526 lines (526 with data), 197.2 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "6b0aa449-73d9-466e-dfc6-64642411a6f3"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696826034.1199777\n",
"Mon Oct 9 04:33:54 2023\n"
]
}
],
"source": [
"# This cell is added by sphinx-gallery\n",
"# It can be customized to whatever you like\n",
"%matplotlib inline\n",
"# !pip install pennylane\n",
"\n",
"import time\n",
"seconds = time.time()\n",
"print(\"Time in seconds since beginning of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "81119ce9-98cb-485e-a642-b20d9e7a2ca5"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"import pennylane as qml\n",
"from pennylane import numpy as np\n",
"from pennylane.optimize import AdamOptimizer, GradientDescentOptimizer\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"# Set a random seed\n",
"np.random.seed(42)\n",
"\n",
"\n",
"# Make a dataset of points inside and outside of a circle\n",
"def circle(samples, center=[0.0, 0.0], radius=np.sqrt(2 / np.pi)):\n",
" \"\"\"\n",
" Generates a dataset of points with 1/0 labels inside a given radius.\n",
"\n",
" Args:\n",
" samples (int): number of samples to generate\n",
" center (tuple): center of the circle\n",
" radius (float: radius of the circle\n",
"\n",
" Returns:\n",
" Xvals (array[tuple]): coordinates of points\n",
" yvals (array[int]): classification labels\n",
" \"\"\"\n",
" Xvals, yvals = [], []\n",
"\n",
" for i in range(samples):\n",
" x = 2 * (np.random.rand(2)) - 1\n",
" y = 0\n",
" if np.linalg.norm(x - center) < radius:\n",
" y = 1\n",
" Xvals.append(x)\n",
" yvals.append(y)\n",
" return np.array(Xvals, requires_grad=False), np.array(yvals, requires_grad=False)\n",
"\n",
"\n",
"def plot_data(x, y, fig=None, ax=None):\n",
" \"\"\"\n",
" Plot data with red/blue values for a binary classification.\n",
"\n",
" Args:\n",
" x (array[tuple]): array of data points as tuples\n",
" y (array[int]): array of data points as tuples\n",
" \"\"\"\n",
" if fig == None:\n",
" fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
" reds = y == 0\n",
" blues = y == 1\n",
" ax.scatter(x[reds, 0], x[reds, 1], c=\"red\", s=20, edgecolor=\"k\")\n",
" ax.scatter(x[blues, 0], x[blues, 1], c=\"blue\", s=20, edgecolor=\"k\")\n",
" ax.set_xlabel(\"$x_1$\")\n",
" ax.set_ylabel(\"$x_2$\")\n",
"\n",
"\n",
"Xdata, ydata = circle(500)\n",
"fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
"plot_data(Xdata, ydata, fig=fig, ax=ax)\n",
"plt.show()\n",
"\n",
"\n",
"# Define output labels as quantum state vectors\n",
"def density_matrix(state):\n",
" \"\"\"Calculates the density matrix representation of a state.\n",
"\n",
" Args:\n",
" state (array[complex]): array representing a quantum state vector\n",
"\n",
" Returns:\n",
" dm: (array[complex]): array representing the density matrix\n",
" \"\"\"\n",
" return state * np.conj(state).T\n",
"\n",
"\n",
"label_0 = [[1], [0]]\n",
"label_1 = [[0], [1]]\n",
"state_labels = np.array([label_0, label_1], requires_grad=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NqAg65FbTnyx"
},
"source": [
"Simple classifier with data reloading and fidelity loss\n",
"=======================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"id": "ZyKIWD9bTnyx"
},
"outputs": [],
"source": [
"dev = qml.device(\"lightning.qubit\", wires=1)\n",
"# Install any pennylane-plugin to run on some particular backend\n",
"\n",
"\n",
"@qml.qnode(dev, interface=\"autograd\")\n",
"def qcircuit(params, x, y):\n",
" \"\"\"A variational quantum circuit representing the Universal classifier.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): single input vector\n",
" y (array[float]): single output state density matrix\n",
"\n",
" Returns:\n",
" float: fidelity between output state and input\n",
" \"\"\"\n",
" for p in params:\n",
" qml.Rot(*x, wires=0)\n",
" qml.Rot(*p, wires=0)\n",
" return qml.expval(qml.Hermitian(y, wires=[0]))\n",
"\n",
"\n",
"def cost(params, x, y, state_labels=None):\n",
" \"\"\"Cost function to be minimized.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): 2-d array of input vectors\n",
" y (array[float]): 1-d array of targets\n",
" state_labels (array[float]): array of state representations for labels\n",
"\n",
" Returns:\n",
" float: loss value to be minimized\n",
" \"\"\"\n",
" # Compute prediction for each input in data batch\n",
" loss = 0.0\n",
" dm_labels = [density_matrix(s) for s in state_labels]\n",
" for i in range(len(x)):\n",
" f = qcircuit(params, x[i], dm_labels[y[i]])\n",
" loss = loss + (1 - f) ** 2\n",
" return loss / len(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Bz83H0xTnyx"
},
"source": [
"Utility functions for testing and creating batches\n",
"==================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"id": "i1-TLseuTnyx"
},
"outputs": [],
"source": [
"def test(params, x, y, state_labels=None):\n",
" \"\"\"\n",
" Tests on a given set of data.\n",
"\n",
" Args:\n",
" params (array[float]): array of parameters\n",
" x (array[float]): 2-d array of input vectors\n",
" y (array[float]): 1-d array of targets\n",
" state_labels (array[float]): 1-d array of state representations for labels\n",
"\n",
" Returns:\n",
" predicted (array([int]): predicted labels for test data\n",
" output_states (array[float]): output quantum states from the circuit\n",
" \"\"\"\n",
" fidelity_values = []\n",
" dm_labels = [density_matrix(s) for s in state_labels]\n",
" predicted = []\n",
"\n",
" for i in range(len(x)):\n",
" fidel_function = lambda y: qcircuit(params, x[i], y)\n",
" fidelities = [fidel_function(dm) for dm in dm_labels]\n",
" best_fidel = np.argmax(fidelities)\n",
"\n",
" predicted.append(best_fidel)\n",
" fidelity_values.append(fidelities)\n",
"\n",
" return np.array(predicted), np.array(fidelity_values)\n",
"\n",
"\n",
"def accuracy_score(y_true, y_pred):\n",
" \"\"\"Accuracy score.\n",
"\n",
" Args:\n",
" y_true (array[float]): 1-d array of targets\n",
" y_predicted (array[float]): 1-d array of predictions\n",
" state_labels (array[float]): 1-d array of state representations for labels\n",
"\n",
" Returns:\n",
" score (float): the fraction of correctly classified samples\n",
" \"\"\"\n",
" score = y_true == y_pred\n",
" return score.sum() / len(y_true)\n",
"\n",
"\n",
"def iterate_minibatches(inputs, targets, batch_size):\n",
" \"\"\"\n",
" A generator for batches of the input data\n",
"\n",
" Args:\n",
" inputs (array[float]): input data\n",
" targets (array[float]): targets\n",
"\n",
" Returns:\n",
" inputs (array[float]): one batch of input data of length `batch_size`\n",
" targets (array[float]): one batch of targets of length `batch_size`\n",
" \"\"\"\n",
" for start_idx in range(0, inputs.shape[0] - batch_size + 1, batch_size):\n",
" idxs = slice(start_idx, start_idx + batch_size)\n",
" yield inputs[idxs], targets[idxs]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B5s1nRL2Tnyy"
},
"source": [
"Train a quantum classifier on the circle dataset\n",
"================================================\n"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "a9fa9139-28ef-47c9-caf3-68e042007809"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.363736 | Train accuracy: 0.475000 | Test Accuracy: 0.455500\n",
"Epoch: 1 | Loss: 0.269356 | Train accuracy: 0.605000 | Test accuracy: 0.565000\n",
"Epoch: 2 | Loss: 0.224954 | Train accuracy: 0.685000 | Test accuracy: 0.593000\n",
"Epoch: 3 | Loss: 0.191246 | Train accuracy: 0.660000 | Test accuracy: 0.642500\n",
"Epoch: 4 | Loss: 0.209470 | Train accuracy: 0.620000 | Test accuracy: 0.592000\n",
"Epoch: 5 | Loss: 0.131571 | Train accuracy: 0.825000 | Test accuracy: 0.781000\n",
"Epoch: 6 | Loss: 0.177782 | Train accuracy: 0.780000 | Test accuracy: 0.731500\n",
"Epoch: 7 | Loss: 0.142674 | Train accuracy: 0.780000 | Test accuracy: 0.756000\n",
"Epoch: 8 | Loss: 0.136156 | Train accuracy: 0.815000 | Test accuracy: 0.761000\n",
"Epoch: 9 | Loss: 0.099383 | Train accuracy: 0.870000 | Test accuracy: 0.826500\n",
"Epoch: 10 | Loss: 0.115455 | Train accuracy: 0.840000 | Test accuracy: 0.820500\n"
]
}
],
"source": [
"# Generate training and test data\n",
"num_training = 200\n",
"num_test = 2000\n",
"\n",
"Xdata, y_train = circle(num_training)\n",
"X_train = np.hstack((Xdata, np.zeros((Xdata.shape[0], 1), requires_grad=False)))\n",
"\n",
"Xtest, y_test = circle(num_test)\n",
"X_test = np.hstack((Xtest, np.zeros((Xtest.shape[0], 1), requires_grad=False)))\n",
"\n",
"\n",
"# Train using Adam optimizer and evaluate the classifier\n",
"num_layers = 9\n",
"learning_rate = 0.6\n",
"epochs = 10\n",
"batch_size = 32\n",
"\n",
"opt = AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999)\n",
"\n",
"# initialize random weights\n",
"params = np.random.uniform(size=(num_layers, 3), requires_grad=True)\n",
"\n",
"predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
"accuracy_train = accuracy_score(y_train, predicted_train)\n",
"\n",
"predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
"accuracy_test = accuracy_score(y_test, predicted_test)\n",
"\n",
"# save predictions with random weights for comparison\n",
"initial_predictions = predicted_test\n",
"\n",
"loss = cost(params, X_test, y_test, state_labels)\n",
"\n",
"print(\n",
" \"Epoch: {:2d} | Cost: {:3f} | Train accuracy: {:3f} | Test Accuracy: {:3f}\".format(\n",
" 0, loss, accuracy_train, accuracy_test\n",
" )\n",
")\n",
"\n",
"for it in range(epochs):\n",
" for Xbatch, ybatch in iterate_minibatches(X_train, y_train, batch_size=batch_size):\n",
" params, _, _, _ = opt.step(cost, params, Xbatch, ybatch, state_labels)\n",
"\n",
" predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
" accuracy_train = accuracy_score(y_train, predicted_train)\n",
" loss = cost(params, X_train, y_train, state_labels)\n",
"\n",
" predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
" accuracy_test = accuracy_score(y_test, predicted_test)\n",
" res = [it + 1, loss, accuracy_train, accuracy_test]\n",
" print(\n",
" \"Epoch: {:2d} | Loss: {:3f} | Train accuracy: {:3f} | Test accuracy: {:3f}\".format(\n",
" *res\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YWspC-9BTnyy"
},
"source": [
"Results\n",
"=======\n"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 509
},
"id": "ZPszGYA3Tnyy",
"outputId": "64e20a6a-a0dc-4393-c9af-837048be4ae5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.115455 | Train accuracy 0.840000 | Test Accuracy : 0.820500\n",
"Learned weights\n",
"Layer 0: [ 0.32841779 -3.42013795 1.40024328]\n",
"Layer 1: [ 3.23552531 -2.01226956 5.02293256]\n",
"Layer 2: [ 0.80679605 1.47318633 -0.16355301]\n",
"Layer 3: [1.40341737 0.26145219 1.77680696]\n",
"Layer 4: [4.14694087 1.18372278 3.0111183 ]\n",
"Layer 5: [ 1.31753639 -1.49914892 3.80131224]\n",
"Layer 6: [1.42366008 4.26104035 0.51179003]\n",
"Layer 7: [4.39441394 0.52649604 0.79834961]\n",
"Layer 8: [-1.52771029 3.02108697 0.98334077]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x300 with 3 Axes>"
],
"image/png": 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JQWvUhiUnnHCCD6NvvPFGmjFjBhWLRd95a9asoWQySQcPHgxs18KFC308SLn3/djHPkb19fWBeHXfffdp/EsQZTIZOuqoo0qYTkS0a9cuAqDxbSqWvfzyy7Ro0SLK5/O+4zNmzPD1m+16IjN/G1N5FLuXA1i1ahVmzZqF4447DhdeeCFqa2vx1a9+Fcccc4zvvA9/+MO+7/fddx9SqRS6urrwu9/9rvRZtmwZamtrS1a4hx9+GC+//HLJdZrpqquuCm3b448/jp///Oe46qqrtDi5StzJDh06hF27duFtb3sbTj755NLxo446Cu95z3vwne98B3/84x9911x66aW+Z3V2duLQoUN49tlnAXjxew888AD+7//+L3JbZs2ahVNPPRX/+Z//CQD47ne/i2QyidWrV+PXv/41nnrqKQDCiprNZkflPvdXf/VXaGho8L0DIFy0o9CHPvQhJJNJ37Fp06aV/v+///s/DA8PY+7cuUin0/jhD3+o3SOsH8uZJ319fVi+fDmy2WzpWG1tLS699FI888wz+OlPf+o7/33ve5/P0nL66aeDiDTX0tNPPx2/+MUv8Morr1j7wnEcnHHGGaVx+9nPfobh4WGsWbMGRITvf//7AMS4LVq0qDQ/oq4XEz344IM45phjcN5555WOTZ06FR/60IeM59fW1vpixKZMmYLly5dHGm+2ZPf39+PFF18MPT+m8aEYpycGpwE/1r3wwgv43e9+h87OTrz44ot48skny309AMDIyAj+4z/+A+9617tK9/zd736H4eFhFAoFPPXUU/jlL3/pu8aEw5VQTU0NPvCBD2jHR/ueo9lnVq1aVbIyAcIboL6+vnTtoUOH8PDDD+Ntb3sbjj766NJ5c+fOxdlnnx16/5hiei2RbY1Gpfvuuw+dnZ1oaGjw4f6qVatw6NChEv8yVvdNp9P405/+hIceeqjid5DpV7/6FX70ox/hoosu8nnbdXV1oaWlRTtfxrLf//73eP7559HZ2WnkTU1ULn8bUzSK3csB3H777TjllFMwadIkzJkzB/Pnz4fj+PURkyZN0lw4nnrqKTz//POYPXu28b6cVIaZnnnz5vl+nzVrlm+DNhG7UFar7vBvf/tbvPjii5g/f77224IFC/Dqq6/iF7/4BRYuXFg6fvzxx/vO4zZzPGQul8P555+PtWvX4tZbb8VZZ52Ft73tbXjPe96DmpqawPZ0dnair68PgBDS2tra0NbWhsbGRjz66KOYM2cOfvzjH+M973nPqN477B3C6KSTTtKOvfTSS7jpppuwdetW/PKXv/TFpptif8PaUM48efbZZzX3UsBzTXr22Wd9c0Z9NoP2cccdpx1/9dVX8fzzz/vcdlXq7OzE9ddfj5deegmPPvoojjrqKLS2tmLp0qV49NFH0dXVhe985zt417veVbom6nox0bPPPovm5mZNgOEMySode+yx2rkNDQ3Yt2+f9RlMJ510Eq6++mp87nOfwz333IPOzk6cd955eO973xu7lk8gxTgtaCJw+oknnsAnP/lJ/Md//Icm7Fea5+Dpp58GEeEf//Ef8Y//+I/Gc37zm9/4lComHK6EjjnmGGOyxtG+52j2GfVavp6v/c1vfoOXXnrJiHk2HIwpptcq2dZoVHrqqaewb98+a+hFEL9Rjft+5CMfwb/927/h7LPPxjHHHIM3v/nNeNe73oW3vOUtFT3Xtj8BwPz58zVB+IEHHsC6devwox/9yJenJ6oSuFz+NqZoFAvdAJYvX17KimujmpoajcF79dVXMXv2bNxzzz3Ga46UOCubZYEXYSKRwM6dO/HYY4/hm9/8Jvr7+/HBD34Qn/3sZ/HYY4+htrbWeu9sNouenh4cOHAAjz76KDo7O5FIJJDNZvHoo4/i6KOPxquvvlqyGIzVO4SRrPVjuuKKK7B161ZcddVVeOMb34hUKoVEIoELL7zQmKhjtG0YDdmeXWmbstks/u///g/f//73S+MGCGH80UcfxZNPPonf/va3vnEbz/Uy2r7+7Gc/i/e///34+te/jl27duHKK6/ETTfdhMcee8yawCmmsaUYp4NprHD6D3/4A3K5HOrr63HDDTegubkZU6dOxQ9/+ENcd911FZfO4uuuueYaFAoF4zmqMGnC4UrIdJ9qvOdocGci94eYYjrcqNy1riY7fPXVV9HV1YVrr73WeP4pp5xSUbui3nf27Nn40Y9+hP7+fnzrW9/Ct771LWzduhXve9/7sG3btoqeHZUeffRRnHfeeVixYgW+9KUv4aijjsLkyZOxdetWfPnLX450j3L525iiUSx0j4Kam5vx8MMP48wzzwwEiBNOOAGA0JDJroK//e1vQzXg7G72k5/8BKtWrbKeF1V7NWvWLEyfPh1DQ0Pab08++SQcx9Gsn1Gpo6MDHR0d+Kd/+id8+ctfxl//9V/j3nvvxSWXXGK9hoWyhx56CIODg6X6pStWrMAdd9yBo48+GjNmzMCyZcsCnz0RmVt37tyJiy66CJ/97GdLx/785z/7snqXQ+XMkxNOOME6hvK9xoqWL1+OKVOm4NFHH8Wjjz6K1atXAxDj1tPTg0ceeaT0nSnqejHRCSecgJ/+9KcgIt9YP/300xW/Q9icWbx4MRYvXoxPfvKT+N73voczzzwTmzZtwrp16yp+ZkzjTzFO+6lcnN69ezeGh4fxla98xbeeOWt8GNnemft48uTJgX1WCVWyH4z2PceaZs+ejalTpxoxbzQ4GFNMryVqaGjQeKyXX34Zv/rVr3zHmpub8b//+78VY4sNQ8q575QpU/DWt74Vb33rW/Hqq6/iIx/5CDZv3ox//Md/xNy5c8vCKXl/UkndJ+6//35MnToV/f39Pi+mrVu3atfa2lBt/jYmQXFM9yjoXe96Fw4dOoQbb7xR++2VV14pTc5Vq1Zh8uTJuO2223xa66DsiUytra046aSTsHHjRm2yy/fieqVhCyKZTOLNb34zvv71r/tS///617/Gl7/8ZWSzWdTX14e2S6bf//73mjY+k8kAQGj5qZNOOgnHHHMMbr31Vvzf//0fzjzzTABCGN+/fz927tyJjo6O0LqmUd+/mpRMJrX3vu222zSNa1QqZ550d3djYGCgFD8NiAzfW7ZswYknnmiM8akmTZ06Fe3t7fjXf/1XHDx40Gfpfumll/CFL3wBzc3NOOqoo0rXRF0vJioUCvjlL3/pKyX05z//GT09PRW/g23O/PGPf9Ri2hcvXgzHcULnc0yHH8U4LahSnGYLrHztyy+/jC996UuRnstZbtV3nj17Ns466yxs3rxZY5gBaOXOyqFK9oPRvudYUzKZxKpVq/C1r30Nzz33XOn4008/jW9961sT2LKYYho/am5u1uKxt2zZovFd73rXu/D9738f/f392j3+8Ic/BOatAQSGmPAj6n2Hh4d9vzmOU6rawHhbDk4dddRRyGQy2LZtm8+9+6GHHtJy+CSTSSQSCV+fPPPMM/ja174W+T2rzd/GJCi2dI+CcrkcLrvsMtx000340Y9+hDe/+c2YPHkynnrqKdx33334/Oc/jwsuuACzZs3CNddcg5tuugnnnnsuuru78fjjj+Nb3/oWZs6cGfgMx3Fwxx134K1vfSsymQw+8IEP4KijjsKTTz6JJ554orTw2RJ85ZVXolAoIJlM4sILLzTec926dXjooYeQzWbxkY98BJMmTcLmzZvxl7/8xVfLOCpt27YNX/rSl/D2t78dzc3NeOGFF9DT04P6+np0d3eHXt/Z2Yl7770XixcvLsXAtba2YsaMGSgWi5Hiuct5/2rRueeei+3btyOVSqGlpQXf//738fDDDwfGQgdROfNkzZo1+Nd//VecffbZuPLKK9HY2Iht27bh5z//Oe6//37NxXYsqLOzE//v//0/pFIpLF68GIBgpOfPn4+hoSGtZmbU9WKiyy67DF/84hfx7ne/Gx/72Mdw1FFH4Z577sHUqVMBVGbZam5uRjqdxqZNm1BXV4cZM2bg9NNPx49//GNcfvnleOc734lTTjkFr7zyCrZv345kMonzzz+/7OfENLEU47SgSnH6jDPOQENDAy666CJceeWVSCQS2L59e1mhOS0tLejt7cUpp5yCxsZGLFq0CIsWLcLtt9+ObDaLxYsX40Mf+hBOPvlk/PrXv8b3v/99/Pd//zd+/OMfl/2egH1tB8WEj/Y9x4Ouv/567Nq1C2eeeSY+/OEP49ChQ/jiF7+IRYsW4Uc/+tFENy+mmMacLrnkEvzt3/4tzj//fHR1deHHP/4x+vv7NYxevXo1vvGNb+Dcc8/F+9//fixbtgx/+tOf8P/9f/8fdu7ciWeeeSYQ15ctW4Y77rgD69atw9y5czF79mzk8/nI973kkkswMjKCfD6PY489Fs8++yxuu+02ZDKZUu6dTCaDZDKJm2++Gc8//zxqamqQz+et+UduuukmnHPOOchms/jgBz+IkZER3HbbbVi4cCH+93//t3TeOeecg8997nN4y1vegve85z34zW9+g9tvvx1z587V8tosW7YMDz/8MD73uc/h6KOPxkknnYTTTz+96vxtTC6NU5b0w5K4DElYCZWLLrqIZsyYYf19y5YttGzZMpo2bRrV1dXR4sWL6dprr6XnnnuudM6hQ4do7dq1dNRRR9G0adPorLPOop/85CdamQO1FA3Td77zHerq6qK6ujqaMWMGLVmyxFdK5JVXXqErrriCZs2aRYlEwleCAYYSCz/84Q+pUChQbW0tTZ8+nVauXEnf+973IvWP2sYf/vCH9O53v5uOP/54qqmpodmzZ9O5555L//Vf/xXUrSW6/fbbCQB9+MMf9h1ftWoVAaBHHnnEd9xUMsz2/nzu+vXrteea+kWloDny+9//nj7wgQ/QzJkzqba2lgqFAj355JPamEbtR6Lo84SIaP/+/XTBBRdQOp2mqVOn0vLly+mBBx4wPkMtf2Vrk6nkj43+/d//nQBoZccuueQSAkB33nmn8boo60UtGUZEdODAATrnnHNo2rRpNGvWLPq7v/s7uv/++wkAPfbYY75rFy5cqD1XLfNDJEr6tLS0lErubN26lQ4cOEAf/OAHqbm5maZOnUqNjY20cuVKevjhh0P7JKbqU4zTE4/T3/3ud6mjo4OmTZtGRx99NF177bXU39+v9YENP773ve/RsmXLaMqUKdp77t+/n973vvfRG97wBpo8eTIdc8wxdO6559LOnTtD3zGITGubyI4P5bynrWRYlH3GVjLsox/9qHatCfcfeeQROu2002jKlCnU3NxM//zP/0x/93d/R1OnTg3ukJhiOgzJVjLMtkYPHTpE1113Hc2cOZOmT59OhUKBnn76aeNaeeGFF+jv//7vae7cuTRlyhSaOXMmnXHGGbRhwwZfCVUT/c///A+dc845VFdXRwB8/EiU++7cuZPe/OY30+zZs2nKlCl0/PHH02WXXUa/+tWvfM/p6emhk08+mZLJZKTyYffffz8tWLCAampqqKWlhb7yla8YeZs777yT5s2bRzU1NXTqqafS1q1bjdjz5JNP0ooVK2jatGm+0phR+duYyqME0WGkxo0ppphiKoM2btyIj3/84/jv//5vrXRUTDHFFNPrgd72trfhiSeeMMZ7xhRTTDHFdHhQHNMdU0wxvSbopZde8n3/85//jM2bN2PevHmxwB1TTDG9LkjFwaeeegp9fX0466yzJqZBMcUUU0wxRaI4pjummGJ6TdA73vEOHH/88chkMnj++eexY8cOPPnkk9ZSUDHFFFNMRxqdfPLJeP/734+TTz4Zzz77LO644w5MmTLFWsIopphiiimmw4NioTummGJ6TVChUMA///M/45577sGhQ4fQ0tKCe++9F3/1V3810U2LKaaYYhoXestb3oJ//dd/xf/8z/+gpqYGb3zjG/GZz3wG8+bNm+imxRRTTDHFFEBxTHdMMcUUU0wxxRRTTDHFFFNMMY0RxTHdMcUUU0wxxRRTTDHFFFNMMcU0RhQL3THFFFNMMcUUU0wxxRRTTDHFNEYUx3SH0KuvvornnnsOdXV1SCQSE92cmGKKaQyIiPDCCy/g6KOPhuPEushyKMbImGI6sinGx8opxseYYjqyqRx8jIXuEHruuedw3HHHTXQzYooppnGgX/ziFzj22GMnuhmvKYoxMqaYXh8U42P5FONjTDG9PigKPsZCdwjV1dUBEJ1ZX18/wa2JKaaYxoL++Mc/4rjjjiut95iiU4yRMcV0ZFOMj5VTjI8xxXRkUzn4GAvdIcTuQPX19TFgxhTTEU6x+1/5FGNkTDG9PijGx/IpxseYYnp9UBR8jINzYooppphiiimmmGKKKaaYYoppjCgWumOKKaaYYooppphiiimmmGKKaYzoNSV0/+d//ife+ta34uijj0YikcDXvva10Gt2796N1tZW1NTUYO7cubj77rvHvJ0xxRRTTONNMT7GFFNMMdkpxsiYYoppIuk1FdP9pz/9CUuXLsUHP/hBvOMd7wg9/+c//znOOecc/O3f/i3uuecePPLII7jkkktw1FFHoVAojGlbi8Ui9u/fj7lz52LevHlVO3es6XBqSxgFtbUa71Htexw4cAB79+7FG9/4RnR1dVV0v0ppvMdVfZ7t+eWM4Wtpbk4Exfg49nQ4tSUKVbLuRnvvSu/xesJH9ZlENGp8nKj3eC3RawUjY3wcHzrceUgVI/bs2YNEIoFcLjfu/TuRPKQNH8PadVjykPQaJQD01a9+NfCca6+9lhYuXOg79ld/9VdUKBQiP+f5558nAPT8889HOn94eJi6CwUCUPp0Fwo0MjJCRERDQ0PU19dHxWIx8Fz5vPGgsHYfTjQ8PEyFQrevrYVCN42MjAT+Npr7Z7M53z3Cxmd4eJiy2RXSPZK++zU1zaEDBw6MsifC27J3715qb20d83HlNgwMDGjzaE5Tk+97LpulO++8k3LZrHacxzDsHtV+h3LX+eFO44WPROX1XYyP40O29u7fv3/U72G6d1trKw0ODpbOiYKP8j2S0r14vY8HPg4PD2s4NJb4aJrXjvLu3YUCPfTQQ9Sm4Db3san/u/J56srnx+w9jjR8JDo8echq4aN67ljTkYKPNv6jGhjJ/A1R+fioYoTjrvlq9G9YWyaahzTh48DAAPX29h42PGQ5a/yIFro7OzvpYx/7mO/YXXfdRfX19ZGfU+5m010oUGMySTsAOgjQDoAak0latXKlcRKkHcd3btpxRj05ePL29/dri8m2wGzt7i6TAR8PKhS6KZlsJGAHAQcJ2EHJZCMVCt2BvxGZ3189Vih0k+OkCchogvL+/ftDhfrh4WFqapojnZMkIOVrE5CipqY5o+qHKBuHA1DKHc/Rjqup70xtqAFos/u8jOH5KQlI0wZmuyuf1+Ziyr3XWM3NI42pHC98JCqv7w4HfCQSc3nLli3U09NzxOEjkb29c5qaAt/D9v7ycb73JoDyyvqNKvytWrmSauAJ3CaMmNPUNKo+CGOeh4eHaU5T07jjozyv8wA1KM9PJxI+RnOWASPVMUwnElRTpfcw0ZGGj0SHJw85Wny0nVstHvJIx8fuQiH0XaLwkN2FAqUdhzLK2p3V2Fg2Ppp4uAYIPms0/RsFH6vJQ9rmThAPGQUfDwceMha6XZo3bx595jOf8R3793//dwJAL774ovGaP//5z/T888+XPr/4xS8id+bQ0BDBHVCSPre4C6dBAUieBPK5JiHFNjlUxtGkkeLJuWrlSutit7V7u3teFE1pkLasmhpXbqsQXOXmbpfezfxbZ2fO9/7z57dQR8eZvmPZLJ+TIcAvvAMpSqdnBgr1ROTeg4Xs7YFt2rVrV8V9YdscVmSz1NbaSrWJxKjHlSjYsmVqQwNA3QANuefant9imOu1IdcUK3yHMDrSmMqxwkeiyjFyIvBRZhz7+/upt7eXzuzo0DAyl82OKT7K7QkSaKtBYe3dYDme6+z0vf/yZcuER4py3IFgiLoBalTGIgWECn9DQ0NivAG6NmS9jxU+9vX1UfuyZWOGjyuy2ZIlxsb8heHjHsN8Xx8RH6uNkUcaPhIdfjxkNfBxu3tuFAHYJFgH8ZA2ZeeRho9Bv6lYeNqSJdp+wtbXTBXwMWy9j2Z9BykXhoaGqsZDBuGjrMRVecgVFeDjRPGQsdDtUiWA+elPf9o3OfgTpTP7+voIAO0GqA+gAQjGJGwh8yQI24R5cgwPD9OqlSt92h4HZo1YozsxJwM0A4LhUhcYt/ug8tyD7r37+vqs7xxmbQ2zCgdZ5YP6WAi8cnMPSs8x/eaQ4zSQX4iuIdUCLc5JUJCgDGwwHi8WiwalwNrA9q5du9b4nmEbjWnjGHbHWp27lYyrTEGWLWbATfO1J+T5prl+TVibK3yHMDrSmMqxwkeiyjFyPPGRMYkxUv6rYmQDhLV1LPBRbY+MkVFcvcvFR7mfbe3dZjhuY+ptVuiOiGNmGp8tW7aUrl0bst5t+Cj3jU3RO974uAOgfYZnZAAaMfRHGD5uMfRxX1ibR/EeQXSk4SPR4cdDyut2CKBegHLS9VHW2u6wcxW39Kg8ZBpCmFkNIewcbvjIvFc5PGQQX2L6zdQ3NYZjDY5DiTLGLAgfw9Z7UP+Wi48E0B3Q3blHi5Fh+BjEQ5aLj4SJ4SFjodulSlyDRmPp3rt3rwZiaReookyC0A3VnRzdhQLVwHO7yANUb5mApkXUDcEE8MTu7+8PBWoTsTYsZXBx6i4UAl29TQI54BAQHIMdbulOELBZ+e0WwzVRLOY2wX6b8Xhvb69BKfDPgc+56667fMBojidfUdIKMpk2jhUQGyMLDqFWkYgeDHwPm2Urb5mvNlAMAtRQpqGCd4hCRxpTOVb4SFQ5Ro4nPjYmkyVLA/81rQeTIFZNfOzr6zNaO8NcvU2MKPddmKtomCXnBuX4LZY1ZzsehSEyCX+9vb1E5DGVBwF6MOQZKj4SRYspN+FjNwReseBQbXzkZ6gY2egeLxcfTUJ5qOKpwvcIoyMNH4kOPx6S5xLjEQt5UfGREEHg6OvT8LFSHnITDk98DMPISizdJiwMW4vl4mNbayuNjIz48DHKM9QQqUrxkSCUqdNRPR4yCj6mYOchy8VHwsTwkLHQ7dK1115LixYt8h1797vfPWZJMLoLBUonErRDGfiwhbPWnThhTI7fkirOY6bFBsx5mDWX3TAD8Xb3+HYIgGtvbY0Ug9EO0E4IMFkvHbcJm9lsThPIhTt3RnPXVq09nZ187Xb32u2la4Xlukb5bbrblj1SO6JYzMuzdGezOWl8NhHAwrPjtktuUx1NnjzN14eFQjfl811Sv+wjNa6cFRL8nPUQGvF2X597gkPGHX95XFMQblK2/jUJ91GBTD62x/L8RgS7DjmGa1Lw4nF4btrc5SoB0SONqRwvfCSK3ndjjY+Dg4O+dQH43ZdNQnuQkCQLizZ8zLmuymFMTzNAg+679iHcrdrIiLprwDb35VAj7mu5vRwHWKOsrenSepXbss3QX4RoDJEJD3LZbKmtgGDauy3rvQ6gaZMn+zHNZaSDPG9MLq9DAN0KlKxPMkYuNjy7wW2TKWZTxUjVOlkOs7ceXsyi+vyg8BwTpqYN41pNjDzS8JHo8OQhOceALOyEzasFEBbE7QClHCfwXBaQ5ftXykPy2qsGPi6A4GP6UTk+Nrhtsrlsy+s319lpxce0+3782y3ucbVvolihy8HHlOOUXLvD8DEF0CSYsa8SfBwrHrISfJSPdaB8fJwIHvKIFbpfeOEFevzxx+nxxx8nAPS5z32OHn/8cXr22WeJiGjNmjX0N3/zN6XzDxw4QNOnT6fVq1fTz372M7r99tspmUzSgw8+GPmZUTtT1eioC5KZO3WjnAI/MzBt8mTjZOGkMp4l1b+gTEJR2CRn4C0WizQyMqJlArRpDk0xGCnlPbz/KxFq1xMAGhgYkKy+jq9t6fRM33ch4I6Q31KtX+edF2bpNgnKjQTMtBwXwvHGjRvd2PEaAtidXReeJ0+eZvQCEM/lNnWTGlcuewrMbJrtu+diOLQP3qbYBVAndC21A7FRRrGm8bwO07ZfI89rJdGFGguWgedmpM51XhOnKm2eOmmS77sp4Yft92qu88OZJgIfiaL1XSX42AARFiOP65ymJkobzuOkMoyPtyvz3rTpR8VHzoiqzrGG+nrjnLPho7oOK2XYZNw2hRoBoNPb2gKtUyaMlxktQriSw8bYaMIfPOvdxo0bqVgsUlc+X/LW4oQ5cjumTZ5stHLxHmXzvJEZKTkZkYo9jJFNhj7odP/29fWFYuTAwECpPWFM+DZ4zJ6Mierza9xxOggzRtZDKCXka5pSKcosWTJmGHkk4CPRa4eHjIqPzRBKs4Q0prMbG608JOMjK9RGy0MejvhYlP6XM2Gr908q32V8zFnWZgYePob1jQkf0zDjY7d0XU9PD53Z0RGIj0n3XmONj6PlIcvBRxsPWS4+piH4hpPV65L+CkIThY+vKaH729/+trYoAdBFF11EREQXXXQR5XI57ZpMJkNTpkyhk08+mbZu3VrWM6N2puquoS7IEfjjF+XJJE+qBEDHGhYCL8Y777yzdJ6aaEEF5jBXo1r32TIwqdaAPMSilxMsqEBjctH0mOVK3bdBra3triCaIVX49GKvtxFQNNxjIwHt2nVCEM677eCYbk+ATiTS7vHNBJyszLVuAu4ksyC/z/0/IR1X310oE2666aaQvtlDnlJgtfJ+XlI4TWhHigpwfMBf747VHgjBuR6eFceYSAO6Na27UAjVoKtgNTg46NMYFotFY4kHdWNOQFif5GMLlO+LFi70uUpVI3PqkcBUTgQ+EkXru9Hgo/x/69KlRkGRY4M/85nPECCY0RR09zgZI8MsubXwcC0qRq6QmB6+nwkfeU5X4prI7WYPJTnUSGVi90AwO0XlHhshLBsqU8bWImYG0zAwNW6m7LdCZ4haDf3TDcHIyX2XWbpUe/8ivD0rrG92u39Xw+5S3ZXPlzwrZHzrVs7dAA8fU9JYRcVIPieKOz7j48jICBWLxRJG8v+cnDIII9OWucSfxS0tJeUG02gx8kjAR6LXDg9ZKT7y/nqcci7P6XXr1hEAuhjV4SFZsDmc8LFP+r+9tVVzpZcxsgNmfOR4ehOuZuBhIcd0m/DxCuj42AXQacoxVnQeVI4fLvg4Wh6yXHxkjJR5yHLwUVWmtMPjDw4HfHxNCd0TQZVausmdvKprxGT4YyZkRmcToGmeToenebdp3bogNmKTO0nQJOdkOG0BwGTSwqmAZ9KoNQAEOJobeDLZKGUID7Z0+/8vz9XbE5iDhP5FJARsub8c8mLCH3SPyYLvkPTcPum4fN/j3L/25GlBvwOXk1AYyO1iC324p0ARHoCvtox9WByW6gXBQFcD4fqlatBVIdtGprq4OTeTZU9Pj8i8apiDDkBLlXmtalJN7xHVTehIYSongiqxdDNuqPhYC7GppwxzIANh4VNxcA6i4eN2mBO5WF0Y4QmL5WBkFHy0CbSqtSJobfIaDjrXlqn85JDruP/qoQt5DgQT/aD0DGZchwzH1PvKQmKQJSvot2alTSrjKicjMuIC/Ey6NvbZbGjsJ4+DygiaMHJmOq3l5TCRrb7v4OAgrV27NhAfb4chP0GVMDLGx8opat/t3bvXN04m63YtRMLHcvAxjIfcD4GhKZTPQ34Thx8+FpVnVZLxPwo+AsIrL6UcY3wcko7Jwu+DhmPyvZda+mmi8HG0PGQUfKwWD7lk4cLAUAitXyYAH2OhO4QqicdhkDAJ0UGDeybMmWJrYC4/wGDWZXjOJAiNWjvMMRE5lAdMeyyLNczFZuFCv8sbxyR7SdZ0N+1kspFaW9vca7a5f20C6nTjPbxnBrm3g9hqvXHjxpJCwX/NHNLdyScbjjWSsKBzm0BCKB4iTzjf7gMru0JgMnmu6XK8ezdF8RToU8ZNPomB9Iorrgj8na1pW7Zsoc9//vOaezdrEzMQtZOjagRNLk01EKDZFlLCR1sbjkPzmpspaAOKmpkyZiorp6h9FwUfw7K+mgTyFLw4vKj4OBlC+FsCOz7Kzy0HI8vBR80qrLhgmty01azBQfN/uvJufI8ojBszkACova2tpBiTr2NmXVUsq8dkxqcWeswqC+hyf9v6rCmdtuYp4XPYohdkDTMx3fz7rbfeWlKO2u7BGLlmzRpqX7ZMs7BUgpHdBXt939MM3gFyv6ieC+sBqnUcWtzSMmqMjPGxcion5wXHFNsUhJXiYxAPOcd9pvqsNED3w/MaCnKJPhzw0eelk0xSm1v6LMyjSXZrrjY+drt9L+8x17jXqQoVVp4dbvg4Wh5y7dq19JWvfGXMeciwEpAJHB74GAvdIVSupVt19eqGSKAju4UELeQgzVrYb0V3sfRLz1sLneHMQzC8nCgtKjABfpeS7RDa9aDrzjvvPNq6daumwWLBG762ednLWfsUbulOKPeQXb2DrgMBbybHSZWStpkzox8gIO17xvTpdWR2Md/k/r+BgFWkW9FrKJ/vIiIyKh2SyUapbri53YlEHaXTTYHnrIcoW+EY5kxQNnt5Tq2FP/whBX0jn6pcw+MbVG/Tge6yxSWbal0XdttcYo3rMHRXuwxECSp1c4ot3WNP5Vi6o+BjpRhps+qWi48ZeImJKsHIXGdnCR+jJCO76qqrtAy0phhJXo9qvoUwRkPt730R+hIAXeCuSU6AJo8jX3cAnqcBf2qnTTM+l+MleZxWATRJwdFJSFJXPl8KabkGgqmLauWqSyR88dJBVpgU9DrHJnzMwFzyS57LNoycqVwTVNpIzl6tCkcpeImTwtaGCR8diLlu8j6ILd1jS+Xg42aAlitjl4Nwd5YT1Fabh+Q1WYTIqn+xtE7qobvsjoaHHGt8ZIxkHjJKreuxwkf2tFIxhde56buMjybhkvHRlriuWvg4Wh4yCj5Wk4fkNlWKj+ydUAkPGQvdVaRK4nFyEC5A66UFIScFClrIQRMm6Lfd0Os68mRaBOF2NENdKGUAU0rSRMmAF6Z55c+cpiY6cOCA1m8cq7Fr1y5tkWWzOTd2O0PC0mtzUze7ei9btpyCkp4BenkymzC8dOlp1Nw8T3onh4A6Aq4hEYO9nYR12iEh9M8mtQY4kKJ8vouGh4cpn+8iVXDP57uot7fX/W62YtfWpqQ495Ty/FTpnt2FAnXl8xow1wBaHI+sIW6AAH8+L2xe3CbNQ85iqs4x7l/2klgPM/N3XcizWKNqKzshbwSTATpt6dJY6B4HKjem24aPNQDVz5hRMUaa6k9HxcdagBbCoBSoACNNDGEYPjoQzJSauCUIH4mIVmSzNAW6tT6dSJQYMJur9/Jly4wWLJkZNCWTMTF8MwCaPq3W906TkKTL4QnNzBgdhGB8GpE0YGSaOjvPcvFR7iOHuvL5Ej7a5kCqtrYUm9cBz2rE7ZRxYk5TE6UdJxQfU1Cy3sJzfd2B8MoOu6T2sYBi6l/Zc8HmehplbUTBx1MBmuHOkWqt8ZjMVEnOiyYILxV17taNAh/DsHMvQG3QcWmR++w26MmpDhd8lON+ZYxckc1SrePQAhgsy8kkORgffEw5Di049VRa5FpVWQhd7/b9eghMkfFxNnRBNQ3QWZ2d1JXP6/HiEfCxdvr0yPg4Gh6yHHyshIccC3ycBH9/lsNDxkJ3FamSmG7WbqmgkZAWm29BIppAbvvNpJnfJy0G2a2ESzHs2rWr1HZOqJCBDky8CBlcZVDbuXNn6Xmm61qUSc0Z2MPIH9fmKH89YXn//v3U1KS7f3OG75GREWps9Gf4FsJqPS1d2mq0Opgs8Hopr4Mk4r5VS/YC9++p7t8o5dL2ELCaHCdFra3tEVzPQcKirjOlmUyrL25wZGSklLgkaIO7RfrNUc4Lyzg5FVLGT1eD7XNrkpJR3HrrrUYNKWuWt8Ecx8ZzkF2tgtbCN2GOawzLQhkzlZVTuTHdQfgoMyTqHAhT8Nks3eXgYxEe88gYOTw8XHKND8JIjivj9Tc4OEiLWlpKmnnVFdKBEEi5HZyBPQqpsb8mBmz//v2aSz8zm7wmZjc2an1TD5GwzmaVNTHNM5tma4kdRVJKD7fl5EjZkoeSGeccxy+MO06DDx+D9kkutaPOL8YoHqNK8FG+F58XhpFr3PY5bt/b8FGtY8/4KMdisgu5Ov+4b8MEnxgfx58qyXlh8iBha3Ol+Bj0W4vyrDyE5S8KPhJVxkMWi8USTzDW+GjCyO5Cgc4ylA5LJxIlAd9UeaUeInkXJ3M18ZAmwZEF2B0whw6wVXsHgkuqAkKI3wEpAaTjUHtra1XxkYhoYGCg5KZfLg8ZFR+j8pDDw8MlV3AbPi61zL/jEQ0fN1jGJgwjY6G7ilRunW5ZK7QeoOmJBNXNmFFaJKYBdeC586pgmoY/HkfVutXBXodbnkwcTyNnvmXi8i0mYGpvbaWHH37YCCKsVdtnWcS9hkl91113hSZLsMW1pevqSoAwNDREra3t5DhpUstxNTXNKYF7R8cZJGKk5XvVUGfnWZTNrvAdl63estbU7HbuMYgbN26UrO6yu3sl5dK4HJpfkSAs9M3uOXnSs7ILoZ1peHhYUx7MnTvfB342N+0ZiUTpvCglQyYDxqyk8nnFYpFy2aw1AQwDHid18W1GySQl3HnOLmo2AG9DcJmMaqzzmPxUTsxiGD4ehGD2VLc6R/qrMmgp6LVVK8VHOfMtY6SKRypGti5dSmd0dBg3abZg5ZVr8tLz1PWkulKayBTXNhmgBfPnlzCrvbXViKNzmppK7umnLV2qafhrIKwpJuWjzHgwRoYpCjdu3FiyujsQ1pVwjFxtxceGdBPVG8a6WepbkyVjycKFpbabmPJ58+aF4mMLPI8xPi8MI49DeP1kDtuyzVU+72GY8ZH7lmuux/h4+FCl+LgdoNpEgmZMm0a1kmUxCB8XoHwekpOBBc27IHwkGj0PWW18ZMxWPZcaFB7ytCVLrC7cQ0ND1NvbS+m6Ot/vGYgY5FUrV2pKO1k4M/GQ6vpnIfD++++n7kJBw0fbOrYlNwNE0kZ1DjTAS9BWKT5y1u8wjGQcjoqPvHeF8ZBGgVyZpzfArNCPio99CC+1Npo1ThQL3aFUTmeaNFy2icR1jy+RJkEGBosF7JknwzSbcgbCd7h/5azUQ0NDvsRoHPO4C+YYbnUSqu/G16+Xns9t2mdov0l7xOC0GHodwhRE/Vl/HzOjVyThYr6W9HjrDAGD5Lmg30FAUjvHcdKl+G6ZPLc/S+Iyd/MpFou0Zs0aQ9t0xjGI2UwkUoZ36CZgIPTevBkVCt1GhQTg0HvdsbFlDVXLddisz7Ok+6pgrIJZWLbMxlSqlP00Dd3NtzGVotOkOrSVaPODNuqYqaycovZdOfgoj+cF7t+l8DNk/OF5OBp8/KD7d5f0e39/vzZvGeNkj6GgciMys8XXFpU2EKLjI5EX15aCjo8O4MvsasPmXGen71k5CCWp17YEBeWkkKkcfOzt7aVFCxdGwMg9VnwUIUd+fOyGyOkQFQNsil0T7oXhI59rUgaxdXJRSLKeMHysgyc4mfBxZjpNd955Z2gyoRgfx5/GCx/zEAqhavKQLGyb8LHaPGQ18RHwuzjLGHlmR4evn9Vnm2KWW+DhI7nnJJVzMrAnBFPDB9T1zxg5ODhYSg4bNi6m+6wGKJVIGGuQVwMfG1wFRBhGOoiGj+XwkGH4OLuxscRD1kEPhUjX1oYmo+yP2D+VrnGiWOgOpUo2G1nDZVtsbHHeLQ0yLwq1BAuDF4NrT08P7dq1KzS7ai/MLiy5bFYD9zz8yWKiMgMmrVoKeoKaDAzaVIP2SI5rs7mLphynpLTQGb086XGCnPmbz8lYzsmIhdff77PGmy3dQyTiqf0L0Ts3Q2ocOpBy48yDmM0i+YVztQ75Ust7ewyu3ga/RVx22b/B0scJiEQa22H2zpgD0Gel72HzxFRqTp5nd911V2kDzsCsic11dlKxWCzV3lQ1ue3uvcI2tmqt85gEldt3UfBR3mgZI2+AziTJSdHKxcc+mBmsGoBmNTT4jlWKj8Vi0ejizW6bfH5UfCQiuvHGGykIH6cnEiV8NL27A1C9ew67bzZCtf47pFdQEMKu6nZeDj4ShWOkeHY0fNwGv3J3aQQMCEpYxnV32V3SNq4yPh6EORN/Wvp/tPh4muRNEYSPRES5zk5f22J8nFgaL3zcDM8t2cZDAiglto2CkR+FGR/HmoccDT6Wy0Oq7543PUtqzxA8jwKb156Nh9wh3cNnoDLwkLzOTa76QYpk/r8AXUBvs7xzVHxMQSgbomBkrdR2Ez6Wy0MGtbuttZUOHDgQiYc04WPKvSbMFd6GkbHQXUUa7WZjcyuRJ6xsScxDAM4d0C06DgQzyNq9sDqiJmBIJxIljZB8XAU5lRnYDX+yCRn0Va17GvC5/4UtTgYcU/09OSsiP/NiyDUQVUYvSKAdIGBFyDl67Li/xNkd5JUG85/DYxLk9j4yMkL5fJcb7ygzm2kScdqeAG1u51rLcc/SLW86wfXNBYNbgKP1MQCaP3eur/2TAXorQPfAc03jTUbNas9gxhvjgw+Kmue2TZDjYflZQXPFZBGoAej6iPNsLNb565lG03dhGKZiZAbVw8f18IQsdXOuUY5Vio9cVkXFyBoIRqRcfNRcNKHj4wWwu/SJNaJgHJxSTF2n7zczdpjKQHo5L8Lxsa+vjzo7c1aM7Ow8KyI+Jmiz8n5rI/SljI/qeZvgFzKChJ0lPou9GNMrAboawhOiHHzkuWpLEBTj42uXxhMft0MkyErDjpFyQrKw+3P5v2rwkKqCbLfUrmrgI5HAyOWut0cQRq6GGSPDXKFzSr+bzrFZ5G3WYw73kcc8KDTIlNwsBSHEyvHNpnYOGI6Vg498Xod0fxtGqhUsZgI0DcLrtlwekoXpIHyUw7mC2q7WDQdEjH59mXOt0jUeC90hVI3NRp5IuyG0jinHoTlNTdSYTNIm+MHRgTnFvimZhDFTorTwbZPHJvzI5Vm6CwVjcpcuiIyWJoDJAfQdw/GgxcnaNbZg2jSM3Mab3e8FOJT0xT5f4z7L5rq9lIBa6Ry5hvZBEsxoimTrjpyUTcQ46uc4TgN1dJypxED6GdtsNleKo1y69DQyxZl7TKVcr3s6ifjGbQSsp2SykZqa5hgzrLe2tiuWp6C+6Cs9x0EtFeD4+rgpnfa17wyIbJrysQw8dyqOfzpTiW1NAMZkTfvgB1253EXYXGHiDWJwcNAXM5qCrh1mK9BYrvPXK42271QMuwVeKRb+TcbIauJj4AZrOFYuPpow8gx47p5R8ZGIKJfNUoMU+x6Gj6awkKQhY3gSKcrBIcChBFIEXByIkcK924+R+XxXmfgINwmmHyMHBgbKwMfplECCLoGXAbgxmSztqyrj1t7aquGjrc9vDJkbZ3R0aAriacp4RsFHB142Yvl4Hm5JJsT4+Fqn8cbHw4mHXLVypbZOuuCPu60WPjIPGQUj1Uoo/O7XhDyrFvBZyOUSUwctfS4nrmSvJ9+YOA7lOjtDk2PmslkaGBig3t5ejT/LQCha5Pjm6yAyck9334vHhRXNKj7mslklR0ewl1iYK3aDEgvfherxkCZ8lJWXUeaL7FEyMDCglcg1KUerscZjoTuEqrHZjIyM0KqVK7VFtHLFCm2zndfcTLfddlvgZAY8jYtJq53r7CzFFtsmXlB5HcDTzq3IZmkGBMCqlqAwFxtAAFTY4pSTEAVZpTij5cUQIDcCIXir4BwcS811vzPKNcEZx+X4Tvv968jPbKaotbWNisWiMbEZkCOgl/wuk+tJWHXmKOeK92xqmkOPP/64tcY5ICxLIoFcUFuZiQYB7yBAWIlSAE2bPJnSysbVALERqhrprnzeN4cTEMznxRAgnzHMkVp4WlDWQvM4h8UkBlljctkszUgktDaqmuSxWuevVxpt38kYpgmwyvxijBwtPvb29oa69PYZjpWDjzsgLALq/FcxMqxU3sMPPxwa2ynj43Vu2wehJ7kJx8cd5HkMlYeR7PYeFR+57GNfXx8NDAxUBR9nNs2mxx9/PLSGL+85QQoXZshvgSfUp+C3NvksgBCxhOXgI7u0yyV41kMwygnE+Hgk0ETg42h5yPbW1tAY2yg8ZFc+X3Ll5vmeRnXx8YyODl8fBGFkyu1DtpiOwIyRtntcB88avljhPU9BcLLEO++8kwC7sqIpndYwJeU41NbaSgMDA9oYqXHmfJ866MkWZYHSpORTs7SzUB+mkM5D8HOyUJ+CmYdshFBGT4a/bSaMnOGOURgPmYMfH4vFYqSkvibqLhQo5Th0OfyWfHm/r8Yaj4XuEKrWZhOUbGdgYECr3+lAWANtoCZr90x1XMPc2q+zTEjODBnk6q26Npnuc6r7lwG7GwJs1bidGvcTFIPIz5qkLATWdqUgJ01Qrd/b3e8MkAdJMGwp8scrTpd+l5tw0PfM4HPMGXeLxaLknm6LNZefM4f0WGwRc55IpEvJjIrFIrW2tmnlddjyZM+C3k3AMJmSrLWGJJoA/KWRZE27ClRq2QtTpss5EOVReJxvvfVWaqivL1vTSGQRsFy3o/Fa569HqlbfcayVipGrVq7UEn+NFh/5tyCM3DVKfAxTNsoYGYSPc5qaqNbNfh2Ej1MMGJmBYMLaIAS9cIzj32aPAiMPH3xsa231ZcbnOdWVz4fGku6HPy4bAE2dNIkefvjhwHFtX7YsEj7KyY12wIyPMxHj42udxhofR8NDyrkZ1DrXo+UhbSXwOJSlXHxUvXZYOE8nEpF5yMmJhK89Jh7SsTzLgadomGnw6OFwPfX5+yB4K/m5tpA+q1t7QOZu9T0bYE5uloGnNBwZGSmNdy6b1RWIjkNTJ00KxMdhiITH6pwL4yFz2SwNDg6W5prN7V7FR36mipFd8MrObtmyhXp7e43YHoaRJnxsa20tlYar1hqPhe4QqgZghsXNmBZUCnoyMlnw+cQnPlHKiArpPvIGemZHR2ni7YOeDCsDyc1XmZDdhYLVTUcGCRvAbVQWywAMsS6IDsCAWdsFCO3ZN6XzVA2k/ztbulVrzC2W43IM9O6Qc8wZdzmuM1pyoLtCzhXtj1LKTHxMmdz3kTmZXKoUr2kb156entJGLdeE5OyUOyBCKFZD1F+U72XKdJmCELzVhFYm1yq5fmQQqcxDFIqZyspprDHSATQsSqF8fGxXNtChoSFqXbo0ECOZKUsnEmXjI8cXR8HIMHwMs/YAerbe9e4arIOweu8unRtm6d5rOS8MI3cclvgY1Gdqn8uxpBnoe04KoIWnnho4rizMyImBZHzksakFaJ40R0aDj2oN5CCK8XF8aTx4yOXLlpWNkfOUnC2V4GMQD9ne2mq0YudRPj6OwB9PzfhYjpJzhmE923hI03sCwkq9t3TcjEGqJXuxKWwRKS2kL6gvTPdVrc5RjGGl6hXZbKR5NZb4KPOQGfixz4SPBDNGpiGEf7WtMxUX/Kg85FjjYyx0h1A1AJOD+3fDnowsaJHIWibVkuFAuP1uhqfF2r9/v+aO5FgWCP9+yty5tHHjxkjMynqEt1t2z9sOL1v7Hksf9Cnn74Yb+w5PmyZru1YZFlkCIstrYzJJ691nroYX/yTcF2e455usMRyvqGbTzUjndZPI4qtaj9WMuyZLcgd57pIe0wlc4yYOqqHwuPRtBIjyCmGleoRl6SABf++2z6/lDWK+g7SU/nt4zPwmeFpIeWx2IDxByWT4XSzZteq0pUtLLpUyeAZZZ2Tt/Xiu89crjSVGfiNk3pSLjw5Aq1au9LmzRcHIBED/8A//UBY+hpYfgWfhvgbB+LgNdnxsV541DNBKJEhVuDW76194xjB+3UKOU0vZbE6yNp8cgCsmjGwgzyJ9eODjwoWLSonHbMzfavf/tdK8UfeUIIE9Kkbuhn9sTBi5PuSeNnxsa22l++67T7NyBrlDxvg4vjSW+LgP4WW/ZIxkPKs2PgIisaBcAzts/ZSDjzIPqSZkU3lILk3FGFkP0HJpzRchY6TKEyV8POTNEB5Ccg6mE0vXmDGoVsqMfUvpvmZeaz0EpucMfSEsyUpuIDgll3J5b9ju4gPjmA3zuA8TQMnaHHb+WOOjfL4NH6PwkHVQFDxunHpvb++Y85Cx0F1FqgZgBrnZMGAGabj4k4QAEHliNUC4FeakyTcznfZpPaNYSWSAXTB/fmCbZsCrGcpMoAzqMyFiTQahxwAHAewIzMJ0q9JH3e47cwkC9XyOT1GPizg+BkwzCHZ25pQ+cUhYhvm8EVKz8wpGs4v8zGaGbO5H3jWbSvfI57ukOGx7+9iS09KyOIKlm5lXZoRXu8evpqBNIwG9pmKDe3x6IlGKX2VGkd268pASW7hjnXevZaEiaJ4HaagzhjFWQdOY3TkkFqea6/z1SmOJkSdEmDfl4qMpY/n0kDlYCT5uh2BoU9Ax8kwIJnEnoiV12wA7PnZB4C23SeS5mEL+Otuqx4uuhBMY9MZQDNIxMk8CF8cWH/P5rtC2MT4mEvWUzeZK+GRKisfHmFFnHNuDcCvcydD3vlqA6mfMKLmz74YfH2VrtoqRM0KeFzY31XI8NRCxkjLF+DgxNJb4uAheAr8oGFkDIWCH4aOq4CkHH4HwuvST3GuZP7DxkCo+BrWBecgzDNfUK/jXAIeASQoeer83Ns4iFR9XrFhJb/Ql+TJjkFmotIfksMU+A/9eYbKQyxh5itLmrnw+tA4182y1EJ4NXFUmTPHBXjq3u8evRvn4mIJIrlbrOBoPGYaPaYTzkKst79CUTlMdxpaHjIXuKlK5gGnSkHQXChqIcbxeGJDsAqgHoPNDzgP8MWPyeWG151jjr9bSsz1LTlyRVCZyWvnOSRO2QQC7iVGZBE8by8Cj9pUDv7YrAcFgy4tT1nCpiocdEC6qDkDL4ZCjxTqnqL4+TUREAwMD1NraFgquicR0EkzeQRIMYo3v3YNd1Rvc8wVwcob0bDZHgmFOk24typBcT1ycy8CsWufz7jNlwZz/t7nYe5buvDKOs5Tv8kZRKx2XLTZDEBtnLsKcigLgqoZdzbgblDeh2us8Jo/K6TubBtmGkWFWnGrgIyF8Q9+GyvAxAV0gMlnjvwlhsVbxsT6RoHRdHaVc6wkzqarLJis3PSYmQUC9u8bzpNfcTrn4488F0dra7l6fJ1MN7dbWdhoeHi4JtOONj8VikWbMSBkwT8bHbh+W8SfnlkZLuf1I8FtPbP+bxvib8McWmgQiTtJUr4yN/JcT3rWHPC9obrKS1LRnqnxIjI/jT2OJjw682OcgjLwgwvwaLT6yq2864D4yRvI5YTwk42MfzDxkHYJ5SF14XU8CH6e7WFN9fNQVk2asu1Z6j7RrTY+OkZ4A3tmZKwmGDXV1NB1+DwdZkJUznHP/ppQ+5fNUHOTvYZ45Kj6axpkx8lTlniYecrl0ne2Ze2Cem4BZTkihejxkLHRXkaJ2pk1DMjAwEAp2DsyWRQeeNp7dQoIE5zS8zV8+L4yBUDX+OxBgoenoKL0zx2NsgB8QTe5HO6BnilQZlTAGuxagZuUaB9BqtcrWIdPxG2DKei6+60l9GJBVodYhLTlGoZsGBwfp0ksvdY+ZYxjlcl3AN0lOfiasObprqN8CtE+8u1NLwGbSLUuOe3yIvLrerGnlJHLMoPrLji1btrzUd0V3XNshtOmXwMtAyqC8D577LW++gC60d8BermZmSKbMoN/6+/u1+HLjHA9xE4qZysopSt8FaZCjxJVVCx9tnkW7Q+aZqvEvBx93SGspB11ASkEwBdXAR0fDDWb+wrxh5GNw8UOvkPDFL35xQvHRy3Ce1Nrm4eOIdC8OsfGsRLxnDMGzaLPLquzOarLCseKW95YiRPiTDR9H3Ps48KyFtxvGlueWaU6lZsyoGB9vvPHGGB8nmMYaH2WhQp07OYwfPhL8Aq8NI9kDo1gslvIehPGQGff+5WLkUOk3xr9hAhYbsGOzgkXVwkfZqycYI3m8BwYG6LjjTqDoGLnBgI9+XOR+YkySx30fzDHsAzB7/MyRxkSdcyZ83ALQGyDyi8hVPmQeMgkPH208ZAOEEc/kGcEyhm19BM1dTvwXts6CMDIWuqtIUTvTpiHhWCsb2PFHnWD8ncvWcGxIEOjdAvsEYzBUJ2sHzDX3umB2WTujo6OkiZXjQWyCPbepVnr2egiXphT84FrrZpa09VWD4ZoUPKuFer6tpMV0ty174MVFcn97yZcYpEcM4JohIfj6y4Lt3btX0oTyh5lAFchl8PR+8zKR7yHgPPec68irlSvfZ4P0ekXieEfhpm6yKqmlgPxMsch27n1fgAQtMQj/eSRKCZ6udf9yiZL18IQkk9VypjKnZqbTdODAgdBaoeqc2Ad9w3Vgz9Yq168dzTqPSacofRekQQ6LKxsPfNwOc9baBugx04Ty8ZEQjpFs9bDioyGD+TDg1tjm9cz4wYJmg3vc5t7YZziWIE8ht8fFFWYSVSZ1ovDxIAEfI0/4Xk1mBlk/dsbpp9Mki9DeAafEuMvMqun7IoBOM/zeBS8B3i5pDs53E1fZSm22QLcGJQF65JFHrPjIHg4xPh6+NB74uA+6ZdGBsBLKGBOEj7KQYjJYBOFjETq+mTByMkTeHRZgyuEhZatwLUAzamp8rsoyD7kbYm/YWHo24x+H2qlW7Tz5H1stfGSMZJ6LP3kCNpes5yz09fb2anxYNIw04eMONxeGExpiQ/AszKctXaqNm6rEyEhzLAgf32j4vQv+BHjMQy5etIjCeEjH8MwzXc8yFR9ZAcDnqfumylOEyWpBGBkL3VWkKJ0ZRRNp+02uu8nWEBkAufZdYzJJc5qaKJ1IaG4gXdABV84yuB3Cxa0hYIHIE1MGP27TAHRtWE6qh6e6sJtS+6vPkxdyH7zYt0r60QQoNku3zFQB/izB00slJVQmlTPzsqA75IKdcNUW7kQ2pjdP/nJdMnjucr/Llhn5uRlStaQikZGdkV66tNUFW1XTKichKrrt30WcfEhYzuW211jep0Zi9L3YGK5zGDROeyBAljObs6V6cHDQWFuXs1falEg2jbg2P2JLzphRWN+F4WOYFW488JHnTk45T8WsSvGRUD5Gyv0xBNAHDccLpVCZsJCRDZbjNkuOyiRmpPuYmNSJwsftZE+AaWakdXz0So45LvMsJ7QrwrO+cCjWDphzA3DYmMzQyfHVYXW2FwB0D0DvgFAO5zo7S8y4qWweW6JifDx8abzwkeDhEQtQUfCxG2YhJAMpMzmEANWunCPjVRvsGKmGmQG6J2h5+Og3BmSRoF6ArjD85vf8C8LIaEq7yvBxt4RffM8hYp7Mc0evFCOD8BGl8CR5v7MZq1qXLvWFMjCWbIffY4Ln2i6Uj49ylQ+Zh8x1dlK9y4Pb5vs9EMntpkHw8v39/UZ85P3XFH6hVpNgJVeQxTy2dI8TRenMME1kW2urpoVhLebIyAjNaWrSrNBp+AGtu1CgAwcOaEXtuyGYykboGyxfrzKOk6BnopTjKEwx4KZU/TKQq5pU0/nsHjJ1yhQCzNrZJISCQAWIsEQz1yh9y5kmVa1st/tJQTCxN8PLINmNoGyTLLDuI92yk6REot5ynQrU+yTwdEi15OjuRN8kdRNpaVkU4Vnyb6qmNYgxZ2a5P8IzxGcyBFM5MjJCbRE9OzqhKz9WZLO0cePGkrsP129Mw++lEaa1l2OZ0omEllCo0nUek5nC+i4MH/v6+oyWPK6nPN74OBliQ69F9fBxO3QXTdM1KYAmSR4/JsaTFQa7Ia/nsEoGtSQLpV42cFMW8m4Xn9YTcDMBF5NgBFVmT2ZIJwofBTPLpQ69T4KAGwKeFZyIzVaDuB9mzzD1PHU+neXGDoZlVJc/DkCnLfG/16KWllKVEV4zi+Gv8R7j4+FFhzs+ssu2KeyF75FW5uYkCJ5MPp9zF5gw0oaP3YVCKV49Cg9ZV1rbbCDYR7oAXENCyFbjs8MqH1xDwfiYcp81Gnw8SMLFXcZIFrTDc+3YMTIYH9XymQmIMEsbdsnYwbl7YPldvrYcfJSTqLFicrQ8ZPuyZbRmzRratWsXEZGRh1T3YbVtsleuLKuNZo3LFAvdIVQNS7fJiifX1RwcHNS0NN2Fgq+AvPysnp4eOuaoo6gW4RvssbCXebBpx9V7hsWE88R3LNebFhxgT25QbzgvbLGrfccuy/LxPASA7Idu9Z8D0IHSwtYtJ8JtByTigdLkT8oB8jKD2wD9OKWdvFHsIY6n9pfVUYEZNG1arXKPGhIJiryYbC8BnK0dJ5PZcp4gPTY86D5+wEu68zxsLfRAaEh5ru2AOaZIHUObV4b8DBPjasriW+k6j8lMo7XkFIvFkqeEPHZc7qMSfJw3bx7V4fDCR57H6Qjt4t9UxnM9hAXUz2wcpHArjp6p3J8NHORlIR8hYBXpFqMu8sdLe0zq4YCP8+cvoFNPbTHcZx9Fx8dtpWsXwylZ+uqgJ5MMwiCVGYyKj7vgWSodCIZvE3QrZIyPrx0aK3xkHrK3t5eWt7Vpv0XBx6hCSAo6RmYM55t4wDB83Llzp1aWTMVHYYlX8YirIjSSbhnulh6lGgrCDQlmfHRcLBkNPu5wz2OM3C0dD1Oc2jByfSR8XLr0NDrppGbfMRnjGpPJUIF3LURIagp+wbQSfJwGD7dq3GNRYqurxUPa2naycp4py3m5a1ymWOgOoXJjuk0aEs5IuWvXLurr66OBgQEjgJpAksmUaCMZYQIFTV41298+QAM/1aXE9ox7oLsQ2c7fhmgZN9ltkxkOuU2yFTw1Ywbdf//9xr4rFoslLV+YFT4Hmen1A6q/pJcM2gyUuw2/yYBeS4IB3UbA+lKsDd+/UOimfL7LBc/t5CUo2kS6MCzigfxxRMJFid217O1Yq71bS8ti95gc6xSmdU3QDfBvwksWLiQiMmreUxCgaLLEBGnBh4eH6UxfmY7wec1zR3ZDjt0nx47KiVkMwsdisUjFYlG4iillVyrBx4b6+tL/E42PN0MwC4PwW66DNn6uCrADoL0AtSqC89zSOt5EgsFii4nfOjNjRooGBwdLceZy/xWLRYvnTDf5hWdmZnNkYlKbmuZQW9vphvtMBD5miGPK1Qy/4fjIuOfPDuzAX44uLHtvLUDzURk+bgd8YTph+KgqpGJ8PLxotPhIRJF4SFZSVoKPlWJk0XD+koULS2smhfDM5yccJ4TJIB6yAIcSxvJZQWuZLc37JCwxY2R9fYNxfykWi9Ta2kaJRK3yLFNseDA+rlixUhG+ZXyMojj1Y6SMT3Z8tHkD6DxkLpsNTfxchPCyUgXYJPyeElHwsR5iT2X+24FIakY0Pjxk0DuqYRrVxMdY6A6hqJ1p0kSuWrlS05YVCt2loH+f5tBxqK211Ti4Q0ND1NbaWqr/ydekE4lSSa4gi4kN7Gx17Xbu3EmZJUsi10csMcYQluQw1xKO8QlqGydIkt2e9kGv+81tlLVRMhPPWrOMuyg5btwW7z3Z/WyAYLpXu2PTXSjQxo0bJYDkS2WgZLcjU5ZKf+IezuQrg/zIyIiSfVK+pxp/aC6Lo4Mvt6OR/CV1vMRr/ufJ3ZIhe2keIUCo/cfxZxmlXar3Ao99mBY8l80aS6VMArT4tBTscUpxoqCxoyh9Z8NHzd2xUDDiIyeltOFjuxvCo15Tk0yGYtd44aOMkdeGXDMIT+Oe0ywqgmFykKImJEl4vDSQYKJU5kJc29Q0JxAfPWy4hYTLZBDzN5lEWcMNJBhAkT13/vwFdHjho9zmcvFRvxYQzP+I+3IZ2EvtqEJJufiYrqsjINwKmctm9XkPaCE5MT5OHFWKj92FAu3fv994vFyMzGWzWgnVxmSSpk6aVMqvUglG9hnOLxaLNDAwoGFkOfgo85B6BnL+hLmLc2gK8zE7SITrNSvPtWMkr1c/1rDXji1Phhkf8/kuScHJbVYF7SCM9O8DnZ05n5LFjI/yPcN5SFk+UUMz8/BjXBGeQsU0vpXg47p163w8u9y2RsN8qZSH5HjzicDHWOgOoXI3G7Ym9Pf309KlraQnRRALSHabUctXcU3S4eFhrQSAvOnLoDUd/litFMJLzLB7EWuOah2HctmsL5MmC562SZqBXxjihVkDXSjiBUcRFsr58CdWM8U2NsPTlKUgshiqGxRnJHwUwoXcBPDy4poOvbwCt+mqq65SwEwVTnWry4wZKfrMZz5TsuJt2bKlFLNso3Xr1rnX77Y8T47fAQEnluZXMtlIHR1vNGQJZiuZnAmTN6erybx5yRpiSNfvK/U/g2bJ/Wjt2hIY8sapKkqapXENqyEfNEdUoceBvXxcbMkZOyqn72R8bF26NLJrt4x1nAtDtd7YrpmSTBqxK0oJrtHgY9o9rgpEGdjzTsgYubu0brmerLyHLCIRS8j9wtZudc0/ULqmo+NMbT/xcOJREuUE5evZ3VxlZqe7uGDKqlsePgIOZTKt1NvbW7LicXJF25qNjo9yht9tFA0fc6Tjo1d2LIkUFeAQwZwhXC3J04Py8VH+zntgJfhoUlDH+Dj+VAk+skKsGhi5QknoqJ7fUFdn9SIMw0iZ56wDSoYjFSObDfdnfFwP4S20AWYe0hPsVP4kbP1/gIBLKBgfP0Yyrpow0nMr3086Rpr4KTs+mnlItppvJ5NVur6+ge68887IPGRPT4/UX2HWc+YhF5JcmnGF4mkmh8uY+GYTRlWCjz09PaUcB7vdY5fDnuC0Uh7SZK0fL3yMhe4QKnez0QVl82Rn18UCHEoqgnky2UiFQreh3p+36Q9B1PtUY/VYSMq7E8gmLDvwu0oGTb4hd+Kr52WkxSdPUEDEiZ2luL3J5++FcEkxtS2pPCcPczIkTowmP1v1BuCMhBnD9Y2G69mdZIP0Oy/eK664gjw3bFUTqZagEYDOY2lUoLjKFXnuZLMrpHPC4iBlS3WRgL0kYra9Z8yYkVLaZWKUw5Km2UqWiU8WDl3u/i9nWu2DV2ZE7fckREkHdhNaD8/lUZ1LQWDa09NT2oTY6mNz0avmOo/Jo3L6LqqgrLp2y9ZnOQFPYzIZSTBRsSsKPiYt10bFx6BNPJfN0g9/+EOa09Tku4aZkmGATjHGDJ5F5uy8naRbM1IEtPvWrFpSxosFzBiut8VGsgt2SvqdMakcfNxM8n4XhpH676x0CLJ0yRl+hwk41Xf/+fNPNfQnY6Q9g7FaYslWkofHlKtj3HXXXRQFHx0IhTG7mVeCjxz2cMUVV8T4OIFUCQ9ZTYw0lRqUz68UI5MB16rYNwBP2c6fBEAtytpbLH33eEjmcVX+5HoXV1IGvJHvGwUf7RipJ1AL86qx46OZh9xEeolX9hq6rmwectkyju/fQOFx4ioPeT2pMkV9fYP/mRbDX5ASKCo+7tq1q2Tp5n09D3Pup4b6et/+H/SMoLk/3vgYC90hVC5geoJysMB0MYLcZmShxlZ/VP5kiGPYEkhRp6uJJ5g1OrnOzpLr3vK2Ns3tKOVOdJN1+TiA1oRM5BOPO66k7Uw5okZgB/xF7ZshXOoyyv1ZEJfbI2fFNC3qIvwbjm3h21zK5ezlJXcS6IK4ECgTBoCsIWF5kms2MtjeQgBo2bJ2F8xXu+d5zCaRAMumJlWLOjlkftQSMNX9f5FybZ5EUiN1A+HyE7eQ0MieQgKYc6S6NSWTjZRMctkwUxZPv/cGbwLsnhRWAu60pUspAV3JkgEo7VoVw8BcjWfUhBhX41/tdR6TR+X0XVRB2baOTcJOWDjLAoC+IZ0vb8wmfGxvbS3liCgXHxMQZWt6Qt5v7dq1lOvspMZkkjYAtBT+qg2LjaVj0gRMIj2WUI03VHEivKSMx6SZfttD/nI1KsNWlM6Ngo/15OGjVzZHYGQtyVZ9GSNXrlxluHfQe3PcY5KAjaRbqTIkMFbt50YSwvl09y9XdPAs59fAY8ga6us1oYQtdqwcrpHm2ZymJkq7+2LQvL377rtpVkNDxfi41jC3Y3wcfyq376qNkReHzDO4c/UG6b5hGMk8ZE9PDy1ZuFAPl4QdI98Arp9tMDi5vMSVV15JxWJR4qczpLtdT3GxJGPAGzl7eRR8lAVRFSNviYg14fgYzEPOIy9em+/l4WM2m6uAh2QlY/V4SAcpykEYTKYDdApEKblGwCiwlouPHD7BCsegudtQX29UFm1ynx+GkWrCy/HAx1joDqFyOtOLj9tBYW4d0wPdZmxuvXJmRM/lzaRtU91+GXg5lb7cXmssBMwaJnZbk68bgj++Q10sXJbC9BsnLbAxIWGJONrgacpk7a+p9qTNNUZ1JZcF+Vo3ppuI3Bh9FTAnGcZqP+llIdRzNhEDsYj1UetkpylYkytvNvXKtSnL/LuD/ElF5Db5s6Nnszn3f3VTy5Df7dQrOcbuSbKm3jZui1paaArMpUrk2t9qmAKDNLvnqp4Nuc7OQPfU0a7zmPwUte9kvAkLL6kPCE0xua3NgWHTh+fh0hYyF3sknOF5Uyk+1kDHsiH4E7NEwUd97d5iOR4W2yivYbmkzDDp7t4md0n1N9U18ZoS8xcNH/MUDR+9Z915550kmFUV52xKQb4fv/eJZA71UvtzmPx4p7bLiweV6wxnlLGT9xmeL9+Eh1Eyc2fd11pbhYutYY7J+GgKUcjAc8+N8XFiqRIestoY6cBfVo7du1nwiYKR1eQhTy61jdceK7bWk3+98TkjpLuHy9cXCdhiWM9R8dHjxzyM1DOA20NuouEjUVQechUJzyb5mOotFIWHTFH1eUhPUatW5vBh4CjwsSufp+5CoXR/25ycDDM+OvAEZhsPyXmBxhsfY6E7hMrpTI5F8Ba3KSlCAwGOkp3XFgMXlPjAZP32NPGc4EJ2RQK8hABDQ0Ol+LJtMGehDALThDu574Au2CYAWm5ZLHuk89TfbFmAd4e0pR6epkw+x1R7Ut6Y+PprYd64ZFdQ1nbpySqEdXfnzp3ud7YWya5IedKtU40kXJ5szKY8ruo57QQMkh/wo240efIs1SZ3qwS1traVPCG8exRJZD5X7zlEXpmdtcSbZrFY9Lmam8YtbI5xqRQ12ZYD0BvdbJRB147VOo/JT1H7Tq1HyxlH1c3Qge7BIG/SGejeMGnYczZEcYGTlWx9fX0+fAxyTQua28zoqgyHA9C5CMdHfe1usxzfbcEATxnmMV+mWMIwd8np7rrmPUzdizw3Rxs+Dg4O0pYtMlOck55tw0fOHxGEkZsMv+cI6CV/THdUjAzDbcGsdnbmaGRkxDen2btBdfvl/et98FshZVdz2xyKgo9qHhMn4rVjscZj0qkSHrLaGMlu4vL5OeiCT9CckXlITjTG8cOVYqRY36ZY64+SOXykSHJZP/9vNlfqKPjYQEIQdqTzTO7ktpCbaykKPhJF5SFlfDxIdplgInhIvzJ2vlt2bAdEHpQEPJ55NPhYLBZLfRI0h8IwzsZDThQ+oqw7vw6pcks3kVk7V0P5fBcRcTmCdiWTqs2aIbuD5C2LwIth2wO/EMmT6fOf/zwdd9wJxoXYigQNKhPaBqaXXnppKS485U7eb0CvcVfOYgmqXcvPUTW1XdI9z+joKGn9d4c8n13KVUY9A5TqFjY4DuU6O41jbSq/IzSYKWUMo5SBsDG9qqXpYgJ6yBxbHWWj4bYElwNznBQVCt2G+Sx/N2mBPQv6woWLqSufN49bIuGzBtrmmJwxslgsUk9PTymBiMqcBF1b7XUek58qsXQTzNZduW5wsVgsVW3g+RNW33o6hKu2SYnYDn+Ii03JdsrcuZExzDb/ONlXGn58PFGJl1ssxcep945u6d5O9ljqOSRbX4R74nYLPsiYwi6OXYZ1nidgk1v3ut3IoJjwkYhdxNmqs4HC8XEB2RXQJoy8luz5J8IwMrqnmoqRJsukybU2CRFqBQjrvak0TmMySQ319TQ9ZI6p+CiXlAoThmJ8HD+q1NJNGBuMnOrOQdO8aEZ4xmkZH8MSrQXNQfHJkCfYfoPUnDTB2GDjb2z4mKYwfOzoOMPlx8P4cDnkxpSAMhgfeexUjNTxMey9xpuHVPtAKCKbkPQpuLniz2jwMbNkiZWHTEFYqsvBOJmHDMPH3t7eMVvjKOvOr0OqPKZbXty1xMkJ1MQHIyMjWlkx8wLgxcMJaGSg+SAxw6HWN+X4hvpEgiZNmkp+1+IUmTLKToJDp4SAabFYLGU43GRYPHmAFhsWi5oESQX3tOGaBoBmQdfU1kAI3fJCUbX+QYCvlu9ZvMgf0xI1noNIxNPMamggvyLjIIUnsrDFWHpxQHV1KdJdizIk3IH4uG2jYSb7IHmaS5vFjNt0TWmM9fmcoWBLedr9O4UmIUmboXtCOBCa9zD3uSBtowrm5dadVSlmKiunSmK65fXN2nHTmjNpqYPWdQJ6kjN2G3+vAUMy8JRs9YlEaSPneTrFgEcpgM6MMHdlfFQrVMh1UhfD0fDRvI80SutL9Z7qInPMcprkOMPW1jblnDCXS7aYXKGNgbqXhdHw8DA1Ns5W7tMWoR02ps/DSMeZTLqLpsDIurp0wH1kjJRxMVoCIjlT83YX61ixw/+rro+T3LnVlE5T2nE0T4iZadHesFq3UfGR10E5dWdVivGxcqo0pns8MfKD7m8zoMe1+nhI6TgLQ4thdl0P4yGXLeMKAqbM4oyPeviInmhYxkJbuMkiioKPvb29ihU62MrrYeRY4WM3Ca+d0fGQAh+rwUPyfqNb+7NIlOaYHKpVKT5OhjDQ2HjIG0Lm12jwMZfNRh43oljoriqVC5gm1xG1nh4Tu+hks3KChB0BC0DE2QqguYOEa4k8GR1qaJhFX/va16jNLZXFn8mTp5FXz1W2dJpcaFJ0BhxyIKy9JqGZiKi3V4BBHuYMrF0wxHK4tSdZg+X7DaADhmtY87UDXvy3LGCpjASXVQhakNdCMNvqs3KdncaxCqNcNluyaO0o3S+KpXuPclxm6lLuuE0hczwixwUlDJtPioCEIbEGKMzSzbFIfX19xvkMzAi5/trSuWxxVGs6BrnPpRMJX8ZIua6wfCwBsyLGAWhwcLCs8YuZysqpnL4zucO2t7VZ19zQ0BC1tbZSbSJBq6W1ZY0hdDOQmsJeGG/O6Oigu+66SwnxEZs9r+Hd7rFFljl2hnv/FMxKRSIPH3OGhEG2OqnMUO/fv5/S6ZnKuusm4ADpORbyBAy4/3PGWtWS4YV9yPhoW8OJRC3ZSti0traXvb6IyM0RYcKxICwJYjavoUQiTalUI3nMoSmeUezBJoxsaJhlUHpHxW0PI+U5Le9rQVnH5XnM+5oc83/Qgo8pd56o60TGyKGhoZJQpO5xSYAeeeSRssYuxsfKqRIeUsVIG1/C457LZktJa8vByE4DRp7V2UmPPPKIxkOa8HEHBJ9nwshO9342HpLxUeCXyoMyPurhI4VCN+3fv99dt6oys4v0WGiHRH3uaPhIREp9btP6T7rXjDU+NhKwIqQtwTykZ2wzhcokSeSpcMrgIfNki2tn/JJDEYLwsZzQGhMPWSk+Egmevc6CjwmUx0PGQncVafQlw8xp/XVBJkOCceJM0n5XQdbueUKQickQcSmcsIHdV/7pn/5Jes4O8rT4u0MWsyFuSNKyeu7H9sVzszKhOTOhnJ2TY8pZ27RHuSbMDblWEdKIiB588EFKwJ8RWI6H4sWfgqIsiFgiQCZTMpECHHJKGsK8Np6Ow1Zxe9/7SzUEMaXQ6s7KwN/f3++WquB51kjmbKB+QUAGqGKxWIpxtdf1Pqj87uUWkMdL7iuT+1xXPm+swyzPv76+PuP4pSCsk+2trWWNYcxUVk6VYGQQrvA56tivAKgXIhZRcxN31y1v8Ma1Da9GNq9xFkDf9773+eZln/Tc9RDxaD3wK/tM2aFt+Ghfvzt818sYLzJ5g4QFtkhesqE9vmv8mG5ek45TW9oXiAQ+eonJ9JwjXtkYfZ+RkwJFJT1URbWm6Pi4bNnyUOzr6DgzQh+bLFgoxWUPDQ1RT08P3XjjjUpbdNwOwkh2WdwD0KWlvpPG1o19lI+FxcKa8PHMjg5fGTUTRrJAM9WCkQ319WWNX4yPldNoS4ZFxccMRGmuvlFgJCcplfGxr6+PPv7xjxPgVYBhfNwtzVWTQSSIh6wEHxkj8/kupUqQipH3uMe5j4KrCan4ODQ0JCUmU9d/RmrPeOAjyAuzORx4SPt9auEPRbDxkO+JgH9jiY8jIyPU29trrJbEa6AcHjIWuqtIlbuXr3eBYAM5TgN1duYM5/BCNdXpm6UBjc7M2Sf/rl27LMK9bBUJBqI2d9LZ4vOIqKQNlReHKWt4B4RLE5cBkGM8TO7pzMyyNYmvkRvKC7u9rS1wkcmMMbfrgpB7lmPpNsUXj0BYuPzg7B/PtrbTye4KtcEFVWZ+g92c2IIlj5N5/KeQKAOht0mORTJtGt68uyBk/umWbrlvT29r07JJ1gLUMn++r9/ZLcmkFBFCQ7CmNM7OOz5UifskW2X2wKzsksfe5JEyS/luEnZtc+Ni9+/AwIC2PnJujLVXztEuOHFVgyj4GK1CRYYcJ03ZLFs22CPF5H7J17KVKDgGMZvNlRRZOibI7WA31dWutdt+z0ow0o5jOj4+9NBDLtOoYmQDAc3kOCmFUbRjpOwFxWNl6ov6+kYSGGke/yCM5Hm3HqCTSlgrX1tDLUpMv22OntnR4cPI9RD5Cs7o6NDWkrGCg+TFYXuGnIU6jGJ8rJwqdS9fD2GQ2AA9v4w67pugW5tHg5E8P4xCC4RwD4QbRMJ4SC/cJQo+Cpdz4RXKuMQ8iQ0jH3CPy9eMBh85uSPIK7U1HvgIzfMpn+9y8cqEj0vJ4yHD8ZHbG42HrHH7W1dEdMAr12UyXvG8mx2CTWHYpfKQ5eJjtXnIWOiuIlWWSC2jTNJMaWFzCv3wLLJC4OLEUTKZF6he/qWpaY4i3MvZF7sJqAsEjSjuFfw+UbOGs3uI7F5cYzg/BS/2txvmRGopiPg3mYyLDCKJkqyB3ai0gz+2JAxRxt22eBe1tPgAzWP2bfUau3zjEDRGQIKy2ZyxXSYFkBrzL+p0n+BrA1uAZNq7d6/C4CbJrDDgEIYamgRH8zKoAWhWQ4M5Pt9NDhOlT6NkTS1nDGOmsnIqp+/27t1rDCvhkn+Dg4Pa2LMLmaqNTrjzwIaPqiIwpzBx9fUNGj46SFEBDhFADT4rxm4CVpODWreGtr+qgY3MeE9kz0Yr7x0HXZzmNaW6Bjok4h6ZKeL2+tdksMKXn9tO/nrbgxSWvbYSjLTh2LJl7QZ8dMhch9ef/CgMI5ctaze2yd8X7vg6HNIj3/dEUuPFVe+1oaEh6u3tpYY0x8PaPNEcSrp4N23yZOOeNqepibry+VFj5AnHHaetA5Iwcu3atZHHL8bHyqkSHjKjjD1/z2WzkXmuSjBSNZYYy3K6z5sDoSwPmoNhPGT5+NhtwKUgjMyQl9C4UnxsIA8fZd7rYqUdY4uPROTDSOH2HYSPUXlIlMVD+ksygoQVvt53zLQ37t27l9pdJbQDj+83JUjLuL+r+Hi48pCx0F1FKr/cg0Pm+BSHHKdWElx4ofJiW02mMmC2OEf/IhoikZBGrrMXFre72W2rGYg6Os6M3EfdhUIpbmd3CAir8UYDIefXuv8/AJFZU90QDhw4oPVLUMZyXrQrQp5bbkx3Vz6vJRNJAdSYSmmxWMJtUlZ4FMmLM5KTYLAmcpJxjIBJ1NAwy8j4hymA/J+EGxfpHeOyYZ6mUxVAgu6doEWLlmjPyUG45HK/m9zRuJ/CspOHxexH2fBlipnKyqmcvmtvbTW6fTOz19ba6ht7tjivhtlrIggf5SQprUhQwpflNRgfvbwMZgvKaZlM5P4RruKym3JYZlyZSbIxpXwuuz/uIeByUq2r+XxXGR5S68krndNNYeFH5WDk8PCwyySqOFZLdXVp31rNZnOUSNQrz2aM5HHz8FG4gZqVgJMmTQ3BR/P41tbW+46pgriOj7KgHbb3gjJLlxIAWqBgpPx9tBj593//9751wB++z/333x95/GJ8rJzK5SEdmHPkOBDeNe2Kd+GD7nhusIxzGEZyWF8OfsE9LJEfIHJesIuuTzhyvYCiUPn4qOY9CsNILne4loDJvvVWHj5yxvIUCcF2YvCRiGjhwsXKs0346CkA7DzkFGpqmjNqHrKhYZbruWnGSDmcgecVG9Tka2SFfGbpUl8d8MOZh4yF7ipSOZ3J7gpR3G7FOcMkNGjyxOJsiMHaMrHRm7RcfH24u4r4tJHJ2rpkyWm+55kSETANDAxoSTdsE/0ad2FxNkNbbW4+X1107W1ttGbNGqNrXNgiA0Bt0qJdgOBESFHenclkmZikfD+rs5Ma6pmZC3btF+N3B/mzzfsZw46OM6yWtjAFkHg+H6txLeB6JvumpjkGBlit380KA9H2uXPna231NmnHOEYMdOvWrSv1eRAgFovFUvI6kyZUjkuLQjFTWTlVWjJMHVP+cBKbTQC1Gty7R6Q1bcPH7kKB0o5TskpXjo/svu23oCxd2qq9mw0jRkZG3CQ5UD72Z3uJv4KtzcKNEL7rNm7cWIaHlPrObSSYU2ZW9QQ7qmt1FHwUjHWa9HhIPwPc1sZx3OXiI0jNzptK+ZWy5r4wj69QXvCxDNkqfTQ1zXHfSxa0g/tZxccGpR94z5Mx8kGArlbmfNh66u/vp4b6emu5nRgfx4cq4SGDEsDyPDGF5cm1usMwctXKlcZqDnw9x20H8VN5iEzlGeU+DfX1Gm9iw4ny8fEaBZfCqrFUEx9BwrI7/vhYKHTTD37wA0qXvGnC8JHocOAhZzbNpvpEojSv1XnFgjPncrr00ku1PACyML60ivjIRoDR8pBHvND9xS9+kU444QSqqamh5cuX0969e63nbt26VVt4NTU1kZ9VvqU7aCEIcGhtbXMXaoZ0lxh2odlemhg2oBLlBVKW64O1dm9605ukPjFbW21xb+xaZ/pt+rTawImechzqyudL5XT4ExZbpGZmNfVJVKaeF/F06OUx5LinKAlN1OcygORgL4cg7rU7cHwEoHP5Cx7fDQRMpRkz6kK1qOEKoF3ud3memDPZi+yW8rwOTopkds/KE7CDEq5rEo/R09BrpbMHg7GsXDJJXfk8rVq5khLQE1k5ENlU2V15LGJyDncaT3wkit53YUqxpfA2ze5CgSYh6c4XKUmN6/4tb5w2fJzZVDk+dnR0SH1it2SEJc7Uf0+E3jebFWFI/vJeQTjhdyENw8dwC7tgyBKJGZTPd5WF/6YyOfpziySUzf69TzBtbImqBB93kKis4FhdyvU2BT2DEzOF4WOG/IJ2ufjolY3bAb/LrgkfZ6bTJWWCDSN5XzNh5GyAPhvj42GHj0ThGMlGirbWVmNYHrt+ExCKkeydZ7s+rKSnyrupGfiD4oLtGBKOj46Too6OMwzlD+24diTgYzLZ6HrcTI/wzmyVPzx4yBMlY0vYvMp1dmohDQ0QCp4dEMmRxxIfK+Ehj2ih+95776UpU6bQXXfdRU888QR96EMfonQ6Tb/+9a+N52/dupXq6+vpV7/6VenzP//zP5GfV1lMdzCDNDg4KCXLsQOLmqo/m82V4QpTJE9Dz1q4O0i3asvClF9J0NfXZ4xxSSYbKZtdQa2tbW5SCykm0s2oWA9h1d4DT7vuAHSaW/Befn4S5izjNe4CZIoiBJsWWcq9l5y4KeUuzpGREWuSj6AkDDKpG2W0zUoGKNXlx1HOY2Dz3IfCkuCEK4BYExo9k73/N85D4HeFCp+T4v/aRIKugdjkU9CVE3OamoylUzj7vcxw7HHHthZC2UEoPzb/SGEqxxsfiapn6WYPlGKxGBDrJ+ZPXSLhCtXVxMft5DhpQ4mUyvCRy0HqQlYtCS8ldf2kqLY2bShhZXKdFkpb2aoShckz17ZNkdgXVpNg1gSjlE7PLF1rwkjbu6sJxnQsisLcqvi4m4SFq5ai4GMUZsmexEnGyKj4qJYYMmF7SGI6aT0kIFyJ6wPwkchcXmpOU1Op/je7KTNGpiCEqhgfDz98JIoWIgeAdu7cGXqeLFyo/FKoJ5n7PQO/N+Ad0BO2heXFMeGESILYVjY+Ao6Gzy0ti0kI6ypGilAZjtk+cvCRPWYzFI6PfB/GR48HmwgeUs3TovLpCeU807wcS3ysR2U85BEtdC9fvpw++tGPlr4fOnSIjj76aLrpppuM52/dupVSqVTFz6s8e3kwgxQ2oevq0q5bb8Y3YTj+InxBXGNgIB0C0hIIBMeemWsVDmtt8lw2iXSXP/HcE93/TVqsNHRXbAciWYJJoA4Sgk2LLChxk40xi+LebDs3zC2rtVS3V3fFEeXiegnocb/vI1PM4Zo1a4xtZuCPJnTIAB/mztlGfibSlHE/LNN6n/S/9wnLritvaFHK1Jnie8LoSGEqxxsficrrO5NSrAF6Ka8wfKuvSxnc8UaLj6DGxlnkYWQwExEdHzPk4aP8u9ndT2dC06Qm8uJrZaYxCpPnlZzU7+XH802B6ycMX0z4aA5PCcKaTQR0EpSM30ArARspCB9tzJKMkWGKHb+lO4q7uyxo7zPMg6WB9+iFOZlVlOzjjJE8J8PicVVrZDXX+OFMhzs+ElmscwpGRgmlC0o2FcWivh2gtOP4BHcHKFnHdxvm2BC8ZLlmPmTYsF6j42Mm02rEOOH1qfIjji/nzZGDj9vcNqRJVLpR23cm+XlIlccU302JFMeSh2yDJ2jvgx6SIOOebV72YnzwsVwe8ogVuv/yl79QMpmkr371q77j73vf++i8884zXrN161ZKJpN0/PHH07HHHkvnnXce/eQnP4n8zHIBM2jRFgrdNDAw4Bv8YG1/hkyuGp2duUiuMFxnj2vRms/PkCnBQlPTnFKtT/8iMrmPsMsmuQs65fs9gRRNQrLkUh7F/VvNfliOEExkjjPPQyRvkBM32bRYYZuSfN3Q0BC1tbZSKmIyufsBymtACRJaVD5dTmKhz4G2tuWl59s0uF4tSz15hpdchF2QbJnsOanJlaSDdzeJLMd+C1MUS/cGeDE45WTX9eZxcN6AcuutHwlM5UTgI1F5fRekFOsuFCYMH+3PNHl0pAhwLJbSMHxUf99AwjoxOdT7Sf5wkhqmcpg8IsGE9Pb2kuNMVu6dIcHMibUeho9BXgBy2zyvKLbKBL3n/eQx5klL+3KBcwBwfO9cPkbWSMcyFO7FczEB3yRd0M6RYICLFFrWDQlfFurR4OPNIdfWlpHoiijGx/HCR6JoGBmWBOquu+4iB+aM5g48hWEYL5br7Cx5A5qe2Q1hHfwoQMsNMcg6Dzl2+JjN5nzPz2ajez5NJD4SEWWzK8hxaknwUWH4eB0JJcUqErKFrQLOkPu7qTKS4xNKx4OHvBK6wJyDEKTV2u5W1/NxwsdyecgjVuj+5S9/SQDoe9/7nu/46tWrafny5cZrvve979G2bdvo8ccfp927d9O5555L9fX19Itf/MJ4/p///Gd6/vnnS59f/OIXkTtTJmbkOB7ZVBPWK+nlT8LgL81kBwn/QhXXO06jW9tUBp8VtGbNGgsI7KPwerGyC18QGAQzyhs3bvRNdM6ayQkUrrzySmOCC6LyhGAiL6O6KV4pSh0+jmcJyggaVBOcMzWq7jP8O5fzqAXoOutYnxo6B4jsGlwR96Qm6nO0//P5LuroOIM8gLZZavjaNHlZk715K+aLfA8G5Ab3+Hri+EWCl3U1iqZyeHhYC0vIwEv6Il/HzElYOSeZjgSmcjzwkag6GHm44WNvby+tW7eOdIwcIbOlQK0XWz18tDFqUfAxKpNHxFUUUqQzwN66D8NHwRSb+z+45q1D5my6/HsHifjsoPadHDoHmGwYuWTJadTZ6WfWTYmHRPIiFdtM4VoOAWeQKMOoY6Sotau+t7/PGQ9Hi48OhJLZdG2Ucncyxfg4vvhIZMZImd9wAF+tYjkR7I033hg4d3p6emhFNku1jkPrletnptO+ebQim6Xe3l6txJLwylDXsx5m463RscXHtWvXavl/mA5HfCSy1cF2KBwfFxCwjIKE6rD+HD0+Vs5DznA/6txdkc2WlEWqNxzzzuOBj+XykLHQHUAvv/wyNTc30yc/+Unj75/+9KeVSYLInRlE5rgW3b3Rm7x8zAwSNmHKy6a6wzLpg6woa0mtRehl0d1O4Vki3xH4O2uZNkFkIbYt5LBkZaZFEuTurZ7LQv685mZtnIaHh7UkbxkIdxg1u7kpEUnKBRNA1+plAPomROzIFAUwCiWXcw9kE4kZhv4cKo1DX19f5OQfIlyhTpl/DaV4J382X/MmKoTtDhIbid/dtalpDn3mM59R7gHDd4f2SQ2dA7NygmNymLoLBUonElpfZ5Tr2ltbyy73RvT6ZSpVCsNHorHByPHCx3R6Zgg+Bq2lAullHcvJont14O92byR+vnCzjpaMx85gRTkfADU3z9PGaXh4WGKk+ZNx+9Kfuddc8zYlYYeKExkS1uLV5CULCsI1Fnjt+Bj1XdPpJhdvN5DMdLa2evXDTVjmD9dihjdDiUS9VmYsnZ5Jf/u3f0u6krvb7T/xXVYsjxYfa5RrGxzHlyclKsX4KGii8JFID6/bDD2+uiuf9wkXNiPFkoULfdexQDMznaa0a6wwuQCbeZbgMMXOzpybJPEaw3o98vBRF6LN+EhEbv4OFUNS7ruo+NhCHj7WSsdtbXyzpT93EyAUL1HfNZvN0dKlpxlzOJXDQzpI0SlwqD6RoGmT/V4EM9NpuuqqqwjQeWeb63m18bExmayIhzxihe5K3INMdMEFF9CFF15o/K1aWkqZwia1rJ3zF6OXr5ETxZgzoHoass3uNaqrzgIK1rLLDKUAut7eXkM5hzD3RzuwcVZiRxPoRPsXw6G0xfXNlolQPZddmoJcRwC9Tunw8LCWeCQD4T4lu3dFSUTC18r3UrMsym0cga6I8GuJ9VgoLn8RvJFtk/o3Yx0X/xwNc+1NuJvoBvf+a8lv8dlBpmz4d911l9ZnByBixHz9VGb9dZnhKMd6I9ORwFSOBz4SVR8jxxcfmQkaIR0f11Owp4Yc/uGtMZHIpzr4GJQThP8mEiLZmkqma02Je4aHhyMkETPjo55kLkMi6ZHoW2Z4oykDM8q91HsHt2/ZsnbpGWZ8HBgYoLVr14bcazWVh49FAoIFgHS6yR2LDQTcTl6ZIf5sIA8f/f0yVvgo71/lUoyPHo03PhKFjzF7wDCPpMasslfhenglW1UBhHmcDAQ/wsmu5PNq3HNvKc2p1eTFDpvX16233kp+RdORiY9ERCtXriKzu7euEAjHyLXkLymsKuvCcuio/aljZDabC/CC9fBR9JsTODbl8JBN6TQ1JpO0AaDbIcr3yu2y1eCOgo9y9vIoa2e88fE1JXQTiUQYl19+een7oUOH6JhjjrEmwlDplVdeofnz59PHP/7xSOdXutnICQnCXFvWrl1rSVzAyRJU5kQWrNWFOp1EwhbTpB9x72dimtLkJbPwLyav/dtIxKj5gctxGkoWgTBgC0te46C2VFvXVAYoSgmv9mXLAhdZLcQGk8tm6dZbby1tWFz3WXVJz7jXye4qrHW1CfYJ+LMqZpTvtkQOfHzdunVKf/Km4gkSQvBl4A2KozZ99+YfW4Sy2RwJ5jNMI60+j+Ned5DQcuoxsJzgyqQ4SSf8iZLUMZVDC5h5KMKLnWemYzR0JDCVROOPj0SV9d3E4eN1JKwE7WReN4vIzDTZ78lul0H42NmZo76+PsVzSMdHc81aTlTJ7yAUDiZ8jFKiJpvNuUk6g3AjQdlszoePol0p8iszPKWtjI/hiewSyr0yyvdg69m8efOV7PA2fIzC7BepPHyM4tWgzlO5bYtJVX6zoDBW+MhrqFKK8VHQeOEjkRkjVX6Dx7inp0cTLrphrqEd5Fa7AYJPWQQzf7IJLJyrAmAUfNxDAnePDHwsFLrpvvvuK7m1i/5npW1wDDWRnJ/GhiGTyLOC5w33Dc4zsW7dOgkft1vukaJoPCT/b1Y8V8JDcuimqtxZDN16nU4kqCuftxre0nV1JI+rjJE2fJSTDl555ZXjho+vOaH73nvvpZqaGrr77rvppz/9KV166aWUTqdLZRz+5m/+xpfZee3atdTf30/79++nH/zgB3ThhRfS1KlT6Yknnoj0vHIB0+Reks0ySMiTWs9y67lE8kQdIaH9VxmdlLuA5Mm8j3QgNE1603kseGfIBHR+ZpctRN71bFHo6+ujwcFB7f07O3PaArAvxmtK19mSVNhKfMmu4RnYSxIsBegG+LW7YaUK1PaEJTKRf7OVEMsgvBxHVz4vudPaQLGDdEG3kbzkJHL/9mnXcz8KD4EoTGrCnUdEgBq7xHPWP78cJ13a4FTFSQ0EE2DLSM/zz8Q88P+c8KVSOlKYyvHGR6Ly+u61gY9Jbf4KfJSTa3kYWU18JCJFyakLgSzw2RRNQfjo956xlbWqJ+AG8nsHhDFmevK0sPP9Xgqmc1kQl9tXR4mE3y1RxEkHPWuP5V4yRlaCj2Hvd5B0fCQSSY50xU4+3xXj4xjT4Y6PRObSqDklCe0whLAin9PuJo6VvefY/Va1auf9k7YkgLQr9zQbFRwyh55Vjo/sHdPb26vFEHPCS6aJxccUCV7rClLLc82YYUsktt3YnnB3ef4tCEt5LOx5JvzeSbZntVA0fFxtvL4yHhK0zP0r507qgs4Hc9b9oBJgtqpGNnxMK98rtXITHeFCNxHRbbfdRscffzxNmTKFli9fTo899ljpt1wuRxdddFHp+1VXXVU6d86cOdTd3U0//OEPIz+r8pJhHiAlk42GpEDMBPjjI/wuHFFT9pPhfrKlQK7RxxlU7e5tgKcB9JhkdXGvJ8eppdNPP0NjIvW4SwGcDz/8cIQMvXtK15SreeouFKjWEQt+H/RNqVb5zotwg/RbUAmO/v5+XzmFsIQPfC9bCbF98DNGnHxNBQ8vC7tNUdFLwWU45P71kkqprlXe5riBTBppz6qVImERVJ/ZTcCAdA99fvGYFotF1+0sWpz+nKYmrW8aIJgHW/30cuhIYSqJxhcficrruyMFH1WMFIxN5fiYzeaot7eXBgYGImCkaHu53h2FQrebWI6xRGWATcpY7gv5OhMG+fGRSE5EpGJIs3IvmyJWV5Q4To07D0zzwta2PvddM+R/P7nUZbn42EfCqyGtvJ+oDSyYStPzZE8vPQRHxscwxW6Mj5XR4YyPRPbSqHOamkpWvjz0zOSNySQ5CFf2q7W4+ZgDz+K4W7mWLYSrS3M5zEU8Gj5mszmjIraj4wxauvQ037HoPORY4SMImKl8dwh4wG3LVAuOCRy68cYbffjot4ybwpn4XmGGKkdpj3/fTCYbaeHCJSH36LXglR8fvQoUo+Uh06R63Rbg0IA070zu5TI+9vX10X333Rc8x93zTfiYgpcTaLQYecQL3eNJ5XRmmHY/m/Vr8OygwVlPw1zYrnH/t5UiMblKTg6855o1ayxZDTeTmtjBX1JAdeur8R0TQJB0f8u4i+8W9x3Xk6dVE32Qy2bLGifue9VtuwjQWvg3FNl13HEX9i3KderiVTN6dhcKdFZnp1EjN0m5l7z5ye4tfO9/+Id/oHnNzYHPD54v/SSA+R4CRIypHveUJtWCx5YVpuHhYbdOcYpEHcoOZe7Ibly8WaiupmyN3E1+oUXML9ka1qZo5vmjZqQPTaJnANly6UhiKsebovbdkYCPLS2LqLe3V8NIW83w8vDRIcCRMDLlvsMe8jMsgikrZ657fW9y2+4JWM+Ou46Dy1151mZvbzhw4ICWTEy0f4ZyL3leyAoQce/bbruNmpvnRZgX5kzBHj4KhW5z8zyNeSwfH7eTSGqkKiq6SWCnyRpYHj5GrdgR4+PhTZXwkNaSSVKS16Dwue0QtbaD5s818HsBqvfsdo8v1ua4Q56n2+jwkV2+q8dDjhU+ggQ/1GhoU7t7ng0j79BwghUSIpGaKZxpknQvdd+U85eAtm7dShs3bpQwMop3kQkjxX5qwkevFrr/PVSMfPrpp13MD+MheX+TlANIUat7f5sruOr9GoWHjIKPvu9jnIg3FrpDqJzOjFKWoFgsRkjscrL7N9ylz/9R79epTWyPuTPf05QF3H9ukdgF/M4776RgZkfN9Mvn7ifdBbmRgM8SkKKZTbMD3TzkWCe17w/CixFhCzRrZ1dD1+4CoF3uebYyX9MmTzZqnltOPZUWzJ/vew8HoNOWLKEzOzp8sSeLYHaZSUC4x8xw4/Zs4NHa2m4QpBsM/ejQI488YsigWUNCcbKHgNXkOCktgYi4Zgrpm4DqxrU7ZF4mlOuZEYWi5dU3+iF4ie78LksBwAo7KEelmKmsnKL23ZGGj0QmjGTrpVy3vhx8hLtWurR17b1vjTFRkNouGSP9fa+6Ta52f1ttadMu9zwWJP3ulpMnTzN6Lyxb1k5XXXWV5HYpPun0TFq4cImCZ7ZY+oRUkYPbaZ4XwkolM+HjhY97yC9Ajx4fibyyQyqzyArirVu3+sY2xsfDkyrhIYOECLV0l3rOUvdvlJA5/pzs/pXvOQJQyqg8SlFQ0kGZwvCxujwkK82qjY+3SM8IaqcNI2tIzVAuyqmJnBnz56tJFh1asuQ06ug4U4nH5n7X37mpaY6l2o2Hj62tba4wrSpy/f3Y0XGmqwzwjvmrItkxUng0cI4oGw8ZHnqUUeYnfzfLJ8E8ZBR89H2vACNjobuKVB1Lt1i0nEghPN6tSF6m8QypLhrs0jE4OChlWZTvN0Rh2VVtiVxksjPJ+8is4WdXFGaO5fg4PnY1ecmGdGZXjW+UyRTrZMomzpk3+RwH/gXc7Z4jL7rtlnMb3AQN8qIeNoDCwlNPpY0bN5ZAQY09MbmOpwBqcf/ypmfbIB9++GGa2TRb6fNJZNoQOamd7KIYJkTIAOYJDBdbrg1LkjFdaZNwuZQBmudWHkJBcgf0MhHdhQINDAzQopaWwL6JLTkTS9WydHPCrmrhY7FYpN7eXiXLdWX4CKSMjJwZI4dJL/cSFR9BdouKwFxT8h8mk6tmodCtJLAMcyvntsrt3G48t66uwdCPejx+fX0DXXXVVaU9UE9qZGPuOdZwQeiYiVraKkNar93ztYCPPLfYQ2s7zCWcVrjJQGN8PHypGpZuVrR4CbuCxzsHL4mamtuG3WgZH03W870AtYQqNb1QDDlZqkzV5SF3u8duJj05oYeR44ePaju5X05Tzjf1oW79PvXUhRoP6Y/HtmEkJ2Y82fIs0S41pEncjwVkf7hOZ2euJLD290er9+3VJmd83EBmzA5Wvtt4ZbUMWBQeMio++r7Hlu6JpcpjureTqQ4sA0Kh0O26yajxDjllotoTTzAVi0VqbW03uvDYN/425TxdO2hnfjOGxd9IXtIFVUupM2FRLUky2WKdOA5DzWy4FqCkYQE3QgjevMjWQ8RHTYFe1ou1xbKWzFRKI51IaPEg3H8Xw7+hqYueXeJNlvYGt3wav9sGCLexj4X0I881thIHWReJ5M0R5HfbtSVbCnq2WRssJ0Phvtns9qcKtJvg9www9g0EyMqMhMkLIgrFTGXlVFlMtz3xC4etVAsficitFctCeiX4OMUodJsxUs7mX118ZOtmeP/6rSqFQreh79eTcKe3YbnMXDeQsPKq1iVT2RozQ9zUNMfSdzbh1R+j6TGK/nnhzwkQzSI3tvgYlvk4HB/l/skoGGiqoRzj4+FLlcZ02xQt3YUCdeXzlE4k/MK0u5c+CI/fUA0QgDmpXrFYpPbWVko7jvs8h4RyKAgj5U9zKVmqTNXhIfVSV5XykNXFRxWjUgTMsrRV7cO8dt9Ewt9//vjoMCUxY6RuaXechgB8dKz3zGbFPhrFQ42IJA81NezL5jlg512j5LEI4iGj4GMKXkz3aDEyFrqrSOUCpl+Dr2umeIELzZqqQVMZGnmSF0uT2eT+4GnF+Hm7QyZ2UVqou0gGLHnS6UAUtmDWS/E4fI0MsMFxmAsXLjJqKUO1u8WiMbNh0DVcOowXZRN0YbpWuUdochKDy/vN0AV3gmdp59+/BH2DzXV2lrSw8jP7YJoj8oa42nVhCk46EmzJMQs1Xj14VShiEA8GZyZmLtR4+mF4WVc3wdNeql4I8veufJ668n7rYjnZKGOmsnIqp+9MFk6BF36M7Og4kyYeH71QGrkMjbop+zEy7L6jw0fAbukO8xAwZU4PbqucndchoIl0YVpVwIXXXGfymLmbQ96Z3cq3kXAB9ytpstlchLGcCHz0M78CL6PjI5GHkdcqGNkFYcmM8fHwp0p4SOZjbMlVz+zo0MZ7sTsv+LvMbxThxXj39vZanzunqUlKOBtctk/wjYyPXg4GNaHi6HnIPPmVmMFhJmE8ZPXwkQVtxshZBKwkP0aa+jC4HWaX9y0U9M4ehn6JVAWuSGgZ9B7mMmCOU2vIPm9vr27p5vNNGJkkHR+9BHJheSyYbDwk4yML4IsM+NikfB8NRsZCdxWp0s0mzCXDX7uwjYRGjYElmHEzaWCCLS7qxM6TZzlijZ1YaH53dZEoQY3vCGMKzddEY8oSiXpNW0rkAdBueMkVHgToavf+ch1SdosJi31atFC489x4442ldpqSnclasrDkJKayOWGW7uPcv+zmwpbv9mXLaHh42JgsgoV/O5DmiOMEw2pfMokNp8adIywIcPzPAhKa7w3uPXTm19t8os1bVUnC77dC6i/Vq4AzzS9wz2GX5DAviDCKmcrKqZK+i+LaOzH4mCK5dKKHkR5DJ895ThimM2tjg4/ABuPaJTKV0nmQgLXECRYZI1V8DGJeVXwU7VKTnckCZrDSYO3atYYxCbN0M7YM+o63tCyigYEBac+y9bleBqw6+NhIXgz5tSTCfWrd99hHuvcCJxWKPm9NGCkrfm34mIO3j+zatSvGxwmk0fKQQTHZeyB4lRyEZ1gD9GSy6nW2JLW8Hr0M5TZPjmAeMho+louR0YXWMB7Sw0cOM7q5hE3l4CMAamtbTh/60Ifc77aKGMxLcR8Gh5+oCRLF/cL2yuPcv6xgFG1Ztqw91JvHVgZM7B2wGN/MGOkptlV8ZK+BGSR4SBM+egJvFKMWUTg+koKRe9z5nYJn4KoGDxkL3VWkSgEzzCVj3bp10iKS40kYVByylRIwbc7m542QHlsoW49WkCgV4GkpgTqStXRc1sEeH+lfqLIlY3BwUMqoeJA8NyGTFYDB28yA7N27V2l7Unkv8V3WTIVZx2V3Po4bVmNC+HuzcjwMFNgalstmKeU4geXFaiEsu3wvFuB7e3upu1CglFsGTY0rrzVqCxvJy+DZUbqPKY5J1eB52TQTpFsZHRKCtgmce8gD+eVuCZ9gcJb76K677iq935D0zN0hfT1v7lxfDHBUoDZRzFRWTmOFkRODj/XKd8ZIZqhYqPLjIxHRwMAAtbQsVtpqx8disUhr1qxR2jVMXu1xEz6ahTRRYzYntduMj/Laj2L5YfIEW7W/+Huzcjz4/XntZ7M5N7mPfTwFk5YmlblnXBPXBzGkcuwp42MjAUt994mOj4yNah+bMFL1JnPKxsdisegTwLZExMfpiQTlstkYHyeYRouPNgW/PKYDyvccdHfaRpgTUqnPu7l0f07oaOIFgnhIVszvIMdJUWtrGxWLRVdB1i5dG46RGzduJB0fy+chdXw0vVPCt/bD8PH+++8v3d9fbkzFyE7D8+z3lXlIL/nZagrGyFoS+4Z/D+3t7Y1QYq3WcM8aYnzs6+szeKiZMfLAgQNSHHol+CiMTbWOQ+uhu37LFIaPJgGcP4x97cuWle41GoyMhe4qUqWAGZ4MSM4IqGq+mEE0Mziqi4UAlLCFdZy7uNYTsJBEfJ58b64tuoOAvSSSM3i/d3bmpPI3zLQEMw5DQ0OKNYu1pptJWE7l5/uT98jaPmbOPDdULtvij0+pRVJbnN2FAqWhx3JMgnAnGR4ephVuIhEWjNVapw5EhvP1EDHerFGW78kx3aZkb3OamkrXycdrINxgbAkdZCBRM7IvLiXBsPUjj3uixDyzBjcMQET8qxqTmnLnn0k4uqYUV2na0FRwNiUzmdk0m9KOU8o6CXga9yCmAwCdtnRp4HlRslHGTGXlNJYYKXDp8lHhIxEr7YKedTF58XsLSGjk1bWVdLFrL3mln6qJj+Su3zQF10z1MFLGRy9mz4yP4vgOX1vEOkyTn+lKETCpFMO+d+9eOumkk8lcVoyZvl3EDFR9fQOZEtE1Nc0xrn2BGwkyZy/vUsbJ78aq7y3qe6hKFBUfvRwAUfGxWCzSwoVLKsLIhoZZFFZ2x5bsqSufp8Zkkj4Y4+NrikaLj0GW7hqIEAPVA68X5gSy+yxjrgulIH+i1j0kEnWF8ZCL3GbqPKQ/A3aeVAHSVPPZjI/l85Ce8MrPVvGRBc2Mho96bhGhJOTEcXv37pVqigdh5CUEgNra2snzmPHuyzHdpvXvKQVsGKmG0qgYqe9J4j0mU1A1BRkfiaLzkG1ty7WwsfJ4SPm9/cY0W0JlFR93wAvDDMLIrnx+1BgZC91VpNFsNuZkQGkSFsgdhklmcjEsklq39Morr/RpAz3XD9PCaiDgFALuJ4+RYy2hKalCp/F3oZ13yItj00vaMPNgBg0Gi03kj+8wl6kZGBgw3CNDwH1KX/mv49g3toDeeuut2uaTgRC8HYCalPrbtg2uR1qApy1Zot2zK58vubqoLirpRIISlk1QzqK+DX6tnqzpNiVEEX2gukypYDatpGmWyZYs4r77gvvXX/bhmlJb/BvqQQI2+KyAMtmSmcjZ2XmMgsZkj/tbXch5aoKiaq/z1ztV2nfDw8OSO5rJfZHrtbKQUhk+EqmuwSoeLyfhSncl+a2YJoxspGD8HA0+Xk9mjDQn3spmVSY5HB9li0J/f7+b2VW1wmRK76M/w3bfHmLMufPOOzXmqalpTsnN1FyT1+RdkyM9S/A1JabY771gihs8kYBW5ZifMQcuNuKUDR95TwnHSH8tXR0jg0uTmfAxn+/yxfrWx/j4mqDR4OOcpibNYp0GqAMebyHzFWrumQ3wQuXkMVcTMprmnOcmzGEqUXjI08izjpt+z7jTTy/5xRhRPj6Ww0OGGadQWrN2fMyTEPpTlE7PVH4LurdwY+/t7VU8ZsSH9wf7WJgwUsWzbSQrMDyM3Ec6PiYIeINyLEdR8JHIjJHR8TEKD+llUpfJ5gbelc/78LEB0GK9+cPrYIO7hiaPEiNjobuKNFqh2x736mmTdu3aZajDzIyPrmGTJ+nDDz8sTXBbUhf+f1YEYJge8jsnXeh227PaPWbKAmliSvPkJZqwazvN92gkQHZX1zVmHOPd3upnthZAaH/VzWequ9DCYrV74Hc1KRaL1NPTU4oHIQrXTC8AfG1ioVutm2kqg8b3KgL0jtK5B8ke36SXpCgUumn//v1GK8rDDz+sxPOb+xf4KKkWxrBEHXL/hMXx3nXXXTSvuZlqIQRvW1bebqV/HeilUVIQloBqx+TE5KdK+0645KVJtYiIedtFskWypWVxRfh44MAByaK+mcwJXWzfgxiz6uKjX7kQjpHmbLTh+CiY530aNgirEbuJeu+TSEx37x2W4K1HW++7du2itWvXllEqcxMBRynt0i3Twe6fcvI7U+m1RveeOj52duas+Lh//37JmywMI0/2nVdf30A7d+4MfHc9GZH5vP7+furp6aHmk06imhgfXxNUad91FwpSNnHvw0J2N4SVGxBxqbnOTl/1loxhbqSk6+c0NSn4qM65DaTjYxQeckbI70XyLNYbXGzZUFV8tPOQ7AJuW7sg4HYNG4SAegXZ64YfpLAEb5y7Qs49ZOMh7X13vNIuHR+DMZKV1OvJbJUPxkeb0jif7zKUJKs+D8n4GOQG3t/fTyefeGJJkDZhJGf6l6/NGTCyISJGxkJ3Fak6rpOLyZ+Eiie3mHi2mAnd1aKGBOPoCbJ1dWnDBC+ScKuT4w9lwTdoQZwf8vtqilLcPtpvupKgtbU9grXV/tu1EC7gtY5DG6CXCeMLWJi+2P0eFvuxHuFJFYJisIxZSOG3ure1tmoaNbUM2naAahMJpQ9MCaFqSK2/aGbWN5GuGArq+4Q7j9YTb5aeF4R5zphiJvVzTYKA+K56COQhlBV88W73+InKeQ5Al8MD47FY5zFV1ncePq4moV2vJ1lAVROYmeZQFHxsappjSCTDjAe7QZeLkWONjypG+oW98Gy0Qb/tIq/GuYnZMjGKFOGd1ltjk5nCY/hN1rFGYgVLfX2Dho+2BD8IrTE8mUz1aU3KDMdJ0+TJ05TxCbo3u+B6woQXyxoVI2N8PFJoNPi4GkLRvhig6YDG03DOGeYhTaFt8vcMhIu5XPvYnmjLFqYyWh4ySgLNcvDRvy+0tCwOsLaGZU4HifwUpvfOW96HEzUGK8xMXi0qBWNkMD4CDrW2tkXCyPA8GGZ8NCszKuEha0ne76PwkKaku/xhfl41tjnKX/6wwUu+9lJ3ncnntcNTbAVhZCx0V5EqAUw543R4CRn/YKoxE8ViUcoeGxWkghd/8G87Qn6vpbDsi/7f2M1uj+E3vf6i313H5jKdJpOVawYcmqRoZgtwfNZk1dK9ByIpmVznz6YZDisfYLN0h7m4dEDUGjQJ9aaNdF5zMwEOJUt9sI/0+KagMZTdVVlgl4G8xti/npY1ozwnY7iv97xsluNNN5E9gy9vHP6N7kQ4VASoH6ArlH7kcVNBNgNRo53Hl5mSaq/zmASV23fDw8NKQh2eQyPa3JFLdRFVho+LFpmSm40GI8cSH0+Sftfxcdq0Oun7bvJc68PxUZT8OjWk/UXl+x4KTl7kMeG2UmZM0WL4bb8tMNYBNimrW1vbaf58xsOgMYiCjzZssmFkguz4aH9mNptzvT5s42PGx/lwqA+gu2J8PGypEnw0CQ+b/RPC5yEXxEPeeeedBAiB3XR99fExjIe8OMLajIKPZh7Sr5wy8ZAmHOOYbsbXMHyUjw1IbbBjZBg+EgVhZJiyoIOAzUbFpwkjvSocleCj+ls5POQC0pWHcgy5mYfMZbOUdhw6VcE4/pS8eRS38xREEuR+gG50r5XXwTD0Mr0OQCfwukK0uO5Y6K4iVbLZyBmnw1xOWlvbQu/nFZ1X77HbPZ4gv6XzHYHPnDp1hhUYxIIxJ6WZMcNjsMIXpRp3I4MhLyx+jqpRrCV9UaouNCa3ULbCStZdpKgAR4ubTrmLS05Sthl69vJcZyf19vZqcSu2ZBImy/R0917WJA0ScPAGqj7jvvvuc4Vtr22LFfCaDoeER0XYprbN/W4D+E1ktqp82v2rjlkjiay8/iyYog5uTnoGj3dGmV/BG8rJCiCm3L7Kw5z4rsb9dCt9Wu11HpOgcvvOHjqiW1q5VmgQ2fFRZiAWKHMuGCOnTJlGZozMkBkfWVM/Wnz8uGG9qPh4EoUlv7HjI4cP2frqdrLvB5tJdQtsa1teFj6arC7iWSeHtKuv1H82fPQqZcifIGtLGD5WgpF2fGxtbTNa5bNZxkjGxRgfjyQqt+9MMaspdzzlCcD8Q1tra+D9GB+DE+6pc64SfJTz/tgSG8q4NFp8tPGQafKEZxsPaVu7Yfh4Odn3gx1kKoXV2tquWZ/Lx8iwdrG13YyRe/fulSprjBYf5XaUi4/zyGytF/tKEA+ZgecdqoXKGCr8EPyKKRUf5RAME0bOUe4RW7rHicoFTNnaWYBDTkmANE/uKElM9Dgvk3avMfKCuv/++6mzM0f6glhEArA2kZ4ZOEnCBXQHCa0ju+CooNtBgoGS6+rKDCMvxDCLi7wZpMnT9HeQZ93ZRcDVBICuuOKKwPuth2Hx1dWVBGJZ+1UESlm01bITpqyJsvZyZGSEuvL+vku4f61xKPBvgqqWe05Tk+aevsD9vh5CkbAeIsHKzEhMPys9wtw9LyGhAOEMlycH3tcrJyc+hUK35Lp2s3StKfdAUDu8zyL4s8AHgexajE2dxZj8VE7fhVs7/ZbWtrb2UOuAOQ52mHSLY4NhzpWDkWmy42MNiWy+YfiYJ4F9k8jDUxM+Tg7pJ/7kSQjDbKkx46PnDbA+5L4szDvkF9JlC4QXN93T01MaB1vWbRUf9RrlDgHfiDAvBB6oXhIizMAhPzO33h0PdQxYqFXfyYSPUTByOglcnEFhsaz333+/sX88V9gYH49EqgQfw3gF+VgYD2mKg9UtfPtIDWMpHx8zJHJx2DByEgmssuEjC7EdFI6PNcR1pIMxkvGR132KRCmsovvpIY83KQcfQYIn5rATFUvEfdatW+cbiygYuX//fkPoVFi4jN/byY+RrFCoFj7K7SiHhwzzItCTg/p5SMGnm5IKL5g/n4DwSg6LAJqiHAvCyChhpeWu8VjoDqFyNxs143TBN+F1y3IUl+W+vj7X7ZrvkadggXaJO8H1ZzY1zSndW3ZDEkBQT3pSI5DISMmLhd19BklnalWgyJDZZXQDhWvutpE/ZoWBx1wruqenJ/B+tfDcxG+GiNNQF58pzkMuO9He2mrMmsgLUq3NvRqeG18G5kQ2M6VnmlxkMm7bGRxkl0FbnIpwGTTXg/ViFreT5ymxnvxuqjxO/cTMdSrVGDpmbPHiOWUuQ5KX5kTRHefgDUWNY5OZhTCQDVtfla7zmDwqp+/C43q3kYyPJqZEpaGhITe3hawlZwbLhJGLCXgvwVjn3oyR2WyOBD6qmCczNoxNQySSkqkhH6tIz2rOFpjR4GO3dO0NxjXkx0ebpZ7f7Z0kmHB1L5DberB0Xx4DLxmo5GlkKAPU2tpGiQTH9LF1Psg6Noc8K5WjPCND8Cm2VYW0am3pdt8NbhuC8PEgeVZmc4Zk4FoCQMnklNAx6+3t9c0pc3ZlEz6qjG6Mj68lqgQfbeN2DfTQtyg8ZLqujmqhW/g8Y4SKa8eTjW+14aNIdraJRAUc+V68v4fh4yKKjo8p6b42jFxNOj7aecjo+AgSXn/l4WNfXx91duYCMdJf3mwDCc+j46QxMuHjTKWPZIzMk1c2cfT46I/pLoeHDPeyqqtLa6UbTTwkh4wW4SVAXtzSQswjyzdXKznI+HjllVdStTAyFrqrSKOxdPMgDgJ0ojK5F8ExxvHy4hwYGNCsqv5kLkFWTAFIicRk3/WczddEfguEuhBZa6ZqLuXvQZliVTDso/AYFXXxglpaFhnd8wqF7lALWofU7iJEpsI09MRmakZDdRxsizrn1vrmz2KIGtyAV95FZoYAsemlINzGTC4ynNxNrsfKrvDc7g0AzXDfh69hK72qiS4Uuktle7zjqhtqhgRIm+ol3hDYx21t7b45ZXYjbpDmxBABH5Seq28o8+FYtZBB4wFAKxtVzXUek0fVtXTzp5nE5m8W3OxCi4xdQXHX4jw1QZYNI4PxcYn7dx+Zy7KAhHXH5AopY+Ro8FFYa0zueTo+miypOWJmC9jofrdltvWeq4+Bub3mseoif3khk3VsMbGnk578R07GpzLMcrvrCGgjr5SXUCRms1HwUR3rvNvONOkYGey91dKy2Oc5FQ0fr5HaoSbLFO68JutnjI+HD1XT0i1/lsKcCyaIhzSVF2uylkdMjBM+TpLWVlR8NFldTRjpx0d7iIdcSiwMH0GizGQl+GhvbzabU85jfOQ+NSl9U+QleVMTpFUfH0dGRgwx4lF5yGB8TCTqtfJgxvKJbsgowcuXBNgNWznLOuJKOtXAyFjoriJVGtOtxvU2uoNfcltWBlgFSGO262SS5s2dKy0gk3bPAyTHSdGyZe2+si0mkmM/RGZcFYAb3IWlggzH8EQBP/X7QQIcSiTq3UW+hzz3FpOg7oG+CQiIuASRnzFJIEUnwClpdBsAWoFghmQ9vMyeXfk8NSaTPsFXvoa1YrWO46/NDeHmtwOgNuhW9ckQQrm8wDn5Hj+jzz2+W2pXULs7lGd0Fwo0ODhojB0qFovU2tpOjqMCcIqEy5TpeJ7E5pMiP/PXSLwZtLQspsHBwQjCVYevreYN2aEvWfobEPH26USiohIP1VjnMQmqPKZbnUM58oQM3aqoMzAsrHnz1HEa6A1vOIaCMZKfITJLh2FkNHx0yFMcmazrYW6LRaoGPqp1Y1V89Pf7ehIKiJzSlnRIW9eX3suzDIfnLtEFzDR5GecblbV/Mglm0LNStbSoyX/Ya2K31K7omFModIfio7305RzDWDeSsDrZ8VH0RXtoCTEdH1ko0b/3IcbHw5kqjek2lTjyhb4p+38UHrLBceiYN4jazAfhKfZt87BcHrKtbTklEjY+0YyP9fVy2E85+Cj4DaFoXK+sN1VQd3mkjjO18BbGyOj4GBb6Y8LHHRRWdlG8hwkfd5AQiqco638yCaHcU0B4ZV8P0ljiI1ElPGQjebip4qOM89F4SLUE7z6YE0f2WvCxWCyKsnxVwMhY6K4iVbLZmDJO5yFcmm2lq2TXZRaygrWdQeDkZy5tCQBM8SXB977BeFxnhlSGkF1GZTDU6wB6ceUml3RRWsDGJA8PD7tgarqn+D8JR5QTc78HuZVkAKp3S3OtB2hLyJjYMoO2K33bBuH5ILu9AKCNGzfSCtdarlq6OdlbWLunQ1fSMHA8+OCDEWvmhlnY/sHQx55LknAhchTwV+eELCztlp43RCLGqod4g7D1ay6bNcbPOwB15fORXCZHu85jElRu35myqXoWRFbumAQ3WQiS541pngb9xplw/cm5VCofH+2/iXrXQRh5DY0lPhLZYgXTBHyHPFfFegp3befxkmMqR5PtWM1knyN/0iMxXt4+o1q62TU9rAbvdApy69yyZUupZu7oMq0H4eNqCi+7pCqTZGa5SJ4bpzt3Y3w8rKkSfFT5xwyEUKHWGCZUzkPugKfYD1NQlsdD2taF3bU7nIeMio+83lSXdA8fC4Vu6u/v1zAyGj5OIi+Ouxx8HKLwUmm2GGoVH9tIhHf68XHjxo2KxX7s8JGPVcZDzlfeh8dLbkcYD+nxuzzX17vzeZf7l41UNnzktVYNjIyF7irSaDabwcFBqXSYB54jhkkAeAJGL4KFK4/5NGmLdObSluped90ItlaIBBT68UsvvTRkkfEnQ4L52E5AjRsDpGrIapT3kt3t9ORIupZyh/veq12QySj396zeXANTHYtd7vePwkuCxosxDb/7SoPjkBMwVrUSMKyG0Dx3S7+z1npRSwulHKeUIE2NvdoEz5JdruCvWr+amuaUyojoY23Txu4j/ya3gcwxPIJBFPFAUQF5Fel1HmuosWGmpvFPATQznfYBYrFYpKuuuorOO+882rp167iv89c7Vdp3g4OD0sYqM04qY3aL9PsOFweCN+RgjNQVlCaMLB8f7RlfvczaQRjpx0e1Tupo8NH/PhuIa0jryYkS5Al5tjjmXe53tqQslcYu5Wuf4zTQwoXsXmrrN7bw7CZPKO2Wfv8geZiSIb+bdYY813RbOcJgpvb0088glYFfupT37SDG2oSRCelZJnyULXblMKz8nvLYp2kSdHxsqK/3JdeK8XFiqdK+KxaL1Nvbq4WuhfGQwxDK/SAekgV0zz03WEEZjYcMtuQKYcqsUP3EJz4R0g4TPqaoPHwkMgnrOg9pw8cMCaUku0mXi4+MkWlSMTKoRnU4PgrFSEvLIte9fIGEFXm3DyrHx7a2dq3P8vmugPruUXlIW/+F8ZDiPOZ3hyEyjatrpB7CYh2Gj0RE/f399Dd/8zf0vve9r6ywm0rWeCx0h1A1NhtODJDLZn1CxB3wZxoFhEB2BoKFq8HBQYN2UQYkP3Np0lKatVRRtPumRWJmuMT3FhJJM3JKe8Oew588eVpLPR5FL0tlup+f8VHdsNkLoQGeBnkYQqMsn2fKfOhACOZcQ3MIfi3bDTC7vHCCNXX8W7T3B01Trk2hHMHfHK/lCeI2pk8FxIx0nw4SG4HJpUveCBxN8BEuVCB/jWHOeOq1MZFI04oVK60xaZzc4umnn6Y5TU2+c+Y0NVlzF4zlOn+90mj7Tk3o6M2ZfaTHsOXd+ZIKXPNmjMyTF497hjv//HXAmSrHR3vG13R6phb+4pUAGzt8jJLzQjCKNtdm7rcUeUzzsKHNnaQnF3Kkv5vJS8QpC583kLk80HWkW7POJK+KAn+mKdf596FwpnYyCTzbQH4G3gkZAxtGnkp2fJSfe7KhrSZ8NDGsoKbGWUYrTYmXKBToBz/4QYyPE0zjzUPOAagO4Tykt7cyj6AqKFdQkKVbx5QwjGFlnpmHbGqa47oqjxU+kvvdzw9F4yFZkDZZ2KPiY56Az5FuYHDI8wIoBx/Zkq7eb67yPalcZ+LTg/BxEqn4mEikA/osjIdcT0IRUU/6nIvCQwo83wZhNFPzHHGJvdmNjaH4uH///tjSfbhRNTcb1W3IASidSGjJvBzYkwIsWbiwdL9cNkszEgk6RavZKoOAQ7lsVqulas8ivNSwKBvJiycz1Sk86AKGCUz2SfdWs7GaF3lPT49URiUqoNvv59UwFN/Z+iwvUAdC4GUNcg6GmHoIS3cthNV6j3T9JOgxJkkIjbNaKzUFEdOdNIw/twUQrulnus+9DqCrAfoY9E22oV5kp1Q32MtL55j7rK2t3dWMXkNCo32HYQzzBFzv/h9Ue51dhPwacrW8z+mnv5H0TcvexmKxSGecfjpNUs6vgQDGOU1N2jilIBjLiVrnrzeqNkZ6wjIzCKb4QHaZ05m01lYvoV9HxxnkJerhj/97Oj2zaviYSMxQfuMyYTvIcdIG90UZI9l1+GbLsyvHx7DqDuK5B8mzxKgWJIf8XghBSeEWkLBqyUJsnWEcGGuWGu6Vcs9Xx5/bAhLJ9s50r72ORHm0j5GKYV7MqNo3Jgbaj2MCH28hsW+tJ69snA0jM4bxBXku8/Ke9U3t+WZ87CbO8q65ThaLNDAwQOk6f13iDEBpx6FpkyfH+DjBNN48ZModfxZGVB6yXarr3dmZo0SC+Th5zk31fTfVmjZjZDfpCf9MwtR442O1eEhdaC8fHzMkhNjVxG7vdnxMkFCCmqoVTSYgafEYdUgYvFrd525w++295MWk84djxcvHx2w2VyYPacPH6Dxkfb2KvwEl9opFKhaLlFmyhCYr19RACObq+oljuieYxmKzKRaLoZnzHoDZQvqJT3yC+vr6qL+/33f9IECtivCdczOkp6BbCM2Wj2ESxevtm76/TmGGTPGFPT09Us3m8mPivBJmNeQBeJjrUtCzmOHxu6WofZ5yHNqO8Hgo0/WmpCXc72FxVbZn1MNjoFSGCgD1wGO+HOgbrKfVM/fZsceeoIxzkoJrY+bJzBwfTzYX3v7+furp6aGenh4aGBhwN1V50wp22d2yZQs50BUXDQivfx5n5x0fGou+Y3wLdns0ZZl1aOPGjYa4XGbYchRUbpFdDe2WYZP11cPHdLrJ8Jtf2Nq1a5dUNsv0jOCYuErwkfcbe38Ohj43m+VyN2Exe6bf2OXRhi3lxgVuIC9GUWXk+LuXG0I8R1XQmFxU/QJCTc0M5d7TKFgpoeIjK4lkQcOPkevWrQvAR75HTSljL8Ff0jKXzRoVxCfH+HhY0ETxkIMw85BmfJSFWvtalcNVzBi5n7wSVTZhKhwf+/r6AnjISvDxIHkJNCvhIQcpTGjPZscTH8P5aC83h81Y8g/kWdRZwV0ePp566kLlvkE8ZBA+Xkw2fOzp6aFdu3ZRb28vNaQbfc/jqlC2MIq+vj4aGhoy8pDMXwfx57ZcBqNZ47HQHULVAkw5Ay5ReD3G1e73IoRmey30uswORHINvpYTb50PLy5ZnkQb4E+upWdrzLgLZAEJhoa1V7wQBDPD9e3CGbwMmesKihqQ4eW/NhvAIgzwVA1rRvorFr+tz9X4e9t526RjwwiPtd6jjNG2CM/og4j3ZmHa5A3RJ50P6MJ5cJ8xyKrgmDH2bzjA8ydPwCaj1toTSKJrn2+88cZS37LrfhF+YLT14dq1a8d9nb8eqRp9Z8NHO4O0WjpWJOCjBEXp6GnI+R5yCRWTi+MGX/IYc5b1GhKWATs+rlu3zr3fHmPb/QKwyVrfQIJx8TNBo8FHZkSFVUR9Vg35612b+7y3t1dx2Q9iXvm3YRJuqmHYIY9RuDVLnCPcD80WJYc8670qjMufoLjCSjAyqsvrZ0m1CIla8/Z7DEoHGf9k5XuMj4cnTRQPyVnti9Dz0/j5HXmNBWOk46R85Rvz+S4FUxhHEqSHWAgBKwwfWVAK5iFnUGX4GMxD2vExT2FC+/jgI49RuMco8A7y8MZkLGlXzleVyYcbPvK+KIUGuDx9kKVbVk7JGMn5lIISKttyGYxmjaOSxf96otEC5vDwsBaX2l0o0MDAAAFexj21/EMt/FbLGuh1pVMQgtYQRPK1Jcrk7YZwl5ZBWJ6MIyMjBneeHSRisE3WnH0EgN773vcSM6mmBeS5Mu4juzuyvfyXznCzBraDbEA7MjLilvNR2zxCnuYxUVp8tgUaRYMsW7qjZBVfDdBeeMlN5I+1LdL/tnPWwbN05zo7qTGZpLUQ9Tu9Z5jitYITVPhBVgDySSedrIyJ/3cvUZT4NDTMIt3NaJLlHnnruPJY5JT5KH+PLTkTS6PpO1P220Khu4SPYnPXE1KJUlrBWnkhMDvuPXrJqxer4oMsyHlCqhkfQcAVVB18PEg2az2g1xAfDT4SEd13332Wdm/yPTdIcCeK4oUg/9ZN4Vlz4bZhBel9HYRR20POuYuYUe7sZKXsegJuJ6H4DZoLUyM8P4iZ9v+2ceNGJWmg4/ZLs2Hs9xnvcQ2kElKu8pznQl7pN/l7jI8TSxPFQ66Hn39MQeEfHV7vjLEDpGfKVtfFNT4sEBVj1H2evdfyynHxnRXpwrqpC3Qcvy7OCeIh/VgWjo97CHC0vBqMkaI/g/AxGJPGFh93ELCXvASiUTByR4RzPG+gzk52Ff8oCcPbRONjvaEd9W47HiQR034tAbq8JBsXmYdUMTLMWCaPaTXXeCx0h9BoAZNrLqqlnFatXKklOMlAxGF15fMayIZNDgd6rDGXlpAFOdXtQly/gbykDAfJ03aqGVhNMR6LiRO42ZP2FF2wULRUlvI2dtfOTdrzC4Vu2r9/vyFpUo6Eq5P/eH19mtKuG7lpgarjpsZDTYPnxr0b3uYWND5qLEkeIvFaDaDVCJTLgXzUPT9IEwd4iXG6CwXNzf1zAPkTaYA8i2CQFckPol/4whcsY+JtOLxZCuaehX2Tm1G0cfXcfB1y1HrM7r1mptNUC8GQ7pHGKY5ZHD8aTd/pGcIFLqxcucog8GbIcdKUz3dVUMqLtfLryctM20hB2cz9+NhHgkEBeeXKqoWP5OKVP1nN2OJjGwmXyWHyM8eOZu2RBXd93FQLlEOiFmsDecrOsPqwy0mPZ8wQMMVgeeIxIxLMYRiz6mJuvkuqz2uy0Khz4biQe+sYGYaPRMItWMSbOpZ2pCjc28gfJmYLv3EAmjppUoyPE0wTwUOqx038yR1QeQNWBMmxxuq68LKZ6+E7zEPuVo6ricFUjOwkYJNWoioKD5lI1FJz87yI+Cie39mZ8z2fMVKNHfZ4yLHAx0YqDx9NcdKcRylFZoy0Wcp1jGxqmkMHDhxw94ko+Bh238rx0fMESxvakSJ9r1D5Ww8feT7YMDIJaPgYx3RPMI0GMHnxm4QxB6BGxzEmOOHJwkIMW0Zswtd7obued0Nk5Aa8MlVD8Fwq/NpEWdCWNXAqaNjKNAgQNZen2U7mmrrDpGqx7Nf7wU3Oduw/dwf5yyowuEnCmtOgJWKQFyiTqVZmC0AHoMdJHYQ9aYlJw8yC9SbDuGXg1eNkYT3IbV2uxx003wDQm6EmYbOB/GryEgdxzI3MrAZvOC0ti0PuXyvdQ0+8kc3mQuLGvHud3u7fKB2AZjU0xNl5x5Eq7bvgsXXcTLZ+rGlqmmPFRzsDcDF5gpwq2IG8EixDJFtydIuJWvf0tY6P3eQlPuI26wkx5ecymWutN5MQ5EfIn7n3oKW/0u6zGg391kiiJrnKoGfIq9LBCYFsOONXYHB93nBscixjIp93MXmhBTzG0fBRMPdh3kayW643hwCvBre8hmTMl/f5ttNO8/VfjI/jTxPFQ4bxjzmfcPUNw1qTrbwpd02PBh9ZYDLn1FBxJhgj9ezgUfGRiMrkIauFjw6JTOSqd1MQPk4hYDbZhWBd0cohfslkI6VSjSE4I5Qr0atc1JLjTJGeVX18FPMqEXL/Bb75k1AEcVXoltcQu5jbQnYTiLOXTyiNBjBtMTe7ESxM2crX2M4/EXpG7kZ47hQnAtSpTK7uQoEefvhhZWLLC38fmWPgzItA1jQODw+7VgUVDBich0mAmR9I5AVoAi0TuHkgYUsWodb89bRuLS2LS0lFbGQr1bFeWrA7INz4TUlLgsZtj/v3EoBuh579HDBnsZdLm8n3C1PO6MyrbVOUz2M3otXunNBLAuXzXQYhOUgISpB/jOzzICy2d7oy7xscpywNJVPMVFZOlfadfWyDBZ5o5b3ktd5DwbG/J5Kwtvix5qGHHjLcl4XUTaS7T44WHznGcTzx0d7muXPnRcbHbNaUUyMp3d/uRh88diDgEhIu4aqrYRCOZYz3C1fQyMyeTXCQ283M5IeoPHwMq/kuf5K0GX4rJ2OczGMMw78HmZJ7xvg4/jTRPKRNMeNfd+0a3vir1viTo1WGj8GClNzmYB6yevgo90+5PGRLyyLauXOnT3hXyV4O8yCxa/To8HGP+/dvyF8PHJTNcriOCSPV+uVR8fE46Rk27K0cH4mI/vmf/1kZbxs++kPOrgvByH0R8THX2Vn2Oo2F7irSWGgpwwL4TcH7qrvzHQBNUl0J4ZRKXsmu5wnD5GpMJmlOUxNNQtJ11TUL2tlsjtasWRO6COQ2Fwrd5Dhp7V5etvPgxBEygDFo9ff3G8HNY9zzZE4WkbcsWtZk6lYoE5ks34AuFK+HEASb4SVMs40zzwM56R27qnOyERUoAKFMGTHcT046Jj+P58JSHxCaxidJ9syTPS7IdbvnXEwi2ZGXfGp4eFiJywlipNcREJxR2Zxh1X+OLRN91FgcppiprJyqb+m+JjLWMNnd+fIk4r/C5qPOwAn3djUD7iZSLR3+fAbR8FG0dRPpNbG7SdQQLw8fea2oGBmOj461zSL7bXR8NLv8q4zZeve+ze73KMInYw8Ru2J6yZj2kZlZ3We83xVXXBFhLrCCxiQ41JBgwrkPG0hYoxaRHx9Xu7+vLiWf8uPj7gjtEJ/NARgn8xhyvdrd7jlRFfthFONj5XQ48JAq/8iha2YvR9NcrKVK8bG1tY02btwoHRstD1k9fCQaDQ85g2QBszKMdJT+W09eIro9FI6PvFfuco8XS8eCcyvlyV95KCo+nipdbxofEz7WkFDqcPUQOz7q/RM0J3t8bV8bgpHMq080PsZCdwhVKx7H53bsJrBQB/wWd8BNCU50oc9gIUSqVFaEwTesrNINEMK6f6InfGUlvJqH4UCnZ51UAexU6V5mIFm7dm3pfsPDw5TN5nztM5etCFqgpuyLcozRBqObi4lUly2TUOxAJLZjbbKt72shlXKDP+kdx7VsMtw/A7/QfYd0H9beqXHiGfjLbOlgeXIEkON5t0g5JsZ02TKu+21itk1aUHZ9DN6ETQIVx3SXo7gKopiprJyqE9MtjW1pDpnnYk9Pj7Yp2hmazSQ2+zDBzjbvbyCTUHf//feXlIGV4aNsSZUxUrYoheMjEdHevXu1eES99FnQO5oTvnmxhpXjo10o7nWfFcbwy14xXrI6fwZ43dXUX85ymPx4x3unGgeZIXsZsmND2inf+7OGd07Q/PkLlDJxJkt6o/bsMIzrLhQ0nqIv4rVRKcbHymk8eUhbkjydf5StzmH4mAiY9+H42NvbK5X/Gi0PWSvdx9zeLVu2+ITrIHz0P69cHpLx0e+iHUY6RqqKPf7eR+H4yOEwjI9esjMPI00W/Erxkfen8jLD+z+LCehSjgl8FHs/92sUHtILgZCNVyrOrchm6XDBx1joDqHRAqbJQtpdKFBXPl8C0n0ALVYE39bWdhocHPTda3h4WEkCYQEv+C3dQZOLy19xaTJ2ee7t7TUI+apWczslEmkf0HhaQ7l9XAZmPfkXWvACXblyFTU26vEsMrgNDw9LpVaCtIEyeHQrv/uzFkchVQPN/ceWas4kaov1duDFbxuT3hWLlMtmqQZ6AohaiBqsnPhBjhvfB710GAv0fO+FC5dImXw5dnt6SB9uI09xUkNeFmDV5TNDAlDTFK4FrQucB+zdMDg4qG2aWZTHdIRRzFRWTqPpO5sbYD7fpQnjIoGNh5MqRuoKOt60bwmcZ+HznsifAddUHgbkxStGwcfdSpsYIy+W7heOj54Lphkjo+HjdNIZrG6qBj5678D9x3uAzMTmSWfyUm5/ynjhCaR+d00bc36yO14ZpX/0uEx/pYuExPxVgo+z3L+rCfgm6Tg4hwQ+mizpGVKx04ZxMj5y+Sfe58MUvibFVRDF+Fg5jQcPqfIWAKi9tVXDRz//ON74mKbR85DR8RHgCjl2fOR+qYyHlPFRVx6EkZ6ITk04xxhpw0eHvBwXcpy3145sNif1ebXxcbt7/TXkhSHa+m+19GyeBzYespui85DCkl6LZCBGsqdFVHxct27dmOFjLHSHULU2GzVxgx9IdVAwJZfwmIxgl5Nr4GXgO9Uyudiqfp1l0nEJKhb2NgOURILUGJuOjjN9LjV+reE3yaTx9wO+CiQtUh8EJ8phxivMMmYGD/l3f9biqGTLcJ6RGjECXQh2IDwMTP2eMmSv57FTY/b4XqbxZeF/l3Stv2SCfxybm08J6cOgDM0q8HcZxt12b12r6rmueddms4JZWA1QP4SCI+fOcxvTYUqQZ6OYqaycqtF3JnzUmTZ1w/VjpI6PspXVpL1PufPVNDeZEb3OuB68ElSqlZrjmMPw8XxDG9U1FYaPNRSmuPLKwNjPWbjQVkpNx8fe3t7IY2p3+c8o7TAlA3LIFkuZzeakmE81TnTY0J/2e4kQmWLAdeJ/z1JXCT6q87aRhOBt2hfF2DuopwIcysDgueSGhsnXq5acIYgSlfUB+FgORsb4WDmNDw8pPhl4inweax7jicfHRhIKKf/6UGN6H3zwQen3cvGxRnpmhsISFo6Oh1TXfvk8pBkj1XhrGz7eYGyr46QMFT4OF3wcCx4SNCMCRkbBx/Q44GMsdIfQWG82YdlUOd7BrxULj8HhLHyAyGaZdieVyRLKQM2TVd3A+SPXh77Z/V8GGHNMRg2JurZcgiJFXuxwRjk3TZ5AHJ6Qq6WF3ZxNrnq3kOPU0rJl7dTT00MLFy6StHMmq7dZS2mLAyIi2r9/v7awa5JJI6OTcMdkinuezfOgrbW1pMFerZwrx+zJ2UqdgPttgV9Ala+tTyRo0cKFpXcTgm5QHxF5Wld1M/X3owDZIgHvCB1H1YqdTs9047n8mum6unotj0FC+Z4E6AHoyTTCKGYqK6ex7Dt/CRE7RmazOekck5X1TlKTAXkCssxw7jPgEjMEQiHkJaix4e86EsxKFHxMkxfjxhhZQ9HxMUpMtB0fs9kcDQ0NUUvLYnKcWvJnzdbXdTab08bIhpEjIyNSmS65z9Ua6yliJmz69FoCuD65+Z3YimZWQJtc9lNkj8u8mLxYQ/91jtNAzc2nlLxmCoXukBJmNnw0Jy0ScZgckykL3V5+ln3QM+zOTIuyl2qOlob6eqqDWclb2p8Amq9cFwUjY3ysnMaLh7TlN0k5TomnqxQfvXlfDXzcRYyPPT09pfcYPT4myRMeo+GjnYfcTcA1pb3FjI/mdV0uD6mXxqwhIWjr+Cj+cvZwGz/VJnmKTTQ+mu5dDg/Jng03B74ze/oEYWQNEAkfE9ArA4VRLHRXkcYaMMOyM+tJEdTF47cQLly4hHp6emjXrl2lhT4wMKDF/JoEN0AI6HfddRcFCYZ98LtBM5lq7voBw4u/0N1XEuRPfmNyMfKDWyIhu/zJ2R9NWkEY6/565WdEaSx2uxseHja6dMlaL7Z0b4Bw098AUCqRoJpJk3zX5SE8BVIQmjUg2GWQf5ddYMLcYWwbrvyxXcslaA4cOGDpI5XRD9Jm8rzdRv74XPt1AwMDWty+xxzILlcsHPnnVwpJ6oXu2l9OUoyYqaycDheMjIKPct1rthzt3LmT/EyNPsfk35cuDc5FYHPHDsdHxsjPko5hNnzcHbImo+NjcHmuRuLcDcxkmZhkU9kfgQHMNJtcF/k53B/sbmgPO/FjhOl/Gz6Z+icYn/i9Dhw4YFAilIuP/nkr5oNDF7v3Wy3hF0kY1r5sme+5vCf0wR9OloS5RGW7cl65GBnjY+U0XvgYlrC1UnwcHByU9ufDEx89Tz1+ZjR81HnIVdq97fioCsf1BCTKxkfxzhtI8Ew3UDg+snLktYCPDvmt6OXwkNdI/GNwLfOBgQHKlRRL4rNewkeCyI90OOBjLHSH0FgDZni5GxEjo1t79BIDtuyJDMo74G3Y8sN4Yl0Av1bUdt566Bqg8PfYQP4SFIJR9jSNIMGY8cJXk2mo2rMcmWORciS0o6oWLUPJZKNUQ1rNGpz3fZ/T1GS0JvA7h5VxC2SgYCgD5pZy6e3tJXkDZet2WLbSWujW9Rq3fWFZ1KcnEtTW2loCll27dtHatWtp7tz55Ld8s3ATvpnyJ5vNuUBsj+Xyb7bbjOPhjZd5fmWR8CmF5PeL4u4VM5WV0+GCkaPFR3Ft0HNWE7Ahgiviei2pTrR3YKzy5n44Ppqs2H4hORwfG9xn7iDHSdFJJzWTanlV3aH9cffmWEn7O98i3cuWxC2nvZPjNFA+3yVlAFcFiDB8ukbqnzR5rqjbQq67uOQVQCS8L3p6elxGv1x89DOrnZ25kmdTBvq+wG6SHOrFWO4PFfJ/t2bkBSrGyBgfK6fxwsew0qSV4iORLc7axOOtVpIFji0+bty4UVHYM55Ewcdu0nlIkyXYj49etQqZR/EL5KPDR5mHsuFju/ZOhxs+imo6rKSpjIfMZnNSP+qyAPcnG8B2QJTfVb2DugH6xmGCj7HQHULjsdmY4zoYFPyxJ+K8W4gTvCQStdTcPC8wcRQvbtam71Em3UFlgiYAWnjqqVosWQr+JB0yQHtKgagCGTPKNaQnUsiTV3rC5EY0h8SmsZsEc1hHnktQMNj7Nw49eQW7laTgj832LU5XAwy3b2SLg9yXNiH3nRDu3vI75To7aWRkRFN4qPW/bYDRodzPgVdqJsxKLn/a3OQrQ0NDbqk4Vbsbvpmm0zN95+fzXdTZeZZ2r3y+iwYGBpR7DpFXRkPe+Hgjt82v2NI9UTReGOk4fqWNipHZbG5U+Oi5su2xzjHxWUD2GEgxx1tb2yvAR5XhAglX65OVYx4+Cnc+U0KubgK+QeXhY5F0hs/sWsgW2qBYSX/SOMZZIo+JC+qPjaQKBZ2dggHTlR6qABElLlO2wERhfsVn4cIlpeRUImt9Ofi4R5snAKgrn6euvBg/lVnMGWK1HeihQnJlikgeamViZIyPldN49F13oUANjqNVLZGTs3px1p77NDA9FB+J1Fw9YfgIEq7eY4+P8+efSiLhovzsDHlu8Cmy4yPHZScokain8IRyKj4yD8kCcKX4eJD8Xn3/P3vvHh3HcZ2J3+khCT4AzGBACrId+SHqxSdGIEiNqAHHnAg/ZJHdbOKsvZvEdrKW7eyuJcXHK0rabByHEp1EAmLxOOtYIpaSuaI2QUTa3thBRFpKyNh5kEjihIntxcSkT+g87JwFTmznYWfXvr8/qu/UrapbXT2DAUBSfc+ZA8xMT3d1ddVXt+7ju7w/ff3xTrwS8XHXrj04PT1tpYWVhPMn6egmPtbro8yD7ho3SIckjKyB7M2+IYCPU8uEj21tuv/xH/8R//Iv/9L5/M/+7M/aOd0VLa0CJs/fSMrl4PKbv/mbAnBsQoBfMCxjbv6HOwAlS+X8/Dxu7L/OPNZT05tevevXOwrAHgC81xqEbhh2mrCRMvrzgnRIkTnZ6FVDgIvolhzgfUFARUBGG3xS9nJO7knEyq0Zk02YnDMzM0btP96OLez/0CZ3CwBuyOWwVq02xw6dswCAbwOVOz8BypNd2LDBIW4r5fMYxdciBvUpAVzGwSUdK4Lyhj8JgDVvSBWgJrSjz/2LaX//gJiPPTY23rSAcuZcN3Q45B3zA/8qANxu9c1K5XRnGClLO/j4xS9+EXt7iw7eAYwgwJPN8bUYfHTTKiTCRXt+2O3ZiQBvRhsfZUIbCR+5UvUa4fx1VIpQEj4OI8CLmA4fab4RLs40r9/TU2QGjKR2ywr4zMwMnjt3TriHYQS4P6E/uBe8jgAHjdxz/Zt9qBjF70HazEZRAXt7+4QSdH1xO8iIYKdqIcresCKaxEx6bTLHC48KSMZHhVN5fArcCCpSxHlqmB06nMaA6vtuC6j0pnIbGJnhY/uy1Pg4Pz+Pt5fLmLcwahMA/gJ7xgsLC7h/Pw+fTo+Pbq51CB/XCHN/efGR9BHzmBsRYBYBvoh+MsPlwUezNBpvB3dCtYuP46gcFW9AgOcwCR+1gaBz+GiOL/s5FeOXDyNN4kmuP9r4iIiOA6xdfARQ+nA7OuSSbrqff/55fNWrXoWDg4O4Y8cO/P3f//3md7fffnurp7viJW1nuuARBrS5ubk4nHhVPOgor2OyOQj576rVfTGZw2Q84UxvIA1OG6yVl8i0xEVQwBpE+DgArgfA1aDCobmnt9Dd7QB5FyjLPAkP69jtre9nEyRQWJ4dAi6TmjUaDRwaGmZEaHXh932oN4dSXcIIAQ7G//8XtPNmaswIgWBav+zJSe0a6O8XiRnWAWA/mKyzH477zu7LTX19zedL4FEB1+MRAeAHP/hBJ898oL8fc9a1iJmeg8sCmAYBOudToAwwebE/6+iWkzjojG1lCLmXvfdbd23RCwWFzibl7/rGl1q0VgOvQ75y7OUZRrrSDj4iqhqr+XwXG4Nn47Gi65PSbzuJj3r8P45qc7caTTKfAm7Y0IvKE32DcS/9/QNCxYnjqFIk7PHrI9lag36MXCw+HkeZjbaGmnNjg6Ww+jwuP4hSLiBFZvk9TWvje6ToBfJM8WO6ECDXfL7aQDfijB+ACHfuvB0vXbrEiJz4WMuxvj/D+oHavSBcn3t77OfTHR9vjzMJH8dRrz0J4Y0J+Ej5iZJBFUGvV+QFlyLUugBwrcU3shLs5Rk+umI7L5zwWM9zOnfuHBZ7eppcPQa/DJjs5XNzcwwnyih5Z3346OZaEz4+G8+FVQjwIGr9tS+ec12YHh9pLnUGH9/97ncjomZ9r1a5p3+T8HseFr/0+IiI2NvbhyoSycaetRgm+PTh46cR4Drr2Dxu3bpDxEd1H53GRxoDm9CNWtyAyTqkHFLvMz4RRh5YJD6STt6ODrmkm+7BwUH8yle+goiIf/AHf4Dbtm3D5557DhERy+Vyq6drS/7bf/tv+JrXvAa7urpwz549eO7cucTjf/VXfxVvvfVW7Orqwu3bt+Ov//qvp75W2s6UwUPO5ZAth/JG5ZlnnhHqzyb/hr/CTJLmJgwA8G4A3GF9R2RgxVyuuem2c4mmmxOR/7aMmoyngG6JF8miqQBraGi4OeDdEkK+30co1yUsxJ93IQfmQ4cOGfdAL279qoDyBkv3XgaZUbwbzIVTIrDriz8nMJmbm2sqR3b4YAEA76pUnE13FwB+ANwcP8oDlMBlPehUgzOpxhL/joD9LJoho5fZ8fICZOfFyPMgOXdbHl/KQNUb31+rNWhbmedpZKUxcjnxETFd37WCj4iteEB8RHyLxUeprAm9vxsBdqA7Di+grbya554Wzikx1YYwUkXutIuP/trWfagxUilYRKoZ7ssK8qgD895rzvNWno48mt4QyVAQGfiYhO+Vyl0JZeZspTVyo52iPuzpoc1CqO7sAyj3dRgfveHfQt7g/Px8YukbevH1ao11LK3dtN7YnqJOzfG0kuGjK9x5UQd3/bc9bhLRq29MnDhxQpgXndIffRhZtD5Pg48LqEK1O4OPAKYxNx1GRnFkzNLgYy5XxHp9FBHR8nTb1yqgGU2VFh+p7+1jC1golJxxoCvFPIV2jvri8HEG/VEAhJ0+jDwmntOHj/Y8SJoLtw8OOs6vMugye4SRreqQS7rp3rp1q3PT+/btw4MHDy6LlfJXfuVXcM2aNfj000/j5z73OXzHO96BxWIRv/rVr4rH/87v/A7m83l8/PHH8fOf/zz+1E/9FK5evRr/9E//NNX10nRmK+W8VMF67pFJJim4/vpXxXkaXMkIlYnRhejVdfzHHgAz76EMaiNXBDcngucH8dC3M6Cs8Kfi7yZBbcCrDhkPB+ikyar6isqlcXFZ3O3fhxYUIsE425zIUr3tPlCLH21486CUGdp063wV/wR/BFTkwD2B46hsBs+b8h1bsMjd+uLngqDCy4lwbXZ2NtUG/QCEnof9XZr8ngk0wVS2VPrZSt18sHy+hK973U2oFzD+rNViQffOy5CklU4qlSuJkcuNj4jhvmsVH02PTAjrcjg4OLQE+Ch5i2mz2oUyWaMZqWNi1RwCEGZQGN8symHgrwi0Xykt7eKjJpP0zeGHm8fOzMwE6m2b87avbxMuLCwwfAwZ0R6Jv/MfI+Gj71g1ZmzFlHvKFK/HyZMnAwpoqN2cvK81fLRZdZM83XwzxkvfFMAlXNt6myLOex3DdonIM8PHKxMf01QpoTGyr1rF7ihqerYBEohS13Vb62yn9EdpI1hCedOXBh9nEOAUa8MsJtWGDhvEJh1jLiLi/fffn/j7G254bWAO39xsQ6v4CLAG6/VRnJubw4MHdfRLJ/AREXHXruHEY118pHbScY1m/42M1Iy+bw0fG+jf25wJ/HYS3co1i8fHuyoVvPG1rzWcYb6qP61i5JJuul//+tfjn/zJnxiffetb38J/9+/+Hebz+VZP17Ls2bMH3/WudzXff/vb38ZXvvKV+HM/93Pi8W9605vwe7/3e43P7rjjDvzxH//xVNdL05lmXmpyeRuTjRYQYA8baNIA3CAM0Fao/5NzTqRFOQn0iQmTcvbsMKgBMMM3toDyrE7G7yeax/raTkQKO8XJlo7xMQmQKSz1gea5FxYW3PJgAE7OO4VnE7lNiLhmOH4/EziOJjhnCPUde8DzXBrWcTMzMzg/P2+UUeBlZk4D4GZQHvlwf9rflVHaFO/ff7dYciyKis7CF36OPgs6xNefRYkE70pQKlcSI5cbHxHDfdcKPmrmVXrRws+Zu/k4oZIvncTHNBgTjtTRG8+yNY753CmjwiQeut4TuEY3LgYfwxvzweaxhI+up6yMOqeTnz9nlRcKESAOB8eEhI/JG4akZ669JnIEGTcY1tDNPaTNQ+v4WK+POl7rMgAW48oVtvhYqZ8EN/xY8oaH1ptWJMNHLUuFj5chrCtMT087pZH2QPIGYmn0x1Y/T4uPgHrTTvNnCyqcn4zfJ5ePUpizzfiMY+QLL7yQ+HuKfgzrkO3go00aLOWRt4ePiBiT3y4GH/Wx09PTi8DH0Fiw10GFkX19m9AtmdbVjA7gshh8vD4wz66ITffXv/51RET88pe/jH/zN38jHvOZz3wm7enakm9961uYz+fxYx/7mPH5W9/6Vvy+7/s+8Tc33HADPvHEE8ZnP/3TP407d+4Uj//mN7+JX/va15qvL3/5y8HOlD05spKoWBK5pambTbwymqHYw6gZdY9Z5yNPjG1dqwuTLRImRxyaIgy4pMH4tvjv6dOncXxsDIu5HB4H5e0+AKr4/CprwPNJoSy5Ebq5OwU0Q2kOI1eQuIyNjTuhL/T7YpEmV9KmHhBgrddLFGJ3744iZjxIDvVrQHrrdRpPt69tM8I5yQpI3mw7BH2kCU4uAKrnMxh/Z1txi6istvpcY2PjODg4FD8X05LKc7hIwrWXfxBzuW7s6SkmWGf1sx+ByLBSrkR4+Upj5HLgI2LrGNkKPrrPuogmUVUdVbhhCRWZ1huwM/jIjyVPid9b7P/uCHKDnjJCFVDhzjFUnos1xtzxKyWS9yTCxeBjf/+AUDHAxkj1qlZrxnkbjQbzzvj7RtW+JZwNKciA2rvlj34wx1HS+XzMyjPOOc1Imw+ha+gbRZU/zj+j8VdAgM2e5yTj49DgYHO95GlDA/39iaXsfGvxWlDVJmrVquHtobUpKbIqw0ctVwo+ck+3LxqiVq1inxXt1g3aAVAGFSpL6WQ3e/FqMfj4LPqNacmRm2F8nESAXly1isKXffO97Gk/x0d9XRsj9XXN369evS4FPipM27Vrt3HOtPio178zCffXOj4ihg0KYXzUx1artTbwke9h+tA1oNDG3OXlGBsbx+3bB9GOkuAh+VxC+Pj/gSqH218sOt5wcjbZGEl8SCE2f1uWZNM9ODjoBcrlkr/6q79CAMDf/d3fNT4/cOAA7tmzR/zN6tWr8X/+z/9pfPahD30Ir7vuOvH4973vfdYgglSdqch4iuha7dQi7ZYQkIgayMLDB6M90Pehyg2UCFsi9BMbyCG5M+zgtAx/9IpAhSuPC58DAL4n/ssnxYx1ff2i9pGRwR9WsrCwILD21hHgKYyiUsygbW/qS6gsc7pcTaFQwkuXLjXPG6p3SQvbJLuPQRBCWUBvcKl/y8JxBTBJ6RBVuAzldEv52Embcc62aC/kvjIz9KwUO7Q9ljiQ2sCqLMJTU1P4qU99KmjssJ9h+oiFNMdE+GZwieqWm0htpTFyOfARsT2MbB0fCSPt4wkjB5rPvjP4aB8bGndp8iFrnvmzOf77nvivT0Gz8xsjJPbfxeDj2Ng4Dg3tRlkZGmz+pru7gC+99JJx7nTz9h40vTO+a9EaMINqLXQNsZXKXc440jndksKdpGw+m5BPWkN/OgGgXbfcjOhJxscXX3wR+4sKX334LSl5ofWIr0n2MTsgTOSZ4aOSKwEfyUD+YVCs4/x3ZVDREHb5uHlwdS96xranz/V034uLw0dfrne4ykgYH/l7H0Ze8LSf46Nf/7h06RKuXr3OuW4uV8CRkVpCiUqtQ65du75NfKT2EUbSRnPx+IjoNygk4+NE89h8vsSeTVp8BKYD8uchjR1NLPnMM8/gzMzMonTINHsV6RjiWXoWlKGqvEz4mHrT/WM/9mP46le/Gr/whS8Yn3/2s5/Ff/Ev/kXa0yxKlgM02/F0IypFRw922ysX4bZtOy3wIAp+O/8sj5okps7+v4A2OPX29uHs7GyTFMWfW5JHBaZuXeoJMPOYI1C5yzQY+aavC1xyr00gE4lFoC3uPLdsrtn+42J7lAV1BLmCZMvc3BwLTZI9ZuXyLmvi2WHPCghWr15nTKxatSpuosvsvs6y+zgIAtMoqBAXAL0Z7gVwmODtWuc0jkbrdeecVMqgZLWtaF2fgKKVMgqk8FGe/qlTp5qlvXRtT7JEH0Q7/EctXhTmK28gOOkTSTgXKpR7dhC1Jy3neJGWu2TYSmPkcimV7WBk6/gYwkha8On79vFR15WnXOtTKEfj9MWfE3OuPW7N8ilKaaO22vcQsbHrC51voMZIOrYz+PiRj3wEk1M49CbTZpbftWuPcP9EMhShKlHDlc/zwrXGUTMBn0WqO263Z3p62hlHyqBgnq+3t4QmG7qtbJr34tbI9St7W7fuwEajYeAjla4ZGtpt4eNEHGWgr7lu9WpcH//v88r4lDyp/jLnG/Gd9wK4RJ4ZPl7Z+Dg+NiYSrhYAsK+3Fw8fPmw863Fwda8+UHoGGdjrALgKIoyggIoc0nR2bN++s1lX2b8ek/f4EdT6GpEaShiZF/ChFXwkAyygYkQnbJY2i6fRxccdyDeQEkZqj/BDKPHPVCp7sVK5y8IjWYdsHR8H42sR7vgqHrSOj4jKoGCn+SkdTcbHUskqJzw2HldVag0fEbFZn3tqaqpJ2DgyUosj2B5AgE+gvU6PjY3HbWhdh5T4mAqgyyJSlKcPd1/B8LFgzaWlwsfUm25EFVbT39+Pn/70p3Fubg7f+MY3YhRF+C//5b9s5TRty3KFB3FJ25npLVw8xDJEJMCPcRXQKOozAEUrtvx6t6Eqa7MalRVSKQYABSxZE30w/vsacDeSESgGVMoJboBcmgpBb+bsMPM6qM3oKjFciYO7rOzJbNd1NHNn9AStVmsxEcjNAmB1IcB2BCjg8PAeow8LGzYYx1J+N93X28Fc9MrxhH0A9Ca7mMsZfUgMsj0A+Nr4s6QQP6pp/ZM/+ZPNPl4A17K9sVg0Nhb2eDwO6fLEqH/t3PbRet3ymkVWCHm6MFKJ9CmcCxWaJxOoNzD+nLY0oZSdyllcSYxcCXxEbIdsMoSPaZ79g9b37eJjGXO5HpbG8Dhq70rdOnaQjVGbvZy8RJwAJuT1WWWdo446dHk1KiXlLC4VPipvTgFV7VqpbM0mVN5xsx+feOIJTFYS3249k2fjdvR67onP/Q2oQhIPJs5dwsfDhw/jrl0UEfAUSsRLJ0+e9OKjejbJqS5PP/20t3+lWum8DjcZnkMh35MgK3kLCwtODi/nGwmtwd0A+F2BYzJ8XHl8REzvuUtDuDbJjqGyoBLxGZ/b8npcR7XZ3oCmDnkP+17CyG7r83bw0T5HGXXocjcq3DKxxMb3dBg5jhoj1ZyPom4cGxvHarWGuVyyDtk6Pr6F3St5sMvxPWh8tI135rO4JTh3T58+jQcPHsQPfvCDmISPlcrepkHRTeUJ4+Pg4O24sLAg9u3Y2DheunSJfS6NwQIuRoe0ddYyw8fQPCkA4I3LjI8tbboREd///vfj2rVrcfXq1fg93/M9wXILnZY9e/bgvffe23z/7W9/G1/1qlclEmHYgH7nnXd2lEgNMU2O6gEE6IonUihn8Jg10MOMv4h8okyiCrGsGZPLnGx5/AAAngdF+MW/q8R/d2zdanxuhz0Nxn/5Zm4e3Nxh2nAWQG3ee4X2DA4O4ezsrDH509eLlOo4KgZvVfKCLLJ+bxOfWGRRPgYg5ryvBTckxTZS7NmlPO1vt87Bc7PomSWVbxkeGnL6uAGasdQHCnNzczg8NISFKAoqZtQWiQmSFEGyYLrjkI9TrmSTMaWEUm1hLo1GAzdvvpmdhzezjn4DjbnoH7Puj5PKdWqep5GVxMjlxkfEdsgmQ/j4LIZzA9/Dvm8HH2dQeWC5kmBjZBkBXkTFrcE/ryHAZ9AOOVabVEkB5fcwj66iWkelENG4LljfqxrUNj7SPbme/Fbxke7V/m0hvlezHzUO+Eq+rIt/cwFdEjlbEX8zAkyh7WkiBSuEjyp/3faQUHSAYhiXSHFMhvxk5b9arTWvZfcvedI4PnKc5QZPMtLaUVRUfSJJydser8USp0cE4HjDC/Hn4wD4PtYG+7cZPl4Z+IgYzlE9AHFlmVyuWanDd+wxa+yZEYZpMPIAhvGRNtLn0cVI+30n8dFtx223bcXZ2VlExDZ0SNJROD4qvXB2djZmM0+vQ6bHx8toerDN+1Iec0BlxHSjlarVWvOZJWGkJieV8VHCnFbwkbA6CR8REU+d8uWm8/1Q+zrkzZtV2pY9J+ogR+6uFD6m3nR/5Stfwfvvvx/XrVuHQ0NDuH79evyVX/mVtD/vmPzKr/wKdnV14Uc+8hH8/Oc/j+985zuxWCw26z6+5S1vwYcffrh5/O/8zu/gqlWrcHJyEr/whS/g+973vo6XDENM48mZjScYn1hJnm5OiMAnsauA0qAwFVvu+ak7gJHLFXEV5MU60xSixDdgZfDXmeZKxjgINSbBLDdG5zkLaiHpBuWxJYukZDEL14s8G4NDN1J++MGDB/HRRx8N/O4xBAAjTCdtbje9atUqTk9P4+nTp0V2Ue6ZoIl89OhRlzHdCi9MQ6wGoFhACXQlb3UEZv6KHTYPAE1A9F3n1KlTeN9997HxRYfwcS/V2SQrsjlWbfGTl9hzhjY902imJay8p/tKwMjlxkfETnm6JXxMOp57utvBR8QQRiolr4hy+GMRTYt9Gd3QeVLK+D2MC9cxlQopBL9Y3JjCY9M6Pmom89BvdT+Gn6XJH1Kt1pohrI1GA6enp1nZMhknBgdvdzzIttdKt+OexPY8+uijBj66/ca9gLayV0aOj0mbFlpruPLGPS1SpJK0NkgYSfjoNZyOmIRGuwHwRet6K+3pzvBxcZ7uWXCZmZM83S/E/0+ASoNz1+7F65C6ZrVksOPh5EuLjwCRgQ/t6ZAHUOFjDYnkbGpqCn/iJ34i8LvHjD5sFR/Hxsab0YqkQ1arNeMYyRt/9OhR0bPs0yGToswOHjzYJj6GKjm4+OiOwTPst4vXIdOwma8kPqbedK9btw7L5TJ+8pOfRETE3/iN38De3l58/PHH056iY/KLv/iL+OpXvxrXrFmDe/bswd///d9vfler1fBHf/RHjeN/9Vd/FW+55RZcs2YNbtu2DX/913899bVa6cxqtSYMTMrlGG8OmqmpKRwcHBKOLcSDmyxfOXRDWvjk81kpeZhGOKwzCbgRUoRoxFb2M4HjqKSTr+wVeVwli1mo3rjrdbKVd9/vlMJWrdaMZ3lXpYLdYIWMgyKoAYBmvoo9IUVvMbjeDM44eybuk4JVOoYWwAokb5j5a6C/H4sWu2kB5JSBfQD4ofj/DesUqQgpjJRG8Ilm3yYZi2hBfRY1gMq5pEkAJuWUcU+Sstj2iPOrByKnj/qiCGsjI2mmeUeUyisFI5cTHxHT951azNPh48zMTJxTV0QXI9fEv2kXH21c9GFkKPyR8gyTMVZ5C/jcSN7c+sq6VKs1oy9bq70bwsek3z7gzF21Ie5GM1y8iIrR9tnmc5Tme7K3Sd+rPuYMAhxwwgv1BuEMJufg63tUJJtFdJX21wh9sg8Vay/gunWUciT3UbGnp/k7e/0rW/hNeYatGgml/EUekt5oNLDY04Pd8Xd1MHN7bXLODB+vLHxElDll+kB5uMfBLGk0NDgoevDWgCZUs3lk0nu6W9Mh/d81gr/vFD5yfGhPh5TxUZf5TdYhXXwsoJnS2Ql8NNeD9BhZwSSjYhgft6EqU2mvteeRkxO7Dhn1XU9PMTBW+Ka+fR0yCSMbjQYODw1hT0p8LOXzuHtoaOU23b/8y7/sfPaHf/iH+IpXvAL/03/6T2lPc9VJK53pz+X4mfj/B5uDxp8/sxW1JW8EZetgHX0kESpPj4NLKKwzOUQJwc0Jpg0ZbaIpBDp0PgqJSirJ5fcohBTgHk8/hUrWFJrAQ5Nrfn4e76qY1sgIFJGZr6YqYthSPRFPZPKEPwmu5yMHgCdPnjTO9xSo+uf8uDKoMH2J3K7suT4petMAWLPOtzr+67aJjEA2uZ8G7ygqWkygrlXUR2jCRZoT3HIrszJHWIccXgLXCEFGhjQslJ1QKjOMTO47ZYmW8JFy3UzlxZeDrXILC6jyqm0FIYyPSlHhIW0+jAyFuB+L39u/p7xFpSS6tceTN7f+si7QDJl0MS2kGPvw8Xjcn0m/7UWAyMBHl2QoQrVePZk410NeoCgqMC/Pk+jmIOYcfFTnGkU553INugpk2XOfgHJqFgT7aAP4lbdiFOG61auN861bvTpxAy2JlL9oY9ulS5eMmrS0Fkle9gwfl0da1SElYtafif9/kOHAwsKCWPd9NWg9gEcx7ki5LrejQ/q/mxF+v1T4qL5vX4ekOuAmPuqc43T4iIj4qU99Cnt7+6z72oQAv7AofASYsNjFZYykUHt9vqfQJYIrx20vYzp8pH6VUg9oDXgK5cpMa+LvXP0xny/hyMjrrbW+fR0yCSOJqLgVfEyDkUua023Ll770JbztttsWe5orVlrpTDeXwx2YvGaxmT9jWwRD9fbiDUd91BgMCwsLLHSvs55uaZMYATRzDaWcNn6+bkguewUAgTqHUohLAZNLITRQKee+342jyj1UYdrT09O4sVgUGUSjwOQL5WTR5CVmyDqoRfFJcPPg6TrjY2NYiCK8F3QOPe/7pzx9KeWi3whKIbQXY55SQOXKyAOvr0f9u+CMadoYE3HHyZMnU4U9+fKQ7LxVWygk9g0AeIjd62VQRovueOy2wkLZyZxFWzKMVKKt7jzXzc3ho7Hi5mBzjLSJ1NLjoyqXxcd1pzzdkgIUOfjoP193AMtUaJ0/P76egHNJ+Pgk+hmHo+bzmZmZwXPnzsXKZAGlEE9prstjQFash4aGGXNuHZU35knvGNFGlJ9BXu7MVAJ9920r7TeiJjWyPU1F1JVFXG/6ZvArb4TlhI+nT58OKoeLwUdExHe/+90IAPiYtRZUQDGeZ/i4fNKODnkWlGPjvDCeqK474SOVMG2A1tVoc851rYsA2G3Vj+/vHzDKpiK2p0Om83QvNT6qMO/2dEjfRpNS2JJ0TxMflRGBjk8Og7clzH1is4vXY2wqG/3a29vXvIYmzLwXXYwso0m0mYSPD6D2mEsYWUB/ugFxi/j1R0RcNh2SQt2JaJgw8iwoHbIHWmMyX9ZNNyKmrmV2NUornelaqVxGXV4n1AUHmnBcwfBNvseM87ntiFCHZsrWpbGxcW84xkB/v/F5GcwNGd+M8sHoo/CPAPCuSgV716/3khoAAO6OCciSrYocNDYH+mkG5ZqOu1HlkdqLh7tYISSH/tHkD+VFU2kuO1dbKv1Ryufx7v37Dauc9PKSPwjXB/DXdZVqtM8Y1zqGJhAnkxRRfpINfD6Gy7QY4vOy1SHX9Aa0k5uzlEolYoaRiD4rvpvDpyz5+zzK07Q1l33z/tYAPhJ2lBIxsr9/QEx5cD8voxkRohWRkZFa89pJZXnq9VHs6SlgEnHgyEitRf6DNPio/r/pplut3xJGPtu8dkjJ9tWb5sRGSb83j6Hj/OvoxYsXhWgI/kq6b2nzkMOk9rkEURqfp8BU3gAA77//fu9ckDBS4uRopV7s/Py8s2ZEADgKagOX4ePySzs6JD0jn24wPjZmbNDpWU6D6aHj+sEYRJiHAipj4TEEmEwsO5hWh5QwTed7dxYfVWlAGx+LyHFPE5D55rBbekvpiMn46PJQJOFjMsbZfZ0WHyUd0rcBHhmp4fz8vBAduBh8TIOR0ndkwD4dv1f6Y6v4iLg4HdLHeQTxXCPug1Yxctk33deytLrY1OujMeAc8Ay+D6OrGJVRWYB47cNQWPRp4709GNRE4+BiXnNoaBhnZ2e9FvdLly7hbitsPGkwUjjL+fPnnd8NDQ7iXhaubecZbQHAXwJdNmVj/3Ue4N0S/0byfCVZWjVgaUZG89x5yOMkaFbwNEyG8/PzDmmabazgYYMcXCkk/0xCv0bxbyVP+C2B5zEhXL/RaOBb3vKWxPvj350Wxo290LQKhkkMly+88ELTE+QTxVZcRHOBUXnB+bit7bBQLrVSeS1LK31nKlVnhLk7j27pKsJHRBX2W8AwPppz38ZHZf0vYhK7drVas8qd6DF96dIly2OerGhVqzVcWFjAhYUFhyRnaGg3vvjii+w6eeucWxDgl5Arydp7wXOqKYe5PXxsNBqszOIEcuW6p6cvLp1zDyYpazY+2vc6NjaO9fqoaMioVmtNfNQhp9IY0e1XNWDt5wiowumT7tu8P+KNIHz0K6M3omb/NYkc6bUbtPJmY1loU51UQYLqr/tyQen3dj1uygm+0cJ3emX4uLTSat+N1uvYA4BvA3mN/zC4/CxEyFcD5cSwS9SlZS/n0ooOKZXS27nzdmdudAYfbZ2kC1WJQW1I0EZRO6eaPOU0h5MJEtPiY7HYH+MjGTTCGLkYfEREKyzfj4/qXG60UHIkkIyPOhohCSP5d1Ko+W6kkpASPi6lDunjXCrHOElljlvFyGzT3UFpFTBdy7s9MOsoh52U0VUwfPT5EWpLlKvsIMr5sXfcsTcmcHMHNIVjnDp1yrG6HQBlxU8ajDu2bXOUieEhVQbMHuhPscG9xQLlcvy3WCgJAPFLQp/Ox31ib6Tl3E45l16D+D6QFzrazO7YqkpTzM/P40B/vxOGXowiJ89qtF53vA+UM06kOlTWg0JdzrB2SNbuQtyHBXCjBjipj63UvfDCC4n3x7/rb4acSmGkXbhz5+3GYh0qGeG34k6ivdGQwt5MkhdeisNUfK9ET861LK303cWLF3Fj/3XW/OPz2fVqto6PNNe0lyItPtpRFDY+8jI0OnfwAKqyV0mKyAYcGXm9wKhbs8Kkj6PyutwYH7MFzb5Sm8vh4T3W5xHqKKDO4uOqVWuta/mVvK1bdzTxUa2DJn7k8yWs10eda9jeav0cOEEcr/HLFbyyZ8zkUYWkmgZWk9THVOrS1ZOfMM4HkHewOQLArny+aYwmCW2qJfySNlkRqHVFYiqeADNkUqrzzM+f4ePSSit9J0Uq2DpXHeRKMhXr+fISdbrEaNhYRrJYHXLp8PEyqnrVpDOULWxS7wuODknH215t4k6ynTwjmAYf3esks6Rv3boDX3zxxUXh49jYOL744ovssyR8lKOFNClpenxERNy1axiT7k9eo10dctWqtc2qFiQhHZL01+Rrq9dAf7+hQ4bwcYL9NvN0r6C0utjoQSN5YtIs6ByY3PwHDTDkxXggcTBwIBwa2i0O6Gq1hk888QRu37bNuNb42BiO1utYyueDtZ4BFPu2rUyQJ9j+3QMQe3LBLS8WgSKmIc/zg81r2H06j5ocwrZ+6k2cFHpC3uZu4foD4NZTLQDgVtAKVX+xmNgfPCzGp2gN9PdjIVLtLluAQV6JMwwQJKCwowa4crY7NnpwmZmZafa7zYYeAeCafB4LoI0B4fwq1b/+kFd1/Pnz5616kXyB2ITu5r4be3qKxrhWeUz2c9Y5+QCAN0j3tsI5i9e6tNJ3NBcmQeWbmmOmE/jIidlOpcZH7cWQQt1reOTIETx8+LDjmdBsr6H8b4Vdyjvtnt/97Uw81m2FhYyuG6zPiQU+wk7h48zMDBaLG4W52YUyqzyRgEZYLJLh0e9Zo2tUqzVxXVJ9W8AkxdpU8jim8DETif8PDe128BERcfv2QZSZfm1PkerHD3jWAOIwIaNniGiTanHbBlhpk9ULyuhKFT8QFT5KHtAL7H2Gj8sv7eAj51Th4yVUSYbGD4LML+AL+02KLkujQw4N7cbnn3/eIUVLj4+KoyM9PiIqbG8HI8vsHPNIRGhmP61BCqNOwkcVUm7jYwkV7toGYeKLKLDrtY+PY2PjrAxaEj6eQb0GSc4KXtkijI9aD/M5BHOomds7p0PqccB1ANN4cyAe/xOguCv6i0Wj3Un4+J747yDITOZXVE73tSydyemmgfmuxAHjB0babD6ImrXXVACq1ZqY0+CGa4QtRHVQ3uhSPo+j9XrTgx2Bv8h8BID3pVgI7O98i8dD7LM5AHxtk5iizPq0giqM0AYc5elNCsMLKUC2B34A1CJmew1CYSih69SqVcyDq1R1x9/fC3IoGQFFN6iQsmHhHBJQUHvK1jnp/cmTJ3HPMF84fWP1gfjvZLzoDicer8P6pQWCj0s3HIkWvWrVt8CZ57MNEcvFzvtylcXUoR2DCKPmBifEFh7Cx+dQKzebjN9UqzI+IlKYX6iGq63QXEC+OdQKi71ZIwWL8Foud+Ped8ii/5D1+ePsPNSG9vERMcmrIOWOD6AygvgMJOazTFvTtlqtISRG3NB17A0x7+9SPCZchV7KZw2x7O+wjNO+NYCqdVApyBDR5vqcUn5tbOZzZh7cjVRtZAQXFhaa5aaMyKv4leHjysli8HEczA3AuwJjTtI1yIO308DaCw4u+PJi29Eh1fx7qgV8pO/vSzi3jSX0XRqM1A4q9aJolVvQzfFejQCqTFiSMSIcFWNHKRGRGMfqxeHj7OxsXDkmCR/vxWRnRTe2go+6Tfa6ot7/4i/+Ir7iFd8VvMdWdUgd1u/vD4l4cM/wcEv4GIHmPkqLkdmmu4OStjPn5+eFQWN7YtIQEHRhEjApz0MOJWWKM6OTaM97ch1XshDxutI8pKLRaOA73vEOZwNIG/QCqPAmfvKkhYAUEt/i8RhIpa0i6y9NdDl8RQqZotBQYsD2KkBs4u0GteG27wnA74EmZTakaHEWRf495XSvAWEzDVoxm2ihLSRkTZ8AFUkwASaZ3fjYGH7wgx8MjFWqmTljjd9QyJEdUmWPS5k0KbwpWo0AER4AVX92z/BwkOG3nXmeiStp+m5+fr7JY8DnwgIAlhyFIBkfVU6/TdrDz0FhxWF8RJRK5EgKwgE0DT1mzdTTp0/j9PS0QEamlE+db+0vB+Z6upPa8xj6KmQsBh8RNUbef//9gTasRbUW7Uadcy8ZSFwPtI2Pvmvcd999gTGxGTUPiu3VitD0ej+EkqfHVyvXzZlX/TY+NoYnTpxo3qPPoEpkarTWhYg2J0EZdwug8Z6nHyHIqUZ9UYT7PBFlZRBCkTN8XFZZLD5uYvMpRISaA9cxUszlmN5G2JBDqcRevT7qtC2tDiljZAgfy0gGTDW/Kp5zSxhAmzZfe96DYXxMwg457N7WIf3XXx/3862oSXsRzbrWS42PW1F57e1NOXdW0Ea2NXzUEb2UM6/w0fRad1KHpGohUlpZAWsQifhYzOVaxsfayEiqKhGtzHGSbNMdkLSdqRdpGsQ2kQ0R0GzG5NA11ypVqdyF09PTeOrUqbgWOKDK+bMnkskEaVrJAqyxAoCT0sCtbgT6Bzy/kT6rVasio3nS4gGgPeu2B9i2Qvnuieq5IroENmkWL2oDgCYq4e3rsdpRBhX2VxsZcZ5B0n3aCy1CssGi2a/suCRv+MzMjEHiJpHn0e/LoGuR65wje6zqxZTnD+nQM5MARBNOnRGelzRGqYSetOnyLXCrECCHDfAbGzoxzzNxJU3fUek7Gs8UPnsqflaUQvIq0RuSjI/9/QP40ksv4ZEjR/DQoUPx57eh2njz2qsmPiLaPAFJC740HvXYJ4zU3mHfGJ4Uz1+t1qy5EwrN40pjEZO9wPI5du3abRghXI9WyEicY8eQF4e372aUFPtKZa/Q/2lTCux5//4Uz+2CM2a4p8fGR0SVu+my/ka4neHj+NhYs6KHnarDjdZ8HZWqe/RFEUYQDiem/31rr72OhEKRM3xcHukEPk4C4JH4/83gbqxLoMlWbV1gY7GIL730UjNE/Cd/8ifjeWvjo9ps8nHRig4p493Z5tg3z+fHQuk81aqNj2QEC2FH+/gIAEZ4dXv4SG0YRW2YJOPFcuDjE4Hf17BdfPRzI3WxvHRirG9fh1QpXxG7RymtLGqSV3YKH6XQ+sXMcZJs0x2QNJ2pJ8aT6Bag3xz/JevWJ4QBXkOdh0jWn8fiydmNfX2bxDJJprLDNtBeK5nfQsQHHW3k3hb/5SE2tw8OOoOX/+YBcPMgpE1eGQBHwFVYCgC4GlR4edKEeAh4X8iAMzw01Gw3z5Wqg1mv2rAKg/Iuj4DpVS4B4HZQLLD2fVwATaCzbvVqx5u2r1rF7igyGMX74naEShQk9fU0aI94AfzecJthnZ7J7l27mjVbz4ACqwL73UsvvSSU46nHY9X19s3OzorMk5/61Kfi9z5LeR1V5MZm61o0tpOs3WbObIP1TxIbbzvzPBNZQn3HDU93C3MoAlW9AADwE8CxjY8DGx/fjsRWC9CLhUK/9ZsI3XHkWu5NjJTxUY1PSaE5gnbOeMgzoTzqplGK15w272ETugpLAVVUxySGmL1D+BhF3Ub4oEliU0ezZrXdhi5UHvwSO2Y7SkqkOk4rvLY3rVrd5zAC+8nz7HvcnniPyvhdxiRPT7VaM9pMz6NWrWJ3jIvHBXycnZ3F0XpdjP56EuSIMWktlEg1+c3UQRl4N1vXIUOwz0A74zlfho/LK53AxwvseX4C3I11LR5z9P7toKLYJuMxu6mvrznOZHzV+MjLgKbVIf0Y+Ybm2CcZHLzdOqf9mwcwHT6WUZGdSRi5OZ7nSdjxbwLtyLeJj0VU3uURNHPJR9ANyQ7joz/qJg0+hrzx09guPqpxy403Z1DpeCpHfnZ2NjZc2uOtNR1S733ktLLToPT11R3GR75/WOwc55JtugOSpjM1MNXRF8qnPSkHEGAvKsXJnnyjaFp/KA+FvD/SpCigqh3qel1cK9kCqg2+qZw+ZQ062wsLoDdqFDYS8tzy35CcOnUKH374YVaLW2Zl5e+9CgMA3pIiXJ+zsHNvwXGQiUYopHvUuq8J8G9ux61jZ2dncW5uDqenp7E2MiLe3zi7vuQtKUC4LNjbQed++47pLxZFEjcKt3lS6APy8tM4+qmf+ins67M3NWVUltBnndwfOyxHWzyJXVnKEZXyNm2LqOQF7YvH9OXmuLDrondqnmciS6jveIrFKCjDls32TBwKEwA4BBHmoBtNJUOyjtP4kZQGYucmDB5G2+uCaGOkbEH3l1XxKSJ+TBoe3i3+jtpy+PBh3Lp1u3V9uz30f3IOnN6QBgxVTn3YUJ/0xq9x61zSOtWHWrHS1z516hROT08LtW3p/sqojclSxE0Btacr6R4fTPy+WOz3koumxcdTp07hm970JnzVK15hHNfcfAvcGhwjyWPe68HyJwFE3g97U29zroTITzN8XB7pBD6WwYx02AvuJqMLlGMAQBmfyVvOdZdbxDlq4iPfdKfVIUMYyXEuRJjl+x2i1iF37dptXd+HkUn4eCTQDlXWqnV8jFBtuket86XHxxMnTuDMzAyeP39ejLhR2B7Cx40I8GjgHt+e+L0PH8fGxpupkUp/s/sgh4cPH24+sze96U34ile8yjomnQ7p85jnoIBjscNwxxLgIz37TsxxLtmmOyCtebqTBjffZEuTr4iKIK2EAHcLg9g+r6/etzlQtHXuw2iTzfQVS7gG5A1flz2AmeIghcgVQG8ohy3WbKk26caY/ZtexZ4e7GX1Re06k/aEaACFT9NmjXmPYu89KUZ8UbOtW3OgyqERk/KUdQ2eYx5qDx1LJbukTXoBFDsiDx17CuTNP/21vfG8r1dZ90OvNOHppBgWwSXvyQPgRz/6Uaf8WW93N+7efYfxmY+AxZwbT8Vj2t0467QM39yh/NwL6FqKuZKuxk0p7p/Mk7M8ktaTE5rTPeJzpRdZxwtoelVC4XeugmiHjSVhpEpbkBSaLuT4HUV9zdB1sxY5/426p2q1ZpRKkWqTVip3WXVuyaPA14xQLepZ1JUBpPA+baQ1PVr8f8o3PI06Wovn4PHIrKS2kJGER62466DOzz+DZh1de97z92TslvqbxpFP8U5qs8LHEqiNb924vvJQ16pVx1Bc6O423ieR8NDcoDXAVgyfBTDCjnlDOUt6V3wOu4154XyEj3xztdg5nolfOoWPnNzVp1tQlJ7LWs75X2i8SzWUW8VHIh2TvM1llPDRPKeMkVQDnETCSMVvpN+vW9eNJkamSRnylQsbxfbxccq6RhqskfBRPQ+FibaRpILp8TFCPz6S46o9fNT4W3TasGFDb2y8NNfy7dt3YqWy1/jMp0O6+qPZN5+E8Oa5HXyk49LqkNmmu4OStjNDzHuKVOE4hsNBtsQTnqxLPkbfOkoWy/7+AaNdOizHVXDy+RKuXtUlept93u/Z2VlvuDiFWduWfalkViH+TdIGuyhMiAIoshneJntSb4QIfyH+3+fpljwYEQDOgulNp2vcE/9N8rzzSS7d0zy4m1sKHUNQG/cZ0DmAtNG3f8P7OuTpTmozP18J3AV87apV4sI+0N+PR48exTe/+c34zDPPICI6OT8kbnjaLCriJX398Ny5EbVVF9Ek/uALUd7oryxncXkkbc5id7x58I3H9fEYOxO/fwQAh4xyJjTXuVclKZw7Qpckp+AwsiZhJB9X/jZo/K5Wa3jp0iVPKOQF5J4CEqk2qVK0aGPvWzPKKOfMFVF7TyaF9g8gwKVmm/2eHJ8ixxXJB+K/9wTm8IzRT34PtGRMjuK+a6Bch1ZSyOrx592e63DFMUnhVGNSIugpgDJ69lmfF3M53LNrF95333146NAho7a7Dx9pTsyCIu7k15cItpDNmxyY63UDNHnbJ8FfqSLbdC+PLBU++tb8W6yxSmkLbq1uuYZya/hYRNO4xbGCr9kaHxcWFhLCxdNjpLmx922wiyhvqgdwefDRJglLi4++9KF54bpJ+EjGUPs3hI++6EOel37B0+Z70Lx36fmsQnsNzuWKODY2nkqHdPVHusezaN5P5/CxBjpVI/N0r4Ck7cxwyAyRRoRYadda55E8OQFSNGugaHIfmbhiEtRG7wDosiVp8hyIjXXSOpbnsIWIxGijaV+TNsc+hWECFAHNQH8/FnM5fBBUnb2HQClBa8CsYcq983XQId12+QAejkJe0zJoL5zvPriH9bjnnnyKWzmhb6hkyNvjz6S+lrwjffE9Unuk81MoWtJ92debBEB7kVIlK/R7O2RWHqtxPs7p0ynIQjjj8xxqQ9RBdGtOqvveW6ksyTzPxJU0fbewsOBlELXHmj13TGOUHSlBPAH22EkmImsVI5WB5xjqcDwZv3meNOGj75yuMiddl5QM+5rJHgCAT8bnILb3SVQK2D1x/23BKOrGarXW7APT+0QecluJImWV2qcU33C0ip2L6FsHZWOyWVuXn7eGSqmjcSAR2NljhtJS7Lrm5rkJH8+0iJEfhuS0Ke759q2PZLTl+JjWsDoH2mB7GAD3WW3pAqWIZkbJ5ZGlxkd6+aLbSJcyPd2d1SHV52cR4Ac989rFR8Tlwkh7w0nvJ3Dp8XECFdZQpGtafETPPcmGEj8+NuLf0OY6LT6WUKdtJZ2bHCZp9j70cg2rPh0y9OynpqaCVSEkPX6GkAAAoFpJREFUfCQD1EFQZcZuFDCyFR0y23R3UFrpzOSQGZo4yYNowwYKGZQmGp33AeEYDWwUEiGF5JjEa/Hx7CSt5DmEymHZod38GFJkpkBmEeQLCyndPIQbAJq54WVrwvD3pOBI3vmkcBQe3g0AOFqv43WlkrdOOYDa6FKb7XuS3lO+FS2IPLyF8sQXwAyJ8fW1reTtA1XjG0CFJ9ptroMCm9B5j1mf93lr5hab7/1Wahq/jzsLmkymVEKtFPNQONuqrpk2bwSVGzlary/ZPM/ElFb6rjYygn3CeIzYGAyxisqeUBt3k0uAcYx0c+ZcjNSeiJACqPOkQ6RqbtgiP+ZM/PmU55rpPQByzp3uQ1JwXO9T0j3SuhY1z7Fv336nnJvLFExemTTGZArdnGDX5dhAz6nOzu8zZkuYoXI2czmT3I7IRQkffQRnPoysgxz6W4fkSDA7pHx4aKi51kpknCXQJTWPg4qkuhvMtcBeF8qg8PG6UmnJ5ngmpiw/PspkfAUA3AER5qGAndYh28FHxDDxZOcw0hcls9T4qM5Vr4+mxMc8AnzG05ft4CPGz2owsZ9lfDzPxolNclmPf89Lg/nOfcz63GdYlXVIaV9FVXFaxUeKbk2Dka3okNmmu4PSSmfKITPEGMknyjAqy5M5+fr7B/AjH/mIMKEX0LXW+Sc9J7ByQ3L4ZIyPZye5DLLnlOc5TE9PI6KZi+SrD21b6fnAp9c4KK8Cv2Zo879j2zacmZlp5jzbBAoRKGWJKzhzc3M4NTXVrD+bFI5SBhWSAqDD8BYWFhxitK2gmJcnQW26uWeZPNvPgrasXRDuX5r0VBec33NooT0LUm1zwL7eXud6FG6zL3Be7sV5ITDuVF6TOw7NeWGC+/79d3uIQgj4KdWibP21x7SaHwUwwyezOrRLL61ipM0TQLmK3CA1DGrhs5VP4kuYBIU5u5rnsfHxttQY6ebMuRhpegdIafDnSU9PT1vlyOT6p64lX8qzpHnAw8iTPfmbN9/M6sjWhTlTQKWIaQWHwvt02TWfEkVzdFtzntGzlUNGP4mc1Va32WdMviD0gaQUmlU7krEJUBkk0tQ21+Si4xAub8kxMlimC8z1kfqNG4Tt9cCeL/Q9J+OkiCwewVUHN5qLjsvwcfmkVXy0nQMVa0wl4SNx5UhkfHpc8bHeGR2yHXxE5JFwconF1jByu3XdZIx89atfg63gI7WX9McwPt7UnGf0bF187EKAR1BX4iig6VkeR437FOGXBh/LaIb3J/eFfh3DdLXNeXrVvsC5uac7ZJRxdcjk8mSt4SP/mwYj05JNZpvuDkqri40MIjVUCoetGPKBXMYoKsb5rZLnpoQqv+I9SGExLiFVnwEOyYNbWeNutcqFcaY/3tZx0HkOu3ftwpmZGXz++eedDV0ZdB1TEju02x7kFA7ea11zdS4nepapLBeFPHnZWdn/dtms0O/4e1sxIdZGX1h9by6Hz4LaYJeta/IJz70ge0BtIiogGDxiw8G61auxAMogcQyUsaMAgGvZ/Ugh7KV8HmsjI3jkyBGcmprC3bt2Na9xAeTc+VI+37weff6G5n34FpuDyMO/bRKKkZFaXHORL25dwqanGM8ZRLVw0AYqmRhlGHKpWPQ7Mc8z0dJK3xEu0aaZWHY3g0rhsOcLx6EyAG7I5QxywQsA2N2MvpiIx95EjJd9Do7aClQajHTD657EUNmdXbt24/T0NPb29lnHKZz3R4LU0c1DL6Ei2TTLceVyq1HKV1y9ep0Vmpd0jw10Fa/Qbw4Zv/Phoy9kNJcjg/MFlI3JZZSV4D2oDNbmPdMzVeUNC6gUTBoHBVR5hdJm3+Q32bp1B05NTWGtWm2uV4ThkiG6mMs1c7rpc8oV9Bl0j4A2wNr42Gg0cPfQkMF/UgbZa76VnXsBAG9j/WdX6eDtyPBx+aVdHZLqDc+ByvMvpMDHYhRhX29votPkPQD4IADmhbDi5cbHXbv2WMfVEeDJQLSchJGULlIyzqfClmWM1Cmh6fCxWq1ZbW0PH7UxNNS3l+O+tMswJuHjDCpHn/tcNT4+iypK4AFU+5IuVEzn1B4XH6OoD4eGduOpU6dwaGg4Tiei8z+JMuFoEXVOd7oo3SQdstFo4PDQEBaiqG185FxLaTGy0/iYbboD0ipg6lCYM6hq4NFEJYCzgWI3SnmpyWyEgKrsmOkhHBmpNQdHKGyH2rSegfPjoEKrt4Cul/0GAHwu/m5d/GoqffHxEtEWz1mzS2elUQRuiP8WNmwwPt9YLOKlS5eMe/SGt7P/u9lEPQ7KI1CMN8d8Q18Gd7Nry5EjRxKv+9pXv9pocw2UB5oIcpK8IKZFWk96WiDsGp5d7BqFFvo3b73fKFzzs5/9rGNFTF4ozAWUj0d5AU+Ty/1sHHYO6CcVjK3n7BkAKM+/FM7ZiXmeiZZW+o7m7BlwozJoY2Mb5HaDjqKhCJiyNSY3iEreJbQt9pxvwMRICtWzww/zqKOSTEVlwwZSWB5E7VlZF78AZcZxFdFk46NZOss3H+y5mMOenqLxWbG4sYmPiGkIPmfY/wfQNYbZoZBllBR0LoSPvmu++tWvte5jN6q1soFuZJjdB7Nor4X0TD/60Y+iS4q0DnXZHNqQJ51frzP8/V4AHLLG3Gi9jq8fGXHwWsJgKc+7NjJiKHN2VFhIKeShlETCRetSmrzfDB+XR9rVIT/UBj6mcZo0xy8A1i3cXBw+EgZ2oyIE7ka16boH1VqeDh8BIiOnd2ZmRqjfHPLYqvN/6EMfcgyfHCNbwUelh1BbyygTtIXx0TSGJunn9Kqgwr0Z1CS0SUYC1+gxNjaOL774Ytzn9lpZiP/W2P9p+pefZxQB7jK+r9dHcWTk9cL1ks4f1iEXg49k8EyLkZNLgI/ZpjsgrQLmuXPn2CDLoWmlSpooesIpkCig6bnpQU2GQK86Ahx08mMR01gpFXss1Xq0QZqXp8gJ3/P3FMbBB/yJEyecMCmuFEiD/ED8/5Pgbi5v3rwZT548Kd5jmlA+8kpTLvVB4Z5sRYs2uzajYhpiG+o3ABXCPQ2K1Cbp/m8EHSVg17oOhdJ/CABfk7J/yThSATMtgFjTbVbb06dP48GDB/H06dOWtZQvNmRlNr1HtOjIRqA0hiHAapXCl5I93fx516TxkBBKmSmV7UsrfXfu3DmDL8H2bifNZRrLpHxOgI746AUyJJEixyOACjg0NOw8f42RZQuj+Pv7UVZUbrHeR8Ix9N4NhT5x4oQTMnf99a8MzAeu9LkekOHh3Y5FPkzw2bD+J+X6oHM/domesbHxRHwMK26cmX4YlWJ5b6AP7m3W0bavOzS0G13Ddgl1zvc2DJXIORaPwb4owih+z9OvJIxsNBo4NTXV9JJTiDc36HaBW3/ZVuZsI3IapZBexG9CcyikkNakOZbh45JIK303Pz/fjMprFx/pt92gIi/OgjL62DpVHZSuZfMHkITxkSoQrEET7+i12Zrri8PHrVu3s/c+fCDDoYuPmzff7OiQreHjJGp8dI1/afEREZlOk4SRHB/3oTJMHg7c/43NSCpJh5QrepTizz+EAK9J2b88BJ+nBbi13jk+NhqNReuQ7eAjkRS24umuLRE+ZpvugLS62OgcwQk2wIlZNcmapiecCr8zJ7QOJfRbBREVUFIY8chITfBWlBCg3Cws/7YYoO2Q7wKYtaClkPA66I0fEX/xAb8GVNF6ewKEFo5x4XqlfB73VasOgA3094sh6Jvi/0l58uVSb7n1VuWJt8LP76pUcLReNz7jYSaj9brhKX8cVEmPLazNxfiV9v67wYwS4BIKpQfQm/xQ//o+Iy9iUh7LpUuXYtDU1121ihY3f35YO55uAum5uTncvPlm1PW6zZBhgAJuh8h49jwnvhn5kFBzMVMq25dW+m58bAyLuVxz8SvH+BEiqyKyRxq3W605tW7VKiyC4iqoJXhvOD6aCoCNq6tQKYNH0Fd6TCuNRVSKp6TM0HilHEhuUOpCgA+gW/s2TW7cuHM9n3HBr+RsYv+PoJTHd+utW3B6etoJraxU7nK4GHg/1+ujwtrTjar831nWh5usa4ZYfnNiTdd0G/0IVenOBKyyxpmdQhTCyIWFBRyt11N5v21lrlVPzunTp3FmZgZPnTqFBw8exByAseGvg7v5L2T4uOzSKj6W8vlF4+MjYBKwSl7yQvy5zaZPY2pmZibeSEr4WESNa2/DZIwsoi5t2Al8DG2SO4uPKiUuEtqh3j/88MMt4+PCwoJwXYoSqKCOhB3BVvicALqNSCou//2///fAb1vpX99nKpJoKXXIdvBxZmYGd2zd2oxmpfQ0H0YOMIzsND5mm+6AtJOvqAbOEfZ/ujBaDVJ0HDEu+mqaaqvg9PQ0VipmeEeydTHC29nnvoH7YOD7huf/SZCt+10g196uJ0wkqb71+NgYPv/88wjCdzey/8l7TRNNWnRqIyNOHfFiLodd4G78yTPBCU+kUC4qQ2CH3/vufwCSLWqhUHpqR+Q5fxlMrzb97m2gvOQ8soH6NymPhXu/XS82WYPPGmAlK+JdqBZlN69MZk6NEMTazboPfDXmM0/O0kjavuOL5gx7fmmszmetsYygqxpIGMVrce7etQuPHj2KeysVcTPkx+UtKY6ZxHAtaIrQsL0mfahKzHDrfhllBTCy5he/nkssRF4WHeptK2783vLxe9PLQNcdGakF6oi7XgmX/MZeh8YR4GZUm+BJdA0eUu3xsncep02n0n/NPo6ggEMQOfi4Nh5HnwB5DfJhJPfuUA4nx25etoYrc7Yxtwx+jo/5+XknmqwA2vgqrU0ZPi6/rBQ+YoyDbwv8lvBRp7jYczUJH/emOC7Z0NUaPh5HSWcwGbXD+DgyUkuNjzo8Xcojj3B4eE/L+IioMNLf51RdIYeu0cPlc1L9MY58k2rLwYMHMQkjN20aYG2R1qAymoSghK1vQ+Ul52sKiMZRLu3okIvFR0qr5LhoY+RWkImMM0/3Mkk7+Ypq4Nh5bTZTK00UPtloM/EJa1Ik57LqzbU5OXOx0nQcVDj1M8Dr3aoXbVB9m7mDge953vQDoL3evkVCylnmikAr9a0jdux50DnT9Ho1qHBtygu32/I4O7Yl7zCbfBuLRcOCLIXG8/B76f5pw/sc9algUQuFtOfiPnkKTAu3BCrjAPgLwudlUBEBafP87Lb5Sm/Mzs4iIsaWX5sUZA2WStcZnxFYy8ypBVRha88hwAMYRQUcGdGhplL5nSxncWklbd9xw9Ece9401znbPz27ojVOc6BCxPi8PGadx8ao9aAWW87sfBm09yhdXl1SlFKIHIbw+wE0vTpSfrFUqWKNdZytoLjEN24pmguo+UX03AR4M7rGXlspJiVY+k5mHCapVmuoSXaofQfZPXElc4GdVzIWqzU1CR+TPOS6DReEPtbX8+FjFyh89YWH+4Rjt1S9g+d2j9brzvqxynpPm33COntd3AKqTvc9oEKHayxCLMPH5ZerAR+1/lhmWML1Tl9ON6CKXknCvzcEvm8FHxFlorYINaN2Z/FRe7o7j4+IiBs2FNDER4k8jYfg++7/ZLNPJYzUtdbl+1ARtmF81O35BeHzcvx718iQJGl1yMXiIznTHgClbz9gYSQn0VwqfMw23QFp39NtKwELwmAqoyqn8q7AJEtL9x8OG+GEGpMQJt86Ffiee7fp/AuQPt+iVq3iaL3eHOhnrOulYRj0snaD6Z2gtkiKTx1M6xa1Y0poOwGaHfI9D8pjXRDaYYffvx2UF7zfakcE0AQYWyg01/Zi06Lr8/TRs+apAatAyDFk7ZRCH6XcJBLl1XJDyHK5YrMckR6jFMGhLdtk7bRDLUNjulqt4fT0tLf8DgfhTs3zTExpx5OD4Jaro/Ie9rw8CIAbwK1uQFjjKy9Inz8IJlbQ91qxTYpASuYRUGP4TOCYCdTt5rgeMqaq19jYeBzFVEBN6OZbZ+xrH0e91kiKZ826X/r9PLrhlLzt1IYpp92Ejy57OnmcXAOxXVJIpWQVUBmmZQXMFp3aJUUJSH2kn0safOxj+IgtYiQpgGWQq0uMj40Z84OiOPj6ykt8peUV2VetZvi4wnKl4+Pbm7+xsS6U083TRNJg0NLgY7VauyrxEVHaCM+j8u778JHf/9sRYAOakX/q2fj0NH8ovWRUoOdCkbY8IqkPlTG1mNBO08jQKR2yk/hoY6SEj7VqNWMvX05pJ6dblzYYEQZ4NwLk8NWvfh17sJEwePviSf1sfL68cK6SNfFlcOIEWn2gLPZ8cxWBa0HlG7AyuCEclNNN/0fQWr1SPkGkOqXF+NiQlXYY0rN20/fSBp0UKl8d8QWh7RSuQ20L1bxOCsXnljifVY3yBe1FlxZNXx/dAGZoeagGeoP9dnp6OpWSFiIk0WUyZKu5bZlNEy7aV+x32nXx4sVMqVxmaSdnkcoxFcHFlg1ghsgCKGOWz6hWyucxDxozLoC/tI49R0piKF0fasUBUQ75tpWLCN2Q6FL8W/KU2N6QZGOqjZFmHhy1O+Rln8FwHdWz8d8D7Ds3J1LdzyjKNXK1l5py6MzSOJfj40IKui/U1FTAJFlYWHDyKNX6+PbEPnoV5FrGR2wRIxcWFppkPklrIoAy+EqpQBwj06QbScSgGT4uv3QaH3vbwEeKyPPho3pJm1wf58WANXd9+Dce+P7li49ch9TtC7XFJr7kbVTPpr9/wDu+pHxqVd88qY/Wowod56HlIUeg1u2mp6edNEEp9DytDkmRIISR7eLj0OAg7q1UjHYRRvJqS2kwMtt0d1BaXWzCuWy2deyxwOCFmJVVOtc4qnAMSDyHFCI9aw1COzS7DAqcCeTtElPGgATA7eAuDqtBhS5x6v6kUI3Z2VkcHhpCADe0z6eozILaeIcm2N5KBYu5XFChqoBA4hb3RzGuEUznHI4ZY3nOVVI7ePg9/3xGaIdkDZyfn28qblTDEyG82bef1YcC7Zxhv92xdasToiM9w9Am2Z87VRbvN+zpnkSAAu6AyGjXQH9/qvYudp5noqWVvpMsyUX2vz3vBwHwRGB879i6tTl36RwFaw4X4s8lT/cOEaMX2KUuoD+MTnkKVq1aKxxDHogaKsXG3Lgrr2yESqmZaH7uC8tbWFiwCHv49ZKUn+nEuakV0x50PUXSOSkMkSubZVYLVlonaDMdIhXtRtejM+O0wYePmhX4AGoFMQ1TcGv4aI+/VjDSd94nnngisdRTKxU0KIqtbLUpw8fll07iI4AiwuPv90AyPtaqVTx69KhRNaIAGh8nwJ6jtqc7RKpF83S30S6qua09qTY+0uvlgY9R5OLjrl3D7FxpSonZIfg2RvrxEdGHkSF8lPYdHwq0c6b5+61bdzhpgtIzTKtDlq3xQ+9bxUcfb1M7GJltujso7S42RPKlNgduOK0O/U1WQqampqzSDYV44nHytRrKOeMFrEGUSnGYAFWe4vbBQYfJuzYygi+++CLu3LYNCbTtQd+byxk1ne1j6H0aqzqVOjh9+jQeOXKkya5ue9pp8xqk/o/DQ/hilrRB950nD4AfsCbi6lwOC2CGcidtfqXyag2hHVJOzvjYWLPe4Jn4GVL4fxncaIUCKAZ524BwW6CdE+APWzfa3UKZoCSmaJ9l1owa8XkYXQ8VN0j42mtLplS2L+30XaPRcDYZ0maZ6tAmzVlajI+zY31jtpfNkQfYeRsAeKTZFp/ysQEHB293mGpHRmp48eJFq9a2rah0YV+fydTtblDVb0IENLwUzKlTp1D2InFvffLcjKIC1uujAmmhT6E6IJ5n1aoulhdI87sLVRgikSmFFNYa2uWDzLxIOTIGUeGFqqVLbZ9Dza1SRml9zMecJ63gIyetagUjQxUoatUqFnM5Z/x3AYgKn5ifDUKKEGT4uJLSKXwEANwIciWZCBKMRDMzzfX5Rs943eHkdD+L4fBuc6OnNsYbsKenaLR5aGg3zs7O4vnz53Hbtp0G1i01PjYaDdy6dQcmRzMtDz4qI+waVLnnevOpKxM9wM6dZCB0S6zZBhAf07aJkWcwhI86wtY2ItwWaOcEJqf2uMaBUM55o9FoViuyDeoD/f3OvUr42AdCKTB2sXYxMtt0d1BaBUzKW6hVq7gK8hgJLKljEMUbxQh17e3kQakmSxFdT+EqVNbEC8J3kZepVFIceD6xXeMvKSeNBv3CwgKeOnUKb9m8GfsiU5npiyKsjYw4/ZS00JNI5AldoDwANJm8rOBWCS6lpCZvjH2LVzeYOX00Qcvst9Q/Ut51IYpEVnGpHT7P783gGjQA/CXR7vOcP+/pLzp3V/zbpP4gYNcs4zxU9wwS0Zmb050MviRyuGiEKoRrAZsLDKi0gEHr/snAkWTIIMmUyvalVd4LTupEm4wzkLx5CW1sCJ/uDYxZOyzTPu8YRA5mc0+NDx9N0r86Snlp9fooNhoNPHLkCG7bttOx/kdRH46M1Jx+ColMUNgVrwt0DxLjrbovrsRS+5IVqrPW5zwMsWx9R+GTXEmTDcSqjfZn9vlkvND4Qtez18ILKBH0tIuPrWAkZ9Elw9LjoGvMl/L5pqHbG9El5LFL6UYRAI7auJfh44pKJ/CRe6STvHfS51yH25DLieP1AvCNsL0hTsIBmzdBYaSNj4gcI2mDtzz46IaerxQ+HkA3dYnCtDleyRvgdet6MIqSjAfJ+pTGyJuFZyzjY/J9SimvfCx0IcB98f+ygYLwURs0Iuec5BUPea/te5aiRprOv+aY1w7IxWBktunuoKTtTImefjsA1q3BXYOo6emsQy4emPJAq1Y1K7Mbtg5YgVx8Dj5QcvgQ6M1ZAZIVhxoAToNmNE9ihg2BPveQ+445f/58yzll9uR5BNwNZg4Ab7E+27Nrl0EiQ/eye2jIawFLajvdP1nAeBhiI75+L7jhLxQONjQ4aHyeB9PzlhR+PzMzgxEoRa8blCXurPBMiGCCPj9r3QuBx6vBZVnvAsCe9eub5wtFELgbjqdQ8RiYY75eH8Xp6eQQLh+YVau12Ht2AHXeEnm7tadbrO0ef555cpZW0vSdhI8RJFct4OOV2PntubIvZh2dnZ01zp+0eX8fAP5kfE4bI58EwFXW+B2CCD8JYXxMQ9xTrdbYudMcE/bsSGuDu+nc7szLoaHdeOLECYfAcGZmhpUJsxW8yNtuHZ46y77jYYg7Ua1zT6KbZhXhzp23W5/lURmlXQXMFh2aWI9/R6XIzlh9TVFnqq2t4mNfby+ujzcuaTGSs+h+Gtxw4YH+fjx69Gji+PfhY61axd5crrkeiLgHGT6upHQCHxHCGLke/PhIOmSIV0C9jiHACYYZSRsrQBWdMo12eScu586dszBi+fARUQo9Xwl8tPOxbXycYv1tty+PR48eFZwQpvEgiTFcYWQU/6YblU5lk9yZ+JikswG8GiVj7/r1PeycYWeLabCWnYej9XpTh2wFI+fm5vCWzZtxPZjcSSVgYenxeRaDkdmmu4OStjOpfpz0wHjoIge7BeD5hOaE39hvllGijempU6cwB2rjRQA7Eb9fDQqob4vfPwJu+ShSJFoJnyCFJkRq1h1FzTI8vmOKPT2J+RJJFsxGo4FDg4PYDRblP2iyN/Jk9fX0GPd99/79hlfA9haT57wObig7PUe6h+H42dlhiOeF89ZA5+TVqlUsRBHeA6qkyyMglBfzGCAo9MY+vy+fvgRuDit/ztRuiQWSPz+pTIn9vExQdctzkAEpBL5czNyjCTRJPGjh6sYdEAW9pIUoynIWl1DS9J2Ej4V4viGkq1Jgj32ezkJz58SJE6KxkcJ0I1B4OQwqnUYqsZeP5yYns0qDj2q8J+elqdC+e1McI+e/hfBxenoai8WNqJSqe1BxhkygUgi7kDPd2t6f0Hv1++3oemHICEbKWBF1+CP3cJ9H18MyjOSdbjQaWK3W4vt/EAF+CQG2Gsf7FGyNQ/b5x1FFxpjh93lWTtM33nz4aBNXJmGk7Z0Rq2yk8HRL+Mg3UHNCO7tBrYsZPq6spOk7X/m3OnteIYx0yNUEfDx//rwXH1c3j+XRKE+hayCjjRbnTfCv5YiIQ0OUu5wcsq7mfnLKZbv4iKgwcnBwCF18JA+8IjpeGnzkG9ZhVBjJ8dHHFr+l2a8q4rUQt/1nMC0+IvLwbRsjJUbzEgLcxdomGRHoOzd91nx+bmST7b32Mac/1yZG2kasCZDX89Xx/4vFyGzT3UFJ05nBsIf4fVkAu4IFlsNDQ7i3UvFuTEnBkzZfT1qf0eezoPMX7RJdIQ8rop6sacKbXmD/S8ck/d7OJbc3oPPz83hdqWQcE7H+K4OuM10Ak0SmC8BV+qOo2d9d1jntPuTs5d2ga2uXQSlRFC64Jf7+AdAh/Bwwyta56T1n4pSEPN0FcC1xI0KbB0Dlc0sGhGEwlUZ6XQb3GUllSui5zM3NWeybYS+fbR22LbPckpzLbUBZib6AAIC9PQXjO9/9DA8NZey8SyihvkuLj2Rp5uO1CC4L8/DQEN7lwcjdHiLGJHxcALPEHuFvq/iYrkTNg6jZYpNIAuX5E1Kw9u+/G13vQ4RSPqH2qFxGzRfiGstmZmawUtmLura2NCd5jW1AgAqaoZMFVDmLFVTea81L4ipgstJ5+vTpxHHY29vn3INmFDY3DmMQ4SaQjZUhfHwsJUaeP3/eqHCRhnskNO6a6WsjI01+D9toRO+LluE5w8eVkU7hI4KsQzoEr4vCxwjVxtPGpwZSznG1WsN6fTS4ltv3p16hEoyTCPAC+7+z+Dg/P4+lkunQ0vhI2HMB28PHLnY+CR/5PXQ3cc3Fx764n44hwETH8FF7uiWM3IRumyltSWKdJyOKzwvOn7FbLnlsbNzAR28EZIsYyfGR0s2ksU6h5bdb0aftYmS26e6gpOlMyvHwPbBj8eDoBcDrrAHQBcrbwjeCSQC8O2bMBlCbOqm0CC8RZntpZ2ZmEmt1SpZC2vBRSIZt1SfQr4B5b2XQDOglCBMidVt54LbCUatWRaKlCBLymSB9rW8iJZsE5aHuBpN5na71Bva7/xl/zu87b70fHxvD6enpZh/am+YIkvPpEMMkPPQ6wO7jEdAKJFfIDgb6Y4vwjLtBGTd2Dw+LZWcUwCZ7+QYHb3fCo2hhNPN6+GIiLRBq0SGiFE6kJY6BFHlfmVLZvoT6LoSPD4AO7bYjP/IAWLRwIYSR7eAj/z6pBJQPH5UyQ0poHW1FRTHx5q3xbecVUnhi6x4eRFJuI3RL2RQxOezxFJvD7vfEg2EykO9GpThqVmG3hCW9tlvvzX4gDDD70Z7zUSJGhqsdqLbfA+aacGMb+DgJsne7GwB7N2zAF1980Rk/ZVBpXEnz4PbBQSdHuzYy0sRH+5wTAE2SUWlNzPDxypBQ34VY7QkfyQg5YI3ZVnXIED5GzU2jPxVMSmmheWxjpJn6Qeu3uWHXTOU2fpRRV4lYHD4iYrwxt3UKX8myVvHxODv2IVQb0wKaG1a61hvYPQ6jec+dx0fEMFGZet2DysBCePqI0L46au4M37m2OM8YoBvXrevGF1980aPrLTjnscemhJG+UojHQenxBXB17jLDvk5gZLbp7qC0sukOgVwEKr93FlxrOb3I25K0MQ2xq0ph4818ZJabMjU11fSwSos6Wettqn7bckTe5kJ8nWOgS5bQseMA+Hyg3Q8lDHhSqnxh8b7cvBkI50IBaCIy8iZTqLmtOPH3eXa8HRa2Z3jYWHjsTTOFA9JzSWelTLbEUYjau4RnJBkGJI98X2+vmJdeAUUa1JvL4aa+PmtzRNbT5NqNUVRwQrJoQTHzeo6xY/xec5L5+fkmq2Va72Q78zwTWdJuukP4SFiyFzRfQjsY+V2e3yXhIw/LJbxJg48LCwtMmSlbc8JWpGxm74J13J7EMa8UOfdzwhhN8COHffoJfpK9DdobUWdzXcrLNktYdnWtR9mzohRPO7rHjRgwQxaTMDJUbgZgA27svw5L+Tx+GFzCHAkfC+ASnvX19jbXB7t0042gMNXFR7UxujkwLguRyl2UIr4oOu046DSvM6FxHvdtho8rK4v1dNv4OAqL0yFD+PhQ83r+VDDaWJ8+fbqp50iG87GxcVbJ5ynUHk8bH8nbTOHluu60PnZx+KgNc0uFjxfi+0vCyLL1XmIHL2Kn8RExjJE9PUUWvfAh4Rn1CW3vRh61FEV98efF+MWPvw0B1mOxuNFDCl1EMrAQ4TQ1kmOklDbRDj7WqlVE1KHoUtpFWozMNt0dlLTh5WRxtkPH1wAYJCcFUB7fUQ8whgbKJKhwtgFhgBTAzAFCMDfr42NjXuWRD1raPPKwpSh+T5vqBwFwA6icCNo4lo0JpN+fjtsyE5/H9hCQt/eYp+3T09PNsKhmm8FkFTzAfjcHeuGZAu35TVrUynE7pBzPQVDejwKYIevdgfO63jA/y/jRo0cTx2FoYZ6dnW1GMEiGgBIAbo6Pfe973+uQukUAOFqv44svvmhs2O8FTTDHj+XelaeAk0/RgiPlNPHwMB6iZdeJDNernJ6ebvbN+NgYFqPIGX82c/1i53kmsqRRKiV87AO9ubExknsgW8XIWWgdHx8AtcDevX9/anykmp7muNdhgQA9GEGEDzbP5VMWp1DnxXGPOZ8/UXxedy4QA6wqjcPnAIU1kvJIpWxIYXsw/vzpxPaZZcmkHM8bEeATqD059fhcUpiqqwyTmB4xfv568z59EvJ0Vyp78dKlS4n4WAPAt8XXfOyxx7Cvt9dox0B/P37sYx8z8PER0BFcPnzk3mdRsQOTrKcQiOwgLz3xp5wB2WNJ/ZXh48pKmr6jKD57XBTBJE4lfBwGwB+EzuNjFL8fgyjmPdA4FEV9uH//3V4Pt2k4V+u79mBzveAsqo3aBlTVdyYR4NdQDssmI95pXCw+Dg3ZdcR9+EgYSSW8DiVii/Z0l9GfBz+IavNeQB2ynhxq3yo+cp1IkhBGvvTSSwKLuO1R34ykQ6rceN6OKA6zB9S56pOoSPZq1rHUV7IRehXkjepENkZyUrR28ZH6i5xVku6fpsRx2jlOkm26A9IKkZpt3Y7AZJ+cB3ljugAmMIoKahQZtRgXQN4M+UqE8VrVDmFHIBzpbHxe3/3xzbQUOk3hUTQJpD6giUSThnuBKT/DPvc46LJd6wHww2zS2J7eVeBnCpcYPXmOp+9ZhSzKXEkkwCsL/VQAlbdqCw/T8norLIIHYglNMjCQAcXeQIzW602ldG18bAXUok8gdw8kL+jf9apXsVqcvsVthv1UIt5ABLA34vKCZBsjbPb2NKGTrczzTFxJS6Qmef/4s5sH2SAlYZq0eSmA3lS3io+0wEqb6xA+ToIypu0QvDeEj+741kqhUu4uo1w+hr/n4Y9aKXQZYLmSVEOtPG5g57LbWkQfU7isrDXQrCtrz3Vk3/vDVLn4w+OVZ1wqE8ajiVTqim3wK2ClclfzN2nwMQkjB/r7DXysg2k0SoOPO7dtM66VZEBGkNeZcVBEgPY6x7kLeIRYho8rJ2n6bmFhwfHgEW9Mu/goOYFC+EjXWwC18ebfj4zwXG47tzl5vVZGORvXIlQbVESVriJt8miDeQw1wdpS4WM3AnyYXdPGSD9TuHz/EkaWUeNjsue5VXysVmvO8Rwf5+fn40hD25hihuHryISkMPQoNqjodkRRKT5/hKp6BN3bvrhvJzFNFOPJkyeDTrYZWBp8RDB1/6XAx2zTHZC0nSnlSUsDQdpwlcG06IyCC64+5j5aPB8EvyJ6V6WCiH5vaXDzCKbicP/99+OhQ4eavyHytNBGD0B7tshjTmF7A/39ojW+PyZRShN6lQddssvu5yKAQ8JGVqxQKYJXevrnTOC+7QlL+fi+4yk8SIpG2NTXh2vABZHrSiU8f/58E1yHY7Dy3YsvJI17WR4BFQpps6GOA+DhwFgBAPzZn/3Z+H9fGNdp9tlldg0OwguoNgK2El00FohQ2H0oz6nVeZ6JK2mVSntM32iNJR8+doHrARoF13gnKaA2PkqKaF9vL87Ozi4aH/n13vrWtzr4mKzEcEW0gNxjnssVsVS6LlYauVKoXmGFV71yudWoNtZllLwMq1evM443cwn9yiHAa+K/dnjmmcR22fgYyjnk+Gh72+r1UewvFNBWlFdBHu+sVFLj4wEI4/ojoMPTbYzcHhgrHB99qVLPWb+V2rMAak0rgOtR7wJoGmIzfFx5aUWHtFMLFoOPaTboHB8BAG+NxxWdbwJUROPeSiUFb0ISRpChXXl3CR/TELCqFxGdLR0+Kq97DrU32u+J5fiIiMGSqACvFL4Pl9Pikqb/k8L8R0Zej4oM08TIQqFklNbVTPO+e3ks0I5HUHn2Ackzrl+14Fi5//77myUUfRh5mn12teEjpDrjy1haXWwajQbOzMw0N1g0aCQyLx4GTS+y6CDo2tk0mchLbQNsOR5cG0GuLTparyOif4CFlIwGKCurHXrBPd1JA/eWm2/GvnhDXQRXWR7o78dLly41Pbl8khBDa5KSxBegUL47z0EiCYVuH084J3n4jaiEXA53btvmgObDDz+ceC8HDx5ERMR91Sp2RxFOxs/mAKg0Beqbs/FnveCStuUS2gqgwu2T2nCjdb4yaEb4EgDuE84vRXAkh4G5nm6p7mUOerEoeA9nZ2dTP7vMk7P00krfET7WqlXsjusdT0J6fOQYyTcyp0+fDuLjVnBJiMqgiNp4ZYh28JHmgY2RZSACLSFcM1bibt68uTnf90IOpdqnIyOvjxUpN+xPEQglKUnHUCuNvESNqzAl4aNfyTqe8L2LA1HUh5s33+LkIIaYbAkfq9V98T3rNBWAAq6KS4ARPhbArYkdwkfyBCdh5LDwjAkjC8L5JXzMg7m54WlWtqe7uc6wsf0zgfsgjMzwceWlHR1yeGioiY+cHNV+jhTpl0aHpIhBHz4WAfD14Nchw8a3JIxoIMA8yjnOyRvWrq71ca7wcQQYwaXFxwhDYd9SpZkwSZkPI8soRee8+tWvc/AxTf/PzMywsmKUGz8R9wH3kp+Nv6fP1e+r1VqKZzkVaIdNvFZGzQhfQoD+wPnDGMkN3VcbPl5Vm+75+Xn84R/+Yezp6cFCoYBve9vb8Bvf+Ebib2q1mjUAAH/8x3889TXbWWzm5+fx9SMjxjUHQS/kvjChRzwPniaT5C0izyfVWDwOcm1RXygFH7QFa4D3gWb2lQrHF3M57AIN/EneaLvcmB3mRnkx9jkOBM4tWcGSFCY77yWJRMHOabe/L4DybEjGCPp/b6XStCKGGMhPnDjhWLr5uXjOCu+7e8DM99oEct78OISZ3AvWM6bf8WNusfqibP2O2uUPA9OMxxSitbCw4JT8qEHULOc0A1qBsK2P4marBZKgduf5lSYrgY+IrfedhI/EN5CEj/clzPcQPnYB4K74/0mQa3b6MCgpnL3MjpMwsgDaEGiHn/P3x9nc9NU+NVlyeRMPeD7nCq+t1MgKk4SPpjIrMQ6D+L16vx1dRVvfd7HYj08//bSBj777OHHihIMRdrkyeqbcYJMGHwuQvmZ8AZIx0h4rNj5SO4rGfeiNeSGKHCwbrdebaVAA2niQxkOT4aOSqwUfEREvXrwolke9ANqQ06oOKVVlIHwsgwrHTdIh/fhDuCJhQB/qGtXj6IZGFzBkCNTfcePfUuIjeXLDGGl6ldvByF50Ccc0eznHxzSebhUa7joqzH5E1LweZGB4DAEmY+NGhJppXuLmCbWjYD1j+h0/ptczViIDq30YyasK8dRIe65eifh4VW26v+d7vgcHBwfx93//9/HTn/403nTTTfhDP/RDib+p1Wr4jne8A//mb/6m+WoF/NoBzPGxMXGRjeL/WwkTokFm57AeABViQcCYNr9YGmB9oGo907Xo1QUq/+EMJCshBN4FcBUZyl+jsOYkLy//ni8qvnPXPedKaisxFvJnlUSiMDs7izMzMzg7O+uGfFvHR6Dyn4+DWhzt/hwfG8PrSiXxXgb6+7FWrTp5hH2gDTYXQF5om+cHna9Ss44rg7Z+S/WQaXzy43i/NVj/bsjlcN3q1cb57f7eIYSB5fMlkb38U5/6lEPslvQMbetjUgm8pZznV5qsBD4itt53K4GPaQxyMzMzIj4WQDauUTrLsxDGSMIwnlbD8fEM6FJVYZZc+n4eTTZgW4nhyo4+T5LCVK3WjGelcyFdcqCxsXEDH90yMGbt13x+Daq88uOoPB9l53yqhq67ee/vH8BqtebkEep7jBVi8GNkEj5GYIbeLhYju/Jm2R/fuHgQzDQrSXnce8cdTh540GPPMDLDRyVXCz4iyuVRC6CNfIvBSNqcPgSm8TGNDqnxwDaulcU5rfOgzyTijs9op/lhLiPAEfb/UuLjPYltrVZrzedk5orLmJaMkTYj+GpUWCufq14fjTHQ3qx2YbW6DzdvvgXlahGkX11Au2a2uSmnaz4iHFdGnY9OBhR7HETWcbZxg/p4PapwfnOs1CEXxEiJvXwxOuRy4+NVs+n+/Oc/jwBghJb+xm/8BuZyOfyrv/or7+9qtRr+xE/8RNvXbRUwQ0QtGwKDgb/KoEMfiRRhakqFdrQcAhkPMnGAgVYiyEu5AdxNo33NC2Bu+iLr+IH+fvzUpz4lemvt9tleJr6oSBtYW0ni55LqTCcp6K2QKJw4cQJv3rxZvF/eJmlRLOXz+PqREQc0NpVKuCeQ703jwT5nH6jNAHlcaGPwACjvzgOgoiCKuVyzPyZBEcsZbQDAD4DptUEw81a54eLSpUuGV14aG7bFlTzaFGJ8/vx5L5twF7ihRQUA3D005DwTu3zJqVOnnBDZTs/zK01WCh8RW+u7lcLHNAa5RqMh4mMZND5yfHgE3I04v64dbp4GH9UrxJJL34+jzjuUFF6f8iPVUC01f++W2JHJgXz4uHnzzegqc/SeiJN425VymM+XcGTk9Y5hrlTahLt2hcoFKY9NDWRCT8JOCR+pDA0ZWy6AMrLYz2UEFL6nwciXXnopER8vg/3MTUWv0Wjg9PQ01qpVb1lKn8feh5FUAi/DRyVXIj7Oz8+LxK4cp0LRfxJG1qrV5jNvO40mxkd342jjDHlPfwLdKBd7w3wBTU+wPra/fwDPnz/PrrFc+EhGhGSM9Hue1f1LJbzm5ubw8OHDeOuttzn3mwYf6/XRmDCS/y6HfX3cwJkUMVB2zmtWnCghQBSHo3Om+dVss38BVQST3f6RuP0+Y8ZMsy2Vyl7nPiiykX6YhJFLoUMuFz5eNZvuo0ePYtFieP6///f/Yj6fx49+9KPe39VqNdy4cSP29/fjtm3b8OGHH8Z/+Id/SH3dVheb22Nri2+RpYXc9/32rVuNQXZXpeIUg/cBo+QR9oVJkHJ6HGQ6fcorawDgEc81y8JA747bwdnSidws1D7yMvnC1ckg8BDoTa09odZZ/dycrEAbQe31b4VEwSY38ylCdQiHJ9as0Nr+YhE3xPlbvrbsCJzT9ubx12i9bowhavsk6JrqpEjaYfv0fgJUje7tVq56UkguAODuXbsMkg4u42NjXmboJ4VnGIEK1yfFVCKcs8v8LEXJhytRVgofEVvruxCR1VLiIx/7IYxsNBpNz8kZ8JRkAo2RbxOuK4WbJ+GjVrwKKLGIIyJWq7U4BJCYzmWFT7EBSx6pdew6vM/GUSlTLj6mYdd1yXvoPuxQ0jqGwhNHRmpG24rFjZjLbUhsC8D6ZtlC37OfAHOMcIygcmI2tvP8cDvNxoeRw0NDjlcxaYM0PDRkbAZJkvCxuZZY95IGI43oqAwfryh8HB8bC/LY0CstRtpG/iRC13bwMUye1kCAt3nmfNnCiUk1l1d1Ncel2lBSHvdS42OEAAPsWn6MXCl8PH/+vIORykN+X+B57Eg8Lw/V37XLLK9mbvZ52yk3vIBuCLkdtj+BuVwfbtu2s4mPjUYDjxw54h2PK6FDLjU+XjWb7ve///14yy23OJ9v2rQJf+mXfsn7u6eeegpfeOEFvHDhAh4/fhxf9apX4Q/8wA94j//mN7+JX/va15qvL3/5y6k7U1u+EgZQ4Pvz5887eb1doCzsSRacUj6PeysVl2p/bMxgtyY5d+5cIp3+Wat9FVBluSiXIpTHffr06WaOHnmek+rgzc/P42i9brTJt6iszeXwILjh3eW4X4bi93b4lO31b4VEgZdaOxO49ySysggAC7mc47nvDZzzcKBPZtj/pPTZzzwNwFHe9DHQhowcCEYM9tzEUmbx8/AZfXgYcNJ9PQaxN0o4n1j+Dsxa6mlzc652pXK58BGxfYxcTnx0xmM8DmZnZx2MrFWr4qIewkieX345nifduRxOpMAICR8pqsfO/R4bG8eLFy8y5YB/71Ow/iUqpZK3v4wqf5Du/yHUeZFaOUr2dLvHIdphlmcSf5dMxBMx0iSujG5JPGcFNKb6sOQYJOMjYniT3LDOF8LIixcvivhoRCgJGJUWHw2PPaTDyNC1Jcnwcfnw0ea/scdgCEND+FgAwHWrV4t5rJIOmYSPyfOcVy65jAA5zOW6UXO6+DbGmtRRYWQOVQ6wP4T74sWLVm51GnysGOdRG17y1L4WQxi5dPh4KLHtQ0O7MYqKTj8AbAyc93CgT2aQGwwkjAzn9fMQ8mNoGjPMZ0P46ONUWikdcqnxccU33Q899JA1cNzXF77whbZB05aXXnoJAQC/+MUvit+/733vE9uQpjNpQ1MH2QtLm4Et0gDL57FWreLw0BAWosgZBDycTbLg2KFpPPRCOm58bEzMH+6Kzz0BaiN3HpLziH0DnUKw6VUGOUSTJjQf/Mch3aID4CdFWg1uTl4xlzNCUY8ePaqYg1notWTZtTfn04F7v8HTfjJU3Aiyl3yjMG4IAEJGDu7pfumll8TxOT8/H/Q03mP1bw0AXye0l/pofGxMLPU2ED9vXx42WYnPpHzW49b50ijHmHB9W65UpfJKw0fE9jGSnvlK4yOiGb7rOy4JI8ugCQ3Pg5z+0i4+ImjFm0IUbeXgoeZvQ2VwJlFSGpVnxMzJy+WKzbrchI+bN98cK8qyZwlR2pyHyufc4Gl7shKuys243qlbIEplCKY+XQw+ToGLTTXwY6SvFCbhow+jWsXHdjEyw8crDx8vgxzJR7rABOhqJjZG9vX2BvGRnvnGuByrhH1p8XEV5OMqDFKON5F1nUe5Rjf9L+OETlOhF5EmIpLH+tChQ4hob2ovo9osp8FHQH9p02SMPHLkCN50082Yy61HiRyWxMXHNCzwPNRcavuNKHvKN6KLkRQ+HsJX7emW8GB+fj5FObGDqKMJ6FVDgHsRQOnrNj56OZVg5XXIpcDHFd90/+3f/i1+4QtfSHx961vfajs8yJa///u/RwDAF154Qfx+MZ5u2nQ/Be5GNW+9t0N+HHIAEBZlUIoeWewPHTqUmHsgWXFK+XwwZ6hUKDTbwUPt+AaRalf7zmEDP4Uv03FND+3MjLOpnQellBTAr5iHrFv3Cc8gByocVXoe/L2toNth6FLZLH7v3aDrkVP7PwxqoQw945p1DN88DIBrSKCFmP7nNQil8RAKVwRQXvcNcX9R/c6k4+k7m5Gee4VsxnH+zH2pAnTvtbif+Nh/4xvfmPj8Z+z3gXqLV6pSeaXhI+LiPTnLhY8AcnkXEh8+kmEuadyvBnOO2hhZBE0MtxT4qPrPDbXUCu8xTFaO7kOXKCeHlcpd8f956zv9ntenRZTC0EN1cbvj85ltD5f2mXbaXLCiAjaB32PSKXzsAlXPeD0oksvQZn8S/Pjow6hW8HEQAGdBG4HIaP3Od74ziJEZPl55+HgcFLYlOTvslDH7+7T4KJUIJEmDj08B4JiYm9xlvZc2iNcl4oQqe+VjwVaYMDU1JWxqiUAtCR8vo2Y1bw0jh4f3oJyPrVIEw/gYYv+ejNvZhSY+FrG31yZe44YIbpy020b/bxL6hDblmrRSEl2KLKnt9OpFRZqZQ7UJL+AOiBLxEcFyyCXgoz1fkjCy18JHiuZMo0MuBT6u+KY7rRARxh/8wR80Pzt16lSQCMOWz3zmMwgA+Cd/8iepjm+lM+fm5jACvTGisK/e+OHbAMdr1jrgBloJowdPoZf02rltmzffIKQ0Jg227lghPAPJykQO5I1xkrIpeSDtTe04yPW8bwPlZfbVPefnpfB4Xm4qAsC+KMIiyIaEwoYN4uLD+5KuW/ZM8hprw+5hXa9QUsylZzwIisW4G5THD0B5V84D4CjIOSp8oW2Gvlr3EQIom5DN7nvfWAmBVpKlkBb1J0Gub3wB9AbGrq9M932te7rTykrhI2JrfUesvBwfC2y88U3yYvExArcEFkkIH0OEbBvi34YwshuWBh9LoBTemqP8lVEpYSHF7mz8nkrvnEUd2l1ESUnesKGQiI9mWZ8yyjmTtWYbtm8nZmL16iuWMLnN5E2awAjWYxVyRkrPeZAJ0Oh9p/CxCAqLbU+Kb6wc83w+LTxrLmnxsQB+fAxhZIaPVy4+XgZloDF0AcuISDW9k4x4y4GPZllPvsk+E5jTOZQ3xlHCb0yPrLupJRIyt+KC8hDPxse1h5FqM2zjo2IQHx7e4+1Ls1xXhC77N5XraqC7iVXEclFUwpAhQt1jD6p62eR1nkIVcSARoPH3ZREPzHug/vUZM6hdZeMaFzqMj4jJGFkHtUYWrjB8vGo23Yiq5MPtt9+O586dw8985jN48803GyUf/vIv/xJvvfVWPHfuHCIifvGLX8RHHnkE/+AP/gC/9KUv4f/6X/8Lb7zxRty3b1/qa7a62IzW64YXBEBZxkfrdfF4G9zsGsxT8f+0mZesjpKESMLomnQ9PsDI8jQDycrEh0AoMxaXSjkDIJIPUR4cbzvvg3PWRGiACqW0FalxUGyyBVCeBl5SQFJqyRsR8tzu3rVLNGTQ5CYr3AWQLdHT7F7JwBLKo+Z9T0RLZVDGGqqDPsie/1kA/MH4+LOefrYtc3w8SFb03eBaxgEA3/3udye2Pek7eh6+MSoxRfPxR68yyAaLIiRHQ0ipAj652pVKxJXBR8TW+m5hYcHxWpdBs5Dbshh8LIAiLZQkhI98zi4GI28EFyNyoPCDnzctPkrGxvMAeH3zGtxr4ypHKgRTUmgp9PBBTFJEd+3aLeKjDu98IP79BZRL00yjlDdIoX47IMK8oISvtzzvNYjwSdB10I+BTks4DrpE5XOwdPhIHpNHH300EQdtPKPPK8KztufKYvCxDDL/C4+OyvAxw0dJ2sXHx5vtThtKfR+aXnHAfJ7en0EzNYZ+8wDmckWs10eNPnBredO1ziPAK6x5RB7iOqrImwdQbarTYGTSRl2VFLMx0iy1RlFItkGA3uvc6qmpKQMf0xgi1P1sQlWqjTbDZxBgM2pjAdUiP4tmzXONzdJ4UN8voIvtm1Fmgg/rkO3iI80XGyMPgMm3IuEjYSB9t5z4eFVtuufn5/GHfuiHsLu7G3t7e/Hf//t/j9/4xjea33/pS19CAMDf+q3fQkTEy5cv4759+7BUKmFXVxfedNNNeODAgSWr001kYLaSNVqv48LCQjNXTsrdsjdxkrXeVyJLCqEMWSr3VirYZV2jC5RCSEAb8ibTwOas4n1R5ISKcg9D8zMrhJs2taSkcrCvCZOmBErJksJSXz8y4pCErI+/f49wfgS9mKzN5XA3Y58lsRdDWmymQC9s1Cf2cwktYA+Ayx4O8bNYxd7z5xB8NinGgxTKw9tF56lVq9gXRU7I5hYAI7KDg1bTuxRgf+SlGt7ylrc4/RS6zxvAfP7O2HuZsPMirgw+InYOIyXCx5XCx0ajgXfv398RjCRseA60slu0zlsOjFvb6MfnyHh8TlcpW0A7l3Jj/3U4MvJ6odbu+viY96BWrlwlOZdbi9VqzXlOCwsLVqkvagcpdORx0cohfy78OUvhqhtArQMPgNlP9nznm4+lxkf67tSpU7h7aEhU3laBP9w9DUYtFh/fDIHoqAwfm99n+KilXXzU+kraUGq+WXwMASbjaBs7vWUc1SZSva/XR41x6xr9bOOjTc5YQoC7UTN6a6whpm4/RvrxEQAwirpxaGi3g49uqbXjCHAKVR70abFPbB3Sf90HUHu9uZc8QtOTzb3t/ueS7OmmY3X5SB0V4PZHkg7Zu0h8pLZNTU3h/fffj63i42HQUSTLhY9X1aZ7JaTVcg9SDsxove4lNKPBXAbNYFsH2SpZZ4NmHgB3Cyy3HJipPcaAji03o/W6QxJUADf0kcre2JOC5x+eia/PSWY4YUIfqBzIGwBwfS6HtWrV6buFhYVmrnla5SkCmbxG6u9K/Dfk6X4Q5AlHz2kSAG8HEJXxEVC52/Z3oRx6AJ2DRQrdY/H9FcGfv14HF7CKuRzeVamIeVr0zCXPsNSuPcPDRhkdY/yCJpSzw3oGaTxMTYnzZG5uTiRpkWqVhzyJ9LoZVFg+vfeV4enEPM/ElEVjZFwnudP4aI9Lu5JDEj4iYscwksbqlDW/OEZ2gyp3mISPHNMkfBzzeIlvgZwuDRjjo60A7o7zEUOebv29XnP4c1L5iFI5Hp03qOq96rWLngu/Lzv/uQYmPh4EhemE/5SnahsjSuDi42i9LhrAW8XHgjVuJYx8u/B5DQB/icaEgJFLgY87QOE19ef2rVszfFwmeTnhY0/zfBw/aNPL8UAKi56ysGYSzfDt1QhwPeZy3QZRGaK0qU2zuXRzzYkEzTUiAgLcJpzfxkdATiTG87s1Rh5AgL1oe/jV+xEE+LDzXbUa4skA1N576s/H4nssopy/7kZCUQSBhI/1+iir1Z0uFWB4eE+iDnm0DXykvpQw0jYupcXHHAA+vEz4mG26A5K2M0OWQSfnhoEX35Sl8ZzMA2B/k4iGW+9MZaZeH3WsprVq1VFw7GvwPMQnwd1ElkGFNE4D4Ii18accQyKymgeZ2Vca0BK7saRMIYRzKSkch4eakHJcBDkseYBNxgPWM+Le6rtAzgmP4pe9EHGWRvuam0HnqHMlkzbaSWNCYmleAwpA7MUZUS2SkmW6V+iL/mIRbx8cNMbtZDw2auAq/OcBcNg6d21kxLASSjURy6BzE0v5PK5btcp4NiGSorOgDTtdAGKUQifneSaudAojbWbTdvGRz3VpjtLYk8iIaMyG2hrCSInIiD6/EP8/A5wULYyPiIhTU1MYgYyPC+B6iXdAZLCiJ+HjGEQxE3ER5TzLATTLwWglNRx+qNu1CvJGCSNeCcHGyCIovLHx8ZQ1DqRxsQBC+hMAbuozyYgII1vBxxwAFnt6sEcwygyzPqV1ahLUelmzzs8xcqnxsQwqEs0XUtyJOZ6JK9cSPu6OjdnJbbUJzJ5Ed4MpEYDR5xfi/6nO9zxK7OcSRqpc8wj1ZtJHKHkm/lzeLOpwbrv6Qxl1TrdtVOyK208YecBgMjcx8i6UyeWUZ1ptbk1jQH//gOB9L6AK7W6gZoufaF5f36NkfHAjoQC64rro+jMyHKg63a6hoK9vk9iu3t4+3FupGEYknw6ZBh8RwxhZzOWwqw18LIDyeC8HPmab7oCk7cxQCPEBHwjGpRkA1AJN3jrfeR4A7pVIIoBQE3X//rtxZKRmDNKhod2J1+CbNmAT4QAA7gXAVUbNPRM4ohg4OLiTBZaD++6hIacPk9iN7UkjhVnyezh48KDz/QJo664Tlg6Al8BcnPgzorbdFpjESd/VRkaMa64CBUCbrbYUQRE18fb72Bm3g8x4/kkwF2e+SJIXqQGAPwPuxt0pJQIyEyqCzonZDrL3n3MZiFb8+Pxz7Jn2W9eXaor6yqCcPHlySed5Jq50CiNnhOfZDj6eCczDT7C5cff+/WJZHLpmWoyMAPARUBuugjVPfSSFDWgNHxHR8Gz58LEBgG8I9JMPHzUxmx3eOYAAl9CtwWsrqXL44Wk2v305fLOzs45CtRoAX2vd6yYAvFe4Px9G9nueFd/A1NjGJYSPfb29qfGRIrJK+XwQI5cDH3cPD6cKl2x3jmfiynLh42UAPAJ6M9EJfCTv97BVuztUWs8l6yId9Eb0s2fbBFyEMeSRNTeoQ0O7nT7U3uSydX1bV5bC0BFpw0z46H5/ARX5m0RGNopqIysTvum2kcc8yWstf2fr8gCrUOVwb7Y+L6JiD+f34CNA60f3Xh5BvuGvVum6x9HMAVft2rZtp3AO9X8ZQDT8LqUOyV95kHktJHzc0aKHu9U5jphtuoPSKSulDwRnZmbw3LlzziS+1eOhMF++/A6yEKoQGkXzfwD5ZhwgStw43gMqHNKeGFubG22ypslkE6eh9bw6RDS8HcRu3AXghP315nKJ57Zr8M2BsqTZ+RtdoELKk8JCiVRioL+/mRueFK6SpOiePn0an3jiCTXBwV+SrWT1na+EyAh4lHarP2y2UcnDNjw0hHdZlkkOavxe3gPK61SMIuwvFpulGQ6A6V2JwFxwksYcvXYPDeHJkyfx4MGD+LM/+7P48MMP4x7GBA+gNjF8fvD+T5uH0848z8SVTmGklDdL+GhvfHwLOX/55uEw+00EKgqJj9s0ZRUBVOgwYSR5jiQWVb4pI8v7ZmgPHxE1Rk6ASSBmbDYDpa8kfJwBZdQwDQpdqELKpbBQvebMzMzgxv7r0Fao81DAsbhUDPW/j6324MGD2Gg0sNFo4KFDhzAHfnyk8RDybG+CZHy0n2mn8ZHC+fdVq4kYGaoZ68PHp59+Gp944gncuW1bho9XqCw1PvoMcb5om1bwEUCRy3ZHkeFpT1NaT/YS96K7IeZebyIr2xy/by33GJHnd0+g8nRvcXApVPpKNiLOIcC70N1wr0Vep1sKnScdUoWsU264Py/c9x3leJMOqXLSXeeXel+y7kGKQNqEkkFDpQiZfRJus4r+CZXDPBMf+1g8XpZCh9y5bRsePnwYH330UfzJn/zJlvCxHYzMNt0dlHbycewcmChhoDQajTgXpegM/G7I4wV2nvGxsebmKQkwFCkDIsCvCQChCSkKFrFBMYpixUkfTyyxNDH0dW1iB6qNqH+7Pb6213IbLxg8d0RiI/TVpIzA3YzzsKtatYo9oBcjW4F7CtKFhfIJT+GN7Xi66dXX29tsS9LxOXAtdN2ggO3EiRPB63FWZJtBXfSweRZS2iicAHdhH+jvx5/6qZ8S83WIPO8tb3mLSHLBx8IB1o5SPo+vHxlx8tgiAKwG7pnyVtMwTrY7zzMxZbEYWQB/3izlFhbtsQoyS32j0cD77rsvOA8bAPhrIOeZ0bitCYSMlCpiL+C/AC6WAGhjq7SJowiXVvARUcZIaa4QxqTFRxsjL4AKTzfvqYwSSy1nH+fHjzHjMZ+jSfg4PjaGxe5u7A7MdQkfC6A8wYcPH06Fj7y/28VHOt9hcI26A/39eOnSJXziiScSx9odd9zREj6O1ut49/79zjlD95zh4/LLUuMjIuJ1pZKXobldfLwAMj4ugBnBIeOjHWKu6kwXixutsUoEXK7+qLDml+L/5c3ekSNHRHy0OSvc/OxIbCMPB69Wa/H3H0bNLG5vcJ/CNKHzpqfb57AKe7oBVLj3zp1l1pak46WSbD14661bU1zLZo33H3sWwrrxYZCjMkfrdTx06FAiPn7f930fHjp0CFNjZBThplKpZXzkhvdWMDLbdHdQWi33IBGmjdbrTXA6A3Ft2rgEhMwKaE8alzjDzFvhFrYItaeblwigzXypCSB2yNDG/utiC6GbZ8JDLtV3L1jtlkOAkjzqUlgnWZeIsZGDaa1aNSyu4qaZnWN6ehrzoBatECBsh2QFlYd+iXVc40m+SjhPAfQG4ThAU5n0EaQRiBw+fDixfEgoxMuuk00Lui/P5W0MeBDkjUJX3O+X4z7tjiLsWbdOZJcfAXfRLkNyqBH/rBvcjVYeNMGc3ccDoBUCrpB0ep5nYspiMXKgvx+LNrOpkBYhzVtp3s/NzckbTtBe6BlQm15p3NIx09PT3rZKntfxsbHmZs/GG18YeZJBNgkfEV2MtGv2XoBkVnSOj8fBH3ZK9zIMOYeozc7pvhzPZSnMvpTP41orJ5nmbgHcze1y4KPd363gI2GkHdlQBlZDO4pweGgIb73pJmesFUF54tvFx9Ug5+XSM83w8cqQpcRHxLCHvF18LIOMj+NsDu22dEhq61PAU1X0ZlHrkFSyKqw/JuvH5vlD+Dg0NBx7uY+jChUve8+h2so356HN8nZM2sSbOd1uqHcU9WGptAlVyLi9US4gheArHZ1STCWCNL1RvvXWLSjlYdfrozg0NJz4WzNiVpVCs3O3IxbJJKU2dBofI/Cz8EsY2dsGPhrnTImR2aa7g9LOYnPq1KlmKDGiAtK79+9H2+t8223bmEXeH7ZB5yHRTIYmYOj3Z1GH6fjBikL50tQBNC115OmmjX/ytTjpEC0Y15VKDuEYMcvaMjc31/TU2jX4ePi0PUFeeEEZBo5DmMVwGtwNpsRefhz8od450Bts/l0eAGfZNSnvxKfk2iGgk2DWmqXvydvt65ODYC7ONkM89YW0uR4HwFFwNwp98eehnPtm6C64oFcGE+zqnmfi40H4CTBLqTWVbTAVArvWZKfneSZKWu07Xgap0Wg0Sazs+bT5ta/F//pf/2vivJXwERHxzkol0XIeqmLAF1wbI5OiljhO9MXjO0Tm0gl8nJmZwaNHjzbxgl+HNs12P3F8REiHkY4n22IvT0qHIa4IXxk2wrBO4+OE5/unwNzAtIqPdI/2xrcE6TCyDIvDx6S+6bWuneHjyslS4uPJkyebupFv3h46dMi5xmLxsVkiVdAh+W+MsnuODrkb1WYyoD92FzCKbAdTEdVmUm/See1uuz85Pqqwd36dCREfdVu5zpukq0+j7a2X2cuPo59skjzTtm6fR4BZo1/U60xi3+nv3DxsHS05gWYaAP12EAGeNBjd7QiCGkR4UcC6pcRHTpTWrg6ZhI/892kxMtt0d1BarbFoD0o+WBVw2Ja8ECmaznUj0eUMCqjzVibQZi9PAojBwduNtofqANLg0/kyBJTl4LVsj3otkC9J98qZCn1hTjQ5pDAj7n0J5U+epQkcW97oPDy80w79mgBFeDYIKgeen98ueTPFrsmVSR/5TwTaikyLKeVdno1/b1uZ66AWTWLyzoOuEe9jfVwA2QtHuTS+/iIwbJddnisPPsvlYyDnsd0X//Up21whWIp5nokpaftOGoO02RkfG3Nyqwug84uTxpEvxHCgvx+7QSl9zfkNJpb4FNXbBwed9geJjmKMJJx4OOW1lhIf+XXsfrLvJ4SRlKNcq1aN8xBGSuH4PQB4azxX1zMeDk5W1jScQmfxcXp6WvSEF0DhYwQ6vPHixYst4yOVoWwHI0N9nQYfad2SxlSGj1eOLCU+RgDYs2FD4lh69NFHO46P3XE0CZe0+Kh1yJ9BU1+V9cfp6WmhxnWEKrTb1Zk5PurfSWmWC8Z1pHXE9AaHolLPIuWLDw0NizpktVqL65CTAWECFeHZIALcY52fNsrkYZ8y2quPlQnSenupSgP1K7Gbn232qxt2X0a1jyij2l/kmzXRfTqkzzmzHPjow0gydD/nGYtJ+GiTKHdyjiNmm+6gtNKZGkxMqv9wjb3VKOVdKFZCPUi41WxhYUFgJR/GkydP4szMDD7//POJ16xU9hptD4W585xrE8gKGKrtyq2hjUYjaJml2nzjY2NYjKJmiS/bWrYblDfXUTitUHw7xJMrcMVczvg9/VYCmE19fc616P0OATiMe7KAIwI/8VIlvq/uKMJcfIxtFcyDylsRw1yB5aXGNX8l1scCAN4IyeDmU+rIgnjO83uybvv642YwCajomUh1znkeGQDgocC5JYWgk/M8E1PS9p3IPJqCtGw1yKGQN4N/7iMqrLIrBuweHsbp6ekmPvquubdScdofJDoSeCkigCb5YtLvlgofG/GckvpJKh1pz0dbEef9a2NkTrhO83cA+COBecvrmFOUTLv4GAEo0jMBIwdA4yOlebWLj0n3cwCUl3yfcI5QVEE7+Ji2XRk+Lq8sNT6uB61P2AavpcLH/mLRIZpqBR9NHZKIxZJ1T8LIRx99ND7eTzaGyPXxHehyJpVQedobqHK25Qgewkg3DN6sbc1/T791nXCR9df+fEfifbl1zLtQMb8/iTrnnF5bMZfrReXUO4iSV71SucuTTjqAnIW9Wq15x2eIdyOEjwiyDhnCxxtAb5g5Rl6A9ISCvnM/AFlO94pKq8yTyWEevsl0L5olWngeibmBp/wQsp6dOHGiWQKMXkNDu7FS2csmpZ0bUkYOZiQapOScFC5UqqJSuYu1uZDqtzaplz1Rp6amWirRxfOMaaGiCcO90+KEHBvD2dlZx8JpA0wZtGJ7G6iFbhKUV+YAhPMzJ8BUYksxmRpvC3n17M9XgalUh/LTOYEaQDpm3BAw2r+hzfh43P/2gp8GjM+CW+M4AqHOOev/AQhbQWvVasbOu4ySpu/S5B36xuC94Jb4K4KcvsBzwWdmZvD8+fOuYjk0hHsrFXHcFoCV9ApUV2gaADwLdKPRwKNHj+LGmJm1YF3L97tO4+MqcHPbfLWxpZrjtZERnJ6edvqDY2QddBghx0fCq25QEUFJbeXetjuGhx0vdVp8PA5qwxG6FilYEDg2aWyGrkGG3jKYBt9QykEr+MgJs4ogb/IzfFw5WWp8fCCeG/Y4aRcfa9Uqlnfu9OIjkQV2Ah+1Dtm6/ujTtTk+piMtW4WKVV3WtU29WM4Dl3RI0wlXR6WL0zVuQGVsmEQVIn4AdQ57sicdoICVyl1x3Wx7A0+Rs/bnXagiA2hz3RPolwZyj7pPhww5V0L4iCDrkGnwkXRqjpHSmG8VH2lfkLGXr5C0WmMxmVI/aTIBbt1K9P/dicdXqzV2zii2tEkEFNtR1Q4E43iATyKAm68g5WzY5BS2qLD5AgLchwCV4G/n5+eb5VNsQO8DtXhQPhM/V9Km0Fdz79SpUzg9PS0uKpISSWIvgHyTR//7vCsbwF2kSsJxdP2pqSl87atf3SQN48or97iU2f2FrICcQA1ArsmbFhgLwv2QcYH6QqqrHgGIpHIlEEoqxYaP0GajBwA/E7+Xysj1RRHWRkZSzWtbMqWyfUnTd6HQwzQbpNfecENzbKUx6vg2KTSGt4MKjbOP/yTNIyGfy0eW6cNIUkIfAb3pTPpdCB8jgCZnSFp8tPGD99OJEyecNBWfIZILx0gJH4+DnAsdgTIA+DzpHB8REU+fPo2vuv76lvARIawM0vdnIdyHSeOsBv7wd24clXLc89BZfARQEVdkXLbPneHjyshy4eMtmzd3FB8BlJF7KfERUWFkby6Hr7U2ihv7rxPxUUWORug6lPoQILLwkQjbfHo5bXbL1ve6KsP09LQTVVqt1lLpkEoftx1y9P5JlPO6N6DrKIuMYwgfG41GXBmGcsGlDf7x+D0v9Zhcp1yFoZsprtL4PBMYZ1vAj49pdEgbwyR8pDFGOert4mMpn8fdLLW0Fck23R2UTnm6d+3ag24IeSmeCHZtwGRGwijqjq9zJvGa6nqjCGAChmI118QWtnIlMYenv+dGc0InWUK3g+tRWSVMutCC07D+RzAXKnqFNtpc7AWQb3Lp/zrIbMRd+bx4DzVQRER2Pp1PefXdK6Y4bgK0d4WPK9/xFZAt2l3gRgYM9Pfj3koF+6LIUWwpT5OUgDeD65Xioe9EFENj0K4lTi/peZZ6ex1ymXbqz5JkSmX70glPTm1kBPtsht54vNAxp0+fbo7lELP1AQgrBAVQm+6aNbY2szkq4SNiOoyU7tkmFrIlCR/XAOB1pZKIL63go29ODQ8N4ezsbKpnzjFSwsfL4Gds7y8UnHu4HTqHj5jy2fOx1S4+SuHvtZERvH3nTuyOc9j5GG2AzmHsND6+1jqXHSmQ4ePKyNWKj5PxNfqtccXxkdq+WB2S2mBz4fijMbejy8q9Bkul66zPXh3/TfLo2iWy+OZTv6rV5I02F9MJZzvk6H0dJcb2fN6+rwgBbkdF1Dbh9AsRYsobfOl+EcN7h4m4bWUM6ZCSgbgAijizGEWODrmxWMTbd+5MpUMm4ePp06ebY2xubi7oXLLxcWOzBPLy4mO26Q5IezndZohMtaom66pVa63JVEdl7SoYZVfCE4LYF0PedbJmkQVMT+xSaVPLXm1b0pKvkXCAvQimFVUKCynEn5fBtZbRgsMnlV0CBkApRE9BOEeDLxxpPN1JixUI1y+CsrLZ4V1ckUrjwabPyiB7oSkMtwy6dA4i4mi97no+wGSZ5O0eGhx0NrU7tm7F2dlZx5odCq8sgEnW0he3S8qbT9uveQB86aWXmjmwtoJ66tSpVIs+SaZUti+t5ixKoYfnz5/Hvt5eZ+48GY+fgf5+RNSYE1IYafFOmk+04Eu4s6lUatljY0taYiGSJHwEAFwTRdgn5CgXoTV87DRGSvgYSoGhVyW+Nt0HYdZi8ZGUQQkjIza26J5bxccIAG/fudP47ObNm/HkyZOIaFaKyPDx5S2dwkcfcWoBANetXo2IncVHHjFHqSKEOQP9/YnEb2mlFYx02b/vRh1OTZtTV9dVqZu2V5wcXlxfnmFN4OHndQR4yhvuzttn42OypztJx+evMqrw9yJGUdFJMdVRodIG394T8DJgbki/6VUvN683Pz+PG4tFESMlJvIIAI8ePeqMj1Z1yFoCPiLK5INXOj5mm+6AtFpj0d7IuuyAGyygUGAxOztrTVS5jp+aEJyNMGnino3/HhC/z+W64+/OIsBxh3URUbZi8u9cMNFsi9VqzQBgu9Z1EUxPqm+ybACdu0iv3aDLcHElhoCgDjofmFuD7fvwLRy8tvplMDe5g5C8WB0AMx+ZX3/vHXc4m1n6zalAP/C88F5w81zL8edkHeQL4Gi9jmus4+ugFEopDLQ2MtIkErHZlem8s7Oz2Nfb64bpxARHB+Jn5Du3RMwhhY0X4s+dzUZPj/PsJDKnNIpAplS2L2n7Tgo9HK3Xnflgj1NaIBHNjamP2boOrrFMmk9kTffxFhSiCCdAeSYnwa1sQOLDSMl7NQd6s2/n1tr4WALXk5oUxhfCR44fncbIOmhvRx10eKsPI495rt1XKHQEH4u5HObBX57MxoYkfERQGFmxvq+NjOCLL77oxUdExLsqFe/GfwKUd78mnDfDx2tHOo2PdkRgkc3bTuIj54aRDHa1atVlVBcwMo0OyR0c3NPNMdJ18oyjZtqm/kgiK+b9VkPlOeae7gk0Q9XrqEnXdESqhI+SA6teH2VOOAr5Jn3+Rute7M3xATRJ39Q9Fov9ePHiRYHNnX6TXPqX54UD5HCVl9RN3weNyw0W6bCtQ/pwrB0dsgBK50/CR0Q/99KVjI/Zpjsg7Sw2FFpTrdYEdsBSPOGn4pfaGLvlFFxGwmq1JkyocfRb8fhEow1xAxUZhD3ZTONAvT4aEzW4E5CLyum2gQ/QtsohaoAlQC2DWhRCoVCFuCQGvfikKIO50QRQljebrZAUbNu75GMMHa3XnQlpb3KTFit78aDrb9+6tXm9C+CGb28SQKMErteLFEdf2QNec9Lu93us9kthoDbRkvQdosyAaoc11kGzDRObui+c7klwQS8Cf9mcbisfrQv85DGdnueZKGm173jooTi+QFcmeE6Yt/QbKbTXHisSG7e90TvL5itXNDcL5+YLsVRmyl6gqa0fFtraBWDU3bbnKVecQxgpzRmOjzZ2SYyui8VI3oZQqbdTIOMjgFLcO4WPx8FTniz2bEj9buMjH0d2Hwz09wfx0R4jd+/f76QJbAHADblcho/XoCwlPp5mGGCXLFwsPvp0Gl/orz0HJYOBtIGhKgz2XC+CGa0ne4/LqPTd5JTMdet6jHOb+m8XuvrwKOqSYqYTy8ZHX9Wien3Uw17OX0lh4KYTi17VKt9XuMRuAJvQ9WAX0c4LbxcfJwHwCCytDrlutWkksfGRt4tj5AK4a8aVho/Zpjsg7S42YTZzsxC9XE5BvYaGhpu5dm4I+5Po5raosHV1XA4BtljfF1EOwyk33ytyti4HSMbGxg3L5cLCAhaLG4XzaeuczQreHenJH8rVi+JBzycB99IUYnB+CgB/EJSHZR+4CrSUR5mmxAUtgBTC8wkAHAZ/6OIIyKE297L3E3H7agJQFcBVkJthZLEVmZPM+ZRwKpvxwgsv4Jvf/ObmsXeDrlFbgHQskb7vaGM/NzfXJL+jWr1SmgBfcENhZVNTU3jkyBG8buPGxON+kH0WtNwnhAplSmX7sliMTJWPbNWYtTcylI+cho2b5lMpn8ccuF7iMug8NR/uJG249rE61gsLC1iLidEKwvki8OPjGTaeQxhph52XQFv9i6A2sA/GfTEIspFhMRhJ+XRnQXsnJIwsgkzOxPHxHlg+fJybm8P77ruv2XYbH5+FdLmvafHRt5EqgDJWZvh47clS4qP0/BaLj2VQeEE6zXZwvY15kNNyyux9MZdzPIw2PlJ7KXRZwjB+b1r/fYC1J5TLHAnOL+7JLqAq13UcAX4QFZHxPtROKjNdU8LHULlcTex2FpWHvYb+8O4RlAnW+PuJuH01lPLCzWpItCc4iFHUHRM2t4ePPEK1Uzok7ScoR9unQ3J8RAxj5MGDB/GV11+feMxK4GO26Q5Iu4AZZjM/1pxk/f0Dzu99RBQ+hvGPf/zj2NNTND4vlTbFeeR8QxwqodDwvEeU6hmGa5CblkGe68YngxgKFWlrnDgJrIlNCpy9gADoeqzSM/JNSN5uIqugDfNJUNZm+9pbY1CyAWMNyKErvnvrzuWw2GNaZ2nTqoHe//v3v//9jse5DICvB22k4FZwXx+EvrOvkWRVlKynvvbblk/fcc+xz4I5agLb6mLneSaLx0jf83oAzHxuWySM9LHnfvzjH3fm06ZSCdeuWuUoeyHmX447fMM1D3JJwieeeCLxfKTgUPsJH23vdjsY2QzTtOZopzHSzvEmjLSjBYogb6RXgTJ+pMXHh0ClHUmh4mnw8fDhw3j3/v3O9a4DFRlg42NSHxzzfC7hYy2Q58036hk+XhuyVPh4DHQqha8kV1p8nJ2dxaNHj2K/RS61dtWqtkqWSu99+Ei1sJM2bTQ2Jf3XDDe3UzIL8TFpiNToZW9wywgAGEUFJ6c7La+R66WfQYCTSKTG5rW3oltXvIC6zJfdPvnecrluZ08wNjbOao+3jo+joKONOqFD2udPi4+8T9Ng5JWEjxA84mUuS+fpNid02hwYEhtQtQVwEtWGfhJ1HgtvQ1qiBYlcoo62R1sxqSedT2a53L1rlzEZJLKa2wcHkycB+/8A+MvJ5EEOa5IURdu7xvMZpRzqnlyumcMUIs7JgV68iME2acNBICONhY3ForeOZgSAhe5uLzEdb18oTzLpu60gW7vrnnviCy3PWTRC2wQPYhH8pHG8bZknZ2VkqTw5NMc6gY801iZB52iv9owXmntpcIdvuHwhdtu3bk08H990073tHh7G3pj9OgkjKVcuSTEHgGDJrRBGkrFR8q4RRtq1giF+v7dSUfdkYb79vLn3LISPnOSJG/NIQvh4y003JXrr6HoNUN6QpHb7PN0SPvZFEUYJ95Xh47UnS4mPtHHhVQfawUfE9BiZltDQfu/DxxCGSTrk6dOnmceW9NsFtD3EQ0PDGNZ3eR415V67nmMpvVLeTOuNPOGjMhTk0I1K7cJKZW/zWYSdWJws7ljg3pRnXtIhQ/i4Y+tWXC3gF33Px8PRAK74votAiCZrAR/5mE3CSCklaSXxMdt0B2Qxi43EZq5zus3QlZmZGS8hQyiJ3ywZQGOFMyReFj5v1dPt+93jiefbvPlmPHz4cNOCRa/Reh039fWJk+FGUF6X0CaWb5BDm0ebtIjk7v37RUWRci1pUpdBLRgGsRKYIS+HDx/G0OJB7UtL8iRZ1wjofbUNQ5vls/F7IlCjMCEbuCgfpxCXdiCiuhKoEP7Qs5Gue5ydW7K6S8/8EmiyGHpdVyrhXZWKQyrUFR9rj6lQfdpMqWxfFtN34qIJKorEIPKJ8bEdtlzCR2mBlebrmRRj295wheZz0nePPvqowkiBG4EMdbx/ekF5hmvValAx53l4oTZKGDk/Py9GzPBcS3qGO+K5xzGymMs1sXR6ejqIj2n70x4bXNLgY5Ti/Jxg0oeRpNw9Ht8zlWsM4aNvoz4JncHHdatX4x3Dw1jM5Yy2Zfi4/LIU+FgDM796Mfg4Nzcn1oD3YWTa0n38/WLw8ebNm0V8HK3X4xJhBeQ6dg66MYLIwMd0nu5kEjKfDrl//90obabr9VFE5PuAHag82BNIjrFcrtg8DlHrkElOLJcFPZlIuR18DHFynIV0+OjTIQuB86fBR0Q5csPGyEvgpjStW70a76pUnLm1HPiYbboDshjAlENhyqgJGvQEUVauWsxQ7uZRS+Ju0vlEnUF3ktKr7ACVmdNN77vi/8+gIpbwgUGo9AAgQIRbIYcXQOf77N+3D/fE3o/mIgHKo0OTbPu2bViw6lNSbiVNiDqEra/dQugkomL1LlpkCqQoSsRvtvJJzw4RxYWLA4bdPilctAR+JmFfvVa7tmFfoC/usa4vhVISUZRNhBKBCjGaDlzjbaAVvUL8O/vZ0r1xq3tIMb8LVOj9+NgY1qpVh8k1D8rDxD+zc4E6Pc9f7rJYjHQURXBJvhqNBtaqVTd3OYHkxFZC+ZiaYdfzWeEL1ty0cWcTm7/kHT8L8riVzkfzgn9v49Dg9u2JtUrJoCkp5mV2jcvQHkaG8o9bwcg0njspckDCR3tsIKbHxxIArg/0xQyYnrkLwr2Nj43hH/3RHzlGiQEAfDpw/vW5nKOEDsDi8fEHQIXel/J57C8UMGeFouYgyvBxmWW58DFEWGWLtElPi5GELTaeldn7IqhNzLMA+BgsDT7eMTyMDuM2RAY+jozUBOcX5XSTrlrHUBSoT4es10djDiStt9NmWm/6Ka2zbLTVjnQlHTI5Qpa3zw2pT2Jbl+pZ2/h4PYTx8R5Ih48+HfL2wPm7o8gwFtJY4Gufry68DyOJlPWNoIlAN1mElmTkXkp8zDbdAenEYuOymZt1vPfvvxt37aIwGD8hgy3agibladvsjnxS9mI+b1vm7NJmESoCNim/xTUaAFSs47oQ4CDqUPc+BOjCMYiMxYIUpGNg5v7Ym8Ao4f+nIGxJJaWwFZIg3rYIhNCo+HOet0MhM7ayLnlWJKbFOpj1YxHlxTGkvCZ9vxZk8osGmGRK0iJOtWvTWK7ptQkAPwBaYfZ5qBCxpegG6mseDlewxofPOm1LplS2L53CyOGhIcfAVsrn8e79+3E4EJos4SONXykHkcZvGeSyOoWeHi8jOP3/AVAep7x1nKQUf1KY62sA8D7QSkTSXDoLOrxbwkeJWwFA5WTSuVvFyDQkapTTmBYja9WqyD5OfcM9HBI+lkGTPBFGtoOPobzUA57v6XeUWygagiDs6baf1XZQm5RO4KN+H2FOJEzVa3qGj0svS42P42Nj+Pzzz7eNj8dBju5JwsheAOzKm2RdtjdxB2hCxXbwsQsUQztt2BeDjxv7r7Pmnc3k/RSGPMdJOqTvN5rU8Vh8HamaUWTlfkfoViSicsH2tRbQ3chrImVy2LVazzqEj2s836fBxz4I46PNLcB1SHq2vhzrVnXIXgCz7B1oT/9S4GO26Q5IJxcbyfO9f//dMfs31e5OJmQgcSe7ZPHqQoBeYVIWEWBd/P9jqEPITyHAe9gxXZjMck5gMB7/voGaVdJmTNdt4KGB7373u50J4ivPsjvOnSZlb3Z2Fgf6+5tKXB385S8kRYYTlUhsvmR1/DcBgDh06FATiEfrdTFcfTt4PDf5PO6tVBJrvaat11oCbUGsC9cqxCCzmV3HZ2kMkT+dBdnaLTGWFkAxvtsleXx1jaVFvgBqAedtTGrf/fffn5iDY0umVLYvneq7NOWVvJ5JDz7S+JDmXheoBddW9ooAuC7+/zmGC6cA8D2glY4C6HxaW6kgjzgZqai9E9a1APQC77u3tPhYGxkxWGCJPZ2YgVvFyBA+zszMNPvZh5Fk3HvmmWeaz1gKV+8FTbJmz/vdw8M4PT3tpCjZuc8hfCwA4DbW1746xt3sGr5nMj09HVTutkB7+PggmIqrPaZD+IgA+GvNe5A3Axk+Lp8sJT7uveMOw5PYLj5yXEmDkT2g+Ru4w4RjZAG0jtAOPtrXXDQ+MtZ0rkP29w+gjtSknG7mGIMCjkGUqEP69HbtuX4wcT4SPiJiXKrXDVcH2I6Snp/Pl7BS2cvy19WLp6amrWcdwsciKB3y+sAzSYOPFWG8SZwVE6AwucZ+K+EjH9ed0CEPHTqUem5mm+4OymIA0ya0kHK21YabNsf+ScmJMhClyX4eAcxJV6+PYqVylzV596IOFafXqPDZzsT26FcdTc835Z50o2TRAzBJcN761rdiLp5UExDOp7RJc+wafz6rqi9cWwJ3em8reXXQFtpzoErv2ErgpUuXnIVxoL8fi5EKeaoIvyFglMhN7MWRlN+Dwr3eCHrT7cvXycWAE8qBD5E/0auvt9e5BrGXz1ttcJ6NlW9GY/qCp+3TVhuT2sfJqdJIplS2L4shCrLx0Z47tGG8LTBeffhI4+M8qI0NP/dovY53VcwInb2g0ieM44TPSqA8OUltsjGDj8/18e/OsGN953nl9dfjekiHj6dOnRIZi9vByBA+nj9/3nledL/nwK3uQPOdyqjZGNmbyznXskP7bIyUNg9zoEqQ2feZA8DXsP6TiOnyoPAnDUcIlXZLwsiNlscmCR/p+j6MTIuPCICvDRjxM3xcPukUPiIqjLTnchp8tD12EjP6eXB1GgkjNwp4cFEYk8VAmwCUPiThYw501MyzgfNct3Fjanw8ceKEiI8jIzXWdjdc3adDnjt3Lj7Onweuzrc+cT7a+GjvE/r7BzCKiqhKBJvh2nxznVaHnAa3VGYafFwTj7lQP6fFRxujOXu5hI8l6/2VokNmm+4OSjuA6SNEq9dHrXqBvOj9cVRWrCK6YSVdTl639nQ/iVJNv5MnTyIi35wfQ+WJJkuZ7b3usj7zsZKfsa7l25RPosTmCKDJZiJQLLL2xA9NVGmycbChCT8BbhgW9R0dS55y2/PQlc+LodV3g0k+5suf4u1ZWFgQyy/sKpcTQ1d4/s2vgbtpiECVBtttecmLoMkgzoIKGe8GbZlO8v4VAIw8ex+o7ty2zbjm8NCQQyTHc37Iw5aUb2YvDpRrRNbvJpFbXF85qX2teHEQM6VyMdJq3/kIf0brdcfCDWDmC9O4pvHaF49rO9eOxtKTIC++Nj6St0Zi2C3E17BDiG8GGavOsGv5xicRmx2M39c9c1HaOKbBx05hpFQ/twDK2OatpwphfLTb48PI0Xrdi5EcH8+Arg/Of7/l1ltxKK6CIeEjjbP14DI1h/AxFIK5Z9g0gtuGTHuslQN9FsJHepaPA++D9OlqSZLhY/vSKXy0ozrOsOdO+GiP1xJ9nrDWSpubndu2NQ2ZHCNrIONjUficIkaS8PGAZ+5Q2wgjI8+9rTQ+1kZGcBXkMRI4jfqKymP7FADubhrB5Pk4GfchkU7a7VlYWIg94Pwecri3UknER85x8SFw8REA8KbXvS4VPuZAG1yOw+Lx0dYha9WqkY/dKj7a41rCSM6hcWOgfUsVCZRtugPSzmKjc61NQjRl8eKT7ggbdGcQ4Al0c6jHUW2s3UGgNvarUW2QJ5GTONAm3S1pkLRR5jW5bVbyebQ398pL32uBTRFVqLxpjdPvFfiU488LVr5Hb2Ai2AzYkvhqUV68eLGl3D+JQTFK0UZv/pR1rwUwPTkEkrYHKWe1tw7KW1IAwMKGDS55CrjWaLLyEXA9CzL5xUB/fxP4pPCfAiiFWyJs4eFEPH+0lVIMEltrMZczFtfChg3NeyoI7RsaHEw9V0kypbJ9abXvfIQ/kTVGjsTPm8pHvQ9kL61ErELXWQ1K+Ztk1+J1be2ygYnjVPiMHy8pr11x+5pjOZ7Pdeu47eB60yPQpb6o7T2BNvL77BRGJnmq7O/KkKLOuUeZGa3XRbIkrojOzc2pUHPm7bPxsQzQJOzsAkFRg87g42VQSqzD4RFFTojkcTBrqttjLS1GJpXI0e2M2F93MzA4OJRqnnLJ8LF96RQ+2jWMieiM8PGXAHCPNV6TIv2IVfpGaY4Ixp7QJsrWl2zeGAkf14CJj32g84VtjCwK89zGvJXAx6dAecNNvTJy2jHS5FhwQ9fT4mMPmPnHafDRXjPbxcf/ChrjC/Fzs5/RYvCR65D2WOu0DkkRRT4dkvdpGsk23R2UVgEzXKLgAKpQcL6BtSfsWdQeYkQpr3t+fh4HB4es342jCvU2rdnaCED51kk1C/lnfNF26xdGUQkB8lYbaqiJILjnXBFB/AyYbISkgPAa2ZE0UcHM/UkKpyTx1eqliU5kOT6r6DHr8zPx56Hfpcmf4vewt1IxgJyA4ElQBBI2YBZiMJPI0Ph56UVhRA8C4IZcDrticiVbaaUQtHPnzjmldexjvd6dXbuwLy4PQX0UYk0+ePBg8xlJi93uoSE8efIkTk9PYz/zvP0aaGsl7zs71Hgp5nkmWlrpu9BcmATXY2mHwXHSHN+8m5+fdzycPgWUcIGPWXFeC5+tAr1oS9EchHPUhhrI9UkL8dzmkSmt4CP9XupTqY41SRJGhmpl29+RYtQqPqYZF9wQGcLHIqjQ9lCIeLv4aIfd2xiZVI88AnDwESE9RvrwcXZ2Fs+fP4+9vX2oeVh+DQFuNI4FiDJ8XGbpJD6SXnIeNBmVjY90TEMYRzT35ufnHVZpHys6osKFULiwrS9dBnNTkwYfxwHwZ8CPkVvAZLQug4yPdM00+NiuDmnjo80CbvfVAgDu9oSu836UwprbwcfjADgCIHIM3Q6t4+NZUDrnhlwO11gkejlQkT2LxUcA5QG3x1ondMiPfOQjODw01GQo9+mQtZGRVORp7c7xbNMdkFYXmxCxgnrlUXmEjyPAJrZInom/Tw4Jm5+fZ+QPdt70ePNafKE2w919BoFTqDbep1Bv0LtT/O451EaCkNFBvUghti1l9H6z8DnP/TljfQ+QXJPSl/uXNPltyy0pSmcCv7PBW8qf4oABoD3+/Ny7QYX1GNZZ0GB2awCIjoG2/tq5gltBWcYfAlWeYfeuXc3FZXxszCijNglqE7AaoEn4lnQvHGTTeBCl53f+/HmHXI7yI30hwxvADTNeqnmeiZZW+i7NXMiDXhQ3sf/TzjuqLU2/43OHE4b5NjK+858CrdyRwWsD6MiXpN9OQfp6tQCA2267rSV8jEB5L/g5bYwM1eyVcv9CbeXfkWLUKj4i6lI5vnGxfetWh4k+hI/06jQ+IqIzvibjtvQXi8ExbuNj2r7mz1DCx74iEQ7KKWcAG7zlRzs5xzMxpdP4GMWvIrhEjiFvNCd8dUqlgoq24dfjGBkixqKUmRlQWEn6EqXhpMFHjluhuZADtamU8NHe6KXBxxBGLhYfkfUJ9Zf0u8OHDzvXbgcfX4j7qBfMdZBX00mDjwXQY87GyPcB4NtB6a/7GFHdYvCRnu1i8BFR1iHJAOHTIW9vI0qy1TmebboD0nlP99vjv5MI8IJw7Di65QIK2N8/0BxM1eq+wDV4rrgmWlC5HVS2wK6pbVr3XQ92hAAX2LXmUOWJAyprOpGpJRsd3vzmN+Pp06dx965dhjXzDCgPSS/osMqDoDeWSSFKdVAhPknhQr6JXgfXKloClZdYAtdaSm2x81kej0Glr6fHOyaSAOMeUIvVVPzeIVwD00sH4C/bYIfDlkEOIaqBTKgRgSb7sc954sSJxGuSAnwQ9OaJW7kl63OzTez5+eoDR6DzX+3v+np7W7ZQtjvPM9HSSU/O29k4ekEY3xLDbgHMNI2QcmgziNNC3Wg08JbNm13GaQCnHnwOwordHGhvCBG4hCz29913H05NTeGObduC+DgBKk+PLPVJGFkGwKKnziyJVN80qVZ2DUzs5JE3reAjIuKjjz4axMgJ0DnwafDx/xPGTyv4OA6KEM42cNhhvvZ5T506lfj9WXDx8TKYoe0hjLTx0RzTdZR4W3p7+zJ8XAHpdCQQpW8Qy719rG8cEUYGSwFa84hj5M5t20R9qR9cj6ptyErCx2HQTpVQxNGb3/xmBx9pDnSDwmYi530XaHK4pdAhk/DRt1YRHtu/6wa1zuweHnauTZvutPhoG2ulKIaqZ/zY+MijBzqhQ6bBx+OgK4S0g4+Isg5ZhGQdsp3UxFbneLbpDsjicrrtXGepFra9QV1ANx+6jFGk8rT1pt6/sVVMiWXk+eTmb8vW+XtQ5WbTIl1GuVRYEQEuomtFX4OqnMFldHPBbYOAnpQAfosT/e0Bbc31hSgRyEmeFMqT9k30J8FVnscB8BII7LugLMF9YOaz2L/n4SlEikKgZYNz2fotna/ouccL1vV856VQ/JCFkIfacPCpW8fTgjczMyPmzBD40fHj7DP7udKrDGb0QlpQBlAbEO55tENpJebXTs/zTJS0m7Nob2ztOq80XriSswCuQlEGvaHk+OhT2taDf6E+f/68M06vA9dbIOXCFeL7kJh8e+O560sJ+bAwPyIA/AXhXPy4XNwWjgM+jKS5KOFjo9EQjRVSrWxS4KaFNg9Ae/jowzLyyvC87ZXGx74owgiSw+elMc5TpCR8BHBrH/sw0m7zjHUeZXg/ggBTyIlMeapBKxiZ4WP70gl8LIEmcbWxgG9kUZgDNkaGvKb3gH8zI+HjOCjDFJ8nZWHeJOHjGlBe62dBzsueF+YKbeykTTRhxlLrkEn4uACuwY50SHvuhzCyVq22hI+++7NrmIfwESEcedApHdJOId0OriHHNn778DGNDnkPmBEWtDZTKbKlwsds0x2QdhYbifJfbbht6zMVvPdtUGnBRKTN7KFDh9g5fb/bgmYZLx2erg0CE6g81Qesc4U89T0J90Htysf53qY3fUdc6zDJa0lMia9gk6oIJkAlWchIsZAYQG3PAin7UTzRhoeGsBBFjufmAJuYNpBKFsA+tgHYPTTUZLCUNgu9YDIjcyZanrdK97jFut5T4AITB6KQZy2pPyXiKF9eIS3+c6AJsOi8UrmmHLjKArWJGIntNkvKBL23czv5caGw2nbneSZKWu07afxQZIkUBpcmJJHmKsdH3++2gLxQ87y9Qpx3e1w4V2ij1iPcC+Ed4VCJ4cyzoOtK27/ZBDI+3gAqtJ0ULBtfkpSNgwcPiuW+IlCKTglcb8Lu4WEvPk6Cxqp28fE4yMRlXQBY6u11UgyuBHz0hYf6MLIeX9eO4JDw8ebNm4Pt4t/NNT9/JbocMTkEMGujq9xv/Z6XHerEHM9ESyfwkTZrEq6UPePwIWuO0Hx997vfHcQIPkfssc3x8Sy4hsR28JGHPEcAeFelYmzKyuDf2HGW6zOgI4LI+7rUOmQfKH1m+7ZtBj4+C5o8sR0dkuqKU5UISYf04eNxcHPdATSxWVp8RAhHHnRKhyR8tHXIBsiVKTqlQ+ZAGdb5Z3apx5AOmW26OyjthJeTdYSsLQAPxX99G9kCuuHe9fgYlzVcvcooh4lH6POAz8zMeAwCwH6TJifddx/3oyKBAxwa2m2cfwcjjOATkCaWz1rJJ/T2wOR/mzVppPBk3+Zs99CQWGOblFoOpJTDeY/VPune7O8bYOb15ADwEfa95D0iD04I4B4DXdLjWVAA/LbAb5L68wF2z1K5jNOnTxsWYL6o8fP6yjD5lAWfUiAtvCVw89r4JqcM4bDaduZ5JlpaDZ+08fGhwBgtgLsJJCu6hBs0FqXNY5Qw3olkSFII+G8Ws1F7DjTe2G1O2txJ9/lK4XchjOS4I23yR0BmUb99585lwUcEM1SaSHdog9tJfLwMmvStHXzsthRsH0aSZ9GO6krCR4nN324X3/TPA2A35FGTn3KjeJfwGekKdQR4qhkN14k5nokp7eqQTz/9dPM5hzayvLSVHYnh060kslqaW0kYKeEj/81i8JHm4/T0dEtVFJIiJvlvO6FD9gvXyQHg0aNHnTbviz3US6VD7ojLbhEWUN/bm/O0BtkKuGlBa1P8bjE6pA8fQxhZgM7okL6oNRo/ZQjrkNmmu4OStjN9tbkrlb3xogfo38jmjN+pxfCp+BiprnYXqnDwsvE7bb0OE7GNjNSsa5ZRecfTEKH57mPKuNb58+dTW+x9k4oWjzl2fAigCAh9OSW7wS0jJNXYpvqGNoANQpwzlMsl3tsPWsBBL8naRiFJNQEASmBuaH3XOxCf427r/GRJtT38d7XQn7WREbFcBlkAOXHGGXbetMrCh0Fm2aRyIr6w3BCDezfIYbXtzvNMXEnTd0m1udfH8+isZ1zbJaH43JZwowuUUlO2fkfWa69VnuGj3dYyaOt/GlKXJCWE4yMnevH95pjnPotgGhFaxUh+T/wYwiBemmY58ZG+t+u4jgPgXlg8PpJiF7G/Nj4WIIyPlNvNx7MPI0frdewCpXxzsqPQWOKeMwkjy6DWkx0QoSY95Wt3aD3vRqVjJNfvzvCxfWlFh3QigPr7sSeXwzcExjZ/7YjrwdOYkrBjjTCWukAT3CZhpNROjsmLwcd72HUQMbUOWRfusQCmEaGTOmQ3mPhYAKUjIZqM55QH3i5GvsfTXz4d8jzI4eVpDCoQ36+NjwCyh78E2vucpj/T4uNy6pChcz8E/tSsduY4Imab7pCk7Uy3NveTqDfb9CqjFPatSNV6ESCHp0+fZufy5Uc/iXYIWbFI4WO8zNcZBHgAo6hgWLGlOuLqN+X4/RZ0ve+l+POkBXzCsJhTGFJoUqap/8gteNwSRwqSHaK9HpTVzgcuvs0b5XMgmqQm58HN8e7r6Ulst68kQxn8pBRJ51sf+L4b5JwlKTemC5RHSyQCyedxtF53lMpQbUXeLiIRSROaRAAved7shcU+T5qyRnQOqVRRq/M8E1fS9J1IahJFeF2pZDxfifBlEtQmOhfPTzqXz+Ai8TTsrVRwtF43Nlhn4vFZsKzYPhK/MpsjNskLKSFbPG3imyib6CWEfz7CJDvMOS1GngGlMBbAzKG7UvDx2fj5OVgDyekGoXKGhI+XwQxD95XV8eFjAVQdV5/yKGHk3krFaBudN4SP3Cjjw0htlKJUMW4UTxO5FiHAJxMxMsPH9iVt39m486QwLssgG8oeAk0iRiHgrWIk1XonjHwcdHmuIEFVLoddwvxaDD7StdLokKHvj0Pr+NgN/rzkA9bnEj4iXnk6ZNJ3lJv9LCjduRgfWweZf2IAtBFT0iFpPLWDj4SRS61DponK4PfdCXzMNt0BSdOZMmM5sZBLG1u1GVbvx+PjtaXZDQGXF8ypqSmcmZnBarXGNtEXUJGamZvySmWvwVwZIjpz2cvLqIjUiiiHw6vrUW4YBxuf4hKBsqDRZtI38NeDBjVuieOTzUeoUQNXiefXkn7D8zdogSmDHAK4sVjEPjuPBzRYSyy+IVD09cOGXA63gBuqVMrncWhwEGsjI81z0Pm5Ja8Bbo7PLLhgWhsZccAxtDGw220TXvl+x8OLfIr+GxLOE+rPR0CPtczTvTQS6jsfW66kOPSBWvAfAHNTyD0tdnijb74QPnIl9ClQmyxpU56G2ZdeOXDZecugUx0KIOMdYYt9HQkji7lcanwkI0ISRn7A8/mscI8rhY/PAgSVbF9ExO2Dg01viX09Gx/p/GnwUeqzvXHOqaHgpdgcUL8uWOdN8iw2Gg0RI+fATFWSS46G1vstqAz+m5vXa2eOZ+KXVnRIadNhbyJuA7YZhs5gZK1abY7nT4PCMT7miz09+NJLL6XGR4DF4aPdJ0k6JBmdfPfIjQiLwUcJg0L4iNgeRvLn2kkdchXI+Lh7aAhnZ2ed0pm2J5gwkvRB4vGwdciNxSIW4zK47eAjYWSndEj6TsLQpHNvAR1R1il8zDbdAUnTmW5tbt9C92F0SU5GUXm/1Ub6/vvvbz7Yo0ePsvPMoa6FrTfoL7xAZccm2XXGUW/w9bX6+weaZWGSLd/PIcA0mjW6IT5fL6ra4hxc1T3VqtUm2HCGTFvBAFA1n6l+422BgU+gyr1UZ0EpHFwh9YVg1kErsXd5PA42EJK1NU19SluZIyMAAYeUc5pkWfNeJ26HvWHgIE8hn3T+6cD1aHPTD6r8EADgW9/6Vqcdi8nT2rltm8vGyvrYLsXhy0GTSJ5KYDKT0ud9oCzXPkbSduZ5JrKE+k4qtSItdhJD7Wg8h2icSfjIDUyGQhCz2NsL8ji4xDoAytMjlc2yx/lZ0OQu3dY5yqCwTfKcllhJO7tPJJxYBenwkeOBhJGUk0ceHluJHwStfNkh+CuBj9vj8FjfM7C9TM0N8uxsM5rBGEP1erPfG42GQbITwrVj8fm74zHzCXZem0gtjUfG7qOJuD8cpdsqXcTHyzwAjjl6BKWkjaMyjHOjOOV025FrZfYXsFqttT3HM/FLKzokjZ20+DgAqtIKH2eHDh1CRLVp5aX4JGItrkPSeKY5zysQ0IsiL3xjPAeqvv1pWBw+IramQybhD4Aur9oOPm5m87IAZrRNCB8R28PIMmhnkbSpbVcX21upJOIjoqlDpsXHApg6ZKfwsZM6ZBmUPlEEU1eknG5bryyzvwCKX2Qxc5wk23QHpD1Pty+kq44uoUkJeU4VvYrFjfH/OXTD1LuwUrlLIETbhwCH4/9pQTU97bt2EcGZz/Kdi7+bR4C96Oabj6IKbwd8NygGYVJyC1GEd+/f71jL6CKcAGILaEC+DJpV0dg4RRHeetNNzQksMRgSgKTxxHaBDgtMCr2yQ4V8Nb4JLHj+DveqPQsyI29SO3mIjwQudB0iMbM3krYlel+KfqmBKuNht7MC6fNY+2IGTduCSrVBfbngiNhc9EkxGAaZ2GKN0EZ6/rZVfRyU0gCgGUmz8PKlkXY83dJiLiowYBpO6EWbwwgUG649LtatWoWvtxSZOgA+Dxp/JAWLiLuS5id9Nw8qx9jOOSc8ehQA7welgHLD2fz8vFcJ4yRiafBxJyPSmQHZM7ua/Z+EA2VQIf+URrIc+HgZ5Fy8pLbyMHFpg9poNHBqasooj+Ubj2nyT5MwsgYaI88EzrVaeIbFXM4bqs49ftxwVIUII5EYrQvV2myOe4AcFgol67NxVMZ+rXdMT0+3Pccz8Us7nu5W8dEes5x9mcoK8uffBYoh3B53FP486bleyFtJHCqLxcdWdUjaMNv6x42veU3zHD4W7LT4SP22qa+vJXxEbA0jKeqgXXxMo0Neifi4BtwNcTGXa2JhJ3TIHaAi3fh5cgBYKhTMc8ft5vfZKXzMNt0BaT2nm3KpAVsnNCmjWkQnUNXa3oKq1Ie7wK5atdYKKS9bkzJCgEfEa+3atQf9lm9ATdJG5z+LKl+sgLqGt2lpjwDw3vhvIYrwAKhNm6QoShtlyZJZGxkRPU8NUN4Z8nKXwVRQJVCbYhPIDqM5A2qROy+1oVrF8+fPJ4LF008/bWyAOUCQZZVP/i4BXAgUZ2dnHWbjULkCzgjdLMMTRU3wL4O8IK0GTUJRBnnzQnk7CDoc2G433wDx39K5qF+44o3oEsessn4v5ffy1ypQIaVkmT0LZokU7s3n7VjMPM/ElVZyun0hcqHFnZS5y6A5G7aAMmr52EdXsc+k/MitIOdH7iqXvZZvasuzYHInnAXlgV0Huj4svxZf6Hfv2oXdUdTc+NvziYiQ2sVHBG1oot8PQxgfeR9wPofL8fORFNY0+Hj69GkDo2wFqhWMvKtSaRkfEV2MHBocbIacRuBncl4P6THyWdC56FIYrISREaj1CDGMjyZGhtLDAAFuQIB1WK3WWDTeMdRlSBF5hFuGj0sjreZ0PwvuBiUNmVQfKEw6DgqjboxxoCDMrwIArl21KrFcXwRmZRW6nhiZAaZxdALax8fayEjLOqQvZPzw4cMdwceDYJXeSomPw3HYdigsn+uQi8HHTumQy4mPpShqOk2S8BGxszrkDfF4pNJsAMpzT/ojf/6dxMerZtN96NAhvPPOO3HdunVYKBRS/eY73/kOvve978Xrr78e165di9/93d+dqtA5l7SdubCwgPX6KHuonNDsMqocbkB/WPeNCHAR5fJgvgX238QLqMRwThvjcQQ4j8r7rsp5Pfzww+iGuY+j2ryHr7sBck2SBduKZE+aPuu9DVZ2vsoEKEbHWrWKiLKXjBYgKi9xAcIe3QabQOTxfP755532ronvwwCN/v5mjrOtTDkeVgZuVBLJbpNEYmKDog0uktiAw8/J/78A8oJkW6DLIHv/KvH9kheM/6a/UMBe9ptJUAt+TehvW8bHxrAYRQ5I10EtotyKzzcHdoicr5/pe5ucZTHz/EqWKxkjFxYWcLReN55zHrTiECLEuxGUJd0exzRXfc/+UDxWfPmRA6CMbTOgFbEbX/vaxLJUxZj8BsANodsCYLCw8mtFwrx38HFkBJ944olF4SOCmct2Ob7HED7y/k4qBVi03ifho3TPhHW+XOU0GJkGHxHTY2Tduha9t5U4H0ZuAU1AaW+Uu0DhGT9+GMIROD58VC+fHnEP6jQ0vWbr8qXyml6t1hY9x690uZLxEdHFSNpspcFHwigJI5Pm/b8BjY/SuI5As2JzjLwlZhTn1+ZpQO3ioz0vAQA3Wu+TdEgKGe+NPaQrgY/bwfWmchKxtDrkYvARsbM6pA8fJUdLq/hY7OkRdcgK62uftKNDkgGFP9+QDknr7WLnOOJVtOn+6Z/+afzABz6A73nPe1ID5s///M9joVDAj3/84/gnf/In+H3f9334ute9Dv/pn/4p9XVb6cyxsXGMogIqr/An0fU++xc9tSG2N88SG6lpnQ6fd7V1XIS/+Iu/GP8/idICHV7Ykz1SPmXz8OHDiIhGaKXPgyMRURBYkefyjNWWGsgWOYlohM5rsx2SBdG+BykUqygcy8N4eE4S7yua/IcOHUqlOPqEs4hyqzK1ZYPVPzYJhvRKCiOikg82q3mS9Z33NxdaDCWQtq3mDXAXP96PMzMzzhjhSn8aT9i1oFRe6RhJTLRUauUpcL3PvrF0FmTFkMZ4kjKaZmxLigaFbNsLtJ1/RwpnmpI0PnwcZGOZK4rt4OOzoGpI220RydrAZDC3MZKXAiRvBn+fhI9lUMqnjUtXEkZShBSFn9oGvVYwslatiiVxOomPGruT9Ah3zZ6ZmbGi8TQJan//QIaPHlluHZJj5CfB3dD5xtJz1hyn8UKOicXg42rruAigqUMeAHNN/jD4y6GmwUcKjbbxpRR/PzU1hYit6ZDLiY8+HdK74fQc+3LCx9Da7Lu35dIhKUUySa7JTTfJM888kwowv/Od7+D111+PExMTzc/+7u/+Dru6uvCXf/mXU18vbWf6WcEnEACsUmB60YuiPlRe5wnh96GQdAr7Ttogd6PtAd+6dUd8zT40F2DVloeaA94fwuYDAR/JTTfoOoa8rrPhwYki0aIk5XPQRK2BtgZfAHeRohBUO68lyYPutXY2Gk2LWKiMT5KVko6hBaQdSZt3w63lBCQlMC2Xj7H/fc+V2spB+rHAb7oTPMw63DF5owWgyd7KCX0tjRFi5Ewj14JSSXIlYmQovG5qagprIyPOokehhD7OhhCTK4U1hjCLFusiaCOhZMDLg1zKahx0HmY7+NgLJsP+aL2OxVzOwMcNoIhwbJHGPimlvHSPhI9rQNcxDWFkK/g4mfL4KwUjnYoQoDcPZwHwSIpnyxW3pcTHHU4UHdcj5DVbrooCODJSCyqUiBk+kiyHDmk/b8K+PcPDXnz0jfHQHEyDj5KhcMfWreK8kVJ9WsHHpLZSOTTE1nTI5cLHVjHy5aZD2vh4GXRddt9vtm/b5m3/cuiQtoHbJ9mmGxEvXryIAICf/exnjc/37duH999/v/d33/zmN/FrX/ta8/XlL385VWe6DObm5ndmZkZc9MbGxrFS2Ysqh1v6PZGv2ZtjKjUW2phPej6H+NzgvJ8BxY6aF0qD3QLtlXMh7zQHTcdjFPBISmQ8TwLgJuMeFJCuB10DUTq/RGwRYmqcmZkxcj9Cx87NzSXmw7RrnbTbH2q3ZHl+kr0/DinC8+NcH37MkcBvdmzd6n2e2kjlb/cDrI0D/f1YDDD82mOkFXk5KpXLiZFpiGR8ZCl7K5XEslmSUsAjXIKKkPAZgBtSVwmc5wCYc2qx+JhEHCOJPfYp9K5o3Qfh49DgYOI17GfWCj6mwaUrCSM3W31UFp5lCCPtEMWlwscLAGinh42M1LBeH3WM+vl8CcfGxhPHSRrJ8FHLUuuQvjE6PT3txccC+Nmh67A4fLRDxJMwcrH4mHT/HB8RW9chW8HHDbkcbr3ttsTzZzpkazqkFMIdwkiKjpXkStIhs003Iv7O7/wOAgD+9V//tfH5G9/4RnzTm97k/d373vc+B0TSdGao/jV/gPZDXVhYwGKx3/P7J9HNv66jYh6lY3wb8wiTQ9OPowott0qRgQrZscuSrIYIe0Em5uIWV2ny+PLX2t0k2UqpnZ/M82LSsHwjpLNS0u9CbOncYyUxh47W6y3dry2tMExWQHnSHoifAwcb23hRsJ5rUlmGOZC9REVItyCEymgUogh3Dw15rZBpwsbTystRqVxOjAx5ukP42G+VsrJ/v3bVKqMtdTBJ0iTFswC6XrS9WNO1eEidT6nlv1kF4K0VvZz4SP1Gc8aHj0nXaMfT3QouXUkY2QsK05v1jy18JE9YURhHdOxy4+ODYHr2fEb9TmBkho9allqHDM0XCR8514r9eyn/Nw0+0sYu5JUmjJyCMD6uBT8+1gLjXcJHqT/SSqfxsVWMvJp0SGKKX4wO6SsduhijwpWiQ141m+6HHnpIBCf++sIXvmD8ZqkBs10rJaLNYO63NEuiNt4bUQobGxmp4enTp/G+++6L+yXNxrziOVYTp0j5XTsgMgYvAcNpAPwFAC/LoI8ogkKLJOW6E8IBsVXwlXI4xJp9ljWMflcGmX3YrtG4VBOdt78uAFcxl/OSk1AbJKZMX1ulRWYU5HIkaRYEKUSMb1CkflrMJiRJrlSl8lrCSGm+SZZmSRYWFnBjsegqhlHUTFvhGJlG8YxA14u2F2u7bMuzEC6Xcz8o5U1i5x2t11cEHxHDZQaTxH5m5Rbw0YdLVxJGEtakxUcAsxwTP3a58dHXT0uBkRk+allKHbJdjFxYWMBatSqHAse/bxUfQ5E9NkaGUn1Ih5Tm2/59+5r34LQf3EoonZRO4uO1pkNyrLFJdFvVIX1Gpe1LiJFXIj6u6Kb7b//2b/ELX/hC4utb3/qW8ZulDg2ypZXOXKyleWFhAUdGaom/T9rYz87O4tDQMPu9m/tFx0pt3dh/nRN+QWUobPA+ePAgPvPMM95SWfSqx4CeVrleTpHaO1qv412VCkqgIf0uaZPKZSkmehqwm52dTWWUSGu8sBcZqRRTKwuCFCLWSi52p+RKVSqvJYxcrPKQNpzQp7iO1usGCWAEYORNc4zztdW3cS6z31Id1EOHDhm1UK82fESU2+zbdPp+cyVjJJVdawUfk9q6HPjI27xckuGjlqXWIReDkWnKRSVt7GdnZ3GY/T5pEy+1daC/38079+iQ999/Px46dMgJGbc3d2VQlVOuFnzcW6ng0ODgNaFD2lizWB1SGntSVZyrTYe8ajbd7UirJBiTk5PNz772ta8tGQkGl8VOjqTfp9nY0+9nZ2dTH+sLv4jamAg2cLc6iZZbpP5O8wwXYyHtpCzG29+q+JQCvrlvR5a63SG5UpXKduRKx8ilxEfEsOLK8TGk4EqhnD6MvFbxETH9ptP3m5We38vVlgwfr3y50vERcel1yLS41ypGdkKHJI/31YyPvs98v3u56JBJY2+p9YKllFbmeA4REa4CuXz5MiwsLMCv/dqvwcTEBHz6058GAICbbroJuru7AQDgtttug5/7uZ+DH/iBHwAAgMceewx+/ud/Ho4dOwave93r4L3vfS9cuHABPv/5z8PatWtTXffrX/86FAoF+NrXvga9vb1Lc3NtyJ//+Z/DF7/4Rbjpppvg5ptv7tix9vEA0NJvF3PdTK4eudae7ZU6z1uRDCNNSTtG2xnL9JtVq1bB//t//y/Dx0wMudae7ZU6x1uRDB9NudJ1yGttDmWi5Vp7ti3N8SU3AXRIfvRHf9SwjtDrt37rt5rHAAA+88wzzfff+c538L3vfS8ODAxgV1cXfvd3fzfOzc21dN1rycKbSSaZyHItzPMMIzPJJJOlkGthjmf4mEkmmSyFXJOe7pWSK9VKmUkmmXROsnnevmR9l0km17Zkc7x9yfouk0yubWlljkfL1KZMMskkk0wyySSTTDLJJJNMMnnZyaqVbsCVLhQI8PWvf32FW5JJJpksldD8zgJ/WpcMIzPJ5NqWDB/blwwfM8nk2pZW8DHbdAfkG9/4BgAA3HDDDSvckkwyyWSp5Rvf+AYUCoWVbsZVJRlGZpLJy0MyfGxdMnzMJJOXh6TBxyynOyDf+c534K//+q+hp6cHcrlc8Pivf/3rcMMNN8CXv/zlazJ/51q/P4DsHq8FafX+EBG+8Y1vwCtf+UqIoizrphVpBSOv9XEHcO3f47V+fwDZPdqS4WP7kuGjKdk9Xv1yrd8fwNLhY+bpDkgURfBd3/VdLf+ut7f3mh2MANf+/QFk93gtSCv3l3lw2pN2MPJaH3cA1/49Xuv3B5DdI5cMH9uTDB9lye7x6pdr/f4AOo+Pmckyk0wyySSTTDLJJJNMMskkk0yWSLJNdyaZZJJJJplkkkkmmWSSSSaZLJFkm+4OS1dXF7zvfe+Drq6ulW7Kksi1fn8A2T1eC3Kt39/VKi+H53Kt3+O1fn8A2T1msjLycngm2T1e/XKt3x/A0t1jRqSWSSaZZJJJJplkkkkmmWSSSSZLJJmnO5NMMskkk0wyySSTTDLJJJNMlkiyTXcmmWSSSSaZZJJJJplkkkkmmSyRZJvuTDLJJJNMMskkk0wyySSTTDJZIsk23ZlkkkkmmWSSSSaZZJJJJplkskSSbboXKe9///th7969sH79eigWi6l+g4jw0z/90/CKV7wC1q1bB3fffTf8+Z//+dI2dBGysLAAP/IjPwK9vb1QLBbhnnvugb//+79P/M3rX/96yOVyxus//If/sEwtDsuHPvQheO1rXwtr166FO+64A86fP594/PPPPw+33XYbrF27Fnbs2AEzMzPL1NL2pZV7/MhHPuI8r7Vr1y5ja1uT3/7t34Z/9a/+Fbzyla+EXC4HH//4x4O/OXPmDAwNDUFXVxfcdNNN8JGPfGTJ25nJtY+RGT5m+HilSYaPV49c6/gIkGEkwNWHkdcyPgKsHEZmm+5Fyj//8z/DG9/4RviP//E/pv7N448/Dh/84AfhySefhHPnzsGGDRtgbGwMvvnNby5hS9uXH/mRH4HPfe5z8KlPfQo++clPwm//9m/DO9/5zuDv3vGOd8Df/M3fNF+PP/74MrQ2LNPT0/Ce97wH3ve+98Ef/dEfweDgIIyNjcHf/u3fisf/7u/+LvzQD/0Q3HPPPfDZz34Wvv/7vx++//u/H/7sz/5smVueXlq9RwCA3t5e43n9xV/8xTK2uDX5h3/4BxgcHIQPfehDqY7/0pe+BN/7vd8L+/fvhz/+4z+Gd7/73fD2t78dTp06tcQtzeRax8gMHzN8vNIkw8erR651fATIMPJqw8hrHR8BVhAjMZOOyDPPPIOFQiF43He+8x28/vrrcWJiovnZ3/3d32FXVxf+8i//8hK2sD35/Oc/jwCAs7Ozzc9+4zd+A3O5HP7VX/2V93e1Wg1/4id+Yhla2Lrs2bMH3/WudzXff/vb38ZXvvKV+HM/93Pi8W9605vwe7/3e43P7rjjDvzxH//xJW3nYqTVe0w7fq9EAQD82Mc+lnjMgw8+iNu2bTM++7f/9t/i2NjYErYsEy7XIkZm+Jjh45UuGT5eHXIt4iNihpGIVx9GvpzwEXF5MTLzdC+zfOlLX4KvfOUrcPfddzc/KxQKcMcdd8Dv/d7vrWDLZPm93/s9KBaLMDw83Pzs7rvvhiiK4Ny5c4m/fe6552Djxo2wfft2+C//5b/AP/7jPy51c4Pyz//8z/CHf/iHRv9HUQR33323t/9/7/d+zzgeAGBsbOyKfF4A7d0jAMDf//3fw2te8xq44YYb4F//638Nn/vc55ajucsiV9szfDnL1YSRGT5efXMrw0dXrrZn+HKWqwkfATKMBLi65leGj7J06hmu6mSjMgnLV77yFQAAGBgYMD4fGBhofnclyVe+8hW47rrrjM9WrVoFpVIpsb0//MM/DK95zWvgla98JVy4cAEeeughmJubg49+9KNL3eRE+T//5//At7/9bbH///f//t/ib77yla9cNc8LoL17vPXWW+Hpp5+GnTt3wte+9jWYnJyEvXv3wuc+9zn4ru/6ruVo9pKK7xl+/etfh3/6p3+CdevWrVDLMrHlasLIDB8zfMzwMZPllKsJHwEyjAS4ujAyw0dZOoWRmadbkIcfftghBbBfvsF3tchS3+M73/lOGBsbgx07dsCP/MiPwP/4H/8DPvaxj8HFixc7eBeZdEruvPNOeOtb3wrlchlqtRp89KMfhU2bNsFTTz210k3L5AqUax0jM3zMhEuGj5m0Itc6PgJkGJmJlgwf00vm6RbkP//n/ww/9mM/lnjMjTfe2Na5r7/+egAA+OpXvwqveMUrmp9/9atfhXK53NY525G093j99dc75An/7//9P1hYWGjeSxq54447AADgi1/8ImzevLnl9nZKNm7cCPl8Hr761a8an3/1q1/13s/111/f0vErLe3coy2rV6+G22+/Hb74xS8uRROXXXzPsLe3N/PitCHXOkZm+JjhY5Jk+JhJklzr+AiQYeS1ipEZPsrSKYzMNt2CbNq0CTZt2rQk537d614H119/Pbz00ktNgPz6178O586da4m9crGS9h7vvPNO+Lu/+zv4wz/8Q9i1axcAAPzmb/4mfOc732mCYBr54z/+YwAAY5FYCVmzZg3s2rULXnrpJfj+7/9+AAD4zne+Ay+99BLce++94m/uvPNOeOmll+Dd735387NPfepTcOeddy5Di1uXdu7Rlm9/+9vwp3/6pzA+Pr6ELV0+ufPOO50SHVfyM7zS5VrHyAwfM3xMkgwfM0mSax0fATKMvFYxMsNHWTqGka2yvGViyl/8xV/gZz/7WTx48CB2d3fjZz/7WfzsZz+L3/jG/9/O/YNUucZxAH+k0ooKcTlBECSVQxDmULhoYIPVIEFEZxBxaW4RDoJIQdAQLdLYv6moCBwEa1GoQKFOVJgGlRVBLQUi0VS/O93Dle65V+/l7S39fOAM532fA79neL+HLy/vO19Z09TUFLdv3658P3v2bNTX18fw8HA8ffo0urq6Ytu2bfH169c8tvCvOjs7Y8+ePTE5ORn379+PHTt2RLFYrJx///59NDU1xeTkZEREvHz5Mk6fPh0PHz6M2dnZGB4ejsbGxmhra8trCwtcv3496urq4sqVK/H8+fM4ceJE1NfXx8ePHyMioru7O0qlUmX9gwcPYvXq1XHu3LmYnp6OwcHBWLNmTTx79iyvLfyrpe7x1KlTcefOnXj16lU8evQojh8/HmvXro2pqam8tvCP5ufnK9daSinOnz8fjx8/jrdv30ZERKlUiu7u7sr6169fx/r166Ovry+mp6fjwoULsWrVqhgdHc1rCyvGcs9I+SgffzXy8fex3PMxQkb+bhm53PMxIr+MVLr/p56enkgp/fAZGxurrEkpxeXLlyvfv3//HgMDA1EoFKKuri46OjrixYsXP3/4Rfr06VMUi8XYsGFDbNq0KXp7exf8IczOzi7Y87t376KtrS0aGhqirq4utm/fHn19fTE3N5fTDn40NDQUW7dujdra2ti7d29MTExUzrW3t0dPT8+C9Tdu3IidO3dGbW1t7Nq1K0ZGRn7yxEu3lD2ePHmysrZQKMShQ4eiXC7nMPXijI2N/e119+eeenp6or29/YffNDc3R21tbTQ2Ni64JsnOcs9I+SgffzXy8fex3PMxQkZG/H4ZuZzzMSK/jKyJiFjavXEAAABgMby9HAAAADKidAMAAEBGlG4AAADIiNINAAAAGVG6AQAAICNKNwAAAGRE6QYAAICMKN0AAACQEaUbAAAAMqJ0s6Jdu3YtrVu3Ln348KFyrLe3N+3evTvNzc3lOBlAvuQjQHUykqWoiYjIewjIS0Sk5ubm1NbWloaGhtLg4GC6dOlSmpiYSFu2bMl7PIDcyEeA6mQkS7E67wEgTzU1NenMmTPp6NGjafPmzWloaCjdu3evEpZHjhxJ4+PjqaOjI926dSvnaQF+HvkIUJ2MZCnc6YaUUktLS5qamkp3795N7e3tlePj4+Npfn4+Xb16VWACK5J8BKhORrIYnulmxRsdHU0zMzPp27dvqVAoLDi3f//+tHHjxpwmA8iXfASoTkayWEo3K1q5XE7Hjh1LFy9eTB0dHWlgYCDvkQB+CfIRoDoZyVJ4ppsV682bN+nw4cOpv78/FYvF1NjYmFpbW1O5XE4tLS15jweQG/kIUJ2MZKnc6WZF+vz5c+rs7ExdXV2pVCqllFLat29fOnjwYOrv7895OoD8yEeA6mQk/4U73axIDQ0NaWZm5ofjIyMjOUwD8OuQjwDVyUj+C28vh39w4MCB9OTJk/Tly5fU0NCQbt68mVpbW/MeCyB38hGgOhnJXyndAAAAkBHPdAMAAEBGlG4AAADIiNINAAAAGVG6AQAAICNKNwAAAGRE6QYAAICMKN0AAACQEaUbAAAAMqJ0AwAAQEaUbgAAAMiI0g0AAAAZUboBAAAgI38AuN/sMf9g4UoAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
],
"source": [
"print(\n",
" \"Cost: {:3f} | Train accuracy {:3f} | Test Accuracy : {:3f}\".format(\n",
" loss, accuracy_train, accuracy_test\n",
" )\n",
")\n",
"\n",
"print(\"Learned weights\")\n",
"for i in range(num_layers):\n",
" print(\"Layer {}: {}\".format(i, params[i]))\n",
"\n",
"\n",
"fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
"plot_data(X_test, initial_predictions, fig, axes[0])\n",
"plot_data(X_test, predicted_test, fig, axes[1])\n",
"plot_data(X_test, y_test, fig, axes[2])\n",
"axes[0].set_title(\"Predictions with random weights\")\n",
"axes[1].set_title(\"Predictions after training\")\n",
"axes[2].set_title(\"True test data\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sYQWz6IdTnyy"
},
"source": [
"References\n",
"==========\n",
"\n",
"\\[1\\] Pérez-Salinas, Adrián, et al. \\\"Data re-uploading for a universal\n",
"quantum classifier.\\\" arXiv preprint arXiv:1907.02085 (2019).\n",
"\n",
"\\[2\\] Kingma, Diederik P., and Ba, J. \\\"Adam: A method for stochastic\n",
"optimization.\\\" arXiv preprint arXiv:1412.6980 (2014).\n",
"\n",
"\\[3\\] Liu, Dong C., and Nocedal, J. \\\"On the limited memory BFGS method\n",
"for large scale optimization.\\\" Mathematical programming 45.1-3 (1989):\n",
"503-528.\n",
"\n",
"About the author\n",
"================\n"
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since end of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "8FGVwF_QUYza",
"outputId": "1a30beb5-063a-4afc-dead-903efe8a5ff5"
},
"execution_count": 88,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696826782.208794\n",
"Mon Oct 9 04:46:22 2023\n"
]
}
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
},
"colab": {
"provenance": []
}
},
"nbformat": 4,
"nbformat_minor": 0
}