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"cells": [
{
"cell_type": "code",
"execution_count": 25,
"id": "e9449b52",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "4b6663b0",
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_parquet('minDist7km_approx.parquet',\n",
" columns = ['THICK', 'EAST', 'NORTH', 'vx', 'vy'])\n",
"data_smb = pd.read_parquet('minDist200m_approx.parquet',\n",
" columns = ['EAST', 'NORTH', 'smb'])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "2b19a5bf",
"metadata": {},
"outputs": [],
"source": [
"mask = data.EAST <= 1.412e6\n",
"mask2 = data.EAST >= 1.293e6\n",
"mask3 = data.NORTH <= 6.598e5\n",
"mask4 = data.NORTH >= 5.4e5\n",
"\n",
"data = data[mask & mask2 & mask3 & mask4]\n",
"\n",
"mask = data_smb.EAST <= 1.412e6\n",
"mask2 = data_smb.EAST >= 1.293e6\n",
"mask3 = data_smb.NORTH <= 6.598e5\n",
"mask4 = data_smb.NORTH >= 5.4e5\n",
"\n",
"data_smb = data_smb[mask & mask2 & mask3 & mask4]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "db395350",
"metadata": {},
"outputs": [],
"source": [
"east_smb = data_smb.EAST.to_numpy()\n",
"north_smb = data_smb.NORTH.to_numpy()\n",
"\n",
"Xmean = np.mean(east_smb)\n",
"Xstd = np.std(east_smb)\n",
"Ymean = np.mean(north_smb)\n",
"Ystd = np.std(north_smb)\n",
"\n",
"east_data = (data.EAST.to_numpy() - Xmean) / Xstd\n",
"north_data = (data.NORTH.to_numpy() - Ymean) / Ystd\n",
"\n",
"x_data = tf.convert_to_tensor( np.column_stack((east_data, north_data)), dtype=tf.float32 )\n",
"\n",
"east_smb = (east_smb - Xmean) / Xstd\n",
"north_smb = (north_smb - Ymean) / Ystd\n",
"\n",
"x_colloc = tf.convert_to_tensor( np.column_stack((east_smb, north_smb)), dtype=tf.float32 )\n",
"\n",
"# Use original THICK and smb values (no normalization)\n",
"H_mean = data.THICK.mean()\n",
"H_data = tf.convert_to_tensor(data.THICK.to_numpy(), dtype=tf.float32)\n",
"smb_data = tf.convert_to_tensor(data_smb.smb.to_numpy(), dtype=tf.float32)\n",
"vx_data = tf.convert_to_tensor(data.vx.to_numpy(), dtype=tf.float32)\n",
"vy_data = tf.convert_to_tensor(data.vy.to_numpy(), dtype=tf.float32)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "983ebb90",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(16108,), dtype=float32, numpy=\n",
"array([-0.9054229 , -0.8908542 , -1.2522389 , ..., 0.13198702,\n",
" -1.3902622 , -0.51392144], dtype=float32)>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_colloc[:,0]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "025de49f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x1a3f025ae90>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x_data[:,0], x_data[:,1],\n",
" c=H_data, cmap='Reds', edgecolor='black', s=10, label='Training Data')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b12eb747",
"metadata": {},
"outputs": [],
"source": [
"# Set random seed for reproducibility\n",
"tf.random.set_seed(42)\n",
"np.random.seed(42)\n",
"\n",
"# 1. Define the PINN Model\n",
"class PINN(tf.keras.Model):\n",
" \"\"\"\n",
" A Physics-Informed Neural Network model.\n",
" \"\"\"\n",
" def __init__(self, num_hidden_layers=4, num_neurons_per_layer=64):\n",
" super(PINN, self).__init__()\n",
" self.num_hidden_layers = num_hidden_layers\n",
" self.num_neurons_per_layer = num_neurons_per_layer\n",
"\n",
" # Define the neural network architecture\n",
" self.hidden_layers = [tf.keras.layers.Dense(num_neurons_per_layer, activation='tanh')\n",
" for _ in range(self.num_hidden_layers)]\n",
" self.output_layer = tf.keras.layers.Dense(3) # Outputs: H, u, v\n",
"\n",
" def call(self, inputs):\n",
" x = inputs\n",
" for layer in self.hidden_layers:\n",
" x = layer(x)\n",
" outputs = self.output_layer(x)\n",
" H = outputs[:, 0:1]\n",
" u = outputs[:, 1:2]\n",
" v = outputs[:, 2:3]\n",
" return H, u, v\n",
"\n",
"def physics_loss(model, x_colloc, smb):\n",
" with tf.GradientTape(persistent=True) as tape:\n",
" tape.watch(x_colloc)\n",
" H, u, v = model(x_colloc)\n",
" \n",
" # Calculate the divergence of the velocity field\n",
" div_u = tape.gradient(u, x_colloc)[:, 0:1]\\\n",
" + tape.gradient(v, x_colloc)[:, 1:2]\n",
"\n",
" del tape\n",
" \n",
" # Calculate the residual of the mass conservation equation\n",
" residual = H * div_u - smb\n",
" \n",
" return tf.reduce_mean(tf.square(residual))\n",
"\n",
"def get_data_loss(model, x_data, H_data, vx_data, vy_data):\n",
" \"\"\"\n",
" Computes the data-driven loss.\n",
" \"\"\"\n",
" H_pred, u_pred, v_pred = model(x_data)\n",
" h_loss = tf.reduce_mean(tf.square(H_pred - H_data[:, None]))\n",
" u_loss = tf.reduce_mean(tf.square(u_pred - vx_data[:, None]))\n",
" v_loss = tf.reduce_mean(tf.square(v_pred - vy_data[:, None]))\n",
" return h_loss + u_loss + v_loss\n",
"\n",
"# 4. The Training Step\n",
"def train_step(model, optimizer, x_data, H_data, vx_data, vy_data, x_colloc, smb, data_weight, physics_weight):\n",
" with tf.GradientTape() as tape:\n",
" data_loss = get_data_loss(model, x_data, H_data, vx_data, vy_data)\n",
" physics_loss = get_physics_loss(model, x_colloc, smb)\n",
" total_loss = data_weight * data_loss + physics_weight * physics_loss\n",
"\n",
" gradients = tape.gradient(total_loss, model.trainable_variables)\n",
" optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
" \n",
" return total_loss, data_loss, physics_loss"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "70943a35",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting training...\n",
"Epoch 100/1000 - Total Loss: 4468012.5000, Data Loss: 4466385.0000, Physics Loss: 5424.8076\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
"\u001b[32m~\\AppData\\Local\\Temp\\ipykernel_2732\\2201451446.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# idx = np.random.choice(x_data.shape[0], size=physics_batch_size, replace=False)\u001b[39;00m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# x_colloc_batch = tf.gather(x_data, idx)\u001b[39;00m\n\u001b[32m 17\u001b[39m \u001b[38;5;66;03m# smb_batch = tf.gather(smb_data, idx)\u001b[39;00m\n\u001b[32m 18\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m total_loss, data_loss, physics_loss = train_step(\n\u001b[32m 20\u001b[39m pinn_model,\n\u001b[32m 21\u001b[39m optimizer,\n\u001b[32m 22\u001b[39m x_data, H_data, vx_data, vy_data, x_colloc, smb_data,\n",
"\u001b[32m~\\AppData\\Local\\Temp\\ipykernel_2732\\1096726604.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(model, optimizer, x_data, H_data, vx_data, vy_data, x_colloc, smb, data_weight, physics_weight)\u001b[39m\n\u001b[32m 62\u001b[39m data_loss = get_data_loss(model, x_data, H_data, vx_data, vy_data)\n\u001b[32m 63\u001b[39m physics_loss = get_physics_loss(model, x_colloc, smb)\n\u001b[32m 64\u001b[39m total_loss = data_weight * data_loss + physics_weight * physics_loss\n\u001b[32m 65\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m66\u001b[39m gradients = tape.gradient(total_loss, model.trainable_variables)\n\u001b[32m 67\u001b[39m optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n\u001b[32m 68\u001b[39m \n\u001b[32m 69\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m total_loss, data_loss, physics_loss\n",
"\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\backprop.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[39m\n\u001b[32m 1062\u001b[39m output_gradients))\n\u001b[32m 1063\u001b[39m output_gradients = [None if x is None else ops.convert_to_tensor(x)\n\u001b[32m 1064\u001b[39m for x in output_gradients]\n\u001b[32m 1065\u001b[39m \n\u001b[32m-> \u001b[39m\u001b[32m1066\u001b[39m flat_grad = imperative_grad.imperative_grad(\n\u001b[32m 1067\u001b[39m self._tape,\n\u001b[32m 1068\u001b[39m flat_targets,\n\u001b[32m 1069\u001b[39m flat_sources,\n",
"\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\imperative_grad.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ValueError:\n\u001b[32m 64\u001b[39m raise ValueError(\n\u001b[32m 65\u001b[39m \"Unknown value for unconnected_gradients: %r\" % unconnected_gradients)\n\u001b[32m 66\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m67\u001b[39m return pywrap_tfe.TFE_Py_TapeGradient(\n\u001b[32m 68\u001b[39m tape._tape, \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[32m 69\u001b[39m target,\n\u001b[32m 70\u001b[39m sources,\n",
"\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\backprop.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices, forward_pass_name_scope)\u001b[39m\n\u001b[32m 144\u001b[39m gradient_name_scope = \u001b[33m\"gradient_tape/\"\u001b[39m\n\u001b[32m 145\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m forward_pass_name_scope:\n\u001b[32m 146\u001b[39m gradient_name_scope += forward_pass_name_scope + \u001b[33m\"/\"\u001b[39m\n\u001b[32m 147\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ops.name_scope(gradient_name_scope):\n\u001b[32m--> \u001b[39m\u001b[32m148\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m grad_fn(mock_op, *out_grads)\n\u001b[32m 149\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 150\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m grad_fn(mock_op, *out_grads)\n",
"\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(op, grad)\u001b[39m\n\u001b[32m 264\u001b[39m @ops.RegisterGradient(\u001b[33m\"Mean\"\u001b[39m)\n\u001b[32m 265\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m _MeanGrad(op: ops.Operation, grad):\n\u001b[32m 266\u001b[39m \u001b[33m\"\"\"Gradient for Mean.\"\"\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m267\u001b[39m sum_grad = _SumGrad(op, grad)[\u001b[32m0\u001b[39m]\n\u001b[32m 268\u001b[39m input_shape = op.inputs[\u001b[32m0\u001b[39m]._shape_tuple() \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[32m 269\u001b[39m output_shape = op.outputs[\u001b[32m0\u001b[39m]._shape_tuple() \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[32m 270\u001b[39m if (input_shape is not None and output_shape is not None and\n",
"\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(op, grad)\u001b[39m\n\u001b[32m 181\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01min\u001b[39;00m input_0_shape:\n\u001b[32m 182\u001b[39m input_shape = constant_op.constant(input_0_shape, dtype=dtypes.int32)\n\u001b[32m 183\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 184\u001b[39m input_shape = array_ops.shape(op.inputs[\u001b[32m0\u001b[39m])\n\u001b[32m--> \u001b[39m\u001b[32m185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m [array_ops.tile(grad, input_shape), \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[32m 186\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01min\u001b[39;00m input_0_shape \u001b[38;5;28;01mand\u001b[39;00m \u001b[38;5;28;01mnot\u001b[39;00m context.executing_eagerly():\n\u001b[32m 187\u001b[39m \u001b[38;5;66;03m# The shape and reduction indices are statically known, so we use a\u001b[39;00m\n\u001b[32m 188\u001b[39m \u001b[38;5;66;03m# graph-level cache to avoid recomputing `reduced_shape()` for each\u001b[39;00m\n",
"\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(input, multiples, name)\u001b[39m\n\u001b[32m 12179\u001b[39m _ctx, \"Tile\", name, input, multiples)\n\u001b[32m 12180\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[32m 12181\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m _core._NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 12182\u001b[39m _ops.raise_from_not_ok_status(e, name)\n\u001b[32m> \u001b[39m\u001b[32m12183\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m _core._FallbackException:\n\u001b[32m 12184\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 12185\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 12186\u001b[39m _result = _dispatcher_for_tile(\n",
"\u001b[31mKeyboardInterrupt\u001b[39m: "
]
}
],
"source": [
"epochs = 1000\n",
"learning_rate = 1e-3\n",
"data_loss_weight = 1.0\n",
"physics_loss_weight = 0.3 # This can be tuned\n",
"# physics_batch_size = 2048 # Number of collocation points per epoch (tune as needed)\n",
"\n",
"# Initialize model and optimizer\n",
"pinn_model = PINN()\n",
"optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
"\n",
"# Training\n",
"print(\"Starting training...\")\n",
"for epoch in range(epochs):\n",
" # Randomly sample a batch of collocation points for physics loss\n",
" # idx = np.random.choice(x_data.shape[0], size=physics_batch_size, replace=False)\n",
" # x_colloc_batch = tf.gather(x_data, idx)\n",
" # smb_batch = tf.gather(smb_data, idx)\n",
"\n",
" total_loss, data_loss, physics_loss = train_step(\n",
" pinn_model,\n",
" optimizer,\n",
" x_data, H_data, vx_data, vy_data, x_colloc, smb_data,\n",
" data_loss_weight, physics_loss_weight\n",
" )\n",
" \n",
" if (epoch + 1) % 100 == 0:\n",
" print(f\"Epoch {epoch + 1}/{epochs} - Total Loss: {total_loss:.4f}, Data Loss: {data_loss:.4f}, Physics Loss: {physics_loss:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d082f253",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training finished. Visualizing results...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Cap\\AppData\\Local\\Temp\\ipykernel_8680\\1493822550.py:21: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
" plt.legend()\n"
]
},
{
"data": {
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4ypa2zyrvTq8T/d2KStuLPI+svymV5af9Dh48uPbYY49JHUfSxuDcc8/l69Pff6GFFqqtt956tdNOO637PTllyhReup5K6fft25f/P27cuNqrr77avR3R9zUAVeGss87i59pkoXM5fTbSt9W3sqBWLrTen/70p4bbP/TQQ2sbbrghbwPz7LPP1k499VTequP555/n9//hD3/g5wv6bCbMmzePn3epfUWa9957j7fL+POf/1xbbbXVaoccckj3fbQ+nY/Sx02/07apNUnWdfmzzz5Tft2AG5roH9dCCAAAAAAAgA4o4p6Ouu+11148ZZrmUiZQtCqZd0VQxOnSSy/l6YWUEZEHNXynCBilHKazPSh1nm6/4ooreLSbouWUZphEsAnKEKD18tIMqQk8tTahlEXKXqACPhQdSx93AkXJKEqfQHPGqMgVVW1NWmKAMECaIgAAAAAAiAZKA0ynApLULLbYYt1phPVQXIIKJ1Ead5GIJXNtibQIJXNkkwqylFZORZkoPT+RMZq/SenPlG6cR/L4ZD4opU7TNvKOG8QBZAwAAAAAAFSW+++/n7dKoX5+9VCkjCqZXn/99bx6IjVqJzmi1ha//OUv+bwxKrpE1X6pvQRBPb9oPijN66I2FSRgSZXE3Xbbjf9Pc00/+OADtsEGG/C5ztR38Nhjj+VzP5OWLdSeYqedduK9LKloEAkgVWGlqByVxCeSvptUGp/4z3/+w+eK0mPSQgr8BTIGAAAAAAAqXbiDKiSSaNVDES2KTiURMYqckUhRYQ0quDF37lwuZ1R0iqrW1rfBoGI9VJyDUhRJ+qgycBKto2qqRx11FI+EkbRROmK6LycVEyHBo0I+1CKG9k3HmJZGSotM9xOk6rAERfrq+6oBPwlmztjEiRN5yU+qNEVvYPrQ0BtzlVVWKXzcrbfeyhv4UWh4pZVW4o9Jf1gAAAAAAAAAwAXB9Bl76KGHeGfyxx57jIeC6ZsKKueb9GLJa8pHJUOp4d4zzzzDyxDTItrcEwAAAAAAAABY1SNjWc1CaTImSVoSkq2HekCQrCU5vMTGG2/MG2ZSWBcAAAAAAAAAXBHsnLFZs2bx/4smJ06dOpWNHz++x22Uf0sTLfOgvN2kik1S2YbKo9IEzaamJi3HDgAAAAAA9EGxhTlz5rAll1yyV6VDH5g3bx5ra2uzvt++ffuy/v37W98viFzGSJCOPPJIXnFm9OjRuetRdZnFF1+8x230O91eNDctPRESAAAAAACEwYwZM9jSSy/NfBOx5ZYdzN7/sNP6vocPH84rRULI/CVIGaO5YzTvi5rj6WbChAk9omkUgaPyoMsfeTLr00/sjdyl8KrWittaZO+nr54M01qz+UzVWs6x1lq/6qmhgyaN28qjubXTq+2o0O//9z2/vVn6MS7o19LhbN+h0L+1Pfe+ee0KJ5fImN/h/lIn83lTpVPjPnRuK4tau77IRZOmbTW15We/NHXqyYzpU7CP7n21C2yn4LTYXDL40qe93P3d6wmeupvbalL769OeM574/Ev21OSzeFl336CIGInY60+OYAsuYC9qN3tOF1tx/Rl8/5Axf3F/hZLk0EMP5XPAHn744YbffNC3AdTDIQ39TrfnQU36aKmHRKw5JWNdBeObvEtY0WOaFCSs7Me51vLVdposCFhTjog1BSBizX3TIlJuYNvSLTX2B8j9Wjt6fOwH8gFii+DjWhyKmPlBrC0G9G1nX7a1atvW1/Q+ZyUMFtyeruPykYEeCJno560M9Enp0CRR6etLZ5uBz+AAjUI2QJOYDWgsZXk0dQg+pn9jIesj8HIXClG/klLWr3j7whmADbbTTd9iIevTJLZ+x6Cv/vd5SgmJmE0ZA2HQJ6RcYBKxO+64g/dpWG655Ro+ZsyYMWzKlCk9bqNKjHR7GYqkSudjTETDSMCShRmUsLxIWEgRMZKwniJWTsK+FjG7kEx9LWK972v0WO3H09IhFO2KISJGwpRektt0bFM39cdpYtsuSd53ou8/I8dg4PNUj4nzTHIu1HlONHXupmtLsihvQ+E6K3NdbXQdFxkviKzT2ferRYWi7cuMZ0TX7ezbJLWNovUBCI2WkFITb7zxRvbnP/+Zh6CTeV9DhgzhfceIvffemy211FJ83hdxxBFHsLFjx7ILLriA7bjjjuzmm29mTz75JLvyyisrJ2Im5at7H4LH57uI6RxsuBIwmYEfrZf1jb0pEUv/nBetcDFY1hW1EpEO1X3ZEhqR/Ygcf/12kt99iMKJvMdMRNPyPm86ofOOrgiZ6DlSNXpG53CdKYu9si8Ut03XM5UIWXfGSYNIGV3PRVIWdZAImWykjMYuoimJOrZDgtUoZbHM+gD4SjCRsV//+td8/tYWW2zBllhiie7llltu6V5n+vTp7L333uv+nRpDk8CRfK211lrsT3/6E6+kWFT0owiIWNwiVoVIWNFjin5vFGVoFHHIu0/0NlNkRa1Uoziyj5Nd13VkqZ6i16rR8fr4fLIwFUULNUJm6vxpMs3cdoRMJlJWdF3XFR0rGynL24fsvstGyPIejwgZiIFgImMi7dAefPDBXrfttttufCkLFeVoDkzEfJIwn0WsapGwso9vNDhN7qeogq/piKLRK5EoThmpSD82bz8hSEv6tZKVTB+iZI1Iv6e1bTOCCFnR+VQ2UmY6Qsb3obB91QhZ9+NbaoVRsqIImUhEKRlnyESwZCNlecchu+9Gz4dehcH9W9ngAc2sftoXDQG/bOtgX8xuZ/WjDkTIQOgEI2Oh4VLEfJMwX0UMEqawLwlx8m3Ol6rU+JQa6DuqEcU8fBO1Ru9pWVmzJWR52EhllJEyk0JWRspcCxnhWsqKREpl3/UMHdiXfXe9kWzUUguzlpwKIR2dXezltz9lf/3XNDbr87beEbLwT6GgokDGNCMdutdUnt6GhLmKgvkqYK5EzKZ8+SxXVZAbFQa0tLMvO/ySGFVCiZyJzIMU/TyblrO8c5cpMUufc0XErOhcr0vUVOaS2RAyopGU8XXa9UtZWSET3XfWNpr7NLFDtx7Nll50KBu4wFDWxMtJ1o03aozVujrZkMED2dKLDGbn3/I06+zquU5XK1IWQZgEM2fMd+gEI1VhqG9Ni4j5Uh1RVxWr+otyspTBRBUwkyKWzPnKW2ziuvqcTKXCrMqFVRSx9P8xENrfs+xnpdHn39S5IZnnavLLpbLnYp3p6SrXqTJzyPjjBa7TImODZLyhq+IhITqXzMRctkUG92NDB/Vjg4YszJr79md9WlpZn+a6paWV3zdoyEJsyKB+bOhC+e08QHkuv/xyNnLkSN6bbKONNmJPPPFE4fq33norGzVqFF9/jTXWYHfddVevqUYnn3wyr/VARfe22mor9tprr/VYZ+TIkbwtQXo555xzejTO3nffffn2W1pa2C677JI5Xal+G7QkRf+IU089tdf9dOwugYyVQOSEmHWS1SlhJpGVsDKylbXowISAmRYx1/gsYAkhDc4TSJDyFl3bL/o9dBr9zX2SttBL59uI9kPIihEdK4QoZPX39aEBMZ8xlops5fUK+//Bc58+Tdqmg4CeUGG88ePHs1NOOYU9/fTTvADetttuyz788MPM9R999FE2btw4tv/++7NnnnmGSxItL7zwQvc65513Hps0aRK74oor2OOPP84GDRrEt0mCleb000/nhfiS5bDDDuu+r7Ozk4vc4YcfzmWuiFdeeaXHdhZbbLEe96+++uo97n/kkUeYSyBjisScjmijOqLJ6lllLvbpb4eLviWOTcRcCphsZMuXAbcoIsJVVs7yHlMFIat/v/giZRCyxkDImNZIWd59oUTIVICQ6efCCy9kBx54INtvv/3YaqutxgVq4MCB7Oqrr85c/5JLLmHbbbcdO/bYY9mqq67KzjjjDLbuuuuyyy67rDsqdvHFF7MTTzyR7bzzzmzNNddk119/PXv33Xd5hfM01Lpq+PDh3QtJWwL9TJXV6djoviJIvtLb6VM3D5Eia+n7hw0bxlwCGQskHZGwlY4YuoippME0Eq/0fboHKS7SD7v37TACJlr+PMT0w7JRL9HHyoheLMhIu+v3SwxCZlrKygiZrmuJ70JGlBlHmBKyMggdU150DEgze/bsHsv8+fMz12tra2NPPfVUj8gTiQz9PnXq1MzH0O31kSqKeiXrT5s2jacJptehHsGU/li/zXPOOYctssgibJ111mHnn38+6+hQO5etvfbaPCVy6623Zv/617963U8pkksuuSRbfvnl2V577cVbY7kEBTwEqSlImLZ9GxYwpccppiX6eFEXHWyYkDAt23GcTuh6wCtKiEKi85hDfP5E2WIkJt6fMkVFdFdglD2/6CgIkpz7TBb5cN002seiHi4RKeqhqyl0QyFr0NrIynFoYk5tPmM1e3GQObWvxl0jRozocTulINLcqXo+/vhjng64+OKL97idfn/55Zcz90GilbV+Mk8r+b9oHYLSDymitvDCC/PUxwkTJvAUQorUiUICRpG89ddfnwvnVVddxfsTU2okbZsgCbz22mvZKquswrd/2mmnsc0224ynVVJkzgWQMQPIipiNtENXEmaiMbNOdAhWWqpEBz5lRMy1fIUiYqHKB8j/O/pSJVJnlcf6z7NuOdNZrdFk37KyQlaEqKz5LmRFJfAbPtZDYRE+JgEhA8XMmDGDLbjggt2/9+vnXwGU8ePHd/9MqYx9+/ZlP/nJT9jEiROFj5cEi5aETTbZhL3xxhvsoosuYjfccAO/bfvtt++xH5KzZZddlv3xj3/k895cgDTFiomYTBqiapEO3UU4QhGxrN9FHhNqUQ3fRMxEUQzgFz79bU29/9OfdROf97IVGE1XWzSBzDUphJRFW4ikK+qYs0WeVRNNWaR1a7Veboa5Y8WQiKWXPLmhuVPNzc3sgw8+6HE7/Z43T4tuL1o/+V9mmwRJEqUp/u9//2Nl2HDDDdnrr7+ee//QoUPZyiuvXLiOaSBjkYlYet5X1mJDwny8SIsW5pAhbyBTNMBRFTHfsNYIuaACIeSruvjyN7fxOaiXs7zFppzZKn9fv+hA5Drls5DZnDtmAzqmWV+2sfbOTtbV0SAvsqmJdXW28+bPs74QaIwGpKFo1HrrrcemTJnSfVtXVxf/fcyYMZmPodvT6xP33ntv9/rLLbccl670OjRvjVIH87ZJPPvss3y+Wn0lRFloO5S+mMfcuXN59KxoHdMgTTEyEVN6nAaBMjkfrMxF2NSgodGghe6vTweSFTEfJczWANSHgXYeg1vlBgFz2y3Mhq8oyfvEZfqiL42pk/NF2TRH0bRrkymLJlIZZeeauUpZzKI+jbFMuqKPc8fmtXeyR155l23dt5UttDBjfVqyz5kka599+gn710vvsvntnUHIZohQuuA+++zD511RVIkqIX7++ee8uiKx9957s6WWWoqnDxJHHHEEGzt2LLvgggvYjjvuyG6++Wb25JNPsiuvvJLfT60IjjzySHbmmWeylVZaicvZSSedxAtoJL3Cpk6dyuXsm9/8Jp+3Rb8fddRR7Ic//CFbaKGFuo/txRdf5EVGPv30UzZnzhwuWknBDoKOlbZPpeupbD7NGbv//vvZPffc072NY445hn3729/mqYlU0ZHmz1E0kMrzuwIyVmER813CbBTlUEFUqhIhiyUaVnURk5Ww+sdByuKVMl+ETKeU5X2plAZCZgfReWUiUpQIi4w8lREy0WO669kZ/OdNV2lnrc3N6Y5jHBrhUPSMpO2u/8xAWpdBdt99d/bRRx/xJs1UYINEZ/Lkyd0FOKjyYLpUPM3LuvHGG3np+hNOOIELF5WsHz16dPc6xx13HBe6gw46iM2cOZNtuummfJvUJJqgtEmSOCoqQoU3SKhIxtLzyIgddtiBvfXWW92/U9VFglJXCRK1o48+mr3zzju8HD/NCbvvvvu45CW8/fbbXLw++eQTtuiii/Jjeeyxx/jPrmiqJc8AZEKhVCrBudypZ7M+//+m8UnEXEXDfO4TZhIbJeh1ipjLeV2+SlUj0cqSJlURy0NUzGyIXJnnFrtg6pI7V6KmqxiIbNEP04KmS8ZEC3zIClmZ6FjuNjNkLC86JiNashGtRlKWtz2R/dA6/Vqb2dABfXtVtaeR6swv23pExJrbeo5/OtvmsWdv+AWbNWtWj2IVPo0l33h5OFtgAYvVFOd0sRVGve/lawK+BpGxErgSsTK56b5JmI55ADaak4ZSih7yVV4+dIuXyD5Iaor2m3Wfigjpfm5ZzyMm6t/TqnJW/7m0JWd0rrERIRM5J9uOoOmOkPH1PIiSiaQrykS/ZCNlSVGPPCkrGyEj2fqg/UuxgwEgEiBjkriMhJWeIFxCpExEwnxMQTQpYqYkDAJmT6J8OnYfn69sJK9RRDLWdEiRz6zO0vkuhKzovO2jmIn0K0tfQ4vErOzcMRmK5o/JSpmu1MWyQiYuhk29omMAhIhfX/N4DJ3wZESMJEyHiJWphKhSEdFWhUSImB6qLmI0mE8W4A8if4/6dUL6G9p47+v8bOs6/5Qti5+gq6KtbmSudWWuq7bHJ6KFLWQLYJCQ5ZW/z9uWyD5QiANUDciYZnRJmI5IGN+GZxJWNREz2RusyiIGAfMf2bTLRo/xjaoKWY9tlhSz0IWs6Bqr4/qtU8pIcEyJkEsho+gYAKEDGdOIbymJZUTMFFUQMRsNmqsqYpCwsKj/e4n8/UL6+9rod6ZbyEydl1TFLAYhy0OnkMmMLxpJmc3IFCJkADQGc8YiFTFVfBMxHy/ULsvRxy5hIQ3Egbm/a6P1fZtfZrq0fvK51zmPTHfFRR0FP0TnkqWvJborK8rMIRMpga9z/phoiXuRAh9l+oLpQtdxYO4YCB3ImAYgYvoqI6oKWNl0QtEGzbqEq8r9uhIgXkAVXdUlbX7mdIiaicIfOvuSlS34IStlItedMrIWs5A1fJxGYSvaVqP9iB4HT1fEJQUECmQsAhFz1bxZl3yVjYBpKbAhsA2ImByQLWAT36Np9Jm00ZRaNYpmIlqmWoFRZzPpslG0WIXMl+iY7giZ78zp6mKsy/L+gPdAxgIVMZ1VnGRFzBcJs9WEme8HIiYFRAz4Br0nqyJkWVKWde7JE7b6810ZOfNByMqKWaxC5kt0rNH9PokjACZAAQ9FIGLVETFdQMRAVRnY3Na9VP29afszSuedvHNP0X06ixL5WG1R9lomW/bet6IeRH1RD98KZPh2PADYAjKmAESsHDp6zNgUMR1RsSqIGCodAhEBcy1lPrxHXX9Wy5yfVKUMQpa6r2TvUJ3jkUYCJFoSX8e+itaBqIGYgYw57CEmczLW2WBStocYXbB0iJiuRp8QsYx9oNw88AjXEbBG+PB+DVnICFUhUzl/h1RVN02ja7YuKZMte69CIynL6zWWtR1VIGQgVjBnTJBac43pyPSOORJm+oKpU8JE5kSUETFVAfNtgObrQDZE8fgiNVopKyvpbfkiSskx+S5ivs0ha4StOWYq0DlSZT6ZaqVF3XPIVJCZP9ZoDln9uKDMfDKZOWT188dk5mQlQpS1Pp0CRD7+ZeaAYf4YiBHImGa0pR5ojIKF3A9Ml4A1EquyETAd0S8fRCw00Wo08BeVFhsCoXMfPgqP7mNKtldGPEOvwJh1XjApZ3QeU6nCGLKQ0fXOVK8yGSHTIWWyRT3KSE7e+hAyAORBmqJGtKQbaEhHTNIQVSJhvoiYajpLr+2UmHSeN9k9aym9bYiYkcG/rnWAXdJ/k6rPL7N9rlA5n9ks7BFrymJW+qLKmEI0ZTErXVE2DTBvfaQsAiAHZMyjZs2yEpaWLlUBKzsnzJSIadmOJgkzPe8LIiaPzAC9aF2ImH9k/U0gZD2BkLkVMpXrLKHyRauKlJWZ2+6DkIkeA4QMxAJkzLGIyUpYGenSXZgDIqZh2xAxaVQG5r4N8EFY4lxVIbNR1MMHIdPZssVU9osJIcsr5uGDkJXdNwAhgTljjkRM5WSsQ8B0XXx0i1jVJMyXUvQhUXYwDvnyi/ScMNG/jY15ZCKfFx/mkqXPIabnkfF9CM4lS5+DZeaSuZ5DJjt3TLaQh+o8sjKIzCHLawata16W6BwyAKoMZKwESvncikLlQsRspIL4JmFVKEMfCpCnalA20ulazGQxIXLpc4opMZOVMhUxa3Q9yJK1kIVMZcwhW9wjFCGr3xeKdIAqARlTBCKmBuRLf8ntkASLgGT1ZoHWefz/Oe39XR9KcNS/n1zImQz1n1fdcma6+qJKxcUyETOR6JluISNEpazo+txI1GxEx0ISMlP7BsB3IGMKQMTcNWOWlTAbsqUz8pUM1ESFDCIWj4glP0PIyr/HfBcym/3O6Hzki5DVn8d1lsTXXfa+/nqpUv7eduQsdCHL2k9sQjarq5V1dtl7T8zt0jeGBOaAjEkCEcum6iKmI/WwXqwaDdIgYn4IFKEqUPXbMSVk6f2Ykj1dr4ktIVN9P5oQvSoKmYkeZSYbQ6v2I9MtZCqpiiEJWeYxRSZkANTjx1c2gQARC0PEdPX+EtpXS7sREVO93VdiETESjjyByrq90bZU7pPdR/22VI5Vdh8m9qPrPUe3l3k/Jo/X/Z42/Zk2MVc17zwr04dRdwVGk3OdVQtfFaYyarzW+1ZlMY+i7zPy9oGqiSBmIGOeiZiusvWypet9FTHZps2hSZjIIIzuT68TkoiZGLS6QkQsROVD57byHtvo8ck6qvtxLVuy7z8T70Xd27QhZCYLCOXJF4RM7xesZfualkGXLOkMMEPUQOggTdEQKg2cQ+uZYlLEdJao10WVS9HHIlSqyEiHzjRD0dS/slJkSqoavQ62CpeYfv/qnKcm8jkvm9LY6Fwmk9KYpCuW/SJMdR5ZSCmLOgt5lElXLEte2qBsOmFeyqJKWiIXsmpfpkDAQMYMINvEWScqIqbyLaIJESsrYLqjYq7lyxeqImK6U/dMCIbvkSgVEZN9vXytPGmzcEiWsOmccyY7x0zk3Cs6x0xlHlmRkOVRVtRUhKxo/hiETMMxYUQLAgVpig5ELElF9EHEVNApYkkaYhkR0z1HzHQqT0hAxPzaZowiVnRb0To+vr4uPy+6I+cuz4Eq1wPZ65KOVEbd11yVqQ2+pizKpA5mfYeR9XikI4JYgYxpPIk2OpGaELCyFwXZC5JuEVNBZnK47wMQn+d/QcTMbNtHiXAtYqr3+fhaQsgKtidxvo5VyBqNAWIRsuQ+UYEKqBsFANqBjFmMhpmiKiJmukoiomFfAxELax+hoFq4xEblSZ1AyPQAIQtfyJL7VaJaiISBqgAZq6CI0cXHlYippCTakDCI2NdAxMLeVyxFUGQe5+PrCyHL2ZbkuRxCpl/IypS3LyNOjdZBdAxUFUx3rJCIuSxfrxoJM4kNAfM1DTEZKNoqOGCiAIPoADxrPy4G7z4Kgwl09khz/b4quy2bRT1MN5LW2ThatmG0zqIeeeiovhhSlcUyDaBtUl8QBA2gQYxAxjTOCfNBxHT3VykjYmWLcoQoYr7KV9439ra/vS9bQa/sPkH86Hxf6aiKCSHTI2QqqAgZUUbKZISsqLqiT0JGiEiZLVGCkIHYgIwZIiQRyxIuuoCpiJiO/mC+ilgMUS6XqVN5c4HKFHwAIAud7ytdQpaHaVErOm+piJorISu6thRFzWSFLOtaKitnsUXIZKVMN5AvEDuQsQqLWJFsuRAx0xIWs4glPxcN7HwTsfR9ppoZg+qi831lqm9cqJEz3yJkjdIYVYSsbMQsRiETkbKy4iTadyxUQZvV1Z91dJlpRp7F5112Wh6BcqCAh2ZiEDEVIGL2yZKrPOHyVcSK1oGIgbLofF+ZfD+GWOzDZVEPlWuQjmue7LVWZF63zJjBdFGP9KK7wEdZ0HcMxAxkrGIiRhckE02bywARkx+4FQ3e6u/3XcSy1oWIAV3ofF9ByHpSRSGrSmNoGTFzKUkQMhADSFOskIjpRMfcMAIiZm6w5rpEvcrA1aWELdjy1b5nd/QXXtcmIscF3PaTq2KxD99SFhtRNmWxzDU8L21RpJCHrZRFGVSqLYqkF+alKuY9NtSURQASIGMei1jRN2ohiZjPwhWriNnExyhWGbFyIVoiyByXjLjJvFZAff5j7EKWpoyc2RIywicpKxpHyIiabSHLw9TcMYgXiBHImGciJpLSABELX8RCqYDos3CJSEv653rZ8FW6dEDPTVZERR8D3FZfJEzKmo5y+OlzsoqY2RCy+rRFm2ImW9wjK3KmEh1TETKXvcggZKAqYM6YByJGJ+ZkaQRELB4Rq/+50bpVFjFRcSpaj+5LL7HT6Dlm3V+F1yWGz4zp84LO86DqOdrG9cPkfGrdc8l0fvGrModM53wumXleed87YK4YiAnImAciJgpELC4RK7qt6PYqiVhanBpJFERC/DXB62geCFl4QhZacQ+VYh4yQiZb7t4UIoFgyBkIGciYAhCxYiBi2TSqgCi6ri1cF9MoEolQ5nn5gMprhddTDzEIma5zYyhCZrviosw4IGvsYVrIRMkTMh3RsTwhg4CBWMCcMc8lTKeIuWjkbILYRExmnapEw3SuB9ReKxT20IPJhtG2Cn7omEdmi+SaVdQEugwqDaBNkwiZyhwyG+iqgijaEBqA0ICMWRQxnRJm+ts7VQkz9S1mGQGrl630oMIXEbMpVemBoWvxqgeC5R+6/iZVlrqsz5lOQXMhZEXnVd1l8FUKetA1zJSQVUXKqMeYaKpio2IepsrSpx+PSBkIFciYJXQW5zAhYr5FwHRFv7Jky4SA+RLVEi2/bRpIFdD1nohV4uo/hyYrMOoStaJzp4/RM1Uhk+lFRtdtm0Im0pNMp5TJClkROoQM0TEQI37GtCOjUb+wZHEhYnSx8q2Bs0kRM0FIImYDiBjQSVXeT/S5TZZQz1Emz7l0fUkvPn3R6HJOdxFl5pP12E6L+NyyRsU8dM0hAyAmIGOG0dm42YSI6QIi5gcQMRAjVXtfmZCyGIQsjYyYQcjsClkjygpZfaAXIgdCB2mKDkRM5aQNERMHIuaGqg2YgV2q2JQ6+Uzrml9mY25Zo5RF1XljItegonllpueQuUhZFEW1QbRqyqJII2hTc8h8Z27XANbVZe898kWXn18SgJ4gMuZp/xBT6E5LhIj5AUQMVIGqNOw2+fmOLUImc02SufapNoFW+bI1pAbQohGyMr3HRCJdiI6BmEBkrCIipiJgOisj6i5H7/Ki71N1RJMls0Wp4uDYBAs0m38d53S6f7+Yes9VLWoWQoRM9XpQNnJWVH1RNkKWCJloQQ/VCBmNGzrb3ETVSMiaGkSybBN7hAyANJAxzUDE4ugLFoKIFd1uC4iYG6kqc2yiQpZ+HiFInIn3YsyCZ6v6okqFRR2pjDqFTLbCYhkhI0SkTKSqoikh01lhsQgIGagKSFPUCEQsY1sQsdK4Fq48IGJhiZjMMdavE8LzMkFV3+M6z3Wq52wd1w5dKYvdj5FMW1Qt6kFjiWSxiWzKoslUxe5tFDg5UhVBLEDGNAERy9gWRKw0ELFwCElYio417z7dz4+2l158parvdQiZWyFLKJIynXPHZIVMZ3XFRkCyQOwEJWMPP/ww+/a3v82WXHJJ1tTUxO68887C9R988EG+Xv3y/vvvaz0uiFjGtiBipYGIhYPPMiFzzI2eh47nmSdfPktZVd/zEDI/hKwI34VMR3SMbwdCBiImKBn7/PPP2VprrcUuv/xyqce98sor7L333uteFltssaBETPbEH5OI0QXclYjRQAQi1pOqDkrz8FkgTEWnyjxf0RRJH19T1+99V+cEHyrHmiwARUDIAAAuCaqAx/bbb88XWUi+hg4dqvVYVCXMdKPnGESsquXpdQ+6RCvOuR5kylBmkK5SuAKIv0aNXl/Z11Wm2Ihrks+Q6YIfIucGExVWdVVfVC3ooaOoR1FBjxD6kDWqttiooIdsrzEbFRZ1FOigt6Wnl3IA4oyMqbL22muzJZZYgm299dbsX//6V+G68+fPZ7Nnz+6xyIgYnWyLFpn+JlURsST6ZTMKZkvEaPAku+ggT7CSPk3pxdcoTdZievsQMXVMvK4h/J3SnyEfPk+mzik+fHlF15j04roPp0ofMpM0ipCRkPkG0g/9hDLQRo4cyfr378822mgj9sQTTxSuf+utt7JRo0bx9ddYYw1211139bi/Vquxk08+mY/FBwwYwLbaaiv22muv9Vhn5MiRvaYVnXPOOT3Wef7559lmm23G9zNixAh23nnn9TqWiy++mK2yyip8P7TOUUcdxebNm1fq+ZkmahmjP/oVV1zBbrvtNr7QH2WLLbZgTz/9dO5jJk6cyIYMGdK90GNERExGtnQKmA8ipnphdJWCaFPEXODDgFAWnwbXIAx8eM9kfdZ8/PyFImSy1wSTUmYKk+mKuoVMd3XFPCBkfnHLLbew8ePHs1NOOYWPl2l60Lbbbss+/PDDzPUfffRRNm7cOLb//vuzZ555hu2yyy58eeGFF7rXIWmaNGkSH5M//vjjbNCgQXyb9ZJ0+umn95hWdNhhh3XfR8GRbbbZhi277LLsqaeeYueffz479dRT2ZVXXtm9zo033siOP/54fuwvvfQS+93vfsefzwknnKD8/GzQVCNdDRAy5jvuuIP/wWUYO3YsW2aZZdgNN9yQGxmjJf3HJyEb8ZtTWOuQViMn1rLfrrkWMRVczgOzAUQsrEE1CBfVVEaVPmrpNMRGnzUfe5TpSmEsk7KYl6ZYf02QTWcUSWEsSlPMQjRtUab/GKGaqijTFLpRDzLRlMVGqYoi/cb6SKQ7FqUt5t2XXNY7589jr1x0Aps1axZbcMEFmU/QWJK+4L/x2dFs4AL2mnt/MaeT7bn2C1KvCUWKNthgA3bZZZfx37u6uvg4mMSIRKee3Xffndd0+Nvf/tZ928Ybb8yz0ki+SDOo8N7RRx/NjjnmGH4/Hc/iiy/Orr32WrbHHnvw2yhSdeSRR/Ili1//+tfsF7/4BS/C17fvV+cHOh4q5vfyyy/z3w899FAuYVOmTOl+HO2XBPCRRx5Ren42iDoylsWGG27IXn/99dz7+/Xrx9+w6YVozpAuiBhELAuImDgQMVAWlbloWX3UZLYj8lnz8fPoQ4Qs6/wvelvZ65HstU/0+upTMQ/dETJb0bEERMnMUD/9Jh10SNPW1sajTpRGmNCnTx/++9SpUzMfQ7en1yco0pSsP23aNC5Q6XVITEmK6rd5zjnnsEUWWYSts846PPLV0fH1Z4vW3XzzzbtFLNkPFen77LPP+O+bbLIJP/4k7fDNN9/kKZM77LCD8vOzQVAFPHTw7LPP8vTFskDEzFa30g1EzD8gYkAXosU+REr3F21H9nNG6/sWIaNzlI4ImY6iHo2ES7bgR9kiHzqu56JRMpViHo2KeJgu6uErIRXxmNU1gLV12ht6f9n11fuyfsoNpehRil89H3/8Mevs7ORRqzT0exJ9qodEK2v9pI1U8n/ROsThhx/O1l13Xbbwwgvz1McJEybwVMULL7yQ30/rLrfccixNsk26b6GFFmJ77rknfw6bbropj8iRzB188MHdaYoqz88GQcnY3Llze0S1yLZJrugPR6mH9Id755132PXXX989iY/+cKuvvjrPS73qqqvY/fffz+65555SxwER81PEXE8wdyFiIUkY5OtrhjR/0XCdWZ0DpR5Tv37VaCRSrt5/skJmozKjayEzmR3RSMgaVVUsW2VRRsrKVFe0SVFlReo11ihVkXqNyaQqAv3MmDGjR5oiZYH5xvjx47t/XnPNNXkE7Cc/+Qmv5SB6vNRf+Oyzz2a/+tWveOSNnOGII45gZ5xxBjvppJOYrwQlY08++ST75je/2esPt88++/C8UzLo6dOnd99P4UjKFSVBGzhwIP/j3nfffT22YVvEbEtY7AJmSsR87PMVknjFJF8i4uTDfl0cp28C6Ot7TlTI6iszmhayPGRETVfZe5+FTPVaLzuXzASNomM6SJo/F0mZaSHjb8HszDtA55PUtJsihg0bxpqbm9kHH3zQ43b6ffjw4ZmPoduL1k/+p9vSmWn0O80ry4NkiiJb//vf/3h1xLz9pPdBwvWjH/2IHXDAAfx3quxI89kOOuggPt9M5fnZIKj4NFVCpLBj/UIiRtD/ZMUJxx13HLfiL7/8kn3yySfsgQceCE7EklK7oiV36SKTXspgsoSwTmIWMR9L0Ic8KA5FxEIBr484jT7DPlVmlD0Hus5KyKLRdUvm+qhaXdFE6XvVHqdlEZk7lkhZkZABv6Fo1HrrrdejAAYVuKDfx4wZk/kYuj29PnHvvfd2r08ZaiQ66XVo3hoV1cjbJkGZbzSfi3oFE7Tuww8/zNrb23vsh0SNUhSJL774gj8mDckXQb6g8vxs4P5rm0D4qoBHq3URC61nmG1iFLHQxCskEUvkQSSiA9EQg14n3yJkvpIX7Sr6zLuadyabyhh7hKz+eiyauuhLhMwWjdIWTUbIPHv7BQtlnVHG2frrr8+L3tGUH4ou7bfffvz+vffemy211FI8fZCgNECqVH7BBRewHXfckd188808ky0pOU/Vz6lC4plnnslWWmklLmcUwaIKi0lF9KlTp3I5o4DJAgsswH+n/mA//OEPu0WL5oOddtppvIT+z3/+c146/5JLLmEXXXRR97F/+9vf5nPMqABIkqZI+6LbEylr9PxcUJ0zhEMgYmaITcRClzCfRaxerBpJGURMDgiZOPVyJVqZEULmV8pi+hrdSMyKhMzGvDFdqYpFc8d8ETJQHipV/9FHH/EmzVQYg1IJJ0+e3F30gqYDpaNPVMGQ+nudeOKJvFAGCReVmx89enSPTLUkXXDmzJm8wAZtk5ouEzQnjCSOiopQpUcSNpKx9DwyqsBINR9+9rOf8egWpRzSMdI2E+gYSP7of5qitOiii3IRO+uss4SfnwuC7TNmuzfECjdMYM0D5S+GEDEzE7djErEYJMxXEROVqkQkIGHliEXIVHuXyUByJfvZd1WZUbbYh04hk+03lkejKos65pA1krI8IVORMZmqikSejKlUUxQRsjL9x/L6iRX1IEv6jL1+rt99xn799AZswGCL1RTndrBD1v23l68J+BpExgwiK2KqeekhiJjO6lk+zk+QBQJmHhmxgoSZiZDJyrAvqDSEruo5oOgcrUPKZMvb+1zUw8eqiirl7UUiZCJVFlV6kDUSMgBCBDLmgYi5ljBdImajmbMJETMdFYth0GVDumTmc9U/BviDyt9EJs0xvX0bEpf13rcROQulb5kNKdMlZCaRKX9fVCBMVMp09hwzJWQmgJCBGAmqmmIo2BQxHSmKVRYxk4RYBdG1iNX/LPoYED4if8+suX8u3gcuI8Iuzillv7DSce62cY1RvZ6WFbF6KUsWm5CQST+mQXXERtUVywgZADEBGdMMImJmCFHEYsC2iBXdJnM/CJOiv2uj+2yLWdWErCyxCplOEaunkZRRdCxvyYuO2RYyU0DIQExAxjQCETMDRKxaItboPohY3KjIef26tt4jELKwhEz0micqZCZFLI3OKBmEDAD/gIxpAiJmBohYNUUsbx2IWDVQSVvN2oaN9wuELCwhc1Gl2AVFzaFtCpmpVMUECBmIARTw0IBK+XpXFwtdFRNjETHXvcV8w8fqiFUQMBvV+0JD19+90XZ0FAGhv1/sRT1ke46ZJrkGyRb2aFRV0SRVawDdiLx+Y7JFOkIq6kHniY5Oi6XtO91/+Q8ag7NCYALmQ9l6X3uFNaKK4uVSrmJvBNzotRUdnNdvR+ZvJiMAut8LOvftUkB1vU9dPscQqyzq6kWmUmlRl5BRlotsqqIvQqarEbQIRWXudQhZKCIGQB5IU5Q8iSaLa2SiYlUXMZIwiJhdkmhErFEtkddW1zo6Hm/ivaBz364jsjGkM9pIWdR5HqVrQN5i47qk67qoMu1AdgzhqgeZLUjIMm9HCiKoCJAxQfpZLjObCFfeIrwNiBirIj6IWN7vVXpti9bV9TdqtB2T7wWd+4aQVU/I8vBFyESvtTaETPe8Mds0mjumKmSIioEYgIx5ii/9wxIgYuGge8CXLhuuWnI+FkFTeW2zHqP7b5S3PRuCo3PfELLyQMjiipCZjIo1KuRhmzwhAyB2IGMeAhErD0SsPHnyVXR7o+1V9bVNP9bUYLzM3DOf9g0hKw+ELC4hiwWRyopZQpYXHUNUDMSC+1mkFaPoZK2rlG6VRcylhLksM61bwnSuV/YxPqDj9XUZpbJBUlFQ12sVQ1EPl8/RRlGP5HzrU5VF3UU96Lr8ZVur0aIeplMVO9uapQt5UHn7JgMFPoqKeTQq6hFyFUUAivDnjBERst+O6exnEruI+RLx8qm/jwsR853kNVGtaAjClMEiZMXHVkn8enQKWuhl78tUXExfv0TFzEcho4bPZVMVbQkZ9RprEpAoFfLEC0IGYgAyphmIWPgi5kuES2RQBhFrnBLY6HWEiIWPaMVGnaKTfF50R9Bkv0iIVch0lb7Pup4VyVmRkKVpJGe6hSwPUVHzSch0Rsf4uhAyEDiYM6YRiFhPIGJm591AxMoXy4CIhY/rio0ihW1U0Hmsts5rus7TOkVM5Roncj0VqW6sModMp6jJVlfMK+hBQiYDCZltUAYfhAxkTBMQsZ5AxMxWpIOIsdLSBRELH58qNpqQsioKmWkR0ylk3etCyKSFTKWYRyPh6kKuFwgUyJgGIGI9gYjJIRsBg4iVbywMEQsfXys26payKgqZLSBk8UXIAAgRyFhJbJxk84CIVYdkQAYRK5+mBhELA9PppabfB7qFTNfxkpD5WvreVlTMlJCpjhV0lbeXETJVIGQA6AdBXcugfH1jqiRhrua7hCRikCezDP3/98JMw6Xcffzbuy6hX/b1KHPsaSEzVeBDtvR9o2uICVkTqbSYV9RDtupiUVEPErL57dUakokU8qgHxTpAjFTrkx8wOuXLBKoiViXx8gWXIuayWiTIF7HkZx+FzDSuhExHNUZdclYfKdMtZ1nneZXKi3Sd0SlkMr3IRIWsETEImakeZFVgducA1tZpr9rIvE6/x47gK/Bp8jgqRif/ZDGFjqgYRCwcfBGxrN8brQ/MiVjRbVXA9nvN1OdQZyqjaVTP/7oyMGSbQoeWsugLImXuAag6kDGPRcw0ELFq4ZOIqd4O9FAkXRAyu59DXyswxixkKiKWACFTmzumE5SxB7EBGbNQqAMi1hOImH18FLG8+yFiZhGRLQhZ2J9DCJlZiq7RMtd7CNn/bwfRMVBxIGOGhQwi5r+IuSpr7+qbeB+fY1ItDiJmlqpKlsxcKhOFchqVu/e5abSN6os2haxMVKxKEbK88va2I2T1vca6b0d0DESE/zNFPaNo8q3JfmIxVk10DSRMLxCpuEQspoIesu9NnUU9RD+HtF6ZYh46CuY0Ol+aqrpo+zqoS8hcjCtsC1nN40IdqKwIYsG/T38AJCfOom+0YhKxWOTLpIj5JiK+zkPxSUjKioaJKJPoMck+D53HqvM19Jmi97yp6otFQla2+mLW85F9Hj4KmUqFxfrroW45a1TiXgbZCovUa6yjvZn5ImTUZ6xREY9GZe4pOtZHYBsAhIq/X3l4TuwiRhe4ZIkNiFjYz0+VeiEpIyim0v1U5nM1eowpEdO97ZAw+ZlIf36T9Mb621w+D1cZBUWUvU7RNTJZQpyX7jplsVGqIho/A1AMZEwzZUTMxHwxVRGLFYhY2M9PlTxpUJEJ0wKiUulQ5/PTte/YMS1ktuaYQch6okvIdH4pKzt3jKJjruaQmQJzx0DMQMYiBiLWE4hY2M9PFV2RI1rPlnio9AAzGbWyGX0LCZefkUbCJgOEzA8h87WYhw4hQ3QMgHwgYxrxKT0RItYTiFjYz08VGdHSsR2dpPcp+zxsipjserHh+rMCIYtLyIqQETIT0bEiIROpqlhWyBAdA7GCAh6eoDNF0VcRc1XWPhYRC61HkW3KyoCPMuE6jbJMBcZGj42p+IfO6osqmKjEaLuoR3J9mNPe30lRD5PIFPRoVFlRtqBHHs19O1lnm75CH7oLegBQJSBjmlD59ktWwIokq0w1qBhEzNY3tLpERbYyGiJfYcgTsBdJ803mZIUs+QzqLKEfupAl1wofhEzkmkrX8C87WqXGCDqqLIoKWaPKiq6ETKWiIgAxAxnzPPolGuVSTamAiLkTMdEBFESsNxAx4GMfNFEhS38Gdfc0KyNkZY4lJiEz2YdMV9n7kCNkVY6Oze3sz9o77eVVzu/0R7ZBPpgzZikqphIFM11mFyLmVsSKbhO5TwWIGIgJH98LjT5jWffr/FzabvZu6nys69ohe52TFTETVZB1ziETmTtGQuZTdAyAqgEZs4DOdERdQMT8ELGi+yBiYQy+gVt8fE/kfdaKPoMxCZmuc7NtITMZEZP5Alem55iuKou2hQzVFQH4GsiY4ZMqRMwMMYlY1joQsTAG3cAPfHxv1H/mRD6DsQgZEaqQqeAyOiYiZKKVFX2KkAFQJTBnDERRMTF0EVNZV7QQgE8iVj9objTnx8dBNgCyc7B8+gzaROc8MmCHMnPIqLx9Uzu+4wdAFsiYZ33FKE3CRnTMBBAx+99O1xcC8AkRkYJsFbNAny/5/3O6Brg+FKCIb59L22X6QxKy9LXXdMqirkIeojSqrCjba6yWI12mhYx6jfXJKP7Be42F9VEDoBt8heFR0Q7bees6gYi5FTHfgGTpE7H6nwEwje5zi4/NoUWLaIkW09Kdqigzb6xq5DV/BiBUIGMKEmYiImZLyEzkzUPEegMRA2XIki8XQgYJrC4xCpmN+dI+o3PuGEXH8qDoWMPHlyxtnyVkNXuBRgC0AhmTwIaEhRYhg4j1BiIGTAmQLTmi/ST7Sv8MqkWMQqZKqNMHVIWsLCJCVhZEyEAsQMYE6d/qtlqSj0DEegMRA2UQkR6TYlQkXpCy8NBxPoKQxfMFr24hK4qOFT6uog2fAcgDMmYIXfnjOqNjOlM0XIqY1saizfMgYhqAiJVHRnRMSJHoNn0Vsljegz5+zl0fky9f/DWKjsU0b0y0zH3ZdMWyIDoGYgDVFD06wWeJl47UiFDy5E1/Y2pyQBGLiMUyoPUR3RJD25OpsliFyowq799G7RVc0KhdhY5z0yzJ5627yqKKkM1pd1+Vka7JRV+SkpB92dGqVcjmd7QI9Rqb344hHQAhgk+uAWS+HUvLlm/iZePbSBspK6ZFBiIGXEWTRIWsvjJjzEKm8r73UchMC1By3pKRMgiZ30LmU8+xImp9a0hVBCAF0hQ9ETEdQMR6AxETAyJmDtNpfY2270tlRp/x+f3v2zkMKYt6UxZF542FWureRqoiAKGDyJhGIGLVSElMgIiBRtisfpgV7WpUmRERsq+paoQsOZchQiZPFSJkiI7pZU5HfzZf43uiEW0d+v92QD+IjGkCIiYORKwxELHwsR19qt+f68qMIeLz5wERsrgjZKIURcho3hgAIDwgY5aBiEHEfMHngWfouJKcpPy8bGVGSFnPzwU+G2JAyPQImWyJex0pizLl7UUrK+qi1oIKiaBaIE1RA7q/+YpVxKr2zbHPA5zYB5sm5EI0pa8KYlOFSo4+VmZ0nR5o4ku52R39nVw3v+jU1zbGJ3ysqkjzxprae3/3X9VURQDq8esTGzk6o2IQsbBFzOU3yhAx9e02kokqiFgCKjm6mXdmusKibMl7E9kSMoKmOn8s6xpaRtAazR9rBEXHvmyzN5dI59wx6jVWy5CtstGxpg6IGqgGSFMMkFBFzFSKIkRMHIiYue1XScQSUMnRzWfMdXqgb9cKXdcxkz05bWbQ+Dh3LK+qIkXHAKg6kLHAomKhipipAQlETByImLn9xC4YReQ99yqLK4SsPDEKWSNk547ZnDdGYO4YAGaAjFn4tgsipp8QC3VAxOKsXBi7WIiASo69gZCVB0IWTnSMUhVdRce6EF0DgQMZMwxEDBAQsXgrF4KvSF4L2UqOMVMFITO9/5CETNf13mchQ3QMAP1AxgxGxXSXsQ9ZxHTOF3M9+PC5Z1jWEjOxD+ar8PeQfUy6fL/MEjM6zzE6MwJ0HZeKkOm41rmMkFWNougYhMw+l19+ORs5ciTr378/22ijjdgTTzxRuP6tt97KRo0axddfY4012F133dXj/lqtxk4++WS2xBJLsAEDBrCtttqKvfbaaz3WGTlyJGtqauqxnHPOOT3Wef7559lmm23G9zNixAh23nnnSR/Lvvvu22s/2223HXMJZCywcvZlgIjZx1aZ+tilK4vYB9hVQvRvWeZv7uL9Yrqyos9ClhyPKyHj+45YyHT0GlMhr6Ji2VRF4A+33HILGz9+PDvllFPY008/zdZaay227bbbsg8//DBz/UcffZSNGzeO7b///uyZZ55hu+yyC19eeOGF7nVImiZNmsSuuOIK9vjjj7NBgwbxbc6b1/Mzevrpp7P33nuveznssMO675s9ezbbZptt2LLLLsueeuopdv7557NTTz2VXXnllVLHQpB8pfdz0003MZdAxhSAiIkDETNLFSWMgIjFh0plRt37CFXEfBOy+uOAkAERUFnRDy688EJ24IEHsv3224+tttpqXKAGDhzIrr766sz1L7nkEi43xx57LFt11VXZGWecwdZdd1122WWXdUfFLr74YnbiiSeynXfema255prs+uuvZ++++y678847e2xrgQUWYMOHD+9eSNoS/vCHP7C2tjZ+HKuvvjrbY4892OGHH86PV/RYEvr169djPwsttBBzCWQsABEreyGAiNkHImYOiFi8qFRm1LWP0EXMFyHL239VhMzm9ARX0bEiRKJjKiBVsRwUVUov8+fPz1yPZIeiTpRGmNCnTx/++9SpUzMfQ7en1yco6pWsP23aNPb+++/3WGfIkCE8/bF+m+eccw5bZJFF2DrrrMMjXx0dX7/Had3NN9+c9e3bt8d+XnnlFfbZZ58JHUvCgw8+yBZbbDG2yiqrsEMOOYR98sknzCVo+iwBImLiQMTMAhEDsVLfGNrE39xk82mXImaiMbRMI+hG5z5dx0XXF5nG0GWaQ9cLWZnG0KFTlKKoMzrW1BZvs+c5Hf1Y3xLNwWVp6/gq5kLzq9JQCiKl+NXz8ccfs87OTrb44ov3uJ1+f/nllzP3QaKVtT7dntyf3Ja3DkFRLopiLbzwwjzdcMKECTyFMIl80brLLbccS5Nsk+6j6FajYyEocrbrrrvybb3xxhvshBNOYNtvvz0XtuZm8+/xLCBjUiLWT3h9H4p3QMTsAxEzB0SsOiSyZPJvbkLIfBAxV0Imeu6DkOmPjs3vaPEuOlZr72MkOtbUEa+omWTGjBlswQUX7JGm5xvjx4/v/plSGSkC9pOf/IRNnDhR6/FSemMCFfigfa2wwgo8WrblllsyF/j1CQbB56rrFDGXyKTn2BRG1yKWNziWGdTGIlVD//95zDQUYRHZv65969yWLkJ8n4h8Pm0X9dAlZI32UyVUhYy+1P2yo5XZhMrbz29vKWz83NHuJhpQ1eiYC0jE0jKWx7Bhw3h06IMPPuhxO/1Oc6uyoNuL1k/+p9uommJ6nbXXXjv3WCiNkdIU//e///F0wrz9pPfR6FiyWH755fnzfv31153JWFBzxh5++GH27W9/my255JK8FGX9xL8syHQp7ElWveKKK7Jrr722Mr3Fyn4DGIuIyc57KFqfBh31SxVErFF5cBvV8HwUsfqfbe9fx751bisWTJbDt916omqiFMt0B5leY2XnjpGQ2QQVFf2FolHrrbcemzJlSvdtXV1d/PcxY8ZkPoZuT69P3Hvvvd3rUzogyVB6HZq3RlUV87ZJPPvss3y+Gs3tImhd8oD29vYe+yFRSwpwNDqWLN5++20+ZywtirYJSsY+//xzXmKT+h+IQJMGd9xxR/bNb36T/1GPPPJIdsABB7C7775bm3RlLT5FxGwJmdKkagtCk4iVqJA1EjFXuBYxHevFKGJFt9naf5l969xWLKTfpzbesxCy6uHj/HMRIRNt+GwiRRHYTRf87W9/y6677jr20ksv8QIXNP6m6orE3nvvzedzJRxxxBFs8uTJ7IILLuDzymgu2pNPPskOPfRQfj8FT2j8feaZZ7K//OUv7D//+Q/fBgVWqOw8QfO1qOLic889x958801eOfGoo45iP/zhD7tFa8899+SySGXr//vf//IS/FQ9MZ3e2OhY5s6dyystPvbYYzziRuJGFR4pWEOFPlwRVJoiTbCjRRQqx0lGTn8UgspcPvLII+yiiy4q/aKbnBOmOzWRhMzk/DFVETOdTlMvVo3mPUDE9DTkzUpZjFnEbKb65e1fZd86txULWe9Tk8U+0p9x0+mLtlIWgZuUxaK5Y41SFV2kLFJ0rAnS5iW77747++ijj3iTZip8QamEJDhJYYzp06fziFXCJptswm688UZeup6KYay00ko8c2306NHd6xx33HFc6A466CA2c+ZMtummm/JtUmNmgrLXbr75Zi5PVOmRxu4kY2nRogqM99xzD/vZz37Go3eUWkjHSNsUPRZKwaTG0SSadBwkhNS7jErgu5xH11SjBgABQqZ9xx13dFt1FlQCk1IUybYTrrnmGm7os2bNynwMvQnSJT8plEpVaL7194NZy6B+xmXM5BwxE0KmQ8TS1A8WVCWoSKyyhAwixrQKlOlqeC4QjRqZEhmR/YvuW+e2QkbmfWpayGzNJ1MVMtGKiqb2n4VsAQ+dmSJFc8bmSlTLKxKyL9vkZK2okIeIjCUUCVmjiooyUbEiGcubM5ZXwKNPWxPrmjePTTv1BD6+E5kfZRMaS5JM/GDKj1jfQRarKX7exv645Q1evibga6L+WiKvxCV9KL78MvvCS1Vb6AOTLPXlQE1ShWIdIqWPRdfNo1FKYlbETOR4bBOqiKUfXzURk11X9zZF1tO5rVgQeZ8iZRHYTlmUmTems+9YUcqiaKoiACDSNEUbUB5sOiyaRMbSmIiKVUHEfEqnEZlDVkURC625rgl0FsVwgc59x56yqCsNtwopizL9xhrtm4g5ZZLGCDLRMR9RSVnEXDEA1IhaxvJKXFKodsCA7Asq5YzazhsNLTWxDHQBFpGc2L+9TWTLq75EgcpT1aM6pvFNyFy3FLBB7EKmun8dvcZsEYOQFUXHstIVTfUXE6Grb42xuIcNIGKi/hpDpcSlbXSLGMlXevGRmL8RlY16ue4blgARAyG8pq5bCtjE15RF2VYhuvdfJkvD9jVRRxaNbKpi7FDjZwBiIygZo5KUVKKelqR0Pf1MlV2SFEMql5lw8MEH8xKZVMWFSlz+6le/Yn/84x95hRZVdKYomhCxUKiqkGUNsFwLGUQMhPDaum4p4AIIWdg9LmMWsry5YxQdcyVkPDoGQIAEJWPUK2CdddbhC0Fzu+hnKm1JvPfee91iRlBpzL///e88Gkb9yajE/VVXXeW0l0BClUWsqkJWNLCKYY6Ya2IfmFf5NW7UUiBmQhQyui+9mNi/ayGTuYbHKmQAgArOGdtiiy1YUSX+a6+9NvMxzzzzjJb9+9bQOWQRk51DVoUBlY15ImkgYiCEOWSiFSIxh8z9HLI88UpuL5pvhjlkYkImW+4+1rljFB3LK3PvM1929mUdBW0RdNOO4pdBEJSMxQBEzCy60mdcCaKtCBlEDPj2mmfJlGxLgdiFjDApZapC5qIAiKqQ0TVTZ78x2w2gfROyPFwW8wAgNCBjAeNKxMqmg5gQHVUJCykqlwiUjeazLoBUVZuyf3/XImaj7L2NKJmN1iKu0SVk9OVqUfNnn4SsX2uHVOPnstExAkIGgBj4lAQaFQtJxOjinl58EDFTx2IjkqWrIbNPQMRA6CKW/j/0CHpI58aYskqKGkD3WhdzyACIBsiYJaosYiZRFbGQyBrgqQ76IGIgNnwRsbzfTQEh8+NaqnvqgUkho+iYzcqKAAAxIGMWgIiZoaoiJnKfjvVtABEDMYlYo9t1AyErT+hCJospIcvDRql7AEIHMmYYiJgZqi5iMuvIrGcTiBiIUcRE79cFhKw8MQlZv5YOJ0KG6BgA6kDGAgEiZqbhqK/oGsjRdiBiIDZcipjMZwpC5ud+TOGLkPkYIbPZ/BmA0EA1RUEGtdJJ0V5vCB+w3UQzZFw1bXY9EExLlegAORYRG9rnq4HSzK7WIPZh43htEXsZ+5hpVN6+CtUcwVfUWrtYE6otAoDImGl0lb21ja8i5mNUzISIiZbIzlvPtohl/S7ymFBJxKb+Z1/3YeN4bRPLeynmL4SqGiHThWipe1O0tCL1EAAbQMYsEJqQQcT8GAA1EjKfRKzR7Y3uC4ksmdEtODr3YeN4XRHLe0oXEDK/r2G2EJk35kuqIkXHAKg6kDFLhCJkvl7EqiZijYTLRxEruj+WQXORxOgSHJ37sHG8ronlvaULCFl1KGr8LCpkjUB0DADzQMYs4ruQQcT8HPDUi5fPIpa1XiyDZRF50ZlOWHYfNo7XF2J5j+kCQgZCEjJEx0DVgYwBDkTM74EOCViy+C5i6fVjGSTLSIvOdELVdW0cr2/E8l7z9TzlUoqqKGSN5osVRcVCFDJVUFERxACqKQZc9jZ2EfORmL5xlqEKA12SEl2VBnVuq2gfOrels2JjHuZfE/+qLNIXJaIFeYDe69rsjv5S7WPmtJut4ji4tY3Nbe8rXNbedQEPl9T61lhTWxOLjc/bW1mL4HtABx3tkNUQQGSs4iKm+9tLX7/B1HFcNkQs6WEkE+kyHRWrioil/9chEaFFnHxKsYzt/epjr78qIPtFo6t+ni76jMnS0d7s+hAAiBbIWEVEjC5KeYsOfJUwXdgSsaLfRR5ThYGtbuoFAUJm9nEQMlBlIaPoWGhCBhEDwCyQsQBErOzFwXQaIkTM3ECtaAAHEStPnhhAyMytr/qYGN6/ELL4hUzkmm9byMrMG3MpYpSqCEAVgIxVICIWqoj5ULzDpYgV3Q8RK49qEQwImfx6uh8b8vsYQuaG0CNkIB8U8QChAxkzBEQsfHwQsaz1IGLuZQNCpuf5QMiA70JmUsp8FzIf0hMRHQNVANUUIyX0Cok6o2Iq0uhj1UQfy9eHiI00PBvbcomPlRyL94Eqi0CtyqJKpUX6MlZ3X9H66ooD+rYLl7cvol9rB5vfbnYo2NTaxWrtfRr2GmvKWSfWyooAJCAy5nlUTPVbOdmLTQwillRzrF9CaujsEogYSJMnSCZeQ0TIwkBXNoTKOd51JoZvlRYTITOJr73FAIgNyJjHlD35hyhkZUQs5IbOrvFxoKobiJifImZj2z6/zyFk4WSOyFyTbU1V0CFkFB0zLWQUHQMAZAMZ04yuE7Cub+FCErIqipgPQubjAFU3EDG/RczuPqojZKbOaa6ETGd0zIaQ6SavqmIVImSYOwZiBjIWsYiFJGRVFjGXQubjwFQ3ELEwRMzmvnx83yNCZne/NoTMZiGvskJWFB3TJWSIjgGQDWQschELQcggYm6EzMcBqW4gYmGJmM19+vj+h5DZ3W+oEbKQhawMiI6BWEE1xQiKdVSRGEUsxMGYr0DCwn/NbFVZTPCl2mJoVRbpXDyns78WIZvVOdD6foFcdUUSMh9K3gMQE5AxRXSnH5gWMd2l7mOoquVSxGyWqc8bZNLtPkYHYpMKEAZZnwVXgqZTyOg8N1NCcoAYoqXuG5W5p15jc9v1lsF3RXPfTtbZBlErgtoTtKRaFJimowOpoSGANEUFIGJhixgNTqoiYvU/1+NLNEAXEDGgE5dfVug8T/iWAeATKin8Mj3HdJLuM2aSRumKqjTqNQZAVcEnQxKIWPgiVsXGzVUQMogYMAGELF4gYmaiYwAAOSBjEkDEIGIhilgVhAwiBkwCIfOTMtcFmyJWduwQiogBANSAjAkyACKmZTsQMbcDxhiFDCIGbAAhi4eQImKuKJOqmBUdQ4oiAPng0xEhEDG/8EXE0uvGUrgDIgZsAiGrJqZFjIp4mI6K9WsxMw8MAFAeVFME0YuYjYGHy5L0qgNEVwPLRgIlWs4cIhY/NsrbV5Wi86Js9UVX5e1V901fWMpExyiTxVVkbEBLu1Yhm9/h77Cv1trFmhBBAxUE73pHmDyx62rwDBGLV8RcISJQutYBcYC/tX2GBpbFoLJv2QwS01MLiqJjJGS2Keo1ppKq2NSKEusA5AEZc4jPOegQMTEgYmYG1UXrYnBePfA3tw+ELE4h8z1dkaJjAFQNyJhjfBQylyKmC4hY+IPprMdgUF5d8Le3D4QsYx8QMgCAZiBjgnzZ2bcSQuZaxEKpnAgRszOITj8Wg3GA94B9IGRxCpnJFMUEpCoCIAZkzBN8EDLRi1ijydKIiJmjSiKW3gYG4SAB7wX7QMjsC1kROoTMVXRMRMiQqgiqBmTMUZ+xkDF1kS27XZRrBqAaJIKetcT8RYnLL4JUiKE1iS7KNn72iZbW3n3EZECEDICeQMY8weW3bDFgU8TmOGyOLNOYOdQmzgCUwYWQJSIWq5DJlroH5YRsbru5aRG+CBkA4Gv8bThRISBi5b5BdRERIyFz9S01SVajQR9EDFQZm/3J6j+L9Lvpzx+de2x9KVRWxHT1IItJyL4omINuS8R09BsjIetob9ZyPFVhXnsra26z1zuxsx1RyBBAZMwxEDEWbGqirxEyiBgAdiJkeV+KxBIh0xURQ7oiiyYipgukKgLwNZAxh0DEyl2ofZgj5puQ+SRiKLQAYn4PNhKu0IVMd2oihKx688c62xA1A0AEyFiEIiZbFSpEfBAx34TMJxEDIGYhExWtUIXM1BwxCFmcUTHMHwOgHJgzFhmqIhbSRdInEbM5h8zlN/GNBrS25ucA4BrZz1lsc8jKYmsOWWhz1ai3mA4hG9C3nX1pcU5Soz5jiI4B0BhExiICIsaMD3iSpUr9xUQiC1nrQNAACDNC5vI867JPpSyYavAVKOIBQDkgY5EAEbM70LEpZL6LWNG6EDIAvgJCZl/IbFzfVIQsr5oiRcd0QNExW0DEACgPZCwCIGJuBjg2hCwUESt6DIQMgK+AkIkTu5DloUvIfIFSFQEAxUDGAgci5nZgY1LIQhOxosdCyEDo0Ps6vahvB0ImCoTMXHSsX0tH6X0gKgaAHiBjAeOjiOnO9/dZxGTXq4KIFW0DQgZCo0i+yogZhKy6QlbU8Nm2kJUBIhY3l19+ORs5ciTr378/22ijjdgTTzxRuP6tt97KRo0axddfY4012F133dXj/lqtxk4++WS2xBJLsAEDBrCtttqKvfbaa5nbmj9/Plt77bVZU1MTe/bZZ3vc98c//pHfN3DgQLbsssuy888/v8f9jzzyCPvGN77BFllkEb4fOqaLLrqo9PMzDWSsYpN+Q4qIhYTWgU/gIpbelq5ogo8s0KcPX3RtSxc6txU7Re/JsvMlff6ch0ZIRT10UiRkA1ravZs/BuLglltuYePHj2ennHIKe/rpp9laa63Ftt12W/bhhx9mrv/oo4+ycePGsf33358988wzbJddduHLCy+80L3OeeedxyZNmsSuuOIK9vjjj7NBgwbxbc6b13tMetxxx7Ell1yy1+3/+Mc/2F577cUOPvhgvu1f/epXXLQuu+yy7nVou4ceeih7+OGH2UsvvcROPPFEvlx55ZXKz88GTTXSVZDL7Nmz2ZAhQ9gPpvyI9R3U1ysRU4mMmZYx2Ytmo+NxXcbeduXEWEQsduqFZ05Xl5ZtldmO7m3FRFZUNv1+z4vain4mVKO+pkve6yx3r7P3mGrJ+Vklj6HRfmd3lCuFP6e9v5aoWJpG5e6/7Gj83ssrdT+/I7u70fz2Fq3Rsbzy9rX2/C+OmnLua2pryr69o4l1zZvH3jrhF2zWrFlswQUXZD6OJdf503jWPLCftf12fjGfPfP9C6VeE4oUbbDBBt2S09XVxUaMGMEOO+wwdvzxx/daf/fdd2eff/45+9vf/tZ928Ybb8wjWCRfpBkkV0cffTQ75phj+P10PIsvvji79tpr2R577NFDuEiUbrvtNrb66qtzuaPtEHvuuSdrb2/nUbiESy+9lIve9OnTeSQti1133ZVL2g033KD0/GyAr1AtEnsZXN0iZrL5qCg2e/dAxMIgK/KkGo2qf1yZqJbObcVO/fs97/0vIlm+pt+G0nfMpwiZauq/iIiZQjRCJjNvrF+r2HwyNHsOB5LB9EKpgFm0tbWxp556iqcRJvTp04f/PnXq1MzH0O3p9QmKNCXrT5s2jb3//vs91iExJSlKb/ODDz5gBx54IJcmSkOsh46Z0grTUCri22+/zd56663MYyOZo8jd2LFjlZ+fDXC1tgRETD1CVwUhg4iFQZHgyMpP3voqEqVzW7GT935XETKImH18FDJXIiYqZCZTFSFkclA00vZCUOSHBChZJk6cmHl8H3/8Mevs7ORRqzT0OwlVFnR70frJ/0Xr1Go1tu+++/IUxPXXXz9zPyR4t99+O5syZQqPZr366qvsggsu4Pe99957PdZdeumlWb9+/fi2fvazn7EDDjhA+fnZQCwWDUoBESufKklC5jJlkQY3plIWIWJhICI2tI5IemCjbYluR/e2YqfR+53uzxIsuq3+sRAxt9ecsimLjYRMNGXRtYilhUwkZdGUkKGgh9/MmDGjR5oiiYpPXHrppWzOnDlswoQJuetQ1OyNN95gO+20E09XpOdzxBFHsFNPPZVHt9L885//ZHPnzmWPPfYYTz1cccUV+bw23VC0Tsdria9NDQMR0zdnLcYIGUQsDGQiTCJypGufOrcVMzIFZEQiZFUUMdfzd0OIkLmmTMqiaqpiAiJkfkPikl7yBGLYsGGsubmZpwymod+HDx+e+Ri6vWj95P+ide6//36eJkjH1dLSwuWJoMjWPvvsw3+mOWHnnnsulyxKS6RI1oYbbsjvW3755Xtse7nlluNVHUngjjrqKC5sqs8vDc1po+Oh/bW2tvJ0Sno9KQ3yrLPOYu+++y5TodpXaMNAxPQXD4lJyFBNzW0VRJlF5z5UtpN3n+y2ZKmqxBUJWRVFrKpVfRsJmWxUbGCzfw2ddfQbA/HQt29ftt566/FUwARKCaTfx4wZk/kYuj29PnHvvfd2r09iRKKTXofmrVFVxWSdSZMmseeee46XsqclKY1PlQ9JctKQTC211FL8WG+66Sa+jUUXXTT3OdHxJ3PkVJ4fcccdd7CVV16Z/fjHP+ay+POf/5ynTN59993sqquu4jJ23333cUmjVMuPPvqIyYA0xQqh8wLoqtSwb9/OqgIRc0OocpGVaki/yzwf2VTFZNtIczSD7kqKRWnUIYlaaF+4+pKmWHVqLSgMrguqZkjRH4pKUeTp4osv5tUS99tvP37/3nvvzWUomXdGqYIkIzR/a8cdd2Q333wze/LJJ7vLyVNE68gjj2RnnnkmW2mllbicnXTSSbzCIpXAJ5ZZZhmWZvDgwfz/FVZYgc//SuZ7/elPf2JbbLEFL4l/zTXX8MqKDz30UI/+YbQt6i9GUIn7X/7yl+zwww8Xfn5ZUMVGKqO//fbb90qJJH7wgx/w/9955x2ecvn73/+eR+REgYxVBIiYP0DE3BCqiOkQMlURK9o3UMd0SfssUfNNyOiapFrinjA5Z8ykkFF0TKXEve9gzlg8UKl6iuxQk2ZKBaTS8pMnT+4uekFl5NNCsskmm7Abb7yR9/M64YQTuHDdeeedbPTo0T16h5HwHHTQQWzmzJls00035dusr47YiOuuu46Xx6eCHxTJevDBB7tTFZMoF807owqOFMEimaPUxp/85CfCzy8L0UqLJKnnnHMOkwV9xgz2GTOdpiia0+6LiJU5Dp9ErEwhD59ErEpzxkIXsTRZUlT0/MqKWJlthYypdETbIpamrJCZSBNXETJVEVPZl6lCHqJC1qjPWD15RTzyeo3Z6Ddmo88Y0fXlPDb95yd63WdstZuPs95n7MU9zvPyNQFfg8hY5PgiYmXwScTKABFzQ0wiJhsh0yliefsGYYhYLBEy2xEx0cqKPkTIXFRTREQMVIXx48cLrXfhhRdKbxsyFjEQMX/wScSqRGwiJiNkukWsaN/AfxGLQch8SU30WchsAhEDVYIaSKd55JFHeDEQajydQPPjVICMRYpvIhZK9SwTKYoQMTfEKmIiQlZ/eyNJU6nMCCELT8RCFjIdIlZ2nprQPipS1AMiBqrGAw880OP3BRZYgM+Vqy+rrwJkLEBE5orRBccXAfLlOPLESnRQojpXjAZiEDL9lIkCxYJK2XsdklrV1ztJ75WdT6by+TctcD4KmQ1UhEymCTQAAMgS91fHEUAXgfpFFNPfAMYgYvU/i6wfwzfjoZNV7a+eqgqDaar6uqbnWdqYc2njCxzZ8xqljectIaFyXQqxCbTJxs9o8gyAPiBjHqPj5O9SyEIQsaLbRO6TAUJmPxpUVXEwRVVfzyz5qqqQ5VEFIbONbCXFAS12CzNByACoqIxRQ7eRI0fy3gQbbbQRe+KJJ3LXvfbaa/lkuvQi29PAFTq/hXMhZL5e6GSlS9dAxTchM1W22zQi1f7qqapA6Kaqr2ORdEHIqiVkuqNjIRTvKIqO+SZkTZ1qxRNsMr+9mbcMsLdgbp8unn/++R4LdQZ7+eWXe90e/ZyxW265hZeWvOKKK7iIUdfsbbfdlr3yyitsscUWy3wM9VWg+8tWOrGJiXQIm3PIQhSxrHkUukUsAXPI1ChT7U+0OTLIBiJWvI7pLzfofBHKHLJEyEz0JPNhDlmj+WMxFu8gISvqOUZChoIeIHbWXntt7hDp9sw77bQT/z+5nf7v7OyMW8aodv+BBx7I9ttvP/47Sdnf//53dvXVV7Pjjz8+8zH0wgwfPpyFQuh56SGLmMq6qkDI5NBR7Q9CpgZETGxdCBkLotqhT/sNCd+FrKmjiX09RAZAP9OmTWOmCEbG2tra2FNPPcUmTJjQfVufPn3YVlttxaZOnZr7uLlz57Jll12WdXV1sXXXXZedffbZbPXVV89df/78+XxJd01XLW/rGz5VWLSZ8mJDrkKjCk2fIV7+iFjyt6iq2IUkZDrP3zqjY0mLFd97jbmaL+YzzX07WWdbb1Frau1itXacp0EYkEuYIphPwccff8xDf4svvniP2+n3999/P/Mxq6yyCo+a/fnPf2a///3vuZBtsskm7O23387dz8SJE9mQIUO6lxEjRkQhYj42ey6qzBW7iLmMioUoYhjEhy9i9T8D/84dIZS6p2tS/VIW36JiPopYUVTMJrW+iH8B+0yfPl1q/XfeeUdq/aivjGPGjGF77703z/McO3Ysu/3229miiy7KfvOb3+Q+hiJvs2bN6l5mzJhRWREziY2J3hCxOEQsAUIWtogV3Qbcn0NCELE8ygiZrIg16jdWdgxQRsS+7Gh1JmKNUhQpOgZAqGywwQbsJz/5Cfv3v/+duw45w29/+1s2evRodtttt0lt34+vOgQYNmwYa25uZh988EGP2+l30Tlhra2tbJ111mGvv/567jr9+vXjiwoQMTEgYq72Ha6IJWDeV9gilr4Pcu1PymLIIlYG3SJWFkTExKNjTW3+F2MD8fDiiy+ys846i2299da8Kvt6663HllxySf7zZ599xu//73//y6dDnXfeeWyHHXaQ2n4wo5q+ffvyJz9lypTu2yjtkH6nCJgIlOb4n//8hy2xxBLajw8iJgZEzNW+wxexBAziwxYxmXWA+XNKLCImGx3zLTWxLEVRsS/b/GhlYjI6VmtB+iIwxyKLLMKLCL733nvssssuYyuttBKfPvXaa6/x+/faay9e14JqWMiKGOHfVx4FUFn7ffbZh62//vpsww035KXtP//88+7qipSSuNRSS/F5X8Tpp5/ONt54Y7biiiuymTNnsvPPP5+99dZb7IADDtB6XBAxMSBirvYdj4glIEIWtoil14Vcu4uQxSJisqiImOmomI/4GBVLQHQMuGDAgAHs+9//Pl904u8nLYPdd9+dffTRR+zkk0/mRTtoLtjkyZO7i3rQBDuqsJhAoUMqhU/rLrTQQjyy9uijj7LVVlvN4bMAVQMiZqbiHoRML7KvJSQqbCBi5kSMvqCV7TfmY4qiKOgxBkCFZIw49NBD+ZLFgw8+2OP3iy66iC/gK1yXtacyx6ajYzTA8DU6ZpsQRSz5WWSgDxFzC6JaX2O6x5gtknNnrKKmmprYqNGzDga3tnknZI16i7nsMYaoGIgJjGYqgmsRS9DZdyaPWAcSVRCxottk7gd2wN8hPhGr/zkUfOs/Znv6woCW/HP+gL5+XA+yeo3pbPoMQKjgSloBfBGxBAiZWUIXsUb3QQD8osp/jxhFDORHx0xD0THfoOiYbyAqBmKjulfRiuCbiCVAyMwQi4jlrVPlgb/PVPHvEruIhSRovkXFykTHyghZUXTMtJBRqqKrqBgAoVO9K2iF8FXEEiBkeolNxOrXreKAPySq9PeJXcRCwqaI2YiOEYiQxRsV62xv5vPrbC20P6CX6667jv3973/v/v24445jQ4cOZZtssgmv2K5Cda6ehvC1rL3vIpYAIaueiKlQpYF+yODvFJeI+S5rsiIWynXRVyFTJS8qVmvH+QKEx9lnn81L3BPUV+zyyy/njZ6HDRvGjjrqqGpUU/QJX0XMVRNNXy90LgcUNsraxy5iBMrYA5+gz1ws0bGqf5EFwu85RqDpM7DFjBkzeP9i4s4772Tf+9732EEHHcS+8Y1vsC222EJpmxjdKAIR6wlErDcQsXxkSqIn66KMOvAJXz57qg2fgd+pij7hu4gBYJPBgwezTz75hP98zz33sK233pr/3L9/f/bll2rjPnzCFICI9QQi1huImJ5oV72AIUIGfMJ1hMykiIUQFaNrla/FO0QY2NzGvuj0q7dYyNSaER0D5iH5OuCAA9g666zDXn31VbbDDjvw2//73/+ykSNHKm0ToxpJIGI9gYj1BiImTlG0K+8+RMiAT7j6LCIiBmyAqBgAPaE5YmPGjGEfffQRu+2229giiyzCb3/qqafYuHHjmAr4lEkAEesJRKw3EDF5sqJdjYQLETJQ5QiZaRELISrmOlVxdkd/K0U85ra7i5xBxADoDVVOvOyyy3rdftpppzFVMJrxiJBy0SFivYGIqUNyJTs3DBGyOIhFqm19NiFicV67qkhTK87hIDwmT57MHnnkkR6RsrXXXpvtueee7LPPPlPaZhxXwQiAiAEgJ1ixDOIB/pbA/jUsWXwnphL3AMTAsccey2bPns1//s9//sOOPvpoPm9s2rRpbPz48UrbRAxagjnt/Y2kKvoiYjQRWjZVEQAXYPAe598U0U6xCHzM88VIkOZ09re6vzQi+9aVouhL8Y5+LR1sfkdLZqNn06mKFB1DvzEQEiRdq622Gv+Z5ozttNNOvPfY008/3V3MQxZ8AhSELEYRSwi5MhWoBhCxeMHf1p+UaJf4HLGSEbGi8YKMiIUQHetoz27sDEBs9O3bl33xxVeBi/vuu49ts802/OeFF164O2ImCyJjDiNkvomYSITM54skiB8M1uMHETIxECGzj+8RsS871IvIIDoGgBibbropT0ekJs9PPPEEu+WWW/jtVOZ+6aWXZirg3e8oQmZSxHQIEyJkwDcgYtUBf2sxECGzt09ZEcsbI6iKWAjRMQCqwGWXXcZaWlrYn/70J/brX/+aLbXUUvz2f/zjH2y77bZT2iYiYw4iZL5GxADwFQzOqwciZGIgQmYeG2XsfUA1OoYURXE625tZrc3e69WFv412lllmGfa3v/2t1+0XXXSR8jYhY45O7D4Lmc0iHkNRMKRyZe0hVkDHewWiBpLrlelMDp19xQY2t3lTuAMAoMYbb7zBrrnmGv7/JZdcwhZbbDEeGSNRW3311aW3h1FRCcrMGzP1TZtKWgddzNKLLSBi4kDEAOgJ3ktfEXNUTJTQqgCTkMnSqPlzmfliCVlRMX47mj8D0M1DDz3E1lhjDfb444+z22+/nc2dO5ff/txzz7FTTjmFqYCrmSI6CnjoFjJVEXMBREwciBgA2VT9PQUR009eSqRsNkujMYKKkJkUMZegeAcIieOPP56deeaZ7N577+WVFRO+9a1vsccee0xpm/gEKKCz15guIbM10VlH7j5ETByIGADFVPW9BRGz/8WiKyEriorpErEYomJNnU2uDwFUgP/85z/su9/9bq/bKVXx448/VtpmNa9iJTDR9LmskKmKmIuoGERMHIgYAGJU7T0GEauekMUYESMQFQOhMXToUPbee+/1uv2ZZ57prqwoCz4FjkWsrJC5KP2rGh2DiIkDEQNAjqq81yBi7q9tNoUsLyqmU8SiiIp1ICoG7LDHHnuwn//85+z9999nTU1NrKuri/3rX/9ixxxzDNt7772VtlmNqxdwHhWLXcSi7/fTp4/0YLcqg2MgBlU/rF+AfywQ+bksD9fl82UZ0BL+F3aIioEQOfvss9moUaPYiBEjePGO1VZbjW2++eZsk002YSeeeKLSNsP52iNyVEvd0wXERXRMlpmdA60I2ZyuAdYHE1UQsfTPIoNoiBio4nuEItozu1qD7ytG51A6lwI95DWATlAtdU9CZjJVsVF/sYSW1k70GgOVoW/fvuy3v/0tO+mkk9gLL7zAhWydddZhK620kvI2IWMeULbnmKyQhVYG2Gchq5KIiQpZjINsoJeY3yMQMneY7jem0nuskYiVxbSQAQCyoZ5itOgAMuYYXc2fQ4mQxSRkVRSxRkIW8yAb6KEK75FkzqdJKYOQuRMxUUyLmA0hE42OAVAVOjs72bXXXsumTJnCPvzwQz5nLM39998vvU2lT1hHRwd78MEHeefpPffcky2wwALs3XffZQsuuCAbPHiwyiYriS4RS4CQ2ROyKotYnpBVYZANylG194jpKBmEzF9siZgvQoZURVAVjjjiCC5jO+64Ixs9ejQv4lEWaRl766232HbbbcemT5/O5s+fz7beemsuY+eeey7//Yorrih9UFVAt4iJClnsKYo2hAwi1lvIqjbIBvJU9T0CIatmVMwFroUMgCpw8803sz/+8Y9shx120LbNPipGuP7667PPPvuMDRjw9cmZGqBRyA64EzEAXFDVQbZLFmzqxxcAqg5EDABgu4DHiiuuqHWb0qOof/7zn7x0Ix1MmpEjR7J33nlH57FFS9kmz64vXEiFjBuUHPebtIRByMJ434beN1A1Kmajgm7Vsj18BSmK4uX8bS9AL0cffTS75JJLWK1W07ZN6ZgzTVSjyWv1vP322zxdEYgLmasIGQmZ6gUsNBGras+csoimHiJF0S5Z8kW3za7NZ1XGZxEzna5oOkWxbHoiCRm1NjEJXc98i5BRo2fb88ZcAREDVeKRRx5hDzzwAPvHP/7BVl99ddba2vO8fvvtt0tvU3oUtc0227CLL764+3eauEY19k855RSt+ZNVILQIGUSsWjQa4IYwAI6JoihYlSNkIb0PdUfIfBcxXREykWuPjxEyErJYyJsvBhEDVWPo0KF8atbYsWPZsGHD2JAhQ3osViJjF1xwAdt22215x+l58+bxaoqvvfYaP6CbbrpJ6SCqTCgRMohYNYp3iEa+QhoAx4CIbFUxQhbi+1BXhCwUEYs1Qib6ZarNCJntfmMQMVBFrrnmGu3blI6MLb300uy5555jv/jFL9hRRx3Fu06fc8457JlnnmGLLbaY9gOsAr5HyCBi1aZ+wBviADhkZKJeVYqQhfw+LBshC03EQphDZvI61yhCNrC5jYUGRAxUlZsKAk/HHnusHRl7+OGH+f977bUXO++889ivfvUrdsABB/CcyeQ+AFwRg4j5ONmfBr7JAvymCkIWw/tQ9XNuWsRMY0PIAADAFIcccgifL1YPBah+//vf25Gxb37zm+zTTz/tdfusWbP4fSC86FhMF2Cfe+GEKmJFoHiHf8SeqhiDiBEme49V+RxapkenjwxudR81Q38xAL7mD3/4Axs3bhwv5JFw2GGH8d5jVNhDBemRFJVyzOo2/cknn7BBgwaxmImxMpJISkfZi5Ttb0JDHUxAxEBZuQpVxEQFq+oiFnpUrKoiFuPYAYCqsuOOO/KswO985zvsqaeeYj/96U95BUUSsVGjRiltU/jrjl133ZX/TyK27777sn79vk6FoVL3zz//PNtkk01Y7NBJNZYKSTK59XSxUsmpd5WSQkIWUsoiRAw0kqxG6Yehiphom4SqixjwS8So8JZIRosOEaPo2Nz2nr1dAQDuoOKFM2fOZN/4xjfYoosuyh566KFSjaCFR1RJyUaKjFE/sXQZx+HDh7ODDjpIOVcyNGL4lktlkrPsRcv13IBQImQQMSBCkWyFLmKNhAsiVm1MRa4QEQMgm8svv5yNHDmS9e/fn2200UbsiSeeKFz/1ltv5VEhWn+NNdZgd911V4/7yR1OPvlktsQSS7ABAwawrbbaildiz2L+/Pls7bXX5sGfZ599tsd9d999N9t44425h5AEfe9732P/+9//uu9/8MEH+ePql/fff797nVNPPbXX/Y0iWuPHj++1vP766/wY1l13XR4pS243GhlLSjnSH+eYY46JPiUx5ghZmX4sohEy1yIWSoQMIgbKRshiEbG8CBlEDJggNBFDdAzY4pZbbuFSccUVV3ARo97C1NLqlVdeyaya/uijj/I5VBMnTmQ77bQTu/HGG9kuu+zCnn76aTZ69Gi+DhX8mzRpErvuuuvYcsstx0466SS+zRdffJELXJrjjjuOLbnkkrxye5pp06axnXfemR8bzduiWhVUNIMy92hfaehYF1xwwe7f64+bmjXfd9993b+3tBTrEFWMz4KiYbNnz+6+P2salwjSszKpuTMIV8h0NMZsJGS+iJjvQgYRA2WFLDYRqxcyiBgwga/FOgDwgQsvvJAdeOCBbL/99uO/k5T9/e9/Z1dffTU7/vjje61/ySWXsO222667rPsZZ5zB7r33XnbZZZfxx1JUjITuxBNP5DJFXH/99WzxxRdnd955J9tjjz26t0VVCu+55x5222239apYSPOzaFrUmWeeyfr8/3iEgkO0zfb2dl7VPS1f1Jw5D5IvyuoTRbUwhyhKo6s//elP7Ac/+AEPFVJ4Lr0ANVw1fq4COkUME+iBD5CExSRisTcWD+2LF5OYbvwsQmi9M30DPcbCg6I36YVSAbNoa2vj0kNphAkkPvT71KlTMx9Dt6fXJyjqlaxPES1KE0yvQ1OcKOqW3uYHH3zAJfCGG25gAwf2Pk+st956/FgoU4+kjCJjtC5tNy1iBKU5Ukrk1ltvzf71r3/12halSFL0bfnll+etuqZPn85Eof1mVZWn2+i1tSJjFGYkWyajpbDchhtuyBZZZBH25ptvsu23355VCV1RMZ0ihotMT0xExKoqZDENjoF/xB55hZDZEbFZgvsI7VqJFMU4aGrvY30hRowY0aPWA6UUZvHxxx9z0aExfhr6PT3vKg3dXrR+8n/ROrVajRcHPPjgg9n666+fuR9Kb6So2QknnMCLCFLk6+233+Yl5RNIwCgaR5E1Wuh5b7HFFj3SGEkCr732WjZ58mT261//msviZpttxubMmcNEoEjezTff3Ot2Oo50lE8G6asfTVK78sor2aWXXsr69u3LczspHHn44YdzW6wKEDH/MZmaCCEDQD8QsnjxScR0XDMbVVIMbQoDiJsZM2bwMXqyTJgwgfnEpZdeymWo6LhI3Chyts8++7B///vfvIIhecj3v/99LnPEKquswn7yk5/wKBpVeKfUSvr/oosu6t4OBY522203tuaaa/IIHhUbocqIaakr4vHHH8/sq0zSR/epIH3lo1BeUsKeKqIkJvmjH/2I3XTTTawKQMSY9/PFbMwRg5ABoB8IWXz4KGI2rp26xgouo2Jo+BwHVMwivaTbU6UZNmwYa25u5imDaej3vDlWdHvR+sn/Revcf//9PGWRjovmcyVl4ilKRvKVVHikqB4VA1lnnXXY5ptvzqu4T5kypVCCKIOPKh/mQRG2lVdeuXCdNJTi2dHR0et2mrf25ZdqY0/pqx69cEmu5DLLLMMee+wx/jOF+RIzjRmImP/YLNYBIQNAPxCyePBZxEISMgBsQJEmiiqR4CR0dXXx38eMGZP5GLo9vT5BGXPJ+pReSO6QXofmVpFAJetMmjSJV0+kUva0JKXxqbLjWWedxX/+4osvugt3JJA4JseYB22P0hfzmDt3LnvjjTcK16mXO8oQrIfSI+m1U0H6K49vfetb7C9/+Qu3Upo7RmUlqaDHk08+2d0YOlZCFzEdlRR9x0XVRBKyoR5Wa3TdpBeAMsRUTTFPyGSrLNJ5JqQvgEIQsfS11FSVRRo7qJa6R1QM2IZKx1M0iqJSJB5UCfHzzz/vrq649957s6WWWqp73tkRRxzBxo4dyy644AK244478vlU5ASJsFC59yOPPJJXQVxppZW6S9tTAQ0qgZ8Ed9IMHjyY/7/CCiuwpZdemv9M26Z0w9NPP52X0qfMPJo/tuyyy3InIehYaftUun7evHnsqquu4lE3mmuWQBUYv/3tb/PHvfvuu7xKPEkdbVMEeh5UNITkccstt+S3kWhS6mR6PzJIf9LoxU0M9Gc/+xkv3kE9Br7zne/wPE0QP4jE6REyGoiF/g05hEycdH8w0UqIPpawV3keNoQseR/GLHC2vtCidiBAfb4YACGz++67s48++og3aaZ5WlSZkIpdJAU4aLpSOkJFU5eotxiVric5IuGikvVJjzGC6kuQ0B100EF8ftamm27Kt1nfY6xRMIj2Q2mKtFDFRYqs0XZo2lRSDfLoo49m77zzDr+f5oVRP7H0HC8q+kHi9cknn/CmzXQslOVHP4vwjW98g6dUnn/++XyeGe2b9vO73/2OP3cVmmpVyC0sAYVSKUf1B1N+xBYZ2hV8imLZ6FgIDZ9d9RRTjY6FLmQEhKyY+kbNIiLjY3Nnleehg0aCVf/+C0HIyvQesxEd0yFkuiJjjSJWuqJjIvtSFbKiyNgXnX21RMa+7Mh/T33Zln/f/I4W6ciYTHn7zrbsdWv/X+mvnqa829vyG+o2dTSxrnnz2Fsn/IIXqEg3/PVpLDniojNYnwH2ZL7ry3lsxlEnefmagK9RikGT1T7xxBPsww8/7JWnSeFLECei4kcXYJdC5muT5zwQIYubLIFJbs8TmazHFK3v6/OwESHLet/5nuJYtgm0jXTFkCJk9CWjTiHzBZSzB8AfmU5ktlEvMRXplZaxv/71r7xBGk14ox1SLmgC/QwZixPZCByETA4IWZzkCUyRyBQ9xpWQqTwP3WQJVtH7zVchKytiVRQykflcuoTMxNwx1aiYLANa2gujYzppae1E82dQGRZaaCH23nvvscUWW4xXX0y7TwIlGtLt1KfNuIxRLuaPf/xjdvbZZ2d2yAbxoTpHDEImB4QsLhoJTJbIiDzGtpCpPA9TpAVL5H3mm5DpErEqClmVI2S+YlPIilIUATANFQFZeOGF+c8PPPCA9u1LyxhNiqMGzxCxalC2WAeETA4IWRyICkyZ9W0ImY/HJfve8kXIdItYAoRMv5CZrKxoKirmCkTIQBUYO3Zs5s/OZIy6VVPJyuWXX177wYA4cS1kvhbvyCttnTdo81HSqi5dALgUsVBL3ruGRMtWVWDVkvZVJat4B6JiwDd0186QljGq83/ssceyF198ka2xxhqstbXnRYZK3AOQxqWI2YiKqYpY8r/IQA0iFhYUHZKJKsmu7/PzAG4wKWShRMVEaSRiNqJiJlCZL5ZXSVEURMVA1firgdoZ0p/CAw88kP9PTdfqUZ24BuIFItZYrBoJGUQsTERFJhEYGfGxKT2yz8M3fEhRDFnIQhKxKs8Xs1W4Iw1ETB6K8jU124v0IaqoHxO1M6RHVBSOy1sgYiANRExcrGRvdwlETJxGglJ/v4jQuJAe2efhC1USsbL9DrOooojZjIoNbG4LVsR8gnqMAWALE7UzMKoCRoCIyYtVVsTMNyBi8uSJiuztje4zjcrxuqSKIqZTyKooYr4xuLVNi4jlNXwuk6KIqBioKtv+f+0MnQh9EidNmsQOOugg1r9/f/5zEWSLwN8qUzaAiGU9RkyskpRFiFhc1Kf6iUSa6lMDfZAe2efhiiqLmI6UxZBETCehzRWrekQMAFv85S9/MVo7Q0jGLrroIj5ZjWSMfs6D5oxBxoArQhcx1fVBGCQikyUwieCk70uLjw7p0bWtoufhAzGLGMmVzDlIRchCE7EQ0xPrUxVVSty7FDFExUDV2GWXXXrdprN2hpCMTZs2LfNnoMbsjv5swZZ51kv00kXLRnTMVSl7Gz3FZAdDXz3Gz0gXsE+RiCU/1wuZDor2oYKvIuZTX7E0olVTdZ+DYhGx0CJWulMV57ar9yPLS1EEAIhTX75eN8g7cihkLi5UtnLrSchcYGMwoZL6Y7rPEAiTrCqFukvc29iHb/iYUqvzCxmRcxBEDDSiX0sH8x1UAwS+8K1vfYv3FzOBUGRs/Pjxwhu88MILyxxPpUCEzAyIkIEQKBIiXWmANvbhK1WOkEHE/MB1w2cfomKdbUhpBHHw4IMPsrY2PRVQlWTsmWee6fH7008/zTo6Otgqq6zCf3/11VdZc3MzW2+99YwcZMxAyMwAIQM+IxKZKitLNvbhO1UUsiqJWKxVFHWkKvogYmVBVAxUBSEZe+CBB3pEvhZYYAF23XXXsYUWWojf9tlnn7H99tuPbbbZZuaONGJ0CpkMELLyQMiALDIpgqqyZGMfoRCTkGUV5Eifg6okYjFFxVSLeAAA7EIVFN9///3CddZcc03p7Uon1l9wwQVs4sSJ3SJG0M9nnnkmv880l19+ORs5ciSv7LjRRhuxJ554onD9W2+9lY0aNYqvTyUo77rrLhbzHDLZi1js3yxiDhnwCRtztWKfDxYLuueQQcSqxYCWdi1RMZPzxpCiCGJjyy23ZGuvvXavZZ111un+XwVpGZs9ezb76KOPet1Ot82ZM4eZ5JZbbuHz10455RSeKrnWWmvx5msffvhh5vqPPvooGzduHNt///15qiWVpqTlhRdeYD7iSshix9dBB4SsetiIQMUc5YoJ159/G61AVBBNtdeJ7mum7Fyx0Erbt7TKl+4Woandv8I7AKR5/PHHeVX5+uXNN9/s/l8F6Xf+d7/7XZ6SePvtt7O3336bL7fddhsXnl133ZWZhFIkDzzwQL7/1VZbjV1xxRVs4MCB7Oqrr85c/5JLLmHbbbcdb8626qqrsjPOOIOtu+667LLLLmOx45OQuWwCbQOV3mO+DMhk8C3VK1RkZElVrGzsI5T3oo/vW18+974KWZWulUhPBCAclllmGbbssssWLlZkjARo++23Z3vuuWf3julnkp5f/epXzBRUweSpp55iW221Vfdtffr04b9PnTo18zF0e3p9giJpeesT8+fP59G/9BLzRcb0nDGIWDgDs1AHtiEiIkA2eoGFKGIy70Uf36++fd59FDJb0bFYRWxA3/Zgo2MAVBEpGaOu0k8++SQ766yz2CeffMJT/2j59NNPuYgNGjTI2IF+/PHHfP+LL754j9vp97zJdHS7zPoEzYcbMmRI9zJixAgWMi4vNhCxcAdooQ1wQ6RIhHRJko19+Ppe9PF96uvnvIpCFpKIqTR9tiFkvlRSrLXUWCg0dTaxpg6LSycqUupi7NixrG/fvu5ljMrXb7PNNrzpGYkXVQyhxaSE2WbChAls1qxZ3cuMGTNY6Li46EDE4hmohTLQDZEsIdItSTb24dt70cf3p++fbx+FzBQhiVgZTAtZmehYDfPDQGA88MADbOjQoUa2Lf1pGD16tPIEtTIMGzaMy+AHH3zQ43b6ffjw4ZmPodtl1if69evHFlxwwR5LrBcfUymKELF4B2y+D3hDJC1GpiTJxj58eS/6+L4M5XPtm5C5KObhm4ipRMVCjJCheAeoMtLvfiphf8wxx7C//e1v7L333rM2v4pCg9RUesqUKd23dXV18d/HjBmT+Ri6Pb0+ce+99+auHzMxXtRCHWDEMnDzdeAbIiRIpiXJxj5s9g7Lei9W4f1o8sugKuAyKuZjsY75HULtZgEABpH+FO6www78/+985zusqenrXNRarcZ/p3ldpqCy9vvssw9bf/312YYbbsguvvhi9vnnn/PqisTee+/NllpqKT7vizjiiCN4jif1P9txxx3ZzTffzOe8XXnllcrlahdonReNiNlq+uyS0EQMABBuM2dbZDV/BvZETFcbGhsU9RwrErH57Y2Hhx3tzUo9xnSnKNLcKAAqJWOUM+mK3XffnfczO/nkk3kRDmqwNnny5O4iHdOnT+cVFhM22WQTduONN7ITTzyRnXDCCWyllVZid955J0+1VCU0IUNEDAAQKxAyCJkoEDF7ETE0ewZVoK2tjfcWW2GFFVhLS7nPk/SjKdLkkkMPPZQvWTz44IO9btttt934opNQhAwiBgCIHQhZvEKmS6AgYvpFrCgqVgSiYiB0vvjiC3bYYYex6667jv/+6quvsuWXX57fRtl5xx9/vPQ2lT4VVE2RUv8OOOAAvlx00UW88mCVICGLQcQoVTE2IGIAVIusOWRVAXPI/BIxH8YGZUVMJEXRh/REAFxVXX/uued4AKh//68/79TX+JZbblHapvQng+ZcUUiOBIz6i9Fy4YUX8tuefvpppYMAekFEDABQNSBkoJ4qRsSK0JWaqBIVMyFi9VGxPhK9yQBQhaY7XXbZZWzTTTftUTtj9dVXZ2+88YbSNqU/HUcddRQv3vG///2P3X777XyhnMmddtqJHXnkkaxK+PANmA6KomOu+7EA4JoFm/rxBQBVhvYJo7y4TmY6zrqoqojlRcVsVE3Mi4qpiFitbziNnEG1+Oijj9hiiy3W63YqKJiWM+ORsZ///Oc9JqvRz8cddxy/D8QXFZO9qLm8CM+JZP5ESOXtYyYtYRAyv/F93piMkMnOA4tx3hi+CCzfY0yFMimKAFSB9ddfn/3973/v/j0RsKuuukq5dZb0p46aIFPVwlGjRvW4fcaMGWyBBRZQOgjgf3oiXRhltk9C5qr5MwlZDOmKJGRV/EbdF7Lki26LpVdXTPguYgn0eRb9ooUESyT90DcRcx0Rcw0V99KRNVMlEUPDZxASZ599Ntt+++3Ziy++yDo6Otgll1zCf3700UfZQw89pLTNPirl5ffff38+SY0EjBbq30WFPMaNG6d0ECCMeWKIkNkHETI3FEXBECHzi1BEzESELFYRq3pUrEoiVgRSFYGP0FyxZ599lovYGmuswe655x6etjh16lS23nrrKW1T+tP3y1/+kofkqMEyHQjR2trKDjnkEHbOOeewquFDmXubBTsQIbMPImR2EZEtRMj8IDQR0xkhi1XEfGPBlnlW5425EDGXlImK1VpqwZW2pyIjffpYPGYUNTECFS387W9/q2170jLWt29fHpKbOHFid9UQOqiBA+M8EftOCJUTIWTlgZCZRzbiBSFzS6gipkPIfBMxnVQ5KuZKxHyMiqWjY00QCuARd911F2tubmbbbrttj9vvvvtu1tXVxVMYZVH+SoLka6GFFuILRKxaVPli6RIauOUtAIB4yIuEJYtvxBoVs0nMEbGm1uwvTjBXDITI8ccfzzo7O3vdXqvVlBo+E9KfBLK+008/nQ0ZMoQtu+yyfBk6dCg744wz+H3ALhCjYmKIijX6dh0RMz3IRrkQFXNL6H3FGn2J4qJ3mOpcW12ZDzqvZyFkjfiAz1ExAHzktddeY6uttlqv26mw4euvv660Temr2S9+8Qve7Izmhz3zzDN8ocoil156KTvppJOUDgJ8jUpuOoTMroj58s00JEw/ooIFEfODUIVMNJptU8jKFj1ylYpeBIRMPyoNn4uiYw0fV5CiGNp8MRAHQ4YMYW+++Wav20nEBg0apLRN6SvZddddx2vpU8GONddcky8//elP+US2a6+9llUNE8U7IGTlqUJEDJihkWhBxPwiNCGTTSu2IWS6qs+WETJT17CyQuZj0+cvO6qRmo65YsBHdt55Z3bkkUd2181IROzoo49m3/nOd5S2KX0V+/TTT3v1GCPoNroP6AFCpg5EDJQlT7ggYn4SipCpzu80KWS624AgQgZsiFh9VIyqFAJgg/POO49HwMh7lltuOb6suuqqbJFFFuEV51WQThZea621eJripEmTetxOt9F9QK+QUVldk6XnY8OWiIk2ZNUNRMweJF7pCosQMf+FzOcKi2UL7dD5RneKtKl+jCRkMkU9bHyRSNdFG/vR0fA5thTFdKpiTaBoh6yIAWA7TZEaPN97773sueeeYwMGDOBZgptvvrnyNltUjHDHHXdk9913HxszZgy/jRqdUfNnKvcI9AIhEyf2iBhwJ2QQMSAjXPVfmvhY8dSUiKkKGQAERAyEAPVb3mabbfiiA2kZGzt2LHv11VfZ5Zdfzl5++WV+26677srnjS255JJaDgp8jayI2RIyH5s/2+4p5io65hsUjQglTUwFiBiQxUf5AsBlVAyA0JlUlxGYx+GHHy69baWapiRdZ511lspDgQUR81XIbBBLk+fQmj/HLmTAf3xOUQT6meVh1K0oRfGLzuI+YoNb27ztNaZLxERSFAHwlYsuukgoYqYiY31k6uqPGzeOzZ49u9d9s2bNYnvuuWdmqUfgRsQSTOfI+1g0xHT6jesy975+647BMKjye8/Xz2WMxCZiaSHzjRAiYijeAWwwbdq0houqBwnL2Pnnn89GjBjBFlxwwczJbHQfrVM1TEzW1SViCRCy+PB14OfDoBhUC7znqoVvIkZjAB0iJiNkA1r8y47QRa1vzfUhAGAdYRl76KGH2G677ZZ7/w9+8AN2//336zquyqJbxBIgZOZw1QSahMxHKcPgGNgC77VqSZhvItYIWRHzLUKmMyqGFEUANMwZmz59OltsscVy7x82bBivqAiqC+aQucHHeWSYQwZMAxGLk5CES2dEDFSDpnbG+ljM/Kz5NTQAOQiPligVMd1tuh7qPp2Vwgj8wIYk+SZiNiNkrqJjPoPBMtDxHspbgOfnxICkqqi1DCgPomIAFCP8CaFmZpdeemlhycfNNttMdHPA4sm/yiIG3IJBM6jCe8fHdOFQRKw+xX2IoRYosqn0Za/FA5vVUw0bVVX8siOc91seTTmCVtRnLKvHGIp3gEqlKU6YMIE3ef7+97/PjjvuOLbKKqvw26nXGDWCvvvuu3lHauCm0XMeEDHgGqQsAlkgYtUQsTxIyHSmK9oWsbSQyaYrhipinW3NwlExFREDwCVZleTzUMkSFJaxddZZh/3pT39iP/7xj9kdd9zR475FFlmE/fGPf2Trrruu9AEAc0IGEQO+ACEDsUkYARErJ2JFkqRLyFyJmIqQhSpiMkDEQIgMHTqU9xATobOz02zT55122om99dZbbPLkyXyOWK1WYyuvvDLbZptt2MCB4eeHxyRkEDHgGxAyUARErFoiJkJZIfOlyq9KhKweiBgA7njggQe6f/7f//7Hjj/+eLbvvvvyjEFi6tSp7LrrrmMTJ05U2r6UjBEDBgxg3/3ud5V2BuwAEQO+AiEDWUDEqidioqKkO2XRVcGORkJWFBXzXcRkUhR1ihjmiwFbjB07tvvn008/nV144YVs3Lhx3bd95zvfYWussQa78sor2T777CO9fYyKPAfVnEBshDbwBuaBoIeLSRED/otY2aiY8OMzincA4AKKgq2//vq9bqfbnnjiCaVt4goYISoXOhvfPLosdRx7efuQvqknIGTxovq3DUnIXPT1Q/uMsFGNig1oEX+vfdmWfR2Y35GdBDW/XTo5SplaK875IA5GjBjBfvvb3/a6/aqrruL3qSB89Xv33XeVdgDcACHrDYTMLyBk8QIh0wtELOx0epcNoH0QsSJqfWuuDwFkcPnll7ORI0ey/v37s4022qhhxOfWW29lo0aN4utTut5dd93V436qMXHyySezJZZYgk932mqrrdhrr72Wua358+eztddemxfMePbZZ7tvP/XUU/lt9cugQYN6PP7iiy/mFd9pPyRHRx11FJs3b16p55fmoosu4q2+6HkecMABfFlzzTX5bXSfCsJXvtVXX53deOONSjsBboCQ9QZC5hcQsniBkOkBIha2kOkQMZHoWFZUzLWINSEaFiS33HILGz9+PDvllFPY008/zdZaay227bbbsg8//DBzfWprRfOn9t9/f/bMM8+wXXbZhS8vvPBC9zrUAov6EV9xxRXs8ccf5wJF26yXJILaZy255JKsnmOOOYa99957PZbVVluN7bbbbt3rkKdQcQ069pdeeon97ne/48/nhBNOUH5+9eywww7s1VdfZd/+9rfZp59+yhf6mW6j+1RoqpGuCvCrX/2K/fznP2fbbbcd+81vfsMWXnhhVpXeAkOGDGE/mPIj1ndQ9kl1gVbzJ/syZe5VLkaNGm/quMANNdTcU4QF+nxpfB9DLezDpzSqMoQ0AAdyEqb6tw1J1E19CaJDxEx/AWVrzljZLwpF9ik7R3tOe/H6ukrai8wdq5cxHSLW0Z5dmEO0gEdWEQ/ViooiDZ+75s1j0049gc2aNUup15ONseTyvzibNfe3N2eyc9489uZZcq8JRYo22GADdtlll/Hfu7q6eITpsMMO46JTz+67784+//xz9re//a37to033phHt0i+SDNIro4++mguVAQdz+KLL86uvfZatscee3Q/7h//+AcXpdtuu40HgUjuaDtZPPfcc/y+hx9+mG222Wb8tkMPPZRL2JQpU7rXo/2SAD7yyCNKz88GwlfJn/70p+z5559nn3zyCTfRv/71r2aPDGgDEbLeIELmFyENvIEciJCpgYhY2BEynSLWKDpmQsRCTFdsCus7SOsymF4oFTCLtrY29tRTT/E0woQ+ffrw36lwRRZ0e3p9giJNyfrTpk1j77//fo91SExJitLb/OCDD9iBBx7IbrjhBqF2WTRHi9prJSJGbLLJJvz4k7TDN998k6dMJhErleeXxT//+U/2wx/+kO/vnXfe4bfRcSfCJ4vUJ3O55ZZj999/P7fJXXfdla266qqspaXnJijkB/wUMtmLEQlZowiZDiFzFSEjITMdIaMBlasIGQlZSBGyokF7SANz0Bu0NMgGwgVAtejTwZjNy3Kt46v/6wtLUIoezcGq5+OPP+ZNiylqlYZ+f/nllzP3QaKVtT7dntyf3Ja3Tq1W4327Dj74YF6VkHp5FUHpjX/4wx96RbL23HNP/hw23XRTvs2Ojg6+zSRNUeX51UNRux/96Edsr7324s6TiC1F+84+++xe8+VEkP6ahJo+33777WyhhRZiO++8cy8ZqyKUqmAjVTF0uasqLoUMAFBdbGQA2MBWnzEXDG5tK5WmmFdB0RZFKYoyyPYbq09RJMHp1HIkcTJjxoweaYr9+vVjPnHppZeyOXPmsAkTJgitf8cdd/D163t6Pfjgg1yIaGoVRd5ef/11dsQRR7AzzjiDnXTSSVqO9cwzz+Tpl3vvvTe7+eabu2//xje+we9TQcqkqJQj5V5SOO+///0vW3TRRZV2GiMmhazMfLEEE+IEIRMHQgYAsAlEzG1vs0ZNnk2Rl6JI9Gvt0JqqKCtiqvPFQHlIxETmjA0bNow1NzfzlME09Pvw4cMzH0O3F62f/E+3UTXF9DrJfLD777+fpwnWSyJFySgCdd111/VKUdxpp516RbhIuChqRRUOCap4SPPZDjroIPaLX/xC6fnV88orr7DNN9+81+2Uejlz5kymgnDeCBXuoAIelKJIkTGImPxkXleYFCY07xQHKUkAABtAxMxfp0Su9yRksTZ+FhGx+uIdqqDhsz369u3L1ltvvR4FMKjABf0+ZsyYzMfQ7en1iXvvvbd7fZriRKKTXofmrVFRjWSdSZMm8YIcVMqeliTVjyofnnXWWT22TXPQHnjgAV69sZ4vvviCzwFLQ/JFUNqiyvOrh54LRdzqofliyy+/PFNB+CsSyrGkAh5LL7200o6qgm8pizYiV4iQiYMIGQBxQvMzfSicAxHz6wtDkxEylRRF3dGxsqhExepTFIFeqJohpf9RVGrDDTfkfbsourTffvvx+yk9b6mllmITJ07kv1Ma4NixY9kFF1zAdtxxR5669+STT7Irr7yS30+9wI488kiewrfSSitxOaMIFlVYpBL4xDLLLMPSDB48mP+/wgor9PKOq6++mkfYtt9+e1YPlZi/8MIL2TrrrNOdpkj7otsTKWv0/BpBRUboOdNx0HOjPswU1aNKkaqpkMKfSLJcEBY6BEm0iAeETBwIGQDABFUXMdfiZULITETFygqZrnliwE+oVP1HH33EmzRTgQ1KJZw8eXJ3SuD06dN7RJ+ooiD19zrxxBN5oQwSrjvvvJONHj26R++wJF2QUvmowAZtk5ouy0BRLCqHT8U+ErlKQ8dAgkT/U5VDyuIjEUtH1xo9v0ZQ0RA6ji233JJH4ihlkdIrScaoPL7RPmNVRaTPWBY6o2Mqc8Z0ipFMRUWV/cbebywPG0IWUjVFgiruZVVVRCU+FnxVTJW/YWgtD2QjYzpTl12JmO4+Y7IiplPAZPqMyU5LaCRjeUU8ZAt3FM0Z67VuAyHL6zMmKmPpNMWs+WIiUTGR/mLdBTwUemrZHkuu+POzWXM/i33G5s9jr5/r52sSOm1tbTzyNnfuXN7yK4nmqYARTqS4+oZQZb+x9xtzRUgiRgP1ZLAO8QqXInmSFavYRUwnMZ/HYkBVxEziU6qiDEhRBK758Y9/zCs50vwzkjBKdSQRo8gf3acCRj0R42vKRhYQsmqLmMhtwG9E5ElUsCBiYeDyvB0KVRUxXcU7APANquz45Ze9M5votuuvv15pm2F+NRIAvhTyCGkuV+wNoG0Ruoil7wttUF5VZP5OjRpAh/Y3h4iBPKoqYgDEyOzZs3lFRlooMpae70ZFDqkC5GKLLaa0bXwqKwCErDpCFouIpdcJbXBeNVT+PnlCFtrfGiIG8qiyiCEqBmJk6NChvDgILSuvvHKv++n20047TWnbkLEAoEnFZRs/Q8jiF7LYRExlXWCXMvJUL2QQsTCAiJXHhYjZIkvE8po9AxASDzzwAI+Kfetb32K33XYbW3jhhbvvo/ljyy67LC/XrwJkDHgJhCxeEQNxoEOeEiGDiIWBaRHT1VvM56iYKxHDPDEAykG91JKm09QXjSJhuoCMVQhb0bGQonAuhUxnvzEaHELIgC10yhNErFr4WljKZFn7NINb24KIjOWVtQflaG5jrNlmQcg2i/uqCPfffz+vnrjbbrv1uP3WW2/lfceoobQs+BojEGQuFLovhCrfVvp6wfWtyqLOPkOig0QfBpOyBR9c7Bdkg9ewmuiIilVdxHxo+FxWxHxs+IzvIYFNJk6cyIYNG9brdirecfbZZyttEzIWEBAy+8QmZD6ImIlS6Lr2B4rBawhU8fV64ELEKDpmgryGzzIpij5FxLIaPgPgmunTp7Pllluu1+00Z4zuUwEyFhgQMvvEImTp232RMp1NglX3A8TAa+gvvs9x9fU64DIiZkrIyuCTiAHgKxQBe/7553vd/txzz7FFFllEaZuQsQCBkNkndCHzRb5EB/kQMb/AawhU8fX872tqos8iZjNFUTQqhhRFYJtx48axww8/nFdXpP5itNA8siOOOILtscceStuEjAVKFYTMtxLKoQqZzyKWNdiHiPkFXkOgCkRMPTpmc75YLBGxPh2ujwBUgTPOOINttNFGbMstt2QDBgzgyzbbbMNL3qvOGUM1RUG+7OzLZOsf0Ul8gVb/qwrGUP0wFnRVV0zIEzEfKy82SllEvzE3hFh6PgR0fvFi44si09gqa6/ri0zf0DFfzBfyomJ92jCHDLiHeordcsstXMooNZFkbI011uBzxlQJ71MaGKEIma+47DcWuojl78c/ERNBRciyRKLKYqf63GMVsuTLCtHPhK4oc2gihv5ienqM1ZNX4r5RVOzLNv+zHWw0fK7/2HZhRAsssvLKK/NFB3jrWgBCVo4qCBlETAwIWXkgZNkFbRp9NiBiZohJxGRQFTETtLR2lk5VlG343IQoFwiI8ePH80jYoEGD+M9FXHjhhdLbh4xZIjYhowvoEIuCFLOQQcTkgJCVp+pCliVWRUIGEfNTxHybkyYTFVMVsdijYijcAXzkmWeeYe3t7d0/59HUpPYlA2QMBEOMQgYRUwNCVp6qClmRWGUJGUTMDFWNiOVFxcpExPLmi9mOjpmMimG+GHAJVU7M+lkX1RyFOKIK5XFN41uFxTJAxMqhIgRZ8hGyWJRF9bmHKrAiYmWiH18VRcy3qJXp4h31UTESrrxFFUTFviLSSxqoMIiMARAxsYpYAiJk5anKc5cRK1/bQYQiYlWLitlIT1SlX2uHVxUVMVcMhMiuu+4qvO7tt98uvf34r8Ag2G8xfWMBjZEsW1Ex2UGlr4NQGxEyX9EVuWvUOkDXtoC5qBgI55pV1ENMZ1RMR4qiCEUNn+uLd+iuoIgUReCaIUOGdC8LLrggmzJlCnvyySe773/qqaf4bXS/Cv58XQKcg35jcYmYTKW4ZL2qR8hijV6JPE/RfYTymvkARKyaIpYWsjJpiTYpmi9WJGIyICr2VYqlze8Ca3Enx1jjmmuu6f755z//OfvBD37ArrjiCtbc/NVno7Ozk/30pz/loqZCOF8PAyv576IXOZuVFF3PGwtZxERFK2QRS0CErPzjGq0LERMHIlYNEWuUoqgjQlY2KkapiqroEjEb9FF/mgAIc/XVV7NjjjmmW8QI+plK3tN9KoQzEgGVvdi5JAYRayRcMYhYAoSs3PpFj4GIiQMRM0Oo1ybdKYu60VlFMS9FEVExEAsdHR3s5Zdf7nU73daleJ1EmiLIBCmLcYlYXspiTCJWNmUxFGykE9bvAyImDkSsOiImU7hDNWXR5Vwxm1GxspUU6fZOvYcEQCb77bcf23///dkbb7zBNtxwQ37b448/zs455xx+nwqQMZALhCwuEYtZwKpWIdCGHEHA5IGIgdCw0VusTFQMxTuAb/zyl79kw4cPZxdccAF77733+G1LLLEEO/bYY9nRRx+ttM14Ryselq810WvMZN8UnaWJqyx1MgM0Wtf1gC6GEuA2ZAJCBHz/4gWUu1bG0htUpbS9aFSsvpIiALHTp08fdtxxx7F33nmHzZw5ky/0M92WnkcmtU0WCJ9++inba6+9eKWSoUOH8hDh3LlzCx+zxRZbsKamph7LwQcf7FzIdOO7kFVZxBJEBMu1hJVpjusrpmSmbAqg6X0Ad+9VCJn+lMKy23N1jUzjqqqiTz3GyqYoAuDTvLH77ruP3XTTTdwtiHfffbehl+Th/6f0/yERo3Dgvffey9rb23le5kEHHcRuvPHGwscdeOCB7PTTT+/+feDA8pX4SMgGNvs1IZcuNgu2zHMiZC4rK4YEyVbeQM0nERMpiR+CiJlKWdQhSY2OCSIWNvQ59/EzXcW5XaZETOaL2VhFDP3FQBV566232HbbbcemT5/O5s+fz7beemu2wAILsHPPPZf/TiXvo4yMvfTSS2zy5MnsqquuYhtttBHbdNNN2aWXXspuvvlmbqJFkHxRbmeyqPYAqAcRMvWUxSqTNUDzedCWJV0hiZjNxsm6tgURi4OqRchIwmISMR0piiRhPotY3nwxHwt3AOALRxxxBFt//fXZZ599xgYM+Hr89t3vfpc3flYhCBmbOnUqT02kJ5+w1VZb8bxNqmBSxB/+8Ac2bNgwNnr0aDZhwgT2xRfFURyy2tmzZ/dYQhOy9OJayJCiyArly2cRy5KvEEVMl+SYkKT6bULE4qIqQuabhPmAy2bPNlITTc8VQ1QM+Mo///lPduKJJ7K+fXt+xkeOHMnnjkWbpvj++++zxRZbrMdtLS0tbOGFF+b35bHnnnuyZZddli255JLs+eef512zX3nlFXb77bfnPmbixInstNNOCzplMU1ayEynMSJlUZwQJCxNyBKmI2XRpCQlxwQRi5PYUxZlRcxWJoXLeWIQMTEQFQMh0tXVxTo7ezdSePvtt3m6YnCRseOPP75XgY36Jauxmig0p2zbbbdla6yxBp9zdv3117M77riD9wbIg6Jns2bN6l5mzJjBdBNLhSaRCy2+MTWPywqMeXPKGoldSHKH1ERQBl2fzTkWPuMzJUUp1vO7L9fovB5jsZMXFSvqMQaALbbZZht28cUXd/9OrkKFO0455RS2ww47hBcZo3r8++67b+E6yy+/PJ/r9eGHH/aqZEIVFuk+UWi+GfH666+zFVZYIXOdfv368cXGyX6BVnspfDaLe9RHyEz0K6NBw1DDUTga/Ohs/Kyr2Ef9erKPcS1i6Z9VtmEzOtZIknRuC5jBpfhDxHqC+cW9+bKjNdr+YgDE2mdsu+22Y6utthqbN28ez8J77bXX+JQoqq4YnIwtuuiifGnEmDFjeB3/p556iq233nr8tvvvv5+HChPBEuHZZ5/tbs7mA7aEzIcqi8mFW6eUJYMHk1Lms5DlFQOxIWRlRSwEIZMpQa9rW0AvEDExIGJuomKuRMwmeQ2fZVMUY4mK0fFqLO7bkFpgr08IjBgxgj333HPslltu4f9TVIzabVEGXrqgR3RzxlZddVVuoVSmnkpGUmn7Qw89lO2xxx58PhhBk+a23HJLnoq44YYb8lREKntPIcNFFlmEzxk76qij2Oabb87WXHNN5gumhaysiJWJamXNITMlZVUTsqKBnmkh0yViPguZrDzp3BbQA0SsuiLmQ1+xRlRBxACIjfb2djZq1Cj2t7/9jcsXLToIoppiUhWRXgASLhIsKm9/5ZVX9niBqDhHUi2RqpxQQzbK7aTHUUrk9773PfbXv/6VVeWbOBcRMdGLsO4yyLIDCh8HRaIDO5cNpHWLmMw6JsgSJVV50rktUA6IWFwiZqsqr62omIiIxTxfrCgqljVfLJaoGAif1tZWnpqomyAiYwRVTixq8EwlJWu1Wo8w4kMPPcSqig8iJlJlUed8MhsRsiJMRs9UBni6I2SmRMynCJmO8ve6tgXUgIj58QVWSKmJVYuIYb4YAOr87Gc/4w2eqfcxVXavlIzFju2CHjapSrl7V+mMtop2yMqSyqA46zHJPovuC6FiIghLtmS/zICImcNG1UYbUTFdIja/w9zQzWbDZxEQFQO+8e9//5s3d77nnnt4tfZBgwb1uL+ofVYekDHgXMRMVFusipDZbiorKmS6BszpfdHP9dt1FU0D1Yh6qVQ1LQNELIzy+dRf1CRFKYquRSyvx1hTxu15xTsACJmhQ4fyaU86gYwBLyJiEDL/RUxUgEyIWPq2kHqUAX24+rurVDVVASKmT8QoTV+miIcvfcXKiphIw+e8FEVbETGZ+WKyUbHmtlKHBoAQ11xzDdNNMAU8QPypiT5+A+rrwMqViDUaGJsUMZH7QJy4FvA84YKImZsvFur1YG573+AiYmXJiooBEBtdXV18rtg3vvENtsEGG7Djjz+effmlnrEYPkHAqzlioV6AbQ6wXItYQlbKoA5EZAtCVh1ci1iZqqZVFbGy2LwOyEbFTKUo2hAxm1ExUymKiIoBV5x11lnshBNOYIMHD2ZLLbUUu+SSS3gxDx1AxjzCp1QJl8U6YhMynYMtX0SsfqBsU8RU1gVh4ouI1QsYRMwcMZ3/dSAqYiIpimXJmi+mKyommqIIgCuoj/GvfvUrdvfdd7M777yTt8qitlsUMSuLv3FvECRFZexliGkOWd6gy1Ujad24EDEQz/vE13mA9MVHlnSZ6uHnO1YqGloWMV++ALXRU8z1XDEd4BIBXDJ9+nTe5zhhq622Yk1NTezdd99lSy+9dKltIzKmAZ1pC75cHMpcIF02+QzlW2BVEYt1IGi6hxnw++/uq4j7Fol2RWjRKpniHTqv9WXmi5XBRlQsi7yoGKooghjp6Ohg/fv379UEur29/PULkTGNJ+mBmpKWfew5Jhup0hUh87H5c1nKRsR0N3N2DUSsmtT/3UOLkFWFWEVM9xefPotYCE2eu/rWeqUqAuATtVqN7bvvvqxfv37dt82bN48dfPDBPXqNoc+YYyBkeoRMR3qir0KmKzUxFiGDiFWTvL87hMwvYhUx3VExERHT1fDZh4gYgaiYGn06GOtjMSet1mFvX7Gzzz779Lrthz/8oZZtQ8Y0AyHrCSJk5uaIhS5kELHqIVop08e/c4hCViZtO2YR01lB0VVELFRqLbXCXmMAVKm/WALmjBnAVOlbX6CLdP2iaw6Z7qIdvswhM1WsI7TBoW4R83WuEYizUmbIX37IABHzX8RcRsXy0BkV8/D7GACM4d+nORJ0Rch8jI4VXbzzZAoRMnOoCFlog0ofIyUAxIgrEVPdrysRC4UQ5osBUHUQGYsUk7nzqhdUHVUWQRwRNRm5KloXkmYWXa8vbSeGv5XOz43pHmMuswJiONfrFLGi+WIqzZ5Fo2KNREymrH1WjzGTeBocB8AIkLGIcSVkVUqP8am5a2iIDM5jGMCHiq6G3unHi27Lx787RMyciNk674tmmfggYmXRKWI6Gz3nzRdDJUVQZSBjkROikMUKhKw3iHr5Sf1rr/q3yHpco235+HeHiJkRMZE5x7aBiLmPigFQNfAJqwChCZlvF2edQMj0DNiBOfJee9m/iYpo+/h3h4iZE7GYKStieSmKLiNipkBUDFQdFPCokJAt2OJ/IRCRC7Xuiou2oQGZqeqKoUKD8KSCno8D8qogErUSqXQomoKa3paPf3eIWPxzw2wX7AgpIuYqKob5YqBqQMYC6TlWRSGLmUYDsyrKmq3BeN5+fC2nbgsX87l8EDAbxWxiETFd8lXVqJgOEcsr3hGqiCEqBgDSFINAthyv65RFHysq+tJvDPhJlUUslkqHVUXnuc3Hc7et61kMZeyLaO7bqUXE8op36OwxJotH33lHw+WXX85GjhzJ+vfvzzbaaCP2xBNPFK5/6623slGjRvH111hjDXbXXXf1uL9Wq7GTTz6ZLbHEEmzAgAFsq622Yq+99lrmtubPn8/WXntt1tTUxJ599tnu20899VR+W/0yaNAgqWPZd999e21ju+22Yy6BjBlG1wkeQia27SIgZCCLqosYCBdbIuYjKtcxnddRkw2fVeaLtbTKyZZOREWsvpJiVlQs73Rc4dO0dW655RY2fvx4dsopp7Cnn36arbXWWmzbbbdlH374Yeb6jz76KBs3bhzbf//92TPPPMN22WUXvrzwwgvd65x33nls0qRJ7IorrmCPP/44Fyja5rx5vbO1jjvuOLbkkkv2uv2YY45h7733Xo9ltdVWY7vttpvUsRAkX+nt3HTTTcwlkDELQMj8AUIG0kDEQKgpihAxd9fPqkTHRHAZESMQFdPPhRdeyA488EC23377cdkhgRo4cCC7+uqrM9e/5JJLuNwce+yxbNVVV2VnnHEGW3fdddlll13WHRW7+OKL2Yknnsh23nlntuaaa7Lrr7+evfvuu+zOO+/ssa1//OMf7J577mG//OUve+1n8ODBbPjw4d3LBx98wF588UUuXqLHktCvX78e21pooYWYSyBjloCQ+ZPyAiEDBEQMuG5+rgpETA6ImB4hq09RlBGxvP5iaRAVM8fs2bN7LJQKmEVbWxt76qmneBphQp8+ffjvU6dOzXwM3Z5en6CoV7L+tGnT2Pvvv99jnSFDhvD0x/Q2P/jgAy6BN9xwA5e/Rlx11VVs5ZVXZptttpnwsSQ8+OCDbLHFFmOrrLIKO+SQQ9gnn3zCXAIZswiErCcQMuAKiBgIFYiYHFUSMdFURR0RsrIRMR2FO+qjYiGc1pvbatYXYsSIEVyAkmXixImZx/fxxx+zzs5Otvjii/e4nX4nocqCbi9aP/m/aJ1arcbnch188MFs/fXXb/g6UnrjH/7whx5RMZFjIShyRpG5KVOmsHPPPZc99NBDbPvtt+fP2xWophhohUW6wCzQOi+YKot00fetJH3RoGZo8xdWjwXYAyIGQPwiViUJ8x1ExdwzY8YMtuCCC/ZI0/OJSy+9lM2ZM4dNmDBBaP077riDr7/PPvtI72uPPfbo/pkKfFDa5AorrMCjZVtuuSVzASJjDqhqhExndCy0gQTwQzBUKgeqCEyyn6xF13ZsPI/QCDXtUJSqRvRdXZ9UincUNXsuW7wjr6y9DfKqKLoixKiYS0jE0kuejA0bNow1NzfzlME09DvNrcoimb+Vt37yf9E6999/P08lpONqaWlhK664Ir+domRZwkUpijvttFOvKFijY8li+eWX58/79ddfZ67w69PlMZ+3xz+QcQWEzA0xDVwbyUn9/bI9tWSFr8z9Muu56A3m+/tZ9H0dQ38xYP8LUpMiVoSIiDXqNSbac6xRfzHMFYuXvn37svXWW4+n8CV0dXXx38eMGZP5GLo9vT5x7733dq+/3HLLcRlKr0Pz1qiqYrLOpEmT2HPPPcdL2dOSlKOnyo5nnXVWj23THLQHHnigV4qiyLFk8fbbb/M5Y1R23xVIU5SATsKDW1G6x5SQDVFIDTSV/kjfQFchVZEGpEMjajCdSEeSithI0IpSFusf22j9Rvsrs29b2wqVerFq9L6GiMWNTNaILRETaficFxXTGRFTETHfo2JAL1TWnqJRFJXacMMNeSXEzz//nFdXJPbee2+21FJLdc87O+KII9jYsWPZBRdcwHbccUd28803syeffJJdeeWV/H7q43XkkUeyM888k6200kpczk466SRevp7KzhPLLLMMq6+cSFD64NJLL93jPqrqSOJE87zqaXQsc+fOZaeddhr73ve+xwXxjTfe4KX0KRJHhT5cARmTBEJWHSGrCrEJmQ4pynt8kfiopA3K7NvWtkIjT6zy3tcQMeBjREylt5gsZSNiuhAp3oGomDt233139tFHH/EmzVT4ghowT548uTslcPr06bzCYsImm2zCbrzxRl66/oQTTuDCRSXrR48e3b0OCQ8J3UEHHcRmzpzJNt10U75NaswsA0Xprr32Wl7sg9Ip62l0LPSY559/nl133XX8OEgIt9lmG14C3+U8uqYalTABuVAolSrPfOvvB7OWQV//ocoKmY4iHoSuIh6E6SIeosKkImQy2xfFZWRsAQdyFJuQyZAWGRGBqRefMtIju29b2woBEbFKv69DFzFdc8ZUUrxnOdy3ieIdtkWsKDKmY56YSIpikYzliVhWVEy1yXOejGWJV56M5Q2daP3O+fPYS5edwGbNmtWjWIVPY8m1f3QWa+5rb4pFZ9s89uwNv/DyNQFf41fsOSCKTs6g3EWYLvouy95XlZjmkMkiOzdMZf6Zrn3b2pbvyMwNSxbTICJWrSJaoUTEdIqYbmREDIBYgYwFLmQxl++VFTIU9CgPhExu/azHUHRKtny+TnmCiLnBtIi5rKQY05djsiKWd52XETGR+WKuqifKiljZqFgZiqJiAIQMZMyRkOn6Zk6nkLlsAO3j9l0Oflx+w+7jQDcU0hJW5X5mVXt/hiRisudInSJm4wszG19QmqyaGAMmRAyAmIGMaQBCJgeETAwIWVhkyReELP73JUTMzL59ELGsa7usiKlGxWJFpHgHAFUDMqYJCJm5+WMmty8ChAw0oki6IGTxvh8hYmb2HUtELDYRE0lRRFQMAHkgY5EJmS58EjIftg8hA2VkC0IW3/sQImZm376IWP31vEqpiaYKd4hUUZTeJk6tIALQZ8yTPmQkZDrK3dNFSVe5exIy0+XuTfcI07l9l42gGw38TJbCj7EPmS5kJIvWrUJxjdhFzMaXI2VFrIwA+ZCNUA9dh/K+ILQhYiogKmY/PTEEMaNj7GMxeFgL4DUBkLEohUwnsQgZoWMfLoWs0QDRtJAVAVkDsYmYq4i0iojpEiEfRSxEYhOxsmCeGADFIE1RENkUBZdl72Mrd68jZUan7LlMWSwC6Yz2kYl0ISomRxXfU76eW6qQNu/i2q2rx1ijhs9FPcZ0ITtXLC+KJRPdCiESBoAIkLFIhSw2fOtz4+ugCUJmHxHJgojJUcX3kq/nlCqJWBEDWtq1RsVsiZhKj7HMdfvWjEfFiuSqUdIQxAyEDGRMEgiZOyBkYkDI7FMkWxAxOar4HvL1XOJawnSLWNliWaJC5pOIhRIVk5UpyBeICciYAhAyd0DIxICQ2SdLuiBiclTxvePrOaSK0TAb12pREZvf3mIsIlYUFTNVSbGMWHk2lR4A7aCARwkhk/mWTLWoB8gXsiGeFNJoNJhyWYHRZFGPIqpagZHkK6mwCBELV8RsfZkBEQtPxOi677rMvaiI2YiKyYJy9gD0BjIWqZDpLHHvs5T5ImS+4lrIZIlB4CBh/ssWqJ6IFaUo6oqINZovpiM9sWxETHaumOkmz7JRMR0pjgD4BtIUPUtZRAPosNMWgd0BOwb4IFR0RcVcl5XXtX+V60xIlYNlRCwvRVFGxFSiYrIpio1EzESTZwBiBDKmAd0pC7qETOeFKnQhcz1gAfoFCyIGqtrU2cR5TeWLLZfnVZnrm84vOVWu975ExMqImEjTZ1kwVwyAr4CMaQJCpgeTF3cIWTg0Ei2IGDABRMzN/kOJiBVd5/NSFGVFLCsqJitiuueKZYlY2fREAMDXQMY0AiHTg8pFHumK8ZEnXBAxYIIqihidN2MXMZep/zpELFTKpigiKgaqBGRMMxAyt0LWaGDhYhCBimnq1IsXRAyYoKoi5nL/oYmY7LXdVVNn3XPFfIqKYc4ZiJV4voYJuMqiyAVloIaviXRWWCQhW7DFbLVGuugv0Dyv9CCjvuKi6nYBc1omHyIGfJcw34SrqiKmOxrWSMQaVVEMNSpmYp6YCIiKgarh36cfZAIhUwcl8MOnSMSq2tMsJGyJtEwrB50iFlL0O3YRk6VRhWMbBTt0VE/UXdY+T8REomKoophPn/Yaa2Y1a/urtdvbF1AHaYoOUO1pgrL3fm/T18GarQa2LkHUzF9s/21E3u8QsXCuI6GJmEt8bPIMAGgMZMwREDI5IGTlgJCBKv1Nit7vEDG/CF3EdKQo2iIrKibbW0w2KgYAaAxkzCEQMj+ELFlsACEzC4TMH1z/LbLe7xAxvwhdxEJCND0RAGAffDoDFTIfiaExtA0pg5DFLQHAn79B+v0eiojpPgdBxOSQEbFQomK2RAxRMQDUQAEPT4RscGubk4IeugmlqIfL7YsM5oYaLDjSaFAqWgAhVBkwWewj2W8VCor4IlyhVkwMCd2C6FLEir4ADTkiljdfLPaIGIqDgBiAjAkyr72VNbe1sgF9242Ut1cRMl+BkOkZ8JkUMl0V6UJEV/XFPBnRKWIqctdIkkw+d5dUIeobA6ZFTDWNP2QR84G8qFieLEGiAPiauL8ycYzsyV02ZVH1omMjBcTHlEXZdB2XpZwJpDOao6xM2BSxov2JHpfsOiYfH/L71fRn0scURZ3H5KuIuURHfzHVKoq6i3eYFLGsx0DoQCxAxgIXMlUgZGa2rxsImTlUpcK2iDXar+j9quvqeJxJIGLmgIiZxWWj5yIR09HsWVXEsmZfiEpXnw6x9QDwDciYBSBkZoGQmQNC5oeINbpPRZJ0PXeXQMTMARFzK2KiDZ9VomKhR8RUtwWAr0DGLAEhMwuEzBwQMj9ELG+dMpKkM/3RNhAxc4QkYiGiKyLmi4iJVFE0LWKQMxAyKOBhEV+LetAFcYHWecEX9ZCFhGyIRJGMqhf1kCWkIiCNinr4ImLpdWnfOiRJ9blXQfQhYv6LWGhRMd9FrGyKomlRgnSBGIGMBS5kukrcxyBkNmQpGai4kjKXQhZ7VUYb0R+d+/B1WyGJl9OIs4cippPYRUylx5ioiDVKUVQt2OE76aGMjNRB0EDoQMYUTsCi5e1tASFTFzLZ6JjqfnQCIQuzTL6PqX62gIjZR6WSoi5BtJGariJieen/stMIikRsfkeLN8U6ZHuMNYqKNXU0FaYoQooa09xWY821mrX91drt7Quogzljlr4RK4PI/DFd3xDGMIfM9Pwx1f1UdSDp40BcJzbKyYeMj3//kD4/KkDEzM3DVhGxEFARMZ1o+D4ZgGCBjAUiZCJAyL4GQuYXPg7IdaJSAbEK+Ph3D+lzowJETE7EZKJiqiLmQ1SsTCn7PBETKdwBAIhIxs466yy2ySabsIEDB7KhQ4cKPaZWq7GTTz6ZLbHEEmzAgAFsq622Yq+99hpzjWxKhAwQsq+BkPmFjwNznaj0BosZH//eIX1eVICI9QYRsXLYELH6qBjSHUHVCEbG2tra2G677cYOOeQQ4cecd955bNKkSeyKK65gjz/+OBs0aBDbdttt2bx586KNjhEQMjdClrfYGGDmLb7h4wBdJ2n5goj5hY+fhzLQuap+kaXqIlb/xShd0/MWVRHzISpWRFFUzIWIyQJxAzEQzNc5p512Gv//2muvFY6KXXzxxezEE09kO++8M7/t+uuvZ4svvji788472R577MFiBkU97Bf10LX/2It9FA3UYyj2UWUJq7KI2ficuxSukETMVp9OHyNiskU7dImYihRhnhgAgUXGZJk2bRp7//33eWpiwpAhQ9hGG23Epk6dmvu4+fPns9mzZ/dYTETHTKYqJiBCpo5qhKyqZax1DuR9HMwDMaoeERP9nNs6H8QgYrISJipiZa/BIiKmEhVrVNZeRcRMNHoWFbG8+yBiAFRAxkjECIqEpaHfk/uymDhxIpe2ZBkxYkTDfUHI4ryw6wZCFvagHoT3N3ORmtjocx7DeYDO1b6dr8tGw3yYdiAiYjp7jJVt8GwzTRDpiCBmnMrY8ccfz5qamgqXl19+2eoxTZgwgc2aNat7mTFjhtDjIGR6MHWB92UA5MtxhICPg3sQzt/Kx4bOMXz+y56jTVxHbKUlyiAbFSsrYjaiYvUpiqqCVBQVg3SBKuI04fnoo49m++67b+E6yy+/vNK2hw8fzv//4IMPeDXFBPp97bXXzn1cv379+GKzITQJ2YAWs2cgzCGTR/fcMR/mkIVG7E2jYwAiJvY5h4j5g40URZ8jYrqiYrYQFTRqqAxAiDiVsUUXXZQvJlhuueW4kE2ZMqVbvmj+F1VVlKnIaEvIbAAh84dkYAYpawyEzF8gYsXoFDDd81hjIfSomA4RczFXzERUDICq4l8poBymT5/OPv30U/5/Z2cne/bZZ/ntK664Ihs8eDD/edSoUXzO13e/+12e4njkkUeyM888k6200kpczk466SS25JJLsl122cXosSYpizJS1ig6Rhecwa1t3giZDWIXsrzBGgStNxCy6klVSCIWQ9SrEYiK6cdFREwVlXL2OlIOkbYIqkAwMkbNm6+77rru39dZZx3+/wMPPMC22GIL/vMrr7zC53klHHfccezzzz9nBx10EJs5cybbdNNN2eTJk1n//v2V0hIGWpAyG+gQMhvRMd1CppIeaCpV0WYao4/l7VWAkJkBIgZsoXu+mGxULE/EZOZ86+orpkvEVKJiZVMUTQmS7HYhaiAWgpEx6i/WqMcY9RZLQ9Gx008/nS86oJNwv5YOZ6mLuqJjVY6QhTJfC0KWDYRMLxCxMKJirlMUdUTFbBSAci1iNsrXl0VExPJ6jPkcFaP5Yp3Mf+g4m+vGqiaptWMeXQhEW9reFKonY5ETvkj6hM78eF1VFkNLkZEdPLkaCOke5MUSOYhFIFwTy+sIETMLRCy8Bs95UTGfi3YgKgaqDGTMMyETAUJWHghZ2MQiEq6I5fWDiJkFIqZfxFxGxURwERXLWl+lkTQAoQIZU8T1t2QQsupMuoeQxS0UtonldYtZxEjCQhUxkq/04vL6pEPETPYWCyUqJlO8w7QoZW0fJe1B6PgXdw8IlTlkOkvfu55DZquIhy80GhyZLPShuxR+0UA2pLllOsUilrlokC2zVKFcvayIuY5+mRQxRMWYURGTiYohIgZiBZGxwMvuVjVCVtWBlY1onq+DYN8khtY3LT6y+4CImSWk84XKsZKEQcTUiDUqJoqIKAVSMwwA60DGSqBSWTE2IbN9ITbRdyykARYBITOHqMyk1zMlZfX7kFk/ZHx974WS1lxGxHyj6HokXdZeQcREo2IiIiYbFcsra58nYnmIilheVKw+RVFns2cdkS6kKIIYgIx5CoSsNyYbQEPIwhkUm6aR1OTdr1PKsrZTtG2ImFkgYvaJScRksS1ieYiKWCOpQkQMgGIgYw6iYqIXBgiZHRFLgJCFMzg2TZFwiTy2jJjJShdEzCwQsbBErP66aVLETKAqYvUpijIipqOComkRQ+EOEDMo4OFRemLehWVAi3gsv+hCJVvsQ7aoh6mCHjZEzESzZRIyk0U9bDWxjqVhdNkG0yrSkzxGtDiIqOwl24OIVU/EdB6TLyIm+uWfTxExmaiYaIqiLxExXVExRMQAEAORMY9FTDVCpjNy5luEzAa6I2R5i87jzVqqMFg2TdkoV9Z2ihbZ7YUEvYfyFhvkfU6KFt+IRcToupJebGd+xBQRK0tWVEzHPDHTIoaomFkuv/xyNnLkSNa/f3+20UYbsSeeeKJw/VtvvZWNGjWKr7/GGmuwu+66q8f9tVqNnXzyyWyJJZZgAwYMYFtttRV77bXXMrc1f/58tvbaa7Ompib27LPP9trOL3/5S7byyiuzfv36saWWWoqdddZZ3fc/8sgj7Bvf+AZbZJFF+H7omC666KLSz880kDFHyH5jp0vIVKiqkIXeS0j3c5AZNNseaAO7yP5tXfcE802sfChpbzPjIEFGvlRELH2dNF3CXudcsbIilldF0TR5kiYiYihT7y+33HILGz9+PDvllFPY008/zdZaay227bbbsg8//DBz/UcffZSNGzeO7b///uyZZ55hu+yyC19eeOGF7nXOO+88NmnSJHbFFVewxx9/nA0aNIhvc9683ueh4447ji255JKZ+zriiCPYVVddxYXs5ZdfZn/5y1/Yhhtu2H0/bffQQw9lDz/8MHvppZfYiSeeyJcrr7xS+fnZoKlGmglymT17NhsyZAhb7ebjWPPAftqjY7I9x2RSFrMo05dMtg+ZzpRFFwOHBNNpgITpdEZTzyGdvhhL7zJQTP3fudHf1rWI+YaqiJl6LmUiZDJfvJVpnRKyjImkKOqUsbJzxbIaPMumKJaRMdG+Y1mRsdrn89jTN/+CzZo1iy244ILMx7HkJtuezlpa7Z2XOtrnsUfvPlnqNaFI0QYbbMAuu+wy/ntXVxcbMWIEO+yww9jxxx/fa/3dd9+dff755+xvf/tb920bb7wxj26RfJFmkFwdffTR7JhjjuH30/Esvvji7Nprr2V77LFH9+P+8Y9/cFG67bbb2Oqrr87ljrZDkFytueaaXPJWWWUV4ddg11135ZJ2ww03KD0/GyAy5jiVAREy/1NqbAzofOw5pDMChghZHGT9HYv+thAxv0WszBddtkQshGujCRETRWdULEvEfIyKIUVRTQbTC6UCZtHW1saeeuopnkaY0KdPH/771KlTMx9Dt6fXJyjSlKw/bdo09v777/dYh8SUpCi9zQ8++IAdeOCBXJoGDux9rvzrX//Kll9+eS59yy23HE8zPOCAA9inn36a+7xJ5ihyN3bsWOXnZwPIWAkh0yVlEDIxIGRhD04hZGEjK10QMf9FTFXIbIqYjblispjoKwbKSVooaY992rpYn/kWl7Yuvl+K/JAAJcvEiRMzj+/jjz9mnZ2dPGqVhn4nocqCbi9aP/m/aJ1arcb23XdfdvDBB7P1118/cz9vvvkme+utt/j8tOuvv55H1Uisvv/97/dad+mll+ZzymhbP/vZz7i0qT4/G6CaYknqhUw1hbFeyBqlL8pWWdSJyyqLRUJmOpXRRuVC0xUYbTyHIqpamTF0RMQq/beFiIUjYunzp8gXXiIiZiMS5ntUzAauomJ5mIiKhSJZvjNjxoweaYokKj5x6aWXsjlz5rAJEybkrkPphBTRIxGjAh7E7373O7beeuuxV155pUfq4j//+U82d+5c9thjj/HUwxVXXJHPa/MVyJghOSs7ryyRsyIpq6qQ5UEDiRiEzDSunwOEzD8Secr6u4RSqMO2vIQQyZY9JzYSsjwRsy1fvotY2VL2NkQsPV9MVsRMCJLoNrNSFCFsjSERE5kzNmzYMNbc3MxTBtPQ78OHD898DN1etH7yP91G1RTT6yTzwe6//36eJlgviRTZ2muvvdh1113HH9vS0tItYsSqq67K/58+fXoPGaM0RoIqO9J+Tj31VC5jKs/PBkhTNIStFMaqpiy6TGW0UWUxlApzqtvRNWi3MfjXuQ/XstIIV2XnQ0olNl0FVaeIpf8vi49Vcn0UMRvkiZiO/mKqoIJiHPTt25dHmqZMmdIjIkW/jxkzJvMxdHt6feLee+/tXp/EiEQnvQ7NW6Oqisk6kyZNYs899xwvZU9LUhqfKh8mpeupZH1HRwd74403urfz6quv8v+XXXbZhhE11ednA0TGDAtZ2QiZSLVFmQgZ5d+XqahYDyJkZrDRMNr1gLEoGiP6WNORtmQ/OvfhW2QwJOGKeU6nTuoFTPScmCdurkRMdb6YSrNnXfPFbETFypJVRdGlQKk0lJZZB8hB1Qz32WcfHpWisvEXX3wxr5a433778fv33ntv3t8rmXdG5eapQMYFF1zAdtxxR3bzzTezJ598srucPPULO/LII9mZZ57JVlppJS5nJ510Eq+wSCXwiWWWWYalGTx4MP9/hRVW4PO/CCqyse6667If//jH/JhIomg+2NZbb90dLaP+YbQt6i9GUIl7KoN/+OGHCz8/F0DGAhAyESBkPYGQmcVE/zIRQckTBxOCU7+vGIUMIuafiJV9PnlC1eicWEbE6PyvO1WxkYjpjIqJZrLYFLG8svaueoqVRYc0oYqiHahU/UcffcSbNFNRC0olnDx5cnfRC0oJpAqECZtssgm78cYbeT+vE044gQvXnXfeyUaPHt2jdxgJz0EHHcRmzpzJNt10U75NarosCu2TKipSCfrNN9+cl6vffvvtuQQmkKDRvDOq4EgpjSRz5557LvvJT34i/PxcgD5jCn3GVFARMtkeZPwxEnPIdAqZ6z5kLvuTmZ5/ZVLIVI49a7Doyzw6nbJkYh+yPbpU91FGbEMiNhEr+5xEUhKzzok6ImI6ZUwkIpYnYypRMREZ01VBUTQiJitjoimK9VGxovliMhGrRpd/2eiXSG+x+nU62/zvM7bpN09lLS0W+4x1zGOPPHCql68J+Jowv2KpwBwyFRGT/bZQd8lgzCGLPz3Kx0p1ugXDZs80nXPn0nO8Gs33goiF99nTda7LSmHUcX6W/TLOFxETwXcR8z3ChVRCAIpBmqKHKYuqIuZDyqIsMaUsmowW+ZCy6LuI6Z6L1mg9G1E4XdtOH2sIIubDey0kEVM9J8YyR8wkIiLm+xyxGOaKNYqKARAykDHL+DqHjBCRskbrys4fi0nIigaUOsSsaHBoWtR8GBzLYlo6fJn3JYLPAhbie8s3VDIAQhUx3VGxoqwVURFrFBWTETGVqJhIiqKMiOkE0gRAY9zHvANhvuDE3JBpdDFM31+0rsrcgVhSFkNsKls0GBYtXV/VAbVM+XefhcgVvr5vXEXFXL8evouYLUjCQomIlRGxvPliZaobimJjHwCEAmRMAl0nZ5dleVUvirIXSwhZWAPIRLrqF9ltVBWb88xiwdf3C0RMDRuNn21ExWSv80VRMVkRM1W0IwudjZ41TRcEoLJAxhwImUgxD5+ETPVbSwhZ9ea5uB5QuqSo7D4I430CEQO68GGOWF5UTLaCou5oFaJiAPQEc8YUhaxfq4V5X22tpYt5qCIiYCLFPzCHzD6ui32Y7r/mM/XzyCBi/olHHhAxN1Gxsv3EfI2KyWIrKqYzIkYgKiZHn7ZO1qer097+OuztC6gDGVOkCkKmC1Uhq0e3oEHIzAEh+wIi5pl02BYxW8+3TKTftIipVOvV2cxZV6uZWKJijUTMtwqK8ttG21wQJpAxR0ImU1Ux+eYvZClTEbKigYMuMYtdyFTQJXFFg1Fdouar9LkUMRuvic9i5UrCQntNbEXEioSsbOEO2aiYjsqJoUTFdFRPLCtINtMOqfS9+a/HATAD5owFhOyFx8Y3jDIXU50TvGkgoWueQwyNoUNL19LxeiTbCOm1NY2N1ySW1xsiVo5G5/P66rtlmjmXhSTMdDRMd1RMd4Nn2aiYiEjJfr+qKmf1PcYAiA3ImENULg4qQmZaylwJGQEhC5cyr0f9Y/Ha2nlNYnmdXRe60YXKecvlHLFEyrLkTPY6JXottCVhOqNiukXMBI1EzGRUDIU+QGz4/4mPHBtClpYyU3IGIavWYNbl65H3mCq/tjZek1heX4gY866EfWgipitFUUXETKQoqkTFZEFUDIB8IGMBC1mZ8vcm5AxCVq1BrYvXo9G6VXxtbbwmsbyuELHqiFiIuIiIqUiSrahYlohlbRvCBkIHMuYJqt/gJVLmo5yFLGTJYgqVpsoxI/JaiL5eVXpdbbwmsbyeEDF752vRL+ZMiliI6YmNsFG4w0TRjkbbRNohqDp+n60qhkyFRdOVF5OL5IAWue3IljLWUWXRZI+yrIGPzuqLeQNdHysEmsaHlDrZ113nMZvedyxSVVURcyVhBCJi1YiKlS3aoVOqRKNiAMQAImOCdObkiMuWw7X1bZ6uC5fsBVO2p4zPEbI8kMoYLzKRJt1/I53pmuBrIGLl0H1+Vsm+0FnCvux1XMd8sfqomC4Ry4uKZVVS1C02JvuHARA7kDEJOiwJWRWBkPk14I5hAKtKkWiZTi/Vma4JwmnTEJOIFaUoqqTAxyZipnCdnqgTmagY5ouBGICMSQIh0xsVSwMh6w2EzB1p8bI5x69oPxAxcSBi5YCIyYuYSI8xlblijeaLFYmYSFTMpYj5JIEAuAIypgCETL+IJUDIegMhc4uL1z9rnxAxcSBi5YCI2StlX5ayZexDAFExEDuQMUUgZPpFLAFC1hsIWfVI/80hYtURMdVKrqbPc0VAxBpHxVyIWH1UTKXSoUlQph6Ar6ieOWiETtItrZ2ZJ/p+reWqIlZVxESETKX6ou4qi/XQ4ElnlcW8AaCrKouNBrhDmr+wdixVAhJmXsRsvMamv7DRKWKyX4ZBxMKIiIVKbFGx5vmdrLmz97jRFLUOe/sC6iAyFnGELIRSwDKNostGzlx+cxz74FxmEIxIGzCB7PvK1jzAkERMFohY46iYCRETAVExAMIBMmYQVSHT2axSh5AVXVTpYpwsPkXWXAxcbKQr+ixkMkDIgGsRiwHd5zNdjZ1VRExHf8wQI2IiZe3ri3fIpifqIi8pRbasvagEykbFIHMgVCBjhqmCkOkSM1tAyPwAQgZ0ABGLQ8RMYlLE8qJipkRMBR0RMNMiBpECVQYypoFGJ+8qCVkoYgYh8wMIGSgDRCw+ERONjoleH12ImAiqIqajaEfR7b6IGKJioEpAxixRRSFL8FXKIGR+ACEDsYqYifNAzCKmG1ci1igqZioiZiI9ERExAMzjvspEhVCtskhC1q+lQ5uQlc3Lz7roDmhpF76Q254n5gobFRYbDTJdVV9UGVjXV2RMD7ZRrTFeQi9FbxMVEdPVKsSmiNE1quyXh7FFxHT2FBONirkSMUTFQNVAZEwToifzWCJkvbbZ0dpjKcKnKJmNHmS2omShD0JpUJ5e6u8Ddsn6O4g+zsS6VfgM6CZEEdNxPXQpYkVRMZNzxETTE0XxTcQAiBnImANiFbIe2zcsZDobQ9soDe1SymwNRhsNrFUH96LbB2ZQkSuRx8T+99T9eZc9T9kQsdDbsuiqmihCWREriorJpCeGKjuIfoGYgYw5OrFXQchME5qQEVUQsqyolq6Bt85tAXFU5KroMbH/DSFi5tB1DbQpYiLoqJqos2iHC1SOD5IGYgAyphkImd0LvW4hsxUlc4HNdK28dEOd2wd2UZGrrNtj/9tVScRMpCgWXZNErn0i11VfRIwELFlUMdVTzCcgXCB2IGMGCFHIksU2Oiot6hQyAkIWBrEP6n1ERa7S98f+N6uSiOm65tQvNq55tqlPUSwrYEUiVqWoGACxEO7ZLSJ8qLKYUH8xLFt5UfXiL1txkQYhA/NmHJcYBC3QOs/awM1G5cVEyEKpsqhShRHYe81F5SomCbPWtiJiEXOJ66iYznTEKiATFUMEDYQKZMzgCb+ltdMLIUt/oygrb8mF05aUlSmDXzQYURU1GhCZFLK8QZ5pMVONkPkocbqETHY7iWC4kkGXIupSrpL3ro/vRVdR+NhFLOSomClCjopVWZqa2jtZU1eHvf11io9DgTuQpihIp8K3abLfwOlOWaTby4iYD+hqGF1msGJrLplPJfFDS3MsW6VRpgpg/Xq2xUTleFX24SPp95/Ie1H3+1XnZzJ0EZOdL2ZDxBpdQ8tGxVT6i6VTFFWjYrL9xcqiq6y9KKoNodP3V1nwQPhAxgyfiF0JmQ/fJOqc3O1ayAjbQkZAyMTR1RNLZg5UmX2rYGPfIYhY0W0i96kAEQtbxGxc/4t6jLlCV1RMRcSy7oM0AdAb/84cnhOCkGVdwFxFxSBk5YGQ2W84LFMdUGXfKtjYd0giVnQfREwciFjP63veoiJiIr3FfKugmBUVM5n+iKgYAJCxaIUsjev0xBiFzEXaom+ELGS6+2bJrKOCjX2HKGJZ60DEzKWAhyxiJgt3mIyIiaYompoXpipiIlEx1WOGfIHYcJ/LFrCQNfft9LKoh08ilr6ID2jRc7XIGkCUrb6YNbhpVPTDRsXF+kGhrYqLoVdmdBkpcik1vgpVHjrFycSXA7GImIvmzT6JmKkvWYtEzFRUTAZZ2RGtc2W6OXPRuvX3QcxADEDGKiBkvqBTyIoGG6Ji1mhQI1ouv9EgS6esQciAL/gaGQ1dtnSKmE4Jk4mKhSZiqsiImErxDtuFO0SkS0TE6tfJEibTQgdASLj/6iZwVL9Nk8GHi46u3jA6UxZtDEB0pAPpHqQhZRG4Joa/t4+fo6LzkMy5SFcV2lhErFGKosp13HRELE/EROeLlY2KuSqHnydcWbdDzkAsQMY0IHsiV8ld1132XgUImToQMhALMfyddX9+TEbFXEbDQhcxutbaFDFdyIpYvTi5FDGVyJku+rR1mdkwAIaBjFUgQgYhKw+ErDoDdRD339fHz00eELFyItYI3SKmIypWVsTKont7OqJXiIqB2IGMaUTmxK5a2amMkNU3gfZByExLGYTMPjEM2EGcf1cfPy95QMTMpuvHEBHLXLdEVMxU82bZdRqROQdtPqJiIFzcT0aKDJWiHrKULeqRvgCqVluki++Avu1GLvq6i3zQIEW22mLZoh6NhKwKRT2KkC34gSIhbglZxEISsASIWDhzxFyKWFmx0SliphCJgEHEQOhAxgyQnOgbSZlsZUUTVRaTC6KKlOkUMpmBgIqs+ShkhC4p81HIdA/usx4DQTNLaBIWonjVAxEzJ2K6y9frSlHUJWIyQqVbxEz2FSvaJgAxEEya4llnncU22WQTNnDgQDZ06FChx+y7776sqampx7LddtsxW9CJ3+RcMp1pG6rpi7pSFqX2qZja6FvKou60xRgGoiqyEJowhEJor6ur97/Oz7Dr0vWhi5iJa7GriFgRrqJYpver0kMstKhY07wO6wvwn2BkrK2tje22227skEMOkXocydd7773Xvdx0003MJ1TnjsVY9t4GELI4CU0cfCe01zN0EZMtXU+4Kl3vs4hlXU9tFNdqFBVT6TEmW7DDdFSsaD1ExQAoh/uRvCCnnXYa///aa6+Vely/fv3Y8OHDWcz40BjaVMqiCXxLWdQ9jyy0lEVdYF6ZHiBi9kRM9QsdiJgdEdORnlgkYroqJ5qOWMmKmApViIoBEHxkTJUHH3yQLbbYYmyVVVbhUbVPPvmkcP358+ez2bNn91hMk/RCKRMl8yVClrf4BiJkcRKaSPhGaK9fqCKmEglLgIj5g4mIWB66REz2u0MVEZONiiHiBapOOGc9BShFcdddd2XLLbcce+ONN9gJJ5zAtt9+ezZ16lTW3JwtPhMnTuyOwvX6hmyA+WNOC5lscQ8dVRZVqyuGSNGgRlfkzHWELI+YI2eIkKkBEbMjYb4gImI6vkizIWIu0hPLiphMVCxkEdPVQ6yX4CEqBiLCqYwdf/zx7Nxzzy1c56WXXmKjRo1S2v4ee+zR/fMaa6zB1lxzTbbCCivwaNmWW26Z+ZgJEyaw8ePHd/9OkbERI0Z0C1lTq70TQHKBkZEyH1IWQ09jVEll1JWuaKL0fRVTGXWX1hfZZpltV12sXAqX7t5/JiVMV1SsSMR0ZjLoFLGy86t1goiYRP+vgu0gIgaABzJ29NFH84qHRSy//PLa9kfbGjZsGHv99ddzZYzmmNGSh20hUymB76uQhQaE7Ov10sQgcaLRM10l+EURFblYZMuliIUkYTqpgojpiIrlzRfTIWKiUTHTIqayfZMRMZXtABADTmVs0UUX5Yst3n77bT5nbIkllii1HVkh09EI2paQmUxV1N0oWndzaB+FjDApZY2ELGtAHEtUrZGQuRAeEUmEiFVTxHRExUIUMZ+wGRHLw9fmzDaLe+SlKPZpKzfOAsAVwRTwmD59Onv22Wf5/52dnfxnWubOndu9DqUz3nHHHfxnuv3YY49ljz32GPvf//7HpkyZwnbeeWe24oorsm233bb08cj2HtHxTZ3st4OqFznVnmMi6Lzgq/YbMz0IKjM5P2/gaHLwmDfwLRoQx1IgJE9sXApP0b4hYuWBiMUtYqaiYrpETKWvmAl0VmZEVAyAcgTzldTJJ5/Mrrvuuu7f11lnHf7/Aw88wLbYYgv+8yuvvMJmzZrFf6YCHc8//zx/zMyZM9mSSy7JttlmG3bGGWcUpiGajpAlqEbKECELI0KmO0qWNYjUGTGrj3aJDIhjjZD5IDxZETIfjss0ELHeQMTsYlLEihBJUaxiVEx4f22dDCU9QKgEExmj/mK1Wq3XkogYQb8nc9AGDBjA7r77bvbhhx/yhtEUHbvyyivZ4osvrvW4ZCNkaTFLFlkQIXMXIUsW2ShZ3qIjYqZrgJkMhGUGxLFFyHwSnvSx+HRcpjD5XjIdXZb9TKfPJUXnE1URo3NiesldDyJWGhkRcxEVy/o+0FW/MpWoGKoouuHyyy9nI0eOZP3792cbbbQRe+KJJwrXv/XWW3l2Gq1PBfPuuuuuHvfT+JyCKjRNiMbnW221FXvttddyW0ytvfbarKmpiWfAJdA4nm6rXygDLu0K9ffTMaUhT6hfh6qvuySeM6JDyhb1UJlTFkuEjNARJSsacJiInGUNklTK4WcN3lSiaVkDTZXImcqAOKYImW/4eEy+i5iP0a9GUmU6+lUVESuToqhSsEPXHLG8Js891kFULH9/mCumlVtuuYVXFb/iiiu4iF188cV8eg9ln1Hf3noeffRRNm7cON4aaqeddmI33ngj22WXXdjTTz/NRo8ezdc577zz2KRJk3i2GrWbOumkk/g2X3zxxV6ydNxxx/Fstueeey7z+O677z62+uqrd/++yCKL9Lh/wQUX5MeaQLJVD8nXNddc0/27roy56CNjvqMaIUuoaoSMMN0Y2nbkrCy65pzpjpxVIUIGzL5H8hbV93XWYhKVz6bOJs15QMTKXbtVrt+yIiYTFTMpMyFHxYAdLrzwQnbggQey/fbbj6222mpcygYOHMiuvvrqzPUvueQSLjdUp2HVVVfl04HWXXdddtlll3VHxUjoTjzxRF67gdpMXX/99ezdd99ld955Z49t/eMf/2D33HMP++Uvf5l7fCRfw4cP715aW3uez0i+0vdnZcSRfKXXWWihhZhLIGOBC5ksvgoZEYOQ6Rx86S4CYhoIGchCRbhcvo91fkHio4jpxFVDZ13Xz0bX7LyomE4RMxkV0zhludRxaNt/RooiomJiUM/c9EKpgFnQtJ6nnnqKpxEm9OnTh/8+derUzMfQ7en1CYp6JetPmzaNvf/++z3WGTJkCI+6pbf5wQcfcAm84YYbuPzl8Z3vfIdH6DbddFP2l7/8pdf9VMBv2WWX5T2CSf7++9//9lqH+g3TNlZZZRV2yCGH8ErrLoGMaQZCVg4IWU8gZCBkQks/1PkZhIil1oWICYuYr1Ex0f5itsvZh0ZTeztrarO4tH/1ByExIQFKFkopzOLjjz/mFcvro0n0OwlVFnR70frJ/0Xr1P6/5sPBBx/M1l9//cz9DB48mF1wwQV8ftrf//53LmOUDpkWMpIriuD9+c9/Zr///e9ZV1cX22STTXhrqwSK4lFkjqqsn3vuueyhhx5i22+/PX/ervAvZyACbM4hk5075vMcMhPVFl1VX1StwFilZtIgXnRLOURMn4jp+MJLVMTKpCWaEDHRL0t9EjEfomImBTErFVE0PRFRMXFmzJjB51L5MkeqnksvvZTNmTOHTZgwIXedYcOG8blsCRtssAFPdTz//PN5tIwYM2YMXxJIxCh18je/+Q1PnyT22GOP7vup2AilTa6wwgo8WrblllsyFyAyJkij0rb1IEKmZx6ZiUiZ7QiZT/PICETIgG7KzAFrBEQsrIgYXVeSxQcRS+aE+S5ivlZQLLrf1pwu0ahY83zIWR4kYuklT8ZIeKg1FKUMpqHfaW5VFnR70frJ/0Xr3H///TxlkY6rpaWF9wQmKEq2zz775D4vSnV8/fXXc++n+WTUCqtoneWXX54/76J1TAMZkwBCZlfIssRMl5zZHsyIlrJuhK7y+DYKH5QdlJsc4AN3BTh8FDFdn6tYRIzO73mLKQHTLWIqhTlcipiuqJjLiJiOCJpoeiKiYmbo27cvW2+99XgKXwKl+tHv6YhTGro9vT5x7733dq9P1RNJutLr0Ly1xx9/vHudSZMm8eqJVMqelqQ0PlV2POuss3KPl9alcvl5UOrhf/7zn8J1KIWR5owVrWMapClKQifrmkQKoo2URdVUxRBSFk2WxLeZspg3aNOVxliEaIpjeuCrK5VRJmWx0aAe6Y/ir5eu1wlNmP0RMVtfIMl+0aazQqJOEZPFNxFTIe9UrxIVc1ldUWlb8zuZ/dFInFAqIEWjKCq14YYb8kqIn3/+Oa+uSOy9995sqaWW6p53dsQRR7CxY8fy+Vw77rgju/nmm9mTTz7Je/sm1Q2PPPJIduaZZ7KVVlqpu7Q9la+nOV/EMsssw+rnhxGUPrj00kvzn6ksPskiRbqI22+/nc8Pu+qqq7ofd/rpp7ONN96YR9ZmzpzJUxjfeustdsABB3QX9zjttNPY9773PS6Ib7zxBi+lT+tT0RFXQMYiEbKyhCZkuuaWuRQynfPKdM85SwbJOqQsGdDnCYJsk2kImXlxtRGJhIjZl7BGmQU2Mx5sFusIUcR0yZDKdkQeI1u4Q0S8VKNiSE/Uy+67784++ugj3qSZCmxQA+bJkyd3F+CYPn06r7CYnpdFvcWodP0JJ5zAhYtK1ic9xggSHhK6gw46iEsSFd+gbdb3GGsEzfsiuaJURmoyTZGz73//+933f/bZZ7wiIx03launKB/1QaMS/QSlYD7//PNc7Og4SAi32WYbvl2X8+iaalTCBORCoVSqPDPiojNYnwE93zQyQpagKmUiMlYmOpagImT8cY6EjNBR7MOlkBGmhYzQVQTEdAEQEaosZDKiJPs62UoHhYj5J2Iuo2K6RUwmMqarqbNrEZOZJ1bmPtUKio0KdWTdLytjHR3z2ANPns1mzZrVo1iFT2PJrZY/nLX0sTfo7+iaz+57c5KXrwn4GkTGLKMaJRNNVywra4iQxR0h0yFlWQNp24JW1QiZrCyJvk425+RBxPwUMVf4GBGT3o5EQ2cfImK+iVjmcWCuGKgQKODh4ERetrCHz0U9iiZ5m0THYMNlhTJbk/91V2ZMMFUEpIiqFfVQfb55j3NRHAUiZvdcJFP0yMV5u0jESL7Si+3rt2hUrJGIyfQSszFPq8w+fOwphhRFEAOIjDmi7DwyFWQKfRQJmUjkrP7CbiNqpitCRrgs7GEjZTFr0KojjVHnXDMR0iLRKAJULx02Ika+FNJIR8hMyFdo5edjFTGbEbCyKYp5ImajinDMETFbZex1Sp0ojaJiTe0QMxAmkDHLxTzKCJmNQh4iqKQyJnIWSsPoRgMik7JmU8hMVGUsMzgvI3EqKXxVit6pHIsrybIlYbai0SZFzOcUxFBFTCQqJiJi9VExUyImcmp2FRFrJF6IigGANEVhZKsq+ZqyKJqfbzqV0SQ2BiemUxp1NYv2Pb0xlMF/1XCRdioKREz/uc5miqLvIqYL0cbOulMTVfuV5a2jOzXRWFSsDYXtQbhAxjQIWdkTvIyQ6bhguRYy/lgIWZADRsKGkPkqAlXA59ceIuaHiKme+3Vde1Sup6LXaR1RMdF5YnqaJJffnqm5aiajYgDEBNIUJaETda1vTWu6omzKoo50xbKNonVgugKjrpRF11UYXaUt6u5d5vv8MwARcwFELCwRa4QrEfO9KbOJ7QXJvPl2wyBd8y3uDKgCGQtYyBJUxawqQkaYlDIImT1BgJiZAyLGKlOmXjUzIWYRM9VPzJaIVS0qlp4vhhRFEDqQMQNCRtgq6pGVuigjZ1UQsqwBjG45syVkIuRJW9HjVUXPREXGIhAt0yuuPgtYyCJmKoVZVcR0pIX7KmJFaf4yIlY2KhaiiBXvA1ExAGwBGTMgZGWlrGzZ+0TORKUsuViqSJlqk2jXTaNNRMxkhMxkCX2VQWf6MWUicKIDZxONp01iQ/50P6eqlZt3LWKm55C6FDFVTIpYo7nWJop15EXFYhOxsmCuGAByoIBHSRp9m0YXBJWLgo4qi7LFPujCaSOdJI9Qm0XLDsjS69DPrptNuxi4ygzYbVRxdFlUJJaCJT78nVxVJY1dxFSiYqauJXRt1C1iuqslhzJHTLSCYqP1Gz22bFSsvpIiStqD2ICMaUCo+pLlEvZlqi/KXkTLNv+skpDl3Qcha7yODwP9RJzKypOu7fiCD38b31tDqFIFERO9Tol8SWlKxESjYqZELHPfJUrWm9iXiIjpTldMzxdrmoe5YyBMkKZoIWVR5CKRlc5YNl2xTPVF2blkutIVXaQsmqi8mJWy2Ei4bMw7861oSDKIr09bzBvc2yoaIkK9SOWlMsYiXL6LWCzylQYiJocJERNp7mwDldNekUDJRMVURUyERimKRVExiBiIBciYZSGTJf1NYNl5ZKEJWR6mRM2kkIlGvqooZPWS1Whw75OQVUm6fBSxGAUsASLWE5upiaICJtrYuSxZp7tGguQ6IiayHkQMgK+AjAnS1NnU6wRda6lpEzKRsvi6xCwUIXMROTMhZKFjU8hk1vVRyGLCtWiFImG6PuMuS9b7LmKN0CViZaNgJlIUdUfEiu5XiW4VPaZMSiLmiYEqARkrAZ24dQqZDImYyUiZarPoKgoZYbphdCjRsTKDX5MSZ1sWXMmfz1JUdRHzaa6qKxErkjBdwmWjfL2siNmIiuWdclTSD0Xvl1lXRrZkomL1IlZPXlSsqd2v6yYAokDGPBIylabRslIGIQtHymIaOPvWsNq1FImIXewSVi9VIu+RGERMt4S5ErFGkTCdka+yItaw6rGheWFlomImImJSslRSxJTnkGWImEh6YlMbrtMgXFBN0SC6y+WaLoXvc5VFl9UXTQyeqpDeGNsg2kQZ+Hrhyrs9NrLeD40qIPr2HlJpSwER03dt0yViKoj0FTMlYnnbFYmI6UxPlAF9xQAoBjJWQSErc6GEkNkDQlYNqiJgou+DPFHzBdXegBAx+18ymqiWKNrg2aaIqUqYiXli9feXFb28qBgAMQEZs0CMQiYjZXShNy1lELIw8GlQDfz9+6fXc/2eSeSrTIP2GERM5LwfmojJIjpPTFXOVEUsr3CIzjlfojQSMZm5YijaAaoC5oxZQnQOmcq8MdX+ZKrzxxLSF2aR+WTpi76J+WT1gxMT88l0V1ssW9CjfnDYqPBHen1XRUIaDa5jmF8GeqIiVKYlzNYXHaGKmMvqiLrL2JsUMV1RMZ0RMZN9wUxVT6wM89rshkG6cD0NAciYRZJv6kxXWrQpZPUXbtEiH6bFrH7AolPMXAmZzvV8rdqYNwiHoIWD60iWT1FmHyom6qyOGEJETCYqVrafmK5S9mVETG1/NePblI2K9dpeXVQMKYogZiBjEUuZbSFTqbqYHiyYrL6YDGJ0SZkLITOBr0JWpWha+rmJPg8bVSp9lapQUn11ipgNCQtFxGxHxUKKiIk8pkz0Ku+xIiIGAMjn/9o7D3ApqvONn3u5dKUjJfQiGoJ0CBopgQiBJyIoATSIKL0EEKVEEcGgUhQCokCiEJ4YIDwBVBRIFCFSpCtFIHRCCwEDinAp987/eU/c/e8uW6ac2Wnv73kW7s6enZk9Oztz3vm+7z2sGXNYlMW7g6fyYpOOGjIVF3Qvmn04UUNmB36oS0vlxOdWYvdZj6Og3fVUXu3LWKzUeHlZiJmt0fWCEEt3VMyoEFNp5KFnXUa3p0ckWV1nom2kiopF1osxKkaCBsWYAqzOUWK3wYcRQabq4kpB5i38IMi8JiSM2rgnaq/y83ql79x8PDstxNJ5vk73NSrVjUqvCTEzkzonw6oYNOq6mI4IWCIhxjnGiJ+gGHMpqlMxjNx1pCAzBgWZe0WZW8WZnn0yEgFT8Rnd2E9moBAzBoWYuyJielEdFVPxGeyIisWDQoz4DdaMKQIneC1LU+q2qMJZ0UwNmWpjD6M1ZKGBhZ01ZHIbN7OU1pCF8HItmZmBrBdrzozUWjkpUoxsG22TfS6/iC2/mHGovEnkhBBTcdMuyKmJqqNiVjBTZ6YqKhZrZ58qKkYhRvwIxZjLSXY30IxQMyrIEmFEqAVFkCUatHlZnPnJBESvOPOyaIkUZF7+HH61pLcjQu/FiJhREZbsOuhFIeamqJgqN0YzUTGj84pFCjFC/ATFmE5wks7MzBC5SRwQ7YiOJW1vMnJmRJCpipwFTZDFG9T5WZR5UZBF4ifh4tbPkkpE6Tl+0iHEvCLCgiLEkuFVIZbMQTGd2DkfmZWIWLyoWKwQY1SM+AnWjJkRZUkuAFbNPNJVW6bigmg0TcVKDZnddWQqa8j8XlsWhJozkv5jI1UbCjH3CDGzKYpmrjuJrnFeFWJ2OSgmWq7ayl5FVCyeEEsWFaMQI36HYswkyQRZup0VgyDIIkWZXeKMgsw6FGTE6kTkVtdhFgoxfVCICUtYqRVTKcTsnI/MKrSyJ0GDYswGcAEIPUy9n4JMF3YIMwoy61CQESvHQux7KMRi1kMhpuz66xYhpgenI2JG2zIqRoh+WDNmAZzIk9WQgdAFwWgtmdH6saDUkKUaoKioL9MzaLJaY6Zn8OflGjOzA2gv150RoUxE+cWkw2kBFiQhZvTmp5uEWKp1qxZiRucT07tex6Ni2ddU7gohaYNiLE1EXij0CrPQhYemHu4y/Ig3yLLDAMQvdvlWB+EUaN7DrdFRr4gvuS5FEX+3CjGj2RyJhJiZDBQ3CTGvRsTSERXzoxDTrl8TWkb6UkA1zSVuMSQpFGNpiI5ZjZYZFWVeE2RAryjTI+DSJcjiDcLscmX0eyRNz8DeKVHm5PZVbtutAimo6YZ2CzFV6eAUYsJWIZZs/XYIMTtEnFlihZhpB0WPCDFCEkEx5iBGrfCNiDIvCbLYgUOs2IodVOgRcE4IsnSIsiBb6KdbFMWrXXJy28DM9oMgwgCFmHrbegox4auImDU7fk15VEwJFGLEB9DAw2FnRTMXFN1OUh4y9YgdTEQ+krVLht12+E4P2hJBgxD7tuHkts1sn0JMHRRi/hNiECd22dTrEWJumUw6lZCzI2qmJCpGIUZ8AsWYhwWZHlGGi58ZUYYLqlVRZkWQ6YWCLD4UZPat28ltG9k+hZg6KMSsXzPcKMSSvueG+4SYlX1SKfDsjopRiJGgwTRFhYRO+EZryKygN3Ux8kJoJH0x8uJqJn3RTMqiUVLVkaUa5NiZzohBnBMpi6FBql9TFu1KGzQidJzatp7tU4gFR4g5WRum94ad14RYUCJiTkXFjDgoGhJi2TSrIN6EYkwnGUhniLlW5eZVa+ph1P4+6v0RFxwjwky21ymyQhdeo6LMDYLMyEBItThzWpBF4jdx5qToMLLteMLJ6r4HRXAlIqhCzMkImNFsCTNZGW4QYmZFkZ1CzC1CLZ3zihkTYoyUEe9CMWYBnBxVCjJV2O2+aMbkw8xdVzMmICqs8lXOWeYGQRZ0u3w3EHTh5MUUXLuFmJlomFEhpjJV3IgQUynC5GuMiFnCjOlHutwVI6NiFGIkqLBmzCJJ87sN1pGZueCoqCszc/FUYfJhx0BC5V1jDJZU1pw5WUOWaFBrZWAber/f69OIO0jnsWbnb9XsecULQsxMjXKq61S6hZgZYWS3EHOLoYej84pRiBEfQzHmc0Em10lBZgk/CzKzg9zY9hRkxE7SeXzZLcTM4BUhpvraRCGmr02yKJbRqJjR5WmLihHiYyjG0gAFmTUoyNKD3khXotcpyIgZUh1zFGL+E2J6sjYoxOwTYkbXlXQbCaJicdcTERVLZtpBSNBw56jQZ/VjZmrIrBp6WCEdNWTpmkhaRQ2ZHbVkyQZ9bqsti6wv0yPUWItGVERXcRz5QYil0ynRqRoxlTcEZVsKMVuFmBFjjrS4KcZY2euGKYrEJ1CMpRGnBRkuhm4y9fCiIPO7+2IijAyKEwkymocQI8eSVSFm5IaHSiGmKoruFSGWKipmRISpFGK63+tBIZZKIKUjImZXVMxKiqJ2neKMeBOKMQ8IMuBElCx0kTVqfZ9wfQrEmllBlgi6L9oDI2j+x41pqXpFlRXxpTJl2ck5wtwWDVMtxOwyxnBSiOmJUlkVckbbJdwPnRM9m46KeZTcq9kiNyMnfdvTeJ31AqwZ00mmjvOF7pQIE3fxcJFSUUtm9OJo1iHLzgu9yjvAqt0XVeLW2jI/D+aJN787/FbcOCeYV4QYzs1mzs/JrgsUYur2CcLILiFmJGXRzbVi2jVGxYh3oRgzgMoTvNm0CqcEmXxfGsw69EJB5g/cNqgn3vrO0iXC/CzE7IBCLD375FRELJUQMxsVS5iiyNow4nPcM7r2CBRkma5Jg6Eg8wduGtwT73xX6fxd+FmImT0nq7o553chpgenUhPNpCIaFWKJomKW0xMjBBqjYsTrUIyZgILsf2mLboiUUZD5AzcN8on7vyMKMWcjYqrSE1VO4+JWIZZqm26JiBltm0qIQYQpEWLxomKMlBGf4e8RnoNW9naaeqh0WzTisJjqomzE6EOl86IZU490uC9iEKfa1MMMRoxAYreRThMRq4N9GoKom8IgVTsnoRBzrxAztB6TQsyshb3XImIqXBNV29Iniooli4YBzilGSHIoxiwQOhEmEmVGBFvoAmNUlKkSZMCKKDPjvqiS0ABFhSgLDZpUOi3GQ7UlvlVnxniD3Mhlbnd39KuYS6cQcpPocrMLot0iTLVNvYqbXyozIYIQEUv1utMRsdjXzQo3o0JMdVSMKYrEDzifZ+YDcFK0Urgb1f56huHURVUXNogys6mLUevRkcKo2kI5chCjaiCDQZTK1EUnB4epBrF6BrnpNEtwAggRt4kRt+2Pk5g99vA7c0KImT1/2DFfmFm3RGAkJV3P9cPK9Sr22uhmIebUhM6p3qsyKmarEIsH0xOJT6EYS5MoM7wug6JM5Z1GFYJMz0XcLkHmJVHmBkFmdJBLUZa+/SDWhZgTmD1f2D1xc6woS3YONloXbKcQM3OT0mkhZuUGrRUhpgcVYk21ELvFSVGH8GJUjPgF/46ofFJfZqSeTOUE0VZryaLWhQFAgjSZ0GBAZQ2ZnfVkQFVNmV21ZUZSFi2lfXl8YupUODkxdZCEmF3CnkLM/OuqRZgRIaZXdNlhjKFHiNmNVSFmRmhZFWdWa8R0CTGmJxIf44nI2LFjx8RTTz0lqlatKgoWLCiqV68uxo0bJ65fT37mzM7OFoMGDRIlS5YUt912m3j44YfFv//9b9v3N94J01JKg4m0RSfdFuOuS8fgwM7URZXYESlzIkKmYhDs5wiZU6LI70IsFFm1M8JKISbSkpLoJyGmErNRMTvSC1VsIzZFUe88YnqiYiqFWO7VbEP7RRIza9YsUaVKFVGgQAHRtGlTsWXLlqTtlyxZIu666y7Zvk6dOuKjjz6Kel3TNPHCCy+IcuXKyXF8mzZtxMGDB+Ou69q1a6JevXoiIyNDfPHFF3HbHDp0SNx+++2iWLFit7w2ffp0UatWLbmdihUriuHDh0s9YOXz2Y0nxNj+/ftFbm6umDNnjti7d6+YNm2amD17tvjNb36T9H34Aj744AN5kKxbt06cPn1adO7c2dQ+4I6Zk3fNzKRohESZFWGmWpA5lboYSltUbYXvZjv8pNtSOAgOgiBT/Ui2Lb+RDvEVtT0KMdeIMNVCTE8pgFkh5nRUzKpzop51xHvdyaiYmdTERORevWr6vSSaxYsXi6effloGPXbs2CHq1q0r2rZtK86dOxe3/caNG0X37t1l0GTnzp3ioYceko89e/aE20yePFnMmDFDjt03b94sChcuLNcZK5LAyJEjRfny5UUibty4Ibd3//333/Lan//8ZzF69Gi57/v27RNvv/22/DyResHo50sHGRrkqgeZMmWKeOutt8SRI0fivn7p0iVRunRp+cU88sgjYVF39913i02bNokf//jHurbzzTffiKJFi4paw18WefIXkMty8unbR6OpiXram7HAj8RsCqOqlMVb1msiPdGulMZIVKU2qk5rTFc6oxn8nLJoN0iH9IsQ87PgcqsQS9fEzclEmEpjjrhtbDLrMCPGzLgk2j2xc6r1WBFikZGx2LnEUgmxRFEx3UJMR1QsJMRuajfEpzlL5fivSJEiwk2ExpKt8nQWWRnpO9eb6RNEiho3bizeeOMN+RzBEESYhgwZIoVOLF27dhXfffedWLFiRXgZxtiIbkF8QWZAXI0YMUI888wz8nXsT5kyZcT8+fNFt27dwu9buXKlFEp//etfRe3ataW4w3oiGTVqlAyutG7dWgwbNkxcvHgx/NrgwYOlCPvkk0/Cy7BdCMD169eb+nzpwF1XNAPgiyxRokTC17dv3y7VM0KhIRBCrVSpUlIxhvAoHpHbATnXItT7NX2CLNfoGPz7TWjJfqfZ1gWZ3EYeg+u4apMgw3qNiqur9guy3O9vsuVRIKRC13gV67ry/f/5FdetqQD7RkFmjsvyi73mWvFkbGoE+47Na2HRY99xlhMjrFL9bv/X3rgKMPs+lSmJmtE64njLc76vVRYW6qJTbftG6qMq86b5Iw9CzMh7tRvGX9MSHLIQRCk/m47P/79tawm3cdNA+6htX8+N+n4yb/7/nmgRf4OMnBx9QuyGXiEWrZC16/GEWHaU8JDtXBxjuInfvJbm7X0vBiPJnz+/fMSC8h+Mn8eMGRNelpmZKcfSGDvHA8shoCJBpGn58uXy76NHj4qzZ89GjcchTCGK8N5u34sxlBH16dNHvq9QoUJxt7VmzRqZ7Yb0xaVLl97y+r333iv+9Kc/ybTDJk2ayIANUiZ79Ohh+vOlA0+KMeSKzpw5U0ydOjVhG3zx+fLluyWfFEocryXilVdeEePHj791m29OsLjXhBBCCCHETi5cuCAH+24C49GyZcuKz85+kPZtwzMBkZ9IkKL34osv3tL2/PnzIicnR46VI8FzZJfFA2PqeO1DY+3Q/8naaJomnnjiCdG/f3/RqFEj6RUR73tFG4itRFG+Rx99VH6Gn/zkJ3KdN2/elOsMpSma+Xy+F2MIB06aNClpG4QbEdEKcerUKdGuXTvRpUsXqaBVA7UcqfAR/qxcubI4ceKE637cbgV3YPDD/9e//uW6VAE3w34zDvvMHOw347DPzMF+Mw77zBzIZEL2U7KsKaeAUQQiRKmM5+wAogRmGJHEi4o5ycyZM8W3334bFbGKBWN+iK3mzZsnbLN27Vrx8ssvizfffFNG3hC8GTp0qHjppZfE2LFjhVtxVIwhjxMqNxnVqlUL/40c0VatWskw5Ny5c5O+D3cgcNBDTEVGxxAGxWuJSBS6hRDjSdEY6C/2mXHYb8Zhn5mD/WYc9pk52G/GYZ+ZA2lnbgSCDA83U6pUKZEnT55bnMeTjZ2xPFn70P9YBjfFyDaherA1a9bINMHY8TeiZI899pj44x//KNu8//774aw4iEzUe2VlZUlN8OSTT0rBhZTE3r17yzZwdkQ9W9++fcVzzz1n6vOlA0ePWBhsIOqV7IHQbigi1rJlS9GwYUMxb968lD82tMubN29UEd+BAwdkhKtZs2a2fzZCCCGEEEK8AsbcGD9Hjp0hePA80dgZyyPbg7///e/h9piWCkInsg2ivzDVCLWZMWOG+PLLL2UtGB4ha3w4H06cOFH+DbEWeh2PCRMmSHt7/N2pUyfZ5sqVK7foA4ivkHgz8/nSgSdqxkJCDOmCUMT/+c9/wq+FlCzawFllwYIFsmgPkSzYbCLlECFr3F2CUwo6W6+TIiGEEEIIIUEB4+aePXvKqBTG05i3C9GlXr16ydcff/xx8YMf/EB6LACkAbZo0UK89tprokOHDmLRokVi27Zt4Qw2pEjC9fC3v/2tqFmzphRniGDBYREW+ADppbF1bgDzCleoUEH+DTf0SLANCK8f/ehH4WW/+MUvxOuvvyeLWVYAABDgSURBVC7q168fTlPEtrA8JMpSfT4n8IQYg8JGh+IR+lJChFxz4JyIyBdUcQjMR4YvCpM9wyER7i7IIzUCQqYodHRbfq2bYZ+Zg/1mHPaZOdhvxmGfmYP9Zhz2mTnYb2qAVT2CHpikGQYbSCVctWpV2PQCGWaR0SeUDmEaqeeff14aZUBwwRExUiRh7rBQuiDKh2CwgXWqTtt8/vnnpfjD/wjSIAMPQiwUXdPz+ZzAs/OMEUIIIYQQQoiXcWeVIyGEEEIIIYT4HIoxQgghhBBCCHEAijFCCCGEEEIIcQCKMUIIIYQQQghxAIqxGI4dOyYt8WG9WbBgQWmrCXeeVLOmZ2dni0GDBomSJUtKS044OMZOKudn4FQDR51ChQpFTbKdDEz4DdebyEe7du1EkDDTb/DcgQsQJk/EMdqmTRtx8OBBERS+/vprOQkkpqtAn+H3evny5aTvwdQYscda//79hZ+ZNWuWqFKlinSrgsXvli1bkrZfsmSJnNsR7TFRZmielyBhpM/mz59/yzHl9gldVfOPf/xDOpXBohqfHw5qqVi7dq1o0KCBdLyrUaOG7MegYbTf0GexxxoecIILCrBRb9y4sZxX6o477pCW6HDQTgXPa8QLUIzFsH//fjkB3Jw5c8TevXulPf7s2bOlXWcyhg8fLj744AP5w1+3bp04ffq06Ny5swgKEKtdunQRAwYMMPQ+iK8zZ86EHwsXLhRBwky/TZ48WU6QiOMSkyYWLlxYTtuAGwJBAEIMv01MebFixQo5sIFdbir69OkTdayhH/0KJsrEXCq4kbRjxw5Rt25deYycO3cubvuNGzeK7t27S2G7c+dOOdDBY8+ePSIoGO0zgBsCkcfU8ePHRZCAVTX6CSJWD0ePHpXzELVq1UpO1Iq5h3r37i1Wr14tgoTRfgsB8RF5vEGUBAWMq3DD+/PPP5fnfkxn9MADD8i+TATPa8QzwNqeJGfy5Mla1apVE75+8eJFLW/evNqSJUvCy/bt24cpA7RNmzZpQWLevHla0aJFdbXt2bOn1rFjR9v3yU/9lpubq5UtW1abMmVK1PGXP39+beHChZrf+eqrr+TvauvWreFlK1eu1DIyMrRTp04lfF+LFi20oUOHakGhSZMm2qBBg8LPc3JytPLly2uvvPJK3Pa//OUvtQ4dOkQta9q0qdavXz8tKBjtMyPnuiCA3+WyZcuSthk5cqRWu3btqGVdu3bV2rZtqwUVPf326aefynb//e9/07ZfbufcuXOyT9atW5ewDc9rxCswMqaDS5cuiRIlSiR8ffv27fIuDdLFQiAsjhnFN23alKa99CZIv8DdvVq1asno0IULF5zeJVeDO8tITYk81ooWLSpTqoJwrOEzIjWxUaNG4WXoC0xAiShhMt59911RqlQpORHlmDFjoiaI91u0FeekyGME/YPniY4RLI9sDxAVCsIxZbbPANJjK1euLCpWrCg6duwoI7YkMUE/zqyCyWmRnv6zn/1MbNiwQQR9XAaSjc14vBGvkOX0DridQ4cOiZkzZ4qpU6cmbIPBcb58+W6p+cFs3kHK6TYKUhSRyon6vMOHD8tU0J///OfyRJknTx6nd8+VhI6n2Jnig3Ks4TPGpuZkZWXJC3Kyz//oo4/KQTNqNHbt2iVGjRolU36WLl0q/Mb58+dFTk5O3GMEadjxQN8F9Zgy22e4gfTOO++Ie+65Rw4McY1A/ScEWYUKFdK0594i0XH2zTffiKtXr8oaWHIrEGBIS8dNqGvXrok//OEPsg4WN6BQfxc0UEqCFNf77rtP3lxLRNDPa8Q7BCYyNnr06LgFsJGP2IvuqVOnpGBATQ/qTYKGmT4zQrdu3cSDDz4oi2qRx436n61bt8pomZexu9/8iN19hpoy3BHFsYaaswULFohly5bJmwCEmKFZs2bi8ccfl9GKFi1aSGFfunRpWW9MiEog/Pv16ycaNmwoBT9uAuB/1LQHEdSOoe5r0aJFTu8KIUoITGRsxIgR0r0vGdWqVQv/DQMOFBnjhDd37tyk7ytbtqxMc7l48WJUdAxuingtKH1mFawLaWSIRrZu3Vp4FTv7LXQ84djC3dIQeI5Bod/7DJ8/1lDh5s2b0mHRyG8NaZ0AxxocU/0EfkOILMe6uSY7H2G5kfZ+w0yfxZI3b15Rv359eUwRYeg4gxEKo2LGaNKkiVi/fr0IGoMHDw4bN6WKQAf9vEa8Q2DEGO5Y4qEHRMQgxHAXat68ebJ2IBlohwvxJ598Ii3tAVKgTpw4Ie+eBqHPVHDy5ElZMxYpMryInf2GlE5cSHCshcQXUnyQrmLUydKLfYbfE256oL4HvzuwZs0ambYSElh6gJMb8PqxFg+kTKNvcIwg4gzQP3iOgUyifsXrSP0JAccyL5+/7O6zWJDmuHv3btG+fXub99a74HiKtRYP0nGmEpzD/Hj+SgS8ToYMGSIzGpA9g2thKoJ+XiMewmkHEbdx8uRJrUaNGlrr1q3l32fOnAk/ItvUqlVL27x5c3hZ//79tUqVKmlr1qzRtm3bpjVr1kw+gsLx48e1nTt3auPHj9duu+02+Tce3377bbgN+mzp0qXybyx/5plnpNvk0aNHtY8//lhr0KCBVrNmTS07O1sLCkb7Dbz66qtasWLFtPfee0/btWuXdKSE2+fVq1e1INCuXTutfv368ve3fv16ecx079494e/z0KFD2oQJE+TvEsca+q1atWpa8+bNNb+yaNEi6bA5f/586UDZt29fecycPXtWvt6jRw9t9OjR4fYbNmzQsrKytKlTp0on2HHjxkmH2N27d2tBwWif4Te7evVq7fDhw9r27du1bt26aQUKFND27t2rBQWcp0LnLAwnXn/9dfk3zmsA/YV+C3HkyBGtUKFC2rPPPiuPs1mzZml58uTRVq1apQUJo/02bdo0bfny5drBgwflbxLOsJmZmfK6GRQGDBgg3UvXrl0bNS67cuVKuA3Pa8SrUIzFsSvGyTHeIwQGdHgOu9kQGAgPHDhQK168uLzYdOrUKUrA+R3Y1Mfrs8g+wnP0L8AJ9IEHHtBKly4tT46VK1fW+vTpEx74BAWj/Raytx87dqxWpkwZOXjEjYMDBw5oQeHChQtSfEG8FilSROvVq1eUeI39fZ44cUIKrxIlSsj+ws0WDAYvXbqk+ZmZM2fKG0T58uWTtu2ff/55lNU/jr1I/vKXv2h33nmnbA/78Q8//FALGkb6bNiwYeG2+C22b99e27FjhxYkQpbrsY9QP+F/9Fvse+rVqyf7DTdFIs9tQcFov02aNEmrXr26FPs4j7Vs2VLe+A0SicZlkccPz2vEq2TgH6ejc4QQQgghhBASNALjpkgIIYQQQgghboJijBBCCCGEEEIcgGKMEEIIIYQQQhyAYowQQgghhBBCHIBijBBCCCGEEEIcgGKMEEIIIYQQQhyAYowQQgghhBBCHIBijBBCCCGEEEIcgGKMEEJIFE888YR46KGHws9btmwphg0b5ug+EUIIIX6EYowQQiySk5Mj7r33XtG5c+eo5ZcuXRIVK1YUzz33XNL3Hzp0SPTq1UtUqFBB5M+fX1StWlV0795dbNu2TbiBpUuXipdeeknpOl988UVRr149peskhBBCvAbFGCGEWCRPnjxi/vz5YtWqVeLdd98NLx8yZIgoUaKEGDduXML3QnA1bNhQ/POf/xRz5swRX331lVi2bJm46667xIgRI2zd7xs3buhqh89w++2327ovhBBCSBChGCOEEAXceeed4tVXX5UC7MyZM+K9994TixYtEgsWLBD58uWL+x5N02RKYM2aNcVnn30mOnToIKpXry4jRhBwWEeI3bt3i5/+9KeiYMGComTJkqJv377i8uXL4ddzc3PFhAkTwtE1rAPiMMSxY8dERkaGWLx4sWjRooUoUKCAFI6I6j399NOiWLFicr0jR46U+xVJbJpilSpVxMsvvyyefPJJKdIqVaok5s6dG/WeUaNGyT4pVKiQqFatmhg7dmxY/EG4jh8/Xnz55Zdyn/DAMnDx4kXRu3dvUbp0aVGkSBH5mdGOEEII8SMUY4QQoggIsbp164oePXpIsfTCCy/I54n44osvxN69e2UELDPz1tMxBBL47rvvRNu2bUXx4sXF1q1bxZIlS8THH38sBg8eHG77u9/9Trz22mti6tSpYteuXbL9gw8+KA4ePBi1ztGjR4uhQ4eKffv2yTZ4D4TQO++8I9avXy++/vprGZlLBd7XqFEjsXPnTjFw4EAxYMAAceDAgfDrEGlYLyJ92Lff//73Ytq0afK1rl27ys9cu3ZtKVzxwDLQpUsXce7cObFy5Uqxfft20aBBA9G6dWu5X4QQQojv0AghhChj3759CCtpderU0W7cuJG07eLFi2XbHTt2JG03d+5crXjx4trly5fDyz788EMtMzNTO3v2rHxevnx5beLEiVHva9y4sTZw4ED599GjR+W2pk+fHtWmXLly2uTJk8PPsc8VKlTQOnbsGF7WokULbejQoeHnlStX1n71q1+Fn+fm5mp33HGH9tZbbyX8DFOmTNEaNmwYfj5u3Ditbt26UW0+++wzrUiRIlp2dnbU8urVq2tz5sxJ2keEEEKIF8lyWgwSQoifQIQJqXlHjx4VJ0+elCl9iYhNB0wEoliIsBUuXDi87L777pOpiYhGIXXx9OnTclkkeB6b4odoVqTBCKJSTZs2DS/LysqSbVLt2z333BP+G2mGZcuWlRGtEEiHnDFjhjh8+LBMp7x586ZMO0wG9hVtkS4ZydWrV+V6CCGEEL/BNEVCCFHExo0bZSreihUrRJMmTcRTTz2VVNSgpgrs378/bfsYKeiskDdv3qjnEGQQh2DTpk3iscceE+3bt5d9gVRGOEpev3496TohxMqVKyfTNyMfEJzPPvuskv0mhBBC3ATFGCGEKODKlSvSjAO1U61atRJvv/222LJli5g9e3bC98Bk44c//KGsvwoJmUhgZgHuvvtuGTVC7ViIDRs2yDqzWrVqyYhT+fLl5bJI8BzrT0TRokWl+Nm8eXN4GSJYqNWyKkorV64sBRiibDAoOX78eFQbmJrAPCQS1IedPXtWRudq1KgR9ShVqpSlfSKEEELcCMUYIYQoYMyYMTIKBkdFgPREmGnAnRBOhvFANGnevHnS1v7+++8XH330kThy5Ig04Jg4caLo2LGjbIcoE9wPe/bsKfbs2SM+/fRTaRYCo5AyZcrINogcTZo0SaYHIpIEow5ElWDWkQy8jn1evny5jNDBjCMkAs0C8XXixAnpJon0QqQrxpqCoH+Qyol9PH/+vLh27Zpo06aNaNasmZxw+m9/+5vsNwg7iDq3zLlGCCGEqIRijBBCLLJu3Toxa9YsKaxQLxaiX79+cjLoZOmKSGeE0ED0p0+fPjIKBhdEuCxOnz5dtsE6V69eLR0FGzduLB555BHpMPjGG2+E1/PrX/9aWtTDpbBOnTrS1v7999+XwigZaA9RB6EHIQQXxE6dOlnqD+z/8OHDpdsjon8QVLC2j+Thhx8W7dq1k1FE2NgvXLhQilMI0ubNm8tJsJHG2a1bNxlVC4lOQgghxE9kwMXD6Z0ghBBCCCGEkKDByBghhBBCCCGEOADFGCGEEEIIIYQ4AMUYIYQQQgghhDgAxRghhBBCCCGEOADFGCGEEEIIIYQ4AMUYIYQQQgghhDgAxRghhBBCCCGEOADFGCGEEEIIIYQ4AMUYIYQQQgghhDgAxRghhBBCCCGEOADFGCGEEEIIIYSI9PN/EW0qawJ4ckwAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1000x800 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 6. Visualization\n",
"print(\"\\nTraining finished. Visualizing results...\")\n",
"n_plot = 100\n",
"x_plot = np.linspace(-2, 2, n_plot)\n",
"y_plot = np.linspace(-2, 2, n_plot)\n",
"X_plot, Y_plot = np.meshgrid(x_plot, y_plot)\n",
"x_flat = np.hstack((X_plot.flatten()[:, None], Y_plot.flatten()[:, None]))\n",
"x_tensor = tf.convert_to_tensor(x_flat, dtype=tf.float32)\n",
"\n",
"H_pred, u_pred, v_pred = pinn_model(x_tensor)\n",
"# Denormalize predicted thickness for visualization\n",
"H_pred_grid = H_pred.numpy().reshape(n_plot, n_plot)\n",
"\n",
"plt.figure(figsize=(10, 8))\n",
"plt.contourf(X_plot, Y_plot, H_pred_grid, levels=50, cmap='viridis')\n",
"plt.colorbar(label='Predicted Ice Thickness (H)')\n",
"# plt.scatter(x_data[:, 0], x_data[:, 1], c=H_data, cmap='Reds', edgecolor='black', s=50, label='Training Data')\n",
"plt.title('PINN-Estimated Ice Thickness')\n",
"plt.xlabel('X Coordinate')\n",
"plt.ylabel('Y Coordinate')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "539c3a1e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(283,), dtype=float32, numpy=\n",
"array([79.95133 , 78.53927 , 63.72257 , 74.53361 , 73.704285, 71.80577 ,\n",
" 68.12964 , 67.4809 , 68.8757 , 77.746765, 80.120674, 77.13182 ,\n",
" 70.50465 , 65.60452 , 78.41661 , 79.90574 , 77.03913 , 75.82967 ,\n",
" 65.46358 , 79.317795, 71.07991 , 71.22533 , 71.884575, 74.263374,\n",
" 74.483925, 70.11819 , 76.56166 , 71.93146 , 78.5013 , 80.83161 ,\n",
" 65.573784, 64.985466, 69.719536, 76.979195, 67.2006 , 77.24953 ,\n",
" 75.82853 , 77.01775 , 79.14941 , 68.508675, 78.79717 , 77.34438 ,\n",
" 63.874783, 77.78103 , 78.78632 , 65.821815, 72.21952 , 66.36779 ,\n",
" 78.13761 , 68.33177 , 67.92291 , 68.17447 , 71.54735 , 78.28899 ,\n",
" 76.244194, 65.244316, 69.17073 , 77.4694 , 74.83061 , 81.05318 ,\n",
" 75.15418 , 78.24803 , 80.29803 , 81.48914 , 78.998146, 66.58618 ,\n",
" 73.49835 , 79.7987 , 64.88543 , 76.68885 , 73.29893 , 64.32176 ,\n",
" 74.11459 , 75.34944 , 81.88723 , 71.33085 , 67.31428 , 76.19049 ,\n",
" 76.27586 , 77.13156 , 78.83771 , 80.44574 , 77.47708 , 75.63483 ,\n",
" 81.51993 , 80.591736, 75.51759 , 77.18567 , 65.04841 , 79.20849 ,\n",
" 80.03261 , 77.457245, 78.01297 , 69.05675 , 80.19425 , 80.7137 ,\n",
" 66.283676, 79.412796, 79.42351 , 79.93085 , 68.143684, 73.48873 ,\n",
" 75.50619 , 78.720604, 77.86678 , 62.38217 , 78.17037 , 66.86765 ,\n",
" 81.33264 , 64.48578 , 70.954735, 75.94949 , 75.01903 , 64.04712 ,\n",
" 78.7372 , 81.89543 , 78.17887 , 69.279526, 71.53013 , 78.1602 ,\n",
" 63.35933 , 67.79604 , 79.15022 , 73.20879 , 78.47713 , 77.173645,\n",
" 73.00993 , 67.25017 , 72.24619 , 78.21178 , 75.06644 , 74.33984 ,\n",
" 65.849495, 69.62298 , 71.70737 , 67.35578 , 75.21875 , 79.13674 ,\n",
" 75.030396, 75.47156 , 79.88035 , 62.92559 , 79.67811 , 63.295856,\n",
" 76.96324 , 63.36444 , 68.56592 , 72.71197 , 75.51446 , 71.0684 ,\n",
" 78.39598 , 66.97353 , 70.03853 , 80.02113 , 65.19196 , 72.83953 ,\n",
" 77.48278 , 77.411705, 79.12796 , 80.27173 , 76.07913 , 77.134155,\n",
" 71.23414 , 76.62395 , 80.68029 , 75.18299 , 70.16412 , 79.809906,\n",
" 79.12245 , 81.25663 , 81.93967 , 74.550865, 76.67094 , 80.499886,\n",
" 76.70837 , 64.6442 , 76.70221 , 81.041275, 75.07984 , 79.04107 ,\n",
" 63.691998, 77.56036 , 77.70403 , 79.335266, 75.84438 , 75.83859 ,\n",
" 62.818756, 72.33183 , 76.383316, 78.28504 , 61.965504, 72.740746,\n",
" 75.556786, 66.29001 , 66.78955 , 77.6771 , 76.45348 , 74.73707 ,\n",
" 78.10723 , 78.62455 , 67.95378 , 68.32034 , 74.21404 , 75.51784 ,\n",
" 77.18287 , 70.73556 , 78.44168 , 72.94826 , 62.876373, 78.03125 ,\n",
" 76.4412 , 76.43703 , 69.00498 , 68.80357 , 73.03741 , 63.472164,\n",
" 72.40581 , 64.905136, 81.006096, 76.15461 , 74.28737 , 78.143295,\n",
" 67.826614, 78.16557 , 79.887924, 74.077545, 70.450005, 82.06128 ,\n",
" 63.74009 , 79.75677 , 77.87779 , 70.21184 , 71.058014, 69.49563 ,\n",
" 78.723724, 73.34935 , 75.574745, 71.54275 , 74.21908 , 77.186325,\n",
" 80.29052 , 72.81478 , 78.49678 , 79.16416 , 79.27243 , 81.11414 ,\n",
" 75.47006 , 66.437584, 63.301437, 72.84353 , 79.457565, 78.878075,\n",
" 76.01766 , 71.36792 , 63.484745, 77.943985, 75.70132 , 75.080055,\n",
" 78.3626 , 75.43825 , 70.06755 , 76.49877 , 64.38955 , 72.9687 ,\n",
" 76.11136 , 75.06943 , 78.10337 , 74.76215 , 67.56177 , 70.89073 ,\n",
" 64.50074 , 78.68995 , 74.26585 , 80.22848 , 70.63201 , 78.03019 ,\n",
" 76.876465, 78.12322 , 77.68086 , 73.057884, 81.31327 , 63.883324,\n",
" 68.71147 ], dtype=float32)>"
]
},
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