMAAAAwKqoRAAAAICDsMXjIpUIAAAAwKqo\nRAAAgPVS1Q3PbN/ejS/s3j3mbNhsHv7Vg4d++I//vBt/w/UnduM/9zWP6cbPOP5T3fjcGvoeHIla\nWuYn2KNkmqlEAAAAAFZFJQIAAAAchC0eF6lEAAAAAFZFJQIAAJvHQM+AbJW1yAPPQ++DI8uNz3lE\nN77nW24ZfMwffMOju/G5XVetaeyFO/Q+OBQtybxKhCQqEQAAAIBVUokAAAAAB6EnwiJJBAAANo3a\ndlQ33vbtXeeZwOGb2bmzG993XH/Zzqmv3DF4rflrPzmROWVhfjLX4YgliQAAAAAraEnmt0pvlsOk\nJwIAAACwKioRAAAA4CAWNnoCm4QkAgAAm4beB2wlddrdu/ETrpjrxmf/74cGr9WU0rNJSCIAAADA\nClpa5u3OkERPBAAAAGCVVCIAAADASloyrxAhiSQCAACsn6p+3Hr3qTbz4Ad24zt/5/r+A/79Ld2w\nLQSZBpIIAAAAsIIWuzPspycCAAAAsCoqEQAAAGBFlfkMLEc6wkgiAADApM3M9uML8+s7DyZq9tS7\ndeMLv9HvcXDnD57Ujc/f8MmJzQnWmyQCAAAArKAlWdD3MomeCAAAAMAqqUQAAACAg9ATYZEkAgAA\n668GfhlvG1gvPDSnodNnB/oeJJk9/Z7d+NynPrOmMQ7JwPOobUd1423f3jFns/HW+HrMnH3m4KU+\n/8o93fiOHz+mG5+/5OMrzw2mkCQCAAAArKBFJcJ+eiIAAAAAq6ISAQAAAA5ioalESCQRAADYCBvZ\n+2BITa5Id116HwwZeB71gPt04zedd2I3vufE4T+Ydp/c//zNzK3tj6z57f3rzB27MPiYOrHfw+Er\nTr6tGz9l5x3d+K17dnTjuy7rvx5J8oD/cEk3vnBbf+wtr9dvYhP+12ayJBEAAABgBXoifJGeCAAA\nAMCqqEQAAACAFbRU5r0Hn0QSAQAAVtb66/Pb/DrP40C99ehJZo7pr/W/7QEndOM3ndtfxL5w9HBf\ngqM/1/9jamHbwIL4gSrwmX39+I7rZgfH3v7JY7rx2eu3d+O7P3t8N77z1j3d+L1PGf7Ett39x2xK\nA18fh9KPpLb1/2ysY778c1G3+0N7q5NEAAAAgIOwO8MiaSIAAABgVVQiAAAAwArszvBFkggAAJCk\nZvp/ILSFgeLdgV4J66W2HdWNX/VDX9ONz+3sX+d+r7+lG5/97OeGB5+b64bb0Hr7hYH40HX27h0c\nug08JvP9XgZtYOw28PnbPjvcj2Fw7M2ohr5u197Mow29trff/uWxhY39f8H4JBEAAABgRZX5phtA\noicCAAAAsEqbPolQVa+tquur6qPLYr9WVR+vqo9U1V9U1V2XHfvJqrqsqj5RVU/YmFkDAACwVbQk\nC5kZ/TYNpmE5w+uS/G6S/7Es9rYkP9lam6uqX0nyk0leXFUPTPL0JF+V5J5J/q6q7t/ahu/iCwDA\nOhva23727qd24ws33Ni/0NA6/4F14snwGvLBa8301+FvO/UrBse47slndeOzu/vnn/HGT3fjc7uu\n6scHR97apqrvQZLUGpv9rfX8JDXQJ2LqXismYtOnOlpr705y0wGxv22t7f+KfU+S05c+fmqSN7TW\n9rTWrkhyWZKHrttkAQAA2JLmU6PfpsGmTyKswrOT/M3Sx6cluXLZsV1LsS9TVc+tqouq6qJ92TPy\nFAEAAGD6TcNyhkFV9VNZrLR6/f5Q57RuzVhr7fwk5yfJ8XXSQF0ZAACbwkC5/1CZdZLM3PWEbrzt\n29eNL+weWAcwQTM7B/ZZvP+Z3fCnfmb4nckdf9uP3+1V/68bV3i+RQ0tkRla0X0IyxksW0haszvD\nflObRKiqZyb5liSPa1/ckHZXkjOWnXZ6kqvXe24AAACwFU1lKqWqnpjkxUme0lq7c9mhC5M8vaq2\nV9VZSc5O8r6NmCMAAABbx0Jq9Ns02PSVCFX1p0kek+SUqtqV5GVZ3I1he5K31WI5zntaaz/UWru4\nqt6U5JIsVmw9z84MAAAAMBmbPonQWntGJ/yaFc7/xSS/ON6MAABYb/WQc/rxfSu8X/Tp/qrW+c/d\nMokprWhmx45u/Prv+Zpu/OZH7O3Gz3nersEx5q69eO0TgyFDvRVIsthob346C/knzqsAAAAArMqm\nr0QAAACAjWV3hv28CgAAAMCqqEQAAGDT2HbaPfsHrrqhG5679rrhi61xjffs8cd34/u+5r7d+Kef\n3O97kCTz2/tjH3d5v/v6/X/wo9343J49g2MMqW39X/Hb/ED/CGvhV622b+/G295+T4sNfW1nZvvx\nBX3nD0VLsuA9+CQqEQAAAIBVUokAAAAABzHf+pVERxqVCAAAAMCqqEQAAODw1ArvztXa3rOau+rq\nw5zMoatjd3bj+47v/8p8+jv2DV5r+7v7PQ4Wdu/uxie5cn72tHt04/NX9/tHtH0D6/mPVCt8PQ/2\nmziE3hWj0/tgoloq896DT6ISAQAAAFgllQgAAABwEAvNe/CJSgQAAABglVQiAABweNoKK/rbJlyX\nPbDm/XP/7sxu/Ohb5rrx7f/0icEhFtZjjfzA85i7cqCvxJG6Rn6ox8FQv462MHiphTvumMCEmEYt\n0RNhiVcBAAAAWBWVCAAAALCClsp8W2EnmiOISgQAAABgVVQiAABwRKmv+6pu/Kjb+z0Djrn8pm58\nhU4QK/eJmJCZnTv7Q+/d148PrfVfh7kO9iU4lEvNzvbj2/p/2tRdju1f6OQT++ffuXtw7Lkrd608\nuS1q8LXtxGu3d+u3OkkEAAAAOIgFhfxJLGcAAAAAVkklAgAAAKygtWS+eQ8+kUQAAGCrmumvnW8z\n/TXbR90217/ODTf34/P9HgpJhnsArLX/wMBzSFZY63/Lrf2hB57eSmOsVQ28tqmBP76G+jQcgjbX\nf4Ltc7f0H3DLbRMbe8sb+Px1vwb3+UN7q5NEAAAAgBVVFqJpZKInAgAAALBKKhEAAABgBS16Iuwn\niQAAwJa07Z5378av/vrjuvF7/vnl3Xgb6H3Q9u49tIn1DPRQmD3h+OGHHLOjG2+33b62sYf6Eqy1\nf8MKl2IKDPXxSFKz/T+e252f//Lgwtq/bpgukggAAABwEPO6ASTREwEAAABYJZUIAAAAsIKWykKz\nO0MiiQAAwDQY6hlwyimDD7n0R+7Vjd/3jTd34/OfvaEbb0NrvBf6vRIOxcwxx3TjtbMfT4Z7Hyx8\nvrNOPTmkHgdsIkM9C9b6eV2h98GQhd27V31u0xhjy5NEAAAAgIPQE2GRVwEAAABYFZUIAABsetvO\nunc3/plf3zn4mBMu7McX/vljk5jSIZnZ2Z/vwlffr3/+p68fvNb8jTdNZE5MiUktR7Gs5ZC0JAvN\ne/CJSgQAAABglVQiAAAAwIoq87E7Q6ISAQAAAFgllQgAAKy7mR07uvFLX3NON37ssf0t5k7/6eHt\n5BY++k9rn1jP0JZ4Nfx+XH3tV3bjc3c5uhuf+fuP9M+f4DaSwKHTE+GLvAoAAADAqqhEAAAAgIPQ\nE2GRSgQAAABgVVQiAABMkdrW//Wtzc1t6LWGbLvPmd34r77jT7vx5/zUg7vxE17/oW58uCPC2tX2\n7f34QE+Eutdpg9dqe/q9DGY+1O99kEPpfTDUq2FIa2sfgy8x+8D7d+PzH7t0bRfyuZg6rZWeCEu8\nCgAAAMCqqEQAAACAg5hXiZBEJQIAAACwSioRAACmyCT7FbT5ta3DH+qh8OmXPnTwMb/5va/pxl90\n9qO78RP2vWdNczokQ70EBl6POu64bnzh8k8PDjH42q7HWnjr7Uczf8knN3oKbJCWZGET7M5QVU9M\n8soks0n+sLX2ywccv1eSC5Lcdemcl7TW3jLJOahEAAAAgE2uqmaTvCrJk5I8MMkzquqBB5z200ne\n1Fr72iRPT/J7k56HSgQAAABYUW2GnggPTXJZa+3yJKmqNyR5apJLlp3Tkhy/9PEJSa6e9CQkEQAA\nAGDzOy3Jlcvu70rysAPOeXmSv62qH01ybJLHT3oSkggAAEeqNa6dnz39nt34/I7h6/zm/c4ZOLJ3\nTWNP1MDzHuo3MX/Lrf3rLKytp8TE6X0A66YlWWjr0hPhlKq6aNn981tr5y993JvAgd8InpHkda21\nX6+qRyT546o6t7W2MKkJSiIAAADA5nBDa+28gWO7kpyx7P7p+fLlCs9J8sQkaa39U1XtSHJKkusn\nNcENX9QBAAAAm918Zka/HcT7k5xdVWdV1dFZbJx44QHnfCbJ45Kkqs5JsiPJZyf5OkgiAAAAwCbX\nWptL8vwkb03ysSzuwnBxVb2iqp6ydNqLkvxAVf1zkj9N8n2tTXbtk+UMAAB8idlzzu7Gr/+1/nrg\ns19wzeC1+l0GpszklhIDU6ql1qsnwsrzaO0tSd5yQOxnl318SZJ/M+YcVCIAAAAAq6ISAQAAAA5i\nwXvwSVQiAAAAAKukEgEA4Ag1e/JJ3fjXv+GSbvzdL35kNz53+fsnNqcNNTPbjx9KT4QaWDs92f5m\nm8/Q8x6yGV+PlZ7DZpwv66K1ZH4T9ETYDFQiAAAAAKuiEgEAAAAOYjPszrAZqEQAAAAAVkUlAgDA\nFjd7ysnd+OPfdUU3/nePu183fvR1W6T3wWC/goHeB4eyDn491s5vxr4LW6FnwFZ4DkxcS2WheQ8+\nUYkAAAAArJJKBAAAADiI+eiJkKhEAAAAAFZJJQIAsLVM2x7vM7P9+MJ8Pz7w/GZPOH5wiBe9913d\n+G885knd+Px1uwavtSVsxq+DQ7FVngdMgRa7M+ynEgEAAABYFZUIAAAAsCK7M+wniQAAbC1TVuJd\nM/3y2MHdBh/5Nd34U/7gbYNj/NpXfl3/WnNXrTw5Dt1m3H4RYAIkEQAAAOAgFuzOkERPBAAAAGCV\nVCIAAADAClpL5u3OkGQKkghV9dok35Lk+tbauUuxk5K8McmZST6V5DtaazdXVSV5ZZJvTnJnku9r\nrX1wI+YNALAabb6/lePsySd145c+9Zhu/H8/9tzhMfZdt/aJcXj0PgC2qGlYzvC6JE88IPaSJG9v\nrZ2d5O1L95PkSUnOXro9N8mr12mOAAAAbGELbWb02zTY9LNsrb07yU0HhJ+a5IKljy9I8m3L4v+j\nLXpPkrtW1T3WZ6YAAACwtW365QwDTm2tXZMkrbVrqupuS/HTkly57LxdS7FrDrxAVT03i9UK2ZGd\n484WAACAqdVSWdATIcn0JhGG9D6r3QVprbXzk5yfJMfXSRatAQAbYvaE47vxH3/v33fjv/ase3fj\nc9ddP7E5AcCQaU0iXFdV91iqQrhHkv0/NXclOWPZeacnuXrdZwcAAMCWstB9z/rIs+l7Igy4MMkz\nlz5+ZpK/Whb/3lr08CS37F/2AAAAAByeTV+JUFV/muQxSU6pql1JXpbkl5O8qaqek+QzSb596fS3\nZHF7x8uyuMXjs9Z9wgAAAGwpLdETYcmmTyK01p4xcOhxnXNbkueNOyMAgMm56//XLwz9rYc8ohs/\naubybnzh6KMHx2h79w4c0BYKgLWZ1uUMAAAAwDrb9JUIAAAAsNEWmvfgE5UIAAAAwCqpRAAAWAdf\n/+H5bvx9z7t3N163frgb33b6ad14u/X24cH1PgA4PK00VlyiEgEAAABYFZUIAAAAsIKWZCEqERKV\nCAAAAMAqqUQAAJiQG3/gEYPH3vuju7vxmX/80JrGmLvq6v4BfQ8ARqUnwiKVCAAAAMCqqEQAAACA\nFbSoRNhPJQIAAACwKioRAADW6LoXPLIbn/3GGwYfM/OHl05mcL0PADaESoRFKhEAAACAVVGJAAAA\nACtoKZUIS1QiAAAAAKuiEgEAOOLN7NzZjX/8N87txmeP+3w3ft+nrND3QC8DgKm2EJUIiUoEAAAA\nYJVUIgAAAMBKmt0Z9lOJAAAAAKyKSgQA4IhR27d345+64L7d+PZL+r8qnfXCj3bjC/oeAGxJLSoR\n9lOJAAAAAKyKSgQAAAA4CJUIiyQRAIAjxq4f/7pu/Jh39Jch3O0P3teNL8zNTWxOADBNJBEAAABg\nBS2lEmGJnggAAADAqqhEAAAAgINoKhGSSCIAAFvMnid//eCxO87s9zI47Ufe3403WzYCwJeQRAAA\nAICDWIhKhERPBAAAAGCVVCIAAADAClqL3RmWSCIAAFOptvV/jdn9vJsHH/OV335NN76w1Xsf1MAv\nvlv9eQMwcZIIAAAAcBB2Z1ikJwIAAACwKioRAAAAYEWlJ8ISSQQAYCo9+5JLu/ELHnvq4GPm7rxz\nrOlsbnofADAhkggAAABwEHoiLNITAQAAAFgVlQgAAACwgpboibBEEgEA2BRmduzoxj/xa1/TjV/w\nhHt343O7Pj2xOQEAX0oSAQAAAFbS9KjdT08EAAAAYFVUIgAAAMBBLERPhEQSAQBYb9X/Jeza739I\nN37Urf360bkr9D4AgPUmiQAAAAAraEma3RmS6IkAAAAArJJKBAAAAFhRZUElQhJJBABgDAN9D5Jk\n9pyzu/FbztvTjZ/9fR+YyJQAgMM3ehKhqu6X5KFJTktyVZL3tdYuG3tcAAAAmJTW7/N7xBktiVBV\nO5L8XpLvSTK77NB8VV2Q5Hmttf5bDgAAAMCmM2Ylwn9L8l1JXpbkDUmuS3Jqkmck+dkkdyZ5wYjj\nAwAAwETYnWHRmEmEpyf5udbaf10WuzzJL9biOsmfiCQCAGxJn3z11w8e++lHX9iN/9mD792Nqx4F\ngM1jzCTC9iTvGzj23iRHjzg2AAAATERrKhH2mxnx2n+X5JsGjn1TkneMODYAAAAwYWNWIvxGkj+u\nqmOT/Fm+2BPhO5J8c5Lvrqr77D+5tXb5iHMBAACAQ7agEiHJuEmE/7v07w8n+aFl8Trg+H6zAQCm\nynU/+shufOdnhh/zZw86vRtv83snMSUA1stM50+4+fWfButrzCTCs0a8NgAAAKybptNvkhGTCK21\nC8a6NgAAALD+xqxEAACmTa80Ncnt//G8bnz+sZ/rxu/1XZ8aHGJhbm7N0wJgE1o4stYu2J1h0ahJ\nhKp6YpJvT3JGkh0HHG6ttUePOT4AAAAwOaMlEarqvyT55SSfTXJZEt2SAAAAmDotpRJhyZiVCM9P\n8t+TPL+1dmTVuQAAAMAWNGYS4fgkfyaBAAAbpIbfMdl22j278U/86ld04yedcFM3fs8nX9qNLxxk\nalvWCq+5tt4A08138UUzI177rUkePuL1AQAAgHU09nKGv6iqluRvk9x84AmttctHHB8AAAAOX7M7\nw35jJhFaktuS/GKSXxg4p7+PFAAAALDpjJlEeF2SRyb5zSQfj90ZAODwDKy3r4c8sBu/5Rd2D15q\nfqG/ovGuf3FMN37KX3ymf53BEba4od4H+h4AsMWNmUR4TBZ3ZnjdiGMAAADA+OSJk4zbWPGGJNeN\neH0AAABgHY2ZRPjtJD9SVaONUVU/UVUXV9VHq+pPq2pHVZ1VVe+tqkur6o1VdfRY4wMAAHBkaK1G\nv02DMZcznJjk3CSXVNXb8uW7M7TW2ssO9eJVdVqSFyR5YGvt81X1piRPT/LNSX6ztfaGqvr9JM9J\n8upDHQcA1ltt394/8MD7dcO7T93ZjZ/4nVcPD3KPr+jHP3tZNzx/05dtsnRk0/sAgA1QVU9M8sos\nblLwh621X+6c8x1JXp7FBRj/3Fr7zknOYcwkwk8t+/j+neMtySEnEZZsS3JMVe1LsjPJNUkem2T/\ni3RBFl88SQQAAAAO2Ubnj6tqNsmrknxjkl1J3l9VF7bWLll2ztlJfjLJv2mt3VxVd5v0PEZbatBa\nmznI7bC2d2ytXZXkvyX5TBaTB7ck+UCSz7XW5pZO25XktMMZBwAAADaBhya5rLV2eWttb5I3JHnq\nAef8QJJXtdZuTpLW2vWTnsSYPRFGVVUnZvEFOyvJPZMcm+RJnVO7+aKqem5VXVRVF+3LnvEmCgAA\nwFRrWbeeCKfs/zt16fbcZdM4LcmVy+733jS/f5L7V9U/VtV7lpY/TNSYyxnG9vgkV7TWPpskVfXn\nSR6Z5K5VtW2pGuH0JN0Foa2185OcnyTH10kWNgIcqWqgidFQzeLQ+UN9hNvC8NgDY7Q9A8ntD13c\nDR/zsR3d+PyDe6sJF2279nPd+NxnP9t/wNDzBgAm6YbW2nkDx3o/jA/8ZWJbkrOTPCaLfw//fVWd\n21rr/+A/BKNWIiy92/+hqrqzquYPvB3m5T+T5OFVtbOqKsnjklyS5J1JnrZ0zjOT/NVhjgMAAMCR\nrCVpNf5tZbuSnLHsfu9N811J/qq1tq+1dkWST2QxqTAxY26/+L1JfifJ+5PsSPJHSf4kya1J/jXJ\nKw7n+q219yZ5c5IPJvmXLD6X85O8OMkLq+qyJCcnec3hjAMAAACbwPuTnF1VZ1XV0VncnfDCA875\nyyTfkCRVdUoWlzdcPslJjLmc4ceT/FKSn0/y/Ul+r7X2waVeBu9KcuPhDrC0ReSBOzxcnsWGEwAA\nADARG707Q2ttrqqen+StWdzi8bWttYur6hVJLmqtXbh07Juq6pIk80n+c2vtsP/2Xm7MJMLZSd6d\nZGHpdnSSLG0z8YtJfjHJ7444PgAc3GAvg4FVd4O/QQz0PpjgbxyzD7hfN375d31FN37f1+wavNbc\nZ4aPdW30b04AQFprb0nylgNiP7vs45bkhUu3UYzZE+HzSWaWnsS1Se6z7NjtWdxRAQAAADa/tg63\nKTBmJcK/JLlfkr9L8vdJXlpVVySZS/LyJB8fcWwAAABgwsZMIpyfL1Yf/EwWkwn/sHT/tiTfNuLY\nAAAAMCGVdvDdE44IoyURWmtvXPbxZVX1VUkekWRnkv/XWrthrLEBYNUWDnfH4SUT7BnwyT/sbw/9\noLP7fQzu+927u/G5K1foe1B+EQIA1m7MLR4fVVV32X+/tXZHa+3vljpGfr6qHjXW2AAAADBReiIk\nGbex4juTPHDg2FcuHQcAAACmxJg9EVaqk9yexT0rAQAAYHNr0RNhyUSTCFV1Zr50K8fzli9pWHJM\nkmcn+cwkxwZgk5uZHT42qb4Eh2KoN8CEehzUtuEftd9z8RXd+C/90SO78T3ff+3AIGv/paZm+5+P\nNje35msBAEeOSVciPDPJy/LFFR2/ky+tSGhL9+eSPG/CYwMAAMA4pqRnwdgmnUR4XZJ3ZTFR8I4s\nJgouOeCcPUk+2Vq7acJjAwAAACOaaBKhtfbpJJ9Okqr6hiQfaK3dPskxAAAAYP3piZCM21jx4iQn\nJflCEqGqfjDJuUne2lr76xHHBmCTqaOGf+S0PRvZE2Fgo6K2tjkN9T645dvPG3zM659yZjd+2if+\n35rGPpT+DW1ef2MAYO3G3OLxtUlesv9OVf1Mklcn+c4kf1VV/2nEsQEAAGBy2jrcpsCYSYTzkrx9\n2f0fSvJfW2snJ3lVkheOODYAAAAwYWMuZzgpyXVJUlXnJrl7kguWjv1lku8dcWwANpm2Z89GT6Gv\nLUzkMjP3v083ftO33Tn4mOPf2N/icV1MaAtLADhi+NGZZNxKhBuTnL708WOTXN1au3Tp/lEjjw0A\nAABM2JiVCH+X5OVVdUqSF2Wx+mC/r8zSLg4AAACwqbUkze4MybjVAP8lyZVJfinJvyb5uWXHvivJ\nP4w4NgAAADBho1UitNauS/KNA4cfn2T3WGMDwGrVtqO68bZvbze+7bR7duPnvv6T/es/6dTBsecW\nJrTNYg28MzK0fWWSTGrszWjo9Uj0ggDgkPkRsmjM5QxJkqqaSfLAJCcnuai1dkdr7daxxwUAAAAm\na9TmhlX1vCTXJvlIknckecBS/C+r6gVjjg0AAAAT09bhNgVGSyJU1Q8keWUWGyp+R5LltYV/n+Q/\njjU2AAAAMHljLmd4YZJfb629uKpmDzj28ST/ecSxAWBV2ty+bnxm585u/Nnv7PcFfs3Dvq4bn7/5\nukOb2FoM9T7Yyn0PkuHeBxatAjAGuzMkGXc5w1lJ3jpw7I4kdx1xbAAAAGDCxqxEuCHJmQPHHpDk\nqhHHBgAAgIkphW5Jxq1E+N9Jfraq7rMs1qrqlCQ/kcVeCQAAAMCUGLMS4aeTPDbJR5O8N4u9Jn87\nyVcmuT7JK0YcG4Aj0cyBLXgObu4xD+7G/+R1r+zGv+/e/65/oXbzmscenG9bGIj33wKpmf4azaHL\nbBl6HwCwXqZo94SxjVaJ0FqYWXkBAAAgAElEQVS7Mcl5SX4pyVFJ/jWLSYvfTfKI1totY40NAAAA\nTN6YlQhprd2W5OeXbgAAADCFyu4MS8bsiQAAAABsIaNVIlTVTJLnJvn2JGck2XHAKa21du+xxgfg\nCDTUBKCGc+a3vvC2bnyo90HN9vsYtLm5lefWszC/9sd01Lb+j/NDmhMA0KcnQpJxlzP8apIXJvlQ\nkvcn2TviWAAAAMDIxkwifHeSn2+tvWzEMQAAAGB8KhGSjNsTYVuSd494fQAAAGAdjVmJ8OYkT0jy\n9hHHAIAvav23CC77468efMgDnvnpbnx+4FptYeBtiBro2DxwnUla2L179DEA4IinEiHJuEmEFyZ5\nfVWdn+StSW4+8ITW2jtGHB8AAACYoDGTCPdIcp8kT03y/cviLUkt/dtvcQ0AAACbRUvSBqoOjzBj\nJhH+KMkpSX4sycdjdwYAAACYamMmEc5L8r2ttTePOAYAazG0bj9Zl7X7Y5s99W7d+HHvO2bwMfM3\n37K2QRbm13Y+ALAl1PT/qjQRY+7O8JmoPgAAAIAtY8wkwi8keXFV3WXEMQAAAGB8bR1uU2DM5QxP\nSHJ6kk9V1T/ly3dnaK21Z444PgAAADBBYyYR/m2ShSS3JTm3c3xK8iwAU2io90GtUIDWNuFa/4Hn\ncdOzHt6Nv+Qlr+/G/+BBw30PWltY+7wAAI5QoyURWmtnjXVtAAAAYP2NWYkAAAAAW4LdGRZNNIlQ\nVfdKck1rbd/SxytqrX1mkuMDAAAA45l0JcIVSR6R5H1JPpWD9z2YnfD4AKxkYRP2PVjBtnuf0Y3f\ncY9+r4TzH3Df/oXanklNCQA4UrWBnlNHmEknEZ6d5F+XfazgAwAAALaIiSYRWmsXLPv4dZO8NgAA\nAGyIFm+RLxmtsWJVvSPJj7TWPt45dv8kv99ae+xY4wOspLZvHzzW9mzC0vehLRvbwE+zofgmddOz\nHtGN/8xLL+jGf/+p39KNz0/Z8wYAmDZj7s7wmCTHDxw7LsmjRxwbAAAAJsd7FUmSmZGvP/Qy3zfJ\n7SOPDQAAAEzQpLd4fFaSZy3dbUnOr6rbDjjtmCTnJnn7JMcGAAAAxjXp5QwLSfbvH1YH3N/vxiSv\nTvIrEx4bYNXa3r0bPYUjzrU/8cjBY8c94dpu/FX3f0D/Ae2Tk5gSAMCqleUMScbZneGCJKmqdyb5\n4V5jRQAAAGD6jNZYsbX2DWNdGwAAANaVSoQk4zdWBAAAALaIMbd4BNi82pSlkqdpvjOz3fCt958b\nfMjdn3j5WLMBAJiMKfp1bEwqEQAAAIBVUYkAAAAAK6hmd4b9RqlEqKqjq+ovqupRY1wfAAAAWH+j\nVCK01vZW1eOTvHKM6wNsWVX9+BT1RLj+Rx7WjZ/z4o8OPmZ+rMkAAExKG/g97QgzZk+Ef0zy8BGv\nDwAAAKyjMXsivCjJX1bV7Un+Msk1OaCfZWttYcTxAQAAYDKmpzB0VGNWIvxLkvtmcUnDp5PsTbJv\n2W3viGMDAAAAEzZmJcIrIlcDsGXd+AOP6MZvffjnu/G7/e6tY04HAGBUdmdYNFoSobX28rGuDQAA\nAKy/MSsRvqCq7pLk5CRXt9b2rceYAAAAMDEqEZKM2xMhVfUtVfXBJLckuTzJg5bif1hV3zmB69+1\nqt5cVR+vqo9V1SOq6qSqeltVXbr074mHOw4AAAAwYiVCVX1bkv+V5O1JXpzkV5cdviLJM5P8z8Mc\n5pVJ/k9r7WlVdXSSnUlemuTtrbVfrqqXJHnJ0vgAm0OtsMdw27gU994nnNeN/95//+1u/Mnv+rpu\n/Ozv/tDE5gQAsCk0PRH2G7MS4WVJ/qi19k1JfuuAYx9Ncu7hXLyqjk/yqCSvSZLW2t7W2ueSPDXJ\nBUunXZDk2w5nHAAAAGDRmEmEc5K8cenjA3M2N2exR8LhuE+Szyb5o6r60NISiWOTnNpauyZJlv69\nW+/BVfXcqrqoqi7alz2HORUAAAC2tLYOtykwZhLh1iSnDBw7M4sJgMOxLclDkry6tfa1Se7I4tKF\nVWmtnd9aO6+1dt5R2X6YUwEAAICtb8zdGd6W5Cer6m+S3LYUa1W1Pcnzk/zNYV5/V5JdrbX3Lt1/\ncxaTCNdV1T1aa9dU1T2SXH+Y4wAcmpnZfrwtjD/2QN+F2eOOG3zIOb/w0W78Red9azd+9g0fWPu8\nAACm1ZRUCoxtzEqEn0py9ySfSPKHWXzJX5Lkw0lOT/Lyw7l4a+3aJFdW1QOWQo9LckmSC7PYtDFL\n//7V4YwDAAAALBqtEqG19qmqekiSn0vyhCTzWWyE+H+S/Gxr7eoJDPOjSV6/tDPD5UmelcXEyJuq\n6jlJPpPk2ycwDgAAAEcwuzMsGnM5Q1pru5I8Z8TrfzhJb0+yx401JgAAABypRk0iABzJarbfE6HN\nr/CgFQ+u3uwJx3fjj3z3dYOP+fsHH9s/sHDjJKa0Pob6UCxM5nUFADjSTTSJUFWvXcPprbU2WpUC\nAAAAMFmTrkR4bL60Z+Vdk5yQZC7JjUlOXhrzliQ3T3hsAAAAGIeeCEkmvDtDa+3M1tpZrbWzknxP\nktuTPD3JMa21eyQ5JskzluLfPcmxAQAAgHGN2RPhN5L8UmvtTfsDrbX5JG+sqlOS/FaSh444PsCG\nanP7Rh9j273P6Mav+PUT+g/4DycNX2zhignMaH3MnnhiN75w553deNujJwIAcBia3Rn2m2glwgEe\nlOSygWOXJjl3xLEBAACACRsziXBtku8YOPb0JMMtwgEAAGAzaetwmwJjLmf4rSS/WVX3SPJnWUwa\nnJrFxMITkvz4iGMDHLI66uhuvO3bu7YLtQn+JKjqhm9++Gnd+N1e2y/fn7/soxOb0oaa6b8ebc+e\ndZ4IAMCRZbQkQmvtlVV1e5KXJXnSskNXJvmB1tpatoMEAACAjTMllQJjG7MSIa2111TVa5OcnuQe\nSa5Jsqu1Sb49BwAAAKyHUZMISbKUMLhy6QYAAABTpWJ3hv1GTSJU1YOyuJzh0UlOTHJTkncl+fnW\n2r+MOTbASob6HiRJm1/jdoAzswMXWhiIr/0n0L++/msGhuj3ADj7WRf3z1/zyJvTwq23b/QUAACO\nSKMlEarq65P83ySfT3JhFndruHuSb03y5Kp6VGvtA2ONDwAAABOzVd6NOUxjViL8UpKPJnlca+22\n/cGqOi7J3y0d/6YRxwcAAAAmaMwkwsOTfM/yBEKStNZuq6pfSXLBiGMDAADAZDQ9EfYbM4lwsJfY\npwAY30C/grZv78SGmL3fmd34/GWf6j+gDfdc+PxTH9qNH/cP/W/Xp/7BRf0hJvj81qyqH5/gxjwb\n+vwAAI5gMyNe+71JXrq0fOELqurYJC9O8p4RxwYAAIDJaetwmwJjViK8NIs7MXy6qv46yTVZbKz4\n5CTHJHnMiGMDAAAAEzZaJUJr7X1Z7IvwjiRPSPLCJE9cuv/w1tr7xxobAAAAJmoTVCJU1ROr6hNV\ndVlVvWSF855WVa2qzjuUp7qSMSsR0lr7SJKnjTkGQJLMHHtsN77w+d0TG2PbGaf3x9h1zdquc58z\nB48d84Kr+/Fv7I/RFob7K4xuoN/EzDE7uvGFO+4YczYAAFtaVc0meVWSb0yyK8n7q+rC1tolB5x3\nXJIXZLHFwMSNVolQVV9RVfcfOHb/qjplrLEBAABgkqqNfzuIhya5rLV2eWttb5I3JHlq57yfT/Kr\nSSb3btoyYzZW/L0kLxo49hNLxwEAAIBFp1TVRctuz1127LQkVy67v2sp9gVV9bVJzmit/fVYExxz\nOcO/TfK8gWN/m+R3RxwbAAAAJmd9dk+4obU21Megt4/2F2ZVVTNJfjPJ940wry8YM4lwYpJbBo7d\nmuTkEccGtqrqfe9Masf2/vmHsg5/YK1/Wv8nR9s315/TQ87pxj/2o0cPDn3/J3ykP8Zaex8MvE5D\nz+FQzH5F/9v4/HXXT2wMAAC+YFeSM5bdPz3J8oZaxyU5N8m7avF3wbsnubCqntJau2hSkxhzOcOu\nJA8bOPawLG75CAAAAJvbeuzMcPD3et6f5OyqOquqjk7y9CQXfmGKrd3SWjultXZma+3MJO9JMtEE\nQjJuEuHNSV5aVU9eHly6/5IkbxpxbAAAANgyWmtzSZ6f5K1JPpbkTa21i6vqFVX1lPWax5jLGV6R\n5FFZLJ+4NslVWWz6cPcsZkR+bsSxAQAAYGJWsXvC6Fprb0nylgNiPztw7mPGmMNoSYTW2p1V9egk\n35PFfSxPTnJZFpsq/slSFgVgIuZvvGli16qj+t8a566+tn/+TL//wBXfdlw3fq83Dn/7a3Nr/NY4\n0PtgZufO/vX37JnY2POfvXFN5wMAMP3GrERIa21fktcu3QAAAGA6bYJKhM1gzJ4IAAAAwBYyWiXC\nUrfIn0zyjCT3SnLg/muttTZqJQQAAABMwmboibAZjPlH/K8leV6Sv0ny50mGF+ICrFYb/7t329fv\nDTDU++Cqn3hoN37Pf9jXjW9/50eGxx46MDM78ICFfnig90Gbnx8ce80WJngtAACmwphJhKcleVlr\n7RdHHAMAAADGpxIhybg9Ee6S5J9GvD4AAACwjsZMIvzvJI8a8foAAAAwvrZOtykw5nKG30nyP6pq\nIclbknzZJu6ttctHHB8gta3/ba7N9fseLB7s9xnIg7+qP8bA6TvefXE3vrB37+DQddTR/SntG35M\n9/yVnh8AAByiMZMI+5cyvDzJywbOGegUBgAAAJtDLd0YN4nw7ExNQQYAAABwMKMlEVprrxvr2gCr\ndShl/XV0f0nBJ5+/vRs/+1n9HrILh7Ad5VqXLQAAsE68RZ5k3MaKg6pqpqpO2oixAQAAgEMz0SRC\nVd1UVQ9Zdr+q6sKqus8Bp359ks9OcmwAAABgXJOuRLhrvnSJxEySb1mKAwAAwFSqNv5tGozZWBFg\n/cwMbPayMN8ND239mCTzb7lbN37O917TjQ91XZg98cT+9W+5dXDsofkOGnreQ9tUHkKfhk2pBvoj\nb5XnBwCwSUkiAAAAwMF4ryLJBjVWBAAAAKbPGJUIpy1rpDi7LPa5ZeecPsK4AAAAMA6VCEnGSSK8\nuRP7ywPuV3wKYHMbWnOebM5150O9D7Zv78bv/ffDhVhX/Fh/B9q5q/65/4CBvgTzn/tcN35Ir9/Q\n52OtPRS2is34NQgAcASYdBLhWRO+HgAAAGysKdo9YWwTTSK01i6Y5PUAAACAzcPuDAAAAHAwKhGS\nSCIAQ6ZszfnsKSd349/1jx/uxv/4u540eK36wEfWNnhbGIhPsPfBlH0+AADYmiQRAAAA4CD0RFg0\n3J4cAAAAYBmVCAAAAHAwKhGSSCIAU6a29b9t3fonJ3Tj//OxD+vG21UfPYTB9SsAAODIJokAAAAA\nB6EnwiI9EQAAAIBVUYkAAAAAK2nRE2GJJAIcIYZ6CbT5+f4DNnCd/+z9zho8ds4bP92NX/yDO7vx\nuasuX/sEJtT7YOg1Tw0XgbV9e9c0BgAArCdJBAAAADgYlQhJ9EQAAAAAVkklAgAAAKygYneG/SQR\n4AjR5uY2egqr1nZsHzx2ybMf0H/MRy6e4AQm8xNiml5zAABYDUkEAAAAOBiVCEn0RAAAAABWSSUC\nAAAAHERt4Bbom4kkAmwxMzt2dOMLu3ev80yWqeqGb/tPD+vG7/m8ywYvdee37usf8E0dAABGJ4kA\nAAAAK2nRE2GJnggAAADAqkx9EqGqZqvqQ1X110v3z6qq91bVpVX1xqo6eqPnCAAAwHSrNv5tGmyF\n5Qw/luRjSY5fuv8rSX6ztfaGqvr9JM9J8uqNmhystw3tfTDgzn//0G78rj/4mW78jm+8ffBam/H5\nAQDAkWKqKxGq6vQkT07yh0v3K8ljk7x56ZQLknzbxswOAACALaOtw20KTHUSIclvJfkvSRaW7p+c\n5HOttbml+7uSnNZ7YFU9t6ouqqqL9mXP+DMFAACAKTe1yxmq6luSXN9a+0BVPWZ/uHNqN5/TWjs/\nyflJcnydNCU5H9gkBrZsvOJPH9SNP/j0/paNt/zbGyc2JQAAGNO09CwY29QmEZL8myRPqapvTrIj\niz0RfivJXatq21I1wulJrt7AOQIAAMCWMbXLGVprP9laO721dmaSpyd5R2vtu5K8M8nTlk57ZpK/\n2qApAgAAsFXoiZBkipMIK3hxkhdW1WVZ7JHwmg2eDwAAAGwJ07yc4Qtaa+9K8q6ljy9P0t9PDpiM\nh/V7H9zlXcd247f8vt4HAABMsaYnwn5bsRIBAAAAGMGWqEQAAACAUalESKISAQAAAFgllQhA1+yJ\nJw4e2/WSfd346d97cTc+P5EZAQDAxqjoibCfSgQAAABgVVQiAAAAwME0pQiJSgQAAABglVQiAF0P\nesfNg8f2veDMbnz+1ltHmg0AAGwsPREWqUQAAAAAVkUlAgAAAKykLd1QiQAAAACsjkoEOELUUUd3\n45f+0Vd14zPP2jN4rZkPf2gicwIAgGlRCxs9g81BJQIAAACwKioRAAAA4GD0REiiEgEAAABYJZUI\ncKRo/UVcJ/zjjm58Qd8DAAD4glKJkEQlAgAAALBKKhEAAABgJS1JU4qQqEQAAAAAVkklAhwhZk+9\nWzd+t9d+sBuXZwUAgC/SE2GRSgQAAABgVVQiAAAAwMGoREiiEgEAAABYJZUIMK2q+uHZ2W5816tO\n6Mbv8Z9unNiUAABgK6roibCfSgQAAABgVVQiAAAAwEpaW7yhEgEAAABYHZUIMK0GMqF19NHd+Cm/\ns7N/mb17JzYlAABga5NEAAAAgIPQWHGR5QwAAADAqqhEgC1m38O+shs/6t3/0o03DWIAAODg/Nqc\nRCUCAAAAsEoqEQAAAOAg9ERYpBIBAAAAWBWVCDClZnbs6Mav+bHPd+P3eKetHAEA4JC0JAtKERKV\nCAAAAMAqqUQAAACAg1GIkEQlAgAAALBKKhFgM6saPPSvL//abvzsH72yG5+byIQAAODIZHeGRSoR\nAAAAgFVRiQAAAAAH05QiJCoRAAAAgFVSiQCbwUDvgzv+40MHH9IGUoBzV+6axIwAAIBl9ERYpBIB\nAAAAWBVJBAAAAFhJW6fbQVTVE6vqE1V1WVW9pHP8hVV1SVV9pKreXlX3Poxn3SWJAAAAAJtcVc0m\neVWSJyV5YJJnVNUDDzjtQ0nOa619dZI3J/nVSc9DTwTYBG589sO78ZO/88rBx9zn8VeNNR0AAGCZ\nSlIbvzvDQ5Nc1lq7PEmq6g1Jnprkkv0ntNbeuez89yT57klPQiUCAAAAbA6nVNVFy27PXXbstCTL\n32XctRQb8pwkfzPpCapEAAAAgINZWJdRbmitnTdwrLelW7c8oqq+O8l5SR49qYntJ4kAAAAAm9+u\nJGcsu396kqsPPKmqHp/kp5I8urW2Z9KTkESAdXTVix/Zje981Ge78Zkn3zB4rYWNX5MFAABHjE3Q\nE+H9Sc6uqrOSXJXk6Um+c/kJVfW1Sf57kie21q4fYxJ6IgAAAMAm11qbS/L8JG9N8rEkb2qtXVxV\nr6iqpyyd9mtJ7pLkz6rqw1V14aTnoRIBAAAAVtIy0H1gfbXW3pLkLQfEfnbZx48few4qEQAAAIBV\nUYkAh6N6DVKTZ3zsqm78Fz54Zzd+2pMv7cbXpwEsAACwspZsfE+ETUElAgAAALAqKhEAAADgIEoh\nQhKVCAAAAMAqqUSA/Qb6G6SGc22f/L2v68bf+PRzuvH7fvjDa54WAACwCeiJkEQlAgAAALBKKhEA\nAABgJS0pW6clUYkAAAAArJJKBFhSs7Pd+Kdf+tDBxxx7RT++8OFLJjElAABgs9ATIYlKBAAAAGCV\nVCIAAADAwShESCKJwBGotvW/7O988kO68d2n7Ru81jkv/ddufH7t0wIAANj0JBEAAADgIEpPhCR6\nIgAAAACrNLVJhKo6o6reWVUfq6qLq+rHluInVdXbqurSpX9P3Oi5AgAAMOVaG/82BaZ5OcNckhe1\n1j5YVccl+UBVvS3J9yV5e2vtl6vqJUlekuTFGzhPNplbn3ZeN363H+7v13jMN9wweK35Bd0PAACA\nI8fUJhFaa9ckuWbp49uq6mNJTkvy1CSPWTrtgiTviiQCAAAAh6olWdjoSWwOU7ucYbmqOjPJ1yZ5\nb5JTlxIM+xMNdxt4zHOr6qKqumhf9qzXVAEAAGBqTW0lwn5VdZck/yvJj7fWbq2qVT2utXZ+kvPz\n/7N393GyZXV97z+/tXbVrqruOcMAgiNDBBW9KkQDaDQm0WgSQL1iEjEYo/jI1Rtz82BuAvEmEONN\n9OZG40NMLlEjJioi0cjLpxFJfGkioqAGBSOMgDACM8wwAzPndHfV3vt3/1hr7b2rurpPn3P6PEyf\n75vXcLp376patR/X+q3fWhs4Z49+ZAw+ERERERERkWvOcD2dIXtEBxHMbEIKIPywu/9EXnyPmd3u\n7u81s9uBe69fCW8eNpluXe6r5VX/7OoJH7F1+fe97pVbl3/F2+7Yunz/zz+w/QM074GIiIiIiAjw\nCB7OYCnl4PuB33P3bx/96dXAC/LPLwB+6lqXTURERERERM4YPZ0BeGRnInwG8GXA75jZb+dl/xD4\nVuCVZvbVwLuA512n8omIiIiIiIicKY/YIIK7/zfgqAkQPudalkVERERERETOuEdIpsDV9ogNIsiN\n5bTmPogf8+Sty//0T7zlyNc8c/Garcu/6uOftf0F5+/euliXBBERERERkeMpiCAiIiIiIiJyHAe6\n612IG8MjdmJFEREREREREbm2lIkgIiIiIiIichGmOREABRHkElh19OHiTXMq73X3F9y+dfkvff2t\nR77Xr7zxtq3Lu/3zl1SmM8+OmIdUF0MRERERETkhBRFERERERERELkadb4DmRBARERERERGRE1Im\ngoiIiIiIiMixXJkImYIIcmKXOu8BcOQ4/P2fvWPr8vNv2f7cFPv23z7yI/SklRPSRU9ERERERK6Q\ngggiIiIiIiIix3HUKZdpTgQRERERERERORFlIoiIiIiIiIhcjMZRAwoiyFV23ws/bevyycu3pwJ9\nzI+8/moWR0RERERERK6AhjOIiIiIiIiIyIkoE0FERERERETkIkwTKwLKRBARERERERGRE1ImgpyO\nT33a1sXLZ39w6/LH/qW3XM3SiIiIiIiInC5lIgDKRBARERERERGRE1ImgoiIiIiIiMhxHOiUiQDK\nRBARERERERGRE1ImgpzYe/7PP3Xk377or//S1uWve8Zi63LF8ERERERE5JHDNSdCpkwEERERERER\nETkRZSKIiIiIiIiIXIwyEQAFEWSL83/lT25d/vBHN0e+5lf/xHz7H7qjX3NJzC79NTrJRURERERE\nTpWCCCIiIiIiIiIXo05KQHMiiIiIiIiIiMgJKRNBRERERERE5DgOdMpEAAURbmrVk/7Y9uX/2z1b\nl3/8F33gyPdqu/ZUynQkpQ6JiIiIiIhcdwoiiIiIiIiIiBzLwbvrXYgbguZEEBEREREREZETUSaC\niIiIiIiIyMVoiDWgIMJNofrIJ25d/tGveu/W5W/7s5Oty9sLF06tTCIiIiIiIvLIoyCCiIiIiIiI\nyHH0dIae5kQQERERERERkRNRJoKIiIiIiIjIxWhOBEBBhJvCO758+5wI/oLF1uXdhd+/msURERER\nERGRRygFEUREREREREQuRpkIgOZEEBEREREREZETUiaCiIiIiIiIyLFcmQiZgghnSDx3butyt+3r\nt2/W3AciIiIiIiJycgoiiIiIiIiIiBzHga673qW4IWhOBBERERERERE5EWUiiIiIiIiIiFyM5kQA\nFER4xIkf9mFH/u35v/JbW5e/4lO3r9+eRoFERERERETkpqEggoiIiIiIiMjFKBMB0JwIIiIiIiIi\nInJCykQQEREREREROZZDp0wEUBDhurO63rr8gb/69K3Lv/Ol33Pke/3Tz3zu1uXtQ3906QXbJsSj\n/9ZphgUREREREZGzTkEEERERERERkeM4uHfXuxQ3BM2JICIiIiIiIiInokwEERERERERkYvRnAiA\ngginz2z74qd/wtblX/WjP711+fe+8/aty1/yUc845sPvPrZoJ2XV9sPCm+ZU3l9EREREREQemRRE\nEBEREREREbkYVyYCaE4EERERERERETkhZSKIiIiIiIiIHMcdOj2dARREOHXVR2yfy+AtL1xsXf6D\nn/vZW5fXb3/XqZXpUmnuAxEREREREdlGQQQRERERERGRi9GcCIDmRBARERERERGRE1ImwmUIs9mR\nf3vHdz566/LH/2Tcury96x2X9NlHPX4RwI96bmnXXtJniIiIiIiIyDrXnAiAMhFERERERERE5ISU\niSAiIiIiIiJyLNecCJkyEURERERERETkRJSJcBne/SMffeTfbv3x3SOWv2Hr8kuNZR057wFo7gMR\nEREREZGrwYHj2mI3EWUiiIiIiIiIiMiJKBNBRERERERE5GJcT2cAZSKIiIiIiIiIyAkpEwH42D9+\ngTvv/O0Tr/+Mf/LpR/7t3I/9+tblflrzFWjeAxERERERkWvKucj8dDcRZSKIiIiIiIiIyIkoE0FE\nRERERETkOO6aEyE7k5kIZvZsM/t9M7vLzF50vcsjIiIiIiIichacuUwEM4vAvwb+AnA38Btm9mp3\nf8tRr3nrmxY86yM++cSf8Vhed8XlFBERERERkUcOzYmQnMVMhE8F7nL3t7v7EngF8NzrXCYRERER\nERGRK3KxrHszq83sx/LfX29mTzrtMpzFIMITgHePfr87L1tjZi80szeY2RtWHFyzwomIiIiIiMgj\nkHdX/79jjLLunwN8AvAlZvYJG6t9NfCAu38M8B3At532ZjiLQQTbsuxQ3om7v8zdn+nuz5xQX4Ni\niYiIiIiIiFy2k2TdPxd4ef75VcDnmNm2NvJlO3NzIpAyD544+v0O4D3HveAhHrjvF/1Vf5h/fSxw\n31Uqm9x4tL9vLtrfN4CBNVEAACAASURBVBft75uL9vfNRfv75qL9/cjykde7AFfDQzxw5y/6qx57\nDT5qZmZvGP3+Mnd/Wf55W9b9n9x4fb+Ouzdm9kHgMZziOXQWgwi/ATzFzJ4M/BHwfOCvHfcCd/+w\n8rOZvcHdn3l1iyg3Cu3vm4v2981F+/vmov19c9H+vrlof8uNwN2ffb3LwMmy7k+UmX8lztxwBndv\ngG8A7gR+D3ilu7/5+pZKRERERERE5IqcJOu+X8fMKuBW4AOnWYizmImAu/8s8LPXuxwiIiIiIiIi\np+QkWfevBl4AvA74IuC/uPupZiKcySDCFXrZxVeRM0T7++ai/X1z0f6+uWh/31y0v28u2t8i9HMc\nlKz7CPyAu7/ZzL4ZeIO7vxr4fuA/mNldpAyE5592OeyUgxIiIiIiIiIickaduTkRREREREREROTq\nUBBBRERERERERE5EQYTMzJ5tZr9vZneZ2Yuud3nkdJnZE83sv5rZ75nZm83sb+Xljzaz15jZ2/K/\nt13vssrpMbNoZr9lZj+df3+ymb0+7+8fM7Pp9S6jnA4ze5SZvcrM/mc+zz9d5/fZZWZ/J1/Lf9fM\nftTMZjq/zw4z+wEzu9fMfne0bOv5bMl35frbm8zs6dev5HI5jtjf/yJfz99kZj9pZo8a/e3FeX//\nvpk96/qUWuTmpiACqaEB/GvgOcAnAF9iZp9wfUslp6wBvtHdPx74NOBv5H38IuC17v4U4LX5dzk7\n/hbpUa/FtwHfkff3A8BXX5dSydXwncDPu/v/AnwSab/r/D6DzOwJwP8BPNPdn0qaWOr56Pw+S34Q\n2Hwe+1Hn83OAp+T/Xgj8m2tURjk9P8jh/f0a4Knu/seBtwIvBsh1t+cDn5hf8725Hi8i15CCCMmn\nAne5+9vdfQm8AnjudS6TnCJ3f6+7/2b++SFSA+MJpP388rzay4EvvD4llNNmZncAnwd8X/7dgM8G\nXpVX0f4+I8zsHPBnSbMR4+5Ld38Qnd9nWQXM8/OvF8B70fl9Zrj7L3P4meZHnc/PBX7Ik18DHmVm\nt1+bkspp2La/3f0X3L3Jv/4acEf++bnAK9z9wN3fAdxFqseLyDWkIELyBODdo9/vzsvkDDKzJwF/\nAng98Hh3fy+kQAPwuOtXMjll/wr4+0CXf38M8OCoUqLz/Oz4KOD9wL/Pw1e+z8x20Pl9Jrn7HwH/\nL/AuUvDgg8Ab0fl91h11PqsOd/Z9FfBz+Wftb5EbgIIIiW1ZpmdfnkFmtgv8J+Bvu/uHrnd55Oow\ns88H7nX3N44Xb1lV5/nZUAFPB/6Nu/8J4DwaunBm5bHwzwWeDHwEsENKad+k8/vmoGv7GWZm30Qa\nkvrDZdGW1bS/Ra4xBRGSu4Enjn6/A3jPdSqLXCVmNiEFEH7Y3X8iL76npD3mf++9XuWTU/UZwBeY\n2TtJw5M+m5SZ8Kic/gw6z8+Su4G73f31+fdXkYIKOr/Ppj8PvMPd3+/uK+AngD+Fzu+z7qjzWXW4\nM8rMXgB8PvCl7l4CBdrfIjcABRGS3wCekmd2npImbHn1dS6TnKI8Hv77gd9z928f/enVwAvyzy8A\nfupal01On7u/2N3vcPcnkc7n/+LuXwr8V+CL8mra32eEu78PeLeZfVxe9DnAW9D5fVa9C/g0M1vk\na3vZ3zq/z7ajzudXA1+en9LwacAHy7AHeeQys2cD/wD4Ane/MPrTq4Hnm1ltZk8mTaj569ejjCI3\nMxsCezc3M/tcUk9lBH7A3f/v61wkOUVm9qeBXwF+h2GM/D8kzYvwSuCPkSqmz3P3zcmc5BHMzD4L\n+Hvu/vlm9lGkzIRHA78F/HV3P7ie5ZPTYWafTJpEcwq8HfhKUqBc5/cZZGb/BPirpDTn3wK+hjQu\nWuf3GWBmPwp8FvBY4B7gJcB/Zsv5nANJ30Oaqf8C8JXu/obrUW65PEfs7xcDNXB/Xu3X3P3r8vrf\nRJonoSENT/25zfcUkatLQQQRERERERERORENZxARERERERGRE1EQQUREREREREROREEEERERERER\nETkRBRFERERERERE5EQURBARERERERGRE1EQQURErikz+woz89F/583snWb2k2b2xWZ2w96bcnlf\neg0+52+b2V/esvylZnbDPVbJzD45l+3R17ssIiIicnXdsBU1ERE5854HfDrwucA/Ag6AHwV+wczm\n17NgN4C/DRwKIgDfR9pmN5pPJj3bXUEEERGRM6663gUQEZGb1m+7+12j3/+Dmf048OPA/wP8zetT\nrGvDzGp3P7iU17j73cDdV6lIIiIiIhelTAQREblhuPt/An4K+FozW5TlZrYws28zs3eY2TL/+02b\nQx/M7MPM7HvN7N1mdpD//Q9mVo/WebaZvc7M9szsg2b2n83s4zbeJ5rZt5jZe83sgpn9kpl94rYy\nm9knmdmrzeyB/J7/3cz+zMY6P2hmd5vZp5vZr5rZHilQsu393gl8JPCloyEfP5j/dmg4Q/77t5jZ\nN5rZH+bhIT9jZo/L/70yf893m9k/2PJ5TzazHzaz9+dt9ttm9pc21vnYPNzkXjPbN7N3mdmPm1ll\nZl8B/Pu86ttGZX5Sfu035O39ATN70Mx+zcw+b+P9n5Rf83Vm9s/N7H1m9pCZ/ce87z/GzO40s4fN\n7C4ze8HG61+aX/80M/uveZ+918y++UYeHiMiIvJIpBuriIjcaH4WqIFnAphZBdwJfA3wncBzSGn9\n/wj4F+VFZnYb8KvAXwW+nTRM4u8DE2Ca13k28DPAw3m9rweeCvw3M3vCqAwvBf4h8MPAFwK/ALx6\ns6Bm9vT8mY8Gvhb4K8D9wC+a2TM2Vr8VeAVpyMZzgB854vv/JeB9+Tt/ev7vnx6xbvFlwGcD/zsp\ng+PPAD8E/CTwplyunwW+1cw+d1T+JwKvBz4J+DvAFwC/CfwnM/uC0fv/NPAE0vZ6FvAi0vCTQNqe\n35LXK0NUPh14b172JNL+eh5pm78B+Gkze86W7/Fi4COAFwD/OK//b/P3+Jm8bd4E/Psjgjr/GfhF\n0j77EdIx8o+P2GYiIiJyGTScQUREbjTvyv/env/9EuBPA5/p7r+cl73WzABeYmbf5u73khrBHwU8\n091/a/R+Pzr6+VuAtwPPcfcGwMxeB7wV+Ebg7+ZgxN8BXubufy+/7hfMrAW+daOs/yKX97PdfZnf\n707gd0kN2C8crbsL/HV3/6njvry7/5aZHQD3ufuvHbfuyAHw3NF3emr+Dv/I3b8lL/slUiP8eaSA\nAqRgiZG27f152Z05uPDNwKvN7LHAU/L7jwMpJQjyfjP7g/zz5hAVRtuQnBXwWuBjga8Dfm7je/yB\nu5csgztzRseXAV/m7v8xv8cbSMGOLwLevPH6f+fuZR/9gpmdA77RzP6Vuz+4ZbuJiIjIJVImgoiI\n3Ggs/1vS9p8N/CHwqzl9vsrZCb9AyjL4tLzeXwR+YyOAMLyp2Q7wdODHSmMbwN3fAfx34DPzoqcB\nO8ArN97iFRvvN8+v+XGgG5XLSL3hf3bj9Q2pR/9qeM34OwH/M/97Z1mQ/34X8MTRes8mBRQ+uLFt\n7wQ+KTfC7ycFXr7VzL7WzJ5yKQUzs2eY2U+b2T2kbbAC/gLwcVtW3wwqbPseDwD3bnyPYts+2yVl\nm4iIiMgpUBBBRERuNKVxWNLhH0eaI2C18d+v578/ZvTvcZMO3kZq4L93y9/ex/BkgZIBcc/GOpu/\nPxqIpIyDzbJ9A3Dbxnj8e929PaZ8V+KBjd+XxyyfjX5/HPDlHC5/GSbyGHd3UqP/DcA/B95qZm83\ns6+/WKFyRsNrSdvqbwJ/CvgU4Oc3ynGl36M4ap89YXNFERERuTwaziAiIjeazwP2gTfm3+8H3gF8\n8RHrvzP/ex/HNxYfIGU3fPiWv314/hwYggyPZz1d/vEbr3kQ6IB/TZp/4BB378a/HlO26+V+4FeA\nbzvi7+8BcPe3A19uaQzJJ5GCJN9rZu90983sgbFnk+aC+OL8ZAkgTZR5GoXf4vGkrInx7wB/dJU+\nT0RE5KajIIKIiNwwzOwvk8a7f6e7X8iLf540MeDD7v4/j3xxGt7wf5nZJ7n7/9j8o7ufN7M3As8z\ns5eWrAAz+0hSD/l351XfBJwnBS3+y+gtnr/l/X6F1Kj+zY2AwZU6AOan+H5H+XnSJIhvdve9i62c\nsxJ+28z+LvDVpGECP0cqLxwucwkWrMoCM/tY4DO4Oo+q/GLW5614PmkSzd+9Cp8lIiJyU1IQQURE\nrpdPzpP2TYE/Bnw+adK/15Bm6S9+GPhK0mSK/xL4H/k1H00KOHxhDjh8B/DXSE9G+Bbgd4DHAs8F\nvs7dHyINPfgZ0tMBvpc0Xv6fAB8E/iWAuz9oZt8BfJOZPUQKTnwKqdG86e8Cv0yaBPD7SVkMjyXN\nvRDd/UWXuW3eAvwZM/t80lCL+9z9nZf5Xsf5x6RhIb9sZt9Dyuq4jRQc+Ch3/yoz++Okp2L8GGlO\nhQh8BWl+gxJkeUv+92+Y2ctJQYM3keaGaIAfyvvudtL2fhdXZ0jl1+YhJL9BeorE1wAv1aSKIiIi\np0dBBBERuV5+PP+7T5oo7zdJPcevyj3eALj7yszKYwVfCDyZlCnwB6SAwDKv96CZfQbpCQwvIs2R\ncA+poVvW+Xkz+zzgJaRJ+JbALwF/393fMyrbS0nzJ3wNKXX/9cD/ysbTANz9N83sU/L7fRcpdf/9\n+bv82yvYNi8G/l0u4xx4Oanhfqrc/V1m9kzS9/1nwIeRhjj8bv5MSEGMd5ECJneQ9tfvAJ/v7m/M\n7/M/zOylpP3ztaQAwZPd/c1m9qXkJz2Q9tmLSMMcPuu0vw8pYPTdpGDRB0nHwsUejykiIiKXwEb1\nNBEREZFHnBzAeAkw2XhKhYiIiJwyPZ1BRERERERERE5EQQQRERERERERORENZxARERERERGRE1Em\ngoiIiIiIiIiciIIIIiIiIiIiInIiCiKIiIiIiIiIyIkoiCAiIiIiIiIiJ6IggoiIiIiIiIiciIII\nIiIiIiIiInIiCiKIiIiIiIiIyIkoiCAiIiIiIiIiJ6IggoiIiIiIiIiciIIIIiIiIiIiInIi1zSI\nYGY/YGb3mtnvjpY92sxeY2Zvy//elpebmX2Xmd1lZm8ys6ePXvOCvP7bzOwFo+XPMLPfya/5LjOz\na/n9RERERERERM6ya52J8IPAszeWvQh4rbs/BXht/h3gOcBT8n8vBP4NpKAD8BLgTwKfCrykBB7y\nOi8cvW7zs0RERERERETkMl3TIIK7/zLwgY3FzwVenn9+OfCFo+U/5MmvAY8ys9uBZwGvcfcPuPsD\nwGuAZ+e/nXP317m7Az80ei8RERERERERuULV9S4A8Hh3fy+Au7/XzB6Xlz8BePdovbvzsuOW371l\n+VZm9kJS1gLAM6CMfPCyRv7/iFkEMwzD89/dO/Au/+6jdx7/vGn9b2YTqjDPf+novMG9JcVAALpj\n3u+4zzmp9B1jmBEs4u44Xfpkb/P3K2UYjwzZ/M5XW8jlrIk2yb+nz+9oaLsV7s2oTKdVts3RMNfy\nOxtmk/STjWJ9Pto/Rx4fdsTy4wSMMNrfl/r68pkVMUzzEsvHdYt7m9e73DIPx2q0KYHYn4sdDU13\ngPvqBOW20X/06webUIUZFansIZ/rLS2NHwDQ+nLjOLuYQAwzACY2IxDXriEtDY0f0HZLoD3R9z/Z\nvj3d49SoqOKMyBQbxZ2djoZSfi5x26R3XleuM7E/5m20n4Zrr3P89gr9azav54dfMyw3mzANu1T5\nttjR0bCk7Q7ofHXE64/7Phdb/7j3sK3Lhr2/ea27lM/Z7DtwxtvKbEoddpkwIfT3wfTZHU6b11ux\npPF9um6F047e60qVb3mx0YinfT2+1NGPm59fEcMk1Rf6VdI91fH+Gnix86Rc94NNRtf+8bGfzoWu\na/LfmkPvce0NR2YMc4JNAM/X/lS+4+9Zm+9VHHW+brra9+Zyzwj9b1j6zsO97ehry5V97rBdAaLV\nhC39f57vWJ23dPl8TMfIxe4tsi4SwgTD6PKxO5yz267Lm9v2am1rG9Un5n19oqNlRa6jdPv5eLya\n+zuVY2o7tKxYth88buX73P3DrmJhrotnPetT/f77j/3ep+KNb3zrne5+Q2fU3whBhKMcVSO71OVb\nufvLgJcBmAU3q8HbvqJaKq+TyWOoJ7cRbahYAqya87TdPm23198k+6DC6H2OU09v546dTwHgwB/m\n4dX7OGgeomn3AWi783i3HFXQ+sKf6P0vJoQagEfvPo15vI3GDzhoPwTAfvMgq+ZCviit+opN+vgV\nXbd3xZ9/UjHupHLufDy3Vk9g6nNaS9v8fHcfDx68i73lfXTdhVzAYXtdyXayLYfUaWz3kwihZl7f\nDkBd3ZI+2zua3GDbX95P0zywtTzjhupJxXiOKi5ou3269vyo0XRpppPH8ejFR6fvYBOW3cPsrx7k\nYPUgAE37oeF8uYQyl2P1tt1P5LbqI1n4uf5cfIh7uW//bewt76Fr0zEwfq+yHx3HrCKERV85d1/h\n3rEzu4MPnz2Vx7Zpm8+paWn5UHiI9/lbAXhg7x0sV/fTdQcn2hYh1Dx692kAfHj8eB7VPYoJFatc\n6X8gfIB7u7t48MIfsGo+kAKTRzAMC1NSJbbLZW/7n/Hh+572MTqZPJbH7TyVx9hHMvdUke1w9uwC\nD/i7uX/vLgD2l+/bum+P0gcK8rWl6w5SMKe6jUm1AFLgMBBovaHN271p92maB448RmM8B4B3e/06\n42NgrQyj5fX0dj5u8Rd4VHcrAHt2wPvt3dx38DbO79+d33P/mC8U164ZTnvJ1+pQAodhCsS8LN17\nzCrMAu7NqHLbAW0OajfD525/d8wmhFD3wSCny+dA0x/X8/oOPnr+mdzhj+eWKpWnCul7LVvnoTZt\n0/fYPdzTvZUHL7yDpnkA4LKvG9sMDeiwdk0fO+1jfds1Pxdm9KGeft/Yt9PJY7l1/iTqsNsva1nR\ndAe0fsCF5f0AHCzfd+y5Ppumvo/d+sP79yrXusYPcG9puj3OH9wDwHJ136V/0VM2vn7ftvs0zsWP\noOGAZfcw53P59pf3H3n9L++Rfsj/bjl31gPq2/f9ad0Tx4JNsDDvA+QQqOKMzhuWq3zsd3v952we\nR5f72eP3uy3fSx5TfRRTnxPy/wAaGlpW7PMwe/4Ae6uU8Hth+f6L3ltk3aR6NLfMn4AROb+8F4CD\n5b39cVvqImOb9+KrUU80q/pj4Pb4iTyqexSRyMN2nnvs7QC8/8LvsVy+/1Svw4fLEbht92k8KTyD\nB8O93PWBnzpm7eYPr1pBrqP77/8gr//1/++qf04V/9xjr/qHXKEbIYhwj5ndnrMQbgfuzcvvBp44\nWu8O4D15+WdtLP+lvPyOLetfnnwjC2FKFaZDhTdfjBs7aiRId+ILSBVmzLxUElqqMKcJy76C2Ha5\nR+OIm+WVslw5rcMuU1sQCDQhVSRjqOlCk0rWDRXZVFYw9q9Zg7o0MiZhwdTn1Mzp8n5Y2gUmcc4y\nTPHcY1z6pwEsF/FyyrqtMnCllZGTC8R8s4pWYxZHPR553+WK7CFHLT9GDFNimKbGhIXLDmTHMGUS\nUgPQCHhoacIeq5Kd0MVjK5FHb9t0vgWbUPuC2md9xXo/LKhCnY6TUQV0eOP1ZWahP55zXgfRKiqf\nUOdMhNoiDYHaayZWGrTT1JBjecJjIOTMGZj6lAkVtUWCp/LUPmNqc0KYYkRKFtD2jZN6wVIjsmyv\nDi9BhdF3PP1jNFBZTe0zak/bJ+UodVRWD5kndvS+Pep9x/8aBlYRQtU3pqu8bcyH623nDVgFWypK\n433b5nXS+245LjbEMGXuc2aWv6M7M9tlEmf9NcjtoH+PzYDzNpe8H0aBldLQL9fpEKp0TlmArgQB\nmv4Y8C0BpbW3xkbbp2zzdAy1o0ZGsCodmyFSx7TeJAcRAs4y35fmvmBqc6o4o+3yMdA2p3jsDcfF\nde1H3Zyf+Yj5ms0qqlBT2dDIMGKKgXTdEAy6yLlezqdJWPTv1Y3Wd2txuv64uNFMwpyaRcpuDB2T\nmHpPl2F67PV/XQDr1o7lQ+fZZmCnLPPhvLzYsZiupydoYOfzxvqAXr4/dxuBjW3lPGFZjns9Zv2x\nMPF0LR5nI0QqWqtwOho7YBnOA5zs3iJrUp1/uOfDcG/bum82jp+rWT8sx0DtM2omRAusvGZi86G8\nVmF+mtfhw4zAxCdEJhdf+SxyoNM5BTdGEOHVwAuAb83//tRo+TeY2StIkyh+MAca7gT+2Wgyxb8I\nvNjdP2BmD5nZpwGvB74c+O5LKch672WqKMUwpQrzPkWxRPhibux3oxui0+RK/MkikZM4Z5GDCBgs\nw8O08aBvLHZhSeMryD1aJ6kIX4qSHje1XeZ+jqVNaEMOYIQDPA4V05Avpu4d5oGuu/SG6uUqla+p\nzZn7DjOfDxkh4YCHQ26IdaXydgC5oeaWtqX5tcsiOA1pqEtu0JbeKBsuWjFMWY16pS/7cxiCZTHU\ndN5gXQWcrLd9/D6OU8U5te3mZamSs4p7xDZVappLaoSP3r80KnNjdu41Tf7uM9+linNimNHlz1mv\nNJXKVodhBKv645kurRtDzZQ5dT7WZqGidWfZTZnmCsUkzDkogYoTHPtmkz6gMuvmzKxiEgIxN4hn\n3Tw1FMIsBVna43oPUi9yqrCu8ncMmI8CCelDT/28DKFiYgvqbjo0sHFwqMMtVDnYZVZf0r4t19Q+\nK8RiPu5nfcOjCim42VFjlrJMupwZtS2QacThWpV72df/fvS1eRLmLHzOIuYAU+fM2GEadvs0Uu/2\ncEvZZps9pylYsd5TetJ7QXl9HzAYNfRLRbYPIoyO7a4rgYCuT2HerNSmNw8Mx1DVX1Odjq5r8vmV\nssuqUFN7zSJGZjFn5AXDDKLBynMQYVmn4R9xzqpJx4Db8tA277cNl3YNHoYFBPp74Ck7LgA0OKLD\nYKORGyydJyXoCBB8lY7vQB8UPioAVpTzaWpzaktZaG0+59O50OHWUZXG+UkbwldZOY9r22XGDtEn\nuXMkbY8qzmjak9xbQh84Orx/jtkX5ccTNtjNwrEN7PFnWw4ghFFAr3QulWO/48JFP/NyGXG4l/iC\n2us8ANHyZzsrVnTWsbIDlmG9UXnc8SbrqjijDru4t+yP7m1bj9treN6ZxVEQoWYWKqIZbTulZlRH\nCTWtr/rMtKsh2oQpFcGPOB/lpnFNgwhm9qOkLILHmtndpKcsfCvwSjP7auBdwPPy6j8LfC5wF3AB\n+EqAHCz4p8Bv5PW+2d3LZI1fT3oCxBz4ufzfZRY2bZpJmFOH3b4nuCs9A3FF5yu6rjl0C3K6E1V4\nJnGXXR8qHPvh1pSuGHO2Q7ePdXvDW230gF2pUtlecCtzT5X11tLNpon7OGkse0sghOFQabvlNY1u\nl3LWdguLbsHMpv28EUvfoQ677IUZXVjm8rW5bN2QiWCObaTEHpf2ODQQ4pZ1rm7aGqQKaal8TWwx\nNB7yNbuK8609dKncYeikuUgZS6OlNNw6X9HY5UeXp3Gnz64xAhg04YBlTI37VVPT2d6xvaXjHt7y\nc2lITW2eG+TT1JAFVr7IjZkZTXu4R7Q0Rjz3YI8rg4QUGJuEeeqFDmndWQy07qx82n+fKs4JNqU7\n4tjfbCgFq5iQKnM1E2YxUofAqkt/n3dTZuwyiXMOLG8XOLZBkI6Dckx2DEGkfGCccCjVpYhhSu0L\nFlazCCWDwwmdMfMdJnm4UQyz1Mg+YYV16NUrc380KZMlzvtjf2rz/tpbAoKT0LAK0+2BTKuYxPRa\n945u4/w97tpcxTmLMGFepW3ZNRWzdt4fWwBtNwc/WMt26rd9Hm6w+XknzoayNB9E2Tblvcq1N80J\nU9GOKodGyIHsrr8uuY2PiyL0WQghZ3uM38O9ocvHbxXnLKymjoF5lbNm8maMZqxyFsTC0vFbx1tY\n5fO784ND2QgpuBIp1+OTB5lKULhNu2wtA2A0Nn2cgXFMEG0zs2xrOTYD9eMA0aH3Ww/ixjBlaov+\nepFKvmKZj8ESGBsHbLapYrpmTCxl3qXPSttiZZMUUBgFJW6UnubS8TLnVhbdDgdW0VnLKqTvugwf\nYhXmdN3eRYduYSEdK9Ye/lu2vv/G5127df3N4EIJqFnXHirPWvZSDr7FMO33TbSKKswJ3nBQAtKb\nx94pdvpYmPbB+Xk3Y2bTHERIGpyJ5wwsGpYhBTRKo9K7a5c5+kg3jTvM7BwtKyajYEzXpUDscK8d\nX+dOVue/EmZDVuSCFOSNZrTuLDwFGydxJ3WmdAcMwx5P/9pgFogeiUwuK0D8yOfKRMiuaRDB3b/k\niD99zpZ1HfgbR7zPDwA/sGX5G4CnXkkZiz6FPu4wtV2CRTra/mbdhAO60OCxoykdQIQ0bZC3ffPu\nuBOrDrvsMvSI7vk5mrCfoohAFfZo7AIpArp+YzyNE7bKvWu7fgsLnxM99pkIje3joYMKVm3se8Xd\nO4zA8gpS3i9VaUDOfIeF1cxC7K/XB92CWbyVujq3lhUyjBnODRpvNir+Q3bCWgXRLKVP90GEw5Ma\n+njdK8hwOG6cdghTpjlFbU4e403XNyz24wNYmK43lnPFJ/1bAgFlIqvhJrdWkSqN8yr1uHahYWUX\nLq/nEGMad/rsmtJL0tgBy/gwAMv4EG17RKU3V8LGPbylAVZ6gufc2jdm+yBCmwJ9kzBnWYJ8o0DF\nkPZ70DekSgU8eEoBnYZdZt2MRcwV4SoHEbqKWZcbtLkx2XbbswY2t1UIQw/BIkxYxEgVjCrkAFg3\nYcZOqoyGKbRDxsS2bWsWcu/ZMAzAqVJPdAmQnWImQtkPMdTMfMEiVH0Du3zEot1hFtMcAheqBW13\n/sQp7THM0/Wk9IpbRwwzJnGXWUjHfEWaTLU0nCCdB0el6cYwZ1qloEbKFFv28wakco/Grm6Yhl12\nqorF6DvutDPmyDN+wQAAIABJREFUdivTHChp2gu0XZl0dn3yx7R/xnPHdPS96CdgxP5aF0dD6Mqx\nWoUpwSYEn4zGQh9gnnuiuxJEWI3awOVYifm4n6WsnVEqfGsNTtcPSZiGXRahYmdi7FQlEyG/izlN\nl5bNY8WivYU67HJQDdunswMYp/5amVD0cIr60dvCRuPPcwBwXBm2kPdBu35du8h7X/S4PO71ZVse\nUSmfxDkz32U+6hhoadIEaBbYH6VId+32e7hZ6BsuM99lx/OxnNc9oKKlAaNfzy6axXR1lfpIyOVZ\n+C0srKbKvZTLHETYjzvE8BDNlvN2CDTRB+Pcl8MK40zM8WvKn8fbcst8FX1QYHR/sTwUy73ph0Km\n5RvDrKycO9M+EBRsQh12afxgbZ4EaClDz640S3AsWN0Hp0odKGD9Jkn3qg58nrIS8rDUvfgAMcxo\n7eGr2jN9ltTxHLv+KA7YY28UIG9scnzmwRUEjTaDz8lQH3DviCEFbQHmlu7FKYgQma1y4DEsch1l\nn7Yt8wFdnc6ugF18JTnzboThDDek0mipwy4Lbk0VtTwPKuQgQlzRetNH+tqcWmos8RNcUGrbZSc3\nWrpmwm53C8u4189LsArnU89nGQ9/yuOeS2V74akHjg7afKNahQO6kOcVIPYVWfc2V5aPSO+6Ckpl\nae5zFqFiFkN/e16tauZ+jgvx3NpkMiWI0OWJCNOTL1YbUdnDs2QfqpisZS90p599kIMW6QNLQzDm\nXq18w8gVybR6urFMw26eAyAOmRH9SmFoOFvEaPMEnYcrETHs9O83Dbu0cUUMD7EqFboTVjzKpIW1\n7bLo5v3yDqexAw7ykIwqzFhuS6/sx/yPAzplvH9g0h+rt/SN2XJqrboJc87xcNzps1asjblUPurp\nrohhzqTaoY55ssp8g97hNnbDtG8kzyujdWPVOTsHOZsg7DKNO6yah/vU1eOOhRimfQ/BoorMK2Ma\njSbvrmUXmS1TAKSKM1bNEPg51HswGpO7HmpYbYx06k4tyFjedBLm7PiceRX6BnZOpmCnnfXZFiWI\nUxqRx791IIQp7t1ovpUuDYfJc7QATH1O9BxEyN+xC92Rx1GMM6Zl+E9saLv0RI1yPTBrjszWqG2X\neQzMcwp/0wUWYbKRbfEQ7h2tdRvnXZ60cNQ4d0uTHZZ7wTjgeGieBrM8cVsexjFq6PdBBEvj7Tu6\nYe6a/OSI1husHQWX+u87jOEOVjGpdpjGnbVx+/3TR/KEvrXtsoiRRQWLKpV3kosaDVb5K+5Ukd1m\nh3m8jYMcJFzF87TdHunoHM3vkBv9afn4KQXr+mPXhmEpANZVuI8mGe6fdHB5DbVLHqNuox5IAwh5\n3PGgCvMUbPPh+tfSEknDUPbzcRnCNF2XtxyHZpO+x3nHd5j5jIDR5u8dUsgQI3A+H5PBalrOX/I2\nOKltAeVtmRnl2F34gkWsqDqjc2c/1ykO4q3sxw9ysHHelmEFbG1I9Svlf7evY6Pj4Li9Og5WxDAj\nhmnOrFz2n9Pfj/v5SWrCRoZUZTVT26Wymv2YJg3etwllCMY4oHWp1+JtczFVccFuuZfkOpDZ0JBr\n3WlDILRG181Z5XUvxFu5EO9j1Uwucb6am9csnGPR7RCt4oINWYjLpsYZJhlPWW2lfjgEjS71/rsZ\nfD7EDXBCmDIjnfPzKg01S0GEwHxVsnVTZ8rKLvRZeCUb7jTrrempdcb4aU03HWUiAAoiHKmkr87C\nrSy63f7m3eaGWBNWtGGFx6F3OHQVqwY8rKArN6WjKzpzP9dPXNV65EJXM/MdVpYqc5P4MFVMT4EY\nj/nz3D1rm0+TOGLCp22BDMf7ynbpJQVo87wCB75DZ22a38gCleUxwfmiGcOUdtS+Hj5/1MQ5pQZ3\nSSGc+YxZDMyr0Ddilm3FvEsTfB30j8aqcB9NNEbaB14qsX1GQe6hT68aLcs9vxdLfb1CpaLR77bS\n2LBIFed91Hnm8/74K1Kv+CKnhpY3LEGIQLAyli+lO7s19K2w0fEwHOfnmNiCLqzYj/PhRnlJs+3H\nnClQbrKOe83KdtjPN+NptcP+aj29criJbkxQZSFnX0z6nuCZz3MDZzgGVl3FvN3po/AAq6bqe0ND\nHsfvoU6BBKv68aXFwm9hUQ1jwGcRWodlDP08ADPS+O/SCEhvevT2qeKcWW5QzKuUGl5HWOWe3P3W\n2GHG1FJwooxjbdvU0ziODliexyFYRVfGIXYl+6kZDSE4ec/3xeWAVdxhxpRFNTSw80wpzJqqP07L\ntrEuDo28I8+b0GdCjZVU0tLrViYQ6+gIpVJk3dpxNP6cKgxBhDbmmfG7JTZ6moHb6tA10TDmnGNW\nGbN8V1y5MWsCi9VOPy/JQbWTzqeNQE+aaG22FkSA1Igs9wI3z4GHMGqY5q1pgSou+iexTKtzfbZB\nOVYrUhDB6Vh6CmKt7AJNd0AYBbPd02S4MAyFMEKe32e69ujAkllWvgOQhyikfT2POYhQMhG64fhd\nVMbCJiz8HPs5G+WgeihnawxBhBDqYQ4G7+hy0CLVwTYmJfOhIj7ORFixOazsCnt6S5bTcZPTls/p\nh5ZsGxpTVq+owy47Pu+vfwAtVT+R6vmcWZYCYOsT5Q4fW/XzIMy62ehamhu+HvuAQgk2xDijaa/t\nvAjbzuv+XmJTZjH1lDedM8uZGfs5CLsXNs/bjQyPsq3zkIay3njOkL4ca68B1hp2m9Lwuj7LtFoQ\nQ52eQNWN3ncUeAP6LIQqD22FdC7O2KW1pg8whlDTteV+cGX7YnPS1km10wendqrINBpxPK8kKeBt\nZnQrZ9WlMtUxbfODQ9tcjjLnVnZtRuWB85aua2kbzoZ7L+AeNgLJ5f6U58u5WEZCCViNhq55fsz7\ntrpnDFNmuYNmnoeaRYPWLXUCQh4euTO0G8h1djv9QIIIKIhwSGkMD42WXRajRly5gXfe4aEjMMwG\nniaNCywbaPve5RVlIrRxxdosMPcFs6pEkgOLdsIFn7PKlYP9sKCKc5p2n5Z2VMGCkuBoo0bwoYm9\nIN2Ybf2m5KSLVKlsz0azcLd59vVlt5MyESxN5lSCCB0psBDyTPXle6WnBYxT/y1dTE/hwlV6zUoK\nV+olJpczMD+oqUPK4oAy8Vo89JibtiM1tkqnhpdmeWDt2d55/DCMK7Xd2neCU7wgj3osyj5MY/Rz\n1Jlp30NfKqTzcGuakM+q9cqopbT3WMbfEqBb4jZKeR9FzEujpbZbmPkOra36ih6w3ti/SI+UWc2M\nHWaxzEGQGgrLbs4sH2t7eZK61h7uj9302Lk89ndzbKrF9EziULIyUrbAPA5H/rINzJphSAPkXqE8\nv0ep3ELXp4mHvuEUiVTMunkeA57WnEejcWdVWT8PwMx3+iEN46yB7Y2QmIZ2lGO3CiwqmAYjn/Ls\nV4FZqFhwKxfiOfZjeu5w5w3kCfyGIQXz/ASNmq5L36vN4+HNQ1+56Uq75hR6ncpn17bLwirqODSw\nyxqzEJm3QzZLmpeiHsYyH/UkDpv0mVBF002o4zkWfq5/lGTtUyKp4VT2WWct++WRpO3D/eek4/kc\n81zx89jR+gGrNvaPh+y6JW4TYHUoRXruO8wi1PlJBG109qrAfFX3WUGTMKeLKzZndTdLQZEQSvBt\naNB35bLY98TDWsDMSpbMdC3roHzfilEmgk/orOtnxW6I/RNASuZCa8McNv1EcPnJATFnM5TXYGC+\nTxWmtHkuiRk7LCpjFp1ZDiLUeQhONKPNx8DDlbFbVeyubuFCfqzmfryFptpjnPQU8jmXtkkDeWLG\ntTldts1tUdL1LbAKF2h81V97IQXQrihVtwS9bVuDb7g+lAyTccqxe5rat/Rgh5COkRnT/voXMFbe\nEbopOMxGgdS9ZZ3vJevBtmB1Pw/C3Kq+Md6WoVmdpbqFp0lNoQQlJmsp+Sf6+luu50dvJy7aKCrH\nbiq3EQisPFLnjompz5nmMdutpfN2bZ6i0ZNJglVrw0f74NvoqSVAvq+N918ZanTEnDUW+kf0pbLU\nNHEvZ8+k/Rr6uSbycKI46+fGKoGbidcs/BZaGs7ne1Mai76Xz/P1YO7lZoeV10zjTt9QLBlta0EE\nT49hNRz3qp/sce7nmMTdQ9tcjcnDyhOBSrZj6OgzCet4jv3qwdRJ2Gd5TbZuRSMcqnen5RudfOPs\npn4unL40o9eleuokzJnne8GsMmaxzFGT5nACmPWdKXOa8rhrX+VOhtPtXHD3PP/OTci5eJDoJqEg\nQtZXaMwwq5lWqVK08F3mVjGxmIII+YbVdU4XOiyE/uKzCjUxpMfxlQne2m6ftluSJofKFx/vwCoW\nPmfWP8XROGgju6vFMNY7HNBVKQV/2YwrrV2fnj+I/XjpzQbwZq9ZSSUsN8RZDLkHNrDKEfl5W6cb\nKoElE6LnZ7nTgqW5Ilb9EIdmGNc8KmOpoK1X/A6n6h2lXIAn/SRrqae4DtDk95wEY2op5fgg9/Q0\ncZdgB2uTBLb9bOfT9Wes59mzx+lkR2/DNONtSald/xajyb2O+B7bjDNISs9YmtV/ztTzDSPPwgsQ\nci/gzHeYVjscrOr+Ql5SKYPVfS9vqgSX7zI+XjqMSNWPv91hngNYe3GHKjcoum4v9aSOskwsH082\nugmmnsMZtdfUIYw+JbJqa/YZGpqTuKBpF3151noqWW+YQZ4t2cp40Amz3JgtmQgHlTE/mPaZApAn\n+cv7tgQE03ullNQyQZERmNiMOdP0vn0mgtM6rDqYlhv0cs4snGO/uoVVk27QaT6UjYZyHk5SxpYD\nLCLMY2qMrfLmOWhJWRXNbv++kIIIbW50DePiZ32ltwt5SFU3oetWaf1uGD/cdak37sjU+eyoiUX7\nv5XJZW3BNAZmkSETwaGLzrxKAZy0b+cplTLMKBWWroPN4QPpWJlT54ZnEWyPmaUAQul1qy0SLdD6\n+mMe9+xDa9eg9JQCYxLmzHLwbcV+GvZDzOd/mSehKQVZM/M50wB1bji3no6HRZiwIAUm9mJ6JnzV\npWEFfdkJaVKrcr3xFavSqO/KtXE8rGK9QVomPNwaRMiBqMonVKTrcmel8ZlG3QebrJ07ZfLFElhI\ns8mnjK2JLYYgAilI0cYhk2HR7TCNKRtnFjaDCIzmRDDqGFg0Nbv+KAAO4sMpiMAwpKxkFKQshOE+\n1lqVr3njrIKUHRbDvD+XjUAVZ3n7jRrKeTiBbQRRD9ky2d16ynq5CY97tYcGbXm066FrVLuP90NO\n0mMNa4t9hR5S5kDA6DqoKZPy1f31qZSsXFNDmFJ7unZPw/Be7iUjKaWu0037YMMkzjGrMWvXeuGP\nn1A3DNtgc14Jhkbv2nqjrMrNeYRw76+zVTCmORA37QLzPNfGvu/wsC3663/a5HsMw++GJ2KZVSk+\nW+5t5RwZzRUyDtSNJ5FrrTv8iDsrPb6TPjNtEtNwhCbujRpc3TDMKownHU5zY40zpHZ8ztKbPsAd\nw5QmP2ZxnDdoZSjqxuMqL2U4zSTMmeb7arn/GRD6ORFSlhCk9PZFvubsdDvMwjkuxAVNWzoGUn30\n1IdmPkL1T6gaZTume37FIh+nZShjyvJK99px8P5i9e7N633SUurt5ZgsdfvxU3jS+ismcYdZeXpU\nJA9nSPWU0gk4W6U6yipeoIlDYCxdd5vR9eHSM4X7uURIAcLSsaVj6OamIEK2OWtvP6md18yrisqM\nzlPGAEDT1amyHujTbCMTLMRDQxys3c8nck5rzTfEFLHvP5X9NmUjrHLk/mFPvUZVmNLF2VogIL3f\n0HgpPSXjidfKRWyc2p9+TxXGhadKfIpqDuOAAQ66is5n6VZoMPHhsWkpiDDrx9N3vuwvekNbZYKV\n7Inx45e2pFwedREqfc2lJ64OgWmEaYRcz2cajAmBiU9HwYYDliFNmmd970baNl3XUGZU6LoGmALr\nT59Inz1sw2G7VWvBm9LIOzzPwsbxtC29sswBsJY9koJLped9XnpwQqAqF+z8tnNPjeEYZ30Zuy59\nz/Sc4zxxYFhPlQP6inyaxCvfJH1G7VNqS49oLEGIldW5Pj0EWlLjvFtbZhaYVAtmPutvaEB+TGKk\n9vJdFkzinGWY4V7SRqtD278IVuV0+nHAK/UWly28H1OgpTyOD1LgofNl6oGNwxjlKs6Z2rwfcx+9\nYpIflzSLxjQ3lmYRmo61wMI8p7DW8RwH1UP9Puvy+TSuKASbprT83CtZR2NRORMbjt06N8Lmq5RJ\nU9Jim+6gP676x6ZVt1DHcwSb9Bk2wQ7oQnpCTDNKwW1J/61lKW0+NcDLXPzD8J7+bzjG8AjY2hf9\ndijbJ61n1NGY58lh59zK+bjDpNqjn/gBRpWXoTwxzvpATp8VEiI1aUz5bpnwMwSCQeOBWLr0O5jb\nuZxiOs5mSI9BK42wle3ShAOCTVjlx0OWOWy6rjrUWzlnyjRY3/veuafsixCp25KqfStEWFlYy3RK\nAbm6z9gKnoI7VRxP9hr6QMhmWnbKZKiHgAFDtsAknzsTaiY+XS+3QWBCN5p4snw+pAZrev+Yj/td\nahZrAZnWJjThAI9tvx1mEabB++EM0zw/TuyM5WjIzyxauo/lNNs67rIfd1NlexTIKMdz0w1BgHR/\nmqxdQ8s9Yzy3RbQJq3g+P352FKD0Js1CbqMgwLYx88ahY7w0KNeu3+VxwKPe7pIhEkLVdxa4Dz3k\nJagRwyxnYYW+sReM/CSWFAQr18Ay7CoFRYZrs9ExiYt+vVmM/XsN95NhPqB5nvB1EtPQtibPfTPI\nw/W2BVHykwnSl1gOQyTXMhtLELpkHDakrMowBBFG966SOZLu0zb8nPfP1OuULZav/5CG+5TtbX1A\nJm3vNt+vc8HSORJn65kIozpO2h/5EdUbwxpStlud7wc5SGNzKqtZxvkQNPeGKsxyICAPJ4rz9NhT\nhmvL1GumNiF6XBvOtWrqnB0xXBtKcH98bKbt51sDrOP9VFQ2YxLK8CaobD0TIRoYTufQRKPO86PU\n7XR0z83D5fK+TBmpl9f5cRaVIGF6klJqKNfteJhAuq6trGT9NZg3WzvvfK2OOTbOkil19mot0Gq+\n/ppS95yERV+fmIYU4I0hDS/brKMsw27/xJwU+AiH6q7Q5KOw7OctWQXH9La7e/80t5uPa06ETEEE\nACw3hrq+56FUcFN6YmASjLajTytsvaL1eq0BGfNFgThckBqr+or/0COVgwix6iP24CnVORixy4+E\nok6zcef/fJyOZCkSOozbSxWi8azbKeWy3GSH15ZGT58y3KcpD4/vqttI26Ux7QFj0mci5J6pUUOq\n7dY/c/3n9SyI8c15vGzztUmb90FOAw2hr7iu8qrTCHWI1G1NncdeNnZAeSxc42WehANaq/DQ9T2I\nXbfqy7M+nGGIHJfU8VJZSRO1DVHjtTkgGG4g69thPQMgKftuyBRI5ayYVAvmdmsfdV7EeKiDbbaa\nMQvn+tmDAbqcbTGJi2EiOJsQ7IDAqAewq/qyl2BZ3U1TRkc3p4639Knmq/YCbbd5k6xG221ovE7j\nDrVV/ePgIAXdDrrIrDTCPN2MJ9X5PqI/7oFNE3fG/j0DgWl1rp9QqI4hHa9xuPUtqrSNShQeUqO7\n8yYFIcLwyMlJWKR5TnKaYgoiTFhMIrOKPqg3i05jxsp9yE6wioXfwp7dykH1oX5/9z0TJWCVx5/P\n2F3LbJiG1KObn+DaH8sLSxMwXsjjyjtvCN1kLYgwrc4xC+dSgzHfuJe+l64rthqeNtFNWOV9td4L\nsr7suMc/leO3VKxnfRDBR0HPVPlIjezcO0KaO6CJe6PPrtIQgo1zIvXsDdkgxYzU21J6YKchTSBW\njSozc6+Y+06+BqX9sCqVZhuCbx230IaGJRcIuQLexTTmtNkI0kAOREXve9+7aHkfBRZNKusF201D\nNQK0PjQqo01SDz9DUK0L87Vr9jjoEEaZAOXanLZJzgxit8/+mubrzYQJE5/Q4cR86455tv6WVf+e\nIQRaUgZbNQoilHHcNfO153s3NKkyWI59UkBtETtm+WDtgwhmLLtRj2i0NE9N3uYz32U/7OKxpSoz\nZ1h6mkTrqxTUbMtwhjwcZ6OXzj2NMy/ZRykTYU7VHdCO03zz+X3s4wJHx/z43lMq70e9ZrxemeRy\nbZ/Rsmr3+0ZCFecpCysGFtVwsZ4ECC24Bxar9QlaU0dAaTykLJVJnPdBudTjvB48HnqwI3U7PPa2\nD0ocejrI+uMLh/pCPdzjbMLhx7LmIRl5MtehjAeUJ31sbuNynZ3mbEEw6i5Q5/09b9MTBup4jmWV\nJ4JscqB7dH8pjzI1QnpCVPkcQs662Dxe1u/p46yj/tuEaT+nTMmYmNiCqS1Yhr2+17b1hknusBlP\nYlzbbro2lToTE2qLtFifYTIJc5ZxRtc1pLlqyn0s9zinT+i3bwmYbU7QOt5PZd3advu6Yrn/rc+J\nQM5WdFq3oWe6mea5t8bbPDBkRY0nKF0/j7Z1+KTP2tawPKZBdcwjV28IeW6CfjJ1i0xDmhNr895W\nAq0wPBkHYHNC3f6c2xLTHBseuTuct+M657CsobbdPstz1h8DzirXIyDVUeae5qCa5MluOzqsDTno\nOszZsW3esM0hD+vX1nJPT+0SHLqbdTiD9BREAFJPdMosKDPxlspXTcxBBOhCSnGFFExoPPXWlwvJ\n0lKOmVvXV1rT44HWU+s7T5X+WQwsYhkLabmHNVDnRlTtM6ZhwX6oqUZPFuhIwww6hkd9DVkIo1q+\nQeDwM5BLD1FJGe7HAVfOKn+/gzbfBLsp5oFJLlPrHZ13fUUIyI2W9Ymi1nvwLy+IUG5sU8rkdLnS\nGnwYV54r+XU7GXogQ+ppc+uGirVPaGyy3osUVn1lYW27MQoi5NTxrmvSdrdqrXKyaW2oRP89mtHP\nhxt24wh23/PuOyxivoHlCXRS+n5ad96kJyGMU/VL5WkS58NkbFYTPPX0lIpW6Kr+e5dJvGomzC0F\nxqY+7yuEyzjM+j0EVhqCDc+dL3+bxJS+X49qN52npxAsuzKvwII67LKKD/fnQ+qFHc3EPtoXIU9Y\ntsgTRY0bs6UKctCmStPOatYHnCZxl6ZbEq3qG2YhBCb5Oe6l57QiMqFKwYlRL3Sdj7GVW/99ZjEy\nb2bMbJe90RMAGqsYDxuB9Di+NMa+9MilnoM6DJW/WbR0TIeKWTunjuXJKOXJD22/LUoa+sRrVnbQ\nb6fWVunaMk7zrkhDz/vxxNtTLce/b0sRLhkcc59RR0u9H3n7uJd0/zSnCkDdpnHhy1EQobEqBe8O\nBRFmfWrwsK8js3bOrBp6YKfRUiZCZ31dtPW8XjjHfjV+cklM+zanhnburHwnXSPzNi9P1ek/czSH\nQXkiQel9BzgfQ8rUWuYGG3MaW9GlgTrpc6wj2oTpKIhQ5o7p8rChVJ5Vv61tFDwsyydhMZyPPid6\nlYZJ5GDChDSszt37uVECxooVnQ3nD5YquBhDUCMP26k9NXajjx7xSNM/NhBgESKz4MxCxyw34uoc\nTAiEPrAwi4FZlQJ7syYPg/MFe2EXD21/vYm5p9npWHUX+u0xzrYZjrs2N6Zna9lCe2FOGw/Wgr1l\n3eH3bqPxxdrfxsbX3U3joHLMmUzBJocC9MYw10Y6nmf5+jS8V+PDBI1lG5XJzzq6vsHQ9UGE3aHh\nMnqvbjScoX+8aq6jlIlZN7MTh3vv4UZqyqwYDfHZ6DEt+6RkBQBpgtKuWrvuj9cdhkema6ZZatAe\n5ADffheZ+4JJWPRZCx67HKgYzocqTPOQj3o928cC0epDx8D42uJhStOVetZ6pmb6LpNhomZ2mfqc\npe32jyCOnjIqWl8NmUD5vKy7mjqfT7VFZiHSemDW5uFcZfI9S/syjK6laRuNt3EawrPtuBzvp7Js\n5usB6VlMmS5DF1R6/GoazuB9ttAi5Imnw3pdoeuavjMplbFkUG6rg62ztcDDppMuK6Uuf988F0cZ\nNOMelG2P7uTKAhIlK6ccF2kYkREDTFclGJPuz21YDdkuOXsovUdcv4da6RAZOhYOf27ss7SGTpSO\nbpS51S8LXT/hLZRjIAcRfJiraBYjs2bOzFI2AgznQdet+qDcZobyeNm6w3Vzs2r9PnezUiYCoCAC\nAGZ5BveQ0pCrMOt7tVPvt1GFUnFOr2k90HjE21GFrqQm2lCZMYupIu0NIVf+x0GEUkkw86FClmeu\nqts07nFq877BC6mPYLMCVHp7Dk/+tJ6FkMqZen+G8VWlZ3dIV65Hj1EMHX0QoaEDTxPxTXKjp0/z\nPCZgMS7TNptlHFcMSsbENAcQZhFWeT/M/n/2vm1LchxH0kBKrojq2v//0dmZynQXsQ+AASAlj8ye\n2afJYJ/qjFDowisIGAxgtyRDh/TMIaD/Mg+dvOJIzo4d3UEEXlM9ApBZ+y03BHvnKcZaIH2cZQ1n\nOANEmJkfa/tr6AmVItUTre3Y+9/41M+khPbrFvTRNnzoOxAhE0BtcqBjR2sZY/cqwBPjaj/82KiX\nbvjUv9KrtP0H1jLk5YpkKtYiDUf7e5rT1ibBzyH4eTLXxoedK+95K/gsQTuOB6937NjxGcobvVxH\nMzjK+sfYAg/pkYzyIZ84+78moGSTD+x64NCPSFDUpWFHS3CC4QzNYg1/tFTkP3rDx/mYwibsSMw8\nLq+2x2LsPTbY8yE82oBoAgtHgGAlxtkBoNoXBD8e+ogN/Kd0nHjixPPCpMGWHo0V4ADuvR05ZnaN\nZ6If2L2eGRtv9ROn/1PRMoDl2f4rPDYWXrOjsqEU5rVk1naWjs3omG64AxZf3UAF2Y1uAJ/nAwf+\niiPXGEL2oX8FCDvwwA/9SE0bDiIgDdfqXf7cGj4Khd9AEmOIfQqN5M8wuF/liNsuux1HiTz+S8WS\n7zLUpMrC2SA1OXTI37kexyeMhyMx93dp6NJgR/xlolWyEWo7a4id3Wdr/dADH/qBrUiUl254FWXw\n4WFjtq7f7rtCAAAgAElEQVScJdGGGy2KH4PzV3E0rh0Hkl4PfOjfGHJaiAVs3YkzEdDmvasCZfx9\njJfPD4+xVztqjaEpQMrdsYDrX5Vbj/yNcm+e8fTEb+0wT+XCREADzsgD8BcO2fDogkekEBBn0CQI\nCQAfw72aGDhdBjL842h/J4DWDLhrASDbz+ew0ErO8w/8ja19QnuGixDUv+RD8kJvP2DhU0OuIIJ9\nbw+A9yU7RntaRHTxnBrL5BWg4Kcn5WxiJwaE4XNu+DwtpOGf/q/pW9xPgDzK9KU/cOqsoq6n6qgw\nZNJ+J2infUwARG9HnEwSCaXxLxx64Kf8HUcQK05s8oGtMOIOsbn4oR+hMx2er+JUxafnGjjkbzu9\nZbzwGj8uQK3qDLzUv931ff2ZbDD7tv3XZZbF5mhQnF3wj8/BR2/R5//VMqzvJT+ntbOGvE59/BuG\nZdz7xX4ylzMBkHdMolsv96hibvry+3IF0eq11j6NAcr9rhk4KmcmLDxetj+/5J90LOkzwpsJBlxq\ndSNzqg5IljH1H8V5q0MqhjFhtgwlezTFLoqny2G73vD5euAfzROxTk8EXHXXUcbwnfNvbQP/1mSb\nZOF3+bPLN4gA3+z7B85hNOS9f4aCS6Vgb7aR59n0pjyrbpCRCh0UeMkrkMfm2d9f+IGXL/RTTTGZ\nY4zFPKEbArn/GA8c+PSj98YczoDZY0tvT1WKhpBaPz934glBL/FViqMbmsswgR8dUAhUTZFlYr8N\nApwIcANAtAu4KnJ9oUnebzz3yDaF6eGUz6OZMfZoGobYo5mS/9G22MxPPdGkYeiIIzmf8iPYCZt/\n7xQ/Tkf6jBRLKpekaw01Y+3UpJPbrb4x0OsWAreACCOTqa2bSmVBKE402c0YHx+5gTUg0wxwE2k4\nnpZwkOgyQYka87/rEQbF4H3MX4GWMZ4ey3u0js/zwINMhvbXlOOD7SF6nrTfjh0GfFDZsbFQfAzB\nPz3nNOfOK9g68xGiEc4AAxE+8HdR3jgH0vvwaA4ktY7HmXHHr/ZjAkpObNhhxj0V8F06tkZaNoqX\nddgab1KUemMJWciHbdCvZiyjavDz+8fYw6Aw8Gvg0QY2Vzz+q6sbbGJhIM64+QefQJv74gN/49AP\n7DpnJz+x4ZQ9Y9zLNNaF3TF5N0oMZi3VINtKQtNHExxtJNWfY9sRYNdnUCn/A2dLAKPrc1JYs39m\n8KWhOZW0Bftjb9akl0o4pNRppqSY1nLoEYkwx+hBs6fmeXg4QmvGzKnGPPMhcA68lF4pxBw4Xg+8\n4LkcCnDT1caPuWNCDgoi9GCVN5wvvL7JgWMQuHmEoc+Y8r017A4iMMGqfXtgFANlyIj5ECACLG+M\n5T3Zglxt9zQ8NcfiYzOw9tFGMBAeLeXZcXLdKY6uvvZ8fYslBjzlGbLTGBXdQy5S1la5VefdkOeU\n26Jrx9H+D17tRwDAAA3v7WqsTVnx+2Xf5HV++/q3mYmwy18+WgVEkAGRjmczGvxDPj2pIib59xp+\nfoRmHx3ngV3+wtkyBGXoE02f2PGZoTzd5mQFEQCFeqgA7/t8JSix5pYhOLEWGtSAgSbvmAitbSWv\nRsMY2wSyRAiCDnyG4wXuKTd94ueW7MaPYSDTfxL8Q4YTci0yLEikR24Pgk014Shg86VmiFc98WrN\nHRop/5rsAZxECJ8eOPThY+cGF554yF8YOGN9f6jJ3kM2HM66enST22cBc3YxUPPEj2lOn+60+cqg\nXK+v4JbJNfvZ5pjlrWnC/C3mWBldQi7bWFji6cMZILU/zAiemZN3BuVdPS+/fwUsvA03eg/6VVDj\nLj/VWuZQkfffvzPkeTQv97ujNw9n0Fxjvj8/5QfORibCE6I/IB7eQ8N6YJ6P9p0F5IeNMZ1/1YnS\n3SkwOaIw8DE+y7gaU2xrwI+hkQz4o9uJT49xxGkwQ56Q1iP0kX1pfVzGW6/9ewc2tLbHvvLHFsU3\nE8HLN4gAADSI/N+tfZbkRg2PZsL41DRcPoZlwZ3CvQYwdMdTP9ConKgjjthnhUF2z4DNcAbzSj5a\nKuXHy5IxPdpfs/LkG2/daOz9szGsGBh6Xo5haXiawh7oJbNwC36c9O7CM7A3dFFsPPbMG/x5/oX/\nKysT4So8h6RyesdUsG5bNygXVr7BPfz9Fn89jMblj9S43B+DHr8DXTtURnjYdjzwxIETqdyeeIay\nsPYbfz+VlGWnIOMZGbmBVEZXIGTaSOS6MVNxqiwIekYP+RuHH5PFdndxucVEZ13w+fMDe/srNjUa\nDrv8FYbzoR/oMFp01KOlUsZ5zmRYT214jD2ef8gntM0b4ZBnsBDCY4cWOQAIjDUxb64dQZRz+lP/\nhf+Sv1KJLQq6OjWcpWM3CjYp8zEHBpix/IezE+jRB8zofrUfE1ByymaKI/agDHeRAAoPDzew/jCq\n4M/RYo1+bIKPpxmm9Lq95Ade8uMCfmw43Kgg2GUAwmcf+MG521p4uj9aD1rsj/Y3mvRJkX3oJz71\nAx09GE8NDU/5iZe+wlDl+ALzHOQ8W+fnHQPIro9UtlsPlgYNbAMY1UNA2EYDcT7k75A5XXa89B+o\nHrGWAEzskla86o/e8bExptoMqfToWjkd2CHF1OaN9f8xdtTs+D90xyjPPvGJU5K5wX4bGB4qdQaF\n/3R2ysPZTgDwcT7wCkZYPrv5MaEbQxegJn+RlNbNf28BIniImLNJzFihV9MM/SYJYhmIYHnfo8+G\n4AU7DA/Ko0yBreRMYB/vuuNDHvhoPUBhwGT6GEcw6izm2ubqhyeGfDCc4VT8ICDo3tiPntnBj5eB\nbC95YXg/bdjQtGHIY8rjY3JooGMLmawYON2YPiIXxIYP/Rd+yn+ilX1wYDlqs3ijWSzs77rnVLbT\nWirrwEDZv2xNa4lbdqCGtPyH/O26QpF/AF7iFduKR/7nh6+RM9gsQ4y6/4FkIpAdNdXbEzzX9z2e\nh+8Dz8jTwaJ6ohc2Avtrb5lbYAXG2W7FmLykXXe85IcZ8osncugzxqsmJ/3ogp+OgPzTGz7Ojo/x\nVxjt2mwfokEFlKSiOo8Pj5q+03FyXPx458V50r0dzXOXAMDHMGDgUz/xT7C/Tux6mA7gcjaSDrce\nACWB63OY3LP7/rJvtIZXOYVElKenzOCW1f/eMbTS4A/syWprAw/fn6IfNBP2ns7+Y/9/yuYMLdfX\nWguWR69G4o0z552+dg8WXI34/x64cM9oiHq+BQXuGQdryF79tuU8+TB9XDJH2KMZ2+sRRrvtz8/2\nM9bs1g5gmF60glsslGt3f+Px0gTHWAi2zjr/iU898PDXcC/eBHj2ypb0Y5fHYc4IGEhdQx/5DfbD\nHejxK2B219kB8F3+3PINIsCU1L1/4DVMKTAvSNKnH01wdIvvY2zis1ss0qmCUTe1oXjqI5Q8CNA0\nvTCALeAueyRaY7HvoGwWLWhUJgByIbdisAB2/JdoKqYAwHOW1+QnxkSoGZQNvBjI482OYZsRa7d7\nnbpabPLxemD3RDRQq88o+QbY/Mkjv3gNos8IOCzPUuBFjghnIRxNo8ePZoJ+a5LZi8/N6bNnMkLQ\nwisYWcOxhbLQQvl3AKMo+ACZDK60FqF7ye8QR1JVxaYI6TgessfGEiCMmEL9cE85N7CjG3VxaMbG\nPprRnHc9cPpmAUml9+HHakVCTBnlWMP0zk3zvAuew0JDIlmUs2CiD2HGeHdQLNqAHQc+XYHM/ng2\nxd7mOb2fptBlUiLblKrnkqXDkj1++O5Z5wCNw6NZToRHT9r5A5/Y8YkmeU750B0PbPiQLZTBXaQw\nEUYkkPvoJ56juTeWiqOHU5w9gImfnn8DMnumLa9Ez/Cgpvj099N8M2WwhSeXAMiBT0Bt82dfHPqB\nAzu6tGAyNBV07TjlxFNqVnYEk4ElQmZwXWuVUs6/n/pKZduB1I8+Ql4NKF6NHjl4G60NBz5xSgJb\nIp6fpIAIPA7PqhDCApaRHjMTQXisIPMxGGhI2QggPCy2bsTrKDhGB/AItutTPqEY+AHSUBMAtXAy\nDe/7073Hj5YZ9w/peKoZOMFWwsCGPrEHBhRDTfYyQeKJ5yRvKDteaOiy4xgfU8w1gVuCvXY2PPcg\nypSGruZ9HNE/DzMCZcT8YVjEIR1HaxcQ4TkalN9uDL05Azx4lIRi7J/Dk4VyfwQMWDiGAemcYxvs\neE6e7NPU55qYAdjL8ZT2+2bzo4Rx7GonztRTJQjs3jENflVk2T9rce4dgAxTaegzUOfjTybCLh/O\noEEo+k1o2FmoYOytskVuDSn7zEDDgc9ITEuGYM58vstAPO7JBx54yF++r/+INgzv76HPKdQFggkI\n6MhkhGvpSBDh5UDkJh9zX8BO+Dg8F8FHN/msKiH/AZPdNpZ7nB604SMcIi3G+8PmbcnpwXmy63Gr\n47C8xBgdKue073bZA5xgqOohm62HclymYmDHY2KWPRx4NjmQQNujAackw2Q/LZcW9UF+nyBCLVV2\n3F1PEMHm94E+zwsHEZiX+zXUdDMArwJkmf7acLySXWjvN5ZHBQ7ugOW7HAkdV/CDf6v/1nIH5H0J\nIhRd8neYCF9ev9HX6rW9fdoRuMwx0iTCRbiWj9bwODcc+oEfZe6aWvkeRKjlkncLzZnK+6TzNDFH\nQXWmDJzYnRFo9ck5YMAlGTJVR+G6tVC3p/4DDcfNM+Txna565xSrrApLIn8fWPJnlO/TGVi+QQQA\npsZ2z8BstKQeSiawNXgyNI29uAv/k0CE7eeGrj0MnI7czGaK6748W/5zQbGJuOqyOb2vovLuAS60\n1ZWdQG9C1yUBisCFlAMD4hsSUK6tbfTvCvBzAB09BN+Q3ZTixcAGEEdY2WevIRn2fIu/s6iegKP3\ntZ6buEEtiGtWPyn3Ne//noo1hvWVAJz29SzevlzjZpMJZEZoc3cehag3jbTq4dL0fGrtD/dIZhz1\niYa9zI3aRvt8XGvi8fyZaG+oKaVNW7RnQzfvHrbwpKkMnOqbGNhnUsZa0AeP29pjbpVGGhPB80yw\n3/rY0Bsi6eXAmzmtXhfOKdhGKq44cG6bR99YFBFOU+ZAm+Zvrj/A4qh7o2eLCpuvJzGPrrXPDFXW\nc28a7xyiZe3Dv+v97gpz1y0yolfltmP3GHb7ffcNv4tC/Rtbm9cY69nVZFDTNDS7dvtumVfdjTEo\nMOh5lh27fFiIxeLFs051tg4qfblmdOd9aWDn3Ejvlyz1530bmtUfKRtURwCnLIIWY5V9Zv21lXfu\nzWSutExq25vGt9jnUVe0qT5dBJvauNe+5f1bAdnWNua8snmQ7zQgZyjj5n1OlzYJFA/dcOpjMuAq\ni6zK6lOf2NBz/jbBJgJxkIvfjrA6EngVgSdsBLj5bm0xp4z5ZCEhUmSlnfQg2KQZmwG2JtgPnacy\n8F8V9GCZcE7n+5og9sBYn7qhQTC0Tfsh5cd0UoQ8AeXfuAc3bNonmeGdaUWSEfK7JYy1RX6zr2pO\nie4JjDe9Gs5xSoZu0W+UfzLJjbK3QrDphg17guqeOmHel3MdsGwyy1UAZc3l+wT1iNeb9kuhJM8o\nxeU+GvdNhrEDisFfS8zd2Kt0Ooqwi6A3wYYe646e2KnP2RZNB4ixOXuMBUDq96JzsB+xT/uuOFBn\n40mWju91rmPFO3VDKzoA16XtuTk2W2lX1Ft2//6AoDIh1iOkWa97JsJskJpcoVzi/ldBBMa8UKec\n5lBLuWzXdtevUt4Pyfpq+faFJXpzT/7N9LuV3WPAw/39a+H31vCP2je3AAbuk6paAtQ7tlHZG/34\nVs5p6zOzETnGW7N50oseJQ42pjT/muJfnQx8fvPwxCmXjte5+BdB2Dn3IfV9KfUKu05duIUO16SF\nnkI2YMd+0VWNFcYwigUIwxljbmvxa8Dku/w55RtEgC2KvX3ipX5Wt1ORAbgX1dD0XhIr7k2wN/PG\npaE8cGrDIVtuyvRYyM+41uB0555GC0DvTwsUeWvuFdYDL7xuEVUu5k23W2F5x0R4MUwgaMiGap4q\ngbxuYp4To4xnOIOq5UDYi7eanjbSd2s5l4RN69+rB2q6LrYhDYxE+iVp4QwXeZzpCePZyK9hW9YA\ngqJ7aguAgMCC/TSHhtRrtX6CFvHntQ0rg6G2pzJP4n0yjyGRaL5jUztl4rGl1+PRUlnIPADwufGR\nBqHfYwnU6NXeow9eHAtFKO+PMs/jP2mBYj/0E5VWz3qSPVA9g7sau2Yrc/rsgsdZPCOtWTgB/oWn\n/ox+eOgDQ8wbQmXdQISOTzxirtp6HL5u1OdFw96Mdk4j+zHyyE+Gw6gqdmnh/QdMYXg4ffHwREX2\nHUsk9xgjrj086/ijNRwjWQP0rNb1d+gHdmcW2fsUe1Mc/YzQzU0G9mYeuq1l3XfdY90GEwGP8E7X\nePhT2xTiEEWSRXPrbb0c6WZFg375DMqiMSVYf85vM+ops3jfw+XVWeYaqZl1jRtrxcLBaGy1IZEP\ngV5bJpY7R4rUU90TKMkI4fqrY6tAyASKzlPzDHhS6a09I/I+MP7/aIJd1JlavhZ7w6F2QgLLUPWT\nE/IUm5AxmqEH1XvLsAdeP8WOGo2wHWcLiCQrw+bITNodCpwCZ8T5t4fJvQFNEEEkWAiPll5sQIKJ\nwBebZ2vg6GcwEPa9JF4kE6GNmBdUbpk35KmPMAI6upu0emFwALPB1HXHiSd2n++AKcZ2BO3nJG82\n7Hh5wlvri99nJKxMvlrI6rO673g4s2PXq5Hwg3HUnkT14fqCfcPyagCCl2ph7FjYz0te8Z1TrN2P\ncUwMNL4rplsXZ6Tl0bMP34vP8j4WxbgYsANjOkmE4TR3ZZMjjCauYzICpnfKwOHzb2tkWAqeHXic\nnL8aDDqeuPSSZwKBLut2HNj1gSZtyudQQ9PWNrK8ZENXA1Nqm1hnC49jAj0LTzhGem0HFLvuEwts\nxxbH/iUbwAy6jhJ++tO8/U/9x+oOMh5PD2cYF73tLRNhAbdML+TPnlunAEwEDk5VX992nczWh+yh\nr4EhtvpM5iTyNJU1x8RUz9BDbo4ClHeMg/dhDvEs2xF/f5/f4O4UFjIpVuO2hl68O41la3+B7CeA\nOoY1NcMZLBH5Ux/44WEPTz86G4DnTckxawWcid8XoE5gTqBWAB6FJeJdw7BMDy7MRmfMdTH9eKfu\n16qOYuv26cmFyYhmvVYWTzCJJ9D7fl50/TYdv4uV75kA4yFsfm4yzw5+0BhxgbI328i5jJ5DcXbS\nyykdGrTbEWQsTcX+c1MVsI1u0w0PT17FOpD+fBTBtUvHPnY8UI7wwhVE6NovAhRwI74IL4untdpQ\n8ISBo4p/gq5noMLuWfC5KQ21jemQHknE+L47wGJUmtwNyBDX1+eKgNtCgbd67U3j+KS9uQHZMm75\nORpEjabLHuHRlVA/YQIZXmH04na5BmDyFL2kTYIYSKZC9HdVxrzfq7ettrNpg2ib3tHQPA9G9jk9\n5eoGA69tTbC/duxxpN1pmLY+sPvSfkiH818zA7vkxs0QkM2/t7uBwed3zPGhHK+u+2QUNTQceJjh\nXM72fg0zCGmDmCFkdUyFycCCYO+U9fOgcUalqNl8PdoooR3D48Uz7OZxbtjBmGqOpdoa69m3m1MX\nCehFIrk+8BrAXiiNm4iHIKWxyHYMGTHGNDgM1PDxEqOH76Vvjrb5UZLNN343IE+j3584oy+YAHJv\nDY0TawAv2HnWcalkUq79u5bVK8LC9SnaQtGnUnW0gY1Kpwj2odhEyzy1+u3nlgkGZaC5bEpG1ol9\nPLCJuSyTFpmgyqOMt4jN+zDMO8chE2m+5AXmeWF9VDOfQPT5ueOlxxVEwAh5fLjh/HN0HN0MZBrJ\nu+coGNonudql4VFyDaiKZUrHFiCeRWoPtJCY4uPjdXfKNMD1aEn1CGBsZR+ipqcAngNoAzgZ485k\na9CY+1ZHiffupVu6Cp5dQO/TI4CBgc3Xw+Z9okNiDu9NsUueMhL1djCFXs5dGixNmYMIShbEiDEI\nQMGTw9YEmb1Jzqmi4A6XSwEi/MaZ5QG26f26ADB5uzfdsGP39bAkLcRIarweLvvS8AdsX1fRoL8D\nHgJ0mkESbCxfIyZD4f1m/zXRkHUYiiGWnyllYsc+HthxlPe1W0PBXnGGQe2VvJ72BMvJsuth74XJ\nhFP2YApMfaEjQ35Cr2n4Ocpa7i4jPDwFQLy7hovs+jCQzYGEqJOaXF0N8NW4ecpPDP3EWZhPTc1x\n89BHHpnaBLtI6Fj2TgMEN3c62Dj0WDfURRiiZ+Pg17AZuCLAiQ5hUkgxhuEEIhBsXE/OegNu7UWu\nUV/bJJmxJgcajq54eq4aAAEY7miTTCa4W50fY4mHZ91rX8f1y+/399V3XK7r1+v2Fnx4e+9Aw3zS\nCwDUI87fgghiYS4bCJrbft0asBHYbxp7W4Qy+nqz/X4Os4nxW9gEc91aHONbdZ66J/G+geGOJbtn\nF8XeBrY2sJ8ZZhh64aSj7LbvSM5pA10WEKHsz9Gvb+YF97A/tii+wxm8fIMIsI2+yQ47LOuwjZMx\n08442J1CT4/A0Sx+fHcPvhX7eWsCjCLM6DQlzdE3Q/P6Bj3BBZXRWO3bxgDYx4aHHl8igRtSQHBx\nD6jRbhcI9IWMn7K22KY/FNPmc7qeLJIKy6mpnB+vx/Q+lXQXVm9/dsPAJbSDP+t1Y6JBHonFRN0z\nOKLPLTaMXnT2mwDD6H+vQUXd/qeq6EglOzaYyjwoCubJODBYiMBTfl6MZypswHUD8JfGvUNXQTyj\n0B2beT569d47GCJGyef47K3ZvbpkoNfKpHEPo2LypLGvI4+EK0VPZlvnaQjliLwAwfCyEBvfAFks\nZk/DcBY/fsg8dAlOHa60ke4s2hbPbF/e2cMbS8bAo9lBdwBwDE9+2stcQRqyezEkzcjM+uxuIBpY\nWL3QJxoUR++ZbNENykcXHC83+McDLw8ZqUDLgQdqkrX07I7wvB99BGj4qEwE6VDdMLTHWqYXeWsS\ngQgDYgCYpkFKWaP4iKMF69hxXlb2EHCNFT3xzJM7wkOfBvZQwdnJRNByX8MxNssb4OXECy/ZJoDD\nWCs0ErOOjy7Y3PsPmPeywczbUZK7Wuxn5pHgOt1aGmsAvYR54edoxtJRTDkDhgMYVMysPSc2MQUt\n5p8nHz0LoqzQCDVh2A6L5cghiKBgUsSqgAkanvpysMwNMU+mKpLJvfZmQJblP/C+5bslf+axY0P1\nfu4XcMvu57ohw8AAo8d2Yt9ttu07v/jC47lF/zzawKO32LMM5BUcmqFkmxirYsD6LEAE1Yv38HR6\nsO2PycAgw6XeOzBwSgIQ44sY67VkLper57Ky+jZ07Lq7ieugXB4Tgifcq62PODloPgZVYeT2mhfG\nPMMvzbZzjdS450dL4wCRRC9Pewgvqa+DpyeujKIJ3K/GwqZHUPhFW+y1bTHCdjwyr4Y0vDRlfy2n\nvCa2mDGJhstur+cp3kcd+8ncHwZU1hC8XXfXZ17TviBqIMta5mzytr4IXrJ03w8sd4nnAXLW0uNM\nr+2AYpeOU0eM8yEdu9+b4DPcmVH2NjHgWrRFnhPAc2AtoEvd82uJPEGl3QPntJccDqLXcIahtk0P\neJ6oMCp9vZecOzQoX/qa5D0wG/R3AJR96+a6/AJIuL32njlkzpxfn7YQ938FRBRZV4/TZuEx2A/X\nF8xZYDpmMOI8b9HP0fFD8yhS6oo7jku401pWYIjggQVC5HxgmNyqt9v+mvoEnRLmfOSelYngqQMy\nh89ZgDoyBQdKYsWig1+ABJkdgH30ObTsu/yx5RtEAAAYSm3Uv908uZIboqF+5g0gXsBN5FVCHAB6\nc1MhG6MgktQH3PNqnlNfwKP5d9Jg36mUS8ep25RlvC+b/QaLea3KqRnJVxCB96zI9qmGbvPbz2Ex\njWgaSqLE/Sn4ArTQ+TvDVRgWS6R1Dy5Mz8ls2LCeNPRs8+a1gUe3JISkTf5oBFG4rfrmPMwbnQr4\ncN+AhMJA7xl/p8LbIHhROb9BZO/aVdt3l9CHYRS9JBrruhXPh923i9EVFXM4jW0UW2TkDqUdBYWW\nZkn2hsVoeyVwyhnvYR/nf8WgHftF0bdNb78okkfrEW7AQmAsx9AAqH1UD22f5m2Nld/RJiaCvW94\nDCDjAHPdhKdIkn58FENq9cRujQb8CHQfQHpghd9yz+DSPwf2GP/ahl26f4dgzXDPblVe7JubJDgB\nAPvZoNhCIWZ7WPcszano1WgxAKKCGrcMJYciKoOCvwPJprC6Z7+zPUMFzzH8evalxY4mk8U8XnLx\nGB/Yo721SUeMYyrMAtL1rbwG5bLEPA9lrPSRwuo2VAJ826XjBXWQRkOOKoYfH2rGMwD8PLvT9dMA\nNEBZzOtP0a64eCpVAW2CUzuYOPKcQIRSFABs3XPf2ZqBCA0p/zbPidDUvfqwMA80A0efrKMaiArJ\neG2+c/eQiMpEEPdqE5Tbynhvm4NO/q9qshJ2l8W9vG/zPthHw4h10zxkO/dPAHhxniLnRvO8Bzu2\nEnJkx7fuZwkTRLLskgL9+yDCyuSrpTk4BziIgM3yMgRzhPPLWAqAg5bRbzXUBdCmHoID7zf3DCPb\nwzWy4+pxttHmnmbvNLlF0FECTL6AIjIssVopqgOHfkxAyutNnPOuj9ifmJy4Xss+a7HueH79aPAQ\nseq9t/lAMOAsxlywrjwTjvWflm/Yeq9OEmtPBZY0WHeVxdB1c3Cih8zYwsBOeT7g8xU5zlswFmbd\nbG+KrhnKE2GAxJg0PckEEX43nGEFy7hu7dtcd6MkVhTADd86BzdRZ7S0kHVkc9i4v6Jea/4sJp6u\nZfjpSW8ZpXrezKI3IMIbkIBtvn1mAQXq/Xdl/UbkVSjXmXAz9IVgu6bcP1plUTqIqg9jraJNzMo7\nD/0aZmT3tXD+bdFrDS8MRPhXKVVv4V68dQP2Od5VhwtdyHO0NZXb+XfPRpj/JXMh++wuy8WfVBTy\nb1NXLjwAACAASURBVIDW/5vLN4gA2yx2+bBYTD08E7RvIA4g7AIMz5AP5II+VaaN7vQM99X7FWs0\nPNKezKel0dKECupVKa+CK+pMIxc0xCQ8u/FZVWhQW7PQeCMwQGPmLJuP1cOV1TMRWZ7Y8GipwCc9\ndQn6glGya31uGQqXEAj1thFEoBIy4j8ZVak3SvgWgjQVebii1YaaMa2II9qamIeoIQ3XMzyzcx89\nVSAYEWscfbkoNF8l91rj3FZac6LaLWjCbCMN2aTwayg/nBvstx1boV26QdGA55lziIBIznPxpIWM\n63UjeexYh9Xw7H4BsmjsVyX6oTqFBHCDM/AjAZsNaSTyDPvmSYKqB2j3dXL0M/riaCPmQM7fXDNs\nI/vDDJ38nUpXNZLrv5z7BBzM4LcXPEYPhkvNVmz5CzArfj3fH9daAiCRbb116LCtnX0RYSYN0zoX\ntf6LJVRkDTkLAXTVeVu8qbXUtUcgYDIqG5UKmeo/jW3rOCkonb7O0wL4jV6AIZkMXQcS2G9ijJZW\nwNqns7U2N8YAC3vhiTNJJ7exGppG2KM1jGE5DYYzCKw9LeRxHXvLfl1lsnlTTxrqsH0h6kMAUw3E\n3DQN0qZ2okhbwF7AEkI+nGnCdu/NWGBbMcLMcEk5sDfz7g/JNXJq1oHMCJNxEknDOP8Bm0M/m4Sv\nNBgZfQR40PcCImzZP3swg+zZw2XH2RF5I5jbQb1PyNJ7QZEQgOePUWOCEYSz533+jzmEZNMWISIs\n7+jOa3mn1Nu/mYhzcxDTQh4pr/yBgQTaxNgYlE8slgRT8VCEMW1ezY7nSOYe18jeWwL5/q6EjHxe\nCcJYBDJ30nNNPAk4uHkFFmhQA8YkvB4ZbfJi1z3kwFNf1je6FaPHyksTRNjF1tAAQw3pPXUgoQv2\nMw1aFr5zF0u3KNhtPpQ67WiT/Ks6jg+JgQgl7AEwgOLhLL8AgLv1sbEROP8sx8tZ2F1HYSHUPEWP\nhimsZPdkdjuszys7MeZW5NpIBs1Xpz1RZ6j7agLpyWrrYuEMpwq2IXMbGzP2u7dbB17aArhiHd6y\nREvJbP2/F5aa7/k6L0Lcx+O93/z9HfCw5rL66hsVtAQAngaSa9n03wnALYb5HmCXO1icSTmdsLAs\nxFtggSCC6z95XXAPIqA4+Uw+p05M0N0dNT2dJ69hrLDKWKwOsSozV2dYBf4rsLDqft/lzy3fIAJs\n0UZsEnZ07ZNizAzdBg1QsTEj7jkyE+7eFM/hWbSpo4tAA+VOxdror2msNOXmUDOTz9lW+Tw9WVIE\nzyYNssipKoiksgRKnBf/3Ylih5ACduUmWSjqyGzc3KgotkdtI0v9LBQtBFFVBGbB30HKXXrGABPs\nlpF2RNuTvZGKfhzxpgijysgh5h2jx+P0PhHJcY045fAmXNvScN30V2/YO/ZHbXfz7aIqZCJGPWSm\nb7axhbJAD4OzXiQzwvPoPyq01nf+4CiZ/TVN9spE2CTnO59nJvr1bO6OzCTP0m+U6H3M7JrqreY8\nZHZ49llkegcN5yXEx9kBUvokgYDiPeJm39KDSG9Y9SwzjnlveokB30seALbDvGl8vtkcW9beCqjQ\nIOX7ea2u+cyobetdVMNwITNlaxLM5qEmNyz82EEwVezacKIH8Te9D0WhekO9rGsvlPrGjN+KPRIr\nCnZPOpkyI8erE+STNPy4zgUjmBVWLy/hzR+ossnkBsAciYcmiBsxq0yCVryF6mOmxf39swlObRjS\ncUIDpFF67NqIkwiopHWpDC3g1QzIYG+2wkTITPTm2T+1GYAJmOoqxkSoMof5W2oizke3dzQp+QZc\n1p2aYvVUzxmhBmDbtQRROadEUgl+FMDK6k8QgEaT50PYzgIiuHJ5aqyNrSWTJted99GQcoKORNI3\nM4v8XaMHqBLglYiDCDOgwoSNtQRY8sVe8q58Fc9bQxe6y9eG9OzF8w3GjgBBjwS1oz5iIMJL6z7m\nshKt5DAwBX9lXW3ubQ7lvjVLprl4+AM0XbZf88jPBonoCIZFlNjbiuENuNHd456mMvVPvBNjCll7\neM6a6hXnmu3FaNqRbBS+c3PGAXRO6GtG3XXPqXs023nqhpcmaF8ZJfsiUysTocFYlm3UPTfBt7q+\nN6Hzh+MlwZapc/rEK2jkueaNGWAG3ByysRbba9dTURjOQKNPgDbwHG3q8zxdTBKQxgbg5XqAgwi/\nYInOYQ6ZS6ZeA96EGuC83H/n/bb6ZpLpu1Jj9Gt5BzrYM3ms9t01yw+yXUDzpoi1bKC36WabM14t\nN8gJhqTWk3Cu9bvKG673ClAOaOjqfEtlxFQQgXtVL3sw7RLKGLsm2HkymfKdTDIuQAmf4K4WDD0Z\nXqcx6Q9W6/cy9I8o3zkRAHyDCAAcRPDENw99GGJdvSARzoCgY+5D8PINJMIZBjfN3Ms3iLMX2qQo\ndfBe0u1xUchIE92bC4FSKk2Vv6+CaojFTg7ofP8wIyERdKsHNyB7nynVJ+tSkAIaU/nO9HSuIQ2T\n1wCZP6Kqg6vBrc4PIGMgj1JKT2GGZIzos+qt1uYgAvtkuENe01lL79ik0FOp89957BnPfobOSuul\nz3FtX72vAkH0wK4odBqg3p5WzoP2rgrFgMoojF5r7c8z5sMzCpgnD7R5c46xb7cG7KphrALADqPW\nV2+xqh85Vept1HGJI4esjQ4aSAXaJDbjptkXVcmZj4vj/TPgZZ6uBAJs0yyKetnsE0RIRbbS/4wx\nMCc+XL2t1q/3YRNoOY+sf4riWTb3rZ/YtkLfJfNIPDyIa8/X+5iO6vKxbg4awOZ488Su3M92aVAB\ndvRgCLGM3970be1RsU4ZODMRXuEFsafMG5+JnfyjgGBi8Cg8EWbj16KCbhABxwoilHVrp8hwvvk4\nEEAr62ZoesgpBwxcEgzY/MtEiHWMfOxf3r4peSTw08eBom7EPJ3lssLCDdgVzRkETeChZ2y3GdZb\nbU+z/myS3id6Tl+aRuWjWeb/ppZg0Z71Na9zfbif9CL7AUCGKacRqsex3kaAB83/7adm//T0gs00\nb+BZ9kCuhW2Zf6fPcYth9++QTSU18S/DO3IfVKiBkGrgdLQR742Jd4kUL6wQyaRhPAawIYFMysIx\n8khZ69NkNLEMtfj8GRwoe3psRjDwZKIsz8Yg2z0cdIoTMTpB/ZmpwdCZl45lvx1hUNtLt0sf1KSC\nkZzT13EN7bD3uQ5V9hLu1dUrvgvB72QxPpVcwGQD7sIEpZlXg2DTqucMZ2lwmp+ua53qhnKA/HlE\n7gSch7zyPUcdIG4plyJUSeo+ZCFXqpiBXh+DphKJjHkEbA0LvAvXqGVtI/co+/bAwzPzs6gn8D76\nrMfl3pIyWYeGIyFPNlEw/LQakByDPs2r+1CDOxYCAYev8iT0my54m4/Bwyne3X/PRJiPFq7fZigC\nw0gBrk8HEQhc9xrW4iwlB+stDGm7OIPWcmUnpKypx7+e0NBN7V3cn4Cqn2/9RHfHR7Ln0qFWdcAB\nkzXUxxnKa04Df2fpl3R49pgXwSQsDL7v8l2+QQTYBsgj5nbsTge3v9U4x7OcxLA3xVOBXSU82oDg\npbOXBzABYEqwo92uvNKzboUeLxTlSRz9lkkZpCeLPwNmeKzLesBo1mMxJmyDzE2/hglEoj0xz99p\nzqNkIjSgq5qhU9B7a/0V3CCF1z97yQpvNKt1F2mOinJzZj2TxkfNPPqsFe95U2wQVyhybDAwJcdk\n/1SFvl7zC3GdvlwqrZfY5rw9FLj6cyt/Z3+sKHScpX3xPKRybX2RFLtQrEmBl1SKGBO+ixk/gAED\npIn2yWhB8bi40SUNRgYvxpFY8qm60dk4SVAB45qMSTHm+eYWd+rzRnLsRlk/4sZZzQ+xyYiNkx5m\neovZBn6nxr6yv+kpnRV6m1v2Xo8HbQPYzBud6L95/+bnJRguUaSCJan4bdtAa4reZ7o8jbsEJgTa\nKEtafMeAI4R22ymPirw51Yy11ynB9Mi5XUCsVSaUwrUXSj1DGXp66VUFvc1nVCeYk7JBxWjtlgnc\nQQRtYVguojLCx5JxY55YC2cgoJLnz7N/fg5XxoqsOoWys8QtN/EQNJt/nOcn8rscn/DyNA1ZuYng\n0S3GnXJVkTK6hgkM2O+cGqIzaDmJyjEbkDRYJk+nmOEiKgFcvxTQIQ6Q0QsrkWiN9SGAFnOtzFdx\nWULZZPLUQhlk8zXqmkLbB1pPRZbyd/LQ+vpgCAkTRKomQA7Y3klQhX3ZYn+d85aYEdUC6LDY9wFI\npqDLYzd/reDeKfksG2ZwgIZrZXUYE6FhP3N9EjQMEFUsfwfBtfAWOljUR7IUzRJOwIX9ywz8bJmK\nejiDzrLO3zeKct/UzKqVRTbc01/DxqCzkSNlr9hKHekAmfYswJN42m0EHNUdJROQGv1EWZd1W/sc\nSIIXx8BOp4F/24yuetyqtWGgS4sEdXz3zuSnkvMyWVbZ9t7Mycj+oNzdi2zhzydyjZFhocv8G0qW\nRWkreArD4nS5GJoeVtJyfW+uA/VWmAhqCluXMQH5+1R3+z5Pb7EwPLv2wrgAcAQWVgAqGRRzYaLU\nWr4C9e7CJ1jeAX5rbH49YvuSpBLziWGXY5BZR908PDP3lzpn8xo8FMzn7tgiFxJDcPLbNyDCqhsj\nQeOt/I2iOTRXgesU1fnhIG8b2J/p6KDDphcdZWuCl7qsWUJ56/iqywsgnWDD2cMDEiyv7m1Z2/NH\nFQW9lH98+QYRADAKqavRkrq02KBp6Fs4gxbFqCiyVGwiNrYsRvVNZqxeId+8fPGL5ndScOWGu4II\n1MWkbIhrHJUJ8FlZtdMJ0nBkW67f1jD4BiSuiwheg8ZmGtpUBO/yIlRjWnTdNIFxI4ykgAvJzLD+\n2vsIb2xN7lWBH0+BEFaK0mOhuW1zPCcmAkzLj/CG0FgIAbTJe3ARpJpqGzcS/mwxiPOzKwotNOSl\nKiyplmxlzDaRaW50V6jna6bIaenHUXJ2UGFt7F+ZlTzLqt6m+UdPu+uepT3JFmHZRnNgLJUaA8ck\nrJbuiiXR9jWHwZzQyoGkliACqZ3c5Fnvqizy360YlgDApJWsN9fjtg3gBbRpTSTAspfvGMg/zwMC\nH/FsUzNK+wjgpRcQgf/adZunW6l7gh2IpKJD7b6KX5we2nDqDM6tTKFTrusQoGJunorqQbS1l/2j\nqlP91/6hbOju9TflxeefZDgOUBSuRoO0UqBz7se1IWGo5rfTW0zwjWNQW2rMMTPCm2ICC3dR7P2c\nQIQKUEUbheBWKvCUP/Xs9pPzdtBYc7kks5Kpvhz5bqu7y3SZ5bS9P43xp7+QMpljyWd5TWJdY2pP\ntL1l5P3uc7U1hZ86CmK/rQMtGDMc/3mNbL430LjbHOA2Oa8xD06VydvGugyXiXUtb82N73jWDAjR\nEfW+20dq+TW0YCU94QwDSePW2u1VHfm3oMaXucuPnrG3sz2UIa3Qv5uvqSIry7uorw4HFcwgz/0x\n2D81YkkUL99Hc1/uEOgEBEAHRmFf1L7aW5H9w9bxVg3xonOwX+iQUP9G9d6bHJn3LJZ5z5Jg7gDu\njReJEA77NvuktFmtnvWYU4DAEIG+1H9WZployvTK1su6Z//YnJcy9xl2igmUOT0vvj03t/tOX1pL\nx8xqI5DeW/AjIc467eNOj0vnBAAHn20sX5Q3ESNfxhN0m2ThPreGDQ0HIe4My/Fm5Ukknvw9VgO/\ncwcwdMx5DupfJPIs3MfxM5/BVhxoe5Fn/DfkftW3NMc19L03LIT1MvNhkXFZC0HX+vPEbCwyurdk\npVSH2iQ/hUltHTRydhK1Wv9QgFr8tukNgjkUWT2Lirzt0+/y55RvEAHusXHaliVVqguwIpNUgkzI\n/JSZ8r/DKKWbmAIJpCdYJdFGo8BRMWI4Q1LBKU+auAEqM1Jp9Uolh783sfewqJrSUZe5FkUhFVb3\nwraM/a0G5Sj3CqgEpKKhQnquRrwHFWZlBwNVf4wylGBHeruVNEyZGRNmYHi2/DZ76fdFSWOYCWne\nKqZkChCJmAgo0OMNZGhK2LGuoZyi7vWY23CJdaNyoHUzkdhEZm8P21eUtzBQZhScfZ2KhBtsDdi8\nji8qKG2OzR5N0IvB1CCApNFuz8wbZdLmBDzBIsbM50bBAYJFsU3smhyfVuojnDuFIdAlwZ2Vgl1j\nUe39Nge0fIMGXDUqpXyz9lllEjBsh9muaUC2pmgeG17HgYBGrh3rBFmYJ9035Nz0aZRZRu+sdyp5\nW6m7ta96P6uykXOAhb6Z3oDz9P5cDLSmMq1F9mA1aOkJRjUUGvsnvfRDBf01K6zs/60BflJmzB+g\nTbGe1j8us7JKNp+LstTgQA7m9cCxrLlMKgCX9bG+HZLzmTL9rOqoh+K0ruFpb93lYlXgm6C7wS75\nqDHBBJOiSKCV/fwayb6oDAwFYKFwlYlghn4FAlKJzGM+N/Fx9HrxvtGoeM7yJsDr+v2l33af960P\nNGcCk4kgJ9AWkKXmzIn1OmYmgu1NgC5jUQFddqaFCdQ8KFm/k70uxh6xk1H80V8YZL+K4Y0jZ8s4\nRv6hco3zlmEsbEvqCmnMCATDgatcJ5bEsxfHginmydCxd87gDOC0fuQa8C6LfeQs400gfQim0DPo\niNCA6DmdZQH7ooZx0AipAC0NilpHgv1dx9QGQdljKPsLAlBZlSJ07aQxIy7Tw3fBcdeU9afrJ21w\nbbNuXu8JwMi1EAyiwf6ve1DKFolrCegFa88B1FNN10gsKfMH1TAZ+z3LWH6vZd4fEsAlE0FUgH6i\njwaGYHFM6vrhu9jj9EwPkTQOC/gcoMFCeV/DLYIRcOPtl0KFn0tb/i3P6H3IRE1YWQvT/t0d2VrP\nEVhPxaj5DHJe2L6so+45halZALRWxrGumUt75HpdyjqY9o0A28scEYn5CgBbP4PV2BcdJfU4f58/\nO6SCasxBtTi22G913wggUvL3XwC2//tLMS7+8PINIgCwheReXHRH2u0v3CxsIedi21xRmpkIfr0h\norDUwQEFHAFO5SMMYhiSXI0hfru75/ZVNtsqkCqIYP9mq1Q0QIUScRGbVQqeUf6jsgwLW+Dmwveq\ngy5SNyO4V1qm73eBe5a4AV3VuGRP1L9YUqIt3lMEpCOvzIyelGoURTYVVXYbldcuSYumrjAp9cVr\naD/4t50iXBUJ1r8WKdernOXvVc0NEKFYFDSwZuMsoZ95s1iZCLnphNehmay7KBHe5ZUSWzfKMPpd\nqd8klXXx61Su2U1UoqnYkJnwjl0DrdckwJ2Vgk0PX7Tb50AwEQr4lgpdHksabSRCX4woepg2f29j\notNux13VDTpZSZUqaDWt49qRoQcTKFey3Vt9MoRpZlGkgVXXdY5htlEU4UkGkmV3qlx0s5pIMHvY\nx29RFkVn4MfosyP6R1TQ2/B+zv6tQBRgy3RwTS0gy2pQ68h5n3RcP8ZsJHti9q6xjp4/oCVjIUN7\n0qO5NfWEsQKMBH4I+BDs4fg0JGU/v+2hHCFbU3GrHn5VB5NbfoOSzgCHqqhxvoi3W6N/ksVjxrog\nwzO6iusyacz0ZjkyVnkcIE9Z94Dl1uglQXAYKbtmGMPD2/pSNF5zkGWWVcl40PJdAoda+uNU89IL\nMjwDUKgIKnBI5kQd79nZn41559n9KpFiLZQhW5GpK9i7NRuvsdy3L3O3ieLUFkwE9vnWco1kriHz\nCU7exgWQsGfFQ75qn7cAk2sS5wYJI3Fu5B0QMBu5nJp7MbpHuZZHIJe61fkjAwSjr3vWnUG7yjrb\nX2oKPY5NsDD9uko6IExWUG+pwKXE3NwmOTvvjWi+dxaqct0bZ0aSreaVYdKdNk5DTKCX40G3GyaC\n6L1xNhYvNGPgZQURHED4KbNRafpA2RvFAGZz8LT8Nr+3AESquqyxK+MVuIILfEdDfwsurIVgg527\ncn3GTkMAVjZCK2ELACJ0oZ4qsB5Tzfs2tEheaf1j/TZk0Y98boTOI+IAlo156l/XMbwDEdIZl3Ig\nHG/Axcd/0YO6hZZZWEPuEZzjldX2GjLtxVXjvpOfXDd06JkDzteSZnLg7/JdvkEELzXhTd1g05s1\nR7pNiuxkINkiHtPGYoI5vAmNG6JOTISkZef71sztwOw5Z6FiU1WoocVop6IOjXi/6t3ukW+AisnA\nJh30iFIxb2qxrPSC24fSQK6JAwe9Al4po1bmcV+s150sqqEZafA56lrp8pLhDNUII3WWxAzbY5f4\nyaKMJGiQ1xRpfJh3D+51nuuJ2m7NvlhBhOq55/dFZkW/uQFa6yRAUFfTi13nWyqy/HdiIgg3LP+7\ntyN+jvelMUR6ZxcDYOo7LaZWPCs8Nxt6eeZwBptTbfJU0ovMRdIb25rfBLJvZqVTg1afR8hVWvWs\niF7bWBVAGiga9WbSuNbV6cVJG07lP99p/XTNO0KDcq8GqXu5deS1GorBN3T3KqvoovBS1tidp8+z\nynySGP/ioS1Vq+uOv995QYBVqXeD0b30Y8BBhKqw5tyjUnOeNqcVKMnzbO7YPNey9iQo3BV8aQKg\nAS9dx6HOaQlgtDIR6Kmv8vzFuVwEurin2NgiyUbZ+iyT95s5yewtq5w2oCG/gTbnYOGtEc7QZgNn\nb3AQo15TYMik3Na9pl47yzea5DqrJ78ADB9Y15hCtgQROC1kQ+ZE8JNLJpCl0QjXrBf3Jsq/wpgI\no6mA5Or9eDX2cq89YfuKlrql0YOprHK4lpq7hmWr80rS+13rIyLzvCJo2Gb5Z8AA5WLO6cwNoNlm\nl6Gc+2QpNUn5ssmAijgwUfsnvY3sEu7EK5AQQPUU3jb3j8S7C4jg+0ZleVVmY4KOZCIYmBChoTRw\npHjkRZIBGsw0Z1UsoGfKjPl7zKsBJNtnZdBZned9jPWpp6oAzqppRT9qDMGbmSEJdGZfbUI2yMq8\nmo3eYJhN4zK3tV43cLTkauF+QmeJCvDinlJCXXwPmfbDZZ7wGwx363HN+3fRNWuoaS0ruACgvOuO\nPeDzqj5Tjh68I8o3B4du/hLhEdP3tcW33+VZYNLNcLwQvCvJjpOFsOpZZjlUkDHYCaXb7hMtpnOp\nhs7EsezL/j3ZBhx/d3zE3CiOjhUAOc8E+l5VVhb5yVxJlMt1TtT58McDCIpvJoKXbxABbrCBlPOk\nrgEItHdrOuXREGgBGFJBjQ0sFjvQVYwO76uQdGNDiCuIoO4Rs/toWHTfwPLbcqHEhmKj9b660ebV\ni9IZcVUthBTbtoWylNZH9YIDAJp5AirzIUMrdFI0Mv6Kv6fgugyKGzhsAz3bpsSuQnP2pNnGJ9Di\nlaR3LhQg1YxR5rOrJ4tjpgagKLVh/pl1iw3R2yWzcspvVMptGsnLfaX/gVQmgcWzLXBknHNNIq42\nASIzMKk4Wl0TXKmGA+nYXerzEkp9Dk3OZQ6kgkr0bKBUIx1IAKGLJMtjAVJCwSr3zt4sQ+ED4Gnp\n6UtmzzWcgV6susHa2qfim+du09jvJQv2PVXQw3my+g4gkapfNv2mkAYIAbmec7fGRTY3rCsIuEld\nz9lvHnwbxmpvNDCEZJ0oG8r6W8CEXBN5ffZMu+eDOVx8HbYiG2aQJpWiFXxT5JjOMkpdoZq9l6zm\nZe6XcaBRPgNE7rkdxQBsCAZZNT6AnFf0tPc+QvanrJxZaNEen6d1HtR5ll+hZ7uCCDaONYcG5zLn\nJpBMGDTgyfEuBkGVk/y5fl+WvmMZSNka/dt9LRBU9RfJqRCGe8S6u5kDDUHcs33M9pGSjgVdy35V\nAOkz1sOswG8tWWQ272ViunEfmZKc4moY1zIzcxA6QJVBzevPOwkCdcEkY2J/Chlknbs1xaYViMr7\na90qmyvGoc3rQKnUF5nI/CA/RTLXiUrkT7gAJ2rGBLucnunaRzVWu+aAQKN+lHLfsh/U+rg+UZhF\n9h1nfhUHRCsJDKvRTlD5LPsl98aq95AdyLI5ZtKb4DyvYUxVzgpmmeU9EGNRx5b3VscLZTGviXBu\nyAxu6XUOpuq0LNC7IvV7qQNVZ0oXhTqIX8GOGuaXz0umePLuHWJ62HwcqIQBqZNeJNNiqkzTe3BB\nbw1/Gvd3nvs7QAJAhL+uxRgM7TrXkUERrfx9Pi6d//l93t9D533Ikg3PTpte128BD1bQoMr7vCYO\nsCZgOjT3Jd5e5Xo4k1oC3vbtAlAWGcx6cv6yLzZ+q+pqoa0sYxoAE+t9BV6/y59bvkEEAIBE4psN\npL3lxiegxywXziZ5bUYKJQw3YFZuI2a9KBy5aTtYUTYqbngNc3b8GifLkpTDcl9Z+FXhpVKUAnJ4\nXRLBZnhFl+XgHDeOzSBLFJxGTyhQsH5QzWMSVyPaKmQdvJ5mAKSCd/VCZ/Rg98Rra4Zw9U0yhKI4\nNbII51uFHjLFJo7wdpuCVuM079Hl3JxqofemAirceFIhc2UsFLW6gaUxBW9vkys4YLSzmeZNwyGY\nDT7+DYmAJ21bwlsJYFIkYy6VOrOHa4hMVaKbG0L126zLKIATAZDKzEijsBgUkVBoFMBKvS1VUUqU\n/+JRL+/jySuc//RC09jnOrC+r+wjvtPo13UqVHp7L30hjUwE9q36f1dgIoyM0hf0KPFTdr8ASE/w\nWRSIFSg/y9ydGDF1rpZhTdlk80NEAwDBsLU+e+f8GLY213sF30Zpc7YDQR/dSrvZPxWEEGS/zfPK\n6cqlL3KNFCPXgd1VMaYckDJmFaRk9/Tl28wZU73VgIdnNJ06W9knRc6rj+PKBuDeM4dSVADJrp3C\n0JdsY8i66EeEzKYBzMLwEoIRm3CsAUmU1NrfJOaAAQ16XXeyzlPPRQOZldZJHvq8LONYgRvKMX5H\nffxOFABXZjABmI3iWhbGelxjvdZ+W/9u28ucs2QNCWS/WW6ABOil9NFqoE+yEhp6AWVl1TnqvDDQ\nVEt9EPvfxRSTGWQV2LO1j2qs9hzOoKghBYP9LdX5MdKYbbP8pC6Tntcktdf6VKO+js96CpUI40A6\nhQAAIABJREFUptOUVG0PNHmVOgQBm8qiqLKmHmG9yeQniHVd102CXCsTdQYpgHmPz29L9B9LNZpX\nijnljn3b5oQU/VE19yvr4+zzOt9Yh5DJ/I415erU4ThPU3qdUaWugktplwnIdt0zA+wb77y8dlbA\nJfeBAq3mInFGQ5PU5hpkuW73iYMBKVMTPA4ngPffVmS8AW05j3MvunIeVuaw3ed62CQrZ9AAmP8W\n49psL24bPC/CDD5Xxwx1BzrCAMQaqTll7PpVhhJgqnPB1t2lSX9WuQsV+wPLN4gANz6IKELCi2i/\nmzCpHkAAbnDDKXt2bcQmUtBKp7SqZDbvU4txVxOVxcaQgis8p0UqCXKTz/ogDCkWGsm6rPYXZiXE\nFGKnsILCKL1BpiSnh+OC3rdqwM31UuuY6EsqjVx+3es3Z4aWQElrYsU41qhS2W8yhJM9gSbQkQom\nFYVoCjj2ubmfwEzd8n5W5BGfbMPNvjC1b1V2mNCwXqu0Xf6Ryso6BwFg01QOgrbmz7+GgPHSMxBF\nRZz9KEDZHPkv589szJjGcedlrXW2OZn1yvvmsbGfXaFu+a4u2aFzBuw0qOze4QoUShuubJRW3pOb\ncNL8WP9g3LjCW5Pq6QBq3CmVzvV5NJSY7oxF776ugDS40DQMs1bmbisgJZW+ofcASO1b8xYlU8ny\nOAi6zgqd0RZnVlAtVQkXkaAtx7d93YWB3Y1GSdZE7ct1/pDGHsqnrgZzXA4ZOAMTiuHJ6ABgG9kf\neTpNskxWxa8CKmGMYgad4ls9jzVsYSSXePaQfWk80PAT5Leb5H6QwsTGpHpc/XL0d4Jb3r4Y55nl\nM8m6MGZKGz1fRp0vCVblnAbgHshZaW3NQxl4OoN3gP7MEAfxebG1dX0ncMBx4HweRfbszU8Tia+a\nnB4y08771OdRaWDMbJtQkDGX3/UM8noFCWewa5YtY10jnP+VNRisgTXsx9apxLxEsMhmwFTDILa2\neZhVASVq/2iMoYJMjbWkjGcHqLNhUNZTtrnqN7xW54qKjcUl8Z+KhUVewsFSF+KeVft3KyBkXZ+s\nd1lOlxOoCFQxtCDy+Ejm2ZjBqYWZ4yyJ2m0pe3PdVCZCBZzMsLT1yFee9Z4Cnli98jvvjG2ogEml\n7ZkEEFp5SJXhnqkv5L7Y0rEwKkjC5zOx4gytzvUGkuIe/RNvWnMn8CWyvJXtJaB9/dvAfd5/Hjd4\nvX6fdwFqfnS7p03X2a7NwQCp81cUwChz2kNppPSJZL+sCbJF5j7jnl4LGQiC1Oerrr6GM0xguIcz\ncB4k8F0dGJxrKZOrfKDDrN/MwWDUljlR50IjIPynAwnf5RtEYCGNkUkVw0hwwyiOAyxGNj23ZB2c\nMsdOAQij5SzCh5sw0WT7PtJrG3VyxbQ8C8xeLCnfAeaNaIj/XoTbABxhTcETMXZFAaLyasljCogx\nkn5NhaMmYauCz84qL6EYo9AL/XWqqZxMyoFkW6pCIQEi5IYaXsnSJ6Ict1QGF1vdvSj3Ap6F9Tz9\nHarZhppcRqOJc/viPSLRJyxU3NdPJ3iUyiS9r4yt5OZT54bZ+xIGPd/FcIbaj1pYAPZvgkaVXZMM\nE0xjHB7BUm8yQmJOS3pNVmZPBTJW5L56e6rhE23sBr6dfkb72zlAsKz0RU3yBrh3zBU0KUYyjf3e\niyetGLjhgSgGag0FMEbR/GzbYEfmMSeCJ6Zb28i4fZnkiERdWegZHJIqGmVNHS/WaV5/6QFcmTiA\nKZnpeXWQo4As6nlQZk9jzt1IvCb0COa7Rea5PzNk3BMexjgTZlavkBYD29twM94WNjZ72SswO0oH\ndWh8S0p9KBfrt5tLleqNFczJrGqdwnsks7zhrc3/r+4b0ZeYgQNmhK/hOOzftjwLzHNAyliHYahZ\nl9zzPJyhozARfOy2DJ+SptE3s6I7j6u93xRZhpnZvZk3gdVkOE6VyTmnqgxyzzuq7JW4fy2recGx\nWK+xvjW5a52f8SzSsOA3aWSm/FNgNKysGf5cZbIiDfaL514098Jgm819HkAWrzlTI/JQ1L7Q+Whh\nII3KauyKXA16LfVnIYgZe47Pi5xrs+E9zXNBxGbX+UvW4rpfTiAq617qzX2ax+QmzTv34DWZcAXs\nVed1AqTsreuGgKmgOGP8mfWkKtsPKPtSBtpYlH68Q7VYzzIOBHQj5Mjr3cj8KEf+sb0VfOE8EaCE\n8xSvdMi1ZCVUBhHDHqLeFYh4Ay5ctZwEudeQIvuGvgUXGq7HPK4nOcys1jZdq/m2mktohu8AqbcM\nyLSWueZSLs1g47p2eN16Rqbf+XPI6eiTmjdn1o9sDhRdpAGyGfDN0BbaJdP+wjkpOsmHschPft/q\nk/0TDr2FSTgUkcDyzyt6pXr+oeUbRAAAZLZhltXIMEphWcRIcCHRZQ0WQxUeQnBgUqq4uKuBksac\nXSsKR6lepSJGfUPwl2vmIHhLO0qDKxV1Co+tKZofcTOKIoAmaIMKjhUe8WjvLJuN1/tcvl+FeK1H\n/T0pjjMTIWl8Evcm2s77Mulc9KWWDaEYewNL3HIR6gPZd82VEBO8+cAKDNT2reEZvD8/dd1Yah/U\nOZjgTlEwl3lBx9NlA/E5GAoMZkM/rrk3vJfn6UntpZ3sCwCT0kBEvHrietnU+J1tGfCqsM1rh4pf\n9XYnEyGOYywAwt17emmrGQA6tT8UxDaYrDraUplB241iLEgFjUoR6X6ViRBe/IaYgJV6OrW7zMHJ\nsBFMYxOJFRtwOpJJWZMGW63Togjo/L36M88m57dXJgJatimVU5s/PmXi2bvCdV3bOKTKxmJ4w49j\nfCPz6u/VCOP7V9nbOQ/KuqXx0XqZAy0BsG2aF9c1xnlU69RhAAY/Y76tWf5HGzXH2Oqu07wFOE8J\nYqRyS4NxSuilXNOzrJLSJ7x2qlO4Ub8D64dKVwOmh2P8BaU+c3/zfb3I4/iOZJ4OipEwAqUCaNcc\nGMPfM4G6BBMwlzvWQXjSlmsAgZ/aZxLvAWYgcpoDrGuJVc+wwCRvcw1XUKQeLVyNxZClXtcBBfMs\nVHCK8oYODQJb0Ot+y2fWb9/93eopXm99G699arkGj9kfZszWPavuBXye8rLuhzZf1hOOsp/ZRv5b\n36khg3SSgRzLKl9z3PxdbHeVx8i1OemF/kd+gYa+7QUldFPlMofWnBIA3jIRQhdiP4Ysxhxe5hT3\nGuZAoGMycuWqlzGf1qrPiDhAUeTIEJ10yjs9Zy2rjjd+87m78i5BYq3Lv/N3shAk1r+H8Wom+I0+\nRI4d150lJ/76u1+V+mzOnSsoat9OB4QxEVIWRT2BSQbX3+t6vZMLa504JzDJ2ToXvg3pP718gwil\nEEFckcRUDgv6X7wLAQRoxhmlUpXnh6ewSMVr3iQ1jLm4RmVlETQrE2H1sgPpaa+05qYpEKuQ6X1A\nXrOxWr3SNSafSl14Zlysj1JP/j7Fc7rCa0pzSsY7QWb1pqFRDAqncXFrfRfOEMqUXzv9XflkKu6T\nwlCq0pF9Z+2aN9lqmOQ7s33VIKMxP7VRaNDM19NQ8Q2jzeAOQMBn3izME5tAll3LMayGQzUYeG1r\n8ERX1cARyIKMs461D2ggkFFj9yRDoHpyw0jhpr3OxdKeShOOe5uxBiJMJt65eOeEhpSPg2SfV2PN\nQhT8yCz3tMsGyHlVJmusuj1v8+IEJnCqek8BlKSKZd0IkEbYrJh3mQEuYduRN/JnJlqLZ+mFo+Gh\nqTQWEbTIHvuF4NdZZF2EWXkb+NJGj+NqtE+ywefbYuxx7hIkABC5aGbmCte5oisTXLZQ6FN54hwv\nIVktPbukwNr8m70yfF7g7aOR3BPYjXWClJ0B3Crn8gyMRsJC9q3mWE9AnJjHeJvanXl3aobwzNzv\nY+MhEgPzmmQbV4MtZU4F+rjOy3j7IAWgYnx7aJMMZ2gErMpRxRzT8t0azsAQHF5n+Bj7fIwZSGC7\n+/LO1Qiq5ZZ1sMheyugK9rL/qve97meyfF8VJaN7MhEqOI82cJK1w/kPY5TVkzeGI0mUMdbu9DBG\n/hj4uIwaQpKx/WmIK5R70HpEX9Fd2JbUI1KOcE2xjiolL4s/cA7uiSU3lHvDR5OJWl+Tw1ZAux4J\nXPt8BfoCuGFdl32YLwwApDxP9lCdR6E3tJqjgcylWQcj8DMD0upeec59iT0LmgBGQ50Tc50jpwRw\nAbXi+7oA0lUe89tdodpi/rVJXtHZFV2UOluRyasXGjf1IqCw1jWAhnmZ2fNKNgK/v+bGuj40lrGf\n/ga9rGcyFPKNZB/ktRatzusCS7ZYmQhkU/aWwX/z3M33Afe/t6VJ6+9RT5nfASDZajF38x1Vt4Lv\nx5WJsDJK+W6RHPd4HghGQv12LWRGEEgAGJ5jAvCPZSIort7DP7R8gwiwxcVzfJkxNQ2ZStms4EBR\nKP09qaBVL0ox2lGuuWFHb6pWT3kIJYYdzIoR6W1TGyi4KjAoCSSwJNJeaba5ObHcGaFAqsm1PQrc\nb/gFsOB9VKxZzRXlznqqGxvVc3BdtPJu45UFaS//1g2c98ryLDDT9jnWa/JIKsfRd1U5KC+VsknV\n77RlbOM6dKmTXu6JzSEMSPcUYVYmBanc89ma1JDtAxBejPUkBzNYJdsW9/umpPR46DQmtnlV8C3X\nSU3M3qb7l7pNit8I4zuMPSrFKONc1115z91eHm3uWrzQpphVRS0BkdlBy9YGLqYo9/nm3tTeJ1g8\n3anoxaaPsn6o2LSrYsyjOqsHMgwzyTPWq6FCZU6LN6EqMJFtGrMBlYrrTd9Nv89KIo2RlQWRHpQ0\n5iW+Nc93a3OVTQWIWsa2GgkWZuUAWFmeBKbqGfQG/CTQE5fbTK1PwEenNbauZcrsCjr20v81Z8Cd\nMSex52CS0+yjCmrkGLEpNpfu5N9XJeuhAXrNQkzmvpEE7qZnJXM78L0EvBS5XgjAKWZZkOenJzAR\n63uSN/O8sv3uuh/cnWnOPaiCvRUsXD3HtYRCXsY75drMRFBNcKs+X8c6rmMGe/P+GZhomnOO3+Y+\nMOUa8Hsvhx9pTW2YBiXrVuvCU6TgfZKAcjb8HDqF8tSY/bVwPs/hNnLZa7k/TA6Q0ndeyesxtgUE\n75KnO1SWQA2lsLVcgBLMOgLfTZlU+3wTxQuY1nEr706SQDqnWKrs4GVRuWWNDiFY52t+iYePd0pd\nkwVwwDzfYq4aipDfKPW1/p7BhOhzlDX3q+tgB83Xv07UaPfHqTP+rL3/Xl+0v9+dzjDnVsi/9/id\n+0idvwYSZj+K8NSDGmaQ75vALbnXYa69MMvvqLPP0zr3VwAt9r5wTNT9NpnFwAzu57z203KQez6P\ne5yYs67c15xGGQb5O7vKd/nfXr5BhFJWTzEwbyBzOENN5mb3muG4nM7A5zErDVQE85oJrq1dlY1V\n4VhBBWDeGL88J1sRSkB6DjzbvRjtsraviVi8cLwsT5BYlf+vQifelbv6rvVORU2NxlX6KI9g08m7\n/NL7cQCuiseqNLD0pW5V6brUlYL436TmvSsiOdc2ydjHiHXEPLfW8qu28Z5oU3gr5tNAaBSuc4in\nNbSiZ4QSPXk5x5R4bTX04xs342TK3eyda4KIzR9uBbY+x2XX9vObtS+qYZaMDoYbpILAzbl6yigL\n5gRkAkCneWUeqRw3evFlQ3h8a7gE11D9TgWiBNkP7N0XWS/TfdfwKeAKmq/r7rI2imHBcRVB5kQQ\noPURAAP7+U5erYAe761edyCpk1Uu8h4mXZzH4V7Z531D0kur8S25rSOU46oQ1zp5Dvuc7fz6bbZN\nMM9ji69OcMicF0kHD/nrRlSVYVMW8CIHm4//3G4tRx9mqYai/Z5AH79D460hk2428flaj8lgZZ6j\nMBFopOT+EFT98t1g5cQc4DhKOHSqLCBDLA1NRbIEvc9xP6/+ncI1Vkso//z9RqZw7LX8PWTVAqIy\nwWBv8/HJHP8ZAM71C8yyNI+MVpwj5Rbr+m4f+Krtd+DV2sbaBwQkJl1Gr+MQuUWW+Px+I5fuyjoX\n6vW6Z9X61j1Flnvy+Zm2vokG4yfew/VZQdZgiKLIAeZDuO47jo1N/XbxWsf6k0lnoFzmfXSyVH2A\nYAGBBLtP8nSGMm9ingGXet4x0zSx+ckLfbd/sKjeX6/1X8G9JnP+hLtyZ6Sux5j//ygrE8Hm9wIe\nI+fVra7yizkN3AAIv/FMfTdBd6DoEy3lsH1DL7LgDrB89511vO70c+oG5/UVf1BRfOdEsPINIiyF\nm8yULMeV2uo1C2OiKIlTqADfh+IxQT5r/86bR/U+5bfTSEd5/s6zAlzp+pRcmaUYsWGkUs6Y8BJ7\n3hRtMDYs7zUvxixgI54TKOjxjKSu9VyNlqgbZkFWEeKJVl3aw+zpmV9CUsmrfVbRf+Bi9NW/iSO1\nacxkwrq79rw7Lqz+vl4TpGIz3Xvzjop+vyt1Q6vtXsGGOyVsK/etYJdiAVT4rnIvfVtcF/X9E5Wy\neC5rvomqDK51J607no+4zxyzNcFbfLv0L/ugxringTx7tkmP4Lq0umVCv3UeE0gAimFWN30p2ln0\nBX5Z7sZqVURprNq1a/gUFUFTBlNrfPf91ShgqIi0eZKHMhv1SmU7r1m/1HdWg3AGsrKPa781gXkY\nF0Vp7Rses0fK/ymSMrT03TqG7EMznjGxUZpnOw+DnwZt7Qf//TKPOc/9vuptZZ15fQ3bqQbkem1o\nssgImK79sQ5tlQMClOeBU2WSGRxvSPZFnfBMtigNmXjyBkiaxgbFuwz2ZTK7tN4r8xyKHBZlfXI+\nVSDgzrZIA2zuf8r32rSpvvwOquct+5bhLwmeSOnjlNUnT3hB8Wqyj5Z3XuR06ddIUudypJVwhmrc\nVNmpcW1uIE9UqfKTpXpQOWZ1HAaueQpO8N5ZRjPUMMfsHjCtyT35zlgbtd7LvACuDoELwEbjHXKZ\nm6yzLG28m79tuc5xJvOP38g9KFkFQ2a9sf5rbchxqICa9SX1ofIO5kOQa3iZ6XEj1+MNcEMqeg3B\nqvrWDAoUOjuLzqBAgiX3uTfujNDp+RuwoGFNlTg/d8lhdvMNlppEcc3DQP1gXQ88Rvwr4JqJFZnf\nocqWO12P74lrRTeZ1pTPgTVcp6GEG7sckE45wTpdEyvW793pvnes/LWfTJ+XqMd3+S4s3yCCF1v4\nMyIJpAFhPxejALNwyZ/Ta8Jra3z0JLhcKGixuKc4db+vhi/cofk1KUzdgFdBRCr66rVoVThhfmYt\nl00WdSNhGxEepbpdXI3t3CzinVQs9CqM7+uTgq7Wid4qAOExyfjlouyVMR+TByKFrrVJYqOv9b1T\nCK51fKPY4zoWNLDmxFlsY46TYDaQ6jtrP7Atcrch1u/iOvZVoZ8iZTi+/vvU13HPHArA+vA/ItmV\nnbCuJ7YvrxGJB0Sv9Z0UfZHLWrkDbCJMocT8J8o/g3+rAUsQcZ1XwnbJOuGLHChZzCf5AAlFf5It\nwHVsZe4fzolpbKnolLrUWlUlLgC7dlV87RednnmXL2BqsiCy6d+VmBu/vd5zHFZgTMrP7P8mCJot\n61dpqUABPN8E4k7rhLK71Id1qdcIvgW4JDIBTBX4yXbNbazfzrCJG7CsGMh8/2pQ/UbXxnciN4TX\nTZpAh17CGaI/yrV1nob8cOWYSsfL90ZFeiVDPsg8jmFAxncoi4tiDFzo1DlWngejGMn1WasnAbiZ\n3n83JaqRns+/p/HzGfuXRvxVdgvKepD8ed1XZkPeQw7LNxSZPG1lx92dArDuZRyzOraMU69jO9i3\nde/AokdUFk95b/3OBWCLhs7XOJbWxgJgVv0HuQamkMXLGs09NT/jTpLpyjwv+Z34exmvrEuRnyo3\n9cT0DHCdj3Edi+5JR0pdi76hEkiY9lBhOET2I/tdyzd4SkY9YUp9kk9gDvQmRoYhEffX1xMY6gkP\nt6cwaJ03Od6x4V2+gWDZzt+p9Zb4O+tDYKE+d7d8ZZnfd6XKlrqOeO0u+eLduwL0XO5bwaVZV1nA\nG8zzkXpGrU8Fr1juTnKBZCgJUMJzfndD+d9avpkIAL5BhLdl3tyvQm4yFMomSer3ZTNGEezluZrt\nHLgXQNyUqmKzrt+qPEwoo//9jnIW9WF8XavekqSpblWZqe3x941S57ppM9GiLFD0in6+E0b0osxH\n7PFf34zvwA8+P/1sG1D99up1WevyLm/Kqpi/w2X/XSE7ecyrInDjMauKUm0v6zLX0TffpW5Svlnj\nr1fDO4wCmZ+vydy6/3mNCU6WQPlu+Y/tnje8Wm9XkqOP9DLmqxJXf568c6WfZBm1dR3elWrU1WPC\nTjUlPhSyG2O4bvp3pfb53bzhWE3z1wf8rUFLmaDXZlVPbP1mTcJYwZM118VXpc7JtlznN5gsj22r\ndbefr2O6GlyXa6Wu9rdUnO5AlvXZdU5Qa1pP82DyXL6THvFJeUcCtul5TxlU48K5amvCz5VVw29X\n5lv0w1LtO7Ez7T0yj0X2QQEtv5iruBmTK6vu5tsyM+Xs5J85Hr434KWUA3mM7++KUno+7wz5i0de\nZuP8bu3UNq775uTJlZx7VaGn0U2WoT13Hd9qSPOyLPN4/Xmit0PmfXHxNE/tRsZJZ32uHXY5jhA3\nY6sOWKDM3bJP18SrNXfNbbvL91anRG1nzl37YZVj4u/iX3md9a+yYGUirHvv/M4CmMY9c8LC+p4q\np9/FyK/gyQoWESh5a7j6H3RgPqI46qS385kyvoLBBPkid01hJoSuB2BNshztwDXhIoBfMhRuWQNy\ndTgRrHpnut0Z6VM+nHfoHnJN2LcrsFh/nveNkPmKyxxav7QmHOfP8f2bqlWZnfX8eg+egOYKatze\n+55BW+eulp8BvFd4v8sfWb5BBFA4JAuBiiJQleHcCO3fq6Jbn323acQ3ZRFWMhvw/FYcGYmrkbm2\nwf69ZmWvFLlUahYBSQVo3fDF4m35/tUjB+B/JFRWY4alKhf1WsSCae2jVI7jvhvBKeU9LOZBzmvT\nUVWCKWwC/revmnunnPPb1zmQSX2+KtOpA4tn/3IvFkWpKEV5LXeGdX5eDHgpnpCq0N20pz7Hb3/l\nmfudknXO9UgapxZtIkCQL75VvTl5TS/fiA/xW6XP43zw0l+KK3jXZH53fK8Burz7biwT3Eklb+2T\nphbHXlkgLRTWVG6Zq6SO2cycwOXdNQ9GhjNkvbSlsTTJv0tL7o3a/N7d2r+O0erxBa5K2EUOoIzF\nOjaYlSgyUkTmF1cDiv+uc6z+OnmwS92AAjYsLJMA/gpYMcW83/XR5cq1vFuj03s4T30exbOiFrZQ\nBlcAaPXYBVNHp/2hFcCW10LkTONY5lsJs6ljFO+MNRW3vW/3umcU8PjOyFuV/xUMen3xrWqQJvMl\nH1CRkAOXvQx1vlgHzXpFyodqeFiivRlY+Wqc1/l6MQpu3sO1PM1TndcXgDhKklT+qHel3H/xnajj\nm79VIHT9W+g2C6AHXNce37VM6dStyv2yPHsHiFqdZ0DPvnHDkPnF/FkdKlOIZ9GPKvNnTXJLJoL9\nPIMJ73TQX5W7EIdqBH/Vjrt31fLuaMHfPfbxKv/WY8Pfv6eGRazzub5flz6sstu+iABO/t2+jVq/\neW7t51WfjfXVALRZ5lQGK+u5fmsCucrYrCFeNUfSuNl3/9iigHwzEQB8gwhTqQj2nYdlLVTAUkGV\nC9rN+4BFwXwjPFZvGDe1Gs7ATftdnWrcc/0+MDMFVsN0Dd2IbxXFSDEL0nhXrRzbKhTGtTZLffS+\nLZWq+EtFGBkTzyrwbPk74GXdtNe/vb3P27Iqn+szd/VuPrfmes+b2OwZWg3yeWPg/WuYQnxrGfuq\nsFbvbFWY7hSvGEdWyksXcQ+8/a6l3evGOMcD66XP0/C91j3W0/zKNHIwz9ukQLZgMKzzZ/q218ni\nSDFTBH9HBoCKdCqUNqfpAfL6tDRQLyccsD3l99s55IZDrY0IcFe9dZ6s+UnulFfWff12NWyu35mV\nF4KTd97bSp8M0LJ851dEhynDPfRWjv4yZAJJ+67fvkvQJ4LIv3H7t/Lti0yEATwVfLM+yHCY6t2L\ndyLvzVwc9RvqGfLLegLpqrmGrkZagmx1TdzlpIl+vlCSuHDnd98CXDcyuwKx0UZn8dQ5sLJHsg1S\nxjHvuaPlzs+8l8nzvdm8O2/5ei+Ai8ycQfZk970LE6ygyK0xs4DGIuonSlSZl+/lz0x0yz7Pe8Vj\n9PNjtX/WKlQDe2II+DWybdaeZ16Eu/w4dZ5bHhPP2zK1299fAI+1jorr2ExzpMyNKluvYUJVBl0B\nH7t32WPj/Vc9pYIb9vv1nvnI7tSt7gqZbfdragXt+Z2rTpDfS7Bj/oaDHn5tzU0Vz+s1JGY9BnCq\nv+h9XoSb9TqxE27aW8McAATI0vy3tdh3yjsx/2z6k1xkLzDrvbOst9Kb5HqETPNzHecKPk+nRdzI\njawj35FzdGVfXp65qUOb5oX9uzroWFZ2T5NiM5QjHttFE/kuf2r5BhG8fHVcSRXM1Tv3e+/l/feL\nbqaZzX+vytb0TpmFQb1nVYZ/d6mHV3F61umIU56AomiVb660t9Vz/d8tVTGKTPYNkFGUqhtBv7bl\nXbkoTfJe0FIh+xWo8TulUkPXetJou2W9BPVZp83F6nfNXCy43Yunb9l99HrZ/fW0DyYM/Z3hvNB5\ncVWi3tVnNewE1/6pnq0w+Ev/rGVSDnBdv5ViG4Z+VJxgQo5DeBUD7ZdQomsNEigpm36DxZYzHKP9\n3hxldaJNpW2cjyln0ii78+L9T7LZT8cfMmFcAe+irm8U3sv7MNe9JsdMEMK+UT3gX4E6wJyIkEZ2\nNdTfeX8IIs1Akl7G8d852up3+rwarpOREn+vcmAxCMpa+e+Ud4brJZyh3ChRyXE1cgNIyjIxgCTz\nDbyQhtgdc4/33WX1p+z7Vd8yjpr75OVIwOne+f23fyhlHYdVwY/1sayTdyAJkGAPn7+XcY9QAAAg\nAElEQVQAss5KmPYEys1a799Y5xML4sLQurLk7jCkOgZ1jVQWQs0j9TuyoYLpd2AXyt8JJEy6kt6v\n8XX9iJREufUezPpdd0C5lzGUkFWVgTGPbfX0rut0BRBre+6KoMwrsjFv2Ahy45Wup0v8OyV1ums4\nwvXer09NuGPD3LEF1j3r7ftKa+7YrHf9+9X1+m06FTDapB+tzJwAkqIe16KF9sMwq8oEemfM1/um\n+td7qaMU2cN7/qc66pp35A5stvv+VF6C/nqS/iHlG0S4KbNSzmuroqyTYAHw/9h7m9Ddnic/qOp8\n7xiTgEZ0IU4CBoyLxOUQxI1gAokgxEWE2QUMBCEguApDFtk4i6CYjbgYyEJEnYSgZMBNEnRpEnwB\nMcHgYEDHuFCMbtSQ//e0i+7q/lR1Vb+cc577u7/ffepyeZ7vefp0V79V13tXCxFejyaWuYPBKo6/\nOYkVPa2nYspAJWE1zfqqpeDw7fDotZtoPUYhcOSy5hE+hz9RgDhiv9GCgO7kzZVPU0lFW8XSAgRa\n/677cjAKlXoexM0WhUY8LDmi9hsQCTVXITIgWoYQE4jhYenhZ9fSwWX9lb/Fpf7q4TVlLHENFCYq\nmTuG+jwH/md07Imgr54pD6BUGUzU7EvG+dY+MDiV8Rv3T+rCT4/xE1fZ+nfxuFE4D9q4Mj3Y3xE0\n5cq4MFqHPeVIXL9hlExb1oIs5ZhTtZDVJHRmTVdhx6tkA0bjJMJF/ZT5TiKktDU0Uo56EAsfY9qk\ncyKUd7ggdRx94w4yveeRLqoyy6dUrZjMzSrXztDm/q+Vd7ruWZ/0WbJHl67SY/E8mnnD6PC09u4p\nyj9UNjj8gV+3/ttTDPS4lj8WlFxELU+KVbCI0kgef2xYD8STy8LDRyII+KzGV5QxLZdEK2/xxOeo\ntFV4m3kdgSjoq1UfQgx7b8mk1k9VdrZsd02xAGvQJmFV7Xd/c32HqIXBWoHfMxBFuRJyv+a3NngK\nBVdZG/C0uXz0vK+nzWfmgeR2lfwMzm2j4MjrXOhXnHfEgnjsWBxW1snBbX/t8BOW570r7nv0Is5Q\n8YbvBd5KBAB7N3r+TCBAkiKSlbhj5m/yBba+LWGuUn1QrbaqfqpXtpB5HmnnrUIByRwegspiAjGM\ntd+lnZwnAK0e2tqExBDDJUZWIhTkly0TQEBl2Kq1A8aIa/9IETxPeZDLo5IE5xcsCGVuE/WufJ4i\nBK0j+X2ts5X6fGsUq3nEebIJ3hjXLOAm1X7hZvHDvoi8hNp3ZKowY7zgpC0cHt6pafGJ8t3VovhB\naw23OYrqQsCxrGNwJCp51+rarX0jPQ82v4mLtzmc+WB173auK9V1hrh/HETn2ZaaiLYY184HEX/J\nP/SW7mQsz5jkKX+KNUzwIKKcsIqbEFr7K+MLe/KD5MrDVHH0xvdDjRv8Lm79gjuMlbKIGoFC1lQi\n63asaVBGau12BoS2LlpCW7mX/jCMYS4n+PXESSzwTUEJ+870xyoe+/1e2sG5oZgmtn3XytqhaFbF\nZGhJKsK3Los1REwkjo+1Nqmx0BuqlTGKjqocwj0C3/N6bePmjYeM7RfVZKZ1OJaeSzaGweVx4Lqf\nPPWWLit1EiS9bLkcNC3UuYaq157xHtDeCA13EWjbGiN1RbS8m3FMdMqYny1MSp13nGmE5LRFpW4y\nLuZ47WXFx04zPMebDzyFkb1diY9Ex8dJ/LNDxecjbfqo/aEqSCvvN6FdStGv58X+Ln8LbfHWNJ6D\n4lmAY15pmKVVZk1bT0EZH2xXcE2k57XiQlTpN1ELlUymrPTJ5kTQVw+kfAd1KecpUxUvk/oblSLh\nHAX+Fa+EHzugIlfA0jcifbZZ/rzWJXwUAa1wzrjRs3r2oCLpMP9B6YF7V/BhknUji7Kte9+IJ237\nOcveQPS+nSHDW4nggO/eJ59AyMVNTJ4lFJAao0RkmYNWrz3gEaxRLMqDkH/rGTMR/OQQoq5XGh8b\nwyj1jtodwR0LuxA3RZyJ3Lvq63fPig71cUlEZwWV6O8TMoFVxZDSnmcwBvEKUnR1GEYCVGTd8ss6\nz5SGHw4T1b4cRPgexp4ylKXipdH2g4ezt55cnLFe+D4ckxXr/uR31VfccEzDl/GAFgudrAMvs/4y\nQhPoKVOMX22KfQbRCiFEbc/Z+8pHIOti5KXkgWV2dxUIbp1GyUuk50aei3CEuV46ONbHoL2S1He8\ntUOStsrcoJIH1/wI0kVabEGw8uo7qLhJW854d4KjtnHdEVX3fGmGDN3H/BdWELJrV5hePLO8MZVn\n1nMvwnfFuC68wAGFxYXZbvvaHwcvHWqV1D5p4QxJlbPJ1CJ8dx3ncHwTteSgAjUnQtJ98VzrR2Dp\ngGcVt7CyHNt6gfVgfldnDvXrwCpl8bkX8mgt8lgGhcpV6JRyMLnoxLCaxBLBhvl8yzBTXnjW/h3Q\n6yJRMvyL3Mojw9k8NBuPeKdN9XzCo+5Conjfj+ijuuntR7BG3vD14a1EMGC1f0TtILMM4nKdw/YK\nMzcgRI3hbfWNNrmt4goB6CwOwEiIZUhauuuO73kjRFdAWis0kTDrfXnPtVpYzP57e9cK0ep9h6F8\nIqThCsghhhpuTOKG5YiCvoCHAIJWXpnfnLqF8bdJxHJd17MXS/s6nKEwwxWHcR/QSh9f22niwJWW\n3+KimU65vQJdYZtHUrNqiGW35kSwFro6T6LMMXve9AGFfY82qHEoOFVPleTbFw5u61lbXJw4eZK2\n/fEfwZ31IOOL/a7P2a/7MJ8eMFPAhPuhBZ4giu1Z5SJabYl6ZRlTL0CeqblfU/19bfC8sRidW03Q\nAYlQSe2kbmyoniieV4f5XvFI2nJtFbMM/z9wf5A/9hb/KFGv1G3f62ibxbegFyl6UPlxGKGDi5t8\nvZI2oLV9P3Qd+pOqByPuea9/o2ZWhC27F6w3Wli3pf+T/srx6Z1To9tpBKLExj1elq8rn/BdPIjU\nHFdcMKEpkZsTAehsgvAEqzxAZY+lE2ZLdMBwBmKOGm9S52utvB4omX8KgmO7VSCgC6w/9f4rn45y\nOj93nl1giEfzhPtc0QNrZaSGe5fweBujAT6P1vYjhURvT4QCbyWCgS6L7HBz68SDaEUQGB7kwYGs\n3dd7PEaHpMfQrsSQ8eETnxGeAvbqLvX8Inh93LHGExGdDlJITldIq21R4t+J2vhhjgS05KLiJUWX\nlyO+VSr2f48YP2neG++d8wyFmNpmZcz9OZkmQNqcM4QzTZiYBzJ37tRwRVmG7oc7cNX7h6gfs699\n1N21mswA+xd9JxLmaV3ohhe3i1zp8uraa3Qq1YAGmdMzZQW0Z2lCD7QqLBQRReocjd/KRNrY4Scs\nZivg4Z1dwe35i/Ted91V9S627+PBylvrSRh5LEb4DMuRzhLvt2n+ptQJ6SP63yeKbolTR+1cgavC\n7k3HsA6eELqjKlbPUgwzEni6nxasgnTv3UWlKI1DKUKr/ouEXuWRkyB8ZYMRq0kZB3LHNOTXdHx1\nnUipFS+fn4Iy6Q2vgbcSYRNWmKQsOIwLVg1n1STf36WrVaDVk4iq+6VNrChlR3G8T8Atq2SJufSY\nk5Ukb0/DXQsrfveUOE0r7WjLec9lDd99yq0crbmvcM5osdo6saLNe8BQflgfGcuDZ21/4poRooqc\nTQq6A1fJhLwnDE5Ujb1Ci0iUmWsNR4yca+kis9ZSW9dNMcaVw/IENM/tc/XmHBegA+KS7QEKpPI3\nQsRQe1as5Pz2tSBSTtd94LodyW/jukPXahHkvXfGVbqA1lQvnGGrruA5KspRYNB4xO77VjkfGgKc\nd1b2HnpEWDip3SiSBmNP1Na01z/PEwE9SazX1Srud+AKPbSGGS+zfR9mEsyr8hLyLdKjWwtWYVUJ\nsHtWjcbvawmOuD5n5ca/X1eWdLwX9+si8lSr7U8UlFdwiUDdlLQITpT2FIe38iCCmYXr+4G3EsFA\nRCRUEkQShkEOnSLAJJ2Ib7etKgzXNnO7H6wT7R0srp66goN7awve/0uk173nNqnr04wPlRYxSYz0\nw1q8vP7J7xu0LLuDYuynuJdPCGik/BjlTXDb7/DpDeBeOEPnNitu5CC0S6ysdUt1mZgBjhbwfYnL\n9nDCemuCP7N+hXG0ro3VBRskISYtgGIeEXsNVmgJXFCC1OsST91OBHbs/OudTOLAo4QdQFyv3Fgi\nSdTye0xfSCdXkitP0f2VjnKjiBFSpb6872WM2nxZN8qrLqZtXss6nIQzHGD/rigfTThIziCOhPcV\nvLUAJZ+a5kpdURt+wkS8hi4WXiNXf881FOmx5DqwfZGkaSsWOutG32hBf4Xmq6HiAAjxwZSKSUzd\nWMJCO8rfMj6WKS/fcZ/Ibwfn9Sg1tHO11RnhaJ/ZcAartLH7QNOrRqM/yvczyT7u28P68We1ZpF+\nqHVE3TiJkNLOeXCfrx5NiejMyXSt90esmGg/qHkZ9EfK7ghE0Rnl3RSAtOXsj89lIcajK5HCEuc/\n4wBJH+VdF//2LuamkHCj/qzX+x6TKtt9IWcFEQ2ZIswvJLcyYA4fpqyskJwc6spVCI+zDVmLNwKO\nrTUM3IngtB4FT3ki3IXZWrd6VSGPHb1yiAXyfbjGPF0tkeaRM/1kfXbVa6IlAXSP64xnlDZGa6DW\nF5zfJ79d+r93eCsRArD3Xu8AFrcEZhe8cAYEj4m5A6uJkHKbC6ffRYhyIuy8j+EM2dXVryiFv7yi\nZ3OIcLEW4ZEwvgpLWu8Ao+xKfa99BFGLNXHCwWXT0mL79zWOvCYABvspEIBGfz8FV61inZJx0xLy\nCqV9nJTKZ5JHMENvdz5OaqFPWPfOdYMe47aqUBiFauEekHAIL0GcBT5K61WxMMbBU8qo/Y1/GJxy\n2bivVxPtafyceHRzVkfKaLc+7pUC1fIfrMmV7bgisO0sz7s0W4XqeeSN5ZpB//2Xu9ZPFBBqfst/\nlduH/PXv12WUvJs47liHETr8vMUM+M36M9s7XWiLVdyMXzdl0/BvfI4KhpMSnZPFa0NNsf6RskLf\nHIQ8Vm8cwiSm9oaYCCSa1Y5T5D1hz4x2cwkyxtNml2A3nGHVc+QnDYneOREKvJUIAJ5r9xWwBCBK\neLgKXY6rag0bib+Cy5zA8eSgeUqg8c7LkYVyScAFjxAPfgglwFPgJclpv/nv2CRBy21Ncdmoqwj6\n57l2wH4L4CdWvMbheW9cyaeQ8WrfoxwRV4SCJ2bF80ZYBUwa6dad5JOvx9peGHNeMeE8BFfmwKP3\nJ+lQD8wRIF4tZ0rK4jvKyl7pTRiPsAbnQ0rmO0qXEUTrKtpjyezFFJS9C2OPnkSfZVxfQVtH4Qwe\noBB8lU+oXjtmfEd/4/OVZ26728YhnWA1Uu6gFxLRWMBOG4p4T5E7A6ELkdD/6nCGu3V4AuudcKUI\nOk+A4vFTf3fKotKIqfdAyJ4hReEayBZf6agZgueJsOIJ9PbmfwPRW4lQYRo7XYTV/orHRtAwc6/A\nSMCtV2kFIETKs448AVhNi2GUzwR9bAxLIqarV7Ml80m0R6RahvtmUVWWH4iLP5joSM8fNhqf8mWD\nmILX7stAzSs311l8VuNYy7MankPaw2GXMVeWmQOvZpO9IeuKl+Nlu+vMxApeQw8Sjdy+p26KdRCS\nzlfAYm1oba8A189mjWP0p61XaGjPEi87OeIYhTN41qxozrxwBglhIPI9FQ6hBQxrvsxBHnuDJ/me\nFa7AuqAIPTjRp8mHEK0daw3uy8dtNZfxRs/9O9eTquepcAM26/xrwUmORQx9dYkM59wY48Ob/zI+\nYsXD8yPfNmHaT71y5AkvK1Wfs/4C420HdiYS6fkXYErKtZiPRJx6y/yqZ2INbzgSnSDQsKEbVwSR\nqwYDq3yKcsgJbUA3fLHi7oZJXIFRThKkz+3cExy54lcFKopzWAiP9BTYmkR5opNgt/8dLkdbb0Tt\nXPfc3Wdrf+fsjxIrrtYhioJZmMPBPKzvyl6QdmxrNscJrh0sHy3l2S0Nq94hotA7CA1L5ZwCGiHP\nbeiMIuPlt7TN1+F+4PrsR22luwtvTwQieisRKtgbBppw1Q6Y/LcIqjqGnKB8/n0fbOwkJmB6Oi5W\nCXZw6KvfSx8/Ie7vTKnjLyntHThIexRxqm3ocWz8a4tbx1AsT6CQcAYPpd5FuGdqEo1dgnegE+DN\n3/iouTkmUy6pQ22UnAvrGQEeSlKn13Zz4YsrTakJcD7uY7zEquevB7vJ+voR14ozawWKlEKhJVyy\n5YRcddlnanteXeOHuIoCrOKtcbb1eTDzRFAKuhHCGxAzSddpknILBffQLY8XpqokzHd4azqJ8cAR\ndGFT1hvF0N/ZOh62BW12lq+LQh3SKBumEOEQebS44CzSlnQRBGQ17vrMHIHFA/eGVQp7YM8dV8kG\nv9mr9qI6kWZ4TdveKd0jJCpm5ka7zXr3+vSB5/KRauLgoyB8nqnS7auGBeutdtVLqqtX+lVyyUjd\nbM4sez20lw9B5hE9Ka0Coyoy4N3RqvP0YR+Sk0aeJSr5YHR+qwPK175YmkB6LLHOlTF2czkoQbA/\nnzFXgTUEyXfB9XCQsOsGc4okarmQvJwIAiueFFYh8EPnRGjnRmtbxuKjGCUSdFLnxWnlvTMrUqis\nQESXQ4/im64Mnh7A8mBn4roWiNbDu97wfcBbibAI6IWwAitExNYXXQ/VMZzl2WwzqzimH2Dje4dL\nlNlafvNgRYGyUmZlDDw3Svm0rocLtzZqHO1hE/x910qjmEtsH1hjdMNTn4trXBi8sasmq8/+9/LJ\nPWNoy1hgJkoLB6hxLOjcubs2L459osmeVwkVrVpDlHY9jNCJ+hDFM+Y4/WRaLmve2T9qHR3rChWE\n2f5YUT6uXpvX4mHXaME01Ot03ICd9ZOKP0VeA4U5TYlOYiWwiwu84IpbLVrnHr2UPAa6L4hP+2zM\nINd4Yl2WK22r42dDezbAuzVmBneOJtt3V8k2eB+thbtd3s7BsOg9MBo35tTR0yvKvCtnjOyXaE+E\nCmJn/87yKYVKMHM+J3g+AyZW6xPDRNF6G3mEWe9LSazYtcO0nFB1FbrcXDeEx2iemlKmV9x4sBNG\n94Tw/2RSRe8qWM9rlQ3/YMGbBpl39OrLHrwGh0hD2eHawzQfjVPv6p5f5Su+b0h7G+AnDG8lwiYM\nbzG4eUe058KKWs97defPu4RgdI2Vhc7qDniMWQjfWue2Ybwn1HdT+d0xEMu2p413y2Pb5vPjaNaN\nNiZc8USrxmq+Aq0w6H8fvoveAvA+rr8RwxbdeICftc2ZV4O4NqbUFdtlmNnVtS+8dxQlxeF7OsxA\n5lVfH8ndwvT2UqRMOsuwKSEwtFyMAbFAC20UztD+KJ8rfuAD/F4BT7jAd3kxJoo/CxiulIqAi2OA\n3jZexna3TvCasYnFPCYP967nyeY1hxa1dqOIIairRNmAdgn2Ba+RFXa051c8ESLwwoCQ9hGNqJSP\ni3uG31iTXt897w+i67TuVbBzRaWFnXncrtu0M2s/n8U+MgeEO7XQvRIeJMpDbtZpe5ZfSawYeRys\nVuPRjNlYzxIr3oEfIlEfKgz6UI/+SmOlcAJ+TbyNUtJhb2G+FerXgGqH9RmR28G2AK/KnA0qhPe/\nJdrwhp8OvJUImxBZVSMQS5S1mCy35zwbuYRHONgyUUKt1ez3FoWZm/UIvwhCzwST/GxHsfFE+18D\nVnl1/8rKltBQu0PO637CGqddLNcHcZQw6TAMEx3akrvDqFuMWhgGOR4DvtC2ah1vdWGDzjMHpyvw\nKssBXn+Z/6Z6RWVf1v/uwSqOS15Eg9+U5R7qdKvtYo3njVc6bxSCJ1gkkfaid8ErFS3oiTCiwJ1V\nrGhk2CoSwFTLjpLFx+F+gtWV7fCE4DlaQ5qpj0HlTVJu/Q1PC+LVpoe7vStzlwUW/btDWtywuScg\nTNBHDo12wLr8S9kj7Z89d6FeqY1nIwiDfcjRPLzU67Y1IMj3FU+FSG/HttJTP5/tN3trjIDnhaCU\noJHnCPkK6GX67tTr3apwpjTkE7zbGTyQfp6UStJU5xw7WsgKkYwp+wqFhQ1mb2i4E/LceIhM1++E\nFs7Aem28b2egotF/50QgeisRlkHiGlG73uI284by3OOaRTKyAGmrT04Q1x59cKIP7knmjuuz555v\nYzht8rgWgy+4NxMbWrrkc4WBm+lC0VPAPTi5PzxVEiFqzzKxv5dY0QreBxe3X3lWyiVqTihenOYI\nunmdoIvzZIu6CeUKnu56MetUJZqCftt5tTFzRDm0JrcDFs9aBySMk3bZx3cGKrnmJ/YjqT2m+1AY\nhoHgz4fUW8p8OYh+9qkEJXbqlBZR/4BMgrr55CDiL0zpZw0PoSGY4yJiLkI31LBX5n0iOkHDU/cv\nN8boLN9Pbu6dVXaEZF6JiCQxJMZ4N5og30qGasNgCs0g0ha/EHekVyT5D5CJi9/XrsAsWJHdpV0u\nj5Inxst9E+FncYjCzhLNmUkucczjbP1+3fi5Apib5SBqe6F2LGPJB1MynXUTkHnC8AZO6gwVHEmf\njZFepBP+8Dd5VurH1y09JGpnkd77PV2UfByup+LRK+etwkCNl0NDtWKuV5rHa0gnsPVody63Ycke\ntCf4ERUlo/IU1HggPjqM4J6A4vVjnH8GaaFee/JOdH7mz0b/alnu6XKX+4HmtBvHqa4hSd7Z0WM5\nHyHHg5yLpt/W46b2ibgKjTg3VXFFfl4COTOsImFVsfe1EytiToRM+9jsR9uPVM94GcuPMtcfrENb\nUzJnLbyjvEWdvZ3bivtW3xGmDtZALpOc736F0dzY57gmcr339ucbflrwViIUyAwEk8o8SjFjgGAZ\n3BmgYuEK3NXYWwZcYEWo64UoDasx7TPoEp4BeK573lhGiRVtOzpeOLNT6EEi5dCqmOsfMW89bp4V\nxn/3haZJacMwQDtt2jHp6i7MTtq44jGVMUd354/JvmoH6p6QtQPMmhEegWUIlfuh52uvhDDqGFki\nvXZGECVXXIGUkmJ4LDy1Hq3SwC2zXFciT5F2UKNNK8K/i8DCSzOFzkf5bj1DGsape2cHrDfFjgU+\nEYYMQcgF0gQrTQccp+w9z8spCq+qeKSilDV0NsZbM7Pu9XpWuK996gWcilsgzEdwJhG6x/XJ7QwV\nF/EmWNxPTRhPlIrm+jgSfX6yamd3ja+WH5Wza3aHJ4nK1hw55Ie8dOVHvy3wIaOt7ug9q6KIyOd/\ndknN6eA1CuuxbXa3Myy4tWN9P0aw4VyvADw/+iTREa9JKt/zDk23XmAdD+28k5X6vZJiBEoJQY0e\n9icSvDNRAh0rial+qvBOEkFEbyVCB7PkLdolrVgENtfS6tqznqQziJjJLllg+fSy8StLN6OVND//\n3HBN7TTxS281sBp0lqzP4Mo+skB2+AQHtigS8LkkHPMAcyLI9Wj1N2pWndWz2k3oQ70r3Y7LWpMB\n1jDJSi2/fs+LxnN1JOo1+K2Ofq3VJIfkJ9tMjtWjhhw4k44CJFqre9wZhKj8max7SUba1A+Cn2Fy\nU/Bd4V5O8RrTeowVIApnKutycVE9fb4xWL+IyrhyYBWd4BgpUe9mfRZ8VgVBpJfu+hfLnrOmI1oi\nkGB/RHkLVph6TZuKcg7dewnWMLwj/20bW+uiU3rpibPXyRG1rTlTICD+GN0vCSpnMEq8Z//OlsK4\n1pUcN5h4MkoMHAkd1osxW6s1Xa7hDKTXYQ2HgAB7sdpbjwXfKyWgx3FXNf5m9qxiDJ/jLSvaOgr1\nbZxholjO39toRXPc4QTldM6PgpcKZyhzsxGmdlBOnnpXjLLhTm5bDEoloMNIj9Mn7Mmq3OuvqF3G\n6xuUkdDg8zSgEr8LuXFx8T0MBLzEisgrHhPWzF4HrrxREAngI5pyfWefxTBbAye/Xfq/d3grEQyI\ne1P33DJLG9pczx2cSIh9K+QmUaKeQHla8lp2ojkkpz6iXqCJDvt2Ldu+RluEa89aFI0RtnmAgGCt\n0DOiib/ePRw910DJM8fmM3rHrSewLHl3go/GvlO+VO2zrsN+lzIigI/qj3IhILMzEsIQLwS0Snp9\nVLcEOCEtHq4YcnEGjrtsgnLrVYzHnPFlzkotP5yh1dchBI9QUYcu7NhVUeY8wULhHGI4g/TnSHo9\noosyBTi0PowJw8r+s+7wn6nRZXSPR1jlLcNcK5yID3YteqNr+Zqgqtdub1Fi9b3G5SZatlyJwqAT\npKbvYcjVnBGvoQydSww+ax9Pe09lJVWj+1dBuyjDGqrPoCy8o+Y5NdoW4otCKlzxSEdTJExxJS3A\nIF2TnB5y3aM6qwdjP0qotnl8T5Xqqv9HE2hre6zpWIdPJ7j5uCM9n4EX2tIp562iupQR7yzhdVTy\n4bqOnLOEtYBYE+U5ZU4pOwGVGNXG+UCdEbzaXnxXlPTi7N1nXzFTL+bAIGrKP1GstStLM/+H4Qyr\nYL1Zp7y78mwUvj3CH/De3ewAXk6ED+bMN3y3ngiJ3jkRMryVCAaevEaGKPYMiIBBK67uT5/Un4KD\n+WuBTVLmEdIEZe8AH0TJ2b82N4IHkasYPldJz5z1sHo7wwh2SG9KRUol7ca8Aqhd73BAxpd8128R\nhg4mYhXy0c+3O+cDy47y6Ei994ZWNBlGz/FGYJNP5BKAuWp0QM8gM56phUI4vrO41+WdJwHnY3Wt\noltmU4hpL4Ra5xEox6iXPbFYryBkU7ZXZjJrzxw7z940zdbA1SSU2L+wbvj0FJioZJgpBHeez8Dr\npvWqOTjBAFdNnbvBO8GvCL7RnfTRefhDWD2jnApduQ3V3VCQg7VavfygfB8OhZ+yD/rEindhl8Tt\n0JXdRNTqXTPmifZDd0bAzvfmldB+jcZZrtvEJMa4Vuq43iTs7QzvFQmdUvfQa0orY+/h8WpY5b1H\niRWfguNIdJ5+G0q5TrKX52CvBD8ph70R6TUdXmttFUkPTajUEo0+E9OnQSrze4rWqPUAACAASURB\nVOntifCGtxJBQOKSak6E8ry7+g6Z2hrLvGYx8ZjJEVRjEJXkZPB8Z+s+fXiMhE0Bj0GTR7asFw+L\nIRgz/EdWHm+c7TzY6iV5j311RcMs7IcViCPwLCXe89l7u2DdrK+2Za0rFtxEY9QO3w4vetZrRLU7\nsJIf6BW0AS2Otw9reQWosSHxrNBwOgqdUeytlxMhyt69C16i0bEStdBVq0gAYUSUL73HTi8QVV4r\n6P9IIShKJEwc6Fl9V/cijkXkgu3RPHW3fQmfe2pfNGXHgqAsltlN4jML17B039LeO6QOxzml1LxB\nfJ2IC+gxQpT7Mwq6QK+d7GLue0olivM07EI0xp0VMfVld3gJL2zHi+3fgZH3pDz28Pbwmf3ttR0q\n6pTSMlYkeHWujgmGyRD5HpnCM+2AUk6zXr9DfAZGE6KxoB/dzuC9s+w1tnAKqRslyudB43XdK6iQ\nz/fmNFXlHyrZZf2seMV4Hqyt/oK/U8+5ykxeBNtkb2jLD9y8It+rJ0KitydCgbcSocBMo/7EFSqR\nNc4CWuQ+OCsQNFPZyiXzzNYzIm4qRhBdMBfAerquWmuskFxd/dKYkNb3HVf2XJ//kjfOM81r2HY5\nQAIFtarbK2JdYiNr7ep5cbBkb9d1MrWDUJRQ1jIYuedWxRisQSJtSD8hE5BMxyfU0ZryFQgRdLk7\nnAmqMe8m9MCd5wGDGEEXdgDW9tHeQJRO6q1H1XLguINLc6quh5kGq9Szbt5eOEPbq/hiw1HVTf1z\n284rwF3Dm+tuqVIH7gq3sifP1GK/pzGoA8uuUgyR5BfYx9LeGKQAnqEyYccqbm82IfKFF81k6989\nOvoJZ4n9TZTw1kq75krun212rvzkr70wxyKQBCeQt35tnRja53n7yHOLu79fxnBnnU+TUhPV6yFb\nP2CNSbkX0BHvtghxwvEcb5janCmrsNRR6mFZh/J8wKRF3jDD/hovBPk+kufuuLTjWES3M+RyvlLM\neycKyfXAu7lB1QWhYfq9WJFgPRlWPRswRPFgog/Ot4CpkITKJ8I67o98928pmwwNk//ufloIuRyB\n5YcjpR6m5pHvb0+EN7yVCAXy1X2p3hv7NeDg1E4YkEybxWpM2NCK44UzbLnoGgKVvcXF20JfGfSq\n2wM8fNFrvWYNP6xCpgm6AqORs82sKnqtUsYToEd1dnexB23Ip5eM8Ire1zKb6rcyGl+Oa/Pq3Uhg\n/8aDN8IDLU6yDqIEZq2iNRztPCVa3BsG0VQT2Pkj1Y0FQb8lNOLQMfd4ReIIMK/F1d2HQhNex4Xz\nE2W8t1dgpjPvxdWbK7w6PyTeuzybWXPuMEpeklcv+7qOxaAwZEPqIIq9CvB3+ZS1p8MZ2qzOPZDS\ndAGsKA6mrB8qvYiohjUQGUVCRmYlm35tW/I6UO+iLvOSp2GsIOYiJGOW8UT9GN4RQFffbcOUlKt5\nOpvic0XRkr0OU6ur7D3hD+T7Tp6ZpxSsArFg1s6rlnQTFP3UblRZbRv30A6+q6KN5P9Bz1MqfwtL\nFuF7V7ExCqfxFWHYuPmEDq8m610F64kQ8ceRV03kibAavjArlyi5830O3vU8ERAkLAuToeLV4URN\nuRHiNUusGL7X8CcidcOLyje1GdKAnnoezzoDdQOUoPG9eiIQ/TAxeN8gvJUIAEIUvDjPl4Fj1kZP\nhIyXFZobs+QpueX9RDFjjmEYOqHiGF3UqI5ce3Vow0BCmbw7AxTIZu7kfmhDPhiSKYf/R++HeHnP\n2P9u4SpzUgznypOgKmACPDyGW7wX8M3QYlI+P6EudhRjrf71QRQBw2uUj2Z9YEe+EcZPXdE6EXRY\nCZDCtLf+jOhA9EvLrs7dpNvwCe3ZUxRRZutkgUvXoy3QNzlbBx8iUODVHxv+6PUin5bf9ejTHi6J\nPqv3S0to1fBr5eqzBSYVr2SsyF4EDAHRioNeQWy/Z+F5fAd6fufa/CqFMzxPqY3ScG9WdzHUgJUP\nCPcgujWEy3CVf2sK6f63O4I1kT9+XojUJUWw0BFYT7jvIrRXXa1Xwea+mSqHXaWkfhhZjL3QBM9j\nccfjRp2NRbFj6Xq0BjCsSXsxyPnQzh2Fp5RL8J3X1rDlBYlgTTn02HrNWSv5E7DjRTCCmYfBEzDy\nnEBgat6+7MxtrouUh8nHkZXHHwepW0uOJIkWHf6HPMPLYl/wPAYlLx8M897K4jKxTWQlWVaI1ATL\n1K99pB/oifCGNwi8lQgFcCNfJZDI4OG1PZnxjwSiWOCat+d7IljX2N3kjjMYMVquoP6Qa4eKNXW4\nDu+w9BIpeszJzI3eaofl++iARlfaqFzkRnfV28Njxu4wEbO5xgRyuTxo3xe7YONqm1Vgb0+s9lMJ\nclstOHUN2vWFCj2zLQeAD+uxo/EzqfsVHlYzBnxlT3rldzPza8Emqe9W4SA04Mm8MsnxEUHPA2m3\nxdfztM4IdD4Ldr0q5GyQJsRTYjT2VxUUCJ7SUrn6dm3qs3FnXiS5V/17EbcR/FDGJUyy5oFcQXsn\nUeEVeHU4EtF4vlfmYyfJXlRf5S0m49tuR/HL5XCGQONO4zUY8WlLc3D6PNFd8JMlP1P3K5UHO22s\njK+6lWyx7eRo25Zv4YnwWML1OVhJ+Ph9QnrNhvsRwluJUCByNe+scQ+15bruB0noLB53YWXpj67N\nW4GdxH0Cu0mRBOzd8CPLDILcQ31wUkzBB/csgFjiUZO8K5wr7TDgyPi7a81h7dM2asN8igue1O/h\n1L7LQemsw8UxRdj1thgL5H2dNixgBWSfLzGngwmeKSI8q0arWMwF5h0Ph6uCZlDXjBEQl0fUaar1\nIP06ekYZ194deuUloDoxwW0JAVkNBYlAuQyP8Jm4B6OyViXfc5S5IjB/GJrj0b6aENDJ4uJZXkdz\na3/yhFG0slbPmclE3goxAaWhTXS5AlE4wypYK73Nnj6C3VDBwyRXjIY1at96IqzkGngFnGRcsh3l\nOkLn1XmxXW9uVq35u1DpF+zl1uaaclOHK+VPVJSPxiG6fWAYOnaMzyu/Hd2mgLcXrTAezXeYIHNE\nm2Bum7Gmf+FUCkPZ82i4a1dCKq/U8l4+03qDjheOxuyvt4Ob18sHZzr+wSU8oJSPPH+82xnqbUi7\nGwPzYnB/Rl3JgYEK71V450R4w1uJsAg2Zo5oj8G/ctYZz2qFy5WYphm496MHxAjv0I0sAXcVH8P3\nJwKeHe9XWpiEmSUiopS2mJslLfhN5dXKgWITRq0qANaTI71uAqIM2fJ5iaEOBsyGuFQcqLWDLqu6\nQF+nMCtdPhKzhjAnQqVBqSmHmgU4cySh54uMS6J2D3owRkpod5hUwf3KVXN6vbF6pttYq/uqsmXW\n1t29p0MWNKPuJRh08Xp471z2xKmxTloJhjlzpNioCiW4G9H/LktqFQlVQUu+YOAJChakyzZOXe1P\n6vcx87XElgq/onzwlD7Veh6sDx4F3b8QVvaiEszlvdX6y6c7z4MuWxIsV2zeUbggH3QALbPteiiF\niiRPgWDXbRUi8w+pLEbJRYH4CQ5dHabN0Y0jT4EX2bruzs9KkTAsS42WSFjBEZyLXfjwlFdqbRyV\n9zNyQcrtojeCpxy3+aLG7Zq+O4hK3hH1HpDtUUiCrJEdXvm7zYlwRePyE4W3EsHALFlKLecc4CNm\nevWAxMSFjUg5mtJUrF1O/UJbErWM1QIlH9oU310QXKTGk9ofjYin+rcNcYjCCSxjViqqJ4Qbc8r9\neD/hySHxcEODQMQYsPmEsjqWPHV5DVbwIsJ5dZ6RPiSP7lnAiMKnjUXF/lASRnq+pmy3Zv1UijtJ\nXIZSO5Y17URt2fFpSTtZFwamzMpQ2KayzpkqVENQQS+Uf91DaaRoyLczOPhMMjOtMEVenG9UJuOT\n6EjN+lqZIurnotv3tR2uvy8LKx6TNhAyu/eD5yNQyi+mkGneVSx45wORvwYY9xZumAWmaXgGciJK\nvKw8GbeDsbpaMun3vD+PUSz+3TvobdZ8e8XjAfSkwwueh8kTnQMook1EvYAo8d+tn+N5XVnDcp7U\nNlYFI+4ttE94GGx7CULb2cNJhxt549snEc6/fzDRJ6xzCSsS/kG1u0Ar6xkM9I/Ip08eblf5PPRc\ns/kLOmu+wbk+r3t0vU0ioiONcxm4ipZHHfkFn7ZXmWRdND5W5lUlLSatSNA4jhWlVsna2i7fH0qa\nKfhiLiv0nFndf29PhDe8lQgGduO0dNKsTGY819QVxZUXZ8fUYqblTni8sm90O8PXgicE9C1YoFt2\nqKP8EDIvKpa/zGGkmd8NZ/DyIL0SbB/t9PixlxwyG5WBkrK3MdSg3SaB8QDcXDCIrCR3s+EMw74Y\nAc5bQ6dvfCKiPUFvlQH3avQSK6Lr7Ml+vPUsp8cuRO7aT3gJrII3PkijU/1vsoy/AEcvpKvRG73O\nbehDK8/uOrLJGk/zd599nNTv9ftiX56AFMSTn6nv/2znVIaXtOfPE8YhSba70v5SfSe7ngQ/VkMW\nGgDOku8nWkeeDmym2J22v1DmhxJttMKy8GucurkWYbHf70m9K672S3R6UOiViU+9PCe5zf26no67\njxIr3lEUNrtAoi+gSEC+0CoClJJsUOfsfK/lZ5pgcvaZV8Yx6O3Ad+uJQER0vhUoRG8lwhJIBl8v\nk6/1RMhC/1q9yu3s6C0U4irHJJpD3wKR8dgDq1GvmdaPZpnWdAotGXLP9XVYvbFBv1OsPIe1rK3V\nkepnsxAIRFpYW/PXSC5zV+iyFpSV38Ul2SoMorU8WuPqGiI263zwfpRESvZVe6A/ce94eO7sEXQT\nTawzH+8wRdWbBG9niEyFUR1Q/Eo4yNQjwCnrMv2w79rD/Dcf2pXds865beO81Ea5KEyjuRQrWDzX\nwtBheQal62hIJBeAtSR3628T5E0Va8z4u698EdxXwb3G0vVVM+8FTWhGtYiKypOGO/dpwRnzzKjf\njSdCVbDbtkNsDe5EQyZ4xdLbK1214oaJ1bqK6onWpXgi2O2zA80KzdVybz3ZIrBeCH3d/nveGW+V\nPbNreLWHgeYhZhCVGAlcK6CcwsRII2OZhHa39TvKVePRBvHKbEpCrooExXfR3pmi5snEgMxuK/qa\n8EO0+QQg381HouPQe7b3RNH78GvgZ2UGWRSNPrQzD997BY6eh9cbvj94KxEKtFxnrASfLSZuktk3\ncnklGh+I1fUSD+TUH/AHC0PefhnFunUM66JSsVN2kB9WQdTcuuK6/BwGC4rWEC8PjxHgiNkkO15/\nxKNsFF+WCT6rv/Ez162ZU1RAzdYdF1ukO97meVYQjJlHBiYY8VDhEPbZQ8qO8TWkFmcwQTou9dbl\nvf9dty3u7qhIqwU2O+itByVA2UVwUGFURDiGohS5WrfqZQj0msqrecc+8LQtIYoNtm6Ttu2mBGjK\n2o+ivD2hRkmoqNw8jeKg1SnlmnJ02Qq1Mf0jj5Su3AAwtwUqP+SZ7Q9R6Xvq9yWig+Nkaau876J/\nrK0OT4B2rZ8moaSlM7jm7dz67bbcHiNlwkgReIWOybnVnaG4Fw9yF8ZKv1T5g1QicDcHzBBPh/ZP\n2o9yXMh57tNWo0xyQw17gVvGcWVf2nYtSf1MVb+5BLMwvva3pywwf5NWIGi3fs0IYRy9Vi4tLIpR\nEglRgjs8jFKMPSj/WW+g9fdy4dPBy4ZR2HIITOWqQlo7Q9W7Ab6RsfCA374wq+SKAqnQYqsctWvy\nlUqIKBfLjOey5Yja2lWhqxO+/icPqCX8zuGtRAAYWxq0FwKDxSxiYK/hoHMifHBSjF5rv3w+sJM7\nKyPiYw7PKJ4zC7RwMDIeBBmE2Vsh7i4uYNWtiYTEgm48JQR3YcI9gVRcCu+CHBhEfqi4zYcgbeNn\nLUvXNcfegVjj+IxAgr8TaQWCVy8edslhImWNHtSYxwSDYe/V3lm3IljWv8FboOEYKMoIBcjJoHoB\n/Ys8nS/wO9JyMLnKGhYI4T7PmOiTOGT6VdOAJ8a7qja8NVqVLNwSeVGqVmhPCeJZ53CvIaPvMTZH\nR2ubO3weQu31hVZTVCRxxWU+kTUvhlkD4oWGfbFW2tX9KuPQWSVZr2Hp42diQgWIi3PZ34iR0OJV\nhcmBCswj+ev0OKZcL8MnKrukTzY5oYB/w0mjSbu021UWxMWHEHUXcRDrtcp/kLOX1jNqJecJWhuV\nMUNoXkqUPrm77cHiicruqyBT7SWcG3kisJjzK149nkqRtU9yY5yD515IIV7bhwmFLT+nPen0eZnL\n6fZF+WdDc7z1b5WoXtiDxSGC3dsZXgHLV1LSWugw3rjwtcCb96q8NYaqo+4RVmuoKttM3dG5a8vY\noZF9WI8mbj9Y77n8s6fUhe/0muTsb/j+4K1EmAAqD9wrGCfE3XOZR6gCV0qWd1XEzCb6EWuAx0it\nahuVZvoQgRyF8155IPVLSMNOe6uAh6kw0xXPfEpX6lzDMHg/D7Zk2n+FJtiCZzyIrCrtnWsDKhZL\nqdNV+DhKDHynY9pBSPHi++RAQos+n8UlMFhHVwCTSqXKwTdmcIWPGnkodAWLy34u0zPTqAxCgYJF\n0FWu8UY4PbgTUBEfz9uIaMxkf421PAIrg4+tncJlG+UAk6K51RMBaJOsazvfSC8Osw+IGuMXdwCl\nCCIywtpdpW10F3y9+i2wgCF6CF6sPVFzYV8JZxguGVzgnYRDbiiR5wlhPQ4EImPO1XW8qjxYt1S3\n5MTe+VZ5ehQkTTiMJFc8zJqORr7Lrt55IlgctGDzQ4NSoqy+M63TP8e8Fg7ux0ie46fU68GqgaFX\n/AXJrNkqSdfqR8/IFhoHZ4njwuNdffwtrItvFVqiUXjmKG+4KDRQyXRQ8yaVknf54OWQ2ShhrFWC\nEd86t+pZ8hB//9OA9M6JUOCtRFiEV2VOr8wBxCocQPiFUcYYPSIt1BH5wszOwZEW9wNzzEgjIT1T\nOfgJuoaHaPkcaUPvCEQjJs2DGatdPSvYuKgZZkCFMMAn5hvI7TTBCS0icphFTK57DV2tK5gXknnz\nf8N3JYknZiAWpkyHedy9vAzqYt+7IeOjmUJl5RogUK0DYOH3yzXmnqvLDw0Xn1UM2leyW2WxXFhN\n28Kidj1KHFnuKGvxMGPH1PaeAJatuFdGqUcLwweUMkQURMRFwLYCv9AraHvQV/QUqe0p91Z/Te/k\nnhmVu+ru6tXfCTnBuzIP0rbsK2slzfX1Fk8V9jShc+ycEUxtXFPNW6BpAdtJlArEGwH7sxjGV0Nt\nBsWnVjqsl/IarlnRIUmYlxUd+1j3E+4HChQOMG4ND32oHdQbAZh9IdfrS/TMu8ElUa98tOeyVZp5\nHiAMf0uZdpNSe67PrL5Ho0TDeJNEPQOdcgecL8JHMNA1TEJqBXQyz7i4X8/og+RfsTkRRFjEfrnK\nePCUqbiR3M6gPf+yMqCNH1M7n0Y0JlK+RQ9tbiuvrruhDLEXG5Mb1kR9csOsJM+IoLcBlos8EDRN\nW7/20b6LoG+cEhqCfBHVT5nvn2OmD27JFZEPE54FvXE9j9SMk17n1osI92HdTzZuYxGQ75Z2modw\nChSlJaeH1DGh92/4vuCtRPgKEMYnESlPBCLfylAFS8U0sMusX3FRwjvgPW8Iew84kRbsFAE0zJV6\nx2u7UFqsHt/s3JStK/shLqSa4IsgVd+lxpzUhEns2+laf8w1TUJ0TTkloA/OM9tGPnhsAkuPd2+J\nPT9NHR6gVl0ZVc185TnU6w6fyd+SHRqNHh5T1bnugXeLqt/g6OFPhEIl9cyJsrbE52hnWHZcc5mB\n6cLBKd9HbsMzaO7IUB90UFwfZS2MoAslqtZr+O7i0H9PiVw+pApS9p1DNkC/v3eVfZUpMu+LBa9L\nQsd53pKx5KJ7J4Z3jZQa8rcIRFrRotdAnqdU5wjfn8Eum1XrB1w8obvzTIC2dBw0LIoBXtWrQ12h\n6UicVhItz5hPQkWcF+6G7vCJegZZ4+M3jcy79FsUCJ/q/T7b+B2hqZ0f/njiGSBhP7qDqXocsTmf\nuroqvkIzYD5KQ+nUgrnUd3hrxRnL6O8IUFkcKgDISYzpWMN1vfoz+t0D5De0goqq4O65keMn4qbO\nCPO9b9usc6edg4TP0Dh9Kv7IWfvUttnhzJ0ax401bZNkf4vghS28IpQhBSFelp+JrlFlSvSlTPrf\nTznXlByLlZcEOf8coK9pZDtXD+5D/6S+/rpzOas8XHU7EQ1D6E/3N4TwzolARG8lwqOAVzxa5ejq\neltRqjIJQaG62/145JiC4X23HiBjqJ4vCg9CCL34LqvJ9vjTrw04YtPkZ4bh95Ln1LuCzTuRZRD/\nJiLFdPo4NIYU5br+4JB62LwfVq1wmpVnc+pg+M9zvgprgMySMGR3oQtBCNzLPQXeE/lKEM6ULR/d\ntWDfyFnG1JRiVkhgs+nR24UGlo3mEeMLI7psgFcVyHumXyXyu7he8MpGL0Z89drCS22/oE7dwD7W\nT+y7nfBu9CrIdBdo7H1UiEjfcPAUKEV8fbbWjrcfrDJanmWBti/3pGCpvNcGg45KPv2cFV6jZXeX\nV/DO2xGEyvpJG03Zkc9frMYKiivtKXhgOfbCak7CjUqaJ9pYqcdTFrjPFj0PvITiTXmMyiavXa++\nlui2nSVMX7jxYDZh8IpBzyovRutiBcLcUEaJ5vHlb3jDFXgrERxYYcpdrSA1JnoELbmZsZhUi3pp\nQ9yiyE+K17XNTQu6AzXxk+cex83KItDwKmVABLdWxbvgWVHR9BG5nL0CDi7NPkB8R4zMU4yv1O95\nNwgOzUvdCni6DJNmeG11s+VplU/IZA0TpHXtpPhHBxAtUaaEqE7q28nnEI6H2uC+xUO3OXj2EBPg\nCR/4vM4deCxli6jQrGcQwf2rr7Iq+1u8LQYuwGgtRRfvZcurdR8X92DHHfcK4Nki3xP0O8ovQOQr\npbaE7VrnygHnEFPrSbMBjdHWCk3JS6OuvyR7S8P99eWF7HjgZZmX5LSYNFiEBrQg2nXJ5Zzqnm/0\np3kIioDOKmEt0XwNuLmYFhS8QqMvnadHr+jwDBCzM9vDfSbkhkL/htKAOQ2Vm7p88044mFvY46ah\nBJU9veeIOffkfBYPFUruQhjt9U4ZXeZnimfwu3c7yg9tHNoB7f2W3LMN90O+lSHRRwlrqGEBg2Vz\n1WBmlW/o2UhH9gjDsl8L9u6B+glBovUY8J84vJUIAGLBsLGT+H1FwbB6jRf+ITQAb0NAAfCDk0qi\n9cFNGLsKbux1daGM3nEYWWAEWj4GYLpKuZ1QC+2OhcST1O0MgpMfi3cdrIC7qyG27thYp20Drdl3\n4xW99qvQ7pUzLsyepV0UNWJlTUkSEWl1tjCNLZP4fn/8kBedt6AdoPBswCyh5QDd3vMnNTdk6XD5\n5LIBZ1mvhS6gYkgsc/wF6i11Wky7uFwChRXhs6Rch6uCh5vyUIYGrZzyPjK2M1rWMVRshEcZGxX6\ngqFXcd3Sfh2v8nlSZspwvo+P9j2VDn2U/f6BY1bXY8NJ8lJ84UR/n9o6D2mbvZWgxPBgfLGrZFFK\ngHG/Bbz4e+s6/nEk+oRFoLyP1pqJcVXtWuWLI8lIdj94VpMGwlqrSm/TNirD2/zEY5blpPis2bGk\njWjxScYrzmkv1fJNYLNrXCySYgiQyljV1QvRFpBO480M3hWPXr+UW7TfRAeZhpQbfKA+e24wtBPh\nLvjOYKQckHUiY4/1yzrK5bl790xZ4bOiNGpeU0mdB0RUQxKwrBc+pfHO/z+ESEMZmyNBhEm172iM\ncxeKYQpj2EyX8FGj883BajjDTv6Dvg2iD+f5YeZUeBh1DsnZlvK5Q0T05WDiz8yL49xJrgsEbRyM\ncZgmQ7fvVsWu/Nn4OI/WPwkpJUqv94V7wzcObyXCw2BduO+6Gs8Sw7VyN9oozLLNphwmNYODHMFq\nWYWwVsUCCC7WDV7VD481H9tze6+81ggtTega5zKa5edIUYLCwVPQCyH6M3/vBxiFf8QrMzWpCqut\nHb2qPQZqfN0aKBo2tOQoCLPuVP449CKKDufVMb9iYa3vAjN42jVuEQGL7tRFH9twnuW/tXW3q8OZ\nL8nxIbkRMFcCPl+BcVhUE/hwtvJwDBQ/uGaMAnUEr7LCXLlVBBU6RLTlOeJdq/uDwIHB7ZVTnb5m\nhZgr89K2iy8Qi6LtANor8hsKw1I28siq9Sl8G4hw2P7m+nylV6++em+WgT+yfj55FnU4QZ/9W61M\n+YU6+3dSx2/d7VNbq62i0dod8Ug14Sc1ZVRLEJnbWEW3K9d5TMFtRQv4EWne7Anw8pGswmo4w7cG\n6kg3XZ8pOquHp9NPj5/vyIhRIIzAU1CthlyEdT5l8XrDjxreSgTKm1gIlmc1VpbQzX0zskJj1vPM\nWBshvlgGmHIGWJW5lgvejoCAfyfqD4rmlj4n+Nk6oZNCiYUPNfAVXxahWysP5KEIW5j5+JNiRucK\nKC2s+dxhVmY4jRQhRJ6Q31r38KnWH4fhtQmdsA0rq+a62nzJlY9dxnIjyCmLCli7BC/pTyJ91ZGU\nq1fqiTeLZz1z+zYOE1GCNCiPEhRADXzrtydwi6JE2obEoQyKhNppVgnO2njqfp8OM+tlbLcg3huO\njmG4twX3T7geEcsuM6eWMXfbQUWjCFCp3k+t5pZF6IsxkF/sWiVIeNrjmVpYgyi6oJ+tTqTVqVr3\n7XzZBGwYFsUwATMvl9anpPZixKALY4mKG8EP96NkjkfdCXrR6DHPe9tacpsFHeqH9oiaNZQTzoe8\noCRBMJ0Gh9oE5DyyAj5WdTq/2TIRRMqvr8HrCs1UnljgQYAK8KgfrmcgeCMQ5XnykivOQPEzAxy8\n9ywPg1n4tceJ4V8O3adK5+A975aiaO8Mz+LyaT0XrDHC7jFLe/VNS+as7azL9ne9TRSPc1GJk8df\nF7Q35RCRaqwbd6Nkx7NcKZWpz4mAyuVcPdPJqTOOIU9n8de5Arigm7pnYIeFdQAAIABJREFUqo/c\nG5yOlAn+2Xl15DxkRw07kn7LOtW03gsNXqEt1XOqPPsoys2Pct591HUF+T2wDa9O1ue1dx2j4rXN\n7QzVs9HwKBHgGpCcZXi7jQw6nmM2xAZDlb/bcAZKP65YnRfCW4lAWSiyV9AIrAjaw7qTjvkkysR2\ntP4OwyA2wQUJpH8YCKNGpEML0JLuXs8jROmi1UuIkmVOo8PFwng88A+/DCZPc9+bwEpRGfOR9n7G\n/OLPUbI3D+SmiNF1alF/V4RKsfZ6QqWRH2vSSAbcL90K4rJ+mrFswo/iRPTntB3NRCF0h/NFONAq\n5gkDge/200kRPXd7txzPFWTs7btNwTGsP8BJBGTL6Mv3jNtcOdPq1J41KBS5IR0X+nfV+8HmRPCa\n3xGA7dWjWTDof7MKpxzF0dNPFyFbmQPRPmsKHTzHFhTZX0EJQKQ9GFZndIoaOnKwfhx5V9gbc1bA\nbs+2xr1FvgYihO2OP/IQS/mlWJNGfUaWeog88tmtHw7K7QAqA/DZ0rsFCbxhISWJbgDlJfBpqKR5\n0jPAgrdt8ZmXE6FLEB7sjFWn9tWwBS93TFhnShV3+Vz38egBjXtWCcaJqrLgZ6kpEDCkEAVwDIe9\nC0OPtME1ECuhPRakJXum4PerXidv+GnBW4mwAXwkSqM7WwIYCS+t7vJpXL+Z2uGD8WAH9zpAEepm\nAl0fV9UzLXwUyxow9GT+9hQYrteDVp46+Oj7hVHzqRgKbhXVQ+LorQlKuYLdHAhO9UA3n00p0jpi\nLTMpAQ0P6reMjWfMk/5Wi/4DELnwejKhuhqvlstjmxMI1ZIho6aEz4N0jDC04zLQtGk1vBCOt6JQ\neQIyMztYDER5fAJkYNv0v4HlXixeqAPxkruixYGoudhalEL50BtrznSrWRWb5R9jtKNxiARIZaUH\nhYFenylc271nBAhmxmPAXUKlUmQkNT7SfrySnLBoItKJA72wqNaHsn5cuf46bej2YvGW4Ug5uWlt\n8RSjLbO57g5aCTGxInpcNLw38bixySPX/2T+1r8Dze4m9jXM9iy3xMydms15huApvvGZjJF3G0mH\nwyJg0V5ZEP+Gz+z1iAJxEsY1w8PYC6LNQ6WzwGuIIj5RPx23hEuPiEQ4LobF/hjhYKbPakG/zp9b\nXkgr/dp5I0qEnzuIfu5I9HNHTq4oJPTLkRUMn9TL9q63kcHFrhPPO6HrANAYobXIw91Tq8TwXedE\neHsiENFbiUBEonEfO+ZUxsa6NrNhdhY0982d02jRjQUC3XZtnR+s7y/PQkv7TqSF5mbdTdVjYIcZ\n9SwA1rLySXCgMhfGtI0HKhhmFmylPJjYhUaCmG3vKghxX6UbKAhloUXGX9dJFOMvIIob5XIdMErN\nct/erYeKOSzxcGqKIROqwm1tSZNM8B3LTQS6/C4qwsqXIYMma63tu/qy7MVD9zP3g+r+9NpuibR0\nIrQmQDKlw7jrA962qwzPlEDv+W4baFZawUnXq9vBPvSwbKGnset412YZmxZ+xUUJsrYhrHJTrUeS\nOom+FKXgx8dZ25bEhuje2oR5XR/StRbO0Mp9MYRACbtggWrPqChUQWDz+ucwoh5Egl1d59T2YRY+\nYkUL1XdYKVRkHJJ9Bu+OXGYPlXumPiSiM3+qSpMaH8Gp83ZgWXOBx0PXP2+v6nOsZsGXd1LPtKML\nsnbxdizBnvJACrcPNa5YX03IhlbMA9br0WhOrR/oJ85RrffQiPFJNZeKG/Zj6rV9teW69828Wdow\nWufqGWwUVLrXZIaTergkSNT5dLgqKQXk16MVquFC8ie27QGrNdlqt7QlP8NzMqhPjiZuOKak+SMm\nHx/h2ZYEfusr740ljetC5ctyOINRPkkIUTtPWoNirR4rXLn7LiETuP48w7fgY71to6sg7dWOlibk\nZ8JXOCF0qa3JoyaYzolVP0fjTP1eym0KPct/f6bmRTZcA3jgmXZG0PEWASnGfeOFM7Ry32s4wxsE\n3kqEDYjcyfFYS8SdoNn9TeIO1Nd1AOMoNEKuktFntLgaaiI0sz54IMzOCjRhFg/U7M6FmW+F0TrY\nT6x4Fapb+NkEyK5MFTrZHBL5WTsk22B5zBcS0Kd1jmi9QOWPx7gJzFz88oHmL4Dp4VLCGfwkWO2O\nZKKSE4HZPeRWwgI8DxrdnjxP7ll5JwGi1K/mGZQUI4h07r4lL/X7asMsh/uGqK2Nrm3SDIr0rcWD\n7oFn0Tsc4Tp611N4roJ7ZS63tWkFnFWwCtPpu6ZAOrnmvEDFixaODEPKbW9X5nQR1/yZBfNP5607\nyx/Plvostbw0AjyTPgzYnCAeMOtcKsxtfFxFM85ZUGcULrdynEUYWyWhOXJgPvWaOsicR6A565VT\no2tKmxDj4m0VYUbJ+SQohfIiPdlNJCkCtV1ulgZ69MUmsT2SnP96ztx2XXraFI9rOxbmiyV/VDmB\nU6MZ6O4ufSkcCryfGaRo2+F62DkDX2Uw3brqEXjF3TZGx2YYXhEgJ/kU2nvIu88BPTqOwpd/4UbL\nco1yQ0h/S820fmprA6GdMT3eI1yl7KtE/e/WEyER0fmd9t3AW4mwAAdYER6pj0RYbIxEEmsqWBjQ\nimZzIoiwaZmdO2AT8qzCh1gnEzDZcAgLXnKYStkVCAmvx5R11rWGQxR3uNNdOfCRjCdqAgPRuF9W\nEIyE5xW8RLiKAK28da0NyuU69bqT9z84W3A/SufOxLAmWz3V+iPrYRBXc9XdeJdBnQnSKNCw5VC7\numBPAuPXLOllfJiLcA/CtzUTl+9P0pUIoqutwvLbSsjYwror8LYkoGX91XCYVPP8fXyclUHz3PMb\nDqkqwFDwixQyHZRFbT0RbB9ZFy/l+szxqmqFc3sfXczznjUKjWoV2pskfT2dpZNN+Op+V9qGo3vW\nvK165QS2LYZpVFZ4W3lEF5RAm9aU0v362IOoje6sWajLS457hQx615PjGnoC0LCtLPKkdCO3Qfai\n9cJY7cfUI2LWPnh7Et3PSaA88wQfavmi7lTvhtKi1rc+c/Y3wfm80+YNhO/c1tDqGP+OnhHq+aIn\ngmoLv1dS5yi2S3sfLHRVjHylboeNiMIYrGJsdIvKKDwSabu9zcbz+hHl65N7+Q3fJ7yVCBPoMvma\n51dgR38lVjRLFNFFdwW8pDnjdtess7sw0yyPwHeXp+p+692j3oRIrgqEqYBO/UEwGzf5Hed2R3CT\nOlYZoBR4sngwvKIKhCNU8iBUhQEckmhFDKGGG1xbRzsWY5x7It2PURjCsO7yH/OVRPv+YHSr3hTw\nTMiSKL9m+jOrEItgV7kYW8KoDixDKMmVKzzRpV7eFy8ve885kS/M9a7OQrfknd7a2+hpMH53guk3\nwIYziwKhv31F3/xxFz1vPM7ULLchiFnXKeTFlVuFkg0J2gEU8iLUiPRcYogDbiSrAPbA0nquc4P0\npn/PS4iYlZO98tM6erSoJ0dogbXPR1OgeOXq9wBHhcPCWoqKiMt67+lHlENcRiM8aRNe1dn2qXpe\nCdR5Zvj79XrZEBC3luwv8x4S0iDlbF6aYb2eIct5ud0MMh6EVTqya/SJ4MnrGr3bFe4C1oieJfX3\nwjt8KQoDgS+c/x/lO9Z18LVxm+ZAeABO8xmWG6CRQ2C+43CGd04EInorEUKYJXUaZclfhVFMHRHR\nx5GIz1TDGZAQfzm0oCsJjrS1BizxVQBMVZCuzLbD5DQcm1Us49aSmlmmRVnkqR36Xt/U387ZmOPx\nmPAaSz4SUcG1vx8amFbB29SJdct3sRDM6IFYEawSAg8MosBq5TDoygIOz7QGuRekVsAKaJKE01pO\n5Xdp60gtGzeOlxVyP6hp3q3HQ77isbRzomUG8fHwaHOhngMO+UEC4V4z7Zopz/3uhaZmFSVqlgRZ\nWytcHeIo44XM7VnG6gOTSh7cPB2qIA77Crw/pK6TmhLMc1k+yvNEXBUXTZjuGcBuLGAf4E8oaOX3\nJDQDFsFJxF+Y6GhsCOLIUId1xhBcbHiY9KdTBBy67zVHiBofQydqnanWmfGJPXJUvg34RKVGVUA4\n70u/VB9Nv7GvHg5tP6WimEomIaZmMBW9p/aZ/3O3x2Ttt3cKfUiNZnAl5qwPhopcfoY/saG/WFye\nHZxqlnr5nakfH1RSVnxIryuhxyg0Mrc1bZWIVunp6UTqlWf4rtA8bg31/iElS7uskyrIlTdACdmN\nix0jKJfrKt5MZZulI9WrcxvtkL3Xh5gdlL3q7U0DFgfPImnIleIZVoCLF4++3lLnrqllTd2yhjs8\nWZf7gPJSptG//ipKxrKOIQZ0x65LuRUwZ+s+Q1YEnkwtgz+39Wy9kpL5u1MwwQGmc9R4Sqw23vac\nxJs7Ks9FOrdAq6e0E5wl7Xm/QDz+Sq6KlHc9ZYXHKx6J3dsciKjblXj1I/5u38W1U9st2kc1v5yv\nVEZeU3jzDxjfL+Y8lbpXtk5kGKh8EMw7ek564VIHvCt0Dfk1UWRjUnA7V/W4T21dePzWG75feCsR\nJmAJGTK9Oa+BtkKfSZgbNs/03bp1owpRqAyMTlKFTOsJxItJiJcQxsb6WwtXIl9I7q7NO4gwcWQE\n1qokh3Yik33cYQJ9PMYuVePMtI3Jj5jRERh5gdAlDa1POkHlngZyOXRjo85lS8KgXq0sSCoUBQWT\ng9phSdTCGSJrXGNsWt1e2/hdGHj8TRhLz0JbGfTarhbEaz+oZ96tpXvnMOyYXyOYEFFN7IUCxaih\nkXfDFaOLh+OKoqyWt/XNypuxjKypnrIQ1YGYNPQAhljqk5Kep4JqB/AW+vlh10C0h83gRTlX8qd+\nBd/0+ovQeSKQCBWN/jInotQY2ZTiPBezNYx0Tmib/J0W3qfjyDGgRhOEijAiXS/2Lf9GId2oCjh4\n5iVCvQure1110zw7nbh1WfddTgQj8Of6muKxPlNCXlkDBwjwQlNrjgXNI+z0reJrnqEAqPdRU9TN\n6qzvBmeD9443lvLZrqmmpgSD8jbpmyiGPEyj8UGloMeHeEpDIlQk6LLdeiFSClvbt/outO8JoTuw\n6kX6lgNj8Gk/zkUCJUK7vYq5JVychWdx/bSqe93mKqCR5tVCfpZrvtO8ACj4fefwHfui9DDa8Jiv\n4Inr91Y2eLNy5ja/wP+PiIm+2rahBdZV/ql+e/A0GfLyIwR8zS1YYdw9C4D9XWBnHFBZkyiymqwz\nmJ5lJb/XlFhS54diiMs/YJ7S6cftRnAO6HEkNO3UP4KlNX1kiyBa2ntr3jM0YTZPZ/BdlTGoyHnn\nKbNOUz6Rv55WTgrPaog46OrmSoB5e/qGmgiU5R7wtF1aabtTGEG9bnlnTI5B+RlECQxX+Bmun5Y2\n9je49A2MNxy+a6/YzN8dReAc5dtg+zOiNVfKjStpX/E8HeXKQEhn2zcjmvqEV6TAytpc2qPO2I3C\n79ADQP4m6s9tPzxpAR+3zbUJPtP62cwU80p3hbqUeDmE0QNvrFxa/wPA3XAJohY+sgP6Zoj52m85\ndkpSxaN4CQNdt2u5vRv/NoOR1/Bq0uM3fF/AzP8WM/8PzPzfMfN/ysy/DX77JWb+dWb+W8z8B+60\n8/ZECGBJyL+h8XMt8uV6s5nVQtpkipnxFfDc4/A3vPavtmvfJWO1AQZdiOXdZEUdGLNqc7fUOHmH\nAnoXRHg9RXBPGN7sKrgnJD4FQ68S4/Uin9YKiIoZcR0PBSf0ozMgSjEcC7GGyhhh2foOWn0O1p4I\nnsUAhTfsB+l1US2InHwp2FqmzX4Ta3f+TdepbjWo9R0ts/aRQzJsroWqeHIsVUoITrHlbHcJeyvE\nCoWO5qR6T1kcbR0R9MJ4qmNCROV7+U2uffzZEXvXgNeKhGGoEBpOagyXkArKe8oIdJ9ONahLg3e9\nO7OhuQXvxO22nxxytIyyy6yyef5R3OC/HLBPbCIBIqpJFSeA+CuvivLsw9CW0AqNz26ccTt5ZkaA\n5xjSPoZxPDD8gIjoAMWNOdObN4uU6JW9q4mOJbTH4nvXQiR1sDMnHija+LDQotaT+S0R4Dbo96sN\nh6j4wE/rnILf0cggEA3dnRxVM0+e2dhYIT8KQUgpdSENXt1eMsRRG/K3vLdy28OZEiWwlR+FHmM4\nhIcHnjkE7wrbaXMACV0Tr2CmNt52TayA8EP4t7SlHiqtWykj4SsuDvP1485Vuqag+cnDt387w18m\nol9KKf2Mmf80Ef0SEf0JZv7dRPSLRPR7iOifIKK/wsz/dEqjVOgxvJUIAJBCwMQFrRHglObXO9Z2\nDFjh27bbJ9zyhXcPN0sEPJz48DPFt6sSe8ZPyROkQxie0CxLG0ooKA3o2DARDHK5LywxgPoKTKvQ\nGBF2rodGE5yFIbUEPtEzihLwfr1eB/efcqB4zBUqDmT+1LqCOurc0pjRYSznCMnLfSCqYQq1/h0J\n1RTRlgC9t7pbFAZgr25qCpFc5yckCGx4+/WqkHOLu2njLtzJR7Vi7bAW/1G7OK82saLNiRDBkqKX\nkm4rKNfCy1qlLCFeag35Sl15tcuJYApi6Js33yoXhHi/QWsntXwQ0mbe32bPO7RO1v1hx4NTyUHT\n6Cwdh9+BnElTId15L1G/FppiEvKolL55IWK4RyvOgA4nrjQavbIs7CoQPC9VGWMVauHQ2eoVA2dT\nOqlaDzGvRoSbukXDUUbgwvE8Ozovk35oVb8QUpD1eJPkqn2jk9M1b5dO6UH6meVvNO1uz63iObfT\n5gy5Yne8Ha9FKWuVRitCGCosMbEiM6krHh+h5+6B7uFj1wlcK1nLjRUJo5wIkFphC67yiHJN46vB\nhmoR9TS+5SvDfS3hDIPNRzIPjdckinnJTqE/AXsDDNfv8i11Z4S049E/zInwhh8HpJT+Evz5V4no\nD5fvf4iIfjWl9PeI6G8z868T0e8lov/ySjtvJUIAK4dlFtBbqq6a8TRxRyAjl7ga63j2BII5J2f7\nSCedfLSYYGo3RazE/OdkgLngjBDMiJSXZVvwEIt7LbtAdFwX669Eq+42I0mIvpXQKI+hUr+XT0S3\nCS35B5s/QJg2nRNBBL0+kZcCWCSYQNAyg5KN2LOcDuEVA79Z5xNWzmH98D0npaPLC3f1ZhRRli3n\nUJjUWYU8YMp3gI+krjc7InO8gUgJe8WiZ5WoIgDn38pz1bbfXxR6Fa4kV3PFuOWQFH/wVnqEgprr\nudA35sTGnN2zHRfrnesNFW16IazfWKTLP7n3I9q3e6WthVVBLVJer4y92wZUOFIgo0Kra5t6YRF0\nNO0B4DkiDd6tGytlhXebhaFkTrDlpGo5hqhawVcg6sKdUIZvDWxiRaJ1ZUROFL0/FvF9R7b+1O07\nxb+A8vvLcdTwYvFE+OCmtEUnvj7x4x4NyWfH2gtRiJNnTHRztS3Cd307w1OW0jH8Y8z8X8Hfv5JS\n+pUL9fyrRPTnyvefp6xUEPiN8uwSvJUIABER08kUizAexKd5B0201PoMtzGBQKs4UWxRs1AJhbiB\nOcggwfRw6JUbr908OwLMsJ7J71eOZIyVPbgpEOwVjyvzI3VJQp4q5G/0/ZHY3QKYwX6n/auAazPR\nXAlCdC0XQm+JSe53F7mHIJ0+C/pUbodp+x7DQEVhQL0Q4cmOs7o9iHNdyP6Z70J7P/rp3Zcu9dpw\nMCOY27WwOvzJJNQN25/Ug8rky3kRlq92ja3d9TuXW1mgXHgOee6bgwH0POpmcCX8wENBeSdwFi7R\nU87SbSl7dU6uJn58pRKyGTV6HiTCd/fKt3Dvw0CO1iuef0/DLH9MpYmGMl8l/b5PwxgwV43kN4rg\nLt/1LakgvDCCK+8/eXXkSKkkNNE7xYXOvlqcdtflwmJN9f/YM+IN3yT8HymlX4h+ZOa/QkT/uPPT\nn0wp/cVS5k8S0c+I6D+U15zylxfGW4kAgOEMAmIhwvhaIupyF6h6LraPLqy1bcpazjMlddge3F/7\nuALimRBaPkz/Dk70CfrGkSUJhZGWp+A64bIe5mz9dkshhvkRvMR1lqEucZEUbORKI7dt1k2dZXHY\ncIYITmrXT43g1ZZs72YD1b7EmR6J0qePjNSh8gjA91oXzEF9d7BPIk8EvGruC+sDuiq8PFdrF3fT\njw5/g5+NBzF9kXFolgm4WhDarAxFdW126j249scsaToo77tdeGI9eR4LNaGTcfefxW2P8BHvl1Ym\n9fNBvifCLkNtx9dD65BcAE6HvHCu7jYRJrUWpB2Mfz84ldjeHrDVp5PYYjgA7qeDiE5OxEmfbW4O\nhKNM+CScQdqzfwvtHIW6ROtlZLWzlt8V2FVUM4OVu9IpTS645ENQOWFgf3RJNgO8vLBC28nd22Wk\nyM6q8kIjRm3Ft8yk8HfXIwbWKhpOGH6rZZ29VOeEWjiDDY8gQh7lmb0mnggyN1/A+9O7+lD6iJ5M\nK9wSXu0nn1wJ6fis/Z4hylFjwb9dCDwRyvfsfXBSC7cCvp1Z0QfkEywgP1LXEOApv3/g2Wjn34Q4\neeEMT63zN1BL4vVDo5HS7x/9zsx/hIj+JSL6fSlVYvobRPQ7oNhvJ6K/cxWHtxIhgK9BdBWzUENQ\ndWKwA/6rd6lnrDwrjhfO4B6mPu9c6xj2gzPhPLkpG9LCeyOwgplq72iWUSSe6GYmLvhtLFtcIiYI\n9MgA0uc7uQ6qheHiuCIsuZVi+drH2CLptWG9EYT5wkOSHSWUql8E50SdWyDGjLo4UIvXRGYQ6/ay\nE/ORVN0e05j7KMoKK6iaegt3h0KyKEnyTSnQn/JKtTBDyE+fWLENnMRcSrytviGDewGr744Ldywi\nrqv9SKA9dDkZXxszm3/Hsn7dEkfdrpoVoZbqRtKupeU96gUMnGtMymYVCqazCpl+/QqjR9TfP46C\nXaI0YhxtnR7tJ6LEmBiMiwLYYXJN3Y1+trUon7jWjtpOY1sZvwTKL/mU/SH1Cc3V3j4yZnncvJCp\n2mZqwlRLUpkUPZPPO7Q5umo4AmzP02VX4cKhTekgoiOpa0ulz2IYwDq1MsfSpZK87tD0wZAW1R9U\nmOTx3zuYO56C2lkj3puqysFAssN/SD3heULQP2rr4qz7Dp4x57C8gTi+eu7KmHmeEjhPet9Txclm\nKVMKg83163IqnqLbKHYxDwXuHTGq2JwROhfYNyEnfTWwV56jYQpB561poZ5fJLEir53BQhd3oONR\n7KFHct7pM1bCgmwiV+zPW8fw0wJm/oNE9CeI6J9PKf0/8NOvEdF/xMz/DuXEir+LiP761XbeSgQH\nVtzJMZwhco0kavsSFVcntuGYUbQ7LmrlW5mIsY8SO3rhDDvWPG358K3bXh4Im4NhlATL4lzv3a3W\ncnKpszCxfWZw9ERY00JfgVlWYSJx2WfNzJFW/Ijb6Yej3Iji2RPpMcZsxAjduiifqLkWy3doUXKf\n6vqjeHX77itc/zy3WZuEzkJ44G9yT8yJyAtvGsQJ4G8r175Z7xZ0V8bquyaJqpcQkpsa9s6kYonx\nXvMV8CynOmO3xnNXuWjd60eupS5+Sji5xik97Rkwg5SYJKC2P2eu0bGDmpIHlS9Emn7ehdF+2wEl\nTJOm3t5Zi397exo9QD4YzmZzJiVzaomwZZPLyW894t5D/Nm5gnh1rBzpNEqaK+AqTJQC5D7YM5AH\nVnHb/lUQw4XHr1m+A6+yHZF2Ca+wNNUrF+OVoSndW2ctLfyWYCUp+PcM1VsSFJp4Dfssee9XwbHX\nKSjwcqJ9ndD+nxqkH8MG+XeJ6DcR0V8uiuO/mlL611JKf4OZ/zwR/U3KYQ5//OrNDERvJQIRieAl\n2u1e2B6/q3esF6PexxFmgdbG+rbf/edPeUfUGxuWk7RAWc5xzKc5GM8FLsi/6iePR/QbURvjO/Hj\neL96TYTpzNVsjHVOhJ4Rxc+zDVkX+5mPnOSG0KyCMEbIHEkbnoCF5Wzfrwgm7pwFa3o0dXez/uY7\n1P3+5sSn7e9XgqwdsTBli/p+PVVIhrWR4LcThD2Zt3OyiE7zKfWfVGRVeP0sf7dxG9fd5SwY7GX5\nfSYQ74YrJNLxxauAeUjOMza9reyPE6yfpxH8M45yxmSoCh0oY8lonCBrDx5NvWEUYatjfnCqN5cM\nq6/NGOWRKSdzXv92FGj1mVjlhD5zv148T74T6LiA59aPnlozOKtPBij5y54TmmUhmQ1sz0JXoQjP\nEbN83jKcFf4ZLDHUiKMoKesadsacTqLk7CNJQm3PrIO5a99TOO6S0qtrfnx2MjyLaa9nxffySdic\nCB6ESR2dBWvXhdwYFhkRZiDGC89QEtWXHILg5TDw6rS5Ejzjyde6nWEFxOvKetGseo4+nacglfVk\na4x4Hztv3nvWGLiaoPkNPyyklP6pwW+/TES//EQ7byXCDTgrI9BbxkYCi7gTurGPtMaMNGv7Ndhh\nepQrexBe8UpQngiI1AA6Fz7wRJiFMzwNaH1ArJkyg4phbshUNGGyFWrCSX+d6Ai88APtXpnohPhm\n66rr3e2+o9RScdCeNjx4Dy2lEUSCenNl939TdYgk7fneOzhhnPtKTHhG5lATUa8pFUYEcGGOc0lL\nuV3V8cwCF4GKrzyE1lE4Rh4TpeKTcV86sd0zBgXDGVahuyLXaeM4Erm5AOB9lbuj6+PCOiDHK8cp\ncyWJ2tAdHz5zuIPs8/IJyRWjc6lV1hAW12l7E4amNeZvxIV7HeoB7yHYcqNlcj955bxMzo2yX7d4\nIkjsNFFTTHihVkSNVtgwPiLNd3ihB5ZOrIYzRFcfds+WasM1t1KWlHjVzoHeI8R1OScueWWuQ/Va\nufj+6rhgToRP0v2W5zvhDLpuCGeQZxdw87xw8Jl4uul32VUofAtwdiurwSrtxTxpXvjkxBnHDWeo\nBjNKZDkAfQ5HOJX/Bp9RWJ3sEWYmduYr86Xtise3AqGAl2j4O4Qf0vNGATP/G8z8N5j5v2fm/5iZ\n/0Fm/p3M/NeY+X9k5j/HzP9AKfubyt+/Xn7/J6GeXyrP/xYz/4HQ/4gPAAAgAElEQVSltqm/MqYx\niqmFFBjmSsfg94LWjrAlcd0WB2mHnf8Z931D9lMeDasg2tHHQPknU42HbwS0EXVhmpFJW2riQXSJ\n+rCDEWQPg0hA0wocWQnZmtTKWcF/N4nYVcA94iUUixg+q1wZrmvUEFWBfFEhRvrgFhfvWq8DXb6E\nRagx0qMYYec3r/hT63FnD+gXKVwYOaGcP/4rVhm7Vmfw6qvO+GCVvMzr24H/YUxl3fp5Cxre1jMj\nyuFhAa3YIf5CA2GLYFic6kckuNbD59CKlRXOdACrryA9H63ZO2eZy/RPyluBrP5GVD2PZP0wVDjK\nN6TXjxYUOvpw9HXNwhmidmcQrTOvrXNWoMCTanvJxSTfd88tT+nXhLivxyQ9zh8tgt1bbQ3mf6M1\nMjrTc/4pw087PLadszyfupw3p094Idh2OqPC5pmPyhoU4HEsV3h1NE74iCeq7lRC2A9eXv82aeMM\npjTxsortDT8V+CaUCMz880T0rxPRL6SU/hnKyt9fJKI/TUR/JqX0u4jo7xLRHy2v/FEi+rvFXePP\nlHLEzL+7vPd7iOgPEtG/x8yPrHLJCN79n2jrGT5XGHirBR3dGx4BuhuOcBtBdIXlU3Dr4PyBtNu7\nrXp9FIHfKzdyxz4TlzIcKhh2wCoVnrq2E8MZvGsFXzV1UWiQB+LFs8rQ5nCJ8i7GQ14VyE3dq4A5\nVdBNWbkkkrgf6nVlQ1mknur+aP7vKJ3SGYdm7YYYRPQundztpxmjf5V+pTMpd+zZ2rpyiHpOVdo1\nn5WLehR+tuWNVD65eOe0s6l5WXR74jxji8uD8UGecD5bi7LWu+fwTNZ4DeEiUdL2+8mLmbfhDvie\nCLF2zdr1E/V3yeX5BzJ2zfYsKuc7wFwv5byS/08L5+IN2tMGPzeQhZpvhPyQkFxXC8O4C9LOrKoV\ngXMFIhrYeVEZRcIInrxW8VsAr8/evvPWgOREIOrpZzSWu3MbJSPNSMUryQtjSWQzvrzhEsjh8+r/\nPwL4JpQIBb4Q0W9m5i9E9FuI6H8jon+BiP5C+f3fJ6J/uXz/Q+VvKr//Ps6qzz9ERL+aUvp7KaW/\nTUS/TkS/9ykERbBW/6k/IHUcbPu8si5msc4olNrDCV2jan2LOERX890NYxgpOO6CMHTVg4Sasqdl\n/H5G+CNan0tkWvG/dyjFyp/7BMWbe2SC8aaBnfqicYg8Eapl1By6qMEnahbS9bCb5qXjukkbq8vS\nGpAbGo7WRucav4ac26CtF292QOHEGwEr7MyE/0iZ4yWm24G7ngi67fsb89UKUKEraj3RvJ+vYN6E\n1nmW+3obArVnH9X1tnnPofWsLtGLDIyELqCnxlWIvCcs7JyrWNbLobAUxuDgo5IhGy+EEWivwufX\n7N2zVsbIKlrs7xHYG6bwlo3REK3O58zIMlobVQHxIqFYKXuTE6dO+tkr+CIM/VrdTxZ2ynuhMn4I\nzLUx/1oKjHou460qRphXIW7UP5/BrXnwmCunvOWDdj0RlnB5w3cN30ROhJTS/8rM/zYR/c9E9P8S\n0V8iov+aiP6vlNLPSrHfIKKfL99/noj+l/Luz5j5/yaif7Q8/6tQNb6jgJn/GBH9MSKi33z8Q0RE\nKiv5ipXyHAiBY2Y+DZnlGeM1I1JoUUnm2VVQ8ViD9q01J7LqXG27IEDSs3zVlb6KB8MZOvxoPBah\nwhcUMsmUVa7JiDe+n3TlolQibu8cA6H8TEw2jIHIsag5767e5NHw1j+sMFkytzbk51oYwP47RNR5\naJghr7DlUhvECKLwKJ/T+FsnEVZYlDJ9+XhA8MRESJjESwSAI1GXPE7Hvr5GI55jllvDNU78aPuZ\nnfCYW22ynn+PJqm8D877HuwKAN4NAq5FDBO5EVOzqrf9bxUmM1qvrkJlYY7X6PsU4UGxg/tr7Van\ntI8dTp2Xi13T9TlTd9uIm9Ef90nFrBVUHoZMJRSq/EaBSz0oIPFaTSt4aPkAFRI9nk/D7rZSiRUf\nxMOeq913FqV3wwN/w0okJ8JIl+gpIM7UbmfAZNIdrgtKyqtj8xpqex22vNJS6hQJLj9zsZcnJXoi\nsaKXE2Eml2ejT4y3JYezcZO1ZtsYQYfbJlP9lCL7R2Ikfy28B4GIvhFPBGb+Ryh7EfxOyvdW/lYi\n+hedot45g79Fz/uHKf1KSukXUkq/8Jv4txLR2Jpks85bF+KZRlkfhuzHPV9g1sXS5EFv1dXPq+Vz\nFre9nHVaQxV0nd9WoCbeqv9TZfC9mDXrPVGVCaTdZVWsMOn/d2F3Q3nrxhde+juLV1fLbvjDNNO/\no+iQZD5E/txYEI+RXYXBqN4oEdsoEWi1Jjj1sjGj7/Itsl5ZSWwLzKes0wfZyR1eY1jWmTQvnMFz\n80aIRuFVCosQD5iSnUSzto6VfX/VArTrWRHRfftbxknTy/r3wVTzIQTJJomo5gpqIX7NUwPpsae4\n85K4Sp1MbQ1F5XZhdgZ5W3OmdEborrddwFflWZAzScZNLKB4BsJUWA8gb5ZeweBFXiZ1rR207Inx\nQ8Ds3FlRmu/QKXXGDwwFtW74/yqQM8bLX9CVM8qzEax6ItyBV+REuAqoGFTPad7v6PfllbXh5teS\n2DZPhBqGsV7NG97QwTfhiUBEv5+I/nZK6X8nImLm/4SI/jki+m3M/KV4I/x2Ivo7pfxvENHvIKLf\nKOEP/zAR/Z/wXADfmYK9hx0BGSUifYA6xod9wgluzeomhGKti7KLjwjOtqIsEMxRnV8ZbvPqqpDS\n3cl9xT0BTVosQitajERonAvDs9ZtLLkNVTlNfzyvhHqtGKEmuOEmdRysQ1NWYffuadRG41qTT7T2\ntHf6sbTu8YkMcwUugW3fNEGlj9e7DqPEilaJYq3RdW8pgb/4FpiN7IVhLEGVEg59elPPgIwgW1n1\n2kGXYpmHM/VryPs7FfOrd2UXzg8bosYHUzq423sjaN1m8iy8Lda8CUct6WXLTfBKRQMmVBROmw8t\nDNd48M26V6xAek/1LXgKQUujPUChJH+39HKCWPPvbYcAvNQ8cjaEq13lZlD1KDRHfreeNp4nSL3O\nboKHh/UB6xQL5H2S6i0WRGRCTiZtOQWsklYUDx7ccZEXGjK6zm8IUQZ5zwOAtMHfuv/PxqnPPZTC\nPBcKlyCv1QocnIYKes0LOF6ryafTETZ1zkWZZDftAWvQw7eGkvg4H/z1jKsH8WVvhFdBpWHFE44/\ntAJztk7uJA21b+LtDO41kNWYls9hO+c20bunDMWwrjdswm7iqJ8wfBOeCJTDGP5ZZv4tJbfB7yOi\nv0lE/wUR/eFS5o8Q0V8s33+t/E3l9/88ZWn014joF8vtDb+TiH4XEf31HUSqW7ajrcZcCOo5OfFu\nC2ssEh6wHbxGEpPqeQShJueb4LNsWTEurp4ChUiHcEjbiEt0WJJ5T2BJkAb1aYsr961wVzThqCBo\nz5LqD+I/A5sX4TMl+kxEn6ndrvDpKCqm9T4oiM/aaEmhMs75+zhRz0jgw7nZwru86Hk8IHPe5r/3\nRJDsCbl/wQLBmIWIIS71XmbW0dpITcBTcuymxSLvJ81AEJGTQLHhbv/H+M43lbVuS/9mgFWKd0Ol\nhZBY8U6+A8lFUttJ95kocWvHJSJXsEp7KvzIwQk/iYg+z4POxPR55jOghjJQOQcI5zUWCl4OR7/H\nHZ6WJP+L9F8lBrU0bDAfo5wGLZFr6tZyzRkCzyxNXoVXso5TQYVJGRyu5spZXRqnrDli+oS9suNd\niF4pHr5P8+J+SJ+P1712PCVfW1eVh0tAu6jxRwnfkbV7CyOqawO9VIaJ+Qrgz+0M5e7ZQtOXwFWW\ncf+3eA885UlwEE+9MSLowyf3Eod6dF+vhcZbVQU78jwOznz0HoDogWuf74DnxfCGN3wTnggppb/G\nzH+BiP4bIvoZEf23RPQrRPSfEdGvMvO/WZ792fLKnyWi/4CZf52yB8Ivlnr+BjP/ecoKiJ8R0R9P\nKW1dFexlre2sYgDxVXxrz+z9z907pOOGnwB0m0QcFF4gPyUVo1kUCQ9aA60GXHprrVs8EObsffN2\nxHzr0cBCQdrhYQSzcjb0pfs9tXpWITqu6thxK9eXyZptjzGIGNMVRq9W5QSt4r301gvvYPLCTh+H\nbCRsjBWRWAUTqWuT8BOgrf1eWUWpz4lwcALzL3T6AK0XgfeHdYlEi6bTn9FykTkmatPRXw2X7/GO\nlIJtKPzJWY3X9mlh7u+ZsSCiJty/+gpHhKl77sEkiVkRRDlVSsHzNlbSH5ux2wrJq/sezxrlCceJ\nOHGjkzD7lYZyb50SfMXKpkJ7LmpgVfK8qAziTn0TLfmqphmua7WzNK8IBOr9heeH08ddoRT77qU7\nq904aCixjxQPNlcL6CAM7s7C3AT3rHEWgVUyXRVIlvLQXIQVBYl4Itgbe3bAKuDt6/Lr5TFShiBp\noyRc5XbsfVJbj5oX4w7P/J5G6OTUDHDqOStvU+t1hnjh9yM1TwVRFnieCxWXgrd4WRwEz4AuYn0R\nsIT4Aj64t71wTybkLRq0/d3DzpyueELsgNBODEOV6j2+GJ8NWKTvCmY38Hwv8E0oEYiIUkp/ioj+\nlHn8P5Fzu0JK6f8jon8lqOeXieiXn8bPun0LPClMW4LFCy75FiI+ADf8p0Psc+x2ChQKfuItj4l+\nGlxCpSS4fm6EIUahMf/NIeNpYWdWlzwR5Fzi/rkk16lWy2T6M1ljnpKiabO5fqZaZn/NnuCBg4nd\nLA6KISha8+imDwteiaWVNcu/YBUlQbkVobjT8s9fWYIrtxLUuQZX2Wbh9cdbu8vn5HRhHpgVxdHD\nW3/kmj3EY7P8kiunQ3wk3IzB3TQzsns4e+70EVyN+AoTp66EL2zCbM6uMMAzHG1oXH4mNLO9LGON\nCRdHOU4T7a99VRVO7GdTflclJMW34FgFayfxn9L2Gh1a9RbwwgpXwv08GCn7PbjqVh2FrozaWMUl\nqt8C8kViRe6T5YGXl6w/Z3+m9O1dvudh1Ie3JPN3DN6+8xP/otAej0r2LsrJFiXZKhEVBUJ5Vh5J\nqPJJyVVMdMrMhTW8qvDe5+Qn8EIJ3k2E+a0tzDd8M/DNKBG+FYgOoZSyRQrvFU5GmBIXtigZUzss\n/dsZmjtms8h55aTNFdepUQhDF4t3I4DS3kqxckvFKrR40/ukuI+d3K/RZrj3y/RZihHOlNv9YG2x\nvONWG+VGiKr03Hg9zxoMX1B/O++HuBor6lXhe3Yv+dcCKxTp+NtxFuf20rxMpRnQ7+g2kOhdEaJG\na7V7trgrUqIuqeKOS2fHkCafMcMwhvP0Q8pEudVCvhjCvDSdtrC6hCLPM7lJw2sHM/1jOAPSyJMs\n7lTihv39+NSSRwtXtZo9oBmz8eIY1/sE7rOtg2uambsxl9AGm0/B3pDzmdraQqviSZwVRwuCR+hl\nCEaCV8kEnrV+Zfy9MBEvR0LNqzKqYKU9I1zeXSM755J31fKsroR71SyClJhOTppOp2YkyH+/SCoz\nA7eaVDlSOkaeCNdv2nLacJ5hzoSRJ0L93Xgi5LZYeSKE7xZYWTPZyFO+T/PQtP3N5I9xKp54lk/H\n/aDeuUmbn6AzIwXsdwXvZBJE9FYiPA6e94D8VZmQ8HYGrKO8czOcQVztZ54IRD0DNLIyGmeAEh+v\n4SXaS0O9uPiSo7dEc5nvLW7WE+EuLRx5NqBVTJJ7ydRm5YFmtK9kHV4d4q+pSfZCdJpXBTyz75VP\nz922A7sAV/BizDWwMCBXE2rM6nS+7ySkk+7uuPLOkiw+AeitcgUkdtezaud1Mx6jO1O1ypuJhxMu\njYPyXsYQB9zXXjjDjicCCisrfAu6m3rheZdg5NrwYojCSfyy7Au1DwNe7ziDyLsv12PrpXYvfY1v\nB1p6tDPEegQq1+SLoDyVqAi/VyqdLO7oJoyn4FVCjqfUm9HuXQXFnXICVrFrYXV8VjwRdsBXyPTr\nwfNE0MqrpmDwPBGyAkF7Ihwg1Mv7r0ruGIUxdPwoSQrFhoc35llw18/5An/yeP6RlwUUveHHAm8l\nQgB33C8xicnOFrcazpyAj0vyPQYCmZ+jxSS7y2kviGVL8U3tJiaostZwTFxmE2HV91MvHgzH7UGG\ntlf5WLzaXGBiMKLsHuffN576v1m8S8qz0oVkuvIZjFGPd69BjzLwut4Fq8wLEf3sJPpZWYdU/pY5\nk2qemI5a1+6LgxdCN9+bwu4I1K+jvRUMGub0sDzCCQu2eiQlPQ/RfdbW7Vvm0LVA/QCWBvcGkBP6\naENozF6pNMdhoL0kmnbZsJiLzKCvKkayd5FY7ppideaJoOqQ90tMsCRTzM/Qu6LRJQ8PD25N6QMb\n/CznGHrzRXCQr0xSZxuZM4fausYpdF31mWnxUpHlrrt7ZjDobL6P5IFZ7iSBGaq7s4hJyN0s8QBP\nhXZ63pNN+cYu/UXh/k7idKGdKYF3woTdUG0T0c9SFm4/cV3KbxKOckhS5bR0Fm/JipM18og12szB\njpebX67fPzPlwhU4U5oaa+xVqn0dmj8Xz8yEtD81j02ittftOhJcvLPPPZcxyc0FuDP3316wzQ8I\nEeP0HcK3cjvDNw8Yny6WkZqUbQHuEm4Vq6c0su3MGMUD7rieE4E1BK0cC/21mbevwNJQAbN/1/02\nme+uttx7L+nvw6zi5M9h9Ozp5HKuwPRgfbVe5MwnNxpchW6+3eRdcdyxCwPLjXcLhHq1Mtnxms9W\ng/4qidk87O7bl8KLfRhrgsWNtb8SGrOiMBrx3a+8UhIhkVW68hLudrxs4jqiBw76jbm3Z4RSdpuh\nHI2sdxPSynsR7IaK4Vlw17kB6Qeup6t3tLthE55Cab/q/F5Ad+7SIrki19YpY32lfhte0Z73lcne\neBXjGynsvyodD6941D+tbGnMyK/rYnVDQrSGvZDO1ZCGKMwB29+F8HpL2nc8HOWI8iCaf3zeFBDz\n9yoc/k1VM9g9I7y1cNRME2/4XuHtifAw+Fm8iSjN3bTnAkUhoA5Tix4AVqvpEm1ykt2Z67r4yCEC\nPZPqUzXrChmBTTY9cixgcBsVDTEf3DKj1iy6SSs8SAuRTMFBFeAo1ixL4PukQnsMpvJigHotLq9I\nVLYC7toiVpYZ9M6YV6jdfjGTtW6DujqnDDDP17n/mnMDhXc7g+EqDlhj2/K0rVf9FNd5RW6vXiJG\nyWXBVYw5z/BaOYUQc9mT/VsM3ZXXhaHMCU9bORsCcFVo7xN2eR4JbV0phtqpjwvyT+QIQKhJ/gZl\nTmpZ3xttF6uX7hdzItpRvDiCnIXqKouKL3n25aOWyYlT2xx+1Nseeqh0A9sxnwqHmcI62TXexxYL\n2vjOLljvuoMpzIlA5Xyq757t4NdhipoUyHeVhFYEA1RAENUBRFrkha5IO5J5H5+tAoYpemN3EHUZ\n+CuukFh3p912LvpeBxq/64rN5i06XxTNy1QbV/CZQFOIyHovVzxCS/bKUwsMddVnyECWvdfalP0q\nZWVtpNpH9I6V+1sYGhJ+x9sv7Wq/1PE8On+BBnvTD0LNdeDcIOHezsC+18ZqTgR1ZSUoIyzU8+zo\nxzHP14Jit3wizfDeipSrWIcHjT5z+d6XQc9oG+qEYc4ypp/UzxOGQmNf3kDfkHXnh4W3EsFAs7jL\n33D1VaRtrZ9wqHdliiYB2smFdR0ejLScNWmzKCqYiCul5UosPHz1lV6mjLmNwV5z6V0xuHrzwQ7k\nsUQ8geuqhXyLgy0mf4/Gels2pDz29b1Et+JyNfPbDjBPWHP4V0/2fTTJ5QhqPLuzpg/DQDMZeZTy\n0llNdcCHZv0Ow6gyyV7ur7VTOJPcYU5247e2ujXUFFT2gFU4shYo3MVo8JfHB6fuGi2vCi9RaId3\nKl0zDKu3XZ/Ywo9eRXWkaXzvCDxFglIu4hq0iiRVDgW2pGh8FirXmKvKvJlnq8Ck1/NR5vXglhX+\nE+bbLmc7N/UmGxwHT3g/DqLTiDvVE+zefBu9VKUjdk977+HZlnMN6XCG3n1YtxWR6e1zIFJ4FCne\nXmkblVdrs5zLNidC/u+/v4q3XXPeFY/2jvrR+cHcvKpaWJDTLsnekf20PtJdIujJssM+vULwGXll\nJjLWZOo9cqK32Xz3vDdGYJWe0W0smg7ezyUS7Sevbs9whMoIfGYBn0VtenXF9ZW6vIe2bLCOunwF\nJLSV69+2z8KnIb1S9My0seK1NDJIXIGIHxCFzDsnwhveSoQCnaAQ/L567d4MQubF4IGZxj14OkEa\nHkDV8rp6jc3ikOzirMZatK/4NxXmUx5VBgXuLH+QidAWqXm/xWKWiOrJkFL+jskV14SQuLGn1kKU\n9Ao9EFbnWpQfXtLQWsZ8eud3u6EDEKxCzBouVclCwVh3ltdDKQEkMeOTa+lpKzeC7zXTBINEveWp\nvVsWKILX+eWkltRxI/KsVdkUOiiY8pEofY4Hncm3Em3Taav08ZLiERlPrnsL4opy78r5g5ZXVFDW\nOsnS2cOMx0F9SI7GR5R2B7cI2pDxXsQbQwhnMAoTs99HCS690UVLriRWVEoJuSJZeQ7IGuppIHrk\nILgecyYnAgoUVrFU8XXq9tqJFO5EebzslYUeRAJoVdDSfM3KrxFNuksqDwePEU4Dne8QzsRqX43O\nyh253cVVhNQj7zdMNP2tQHR9aPfMWcN4HaP3LAzhDZQu1rvBg2w0I+VFI2c/Cvn5auUxfz4CT9mJ\nyqXpDA6YkbsGIwzRfBvcHbiTeOUnBm8lQoERMa+ExGj/vay87uHnMQU3+E65CmxVaOxj2oxm2nGP\n05YT9MZIZIUpcc9d9UTAcIZIAy7umaLBrS/eAMzOuwLWchC6HroCUutX5Gr6BNhpjOBOjgUvp8ZZ\nfVHaw+o2VyYsSsQj6+lKTKObSW8CW0L/iQEnGkZjOB3fAAl1g8XMxdx7trjnWvmisKJUXRW9Ktzr\nSb3g9pOWPQU867tHQ/HayB1PBF8Z0j/8AW4EHYK4lOIVj/k2l/6Kx5q8a3E/X971X4FBWk2sGPXV\nda/n2Psgcsf3YHfcpjTGevo576Nyp5bbOPOi880bEw8ihcGqoFv38iQ+G/XAHOC8gm9E+1qOqOtr\nWOUwGqwfC9KbE8L/VOLp6t3RXyuJghtDfeMGyx4pnZYwT9wzVsD1vP1eaU++4+VwiUe4ACv8WZQf\nZaRAGFU7clKMFGluWGFQt/AT6EWHb414B/SMWKUfb/g+4a1EmMBhFAi7V2ZFe76jqQd1IQO1rAke\n8IQNG7ep30+OIgGVI9wsKGgBLMLeaXASC78VCmzYxNNQk8egdVhicg+MV28jhooOxJWIStjH1zmk\nPLBMm3J1I2MxOTSDiS6KnivcFdB5JZq1rMMbv1vlyZHqkOZ1lVQ4jBxkTNoSFh1U3f7CDbWgHdDz\nHljslKfLAZ+fqhLPglWvaHI8dvhI/cIz1l1Zv+o9WLtcn90Hm4tkWr7OY1IxmKqAiRu162bIRFG+\nvrZ5CzFFoTsWOtdmipk8D7wiORSHYQ2IIgPOAcezaUtHFeC2o2T0Mnm7OR0WFdrde6GrjqkkKON5\nS93x3tmxqkbJACOUrSdCqCS+AkLUYB/pentkGcrZkEf5XYT0A/ad7Z8Nn+xQC9AdQaKxd5Omo9wp\nEtAYYcnipi50ClFd2oUfz1OnnBnTKa/Hum0MI0wGJ/V9s+Pqaj/wRGj5OFLHrxEZg0PQF8/lXvqg\ncOB9vL91sGHLNnzA8rh4cw7RRY8yCHmQNrR3HrRvVzUsTOtB7L+7T8ie3pc/KXhrVojorUToYEYY\ndSbgWJvcyvjPuwKqjWZt8ojUweVKmcQGn/L+JmFLZ+qujlqJc7XCJHr4jCz3M7ByypOueatkNLq7\nF135xUA6u+axvoMHNGd9TWZw5ZBnZb1/6oaGlXpwzSGcJAcJJFYs6262ttIDRHanii5WPGSUxnkS\nRpVEORbuztVVQWWXkcNwhttw0xPkalK0VffgdpViT6f9d9dMLrNYfcTFs4pbZV+lnYQhQzm+XzwU\nLHg0EeWKThiwscaoBFmZBsmJ4Eymyl8AApdy/Q0YY4vPriJU307k7EsSy3DDI5elZmGm9kzGwhXy\nVperY+q1TL7kVcEQE7+uxTbJn0drbfS84iKFQw15WkdBv9w9igdQC9iyB6gKWGdKdLKetyeFWI9v\nw2cr54W7xqgpONGR64MzT3FlbNOZiGcbagHYLFObIPNtgdbQ5rANnCTM1Lww/F4+o5lK5mzCNWSV\nUm94w7cIbyVCAdQ+ohVYZUs2sBLOgIZTlcTLWhkmp4klNGMXqka68kGgy4rmv3lZ9FaDjGPfP3vw\nXAWv27Mz8f9n7wu3HGlZbrHyfvd/vWdNR84PRTYIllVJ98wzE9aaSbpiqWUpImxgCn7XbyrDcKia\nZLR6mPsvkD8uhim03sjhPfy/OM24v7Yz/pHV8nK/JDf9CKazJ1iKVeSMvKsE0hE952KixNY9UdLk\n5Yf1NTA7yfzy0dU9Yf7oqa+Zn9MxF71CK0giBlbMyMuiKgCDtJ7RTSH2VdeeaO5HVfoYKWflBxIB\nr7m/o2jnURDM7xC+QwSCKAycpamQnc5E52iFOWNJcaffuYZ439A+eDrzHcblonvntXSt2cw9eyXZ\nvBS0AsbCie9ftxBZvuN3wuZHk+2BWK+b/S6qR79n7OeMEC2XuYP4wyciWTxiAvvt24gOUBEtUykH\nU3iHVkiEK2RcGEAZwt1AwFAu2/dK8N3ELtlE4SEdUMdPuQj8pykI/TIrRuH9skUL77D/WGEQZ5gR\nKnigIKIpbo2U23SNBLzlZdfIf53eYST7G+ijRABqiP6yPKCt3BnUquAOft2u7RBoc+MJqa9cGfVl\n/fd9E6aH/cysPeUoE5y9jcWsSEHo8hXmEwtNZRL02zW7+YW+IQKpnvrX6zF+YbEQHgW40XvPhV4c\nT40PUeAah+98dZhaBVD07e9QdujftaCjxjyHZPc5gY6WDsXAjEEAACAASURBVK4vVsvpFRaD/O1l\ngzZix/r5/ZMepG5TIqz5TAq6xtoX7mt9PpQdoSmwHGzmvzTbzrQ8rslzmX5wMetvxVqepPMSo9jj\n8lUeBfM3motXhdj5sZul3fFJIhouMPgAUWDF6ZC8XE/zNeTzpm8R+qTM78fXvzsiHgp8dY5m/F8F\nw5lvnR0o9xs/aHL3gHeLbj7ZiERwadOE/7vzjF3lU3QIm74H60mUBf5d+m0HBXD5Pt6JiwfAAjkz\n8Ybsmsbvc0wE25l2gGTDVyMl2VWSw0QWCuWMMDuDqXQqp2NMNO+f5+24ucxM1cHB5b14X3x8b1o+\nSPe7oMgw4Xk0Ilvw81WaFHbhBtnH6NAiSTFbxUkfVT5qLmeV/XvIsiFY2cPvP+27VlSHvEaTYvag\nQrUrbLFtGV/jFkB72RlkXzJrebjvcYjIVWNKSZWixX1ie6iMlL931lgoT+iDtfYWikrvpSZrD+eA\nvJ8sThmRrtcwbtKH/jn6KBGAGnQ7zkUrNAdV2aubz8pewLUJfAq13VgFwgFfoTs8gsenQvMN1Ivm\njUXyex/FXUtNQzHXVX/sWUjLaHUwPrPih/63eD9MlsiwG13T/ryHQYtLCbphZMQGpgdzqGg9RBae\n+S5BCYNK3aHIDQPrFXoRAdrrhDWXWCYmwg3/xmBl1rkh/N+o76VXBg8pgQ8rl5Pgk+1zVsTyuF/q\nk3pW1t0z4SuK3SF9ZFIXgjPik+ca5ci+h8xdI9pDasCz38UDIopSPQ46kslGRJPfG3VIr3umiHdm\n8Qp2yI/FDj9r/WgkirIhLwtfo/msG0HbEYmwk60gohVqI7q2Ck4Y/fYKMlAIIfjtn4zc2X2vM1rv\nhonvQcZ8ByB1xUUgWmNDvgrnqy2P7kayRw53R+iPRx5wAVTC1P4tEIStAyp9hxJD9+fgUH2h/jBT\nA81Blhtv5knB4OUNXD+VNb7AKjvD4ebZK+TjlbVr+3tr1j66VhmKDGlEYxJl+9Q7zvu+r8x3HXL+\nAjo90P079FEidDrPOawaUiKrfU7TZOH9wTWzIXVr6mSFOyG0QjwdUuEoZanlH4ylkFqVDvtbOZgI\noqMfx5zaimj++wxO5d0ZsgNS6wsKuEHjZJkk+q2jNnYg1hdVZXzB+zlmdDV4XVpP8YErbcOIDhlj\nWfqrNPfp59lGkqW+UoUBKBl2HsJova2GPIvd1ixI676NSha/x6AV9t3q9xJMDjdgMGEiN5kzS1qI\nnBjBsFxGlOF2MVeot8JvLxwyxQKyeo+DPRRuFi6cRODr7QMr+mG8Qnd4oKfpwLkhkofrelja4/7o\n+xK++xqZiOo0o7MyWln9zw6WB/EYn9mSHLzI4uCzR2AdHo5g5yPSDO3JnhkEh3sXrVwXMjqby1md\nEmB21ENEWVrG+ebFT7juvhGHzOPzWhvDmuvQja2/vQzFe/A7lL1hnxIFZkSxC0jEm+21WCHYPn0A\n7JcP+Ah/gA5HmbaK+ztGZ5X3BpvYpEiRsEN3urrblk/xSNT2lujdZUqnZT8uzvOQP8snIBGc2BLS\nVQRQ3J9vWqQf+k/RR4nwAuUHF4YDcJnkMD2v2PuZi7HCtWvdekY2AIv4qUcC8g4SYYd5HSD4hJG+\n4TtaqrUPvMVYoxRAwqBSPvWClIFW6jPrWHUbBX7OSQB713qZFTRMaEehmWXt8P3i4FCY+V5eHT4M\nHjQsxmFfSVM8Bg92dhh4WaaKroXw4ddaetehJjDstvrJCtsRXXmFPvicKBBEkImtDBuHb+h/Du1s\nvCCKp4IH0KuwbH/G9ZDws5gI3p1hHy4f9zO6+mo6aeH9+vds5c9iJey0K/sHU7l9digA+x1BA2F/\n2zngavag/XuIBAVxXtaXyCzameBdiUiW/AqJUCyTATyePdgdbs7L84d8OfC5yNwqI+Wp318P6vM9\nLGs/d6kEizvk/6TzpN9JRHau7uyJKwr3APzu+ISn0g+ZnjVHczIbJ9yLhdfi3atnDKdAIRqZs444\nhtWKMncs1GsfbHURuDegO8Mcu+l1vUN0qP/J2A3Te4wULO65RaHgZaxhdIE7Ci1QB6ZORcgdUN/o\nw8Z7z0BBkfHvkityv/e7s7D9Z+iDRCCijxLhlGTDfbkeqONMa4lRxYloKBAMjJriA+OVaT2sI4Ej\nKAqF1flzliO3fGQHcOnzVr/cgA9Bf/TTatx9YMXx2zdaZ4gsbNFDGL+LjBWcrMZ5N9czRuMecwzm\n1kFshNGn2yRRADKBMafOqqXaoBxotuyKsLK11JwEJLE8JrRGt3JGgvl3QsUNamDSINp2JX3gjuU9\nVpAQHRsYXwyg5O8v0K0RJ6FESWHnm89SS12lxm9hrhxE/JQl33nSRuaYM2K6J/hGvvQRIU/YpbYv\nbFjIAleEg4oe2ku5pY0zB8hMy+idapd93F9jkZVblX+vv+9dWdkZdFOL3io20jQuAoMb985KTe0n\nu33ZKiaYmAqLUk7ed9ysVOHX/TW4//wWo/uZbVwXVSbHCry75Me9DP7X+7GoPZuuPn7V9Huwh2TI\nPnHtsQaVa7S9crIXjnMN4u3MaNHzfuyck/6WVI8R2i+SLYVHN5mlDEUvBp72JO8Uh2qlWLD3BoWc\n9lx4hCARpuJBvVfXYWRoqOUfdWf40KCPEgGIwREpE1wOZxXGDUagvCt5gkgPf548PJ3ICpVWdy99\nlnvD7l6jQNgpPXhbJLijNeWQQIigrYxkqUwA8X+PADwFmPqAUBe1cvSATuVgI0wNaxjhNfuIci0L\nrHhGoz1Wj4+hPd6vJq6bcuTBqj870+AMlloDK4z5XQSloJqCAxxUYpUJa0JlxhntBohs63VOSUdE\nDq6vweO89v93oPgO93mH2vzonefY23nFl95Ng7f0XjxhRhTHh7LDQXR1+Cjf6VTAtK5a/e5QJohL\nEDP9uxi+JnnGs4MHKj1KR8jJGmjXdR97B2VC7Ol9Dokw/677DJG1rsm9GSKhuE+5T+gVXn1XwfHy\neJuYanqE2QmguZrOPi0hUoVteDVmHkqPLqAqN0EfWZXVpi9Z/4r7e5NW/ufaZtl+N6nfetDesFb3\nvx80Kxv+ZFrFRFjet11urjdzO9h1R6iLyRG2N22G832r9T5caBd98r3ecyuEnTqC1CR9sUFuZ7qr\nSB9t/MvuDK9CDP8i+igRfoAiIYbomjV0t2xUatZAJ/62F3a0iJn62+8iEVKS4DEnixcF2dd8q3XT\nlFrQwojXtuojq2SQ9xIph95JghvZ2TCiAGZEMfLlLr3b3zWaDysf9lU9pmsupd1xxEKwF35P95Yg\nVV6rSxUbpTSL42qsRABHmGIEVc2s4lE3wwCjN1AbY3zcp6eDWGO5lP31mmVjsXWv+xfKQAHT8i4n\nCahkmyL47++Wx+6mgvUpMJG8Rf+KzHU2tmc+5Zx8331Kjxwkmt3XTt//KGAND3ada9GrMulqzvif\n7gr8TcGuz7FUHry413qXBqQswOMVKH1Tvr1nFztDkmLgT5Ej8NxhXVN/9jBSaJ93+XlpHXS+n3YU\nBqgQEMXB8RsY6oqHGsOfu5bxKr2G2qjzfmSp5wWZ2douXdGQNLpJB/+Ahv1DfzR9lAjUFqnk2Z6F\nOx7BBL+TBhxpA6obWQlWlEZ2lwORWFvRNeDBQ6h/HNVAJyXdlj+UeejkaqNaCyP2xtHP/5GBbhER\nMcK43Nhh/84OkC9H6UXlQFJmigJOeVwFc1+34KByRJAWVyxaGmAp3zlaPI/ZnWGHFDUic7lAv1VL\nX04QBjN0FDoQOP1ialJzH9mNEw/YYfDGO6Y7kvoVTs5jg4aFN3Zx6xgta2xHqPPlDhYL7XxYnw8R\nlAoLGT+xbhn9e7/IHvcN7UfQX+wbPksFq3izSHLjPR79wTzgPiPl32IOS7kopWhGRbISmHdGw+Wk\nPYM9EF6hs4OvxMGQdo7CU4DFYQ2Gg+iTdQ6OeojmSRC1WZgqJ9ZXk84RPt0iQleiA/ruq7yqdPF7\nzi7tltwVoD3LMeil8dn3HzihlaMfLPqepUGL47SCg0WMfbmvN7Ts90McBjTVYKY6B06fCXkhWUTH\nGUV79xQToRSiB7l+0ujnUjYI5BXZM4odjh57CMtBO0RhhidM+zwC0bnf5DGQMM0oKoC8O8mIN9Pv\n281wsU1eOwffJYin558OZLpF2Os7SE1p93eiLfAAzgleK4ojoq6Y7c9szUa7UObOEFGkVPKxt7RL\nDBMVJh9AUI18KPd909nln3dn+Fv8eF6kjxIBqPJ5+qYoDyvS6pAid0YxEZo1tQVWPLPo63UtK+fn\nV6a192HMFBq7AnTab/iOgd6GZR58NKbDEPogCPkNs1j/zHHvRp9lmxneEicw258isQZHfKsEXdTN\naY4mzMSnBxobfLLYVFYkyAStx8xZlDg2JJfIiq7P9vq4XwnwFtLmPompvlqgurItdb0UfO/+rSGF\nQseFRnbWPRLO39qDy56ttx1klqRmxDWAaUon3+Xq6oQH0bm/J5FJ3Z7XR+sF+3InVseuFdsqcfJy\nV903bFaJnBQS/bpUa/ZZ9+x3j2wZPP3VFI/loDCRSpqVAjWCk1aOJ021d9u7QyNAJQzAOtV137ch\naOMV6/b4Ti/w5RNqCCype925aN7OclgcfBQRBxl/mP3J5/o5+S60Y8g6Q2leIVQuR+4MNjtB3G7G\nD3ZcS87qtvVxU66F7g+Njl7OoxQiY045zhEXkbvKiiK+lBlpMsPfGX3OtR/6afooETql0Zqz2AgA\nTfyuTXDOJw4b/IVWjxJvoSbWgMuGYFM9gntAoFh4BRIlkaONdccxeR8ToX33ZeL6TwOgRUIBEagO\nrj/Y1cBV6NYg7Ubz7szfONp0huIqzIc9H5IiEquKlF5tVDaloV6LApHtbJJyQLFoBLH0gSlt/MTY\n9KLepN8QC2GYmzYOVYjAEPSDQSK0wB2m/zgAxgAu/9BCVmL0xlGYDi7OGufGoKg+5znuU4tZofld\nTAfOXXhNUNei6CREHWVO8chUDBpCyuHnXR5sntPzNvN+ZEx/v5R291mt8lauFarDox4oeol+c2id\nsYr1kz5kPfd8Vi3ruTL7CilfXZQJDLzZ1I+t1DwsmFJhOeDJMM0jrHXbz/NnFWW/V7AaQ2qy55s+\n470lRiLgQXL3LYiiX1I8nrUt/b0T9X1l6Z6QkaQoBfl7tV+gYkbKbh3kyR4qQ0W7lN185mleKKxD\nLx3z3PApidu1juxA5RPNAThlX8jcGWQsmIlaNovfyxt3kTSepjV4EguBQTr0Ac+vUhZ3JLtm0JKl\nEB2HuZbNz0K7GKUP7RDT7Ob4r9JHiZAQLjiByk+WKkeRNQCFlwLlvgti5AWJLGjLzma4sgIf5KDV\n2z1cb/x7FcSmt6s5n38HrSJpE9l3hc8zCYwXnkcUTqG1ZYqBUCZrStSUH360KMkmHDHZMyh6VD5r\nnAPpNtrQPdIWoaitv6KkWg9qCQ6S21HomduAoIRyw2pkXHQKEUncBIZrFCgFupAurZ9l8pheXRaP\npM5/XkUinFE57Fy6MvdXPOyui5pAq40uaPEqPfr4lRRZqjTRvqDyapcQ9vqOTCUl4FWXXK02EF9Z\nNPzpWnDvlSH3zUQxEVZuaxM5l4QoqKtp37vywN/cNYI+8Og7EFuIRLizVn0fPK8Q5aAJ+tbHwGb/\n0Tmp8pMeUo1l+UY/z8hH2UdUnynHOQ+JUjzKdSKiGosw12mzkt0MQPn9l5r7FoqQDxgvAZEIy6CK\nDo2w61a67Bv2g68hAjD2lvyNn+uba4o+eVca6mXz2znBPvS30keJcIHQIiK+zKjZJgqsgBQLmZU7\nfBZ3xAX5Q+A7mPm0qQSOc8fB9HwW4395xc84ov1Uj94iVZzkTt3iQcZ6qf2zWvi2kYLVlmN9RPZY\nu4EivXBj2nTlIovR7rgOa0Ivf3Y42RV6fVaEybKygAxOfbzqiIn3gjUwrnv+Za1Imn+7K1jFPs3t\n2lME4GKtkrOjajtczAoiHjwjytgypj6rhUHTeM3BGFfCtlib/NyZWMFAlbTKU8GF8vkr9UlfSmFi\nyM7ARfmLOTg9aaR6NPWdrBMfyA77QLQp6B2F6Ng/qpxZks74tlUcEh3Mk1+3zy5SnFB8lS+PuSOb\nVLRmfdYS0rngD7S+D5FS/VL/YK/ZstRTojjHOuHz7O1GxoGDdO3439J+BXIC1tfCjMD7z+BagPCa\n2gjaPHj9jAqWgjm0KL9LGWIi68N4VLbXiVRx5/vm+8nU1oLE/ZFnWiL43LsYMW5cH6YU0kXkjrzu\nFb3FsOFP93eCH2zQGd/63SiEV8ij/vQiKESDpSipHbMYBi/3y7VXPG8gooFECHhwVA/yq1eU2f88\niVD8oY8SAakSb/u8/hRl/qNMxfphFbmuFKEQUCO+irpuMxys+xjtWehzv+VbycrgJMVjeJ9IGxph\nbDB7E7yv2AP6lQ17ZVte+UQKTb6OZ7mgdvrEcfDAKxT7fcqhsNAubFisLDV6txt1RFBeA5PEsvK7\nueh31/M+y21iCUPBMYSpAiQXlRXbipisT8kP0i9TNO7SdO3ppldTLOh8MWlgN17xpZl6QWCNA7IB\nnyE4lKLCtgdxLFWUiXOWjNNu/gBb34GmEkUWzrmMcdcwFrh1xo5Vu3oo0vkvcyI8nB+FwkwiJx2I\nDtaSijIihrWPpPsPm0PkdL9PEQzf7/oW36EBJzcpF6jtTwcq4nt5OlHEi94R4crd3opuExJELdKx\nE+0fbl5FImC/pQMxNDxWiu6Q5dvtE9/xTx6MmN1e5c7zZ7RSOEZj8hOpZpGic5LEQVrGhpiuc/ys\nojxOfsuuI5JA/tYsWrIvtJVSoZ2mGGpoBUEq1F4mE63Oxrx2OVo+W51l/IYUyeIrOfO11LN+bq5a\nOyevCL8aF+ZDfy99lAgJ4YK/k+JshRrImIMGz7PtNYVBsQGsuG2Ypp9ZX5JF/6787xJATNpa9UcU\nBGd0evBeSB5XnuuOMjGDt96pB93mr9KkSIJ6BySTC33V2BqKwgAzkUS/r6BdZyL64rYpi4A2UliR\nPneq8CHqQd7hUETzM0eHiFdlJo3SrXUzz/7fWcCsiNaB4+z6/F3E1MbTP1MaRPNE2PlJehWCmUHz\n0S+5ur/fSWqVUkFyKG4JBO2NPQHTX+K1V/sW0e1gh8ki9UoJ4VX+gB8pXm4j3Dieu1KnaZsa//Zx\nIrLAikR60HiHAJ3HWtrbvzAjzUDeZW3R/f2pwkFZ9gs8FB4UHxBHw/5S8GzyfqzskAXJK/S4sAZ2\nAyuae66UrZbni9EEeYwcxivbueTLRXRpH4HJi1kFhk6L9PNPce88I1yPy1hMiSKSiOgsReRVUkVP\nXu9OQEnhST7Q9bRv4x9GM3DElgVHmJ0KKUMjoFI2UiR9qNNnYIjoo0TYIuvvmZcT2KVHlHl3htXh\nyPh9eX/1nc6G/ZqvTZZdV8iktjn2x+CM5qCJeWBFiWk3bhmwgoOo1t5PcWew/TrIWnaD0GGDVjpa\n9NW9u/FmSpNoMx+P6K9HUPzEnQHnV3g4CaxVtl/7zHFA1F8xyQS32mvwHiNsMXVoKb7/Ej9HZPGX\n+8825LMYi+jOMKpBGHjmilPs/RpfokzteWjiwUSPwupCQe2Qg0gEolYuS3pwBm08omCqJ2Ol70zL\nzM/SynAfgKwP4sqwsgrJISQKIpaV930qheeJRPZSFKgsi8x9R8RQaLl+2hSPxViwhYcwrefmZQr9\nr0RoHSbwVhT2h3f4Xhfq/DqYd6cuLLQ37pE7w2YM1dEPPLi3/rpOyvgwE6ZTL0OhGqOuwsaAzuPG\nh7e5OuZXfKbcHyALxnmq7khjXXj0hONvUR+9knu1N0WHIWS3wsp9sEh5v9r32BVhO84N0TRHUUmp\nivz2253I+ZErxUCnAEqO4YYdN5d2za4WcX15wj1euSN9apkvXH2FepBFpQr3IJlggYmiIA2WPbl3\nKbLAK55wfR/h72WSw2R8MTBoxIskqKK878ig8g4FUSgTTvJ6+/s4NPW3mTPuc9le0YwrMgewv/8V\nBdSHfoY+SoRONdk4BAEgGme03PJIfadlhZmgJTezOhkmDKc9b9FEBmWvaeqy4n4jVx4X/mSlDG7C\nQGa1Kgqi+THTbfJIBA9FvVzfSYDFdweX8ZDwHdjZFXcGtFrYOoL5B+9/XWcxFg/8NBk/+pyOXHqY\nrHVVaIlEAKonQUmzyMoTYiEZ7EiDv+OaFB0KM4qeUc7SKGOMTd8LHt7kVpm4onX62u4cCbt4ftnV\n5UQxQYKztCGu3NbeDc2mopYKtcB+1+tI654ChWblbF+I+hrDidQL+bmFqTxNneSQPZRbxiko62X/\nUpjIZd4Ybggbx8i3w0674nZ83q3m4vuWGCGzYpWmAZY/ozbavF4PyH1F/bWHmg/wAo+38UB2JHaJ\nz5BlcNmlO253GVT9CvmDavbIL3oEhhQH5jxvaKzhmqPYDLLivWKI1hsIe3ejxk8pbyleR1fdGeSe\nVbtXrk+BERN3BulL5M4w7u3uDIgqlfFr+1uXu0B+kfkhyjxRGHlFWISsQfVfAXlq8K34kS/RSqF9\ndxp+3Blm+mRnaPRRIiSk2r/csuKDSEXpgoYmDzfJqLIgwFolyXEu+c71t2yjQ9gmag6xtFgpS4Gb\nhma7l/mfpFojejyqCTAz/pE+rzfCPDnRfpfirD9yOI7dHKR9bUikyaP38yDxDz0g+OPjaP/8oepw\nB69I0x6RUQK7/p0FrjIBq6Q/i3ZigZmnOBXep/YMpniGQJB2fGDFFSESYViqhoWSx7yWdzj8m+GQ\niv7SxspqrAP93YrfsXkgnJPYN1mf7fNRKkyhk5feJlQr3CWM47EOKhoaCqYJaBEJ5X9Ej4cNAir1\nF9dOceiaoxR6FDZ+8llcLY/qKaQWxFIKPSvWK/XoepqHZyyo0e/Sx3XKIAGf0kd5TnzGxgNoQhXx\nwSMugvTngHlk+o3w3eQ9SVlveZ6QCKM91//VHJBrfQgKWSuQtFnZjg1atIn6XC1ERNW6sZXS+FrV\nPheO5940NqTvElNWepcJk+50VNYvPp+ESIQCYyT9OeD9uNGZ+idWM5+dwa9RQZIVU5Os+TKeJbOW\njeUI9/2vEH2RKgAl44L0CcfN9Bvq9FkGQmd/YY4Qw0CKImKnuGeWzkzZGYiIHm2PJoIxh37ImMp6\nwGdUeLs8Y+n3LjYGIGOpLGXiEyOOzFH6ekZ+YNctssnDTgFzTdED0A/o0xjTjgLzT+L5qPZH+2V/\nd2tigXCKhm2a+cJDTradiKcc0xeoF25glOFAFjrI8ivcJ4u7liERVCHdFb/4rEx0UKHaDUweiYBz\nZFyHP3eRCEPZGiIRZhcY3eOEPxBlSITWnsoY7VMXTjmUn1mEa0wydwVdN+L9BHegDHpQnjnJpDkG\nmR15i8i++L4fhenRi8jhP1p3I9ZV8jwfJMKHIvooETboTkyE74iBg4qDTBv8TroDSz2LiUA0MykR\nSFAwEfruvOyiBb7LF68iEcb4wGHCokT2kAit7Nzr6D6ZKx6JgHWIcCBIBC8kTO+sQyH3g3bdG+HT\n4GMb938nrQLGEVE86Ob3GKWRFRd7xlVaWctWAl679/1Sw3R4b9/S8pidYT3c+31N+ecJY70yH5ms\nFcisX7afchgUagcAm3/96O/LKHCLBNc8H8eMXrEmG7cZf6ju5Pcu+RQrnljoIivXVQs0PkqmSCdS\n5IjnYRlyUB4zA1UVCV6WTBBUBiz3tXFw+eFIegFxP5oRqcyxHRNB6kAr7mJ94ryQQ4tckznBsFnj\nvj2lX0zq3qGr8UEmBJznnx356df+ymJ/d88rmRYZyBtS3kGr7Ayvok1P207q33P6eY1SGTeQ4Vp5\nVEDAupL74PMx7nkfvduNf18C/MtIBPUPfZQIGWXTw1iPupZfNZRN69eEwa79LKIBna1mhur7hXXZ\niKNnCa1owQZUDiZ+xikKszaJrkVJFsvGVoqpxJxusjPIO6BZe1xAxhNNuw9QmTa9+TyeVhae0PKQ\ny6GntANhzxAJZwewFUT7jPxBYzdV26JC8+dx2IwLLRf5hQ4alAte36sktkqzHexhwQWfcoOgsfVF\niS6Qj2AMjMmyBNeO0oWToL6dx0uVic5KinWW4Psu+RSPVPuBwcVlsT7p1+dlOD9OBN6zObUjL+/I\nHmKJN/E1SIV1g1rh84wN2r9AYXXlDSE6gUit687ajH28otjZfQ5v6UPyc+GszoGmutiHtGPB31Ea\nTF8snRcw6crBW4dcZ6DU61H1F1ZOZE3PxkvkiSg9tLfmnvXgzGgypdeV69DHbF+dUjfePGRj8FB/\niHz1qGX4zkAcuAeC7wZhIMg2JwNNt1Ozgg95hXlUu+JZDelSQkXCQQ09FSkSJOBhfF9Wn1OqUttg\nIkXCMtiilx/8vYfnIfqXX39ToOZxz2vZQqL5Xvz7dsEVPVJ1FCMr+x6FqHC+8vwc+NCHMvooEUiY\n/nqxiDF5FfgQaUdQrFyGX40IGT4Lw+ifq3vyRT8TlOBwGW76fxhG6cqmO0Nq43Kv2nV+Que6+xZ2\nI+2PQE8LS2Crr7m4zFa5hkzIBE0vgL8jDdU77W+3o84TvX9N1EphyrxOdwNvPRmFnH2FX9gHeg2V\nQ+QsK4mC7vJaGpEhoQ4uYT0/JfdcjTWBlPUR4dXyD/cDD/Mnyi3paNW/Gr6ihKeto7kzYGcWJPuj\nrEHJMnSHdoNZtnbcAQ72vDjNrfub4+8HXKtMhI8SBnE9SvOrDsZpNwBlxk/b+7HKmqtUCd9PV/j0\n3x6kzym9j+LOEF03yGWoAfysnadJ2TGPSYPovYs9e5kr2jM8ElDiCT2SsUc1CcZf8fJaZqUPY3rs\nbIyiXNhUDL1D0bHit8tsRos+Zr/560sExKp+9vW4ezcHA+Pj+NZ21gUisOTzFTnTo9t2+4GE7sYf\nWtBnkIjoo0QwFG+QZcCNIygqEQpJffPwgRGDtrYYteKqawAAIABJREFUDMcw0AEvTOpFhuQ3srTt\nN0Bzdnz+IroEdzuDh3fyfsu7AscK/nqXrgZWxH607+WSoOiDKEoAUKzBB/OrrL6BEmCRXPk4uOd9\nUsHbHpLOiCtT8UJAjQNCXqLJfDSPeYYWioQI5hLPVxeYTvgLoghepaVvraMC2tGo7fYc1ANN9XLd\nPMXVHgp2DomzO5Ofa8UEDGMmIgjuKocZ5I16ALF9EWF/HGhZ06JOqbSkH07LhoenVHlHskZy3hzd\nc1Z29j8ODqRk/WivHK7wIInrh2twlPIBFYEB1BPIulFYwn4ZlYv6f1X5kCGydhWU+vRawRavw3mb\n7FUMcY6C22zZSlQe7mJlsz5kbJrFceapU52RnENk0ICyX7R/jtcmfb2j/JV9lpc9zknuS1O7yifb\nOTjuhzXb6is4rce95NpAXuQPkisFl+/XT1KGehzfKffH/5PpzACIFKUOFdeB8U6Dl4NrbJeOEstz\nSD5DxqXKF+RRjUTUY11d56OvoCk+9PfTR4lwQkfhMMXZVWLQoN+hXT/l24vewS4lwJl8N0UddPwV\nMjlpg5gI7W84uEQQvt7XgkFwnKuJBBXbYaIejk3Urb2bz7EboIooF5rv0jvqEgjkWTsIl4W4d6aQ\npN/US3O9K4WNwDBtiqtisXmUz8lL8QNwbgXY17KwHto5w92dgl19zp1BXJ6OuV51uWnXmWbIejvH\n93k+LHbNlQq7L3MM53A7lycwU2l7fHKHJWsgPYXU6jvFwHD+fWIzE3TU+fKH7gwSXBH7FUCibeBJ\nieq/NwdGP/wzljXKCWuXOeiDFcKjpIoIDGb5OGpL3UlkrJ3MhR5VLaCPwvTVgyveoYyPL5EIzh3H\nBrUVPqtrVlwysi76vU1aHm4RB9P/RrAwODC/IOAGHhkGhuxzq1fH/2W9m9geziVqpN+DeSTP5ZEI\nAhk3QTwPqFOoEpX/EZX/aQrXVQDRiHxgRaJrgRVPCcYB03+Ony9MVnyu0pUk5pqrk0mV4XgoztwZ\npM4hY1CfY/C+lR3gHq/zPeorUazAesL324oEfJgJ3q7yEBGlsiu6wP0uWrkzrO7x5THFI1LkzhAG\nNiSVD4loyCwMa3E1Z1cKoyUa7aIMPfUBUu6irK6ubuv6svYjBVpGcxLqf4SYw8xw/yJ9lAidRooY\n5m7J7QyuIxEYLFmvUrZ5cLVZGNCS1voofVJrM6aSlN+2+rBZrpTZB/MsXd87yRxoMfz0dKKyMRFE\nII+Y/27vUUhVba7V1osFx1OWbWKHoneDSIFV2TmoUzHWGE/DKgjQ8AiJ4NvwSAQTVbl3VFMlpVUZ\nEsF8Z+MeHdkgH8dki05QCHjIKMw9vkb7fftgcyOAn/Ft7f/7c8Y4hAXWVE/ZHD2duuH4nNwztcGh\n9YsXvpqmXIBEQLqDinAVnN5/l3y7I8aD8DCCg5HJzqB+5URWkXcmzuGhKIuILn35CVo1U/Bglyim\nbrUZVJJFIx+/e9k9XJd6ADEFAy2xzQRgi09KHacsJaKJwUjGA4wDc2esruxZKG8wCd9+73rBA9/q\nQHTVnWMblej/TpAI+nvcDr4uP7yIAnuJsPGT6n762Je6bNwwREX3rAIrmgMxdYVRgEQI9wFevxqU\nf/DMgL9jXzL6jl0G+YgqSBwfuouC+NCHgD5KhD+IygLxgHloFRZq6YrM+4eFQCCiC5YQ3HjFGgaC\ncZiS7qagcVcYu0Ne3owOQUa5tdRySzme4JkMn2cIF6YO2Tx5N99x8GgH8/v3XxaWTtwZXhaST2Ii\nrCiNSVH6+wQ9myoazgdv++AQuTP4Pt4Uzud6NpVDJwLQzuFi+TbgGVe8+Uq9ojSJLJGoQBtB6Fy5\nVRBFA93u39EHe6VMNAIvb7ozEPUxclb1uHsvESLK7lI2bh6JEN0XjRkagkshCiOhfiO9I/7M7baT\n70T0Y8JF1Af5vNqFlWtJ6mrChajwFG8IjT136BSdeG4eNoSK5w9doHK+pzEXmoxsm8gEfMfeXY73\ntu+JymFRNd9N/2x2BqLf45P0B9JHieAoEqibUNU3BvnsPo2758VCs5AlvsZykUfshUBTCucbpkJP\npunM8w7atRoTvUdWUB/MCw+yETVGI0G3vwWy5hHrRGT0sd6VAauPIM2RJXV2x9gbqLPxvBuUrN0b\n+/lifI3jpP7ducZwWsnmUwTNl09vjbsyz3ahfGmnPDyU7IFZMrKMv280g4RwX83bzkMJgHDqqLuY\noQG6bB6FSVIABu0DPPwyBZ3yih/vGoF9fZVQsfqW+qKYCPJb582Y8eVdZIzXMAceB9OzqjuDoIVK\n4XHNR+DG763s3M7Ouw7dGW5S6Z7IkTLzykhatE3pPr7nftzI08e1/vl015oLnO4IBxF9JYfKV+Ze\nKfa94x50Rzkw0O3wtyilWoT8dn0V0+MOZQqWnfuIdE++U0d2znqF16ACwHmREAYGLYNVxOvkAjBg\nohDpcgovOF+vazQH7BH/YX2DWPwjt4VL9bQXbmgO7qptNGUC9uMeyfhPLsRDKN2rB1FtooxGQ9ty\nLhAcS8oaWfnPujN8aNBHiXBCvKEseIcgK4ctw5gEpBi4M6z6kCEVsvJppZTD2usFAX4wxHduTjcG\nPYOH+7HzecqF5rzus3XvKHNsB+buEuLqlYA+mfvCaFcC7x0aqf0siOBdIUaC0M3B7+z3szm4qj8q\n++7YELfoBInQLr23k7xI7fo7rEc/+R6YC1UQzEaQPxdYsf1oAytGSByrKLOoHR+MViLST4enk0lc\nabY8+d+xnbycpeyQgBHqRRj0wuxRmChQPGdtzuOm4yoUIhFuklf2vGW/7O/O++8yfMfrE58/qduX\n45N77tIra22l8H+ncu2Vulb8bXkfyXvw8zrOzsB434vPvkLsmHIuO4O/b5kdoH9eMZ5orIzXVqZ3\nZ1qVwzW16tfqMSSF90/Qq8oDIruuomxsno+gezHRVXnLtZ0p+X6jYud3i2Uf+rPpo0RIKIog3tJt\nQZlEGAvvv+B/VEFZIAzKuDOQICHmGjNolRyMpfzuPhTFRPhtqWMXUclMYEXIzKCWOtFQK0U6VI9S\nGJbU/v5LYNW60n0faDuiIRC94cC6ioROBBufO9Dh782X8Hz2XkMMzH9H1zS3tWhp5koyq6lau3h8\nFrKHcxMs0cNUnHkvi6lwW6YrceAxJEQf+TdwR7A4U+hdepaNwjq/8jImvRrLwWNdXhWrth2MIYPX\nzf3w/aod5V1IhMidAYPtteCSTPzUzCyVbTC99nmh/tFOfI/JMR5qXA8yLg0jwCKspzdB+vGgM+JF\nYB8DJAIeeiIEz5kbCFHMn/0cY/mejeNNhjDddVKPRyxECKCrxKAhl4NRplTfoTP+lt5H9kCYKa3b\nO7+WvegqGcVk//5ANJ9TsLd7LA05DgwQ3ypHjT3rfiM7CgSi8+e4q0DIklpV1gCro2zv6VUkwjJF\nJEy6SIEQKSyv0srSvwraqUEV4R/F73s3Zs6HLtJvVOz8SfRRIpxQY/wtuCK6MzNYuW7XzWVoPQvR\nCGiH6ffE4vZVUXvd3RlcfdkGlhHf4IJr4d4L88tzP9z33tX4nYHBzvJkoyVXngsFsfFue/ksWCI2\no+/J5llX5RIINDS/Tg3QGfVX5/FBApm2bYjAXF3f/VgsYzQE6+RWELAb5kCP2jAB+VyqtV1SmD4T\nLfzU71DkchMFj5J4CF4I+ZOEBYRVnxGmSzNUG5/0ltczWzmidsK5T8l0ckzszDokCt1KyLtzxI60\nHZG+96YIZRi4g9goEla0GxNBFdYB2u4oFMbw2IJORwrJe2sto10kwlkdnqKAY68epnfplcPw1cPM\nCs0WKbzvIjF23zm770yKwnmUvb1TyO9V0W+tb50/LPoi9/j7sr1jyD4Bz4no9hvHjaJyz15F08AU\nZ1DB2/8EEmVBxmd/wmhVWecpI6wAU9cCr5TPZ0e5eVetVv4EjYLbjFyjWI6/srZR2TqueaMYNUVM\nT7zU2ih6Dgnr7fd+Uj5+COmjREgIszFULnQwmSwNKBDxicZ8izYkfxWKY7pyeH4Jopi4d0SBpHfo\nVbhbCYJ7aUwEuEYdoRAIT5GBNgpOd9dncBeF4O/JaHe4Vu8ZhW7xt84OP1E9qd/e1NedQ0d/P0G9\nr1p+z9LuhYeyxcB56LmfJ4WCsfgGPCJWqcGUyuiDdKFQmcawlGZAFpTJ0/xWjH/1q3T25BOks5IR\n3qb6qipXI6XD7qHlJ5Qt1o+/fxaaYvAJ78TUcQONIO5MhYb1fcXrV7Bar1xo2eycPy+exAV58LjK\nvfbpzILegoUF9727H8l1P56KDsR7r69vQVe07wmLkIOiXexTPVjU357tWZjicau/UKd2JTn9RZ3p\nhGt2xGzY7AUGnFsh5DDmQvZb3LeZf7yTdYdxafrnlWYmtIv7exVXoyTfPWXuDIVKd7Pqfy8UAC0e\nR5JKWA7lwPM4mObyqbG0/fwvvZ8284KglVDp9A53h10q0N5uOkeJX+KHax85vN+/VR2yP3mU14c6\nMX1SPHb6KBE2iGtpuZ6Jpw1m13P0J/2bV5uS/Gb84hAOFWw+HqIqsP4VIRJczgN4aBESBp8FHzQC\nc5TiMZAWotzIUe1laOe/d2PJDmTvsgREGuIpgJlTDKhVUpRia0ubWL2p1+WHfbR36BxigNi9O/3X\nqxSl65rMTdm9yXw5i/L+DvLv6W/Yx4xvs8Sg6Q9WDhp+1SNlaCDoE8W8OFb0ft96L7AuRpCsjfua\ngAvBsA5VglbDMN1hAZ5sd97ZuW8RFeN6FBOh1jW8ImlDYlUwAbLJlLXfZX5nOe6RvDuDT8kryDDc\nQqLPUZ8cXNy1+8aBG3PtbLNAuFun00wl/VPGqr2L874xkwm34RUSeDgM01zSrKR91fXAxCRieT+6\nj+HnXcpcJBQNUUa5igYnypUB3sVo1yDh3eiuBN78DlTm7oGYaM2T7qR4/Am6E9j0zpMgX/HrE4Mg\nblU0+AGby+2T+95yo5Mf+tAJfZQIATHbQxYKrXbDcDBx6vByspAkEaJkgccQWrQGq+DLRPTssCmE\no0bZGTwMKvLfIvjNWpzaH7vZGc4s4dg3D0O8cshK3SFOpIUdQYXHe7FjpvEjvMZ7UVf/LNAlFGSH\n5dcpVDI5zgrh7fvhlFjsBCfsi/4WpHgMlAkM8xwD0UncjSfbeZ7CRRF2fkGSWwnBU0yOylQY5+y6\nHRSWZT2O377xQPlOEreVR1kflvP7c9paj37gNsnzgFUZEcgZgyh2qQoDtCGfHIotBzUlmq3utty8\nJrgWa30PnlcCK5o1ItfdYThzL2Oex3z1DjAmQkQTbyflv8Jvhlse0+i/9rXQF5dwXmmhGrs2/KG0\nM1W9O8QB9/mtEd3GmEWXYl3JbnfkCgX7XjYvjN7hWpXGfQDr8Dqk1Z5YHLR+V5Ec6Ei6XMUdsdD3\nQ+Y2vzl+1tRdSepMxm1SsBNPa0bKPQh4iunvfNTO3E+3KdJ4TdqyMpCZWmw+RL56oLwyrd8dE2Hu\nS1y/BF8d5Yh6TBsnTzAZOSySgTlAxiGfHfUQ8ox9iubG1v2BxqMF4I1j9/w3pJ3/CH2nteg/RB8l\nQqe24fDb/fORZmHSWRMSP1L9Htej99/sWIBEKIflT4JG4KdYl+e2/SalfuP9WiA8n3YtaGs0gJ/Y\ndwg+psAFnvqDSIRhqUKrC80BfA4Qrr6bh6wOhruH3+hgcGeeZLdE1ndEIuxo9cWCaNAxpm0744sU\nHHO2TK4rZ0LSjhVu6odYh1FA22zvOyg79MnQIEKlUNzHKNVdSqsKRhErsF4ZF83OoIJcNn8kpZY/\nAE0HZFLXByx3l0Q4e3cgN0l153lXC+ZbxrV60DQHd0gUF+q/i4q1jZd0DOY68dzIajanQ7umsJvO\nS+RS9pa57JNoqOqjwIp3aBX4bOvm7KeN9xfOfTFfQt0aNFjf5Dx+jaJ1vjpc3nHD8xXhc8QuEba/\nuzyjUjEBDtu130srMFsU7DSr43QMztwZRMZZoHl8FVdcNa+gc+66q74DRWHSFdJ9d4ZoHP0aQ5As\nop8yWo1fxFOn8wEItCq3M2GAXiLd++88eequ9KEPdfooERJS7WITQp/Pgx6POjSSkYUcLcOYYSEi\nRQH0v0mswWW6d6AbRt9Kt4jN9c1W6UDz6neKAIngA5nJc2cpLye4fKShpZmizUVTIwbj7B80GOA4\n2BEIzcE72WWT3oIZ1RMxaw7U6l4DrnU4RArEwqhTGTu+IvBFqBV5Rob38ux1P7lQYQZLq9RThlCz\na3RDJMKAorsYI69QhETw9UbrLlIcjPvOInARDQt5VM/2YQWj2zPTWQo0nOaKQmDz+27b3+L64MYN\nLfQrknF/lBldg64L43olql2B+XyKYmAOiObbELSRTZ27ep7A/FohYCIgEcLbV3XfJBEIcZz8HERf\nbrFiGZ5crFV9Rk2cpOwVJIJj9JJJYw6yCjBvsusyRI0Ew/4KZS4Sd+rhwQOFDwuf3GCIyaBuuRLU\nheC/gUQQ8oqQKCCu31YP0v3qVb6hbki6r3kE5RUeJoc3Od9UyxJPrcAeiWZceoL7IwRPaztWZL5K\nW0qUacG4vWrsxed7i79nh/4kJMKK5mCHszy81Zc67x8G8QIy8dnYAEue+HQWWHFdIYPc3lFy34yw\nvOLS8rfSLmr7b6ePEiEgsdgQoXDGp5v1q6QMoP9tDooolCnjUQHNHixMvXC03YEXR/3CSGDvZFCq\nMOA0LsKgH1i0ZwJnhETIhnKCxpI95CN8Fut/hTK5FYUdokgXs954vFD+k5QeSm8Mlhxy78g1ERJB\numGsyNQCGZ4KQIsUjx4Fw3DdU2Qt9ZY9QXrIkInwLYIaClY+wLeJGSGQ7hfWonUjsMq9SjS5M4g1\nHhVRw/94mscxf+Lxmb8U496Fn8kz2Dbdc7mqdsinHj0OHi5Gns4s2aLsVN5TOsR6Dkgp+8z4e0jH\nVR8gMUcXACi8Qj6uy6uOE3GAwbIdy6fA32+Rl08mwkFEFFgfd+gOKgYVB7+TGsKkpO8buxgdemuZ\n0QhX6cqYq5tfa3M+pDZ5a2fd3w3STEQvIRHafJkzCm3HaCAbWPG0/DcjEVpgxWgPtYEV5Zonb6hi\nLoOB4j438c1NhfmKdvVF4b5WefDlFRIhQmheQSas0FjHPZzSh/4i+igROjXBuYQb2jjYR35RJ5ZV\nC4+H78Uyr6yGKCPDmTV83JscOCRN2OrQqNbqYv8WJMaGIiFyZ/Akz3GqQDA3rU0XUeBHhXxG0Fuw\nwAwh8nzTk6BeV7YRDAq1SxoTIT5QjLrgoDgOOCW2eo2Ix8Ofs1nYn1WtLtLfK+4b0+Gyb8R1YREh\nOt/UVnM1ysuO9UWWt+i7bXD9tNH8mIKues3NFk41JkQiiMC0OuvunvG/Mx3qaIMsPFPcmoZlEizT\nz3pQrWWgDkb2ACaDhnrWMlA0RDTUpHggFt78BalRhXWEijYu5geu3P9hmWuKAT+vZRyQF6KHwDuj\nDjT4riURhD06wAvIIdV5Vu0ok6RuVM6nfRb+dV7tRAK9xRZUfe7Lth/PIObRfBlKo2Qf1qCgoHyj\n+NDuFYiNZyQdwcZJx34VE0F+wXS6octYcE0UZahU926LTV5iM39sijyvKCyjb3fID8/BPetO0THe\nXZ5RzJTRRsRTp32kIanQuBOVz87P0XXvJnBrnBImYpSUrn5ZB5jt4gllfVxPvFfRK2VkWiBqz5et\nr6acE9kjkFOpLK9jhi0pZxThoFjY5aljPtRCXKuLw9N/I53nMs2fgkjZlARx7Ctbdx5U7hOUCwl+\nsMqOpmT3ygPMTIZ9wGDAQ9YDmVLK6/vGsd9yhPz7KOPT/yB9lAhAJWCUw+rFxVh/Mlh2hDKTS9Fe\nEm7ePoAPMLA7dAe+pXED2Ah+mY9dFBPhVbpywPExHNr9zn99MMu+ufRUdpGWVRmmlv2JBAN+3p2V\nQ9rJm523h1ZddGfY45UDYnqyY+PcxgjplfQAMfka0vuzO4SC9xXfgGXdcvAtIOnjLj/wvZfqFVcG\nXI8+qNfV3u9CRsPglZtjNfgfCkZhuf4sFee+HA4UPfJ8Hqp4WCILrjGhMceC53oHCg15DbqaymHP\nRGAPvkfuDBjksbrfEIngnwEPApMLSuyr1zXflhtITIT5WS3fvbqscH4owmdNaO28epC8Sqv7IqVm\nWC77YZWdoc4LFlM8jrpfnK6RMWX3MFYkwEe/acW7RW7gC/pVf4glQkX3aw+ORhuM94PIVKImU53t\n0b+FgkGcU13vGUpQQXRGkes8XkMj0QqVMKVzRGVB4dMt08ewMnVTScvsrpeo2Gpd4MH7iguAj4kQ\nrg1BIhQbe8ojEUad261DXZTHUWnz44NE+Nfpo0QAWvvuN223FyYxBuqdoIG7hOcb9dvbYwvozqB/\n7xPm6d4996SH1gvtTm1JlMUTaaOUWdmxK6CcFcOUlQYC5+rAEa/cN27wMyWiKWjVkNMvCtw4b8vi\nHCzvPTojrGg3dWE4xl25I+nadqC396yQebA7RGosKZskEsCxKFRQBXemg/V3IgJ3huSEboLUWUsE\n0Vq4QzDDsJK6Mivkhadt4SkMqij9l+fu4xOiNKK253c24hiMNCb9Oro4sFqC0Z1BXTXsYduiE67P\nr3IAkgnYD/rEXqVL7mQbBxWv/4p0AFLOKhYCy2sazZba3A2iGxZY316AvauXyyC5+rsiCu6St5Re\n2pO3G2mTxhwKRJHd/xZ0CpaRw3g5NOXp0MgcwcGBcD1ZZe0Z2mKeL0z1oiZiekfhYS3n0wO5uGgD\nkRG1b7It65Hyhl1d8Nm7fpehUfi7N1YMN7IFMxhKiwIKejNJQBjBdQlBplGnc4WY910WmjsXj+9R\nXUipjDJkGTafvt461o+Va1eoPMnMELk+CGqCKDeU3SE1AO7Xma3XUvw7hs2oqAwiAWgx0OOVFPM1\n+W7KvG+I/pPEtIfC+xfoo0ToVKjQo2vfTVruLoAWLlQiKDkc5SNmLVamVeRkpCuabetDG/1eKAok\nU8Yx17XtYLt6vRCLNazOCIxHIXpya2c8I/A24cm1zK61K2HaH67G6XdTSmjvAzXwlrkWmqF6pu/B\nd+xTpfZsiIJenUPRJzeM6u9kc0QlSHt+7I9C9IAO4b7iaWjiwV9fZuWTD3oEgRWzZ5F/0nZa3rkz\nGCQCzhGiAb/eFuQFJgyN6/vKhVWbwrLgD7stDyrj0yrqBmEUS65E9bBIhIoWafscZxbc8BnkfrLy\nhhwUx1SBddnqgmeSeQ7VnqXRjNon0jUWRZuO4hpUJqr1oCqQcFaL1vOpc8hbz/2a8d993w6Yc/LM\noc9pzYWFw30iFfgcc7JDTI9CZqq0MsKbzsfZ93NXlmFS5MKTtdcjToh0yvBZQdKUpkDAOAk3JUnZ\nt9rBt/Q9sozfCpV0HPy4HgXh9rqveUi4WNbNmmC7Dh5dofdY8FAiu1bC+BuROwN0SIV7WO/EDZbv\nqbvURNcjwv23ENOjFHr2g2Tr73zP2NfM2bRM8ozwabQqN6W6WKynBT61JeM15gApf8D+NMV7jKH0\nSrDVu0I+5ElcqnYpMtqsAsl6nmRkoUW5TB7Rjiy0hVhR0p/B+3Dd9TlSCdeYom9nFwbkvdwNIj1I\npVu7qOQZ1/AtotK33yt1mccSZTWUb2NaeqdxXvaUoGxlzinb1nh+fSct1a98n5/X9Iln/qvIMp2r\nwyXVj00vg24EotAwsic2OBpmGvDYFbqjy71esfC/o+2p0qcn2zlK1OUGh0h5B8r4Q38PfZQIjvzh\nKiOMPD0YBc38OxVkXfWYnhAZzY4fqa/XIB+HcBaXwYvoEoCab0QiSOCW6Bl8P4SRDiYbjEVm7EDF\n+igTIRGGNVQF8DjNDaOCXm8tRIVtEDsRC/AQ18o1hosWBbEm4KY4xXdgDR5lYbrxs18hFGFKWStG\n/KGd6No5wChPyjynRp/EeizvJkAiGKVaURSGebWrzkDh0q18fjzfvddl0O31TTDopVtxAyRCRnc2\nbFWSyVgXkmBYqMSSs2Lx75Lm91ucFFKO0lN46oFPDsJ4GEaFU3TQ9yRuYqJ0akM1B1aUrCHmuUnq\nL1Bf3J48Sjj2TtreSVMa0fC7h+oK99gW7pC7SxoXov0dPZux2Ml3GHu/T1VfFknm6hFMVLGwB+PT\nlqfOAVFgobJN+BQGEWXKx8Oz/YgkiNvE/4gM3/N93aEdBRURWdSOWQDY5ryHZnMR62M5NUN9pdj1\nRWSVLMJfibrSJGgnUg7ggUOe2R8y5G9zSAQNs5UpZpTK6B8pv/LjMp59vtT6RbNxQoYHodiyNyK9\n4ibn40VkNBRWeK1/MvQR5Y1ltfjCT9akyEWFVJGEipVJqeb6Ju8VlQmTcoRsoEUfr8DPGer1CjrB\nvGuIkzA9GpfwuigSfKyEg9omhi6pRzCyIXsfsm//c2OeqAyobaCMM+9XYuSjcF+OG4l4i/JmQc2h\nK+/gsdBHGYej71HYtnVD0fUt9zJ9kAhtEH53J/4M+igROjExPZnpqzaruiySZ23BvujoVkQpzy2w\n15MLfXXG94RrctBkbhq+dk01fs/qmMVXs9a2gGGtna8e5O5r3E/QTvsUnsLctMHSfyIaUFUV2eBZ\nMeBRlyoxraMYTolmJIL4JGt6QCJJOTkep9rnlDpxk5Kmw/fB3IL84RhFSISvOqy545BRxdpm04y1\nd6X1Pfu7MSkNmbpAy4ZZtrJt3J5Vu9Oe0YHVAuge17ZZ1EM25TZYE7KhP9IXPM+A48FBqvY58KsW\n+uqdlHLyXK0dLfersvn9QZri8as2JI6MmyIUiH4FGwa+X/ucYLkFSy4iEeRdjPncn/foD/F/fVr+\nYhp90XcLYwxIBIS4t6GV+Qi+9LXQL3he6s9fn3vZGiSdnblGqujDFK9TTIQTJAKiTDSCPhgeSL7r\nuAlfqAzvsb/bL7gmc5eJzJijcs+kgOu/T7F62GhZAAAgAElEQVRCAPcswdMkvSb1/kufTbrb/g/n\nyhfLwbLQV+d3Tz7aGv06htBWaxOIWjDFVu5XPeirtrIyN3/VdnB8whpp9fVy/X3/Guum/RvrhDuf\nq0zla5ifiJ5E9Ql8hNvcEf4v4yu8AIdK6pfqnsyjbaQHt7H7gmCRtTaRrtZjtC39aHxMxqz0ta37\n0BfrPBDm8ouJHpXo19HKPkEYfXKhr5ooqz0SwfzGsG/Ye5l1Pn/V0uee7o80/p73RlEaaV09QCbr\n/vKEOaX7Io89b6aGcPiCvWSMEfT5OLTudpeun0LYb6avWujrEN6r/MkgEWAB8FPbGcqysb7XPMig\nEUadONZWsJe9S8YE94bBK6E62cfG/aUfDqvdn2QshFpsAAcjx2c2MoXGl5K59jXa1vmnhz7W4Kld\nc/1k9YuX/fJxzO/rV+WxLuW6/DPj6vrTnrHNUTk0yRquMKdV7tJ3qPObkVWO58QxVxmETd9l+ISi\nw+cdsjFTtD1j2CaVV6UcuzU1+DuMJD5r5H4g9SBZJeZ8j79u65sDK45gw+Pg3NwWsJ/PjrREevT3\n9WCnlK40ZJfWX5kndt3IXm322wr8abxbhnsaFZhfY8xhngyQAbQ5aKF5zmKqmWvBbwYV2K9JX1DG\n/Zo686F/mT5KhITMhtw3P79PbtVzsV3clNqmJwKrMimsc4V0xJ+2clq/QLteBld9TvcOd5ECJGek\nUY71aMxkk8D738U/vTLlnYT5rOtmKiZ/gFjNq1Z+QcHNiurHzX++7VHw/fB6TgESYd0d+zseZMxv\nP61ed5a/cRngh75Lni+JUMLjmhUMz2hnfdVa6Ko7w6odFVDmCNwSWfr5bCOCyCfJ2PCsx1CcLPnf\nEOSs+wqu/+Xz46HKzZcKz1FB6YP1Dv49V5e2i+0UEVxRAdeVKYpEUFcEu28IH1Dhtfa/cf4T6XoY\n90eQuhPa8SPe5Z2rQMA7Y5jdt2M1bu3HbepBoIwDKq67KHvTVbryTDuUuS4SzXue3/skhs/vODMw\n2Xdm1lCx39FVfEcGMbyAF3MN9iF/b+Wu6HKBp7+DtpFKYz8ksKS3vkdIhD+ZoswM39vePg2DBun+\nG9XReLDyX7nmy9i6T/qSTW6YJJHLxFR81cYFOvi/MJu+h97Nq/+r9FEinJAIiIWtn2Y72B8UWd1k\nAzT1bCsdMB1ZF+4oYFC9Ps8gfDOV5zI1YlSLnSrNyHDC6Afa7uZi+y725GFn+OgrHv1kMoEVj/6u\nszSPUVWikS9dOHuQUwqNe/WwM+ZXQavF4n1RHg8AyVi6WQ8kZwoEdFUJA24GuHVF9es1/363haXF\nKVn9CNl84q3mfZMTJIfllSlKaRe5TChMG69fP8FH72z0VfiMc3VqXY0HbljLKA7q1WCKvWzQ3QF1\nPTkgnrkzSHtl0VZ7jvY+nvUY9z+Ihq+4IBEErRVZXuWgrXVaJeDc9/lapDA5gz2bwyTFguDgIWX+\nPXr3DAoEIdwbpF1nmE4PUqjY8JkatoXoYF3suHsgysbcS8JP+gFoY08Z9xbrijPywidz2fgZ979R\naE9jSZY8T/orFMVEyPogilITYNH10bisQRHsf+SmUChwaSrWvWHqz4C5t/3v8DfjgdbcZ4OvRjER\nfP/lbyH/fk8PXm+iNofn/eQOqctZGUwDa7/jxpZRGpSU5jli5gFMtRkdodkGhJ/5QxXC4vWauhtF\nY6d7RJDK8Q3ZGeT3OuSwuRw3Jtm+A4rmj6Jk4vlgq0RqlGjuRPeeA9+XfF9l2fjQv0MfJUKndlBn\nEoihQpOa72+D4gLMlrScQCTVvUE3A0EQMFkBszHdovDsHuSkCXpFy4ligmm0w/Cb6T+L5a2Xg/aQ\ndchtmtcZT5NQroIgK37uUPfqICvC7RQcx5dLGJE8R6XSob1ELBhoFGRFAp5gk7PVcSh4gn6jprgd\nNO3hQ+8N3mPSf6EG/3RCn0DZaE6lNR1IBEJJXmE1P4e8F/lNtvoJkijfR5uqQBDlFZFA8/xBhsP3\nO/7uDy/oEHQDUOv5bOFq1i8tO1wZ8NAT4PDw0OeVLKiMkfq+WK1Ho1+ZFJooKqwiwj6PHNSmeit3\nt6hszi/mZVcmFtI58BzvSodl8Bqyc/cOjftPLKy7qUgNvJXaOjy48ViiNmbPWugXKBGYZ3cGcVFA\nlxhRDj1hzX+JewTMZ4FPy1xl378vmKy1u2tUbVvcuIzlHurzB3mvqPNjouNk53l9FqKH1K1KlCeL\ny5s8j7oHed78VcGnlxVy+1VpcmdAq+wYk+aH1P94hEq1FekaXJVZ/Ma6hioVQpReXH6GhiPhnnnQ\nrDwRNi3rh0j2IauYEPixQQfKXu5euOytxs0LaDICyCbTn7tIHdPD7C/q6NCN15lUwWTuYabHo2wl\ncUNjCo6Bf+Yz67+vU+QtnM+oo5bhwj30CdexrlS5BopzlLf0XnB36NdL31ceoLTCNs7OV7pWYR9b\n37JHifYBY0RFClHkZZPMFswNnwmh8b75kO9dGeR77eXlXm+MkLqqMdyJjM5GnEK3BatYZjMHRMGI\nqFzk3YPtcVF0K8xV4c+Gx1OfH7jsSeeU50fYv11klJmzqPVJCOPRjNvgn5RJg0E7ilxamO4rJP4W\n+mRnaPRRInQaUK9gXchmQQQH7xOO/6qGevRrnJ32F6zeY/+W73f6phvPnv/4fr2FMkXCZL1+AZqw\nOqDdfVdRXdH80fzE5/Xc4cu4qQ0tsSuTPeLsu34lGZDd/K9YT5ZpDOUzEfqIyDSG7gweiTDVzeIv\nqdeWVgbTTl5si04EgChg6XzIQCGmgOCjZXbmc7aG77jZrKz0UeA4RCLMSr7uOgZuA+iTjWWnfvRP\nPEy3z/jwuYWSSgSFa6tkTUbBi0Jrj+2CASVbl/DAX0CPqmOWkYzxF/Arf5hO6TiIqssa7iYoBgZW\nNNDPUIlMn5fu758099kfonZ5E/694h8eCYD3ToEVozYoXrc+sOKT8j3GL4ddfl6KDSz8HVS5ByQF\nFx357mUaLCPkFb+jbML7Iz74vU+4JjO+wd5nFE0msOIczFLqQ6v9CKyI8/DkgT3g8KBCT3eTZO9Y\nWa3T+ZhcnzIsFDFM2DYGMsnXG6w0rPKVYJu2X7PyAOfg2+ZTslAVcXSyP590JOKr4iY1m8A+9K/R\nR4mQkLHQMtGTWkA0ZZxl/EMt9a4fv2+Iibq1DQSGYbXVMqOdRRuoVZYmMLfyqA8tjOwqnQJlWUE2\nojN0wlWa/OjRlDQsHe2BUGtcK7wb0ASPQ/LJgYtpasZo3q31aj74yd/23lYg0j4bbTm0W6fDQ9zX\nFelzx8+KFmy0hiJSwd+Hm/ju4dWnqfTzRG79YqL/wThX6oHZcD66h8n89f2hUizYTBh0sAeuq62e\n4s10pp38+XwXKpO14EufcWJ0a8d41gB6j3OucnsoDSSn/AYt7cO6Iv0GNIrp4+I7ulAN6yiOu5hy\n6UQJsyBdm2rNrdzdFeTgBfVjYEVBfNnDgx9HRYZhMD8ROD2fZi72GZkBSaPKY0HHDH4Mn8a6m7gG\nTePQ/wnKQJ61FO7xD7Tvz3o4JAIGKOz94A6oIH3ug3tQR26BFf+vB0kszCPYpAaxCzTNVUaXguv+\nUh8rQCLg9EHEzg6JddSgwFivR1bdqJuYFWM3fIEcSM08637wT+ahjBGkxGgMn89Z5T1CDp8x6vgu\nEuGKYQwRdNkclTG+qmS3wW/PZYfIXaSzutH2o+hehihBCaDnXRKjsfQsvXKhh4sZFM3y1eMjsmJC\n1i3uW9Gp8mZj7/OEAZzRpWXVFLqq+ENw5s4QJKZa1C/jNheSFI3ZdR9YsT0LKkVUkbRzzLWItGI3\nQCLzjpXHa6y0Su9UK8s+g+3D/iQX8Ed3r6cCroZE8Rzz2UzMb24OvMPw9p+mZDv8F+mjRHAUWXEr\nd027Z5AdwoxMpQlJxQg7Xngy5FLJyD343cOlPIOxfbLRi4nawdTv3xMTmPLryTYuP7P7TDpwkSLB\nz1qJ3Y+OCxZx0A/rXnfSbBzQtjDJbDNHy2gphQrPYSszJusFtwNgYdx34VcY9BW+5gXElQLo6aZ/\nJqjtEPrEhv2C/oXv0Fs/ZS5szEl5XolkX1dtVNvWFSTCUSiPJQBzWNJGNn9tFcokzoIIzJJ+yRN3\nfoMHM4FTXiGfISQsYJyuy7CSTmnbXB1+HUVuLPq9HbBEYVAKj8www+1BeCLpXMED9HRAc+Mj7gxn\nyAYk/4y+fAQBRiWitO1REhk960GPoxrkCQaUfMJB/YtVAUMEcG5Ys+2+1u4XY4RtcM9YdymmsfYY\nLjHAZc/pfOz3uoIWs6FAJOv77VOpnbW1QjR5HtjGAPmFDUhq0jh7q2m2jW0gEfCZbN9jyuYeu+9e\nCYsGSYaLAneexnOksLWyg8ROcY8R9kffo/7lz1F4YMTrbK7lDWV9iPgDm5kl99u2ORgLrwQzv/l2\nA9kjRKIWNze4/c1ERq7MLNFn2xkiAlFuESUs9nfXncH+1vli8LPU5dfjmTtDGXOkXfPvwa+7OuEX\nzskbDT0fMDL/mDf62+hLf4ZJDjvZW8bNLXe22ZeL8F6X4pGIyKQDp/Xa8+TnwIc+JPRRInRCyDla\nv+ziVu3oFfcCoZBpAxLhXeRhw5GAMjGQi0iEWNt53jc/BhUP0NC3Cn0PaVh3GndjJpND3UdTPyMp\n5S0a/nuqDHKUBg5yZdCvbASrKnLw0Tm469aye87FXNCtDUw35p43mZzhu0ErZjJYERIhI7SYEVEY\nOf5q5oCazQ98WC8FbNZrqoosur4gXVPGiFKRSGOGcNDtoJn0WkReEXCXIjexBmKI37/ERRBLUynt\nBnRxEEQJztmIMHaB5+eiqDE52mshIh1An5qOiNI2d1wjCpSbhGOWg35vh4io6nNKGUxbic8oigMk\nPHiJRV2RC8pr0mke/XAxLsIOxS6ENJA8pfA0XzLR3/PdFZdBmD/2I0KV+fSdIlRr8NtZGen50l0f\n2jMkgnkG6DORumaoa2DeDk5hLGah3tTnr923Vu4Mu0ilAoeeyJhDFEzHsZ7hME00FPxCK0VBa8sW\nCA/yrCM0UgeXPRnDB7LlZI4yqQyKdBDblzsmb59/0enU3T8pqYubJ/26QW8WG6x0hUQ4uJgYBkTy\nzLE7w0AilHkuHzTXNe4peq903is2xMUB3RmaYl74IY/6pKaJxxiZ0u4jrbwVcaJXMLl8BFMFUSIS\n3NIqJ+d7WmfqJKdnFO3lBf5hW5E8EiER/nVHhk9MhEYfJQLQkYgclcoQ/B7AqISp+KA7Yd1OiI7/\nyOnb56vsKGOj4mFlZFJrY+hfV3LhRDZPosbvPIMSi5AP0tIs9Iv+IhLhJl1BU4h/GwqeYrVFlFnQ\nRapcJp/MbD4ozJCTsni9zbxxf5kBJdmzYFvyLFcoqmP6EVNNeUslcTr1VZkxn8yKt4gT9Xna5wwI\nNqEP7IBQ2r81wKl7SeBf+i10xGPY4IcQOJAAoQHlvNXLe2NMzcmcpC7gzd0J+jL/UY4WcFbmnPxU\niAd0Uq6JUIxttz6WyUomSAI5nLXAi+23Jxymo36jZQcpypEePtuorLgy55oXFLxt23H10UG3gsKA\nu0COMRGefAyFkXd9iRBxYT9JD1lyf4pCiBbREcyQzfWx6td4d3TOv4jkMCCIh9GAqYvICfZ4/zgM\nz/3Ax0blwZhXfS84RVAkSAT7HOdzy1ibTSfhWQLCZ2G45vst+7c/KKT9KeoJLft7BoMuh10/V/zN\nfRcqW3dAhus7tEI/7LwH5FVS/o4xybTrTm2+NhTJgpv161GIe+qXcuicGUi3glZonua5zBGJiyB9\nESWAyB3GoAEH6qPwUCQg1cKjDqTmWiQMq5jyo06vnCnUXBnYZjE5WJgA9KcrGyqTCQyqyibP4/U7\nD+Ul/q4ZDiZYL5EHLfa21lldZulmTWbtZEiEg6ZsapKt6yg6hwfCEeYA8kSU2f0cwNqPrbCrH/qb\n6aNE6NSgT11og+vqC9WCK3rf2LkeK9h76FOrCxvon4cgHYr5SY5FCJnKDnzjkOHKbbkznFV+Qt6n\nUyGFMBaB8N+uzweJqM6rhBYGE7sC+hPdQzRvCAaihtdHO3ldGBNh0kwTmw32GPfAPOjXHsUKLTJX\n8UAAruqD2ly1yi+5jocWzJ/tn8dbZrCO9MFNH+yAptkZQKbCA9O45n3z8f5gPRb33N5KPx1IJzgB\nG40z5t3ObwsEy8zc2+sWGKKvG/UxIqhLNXL4e+JzkR5woojVQmfrKlJM4B84JuK2cRCnh4+git43\nODgTDSTC/2SdlAJxQgrcI7B9WdNMheWgWkzdOPTDvz6bvxMiK7ek+veMt0UH5rE39D6MoKTw+8gk\nMmIiYGBFyTjhkQiWV8q7r8D3K7f5U5lMjAjsF2ZDGDedIREGjJb6J4dwfTMOkRAOffdxY0zw19Fn\nHmMS0WKpmfvYvur4kYP3KHuLRpt/7TD5KonyTmhC/Dm+53+bpj3sOd6dgVn3rEgRiXQbfRH08Sia\nVQf76BUjAynCNoL8agx2kHEeiTDVcUNWybIzSO2RwvQKlcJUDh6KBKJzJZ3Zf3sfVZaB8Z0MajTF\nMcA6POmexab8+D3gIdIGKhiGi4N5cCLv0hDJRrXw4H+mnWqvIRIhU1pFc3AFItq1f+n7cAIYV6Lk\nEN+QJrbxCOGC/fHvHb/jHMie61+jV84mfxN9lAgJoRLgWZvF7AmpDodwSlaAj4KjNeHYzji0JsnN\nclgyFrfBsDXFo/wWzeFs04kY11gEER6LiHyKm4zexVAu1dMLt1Q8+zde7Spunlu+aid1hdZxh11T\nIUh+74gQPEynqJmcVv6Y2TyWgG2oCGn3xTmbp5SGbg5FKb4m39Oi198xtTDQWyWnMCEXEG0MQjyS\nA964a4FaSRHw29k6M7wFrj3dXLmywZ8Jp6fCa2dCZykgiXJYuVeKVeYeDLEx2oN5HITx4Lwz/pL6\nEHklxo/w8467XFZkAL9qD6xYzH7g3XGE54qAPeojN6UW/EPerwZWbNY2QR8QdQVC1WCRUiemFh79\nYdufQtSRLdzLa+NfbMcl7GyFaBnBaXP2hb5/qN495F05XEXKCU8R8IK7vRAF62aM7OmgF526si+t\ngppdobuR5TM0Ax5TVvudHLDMhYBelSesXMX9WrFyzeaukfXlDM01lZe+kSr1o7X+J6Gfy/jH/W9V\nkJigi3/BQSlCckzyeHIv2taG/EC4D1nFcKs7ai+mSIF31idbqJ5OVEShjGu0r8SI67x/74f+Lvoo\nETqdpeGrHUJ1iCUOEQZw2COKFz8yaWnnKHwKBa00b0ry9Wwdb2s80UEO+jOgiAdamuDfYocxcOZe\n7EmBhYQapI95jrCcVmzgWxJcaG+3K0EffPXY9ngOVrjXCEJXFOURWau8O4O0P9U9pSxa9B+s1dnm\nYMqTvCsIgCcHi0U7HvY21UlzQL2pN30+eTird2d4hwBdDjaWcFO/SznXDt+t4NdIo3ev3VX6pMVN\n+hm4exDRCKwoMwZjWFztawSrxHVJRMZXdwR6hLKjInRn+N9B9CB6PKp5Bu07dRcTfSZsS37XfvaD\nGRym7blEhbcrgQARneD9xyu8ilJI3WX6xQge2vqiHAcP63PWkfxlrdZfpYbAQLTQUCDUYgRZVGIQ\nrTmhCK0y/0uv49Kcci4/5ZifBX2wr8ibmRsS0fxcnm9IOjlpP6ufSOe/nYvqdiNz9TFgzNqB5X5q\n9s/uzuDGqMCcs7faPoeuW0TEY15KOfB3l3KyhjvvX1mhVzB/7wLiD5ZjT5w2ogLrR8qqqxOm//R7\nluzB7K5dJeN2QfNzZgqX7P2k7UgfO78WF00jz5BFbeg+XgZTyprb9qazfmXtaw+siHvjowi8vZgx\nERkH/fNlTb3bncHIe5vuDJU6oiJ1Z4Br4M6AQRcxs4eUW8mEYy5QLO9KIOCIIt6fybWR+9TWe+/u\nDMPl6eBpras7i3MzJMterrgz/PNIBCbajUfxt9NHieDIw5DE0tNg1kxffWnLdbF2ETnLYK/vq0pZ\ngISxCsv8pW1LXnAUlrkLzE3w07ICY8V+t/5gO/03ZArSFsEmUKn5032x9qeWgUTgWkaEdOYuzIIg\nW/EfjJuU9/B/JBtBGa6bOs1JwryggUSoYKWXsQXoGbodoCVSYMcM1Zv+91+GRX56DxyiQrzFpGU3\nwE1XMx6g8FgBiYCuNG2jrQaJIHPAjHWx8/dJkr4tSmkH5Vjnsf+u/ZcKxKIZvc+iaIQvJn72a+A2\nIan3ZBw9vPqrd/TBCtnHuerN7W0O5AzdHz6frNHpufdJB67KTVM7Wd3t/evoMAnCAZAZtdKw5hpc\noI6bqZe0iPwtm7eM1a868xu1TOMcni0eMvahFxPr2sMyaG5RtxIy7zajSCAfQen65xcX+l9plnIR\nIDHn+6+esaEdpPVTunbQPHe/WOYX9TZ0LXv+OZAIUCl/EdUnBlaMEQZMeqCX8RXegGNdYTyN+wG7\nvtdCXNoz1/G+mxuDpGkkIvrFkrbRIthGGlBUOdd2qJAxERrZKoB/KpxBxkLWn7N8VT6FrJvpg0iQ\nYIlV1vOAjM/zKTyxmDGXOWqshcO9wLYvfHakUD7MdB6PON7tGJvG6345eDMddg49he/BJsjwgFyJ\n6tM+1wT2IIvSGqgYx+ukvhH4s85pBs9QA9Fn1CeMOaFKLYIDRbSw/XMrf2Dgl+1+fYdqmIlT0hKR\nSckIo2JA3ereo24XWB/Oc9mP9LfOt115L9+UXvaAo2KlYuaOmYNu7dN47ljNeOt4Evno3KDVlud5\nX7vGur+4p/HuDGIwwnZWbgvZ9SvuDL6f8l3uHXMQ7+/ra0IisMoOco/IhMJTcf9F3if9hyb0u+Fh\n7XNrDnR+PFBPEIzW0zK+RkKZO8OHPiT0USIEFAq83DWZQyOrhyEVOKywTyQHinzV7QZtQ4Yjm63v\n58uZasFqICkeBYlgihXVdvbbbhNuKhgvYKUdxsKrwIoY4C2Cb4l2WSL5SrWV9VMRKjwO6ViNzAnP\nWD2iIUK6nG0SO5Bt1CQL4NgGTGo5lc+sKravPH2vbOHAsiGt3xEaR+wAgVG7W0F4+bxjvuED9k43\nNAqnliVPJj6GP/j6wHFu4KQdDeCIljUtOyxtZ7CXg8K+R1kNiNxhf5NKrwcA6UbBSEQUGNvj/iIS\noXdU+v44xNoxB1b0JM1NMPjOUw8WZa3WhcqGER19gzBNIt6CMc3S9dHn8P7csp+7w6r3q+JwKJpB\n2aLKQ52/zHYcTbyAgtdF+cianYFiJei2GfawgUeH1csEodunCImgfEIfCPkPzuEIiSD8vJRiLItd\nr6Jt04xEkNE/oOxA1LjDlHkIwjUif/vnsdbxg+2+GiER5GEiJEKIHsq7tyTcvyKkgKDnwsCKiJwI\nkWizrILIRtyDx+/90ys8UGYInwPuz57bppx0i+YiZUiPgUQQXtbFqudLrTkaUWxhAst+ZfYsla+u\ntj3WQjDeiNZBQoRQKzdnaxiZGii+Hv0WUYSc1L6X6fsxPpWka8jXVFbEcq+/Ob+HCT87ZrE73oMc\nEqE8lPcqMnBec5ha3FTn5dZgfQ/ZuAju5t8jJjpVnv8r9FEinBBCYds6Zb2e3GMPZMKwcCPVxYsT\nUbTxyCrO5ukO9B2/K9w76jhaTq8zyIidRBsLlkN3hvF7sknZgenvYXzOFtEIibBLXmM9usDx9xVd\nUSCE/nssBx6b7m1FajFZv0fv601kLapoCb6sojJz2/Yj639lMsHgmoUDLHQBEiGio1glUqtPUSd+\nXhjEwGi8KpRIngMC3a1oyvogfTaTn/uas/UhHLEEkwytjf55VhHwkVxQcA3yt7rXIxGwM5S/EuGB\n6O4QtYUZCcTCiyPzDNZ13M2ubKiq5DUWoOC5pV77rtvzMev8RaTYGaXjUeL+ICJnxEZwyDR5pq/x\nviWmgVVgeP9ciWkiKIUnzBf8bjqFqIPCbTDkOhFF/rhZ3JUoZbJQprTXdcbhuL9LhjtTxo8xlPEt\nNOIirIjxheD1TUYaIRGwPkQiCKnSvB0SREmOOtho3JpH4zpV4zb5NbSg1UrCx0a5JRADzO9jyq40\nKaaOEn5flW2KgULEei31bV8Mqc/OIHTHfSPtAFldlByGS7HjdrTHUaUt64HWuxuijCbuMm17c2u0\nxLGTpI7WUPywUbY0cWeYyhWa0AhiHJpTP8Z7qkFXDCRCGb9He6sgTwIgxECb4rP6svL6I3eGLf7m\nkAiqFLcd0nfOvd0yrqOhLMsmgbxhlXHiQ/8efZQIQN5yM64DzE5EYBFyBFbbrqnrAcL1dgP9+D1P\no+XLYWHvAGmeh9ozISxLNgto6EfpjDl6a8MKqj64ONlykukiiqBuoGM0uzP0pJbmvSFM8SpakIFR\n67VG/snsoV7a7gx8Q7Dwm+GZZQWhtZHbiYwNro1sPo/DOCh42L0/H5RO20A3Du0bwup36AyNw709\n464hc2QhhI36LyAeiCg4ANw/+gzIKBw65XmQNwjPwTnm7/VuRrcRsIA2eRwapwMRQOtnsoIZc2mx\nUwY6CdJcwrodUPapPgrnV3W8cyWkY0A8VCCYMk6JvKHXSmkcSMjOy/Z56PuuzZUBkQji0mIVA/pu\nZarKbnRwMWWfvQ48FDQBegMHJenFiDRrSXBTIRFer+1fQkMpTJZPe1L+bPm2fB6k2ZVwS/FzQoIp\nmrp5HtMHjGPt7gyME+HGSVDqOaM/0Qo2KXo3mEpUIkLG4JAiDxu/k/5uIPSbioQdYi768hfkH72U\nmd9qnfleSnRxCo24VmCZLkzHhJ7rcRGI1RJfiAp3pZP0AZaBHDoRdXmGAkE6MxxpOVmfs/LhoDLQ\nCFb5K/0pcVnHyTJFwi5NbrxBVeyu7xp9xt80511oSm4p0BW4D3LvGxAI7pPIvoccmQPrie08IFqj\nev4d2jMm/Qv0USIALQNdsWpciWYhsrYC6ZsAACAASURBVF2z5a+SHujw0KXCcmLYgPb3mONdYfcq\n+YOEaLP9hhLdhxtVFNhsagTrjIR+zpEjuzTgh1j9xoZ6hgbYpZ2I595tI4wgHtXdD0ulxG4FlfyB\n5NrhHsnDAr+LogOsjMe0dv2cqd4JupdLkAinRy4jUVwfOQO3dNVahVNyfxcMpqBZl3ty0s9gGMQK\nFllbMtI1zKTBsKzi54yPtUOnKmLlM+KTobJjk1FaVwL5zP268/7a9JREPs5CGYprVaiUoRzaaUvK\nPREllPUTUQePIJUYIBEiRe+pC1VQNpsb0foSfoxBR/0BBaHx3p1BFOqeThWRXR+y9WovTIDhCrKB\nEPbuDET2sFDcmrvDb8Wtw7txhP0xke7227BbqVpEs2HDA3rUL4syOn/oXVTRnQPDHGx0n9++tD/e\nEPA+h8KAkpflEV/CdyIFp38VZ6iMJ+dl8n7ON0Tuo0aBgNd1q13SWE8vxtz40N9FHyUCkAgH3jLd\nFHE9fQ8EhYkEeK9xfsVCJf3wFlqi9Xqf/YzjTWInJZOQz85g6nefZ4SHmdOyvIFEoHub/IowCE+r\nHy1d1+taKRKwnYd5JcV8fxSeFAmvKoQwuF3z2yxWUIO5N97Z0opLpxNhoD+SzVY1/TkUcrrppL32\nqagKPJiNMQjMClfStHlXk1kxESzM3brHJ7qaWASHXIt4xW5TV9ZmRoezeqVtRagUIkJ4K/b9F8L6\n2f4m0Ft8doXvqyLtK+HH2ZQdcQXZrpP4eWJFrz/0RPcx28CKkuKyZWJoJCiEX1zoa4yFukFoyjtQ\nwkofhpVdUmhqB5/dArkkrkT16J/XmY6kVd1xAcR7VuTrGhkXSAVeDOLmUzxeOdRFe6pdd6JcvKA0\nAGXqUMxmSATUUh/Ar+hkf6ScXZ/tY5XbmMaZ6KP6pI+bNxAo3fxhK6zfyh4yFG3fguujPM/CChDO\niYg1H8Hj+H751KgmkF//Wq4PS9LhzXIuJkW7FGfwMLqfcq7gjVAiO7QvG+a178REiMpidgYileGz\nOo3F3/8G75tJ92O/5+6wyGz9+cxJev3ayKvrAk37scTFOCOLVoRxG3zvD4RE/QTxn4kG+x30USI4\nCpkKLLcIHi934Plp0kDi98xvYpNGe/xKLTFhYMVV0EKkgxLBfCE8Z8zp7L658biPq3tx/MJ7E6XL\nqt5dK6Cvd2cmVFr7qhp4Z1m/izskgdtGUKj7VcX1909UmsnfeGieCIUlF/8Ag595qmQPXD+qWF+s\nKZ/u0PuKen/KSHghmq3gxsc1FLbt/RlEczjSOloihS7QaBcFHAb0l1MuRCTBB0edXtlAJ+/8Fa1v\nUFUYI2BRvd8/mGgoDIbimp3i2nU7qn8cuFjvEfqqRP87ZoXOFVrNAf8LHhw3PIhOaVe5y6zuDNGj\nZudNP64jtS/T6V4SURao8yof8kgEohhFgfNfaLXH2T4pMm2lq8hg+XcoUsChEdQbaVoBe+2V7uwg\n/qQv0ZyRPkaxX3b75Xn3K3QMpApce73al+hsvmQ/M+e/tyC8xfwdlZmvLbuSlsXU0Exg/IBrvt9E\nOn8P+DsyWD1ceXOTuWaVRhkfy5QQEW/YnbP/amDFDyl9lAiOhk8WwEoFXldI/a7qwrKSMaWdDSS9\n14kHkZJisu72zzNLgvEBHlLsnm/mOylCWpwO2ZsE/u8g+zwNjfAu4cD7ko/r7vMR/IaWUY84ECWE\nIhRUOVag7DiQ9Dp3fN/Xv+scFuvQlXe/A28fgRVZ+2MCIE47ua3wynoYt/p7bs7XKHo0HvruHGaw\nrqXSDSNdeqmezi2hEUXIE/wNfWNFmeIP2KfvG77fGp+FpXfmv8X9HfcjU5CqG7ndf74gZodkZqjk\n4h+wCLGuT6wHvwfpumWyKVWjQ+YWmX1DLP/hz+PvXcSYV9xnCrOpS3L/yWYb+6fTpFyQ+AhoINi1\nzGedlTgbl5Q2gZlzF4mQVaUWVR4xBKI9+CrxzosK+rOsMyiTxURYtTOz+f33ECovkt9Nn07qix5f\nkSnRjdffzFUrtqdoPZ2vxZM1+CbRbdVOFr9pDpi93xnPw15TWM2UZbnIK5kL+8CKo+6g4l13hg9Z\n+iARGn2UCAE1PyFdUZVFmaCaTD2027zkmUBXCF0CSm8HD0FzP6TduweEn6TMF5roPCYCBu0Z15lu\npSBCEv/nq2TS+MD11fj7538yIlDLKDNHAo/jKVToezsMlBFszrcZwS5DhbXUHWxSGYIGCa0AmYDE\nXIhH9PY9RitjEB0OdzbolctBGPmd/Ht1ZixTOcc7/QlN7VYmqpXoeJiHwvHxFrAoLaknb1Ve9SeM\niSBzaKFMOBNA0SdbINom6nf/lEfB58oP453PcsvYgCR8cYe8u4fWrH3Bz4mqHvqkbc3+4tuywmk0\ndQQyHMfrsAeLKrEf+jVxd2iBEfueU3XfwcPh1Ddu+090GH9y4G7lC1YmOrorg5m0nK7xZSagEyrE\ny3eczUhxUfDuDKuyCO8efR59n+8frjMU8Eu86CBAfpyiNId5Z2HBBEyhFDvOuHfiHoNVeR9pn52h\nso0jsaLdTAjTfXTO495F3i98N1AikszJx0BHbd4X9YfyFI9bY3IT0SqoPeTDEqXfgM0Y71EZzU9v\n/BvdISS4oYfny/rUdOn2HiLKAyv268YNg1s7PrBihMpUI2A2OpZUOTqPM/dnkSWPCIRVMPUoNsG0\nTyTfM6OEzVJ2vj/Kuiv+Wv8y5LvO6zB9LrHMnx9auB/6o+mjRLhAaCWN/HmJAmuQCMNhfXr/2R78\nLsinsUyvGE13Z2j9Yo2+fXCPUH8egGrVl/nwaYUXKTe14HfXxW77Sh891eC7Cp8ayCt6T1lMBH8F\nhZx3Clarw6i11kpOimBeJ/IKjsVdxexQyN28v81TObyua9m2Zr9gTUvdAZYNbraz037Q9R3L2XfQ\nUeJ81Kgki35D4+oBB3gij8CYH2yU2wiAicgdg7i4QBkabQVCCS3hgFQSF4YvGCd5rxjjYSAYNlaP\nCN8SF8H06wfkwSs8bUf5+x1K9e3DRbDGloiAo6j7QblvGS5HGQeGyJ0hUlJ7ytAwGQXgo6BMHgxx\n9EV8tDc5feRmRbR+RwiWeifNa7mQoFSF7gK7sbsyxNNB/eoCBe2oH/eDNB7HUOy+Yf1n7+WKHv5q\nTITsXWcxEdQIGOwb8hmsY1yvOA/P5nxoHFzfsqZkko3sDIcoQxmmAI+/40CyOXlkq/3tGxbaf4SY\n9lF1fzt9lAgBoaWLSXN145J59ijZX0wjZ/eTrWVI6pL7lpC8WsbGhMgGrbNZjJAaNNUKmAh1HW3Q\nfPA1zK9LooySb3dnGNBLgE5yLfSs2k/UxKqgz5OwEh3E2xgEm0MwRly5OfDW2j6JiL4q8VfTwArz\nr89mwcM+frGgRqzGWN4P9htTyiHcX1KjDYXBibXLWj9jyxpq7wtBECbop6R4lEjs2HdsBw/6qDQa\n78eNJ5LMWcmtPFLAMfc0clp54TaPGMenyDwBNXafeMxWo68HIbDukoyxtiMB4J5c6FkP7fuw7HWB\noDLV52HWLW70X1XrY3iP47mptDnzxcQ9bH0R5AC2w8c4POi6K9O6G2W4mD4W1jr1JVtLt9YZZH/p\n9Qq/+ep8wQcBlQjP1d9LGpW/ki3j5zCDv2eth13kRDQCodT14WmlqJT3VeFdjHkI3RFhZQQbhHeI\nPKYwmTkgvPkXa5rIrz6X5fmntLpf3N59/16fRM/nQU+Zv1RGIEOcv1Kvzqv8QBO9VwmQKM8oWRG+\nIMXj/+v87FdtaRmJ4hSPsl9gYwNxQjK2bPp+FB4sVV3a6ugHfZHChZD3yj34vialT+vGV5V1Imu0\nTEoQUdRXwzN0L8R0y9HhxM/nSZnWPzHzLVrd5Bq6DMl7fcq8KE1xbBAhsl4Ybq7Cp4joi4m/ZFgL\nPfkwSiNps1IhfvZOfAGfq3Odo76npJW264Hh36TwwDGJxpHlUXjscxK0srKiBuV9GEVhJWKZG1V5\nG7rlYLDbCvX6a6Y/8GwyXkKeyzy7UsMGKvYySKHSy2Hbsq/5dlC5OfzV2coYuBcg8i9zG8vey+nx\npG3WFoUX+XfA+L+DXlH2I92NibD6fScmwi7VJw35l4joWQ/6qooEI7J8114XfjG/79av9pkpbmTu\nhehAr8HofmoRGvNdRgJFoNo2PkiEDxF9lAiXyQvFKDOcUQQTG5QEphK41A5FwoLUse3HeaJOR+jy\nyoWhOC29VB09i88ZjND2tAHXuIeK7sKdfbWVxRovfZMG+sGZSGFdQf8j1OnKnYHovhHwSuo8RDk8\n82IpVThw3URRvkQRwgADf/rAbtE0FmtC7cK6fL8ia+zOq9Sycxw2v96qDrLvahcxHEXZHpHrYT4j\nzHSe0/idFc6CeO8+qaKgehoNuk2WHUtXu8ceQFsd7lAaKFiI1iAlPCT4+rWMrwBd0OYbUKEX1XtV\nhm0KNjmoznEARAmKAit3bmXOmYs2ahducYEskTMrCl7qlQPLnYMCUuQ7rAe3znOdgs2kNovqC74f\n/d+IyXgFur8T8p70MbJnFl4XHRai7qAL0VlvZSye7trVOWHevQt662mFRpA7TXpJ0E0Lu4n2MXRn\n8cGId63tUQaqiAS5dwU96OHrfnqsqli5BpajED+jid7dF6J7fN+S9fTqvMDuEOn7iRQJp+4MgiqA\nzku5w720ZhCxKNDo2vYzDPlBlaCi4PR6Pk8iD+6M3VZMBOef5IOgX5V/02aIQncG/f0fDazIa+PJ\nv0QfJUJAVvgEyyYcjJum3y/czfpJ5PF9buw1wFem70pY2qFS1J2BallG4kaLvm/fbEZS/lpX1pz6\nG8gr95sFUzYyFJTE2vK9/bJpH+0wlEAwxrJYjoJh5A73w0OFHLKbsqzP/VJSpdSwxlEs8F6hs9t9\n/X5NqZJFr23DQgUxAOarK5vG6BqasyoTlXo6hyUdF0LOC5pOd9se9cVNbR3SMorw3HIfrP1CzRp4\nFKJHr/ApqcaKVbZh/3ctSpF/N9aj1k97fdwPn0dxnQFLFMZE8LRzUBtlRSAL+orrTgRTRLUpAsJO\noSuKMFEAqmJiVipMHSMC5AmgE060FmE8khssQZA5Z1TKdcWNJwxjMNon+27G4bScB3NN1/hQsvWq\nTqrZ4aUF6vMKa6P7S9rzB8aMmt98GfW1DmJnyTz3yq1I5Bq7D8WUWfOR/G9nsk4WWDFCKZnfF236\n61H6yfF38rBLhQRCIx65cmmuk8en7IvCZ6W5FhdB5+aT40NttKeIosevwXesy4yu1ruDTlCe7++N\nkEM6h1Hm3eFz0d4XtZveTESSctfHRDij5t5QJh5xNahj/RGnyA/9yfRRIgCJltMf2BtjLDZSeBfe\nTJ55UkjbMhbCuD/eXBH+z1Cftc6ZPSTcCEU2RCQCwqTOSPyrDGwwUXzA2VHbd5u+HMBLcbmVE+ip\nXNvVqnpftkqxgBD2UXgyaXo8hDQKRBx9P6O0ix6SKXViZgZPJpAjs3E/mcsu2uSmNc5e78qdQfsa\nzyPs+4MVqoeC0tXtBCHDMsftZvyuiBZzuzsCaX6/HSPJEa/zvGduiebtxg4fuTOIewETOTil9e6U\nefvF8zUU8PRv259xiC1l3VWMQge0Uox6P9R9Qcse4PE58Jq47mhfervQzjRe8Fk51racWsyJDM+Q\nfoVoCfce8HlkP8EHEzes1vdiXDnate46QfO7JQKrLozTF4MSt5fdMJZfIp8tpa3vXBGDJGWndMpU\n1OWC7Tpuv2P7di+vMF6jsFOcHCzuQWrxVCUNA/qjjesT5IQrlr9XI+Vrh9c/Z4rDSb4JeCGO2wiA\nB+vQuzMw876CNugnviPp55f7e3IvgDq8TCPv37rZxO3f2WXwSXEtMamcMNrFTDaOmeBzHwXnk14b\nbfoIoJvQConBocFC18rXHcpktYhXRm5HqQsoze8Nf4sO+LpWZ3cG5PaIPtiJIZMRspBhZ2Ar++Hv\n2p9AMSF17sojJ7CXVYrHHbqqlPlnkQj0fYqx/xp9lAiOBlwKrskB4RBFAlmLkPGhJ8tQ5Hqr55v7\nzjPDvuTK8Bso20zSoUrcGZBWyhmMK3AGapg2gVXZb3i36jfaBPIWXV0EOfmNzQZU3NzzwpmUi5RJ\nohlniP/RhCKexuFB+/PZ+vvHigoTQ+PkvUw/ViKuAinMkRrYf928e7yTWsjE2kDJANYvSwyFQGAI\nLRgoFcoAd0su1+7b3HnJajyvIBFWlFrNzt7lLAWGN70LRnnPYt0+vSAe1bfNiwP0SZbaNyJv0Vz5\nyDL8fohiDZRJckCRuDtyr7wKr6yVvhJRQx9An/D7UJDi/JqYRiU6DnMdffVxjcv30MpGtp/+AMm0\nJ+BX+GfqdwppX/dd2pmPGANldZPwkKmf3+AjFq2FqHv789lWlN63ePa07vWtl8kaWbCdQAl84t4m\n+9bg8ZKd4RIvWD9YZJmWdc1M6s6AvPcR3DC1az8jWukkvCJm9z1dTfHoy1di48KwrovNZ0RizJky\n6HD+3uc6bLwzUSSiugJ5sjXABfUl8gnGIjFKv8E8ucsR9hC/i0SIyCvxtC92T9Gu8AeJ8KGPEsGT\nh/gI4QFLyPvmqmU1Jg/9C/28L4g6RktNTQtr/Jxp1jTf1Ruq9aTB3SKG6C/lm9Icsf2y4HBT0jiG\nHWmnrBaVQEmJsdLEQvDXnmz/XlEp5ZK/XqEy3s0RbAJnVTF8vkN4m6z0m3uMwCgxNWbaRu2Ptdnh\n6GAbWd+mAv0fHgy8Ekdoe11dGOSGIprLRxHYfSkPNxWYYhYXY4onEHXGNHodiaDz+nwMovW0QhVl\nkadviTiOkdtDHyipNqrA74FLqamrYjBL4glG3w4Ubs8hDRJ5RXi0QmJiRTYnlwdNbgxdusQ1Hh0S\n0fJ5h6I6o9ScldTX2semqWxZoY95gH3114gU2u3LvPvYXyGI8RWS55ngyXRfefLqe8soU16McS2z\n7IJK77pgI4ic3On7SnGja6SNYgXRQaDtkctIRhgHQL4bRMOdsfbMZdm++/tGc/86fQc20qN9WxyC\nEvKlkDBY8w/Svx5Y8ZOdodFHiXCBcMMTaKlYcuR38WGNtLVnQl4IVe9sa9KUFq9pL6PMSgaJFAtx\nZ3THzAJOeYbKNBteM+H2SuyAaVyOg+hYS1oC/zYCONv0aExocWdTTsbRycvXNPG8/juiZ83tcAcx\nPUmsfDz6jQeCzFUlUhJY+OqaISIM/FmJjqOMAwgRDa25h+oWDxlOFHQYHG7IiInCJqpsFacDycN5\nDTJiU3ifIIkna+4qKWpmpghd4d9thJjJrNV4yDLt0FrAjuhVmHYU2Gv052ytEW2lfMfxW60XJHyu\nzJ1oWNThuvCMA/7G/ppy7v5CjYe1LA/tPdho4DSuyXtVQ6WiehDCPCxK2HesYzUWkZl0k2R+ClII\nkWAMn3LtEcxH37WlhQz3XPgbx5fxQNivSeBcvIZWRd8XP5ZX6YrcPwIrBjci7/NACN3f+jXaVzZ5\nvqIuDHpdU14v+g5rRlLxmnYinuzHnN27o9gYgeUqn5+tI5fErIxVGOhzZPFYVm3IGmV3z3DB6tdO\nV9wN5cEVQp6SkezdbextuR1XBpOOcdqH5t8EoeB/j/q1c01Ig+jaT3s/GBFJ5W7kn5kMnrksHG5u\neySCtLWkRZDbmrlWJrTLl/7lFI8fUvooEToxWKjNhkaqHGgl2JTBwFcjrRexYShEe4y8uiA/K01X\nJmxN5chBz4sIP+W2Jq0AlG/6rX9uBqXeookBujR5AglX4QIhZ+fPmG0sXnHz3UETidZyOh7q5LkQ\niXCHp8+W3jmw4vamwjRBeuesGXO7nvxPofuD2bVpK+ghBtmLDtm+gywohGrvxz5hOrthrfb9HAX6\nkdKalqnWHlNhEYAsuobxO6bngO9vmbYyKaQxCbSH/VqsNcy8kDYx+KdmKYjmyc4cMnWSHbMr5HnL\nWcrK8Z3gMNM/z5S3dq2pe47uRbPAulLUmudIfh+Hmez+cVqUxjzv5Q5Y0PVQncvPVdK9cz4g+mVK\npEH+MEPOnbZFERXd69dZQyVq2l68RXhe2fAjXPJBeNkc3SB86Q+LEu6D/EV8YUduaffmv7XfO58Y\n2Tj2ibndt7vHVWBhiES4q0B+i4zkfQ3wc0HCVQaCJSl3JdhmU8rO7gdZVi79XRUCHq3lMzIwWwv4\nyt3Buy5k14T8fpnxRFRSEek+vCJRJt+dK9MK8u/6wl58Ru+U3f9m+tP47u+ijxIhoMgvzRMGfjoT\n4LxmM7N+HQVt4+eEJdG6Nn4PNPVEAsOc2ymHzbxQevo2rkUtyk+pY93Xs30sEvzw3u3leZTJ2q31\nOCH0Qj89s0e/ZNf8sp6oLLZ9BQ65Q5my6mrVPvSEsQSZ9gJFEqQjy96NJ2+VOaNygLIiQjf0T/R3\nNddV/3CJssP+1L507cZLFWWMn1MYz2Pu1/rvs/bGfZTMlSzF41SXvZb52kbClJTdFWDM2jkp4w/d\nw+IPdURCQTR/V4eO6LqBpZb43bD7lO94eJVDNFOhZ69kIKQIDlSs73EoPosihyrAqsc5NX6cvc2Q\nvPLKKtio93mySGZt/iZKE1RE13h2Rxfa5Xm+vvZ+XaV9UXgkQpG8kzdp1X8kzw+WSo8bL1T5cDHz\n/NX6ryiRLsQo3Gt7t9xmH6dy6LsS+eV8hw/KTYr4XZbe0dMVuLykbtQgvIF8K8jahSLB9nP9+0D3\nsa5fItW3e0TCW9CKG+/Y81/hK5/oBR/6DvooERxVskHk1HrpmRQNeKSJdeIYSqtjEVxLBNeDJySC\n3IttEeWyw8gI4RiogW7275l11/i3LriOz3wARmHbrvmbw+94//h9lDnh5IJE+P/svU2obU23HvSM\nWnu/N61LIv5ETCAKQRtKQC7RlgjRi9owLW16o0I6GuyIBhUCas+G3JZwkUgE8Qc7uQ1RYsCmQQ2K\noAgiIYnGnxCxI/h9e82yUTWqnjFqVM2aa+1z3vO+Z43DYe01V82qmjWrRo2fZ4w6xDBPf7Rb1Ffj\n1KH+syXaQ4S9Nyp67uh5VtdY0ZkJdooSGK/nYa7FMD5Xpikh3js/nnmc6T695mGObEzo3vc+h0KH\nSfScbq5OBUqPRKiKd2QwYFpBT02iuCYF9DWRDzFrXdtQcxqPj5a3SIS6itmT25rhU17Gdq54FiKl\n0MfNc7Z5bkfJlpUuGWnBduTfRl+e0BZ3jXSKuIraX5F/Z2bOHhnHPRnDUUbnJX36jUIpj/PMS+q9\nqGWI9T3n5u1kRMP9sGuyv9c8vkMeB9eulm0nfETzy7y8e2d492pJ/jjKeH0Qvz3WSULLChh5cdw0\nl7NHWxrY7+q53fzjMfeRKQeKQORYS9uD7+2hWAbIrX9qhDJIhMZDiI+crOVMc0A707ra+BO2tRLP\nz70xuPP3bJ5b96boFfE9gyHZ8wp6ruHUDtj3On0G96nPMti4XB+7sUyI17p7eHzofz+Guq9vrf8M\nZOLn4Ixm/NGHNdjK6WWaxBHBS572b28/mVUTjeEqnGEl783CGaK6outsMOC//TvjsJKdpaMyRfk7\nDitWXsyyYiTfsA7A98sTSBZTufKFNgXi9xu9t7MyQ3NYy2bfA+2g/74XehkRHM0SKyqtFuEujsAI\nk+T5j5AIkTV0JjA/RdXb4ZEIuMNdK38zEoGTmkmvbqCVBTpCIkRe7s+kCAVwKLN33ZRqUmfDAffO\n99Q+z3pORfd4imZWSZy1F84gJ/WfEcchnlH3mq2RCF/6cKAxF6AKlDCw1F3qQsyDAxlJva3uGGXQ\nHf9rzsICSgq8PyVZE8wc0a+zuSHctkcicMUBMZKF//Nv02dBHowHjFBgB9zZUWXnyc7Gcn7OPnPi\nxC4PmyrTGA0dkfGTiRVA/X3H67ykaZZc9/VHEKz8XInNrfW3J/aUcI24HDXP0jMGt9KP8slr2q+9\nHOxfQN/j+P5oX7vnE9P+jFeERQsH2t2bGBI+QyI1lBHW7/vIspUTYZfO9rMzJGJU5ktIQCpnJmHF\nWqDJuXlIZuEMPmRoFs6gib4P9x4S+KhsNaLacAaf9yAKZ6hdL+UcEkERB+1kTBMGce29i4xml7Oc\nKDxXo7xiV3nlcn7ViXOaA2Qx33RfTVijAYXKvuhFLyPCBkVWWx83bsufU1mEltmGbT+wx/lQAd8u\nUAT1UDFJvT/mbGG9fmQkNSQo89Z2h35c77vWazeouok0Seg8Uk+fLepCY5z5OnyaTw+YJQlc3R+V\nGwUHmYacRHVyyIGIbpZWGDRKktgN1bZtz1o+ox2hnOdRqIdKf+efvTHJxLS3bf3flOq5222NcdzS\nmea3QQmj10/f76PKx1PD3QSX3D6TS5w2Q113VGaGaEjTM32p3XmmDknZVCBJ2jOZcrtQ2CYkz8rY\n0c8ZUHuF5kIoHq5eTgVX68mr97v6V8gmLrO1xywMYM+SzyE0s9kYY3VVTngXiNbBZ4SLRTwp4mOr\n5K5soH82AWmpD32vbvvxY/VGvGvFTy4NY7L8YefZ2wlTQgYDetcNITDZLKJTlELDY3DKVMLIs9K2\n377yY8RjasqJGEX60+kiI/zMfXcn6eG3SOGJY5trKgx5mpQ947e7eSiWdTywZ12lgnr9XoMkHs8p\n93OjlxGB6MgZB3JBaTavY4ecsXLVznYHQUsz/Xd1JxLWIvhexFharGsNpQi9Ts27KhVyWTL8t3ZR\n4LSt50e16M4WAYczTBbJANuuz6xn5upv99yfoZTb2zS1SK4w2wYlbi8lZlwMPbvnsvXzeepHPVFD\njQYfR4GoHkG/74f1Air8e3fTt5DYDk22z1ms5extFS2Pc8ihQmzvdTjUQ3TP/cg2fT7uu86RItzQ\n+ADtfHrfTs7AvY6Sniuvv2nfZ8TziNeHvsXyHmgO1Tpvt0zrT+cAV5LrtboG3Hj1UIM+L3g9ATzX\nisSqdQnjXoM1Yeqs80eL6bzPg2DxTwAAIABJREFUh2BgBBeMCRkj9Nd0i8aLm9G5y+NQ+tr/bn3O\nsfI1QNKHcAZpL5LX3fCIwa0ztG1Xhvv7ab+5+wfvPFCS4vk67VTpyW91DaTeZ31fHM6Qc4KH1bfw\nFdeGPzVB+bDeLiItEzfzwpQKn7pnGIPKAXuaTD+ZIQ/t6Ltsz1J5SzcwZvqNxyNXfjEReof3bcNx\nSt2Cg/JJ+LwhyvvaWjH9LO+a90bJ8f50oIcU6Fy2SMCx2zweRfDTdS9N0zPwZBn3dH96RTuKNvfc\nFC2cgTtFitxZOIPZJyPsvqOdcAYjjwRrL9OnhsSsQpy0LNuTjlxkDFPUeFhO+kjhcz0cs1dhZCbt\nA83l1g+nAepzaNn7ISPPCJAIZ+EMEbG+rvvAbC72e7rcceZ40HC3Ldocd08zxdZPM+0vvy8jBwah\nCbNwhlm4QrlnrMdf73XpmuZwnNzaVroazjCjg2Qmlb/8XNV+n7Xj7Y4+7Kx8Tt69X9hg3uoM1Bj3\nZv9dn8HwiGC/OpuzL/q+6GVECGimEEULJxZWYuLEioMXo1rK2YL4SO6kz7Bi9j4VwWu8DuNx1Bxr\nHv74peHqMxLJdCyTfR+73igf0hDpflc9W15Z61DYa/VwfYw6mJUpbdE19DkSbXIhwoL/zs9tIlc9\nxonWhYGau46eQRTLvIit8s96B5+BvI91jePbj2vaIy8UjgasUAYBcHE+Ok/jtJjz3K7QCbNwBq39\nKpTybD574jnmn4n5ibRr9RP9Ge7YR4g8+k5nVNqdD1D0S855f006KNZwrGuA3ojeQRSffkbdHE5K\nhisz25N5vt9kYrjPk/UHd5Sue54rSslqnZzykTqpfGLFiPdtRhTYvtXPqIdnr2rW90c8dk2haXVc\nu3+9Kxa6gtzYeQJx8k8kJ4j77dOVsRY7VttLejmbORLJny+ydJpYsX4OBvcn2uLjHk9RnsECf73H\nr0fH63QGAC8jwlPkLdvb5aGbpEATK+aUm8V5iOXW+LUTIbz95gTwLTpyeDSTSfI0a+9CMzO6xIO5\nr1XSOI7u/SpePcHHIfgg72iG9cirIsXeF0ZVRGEOj276PgTizCixG87gKVIe9bm9pXxmFGOr+qxc\na8+1AwDREY9n9bUxJxeQ9aTNkQjqDedj5aLxZSQCzzhOxGlddrnONbo/OzQAPbvxBHtPwJEh6i2g\nuatHCPIzzsZpQABh9HaoF8Ff075GIU6Rd4KTmpqx8QPrkAh85OWKVkiEYhzL9e+9+pRsEkQx72XV\nD02UZJLLHsBxl5osUPtBHnctNql/fH/9ZASzXnIu/CoD0vpe0EQfPNzQI4Ud/B/jq+GEf0APe9F+\nO/ZpxqJ1PlpERy7QIQD540D+0OST+nM/rtQnsy39R3umcA5khDk7+zrL7ftnH7l7YMw1VIwH2XhO\nb+Q9N2UzoQjAiIJxX+312zFyjcdChs7RTSQCG0BWQ8ZzUu89c6joMdPhGljKE3Qdto4Zb2AEQtSP\n3k5H5hilHoA/JjlJRQlO2uQ+8r4xG/qM7rWNxuQLRQR1avtW7c+hlwU7CU1X1a6I55VFDlyv6wox\nCoE/w7IYZdWj3TdSpn1cEytGpPmGdsIUtB++TxFqg0/WCSmweHKfezG3j9O1F73oWXoZEYh8pntg\nISACDV7czyDvCtgjnj2PRHiEPMQzopDZJT1Gqv8oQcxg/80quYL9WMkNeaPVGdIDO7E5QoqgXV2Q\nr/1AV7jNaRKtfJ7mE9A6QWX99V4fwZlrdSXfwv756r4XbUM6uU+FrIgYXVLKlr5yiMxdrPAL1+bZ\nEY9nr4+VZP5uKmA378SKVTZNO5+B0TiTOFb3E6U8jrG/ctQjK2A8zj6mV9Djh1f6xEyBiOYZz++h\nTrZkXnRzRkI1K/tAN5wlETK25dpcD3FINWfIeNqB/a6j6LuZ3fyaUkI75naX/Jgp30DQD0+sM0LG\nfvp1gV70EkXl+d3IKjHehjV7UFhg+e8OWRhy3M/wvtl+ncs6zzS3bwjel/K7ZpDOtB/UMov2DYql\n/p0vrJMMiU9FMvEDuga3q22kXtPBkDor7x52WEs42aeDnAi7w+E9vJFRY5ZvwDODFX9Mso/CiYwW\nHllSDOI5XK/K83T74p14JVeUmzc7eUKRAsm2qsgQ3+/t/R74uZOfOfwkKuvbXl3n33yoWyTNTOdz\n1M5iFpu5i3FO2rLl82w/ZpoeKxvM9ZLgmHmvEwhcn78UsZ3q+DTc80+M8nwefGskIv88gH8DwN+Q\nc/6rUiAuvwngHwHw/wL4IznnP/9o/S8jgiOvNA/nzLOSqAvJMV1mNF8CXpSo3rZRyYZQTHTkc2YT\neQ0koSVWHMr7Nib1WvHlIgXaSPEA0RFb5Ak7g1IeeRRUTExmvf5x9I1TDT27py60fjqjgrZ7p7ly\nRdBuysVJuZWHZ1q3GxO7ucvSSxXR9pnLeODkEXqw3dwMUZjHI3TkriBtv7rJ7jMaZp7rHXsyvFO0\nC7g9i3VvN8eeiqnL0bX7YL8bjLNhCBiJECf83J2DPrGoPu6pt2dBekcM1Z+84wkSpA2t8nVnmI7a\nZeI+7CIjhjrWP9vOtu87N3VeOuQt2Vg0HMOvxVtui0kdZ2PAR2/6nAhFCaztHD13zp0K3ibzZXUs\n8sM0Wt/qJ11C2fNmhqYr1TPZPevCGkmylbF4Joir0SbKg/AoPXrvlZwEPCd7/g42BNdrJ/V8JmLB\nj3HkqFolPvb9+kwkwbdAK+NNhESYLXHjDEINaYvKndSj9IjcBtg++7qGvjw5z9LnnxP3ok8kEfm9\nAP5BAH+RLv/DAH5//f/3APi36udD9DIiVGJYkYdSAqNlMKMrmrxAV/y1yR0XmHD34pA1t3ofh+OZ\n80p4rWUEJsv1imY5EZS8IfQL2Esu0SypDJN9V1UwcXUopGwmoK7ICvOqCJRKWFnLTjEHUI5HImX0\n7B01JaZ+rmzCuwgbT1FMaoR2WfWVjVWhwkVtZHp+ixRp2hW5Q2KFzH9npfZUURogI50ZaAzcMSSO\nO6s0JoUke5jvbnWaJ+AIbmI+8CX0mlLxYw8+UxyYLzESwRslijdTpsffRvX69tU7tjq94Aw94ucp\nG49ZmYhCmJi3FH0rG6Vp5s3aNVZpu9xGiEBA7rxuo94zWhltNRSIxw3A0HbnOaUukXxqDLbtbBe1\n96GMO9/+kSn5ZP2ho/AmfaoF1ai9ZXibvNd8ALIhp5/lg1l74id9ap7f8v0WPA7nKnmEwjU8MQqt\npJHoiMdo7fnwrxldNZL7e3U9Az0hcUGNWWFptx02vuUDJTRuSMzRH7InodZLvF89LqmF6LWfmVEB\n6Cgi72ibJYnW7+039wmswxk+k3ZyFLX2L7y7n+Frfpgy1nvdN0T/JoB/AcCfpmt/GMC/m4uQ+F+K\nyO8Ukb855/xXHmngZUQ4oUGnWMC4V/FYEUVHPIZJwJy+pB6rlEFHgsV9OnIVMB7wtH3GIok2mM9k\nRlHMPRB7vCJmH4WcRt9ntPJ4+fkQQQDNPWRuuEJnQpGHru4QTxdv8ChKx0qkc+1/48zWrLkvHrTa\nKQr1UFpBSh+leRxvPC8fbfvZ46T4/qIIF6NBM67h8w2W0djkY3+Oh3WeDMMOMsAbBmfGgFn9kZHE\nN/vsHNO8Hru0257dH/duGvfrbNaSV4Sj/WkGuOGcCFFbn00+R9FZoybZ8SPtYeQ73uh++ZSAC20P\n8xLOGHpi9PtM2g5BZV51YU98hlb7xvSeLw1xZ8QKXQ+RV+RImdUxC3FYhTLEeS34/ey/m9VJKr1t\nafzFO1kiY9UqxEJfaT89ZnMmXZRXHkYJviwIPzkSkX8UwP+ac/7vXJLOvwXAX6Lvf7leexkRnqWz\nuEBjaQyO7LtKxvsYtfFc9WE8cKs7kCRNQrwmZFn4xFW4pj/lYKsvmFjoIy/0kZE/0EIYuM8RooTz\nVcSC4hzNcZUUJsdIBD8Y3FSELGnlIFV5n3usFaJrhufE03PFeDIzuEzpqApGcLzWyW2tPUUSGMGk\nemL6UXzAQW0A4z1DGzq/fZ94chzju8ouYRz3eaWY5FqfUBt+LT1qyPoMCkOu2ZDUJKNBS2se189M\n1sRem01k9GkcLLAe06Me8zm890/MwjyDA2fYphN1Jbu5aqYr/70xRrl9dkH+mBk3hwl51HXRsrWF\n+8ESjXDWvyo9MzrnChIhGt/ZtR1qsHrmLQE6plyX8AE74ui8vSMH4xeiox5nDtfh0fWPyV7ix9ck\nlCQD01VjsjeiPWvfnY3Yp/It0VQQYhAIQLmmfysq8ZFnygfMPmJ/s2tSDXw5y9b8+ylThEYp188Q\nZfvGn11jzE6uIiXut9TEu1u9aVAIFmrPQ3hf9Dn0lcb5rxeR/5q+/1bO+bf0i4j85wB+d3Dfvwzg\nXwLw68FvUccf5g4vI8IGqSITedhYqDNrul5TRnIKTZeSwGv3BIaIBmEHsTLu4aKlMZtYsRw5OSZW\nbEcGbc65s41rx9kSLlYTxLfvtdLjKHdo9MLs3cf3rL5rf66QT2Zp69oXtFUpixJV7czBLa/QIjFd\ndOzVSVW9fMW9S51cqzwd/M62BIBIg27QxrH4s3kLvhY9sg4H4sVDLs+dxINXPHt+6A9ongStS8I6\n/Vyd5QmIwnFWnb2SWPGz6EpiLlW0rpzKw2jn6dwYBjiVDS21c+OmXtFnFJao/89Ac9UQwzlnImN9\nPz1jH1XYjDKrIzVT9Sw+yiqYKR+5r8MTAeFLgKr8HDNr7IQXLvdl2Bwj3mg4lWWy78N1ejaZdTua\n9vJeLuE8O5UVOfGpY3Li5oWYxJbX+geoEW1vNczW/J7jYLJ/L1AIttz83lndSu2obb6WugwMxHOX\n/Vk+LGE3bEb7/lBIQ5Tk+GSvGpNKP9Lwi74i/dWc86/Nfsw5/wPRdRH5uwD8rQAUhfB7APx5EfmD\nKMiD30vFfw+A/+3RDr6MCFAvUEaCDAyHM/mDvJt6BKDN+t89F2E7wfVmNU7F47USdn0OsCgJHW/A\nikQ4Uy712Dy2Yjev6wSJcCYwnNGu13XKiNm1fRFOu0NsGPpS5OfD6iQfZfacXTzDCrs55+m7nnks\n7fyS5pVsw2vqOB+MkuCy36xIBP88z4zrDIkQkfGmT+ZIWSPUOd9JNwn9RrwUFlzCjexeRD6KJ8wf\nPfaZxomOKrLCYE8U6ro8ezcnSASmZ4UTk0cjS/FOm/ozvPnJx6HOnkNl8PtsHgZIhIh37z7iTrkm\nkM678UXJGNumlsNzJMJn5AoBdO/rwj2jywCLFhunZPxbdlMm3I9doQIiyODM8wcKv5h5MHVM+pGu\nKOuksZZNrzAj72wnjadihpQJES/BtRgZsxfiFPEP7iPzf5Unvua89mT3M0HC/IjH4d74dX86DWPs\nfm85EQJqMhztjV/CY2plBFpv3BeTl8fxVCPn5tPr5t6N/fdMVmmJOpFbmI5HO6oMXMr3zx9r/iZx\np10dGf08XH0Z0V78GOIFUCOzG+8fcf1+S/QtH5OZc/7vAfyN+l1E/gKAX6unM/w2gH9WRP4DlISK\n/8+j+RCAlxEhpHLUYxdaFBpthJlatvye29/KvxlO7o+SYkFFFl5OJa4XsDCpJlQJ9aFeU5hhQs9E\nHSVkZPL98ZZYTTZ2hkTIiJnN7PoOeQXsoTqcTDZAhnMfw5kQpt1gTzyX3UuUNFaeF+168lbvWR/9\n9dZ+IPxFOTrCe2m+s2eqnUlfL7YwDmcdX43PgW5ML7K3GGTPLp3F5euv7E3YpRjKbL8P/X1gvkbe\n0h3yoSNfK454lx71yK4Mj+ppBtZeHV0b+t51bFbvX9L8qNsd0qoF3VBg942ecFCfqZlu3dzvSnT9\njn5fN2bnNh7cBq/VlvTtKEkDjTHyzAqzoCte3Rz0ndf/Dq0MCHGb/DaskKz8zIcmreaGWaPH5HjG\n6b3OaVBljavhM2dImd3XOLVXKB/PEvISnsvlAhpawiPRVkc8Mk/WaiKb7tb+6njgZ0D5I9SeJ77E\nPENDGdh5fGV5Xe4/oxHcmnwGeTG860omlHBh4OOyu4YFRiCsDAjqCLR1zh0rj9CYaLHL+0ewl5yF\nxs76lmH3AKDuEzP+solE8HRlDpr99nH0+4t+fPpPUI53/J9Rjnj8J5+p7JsxIojI7wTwbwP4O1HW\n0D8F4H8C8B8C+H0A/gKAfzzn/H+vzrkUkd8A8K/Uav/1nPOf2u2DehuK0tKNCB+5GA/u6Ewr5+7J\nUs9/i+EmwU+zPbMRQMti8v1MhlNByx+HrAJl+577ZsWbv3NkQD3G7TPs3zkjVqHQtAMrpA3PMnnG\nZ46Z27W877BBn6HadyWi2UY7IwKjmGv+aFEh1AsrQjxfriR3GgVGARYChm7mTbAmQcjGz1MngYqw\nsZZ8I6jr82z0+ZEj1D7bcr6TOHALnkiDdmaUuwJ3jPKQ+GFbjUkopFfeMPzEgrLkQUBN6H2/4g3h\nnAgiGQiOU30G7ZDdJ1Dnv2Gg9fpEqfvSYpSv/+o89p53reMgxVC97FM6ri84DYN75v0c2RrsoxAU\n32vm1cnvd4GCMWs3qvv0vlllJ4NweYwWk6Ak3Yx/jwxwu01H4xYZp7JdTPBIhHJZTyASLhb2OgEt\ncfROThRzb6CkP7sPzJAIyvNaWAM0Jr4X1vXQoPPNoEd7wMq5Q7/JCUOVNP4tks0eo1EPu0c7fml6\n1sizCl1Q+VMV9nPFft2WcZxM+1PaiZxLLDOpkYNDqoC5MWrZoVY+h3txFJLxCmd4kPJ1Y++PSTnn\n30d/ZwD/zGfV/dmnizxDvwngP805/x0A/gCA/xHAHwfwZ3POvx/An63fAXvO5R9FOecSIvLXAfgT\nKBCNPwjgT4jI7zpvOp8ylmdpJyfCdl1UvsRtUT2Xe3aNWIlTBUGgG6SNbZS2UclgcV3ZAi5ZORW+\n5+u4UMWVsi0cVb7exjuDTfUkTucLOTyveNH33WfjchnOCn/Mx5bDOtt31I0XsdLQaAM6YI513H1P\nGaRl8e4PM4AREmFr/TYLY6//kUzbpt24i5foTNg28y9YuJKy8TY+uibECeP6d+czmf6PdMnQsvrR\nPePM02t43YW2PWVkg4h6dCvKDZp7/V5V2pcG2KDi1fzVdzU7Ung1Zr6lqI7Z/VFbKwNCQyPR9xXp\nsZhhOR+CYP4WeLhxN+qIcULkrIb9vQkxGO82vM2Gdy/jzdeGJs8SZoZe5iM6N7QfV2glO+zwAJ7n\nVyDJ4bxyxxdHcgEbEMyed0F+CF/PZ1vHn6SZoXoWrhBd47lWjjWnexmdELQ31puH76t5/ugeusOv\nTZ4feu9eLl6hJpZrulbKeRx6nef98/TMPvSi74u+CSSCiPwqgL8PwB8BgJzzLwD8QkT+MIC/vxb7\nUwD+CwD/IibnXNayfybn/NdqvX8GwD8E4N9/tG8lK77gozoV1CJ+rxZ1jqvlMIaIbBZWu9gZsmTb\n77oNh0h4JILx6pKl09PDSVwCesAp3GiVBGyWcGiXooSQ/Tdi4HSN31uSPo7dc/A5x+D9GIxZhTVx\nGxl/lt/XQl3zPrWNflJwY2JwEWt1l4c81+VerjOH83y5oW60J9KF31U7uzTLMaLeutURYkUozQ3J\nUcoUnlTG8etONl53yV3zQ2t4oSoTqMK2ZNyYN9ZPHaO3BKRDlXhp7fQ6yt9vIs3I9iytxpINYX5+\npeDZ4/r12fs1gXo0e13NWKtt5m7EbTkEJIee216u86GiuCp/ODFsTTDYnLytPw8rV9WYIAUNkNwz\nnhkYrsCv5+g9i0TQYoOiMdy33teTm++PTraEBex6dt21NSQlnfwd/SYiZo9T3lJQj9LKzWD45p1u\njMEq54uGlPDLiPYr/q5DlHN3bETlS1nlFd1j2/Yc+n+08oGRhn5jQ6LUsAXhdgIDwqx/K7loKLi5\nQfr+K2JFpK8Jcf9bOcHwHkLeUvkU7+s9nLbnGvOyll4D9N1V4w7Ksb4A8SeSCxmpVr6P+cz0pIMz\nkmAu7hr3Wfb3+cmicp6W657L+SInE2QwJLix4ZxC8Rqpf0yaiU4a+54o42VkUfomjAgA/jYA/xeA\nf0dE/gCA/wbAPwfgb9KEDznnvyIimihids7l7PpAIvJHUVAM+B3yq8PvrJzbuNNREGFa6U/nltOg\nnyBPPy32FjvK1x6c1Jndvk9YBrxw2Op/crW1MT/Qjsr7klZ4rv6RcAZgbgg5Y85APA8UNjqEBfwE\nGBknKAJi74+f26XcScVBgUgoe3iMltDhi5V+hgVqUe3OEosQB5xLYFXe8AhHkcASQSdngjMnCw3r\n1/smv/d+2L48Y+RctlN7fKak+RHQMvdslX5gr68XbWoDxftVubrFoi/O1wFOC6uUROPXjBum2Wef\nfI1E2CUdq0RJ2IZt0yW102t5hoXfIZcYoyXOW0ya5592Tax4ecUyH/3/Z1G01zUlNVBMM933KJ3y\nm2BFNWQgrf9ISeV9KUY4lM9m1Jm9UL75KKczZKDlJeKVpOvRG23PSJzm/y0qkDtJnyNKk4Eta2w9\nON24XVEOG6tuNt6DUeQTFvBZ+EJk8F7REI7xRN9e9POhb8WI8Abg7wbwx3LOf05EfhM9dCGiaLrP\ndumQu9SzNn8LAH717XcvOVAo/0O9hXlAA5iMps2aul50GpYwU048qjHifVcZjz/iETjpJKyFtnuM\nu0U7EgyFTc2r/mxsBJLKUZT5E+MIfLcYibBdh1raNdbz5Fn4HGk/5Dvws5myNAhaF+o8I84HMqMZ\nHHr3fOVLRHMgOuJxFRLhhf9wfNwc4zZWis1OXXpN0togEQkmfo0dZAhIdQnfh7u035N2At4yeBcD\nTKa89bPPiwfPeTw2+qBC7ZuUhLZvkvHm3ydN8ltFKsyccULlIlTLqfDj3MscX5rIu+j7P3UYB9d1\n7/C99h7XDHr03B2Qe8L/vF1ey5dRX3TE49hmXNcuu46QcklyRQDF9e7waY9EiOpihbQb52rowik/\nH7/oXpnbJDzZ//KErzB87igZ2jNhrxPloNjJiVBCMcYkibv7XTEa9P2u2/VpfJ+Jr9I6NnRVj0QA\nqmH0Ylsr5WpWrl/rvEENZRYZ1O/V2xUpNssH4cnsuWeMxmZ0rIlhc1h0h3yixFlixSMoC1gFP0qq\nuJNQEWAkpKVVHoQZcciEnrIy7nd93KJQu0coMoR5WiEoTD6MBk3r/EHeAuTJJxl8DA+hPeR7pW/5\ndIavSd+KMekvA/jLOec/V7//xyhGhf+jhimgfv6fVD465/JTz78EGIUgLbSAFUxFKijkcZnJGWtF\nbCpEnPSPkRK7NBTdvJmP0+ux63Gfr3oBhP6F9K3M1gV9ray1q9jlL8HcH7X0K7Xjvb64j6zTjzld\nrhhNDorPnR039mNv2INXsS7uvLHI0wJmbasMlFI1tEVeldOW98k8nnN7fk1h4Sq6yIco+bGd8VOO\nDw7n1oNG2i9znNzn1KnH1+4qbkxhcsGrlVBOhBka65nM+SsyUHpybHxtJNvXXEtXDG5+LKweXo0D\nEfIAc2Mmt68GBP8envKFfKIjZbfKs993T0LYQQbNUAIccjKr66wfCTLcF60HlVv8qWz992Uz22R1\ng4VTgRE+M+8mPMptPhaP7qEC+SmI4y/6CvRNzIOc8/8O4C+JyN9eL/0hAP8DgN8G8Bv12m8A+NP1\n798G8E9Iob8X/ZzL/wzAr4vI76oJFX+9XnuK1ICgRzKpEHLlLPcGcwNqosG9wVejxXA9P6ZYcAbh\nkHbT5IOYuXpC/O8bwzP3JvYNNso0e1UCSpvjfdbPmTLEBhCNgYyuMfmkmJ9Jj2bFVxLYOfIQHHgD\nEgjEDpRnib0/F8JHn27Tzo+9+Zlq5mwfnwsodN7G7HJ7HGvL48gJTX0G7siJIfS//U7tDTGi6vmg\nB+as0J039P6MwrRd1+xx5sRr0gT1jBZf38an1gfqc8QvWh/tWLS2/bMxyoWPPu2Pbp9RkQSgsUfM\nZ7mOK6SeZhXgkrnu2pDo3rHdVVbzR0jIK8vvW/sAWN4S9X1Ffd7GCpif36VPXWngPs3gvLwGoqTA\nEpSLiMMZ7PVyw/aIn3lVA8W8r4VzBZevReUabzBIjm6c6nXV9el4RU8UKdP+mjYnffF91yLR+7m6\nvqLxWO3PYfZ79HHvPK78fXPz6cY8GjD/fb+0L1ubumNO5qQG5u+gdQD77Mz/4fpYnnNsW3x5Wp8G\nAWR4blmTPM5euWdDQmQQZUMC/+d+RP+5fr+HRqjGiL6ZUNKUGkIXqfTf7MGg7zQnUzPQ9z3Mrx0v\nb3wNOepFPy36VsIZAOCPAfj3ROQHAP8LytmVCcB/JCL/NIC/COAfq2XDcy5zzn9NRP41AP9VLfev\napLFRynn9YFuBpVwod62GM0ZyrWeGUqB77/QFhPDRU0irSRoWGjohpObktDLyo/LZHmnq5+a2Ith\nhc1DMLmdKUr2036DCnx5uKY0nF2cx3t2PDCzqA+OZexCxTU4GY9Fkh6XndzvOg1WpEeh+vq1X/0i\nzitz909/r59troq0rPnt/ZPiOfQFvUwSez77sm0Rc0xWv2dzcp8t1JPfw5MgKNpW32UXqAHkKtQF\ndjf+W0OkLhkjvdZR4bK+iI+/9bcKMCzlJifTWfJ6f0a3R71JD2lAExi57fo3Kg8LlYM6UODr2XRM\nktT7KWEkGTFCRV2vZb7WC5bY2TjsyIxJvX7QtZz784l5RsvAdmKWzVyIxkE7r6ELx1G+043S3r9T\nDoKTAtopDegJ06KpH147Sfg6ey6l2RGPnoetwhmG+1xfRHLZF1lpiyx/6Otl9jjNSGGscFT6Po7v\n1yI3BQBM9vnaZ54bYsLBHu+D3uvDMaK+uu44PXwMA1HlyiivrGDqtXY9DzyuhzPwNeu5TUBLkN1S\nPuVe93J4/INxpfp7k9u6bJTaM6qxlmQmNUx6noY4NC5J3zdKjpDrL/SRewZZ66x8HhMXTkMF+G/p\nY1e+6zjFbUQUIZ743bbxCMfYAAAgAElEQVStIujjFgUWwC6rjH2PaGokq593jPvLiwp9CdTdT5G+\nGSNCzvm/BfBrwU9/KCibMTnnMuf8JwH8yc/ql4iVGFbx57ukMYkz8sJ227AWxgrBuAGEda9+5ORN\n7tl0wXA4g2kb2VqfWdilzfTyec9wxo6hz+MRj7ppcoI3VsBNH2HH0/f3GWHHJ59SIRXapgoNbi+Y\nMXZWKFcMbPRWL/pICtysrkFZIov16ZwLkAgZX48Bs0cbqJsr/d6NExmhFEWUswweKC9ctmu7HTyR\ngthg4N/DuapIfUIRVnn9rdaj9dYEhdiIxzHBomNu2/ZzvPQ/TkbFQr22XdaOCsbjWm7tGIH+fHTC\nt21CNeJjD/nc9Vv1LN5pfet4j/cJJOfKD4J6TdnKn4hPODtH2xey2LmgfxsDTfC+fdwxQMorUIwH\nvbApl9s4ubjmiWdc+8t8z6+fO/oaXSbnxKjcfCnPYGRwAj5HrjbvccVPFw+n4+1PZ7hKY66SskKj\nfdMnBRz4SLXE8N6cj8laov9X6LNet3cT6fzjq97xUq7lga/dJONNgA9GgFG93EbK89w1YT+HjT0Y\nsckEYKUyzOtiZM6m3l7oXUzPylBfg/yQ6ThxQugdecU7gWbP7mu6YkDYOTWiOP8CGcXJLS960WfQ\nN2NE+BYokaC+s653F+Ig/JMw3Np7K4pMpKCs2oo9N9f602Bvb927m25FfmxOqVtXZm63o3notL1d\nPqjC7CzT8il1jti+t2QytY/po3sJPOT3RgpKgW9la0jIXdAf+z7ZoLF3JCUL8cXjJQMKokHrwIqU\n/tY9JCZpXP1kgYSNA/rsg7ECPIy5KW8ixdur9Xhj0Ioa7LK2LbdsBJh2PXg3M+U7nM/GFYKhDS7G\nil2EBp2dJuA1nJTGTVjfExtr2kohJM/Ua5Dm/WbB3Bsze991zuTWdvOmUh+9sKr1e6+ZqZubUx5h\ntB5A3gTprSrT6cBNDiRJrT9vqQ+jV/D1SLmWWDEVpYM9ZHzU403L6Vwd+GoX5AHgQ71rZKowc8EL\n9WINSVI9qak+l97/puvT1eXr4xAHpchmxN5LO7zFyKLX9N2WZ7T3CqSND9AVy2TKFb4242PcT2lz\nP+i9GR8JkQhqTNLiCcqzrEc2UuC4HkAVs/Eo1ZmHLDpxJI6bxpSn9fcgrWw6MdwaBMdnEk+Moy9a\nVSjYy2z7M1bVkEq0JnhtzAwJoLKzbaC1lwDcEpBKaENHjFmZQe8ZlbhFI6ZvnWZzww8BtyeCyq/6\ntZvYNQeU9d4893otde/+jeap1Ee/HWP/xNV5CICjx6zrOzyVg4j5cThZM9jQ3EAqctGAFtPxcGNT\nz3Zo37Ws7h+fYRTQZcvHOXriIxv5qEc/H70TaGwrWvdj4sIif+TWPy9TaJjdjoPgEQMRI892j6ZE\nqpvrWyr/AciN3puRuca8Hh6Fk6QjG5fNGtnpUUz0T5syvm5+l2+ZXkYEAICPk+q/6FF6gIXPnYXk\ns/ARkVoG2bKYD1l6cXbIbFwyKmedwYzMcCdJGjBaZa0y2BX0nbPVgdGr4RMcNWb4SfxqNZ4J1VMu\nNlTFowr4b9PfmYBey3F4RKQEnVEUOvNM0j0vSAJOQRJ9l+N2wYqzr0NpZy+cedCsUrY3Bzo8syv4\n3qs92yjFd+JBaUnX3KV7jHdd11BgyCKBV+eUfx6GyvJ9u46laWLTTfKKgcCOyU2kGbU0XhNAVW6s\nzmTRBEetLzUj2o3eqRoQ+H17Y4NVYMd+q1IcFsCoGPPzMs9TPqK/t/Zz4S2REdUYuaCKeF9DVba1\n/Ifa2hVaZwbPVm8UgqSIhJM14XNcAGhGumZMovlbfu9oFmT7/pm8ApjyuN/xSSXrfs6v8f0rYT4y\neOyeL9/qoPmiQn5T+EJ8+VyL8/lU0Oq0e/EqjCQaF2+QmpVzN7U/80ZeHK7znu1auEozXud7wfB+\nY+vNsXIaGTs4jBKoBgf0+anlontTtnx6tn7VEDqGxUwmMS0gc5oWy360HgHlwzKsPR1L824m++ez\nNLEJflGKTmzxBpcVfVbC41U903VwRGfZ1HvIyWdDfXudszWv8gNQx2Tyzi8AKF70M6aXEYHoM21q\nKnxkyLh7TcgjEb4Erc6FL94n1kYy5EKSRdMO9mP2vVzUFe7aL2VoboPUwurtSMw00ZOxaZ16FKVQ\nO0k3rnrxQBe8ruxoq9AFvq50VGi8QJA3YWZJOvzbIBvr5x0wQoHeU+DWQuPTIdXGU1nHLNP9LSmk\n9Gcsyt+8oyrc5GM0lHwGeUG9KH72eVgpf4joZpucynpZtR2xt4QGgIGE83mc3zNDabBxSpEkCX3T\nb97HCcJG6/FtJXpGUcsSeaGB8r15F2+ah6Qr8jeNxxVrmtByh3SvX0d0UFJFrU/QJK23lCH3bIy6\nqoT5Nd9i8UlBZB7IQlU8MDanQpJu0Equjoj0XQBzpUTnjwjxOr1Pusp/QNrc62txz1DrPXLs/evJ\nyBwTPqs09T4wSodDPXobpd8ll0Uth1FpYWPK4AUkL2nUpTPFxvKw+Uuzx50qmkVwLMIr9P2FHdOx\nSJ0nT8OiaH41g9ZszyNe13iI2wP9eKlX2fP+FfX1Ge/VQN3PdM1ynwmJcNoGORP60bt1vwsg+Pq7\nMQzSemaezPui592c10ANkzdBi0cSdPQRow78yQzKIm8ieE89b1BoRKjVHwe9RxQPNI+xjpxxOCWJ\nEW5Hnzc84i28k4166LyRx037xlWzUWY2VXR9pGzfncrBHnXwmQaD0OCDdZ4G7zQMjZbGAMMhsnTf\n491u5HMieMPl9PSclHonyWjkj/S0R5D2uW+lVpV7g6ZQw8yuPNTPnfIrJ4LSy4gQkCYcW1Hb8NEZ\nUM7dmvxIopQIibDyGgBrC6YqsuYcefrtSx4bt0Ii8BP2TTIWTuYNrAsMSfUCSLMyTfYaqO0gFlc3\n+jXrT3B/MSRYWBTPKVauIrpipOENz8AT6VPLHXmM9SyZzatA94W2k8sb8lNWgi9PLVGf0qS/inby\n4FsFbz5C7E3o7ewdQDoK6iTds/B6KFzZluXEiMC4jgQ87xg5wOE+uf2epCzITOvhTVSws7XfqG2T\niJSebbZuWl4MYpYiQArOWR/urRzDCOPuwXndRaSGFS2rilULS6n8/CZROAMpyHl87rP2T7erlGx+\nhAUNSLX2rty7l5EvekRNr6MbNctv+RJc+FEqiqPgcJpKrHSM3uL+G2liQDvOda8TYgyzV6gbJs7L\n+iJrg/ZGwwuKY8X1nq5orhjWrvHDf7fGtdH4LFnamirle34lAxGXnqBQ670J8J7qPHbtMRRf/z6k\nr8kM4HBQ9ugRjQGhW3GK8SC7RoP5mAIerW09sqP+GAiCKOH1s2TmQNK/g3IdY3Za56p3evd4usi5\nDmALPGbKiPaI0j62UYvpOw1neFGnlxFhQrqOKcysMF6yTqvA2hRzFZKMNXasO2cVOLO7HvdF29C6\ntFxUPmMMD5jVuaQzifeEdpk7iw20H5Yu1E25eTK8dg0Qs+d4S2txN+1NnrvpDSq0uzGMkAVMLBzs\nyHk3egyu9ybzdnaFKu9V2BUOilBfYY2tP1I8M0mQqjbDHtOhj4wxvrC/aEZ5L5C0Iw49XJitThfm\neTS20bFk0Q0rj5qiXGbthNDTixR5sv1az/U/Ax17Yr59aW94xzMYBFngIhQVe/h4vqtAPhhNxdaj\n8fU36Uz11oR3FsSIT5JQP/anlxvnxsToOXmu1jb1vd+DBotmvs1rmIW4EuLRjXc3UeWlr8UsGYfz\n3uvzsAF7h3SuvkkC8tHWfuu7+QPzcIYL07oZM0m/YUNCq1IsP7+lY6kI7/Bbjzy5QjrFI52/z2ny\n9EZxX2chJOhzN6lUJmPHBUB2CuLKwHWa6wSF93p0Tf/N9nWLhyz4fjntalSSvNHx6nvyRm+/V0Ye\nd+U1JuRNrwNAkyNGntFzKbDCn9vJMT8kCVVNffa62+EuZLCfDGvjxdaqZgu5zcAYdhva7brCrYZd\nYSaGMmc46a25p36y7Ny6FiAE+Bo349EL5VQlm9MlypGgbfu2eH+O8zDA9OEKecfjMw46NSJdckQ+\nCPlMw3qwCYLP9pXji7oiv236fp/c0suIAPWO1PNiIcPGroIOb279Gm3A6AnNVgg+wxxUEEgjH1AB\nlCHKQKl/FpZwxkqCpmtSxeJpEt+fprRVxnaQ9b7VOfrtVSlM6Hsjw+2VomONeNOZPod6ZqSfM2xj\nwNAs7lqnevqaAJ2Aj2wNL1pWlRIPkZ0JNx5N4O9ZCUU2B4SehjzG8gMw19rcJCHE5+JgwX0UtMQl\nhcotsRR7OjX7/I2QCM2raOqsgrTOoVzCDDjUhNEpjwj2MpEw17Bufkb/20ajTiieH29pfx8Ev4B4\n3u52wwv2AIUuoKMXWtPUL6H7OKTGPw/HTIqghRLJrV59S2XiviU3r2y4B/fR16+hCpHRz8L6y+/d\nyZZbckXLj9Xgpbwqd0OvU1La39R5kQx5k56kKtX5S8oCQ4DN87j1EBowKpMrEOxskqzyuLRxrJ8t\n+37dVxhWrffwnsMnNbC+KdLHyfa/n5VuHiqiyXU9sUc97IdrQ9CP5eSwqKF6+vQwXB+WouPNa8Ab\norXsMAepLd9P9kKrUVfCcRv/HjLoA1hZfqf5f7STQGyAPGFcaVKszUHEz79qpocWML+xkPvW3yQN\npWRPb/EKZK57XkcVhflq0NfYFVXYwNb1f5tDpT+3lFsi4aRrjBp6E90HLeqA79drN8l4T2L6yN/u\nlF/i47BOg8jAGMpBM4EiyQA/O8vR0WXXbpxkA2X5zSqSO/JZr39MEnhmSHiEfEiDfp+hJhPxIKn9\nFI7drZOlJQWlfURofMr+LRhO6sg2BGCUkGsdMuaaOgLGmLCWEzo6ZfK7mzIa7gPE+/OY/0CaYPOI\nIfZFP196GREqnW2ow9nXwMB02YDAmdFV0LsP9/c69Zis0UrfE94M3vJJv2dC+/JZXaZIf2xiu57F\nJJv0VWj/IyRCuKHQZskM1wjWDU6NUfJ8AEdnPfUqwNR+aAfwcF69RqMAN9sc+vcVEoGpCOqjAcK3\ny4KTF1a8vBsqPrW85lUAgFsKlI4FcZiOVy64/a26Ju9EYZz9fPc6Fr5c/TxtjmOB6lrgI8qiowmV\nIqVl0ul2jBSvqStZf/0czRcBnpFi5Ftv8e7RQy2SvPEtbATV65oTYUUJHMtb558q2Oh1qpLhM9X7\nHCI3qQdLijcgzPsQzTnmS5xnZRg7unBkuw+IK+c9sl5Yb0Zlz79quMfKo8n3mGz7Atww5iYoBV1F\nukdwp9r81VuEisf8rhlAQML82OVTegRCbd7HRnnlfWW/LQ3Oj0V1F/qCLmEIZGjx/WGB3igLm4wx\n2pNZ2fF7whUKM9y7v2fdLHJNf+6dOGKe99pXc/JQ1A4bboN+hwarGcJIMOxv8UlJ2bSt7RSD+2yH\noHqS4H4fk176kAbllZ+BZuM6d0LllF91NJQ1WJe6Sv6rlAV3/yyLur3R3SMS+DpQ0AOKRpj19bNJ\nk4JG6+uS82OznI6Xz5NwlWbrbGVM7QbmbihXdDXIQHt37+X7pD1e9j3Qy4hAxPG1JqlYXVpvQrD1\n5p3tDEIFZVZEI8o5D8pUuqlVlAW8Yr8U2I36S2TG7RaR2nblLvkOeFgFe/r97f74xGcoFF6SoBxt\no9jm1I7H7J7tCndmxbkyxuGaSIHr0XPk3JV5E6MbKFg7TyjoFufWR/r9Vj8P9Hhn6wFSeF9u3lg9\nj5oNRN5IVe7tycyEfvOIBUU+6Bx/r+/8LaGhEG6pK2SXNlCHRPBZxP364XwN7DVq/fUaD3m7Wp1u\nbupn1O3iaXcNahtvqXingWk4g3rQV6eRhG7LW/fib6MRvBAgLgkpGcX6Zr8W5iLP0mAIETQPY7kp\nlXH/4QZ500vl6FcvFJf/XSjSuSZSEnHpGJQjHqUlLSv96J4+hdQnSXhL1avt3m2ZHrnVp8L8jjxW\nUDT8jOV/utlQqY5KsGM4KGqba4TXot6iiohNeCi4Z02UattVvqX1yQISywpSIs9o0DH7XSeKW1Bj\nWF4xKviwFEUiJMd7QqM31ZtuHVHCfJo9t/pcuj/HBtF+UXmNehGB/mj8Lt8qPy6Knd6cK6pH4I1w\nw4PUT05avAsrD2Pf6TvHbT+SSNag1TDud7xvMUXw5oF/JCkoQYdEWPbHfY8MasyruG/DswT9VJ52\nE+ItkvEmR7v2lorh4ya5oQZ07r0liw4UmtNaTufLu4zLB/CiVE04S+xmKyRpZvFHHXfjcYJ5BzoO\nrSqq8ip9Zj6CZ2j3iO0Z8ZwBqiyp4xYgESKw0SMKv9ZlDSZoxzxObwLQjnd0DEvf9bOy9w49gxx5\n0c+HXkaEDYo2P858rOs6o6IRgjoOrAX54pEcPZwzQUvv8XVmxGGrWs+Nr7OHnzu6Qd5DptcYiaD9\nY+vqVWIYV5iRGHPUhClz0Wo69XhfqMMoGRiFikFGz/ubOb/n2dnzp/0TK0SwcaWVqYaFtwSke9zv\nXnZPWYs8lHrFH115FQ3ivUec+Coi0xNu0HsRgfCosilsGCVjfeg94roDD+5nkff0fA3S9WqNYJ1X\nAlbuOTBm8j+jlXz1RsbEWZn7ZJ0t7wvq2bkvajuiVRVsPBbxa/U5MtDc6D34XAjHYddIcIsacNt3\nRdNhFMLnvGQvhtuCIvKUXyRRpafuT1nDAu090dHNOt4ayaNDkgn3Y45mnHXwhDTJ6NVjIq/Qav4z\nCaySo7TK29TKTepeIayuKLGrsodlNet6TJ30HgEksYqpDy20e2c3XGk4Q9TPaCrcNvs6JOn1VCfu\nKvGmMSRgNDpFdvQZFWNOl/V6G5+z33ytPSvKYxQp4AX+b0+XYVvzmQg6m5YFwWG/D3KyKM+SkQ8v\n3rc/rcyHGBZD2FwWO6PvGYlQ5O/v9/mZXkaESpoPQaFskRPG08wgbL1C9RrI0kleaetNQPOY8z1J\ntH/VYyJo8FUfpqCbv+/jSggGQJ7Y3p+c0Dic95jOnpuRCD4nwgq6zIoEQxnVy9IueFdRomPyqK9e\nAI0EZN4sOTfDLVVrcBZTT9R7/TVyNPN3hTkqvbXx7HPlEPJA0jMkFAGiwAal9YU3MaDDDoX6WuDD\nY+ylblzGown10nCuBJ2XPJc1vKY/kMkoXRvKFb9tvSBr5SAhCAUSW6B4CChxVRI7T/heWAFREyDG\nxybx38HmenJU2VKZnEyQJxCLJNjSe0A3MGkzs9Oked5HgmPTCThXQM0X0D6TIN36s/jjKm8VdWLm\nH3RN5DqX+vspCcs6+uCWMm5JEQilgo/jaN5Dnb+aAFTrKNcODN7vWjYhD687VSueuGezSVtpbfEr\nhZ2rvMaUDip7kAe8z9XRq3kgt2QQmq/kA6zA1PEEzCkOXr7hvQeECokiVdoRfQB65u8qtDIKjAbj\nbB737Pa0h4QCPM8P/U2PV4t56y4yj3ED3oDq6+QjNEs4Q89NcRNVCnqN7blo7No4HgVBtjZulXeS\nJNuX8kYeR+27d50uqL13Hgcx3Wy8nREsyt+9jHHo2FC5huDgPTYQPKKcCFqUP3XfZQOGyj/22ECe\nI72+ghyx6y9JR43yMXe3dJjQg5tk3BXhIP2aGgj6npvb/RbNV9ep9D0UKGF8N8GgbWq/AJr7uctK\nhk/pH28JY0b+UnFDIrQ5KINRzyDFaA7MaET8rImN157XNconyRSjeVLv8SfyAGrPtPl9do3TOs/b\nvKhri2WKCOnj8+P4dXJlzGb9mhIjEezZ3kuKfub525C5VJ2eVsZheF/RL/Gib5xeRoQTaoo56qaD\nLkjohnFriz0PORG0DqZ2OoPzmk1jb+GEKswRDzs0eMS9gFJ6E5adXdtp52tTtIno+1SKFIKVUMqK\nl97vr3FZAM1oxDKVEX6a8GTlxTPvzCwrsu9DFxTsuzUewSbkFxgn/3aTkgfBJyMCJuiSoCM8FzSf\nw9emlaFnhzS3Q3iiAPpjH3BKxarOcN4svEnofe+xqd3PUaaT1Pfo7pXx3oi6nY54QNO4yMqohcM6\n1oKqGnQOU1WHEJu66mc/PlcVy7F5c8Qj/cbrfB7Tbr3A/LdHihivztTPtE/K31kWVKGuZSeHNOOx\nUQr0+95SDNot+U4ur4mKROCcCBr3HiUffRNNkCjt2swQD8BAiYd+u7q17ekJG+79sFHFGECJVwPd\nAHuk7pnOR/fme+PLKm595V3l5JnCk3V8SPrb1zGv/4yi9Bc3sYp6KTh2bWjbTbrPQlbou1EFOwwX\nGO6pn/o7GwLUOOUM283ASaE+uh5NYmM318ocL2XfklUwbyi8LtcOHVBD1N5LO12bKQHHPbDI9PFn\n5ISz75Siont7dwT5fCtsQGr31f3Gh/GeoRL86Qs7KIZVAka/xnfCHFimbtfaOh7vtQip9UvZOclE\n19cO8qKF2w2VXN97BHryz7h3zfZIf0LH906vnAiFXkaEDepeM4rXNsqZLcvMlNf3eETbuBgZdh8J\nw4+QCgSeHjnyJyKWb87oqYQxF5lllMTvTHl9RBDbPWLMe05UIWRvcFECnZLCEMqczTWgP9My7n12\nPUCWsNWd8y6c6I3l3om3/lnY/nDEI9YCO98XKZMhmViKyYa9Qacy88mE+ZIwOQ/d3iGJ3NUAujdE\nv3ahvBVZeB6LgOoSiw0KMit4qkwHcxbd8MHlEvZfowimzHaI+8cYK/4sTddo/eRs354+Q65rPCfq\nSBTOYNqXoagSG4dWRgN7z06Pr1NUr+5H/Wg1a3RSVIlei/L9GGUwasQr6dvqo6vPbBjnt14x1kYG\nYTZs9aS1GR7BtKyXOhHx/yhfp0/wlvGY02S1LqZ5c3IxdN1dWZMPRegaLI+6VWOHQSJUw3xuqKvS\nYJRI2UPah+eW6Kbc58aFxbOahRqSekYaRuTLnt2rxgA9aWyeNHm8x9STOayoP3uuhhulHYPqGa3K\nMNIM2A/f/dZ08plc8Eg48ot+/vQyIjjycT5skExVmADQ0QlU1hyvtmEl9xfZCuzpikx1ZYM3jRth\nBc1iEnnn9Jz2q+QTNR10fcakjFw/uOyKEhN5O3wcWLm2prYPB9e9kZ/HOc5DIa3NJD2mFlAkSz8q\nEehwPPUU8LntPrGi6U+9n7Mo87GGtwaVtf3XfpbPcmzercJ2uW0VmgcdEt0rNEMkcLhO+RwTK65I\nlUPTqJ+rb4J0KzD4MGylCRj9mYzMv1KekjRjxTKxoliYpWb5dtaZ+iL7i1jZ0zhu9Sxfhp+TzItW\n96wMCaY5nXAtnKHGMNTEpkAxIuj7bcnL6Dm8LqRCtdBcPXKuyr++q16nKJy8JUyk0C/pc6WHP9gw\nBH7mPh/rdX5WCmdAAtLNHkPJn2dkDCI5TpymfJ+9gzrUR+6eojIH1DDSlXL2GLY2PT9y86ihZkTC\nJVUKJft9Es6wk8hL44nZmytwcwxkYAKFsaTrSQMj8ksRgPOcihl/AA1a77ec7OoDKq8T2oucxdhD\nyu1wF893ycLf62vJTHmi1u/Nw5zsHDfPPLnuScMZOEyh7Vt0f1H4hNZn+TujK+Wtz5V3GpnBG196\n0dbPO89jLpf7Jz9fRN4Y2a7RPUlySdqZbBhRCSvsjdwk87DXdnObF8y/3kTDjuy4qU/8o1ZwQ0bO\n0tav9keh47yvtul0ugEkGiSeL7nxZh1L3bP67Z3P9D5bvhI2iY4AehiJQP2andQA9HCHg9BM/cSG\nsX4OAeZrHp0wrHnJbaIwSo751ooO7BsGZqGGRya55CysgQxHcotliiZTboQTKX8DXKLVFxLB0LOn\nt/1c6GVEmFBj3JVhzWKJePPL6DBHf+zPDg35VNDjQE072SqMgGUAg7JnhJde95LYe3CMSnrO/izk\nUUkAYoE2ostWTpcA7yy5YmegXZnW7u7A3mZojh1qKAT0cdEQGL52VKFipWC3oxJjnb18hpvI2Ccv\nGEsrm831W/vfK7mpAtz2uRhKHL0XH84QGTc8hWvIJCgsYQZDYlIAHW3BMa1o10r9oxDV6l4kVmzP\n4L9rUZP5zc5ZTayYs+CADZPoSUqzu0YeQdjywNZSW1I0zxvMP5LKN2gQ3tGFWF/DrmmyC0Wur2fw\nVYynA7TfUsYYa7x+71pPE8xJIYhYkq4ZRrXtkiI3zDWCo3olbIeKx1QadB0Yeb2hg57KhDOo0iNt\nLbY2BAAZkvp6L33nceNyYX+DvSzsZmBIf0Toa8o0SIh+epWdIBFWjDw4hvlYzM9ZctpdmYSNECp3\nbFPjnX5uXKnkpIn6aU44AppnPELPsTLtwxJmRhdVItm4CYyG5WJIkHrSEtdbrmkupMJnNSdMb/sO\nDPsqt1fapAVioGWa8XN/gO16muyBPwLNQhb0evS7z4kQ0UzOM2EcC8bC80P3H+bprZ9SjUMPhjOU\nuvs+pXN1oONwUJ9lcwM9i3J+0YteRoSLxJ6tIlyIEd6SlE+jJKnOENXnkAjcxhVindp7yAVdMdBN\ndkpNkwTUCB0xVVYAVAWPoNIRD53p++748UnyOwk/z+IuH4FxX4ErR3u6ibMTVcit8YC9wXrtllTZ\nyeb+0k6ffwL1YowP1A0CnFBq/lwJXTFTAQfocMuVYK8UCqsRQsTNbwNjNQLUpKHArX2WsHHoq/te\nMh/HZT0S4TEMTkAp9hrswnZV6MnEg/wxaExR3LP/O3rHiqgQ1torEkHeOhJBKJyBDTcRRQbHKZJk\n8F7mJvT10IZqgBPrVVQF8FLoAfEWPeZrhydnMkae8ZjZO27KSa3NnoseGz++CM0aSQm4j4kVlWbP\nldr7IgMz1jyl7bWpJ9zcaWtGM0+9r8/raJpo937vv3tDTUO9eMv+hdCDrU5TnR6J8NnUjWP2+wxR\nM/TzLRXPqEMibNoeW5sATFy+aWbyPUQioMttQF/XBa3Xj49N1ZifuS5YgwPXr0lg36QnYBR0g0Fp\nLOODDLRSjQplr89vFBkAACAASURBVJ8/1+kcN4w/AccdBaWie5ZghRQye8DFfa0brp8PZ4goT975\n2I8cGgeeCmeoKEp7xGPQBj33MVy3vaKp3PuOsp58JCUwcVoN/CD1dw+cCPdjONEzoIKCCPnsoL6f\nBqnz50UvI4IhG4vbP23G8b2J4xVIf61cP7FSSk/cqMogUKyct2RhubqpWU+P/dQ+GNgW1o/UlMAT\nXuEFQu1HhESYhTPwed1C5dqpC/0C2GPIQkr5HidL4n7x38UA0jt/E+BOcGy+n/M5cH+zK8djwZ58\nvfzWDAXchnrlLBlotyoSS4s6oy2K4P4m8TzgM7N1jmtCcC33njLek93U1HPJvWhoj9ZQ9/h4SvRc\nqgDyb6ZO9OPTzoxF/hgjbr8Z/oTLkwIAiTVrfZroiMcaxsBjqfGvEsUguPo381K2/vM648SKOmtU\nwWEhOCMWyIyO43kG/d089EkwQNyJVLln0EI3QPVcKNLeQ0GQRJ7BntcA5m/U+xIsEkHn+W1Y97n1\nS/uT0edC7zva6QztmuMrrW0HDW2nlTgkgieF+6oi1jOw07uloS3vpyMnNC6/PKcfX4YCWy+Wlutj\nbOfCLaHBsPmekI6rqntfk13B6t+bYqf9ozntjepCdQm9P+bR96z8jwywkNaGjosqeHfmO7krzc2L\nnAQfGe3kHECz/4s1zArxJi9IBOtEwxnGPZOSe9Im2k+8ievkdeu/D7y1rZmxrG6DnGCSE0ff6Xe9\n55ByqkNrp2nAedj7Ixr2Oze3ARcX7551+Fs6b+xzpBqa0ddm5w2UjBV9L1TjnRr0m9e5llGjKT9H\nOeKxPPqbY+RyAPfmROh+c16bkbzUvNDeQnJRA+TTnnpYVO97Qt/L7H0xuqnbLz4nnAFw4QwR9B5j\nYsWr4Qylm513N56oZVOX84XmStMFWl3WsBYhESQYAB5e3SvZ0RgbOycD6ZEIBwajt93nrHw0Ivl8\nuyM6pskYEBw/SorsF31L9DIiBDRs7Pw36d0MYy3X9N85cxcVQhmJkGJrZ6n7CZMhYgViuLDjIkh9\n8/RJ+Z7LDr1xs1digHbcVaRc7iaoW/X76iNxXiMvS3ISJfVWsBFBVUFV+B/xOMawOPueVh6dLojn\n1k+FO6swqXF8Z6EqnOE4yiegno+T447n1Lwt/f1HEP/WH+rs6XSbJKjKi3CSz6IVUidT2xkaiBM/\ns/UyXfc6dJ0o9+PqvISfyOs1ma/eMMZGTCvsdEF9FGSdV4mEudaOF4hkNMidkrFMEPP/wlR4wd4L\nmkHUowSrHEETC6d1z3qA1zBxOMMBCb2a7YjVoC0fjlNYvTVqZPD67vMhD3U44181/p+t2W7csn3W\nIx77tJgzPhO3zlY6zPaosQ+mvLfuBVA3kZ7osM0B+u75gG/T3wvU/ZiM3g8Rb25PkiqkrHAx+aR2\nYXdg+U0zehrDVslNwWFPM9EoQl1pToUk7ojHDLwlwa0dT1k4d3Rc4TaZe4ON/wLx3bP54emzkAhR\n+bNwBtuP3PrhEyuWewoljLyByRhz0jjR1FD9CFK4myWC33LvIyctHcMKNxpKxdlhEt1Oiu7IML1f\n432zd/S90FXkzs+VXkYEIs1hNWaXtt4U/Yzkap8UqGelj4W7RgfaEXK+bbaml2vj8W0RseDIiNiH\nlTZT91xZO70X8Wa/Fc7gKB+5jh0Jso6Jat0+HnOahTbcqK7lRDCynnRvmAkTgNVTWDn31uMIOpWD\nvkaoF/YMe2Lvbk98ZjfUhO7JAdZCXEQ5Wy/+7IhHFUQiaF9IlA8jH25ORv0go0W/Vu7LWepc0jpr\nac6JkMe1045mIzHBKIM+zijIkXDUI/EeCZOInsl/98/L3YnKXMr94b2sigJqhtWJsmU8PKMw+Qh/\nKjkrraKwnD7u3rDCDVLF7CycgfeCz6JoWpU23EYEWl/B/qZe8LGBo9/sK9CcCLT22lzO/Zoau2ZG\nkmf3In3CorNrm6QsEkpnBn8H7HzQOhVFdpeOXviA3fenNCCZ6mc1tAwiQDQOR+5B/sNvvb6zXAPh\n9Oa/6ZUCoyMFsN5onV+HyADbHtp58ChKP7y7+vapsYj+9qEgLMvpLx2MyeFTcSPd7pPxzkaEKhu+\n1Yf4QEkU641WHhqfc7z3m8IRLQZhpvwcztDGpzP0zzF0ddUlPWliOTd5H9ow9DM6i/vKn9w+/+3D\nRK4owqv9+QDxISAMZ9hvp9TVHWuTds0Grnyaf3YyV8YwlwbDz6pfwQuJM/+86HuilxGhUrdaOqWM\nlCqGuCVIEzBYiCkbguBOx0Xx5w55SKIPU1AAw7PLd9icGH7oUFKd60pTCJlJXWGWK7i2dmNKjVn2\nzmXfVy06+EB/POpw50KKShCgQ0izCrpeqZo/wywnQv89tzq3DNlS7PkcxXJLXZhGbW1nPucTae6K\nMnWW3G6H2BDYwmhmnToqRzi6VYOTxkUoF71SxicS+kcNLpq3rBBExgE2BAz3Tq77PgLWUeodnkB/\nhjCp4qxSxAK2ndfzutSQ2xAfUKVrvQ5w0sVJVwltEaNlniXnjG5tfYkE1yt0gu+TQol5bS+H78gj\n720GNhmMhIAVuvWYRwGtQ+rP0Ec85vGbkW9jhsbq4WC9j/eKxrLZ7TfpAYv9Vef0KrHisp2NMgwm\nsLyhGmawyI/wCXCtJitId4p4B8oBtDAYyGlYeKP+PscQqKHshoSjqL/32idGIvR9v3f0LjqXMt0P\nIJOyK4QSiiax0cD3JMIZNN4nVuTTGXrYWA9dCUMInuRtfOrCM7Q6hcH/vV+nPVnKOBCpXGSMjJ7G\nhJyFY9mNn1O6wF8uglOm/XmdzlDoNQyFXkaEDVIPchK0ONYC8y5MTz0UGbnHuS4U4nDNJ23LHid2\nq149jqfXzcbGtK2VSf88hvg2NqeydODCLiJ61Jt6iTScoX7qEVKzIx63qhxsKf1YLzvmYow3u0y1\nePKLoK5zpSUs5Pqgcw3Wm0vhCGwN1r71Db7PCb5H4zQ5jtrPH00uVb4fBjFxk5IXoWWWdv0+I1bQ\nrCAalzVHeOmz6UUP/wHa+9+Bg8dHHDn3z4V7oy7p+r6iBB2BJzFCv5wJAjqH5igbW//YjwBGqQ/4\ndqOjHetnSp133XryO5OAdtLnpljSPUtbhSsnTqArv/X3qQoQ599YqU4lbIPiz2XOW740qdExiVw6\nbWFGCgXX8dXjZm8ieE+CN6E8Ak88byZEgqebZLyljLe6zmbv24PoC7oFxgBxlWbhHDPSIbgl4JaB\n9wT8sva7GYB9/DjFUl/pV7tdgj1ro9PJxUB7OlvzQKzIdINK/Z5jGUNmmiOFm60oVLBASjX60JYc\nDK6ZzXfb9zwyBknPdwCUOfoBm5tIcyQwyklDsTgkS49J1uTV76lvKLoe7NGRo+I+77ufKKkYDShf\nhSZWlCTIpN0+w7/GEIXHvOu79GNB5BMc29NQoRYyVN8/RsPBypC2ehoNGRpPZRtpui8mKV6oJhfP\n5fMdW8Mu94rCV170fdLLiDAhVh4zrFAHqFGhxL01i2LuAjzfDywWsHo5DwsB1XsTuiKniXru9yLw\nzY6FCS3pZ9zhfgxIBO2XOI1RwwW0aMYssc01OmVygTcsH7nD2at0oePI8LyM4s1cxYQCVhgxx3Re\nfBZPqkyp8P7WFHlKxgbdrGq/oM9TBXPkfq2K0ww5ZNrNHHsmlCfKidDmn+oa2cowrV0KstV53dEr\nvVyDSGK92Q7kMNwcznA2h3RcvOc9H2jQbAAQrfue27X7PTV0y6qZHkLU30H+OCBvyUy2/JGRP4Dj\nLgY1U7pi3x/PXY9SYKNBxsY6CiiEMYcF15aWlaHFG7t2SJEfNwrpyY5HtjojJcz3Q9CSXflkbGH7\nAcppZ55doWfzEQCWV3HEQRSaUpTx8sN7Kuuak6k2Oo6usR11IX8ctO6yWeOAAneCo1ar/snn04fj\nTwY4HpecL/KICQmt+yPHY898WlB4da6G/PJ73/MNrRABBwY+vZWzR8MZAuIjHtdoNSuPXJm7Plxz\nm1r4ywQl2PaB8ZpvH4A5Vcp4fQNeuLBnXKJH7tFcR0AxvGvfkpRxaHsDxv3+EkXHOR5HRwe5PfhR\n8sYlkThZ4Nega6EHY9konCEilnejsePkrjdRY9vn0LHL546j/L/fC0/+qGaMo8vnSldOEXjZBfao\n6Dyf9dZ/2vQyIjhKjh1onHiPpyxcRVIJWXhPgnfaEBWNwMYDvzCt96J8CAozeksZctdremRQN2KU\nPu17p3ameVZp6sJudpa0jIUWr2gO7S82pYRs4ZoLJEJ01NXOI83KGIU+06bf9+d2fwQLZ6i0ogHe\n6+9vibMjl5s/shqN9Gi67vVofdVrVUznXAV2s++C+q3mO1ADmLQyrr8C3OSAQAwS4V3qPK+FU41x\nnABVzMVmySckgo9bZyHX3L45HSNPSxQj3LJxT/qdIw19qNd+5zngc1uYArXOsxCPRdONRuRMXC66\nPBvSFdy7nbihHjC+iTz3pnxrb21y8WiCyAjg0Q2K0lGEDz8bK5/qZTQeTSo7PKeHNUxQTrPkgDPS\nsnezPjEgboTKKu83pzNIn2d+3Xphf6YslvCkXvgtSTEkSD8aM8zCuZmoQo2DkWH2ljLSgeF9M/Kg\n7RvoPKMf6blWaK8oyFoX16PKHvOjdzWu3MvfAPALeoZTope2MhZNDVP+ganQ7hGPl420jnRe/BJy\n/bll3xPu1wknqb5JfwYzNaWfRsPU0WBUP8Z3rugc3ld1fQ8nPEzeX0OtGARUPzZSn+IQPsEHSGLH\n85SlmEWV+pmj/reocxsU5UoIHRT7VX7zlOqcFrEoEZVdWH5R+bzxMwE09OmeYwPFieg7JS+jdHli\n9p7dBgdrPGhJrFt/+q78mQbxF31/9DIiVPIQSn884K0Ju6VcRlfO1ENxS+q8sUd9LSGEASW3GWmr\noeBIfdRcCZ5xhV4TOE9Ilfw4idyARgAXDxJDxY9zSlKTM3k5adj/Asmx9feBxnUcxzO/MXB9P97c\nvxmUTWiz6f+r8F6F1ZuMKcDKu7Yd0KPweKMfjFMY+6hHFwr1N/IuqbLXYjKbESFXyDN7NUt99zOP\nxCxXhU/uEzzLFQq9BaToxYppLxdXmsn1v9ePnnhS1nUrMSrhC2/k0dzgOcBGsK1YzIAk0Rh43iS5\nGWi7IQnFO30yTh6J0Ou1PLq1FRkTtY/C7576Xvv/mFtQ+3l+XT2lPKXOmuycdq9vfQ3ba0nQziXX\nH98T8EMSvCcSKyNXb/vekQhQFJjLEeLXd8lqbpU0/ZvRKUo6P5bPCJ3T/Rg3ViajBMXMA7nNRBda\n5E7j3WXfP5LgVp/7PQG/iIwZUTgDI6Y2jIP+xCZTz4PERhFu64zUgaFFGdViFOsczMzNWO04nKWu\nE5AMA4pxT72Jwj/On0XaJ/GMxPzD9rU4o60xdIfUKaChqHrtyBmHpG5EkG4oPOv+8K4MIjPb64BD\nJ2x1e0phmMtzVT5FmpPsCBaTMd6f0E45L1ck917fpCfIntW3mjUzuXyLjMODefL6th8hMu9nS6/T\nGQq9jAhEEggGzSsceN6PBLwfXTF8F8EhMGdK32t8fcbeAtaNRdt+SwUW954EH7pvNCPFeHTa4D13\nTO4RvrXtSYAVeP3m56IibBvVkNC+831eKZhIQFGCqd04sOz+VoHzRgPWLM3O9uIF2Sac0nzSDUfn\nxZsoyqQrRx15orF3NB61/GyM/PNwvN5NcgufKL+X/npFL1UPb2YjQ73/XbonTgdAN1BgYkjZnDcq\n5EbKndfrpHn2hK6d1G+QHH1N9N/py8EzwdKuou8TdbV6navUhzDt6glROe9VYNOUV1qBoStkTOrf\nmwEzoYRiJOkxLYxKCDz10d/tGlRpz0i5zx0fn7wicWskBfwmIu/xH+7xrsoAiTDUSbf6qvjTGytj\nBaorHqlcIM9soKSekO+6KoYKDX9P/f9b6u/70RgLDWXwYXn973EfHQxOQEOVwHVl1avZb96YwtdY\nMb5ReS33LkCu76AjEQS3lHHfUdD8fLpCihyjOgTdwxiF2UT0iPmBdZSz7icskisS2eMQg3pIbgF0\nHkj7zZ6EExvkmLJbLnv8YUQZhcbK+jzWkZMrm6/7eFLjUZHX7mJ5xcP2yis3Pqk1fmtx70nEGBD4\nuzUQjokVP68PtY26Hd5SQZFqt3g9ePn8UjtaR50rwsk/TiZQZNTVkF5TDlzuWv9e9CLgZUQwxEc8\nNmEamv+gbi60CtWzrMf25Oq1vdP6zhiFxxlJqkK0s4ArtJ0tlzMewoz0CaeaIUYizOIbNWb77Bic\nsP7w6Jhe/tHs01G8JcfWchw5tzsjk+QJ8Ej1ZR0Kl+PkhqrcH8aEoYpuHoRCz+R3eX7zBC+UFvUK\npop26DDSMr9LWEPtR0U3/PKs4YWQbWL26v+dTayFA7icCFH9K4Qnw9tLzgZY5MRRN+0jm/oVdtw2\n5ongbo6NrP3NFE/en2E88SFD8NEEkt6OHp2na4wfj40H+rcqOQqo8OthFoNcjuSiuPbNUwt2jY2j\nQcPW74X/cu2TmNmCdhJhhqHw6Gj/yDE4oxgqLO2vL0W31PeJNykohBsZjVrncsZwxKMP+XExuDoO\ndh9QpS/mQVdj9PmTyfLx/q0ntVsrRZp8EgD4KD9Fwuha1KaHubC5T+kxmKWffS1Pw5KOPIQMmfoO\nGfLn9La0Hfv9yNaAokfSte9bT7JW4tdKTr/fX9Nb1e4KWMOfFrtP7sXJe76GKtC/O5qGacxLlZu8\n+FbDGT6Qmhf7UyiaKBEyATAvpxmrq5GP80Md4f6iRpD+nKsjHg/E+ZnCRzgpuzre0X/P6AcNKl/T\na4wmSaESPWvIfk2kA7BspAZPRXkBc3l/ZZRbhfSGlXh+HKAaWUbhnDX9djHv1ld71scnQS4/efrW\nDGw/Fr2MCAF5JMJb6nkRWmx3FhxIJWt9XXGHlJMa7sKxbyWy72wDyVUYM/1AVwA5E7RCDHljVau9\nlgWqBVykxf1NSZNjqSUA/XtbKJxYMXcm5H7qfRfUc5AzMfYnGY/C+BycTyG1zDyjo/iKzLuvjHDC\nHH0Xnrl66HCPxezxjreagPNNerKld7GoA4DfZRdglXw4gz0+rbaN2LikSBTfzg6l2uf3XOKntR0c\nNtbcVHlM/nY0mzfsiapNuRtHZYaPX9yhriSU7zkL8gc19nEgJzFr4jh6YkXf92Ls6+MznPjAu3fu\nv2lCSH6fXcAWYKHYrpSv6KdVOIMtJ0ZYmneArBSIjTmr/kXHdurJKPxdx2F1rKbPg2DLZETKCFOa\nTTYXkvOZgsNVZ//MQDsYF2mqidA16UK2Jg4s4QzKj/yz537Dx72tuazWqfvR9oj7keotynfj/nE4\nQ0PJhe9jFIqZdK9LGe1EJD56Thx/9CgnDjekSsvYkLH3XYBDyikiCsJ5S1YB98/YOyxziRwL1tgC\nl+u9t95AXtR3hXb3AEWuzO7R3B4hEqFYkxopfx6Sxk4exyNUWOEFuuF7Uw+cPgOwtxYPCG61sWI4\n1D25163zW0Nd9c4PJLylfu1o5S/sx7yXaJJT0LWjJ1XMNF+KorhOvvkZlCC4f0Hj56PEec583gIO\nsduhmxwt1KmFM1Q+xoaMaD1cdeoxamq4dWZE0HcdJFs1PBnjsrniAHyhFl6k9DIiBGQt4roh9CQ5\nSpqMShXDe/VY3EjhTxKjEYbY9YSWvKUntSvGizfpDKu0WwSZD9PP0g5DtQ8ZEyHNBDdRTTPZ7/le\nlWmqaLXhdgVYmpJpsrJnd3+GMXr0eiZIvFRh1Q1KLS0We1SSAsE47HNnqPr3gGqWDt9X3UdyRzas\n4JyC+s4k411DX5J6LHo5hi3f6HmKlduGM/SQB7sx+jHjufRG78EnKntLxXNSyh/mPqCE6iicN6ML\n6jq+nFjIwmHcWDTIeg7npk+S5fN9Ci8sunHXizzOSRW2JpOamMGqDTNf8qL8Qgv9kvuyQn+9Nw8Y\nIZdqcDKKV5JyrGPP5tZvqBMr3Y42tuaIR1iew+EM+S5Udh7OEBkQ2LPN68DWZ5/xVJDzi76GM/Cx\ncJ68l2eHfGhX67tBBmWAnnsWzrCtEKKvAUXP/ZByNSSw0rNJk42gZlcxRij20K7QBL28rT7VfZAV\nWi2jQBk9GCZGXJmp2hL18cEHbLy8tXK56PCphDEAlY/XF8jQ+nBcAqvu6Z7kcczcQPCAKcXHAYdV\ny7V5ag1wF+dHEuDW+9R4/0k9qozfxF8rpEajjL5XKxnZbajTG5Rykblo/LTtYqCS+txaX47rrp/G\ngJByC2eoVnfcIS1k6H4XMqhtLmBPGy9TaG+8cuTwz5UiGanLQuP49CMec1uOVqYS3KSO8cnwznjT\n0CbsflYMFXuyLGR8z+zwG/qExxx73XG1E8j086MiAz+4bn9m9DIiVJrFTxWm0ZWq7rHqiiAnYVID\nQsuJMBFsPIxIvV3JeGpUcBoTOMphs1q3/oIsmFVh90aDDm+W1nY5bi4PSAQcgpyy4TRq2e4Ok4IC\n4HbOQjii84fN99mNnEQGCJEI9TI8RDzTb9qGc5aYvzUmWf9ukLXNvdhYkoMNqIczjBRtNt67l921\nXaH8bCPTsAbtZ1HuuwEku5Cd1idUZdwMcPQcYubPqs+eonAGRSIMZZ24qo49RsfwuOg6aD9UuPZR\n98n7kTpcO/SKdwNaaU/aXMn3DMk1NEK9RB8Zx10RFLBz1cNNUTxhfC08xoqHx18Lyg7vMMfHApYf\nyS3fjv7LdhJO+uMpY2G48XVgespdXHfLMRGc0FE/NafMuuFMf6776sdWeZGIAZ6Yd/FI/jOf02P6\nqiaPpnuTGgR/5aaGBFJ6uAK33jgkJ9/LJOO1rMfqliJ1vwoSLT5K3ih0RjrHoxYFQBQ7nUL+1xXA\nJGIMqNoOUPlTdgvOvYxoLpkiHomQ3Bpr3sf6lcIZovqiv2fr3O+ROzTMXZ3oR58f83t1rdrrg5OD\nrrU5kOM1rEMThRsap+3k/h2age1ESi6Emxy4pe6bPo7x6O0rnmkOjctHLkcQKzoI6GvTxVUpEkHr\nKJ99jzHhNIDlUTl4t9wnGrsS1uLm+eazzUIKDkL3PUNaPyMFmdQRNKxLh0IDLJJKj73WXFf3Tdn3\nKkVyPo5cj3cU4Dj63Lh3WWjc7+l9586nBySba2YYL1zjDy/6edPLiFBpPJ1Br2e8p4NgoWX1SCqQ\nofeU8Yu6ot6qcsUJ59h7tPTgN8ee9dRoLDtn9i9H8EXP0I0GQJfzZ+0ewx/ntAuJU18UW/7vbTPz\ndc42kSJktE3Q7NzzTq/66I03rTqvO+p3fR4yDDFataEHxP/YSY0HmigTKAYozbehvWVEwY6gzYJV\nuxYo990I1YXg5skjz6d6UHDAGc1yTe5Zrt1zOSZOqM9KJb/AvO878+fRXFBH7pC8Lsd2WF/k2Gt9\nVimKK0syPEuDa7v+FuFCBeIFMJDrM8olKWInwpvSTAHy+h+wBECct9M0V8oymFJfgx6CMaGZMcO0\n1XhWr0tzhCjl3Ofvaq4oL2WlkMOMlp0sFbS+eAWADUmqOGcEENGI16za98+AeM5GFOZYYGsG1Msu\nFFYF/EoqBsIh5jtSXN31st77GrsfhfPfcyj6mjXIXu5HaPQoyjAIK488YJUHk8i2fr6lzhsVvaEn\n1XjU1JVwnh5yV79rHdnud0LKYC1wSYJfGQWy+5xR258io0NgoPA803tGFanCdUQck6+wsf1O60Ey\nlgmblZQHBMAQ16YqnOfJIqO9rBioNdRPeU6uOa3Y0PwAQ46sIv57pBkeYvJKcRjq6jZu1uxNCzbh\nu1ny68wNBZ48D+P7NB9CRneg+f1vNQ8io8TR3kt/J8WBViYWIxFuciDRCRu3JptnsxfpvPEOKkET\nK4b+st8loiWbZARvIkMR50SoM886JSyV/mZfpfndNvt9WxLWh9x/P/QyIpxQ8yLXxaVQOk2Uw0Ke\nHqfHSRCbsrZh9eajr/Qz0afP7OuRt9xfAPYc8voZwbl2KBZQY4vnGQWy3knbwWJN66dgxgl0hXLm\nnRiqp3HU70A31JRGNjrPdbrvTTnRcVTFfdNQs2q+z4cSS34mrPuTIHyfGV6qOTnM/Nvq8Tl9Vj0r\nMrku3HuOJ0j52PWcM5nEitoqK2Fa92RD2vUEtfKqzG4aEJbIg69EiYRtAKMyG5CIHkmqvNL+ZuqT\nWAjbgpZe0HIFJ8Ketov5e/XhGYdkSoshbY95hDoaysL1I2PklGaZ8HB6GcDeuwX20QocXvYowMEb\nuc2ejq54+iF6xgCi9AiU+EsQgwcAa1T/ksSJGaM2u2yF9n6vHnk6o8+G929Dzn8kWtkgfmr07MkL\nZ0aNwvfHMmwE5ZwIX4K2kKNsXESXN0wuMMyTTGc8zDZf9CIALyNCI2ZKNh7pwJscTfFtxoQa38Ye\n3reaQI+TIEYCWnQ+rCoTDCVn67lwOIOo95PqrJ7Qm/S4zw96nsiwYJRzhR52XCo0a71A2o3tXHAE\nyeWqZ0D7mBPwi4OMH8BuAuuYmrn8KFA+0+/uDbsCPV5Z1VlwLMJ32ViaByejnveMJSfWvBbFm1Va\nL3/b+XMno8yYsX6eWLH1d9K+xqFGv7OQFglVmrvgvSZfA4B7At4OwaCAKzWlm0JN6MVnxMKzluDk\nZ6FQ5r1zGBV8fo/8XPwugP4JOGWb18HRf59F9LJRr3vkZJCQMyUyKeEMN9yPZD3bJMzfqM7dEwAU\nyeBR1Z9KCyTQo/TZib9mnkJzyk39bH5z4ybKFQ6cGk/ZDUE4M5QyL+ScEYmviU0mGRkymUdFxk1/\noo+iEH6oD/5DqkgEDmdI6Hy2PWxP2mYg1A1B3fnSkQUfWfCmnr2GxLNGcu27CbfL435ZFE2peyA9\nt/KfC1KwF8wNn6B+9v2fvY3KM0qSxQ9yVXfEUz/NRZKbLPWF52NMzjqdKzreib7XG1pk0SGhN7+f\nAMGQ9jUiaFhLmAAAIABJREFUMrp/6bmfjb/2m/haT3Y8N9y3egFzLHFDjmS7DmZ91tMm1qhECRNa\nA/HaPiAQVdZM5vsud2g4A8+hLDncW+M+bRZqCaZpL7zTtQ3YFyPeIpkoI35Pn0FnzzlT8kPDOd2z\nG/7QEipOyucJHDAhw4ek3jgvwkVSg7LnR2c6RAtpkWN43zqfec9SBGVDapI8oRShUdqJHUO48Xdu\nepjwsO+RXkaECYlnFCm7xVUU6fd04L1mpr6njI9clMFfkLXynnODkE/bSx0y1Y0F5e93KXDTX1Is\n1i1VQYYQxQVGLS6hj7WefgmFgj0Ib/SMGXrkZW79aN6EQOA9pQbBSMDbja6hJqVU4bQLq2JuLZuM\nR3TEMlA1CIEMN1JqVVidho5EMXAehqwJOBVG/CvpaNeVbrl4qz8OfSxSfp2XVp+NFQptizEYKgRz\nUrJ+NBGNBcocT5Ihuc8hNXTgSEECUecp4odHQd/oezECdUC6Ud5SFw7VY8pjZM5MpwWl9ZtN0bWh\nMFObZNLBuJsBoB+r1iGNk75LScKlY/6WivHj7Jk9RWvT525gRVNybGTJKBs/w3F7WE59pjxPcOfb\nMRpuu8kmNgXKOKU0f26vJJQjNPtFTmDLSBpxQvgtHW0O+vG5pWPg3Wo4Ktfcs606mqQlJlslVnS3\nALCGMi8wRwYG5SW8bnXe59roPRdIfeLndiFjrS7xhrQy5sUYWAwHQPn8HbcDPySO4cbJAI3EBrAM\n3R9r26CkZInGEppIk9YxPY/Oo6JQHgMPa/yblB6fXFH/LnySTziqPDCR8n2UOkrOiFLpeyrG3iMD\nP9Sb30WTHQuFfSmf6+uha8D6vz5P6vz51HDWBoPqOoolwCd9s4ks19XquPLn8HsdN4NACwovof9m\nXu7xw7Zf0V7wJoVVsG3rLZW96C65O0dSHR6UucT9Ldf62ItkpNuB263zjBbyEDxThKJh3nSrCRpv\n6agyo1p5Eu5S5rAxBNaZl0x9CAWSg/Lr0MVQk4nCCRnefqBLRjZPwr44NlMifyxkTWR0KMdIWv4S\nlY+QRkXOIKRb2zf6HqVyRITMfIZ0/rVE6qnyq9n6MZ6v8uGNW2q0NAYiyDQ66ltBSL3o26eXEWFC\nzRORMt5uyjT67/cDLZu9z9aaCDUgAYOKKIqn7PKDjeV7k74JaRy2cHmnqJ+1345zJOtrS6pYpbIW\ng3Z0z0fEfAy/JuOCfs7yImwRIxGMlyxGIszCGTx8kv+2io4YRi6woQEZowGBhXcW0vgooF6/9Wsn\nKQYKvxkdTgDwz7ZD0QakcwtAMxpoRvhurCjz+pBuAPnlUea30DOaPusc+hiRCDPrbZm3mrE6qNOT\nekZre/r+M49Pi3V27aAbJlS57F4pHYeabyBbJEKbRzRfFHaemrJXyx/S7/3I5OHV37twlwlyuBtr\nx4JcFJd6uO9XKZxh7cHRvGGed808e7N3740OPC9ngtNKIfEy42Ak0etDP7SzgUDqlL3PjIdse4Tw\n3KfjCpuCI9BcMz13RP08aUNRCJqXpSMRjmLc9EKqw7W3hIHG+wlo4rxevOcM8ckw/ckBfl2ashSq\nwop2z6Lfc7KchTOwUaHX3w3IahQu/LcbYoFuRNUTkgA0tF/EnzKtB7mBxqx3tCEReLhReO6BzjOM\na3CBRKhfh2fmKjxx31f7yI5iFPGWhlox/HP/mEE1YHDSajV639uc786Zu52u5D1dt3flWGA+4rG0\nNeZXYaOCQcA5FM5TRPOi7YGKQtDEp2RZ0z1YqSUHdmPDCiZ/X3WDaTfXVbvfjcWs+HFBWPRJFLkf\nIrIMY+BfVBbmcROHHFUDoyKT/j9y2u2+5p192aBIeYIfuRgmOdm4BY/VS9JkSObTfYqozNQdEBHt\nzovvgcq+8XkywE+ZXkaEE1LLst8skpQNkc/+tfAmvSZdwV+005AIKeOmx/jlAzdJ/ShJhZML8J7E\neNETCTj9CKSuCBoPGTGSHVJDwnCdmI9vR/tyE6to7yRBCtv/ytQMNvWlqecqJ4aL6YZlk+lEdWkS\nxeF0BlKgFEkCnAsbkddJPTC+7RKS0xMCqbdP+14++zzP5HnVI6tAiJtyKkksSJt3tZjws3HKtX/6\nfUjc6ODUq/rjkAlrkGuKCoceAM2VzNmtV+uFvcimlMGL5lanXtON/eNwUEN/e55v7oB9D96wpRDm\nhF7vwwczBVqFBO+Z50GcwGkkVaKjRIal6dzqU7g/z+OmYJJBTA11zI/ZqMqG1qaUuWfkIx7bNfcE\nfioyL2zPn0eEgCkPO4faXNXEoKQ0eTQVd3uVHVxPZmhIhFvGr6QD736P89hWMiCYPB/NqNyNdy2+\nvu05oxGAn5uJjSitDHna9X3dqlHkLn0ylTKyXCjNgKRjSHOh7aXUD05K2+dQaYuRMBzOEBIhEZgY\nrXJIFey3NAsJ190O6atc5ULJ6Ap4W1cLhbwoH6RwJUF2SC4mZove0KHz3oadlY4fhCZpCBOMQA1+\nVqUIgTQzRibJ6yR9s6SKdV5FBgRPnASY+zvLPxWeTHSSo0TDsbjPzdCX+T1IWMUMzu7L7CZO/NL0\n6GkOYRrYRIZPRWlKSbQOFERSMSQon1i30VfUnMr+V8NiiNcN225zpsmwgDR8wZ/GccDK7B+5/I+m\nFdskZg6Aso+/MAvfO72MCEQM422bV9LTGWx8nWTBcRO8HUdjKvcs+GVV9jWTs26GH2I3Oo3n1A02\nH2hw3FuVvkRSD2lIGb88uiDcj3ykTRbqNW4iMjSTbVc0H8uIvRJYdBNnATlJUVSMkIvC8wyKOaPB\nzXSjmgna6/6NN0ThDOds3KI6NK63xWcefXyP+h70mbShAqGzz+2NBu81nOEtHU0OKMiHjHzcjBDN\nnhbfT/7UzcaEGcBmEG7PUlEWPM8TyvzDkUhYL31OmTbPXOaj1FjA8ox5EJ4iYgGwx6P3d87oj1tV\nHlRhBGDCGQZLiGtH7/NQVXNSxbK358R9e2vr7rms80qqWLbv7Zn097gRn4j1aIKtVaIaYsa57st8\npZhMH7BvCtM1Cl0ZTldwt4j53t9PO0ue3k9q17S+PHiFlJ/ehvdtjUYtpIn4cRkzek7f0WTbjqZd\nWcuMwsgDD2MDqhDP0P7pXgHAGKfvNL8+2lqW1m+fHby8a6nxu/2aGnV/JWX8UNfyD+moaIQezjCM\nw4IYUr9LnAFfn9//ntzemFAQFO8kWOt/3ZYbJNu9VyWd1+X+3AzcLXa97hcaegaowfTAL49uhC1h\naYJfHgVyX56BPNDRot0gzeC/LD/ZHKXub7pfNd7pli0bzTof6XzlTnsR0PlGb8Oum1YGkzlg2l7n\nBvDGgfIe0J4n5YLUu7d3WNbcBxklFXygBiXzvNC51XlG+a2vaR8W4n/naxHpM3KSxTt0fvihGfcg\n5dGss8/G1oQtMGIoQIftUGQn0P60vUa9+uihdAdQ3o2UsJLO46TLdM1gwvuCvcZhXgkdNWBPPRpP\nJOK6xv77vc19pz21yVMJLbEir2Xv5HtPR5H1kyIzSw1vktsR2Hf3HnfYqvJqK6/t8ddH3vujVMbs\nWQnqp0r2hJnvmV5GhBNSgXWwTGeNTR0hqMyQuvAebAJeyEz2tIcO3bTKw00FYXSGzUiETMKX9oSV\nh0Ehbe4A2zcNc5DkIYkAQ7qb/gzLJG8JSEffgIS8RmcUFotMom3zvL6go7HgcbJCTO2XFIOQlhk9\n8bGAB3QhRu/VePyDBNucx7kSGVRm4QxlrLO7ZpUMJs6MPwt5uMnRDBxAscCrEN423iZj0IAEG5qx\neg99rJswjXkYCxiZy9Fhe1y/9xj5xIos/Cs8G6gKRRIYGDJBtW2dc2rr5qgCkEmsCBx3wT0nixCq\n/fY8J+d4CZzFp2oC0BvIcBMF5sMqDe35Zg/oXYgaUkKe6fY87raLNsKnyHr4JVSuHqGVvKZCNkPp\nW/b/QA/UPjLsOQkfZoZ2nc1H1qBEyhEZZ/XaWyoohDdWkqtn7Z1ySZSHy6NrSv9/tAWH416m0b1u\nOvcs7X+iW8MxYuWN3s/svbACqEauSKllyu1eSyykKypHH5/36+YVT9bwqEgsrneJRHAUwskxQSLw\nJGphAnnYk+1ziyne+zgfL1Nuy2+KhsIb9kEKZ+hty1b4gCpbZn+h62wsK7/1fDSPEMsyZ7+bOPND\ncKQYKRkq/ZN3tKJm4DL7ap8DvD6zhjKYdYseUujCPE0IXd1b/LVVYr3QOz15jh2kgk1aG5ef73XW\nWNHrjHMixHXXT0ILeqV8yImQrp3SwEuZyfPqMv+73PyWgjlF/DgTT27rrI7FPSd8HIUn6ztTwxvL\nHTq2u3aIFxLhRcDLiBASWwxvlLW6eQoAJLkj1wR06qH95aEIAQt9TGKTX23ytGaUUOGyCzHSIJXs\nzUXtt5C1eEXR5mfjLKVfI86nDKoLKsrsqO8o8Z0dMdEF96vhDY/GHp1t0rFy3g0GQn+rh/BOCugH\n+nP12ORufe/jn6HZvS1CIDdDVOmPVLjm6LXw1JIynZRrXlxWUEiJsgawnsTOJHCUMRTjbbjfDeIJ\nRUYNr1Ak+u8pe+nY/45RMOz5Sfg91PIqLGlcY0I/XYLXBGzuBRZ2hNan1sm4Xc2x0OrKTiht9wXr\nkv4ePbexd+azKESybp7OcDhjQkReOOLM5vGJIX2+eoOr/g4UVE1Zm5t5Nh6kgsKpPFCvSTcErcIL\n9J62Jun+UgcrzjWcAd34l9DD5QgIE/JVqcgEk+D1VlEI5Dlt5EIX2nf2emK4pSnIHMvc0FZBO9Er\nkcALqIqljk+Lk2c0CjwP5josv7qJ5S33NpcsXyxG1GTi80vivNjgYXIiLOjIFMsPNKML50Qoxkec\nQvOi/eKhvEMBrXyNHG71KLEiAwCQ/g4Y/cGJL4GieKnxuSEoqh3H50TQ/S8yHkZIgyt0BYXjQ0Aj\nmxHTwWU/+vXopJTIuNBO8MjOwF6/84lQfe325/EG4OFdQQ0QeZhvsxCIFh5I7TyaD2Gwt4XOOuUP\nMhgbzuYtI61K+FPGkTo/1v+ao+UzSGUere8mXSYa86WMs+c4augCvW99b8axkPtvrRxNIS2Xcx7e\ne0MxfrdIhOd43s+JXkaESgzBAljhL/Bcf9wWDj1y6mgLvDMUazAogo0VaKbeFrHK3k0OJMqLoO1o\n8kZd7C1BXCLLYrC+7/fRat+UHaP0yKBAAd3jHyVWvLnNNGVrkIkeuTA7e5/3nPS2x02yXNeytoVn\nvBPqsWbBsXxaL2AoAHsIHX0Vp+wYCF9FakRzQxNvcTIcrWlXAPLTwWeqnsaHQudbn38NMr9ouyUO\nPCiJTxCPmUQ3MoUTl+u3NPFwDQneMiX7HIUDExqCnu0YgMlJUW4cP3eODi3vjhTm7DyEdW1lN2+P\nQ3A/RnRDKxMo4WdIhLacvYKyOU+2lI8j1wew0NlZ0tVZXpOrJ1ic0dk57Zo7YTkUA9Jl9PR6I5ga\n2nTC6gkrR557Sb3RNUnvv+4r3tCn+R06uqvzIQMHl/LOOa72/2fv7V1teYJoodWzz8XM0EBB0EBQ\nQTPN1GcsaGBgJiKY+BEYCIKIIPgfGAgmYi4vEXkgD4w00kARH4ihkYKZ+H5ndxl0V9Wq6uqZ2eec\n+/s8BffufWbPdPfM9EfVqlXVjwbb6efv0dCkw0GEMG2xO1L/5vkXgLJ0ej/wpG3FDMDTNenkab+i\ngnKCMd5lQ195oOM7NWpb76NN5VjB4HlLGczRT971g5X8rcjaj/RwnkvUQ8nHpWNs47Zh31VVlc1A\nNLaqdcuW/9Rklk0kBbUhT9Zzi9TA5Irn7lkqDlYDc95uw4NahWRQleXfCv7Wj7Mt3T3cz9T9NG8F\n/5ZZVxzGsNaRE5Cm9w0Hs9fr0x/FGuhAXwK+e2ynJZgujMKvENbnXi33PPHh11ttp7oTPUfdYhuA\nhX29zYS0P5qH1ozfMRi4zbcE5fqqLYZZVC/U8nZJXGtg19vbC70lJ9hcE7xei54/5skLpPRb/vTy\nDSIkyQbt0XyrHo75ltbw6MMTYUyF7tmcmTWQF7uc/Eelzboec6A/egPnRfB4WeBHn9tHElih3n8D\nFArKGaP2pZj2NOjcuxirKi5ylD9/ByBNLI5Xf9O8CFfrwVgnhyZ6GqpwtBnDFo3g3XaP7NnehTMc\ntCiokanPtMPZvE8FjKhMVdgqL3vYa3xuB3VAgkbX2lQcq2cCyspOTATGwFeGQQS3YNek9tGzkkbs\nm/n9gDNu3uWYscJuHpRgyjHv51jfi9Zpx6ldnAXdtunT92gWUhxUFQDyTEptGUuPay8Ul63gh76i\nH809WRqPLLKhZGsxGaigsvVw1QMqw2ZcFyt7NMET0TNtXth57Dk5yCKyGMMOfIodGwUcsK0d0Y3H\n3GjOOI6YD4TvyxX6KKyc6faFeSwv/ajv2Tp63vGQCQZy7oQLeivzTW1wx7rOwEkPV/JM/+wl1Zhh\nNsi0Sy+JV+fJzHxShTJ6xc+3D1bRee1HyInwxNvRB+Pu0ePJSXbsnzz2mI2gz0TZI9J8Dntr595h\nK/dQxp8zKEai4YZfWmIQIBrN+pyNWUjP7a0heFofx3DsNTBorDswjeS0gIKQlCcHEWSgB+MNA2Bb\nPOqzgCv63OYSEPDBbt/zFo88r3MSyizqfVxB5VV4LOscvZS3O9Ba3PJyIzwO9FPD5TITAaTfcF4M\nM+45KB/+CiomQju8X7LTR+81rFXF+qKhIaxrcE4Ezrmgn0dxzNkSF1LpYhn1yQhucQ2z6cSOkbda\n1ksXXHUDdtTN9hM/y0IoH8FmPl5CMCi0Qa85BTkOH2OcWBFd5wEHYJktADgj5qxdOYTBqm2RfaBO\nm7EGpsJuvgje5hEYuqTqMQrOWF+gIVSFT3LV/eMpmv/w8p0TYcg3iJBE95IOA5gUUBYFGA47d/iL\nF4+Jnc/X7tvA57+1jmdri4LJisO4Rtz7NAtQhYhbvVMEJK8cykSgzOCAIuWrt9G9YWqcTa89Pcsq\nXv8lmZ7cYGQXM1xcoL19O8nPhLNDeyIvMSrlY548mBbN3gUAKJWY69Tyj/S+gqKs10itsKhUTIQr\n4ZAGbY+2LxpXsO8I5/QAlj26ZtFfDdqxreHsK8RE8PavL+KYjTIKK+VE4D4VHkLSdhR5Nzu9eDh9\nku94fAbln9Atz2VAXicKOQjttz5eGV7zc27xGCmpmNRD95KF9kozN2uVi+FKFu85gn4/wYV6YGzH\ni9Ink+drHGsLE6HKQP6K7MZCuXsDYEr87jwLkWlxbjx4UKQXcRlaRAacjYP525MMWMuJgPV5aL/k\nXB0HBELzp84XIXmketHJgNR77Gl8qlH2g0LwfhwdPx49PreJcgcPF8VbW59+CvrzwLMfFH+rxgmN\npReULb2PoDB3vVdiEM154tGZlSHWr+3S5tvVVsyRhoYnJ4vTZ5/6UDbQHxOM4jCauzkRKibCpVTs\ngw0Dj+upZMeg+6hoXoTQf7gdLw5+7b/BGTP7coe/7zsslmoOzL9X7yI/OmUSPGw+5jLOn2blEXbG\nDq2Np6VQfaoD9RRu9Owzrw+NW4uRp9AFceOQL1dwidfQKh/Ccn8/gSGwkzvhr/fKoXnz5mCwUCya\nO3U+5bBd3s5bXy8DmcZ2Ltvl13vYDjGNd6FOISfC6F9PAyjb8r6zCgWsf7N8JVPlW/5c8hKI0Fr7\n2wD+VwD/roj83fTbPwrgPxWRf/4L2/eriGCdCH3AT+P8GJRp3T7s+XQU+ggLXUFBbfcih9o01J49\nghZepoMIb20Yonnikiau0AHAqhObvDIxZEWgWvwO8kyz0ZwNzVeEadlX8kps4lLPvDRmM5egtALA\nL2Bl0iUmBRpJ7K6eb/bYjmvFWCVedjOvx5XkU9wLGN9DBn0eR3dPb8H8eBwdby0unkzpZTZEs4rq\nB1B52g9MT6DwO3A6691322nhFESFIyQqJSYCC+dE2GkrWwqufqqimbeNtALin5zUSsvX52DeyZN6\n77RvN4bkpGOV45WZCAow3uSk93B/HzdjjjnHHS9Aabnv3zr5BcnPPCdAPBNNWhq8pM3vU9lQT/EQ\nopw74a4MYNRBhMfhoISDafPkvYtwORQiHCRuHZaL4rktx/9W5wDjWTCbyvMhnN+/SGTjOYjr81/2\nCHJx7MmO6zzAOZI+Imq0hWNoPv+yhVdRewrhNt4V7k+VtOJ7yCVxvyoAiXVx0SbeYnnsZuc5MIDX\nIrItz0ryGF+FQO2kSwSR7sxpJbj9avUE4C6FKMh3YmkrhZ1Pq6jtr8hX5d/4rWTR5U4YuMxSUgBh\nG3JAkofxeXu8n7+1Qv8xBsKq4HMSTT+1hfmGdbDFeYH6feb5/PeyredvIa/oY392eZWJ8M8B+GcA\n/JOttX9JRP5v+u3vBfDPflXDfivJC83RnOb5eHiveTyA3juOXzL18Uh06ansNt8CZmcQtcPzL1jd\nTea2Xh1xeyrfomqcqwuteywfx3Bksj10NKC9kEVaelviltlQG/c46yNlpBkdsa1K2a8w+LKnXNkF\nSxhGi5O7KoiPw6m/wJjIh2HndMqs9AMI95bZDfzJuyKE9tz0Eamvj3c18DJiPWfGFoNdeas9rUcN\njTc1PLpT+PhZaP6Qu9LSs2N6KjAyyXNC0SAXNL4dqv5oI58we3yBufAG/qZ/nnnS2mR4MDChxjUn\napTJCopxlu7FCgv0vrpSMjW+uj4zq/14C79rGbECAoZUztwWiPd1pZyGUIHmdGBvT5wX9ZqSjUCM\nGv2udFDAx42CrplKvW0jhfco84TZALzVIAAMwskEEjQ8rYkZXJyjQvuO0uaBwUAbfbTjSV6zB/U3\nzCJ8HvBno6w0Z8Q5m03zIADA2+OJt8fTrlNZEvqZq5Le+wXIxgyEiuVUYlTNn4fR/yfbK+Zl8d0R\nGl2bJbMFfF6EhTPwHCZwgBVwoICp6L7+tlDumP/qjtQOj6mugAcGT8uHckM+kwNoV23Fniw9qCdN\nvAsycvjUGKM57KwZeGTntdjOMzxA16jLvCnp9+rsK/bBOCeeH5JRowaRTsvL4HOag42FsFyn+Rji\nnJx39agiI6yMi7a1dsJ8QVsMTj62u3aEhBVzPLJzYC1/lOsJVnPdsZ6kqzUQSjvbqOEMtA79mElp\nfxwaMqfz/OynzWHkDIrGdtI8fax6kDpsABDDZLBRBD3oQvF9up6uTgruA5l5UuXj2LFQPgOefsuf\nSz4SzvBvAPgPAPwPrbV/QUT+zhe36TeXiuK7m+ju7H1ceZyBaLjwAsHhE60J3lrHe/N9qs0bRUax\nKlPDe6mKli9YvCc5129190G5Nrp8VyocYBvNW5tXWrfGMjP98Bdqq7bxTFz53Aj/8AEY0JKqqeFE\nRbAiqs/xx0ycA8z9qLuGkjQrb1fP/hZ0Eh8LDSfsydJpAVKvR/a019fVFOLcB52GP6TN9qzXeTzp\nOE/fK1lCoL5BVEpQwjW+p7UdbPCIfSrCHw2c2VZyZ8kMCwixfEtNsz56Fst40LbbMR+brvjVYQ1c\nvpfpDRIgeInkvU3a4REMbQ1d4Nwp+fMVUbZBAArgHsF8PHu7g3SasLoAvafQD22/F5qV0l3ys/F9\n9kgaE10aWoeGouJoYvXcpcpHkDD2W27jwvG9KQo86iW6sKpRChRb607R+SbMlRe3xeP2zBvP64Nu\nbVrmXsiSvZ0UysDvu9M2peMemxkon0WLte0CLMww/f5G+SAafK6o12r6jmlQCiXKhXvVYzJWN2T1\nPH5XLAa+AETPmAkGqf+bNzgZcUu7N2Dd3RCBUY+zMZQll6Vih6nwTiAvSUqs2JOHdCcKlnEC3DFf\nDScJ5wMZWfEdWACux86a1LL+mxMrDkOs4dAX1BQYuMdAyBKSTFq9Abs+u4FlfMrTxyfeOczBwWpn\ngg2q+7v4eqzMIUHcZaHCJaoEh2ee68rA/8zODF8RzqByd/vHce7Uc9WZMkFfZUjxrl5AnE+4voPK\n0/P4uzmywrwmxoI2yewT6BqsrIMIGDBoJakIlmV9zr+nPvJXlc+wKf9M8hEQ4X8B8E8B+JsA/vvW\n2r8sIn/7a5v1a4sY4jaoPNGw8u90xUZJUeWbUfuXKHfsEYYrRexpsi374AqOTmSCoRjp9V8x4Ypc\nAwANUckb7WwBZf0S8a0M/PNQwGW2xZ6fm9rmKSelc7AyBJoEbdyH2L6/PxpCIq92NPwQwcPCTXx7\nNXsOpMBG7/L6BHnHAm43MBb3B8S+Z4mJFbWO+Lu3aRgKQQkOQABGEroTTxknGFUvaducGy+sVxvu\nu0pLVvTe81BEL/IihMoD0bPPyiqDcm8pWZ2W3S0HyLzmhYGjYExmhAxQolkbG2BKnZ3T3ZjwGEYY\nU5EwCPeSUgw3t2Ecc8+tG5DrebGd63v8Kq+m9nsFLIB1TqqYCF8hulVppv/vuuximO28ykWZOteq\n4icNEziSsBWsoFbc1Ej2MTo926AtDOHbWgYG0XzD5vUqxrGOL82H8DbZdT8eTzwefYCUh46neVEG\nEapA2iSmsFbPrQlAdHRjdRSPORw7PBeEgro/2kxmdgKgVMJsgsFEYHC0GXOvas/KROC1bj7zqjE3\nGqjjHeDnf30/VgX1jTtV7p57loY4Xm49608qHQ0R8AJ8fPXG67uvv3zfT53PNuMsswBfCWeotrO+\nEjPqeDjBgaSljnDt6BhjLbFGbEE+BawMqFYAgVgHOgcJHNgUrHNTGYlnOkvd7hw+5PfB5+3AAdJ/\n6IK85eTZdbHOfT2+DfNOt6gPZz1zzCNr4upX5iS9ZpTpTAZLpM5rFveBKbuEt+yU4LwI+ttYbSLA\nmKvhskrw6EshnW/5I8qHEiuKyP/VWvsbAP5zAP9Na+3fBPA/f2nLfmPJhmE7HFEfx5x264CBmKLH\nCeuOwtgEoqFzNRYDlRfuUeMtKR/Tw6bN1L8BR0QrY4fdEJysiWOrHOlsW11yoT9qnR+YXPMaOepO\nC6Z6psViAAAgAElEQVR9vy7vCsjI7zsrikZ1vVFWFl2c33vDO1HetPneRlc2ADJoK8NvY+bt1CH1\nfmVh6jefu54XD2ZjpnytU+uryuNDaoQ9Ult8V4liwQxuevaAUx2bh8FJJj2cAXFrLGXe9AhM+NZc\nddl261N58yR03iZjNjzn9o45czJ17UxxZg+Djm5uSvhu/WhFCc7Cipb+1hkE4XHXw1jU+eLZjwCk\nKY1yp6D6uZo1PP5+6D2oR/PQd+0NZdDtLKSGgbStFEyEvEMM30uDs3P499YGocLmljxf6mdTz2sO\n41jZC5khsptONVRjO69NQLA1BVuSsEECTK+yHp+H3uOYGLe4JvHSOe3MC109I6XBy5PP8/m4Wk8q\nb/7O8Hklb4Wf7+3lIWR9txez7Gay4DHvp/Ka6++Ay/Tt/PS+4lg4qfJT0lpd7rL85rmZdIdezAU6\nGwdwf87RPkZkAlCx/+9AwZz4uWqrsgCr9vBaor8HoGeuNwe8X2uCXKGkdlqnTMZZSG44XyH3zzBn\nW/1att+cJjoVnSC6jOS9yiqdx3l7RwVA3mf74paTeu8ViLF7lted7G7c/FfE1/PuC69KFweIzqQR\nI0B3E9JwA57PX5UMcjPIl3UgavCiKMs78HweeMph96P6p1CfPhsfAUz4CfPIn0W+H82QD+/OICK/\nAPhXW2t/B8B/BuBvfVmrfnVpgaIe9nFtYtsHNsqJ0J+HAQm2BWQ78HbIzBg9zn3jScHKdDqlV+TA\nBCOd6k0bCqDWoxQ+mnz0exPa9nEqSHJjYtNrDq8boDjOsE3fWp4qwiGcoasR6ECHfp5uM5nkdHJX\nJsIBm9xt1wwKAWlNk1FGupmCAyoai/bWELYUY6o9773Okz4Qt3Orm3ti4ExFKecKqK63DOFwZkU1\n4eu7CvGlbTIZGixZ4nF0PB5iCUOrdoZ90vOzrO73APL2eFYW3d/Yz17m8xRjImgow9GwUvlyPYht\nHothrJgZAx9JppV3rdCqdfeK59P73zDiPqbQnAkzETgmO4BR2i8N4GkBpLFyphXEBnBmBYyTycOq\nn5IKA9Bsfjp/trmfVkCWisi0xJNoro8sahQDo88oA8fnJaa/uxlY918/mIGJlQ2A8PD0eT7pPK3v\naGpYxLJ0TI6idN6JOR5A1/A9B4OaztFDquiOea3b83g8Oo5DsHjkvlB7vPJ6HwaH6d8I77a1obD/\nODr+7oxp4bwEOYN/YGBM63QYGHG+epvGrq5PjyZ4Ns2/oOf5e8gMCs2poMfKnAhN16c1JwIbyZfC\n5So6RGv1o3W0dpjeUeaRQTLAQWNic27o5xA82hHmQRGUtG17EQfpFLqtMT3fXRvfmrMRRnFRtxj3\n6G30MJeGZ/NkwiyDZZj0q2Ntz2dYWJp7IILyK8hjQPAXWyJyMm4zvJXPfGXItwZjvO6Skg+sf+Qr\nsNAkOhauQSyL8yEozCc0XqqcCBlAOMuJcNA5DWMuCOvP7Le5r4T1hRgID+5XcJamLhu75KMaytCo\nT+sWsjl0a5GMEgO23XlO+LkL+6tA11fktdSm3/JnlE9v8Sgi/0lr7X8D8F9+QXt+UzmSIgOsyo9N\nFLTHMBtXvD0g4MYe7+UdPEnqbbHyJSXxciCB9XhdZJ82+cyJRpoZC29toLNvbcS/6bXLdKIB0iR3\nYhftckViW1ResqHJU5kp2TfLP5NmEz4Zyc3fj9bnCfDg58nqYWKj+wctGOhHWGzYILHrgWpzAxPe\n7mmEPrhy6R5vhM+74PrZU+oFdZLflz+zNZFmRS/nMJGz86qHkZcdXUR5QQY8J8Ujxxb0yApSjWJn\ntGfPFWgscX9lyqgdpap3+SIcnJj3g/Ua6aM/5szZum1kfjfqWe+pT6z1r7pEFwSAUVADf8D6LnYe\n3kWyq1nvxTxcLZz6GekykhOaYtTb9l2P5LTxWDYQVHG7BDO7Aqg+5i0uFj4Gxu4KcR4RzHfYeKy0\nmIdlWSPEQT2ew2isWQI/OnbAQxpGef5eec1QxfRxMBNBbOehZXcG9nBNl6n02lAJu/Xs3k3zZ8/3\nA2qvAimtiRvJ8LUwKtaAbgM7zotJFGPdcScHXw+iGpyZZpq7ho3Ahrjegu7py6WKjTuRcozTfT9l\nvce75bxcd5q0HADn9616AfVf6ueBeYK5lSSxyKqdr6qmZzBE67kjkebdQhksVVI7/e7sTV3/vVxP\ntrh/x2G3po6xxaPk8Tn/CTW6h4/ZzjUfA8fPf1SG7uOGu61NG9Dgo4kVPyoVS0H11jv3ncPEjocn\nndZ5eJznc00OKcz5D4DYN8c4WJNWG3ZPa64xlN5TXiLdNjqzk9L3Reegv6+29fyriwi2OshfTV4F\nEf4hAP9nPigi/1Vr7X8C8A9+Sat+dRmemLNJfLfgRC+eKzqqkL0dwNGnotdWhSMmVKzrOcgYBkiR\nbAlYOIBDxHKfjdjoddFe5CQcIIcz7MTjw/yYe5e9jcu9wRee7M3fXRPb7sm+5I4ie1GctkkVbt1e\nrUHQzfvl57HxmEVjyFQXF0RvwNgmpxla3SFmhH1026n9PdXtZK/tNmxitif28wgaHO014GltnyuK\nnK9g3dpIPyX0ybzLwQ5hH1GApOCZR0iTQup5s9zuVPY+qfZ3k/lxeeiAzMBeDmfg8Ahjpm7K03vL\n4+RKCWKvN2eLVqWcFRuV22/yQvtiSuWdHt0nqOZeLuBQcGKeo8paBYzthLeFO216cXDkkLx+IsH4\nbCOBHTMRrDz9TPNc3H3CgZkqJ8rdPmiZ7M0D7N5grkfrAmaff8rIJJtDyNj6Iaq0tuc539MzU8QF\nNrft4qn1vngtCPfSfG5QLzWvqwwQXEW46Rwm4RnUz7R6/lrfJQ274A17bPoazlA3QNZOlMIZri7f\nhRLtju1YbaO++tpwzQnQxDauzqd8tq5Vb42B2dl3ZQXuATbCtIyTtmndMs7PYzsb1I823xUBmRq6\nEOPMR3ja84jAhvZ7DmcQzJCCNIdpXgH729YEXkvE1z89QZMpCmYyy1l357HndXTEbVjHpRLq5BwK\n1bPLsm970RcYFNkMoa9OrFiFO0Tqfgs/VNuaP4Dg5FNANzIRdN69XpzUfrB+26IjC0h5oVKbQogv\n9u/FdND0ez79LEHm7u9v+WvLJYjQWvsPi2O70wXAf/fJNv3mMpTqqWQdEr6rNJmGF/2uyKPS5gDQ\nBNOi90iNroQYZ4lbYtH1iJOPsyBi9mJVykCKlv7bKUdnonF7i1cbq9GninFgeJxUWcZoZcRvM4Mx\nEwFQr1ekpeb22Hugcsxb18T2AMa8t6c+Y6tjfy9Zcly4ep9HqPco6LFZdAxsKH5nlFqBio9INtbP\njCbzvGP1aAlriHyMy9Zy9HeIgzbN9wb3rZOoXx3TE0Nxt6YwEfquzagU7EZq69srGRQLaXAFN3jI\nENt2JRlcqmTMGZxBPnq587kcLjIMMD9HAUtWXgCfU4gtvZfNWGTDVxDtTz2moA3fb/DecVlN0MRp\n63r8So7DtzfVs834nOOGAcu7IFgFoFVGjMhgoOkxfa76HLJHPjO5rPzMlMLqwV/btx5jJgKHTA02\nwjoHADjVGH0tSIbQySTEW3DqPQcgHgWYOWnFb22EqAE+V/C1ej/zktGWNvp/B8J8pc9wUOLHRcrs\n43FVCYcs5h0LypwIL4gEY3FzUnrAHPpyJR317gxnYz0zuYITRLCGM5zMeWc6BzMElTH5CPqPgz8A\n9+eG96eX8REWxdpOl2pe0J2AFOAEHHDP12tOg4C/JUP9Sqp1taCvhaSKS5sJaNftHSWN27vt2bEH\nKvkqJkKbLoA7IGHd5vU9ZsYWcH5fB9sDE5QduWxW5gDPN2ePitdm3t4xbPHYom5tSrgqBYXO5X2M\nASTqA9Bj8b6/WQj35HOa459H7jAR/qPimKCG7QXAf/yZBv1W8pHELp5YkSYVMyiG8GRyZ3E7jlie\n1+WeqjpLt4IGukc58NbakqnYlCu5VjqGJ3Wlt6uEuLTZlrew6N9TavKkpaj4qTp2oqmOmLDYBlXg\nhyIyf1NlPt2HKqZvRKUfBocqrlGpr4Qn8acaUfAM/O+94e1A8G4Mz5TT23XRf5WVcBcp1meytJ1C\nBbiseN/7zOqvinoY3+xzPPMqsWKkWvuxTrscjJ9rLzXHNbOYUqXxQfpOhBdXB4L4ualhxNRbvdbK\n14RXncojL24GyziJ1/K87FNzL8SxNnKm+KBnpdzPmZ+I71AVmuCtjvxu/7TEin6PmpAsJEtEPlbf\nV07mqtdrtBUr6EwRBsYYe7TXx0op4R713goFlJ7ho43QEzV6BAMc7IhztM3DDCZg3YXkaB7KcgX2\nKiB0UNc1KjG1Udv5ODoeD82FokZ7GmPZdWWeTz9V2V/MRHjvY457yurRfUVGWJ/Xo+GD2TsXAIST\n8nLssSr97xvWw3p98opjgJ1q8BtFflNYDgFZHi82Ht9JXT+NdafxktfcO8/+xO4MDpXdFo9XyvQd\nIDXMTXAWIAOzb0fD80nOGG0f4hi76wUO91AY9a5/yNRJfE0JMeepn2uIouT5HGlqSc999Id1d4PF\ne/wuExmbyRQBYyGYB13nrWcbifa6syB4e0et/+mXhz65AsDizg9aI6tn6c8nbxuZ1976uAIIeVeA\nBUvZOl9yPcOZ18l8CfdYlqGfa9+3HGYJqPbf1/IiczmtDWCwN7KuFjbbnBd0XnZGQkzWrPeozBdm\nl2SGkp4LRMBf9fGSzfNtSv/l5Q6I8KO45v8F8E8D+B+/vEW/keQF0hHKGaN5SDLqm3lxONGKhjMo\nDf7R3dh35X8sdBz3CQDHYyZHSoqwghN23gQpYpI/37LqxyzzKWJUPDcoYmyo3WyapCyRzBIfvz4j\nPaTb/oH+1m24+Dz2XCxeDKgCTPt3V4YBzbrKQjge/h50W0O99o1i13RyfjbPH+Ft9qSKMVHk9Co2\nyhi9xdJWUa+4Uzrn/t1opu8wIh8o92Us/lSeEL3RSzZ3eGypK1+UTEyTgs4tHo8EGmVjWa/R5+jb\nNOpYACFVQJVY0fNn+N/8zDWZ5Y/mNMFQRr8AmEh4fD4ooRx7Yn2HhlXpQ2+LApGfSU74FWLjKaeB\nkR7OkmZgbzQ2AI8DFq6kve/hXWhcL5GRJGZgx7LYE5/jM9lg81iTVMhxhItb/DPIHefGK1s75jnB\nDAoyPnkHHZ2Px5woFkt/12tpFFa48eqeU2dK6DPvAHCM0DL3Mo3Y76PJiE1PBu2Po+Oh4VPLPJPv\nH3ReS+92zPc8xvR9Plpk9ox5s6M/j/iOmDINNUxkUqXJythIZMk440EINGWPHRv3dh+0YBxtbE35\ny+xzb4fM/DQfAzKVNfcGN0rH1OV5jUZ7xhz3RGQS6nO8GMp2U40QJDUeAlsId1cSLnd+zH7zEF/v\ndN4869/8zKvcKAZO8TFEAGzbLrq5nKNkacecyWI94z280VrEuUNG+zVJLusi49guLHS0399D3mGr\nEjasnPElxpKK52rYg6/bzFSAXX0vfSOfk8GYkF9nGpIMVq9leTsE0dER7+HnSsVMOBNOrPhKeXcS\nK/I8ftZNTYciPVPn1GPO+55/6/6OL0DUhTzXiwRGseWFynN0MRjZsXlHPhOe8FdOrPiZ8N0/k1yC\nCCISbDyiBD3zb38mqdgALNHQ8nPVKGG0PKOUYcFKM3Yw0pvHLfNvRsunxdOUQwzj2M9D2Pv6iqqp\ncmeAcNNdsf7YjJTpsJdMBJYDKbGiT/hMnVUlxMIZMJ5HiE1GVLYf6T3vGCX6fHlAmB0q43m+iyNy\nwytwQKRbOMMhw/v43oehcZbESR0PVwp0T4oLH2Nadu7n+v7fAyvBvzdSiLWsMzndlQLxmSub5c2A\nnBycOO5GjRn17jP6Lqh9UZxtnT9VPCfCekOBlUEGxe7+OlH20RukDY+Mb6NKnpDUXtUP8xJ9Z8HP\nrAM+flfKcwPVowPPZ9yTfH4qUyC32eOgY+FxC02MJIoE7HS4oQ5M4LEfqMIfru6FDQZmBnjM6bnX\n18vgOT6GkI3yxv9vpCDqfDwAUppXEOesnbBX9uw+dc7nY5zjhbcpM4X4EGPhaB9tbGXoe2YvZ8dM\n4sWeq0beq7UTMai3W4d0ffNr/LrMUuLdf14VywlyZkDKMEg/EvpHhZSHq/sPx67Gus0j3rbboFg6\n747hWOkwmqhxeY4bbnyebw0nIkCFWWn3TO21Xa9IBdIzBfzR1nMW1tg8ZvkHwMypkQ/hvfvWj8o8\nC17+k1cfcol0sXwltgY+BfKOASC8Uzm2NmYWRAtrTmYcjPZ8wrr8gLwKLvwsKXNDVY6cI+ZA0zMq\nkLChBtIzk0bBft6ZZOdIGQNvvjTdQlrZgGn9JaKKHcty9ttOvpkI3/Lp3Rn+TLKj67FUe1fHMqJx\noTF8Byk7hqy+aKiHepqj7qPeqXgJe1bWmM1m15aNJw+t4Mo3kmMlM6LvCK/OSoVhdlrDx4Q9kJXX\ny98NAImTs8aJ52zpXZrdYzags4w8B5ES9i4DOPhlblHW0IEDeMphW/bhGN5AZo6EMuHGFb9DVuhO\nn0swnL3fXElelFSuFDbe2suugb6XaChYf4bfF4ftqAQPy1nsrS6cDHy0+r3p/QmxDjinQQjvQPRI\naRv5Gmsr0wcVEaCyeDcDbfM4XtzPhTrNt2Sjjfr5K8p17ld3JT8rgO3ONYFYFvbcBe8fGp79AI71\nqo9GoJdsienNOytRgUlW/I42GGDZq6WJ25yhUs+ollyr6JeAG7AeHlAY5+D+x9tYxvlP+yuH1LSH\nhL7POzEwAuSJ2+a1AjeagnFUv18Gi7zdGwObke/592ASHXZdRSF+VXKIVvWGKnaYrgOe1FHni3tt\n2t13yIlwU+7kRHjVHtwZErqGahM5VNKu0RieNhgYxlAigO2r/JdsuAHelp3sALjsxMi/DWDMb1aB\nhcA4QMO7HHh0weMAnnMufM5QuxHmM49ZyM/qXF4ABMw5wABpYiH0cFIYm+OzleuMAn0cpuDj2eu+\n223+SKYkhzNUIvml3JAw9784KQXWFtyRldlQA9z1wWY5hTYPv1pvI9gry7ygwJGk8yO49NLt/SmF\nAZm/unyDCCSc6CXPA0yT9b8jvenxnDR6MjQ1w3BeOJcYJ4Ht8brzRjl13CmXbtA6E+FtrqhvB/BL\nCGlY78kbgLEI8eJDORH8eNEuU1DJ+CqM1KvJ9dbc1AUWi/1BacV3Bjw0Bu2NqPiXWzqlzxxGPtYl\nR9p1EdfcCKM8Me9EgxugnayL4AGh8vWT6/J25hi5NV9ACKXoMQHkR2lbQ9FpszyuO2Yl135jORE0\nnGGGH4R+aoZeswcgT6D3QUR+J2941VedOZEM/t4iS2CeF4CAwvjZ0alF7/N5zGs72lTyunp8LXbR\ns9pr2z3uNAozZ1RXB+JwYMM13Hti96ixVyVaK994Jy1VvWFCcblymFFpjBBTYtf+On6PxqVIA1qM\nJ+7i3nw7Bn3GscyW+gtTo8d91dndg3AAswDybAs4MspSg0IMZOREcL2p0aWG5nx/iHOd0+N7Atbi\n1oJVDOuZaOgE4EpuZmiVosZJ2DpMHEigsByfW4ZoP67Hnli8eJZLQNIAMR139yjoZ3LQe8v1eDnj\nhe3WvVb9oC+JCt6xW5akhXmdqVzE6W/dPeezwlVw5NIZ24qlYiLIu6C/O/h1BYYCcz1QJ8CsM78n\nwJ9dcAxgP/9VslvbdH4BfKyGnAjNwc6QrE7U4Kfjk4mQMLkitr9oR/Gw5H3oovLuk6rtUPUkgGEe\n13wIPEaHU4OBydWnzHMr79yg+RAqA3QBYNLb3jENPpoPga/l3Rd2LIq8Q8OpT2LqLwCCDnMl2v+4\nH1puMrAOss43yjhoiEwEG3v0Xps+7Hdfg0dY2tz5gwArZdbwU8lOC86XwCL2fOlaW0//uuEM3zLk\nG0RIYkbgzUV5ifdOC64aSCH+ka458zrc2lbMjFz9/nFlQnrdHvVWnbUre427yDK9fCb26lJS7L3F\nr5GX2LOy6+Q8Qj8EHJOGAATZlk6tveT5YkVaDcr37vHR7+y9UAr90cxbkZUgTto2/tb7jMpeZiO4\nwVknGszJO/WavGUiJ7zTLShzYrOPygBsIkUZ8EzyR0u5Ofr0nFA4g7bVjddNXVDauN6z95EBnM37\nPVR54GprQGXnAcxMBGlRaWUvLrN/K28UgIKfovVjsfpzv1CFhrNdV/2l0bWluh8Qqx4aq4oWJ7XM\nDjMuwv9mpa4Fw+wph3thbMzAlCIPkTg3HHleHvHwJ4YGaVejj92bh7nvAkP/fFB8ts7R1TPXDN8t\njPHzvrzem5enYIm2nHMibL2x5NmSdxmcWn6R8yFznoTnc4wJnc903ntt+83iGBBA+4Ny2ug1u9wb\nvfibw/lynVdeNgU+WRqi8fqK3PXkXYXVlEANgZsAwrxyq04yFgFYjohR9h6AqzLfn+o2xbHAYrzT\nWOg632wOYsYht6maz5QpxCwcSwRLOI2yDpiBZkYuzfFquIkAT+ozz+7rujMRsIQZADDnQiWSJzuB\nGY9IAAJvI6zzMo9RTbLHbEmOWFK5M4Z3r/nXDk3g973Lh5D76Uf0lwpQDr/Dl8VsqLOzhpcVnpc4\nnAfwcIZR97yAFj/pYiEsvB2xVjOcVx75oJcD98DWb6nlp9ozfyC5s8XjP5wOqePqH2it/T/5fBH5\nP76iYb+VKJ2IwxFGgsGo1EiPXhH9zIl6NGbqarIayRudLqvXHgAkGcSWGR+UII/olbwtoXrBdL7a\nhjIAyz1un9EXeD1ekTDvF1pMO5q9Jz0/U4T1WQ4VYRzUd52VzLc2d2bg7TtFnztl6SZFrRKe7xX1\nZ+VDF3MFMJ7CRphnZ98lVjtbypacBVg9dwc8sWLYvnQBMHLdL7z/I44RrptZGw5CeQ6ExzHCScJe\n9pnyQZIhnlpRXdtumf4DpVtujYUHtY9DTbJnR8jl/ypdmaVhJFccDaf3uxim/jQqY+fM+MlhP6ey\n6fw5RKM6dcnqfVHN48Z5OTTJvptxpYnYNuDADc3AQnJI8RvJLCMTQY6Go/v4VQVWjXwrr3lcbRbN\n9TGa1kwhXMdk/K53p8d9fbi+vxB7Xbv+4p+bInNIxfr7vi1VOAPnjBhAkGznwR1zINSBtZ9ncElk\nP8+PPB1+3baiJBULhBMYflTuJCa9AhRsbdywRQyAK4yQJSH0XGDvzKOj7iGapC44Xmz8ppwm6dqr\neYvLtFwbJ+zPMxmgwjo+3yfwyWFIOczHWBkXABaLA9Br3hZeF6v1JeRGIjDhak6+I2EtJ9zxamvH\ns+O6vaNu61jV9RnhLsn64JWUTgRie43yXgcXG31ajhwqf9EBe60L5Z2N/Lv+Hv/+qBzWt79hiL+6\n3GEi/O+oHWB/c3N+xY79Q8idnAjLNYfgOfMjNDOCKOlW04WLt4hapVpMlPbdl5g2QlzT4po9jU6h\n1Yluf4/MRNCEPKxEqZihe/JcgC+a7HNzN7PfoKK38DfXXyke/BsnwOF8CExHG9W3MCFnZLcK+1AQ\n4ZfeSEkb7Xv05qGObfh9fpE29p8mmiQwFAFjRiSk2+vx7ai4jewZ3CkKFmMnCArQU8YWUfo8Oald\n9PrpjcA/ewuGUCU6NjIT4VCFlU/ugDxlZDunep4zbICflYou8uoFamZEUrFKuddn0Dwz/dNCEqKy\nBPrei3HC7A2Rhv704/qpXioBP/Nacl8M9RfnMtjVpjr2Ec8pAHeZ2QPu9rcxQsTviTOBe5jNen9d\nmu1a4WEPaRy3Nnc0G8c1fKDT8y1pv6rMvmogMMOlT2/eRREO6vqxJgoO5Pl6fQmVgZ/DkPRZ5nCR\nM4WwpHwnI3nZM763McZ6c2NF4NnfqX3rlpxx7OX7uSvVO8tAupVLn86EiJINpisl2uf1gchkgPJo\ngqNgjIWLPzzYyoaEY58BIoEJhOVjLVbTEEGVl5gX2saNUXslA2i7HrfD6KJ8UwR03BEL2ywMrkC6\nQnQCNFqbeV571zWeciK8F0wEgffT3a4DuT3W5j7mFHmXBTzgLYTHuT62Lamj1i0+dr7Cq7rz/n+6\nXHu+ddmv7s6wE32U7PUP+V9IPwIwt5XmUM15LlZ9UEXq6d/vRXV2OCAdw4S0beK7PQnr7W4r6M/v\nXefGqLN/xZv664YzfEVGnj+H3AER/rWf3oo/gHD2//F3vcWj/q1Z/R9dzONtaHlT/XvV/jkLa2Y4\nAH5cFVahMocH9whMBGUj8AS5pSS+OB/o6WL1M3jSDFE9mzV594nPyGByzLppT3H1anO4hWZSf5/P\n+hA3KsPODOz50nPbtSGmzIZAFZwLHccgNvjCDqxKnB93VkK4Z+w9zK8qBdnzZ/dCxkXMAh53B9Ff\nwqJ50Z9YOa12xPhx9CXGnSUrp5mWbPRMY3SsAEL0Osb8B3mv7909jPAX4Hj6uNMn0hP1MYRH3Nof\nbtZDfYfdbvqVcwYINBv/OPikfq/sV9sG0O5By2m008u8n8p6qLi4GxFk4OCGEWfjZAJnLYIvlVhf\n1ISeNj/7XPoAbRvKrCQFjW407mhr2E0DEgA2/vul81ZdDOrGdutOJNrXny8Z3PN+6W/1knIIXZW8\nsZL+bA4OkdKqXGcO0VEpt0dNYls8Upv02enV/N2uO4DjEZlhuovLka61Mc42N9wA1nPfjvHMpRNr\nq2hzNfcy4+/qPe0MaJs/6Vl8VDyR8OoVvVOu3v8lo2S+Kw7JyfPnctEHhPMh5HBC1SsAjRUfTD6z\npRuNs819aJkazlBJADKxhrHxMQacZrAUnp3BBf/MwEQl1Xt49hYAAxwOcuq92NjMzpQcYiZjHcjG\nrq998/ls2re01+pacyJ8hVyCLJvfzwCEM1zrDHc4W7MzE6GB5ltdd4vLG5rtSMW6kIfd+ta/oQ/k\nuRkMTMWKwu4eiO+2Cl/ZOcf8mp/wor/lDyl3tnj8L36NhvyeRBXM5TjTKwVEn3dD0xJYmWLCsfDM\nDdEAACAASURBVPh+/StrLHvcWAa1y+txWq0jmMaCMMpWBBX4vnYKz25SPaOLPy8U/k8JeT93q1bL\n/G6s3o2hdMpQUFjZ5uc270dOtO/ddMrUsef8p0bcg2Ij/b5GWc+06Hs2hlhTVS97RsbfnNguKkZc\ngnpkFF1XL4oqIJyop/T6zs/wmC48UKzo6uL5dviOGArGBa+jYMT/HWzgkLJEVQsKRRLn1NWQof5w\nTw4AeyfRWyeLoXFM44IXbrEtHhsqxgefe6aItdQN8jzSZeb6wLlhxoqZXgM+nwySYFz37hfNcWjx\nmMQOCMpy8dxCW8LzXfd412OaK6Fpny5MgKMY9wCDaw7qbudgarx6owL7K83lZtw07tPDU625Psax\nthhhWl4r+mVmEBnwfEN4Hhtt1HXgXt+XDjRGd9nTSfG3MmnZwQuHS/zwVAQox2g7rsMCd0yEjwrH\nyIe2TKDwdC5JjbgMJaAxt154sv78hDVW8x9oqBkwPL7VfFOKzQ/36/wI48GZNj7GrkL9skTjKq+R\n4xugwJzO1zx/6lo+1snHPC8a7b5Dwzi3xivP5n7zNr/PNvFYfAf6E5D3xKp4DhahwOdLY75RXZ3+\nLfXiY+BAZWjeTazIdX+l5MSK2/OMXed6bHamvEK6CLkQSFzX9PMaENYN/RR2OoqDB/ruvZ3OWBun\nNpuXMxsr94FvuSd5/PyV5Tux4oVktoEfF/NO2wRwOBMh5i+ISeh2HmeO67/XNgIHmgMJvBjfL+x1\nJkK8PNbdRei5/QQgQWVWyEyRKoM35zNgmlhrDYdI2OfdM6WvyuzikJ3GuCcw9M+HnSNmAKjxYOER\nEOhWAGqMMs3wSthA/Ijz56BnVhllY/GJ1HGvew3R+QibUYG24Y3t9n5+PM6ZCJUs3qFcVzGOX6G7\n26IP72PqQVZvwdFfUYLvRRWqcjG88gRYICooWu/jgO368VRj9nAMQJkIu/vjxE6NJ64sNJGFcCLy\nvgmcuDCOCYT6lHuy6Hp7f3NsFG5D9uZVhmM76nfLoWWfkTifR3YC4HNJs7/nI5RcjnteM0jAoUDG\nYrrZvpAXQ732d9eYbABvKrV8IgQEnbaJnk8VXsMGoPc7+J7sM4fL4xADRqyNddOHR/Iuv/0FeTsE\nz7nTyhk4oxIT/8Z7/Yw0frctHlO502f4XbB+YnNKYn7ka8LcOm/wSqfQKxiAUxYUgyqazHdMf7x+\nDwDPwsykBiHGOunsqvBbAVrGv2ldF22jz00RtPe2VuX8WpJZQhpSAbgBNEIVafxIZBNUIMeYZ/db\nAwIrILANnzx5Hn3z4xqmxO0/Byh2obyVvtyqiRprm+8CeCHMteibPMZ07TUQ4Rjz3qIH9c33JIb/\nk1551hd3wNbVc/yWv6Z8gwgAhkeBFPMWFyFFIWt2ggTvl4EGZrz6p8fdrzNJmQyHUNDh7WnheLyD\nuf/39FsDrixzyEBWZLwBdRsyrftVr8fV+ezBsmNni23mV31iZR5GGdBJmbvyUkbkti3Hwrl0jWAA\nB9pVBiNhevj1fU2jihMs4qQtwJ6N4AZbMxCADeszOqX2t9HOmW06UCSj0rRrB7A3PliUhquKI4NB\n412woTpuQKhsNbQEaV01w3p8eRbPM7IciCXQBLB4UjXeCiaCKrLsqZ5PI+ZE8PKl6EN3xHJEGN20\nWXhGT5pwo/nqaPXOIjovZI+1GgqLAZ7cGPLsoSPpPa7ecxgzpBIGfjqa3Z+WCYzj/CxVCd6BRYCD\nGpxhvgrByfdoXqj3wbJQj3sWA4plTCBLX0VtKFd1W78Pnq+Zj4SejxoAeSyvbYuf+j717+hVS0q/\nsQ4KpTXECTfapjQaKHyDzMwb96rvwj+P1H934M/VOWfCyjofOxPP6dFCezU+/quSDI9xVxgtfbbR\nJ7PwGxD75h0wYydn/cja2eLntroPMBFyvdW9sDMGoLlq/j3C4UZeggyk7IQZYdz8LNwHcs4hQMfi\nTK5I4Qy6xd4w2vXcOuPDzsHE4RO2Br7TOO6DheDbB/O9pblSYrt393slv6Xn+ivp9MrEC/kC1Muv\nxDva4lHXlT7zRPlWnvNaRIP9jrD+2dTpaOCGO1L8fc+8ULNi3o0j52jI7TqTO23+DmUY8p0TYcg3\niPCTZZd+49QwFP7eyuNWjpUngfK7O+9UQpwVEIGDZGylu/oKz95tycbMDWWFAZyzONStZwU1bnFn\n8TXFHwwMjck47s6g518nrax6VbVwqcKQlYilPA15UIM83OMHAaTuxlAwAItzfRHlxbOmNY9GgVZJ\nSiZ0AWps21rcmyed87ZnuVLaAx2yF8fE3xt71T/jOOVEY3asKG/Xx7a06k5E9ZBYcRxiVoFdUrzv\nXvQtvX7H3GYQtYk4ZXNzDzvJzJlSAgIC9OexjIFsAuyS1IUQBwzmWAaRq0SfQGXYrCFFZ2BgDK/Y\n3awrxWG+z9xmBRZkve4VIOxniDYpgKc7r2Q6/BF1+JRtUb0MNUisbXcrmud3oNnWJIUF+AVyhskz\nGDk+1+Supdh9R+Mrlu3lZcdL2c5ibVRQB7jZLi6PjENtsmg94m10AE+fgXvjOQGyrvWPBAZksO8V\nxt5yroIIEsECXWN4LXs+D5srA9hB7dXrK4eOsijP2qtjrdYrtM5al+LfMvsghzL0dH7Vhix8bp/z\nb8VMYvp/1cbxdx1W/BmpgE0gsh13u1Pp8wlgkqy60Dw9iBRsEhZmDY7rv4GDb6nlG0TYSIidn3Tl\n9pBwhm/JOI4/jo73doSEfkc7jPLMcXvLXuEH0N4Ex1PweOh5Espn9P9oI7HUDImz3QSkeWJF3zrP\nvd2Hei8Fa7nqhtf2tHUSs+dBHoGu5yWPn197PvGe6UMHcE6/PdpIcpnOOYq2L1R0DAOeM+EqtX6E\nM1wZiP4ZUOwZdxfBoPnPwWNTTvTpDKjm3v7qnAzsM8m5mP5a7vW9ARA0LjzHXIdt9Wh85LwhWqe2\n++2QSdtjynJfylRjph3RC1NJBlpsSzimVFM/CTkMpI53LrO0txGSwGO+IcaRSh+Dbih+16BMDk9Q\nu/MIHhOx55mN2gc8z8GjeT/JeShyUlOmWYcs/n3eecqJUKFCTMFXqaixo746aWi8toUwgTMjZiQI\nne+UthfVUJNn14RsnscAoBnqQrsfrBNlzgx52PunxIoNwAE8hBMwxnwPmRFzNBkMmHDv3ucE7lWs\nxO5F6tAkHaPjNqfh07HkkbCM7nOcjWNYgASd07Sd3t5aLBFvmgvuTFscKjjK+phSm3NZaOz/a2XI\nBIja2vYdWs/5lJqvlc5uaPZbljthhjksa8em63b+OYg+1qW4o8sIBbxuSyiM9YiTdXzoEz529Nks\nielI71DdpgE0Fv1fYCzQehUTZL/GJtEzt+xDcS3IwYXmTgQ976SMWN+Y+HtYN+DeZwbSFbQvEity\nToQAXKc2fHXIxS4XQCW6reOvLdovHjQv8WeWy9wm8DV0+a0RWJbqB0a/1XCGt8PHQ54CjKGk7TF2\nhK4V3AfYWaF9Uuxan88nYPCNF3zLC/INIgAAZItkvip5QeLF+rPeRa7DqNl0jCcoPXZbQbp53s4z\n7Ds1+MJpqCgpwR8RAwRYE6ja9omcDqwoVUqFUlqZusthAXkiZrRbz3lKM6bIwImUEktGgjj98UzO\nqFRxsXBUeqEc5zKFy6BFyO73Ex34REyhnv9COEOLMdx1OINvacRKXulVy5RoZACoeT1H9FTF5Ila\n3gA70OvOFz3t9e9OmZ7HUHu2gQga6Xk5fPNoo/9pv7LErmULvVy91gGH1ODYQaAsBE3q9Hy6F0TP\nHDRc9e7NsXHSjjviBut5sk+geN/ap3D+PLJUYBqHyWgog78nmQqhpLllncI0rwXP3Z5rYMQz+7F1\nLKuCyCFMow1RYd0ZSxaGUrFxSs96PG+3dDrQWec1CWtavpZekNJ2K4ZMzSByAC8CNXU7c9/J5/mc\nn9Z2AgHqRJA6j9QAQRUqcEBCLojfSrzPksE9AYUBvGjf52uuR/axWblyuOetsmhc+dzStgDKZ0Rz\nuOx0Az0HEzxu4VqdB9t2rNxqg9bT/VmFLbm7A4B+zUiSzPMDJ1Z0psAqP9OYfAVcUNG1utrS8bPb\nS+Z7zTkRsqd/XPP5d6rCerz+nZkIIs0SaaoOBGCuwREgAlhvwwwHXNkx3/Jx+QZbhnyDCIVUCpdt\nG6aeGRHa5nH+FowfLSvGN1l5uuhRNa1FBoC2pdoC0ZV9R0/VCFvajpiA8TYd8QPi5kO9ir868Bbg\npbIOb1gE9h7ABqDuaBG3jcrbOwJkRFy0l5VXpqc/E9jQZXh5ntLCRXp70eO4r/UVZamOwoxi4Qe0\nAI3PtaLP6Lgcr8pbGvFxBcZWwwG+R3JqN4eLqDAFlT/v3ocU6H68lzjmt+EA1s7Yrko81lINMVjb\nectKNvpHmW48WpjM/L4Yqpu61StzqshrR33vVlBliI6+vN+dgRN2ch/LwA17TNAuAK1k5HIeia67\nKBAoo2IGKaE5od6N8G44OsbeDsH79BY5y8lZIUsZmGAZZdnWXVEMGINuzdZsl5ddfhxtz0GKqOVD\n6M12m6kUb6NJM5rV1+cQxlsCVs881nlL5Pgc9tcwY6JiFWnbWfRV7tojxfccrqYgH28Hau09uU+n\nHmddgsAHLQcfn08z6PrVwvN0Bi1P23xzsc/wzImfoLhW4G/G5z4GJazc4voczlBJBSCp5FAIDVnM\nuQ2cidDsb78HBZlefHeBFcTzpLcp0Nv9stmv89j57a2iaveL34ssSW/ZwbCcew8s1zXDATRmyvk8\nqezMWIl/hjw1OZThBCjatWkBVgvg5lu+BfgGEYJ07DOlSweOH/VvLSg3tXIE+CJmpuENjw63RxXC\np9EM2aBY63wJjGdNFdgmE/uMhxxYlYMDgNrRQucMI/uGUED53SROIeYec5ImQ4DZCGzg5K0XzxaK\nLv6+1Ih6CgzMec7gb43TG9cIKSJrebt6zoQTKwaKm7Y/KSG53GEnRrT9LqOmSvxlv0kDNqwdDn3I\nFNhqjOwzx2dF7qURsXh1tEyAPAfUAzLVOm/nWD0HTkJ4tUSbB137Jpr9rff2mH33kEjVf0Up93AT\n+iG5MUYeEt8C0e5XvL+N+9sbdpIUMPW8Xyr1wuyGeQyFAk3zqxlX4vdY9eMQf071VV2MgRsgeaYh\neDuAo6cdYtAWI6kyhoHpPULDu+icQUnbCNSskrRl9hsbUCIjVnr8PvNLdO6rLSioXymcPFLblQ2+\nQ8d9p5OS8DZmOX6X5bMe6RxbrMe4P8Uf75e9S7r5qnw06fGVxJBEANKCsTN+8/Nt3OriAtx+Hg0b\nI7+YFDOIc3CDJgMoz3mXQDGBchx+wNd2wPpuQwF0Th3hrfluTPq7AlFC3WZnJ7MR10BlGMgJyGZM\n2Pee25Z+3wAjHzETlQVQGZ8qpuNUY3RjoA4tdwUZlY2wK29X9qu7CnDOCZXn88AxDfozBheXodKB\nEWl3oxkKhgFxbbD+8w7rmLwGP/th60aVg+O+rqHn7wG0v7L8hLQ0f1j5BhFeEI4Rjcf3s8IrIQWV\nUlsxEYx1QMau1pPr07jBpy1KVMYN5Lv3huMAqolkTdbiMW2aACkbEV828D5YkOZzAAbqrQufx6T5\nLgEsauA8yaBQXYkVjl1T9dkoG+0N892GSdr3kq4SIYrAsvCzknIlYvXH8tbzmhlM2cNYSWvX+Wnv\nKLjWbycQ5uErzk4IZWqoAe1Xr+8lYWFFXbG8mLSoWUyh5mJQOihQPzfdMi+H++YxXyVW7M/m74bO\n5f4Y2o2sYK7AgoJvHJNpbAW4BnM2fFobcZkhnKEL8E4z0bMPFsK7oP/iCqsqyg4M6Ht5TRnJ26dx\nf29tDzZcefN0fjzdunU+HAdJIluCk3/mMhgwGB55n2+UDaJUVR49UWkbu6E8e9yaTfeh5znoXbA3\nRkBg12b4lQCfggmcE0EVagaNKKHpmeRnXoJUJ8d2YiFMNK+5l9WPATUTQZY+RjlREgvM32JkdN1h\nd43CyQDQXUN4nW4RvNZrxvkR2DKWSJrkXvFkcy4KBnFWxqSLxmpneF9zLIW2iwOMp7lf5j8+o4Wn\nbcUt/cxYCI12x2kOTOVQtaXuufbeAV8yJmJ5LqmlHGbFs91tDzDuOX5CfpL32Xff5/r1PAKY2p9j\nvnhnJoLQPy3zC3W0bODvQhcsJKMEiu7lR+C6OKfTGauCz7ubg+QsqeJXZOnnfCCaD+FNcyyBxrYB\nvYKmTLnneO+Azsmuc4721Y4pFQcGNy9qaes3K+FbXL5BhJvSjji5hK3oDp8s2yFofRNScDLXaCjD\n8VgN2O01wQtaKcZC3/m6JCcwfU66VV12N57uI4sTGzJyZQWHuq4foiohTEPO8ZSLNwG0QJNxX7ch\nfufEiiOOeibomm/kXZkLkq9d78UVzra8PgU3vKz7i1wvFkuNB7W6IfhoYrOdRAaCA2Mtgwh9WNEf\nqb0Czc4S+515is7Kj9tGrmX2DehYghT0mb1uaoYysKD5Dx4QvM2J5Nn2be5whVjv7UhKvKgGsvBz\nfTvEcR4bY6rc0b3XTVj6m+4Hv7Z1nPegH0/BEE1MhbgF5qN1NzSo6jMw+GwMMfugkeLsQO88b7al\nGXip12GhoncqjWOZNXrEdnIpDOft84CY0W8sKWUh5H6qhgrW43eMLp5H19h/fy+HUuTJRC/bniY5\npoWfGoqzxIpuPD6v7+WukSDSootqDsyKKVUxREq5WmRuymcu5wTKedxwsa8yvVQYzLiSNbHtBAu0\nT00G1iM9X9WPrvQwBnR9DhP72+LT6ZolpEHbRAA6ESYXuXvfYX4qw4v0Xyww543hOeVMfkbY688w\nQD9bnq2tN0C4ANAwCyowoqo23pNqPqh2Z/CcTTone/vWHXw+vmvVt+zlK8CjP4N8gwgbWTyJvfDM\n3DFUb/YzTpqTKVBGET7ptOUWYXcX9Y5lX1ylYg8mgtDeuKuB4N7r0RJADc9ayeub73XT0j2o4fI+\nKWXPaMhoe8apdbx1RXNc2BukOOj1zzkZv9OE/RSZ78iVi/zUnzL+vZMW8WwrE+EgJoKyEbxMSco/\nLRbx8cRnMY2RwKCQ4rxJZ9Y4P96bXtvEmbJzskwJZWljxnc2mkuvA39nJkJiCWjZmpmYQzGMJULg\nibWteE470TIHbVFif6fxxDtLjN0Z1rHGtP7nc30GIeklPRdW8vhxqUfioPvJwMIApcZv7F2slJMr\nRdK9H/Pkd3qC7x3yS4f8Inj+Mkp/9oanHEERZ69Xvv/xfU1c2SXOdYOZ4eNExMdjvpeK0qs7KrAw\nUwOg5zPoA/6DenvMIIABElf5XzgLv7aj2gGFhet578ODyGNRE7R1mpdy/+H6d2KsjqSA6qeVx0qr\nPovu5zFV+0zKxIPEZDrb3m8wQtYxKIhj5alGFB0bSff0njO4Clq31MiL+Tukxa3+Rpnj2BsnfCWm\nkQl3fgFwMQ+u9+3lBIW1C+RdLKEps70+asiX/QfRuLrNfFPg5F11Cp27z3UYlczKuVM3Ax1jZ4a2\n9YBX4SlVLhcWZVLZ/FLM+e79nc9Lj8PX6xf8IIvY+HzOtrw3S6jXn8dkIURQUOntIbcK9/eTdvwR\n6dp38jrkLR71OZSJZbkPIuqUz37gKQfe+2Zr40L3vSM5T4KVSboIg5OdmAj6vvM8yZ9nUo2yDCZ9\nsxC+heUbRLghSnvb/s5xz3315O7L9e+tATLZDmFrtU1ZI3GaKxeWyf6iavVeL+Uda/xZef1R53sA\n4gL6yu9nXrQMjthWc7nsFzxkHM7AxiAQE8oFdFlYSZjtprth5WClsqsh7sceMhQdzY0wzzTFmMt8\ntHPUMwMhvHhlAy60C+wtScaDnaNGggMYgzIqgX55V3Wt4vzG9bWHaE2cNhWoDlPKpQK2sL6vnZx5\nIK50Et/C8rKa0T5qKyew5Lq0D4lkyrEEr5vF5yY6b9PYezVeERPAVsL5E8rt3xS8U7DsvVtHyyEr\nwQjDTCqa+qEp1XxdBcSYcuf7Wmum6fGdzlVg60Jz0xCDhYlgnebjSpJn1xb7+00NZP3X3EDja/h+\n1DhXQBGYTATR5IreZkE2huPzsvtDfE/j+6+nEC4JTZuzjXgGWXZqOHHV8k4gABts8TyRWIfOaTzH\nG1ssAVhazyO1v5pXhZT7s1AH3uoWyIy77WWflrFzxceAhgOac2VSn0eJL5ZRn59BPSDOB7oO7a6l\nE8Oc2YtzOMFo3LZ7Ht88n+n8Hd9PnuEuh0pVHovOu6wnCISAwj0IpWu3AgjRkRKNZAbaeB79WaBB\ndIpJeTzL3cSKsbxCt2TWGjz/VCWVHlDmqtkxF+33unyRwYizZlIxilszW4bXENNJGdQNZXvbMpCq\nbcsgar6W74Hljwgm/Rry/VyGfIMIhXxmK8Zx/bmB9CpN7Eyv5UzcWc6Mo5VpcXPS7jF7P7cx//3l\ndJ/dylwcP6Mmq7CHtoUJm8oJ1LWo1O+YpsuzwFAGnuIJuboA7/DcCAAW7/+ZqPK8Oy23m5UQbU91\nTZcRix1j0VXpHuedjQ8pFqf6WAMnVmT2AQMNZfI7maAbeUS1rZXkOFbAn/nj6Nhu3bUkp7rXn9UD\nPdrmhlsvKKja7lseOsxxO8tg4zTQeecpkYmg7cG8F1Kuiv4WKMt9gAbt2YMWIu8CeYd5w579mNuJ\ncf+hJIiF4Z/FDV2tJvZj0H1dKeoRpI197e2IgOIZyBINzdWY3MmSC4CAGv7Uc7NkFkcYwzeeZdhe\nmBgRrAQ/qgunJIfdy8Yt1+1tit91Ds5Jh0N9fQAAEYDzOXA3D+ul23wQ9rmuZZawuPFcGw3nDEKd\nCbMp6n5JOwwwunAjVrAKQQu/32vipXDYjr6pBljepls5lnZAwOb7eXuAJpLG0QxnOCkkGlARwIxA\nnLZ5HW8Pu56B6xnHr/NmZhAhgn+vSic2XpuMpJzUVgEEXneUHWZGrrZpM15elWy8/0xP9U/E11yo\n7wTAX/9RAlrVlT5jUFbM2GrOzCwwlf5slijXk3/XOlxe91l+D7tzfMsfT75BhI1Uk0KOy9Skajkn\nAjp5uHkyoGu/Iitzjuc+S6zE1NsdiJFDKcJvSfGpvCxdsFERqJyL31nKZ6SrHxszRrOdkyZN8tV7\nNOVWBLqlUFbw13bXfqW7i0c2CNQxxkqIMQngnrJx7f3knCUdGNc017PdGZyGOZ+VXHjeaaX/jFfN\njGAaY97eVOWJIeHX7DxZ4mP3psRtKOc8kGK9r9pRZX4/q09pjrYNFgOI+r4IjLFtXeGez5BEaecV\n3DWCO/BzBOcPEOGY7QFyZug7t3f1DEYqN6Z0+7znoMi4n37BBNMkckoXXZgIuzYUZXJcOIMMAG/r\nKEEx5DwBpSebWAf8D9DnixCatAMcd0/AxwmDPO2aTWMVrglCK1m219vkI8mMkFcSQH5EzJAqQJie\n/s7X2HmySdRYgtmvt60Mi6hODmECcz75IHCfd10Iv9H3wdTznV8Czdmo1pjrsizstrNcOlUfuQKI\nM5DTZcx7Des7WnIHzWe4rH3p8wxoG+Ws1999CzkBLn8CNYAtMufDgn1pyZF5zqjAyC809HV3Brsn\nNV4JoNWfOSfCWWLFnVwly/yILO/qpAJOvvoU3Ylor2OZgwjjOZU5DxDfvemi2RnJxXNoFIWvqIPv\nlZDhK1F7gVkclifk14F1fnci+JUArT+AfIMIJNVk9llwbreoLzFYtA3DlSHPSbvO1PSd4eiLyvVS\npxS5B9H+FvoyfBLleMazMIVbdD/BEt+1OWkqA97mZ49UaaWprrTfvYIyYt78nHcZic2etojMukSC\ncsphBaNuMRBCy3sXQZO8O8N8lhvDIIeD2HtMf3OXE/q8zD+xMcBGu4/gyczeWEH0gABRGa7eYYwZ\nXSmbAE4Te56JKkorQ+bck6z1O/qflLQtpVYuPWhXc8m5V9kNLqV66qLOwAKkNlBHLD8b3aTgt4t+\nUmiig50QjYR1CzEanqmfamz/lXGogARn67axXNxjJdXzcHZGVM4BWSxgVuY6Go5NRVXfYCoqcN+4\nAGDeJH1uA1DQsaZ16hwUn3l+rEoHF4m5JKLX0o8B6THY3mpi4QUWQsfgCPzZMruLz7dnoUyN3ahK\n1tyY18coe5fDPapktJSUZtHzYtE5ARn/JuFaPW8c9fCyoh+UzIHq5mr5aKgBoO+yrjKQOubf2Tj+\nqGPjM8o0P3s2fEdfle25QFCZQtu1X7HcvTXuiXk+P2O18PVdRl6GyFC4BkuBYWhWE1lmDix09u5M\nnZ685fm+rExQG5MOs5OKZaDvyfsW64r1dVWIQw5hyEBHT+dXbThrpx7PyR2TyqQUNQBp7s8AzRxv\nmovJ1lD6Xo3DR/otzEsnz57DVKQL2oEF0GU9aheywH/f1cPHuWvjaljkW/5K8g0i3BCNua4S+DFt\nPtK2EzqdlJhKqq1l/Pqd8TKvvaASnlGxRS2HJc58c24qL7AvWbm9MNLvyBIrOldjfReWnTbFZhsi\nT7TqLErLVEU2Hke47imHKeBBObAFEHTNeh+qRDS7FsZCMCKrLsaI2xVyme6ZOK9vrTvG/ppiLfF9\n90l/ZA/Ijs6/87aHxIoSjcUurVy4bmdA5z6a3zdqT5zVnQyY8Tn/LpgO5fZPq8qxnFeNRfX2shJi\nCh/iWDNaLPwZc1Z0ft96jD2CbSpKPjesIQBnCkYpOu40ueJ7BwSQdziVcoYzhASXcEBnJzxXDoNU\ngqGr1atn0QFKHt9+L5WnnOdJNXAHI6Fs0LZ9V3Kau6QpqNBim4pzmaofc000A1ft2O3WjetCsrCT\newtjgJgIazvP1yYghzHM3+k87+etHHe2nSS1TefiGnDl+ZzvV69vC6tjB976mPH31iUClOG56trE\nz6UjzIH3xtw0FhAHvczjIReT9oswt1wYJRfVZ4OZQ/0yvsTbUPs6nddlLPNzDPOsjGd6wEwzmQAA\nIABJREFUZgk4zAw+/a7hMW0ymO5ISe2fnwy22LxW2PpxTq3HxBkzsgrpKdvax0W6/eyoT/tvC7qb\nr+fRaM3MvasxFI5v9BugBhDuXH8m12FrpK8twEP8WwGER3rK45m0eACFHg/uz1E3qthNdySMJe0L\nd3aKMP1qbVMEiGa7LuaDj8hflYkAvKYX/JnlG0S4ENu2sckSq6g0ZlWsWPnJ4QxV4qCgPLOnpyon\nKdbqAeK/zzBBDmeo7tGzfk3jauMB5q2SVNxQiIs+fwJfNIGpF8w+z8GXkyLwFH8uefLW556VdWUK\n2LE0MQ/OQbPv4zz1IDolu89u08WVB836frbI5sUlUEXbuaekSi5VnlcoD9kLc8w78TjZosCwJar3\n49sxrydJPHHI2OJilm2e+pN+Hj1X3s+zd3R8xr9DOW3tJ7wVpVLms+RFhz2z1fm8POu7bRC8JapL\nYCdMqvEKddTlVx5JIAJpIuM/SZ39Th4V9srsyq8z9/N57bS/7kQTUXL5j6PjcXgs/tLePr2BANpb\nm/PxST+8bEOkK/NUCz6+KT8r/4I4B70iV68r9/kdE0HvqDVfh3gefTQPGbHzMMf+I773R+thbbzK\nR7R63O7f35X02eKFDYXXDYNF6IVb+NRVnzp8nUsc9yXZ5GcYDKHKi/VDkytW54fnNk8eiaK5H9Tz\n6ZKJXtY1JQO4473oOuTXaf/TeZDbecc44/Krtupv1RoxWEAN1xDNvs4n4v2ovGK01DsNUBvx8fFS\nkSV24Vm/hnA4RQ6lYNaBh/e1JcEi6zJ6IGzjftJvSpalrM9IRNBaK8Epvq4SIxpmB0oKaWFmFuf7\nYB19HPvYGvIt35LlG0SY0iFGh4yMAuBOkr4sVcIeG8A00O8IexSBacC1OOnlhWxpT0Ze0z1WTISd\nVIkVR700mVE7PjNXVVs8jvbNz5ncLSe8seQy1oZ78ecW5zwN/+cs8ykNv/Q1seIZks/330W9xULt\nETMKgKGfO3ocUe6cFC2jzfs2OCX6LlWuo4W+mhXo23231/1I233QcxxtiKErOYzG6u9RwTxjIgTd\nG4WBkOiKzX4XKzvLHe/WLvY3UP4DK+H8/aihllkH2cZQMCEAjJDFEDLlumx70e4OtC4jFwJASRVh\nOUg4yZR7ZQg0s/fs5e6SwvG4FbR5zwa34Skj1CjSYnUe0DI8pCkkVoQDWfz8ruY8bnMl2TPN2bWB\naFTvtp9Tur4myAoeLjKcs/d852XKx9RbGc+JIT7sOT5jIpyCnWCDK4ISxjrgvBHp3NDmPup6dmdI\nPWm8ZxbYcs9kfDKNV+x5xjHIoQ6NzhVKilvlRChFWXPTKw9onx9lmve5YZ1VJ+MAPXnUu+YbOKg8\nnwP1/oA9KMA765yJgj8qS5JQHVv8zDdMhFeEQyMBnwcu29sUv5DJRIjz+VlY3b12zc8bt5RZE7me\nPAfpvLDLWaWiW42LtJD/SXUgXstHotv7Bv5ngYCc86CaI3bHP1LH3fNfkZZR3ilLvgnS4+88N82J\nYO+9amvR1DJXSPdP3ZZb27h739mReRfruuqPf0Vhvf2vLt8gwkbyYmPxsvb3/ExKVaWI7ha/JSfC\nti318WoyjrRorEq93CX4nUuk/GMq+q6s5zgxPjei666Y7cSe6cmo5aSQPKHelegZpPdsiwQZSPYb\nlrADvy49H4nK5/BYqMdlSuF92LY3tDH/Fj3/Z1L18zKMBU6xB1Zwx0CH3pb3lGPfq67OFG2t5iBW\nTqBDFwWsiUzvLe7RuKxo1/Q3NCcBeZdnO3tfd3g4SyL2EVEmghkwU+MYWxZ6Kx8z4aq3pwYqPJyG\n5wft0zSvVTEQm7F4Vwkt8wdAIM2V6dHyV+DWuq8x40P/ruKmK9Etd7MnanefDJTwVpTWnmngHOSR\n2k392Xuk8w0bzrspMXWR2Tbf1lClBMlO2DHxPH+2THP3vAjaBk8+6ls6KhMhskJ2BoYbSASiznnV\nns/ZGoIIiD6pn1P0lcm5t7DNeeBGBzraMPgQmYxnTITIUGxbpt3SJ0tgvyp/grgnzW6tZv/wNqUA\nbo2hpYybbICt3qOfTfA2NREGwZ6t2VaUavxwX1zac6/ZL8kVuM+S2ZDjGMKxr2KaAOTAkOt2OmgZ\ndd8l1t/mo1VHXur/5LJ4tq1jBS5kJsJtIZbrjo3GrI4ejknw9pue2xzWqsJLPgLivJIQ+lu+5avl\nG0SYcmeCUQR4PV4BDj9fDohR4Fl6VrTYIEDhqTGPASGc4gr5HSqvTqLsXV4BjJN7CcruVUXRGuJ2\nj/tRxJifhXtscvmVIqQGgJ7LoMjV/VSxhLqgPOlaU4IZWKDzq1tnD9jOe51v55yBED0Zu3O6wGK5\nrxkN8/sFVBtyS7wwZnbj8A48pm2/6tM8rGJiPM1Ovr8+J5C7K9YPSfHVQ28HgxjTCJvXDAW62aUe\n+rDei/WvD+od7gGZA75imiSm0tbgLo4x2+pM9kY81TvbtmNsjXpcJE+OxxqfLwKbc6MH3MGbnaix\nrF7nMwMshoEloDrP3yf11WV/zPjbyTpWhqIcaOwtAgh67hKWh9GlhHaBGAnEZhIz9byineZE2Elm\nLWQRGnvhOvh7OE3MWlhlI39BWqNutlmZCIv0OHczUK1Vf8RT1lKHb2nuG8d4ruVkytrmea+CbSPO\nDHfWHU63qcbMeo8YImfAVPt4P+fQiRx6xMfyPKXr+hVTJY4ZrVPnhxpJ2yUt/rWltcia/C1F++su\nnIGPbcvAvp9kPaMTEzUwXgkwvhraVQ4xpGN5/r+7rbDKFfT+M3a5+KvJZ1k7fxb5BhGSvNIxPgsW\n5JwIctTeH40Z1Kzgx0wOxuQII/pK7btbkOMX2h8Tt6xlGhIrSIbheZn2HdeGKbBfOKtEau5dckDl\njmRDlDNG+5Zr2m45paHnUIcOoNFCo4kZ9dhZXKHFuaX72T27SvH52bKEPZhiy1vS1d4yXYQlGYHV\n9o4vt0uiMsjPhpXkq/FwBAMoXh9zItyMeb4hrmBKCRioAm0U0nQdC3sqg4ekxbCfcr/oDN71ddzV\nSS1v3qi2GyNOXqb2djS5nEu4icAK6q6G6306N1B7eqpcL5yxv8sAIHJ/1603M0XUFdJ0rLi/iv1U\nh5dFo3LYdF87GSyhGnDQ4G3+9mgdj6N77hIbO07f9ne0M5h1Hhm/81ysoK3uXLETZi0EZlmep2kd\nUyZCpvXfkg8EiocxelPL56zxuyS4V6LjIcyNyUPOhnmVYNETSwLQkAsKaat0h/09DUM876Ax6p3g\nzCznDdHR0WT2Q3h+Ikssm1g4p9ti33zfGRDR+SWHlDCDI9RD56mBrs10XYPWfFkTRvbE0qnkbOxL\n+H5+3xUT4beSzzIRdgDDyDlCfepkPa2AzKwP6++8a8eDz7Uyz6XShRcnZgpp4jbZ/Ae8vDbndnzn\nVPgW4BtEMOHJpxrIHIP0U+SFBIFmoFwYPUbX5MV7N/D7x+9NFbKnrF6Jzz6ucqIqaNX9eVB84Fho\nx44KsxzyXF0pWBqz+i4H3mfc6S+94V2Ad4lbPDrlWI2HWnd8CvAQVzieIngsRsP4bOn6neeQ9yjO\nj+VMcp4AYCghu50T3AvnBtJOOCZd/67L8wVN3827HHiTfl5+h62+H+6vN87hePCWwIO8FVUjw35b\np3j88y72no2YQXeP9S4gCAEYqgwpsJDb40wY99Q8RdyDSW3Iceb+wsjiKpSZ6l5UcnF2nBR5j5Un\nRsJ83ke7l2k97yqj82QGjtwzHo2KreeUHqfMMVcxvJ7z2BuBMVV/ZiPjrrHCoQzB4KV/2lZBeq/w\neW2AE2LHpbfVEkICeju2SqNvN+x/DyPO22MJeem8dojlp3ij9717zbr7BwC8z/n42YndBZ+LK6X+\nnZ7Rk57Zrm8iHb9iIqxhdx/Tspf5IS8ARSNH8rQW1oLT8I6LKUvBndwtWvFu8zmWY4n/TlKHtEWA\nSMO3zsQSEfKxpmyENSTjDhB0xZIAUD47Bld24F+WFr4XIXEEfoTQ2QnOrCxYP/+OZCP8CkCorl3m\n+jz2NiEOQefetOOz+mPU62UBDfj3/MQ4KSgw+s4S/gkCb3jsQfXQdF8h0QyXIfP3ZmtL1p3q3EF7\nXcKWalnfUTiXrtmVtb22/V7gpF9fvjGUId8gwg0RGozVzgkfkWVgkhGvnq+zmFRDrTMiTQbAnp6+\nqTtTJBndJq/jju3gCdCs2Lq+C1FFMHvdzRKiGVK6JlYEJXg7TGF+WvKpGjGu7sG+B5tJk1dFBb70\n2up9kMcEGh5BIMtTUsIaVaQ3SgfHueujuOuljeX4d2aYeBgIJQ/S+4S/h7wl4VqBfg4lpz/jQjsW\nytgeVXrPwipYKiQ+J1bM44DZN3lXBEnn7hgKn6Ww5eSjuzIzRbdheHHfGm2qNA3wA3CKi55fvCDu\n/wUOZ8czg8aqy0Bj9/mA7y8bZeqhDwqkxLnG2t0UFLFRM73295RiLq+ipp9m2e6pM/BvSTnPoQxd\nWqDFqid1mw/n8i7WtnPm9rNEVzVNX6n/Y4x8lNxTgUYL6AQ1qHSMRTbRcVJ5eFq65mBsI8pJbfX7\n3XCGyBYZc3MFBGW5e2xfsf/TZ+fr00Wfpmu5VvXw9/f5t651vYX7rN7LXTFmwv1hZ3OvbkGpDJKQ\nxwIRrGb6vzZVw9sUPFzaRmNM+xgDOx0ezpXbv+YA+pihugWb6H6Wdqe/GYgJx5sGBKGci6Q3tJRw\nWJk6y7nBKPU2nuU1+CvJQTBVOdbz2kbjVkF5ZalKmGN07pv6RsWQEK+z07zkLAZfH1cwIvXjDRvh\nZ8g3A+FbWL5BhCkZoQypys4Qe6Jb23aQaTZWRZiLObLTa8NE4GRvnhxMTNHW+Fzdgz7U+zL98rXT\nWXTiq7YmukrQ9bIcqyLAk3ukd85zzHhti0JZSZdkTMv0ZPGiI/XCWyvx411xOMNXSECci9+rOzym\nksIL06uhOSW9T2I5lzkRskEgEQQAViq5KsytyWn5Zrh+cDHVRKol+r8pM2/7mIV3Yrisf7afM3i/\nzQSKj+QdeTRZjOznJjHalbdBcAG2MXj3RZ3YQkIwdjR4HN08zh7OQMkR2964yW06JujCOwIczb2p\nZ0aSZunO7zMzf/SZQZqFnHWaf64eU0gEqUBiCo8Bxjg5IMZ2OJMu4wbNcAAZFPO3O+3xY4Dv3HNZ\nfaBuP44++uljkxMh5UXQ+oyZRcaogsKao4ZBrzNAV8us5+v9w6h+aeG7r8l35cwbfkkTLkEcBe+u\n05BaX8X6LJgBpH8DHKpynmvA2s2szZB4+nOKQO4j3IY3Wieke14EvWSwZeoHu82SX5y+5ES4kf+m\nSphYlwVLXPlEFKafu76z9pVs6H5Gaj3mxTI242J3/IoJYY6ZEiSX07/5+OPiHBWbgw+d12E7OIV8\nW7gGo1Q/tqbvdKgE+rAYgHHB9AEYoNjLZ9bwV3bI+LOJ4Hzd+CvJN4hwUzRLt/3dyjWGft8rF2eU\nzVhnfVLYu/aCWl59327tchJnVcmZ4aEG5d3Yqa1xQ9+HtxwRpDGFJXtE75V/Jqza6vdMCR+/rfTZ\nXLd52vMxNHAoBICh/J+UZd+B0kjJSl6V/RnQsAlGu9f6HHiJz3b3Tgftsj6+lE3lvW/YLaOdH1+s\nKjos7/FtysEmjr7KylyVGZRvnBsVdXzl+ZSgnsG3Y+1rHD5jWe4TkwGIfUfE+87Y1pYUohcf92eS\nSIZQDFFwFHgwG0v2dXDYw12p+lODzi8SXtDdEDORhk5016c0vGXq7Okcsd6AGuL6i8bTHuKezuec\nK4TKfyWh3IF1nXl1vOVwBi3jYWugj6PM5GstU9FXQAzAAgqr909DylSuaLuZ4lsaSsv9rcfaNEr5\nvQ2HAlb6yGbLuK8UplLzZw43uOoWvEvGmHNo+00GgPSTCuxs4NBLyXPrldi8RB5dneNCWBF94zVO\no3MO1KEn9Y4k8xP1PVZjSrfazsCAPXMdo+JjOYb95LqViYUPM4V+S/msUbkDED5S7hmAcGuLSAWQ\nz3ZNg4eTVnNJ1rPP5mRnMby+Bqvz8ivkbr0eJpfhrm/5q8k3iHBDeICGbZqOaGQch8faZsUoJmYi\n4541oCPGYQVvFOJODEeLHqmz2KhX5CMZpLV+aSjDGc7kzoR5TftsRsfn8zuYSVDH+2+LtIVhXP/e\nx3eOq60MeA15YfDElHz4mx+AwlBC2Ui4K5aRG+fPjxPJ2TE4In7XYxHAnAQq+HGEmHQP/+B+Ossj\ng0CZCO99Pd+vsyc3+uhNKykDE7vkUrmva6x+5SXeSQYHrs7PHig+3TOhj89HE7wdErZz1Jjho1Fh\nXX+LlSuAxfHjAdDS86Yqd8ngKAaTevuCUr27nPqIim67+Di6GZJvrU+c1Esigm5RbowTVlCi2raw\nNkhW0fjwKxlzoMzyair51XjzuX/O/xCcmX4/M8t2BgOl35sv2AAdfzuAcFfZZTCyTbfhs7cQnvY0\nBV7sWJacn+ZO7TycBhMGC5sF9DtLydL4zAuyQZrKy+/GQJG2HKuLa3jQ06jmLkt6yYZ1q8e05uvQ\nevWhvZK3htdVzsET2kTHdL1scLDgaILeDjya4HEAvzzr+zuTvP3rnVwsfJ6GntlOVQXIFoBs+twz\nrM53MHqFeXDFnlzAZqRQtKrMai5mmEf25+Z8CGf11Of9eohLdrYw2yysp4JFp1jLWvVfLS+yYFu5\n1WwlFavrI7I44VAzD+WnrT6/f/kD4nw/Rb5BBABAQ97aaDmj8s4cMvZxbn6OKUsn1GZLsBa8GNMj\nTYwHTgAWrm++0CmtSw2e7AE9pR1eyKthCDmcgeO6XpFbyX1ollPldqHubhbLa8qnKzCdjlWAQZaz\nZ5aT6ngIyFS+MiNhyiteVj2fPTPqNVtiMknB7klJqaj5wcv9CXaAij5bBWbubPUovY0MlfDx8xFK\nnocgsRe+kXdoDWW4Ewaj137FzgzspVWDskFMeR7m5QAXmhrOx9zVoDMbqpW7xH2F8L1m5dvO2bGx\nkPtXsyz+Dytz/qOXHD2iqrjv5XG4ojOS+VH/pvMssaIlU/AXwMDuWX4G77+S/qZzUAOQLMrLGWEs\nWuIwTLhfsLfrM2yMnLdgdJj8930g4WhOMX8cfYQyHHFc8DO98tJlivZVMtng8T0pm3M3VL3IPOD0\nqybuezQPl7IxkDP86cWF7MbLLSnKrJh4mTJfVafHWnNdhj3nHEY0zrto2wvJ/rTM3Rye5w4OW/Ct\nbht+0BjvTeaWuOfzQu8Nj4fOkX6PFcuiYnbpddWuPcdsx/jebDw/ml+nz/GhcfMNS4iVfuV8Phpq\nB8SxeMYeZaDYzi9Apjvye9oe8GcCCINVxGvb/boMhJuXqL9+Z2dkJ80rwmOHQ5Bsvtpcd5bXw9vy\nbSp/y7l8gwhJOLEJoErs5waSGv1XU8MZdWog2/5dP/OclI32O4tEph/acXHDKmSPldXLpl727PW5\nm7goT1alF68qaCqYPcVemlfEPl9fMDn5lYcfrNnAM3JflzWeT/YEc7tYSc0K4ZIgCtoWWRRG/l4Z\n/JxM0GPsPI/EThhQ2Z2XY6e1v8TkWnSu3YcmSovGwhKPLnU/+IoYUG0foJ6in7+A3o2nVaMlK9HD\noOkGHqAPL37V9tF/dRzH3zjp1jJOdnuTIT730/wC+5/ieU3w9tat3MdkIkg7EotAyv6ewcMjzZEV\nJfuzon16PDeeL9ZQoJ3khJ8KHj8IpHlMMOgpbryqodco0cErjJlL5Tgw01bj0JPlFfXA74UB9vB+\nTgAmq/vwemJixejx293FmD/F8iiMouN7WtugBuoE6zdlc5t/1nwhXeaWc+M7gOW9eE6EVe44A6pQ\nt0ej3AJwBhCX2cgY5vdq2zvS3G/hjVRPHU6ysgbj+3DwQNv+4JwIs53s2T8b6wHYYjaB1b1ew84d\nZhLo59EEGmr6pGM5XITbxqEjlQSWSW/lmmdhP3rNZmzWqtR5WCZfK1Kf+2sk3bvKffKzpErKOXS4\nFGpF89IK3FAes1kGJ9i2OQ3M7NnrWrst4X+mBP3tJ9f1u5UPAnB/RvkGEaD0xeb/ePGZcY76zzxT\nx/gqD4HMrQuO3nA8BY9Hx2MaUo9j7HvOenhrm22irB4GCcQ8Fp2UskcTSBOIodgzgRhq5Zr34c4x\nd0scZ/WM0mQRqHeiDpd6RjkrWhW0PEF7EsmPSVZOFeE/y2ru505DC7wYgwyFVFeLoBNQL6YVFTQ/\nMfdGxKR6S1Ipu953bGjm8XBPriqBbIxoHfmdhq3v5rG3VnthFmBivd3b4okV7yshd2mykYornpSU\nPN1mhPR2SrfeKZOt6TzBBuB9o0IV8UxrPdJ7/HH0qCxPJeKtddqc4cAhgrd+hG3z7i54pRFyAW4G\nUFNZLzTXZQ9mAEOFtjWb89rj6JCHz59D8fJ3NnYpiQaMju/Vw44YjvYBlkg7Xmfe7HawARyT4blI\n+ySHrxhwRHPKmPt9XmoyjL1nA/jtPdq58Zjj3MtziY6uO3Po1nJXEoy9BhwPwaEZ5ef1VZ3hUOlt\n909NcltNBY1KGvNZK9cSnRv1mkfz+VF/B0bYmTErJpinfXPci3ibN1q8jqMKuFF2zO6dyYsa6x1v\nZphvEJv+mMcYOPDQON81oTLSr+Zmbln1qMY6MI0yamPDeObGPrJrBW9zqzmdz8fYQVhD9R5ysrwI\nCPj8lefjCDbMzxbzMehaGe4R2s+8zW+Ny+B7bIGlwM/E7rYnBa4QByjus11Ul1iYkGjGqhDBZFk4\n65V1v96c9s65B1gvyjkJjuYhSfkeyrHdPCk065wfYSXo9Tzm7UBgBce+Y3UWDrWdVM6g5Rz9d+JI\nAUhn0We9rMHxfOvDfAwxgSeDpe7YagAk9IGjAEe+5a8r3yDCFF4ElwQpBBlHuiKBC5jKwSHoPS5K\n+lnR5DwxYD0y9TpOQHalBFfxbbe2kzqRXWjAWRG8TaCKL8hasB/n8lpQA2OlO4Uqbp8Xr5aivTsZ\nmXFfW5A4jGMnnZBjp8av57yUGwHMRHBlhpWmR+s42iPl6Zj1GdqdvcqynBvb+RpsUHtDZv8QZf/s\nyzTD5fB29qe2Xcu5B0JUBqF5sGc9/fA5IZT/AlzCzA4FKDhnhJ0HzQWxL+uhyi0ZZ30qfcMIGqNM\n8wfcaeVQD6p2n7QlG5Ub8O+uZMXMvNZEhX/2FgysK+pxVQfL3rMztCg2gioApQq98b6Rlcw4/417\n3RiZYY2QAAACo2t2NDwbLJ5d2lXWhFQHHEjTOle2T6P3m44R08NZRj4uuCRTwA8J9dweQ8REGPXR\nmiL7fppXDv5bgvGCaby4IeFJ8BQw6AOaPcgLr55uxDxFd5Nw/gzx8I712fJ6WwHZKi2NJwWwGAxi\n4I7LX2RaQ3mOyFvA5ktyuQrcKCj11jqBkQyaeX9+tDbaDVno47utNaMhr/XRuy1aXSV1VbCB55yu\n5cFDkxpovbY6TtY/MKNDGXnOwFxi9e2z2Ry00zl+puySGFbHhwukPlfFQQm9bwnnXQEJ7CzMcnZl\nDsUC0lyEyIg600OCrYHI/lM9jKPqApNHAdhHXcFnwkxLFuLlNX/NxIqucX3LN4gwpaGlhSQqWe0h\ncYcGW20Fx8x6oh7J43Cvy+Ndgldp1DX+heRRx5hA1WsDYG6JRYsSLTpMEdV2Kn0uL2rMYmB18xWP\nXM77EOqR8wm4UvSd1tdstrxjiNdti4DNKF9M6QHcU/kUPyaJdWLlFYrSLsdCDj1QJD9nba7k7D5Z\nycieoDNhJQiIHs3gZWp6X9oW7hf+O9et5+xyIlh3KMEw/axjS6Xol8dRvIfetowBVpYrNJ6ZPZXh\ndFc+Q2Nrh4Bjhtmjbs9cxJgiEQxaPXEDIIKtaH0CCpqE8fY90b2Z7caPJLkcjZ3VxOKKRz6DOH8e\nmF5guhceg1SklRHDGWSyL/wY9L5Ts6p+9Xj06EFcQIurJxOveTSZXv9aPORnLz62vPw8n6u3+0cT\nPDmEpSvIOBXLJuhoZpxYHVBD0P+OuSt8XCv4vYIt9N3AIwpN6vtQDfVYA94vDGyX9D4Q34N9vTDK\n8zSTy7k6n9uq1bkRyL8JmsRdCh76vOatcN6NO5KTT7KX8KO5XjRsg2XxsBaiSf442d9gZQg4P0lo\nf/6kdZBj9pe69H2n65d7qa5r2k5ZLvbdXAQP0TCgi/7QG97efJ4aRToTIeZhQekZD7qigUvebwDg\nUOZUEwMK8npsdTR/F0Bsf4ibX/L1NPv8aGjfLpxhl1RvJ5mNeQYkaPl2rYGRm/5zAyjgsm+1N33a\n8aMFVnAmGW13DALZCdymYo4Gxr1WiZ0/IraWkP7TUv/SutX8t3a015xt3/ItwDeIUMqWiQAEJRod\nOB5DsQOA49HRewsKWUldbPrverS2Q0K2ci1TJzWdCCxXASK9asdEqCY59T6N8+M12ftEj2LG+684\nsqJ1X52w+qslv29mIiy/bdDanBSRd2dAcypbzC2hz27I0RoBHB8XXiwe3BeLUlnxC0DCPPftUGU3\n9tczj5IKgwmcwNF+n8eecDS/LIefpV6XjRnEcIjKKwz4uGNDUr05I/+HNrQNNsJmjPL9ZAWAKY7B\nIOj0PZXD/YrbzkaGs0rGeWrYDANmjMYOQe+tfNe57wp9cr/Mnqt4c/Oam5nXzxKravKxYNQ/PDxk\n3PcMb5CHgagG0jWHntTAqCQnybzK/bFr66ui7ATrA/Qs8tw92unGqIbBMfjcG/DWBhOBw6cOiYaH\nHm/h7whELokrm6Sx2dA47r5gnZjXLMQD+3gIFNsZFrjs+DDnEfaSVt1OesO7eJCahT/R2VzOzni8\nUpDdoGCj0sNntGwFqTlsbgBr1/1qN6d4uXlCSSee3MMW1LlslbfB2tKAN9JfNIzoKiQPcBZLWAOK\n9vFaZevBhiJ+JBYNC4M4Z9vrhvLIQPT21FRwL9vPW8prMOYGMxmYcaDf35rPS1WXDAvTAAAgAElE\nQVRISBYN8QAcpMnrYh6/49OBBaGydI7nY99yLmUog33/eLldRnJpYPT9dxmhybYTDVhPw/w8WVc/\nhj98y4vyG6Xm+N3JN4gANZDqkeeUTARvCoBy1l89XY6IZ68ZK1xG0c/hEfqPq33Bw2jed7umIYcO\nnImoMXXn3PT3Eu5Af2t5FRlK2xfCHpbKSHn8IGq7E2YiBA+ZegluLPosfRKOGTBwj6UE40I2lL4z\nqc7mmMjgsYZ7wAZDZX12QTGG5xA4y0qvkpXEM2/llajxIc+oHJkSRUa5kGLkbYnlqTeWDU71NKii\n5QxqMSCBvTyq4GbmRk7mZ22wtqw7p9g9bQztGKPczSuvCrNuQ/loq2LNxudjGog5P0E0MvfvyH7i\nUCKlK4tTase8piAY92m7fHOPSYl/dByWU6bj2Y84p6JNg4IU9O38rWM5Vi56C2R0DmYYIS0Xk6Td\nF/bvcLnmBKRh8PlxdPyY/2zcHeQtNAR3oAjvCZyrFEn1NAcG22TOsfQ+QxeoDKXSKo3a7gVD8eVw\nBp1TOJ7Y6jxaPemfSM5rcSavKtBsFCsQE8bdscIabzM3yaN1POfV7K1nHWEHZsbf6652lRDxM+ve\nkXqsrrV6TJMqPpqvha8wm1iWhHR32jdBm8dEsn5MNsvbo5f6D4cz9EezUAwHhc4ZGTm54oF4v3me\nGsdifh1mbjAToTfgxzz+Yz5MPafDd9t5HGcBDbQO9Yajx4TFeUwGZgLifKOe713I5s80jvJaqZLz\nIuxCG35Ge0Z9m7w3RzovrVWv15f0I4nAqQI7JSOE5/icSPxkLqiSJrNz70xnqiQwYn5X7sBvydJa\n+7cB/FsA3gH81yLy783j/z6Afx1jNf53RORvfbSObxDhjhxC2sU41A6M13LA4pN0u0dVmgD6xJrZ\nOIg7E01c+UqLHNFfl6ZeTLzR0xDP5fwOZ6L18iSkoEiWABzMRT0ndPmQKsTxii0qws7UqBcGVp8U\nrLiaCqtY5t01IyuvT68PqMfs+npVx/Mzuasce2ImNa70Oaznsuf+2ceL50SeWu+uS+RtIZe2HChp\n0nY9tTnmzSjeGSlQuz2TBS3Yf4LVuNu1RSbYYfc6z2P6tpbPrIldmAu3eSevLL/DSz0ABFVuBeN9\nPY4OzPcnR0eXEUt81mdW+qSOafdYLdcXBd5NcBnYUZjPEg7sqLQ2QsGOmXXweAgevYdn9eyHgats\nJPws0bCNnXDOjJzPhIGtzBAz5Q407uZa8vbseMsgQj/QW8d7O7zMNoz4txYVUWAFrmHH13AGTRxm\n99QBPGIfVuZBZqV1aXjXcBoC9FhsbeFsecBkEEUmw11RZft2MlbOfwD3FttcOQHdY4KuACxhnxDw\n+Nb6APUOgdIQD313DXGcdDFa9NXaWhmq9gOLhkCe9MnT3DzQcX6ij8BvRZ+FyJqc8hUzqpoPzbBp\nDGhr30QAQnX+qx7j28M1ioeMJJjVbVV6E7MPOA8JA+zKespzI187ch5o+GC3vvyY+tvbMXZW4XrG\nWu/15PBKZqflnAhZ4i4Yc06i8RWMyaV8KcdSDmUw58fJoKvKqH7j4wog/EzgoMPzilXtWaSRQ+9w\nh95HnSKjPrEB1zFMi4xb+64+1fUK0O4brit4xUjOUgEIka2oesFrIS1/fmk4Y1n+HqS19jcA/IsA\n/gkR+f9aa3/fPP6PAfhXAPzjAP5+AP9ta+0fEflYgotvEGEKJ1thA7sVcdl00aBozj9ZSYies7Ro\ngRL+9Lhw8rWWkOpQSq+276RNYG+jmFFqSRlB6HxlrFk4Q0sKZP1dRY1xuwfcM5IyoLD+vrXQQnt4\ni8fPCE+gV4T9QB/G+h6rstdcBG54jN+HqnI1X1eeTC0r9w3OIJ2FKXOjXeO9Rzqv3GavZEOkYiIs\n7FyJfWrdSokTSAH92dCaH+v9wLsc5aJZJsTiHQ5mcipOaAQAjZgDeox3kXg3JW3W1z3p0bMf0NCi\nJxv3S0vIY4S654TcAi163zuGx/poY6cWQAHGBFoW5bJwvQaSVJRi0j5Dtv6i4RymUYl6+8D3M5kV\nnFhRheORd0k//3/23ublmq9ZD7pq9b5/PxU0GjMQEweK5h/wA0EECY40mIkS8YPMAsGBIEEJThx4\nQCeagaAcSEAFOUQRzVjkzCIx6sSPiahgdBBiVHDwnvfZvZaDtarqqurq3n3fz/O+5/dxF9zse/fu\n7rVW9/qouuqqWhk0PN0CDQqA1XXTH2Ibjx6kuxIURExm2KD2MFU5ZomPRsX0rgoeFBYwZO7MIGK4\nlxknWRRQPOTSsXqsOuj7p4m570JsBLHz995sXGC17WreD+N8jTtmcigwcCZVst7jObUhdhUyBbgn\nPscTzwSKfqXlJhHfHcme4zviXm6DiAFhhTUizpn371/lI1DAIPa1GLuv7zbPVeUzpXGj86JvBxpB\n47N3zt5hDuGs8k9chYjw72Hb5hV6+jVMOa2n1REOflgf7AwuzEOPpu0TaHO4FjH/QSzXwLtO68uA\nPdcYTufvK+drYeO1kjqPgYKeHvL5SvIuDZwrQctRJsJZEtRvIVd5Eu4wybgPvrrWclvo3POCVuTz\n37DxwKGmOo5U56nCMfXcu/ItQIF6z5tP+YHInwDwb4wxfgcAxhh/ZR3/IwB+ax3/X0XkfwbwDwD4\nCx8p5BNEuClnXoSQMEo09pPp8KoU1t5k3kIrbCWp92NAo2Ie3KBUaUiDnj8n7JeXUR3FNI+vQWF/\nN+WjWWsbfebEOoArXx+JiTtTnO7W6yp20+4nrEgdj+V77jj2s6s3znTyu2yJq2Une0RVjjk5jsYP\ncFRoc8LGsyqqAtZo4WZgxc+7p/xnlFpZDvxsXy34wZNM7CP3nDqgyMc8DCrei0X7wE3m/jeRkNfC\nKO80V8L/ZyYNtw3QPhr7pbbvzvx0ujvD3P7gINXcz3Pqe+TqOWcGm4jmRVhA3/p9k4EnXeNsoW83\nN48uBkyFY8OVVqNQv7PcaovIu/0vZ7T/6H3mvXw3hnxcRb3wDNxXiTDPxJwM30rXfgd9qUrOV553\nsh7UTMf3ydl2oHfvE5PCjZWkswCGA5jIAEQsPwCQlHflSoiAeujpOeeV50PgJIVC4NS8g+U5GSe7\ndiWpDP4qH8JInxaml0DTmslwXr6V8/oUexffwkhl8Pje+Q5MvEde6V2ZLTc/ua/Fvqp6oR0r9IZx\n7J4fEg4x83XzdX/6WumTv/L1N/qRyq+JmfH7ROQv0fffHGP85s1r/yCAf1hEfgPALwD8yTHGfw3g\n9wP4r+i8v7yOfUg+QYQXMoEBBwvMwH9MDXyskAZg/ta2GTfsSXvcE5yNuODtL0a1JhjTLSMtO744\nvaqss3kvVzkgA1Kre3PyIvbVpXQAIA/Qr2R8qfudntXoH5+JI4Us/uYZ8FlByEyOe+UovXdjo62o\nx3u2dqykolPnuM3qmllHCUnuGPwCtE+xsR5jL/Ved7JD52WnF8esnB69n+qBaS0aM+pxcXpmpXQx\nYu+fvlWd74UsRN3VNj67x4DvVJ+DlxruGRqNlDoZ5bN55RECWEHhLR7Xb22Yx8MNnOv7nQlv13dZ\nye7PrjqVTrtUfu8KJ0eMLC//rAAoT0JG/YcBolSvkDDyGyMryg2ZDBUHIapS1GBSajQAdGl4yMAu\nAw/tV92ZCLpDw3NcK5B6f+CEjTbk8G75GI+dvU/jxJkImvDwWIEqqeJzTCYDP/K+fgvxYEmqfhV+\nN9B8lbVCFQ5hFjSvNbj3M+yaICN4XbfmeUgGz5ftZC0ip4C3Pdbza8QMyBQWdjanXjEU1BOseZg0\nJ84UHUNc9us2HJLiFnUYxW/KnAFgY+FsLZMUzngmYb1aTIR8jbHv6NxMZa+MTtW3Hi3mkhgYeIyB\nRwd2Yi/MvimX9fV6R4Cga06EwgtdrkeIDAQgzjuul5zoCS9r+OuTuzs03BV/Lj6nroIiOLXGRH4W\nmfmlBvwmzrJdqWsO4rqC1gE2f3q+FB93rxIrVuykwHg7nF8/xzsskPfsgPEpH5a/Osb4+85+FJH/\nAsDfVvz0r2La938LgH8QwN8P4M+JyN+F2mD68ID6QYEIIrIB+EsA/o8xxh8Wkb8TwG8B+L0A/lsA\n//wY45ci8j2A/wDA3wvg/wLwR8cY/9u6x4cSRgTEGydKGEPSeh3nESAlIiSuCoaYLtYX0vxao/eK\nG7aXlxaoOBu+Mwa0ntBCJuWu6PWkdVd03kxZ7lNfX+WoQfIxRWkqb3TDCznbUjDEK0rtcbqqGye0\nPFPKs0dS8yGU9cyGeDWpU+RlUA7HCABBpUSfxUDbFmuptDEonGFEYlqI82zDnud7pIrRfEVbPk0+\nqEbK3tDa3AWFQxz2ZYywcp5js2UphtnIVlo2P4Np7KWyKZzBjKbejJa9c3IrC2dYisAGICUIvOM1\nDyEzy7DUuHnpDiYqrVp3ctHEg8B6l68Ufa0TkiGHNb77mAb26uy2QwbT2+l+UXGRg88ivBfnfM4c\nGg8Ylb61jiYtMGTaOE9SCeBUmebfZzLAoj48sBa6lecXDxV5vxI1k3OmYqie1sZtYNtXHLhZxN3m\ngk7AwibAmzBzgO63Pt07Fg2cs10tpoFCc/vuFOq+KNR7b7OPD7Hs4hqucTavhnuu8coGkirVd4XH\nKcd3iyng8/uVsctrxD4Q5jo1WjdaMLfWjVLPXslTObHAmO1UnTJBrVHu+HA3FwlwA6CEggcjHFOD\nuAKcbq3pKdSJAbwM9sawJNfBmJmzrYSWOdRpdDnoPfmRXc21X7Mt4qzb/PTtlDVfhh7vELQYzrDa\n1kcEQmd9jmVMgCiugQzozbG4PscR1Mv62ntBAV6Hrq69s53jIRHxuuYjyRVzokI1iq8MXBFf55nR\ndqXfhGTnQ0HHzELRes/5J+gZJwBCxVCopA+BhFAc/y3nZarzgKXvH+gDn+LC687vpowx/tGz30Tk\nTwD4T8ccFH9RRDqA34fJPPg76NQ/AOD//GgdflAgAoB/EcD/BOBvWt//TQD/9hjjt0Tk38MEB/7d\n9fl/jzH+bhH5p9d5f/SjCSPmnsgzJ4KIYGtk8G/DVrWwhdO2JrDHsK2w2gaMbWB0zy6uHiVOdLaJ\nLyKhHm3dT5WabSZrkn3ud77vcybbNldgMv1V1sIPAFsfyzvlK9WXMRe6vYhmupNUkYWZFZXipxN0\nTOYYFVf9vZq+G+555y2Bn+5Xv09vHXvxNvFF3OyFAfg2TFxHV9458zLHJAPzHf5SJGRx3hpgW/lR\nHOBU4uN2TlvTLNiwMpr2DZAii6NSZEoWOH/BVAU3UoLVa/ag3RlmW2Y/PzzL1Xa95xt5Q/vqIPqd\n4/O3i3CGkn5Kz9yehz73paC37agwAu5dfiU5bKPJAGRMw/TkvubdX8qCKnoqr+Kq7TwoO4IM2gsl\npQpNEfiz090Zgrd9O1Kqx2j+HNUghT/vO7k7VMJ7uxGzU00fnp+BlGQGu5hZ0XweazbHLYOarKYx\nBFsb5vED1thJ9a4oqJZ7oKinyYV2YLvlkPIpMiBrLtH76nwxaMybh9O8vX5PbfPDFH3B27bjrXWM\n5Yn/0hXwAzy9XEcfDXvj3VN83tHn80bPq8mwNWTbOto2DZIji+PY/mN8dvxeAoMrqaC0yyFgcjDI\n28r9QfwGppczWNYH9/Fz2UTfB8+fcVcCwD3g8dphwKwlz9sWUFtlxlt6gxsi5LSg9nBoT3gOuTkj\nrtVhnaDT+Lk4aCRLneHkmiuZZPO1SPMhzB0SdHxyEk4uvx77V/KKKaOhPG+rn74t4GY+59RPRdAo\nTwLngvLyPDFelVgxM/B47tXvTXUpuC6jz1Kv9W1ZO9A0BnTW/0tveKwX8ab1bXG7Vb5vlhKQSWwh\n/rTji6EyLub+JmrUHl8M5wMZBHponP8B0NV7yNGhovfv+R3QNaHsUTsWHJTIdT2cWl4rNAdfUv8T\ny7h1B8M1sa/qjPmeMpwdBhS5v16Ce96vzubmqXsf8069Go9n/rkqLLDJCCzNRmXL+MyJ8AOW/wzA\nHwLw2yLyBwF8B+CvAvjzAP4jEfm3MO3kvwfAX/xoIT8YEEFE/gCAfxzAbwD4l2RCjH8IwD+zTvn3\nAfxrmCDCH1n/A8B/AuDfWed/KGHEVESicqmeh6ncAfKAa+MqBC7od1OWdMCtGD5OdMaxepwTYZwo\n6a0N7DtKyt5BGQ7leJbhYb/Pia2iMVUJxI7nHPfGVWNp+4YUM5Y7dL9KMZjHP1KeK1lmiJlR55O7\nxsQN8fn4Kk76Ts6AYMt8hXfE7meKEyAX7ycbuaxMqWKkOy2+YiSMgJKf96mSSYGaih8pfVFZ0nhP\nzleg21jd8Zhpn54KS1LACP1XJsKguivjIWTENg8rMyMkeDFePQt7Jum7spO4/UD0iPqOHGxMnysZ\np15jrmtxku0FXzAR+DJlIlQSQBClCjegrZVJwVP2BO/dDYXQJjkf74e+Up+22kVKsyY9C4BEfd1Z\nZJV7XaMndqc66X3NuFyg6GPbjc2iGdj20cxjNxaouY0JDFqZcKARgK0/6tHNHvSDYUZ5cAAYC4Hr\nq0bMc1B4T/Vc+nFMaRm6baoyGcKylDp/nnfu5mCpRBX9PjxOvYlyGJx5YKAL9VNmA4XEf1kfAGY4\nUwPuQY9HqVgIZ+0pr79FS4axFdkxoGBktf6WYNHJuuD9pb5e2TWHugsOz5zBgnwu8D4Wy5m8yrtx\ndr7t7NBG2LkDraH16dxRE9RzIrBjwc38y/mpWFMHPWNNXqn3z6y8yfYZxtLR6+7uqnBVvxjKdtIf\n3nm8PtevqfIgnNH0r7YyPuQNKih8mpMmHMOIYwdHJwnLq0TEuj5wOEMf01mVQ/rOnHflvVM5lWTG\nlm0pfNKOn3NOhG8c7firkD8L4M+KyH8P4JcA/thiJfwPIvLnAPyPmHsM/gsf3ZkB+AGBCAD+NIB/\nGcDfuL7/rQD+nzGG5pDi5A+/H8D/DgBjjKeI/L/r/NsJI0TkjwP44wDwN7TfE3/j/4sQhnncZn4M\nCz8A0IZt9TjLUWVuxElmIYgef5vKtHq6t7GlY+cZidUA9Ng+nWDfq3ixd3bW7V4ioG8hZwkAj+ed\nAQik+N9coCL93cvnbNWbKZ0r7q1aFH5VgEqinAK1epofG2/zyF2ajWEZzqrQT/Vebs29wYZWf1VL\naqljtBEzfK+wA04mZfthU3ty/aqMxq1FEEDfGyPv3ZQUX4g5J8KxvmpQSahjxUa4m5TOw3OiUq+e\nQfNMtAHpa844uc+VXAIvybi2Z3bxDMLlJ2XEJIqYro6dDevISGmDFP2L9ngduN5aZ1ewgTleus7H\nnDAhAauVB2eM7PekNgdl0PtPUOjofG3nts2QnU0GHssbi72hLy+n5kTYF7OswZkEA45q5hAlNxJT\nP6T32AALV7F20A4mcU/6FZtN7T3bocF3uyBAMMDrc1xlJVhBeWbhMHMss9py/61Cm+zZ8DOhe4RY\n/GRIM1uqt6jUry+wBtoxgMMUrW7p807IIk7eYWjbnXXz4vzqeutfNwCAu6JGS5NIg5/Ojm4e+7at\ncIYtltX7AmfXM+0rtOG9oXfsdNC5dqTfOTGiSkgMKwq497VrzvphBx6t49HF2HxvWt8Rc2Wdic5X\n838H4RjA1bCMwIBb42sCC36v03J+RXrLj1VCGMPSg7S7bzIo1BNBL55MBMp3cWK8O2Dg38+E52g5\ngAnHfslzZH2/cQglst9u9oPP3Rl+uDLG+CWAf+7kt9/AdNh/tfwgQAQR+cMA/soY478RkX9EDxen\njhe/XV0TD84Ml78JAL/38bcPIA4mS6zH7IIwU3hNmKbYNgDdld7GFEC9VNTjSspthSjyfdsgYPt6\nz9acJZb3PFaq5vOFi54NNyAuXJmJoNRtfvqvdBh91u/KcF6gF2JKXPSE6CTKSpnuTPGKLeDb6Tnq\nrLTOFujJauRI+QKdsur0OaHfcnM43IOF8yRkY0Vpc/kYt/kqi7jlKDgJEbDtzMAgGCnBN9kSV3vq\nKmPG9s4mFpAqSmF3hi7oLQILarBrzdRIO7IdUj4E9Yaureo0h1vIj8DAxPCFH/Z7NKanIdXw7GKA\n095nqFQc89fP7pUybPNKMR80Gg8KYmaa91m/vZRk9WjiSyB5+9dpZ+/htE0p0ZkyL/g5DALxmBV0\n9jSz566T4WrNl3WOIkWYjIQqkVWzsUj1Vmr8DXDjTGZYQVv/A21fhlOq+9volsvE47Ad3GTF0MNh\nnIkQwsmKsB6PET/+Fp/j7NfPAWMSaD6EHKc7OiAbwDkRNH47GEjAMV9Pi2uayjT6PT9Gk/vryZy7\nV8jSul5D/3TOAxZgsCmvY8rjsePx6MuAXfcT6ECjQmQxEwZGO87r7BhgYPdyaW5wmkn+Ka2B3tb6\nXdbX6zUKnPd3G+RXc9zA6/wDwn16gQhb63g8YhjaGAgJdn/dktdUC2VoGso0pbeBR+94tGbGmYZZ\nPpqE9fRKeA1ltpuKgnrMgLNEp2SojnGck6+WgV9TFvoPy3uAj3E3U7iez869BWYLf18ryQxHmsfz\nNKBCka7r5rerQfVZa+1aazKIHx0resz/qsSmFZh9uz4/YybCD3xY/NrkBwEiAPiHAPwTIvKPAfjr\nMHMi/GkAf7OIPBYbgZM/aGKIvywiDwC/B8Bfw0cTRgiS4ZW82brCCzMQ9HPgoAmQ58KSoRFowBPO\n6wV1ef/ZE9fG6cJZGYtNhhlHCiyc4YdsEOgi1cLxdV6hsmcg4UzOlL18qcdyXw9XaW5sAK6csfGh\n8WuzHD12DigYLVEXezN0OYfB8noUdT+756HudGWlh8b6RgOe47KvyqgSTVVi8eL03LblDWrgmOt4\nfi7rI6JenIpho4Y+4MoTegQWZqgBU8TrceCAXFS+rCwGVQrWgCZ+M+8yjvd5b5IuZjhkCQqLjBUe\npeXReTYtOVsheGsR+8tZTGQom42BIslgf6qBzm2Jba8UVVY7qp0kzCBbddi2mB2fx/qhzu14P5YM\nCJXnEO+XPeevRDDwIKP91VhgJc+WkOVp7fvsp5xYcSwWgkgLRsikSs+QBmCSOJhVAPia1pYizFnp\nKyypd1nP3ef9kGgTzsAYEBp3UAKHyZURYnTr4rcQ97+ATAE/3/P7Wtnpzswsm0q9e5dFQWLAjdet\nW0hiqMvqg5xbwpIsM8g9hu3Q4nXwXQdy3WIeEtSLdfpehfbM4/UzqM6bc4OEtUYBbUsi2ZvpLvmd\nVmNOx041L/LllYdcvb4WNrKeueZKAYC+twNA3gqw5iPCOorOnRr+Mo+5XtGoT1ruhpX4UGVvggdN\nnpt0o6hz7pq767SGAo3KaIRQwuT596T5jnDSW+Due7BmZgJdJVn8sTAeMiuY32vEC1+3R3WHg5Po\nJqCTHTZjMR1Gj4zHLAO1vv4t6PifTIRP+UGACGOMPwXgTwHAYiL8yTHGPysi/zGAfxJzh4Y/BuA/\nX5f8+fX9L6zf/8sxxhCRb5IwYiZaXItFZiFQohUA6rpex4ZtB6kxvW3zhD4cjnCIvWYaLU9cbSbt\nmgYEXV8slu559IXu0QaenbLRwulXd+XM28zI9qwXe9U1E37UebJXhAgd9yRz/wTmuoq06BFOYyr/\nHew0MxHe2vQk5HAGBoRCNWmhdCUt1oMTKen9JJ13VT8rK7XxTPJ034NiMXNaeMzdPG5JFAVmzFyx\nCgAaG0WUVYV6N3A8aZE4iwyYfQi2Mb2P7L3MDAE1bnnxVEAu39tzKrhXU8M75v9kIA0EaqjWjb3v\nHYJnV2CjrXp3SJcDHZYls3OOAJEr1ACw7/W9lJUjNA/ocv+ecX/wIlKMiOVyGRHgASIg4tn3z8vx\n9pyfE/u7bz93V7iOe1eKr88FYS7jNr7D0RJAGozbhq4l85wxOvN/mTtVbFvHlhhhb6NbngRmIuia\nMkHOYd5kIBooZ+BGBks4DwKXn+OteVcUfffMLuD75dwhu7GI5jn77KpBNJxhaxEY0za9MhrnmJ03\n1lMVFAYxEdqqP+c/aItCz9nQNRmlGrEAnN3BEzpp6TOx4uv+ykysKwm7QukxHIFyWKvjPcNabdcl\nsAOuS+h3IIGG9L+zW+bJCu5fCYN6XI6yEBjMUYCQQ7cqgI/74teIhP8r0Oeod2xNgYQRarC1hkfv\nlnNQk+RKf007vyOcMyiLsQ2/oeHOepSI4CwHQc5XoO+P9V8Ltf0guFDdS+sFIALQprtpjy8e+smc\nr6F1er8ZskJzkbVRjJkW6xmZCPoM3+PL5xxLeo95PJaVk4Ke3aeYbm/LHUfET1VYx/m5yw8CRLiQ\nfwXAb4nIvw7gvwPwZ9bxPwPgP1yJE/8a5o4MGGN804QRpfCMry4kHYUZYFiiCkYrFDpbBNnLV8wq\nshTDKzlkaJUBWTF3euVmCriLJ0iLXqaIdHsZjHpzdTMTofaox8lvbt0m9eqHQuHNvKwBnKXkPSbi\net+otwRJ4N0ZPOPuJjOjdeu1AsCLm3t23EivwgPq+LS1rdvwxa+MOR/Okhh2jJR+Phdz+3VWPthw\n9vqMo4dMz6dyQv9gqvtw7z3Xk+uokhU1ZcOMFW6gx/beaDs72PaMOXmUgQtk0Ar8fwDoK4xBt4jk\nsveuHilVwsT2tPeQngUg7F7H3QCECSYAQG9idddnmb3hrxYlG3tkwAMjbHfJVNa7Uo3l+f9qd8dy\nZRF4sAv63rDndlOyPL2fbWkIrbcna+qFoTpDqbiNRZ1Re1diuzzUoi/ARcNWnh3GXgGAbczfRgc0\nC8/okxHQKYSL442rcRXaAQnvmONf2fuuY4lF1lZ2zMzZ4Z7PnPcGoLmjeP9XnrIxsLZw9GO95+/N\nnqcBel1W2A6HMziQZH2gN/RdIH2g74Lnc/aXp76L4cBZH9dKLQNjlyEsBAZ2jAUoHlVmDXGb/89z\nGfAHIoA/v19U8ILikrdlPNvisWS+JAfD6Mf7nQkbj2MMdHn/HJHrWOa8GJQwRQEAACAASURBVPEE\nZSL4OJnzocbv6310W02+XNecCNIcK51Dlfbddy3gnCd8LLJrFFhsdsxUjHXPOcseLaasbwnVOSTB\n7p6c0zLcyyzgwLo77z5JB4v9h0PqwjpoQF9cB4FvCyqwwV5gaAcw4Xh9DS7MbR/juQM8PrOhvsqh\nw1fJFE+ljxnOdqqLnx/PjiH9fFfoLkkGXgHYjnC9HXWrPCZ8vbkOPvgImNBke33Sp/yk5QcHIowx\nfhvAb6///xfM3RXyOb8A8E+dXP+hhBGMplZGk4YyBAYCAA1fAGAshAHQ4qfAgau8BiCcKMhCLIb+\nxNw6cgxUAVXZQxc/taxopGYP2dXWjifhl/O3YhG1/9df5am/M1mxV778kW+W6M0RsHGlM19+Hsow\nF/2OmdwJyLHHCiysdobquLJxzE3hMbQPO8bhEWt7xtOnQkr0UnRnTgRXgjPzQ9szn8VRGGxQo1S3\nqsKqs7IDLEN+B/YP49daHnlttd5JeQemEqp7YHM982I4APAuJM4aKMqmve71fjspuwCwQWZ+v0EK\nrxrDtEh3HKml7l113+DMtyAT1OOQiRdSAV/ZAKioxTmcQcdTo/73BDAkDiftU8EL2TFZCPMhzzL7\nUVmp5DqJ1yrzBhMhy3uYCAyk7cPfS2lIBaAEJUB5lvyuwbfX9URsdZ3Y+37G7DlQtQuGFaBz7cc0\nVKXEAnE+z+/VAQQGHjUUIY47PT+EIK3dHvregkKsgJOHEQGHLrXWVd0qeT4LXccoR81yjUWjedVH\nb2Vz7QlLDxLW/5nnKBoTbdPdGWZoCUAe90zzoz7NSdr4M0vpvc+OCWIizDHrjEerRuEN1fND/xGv\nNqs1WkdJx1gYzw9FcShK6sMhkZwaN/TSNbRto7JlhTaIDPTdDf7K/1AlOr3KL3NnHr4aXz6naiiD\n53IAgNHEciW4132F1vXr9f6svhkQAeY8ojkRAisMa1zZea8B6zui9eY19oqV4OfEd/brDm+onved\nOVRDmHiHBN/qmsYMXLcznR9zKWmkk8z3Mg4KsjNXj/OEA2CLldMjC1KvvxNekc/4yBvo39hH+6OR\ncZx3fq7ygwMRfrflFZ3ctvzSAwXF90yqPmeosLrqCmUBfSmP9NvYa0OE6Z2bdHSJlC03gklh7GeU\nJzWUsuKYk7mwV93rsY9ouKiSwsY7U6LyMz9ELlR87Hwv8HeipbJHvVAaWJEUcUrrtjQ1BhAy4swM\nAyD+Pq8VvDWZi4oea8tAX+yGeZ4DCkwXjAyDcwNUj+Ru+MpjeiWaJEoXTAAY0g6Gj6jh+WLx6pWC\nB362hdGc4mo7ELZKrDwwfu/0PXt+1HOFSKuemN0Mb2AgIMd8VwwLjvPmY3sXtIn6eN1xZPZUwlTw\nrFScGiOqsBIowE++oiOeOm0WkBCS5SkAYwb6LCEwVPB+j1fOiTD/hH6v+8npfto9KtvBgD8BEgD3\n9oYs6GTwhnKSp1JBsVgX395RjSc7P91P26xG7Cx7rHj9uV+5tvmOnIEuZ30nbynGAMIRYMDheZZr\nXQFC6BhhY7/ybSkrg5kIV+E5r2KMq9AO9YYHdkfztTd7xfX9+A3SzfugiRE4M6z1Uk186W3WT7E8\nHZqTKY+ps9A4IAPbJ6ACjjqAApF3wivmfXQhT3Ns0BXSPAtf3/QsTqqoOScaOQos+erw9fCjyRXf\nm7/mSlRv4MSbwAzF2PaZINXyBQiAHt/5e2pSjUEO4bPncgK6V3UvmQDLwK/i+F8Jj82cJ+Esr8Yd\ncGEGCxzHzx39xrdyF6vje8JI2EC3RNAJMGa98M69Y5vP5+oOdxjJmJNVW3M060eH/DY0NzMr7tC2\n11U9yCcT4VM+QQS4x3wHDp4IS5xIcX4mNlPo4lYrT7boWXlHIm45Ka/tIgFVCEjBECkTjGksv15j\nrIM1azxkzCRcNIvE5DFFPV5IIGWQ19tDJ1YbQj3XP+vx5rVcZHqIHqTESHUD0YkdnixsbXOXlUxV\nPPWJKV0ut0WTYG6Aed8fbdizY6Ofs4NnEVqoLPQBfu1GxwGK04W+R68Tt0F/LxVHHL35lmivqCNT\nTfNz0Kf02CbarM9i0tFjWEzw3pnye27ccr1l1Vs9ONWuJTEvwTTEGWQ5kzPa7SFGfkSwRo2oV/kf\n9H7TmNb8ByuUYQjeFPRh5YyBrxservNyzwEEHouqqE7Qav6+kdalkVlcbnlfepjvTh55Q+mMSeXY\nYItGBNDMK/0gb6zNr8QGGGl8vwJsAhDcXSkLntJSsR2HT+3XB9wzG9xD653uOVYyOQJCLcFmYJud\nMx7uKbEJZFHWgdTbd4Zrb6qe7jWmEBMFJXCi1Or80FZ+CGJDPeR8vtPygDnHm8cPvEuOAhJxDOn7\n8pwIakBFr7g0fz+zrsNAbjf01/eVXNGYA+ueCVegdh+tCkkvUh5i4JImbt6kQ3kV+lyqt5PLDmB4\nOof7Gv9/NYZEUOZssN+L8RMANfFQQm2jPnNgMYROZNA6Me97LVce2/zsQjimjDAeAfjWjpR4c9Zp\nbRXaGzrNTXMeq/tvrOOLE25KfmcCCe+zMuL1MxvzXCde5jscqGJGQhXioPdQ8CKDFQZqBDBC51et\nG5WBOI/kvGOv5NVcmcNUtja38zSd2nRfcVBw1WFbu6uVCcWTLuTjkftKlEvngY5v0gu/RkrHH2Y7\nf86JFT/ilPspyieIUMidQSftNfPgjpjy1gV5yTPUs62kTGmHBJYmM3t+Y0VrdAxZZvMK6ty6b0lj\n3lRiImSjzTLAvlAmmcoF+LaSmYkgOE5Kd+Vr6EOvFuLzCZkUhIU4eziDswtYIdMYT2ciTKPtjebb\nyU4YCzjQY85KYAm5EGhhVM9yTg5UMUdy/K2m81DRWG89T2hB3FpczIQUqCs5y2p/ZCI4eJOvH2bk\nx/aE81adn8OVUcvZQAaSMmhkSIiRVwBgggDL8zUE6DD6LdYz49htvifnImCqqedE8JAGjwoR89xn\nqZyaXH9uO39WYkwEcQBMoEbC1BwZCMyG2VjUAo7D5njnHE9cbS+VRRkY4dhFGw7Z8ckrrW2s5AhC\nOf3zwNjRNuzcxtSHLkDDSioAgXMjhLI/uJ5w7DfgfYX7LqAMjPXHu/CsnBo8Tox10HWc0Phb91Nm\njsa1zzK8Dsaaom1aOdeCMzNiYsUtgSsqnAlfjBkmBsJu3edfBqnjPfxTmQd5rDETAUDY1g3ApNU3\nQLrPgYEtZ9fJ3Jmhi4U6Ag52V/WKB4tjJ9LWepvbV927CvPKz0DzbrxiunzFkgzgOA6A+fwf0tfO\nGGvt2RxEEOvU9aBnKv8rcWalX2vzdyolAsHHcAIFP3QXCf1tGxMM2XpPtPO4Swfn57gSZevwWsgM\n0exx5p0ZfiwSQMBC8bOcJzgfJmc7Q9ySTn9L2FEFKKjrzBnWfbcm2IbrdvvStzSkAfBIp4MzBkfw\nWRm01t+WTu7zp69PH5WzLnLWd34sO2x8yq9WPkGEQrISAZAn4GthPZI+3NBShfXgjbJwBl/EJtJ/\nXORVsYJmNO4CtL4mGI1xb0Ybv6ujhLi7E0VfljH8RokIZ04BwcpTZoaMDEfAq8eZqcstKWpl+S0v\nxtFL0Mz77R7shukl0roB0SMOAG+rvW+t462N+UfggIczeF3mQkBe3wU2PCh+922BCo8GvK06vrXJ\nFBlACSawZC+S/n/6fM5oy/pphoMvfAACpVTBE2y+T717u4+Y9CsaLLMy3mTgsc29wDfyPoG8liqZ\nnjm3tHJqNLfrTALFFl6G2P+LqjwQGAacz3N+niiy68+NKjW4HXGqwi9YPorxe0K+9R287/pSbEXM\nOzLC+1aGTASJbGeGlBNhJuSbN3gOr7EBlIt6OtkahTKY5hPz6GrY2EowOHamjpNXOAEJZ6EOZ2CL\nhwexFb5+2yfY1FfCyDOp3uHZNn66s0f+paGY+3ucwzTXS7Ut5h2p1rVQt2SQSEcw+PWcMqdBAtf4\n/7HWOWkjsoAWkDQQQeUqxr5tHm8OzHlyE88tA0RlvG6/z8lva/vePlpg7DQRPHhrwc2ZUc6wwsqT\nIAQejKMlrv/bHx0u3oWuTxmIqBdJMqrX2O6Dc9kMA+wPl8aqBAMGxfGw69HxdpRvgtpzMXmdzZkP\nejePte7olo4MIqjOpHrQWWLl98i4YYQxE6H8XdZuKo8e8miMpU/0lgDw1m8BNSo5LERBcq23h+TF\nXDwDc77xsTz/+ZaJFX8I8uvwDDP7t21rJw4ZwQHEIdE5vKghe/XnHKKMOl17p3Opr3PW+xpiiTkt\nDxYiyK0hLZWE+lRtW+fcBZzey/T4KQnrmj93+QQRlqjBYElNbKLQhRyQB43CFU7wNf2I4w19K8no\n1VDqntInrb6pYEVEx+jT7YlFP+/rfmuG1QkqKBFFOEMGJ7zOFboLi2vXWz0WU2MaI77o38VgGnzS\n9dASQOmig2ZD3QKME1apwn12b2B5odKkadR/0bg9V8qUjaDGtIYfcJOYiaAG26M5aKDV/q4NO6YM\nhcd6Nx3ROELxfdZp/nnG59i+s3Zr269En90mvhe6hjVg3+aWl9R2BlO8v4iBVdyvVMXhY7y/9nEn\niGjwc3LC+fv6jYyzM6O1ilPXa+dzEfvUTObvleyxsvstRa+ter2iigP1e+d7a0xkFvbU67RVJfHc\nMQEFFTXMHtRXeX7gXROyF69qU8VEiJTWOM9oPgQJx8ach0mZ2rYetvw73KO4dz72SjwfQjxuFOAX\n13tfL8AEei7vVX6zB5PvqTKVHH84r7Z1rBTPHMbR6Z3PenvL/HNSeMswosLYM1bGusE+fHtlE3HD\nXRXrNwtv8P7rseWCPMPJAnA3mtdO7XNE4DpT8yc7cBy2bRRbtISOrbXqEM5wNPINBCOwQtc3y8Wk\nhnSDuTS3db+tDduGN2/7WYmDA8uYIQaSrp+noNz6rOjptiU2hjk7QrllOJDTszcZeFuAsm6VLQ8d\nLNdz5qD5lut3Ndd+bV6EwNDTcIZtOKUGwKPHsQMAY9/M4AQiPHHFulTgjRlfvtOEMo7mcWewscMA\nqEC/H6t8DXigIVHA9VoLwJJ7KsD42PagE7pe6Fubn4F4gDMSOIy1re+vEppyEuecKNPA5nTNqzWn\nMoyvQ0UHWpnB5lN+TvIJIiwZI37e8QLmLRnP6KimqOn3YmHna8MC0lZcVYoD9OROCkKsz+5e9G1M\njVzGgIYzPIo8CqEepTJ5NIqyyDI+AhNB1Pujysq9xZqNtxxrXyZXpDpkCfHEJz4Qjj/je0kb2BRE\nWErrGy0WnB08enYcJQaWUbbCF7T8twa8yfLAr/tpeMNzHJuoHoYSxEnnMgtP6eVxO6i1oNB1ev8s\nW5vKHAD7HEMgvVZPOXb1laghpOj7WWJFLVPrPo1xP88SGZ4sktegBI19iFEnlZXQMMI9zxSWYFwN\nUvBWUfuY9EYNG5n3k1D3O6Kx6tz2SVsX+51zAqg0e8bz+6MBrSMoscACEdqRiQCj9ns9utHUoyHK\nz5QuPygkpyFEy3gCgPYA+q7G1Xo3+9GQXtiDeyfT/SsmQgY4crdVCv4cO9RGSfckwjPHttp97H1r\n/4WxM85k1l3KOW3e68IowpFRdNgpIbQztsc8nPT8OCSqSuoWy79aX2J/SQQXk5BIeM37HKrnczEl\noy1AAQUTHUSAXa8Aj14yAeUYqmX5DxiQIgZAqG9VCQYQWjwMsAEDYwxl0BxN3MAh1JtZENNh0NFW\n5R6iuR1ieaFaWvfidzuP/j9PwqnnvkN5WjII8NHrZ4x5p10wvB2D8kQBNQh2Bqp+jZw9n8h2BLZt\nhHrPOna01gzo0frqHFYmgy3rQOMRR1aQfipwYPdDXNveKxVtvbrVlTFfhrSF5eU4H57R5a/mF71G\nHQg5kagmi+R6cR8EELxMh+SPBChu23yfmi/Lx7SvRXZdAWwCqsf7Gqzj/3ECFNv7Tmtb7gfz3j63\nAPX8WMlACoMb5+/i5yyfT2TKJ4iApXTh3EPG3KRINTx2pEndPCqtldLH4QwstmDqgbVw6qQyViBY\nTLDln93uo2EL4pMq6qzW7r2LzyArDlyOI/CTyh63P5wgArMeVFFqMzbjVPR+Wl+rEysnqwGTJfK+\n4Rw8iUnhcErpbJ8abEqlbTIS9eysDASD7a0NfEfe3TeZgM53K0RiHpv3lh6BjWri12Rqm5ASJ4VC\nP2pwgH/P36eh5IbYpPTCwKne6nCUA3viheKm73UTZyLooqrSbQsjNvzl8Mo1t0C1W0KWHgyZGEuq\nQNc0zlnJyB6cYjwTi6Hbp4MS+2hoAxCJ6pbOP4HBkO6tuyCE7PYar57mljynWPZoAvQ0G3qT+Mw2\nEQPcXsZDr7pou3V3BpZ9zL8z/fUQztCUAUR1T6BA23DIi1Axp7QvnQGjfJifd8j7MDyeH0DoKwzU\nMdj5XqkVbDeOotGd60NGgt1v/T9in9I8FEx51ftHkHG2kX1MFQhj9QGPi2Of4vMj2LbMeBpXz16H\ncqmX1wBy6RMIQ83AYkBr5n+J89VD7sX8v0sq41mZc0JMhBUicVY278ogBCSEez6GeecnuNIxxuaJ\neUHZ47+yWTFULyabrQw60biSxBBko+iQk0HierC1ZYhz0urkrLF3fNg95QgQnrdNdYE4TmLd7vUR\nZdG1BzEnALQhaM8JJCirrw9B60f96kzOdACWyHqLbbnzLK6M+Cv84bhW5Xq9LuuyXhXryj5frVFZ\nJzmypKo1RGOjsj7ctgHRUKcVpjITmhKbQJx9y2LzIunaOrwf6ZhQfc7mCs1XVuki+cg3jML+lE8J\n8gkiFMIDGEQpPED7H8kOSHJnYFtxmy/e0oD+rA22tg1jLWh+hDEUG4UZwtngOyCuB8/8AjBOvOHs\nTQamQdztmFh7ValhXGAisfWk+2iwhbeii1r9Et1Ud2iI9xvBazsna1e6DufSXs8zDKQbjVbbPMET\np2KqYSbwXRc2EXzfBr6nUJU3GZZj4XsFEdp6L60dwiTUqIueq7gAcUMYvFKvuHvU1f923n+NWtp8\nf+6Q5OoZFzYPB3bFTz2p7DX2EOFh2zS+LRBh5kPogSLMRhSwlC8M235R23OZcf8GXdWZGKufiyyv\nJCuXL+5BhpT+xfjxcWAdnN0zhPEgKbnJ+xa83gP2zJkOmeM253hdoELz96f5Kd4SY0m3oB3PaNxm\ng9b+Xpgu1o9Lb66PR6Wx6//AnPssw31W8t7BhNE62/8A0AXDGA/i+3CTgn7YDpS8fKaQMvh5VvbF\nb3MOq29wiI02NovWcRn4DMale9xJapkTxzFokuvBbIxZ/7pdldxJBqZhfgxSG2X/ZmFT0Yddn0P3\nFODmkCw1gm/3qcrdV6AcCgI3id+31n3+XB08lz2dCH5cQ8Ym0O2MQzdGqFz4+hTitfUzYhWlkTvn\n3aWL0DqSk0zWj+fcwPQ5aMxQhi2OIQ7vtGeR59Mb+RFymwKAeDHJN/Htfk9DJbcR5l4tT2Qanb3r\n/KDG4blDZ5ZTY1NADWSf5Zs4tsVZbHa/fM4QB2HDXBLbPpM80gEF4vV3Scb8mB2O79OGBO+6Hpv3\nm0+AmRSZRK/PyOaok8dgOyfQOtjoj4oLYsfagCyrqW3d7vWg0Lqpbc+HltNzivjw3uh8rY+GNjza\ncX6az8B1V+DopOyowu+q50A6uOi9z/tFC+uA9mFBG+9YbH9i8lMIBfoW8gkiLBljzEywX0NSIYS8\nCm1guvOxfDLqiMaJlWxxjAF2YEpTZWd9D8r4ulxWBtfEKHiHfqdVOB4XBgIW1Z+86vN7ZicIkzou\ny1SKVwA7TuYrpZhpEiZ88RPvKDazPa5ImuIoMCDhse14tA2bdEqEOBPVZYVsYOZD8CReEwx5NDfi\nHuIAwndahirJvQcmwlWdt5wlq8evlx6E9FulzG9bx/Y27H9gen5OnG4l7fe8/vNzMjWciVCJexbn\nA1YgQX9To869qYVxShLon4HeqMeHKch34moPRpot6vrdgYpj3oD7I7KPImYc9Xt24GYaRxEAE7w1\nZwko4PW2cnU8JMVU9gjo9L1h7w0dnlCxZGcYqICkTtVyeFftaExnerneUxXzEHu+Eq/lnAgWi39R\nF82HMLocxkZmqCigGJhaN9gcQF2H98RpO+vF6zZ3Aom0VAd5yPgfYmyfKnliKGMcM9IrrZrb4qFF\nNTDh13ooz6DryyfWpmH5tvKyaEIzXWMAWH6EkHjS7HEJc7KCws9iksoAUGlUrHk+J108leZbPxrI\nRy3VtS7UPTsurD5jlb1OWx5RGWI5Ix4rVrtiaXiZZAxk3IP/T+FRDNpqWE4A9NjxQtex0RzCJBBZ\nfW9tbivMyRRPFZElI/fpcfQ435FIQef/CzAljW3d3nGyROjasUIcWrNn2fv5+p7ZdLkdE0hf7WTj\nEXIY4xHQXveHzl8jlJPL5u9XenEVuua/HUGjfK8z1pKWm8MwrpgJudxX4QzvERFMFtxCMNo2x91D\n5tN3JgLC55noODAwHwogeA6uWe65/gIomK//61h0XVOdZWfbkffUL6rfgON7+wxx+BTgE0SYMuIk\nOgeTwnMXl7GFsL5Pj50Q9c7po5xCgZUtu/aJg0GoIgKAkwud7JXcZAR6syyP79AJLtE6teyz+Y4p\nruF41vWXcqDx/Y82sxNr7LWW/Z4tjLwN+YSzxfdji8OZTM/BQn5JAQoxkBeAiB5XZff7kBNhmMfX\ns1JPleDxDs9apVvFnAjHOOYxJm14o4fMLIWtCEsIHuHnkYYZcxDcqr49C/MoLqOPWS95JwatI1sb\n5onldrBhkkMX9hka4GVMo0u3SAVglFA1yAAv8nSsBNaB5kEYduzZJ/inqL6i/lpO9oqP4RHb0fM/\nT9StFQ8hR5XCiwT8sbKcjktqo211+ESgEo8ua4vMeWxXejp5Q3hnBg67Abw9r+QILIi1844fJIYE\nxGeaE4zNudB/3/cWvJ19zO0O47aEETCadX5drxJMXjepthD2UAYHonb7DqrPrIvA+/ns33Mb0703\n6zutDwdKeJysT9++0wEED1/x3RX4OeYQL935xdqYGAwdx/dwlqOGAXJla3Gy0EsvOJy19UrBj97t\nI0hwMLbeYbFK835b1eMASOQ4NYnsBE3UONkIbvCrkfIRKrM25xUIdmZ83r0/4PN0YJlsK1zDQjtQ\nhDMcxwOH2JzVq5pzxjvbUYHBs55j9VU6JuroeH3zq8R3x/JjvRXYZABlQCZQjPzMdV16WaXz3ATF\n4TMA4WtyInzLcAYA6HL01gMFUJT0c3t/xADyJN5+PScTr8TmMDP4kRiCK4k3jWWem7PsNHcrUzPL\n3XwIs9mfORFeydQFv6298WOVTxCBpFxwlMbbMenxB97UmNufwY/nJIl5wfmosZuTC5mnDVF50mPb\n2qlhKpRTlNLPxnDM86D3W+dZ2W7YzfvEeuUMtZoTITMRbPs4o3fNMqopyoz2vPhmz6T4hK7fW6q/\nb/EIK20ABmoEWin8fq3FMjbpBpRo3dmzIjJ3D4ielUURF1eUlLXxXXNmw2OFMwwAz+HeoTMviDqp\nglcnK/BwxT97x/P/BnaR0WWe3ebhDP58I2nmlP7K7wZMs13PQobtrz3rsRbMXiuJIrNdliOEPMJ3\nlED2pvICrL/Nz7HqmPoa6sU4GkZ03J659nMub5TKYnX/vgy63uaY5roeDOnCSy8yIKNSVgSAhxwp\no+gRwBzYvBYV+GVoLiYCU/zZK/5SIbbwjHpeVAPCMtPvxzEf6KiJdVCWefLTGG6waRt5/tZcGQME\nHowanCnbUr7bVfbJg7qTf4NBs74MBwYY2Rs5hidXzOFCXJ96Z4as/MdrroTLvSMxN8CcH3i72Yet\nOWuugljy0F3ncwBDnN3ma9HKv4K0H/uVx5VBfcSkonUD5tjyhUwPj7WWcE4PzcrO7SewgCupvGMy\nZrY2MAYlVdb7rzY6YzEyZvT62aazZsRnws6ARvetxtuZLXVkIkQwvRFgBNCzp/wHmtx17836VR4r\nd8C8s3d4x2zia21Oavk9+v/ORLiumF6y4zi/ayw8SwhrCsfr+8vSNJjKfl4XKY1I00cHn+tzgogD\n3lU5fH3DZMzGe81yOdRVAYWshp/V8Uxu9YvqoTDD5rHCPaF6oZ1iW8+6I8l37bLVX7Tvk14onrxb\nx7JOI3knJBXd3vPQRri+JlA9wNt/9u7PkkBWUnNSP+XnJJ8gwnvlavYJ4ME9ZckGNVMAM4WvpQW+\nk5GrStFSrqbCv5R6EaM52raPpLxw3gdZNK1giG8Dsjsaqk20RG00aTZ4LDUwFQGg2aQ4y3bPPWfU\nljRnqaKjSqHVid3+xoUXe25M7+y9iEuE1tuVfzP+CUXeWl9Zlge6bbE5PPEfvOispLHSqoyCt5VA\n8bvWLSmj5lfQfABatsZdSveFRYVDHJowQOMN3V23NKnQ6YFrxSHEBC9aqWX9b6QEU39xD9Kq7zaV\nfqWxznqP8F3b/dj2mSyvkSJxsj4xkHBHssIVkrsxDZSKVGOROyaXxuEvVRnHOquxN2h3hetrzvKS\n2PEXc4zWUVlBaL57ytsyvuY4jKE3c8cQT2I36x8Nzp688V5majeixz+3h3NtAFhJyWBzqYiCplQG\nMOnB1NeVstnUgwkY3RRoNo+d1TMDEK8k0vKnEstz/qTEa2K7I6jDZSr41rZ6u85Q7voMzIjFonHW\nwTz+BAEU1s9jAtL75XneBQZU7ohT9AtDJLFJjAkjCC9XHuud61zZPMcHMxHcXp8HZQHWGr6j2+m+\nrYRoW2u0xnl9N8rDEbZctIrO8EIG4oUtaszvQxdx6qzT+OgGxK4mzjl1606F1wXmkSbC+dLtvO3R\n8XjsGEOw7b7+cs6IQ9w9/Lb77iBwqA9GCDGbW2zWuT6aeOJdeQDykLVuMHvE3zf3fdUHWHfYHn29\nc2q21v2wLo5DctksbNDl0M+cw4CvyW3UaUSdFOpIAOA7eTwQjE0Zf57s0QAAIABJREFU89m1Nnw7\nZmUsJCDprhzAdXYSFOBWnn/fw0T4qFQsgDP5KBMhn5u/3wldyGfI6iy6TSmg+uXaali3HZVhO6Ow\nA0UZUpn5dyAUwfU4n5emPqjjF1Ddvh36+SnoLnks0y5iNL7PhJ/3q1dYu0F+HvKZE2HKJ4hwIqfK\nFWczU8uDKL59z56emAleRTtg8LInb9/8va5GPh4QRs2iPwTo0xDXCV0nvIPXOJWrwlSqw2/rUxUA\nnfwA4NH6orbybgYDj2W4uJc91j+WvQzr7WK0vnMkZ0WAJ34+lnMpKEPhIR0P9cijVtJEJOh+b+v5\nfL+5wsAAQgYR9tFCYqDpM5DoUYLWB4iGrgMkgBt+e6Bf14+toswZNbNFI9Yy+K/zeIvGOoaYwCm4\n0jyfxYrfXUkVx3Ijsncp5y0Imb6HUzmzoZIBC71+L7YGA9iz4R5mV9T8t4+Ixo97Ej7y7ob6Vde6\n5/MQ/0vnRQ/j+sQAx4LPz7X16LJxcl99hHlJplemz1AQAOYB3EfDcz3LZ9ftJtmbjRC+NesYFeZe\nvQumMSuQt64ZpIDneOsyUSNJNiC4HgePHzFhLASC+piPMUFmf2g7GUDQ+5/NWHPd8HZXv3O5838H\nwPiZ6xw34MfmeREAqHeBkLJNymjq4VnE+uQkbHycY3ftOOrY9cw+M0q49mPLiUB5ZponDNX+++xr\n9hTt8z7fnxltpZ9g5dYAUIc3nOnSJ1q4MckIhHQGXKpPk9IrmpMJA27Ia0y0sr6sXHi/d+P1fCvR\nWQ4ZFRdGmRnTqlWmZ1KOx8WC0hxBAPDY+loL6nJin70PZp1J1S/P5Iqp0mRYMsizcIaKMVqNo6ty\nOfSw07UKKHI4o4KLvKXstzJ+XoUzhON53L+og+9qdA0gvAIqSgZFAix4bfILB8ZzHEJoTHkBIA8F\n2SJD1R08YnPTvr7nnqq6M2/z/baACWci6LPI+Wjm/3vn8MHJ/mAnX2arZtHzXuWJZ9PnveFLn/LT\nlU8QAQAUJQSCp9nEGAJyomHQrXTgUliAGV1B4ZWg9IoMKkfvMSe4tq2Jk+NlidLL5W1bp4lmx2hx\n4tn2abQqhRzr/vIAZJ9eEr1P74KnNFM85qNwT4JT82FJ2zix02jA91vH7yzl662JTa4c4vCQIwq9\nLfbFg5/bwyFW8/o0Wcj/QN4Kjv9XLz4bVFhkrLhdpXvOWhueS2Il/gs7UKzY1o36jP7/oOfzoASK\nvFjo1oaP5s9sYlM7gM2e5USwNTwjL1RRa7/RRU/VQDbI4g+qBK2vi6XxaLw9pQM+srxH8hjLG+zP\nV5PNsRL/1nxLrzle5g/GfKB2f6Ekfoe4aTbYpKDprfOvvK+e7dg9BX5/f756OIfNvJIR/iewJpVl\nQAv8uTG4xW2q4pb5mTGjxb19A4+2cjY0sX6pW44+msdkTgMKh61rz55j8HiQIcPAYWARtDhemYmA\nBksoa549ANtb9NboeJsMqnXppswW6n9rjvI5DOsZqWfpwiDMz9eUtFqj0nfzoPNaouf681jv9qY7\nMhtODCapUjy3fz3e7yPhdApAvFIeI102AjbKIArnJ8+09Q0GI1dnadtMrghMkHoCsA54KSuBQ8m2\nhgWky9HbvZKicQge4GMN0PU4jTlanwOTUOuaH8hiKcjqCDp/btKDZ3DuCOQMA1GUr8UeMwDI5veb\nTIS57u/dn4+GdjBbw7yUVNVt9ctGPdMdDbyGrnkbPv8JxAAQM3pWW+UBY1fY9aYnOICxrWY+1kB/\nazOZr+QU/CkP1CXwgdhGY0GkPqk5eLjf5vNYdziwh5JeZ/obrZeTBaYAe+xLcZvaY/d5NR04MOBM\nIwWm4/MQsC5koXBp7srhjTpu1aOviQk7OTS4HrwOeuLjY9hEvrZBsAfAdZbRRLCnSadRPU9zDxQT\nlbYhsxN4zvGTBWeLuqzBpCy6rBcqQ+pNBr401yd0XuZ382iuPwOwUIa3xVTScqTFPslzccdJfg4K\n/TCmKPdzej/8HFi3YtZaZPbWz+bnJJ9MhCmfIAKw4o3ivBP0yHcwdmaSKpjHbibmilmtq0led2e4\nYiLk/ZGBBEJgKtB63vTwzcVLJxmOwwyJYtRQJGMamBNr6SmiBXXD0av+IO/6W1LyqsXyDN0UfgZn\nllqqYPbUApHWy8LhAbOIM4PAAaG86AclWKbOuTXKibCU1+9bVFgfy1hzJoJSdRukR0+ZKnRxO7Jl\nUBFS3bD6IHkcNe5fj81ES+PwLDr101eGhhlj1E8eqgSrki2+ALIiqosVxydv7fjceXu9ah9vjt12\nOucIv3GXGWN60ju4X8SxaMrJupZj+rmGkXly9aT8vk2p5Fb/87FVlZM9xAx4+Pk1oKDisZdTeXn2\nWV6gU66xbPNDFwwZgWHFtHj30lN7h3/29NvV2ns51ls8j1kmNh7asPOuQhNErnyqUbiPmEe/mMOZ\nDeDlxHr0S19mnPuNBUHP/MiMmN7G5/AcDXtf8bfDvUu7je3o0err/qGNeu+SFk3K6zi2F6j17/cm\nrquEd+BRg5Zj6eecIhGIxHz+k6UgHlqXjMePyATV/P+DLON/XLj4GHsQLEAtgCfHxdJmOe37zfuZ\n5URoCtTOZ6HwQKN+7z3x/kO4ynFi/ZznxsSWyOurrgUPoXwtrc8dDl4Aeld9Kbfskr5NffrsnlfG\n/L3ksC9PeZdk9gSH5Ol3gOff+wPvLkvg7Pz3MAXO7nl9bpQxfLxfladl8Aw8wMa9KYF1QUmkzfHq\njqh1uY7FwESYDrEdEzgDFOidubI85HfqVXlusjna5mbY96jvHXVsnkZ4HmAwxp7RiJ+f8imv5BNE\neIcIW4uafKsnytPamYEpdzrYLTdQdW9WeCkemEVp3lZWkrYtEEInpLEMp+7IYkbPz9tKStZAMCg4\nSR6AFaIQvepvlgCr47E0CfU+c7OuFueDR+obCC8gAp9U7ZgoHXEZv+v4tvVFMRuHcIaF868zh3mq\nPc58JVBsztR4W7GmIZxhUeP20fBsLS12R/Aj65cz3KFQLvSPu9iJAZAlZyk/PCdbmOm9kqfO6mrX\neV15SyRpFLZysvMI113vom3MUjER+pDj/tK0+DO419Z4FRyBKBbtK5UEyv7sJhDA6P+PFpVyvf+W\nr4MbdtqOO8KeJ1Wro7Iy+8sDHiu+iceLM/gwBtCfsNCDvq9Ej8HArrefPNQL2lcrC3RMT4/9tPoX\nefa0bRuBcupp5HAaac6oiiww2DURqDsqx2xsnwkDO5pkcu7MkcYs/c99KTDS2HuJ+bw5f0G1PSiH\nLgEwoCqDVONFO/J9t/D+FwBx4vXKws92Grg1E+EulCMPQDafIx6bM1Fsji5s7hi+xnW6zx7Kwjt4\nHOQyQ512vA5lu/FaaqF0uWLVIklITQBXVibgrc2wu7x7kM/D2eg5FnHI5aFrzknztuCUkINjwrER\nf2dNphPi0Tw30GPraG/zAltV3xl6fdWrrnSOq+cwQw39hPI5aF6ExCjVT3sWJ2vGcc3i/1WnFBuH\nuzHzHBx83ljbq/VRxcLGhn+/wgJ+KPT2w3aSizVxV17tRDIyyNB8HYq6kAMLCm5sAkthNEhnehNE\nRqeyGlo3Bk+lY/QhlNg3rsFZhP6srZctPZg5n5JEwadP+QQRgkzm4cmk467g+b2ywojyezcLNdMh\nj8qD/3ag8jU19NepS8FSjyEwJ9W+A9vm4QJG1Qd5tW2xpwWPjOmNzu1NDa9B+Z6chaDggYEJpOgr\nhV/zIgDL2G5A7zW4wgo2WjtRqN6H9PM2fjlBnhq0mTquz4Lph5qBd6NqefZ7CRS1Nxn4ru2maL21\njoZhyjDgdNBfSjewQWUmgaT3LdEQn49h5grYET2nvOsCoN7j8+eSJfcNTYDIVODHWgxDLOuiIPOz\nbFAGRmYidFK2qE3ZsCOGRVVfp6UKdEcOZkH4efreZf0W76MAQgYd9Fxt4kPUIKCQGLjxkmNZG9zg\nrO6rn0Y7Nr3l+Ex6+q7t4j4CrEz0EAMOAGUcTPCgDwcXvl8KzdtKiAm40aSA5GyLmDJ736cf28rJ\nybz+gDw8Brxtc24IY7xhhsrQWFSAsj1gNGiLTxYK3drnO1FWVBj3MqYRsNqTWQRn4jGrRTtRG6sh\nnAMEllLiCOnj1NjIMhDBqD4Q8m7GvAWUkK3I3VPJGXiQneXu7RqojFD+bHQOG5V2TnpwM9Heet8K\n6grTiHX3n5i4l4/rnKyArYZXzfrAEgdXcsuQrdanlHzD8nlQ37DnuA0LU7BkjYfJacz+sY7b2t8l\n7F6h6wM/jxDOoPXRd1BVPcXx52YGEFvB9VVnNZr5vW/NmWiA4w1v/G62PkMsCybCZGpezzfMLgz9\nKZ9H67y1kQAnbmtOfsjPLPfr8HDt2RT6XVn34/fq+SugF44V96uZbn7vPKY1pw1Ql1vpxxzaMGjY\nzrHkuqiWqRNTp2fYhljSSb9P3J0h78yQywihDaMGEKaORGPx2MTTd6UAn447dXxsBrrP8zS0jplP\n+qMMBzUF05HwPYEI2xojDXWfrUIYxsnczODXY4VPx3AGrFQv/vIqcEnfT5YZ/vQNPXyf8qOUTxAB\nqoAN7GP+lSqEWmNkiY0OYAfGcx1aYQx9J8pnbzO5TU+UWFwoKzrJLOX5SnnxbfccAe9POpY80yxh\n0bMb0sS+jOYdtKWQxdJFA2nS852JsLUZo8lKnuYjeI/BLxiWbf007rQQ95z593KrtINnj8ounn2Z\nzA2+GLEBx8nsLOOuLRbKbPAs+I+tYwyl2uYGHduYvUJzEY4X7qOZx0KNPfVUnLFsT2PsqDDNLs1K\nmmD1QVOC+TlFRSsn/lFlrqJPA7R4jujt1c/sjfU4v9jIKnO13hdwkGKnoVExiHLeDc9AT+BcETLy\nHMM91H3WPYM6Z5TCWqmQAEpoWFQWrR8n8VRFpyH21YfRKQkAWfOaggh7b9h7mtfgXhG9ch/A3mOy\nxdBn0quwJJ763Ba9O3v2TAlig0HvyeflZGbUF0O5hyNRPHxAFWDuKxOwUkoxcM/jH+KwZbZ17LPy\nCkbrLhhWpnkhEerB3qh9aDiDUIJFwXOI1fHAlEtUWc2xsOtOP8NDInTccXsPbeP/z7yuBEDpGZfJ\n5Ui5b0txf0gMZ7A/qsc0xOJaxCB6lhJM6Wne6HGumo6D/lWuO8ECjXgxMUSRTsyNVGN4G5CnMxHu\nsIJYzpJislyFM9huLg/xcUzgn+Zpycy0Jppcbs1NukNFthaLsMWXYXfp8yNy7rEfVg/+nBd9rNCK\niaB0dZ5vNKxIWW0zpMnDmnaqk4GKNhfwejcOZZX1uvC7noUBMzMghgyOdI0+x1iOJlXci7L9mlEe\nP6tvx1yc+ppI9KxXIyX0NXO0+Ro29T1lDjhjx8CyMfM7DPjz0jAe3vqb19+y/jRvWwgLhbfxOSoG\nGp22bdgzmGu3A0kOWvg70/Hc55knd/2Jy/iqqf4nJZ8gwokcFqcDEyEtGFgDu8cJJ+RCSArTQdKi\na/cmZYEloPxElTOkVJYC1+vsy2GiqtBzRemTZzhPcGYkbz3EZI5xTH42i3GU+HU4Az0P8r68V15t\nB5hzI9yR4pGZCGDeY9254rvNmQhbm8ndHttuycIUeHl7dlOogNWHxJWwWG9CnLHe82CPfd32d2dq\nDv1rHDxpVhc6wIssU+sVQGBv/ra2kRwnoQy5/96JR/d6VUb1/MyZjNnQ7XPoXBs2ZBDzu5nv5AhQ\nNfH9rN1j4+8KiOEMdZl+fV7CdbyOQOFfbaJ2qud+k6lGbuF4ZLjorgEzcZsDOB1yUFjHifJzaAO4\nL9EPpnmt3x9AUyaCJpxb/29bTwDliGDDYsFsm1NDt+7GJO93DzjQMm54/zn0pQ+83GIOULtvetaE\nWFANuhUqA7uTiaD1+oiwUfAcXmdOwniVAyW2UQpl9VhmeJUMtOk6lJKsCjz8a17jYFC471oXlYkg\ngoKJoB74lVARs+8M+82VdfdA33i2GqJIYYvtkQ1Hus+NyVXBrOw155wellSxSQSz0yIlAkuI3J6e\nM0KN9TMmjM/J9xa9q3CG4JGfL3AxEkZ0dCjwTu3eBGGr48ejny6s5Q4fBTOBQya4zYHZeFMY7Ham\nwsU9CgBB+w9vkatx7iPN/wVeUhSR8xx5rpQAZq4qv2rxRwyiq1wIZ/eutnLk3+7I2XaOh/qtGl15\ny411cavkJMbQ9H69Dl8KO5oexs5dOrI5Z4i92RQw4d04ZO08o6D90vkLxwWX+RGp3tkPIXzlU34Y\n8gkiLLk1KDInPItuCdbdY2d00ZNpypMbJoMeAFbiJstOnm5hHiy4l+aMtRBi21PVX9Hs8oLpuxw4\nOKD5EMxIXl71mdTKUVaNw460/NVkMuSYalkyEJgeehPxzwvsVfvsGkZ+T2iURzqvbzcGOMiyUdyn\nbQ3UPBnW49Gx72JeJF0wGub/h/ePaOxV2yENTG/xs08vxTxWG90cB115VzjU5fAMVn1ERliROS+C\nXptDMTZaMJmuyrHgnIVamQf6zJ/ZS8j1url4egIqVcpWvxzHsWv9FcsQOjEImOmhC/30Eut7XX3y\nCqQgSiLwfgVY66gGsu+eMhX3h0yDkt+FZoY2luNiWO3P5glje8OXtcWjej6eA3i7UR/BMh5Sxmnb\nZ72RUtRgq9TVOG9FnwMiyHOoRzJegdXnXtClNakkM2GAsbZSW+/25D2FUJPhx5hJBjiYVoUzKHtg\n/u/MDz1z9rMJVgQmFhYzCRLG95HtM+8pBC7sNu7Y6DmbT2P78/8cXpRp8QoaT+Wc53sE7VxZcgqE\nAUQhbp7R/bHQwBnKQDlB2rmZdsb48cTGHm4zeJ3XSdXQQMzzWG8I5RDpAJRQrZi09JiWm8McMmDo\n3tHIGJtlfYVFsYSZHsAqS3PanNSt0dyv1yuAoDRuAJbfgcUcNPRTv5j3vWyv0u22yXhtdRcyhsC3\nw/Vnc9gmEMr2OWG9FMLd6jkWC4wAXGYhsFGp131NjoQzybkTKlA737thhi1Ua56GL7ySfB6HJ/Dx\nu1R71iXWDd8tcZeNQTrNUXfgUCsPfdA5rIN3h1EZXQHg+d237szzr5y+b54HFAzcAVg4iLE3aXFC\n/c6+BpT4qch7kpX+lOUTRMCi7yy07dSjqZnRYqabmVhRjR1mJFQYAylpB0O+GJR9x6yQbfnEP6rS\nTcbF8vZcCVOdanbC+mzRg5wntbzFWU4G01qHSFsxkFGJYJrXaT2hHusRJ/UUJFglLeI4x2DsYfg9\noYtHVr7qiWEUz81i4ak9aiBp6AaQtnPcpnWwtel9bK2b8qUxmhavq/1FZNLhaHExb356jg3H98pU\nSG230tbYIFDgYismR97qTJoQyyAZCKzsL6NIQx/sHKu7KvR13C3gSnqM/9PvS5EH6e/URqt7eqdV\nJvosfegWea+VK1bcAVeSY8hP9FwDkeVwR4F7j0f6yB5aITRkcLkB5lmkTakhI0uBnd6JibBCGQIT\nAbW3esD7m57ndOY4h8kaQMZUeKz3TLGo2j4OTajo6RzOINTXeO56+RxpvM1Pb5eCcl+W0v68yezk\nuULrfvC8F8bkK902hMsNYHD/ot8DlZhCJexYUMp9/GXQ4Kw3VjHVIkcPmZhBeYxfZlp/AI+SV3uG\n0c2fHgso4GSCM1RH7DcFtKu4+Vw3LUfFgJf+DsZNk7k7QwK959rEY3T9PYAQn/FonoEVWOp9AziH\nzNqeed579fOtE+MrMm7mfT6WWJJZW1x3gJ6VufwdEJq/O4jsjCjPVaEAe9sUUJNSUb8yvD0/DYyJ\ncdoWOa47DT7eub/m3ElW1t2cJaYjvs/60rUNYOBQgcJGY1psDuIxauFyaW3U58rrTg4nqOpfyd0Q\nhnyvfKwKZajefzjvBQuhlnZwuNxZf0t2lObJIhBse7G+6G9vi4Ewt3n0ax+a78VYV97PbL6muXj+\nv+pUlMNzwFW9hn2mZ5OYCK92MvmUn5d8gghLDOHrGtsq4TgAjGcyaBfvjD0UMx8Ce3qwspjfrAh5\no9AnkNDScREcciqYByt4TJbCM4Busa1r8QF5uYqV9qjU+j0tHnn99lAD+TGNYj1/23rw0FoTWbk8\nWQSYfMBAwenJLf6uEy8DC1Vdcn1YbiVxgivDVt9FVXamhgMIzNRQAMHonhR60sQXyLYMgqquUamT\n4OEEoieCjb0xRtknW6VcnDBhgKjQNZmVlZUhPBiHVX4ErfU7lDFeNHN24oF6YWODNdf7QMkkQ8xi\nGFmZ5LZqe5SCyH1ARsifqAt+E7+fOigrdacCGXMfVtZsBnJmcizekUWVCARFZ1K/c8LFfgB1NDZ/\n3yf7AJgebfV6hYR+6TlqG3PsLY/PQ1hVaDgsx4a79ooHZtfHObrqW+/xolQGiwIn5gVcRiXn5lDh\nXXkAd6jnu07wBG4kQyD6nC7qq9Tn52AmAdb4GLTFY8wd0sO4PeYiGWR8AA5EspdTy8ri7DIa/3IE\nfrjtAYCT2Kcr0fVnC8ldx0qi6ADuttyjj6ZKuxuqDlBeFvU+oZuNYEWlNTCBGQCMyeWJ24oXf+Yy\nNi9+Mc/SbV6BZ5HsN04N5SMrjspOILKdQ4mJI/NprORy/m6urP+cjybUr9f+Z3UgaLvuSNZNNGnx\nLNfrcZi/VS+8Vcr7+1+VjT87vyIoOOWQP6AwDN9Vj9vn8bp7fmzWiY6rLlICScdjVZjDWSLIKzlL\nqghI2S/NkQTv0xVIp/Mys3CcieBhwOqMKxlRqjskAEH7ga79LJVjKdzz5svP69jPWeYz/92uxQ9D\nPkGEJZyQJXSOLsBwAMHYAMpC6LDEiv1JCvdStnXCZw/dwLkHNIASHRjPqaAKEDy8Z9vgxe0lVREk\npQ9F569i+A5K5bEs99K50vJDoDl9pA4514CKJ8isaecd7hlYV8w63Cz3PQY0cD1xVYBM5UG8ukfH\nZD18A8brdUJQ/n8Zkmfn/yomax5/0eP0+tr3UGNVpPAGatlqmBnlk4NvX0i1rWKZFDMb69CyFDCh\nY4h9cqz43VBvUmLdMy0Ud39d77s0Xm/AO879QK6nV0oRK2/qpVQD/TkAWWEh/OxfPYeP9KNcnz58\nnjdjYR3fJL4nBwLuF/zR+fwUmGXwwjzlqcyb5XIc8v16zQdSAQgc+pElh1llEFLp9uGmeWuXGxPZ\n12xnfMrmesf8GXGP1cZez5XA1/VhwAE1ZeCx5O2zObzNzhlxbrpq61g8jqv+X68L4/BbrgcQx/6g\ncIY5MYiNU72W504tRet4rNfHROeCs+vPEhyeyfl9/KKKgVCVE+87DuyAs/LOkinW9fXfSkBh+Jzu\n/R3hc5b5uqPHsK2XpxMjjfMpXO3McNVv9SQ5jFHVUe9IZqsxQzvf824Oi0/5acsniACdaKOSP9KE\nMkfRMMBAj41nXNyU9stbZ2U51SWSMa/KyoyPjVPDlbIR4jSBkOFbFy/eru2VjCGXnqEmc0u41nqI\n4e2poQL1CJzQrvhEOl4alxaTCnfHfSPpQ9D3Zp4Vq9ZNhcmoqeRAVZCFwz022oLOLyaP3XoQtiVQ\nUpqVMu9GpJgHkCnHVdyy/lYd5/2n995mH9+on9OzDl4hRBDprI86zc6Re6tTUhbz4pfjrT8ifN/n\n8Eo2qovAn20MjVifxDoAEIxuDmfwu/u707jsszGVKbNcP/ZMNwyjVVcx6J6Akem9fv6mdF/qQxrL\nGfOCeN3ci+LtOSgtN14Pg46HRLKHk6/u4/ez01r85GdmjBh6R3yf1+Fg8+rJ7JnH9okKhz3kezJ0\nrMzV3swYeo9k4GZOf8dtXffhIRf7gDGSuE52z1SGGnV6vu6646FEwBPbLWW5CidiyfT2fM9B8zu/\nx63p/g4+Fj2MKBqAzlAYdv3oNb33GI8c56VK7HlexTHS/fLae8bIM9GKfgBRvbNuVYbCLO7Yjw9V\ng7OvVomXZeVcGJqfxXbO0HWX13U1xItKZibCrMEIYFQMLfP2mI5VgCfHdo6QQNXy+BDYMnexmXOL\n1rXvsLwylmOkt7m2jmtQb67nqR72nPvM+QHgbQxg5ZJ5dtcd3oPxVDkJct6D03qKIGzjiAVOr3vy\nfapy5tbHkYkwj3srfEtIL8uuXfIKNBC08niWqPeTc67HY3bfdwAI+ix0PppshMhEqHIixHYMmyt5\nTj4Lx36P7roPGjdj5kNoND+c5b74lJ+nfIIIhZyt0+M5gmE09oGxA+O5BvDaAm1mMafFYm2vpzGz\njAxWCosvnHL8DXOAnyZQTMd5gZttEytXDdpX4Qxnxg4bFJuMZRTH696TCK62H6KBKUCCta/vX+Uy\nYMnKRTCeqqzPqFkLHjMtdN6UR1t7/24jhHuIDLStB7BidKeR7vSufO/6DMw4ba5Djb+jUbdT3Nwp\nhR4rzGHlYNBnYLTmBIxF6uvKbZD6kuWmMIPWj3NIgIgqc/Vqx57X+el0/Ssl7JWRkEMQqi1MD963\nQ/uS0b7eH++tbIvzibEwj8e+ynXXpGuaSwMA9r2VDCBZ9HR+1pqsM4czbDKwwUGEtzYBwRz6oUCk\n61arLwDBeE26V6TVDm2L1+uQOFDDstitDuQufZAqJwJQA1khFr241xWDBlCQLW2JKOrVWufgddI3\nq4NghsRkNhiFpXnZuS4OGuhPuvHWhnOc4pDjhssd116vV+EMavicgX0ZGNNrqroc7r1+bDqnCm3x\niDWvIgLTXRQQ9HAe3br4vBxaxxYt/8qgssSKwNGdB1iyvbPrtU7vkWjEeD3n92jQHaj5cq+8+9R/\nSvyb8y/wszSQ1uehtzbw/eb5giwfQj/mmzqUu+ZsWwuary0Vy+VXIWXoKxli6Nfj6SOS16RZD/rf\nzjtKlefnjIXgHumLupywEM7ue3bsjImQAYe7TIQqnGGglyzg6nm+11KucyL5b481ReTdZEJiRQIQ\n8nzDQHGcg2MC3Sw8x74CK3k9mWXWTAQ7X975kH5C8r7Z+qdfJ+bxAAAgAElEQVQrnyDCkpGU+DwZ\nWnwj5SvQ0AMOH9j3GTs8CBkcZsSlCe3CaJplLWrTFmfyjsmA6Dv5OTspEbveHyUNf5wpuCf0rYqJ\noBnfAVi8/1n8LifVM4/oR3mya5az96HvgMNAlhI8epxc92UIsWKkse/+XYLhHHZnGPraIyAD1ItQ\nTIIYjT2Noy+ZCO9QJtkD1Ir+66EMnABvtWV4vec5HiMfFKMuGM+B/YsDUt8iqU61oA3KuF0xZTx5\nX0Tfq+rkY2VyTPJksmi/6CPumVyH9UTvWiWuuFH/U+o/Yv+5yllxJg5KIsw9Z8KeQP60uGweI/uc\n1/oeEyvOHT98XuvUp3JOhIvw8CgZBKWTX23vegkmFTHonKNVgYU4nx/r7H3Pf+Ms+8/i2rvjeSxv\nq9Vj1SX083FMZjkQPVD6vPfuxzwnQgQCryRsT2zvkfsprF535T1zmxXwHBFcRzRMGbBSMIWWh1I0\nuTG3KYQBMkW+u4PArl9r7L7W4G3rk7H1HLY1Z34hvmZp+Am9j8qw6Sf3yoNKWRJFY7PR0ccok0Kq\ngfLq7VQG6Lx/sqDh9clgfMyJ5DHhCvDM/A4SQkXHgOebKsJKqtCLO/IKtGYpjU9er4ZYP51z+rpu\nB/rS1/ZdwnWdVp9q/tLXOsI8q0yoRuM75izxOvl4OOi0PwIr6I7XW/Wekt1wQ3I4nx1EBK+GbfV6\nPbnoe+LcHwMCtGmQM+g5d/Hi/CWuJ2ZG5qC5itfZgbjml4DIO0QdHp/yKXfkE0QohAelJ/UT5+0B\nYUYetFhafCrR1vLiwCyEKqmY/t7eZjnyUNBgnbcDZ7TbuzGVpcFfJiYsvN9GCZ6yyZhJFQv6v+7H\nDkTaqhX5DQH6vC1hUDIg5eT6qvxjLPjxO68lGYzSMvRdmzGzdtYQ8kBKA/pznrfJQCdwYIxzI9XD\nQI4tVOMiGxmz7n5eYHwMoIv336lEw7b3U2VaEwrp9Q/pkYJBwt76TCWfid3qtgGRFcAee74kx5de\nSRNPZDSWUV8lQspi4FHxG49l9UCEcROo7nxPPzbo/ypC572eypCcVPtfYN3M56kxoVpGfg4KdrJR\naUTycfSOZGPkaiskzgkyulilObmsKXKPPL/E8AxTxGygaJ87lmnJ/xQ8kfh7Phby2ShbxcoWm9t8\nS8SGvt5gTsZp5ZCymUUNpj1RoNXwrA0OrePyHgm9E7436vC0u7kS6hAorsdRpA1LFBmuW58ZzGJh\nL77dj9Ynjl5hQEePeRm8ZhNgQM/IWFsXQ437qR1jcGU9jAMAcEeq/Dh3cinIvbW/er7seSzvHZLi\nnt/PgB2ji8Tj1dyiIWMzMfNubZkV40qy8X1d12o+vwZ460Tar2S+8xG+v2IxBVDuneWFEKbBuxTR\nOj98/eDjZ+XU+QlcfpVMjhziUIUzzDpEkCCHM+Tz5rkN1Q4NVThDFeqiHSYwjzNQl1g/GTh+tBVm\ngjmspc82xi1p1/bfdOyuf03ffx++fbcez1LlnbmSq3CGT5nygciyn6R8gghLOgAZdTZXkya+CwJg\nSDl7wDXWzfYQX9ugTa+dliWHAWn7oz8dfZcHIG/z+P4E+pc4FYRJq7lXOyfqOyb00WuSNdPPF5uK\nyslMhC3lQxgDtJe3G4AvYz+xBqfcG6SVgqkhJawcqOf0LHERS5XosK/32oeHqrxGyNf91GhuBCJs\nnUIa1PiZShIzN4AYa23tprAJyylQnTecheAUx4F9DByTSMX3B6jHToAnsD9XstDdr+Ot0s4U2bzd\noH4aMHCycPLWkFxPNuS0jWaA23mrbGqTslBEJmVfj229HZITzt+UjeDPJBs9W+szJKU1Swy2Scej\njem9T3XnumndWfmbdfb/vZxBhpP2l7m4xzwUw8IZ7BiDPcyOWffvBFA54EB9YCWKnX9pXhuxf1qM\n/rp2X4yK+eyoH1BduO5WCfIEjefqVxSUmceIAgI5J4dv87j6XeumNM4+yM9JxyDd4wK48djWGdPN\nVXwuYyeE8tAcynPbtsBFLlsT+TETwTxS7/A0VQyQHI7FdFj/7p5SOzZ0BvdjJQMLMCCUn5Mzr1Y5\nFai7Pst1qAAow7a3BirFXAcWyiCw3Rm4nZWt77kXopNA62YwFY2RaWTQy83UEH63J4DNqQFh4+HF\n2vUCaOzDw4nCcRxXxQq0LMNpqrJDe4v3rOMXvr2j7VJEWmn0BMd7xJ2Bxio2AmTcD3SNu2LhMCW9\nZIfoHH5C4VcgQbRxAHJSRa1nJSXLQwGHVfaTttb9YqClpD+E4x28U8uw+TiGFMR2zaof23tV52Po\nwfF4vpUlNR8jAM49/X5Wh3L3BvSSmaDhDL7d8LB1XuV2ctMXCuB0rNhMgZbnLgLQOMwKKJ5RYizt\nNh/HvqHnvtqFTK+rwg1fhTNoP+ljoMtJhvdP+dnIJ4jwSha1Dg2Q72gW+OVuRnemQarCrcd0O7RM\nDT0sZklJkg1o3/lvnZI6SjteLw2Qx4B8icpbJe9RYnKeA4v5JgNHPesmpDh8TYbvucXUvfNZydOQ\nkkGL7MBcUNVwYNq1lxnDDtTrvu9uRLki4QZTvg/HtKvhCrBXB4GFME8ckCbHOPET6VByjC6IR0Mq\nPJ+L22Zwp/Nz6/PGDB6occRFTYU+3Teh9Zy077jV27BtLvX7ti2AiozfjKO/13PSAMgy+McQPFu9\nFenV9bHek84ctklUIyqNs04GyEsWTFKSm8SkS00GhsgBpKlyNPBnPo9jhy30po1A32b6LeBj565B\nWyl6VTiPgamPlVKr7dPogj+3yQrzcCC7Fms8kdGqz8GAun0BPxKT+FX9xjzdJ/3Cc8J4OzIro508\noSZutDU5ApdVUlHgaHzobNRQeNPC/c6OVwZepZRLOV86gwX2qf3JgZvV19ocJ+aZvmSoFMco5j/U\nlz513rW+MruKKewhwWZmmVVvqkWg6ayed+fs0/usuVOYolWd1wTjjGt8MvlV756fkb0v0HPTc8J8\ncj1nna3TYavfom9tMvBYW0QDQNv42tWGwlbxMD7/fzL4BA8FXYntY3+pDmHb3GWATT3B15ycULdy\n2ISxWTyLl+GrqJ9vNRaUjeBlO24TgGtqKo/Rsy505uW/KznJoYLR4bhE8IITLfJY8/lB1nV+zVli\nRS+7oZ1Y+Q1C/UICqLxOWOMQ4T2OIeh73HQl53wK5UhM1Kyvy50Fng8hr9nHe1Vzsjt5voVXXLH7\nJoTXD8F+AGMmuPThkOSfiPwYwoF+HfIJIkBRuYGNPBjBS9tJkSCPgMawDjM0xTx05rFbLASe3M9i\nuEtZSYbClmtrgYuhEFERD+1LHnn2SsfzPjYwNpn72s69t6neSWnwuOuvlGKl5bpzSImxBhSRH8AQ\nPeZKQ/aK6fPu5Hn9spJkftHtO0+qVBsNw5Xpdf+8r/bXgC3aniza16aCIeG8SEEWYHlf2Di8omhG\nQOC1F+yVWAiR3UesD+VnkxH09yyimmhQZdu3tbcz1cWelXukqvc9AYlZv42SZm7S0YTNuor2P04p\nrfmQGsITWFlz0BhG373TZq73Hcnjiec19oZlKmWM3z2GqkQGlQNp1s8SMHIHGSqVrKGKvnsndf9t\n3ZmiBg/ovhfGIUdNmAcdcW7pcK9QNvLVI7yd9HHgOKaYwaN1UOAwv9sUNu9zQVD0V70JIPqI+Nwu\npjxz4tT2wArV4nEyjEm1iY59rY973YUejLP+fN3lXV4eCxxaGBS2PpV3/a0CLjJ48CqsowrxeykU\n46PG5FcrocRs0O/fKoGf9jMhEN/zivB5/n8JIhQ5fsKsKAMP0cTD3a7h7R0v749Cz9g4ZC6Bc5XB\nWQJpdVlZ4vaSYuEWOd+c7tp1d7eLcO2IY3m3NdpZYAqRqI6jxzmc4U50zdcACO8RZroBuAxnqK9f\nY3/UADUAyJ2F4ytFmoOkObHiBLTmMc2vpGfMY/C+X65f6x1Sv/F36ImOMxNh2O/va8snPf9TPiKf\nIAKASXEbaxIrfq44P/tEx/cv4jTv3vDcN3zZm22R92WFNnzpTDErDJ8FFuDhGqlSeJV6ZcYm1KBI\n9WywpIrWMjOm2yrbF5pw3gtqVktGtdYDWIrsNutkimjxIN2IisdyzP7I378iGOsQgzvk9kIFMIgw\nWQgzOWOsu/5/KDt56cJCs5Sz6aHz9+tG8zDoerYhK7rH8sqY06JNCipUdVVE/vB7wyHnxd0lujKQ\n1NNldbJ3XSh0J/cdpEDNhdSjIF8BC7xoM7vGFdzo5bkS9UgpJZGZCBY/XwwLDpUAfJwrkNPh70WT\nL/EOKAzihLokBU3bm/M2VE5PZihYvYYcw7TGBNWeZLhUhtU0Wkf0fGUP9YXIQyDPcfAK3QnQZEo6\nU+oVQHhZNj1n7hfutZxHxpjTbjWvvldKTzvXQ6Ln9JXoWYf5PjMaZBjV/8tauwKtdY2nHFp1xUSx\nvrb6bHsItr2jrfXS2AFw476J0HvLnfNYBrOhOM8Fz73K/qm80CwKnjKY0jb1qLrXuZqjuiVdwyEX\nwjh8FiD42XN8Vz6F2KfZkAhra7qlzjVhiELnAga5x2FNW8UWdTm2h+crvc9DPJxhW0wEqbTSNiDd\nAdt5P1iteX1hpiT3DS43VvXjg7YKfaxZNMPCO/28tJ5e1EMNw3neck4NT7D67HM7Vx2jvLXr/IsJ\ngncDsLN+lAtec91Nt9dZqEL+nY+rPlaFMsw2HCd7C4E4qVcVzsDytnrtVb4egPr+GUC2QISteZ6l\nR5tr/z7cKcnrH6/ph5wIa/clBipbq/vGfH9y0HX6kNPwTJbpeBthjhgYMyGv6iI6N9CL3aHrwcD4\nme7OMPWaTwE+QYSD6OApqZZ9YKzZeTyH5UMIlPflZXAmQqPMuY4mjvEa7e5fAHliIfPRiJPHRO5d\ngffrql0KOIPwWSxryA5OymKVmIw9X0qxlmb2wVSmvo1T5H6MWqhjcQx1csKJCLt3zBPlcAjKiddA\n/G+WIYdjuj3fIV6bvDx1G/RhXivq2fCtjMOB2vN1RoEOyPYQiHjMKicGZeWsCV4iC3k7Qpb+9D5r\n9ZPjeDQKn3135TgrRcyQaEnB1mPl1oCLlVGNEyEjhcEgpu1ubaC9WGXIObnKrDxiXvfWYj8qk57K\nOOxEoJ7uoMASaDRQJ9rjOUNBNGXhfOnN8iEw9VEBnVe2j+UBkBfjW5ZRoSDrOpbFxtsrSvVS5N1r\nrc+ouEYYYPKitWmqGH4Z07v/yx7HTbWLSzZqsKosWvdA/ZbwjrfW0fZWsg5CvU+On23/xXR1/qzm\nEG4Pkc78fmtsPFpkHExAfJgxZe0h9gDg2561bdDDWp8Hz3SkEmv42yaS5uNZxqyTd8wMBuV72/95\n/SsMx4qxEvIXvACup/dy3Edmv0IyfbtiFhi7JQF9CtroJUr130RcP1EaSGLYVeNJDanHtqMtbdTy\nnxTzJ8+zW9B/vI5j+H2Vcm1hWi8AuKsQm8O55ZwJjJ1nCVh+kzOR8L+ud0fROyrLiWnsnA9hIIJ/\nrwzlO5KTG+awhR+zaE4E2/58LSblulT0S1mGv+ZZeuwzJ9KXMXCWUBlwEHXuTNLtXkJrIxCnDmOI\nUnWqMDO9/9X3K7H3LUlJ+ZRPSfIJIizp8KQuZ2yE8YuOsfIN9F8CfZ/J5p7Ls/JccfNfyGNntFYa\ni9nTDhDiuflivP8OoMm5AKC9LWVvvbX2BejPEa7vX5pt/chx/Kz8fyEEW9t2oEbieuEDkiElmAow\nxc2rQaiAyplRVgnTdMuyG0/4yxgxZcVp3py3YevTYOLJVFFje8ap7Vlh1rrxJz8LV1qYgUDexKxs\npuSBRh1dSPZ7pWItXMmd9zH6XFC3t5VHoAN9b7c8OFdJxNwzshQi2hr1PZK9h1eiFOq8DaIaZpXn\nQGVSFKeiGfZ1bvEdb63jIX0lWFxghY4v1EwYjlPVvmPG5brntnlIw7ynEKCz7mNKfw0MqLgyEp9b\nFd85OifsiowmTuJVKSnszc4ii8JsoJ3WhS4Q2g9Lx/xlcjmevwbceOW5QZx1khM06la5/DyACLYp\njVSTmv3Oru+rYHjxvA81eOL9bJ4gxsT2NgD0lRNk9auu4JQDh2cgoOV0KftxZLOoAvu2PvcuZkw5\n8O3Z4CPw5UAVADwpnEGPtW3MbYobIF+87Efryws98GbGEPB2MpTzNo+TeZVAtPrS4pkfQ9gslGvE\ntc8SdBK4fJoLoaJAcUzJr1Jorr2aR0smQl6WEPsJwHOkG+eaV0QAVDtLlIl2xddZ3t6xBDJPgIT5\n6cZ6WEOtjl4Oz+2/SrHEittL3OjIBoLrPD7vTOEk0YLZlTiprYUtQJkl2g++RauOUiYzzP2KgZRx\nPF6tg/l117tH1I3i3RheARvsgDjchwYErxv2+34cz0Lz3dta/xtBQc/u68Ubgfsa9mVJmVeC0Tju\nascLsxBsbSna/V6d8FPuyWf4x5RPECHJnjLEAoDG6GEH+i+WIrED4ylgb/WXZbB3sLLdDrHDzzXZ\n6znAmqxklqUgQf//BM9ftkk1/X5g++uXUvbmzIRg6PccXjGT4T33LeRo+NIFzw5LlMcxlSox1g8h\njCHLGB7DzPHsgG9LBsw4agZVAAUWxsHYsMlx1IpEJe6hGHiusA72ZDcZEDZQsIwtRGX0a+NVK+qk\nJlI81Flc0XrVzj4IIV7PsBEwc3eLNgDBW3FL2rCEVx0SnuO833qnJ4BUVX4wUHY5Ba3CAq6ebiAA\ndWqsMTWPKZz5Phrew/2D3xej+xloyNRZBa1ioq9zo/puQiJ+fObFCx7SAYsN5W3YtnErtvXs9fQh\ntsMJgBXCI3iOhi/rHX1Z88hB6RvHzyMd8mIuUUq4To0PcSBhiTwBYIQ8KEpZ7zsOnpMMCpiBwVPn\nAjs130y4noAVBffGEHxZjf/F7oazttvDV2IctBpgsexjXaUB7THL3paHa+ty8OSy4etlOPjsITJe\nH5ZtJbQbXUyRbeLhDJxIc09t0dwX2ZM9l7K497l6lwNQYmCb50TYGMTipGZ9rbkdfs/K6ISvHfP7\noPnBK8pb+un/z67P8zj/c72BOd5Hd6AuGITjOAi0X2tbKumQo3cBxffcZnu56TiUGRTnsoFz8Fgf\nuQFtxWkCZ45wyMD5ttMRvAnlrXE4E26ueq9+Et7BArJDaFLzVgS2RJcVJvM6XMrqqDd6x5rYxwTb\nGJD2bb4HmNWpOtSdNbpaN3RpBeY7cnYrvAzoukddEKRLqfF+0kjvI/QsEwOhkrvgAf/GAILXq77H\nFZCQt3F8FeYAAI1CHTi8x5w2FXDFoULE9GXJuzMB8d5f+nxHukvDZmMsgtzO0FNb49ghFMi2MW7r\nzjy+Ha5YdTZdkZ//Ufj5cDuO9fjcneHnLp8gQhKdNA+LzwIQ9t+ZX8dzGuF9F+zL86/JqXQ7QcCV\n6CuPHIs0gTxcMXn+ckPbOh7f75A1M8h3AJ6A7RwB9fhOZsSXtRXk87lZHPOTmAi6AN0VpTUCDiao\nUQ44/bRtcyvKLK/arQi8zt0a6mGoPMel5Sxiqlg84IkKl3etNyGEd/z/7L3tduS4ciwaCbCk7rGP\n/f5Pea/tme6qIoE8P/ITIFhSz9728pkR1ppRq8QiQRAEMiMjI3GbrLdCAg5tyQGcjUJ3HvQcpUfu\nmyPJ070YtT+6yEsAwe7Jc0CP0dmKfpoDM3+uxjTi7zZEHwk9ErA0EE/HZaqz3ehKKRspVSQ9L9t8\nw8mN689VA+aoOvmjHnNpZ+mPUWdg/LdFzvJ1QFmrgH0zz5EruR8Dauy2xNnZ0vja/MxODWmE1NTA\n/5Fm4JYJl65o1oNgJ0VfshO3VI+++vfMRFAH20RZgVztJDmmOobz8yiAlwqza5nQaNmSk58eqjuL\n5fyZ9Ok62hq55/GZryHMHpWWNCb5vOb339JBalc2w1gC046z+Xowg5SRYX+3aBRTMHYqMW4kkfY9\njVMxgzHpPlCVHPDM1KjFqnPUgYng66TdK8jZC4M6vc51O4+dc6sNXAh137yf5O/D9fy1tSc7lU3f\njY3CqC6WjgLoPpGubUwEHYxWZHyKVqpJT3V4nmWD5yJ/JsIcz3wNPMjPUZQuLmZAiFnUCh4M4NSf\ne8+HiimwObBYxHLri88wAu7zuzHvBx8xWAwInSsI2XxzlomCSFtKxbGLmaCpz1/N9c4Mh4oQZqa6\neDb2kVbTEG2ecKfnPQOI9dBSlgAkUGF1z+frWprXq3aVGpHTQv33T7QA4EYGBWD2Y4AVcwRaPksp\nfovz21og+ycv1mVDJNJnDq5cj0XPID+PwMNMybdKDFloMPrFCSiJig1z9YUOTtfI3zofa8fP7WUp\n9zhoesfl9wwG9iMCbllvaF5rDLyTvVI+35LtYUCx79+Lff4yDfRPLD0eDOHYN4wNSQQUm2vEAJ+X\nm799FP6T/tzfoX2BCDCUV40Y5mHBls1AtBD6DvRHvE0GIFiUf29RAjAb21Gr1z5TBDFPwiuLPrUV\nNdAXtCY0unaIuCMg6RW7CqIdEw15uMeLZvmrmYVQShjP2aC+ymu2zQ2IKBFjRD/98/Th5SJVSMpc\npd8dSEg5mCfRouRo+/1hdMDndnIMyXJwL/qGHOVJxuGAOE83Nj136tH/7FvNIMeqvUgZvzag/P4N\n+GDfXHL3hry8RY6nG1zzPPBUk8lYxjkqKjThcQOdlY//kZYjWJUt6hoOXo5cFQqafv5+YYvEsZ/L\nSnMOqQflWhdhFkGaG6djBiAgVfQAxJiuPBoX5OOdHIIMBOV+XP07MUS8TwoaHGldO3jd//leZyYC\nNBJvFUrIyy8mi9mueyQLxvRo0t9fXZ+yxoDnqYygwNwyE8HERGvpDhyuFL+lOzGP5KdE2I/BuY+5\nLGkx4+eX97FYyzKwWBW0GijmdizFTwdvENTbbWvYto7WCrYqKA4d9VOOuZ+XwqgnsOsaDNG1CnAB\nqMUclrKoqRwqJLf+JAq20ESgbHhjnA+xBkfEbWYi5Ja1irz+uu1ZHQ50+RpcjVkR9/Iy9c+d6vHj\nAQtfrc0Xf6RC4PQZ0ccG7SDPcHGM+Egxrwo0Kqr3WDS1pqbnXUsCqnx9TXvBtC9b830FCsYnJoKX\n15ubiyvq9XjNRGCGg0tzCks+NjPH7L5Zn8lHTtKvaCcM30v9DG2p1A9KDt7UnBGHYAadBRRpiq6H\nvZWZCBGwGe9D1us8WQIQuGqrVIVXn18JKZ5FGHn47nBuvYNLVgVEWnH9N3Kxxp56MZbopCGgRxnA\nT0hNbwVdy7qv0lPmNvf2JHhcLuy0MrJlM5CUu5Tfq9N9T/a1Cbxz+sz1pRITIYsvxvUXjO2v9rds\nXyCCtsslxyhFB1QHYQQRbAEBNGLH5OXPANUg6GPucFsY6ctL30SxuMwofZf+oGdjR2h1rZFrNBiA\nYGkWdu1TtKXD6ZZ5IfysJkKuXX+6h+X3PtdeAQmDYTXRRC1KmY1BMw578kkijvZqgyT/KRtF9Mtz\n+T95P8vWcQka+SF2H8TD8+m8FoTLbTauPtuGVAyNArVdx9LSZX4lfWJxbBn6tj6XRxtfgD1XzZ5T\nsEvCKbSVrxADhxyzFR4dOwBz6oEzI9I9GBshCxoaG2Huz6u5EposQVH0PFgFVyhFQq6aHHd+2Dl6\nP6cdzMeMgF5QovP3TcTLr5ufZ7onZsbB4Xi8et/SReXIw0xgDIO30tpgkIO+doz0a93HzMdaO3EA\n9RHEyowpB43ceefEaNBIOwe1detwMcdK5MG9LTlavpY1unwncjN/q9JIMa8FnuJxuq+JpZKZZvb9\nz0TWC2IMq1F0OYAFd/I3FrZVB0odnTjLj3ctvo5gZli7mO+5OktuYzoDpr/ZfhkpOnZ6c8qapvPM\n7bNpZ6e2YMas2lmL5AL2mCbr1br+Sp7ho0aQdTczETJLIQPWs7P+UrNEW77XauDoB/sgoM/gBYM6\n69PkMqP51KNuxHVf/wyLbPUs8lr1UTlJeadSWVgyJz4By4kxMwargFwBDJgcTbdb7OfaOVz9/sp2\n+GelL5wAjQk8mFMXVv2Nz/tl5YDO5XyfPK0j08CsgmS9Qex/YyRbBbR+5iBuZHJhef+Jlss5EjEG\nJssH03AICGb2Q7JTgwlxPmdO/TIA4ZQOcwJ47Dv/kPX7/2xjvLbl/k7tC0RILa8Tg+iSOu3Di3cI\nCpnLnpkWwsEFMxPh4HFht+NNBBFdpiQnT6NUYHvvg9AiAOAh/Tke5KBG29WhVeAAgGshZBpyLgtk\nhtIsWAXEvfaJqmWiUtkQrYUdsY3vhxEcVPRfcwKXx14oJFl6CSCCkpbKsev4Sj63PBenYpJsokdP\n4o883m9Pz3Hv9mzViUYM2yvq4xpZ1u9NY2953p8FJ64cDUI8LyTH5lV7BUr0Rtgf1f89n+sjg8sd\nh4u0jtYIdaKzGmU4i8s5TXBx3/Mns3HrnxfGth2pXxtutYP2JAZH8s1MUTf7Vebxua9WoqxUKdv0\noDAllrnTuDZhLUKwYhEsxcoGVf/QaQCk0xatzJTLlWHdk4E9g4iZLmtg5MGREmDt6h2f6ak5dQBI\n70IP6nr/eXYmaVMQdeqbP5MFpzdrtZio4pDqgnxsfIeKzEsDhCydpgC46Zfeq3z+rY73HpRqS38q\nTrHeEhixUURhuzpIbScXMO3ZOE2MNjknhzhhyWDGuN5e+WesDnPWwLDPx/GNIT2RjSjevUzJnlOh\nqADYVxH5eE8qzeZ3vhACZEmO/pjaMWpO2BjUgjPDAfJOxN4Y+/XMvFq1QWzRRmCk9Cy/ZwD3fGu/\n1OYXuBNQ0nszPMvXp8p7VwbLfB3xqgnGeDk/709dZ/F3cfYVbBoNsA/bAAgOKXSRZpj3+mWfVmAR\nLC1rHEPRGAlb7x9pBkRnLR5JNZqYRDjv2x05GCSfxdyN/gIaKFmABf8T7f+lqg3LPYvSnCzjfAMA\nDJpjZaxc1GMtBqSMqdlXeR0yENXTK1ns16vyx/Pa+Mx7PiEAACAASURBVCtpyZ9pmYV61TLA8NW+\n2heIkFpHLNiDEXGEceefsUT8j6MMCOTRRUDFFpSH/v7sIcR1MIM0nYHnRX9n9Lv8u9yA2yZ5hH0H\njh/KeNDztKN4VDhTGrMg2u7VIiKdIaPa4z2dP5vTGUzpNS+6tcZyEsyIcBQyrdop859Y/E59zBbO\nRBEVgztABANO7L6PXvDsRZwN7W9G8mejwC5l53weVTeHEJezjXymia0iQKtm4lEOmEuS4svxeAXC\nzI7Rr7TBCVv9/QD2p4IInXC7Nb8mkIzOT6Tl5Dredm0g5oo7ulWM4pKYLlZi7Cog5UKDxKlqQNyX\nRUi3mxmuwu6od8uvDoPONv3DvwsUHo1ov25iIkg6Q/d8x9NYrofFm4jBkf8X9yaGxasIdc6fPNN3\nM6AXz+AcrbR3a9H3BITOueMWmZbzn78z/35Zg7uQqNwB6I8EGKRotQni5fe2M84Cdp0kPI641shG\nuG5U4PnbQ6lNHbc3HZ/vCh78VvuQ/lNI5tQtVT8wIEGcfLn6luY8H7q27MXP40Cmrl95nfZa45RV\nvwmVaEy7iSXzBBa0NgJIcvz40K7AiKzDYn/fbI134BBRUaien/fw/YsHYulq1qt+QIXqxr4YIyMc\nM3Iwcc5TZg52GgAHDT9TFWeOzvu78plo+sSGWZ1X/k2XoPny8wXo96oF0HImJgvw0l3cE5CKIbWM\n42iaCFcU/OHeSIEICteHgXNlhhLv/MoemxkhYyqjpM7E+mv3aKeex3taIxVkbNN+SGmszvf1er/N\nz9T1f0gizpYeAgT4Z6AXILZBUNTtPWVnteUgx8EWlfbMr0s7hP0ck1P6C6bDZ7/7mSoM/12NuFwy\nEi6b55XARX2HvTDNPwPbWy84WgIRWG1NnTcbOqjKKjSk2BjbwtZgE7XtoSO1Kh/7UfsVluhn2yo9\nhV/Rgv7i7UsTQdoXiAAx2o+0rM1zQ1IH1BBTg3S/F7RW8Gx1WDye6rQ/dEN/9oK7gghP007orFHw\nYCK44XsE46B+B+qbLGT9/2PsdwUCjoJSGfuzhj7BQpSoI5dhi81mRqsltwuCrB72t1AUvmpDxI5G\nAx7AIMIIhNGSy4SNeVjRluvmB29tLm15tIqj1bG0ZRfqarF+URiMGV0VQ6R7CoN9/9kEjHn4Z1GX\nOVPC7P7cwR5SA2LMTCyKE1PDNqYhL3dKYZDrXKdiEJKBeOHILsfvitWgjqszVz67QZkT98lm5QuH\neVUZ1VIQgMExj1l0vkYWrRuuUYTZc/umIMjGaIfkg2/UB2fIhIbWNvz8TCPyHzoOY+69aAqs+zkD\nT6zXNid5ZUi49sH0N6P8jpE6XkbdvB+pjzMQmPufhbwY+t6csb3x/U70SACDENfseISQmvbhAJrq\n0Hh+qoJtH6VbrRwP4ngnPqQrFzH5hlzu5ExYKcLfqhj/v6Xym/Yu1dIdRPByXvrcbnr9W8lMBF2n\njxLq3AO1lQZAwIQJ30pHVeXdjYJR5CkO7kQHGJXP+WcM1UKMwloxQj+zOSl57uFYU41nuNqrThG2\nlJbi50jPoSvb7DNrUV4P56oMeW+U9VzYei+Bul+mDYxtAHFewFjcGTQvDEMa33kc53c33mv7+/k0\nSMfYrblocGURcoasz1daAENXBlR9efiwcntKmAMy5CkRWXQ4f8/asJ/q7/aOy/nHb0ha1njzeZ02\nSv2lZsqUkpW/a6yBrMVi9zMDDeQiqaO+ibEnAvRY98E0EbTieNJECBYbgAQ2jDbOal2Wv31+Hfjo\nu6sUBr/+ZRrC/Lz4l9MYADhwMAMIHYzM8SwpnLXcD9wWSR+V+ad8L6cwH1q5yIkMJEKKAJ9sACDW\npa6gRK3d5+LMBraW147Va/YRS4H5DB5emdhZJ+GrfbXcvkCEqc2qrTnCT1tsMs5EmBzVhwIIFq22\nf+8MZyLsmhs70Caz862559u/MOidNCLBzjo4joLK4wbAnVQI8axP0DmqRWSjySnMDQokjA6EAQlX\nisYODpTIec0LaylnB9aiGK7IS9cbdtzb4sMXoavVQhgCb2eHfDgtEijCI9Jv5TpzXjhP5+rJqbIW\nFPNxAyICeIFwr1oHiahfGs/OZx/dqgq4cBqNKtXAaFRftZOT3CeRSRecC2OtEA/pGaOa/gtjWcfU\nc6SVKVIsellymkE4QnNKQ2b4GgAg/U33TkDdOup7ODh1C8GwTDUU9nQI1pF+P0dOs/Fatdj9puc7\nBQp1vuTKEE5CWRw7T3uPuC0mbk4XMUpvZm98tuVTX6WeAAZ0ybzMdHKj++dWQGgrsCtFEIERhMyf\n96bl3Sz64fotiX0FBPDmL2P2/nUsICVgzaH/ML3K8sITeBBggJzzWxVn/VaCJdIUgKylD+yWnA5h\n164GKF60Ual7dI5szm5Frg8AtRCqLkQxT+ER+RVbqaSKDavIqulzZMP4VX53oQC+SpUvuVBZiv4W\nH097x9aLMhWA81xJzz4DO/PYzqDg+b4yGy2nM0wHT9UijCXlkexfsK/NcZzLygIXe91V+2DyrpyI\nzwrUiiZCDxFJAFQZW+3YCmDkw3y+sGVY+vbiXtxJ1vk7rDW0WAcs+svszD0XWMzriIovWuUPE7YN\n5sQ83mdAgQgg/rg6Q/6Of9fEYmn8u+mO9B7rkDCVYg+9+R40vrfW8tps6bE25vbvOWWWsbZzfrXN\nNtXQrw+cy/9JAb6iE2UE5EfQoCwMrSENTTcGEfAOO2GeiyiSwri1hlrqwLi1wzqNzxsAKO3/mbF1\neU8lVY9agn8h95v335xKmcESv0+SUt0ZYCUFhh3oTzaUV2z4AhMA/M+xaf63ty8QAepU6Yu4ioz1\nJ7SEINB3+bw3wt4qnq3g2bUagtZRl/SFiFZLTXWp/ADkhZ5GR57hzjwAFRWTiAQfGEpJLiPQNEZt\ni0fFk6HEaYMxQ6knw9yjzSMLYdYKyOWNPIo1UIUXBsyC6hx9H+3oyxxwsU6RVmzQpgCKOZ+VQW0U\ntxNHWpy2bHTa33KJN4/uDmkc5iSd2ypQlB31oPqnDcMEq3qKtsDAhXFzmaOGc8sbS45m5HsbQB8i\nEI0pKTPNbn4P6o2xpZz/ukl9eRONu1n0fQGEzGAXcHZAZifYPrOSXlkbYE0pPV+XMGrpU5F7KDXl\n+ZLdW3OqLqDpDNBIBac5z1GD3Pt9ioap2vj03Ix+Grb22khl/87yz8t20qj4k8ABcAbbVscOrCED\nrMo4/+Vv53MUnWt57IaLpy9SUsaPSGUcTtP1TukMAMotzssKbobKvJ3nOk1EtA107lNxYy4DczYn\nXI5BQZaa+lgUzCN3FNbAivw04DGOq6WjMqF2QtVi45U7NirD2hQii+xMhBB0lP0hV0goJd5nAKit\nL/P2AZ3HyTCOMdKfDHeOM8WfPO9BQQX9jpd55FhHhA2TLjDn/+t3c+rT3FZzWoCdcU9jRDpDFlb0\nvbYJ8JFZiNInY6lc73dzGwHkkam0zH+eF/bhjzSkVBgYPadKmE2TRTcJAfjNbZgvBkgapXsLp3dF\nwT81Q3Tm+5puy0DpxNv3vdGaAUiUUCy71aytUqpGmy3tYrjOoi9l3GvFDmS06XleBVPk3LE/WF/G\ntJT8PifHrhJq44GpdCv2rsrFzGYZ+qJssQEAY3g6gx1jn3s/fJFc38ertkor+Z9oAkCvP1+1lYNL\nOnlmMOETF3fXey51Cshcq1vHrROO1nDb4zlupk+SU8SAIZXH5mQGmgG47TAyffU/jO833G4jP2dZ\nrEkzqGrBEGM85uOG3/WYKyDqqgLGV/v7tC8QIbWM1fmC2/UVVSqt5aweh+THP1rFQw2On03SGZ69\nOM3MGAj2H5AVdJMK9KKucHsApMJinKpCmNEHlKA6pWjZ3HJU09IZiBK106J6PUX2BrbCayfWxyyh\ntTOtF/iYXnXVXkVnqAAgA3nUmakNvcoGa4J8Zqh2DqGzQozKhK1IhAXQBV2p3zlKUWAbQxiO68iW\nLLrZBp5z/eUzOSDnnIrhfDYG/dxq4MzXy9R9i5BuKbqxFcaNwqGoFOfJxsZciSCPcbkx3r4Jp7To\nWN9+NLwV2eI39crFiQtP04W+0ljmkmp62ABSjaW+gvUAwIW9VqSNbNTnXH9PMyABmuotBMioyr1s\nW8dWmxt0z15Q1OGPiLH0Z1bhXwrFqbN2ElbE+LxiTEZWCxOjmzP1C+00x1JrqZKMOEyEo2MCGYMV\nEgb4606E8xpjE2ACfRSU9DasLXqCcgPqmz57pVUra1+/oz/t9w6wWp3GYADSnIKAUrfSsU1RIKGS\nrqNu2fiaBfq6nnfvAcxlQz/fUh7X8SSyzrpzrw49s7DLAGDblB1WCYcK8R5p8uV5agBHZjuIk9Kx\nle7lHN/eD6GpV8b7Ie/3vgu7boXvdEREyrw48RXDcDXtDQOXqcA7ZKw6uR97Dh2HTtZKPBixABQk\nlvHh9E7PTBlLFzo41rWm+kOmddASncFo3ZHqF5oILrRr68QmV/A+FXlHy2b7ZHIyFutoLltoIIIA\nWbau1fX+fc7Hgr9NCWgzuv9MsZZzh5OxOvUM/AGp7OIGQcKgThPJXmL6G9sVgPBBOzEUy3kdRbpV\n/1tig/rhhR2YQlEQfQI7XqUu5ZSCjgiQzA751XcHJoKBWzkw0NnfsZoo6s5EMCCejFVELtp6j9dl\naJZOZo11/1iBB8DZOfxntgL65ej0r3zHQgHsFtCfc1yzPsLq2jZ2Q3WRmX3QA9yjjdWW6LhtDW+6\npr5XCSwec7rBHDiBCZXyYLsCMocM1KUiVX4y2DC/KtnuySDFcAsX0zgfb76BrfErBmFuK1bH36FZ\n1amv9gUiaKMBmcz5Y1bWjiqjd+D5U3ar++OG+7Hh3iruTT772Sp+tIJ7C92BRyfcG1RYUT48OisC\nGE5CbwQ+zHCUa+4/ClhPtN+LCxiWythM2K6F88UsKvfmKJgmwp7EAI0ZUTGyDviw9AXdhCbF7j5p\nHZjIl/2Ovga4B/FCDoclO0yfbdxlQ161HCnato6jMWoLI20rHW8sjoJFzyt1dCbcct6yCkm2o/hG\nb59X7jhA7qBb/60WM/Aa5M+l84yWSUr1BQDe4WDSLH7m8zEZCZ6W4p9llBp+j5X62Wgz7CoZSiLY\ndK7Tzl0cuJtu3pa6UutI1fbtxKK+DJ9T3v9OSasjmDXMog8iNGE9uNr1x93PU1PcUVCR0uRkXM0r\nB2yOoasn524dQTcjM33meetrgM0NE7vHBOhB15mIXo/3KPRUfZc7eZTdRZi6jmvS1cig38wAyQrS\nz159LWAmV/a3UqizA8wc76/1b07FWBnqn6KyOnOA0BvAB4PedF17A6o5SGm3Mr2SnEsKAL0VX0d7\nU5OzxXNfifpZF1hBhFUahINACbRzoLiLkOEdQKGm/ZGxpFT55TKaw1plp7OX8jWgi3swgOyeai/n\ndzRF1KW/Z0PTolS32lyvYHvrqO+6Rj6l77V0qTo0nd/mL/x5X4NDhZIje2FnlipghgC82odeALDq\n24zOMqtuT1xDyxqnFBIDxo4BsGfsHXi0/CzImQgZRBCwn/DUPb3thPpdAQKbfxYNv7p5f/EmpyEt\nHFkj+PQ1+900ET7ZLBAg3x2vPTsUV22Oqs/pCsUdXflMGDiv1UWG0su6ZrlmCHUHLf34g0/j6/1I\nJEQGJOr/iyDGqoy1v989RiivdZWm9XTlZzoTTfo4a7KMWiTk4EUwBEVnxVhDgFy36TWGtZeDfQAE\nIDakVSLW3380peE0N/NedQps8HDt4W+L4+V8vPx81kSwf8/lHz/TAkBI66n2chAKzu/1MAdlXmaQ\noVRGvXVsR8ebVnx6axVvrSmjKexuBgkAZwGRNG8jlcyYCCn4UXjQEZLvpoU4NUsr/e8QVpzb3xM+\n+Gpz+wIRtBHKMhLrqQWN0J6E+88bAOBxbJrKUHDvxkTQKgycKjF0K6sYEZajy6bSmbx8Vz/UsSnh\nDD9+bHhapYbKrih/+9acGbH3eITMhH3fwijqQXkby1jppuSevBpUKfoezhjpsTxcJ7eIDk1jp47L\nUBmCx2jslbCiBRbEScJ4cE/hQk9Sj5z0unVsR7ARrM8FvCjxSHirzRFgo/daOgf5Zt5x02dmqSqf\nyqlGROVmQTMALloFANTgTsRHdHR3TpPzYKrkJVGgDSCxUnB23Mw0NSaCOAsTla4A5V3+A3Tu3nUD\nnVkZc4gEZjhODiliDsyq8zm/ddUih+/lEHn/879LlTzfYPbYeGOIClGTzTgrtRMxNtC66kJiXeTr\nnoW90rt1ESlY3ddnQe+recMcZUoBKWtqpWg7h9OVo24rdfKca+s56an/W2IhAHB2V448XeZ2soCZ\neQDK9xAYtK7xHu9R7m+Hgp/q/XICV3KTJeMcX2wKZPWWaa/hPPl39YsGzN47uc/zf3Q5tvdiLsm4\nagy4oK1XMeCuE6Q7i+dGHcxNgFB9jpRUuqxfN2K0ohoqsP4gmAhbd6ZDfe/YfpPOd920th8de2Ng\n0XcDG2Vs6DRXjSXk6SqY9gWK3+vW1SHtrtlzKx29l3Hdt2sf8Y4RBUPD+iB7LYZyyvYoGwP74n4y\nGGZ7054YOwZmECUGjP7sAGiPeXFqpg0wtZwS6OK7Nl8OeBqjf3MRGaX0EuWqFbnZDJ9TwF7tWZR+\nnvYqUgYFjWVLnfXlrMqrNWjNrOoY5wgf+h6UtKYaU6HnY3WctsTAID2fOeivWIP9LCiaGZQjqDsC\ngatSyMMcKGksi+097GuT2RiW0iPn5PQfeb/tVchpN4wxeCH/VpYpxvlwtXdcadV8tr0SUhyPO7er\nKO6p4gP6LwEFs7Di6vyFegAdbGnGoRvWn0A9+gggZKZMPrWyYErtXrHqvR0qsl79UE91JsZv+tm3\nGsCBV3aqfSgVLX9n0FGQmTWAzml97ta1YAH++ed6JaLoFRk43rO/c/tnaI38FdoXiJCasREGQb1D\narb2Rnj+rPjjp3hS92PDo1UcTHhYGUAt47inSMihBrpRJQFZtIo69kO079DImzrDx1HwfG4gAr5/\nf+L9N7E0LTLS9zBiWKNZj33D8xArx0rNCKhhIAKhJRADGJ2fP9u466bpFNbYoOcI60dtRjg/6h8V\n0UQwB9zo6fueEP0iW7Goidt1lOJbk/O4RHzl+8xjacKP2khzXOgF2MZknz8iipFbT/NxbvlvmUae\nacybAwnxNyshFWDObHrYseEIkMnRM5YOfnaQr1pOkxkYKj2LK4ZhHvmkY3/m61rLET4BpwJQKYUl\nfUTfHQAoNzFoiuYhzpVE5mZ5zKtc5rns2wDSIMbHKy7w2TGd72v1vowpQ6YHEs9J2Bx0mkecaNpN\nI7bmOOUo16x/shbaC4csgDYe/gM0Im3pM4s5TGXxfqcbJ+NLZ2/vwhExIHTQMjGWD8eBV3O0dWEf\nmUMv92Y/zXE2By/iY88UsrcI7Xtt2Mp54cpRy+yw2R9LzUfr2q6ex8jokTMJSGhlHuWPrYjDnK9n\nKU5VnW8XPbwB5ZuMS7mr8VrOzKWs8m49XoFMhYPxMNRYt+dQwhkvG3DTFKJWbA8VY1mepb23eZD0\nehdhsKB1696jn1saX4gxnieBpTIcDK+4ZCw73M7XLwC6VQxpFwtGBhIofTelWF22DzbLeb3Jn8nl\nzqlfV1H7+WOrmOB6FgCgWiJbYRg5xravjuk9foGwfypKWtJ/CHCN0r+dqJLYfPZcal2X4u0L4Gt5\n+QQgW6m+DMRY+d3MovAUmwJn61nfqTFKC90m63Dv3aPQ/h6XXF0lQLsceGm6/hpIZkCwa1qmY+V+\nwv4sgOe5z/Oh8K+nJsh14jtzJYbhuAsaeMeZiXCVttCH84/HrMCDdeqCsRJsjNjXTFY2nDNirKku\ngrEY+QD6XpyFaPPgVpszQA1xPDq0Yht5MAeAM4g99YvM7hlBWNOFispUEVPLzdJZkYAFew/OKRAC\n8tue9ird5WrP/NJE+GpfIEJqPeUOmoHcmywOx6Pgx483/LGLNSHl/s7lEy0LOtu7DDPOR/RTNl67\njkTAaoc7w8wi3mgbuinKZ6GwnoyXfa/4uW/4cchjvateg+k0AApwsCxt2aFbOeqcNlFKG7+h9B3p\n+4sNqU8R6PHc63+/PDbz+KwlQ2U85yc9/UWbaZx2PgN9ZhbFP9zS2F/pUlzpIVikAhijstYM9BhE\n5BCCW37dD7rIHb7L8FOc8N7CkTTnc9BEuLrdiYLeuDjzI7dZX+PlOQGNescmaznowzmVkjg4ON0c\n+bF8ZDh519dvrXiEdGQiXPf1gu28vq80B1btpLWRjekXiv/eF7/GeZzmsZsp83NzJzX5Dybyd2Rf\nahoc183QNZB3BmYHvEeElg+huzctpQUEy8nSPgCNNHYAnXydJJLv7r2M67QCLJJSoVH+EukxswGW\nI0KHOq43At60399qw7dNKK3PZJ15dRGMPpZUlYi5kRXxiys4sowPE26maVAbtqMrTdaMQVJAIdYM\n88MH4VhM78EJvEzpds4i+4i8nu7JQ/zxmWkbIPXF8oKRxmQQ9bX0gRyt7muWB+nYBgNmfJ8DFIr3\n5pRGhHH/j77rP9JPA326jV9aB6gsNsWL5k/YjIVXrRR5PxJaSQWuBbL8CsXPs077eIy1V/vyrAPw\nZ6nNriEyjBvgjIO0ngHybE6pDfN6vmm/sGCMIe+r53s0W2sGOoyplwH7871MwYIE8pQqcyTEOWVS\nl1SdQdiOIxOhEtBp3Ptjzcp2ZrbPTl1bNnkVzd5NaxTOn/2ZNmYFXJ8r/20GDlbggnx+NlgzgPC6\nBGTxv7fOzuhw1qqWdB82CISD7/vLTjgeAjzvzxqsYgWd5vfJwJ89r21mU2bgW5ltlAIDxpTMwRS7\nRi5D7SwwpHf0H32O/wwb9y/YvoZF2heIMDVbmA+j/R6Sp/18VPxxf8NPddCPLtTgHCA72FgM8eK1\nlMoQQnIMspxMTtfdsRQnNGX/nMDbd8nXPLTsIzPh8RQA4WeTPj6apFo8GiWhR8kb3fqYN3UqmeYs\nBf1JYYTn8o/eOqE39ijgSqDMD83OftxS/B0S0RKnheKcRxrcdBLumj+sopftKGiNnMJt19x7EWfc\nwBy9/Wb02dRK7aicHN1E/87nvGozA9U2HzYKqkLg/ZnGwp/BlO8NvRdEdLn7+OTnuO7QXO1hcF4c\nBAjTcqmLYOk2ANpP2UD3vQ45xq2Xc+gBcU92nNxTEjRL9OFTcyPS3oXYFLPewHT46T4BzTXUA4qy\nfUoFejfk/9M2vzenzw+GQXqPJuOuI6pUD0Hn1bnT/HXNiK0vAb/B4dcyZ5nRsYrA5khdZqQYIJRp\n135vrkMRaVHeBz/nqEBddN7mCJSnRQygGQKE0veCG4MqgRtHBOgJtP38LppQraegeZoWJ3aBrJmW\nP29z57Bn00XHAIDXm1+NRY7wmiG3FeCbggjvtwNv24HnsQ3fIRgrIM3ZPL6ZBZEqU8j4kFNfTSfh\nVju+bQ3PXjw/ujHhNhnAo2p3Aq+fCkqXcb3OlYP8OwoiWdqfVxlKx2T2zJh7ro5uXrfnPQQJjJwN\n+EIolV2noR0FhzLu7D3fiuSTb5QiuSXA1iHNprAb8L4HcTb05em2VtCbYlpp/zW6PdJeLfM3dAxY\nQU1iU2qPsZgBU7m+7nUOvqS9LtCV0/dym9cGYyPkqzF4CQj+43nzhgjw5cmMwZTHHBCwxvSfjJ00\nsPSAoDzYZ236W/4u4VRm1wEx2/N6AfN1VRb/no9VlH1kkNoNyflLAMLAlNmkr1YWNI2GVlqRD138\nlEIYtRKjEalPG3uBpe2M7ARluVJ6BzG+k/+sNrABLlgIq2NtDzilQmDUPlgdE5/HcTPzILQSuov+\nrQCHrKlgDCVz7vtO4F2YCJRK8MzzsTfC476hd8JxhC10tBoMED3W1h+r/gKEPdB68WowVgIUW08a\nKrGPz4GkuZmtl1Mc4p4/brF2f+47f1bk8qv9ddoXiKCNIXlSnRitwysuHK1iax3P54Y/njf/XL4j\nC7ttJGU4n7RsB8yvfwewq4ATM4EPGgRUtq3jVhu2TajW/REO2PEgPO8bnk95hK0X3BVA+KHGlQEI\n9064W8pFU9rbIqrDk+FokahRXGn8u33WG4EOdiFKESa8jk8MdpKfW36eHOVU8pKPLkbatCtyw3Dt\no1XsreLQ8X22Koq5nVzYJjd7DrmMITO7c/voUc7zYawOpuUSOhvRPd2bGxFVjclHfMYMtEdxJ8ep\n5z2lvSRnbzWW574Y+BPzkzCOsZ2j6u9ZRMoi0v0hmysAPH9U2XT36uVNd3M+e3ToFbsl97vxBYgw\nzFGbaxn4iPOsaH2Zmn86tdKT6Q1eKuwzugMujmgOWy/oe1FwbTzOouPWV8ZoX88RpuE6CHo1oIyH\nxaEOfhg4YjXKt5FNY5FX+yynvgDTs+DxWdg60Pks5JW9E8vxHqozEGEFbvm62dkBGLlHcaLM2Ww/\njT0QDjY3ET7dlQ1m/WYFAfL7JD+DXcAs1zGxWbsfhoJZjWATr2iaSBapy2Nqkf9bEcbDb5XxzbRV\niHG7NQXkZI3O5UNX7Bo+gPaIC9SZ3jtExeRvW+l4Lw3faohmHsyondCS8+jAmwEtNubPAiqiwt8e\nZsgWn+eznk2e+1cpBcC4Pi3TVQD0Q98fxPrWdU1lDqcSRw+KuLZ2FDyT4W73KCUso0qGgDZY0nln\nvSADxvaOAEc7oe8F/dbj+7Mzm++zAfyUGzQHhHUxCgAlxAV9D03n4FhcgKODb0lgcVB11bXdnJsX\nTIS52d4a/8bQD39+07OzdSCDm3YOA+rYAEzd3LNY8wwmOyi5hyAqbvDUzmGOLZgtpzY5enMKAiP0\nqloTJsBnRehmFuKp+oquFzRPNgsMFXbHsICdFeUgQokSsDktz65g1zZH1PYIwDQRePgMkCn0mX1t\nVS3hz1Rd+Ez77zjn+joBJMyfD7+rDeoggoqcup322QAAIABJREFU88KwoC0FU3bC8ynpw0cK+hyt\n4KG2ppt7ChARaAB7mSVg+VA7nojResettwCu6/V4zRhj0Wt1fq0HAliwg4c5LePz8fP5rEbTX7Ux\n/vnA3P+r7QtE0Dbjdr5J6sb3PMRhWomfRZTqnIecnYe5ZSenN3GWh9yq94bf8MR266DC2O9qJD4r\n9l30En5qekVnwuMQxfWHLoYm9PhQsUcgRKcGNsCFs5cdN1sgC/HpXtwgaMnhd+f18xoCc8tUR7kQ\nwhibQYQOdxT2vWLfZWHPavT3Jgu7C04Z1TflP7ZWYJUeuEeUonVyRXtLDbkSipK/jb+vItzMQH/Q\nMFZdq2vMUY65z0B2+MeTz/Q1qU8ftElDxUcAJyn8p7QAy23uO+H+uzJc7htKYQFpUjWJ2fl87WQk\n2qQa8sYUmGtsi44BD+M4gllh/AZd+VwMSow7FYr8pp/dCPz8/G6QI0KAvrcHEuU8ALnRSaBwkPw4\nDE54jgIY8JTZGpYuMuZCM4Ya1oUlgt3h+be1do90ZCVwi1K2vA4h5u6sQ5ENZssxt/uV46SUao4E\nGwunsIAm+V5faZ3Y344/dCyP8ZkfR0E24l20NQGhs5infdaaiL2OVWJImQgFlKJbs8p9pe7O6pvO\nye9VhAz//dbxXqPCwe3WwEx4O+SzR6/YignTxfP2dY4DCGUm0NaUwZEcLR7Xlltt2GrFW+941yj1\nvZsIVzyHreTrxNou1ysot462Bx23+RzW8YECP2nCxjw+L26MuMZMVXBdh6PoWjemBNq1BybCRoNT\n2VrBs9VB+LRQpM9EOk3sW6f0HL1WNuobS250vHdFKjQc0Z2BfXMCEtiZLJZ+kxkM83hlJljPwNrY\nUZws/dyG9cD6tabyX23Fn9miV3o9H7U5bTGnyAAJjD2Kg9T1+zRuADxN5GLNCAaALDaldhd/9OvD\n1ojozyXITKOorqXxjM9L1gfXTaod5abr70D/YNABtyvkYNk7auvBKlLtohtF9ZVKAYj5Xo2YEhkI\naiygQX5GvxonnqtKGJAQ++74+WdaPpZIBF9X35137KtrFJQTEGDlG6PKWhmOt2bAQq7GdgqUdQyM\nsHSigY1gGmR3BREOtX/M7szzpxLjvYx6ELL2yBp412NrkXS1Quz27K88RSkbqlwjE/Fc2BgfNR+P\ni+NtT/9qX+0LREitEOnClZF2FThUilKlMCXcsdZPIu98NLSW+74ekp3X3gj9mRaereP9twP1JgDC\nzz8EMLg/bmhd+mSlqABRgBX3LwyyvYvq7IxY52b5lJwiVHPeujkffWEAAerEZ8PR7zPKLOZ86YHm\npWOY1eoHNkLePbvulJ9YDXuK5h86DvlbMU7l5HCYoN/qnNmw9v7qv0nvKW9KK19ahCjJnQZADI92\nFAeTnHaJ9ZjPv495r8lZtLzVPL5+r+P5OtNgfHaW3aLvBc+HzLXHc8P72wErkZbH4qp9JtpjtbRD\nnDOBBlPVEDnn4hzpHvtk8eaUBacpKo27NzoZuIwzaGDXHSKnquBum35v6qhOzhH7d61Dr+9FKKvh\nzBjQ+BGl0e/XARVezuX5Pq2f53zgoH3ndZEZYAoQVZzrPgCH4tQRGvHS6Mi50EIH1wVBjbjnzypa\nCbm0X2EFAsbxXd5bJwwUei8xd36ufr9prtV+DRS+2w1tYvj92+1wlpMDK6W7wOKmAEQWv7Jx8/4l\nUBmdgESBNqcnAyWWK3sbcqsFpGgEF1usChplETi/30OMe1/7W1T1GdhmPM7TAWDIn+vffBwN/MWY\nltKUcTUwEZABfPhP2mgokZgdQBs/Z8Egr3/jHh2MMNIUtZFeLHMcweo4CtpRwK37mHERkG7JF+5n\n8MBE9mbQ7EMafQegzDsDeOkCTDDnxhlJaa/N6TN1gQJcRyp/ETG4aLNNsFJ2H9OQ+HSAAQhXwGME\nGsZxOeelj6yXmX25ahbpvVovrMm+os+bEM+fKQG90b8CAXqLpzNYtZJIZyiLe7CWsSUe1pCp/6t+\nYrTr5Is6LhP4VDJgn9PhHDzOzn4IQdbUr5LshE5AHZx4ToBB2LMdDNHMnJgDYBQ9rufABQe4sEoV\nFJChgngEEfzvli6S5hmlNShuPPdF7Me9FzyM9WrVptJXbkWqQhQK0XU/B2MoLVow2dm/gATJtBv1\nQOzfBTTMpVlYMfS10p5HOvlT9KP7eWmRNPE3aXxaav627QtEmJqox4YRYTT3oxXdkOWNFlE5oUDF\n/mX0yAQOMHtkcWWAGY3bQITnY/Oo4b/+2x31vaNUoP1e8J8/JHxq7IPspBiCnMtGGTq9M5zCt3eJ\ntgzOdNf7SSCCGVmtF2EfJEPAo3bJIAOg0cL47J9Vq9Yji0eXHOmjh2PQOaozpAoL5xJPUT93LqtV\nc+TR+y8/M9XQyv59hlmRRY/MyM5Rpg4CmVPgOdiZiTDqZRx9dO6bRkJmf1LuLQETdLZ1c7M+HizG\nDkNU+0USPY19h6eGyHlfOKVp4/VUmR5GhEU05yjDKVKohnreREPUcwTL2sVGOzOGXKAreUD9iRDq\nS/Ttg0e6c6buDloFncBcBiaMURyjpGKwgOy5iq8hz9mcFzv/BnNm5DrPVnEcxcs/ybVtbPP9yByb\nBTqNUXWi//MIbDhVPTGOmkZVJLoSfZc5kxzmF+/FXE6sW78Z7jx4ab/UmlaoaS1KPW614TjGaI+n\nWeQoeyNsPM4fj4ZeAQhMbkxLNKh7elFuzKEb8ptGF7/X7vPHnpdFlWRM83WyA6DP58DA5BLAKI1P\nCwHScO7Xzp441BiAhapRqlL6pLejfWoBWGUNGBvfxsFoky/Ij7zf7V1BIy4B/KnaORWoKKue84KJ\n0Fn2wzFPy7yqGMu5zKelyOS1YaaAG1vK9ni5RztGUhn3DmebPVsVUDc7FjAQOD7rjUAOkIwggqUZ\nGHhirJohLYpN2BOexsHPDqokOYhaBYItbDrpDhApuDEt9pFWNH6WNREM4GRgXB8UVLW1hdP+PgNI\nw2srNDN3+od1yO4zOfJmAw2MwwVA40BC3qMN0EnN9pzcTwAaTCF3fg8VrT5VyWJxDDOLTEonsjPv\nqJgmQpyftNyfVJSBg16+NlsaDDRww2q7vFg3Bw2g9GyWmgh9FLAFLJ1BItPZnpCfeT22YY7PRmDw\ner+/+ttKH2HViAirig3XTITr1ItXOgjEa0uosYAOR5qnNs84peiYgLJXCiELHEpwwtb+mYUAWABN\n1t5HYrPY+t0Hm03XuCx+28ymTDZPsoWG7Bka7dRVlZI/0zI7YQXCfLW/Z/sCEWDoXXKSEfYLs1RI\n6CC8lealAPeUN+qOFsJRMLTRHZ5xzz8Z3AIiiMDKz4eABN/2Hdv3jt6A56Pix1OZCG3DVrobxIDU\nEO9EeNOSX/M1wmCNfw9CW+4MnY3THKHNtLZsyNo5fEwTnXJmNVw1z9mnse9ujGpkBj0NMIvBUm7A\n9qa0wFtT4zSigDfNfWSEJkJV9fVbihY6A6KJYe+1fx8Nrcr3o0Y2YVaaf9U8yiqDcUKYLYIdIM04\nbvP4MdMpamAtIsHiMNSUzlBLRp3XbXS+hSWwJar223vD2zMir76ZfgBMZ+MuSk4GWyKzDz5Kh8hz\nem6Zbpd1IYz2wibc1wFusZm745wABH+XYdHKxMAwBx2EfbfoZQ2qYjL8DGC0flfEWjOkF7FgAWK8\nmmNHnqv++smNhrvcN+s5ijtRh96XgVEOikwAlPRHnK+exjKcjvHaUUpQfy9Cpbf65a+aP6dC7jSZ\nUjrzWRHdKMWn+5/6lOdUqVqmTlkB1qeV6GADuU5DXtvmXNMc/X4qkHQ/NvyLOin+PYRhx4i+Hwkw\nNLpzbxgcfbkPjrnqeinhiM/7yzb1cSssdOlb97Wy3hhQhk6uS27jmB1029vifkanxr+jc3x4Dl2c\nigyUAPE+5SYOEY2U4kFTQKjjtvbkCh37tP8e2olnl9S+I4+bvwNxXbtH0705Wg3Gw5RmdWqdgoUg\nA6Q/dTwXmgUzs2+g7HcWQOFILoZVt0gDTEUBBI5nKKr/lnqT58G5D3P622fah0D6B/ui3WLW/glA\nBqgbRFDVFKGLgjaAgzKnahnpxLOI8BjAIT90uadOa2HYS9OzmsAHq/wD0jUs9Y82oKSUGOt2L/Aq\nOrVwMIam8cpBCQejETsBI+zMxsmOwXXLzrulN46pB3HtVxUbrpx9SwD57PHX51GQaXE32W7P3/+M\nFgJw3isAOFttmdbQAxyqt443rcCDHfiZRFvmPR2I8tWV8jGk/bU5oDpotaV1PwCFub8BnNpzZAc3\nMkNrfl9tejLgjE0B1LS3A2Cwllspv2D7/hXbZ0Gyv3r7AhG0FZD/l8VGRPG7oIBx28ZFyIxvr3zQ\nxXjZp/SB1sfFfJkT6DRbwuOI3PNN82rvjxvuLSpDvBVGQ8rHK4wKBlfgXSPG74VxpzD68/UzJXsV\nyZr/lhc7njZZo1rPtEX7Wxii4fxdsA+X/fDx6hBxxWeDhZ4lGkGgLUpgvu2HR19zxLimsQLEwWYm\nbDVqNZtqd9OqF+Y4v98OdHXq3jVK/5Ow2Kpy3+HX9qiOG9CkTnMwKNqzDs6sl9DU685CeMJEWDtS\n1grJxjRvIlYOcYiG6rORUlbyuUVb6jvj/ZvsqETA9t5xu7cAaRY5xxY96hMl3ByHqH+dSHGprF6O\n8rnQmZpY2QkLZsJ5Mg252YCXD2sPOzew35OYZWIihJMxGpOCYSVHSBk8R2ItHa0OEdY9AQg+nVlL\neLH0fWYqZcbDoQrOUnJW+2NigaqlYfdXRIAgAYIy/i05Ubv2jaGO3zw/ckoLIoqXHS5AbeaJNjk3\ngoJWg8NkER86CVIC8HST7a2hPisKiWYGEADmHE0twGkNMqZGvi8DOLMApOs7pLlqpbdypRSe3sM8\nBntinlRi3B83vG2HR1qNvWVOt7FMdg7hx+pVQ86AXCnCCMu2W9NrPns9zdNsPBaICONWm4ypXqd+\nZ1AVB3dkuZimB/ycFrG1J2z/yuCCRUhZARhAI3iNgV3eu2Mp4Bh9Z0xRXotoJ4O+bt3XZjvM9t49\nAQO291ZiPNL72PVdzywmYwnlMmx7T6k0Rk835yo5/Mbm45mCBriVFWkp6d3UNf5gA5MCPOFDWHeo\neczhnvGpEgMBc86QOQuUfg/2UfTBPsvCqVa+1jyIvmc7INrZYVo3Y4nkJmvLmK7EB0D/sjiBAgk+\n5slZHnIL+3n+2n2GaxXPoWmVreiTrJMx92Nu2vwpaiPOQIL3YWUYTN6YCVAuRVan33vaL1eN7NYX\nXZG/U1qnoboE8L2EmQcgQa45gglApDBknYT8uV8g/ZL7bSkOBdNc0JQIS3NwcGMAFqpf2ziRv1Id\noKQkh0zD79rlYX8y1kuJ3w1Y8PNVxrdvu+wnkJLqgK6Trei9y7E7EyrOgsaWznVLwa7breF2674e\nk6a+zak42Za2OzO2NCddDV8DaAQM5Xf4ZCPIPBHGro2ZMAhzOVCGsc0IFbcPx/2r/bXbF4gAQBy6\nNapmeXO1MOqk5JsNK0A3H3cUXiz4i0tlw8Lz+I+KYxc65VA2Rmmpef00td9K4SybETlfbnn9BGLk\ne1+NR+fZwLxGJLPD5Yb/5dGLfiEZXIcYVmipzF3yZgwILpsYmbV23NRCbDWuSglMYBYnuzp7g7W+\nvDozemu30vHUaH4e37ldlU/MLI98xMrpMoT6lNvIcf4VXTNff4xKWnqH3r8ed5WRYEBCbrQF06NU\nRrmxlJpTVsaQ5jHTSwfnODkLn0GyX0yWLAb4qq1sL6NUZ0HTsxbAGDGWrTz+bX0wEGGmg//KXLfN\nGxijvfkdW1HX47P8JTo5GNYy4yDokNP3pzZG4ePn1dPLnxc/NozYnPI0AAidXMOANFxTb1LOsPcM\nInxi3sAABANXbCBpyBd3YlD6XgYMWi+eChL3Z+CXnxIVQoF31oeCUC1RWy2yb86HjbipdHOPSDIn\nh9AiX8RnbYvMcnu1DhdlClVTkDfWQQXKG9CfWDozwzkWnzkIxel3HZsMBlrt9XzsVfPxcQedtSRu\nHGMl/IypItemxBbSZwQBElqx6G082yvHN7MjclrZq2bvoJUXHE84/Ry+A+/7DHgaeEKJxshderYS\nF87NtF/k3yO1+TMtp7EZu8LSBD51js8MGkYn3K/ZYj36lXzwV8dzWuPymrpiIsRcDqcJGNc1swtz\nv+UPFx1b6Wf49T4e0MxiskapTxIsiPPmKWcVcrLt0FWjxo4zVkIuD7liH3jabBoNAxT44nsSlFjY\nSuNQjPd2keJg53eAIw0so+MVzT7fhx13JXgNjCChLCw6h+y92hjbrWmJx+JAwEYdGxlIn+00GvCl\nPPfDJhWhzVI7iqVN/AnZAWHzGhCja0CyYOY9GhB8q+h66ixiBLjgzKdPriF/5WZ73Ff7AhGGZhvv\nbEQ0Lvi2HdhKd5aARTEOBrSiE+7NaJNBC2pshh67YbNaG6VWbIARAPD7400jiAU/9803gbfS8G07\nsLcQBHzbmvcrSo+xO4zWRtq0GQpnB8U2tz4da/+2qLxckzRCOt5Tjvb4cWpEeuTvBZbsSKvnBANV\nS2gNxkIfF7dy66i7OB01IbymiRBaCHLtWwnRLFvMexenJ6iGHbfaUdvZ8Z+74nQ1mwMG0LRxgIgI\n21vciwENlrvu1PMeTASP3kPn1hQtNGDrQxEo/ZkdmUpZfTqcIT4I5V8Zb/+mgMFNNthtG8XcAIxC\neT2DHdl4H6OPjWnIwc8DGxG+cMRWi/crh2DIH9dzPn9oVEOdf6vzbICX6SEccz9hWgXBWLA60U9d\nGywn0soIAvasFhGm/B7w+W+Ra158jahb3M+rttIxyToSZiPlMZ3TKoCgfg8pLlBw4E8YFDlFIT/b\n6Cw8tLJ963jbD9EHUMf3eJ5ls7KdNa5VI5gQ/eeBRQEHaeL7rRfU1odIZc51tWdryu33Fmvv99px\n1/nw1JK7BxccXaLcolMTz+JQ5pM59+UmN8VHSjMoMjjcCc3C5xodXuXhnimsPFbyAFQIjkCd3WjN\n7RUwwbA1fqxw0JlHJ6sJzV+0EYJ1sT6naQ0V9N06AS3vm/pdzoAKYEDNdA9QICHPc8Qe3tN3DYTI\nzyYcPfeSIuI9gGD62Xx7hxwf6yL5+5gZGJ11D/BSpgw6tF592mW8ZGRy7oPGr120/Uznue1JElgY\nn6ntyYxw3GTMyJ8dIMCrvQsZw/cUL5srnR0MxLQHsAUSkme+z0yEnsbYDrOIcOr3cBvTmK9YkdZX\nGwvfZ3vkpJsegrDQxr2rUNgq8vh1PE5XwvTy5fc3fcrz7x/v23btShDh1ASOwrjnGAMEBuTOqWd/\npn0SG/pvbZ+pCtGV7ZDbKl1C1nsemY3KwOEW78PAeLFl4AbcvnWUKgtVFjlnhJ0DwEthtxR42Sj8\njJrSaesmLIQM6hqQ/an5QVgCMHOa8K/OCf/uAKj/CYTjq/2l2heIAKOCRTpDblb/1V5oy3k1OvDB\nITb17MCzs6Tt6/dzRMYUced8PcCMefKIEQD83DcpY6Wb3Lcq1sVvbzu+3w4XbwOEqmoRLYtxWdR8\nyBuj+DksUpOx6DS7hREZjkcY2+0ouL03GJ/O8nYtImvfk//yIqSFfabFzTa+3DdjIvCRrJhBUcb6\nLv2vtbsBXnsHMQ2GNhGj6LMN8TMBDq6icqtNNPc1R2iHqFcvaFNiWa0duTRlS+UdRdhJjgu2SxiA\n5hzn7mSK9yuU1KOwFM/Q6az6ILYS5+yNQG+M7ZveWQX4IWW0IjXkesxsDPzfsH6yX8Ny+WanVBzN\nkvq5oKmm98uVhuFT8RQp6g049pg4rVnpvIXA2wTSFBLKr73ze1uIwyVRurk6w1XLTIQBlLI+slG8\n12lDPhZdXtn5fV4xXl61/H13Ek5G2XVk00ZXNBHkWZepTxnEjM/kPXdW0Ttwaw180EkAMNP1hzz+\nOaUhRZPdyaJQ8sd0Hz09s5zKYK3q+uAAT5Pe/HEU/Pstnl7rhL1lcU2ZnbOQm6cfJMepiCy5iuWZ\ns8iRyqL3WB8hohVaDQY4Rp8J47s1pgqwi4bJ30bAaRjL3F/daWYnyhz5HM3uOwaH8lVjJhytxF7S\nOCoz9Jgzq/fAKLor9fkRUNTKRYu1khG0fgPv+oEAMUzMJH+nE5hU52bOo94w0tgdPMsgYjARupVm\nMxZA0lkg9FjYBrrZuU8FVm412HO3IsLKdp+vGuva3zVf08SkRx2HcQzkJ0cViatzJ7Da17aJhQHg\nrHvwCWbCK/bCiUnqAKnZKJRSt2IdsEvz8O+xO85imFmSL/po6X6v2qnChH2O0e6oyrbIQAmgjikB\nbpkUVhHloMF3MEBqP07XqwlcAp1vK+yd9efS1/GkA7tBz10xaihYrv68c5YLJCPblavqDPnzqhPL\ngoaUHOqu4JkJ29q9zfOqVKC+dRAR3tqB78948Q1c3dkA5Ehj+00fzjbo5ehY6N4yl7o2e/sygGL7\nIEJfKu+NtrW4VhGJrVQplqZaKOnOUPq/jIC9s85w4jFF5O/W/jcAav8b2heIoO3ENtMJYoqrlSRX\n3sq47IbIcyw+jyYb9MGvJ1gRcu/ZeGFCKd1ZBT/um4tBfdsavt9kkfrXb08R/LsDvI+LsylOA2aY\nnPfddYoDD870Z5odySz5rze0RAWLvP6PaD9zBYE5f+sUpZhvyCJApoLdcvQomhkrThtP/c+taJ1p\n0UYI59UcipUInd9LpvklEKC1gqI1gOWetQ9aQkzus3j+v8yr8TnmcwJwZkX+3Y6c86Pz37Lx0VIf\nV9cxWjEVAt3MCBEQYdWu5n0GGGajgEGoygaxcmjeUXum05hfpQrYBlcXRrXnMh90eg4ABscpAwjZ\niGw6FlmrYI6mM8cznFkiOSUFeOGE+/MxQ7YMRmy+n5xX68ZHZ4zVSlRA1KMgHZUIG4nzM9OdiRgz\nkJDvZ/WYGeQ0Sn8O+m7X5NWu1qThPJa2BKHabx3oqTTgVTrYK+AvM1laOwMDco80RNDn362JcxZR\nyadGlp89Usm+1abspzBuzYlNwSwdj3ivvN79Fr/b5Zmhmi2MqsqBtyq6JLfSPXr+KjZkKSSs61p7\nMIo6+c6I4nBwfY1HpAoM0dgX82Gkqb/eBAYwE/ouJioxFQEycQ+wyNL+wkFm3Ai4lWQsF6Cy/kxA\niz/bi57lOWBlHjermsBq5CPArix85k5VpkIj1sZ5fsUYYHBCJZUBwDElR04OBhW9hzx/pr08++K/\nkiGwYpYx8v5yAY5eLGyn9AGL2LYE9JmoqqmDAoOo5q+0vJ4XGvvq2kNTcMH6lNd9ACgcew4Bp7XB\n1lm/iKUhqQfHmQVUsBTavGofaSJYVHnOaddLYRRSVkWkdJwABXJjeZT8khRjUQlDpLuYjWfYlgWx\nErsjm0qmfeDpD6lEZO531k+I70pfVmyEAez4oM2gBnPYUt3WyB5r98gKGK9hwr03tc9bJ7zVhrfW\nHKwAVB+NCTcdy/fC8c7b/TVhanF//ZbKHrLeSwoxmMirjtnzn+0Os7tzKhrh/OoyFFA0kJrtu6/B\nr6/292hfIAI08gDGrjy6vQed8ehFKyEQ7kfFHyZ62MnF0h66GTw7u7hiXgKapjccuvjsCmEP+f5q\nrNxujLcau8tDS0veSsf3m9CmrGIAkK7DhOdR8cdxww+lz95bcaGpEIpip3aexmEyauyzU+k9VrX2\nFHk1+j+zOcQhjjXkwfO5BKaJT/o96UZs0QBf5BqBnwx+T9atAqH9GWJ5x4NwPKXE2r7rWBzC6shI\nu0WpWg3q2NsNoNJQt47eCh7q4D9bxf2oePYayt18HkdWxDYLpx0skWpKdLdCjNY0IqZ9PI6CvVUX\naHPRrS6MF2BkOcwq0lYRYOc4/rBIGoeDbNG3mZba0veQPz8K+vMIZsjOOP4Ano/NmTl2j59pY55g\nAjqq/UN+XOUCzkCHCf7lMlYmqJaBHokAAm2POV0quwHBPCq954oKgPytQyInpv6+9+JlYL1cq0Uh\nek7lCSPC+t9Y8lIbR3k5O1ZKRnF6b00X5ZyjPQJmayPKIhyblUlUA4IwGqheOipF4O1dz2PRupSO\n7WltWbFDbI7ZGmifGaNiLAGna9Ae50GBsxNMx6JZacDZ0HenfxwXAQNjXTqUHbAP0cY0L9O4hq6B\nfp8lSp9nei7z+011G75vB95vxyAQaNdkphxcHppT2Skc1LmVysP8vdWOrXXcVPD10cMgj2oQkVZ1\nPIuvgb11f8/2h6XqZd2bDIBEBBaQgLulpjno3oFe1MHyyHQa1NRaK7AqNDYcWZQ4GGh6r2/x3raj\nOH04p6dV1QU66b9A5rxfW/fvR6dxb2T5mdMznq3i+djS+tRA1cA67Wsnpz+dHBwXStSxNMYZZzDy\n7JTyIY4nKqKMYXLoB7BhitgbqyvrIfh5EYCW3aPtCSMTi8br6Lswp2nNqU7STx7HZv6TOy6yL+15\nb7xhySZgZYPknHUqGPUyLM2EwwGUa4pRMYA2IBCfSzwaeHYiFFCMT05nuGTg+cbmF1zfV14r2fb2\n+NsM1Nk6nFPtRACYh6CWHBv/Hb6/xD65KukY69/5vvqL73328xkMyfol8/FzHxxsuNjnPiu02I0h\n0CuenVUoUP+mKY7CRLBnmDR9rFRrA9qzoO2EYy/oPQyWWsb3riDek6e+rB1dbcVgq1Gr2PeCuhU0\nrctOBVK5Lc1T03SZbRR7rykB+UvdLTYgNgeS2NdyfyZ2HPMwL+gLQPhUWs3foX2BCAAARuOOrvK/\nDA66soIIey/4Y7/hd3XQn0aDS7itbc7AuPjlz3PraaPrLBuplNLTKJNe91Y6vm3HAB4cR3EVeGs/\njw3/uVf81M9+NMK9E55dKkcA8M1nBDAQpcx6LFIGKhSMEYTWi1P+5JxnqrXVAM8CL8B5Y3aDJo2L\nIZ3u2JqTthPKzih7nI+IwI3RH8DxUx1D7n0WAAAgAElEQVT++4Z9r9j36obm46h4NFEwv6X8M+u/\ni+KoIm69MSgxB44mAMLeyWuIzznIc7N7Mke+pN2dSRb653PznGlAwApRW48NKUdF7FNzRimNZWfy\nCphZXdyezWoOutPNhKMz2mRxNtbSo38A7aca8D8J+73ix883rxjyaEVSazp5h4xanp3cMTUgR3Gw\nVKSfx3IGHuYW0XmZR9lYBuA52Z7msnXsz6qOXRkMNUP5/TmqMVaQqi50uW8iDkFUDifV1xEHz6Lv\nG2WjMTnZgIML9pkBS5n18JEQXm5WO9pWKzI6P8ksyuPrDqQLRc4ijPCoNCHeA0ZE9oZrL/rjdGAt\nbSvXkdzovhP6oC5JaDthf+i7/NA0ryHlR/oyzjUFN1sAdZ1JAEUr8ZfeHac32/vdCURlKa5XUioE\n9F6+V8a/G1vs7Ym3reGxbyfmBE0pZkJBlUFpD3O8RYE8Awk5Imxtqw1vteFbPSK1jQos992ftx5v\nqWe+9h0y5qUwHo/QcDBmUp4XNgQ2BwqzM81mVlYGG87RbOtLpLsZeJmB427G+g6UzuJM66A33fuY\nAyipxMJGKMFOMKHhWb+jsayxRwItpM8CAuf3+7Fv2I+K7Rl7A+fqQHav3Zzu2EMBOD25GQimIFbW\nC7I3szVysIw2Zd9kBTxbHEpoFSzzn7TNUUgiYTesnu0KVO6tDOkMwnQEZq2XzgjG4CHmNTdGb8k5\nTQK2M5D8bNWBvgzO+LTqCiC0BLRJzAdcEh18CzBybsYCMmpP6wQUXVdtf9U0pSPtl6ZHNIBliPlb\nJxrlKZ2jw1OGRhCIhnd53iNzG9JH1bEb7E3Ecxycd461ehb7/sgJeiVu+Cvt2uFf2IUv+vQrAMJK\nE2FIoUjnkjGM9DSrNibzUW3FbilV8ZL0B+H++6bBqk1SsGA2l6y9ntan++0x7Dny39HLoKdwe26o\nNfpaag+9rGn/9XOkubpqr5Mpoz/Lz7+c5a/2on2BCFOzFyYLKxUwnk1YCIdHpCzHSFBMQGhet2KL\nuH0/xA0dGbx4JxuLsWtgwbetwbQQ3rfIt3o8N/nv2HA/rOwN4d42/GwVv+tmfO+EH4ekWVi9bNuA\nZsf9V9oq7cFyVPtkQEnelRw7lE6eIkV5rzrlxVuks0m+6FC7Vw2sUVuguDNgDsHBUgatc6o3r8YU\npwhDLpVXkzbCK4dtyD9LGG02ChoTSjJWaunAUcVAtbJwpeOYEGfgpY04NIu85wiXO5+XG4RtSjxE\nQGz+shqHz98LDmN1/LyhtYLfH2/4fZcl5EfLTq7tktcgy4laVxi0jSrPua0owPkwqwBg83Kjc2qy\nncecpvjMgLEyOTNjtMec+p1DE6Fx8WhUTnGwaF2OEJgjnqPDThuf7rn7dxD96Wem0JWw4UB3TnO4\nOhMhKreUCyMh5wnPEY9gEwX4cSz657RJSk6o3zOpwR/HW754V2fa5tz+rNh1rXs8N2UT0LBOEwI4\nADAAoiZcuDcBFh+9aPpZzKUo+ZfO2UfWFYATjb7q/f37reHf3p4AgO/vO97eDjAD23MsgzWnd2cH\n3BxNZgJtXQBcK7H3yOtgPGB7rpVidTCnJ9bePN/VGIaB0RW1dDyeWTS4wEQFrY99el5dc6izc+XO\njALL0t8CVm+ptyj9+HTW1QhIm0Hs7/xTBos2GjbPeU0rMIZNaMdIyTNdG9LquScQaY76ZsDEGEXH\nUXwO3VQ/Yq3fkgDLLvdQKoAW+5Ox9I4e93qok2jpY37fel/Y0gArmMKWXrFiIhS+fK9t7HJ+v516\nYAgYmOPCxsX1XmZwVPoRAFgwJ87pQHM/BLzL4GjcB/zc8H1+BG50p6pxXC4rPLCjoOu/sVmYBLhL\nYKTtn9lhy/o9uWKDBZEsp3/W3hlQxouNewA9LbUjrfu2p/fkcpvTmBkd+Wcfzh8BLdcJ0VSsrFGT\nKyzk9SmXgJTvmu2bvpv0aFafz9exag05mh1zde5T2BMf0ed7GnDhOI3wdX7/87mUGOy2ECcmgt1/\nqXCj3+bffi/48eMN92Pzks5AMEKzQ/9eGTuPTJjMNI3SwB17q7gdzW0UCfKNpUgNuJV5mPdWmU9R\njWFMUZjt76uW5wqzlnm0a+Tgz99YWPFLE0HaF4gA3RDQ0FBRlFb87PGCAxLl//2obrQCsU8E2kho\nk7OZUf5MFyN12jL11iLGTq2vDUSMb9uBWtiNvJ/7TUQXex3q1f84Kn42wk/d9O8NuDfCo3MwEXo4\nQzmVItgI1u/YTBunspG6qDiqr8dSYS91Z5+t9s2MoNrYr5gIHcaaCKNeRJ4K+t7DgFusYRYBz0I0\nlqO+auYIynHqCKth5syE0lGpo+b6uwhDMu6HfXEPdHhkjABAaxWlwYUzrY+PXj2KnTfzmYlgY1gp\nGTZMHi0P58pE/8pkAAWglM9nEUgzjMwpe/y84fc/3gEA//l4R6WO/9pv+KPl1JmyjDrmNBljRGQw\np1If8ug/ajGHwgiWfkeOp+mSZGM5G+h+Li2fuvdQzrfv76pHEs8xGD05zcAMANNLeTSJcmbn6Oj6\nH0eNa3F8OYEVNuZxjdFYJ4/WA0ApYfQ5+EVmDJ5ZRfJvG58kHAlCnRwO7uEEHs6qGO9/72JQ7D4+\nATaMEU0bO06fkVeasL4FmynAg9//6x2dlVJ+WB3ugvshQIAxdh6qR9AyiGBzIIEbEvWR7z178b7n\nueJVUbiAOCJLgJaFVWPfxEffixjl/347POWs1o7t1ocKJnbvNjZ5bniqjaVdNF1rb+yR6eOpa9RR\nsO/mkGamUay9gBiKRuHPWhdEHBUfmgLAFPPYqpTMTq5T132SjHRpuac0nw1E6DRS+S0FKqVu+fvE\nyQi3sdi7O8zmLNck6prfeQP95nQ5iVOOQJCBfAGws6+NPs/1/chpKazOJ2paPw0gaJzeO13vmryT\nJubqlWA49ItEhE3THAadI71v7Q910Y1hMFhBBsutv0p/GfZbfVbqk/u1Q/9Fx7IvQI0eFWfGdAZx\nCN2oNoc/iXXmvtj57doeXU3jVjSi7/nnxmBJTBZ0knSPHkBbgVzX3vmcsmFdcyYCF3RNGctMBIsW\n5/fTQCl3DEvsrdA0okEgtoeTzAdrkCPtjVZaOK2znOZuBq6PDn+nYszH1J/GIehtqXz52JbS5Tpy\nOsPYMvNgJFNwOub6s/nzz6Qu5O8M179gHlylLAzXVQ7hqiJDbo0rdt2TI40KCnwmgKcpkJC/exT8\n3G/443nzdUzOmcAnPbYS470wLOUEGO2tdeBEj2tF1/kz2HoG98ntwmFspuftDJX0GcP8FZ7WC3aW\nln33s6Viv9pfv32BCABkWdL/WH6acSpLEeHeKu45+qN0Ka/PC2EhNJbfsxL/KmJSFN3OugJEwJNj\npdo0jeH97cBxFPzXXZy4/3i+4el57tYfMXgYsdk8eug05HQGywF3x65LDrRFZOU4FRfqAmqE4Rj0\n1XxfhVic/EX0OVMXM6hin83RyE6xeOV0hmOvEtW8BYhQixhPVIT2BUDrynfUHrmxtTBu3F1fwto5\nH1KFtHZCvTGqggjftgOPuqFxwZvuxrdSUFoY7PIc4l45XcOo0Sexx05h3OpxQR2P8TOH0w7NIMww\n1mw0/NhszNgxwy0iwSMNLue3ZqOJGbjfb/gPnX//tb/hWz1EH0Lv/2FARdqVckpMjrbMzSo7nEpg\n5YjTfJ+JoDdEmwZjZnQocss58vtenQGSN2lznDNlXp4Fu/PZ0zO7t0h1Mqff3jtzwgbDjIGGeBbz\nMzWD3/rjRr1d26JtnXwhl8fWh7GwZ5Bz+502Po1L1ah2Lp+X1cpHJgIDyQDyXHqc7+XsyKjuQuup\ntG0APfZ8fn+8S3pPqnIAwFN+HrrePDtwI1KKezjTFnXPnzUursyf5wars5hBvQIajLeqBpbeOQDg\nTdeTf9mOCKCyPY9gM+VmICmgjmovg+Bnb4Syq7irgwhC3z+OgqexBpQKm43YPP65WUpL3aJqjfV1\nENEE+fuVAcoOS4WLJqB0KP47QJTABk9pUnDoaeySXk5VTDLbISLghH5nlLe4ct26agfdwtnT57pz\nOKm29zHFeAOxzp0YQBz3IN8P53LFPJj/PZSj1ffTopkBnsh7eKQ12QBHiYIGfCzve3cHpqhzTgWu\nHcIdIP17FlZ81XJ6WziqPLzLrYduCaApikynNQwI51B/Gco75sCC/HkMxFggxCOvxjrRe7XPXrYe\nP2w+5z3C/pb7fnRLUxjZbXvag62/ki6VQOqe5slqn+ppTBinYwxst4CHjc/s2M0OJxBgHiOAZ7uE\n2Q2x9rPP6XDUk20xvQAfAQfz5/lv8+cZQFilLszn+wyAcNWHq8/Pv3f9mfccHuakAfUtMRHI8s0s\ncgh515+qk3VvFQ9LZ2DSABMP+4sBCbZn5Xlna8utdtw0eJjXG9PiGVMHZY7mAi62h+ZV+iOx9AwO\nGICQn6mN0eq9/7tWZ0iv9N++fYEIi5adK3MSHk0iubdkXDeWEimWzrBpaoOkOOj3kyq5gQ29n6mQ\nHYTCUkKydaG//uv70+vH3o8N//9TQYR9czTQzI1b6Y5AZpSQgZOxZItFjk66k+GoOLmDWzjAheJM\nhAQOQIAG01WY72ugSE5jbXTnK2BzcOwaoVpUy8ZyZ9QiYkzbu21GgeA0TaztrLnCM4igBkNNnzGT\nABalSdlKAN+P3cWwvleLvhszIRyKQuTPIKKfCgL0iHKKcalReHMmktiW5WFa3zsDTEGrNIcuL2Rm\nfGRKcCDQiZ652FMYEcUbAZAwfKMyCeGtjHMg5lmKXB3waEsYLYHQ2/ZzKyFsKUZV3JA5Vn2xeea+\n2+/+XqVN1SO0LWiK94e8Y4dGE4wBkp0Hi0iGsR0AUabBd0jVloiKBwuhp/Fx4cf0XEUx3OaOXSfu\nzaJHR48o8MwqMMcyt/xZqJ9H7fMcfQRGmqNUFYj1obE4O3uPvtkcYfAUrT4LPeV78/saQJFwVG0N\nMWDhflT8UA2DOW/ZHEYbp2oiq9bvHuvR7FQFvdOM1WDIxDMzoCW/tx2daQA0tiLvfyX23Nb744Zt\n6ziOOqyJOd3o6YK8AlxmKjwgQEpvcCfsUP2Cfa+4q8DvkQRf7+n9PGkU2HOoAiLUG/vvpTCOvaDe\nzwtDjmCxPvNBBBfhUAEBlBkgA0j/2y7rwJEEEb2CSbpOXHcEVPpDLmZrw/bW8f52gO55nTXm2gjE\nHR1oZH2zOV38fbJGFOlQGVwtUPZGiTFL3F69TgJbWvp3AtUNRDCginF2DE2kWL5f1MAvPkCsTAQq\n8HQH5nOUNM+5WZkdiH3Cxzp9bj+t/wZqHEf19yAck0jRmun82VG2PuX0GBvfXfe6QbiO4jzWHCgx\nh3yVF9oBTCzEfD+D0K6+i0fqV95/A5zScUxOnIEPMh7SodZKVFxIXoZrV6Rm86Qn9mdmfr6ymXyd\nWu3jqyFZfGa9pmSIvtI/8MpGi0MiDWKM+pdkr6yuHb/L9+bUhVnXwM5NKENqQv77q5QH+Xvx85/+\nnueLB9Z8JYm9xIE6TXlF2GRAAI+V4jPTZcm+Qd6DrWrSW214eztwu3UPikm1BlL2UtggBkQzMMyh\n833Fd+b2Zyn5li5X+O+bzvDVpH2BCFNjRd7mnDSj9H8r3T/fuxSxsUVhS87w7MzYSwesFzBzLhoX\nZzy81YZKjMeT8ON5w39Y/vlRsBWJimWU8Vakf9/UYvhBdNqLM+oYkUEK+r8dnwGGCfFYRTmKpjMM\nn+n3c3S3T0beiolg/TUja/dc0oJ2sAgsWh35LgYdbUBREOGG7kjukMvMYwYXEUuJPiZs+lwNTLCU\nBgMmbm8N344d92PD9ybAxO+l4lbY67IDIxgSjrhQV9GjZKiDCDU2QxPbmhd9Tg5OFiMUgCdt2vna\n2XF5sVFc/WlkitCgg/GtNnzbGp69RpoLPne9PJVszG6le9oI0dgn19a4UBi2n5ldYX2wuTemM4ij\n+kNBhOdRRQuhlwGMkLkXxj0gzshW5HkYNV8MULgxCoyR2Db3ESJsBtgmPN7H8BPx3s6MmXl87L02\nZsJVO1XG0M+zOF2lPrCPsuM70MQ7orxc6uPKWMnNxKxm1fbcPE0Io3q1XwcjwLizlPKz6KlfS99t\no77fPC2JZb02MIH5NL6dZV4fUyrUPBcdQOgV/6WOnQiusTNcbBy7zqm9h3OyK6WZe4rGDqBafrdG\nsPfZirLkAsQ6uCgFewTDre9lA4qBCDcWEdk74/0h61q5n10XZxj00QEwIzZTge09GtYgdY4zsFMW\nlQMk4q/vT36O5pxZOsON8f52OOCV+5lBCZknUlJuAD8YCzdkvC87Lvqla12Bl3Wbm7EuAAG9c3qi\ngwgKLh5pv3U2QA8wKZyq7uBJhbzrRABn/QWWN9vns+r5rNbNmUXI6efoeJOzneQaNhbrMVutO3mc\nVqwcm1e1jOwY8lIaMebaqeHfw9+QgIuJ/bDsb3K88l5rIJxXnQG82nBmCMT3Yk938cQC5FcogHE9\n52FzJQI0nXOVjLiO/e7vmP836iRkplfuZ65a5PeOM2gQ78xizryyIV587zPto+99Np1h/o7Z2R+J\nNVrwzT9LcyanDppIqNme21vDb29PHzdjIhQKID3AMujfwk479DkVgtuft1vD+7fDGbDABM55H20N\nk3VtT8zRxsJMfGUz5ZSFV82f7Tx//tyj/ku1f4bo6F+hfYEIMNBYS70RoXVOG6ewEDqEtmrU1Zzu\nEItodjzs+7zYnMWoyVGvzoRKDZ3hJRrfijg6jQv+4/mGP/Tzg9VRRmzKt8KozGgb4a59e6+EWyM8\n+7wIW7/D2BG6+1i20RzaklDYrv3JCtbmsA0R42507nAAnGrKQcNzxXqML2Vny7smVx1/7lXK1B2B\nEJdKKMdoaFAVJV0q7KDArbZB2BAIw7BeGFtykF2nq4BZGK0evUWAB5k9kPNqn62AC4JyzIStMLgk\nRygxEfY+MhEaCyMkO2zMQEsGYfdjx0ialV8cI8asecvxGUEEOHkwbsXo37aO7yru+Y0J77cD96P6\n+7CRlCLlTmEoWVS5Z0c+Oxj63CyP36JWFsFRg7C1nO9tczCi3RYhT19NrIwY89YItBfsR8XPXUGE\nVjyyfHDSIOhSuvXRKI05+7097dl2Gp5bjPm4DggzQfNSS5yvMPlx2SC0sYo0pMwkScBYy9ARcBzA\nZpRNW6OScZ+dwyEKi7FlYTzTZtg5ytke5gT3mENjFC/uIZ55ui+dFyYYBaQIbepvUWMnDZsCuJHT\n7tfu7O+FfdYbgXUtAEzbhD2FxgzNhlgTQyBLYmYz3T73AYh3/j/2DUW31GevXl7PGDymufHoUjHH\nQCepniNVKjyNpJOvqfbZtnUFH+MZ2vr6SKkdR5f9iYjRFbS8FZZ1oKluiTmqleW/aQ003Yd5Ts9l\nbQ1QGwVEg0ININJuWoDVc+O0DwooF9ofvQn7TByDaKSAVxbu/Wxr6b2z+WuU3VHjQedzH1l2Xhmg\nj/cIINJzuoy3PT9Xb9dKNrbOA/BKLkcPAUeP/nbyqCT3jrIp80/TXMqmYHp6oVdpGkDeI8ZI+1we\n0N6jnOqya3WjbE74upvf74M93JyZCKvGCJD2ZQrGHL7O95mv0Y11hSEoYv3Okdo8tzPLbC7DmveV\n6KGAQKarAEDTs3ROpEFyUUhl5km/FVRrSY+JCQHYWh8VsD3NybE0doeB5vEs7fsyNUYdhJmebi0f\nN38+jPnibyvWwUcaCMBk92H9bzlXv/zb6rNIiVixFgJ87ziPrz2bzJCsFZFqA+D2W8P/eT5E1LZw\nAN3HpumdyTa7CAA0ljTVW40gljHFBpHW6Z3N+6wB6MCoZfIZAG0AXNM+3dNzszH6al9t1b5AhIs2\nloMyFgLjXV/22biSf1tt93MkIzczXAWVZD+PObR27j+OzR1xqQwh378R47dUBhIA3kqD6RzY335U\nwo8CPEtyRpBE8+Y8MJ6itqzHJIPwFV2MiJHLJ3lN6cFYoQFUCYdp3jDkb2K4hpNxHBW9RUxgrpct\n57Lf1wt3T/+eUV4DFqzcYAiChdBOXpyrOsAWODlI9TIoxtdpjz0cWkCin/Z8ZXxU3I/HfPUcXZsN\nZUmJGMduiJ67UZ6ppOP355Y3FjvPbWv4t28P6XcVMOWxb3gvbwAEYKvUh1z6PKar5pRyUpAmozLa\nEVM4H3K2fQP8Ba8Bumm3oqU/zXArg1Gd32vGOTqXjTs7zii+dpylHljkFpjAxg/245QBFfdsf8No\nbFPhwTHLTtYAmFXGVhs2j8gzbsTYNNo5A2OthTZAiKkF+GdGa6cxom6Aysx0AFJEUn9fRVhmCvhN\nnf5bcnL3LiDmZKtLWtTFlDAwkauUc73pOWcBKuAqgmPvDvnzySyIAsJ/HTS8i5XecCsdP5sBC7IW\nPnUsXaNk0d9Su9LIU25sEQN/ax21BXPHxs2alEyErMX6QHejw7aCthdwl++XLdY3v1cHuMfG/p86\nfYtolq+9F+8mlXiOZGslRnALmEB3re5A4Em0lTT9Rg4WkL/grZA/11sBdv19K/FsOxsIFRTs2OPG\n1pQJ4+lBGm2+eo1HTY4AbHKqoAFKwV4yIGysN2+tDhfrAEbthJXDPqc02L1lcFqOe70mZYHWDOK+\nbB+AB6tmTITyBknbyGHbVb9S/z2tpIuj3no5zcFzik+klBz9/7L3NlmS40qX2DWAdI/IrOqvT+9B\nc8001VS9Co21C00100jaifaggbbSeq8qI9ydBEwD+wVIj8yqep+638vAOXEiwn9IEAQBs2vXrsVn\nZsCBYQr3wVYiRFqnsQM7C9jF+6Tv01OgxecvBTvH50A5tY/mYZxtp3SaP9QOKS56vLPUhY+i+p56\n8CR94R/dPgIQzgCDH23zs3N8lnTya1rN8gV4xQPLpWH5vfl+uZaOb/siOgkebCjDHm6NwFjKkaEz\n9MvTc8Z9VeZuOP9AzN/h+/q6fedw/Kdnft4KCE12wz/x7X+B9p0182dqnyACdIPQhXAFdEGM9xsT\nrqXjpSa1RFR3cs2huDeJyuw9qZsiHvIfmXQ5X/9NBbPuXaLJqy40vywdX5cdDPKyXpcqUaqdi0eH\nr6VgVQPKBb8gtuUYGaQhAgeYIakL6ZmjeTAeBQQxg8OiigNV0ICJk43x2bHzYm45ozk6YurX6AgV\n8zuhaUkuj6Y2oa1vSbehFnbhSK8UoNT69dpQ14j27Js4n1sbBcwKBNR56IRZlHlCiPG1qGmnsexP\nZculj1xmi/5l49avlQLtjw3pHHxpw7lVyOvEMBno/xSGYlD4xfF+ed089WJZO9pecFmaz9el1AOY\nYdT6M0ScaKRMu1o8jX0Lg/xwiNOWcQgRLUzzR3McH60OxqBN8Nn5dWQ+ORnhhMjncgWG3GxtyLRS\nw7vq9LnT6yAzFOL6w8gb58X3mjlua+m4FFnDrqXjUQvunbFPnncIcRq4dWRYWZUX4hlQodPn+5nB\naikm9ppXR9H3r+uOl30Z5tZHDoxFIO3YueQVIOlD9ryvJZ5701IfABrEe/ZqZ9OMQGKtKOjbCBd1\naF8r421fcK0d7820MgJAeHYNDnzqRC51NDArA8vesGoS/Fq6/2xJlNecEcP0rMTto1Xcb4unTWSq\nvTnJf8UPCKDoeBDJIU7Mgfz8p+E4gBf6sVLDCdsfsh6TzmtAqmS8VsZ7Y6zO9hEdo7XI+9Ys8r8l\nJo3fY4wg6q1JKd6L7v/XbceyMco66goB8HruQDiz1o5RxGPN+M7BLhqdmHF85fhxbO4KJJgQ4Xei\nkOZYyPV+vI7kfvQn/a6U9qmTyPtHrapz7iDCFbJ4l2AX8m7Hg6e2GMA/M0R6C4dr3j9GQNj2uPjb\n2GtynfFZ0zjyvck/m9YGTV/hxkNwAwD6Q/obTISYKwPLjkfxUqOdD8we25em/eVHWwG0POvxS1nX\nIM+1/FHTQHimkxCgilbt+ItR7I/SF/5IhJxTCVxjaBnzIt8uuyemE2aNGkdaFYD6hUALo6wNwAO7\nMoVrAkp3thLsplUDrLZGeipo2tu6ldJuh5KwMxsx7BNycH9TBvEg2P1sPH5g6MyO+myf7Vn7BBG+\n0zokmvtaGy61ewTTjJCHUlMB4N4ZtxaU39xyXlqH0JhzOoO1NaVMfGvVHdClAF80T+rXdceXZR8M\n0YU6dpRhMyFEjWw3RCeDAEAsUHkz5gASKBkIvuhmh4sJ+169LjqgZeGU3ZBpk7bxet1lW8CfLGjy\n+UR3VgFHomAi8C4AwnaLmvL7rhFnFSC77RXv++LHkvFhZ5n4a+ZsXDvKKoAEIKJmt32REnFTKZ9C\nYzqDGWm2KQmFWUCg+I7Qix97cUE0u16jPM9R0spHPIdojCyZ8WMlSqWUXZ0iK4qqpz769xFUX0Dm\n+d4KSmEsq0zsspgRccxpzmreM5qf2xgxV2fJVqPJZpijrHOzU+R3SC+REfO8N9lYt1Y8R36hjnur\naUOOPvFkADHCYbZ+bBpVnKng2aGW157v2NkYtHNbiwjZ+bXb9z0Sd8Lp5k6gpaOqswkYc0R0AbKW\nCwCNWNeBIeOK4AnEMoN7pEWP151bFo7M9yWDmY1FONCMt0qMa20qpqg073a+Xrih70a5RnY5KoiP\n6UzRp+8IWMfYcEFNqVgAcFNg89sekX15X1LhTOMmyn6O/TeQlxlelcLAA8KY8y06KilNy0EExqoH\nvUHGd+uhVSLCmITbvuB+j23fxqgkUcjtJIoLyByRn9H5zwFjiZGrMv7kyFIRccIsYivXND5j/rft\nT5qCUVag3fU8TUQai6Y0AMC1drx0YSMEiCA/lQLgkWsMZs0A9PFoXO8MvYcVF91LtkfFujWgMFS3\n19kDpfRJQDFunmn7GFvHSshaP2V9ydVB4ruL3rJ9132AEIydvWjJwHByWJ0I26dnZlB+rZ3M/RnI\n978xPvOMmAMOIuwAq7NvlZ/sOJBxkagAACAASURBVGegcE0irgBQLuTVHfy8O9A3PWYqF5r3Gu97\nC4fLHUOM42BjYWvOCCKEdkluWWOEkL+n64EFFx4hAmkH5SbiolFxRNJ1JNAxgkuZGZIjzK57A3yw\nmzxvs9NPE3jsoF6y9ZyBYMAfj//bce31vGwYM4FAh2BH/mD3fo06Bi64aJoTJ2DCj+ge+BmT7eds\n4NyPZM9Kf8bUOi4knbVuVKBc5f6vW8Pr/aHfg5aQlTQza2ZXGVvzSonBauWhH7I+GksFENtzd70s\n7SN0zWBZ281WlMo0MY/seuZ17Y/On48YyD9jE/v+zzyF/3rtE0RIrZ8sU4ayX6tEXb+puKE5948G\n3HRjeNsZ73vHo43lWTzvNlOBwQdDjVnEuIzx8NYKGJJrfikd/7aKxfLr+nBAY08LY+s0CGzZojUC\nGLH4eF6YCwjlBXV0AO29OhmQ+dylBN3PIvbZoWgW+UkbtOT1HV0s28QbRwmo1gmtqGJ0oi9yI4lM\nPVQZ/bZib6ICbirm7/uCt70OoEGhOLYLr9WuIAUGxeucyjBH1mkaCx+T5IhvXSxtBxGYAPSh1v1C\n3aM88t3s8LAyH0YjLPkssFJUlicHALdWXPDHxnjX6Nt8f/O9zaj81kVB2qNCXZydXUEuPyZPLJF+\ndCSAMDoH8OZMqEyPTZOR6dS8YVMcZ1BOHZjnNQC8VHmWquaKFzJDM4zj2ZDLhuWejFdziDNrgLUP\n1qvsoNhrYpCfl1CCvpZzxW0s4n1KVON47VkjAhbLvXQ6v1D6bR7Z8TN4Z2PoOgiI5xYll7EkBwqj\n3zywOqyvc2UGawZIZlZUJUYnDuAR4RQchSvTPbRnthGYYpzinp2DES5O9wGyUBD3RDQNoGCvMnNI\nK1wgnvl7AhAaH9cMZvLSjUSMZelYpjiQRTB9bBREuJSGe0IbYt6rg6PPceuEx774fX40YSespR9S\nfJ5ee173Txyts7VQ1tOO3qISTs4Bn5+xXN2GejhfvgY5QDSvxRYFJO9P1Yo5eb0JYGycQyKsnDQ9\nYOt3AALdgNIn6XLZeM9aGlFeNfbG4XlicgAPSD50m0ovaLN5sCwyrrRzONimQ4ERFI7nZAQuPzKH\n85pj15S1WloX+r7fC0ut2M9B5HlurWRCq3ZCHDpoqQA5mm8aG/Z+HpccqbVz5kiu9R3TqfL4DH0G\nBJS2eUH22bM5ICBKdtqjEkNeh8Z5Yc5eBjXMdst9mtlyw/EQ+8/ZWP+R9pHjeFY14b9W+9HzP2Mi\nmEaZ78sTeGDtLBBnfxPBxRDXteGyN6x7d/bwEo/GEEgikj3eAHtqFbhdvKoYICDCdihBjcFWdG0V\nZS1nO7X7T16PjnbHmQDnR02Ci89ZIp/t52ifIAIAQOqBl5Thkx3fFQIKmJMOiHO2qbNmSu2Pxioy\nZeRYjdB8AOLlh3brBbWI8j0AXPcF3BmXwvhlafh1FaTzurTD93dIxPn3vbiw4q0fnXZr2QkT5FQM\nTKdrG8CQEHD57HOVeKNdAcFEOGvzxpYrV8wtL5x7L6idJXKmB+lNas1bZAoIxf3HHrnvpmAuJ5SF\nL1PRnWHSVHyMAVoQpdCS2rXn9ILVaOaEbAMgo0TaNYjRyj0W7EJA7wVve4r6leLg1Eyl9AgFwpHS\nUw3jupkh4uwYYTUUCl0Mnr6D6X9GcsJtPLeaSn0VvN9WfNsWp2o/DClvBU0RDDPmRj0DGsbAfhMJ\nE8Fqnz9rZ7RUGQdCruUu0RHdtE8iexd9huwVq9k+T8NskHkqTjbSAOSqDICyExiutP3RtXxPDC42\neDt3RPZaK9jb6EgRBR06l32sybHOzVg0ZiBJzrSANmM5yHFemGAs81ji0aLte7ItSjqXNTOCsrHT\nWMqg3rflUI2hMaW0n+LU0AwWmSHthlYn9CqpTNaY6VDO08aXMTqB1ufsLJgzPjNHGkPWaaVk/7I0\nrIXduMttIRxYRoA4GvdHbMmrhpWrGmpGl26tDNdkuigZ9CiBH8TxMQJqNh4GEt5Uu8Huy5hTH1Vo\nztbqqHJxfG/OLXaguRefY2clKc2Q31rVcpct0jx0PZ7p4N7fuQ9+3DC2Z0ZIBi6caeSpNlNFAyal\n1sezCEi6TJ4robwfejgZQJid+yxEbOtWLUnYmBsWlrUu0h4Il22XqhaWfrePIP5YajbANOknDm3w\nk/L1aL9nJ7UQwrnfgL5JX9peBmaGlRoOh5i8SoenZ2idRG4c6Rk9AYC2jw3BhHDOTax1oH8nJkJ2\nxg3E2nqMT07PslbBWp0hnLBsm9j1OdBBPIDufYeWy9SxbALCm92Vx/cZMzM7e2esGb9fx5cOaQqn\n1THJbJizg9rGenTaC5O/XphGxoM5z7PNlwJ2wXbIx8TAhAAA45OdlXfMRz5cl37+jIkQnwnbTOyn\n0c4dQKqHjmMB+k3m+34rziJrXQpJVmLXILqUjmsZwYlKrGB0slGI8EAFHnG+vUtALFcJysEO6zMQ\ngELe38wGm/e1+e/GeR23e/KMSSLn/Gk1EXD+7P2M7RNEmFpQ0OV343hMtl7wlvJbjTa06xPsEf5s\n3RY6zQ8FRuO0FvZIhEVJvywLahMxx9dld/CgEOOx18Hg6Ex42yu+tejj+y4siXvjYEFw/AxpApZD\n5U5CGE6EtADR0RB1RDUZ8Dkya202+vM4PGvZ+Nq5YOkCGPQyRiDmKHauB57PYRRh63ds2vH51gr6\nVgB0VC3xeLnueLnvaL3gshmVuODWWNNFoh/mvGYtB9FEmI1WrZGt/1uk14wJjwqzbI17WuSz8nTM\nrxBR3C1HTh27LNh5FmmZN/wwlmQT+/Z2cWPyfVtx2+tQMeT3RrhpXrUpibcmDs+Iio+bKCDRVCrw\n/T+Lp/k9+0G+eQYm7LrCqSwSua2Rl22b69nRDUA4UIHza+l+bW4MWtQLyYgd2UDWt3ysWfDMvpdf\ny+Ng4qV5rlid+Zm9YUKJ2WA1kKNQREpy5YI8Dlkl3K7RDRdjIvSg+Nq3s1r4EEGDGd1j+oEYTJJ+\nZP1sXJxSDgC3HmBbABgCZgwRY6gTV/rhHMYOGp4J/n4eaWd42piLUWop3V9X4D8oW+zrsqvA1urC\nf6WZLsAInPozuxdPKchU+KqIjI3XfVt8fBwEwBHcPas6UyB02SUJBC/KvPorUcvcCOMaYi2XeZRz\nj9G1/H253jCCt0dF3zZnh9W147I2vD0SeKesrq3H2rhryoI5z9bOAIdsWNv+3/X9VdcNYFyPMhBv\nJYUHvR51fIXJYyBCEs9Ne0RjUXjfnREi93TljiP4x0moERrpDrHFlujPeS+Kvf8oKDm3Y8nTeN6O\nnw2wzgCEroB8XnMCiJ3ueWZYaXUH00GIz9jnxr0/gwld0yfsec66B8Fgi/7I7wTmpNeHPVKdYy/V\np9cs4xHH641EkLkBNuP6Rh7d9nWok1ewyloZZ0wCZ4/qa17NZ9rHs43n3TYmw5kTeHgFmCtkHbRs\nLIjBo831YfWG6QUro37+XT68Nryv3/wjlRj8ux9oIkjgJ57bjxpbGd8GbL8X7I+Cx31xFtl9W3wd\nt53wWhitdvSUclzdvstgrR47vZZZCDmNKOZKMBadccbBcPGABka7Jcbiw8t92j5THD4b8AkiaGPN\nvWJfVCy6GHTtgt+3xR30Rw8jfKDCYURQmRkt/W2fYwoHV95TJgKxCzi91B1rKXipO5ZkCL/vFbe2\n4N7GaP9bK/htL3jTjfe9Ed4b49540GloiqJbFMg2W2Z4X+01iRSMC93sdNlmsreaqJhBzbMFLkqE\nZWdEDLx9WvsZLHm8HGX3Hq1goXJgIpjSOCWntCogY4u40edrEuISw5sHwGPrBfdtwf3bguXLw/P0\nl0vH9bpja9WZIrdWsUzKM+YcMLJqPRxE2PKGAZ07aTN59CiDSXnMIfes+LiFsZGjLXNpwZyjvqUx\nd+Myjbn9P7BUIOKPv98veNfUkL89LmAAv+0LflP0/dtOeDejUV+TSItVKQjDb64kUIm1jBu7voXc\nWzWMe9BA54igvDZGZwCo8B8Pm2lj8jF1g74V3NqSHLF8TDmOl/pKY28gDetx743w3mLNMGPZFfh9\nbUASu5MxOKvkItczCz2KEeqMm1bx2Otg9IhivTAy3JBJVQ3uGs1/9KKU7nE8AxCcykkpoymvdU2f\nHZ/nDC9faOwsN3gRQGbrLICDjlOUkiyugP5tlxKcNwWhbj20BUyg0H7kuywlOpMTJutpB3MdHBlh\nJYlgbTwTpKXdxrVuBpCMlZGNPIJUAfh16fhF14YXFR199BAczSUl5Vjs4/bYKx6PxdOvDCDuCGPT\nItv3vXrZSKPCbj1K+279mAPbknNbS8flEt6ZpQZkEGu2K3Nu/WyAtnSu0zUkO8utDNoLObqdm9zH\noPXf7wuuvy+4fFEgvTLWVRTRjaFic+SRmFyPzkmzKOb0xsd1JDe7N3uX9LeaRDirsk1ExC+cdiJG\nRcxnc+RtTu099hibRzmdQeZ1PKM2ZzsI6+Qc0QAKS8SfCLH27mUA9XLJX0uh8sg2BxBobRAnNZDQ\ntAbm9RexDgJ53R7T22StGJlT9r2sk9FvQP2F0HcemGk5lcX71sY10f62Epq56sLOBujEWMj9CBuD\n0z6Z50cl6ay9VCDfWVnfgN7vvaBUmZd2SaaFYCwJuT8Vu7ER3KGN9TJHlm0vc5C682E9tv6y/w5b\nM4sH+rifhFFjrePDa0Mk3iNN43sHx545+jGfC3zog6UWeB+/AxjI33HkASQ4BU1SL3QKLlxgJR5j\nzDMQeDiMz8ntreDb3y++bj8U2H206qCh2WaVGJfSh2BDttsM7F1qZhmnwBYXBx6BLHZsooqxl1j6\nVdjdsd5k4JqnuWZ7er7uOeVhHM8PFtB/8cbTXP2Z2yeIACCnMwDycAyRCGK87RW/7dXp2xur+jWF\nM7QUoHYApZwu0t9rjQtuLeXnl44LdbwsOzoT7rpI/bZd8LbXATUHNMWih0bDrYXQo7ElTFlXgIAc\nMZnoW38AZSyQ/N19P8+lzRtRLHfH9+fXhIUQQI5UGBAQYVHLxgkftWNd7bXd6WDmLDLI85TniG/t\nUeWiQKJBj0fF652w/KKL+0vHddvRWsE96SzUxkOpzpxgYK+Ig8JaVzqQZALwUkP07Z6MPjGubHwh\nasqZcocwGGbHtw/3lj1aEuyak2gBwsHPBocd6/ftgv/yEMfu79sCAuOtFbz5XNOUhhb0VVerTpty\nbvPGaWJcTvncyqBAnps5KofXJwcnPyNNQToA7qzd9op7K4fUAwMBhigelO6PMLrP8lizccfpePKb\nHXXL7A8Zj+h3vs78fYvgAwra9VEXhRpjKXWIVFo1glyBZU9OVNanqOn5CPX2SM1oBt6lPkYlkBxl\n1ff6OfXWqPWZUeOAF7EDBn/bpMztvZOvazK+pCwbOeijAXsNB03OTdhpZOEYhd5YCDmS3NTJyToU\ncyS/M4XGiba1CDD0yxKiuB7xL5EXayUlPdKexu3WKl73JZztTmAuAFYHPY0V9LYv7jhbSdiHpkYB\nUHCE/NjSoUg7WZaOy3X3v5el4f5YPKXrbuwhnM/F+H8EuWKMps9xRGJzZPqsGStG1r8cjau4vS+o\nusbXa8eyNt8rAVtnxzQi1jEoGJ9RWysllUo+m1NM8nUVYizUfZ0qRcv/9vRcMqk45vm+bzR+6+fe\nRxDXwBiZn5YSKN85AxAGJoKKOZbKHpHPdP5zUCid+7THODBJXBeJYqyy/eOgzx5AQk43ccds6sfO\nUnbVGGx9GyvYyIn02k+2g8xusYoYu1YjGnRq0loTY0DuvAN2DxLAn7tAaV6QzmvktY5c7JHKCHbM\n5WcNPJ33b3//4JCPc9J+hlRTv6Zzp+8wbukz30ur+/do5PtgdCTb4KRJDx+XlyzI6QrsdlwCgi2Q\nBANrjjO+JD7nWbpufDCtN1vB7b7ifVtx18AeMJbxds0tYlwLIGG6cRIT2LV0XpbdU6fteSqT3Qck\n+0R/rMfZFslpXo3P59BfaZ9MhM8GfIIIT1sgcuYkVXzb6xAxIVjek/y/FMKlFCnxaIZ4NpxTztms\njJvzRM2Yu9TuddK3VvDbdgEghvWj52XP+kzDZiBgAQ+oWWGCCUiOC4psiPMxTcnfDQo2xylH+aUG\nfc7nLWAvU/e9xSpvznIKtlMNUb+tEy6dTo9XFnjt9JXDcL1UJ7ZiL+PibfemUvHPmVHaekHbCIuB\nL0UotKWEMVlJRq1gNER7BCfkzCx9rxRRDmZ1PjBS2GZnC4BHr43Bkvs+j0XeTOI6R4eAp99nLYzw\ncf4DwO97EXpedhY5GAdOLT0Bp/Lf5rhetOrDLJzFrAZiAhJCj+LjFo7paKRZRN/o4LdW3dge1Prp\n3LAiGs99NoRET97AURH7WaMnV1gQY9g6KagWn90pKq1ETrUZskFvL9Mx5zQQM3CBc5BvZicBaoDz\nyIQZronGz7ZJM6P7fYi8cmMMPHqweDLbJK9hcn5yUMVAAUrHAxIwg3CMnBr65L45CKYicNlIC3HK\n7mvVvVcsmo9j83wpNn9Pxk2ZB1nUses9HJ1bGqLaErkWEOFmke5kNHIadKFLk9Lx5bXl0lC07OV1\nE5CwbowN47zAodfWT/1t/cPxucnU/xyZrtSdHbbQOJZ7Gl9LO9tbRds0xePaUbRKhe1Dz9IGpQ9Q\nhqH8n0G04XMYn42e9rvcBEAYUxe+V+Jj3OPGucwaDbUoIpBAlOSUFDC4jOwuQIDb2rpHujMDcO4D\nwxzOeP8HlyUAsset0zrcOZyntktEfgaQz54vq+qw1O76Q7l5Slvy6l3/QPeGQWtA04BaDwDFxuMI\nisV+O+wRPAI8NicGQJljzfCShhj3qrM26AMhAjjWn6ffw9MtxRsh1uW5FDEwPqNExzz32Q4bjn1i\nz+b3mEfdBTleAqoPvc8aDfqcH+yeqMwgx3hmScZnzveq8XsF1fURCOUgVj1f2+F/Z8TIb6/EkPSh\nAK06ot+zPWAtYd/K3OEhVWpdG0rhYR7RXj8EeXiyC03kOmuz5Lk7XM/0/3wPnzWbK+UI9/007U/E\nif8l2yeIAACIdIbClts/fsKiruF4MpgIlwKs+oS/VDEQHy0h285oYF8kO9EB6c6RMqPyAhsKGA9U\nfNtW/Bd10v+uFSLMAAPEQCVirATPwf3IMMjK77bpSqk67WPa+PPmLxHx0ZG3smNWhkauVyn0PeeK\nm/HD42acDCl7zajSW49F2WmgSYwoCy3lZnmrRpOsnQB0PaYczzbaSt03OQE/5Kc3cupad0ri6PRY\nm02HzAbYO+NBQovN6QxWKm5zh0DGbWd1kJJj1yGlvUraGFoHuEzRFsjYmZCeRZEbB/V7Nzorjhun\nOVb2stFB9y7RYPtM/p2vOedy59KhLRlK9rfNtZoiUEZRBcQYtRJYodEQfchAx5x3mT8TebEizPlI\nIpsPpYxnVB+YDEvEa7br5lQVAyDsWXRxy+kYBuoZmEj6xkxhlb7zFOlNTrDfW/J0EWs7i/HekJ0Q\nSD1rjsoktnZUmvJTkzOQnZlIS4jPscKOttbdmzj794aB9WLrx8wSiWc5NE8kqhLXdOuEtyZr6vyY\nN87zU52wHrnnj14nllA6v49lvm7LJx11VPLnScfDKi3k9ugFv+0hTghIClb+WOcQ9MsOzqNXT8MA\nAogaNEQKozJ71QcAzkJ4b/F8PrqM187xjO0smiW3vWLbKhbl+NelO6gwl17MLB5GzNM83+ZnxNbu\nswi0XRdNc1ALQur45vUtIRSsNHUDEe5FKfycDHTZ/3LFGiufuZCc2wAm2VdIhZDtPsS15DXurikT\nloYhui9loNFbqhSlaHc4qKa7YuvvqH0jn435m6vpdCZlopjTw6idnTEn/RWnY98jBWRPQmzjWhlz\nL1/3vHwyMsjHeh/hQsKz+8Ec4sT7JiWWbXxyrrmkNJwHAmiJTvQHDw+8lI0MsUa7D8Z8s1LOXa89\np8rYmNt4Wts50rYyK2hnCZDY/fLdMI0RpWfB9p5moEaT+Hi2S6yilFPUNUVvZsecRcHz/iTfNRB2\nvGl2b3+EifAjFPVEmktBi5N99sl7UU1nev1Jf54e/4Mw1EcOL9Pxe/b52fXtiNQLc8BnMUK7d6bL\nUqowcFetyGZt9/t5IiCKnJ6ma1lKlSJi1Nr1WTM79WifdISWUwd8UHM6Q/QnAQnJxgZObLgfABDy\nZ/tUPeiz/XztE0SAPYisSKfkSA0GpBqNHfD60wQoRZT8tUuBO3ZeN1ePVZhSrd187mQkkpzfUiYq\nLbKId+DbvuA3jZ7eO2Etkn/pjpjSpb4uHRuHbsOtSYpFdkjtn1w60Uy582hJigp9Z5EZ6LiDeThu\nHj9CpTKq4whCRITDP9cK0BDquK24c5IF/XYWlofXm1dDoabNxozSqtG5dtdzb4T9IWrKVu/bNn8x\nhu1voBGGagghbBPGivW+MaUIol2nfH7xz6hoV3KW3amZkfL0HfttRlJ2kH8URW1qUC2FPfqE2nEt\nDKDgphPwURINPgE8wBGoyNUtALjSOjqNecZ6H2eVeLtOZw0Y6+XkmphjUzdhzixISgCW5IjMTARL\nXwBwVtHN2SNZid/LOfHRgJr7JrRYi27PRljcx49ZIxlgkP+zQ2gMBCo96a00TcmS58vuRS435XnC\nJ+cclOzNiOaoBpOd87Mm89JAwViPGpODoPb9rQeDZzzGKA5nVVBciLUTNiqSUmDj0c3ZZGwYlcSz\nfsZHTcqBjvomlSS9x9IwHkscJ/oTzjhjTF+xa/dzgJX4ys6Osnsn81eu5+6MtNGxv/eYmwDQK+PW\nRQ/i2+Pin7XIV+8R88tslRzdN6YOp/N81GY6PBGwLM2jbtfecGsVK0fOvueRpzWeWXVuUoRuu0vF\nhkqMq83pVvFSOy6NfD1eiHCpjLVI9NxAp7sCCFsPZ8zeI0Sp4s7Chrm1iruVd97rUB1CPneespWv\nfW6Dg4IAsrIWCSC2gz0SrMAh0wgStk6ovUSFBM736XjyGdB71kdL0wAy6HO8Nma4Tse+F+xqq8yi\nuvO3CQEquXN2hYvXWfqCiTXujxLAjQInm4IGcl0hLGdsARm3GK8M3unwBTvP38/pXsfIfjHHjHlg\nInQWsKBMXqqwJmIszoSCM0AT1b2SU29rXT5uGsdn+5+1j2yuj1gIw+dOItUfMxGM35pe174MqQsk\nYFV+Lf6OtTSnN8xNvhMpCzSt5UzxXftc0T2gZMD+g2eZClBe5O/L14av7Y6iz0UOlOSqLoABQXZu\nPZY+DQv1ENHuBa1h+K6M5ccpJ8G8HAWHgXM7zAIac/Ny9D8IJvzMTIRPTQRpnyDC1ITmRcmZiFI+\nBcBLMnC5C4CweCSEsBdbTNQwgWxAHf5SUFbTeS3iUIg94vG2R8Tut33xSDIjcpc9CkOMWtkNEQC4\nV+CxjJEjOfGYTpFrKpf0ekTTj84NMC5qfVo0TexrFkz0LujvhucbmIkrhnBbosybodUItZNHrQFg\n2yoe+6I10C3PV6LPIig3LuZrSRHawiDasSwdZQH2Rxitj8cixzYFdTtOug9ieI/jPdciByKndM71\nrmQOTRorEqPFnM58nPy35aKLwSQvmgOWoy12S77vLsU1LtTxi9ZBjrgY8GJzTR3RoU46cNiMLdpy\nRj2OPFr5n1mesdkItg01938u8Sj3YeyD5cjvvXjppYW6Op7jfJZ7KqkA7uAwDs68vUdI0QTQYcPP\nBu1ZO40KcY48RNQhX0+OZAEYQLYcCSYVh3PapObqb9Sxo0wO7QiSBSX1nNJu7+/MISJ6Ymjad+15\n33tUl7Dz2TMe0eq4F/l8p2XpOKicgDwPRVOgDOgtGvk2hygMOsuNjhPlORpjEH3Nz3SjeI4B4Nd+\nBJekcoCtBzyMh6Ve5FbsnqlnvaqznKtsPHpBq6G54mMBUpaM7kMKhN96wfu2RErBpikFpQ/XbgKb\nAdTlH13rEqPGe04xlg4oN6kqQYVRSuT/RioDD+unj6k5XCyAuUXoAOBxr9gSIw8ArrXjpXe81IKr\nTxgBitcicz7YYTZXElA3uGTadxbBxvdUHeT+WHx83KHthPrBgpqZTfbMziUeGTQICbtzzwTDuXci\nLIrkDFH1JjooViFh130uA8e5WfqPXLc8rxWZLaGVixqhnwkRnDRneWx1YKN9JFIHiO3SejAKaQHa\nN/ltr/WmAMJWHUh6bNVZB7YnC2gcLITsGM5rcGZpZOHJOUprczGzEzpzmqPs52a1ReoysXq6Vo3w\nVL3jSprtqgHMPh+2H2rGaJrLOvYn6QxIwGwGlTLYelYeMQMJ+fUMJFhj8MDEfVbi8az1aTSepTgU\n1BNmRH6f/LeLoHv/yG2cnFJlrFda1Sb6yngtO+rSUWsfov93tTV72otmu9lAI9MrAmRtsQCAsYpa\nz/a59Z8xA8dA7H87R7rzWSqDMMJoeMMYr5mBUjjmyiEY9ATM+Ww/V/sEEaZmyGR+PGxTvxTGSyqN\ntbFRJdUgI6X2E2BHWIpFNeKhzw9ppmQbrSmovFGK7JEWqEthvFatNGB55UWWzF6Br2wGZnXKZpxb\nPNECjMa7G/MJJFF0lonS5nlEapklOkMU2gkWEcyRB4ts5c3JqhmMOdbRB4kuGjARZTXdwekFbWfc\nH4trMgh4IACCLeyPXvG2V1cwB4KJcEnRz9pl/KkwytrRNl3ILerRx7Kadhw3qNUAmJfXnLdm1931\nmu49vcZGvQz03jaPmV6cnXm7j23abLYuzpp8N8Z12j/kvlJiOrhjKL+tIoVfj2DoTqG+d6lU0YGD\nAZkd2tw8/z45X7kMVgYk7LeM7VGHougz15wWOI6njY8Zui/qkF1Kw6NXpxBbT0Q8zHLddSwQLI4U\naFZnMRsH/MMbbHYucnumiZDH0iLnQzqDO5jlJAWCB7BsKYylayqUO4bn/TQBuprQBhvf0dgeIccQ\naZSrAoSpw9b/HvfE1ro8Vgb/IQAAIABJREFUHtfCuBQamCc5XWw+TxY93LpkvG694Kq150nv61oY\nK7MDs+7IJ0t2KaNhCcDT3uT48p49c+8t7pqOMl5qOOcmOhlAou0brM/NmGol+eKjkWwlSlfNk3up\nHTsTrsXW+diPMq3ZjMtN2QjuoGuHX+qOSOOgYOH4Mya6BSZ6KJ9jjdImh8OftaDrWhUOggABBoYQ\nLZoTLBT93ERN38LQBu4mUKcXPHa5jpyWtuqeuNo0VRBwca0FP2Q4MbYO8flzF2yulN7TCqiEuKGA\nd31gyblgsUWoU6TyyM6S3zn9z2K4hRPozkcmgqWIZUDO0pqsHUT9GClFLN7LoENTEWOyyGhytuxv\nA/ga4GvobV+8nK4xA6xPex9th0KyDjETWPcBKizgwZ4YTTu0ZGToDtj17kms01Tx8z5pvwNEt/Uz\n3su3I69ndo3GTvDPUDAGc9llZtnPDDTzcT0Bw+fmwDUFaGo25bNmbxlD6CzA45Ho9PyYc3joAyZD\nZWr5GHEsciBhfn3opB3D+ngIJMQHnwEKM9shgwoCgj2H6g1YyMBBDqbFWin6K1blC5BnbYGCmAZI\nvxLKC1CuHWV5+LwsxPj2WIF98XtuZYmBcxFjT3/b1qFcM2BpvEcoyfyUDMjYPN+7aLTZdT0blbyv\nupYGwatYNV1/ZtDJAIifNZ1BnrUnSNdP1j5BhJNWQK5zAMjGvmpUyNS3RdhQDYT0XebRQLGHNICE\n8yaOEeOaathvnbAlapSxIF5qx5cUUZTfpoJOePjnGJcSaRYA0JKjZX00RXIzHnMT44fT5hSAgiOd\nTO7At2TAn5XRYkSO5Y80Bg9OoDmCbry14hGhe1LcN8fQwJf3VvDeRmHMovdu69XBmEodj716xGAw\nXNVAHIARGqPiZ1EDi/QQp+gcy2Z1a2HYXmsYNHsPKqcLVCZj8szwy/WjI8dTopTZ2TMQawZzcsvO\nX2PCl7JjvcRMfzQRErqqWOWqEd6zXOiOo6bByGJRB57YjTAgwIhnzXUsiJwvc2b+8HRuAjsocqkN\nfSPPzQ4qt+kckNPJu7I68vMuxtuYLvK8v0fD7dnmbnoD/jkzgNVoBoLtc8/CaySAzhUtzbVQL3ed\ngx6ROUsHGfo1XQsh8svtRHOlhkgJiPJdGdCajx8aKTS8liM6L7VL+hbFWHUFcGcgwUA2Ax3tvmSR\nqaqpHeIUZwNqBETstTr1fWR9yG8BaoG/P9gZDy8VmupT/HOhKzHuBQR5VgzotH6KqG44RJtGm2X4\n5fdaugAIJfasTDk/CO1CnGHX5+nmAMfzYd821oYdRxgL5CX/CMaoCEee8zz1dDlzaMcb1rTc3zzX\nxBGMvYRJ0pCMUWPHFwcyqOyNy7D35mYOVjBJzuZ4chL9OsJQz8yU1glIoI+x456lDgxpfT730zXz\nCPRYK0RHEIHJ76N8V8CMjTg0EXpJYPQJE4HH1+aVtidgIgtjRhDheI1eilnLzhrD0cur9tBTyutZ\nJRUR1dK+bOvKFvo4vekc7VHCddfUwrxmGNizs5XgNlAFnkfufU5jkcG2COzEXJvvas4x5+m5kcBG\nsBEMeM6sujPR4Xwvxmj18X2G+Po/koLwV1peHz/83OTcAz+W5mDtKTsBwZq1PeUMoHf2hM7ks7SH\nnCIxV0iQ48tv25OIYt+sRfS0eI+NiBYCFkKtHZfe8XqTHJycvuP7hQK3O8MBTisV20GeIpurLUV/\nxkoruTmAm9YrYzMZ/pw1jZ4xk561AknDzqDTn6k899n+ddsniDA1i3Dm5aczcK2MlxK5Sw/f8LPD\nZpFfThvRjy30JuZ3KQ0vxdgO1Q2Kaw0A40ttyjwIWvhS2CPOmYK6nDhChGNk4kc3o2efmynQuZyh\nfxeBioZDEL/PyhplIS8TiJFNOQwTSorMQCidb724g2Uq5sBYd31niYzae2urWPcFj8eCL31zxej1\n0rDtFXsLOu5SwqM8M1xnRz9vkuiCtm88zjVTnGeEUWI7RB4du2eDM86WD5eMXU4R0z5+f26nAnRq\nlAmAFqhz6yuWcnQ+cxnFvGHNuZnGKLBz+HutOKjQ2/ejN99r8/NXwFhqx+sqipmVGPe9euTXzmZr\nwPfOXvT4udZ4TnPx3NaTCM730hsstcWOBch8N1r1TVN0tmmMLqVITr1pf3CIkhrt99Er7j2Mk+AK\nmNsxHpMxz7/xHgNRWu/gFD5ZM7oBgil6aikNkWrEHs23dTazBeZzmDgdoL/LDEz0U7DUrnHnMQfc\nWtaMmM95a4xHk9/myBvDylKJrG/2fMuzy/76xiKo903vD4FxLZJmUh/yuUttctzkvA4U6HQtlYLp\nYO8TWdpN9zVs9xSL4uOaHbyzFiOZnJzZqUTQ2/cuEWQicp0TIObkDCQE2BHO2d6FCXZRILMWiUE+\nmpS8lPtQ8d4qblqyFYALfVZSYVmrvnMCauS0uhzZW0jTf5LOiIGlLTuvndDK9Fq6vswgyg5sHrM8\nXzrUQOvwqHYwInr6rlbxKEnPxKL0HJo88v3nsdox5SJ0Olyk1wGM82bP3e407j44UjnFwI4hK42k\n0zjV/yEA+n4jr8ZhooTDmGuENrMDbT0zbYmxetDY3133ZJtvcR9GBzf6ebYHEzDtdwaGOxDVRlZI\nbmc6Ec/aRzba8zXWzvPjjt9HwMFHYMGHx8zr5klfZt2Ej8737Pj5c1Ye8lmL9yKnP555c5bzWp9e\n801ZgQQUlJeGy6umm+0b9l2YUjeK9dzsBCsStuh63DqhlHjGHlpiN7N/vSSx9/V8zgioNa5vtu/I\nmsp+rSKu/tfaR2P8r97++uj9a7RPECE1Mx8LyOnKzEIdvJQmYmQniuWR88qijvonnquaIku/qoMj\neXhi8H6pDV8Xqe39kgS2MiuAUSY65Mdt3tiyw/U9oR1KlG2rWU3EB8MzO2YZYc/te5tjRogFSCgp\nulFQGnuOKqDU184Hx82ij+4QEAAfqwAg3vYFb7cLfrnfsH5Rpse14brv6I3wotHCRyuoVEYmwpPr\nY1ikVK9FTioAVabGn4wLISKicxR8bsZE8BxGd9QTcINjfw08M4dgADZSFFeuu7oxOBrBVpUgGWSH\nOUYe1ctGuX02l3Q0YcyPBMvyWAxgQQbJ0tVYjvl8bvtOzjl0qra9rz8NMb72WzZ5+btzpDUdxnea\n67ap53QeTsyAmSJqRjMgonp3fd7zNW5d5mTUqAZ2ZexYact7i7Vinr+1sItj5kb5unFuOVo6w/dA\nyRwhbe5sRjnUAEIZ18K6ppKPybMZYY4OEM761gsePYzFHNGh6bvMIYB7xh7L38tzbGdJ9bBcfGGB\nsc8Xa5ZOMc/XrRMeJFUW7AyNGZWqr1ePXrGUPoAI2THLM1oc33ResIrvMl5qcyaOlJCsqMTYu5gD\n0jerUiPft/nrJWz1tVkodkgjSmu0CYa1lGLW+tEpzdUWgsIr+ca3fcGi0d1aOy7Ljv1+dbbZW5Px\neyRg/95I8/sFqKnTGlFwZJsAI7i9KgPx4qV9k0OftIBy9D03Y/ll4Pzs+XAmzrDeift6BOcDfLNU\nHGF8xLGE9Xe8OHvP5vbZWmN7VusF3YMass5YmpeMRTyrkbajdpQ5/Z4GUtK59ZwQ+2FZuq85XeuL\n9haVGKR/4QB5H3l85vNzYUEI+f84V01jKDMjAtyIG8QOdYzzIu+nMW5HB1T24/GGz+LQ1iKIxdrX\no3bBWTPQ0r7D6d42PjrxZ3sRINFm5nFe5L/7dCw7nvT9/PU/0jJAOTdKfTv/7seAw9n7zFH+PKcz\nNCYQR2rxQmFP9lsan97BXdJvjDlal47L2nDZG66qkngtCx4ljifXk/f5sP8fPewrIJh0H+2pQzAJ\n432yoN3ef5yB8FH70TLVn+3naJ8gAmTRXSD1Ys1p9wWFpbTeqg985ICTltcK0ODRgEdjbD0LUo2b\nCQAVMTo+iFsvqLXhi4IFWy9Ye8FaGK91x1cFFwpZpCFEuwCNZO3V1f6lj7IJntGYbKE2ulSm7eVN\n1YwgpPeYj4SwvQWoEfTPP7/gGC3fNAMemj89Rx4qC831ooYxMwELUNq4aGcBRf+u1jJek4Hy6AW/\nP1b8x9uCyy9CUasrY700tEZ40cjX+96xKOXXNgECufM4U6XtnPZaIc3VLePnzMmZxeSMWguk3N7k\ntJuR03EsYfQ9uv0z3KsQS1pIK/79933Bt33FW6tDWTlxpIt7TcZOkQhzRE57Orb9bpsYVYbg21id\nGVpOiZ2iwmeXJ3nu8TezRDFzNHRLAkg2p58JWq0F6H0EGzqOEeczISNJZ0jXkZ6xmVGC6TW/nk7u\nED96UbG+bJQTHrW4YwEApStgw+dRwfk8rA7gHJXN48NA1Kb2iIc4vhL1iPfmqJ4dY+exvrbrvyQh\nN9OIyTRvcwgy9VjGRktcJdFBoONOhJIqqmy6juyc+xmMAFsiSgkDzpoBNox4Pi2Z5qUCX7VM3VfV\nrGHA6f/MliIzGvqNKao69Lg/pnFg47+ofkCu5GDrlZTXjO8aWDGycOS1S234cnl4n67G5EqOWH9S\npSKDuh9FPzODbOsFj62iNnm2rcpBGMsxvlGKMe4jSgE68L6t7si/1o5SJeqWNXMeBkxk57HLMbee\n5l4PsM/mJ3Hs23nNOGtbl3KKxuyxdaT2oLfbujfr+OSoeH72mgqT2n0kyHrBqY+VCAsROo17yc4F\n1MOJtnVBIo4xz7MTbHvI7vMzPifBEbsmBS1bxd5l/l7MYSJjvIQtcmtmVu4Y0lqY0roTFy5Mt+5s\ni+2tYP1FmAkmXGkVRLJmjqQtjOkMm+4ZFrnN+gdt2uQWIoBjPbF70HhkHdjump9Zc+oaA1aQ21gS\nRNLXXGUo62QA4eidsUANwAYSXT3153usx++17znbOX1gTiX4RzVLafj/u50DCWKTZ9bK1ovs2Za6\nYCk9HWjv+r2dBYnpQLsDu1Xy2iM90+xNq4Y0r8cE0RGzdWBZdn2ul+G5me95DnKIdk68F2tv7C/P\n7CMB8eTvZ3MopzQAEYT6OdUQov28HIyxfYII0Egv6mmulTiaEbl8uPNhqQxBHxdHgjW6aJvAscRN\n3qwj903ox0Q7XtUZNoR9LR3XpbkBJbTkMqH8hLdW8K0VVwm/NYnE5FrYksc3bl5/ZAMCxkgCYLmy\nHY+2jK95Tu3o7P2Rlo3WnSNvtE2bci2MF9r93EvrWKi6sWKaETlCywA2kjxaSxWx7m29YNuq55WV\nlbG+NLS94KIgz6WuHlWMSC5rxHocIwEGwqg3MclrjRy5tx7O/pkDmfsn5xrfI0Teum9KJMBJe3I8\nPxZsUxuNGII5OYvvGr9ti2tMmPNz7/ZMFBRLneHIlc7jYH23Z0pU10mNxhqpMUkrYbzO55NoNpbn\nc5tBZsb/ba/K6NFc8qSJkKPG0g/tdzpmV2ZFpRSlprgX5mhKhB9AHzefrJA8Aglj3+3e5PcMWMqR\ntMrkquzzMZxpArguABfyZxUw2qU5yhmcGA1agkbJJvbRjz7eBkT26Vm2FDGviEHhIBo4KsKKNBn/\ncE2E7GDb/5bW1Jk1dSAU8gFxMHo19pf2EVkLJvouJTEj0n+t0t/XSvi3Ve7uL4to29hnAeABAJ28\nBKyV8toIuGkO7pDDreCCnftSuqvmZ4f0vZlgoo5Vck6yE+w10gvjssrJS+l4BbBtS0TVt/W768X3\n2kBj7xYll8pDWfNCPhuNwC5kml8DwnGXtmGpTSoaHc4d84cV5BPRtRHoO+23rr0BsIfQrbXMOAix\nQRNQxHDdzgjAx5FEaP9yNSJd/QDwIPAWJWvl90JCiV4oAMie1oA+ndvmxll3crBh93kU127aGHAQ\nIeb3nM5ltklUxIi1aT7nsnRPHew7gD4xI7qlMeZo/5GJEAyEcX2YmTrAH3cCntGXz9bZWgMUKUXA\nLlKGJHAOUMt3xV6yS7d1t3X8cDwm9/MsneF7or8ZMDhLW3sGQHwETBwrJaRz5P6efGcGMA7HOjkv\no7sGwtn7zj4Ap/PIe5YyxNyP97YAfbN1EhL86AIcGHjweCx4qCC3pRFfa0fjsN8BeAqzpFhqmVq1\nLWmDVxazNITMihz6NPwt6wVjfL7dLrL/eXz9ozaPX567nT5d6Z+9fYIIqWWaTjYyTLn/nuuA94iG\nuWhhZ//b1z16vvlQEq4igotcvagc8uu6g5lwqQ25JvV9W3BryyAcuKvB+dYIb7IO4dEJ9y75unuP\nzUCUW6Mftjhlhy/qIh/L8UWfk1FaG35L9ceXwo7i2rfPcqa/t4iZUGVuZ8YYlTDzVjRxSAp5DfHS\nwzmyAJeV1STIIg9Y5FqM+W2raFrisazdx39mc2VhOtL3s8MWrI6J8g4eBOO2HiyEjBAb2jxQ6+lo\niIjS9RhFz987AyY8murnYFQ6Rsre9jD+/7ZVZeGQ069vLZyX3GYRrjyXzHE1KmtXle1ZBLBSnxzV\n8ULMIM5zywzl8d7EnLY86l3zagmjA2MR41nXoArKEuAAeAAa5ARCu7RIgY1zk7cC0Zf9XoGacdzy\nPbexEg0KPsyL/BDZvCg0ApVL6WKMqq7FSxUQsihKlp/9zFLIbRbQs/Ksc3vOahn/JhpTjgzgzGwC\n15thESEFQnvGoobACDb6eZJRmBS7VIySBt0Wi9ie9zkbvOQMBnvGLoWxF8KvKzsT4aVqCc1CqO6R\nC2BJnQbVdROPbQl0Kxp92nlaLyeAyBgItxYgi+n0ZH0UgkVmA7wBgLWylihjZwCdOfpnzRh7leCg\nwzPnHLC1ICZBJYPgxmPmNQ6QvSQDCYBUA6pFtB1s7V5bwdrHNKKqIOqliDhy5D1H3ro5Kc8AXEv7\n2X1dYk3FSFUBfN35cfTle1pEFvEbnQEFMPK50l4dVUTs8+O8zmtCHudDtR7YXpTmpAuS0uFZIxqv\n5SOg144ln7PvSzUkAOh7QZ/CnF33ha3VIYXEWJTfAxEyE+9szQqn/bz8sO0B2Zab12gfp8IoKW/G\ngIHhuk2Dh479GcHeeC1Wkb8WBf4r9e1PHXb+Y6935lObuH+nb8/AgLO8/AwSnL2egZQCws79kJ4y\np+aSBhlMu6NthMdtwb4Lk8tYM3etypBL8VbquOjNnIWQa4lSw6UyLtz02Vaxcu4KRh/THLP/4Nfo\ne6LO2WR3Z3bhjwYRf1SX4mdqjL/2HP0rtU8QQZuVfbHIl+UqM4JS+6ZOOiCpAmdGp+Sa0cHRPD8n\nBqPcRFUuym+vRaJZa2147AvelQb627bibV+GcmaNScvIRITi0Rn3hkHJXDZYPvRdnLCR7j/0dXIy\ncqulY1FDLnL5oHTeozhe3jTNuf0RhsIsgARkmtlYUkk+H/fB6N1Zzd4WblFBt/4TKIEPj3cxrBct\nMdV7qOO3HnQyd4iJ3IkLY+Q4GSpJJJMoQKg5WpXHMo+V9X3Ou7cxzxHjrNQ+twFs0JG1c8zz99Yq\nflMn47fdjGm4Y2fpDFbSDIBG6c9vrIhpyXt1kdJJZihmNW/p3Fiu0NpxDse8Hij46avM8PKfQIBk\nIayofSIBAfKYV1i6SlD0O+z+9xQ/MoHAmBcEUh2R1Dd+bhDm+wyYEfnk2ZQTDC3zCGyssw6EpzJ0\nUTLPX996GdIZrD8ZuGpIzJUP1rqzknl2LXP+a6TsRF74+y7vZ5q3ULG1Tns3B1AAhZyT3lmAGnEM\ns4NNrt4+Mhlo+Gw+VnZ1jcFgz8ilAL1G2V1r/ne17zF2pqF8aD5m5/Sa5s9mJ2VV0IWYIn8ciSbu\nFHwDFxiPHn15dODWCm57xeUufJpRS+aPR5UCpBydpLNmTm+uLLGWDu4FLdG352MQGNfadK3U/fex\nYK0Na+2uEfTLoqJ6DAf7L1pi+cvCeKns+7dR0TvGvVFS/wLaIBgYX3A30UuK/dpF2BD2wFjK8fnD\nkUfbyIszmNWhLCf7X/tM0/fls+Mz+9HdHJ1f+HX5d08AjmfsQr+G9F1gfA6Hfqb/rUIKM6Hoc1JX\nRrtLCU1jtbUmzEADfYEA/LceQpqWvmagnD0TAeT/uPP0Aybc0GzuUnqmAMh17fNnWfeDBDbAWIy5\nxGOsCcN9mgCR0W44Fyn0c/87OIX/LI7mRyyMzHg4AwWZCVQAtnTNR8XttuKh7EkDYe+abijixvH9\ntfQhCCAArKyB2Y41sM5s0kWr9MyBI98q6AjeGrgLfLwO/Jlm4OY/w/3+bP/+7RNEAADQU7EQgmx0\nO1uenbxuj092uMSZILRD5P64qBvaPJbZY0cwAaE/h4I3+eL06BUP3TjbYLDIcfJvi7R8b+MkPf9s\nwOVFL17T3+m1WseI5rNc0rP2tE9/AIhhBpAiFIJqjwfoHGXxAHjUufJo7LmxRIgSU6Wo6N8JKIK8\nwYfz6e9PoIm95g7ZiaFzdp25ZebA8PrUn2fNKL7j8fgY2dZryoKdm1JNc67es3t45vw7yGKRm9rR\ndhnf+TsdhPKd3EkzDOc+sJ3Hog962EyJtWs2pe4RkLHqJnaNppcyqvtLakacfGNhc+QUBDPaP47U\nxmfP2iFihXOD3p7jXKqvli5gn35mbUUcOPVc8jzK5egAuGE7nHuaJ8/amSbCjzS75ZveVwGp1NBC\nWtumz88GEzMBSRsgp518LxJ81lyUdQJY1iJzJfK15TcRu8bCDOid3ecMrNiznOuKL4VBHWAHLc+d\nHcaUB17kb8v3tXSeZasopfvzL/0y4Ou8b3+05ehrLR3VwcHuY+LXPThDBm6oUV1yOhihaP65Gdsm\ngLhS8Wd2LZq3TkL7j4jwsZ95j857uuevG2DAtsf88Xn90TmftZHNdfy87d0fn4f8L/kOf/d+zvBz\noWOqiX/25PTGYjizH+ylzMa0DtHC4C2en3y8UTySDmtV9N0i2+Nrz5rvDelzeT22QAel+RPpf9pv\npHS4GuK0pLZEIQZTvGbrdN6fjuwGO3YGZI/Obv77e3PqeTrCk8//wBo5pwQcj/H8IM/O66kUT9gE\nAE5ZCH+kWdrij+xlRAyU8blhB6tSlSFLw0TswfkeR/UqUtYqD8ebNZFKemaj39N1/Mml6OzeHkA/\nDc6MqSbGNPnZlRE+2yeIAADQuuZkTmi8s2oE761VfGtRJtBaRoqXItGkMjmZZzlz9t1FDSBjATQm\nvO+hkl2J0VvF+7bg/71LusBvew0a7okxO7MJTun/A9W6O4CRj+WfxWgIdOZBNIaIUWvXXPr4nEXy\n3NA/dsPPNffR6/n+4OJoSv4AHBW+JQr+zgVve8VbCxAh09AtSitj2HFZGpalOUUNAPa9YNsWN8C7\nwkM5p7zkeLT+EarJoTWwFkk9YBZHKX/exiwbEkQhOgYIfXglibR5uo3mxdZypARn0GIAf/Qfc0bM\naYkIOAstG5E/3kGo4NN7Yzl+/j8YnUTp2PtBjIqIfNaV0dtJKoRG9+b63tZyfn58RxpzvBdOWFeB\ntSLlOQEvW9kZuFMZxkscF0K1aDfZvIl0ILv319JRYBEy0UV5dCBrS7jBmcAM24ItCip/hxFh15ir\niuT0mGPd9fi+OVd2P3Ok+VK7UDiZgFKSkcOnD6qxXty5Ohn/bFS7k/DE0ap0DlB2mMipvGHq+o/G\nSSfhGE3sU9QnN8aozeHPWTLLnhnKBkK5c6D9bkl4sEBZRYhn+d4IL0XYJ+Z4LSUYLiJOZ4CD9DID\na4v22RxjAPiy7Fi1Xvm76s8YG01y93X9azIHtx79QQ+xwlurKtAgkf29VVzX3dOI5DpHB8f6vRBg\nl57BEZ/nfG7QVo/CxVr5aBVrs/Kd8SWripGN6MvScF12vG+rXmOVyjwUDJtLabgWwqV0XEvMQIDx\nugCvlfHezCAXAAJ8nEtrCb2LS3IaMk04HIbjPvy9Jk4lTa+dfe75MXjYqy2livFAsOzG5zCAIXuW\nY1087sH5crJyvH1nn8ZsoNv7Gjo6P0thtDaya66FXUja9FSXF2B7E7aNjYFXTuEoHWrzZkhLYmP1\njGk/Z7fH9kSpOGKfsJV63FdIP5fT/GzMc1rnsiggV9mBPkCCLL2TB1uoEFrvg+1gLA95LfYaE66c\n7YFcucHScAzo+K9Vfm5Oi/lHtKgyEDZW9/n8cSnH7x2TKBjI9kzOQaHhexWwEi9FxV2LskxyedV8\nP4CYK5UyI1CAfNM6A4DHvgg7KjEy4/u5P/EcmQ4CMAJQca1/cGxO7PHcBs2MfzjP4Z+l8T9cbPSf\ntX2CCCet+MId0aN7C+EqQBkKRY1efWqvRYxfiQApMqufn8uFuZOSFqm1dLRWXd3YHcZO+G274O9W\nFaAdKfNLAa5gfK1hTAu1T2mxKe+Tuxq+afNai6kSx2JkbSnsdcXl2iW/1ByxnBZgC2ko6CN9Lzbu\nTIu28+XosNA4aYgEGw162Hg1iiYq0KpXsVe87QtuKbf31qVyxVtiEmQQoWo9XzGUdEzWWNxPHebU\nl2wISMSGIoJtGwDCsDLD7d4jaukG2mTEmECfsSOAyPW9lgCiCoobiMe+JqdPN/pKIepoyu8LAegE\nZQOjIJSFDax4YfnsowMPHUAxvGWDtDSFhdWgYoYVR15h+X2J6r8wlmtHfYTQkPSpY5+2QIs9FQRC\nXz6wtq1f8ncIpF5L6I50ALhf8LYvPpYrEVYi7Gk8sxF5McGkIiCLiI2pM6POmlQD0fEtVgYQ/lB8\nhOGbEZJprfa85XrzljNvq0hmh/jcLnwwhqDjtxQG9xzNtW9XHzebf7k/zKOxJeeReclFwBfpD58a\nJFKCUM7r4FYy6n1tIULv8LSR3DKTYGYG5CZaF93PuzMLPVTf9f67Mxb/LwqwLrqOrIX9mTEH3cbk\n0UNMrlfGS+24lmOkrWg/TBxDIuXjXkAKtIlDLEd4qSKuWwio+wgoEcWqyhCtEhN5lfczqBkVPrCH\nM5bniI3FMoyFAeZkX/XrtzFolEc0mjhYDStagBV3YC3V2RFA9DGDZqL10nG9BCf8vi8CHGME3eQ5\nif4sJH26KtiaxTBUifAgAAAgAElEQVTXJtTks6hv/txLZbzWjos6gNUqBaQ51yenXo4TpTwJ47wM\n2ro+Jzq2eSytT9lR9Qg4RbDCHBNKDkrMZTrk3RcaS4AKS0MYlGPkewR9LtXWzzowMfM9s3PbezkA\nIevMuF9eSsfruqOq8w0A5UWc8JZSFGuifH/UMmhgP/Z67MHWT1kD15LSltK4WtWKzESwVtP9Mjtq\nLR3r0iQNo/JA9SsbY+GOpjYkMXkaqN2zxeZujzlgQBDl4MR3r/3POTdDql1+nT52KufmWi/DmhwT\ncGYlzGltcRzS45ztXaTfsaLRek50f9+EFU/7qN9ZoBW6EHu1pRLUMpe4ZdAClKu8tt46Xtsmz8hj\nSSBCR/eKZ/JZS01bqPveZmm0uVqU6dFkRoIxnrLdnPfKQgHoEpKd7UG+qBhmz+iO0DT6KN3ooybC\nw59MhJ+9fYIIU7NFxxaUq4qZbUpfzBulPdybGfdVDLFHA4quPlbusXNyRBM6aQ4XuuQ+bb3gm0e/\nF68J/vteXcSuMWEp42Z8VQdQi4/JIdkc1Ci/ZPTgpSR1/BJa7GFEyHULQ6IP0WXWBWpJyqzMiGsB\nhjJkw/g+9/d+uGWapVE9mQmbVwqoePSKWypB+N4qft8LHj2BM96/4g6TXVFjQlHRMUBy1UQPgT2i\nuxbZEBZm1BRpJ4ygw2y8AEGr3noYyxdlJrQSBox931gIeRNYixhh1vdcZjKPVj6OXG04sLGhsQI2\njE4jbXMtHV9qw6Ynf60yX26p5vu9iGOdKxyQGmlyKLmg5gj6mDpTav6eWUPjfZarSa9NBgIjR6Zj\nnJdk5C0qUmoqyK8vD2zbgvu2DGKgzphQAxsAdgoQwdYHu+bcMkAYTASj9WdHcfg3XUdEG5D+IjB2\nDjFVE9XLaVa9SAWZxgVFn09mYedwctYeeu8skhqRFwUzuQzzde6mAYRmnAMJVBicq/H3cO3zMc2Q\nR4BbFwWrZmDsxw2d0dExIO5S+MAqe5YGZM+vfX8t7DoNAFyo9cHB1gFEQyebxnsfadbuXOn7rDoa\n8m1Nj5jmey2y/lz1pC9V7mOr5Do+j07KSAvGjEXXZxFQqUxC8az6NY+RLxvDnJ+/2HWka6Ghv/rb\nnHxfp5r+DsZdCPmezDX9/rJ0MEcZ3xuLPo0BIvdWcNd0NetPVBiQ/zPYu2qKh82tXSO4+X6vBHyp\nHa+p9HJnWcc2DgozQwCkjj6sVVk34Ufa9/bHId0jXYsBhZGGkcZzGlBhJaWxKDJe87g/6/dQASWN\nbew/lsRkNgT8dWO3ODuHgNd1Q126iymWIuy07U6eTij9FHHYnM7Qde+fS9fuHOk7+Yrs+m3czBEz\nQIXZ+h/MCBlP28NtPQ9gP9aWLlUZiOW7SUG3mniwVYZSe6kkG9DW+AyQG2P0LA0up6lmsCT/BoKO\nbu0ZW+CvpDP8t9IMHGD0D/UP4vOEWiRNNRz0eI4OlaEIKK/y5vpLR6kb1vuOy22J557hwuc9sWYA\n2Z+Lrn+rzmdJc5HztE647fXw7J0F5eIaoo0goL4ms/kPrEI5PTDZ/VOljp89EP+pCSHtE0Q4aUTk\nkcZVhZlY0w1ssxFKqzpkbuHKwypOu7zSQWgKJOSNgGDU86A2XWrDo0VZwre94k5Flcmj1nAliXIR\ngsr+Wi3CmpB/VN0UpUoDAMj6RchpD+YM59zoTEkcwA65qCEySsTYWx3o0tmh88UsOSqj4OE0/ohc\nykGp2N8/LuxiYAT0bwJURn28q6OVxQT1UtwRk++JYfi+LeBOWL8kNsKbnJeT83HvFdTiiGZsl4SC\nm8MwR5hMPXpNhn5jaBmoUdnf0xlgr7E7NBYJWabjz2NE0/8ZmbbIMEFAhBxNswi+HdPyg/++BUiz\nMQmQVXukKXTTEIgtrBZG7UrbTPOlrKFonedJ4cgdtf74d06c7bllJkIlqSJSlh2vr5tct7JNLktD\nfXB6JhhrJ2wET/mgzjo34569VnaKeY5GyvfDGbaoFSOcTmOHRKTLnI8wKi1Sueg9aT1EB+9WZaCn\nOtKdtaxrYsJYziaCcnnXiEdjK/E4MhEaB3XZytTmqKJFReQZ1T6WzJqB38vZ6DAWzJmIa9f7dNXx\nfalCV85zWwQUOaj6iPmdn+28BuW1rrMwqVYCHqmfmdll120sLAfbWNILCupB+T0z1YSpQ1iSJ2Ia\nNrN+ByGABTsGkcydtQfwsyvFtZYAbC+149olJWDTm3MpjJcCbJXSdRNeSseXpeO17sN63jSqvrGx\nAdhZXwbAVZJ1JuMiDWaEw0HUSuTARQB65+lI7vBNYI61qDSglSU6OTvMjPu/7Vfcdf29tYp3BY4N\nzDGRvY1FcM+1EkjAwbUEc2/RvV4id7bXdvy6NPzb5YEvF8kB2ZsIqeUyqOHQnl6KjOv0O6/JBBye\nsY+ctwxPmeYJJWA/A2cHQNvmVhqLquvPfCdm/R/TG7LG9sOxl8hazT6PgjHGh+dsLR2XtWG59IQ6\nAeXK6H8jTyekBJR81Ow5shzu+TmbU7JsrerpRpABT/MeilibDXC33za2dekSeCjsQpG9C0heajcm\nPJhFOFKADD1mCeDOqfUUtsN8P4WKPzp21n5kDv1I+5jW/sde/6stgwJ/1YGzYxWi4XkEbB8Z0zJz\no4t8o35tqK+M5cFY3h5j5ZkHA1h9fXjspmFWfb68aEDD7FfpAzszKwf0nqbbweaHrVfstsJgR1E8\n+/la53a2hp1VSAMUnPpp0xk+m7VPECE1M+orCY0REKoaQyK+v6DgpcZDc+9BhQJs4Q4gAVDar1rS\nPT25Qb2MxeOy7PjKhN807/NN8z43pUJ9WSz3s+OXRYwpMxpe/P9wpAWRF8pnMePUN+/MRJBSWdmE\nCCoUDsruQBkcj0KMfS+nTkHeZI2Cb1Ssw/inhc3E63JUaP6MNavJbJSwztASXORR25VYUeDoD6fI\nYab6cif8vq3YtoJfX/T9F6CsTYwBM8DfgPdtdaqvXKNF48mdz95HI8D7qM7Ui96yS2Hcmjh/nCi5\noeYe+hvi/Ml12b0RerjcWzvXqv8L8EP+uU37ZGCZMRrsvmi1LWesXNfd5xggkWzCZchPfVFqptU8\ndtALiHKfrBEbkvxRv6+rRCoPOYjm0PhYKOMmGVpmCDYex5j0vtszUgvjetmxrg2Xl8jDbbvMf8up\nBiId4cJRFUDGVZ8fMwRKx6V0jf7rOqD9vJSORY93tUjxUONR/mVBS9wpNaeqFkoAk9zne6sjZZet\njr2OF6wmfYBQmQ49N2OaxByS31vpQdOlEJh0Y6WwVijINFBy0DUb1tZXu9sEO14f1gxbF4q+BwC/\nqmeyJwbRrcXxbHwaS0TJ8orlPLGGBBtFYjpfFmF0RPUC8rllzdbnVecGIM/tC+S5sfVSQEjgvTEu\naWGzFLasHH+Wd05QBhLDyzQCwkiqjXCxVKC9YimyZc+lBLNAna0pO/PwzL9Uxi9Lw3+4Pob1fFNt\nAVMXNzHIVsif72sRnY+OWI8be8w2xGGLre/H+RYgEvnv2Un19RNxPa4b1AnrRfe5taEUxu/3ixvq\npg+xpVSDjQ2sGDUzagnQtvockN95b5KxbHhdd7xcBXi83YGqLIqBBm/rlL4WgIDMlZEFITR6Azjb\nyX4XjKYxeu6ifPqaaRXYPm5j5g4FYh5YP5akpXPxNI+8ZuTrMltgdKClP1GuNOtnyOelH16tx/cw\nTgwZ0VNavsZ84R2or+J4Z4HFqgB1BjdtjDK4+cy9dIDf9kZNTWLE+HYQlgmAMIuuJQdrKUnI09L3\nqtgHy1UAhLLq93tiG0z2y1KL58Qve1dwM1Kmdlu7Cg3Pt9sBZns6UBOAlo0RMyXNB9kj8v/+ul53\nTiOYwYhBj8DWdv74dRnXvM4fywczbD4fDbzuxxuv4aPWTy3MaA4igHQO02HOG+tLrknZCSVAhAKx\nHyqA+tYB3OX1wqi/d+AW57u3go2Lr0cAhuObLfSyNLzvwgR0EV/vawB1BGMgiWZTH2yhcc3IaQ/D\nGNA5w2VmqjxjTP7MTeyPz0EBPkEEbRJ98GgpkRuDgRp2vNTuEX+JDgk7wIztlnKP3LAu6kSWoJsy\nwni3RaRwRFm+ruLgNBYRLInMxWb1Uhte6o5cw/lSu6OIZvi99I6XRnhU8jw0M6gkCqMLmDsRKaKO\nMB4sbyu37HgstWNv5TSnVgyZvNF9vPgf7wxODJtw5goxSulYa/R32apTBc1IJTBemAaDw0SaOosz\nCBhjgfDeKm73FbYT0JU0GtlBqqSz7wWXe8NSFhffMzriyBoI49BGyBbpLwvwRcMTWQ26IpV2Aicn\nbgZkInq+UHcDz+n2NbQ6zOF6dKOz2nuWM9w1KhrpHcZC+XLdAIgRzV2YGpJ6s+j3C66l47LszijY\nmxi8zASq4YTZ3ImoIiTXsHa/h3OLCFts4Dk/8Fkz5xsQI+9yaVivO+qqY95sQ++41ubU6LUwdhYH\nMKLQ5JGkEF7reFkaeI9nTJo4CDa+u1LMGcFk6cRgsjxyikxjX3uQDP3u9zvOEFGGLHAmYzKK4uUy\nUkDQaZlzrrOMETO53od9357jDGzZOpeZNNbnJX0OwLBWsaZjmX6GP8uIKKU5GV+qrEvbMC2MaRHa\nC5ZHnCn8aGroIY8ZD6lgWUxrZkeYJsKlNmcDWJrISxUnEDAtBNa5La/J+2P1jPx8y7FibZLoU6RI\nWGWFAnJguKT5YO2hQrEt0eaLOmnXSsPYvhTG12XDL9cHXl8lqk4Erzrz7f3q/dobsJXi65o5vo3J\ns4yMqt4JzoLYOSJeMyjce0Froa5viuZ7SguwdTKn212qzP3Wiz87y7XhSjuu647FkCBUT4fI9PaB\n2p3+PkbnwnrOfS8k60N1plRa69NeQha9dGCLx2PYOl1ivuY9LWvfAOG8Gihpn1vSmm/3ZqlNwP5q\ne3qkllmlGPmsHFdEeQ04FCeqJSAgu2BDuUI99yWtUTaOvmeYU61Aho1vB6Nyx0IlaZRICkD9CrCK\nffYNqF8RYD1GwCmzJXOKhPYWZ83mlT3ngDEwFJTWG8Ea9TU2g/Rb5xyHoORV13ZZ4wOALZVRX9n3\nNACg5itAuh75fekN6x73TFihsf9ubOl0OW2CsBB7WVBA0wS17/Nzl/UzfDxOnXVGXl6GoFd2mGZ2\nivZlPqa9Pp+vgxVIyOsyJSAhHSP9ncVI/4io3eHc6f+aNENGTQQrwamvFU1VKYg0l0sBXStQCPWy\n49I2feMR57vJQW+tYlPmVRY+9j1Wn9svLw88Wh3Apl3TDjNAbnsXQZ9p7VO2E+OZJ39WBq0NEsHr\nvIY5c9Q+x6YtEfPhGUPjs/231Yjovwfwf0LiHjuA/4WZ/2+SB+l/B/CfAbwB+J+Z+f/5s+f5BBFO\nWqGINMoGBfyytMEh+X1bcO8mRhWG6Px4lfR6GI+BJBoC2Zsc63rd8R93QTRbyu38umx41ZzMy9JQ\nib1KADCKGlqz3EPJ65bX8qbvVNfaJPJXYnPLNMRaJmFFYtSyRNRjaaIkmxwFc6AYHKkUuuidbfOz\nI9h9jCKCQwjDYehLZSxrC2r6XnB5NI9iA8B1X0Q8LH2XQdj7GIGtTSo47Ey47wu67gtLIdCVUB9d\nUCEA63vTTT9SKWxcCTHmNkNE1C3GdyXga42IUKZD53tmqSc5UmppDDmt5FoYl8K4lmBBvBTGi6fm\nxLGFEUCDk2opCwzgodHPiwl2Lc3n6r4X7K34RivHVGHGJZzVJakOG0tkrR17lzGz+1UuEIT/JHhg\nUe2c/nIqEkgjfd2MjJWAa40cxLp0lAVoGuFquwCBhRiX2nFp+lkq2KhjoTpEhcjH0BwCFpHGBSBY\nCdYC9CJsH/3uUhi1E0IbQcafC6Mq+GejZfM8C196qkgPxs9CIRzn44Ck8O0RMmGSLEvDZTeQZFEB\nJxqie0Ts7J2S7m0lAZ4ylmgG+ciWkHvgKSBPEnAlfUaEpaz7Ek03Z1jndGV0dKwcKUfGtMmMJvs7\nO2HBgjgvyzq3kq45rluiRbY+PEhAjZfScdVn5EbWV+A/KDj1b0vHV12nTaWU1VEzsGS3HHASQy0b\nzKZl89YCuFlLwa0VEKr3Uyj8wqqw1KIQao05ZTnwa+m4XHZn4ixXBgqjb8WfsffbKkAPyf2RPhrH\nJQxRsHhmea06Y5mZZs1jq9hb9efXKonsnQaw14DQABEaLsqC6ompsVxFlM+0Cm6t4lELbj32ZOuv\nVbGxMcq54wFqMPYu2jR2FonWqdq/jW8vWtkj7yfHXS3voT2BkRlgJ+9n2B02lgbr58i9fW9OCVgW\nLeGqz/dSurIQRqBEjkEK5MYa+Sy9SMbA7IA+gEmArTdhz1h/ZH9RECOBo62THwOQtZkIoJVAFrn/\nm55vjb2k94Le6MDA+Si6ekbxz+mMVmFqobF6S3xfDm4pDDsDxLEGmejpmtbZujLqq51QxxvCrhDq\nlJ3H7I3EHinGHulYPY1N04MSE8uZHwR3+hnq5PGobZWv56OWo6ofEdT/UfoIz0o+fnTuHwEO5vKP\nhHKIGHeIsK41mRcjC8fYPh7ko2AisHbSDkGFwLWgvKqN80tH33a1k9QmaEVT3mqsA4WxVhHiNBBh\nvTT82grqI7RVHq3i0Yp+V/vY49m2oJMcU218ZIAy0jCtfbIL/nr7J0jl+N8A/K/M/H8R0X/W//9H\nAP8TgP9Of/4HAP+H/v5T7RNEOGlC+Yv/CzF+XTdcaneD494KCtVhU7Oc1zYtdpb76MayPtA1LVJQ\nVsPluuMXluh3Y8J1a1iKGEuvSqcki8rck8ARZIN+2xe8q9G6c0lCX2OT6Ioa21UjGC2oU3YeAF6e\ny1/rEknwqMfSse+MRwuH/1IaWpX87TOD/Sz6M/fP+zlFRDM4YFRtyTnUz6nSc2tR9nGhrjngZdgI\nL+agl3ETXYmxtYLH79L75d86aD1ayJaHmvtoTAlDzolDGDF//VoYX2r3e7hxmKT1cN1qYOoBOouD\nldHptUiE9K0F6+BFVeI7ihsmEnWSfkY0KkCEzhH5X+l4b3qnMKTT/AOAkiJ2e5p8Jibk2iK1Sx4s\n4LRPua6kaGzinB3D/cmbJzCa7/NUWgvjaqk+mvbSd+Bxk6Wv6bNSqjyL7ngXxsrCRhijQnK+0Dpg\nXNcdF25YNPT0ti/St1Z8fJdePdLo1QH7CC7GWIVRE7Tf7s9hBpI2tghKOI5WSnBJOgcvLxuWtYFZ\nHK71vmPblqGMlN2f2VA36m7tmZItIn/oITxpNNxeIqpTEA6SlzolUyI/YyJINM2ERZdiDionsIRO\nxwyISKndG2umI7FzAWvFmsZjrj7RKGfllNbk0FZidGZcK+NV57mxl9bC+E86p//TZcdLAtHkCiR9\nKqeFAOZMh1aEXCH7XhJ9h5ezszXj3gVYkNQMXUc6nNngj2AfwRQzgIs6Pbx3tE32l9YKHsqAK2me\n23xkt51kvDpFlPAsJYBIHTLP9R1Bnnw97POYneZ9XXe8vGxojVzdft8q1tLw8rLhl+2h1y0u+94X\nBwu+LOKmG6jzZs97utF2OQ4HkKytgMzTvUvlH1srrPzarJ/he5H7zEIzLtC8525OuwDAo6Gf1vkS\nwEO8Pu4ved1fqqSQ1SpOCZBSHGh0Km19tz7IZ+W80yM13D87dz5ufk/Gyj7X/WCjllJHLQKC2rkv\ntaPUDlrIaeL9fZc1/9oduL7dKx77gi3t6TZGOXXrWXPBQoo+rwpmGPvAxseemWAi6LVwMIUWCiaC\nsyqWhrIy6ErObAAArox+t8iwnYj1HA3rfbxnOV1kUZADJe6hMVmYokIK2SELDYKLtk9mZsWZyZXT\nEdL29E/dzi3PkZlQnYH8/7H3Nr+2LEt+0C8ys2qtvfc5997X/drdfFjyv4DwCAZI2ELMPMIzZBCi\nJyBhxADzISEBQh4gJE9b2JJbQnzJSPQAyTJIHjAAgRESAw9ggKDlVuPX777zsfdaVZWZwSAiMqJq\nrX3Ofa+77eu+J6V79z616zMrKzPiF7/4BQf73DtnJxKtTARbnBiQwVIIXHnXYSkzprnhtMobelKt\ns91aouDfjumTGOfzBko89EBkvixjfMgzOGCrGJM+z36+kOchL+F9+E4YnweGEqRyi/dJGNufrDH1\npf19bgzgK/39awB/R3//MwB+kwWV+5+I6Bsi+oeY+Xd+kYt8L0AEIvrjAH4TwK9BvoffYOa/RES/\nBOC/BPAnAPzfAP4sM3/7KToGEf05AP+unvo/ZOa/+tnrQz6UWIYmItNTYjxNFSW3UadaDDJxLMzI\nW5WOun1m9pVFSyi9Iy+8Wl8wzg+2NFzwsBZMU8M0tVEpoNWEtrhBA8hEcKkFH2rBdQhNJVw7yX2Z\nY8Yxv81BgqKCY77QuHEvee6aG5YYqfFAzQHJT52aUNxjxQfJ29+rvEfnL/b/sQnIQbsol1WfkElS\nHdBsOfMckGFF+EvDXN157YMynMZzJZJoiEUDjP2RSf5mzub5ZQXNDF6BrlFs7h7FHfeDHKiTZgiQ\nam3weNpMIsp3yoyPVc/HFtHx/wBfGMShsgTehIl4RHLkeQSUeMkuRPeYu1LCgVnDw6fugo6xfNw0\nwKKCk3p8p9xF/bqlEYl7uc64VkHHj9FPIgwaagkaCgNs4IbSk0TKztpnT4R+MceCAzBBSsd0pD3S\nV23cxDFln57RmqfklRjmWVIt6pqwbZ7/nbPQgefcRunHK2VMRNhCVGjSiFWkAhPE2SnFI0pE0BJ8\nBZN+i2YMiuK+W3jcA9PixrDDDjCQlIQ9uDRpdNsO9nQAZw/NpWKaG6azv49cOsrSUWuSKJ85cZ1A\nVfp4RNioY04mRqjvOylVduRTS+rMrKkzZTiVjJ4I3D21yM5tjCovwcnDIXc6ulVCoDDWbg0fF1CM\nBrg76MZiWJrU5l467fJTBwgW5v3BQtAUDwDIqaCpuKJ9VyZI+LYwfuUkY+2XTutIOcvkKFnnjMqE\niR0gsvUi6qNIWpqpx++dz50+TcsDnDDnp+u/BUTwPrdqPVvNWC8yr+VpQzoxaAbmJx37LwIo1hR1\nb/JwXB3o0ChrYIvZGrpjicBTy3Ygj845mTyyHYUwDaSe54rTwyYVGV6kL7dVGA3T3PDmLCCCrYWV\nycEC7ddv5oqvpw0f9LufUxoMEn83t+/hUVOsiHz8djhosDPWbR7SbR2MxDzmr7oDbGlEDK2/LL3M\n5pYIqExhDFjZUZvz5twwTeKMT+p4nLeKU+4oCgZF4bVEhFNmnKx/W8eUMmr378BBfowo6Skxztcm\nTkvoOk9n0OfTCgUGGJuTVCmBuQ2WGwCcS1UhQkJ6O8s9rh3tXUeagOkkY3JdizrCrkF1z/mx9KGj\n9oDYE4f5IRlg00dfExiZTRDYwRyGMKEozFWn3HEOtsM0NaQTQHMSQGSkOALgJuMhRJKJOrhjpPVN\nS/dUP325pRujg4I94Sy26Liy/q+HfV3UmHd9YkBC3JZUO8FTEKRFc3afKmH77TUW+JXtu78dXpD1\n9afSGeS+zVa67/ne1WD4xDYDRlMo9VqSg8cjRbaI3gUSwKsCQJXBjT0VRifgtgjQmhJj0tTkR2Ut\nn6qzhzMx5rnu30uSimAnVJRsa47aYrVgqj7+LICQDSWCMQM92AT4GCohZdtTso7vdM/KMfApsl52\nKSaxjukPqvHfK02EHxPR/xr+/RvM/Bvf8dg/D+CvE9F/DPm0/gnd/o8A+H/Dfr+t2/7BBREgLvS/\nwcz/GxG9BfC3iOhvAPgXAPwPzPwXiegvAPgLAP5NvELHUNDh3wPwJyFz6t8iot9i5m8/dXFbRnoQ\noopodyZB+hOiurnnopoRuHVg7bx31MnonLc0O8K+rF1tGb0lTGqIns4V09RRVEzK8re3TfL1xZGT\nj7gx4blO+LDlIc61dMK1EZaGnVFu9xevbUbR0ZgmNQJs8ZR9aPwNEEe+lL6jqiYOjt13+Nbcvd63\n1yIMHJye3gl1y0iBt7etGdsmKtqA0MleasGzpjUAZsypQxquk0lqgufUsa7yiWwfN6SJwY2wvkif\nV6XCUzDoYhuGGNSQBoaBWRREANzBsedtYewAjjbHyCCxU2JjqcNjJMP2sWiT3I/TYWMea06sufCB\neQKpCPBynUe/fVhnrE30EF4GYPW6yroZlPJMwei2nFFF8tuWBsMBwChvGute36Omm6MYqcl2RGT7\npCTXWK4Fi77X01yRUt/RpO0+06EvI71/Ct9DIgEhunbwnCW959o4HIsbwza2WIYO0ChmMIYtOjLY\nGfrc9fDcFbd54H4NvwHugUXCtzcmYIU5kO6MWZpCHnNlZCc41XaM0zvgiERGPGfzWJbMIu7x3qRC\njfw9grWbHmTCefeeu4VUCKOnvygAtoS5+9gX91hZsq+Ccfr9Pqqz//XkgrfnXPEwVRHG7faN5OG4\nCkCkzlW4ztDXyQTqahTqn8/qtJxz84oC5LoLg5bP0kcm+Ch9bn1Y8LJMI8rVO+GhbSjnvotO3hsT\nn2p8eIeWVy73Q8PBB7DT1xnaNWHfjfdjFQDyxKDSsS0y916XCb0T5rmNdzPnhlNuOKU+tEgAA1eb\nan+4M36k+Nq9l0OfTweHwvonlm/clx2+7R9LMwJ8nbu3LMZx5vt/egFNJMr/UfTXhDZtro+ANINV\nP8fZNUe9IpuP5KfP3Yl4VLsAnEXSOERyyXKo94KuZktEdmFJkgIwShgBSI8Z9fc6uGLXrJRjLPFo\n19+XNDTRz9u5ZdfXgZ2A0NdHhlvSSUzGtH13trZ6+etcugcyckh0SX4y+ztD1r9UHHS3EpZH5olE\ny3kHOCUSU2IwMgGvPsX4bHz4ngL/L9pec6Y+5WR19vLH+2M+3e6BB1Hn4LumM9xrtBv/99ZEAQwG\nWPDMqC9yJTmxHFMXQqsJPVTtKqXjARty6gGQ1vTKzGNe25Z8O/flrkG7kHYIE2tVlvMYB7HUe7hv\nogGiyX46ZsIpPHIAACAASURBVO72xO33Eis0MDvL9kv7Q28/YeY/+dofiei/hwTfj+3fAfCnAPzr\nzPzXiOjPAvjLAP407rtav/AM8L0AEZRG8Tv6+wci+tsQZOTPQHI4AOCvAvibEBDhLh1D9/0bzPxT\nAFAg4p8F8J9/13tJwyiQRpAorQEIz5t02VWrJmzdnb61SznHFmbC1yZFhi3+Oik0Mey3LSFlp1iX\nED1dNCr+fDnh4zrh2spw3BoTXqrk8xuIsHZgaWKQR8GumxJjFBb4sc2N3aNCc8pO2bf7nE5Ncwz1\nOl0msjgh2sRlJRxji6PaKJeyb6RO3nfWeyf0Ne/+vW0Zz+uMRZHfj9uE99uEj9WyOCHR1YNQmdFt\npySCatYPbZOa1b0R6qbERPZ7GoJUwWn052ZnF+g2M0xrGD8lxVHnfRJzaK0vmkb/5hAlXSmNfUb5\nLo1gN6ZAXyWs3dToLcrUBuI9hfJ+dk/P24SLiii+WyUaeFUqNYAhGnRc4yNQNfrDQCvtDInaiCPf\n75yjdkfQ7xro98YEe98Z2yclibJIOVJ1PFRoESjjG7BzmkE38pG7U4zNSZlz9wU8uzPTOuHUM6YW\njUERAuzDYXVHJpPJVu4phkdqfmOPyK9dtDu28H0ngtLb8w4kaMFpBARka80p2gYSUnIa+ap/m8bY\n2zsjlqtrTASj91rVkdGPuF219qlc3ljvdwlzWFMm1TbmOonSx7lXasLfMzD3TITUElpiYWm1CADT\ncDzGsQcHChBWRmMv+QuIKOtEhLeljSoO9m321EfKyNplfrYxddZOMjHCHOZUOYdcwxgPb0rDm2nD\nm3kLoLCDFGP2bsCKvWMVo95rzaMSQ+sJtSY8PG5Iep0joPZas3k6tkgBb2H8deuT1HGa69g+LXt6\nvAE+NfQ9M0CFUR6B+aL6B8uE6yLaDQaIOFAZUgLgFWymwB55zIyXTFoNx+ZFGX/ybnj0+TfzgqeH\nZcwjeZnGezpqK+yqbxz6Ju3mwNtvwtbHe+D77fzpa042Qb/J5zpjeDjl2eY10r8HwVkyGwA7U9LG\nv4lZUhG2YuV96kwd86LPVYkYxLT/xrVsXRROnUuTygzJ+fr0MAFpG4w/AEjKlhypMbgPKBtD9Pgu\n7jXLfc/Azu4ZY3i8BwZDUwRHxJeHUzfYl6WDZj1RkQQBuxAVAm0cUoH8ehH4EQA/ewBB1yGwjwtz\nHBP564qFfzoFh1iF8+KaItc63sfvD0j4eds9AGH3d/35XZgIMT0hggmWznAPYPhce7WUaMbwvOsL\ncH0/uVCygQghuBSDJ6X0fUUqYkznjjT1sT4v14KmNmi0nUyXJ7JoShc9pDgu52TfdkhpAW7SGaLG\n1HfRzDg2SZnh++7oD6Axfr7x9Id2H8x/+rW/EdFvAvjX9J//NYD/VH//bQB/POz6j8JTHX7u9r0A\nEWIjoj8B4B8D8D8D+FXL02Dm3yGiP6a7vUbHeG37vev8OoBfB4AzfXVvFwBC535QStLaMl408r80\nRcARkfHXQYM4Z+7ECw2FzqL+WlsepWHmUwNRQwdhvRa8f5F6g++uJ7xoRD2WrbKc+r1h7fnFr7Wk\nUeiJ+4gYuTOltZ7TfvLLySs2UFJHOaCslcVZj47/nlIo2zJhiP1GdDNGNG3rvQw3Y3l0NYbluROu\nteCyFTxXcXg/bAXvlKVh56ucUPoeVBHxRQGOSu6YlQViav7c/V1mZWCU1pC19FrMVR0LYXBIrS/M\noV9DPWATQBTUd18SzPLc7ThW4+VcPCqZaxlRtB3lPTGAhnOWe5xUMyGW+TRqOSB94dUZJJXleZvw\nQQG0lybq0Vt3p6vdGfujLJLcsfRJZlBVdosxEaYkNEEzVsd40WcF7RwSa55Lfz9HvrP2uTER1OFP\nxMj6Xk9PFWnqYG4aFdV7gkcfPWLnRqv12ymJMFIPyvGJhIp97hUTWfUKxpVEnDEOZIY4kTkB2RzD\n7uPH0hlOuSFrypE5xOJYuyFvfbd0wtJc8Z6ZBtBm38i25fGtx9JwSSPDaWVUAxFSx6SK4Vb6c1MH\nl1KsVME4p47OaZTKHOU3yQ0YA2IiYGM/SfvP5pGXCnUqeQcSCDU/frc8QIAd42I4HHrfoe9qoPs7\nuOrHdna2hs119o3UcO0pMSYYWKdzUDjOUsE8n1zerQFRV3P0yKO5BigU4sF4sFKDj+fVqfUahSWa\nwtgVlsUpe5rAKUtUPZPM0QN4YMKqpYTNuY+sAWsOkn43o5N5P6eOvihelUXKAqsWi66r9l6lQoSm\nJ7QMroQ0M+Y3mn513VBrwrKW8Sy1ZWxBydzv3L9b06k454xzJpyypHcBAlZ2CMhqKQWPpeGbxyue\nvlpHFHldt1Gdxt7YYCYcxo+8y32HxcpCVl2EuvcvHRx+6U97ubedbwBCnjumqizGpe70CzydQY4p\nwfmdTITx8G6lGKrbN5ZKcEwtsv/i8+XUxTnPPOZFQBidU8ojoj9NFeUtIZ2d/o85I52B9ns02Jel\ndMxFKhms+iIsbau9EtWWZwjPEtgS4/gQLJH+YfD4xnU+13SATg4sOCjsekp5YtFNkk6Q/C4A6Aya\nCVTJ76gCKQO9OIhgaW07zQkdYRFgzCq2yN0rHBCJMV8hQEKgUYxUstcqH+ktKovhNgVBgInb/h0q\n/UdmoPXpnWi+VIC4867IAkf23Nb3/jCdAiBg3bwDRuguU+Fz5SBDV+3v9bhfUtZkYB60mgZoYGvr\nqsGWPeNKgD7TLpH7ZaSpI5/cD6gK8Aq7UdccTakSO9DHytBQCeiAaXRMxKP855QYpe9Lytq63BGq\nQsE/wU+xiKOd/pruxJf2vWh/B8A/BQm+/9MA/k/d/lsA/lUi+i8gTP53v6geAvA9AxGI6A2Avwbg\nzzPz+09QZu79gT+x/Xaj5JX8BgB8VX5NSA3kDoydaE6SK33ZCj6sE541giOR19vzCo3vPl1sRC0Q\nSg9GccOp4fll1tKCwLltOJ82tJbw8eWEn15E9vfdOimAsV/8ACv/dHTGjot8QLihSGfuN/c8hBVD\ntHXsT4FFQQzKYoxYekUfEYFY3/iWQnqvGbD62vS0p4566TAzfrcu1QO2ECk3hkZjGoYDK3UWIa+r\nan3eU57wYxYnEwCmN/qeFh4lHtuW0BohUwmRHo++judUwLYcHKdN87LtfkxkcU00aNq2r1BO3ZAg\nNQLnUoMz6H/bj4eOTq7qbyBDJq9cMAcnVSoAuIE554aP6zSMKlPPjwBPjHTSAMYcTR8ChvocObEf\nkAiUDBDaD45ERlM3o9XL2cUc5WOLKPyRKTBNDacHiftP6pS05b6hH9bnXSUE68vTVJFTR615OD0m\noFRSH3nHU+ORx2gRi06RlugsHuuCnHxsWPmwCKg0ZSGIM6w3zspA6jRKvQJyTWYxUgBga8JUMMdu\nRFlLR1GqhH3Lxpo5OiMWERusFwWsJmZPewhzqjVWmrWVmDwaKnH3tRMuVSjUNj9l9Tdad2DBGAcW\n9QbuG0C2n5QWjCUVPWlm0DbDndgzzKUhkcwTBkhY32zKDJFnmPAwVZQArAK3hqptk/uiu/P0bTUa\nSbcCxJFofe/ANiasncd3CkgVmMfc8ViqVDqo9qwCIrws03DYAHUY7/XfdwAQjIUQgb8YkaMw/8bS\nfcdj7fhaE+qaMPeG/CT7PSwCInx8OQ9WxdoyXmrBGpz7rUtJymvLODUn71r1oDm5fkdRFk18RZk6\nnt4sOH3t62N+J4KgUQSWD89sfTseKjmo4JUo9g6Mjc1jf2c6bqMdOxBQh2QGijGftGyrrUMRqLPn\ndxDBR/q4Ptk3FlJRqpSbWzoFBqR8zwYMjnvRksspydoDAMxpzP3JqudMHekpAaeye/j0II6ZRWgB\nyUufc8N1VKBgcN+LC3vfOyvBniuWRBz9BksJwjinzSM294JY53SABzhqefN9VCGiwqCSdqr9AMCd\nRSdh5WGNGiWeyNdLIDBG7N/jvfn6YKkUFQQXaBWJu2QP1W2rP/KOrn/or8gqOkbu7+XDi50bHf3Q\nbJwrq+NTugv+3DTGXGwECuePLuseTBhb7Z7u2uYuHvmptkuNOdgjoneh5zuJnoeVyN10vRxVj8Kx\n1BOm3jWFQQNTKgBOGciTBeTE3qkByLWUomjXWXBIxrk/71lTuabUAbMLiTW4FNdvEUZuzd+jlXuU\nNK17YIz2if37B57S0L//0qP/MoC/REQFEpr+dd3+30H0BP8viKbgv/j7ucj3BkQgogkCIPxnzPzf\n6ObfNdVITVf4/3T7a3SM34anP9j2v/ldrt8hKr0u/KJOQm6YcsO76wnPtQzDkdlqtHo0IVJ8651r\nHJuoNuuikRg0CTX9WcUbty6RlsaE98sJP11k9rpqmTczxKH3cU4db8pOMxrMwMaO/nadxKPgkTEN\nUg6rz2v3nGxh2NORmQnT1FyjoRMapx0TwRyynVGDMPHrueKi9blpyoxTComAZqjkxAeDUCbSOd43\n3PAGjGEiUddrLUg6ueevhZKYLh35RbbVF0arCXnpu/dgWhcuYrOPLgCyUFle6ZtiFFsRfJsTqRGD\ncbxFxl25W2tKl46i7+y8SvRpohQQZyldGCn45gwWwnBGpklKYnYmTM1L2mVinOaKee1eqUHzjjMx\nXpJRqfW8IS+XEwcDyanSQ2MjeHvGShB72++zkUcm7T2ZiNznzAFjIpgwWMoSOZ9RMT3qtgngqlGG\ne5YH9kBdpOkDXl4tRvYNAJuy9+MoxzmEEMUAspKrkZ3DwVEfpdCKgDwxvcfu7YaGjNvUkjyo6no/\napjkJOJOZXK2hjEXyrKPVJb43ZIDZbPdYxbHrPG+n4R1sL/JpMYzx/qU2qIORbf3HRhANm6OIO5r\noEFMp4jAJIdj2gGAkG3kDoVVokmSS147cNF7n5KIp67h4ATgbS2YcxtpIVIdhnbR9tgYGKloCQ66\n2pqzdimR2Duh6PcyTRXnreDc2ijNuSlbJToGj5nxWBoe5w0PUx0pKwK4qhMeImgd2Dn4r+X8s5K2\nh9gdnJEXmQgDBAwpNLUm7xM7n/4ueh/Olmg1oS8N5Ss5z/wN43FbJaVBGVJrz6PUpTFzqhrEa09Y\nVITSmqzf+zHdeD8uOhPK1MRx0EXdviX7u/00qr+12gWQRgISM/iwmh31GF5bfTk4P3GIRweHks1l\nsoeUfLR1KCrP6zOQ2w5mMeydpwDmDPFmYzjtpm530MdziW2Qkq0FNrcQcteyoXojuXTQKYOeZuAi\nwC5fK9JjBlFz8FnPFcutNiZ0kvLLdzKjdu21NBNb82KEFyCYEGlshbzyViJJbRIdIcQTihHY2ZVT\nLcwfazxr7gqlEKxRYeMjIBKr1wAKPOB+cMhy14/BMOAWOHitfSdH+/hv3o/n2H6/aRL98Psfduzb\n06gc/GQmYaFOaTBMylPFeanI146q6YGAsajEHohjqKWEnDpOWl53sFsbkJUOdZorlrUMMDrez46l\nBBuDEtjwCl0i+EngwQiJAYDIAAZubYdP9st32+1L+540Zv4fAfzjd7YzgH/lD+o63wsQQast/GUA\nf5uZ/5Pwp98C8OcA/EX9+d+G7Td0DCL66wD+IyL6ke73zwD4tz53fVnm9gauUcsfJtEkuLaMa0+h\nlIpQzxhuDD4WRukiZJh15mwKKNikD2DQrxN5RYFBd/rQh9G5toRMQpt8rgXXgE7OGmWwSKfRNE+5\nY9Zk82kryEQqsGUGgyCIUwqCc7kjdwKzT9EcIN5j3p4tsik4XdyBqdzKtMSIvDtLvJvEXvHdPtmG\nU6nK8pH+VbJE6HPqI2px1rQBq+1srbIb6dY2Bj5sGR+XGayVE2hOSE8FNDfQZBUfGPmlj4gEoPT/\n4XDtx1Q06ptGHefkOc9T6qicxoQ/QB5YeTW/jqUipOQskah2b86nCLFVXFFuqGxzyH+fSkNKLCKV\n1VkLJXc8nDd8tS07QaucGM+bg2pTy3gsVYy9YGRT4t1YSo2V4t2GNcCN/fd7VEfswZ6jmFY0ZONC\nZ31mDAQDEWIqBVegb0A/oH4xyhjZIxPx7j1GcSRzMpgJUJAuMjomSljJ9SpYgRyj+xto1NRBL0RB\ne6GNVAOzTQuZTgGFlKC9A273OJ1EXTpWeGEmUZA+VSRLLSFG26z/bLxEWrRtE6EmgoMI59Qxp46N\nXX8jUxqiok6RVOZF6uC+V3e2fHZTxz9nHs55CUPDdAQiGBkZO3auxPv5xvYxYb143ZE+MxxD0RyI\nY3IuIt7HoJEXXvV5tu5G2kMmXGsGs5T8BESrwsZudHSNDr51DPFIAKrQzkO89P2W8ZAnPCzTLpov\nWjb7coznvDe3H0vHQ654PAm7bbAWGmHbCpgdRGgaSes791MN6c/Cut6i6CAPw9rnxMs2ybra8s0c\nzIFGb5oe9ZmQn/RbfCDMX3WcP25YqpdWtRbFRwc4B3GCAU9H3DvldxiEIPSWwLWjL7KtVinxeOwL\nBu1KplYFctABpoQ9qHILejH2fdz5loZvYoLo2CmwW7N5LWUX/AOCI0qkAJWnI0bnz464uR6A3qWU\naGe6AePkGL+e2Qy2ptg9DGFFA6mnDqQMepzBi0yg/LyBMg0nC5BxalVkhtOtIHMUHTSR0dea2Ho+\nLp0V4mst6d9srrMtNcwjZv8ZYCLPrPcwHV7MvYH1iRZZJp7iFdYhSPqG2VP2XFFY0bogijTvRWNv\n78fYMMd0g31Vg7h9f+y9dsNC4/t/u9UPsW8gCPCG699ExQ/sCb9GnL/uvwPpu/3fTMSzaZ+3putA\nIqS3JwBA+ZUKmiqmpaFdGtJP1f68TFjWgrXlAdBuPYEbY60FvUk1GUmjBPqKoQ81nysetg3r5rpN\n9yqmRXAwiu+ekqdftjCGbH27Vz3H2udS1Y77ZyKkH3B1BqYvsArwPQERAPyTAP55AP8HEf3vuu3f\nhoAH/xUR/UsA/h8A/5z+7S4dg5l/SkT/AYD/Rff7901k8dON0dAgMV1zvhQZLHWg1YAr6pvA1rSb\nJROmLgvNiB4RgyRUM3KHTXhMqGx6ZGaUR8bDhxXTs6QtfNwmWGmhracxOTzkjrdThajr67ZJ7nNr\nCecsjIVTYsybiHmZwZuJkJoY6bZIpswoEBFBUqSck6OwUXCOSGjqRoO21hvtat6aY50DjT6rsUuv\nLPL32FFxcYp5WpHKzp2Q5zZybbPex3ytY7+ShG48pT7Ez+RdEJaWDxTxjI0J77cZy4uWeFwr6JcL\naM4AdBF4rsiTlJK0cmQmtERI8EofzlAYNDzIGHhbGh4VfKlqmFoUMjIRjk6csUli5P9hqngsDdeW\nx7h4LBVP84a0eVlCoaeTltnTdI3ZQYTeCfPiaQ6np4qv0wXn06bvSXLWP1xOo38/VMajlusy4y8V\nL/VlTrqUxeTd2EHtoHxblqvfGSdm1Eb9ER77u6FwT5SMimo0JAzl77pIhMGo/a8KKkEjT3lfH74z\nIZeO82kbdPC1ikhjYhpMjzn1Ud87pkSURKN+vDVjF5hDDShAlPtgHAHAFhTAh3EAFzgcQplTw/zU\nUJ4Arpa+AWEBpH1lEm40VKXzGC9WdiwNYyVGNgbQkfqoHKAl3zElYE1ACD46iyExStDisPE9pz7m\niK+npHOgG8yNgWuTvjNH3CLK9q3Y+SpZP/s7TASccxpl9uycOjSGYrWJfEbje5oqnqaKOXV0GNhL\nIGUi2PoQx8eqQMnS00hhWjvwUsN5ibGRCP3JcfLuW3InLlHBQ+44L6dxXE4dVaP5tuZYVDAyrmxe\nmkrDfHbQCAB6XdFbwkXLJ16XCa3tjcPILjiyEuLwtVQ0YO+Yibhl2lUXudaCtQs4P+4dxhzxkxpQ\n1xuN8mr0BJQn4Omr1QER7YfKNNLYpsQ4J8Y5i+jlR3g1o8Z7Qc6qvx8pzNuaUT821IVG/xjd2CLY\n3L0fIngiz2qRczfDbazaUxpYcK/F2LQzEnz/HeCvry0Xj5IfGQ/ODvL3A+wdOda7PoJI4rREUO7+\nam56Tyl3xE8ityR54cH2AACcJhFUBIBvr0BKKE+M/EH67qoVRWp3UC+CAbG95iAdm+d/R90mHn3r\nx/BIe+Awzw5w9UCDoHMZ86xdnLf9h0LKREDyPvCygjEFwcUxzR6wIEAhHg8mY0DhDh9ysBKAfFhj\nXq++wjfr8D2dBNmu5zocELUSYuUE2Zfvns8i4keKfIrsD9sXTrf/rikKx2bHeKAgAiwhQKHbWleB\n0UTA149y7USgryuwNvTnDVnto9PHiuVDwVXBBAASkDJgwsSMSQMfG4m4KIDT1w1EV2xLGczIVtNI\nl3AxVBkrrBENG6tz6jjltgP/LLCUA8id1P9ICPYTRd0M6w9Jmbg3Wj6nNfGl/TDa9wJEUNrFayPy\nT93Zn/EKHYOZ/wqAv/KL3EfXdIYd9Tu5KM05eem7KTFqp0EhBWTCFrVnwtoc+bfPdJQWZJm0dukM\nEyM9Auc3FY/vZTK6toJFjcpzbnjUt/VUNrw5ifE0FMJP28jNNodWjIMZJQEvps4OmSQnYkwahbZF\njJkHg6J3+TczaW67XJsSj9JCFnGmJCACBWqfiTqVkGbwer9Li4yEDlcq+JSGAquhnxKjWL1lVbwt\ns+cjG8ASoyDW92solWlRsY2l2sX79yJm+ebdB+QfVacqhpZSLOWnBkfCoFhG6veIDEAm/K8mN6gW\nTsPIi5TuaIwPdDqMnVG7eqqi4RFy8R9niTz2XXQYIwIec+FTZqQui5aNoSk3lHNHeew4bwYiAG2V\nVJVl8ynkVBry1JEmH08pQ3JCdQC1KgKixgoAlILL/hzDOGR3UmIVkmPOdBTZc0Pe35Frd8i/+5ZQ\nF/0euqso7+iLsAJp4T2T/CUaqK1LP0ynhlNzOkNXGnAsk2hR8mEgBsdXnvu2DT0RTTOwyiEAxKnv\nhJYwHpzINS+GSBUx8gOQ36ZhTKaNwauohXNzUIUbRuTQ+qsow8BSMgBgIgKT6WroHKQl9irv02nE\n8Tg8F1maSRqOmI37yKT5qouzHiMkS5P+vQZwwIDZPVtC0kfi+SaN5gjQEpgM5OyhIUgJAxJcoX46\nd5xKxSnMdabX0DpGmsFDSGUZYo5adcDKAl+aP/eZgRzGb2Vh8IgzZ89D+FALzus83u1cGq614NoK\nLsZgUxrtLj+f96VgBztnElZOXz3nf9syei07p9KYOZJm4NuHYTzu+3V2bOuESy3DmV5bwtJEtyZW\n3uhwIxeQOapMDZQE/JIb19SkrzreYBn3Qgr4G4iQifBYOr6ZV5xLw3tNFYzf3NFhiOBdSV3SLq4J\nq1ZHWlse2jk+N5OCAnuGVPzbDtg89JIxJizCaHvdaxYZj9UrTPV/BCWK0+Pl/Ptz0XEe471Y6PF+\nY7O1A8AOpHBNBFkTabDAut4nydy/hTW46IcDAGd5N9wBfmlIE0aaFRY4RfxTIBY5CHtkJVgaQOxH\n+8bjqCWN9OddpDEpKOnrvAExN8GPREMPQTos6aS1Ox2O6QwU0k8iE2HPsrC1RGns7OPPql5Z9oQ+\n5E37lK7Jp977L9J+v45mBNYMYIxr+wADDmCFtViOsIN9Lt1d4/Xr79IZbL9HFUWYC6h3oHbk5yso\nf5Rzf6jIp4r8s470rLZ/T8PGPGKFvXnZyPwgc1rZVpjgw3ZNqGvGtLZdmmkiiChi8sCGAf6V0whO\nGOCwYwCDxxwbsLLBeolzF7Rvvw/VCL4vLa7LP/T2vQAR/n63OCC6LtH2sVJi1Jow544nrjgHyv7a\nEkrPY20oXYwW1LSjV1WtvWMiXl5P3R0pJIAyYXrT8fZBjCJx0CYQJMp70ioRb84r5rli23wqtCoC\nKfGgVD5NXv7LWmVTrw70u4ShDu0aDe5gjRx22KK5dzKQNNrcaIgMdQCnnHBt+UBz1J+4XeXiGsvM\nA+E+5mp2pdrKv8VZa41QNJCRMiOfAKKO86p5lky4aATJnnFMyJMjuY+loUOoxx3Azy4CInzzkwvy\nmxXpbUZ/9mguABWeDI5LUr0KM+AQnBtLX2HG26nhIdcRqdwCldUWCSCCEO4Y2vYYOc9Z0hMi2+Jc\nKk5nEV+MC5BRugd7Y5L3zEki68NZnMSYy1/vF+j2oeO8VDxqHquxPCI4QFB6bXdjJ6nYYkreF+gY\ni6i8K72Glq+zvGnrIynHF6qQsAMJsTLJPWZLWxOuHwsul3lca5qEhSPinHJQVfGwGsAKwCJQfq9r\ny2g1ocwdk36DXcXIeLtzA6FZMArQ+w/0doJHDa3VmuT7DeBJdILseTr2Im+Djp/gxm0DWHUpuLsT\nIr/r9x2d+2TpGW5EGxOhxJSNoAgPQAEEQoz2MQLb4pVcJjv+bbHc/ehaEa5N59CxPykwi50Bbj/T\n4bwuVOrnJNrHNCXVScbfbEZVEvBVntUGG40U6LcKyn01bXiaN1xrGQ7cKCNpznok4xRCCRElsEbI\nQ/RoY8LSBNyckuvmrD1ryco09mOmnbOfSaj2W80oSwGzAq7oyEXmyzL7Dcnx6Qa8s+/PmmzzZ1nH\n/vv32li+raVld341Un9P7f/Y8sSgwgPsas/yM00Y+iaPmzDEWidczWAH8FQqfvRwRckdT6or9LFm\nZNqvjQkSjZsSRorZ07ShlK7ll+WctUmaCuNWJNhSYKxv/A97XRd5dv/mj2kX9rN27CLTIromA9q2\nOdDFO2bjsSyk/KRBw0c4p2lBeCoFq4ONXbUOF8Y8gBLDaVdArRFywa46Q7I5JLmGkIDMAFoDHuTd\n0JxQ/66sK0VzxdOzRERb95QR012Kmhra1a82hq8vAojJnGHvhuBr8O75sK8AEffw8rjYIzFmUykT\ngCtL6p7dpM67h2G4myvHNT7BkPtck3J8h3UigF9xW8y8uLd+HoUSP9fuOff3nC9/Jz5OP33ez18X\nEBDjmMbA6GBOYz955tt7apxCWnIShl5kmDyd7aaBuTjYc74CZQHlhlTW8Xx5mXbfErOM/waMcqb5\nQQH/BWjk9QAAIABJREFUExzwnxroI6Ms5Ya1Z5cf2klZS6Ey0HQQGaAQA1O2dpuOhvSLsFhasDvM\nBvlFykB+aT+M9gVE0BaVNiMlltUheCwV51xHucfGhJd1Qmo+OWdidM5ogb9XmeTfzcur1GAYWhva\nCE/A2x+Jd1prxrw2lNTxeF5xVkX5+UEdv0t36rGqxIoKO43nKGlPgx80JsIOGADoxjhwHYT9IkYa\nZaCdISBGljM1ZDK7xyL4FLPg2DoHuhWHCPFwFuX9bFsZpXPy4tHwWEGCmbBWLy/VtCJBXMMzdZyS\nKDN3ppFn++H9GfNPPmKuFfWD3s/mYEos22XPGB0C/7f8XbQQpKzgsqnxH7UDglG2d4xs8eebKAgR\nSxkspbQB4iDnYqW11Nkjy6X3HPk0daXXMfLm9b1NSDOdPWzLlUGlCVV15NXyq4aORFsw3oeBUhQ+\ngn4VRD6Kk1VN49mCQ+zRXF/YujpcxwooiW8V5tdLxoePZ3xUZ6LkjgcWR6E2j3Ld017QjsccvicT\nfYvvoZQuSubk5Tt3cqd6bGMakQDLR5XtkvITSxh2JnATOm+MbBtrxpy4REKVX7sDiNuW0RegP3cH\nC1aIFkQTZoY1MWqFbXTMvywh8p87VBgyRPmTVA2YWxusF4v230nffrV13ud4cukjhx0AliQlzj43\nj+zSTuz5wjXizwjUjZVAQQSrumF9kbJoDphuzpIFKH4qHb9yEqPxx48XPJw24ILd3Gstzg9boMK7\ncKrcfRTcjEwQH+c0nKH9N7IHEQoJ2GBVGOhFtp/PG87bNoS97NrH1ANz9u3c1ipDNXf8WV6LZprG\ngjlpNofJ+WWftROa9scwbjuBCqOcZcwCQL0mtI0wPzmonycpH/kwVbzRdbqzpHSdTxumqeHpRbY/\nbhM+JNaUFgduMyRyZyDCm9OK89N2+zCjr/QeYY5o2vWZCRmzptNZnzkY7sebdsRgo3QDHLwlxtBE\nGHhgT+O7tg+NsrOePvediHDjfq7LZGOQ9t8IGfsEu2e5rUZlQCQGY7F3ciDBWIxmgV434EHSdOih\noF08dQ4QoF5SZeI4T2M8RlZafC5zlkdqEkaA14FWCiKKr9orYoh8ah4TAJbBa9t/BEkizbw5CMYG\nIlQHcK1FPZ5df34CSLDUmCOrAlDWK90KRf6D0j4HGBjbQJjErz+kiKf38TugdgP2IIuMZwGrms0N\nxprcOrDqS5wmP3miwaSh2pHWhrx2TIoSPtYVOTO2LQ9bgSuBTrKeNAMRNkaa5LuIQ7FsrPpXDiJY\n9ZBOXjK8JCmF2rqLxG89DbHtUUkpGZDA43uwYX47gvQRIwNogFA/z8r+R6v9A1Cd4e9J+wIiaEtw\n9gCRC+5UFcN6c1pGnVcAWNeidan7iGhYibPceegfnNiMCRcIi4rQsXFjpFPC6Zdlx6/rCx6uBWXq\nmN9UFEnFElrnBcib0/V7J2xrxvPl5NT8mjXP/uenqN2ABrv75d0+aRJDYdvmsV+MNlizvL5oGMey\neTv67Igq7ifTe4trY8KyOf22d0JvFZQY26J9sUz4uE74sM3jHGd14gv14bhuKsBoDre1ZStYPxak\nXFGvr0+c93INDZSaAqNjImGXNHUCbT9CB1KC6SgAez2E4YxbRKc4DdnQ9UgFNqOtBHZBSZrKkBwo\n0sAmqEPHubIWchdDp7oFxRcRGWvVld1rEKCLRhEBO+0PAxSIGDQHZ+Qqzm6kDVqkMuZ7L8FodKEy\n7ftX34r1D7AuGe+XEy4KDj1hAxdh0WzR8e7pLojQGDilPRvB7ns8szItjnW5h9MU3rcBTJliKStv\nUUywKahiKVTXTvJfww5ESCBcmufir7Vge0ng3gfw1TYVjGPsUpNSAcrU0FNCaw4Qyf27Y26iZiIi\nqsBYbsqEycMZNuqtvHOdX1n6xsAKa06XT0Dfp4Fk8mihF/Dbt9e+yhgdtrb2tAsajr6j43GEpeeb\n7/qcK540d6FDHPcfnyp+7fECAPjRNy/IpaM1AaAB4LllpCZ9IcKmci6LRufkC/JEPMQjn3TjV1PH\nY+l4LM2rqiT5/o2yKs+hZT8DKNmzGMqr1iG3/piXGW+uK87nzWuVtzy+hV3FB5ZvbwnnNUFIF5kU\nR9wi9YA5x/LehKli6WTxvtUxZEuJoHGPBuKkR4BWve7CWK8TuJM7qU0YaQAGE6sxCZit1Po3s5zg\n7VbwoWY8J8Ip273LOz9nDJ2aN2+vOP9KB6/AepH3OF+l4kZvUQTRNAoogHwhhbB7NZ6qaWvxa7+3\nPvv8545vCt/BjtlwSGfY0eRf+V7itY/pYLY+m0MlJ1JtC8aOOXK89cE2JEtn0O+2+bw47i1DhAhr\nH988nQvQBSiy5yql4zRVrDo2Y7v33X9KxzA6i/L7vgrJkQF2r0WbKmpSoAN8qaCH4lHroVvgIJiB\nB1GDpmtlqCj4aGyzhMgSwY1Nd5zHOPyOw++fswWPJICo4D/sCriuQQr3CGBf+jGudQBwAMXG/ZNW\nEAvnlwvesgRi2ccOBUmsb+z4cIiXfSTXuwmsGenj27vqYQ4yp5yvHfhw0R0YqHrG5uACN0H40jkh\na8Bv7h2pbCjXtrMV0iTsg/Ze54yPCXnuwqSN3tmNDa4AIfapr6fURmlXYyIMhmAPZb6TMK52DIMO\n1fxwwEmwMNr5AzKXf+YD+dJ+MO0LiAD5IE6YMFHCnNKOJdCZMKWON2+XEZUFZGHNWx9R/WOLpVSm\npA6M7rjGfFyL9mtmAs2EoiVkHtBwWlbkB4Amv0hfeIjBWR3l3gmX64T3y+z0+C4U1yXQwXdIfRRk\nurMSR0c0Ku4zS9rCoE4+Cjui/SwNejy6oN+WeyV94aVmRkQzwYWXQj8yG1rq/Xs08gGLxNEQ1QKA\nrUm5nZQ61iBs87P1hPdbDhHApLRs75RrEzZF0ZrqIwVAf3IPxlnRRXT1iHwOqQxTWMlOKu41JnsV\nyVuCgNlZqeBXjUCVY7+lWAVCxmU5eXS5tzSM9BQWSUrCVHFRHhHJK9RHfeKsWm1chTo8aSQvFzl/\nv3QXI/wgaQF1c4POopYCAhjIpGMoIaiGSxQqTyzGIgB0Rt/2tFnAKKRiiERqvpV4tAXQcoR3+blh\nMMUF0BwkM2Xm0lBylyoSm1NlGXaN/YCzCGIcF7Vm1JodINJKF6V0qQ0OrRLhdtH4KeOEtFKGbB/a\nKcnF8UpuEpENC7zl1m899gWUXk57Kvom9ctNC6IG9kGZ+hDay3NHedQ5Rs9ZXjpMMdz71wAiH+dz\naThNFeeWg9iigwBewlINIJv7DgZcZ2ANgJg78zqHBRr5cT64oQFDhNKc5SPvfuODAYU9WCf9ltwB\nJh/TgACQX+s3YpXbfvV8xS+9FZ79w9cb0gy0uuCri3xcL1U0biaSMq5vJ39nU2LMybV4pKylRIqe\ndJ750dTw1bTh63kdjLiSpaxu58AQ2KTcoVT50DFJrqWxtjwYVokKLlvBm83PKXXOsUud2bpUo1gC\n68DuPTIp7P1FervNh0WN2fg9nnPBOfdR2nIgyqFtPaFvCekE0Nc2biuuHxnL1U0YZuBynUe1Bjt2\n6glbzUhJmFqACs6Who81DTHMqmlSoqEhDzk/NEy/NoOXhoerABDrKkK8lRNo6FqYRoFrFRgYYjO8\nfde1y3ZzAuT4m8ceDqT04WFbMOyZSSL+s9sJaZJntXknfrcOSNu9Yzd3jm2BiWQ/DcSwgIgFSKwP\nAE/vS5mFEWH6KF3ZCGFbmgF6M++0huhhQjoB9V2Yo+aGB14lqGNzdEhsH2s/uTAxwen6kYH5XZgZ\nR2gkMgPj+wACo7NIB3NVdz+UeKQ5gUofIFh/BfG+ByQDtwC5M2Bot+1u1YXD/cr977eYs3gEvq0d\nqyYRwcuGK4NnnMtAmqH7FZxm8F2wP4PQ4KXIh+3JNIxTEbvc27HRgaEQF69jvPu1hXGTdtsjKBLH\nRbZUvx0YoUyTD8IUprWBlTGDzi5eWbswFuDfY3lSkdHZgXwAoBmYzoR20TloTehVKjkYm5aKAs85\npAHpesyQsVHCGnw+bajV38jasrIQ9tpAEgCgHeIibJ3bgMbuX+TA9w+6OsMXJgKALyDCaAlCaywm\nABdQ/rk0zA+SN71dVd25J2xKL77N16adgWr5vxT/jWO0HyPiS+o95qeE9CALECqjX2TQ1g/A+pyx\nXguu10nvh/DhesJPl9OIHlUzBHdREBoiShEQMVBiv1hEQcWwte230UTIp72q9VHVHPCo5D43y6Ot\nx7xGn+D37ZgDbrmRVs4xJ0ZV1NW2/Ww94du14Npc9O3eQryp4U4A3kwbHmdZJM6nDdO5KULM4/n7\neovc2z3vhZCUpRJKQQrIk0ck96z1fS367UaeKTTvVaSLLkqshje/YJS8HJoRXSNmh3cxhHnCQmWN\nMo/UkJQZ3Aj1HWN70Qj48zRAq2cVKrs2jXBuNIwGhvRVfH8CwmD0n22rSxLaPfvfi7J6gGCAs+fw\nxnSG+6aPnt+evZP2BUa6x6lUlNLG9xyZCJWFLhuNR3NmbcwUdTYuQWDy3MjBtNCOEaCk0RcBE7x6\ni/n3c+IhkDlNHbXtHfkxn9CtQ2z563Ld/XdpzRgI5dRRzpbWomOhh9KYgQGzT8twpwRQMVVNcRlR\ncbBqIvh3bPRaSuyhI/h3aHMogKH5sCrrQp5Nx0DnAJ7w6JEbdf0uzAz7nYi1Yo1EzuPzCACX9Tye\nUhNF7KwKx2kAq6pVM2+YT6pNMwswnEsfjqvkqwZgWY9emoBIMV3BynZlYrzRlKOnUsec9KBK4Dl3\nnHpFbTmAxyTliNkdpnOWVJOcGL2FlKEmKVtbT1irnLP2hMUEZvv+nTTepwX38T7k36079ffYcmIk\nchChql6OrQvWrF+GTd4ytmsC14b0JPNN6YzTtxXrt6ehgF5bxsd1wrWVceyLalI8XGedX3yMJBhg\nAH0/PISMYzOwM59Cihh5KbVxPgUSjtoHXYf56EtdZ01E0p71uB5Z3+pd6Dl9jTJHyNIZ7uXXHwG6\ne2vVPW2V+FwWjeXGuLSEa/PypgZgdvhY29k1RdhtAMAhzXMAiIVApwKUDDxfZb+1IZ2hZWhl/+kk\naXmnuQ6mZd+t25GF53PB55pVYrA7c+YZ4ajbJE6obbJ3SGM+sD/y2ncfyXAojXoGjDW7NxpCtrHy\nkN3PPWZEhzF94pgJf//UYjjOyzgCCX9QLYo60p0BZyJ9R3p8ZDeM/cg7wysQuTFtwImISsZKDhG4\n0H2JJMXxk9bCkY12uP+GASLwhwXtZxVg7KtfhNQidq1l0Cxz4xCQ3kSAk0400smWl4RWE7ZFSi8D\nbqO9Vv46stBKqJpmDC6vlMY34tpTcs2Sruu8LMt6rSDcGQGqESD6DmPtS/uj3b6ACAAAE+Xy/Ein\nzgLzXIezZhG86zJh1RrXVnv6GpSmhyGE/aQf2w71lYQ/9Ev3XPGEASj0S8fyE/n95f2M63XaVRWo\nLeHdOuN99YoOWwAzRj5xJ6xNItmbLcZVqaDV6aAAkDNwvGtKAG/7cnyUCOkk9HpbULeW79bSNkDF\nmRqkFTAwME0+2ADR0HLDzJzKJNRHTqPsFin1bM590NZfah7gSqRaNxYlb1sDFn13Wyc8loJfUnry\n+WHD9BUjvyWY78DNGCF+r/dolFFt2VpjARCuPeGNOhkPpYIZKgr26WwzQ6bTjCFKSIRRfWNoRijt\ntt8xjhPgTBgdZ0PMZ+hdyDNeflbw8YMICb2sE0rqWGoZ/XttGUvPqg8QjUgR+zrmfR5bq6JeHGnn\nFsFMCA7FLvruhvXRcBJ9B+gz2bPJ9nNuI+p6Pm0gRdYbJ09rYRp5+NHBkShcCkarOMNRfbn1hHOv\nyKnvhBot53n3DsiNFpt7XGPAadlSsq1rqo0ZAsJiONKdM2GMYUBZU5MABQYMdEtVyIz8wM6E0nfF\nHbsxE1NMrHUGUgoRZ3LxxRi5lyheoESyG2ccAQP9vitjAAbXRjeUecCdF7PVoyMUDXBLSbFjJRBI\neK5y7s3Ge+g/awbEiChgGn1pCvH23CcF/6bUx1hrC0aKUARZpMRk0uossu85OzPN+sUiTVOKwIIy\nnLKXs53PVfVolgGYLi1h7R0JTl99VGbVw7ShtoQ1WXQvY1EwM77bqqKm8Xuw727TDjYbUvpYxxUA\n4vvz4FF/xjRugAAw3R6GtSVcXmY8Pb+gaHSXfnTG6ZefsTw3XBfp6MtW8FLlv1FGUkH+8zrjlNrY\n/lLLcOZ3ICdkXNmavl0z+ocVvPYhpFuriO5W9sSaKO5n4N06gBxx+o1Js3Zlz+2AAx5jPzKNhlOr\n37yAmgRONMRqty5rN9cO42szm82xT8eytbWHuWiUzA3fTuJQBcfum43Z6OlTlk9u7xNwEIE7eSlD\nALTtAQYAsuifJ2Aq4J+I2BC/bKBJ0lSuF887H6mS43voMNFWa5Z+ca88cNwH8PkikbN4SNcVmdd1\njoLP4bFyy9YTcsvoCkibzgESwEuT4A8UVIj9D59fe3M2adWqH/Ld2TVtneOhcRLFc3d5/HfexR9U\niz6lp6vYQ/l+UcVfTdrxO+DsBEtzAHTOZWUi7BiEMg6PjxJZECB/h3Jtm0+CDRLmmE8l9hz/0jqN\n27Q0S+6M/lEFlJeO9ScamOh0Y+OUh+59VRygt74QW4tAhZAftC/eMZaadmmducq63fvtvdscPIJT\nlrqVAtMIxkaKrGABNBK7LW7piRzAAaY9WOX9eb8axg+lMYBOnzFqfyDtC4gApdaC1LkldXTlq5lL\nxePbFVSAttJQdb9sBdeWcWmeN/rSEq5K97wpQ/TaxXViIFWr71c4rW+GOokd608JH74VWuzHl/MQ\nvqqDdeCR02iseL64Ovc9gBpHqnbfOzm9Y0SkB+uAhJZ1VFVPZ3HILH2ghQjGXtSRd1EUsv6nW0rs\na070PaNIicdyvSTU2ZIbplEvvOMhNyTCcNoJrFFGjHfIMMcl49024VfVMcylC4Dwo9mvfakSNQkl\nMCNVdCDnBI02+yjYupQ3SwAesjq0WaIsJTFKd5DJqJTRWEoQoZ00AVA7azpVvf5+tBlqfCwbJe9S\nd9KfaSak2QUX86kjZaB/TKOc41ozaOLh9AEK7rAYRhwT+bssODFXN5c+2Bx2Q7l4hNIifExp9Gd0\n7hj7ha2zG1bH1pl26tnTVPE4b3g8izs5n5oYci0J1TKcUxyFW0ZM1GjoTEO00kCE1VJiXokc2JPL\nmNh/C0AQ0kvOmMiTfHNzanjUbUsW41dKP/qx58SYEw+Gz5wbpjcd84+9xCMaq5q7Dg4zGiqjPUuk\nbIijBSbC6NfwDgaNWVM4pD/sXd5Spa2NueTO32xcPVcT7mOPMpEBDjyiI8JIUkc2gBJm10emRoeA\nCmv3ijnWV8fvzBypIVabBCzlMPblXYkTuK4yBspLRpm7ALPWjyHq3gPQF0vmGXhikSJx3N0pXXoe\nZUUBiVKl3PGwbnjarDRwxsa0S6l6O214mDY8Pa7jXQGim/NSiwAgB6MwRqelL2kHDtivMVL62rvO\nicec485rcgdW9/O5MlT4IK1M8gKcbG18e8L0axseP6yo6oStLSNVmQw9Zz+yWlwo10r5xjnDwJAW\nwI1WE9q3K/oKrB91/lM9JHunoz/YSkx6vzSNfB4ZCnY/sQ+PYsu2PR5DYGRlDtrurWte/WECTNnX\nxJs1E3uH/jWfs8Nth6b2BfN9sMeZCHsQycA0rsJuMkcHUPD6NInSvYEIzxtoTpjOHVdNP1/XAqug\nQ2G+SQME3NsY4jjt056OQOFrkV1A5hD/HngALKMf9RuOmhHM+jzlcN4uDmOMVlMSsI36/Z7flxo+\nMA1wC/oICBVAqcM6du8qkY1wT18haigcAyU/T4WGTwVDYjuOm7jdrnvcdnwws7mO1WFu74nGz3sp\ncDbGbkqjhp37FaiLgEC9peHkWyBuWvpIAy5zRz51lLPY9QBGBQbuQHqQY+bHhrqlnc6SaGYo6B9t\nOF0HzX4G9JvIHZl9ji+5I7d91SRLK7ZSofLcDvRT3HZT3cL/3T9Xv/1L+yPfvoAI0MUBqu4O3onE\nTFPD9NjRN+D6seBnLxKNfbfOuLR9aa1LSzc5o0Y1buwTW+UQdQ9QLSVCf2ZsH33azROjbQnP72f8\n9MMTAImiSG6ZRwkAM0T2pYjMAIhlXEZNaNtHJ0ArTSf3LUq3N7lwAUwYEYcNQBeH7HKVGbIpyBEX\n2U/2f1ioLN/d+m4YZeTI/NYdHABswtQ81tzw5rRinprnyHGSWvepD6EzAFh6BjDjRb2MpStzoRF+\nuha8uwpw881yAbCBZs8BE5rifhI1BfJ7LRomiwp2nnMbjqIdf3sc0JR9ERf7nFnHjfw7xZQEPU3R\nKgoW7bDzjSE6nHvaGTm2+OUTRJPjW/9bDtHRY7NSn+PffX8/sMoetI/QWIT8XhqM3TOgY6N7SgMg\n35Wk5/CNESX34OfLWXKFzYjtjcRBqWnncBiV25TQAbcNK2OUkJOSoRiiX3JsktSO5OwEA/i24HzG\nEqCREh4di9iPrVkEXO8nGZXRx41pD8QoUAeQMpDeZOeatw5eVU+jMvoie7eLiDu1LWFd9iXt4vgb\nUdIY8QnRj7EtPIunT+33OQI3gPfF2gmXygP8BHxuXhqw6o6NGTPnXUTYmAgxRcbGzdb1eIuoRtwr\nOHsGGA0nTsfu0jKe1SElKBthDVFYJsxzxXWZ8LJO+ix5GP+NgdXkY/S6WxfQRJ5RdGNOSWI+AJBI\nzjOloMFS+qjMYy2R5LxyzO3PQgWf5oaHvmFWJsO6ZbxpYrBaSo6lA+wp9vsIaGy2ptiz0HBc9mtT\nY0JreZzzWrPS473iSHyGkf+rY2x7yWjfCh0gzxn0OGH+8YY3m2xb14K1iubJi67JhUSI8rEIM+j9\nOuu7SPofDWbFoOazf5vMwPpTwnZNeP9O1v6PywnXep9pt5tDbI5SNpM9Yw3X2wbYwUihrCLgc0IH\nST14AAAhsUQQbUxvXeevBQOc5Yoxp8WKGnJt2oFglUnnVHcQqFsVHLcxFg2YxNQFsLMexnhgSBrd\noEfpnRf/PiiCCJ0lncH68F2T6Ozs2gmXqwA3EgDR+25eAjiWvTUmQhy/9nsMVthYZuyZp65d4fff\n2VPcAIBYSmm35CwldFlDU0mytum9c2fwwugr0NfQTx07MdDGpNV30hgjpv8DxPXu1qbcun+fce6X\nMqJ8+JajcF5wCA/vsbE79scUgJgWsBfEdi0deVdeqhv2bz1fCgBGtL+ly4PA8jifH+MgCftPCv8O\n92ojS2x7Hr+PbYfnM8ZgC+D8WIulfAsAsbtSAZqVnTUW7lZEBHnzKiTp2jFNHec3G+Zv9BnPkCpJ\naK6d8IZx2irSEoCM5syEe7bRvSZi1nodS1MIQJr08esAT1yr71t6t2Pih9a+VGeQ9gVEAAB4aZhB\nITIK6tSQTsDyEykN906NkEsTGqiVWAPcCImfVjtMzECIpGm+OgCgM+gkpYBePqgjrqj7sha8rDN+\npmXpOtPO8QTE4DrnhqcSp4WEbIaRXaZ7fdjx9IxRytLuh8lQyduJgtIeGGgvsk+Z2o0TEQWc/K72\nSLYhqce87mO+X1Pq6dZd1yAxo3LCRB2z9snDVPH0uGKanfZrAmQJwi6xtrWMQn2818pJIi6V8KEm\n/N2rGI6/9PGEt+9W5K+2cfPDCTv2zzD7982cGgAjUvhYXGyxdknJEKfHNSa8fzwiOsCGgHilSZwL\nIgdXSukoJ0arTq27FynkV+gyaQLSU8LpseLxIhbQWaPvAPCwVb13unnXrrHh20ZlhgT0q1yQEiEp\nzW8fsds79nLOu7d506LTOAyOAG5sRv1eJGfaqKR1XNuMS8Bc4UT+Xq9qOF62gq86oZQ2xp8ZsutO\nY8EcWnfEJY+TB83S8vObpl20YDS0mrBu5ri83mLa1HBGekJvYrRSPhygWiv9qvteCOuloHca0Va7\nb8Z9xzAaNkR7kEkMGBrCZrKPniuM59iOOfLGJoj/5uDwxPuJTm7tPqcMCmmIvx3nZaPX27UqK5hE\nopch9y4g2NYTPgQBv3PvuLYUjC/CvBZcNqkIA0hKVWVz7ICPqv5VEnBmRgPhRbcxhIlyJWcniFim\ngpHh3k+ninUtuOr9WEUeY33J8/NgfsynOvr9pHm3vRPef5S5TqrPfDfBrOic3Psb4Kw0roRrc12C\nSyu4tDz0KeIxcZRnBW7qmlF/TxNS5ivooSB9VTCruNnb63UcY855JsY3pxU/evMCIsYHneevLd0k\njBFk3J2SsMIAER3drgnXlwnPdqwCFRxSWoxhceu4uqN0XN8Y9/supkg4KOZeO5lo8bA7RDiyXkW8\nDwD6ZuUi92UaHZx359euEZ1LmPPIe7ZZh2l6mPMqdkL8ipmF2SUpFgBUtFccm30AQk4ifU2Tpleu\nAF8YVIBpljn15TJLulsAm5buVUTiGiFL4i0T4V6z92WpZAkYVVHGObu8KxE7tfNJICN3L93ZG40O\nlhRDn7X6VVKcTFTPwHYJ3uh76F61I/a52D17hgsfQJK4LkYG0WcC8rt2zwb7/bZ7mgh3rw1JZ9gf\n+/Pd//3zfr5ZtYG4bprOVh9+gYxpKoT0IOOUrx3T1pGmjr4l0CUAYcskaWNqZzDTsDvzg8xhZQb6\nBvACTF/LcfmRcOod+YWHbVlXvqtrZcy2I6hrAILZaKbfEpkIpktG5HOYBR+jfts9JkLsty/igl/a\nFxDhTktw0ayi6vXXl0lKw6lhbRUPjjmHhgLHz84+s6NQT2fsnFAreWcOzssyjVzr6JBYPfbYzqVi\nU3X+hywRq481jyiPAR0C/IuQV6SLHptFFc1oHot+Egq8OYKAToRVkdlBNewqpOWOFwUkefQX3V+8\n9s4QjeMbaCiXA4CU15RqBJP2yWmumM8V02PDpCV4ytSwXMugnNpzl9aHsQVYHqs4lmsH3m3yLn5o\nvh5dAAAgAElEQVT68oBvfnJBOm+gU3DgLZJ5AxSF9IqYIw6bxKXW/JvJQxOLCpwBRgvWBQz3jU0i\n9jxMSLTZSlbaAjLNDWna09FT+O9eVY7dNQqQfnTC/OGCr9iN9JQZ+We8U0J/mCrK1F04qOk4yfDq\nDAykVS9ukditg4hGdHXoXfRoRO/bMUpq22I70lYlmk87LZGh9K1RoKPxFsXPoEDJRG5MLi1j2/Kg\n8QPAxKSiqxlRqNGYCDZWUlisxXCR36tid6aUDAB1y1hqUe2JpNf2cpcxErx1MfQtara2jPqSML2r\nI+IRo2Pt4sZtXZ2VNASXPsEMOTYypok5V+E574NXt+XVTolHlYIpqSZFGKfCzLH51L5lr/gyzh3S\nRGY9n+gciAZBTkAzkVgzqg70dGNtWWpWKjK/MPZijVuWigtTcrbA1hKetwnvLA2oJ9XZkHd00Zc2\nMXDWe1+7AYrAmuQ9XvU6SwY69toFAPBYN7Sehj7JRSuQREDFAMiYlgEAaWrIE6uRLH/7sISULfgY\nMOfkSH2OzmfrQEoO6NixrRNWTniukzMRWsbSZJ79dMlArxazaSk0KhvyW4lYGz14fmx4rCtaJ2WY\nCRPh6/MVb75eQAn45kVZCy3hpWWUxpiCFkkBybxsgro/qugLYb36XXWYiCLuNusz/YzlfuH9Eeng\n8RRRys/6tt70hziUcQyYuF/bErLSIHvzikXxXTA72+KeJkJsxqq7FaM8lJTV9XsnQNuF4dWbFyng\n9Mp6s1b57+z6B+v7hHLuI62u5A5sWqlDn31tCWvPyvhwsMbu/e4z8VHEk3YAjwkbA+yar4c1QY7T\ngEN33ZtegX5lpCehTfEaxswm4NwI0mhaiOhQGYhAmjLizBxjHESGhGnBNOaxDkWhbHl+//ma/XBs\n32Wfn7cx83cGEv4wWsctHHoUdbx/HO3GyghGJJJqIgAyM+jcMSmTr7yTXp9eGuiDM6Ps+GstKEvH\nWUHP8sTgCrSFRiWG/JaQ3xCo8BBlpGtDvWi67StMhOO7iymIJnRsGlOApzPcq1ayZxUZwHC0A2x9\n/67JKn/UGn9hImj7AiIcWocs8CZoNp3l51atNJy0SZ3jnrorXTMhMyMH9L5DWABxQspj4U27SYGe\nJpS3Dfl3fXA2y0EkEYQDpDzV2/Oyc4hPc9X9E15UaOr9csLzVrD2hEsQfyRIzvYU8q27Rjd45PGL\nQn/S/0Y5wwlgm6CMrlcteoUR+RcUWdD6Sb2j3H3SGtqR+pPwep4bwmajye6cvU5oRDfODhFAOjmX\n1tGqlHyMud5WA9ci9+fcUbX8xMfqehc/W2d8++0j8vQR06M+z4MZhNHxwDC8jpOzCEHZ74y384Zz\nafh4oDvb3+Nzf7IFnncpTahrtil3AQKyl32Mh3ilBE9nsIgRIHTT9PaE6R/uSIqgCwWVgLRgU5CF\niPEwbbuSkwAEwCgOIlAVACJNGNezKLgIB/J4t5XTYPvEPOJje80csCjaELvbErat4OM6D4cLkPdC\ntHeQjEa6MXkFpCTGe2F3wtaecdkmzN2ZQSV1GYvNHQovSxkEk5SCaQ6stapMhBpefGsCTKw94UVT\nU55bwsdKuDa4MBgYNQmd96z3+LwVPL+bkfICWKnSKnnUJuxlfURBlGlQMYnHmN45kNiDf1b//Sgi\natE1j47Jd2eGtDuqGMKRDzpCnwqN0nv2PW2dsXZxTkZGzJ2PxCLBpwCYdjByJzwWwqU5cBOrngwR\nMPgYWnX+TJkFoEx9zCOrzmt0AJQbi5NzDXPvxrJ/ZRqpGFsHHrLcgx2+dWDp+6gvgzBVoFDCpLlL\nZT3JukTCdACw0+mx+3nqwmSZFwG9bM6apg5KFSkzJiv3Sn69CNQd36Nt31VKgY+LyLqyaLGlcY3t\nLBo+1xbPJ85rjDqX0lFmr7G+vRfDPT9gRxFPWdTJn6ZtbDufNkyPMg9+89WL9q8K27Y0gJtzlvK0\nT7nj60eZlOY/lsArg/uKp4tXQjLa+XdtjE9HVSOwaO2eA5jg9oSza2iwU9iYLD2k47CXe910ETVg\nB/D3Gm/P7tei3nJvHMYGxrH3qPGSzphgmjiAAtKqH7TzK2sHlnWgDfmJUH/XmT+AiFufqgRThvZM\nz6FqyB6ItN+NrfQpGrikEahzlVUAF/5OevgePOrLMi8l137qLaFvXVgIASSHgqBRRHGACM3fg6We\n2HcBGIPNIuV6H+xgWwSHjKEVI/iWyhCBPksbSCFF4nNpCzesrUPagu8f74l1nAa7Q6/BjF0lBg7b\n7fx27ZF6oOvlLpXicO7xvu8sCMyMpi+ljW3hmcK4sZ/258pJmXkb6ElRyykPgCIvFfmdAJTlXUWe\nFlzedxTVyHm5zhKoqAl1lTEw1wYoq7cvOv6eAGQgnQldQajMAkLdiJJCxnXlUEHFvsXs63fWsrol\n+fHHCk+j/8I48j7d9+3Y//OW6Zf2A2hfQAQAgFRniHRvy8ekJJE6E28yR94WLiJXaAb6iAoaUr8x\noZOA2YbID3V/xl45fs7IbxMe34hVZOIqOTEe0zaMhsfziofHVQVXpM0PSktthNNVfp9SxynNeKkF\nc7LIjCtXWymYexTDwUJQxsGuLF/CyA22ti15qMICImA3t4RTasjqQRqFSuhVcrAhomJq8Oh/O7dN\nYvK7UK9iZKUq3TgRg3RluC6ToMHEozxO2xLWteDjdR40TlHPF+2GLRjdEzEe9SVZRODaMt4vJzy8\nX/HQxEA9pfbZSD5grAKLyJrB2nAuFR0SzQZCXiYBAN8YPq8ZQoZY88inDgtrE9Xuewj2DY7K2Oki\njHaekH4lgR41UpQIfK2YLxc8fJCxWmtCKaIibJeykmNUMCqOUGFQ5l1JyXYB2mZOqi+IUZeg/oLr\nVWV4bm5NuGwF79d5OFxT6pgToShFPUaa7Fs2g6N3QiOJasdcycsmEYcSFm1A3kM0bodhbufT61iV\nhTg/dEDrOOs3Oq7nBqY5X9fmqRD2JVET4UVAxu7LZUZ+5298RCEbCVhokenESFPTXN1Ax2V3qq1/\nXGfFnBEfYzsRULo1VhqrBkuojGLnz8Q4aV88ZUYr8h5Nk+LahS4s+g9u1B9HeGSEDeEpiIF/ToxC\nKeTd7+ed/Tl8TALAdO445zqcezRxXEry9eFU3OF1UMwp4SLepXeu21OYF4l4sFGsjVWGfV5aWkIh\nAYVtDhPwzejscszahX1zucy4bGXMgXNueFxXTFMba1wPgNH+3v2b2PVRcCQ5/B7TGYz+f0xNsWan\n3FRHp3Z/o+ZklbkH9ltCvQDpo0SAAYnqLteC3pzV1HpS6rgwJOaz7Pw0r3haZnyseceC2JhwyoxZ\nQfn0dgJKwrxc8PRxHee81iIpA2HU2fut4/m9/49VP/InDPBjX8Y5o0KcCgAjUl67s0xcjI327JFw\nbYu29+As3YvYHwH7+Bw+r0WbwY9nAwkj0zL9/+y96ZIcSY8kqHa5x5FJFqt6umVW9v1fbFdk++s6\nyGRmRPhhZtgfAMzgHpFk1fbM9sxXNJGqTHpG+GFuB6BQKCDlgztbDeA9x906EuRP/M7ma2xM0DQU\nHMuKOUfMRdc6wj4W2gCR3RjUfnwUFN8ydoRl5g3NW4HgasYuKXjRU5hqdax7cKlwybWqSapbxMxB\nOZfOtdrTSmxqxkZXQ/ZRu04qKLA5tgEJ/t5NI+iWBfEtp9fOz1xdm3ttd1FbXXNdALinEUiRF/5c\nEY4MULrjBBdmuLAgvEb5PgMIzqEBSWVm+9EHakzAejO2kbxE57AR7wa2rB8Ad/OXU9e0D7rtqeXE\ng2gp2b350f736NytP3j6/S0bgbkIP9oPEAGAAsWC5BMJZU8idpVr2BM5LtGVOuV6rR5D9W0TG7yT\naIvD6tWA92KgdvQ0+74EbBBe7+A/Djj9K0dBSp7hrxzZG8fc6sYOTxyBKTdAA6CR01lZXEmjbkr5\n9wQv6lGEXue+0/rlbyZKus9X7oKKYFqiSWfge/UoQEM/AamnvW4pVMkzHVxR0eQcqiib9zp11KKS\n3Nete9oG2wx0QPq8lwfUTbRW15gRyxLxcj3g6zxsnHUtvWcptlXu9Rx7SbAk5frWNeIgVquCK4+A\nBC9Iuv4enFSMEKdwCEwx47KGsikZulkxq7N1zmwJI722joE8+VZ+rhmdq280SsuO+ZbY5V3EzDvg\n4wnuLIOsVuDlBpcmxEGMPOlnKTIs57kve2SrNGgri8d8SyKcZctWuWbENqOVutPSIjPvPokYwVqT\nuzrc1tTSfADgAH7XVLl041ZYsUeq+dnE0ZC0Bu2aWejjOqYPgatk2Gex+c2PNmUu5yhjOnBE5hDQ\n0pZi7OlB7dnUYH70zEStrOGsYl1GXFNZTM6xOJ+CCHHkHM98w6YCS1UnZWc1PE51MJGR+0ftaSYq\n5kp9vOjf9TmfY0VwvonfAcxmWSuDLkkeyROnPvgHV3QmTcE7/SwwBsJNh63ruaOPmBQaya3FIR4q\nxlgwyB6RvEN0wHPM+HTgaNRP5xvmhQGrzjLRuct59yfZfTn9hNfFszK+HFek8I4j5AD/7RwIp0Dt\n2loK0vYZn1NBbgV4WCDzBmaoaTnI6AmHecAx5XafS/HCHuiig7na0qrb/rHCena66KdsNDuZe/Ty\n72QYJbM5dWMFVaZ8Ow+EI7UP5jlivoXGhirFN0BP21IChmnA+DWzAGVRkEYr9lRMMslL8EDRXGG9\nSQd3SvDHCeNZqugsnD4yb+ZiX6u6sr4BDA0gwyCSrvlo99P2OzmmrKUKdDaUM3sA7eZndRtApZgo\nfWOKEKRsW4+A52/Ywrn2sU9gH2opPUiy1xxp9wMWPtxr4uizbv7tHeht3tAxQqy4Xg9IpaflpVQw\nxtxYQXYtUiBIATBbKvNbTfeSDsjIe6pkSjzqs+7SxkhFK5WJ4FBXh3whxDNtGDIAp0JByoRqJayc\n/UaQV0sL23QGFeK2Ywl4sE/LZ96La9jxpbbJxjF12yj/e5T/fYlH/41+7hU7ut1Sd2COh2NWwjsg\nTz/Xn/dYH6VQsO6C/B3KdLGpRdv5ZNmkytypc+0TZoicgjMObBNJFMAPAaiEgRYw7Mfvap4iC46q\nPTI7pA8cqMtXmYsXPCyJzZoIj9gSW1uuMZKoMzD5Oe9fkjcpDdwn2k89cKLsuHuQQsbLn9TO+dH+\nedsPEME0LcXEk0Mc8eKQV44yHtO6WaAXcR7UeNMSSIPvdFIPjpaF2g3uEnqE0lycP//pqBX7cF4n\nxC+cs5o+EOInWfgkd9B/zag3AQIGjkSbtPVWjixIVQKAI5tcjqx/rkUvdovRo7q0ABp62vLcEzsi\n8xQxHrpoYQxlQ29WnQDLRBiCbmoG1qwOxThp+2YjKw6d/rrIpefiMZeAXAJGEbOZc8SXecAlx0b9\n9hCmiOkLre2dPOE5FjzHzur4MC44HheMzyKQcwZQAfd6v7E69IU7SLpEdBWxVZOoWKvHZe1TcAz8\n91yZXm6NcOA+1uo8wQ3sVAHo9YrFYQCYleEDtcgU0KOhFjTanFeQ7/YgufBGKYABrkvzYHWza4Zz\n3kbDSC1ps9d0Not8pwLzEu+Es9Th1igoYCOi/Vip1qFWQAztWfsmS8I66c5wFICHhQ23QIsacG0M\nNnZRfxYtjTeVAMmCwVx8i0hvyg2ae9Z7ZACTf45B1xH+zkHWHACIAzNXBl+bIxZ9L9ME45QEQ8mH\nnD/5imHswqcqvuQcIY7MDtm8mxmbxv3TUyx6JNM4hbt5ru2RMb+lTG7H9Vpdi/IfpATkxqEVnQQr\nEKvr9oYtYcAJJbcnGznFY1qnNab0/lukcnVITxzJfpZSsUvlUqQ/DQv+209vAIDTpwXT14S3ecA5\nptZntwJw+XiHD3L+S+bnPYS+fiTPFSm8A07ybj6kiqdYcfBccYD7p+AYu7gp95/H1fldqon0XQmY\ncsDXteefB5dwWvt5phIbrXpuQJSo+FPfO/ZOr+2zRy24ijGglT6txM7foXgsZqHf5+lm8lI6eEZ8\nlnn7DPiQsf4aMEtZ4duacFljY3bpdyscwldOc9AxquVq7RjgZ5EUgFUc1VuGi57XeQO6741yjWZb\nJkI248abuJWNju/3t33Oft19jp0G+ZsK/O47WpoqzFsnoDnNj9b93bkU/LFrIpeQ7todCrIG8+Jt\neTzaDQ62HWrfX2JgR+y6gL6qh01IpwL64jDPfX/0nlMwFVjNVNvctPo3hPcdH9sqMTtl02cCwFuh\nvU2qjgkMaLrUHdV/dqjJrH9rt5l8C7KEzrwz87anZ/RrP3qWv9qcSYdox6wj70ywRveSBhK4TeqB\nfuZRZJ+BCP69OvdQE+G97/5XNwv4A9t+Jwiz6wrQK49T5x0QJA/NeQYVAOA4wn1Y4eeCKEbpSJlT\nFFffK1FVwB8cs1ZkouSLY12iYpjRod4zjUgZRCqK2vc6QIN8uqc/AhDU9qDG5ose8FXe+598PX/f\nEo/0N372bfsBIjxoG6NCHLMQKoZaGv0VkIlaunhaqYS90EgJOslZcA3QqAvdbWrKiXXPLGccn2dQ\nrfAJiB89/EfOxXKRKepuKHCrLhQONRPq6jtdSimNdK8X8F5rIobGOdwbAlDBPDXAI4MI3yvlCHQD\nsauGuyZGaZWU3wOd92sbtefbRgy8A5ZS4ESOfspBcih9j/SgU+a1qUhTIcJT7OUXD6FgTBlpLAhH\nuc/Rg1bRjDDlyDoN0jyPgCcb47hwJQg9FgXsIQIyusjWIyClp5r0XVsBBMBE8bJ/qOxrgZhHrTEF\nPIRbXPtJ1wxMGXUhlNyjgIC8D02vIMegQu6A02Ys2b7IvlGe74np/V73Ru7+74+a3VgBGX9ypiTR\nfaa7mnzaZjw8Op99BBLNAGxypDm3v5tK3zMCrU6I/oyOemqE1FdXNg8AJMcR9cIJpv26bmvUe0eI\noSKNpVMcPdBEUne7AGV53TthxUfvZW/gfjuStPsu3Y9LYOu0a4ayc2hq9H+18XLbgcOyWy/u7/P9\nl6XRnRhrE98d5L0cY8Z4ElX/EzCsBUMsGBtjgdhJcAwA6X6QhFFhI+Cjd83QO8g7O4UOIChINfhe\nFeQgkbBbCY2uqoBTkLFUTFoBpC9WcvCF4NDTqtRo3lCk33E+/0zr61+F0ygrXEtva+r4bvuftiz6\nHVLlEm5wCEtB+FyN0yrlVc08XKvH4D3mHFFq3RxXB1ubOoS5oinm12sB/Io6c6llgP+2r0qi1wf6\nuNoK3d3rS/xnmsVl64N5ZKOQSuW/P8d2XtuP0J3L2UEJTjl54Jg8mFF31YuUOaTrUHAtgkszj2Na\neF0CuHoSAPjM41c1jN673l8lGO8BRzipsgFbBhFtf+jreddO2FSn0SiyoQRQBlvaO9tRm31330z5\n+U+MmfcL9f319j1Rwv/K1uyEv5jOsN8P9tpLDYQrAEm6MA4ZDhOzEAazieo6490m0BbHep9SE3gt\nEz1enmOZbfgWhPrOq/uz6zKD+9vmduusM8f5hr5xvv+Fx8GP9v9f+wEiSAtwHNXzrkXJgR5NPZ4W\nHMg1tWCqDvMUsSyxGfpErPw+19Aod9Fx7i1XddDPMT03uO1WQUuBiwHuk4AI/2dG+LQAY4Q7Jbgx\nts/hssh9yHcnQr4Cl5dBojZoKvSWMr9RdVZDKQO1MN1ZI9hEFaF4hMJCeQ9p6WoQPAeMzwWX1268\nKKXKG8dZDcbkWdwMYGM6koMzllV1DklyAB9Ftmz6QQWQwBv5ujOkgouNajjlgFneiRrWFcCCXvsa\nYNGzuWr5xYBf5LOnYcXzhwnHnwvSfxMxwTGgvma4YOq2e5Jn6l46sz+qlCrTCI7HNUfMxeNZos3n\nQeOlkeng+m5blLmv6N4xcONGB1874qyAUZZ3u5RO99WN8U8ZsA3F9qDbCvd6Aya+v/r7FfXzgvWL\nwyRiY7c1IjiS1B8+xboExFRQ1y7Maa0jJ0nuPlCrQJKN8at56rYpAv+eUdRowsaxsC14wsHXlnrw\nlFZEV1v0ckPHps4U4HPyMaWx6vmiqxh8beKluXhwGdZen906bjDnU2fJ3mcSzYXR0063hIGJgziv\nh1DFYerpFVqizoINg684HFaMnyrCSScj95Y+tAo7lRuwXj3WObYqMVtwxURCdn3bAEirrQLjFOo7\ncFv2z/5NLrWX/OO0EAb7WqlDYzTtgbY7WjVBmCfciuP7XFoahZlTuDeq+P56NDavXLZuGDJ+Gjpd\nI/mKf/v0huN/l3XgOcD5gg9fJvxkSvM6BLjiEaprGg8KFCRzXe9ZoDc44Cjv+xQqzjHjnDJOwhoY\nY8ZxXFn/5Sa0/OowV49UfNN2OccVx7RiLQGHkJsmSCXu75vpOQVl2ZnZdoYVf9R792gY1uZ92+8A\nqjNEDSSbS0B0PN6HNlb5/Ja1RcQsg+UWcJLoj/84Io0Z53lp9OClBISV5P2qvgMzBHVN1DKYS+H1\n1yrhr8T/FXIt9ae8ENY/VswvAZdX3pffpgG3NTEgbUBbZU019hG2+hGlzSEG+ez82euG7Ftj3gle\nWDfndU1jRHWSanUNuLBO6cYJNvdOwkPQ8+o7soDeSlxJI1s2CrY/ge6AcNUID6p9DQOwEWrGEIDz\nAW7JqK/C3ppYeHcYMm7CGLlMA7SMZhOEhGrobKszPEpleATEf6vt085aP+76PdMW9PaJUAuaBgcg\nttC804dw7NRRY3R1u8aurX8WoP1es3U/vKypW+fRPTwuf7wDcL05tl+/lbXg6f6mq5OUM9oJTxNP\nANo9r98BffY4sF2fvDAf9DpAD1Lp71X+FeHbMX2edo/60zxzY3HeHMpntoP8XBgoOEa4QwSkRCmI\nQFf5jIj7JE8II5eEtGkuLnq4c0IQEUX/lVCyxzSlFpiKuSBGBoAfpyX031UDxoquewGQdcwBmroH\nYUqjHQvOIboucBmEllDdtj965bC/Ctv987S/87Pb9gNE2DWlyPbav0w/Ov6UAY+GGJZb/04vacfG\nvMsMHABqOPIGsWrZKckbt5OfKlMnMa0s1gLA/XKG++XM/14z08gB0GVB/TyjvFasX+X72WF6S3i5\nHBpVU4V6rGCcpcw1Qyl7rCvn5imIEAG4NcD7ilh8o1s9mjf+4BE/clUJjV7m7JFzgIohct9So49b\nJkIUw9QyER61RzRR/jcvmquJxrZFv+VzupZWoPnESnWfnVGvrsAt8zlHH/BT4hf+YVyYhnlg8EBe\nZKNobqiu4uaqUJ7mLQPdCZtywCR9fRBqshr5yVdMIHSK+rakV7uOeNm6UWmptopu6euG8oh2uW8k\nltIeLafrCtQ31M+cK5N/L8gX4Poy4PXGhvV1SZyKsfaKI8sSWVV9XuB0fy0y1jMAYXS4SFKObFvv\nW5/dwzADHhiI31rKK3WWBMDO3lPKjbp9TJzfnMk3I5XP6d6l9a0VTVMhV4+DOHXquNwkpxxGiV5L\ncD2k9tM2PaMZ9DDjeGWA0vYPO2D8PXXimObPjprtSecIfgD8WVKiBs9Gba6ol4oq/nC+elxfB6y5\n08S/pUKv0V8Are65gofA+3O5EGtVFAFctI+yOCo9osTjmftcvys0352Rr+ewc0UNf9WH8ALmXDMw\nm+oMdrxvAA6JShYzpvPE1NSTgH7BE8aY8fTLDH/ua4MbCoaxNHBwqQFL9VirVsHhCx0CGoCg4Anr\nGfD434whsBGu5UTHIWMcc6O8AgweTyUgudDSHs7DimHISFQ4Ki/g6jUHXEUHoAFe1HVierRX16K+\nntu2n582Ut+ZDdt+ttR/HWHR9XQhvfZKntfLW0KV8mi+EtxPBwxrxVMWwcPS1x5N1wiO8JwWPI2L\ngBG7vdE4oFlEHS0TrszA9fOA17cRrxOvdZOwD+16pc+47xPLPrC6Ee4dZ9f+0zr+1fST1Tfg/pGS\nstk3HZNicqj3YpddZ8a3e987r4V4bth3NBcG2PfCmv1++/M3hmB1W+C40frl30Ng++Y4NiHC9atD\nOBLGQ0a48rHXJbWAALX74XF+lVKhAGBL3pLpT/tsuoSS9LH+1M9ViPPawJD7NRoQ/QS/BURdFPDA\nbkrVYZ16JZx2eLcvb5gqjZ5ujj/o90diipwqtv3wn2Ui7NfrR2kQ/yPa90Cd/wxg8v+1bdd9bBhs\n2srqsH4RUOxrAWXApwx/APxRxmcbRGgvxY0OYWSuVxWwt17lpN7BnXhChOMK/7X7EwCw1gggb9hF\nym71jjb3WOV73nd2kpP0xujqrsSjaCLIdxUAtjoRDgBoB7L8jx8OP9r/xu0HiACdPA4erGfgYPKi\nSMpLfRT6umx0dTUUdnUQDSVTJ2vyXLEhSd4sAETnQBqRVpXeFajXAvf7FW6VMonHBBxHdlZfFpR/\n53zb/HtGfuWI4TLxKyzF4+064vPt0JzTpfrmYGtbyWEqHHmfJSoz3RLmJbboCgAkQTS11GPU+tOB\nmQm1uJYDDvACGmPFIiVt1jVg3eW4P2qPcpJt20RRSKM7W8XobVUD7vuD5E6qkGHwhOQ9Bl+a075W\nj+QrKgZ4icxlMd5zdfhj8XiOHEU8p4zny4DhMsGLGpsbCDSzsq7SX4sBMh4ZW/r3RQClMdRGR95v\nnE2d3Biq+n6CY5EwWmrTFvCBWsRNo3gKbmi6gJ6nCVRq6sFKrdRiXQDSetaZQHNB+bxg/gefa/qa\nUIvDy+sRX2c2rG854hgzpjm1zW/KEW4ixGsBTP5YXT38XOEHtZ5EZErArW+l3qhT+F5Ob9/sep/l\n0o3P4FlZX7UGtFSfOiw9539r9AOdjUqEFhW/5oBzdJybbnQQlCauwILmmGvNb703NXNVsFF/h4q0\niiOfrglvC1da0XOqRsGGPQF1QHsHrZVzyvNlgRNhWLcQqPL4XV+A+VXXgojLbWRWlRH8Ukdj8x6g\nkV4xdkpodc+tFsSjRnDIJWyAEXVYstE/mEoHAaygXyFqDkA7J/Xn1/5Rh2Ixa1VFr2jRM0YesjYA\nACAASURBVJTV+O7ATaZu0OvcmZeI5RaQs2/so9Ow4HhYEUaABAWgqaJcmJnV1n1JV4qemRZ67zoe\nVgKupY8BDyD67mR4RJCY9ZpWl3zFOGROS1ED0RNG0ZI4Rx7nx7RiGLkTnta53fsxR5xzL5kH8Jje\n5+trGdLiHkEIj5t1dtkJ8zAZBabEYmfpOAfUqsydDgAXcrhOA6ZfeZE6HWcEieKlf5HnmjszRNfO\nQ8j4+XTDx09XlOxxE3DBroX22skzw6dVZxiAeY54nUZ8WUb57j1YraDLhvKOLQBgK8x46Fi97ze7\nX6sDu2k71HStPFfnJfb1XoCOJvZov0697KbeVwMb5DM6Hyxgz6UieWW1ug1UGcS0VQpWAXRK9qhC\nsPOewWNOETU3I2mcOhHmS0BcK0KqGKIKzTDwnk2VlLl4TFLytgkr0uM+td3m2rjkuWXXYS17WM3n\nALQghWU7NvFRHUtaBjZJkMlsHHkJWA3A3qpXmP1O940NsABh3hGZ+dDBjj/b3M7CYmr71ikF3N1Y\n0zG+T/FS5oLe4+Y7+jyPoubUWQKb48JOaCwG9HPsP92EGM3n2u9Kd6Ha/PjOTnTwxHa+pjlY5pnV\n1VCAUJ+gkGPR6uKRxfnPM1eD8Z4QU2ki01Qdi8COFYGXDIQzwZ8D3OARlIJWC5ePvXUdsXAE0piR\nVt9TqojtS2X33vedDZrx/ut3ORBNRPkOROiaFU0nwdm+UObgNhW3lQb9pgrOP28jEKr7KzPwn7f9\nABGkeUgqg8yJfUmv8ByA6EFfpZTiCuQlsHFe1FHwmNf4p+tHl2qUnDNYJLEscC8SbfmY4D6sQCWU\nf1wx/9+8od5eEtYlbKKFhRze5gFflgGTGrzFRJR03TIOkEZlRgEQbrnfe1Z6viOkXFCkBI1PAKrj\nqKPQsmipcNEhjRm3SYy0lkbhd07CVsFanSCL8rNCsjU+DSoPccjMRpS8QzL548dQ8ZxWPI1LE5TU\nvNEYSqOI1+IwhEGeM8nnIuYacCssePYPEXY6xxHn1xPSUOCDRCCfKurEubJqQOjz3kXLaVcyrXK1\njw/D0qKKatDsBX4si6RVb/AivjgTVIlTxfHstYMjeF9B1KOPVmxLKztQpc4SKK4BASACLRXlhXD7\nwn11uQxwjtkHKgw5lQAHwrqGliYzr0waPMzrZlOrxcHPTP3kfwsLQJw1XZpXUzFBDSgrqrhRHKet\nwQ7IQo+etqPNuS48WapvAqma86vXURFH6xSodoEarVeJhg2hi5eOoYq4Z6dKZ5JqD8b42zMTVPE8\nk1KyuyN/vQ14XRK+5og3AR4v2eFauMyjBdKiI4yhb++XEvDH2xHpHwWHN1lbRGwzL6GBiABTwlXd\nXsdLboym7tyrc56NYb0Udq5VFZ7fQZ/HNlKWKwNbCrYA7EQvEulU1sEl94oAexE362DxuxaGVetH\n7vNcWRiwjwuHudK2+obX+zSVc9DnozqAa/VY5oB1je3dBKF/rm8ORdJCqDjMl4jrdWiVELKAuquk\nTN1KX8OCI6zkcMk61/tY0woUc9VIcI96q7NxGNf2DnNhWbjg+pj0gTj9yRGOx7Wtgaeytn3oTdIu\n1nqAF+pQE/dy3ch0zZnpTsQ2CtrXcD5fn8cW/L2UgKl6TGV7vEXKdwD0bY34+gdXiPHpikOc4I+h\npUWFY8U4ZZzzglXW42PKeHqecfhUUUvFx4kphJUcrjk2ppO+g+CAUyg4nnlzi8+9ws/9uow2dlXV\n34qP7qP7G0ceygroa7ZdZ+z3rXDgvjJG79/Q0jkAtLLFOmc7QMlVg+y7yDqXzanttW31lAqIoju1\nPuDrGUBF9lplOypQja7lub3QmoEYWiS3ZI+8BgyHjJREY0TG8awsL0gaTuF1sqek6D4rQKPp+z0w\npn3Nz61/YOaPMy9C083snuPQ2SeNiVUZlA9HoM59b2VgzGFZ4gYgLzuWqI4fTavR/s0VqCb9RcfK\nfk3lvt/aAIQ+zuy85b4wezJt57Q9rp+3TXWE+Jr7PqWHx7XtWRL2e100lO5y7i2TqfeFOZfr/7b9\nstfOqKBNRQnL0tHv7PesLkYNrJOkyN4SLrcRzrHgZxdMdHCeMA4ZaRDtmjkj5YLwkVmAAODGykGo\ntwz/xGu3P3qMP1UAC7Kkoa5L4PQkYwcpo6gLK8p9VmEDfi94hz+f4vMtV/lHdYYf7QeIsGs6r1Sv\nIKaK+ERM/10qslCN5teI28Tq22rsTCWyoVgNSqvoMu4jZBW9AgIRQAuQZ0KV/KjweYY/zKgLsLx4\nvH1hA2qaU4sUao4nAFxzbMg8ACnluH0uQneANA9cdRPY6VH0EwjOI/mAMYfGmIiZI7e1+F7b9sK5\n+fFYEV7FaHV9I7Ebp+ZXb9h+tHWolEqp4IJF5XUjtxGBSj36BnD07Wlc8HRYMI5ajpE25b0AtLq9\n28iPa2yN19XhIgbQH0vE0+2I4UtBiMwIOQgVo5Zu8OrzPhShM9dJvuIUM54PPXo2S/32exFEJ9oP\nu1KH1aFOgNvt8Oz8cV+kUBCEoWDHn45BygpiUTP2NpEigabrampbE0furYHZ2A21j6G1ekQpLZiM\nw8SlsDzyhS+SZ65wUqg7Sfx9dj4shVZFL3WMADou6M4ot/cFALV6rIWp0ReK7Vn0c7YKRKlsXOe6\njVREsKOhn7tmj0tOWGu50/7QZ+Cf+l93CDJ1k83CHIX5tOy8ijM0IeJWAt6yx6u8h9cVYkQTijyw\nRlMXY0S8rgG/TwfgM3B4VdYLfz5XjyWHZpxw6dDteFLat9WC0P533jhVld9/2RnGOme70cnvOkv6\nlAXWFGjp/dbfdYswVu0nak4VryG0jZxWCwbZOd7fh64tLTppUyEMoGfVr3MJ7NAKC0efc11ji2DX\nypHzt2XA14Wd87l4SWdgfYfXHoDC4PnZb0XHTf+bRn2vnlM9CoUGlGglmud1bfPOgsFtvRDgVxkL\n6hiPYxZRXCBd+NglJ7ys8S7GpM7L9+jNuk53cIg1GgBsRA+v2eNaGAizgGCuvUQl0Jl9FQ5vE/dl\n+qMgjDPihwrS8sXSn8HXlkISQ0VMhdPQKvD00yLvh8tBTmb8VXA55ueUMXwQEP3nAeefZzxfB8xV\nRf6igFV9fKyyfhC2oMGejcHHHdDWT7tP8pjTXbtQnz8tneFBjpWyCnIJDfzXdXilPg8AyHpGG0Hh\nLA63ZSJ44zTbfcNDU6g6KKHvfJtGxKUPrTCzaitZAWDKFe4yAWOCF0q3D4S3VxGRllMeU8ZUeA1c\nW9DmvoSlBZP32gJ6XEegvp9cOzO0Oj6Xc/15dG3g/a33u65TDUjKAWV2CGe+iDIwALQ0EwWvFdxW\nABtgu2ytPK7mVumHGVNrBTQTgu+DJOigz7119NtSSfeggfaLPqf9xt6/d07Hxd7GePy7zeTQe9pT\n4S1YwNfQ+9k+gF47WzEJ4jmgYEEx90VEyG3u9O8oGJFRUVA22gi5EnKlts/oPfI5tnsBwGNTbeF1\nDZt1NhRqvxMxizCpAO4t4/CWcZwz0s+yV0eH8krAQnBR2a1OKtBURKm85q+EdQmYFzueux1t2Zsk\nNhgZmnCv7NDHdAcxjc2j892A6yvpPrsF7IsA++9SiP8G7Ud1Bm4/QARwZKWXSkNTVgeA4VQQPwqA\n8Lng8ivD6W+vo5SUSk1E0RokOrfUObK5hS3v0GymnNRMKDeH+SIbTfFMDVwDljW09AMVWMnG6X+k\nVAyYxc+AGtq68b+tKQ0wJW3V47VHpvWnpVaVtwqfOc9RI1wxV4Ra4Vz4ptq59rdt1khlWmvf/PrC\n2T+/Voe6A0Q9wHoOQ3eaimPV26YFUfj5nGglAMDBVzzHwmJsxqi6ZI8/loThesQobJQ4XBFGarTE\nff/a5/OOnTO9zVPMeBoXDEPG9cYG022N3eg31EJGwNm4UNHPTMxEyBNaOb6y+AbSNPEp6nnqqvug\n7/kR1bwber0j/cHDxYIgII2WSrPOslae2IBD6GWJLIBTi0eeGTwAmEJfBHzL5Ft+K6cDuI0RbCN2\ntrl3BAy4LzowdssRX9eEqykDp0KbHC2FXLtHU/WshdhpHl137FZy+LoGJO/beZLv/WMjGfucWv01\nONYXG2u/jnfA4LmqAoBN5E+j9FNxmAphKcb4h64v1D53LQ6XHDGuA27ifdqITjCaHSFwfn+hXjIV\n6FVMrMFhI6Z8TPJ+LXBjjA9tznUF8lI7c8DOaxvl0t/1nivwTRJlj0Kp4fR+22h/4P05vH+GeY34\nunRq/JQDDrHgw9hBwbd5wOvKDrmen+vK63u01+F9YpULTeXeCB98d+pWAcGuhUG2pYZ2j4s4kKwB\nY0R2Z6bezktskaphYBDBB0ISQza42ubyI4G3Tf+J1owNfOm7aQ62gIGV+H618XEFRgzQDI38tjNJ\nOlrXfbjdEsKvFelrac+SV2bVLKsp8Vg4ql0kDzkI5fhwXHG+rbjmuLk2wCkQ8SzP92HE+G8rPr7e\n2joS5wFTiZgQWtqXdWb7vrqNCJOJIu5HWHO80MfrXlPBto3DrmNCQFu+dmcPaVRer6MOgXWA9Pwb\nMJOYxWAZCsEDoVKbIw4qdGeeRRyWUp3oVJj7Fge2DZhcQa8T3JLbZExjxvoloF4dBgG7hljwPCws\nkNzumwHd6IDcFjS82wQP3+Z7S5/0vHJmBIXdyTzuU8fYpqMGxiw5YL5GhOMKVKBKNQ9lAcVYscoC\nl3NstlVPKxGmEqHpkzT9F6NNoDbQexHi+wjzNpnBm33pETPj4Tmb80mb37Vv3mvvRbsfaR68p4Pg\nnc4bar9/r2kZSZu64CW44+DvWA77faCQ38yxtlcnTgN81GyqSpGAhbJ9/TxivGV8WCZ8AO8R8SdO\nCS43Byd2sz/KvAqdWRpSRclq23X70KalWtC+itaQtiLaTNmwglX3xrYGOOHb7AOHni75w5H+0X6A\nCNKcQ6sNbDUNwpHgThH51xXXfw94eWE1uMuSMJWIy9pzlG+lLzy6LUfHm89cthM9yya+yiJTy8LR\nx+KwCIV+niOWzLRZu9kAwBDKxvgJnnCIBefSJ7UvfhPRAsC5ZCanc/M3csYx6PQpW4qNJPpoIwzW\nkVXEPaXMBpx/JObSKaTRARlsnKghEnYO4cYQawZTv+/FeUyli+IBQJjFaddSUo4wzQnT3HUfcvGY\nK/et5gOT3OdT5C1oakYi8JY9LjniOovY4uoaJb/dK+5TSGxTpzD5ipQK1hxwEWfkklMbN7lu0WWl\nrFkmQs4eZfat9FgWcUw1KAG0HOClhE1+vhq7DRTKdJ/KoD0yRvi0tsokqRTOAzQU/kIOQYR7yIBG\n+6i2GpG1Oiwz9/nb9YBFNrq5dBX+ubpGb9fIzJ56aJvmder/tUyZrcP9sgz4Y0mNSs4lERkAmU1U\nSQ3EDc2Y+MwH6uBJIYdLVn0LPqYl/2yETFXf2Zh35nw83gYPHINehx2zY6Cml9HP+20aYldG31KY\nm+CnAqVg4MB5XksURIie85CXHNp8UgBBK5fwsa2BC+nnVdgF3UHZvx8FOjprxdLDK/Ga0NKvzDka\na8A9NnL2QBIZB0jfl87N4IACbIxpBd+sQ2dBIL5Gf+8KRKnGDMFh9MJOAtPlX3PEa+5Ord/95HPd\n04U1+mP7sBLnr25zUz2ii63SA6DzxiN5QhKE5rZGhKkil4DLmlpfDlPF8bYihtpYL7MI1dko61o1\n4mWj4h3wtTm0dgwC7BDp/reY6H1nhLi7fYrXMJknkuc8hLKpbnN5GxFutRnMufim66NMQiKHdC1w\nv5EI+sk4qDzHxlCQ5D0meT7nDIiaJVXvUHEcObQ854hMFdXoeawGDNHUGVtFRE5l2j2I22njChSb\n6PkDsMv2VRb7IJhUpMUIR1rGjoIOeTe+gD7eHW3fD1/X3sP2p+oIARxoUIclF9/ZbnKhDTthrcDb\nAvoytRKPYWSw6HUacSyMZA4xt/c1aiqavC8y4nA9wuoEuMe7jfu2ayDw93ksVoemceVkrdinM2RZ\nY1WnYc4R0y0hvFSkQ220dy0B7X0fAAwE8Dvb6OYIQ6qnZFEDo/aAkje/a9UD59yGDRBktGzZAHuF\nhL7+2YBPS38wqQvBahAoULzb43W104pEdq1TwOmOoSB9atf49qxtvenvo4EAlnFgJm52fK8bsMD5\nds8KLATn2vmsbWHT4viYa8G7kHow5SD1rLX6AcDr0EQOZPa2qjbZGzB85u+cjzzey+qAL9J3M4lu\nSAehVLB4y/LsqcG58vPycY+1BERDmcnFt0ChzlHVQXlPf6rpNkHXedsX/fe/b4UC+hs/+7b9ABGk\nKWVrrQwAjJKLF87sNC9/OFxex+bw3XLkaEQJzbCeBERgR5CPRc8bwiLUQkApQkpLM8557BQuYOvU\nA2hU2ySGvi/AIJ84phWVHE4xN/rs6xoxSwTIivTdiugDtPrlFYuUJizl8aLSqg94/s97Y5BlVloP\niVrkX9XCK7nG1NDomC0/t4pBnx9sbKVuc2vVKSjG0AKAqQLIAZPc47V4XHPAUkMrdQgAr8uAi9Gs\nUHp2f2cSDZAx8Bzrptzn6Pne1bGLIyGMQFx6icfoGEbwzlal4HJlGk0DgDFleEd4mwZcxNHPlTU5\nXDPMOwCyVwnW/gAYPADQABJb4uxtSY2xssk9L/z8XeBqd24Zl847+OcR8ZcFRxEAGsTYe5pSczy8\n6w4oJILNZS0rvL835khyRAHgsibMJTQEvEV2qpP77MfUqLpXyhZKYotqS5662ciXGnApAS9r74vR\nE3TIz8aZmUt3lK0RRCAsFZBAJTxUrNRB2OAYvMco6Qg9dQhYKrMGtLykGujKatDc98Fz+aVD6MJi\nRKy1MHoGHAAGLZbK31OHILpWdn2z2R9DxU+H2Yh4dvaBsof4OyaaYgAVLrvoGrtBGSKIPRKnEZHt\nWNNoMzVxw6WSrIH3rAVCj3Zq0/fszb9JKL4auec8/G2KjU1P6QYrW69J5sCW7vx47SMz/gAghnJX\nZcD2FV+bnYOp+FbJA9gKW+l7XFr6Wy9/qMKsdw5kc+5kTBfCFLaCb0vlNIFUHQ5egY4IvwCXNeJl\nGdr6oBVrtIoDAFxEvHMy73uStJn8wG7apAHxY2AkapUmpuIw+A5A7E9Riec1wGt+9FvwsUXZQ8FB\nHHnNM1/XntLHzMCIpYbWzwCAN6YeO9dLppI43Vr+sV9Hjkv1pfp5QnmpyHNsYAWnXPRIP8Br1Cys\nGt3nZ5Omwefv/2gaB9IZSzGVVlq/9tStlm4jjElr1C9iX5TqkF2PamtJxqmQocezbbJSd36X0tcn\nCxCNQW0Xdbj42afi5Dz93ds1UpkIWgGjg9I71EQ7sxLKy8qaUGDdpTFlfJlHrMJWO5Su0aG2Q6qE\n4rfVXHra2DaNowHKds+Q+7YpCs5JmpMDGhGG7Lk7+KzOmzLnluKxrAHhmgCsjfZeMpdjDqHrNun3\nCb0MsIJ2ul7qNff7nerDKNVcb7GKx32XPkAd7IM87z49AehroTnC57j7pH6e2jUeNZu2YJlvetzt\n1rUKQt3MEU5bKLt79WaOlF2qgz5XMRFyhZgItf09kOgNUMBAvs0z/py72w8UEAOAdOJrnsuMQ+by\nuj6QYUR5xNuA2xqbvpDaobl6rBI8qVOBT8xuUMApz4S8hl0gp+sH7dNHdcz4poPGNu+GifCAabxl\nTvVnXCvt0p9IbHDjl6CnmRSYnJ0f7W/ZfoAIUEyJGn0wOM7BA8AVGZaK+RqZBmqQxZ6m0GmBvLx0\nZJxqz0G3BitH2IxabwX86BDPhPTGZ6mVI4TFc43YQfKcxphxGFfk0lMFTmcuQViyx4cL5+p+uRww\nlYjFpD0s1SM6Lml3CB3lX0rY0NOjgAqaI90ogB4S0aktCk/FId+4HrQCCz4QhlKQc2mVHaInJKpI\nEuUFgFQdikcTWwO4/22ZGW0aldxETgRQUGQfYMPgzbMRNZvF9JIjrqbsngqcAdt0jyJj4BQqPgjq\nHBzhOWb8PM54fhI62om4tM+1AypaizeAWjQsGQBBRRSd0IqvS2r3Ez1/xzmlasq7UDAB3fhULQeq\naBvGNHNqzUquIe6v4qATXC+DZSjVWleciPOiNWe1aXVkAoYA/8sRQxXLOhPqXPF0m7r42SQVMFLn\nwKdcRdixVzLRjZYIWNT4zxGzvBey0fvaAQR17q2RvW+ab6nn1/GSm0Aga11MJhrKThv3x1S601TE\nSSVCs5ArAbXy59SACgLwFOrK+t45DJ6QXDfAs2zQ2TgEOowtBRpgZyJ5Zki0fiuuXW9UI9qxc+Yd\nIZjnCUZtWc93CAXnw9KcMCt0SdWUe10ibmvEvEa8Cbh1FaB0Ng7FIpFym1u9ir7CUrbsAtU16Swn\nNWLEiTP9oFTvsQEqbPRaFe21dtDE6qXcR3b5OloOS/uE55XDshtM75UurOjrBJFDjJUp/9A+p+aM\n2++pkVc2p+X3xe+k36cCCufY+y1ImkpjbXl27AbvmiPVUjwIG8e5ksMK2vTTWr2wI0Jjz2g7hNrO\nqWygZfe+F4mI7h2DjSFa2YnZjAuhfHth/LTIogdCZaewCVf2P7e2Vo/LGvFTCRhGrs4wHAuoAtMl\nYX0L7Rm15J82zuGvcp+u3XNwtesAkQJ94sQVj/VNwNF/FFx+jXj9esBFwPnLmlrwoIGbAiBodLr3\nS3fyHpYTNT/rbkzrOSyQxM4wgwsKu6teibVFsooq7pzpSv19WQAky+f0HqLMOxU71fteKgMRuh5X\nvd/9Xq320QZEAHzgAISL2028Xgjz575Xp5ThQbhkXoOmEsTx7tFUtRm0ogvAz6CVXDa6Ob4/W4M0\niBkHtu89jH1hI9HYjvNKW20hQBy4EnCb+Bk1IJNLQMylsa9sH1XzfR478ky639U+BmyknPu9QwPv\nOfKAZbjos2w1Cewz7luzYx984VvgAX/H3MPOAd3/fQ8g8PkfX6Caa/zPalYPB1Bdl4CyOqQP/IfR\nF/hU4AcAHl1TagaGLxnpbWiBkinHFrhrYOQKxBPbkVkEeZcpcjDIpGQFX3k9N3a8shB0nVUm2lI9\nphwRJaUS0P56DJC/1x6+751txWvR3zOdgQBU+ns++779ABFM0819DNg4Q/lzwTwNmw3ACz0YeLyY\nWoPZln3i7/aNoeVrFw/4gviTw2lh8Sf/tTY0W/PqAGA4F4QjkN+M4/ETwQ8OlDMOL3zvx88rrtct\nInrLESknzIVaqcOUCmIukgsrBgPUSJWfWs4yUMs7cDJ6nOdUgpI9hmOfWHq/Ns1A1eO3KQ7i/Gip\nwip0Y4Net74Eiwrp7tYEYIwLUIkrNuzfjYqa9agFG8uW2WCp8/8y+lYi7WNiAOGn04TTJ34/4ckB\njnPXdMH2kFx46hGB4DgCEYyTkbNvKTFNfyOwc1KIkeNq+mhfZkk1KZQVAnDKwlRiY1cAHB1LvgNI\nAFrOZZF3BvAGWGY2+LKpNqF12d2/PCGeB7n5ivr7FYfPC/LKfaGCUTFWVKnj1vLmqtuAB72ShRpf\nXmqvuztDphu9fC6bZ9z74p4Orq0YJkwm30BCncnJ9f5VQTf+nnXItvezVGopF6p3kbxjRgw4n32t\njgUOZSypId9ygs2zafRrT9sHupjlIlUgVgOAOMfGPtXO4lFFf/1dm9aUjgKK+VBRJV95zQGzpFC9\nzSO+LgPmwqk7AAvgaVRaI2SqwxAcGSZWwKGERofX91VIBA9rXy9bOpeN5lZNtaFWgeE59bmsj3Mr\nhAvt6Mqm7xpdXhyj4IBB1qrk+TzBEebQ9VY2bJMNk4HHpL7vJQeEWHGIBYPM25UcRl8xeC4rq+9G\ndUK2VHQ0OvHJADmj798B+J5vBQ3QBIBDcIiOcAjUWCuc9lJxCLWBjSSgBzvQJM9dJYUF7dkABXOA\nqQbz/W00lz/Xx+vmYeRv+m7V0RrMeFZHSNd+WY6Ydh32ET8BS81lVnKY14iPS8IneQ/Dc0V4ckgv\nc1v/yovDmzj4WqHoYNdc8lhyZ2Cs1bcyk0DXa6jkWorY9Bn49Y8nvEwjpqLzIUh1jc5CrFDD2oAB\n2Dp5eieeOrGsA1GP0gvlPOa7soq2qDPA74jTT3w7h1aGaWJp7Z46wNXYH+RQqGJTzaB2IGg2C+BU\nGAxttH4Fsba3Ds3ZzsXfRVXVluAO4EWMKvAm1X9CYMbIIRZ8UVZlGXo/k96Lx7V4XIq/Y0gpgKD3\nRe2n0RaAA3ZjugO62w2F50RPC3Hg/aJ4h9X0YwWv1XGJLWCwrmFT/QaApE31UoL9PWyB8vcA8++1\nb6X9/dnmv/+R/2Xbt0oP7vUQtO39Zgvsz8WxHsct4SCaBumjg/8Y4c+JRY2UFbIUpN9nDL9NWL4K\nQ+DWgQGt2EAV8EcPfwQGWWiXiceLMp4B1fEgLCW0tVKZghUKVst9Vs+MmBKaXaklrG2Q0Lf9aWs7\neLGNWgnMd4CcH+1H0/YDRDDNwyF5h8F3ii9lQn7lvwdfW077AIdKBTa3UJfdOyfHAZCIOyC5ki2i\n3NE9AAifBhwOkkrxa0a5sbMen4H4Cy8s7jiCKiH+PnOkGID/kOA8UKeKJOjgkZidEE0Jt17bum9y\nKRWE6X6R2dPqtSlLwMs65wcgvpVWkoafx/X8972IDbaRQTUaXfv7vWFvW0WP5mbiAxVbRfNCHKmN\nrVQZAx1W3BBeqKDUVZen6nDNJEavwznwp59iaZF2P5gHgaZ3CGPBcw13ArV0EWUhKKIMsDFxyxGV\ngCcBrM4DU+M0r1cNFhYuo807abntgWsU6zNqNEojGd71Ek2tbFTtERxlHNSVDWelCCu4VWaApgz3\n6Qk4yIPfZuB1hosdwNBn82Gr7q/joDEbKov+ONfFudbK9b5V2NDqPii1sJj3/Vf2NXVWtSVPOPg+\nzp9iFXCKR+Cj/Pt9W2sXN6zEUdxz6IwHpfNqKoZ+Th2JbtiyscpU/P5ZAAjiaKuYblJj/QAAIABJ\nREFU6mVJeMsBV2FSADx2lWpo88zrLm+eVf8jrtPQxDFDqMiZFd0v09CMltc14TUzEKXOlRrpnF7S\nHRcGEbqDPRVJITKGsTJeWDtB75FQyDPt0gCsakSvtad2HEIXvrSOr6Yu+eZW9c80xw1Kk+9GVgMs\nnVax2L5v29R54whtf0aASyqeYm73cwgFx5TxfGQDM2c25t5y2ER89XmDo8ZEAPo4C42d4ARsdm2s\nHgOzUPSntnMseIqlGY2qx+FAOMkaf0yZyzvWgEPujvNc0fawVq9enmmThy0/N7R1KCi2dZZVlNGK\n+Wnfj542a39uTBRdC7Y56npsKqxn8q9acrcsSGeP4Qj4xECmgtlxIrzIeH5OKz4dJpwPC+Yl4lXK\nWHLKV7irOJI8591HUTktq8PXecSXZcCtdDBWacS2FJ/tr0fNpgrs3VTd/1Spfv8dbWR+2jnPNPgO\nIli6c6m9ekuukmJp1tlc+71vmD1yDQskaSqSjgMP3MUifZuvkhpigGpAUqlknLvo4E4Jfpha1LZU\nj9NxwRhzA8FeVh4Dne3J69G1OFxzFynVSgZ3zrh53oY7vtPPVfb+fTlN2nyODDtBAxZOyr0ysyua\n1LHbkjYpYpxjvxU4fjR0WtTeAIDfK823mbey9/+ZPdMDdxoGf6X9z2AHfK8SzLe/qyvL+09UiXpa\nRjsm7xqWlSLpaVPC88prfHCAGwPceQBOA5B4/Lpa4c43jKcr4mcOwgwvM5Y3j1p801RwEXCDhzsn\npJkZVuFLFRbPVpDUuS2osX3Ovje2dKzqGhDvHHUmrEkFavpk3xGr1KCn7rUFvc/+vu2HJoK2HyCC\naRUkVRqoR0svhPm1pzFoTnHyQmk1DpNF7G2eLxt0pqyMMdhaekTxoDnDHSL8JxZvdOMVNBe4oxz7\neGrnd683YM6oV1PKJRPqpfR8zvW+XKDNhbZqsrrBNQcOhnJcfY8kyy5DxM4zAPjngPRSUF/79coq\nZSDfWfj+rCowmb8RxAgy37W5uDZfMYFLgmmUIME1LQZL2y1BDBONqIhDN1fg8wIMXmvBJxxCQQwF\npy9itLoKN8Bq+3D/SipDdAoi1Ca8o/04rxGFWCNCU2eOhwVaQs5W23gvV1ubMj6Cr1IeqhtaCQIs\nmCh7d+xcQ8fLygyEnD3mJTYQIV896GWCe7r1+/n9gvKPCfNLwPWqlSXYaOfqF5IHKNUvhqUvM1r6\nKIautE7gqJLSgx9FYSwFsuLeKHpkKCk1dQ8iPMWKs7BwDr5ikhx+LuvVT+LNmLCtVOAqSkZTdRiD\nwxgIT+YGNDWiG/rUQTU5prXlnUNLp9DrORCm4ptz/3Ud8GWJeM297OitcE78Hlhhg4Fp7wCnWfyx\nJMTLqalFM+DEju7LMrQoLKd7CGOl9u8ztZ02DkUhwup6Cshb9jiEAC0tBwjjRSKXjaIr+fJcntBv\nxBozEW6lA7MH0dPYRlO3+Zm2aZ+2e6xAdf2tEjjVxGM7Zhh8kLrvJjLY6OAyFq45Iq8BKRY8J0kN\nccBTWvDz8xWnZ2HmLAzQXHNs8/cts1ArR9udYQn0e2hUbeqMCWUiRAE+Rk846Zx3hKdYcI5r25uC\ni7LnoN3jSUoe5hLwHLsgYJJornWybNRcm3PdydDm6d6B1GbHuaWKb/RwdL0GNjof1+yano5+jkti\nRvzxxnvj4fcV8XlF+JQQf+HvnssKqhfErxWD55S+D+OCTx+vOP60YL0GDF/4+zfRxvHGBAqO9Uie\nhhXDMz9pnphdttaubbFUf+dAlvrYCfzWyl03c7b3293ngF79wN0DOz2tyJvSsr6Jp1kAr5dnM+Ul\n0c/XcsZhyjBCr8MsBNontN/1BVfaafoossfU1cEFFnN11vIMHi51qv9tTc1O0ZRL75KwP/r8XAqE\nIbUt9arzfguu2rQiBcuojXv7LtTO2DtXNm/emzWppQzBNbbhln1JyJJK1I/11Izv7e/WudWfOr/e\nYxzoivee/sE3r/OnPm2/twNb3lmb9W+PjlciPAIM6rvnUcefdp/XMV3bv735vTPP5D3JGKjgUo+A\ngqqql9WuiLlytQVlFzhfAczAWuA+FgYTACAGuCHAfxy7jTwWhHPlKjEa4FEgzTu4I38wDqIjBUJp\nAqteGDO+v2/oOOh7FQDRLuKS7UHZw44akNBsUucbmPmtRjBjrDF4/nMMlx/tn6v9ABFMUyZCdIRV\nHaErsC4BXtT0tQyW94TDEnFcYqOwNkXyamvd+xZpVcG5KhFLpSkBHKnNFyBOGe4Xlm3zg6wyz0fm\n4+qK9PUCep1RrwXlVVDNWwUtwPR7F26ZJ9Zx4OoOEnnKoVGIW35V9ujU4i1qmwrnjiqtuhbX1NUV\n4PXniOGXijxViFgtSvZYtcSRGiauIktOsF5FRSi/hX7vN0q7fik9UViRAJgyewqsaXBohjph8BXH\nUBsNeQkeKbPecNCyghKpmStwWYHfGvofARw2htov5YLxOaOubgPIaOv5t+wIBaOArtUMjjE31e+U\nKtbVN0PkEQCzLe3lUMy1k68N/NEWA6PNM/XcXxXsWmuny5eVjb2ceQNqlO7Zo/y+APULqnivy79X\n3L5E/PH5jJeJjfVLTki+4nlKLXXmmiPn6y+ddUDkcFsThlo2ZSjn6kS8rUeN39uoLL33vWaFnGx/\nDJ6FL59aOTsu6RVdp40D759fo6xKBb5kj8GTCB7yt05Sb57pvC3u1SnPZhxXsJr1Fhgjk/MoVOsc\npFQj8CZz7LKiUYttX3nH1PeiOh1w+I85YC4H/C70YJ41bChdS08p4NnIhkd3HtAimrUdo2aw6/ta\nhB5u88KV8cJAArV3wmwOpoNbwTCO+htRPgdEqd2+ERujfZ5v77ds1osKoNaeVlKIsHp2sJdKht2g\nucjdobAsChU/u+SI2y3heFzxNDJgMMSC5+OMj/82IbCPivUr4Xyb8TwPxjnvqUaEDoq4dn3X3q1W\nMSD0canP59DXoINq0Lht+tQxFERP/R6HDCLgWDyeq28RqakEripEDm+yFrzmgMWMJf25z33XsVvh\nGu31W1FSZTjoIJoKi3WuxsHjCD2/F12vmI3kcM0e/3HjDva/ASF+wRkrvBjg4QiM54ynPLc19Hxa\ncPq0IH0C0lNBSFfut9fELJwcNykJz/Iu4y+yd90qPh0nXHJCVXMp873Z9bkZ9Q+VNTpV+HutbtZ3\nA8bIYUePz0PgewpGNJjnAs87m2NPXlJVWum8xwCINns5Tgdzba/1DihSjtRG2TWdwYIIZXXwdQu6\nUwVcqXCDQxJG3cs0okwjxpSbbXUKBVd/n7pgBXDtPeqaSOZYNWNMjwEqcNj/pnoT+z6xoKOCJqVS\nE2BkBgMhpbLRz4pRdY783Z5mAYzGBtldubE+/sT4sWUg2zF9brMn/hWg4L10wUfaB986rn97pHXw\nXly3vhP17g7tDsAwgMGjz9Yd18KCFHbd1xTkzjLm8XxbEy6vbPOsc4H7lTAcC4aPr4ifBFw4BLjk\n1cAHAPjnCHeoCFNFlXKzlAFaKjM91XY9VBzGFaX6lvriRAC4fNflhzCHeN4lsTWVPb1v3xpPts8t\naL+51nfv5p+7/V31IPbtB4gANrI9mP4zBMmXlehpvnmU4pBSxXjIOHxgK8954HBdsdwixomPUWWK\nWi490jNlFjZMOcJ2t3e+1ZMHxJF/80i/LS3q634+A08HYByYQv75BQBQ/59X5N8W5BcgT31BzLPH\n69dDizbOK1eP6PW20QAERlU5ypl8bTnXU90usMFFHHLpoMriEWLliLqsIu6UEH4mxN8XrFff+kI3\nUUVEQ2WxxkwdPIkeiJVVjrsRzIZKMTm62qxRoD8HzxE6NawHz5UVnmJtObGHUDCGikPI7XNzDTiE\niOSpCY05x8RSAoQmydf5ujokFwAMGAMzQsYhw4f7yAOfB01YMfiKECq80UWIvmKMBR9PE8ax62+0\nlAu/3xx7dLIdq26TQhJMOU3X+oKPTc43Q0uNJgKwykbFLITAwnhGdC2vHvlrRv664Pobj6svX54w\n5YiXecSrjLVb8TiFiuuSsLaotrI4tuU3pxw2QBvAG5+q/3eKcC+zdFfd4cGuxuWjepRJm71OcFz5\nQMfPLDnRmZRmbJz+Bwa70uD18Fz52QtRo90nR6iec/mncn+fNrLE45dwCJ01A8OasRU6uNQSRwMB\ndoJnicJtmAimfJV+7pIdAN8YB6zpck+RHDy1OaNrgcN9OTonJcWU7cF9wWDQNpp/b0wqs0BLEVp2\nw1rRyqlpX3nQJgrIjua9cdNKku2upykf/A+HqlVldvemTATLxtpHKq/F420ekVLBOPC8PbgV5+cZ\n6ZNDnWQ9n/kcQyg4CENglmg2VWxE+fjZeexfhOEyFzIGLLfogCkyK2QVdfGTpIQ4EM7CaFJwLrhe\ngjUEFvSKoeKcVhwkFaMS560XcviysHFcbg5TDRt9DQeOljvbZ64DCTqM1N/dz9cGRFF/T5MAh3aM\n6Bwk9DXVgecCweFN1qbfpwOOv5/hwxuGD90ozzPTgIfUwX7XvDA0hgGw4nmacc2xgQHBET6kgtNp\ngT/LXu0LPn26Yq0ew+0AAHj1CdficSsBU+2d8SiibNlMe6CcqxL1f/Mz351i0/ZAzr7t15b3HFPC\n1gnQd6brp/o/3nWmDIFYL8pbYEvSG7EtSaz3wDoUYkstHnGo270yV9BXpoefTsLwezvxe4FrAYjn\nlLFWZatp2hihegeg6zs0oMBp6uY3+tL2hfkcj9Et60DHpO2znp6max2zKU5paexOvhfCMa1Yisds\nylO+N16cSXbR9BZ7TI/r5+396Nyyonr6EaWjV0np/J5jaJlH++vYtk/B+d5xrbqw+ax517uzoD4Y\n7/6d96psX49eiWGrgeDh0Us/evm7rQBBAkTblB+1B9bq8XrjdZKuLHaYQsX596WN3zQWDB9WxGcH\nl+Q6T4FTH5KHE02E8lpAM6HW3KII8Rk4TwtirC295zYnDuSR/y6QpNU+bECw2Q6Eh2NOm9uMr444\n7ceeB6/3PD7+7lDCj/YDRJDmxHFVB6GYDTEEwvHDivhEiL+0Yu4ILxXh69rKGtbihM7tUWUCD0tp\nJagsXRToTibAYirTJSH8xwK4NwD8chwR8DaBfntD/r8uAID5V8J6DVjn0BYa5wjrGvD5dsRNrve2\nxka93OeyToUVrwEGEW45Yqoe12wAB++EahpxnAVwiAWxdCV/vlEP99MB6eOCKh5gyRXee6ZQaQqI\nOI7RuVY6UfOyrKq8bQ8RcOpGjP794KlF5U6B8BwLzjG3vOVDKBhiafnCAOCKVJ9AdwC51z00F7/1\nRWUDP/nQ8m1/nhPOeUaItQtQaroIGfBEQIQQKkgUXT9gxjBknD8sTXhquibkHJoQjn1eFWOz9dgB\nZpv0zzlhPfTvstNMG6Oo0T0t9bt45KJ56ts66/niMF8jfv3jCQDwRdgHL0vCRYwidYhupoSmVUnf\nihSFDYDWjlemznen0tDQjVNpHUpthK0zrWNejQG9h+A4Yv4qzohGwveU1kYDtscAjA4YfY+cLpXw\nlh28c431MpiflhJLxBF579XoFO0NaES5z4lD4Fx3dUCPoeIcKg7ebRw7PW8z9sAAiuZJA8ARAn64\nrYJ/8oRRHDQ9njzn0a/Vwwu29SpKeJZF8aipo8iOeH8vFfeRTtXusGU1ORrPn2zsBjlRoR6pSo4B\n3/LAfmFDX0EAKRFGPQ3DOQCm6oGeQiO++8hgBxL42Fv2+LoMOM5ri5yOqSCOhPJGBmg74roMmEp3\nUpXODzCNWTPR9D6m4nCRwaoK+DYv28HhUIBbRlunD4HwFBmc+CjrfpA1yIt4IIDOMsoMcOsacYwZ\nYSSU2isXvKwRL5nB1P2c+DNRzEdDpJDDrTCjw1bZWCo2YEpwTGvmOuh8LHrggNqABICZOb/fjsB/\nAE+XuX3/ch1apSGA1zUfKpKonsYD//SBeP1Nayu/5l3A4Iukh/W16/Ax49Ny63NkqogrM0y80UFS\nwPM9innrR2wZJvpZrYagh5Uqv+lzh17u0Xx3v3PqHCTcU5I5Hx8oXufJ/Y1aFoTe5+B57bM4xlJ5\n77aMrzanhOk0yVq7zBHO5c11aCHUKzPxDk9dEPqyRrwuCaOWU/YVH9LaqlAAzFCaK0kZyw6Y0jv9\nr8+pY2g7zzsIoWtFY0s6/pat9gB0llLfVx2mHPEM1pmaJrYTYix4OrCD+SaaHNccee3f7YGPAB/7\nt/Ys9Phv39IQsMD6n4ks83rf++XReey53ju+/9t/JRPhUbOV2QCT2mberQqfEgFXsf/mEnArEQ6E\ncTrgKDSyQ8x4/jLjdF4wyJhOHyrCs4c/MZgAAH4hlNcKuhLCWfaIwWH8WBHSiiQRrBAqblNqzIR2\n37Iu2HejwIdlMXqxsfMuHcIycLjvOlhg37utXqff3ff3368pq+VH+wEiAFJ72CN5NOe2BS8CYThk\njP8KhE+p5T3RdQXNK9yFNuXSahEUXCPOoSLVuhEt5NzW7YJWRLSl/uGwTpLHOr8ifrqCFsLyW8Xl\nV8k/l81pXUOnPDlCLgGvS2qq6pcScCuMiO8FE5V6DHBkWFWqWykz8GKUPPHfxBAYlwggs0CbzqGl\nwB0T/HNAeJVNv1SUXBFjabSqEri8YyHf6sBrxHU19xilFrb+15B0h+ZIW0TWQ0Gg+3fb1M49oUiu\n9mKiAYUYMNCI3TEUMV49HFwrb9Vp7Gh9lA3tv92LpC6wErr0eSBmIgTC4Lh/hqFgPGeEoeL2IpvS\nHBszQM9lm/2XovzzEltlhEfCO2zoqkOl56H2LO27xUs9YRHlMftVXjzmKTaGS3NMqJeNXImrQMyG\nhbNU3yJwW6E/diAV+VZnl52Kbd793hD8q6JP9roc7fV4zV2cUNkG3EePtTo2AByJ870NpqGiO0On\n0KN3j3D/bmT3exw8NXX86BmQeI656WWcy4pzDHhKXbNAFe+t42vniARBcIoOPw8FPw+5gWVqWARH\nGHd0R9Zj6MrzwRGiAIotAkb8j+B3lHf08qGtf3FfetKBKdb7+aPPZZ1n/V3fQwUh7Hq2p0dtn0Pv\nYd+IsIlw2bXjUbSnVzVwuOaAt3nAUDp4HGJF+eLx6xcB2uZB9BD6fFHjtBIL8k1GtT75nh4CyFwQ\nlkkHiEiinR14uXrgVhwqQptHo1ej0eEw97SdOUfWdKgdJDzG3Kr0KHD5mJBv+mLXL5wiL8CseW+b\n6CX42aayfUdr3bJ1ku9RSv1+RMUhMhBsUwgua0R0Y+tfIofXZcBSfKPBR8cpZErpPR6l8lGgJvaq\nfeFdYAc7+5a6VV4qysygS9roIako2bZvHvXcexFxhw7mkISMrdOu+1w1fWHXUsveiAIQboMF/eb6\nd1w758P7NL9rNF9TrZLv6VO6xus9hgfn5FQm10CaUlhk0Zv1hjIhf6WmtQQAp3EFrkdccmwVhRRM\nPXhmGPK1HUJxm8j2Wh9H6bW/98+rP/egtE0heRT1fqRFV8m11M+Uak99qB4psvCq3avnEjaBge19\n9ovqe7D3u2daRK+/bwESL6CllbFQNsveqX70nLXZw2Ze70EYtbF2TLBq7JfN3x72nTCa9vcE1+6B\n9Q3cZv33Zr3h5xF72MASmizrzb+jHAvOIzoHctik6Nj74u/2tFRdb24l4pqDAMCEq9jIg0+4rRFP\n04DzlQHO4UvB8cOK4ecV4WNoJ60rkC8OVUo8+sTH4LtfEWNFjKyTsAW+XAMSepqNewgiVEcb9uej\nfZf7RphFba8mCU6hsTWsFg79KHP4t28/QASI0Y0+cQhotPMwVqQAhJ8HuJ8OLdxAa0VdCGXxyIsI\nLs0Raw6bkkZVJnM1NNlWOoy6mFAFbzbzGnG9MVgwXReMh4ySWcBOEdBSOW9+yQFzldJ6rmKtXJZN\no8Mq5FXRwRGHrt6tkeKUU8u5buJucEiOEJzH4ANOAkwMa2G66JBRZf2gixhmxwA/itG6cklKIoco\nfRZqRXDsPO6NIo8t8v/IkLeRQW0EjmCH0vv3AhZn+2AoXWmtIvjmmmGizpeteqDnOAWCH9CihQSO\nxJxCFwbrlTU6dVGdcH332nhhJkTtn0QIIyFPvokTai1hrbKw10Tg2sVyn9U1o0WjG2v1uEoZwB6t\nEQeiupZnvhQVsevlD5eVgSJNa7E0eqoOWbQ0bB8ttYNOqgQ+2VQeSRMAtpHydRf1v5XQTIdCpiwi\n3actOLdVjed+uY+SEhGqfFarJlyLxyU7/LG4jREcGrUfLVVAI0we3dnUXH4V57OtbKJhPFaCMU5V\nKZxgUX7Xoo2RurF+8IRTqC0Kx8/Nfz94wllqrOfKzKlSO9tC55J+R8/3U8r4P463Jr6neiwquqQp\nLFOJmORd23mi2gbdWKG2XlrDJhM2uhZNJX1noBZyUnvbNb2BSl2A0Ub8Hhnsf0YwjHbGuDZls4AM\n+8npc/Y0A41O2TOsFXjLAWkZkWRduywJS454WxJ+m4TyLhUQKvp80bQeO9a1BdoeK9QBBB0vBRyF\nnso2QsbVLHpUPIpw5FwdRiklU8jhkhNe1oipdABvDJy+MobaRB0vOTR9EqWJL7XPsf37sPeoj2Sj\neC1NhRg0UGBXo+XbaC6Zd9THV/IVz2ndCAeyeGBPy1urZ/CmeOP4VswiMlaqw0EW9HNauU/W1ATv\n3nJAcrwen/6dWX+X3we8vo2Yc2yMwsvKzsNUeiqO6g/s8/C1T3RuaCsV21KHetx+Rs9n1jXLRGj9\nb/5uywWqKKhWX+B+pbbubJhptJ1z6phY4UAFJVczLlZz7rYv5IC58PpxK8CbvJ/nmQUTY3Soi6TT\nzIT1zeP2mlqVoSSVRubqcCkSzc8B0fG9tApFxpntz72tzqCNp3tfd/V79qdt+z4HlLnHvwdz3VZ+\nWPbTy23Ek5ubbbcYdqfalcnXDi7u1spC1N6FBWM3DqQZW49aT6PhNfB+zj7+5iNw6XuaCPyZd27k\nT/4deIcRs7tO11ugzb/v/m6e5HvRcp4/Wwdd9yc7hpIjjL604Fs26XsLgCjrZ/K8Ls0l4CJjfwgF\nT9cFH6YbTmI4h2dJbbtG1Fexzx16xSy1rQrrisw1YNYxJbaXpoS1e6wsGD74PuYA3hOySQXSVELL\nBMxVU//umQg29c/25t81Gk/4O7Mw/l/23rTJkSPJEnxqZn4AiMjIJItVtd0tsiL7/3/ViMzsVJHM\nzEgEAD/s2A+qaqbuQJCs3p4V2WGaSFUwAYcf5naoPn36dNu+gwjSdJHVDUEjF+EI+GcCjQGIGfkL\nl2JJXyLWb8Dt3MonTvM95Ujruy7JbRYAFT/RSe0dT8fb2tXJelk6dG9c8iUmVwEDABhcwpx9Rfnh\n1CmhzaZkKY+ARgv1e35oLVdlqzMoZquLjdbIHtfEDr8vtWRTfkugOIM6B8c2NHwqCKsICmn5peSQ\nfIbPZcNE2JeaqfVq1XmD/qW6wFtDbcmEEoFL/YwZFN+iw7PAyx016rrN187ivFmnMJWm1K0pEp5Y\nPO85JPwwMLr8NCybVAZtlYGw+0sEOJlxFArySpvxo0CEot4qfsb5+ttrFDAAVQrVTe0aA27JS04c\nH/e6OqabmvcdxVnj+uJCNU08lpbsN+PS+VIjR9kYqBoFae/G3JcBy9ZMWKjl8pFEU5X2DnAklcDv\ngWAM4nyftsDI+B5IalQ7u/k5lKqyDPC9XKS6gfZnEMRKaaxNoZmvX6hFUZQCOKUCTZlWVflUAAkm\n4BoJM7GD1srHcZk1Sz2uon2ZjZCi4BQ0OuYq++MmAnAFzZgfJAUiERBM9JZAG4PMO45OjyHhIGr9\nWvK0Rs80uhIDzmvAVRgbADuQmtrRDGaOOq/U+pcpj7qO6Psw4IlhBiSZt2vZlpCrz2CAmxr5qgYk\nR7n21PqikXpjDDoCvAnZWbAyl3YdHXf7NZDHc6kgLPcHl250UM2PgCUz+KYA7iV6TELJV+e8N5Vh\nLMOqyDN3rtR3O0HYWGhRHw7WtvGkd659WMuJZgVHXQWKHXGawufF4xJd7Tdll9iyp5MAjnNqmjBL\nMkCgXKftmVtnR+8v1bHPjkwFjM1aAEkN0oDWmhu7we5Per0PIhTpRcdHxzEApKVDAYOGet+DlyoY\nid/DWYz6WwyVcXOt66dD5wquc4frFz7uH5+f8fONBXUrOCraFrZSgBrf+UFfONNPtm0thfab1odt\n3j3A0TbO+/7c9n401QtowJhWWgDUOS4bh8wVrUHfRBkLRMzQsMX0eR3M/iJg/ZwJlAg36d85BnSr\nsHdWAeqWgrQSXs+HmvY49BGnLqKbC86LjoFQAVId582J2oquMghZpMKC3Cfs+2h9wc9lnpsacGz1\nLO76Xn6Xyz7lh3BdOvShAcBL8kgTBwdUL0gbr4u63qjoanNPLDi332sfgen7VrB9Vr7O+0J5D1MH\nHh7bPnwvbeHxNf6/F1bc7x+sl9CA631/FDTgzX7eucxVbkR19qL2WV0Y5d3mDAeHXHhfAJjtpL5B\nN7Kl6g5c6jQnqoGkNXnMUvrb6mLFxJWUrC2zSnWPxXTclAijc1hyYyU54nsqaJWv9L/t2lEDHWZc\n7ceYNgtcfm9/7vYdRJDmxGjT+RJUvf1E8B8Cym1FvibEX3nqLGeH+RJwufY1EmLzwXWj8pKXys7Z\n1mlfsqtq/UovXrOrBr3PGUtikb33kFylYg4uITvCc46wr7Wje00EjnQ3RP89sRYJ1tUFi+/dIWSH\nPlEVrMq3DMwZ7uBAwsX0h4IuZtAEBLE4tNRhoNxEB8ltgAS+Z5J/08N72zuQqQBr3EZWHLTMntLW\n2m+2ETLaUDYLGMkNDvjUF/xF9C6eQ8JzF/HcLXgWQ3YcVwZTcnOwuX9bP9/1qTpMs8NyY6V3bcEI\nL/rkN320Fxey0ZdsjNubaArM5gdqGMXdqp9KqQAER484grSKLgIAOFfQP2Ux7OnzAAAgAElEQVR0\nb7FpPAiNlyte8GdL1oi+CvG0ZgWKCCqwRhsWQwHTCbXEqt73w5QG2o7ZXIg9G5PXrRHTXJoTUnC/\n8QWn84CwpY7zhe4o/JCoqhw4Bh23rhpHU6I6b+ytK4BnWTiQe1oLQdjxEl1mcEgd0C9LwDl6XJPD\nbAz4Ys7N/Uubv3xcwZQdzktXHTsiLju2SukxHQeX5PFtdYilRTg48n1fBaJgG3GOmRBFONUe11IN\nFCRUnQLaVEPQZ+9dm/ez8Y4UzEkQWiuZDpCWDFtHUyl6BxTVYHG6FtDW0AIqwGgrNuzHXucUuGwr\nKjOr/Mah0HHE0ap2pKabMEDJny6Ze6ZzwFPX3puCN3ZteZSytU/xcXwpYddsjc5Z7kedwCL9xHpA\n9tq0iYhaB6NSnNUJ+wNhRgWGRpRazpCZOls9nFvkqGlGA8NvifAWA166Dh8VwD3NcGrEC9jlLwVL\nZhB1kc1pdLkJW6KBo/pX0/gAfvcBYGV0qXB0Xjp8Xjreq+v8F7pwaf2o7b2usA6JjQrr+/TuMePm\njzRd4xQ0s81J3+p2sBWQk9/rsWbNsPei49cVFXN9/CzbilRaUYVwU6Hd6NH7wO9XGY+JmW5L8vgm\njLqntGAIEU8h4asGaGStsPerJR6vsVVzsYGTTQrAo36Tz+1ayaBj2bA96jqtSKM8936f498XzNnj\ntobK+soFmIRFWvVadvNam0LrjU1m13Fz3G4PtPvK5hmL3CU1QIEKLw77sUooDwfhHqwFICVA1ZHf\nfl4deHt/xXz/yC4qj9kRuRSUB8fX4NgeRCiP3vT2WLb1hbVl0hm2/bntBxZUzjiOS7XRJrWXJAih\nNiQZWzbXruCg4bwGLFcBE24RFIDQZVPeNOBt7bHmxhZTX8KyPNVmsqmDAANsa1HGFx/bsXkjz673\n83ihqqnBRi/Fyb6mr6GwuQOXUNlvf75WvqdySPsOIkirUZUsC4aqOz95oPNY/9uE+YvD9MYb3bJw\n+cQ3yX0FOEdKNzqd+J0r0GoISqtWCq+NbgDAOKzo1q6CCLmwU+SFjhagTmXBoYvwqYnonXpWdP2w\nzPg2s/DdWYwfW7YxFVWedRjECGOnvkjOl/Gm0SJzeo4le/hUMGZXV6SSgXwD8jXDP7XVynUAphZh\nz4b2b9veUM+777Y0s3InwKhR9WoY1/O10oKD59KPgbbOGyA0Llll51xwjRqpZ1QXYJG73iUEl2tu\nLFP9gZwcV6uAPjq/l2YIFJDj/6nxtM6corImX1Xeu5AQApd5jEaHgvUamgp7fe7MFT5Uz0GNSUvD\ns/Rj+xlH+RuLYUmNgbCWZrA5n9F9IhzfFnyU/L7BNePIS8HvKTmMPm/y6wMByrRr75jvjxWQqX6n\nv+pc0xPYO4T695GhrCrh1X4hNhZSaZErgOf26JuTevAMiMypYEq4cwJt07zfJbEAJAB8AqcecBSZ\nb/wiEdw53Vc12Dd9FKbgUr2OJ4fXNdT3/esS8LoyPVh1KOZcatnFbMa85ijrM14i4ec5IJVDrbyQ\noRUxHN6iM0Y4O5nF9BtT27cVEppx1KoIrEXLh1o2itDyi42eq/4FO2I2SuqJcAiNGmrnf3UI9LP9\nulG2UfECHgeDa85aEGCycwUUaeMMKUPGGmp6vvb7UvUL1NC0znr7i0ph12uoyGcg/t+T7L5vkdel\n3jetnMETrnFrINp3aqPVjlhg8SCsqU7mw+CaYKyu7x3x/LpnDmBTek8j2RVwNQNZ86MrQ6LY84jh\nW2jrXJAIjfrWl4MrrA3hCFMNy3OEnMefAAuJcI6Eo+/xsRdH8zhhPK3ojrku6OPriv5LxOk21j35\nGCKO/QonjEBl9uS69hkNGnA1m97zOgygrq2rRL31PaayFV6zQ7GmMezWEv3rIWtWRgVUdK/Yn0cd\nOGeuU5la5t0x+OXqt3vxwL3DGDNXRNJjlNJtgYQiCewtfY11OmK+B7OUpcXnU5CFuLyqsR24fn1u\nKZ+Z9xjvMi5xlOch/HCY8NwteOn4fV2Tw63wutoCGgxqLKaiz3sujbKhbJ9k41zZRtiB1PJXcGr+\n7wcOMlFB73Ot0KV6Np3PeFt7zIaFoCml0dhDDi1wUk0cOz9N9PxRqtVvtbvqHO/80H6uukv/2Yjz\ne0yEP3Lc/4rmLMCxaxpwABpzan+kI2AYIw7HtX52CBFL8hv9EW37vgzEbON1kXSIc0Q4Af1TxCji\nom9zL8B+SynVZv2FWFqKmNVE6qhUwFhB4UdglW1tDbtnwPxe+7OmM3xvrX0HEdA2jYqWUkHoeHJQ\n54A14fpPj8t5wE2qFMTsMMWAy9qieFeDFKrx37lG31bjX+nmNu+dqOB4ZKV+J6WktMRR8OwoKgI6\nDiuGMWJdfI3GHJ5XhDGjZODTWTbeS4/b3GGOoUYgGT31uCWPg4oVhYQxeYyu0R6rmrn0UaMqqrCX\nq0wEckCagbw69K4tKjlxyS2tEx2Tx5oYTNFItxWCsQa41SvYV7Uo5r+12XxFAFXxvObYFoJzHHmu\ngl2FwZ5NDndmh5IjoA69U1AoVKbIo5by9h71nepf/e8oG8g0d7itHZd6FBBhOKzwXQHeOmFtiANQ\nMiJoY7gpIONdrhUoOsl/3ke91GCtdHIow6QZ6o1KiUpJh/y3e3Y4/C3ip3KuzxCjQzhnjBI9ukSm\nmx5CBEEFhjLyLjKgDApVqQfa+y5QdkOzoBzUgW2/12+tQ+UrgqD33QwxW8KNAYRS0xBOns2KC7nq\nLPOxcg2082hObirATW5olTH65HN12Hrn8EbMTFBwSTVXqJRNdMley0bTOMLmDBDgcInALaEK0U1J\nUi5w33rXarnnUvDLTLimUO8xC8AyJQYJdNwqM8OKdikI8IgRAjRWxpyAg99G8NXY3VNDFdzhcSn3\nKX1x8FSj9EBzTmw/aTR0T0V+dI+9o5qWxBUz+LqE9r6bUf44RlPT3Ag4+lxBGG0EbJTj886J1mde\nMqFQQedQ70kN0N414JGdbp6j+ty19Lg5p46ZUwBequPLhuToWxS+dxmjZ1E6R00/QceznS+aUmCd\nmcp0eRCpdLgHl2zzxIylo+eUsJbSUTAnjgqq/Mfste8KLDAzJcJrdPhZNCf6bwneF3RPM7oXmd9D\nQugvOJ4X3ERXaBxX9GOE8wVxcZiE+TXNHV6ngRmCutaBK5McxwWHZzbqP71N+Lr04hQ3cL+ANVce\n7QaWQm//bY/V/cn6CeXBHHvU9uNc2XgxA37n3bf3Vg9GkVmjp3jkL/B4oB2jicdLRoNeFMC16SfF\nrMG8z6jT7yqTUUGEkoEwcAnEqmkUO4xLQu+5IgPA+k63FHApbc6qts9GO8ekRO5TPH6rP/f/fsTy\nytgazHdzBJz7nkSLxwugPsj+bNlLtkqOrjieVMOIqsiuYi1kjnskrFjQjrP/btcwYAWp1sp9X+yn\nN5GAJ/eH/pc1BSv+a86lY+9fO6HbjXNA+nxjczEz8/Cj2FvjG35YCTk5TiHTMV0IKRHWNVSm8Sqi\n1QCqPTyfA/wQEY4MTgCAfys1qJJ2tqZl+Nm5u0lpkf/ZvYjn+mMg4b1u+i2hXGX6clnOPysT4TuA\nou07iABIdQY2unvPRmIYBNFPhPTritcvT7itXVsIsmMhOsmFBXiT4LIpjZLNZeq2VENPxSjONidj\nfIroDwnHN6bLLwuXB+tCwnhcMbxI5YNngDpCOq/VkfcvDu55ABxhOPPGe3q9Yv0GLG8BcRW61K3H\nbQ34Ng81osS1zD1G71u0hMg4ezb6wQBCKQQSS4I61iRIKyHd+LNSgBJZ6TrJQmqnnKVqKT3epjME\n0hrQ7XNX76n1Zcn87+xLFSdj6hk7D2qw9iJId/QtOpdKy6mcUruzBigUvC56Dw6p9PgQfK1+sSSP\nv4BZK1XPgBht96601ATPIpNk1tsiAmNDH6vB2r9klASEJQklTpknhOA4l7CmGZCWZSs1//Lgc41y\n6to+OGax3GDobY7ziDkiqeCQq5ERKwCWE4ECoftbh+dnvk6ZV6xfuOLE4SKsl2nAnD0bUWI0Dq5w\nVNFsYAoOdcYZypKfR2DHiUQ4cCiE2UlFjKS/t1RP05/GiLWtM33eEfAcuJL0B4kSDY6p/gRsIvKV\n1kf2XEzxXgyLbRGKeOcKRtVREWcvFldVyVbHokfOOPdBWBeD4x7S95MKRNS0bNYMTvWw4pr3KQaA\njsFmILA4GtN+VXdEjWTtM50no4eh2evvCcmr3sH2Wkx9bP+ugp7yWe8BYFuSsUj/WkFIfT/qXOt7\nO4Xm1Gq7RodLZPDPRsg7Rxh8O7Z3zEQavdWw4Gi8o4LgGJjhZ6W7yI+m5thR5anguVuFvdKA0N5l\nuOwqMEvgcRVol1qEIs5CMUwwXtdUCRtQjQISdoasiw6VTWBFXTOYtv9JwEgv5xl8xnO3yPvICMJM\nOGWHk28U/qU6efx3ygSX5F6CjjUDmOjaW50LqiCAvjdPVIE6HeNHn4W1w7/iKKyDo4xOTroWj1S2\nVQZI9oaYCZ8XNVlOKIUQugQ/8HNTD4RTwdExcACwg+qGAnJAWDI6KfHYX5RNRfDU1ff4oVswHlZ0\nT/zcLy83/HvyGG8jXjVtUcRrufKRUPWzMAfdPainPWefqfe7eUuNkq7He2rHtXXgHrwLxMEGLWkK\n+3sHHAKhM3M5OBKntN3L6Olu/dT52CpJkTD6dF6zI+Fpm25ZnTBSsJLPO0ted+c8kqSgpMXBdRl9\nH9ELw+0aA16XHs/9iicBEf6SnewjHufaF4SQC/rSelkBwgxCn5vNMXoYcFr7rbxT1UnrI5TNJ4Wa\n+J6yvUjmc+0zl9E5tg10LvddwimsNc1Dfw/wmLA2Su8JB2GAAIDPfM2tLUNIZn2o/Y57liVw7yi2\nlIrtF7k8BlDfy4mv58f+PI+v265//8WetbP97v74e/bq9oM9C8B+pixFgPeM3nPJU127FJyxw0XX\n5hQd/Ik/Cx8B6kkEUguK2czKXJDeJqxvfJLl5jFPXElL1+4UHfIK+APQieB27xMGz/oFygZNCkiX\nxqDsCFiJ99neMAQ9PWbj7FMjlJkWTNBmdTzqN4LQRE0nQYM+mUHqIfn/tejS9/b/i/YdRNi13hGO\nUo4PAPJbxPX/dnibB0ET28TWTcHmSrZ1pBnrgBoHOoFZDI0p7/KZy3BdwfBX4ADeONfXBSUC4QSE\nHz3cT8985kMHxAz/5YYiHq87cfnJkjK8WPAUFriejax4kc3LF/hbrmrsANCHhCEmdK4gqLFBzdGw\nOe1L5uioFQhyRwfXJWAB1qsABsmhFM5X1T6zqQyNmkfVsayqydiyDRzZ37UNVvu3c8AptM86yTfu\nDZ23dwVPIeNZlJ/1Hd6Swzn6mrfJtDYS2nqp4MLryvd8TaGW2NO0kida2r3Xey6NSRAywiCL+KLA\nQsboVjx/mDB8knc4EtKVS/t4ZypYkMZH7XMTG2jZVSdj9AlahkhTq0evmwJtjFk1fDbUzt07AmSj\nmxL8pwH+IHmd5wXuLSJ0jUWxJI+00Oa5dbO071ZTeTq0vMEizBzd5NurLCDP80rnjpZT3G/yNZ1B\nf2n+XU1gKnjyBb3LeOlavfK0Uh0zt6Tz+x6BZ5CDN1rdZKfELIFUqKYKhOo0leqgeWLAh9CeswDV\nSRxdweBaH3Wu4CiABwB86NjhKgBusd0XM5qKuce20Tv5sAOvPWaKmAhaweCasOjR83XXTHiLCpgy\noMCpVe83pb9vDMzy2M7QiMmdw2WcJYCNf32XDcDgHOiiXtfut9YJs/eG+jk78Kt5P/b7Ok+Mw6bA\nopaife7XTX59oIxYHI4C6M2JK+VcTdncJW+jQ9ovgYRKWqiOP5uCo5TnVcC37DhFg5+5wEP1RHSt\nY8Bg9LHqtzgRIAyO6dZlUODAST494Sx7wtfVIxfHwJH0xmqioLZPqWznWtXkoK2jRGjgVjR7yZy2\nLLSO7tPV+H3xmnxRQHoJyDiC/lnw98gMqeEYkROhZKqVD9aJ4FOpqRq1bFqfcBgXqXRUal+MIcGH\nliLRHxL+8vGCQ7fiWRiCr0uPSwzw5GuKg/1/2+4/af3kaMvgUCDURqn/KDOhrXXvXXF7P3/gtBW8\nOcikVwo+zHpR0Fht+3sCsEmLUoHm3ueqYxFnx6r3I4spAk0gGAsqy+5ZBWHRITi+oVvaVv0AZA2i\nBq4+Wq+0D/ZRZm3KvNo+z72jzHtQAx+10s1hWIGpr5Fp32U8j5wKqAGnOXloaenOALkdlVrimvu8\nzZ2Wz66OcFtDSnkfQKiMmAd98ej59+0Rw+s/236ros4f9Ue3kff3wYNHn2VIjr9h5AH3DIza32ZP\nSYUwzwF5EmB2ILjRAZ0HBbetERkz/GVBeBNw4BwxfEuYLx5RmAilqL2cmYEK4HSYsWaHfg0GfGOG\n8xW++hck62QNGtn7pqZPdd93uv/cpxazj0KyV8jxbO5s+xN6D3en/xO1goL0+4f9Cdp3EAHNYS1F\nFnKXsU4S8f0l4evnEy5rQCpuh8DS5i9wv1Drd3uqvf61pfTySqCDg/+RjRX/1wQ4YobB8wg8H+Wk\nGXi9AucZJOGIEjNwWVCmWIGFfE1I18KpBpU+KKUli4Ot/btfEKoytETmVXyKxNh97lxd9enUITwn\nrNeCdZac00zImbBGX2sjr4lFI63KrC01Y0sQzkItL2gAhjpNjJY2Z68D4ehLjY5wGby8oc72LuMp\ncETAVeeWsGSPwxrwFlVF16MU7pmzjaBnpn0TGnI7ZceVN2JTt4/ZsfFlnVBXQF6iYb1QHFNE6BOO\nf03wz2IYzxl5xiY3We+z1pevTilHB+cYqrPuRdsCQH23o/QBi+Txb5XCnLJNFWk1h21EKkaHfF5A\nYalIe/qScPvs8fY24CrpPaoNYlM+FikTGEvbeCZRNNfcdIDH3pKbOFij4fNfu4dl3Keu6DiwFFT9\nrQX2rEr8bMTUrslBK06088maYAz0TPzue0cmpYBwJtZIUBBAdRa2Qk1KOW7pDCTCYGsmKR3amCed\n49J7tkJHKdx3U9VEELDDRInZmdzSzoMDXjrgFLaZm1r+8xhaKgZH0rjmtfabJ2+cHNy1Vo6s5bMr\nc2TODMbxetJ+vKSW0qXiiWsBKDOQo8cOjpkOOTdHZUp8nSmVjfMSnKRb1ffNmgzXuE1XKoGva9OY\nptQEIZMZL6r2rsddE4PHvW+VLrKkFWkZQYCV6M9Lz+UAkzo9HteoZbXaXGbWCoMM31Y1HJXFtm2B\nHAbP81qf2RGDP9oOPuEppE1KSK6OSsYx5KqEr+tFzA5fJFVgzSOuiSsc7O3E/RzTdUSrmmg/WZaT\nAgfX6HAzKQCTsHj2EXB+b+3fgytAt91bp+TwdQkIdKxr76GLLforz2dL5joAx1GqO/iMrCLI8kSl\nMJCwTAH+G7/b5ebhfMYwRDzlRe6P7/lqgPQCLnuY8tYZsvdcUwSJq6lo1Bxo4oVzQs1ljrlVRmhr\n0DaNa//X/remOMTc5gnvl3wvyhRRbYHVGDcOhCTeiTJmAMLqlEmj961z26TG1TQUvo/GcHHw0aNz\nAatoU3hfkKJD6BNeRq589XXu8boG3FIrM3sMCaNPeOlaXnhHzBybcwNeriL0oHuELjmc3iC21s4m\ns2tTLg2o/r3mqIg9pX3OjtkwRKTkaqDG+YLDuMK7gvPEzD0innN36X7CyrNaJfVdbu4XG+Cdf9ui\n9vvUTwu6t+/ugZH3Ivj/Cntgfw/b5yvmuPe/s8e8J7i4OW7/LPeXrtdwaGtQKqyLUkpBJ+CU7gN2\nDVI79DZ3mH7lX/drhnvN8McI6gh05N/TIKDCEGoAjMYMd0gI54jlzHc3XwKWW0BaG/P5cFw52LGG\nOn5ua8CUWLT9VvuFKhC9F1ds96z2HuulZDNH58R6RNHsgVz2u2yEoznlsGyCeiq4y0LU36kIf/b2\nHUQAG0qdA8YAPAcGEm4XqT09e3y5HbBmh1yoGvW9S3AUMXqHXujtkyxCvJg3h+1e6ZVARDVqDLBe\nwPQWMNxi5Qq6T0fg0DN4kDOqfPuXC/LPb8i/zlwZAQDcCmQg3QCxdZAWh3Xym5SCNXrMS8CSWgQ7\nCMPCCrRoNJ7/3RYagkMqhdHRWT4bA7q/dVi/rYgSdqCiPdsi0t6xwbGliTEw4Yg2QIY6Yfe0sm1+\nH/cnR5FPQqM4+oJTSBtl7sEzA8FTMSkkBb1L+NBt83Q7CjgEh4NHjVQSuKSe0jn59/et5RCXmhPp\nfIbrAfJNqKt7WhCegfBjhyxodboAcXLIyT2sIa5AgrZUqAqIAZDxyc+g9zb4gkAZvWv59ZWKaSIM\nrFNBNS9c27oGzL8Q/DlieeOzfns94TL1uCydSeVhpWJb/3pO7CSsxjiZZNMaHdX3xUZq0z6IdQy2\n+9XmiQ1pwjs5ndj+RjUY+B6ZCj8TIUPvW8qiVodZfo8WLa+iZjImR79NC1CDbjHAWDDgA98XbUA7\nbTG3jV31RHplMoTGljh4QixZhAiVCUNSOWHbPzpv9PMPHfD3MeJv41LHH98Ts0YC5TqutJIM0NYw\nm/trZ96jtBJAGQbGISF1PLcHappXZWYUFftsaus3EnV5Y+hbgcd2rrKZG9pSZvDHpmZkcB8nMzZa\necimZcBzoUiOKX92iYRzDPipEE4DO5pdlxC6DN9lrKLqf732EkFPOEl/XtaAQAHf1oC31NIIdKyx\nGCdfZ05FQKKtUc5pGGSYCNy/B+8quPShY+fLau4AHOFNhXAMEcLIhQ8RQx/RJarsstH3cLIm2zXj\nkVPgACTcf8f59K3FIgCMAa8fVTjozaKq/9m5gueQWyQcPMcyWIfoFwE/hjVXh1XXc1udggCMksbw\n3K/ohJWha9iUAuZU8Ho+YJG0ifNtqPukCt1OKdQyzb+Xem0p5vt54okqC8LlNm9+TwjtUWtlkref\nk84fkxKohxTzX/umkW7WFOHPOsdjwlObs7G0Nbk5GcosVOeLP1f9i0GAdwDwa8ayepyOC05HNlw+\nTouACN6IUTNjwQnoCbm3xhzbPnhlxvyBrrTgSzGf7WQWNk3nwz4VYooepbAWxyzaVDE6dCGh7yOO\nak9QwW1lIW5rRzhsxS1/6/YdNvixCD9y1YQ9YLC3ofi77dlVK2HfWBfh/k7qmNqDAdJhPE72C/Vm\nU+TjleX0zjUefW47Jpf7ilCP9lqei3RXkQPYgu71Vnf/ToXLd379zMG88I33zeATui6jP4hWwtOK\n8Ay4kweJsUiDhx883FOCk1SrHCOubz2WxWPIkn7VJRxOK8YcawryMHc43wZMsZUQZqYB1Tlq75Ww\ntU2LYfq2cb7d67Z918aVB18g5bIZN8yi/tfXqf9dWgFQ9tG+P2n7DiKAF5QgEcaDz8gFuNwYMXZU\ncBNHLbiCJ8kxfRq4GkJOhElz5GVjTKVVRJgib4QxN3LRSk6ijbkudkvyuLwNGP5nxOHAdWT9/wHQ\n0AG3GTjfkH9+4/P/Y8L6tSBeHdapvcJSCPMUqm5DjE5qzhrHMxNidjivXXVaHHGpP1X252dglDKD\nUxvaGRhEWLJHnFrImF5GdB9WxJs4xMS6CUxvF8e5sCijCrfx2SSa1hikXIYtK9vgt0wdeaZCWEvZ\n5B4DHtmKsxWlHjdFZGWCkDl3IOAYMnqXcfQOl9iWYwYrSo36jQJM7KmOgKSqaP8GBhDcCPiTbCo9\nwT13QC5Igkwv3xzi4h7QzLR0YrtOFgdBhSrb54LeKziVAXL7DbZ9bwUzbWpJRbGTw/TWYf414PPb\nAQDwZR5rKohGivR3gw+byJOCBnrnc2Jn++pajirJ/2yFDWCXw0jbz/cRmPeaMgAAdmKuNQK+7Q89\nT61/jnZMdSpzA6xUCT8VBiD1uQCh/oPz+m0EkJ+lPYhG9heJvndSCSRQFqqzM06Ta46QWG05NGe4\nPot5rxrFPAYG1T72MwahDKvjWmQMTUmrbHi8rgGX6PGmjAdTteK3mjVcHzqc1vBDczL29NvFACOp\nFBHeahRjLT+1ZzfoPG7pIi06rOdbMztpKlK4YSiULeikUcAs0RiAU1c+LwF/mfvKRBjGCN+xqO0s\n6/Fl6us6rMDN4DM6qahTCm20NTRdQ8GXxha6Fy1cc8G86+tjaBHfa3I81qPDN6HOBlLNDxa/VUfs\nKSQcPJdvrWwqGXeb6gwP3qlSpf+IKaV7CetbNABOwbf6bCIKacutdsTVcT71a80T1lSRzgALU3J1\nnuyB+zmROBEcHHhZOxxDAqExp66JNSzGacTbwsKMv06cxuiobNIwrtFtUlW0rryNItf2wNbWQ6wT\n6tDYT4ABU9Fo+ZaebtdAdSa06TEWBAIaKF/Mff5RDbpeyrcq+ANsx4UVwtTytnY+MYvKYXStjPWy\nerzNPeYY8PGJ46wfhhmH28glbcWReov8rlWoGlAWI21YjDpmLQtBn/suICH9ZJ2wR+vWIzChrl27\n45fkcbv1OB6XGjyZYwAmIIRsgNmW9tfeJ1Umgp5WQRjLAPqtxnu7tdYeC+/uWy6Nyv7wu90Y5vQJ\nmce73yiokHHvnKu9a/vNnmc/FmspX3PdzW/QAH0+r+yzoLsnt+kfj9qj+aNNv5ozBxWBpoumul6j\npLKduhXPpwlPH2eEE99D+JDgjg7UO7iT7PNDQf7mcJ16TMLoHIcVIeQqmM73xTZ0eBBYas+9/fte\n21dt+cPjyoCEe9Die/tzt+8gAhStbyJCa3a4Sl1fEnr2IUQMLuGnF3bwTy9Lza/U1IecmM6WosO8\nNGPytgbMUkIPEBSRGhMAAJbk8Db3wD+B54lpfacv3xB+5OvFLwm3f/DvL+cRMXp28OIWRJhiqxBh\no4rqgGr07xzDppKCGmVaum7JzchlI74Z1p1jg225SerCeYF7GeCfHbqz8n6BnBy8zwga7SsE75zJ\n8YfkiWsUpTmVXt7Jo6iMjdbEDEzy70t1StmBO3pfnT0vRuCasWEiqCSOgp0AACAASURBVH5AZxbo\nXNhJ/NglvGiVDrnHwZVaxvBDt6D3qRoL2pxQ5ls0hFMZqCM4Q3mDI6RflkqP0/4EcE+TFORYrxTN\nZ1cBr9RI09JaAPCNvBh/2xr26sDaHG1V9LWMlJQd5jng9Trin7J5vq5ciWEyRrQaaNfY1M6viXCL\n2/xuTVNhAT0+LlBTno/ZRpzbeX3tSx1L2z5/tBlaloCeb0otFQFgIKM53kzn4+eWPjLn0/sZPeFj\n18YV560SLmL1L7nVSG+/VUXsls6gkcc1a0RNaNm+zV/V3/i2OqH504bW+mgz3zvxXE3C47x2m4id\nqqVfYqiaIJfocY6OgR55+Ju8M/tuiKj2jWHSVwOjGsEPnE/u3yKORjMcFRQgbJ1+yDE6n7oi6Txl\na3QW4uoyTaNGQJrUHHEHTp/gUostbaLRQm06RKNq62WmVPB19fjHNDbR2UIY+ojr1OPnK0ep3tZQ\njUvrwFmAUO+zyP9Z9sjD3F65C0tj5nssHL8kBfS0fKevlHtHBefocI2AI1dFxDjdiR1Epa1fE9US\npaqEb8E9+z7bOtK+q/nw0Hs0oJE5lu9rB+ZIKotl9qyZ0DuH0UccDwzcxOTxTRx9BRZicUzHRQPQ\n+D4YHF9NqgvATijT3BUs4zSit7Wr7/bz0uFVc5jld5p6oloh+oya2mEdEOu4tDGoDJkmptq5YuZ1\nMX0r61FNnWyRxg3Ag63iP0Dvzr2s96kxgHqv272WgVQG6AF2Dpkp1ISI7RzWdX6WNLY1b6/f0rBa\n4MVTwZQCvs4N8T/2Kz50K74sAeeaGkdyfDsfr+ecwjDXcVrqPNqDkwWNzaPPp/enkWl22PjLaH77\nR9uaHc7TgL6PtRT0bQ1Y4gDvtloLd+xPtPWy7b+6LrTf2ipLj9reWX6AYaHgnp2g0ep983SfLgDr\nVO5+o06/x4OUhd9hKewBCXv+jHIHZtyxFOpae89OAMTmA22AB2urbe61bPtoz0g6rx0uyTdwW74f\nXMHH24hPlwlPkj51OC4YniO6Dwmul7EmNsRtDc03mAdOSzXC3KWQpMwau8ykM1g7TlPg9mNDRTPt\n+NsDYTruvsfX/0j7nsqh7TuIgJbOoGV1bHmUQEx5/zROeDrO+PQf7OCHT0yKKrmgk+g7MpBXIC/A\nOAnV9ZzQ35T6Ld0d1XhqM5qV3IGYxpo39/xtxuGwMDgwHfAq7IgphUpBViomgPpvi/y3HHoxTARE\nuCZXnZVArCTODpZGdZoAFhlDPxbCKH9VHCm/cl4DBYI/SJ+6DHIRbjF5o5ngc67US722Gtq6Xgdq\nxuWenlnPZTbSJaMau0DL5xoDMDg1oh8ZVPL85t0DvCifAvC3AfiLUJYPPtWUiCFoacyI4PMuIsSR\nRu8amkwOgOP+aR5rRvoSMf/SdCQATn3g37ST2kz2KoxT2nvVpUzTB6zjfI7MsLH0vrpR5Gb4Kciw\nj9ho7rSW9bQtlm2ZO6BsxhxHibbHqEO2ZqqGKIN37NBP1PJ31Wl1BMT6ru6lw6zwYbsT1Jxj66Sq\nM6y6C9orKlCoY0cjrK5sc0NjAa6x4ANjjPgYODrmydU7cKmIYNyO1o9tZEZplU76Tb1yjeR5KtUJ\n/Lqy6J41MONOB0L7gA2D5ry+LoT/QQG3dKjaB6x1wuPlkhpgGEsbC+qk3CJXpLBzzA76RG2dUCGw\nR9N2H8lgMPD+mEfUW+w+c7SladvfWxBgf56aW16AZHrOU2NFbKI01cnQcwKXCPwyB3hiGv2SPYLL\n+DL3+EUiSlNm2jdX3uCTdG5bqUSvbvulPi5JRM8sXLko4FWqY7dJvTDjXPUppgq6ES6R7932sSdl\nS2x1FlivoGyqovxWe/TO7Ktz2Fbx0OP40RoorGMslVbVgudmwA99jxcRqHs+zHiO80aPZolcKYnT\nEhpDCmBWlI53fu4iIGwrIapR6b0gmQKCulYqOLVlrbTSbJsIuL5H83kmNf6btk9wqMCvdSrr2ND9\njoideVhwokXnH+2XpIsCGghho90Ar0Pe7DnqgOXSHPhSeK+9GI0RncOc46990dJUivE29TwxNLFl\nfYZb8vj5ysbDTwA+jjO+rV1d/6bUUrcqS072fdZzuH/ufduvLQ1AsFV8+NnIlVp+8z0QgbAFNbyk\nW1xjwNMcEEzaxVkcRY0me8qVaWbXBGXdbdarouulvhMDKLxzb5VdR4/T6H6PKXZ//AOH3IAxm8/N\nse+xFPTetvdcgAfXaWkT73+3Zyi8x7TPu+McqFZr2LOC7HrMwssJg0u4ZrbXNOCiukvWJpslTeo0\ns81+vEQ8fZtx+rrg8JGBBSIOMGW0tFQNDMVMRiOp1P26MkfLdh26Z4ltK7WwTfCg/8x8gjyzZTtp\nX9hxZM/1Zy7x+L1x+w4iSNNc4lWE/qrifUjofMZf/3bG+FNG928ijDMGlCUBawL16vUU0JzhOoBk\nAznkBTmzin6N5ju2Iryo+QJMYQZa3irAiGd3VbErt1loepdQsAURWOypIaNW9XrPRFDRMKBRpddN\nFI4XKQLTT9VoJfltKS0fdH0t8MsKf3LwR1lcxgIKGe5S6nPnTPDRcx62bqa5SCS66RIsWcWltvmh\nTLnb57M1unKjBapBx0JQ9v1q1Lj1GUeA9LNYCqYIfHFAKh5DTV3I6H3mcpiSqx585pJeqZ2PhKZI\n5t22BwCKhK7yLWP9UrC8tSnoA+dVA4C/5WrUhZyRQBvgKcs7HXxGZ8bF3jGx79BSWzWye58XyWfS\ncluap+pdE/7rCju4gdwGCHtP8GvfSL6L1RBlNsjoCyZv2RHlTmQy5sZasA75Pjf1UVkoNvoIvW/G\ne+e4HzNRrYEO8MZZducl1wzpq4Spfuo5VUDTXwDgHB0uSY1tGfuif+LRDDzvgMGzBIpW0AC4XBwb\n+1TV3zn6VzYAkUakH0U+nbn3VXKZp+SrI6flFafEzqKCJ961fMt9xMv4IvXftjlqxldzUoHsOE+X\nzHGWPrtXItdj+O99PEnffefUJWoG38YAB4/TzrXcz86JUKNx3O3z2DHriVAcl45V1oxGzd+iQyds\nM33Hn5fQKlqUvXMCqcChgEKroKIRz0CopSj1OqUAUW6yl3uL2axXuSATr5e2HxWYtbnr1thsz8lr\n5ZqB7PU6wjrZAXCPcvuddFypfc733tE2yt6LIN9g+r6TEpxTto4O70G2csEkc+mlG6pK/9/7iNPT\njCejK5QzswCnqcO8NkM/F8IiTEBNTVSDPBbCKicYQDj5hJd+qefUPfQcHZB0LvNzR+k/7nMej870\nsa6xdYzl1n8O27nkRSNpT7dvzqQ6KOZH5gQtGrld97TkW/19vd8215SFea/dJEBs3UvYkV/yttRh\nXffrvVB1jjmdjj+PhVlL0bABgWb7fJMSmmEq+OFww4/DXAMZQIevcHiLVIEtKwKn41S1fkpbGvhz\n157HqkI4YgBnAxw5gitUGVbpHU+9c/zOVSMpqCh3Zoboy8ABp0O34rx0VWSYn7mJJdfye4CUgN3O\nZbg2p/kzqkGP/Z09SjvYO+v6+b4pU+7u87IddxZq0u/tsdq9BQV+z24p5l1trs2O8n4fVyDfXG5z\nzfqRXrOem+p5beqDr/OI/wYidI5kr5A1HlpyvK15gys4dRFPw1LH7pAyV5uCQynOALrE1UWAGni5\nrAHXGPAhBrzIHnE4Leg6tid1XZpTSxOlpOmN2FS64r7dNpsevG/MshOWdGV12H1dxh+RaCCYL9GA\nRx1cVJSBeT+u/jStAKV8r84AfAcRNi0Xpu5OySFIRPg4LiyI8+8F/oceNIZ28JqQrwllkQV9LcgT\nsxGs8/PQocR20w4ui+IqV04AOP/WUxaaN9Xcd9YUaNEHvh0xiPJWPKVt9m3B1wjH3eKPtgjb3xY0\nFFippmSNt5mQZwA510oDFBzIZeTFoKGlLdJt4cLDvrHN3gf/r1HVPHFUZr/RV0TVGBesKr11plv0\nhD9janlBXNXBbov7X5LDKUScOt4gxhCrCvj22lKiUawQ8oUpbA4oEw+MeC61HKbWCA5jhj8AeQb6\nW0I/NRaA6hXYhbtzGWNITX8jM+VdS//Yvtj8BbCCN2ftNTuWHKEKUh4/LEgroX9LNf1Fy5DZjSTX\njYWF34Ato0Sb9Aj3QUU82NBkIIGMIaP1uu+dn1yARxEWbTaap102OB47qTQjbOSsEnbQV6ql7GB+\nV2n0rlGWL6ICfk0Ozx2r4T8JQ+UYPF5XD4LDJWr/yvMXVD0KLb3Iub6G3l4gdHtU5sDgdG43h3+R\n6gS2xKM2Noy4+ULoBUhQ40Aj9uoQKLjA1SeKiNi1e38v0mffgK4t1pBRw/S9yFc2jg9JtMPmWe/H\n7nut+mm7dU2d+I2Tga1zpOcvaDRRfrZ2gFZ+4RQIHi+ql6IpEJfoKrtG3+WUDNvFExCaU/0kbBbV\nBOgccJKjJ3HYNuQPWZMZ6JbjEiFmrhpykvOdAqTEbXM8qLAY4yodae3yQgp43DsmFphNEGfU9KXt\nW9uIWt8PjsVuR58377F3mYHv6HARxhit3Gd72volAv+YPUZ/lPsi/L284eXTDcOLsLd6XkfyNCNe\n5TkzkFYGF3JyuF45BeK2BsxrwJQ85qTpCoRTWPHxMNXStafbit4d8HXpK0A0VdDdCvtt0xWANs/1\n37pTOOL5ljKwmH3wcT9uB/57kWdNYXHGM1MAwzIRbDS+jvO9QyZNq7fYa6RdhJPQnOA9LToKuK+2\nQ8y8Btm9RsskH9aEWfax8xow+g5Pw1JB6kAFgXo4cjjLbxfRV0qm7/assXYd+9xtnO/XJV0H9mCi\n/u4Ra6iVVk04dBEUQ61IBQDHw4JnSSWzoD2BgylaVpsDKQSft4ABqr3TbuqO8aL3XwyI8k7bA8/t\n8/sfvee02u/uouC7o98LJjysuvD40LvzvFfl4fE5y91x9T9J50QT3Fb7mIF4tRMyPgwznp+m+g4n\nKQ0eCzjItktV03LoALOCuazi9t6GMeLldKuitrn0CNlhgWU5KZhu3n/REt9bP8KKH29tJdowrJME\nATnN54+BAXd6FYTvlP7v7TuIoK1AylBJvqxGXQ/HBYePEXTwKKkg/8ziP2UuyEvhaggcHEGa74Xx\nUnSY5g5zDEYF2mPJDiOa0M7QRTj5LlYnyyEVz5T1zaam6QDZ0FELgqCNtqRdwXbxUVpUoa1TyYr+\npvyc4I+qP9DYDc1Y0uoDcWYjLa0Zx7G5iSVz2cpF8h3nJSBKHnarsU6b/wGahtHyTu2mlMRLscbN\nHhBlZ4QjDGrIcroKYfClRgCJgFWdCZNzSWDH7NvSgI9bCvi8eryEjOeOzcEf+hUfetZFaJoRDi5v\nF9zKdM9AnvmLdCPExcH5gu5JIvwfCTQ6pNcE/5prTpx3BS5vHUWSz4/9Uqmhr2sHR37TJ14c0b16\ntb5LWwnBbiaVdfChIKSCw7cVg4pmCq26cwW+DkxRCoZxXEij/+361oitjjWAyNVMuf64ICDBlToG\n9iXk9FxAcwi3Ztb2GECdKgYftDLEyWcUEF5Xt81xNufQ++0EoV+BWpbwkgiX6OFCA15O8nfJTYGf\n04TKJrJio+MnnzdURf68Vdnw5NB74Lw2Bscic8PmmXvHhv1mvQCDCoHuVbtHz+CNpneMPmN0POOm\n1Mb0mqV6Qb53NBvYsBXEA1DvbesMbx2WOn8Lz12/MZbuHf56YjTw0JaV03Fco0m+XV81MB6VBE35\n3oHQyJk3YyAXLsmn+jHK4FpSSw+yQG4t0wd2EhwxiPAS+CAtc6iaK/rZ4mkDyjCtFRuK+TWhMueU\nxaBlXTtqAG0pXII0OH7GPShkmRFaFUIj5ttuL3eObUZ7P9qnxczz4Jit8yxl+rQFVzAnh955dKrr\nkn3dB+y1Y2Yg4X/cVAD0hCl6/J+Z8INn3aD+hwLqCL4DSGgbJQJ+LshrQckJoefrH2aPZQm4zR1u\nwloohTB0EYdxxXjkTX08rDiOC863Ad9mBiDOa4erlOv8Ct3TdT4aR2g3z/WvupexoKYwOLRIPZnj\nFYCrayYasGDB1lx4HOp6XEoDy9lZ2DrOFmDPuB/32jpqY9+uiTVqSy1FrT12A6o2+kUFoNwcGP59\nwfOB01LOEo29JYdfphF/E4ABAH4ss7CKevROUxy4z21OuAIXsRTE3wkUar/m0oIsmt5iU9Hscfqc\nqWgKFJkSywWHbuWATnJYZUwfjws+HCcQFcyxRahTcZwOW2nrHDMPps8frX0KZlggoYJ9kH3Qvtvd\nnH0P1H3w0W+yCm3/bD636+furI+Aj9+7r0cgwKPP7OcW6GoBtGICUAJaFi63mgrQ5+05ybyHwWUc\nxgXHl7XaenMMVXMM8C0N0/SZCk97aoxcTHJfAIbxDcenBR9UQ03YOI+aZRarHa6AR3tWnlO0YzXt\ny1ZWQBDbd1zTI+R3xVyn1D23Hf/nbVq36Xv7DiJIy0VVyHnmjZoL/zGi+4GQzwnxnDC9iphgJpRM\nWFdX603PS6uMYI23m0Q8NB3hKiACO6t8vUMX0feRnWsp0ag0ec011mV+DAmnfsUSfUPBfeINx+Su\nrw/KfC3ZcfQlNyOjd1kiV34TMS1okfpNNFkdltAm0TwFzBPghcLnfEFOwPVbjzdJybisXS2ppYDK\nLbkq0KcO3JIagBDNZlBpt2gUSY6aM0CgzVfAoFG1VDyMI2LtnEumSumG9LbYlDVSCwCfZwY8zt7h\nRWtRS7+ewtruUQyolB3KRi+AQZWi5TelHnw3ZnQ/CIDx0wjkgjJxmsRjpWRjoBVWfD72fP3jnKr6\nuG5+o2fHZM7NOKUH73Kv2qvj142AGxyOvy54mTgfuZfdko39LaW7cxmq2NUJlZzXWj7fCgUI7DPx\n9b2M80NNIWlRV3Vo6R3DkHbReOvM6DMOEgE++owfpM9Gn2ReskiSypusYllbRyoQSUpDey/XCHxx\nPH6f5KGUsTG6UitQ7Gmaeo/KLhh8SyFRYyFQrtHhUyY8JY/eqWwn5LxNHFD/7Wnr2J0C8OOgZfJg\njmUq7sGneh1+ZxnX5PFt1fWKcEtCr989hzUkqrCccVBqrvhuKFsj2baOAO9NqlVqTpQ9VJ0eS8sH\nAC1nBWh0nSrzABDgi0wVDnNfUYysaoAbQ9yZ+aRRXxXoVCV4FWbU3+4dj1hEVJQIHQFDUOehVDFS\nbQrS2XvX0q2OmjF4EvDVEQNRQCvrylRrNaoJU3b4ELZrV0ZjFmk7R8dGtWusoGyiaLp+6CjcME92\nTg3ATtHgCp66iEOIlUG0Zq5aNLiMJAPi4Fnc1zoHnpgJkksDbtLsseYDluzwd9EQ+vT5ypUyQm6i\nx7PHMnvE5FnhXPqcqKDrErIA73o/AJCSQxLWgXMFzx8nHI4LnoXF8Hod8ToP+Iy+Cp92RDVavB8/\nj5q+V2UVqcZBytto+kMnbrfWKdsrm/Fn38fe0Si4d+B0LqljSOBc8WC0MgDClHlc6PqdC4Oqet7f\neu4CYUwUVLZlLoRhiPiYJnyW/PHPqcO3GOCmER973jB7n/DDMEtuOu9x18jrbixNX+cqtkNJu/4t\nDNA8YjTt16dYtmwo/T2wdVoTFZDZ470rGIYIIuA8DVhVkDMTnp5mDH3ERcbQdemxJAeCAc3EAbzn\nhGzbnpXwrzZlfAH/eUfwvZoPv8cKaN/9v2v7yhDvaSLkPcqwuzebllvHr4LQKAY8LuiHhO5jwXM3\n1/P054TL2uGyNkBhVnHr3aVjYXuXxGbqloznKeD4vOAggrGnacWSPbTEut4jr9PuYb/ZMa2M287o\njmmzQaK8AxD4+9aXj9bx/fNkFOSy4nv7c7fvIIK0UjdQpoKroJwbAAqE9XPB9Brw9sYbnTqIMbnq\n2M0x1KiwBRGUMlmNFEk7SGb2h5AwHlccphUxtQ0WQHUm1bg4hohDtyK4vAEhgtCpo6DdKjqVchMy\nmpIAD6aqQ+cyusL5fRpJ84XgJZyUS3PG9Y4d0Oj6DsjZISWH9crXdp7z2eclYDH5YQpyWFpV7Q9j\n2Kqo3p1RBUaP7XrmieD9NmI8uiKCiXoMgwcnnzGKsV1AmBMhkIM3tNQlN4pyNZRJ7qegGvx6zza/\nU53WjdaAhOjJEUiij84XhD4jPBV4UemjYwdMERQIzrd0kfr+0YCFDSXUlJCzjgbATlkvuceWGu6w\njSztW83F9QT35DE8r3i+CojgE5KUN6rUPnJCyS5142aHjVkvxZyXwPRUa3TyGNOUE/kcAJyW5FPH\no42DR/e7bxYwIQCjyziFhJPoWmgOq0ZuW4TtvtwUH79l4xQw0HYpgI6W0T1IIaHW79oUqMtoaQ1A\nY450LldDoJM0nDlTBVnWwMakHX9qRNgUhIMnfAgJn/pUr0EyTjqXcQwJntS5ahTHW9L3eJ9bvXmI\n/8LmHYNHNfpZWo3qmjoDVEbEo2a/YiChVEaS9o9qUChI+EeN6c5xGsreOFWwieTzRj+9d+rUkbJM\nGhTeK4SoVMUtHaEKvCW9dwMjdQRAwAadI4EyDp7L1FpBXZ+KiKxuWyokVYjaunYTjQVNc3EZVQvA\nPrOc/O4Zge2eQcbYz/U9tmowDahTmmxjNHWOxWGtUZzBKR9f1w7d1N7heEsYulhZcvMScFs7meMZ\ng8z7oY/wLnMVHH2MQojJY15CA1FdwcFzJSY9J8/LXOeQ7QsbxdPnffTf+zV4T0+37ZFbadkJmlKw\nYar9TorgvimA0MYkX1XBLID3vUAFfjM/GxvANntvv9fIFYzjWlkqjjrMiYMtXezq/RBxysDRKHT6\nxOCY3veU7tMr9s/5aK8ou2M2320c491xZk448BgZxxXz2kpr50zoOw4MJLUzsgPAx3izv/Ncsf33\n+H7v0rFwvye+1yow+E7/PPrsEWhQ2VYPnMv7a9rvH9/Tb7EQfuvz/8wWpOu3ghD2nlQTgSqoIzpB\nxGmpasMdRDx9mCIOK68zAFdam1LY2rSlscrUmV+zw7IEjGmtAbkxJBxSlN+gHpcKIRmA6bfS+zil\ntK2f7p11uvXF4/+un/3G8d/b9/YdRDAtFY1sZ4wHnsj+SMi3jNd/jHi9jFyG0TQboZmlMsI+hWDN\nbqNaHwtH30ef60bT9wmHvySE/g2Hr7w43aYOXBaRIyhqxBxOC/oPGeubq9oLw8cMf+KocZEwVboU\nxCunWWj5wOu1x3ka0M19BTVOXUQBYXQZWSIE6givudScWQCbxbUbxfEQp2aJHq9fWGHZeXZ4L1OP\ns5TL1MoRc+bnBzinfEqu5gDzccwMUNEkCyKoY68RhuA4Osh5wHwfoyt4CgwWVGPb8b8PIVb6YCn8\nzm4p4E2Al9fV4+g9zpFLR9nIjJYArTmvhR3IlFt95VQcOmQ4KrXSgvOolRncyA/TfShwY0H4twPc\ny1hPmGNGyQVkdonGPmisEtbA4FKieu3RR3HcG4L+0qUKmnhNdYCIy+WGulv69b7RpwPG/wv46YVT\nefIErG+E/p+pVgy5rB1uKWD0qY793gUMmosv73aWMTT49g6nitqzN9WAHzHQfNs8s1cdgHZ/pdwD\nIo9ooI44L/s5RAxisEZRyCYwyDQKO3HJzeDU8ReEzdK7RicnFKF/U42SDo5VuPe6FN6cS9uai5RQ\ndFVTQY32rWPAI/Ep5Ppcg3OYwnaTZ6FIvn9lb/zQF/zHYcHfj9day5rpjlpJpFSQ8bZ2uKzdJjrn\nxYH3rlT6dY3emGf6fXeBWy5bYbfaR2AA4aNhdJ5Ci7Tr8bEULuuWtsKrqWxLgClwcwwtmtq71r+d\nczhLIGXJku7x4H7tZwdh9mgaGbB1qJqQogoWmvKSMiY1faGy3iSVZUqEs7CclJUl+AL0X5p6otdx\nAiJ5KgipUWdVTVzFWZPkjz+5gtHHWmGmc+wMl0J4W7Tjj5hyx4ysqIN/C+jWpywMbtiPHxm4qYLp\nTbR4yQ5v0Vetn3rvxKKFfdXpKHjpto5PBkf2XleHOUkEe+kqk6aWY0seU3aYE/fRUYz1j92K536F\nQzFphixqPC49wq3Nqg+XuQKnAK911xg2QnkPHdPfcJy8MOX07c7/Ymh2H30cHIOQ1mFQB9SCyo+q\nbGgAhctT6gECIBCz9/iTjFK2a9s58rjlknrbVq9vvtgDTKkQ1sVjPK746+nK51w7TIlLa1Y7IfoK\ndirTi+ePQyoFi6Xrm1lTnzcDxT1eowrs/tdShvapAvb+bWusKnEun1ekRJWBOS8Bw2FF6DPGHNvv\n5oxcengJ6DSgeQsb7dfL+k7RDsvAppLQ3TPuUhra826f7b20hYefvwMe/JZTb2nxm89rX/8eAFHe\n/c5+b0HePeBrARG2KbffW6ZVLS2tv/FA+IF9gOMh4hAXlHVGnoD4JuDA5DBdO9ymrgILc/QCLLS1\nbk4eX2WMHEXA+vk0oQ8Ra2pVZ5jJHJCXrlZDIJgxYO7PgYFYy2J0aGyzfRnXgjYGLKOu7u/17zbo\n83saRf+7twKg7FW//6TtO4ggjYjpyqdQ8BxiVcnPU8H1nx6/fjvh29JXg8NOJKX8q3DJfpJZiisf\n36iv6sSRK/AnQvhEGP7KKQHxPAGZ2RD+meA/8Oui0wFwhP7nW1X79y896KkHnfp6oXBb0b8tyOeI\n8cyL1PAlInxJNa0AAAaXMBFXItAqDLEQuoqEmoiqPFMuVMGD7gXoP3Od9IvU7tYa6ZrKAUCMLnaY\nNFdsSk5KNGJbQkv/V1oOac0RJdps7Crc9SLv7OATPnScf6sU8c5lBJ/vUgR6n3HMEU8ilvgcAj51\nAdfk8Lr6KpyVJfrXCyUeAI4hc/Tftag1odTSPZWpERhEoN5xmBqA/wTQD0fQpxNw4fedv9yQXxeU\nuaBkQ7MTh2Obk0mI2eFt6WsUGUAFEOo9eo40r5nQiepW55gCn9A2CZuP79CovfkmSPzfn+A+iR75\n2wL3zxnP09Sic7cMTNzPOs5VMDAlszHL39GVOphyBLIBEmwlQfvSxQAAIABJREFUEDXolD6rY3Kj\ngi49f2eoixMe67ji+Xkz6TRLdkaQsqUfBCJk3YTNXFaWS80L1PdhaqcvmUGn0e+iRQIkWOM0Zk6J\neF0dPPH8fg6pRgLre5B7XTPV3PWDZ0BjAyLId1Nu7I2TjNNDF3GQNC2igpQdciLMa6h54a9Lj69r\nx2J3SdOiWvRe2bvNNm+K2sGh1lhv+fBs0O+dFy/aBHat5HFLOIVcHciPKHVMarsmwlcivO1AmZjv\nnRZ1ShVgPPqC3mUB2zycrC7nyPXhLWjkaGtgAWygPYdsKN6qVUD1v/W3cyJck2tlVHPTq5lzK6Gr\nNNVbInyTNKcplcq42VCyiftY+8eRijI6nCRC9qFjIPtDoFoFJ8rY1yj6gSQi30WMI4+J/safvS49\nPi8Bk2vVbVKhTdlZbYWYmWErqhBa2Va+R2Y5fFsZMNA+4rnYQCLtP2V16HFHzzo0HRVT8s/V/66l\nIFcvuceh9rPqksy5zT8A+NgFvCw9OpNKMifut4NP1UmeM8FdDxidSd3LvIbcUqueou/8USOS9cqM\nc12jq1Bu5HFuy2BqRHrDOjADvgHkCoC2e+R0KAYrOmfYSr8TRnR1HPO87hzqeRWAG1wx+4XD5AEG\nHX773MB9MDRlh7frAO8zXgSk/vvS4Zqe8G0NeF1bsGFc/Uack1OKeBw0fRKp/qD78e6dMBhJ8t9l\n8zl3T2MGaG86c649yMhjqtTvU3JwXcbzpxmrrKkxOtwuPfoh1tSZEDJCyvBrq1RFYmPsr1PBherQ\n8jGbKjMGdzDap5wyW+n5qAe0wEzrAwV99o0rSGxbUZQKBmyGrgHy+QNAgO9TbarWeF+8d3JzwaZM\nZL2GfUCofdbuc3+eer4H93THrrP3K38LWPerzLEaI+7TAOp9naT9JPbRnPD0NiO9TljOfOxy87he\ne1znDtdF03ECXpd+oxFyPC54embGp6ZU3W4d3uYBc3K4iA3ns5RT3gkusg5VESBBx5WMdVN17rfS\nZmoGqv62MHtMtYpsli7RfkZ8b3+29h1EkOaJo6PPgSM4cZEJ/DPw88/P+DwNYji0SaOIsBossbha\nHaEt+Crl0ia6Ujg31RUSoawF/qcB/j84stIltnzo0AHPB2AQFkTJwNsEtyQUAQfYW8oot9V4/Lzz\nUE8gLR3mC9zOGNyXj7GtUq6N49PRln7pPwUcvy54uww1dYHzrThavqmNXvYiimyUrZmNOO5Hjjav\nRUCDHSpvc7Y4sidOkzr8xUMFJA9iNQZXgNioYfpumPq1dQhOIeK5A37ouWQmwAZLkYifUsKfu4hT\nWOFdaQizburWoHIAguP3eJDUhTEALycgJuRfOQIT/+eEfJXnS1QjX5rrmoqrjkcsQqM3tEknzgxH\n6bbH2Uiu9qHSqvX59K+jBiKs3wjh1xvcy1A7vdwi4hmIK6ewQN51KgSPVl2ES6gpYNbeF8CbnTpi\na+Ea9rVSwM5RrZsgWioBR810THHEdz98cwH63M4zZcKvS8DnJdyBANoe0Y/12DlJGT7PjqT+nvNn\n3WZMMiOpnUwN2z2DQsssfl2BVDQiyukVXAuaO2PJ7LD8HpVQA8drac7nNTn8MnfI5QnjtXE2VtFH\nuSQT8UuES3Qbur2WddukcdS/wjRBY2lMu2fMpYlBAuKcF2YRFGydZKaDM/AAsCGkDrNeW1Ovtqkh\nPHZSBnSJtqCUsjKUVdGBnaE29+0a1X6v48yW1XSkDJ8kz83inMx2Uioq1/4+x1D7dkoO58isKwVO\ngQZCT0Zn4RYbi0EjZdpPlu7M98TaIwcxbj90hCkFPIemmaNP6Qk4Ll2tX/7SrXi+rhi65uC0WuVt\n/MXSqoLsGwO+bQ95BDhfEwsOz4a1RRDtG7MPdlRqX9uh/hQSfhynOibf1h6f517qsbeRoAyFxcyb\ntUgaFOxbdpV9omulgmWxtKooU2YH1Y41Ff61c1mrpOjaBJi1To6x0b1cROhVWXwZleWzXUe2n9mK\nNJoWooDE0TetdNsn9toKRGczrjJ03tkep3q82i4KxBVQZU7os9gotq77pf5e+4j3T8seYgZMj/KN\n8OMPbwCAn14ueFt73JKv4MAUHc5wNV1P73vJhGu0IrD8XDG3lEjuI6qizI/SHfSONM1pnzpjtXX0\ns/05HAqWJSDeHMZPGcc3ts2+nUe8XQe4W19TD50vWFePlKnOf3te27QP7R6q/f6ILWFfYyptbbQZ\nln+UifBIPPG30hf+SNrC/h4encee7/fYCfrbZOfGO3SIvRyex9bOqteUuWcDEMsSsHxeQaKJQD0H\nhqhzbN/J+kvBgQYP/yEhvPEeMc4rjucV86vHt6/M1o1vJ7ytHV6Xvs6JHwphHFZ4X5CzAZxiqumq\n7ZlJ7Dgy+0FjI1lAOxcg2t9K0MauAyk3zbFm+5TNemT7CQBoU6jzz9QKym8mBP952ncQATzJekd4\nCgUfu4TOZbydmWZUCvDP6wGXGDYlUnii3i9UTEHeoYPQnF6Z1AUYikbEeEmL0WF9BfynCPfTiX/4\ncgIOQwMPZgEMvlxRPl+Qv8yIXySlwCVObcgAmYyLEoG8AnmWzfgtYJ6ZHaCOYqc5V6WJTNkNxSLU\nSvHaIOXPPcZ/z3j+OqOc9fd8rs7lygZQU8zntkl3xLXYAUI2RgpCgc9Cw62oOeBE1MnmOKdCOK8F\nizGcgvMYfcCTVyOi1JSSZlAzmju6Rv1SKvBziPhxXPBX0tQHktzhVrmg94kjuoXgtC45EXqf0HUJ\nXsLmFADyBIwBdJSX03lgWlD+ecb635iJsHwVcCMUrnZRNTScidRLn1PL59eoeoEaTk0Q7RJ9dQos\ntuQdYAgMd5TTqqHxFhD++wr6x4rEgSLcvgbcrkecbwMmoWJOkYVDc0mYNYKtYwrbscTvHfjQtRs4\nr84obfNn6sBpRGzfWmSF+7eUbeoLv6MGSuQCvK5UNS8AdlI7jUDDRJ9oGxXUpkrFWqFjFF2S3vla\n8m/KhEWqvNj+3T9HLky1H0Q07mIEOz0VyRnnYy8mvcbGhe4ildhiiACwrgWpdPjHHGqEVZlQt8j3\na9X69Rw6R1X8EdgCPEQAlQbUHT07MoCrjI7eS93zXV9qVDOV9h0VDvKUghrhVY0MC8CUIkKp3hqq\nBJ+2VH8FLtaMjeOiI8cRajWE3nGvErZMBO9EkE1+qfotP/QJfxl5QpxEpDP4lnK2rB6nGPC0Ngrr\nNQZ8XZmmfaEWxVZnoAnoarCL4FhEgJ/QNWpxc1QL1lwQs6UcO3ji9DArOKvOYuca6+Xoexw9V0/Q\ndJqbsCfWB3PRNi2bx8yr5sSxOCpVp1MBn5r/b0BPFgFtke21NIaKfjZJ+lvvE55PvFb+pVzw6XLA\nt7nHRfLmlW3BIr0NuFkLMHvVXpA1WQ1r0Gaedq7gIMKU2jglos3FNTdnfCtUKpRhM575OfW55Tj2\nOTD4xigBzPyH7o283+kaqK1SmOWz0TMr6RRiBexLoQrqmWqD8MLiGzyX0+V+K3V/0xYcYfRU9zqA\n10pPRerYy9iVNL9HLptS8JVFpuO7c6Wmk/U+YYoeX6YB44Xn0dPTjH97PuMaPXLh/fKSHKZEuCTa\nODNRAJ01N4fJkaSeoe1j2uee6M7J6l3ZrGsNtG/Psqe8c39ItSe1HSQt7HbpMf444fCR7bV5Dvh6\nOWDNro6/4Lky15L8JsjCYrdNuLJzbcW3FWJiAURMR94Dt/2WpcGc99re6b8HAXQ83Tv/1sLdf7f/\n73q9hyNlC4hsPn8HoNjeozn/7iC7Nz5KmeB36FAK0NdJZq5Zz0NYVo/btw5xbikpOQG+i/BDhmdc\nFm4A3EBwRwfqZfyNAe5UEJ4TfMdBoyV6LMlhyR5nSXvIV8IwpbvqCpqOZkFhfVRHjXUwSjqr1UTx\nD1gHjthP4X1ebCEZx7pPAKyNZoM1+n0pBSFSTa/43v687TuIIE0NyqPPSMXVXKUMwiX+P+y92ZIl\nR5Ildmxz97tFRGZkJlCoqu4aDkVmeeL//wdFSLawZ1jV1VUAMmO7iy+28UFVzcxvRAI188AHIk0E\niEgPv37dzW1RPXr0KNHbV/VYkXjBz1ASwUmZDb3a3kJ3yfDiHEY+e5od3GOC0gv69AQAMH/iD4wz\n8HxG+pGQ+vhlQTxmzM8aFwY7UiJENQRd8mCVzkjxqjpDNDjNHZ6WijRQpMisaIFTJCNUojSqcSIs\naBFKvi4g5n7A4ftLWWhC0PDestFdjXaK2OUShSNHXhVBLfkOBdoQ19QpNJF++kk1xNd0aenv3ij0\nbNFZXd+FbNBS8s5pXYw5ySs9WIvvs8KnrTgKM6xN0I3CN4BSJkz6nKjctIi/ErdKGWAxnnyekR9H\nLH/1GD+TlRcWA2MTjEuIoZbBXGIti+mbDhED7yyOPKeJLCunifQ4qMwiHfGp6gqUjfXKMJCxvswG\nl88Z02jxfCYE/bh0iInmRSsWOjdGPN0XOakUCaxOxrVewYY1LcYIzLk6fxLZE6CInqYaO28GRpvr\nkkGhGgeQKOOXUAUqO6bbF7S+UMzXNcGlLYlYE8Kaue8CBhuxta7oapyDwSlonKPCIikOfGttXrmA\nl5WRQcdDIJq6UFvpnZEGAEUK1oYVB9gAPl8c7+KcQxgmavW+YwZfs0ZnjOKyj1qtDOu32B8yR8Vp\n74o4ZTXKNap6+9daGwEKiRTWfRkDurwbOU/msoxj+Z6ECqTJfRO7pb5AnyoQ0NKy23HZRvxaBhZA\nrKdT0LiJtZSvc5R+sniD0dO6OnuLBAIdu6JJ4gsrTcNS4j/4fjKBFcWgU5kjh6rwqem+1EpngXDj\nDGRVyqBOIeOsxaGvzy7zqR3PVmkqMWo17pzl+8w8T1QpZdrm7Ovml2uqNV1TxDhzuV79fc0wEgO3\nlg9bvyv6bo00dtjZDXYbigLub2ds9wvuJ4tpJgOcIrsk+DrymjhzJSTZh9rUhSVRGklTrAdbQwCy\nCK8uyZDSeqiOc5TXgjo/V84T/xRQM131OcBgbzNxBchsndV0da1yXL2mu9PPjJLcxftYcaCaa147\nYwICtaVVjarvTwCZDGJ9PHu9AsCkrcp85qaMqMxRBlna8dDZADAQ9PeXPQDgB5WxP8z4vT+X+/88\nOzzB8B4j36cKw6vaDoop/Hz/klIDsT/qeiCulVZXYr78LO0rU5By16uDq38Ly2BZLMIFcPQ42J4W\nnKYOl2Are7PZN6UJs1XuSY61/77u8+s9sN0L5LmvWeu/xmZrP/vWHnvtlOerv721b9bvlr5f30RL\noV8dz6rsd29VlXoFmqg16KFVBTjF1m5THaxWxFJBy3CRaggNcMiAzzxbXLjCxugtZg4eGZ3RCTBm\nA7bbBZuDh9nwurchYEE5he6WnvT2NOK0dPBzTe+Ni0PKHQd96vrpyh5A9+45NU4cfl3WkddVGSrj\noC0RqVb7G/A2EHjd39KUUr+YBvJbaN80Eah9AxFQjWTFC9nJ20Ixs0xVF+OvzdMcTCx/BwCfUqHo\nlooNiSj9byG07WRfosHLccDx1GP3MyHYN3//AnvzgOwzlgeF4wNBnfO8g1JkqErt6gRVUM22BaaP\ntgv/JRo8e1NU3kNWOHqDF68hbOcpKqYwZzb22GDWosCvEGamgT3P0Lc93O8d9pHuPVw05nNCzoS4\nSlNXNEtZzIRyCpDhvyQUR7c4M5mMLJVVicQl0HljaGlXme+15oVb3d4/ndexo7QkYCopHeRgWK3x\n6HVJZ/humHDTLxh6v4p2h6C5AoYq74H+VlkdSBk5ZuDikT1F0uJjwPw5Y3zqsCy1fzoQ+BOCLlU6\nKI1Br5gwhgXEdGOQzZyjO8fWSZfzW+FKch7n2KQxNJuLjBMASEljniwejjv8NBKIMEbCn4/BFMAg\nN8aQbIjnoHAKiktm5XJdoyjSfOHIvWLxrk5rXFDPFYpwUusyhrIpXhsi15RLpQhIapuMqWsDRwAE\noZMv7Mgq1bAHFDmuL17hi6F39r4zuO0W9H0suhpnTzT2n2eLJyllFigdYmlusmNdgM6wwcbvRyor\nOA1sJUvG0JwIGVhC7Z8gc7QxonujYLUCE4BKDnbrwEn/yfPJ550GNoYAVTkm9NmYa8RP+tCo15Eu\njTqO5pSxMPjRvi/jNDNk1qkOc8rIQWHkZ5xifiXG1WlyzpdEDrO0mGkMlZxmvrZX7Tk0luak0GtV\ngBuiQ2d4XVkZUwDrVtTcddFI0coB2PJnKW3gYe5Lnr7k/g8N26zTCYrTKAaTytpS3BlTdTWKWKWq\nOdytJVcpqDxfVJ3zIeXi8LZpVS2bQ4a0YUC3fYdOE0V8jrmAB2+V2nS5pu4tZexmRK3Qp7oWalDZ\nSUnzuY6aEsVY2EvVKZQ16RyAp0UD2JZ5+/twxO27C7bvFuzUUrpHKSBMGtOZ2QlBIwRD4zdpnDkf\n+eQ7HL3lqHoFuXcm4r6f8W7DOjVZwag9HhaHk+QoR830/zrOxSCn70F5ltegJjnTVgGnUB0koRW3\n6RCBQbLWSW4ZR07X7/ascdTqTYTMe0LMZT7N/J46o4rjvESaRzE3Gg3sbEdORaGL0pr+7CubT0HA\nyOqIewaSBYASMJOYK1eMB0vzY44ap1DZn39QL7jdjQWIN2oHrTqkbBrxUeoY2uJlvPPdp/W7SKoy\n73JjH7Rpp9KuHayM/GZVIK0y9Iq9kdC5SGDiUwfLYnn9IWB7oooNM9tnwmpsv/utmG47Z8v8zhlT\nWAPxq/vC+vhb0fm3HMZ/NJ2h/E1shKurfSWT4Kv3017/l1IXRPfgl9gJbwkrSrpde8xyLn+XCSpp\n08YI1KypmADPp2hI34JZA09TXwIpCZUNYFTG8BJx87AUllrfBWy2C/pdhCEzCttbj5vTjHOjG+aT\nfpWipRXpfF2D3vLfOqWGK9y0/YdaAa1WFpP/aoUiOSYpMNRnlZ1zPQZ8SkhvQj/f2m+pfQMRIBER\nog/GTCWDxPBzoE3OMeV972hRuOlnbLiU1ML1gEM0hd4eYnUoLmGtyhqzQpcUCf8xhVSDFpDT3OFp\npM305bSgsySCeFk6nBn1TFlhawNt8BxxoXKFGudoms1PvSk2JoJEQtnsNC0ube56u2i8tSeQgBX9\nHr5EmDBBv+vRfWIhwznDfFkoIsF9YULmCIUudcFpkaZFWNZN34jCtVEi4psrtCr3rFOIpXlGWxzn\nNfihIXRnOjaY/Co1Y+Y+mAOBQ0si4OZpsXjfUYRq25QHvGYbaHApIN0wEcQRnCPSM312/JvCdHII\noX67cxHdJkAbqmEuY2NOBjYpOKWKQ2QVlfU0OmGOIg5nGFxQYI21Up5P+gPgygiZKOrX+csyTMpx\nngfXuZuGgakWdBA2SWuQtcZm/Sx9t0SzOk0aA75XACjSD5Bx25b9pPvIhUZ9TaMkNzCXL1Tgcq38\ncIMRZoNqxDFJKDMkhUusZe4sO9HiKAPAzpJz5lOlHh+DwSFY7JzHjteGXlO+vGzcADAZoG/SKACm\n2KqMHSv+73g1ls1+0BkHHgM+K+ydxotXeFrYoQ3kdLapKlYrThWo/d1phfc9UZtbo0sp+ndL53XM\nxtKqsi1+Xgw7f1XhW6qkEAghzpG837YCAqfXNBFA6VOKFFbGg3y2fd/1XtdARc41uticBatQUinI\nx1jPT5kPOVfmhzw3rQVV40EE8ZRS6IzcHzndX2aNyFTrI6//T94UI02D+nswqdFQidgaKi/qroT6\npBb4oGVckSDgW07EEtXKufeJJMUKw0orbA2N+2uw7DpPn4T3aG70zZgxPG9qZJbYAkpVZo8wXoT9\nIldua5UDKBVRyBiumjRtuswQZA0jsLRNTVIgltJPs4Y9EnBz8hb/5C3ubwlIAAC7BcxWwaWE7kyM\nhbQAcalgpZ9Yd2S0OF0GnBaHKVYzaGMD3m1G3L0nyrFxGbfHEcfTgOep5/fdYYoaY9QYee3V0GR8\no2EAxSsKMOp6R2uRWr0Py1F0YZ4YpZB4nb16jfw89T0k3lchOfcQRgila/kyJ3JJwyj7gq7raTuG\nek1rg7xvYZpJxFw+K3oT9uomC7tJ7rcBlYS551zEtvPo54TLQu/h89yje9nj4+GMm+3Ez5ixsz12\nZsAT20FjfJ0uRwEJAsFSVrVUp6r9JEvw9V5V+pVDRjXwQoGYBFQmJD8PsVHpWN8FDBtPzM5Lh82J\n05z2GXf3I4xOhTUzBcuCyfU9LuxAEjjFoJpp0y9rPyq1robRVmZoGQQiitc2fc2ouLp+OU9VcPGt\n8ZdF2+QKRJZL/5Kw4jVjgvaiXxZW1Ay8GFQhwnRtf+XSSfUYXh9bpay1FNf2WqtrUNl3axMUIwtS\nLUvYl6u0lGBx9A7DVIW9D0ePwzDj7o7Yrf0h4PZ2LCVoARZPzgZaVQAjZTpOYFbdmxzv57HZ74WJ\n4JoUXQDwzdyTz4sdVfZcRQyejDpmZKxUm2vtGxjVlFL6jbVvTARq30AEbjJBLkEznZ3Vq03EDUBO\nQufx/v4MABjeRegeUBZItM8hM8qfFoqGAMD5pcPpMmBscmMlj32wsRhS1kQs0WBJpgptRQvJgp5j\npbdbTYtEQl24BDGc45qyJIt16+y+3njJiG2PlY1AzkFdyHOu+VMA5fLrc0KvF2j2hPKQ4YKHPSY4\nKacntbpNKtEfpzI805+NFgeOc7aB1cK/og7y8aGJqFzT6MhYod9tUZvOJQ96WJM2mv4BQqQo6M8T\nXewSDL4sBjvrsGN68sFFHBhUKGJh7UbFnnwp1xgS4oUN54lQbWMyOlZH7w8R9gDkAIQlFnpcryOi\npvdqpd90QmcDdrulaCe8eEcq1qZS9wbDJdNyda46TarrrUMrZd4aLJ7O7SNSVKvKFjqT6vu1AWaU\nHKsbnTQ5M+f6fmoevsJgEw6WSojJexGkPOYawW8FtMq1VyyF1+aOfN/WkMEXci5aGTeOnJtL0Piy\nmGaMUT66lPYEgL2l6iXnUPOoz8Hgy9xjjBYbU8ElozN2JmLHVvklGkyR+r2M06bfDq6uBcSK0Bh0\nwsGF8j6maPCwWHyxkjZBUcJ2XtD4pt/lHg8u44fBMzjAY1LuU2UMNhZAkYyLhJA0njnlKeYBUyRd\nDhlXUgZq7UDSOCIHUvH9sJPxhoF63QhUUSvDRjQS5N7az6b0mubbig46ndE3f5NjrUZGOVe9fU8y\nRGVMOoVSjlZAiGOwbOjV5ao3wrCqWaMZJOR3cAG98cXIuwRT1unYCYhA6v/XGGoGOFJF542R9oY6\n66jPJFWrdfbafii0ak07yGBySatZErFZhlTFQWMCIqdntNdsGRNyj1oJWFtBhLtuwWAjgyJ0BaMo\nddAnjRNH98zkcI56tTYthgCVmDMevDAWOlziLb4bB3x4IaP8Zj9RKb0eSCLSOxOTCgBcF0s99n4I\nUGqCNbFEFqWcsm6Qa9Nn3OwX7OYFdy90ndNxwHHq8dSkBc6JSrzOquqHXKdtSXNaYWszsWyaPcuy\n9/fWZ6S1Y1TGcafzymGg70jodEavFRa9rrKRc145BS0Yp5prd4YYlzVKTKlyna57bk3nyK/GmLQy\n5hpRxcIwsAl3NyMui8MlkBaUTwo/jwM0Mg4DgUGb3qOzEbvOF/YllcxTJXoL0Hp84bVKxB2lETCw\ndqBb+v3XmqQxfC3CLn3fdwGbW4+cFI6nHtOJxstu49HfJ9jtiOWFnmceHad8GpiF513UmJn1Koyv\nUknp6ru1ep3iAtS5KT9b6bdC11d1HW/bW2k3b62Jcm5b5UI+24Ii+o29OHHw6S3Wxf8XjQInatV3\n+RferTSjMjYuoOsCOgaNra77qQTsALG7FSal4SSApjKO3uHgXQkwfsAJwz7gu3SEeqTrPE59+c6V\nfXYF/DhF8ylqBZNzqcpEeiNpVWqbSljrVVqES9UeLv6C4spi9EC/2GhvUTDfXMjffPs2AiBRAELw\nZo6CS7T5bjPB2oT7353R3SvYP1Gim9pVTYE8VaEVxIQ8BWQWCRp+nrB/XDAeHaaJa8Z6iykQ2jj0\nle40RGIwTEEqHKiiFGtURiwOANV2d0irN+h4ExIHPaRaGWJtbGemPfOCooheOzTJgSayKFOWEmK8\n6atMokU6wnYViZteLPJfAvrv2BnRikQdU1W/lZ9GVYpvb9Yq9u076bRa5SZmkDNpVRXtOjgyflvh\nK2EXtE6sRiqRQUnjsCpBqjjIQjpGKjsmjmJxnFGV9CUWR1FbMkysRIAUl5O0CZpBWtUrKMeGPnPM\njU3o+ohhH9D/ju/7foCyGum4oDsH9EcaV0SXq8CQvAfnIjZ3Sym9dlociaJxvjf1D5VGm5qqFBlk\nnJxDxpado70lgUAEqn0uwlf9NiB6EossTq6iCiZ7uwasOp34e6kvtqZqL5TzNFVA2dtc6H8ZtAnv\nbcKgU4nmZs4Vn6IqWh1UEu4qd5gjem8ZAnunWOwPeN8FbPk53vdkzG07j5A0nqYevRngNDsKQWj2\nuUSh7zvqk7Ot9eE7vt/HxeKZxeqEwq5VLt996ygTvtM1Ipky0bwpep4KcOlY7d/qVNYhy3XqD3OH\n9x1Hs3iut+kAEolo8x/vXMAfDyfcbKei56F1hmK2jDZUUhQA/GLgvcE4u2Lw33UWl+hK6g8gYosy\nduiXDc8tpzL2bNX4rAi0unKOBqOwMUSh3vE8MUrhrhOdEjo2l/SB1tERYBWYG2s0ZODgVLmfnqOw\ndL/SP5WC3aZKCQtqMBkb7oudVaUqwT3bdgebcQzA0dfFpRUALYwOxVHcxkkheiytxxsbcNNTBH0K\nFpHTzmTNGoNlcKARoRUR3pVwoMHEWinXoB6BMfLZXMBkoeqW+8+05wlz4BwNzGJgFDBICpRZR82v\nr99qnjgN3LhcKpgcbMDtMONmP0HrymDLWZV0gzuOLlu1xZeFIv3yPBujcLCyd1QQ7BgMwrgpjvzu\ntMNgI3odi5M6RdprMxQGE7BjUK5n4NenNm1MIQXSVZB1rNFkAAAgAElEQVT5MIwew85Dm/purI2w\nJqJjYx0A9lbjHKiPRC/WpZqG1Y79g824cwmzqWCQrITEKKj9+1bJaBlPIoz4zkW87zzP/cTnKBxs\nxOTUqiToxMKEO0ssLPoeXo9QnbutVbixEXeuAuSnYOAUsW1krAAketjpemxjMqaUsTHyzrjfdAU8\nBCB3fUR3k/B7/Yzpb8zoWTr4rPDzNBSm5U2/YOM8Ns6XvTYkjSUYzKlqYEjLzAxZA4a55IYDLEKn\nXzvPMTHTsulvEShtZ1inMwaDUv7TdRHdvYKyC0LQpURfOCv0uwz3XsH0fO55Rlw05nO979FbLMnQ\nvOA9J2TK2fep0W1gR/ga/GiBoPbe5UnE6c8MOb4FprzlO34NQCmSF9dMhPz6HN38+2vXkioSX7un\nt1KC5XnWz4ZX17l+jpq+R0GClFVJfXSKRM+HJh12MBH7w4T+EMpn52ARGoax5z+I7k7r+CsIQ8bi\nCzONu+eAD/sTDt8v0OaFvvthj6e5xxBsGadtCfliOyBByrAapYpNuzERG0M6TdJiIsZE2+3Urxqi\nUwLQemUV9WGbDvsqxSVzWmXUsHnAb7FRCt43JgLwDUQA0FCDQJPfZhRj4+5uxOY+YPjfbqHu98D9\nTf3gtAAxQi0MIqQMzB5qE5C3ZBk7O0JZD20WqOf1bEwg5wAANgcPpTJS0rhMZBQtXEFB8u2lWd6E\ntcpFnEupDKv0Ki8y69e0sSLE0tSMrWJHjRFcOHFVxVWa5EPboaHkPlpMo8Mt0zKUSYiLxuXSlTzU\nKZKx7FMtVShGUkZD/eTrv7WhXUcqB00VNa4N5oGdONngrM7YGGIN9PxuJYVkDqZQWi/B4BQMjoHK\n3pUcYr6+Y4cPECclwXLkmc5jUUWdoYXpwGEmBUDv6H56nzCoBPd7B/M9KzDtesBHqJRh+lCU3und\nJBi9fmfkAALDDQFRHy4XPLMBvmcD/tNmQq8jnpa+5Nn5rBETELQqz+KYRUB0ZVXE4Lq7jDhm9M+h\nGMxaEe03JIW5EYiScSQGl9CdqcnbJGr4xqQyLqekCng3mIR9M9YIuNHo2KrSiij8rcBkzJT20oJR\nCopzfFsdk4jvtyP2/VxqMRubMF0cDDsU4rSmbLAooZhXQKbXCbuoS1/2hvptSVUf4ghDgIqpKD+V\nYQOSqQBIyb1PCudgihEuLI/MGzXAhgAUjM44cNrEHlKmqRoXRlEqgk+VoXDbLbjZTtgdZthNZRzk\nDOSg4CdTorWXscNp7laGuYCIrlkEVGNoSGrIxmTO+1erqgeJGR2hsR5FSI7uWZVjWyOOdwXLgDUo\nqBW4pOBVpD7VahtyPzIPZLwIhZloo9UhFnaWawAMWmMUoDMO7HAdLFVEmdv757WyZWXIetrW45Y0\nsjFaEuKyNAYHG+Cj5ueS9SqVp5O1xarETID61HMyRXhVmpStNSqhBSCsSfyzGpjtGinleX86b5Fy\nD630K0bJdXO6gtzUj3Rs4LlMz0epWdZVJgAABI7oaZ3heE3+mFnwMNvyeccOHe0b/NmsSxm/51TT\nuWSdbgXIPFPEncoFOCdjO9U0AFQNhr2NJXVQnQD3QP0m82mJVImG6Of0WYnAGlWjwgqSurXWP7Lc\nP07VvXkp91j7VsC3rNZO0mqP5me5TpGJWVFggFO4UqM8L6BRce4VAaWt090bqlKxtXXdd9oVMEq+\na4oGkkAh3yD7pFSdeUt3QEQIlQbMDrjZePzT8kx/e7jB09JhThozA0TC9BpsLN8ttsTSrMch61K0\noE0DaZkXshdJ1LW1J/hRoPJV6g9XSSisjHyVlgVAmwS90+h3wE2YMD6z4OdkYJ4DzC6XF253gNIJ\n0UdYzk1yOsGqxI6tKu8mKSC3zp5mUb2vggh5BVSnvLbfimO4NiuBr9hc1w779fHrz1yznVZ/w+v7\nlnOv0y7K8fL76w/Stern2ueW9hYNXzdzyHKwapW+oNbjpzcJm7sA916BZJKBw2zho4aC4/Q/Ac3W\nuh9tW5IuQtgPlw32LzP2W4/dR7ZJzRH780TlJPk88QOOi2vSR+t6n1CrDLmSylBTbQM0ek6vFLvb\n6QybaJ7GJtUJhtJWW/Atq3W/p0zzozMKJvx20xm+NWrfQATIZl8dWqMqjW73Q0D3v+yg/vQJ6Czx\n3AEqt3hZgNmXY3mJwByAlJGbVUw5Be0yrtfIVoyuOySYvULOE9yRS22NDlOwWIKBVrnmEeqEXUcL\nWXEqdeIcwOp4SL5WuziKwj9VXkC5hmyI1zSzunE2C3XiPCkWiEECUtI4jj3iT5xywYbHeepw9gIi\nmJKaIZv+GMmwXhJKtHlJFIHMEAP19fsqVEwFbG3C3sZCJx9sxL5biJoqjplNcI4M2ZaumpNC8BoL\n52OOs8McSMfi5KviPgEd1VEGKMJ20y3YuIaJAjL6ta4Gg7KKagh3pqRsmJsIdeihPt3UpP0l0H+J\nPttG7DKnkZQSXiANDn/RJbpxuJnw/jTjEg223P/3m5HYLi/A4yJRdt3kwfI9qlw45ynX92fvDUzI\n2D/PuGNwy0eNXedXzkzImgUpqzAipajUKSOt07kyH4CiuO2TgjEKStW8XlWQ8cRjg74nqmoQvL1d\nCxC1/uvGeWw2nt4PqCJG5LFIVRbYOGaxpFVpTAA7G3DjchFCkog/OT7suGTFQpi1f1owqv1dnv/Z\nVxDiHCugUNgfci+oBrwCAQ6twRSVeRWRnqPB42mLaXaw7JiJVskcLF6WrjzP2MxNaROzW9pITgYK\ny6lEa3RCpxM0dEOv5LSU5npqZfTVZpQIbKYifDVGAk/asabY8EmhOsApozC15FzHmhJOp1dGnQjq\n1TFYnWFXjHUAiXpTUpjuXEDIFj7XXCgNwBTNhPpT1tU2bYzU7YmGLYBBbyJI0LWWa11SXcPbVgGC\nXJ+bx2ALMA4moDOpOGtGZVgTMfQewybAMcXA9BlKtDhO9J35rwrHJhLWvrdrM96x0yxjOSZw5Lv2\no1UJMakCVBXBzqixeFPSCAAq+TeYiCkabLjPb9yCm35BAkrqwXFxpexyZd5RX4y5apEE3tMTCIAW\n4eAXpVflTuX9tIw7gNakEzMZ2vcofdNShq99HNm/Xqn6l3eUYHicn2Ol5FdwLSOqdXWldu6odpx3\nHh07CgCJGffMDFu0AnclRFxVo6G+q6rH0H43gboRt5JSEAN23tZKRABelq5oJdQ86bxKbWtTxJCE\n+s7jN1A6qPto8OG/0Pd0//qAf/vxDl/mARfef188gftmySsWhLz/ApCnKtx5ncf9VtPNfAVo7dBQ\nCFfn0busnrc4mFrVZ8lZISfAfNxgsDPwr7TqTS8W45OFOSXYntcol5EiCuMFYLtB5dV7sEpc1l9u\na6dZrY5nfP35/9FrvgUefPXc5vdr3PEtVkMFCV6ff81OqPfz9r28ippjXZ2npGfJWBW7qtFjuE7t\nAYh5694p2D/uoLliF3CBMQm7kQJlEogSUPd6zUicXizA49F3eHjcQesTth9oxG0/RQw+Igcgcll2\nPxlMo4U77spa5xMFe7RCqYgEkD1ghGEox0yC4aDhr6WRtCBZ21/rscB9pwCdf+2K//9t3zQRqH0D\nESDpDHVT2NmIGxY/6f60hfrTByAl4Odn5Acqs5gvHpgD0qV6SGlMyLziq6ZnwxlYThrzzGWnOJ2h\nbeZWwf5xj607w36mzdQ9RvSThQ+UPy+gw9B77G9nRF8pc92GatUmr0vVhOB1+ZykEsyLxWVxOHpX\nKjl0mhQPtoa2UOkVrQCdXpdPLOj9QXZoQOsEHzVeuDRmbwOciVQLN1XDOGUSjStVAZLimuTr0k3t\nxtcCBkhVLAqoRv/GBBxYCbczEZvew5gExxR0oW/npEq+bKGnugRtOK2kD2WT897gPBKPeYkU8ZOc\ncQDYc7UGY3IRSIxJw5oI41IZA8oqoDNQzkAdmBfdWeBmB1gDfKYITPrxiDwG5Cki+QoyidHqky7U\nNinfOZ0d7FJTJLadx36JGARQ6T36IWA7+YJWS1nNthSfVQRcWAWAGRYAoN8NULsOh+UJtuf7DIBx\nGYejw/5E73v0DmdvoRRgFb3vvbXwWTMyz+82kaN460JB7i9BcxWTzLTsdWShdeSlukbKueR7aimr\n0jg5CjUHvY3u+aRxOvfwR54PrJgtDl1xIJlRkXOFIRL33d0wF5R/YRZLa6i0BvWqhBe73W35w05T\nFCEDOPMYWpIqTmhRPGe2hm6eR/Hfr6Mu4tiLQ3QOtlC+ZdvzXDXmEqhcW0tvJ6Cifo/Mz4y12BNA\nhsS26Et4OJ1wDhaOaTjSj44Uvsp3WCUlbqvIZadJo+J954szfQzmFRAaEoE8QK27ngAgrCOsJFZJ\n68J1DvAUDc7BFSdFBQIUepMx88MNppZG3TE99OMwwekOQLdiw0jfF70AdsLialxUIGGOlcUz6MRV\nfhSeOQLeii226yKtvfUZ5boKNeVsaxJC1thZX6OkKsNqU/YBaUpHWJehOsByXszGeTh2BIWJ4JWI\nZDbPi8pCkXEVtWIwKGHLfWZURogGp4spbAegRpLJeWd2F+sEtWDtxgXcvz/BDQmetYbGS4cvxy3O\nweHEfSb7ypIqQCDgkIAsbSm/UACYesxB0vsC3ztV6LmEVtm8Kr8I66rQttVrxxQ852XOWEUpgXsb\nC9sCi0XiKLowc9qqKm812QO3JmLfz+j7AC/jeXZwC41BYufIXgIg5ZJaQPdObKE2GttpYtzJXgoA\nW7VgYGFAeY9TMBiMWSm/y9gnEK4OmIxmXbMCZirMjxp6iLA/UFTitpuR4gvSZwUF0T8gkHVJBucW\nNM2q2BBAteXqHsfvBSh6HjJtnWYGnqqRXIBAT5Pr2i2C08R4q+f1BmWeAKBy2scAfRuhP2zRvRBF\nPS4J49FhnizMyI6do7kRQwWf23VcdjLFTCalqr0ifdgOC61q2mp7XDXpc3LNa2C9bet7qG1d3lyO\n5bfPbf5xFcRe9X/ZQ8r1XgcEMtQq+HbNRqj3sg4utcDVNQujbUazmGhSq5LIMoZbJpfaaKjvb2E+\nEHN0e3jG8GVGeJ4QTgrzme3c2TKToK53czCYo8EUzSoF9HHqYR4TlCattc17rt6gAcObrZ0D7CnB\ne4sn1gM5Bks2OIipIgyrTkcYlch2bMAtrZhJc9W/qhkvsnat03sksLr+XFYy1n67IMK3Ru0biMBN\nFqpOZ7zvPLbf0yKvvjsAxiD/3z8i/PUM/5k3DK8QvUJYqnPvlw4xajLum9zjEDR8MLjwBjx6hzFY\nct75s3proP75I+ynG5jPR7qXv5wQHifEmUAJifzbdwb6dkB8nKmoPAB930MfeqAzRaMhnxfks0ee\nEtLIkVwuFWmPW4xsfFH+NpeuKj2iYZLQwtfUPsuOtLnh4eMMdjcLTpehgCPyXNfVC4Aa1QdqCklL\nNQuc817Fel4b6+2iNiWNo3crSvdpcbA6ldwwoxNi0lhidRgFse1MLOKP2mQ4GzFsPbbvFrwzBCbl\nRO9caUDUs7UDkEhEU3IbQ8gwJsG4DN2JtWKgegsMDuiZ/rXpAa2BhxfEf32g8fO3CmCEC+UKAxT5\nJwZJjZw6UL+Mo0PiusVD7+tYZGfhMnWFySDIdE0hUdAiqIeqC9KqWqMzwB8+wN1u4Y5jeYH5OGP4\n9xHbz5zX/eLw8Lhjh4AFpbzDzHl2EmWLbLDtnUfHxY2PwRQmQ1vRQMqUtcZF6+xei4q2+Z/tWJFn\n8UnjcRwwRYMjUwWnQkEkwc3iKGTVOBm5nHsOFrvo8X5PfWFMQowagwl4mhlwYprz1ET1faaIeqvd\nYBrat08KE3+35El3uuo5hEwMnfgG8h+aTV+DohGDrg5TCzbI8y0MHEiOtNyTZVBD5xp39lyRYo4o\ndEifWLfEojhCe85X9k2qVPuurlcCo7AyarQix+rT7lJMk1aQVto5ODwsjtclicxUcLFNKXA6Yd8t\nxRGSaiMhGrxcBnyZBn4/jpyHxtE0Cq8EIW/6Bft+gVG7UqKxzElFolZAq9ugS4m8hVNMCMCpEfRO\nJwyG2BJy7oUFctu1V8r9tYawABMUTWYQwVI61ta4FTtBtDf2l1Cc5I0L2HYeXVdjr2fOSZ8ZuKJ7\nJ0ctprUzY7FmrwkMTcKKzILQlCo0eotzcCtw2HNKlNz7xsRXTk6ItMd2h4TNDwxYYcS7zxeMzw6n\nY1/ue+RqSNK3c9KvNCDknRGraV2lo+d0oXccfRfwNmVbhDNyoy5fGSFfd8xK31wBYQoojLHF6pLW\nVe8RfG1cOULUalpDQtcFbHYL7Cz7kIZR6dWsy7xWtmCHRk0flK+5LhMHkC1jdCL7RYCbpKsw41Uf\nX0eFxcm1us5DgMSnow+42TG78n7A3X8akfMzuidy2J7nHidvcYkCINKaEjJK+oLct9MZKgMm1ZKV\nke8xoWzfBaDpGrFl8LUkvUT6J2MdKRcbpK1OAwDhCKifJ7hDD/Md576fR/gpwvsOgdd2703VJ2Hw\nOOeau97OgaIj8OtDrLTrtfZ63fgaM+Gtw2/tq+018hvHgK8zEt5Kd/g1rYRX1/7Kd/7SsbZdj/3X\nqWh5xdYpbdMDH9/RZ//wCdp72MsMnCfs2T7K5wXpeUJ8DPAUd8F8tLicO5ymDpc2vTdTRTbzSD2U\n4oRuE4sod3neBDi3TlkiVscaIHI6wZn0aj0KWa9sKwFCJWAn17tmrvxSiU/6+280Gp8zco6/ft5v\noH0DEQAAudAL9zbh43aEOdTlI//bF0z/+wnHv3e4sMMWOaq+RFMm8hI1Ahu1onZudVpFlAGiF5+8\nxd5VsUFlFXDYAv/0O6j/wKWB/vgZ9ulEfPDeAXsWMdltAGNg//0z4Hkgv9sBN3ugc1ALfV6dR+A4\nAuOMfCSjSB9GQE+IUcMxPd2aCLPkkm5A90+On0tVKAaoDovTCehp+OjvDth++Rm35xGKI9OOy+EM\nNqLzdbKRcq0u+dEmkSBTVlXx2Wqh+a1LLRE9mFVlGyflFCii+sB0falK0ebAKuTiqErrGDwZdBXI\nImXbgNtxxrt0weaexe4OigQSpY4hAKSMeIwAEhKDOUobaJ2h+wzFoSLVGWIcKIWigvd8Rn4eEf98\nxPgXdlJPHZTOsC4i+Ap2VONinWtndC7lPwES+lkCRYXmzOWyLhtsF8fRpvXG8trIqxTakmozBfIU\n/+l7YuMAQIhQf/8CkzJ6w6VJ4LGbZoRgitO+NREXraGbfFmfyeiikoj8FdHAp66kNbT5zDFTpKyt\nfy5AVhWMqv9HAywAa3o6QKJdz94UZy3kynvodS3L2TrWsk3OSeHJU/6j1H9+f3+G20bsbyZ8ONF7\nOF0GnL3D49zjhfO+UzCYxagtxj8JWu5sJHpiEMYOgTy9ziW9os8KWmmuCqDKPYpgYYmAa5SyqOI4\nWSg4dm7FBiENCLUqBwaIJgAZ1dW8oLJ7Au5RL9dSb/JuOkP59n2IVQVayz3lkvMqEU8y2qqhb9jJ\n3biAgSuW7GMtlysMom7q4ZMiBksx4AnoaO+nTfVyHPmr6UxMNZbSgt4CEn0t+cgaXEa8rOtGJ9zu\nZjidMEl5XQbdNs6j4+9RKiMEg9PcFYdLmA/nyJUXrvYF6Rs5JjXAS637xsCLqzEgUaG6Ri9RYzAV\nzJEoLIENXVnCek3pIy1zIGaFMZImjJRhnVMdawXA1QJgVP6apDZYlYoAnqQqJAZVpGmVC9gm+86c\nNHo5n/vi6B0+P+5g3Qt2t/xO73tsPgIbH3H7QlG8+HxCuCiMLw7nMzNvGEAVh03SIaZQmSit437j\nIj5tR3z69FLeLX4C3DiUXGZhkLRA2RhNyVOW8SzR/WtQpF3Lhqu1MuZKpZfUp1bvZ1VGTtXzANYX\n4PXC2lSqr1jVzgWFEBkAKhRo2odbsEIAuJwVZt5XrSEA4XEaCvsjMrOw3UtsM9Za35fsBhobjtUn\njSMdqC8PO6j/k1ieh/84wdw63P7zAmOr4JxRPbR3lZ3VgOH1u6m0naQqFVZhViUKLvdKjBNa6za8\n/2coaK52IgsjESrWlUk0pA8bsMwmpAgsP2WY2wvUO4r6uO88+ktA5GoMQHX+tE6w/ECdjXAhFeBK\n+tIqivwW+6hxeGv7Fa/5jbMV1uDU/0zKw3Vr07cEbGyrMaTmnJXwdV4R1a6O1/2h3H8DLKyOv3E/\n6o2/FWtBxirq+llLo9Y9MEEhnSNwvAB/+I6u9/4d2XUhAiFAeba7xwnm+QjzfEb3TKViN88Tbh4n\n+M8XXD7T3Hl62pb16Mw23PJgq16IzE+bShBs1WfqdWqkY/aQrHv0x8QsyLya35WNqso12mui6ZPr\nUslt0O9b+223byACwBRsooF+7GccNjPyzIvH317g/zzi4S8bPJ03xSAkI0+vBMwWFgzMea2oLUaV\nTESfKDrVG10M4zQl6OMF+dMH4NNHuuCHe2AcoWIElELW1QBT4wTsT8BpKsfgA/0nSeg+kOPX6jPo\nJpLURIrUleGtUIUWU1NHVxUDqXGIP9zC/eeIm6cvwN/oUBKBMBMLG0BzTXOf1vm7ZTETvYCcEbCm\ntUsrOVuNszjG1xEcofi21NJako4u0mvFNGANp6pwn1EZ23GD+3GD709k2Nzdj+huE8xWQTHPO4eM\ndKHctVbforQWxg4RWALySBtN+jLC/xwxfdG4nHo+xdBmwcbGXIxWU/pNnnMLRVFw/hsAXAKlyRyD\nLZufVjWSW9gAjTMiwIKMEInuCA0v/nyC/fERuI+0YQIkKHockceAxPTMOGtSNW/QcUnDaEttxVxL\nIR02BGwtkfJdPeeK13MrG8E3Y6G6jb/ccrNpAmTALDz3Fu6LQsPOikr0iQp3rmCUGKIxK7x4BZ86\nDIbKkW03C/pDxPAuodvTXNxdFlyOHcxzAkCGZMgaS8orw1ZUoEs5WXaIL1zibmcj7lhEMUNhYwzO\nweDUpD1oqBUTQQQc2+gYpftEaABzURmvBnhLHe85Ku50KikFj4vlqEVlKpERWtXNAaKnW4PV9d56\nRynXMUh9I+OF+mcJBmqR8Uu5nTFW2q/M0WsDuI5h+Un059E7gGw59F0go4wNs1qaNQN5Tc5MqI7z\nhfv8vDhsNwtuDhPuBJgxVOHCbWsKEzGXgJuLQVi4JOe5w8Nlg4e5h1G2sFTEuFs5XKj005J2hTbq\nKkZ0BVkFVKOUIAIThG4vpS8V6jFAnEWNZ5WxMbYcuzCAIGKKS6oVOYQMo4UCv+qzql8hP6s4pjjC\n1GJj6MqaEJJGNELNFqaSxZkB0t8HCu0d5gn2Uwd16GFvCbg2HwO6JWI4Bxwem0zyBKiO9r7IZCp/\n0jg+9jiOPWbe0xMUds7j06cX7P9XOk8NGsO/PeLTZ4vTUVK3LEKkcrCyTlK6kINWulQwSbzWJazz\nusXpmZKGkyBC00/ltleGulodvz5vWSy6eR0ZK+9c1ZHVzpN2/uR85WTxWElQRRg5ZhJ3e1hcYXqI\niOWSdLEhEv8nIFibUnDdtCMBuNFb/PuPtwCA78IRN39aYHYa+99xWol9we7Y4+Uy4CgO12pNkPWC\nrnv0FnNU8KJuXEKr1auUtIB2LSER2HYm8se4j0rfq/pvAcvshoRP/UXD/n1Bx+La+rZH/12EUgsW\npryHxZT0ysKCZVuJ1i3RMiEAvqWd/1qTvZ1+V6vqPV87T9rXHMP/EV2CXyuX+EvO51uf/bqg4tvA\nwK999/W4v26KQVE6l05eooZ/BOyfH6E5mKfubsgmMoZYpWIfGQPcHoDNUITYdYjQ4wz7eEL/N2Ia\n9//XC15+HrAstoAJSzTw3hVdM6ACep2NJbCkGBQgNl+1py0D5rJnApSyabhSUNX7oXLVVq31Pyob\n4esvSdYQmiW/3XSG3ywL46p9AxFAm9vGJNy6gPvNBGMS5p/ob24a8fyXDk/nDV7m7pWgWsg1Oix0\nbNpY6POW6dydrnmfLQ27iEw9R5j/52eozpaIb95uAGuBGIFxIuAAAMYJGGfgx2ekR7aK+AvzGBpP\nOyNNCYjk8AKkzzA+O5ymrkT4uhRZAbZSbH2WZ6lGKN88XRoK6cgUYWuA//h7DMcZypDTHS5AGDWX\n9JKF2GD0ROESx2FjKO+8zdeKCUgNNN1qIsjS1lZiWJJaoaet8VUooY0hI0aNgAw5qyL8lpnGeAwG\nD4vFT1yO59PLhLt+xtB7OAZFrE3Q5vVConSGWlnWmUQ3R4/wI72v6a/EPFgWUyI9AKBdRowKizdY\nOEoaOX9dgARpXRegVIZjq3VhsTi/Utelz7YRm7dEoVpQR6NqaCw/Jah/+Qz1b0/F0s9zRDonzJ8z\nxicy6C4XUvXXKpcI7chiYUuTL7skHltJoWcK9fvtiCMbpqdcUzYkUo12bDA41OBaBT1vDjVpME1t\nZJWLSFWhLoL6IOaaIw2gcYRatf7MACDwI9dzvnnZEeNm72FYNMsOCZu04HapJaAoJcCV6KTcg1Gk\ngL5zAXdSto91ALY24G5Dc97ojNlbvMwdjo1QaVu6U+7xujLJYCLuhwmdjWXOAyjiS52JRXDRWmIQ\npaRwutAzductMgbuj+o85EzMBZmfPmnYpKn8bDM/nVLwilJZ5N0ovI6EyTM9TgP0lMtza2RKBWnS\nXEo6VGNMpoxX27pPCi9zhxM7QsNElQKkxFwB0RpnpM2rlai/AHBPc4/uHPHuMKLfcZ9tE7ShlLNW\na0k7oL+N6BOznHYB7jHCHhO6uceTF+dMlej73IT5BWgRRkllxrTOfS7MlfK9V8AZsM47Fio+UJ2T\nNoIoJVN140x9zUBv11M6jwzbtoxf76jSDOlBVNAT4NxupQtgIHuoUfXdEOvEIuQdxn+nteXD04j3\n92dsvz/BvmMdgI4Qcr1tqpcn2vtUx8el2sQY0X0ZsX+csYwMIiQF6yKG+wTzHZdyvt1g88mjfxxx\n85kYD/4xw180ltEWxgPdJ62zMkeCAYKStKjaR2Ca+cQAACAASURBVFL6k8pY0r33uuazt1VIfqm1\nf1+CxTSmUtEHoMCFMBHaFJ06wuQ8Ygul1KRIKGLlGZXKGnZhMGeMpoxJp1D2bl3WTonsrtMm2iiq\nlCW2W6Bj3SKZo/nzDVI84vD9UlI4N98ldDcjdi8z7rjPfTBFmFDmxhIsFgZ45qRruWBIsKdWutDc\nN9f9fM0cSc2/35pbhXU60BoQF0obNQdOefvdDvaPe+j9hO6Z9rxwJPG85BWWC+/zUWOIhkRvuc/H\nmOC0BlaBFxC3r72XZh1s11Vhslw7z9d6U3LsrTH3tc/+WnvLxSIK/tvn/lIZyV+6P2ktKJLeeO72\nb9J0ziS+iuo4t6mf1Y7UmI8W5s8znP8rAEC5v5UvUIMtzFwS0bbApqOfANB3xCDe9NAHGtRb/TO0\nmXD+0iGd6CbnYAs4JmOAgP6EodnrrWKTP+XCugVIHHozeGiTil4aQLZ3pyOiqXuoTxleVX0dw5XP\nrtkIbf+3vysABt+qM/zW2zcQATRpiDoc0duAebbQTzRjtmnB5dJhDmaVk95Sw9/C4lrEGhDkrn6m\niP9IzvMR0P/9BPP836D/OyEY6uMN3dxxRP5yRvyZNqU0JiijkMYM/1KvGT0J7QkLIKXqDJZ8+Khx\nWYiSKG1jA8ZgcWGaLQCmmJIBS7mA7fOSAxGPvHk+HoE/fIL6D5/Q84O7xwnhMcI8LLVm9+yI5psr\neyNnBVrrai9GI05jLpFSoDqVGtUoGnSCU1RLub6buvm0dYGFiSARXzHc6DVx//Ark/xkcR5OYYvt\nOGDL6uEAlQG9HSbstkuhkRajRsIxAHJIUD4ijx6Ry3wKgBCTLu/GuYh+ICPD+zo1JWqcsAaguiHA\n9bGAEAuXiZP8foAcSKmHLs8oQlutCJ1jlX+JzMj7mY8G6b8F+EvGeGYjLxt0XcTl0uG5EZ70ScNx\nTjxAUf8p1dxuue/MaUCSC9p1AZ92F3YeapSr1SRo69AnqJUnFDOQmmhb2xRqVH7vAjLIUdPNcwNC\n4VaVoZCrFoKUeNxbKkvZCoM+zR3U4x7dCzmn9B5rLWmJUlG5tbRik8j3ehbj3PdzPZ40ht5jJ6Uo\nXUb0CrejwzRx1CIYity2ThlTmNvSsPt+wfv7M/q7tHJyhZWkqh+EHIAcgTij5GmO3uLoHUei68KW\nQPoL7fonzngB+RRVE3C5+awmYLXXGSG351J/nL1dgbVCRZfWcbWFa6MGYNCnGEUEpJxDZeP0JsEu\nxLQYbCzHlcowkKiPzBP6LzbW7TFY5PMOIRq8j+RUbsMCbYEwa3hmHaSk4Doua+hy+Y7N1uMeFziT\noJnF45PG1obVd5+CWa1ngLg/eQX2Uh69rNHrvpBnoD5joxhrx7/XVUCsnQtyrUJJhipgmynXpOsO\nTbWblCUdJpX50HUB/UBAglK5gqOZmEsiOAaQCCgxKCpNXOCrmFURCJ2ixeM44N2XCfsdCxF3Eban\ndBVh+EWvEAOlmHV7D8fVdGXQmj7BRrrPsBikqBFHID3P5TS1cVA3PUzhaXsom5BTgOP33ZnXgpBW\nKWRFAFi7OkkKiaSMUP8yaNmkhlyn8kmT6Gibw6048ih7rVZSbrf+lHuqwoN1nDulkNQaMJX8atEd\necvmsRoIicuZyn0rGU+0Nss1W1ChMB+3Cpsbj93RF1bHJVj8/eGAaZ5wuCEQtT9E6B4Y3iW4LR1L\nnkSSU1TFYTqfaIw7ndDrBCfglMqIxRjL5X4KXb2ZY+29AuRoKnA6Q2NPtPn0AKAHQPUKdkyYni3M\n35lh1o0w3+9hPu2gNpJW6pEuEemckRlkNFNqxlBq3g2J57VAkNFYiRau0gXQOt9S7WFdW+UtsU6t\nvrKmqvW7l8+u0wnlWD0g99eCA1phlVJSjmf8D6Uz6Kv31l6//dne36+Bcm1r3798j18Mpi8JyzOv\nF3PGPFnkDBgTYF0tG203Ce4GMLfMcN1Z6Hcb4DAAA61h+ve36OcHpLjA+6qL4TktumWWxaxXmkhO\nJwLytCa9hCZ1zHa05yg2J6yPlNKnEyLn8XutYLWGTe06QvM+QDUpWW3f1j6vmhK/VSZC/ladgds3\nEIGbLNySV14oZkOAj6bkxFaacDvByzLVqATncr6g8tKIgcDlfHhihkkj/5Th/5ygDecB7p+ARJUd\nxnOHy8jREZVx2NMKIRUfEucuPk99MYwl5YKooWJcsEJ9MHXjybRxn4LGsaFKf40y5jSnctBtwv3L\nT9AhAoct1A931J+7C1R3Qg4egasHkNiTLbma8izXi7tEMmjzWhuo6cpX3NoEp3IpM0X3W4Uar8uU\nUV4mXbNrcm9rNFXE/YihJs1nysEWITDp843z2CQF3eyAOSmKMnD9ZzXHwgpJTHlIrL7ubES3Y2Pj\nEGC3QJppwzLTeodcb/AK2gLdbURiju4SqVLAYHQR7JKKFWOwcLEZk1EqBYghkMsmQoYhG1+zwTJZ\nPB83eOTou1EZ7zYTTnOHBxYT9AyGiIMHgEt31txuoDo7UzQ4MgCxcQHb3uNjnFY0emH6LEkXOrk4\nTVGt2SdVLHLdXxkVpb/pFmxswMY42AKykMM/BYMX74rg4hgrUCDCgbcuYGsULtE0xqfC89zj0jh9\nnU7oTUKnq5MqVHACgqiRsKHGs3dwU0Lu6fO9DSXVKJQc2oQUFVXg2BKfYovapzXHlqqQLLNBEvX/\njUd/l+De1wGdm5tJU0ZkgyNcSCQ0BI2RwYrA5S4lDUHunRzKRoRT+jy/phdfvxujae5bqJUzBHAt\nbVG1ZmewXSM6rThaoq70IRQS6poqTxuzKmJsPisYRaZPHxtTuwElaspUNcZ1c28/zwbP3uKB58P+\nyUMp4NyIQBqVuWRoKOVS98OCrgvouog7PRaAaQ4WvQ3ou4APkfIuRu9Yd6c+d8v4GHmcnrwtbJQa\nNatjVz4zGHJlr5XaRb2fjqH0Ucqk9SPLWlBrNpdcu6SWSF9DNTnF9HxdHzDc0n+b0wI/i5YEgdo+\nGEwsOvw49bgEiwyFTlO/bW0sWgrteHpaiJXjTrvS50ZnWJXKfi3l1rSiuX7T09yR/o5JNyAsPfPN\nNOFu4ShydyrR8MRzZDlpzGOHaaoCg0s0WJJmUVW6yZnTQUS7RJxArYj5GLIuoH0MqjARBLDPaB25\n9T7WjguhOitdDyrui2tnSNqvmf70Xim/ug1EiHZUBaSBCRoxroGOlTAgH36rhLRyCv13Ge8ulzJ3\nTnOHMVhML3s8nQlC2j8s2AweXR9KieacKDUs+MrmO80diScz27EVcAZkTtc1I2byaI2pThNVt6ms\nosAR2ox2PeD7RxOosArmXYfsZ+hTwvjE4zwFDOkItatRW+U0VJfJNpA+y/X+rvWL2u+Wtfgt4UKx\nj9rx8VY1hreOXee55+Z4Pfbaeb/ec9vnub6/9py3/v5W+cd/NJ3hrb8Dv8yiaN9f26jU5pX9lRT8\nbLEcWUdn7HFZHDn8zakCwPUulPLfXRewPbxg+PgE9wPtG/q7PfSnHYZwKouL4eClCbmAajZp2oPz\n6/uXZ6psn8auLuxCmsfe1CCoAGhtqtPX+u86tUHWpa/1/bf222rfQARuEhF8nAb4pHHLNGJtSJHY\nqoTeNPmpjBSHXCN+SzKvImTXeY4AGQROZRIi4wi20N8vp64YVOlHhZA0Js51l012YMqxNRFLI9Qz\nB4oYtk6Yz7X0j9xPZnquONOeaX8+1UjsdCU82tJfe87HSoyezn/2sC8/wv7zAep2Uz/UWyjtS5Qg\ncHkbyr+UjbzmzQtFkkrKfZ021+YH7m3Eu34u5cKAdbR+TQnj3DItFRsoYtvmVor4ok+ac/nrtcRg\nrlTthMEGdH2NPOdMVM2cUcp95inS+yWuK323pajcsA8YvuPF/t4BvUV6nLE5LbBsQBmVoJVZCWRR\nP2S47yxutiwKlc8I0SAkVcCD+/0FWmU8X4ZiiB+VKTogVcySQLSlOCT8fqJGjBpn7/DMUUCnE3ad\nx5xMERsLGSWiWfoM62iGvDvJQRWV4hANDsOMwzDTZifvjNMepmgKhf/ZWzx7nmdNn0vUuW1ilIti\n/vvDGf0QELxBv2Hx0j4XBs/DyxY/Xbble6ZIG7dUSHjXzzAq4eS70pdbG+CT5ooPNVXAaUorkPcl\n8yvmNT09ATgHg5iHEmXd2YCOdQm6c52IIVKqQM8aCkbX9aNtReuE7zsGjfGLRWiuFb1C8AbLbFZV\nVc68frSGl09clrBJSwlJRDJRqPiDpYhHbvL0S1mxBtATKiQJqtZIJWk5pMKKkX4DagSQ3yytP2jp\npk0qS3kHEn2pUTg6XxUlfNdE6MvVm8iMpLPU6HvCi7d49hZPi1S3oXk6N5TjnqtuSN8AwGEM+LAZ\ncdhN6PqA9z29x2W2ZOhtqoMUFl1YTeIcKkVpUjlVcGmeLObFlrUVoGouC/9b7mewcQVct86czHfZ\nx87eASDxRSU5KNBQStIcqFmd0emMjakGt0+KywfGImbphgT3TkFtNLqUkUZaGPNCGgXJKwSuid4/\nbvF0GTAng15ABF7LQqrr9BwMLsEWXSIAuDTgeQGNUk0vtMriM4OeJKoXS4qFfK5lUgHkqM4MDolR\nL8BEzLpEjGOm+dGymXyieSKAm4xPBSo9SpoJXIEnk5dKYqUC6lQgqGrcyF6kyj60cQG7/VzYF9I6\nE2H1a2dIlOhr+U9V9BPK3GH7ZDMs5T3GxFVAGKgp1wsWgFnt10Xno3FwZP9qx57qFMzvtrjpLsj/\nB6VCmscdTgsBCU+B3tfT3MOeMjpdU5G0Qtn3pYx0++6oCgp+sdHztOl8vOc3e3XKxCjRWItetmwO\nfmjo+y1cZ9AdLxgfmB3x0GE5R7jtBMvIr+5oHqcZ5b1JKe6Y1GoPFSbA6/o2tb3FILj+ewtEfe2c\ntwCB1ol8y3n/GnAg9//W9/2abkL72bfu86uf+Qf/1jIWhNUh860yafLqfAHoS6onl3eeWcOoDVgp\nAHrKzbjK2D4E3P444+OPpIlw+K/PMD8cYH7YY0h0TLsZWmcMiynVuQKv56O3OLEdJOCgyhT8kflo\ntOy39V60zrCGWEVi10vangTr5Bnluuv3q1ZjoAUm029UFyAD35gI3L6BCKDFZU6KKLtZlZrCANGI\nOxexjZ7qYtuKLBpD+euSZ+y9gaii1vx+jdCU6AM4Uq7JAXGcDyh5tZodW4CiUQIetCkANjFds/os\nAGjxaxczcVy0UlC8akemtVG0Ppf7yXlNO5focXEmZKHJbIToXAxef9KYHhWGpxd0f6AIjtJAXhL8\nCaWixXnqsHC93LbUzJwk4lyNLxHgo++vGzxQN3AA2JiAj4cznIuVIsnCaTmrlWGlTYbtErRrHJuZ\nclsXpqWGYMqivTTAjRjamo0rALjpZxxuJvSHgBzY6ZYykEkVHQr5qayCpaAZtslDO8B+NDC/I/Ed\nteFIRUhw27lE8pSqqHFVvafnU72BPVD/3mJEikfgEbjb0Xt4/4cLtAO6v4YSNXv2Fn3UWEx1ooxK\nxYEzKa/SVyQ/b2r6wkcydtra6UBCNgqhpAS8Rqsldcg2xvocqaLDrluwHZZV+S9RB99wfWSjhlIi\nTzbKrGlctrl8ikcN6ZHQHNvsPPb/nAAdobd18qRTRPcyl9rdAIDLFhpErZb0lX234GY/4b03OE0V\nUJk4V1j6J7DzMDXmckgoehWt7oKAOEdv8MAVNXrtimK+fDcJQtI9tyBWdQTkuck52jlfBE1l7Mo6\nBLCeAv93CnqVQiLGft/Q1MU5klx8Afk0UFT9t90C0WJYRSWvDctcjfeUc0k/cOx4E2gmegGc0tB8\nXgQDW8dMwNvWKHKaSi6GpGBZODU155OIZwUXxFhvI2WKl1lJD9qaiDEazF6jEoXq3TktDgEBIYuu\nOiYjg1IJwE2esNmRc9wPoeioCJhsu7e0VgBl6DxhUQ3ek6ipr9GwFHW5dwEgXE8UV7PJMDsUcVjV\naSBkZJ/hH+ncp78PMI+3eJo7WL4hqwATVdGyoWO5UPNbx7BQ4aW8bpegnIbeGEArKN7z0inS2qgz\nOtYTudMXKJ1xnrriLL6/P6M/RMRZwU/0HqfRliooIkImaREtuFQyEHjPq+UyKV2tZQMAwJ4npLBH\nktIYLxbPS4dR2DGNNo2wlDQqi63dQ0VUNpcVicbZloFCzblEzywq29oecu5bTZhjAGjdfOeRIgqw\nnwLQXUJxdGu5VxTNCtFtclko9GvB485EbHYetqMoad8FnM49IqdfAWSjYBxW7BfLbDbVfF/73UZV\n2wEA1K6D/a87vNtSqeP+X57x/NMGj6ctzrxnybs9+rXJmpogBH23pPBVu0bOu24Sbb4GA4yixSA2\nDpdStAYVFg4/T0v1z57QKvXDHYbRI7Fa//HzgPGpQ35EKaXa9RHGJmiTERbeV7kcacLrtAIaQ9Qk\nELMud/wasJdzJYL9ypF/1SPyXa9Bg+vP/BIocL1y/RLI0J7/jwAL/zPtl4AFoyoTTlrLtJIm9tdb\nJcuBGoQE1sGTlvFx5CCfCGb/AY846BPMpx3sP7EA4+2E7uiRJ0/jCQS0Li8ax6cB6kgGZJzZjtB1\nPQEklTIjhTWQIKlkZY9ogot1zldNhF9jelCfALGoiX1rv9X2DUQATRSJXsxRkTo505DsPmM3zdju\nFtg+oX/HiN+thhqYBTBJZIVW+xzySgV6GQ3pKnDkSiIqu46uCQDuTkF1CtvnBQufN3oHKesnjlnb\njMklEpmTgkvkBPoV5MrnXlF8W+V6WSC1quVeWrQRWG9QJAQWYTZ8cM54+rLD8QXYfyGDo9sH2IHr\n485SVssiFkOLnW42uoT2ScdqTfSYAc2rvGxuYgQBQG8iNtsFw02A6fl5OkBvFCulXy+DquYppIy8\nZAzzUlgD0klKA8kDcaTvDnPNuxQDqD9E2FsUfQoACOeMHNU6dMq7pOoN7Ec2yj8yaDDYYjXmxxE5\nZRLHRI0o6zeWcqMogp6ePfQ9U6g/dDgcZ3h/wWZLi3v3SUPvLA5xwTuu5PEw9xhN5pKDjTHXRryk\nr00qGhtXj8N0WTGMa/S5LQekUMWduGsxmIStDU15PUfjIClsXMCm51JJvGlbm7BnScSYKepPlGt5\nj4pE+3Qu4l5iNIlDU+5n0CvRyzwlpDkjJ6DfRdyweCkJgCqMUTeOekY3BOzez7gNrE8SFZbRQquM\ngUU4l2Q4AlpBFkDDZypRqnkn7zRwsBFOJ1yiwZGjy5dIYpjkyPP4y8QGGOPreFTrtDudsbcJ90lj\nGwP3GUUal6hL1HWKhkUvdSmtKe9LjOr2mywbW+KMOEX09o3JuGUth7s76hN9yhzNBgZj0WmDWQO2\n0UTQ/D3iaID/vbO+pMXQ/Qjw+tpIplKvcn8ZXqmVA2ZUxsYGxEZDoBXBXeV4q9egV1vRYMcO8cF5\nLEnj2WtMUlkHteKGgLUhAx2EDSVj0sCqDikrXBaHu5nGWudiAc6WRmS1ZTeV+zQZzsYCPgPENElJ\nQTdrjgCpslYZR2VnzQ4w7xz0LS2WauMEzYFhkd73bsQ0O+QM2OBK/ygQ2CSzSap+iJAn3bOGZQe0\nZdllnxGfyTDOHM6NZ9JciUEXTRnjMobew3tThFc37yP6/7wDOkMlZwHEnyfc/G3C+FTLLl8Wh/Pi\ncAm2gEaeqwa4K+M/898iK5QDtPZtTcRNP2N3Q+tNWDTsZaCI/jU7T9X9VNLA3iqZJutQm7bT24hb\nOxe2hdMDHheLS6wFcVtRwpY+L2NS9sDNxqP7pAENpDGVvt2MHptzwCWYul/qDDArT6KXSWvSI1GV\nFdSbTCy7XUT3gb68/zBj97AgzAqJgZvuHAl0Txr/L3tv1ixHklxpfrb4EttdgExkVlV2ssghZ6SF\nT/P//8ZMc9jsYRVZlRsSuFtsvtgyD2pmbhG4KOE8J1wEAiAWdw93czPVo0fPOURTzjEDF/VV1ynm\naHQoOiFxgrgfUbdr9D//HoDtuxe6//nE6k8zL88ypx5GaXM5u8XVJItay3Ob7qEKybo50qjld+eE\n20dVmvWznW1t8ZyBh7pynxlJ167w160Z7gjN84C63aD/+Ib+9Kscc5yYHw3j2DClBNKOni5pheTN\n6JBYQvGTeE3xGun86nzUpwlzrcZxLTBY7+9zyeJr34nV6/m9z9k5cv3Z146RgNWrjp3q9cs3NPHV\n33B9vtcA3CVTJv2dnrFrEKRmoILEOm3v0Ba6OQFBo8f6QGmbzS2IUVyTBExIQEBav31UxLOwddXP\nke/0E5vTC+Z3QlHR9yvRTqhuZhwd7eNA98MJ/ZcU7z/tMLPoR0lbrZxTv3LYVSDMmpAGvTYBrRe9\nDUgsvajwcWn9yhbUAi4svz8DZa85NvyWHQp+y7+93r6ACNWWF6NOx1Ilar6x6N5h33Wo2w3qD/fy\n4bubJRkdx2UHzsMoFngA3S97/K8Dq/czw0vqnxytCG81Htun4Om+BatptgPNS5qkErXbBYW/SMyi\nTH46lopAZiW02pOYfSJ0Rw48anRZoaK6CIDEbm4Jtjqj8K88I3k+7xpXekV1I8nUh+O69DC+PZ3Y\n7Eam0RYhrSz2VlNQ5wpUyMfLgUEWNCsOEdV51JUV7zRhViibJs0J6Y22sVTcQALZOFGC2PJ6tQiq\nFnSn0DcWtbKiuvu5zWiIkXiaUY9ZFC/gE6BQFqYo1EXVKHHfAOgtymgRW0yODdP7dAG02ETNfrlu\n83UVWwkbZfjJ055T/+69FXZDRXHPLAizgT4l59LSEUqVMV/j3GYyB1VocbYRxkOjQxkbRsUimrZK\nlbgxSFLxWtByvfU6sGrmApI8TS1HZwlRMXhbGBNCNQzlmHJppLq8MpEp5AUarFbJ1CRf9KWak+/t\nPBrcrxNuEOcQgPHc4JwkGm3nC3q/BEVLhWFwlmmwKbBOSVMDKz/R/eK4eZSk8DS0RWV58EvvukmJ\nTU5aViZy106sreMwNyiy64LGamnV2SaLx1bnfkZzYdWZWyRMFWgtPf3Lb1lbR6P1BdjQJypwLaSn\nkSq31fFifxl8yMGFUlLtXpvANvWZr946lJHqd9G7MC29kTaZUpVJiEWupmbAqlGx7GubdFRO7jJx\nzZ/LLQ9zBbK4eAm4NTpw042iLZCOvfSWKgalC1gRo1CVcwIk10J0Q6yiWG3edBNz0Oxmi1EZmIhl\nzqoFC3sTsFVgn4FqNzU8zU3RE9lYx66dmYPmJbW0zIk6XgJUFivMTMWX+xELQyo/b632rBpHb11x\n3TBGlPv755n+OGMPCai8bVDr5iJoVVbmkBpUhMSgq+5DpvxmG+M0WtJzu/TkxrA4Gpz2XQElfdAM\no7DtmvTZ3WoszKcmV3gj0hr3zS1sZH2xk2P7/on1Lwdu3wsNfvygOO8b9of+wpaw0dL6l0XL8iYJ\nqS2tSVZH7tqRu7sT/duUjJ8Dm5cJH1RpXVin+VhAvqqnHQhRgAA5tlCkMwBWi9iCVPZzq0AgtxPq\nC8A1i9wWKFItgX1hJLUevTWoVYNOFsJKO5qPnk77wjADAducysKV+d6KA0BkEeBsdcAasSzV26pN\nKzh4icynPA8kqrS+1DbKMQpVUlmPm6JrMIP76wkbIuqPX8kHvvuKZt1y03+g/Q8RL+0eHKtzy3Fq\nih5Ize7MW2ZhxSj2rhnEDcmeNrI8T3n89saXSm5mIgVMSZpcAhR0rDRFLgCetA6dlfyW1qDebDFf\nS4DUPR7pTwKM5fN13mBcpMMV8M9aYQNOOl7Ea9dbdk15rcjzSVVdAVVSmF97jbWQx9lregn5lUiW\nd42fvJf/U1bgV37Ca1ocQUn4+hrr4QLoKDtW1fqcwcs8p+QYJR3vYkxW51E9Y8vxcty2JND5WjQ6\n0N0FlAGfQIT+7BZ72KowZuICUOdTrhlcuSD4PHaYn2/4ajyyO8oc1nzXo7YtqjHFcU1h0bcdjQts\nntNae5Q2ysyuyqytdu2TrlbAj3n+jBiTioSl+BZK3N2EPDdkptCn7So14yYL7AJf3Bm+bF9ABEjV\n0RQImRSgrd4kKuZ3bzB/NPBP3xHf3BPevJEvrVbgPYQAkzzYyjsBFIYRdRSBLP3mI/r2Ad2/oJNa\nb3MIuDFVtdMzqG46MBrTDZi0wNYLSd0X2eiAtR5tAjYvBkYmis15ZlFRTZNaskmDZFfmDTp86rEM\nn6LB1ziCjmLTpwG9SlVCJ31iD2NXko/GiF/tNNnSSzo4Q0QQ2Uz9HoNi8IrBi2gewOjFuiz3304V\n3Up+Q9UzHTQPL2t4WYL/ohCfAqG8Tc4wOnuBLmsl1Ppc9epaR7ea6d/ONN/EAiKo+zWsOmiqRyYG\nOAzACXVMtK4s1ugppQtlIiiHahcqL8eZOAWmHz1PP0tg/HLqpXrazXiveU7CbS9zw8EZTv7SHcQ7\nzemxZXiRH9Q/CbX5cOywQ2K4/HCkeZ4IntLLlwGJMahFsDC5KxzcZW9/uxbBrrZypehMYNUIVXZV\nBdF56BQ9hbj0zpf3tHy/72ZM4sZrBc+zYQyaxllMbReABPf52C4oRq9xoaoqR5V87Jeqr49Lcpe3\nYWxwf9E87DfsU5KRe2kbHdjYuSSb+yldc6cLG+B57HAPmrez4R55vrvfK8x9z/ZdYL0XIMnvz8wv\ncHpsORxTT++5R48dYItY4NYG7rqJu82Z3djQnCToPDqLVkhCk7RZfND0xvMytxzS/ZkSc6rufRcm\ngviNZwaGJrJuJ3zQNDoH4L5Yiakqkc9e5UaHMl7Os9C5pbKS2gKSVWWjIzbT1legOkV79vRZtyH1\nQF/7nOdqT4hUbA1YdzJ/3Ywyp45eM4ZQQtc8XoyKyf0jBUUaBiXJWK46GhWKcn8GW10a5yEqtDfl\nuonAmr6YCwPSUuUV3CW2xXY9MHnDeuwuwIFMXc/gZmciKyPVnwL6eMXoFadoCcBzApXWxvLGy9z4\nkMblueqTzr8nB6KKJQGsQYZ6DKzMpSaHbtHOvQAAIABJREFUSUyzVge2P8ylcrVqZlarOTkbJEDw\n2PJy6nmZFqHR/Ww4eGHE5TUiGPmd2YGjXMerilWYFW7SvDyt+HhclcqXVaHYBuZtc+6LVkEef/3P\nM+bfnmliRP23lGju1rDu0N99RbuXZ7F9PrF9HnjzeMR9lPutNOheoTppL8uMsTjB+cFwOiwMQaMj\nu5uBm38KmO9v5XP7kXccuHkeGJIVpPNiLVgLGY7Tos+Qx8WkTRkXNasARNPhPDYL26KZaVPLVij3\ne9F0qO1Rc1BfhISjtA6qFYvNXAVOhNQamO9Pqd4XcEov7Yvp/DRJUyVA2OeFTFykXn7tOVauPJm1\nVbMLpXVL1vJ87lYLCCVV23QfAozvFfx8Zr3/Ue7DP76Fmw32f4+sb6VXvPt5YPwwcHpcmCezN0UA\nuGiRNI6mcQyjMFIyQDRrAWmu56BrtlpmgF1rhmSnoCKcna5VvUc3ac4/RdrDC+0/zgLOIYWo9WEW\nsHpcki5rPVpHTJOOPWuMDqnSri6OL0lbio+CKu1kryf8V8BfvGopoE4MP319cV24bPe6/OR/vbWh\nvpbhlc/n74Tqu8X6PC5AQb19QrePsbAe661mQV7/1vz+HLgomM1x0QGqWw+bNxq1NpAo/G6SNdU6\neyHmmoHq+vpe63WBrG0vYwePMCVW2vbjRLMd0B3oNs33qRAWQ0TpJf6rC0tFyLiLmI0U1GIGhedY\ngNs8zmctwKEJNSM5g2zFzftiTNTXfGGCvAYL/Ra2L+4MefsCIiCLyNZKdTYa+Ho10P5DskD8738H\nmzXx3dfEroemQt6cQ82TsA8A5gmmGeX9wlJY9bBboW9HTAp2wiRWa8GrMsOq2xXcrrEfTqweZZLa\nTBMhVYGzeBzA7Wrg5s1QfOlBKs0Aq93MdE5CUanCWvcfjoPleO5EQC0lIznhGCpU3ypdFs7XetWs\n8aVCEYNUubWKnywQSi/oZ6NDac/IFcBOK5zJPfh58RLAIO+qtp0CoYHnXtROe8aU5BRall/8oWtg\nIQslXjtnaBXL/lotSdTNh5H79ye230kQY38/C82sb4SBkLfjSDwJIADCcghetBbKNVCpBnKMxbFB\nqnItz8edLCQIyNLowDolEkeXEwpd+unzNdEqYmxgGi3nc2a4iCL/47kv15z30L/M2MYXS8ULlDn9\nnTkEcr9VSQK7d9AR+MbtWaVqbNc6bt+dmU+mLF4vY1eYJvmar01ksPHC0rDR4pJw82bAz/L623PP\nHFYlSZqrYNRHhfKxBINGxWQtWNnWpQCr1WI3BouYWavjQhm2nnEUVf2cHC1BApzcIig4+iSwGVXG\nhZiD5mnqGJ4WkOXr3QnztUbtOvRboSQaF2iOE92vA+tfJBnuf52xL4FmXAQue+O5WQ/cfX3m3px4\n8yJVt+Esyvzb7cj6ndwHpWE+wPmpZX+Q8XKaWobkCpGDrkYH0UPoFjBR68DufpR9DKlv0ycxNROw\nnVDdQYAAs0mV1GThevzZ0n3cok8rmqRV0CVdk00KhPNFFN2PWJIjqfKFojUDwsRode5FXu7j1gZu\nbs+0N4EuiQ7ePK+L73sdDLqg2c9tJWRoMEoXoT+AbTuz+3pEd7A9CwiQE9rN48TL0OErFf+ja8RL\nO42JXkdiApDe7mTuvvv9QPfgmLwuQqO1XkoRMkwVUYBWy/06OsscF5HKnMTlVgurlqQ/JwzZwUK2\nBRSrAYw5ZjZV3p+AsjXzpDC3iHRTezEHdonOvWuWNqKXScCqTB2fowB4Li5BQ66G92YBrKzSAvpZ\nV1oUlI3EQRURvHzN0ZTEOz/zYxBnGa2qNpeXNdO/WG7f71n/k9wH8/sd3CZgd5MocV2LunfYbyfs\ncbFLLZ7tdRbvAt2HPTcfz4T9UC65vrGY79/Ct8I4VM6z/sOB9WEkHlPb4jATzp44BPxRruP5g2H6\nxSSQZQmrWp0D/eXwa+MJUXGe7YUlam88vQllvuxNlRzw6ZbXLGMDYYhgZ1Ru/Qv5ewIeXbYzXLpn\ngDB6gl7Wwd54jAmFRQLSh71/7Pnx8YaTy2yxKgap2r6kbeGqlU0tLQ2leKIVflY8P645PMkz/+bj\nL3T/fAe3a/T3UrTR38zY/cDq1xN3j1K1DcOSdF7H86ePntHZ5XkhPWdOU01X5TpknSMVFkHWDN41\nStEkf0dVJfeN5uKZVyoyD5rDU8vNcGb9T/J79K6l/7sRux6Y9qktb1ai0dQvJx68x56l8JEZLo03\ntDowR11mAXHHys/8p8HZJQPw05gMFvblNRPhMglf1tdXdvFJ338R0b1qBbj+zuf29RpdvgZK6jXg\nNZYsLPFM/uznUtylhU5iTaeX9F7iz1hiDUjM26971NsN+jYxP3dHNg8T/ixaLS7pkTincd6I9WgW\nYfSil5aZuCDxhFUC1j8fUwHh3JVnKrMws0Vut/Fl/c5z6ZxYekUPbCXMIWUDMSEBYRYreWsC3RUN\n90JDLWQ3N3URR9WgAcj9cAEGw2/Y4vHLlrcvIAIy2WysE7V94/n2qxf0P/4OgPi7bwUQ2B/QHx8g\nMQwYJ/lzHhbYbnIwO64j3vh8JuwnQopVwqyYR5lkujkBEKsW/tvvMJNjwy8A2P88snseCF5h20Cz\nTpPKO4353YY4uHIc/XYNm55unOGUgp3RlXOLuZf0YeD0w8jz44qXU6KGGo9Rhq31pZILFCG1oC7R\nZR+TmGSqemirWW8Hbp7mInIl3tV5kU3BjtZCrVL6okImk/blxB51jXYu4EIOREpwbELpycyU6zlK\nxe9zNkb51uQlQhFLAqsxmFmC6A+nFdtfJbC+/7cTm5tnmnUoAZDuF5S4aCIcFNNpWVAA/Cze9G7U\n7F/kmj+fO87Jv36o2ha0iqU6nkWkzt4UYbtlkVSlpSW3i5xnUWl/mdsiJtiOImZpdOCUkh53tXDk\nvx2pglTda7XSmK9XvLkfuU86ANLmsSV8PNP/LD6fdx8sD48bxuQkAtJHfk7Jal1haI2nexfJ4cS7\n44E56GLtls9pTIh+RGwdQRLQoslQKq8sSWoG5aIUu4yisFH69Sy9468EXgGKcCFcLq7LWA1JiLAj\nPMp73X86bvURcy/tLwCqNSirMbcNbaqor88Tm0HsxxRZ1T+IAr+ONPfQ3MszuguOGMDcGsw3O9ln\nZ2ldYP08cP8sYIM/7gmjAFc5iNYt6E5+ULZttBtovl+jdt1lNKCVJFhWg0m/PWTExGM+HtPnTtyc\nB7EdjIu2gEIU4vO9dXsBUMJcBTUqVn9U9Vo6XFwCvVYH+vtA+31L93sZvzePT8Var1iunTT7xw7z\nsllouSzPdp+Cr1Uz09wrzJvmcj4ePatfD9x+OJfncX/uChCwAJyROQGpmXbe/d7Qvgv8vXnguBdw\nIINzTbM4EtgmoHRkHg3blHzuh4793PA8N2hvikilqRLxLN53WWVcgvkiIJnemoOM85pxkwP1OVQO\nJhlEUICHXGudQmTSmrFO7hFALQOX+X5nOvh1/G4roDgmOFKpSCK9iGCwCZgEHOU+YQFNBDgpzjxB\nccLQ6VDcIpw3PBxWPB1X7N7LYNjd7Fm9faT91hR9h9wihlay9uVt9ijnZaxnJlnfoL7aYRqD2mR/\n05D0IYKs5SCDbreCu60UBwA1OfR5Ij6f0amFKYwz/UdHZ3yZzxsdpSVPA+GSLaRV5DC3HNNkm0VQ\n6yq4VZcMBFiqhXk/IACj30fCcUb3aT2apPoYrtbA2lI5VAl27oO+SPiTy1C2tDsfGx4Pax7GrhQc\nmlTFvxx/6rMJ5EzSd8qDKH3tPFs+pkTq9H+3fPP0zPr/OKLfia01m05aBHY9ZpA1Oc6+qNjG1P4U\njo5w9LQncXHIQEmjInOKG+pzzYLSRUA5aaXU9PTP1RvD1fqiLXgnieD0g+UrBOxY/X1E37a0m4A9\nyriKQyhtjjkuNJMkexnEgCwOGxmqkyhz6RUIsLx/xQa4+oyuftMFS0CBeSXJD5FXC0nLfhegQqrv\naS545dw+x57ILWbXW61DEWvGrM5HTv/n8v/1HPnKaVTvyZiv2yleAznKl27XqAQwNn+caFxILcwz\n5HE5OuJhkrGYWDz+LOvxfDLMozxPh2PH5MVqPbN1Zy92jrVtZKMDnfVs91N5bfC2gK81qyjbeahe\no1PQZMaIHQKtd4U9FaNi8gJiNOliCqAW6eJy1VS690HV10fEfa9bZ35r2xcmgmxfQAQk0d00jrV1\nvL05cvsPrvReMo6oDw/wpx+Jvx4IzxJwRBflT+qzB5kkJICWYB4koI8Oogd3TJWiJLQ4z4Z+m4KV\nyRFXK9Tff4fZSUVz/f0jqwQIqNYI0ACw7WHVoz6+LEH/2xvYrGWiS8wINc/y7xBRKZnRvzyzNs/M\n41SsJI0OOLXYXAHJ5lASV3e1sMwBDlMDyLVQ73bs/unM3w1PPO8lENisRowNtNGxSRZdxkWcl2A1\n9+Pnnq450dFhAS8yhSonH00SrprjAhC0xl9UA2BZqFw9ubJQ6XJVzpSgSZXrmK0g56g4OVP6ln86\nrml/DfTWl8r2tpu4vT3Tb12xYxtPlnGwzHPVEtA6GhcYR8tzooE+jv0n4ns+Kky59rr01p69WIed\nKhXxKWgJzO3yWnbzOKfFASQoOgepeOXgVmjgirODmBKH59mmtgLN3unSUxz2HvONQn93B11GTxSM\nya1ESwSkjON8mnFBYxILJwcoMVaU9ZisCncG/VbGylfmiP7XZx5e1sXOFEB7i1XqAvQotNKgrsbq\nQk2Uayn3cqqi2Wbt6ZyAhbZUaBeAKruZ5HuRE9zCUjGBKUhyle0YPzxtmP9NVLWzFkW3OtOsA2ZF\nEVj1TpfANFeep6B5Pne0Hxw7BpqbdHlXCq0VyqoFoOxAWQ23PSbRZPWUgug6ylNKEqHZY1LkqXuN\nuhVG1MWmFViTSvuZTRVgmImzL8AjoaaXZoBHKuq9CcwJ7JpeNHZcghS53cs8kin6Y7pXJyc9nXlc\nR6QfX9316MT20fezVFTcwjn1Lw4YOQ1tYeucUgkts0fqTfW29JeiFSpE2m7CrGfUX+QGzbPhODcX\nmgoBqb7PEc5nud+qHdHfrrndDaXlQiYVjb7vUY083zFEGB3hceDuWdhM44cDzx9X/Lrf8DK1BSQM\nSBLZW1cAxDpZzNtrQnKNVqmlpbIOrYLnRTtG3ryuG/mo8F5dgMcgwOWYWp4ABq84OZL+hnym1/L+\nWOkMjEmINEZVGHC6A+sD/XmmHzw63SubQHtJ4FLLW1p3er1Qb1vjGZxhPzWlDUnvN5gfI6v/x7Fp\n5T607UTbeWzjS5Lh50V00naB9jYxJu41etdAiIR96ocfA9GB+Tiif5J7FueA2jTo215ACADniceJ\n8DwSkn5RmKXqOHpTnu+T12ktkzFZyfOglFyrDFyu8tiuKPNzFKvlvCaBXPsMEhWQXUvcEWbQYzV+\nvawjY9CFUXJ0kni0OtJUukRDacNYXsvnmQWFh7ERa02/7K83gYBO2jFp7lQqibZK62M5nzR2asaj\nahU2saCyfsxwtAz/YXnzcOL2m0e5t99q9H0nz7K5HMW1GLF/9oRRKv2+AsbmKGK1WUgWEuAY1IWl\nrUv/z/ObnJdO4/IySZ2VomdhY2RWgVaRl6Ej/FV2+nY8sv5uQN9YdJ/inlYLe3EKqKlKjlV2AUj/\nZ5nX8qdcWNpTyrWtzqsKCUrME3k9mb64jnxacS77iZd/589fv3+5v1fA+ri8/lobxHVa9lkAo/rO\na6/XWz1ackGjFvO1amk5guwYpi8KKoexxf/ygr09LMynr+6h6z7pl1BI7K1PAxxzAOAFZNifCR+l\nELn7057DLy3jaPEpLgzJMcaF2kkJBh84z7aMtcEZhqBLO2ppl3ByHsqq0m5sXAUglBro66lfjkfz\nc5ud0nK7E2SgWBXNsi/bb3v7AiKQQYSZN7sTb/5hwHzVwTElR3/6C/FffmD8nyemva7owEYsAr0u\n/UbOGaFzV33CbSs95VovfUneaZwTG8GQCx5PJwErGgu3Un3EGqmgXKvg6FQ51EqaDkHON0RJCi54\nv1eawn2DXhuMDSWwXIQLawvBWA55TXNrlAQneeHmfof9Py1fr3/h5j+f5PcEoSyGGbq1fG4eDcO5\noRta2oSyKKDVl4GJ6CEs4MVCvRUWwsZGblNP793NGWMDnXWl0p4F7er+VKD0B+bLszKuvF73EWZQ\nIutH5M9EBL31JcCK9MNM0/kLxf/rFhKblNKNC0VATKoNkK3HymdTFWJwhmxz12hD5zSNXn7P2nr6\n+wAEYkgL1QsYJb8793DfbYTJMlXUzo3x+Cbbf3JxvxslNO6ygP2q0bsTxmhJYkFg6VSpUyliMWtV\nbISKUJ4WAcQINFfrjeoMWUir+faWr27es/l/Hzk/tZwH+d3jZIWSGhRjSq5cYm5kb2YgAT4SHOdx\nKgl7ZG1iSTK6bxTNzczvTgc2iYWjVCy0wXG2BWgZvSn7f5Ou5Vc3R05Dy+bclyAke9i/nPoiCKkU\n9NaxW41Fdd9V/bt5C1ExecP+2OO9pn+RffVbh26C/HlM17mR1V90NV6hEFZzRHYIyPcmBgiPZ9R+\nvPh8TM2yYT8Ts6r7IeIGuccZaJ8Hy3loLgK2XGXtkj5L3tx4Gaq2Wija65D1qqGPKlXf5f87uyQk\nspMATQLhOotq40W10dxAezuzfpzYZLArndsUNJtEBTY64k8RvV+sV1SjBVDQCr0ztDep7WI/04xd\nEe4EEY3M4pVFQOtwwn5rUH98K61NAF2iynftEkjGACdpYTNjEjR9Huj/emL7l4GX51URoXVB8+3d\nnn49c5uCzgzwXgviZqHPTJN1XjMGU/QtQFoE8jyX5zV74aQSS3LlSk/tYoUaUTRzZGUWwGv0iptG\npzlD9nPbBN60M2/7ocxrmRW16ifsOiW+O4VZg27G1IKVBEZtwHvNOFqxCgSOU8MUDCvruF/Jtbh7\nc8LNhsenNad0v8dgOCXLNJKWSFb+t6n9Dyj6F/ka9GnO33UTtztZO1w6tnOSDHedwybtmvNRxn2/\n2tOuEjhvpTQXvCrg8XS25frndTW3DhglNoq5beemnbhfnYtTEaQ2E/NpCtRrgZNqobiQkq28BtoE\nWIZRFdaOMrIGddqzNp65yiznqFibUJ6TaKA3+qLNcNfMtL04HmXr2651rP3M/ewKcLS2TuoWYQGp\n8++ZgmLQCxsiJ2+9rth8ty3teeDm41DGThbX/fiy4XCS5Gr900TXO/rtUFyhlAGCxBjzKY3n7IBV\niRiCMBGiDmDBVGB8l6qvmS2ptNyTLip8ZhgoaEJOpCrAE5kHs6id3YFZBbb7kcmbEo/4XzQ3x4H1\nzYTdpDGxknk5Ojl/qNhklUZDZwK9D8vcSG4HW4AQqJJ8rkCENFbqFeNzgsf5s8s8n+LDKj75nJXj\n6yDC6+/F6lyvj3+9m7/FXLj+92vuFPn1z30/6+tEVAmls9B4bwIqP9/BcP5LZMUjNq1DfH0j2iyr\nXuJ2m9KptiGuerjZFfZSQS+GEZ3YzKtvfsL+jwfOPzr00xInDGPD5E0ppgRUialqHbNeB7QVACSP\nl+Cl8INZLrCyYDoIcyyth1kLrLe+XN/MvtTV+PIpjroAESJAZArguVIp/81skc9zlH5b2xcQodq6\n3mE2mnB08L/ey4tacfq/zjz9suY0tOXBzpU2nxBukKpvTkjrKopW8ncOLnxUnJO/9ZtzUpH+ywEz\n/ZskAau0wlotgIHzQpE6LZ6sat0IZSqptaIVcQqEcyyCLHnuVVYtK0uITO8DL09b9knISSk4zpaH\nqeE5VdDPXl0k8hfJeITnqWX+ITEM/rc9fH2H/mfD6p3Q2+N5Jg6OsJ8xj2mRPUqS6ZwuQWOtAl9v\nrz2eQqu6REDblaPZBm7jmS6BGvNsmFKrQE7481Yr/nfWpWrF637Skzcl2M7tET4uYmyND4yTpZ/m\nxQFgMkyTZfamHMdbsTCLVRCSA/pcgcznk3vJn46rUpUcq2AoXxcXFHoN5r5hm6gw8ywIdqNDEU7b\nbEapgh8VIcr9jqQCdv4PLFWXVDXOlfaH92vmYaD7yzPaPMs1bMBsFf4UcYf0HMyK07nllCzWIPki\nu0sLQU2ydHyY0bmc8tUdBlg3H2jfj6wfk73aoHGTFt2HBCycphamVsZjTqQuAoolyCuiZtnm7sZi\nbuDN04n1QY7RdB7TiNPFed8UJs1xbqS9hMj9VpKZmzcDfRIuzfTDtnUSGzjL09SVe2N15OO5XzQ5\nUKXymCnrRkX6BO48Dh0I/kZrAq0ROm4eD8GrMqZMoTFHanV+kASmbT39dkYn5MZPGj8HzsdG2pCQ\nikdOSs/zpnJLydTGhVLrgyoJWxbdPDrDHBcAIG/eabzTnFMSfHKWszec/FLVnoLoV2R9lFz1ODnD\n6b2BRAMGipNK8Mu/dSusrnGyZawdneWY9CGyav15tpzeW5oXX7zYTSMADUGhu8h8SL97NqUVKydD\nAmzKeX9M9p3v/v2FzfyI/f0K9fV2+eHGLO1sIMymXEZLzBy167C/j2y7gf7xyM3HxIIYLZuvJ5o3\nim3KAsN5EEeXV/AiP0rCCKJx4SYBEXJbRe7HrceFrUDjGBXTvKxjkzfMXpdx5bxUwaLXhULstcLE\n+Ak1Olvi+avx4xLIDqBaje41+haaW4dP65huJXHyxyUJ3D/1HIZWtGHWCfy7C9hzpDu54rEevYjg\nnb1e5m5IIp6xEg28TEKaJNrajx2b0wqjY7nvIOP+th/LnPx4XnGc7UXffA7olYoFPAF4HDqe5qay\natWlHcZH2GRHgiitLzk2yNcxq7bXAoWZrlyryQcy6+rq2ZsF2MjnNo2GwZuLOWdhelzS9acEuGfW\nX1kHNaUtRaXkdmUdq9wVYhwuaE5xCSVjPvewnH9+XcV0rBTKqHWD/RZuPoych9RuN8j4O87L8/04\n9NLGqOv2yFjsGPM6rYll7Xseu8L2OSVQePQLU2m5lkufugu5pW9hVmRWZtYeudxUWQtUpzC3mtXj\njD0EhrwOnlY8Dj3dgy/uM303S/tT58v6NI8mFaKWuMUlNlDNFMpr9PV2XWuqX6tfz6PmtRjrOpG/\nTByvPvvKMeBvMxQ+p5fwuePKd+Krr18f67X34W+DCGIXLmPI5TYMUktsEItngMep4eMvG7ankf4n\nWaht/1TcvFSr0Os0DjatMP82FXtps5I/XQdvk8Nb29B0DfrmI/bPUrScDprmKNpNrVlcSHxQF+yE\nuvWsjmmjA7fP81P+gLzuZ1UYslmbJlbgQBZyHavx5aMQAGs28qLH8+l1/bL99rYvIAIyQY5O/Krn\nB8d80NheHmrdwcPPG3542nF0ttAP88JeI6VZSRkqGycdi091LWSU/V2/Surtm59G1M9HDh87fPJ3\nb1JfrXea87lhcPLZ1ni++uaAaQPjPiPvmvNZ1K9VWSxiCXSyKF7TeObZ8MthU6j6jQ7sneXnwfI0\nL/TVTD2rg5i8vTjLy0+y6Nt/+VWErN7u5A+ghgl1HMEFwjFVcM6S0ClFWSTPXnN0ovydfdcHL4lG\nnqTaa1ZEo8vkrk2k/UpjN451AhGiFOgvBA5zQG6auFQy1FLF8EnDIHuuO6c5DYtLgMIwBfMKqq4K\n3RPE5/04tTivS6+ktUHYCMl7GyiMldu1iGQCdPcB1UpvOT/AS1HrzoJsizbA2RsIYL7d0nWpYhcG\n4g8S0G/Xkoysv5L+et5LTzYILXoKWvQKypmLQM9hVgxB8ZAqOr8cNhzGjulnXVgZm2bm91+/ME+G\n51RNBaH87eemPCMfRsPHKetTLNuHsWX/Z8PdV6L9of/ohCHzuxua7YA95P6gQBgCYT8wPcjvefnY\n4Z8lIR9iHi/S+jGEJSH1QejAdRVFbVrUt7ds7g9s8mudlar8cWL11xPNnxPg9bQqlaRVnwTy3kTa\n4LDtgeGYWCKtZxyszCFpTI5B4Z2inl5zUD2EZZyvTGRtDC7qBDAsLT5WBbFPTAFxds6o2zjyZ6Fm\n6whL55vNsVTIXKpqfBz6kiDnhCvAxT4zCJppnnkbg+LoDM8u06LlWdwYCXxhSXgFJJTf/jI3PEyG\nh2n53XOAjYX7RhKXPOc8TJYPH7ZsTyP7NK4OU1PaQPK8tmsnWuP5eF7xSxrTHyfL06wxaqFiPw8d\n7ZNcgzz2jRbmyew1m3YuiXXWKwBp7QH4ddTsZ5mH8nHufrll8zyx/veJ1a0Azc2tXEx/BH9O98NE\nEblaHg90pwuTpPlGY9/m6M2htw3qzVra1uAz0b8SphkUJlAcHQxuafkA4jgTTuEiwlaNuihRxvKg\nSNDpjiIOBjCcLf5J4WNX2oNckDm5FknVTmOVwaiujL88vlZnx32quDUhom579KbF/D4ulTyA2RNP\nM10qA3a/nFi/n5inpdXP3insDeyGoSTJoiWTkq/cYlMliLUAZWShcudL2WnDU7rf2S1FK1gZj4+a\nVQYRxo7H7N5wpaViFcU1ptGRx8nycRLgFGA/53VMKnk3qZ/hMDd8qyTZXSXQaQqmgCIHl1vMFPtZ\nleQmb5kZ+Ic+Pcujwu6kTzlM+ZeKbtHRNbzMlocEou1dFnmsRGSDYu8kodgkULDTATcZdOvp7nPc\nMmH2YumY2UdaR/bHvljFwgJKHFMLzHXi+DwbpgTerbRCf/+GTfvMN0gLifoxchhbzs5Wdo6LdoG7\nWoPr+9oosc01OvJcFUWys9EUlvHS60if2m8Wur7i5MWVp24nHLy0YdXzrQiLwj6tEwTQtx2r787c\nHs6MjwlECAIsRaAZZUJYGcemmbnbDGV9CUFErGuNhsEbiY/cYnN7dMv8m7frBK9mKOQ2gRylXDtw\nlX3Ez7MLXmMPfI5RkN0UXm99qK/11ff4VATykvkQP/P6p1vt6KCufnCMsbzWakVnLu15h5WsdU+z\nIZNtV8by037LZuhoHjK4rkuhoQbUSsnLAAAgAElEQVQUW+NZ9Uf61TPtOsUT64h9qzHfrFG/v5Od\nvnsD373DrDtWXwkw0b0/svo4yFpSgGLDmNiAeR0bvYwnV41bQNqqJkVwVJo0ET8rpsGWNoYM8tba\nH1PQHLziqVqrpxBFdiR+er/PLuJZCpu/qS3yRRMhbV9ABCTRGL3oFEx/MZyGlpttSuxWM0/HnpeU\nHOWKrUw6lzNeLcZWPGfT35NaehBhsQPLFRw/asaT5T8+3HFMQexizyPBUa6ubFKAs9uOF9Tv/djy\nn8f1xUJn1SLcBbBJScl+bkpvYqaD14h3tlHMv9BfTfwhwv4gC6L5Hye6vzzS/+G5eCPnVSycvSTF\nwLg3TIMtlS6gIPx1n5+Lucd9EWPLx5RrUolPbSLm+xvstltuhNFLsJ2oxAUJ0epSRG6cicdpYXmk\nHxhnh388M31IiepBtA580IXC2jSimmvbUEAIO3vMHBJIkCsmAdtKxaHt5fq/VZH2JtB+pdG7TIOT\n/nS9mtidRnaHRJWfG85e6LCL77WSfnut0El8rwfuOWN/Cey+kaS7+6cVymrs5sg0SVD/PLUMXhaM\nGnEOiXAe4hJonZwlRHVRhd7NDTf7kdkbPiQqcUg6FUP1jAxBAARXWXIaJcyd/UtP969Sce6ef8b8\nbi1JfvojY0ijU/+xSYrIuhnkOXhZAjqXzjeZCcq4gk+ji9bAd+9Q3765fH2cUM9HbGfZIUwa/nzG\nvIiGR6Y2m1uDue9ovg9sElsijIHu0TE7U4Iz8Z8XbYqS8EvxG6OkCg9Cl18ZT2c8CrvYnnr57T7G\ni+dkSr3N+RnNFUm5rvJ3pwWwPM9NmS9mL61TdT9w3cLjo7qYM0wSX8uXL1fi6kACUk6r5TkAaLYB\nM0aCX7zuVSWcutgSqgta9lLZkKre6dzyPo2rh6m9oHwD3MwN22bmOAv7ADJrQIL60SyJ5uQNkzOF\nJSLXSqwF+9GzS+0oU5qTfFRVW5UEUVotY21wlv1Ly/SoaX9O7RmJIj95U0Cr3ji23URbuaK0rWN3\nP9LcROytLu42KIXqrfR7Z90RKAyGslktla08fwEqCfletL15j8mTacmu7PL9phKa1JpmmGB/Ij6I\nkKb7jwPqX8A8RdrEOzeqwWpJqPK4avQl0ATVehFUYX/EKbEyrAGjxYoQZDCdJrCL8ny7mTGbgfEX\nj93K3uzf3aC+2nH3u0fWf5Y54+bHgcfHNYex0sVwUnmfq3U4r9PXgH9t7VlEerU8P71xC5NQi+jw\n9TOSWydq0dXP0cTLvUp/Nzqw3k5s7yZukjPE4diJpe/UVQ4K6ZmpAPX6KheGhY6YW0s3OXSy+3Wj\nsFCsCkK5T0Op1TKuQ7zUP4p5/k8PuIvymuoU9ttULPjaszpMwIRq8/oeWP0woX7dFU2D0WsRaVPy\n3GfmoFaqHHcaUozzMqLe3aD+4Ws2negffPdvz7z8ueHxac0hJejH1Go2V797WVMuXVHW1qFUpDMN\nXYlxIjrHDyF/b6Gu57nJRzAhtTjk1iQlAKZRr/f5ZyaLP0asVtg/bLibD8T/JWPVPyj82F0IlSrE\niSlUrRVaR6wNtMEz6MX9ojiVfCb5/q9srxCaPtnqY8ix/+vHes2GkcvdAZfx4+XnPg8gZPDgb2sj\nvH78ED/9YmBZyDS6em4TGBkWnaWiTxKUMENniAn03s+2FLPgkunRaHF7yy1KrfZs2pnbzZn77/4K\nQP/fH1Hfv4WuQf1B2Anm7Vba35yHBLbG44R/HJl+Huh/THHq8xamtmjntM0CzAYn7LYkWYY2QQqk\nlU6XNWLjrNTCaMqFA6sucWulJFEs+HHITKiIU79REOHLVrYvIAKJ0hkMT6eeMZgLeuPGqwu7q6Gq\nxDQpa6jbFMpif0U3G6tgJW9rG5ZJMIi+wuR1sZ/LPrU52M7bHBV3Q49WsVDmznPD89TyNNsLNDrT\nOzudachWqI6VKItRsQThubozXSVg1xQmXU0+D48bzu8tm7/M3N1JotrtHM0NzC+w/yBgw3kQOvV5\ntpz98huHhPTnazv4KCJGEdCxLLyvLWoxJMG53WoJrnPFLrWCAPL3NXKYAm1FqkiDRJKrDvoGEyLt\nIMnidpyJ5xkmT8zK3SESjpFwDLjnVAWMlMpsCU5toOkDdh3R63SKG4POdpFJVCvuJ6m8nyOhYjdk\ntetMLZP7oDh/tLR/fSnq5Ko1NHeK1WleLLRag9q02Hczmx8TsPDkSwIw5PuahChrgct8nCmIZkVO\n2AAOY4ev2h4yKJYr1nIfYQzxYn85WDtNDR9/Ej6A+SWw/fOJ1TcHzL1F7xKI0NtSNsnOB/bWs95P\nrIe2VI61Mlgl9zFTjXX6Xk3zjocJdU5S2Om+isPKJMmMD8Vto+k95ihiRjngDfsRvQvot+sC3OA8\nzXGifXfk7XtJwqaj+M/vz13ppT85y9FZ9pU2xY11fL0+s+knTmNDd07q5MlqcmVE7BXkWWu1uEPk\nPvVMxa0BzU5Hds0sFntZ80RHmiiARd6yG0mMYiGbR5vYzPkLxfXBW07pnubvuaiKXkkWlGxu85w3\n0yXBuU4HOh3p9GIVm6fXRfG/CqSVACfH0qZgFiG0cu7C3BiD4pirhU4qykHHMnfl6ozYCKpyTBBW\nx9Gb0oqmiKntRpV5aPRiddWaKukMisPc8GFsylyUOzpqsbO1DdyOjlYvav1awf3LwE0/stlMbN7K\nGDQbMBuHOs5lglNWydxQT3paCVOhswtjQX5osfPKm8oITwZTc2+uzftKJ20MrLsLLQ0zOLY/DRxO\nXdEi6YJYAFulcJUwWqbA5udtDmIr7KMuDC2/96ifTuj9uAh/pi2eHXEKRedDdQa9NeiHqtdWKfjq\nDnW/o/sHARO7xwN3P+3xD3umj3I+5+eG06ktWipACpQXjZvczgMLcJQ3o6KI5W4GmgQcbvcd/XFd\nWsvkc9La0FuHzS0/UdpHBr9oMISo0D6WhMRW67/S0H8d6BPYu30caX7aMD4Z2pRAtlrTJU0EfcWs\nqHuUAdSmoWk0epNaRT4EzDEsyUy632elcOn751JtXBgmTi/AQp431a2s37o16PO8APKAPkysppG7\n8czjkGk3FuMzm2lhoeW1ZY6L5of7+UizfhIg4Ztb+d03PW+/P3D71xfOP8lxDk8th1N/YU2dtTyM\nCoU5su0mdtsR5yQ5tCqxLZUUTQ5OX4APWqU5LzEr6nwzzxlDWJwrcvyTZywXKWNj+Kixv54xX69o\nvt/wJunYtH96ZPXrjpexLeNNK9FS0Cais72kgiZ4vNdLy0Yau7rSTqrBqhJuXgG8pejDpzGTfu01\ntezjc62keX/y/2UHn2tFWNpYrpL4+DlAIH7yenjlWK997/o86/d0tb7AAmotR9ASZqSpYEqMwSks\nYyAfewqLVtIhiYzKcRfXqCw+q6ltTyX22e23fP0kMcgffnrh/h//k+a79dIa1zUioF5pm6nJYd+O\nmPsj2siYct4wv+hSaMzaS6aLKBsJPiyWk7MR28lk+Q7Clohpzmr04iZjVMToxda6vkoXOhTpWrs4\n8lvcIhBffVJ+e9sXEIHUA5XE287OXqD0IfUO+WqSyFtNEYPXJ7haOO8aQf//gyjXk9QcROBv9qYE\nRbmK5qtzVzEJc1X0Z53sFWsV81zRjNSVpE9/K9SVz4UN4KNKdlVNATXuzmdu3ICbRDgLpJ89RDnX\nuv8zay/kONjHJUhS8W/3XoVZkFq1Hhb7LliC51x2cEEC6fqiO1G3j+PCDVSdhS59p6kswdYetXHg\nPCrbck4O7ABhRCclfNsK1bPx9ciQTVmKj7fSCdOYHP450c73QmGLUTMNi1DXnJgo+TqBBC/jYJl/\nGbApa9IbWwZkEWs6zSVoz72Xi/JzFYSQ77+M2Hz2dc/uMgZUAdry+UxB4xM4lV/LoIcPyzFDlGci\nRFUCycGJmND9fKI/Opq7RJVd62S5VkXMpcAaL5D//BrXzxgsTgHHGR73wj45JJeV4yQCocneLaR+\nkeCT+KkzDIntMz+P6N2M2jpU7nVctai+wWqFTuyl9sXRPZzpHh1D0jzphhY9dIVVBCJKtm5nNpuR\nphIn7JN43KpxRbjRB8VqFnX03FaSe3nrdqNGBzZ2ZtUsOh0qWXyuk4UoXPZT16J6+Zxa63FZ0X1q\nMKpNqs1pTGouqO2QKPNaxMWylkObkhirFSb9bpPprlze2pzs1Vt+P0Kx+5qDYtafPg8+gr6apzUL\nsCT7W+bi0cOoFyA1azTUuhp5X0swGNMcVrlABDlOrfCNg0YZvFEVAAMh9gzesh477hMzaL2d6HYe\nZUNph0BHdJPmiTqftskLPClvXzQ4Vw+tarXMNbUrhdWit9M3i3BlDlZdKBZluPCJFkO9ZhV2zZVd\nHiRR2XTNM2PLj8BjgOdA9BSAE40o6Y9g0pxrb7y87iEmvdh4nMRdyBoRrwS43QhAvh3Rt/Ist8+O\nzX7GHRcWRLaa1EZ0T1xqbwtBMQ62AL4AxgT61czqdsYktkS3dtiP4ULN3OggmgZJNBlkLj5NDRtn\nl176mJlIMi+pai4V5lpAd4nBtgnSJ69DAVoLjlQlaXWPeknoXNpxa9DJBlo3rtgF1pX62mUlr6v5\n71jtv+gEeZbMzBrQaa3MLSk+yJi0YdGK4PJY9dgpyvwJwHBH0D+dMFqh3iV7mq5Bvd1ggXUjg8D2\nA92Toz92Zd2QFif5batOxu56M9HfOtFVcIu4ooua4JIo9BW9vRYyjFHmwkbpi/uQc8wrmeqLazaP\nFvc4odcTatdhbtM8/tXE3XhC7WMBt1QCERrrMQmEVVraKY1ZdG8We9zLOEi96t+yzKnX7QzXn/nc\na6+9l9+X6xPLfl+7Bp++9+l8vuzz8r3XAIzPAQh/Czi4/sz1cerXSlwdxd4SltbDGqjLAoyyLeMn\nf78W3fRRmJCmqugDmKBwwRKiTC7mIaL/PbIbTrTpedL3q2V+thVQrBX0FrNNRQ7rMUUsdzmIPItg\nxlBA3Nq4qcwDWuZDreLFWMvsocVWXSUh3vraLVv85In4sv3Wti8gAhRl6+O8aB5kpeBxsgx+ofrX\nVXFFTJlYFXRGeeiKpZYiUQSXgDdPPNeqs9IPt1Cg82SW+5bzc9woSR5qoZX8b59aASDT0y6Pkd4p\n+wbwKifuCp9mzbmaNHLvNIAPglS2OmBTZfM8NzxO0nu5Sj1bv5safhc0wSsez1mszqbfpRfqdm5n\niEvv55wSz4AcOCc+KRSTSTrT7R9bzL8c0f9+XK6jq044BcPRS79yrR6v9NLHrZtUTd2CuRfautq0\niwL7lYCcfKm6tul4MVAU1AudfDZCLz1HQrqw6ujxZ8/xoeXXJ6nAHHLyaB0xKn5NYm5Pk+Vl1uzd\n0r87Bc3h1OH/pNH/IQff3k3oJnJ4WkT6dueRZjuiWpirpP3kNFNYzD2yzVEeO31Fw4OcPFD+7aL0\nAtaWf3lcTRVA5FI/cL5SDRQLuGNiEvx8WhGOcH/ccPN+4mYtybg1HmsD2oBJFdQYLKdjy3FqSoV3\nSDT92pJuSlUvqynA1vijxzz+wPFH2QfANFvAYm0Wcksq8YeOx/OKx7Erfbn8AJvnke2vL7TfJ82U\nt2vxjn6zwexSe4/z2ONE9/GMS3ZO218H2o8blKLsb20d6/XE9ncO3XvuT7JPdwKCwm4jZrMgJXGM\nhAFxT0BaoPysL4IoYwO2iygT8SmRCo5io5bHf7bg0yZiuliSJr1SAt5oVTyuzz8pfvlpd1HRH4Nd\nRD5zsDJHVKfQDWVuyElRo2BYTpPsejBX46pREWt8afGCpQOprnbl1g4XlgCpTrJKzvMZ0db8uTmq\nUklqdExWiepinIOooa/MUhkMUcZZZiwsVbpFRdsbLU6bcWHmZNs4O7UYFdkchZZ0+zjxZjWgVRRK\nO7KOGB0vnrEcpDY60CbQSVWgy9LzHGmMl6CxVDkFEtF6wNpTSX6NDaIT00oyCDJ/7h969mPLIT2j\ne2fZz4YhLOtYo1RiH+ly7DkoGh0JLJXs83PDPBnOQ8NpaotWR2sdszecpqb8jrvNwHY7MpxtmT+V\nPtNP/4G66ZZWJ7uImJnEItP3Hjt5cfG41pTIfyrGRhxnopsuPqt6g1qtUMlGtZs8m+N0+Tmd7FeV\nIiRXk/GvZ4axYT+3i6NFUHiT791yjJMzvDz3mOZE/3Y5n5xMXhcXXlvBc0wAYiu8+jii1obocnK0\ngKf58/l1l/7k8TsHuSxKLevqHES4dH6MqJ9kbdW7ibCfcR99cYGIAcKscLNeKvKppS3bQy/2vLEk\nW3Oau88Plv0vBvs/z9z8g9D/7d9tUbtegIRd0jL53tMfJ+72AyFpbUQXQQvrRnVprLQa1XX4jxMx\nnAuFe/LCXpLWJNnq9tNc+UdV1f8KbL9uVcmJs1jYyv3eHzqmPxtW72d2fzxi7hOj5A8N97uZm/1U\nhIjzPGy7WARww6wws1g+5lZIq5fzcFfz0vX2KutAfQoM1KwCffXa9f6W77yezL+21QCGSkWJi/PJ\njxH1+BStrHrKDlzqGSwOBVffVa+fkADK9fyoyufNBRAgGgm+ij+zAOkSc0e6pJeSiai5sJjXsrrI\nAhJDLxivvDaoZc05+h0PY8fbjwNv/pTFm1/EbaUD3acxvdIyrjWEZAcaUiFxSIKhc+UMpXtoXCAk\n9tk8GmJUKL2Mq3JF3OKmZQvr5bKdIV+EePX3b3uLXzQR0vYFRCDRyHS4UDHOirtMEiT5EsBl1E4m\nllzBgjzJy0PYJViz16EIDS0VCtl1qBb64DUhfaZWBc6LsK9Q2rxYzL4GERYP8bz5qJLoy/JaBjCm\nKhi0XKooL5+NXIvSgEyMa+NLH1but36edaFIdrpjdXT4qHlKAoG1qFsOrItokV8SWB+WxMJT0eu1\nADdzoARq+0PP6d/bImYo1y23gSznnsWK6l7wYosTVUk4euPorGfVzPTdwOZGeuSbbSiVwXJ9Qq6k\nKYZDm86nYz90pU9Xfo9QXJujZ5oyK6NhP7W8TE0R+Dp7CcpXRmx3Hie5Rh8nEXgbQyzBqDh8WJ6H\nrgRvm2epIH889+W3755nWuPZtBOHJKT5fuz4dbI8TopTGgStlqsyhfz/kK6HL9et3H9Vtw0s1+N6\nnNbv54Un0/qUWvr9j0nI6ugM67FlnUCn3J9f36tMsz+6pgievcxCU83iV/lchH4YGXPf/EehO//n\n402hy2d6fpN0Q/JxpmDYz5bn2bC2i0oyzzv6nz3v/iyB9Vd/eKL7/iCWlX3VEoNQjE2+vmOgP8x0\ng2cgV6aF7RAD6I3G3KfeeSh98iTaumpMsSYsD6oLYtNY04bSBY+neXFuCRG9aSQxypofIQrlPdHe\nValML8fQyRJyxYmb/cDj0NPMtciczDnH5HG9+3nErAJh5hOG1ByXFqkMdO7dojsg9zbSNp6parOU\nMSABYP6VxWWgqu4a9UrlR4WLJFsuT9ZokPOv2xwy2+ea+dToyMbKSVkT0Ff7DOSeckr5NURJzIxa\n5tUQBfAiyL9Pehn/uZ88j2kXdZmnyvoSlopYU0XbdbWs/M7Ln3BRoaxZZNnOVvp3F+Dn5CwPU1uE\n6Y5OhPJGvyRhvZHVzlQifVLBC5yd5XSWOdGMgcPY8TS2PM8NXXrG1tYzpva9Yj97WnP3MuEqe97H\nw5rVn2esCWx34hCzunOJkaFKf77KzbzXGZ+PhZVR2kC0Ks9DaQXJbLTrFpLbXl7LrVUVsqWO8ozZ\nxxNGh8JSkcOqlMznuVH2OQbNh+Oa49Sye0wCuOuJ4dxwdE2JQU5JmLBe12uWXr5mx0NH8+cTplva\n2NxJMwyNaNnMhmMCdE5eRJMjsdIgknlQVXTyk9Gcppbjx5bzUwaihFn1sL8rTLTWBIwOTM7wPgkR\nH53hYTa8zIrBx4vkLIOBOe45n1rev2w4OcvNe7mW3/zrgbs/7mm+bVHb5CDVWdSuI64bdP1AXd/r\nRF1S7SxFgtJqqpPavqraozJwuAD+Wfsi8ikzsgZEMkOq1o45TS1P5x73pHn3fOTr75Lmz7cavbXo\nLdghtU1MkTjHS4vHUZgIvrJwzfd5qkWD45KY17++Tv4K60WmuVeJ13XCnh0IL96vL2sVBxp1CSpc\nn0f5vlrerWNLUz1eIGyFnNRfvF7Ajnjxo15zyPhcOve3BBYhtxzIOuJLbBWZPBfFj7UNbBpXwKi/\ntRWwg8XetLB9qvXFRcPZi6PLzycBlLeP0iKlqWw+G8e6E8Zi8PI8PB1XPIwdD5OA+dn6NsyIa9cK\n2rAAzRfjAWmddk4Vd7K8hfRnKWTGC7ag7E/GjlKi7fFl+21vX0AEJKi6a6fSYw0Um6DWeN60E7vk\nh1wLLjXJbq22EVRFCCeBDTrgg6iV5yR6CgYXVLLiS64JvaedHXftVBLAwSygQu7/A0kyeyN0Jpvo\n+r2VwPOuuZwUctCYk0KbFpRaoE16BxVG6SLAtPKXSXihIUaFUYrbdmKzW/qhdtZxsgsFMJ8fIVz4\nX0tfoWJOE9zOykTbqFjAFR8XkcU6Jsy0vt4sSS4kPYixK5P70iJRo83ZrrIGEaQiPsflNa3aIrZk\ndcT8mhfreEGhz2Pgppvou7lU3bLQ1+AXK8g5aIax4TQ2pdL4NLWMV6BRTMi5SxXfgoJrWNuIDct5\nbqyntZ7BL+PKBQWTCDHW1oIgloWuCkyWaynvr00OcOWYXyUrqjfrM+dZgtG8YOys/G6Ar7Njg7HF\nFmuoFtlWq+L0AfI73rQTt+sBfU6/JSvnK+kyy/dxqZ6p8jzZlATWgmgrI4GqUdCXFp0F7Mtb8FJd\nEyX0JWgMEWYUQ1VRBanK1dUdHzUnZ3iYWl5mWcxfTj13P57ROtCma9KuPKZN3u0pQJwPAhLmY8o9\nsTwdVjQ/ezZ+xt4lKvLWogzEwcE57WDVSBV23V607Sj4tE0n/btYaPogYEQS7gRQdQ+9C1K9RcAF\naf0JhJO85g+REMQxoiSAJpQ5KTsxPH5Y07UOYxfBsM54ehPYmuXKivZKBpTgJk27b9qZ9WaimQx3\nKTnL91ruqWwZ8JmCLv3jWyNV8hBhkyq69/3IZjUWnQXZh8y76zQf189zdr/I/eMisgh3beSbrYBG\nXeeYvGFMIpcgz5gLl3NLbwJb62l1YJO0WmrnAF/NOSJ+u8zPss86Uk5jOeswVu1G+SLVgV5E4Vkq\npXD5fk2qynNBoyONW8TcXHqW80cbHWmjukgWehNZmUsPe4VKvbWhOsayzmTgHeQ3G7VYcoKAwxl0\nzsDucba8TC0nZzCP8lr3QxCAKQkhgtjldo2jaRwmzWe+FoJsPF2yjWzWAbtDKtiJjRWmSBi5kM6J\nTtw39Eqj+qU1RMCHSExtbO4oYHFkWacbnVhYSlglfXFsCsJ8PK+KdejtYSpuPvn6tDqysZeV3MwI\n81Hm4bwdnjtiVHSdXAttxNmpM4G1DVXroqbR8lw1NSAeY9G1Afl7DlrEpisNgnG2fBz7Mkd3JtBq\nTxbWlWNIItXry+c3xxY76+lT+4FSst49TYvGzePYc/848GZ7Zpc0lvq3Ab0RGz2VTzLPKT4Ut5Ew\nCIXR7yPTublgNQlAH8kzSUSKPTXYqFCieaHlusnnFI1WzEaVGCUzOqyC2xTDddYxeWHvDM83HJNe\n0DfPe7bvZsyuruYk1qKDkM2IZpVA5cWatdGBXgd6k9CANC6ugc76/68BiPVWM0s/KRxxOY+8tsn7\n6pXXln187j15/wqErZi81995re3itX1+bouRiyLaa7paiVRUnBisUrQmsiGWZ+RtO/H13YFxsqjk\nQOyjYu1NAuE0V7I0si5U4MEVORaVhAx9VBxLbqA/uV95vdu8OLrEDD05W+ITHxXHBCIMz5ZeO7QB\nnUhbDQFtxaUrt3l5vzAYl0JNZG0Ck1XkSGg0iRl8BaBl9oVRlRDwb2770soBX0AEQB6iTTszeINN\nAmbZz3e1mlj3E7v7EZsCD0i97YlKV7aqPzXTCsMY8Uc4P1n2LxIwHKeWyYv+QhYlM6uIHaSHMQdP\nxZdby41akqbLnjkApQK98eyuQASjIl3qSwZSf5NGYzmxJJV5Ufgchc9fLBKRznraTerln1yh/118\nJ0gAlwPKJhU5fSVudU21u94uJ3xRj250ldgFzeAsh9l+EqRf9+sugXRetOJFW0f+TK6Wv9bnZ6tj\ntzpw7xru5gVM2c8NR2eLEB4IlVJjSwUdFt/qbJ/z2nlegEFX16VJ1cQYK1prlGPsnSkiesbrZA+5\nsFYOTqim2TkBqITR5OpkQb+bmwFziGzHrizy28bRdzNaRe7TPv+/9t48Vrcluw/6rdp7f9M55w5v\n6u7X3bbb2LGcBIUY4wQsDIqT4CiRnUAQjoQVIcAgxZAQIRSDAuEvkECIABFg2c4gB1uJEysWGdpY\nECWAnNhuO4k7tuN2u939+g13OsM37aGqFn+sWlW19/ed++5rXve5fU/9Wq/vOfvsoXZV7aq1fmta\nDC6Uo6pjsjpRjpLVV7GonShjgyb08ai8CbkSJAkpkKxCqqDJyPFBmoS9EytNN8lizizjFXNLDJUo\ngG6caC9VQcEBDHLvIw9AvCbSnFniIiTMmlY4WNU2um4zS86Vq77BE7UahDG+6mZYPHDRRX01G9A0\nEtKiQrAhxmy2w3x1hSbEPUuODRw1M1GdCAy3J7C38K6Lyr2zEmpkrUHf1+jDvBy8gXVG4i2ViGIS\nwqOfJe8PW2HnCI01MSzF7ICq9aiNj5YR+R7kXPVS6oI1VMXJ5FLMaBYOzIiJ0obgxZNb/2ch1ntv\na3QZMWPZwGXzrDIei6WWVE3ElIRYeLQ2VVNwXsqGukziJFJrC3ByogSRxemux6pL5V9dIFYdj9do\n9WyJCdECGaxkoUIsnCb+rPeceiJkozsSxK9f5zIvBk5knHOHBF3tx9WGNMlkSi5H2Fvxost6CA2Z\nUYWGwVNIAFpHb76KhGiPa6FWI8cAACAASURBVFqsOJJyDcW4cia0vsbCeBjSUCqp+rKxVfrGBo7f\nbdqzOCYTzpWNnITWdXReSe6QWeXj/mND3fS8FPM6kIWSoDB5alRGQm/ycx9sVzgPoWeArLMDJyG8\niV4q0rfrIVVkuRganNYOu1DOD0heetN1KVWAkn97W6EbauyHGvNW5vtqPmDbzkLFikR49SHJo8Fh\nCFBOIgy1jM9+qLEJc12NH1dDqsDTOPFEARCP7Z3BzlFwCU8WXiGsZF7XWZJXZvHQUSVs6yqc9zU+\nvz3B/FEw2nzaSTLLysYkiHW2tuYGD11HL/dzbML4XfYNNlbale/3nZPEwRpi5ll+7zMLrRpxclnF\nZ/2nla/mM4v9IDkxdj55lDzeL3D6ziAlPbNS27mFGBDFbt81skdpCFSoNqIhKDL+49BCYKKAYxxS\noIjHcPi30Vw44hWAI9dMr73unCkJduy6dyMNDq491rbDQwcPmuZdMIBUTcqOOZaUH/nzTpsBZx/o\nsNwNcQ/tQ3lQkTX9KM+QticPTfYHY0ZR5onJWCmdm8sTkti6juvNEEjs3svc34X5e3W5gB161I1H\nPU9v6i3gnUEXkkS3XYPO1sGLWL0Gk8FyOtemewyHtbbCbSYRCoBCIgBISW5WwdvgleUeHwjxec2H\nG9ASMPfvSXms3F8sz1gCIGa89j79bdeDtz1mD/dYvCn3PA1ZpB+tVzFmdfbBCs09hw8NV8BjuXRr\nG8l2DbGKq8XldDbg3p2dCLia/Kdx8M5g6Cu4sMj5kCm7rj2MllfzhP2+wYPtCR4EK0jnCQ0zGs9o\njFoQOSq5xzaJWeUwe1kXlh4nDyyWQ5Z8KiRsMRnL33gTBdWdfboblCR65ImQKML64FMIiW7sqijL\nteOFcIrI9RxJEqTuzCp0x3cO1w3Z7jV4rQrAyWpm65GwK+dpMsvkCqeeINONrwp9ZyrJCq1vPkUT\nclJojDaQ+mDnCGpDbIwkOxzAmZCnda9zokS2NMuSUfvuQoiRu19tcdYPWH22jwkyF8sBp68NgAHu\nXkks337d4PHlCdxuhb1Tbx2DxgTFXq3jABa1xfJeH9/lbDvEkKF888wJhMToSy4H2aAR+jLEqLus\nekDo2EXFSUGzQiK03sTcEtNShzEpKaX2Jvdri8EbrIPFWvpSxnTnTAyxQXjdecWg7OthSCnVtVWl\nWc4ZvIleHECy2OYKio6xkHUcz1Poc6oQu3k6G2L8Yxes7lvbxPk3Jdy0v/L+mIaq7B1FBWdjpf8X\nhqIQ0jsHa2swI5Jlj7oGT3p557z2dO84U4b0eYzZS4zFzKFZSLm37eUMzhkY4yPhKmNpcL5ZRcGt\n9xrvnO7bNA6r1yxWsLg35BkZxN2729bY7UTJ2HSzWB5QkZNfq/syX+cfqfCh+SX41wnno5rdaiUU\nhWIRQqIMgNbKui2lP4Oykrnr6/jlVmzifAzSOqWKc74mHLOAeda1Ma2TGkvsMCaFwSSpc3T7YgQB\nNQmPrQM2VtYHQho3yb6f3sUxsKhCKdIw1yizmHvk6+/huqbfPFUpZIND2Emf9VleLjC/d57oTHsp\nT4rG2fdd0yz2u6IixkntYhvXtop7UE5GaPnkFGbFuBoqPOlrbMM839oQK80ygsvgHVEbzvbBEGYA\nE6sxKdnWBrJtmGhl2uJ8fxu8wc7WsZqGdRW2g1SD2VhzcE9DAIX7Tr3egEAiBSNAmgNCwLajtc7D\nhbHQMAEJNxSysPc8UrxV6VJPEe1PIIUotiEMaI00VzQH1XQcFLl34WntUJHH5dBk63QV+0G97cRY\nQCNZxIZ51vkUu66ESJ5oT0kXb9IcmM8slr0FISRDjmEpFZ70TVzXte2LyuG0ESJLoRVDdF3buyop\ndl7HZlw1AEgymh6dRnkcw/UkwnGlf3rudfeK500U9um5z5rwMd3v3cmId2uTeiKMjGVhvmlYnWUZ\nc0OAmk7O5j0WH5sBswrLt8Ur7f7ndxi6KpLxmpdAS5j3Qd4AhIAbgudpF0nU5GF8qKQnmceAYFjG\nvfVaRlXm5M5Jbio13OyHBu1VLd7HmjfHSJJb6wz2gfDvnQneEyZ6su6d5CprXQqdkfUnEYGKGFZ1\na63xJSeCopAIEOF1uegxn1nM5xZnr/eY/wYpP0d3F0IOzBtxG9aVvLcSSzn1lwofGwVXO/YM1Abm\n7gwzK4oZ1QOIGBfbRVTuzVkDen2OV+7tcP9cWATfcihhCJh5Fvu5qEDzCty5GKNPywVgSNoUKc/8\nAw+C395ieKeD+QzHUkkaHy59EZRPkhj8gZLrEiAOPFHoP5Prl/cJX7N+jPnb9+KCtKyFdZ/PbFTI\nfLAq1vvE2u4CmwpQzI7bOYY3qlQd7gQSx6ZCg5Tbmmcl6eA1H8QhcoFS6nEnoRjIBHCWd40uaMFi\nZyb3Ulc2jhtQyk3ho1CePCP0fmpZMISsLjhjYTxWtQttUOswhZjwpPQ3hrGYDzixdXQDdaGySC6c\nGyTBJQlf8l9erk0TIPmgN2l2/eq1OepVg5c/2IFdcEFtDOj0DDAULV+Ld/Zwn9xHS4y8o4kWuC5T\nkGvjMX+FMH9F7vfVOMfs0V1shzpau2Qs5F1yRl/Z8iGzPsm7BAFBPfhZFco0Xkq4CemTlD61UObV\nB7SfK2KcNYm880zYZWUWtWxbUu5SW/dOSA+FhBGlvlhUHBWhvORqPi9SSAyNlCQdW1XKqpFw6nHX\n1jGMqAsJmNY2WRBza5T2s8IEYiq3/osSRwgRDiGuOsVM6nj3zsSwD0AsjGqR1Nwm+rN6U6XnAvWr\nDarXTlB/Rejz1gLWBi0u9OXgYB+2qD7NMQ9Klylg2uN15VC/XMHcnUkZWCDlfLAep+sOZ58TYnfx\n5hz7oR5ZjDovMe2LysfEk9WHTnDyssPHXr3ER6/C2FsZhGoJmJl+ZOKlxpbhuzCGF4T1kzkutwvs\n+iYRCgzc01AYkzxC8vEA0jqSzzWt9qHhWsDYgwfZGI4tjOlnXRM1LIHA4ZtL9moKJe6QKRmSR4KR\n6UAYmNCENSiGERgP06QSE6p0qQV35VLFEbUALwOpr++/s1UID0jWOUMkeWmiUjLOIaSoSMKVgHw3\nSX2YKxTNZE62zkTlNym0qQ210XXAY2urmNBV+2fkZh6eswjvtnd1VFSnVkptN2XXKaJXnE4149FE\n8jQo4qEywZRQz8MhUjb5tD9oBRVm6fc67K8A0DkPVMCKCfNwzUIto0wYBl2PKe4xjckIK59yCMRw\nz9pjUTssjMdgdN9AaANB06OYMGePueYju5/upTWNyY59ULryvDkVIYQo0IhcVcJKEwT3nsQynfU9\n688eMQxlcTLAe8LpzkJzrEj/ipcUGFDqXMkUWWuD16BJBoH837S+It7PZd/hFIkGTjjmHXAdQXAd\nIXAd8n657prpuB3727uRBNd5RlzXD9eRCHm1KD02M0m0H7wShYRl4Jeaykli17tLmJDs8+y1NiWq\nAUPK0IgXsoY66drPFnB7YNgZ7EL+rEdXJzjvFiNiQcJrpVNyQkjLL6a1RUNhhQiLnlzOhHwydLC/\nOp8IwTS/8n5O5HNeucUxH6xjsoczHGcJjApuJQqJAFEu5guLxR2L2WsEc5pcdPh8D94PsI8H2DXQ\nXQamv5faqz5jhJ2XeLa6lvJPADA/sagXkpQvJjXZE/quxnZosA2WsJceb8Sliihm9TUeQhIQjUwh\nVBuJb86lC03mlp3HuV+rXrusYVZuVFJOlbQhy26vm624P6bkSMrG74c6xkxXry5w5+t7fN0rj3Qd\nleczRskIfQ/YVhlTrYVdoyYjG3q0TIaM0ZwWzXBHNAaofVo05zOLppHkfytNlucpsKxpMxVhjIOl\nVi3lDh40qheuCRhTCEBGnjDBZ8KkWqM8Z8JbsNQMnJTJhpLrrR4TCzfjrHEx/8DdeYdFLaTLppvF\njUXcYuV6my3+y5MB80UiaR7tlvCYgUE4C0kv784GeBYmXMdWNwkGx5x8muG4d9JPGmrA+16SkRlK\noTt5TEc4ZhZSmipXRpXQyK0oRNJX7BnVffnO7n51j/nJY7SbWlzrh2QZ0sQ/yqBvhzp6e2w5KKpA\ndBtWa4K6EOt8AIDFfMBd2+IrGLG+++m8Rx1i+K/aOa76lAS08wYNMe4vxIp9784OsyAgKnFjCDE8\nQvtXhUPKrHg6p3O3foaQaIMXd0U9rt/BzORlVDFO4IcxiZASDDJWIbSiCVJn6+Xea5sE41HJJh4L\ndaowqvs1kVgWt5awDffcWbEw3mlSdnLrJUP73om3BgBcDAaXPWHvxPtA+pbROsbCJWIJkH7k1kXi\nFUDMkp9LO2wI1FiYUAVA+1yJDsW+ncE+btHUA7AM2avFjQpoKinDdiaKat0k1/lc2db7WgnNxmw3\ngBY1qg8sUGlm/bDmxqR9U4R3mfUOy8sOr2wu4fcMGwrKDDsjWdrnaXD9QDE2PyZzNQw/GImdDklX\nh8HAulAHPEsKrGvaOK+NrkHJI4oha0o9IRG03GU7GSNVQuU8+X9n0pwcfIrx1bk/n1ksV30ss6il\nZquG47Eh7AubtRAszpvorm6AUe4XHW/rEasmyTGE5JiZEJztH8eUnWk0Ym0IuyoRUZpnozFIJQxJ\nibs81ImwtQaXQ/q+91b2L23CQqsOsyiei8phUaV8A0AKI9G+1D0wt+br93pojU7fomcxDkhVH8Im\neD/tLIdkh+me6ukgVZfk55mhSOzkSW0bSBhIzNtQOfTBy2k9pMTJcTyydXjwItO0zsCF+bc8G/Dy\nnS12toYPnpE7G5KxZuMlOT7GiXunaxYA9KGaQU0+eOUlEmFjZQ3bOx0zIdJPahMJjFwOUo+xrRU5\nKFemdAwqg0hkVnOPE9Pj/qYVUjPII32455QgAgyaLC9LnananK1rvTfYhrbLu4znRN7nevXUgCIE\n9FhJP1Y6e6qU515H+vcpDhIgHvnbtJ3TxIz+yD0Or+WjZMax58o9D4+LB1M6XpNBbSQPjn4Hhiq0\nFYW8UfL+m24O//gy5RkCEuugA2g0CXLYZ5YMk3Vy4xlzyzhpRUh+ad2iPRdS4XIrLPW6n0mYnjMj\n2VU9Xfda2cFW4jHrx0YA/Rana8PUKFAFT8ecpFL5so/hhrKGdeEbzsesc8B6cOh5c7TvbweKJwJQ\nSAQAohz2XS1xuB6wDwfYTwvDtn0yw4PzO7jqZ+gyZjt3Qc4TjgBBkA+ut2e1xVkzxAR8AOAdYdPN\n8bibR7bw9Jc6LE77SDgASCW3PAA/diyQci8Aq0LiJTGPs2aUiTXGvM6kPaZiDK0kdHscFKb1IDHL\na0txo2odByFNNk4t/ahr4uN2gd1n5dwVWtDMoL5vYjZ63/Govrm+DxmxEJqRhQzBGoDwbHEt09JT\n+ac6+GR1BoCTsw6r1xw+gC2Oehdlx6gGYFK/kiGwZ3AfSWSwlT61HaFva+xDhvH9UKO3FRynhDQi\nTIpFQUuhqdCSW6E8RABb1hYvB4W0MoyTWY/VqsfiNCgyCx81w915E93wB0/gvsY+VCDQ+xMxVq84\nfOREMpa/ut1gfbXAppvHMolnd1oQMXabGT735G643wrM1UiwkY0oEAweeLKTTe3uP9yjqvcy31xm\nra4YZBAVAjcQnlyt8KSb4yIQRE/6ChcDYWeTq6Ah4MFuhQ++cYWTYGeiirB41WN+vwf7PlOa0hiq\nwrW/bHB+scKT/QKPQrUJ3zWwnqJQCCRSJBeU5iuLux/t8WGzjhZjasL3MzA+uNlg90javr5aYB3c\n1V99STbKs9cH3Jv1+OBug/Y8eNIEAeDxfhE9eoaQHEk25TSGeydJRF204jGWVUp66gIpotFQBmMl\np0KoyjxxfQyVzgLk/oMnqBwhcb80Fjp13I/IX2pBnEfPDfn/NvtmbQiFYaaoZJw2PWpTg1DH6iv5\nO+TWDes5lnzUNlwMDbafBpq3L8dOVACQC0WGMewqPL5cxZCsh12Dy/B9aDLNB9sV8MvA6rN9DBur\naot65tGcShmt4SoIiesFLvoZrmyKab8YDDaWMLDBO5+TGvavDhvMzvYwC6QEbxRe1CHmwtGXN0sD\ncxIWnFkFOmlQn4U4+zAIy06SXtG8ih4TnEnrkbyLCf18FGB5sGA7SGK50GdiCQtr2WR8Sds66tux\nQ92wIbz1+bt4a3uSVUCp0ZDBxiYBkyHNaJFIwt7Leng1JGLWGI/FHYvmvnxv2m9UE2heC8kSbnB3\nv4c732D/MDVydsfjI/4C3bpCuwvr7H6Gy3aO9TDDJlb6qUKVlkQmVVUi2XJ3dG1//rsonwi10lm7\nJpKQOdQ6eKo5jYJluc5MnJaF3LReFbBAZlYOr9zd4lXa4MNhf9FqPQ/2i+gttHNC4mjSQyB9O3m7\n69rjZNWjMn7krryzNTgbJ4R/O8ehyobcZMhCDmaVtlHcneczi5fuy+L7OgFV4zG/60EhJQj3wP5J\nhcvzJTb2JbkfN+hjeFnyzGCWKdp7xNC4M9Ph7gdanN7r8DVtcP/fzvBks8TjdoF1WFOV0M/DvnJP\nwbCMhzXVYR5ykahs1ntRIB3nxLlcX2dkkBpvJYGiznO5rs8rSLFYaGuTiBtTAc0dj69cnOOVx1s8\nOD8FADzYLbG2dSxLC8iU1zxSKamqj1GyjtO8krmDUQnszh3mbFLvgmPKt/Z/OiZK4egeR87Ljx/e\nIyclcnkuO45DCBlwuPGM7neMkIA/evw6EkH+5pGnS9ayj1ruseEac65gfQWrfW418S1hHi59e7fE\na59YAxiizAMA7AlVbUGGR8dNxaCalVeI+YvMLMkc1V3CySlj2ba4H8LtfB/6OFtzVLYfuhrnl1LF\nYbk5xYO2iZVx1GtL8/0MPpG4hEQ01NHrVc4jQiQ6WldJ4upsDZP1j6PnASDz3npG6xw6f5tJhAKg\nkAgAgMEavHV+Bu8J/AbhYruMiUo2Q4NHnSTlcepqhKQk5kx77maoRqmFabCqFjipHU4z63/nDR51\nNQARgjdvvgbrJUO8bpJNsERaTm6egFiKVrUPwpFuLBTLSE4X7iazMkm8IGNra7wTkqysrcRArQdg\nM+iCwnHDzRlgDxHS32ln+PU37gMAlu9YzGcWp6cdTHgOs3hlMEsSNwAxbmw/1DFh1dZWWFuD9SDu\n3/ps2bD5QPCbV2NhkD3BLEKdaPW2yDWXcOLIqjcyP4lgXmkQmAo8nrEaetxpxUvAD4lgUGXaW6Db\nN9hs51FZXLoqWGvHmXlr43E6kySdADCbOVS1JL8xjTzUO0CTmle1j0n5xH1MYjm78D6XQ43N1QL1\nchfrTM/PLKp6h5OuxywklNPSlPWyxaudCJgX/QxrW42yc+fCBxHwsJV5OXz+FWytxNaqcKuuz1rF\nQsbFo3MGb7czXIbxftwRLnpG53z8RmpDeHM/x/KNV3DvoTA3J8sOp3c7VHNR7BLJI4oGW47u5PPB\n4o5tYb2JcfepEsVh8Evu/eEdgbJNXMdZ/6WZ9CEAeN9Jsj1bx/GmGqjOKlR3CfV9GajltsXddYvX\nWxPPY0/wntDuG+xDn+/6Bpf9DBdDstid1A6vr1rcmUs/qALQWhEAJEwnlWkCEGIYxx43YwsXRytD\n7l0zrdahifv03Cq6AUtixCpULgHE++Nhu8A7nSiSgFioWifrnLql31l0OOUeq34W+3znDAYOa6da\nPL3MA4lzTsk9H3Y1fvnXX0XvDC5VGcri4FXQX1VS9eBqaPBWK+c97gkXvShJy6Ck/vp2ibfbxSju\nXb2HZsbj3ryPBMh2qPGwm+F8MJFIfdzJenjSAJ+6EALurc0J3JE+l3wXKaeCAXBSW9yd9VjNhCw7\nW3Y4Oeswv+dRn9FoHgIA9x40U4+ycJAOXZPBPCJMyUD8cUOjaC7aESsrqDCBkKjHz83MgHJ97XFv\nvcN2aOI3v7WSBI6RefgEJcT4ZPHvvCgRe5eSyO7bGRZrC6otqqUI14CQh2QZZpU8DGhmUN0HZnsX\niSQt5VjfdViFKiJ3uxav7Qm2N3F/cdag6+uRVwYg7v6aHySVziP0roJ1h4mIZ7WLnkp5ZZg8v4Se\nq/ftXYV3tidoaI6HwUuEICV0u5B0M3FBjPlywPyex1lwcLdbwvp8DucpltVcmyoSYtGLkZMr8S6s\nN9YanJw43DlrMe9k/dq0M8yHGWaG0VCqyKFeaGJV59B2+ZkI8FCPupB9PRBAOg5UA/X9Kt6QW4cl\nHJztcHopY3M11JiWW9VnG4i3xVUgaE83HVb3B8xeYsy8PGc5WNzd7vHhfQUb+sJaIzltbIUuyGbO\npxJ6+h3XlcOithhchU0/w2OthjQ0oE7C0PLyiKsKB7LZ3hk0JilhEqIiqlhuGNA9U73S9hc1ThoL\nMwdOXu7x+kzI/Zd2O+zbkMjOqreGlN+uQ5JYQNZ4zUGhhNO8kjCe/JNl1n0trQy5nJZ/8jlRMCUD\nppZ9CZfkg/AAn117nfI/unfWgqcp/f4IxcB0eGz8nCPXPMUi7LP7GTah133MGTWgwsANGq7RkCYY\n9OHn9I29uZ9j8fbL2NmUDLXN1o6cO9b9Npe7DUnoz91Zj1dWkkfq/v0dFnctTINoODRzlVF5lLTd\n9x7zro8hmX2WKwPIQsNmUuZ7MdSZV5J411WUQpNmlYuV4+r43gYra7AxSrEkvjlfL6xndN5jgIf1\n+2v7/sUG47jV8vahkAgAOl/h05tTvLFboXVSc16tcHlt4ZGbJCcFZeoWRgCC7I/GABdEaIyJ8a6A\nLOISmxeGoKthmbBz4k4U75VZUDi7/7xKP2t78vYBeo0oC6owzoKF0THhYkheB70TQXEdqPbW+ZFS\nqQsIsyTMe7ur0VxJqQolL2bGZ+6dUi7GUIpjzV2qNEP9w77Cw1bcpLvQ+M4Fa8uEGa+IUBsRqlW4\nvbhYAp/Zo2p8XGBDD4/GhH1OJMjflC2e5shUjwmi5P0h9ZxDZvXgYeGskez2wX0Y0OQ0YkFSAVAy\nT8t/ndaBt5UQLFkSJRUwZrWD8waP9qI5P+pqPOkJlz2iJ8LDrsavPLqPlzdLnAQlRT1drK2wD8pV\ns3YxIZ0m+Rl8amPrUh8r6+wM4c1w/cOuwdqGean9h5SAK8b5GimxmHuznHeMq8GjdcnyMDMGb+wr\ndP4UFUnekblhnL3tcNYMOGuGqHRVIZmeoVQpwIZM5FfdDFcqbFuDtQ1up+F9LCd3U/UQOH+ygu27\nURiShiCRSQoBINmL1dLZxpwhl1ieDaiWycKgni3NyscdVxXAZT/gLLhpd7saZ9s5VrslLioh0BaV\nw/1Fi7tne9TNeEPynlDVPlo3dK4CAIf+ZQ4x4W4srbMXj6Q+KDPqGt406ZvOE8xVtR/HiFYepkac\n57vNDM2Fh+UVBh+IjiCMV5Tlq3AVmsphVjmcBMvIad2IlxONPSGA5CqreNITfuXqRDJRxwzYSShW\ngWZRST6JzhPOeznvomdsBhGY1Vr4dleH9qVjAAJ5AZzWcyyDUDV4wsUgbsNKZp53jK0VMubXtqKM\nzMwMbQibGCYyxPQZjZlhYZbxW1lWHqvK425jcVJbnIV5Pqsc6sqNssxXRuf9cW+CqrpegCHimGsn\nR1XJfMr/Jh4IFEoCyjE3SMLJfSAPAfnGrgYhmnXNUC8x7WM5Jla+1lP0YqjWZ3i0W2LxtpT9VXKU\nQpLQpnExB0vdOFQ1o29nsWzuat+jXh5RHlhCIyhUcahqj2bmJm7uGFkJc7nPhzJn+i2wVxJavonY\nxiNS0pRY6PcVnDdYDzXWod3zShTQPB8BIOXZdpsZyPSomnSfuhaFMvfc0XCGaLwIoYWOOSozl+0C\ndS2JdmO5vDysDGluJnI+V4jz+cDx2TtXYdfOsFzLPK17D/ZAdeGjgMOW0O9rbNbzmAdl76SSR+sY\nrU3zRdtwNRh8fitrf+tq3L9oce/OHk0oT6l90iw8mkUaB30vXZd8Vko4GS+E9Bm6GtXGx/LbgzeY\nGxNLGwMqT3g0xDEhqmLwhFrzQxnZy8epr0TV9kD06PzsO/fx8naH5WJAPXOxzYvVgMVqiEaV+B6O\n4LLQDmsNBlfFUsBAmjOWE1Hns+PHCIJjeLe/fyG47p6qoDN8zOWUQ4+ZA5eoiacQeVCWU8iQ0lBT\nmANywYRj+TIohQtNILMCWcsmkiDTdnpw9CJ6pzPwWInMlCUQVbkpl89zuXuc/BhYVAuchhJv9x46\n3GssXpp3uL8UT4TlQsIr83Va1maZL0PY0zX0YOBx7q9Z7bCqe5z4ZNTQ/DS63iqcN+G49m/KWaJn\nWdVzJsZSA6lIYX1KkF1wO1FIBMiG8ea+DkmZZHE4VWsoafx4cjGWP2Rx9roxTIRiILiIh3t22ceq\ni45mO9dkankMpWcI4YXx4qrJYabQGOnD5yeio/fAnvS9s+dAhEmtzsCsi23qAzkuLnxPulQjVkvi\nDGziBt0YUSxHygkQLBSMbXjvixAv3bk8acx4QY/x3kb63DNizPWnzu/BPbk/ShpneZw7AFALjPDQ\nWq9aE9flyRY1gc3cSP1zrYiRZ4LOSZHeV9jZKrrwnw8GF70oGao8SAmsBc77w2zRW5dCCsRCylhU\nwmLrZvVOa/CkE5JH++hhR6hoiU9vF7FdYnUSq2jOjDfEMUcCALzVNnjYEs77tElW2UA5L88ERBD2\nLORFzOdAYuXUzRKQuV2TCLtpPmmSLY6l9zwY5z0OrLmGalQ0R2MQ43LVaaSmlKldx6x1BhfB7fxJ\nT7gatI0YwXvgQXCdxeOXsH6nwpM+n6c8yuC+zFwSO0fYOoOapI8/t13FdmmyxXuzLrL/tZYeC9VQ\nTOWjoNv3qZxSXpZw3c1QGY/lYkAT8qgYkwSIUQgJIKSXWnI9gEoIhlxp4OCebELSt9x6lIc6yb8U\nLbnx+r6KVjEA0arLSMnCxKVWiJu31WvFGywqURT3TkM75FvoPU/yVUi8qQgtcvxqAB5WVcjhgdBH\nYRyzcRU3cYpt0L9rDP6nnAAAIABJREFUD+gauB4Qy8HqeiAJs8Ka5QmNSULZ/kgm6ip4AijhOjOp\nLOuxmOK4B4R32GV/E9fTCgaNeHBEwjWVlM2t4sdE9OkOMyKN9bqJEq3IRznGqQchdGYYZxqC10h1\ngvOhwuMgtF70hKuBsR34CFkyVmocA+shEZFP+jrkXkB8ll7rGeP1lyREIPekIeKg1KXvblFZzCqP\nirJyx8ZH74BcWNZ+qKpUKUjvK/2VFG+tn679VtcOTePiPeK1IeN5zCswSFm+1ptovWwdCSnLQq5o\nroQ3dgu077yC5mFSXlchF85FPxuVeNxZlvw66jGVvdMuKNOf267wuJ2jMamii2dgPdSSn2RAzGXS\nOsYQiJ6YdNMYMIsbs+ZBcSxE9T+5uIvm8k58Zp58FhCPnpqkwox6BV0MhItAOHXheUCyll8NhM+F\nMI53uga4PAXeSXPglZnFq4sWp/MeC3XTbtxoXZzCe616IgpT19e46ua40hKPQ4Wtlao8SjTXhkOy\nWAMT3N8IQpBubTVSFtXrIJfNfBjXN/fqGXaKen0K6+Ub+tBSrLRn8x7L+XBA/Dln0A/Js2I/1Ohc\nhTb8B0i5y8vgHaVkTO84hi4o1HbsiUflCtOknYSzhX+neRKuy2vwLPkO5L6HB4/lJrg2GeL0dzr2\n3CPPeEaSRPspuuyTHKtAMWFqYyRPQr62rgcxBBrksvC4vdrTVTC6SY6S0L5wzd7JWgoAb5LBzNRY\nVAs05m7WRjlZ5dRF5XFaC/ms68XW1njYNXjQGlQGeBJCO09mA1aQta7KDBNqLNH8Hb2r0No6VAyS\nGXM51LgcqtF6kSeK1a9elktCh9udVPG6OXzbUEgEyKJwOYQs+FHBTx/yMUFWkVtloTFpk3M1oZUe\nIhbFtXVpocq9BaZxm0ASuPTvNvyNsr9PPfVV7DC59IN0j3E83Bhaz1kRw9NJBKPzPj19XqkFMJUo\nGzyjjVmsBTUJEeB8YnM3A0vyFpfFjmZug8cS8JgB+Pw+uJT5BRyPFchj45TcmVMfTS2hObStegYj\nEyag14uSTUgeApq4qXccXUhF4SVUVGdkDh2MgcRJImZXHrI+Wod+UgXnQVtFRXwSMj06ppaXxtTx\n2HogXPaMqz5ZprWtjhktMZqggK5qQmNSYsjYvybFQQMSezyEOaDu6RLbOcmpAeCql3s02WTNvznl\n1vOs6do+fR8ZcwrvwyJsex6Ra5qP9I19SvKnZSBTNYMkNMvvoS8h30Tv03fwuJ9Fr6DGzML5q3EG\n8nCPeVDKlPwgiCK0dQaXg1pqgMVmjkV1BzOTCIyF8VGJV6U9LymXe/uItSNl9c7HKRI8IcN8nQlk\neRURSUIqx3tvAhFEI+Km84QnvcHjwISe94y9VZpRtpG36hpzw6gz76MrK4Tjeeejl5P1HOdIFdYT\nhHlRU5pv+dzI/3Ukc2XwKb8Es36jYXEEcN4TlhWN1kRPaY3PPz4lJCxzJCY4/k0UHwBYTvLZAtPf\nwzyPCavS86XNSsYwes3fATlvuu7nJJ2coSv6uG/yNUmPTduY7jH+WzqX8MSoEK0eSymZ29bKN9a5\ntAapQJ6Hl8n3Tqi6FJY3r8bvkBOWuouY7LujCTFhKHmkVBqMj0T4T0s05vcbVWdBiuSoDQJZm/6u\niRmnylYVSA4911B6onosLiqpzvBOZ/AgJPvYWkYb1mz1+ACAz+xqPOjqEdG7qBinlbhPPwzf2ONW\n1v2c1Mrd0M+DEfDT21kMK1MZYmGEhH7UCcG6jWGKElrmkCVtDV5/hhE3+r31eGNXYWPno/6JXoHq\ndYW072ytvjewGRDmi4/tVyX1qjd4O5DUMzNNngx8fl+hWc9H81g9KJUoB8YRi5HYh84hDnkZwh5h\nUz8omVkR4bEhOG6wrEIyayDmc0hkkJDtu0xGsYFsNAQ87IJrPNfRYDSvavzKRr2XGKtKPDPVvV33\nH03kCMjaa+MemoioyxBmmkhYjvPATGSYSCZMPTGv0Xd0TnJ4l2m+HUCJCT0vvxgHz6pw6HE2FbM8\njytiAWnvj3Jm9j7TdyGiA7nwuvc+eBd9zug9hCBIxHr6Y1L+GTuby/qHMvN0zeVMQp9E18afWycG\nn/HanvZ8ubZCRRUWVROJZ0Dm9NrKvvGp4Cl3ZWuc1RbLyo0SonpIydZtzDEiibZ7nxI6b514m132\nab2Q3GRjGU7kc913b2uJxwJFIREQSIRe3SvlmFrCZuYwXEGhbrHTxWMaUgCMWStdQPcuEQcVJcVy\nynBdt+ASxgJyRccFysNrKGz6upHJf3vrI0s/ZAsGkDYPFWA2g8fcJMs/gUbCjsY768/ybG1LcnHc\nOQ2nyGKuOAleln1se0Um1PNNXLvWvJ56aui7pr7Ujet4X04FeB3XcflHtRam51RWhBFte+s4ZuVW\n73glOTJZ+qCvgDTn9C+6aG9DRu3WeQzBH7fuJaNwnW1q+izHaTMWYmLcR3vHwfvDx3GeVSbGT/pM\n0PdQRjz1pGd5t1zIU4F48BzDcbZWFM3O+9H7bq0JpdEy4RjJ2nPMBpuqD+R9GvpnSO9ybL4+CUJe\n57SMJR/MEe2bKbFhs3a3bnyuQufJ+LsV0iiSEuHfziFmBwdkfWlCDOI0VccUU9LpaedW2Vo2JTTz\nY2l9SL8nV2dVKuX99o6xDRL/ZkhzR2fbSU1xrukrto6xGTyuepkHANB7D8ceNTWAQTy+HjxqY0IW\nexWqDt/ZkHwrnhn7oLho2zqf1iUtV1vR4fV6XBPweRZBXTwUdF7J/QwIV33wHmkOvQH0nuN2Hn7f\n43WcRuMzXYc4nnU44Acxy5NTrlMY4pPpujV53N7Bp29sn33LU0+HelolgIF95oXmOJF1OaF9bC7n\nz8/3Ll1r8j50zNkseW+gSVvy5+XPmRKbORhJGZpXolhuLOOq1z5Lcx4AfDj+oKpwUk+z9VOY14yr\nQA5c9R4750ZrJ5DG93Gb3O1nZkwozwzDMmE9sIToqGXRcdxDdExsbAjHEo+AwWV/nMxTuWfcF0Lm\nyv2ArfVhzzps/9Z6XPWJYLpuTZ1iSi7nbZpeS8hIRcjc3luOSlG8HpXkdjFpfCVxXJr7eo0qUwBG\nuQYuer2W4vppsqTSBBM8MKvsu1MPyHHbVX5LxIIor1fZepvLSgd9RO/+/ed75LQ6+bt5Ehyzvh54\nDxwQGPzUv193TJ53PJniu113LHcCgJhzQcMkmDwqruHcLMvV0ICI4DxH7+HGVGjMOHHqdXqBtDt9\nUzmOGa7kcxzvy3p2fr+tpREBK3MjzMmdvM95T1hU9Sg5rM4xCVdNcp0QAylUaheqL4lhT8P8UviU\n9o9jDwvGQAOelo/ixcdtfveEQiJANtHHXXIDdJwY3mWtuRA0yunpi8J19XeB8eYnHy/jTpMEAUA2\nORuVyePCosZ6T5+tSa6fJmSqgORYFC8gKcEba7FlSfLWUT9aoKeLctW/jFl4IWPTYpMTB3nCu2l7\n2vDsrXXYeYueB/TBhMjkYeHA8LBkY/xawzNUvsKJW4A5ZObnaqS0jN93OhbXbVRHlNb4Hlkf8Hh8\n5R0puiIDInh0LikfgGS6XtZmJDAMR8YJGIepqKCwc9JHO+7gSPrI9XfQmNkoHjlvo7a/NjnLLVgP\nHltrce6Ts/XSiYXPhuhAg5PQBhPnfYrJ5Khs532RSADB1WBx6fcHc6np74NAuu+N5ux1ChHFzZxC\n/6WNbusG7NligI39o+00INzvX4nHeg+09jB5FJDGUiFuyOnvUqceIyFU54DnQ4thZVJ76/Bvaxn7\nbJAX1VgwyO87JRamBOHTcIzclHtMBDwcJyfy8da1pvceWyua5tr1cHDYuTmcDxVMsuxfOif3Vq7Z\n+B57yNrSUwdPHsNwHzOqsGYx3TKvYKjGLvNyUTIRGAu8EmaQ5sDOyTMcHFYU1gbMYunVY0K3jh1C\nH/Thm+2D4ndue7Tco0KFWS+hLDtrRn0zxbT++OhvR+mH9L1mRQquJYqP7S3H2vM0wXtqvZxe0xg6\n2CN0b+hhR4J4jQpzNKN7NFRh8A1sIHv7mqKLruPxmnxs/qlyNdovOb8i7dPTNefd8LTx0fuKQj++\nqXj7jM/N55SO0956rG1Ibuh7DLBx7zx1kuPGYIF9Mw6d0bnRex/3xrUbRmu+wsLBwMC2EmZQm3nw\nmEztqYgweI9t+P62LlSbQouBBtSo0HAT7+dCXgkTSISVW6KiJQauIn2SsrOP131d+zeDhn0Q9tk3\nP5A8WxWO+/1dnNTyPc2q4Jk02Q+n8x7hDs8CXTvztbpz0q97thgy6+neLrCszYg8VtmsdXJe6x0G\ndnBwmaIp6q2BQRPGwXEF55PHQPreOfZTjiqsP1NFPk9i1zlZ4y5sBxv6b4DFQMO1ivKzQMdieg8f\n/jcCpfPTeakPr1Mk/ZEkifl1x673RyzbT1NU+cj9xs+7bqFOP1bUYEar2LY7/iXc7+9iQCZL8Amk\nelKWqJLHcst0HZqO67HjwCE5OsV1Ro4+GJY8GJtG1lqVJ6RyWDp3GsaoyL+R1ulaIbImAAw0YMAA\nS2kdY5LElFucoxsurm13we1AIREg1sbzTjYXD2BglxTAoUaesTbGxOHQlexYBtvcXSwvncLMGGDR\nO91MxRLceY/Bjxc+Dsuolv059mwiQg2pzPBuAqQBYMFoWUweczQYYHFl1tgYWRQG3o0WYF3INTnN\niVuCulV4F6DzbuQiSUiJieLGywyt09uG+o8b2mFHG3RmB8ehBCaGwD97OB4iiVDTHIYarHAXdngV\nAND5eUjyMs6jcKgsPY3NPoS675kR83y4JVXhPbU8UM8OG9phwBAtJ0u3wGKYocmsWUKRSJvysdTy\nR0QUx3tHe2xojR1dwoc4NM9fBbR3R14Q+s4jEoFMZhGUv699iyuzxqV5GMd1ASlH5cnDsEFlPwwA\naH0jCjJ4JHxVNI63n85tALiiLc6rB6O5VKFB4xp07TL27ZBlOzs2dzVmEUCISyR03mEX5sua1tiZ\nNQbq4hxiuCjk3W/l3To3Q+s8Wm9H42iQ5mudC5PMGLyP7VTBvGc3GruKDq2Uet9ZcA1XZbV1LhJ1\nADCjBrVaIbLrgOQ1lONYfOkU+fgDMoePuXvm8+WYNUnHUd+v5R4bknJvm+oSDgNO+B7aTkq7Lfo6\nft9DuGPLPTZmg011gYGFtLLcwbPHYD6KBa+wrs7luHsNtj2BIYpzwh3JgEzhfRx7bEOhyJ3ZYldd\nwfGAE0jVmKF/BfOQedpO5pVjj4pM/CYZjI4dKjJxnp+bc+zMFRqeo+nle2hgMMCP9ohjOIi9xfg7\nn84YnX+5tSf/m+I6S+CxfrruW8rblc8LHe+GTOgHH4XJjdlgYy7G3zI1IBg0lPKyeDjU3OCuewln\nTojIlaknz0nt0701n/vqcZb317H13YHDmvz0b+K6cdJ5dOhCjYPvZwp9vj5bv+Ed97gya+mz6gKW\nO0hGmApLiLLJ3es4sQ0G7+HiOiLo2GFHEkuva75FhxyaXd7xV8iz96+gNpR5FIjSMHiPLXfY0Bab\n6jLcfw3LHQw1mBnZvy23cLBgdjAhz9HcnMJ3X4m9XWQhXzxyZda+rUhczi9DnWQDwh4dLs0T9LSH\nDSShzhtLr2PVamUd+davW9eO9f0xuSu/3hDFfbEL33LPA/bUYU87WEr92fqXsOqWMTO/D3Oqw4A2\njMOetujNHo6HqLTqu9SYY95/rfStn8Mxo/d6jrY3jMmk3drOHOoNoN+zzodL8yQq5T3vYNEduJJP\nyxlO/zYFwx3wMnx0HcnkwGdQ8o/dY/TM6/52zXVPu9/T8KwkS0U1arOM79FWV+jpdVh0cXWo7Ifg\neD5av9RLVtffYwkk3w0qtx8jEXTu61oDIO5nALDnpOzvnazBM1Mdfe4BcZGtx7pudOyw4RYbs8bO\nXIV37GDRjea+wvoOzl296zu+sHgv7PULjBeSRCCibwPwpwFUAL6fmf/rp53vwZE1d+TgyMbFo/EN\n6ixVTV6eRi0yB1ld87IyuqhPPmJHFpYsqpDUZOHrKBSNGNAgqDiyMcmMKtjHytdct4lE4ZFNtPS3\nRoT6BjNYWGxxjr0XgX7w+7h454sHoYKhGhuzxcyJIDCwQ4d+lE3XwESrxqjvWNrfGhWULrD357Ig\ncbJYMHt4HsDsQeqWbIREsKbDPAiuxokgqBb063As+6725bTPpCdTf2mbjt2nhgGxiWO2M1tsSIRH\nnUMruos5L9CgGbnRXQuS56oFZ0uX2OAxOr+BD320rO5i6eciTGIqTCSSqeIqvo8mwrkwF1jjAbbu\ncXyvLTXQjMY1zXFGooh5v4wWfqvWKpgYqzi1NjB8nOuX5hG2/tFoLhmqcVW9DPhk9bJwR/tD30Mt\nngCw8DN5F7ZRob0yT47OIc8DCBUeViJsD/0ZOgzBipNIgGhR5QZNlg160G9RLeEwR61ABgYVV6Pf\n9d7z8I00JHkJdtxhY1Jt5TnPUXFICjaZvwZmJORdR1pOr526bIIR++/d3D91DuT3qbiO3+yangAA\ndv5cyADjUIekZB0v4/etc3dn1tjhHDt3AeeDdSPMB545zOk01ppuzBzGyzvr9cesWTr/BhqwMaIc\n7fkSvdvA8YChkrWlNjXm6rE0JRHIokIdrbH6fhXX6IwoPWs8QOc3aGiFDb8Un91RF60xOabrj67H\n07Ukf4fRe3GK/80tndNxyr8VXc89jYnlZ1WsiU0cc71vww0qrtChx85sQ188xs49lrGDlp5tRv8C\nomRU1GCoOgxePIBat0KFKq4ZeX+JfWvcbzOuD/pWz8+vt3Bxnozf7bBvj0HffUqw5eu/nnesLXnb\ndZw2Zo0rPAQA7J18I7qH2Urm/wWdwboTODjY3KJLHp3psCMhIbb+UVjzbfZcB2YvJEAjJMDKrdD4\nemQpB8SbcGMuscM52iDs934D63tZs4x688k+m8+3WX0HK3MHcPdRIxkDoqFEiaQwrhYel+Fb9PDo\nsccGjzG43WhNBoC6muPK3499PYTd+9iYmZgT5rhco/cdzXdGbJPOj73ZoaUNWr6KpAahgjceHZ+h\n4Vls+2B69NhjDyVeNrCugwtjCSTFtjJzXFYfkOfaO3Bw6GicsT6XiSYNP/hm9TvUedGZFnu6wsY9\nSEYWtnC+myj3Yc0/QmgfU8Kvi2W/ToZ6KjHwVNLg2QmAL5QseLf7HeuT/BxjatRmH78zV3dwtSjN\nVfCyWtEZjJP1S+UtCy9yeZCDph5D5si6kc9jJQNrVAd7QzghQsd6HuYpw2NLe+wCYen4HgCgcWkt\nnmK0piHJtWpS6ajD1ois2drLcN8O1vdHx8b57n0fs4L3D0T0rwP4UwC+HsA3MfPPZH/7XgD/NiRN\n2X/IzB8Px9+T7gy8gCQCEVUA/gyA3wXgDQA/TUQ/zsz/+LprHDw2tMMT8zY63oyt32aOhlYwUQiX\nBUSYOQ+HISp2PiwxueILBMUbBhRYRMeDCBfewVdfDwA44RMMGNCZFg5JgPZwIzYwh/fpdyITnzNV\ncOTvVTzP84Ce9xisCO/z6g4cD9gPT9AOT8K999lGMxGOqcHy7n30ocb13lyh550obUZrZB8SGiqk\nWO6wt+LxsO0fYLCXYN9nVjYfWT4Gg4KSYswKdbXCvj7DfCHW5cY36KhDhz2GYGEQBdLFn2WMZZym\ngjlRBYKJm4XELzYwqGDYjJQYHft8Y4z1hoOVdecvsB+ewLo96krcV5fNS5ibUzRYRgVb73EdM09U\nRQvUzj7GfniCwW7gA/PcnC0BI3Ok5xSWUFET3wXA6B30vEv7OWzatzDY5IpGVIGoDpvqHPMz6d8F\nnWKgTqwf4dnyjDpar6RvpF9dlrF32z3CrnsH3u818A9kZmjOVthX9+N8PrC0ZQKOtMtgRiIwn/A9\nGBi0tMWaHwAArrq3sO8ewvktWL+RYOE0Zo6Tu+K10tJr2OESPW9G96+oxpzOsOKzqHQCQGdkXuX9\nucU5BqRvQ+ePmXzvgHjOnIbNfeGXcOSwMZe44rdlbKjB0tyFYQNLQ+zfdJ90T48h+GMYRG+UdxHQ\n8jYZXC9cHF43yu8d33/Pl9gM0vZd/xje97CLXdxF5rSCpQGOh9jH2+Ehdv1jDPYc3o/fb9e9g8os\n4INHFE4hyie3cU7o2kqTNhEMWn+F7f4dAEA3XMC5jVj0a1FQ6FS+ZQ7qGiBzy2GIihgHYoxZCNCa\nFpHU2HRvobdXmNV3sFxJ9uyBW7R8JWvdRDhVRSz2I0xcl0OjU59mireuP1MBU/49tDaOBPPopuoO\n+mj6HeXHEgGQ2qfPWtJd1JhjT1fY+kfSF/0DdMM5rNviED5+3/rNbWYfwG7+EQDAqrqPCg08XNw3\n9Xm6huTKeE1zzOkMM17GvvDB7p8j9zw61lfaXwqhC9L+VJkm7utyrouCfb7f1zz+dnw0Hrj4fP1m\ndv4cm0HmZNs/hssI1OVclM16MceO7mCgLu4bitZfoQ/zb9+fY3AbcFDyR31NNeg0GDqqQCaTHckn\nLV9h07+Dtn8c13oOipLMXVVW3IGHizFL4Axoq4+m/o4eBYlE1XnrMWDn9RkOjjtsu4ewbg0OZeDU\nQ6pdXGAR9m9HNlrWK2iFhDRGsT2Tuf006HWWO3RhHersFTp7hd5u4npjaIZ2cY7T+oOp0hRkHe79\nBp0VBa0bLuG5D+vXVBaa4/LuV4bnWQzo0GJz0GadUzo+Kl9IoI96aASZkoe4/nV+g/3wBN1wnhl1\nfNxTRzLTMWRetAXPhq46wa56DGYLExIo16s5WnoFA3Wjb8yii3Klrq1PIy8IVVynkmGlGcsPR2RS\nhkdFDZYcvJngscEj7PyFyF1aRYzrZyJudA2T+S7fdeuvsBueoO0fx3We+ThRQFSD6NllihcPXxbf\n1S8A+FcB/K/5QSL6jQC+E8BvAvA6gJ8kot8Q/vyedGfgBSQRAHwTgE8x86cBgIh+BMB3ALi2Ixge\ne9rhyr0N63fw3sYFoDZLLCoblWLdPH0gAhwPkcHMGX2FCpJAEtw8D3Bs4f2AfS0Wgho1BvToaJes\n3uzgw6ZmOTHPUXFjGxckQhUE1uOsqwqRBkasdW4bN0mt9drbjWxOwESpn3ZYj95vsDXitbD359G6\nqKWSajMfLZjSbmn/4PfobHCXcruwIV7/Uargw34ffTRUONjSKRwGtNiMyJx4rZIInEgEhYk2pypZ\nb2FEoZiwwdcJMbrp9yz9NrgNBreF930cd+c7OJqjojopFEhCtGKsRNiozHT2Ctbt4Pw2CuuD36Or\ndnEeKhyaMUmCKr6D9llvt7BuG/tV+5ggY848xHOVdPKZOxuzgaPgBpvNfQCwbh/v2Q2X8G4b/wYA\n5BmD36GjJm5ejscMfn4+EL6bmIC0gWGDFhu0XubQYLdCIByZs+x77J3M06pq0PIlercdfZMzWqKm\nBSxsFGQZHi226LCLAqZBBcsdhiyXhG72PBIYhVARll/ebaBBBGbaow/fWIUBFRpU1MDxMCJgdG4q\nYaBjTFSNiJtjUMLSZ7+rEDIlmIBEVup7p3Fw+kLw7DDwDoMLY+ZaeLawvo8Cr6Eqvoe2d3B7ON8m\ncieDd2twZtnu3BX2tBrN6Uj6jWJKA3HnNhjsLtxL5jMDsE7Wtd5vhCiAi3OM2UVPJyA9O7fO9kGA\n6obH8G4HSzV2LHPI8YDOXV1L/uUC14hAmECtwMCYbNBvN671k+fo/adjn5MIUyIZyMZalXXWMWtG\nBDcgygxYSEfti8GFNcO38Ru7LszCe2BwO3TB+m1gwjj4A6XfsR2vDzBwZgjWbBlbDZswOCSSciXs\nOiQi2Y/uwJNeGs397Hu4LrZbrlfrcGiz32Cw0mfO7zPF08CGb2fgHWozj/u63E8MEJ27in3ufCvX\nsz1CsLu41rbVRtaKrB8dLHq/QW/XoshP1liZR0+zLncY3AZ7cxnn5LSt8nMT/9aHPV3H1Lp1MEbk\n+7HMjT303AG9F8ONrrN1yGmSz0n1Gnkv6HkfCZnebdHbTVgbkqfV4FoM1Q4cPBvVwON8F/tX1y9Z\nw7x2ULiHQ+vFaluZJq59FZosb0SFmkPoD9J3rzLlkJHoaf2UuWL9Dtbtg1KXex4MI3fqLwOF5ssG\n3u0knI4tKJAIrb8CmWqyfvkoA6nsL8evD9m4jkTQPY3CWqxrJpAMgS7ICvLzgM5vMLgNiAyG4D3n\nM1kl94TIiU9t+8Fe7fewbh+Iz2HUximm60nB8wdm/kVgHFIZ8B0AfoSZOwC/RkSfgujNwHvUnQGA\njtVc/XIGEf1BAN/GzP9O+P27APw2Zv6eyXnfDeC7w6+/GcLaFNw8XgHw6KYbUQCgjMXzhDIWzw/K\nWDxfKOPx/KCMxfODMhbPD27rWHwlM7960414v0FEfwsypl9sLICQ+Enwfcz8fe/lBkT0twH8xxrO\nQET/E4CfYuYfCr//AIC/GU5/V915ihfRE+FYMOQBUxIG4vsAgIh+hpm/8YvdsIJ3RxmL5wdlLJ4f\nlLF4flDG4vlCGY/nB2Usnh+UsXh+UMbixQIzf9tNtwEAiOgnAXzwyJ/+M2b+a9ddduQY4zDfsx5/\nKl5EEuENAB/Nfv8IgDdvqC0FBQUFBQUFBQUFBQUFBe8LmPl3fgGXPU1Hfs+683sLMvvywE8D+Foi\n+hgRzSAJJH78httUUFBQUFBQUFBQUFBQUHAT+HEA30lEcyL6GICvBfD38QXqzi+cJwIzWyL6HgAf\nh5Sp+EFm/uS7XPaeYkwKvqgoY/H8oIzF84MyFs8Pylg8Xyjj8fygjMXzgzIWzw/KWBR8SUFEfwDA\n/wjgVQB/nYh+npn/FWb+JBH9JUjCRAvgj3DI1v0F6M4vXmLFgoKCgoKCgoKCgoKCgoKCLw5exHCG\ngoKCgoKCgoKCgoKCgoKCLwIKiVBQUFBQUFBQUFBQUFBQUPBMuNUkAhF9GxH9MhF9ioj+xE235zaD\niH6QiB4Q0S+dA3yLAAAHxklEQVTcdFtuO4joo0T0fxHRLxLRJ4noj950m24riGhBRH+fiP5BGIv/\n8qbbdNtBRBUR/RwR/e833ZbbDCL6DBH9IyL6eSL6mZtuz20GEd0joh8lol8K+8Y/f9Ntuq0goq8L\n34T+d0VEf+ym23VbQUT/Udi7f4GIfpiIFjfdpoKC9wu3NicCEVUA/gmA3wUpefHTAP4QM//jG23Y\nLQURfQuADYC/wMy/+abbc5tBRB8C8CFm/gQRnQH4WQC/v3wbX3oQEQE4YeYNETUA/m8Af5SZf+qG\nm3ZrQUR/HMA3ArjDzL/vpttzW0FEnwHwjcz86KbbcttBRH8ewN9l5u8Pmb1XzHxx0+267Qhy7ucB\n/DZm/vWbbs9tAxF9GLJn/0Zm3oeEdn+Dmf/czbasoOD9wW32RPgmAJ9i5k8zcw/gRwB8xw236daC\nmf8OgCc33Y4CgJnfYuZPhJ/XAH4RwIdvtlW3EyzYhF+b8N/tZH6fAxDRRwD8XgDff9NtKSh4HkBE\ndwB8C4AfAABm7guB8NzgWwH8aiEQbhQ1gCUR1QBWAN684fYUFLxvuM0kwocBfC77/Q0URamgYAQi\n+ioAvxXA37vZltxeBPf5nwfwAMD/wcxlLG4O/z2A/wSAv+mGFIAB/AQR/SwRffdNN+YW46sBPATw\nZ0OYz/cT0clNN6oAgNR6/+GbbsRtBTN/HsB/C+CzAN4CcMnMP3GzrSooeP9wm0kEOnKsWPgKCgKI\n6BTAXwHwx5j56qbbc1vBzI6Z/xkAHwHwTURUwn1uAET0+wA8YOafvem2FAAAvpmZvwHA7wHwR0JI\nXMGXHjWAbwDwPzPzbwWwBVByTN0wQljJtwP4yzfdltsKIroP8XD+GIDXAZwQ0b95s60qKHj/cJtJ\nhDcAfDT7/SMobkYFBQCAEH//VwD8RWb+qzfdngIguAj/bQDfdsNNua34ZgDfHmLxfwTA7yCiH7rZ\nJt1eMPOb4d8HAH4MEqJY8KXHGwDeyDykfhRCKhTcLH4PgE8w8zs33ZBbjN8J4NeY+SEzDwD+KoB/\n4YbbVFDwvuE2kwg/DeBriehjgbH9TgA/fsNtKii4cYRkfj8A4BeZ+b+76fbcZhDRq0R0L/y8hAgl\nv3SzrbqdYObvZeaPMPNXQfaL/5OZi1XpBkBEJyHpK4Lr/O8GUCr73ACY+W0AnyOirwuHvhVAScJ7\n8/hDKKEMN43PAvjtRLQKctW3QnJMFRS8EKhvugE3BWa2RPQ9AD4OoALwg8z8yRtu1q0FEf0wgH8Z\nwCtE9AaA/4KZf+BmW3Vr8c0AvgvAPwqx+ADwnzLz37jBNt1WfAjAnw9Ztg2Av8TMpbRgwW3HBwD8\nmMjlqAH8b8z8t262Sbca/wGAvxgMMp8G8G/dcHtuNYhoBak89u/ddFtuM5j57xHRjwL4BAAL4OcA\nfN/Ntqqg4P3DrS3xWFBQUFBQUFBQUFBQUFBQ8N5wm8MZCgoKCgoKCgoKCgoKCgoK3gMKiVBQUFBQ\nUFBQUFBQUFBQUPBMKCRCQUFBQUFBQUFBQUFBQUHBM6GQCAUFBQUFBQUFBQUFBQUFBc+EQiIUFBQU\nFBQUFBQUFBQUFBQ8EwqJUFBQUFBQ8EUAEfEz/PeZcO6f058LCgoKCgoKCp5nlBKPBQUFBQUFXwQQ\n0W+fHPoxAP8AwJ/KjnXM/HNE9E8BuMPMP/elal9BQUFBQUFBwReC+qYbUFBQUFBQ8CKCmX8q/52I\nOgCPpsfDub/6JWtYQUFBQUFBQcH/D5RwhoKCgoKCghvGNJyBiL4qhDv8+0T0XxHR20S0JqIfIqIV\nEX0NEX2ciDZE9Cki+sNH7vlbiOjHieiciPZE9P8Q0b/4JX2xgoKCgoKCghcOhUQoKCgoKCh4fvG9\nAF4H8IcB/OcA/g0A/wskNOKvA/gDAP4hgD9LRL9JLyKibwDw/wJ4CcC/C+BfA/AYwE8S0T/7pXyB\ngoKCgoKCghcLJZyhoKCgoKDg+cWvMrN6GXw8eBJ8F4DvYuYfAgAi+hkA3w7gDwL4ZDj3vwHwWQC/\ng5n7cN7HAfwCgD8J4Pd/6V6hoKCgoKCg4EVC8UQoKCgoKCh4fvE3J7//Uvj343qAmc8BPADwUQAg\noiWAfwnAXwbgiagmohoAAfhJAN/yxW50QUFBQUFBwYuL4olQUFBQUFDw/OJ88nv/lOOL8PNLACqI\nx8GfPHZTIjLM7N+vRhYUFBQUFBTcHhQSoaCgoKCg4MXCBQAP4M8A+AvHTigEQkFBQUFBQcEXikIi\nFBQUFBQUvEBg5i0R/V0AvwXAJwphUFBQUFBQUPB+opAIBQUFBQUFLx7+OIC/A0nG+AMA3gLwCoBv\nAFAx85+4ycYVFBQUFBQUfPmiJFYsKCgoKCh4wcDMnwDwz0HKOv4PAH4CwJ8G8E9DyIWCgoKCgoKC\ngi8IxMw33YaCgoKCgoKCgoKCgoKCgoIvAxRPhIKCgoKCgoKCgoKCgoKCgmdCIREKCgoKCgoKCgoK\nCgoKCgqeCYVEKCgoKCgoKCgoKCgoKCgoeCYUEqGgoKCgoKCgoKCgoKCgoOCZUEiEgoKCgoKCgoKC\ngoKCgoKCZ0IhEQoKCgoKCgoKCgoKCgoKCp4JhUQoKCgoKCgoKCgoKCgoKCh4JhQSoaCgoKCgoKCg\noKCgoKCg4Jnw/wEX7lmZWHxdJAAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -25955,7 +538,7 @@ } ], "source": [ - "sentence = \"That's all folks.\"\n", + "sentence = \"Will Donald Trump Jr. offer the country’s business leaders a peek into a new U.S.-India relationship in trade? Defense? Terrorism?\"\n", "model.decoder.max_decoder_steps = 300\n", "alignment = tts(model, sentence, CONFIG, use_cuda, ap)" ] diff --git a/requirements.txt b/requirements.txt index 2c411d2..151e1f5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,4 @@ librosa inflect unidecode tensorboard -tensorboardX \ No newline at end of file +tensorboardX diff --git a/tests/layers_tests.py b/tests/layers_tests.py index eef0247..3fbab02 100644 --- a/tests/layers_tests.py +++ b/tests/layers_tests.py @@ -10,7 +10,6 @@ class PrenetTests(unittest.TestCase): layer = Prenet(128, out_features=[256, 128]) dummy_input = T.autograd.Variable(T.rand(4, 128)) - print(layer) output = layer(dummy_input) assert output.shape[0] == 4 @@ -49,7 +48,7 @@ class EncoderTests(unittest.TestCase): def test_in_out(self): layer = Encoder(128) - dummy_input = T.autograd.Variable(T.rand(4, 8, 128)) + dummy_input = T.autograd.Variable(T.rand(4, 8, 128)) print(layer) output = layer(dummy_input) diff --git a/train.py b/train.py index ac61cc9..0d432cc 100644 --- a/train.py +++ b/train.py @@ -20,7 +20,8 @@ from tensorboardX import SummaryWriter from utils.generic_utils import (Progbar, remove_experiment_folder, create_experiment_folder, save_checkpoint, - save_best_model, load_config, lr_decay) + save_best_model, load_config, lr_decay, + count_parameters) from utils.model import get_param_size from utils.visual import plot_alignment, plot_spectrogram from datasets.LJSpeech import LJSpeechDataset @@ -106,6 +107,9 @@ def main(args): start_epoch = 0 print("\n > Starting a new training") + num_params = count_parameters(model) + print(" | > Model has {} parameters".format(num_params)) + model = model.train() if not os.path.exists(CHECKPOINT_PATH): diff --git a/utils/generic_utils.py b/utils/generic_utils.py index d5b595f..ca32060 100644 --- a/utils/generic_utils.py +++ b/utils/generic_utils.py @@ -101,6 +101,12 @@ def lr_decay(init_lr, global_step): step**-0.5) return lr + +def count_parameters(model): + r"""Count number of trainable parameters in a network""" + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + class Progbar(object): """Displays a progress bar. # Arguments