520 lines (520 with data), 196.2 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "eI_d8C3_Tnyu",
"outputId": "455a101d-699f-4c6b-ee16-1cdbd9fb8764"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1696869990.2910748\n",
"Mon Oct 9 16:46:30 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": 43,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "lScQSFsjTnyw",
"outputId": "0bd9e81a-022b-4eeb-ccbd-813f6161e799"
},
"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": 44,
"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": 45,
"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": 46,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "NqJGMjbpTnyy",
"outputId": "f87e3ef3-4063-4bd3-d807-4c4c1c89bfe0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
"Epoch: 1 | Loss: 0.181187 | Train accuracy: 0.785000 | Test accuracy: 0.713500\n",
"Epoch: 2 | Loss: 0.148237 | Train accuracy: 0.805000 | Test accuracy: 0.788000\n",
"Epoch: 3 | Loss: 0.137929 | Train accuracy: 0.800000 | Test accuracy: 0.795500\n",
"Epoch: 4 | Loss: 0.119142 | Train accuracy: 0.835000 | Test accuracy: 0.798000\n",
"Epoch: 5 | Loss: 0.117095 | Train accuracy: 0.860000 | Test accuracy: 0.826500\n",
"Epoch: 6 | Loss: 0.121077 | Train accuracy: 0.835000 | Test accuracy: 0.793500\n",
"Epoch: 7 | Loss: 0.127405 | Train accuracy: 0.790000 | Test accuracy: 0.804500\n",
"Epoch: 8 | Loss: 0.100944 | Train accuracy: 0.915000 | Test accuracy: 0.887000\n",
"Epoch: 9 | Loss: 0.098501 | Train accuracy: 0.920000 | Test accuracy: 0.884000\n",
"Epoch: 10 | Loss: 0.112304 | Train accuracy: 0.855000 | Test accuracy: 0.824500\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 = 3\n",
"learning_rate = 0.6\n",
"epochs = 10\n",
"batch_size = 31\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": 47,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 399
},
"id": "ZPszGYA3Tnyy",
"outputId": "d56c486d-897f-4e7a-df7a-560e414257f2"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cost: 0.112304 | Train accuracy 0.855000 | Test Accuracy : 0.824500\n",
"Learned weights\n",
"Layer 0: [-0.0558205 1.54745028 -0.13895825]\n",
"Layer 1: [0.73076534 0.14999777 0.20901432]\n",
"Layer 2: [-1.17107021 1.45194103 0.32099705]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x300 with 3 Axes>"
],
"image/png": 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FdCSTDhPD1qgJS0466SQfRt944400Y8YMKhaLvvPWrl1LyWSSnn322dB+LViwwMeDlNvuRz/6Uaqvrw/Fq/vuuy/Av4RRJpOhY445poTpRES7du0iAAG+TcWyl19+mRYuXEj5fN53fMaMGb5xM11PpOdvYyqPYvdyAKtWrcKsWbNwwgkn4OKLL0ZtbS2+8pWv4LjjjvOd98EPftD3/b777kMqlUJXVxd++9vflj5Lly5FbW1tyQq3e/duvPzyyyXXaaarr77a2rfHHnsMP/vZz3D11VcH4uQqcSc7dOgQdu3ahbe85S049dRTS8ePOeYYvOtd78K3v/1t/OEPf/Bdc/nll/vu1dnZiUOHDuGZZ54B4MXvPfDAA/i///u/yH2ZNWsWzjjjDPznf/4nAOA73/kOkskk1qxZg1/96ld48sknAQgrajabHZP73F/91V+hoaHB9wyAcNGOQh/4wAeQTCZ9x6ZNm1b6///+7/8wMjKC0047Del0Gj/4wQ8CbdjGsZx50t/fj2XLliGbzZaO1dbW4vLLL8fTTz+Nn/zkJ77z3/Oe9/gsLWeddRaIKOBaetZZZ+HnP/85/vKXvxjHwnEcLF++vPTefvrTn2JkZARr164FEeF73/seAPHeFi5cWJofUdeLjh588EEcd9xxuOCCC0rHpk6dig984APa82tra30xYlOmTMGyZcsivW+2ZA8MDODFF1+0nh/TxFCM04cHpwE/1r3wwgv47W9/i87OTrz44ot44oknyn08AMDo6Cj+4z/+A+94xztKbf72t7/FyMgICoUCnnzySfziF7/wXaPD4UqopqYG73vf+wLHx/qcY9lnVq1aVbIyAcIboL6+vnTtoUOHsHv3brzlLW/BscceWzrvtNNOw7nnnmttP6aYXk1kWqNR6b777kNnZycaGhp8uL9q1SocOnSoxL+MV7vpdBp//OMf8dBDD1X8DDL98pe/xA9/+ENccsklPm+7rq4utLS0BM6Xsex3v/sdnn/+eXR2dmp5Ux2Vy9/GFI1i93IAt99+O04//XRMmjQJc+bMwbx58+A4fn3EpEmTAi4cTz75JJ5//nnMnj1b2y4nlWGmZ+7cub7fZ82a5dugdcQulNWqO/yb3/wGL774IubNmxf4bf78+XjllVfw85//HAsWLCgdP/HEE33ncZ85HjKXy+Giiy5CT08Pbr31Vpxzzjl4y1vegne9612oqakJ7U9nZyf6+/sBCCGtra0NbW1taGxsxCOPPII5c+bgRz/6Ed71rneN6bltz2CjU045JXDspZdewk033YStW7fiF7/4hS82XRf7a+tDOfPkmWeeCbiXAp5r0jPPPOObM+q9GbRPOOGEwPFXXnkFzz//vM9tV6XOzk5cf/31eOmll/DII4/gmGOOQWtrK5YsWYJHHnkEXV1d+Pa3v413vOMdpWuirhcdPfPMM2hubg4IMJwhWaXjjz8+cG5DQwP2799vvAfTKaecgmuuuQaf/exncc8996CzsxMXXHAB3v3ud8eu5YeRYpwWdDhw+vHHH8cnPvEJ/Md//EdA2K80z8FTTz0FIsI//uM/4h//8R+15/z617/2KVV0OFwJHXfccdpkjWN9zrHsM+q1fD1f++tf/xovvfSSFvNMOBhTTK9WMq3RqPTkk09i//79xtCLMH6jGu1+6EMfwr/927/h3HPPxXHHHYc3vvGNeMc73oE3velNFd3XtD8BwLx58wKC8AMPPIB169bhhz/8oS9PT1QlcLn8bUzRKBa6ASxbtqyUFddENTU1AQbvlVdewezZs3HPPfdorzla4qxMlgVehIlEAjt37sSjjz6Kb3zjGxgYGMD73/9+fOYzn8Gjjz6K2tpaY9vZbBa9vb04ePAgHnnkEXR2diKRSCCbzeKRRx7Bsccei1deeaVkMRivZ7CRrPVjuvLKK7F161ZcffXVeP3rX49UKoVEIoGLL75Ym6hjrH0YC5nuXWmfstks/u///g/f+973Su8NEML4I488gieeeAK/+c1vfO9tItfLWMf6M5/5DN773vfia1/7Gnbt2oWrrroKN910Ex599FFjAqeYxpdinA6n8cLp3//+98jlcqivr8cNN9yA5uZmTJ06FT/4wQ9w3XXXVVw6i69bvXo1CoWC9hxVmNThcCWka6cazzkW3Dmc+0NMMR1pVO5aV5MdvvLKK+jq6sK1116rPf/000+vqF9R2509ezZ++MMfYmBgAN/85jfxzW9+E1u3bsV73vMebNu2raJ7R6VHHnkEF1xwAVasWIEvfvGLOOaYYzB58mRs3boVX/rSlyK1US5/G1M0ioXuMVBzczN2796Ns88+OxQgTjrpJABCQya7Cv7mN7+xasDZ3ezHP/4xVq1aZTwvqvZq1qxZmD59OoaHhwO/PfHEE3AcJ2D9jEodHR3o6OjAP/3TP+FLX/oS/vqv/xr33nsvLrvsMuM1LJQ99NBDGBoaKtUvXbFiBe644w4ce+yxmDFjBpYuXRp678ORuXXnzp245JJL8JnPfKZ07E9/+pMvq3c5VM48Oemkk4zvUG5rvGjZsmWYMmUKHnnkETzyyCNYs2YNAPHeent78fDDD5e+M0VdLzo66aST8JOf/ARE5HvXTz31VMXPYJszixYtwqJFi/CJT3wC3/3ud3H22Wdj06ZNWLduXcX3jGniKcZpP5WL03v27MHIyAi+/OUv+9YzZ423kemZeYwnT54cOmaVUCX7wVifc7xp9uzZmDp1qhbzxoKDMcX0aqKGhoYAj/Xyyy/jl7/8pe9Yc3Mz/vd//7dibDFhSDntTpkyBW9+85vx5je/Ga+88go+9KEPYfPmzfjHf/xHnHbaaWXhlLw/qaTuE/fffz+mTp2KgYEBnxfT1q1bA9ea+lBt/jYmQXFM9xjoHe94Bw4dOoQbb7wx8Ntf/vKX0uRctWoVJk+ejNtuu82ntQ7LnsjU2tqKU045BRs3bgxMdrktrldqWxDJZBJvfOMb8bWvfc2X+v9Xv/oVvvSlLyGbzaK+vt7aL5l+97vfBbTxmUwGAKzlp0455RQcd9xxuPXWW/F///d/OPvsswEIYfzAgQPYuXMnOjo6rHVNoz5/NSmZTAae+7bbbgtoXKNSOfOku7sbg4ODpfhpQGT43rJlC04++WRtjE81aerUqWhvb8e//uu/4tlnn/VZul966SV8/vOfR3NzM4455pjSNVHXi44KhQJ+8Ytf+EoJ/elPf0Jvb2/Fz2CaM3/4wx8CMe2LFi2C4zjW+RzTkUcxTguqFKfZAitf+/LLL+OLX/xipPtyllv1mWfPno1zzjkHmzdvDjDMAALlzsqhSvaDsT7neFMymcSqVavw1a9+Fc8991zp+FNPPYVvfvObh7FnMcU0cdTc3ByIx96yZUuA73rHO96B733vexgYGAi08fvf/z40bw0gMESHH1HbHRkZ8f3mOE6pagPjbTk4dcwxxyCTyWDbtm0+9+6HHnookMMnmUwikUj4xuTpp5/GV7/61cjPWW3+NiZBsaV7DJTL5XDFFVfgpptuwg9/+EO88Y1vxOTJk/Hkk0/ivvvuw+c+9zm87W1vw6xZs7B69WrcdNNNOP/889Hd3Y3HHnsM3/zmNzFz5szQeziOgzvuuANvfvObkclk8L73vQ/HHHMMnnjiCTz++OOlhc+W4KuuugqFQgHJZBIXX3yxts1169bhoYceQjabxYc+9CFMmjQJmzdvxp///GdfLeOotG3bNnzxi1/EW9/6VjQ3N+OFF15Ab28v6uvr0d3dbb2+s7MT9957LxYtWlSKgWttbcWMGTNQLBYjxXOX8/zVovPPPx/bt29HKpVCS0sLvve972H37t2hsdBhVM48Wbt2Lf71X/8V5557Lq666io0NjZi27Zt+NnPfob7778/4GI7HtTZ2Yn/9//+H1KpFBYtWgRAMNLz5s3D8PBwoGZm1PWioyuuuAJf+MIX8M53vhMf/ehHccwxx+Cee+7B1KlTAVRm2WpubkY6ncamTZtQV1eHGTNm4KyzzsKPfvQjfOQjH8Hb3/52nH766fjLX/6C7du3I5lM4qKLLir7PjEdXopxWlClOL18+XI0NDTgkksuwVVXXYVEIoHt27eXFZrT0tKCvr4+nH766WhsbMTChQuxcOFC3H777chms1i0aBE+8IEP4NRTT8WvfvUrfO9738N///d/40c/+lHZzwmY13ZYTPhYn3Mi6Prrr8euXbtw9tln44Mf/CAOHTqEL3zhC1i4cCF++MMfHu7uxRTTuNNll12Gv/3bv8VFF12Erq4u/OhHP8LAwEAAo9esWYOvf/3rOP/88/He974XS5cuxR//+Ef8f//f/4edO3fi6aefDsX1pUuX4o477sC6detw2mmnYfbs2cjn85HbveyyyzA6Oop8Po/jjz8ezzzzDG677TZkMplS7p1MJoNkMombb74Zzz//PGpqapDP5435R2666Sacd955yGazeP/734/R0VHcdtttWLBgAf73f/+3dN55552Hz372s3jTm96Ed73rXfj1r3+N22+/Haeddlogr83SpUuxe/dufPazn8Wxxx6LU045BWeddVbV+duYXJqgLOlHJHEZElsJlUsuuYRmzJhh/H3Lli20dOlSmjZtGtXV1dGiRYvo2muvpeeee650zqFDh6inp4eOOeYYmjZtGp1zzjn04x//OFDmQC1Fw/Ttb3+burq6qK6ujmbMmEGLFy/2lRL5y1/+QldeeSXNmjWLEomErwQDNCUWfvCDH1ChUKDa2lqaPn06rVy5kr773e9GGh+1jz/4wQ/one98J5144olUU1NDs2fPpvPPP5/+67/+K2xYS3T77bcTAPrgBz/oO75q1SoCQA8//LDvuK5kmOn5+dz169cH7qsbF5XC5sjvfvc7et/73kczZ86k2tpaKhQK9MQTTwTeadRxJIo+T4iIDhw4QG9729sonU7T1KlTadmyZfTAAw9o76GWvzL1SVfyx0T//u//TgACZccuu+wyAkB33nmn9roo60UtGUZEdPDgQTrvvPNo2rRpNGvWLPq7v/s7uv/++wkAPfroo75rFyxYELivWuaHSJT0aWlpKZXc2bp1Kx08eJDe//73U3NzM02dOpUaGxtp5cqVtHv3buuYxFR9inH68OP0d77zHero6KBp06bRscceS9deey0NDAwExsCEH9/97ndp6dKlNGXKlMBzHjhwgN7znvfQ6173Opo8eTIdd9xxdP7559POnTutzxhGurVNZMaHcp7TVDIsyj5jKhn24Q9/OHCtDvcffvhhOvPMM2nKlCnU3NxM//zP/0x/93d/R1OnTg0fkJhiOgLJVDLMtEYPHTpE1113Hc2cOZOmT59OhUKBnnrqKe1aeeGFF+jv//7v6bTTTqMpU6bQzJkzafny5bRhwwZfCVUd/c///A+dd955VFdXRwB8/EiUdnfu3ElvfOMbafbs2TRlyhQ68cQT6YorrqBf/vKXvvv09vbSqaeeSslkMlL5sPvvv5/mz59PNTU11NLSQl/+8pe1vM2dd95Jc+fOpZqaGjrjjDNo69atWux54oknaMWKFTRt2jRfacyo/G1M5VGC6AhS48YUU0wxlUEbN27Exz72Mfz3f/93oHRUTDHFFNNrgd7ylrfg8ccf18Z7xhRTTDHFdGRQHNMdU0wxvSropZde8n3/05/+hM2bN2Pu3LmxwB1TTDG9JkjFwSeffBL9/f0455xzDk+HYooppphiikRxTHdMMcX0qqALL7wQJ554IjKZDJ5//nns2LEDTzzxhLEUVEwxxRTT0Uannnoq3vve9+LUU0/FM888gzvuuANTpkwxljCKKaaYYorpyKBY6I4pppheFVQoFPDP//zPuOeee3Do0CG0tLTg3nvvxV/91V8d7q7FFFNMMU0IvelNb8K//uu/4n/+539QU1OD17/+9fj0pz+NuXPnHu6uxRRTTDHFFEJxTHdMMcUUU0wxxRRTTDHFFFNMMY0TxTHdMcUUU0wxxRRTTDHFFFNMMcU0ThQL3THFFFNMMcUUU0wxxRRTTDHFNE4Ux3Rb6JVXXsFzzz2Huro6JBKJw92dmGKKaRyIiPDCCy/g2GOPhePEushyKMbImGI6uinGx8opxseYYjq6qRx8jIVuCz333HM44YQTDnc3Yooppgmgn//85zj++OMPdzdeVRRjZEwxvTYoxsfyKcbHmGJ6bVAUfIyFbgvV1dUBEINZX19/mHsTU0wxjQf94Q9/wAknnFBa7zFFpxgjY4rp6KYYHyunGB9jiunopnLwMRa6LcTuQPX19TFgxhTTUU6x+1/5FGNkTDG9NijGx/IpxseYYnptUBR8jINzYooppphiiimmmGKKKaaYYoppnCgWumOKKaaYYooppphiiimmmGKKaZzoVSV0/+d//ife/OY349hjj0UikcBXv/pV6zV79uxBa2srampqcNppp+Huu+8e937GFFNMMU00xfgYU0wxxWSmGCNjiimmw0mvqpjuP/7xj1iyZAne//7348ILL7Se/7Of/QznnXce/vZv/xb33HMPHn74YVx22WU45phjUCgUxrWvxWIRBw4cwGmnnYa5c+dW7dzxpiOpLzYK62s1nqPabRw8eBD79u3D61//enR1dVXUXqU00e9VvZ/p/uW8w1fT3DwcFOPj+NOR1JcoVMm6G2vblbbxWsJH9Z5ENGZ8PFzP8WqiVwtGxvg4MXSk85AqRuzduxeJRAK5XG7Cx/dw8pAmfLT164jkIelVSgDoK1/5Sug51157LS1YsMB37K/+6q+oUChEvs/zzz9PAOj555+PdP7IyAgVCt0EoPQpFLppdHSUiIiGh4epv7+fisUijYyMUHeh4Du3u1Cg0dFR33kTQWF9OdIorK/VeA5dG7ls1teG7f2MjIzQimy2dH1SagsAzWlqooMHD455LGx92bdvH7W3to77e+U+DA4OBsZuTlNTYCzvvPNOyknjI4+xbvzVNqr9DOWu8yOdJgoficobO9v6jPGxOmTq74EDB8YFH9taW2loaKh0ThR8lNs4XPg4MjISwKHxxEfdvHaUZ+8uFOihhx6iNgW3eYx149+Vz1NXPj9uz3G04SPRkclDVgsf1XPHm44WfJwoHrJcfFQxwnHXfDXG19aXw81D6vBxcHCQ+vr6jhgespw1flQL3Z2dnfTRj37Ud+yuu+6i+vr6yPcpd7MpFLopmWwkYAcBzxKwg5LJRlq5clVAGJ/ZNJvSjkM7AHoWoB0ApR1nzJODJ+/AwEBgMZkWWHehQI3JpK8vjckkdZfJgE8EhfXV9hy651ePdRcKlHYcymgYwShM68jIiO8dJgFKuX3hPqXc9sZCUTYOR3PvSt+rbux0fagBaLN7v4zh2RlI05ox7srnA+8w5bY1XnPzaGMqJwoficobO9P6XLVypXaTHA98JBJzecuWLdTb23vU4SORub9zmprKxkf1OLe9CaC8sn6jCn+rVq6kmsOIj/z7nKamCcdHeV7nAWpQ7p9OJHyM5iwNRqrvMJ1IUE2VnkNHRxs+Eh2ZPORY8dF0brV4yKMdH8ebh5zV2Fg2Pup4uAYIPmss4xsFH6vJQ5rmThgPGQUfjwQeMha6XZo7dy59+tOf9h3793//dwJAL774ovaaP/3pT/T888+XPj//+c8jD+bw8LD74ncQQNLnFgIccpwGnzAOpGgRHPlErZBimhwq46jTSPHkXLVypXGxc793+DtN293zomhKw7Rl1dS42voa9luus9P3/C3z5tHZHR2+Y6w5ywDUiCAjODOdtm4uuWy29A63W/q0a9euisfCtDmsyGaprbWVahOJMb9XonDLlq4PDQB1AzRsefYWzVyvtVxTrPAZbHS0MZXjhY9ElWOkae3e4uJUg8JA8iY5FnyUGceBgQHq6+sLrHnHXffjiY9yf8IE2mqQrb8bIuLjsqVLhUeKctyBYIi6DRhpE/6Gh4fF+wbo2sOEj/39/dS+dOm44eOKbLZkiTExfzZ83KuZ7+sj4mO1MfJow0eiI4+HrAY+bnfPjSIA6wTrMB7SpOw82vCxHB7yzMWLy+Yhy8FH23ofy/oOUy4MDw9XjYcMw0dZiavykCsqwMfDxUPGQrdLlQDmpz71Kd/k4E+Uwezv73fP30NAPwGDBMjWbVUY3+6bBLZNmCfHyMgIrVq50qftcaDXiDW6E3MyQDMgGC51gXG/n1Xu+6zbdn9/v/GZx+qmE2aVDxtjU19Nv+k2rRrNeDU4DiUs78HEtBaLxQCg94T0CQD19PRon9O20eg2jhH3Xatzt5L3KlOYZYsZcN149Frurxvj1bY+V/gMNjramMrxwkeiyjGS1+4e9z0OQghuNkanEnxk3GGMlP8G1jyEtXU88FHtj4yDUbxmysVHeZxN/d2mOW5i6k1W6I6I70z3frZs2VK6tlJ8lMfGpOidaHzcAdB+zT0yAI1qxsOGj1s0Y9xv6/MYniOMjjZ8JDryeEh53Q4D1AdQTro+ylrbYztXcUuPykOmIYSZNRDCzpGGj8x7jTcPqY5NpTxkVHy0rfew8S0XHwmgOxB05x4rRtrwMYyHLBcfCYeHh4yFbpcqcQ0ai6V73759BDjShHIISBOwxv3+LPnnwbO+SSAvsGH3e1EzOboLBaqB53aRB6jeMAF1i6gbggngiT0wMGAFah2xNiylcXGyuenogJb7GeYKZdNSJjQL+BbN2NgY+HKZVgDU19cXAPR/ttznrrvu8gGjTSvIpNs4VkBsjCw4WK0iET0YuA2TZStvGA8TKIaNsZVpqOAZotDRxlSOFz4SVY6R+/btCzB5aQhGLsomaRU4JHxsTCZLlgb+q1sPOkGsmvjY39+vtXbaXL0rxUe+b1h/b1CO6/Ax7HgUjNQJf319fUTkMZXPAvSg5R4qPhJFiynX4WM3BF6x4FBtfOR7qBjZ6B4vFx91QrlV8VThc9joaMNHoiOPh+S5xHjEQl5UfCREEDj6+wP4WCkPuQlHJj6OlYeMioVj4SF1+NjW2kqjo6M+fIxyDzVEqlJ8JAhl6nRUj4eMgo8pmHnIcvGRcHh4yFjodunaa6+lhQsX+o69853vHLckGIVCNyUSaRIW7T3upNlBgMntXFi6e9yJwwt7kU9w977LllSeVMy0mIA5D73msht6IN7uHt8OAXDtra2RYjDaAdrpgsl62BeMFmjhMsgadxvZ2pPr7Az2FZ6rSY17jH+b7t5zr9SPKBrEci3duWy29H42wbPi8eYp96kOoGmTJ/vGsLtQ8MWi6LSCqkvXegiNeLt6HoTgkNHcOwXhJmUaX51wHxXI5GN7DfdvRLjrkG68UvDicXhumtzlKgHRo42pnCh8JIo+dt2FAqUTCdqhzCcbY6Hio+m8oaEh37oA/O7LujUfJiTJwqIJH3Ouq7KN6WkGaAieMtXmVl0OPhIFQ414rOX+chxgFHwkCOVipRipw4NcNlvqq4yR5eDj6OhoqOeNzuV1GKBbgZL1ScbIRZp7N7h90sVsqhipWifLYfbWw4tZVO8fFp6jw9S05r1WEyOPNnwkOjJ5SM4xIAs7tnk1H4JX2A5QynFCz2UBWW6/Uh6S11418HE+BB8zgMrxscHtkyycM+l4SBM+pt3n499ucY+rYzMWHlKHjynHKbl22/AxBdAk6LGvEnwcLx6yEnyUj3WgfHw8HDzkUSt0v/DCC/TYY4/RY489RgDos5/9LD322GP0zDPPEBHR2rVr6W/+5m9K5x88eJCmT59Oa9asoZ/+9Kd0++23UzKZpAcffDDyPaMOZjCem13N2brdTUAjCUH7WfdvmoApBCRKE33K5KkEpEiN/Z7ZNJuIZBd2/4LSCYS2Sc7AWywWaXR0NJAJ0KQ51FmwU/AzNfx/JYDE/ZKzGKqa1pnptBYgtivn6TS0oxHGRrdoGwGaaTieca/buHEj5To7fZ4IOuF52uTJWi2uI/VJKxBIml41xirj3os3xS6AOjVj4EBslFGsaTyvbdr21dJ4qIkuTP00MY9TADpD6fPUSZP871GT8MP0ezXX+ZFMhwMfiaKNnarxVhkWnuvq5jpZmQdzmpoorTmPk8owPt6uzHvdph8VHzkjqjrHGurrtXPOhI/qOhwrPrKrqBpqBIDOamsLtU7Z8JFgV3KYGJuA8Ac/PhaLRerK50sYyQlzouAj71EmzxuZkZKTEZkwskkzBp3u3/7+fitGDg4OlvpjY8K3wWP2ZExU71/jvqdnocfIegilhHxNUypFmcWLxw0jjwZ8JHp18JC6uWTCx2YIpVlCeqezGxu163JOU1MJH1mhJq+7PZr1HsXSeqThY1H6P4yHTCrfZXzMGdZmBh4+VsJDpqHHx27put7eXjq7oyMUH5NuW+ONj2PlIcvBRxMPWS4+piH4hlPV65LJIwIfX1VC97e+9a3AogRAl1xyCRERXXLJJZTL5QLXZDIZmjJlCp166qm0devWsu4ZdTA9YZiFbFUIHyV/fDfIc0VXXdLfTUAvAUX32u2lxXjnnXeWzq0F6MrA9aDTkaD9sLsa1boTWgYm1RqQdxe9nGBBBRqdi+YU2ME6jDkBQO2trQE3qJJW042b2Qa/hozb2AihuQtc5z7TdnjxOL4F62aB3axZtN0A3akBgW4IoFLHT312ZpZvuumm0LHZCw/Q18BgNXKt/SY3Rj6v3v1tr9tWPTwrjtb9H0FrWnehYNWgq2A1NDTk0xgWi0VtiQd1Y05AWJ/kY/OV7wsXLPC5StmyjVZznR/JdDjwkSja2KnubCrDMgp/fLe62fL/rUuWaNcfxwZ/+tOfJkAwoykE3eNk5tVmya2Fh2tRMXKFxPSE4SPP6bHgI3soyQo+lYndCy9MqVx8bIRgYEwY+WYEsbBVMz46fMwsWRJ4/iK8Pcs2NnsQjo8s2LNnRRhGboCHjynpXUXFSD4nijs+4+Po6CgVi8USRvL/nJwyDCPThrnEn0UtLSXlBtNYMfJowEeiVwcPqVMQRsVH3l9PUM7lOb1u3ToCQJe6a1FN1qUK9zYeknmkIwkf+6X/w3jIFIQlVYePHE+vw9UMPCwM4yGvRBAfuwA6UznGis5nleNHCj6OlYcsFx8ZI2Ueshx8VJUp7fD4gyMBH19VQvfhoMot3URCyG4gv3V7MgHTCdhAniW7gYA8AZsIqFEm4DkEcMZER/nNIWElryMgo/zmaXVMk5yT4bSFAJNOC6cCnk6jxsmJTC5HYf2S3dNtMSUmV+9TLdcBoIVAQNPnwIsJl92u1GROG+AHa7ld3vBMm0JPT0/o7x+B2dUnqqaXz1tjOMcWh6V6QTDQ1UC4fqkadFXINpGuLm7OjVnv7e0VmVcRnIMOQEvUMVE0qdqxiOgmdLQwlYeDKrF0M26ormO1EJt6SjMHMhAWPpWRmSOtXfl4xl0zXUDJOr4fQQbP6MIIT1gsByOj4KNJoC0HH3kNjxc+Ztx3kVbGizHyQekejIVR8FEWEk0YaPutWemTio9yMiIbRvZrfpdDhWzvQWUEdRg5M50O5OXQkam+79DQEPX09ITi4+3QhyNVAyNjfKycoo6dyAmkVxDK+DgD5eHjWfC8N3QYeQACQ1OA1iU5bO58A0cePhaVe1WS8T8KPgLCuJRSjjE+DkvHZB5Sx1fKbS8xjNPhwsex8pBR8LFaPOTiBQtCQyEC43IY8DEWui1UzmA2Nc0h4RrOQrZOiAaZYruBs0nnWi7ayJBwT5d/ayAheOt+S1EzHJoLwTipbkk5lAdMew2L1eZis6ilJTDJZRcjnRtiYzJJbW7ZCps1arrybNxGFGCSN6GNGzdqFQq8Gcn3mKw5Ji9sjpHcAX9CvKhgNRlBDatt45KfrV95b7pzrrzyytDfedy3bNlCn/vc5wLu3axNzEDUTo6qEdS5NNVAgGabpYSPCqYpx6G5zc3hYxExM2XMVFZOUceO4xV53WxCUOlly/qqE8hT8OLwdGumS3OfyRDC32LoY8Zyyn3Lwchy8DFgFY6Ij7Ir/UTgY3tbW0kxJl9XCT7WIhizqmJk2Jg1pdPGPCV8Dlv0omCkzlPq1ltvtSpHGSPXrl1L7UuXBiwslWBkd8Fc3/dMjXeAPC6q58J6gGodp7QHjwUjY3ysnMrJecExxSYFYaX4WAN9CatGdw2nNfdKA3Q/PK8hdT2r/Mjhxkefl04ZPORqjB8+drtjL+8xq93rVIUKK8+ONHwcKw/Z09NDX/7yl8edh7SVgEzgyMDHWOi2UPmW7vm+ySCs3UMErJaO6bOYi49JIC/nt2HlfiDZSp6HYHg5UVpUYAL8LiXbIbTrYdddcMEFtHXr1oAGSxcDxAAma59sQB7myhh2HQB6I7zkFfI7lK85iKClp2769NB4yQ0ArUKQ0a8BqCufJyLSM9XJZKnmo6nfdYkENbnx7GEaxgbH8cWH8ycsm73cRo90ngP9Rj5VuYbfb1i9TQdBhQJ7RdS6LuymucQa1xEEXe0yECWo1M0ptnSPP5Vj6VZdYbshEoytVo6HMTvlWnX5mqI7Pwak+/Vo1mkGXmKiSjBSTvQYJRnZ1VdfHchAa8NHOd/CeOHj29w1yQnQ5PfI1x2E52lQDj4SwjEyLDGTDR/leOkwjEwhWOdYh48Z6Et+yXPZhJEzlWvCShvJ2atV4SgFL3GSbW3o8NGBmOs674PY0j2+VA4+bgZomfLuchDuzrIHYCX4GAU7ixBZ9S+V1kk9gi67Y+EhxxsfGSOj8pDjiY/saaViCq9z3fcjBR/HykNGwcdq8pDcp0rxkb0TKuEhY6G7ilRZTHeOgBkErCfPrbyBhGs5KFx4DhPIw37bQ0Cfe295Qi0hYDM5SNE0xT29HGCShVMZ8GyaV/7MaWqigwcPBsaNYzV27doVWGQrslmqdRyaD41WUAIYkyvjsqVLQ5Oe8RjIyRJMYHbmkiUlqyov0jqIjWQvPE2rAwHasw0g05XP08jICHXl88F4n3ye+vr6QoEjVVtbcuNKKfdPwc+YcyZ0+VlqgEAcj6whbnDHh8+zzYvbpL5xFlN1jvH4spfEesP7us5yL9aomspOyOM5GaAzlyyJhe4JoHJjunMQLpLrpXnJydDqZ8yw4kmY5dG02e5BsO4tr+OFENaFBdAoBSrASB1DaMNHB2L9q4lbwvCRSGDkFASt9elEYlzwkUiPkbWJBM2YNi0SPj4LwfjoMDIN0DmdnWPGxx0Q4VNsNeJ+yjgxp6mJ0o5jxccUlKy38Fxfd8Be2WGX1D8WUHTjK3sumFxPo6yNKPh4BkAz3DlSrTUek54qyXnRBOGlos7dujHgow079wHUhiAuLXTv3QZNnpsjBB/luN9yeEgHE4OPKceh+WecQQtdqyoLoevdsV8PgSnjjY+106dHxsex8JDl4GMlPOR44OMk+MezHB4yFrqrSJXFdI8S0OV7gV78tUN+F/Tt7vcoArnpNzXWO0PAfvf8RhLWdnEul2LYtWtXqe+cUCGDIDDxImRwlUFt586dBMB4XYsyqec0NUUac1sNRl6IBw4cCLisMpjyQp3d2Oi7LgOhuW11F5OqUdNtCDL48ELdjKAWkhl2zrxtWvxyuYtSAh/HofbWVqvrOSA0zF3KvR2AWjMZX9zg6OhoKXFJ2AZ3C/TjvAP2jJNTIWX81CV3k5JR3HrrraFJ6LZBH8fGc5BdrcLG5xswl1mrxjqPKUjlxnSz9l+dvwnpf3VNp2BX8Jks3eqcy8DL0MrlR0pabXjMI2MkVwpIwYx1Dry4Ml5/Q0NDtLClpaSZV10hHYj1z/3gDOxRSMVIHQM2HvhIpMdIFmCj4OMOhJcMBAQejgUfTUmncp2dpXdUCT7KbfF5Noxc6/bPccfehI9qHXvGRzkWk13I1fnHY2sTfGJ8nHiqJOeFzoOErc2V4mPYby3KvfIQazgKPhJVxkMWi8USTzDe+GjiIc/RlA5LJxIlAV9XeaUeInkXJ3OthIfcr1mHjJdHCj4SEQ0ODpbc9MvlIaPiY1QecmRkJBiuCj8+LjHMvxMRDR83GN6NDSNjobuKVG6d7mRSLgu2nhKJ6TRjRh05ToqEELyfgknPHAJ6SC+Qp8kf0y3/1uiez21zPDcL2rJgvpcAf+ZbJi7fogOm9tZW2r17txZEWKu237CI+zST+q677rImSzDFtTXU1ZUAYXh4mNpbW7XnzWlqKoH78o6OQNmhGghtocpwyQtL1praEups3LixZFWS3TnDNM5hoDjTjclRNazN8DZEnaauvbW1NIa6TWfu3Lm+fpnctGckEqXzopQMmQxos5LK5xWLRcpls8YEMAx4nNTF976SSUrAs+yHjW2bZmyiZKGMmcrKqZyYRVlrvh6g6YkE1c2YUWIiTAKbI/1VGbQUgrVVec3UQZ90SM3QyvGGcuZbxkgVj1SMbF2yhJa7YSEqlrAFK69ck5fup64n1ZVSR7q4tskAzZ83LxI+snv6mUuWBDT8jI+2EiqMkTZGT8ZHB8K6YsNIU/KeseDj4gULSn2vFB9bILw05PNsGHkC7PWTOWzLNFf5vN3Q4yOPLecTifHxyKFK8VH2IKmVLIth+Dgf5jJVGegxkpOBhc27MHwkGjsPWW18ZMxWPZdUHvLMxYuNLtzDw8PU19dH6bo63+8ZiBjkVStXjpmHZCHw/vvvp+5CYdzwsQFegrZK8ZGzftswknE4Kj7y3mXjIcMq9vA5N0Cv0I+Kj/2wl1obyxonioVuK5UzmKOjo1Qo+MuCZbMr3P9VK/Ua9/hl7t9nSQjWqtW6i8zZyxOGtlnQLpLngn4hQVrkDARyYjSOedwFfQy3OgnVRcLXl+4hdWo/9JpGVXvE4LQIwTqEKYj6szIYqPfu0dwnAxE3yi5EdyAYo8RAqltYqutXYKG6m0+xWKS1a9cG+qYDmTAwTSl1ruECwaD0PYx5IzIrLuRrTVlD1XIdJuvzLKlNFYzV57Jly2xMpUpu82kE3XwbUyk6U6pDW4k2P2yjjpnKyinq2OksALaNFhCxc4CfaZDb4Hmorhmb5UfO0Pp+9+8u6feBgYHAvGWckT2GwsqNyMwWX1tU+lAOPhJ5cW0pBPHRAXyZXU3YnOvs9N0rB6Ek5b4lEJ6TQqZy8LGvr48WLlhgXaum5DxHIj7yuTplEO8zCy3Jemz4WAdPcNLh48x0mu68805rMqEYHyeeJgof8xAKIXVtcK3lSjCShW0dPlabh6wmPgJ+F2cZI8/u6AjwkPK9dTHLLfDwkTB+POTQ0JAvjLFSfFT7Vi18bHAVEDaMdBANH8vhIW34OLuxscRD1iEYCpGurbUmoxyIOD6VrnGiWOi2UiWbjazhCtbv5s829/ge9+8OAgbd/zcQ0E9CaJaTor2NAKHp27VrVym7qjnWu49Uq/okiFhoVTmQg+NLFhOVGdBp1VIIJqjJQKNN1WiP5Lg2k7toynFojfu/ukDzuvvAs2wZ+wLP4jowMOCzxuu0lMPwbypMajIcdVyWWZgiNUOlWod8ieG5ZfAOS8jDdSXZHcjUjwREIo3t0LvbzAHoM9J32zzRZYaX+33XXXeVNmBdv1MQgkKxWCzV3lQ1uVxmzbaxVWudxySo3LHT4WPYRrvH/XuDNGd1SdGY+VTxMUyrrWOwagCa1dDgO5YHKsLHYrGodfFmt81y8ZGI6MYbbwzFx+mJhBEfn3Wft0HyLNBZ/x2Yk9Wobufl4CORHSNVhu1Ix8dnoc/En5b+Hys+nil5U4ThIxFRrrPT17cYHw8vTRQ+bobnlqyW8Vstnc+JbaNg5Iehx8dcNhtQEFSKkSbLbKX4ON485DA8j4JKeUhO2CUbwZiqhY8FBAX0NsMzR8XHFISyIQpG1kp91+FjuTxkWL/bWlvp4MGDkXhIHT6m3GtsrvAmjIyF7irSWDcbff1uIuAW6Xg3ea7jeRJJ1+5w/5cnqkMNDbNK2j1z22zpnk+q63kikabGxtmuG7x33EGKCnCMzMAe+JNNyKCvat3T8Jcpsy1OBhxd/T05KyLf81LoXVZsbiyDsMfKmLSprKm9A0FLm6xxjeLWyfFPqgtYl2Zs1X72RBhLedNRz9ukPGPYZj7vtNN8/Z8M0JsBugeea1pGuq8pCV13oUAPPvgghW2CHA9r6rf8fDqLQA1A10ecZ+Oxzl/LNJaxs4Vu8G+sUc9AMGS6dehACMsqPpraXg9PyFI35xrlmMoERsVHLquiYmSNuxbLxceAiyaC+Pg2mLHQdq+cMu66cxZLlmrGP45XjIKP/f39lOvsNGKkNs4SRx4+quNQA9BVAF0D4QlRDj7yXDUlCIrx8dVLE4mP2yESZKVhxkg5IZmtfS7/J2NhOpEoWRXLwUhVQbZH6lc18JFIYCQbNsIwcg0q4yGj4GMYDxnGF8rvPIyH1CU3S0EIsXJ8s66fg5pj5eAjn9chtW/CSLVyxkyApgF0IcrnIVmYDsNHOZwrrO9q3XBAxOjXlznXKl3jsdBtoWpsNv5Y7z0ErCbHSVFT0xz3+CbyC9gOefHdal3uGioUug1ty4nZ2BXdJJBv0B7nTLPMDOiSu3RBZLTUAUwOoG9rjoctTtausQUzzAIDgG52v6tuz6st91kCL17mWfjLA7AFKAW9NpVBU3dOg+MEXJYC4+KCAsdR6uLMmamUN7zpLtBwhsvGZJLmNDVpgam9tdVneQobixst4MJlyfizHCKbpnwsA8+diuOfzlZiWxOANlnTfvhBV85Gb5srTLxBDA0N+WJGU9Kc4A2JrUDjuc5fqzTWsVM32lvglWLh3zbBYyAdw1rVJdvRZpJV1meY1UA9Vi4+6rBgOTx3z6j4SESUy2atFuowfNyOYDyyeq9aeOWCTBgZ6EMyWSpjExUfmYFUMXJwcPBVgY/LOzoCCuJpSp+j4KMDLxuxfDwPtyQTYnx8tdNE42O1MdK0BkwKdBkjV61cGVgnXfDH3VYLH5mHjIKRaiWUqDxkLeCzkJfLQ7LXk4qPuc5Oa3JMxse+vr4Af5aBULTI8c3XQWTkng5/FQlWNKv4mMtmfTk6QsccdlfsBiUWvgvV4yF1+CgrL6PMF9mjZHBwMFAiV6ccrcYaj4VuC1VjsxkdHaWVK1eRGpO9YsVKyuf9Wc6bm+fSbbfd5n43ZzKXMyWqruKdnTkpttjker7NcFx8GCRWZLM0AwJgVUuQzcUGEABlW5xyEqIwqxS7N14KAXKjCCZxCGuD2+HFLl9jyziui+9Uz6lTxiLlONTmMno6K1UO/jhK7l8awWQ5DAhzmproscceC83M2V0olMY0TKDIQ2wiMiCnAJo2ebIvEzFv1p0IaqS78nkfw5iAYD4vddvMaOZILTwtKGuhuV+2mMQwa0wum6UZiUSgj6omebzW+WuVxjp2smUuIMAq8wtAKeYtbJ2HZZLlDK02l95+zbFy8HEHhEXAhpG2Unm7d++2xnbK+Hid2/chBPExSgZ4tvaUi5Hs9h4VH2VGb3Bw8IjDR2bIb4En1Kfgtzb5LIDuM5aDjzsQLMGzHoJRTiDGx6OBDgc+Mg9ZKUa2t7ZaY2zDSjTyOuvK50uu3Dzf06guPi7v6PCNQRhGptwxZItpuTzkdfDwcZHCz5+O8GSJd955Zwljdb83pdMBTGEeUoePapy5jLMmfDQp+VQFKAv1NoV0OTxkI4QyWlWo6jByhvuObDxkDn58LBaLkZL66qi7UKCU49BH4Lfky/t9NdZ4LHRbqFqbjWeR9izXyWQjFQrdNDg4SK2t7crCd0hkOtcLxrJ2T1fH1e7Wfp1yXAj0nBkyzNVbdW3STWxm0BiwuyHAVtYesda1BgiNQeR7TVIWAmu7UvCSJjjQZ+h0pPbnaBaxLauh/DGdY8ooaUwkAk8DK99nDvTJKTLwylkQCU1dW2urL/OzbHkyZfnthsg6uUh5LgciE7Nts5ZLI8madhWoVFd+XabLORDlUfj5b731Vmqory9b00hkELBcD4OJWuevRarW2HGslTqXV61cGUj85UB4S5jWqg0f+TfdXGcXs12a+V8OPtqUjTJGhuHjnKYmqnWzX4fh0xQEMTIDwYS1wbNg52GOFeT2dTVio2LkqxkfZRfZA/DHZQOgqZMm0e7du0Pfa/vSpZHwUU5utAN6fJyJGB9f7TTe+MiluNQa8FEwUs7NoNa5tuHjdYb5zxhpKoHH5aHKxUcdX8drPioPOTmR8PWnXB6SFQ0ztZ6oKQKcwP33g/l5774FKYeS3D+jW3tI5m71ORtgxscUPCUbv2+5hG1Jgeg4NHXSpFB8rJSHzGWzNDQ0VJprJrd7FR/5nipGdsErO7tlyxbq6+vT874WjNThY1tra6k0XLXWeCx0W6gagGmLve7szAUEcrGAM9rzAdDHP/5xamlZ6JsgnZ053wba0XE2eSXIdKXKMu7x7SUFAFN3oWB005FBwgRwG5XFMghNrAuiAzCg13YBQnv2Dek8dfHK300xf7ZYjg3wYpBM55gySnJcZ5jWkP+/y3IPNfu8bczUMc/AS4ymjmcKXpyi6b329vaWNmq5VBBnp9zhjtMaiPqLclu6TJcpCCZaTWil9lutgRxGKvMQhWKmsnKqJkbq5rKDoEtzCsFkjfK8//jHP17KGM2fdmUDHR4eptYlS0qbM68LHVOWTiQCLpk2fGSsiYKRNny0WXuAYLbe9e4arIOwejN+bYa5TusOgPYZ7mXDyB2W318N+CjHkpowcsEZZ4S+VxZmZM8oGR/53dQCNBdBwaISfFRrIIdRjI8TS+ONj4BI0FouRs5VcrZUgo/MT+iEmvbWVq0VO4/y8XEU/nhqxsdylJwzNOu5XB5yAzx8NPHzqiV7kUZAT0o5lOR3EqYwMFnIVUt3FHzMZbOR5tV44qPMQ2bgxz4dPhL0GJmGEP7Vvs5UXPCj8pDjjY+x0G2hagCmF9y/h7ys5ER+l26TK/l68mK1GwiY5JtIQoPWScBmAkSc+IEDBySXc0f661/4cuz3aaedThs3bozErKyHfXHL7nnb4dUH3wt9wqF+5fw9EC4rKXjaNFnbtUqzyBIQWV4bk0la795zDfzxT2wJ0gECM/eqBUjeuHSlD1gLKo/FCILA3QG/OxA/+2p42trVIf2DNI4bN260ZjZd4/7/927/1MQWYcxomJYSSjt73L+b4DHzjtKWLUHJZPhdLNm16swlS0oulTJ4hllnZO39RK7z1ypVEyP3wI8PX7fMm/XSOmxA0NLrQIRFbIZnzV21cqXPnc2R/qYQZCJ47fzDP/xDWfhoLT8Cz8LN696Ej9tgxsd25V4mfDzd/ZtyMW4vRCmg6YmELz6ULT5RMVK2fhwp+LhwwYJS4jEbPvZI80Yds2pg5B74340OI9db2jThY1trK913330BK2eYO2SMjxNL44mP+2EPGZExkvGs2vgICIW9XAPbtn7KwUeZh1QTsqk8JOMPY2Q9QMukNV901+FKJMgru8ufhI+HvBnCQyjlOKVcESeXrtGHbtZK+HhLqV09f78eAh9zmrEQlmS/hTwLp4SRMuZtd/EhjL+V8TEBlKzNtvPHGx/l8034GIWHNIUv9fX1jTsPGQvdVaRqAOa+ffsoWGO7m0QCtfAF7P8kCainYHK1KQTkSgs5nZ5JjtMgnXdd6ML3fxyaN28+MQOoW4gz4NUMZSZQBvWZEMzTEIIxwGEAOwo9s9iqAEk3hCsllyBQz+f4FPX42RFix21urKPQZOeFcHGRmc0Mwjco2YoFiLiWqLHtAGhRS4t1Y+ONiTcidr+6RhlP9f0mEGScG9zj0xOJUvwq94XbzUNKbOH2K+9ea2OWw55jBjQlyzRxiNrszpZYnGqu89cqVQsjA9Y7gE6KMG/4k4RgsOQ11wCBFTlpPukylrPrdBgTwXgwf9680D4xPm6HYGhTCGLk2RBM4k5ES+q2AWZ87ILAW+6Tio86LNQp4bryeXp9BRiZh5cdeDzxMWrcJgCqTyTssX3Q4+Ne2K1wpyK499UCVD9jRsmdfQ/8+MjvRoeRtgR3trkZpaZ6jI+Hh8YTHxfCS+AXBSNrIARsGz6qCp5y8BGw16Wf5F7L/IGJh1Tx0baeRyESsanX1Cv8dwMc8huw/L83Ns4iVSBfsWKlDx9N/LReqDTz92yxZ0zksdBZyGVD2elKn7vyeWsdasbHWgjPBq4qY1N8sJfO7YjGQ+rwMQWRXK3WcQI8pA0f07DzkKbwpaZ0muowvjxkLHRXkcoFTJ2GpFDopkQiTbpM5LYFDOwioJeAiyzngQATIJhqhfPCX+P+v0npEyiLRCCZjZy4IqlM5LTynZMmbIMAdh2jMgmeNlbHjHGCIFnblYBgsOXFKWu4ZqbTAXerBschB+aYxob6eiIiGhwcpDbJcmACpOmJRKnfujqEYQw0x2uWmEx3YeeyWZqCYHynDEQMHrls1piNNu/eU9YO8v82qwoQZJxnKd/ljaJWOr5euV8fopXZiALgKoOuZtzVxoVGiHWsZJ3H5FE5Y2fSIHcXCgFGjxU9YfNmF0C9AF0UYX7JMbXqeVEsqOoaN91LxscEgrigs8Z/A8JireJSfSJB6bq6knWamVTVZZOVm7z+GB93GK5JIVgWjTN88/rXYWR7a2sgnn0i8bFYLFJqxgxtrgrGRzmDbyX4GMWq8g34XfR1AhEnaapX3o38lxPetVvuFzY3WUmq2zPldRbj4+Gh8cRHB/YEh7sgygiONz7ugOBb0iHtyBjJ59h4SMbHfuh5yDqE85BB4XU9CYF6OokqPlymVxVua0jNweTlXsqTV+bXqxrU2tpOIyMj1NmZ8z2DiW+/VnqOtGtNj3Kd6LcngMuhpQ11dTQdfg8HEz6aMJLPU3EwKg+p4qPuPTNGnqG0qeMhl0nXme5pMhICejkhherxkLHQXUWKOpgjIyOBLOKcJC188cBdOA3KAm5wj7Mr+jb33DDBOU2exk4+z1bPm+/RLYFPMAZ8EpK0vKOj9Mwcj7EBfkDUWS92IJgpUmVUbAx2LUDNyjUOhOZWd74pBuYGDSBwX9SkPiZNrKPpf3ehQENDQ3T55ZeHgkA//ODEC5utOQlN27IFaD+Ph+PQZuhrcW6GACzVYsNJ5BiEfECbTJZqXG6AULT0QzCE0wC6DF4G0kapL4707th9Se1TB8wM8ExLpsyw3wYGBgLx5brzbG5CMVNZOUUZuzANcpS4Mp3nhQPPWsnzLkwLnpbmqXreHss8UzX+Rgu2Bh93SGspB70AnMHY8ZGtVvI1uQjPpVtvprjvL3zhC4cVH3kOqQycjI9yrdorUR4+yu6sumdjxS3vLUWI8CcTPrLl34FnLbxdNzbuvXRzKjVjRsX4eOONN8b4eJhpvPERMHsd5jBx+EjwC7wmjJQTHXLeAxsPmXHbLxcjh0u/Me87QsAi3zWCZ94sPxYFeWOVZ99Mgl/2t/OFL3xBkzQ5Q15uJbWsr2et5vc9ODhIJ5xwknt8r9Iv5vf7SRbAk8lGyue7AjIIt2/CxzUwx+oPQu/xE8ZD6vBxC0Cvg8gvIlf5kHnIJDx8NPGQDRBGPNN+Y/PCMP3Oif+snqMhGBkL3VWkqINpyk7uacZs7uN5w/d+93zOOh4mON8iXa+eZ1r4He49BpTruklo8rznSSTS1NGxvKSJleNBTJYBjmmplRbKegjmMAU/uNa6mSVNG0OD5poUPKuFer6ppMV0ty974cVF8gJntyh+Dl1JiYwLFDvgLwu2b9++YGwdPNdLeYOSGUz5N3ZN3AvQBe4xLgGkJsyQlQpFeNro5R0dWquSWgpI3bDUxBMLATpTOceBcBVl189r3b9comQ9PCFJZ7WcqbnnwYMHrbVC1TmxH3rFxH7DO5fr145lnccUpChjF6ZBtsWV6TZg/s7rx5bkqwg5vk5/ngMNE4FgzDS5a0Dn0ru8o0OLjwQ7RrLVw4iPlgzmrZprWDlhukZXFi0hjYOMkbzeZEXn4cLHZwH6KDzhm2M1de89Kj4SggntTMrPqPi4S+rLPDdxlanUZguCyoQkQA8//LARH9nDIcbHI5cmAh/3a9ahA2EljIqPspCiM1iE4WMRQXzTYeRkiLw7LMCUw0PKVuFagGbU1PhclWUecg8Edmws3Zv5b9mwJFu18+S/rSzcqscS5Fm59xKwmmSrc1CIH6VgEuM8AZtLMgILfX19fZROz1TO7XbbUPn9oADuOClSeXbAseIjwbMwn7lkiREfbTykio+v1/zeBX8CPOYhFy0UiaHDeEidMvds17NMxUdWAPB58voZQZCn4L3JuFeGYGQsdFeRogymLTt52G9Ll7ZLvxfJS7TG1+4lzi7e1DTHXUSy4NxIQBcFtW41JNzF+bx6EmAjn6PGmTM46Z5nJAAc2WyutCD7lYmtS+2vLhZ5IffDi32rRKOvAxSTpVuNZZTjB+sTCS2TukNpk/tcygbZ2alNNNLgLm7ZZUfuyy5lYatxKRkEtcUMJkbme8kSnysatyG7iLHlbRc8zWKt4o6vi31lt08ZsDg2huschr2nvRAgy5nN2VI9NDSkra3L2SvV9jKavqVgztYaW3LGj2xjZ9Mg26xw/BvPWZlB5NqgjckkzWlqonQiEXCT60IQi+QsrCzAZBDMkKtilsoccp8GEbQW5JSY4nIxUh6PYYikZ5Xioy3zrdqO+iwZqZ0UgorOw4WPLAzoEmCalLEmfNwOfcLPonSck6+Vg49yfLWtzvZ8gO4B6EII5XCus7PEjOvyjZiqccT4eOTQROEjwcMjXndR8LEbeiEkAykzOYQA1a6cI+NVG8wYqYaZAV7G/cp4SDXBmAiDvFLzmycER/X4tB0D6SsBgTy3b1WI3+P+vkZqc5iEwA7JHV0Xx93gtsf8frfSn13k93gNPluqyvho4iGj4mO3dD+Zh8x1dlK9qzgxzfd7IJLbTYPg5QcGBrT4yPuvLvxC9lqTlVxOyH1jS/cEUZTB9LKT663Zra1trhXcE5a5RNfo6Cg1Nc2hoBU67QOPQqGbDh48SPl8l7LYu0kI3bqSY7LmTb5mkmZxrycPkHTPE7R8J5ONNLNpNqUTiYAmlRMiqAtuMkBTp0whQK+dTULEwKkAYUs0s1o6nzeYgObLvV+32+b7IbJTcgZJOdZFZVJZYDX22QIUuk3MQdCSo7pcfgNBxl+1xuvuJf/Gsde63+TrNsBjlqOUcOPPZAimcnR0tBQLH6aVB4Trmar8WJHN0saNG0vuPly/MQ2/4sGmtZdjmeS6vWNd5zHpyTZ2NktNf3+/1pLHLsVzmpoCiqe0si66CwU6ePCgL9kW4CXxUrEoBb/lVp3P0yCsKXyNHGemMocmvFMZ3T3KvNVdkwJokuTxo2M8dQoDGz7Knka8LmqUYzI+NrrPXMrei6BALAvshwsfeUy51CF/EhBhRJXgY1g/B1AZPp7jxg7aMqrLHwegMxcv9h1b2NJSqjLCa2YR/DlAYnw8suhIx0f2VtGFvXAbaWVuToLAHPl8zl2gw0gTPnYXCqV49Sg8ZB1AwtJcS6YwSGFw2kzB+OzVFORrPT5d/C74b2HcqqGgZ2iGPF54PQE3E3Cp+5sqDMsCu3zvEfIbyZgXl3lwnVIAJJf5FX1wyO9qrrqj7ynhxpGGj3ISNVZMjpWHbF+6lNauXUu7du0iItLykOo+rPZN3SvjmO4JpmpYuoeGhozx3mzlUxMvFArdvgLy8r16e3vpmGOOIwE8Nrfz48mcAdHkgq62Gf58PPHZkmHb9GUmS8d01mvOsy1230bibjCq5TTvAsgBiA1G/m0OQAelhV2ragZdCy4zOCozLmeo1QHFCYZnkjXRspZNx2zXTpvm+64y343JpBWwToXecp5AUNNtAz4Z8JIQ89ymte+FYNYz8LSg+xEEdfUdqoJRFFCWwbwa6zwmPY3VklMsFkueEvK743IfQ0NDAS02xweb8HHu3LlUBzsWHQ+9RVCn8eY5q7ZpS7olrxMHAj+iYOQOmAVzeT1EwUddpnKVAWd8DMuSLscDyorOIwEf58+bRy1ujVi5nf2Ijo/XQJ97og7BZJJh7ajjHRUfd8GzVDpunzdBkxE+xsdXDY0XPrKluK+vj5a1tVWEj1GFEB1GZjTn63hAGz7u3LkzUJZMxUdhiddVANIZnBrIE4BVoRUUpYpPPt+lMXA5JATeUQJWUdCY1eX+FhTihds337uLhFFtB3kWcJOxS1YKnKDcL+P+Xe8a9WSeXhXsQUuWnEmnnNLsO7YITln42AORnyeFsePjNHi4VeMeixJbXS0eMoxHls/TZTkvd43LFAvdFio/pjtozeaMlLt27aL+/n4aHBzUCuE6kGTSJWoTJcT4/7CYcRPIqFqx/eS3jqfcc21J3ITLh+pCZJrU2xAt4ya7bTLDoWOGUhCJZu6//37t2BWLxYBl2GSFzyGcSeWMm3KfWaMbZePi2uHrISwMqiZajkvJu33SMVx5+Otq8vFcZ6fPXUvXjx4EwWdRS0sghsaWnZI1pfImvHjBAiIireY9BQGKOktMmBZ8ZGTEV+pN/tjmjuyGHLtPjh+VE7Oo0yDLGXuLxaJwFVPKrpiYSCZdIqKG+norFoXNI9Wquh8IMIeqy53pHjdDMAtD8AuLYRs/VwWwMR/crzB8HBoaKsWZy+Onw0dek2mEY6TKmJzlMv6HGx8zkGLKpXcWBR8Z91qUNpMoDx9rAZqHyvBRVmKYFC8yPqoKqRgfjywaKz4SkZaHNCkpK8HHSjGyqDmfvU4Yj1Zb2j/pBCFMhvGQBTiU0BqPwvhbtjSb+FrPgl1f36DdX4rFIrW2tlEiUavcSxcb3kBy6V7509Q0h1asWKkI30R+Qdvm/l5Lwhq+zf2bKj1XodBN+XyXJIPkKSwpMseUy23kslkrPhYhMFflISvBx3p4OM3x2r29vUQ0MTxk2DOqYRrVxMdY6LZQ1MEcHR0NCMUrV64KaMv8iyOYdE33coeHh6m1tT1wjXCBmWxZqGHCsj7+Y+fOnbR4cYaCmrxwDWEBDo3C7lrCMT5hQMxuhbLb034E637z4pe1UTITz1qzjLso2eJiinGc7H42QDDdayAYoO5CgTZu3Bjos6zF5YWvAoUDTbkEjRCharONDBeCZR9MzGlpA5fGoBv+xEImEMpAX0oiI71Hdfw4/iyjtK16L/A42rTguWxWWyplEhCIT0vBHKcUJwoaP4oydjpLzaqVK4PujtL8VTfQdjchl0rDw8PU3tqqvaYmmQydX2EYZKr7uXPnTsosXhy5fqzv+SCsxddarhmCp3G3YSSv083wl/2pBB9vgVAQhPVtMkSitw0QQvIGd6x19ctN+LgHAn9qMX74qHsHUfGR4Hd/LAcfZWxWk19Gxcd0XR0BdmVFLpsNznsgEJIT4+Pho0rxsbtQoAMHDmiPl4uRuWw2UEK1MZmkqZMmlfKrVIKR/Zrzi8UiDQ4OBjCyHHyUechgBnL+2NzFOQlahjxvz28Q0KzcV/C5TU1zAhjJ61Vcy27la9xjG5T7Mj88mYAp7u/bSM4u3tKyUOmzKmjzPcIznQMiDlxWsgRlEHNSZC82XOHhC936pGQI5t5Qechq4OO6det8e5Lct0bNfKmUh+R488OBj7HQbaFyNxu2JgwMDNCSJa0U3bXbm/wc6x20bpuuSZA+M3nCcl2ddM0t5Di1lM3mlEzse12QqTHcI1N6tgRSlINwy65BUCiSGRrbQrkI/sRqOnfCZvgtGmd3dAQ2KM5I+AiEC7kO4OXFNR3B8grcp6uvvjoUXHRWl9SMGfTpT3+6ZMXbsmVLKWbZROvWraMoDNde9+/J8G+kr+/oCGQJZsu97pmvgX5jlS178pjtd//vgQea3FZPT0+pLd44VUVJs/RcuvhYub2w51f75sBcPi625IwflTN2Mj62LlkS2bVbZtC4tIpqvTFdMyWZ1G6wUUoU8jW3QISc5LJZX6ZhVszpNvE0NLWw4Qp3hrwTMkba1v8aeBi5CUF8TAD0ACrHxzw8rNBhpIwlqlu8CR874TG5/MlkllJfX1/JisfJFU1r9sYbbyzdR72fnHBul9RnriNsw8ccgvh4EcrDR9kFv1dpKwo+yt9tbvlh80OnoI7xceKpEnxkhVg1MHKFktBRPb+hrs7oJWPDSDk/QB1QqlSgYmSzpn3GR/Zw2QC/cMc8pCfYqcL1Hgrnb99HwGXu/5somGw4QcBHSebNOzrONpTd2k/AAQLmKL/JmcVZ2J/uXhPMOs48pL/PbDXfTjqrdH19A915552Recje3l5pvGzW873u3wXExr98vqs0Z1R8NPGQ1cLH3t7eUo4D3v8+AnOC00p5SJ21fqLwMRa6LVTuZhNdUDbV31tTckv3hF/WrIW5kKtWaXYfMQnLsuZMvdYhf8mDYQI+ojkvI4GKX3HQlc/TOYrbW0ZarPsgrBspBIFetXrkoU+GxIl/5MWR0mhzHejjx3UWEbZubJB+58V75ZVXamsG6/qcgChpI7u4mOpwynNHBjsbwyVrGYvumJ6q9INrvOoAs2R9QfjGaipZJrfJ2d/lTKv98MqMqOOehIglZzeh9SH3CAPT3t7e0ibEVh+Ti14113lMHpUzdlEFZVMN5zXSO2XGLopgom6weYgN1qTxlq2vts15GIIxKGcTz2Wz9IMf/IDmNDVp1+cIhNBkYoYd5T6dmnWWgsg2XCk+1kNkJFbXJGNkCh5+8ljb8VFN4LmjtN/pQqhYAc1zR8cQ73fHayUSpO5RLe73Xe45Z8D/js6YN8/IGEbFR56XpkzwMj7eddddkfDRgVCIsJt5JfjIYQ9XXnlljI+HkSrhIauJkbZSg5ViZDLkWhX7BuEp22UeqUVZr4uk7x4PaTIeXU8ixNLE38qY00n6ZMPtUnv6slvBBGo6i7HM/3JCtJT027OltSj6w0L2syQUAjW+cQDmu3+vi4yPPHeWLuX4/g1kjxNfLbVXdMc04btHfb2/6hF7tMpzsVr4uGvXrpKlm/f1PPR7W0N9vW//D7tH2NyfaHyMhW4LlQuY0QVlvWu3v1wYn6fW0FavmU/A16Xz5ayJm0jnlsKue21ty8hxTHULg8kYRDKHtaHPduKJJ5e0nSlH1AjsgJ8Za4Zg6jK+tj1BXGX+whZ1Ef4Nx7TwTS7lcvZybqcfQSZzYGCAEtDX510If01bZkZZqGxfurQ0FnvhjzkhEmCpMuCTLc9dC6/01kKlT3mIpEbqWLIm+RYIi9Xp7rPmEHSNZ/fcFIIbbkZpswau9UtyfbOVgDtzyRJKIKiwyACUdq2KNjBX4xkDQoyi2KjWOo/Jo3LGLqqgbFrHRQQ3c5tQNB+gr0vnyxuzTuPd3tpayhGxrK0t4JaZcteQzvsmAVG2ptfyfD09PZTr7KTGZJI2ALQE/qoNGXgu43nlHjXuce6PLfZbtvhGwccRCMZKvuciiAywMkay0k9+J2H4uKN0TN7HvLI5S5e2k+PUkmAW/QI5EdHKlasoyJzWUAsSbn9rSF+D16GNAM3UPNNk6GtmnwGBj2dAKFXaocfH7kKBGurrg8kp3fem4iNjVNrdC8Le2913302zGhoqxkdd/o4YHyeeyh27amPkpZZ5xvzCDVK7NozMdXbS0NAQ9fb20uIFCwKu7mEY+Tpw/WyHkqoCzl2vV111FRWLRYmfzlDQ7XoKiVK4mQAm+LOXq/HYKv+8y/1u4s1vkdo2tbGePAFcFWg9nl4YJRJaHAPmkhevzW15+JjN5lyFwBoSRjs/Po6MjLiVkOR2ObFkWJz4VPf/hcq1eQIWkaokdVyPVpmHrCY+Mg/JCsewudtQX69VFm1y72/DSDV300TgYyx0W6icwfRnMbdk/E7UKwAia8zkxA/8mUNB0JHBiLVbJiH/UgJQSqUf7K9uQeZIrx2cr7nOAwf5w5Ody1LofuOkBSYmhJk70+bTBk9TJmt/dbUnTa4xqnVDFuRr3ZhuIqKufD7AVE7S3OOA8rw6a4psHV7Y0uIrUbQDwv3K5A3A1ilurx5BTaBuLO+Q+qL2qVb5zoCVUY5n4Hc75TG7AV4ZE1lTb3pvC1taaAr0pUrk2t9qmAKDNLvnqpa7XGdnqHvqWNd5TH6KOnZyVlJbeEl9SGjKfs3cnQPNpg/PgttmmYu9kIRId97YsqjmoNfA1yCIZWrd6nLwkTHy/YYxs+GjvIZVfMwp+0wBDuWRCDDDQIpmuhYONbxlNTwGKwo+msvmyOewa6bYi+68804SzGq90q8G5Vr9XnZiKQQryODL4zkCP96pmedVhowTD6kYqfMo+gY8jJKZO+O+1toqXGw1c0zGR1N8Orvnxvh4eKkSHrLaGOnAX1aO3btZ8ImCkSy863jIcjHy1MB6HSZhlV1P8jryzhmloAFIvr5IwBYDBthivxlvNrn/sxeqzuiUJ793p9qGh1me6/Zqn3As8jypQvck5fsqElnO5WNJ5bvX54GBATdenEupyfx6mDdARmpPxdaUYTw9o+B44SPzkNy+aU5Ohh4f2QAUxkNOwuHBx1jotlA5gxms161LiiCYBLVEmD/+I0PBWPA0meNJZMt4eOwGJwQYHh4uxZdVlvmcmZg7SACR3K8ELTMslr3SeepvpizAe2DZfOBpyuRzdLUn5Y2Jr78W+o1LdgVlbZcp4cnOnTsJ8Gpds+ZvB7xMu2o/OmGOeZHvH7DEQSRakpnqqIx4Hp6lWrcZJiCYPfaE4DaKEJYTtc1h6b31wJ/tUXY11703U9/lOJrR0dFAsi0HoNdrMsmr147XOo/JT1HHTq1Hq0s8yFlMAyXCpHWRQdA6mYY5Z0MUFzhZydbf3+/DxzBhNmxuM6OrMhwOQOcb1ud44OMGeO6g8jkFOOQYBFET7steQT4XQZfBMeHj0NAQbdkiM8U58qzSeQpaqFkJrVNAB2MlvU/5e5msiFgF0CTlfvNcF3b2KkjA2xPkOc3eDaZa4u+B3wopu5qb5lAUfFTH24l47Xis8ZiCVAkPWW2MZDdx+fwcouVkUJVsjI/9/f2l+OFKMVKsb12s9YeJDUX+NV0kr5qO+pvJlXpP6PoXnjUNJARhGft07uSmcmTXUtCAFszTRKRPulwodJd4SNEfGR+fJb1M0EjCbT4KPqrntBMwROH4GEVZMX74WCwWS2MSNodsGGfiIQ8XPqKsll+DVLmlm0ivnauhfL6LiLxyBMJlxEtoFg4Q0wlYQn4Xcl4E7eSPFQkCwOc+9zk64YSTyAO3sHuZF9zll19OXlw4g8HXCTjV97yLpPgP22JRywDI1zjQJ+Loktpc3tFR0vrbGFFmHlVGPQOvrmuD41Cus1P7rnXld1atXFlyCed727TVqnVbFvqfVfp2KbwyQepYRmHEuS+2cg6crV3VYsvPonMbky3oC1taqCuf17+3RMJnDTRt2HLGyGKxSL29vaUEIipzEnZttdd5TH6qxNJN0Ft35brBxWKR2ty4Vp4/YRgBCHe3JfC7R/KcaEcw3linZDv9tNN8fQrb8E3zj5MhpuExv19HMOdCOcyEDR91sdRz4Fc88FjuKd2n3Jwjfvc9U7ZkHT4SsYs4W3U2kD3Rz3zSM5vdgX6Jz3oSzHdRace8l7GbrsC3oCICSFETkoFxVDFSxXodRibhZZi/8847taVxGpNJaqivp+mWOabio1xSyiYMxfg4cVSppXu8MHKqOwd186IZ9ozTMj7aEq2FzUHxyZAn2Ab5Rzt/qnpbms53SBiuVGvvHJINVx0dy113dhsfvpc8/lo1huUJ2BRakYjfnYqRQXy0PRcoaN0Ow8dLCeilaPi4J8IYjB8+ZhYvNvKQKXgeVFExTuYhbfjY19c3bmscZbX8GqTKY7rlxV1LLOCqiQ9GR0cDZcXCGATRjprk7P3ub1dR0G1FAIDjpGnSpKkUrFe4SANGaQJOD11wxWKRstkV7jm6zJB54jqAi+AE4jxMbnHMpKqa3VnQxwp2KQtF1fqHAb5avmfRQn9MS9R4DiIRTzO7sdF3fR720mhR4rJSdXXauL56ePHQURQVbP221RWW3WzV95RBuKWc3x+77uhiUh0IzbtNIRGmbVTBvNy6syrFTGXlVElMt7y+WTuuW3M6LXXY3E0gmOSM5/O7EcSQDDwlW30i4XOFdiBKY6l4lALo7Ahzl5Mi6jKL5+HlXNDFwZWLj13QKxDT8MdhtylZu6PnHLmF1HdQDj4SCYxsbJyttGMLiwpj+rxYSceZTEEXzQwB9VRXlw5tp84dx5st98siYcVI9mqS/1cxcpI7t5rSaUo7TsATYmZa9NemHI2Kj7wOyqk7q1KMj5VTpTHdE4mR73d/m4FgXCsr2XT4mILAsTSCBpHTLfN36dJ2svOPwUTAwUTDMu9qShy8kILCcYYEr+vFYff19SlW6HArr2dVvjIw/iqvbyM9PnYT0Gfpiz0/lMBH1T1d4KN33KSsUMdTb+2vNj5ORjgPeYNlfo0FH3PZbOT3RhQL3VWlcgFT5zqi1tNjYhedbFZOkLAjZAGIZAoCaO4g4VoiT0aHGhpm0Ve/+lVqbW3z/TZ58jTyEs2sl+6hiy+pIXZbEUnWgoBHRNTXx2CQp6h1ALn2JGuwfL8BdBBBd0zWfO2AF/8tC1gqI8FlFcIW5LUQzLZ6r1xnp/Zd2SiXzWqTlq2wAIMpA+lqF4CmTZ5cYvx1saO8iaqbdMo9rm6gUZg52Y1M5744w3I9KzHkxHVqTccw97l0IuHLGCnXFZaPmZI2OQANDQ2V9f5iprJyKmfsdPOpva3NuOaGh4eprbWVahMJWgMvGZcxhtDNQHoH9Bs1IDxi7rrrrlLOAv5MktbZHvfYQsMcWw4vVEMnNBN5+JiHPkN1FzSxbi5DfeDAgZLwZcPHPER2YF5zuizXMkbK+GjPObKf1ERFra3tZa8vIqJsNkf6EprmfoQzm6spkUhTKtVIuqzoXjyj2IODDHqKmhpnaQQWM6PN4xiGkVHcu9XfeV+TY/6fhR4fU+48UdeJjJHDw8MloUidL0mAHn744bLeXYyPlVMlPKSKkSa+hN97LpstJWotByM7lbnhAHROZyc9/PDDAQWdDh93uDimw0gOn2uQrPAyRkbjH4OJgAuFbjpw4IBrrFLdprsoGAvtkKjPDfIyequWXsET8/j663PrcCnpXjPe+NhIwApLX0xeSasJSEnGNl2SySSJuuWOFh+BhCY5W55Mce3jhY8EPQ9ZKT4SCZ69zoCPCZTHQ8ZCdxVp7CXD9Gn9g6VPMgQMuoCQI9VNnIVdT6jXMRkiLoWFYnZf+ad/+ifpPjvIi33ZIx0rSmDkMTxq7Ln8LJ47vQ0Q/Eylmt16m7ugWNu0F/5FYHNDrlWENCKiBx98kBLwZwSW46F48aegMMMRSwTIZEsmomZuZ9d1xwI6DfX1kcFJrTvb7sZlE4kNRJSq8CzU/DfMjUwGqGKxWIpxvcbyPvj3bYbf5efRuc915fPaOsw8fzhWSPf+UhDWyfbW1rLeYcxUVk6VYGQgHlFTPk999ysgPEdymvXE65Y3eO3a5nkvrXEWQN/znvf45mU//HNVp+zTZYeWn0PGR9P63WG4Xi71Y8PHHbDXKpWTQRIJfPQSkwVzjnihR8F9Rla8RqXwpJ3MEHr9cJwGWrp0mWVvAXV0nC2Nhfm8oAVL7G2jo6M0PDxMvb29pfrfYe3UwoyR7LK4F6DLLe8DEX7fAT0+nt3R4SujpsNIFmimatZBCmJvKYdifKycxloyLCo+ZiCUb1yNpBKM5CSlMj729/fTxz72MQI8RTrjzR5pruowEtDEnrt9iMY/7vBdyzxoPt+lVAnaRoJ35YRs97jHeYzCqwk5Tq0P04aHh6XEZLrkxdyficBHkBdm48fIsPwbgFruK0yxKZQF6ncdD1kpPso85LsOMz6Ojo5SX1+ftloSr4FyeMhY6K4iVe5evt4Fgg3kOA3U2ZnTnMMLVVenb1YAaILMnHny79q1yyDc50kI93Ygam1tIyJzfB4RSRZ1szUCEHGWm4FSGQA5xkPnfsnMLFuTwhjX9ra20EUmM8Zs/Xqbpc1yLN22+GK1D7zwz2pr07quLoTY3Bocx5q9EVJ/1fekG4spEO5guj6xG5lJ8cDzzjZ2Oku3/PtZbW2BbJK1ALXMm+cbd3ZL0ilFhNAQroyIs/NODFXiPhlWPo/P4Xev80iZpVlPKj6a5sal7t/BwcHA+shDbOTD0jGbIBuGj2wtUtvYr1l/GYhSUOyWzlYDGz42wh7HyYltTLWu/fuD2BsSifBSO5VgpHmfCFqzHnroIZdp9DObQkBvJsdJKYyi2UIte0Hxu9KNRSrVREFFhMdod8CMkTzv1sOL3a80SdrZHR0+jFwPka9geUdHYC1pKzhIXhyme8hZqG0U42PlVKl7+XoIhRvzAnJ+GfW9b0LQ2jwWjJR5yIDQAs+zxmYQaXMFFxNGmvnH/QFMYJdz4RXKuMS8sM5F3SHgAfe4fE0Qy7LZXER85OSOIK/U1kTgIyidnunrVz7fRfX1jQZ8XEIse0TBxzAeMjgeNe54l4eP8rybPUZ8VHnIcvGx2jxkLHRXkSpLpJZRJmmmtLA5hb5/oXZT0PWjgYBEKXGUTPoFOkJqFvGmpjmKcC9nX+wmoC4UNKK4V+ifR1YApAhwShowdg+R3YtroI/tcCAE9W7oE6mlIOLfZNIuMogkSrIGdqPSD/7wZlFOohnbBrawpcUHaMzsm9yjuzSL39Q2Z4rUkW7zTicSPmb/dIBOUvqQ6+wMxCLt27fPZ01nDaH6PvhdTtL8zhrEWQ0N+ud2k8NEGdMoWVPLeYcxU1k5lTN2+/btCy2fNzQ0FHj37EKmaqMT7jww4aM8N3TlAxvq6wNY0QAvsVqTZe3JVQ1MxPiotpGB2RLP/WP3uSkIZtSeBKcUm8kCuQkjZWY9qPBlF8Z28ns5DZEte20lGGnaJ5Yubffho+fNpavD609+5H3MbevIPxZ7CFhDjpNyQ7Hkdue7/UiUjqmWx+HhYerr66OGtD+cZxIc2qS8j0ku3k2bPFn7vuY0NWlLr5WLkSedcEIoRvb09ER+fzE+Vk6V8JAZ5d3z91w2q8UUXaWWamCktiyne7858MqMmuagjYc0848ZMidQVHGpm7zQSdV9OkNeQmNdfHLKYhBjnpnxUeZtddnVxw8ficiHkcLtOwwf5b6G8ehCNtGRzojolRtT8dE7ptsbZR7Sgccr6hKkZdzfVXw8UnnIWOiuIpVf7sEhfXyKQ45TK2nmeaHyYltD/ozkZm1ZcIEOk0hII9fZW0/hC20z+bOP+4Goo+PsyGNUKHQHYr/VOoCnuwyLGm80iHDQrnX/fwAis6a6IRw8eDAwLqa21kuL1hZrXW5Md1c+H0gmkgKoMZUKxGItW7qU6qT769yy5BJGOgGWwWlWQ4OW8bdt3vIn4fZTPsZlw1jTLbui7TG0lZHaW7Rwoa+GIyBc3rjsmPG53XGyeQ/YYvajbPgyxUxl5VTO2LW3tmqFTWb22lpbfe+eLc5r4M9IHrZOdUlS2uCvZW/La8A4lTKsvaWZTOTx6S4UfHGNNqs0pN8FPgYzajuSMnM7hMfARyAEdLkNDtWQx8W8J6wnr3RON9my15aDkSMjIy6TqO41tVRXl/at1Ww258aUy/fm8Cfe14rEzK1wA01q2hbxjGH4aLKQ1dbW+46pgnhra1sJHz1LkCmu3GNGWeGUWbKEANB85X3J38eKkX//938fOs/uv//+yO8vxsfKqVwe0oE+B4QD4V3TrnjPPOi+T5NnmQ0jOWwlB7/gbsNIQHjl6RTwDUo4SxgF+Udb5nA175HN8LPebbeHgMm+9ZbPd5WBj5yxPEVCsD08+EhEtGDBogj46CkARJLJID4CU6ipaY4FI1WhXv0OamiYRW1tZ/mOyRgp85A8r2SFsYqPjJGvFh4yFrqrSOUMJrsrmBfttdIE2kHCOu2Po/CyIYZry8RGr9Ny8fV2dxXxaSNdIrXFi8/03U+XiIBpcHAwEA8i3Ft6iF1PuP7randhceIvWyZtddG1t7XR2rVrta5xUdy826RFOx/hiZCiPDuTzjIxSfl+TmdnKU7bWi4LoDvgjz9XwWm5FLuiG4uwzXuNdKwGwgK+CXpNd33CU5gQ/PGjMuhx3+cpJZfkvi8xPDcD3bp160pjHgaIxWKxlLxOpwmV49KiUMxUVk6VlgzTbXIASklsNkF4qMi/ccUBm0dKd6GgzQzN19tioPnTCb03ypmLFweezYQRo6OjgaRttnvnOjupMZksZRU27SlnKG3mOjtp48aNZXhIqXtCm4vbzKwGE+yoMYtR8FEw1mkKxkP6GeC2No7jDg99EvvbHUob/uy8qZRfKasfizwFleSCGQ3WyN1EOm8y8Vw2JTdomdtXFR9nKu9QF1L0ILxcGTznbetpYGCAGurrjQrbGB8nhirhIW2hW4yPqtAi1+q2YeSqlSu11Rz4+igYmYfIVK7ibEN9fYA3MeHE6Oiom0QMyicsZFHGpW2W8yU878yNER9BwrI78fhYKHTT97//fUr7vGnC8JHIj5Gqu75DHR3LQ3nIMCOiwGg+VkOJRJp0GDmzabaPh1TnFfOQnKvk8ssvD+QBsPGQleIjGwHGykMe9UL3F77wBTrppJOopqaGli1bRvv27TOeu3Xr1sDCq6mpiXyv8i3dYQtBgENra5u7UDMUdIlhF5rtpYlhAipRXkDVrPP14Vq7N7zhDdKY6BOpmeLeOL5c99u0abXkH29WAog2axMJ6srnS3GL/LHFFqmZWXVjEpWp50U8HcHs3rLbYJSEJup9GUBy0LvMcx3vPZbnXg/PvYbb2ACRGKduxgyrFtW2ee9yv8tlu3RuvCl4HgbPaq5R22aFiurmlnf/r1eufQrBUkfswaAtm5RMUlc+T6tWrqQE9MqILnjuyuMRk3Ok00TiI1H0sbMpxZbA2zS7CwVt2Am7NsobpwkfuQay7npbubqOjg4fNpk06jaMUH+XlYhhbuuDg4NK9mD9nsLx6TITacNHW0IdZsgSiRmUz3eVhf+6MjnB+xZJKJv9e59g2tgStcfS1/XklQeS978ZBDhGl/Jgn8LuwYmZ+Jxu0gvoGbIrudeUvBNUfExBCC38XXbZ1eHjzHS6pEwwYSTvazqMnA3QZ6Q5HIVifBx/fCSyYyQbKdpaW0PxUcYTE0ayd57pehtGqjimZuAPiws2Y0hC+l+/Lh0nRR0dywMVesJw7WjAx2Sy0fW4mR7hmdkqr2LkBgKm0owZdZF5SPM9drnf7Rh5sivwy95rxv3PVThH4SHHAx8r4SGPaqH73nvvpSlTptBdd91Fjz/+OH3gAx+gdDpNv/rVr7Tnb926lerr6+mXv/xl6fM///M/ke9XWUx3GKMgXBa8GtdmYFFT9WezuTJcYYrkaehZC3cHBa3aZkahv79fG+OSTDZSNruCWlvb3KQWktujI2LRgZsJ6CXVzYUL3sv3T0KfZbwG/njEKEKwbpGl3LbkxE0pd3GOjo4ak3yEJWGQSd0oo2xWspB7C8Rmuh5eLLu6oal1BG1JcGybdz/82uw9EfvMxzmGTE2GFtYGCywOREK91e7zpxAU9Oc0NWlLp3D2e5nh2Ou+21oIZYfvOcchJudIponGR6LqWbrZA6VYLBpjofnclOMEFGZyHJnVUwJeXLU8h9Oado1ryFUO6DBiRTZbKuWj/l4LgXe6eLZUXZ1bCsfP4IlwoCDOpyQ3zihMnr62bcrdF9aQYNYEo5ROzyxdq8NI096gZu4NKqOjMLcyA7edhBC+mkQ2YUc5j9uU3SvtzJI9CWg/6St96PpsKzHkKbPZDThQO1uaowkIV+L6EHwk0peXmtPUVPLyYCUqY2QK/rrtMT4eOfhIFC1EDgDt3LnTep4sXKj8UhR8JA1G3oGg508YPhLpcUIkQWyTSuDKQlotCS9Ofciiyhe3tCwiwXOmlPNFqAzHbB89+Mgesxmy4yO3w/jo4VBUHtJuTY+KkcE8Ler+l1DO083L8cTHelTGQx7VQveyZcvowx/+cOn7oUOH6Nhjj6WbbrpJe/7WrVsplUpVfL/Ks5cHs5/KC842oevq0m5cW8Y3YTj+wr4gVpPjpBWAcghISyAQ7hKnr1U4EuiTZ80mCrr88e+biBljlQlNI+iK7SAYrxxFCNYtMlWTJSduMjFmUdybTefa3LLa3MX+Gfev3LeZAN0JUK/7fT/0cS9r167V9pmBP+qmKsfMRukzg6QuW6ot03o/9FmbjdZ4d1OQN7QoZZh08T02OlqYyonGR6Lyxk6nFGtAsJSXTWmUrquj+kQi4NbIyhrb9auhF7BnNTaWrD97LPOM8VH+fQT6XAejmt91GvaOjrM1TGiaBNMXZEBlpWMUJs8rOSnf24zXNnyMEs8YPNe2d7WR2DM3EdBJfusXCGglYKP7/37SZy3WM0syRtqTgMqW7iju7rKSQN77u33n3g5QQePq2Qd9MisbPhJ5GMlz0haPq1ojq7nGj2Q60vGRyGCdUzAySihdWLKpKBZ1nRLSAULxcRheslz/fs1rbESzXjPk8Y8yfxlcI5lMqxbjhNenalByqKFh1lGIj9vcPqQJWKjp39kE9JEweoFUd2/+rkukqOMhw/FRVhKEY6TMQ+5HcJ+Ucc80L/swMfhYLg951Ardf/7znymZTNJXvvIV3/H3vOc9dMEFF2iv2bp1KyWTSTrxxBPp+OOPpwsuuIB+/OMfR75nuYAZtmgLhW4aHBz0vfxwbX+GdK4anZ25SK4wXGePa9Hqz8+QLsFCU9OcUq1P/yLSuY/IjEWeTPXDs9lc6ESXP2r2w3KEYCLSuGeKxboZ/sRNJi2W1VIsXTc8PExtra2UcpMl6TYjua/3A7QKXgbHwKYITxjOQO/yvaytrXR/kwcA10NXtYlTXNDhYzWAL7Gb3GdO+nSVBuy6ARqC31pjA7IMPM0kx+CYxli3KXjzOFygKrfe+tHAVB4OfCQqb+zClGLdhUIAH8OwwrQ2cp2dkUJNuJa9uiHbPDpSbp915cB0IRqyy6b6+wYIy/dkQAq5sbs3yjVUicpj8ogEE9LX10eOM1lpO0OCmctHwscomXuHh4clryi2yoQ95/3kMeZJQ/9y0nedy7fje2aTlcur96uzbPGxDAkLUlifLyXgGxSukBbnzoPj5jhR9nU4vizUY8HHmy3XqnXbbRTj48TgI1E0jLQlgbrrrrvIgT6juQO9wlDFR8CrZCLzkCo+1gP0YXg5C+T1FeQhbfyj+vsGd+1NtnqHZrM53/2z2eieoYcTH4mIstkV5Di1JAxhNny8zsWUVSRki2BOJqCLhDDskL4ykuMTSsvHxynkJahjN3ZTNSSRGE/HQ+YgBGm1trvR9XyC8LFcHvKoFbp/8YtfEAD67ne/6zu+Zs0aWrZsmfaa7373u7Rt2zZ67LHHaM+ePXT++edTfX09/fznP9ee/6c//Ymef/750ufnP/955MGUiRk5jkf2yqB4E8sr6eVPwuBPSmYGCf9CFdc7TqNb21QGnxW0du1aAwjsp6CmLEOOk9aAnE3QD1ckbNy4MXSiX3XVVdoEF0TlCcFEXsbgsHhQHdgycTxLWEbQsJrgDvRZj/l33vxMm2IRXpIkm6LB5AGwvKPDV+ZL7p/8f1c+T8s7Okr9MWki+fw0vKzyJU2861IrtyFbMh14SWD4eR5E+POpm4IalpCBZ0GUr2PmxFbOSaajgamcCHwkqg5G6vBR5wami8eS53TY2liRzVKt4/gUTI2OU0pmyJ8V2Sz19fXRunXrAhgziiCjwMq7lOP4+mALKxmw/M74aGLUouBjOeVrli5dRvqcIJ7gaMNHwRTr96fwmrcO6bPp8u8dJOKzw/p3KkVlpE1WrsWLz6TOTj+zrks8JJIXMQMbFq7lELCcgJNICAv+vb0h3Rja52rio+POU921UcrdyRTj48TiI5EdIx3AV6tYTgR74403hs6d3t5ePT4mkzQznfbNI8ZHtcSS8MpQ13MwDNGb79XhH00Y19PTE8j/w3Qk4iORqQ62Q3Z8nE/AUgoTqm3jOXZ89P7P57uoo2M5+TFyPwWVkOL8Ge6nXB5yovCxXB4yFrpD6OWXX6bm5mb6xCc+of39U5/6lDJJEHkww0gf16K6f8uTl4/pQSKYLVxMZi+b6g7DpA+zovSQWouwszMnKQZsWSIvDP09Sop+02Qfi7u3ei5nSZzb3Bx4TyMjI4EkbxkIIVTNbq5LRJJywQQIMusZgL4B4crNpX1MfbwUoBluxkdZCBiGl0ylv78/cvK4dF0d1SUSfgHfcUox87wpcZ9ZaaCGAXRAKCLUcIA5TU306U9/Wvvc6nf5eeZAr5zgmBym7kKB0kr/U/ASEPF17a2tZZd7I3rtMpUq2fCRaHwwUqc40rl/s4JIN5cInhLOpGyamU5T2lXG6RRLYWupgGDZMgfRKzHYNPNmbyTGaIEH0ZLxmBmsKOcDoObmuYH3NDIyIjHS/MmQ2Gv8mXv1NW9TJMrXgIJujxkS1uI15CULCtuvWOCV95th4n1KzWIb1lY63USJxAwSTLLHdLa2evXDg312yB+uxQxvhhKJ+kCZsXR6Jv3t3/6tps/yHlpdfKxRrpUxvxyK8VHQ4cJHoiBGbkbQU64rn/cJFyasWbxgge+6SvCxUPLYCA9T7OzMuUkSV1vm/jWhv79a8DEoROvxkYjc/B0qhqTcZ1HxsYU8fJQTFpv6+EbDeO4hQCheoj5rNpujJUvO1OZw4pj5IEYGFTEOUnQ6HKpPJGjaZL8Xwcx0mq6++mqy8ZDjiY+s1C+Xhzxqhe5K3IN09La3vY0uvvhi7W/V0lLKZJvUsnbOX4xevkZOFOMYmBnWkHHSHdVVZz4FtWey5k6uEy6Arq+vT1POweb+aAY2nvy6pGl5mJOVEZkzEarnsktTmOsIEKxTOjIyEmDyMxDuU7J7V5REJHyt3JaaZTGsj4DnbroDQqusxnZzeaCwdrbBL6Dq+qrGgNsscgkIDfsGt/0e+Df/HdBne77rrrsC7R6EEOZ941Rm/XWZ4SjHeiPT0cBUTgQ+ElUfI23vV8ZHxgA1JktOSsWCsLrB8hrOQFiwVVdvvtakZd9rWGOLWlp889A2T8MUh2E5QfhvIpGmfL4rMI66a3WJe0ZGRiIkEdPjo6okFv2qJzmESsZHe/iU3Jbadnj/li5tl+4RjBXt7MzR4OAg9fT0WNpaQ14Wcv2+5X+eIgHhAkA63eS+iw0E3E5emSH7Hjpe+CjvX+VSjI8eTTQ+EtnfMXvAjCc+7oBXUYXDzsTa4dhh/fq69dZbyW8drZx/PJLxkYho5cpVpHf3DioE7BjZQ/6SwqpHasLaR3/7QYzMZnMhXrAePopxc0Lfjf95wq3sTek0NSaTtAEix8V8BadMPGQUfJSzl0dZOxONj68qoZtIJML4yEc+Uvp+6NAhOu6444yJMFT6y1/+QvPmzaOPfexjkc6vdLORExLYXFt6enoMiQs4WYLKnMiCtbpQp5Ooka2b9KNuezqmKU1e7Jl/MXn930Yils4PXI7TULII2IBtcHAwPLEZ/EyoTKZs1uoiaV+6NHSR1UJsMLlslm699dbShsV1n1WX9Aw85p+Jta4mYTcBv6U4o3y3JXJYt26dbxPNILgBphOJSNkedd/lvrJFiJ+flRJhCgFd3OsOCMWJLgZWzkauKk7SCX+iJPWdyqEFzDwU4cXOM9MxFjoamEqiicdHosrGToePYbFZuuSA3dDXiA1zG7vOXf9c/1tdNwuhz7MQ1ia7XW6DiDlrhN662N/fXyqHYlIc6mvW+ssuskJWh49RStRkszk3SWcYw5egbDbnw0fRrxSZ3C1lfLQn+kwobWWU7+HWs7lz51F/f7+U/ZiZbq9vwroWhdkvKv/7+8r46D1/FK8vdR+X+7aIVOU3CwrjhY+8hiqlGB8FTRQ+EkXDSH7Hvb29VcHHDRD79UIDPm4CC+eqAGjmSRkfRebvdtLxj52dOerv71c8K4P845GEj4VCN913330lt3Yx/mZ3bzVbuGe5N2HIJPKs4HlNu+F5JtatWyfh43ZDGynyhPco+LhX29cgRtq8GrzQTVW5swiaqiJuqWGT4S1dV0fye5Ux0oSPMj971VVXTRg+vuqE7nvvvZdqamro7rvvpp/85Cd0+eWXUzqdLpVx+Ju/+RtfZueenh4aGBigAwcO0Pe//326+OKLaerUqfT4449Hul+5gKlzL8lmGSTkSR3MAu65jPNEHSWh/VcZnZS7gOTJvJ+CQKib9LrzWPDOkA7o/MqAUTJZFPr7+2loaCjw/J2ducAC2CtNfpIWQD+CwqBKphJfsmt4BuaSBEsAugF+7a5NeFX7E8VV3hbrmVHARVeOoyufp9e77rSme3UgKOg2wkvepI6vej2PY19fn7ZcmXp+AsL1jODF1TCAjiJo0c9AuAozEKqKkxoIJqCk6FC8F3j+6ZgH/p8TvlRKRwtTOdH4SFTe2OnyIOQkjw6ea7os4O1K4jKeaykEQzzyytzdr8wX/qhM7H6IEobq/K2H3hWtu1DwMbtsIfI9X2enDx91z68TooRgFxQCWeAzKZrC8NEfY6nLts3lem4gfwyhjTELJk+znW/PFcKCuNy/Okok/G6J6fRMy732GtpqJDWzuFcGx7texkd9OR7T8z1LgC62s4t01rB8vivGx3GmIx0fiaJhpM7zbSz4WArLseCjOC/oNqyrsMA8ZBT+kb1j+vr6AjHEatLIw4uPKRKC65WklueaMcOUSGy7tj92d3n+LQxL+V2Y80z4vZNM92qhaPi4Rnu9GSPDQwGWun/l3EldMCQYdj0ZTSXA1JxGjJEmfEwr3yu1chMd5UI3EdFtt91GJ554Ik2ZMoWWLVtGjz76aOm3XC5Hl1xySen71VdfXTp3zpw51N3dTT/4wQ8i36vykmH+hATBpGnMBPjjI/wuHFFT9pOmPdlSINfou4U8JqCf1Bhu/rAG0FMiqIt7PTlOLZ111vKAkJ3Pd/niLnlD2L17t89lWn6oSlP1y9RdKFCtm9xoP4KbUq3ynRfhBum3MOvuwMCAr5xCmCuq3JaphJgqCDgIbpBy4ihT3/o0z5qBPsmYmjRFx7xtgNh8VaUFW/1TEJpw9Z7dAA1KYyorVdR3WiwWXbezaHH6c5qaAmPTAHtIQlQ6WphKoonFR6LKSoapc1xNmsbzTM1B4MCuzNJ5dajtyZ4msvab3SZN81e3SXNYSkpaL+shMkQvP+ssLT7683YIxWxfXx8NDg5as/Qytpfr3VEodLuJN1kgVBlgnTKW9wr5OrlPnuVCxkciORGRysg1K22ZrOJBBbHj1Lj7pG7fNPWt333WDPmfL5hZXE5KqrqeesID7505EsKG/HyiUodgKnX3k8uUFUmtnSvjo02xG+NjZXQk4yNRNIxkjzL1nErxsZSfwj1/j3ItYySXFrW7iPt5SCH4BfnHbDanNVR1dCynJUvO9B1rbW2n3bt3H0Z8BAEzle8OAQ+4fZlqwDGBQzfeeKMPH/2WcRVDHKktm9eQo/THL1ckk420YMFiSxt9Brzy46NXgSIKRga9GjzPqDSpXrcFOCX+0eReLuNjf38/3XfffeFz3D1fh48peDmBxoqRR73QPZFUzmDatPvZrF+DZwYNznpqc2Fb7f5/i6G9hRTUqE8ObXPt2rWGrIabSU3s4C8p4Hfrm4RgzFASwk0k44L7LRAumevhMQhskS432QuPveq2XYSIOZY3FNl13HEX9i3KderiVTN6dhcKdE5np1YjN0lpS978ZAaf2/6Hf/gHmtvcHHr/sN8G3Dbvcb8vamkJuigiaMFTY6BHRkZoVmMjpSDcyDqU87shBHneoAObPjxN+R4YPBkka5iu5JLu3Cg1xytV1DAdTUzlRFPUsbO9x5ySwDAsPCRK4rLV7v+mta1zJZ9saXNhSwv19fX55ll3oUBpx9HWDDfho8BlfU4O8XuGPDe9veRnWARTVs5c9/Ymndt2L+ndIhvd4/1k3mM4htnPjBYK3XTw4MFAMjHR/xlKW/K+KSuIRdu33XYbNTfPldow7Zv6TMEi1KrfHUeRAEllHgUDmPT1lS3PTCMjI9TYOIs84eEbZK7hq7MGNpIXo7mH/Epvv5smUfSKHTE+HtlUCQ8ZBSOrhY/Mc6ltdrvHF2ldyfcra03M35aWhQF8FMIsY5rXTlPTnJLLd3SMTEr42EgCl7aRwLXxxEeQqKjQqOlTu3ueCSPvCOAEKyREIjVdDPgkqS1VrpDzO4G2bt1KGzdulDAyineRDiOFvKHDR68Wuv85VIx86qmnXMxPkcBBv/HNE+R5f5OUA0hRq9u+yRVc9X6NwkNGwUff93FOxBsL3RYqZzCjlCUoFosREruc6v61u/T5P2p7nYGJ7TF39iyOeiVCkThe484776QwZkfVpALCgnQAQRdkWSDk+F8TybFO6tg/Cy9GhIVO1s6uMfRpl3seW5pVl/RpkydrNc8tZ5xB8+fN8z2HA9CZixfT2R0dPsHXFCuacJ9Xl6lcBo+21lZqTCYDigp1HB2AHn74YaOL4l53HFKaOq3dhQJN0fRzmzJuexC+6SeU630x+z4tb7CdYXgx5X6XpRBghRmUo1LMVFZOUccuihAh46PpvFOVeRYlQYquvU7oXS+dkDZNWW5lJZ9at94sDBY1x0CCWelS+u+Qtx/UaBMFqf2SMdK/N6luk2vc39YY+rTLPU/1dhLulpMnT9N6dy1d2k5XX3215HYpPun0TFqwYLHi+aVTEAuLsVeRg/up3zeFlUpmwhsomJzNoYcffliTYbiGhGJ5LwFryHFSgQRL4popmn7uJb8AvYfC9+2Ecj0L6v75xWWH1LnISqStW7dGW1cxPh5WqoSHDMNItXSXes6SCvCRMVVucxSglFZ5lKKwpIMyBXlI9u4QAqONh9Tj0QYCDmjWNivNqo2Pt0j3COunCSNrSM1QLsqpiZwZ8+apSRYdWrz4TOroOFuJx+ZxDz5zU9Mct/qCGR9bW9tcYVpV5PrHsaPjbFcZ4B3zV0UyY6TwaOAcUXI/5VAAe+hRRpmf/F0vn4TzkFHw0fe9AoyMhe4qUnUs3WLRciIFe7xbkbxM4xlSXTTYpWNoaEjKsii3N0y27KqmRC4ymZUIQbc/vyvKs77JLE/oa+AlG9Ixu2H1Q3WxTrps4mpcpRrLyRZbedFtN5zb4CZosMWbLjjjDNq4cWMJFNTYE53reAqgFvcvb3qmDXL37t2BzOpJiFhTtU0ueVCOi6IMYCwwXGroky3R2nSlT5ydXhby5RJljRDx7Hn1PRUKNDg4SAvd7NCxJefIpGpZujlhVxTN9Hx47mGBMAjXTaxYLFJfX18gseIwQFss6y2FoPKtK58PPJNuQx8BKBcBH/3xw89K55osKqJNXfIfJp2rZqHQTYODsluzza2c+yr3c7v23Lq6BqldfpZgvpL6+ga6+uqrS3tgMKmRibnnWMP5FCVjeJAhrQ+0yUk/ZXy0KaFlfPSsYZcarrUlEZqu9Em4pKsMbH9/f8lDazv0JZxWuMlAY3w8cqkalm5WtHgJu8Lfdw5eEjUbPuqs5/sAarEafbxQDCBVslzLNFYe0o+Re9xjN1MwOaGHkROHj2o/eVzOVM7XjWHQ+n3GGQsCPKQ/HtuEkZyY8VTDvUS/1JAm0R4LyP5wnc7OXElgHRiIVu/bq03O+LiB9Jgdbpw08cpqGbAoPGRUfPR9jy3dh5cqj+neTro62QwIhUK36yajxjvklIlqTjzBVCwWqbW1XevCY97425TzgtpBs3Igo1n8jeQlXfBbunVCalRLkkymWCcW5tTMhj0Qgqm6gBshBG85zrkBona2ajlmbbHMVOtKaaQTiYDlmMfPJLzK95fBRs183F0oaJ89BXMZMEAIrVYrsavV8zZHv1ua6jWwHV78u+l5NhiOy8lQeGw2u/dQgXYT/BZ37djAC0mQk1rpkqTYKGYqK6dKYrrDkgd2FwrUlc8HSgs2QjCTssBsSlymMl25zk5KuaEt8rlGzxLlvCnQC906BrgAh5wI+Ohp/oNCqonBYeumiUw5RQqFbk1lifUkwo1MfZWZ6wYSVl7VuqQrW6NniJua5mjHziy8+mM0PUbRv2/6c6ZEs8jxXsz4GOadRuTHR3/Yly7Zki3zsd5aJuOjPD48Z2WMVAXwGB+PXKo0ptukaAnDx254yU3LwcdisUjtra1SmIxDQjkUxkPKn2ZynHRAcTRWHlJgZLDUVdj6Cpvb1cVHFaNSBMwy9FUdw3yg3UQiHRIfbTOiMUYGLe2O0xCCj2av12xWyBlRPHiJSPLgVcNiTZ4DZkt3FPkgjIeMgo8peDHdY8XIWOiuIpULmH4NflAzxQtcaNZUDZrK0MiTvFiazDr3B08rxvfbY5nYRWmh7iIZsORJFwQi24JZX4rp1iVFssUZLVywQKultGp3i0VtZsOwa7h0GC/KJgSFaVXAtCYn0bi832x5Zv79iwhusJz5OPSemjbXuCBiTVwXYuk2bdqO+0kjCGJOyHOq85aZCzXmdgRe1tVN8LSXqheC/L0rn6eufN73eznZKGOmsnIqZ+x0HiDpRCKgSDu7oyPwvlWFmDzPivCwxYSPcjKVPbZ17H7Yq4PXhIqPRH4meU+pf+H46M/0KzOg9lJUJkuOzYNKV1kivK9ydl6HgCYKCtNq2ZrwPujLit1seWZ2K99GwgXc796dzeYsz6HLcLzGdfEMT8oUbunWK8U5Nj/oZpqmsIRvunnLc+taZb52QeBvjI9HPlXCQzJG6qx+Jnxc5M4LGz729fUZ7zunqUlKOBtetk/wjezV4eVgUBMqVspDehiZJ3++ifAwkwULFk4QPrKgzRg5i4CV5MdI3RiG90Pv8m4rL8YY+kVSFbgioWXYc+jLgDlOrSb7vLm/QUs3n6/DyCQF8dFLIDdWHpLxkQXwhRp8bFK+jwUjY6G7ilTpZmNzyfDXLmwjoVFjYNkTaZLLpF8Y3aTPjJgnz7LuLwngd1cXiRLU+A4bU5jPdwUmb1ShtV5jMSbyAGgPvOQKD0K4qwP+OqTsFmOLfVq4QLjz3Hjjjb5+qsnOZC2ZTWmgK5tjs3SfAI/RJ3gMfvvSpTQyMmJPFqFpMwcvjtpWG5ipu1CgGggLScZ95tUQceDzIdzGN7htbEbQQsmgFtWLQRXA+PlWSO2oXgWcaX6+ew67JNu8IGwUM5WVUyVjFyX0Ae7ca4NIcMaJ+/aUOc+I9Eo7ri8fqv12z5MxQ92UDx48qFH2heNjEFOjCa3AhkDGWKZgKZ0HCegh4B4fRqr4GMa8qvioT3YmC5jhSoOenp7AO7FbulmQHfIdb2lZSIODg9KeZRrzYBkw4VEm4qhttYGZBENeQ2IPzZA/kdO1JJIf1brPEfRy85IKRd/XdRgp76EmfMzB20d27doV4+NhpLHykDZ87HffN+/dajJZ9bpcNqu9H69HL0O5yZMjnIeUP5xQUS/MloOR0YXWRKI+Ij5yGObNJWwqBx8BUFvbMvrABz7gfjdVDMqQX8AMDz9REySK9myW7hPcv6xgFH1ZurTd6s1jKgMm9g4YjG96jPQMfyo+stfADBKWex0+ejLDWHhIVcaQMbKU0wiegasaPGQsdFeRKgVMm0vGunXrpEUkx5MwqDhkKiWg25z19xslNeO437q+gkSpAE9LCdSRrKXjsg4c/7N0aTuFLX7ZkjE0NFTKyv0svNqSOlcP1eVbfcZ9+/b5NFVqJm7+LmumbNZx2Z2P44bVmBD+3qwcj2I57u/vp1w2Sym31JGpvFgthBWP22IGv6+vj7oLBUq5ZdDC3NPlccy4bXco7cj912nwRkdHqSufpwT0VpPNhvv3wtvoly1dSg2OYxXw5TG66667Ss83LN1zj2Ws5552WrQY4AhuQjFTWTmNFSNNyiR+p4PKPBgGQtdTGD7K9xrVrPd65fsKiJJ8PL9r4Qk4XBaMmdjBwUFqaVkUGR+LxSKtXbtWwe0REh5PMpOmMrdBIU3UmM1JfU/6nkP+zpbyKJYfJk+wVfcT/t6sHA9/fl772WzOTe5j3u8Ek5Ymlbnv6+tzMyOnQu/pjz1tJI8hXOJrR+6/zpvAyzbMe6g6xg4JS7zcB9WbzHFLnIUzr/IYFYtFnwDG4RU2fJyeSFAum43x8TDTeOMjaTAyBz2PlQl556pnnpfQUfXItPGQG8iLD05Ra2sbFYtFV0HWLl1rx8iNGzdSEB91JWxljIyCj7pnSvjWvg0f77///lL7/nJjKkZ2au5nblfmIb3kZ2soHCNrSewbfjmjr68vQom1Wk2bNcT42N/fr8nBocfIgwcPSnHoleCjMDbVOk5oeVsenzB81Ang/GHsa1+6tNTWWDAyFrqrSJUCpj1ZmpwRUNV8sQCtZ3BUFwsBKLaFdYK7uNYTsIBEfJ7cNtcW3UHAPhLJGbzfOztzUvkbZlrCGYfh4WGfNYs1TpvhWSr5E0huJmn7WHhlN9RZ0CdaSGsWZ3ehoHWDngThTjIyMlJywWZGXk0C5kBkOF8PEePNGmW5TY7p1iV7m9PUVLpOPl4D4QZjSuggA4kutjoF0AzDOMqZxJl5Zg2uDUBynZ3UoLj8piAEFHly8btaDa+OKI+jr0+KgG8ao7TjlNx5AU/jHsZ0AKAzlywJPS9KNsqYqaycxoqRYZacGoA+oswDrntvUpDp3ve+fftC73UphDU9BYFNMxDEqCQEdu2DVxqPP7nOzjHho4fb3SQEzIyCz5pElW45FBZevZg9Lmulxu/NIjnEiYitt2nyM10pAiaVcnzs27ePTjnlVNKXFWOmbxcxA1Vf30C6LOdNTXO0iYwEk5YgffbyLveZZVdxz43VGzudRS5FXny1Oo7ePsw5UqLiY7FYpAULFlMioY5FisQeLU8xtiatpmSykRoaZpGt7I4p2VNXPk+NySS9P8bHVxWNNz5uQtADrw/6BLL7De88KJSChOcG48heEom6bDzkQrebQR7SnwGbXcbNGGnGx0YSwpua9dufAFLmIT3hle+t4iMLmpkAPgZzLwklISeO27dvn1RTPAwjLyMA1NbWTp7HjNcux3Tr1r+nFDBhpBpKo2JkcE8SzzGZwqopyPhIFJ2HbGtb5o5befjoTxzHz+3nIU0JlVV83AGPVwjDyK58fswYGQvdVaSxbDb5fJdmwaZJuJnt0EwynYshl1jwmI2rrrrKpw30XD90C6uBgNMJuJ88Ro61hLqkCp3a34V23iEvji1Y0oaZB92iYIFzE/wxwqYyXoODg4E2MgDdJy0o0lzHsW9sAb311lsDm08GXvxxk1J/25hZWVqAZy5eHGiTa17rXFTSiYTWeqwqGrbBr9WTNd262Opa5XvOPU8Gi2kQ5cZ0pTx04HnfffeFjgO/K7ksA+AJzT43R8kKKJPJjUfOzs7vKKwve93f6iznqQmKqr3OX+tU6diNjIyU4qx1CaB47ahhC7L2mkt0yRmaVXwk8odO+JRlAC2DSLp4lbRGdQkYUxCKL12cpbcWo+GjvmTV9e7/m8gfA6dPvJXNqkxyhoD7pH7olK+eRWFgYMDN7KpaYTLE+0HwHqZ2e4kZqDvvvDPAPDU1zSm5mepr8iY0/chRMEvw6hJT7Pfu0sUNnkxAq3JMzcx+acmbSyYTPvKeEj4W7LrqlUbiMYhSdseU7Cmf7/LF+rJXRoyPRzZVGx/TEF5sKj7qMHKDhI/yO1cTMurmnOcmzGEqUXjIM8mzjut+z7jTL1jyizGifHzUlzocHBzUtGMzTqG0Zs34mCch9KconZ6p/BbWtnBj7+vrUzxmxIf3B/O70GGkimfbSFZgeBi5n4L4mCDgdcqxHEXBRyI9RkbHR5ZzVpfu7cdHT+7o7PTf28Q/duXzPnxsAAKx3vyRk/6mEwmaPEaMjIXuKtJYhe6gZooXjadN2rVrF7W2tisxE8z4BDVs8iTdvXu3NMFNSV34/1kRgGG65XdOutDt9meNe8yzoJgybTvw0vvvcP9XmWAWOLULC6C5bv9NGimO8W5v9TNb8yG0v+rmMxXRErz1Stdw3E9vb28pHoTIrpk2Wfe3q8c1ZdC4LU7upLPKs4s+QWSF1lmcDxw4oNUS7t69uxQ7HjYOH0bQwrhMKcmkPrc8PrY43rvuuovmNjdTLYTgrQtFaJCek691oPcEUEuVjcc6f61TpWPXXShQ2nFokTKfHAgPEHltLGpp8eUlyGjmBWMMtzOnqYkOHjwYyHIq38sUphI2R3W/eQoo9lYy46NZ8GSszpOXjEdvEdJno20kYK7SD/4wE9VD+nI988lzE/XwPpHg8la2BG+9pWt4ve/atYt6enrKKJW5iYBjlH4FLdPh7p9F8hg5Xek1dkENluzp7MzRgQMHtFbmAwcOSN5k6rtWx+JU33n19Q20c+fO0GcPJiPSnzcwMEC9vb3UfMopVBPj46uCxoqPGQ0+Mv/AeVt6e3sDuVtsGKniY3DObaCgi3AUHnKG5fcieRbrDS62bKgqPpra8RI/mtYuCLg9gA1CQL2SzHXDnyVbgjfOXSHnHjLxkOaxO1HpVxAfwzGSjXjrSW+VD8dHk9I4n+/SlCQzjcOHSfXitSV7U/ExjNccGBigU08+uSRIh4WzytfmNBipK3c71jUeC90WGrt7+RoSlubpJMe6iMktJp4pZiLoalFDQsPmab/q6tKaCV4k4VbHSV1Ua3bYgrjI8vsailLcPgrDqrPetre2Wq2tYb9d6y6yBsnqqgqkBE+IvNT9bov9WA97UoWwGCyddYxjrFLu722trQGNmlpiaTsQKca7Bl72RtWaLCsz1NJctvFNuP1dD6Go2OCOtWN4bkAfU66eux/m7Lvq8Tw8iz7Bi208WXM9uyZXMyYnJj9VMnZy0p4chNVuDTzrHK/XsDmk1q2vgRCsZUXfnKamQNk8to43aNYkr0XTOjbN3z2l3ziJTjg+hv8m/z5KnnUmGoMS/tsuEgJ2ivTMlo5RpAjPtN4Ym8xky3Oit46xB1eK6usbAvhoSvADa43hyaSrT6tTZjhOmiZPnqa8n7C22QXXEya8WFb9swfjytXzdIoS8T3GxyObxoqPRYjM5HIiU8ZIOaRGV71FxcgMxF6rw8fgnDOFqYyVh7QlBSsXH/18c0vLohBrq71clchPoXvuvOF5OFFjuMJM59WiUjhGhuMj4FBra1skjLTnwdDjo16ZsYmCxsWwthMkMNJTSHuetGZ81CXd5Q/vzaqxzcRDssFLvvZyd53J57XDU2yFYWQsdFeRKgHMkZERJWEE3IUxKs0Tb5HLL1ONmSgWi1L22KggFb74w3/bYfm9lmzZF+VFwZnA90pjwb9xYjV5nGZK7t7boC+HlYZeg5sCrG4iqqV7L8ITvKWkBWsrH2CydNtcXDoghAWdUK/bSOXkdDrgSVjuJ9fR5gzOsuBRYxiHE+Ft3OpGrrYr3y+XzVJjMkmb4CV3U/uWgV4Aanbf2QBAVyrX6uaP47bVI71fZkqqvc5jElTu2I2MjAQ2xwz8goKsQJIxMgwfTfN90cKFgd/lWra6a6L8plY58JgiOz56v7Eb8l73+CnS78H6tNOm1Unf95AXeiS3nya9l1QTAWdQOL4Xle97KTx5kceEm0qZMUXJc2L+bb62DrBOWd3a2k7z5nG8Z9g7MN1LdednplZmdmsMY5GgYDx+xnrPbDbnulaa3o+uDymaB4f6AborxscjlqqBjw7MiUxtPOSdd95JYfvzwoW65I/jyUNeGmFtRsFHIn0Nb1k5xZnK5XvocIxjuhlfbfgoHxuU+mDGSBs+EoVhpE1Z0EHAZq3iU4eRCxYsjPAOor57rpIUBR/nk95FnmPI9aFUuWyW0o5DZygYx5+SN4/Gw7YZgn+8EUE+dQRBftYB6CReV4gW1x0L3VWkSjYbvVuL3pLQ2tpmbc8rOq8ukD3u8QT5XWwuDF1QU6fOMAKD6Ks+Kc2MGR6DZVuUauw2T2Z50qvlTniRqLHKOjdsnVtoAl5iMZNAynHTKbc/aoI31XU619lJfX19gbgVUzIJnWV6uqVPXPJLdpFR73HfffeVhG2Z8VeBBxAxfGGW523ud5N1fxP0VudPwXNTVC32DkQMtxoqkJPqhPM4Z+B347EpJU5V+pFyf8tDn/iuxv2EZcOvxjqPSVC5Y2cKHdF5otQ6jtW1i/ExzDI9X5lzF1rW5LQpU7SKpwxAqwCapDAOSSQoGHMXxrSocYnMsH1MupZxWLW4nEK25Df67OUJ8sKHTMzW7WTeDzaT6hbY1rasLHzUWV3EvU619Ku/NH4mfGxunkv+Zw57B4mQe22TjpmY4E2ktzw7mnfWSGyF0lnls9mce32G/NnV+bxwhvtk5ZknwSnV747x8fBTNfAxBXMi07bW1tD2ouBjcM6F85BTpkwjc51lMw/px6Wx4iORHiPT5AnPMj7KbtimtWvDx48oz5NR+rCfVKVba2t7wPpcPkba+sXWdj1G7tu3T6qsMVZ8lPtRLj7OJb21XuwrIgO8Hh+Zb1T5xyjenzr+UQ7B0GHkHKWN2NI9QVQuYNq1+X5LQpQkJsE4L512rzHygrr//vupszNHwQWxkARgbaJg5vQkCZf1HSS0juyC4y2QBFLUDodOhb+urs51M0xjJd83A2HZ5sXRAc+6tAteDPeVV15JgL1GpW/x1dWVBGJZ+8Vx0+pCM2VNlLWXXHZLPsdmeWbru8lFZk5TU8A9fT7McSo6izZpjtsyO14GoQCZ7D4DC7+m52h3Y7vlsWHXtZula3VhBXamQHwWwu8OH/aeezA+dRZj8lM5Y2ctzaHOqbY2q3VAF+el02A3aOaTqR/3338/5To7feem3U+L1s2vRjqmx0evvu0ZJDID11NQoGZGZTKF7yP8yZMQhtlS00Ge9XsXAdcQAMlbar2lXRbmHfIL6bIFwoub7u3tLb0HU9ZtFR+DNcodAr5u6VeRPEW134tMhGGp72Q9iezK6jtgoVZ9Jvle8nGbS/x0EgqDGWSLZb3//vu14+O5wvK1utwsYX2QPxkS88rOhMb4ODE0HvgoH7PxkNHwcT+pYSy2uRzkITMkkqiZeMhJJLDKhI8sxHaQHR9riOtIh2Mk4yOv+xSJUlhF99NLnOCsPHxknpjDTlQsEe2sW7fO9y6iYOSBAwc0oaW2cBm/t5MfI1mhUC18lPthw8fLSHjHTia7F0EwOWih0C2FPwjeVcc/zp83jwA7D7kQoCnKsTCMjBJWWu4aj4VuC5W72djj1rZROS4nrK1Kp5ukBZKnoDuHDEiL3QkeBLWmpjmltmU3JAEE9aSWeRAfLocgL5ZRUjV6DYpmKwO9y+gG2K2/2+CPewaE4GesFd3bW2pPV14rDU/gvxnCmqsuPl2ch1x2or21VZs1kRekWpt7DTw3voymTymAZkr31LnIZOBZ/nfA7zJoilORrYSq5ZljurfDi/VbD32G0wF4yofGVMr6ztjixXNqZGSkZOnmjxxvWHTfcxR3eNkiKjMLNpC1hQRUus5j8qicsbPVnlU9UaK8w+HhYUrX1VEtwjXY3OYigN4NL0O5uibnNDWV2ub5nMtmqR6eslAwLSzcqorWYRJJydSSNqsomNVcLgUmC302y8Y28ntQ8bU3KOdv9+GjuF5nhWqQ8PztJJhwdS8Ili1joVuU5WnXZt1WywC1trZRIsExfWydD7OOzSHPSuUo98iQlxxpBwUV0qq1pdt9Nrh98FtVvJhu1cqsz5AMXEsAKJmcYn1nfX19vjmlz66cl8a4SF4CuzBmVc0V4+3LMT4efqomPq5WsMoZIz6uL80FPy8nEnaxoGbnIbPZnJvsbBOJCjg6gZFrNJvwcSFFx8eU1K4JI9dQEB8TFKwVLQuTUfARBHyKysXH/v5+6uzMhWKkv7zZBhKeRydI70iHjzOVMZIxMk9e2cSx46M/pns7ed628n4oj+kACQWt3cuqri4dKN2oK2VXgEOj8PhHQCRaDeMh5VwxzD9eddVVVC2MjIXuKlL1Ld38WUK6GAxenLoN2Z/MJWwDFoCUSEz2Xc8lGXTkt0CoCzE8CQxQQykktG7HOpfRfthdilUBEAAtVDIYy9nOZQ2xThPWIf1fhEjelA7pL99XLV1m6q8qXC6CyMAMeOVdMvD3KQXPbUznIsPu33I9VtUlfwOES31OuoYFZbVP3YUCHTx40Pc8qpt+xu3rHOW4A9ANljFob/OHSujc5OTMusNAqaZiBnqlRIvhXrb3ASBQNqqa6zwmj6ppyeFPM7yEP6qmWcZI1fPEiTA3aqXzpk32YyRn9FXJ78Fiwsf9pC/LAhLWHZ0rJDOFMp72kz2GT2VuhLVG555XKHQr+5LOkpojZraAje53U2Zb777BfUrfX72A2UX+8kI669gi4rrlweQ/crJSlWGW+11HQBt5ChJhpc9mFWau0F0qa+YdV9913u1nmtSSR7Z4+ZaWRT7PKX0YWoM0xnJJHYfULM2eO69OGRDj45FC44GPAGgJ9LlgysXHJiRJnzgsEUggaOIhw/nHxRSOj5OktRUVH6PElhdJxUdziIdcSsyGjyBgGVWGj+b+ZrM55TzGRx7TegoqR1LkJXlTE6RVHx9HR0c1MeJqKFPG7Wt5+JhI1AfKg2nLJyJFBThE8OQIwMxD5gzryFZJpxyMjIXuKtLYYrpVt40ceZuon2kKLk7VHURoxObOPV1aQDoB2AMkx0nR0qXtvrItOpJjP0RmXBWA5VgdHbglQievKkBzcgIHoPpEglZDaKLUVP6EoNZJzcYpa6G6CwVqkKy76yEY7Jx07waAViCcIVkPz+LVlc9TYzLpE3xJ079aJVt6GsINegdAbQha1SdDCOXyAueSXXwPdv/eI/UrrN8dyj26CwUaGhrSxg4Vi0VhuVf6nYKo7a0qUFIQyoEc9G7tGfeei1paaGhoyMo8qH3dD30OgC8axhsQ8fbpRKKiEg/VWOcxCao0ZlGdQzlIYR2aOaMykWrIxQ6ITPrHve51oWuV77EBgmFtX7o0FCOj42OG9DHYDtndFovK/88S4FAiUU9iz9hL3j6iyzIuPmrdWNmTKrgvrSdhJc4pfUlb+rq+9FyeZTi8XI7H7Kqxl1yRo9HXb+GyXUeylaqlRU3+w15le6R+hfW7IzA2Yfios9x773OO5l03krA6pSi493vMcmtru7WEmNpX8VEVEvy9X7remw+5bDbGxyOAqoWP3VBC36qAj6zYN83DcnnItrZllEiowmg4PtbXN0hzuhx8FIosoWhcr6w3VVB3+Y6OswPhLYyR0fHRFvqjw8cdZCu7KJ5Dh487SAjFU5T1P5mEUO4pIFpb26R7jB8+EnkY6Tg6jJymedeN5OGmio8yzi/y8ZCm/qoleE08ZJ90scw/FotFUZavChgZC91VpEo2G122QE9Drmea/Bv8ntDJZgcnBprVpcmlI118SXjbM3yLJeECy0LXtcPmMioL1Lo60g5EzIXOJb3kHmJgkkdGRqgrnw+02QnhSp5yF1ICnrt2mFtJBkIhAAhhdwvCBV5T/HS70p82gIaU5wJAGzdupBVS0jGCP9FZd4R+T0ewRBgDx4MPPqitmVtupvV/0Ly3bgjA4z44CCoQ5L7KzMAe6X7DEPXQe+EpGMIyouvi5x2AuvL5SC6TY13nMQkqd+x0Gfnz0jrNG+a3HN4hzxvd/Aj7rZSxWTqmw0hdDodKcVnUuzYzXAKr5b0hWCfVy7uhq4Lh1QI3Mcn6WME0Ad8mz1Wxnuyu7byfyTGVY8l2rFb6yJE/6ZHIXOwJ3aqlm13TbTV4p1OYW+eWLVtKNXPHlmldfW+ey6ZQTqSkc3R9VRXuzCyvJxGD2ktBIcTfh2w2F+PjEULVwMcMxB4bZpioBB/7S9/DcKlcHjLMOKN37Q4q1CrFR15vqku6h4+FQjcNDAwEMDIaPk4iL467HHwcJnupNFMMtYqPbQQMkYqPGzduVCz244ePfEz/PDYvrXnK8/D7kvvhKAqEYH+Z3+W5zmGSu9y/pconmvmfy2ZLa60aGBkL3VWksWw2Q0ND0sThT4ZMpcO8RWeqm6gK6DptkQo05lT3QdeNcGvFDAXkcu73yy+/nGwAr24cNQClE4mANbUGCGideIPRJUdiS7fsyrwXwiVbdiPNwIuB4sXINTDV/u5yv38YXrwxL8a02j9LjepaCRjWuH2Q6w+z1nphSwulHCeQII37HVZuyyb4y2XYAGHB5zIiar85RkY9vh9+YXsD9HHgPLa1Cb33g06oX4VgrfAagGY1NARrlLvPIwNisVikq6++mi644ALaunXrhK/z1zpVOnZDQ0Ml5Yy8xtS8DbILGec1aDPMU5UBDUs0aCsHooZHrCn1w8S0mDO+epm1w4S4DAnhbDsJC0ea/JaClHtcdTFm3B8h1f1Qb+neQFxDOpicKEGekGeKY97lfmdLyhLymN6Ur3+O00ALFrB7qWnc2MKzhzyhtFv6/f0EcIxhhvxu1hnyXNPZUlMeU3vWWctJZeCXLOF5GcZYq7/tJy+cYAPpYxxlYTkqwzpCnveBPFfqNfMhWM88xsfDS5WOXbFYpL6+vkCYWAZ6wwTvy+Xgo4etpjVTDg8ZbskVwlRe+9vHP/5xSz90+Jii8vCRSCesBy3dJnzkNcdu0uXiI2NkmlSMDKtRbcdHoRhpaVnoupfPp2D+p8rxsa2tPTBm+XxXSH130zzYr7RjGj+x/wi8N/eX+d0RBMMhMxBhkqo8kQKoob4+kHxwYGCA/uZv/obe8573lBV2U8kaj4VuC1Vjs5GTTfiF5TsoWFS+m4DloZNtaGhIo12UAamRRCZKs5ZSr6Wya/dZi+TVpvWY5ZRmgrdAuHfklEWhE8h0cUt5eBuMrsSYWpZK194u5bvq2sxWNlXAb1TO02U+dOBP8Ma1e1n4vAF6lxdOsKYKmy2aMZhmGecwwd+BEPzXwPMWSMETxE1CsaqQyMCzTne4ba5HUKCRlQmO+37kvta6cet74M2jLgTj69OJBK1cscIYk8bKlqeeeioQcmCKyx3vdf5apbGOnZw4RXat3I+gki3vzpcUwjFkaGjIaE1PA7RcWacqRuo8QWyumGGMCyBcvwWDpSbCOYWEdVdd+2H34U+ePCVuMF5PH9Ota3OX9L/q2sxeWinymOYRTZ87KZhcyJH+biav7q4sfN5A+vJA11HQmnU2eeXF+DNNuS4o+IcztZNJMLZyQjJmtsPegco0ZtzrznDbs7m8nqrpKzOae6Rx0sVhpqixcbYhE7z4v1Dopu9///sxPh5mqiYPmctmfXvqHQjyEHMgyoZGx0eO6VaNOOXykDaMYWWePgdBU9Mc11V5vPCR3O9+Yd1fti8MH/VCe3R8zBPwWQry/A55XgDl4CNb0tX2TlO+J5Xr1HfNWGfCx0mk4mMikQ4ZM1PyScbH9SQUEfUUnHOqMsEJGBgZz7dB8I+m0sOzGxu1Vmz+v7tQoAMHDsSW7iONqrnZBN3OHTfjoxr34JApU+GCBYtL7WWzOUokZlAwC+RU3/dsNheopWrOsr5EsygbiePHPoJg3dpnIYROFfwdCMaZG5ezDQJmLWxvb2+pjIrO1Vq+ZnuE9vql72yt1mU1boEn4OcQjIdqdK/VCbGTEIwxSUJonHWl0ya7v+us/QwM7QCd7d73OojyaB/VjHNDfb12bK6H3hV8E7ff1kYpxynF1Os28LzbDhBee12tpc4uvGr5s9efdVagT2HvtVgs0vKzzqJJyvk1EMA4p6lJm6VazkA90ev8tUbVxkhmBrUxie5xDrloRFDR1y7VrV3e0RGYO+r3mem0ER9VTFmoZVo8fBR4rFpa8gTsIMdJa9wXHRLKUiLBoPSTV8JGzwTJ+BiV4fVnL9cxVv3kuTarexIzZSw46wV8j2maT8KqJQuxdeQlTOIPM4tLNG2l3PNTmuPM9DaTEMIbSQjo1xDwUVKZUC9mVB0bHQPNro5eTKtgIreRYBbTFLQ65wm43v0/o3m/IM9lXmbkvxG4/1lnvV7TJ/N7LRaLNDg4SHV1ap8y5DhpmjJ5aoyPh5nGCx8ZI3U8RCYiPnZ25iiRSGnmrZ+H1NWa1vOQ3RRM+KcTpiYaH6MoBWz4qBfay8fHDAkhdg2x27sZHxMklKC6akWTCUhqZAjuTwsBre59N7jj9m7yYtL5w7Hi5eNjNptzMXK1+yw6Q2IUfAy2zV4WaonI+noV68L5x2KxSJnFi2myck0NhGCurp84pvsw03hsNsVisZQ5zwwAD5BOu/Xxj3+c+vv7aWBgQLmeAYldS/SMCrvSmC0f1xsWmijtIrtbZ6B3c+rt7S3VbK4k5pItXjXuAtgOs9vzsxHay7n9tMUrp9wkbHss5+ncuHUCQgqegFDuGGyAcI/hcZbfB3+XY58dBDfYKZo+NUII0gDopOOP97WbdO9pUgLkodconoigxbtUdmxggHp7e6m3t5cGBwcDQrItQd2WLVvIQVBx0QB7qbE4O+/E0HiMHeNbWFy2rkqBAxHbJsfl7oBQ+PVDYIFOCaZ6UKiWbvZg+QBURsGPj6K0o45J8bB9165dUtksHQaHx8R5JR5ryGNww1077fvNkPW+2Sx7atli9nS/he9L5ccFbiAvRlFl5Ph7L3nWNYeCSmydi6pfQKipmaG0PY3ClRIqo81WdlnQ4HuIZ1m3bl0JH4XQIfcpPOSLPej0zyHGwRR2FOPjxNB485BGa3ZEfCyHh5TDVfQ85AHySlSZhCk7Pvb399PSpe2a9ivFx2fJS2JsEqxt+BgutGezE4mPUXJLcG6OTaS3lP8DeRZ1nTLZjo9nnLFAaTdJ5vrqYfh4KZnwsbe3l3bt2kV9fX3UkG703e9kd3yMBrf+fhoeHtbykMxfh/HnplwGY1njsdBtoWoBppwBlyhKPe815AfCHgoye7IWkMhfIkDnwrPBlxxBn2U9RdORJMHQsPZqOyWRokXu/W317XgzyECvaT0RetdjtfzXZgQ3DaNgrbhdyVm1+a+tzrQaX2o6b5t0bAT2WOu90rFhRLP290Ny0UZQcHbcc2SlQ0YZqzCGy6QkyBjOt40/f0ouvI4TcGts0ri02zwYbrzxxtLvLPiooQ2mMezp6Znwdf5apGqMnQkfTe92jXSsiGD+BcDzsuA25BJ8ulwOnM2cNdzdhQKlHaeEex7mTgngoyzkrFu3zj13L/m7rxOA2SKiWsfrSWWCgq7imynITIUzosIqot6rhvz1rvV7Ul9fn+KpFca88m8jJNxUbYwjn8/uleHWLHHOatIzco3u8X7y90kVzsP2yvWkqyAivmdCniOqy+tnSLUIedbq6NY5v/Jd55rq368oxscJp/HmIW0efiZ89PgdeY2F85COk/KVuM3nuxRMYRxJUDDEQghYNnxkQclbs0EeVST1rQQfzWspm82F4GOebEL7xOAjv6NtEe5zIXl4o2JkikSCNvl8Vb440vCR90UpNMAV6MMMeLJySuYhOZ9SmCHPlMtgLGsclSz+1xKNFTB12R0LhW4aHByUJq8u4Yq/3qo5sU7GXYR95NVD5I+aFdArJ1YsFml0dJRmNs32XbMIDt2pWYAFOKUM1e9+97uJmVTdRGdXxv0wuyMD5vJf6mbCVqoOaMpVuUzy6OgoLXOt66X24Ldw88fmimLTIMvPHSWr+BqA9sFLbhKpL9L/pnPWwbN05zo7qTGZpB6I+p26cVABJeze6jOcesopoc84t7nZd89ZDQ0BV3V261XbyIe8V34XeaUt+Xts6T68NJax02UI7y4USvjIGUnVhH31SpmPGmi8OpLJUuLEPoAWG9aFzKTKWODhIzMWe9xrrwzgo5yhmvHRFN/td/XW1YQVbas1ctnKFFTYsmK2g0yMKBHRfffdZ+j3Jt99wwR3ItJ4WYUxV1Gy5sLtwwry9y3sHrJgaTrnLmJGubOTrVDrCbidgBM14yDvlVMj3D+Mmfb/tnHjRiWpquOOS7PSD1WRTuS5tAbfqzcX8ko73vfY0n14qRo8ZCUYKedc0eFjyuH1zjzoIAUzZavrwh/jLXIKqO7E7J2hn5OsSBfWzaBAx/Hr4pz9ZMJHU0I0Mz7uJcAJ5NXgtSTGMwwfwzFpfPFxBwH7SGQuj4qROyKc43kDdXayq/iHKRiyejjwsV7Tj3q3Hw+SMEReS4DgwXX8IxEZeUibsUx+p9Vc47HQbaGxAqa2uHuykVauXKWJXxFxWPl8V5mlvORNXI4T8btpyOXEZG3iBniJvZ6FZxFSM1TrynwtglTKQrFU82QuQgicquuyqfyXqZzVJs39ORmCuinlADqAoNDfUF9vtLDLpKuVmYJXw1p2Q7fVz1ZjSfLwYuDVGoHsnk0Q2ml+J3K7zyrtcWKc7kJBa8GW2ywnBl4+//Of/3zoM7LCor+/n+677z5KavqRgt7d3vRe2c3X5F7uQMTj1gK+Wu8pxDGLE0ljGTs1QzjjwqqVKwNKuQyEB0VXPq8t5RW2efK6WA9h+duAYCiEms086D6p1j1VM1TrYuAWESe4DE9qliM1WY2p/Jc5NGhT4P6FQjcdOHBAs5+0kXCZHCE/c+wErD2y4M5k8pLyalg3kOdmaasPu4yC8YwZAqZoLE+8pxEJ5tDGrLqYm++Sko7pLDTqXnmCpW25NrbdksPMW7FYdONNHUM/UhS0FOnfqxcG4VAw5lNYFSdPmhLj42GmsY5dJRipHtfh4x0ABZNs2XhIL5u5H4eK5CX22qMcV70vVIzsJGBToESVfz0VSWCW39KZSNRSc/PciPgo7t/ZmQuspQMHDgRihwUmH6DxwcdGKg8fdXHSm8lzAddhZJRYdfFpappDBw8edPeJKPhoa7dyfPQ8wdKafqQouFckle8e/8jzwcRDJoEAPsYx3YeZxgKY4Yvf0RaVb2qaU5osLMTYk+C8m8I1dClSy4nJ2kRZ0GZg1iXh0GpLEYyHJPILrXs0oD+CYOIx0/WqcCxnO5bP3QF/aa450MQgJxKlxGO6+zLpamW2AHQQQUH+WcN4pQxjxsy+TtjMwFNisLAe5rbOG7BO2aGev97tiy0Weg2EYLIennAL6DOn6xQWi9y67ab2Za2kLnkb1+CW15Dqks6uQWe1+TW/DoSVPc7OO3FU6diFzVcHoEbH0SaAMuGjSYl0qbS21LUGeGX85Hnlt7Yw7qp1T1WmylTGRmCzvnzXdvIzqdz9EVK1/Obr/cyfio9+xe8e8ped4cRH3Gdm5PT3ZQomBQUJq+0QCUuEzNg+axivtHuvRs24NZKoSa7uaxnyqnRwQqCwuG9PgcH1ecOvqXXvqXsn8nmXkhdawO+YmdVwhlww9+HlcDy33GBiomw2Z4mrHSa2SC7NZHzXxvg48VQNHrISjLThY84nXH1ds9ZUHjJHsqW7fHxkgUmXFMwJ4Ew4Rgazg0fFRyIqAyPnUPXw0SGRiVz1bgrDxykEyN5WqhAcVMhxNvVkspFSqUYLzvjrl9urXNSS40yR7lV9fBTzKmFpf75v/iQUQVwVuuU1xC7mPQjy3w4EfxxnLz+MNBbANMdth2/opvI15gm42LAoWUN3KnmMilgA3YUCPfTQQ77JKAuO+6GPEY7iyjsyMkJd+XxgQjPoc129FIKWbxbgdEKvTjjmsTFl1lZr/srWr4UtLaWkIiYylepYLy3YHTAndQobM87wfRlAtyOohGDBQBXm5dJmcns24UPuk67dFIKZnafCE8T3Q3g2qM8oA5Q3V839UGufh80DWTE0ooyx7toGxylLQ8kUM5WVU6VjZ4pJ3GNZN1HKe/nWBcJzI5wMUKcyr7sLBdq9e7cGd1lI3URB90kzRqv4KKyuKrPEMY4jJJg9P6bLDIqOqdMxf956NCXTMff5tNPmRsbHbFYth7mdPAvEDgpzow/f20DAZSRcwlVXQ5A55jOjbc+uwJaZPZPgIPebmckPkNhjgyXT8vkuDT6GJ0jzt2+eB34eYyQwxg4cuhKegjbGx4mnavCQY8HIaOUP2wPzzM9D+g0VhUJ3iX+Mjo/hgpTcZzNG8jyvDj7K42PGyM3a/ra0LKSdO3f6hHeVZOE+qAi4VhqPSvFxr/v3b8hfDxyUzXK4jg4j1frlUfHxBOkeJuytHB+JiP75n/9Zed8mfPSH5F6HcB5SDXc18Y+5zs6y12ksdFeRxsfSHZ6QQRe8b3ZXsRW8939ycGizOynnNDX5MoTrBO1cNktr164tfTe6JEt95gREalsZCOF0BaJvGAxaAwMDWnDjBZWHPrN23tBftobrrPQ60ikB+Jlk4XU9RMK2ZtgzrrNVbZf0G7uqc7IRFSj4WUc17clJx3TjukQCm7TmXYdlL++FVxOxHsJ6eDP8yadGRkZ8yejC3H3XAbQlwjyQGQa5HmM5TEcUipnKyqnalm5bghMdPprCQfIAPRhhPqqbb2My6cZzqxlwN5Fq6Whunit9t2O6h+WbKFgTu5uA5aGYrsNHXisqRvpjfoOeVeH1WaeTzkqvI71lR8eYrXfbbXa/RxE+e8ljsIQrppeMaT/pmdX92vauvPLK0LH1ruf3owoONSSYcB7DBhLWqIXk1dSud59rBwFrSsmnRkZGpLjFPRH68TfWeeDnMXRlijwmuACnVC4yxseJo/GwdJeLkSo+frg0x56laKW0akkVcEV4pB0fW1vbaOPGjdKxaBjpOGnSVyaoHj4SRcHIvLa/IpmbJ2BWhpGOMn7ryUtEt5fs+MiyxC73eLF0zBOgdRiZJ3/98qj4eIZ0ve796PCxhoRSp5+Ed4IZH4PjEzYne31975FO1PGQzKsfbv4xFrotVL2Ybk9YFskKzBNKl+DEvGB5kYRp7kUm2F7N5LoBQaEuAX9ZCa/moX2S6ia4LMCdId3HtGH09PSU2hsZGaFcNuvrn851JLRvhmOmrMVhpLps6YRiByJxky0zdy2Cda55DDiuRWfBz8AvdMux9loXcPcaucxWRmnzVEtf5WdbqBzjd9q+dGkpOQu/f1UIkr0fzrTMA96EuwuFUrvcv/6I10almKmsnKoR0+2bJ8q7Vudib29vYFPUKcXY08U2V3T38pIv6jLgOnT//feXlIEyPtoYQbuQJFuU9Jgu4yMR0b59+wLxiMHSkGEMjD7hmxdruEHrBqijYEiUSSjuc+9lY/gT0nVesjp/Bvigq6lgBpmpHCE/cxh0c/Syz5vKkB1P4f2U2/6M5pkTNG/efKVMXBRLunkeMMYJASWcpwA2UBIp16U4xseJpGrFdJeDkSoPGcRH2epsq6STCJlXN2jXt4yPfX19UvmvcjAyQ0F8rCXbutiyZYtPuA7DR//9wtaPKYM3SHXRtlEQI1XFHn/vJzs+cjgM46OX7MzDSJ0Fv1J85P2pvMzw/s8iArqUYwIfBY7xuPL7D8NHLwRCNl6pfOAKV4Y4EvjHWOi20FgB0+Tmks93hSRaEAXhh4aGfG2NjIy4NTnlhWPbbD1w0GWl5nIinCGcXZ77+voCTOwkeFZxBv90IuETVj2tYTCGYj38i88m4K1auZJmNzaGuh+PjIxQuq4udAGthl8A7VZ+V7MWRyFdsjj5GXmsTbHeDrz4bTnOW+5HLpsteSLIz18LISRz4gc5blznrcACPbe9cMECSjlOKakUW+fDxnAbPMVJjXuNziU+A1AX9Jb0GgghiJ+jzjIP2LthaGioZEHn/tkUGuVk5iWKmcqx0FjGzhRG0pXPBxjNdCLhU9q0t7b6MFJV0GXcdXWLZa7o5r1X1o8ZOzkDrq48DMiLV/QwPZFI+xgxDx/3KLjN5Z4uldoLZ2BWrlwluWCmSOdqOTIyIpWiCrNo6xLx8O/+qhdRyFwDmBkqmYnNU5DJS7njKVtNmPGD4q5pYs5Pdd8X75PmuEyPYb2FgITE/G0jzzofNobbyLOMzXL/riHgGxQU4OeQsBLZLOnhCZZkfPQs6Kb+9Ujt6RVXYRTjY+VUDR4yKkam4Cm2dfjoTyLG6+aW0Hlmn/dR8TFNQct4GEaODR8BuNZ4Mz7yuNgxcjWF42NQeWCjYCI6NeEcY6QJHx3yclwEk50Vi0VXXuAxrzY+bnevX01eNnPT+K2R7s3zgCtJ6DKld1E5lvRaJEN5SPa0iMo/rlu3btzwMRa6LVStzUZN3KC3XMsLSCwqfXIIdjlhK4JOMyXXXxU1tuXJxYzodYZJxyWoZGGvHsIFWe7z2R0dPpcaWWv4Deit6DJDrG4YLdL9bInEisWi1gqqnufbrOBZiU1Zi6OSyaU1I/VhFEHh04HwMND1NaXJzszPpcY0c1u6Z2fhf5d0bd5wLQA63S35ZfMW0GWwz8CvPOjS9NPUttYy74Y+yNfKmkpW4uQQVAKlED1kQKaYqaycqjF2OnxUGU1VacPvmt8zr8c17vmyF4punqXc+aqbm2tK99Uzdl4JKtVK7c+k2tFxtgEfL1IwXF0vzBCrjFaLdL8aAuqs/bRZQRcsMJWalJk/wTz19fVFfqfmkKiM0g9dMiCHTLGU2WxOivlU40SDcc1hbQkvsGLIdeJ/z1Jns4TpMtirjGIjCcFbPsdkSWeG2J98SK184sVvqkLKejI9UzkYGeNj5TSePKSKkfJenFLw0cw/8rzQCXaNhnnJgvp12vWgx8dGEgop/xxUY3offPBB6fdy8bFGumeGbAkLWXln9xSx4aMnYJbDQ+oxUo23NuHjDdq+Ok5KUwHpSMFHE0ZmyK886DK8d337M2DnIeV9fhheRSWfYh9+/nY88DEWui003puNl01V7+LH8Q5+rZhqRRgioFWzIARDMrNpdmly6SyhDNQ8WVVXDP6UtEAQ8bwqwOjqSdYAvkQuKXixw2o/0vAE4mHpuMn6ulDKkq1alG8BqNZxqH3pUurt7S1Zd03lucKSNJkSZRw4cCCwsGuSycBCTkEoGxyAplieqa21taTBXqOcK8c0q4KHqb0t8AuoatKIxQsWlJ5tTlOT1jVdTtqWR7SSZEX3c2GE99je6p+7M9NpSitZWRuTSUrV1Rlrf/MnCdAD0jVRkwbFTGXlNJ5jJ5cQMeFRynFKFm5WysjnFwG6EwJz1LkC+AVyPz4GGdFkslEj4KgMxjrikB4VH/XWH45x4zI9Ne6xjOZcdgksJyGXzpX5FnKcWspmczQ8PEwtLYvIcWrJy5otW3T8Aq9KJowcHR2VynTxJ+k+m97Da/r0WgK4Prn+mdiK5hcg+FxTXHPeMD6Xkhdr6L/OcRqoufn0ktdModBtKWFG5Fml5PvrkxaJOMwL3f+3GZ55P6kMajo904139Vvu6usbSChhdPPGU+afBkfrNRZGMT5WThPFQ5rqsaccp8TT6fnHIgF3kposzVMgymWy9mvmFwtMUfFxF5VCHnt7S88xdnxMkic8RsPHlpaFUl9ljNxDwGpynBRlszkDPurXdbk8ZLB0cA0JQTuIj+IvZw/XP1Nra5vkSXu48VHXtg4jg3uNmJfs2XBz6DO3ReAhayC8K1W5QxayayB4dbUykI1iobuKNN6Aac5wLru16LIKBuPBHCdFLS0Lqbe3l3bt2lVa6IODg4GYX53gBohyE3fddReFCUkmd2xdPUm5bS6TxZNbnvgJCIaX79Uv/WZitqcnEqV+ytnDVUus/Gzy8Qw8ZQPHO7PbnU6BoGq9+Hk3wKv9m0okqGbSJN91eQgLXQoe4x/mUq0THmzuMKYNV/6YruUSXQcPHtSOkaoICbOGy+7ocuxZ2HWDg4OBuH3Zos3x97q5mwbobOhd+8sJGYiZysppojAyLHyE542qoFK13+1Ll5bqXrPlaOfOnVp83A9Qi8bCsGTJmRbM1rtj+8vSyAyPfI9uEjHB6n0T5E8OZnJTVxlc7qecHVdnNdEd95hpsdeIJDrMZOmYZF3ZH2FFYqZZ57rI9+HxYHdDs1u193uYMlodC11cpvwxj2Hh/2fv3aPjuI4z8ZoGSPABYAYAKch2JNuknnxhBALUiBpwrInwQxbZzXPtjRM/Est2dteWrOMVJW02jkKJTiIRsXjsVUwJS8la0RsjouzEcRCRlmIythMTsKOEia3FxKRP6DzsnAVObOdhZ9eq3x+3a7q6bt2+PYPBg1TXOXOA6enH7dv3fl11q+qrsXE8f/68sohQRHshxHf96N0eediSQ8njObH8fmVIahumUWgbxcgMH5uX1YSPSfpjW1sv7t49bOHj7OwsS2sMlPEVNwpXAh+3br1GXDMdPuZyPG1kAQFutc7txkdpHHcjQK5hfDT3PIFm4e1+9OMjLY5cDPgovehp2tA4Pmo65CGI9EcEY3u0ge40GoY4z9NS4mNmdHtkqQHTXw7M5MjEiWMQtRIDLvZEAuVjkGz8/HuIr4q69jsE9gqQr2zPRDi4KcSZXgTkrQYwIZ002MnAK4Iehl4BPVezAsYQk5OqGLaZakiXBKBsFt/7+/pUbyvds+9+5f3w34aVe6JSLlNTU8hfoGQ8+NhKO8Huo46wfT4W9Q25HA4NDtaB5eTJk3jgwAG89qqrYp5vL2Mq2MZ+pVzG0Wo1xpLPc3THx8ZiizXUVhkKT88rjcE/LduVItwrUyqbl+XCSNezP83GCe2jlfBzhYol4eMYBJiDTjQr7g8iwESKUMRDFqlOOpwnA6lab3PkiQE0hispRr6auJGRHL9mBY33SHpiq2i8F3l87Wu3YpzADFCGQ8d5SfRcSfc9P8TO5SJxq1j3FAQ9WK2OKvnLdP93ie1S2ed5mQWMQlFdXubI20NRAYgm+mJycjJU9POsjb7rx3M/AUzkQNSPReueKZQ86mdqazRG7O9+hbZRjMzwsXlZTfjYjP6I6MqzluNrAgH2C7LApcXHw4cPC54jwpM0+DiOdj675gmO42NUrYLPubhBvjh85AavCx+HrXtabfhoqunQIk0jGNkYPo6Njcd0yEfAdryNA+AnYel0yMzobqEsx8tGz+sgUIjnnmhM6Fu3Xp1IHEWT+zYGwnzQXRADNAeA26+7Ts2B5CQdHKBpUSCtQUYvgg6wybiqYFalOkAPQ+8Ho1SfCtvaFZ77lGdScSK3YxCRn1XADr3OQzw3OzY5wxVgul/ukeV96eqL14fX5PdUGRnBhYUFa8FDGg+ue5OLCAFENcp9Hmr+IfKVubk5vPfeey3w8vVvb1sbbioUYvuPVqv4upER61yj1SrOzMzEzjkXtl0jj0vq00lofpUSMVMqFyPL0XfjY2PY40gP4REbvW1t+BBEBIGduRxevXVrU/gYpbgUxRy4Ht05kEb5GhwcVvGxEYPMfNajIbyRBpYpzWPC+TRCrnEE+CQa47kLo5BJn1EmFT49tNC8hwLnuThGmuuSRxYxUuKS+uMwSqNgZMQoYPaihzQg0uRlcg9MGuXXfLZv31UnpzKs9dL7lXSe09Y4ATA5rZGHKH6+crkizjmHUZkhaRjQQkkKD2ODGJnhY/OyWvAxyrOOwqcBNnj1R0TJ8H3aMb74hyIulhYfr732OjSEi/zaRYzC4PPoxkfKy85hLteNfkI5iY/Hwm1kADeLjxcwHrXC+9PVH+/E1YiPu3fvwampqVhaWMQLkAYjD2Ej+Dg2Nm7pkBXQvdlXwNLpkEtudP/zP/8z/vVf/7W1/S/+4i+aOd2qlkYBk+dvJOVycPmDP/gDDAQBDylWfGXs3LlzYe1YOTnM/y5Pzvz8vBU2rBGK8U/3hg2WkbQHAN8tBqEMw06zilQEd145D0kfrVZxtBoHywoAngND1sW387bSpCJjmAx88qLmwHhZ0xjqGuP79PR0rDQabwdfQPAZudcD4MZcDivlcn3s0DnzAPg2MLnzh8AYnfmNGy3itt62NgwgvogwKfoBw+dteZoh8obLdADen2sg3s95cC/GJEUI0AooZ86VoXGuBYI0DNQdYMqZ8b5ZqZzuDCN1aQYfv/rVr2Khu9vCoxEwi3P0nCXHgrXa3SA+TgHhawFtwztA2+DahQBvsvBRJ7TRFDquVL1aOX8VjSJklJK4MkKfIQR4Du2SLPxcXJGbw8gInq5fv6urECqOPkVUV8Cnp6fxzJkzyj0MIcAdCf3BveBVBDgQyz2PjtmHhlH8NiRjNgjy2N3doyxM94TtoEUEmaqFqHvDChgnZorCXeN5mDwqIHkxxnzixGr0jidFnKeG2elnaRRg12/XI0A37gxzuhvByAwfm5elxsf5+Xm8oVi0SG43A+Cvs2e8sLCAt9zCw6dtA8aFj3auNRmtLqNtrTL3lxcfAUDJl96ChgPpq2iTGWqLVkuHj/HSaLwdnM27WXwcR0Mi9xMI8FFMwsdogaB1+BgfX/I5FcKPCyPT4yMiWg4wn564FDrkkhrdTz/9NL7qVa/CgYEB3LlzJ37hC1+o/3bDDTc0erpVL2k70wYPP6DNzc3h1NQUtoMxXO4HwB0irI8ft69cxs4gwHsAcACMF1gzbiRYj4+NYa8whHrAeJQfAlMuag2YcGhOeJbv7LSAvAOMMUzCwzpKoNeIliRb10BKMq6w/bVaDYcGB+tEaFXl+B6IjEOttnUAgAfC//8r2DnlSatfcnJy4jGNmGE9APaJvviwcs0OANzc01N/vgQeJdDz0j/4wQ9aeeb9fX2YE9fSDNQFsCMKuDdc3mN32M9F0dcHlLZVIFqMSQI1TXmgFwWF4ifVT/QZ/ESCQe1YKfbyDCNtkYtzaQ3iM2fOYEdbW30Mng7HSqzGfXgs4eNEOHZltESj+DhQb18RoxJQFOaYx40bu9F4oq+I3UtfX79SceIYApRQrzKhkWytRXdubjwfslar4eDgECvjUlWO7UGANRgpZRobbQWp5inARqGwujwuP4laLiBFZrk9TevCe6TyQeSZ4vt0IECu/g6MDNAR1PIud+26Ac+fP8+InPi7OMf6/hTrB2r3gnJ97u2Rz6cz3L+Kca/zAaVt4+F2lyKtEzAhcg/jIXQrxPx5kBdcU2g7sL19XaxtK8FenuGjLYvBx0JXV/3dGOOXgTh7+dzcHMOJImreWSLt5fio51pTyPVT4VxoR4C70RioExhFXnRgenykudQafLzzzjsRMWJ9L5e5p3+zcjwPi196fETEBPLDdRhfwGsEHz+LANI514bbtu1U8dHcR6vxkcbAZrSjcjaijZEVBHg3OzYdPiLGdUjN4YQQ6fMURdlqHXJJje6BgQH8xje+gYiIX/ziF3H79u340Y9+FBERi8Vio6drSv77f//v+OpXvxo7Ojpwz549eObMmcT9f+u3fguvvfZa7OjowB07duDv/d7vpb5W2s7UwUPP5dBIurihUoMof/aJJ56w6s/6jBv+ceVna6Wf6PutALhT/FYFY6AVcrm60S1ziaaUcxYhIivLswGdxiM+xELYZXkM1/FBOHG0cPGO8MNDrkzeSXJflsAY8tq9F0FnFOdGAbUrD/ZCQQARmMzNzSW2/+ZSSWWH/wDYOdD9fX3OGp4bIAqldYETJ1/hfUNG8WmIk1SkCauXeTHaPPDlbrvGF+/PRmvQNjLP08hKY+Ry4iNiur7ji3NJBjFJ2ggaAJ1EpRX4GL18pYJwKwLsFNuKSGQ3XHmNKy9Tyrk0plpZBksqNIbnY3BwKIaP6TxGQUJtayozSaUmoU6q6Q8tLCGPzIrfewVtxauAJgSVe0O0hYIgho9kPNrny2OpdLPikSOviVRaA4tpNwh6sKuLjAVf3dm7UO9rUnxPYzyk3h8yquUNzs/PK9463/NYK/YlD6DpT+kpatUcTysZPtqyWHxMwrvjx48r8yLNnNZK0dl4Ej+GvhfE9jT4uIAmVLs1+AgQd1qlw8ggjIxZGnzM5QpYrY4iIgpPt7wWLZQ1io/U93LfPObzvdY4iCohPIoyR31x+DiN7igAwk4XRj6pntOFj3IeJM2FGwYGLOdXK3TIJTW6t23bZt30vn378MCBA8uySvmxj30M165di48//jh++ctfxne84x1YKBTwm9/8prr/5z//eWxra8OHHnoIv/KVr+Av/uIv4po1a/DP//zPU10vTWcml/OyV2u4R8ZHcvWqyy/H0WoVe5gnRpaSksfsB5YLG7JHy301b3FvOAA7QCcj48YqD307BcYAOxH+NgHGAK+Iwa2FgMu289zvfEguxoVY3F3H+ybd1eFfCjmfnp5W621TJAAZvG1gyn2R0R3lq7ivdX94nds8+1HZDJ435do3r3jkKEKAL9bMzs6mMtDTkK/wvk4TviNZI12ebhfbPTFMyjD6q177WgQA3MLGWVJ/NiKtVCpXEiOXGx8R/X3HF+e8DPhhWCVFtviwLgeAgwMDS4KPhkdAU3Y60CYjIy9LhPXxihNzCECYQWF8s6iHgb+CHec2+KicJBe7yoU8FnDbNlowcCmK99b3nZ6e9tTb5kpiG/b0bMaFhQWGjyXPte4Pf3Pvo+Gja1/jxZOKKfeUGRbcZ555xqOA+tp9mh0rw1F9Sjh5rWux3zQlT/cydqBWyu6664iU9LXhX068Z/dnI5Lh4+rBR0RsSIfcsL5TjCFfmcH99bFmymS59tWwsRd1oy8NPk4jwAnWhllMqg3tXxCbsAjbEBHvuOOOxOOvuOI1njl8db0NjeIjwFqsVkdxbm4ODxzwR780go+IiLt3DyXua+MjtZP2q9X7b2SkEuv7xvCxhm4CtlOeYydQq8yg4aOmQ3aArj/eXCrhlte8JmaHtEqHXFKj+3Wvex3+2Z/9WWzb9773Pfypn/opbGtra/R0DcuePXvwXe96V/3797//fXzlK1+Jv/qrv6ru/4Y3vAF/+Id/OLbtxhtvxJ//+Z9Pdb00nRnPu0ouAcbZugFMnnTSw9+ogHCa0k20TQs1XkzeAzdWz5w5Y3ke+yEevnE9GM/qRPj9kOf8h8IJswt0Yy0Na3jSC4g80Hexc0svOoCe807h2eRVy3muNRR+TwqZ5hOcM4QmGQxJz5x7lWWEBL3UpwHwJABuBXc6QNJYKGrHtbXhrbfcopYcKyiLJ77n6IrCAAD8gZT92Yi0UqlcSYxcbnxE9Pcdz7nyzQVZb7PCxr1WCWCDMkZbhY8R8SJXDtKXPIkMzyK7Jx4yeCH8rRPjoetdnmt0osmPtJWRNIy4fsN8oL4v4aPtKStilNPJz58T5YWSlHZAk+Od/M7U8DHZYHA9l2g/wseorfThBnEF7dzDeAir3tdF1IziW265VfFaFzEICpZhkPwsj6D0MOre8OT+bEQyfIxkJfFxamrKiurx6ZDNYRhtc3krG92eFh8BI6Od5s/1aPgbJsLvyeWjDOZsj23jGPnss88mHn/w4MHE+WPOH9TP2xg+Xi/285EfpsdHRMR7773Xcz4fPkb7Tk1NLQIffWNBvgcNRvb0bEa7ZFpHPTqAi0uHPAK2/ij10ss982xVGN3f/va3ERHx61//Ov7d3/2dus/nPve5tKdrSr73ve9hW1sbfuITn4htf8tb3oI/8iM/oh5zxRVX4MMPPxzb9ku/9Eu4a9cudf/vfve7+K1vfav++frXv+7tzEY83d25nMXKTMZbEeKh2EMQeUmfhLiySJ5qGT5cVQZRAMZznbb0U9Jvbwv/njx5EsfHxrAQ3s8pMIpxFwC2iwGfxmjLs4kSAOBhiBuQXOi62vF9IXO2j8F8HYDTi+5jd+8MAosJXbsWhM8s7ep1Gk+3q23TyjlpFZA8fzIEfQRscKqCAS3KaQ3ALm9WALBy/cfHxnBwYKA+Hrjnur+vz8qL8dUW/UkwzNOFri7Lu09M5kmRAysRXr7SGLkc+IjYOEZqnhyXES2fdQHiOVY0PnsBcB8A/gS0Bh/5vnexc9oKTLICZLzZd9XnoDGG8miUFarDKsN/XUqJ5j2hkMPDyA1ILmNj41ZoICcAI7ZXX0hkuVyJnbdWqzHvjNuTbmrfkmLsU5ABI+9Wcq5zGk+3m1l52jpn3Iv8CNqhsqNo8sf5tioaozePAFsdz6mAIIhRx8bGcWBgMHwucU8Tz3HlYpOoyXtah4ODQyxXlc6b3OcZPn4itn014qMrWqxSLseienw6JEX22WOoijrhX1UZa7aB5F5M85WX8uHjBAJ0W9wDaRa2bHyMrisxMrpu/Pg1a9anwEczv3bvHo6dMy0+ms9+jDy+rcFHRP+Cgh8fo33L5UoT+FhEk0ZA+fgFpZ97UePlGBsbxx07BlBGSfCQfC4+HfL/A1MOt69QsLzhLh2SFuF9bP5SlsToHhgYcALlcsnf/M3fIADgH/3RH8W279+/H/fs2aMes2bNGvxf/+t/xbY98sgjeNlll6n733fffWIQQarOjIeYVFESQhBDID3kebDJvijXgBtC0ijaByZ0WyO0CsBNjCVLSiUZLb7f+PU+oNwHteu94V85Kc4qbaf20SJDrFC9UBAWFhYsVvNqeO+9QYD9fX1q+YwKcEUasDefx/Pnz9fP6/O+0ottAqIV6QHQvcVk4JIxXFT2y0OclA7RLChoNa3zYZ8lGeOcbVG+yAOw88QofwUAsNDVhdozpBc5KN8PHjyIk5OT+OlPf9q72CGfYdqIBdc+0lDSiOqWm0htpTFyOfARsTmMHB8bw0IQWGOpbkQzFn56zvPK2KNn3K+M08Xgo9wXoFlPd/QplysYKSL8t63h3/eGf10KmsxvDJDYf5PC7hYWFhRW8yoCPIpBYMItBweHUVeGBurHdHbm8fnnn4+dO11t2dswvjjhuhZh+DQa74hNAFYq3Ry7vvEodaj7JpXoIYZzdz5pBfWQWHp2cYLTuGdZPl/zneNjodCHSf2mKXnp+tqV37pT6XPqowwfuawWfOxta8MPg2Edl+/bQhDUPdyN6pA6hr0b9XxsFzGW3NeV6+3zdKfBR/7dhZFnHe3n+KgbpoiI58+fxzVr1lvXzeXyODJSwbGx8TC3W2JWBWlhYd26DU3iI7WPMJIMzcXjI6J7QSEZHw8ht1WiZ5MWH4FhHH8e2tiJiCWfeOIJnJ6eToWRS6lDngVb11gqfExtdP/sz/4sXnnllfjiiy/Gtr/wwgv4b/7Nv0l7mkXJcoBmM55uRKPoxMt5xQfa9u27ECAyQMfBJuDqAeNBJOOoyv7XBkVPdzfOzs7WSVG03OR8eM5HIWKHPgE6ix8ZYWtBNxC18lKblfvIh+chpdXl1ToJ0WoubesE44HlBqSUubm5emiS69y7i3Eg74d439FLaf2aNbGJVSmXVSO6yO7rNEQr0ppyPw7GkACIjOFusL3DstY5jaPRatU6J5Uy0LzOfF8CikbKKJDCR3n6J06cqJf2qoyMYG9bGx4Cs2BxAGzjdv2aNfUw36SwYXmvrvFahHS5udeI5ym97MtdMmylMXK5lMpmMHJhYQH7+/osHgmaU7u2b7eedRJG0pyk3xeDj1RXnhbTOD7uVL08dk6t+R4vn2KUtjbUCXkCjDySeu6t2UbKGe3bicZL8JSar4gYx0fXuT/ykY9Y76j498jIlJU3du/eo9w/kQwFaErUcOVzRrnWOEZMwKeR6o7L9kxNTVnjyCwoxM/X3d2LcTZ03dC0mX4voG8hZdu2nVir1WL4SKVrBgeHwwV18tQdsO7DKPcb2PW0BRa9womu/BNz9LGE89pGSTu04aMZPq5afBwfG1MJV/Mhlh0+fBib0SHbIcAA8gjwYZRVBHbs2FWvq+zOTSbv8f0YhRbzevFybLYp+NAIPvaysXt3+FdntAY4iTY+7kRuQGoYGXmE70GNX6FU2oul0s2xvrJLjAXqvPXj40B4LcIdV8WDxvER0SwoyHQTg0E6Pvb2xpnOx8bGcWpqiuFKOnxExHp97snJyTph48hIJcwlvwsBfhflIsvY2HjYhmSM5MShJIvVIV8Bkf6YF3NpqfAxtdGNaMJq+vr68LOf/SzOzc3h61//egyCAP/tv/23jZymaVmu8CAuaTuTVl1IcatBnNiKPmnIMiaUfVSAFbmypNjy610HpoTVGjChE09C5E2W4cYD4d9Xg9tLRDnBNfDXTpZh5uTVyoftuQsiozQPdn1oudKkMRVWIcq95pNpaHAQKyHZyNWgl+vaEV53z9BQrA/zGzfG9qX8brqvt4tnUgzPw++nkMvF7oU88V0A+JpwW1KIH9W0/oVf+IV6Hy+AvbK9qVCIGRZyPB6DdHlirv6VtdKlcUsLK75cfY0UT8ulL7Ln6Zsnh8AsZoBnvzShlK3KWVxJjFwJfERsjGzStyp9LOWzv1v83iw+FgGwi6UxPARRhE6Ej1pYnWQvJy8RJ4DxeX3axTmqGIUur0GjpJxGW/nVjTS9nm4V47mFkQJjDLo8mtq1WtmazWi84z0xxfXhhx9W+oQriW8Pr8Vru1YRoNtxT3SOIppyMgUkT4hr7hI+Hj58GHfvpoiAR1EjXnrmmWec+GieTXIY9+OPP+7sX7tWeiBCyNOG2eukTwsLC0peJa+R7BtjneHzBbwnw8dVi4+IjWFkIzrkowA4VseOPErDl8acnptcRWNsb0SDSQ+hWVy6jf3O9x9g426x+CjPUcQodJnGdRxLpLGZDiP5fDJzPgg6cWxsHMvlCuZynWjI0+xcY4AdTeDjm9m9kge7GN5DhI8GR/h5+LO4xjt3T548iQcOHMAPfvCDmISPpdLe+oKincrjx8eBgRtwYWFB7duxsXE8f/48266NwTymwUiNOHSxOmQeImLe5dIfGzK6ERHf//7347p163DNmjX4Qz/0Q95yC62WPXv24Lvf/e769+9///v4qle9KpEIQwL6TTfd1FIiNUR/fsF+CFnBczlvTvWTEDeUGs0J1tjDXUb0DJjccf4bhXrvFKRvMuxpQLmPebCNeTI48xCtKMm23LBrF87OzsYmv1pPV+RmcPZu3icAhsF7X7mcGFpNxiKfWLSiLPNE6fmsAzskRfbvnt27EcAY6JonvlIu159ZUvkWIpbifVyDeJi8q74rMUD7FkeoLVr/0mofrWDK8/BxSoZPEjGb1tZarYZXb92qzokqJOf/87QJ13zSSk00O8/TyEpi5HLjI2JjZJNp8PEp8DPyvpf93gw+ToPBPr6IJedwEQDfD6QoSAXqcyhDjo2RqimgXFmZR1tRNaHfkRGaF7+bGtQSH+me6Lu7nq5W59bgY1QSqIh62ZpK/Ri6ZuRFd5V8WR8ecxZtEjmpiL8JTe3puKeJFCwfPpr8dekhoegAwzCukeLEaxYnK//lcqV+Ldm/ZChzfIyfhyusfBGCh5HGWZ21e922bQfrc6n4Boo3nMbSOAJ8EgEAfy7Dx1WLj4jLo0Om503YjyZChRtR2vx9NNxvSPwmv7cSH+12XHfdNpydnUVETNQhdYy05yAZgLOzsyGbOXn13RFLjePjBYx7sOP3ZTzmgGYR045WKpcr9WeWhJGDg/QsdHzUMKcRfCSsTsJHRMQTJ1y56Xex9iVhpBsfm9UhxwHwPs9cajU+pja6v/GNb+Add9yB69evx8HBQdywYQN+7GMfS3t4y+RjH/sYdnR04Ec+8hH8yle+gu985zuxUCjU6z6++c1vxnvvvbe+/+c//3lsb2/HiYkJfPHFF/G+++5reckwRP8q5SzYrHpJq5TPsn0eC/93kmiFg4KDNvf88DB1MqYKuRx2gF5nuh7CyQywIrjrTPP7GFeuJQ0veZ48GI8trUhqHldXPV2e0/wQmPB0MsQOHDjgrcP9YPiXh+mkze2mT6VcxqmpKTx58qTKLsqZ0OmZHT161GZMF579NMRqAIYFlEBX6zuKIJDAQ156AKgDous6J06cwNtvv90CJ27wLADgsOgbuncfgBF5iby+xkRZAcCjYJekW2lP92rAyOXGR8TWeLo1fEzan3u6m8FHwqokjMwD4MbUZXGKyn6klHElgzwbmtJHCp88Tx4LhU0pPDZuxcgoWA+h8RCZci8HDhxgTOa+Y6N+9OctlmJtKpcr9RDWWq2GU1NTrGwZfcjbFHlPZF669FpF7bgtsT0PPPBADB/tfuNeQKnsFZHjY5LRQu+auBHB+2oBbU+T7WXTMNJH7iTL+5g8+ues641BYFXlyPBx5fERcel0yENg0sLssWmPuXjaBRlAxzDiKYoTXEU1q7UFOx5OvrT4CBDE8EGb5/6a4/vR4GMFieRscnIS3/Oe93iOezDWh43i49jYeD1akXTIclnOZxsnjh49qnqWXTpkUp75gQMHmsRHXyUHHz4ixkuIabXa4++GVuiQwwD4HMQX3Vedp3v9+vVYLBbxU5/6FCIi/v7v/z52d3fjQw89lPYULZMPfehDeOWVV+LatWtxz549+IUvfKH+W6VSwbe+9a2x/X/rt34Lr7nmGly7di1u374df+/3fi/1tRrpTC0fuAfM6uQ4xOnoBwcGnKsvFAqdA4UcCuxSVnKVkof6LqY8GJWg8IZohKRlpzz7UbkxV9mrJI+rq54u9ak0hOUkcx13m7g2yc2lEnaCCBkHwJ3h/pSvkqb2NC06yHul/U6FfSJDsOkFWIJkg5l/+vv6sCDYTfOgpwzsA8BHwv83rl8f6ydKI6BnxvtXPt8iax+NAVeufRKAafk53NM+NDiIXeF2aSTxNtTnXhBgZWSk5fPcJasFI5cTHxHT99342FhqfJyensabS6VY1QXCyLXhMc3io8RFF749VD+vOyQYLMPK3s94C55Cf31S8jzoZV3K5Uq9L22vgq/2rvTKS29R0rER2zCJMYg7MR4uXkATev9U/Tmmrz0dV+DKZc6cewoB9lvhhZGBcArd+aXx++zr6w9rzUql/dVKn+xDw9oLuH49pRxRH1GYrHlmXV0FdpyPaZmelZ6nmuTVlzm33JNUq9XCdnRinNCVhXJCHisQZPi4CvERsTU6JOEjgM0jk97TzUN905CDuX6reY9vFT5yfNAwJrnmuOynCAuiKBPXcbdZ89bgYx6jcPxD2Bp8jL8P0mNkCZMWFf34uB1NmUppDM9gxFgv+2iu/lsyPj6FcaOexoHOR7IUOiT9L48bHhxcOaP7N3/zN61tX/rSl/AVr3gF/uf//J/Tnuaik0Y68+GHH1ZJtX45/J+8M67a0FUA3AaRJ3gEdO9yVQwmLuNjYzED1ZfPm/Tbk+F3eQ5pkMnauq7zUUhUUkkul8fVFyLd5egnb64xRMYrTa75+Xm8uRRfjQzA5IBrNadJfCvVh8JnRp7wI2DnaOcA8Jlnnomd71GwieCKYPKZNXK7ouP6AHrqAYDJsXe1KWD9q4FTIQhw/Zo1sf3zYAOYj5RCrZfOvP+StZ738wLYixCBco5WzHOXZBiZ3HczMzOJpIO0AEbz0JWDvSYcXzvBLIQ1io+9bW2x8EwXRuolw7iy9WT4Xea8xQ2yKLwvnXHrLusC9ZBJW3nxKcZdaHuZquH/azzHdiNAEMNHm2QoQEPwdsRJXoTo9wIFQZ55eY6g7RnOWfhozjWKes7lWrQVyKLjPgGNITyFxtvFz0V9pLUpQJNnyT2CkXIbBAWLKXnNmvWJBrQmWs6t9GzZJEpuQynDx+WRpdQhk/CxCPEoRo0UUhtzhuuBG6i+0nVJv00rxy8VPprf3VEpvhxyqgMex8co5zgdPiIifvrTn8bu7h5xX5sR4NcXhY8AhwS7uI6RFGofne9RtIngimHbi5gOH6lftdQDwsFH0aQIyN/Whr/Z+NjW1osjI68TuJVurEppVofU+JICxzkWO8dTG90u+drXvobXXXfdYk+zaqWRzqRcjtOg5wwCxGsW04QgryD3uDwrBgV9nhKDYrRajQ2GhYWFei52qz3dLoOMcg21nF9+vk5ILnsFAPU6h5rhrhlzec85a2CUc9dx42ByswFMmPbU1BRuKhScLMtJk8+Xk0WTl5ghq2BeikfAzoOn64yPjWE+CPDdEOXQ8753lUDSctG3gDFK5MuYpxRQuTKXIa+BE7WViDueeeaZVKHzrjwkmbcqhUJiHwTASXavF8AsWnSGY7ceaZDC4G9lzqKUDCON0PwgfKyBzgFBY0UjpyQskkRqjeDjMFskbK2nWzfIJD4mEwfJcMu4oTQ9PZ1Qv7mKjZeKqYXtdjEOB0g5ltPT03jmzJlQmcyjFuIJoLNwyzGQxFAbMedW0XhjjqDM86RrRF6hX0Ze7iyuBLruWyrtWzAiNZKepkLYR1SuzGXI2yHk1FbCx5MnT3oN6MXgIyLinXfeGZ73QfVeb8vwcdlkqXTIRvHxHAB2ivrxfX39sbKpiAYjo/SPVnq6lxofTZh3cq1sLUw6ydCk0oyu42x8NIsItH9yGLwUHz4STsQxsoDSY93d3VO/RkSY+W60MbKIcaLNJHy8CyOPuYaReXSnGxC3iBsfETGmQ6YJnW8WI3naKc0dDOddDmwnng8jl9XoRsTUtcwuRmmkM6WnU2PU5XWUpYFJHheuhLoMuAcTBsPc3JxhmYZ4GIXmeXSFY/T39cW2F8FtkPHruyj8AwC8uVTC7g0bEomxiIDMpUxztmoAwK2efpoGvS74MJgcKQRT4zltLqmLtIxKySQdS6W5ZK62a5zcesstVj1y+Um6b3l9gChMvJFFGDI+TrJtFOXgIimi/CQJfFrOeSP1EF1RCKMQ5a01k5uzlEolYoaRiHokiMoB0daG+8pldQFuSsxV1/i/FpLxkbCjF5IxclPfZapHsq+vX2wvossgGxmp1K+dVJanWh3Frq48JtVXHhmpJOT3HkE7RHorJitx0/X/r7rqWnHsMALMop43rCu+rnrTnNgo6fj4PrQfzy01/Upej3PnzlmMxfFP0n1rxoOr7nUaA4MTwhkv3h133OGcCxpGuhiAG8FHu0Z7gCYSYCHq5wwfl02WSofkBjo9yyR8HIMA2yCPZrHwSXQx5kftCNAYdDxVwfY8apgW5Xu3Fh9NaUCJjwXkuBcRkLnmqV16y5A+JuOjzUORhI/JGCf7Oi0+ajqkywAeGak48GAx+NgsRlKUwUlcDD4iLg4jXZxHEM61ZnXIZTe6L2Vp9GUzWq1iIZer14eTD08z8IpgPIhzENWH9YVFn/QMhtFqNZbvKK85NDiIs7OzznCM8+fPxzxC4GkPhbPMzMxYxw0ODOBeZijJPKPrAfA3wKzYcoOfSpwdAmOYXx/ur63sJnm6udFLjN5cuV4DgBvC8/pYkzmJw/z8vEWaJhcr+AIHB1cKyT+V0P4gPFbzhF+TcByEfabltLz5zW9OvD/+2zzYq+y0WKGNuzQGdRJD+rPPPlv3BLlkfGzMqsdNOW/tDTy7xc7zTCJppO/o+T8EEYkhH8PzYKcJED4imLSIPPjxUc59jXuhEASxa0mMrJTLotxJ9II/f/48Dg5Kwhe3IlIuV3BhYUEtATU4OIzPPfccu06bOOf1CPAbyJXkyHvBc6oph3kC4/V0kxSkGnJlsFyuhKGlh5Ar111dPWHpHCIt05U1iY/yXsfGxrFaHVUXMsrlSh0fo5DTU4ntNzVgC2iXO+vy3Hf8/oiBnPAxOTee/6axLQ8jlTyTWOZTGJMYgKn+uisXlI6PlywjLxN5oExON7+5DB+XVprRIbsA8G0OjNN0SOKyqDjwMS17ORdjrHEDNb6gNzg4VNchtVJ6u3bdIOZFq/BRLix2hPMtWkiIFkVlTjV5yonBO5kgMS0+Fgp9IT7SgoYfIxeDj4gowvLd+GjOZUcLJUcC6fgYRSMkYST/TQs1bx4fEZMx0qdDujiXimD0yGZ1yMzobqE0Cpjnzp2L5dnIh1cFnb27CLYB5irBFEDkyXQNBs2Y3nvjjTg4MBDbRkYRhWOcOHHCWnXbDyaMN2kw7ty+3boeGfZyoD/KBvf1ALFjiuHf3nw+tr0NTM1x2Yb5sE/yop9cuZ2yX+TLa594BvQh5X3nNlOaYn5+Hvv7+qxnWQgCK89K1roGgLq3lhZnLkC8BjofC9pqdz7sQ3nfeQAsdHXFrsUN32effTbx/rTxp4XZd4Ap88Zf1kkGNaI7530C7IWY/r4+K+yNEwXykKCnxLGr0dN9KUsjfSfxUc5n15hrBB9pgWoaGsdHGUUh8ZGXoYlyB/ej8XImKSIbcWTkdQqjbkWESR9D43XZEu5zfWx/Mi6HhvaI7QGa/DzZhnk0XpA82iRjVZS5cpoC3d6+TlzLreRt27azjo/GA51HqRxVq6PWNaS3OsoZ5wRxvMYvV/CKqIc6tmFEKhZ5zeKkPnZYd9L9mQ8nQNPYlvMIEGBbW0d9MZrEZ1Tr1/4wamWSqtVRB1PxIYyXKop78p0pSRk+Lok00nfz8/OWrpBGh+yBqGqLho8+fgrNmNCw4MYb9+LAQNypQvNH6pBLh48X0NSrpsXJopgb5ns+3yu20/7Sqz2CenTRCKbBR/s6ySzp27btxOeee25R+Dg2No7PPfcc25aEj3q0kLn2Wuu+k/AREXH37iFMur/4b67rBtjevq5e1YLEV3aM9Nfka+s6pE9/PMSOzTzdKyiNvmzI8NA8MWnyqznIavmzRXr44fnu8gwGDoTDg4OqUVQpl/Hhhx/GHdu3x641PjaGo9Vq3TPla3tesGZz0jB53F0QenJBhE6F2zeC/VLpgHj+9jxEBGPSeOZGnBa+XKvVcHhw0Hp59YbnlMp8HiKSuwAA+wqFxP7gYTEuY7S/rw/zIeldUbR/S/j3FAMEDSikscr7YThc9OAyPT1d73dtMWdtWxvmwU9c18muNT425izXQPvPzMzEao7zBYbNYCsRnWAWD/i4npqaUlf4z7LvV2j3tsI5i5e6NOPpPga2Ed0KfOTEbCcgPT7WajWslMvY48Cwxx57DA8fPmyFEEZsrz6SHsMGbLzTcYWiXK4ox06jMbSkwtKLceIu6c0M2HnmMSLPkUZb5E3XQvOoXwqFTWjnJnZgFHbKldRtSApVoUALK27PGl2jXK6oipbpW1p81RXruJLHlU6uhAXq/4ODwxY+IiLu2DGAOtMvGUMbMB3bsslBpf71GfRxlmR+L1XlGXQjQHuM0d7kecrnPI7GyMjwcaWkVfiIKTEyCR9dYb9J0WVchxwcHFbn6uDgMD799NMWKVp6fLwbG8NHRBPl0wxGFtk55pGI0OL9tBYpjDoJH837QM7NXjS4G/dWR3wReXa95vFxbGyclUFLwkfCKddiHK9s4cfHCGfk/VG/5zBibvfjI/WxryxiNA4II+3Fm/3h+D8EJnq1r1CItTtJf3xv+HcA3Om4rZjjmdHtkVbk49DDe5cARfpcEANBAioZ8HdDxNorw40r5bKa0yBDfn0KLYTnfjQcaKPVav34ANyEZAEA3p5wbnnP9JurPfewbXxxAVgbSmAID4qi/V3h36QwPB/TuPTA94N5ibkMAPksZc1G13Uq5TK2gW50AgC+G/RQMgKKTjAhZUPKOZLyWWWf0fdnnnkG9wwNee+PngelBHCDWtufwvq1a/P+0ULaadGEyqnEIgvCD99fLkQsFzvvy1VakdP9FETl65rFx49CFOWyWRzjwkdEg5H7HIuD8fkeoOaZiLwQ9LubbMdV7sZWJHwr+veI7Q+x81AbSmjCrKVCZiJhkvARMcmroOWO9yPPGfYpR2lr2pbLFYxI3nTiNvORoZO8v3vRKLz2s9PyWY3ypxmvRxAAcMcOWTrH5b27CwGiUkY+kqRcbkP4u3xevH/scM2RkQoLy5X9VAg/chxn+Lhc0ip8TKtDuvBxFwQY1LHhrDXOXHmxdsiv38to5t+jDeAj/X57wrnlvKHf0mDkHEbs54BR+PQ1aOd4r0EAswCWtBjhj4qRUUpEJMaxenH4ODs7G1ZGSMLHd1tzPr4Y14mN4GPUJolT5vuHPvQhfMUrfsB7j9HzmAgXboYS94/C+t39oREP7hkaakh/DMAuQbyq2MsvdUnbmfPz85bhIVca05BYdYDbsAUA3FQo1Nn1imJgcGZ0Elo55WHM/NoE1rRCRN7ecdYuWnV7xzveYRmAZKDnwRjB2rm1eyajzdWeB0EvbSWvXwR3CLQWMkWhocSA7br+Bva8hiHKKZUGgMsDTcqsj9GcsyjKMRGAqbkpgaKXPftDDbRFjolDEM+Z557rD37wg4ljlYjUpiE+fn1h6/3ifuS4dBHHuIyiotI/PUGAe4aGvAy/zczzTGxJ03caPhJGkoGcFh8LuVy8ZF0uF8OFNjCLUWnwEdEus6jN09vq53ET3ExNTSlkZEb5jPKt3eXAbE93ksLyYLiPq3QLV4T08D5Xfhph5B133OFpwzo0Xo1hjBhwL4i26B5oiY+ua9x+++2J/W6I4gK0Q7ypv7nX+x7UPD2uWrl2zrzpt7GxcTx+/LjjufG2UV1ho1y6SxnR/hNoFi+48ivrr9vhmkHQwzxe8txFlMp0EPTg0NCeDB+XSVqFj2kwMge2/hjHR8KGHGol9qrVUattUcivHIsSD/az+dcbjlUfPhbRGH+ESyXHubWxTUabqz3vRT8+JmFHOh3Sff0NYT9fi4ZwjX7nda2XGh+3ofHaS6O8FyOjmQzZxvDRjIlDGOXMR/gYea3T4OM0xhdXkvCRY2B88aYCgao/FnK5hvXHyshIqioRjcxxkszo9kjazqTSTtL4oXAgUtq2KqDI8xA1z+bNpRJOTU3hiRMn8OGHH0YAE34sB0seACsjI/U28ZXTNIRjclvdsGKrbgT6+x3HaNsq5bLKaO5TsMmzrhnUvH9c56B6roi2xz/NywvYtYiohLevS7SjCIZd3fUMXPeZpOwnHVth+yV5w6enp2Mkblo+Kx1fhKgWOeWsy7EaW5BhbaD0BRmWM5xAGsfHJf3vGluyn7xjOqVC2cg8z8SWNH3H8ZGe9TSYEHAAE9nSLD729/Xh888/j4899hgePHgQAQz/gywdJ/ERMZ7nlTSWJuvXS/ZMRN7h/RgvuyIVh/j2crkiyHN8oXlcaSxgshdYP8fu3cOxRQjbo+Vjqc2xfciLw9t3NWqKfam01+p//326FNv3e46voVHq3Z4eiY+IJndTZwHfgUFQqJPZRYzMMsxyHKVyOT09rbIzBwEp+aeUe+H9Q/+7xpbsJz9TfCvneCa6tAIfJwDwsZQYKfFxU6GAzz//fD1E/Bd+4RfCeUvEX3Fjk4+L+PxMQ8qoG1a259aNhdp5ymWJj7QI5sOO5vERAGLh1c3hI7WBqgcgRosXy4GPD3uOr2Cz+CjzzqNzdLC89DT4GOWgR+kLGj7SPdplxwCCOvN4q/RHLbR+MXOcJDO6PZKmM2liHIEox5g+W8O/RGTxuwooViDKQyRD98Fw8HQC4OaeHrVMEkBkDPLB4vKyasRDeTBgzQcdGVFvC//yEJsbQiK2pLBjmQehGXlFABwBO3ciD4ZNnJRw14S4G/SJxNsyNDhYbzfPlapCvF51bFUYjHd5BOJe5V4A3AEQY4Sn+zgLkVK/fs0ay5u2r1zGziCIMYoT0ZuvREHS/U1B5BHPg9sbLhnW6ZkM795dZ20/FY63PDvu+eeft0ivqmDGKhnfEqQ09vJPf/rTdTDU7qcKZgFjK8T7lsa2awFi2nE+GeLfqnmeiS6+vuMLT7cqcygAU73AhY/DbJ4QPr4dDKZOgFno6hPEi/wccsGMK5UcI5PwMS37r88zYULk4qy0vOZ0XJHYjLbCkkcT/jiB/pziZIUsCDpj4YNxEpsqxmtWyzZ0oPHg97J9dqCmRJr9IoVXetPK5X0WI3BUC9Z3jzwPWlM6p1Hz9nJPT7lcibWZnofZ3olGST4W/s3Xj5udnQ0Nc6mwVtGEoseVS1rwlM9ZJ43j91JFkxKwVVyHFjqai5bI8HF5pBX4eBai951Ph5T4mAepQ2oGVrRgxsuA2nimexnNGNXm30/Uxz7JwMANmDQujQc7DT4W0ZCdaRi5NZwLSdjx7z3taGsSHwtovMsjGM8lH0E7JNuPj+6omzT46PPGT2Gz+GjGLV+8OYUGw0yOfHp8jNo7OzubgI/yHo2H/iQYfX2NmBOL1R+5/bDYOc4lM7o9kqYzCZiq4A51Jk/KfgDcqwyQDjC1hrkHkXKZk4yqPJh8Xu5xdOUTU0kJCeguNtPYAA4NNR9hlnYMyYkTJ/Dee+/F4bAWt1SOte9OgwoiNvOktnAWdunx14hGKKR7VJzrUMJz0IzPubk5nJqawsrIiHp/4+z6VP9cKvu+smBvhyj327VPX6GgkrhRuM0RpQ/Iy0/j6Bd/8Rexr6cntk8RjCKgkUzIsJzxsTHsgKjGumzrEQA1r1161an/qZ98ZG9J+VjNzPNMdPH1HTdsR8EsbEmMJA6FQ2DwbCNEZIL06YdIsSR8nA6PlWOHFrXo/ENgR+4gxjHShQeEj1GdWzsnkbDO55kYGoqXGZPM2YcPH2aEWpqCzL8n58BFBqlLITtkYaTt0dK8Ct3hZ1ycK0BbeevBSLGKrn3ixAmcmppSatvS/RUx8gxRyLXsd77QkkzQ5Pq9UOhTSYqicO0jyv3nYuPoxIkT+IY3vAFf8YpXif2McqnlRnKMjDxC3Y62HkE9r116jKj/00VLZPi4PNIKfCxCPBLMpUPuUPCR6y7XqHM0jwZLjFeaG902ni2g8Y7KOesqPWXjnI8wy3UcYqRD7t49LK7vwsgkfHzM0w5T1qpxfAzQGN2j4nzp8fH48eM4PT2NMzMzzogbPz5uQoAHPPf49sTfXfg4NjZeT410YeThw4frz0zHxyIab/pTFkbq+BhfXMlBHsfC8oc7ofX6Iz37VsxxLpnR7ZFGPN1JD5ADpGa8FcKB0wtmtVMqf/K8rnrfcqCQh/fDYJOv9RUKuBZ0g69DDmBmWNE5XXnnQ4I1W6vfvClk/6ZPoasLu1n9ZV+4Zw2M0UfGmivUanp6OvZSk6tbc2BCR6lm8KS4Bhn5Wk1h2R7al0p2ucLjByAeOvao8rwD9lfzvtHv7eJ+6ONa3ZOgUgUz9ori+m0A+PGPf9zydOc7O2NEawDJHDHLogAAnpdJREFUJBM0N+geJfA9BRALq9PaSfm5Zx3t1J5/AJknZ7kkrSfHN6d5uoY2d6iCwYgyX5JSMOS8kmFjSRjZzsbXWQDcKRS6ShjW1hkEWCmXERHVEGIe0lguV2KlUrTapKXSzaLOLXkUuMLmq0U9i8ZbLhUyMtYij2fco8X/p3zDkxjlIvIcPMrLe9DTFgoZ5V5ZWwk1NaYp1JrX0S3G+if+vYq69y3ASAF3Kd5Jbebntuvclko3h16g+Jjo7MzHvrsIqvjcMEbLONqG81OMwd3Vzk6MPGbxNkbGOouuCPuGG1eLneOZuKVV+MjJXV26BUXp2azlnP/FTcpHHkouEZ59WBlfRDqmeZuL9XkdBD04MlJRzqljJNUAJ9Ew0lRWiL6vX9+JcYxMJt0ymOQqFzaKzePjpLhGGqzR8NE8D4OJcpGkhOnxMUA3PlIIfLP4SBhZsNqwcWO3io87duzCUmlvbJsLI218jPfNpyBdpZ0OMDqofL+nsR9aMce5ZEa3R9J2po+9eUM4KE6lANZRiDzmFJauheTmwQbd/r6+WLsotFv10ra14br2dtXb7PJ+z87OOsPFz4JtoCPq9Zvz4TFJBnZBmRB5MB4v3ibJVt0PgL8e/u/ydGse3gAAZ9kz4yRhlJOf5Hnnk1y7p3mwjUYKHUMwhvs0xFMMQDmG97XP053UZn4+LUJjXXu7c4wdPXoU3/SmN+ETTzyBiGjl/JDIFIdZMOHC/Pq+ubMFohQKZH17D+iGOH3PchaXR9LmLPrIytJi5DXgx8cL4dzqUcavZPRPwsg82PiSA1OpYQaM95v/NjJSwfPnzztCIc+ixgqr1SY1ilZHuO0U6spPEfWcuQJG3pMJ5OXBzKcfAc4jD3vWPTkuRY4rkkRmdBsmK2/T4XefB1qvSW36roZ6HVpNIauG2zsd13HlQWuK7zF015ttR0nElMsVcPfuPXj77bfjwYMHY7XdXfgYtWEWDTFddH0fq69RnLmnscaey6es50gLR5nRvTyykvh4AaK0LrtWtz6mZVRGFNqteWoLGF/c4lixwJoX5WYvLCwkhIunx8i4Ye8ysAuoG9X9uDz4KEnC0uKjK31oXrluEj7SYqg8hvDRFV3D89LPOtp8G8bvPT0+jo2Np9IhbXykezyN8ftxz5scxO2ZGkREzp8CW3+sAIuoyzzdyy9pO9MXdj0RfvflEawTgKol+zdKIEXkPhOO/SfAGHr7AXBDLpfYPp7nQGysrvNKg1fdx9EndI9yQtD3Q2BYBvv7+rCQy+E9YOrs3QNG0V4LUPc8Ica981WIQrpltAEPRzkEURg/eeFc90H7BuE+2j1pzIr58PyuvqGSIW8X44jvq3mPtbrm8vwUipZm3NJHi7BYv2ZN7Dv3fLuePxnOJ0+e9I6R02JckPJwAIzhs0+0pwMA95ZKSzLPM7ElTd8tLCx4y3KlxUgfPiL4V78bxch7wnFH8/AC8HDzSKEIgp66shixVeuEQbYypyk9pGRIhS3ZA2CMracQoCM04CfQKGC3oVGGrscg6IzVeY57n8hDLpUoUlapfUbx9XtjZS6iK9+46rhu0XHeChqljvKhNZIx23us1zWX56bwfE35lQoy3+5aODD/y5QC/dxhviLDx/QLB3MYKduTsfMRPuaUObDYOZ6JLiuJj8gwMu7pboxkLyKIdJGfTYRz/Ccd89oYapJHYnkwUhqc9P0QLj0+HkKDNaQjpcVHdNyTa/HPhY+18BgyrtPiYy9Gud5J5+ZRj83joyl7Fn2P5427zz05OVkfQ2kdT5oOKVPZGtUhM6O7hdJIZyaFXfNw5qQB0rVxozVIJMEPrdI4va5hSIQW1s2J17iXls7RSJ6DrxyWDO3m+5wK95l09Al/sZBhzkO4AaCeG14UE4Z/JwNQ884nhaPw8G4AwNFqFS/r7U0s59bD2izvSftO+Vb0QuThLZQnvgDxkBhXX0tDeB8YpmcAE76tkUPNpDjvk2K7K8KiwL67Ih14Lg0Px0XUyeb4Igav383vVd53EUzu+Gi1umTzPJO4NNJ3lZER7FHGYyMY6cPHpyCKAEmDkaPVeMhkEkbqCqyuLPpI1eywRb4PGXqTqCvI6T0Aes6dbQTa3qekXPA8cg/X2Ng47tt3S6jA6iGj0XXPOu5JbqPQzUPsulwpJAIo/vxcniRpBI8j5WzmcnFyu4gYinJPfaWSnhTbXQsHVUz24sVDygcHh+rvWp1srhej/NpjaLxgtyr3OorERLwlxMfLenuXbI5nEpeVxkeE6L29s75QSJEQbmxC1MO6o3nH5wB5an0GcsQjgegnnmwdRrqiZJYaH825qtXRlPjYhgCfc/RlM/iI4bMaSOxnHR9n2DiRJJfV8HheGmyx+Fiof+cYqeGjqes9XB9HLv2xwubKUuqQmdHdQmmkMzXDriQAcg4MqU83KGHTfX34kY98JLY/glEAi2JgJIEuJ7CySLTAJv7i9PoUlpkX7euFyPibmppCxHgukqs+tPRi8oHPldwd4po+43/n9u04PT1dz3mW9xiA8dxzA3Bubg4nJyfr9WeTwlGKYEJSAKIwvIWFBYsYbRsY5uUJMEY39yxzY4BW1s4q969Neh5OzV+mSS/a06DXNu/p7rauR+E2+zzn5Z4/38v+pPL85byQ93rrLbdYRg/tMw5RqkVR/D0GEQu9fPY0VyYnJ7M6tMsgjWKk5AmgXMU0GEl8CT589JEscowsMC6JNBhZBRPqmaRoTE1NsVX6Q+iqf2qv5Gt5luNoDCceRp5MkrV169WsjmwVde/IAHIFh8L7qOxacth1gADb6/OMnq0eMvop5Ky2UZslEzIpeGeVPtCUQlL8ucc3yRt8GtPVNg8wCtceR39pIu7JSeOVi3sT7X6L32tfX7+jrdQH1I9FjNINZAhwEQHi1SkyfFweWU4dkrhyJO4dAf7u5eMr2dOth3Vzg07mJHOjys0jIXVIVxm8xjByh7huMkZeeeWrsRF8pPaS/ujHx6vq84yerY2PHQhwPxrDdAJtz/I4RrhPOeNp8LGI8fD+tCUon8TG8BERYJ/n3I3g40nr+SeXJwNLn+D6I5GjNqtDpiWbzIzuFkqjLxsCCF4rrhICpFQMuQFSBFMfeWhwUCXP6gVjDL4XjJHbAUpIcVhfmbcjKRw6D+7QZmkcjUOU5zC8ezdOT0/j008/bRl0dB8uT6c2yCkcvFtcc00up3qWqSwXhTwlGYF1QiVRNst3HP8uFRNibXSFonbncnXiJfnM+YTnnuI9YBYuSspzpYWD9WvWYB7MgsST7BnylAQthL23rQ0rIyP42GOP4eTkJA7v3h0jhyo4rknXSxthcQDioTuShIJW8XnbOgAso6cAZs4gGNAkA4rnyadZ7a+P3QSit2bneSaRNNJ3hEsTEC3UzYEpF9elzBeJkRtzuVT42AsGV6z9xCJcMxh5BCKGf5fyMLx7N05NTWF3d48Yj8V6nWeS+Ep+FW2jqRcBdqIsx5XLrUEtX3HNmvUiNC+dEWh/XMccRM14JIlYbfWQ0VyuO/z/LNqhn6QwakrwHjThjPF7JqXYGKd5NArmkxh5nNpRN/bjjLzbtu3EyclJUROY2miHYBqvFeUsyoUDl0L+GJLyLPGxVquxOrXUtiLqXqFt7NwLCHCd97kNQS7DxxWQxeqQc2B4UPIp8LEQBNjT3Z3oNHkvmJKrbcqYlgZmMg7Q/CqK34+gryzZ7t3DODU1hbt37xH7VVFj/PdjJKWL9MbOZ8KWdYyMWNTT4WO5XBFtbQ4fo8VQX99eCPtSlmFMwsdpNCRr9nON8PEpNFECd6FZCO1Aw3RO7bHxMQh6cHBwGE+cOIGDg0NhOhGd/wgaHJT93Aw+HkBjmNsYWavVcGhwEPNMhyyCHnm5jZ18sTpkq/ExM7o90ihgUijMKYh7HQNlcPSEYCo9xASg0ljj3/eC8QLybZWRkfrgkGHdFM58mu3fBvHV0ofAhGVSCZ41APgTAPjR8Lf14aeu9IX7a0RbPGdNls5KYyhdEf7Nh+H29NlUKOD58+fVe6QPDwml/zsdxp406IugK+hcHnvsscTrvubKK+PPJRwLRCCWlN8eX5GOJj29IGQNzw52jXwD/SvJoTYp13zhhResVcSk8+8R+/HxqBk4PtA7Hf5PBDOcNCtNXttp0MPdWzHPM4mkkb5z4WNajKQIGB8+VgDwPChRNewFyvGD8FGmsHCMPAVm4ak7nHedG7vRMsSgCzvqxDwa43ge+/r6LXyMl85yKWJyLuawq6sQ21YobKrjIyKmIOGaZv/vZ+3scIRCFlFT0LkQPrqueeWVrxH3MYymXmwNjcKY1AezKA11Cv/8+Mc/jjYp0nqMyuaQQZ50/qgf4+fZiwCDsW3V6iiOjLwObQNDO7+dxzgyUokpc7aRk8boMc/ChJ7TuR3RF5Dh40pIszrkI03gYxqnCX1GAbAqxiTnG4iHdVMoswzPbkNjuHHvcicCXB/+bUeTJ306/G19+HHjI0AQy+mdnp5W6jf7PLbm/I888oi18MkxshF8NHNMLoY1jo/xxdAkbzl9SmhwbxojksWkRQJ70WNsbByfe+65sM8lXuXDvxX0R/XwDz/PKALcHPu9MXyk88cXYThGSh3Spz/yUPNmdciJJcDHzOj2SKOAeebMmTrg5SBuJCUZW/xh00rlIYg8ml1ge4KrAFa5GhIaoC6W7jvC/9eybXwfXp4ip/zOv/McSLqn48ePW2FS9HEN8v3h/0fANi6v3roVn3nmGfUek/pVhkmTcn1AuSdZxoyMXcmo6Lsu7zcAE8I9BYCHPfe/BaIoAVnr2hdK/wgAvjpl/9LiSAniaQG0ICNZbU+ePIkHDhzAkydPYn9fnxp90KG1jYGVtkCSBvQAoE4w08gqZUUbDwmhlJlS2bw00nccHwOwvdtpMJKUT46P3RCVz+P4eAQMp8HQ4KD1/GkeF8Vx/PsdoOPfNfX/pUIRoO2ZsEOhjx8/boXMXX75K8P/XYoYV/psD8jQ0LC1Iu+viVsT/5NyfcC6N2mIjo2NJ+KjX3HLsf+H0CiW7/b0wbvDOtoV67qDg8Oo17KuhsduR3+JnCeRPDvm/p/EeNirXcu4Vqvh5ORk3UuuM8p3oPH8xL3rXCm3c1eT8135J1632BEynOHjikgjfTc/P1+PymsWH+nYTjCLhKfBEKBKnSotPrqJyKgCwVrUsXCrmOsaVqbHx23bdrDvrjlBC4c2Pm7derWlQzaGjxMY4aO9+JcWHxENR4MfIzk+7kOzMHnYc/9b6pFUmg5p+lyLpAoQ4BEEeHXK/uUh+OnxsVarCY87X7CgSAUdI6UOmVZ/BFicDtlqfMyMbo80+rKhHMFDEJWs2e8ZHNPi4XbncpYiSKHW0qscgM0WTWHElZERlaU7DwaAxwHwbayd2rkBjDKrhYRXwc6B5AN+LZja43IC+F4c48r1etvacF+5bAGYywjcDFHIfQDuXOrrr73WeOJF+PnNpZJNsMT6ebRajXnKHwKT53k9a3Mh/KS9/06IRwlw8YXSA0RGvq9/XdvIi5iUx3L+/HnL+93R3p58XQeLvQ/0CKTn5ubw6q1b6/W6KWSYUhW0Z89z4uvzLKHmYqZUNi+Nkk0SPgJ7no1i5DYxp9a3t2MBGsPHWq1Wxw95XDsYvHgM/PjYFe6/EUiR1JQayoHkBlMHAnwA7dq3aXLjxq3rSPItEreSs5n9P4JaHt+1116PU1NTVmhlqXQzVqujsW3cS1atjiqe8k4E2IJGMTvG2sCv6WP5zak1XdMZ+gECbPDsV0N3nyOSp9yFkQsLC2G/pPfu0PNq1NN98uRJnJ6exhMnTuCBAwfQKOmawZ/HHRBk+LhC0gwZ72Lx8X6IE7BqXvK8Ax9pTE1PT4eGpFzIyqNZQCJcexvquJcPtxdQ5xroxShUuhF8TDN/W4eP0QKcbIf5fu+99zaMjwsLC8p1KUqghFHI/AjqKTiu+++MRVJx+R//4394jm2kf13bkvER0eiQkqeivb0j8bqaDunlGGL4OD09jTu3batHs/p0yH5QyFRbhI+Z0e2RZvIVj4FR1uj/tGG0eYgTcRFr992e448fP45TU1O4t1SyPDJJx92QYh/ftWuO/ycgDOGGONBruej5cPC7JpJW33p8bAyffvppBOW3Lex/8l7TRNNeOpWREYtwrpDLYYfYn3tuk8jBxiEqQyDD71333w9xI1WKL5Se2hE4zl+EuFebjnsbGC85j2yg/k3KY+Heb18qA4GVXKh4io0RLe9WY98PgOfT6hEYrhrzmSdnaSRt33F8nGbPrFGMLEJj+Di8ezcePXq0YXy8PsU+ExAxpd9W3z+t16QHTYkZLYdXY7gl74OfWIi8LFGot1Tcrmf/t4XftTzBAEdGKp464rZXwkcOZtp7NRojeEJcU+YBUn5g0TmPfSzI8TYESh/3oAndrInj1qHJQ/xdqw8145+Ee3eiHE7eNj1v0V6ssMcD9bPOLJ3HuIcs3vcZPi6/rBQ+YoiRb0uJj1GKi5yrLjy7Hk3ahW8/30IXsW+nwUeT+mLwQGJklc2tZHwcGamkxscoPF3LIw9waGhPw/iIaDDS3edUXSGH9qJHB+r50+PIjVQpZmHOjZGbN/eztmjvoCLGCUEJI9+GxkvO3ynJ+Iio65Dxd1xUjcOlQxbBzYGk6Y+UVsn1AKkTbAOdyDjzdC+TNJOveAEio5sMEa2sTUE8cDImfleA45PiXPS5wI4LwK49Taujp0A3uAAiA9V17gOe33ne9F0Qeb1dLwktZ5krAo3Utw7YvjMQ5UzT50ow4dqUFy7b8hDbtyHvMJt8mwqF2AqyFhrPw++1+yfl/qPUp8qKmi+kPRf2yaMQX+HWQGUcAH9d2V4EExGQNs9Pts2VyjA7O4uIBjBl36wFU74m1r7Q4NfY9/NgSLc+Go63fJhaQREQWtm+LGdxaSVt38kcanrejWLkITEvffi4AczL1oWP1B4tp1u2UcM/Ihh8sL6/y/C7C+NeHS2/eAFtBXCt2E8qKK7arVyRO4tReSn6BAjwJrZvkgfEVZ9XZxwmKZcrGJHsUPsOsHviSuYCO68WimoU5CR8TPKQR204q/RxINry60obOtCw9upKtEvibdMNAB4hYKcntMe+kzKrM0vn0Si/puZwEORxZKSS4eMKysWAj5GhVWRYQmzZF9Cd0w1ooleScO8nPL/TddLgI6JO1BZgxKjdWnyMPN2tx0dExI0b8xjHR408jYfgu+7/mXqfahgZ1VrX78MQXKbBxySMLIbHp8dHRI6Revm2JB1SppUl6Y/kTLsLdB2yUi4vOT5mRrdHmvV0S6OTqOv54CgC4KcA8F2QbKi5ymc9JI6R+5yBZEKNCfCTb53w/M6927zdafMtKuUyjlar9YF+SlwvDcPgMXCwdkPcC05t0cqWVSG+ukXtmFTaToAmQ77nwXis80o7ZPj928F4yPpEO7iRKoVCc6UXm54x76MaRMYAPWtqTw8YoCoktFMuMFDYmWu1b3xsTE1lKORyOD42FpsbvO66DAWSoZa+vPl95TJOTU2p5ckkCLdqnmcSl2Y8OQh2uToNI6tgFv42gs1pQVjjw8e7IY4V9DthS1Gcl76fhngOmAv/CCt8tbtt5YkrtZoyaj5jY+NYKt2MceZZuo7P2DyGkTKkKZ4Vtu9pdvw82uGUvO3Uhkmr3YSPNns6GZyk4Gsh+HTv+zEKY9UVMCljY+OeOriyjw6x83Jvew8aQ7cg2tiDkUFgK9FJGBkZyPZz4CXbonbyusLmOrzEV9q8+XK5kuHjCstqx8e31485JMaUL6ebp4mkwSCfp7s5fCyXKxclPiJqhvA8Gu++Cx/5/b8dATZiPLLFPBuXnuYOpdcWFei5UO5+Gozk7UyPj4gYRu3YaQi5XCGGjy4dUsNHn/4odUgNHyvlcsZevpzSTE43GZAjYIc+UCgiZ7gOwDaAKGeaVlra2LnOgrt0hPQQa+elfG4ahAHoJXhon6JyH7x9VJ+6kZrOfILIgU798RT4V2mHID1rN/2uGeg94XZXHfEFpe0UrkNt89W8TgrFl0aqJgsLC1aeeRWil6arj66AeKSDrwY69/ZNTU2lUtKIkCTpeVMbNa+iXJlNE06vEd+dO3cuUyqXWZrJWSQcK4CNLRshnkIAYBazXItqhI+EGWnxEcHkt+bFefPh9WgfDf/kAlUABkOudIYuB+jKD3YphBIj43lwdJ27MFkxnUZ/HdXT4d/97Dc7J9IoVKOo18iNvNSUQxcvjXMB09W8doWaxhUwTaJ8at62KhrlNKmPrsB42KSP4TweXjk1NWWFecvQyoWFBS95UtRfp1AL4+QYmSacvi/Dx1UhrcbHbmgcHykiz4WP5qMZuZoBmA+38/EboJ0SQgZY0u9FfLniI9cho/b52iKJL3kbzbPp6+t3ji8tn9rUN0/qow1oomcaxcj0+IjoJ7VrRIdMoz8ODgzg3lIp1i7CSF5tKQ1GZkZ3C6XRl41mQPKHt1l8p9DiJINxeHAwdq4A3KQYaT3Es2IQytDsIhhwJpBvE7/HBiQA7gD75bAGTOgSp+5PCtWYnZ3FIXGv9Em6jyHQFWo+wfaWSljI5bwGZwkUErewPwphjWA659Du3fVzaeFgsh08/J5vn1baoa0Gzs/P11kYeR14n7Evn9UjnnZOs2N3bttmhehoz9AHcpQ7JV/29N3FnJqUS5uHqMQbtau/ry9Vexc7zzOJpJG+01aSC+x/Oe8HAPC4Z3zv3LatPnfT4mMajKT5dVZpF8fHPACua29n+2iheDtQr/McoFFqovJPrrA8Y7hV2Hn5dZKUnylMVqpIMe1C21OknZPCELmyWWS1YLX3BBnT+z1t6UTbozNttcGFj5Fhux8jBTENUzB/Vo942jgdO37btp1WmLf2DH2G8sMPP+wYO0ese/Z5ujN8XD3SSnwEsHVIKtXpwrFKuYxHjx516o9RhI7L051u8SkqZ0Wfajh28+G4lmObPhV8OeBjENj4uHv3EDtXmlJiMgRfxySXN1nHSB8+apiUBiNbi4+N6JBp9EcXb1MzGJkZ3S2UZl82Tz/9tF2SCpTw2xAEXcbK5ORkrLQNecpdg4lqyvo8xJLtksqS3TAwYDF5V0ZG8LnnnsNd27cjgbYc9N25XIzVWu5D39OsqlOpg5MnT+Jjjz1W9yJJTzsZr17q/zA8hL/Mkgx013naAPADYiISq/xdKY4H0Mur1ZR2aDk542Nj9XqDp8JnSOH/RbCjFfJgcqblAsJ1nnYeAnfYeqzdDYBcElN0f1+fOg7U/ENQQuDD77SgwhckXO2VkimVzUszfVer1fDhhx+2CE3k+KA6tElzll7Gx9i+Pny8wOZsWozcCG58jK+OB2iMx7vQeCCeQoBu7O29LHacbaCaue0joOGlYE6cOIG6F4mHQicrz0GQx2p1VCHlcilU+9XztLd3sLxAUjg70IQhEpmST2GtoCwfFM+LdOcsjo2Ns3rVF8L75kRJvaKP8mhyy6WCfJ2njafZ8a7QdVv59eVUlsuV0NCQIe0dqpERhawzkjXI4xgEGT6uImkVPrp0SIo0dOLY9HT9/bzFgY87rZzup9Af3h039IxhvBG7ugqxNg8ODuPs7CzOzMzg9u27kGNd9OnAnp54JYNW4GOtVsNt23aiHn20vPhowsDXouSFyOXWYNwrn4Q9ZPS6MNKNj4gSI0+hHx/bUA9192HkIWw1PjaqQ2r6Yw8opcDYxZrFyMzobqE0CpiUt1Apl7GQy1mriUkrL9p2esjjY2NYCAIv8ZkMO3JdTzKmk9JL+XKyxh8NYDLutEG/sLCAJ06cwGu2bsWeIIi/GIIAKyMjVj8lvehJNPKEDjC56TSZnKzgogSXUVKTDWNX33ZCZPDxCVpkx1L/aHnX+SCwthc9z533FwDg1WAvaAC4S6Ld7jg/T1eQ7aT+vd3THwTsxBIZsHOegoikQuZ0p7lfRD2cPgDAUTALF9w4mgfjFeX7jsv9spI4SyKN8l5wUifCyFOQjFe+sUP49G7PmJX42AhGpsHHY2DSitqFUtkObTharWKtVsPHHnsMt2/fZa3+B0EPjoxUrH7yiU6+1YHGy0QKlMZ4a5QirsRS+/xGp6ZsbkCjvPHfKHySK2lEbCTb0qFsk+fTPTmR55euVxT9cRY1gh6A2x33SMqmKz+c+vh2TFLAp6enBcs4GTcPoTFqDtXrjif1uZbHrofTBxk+rjJpBT62SofcmDP5vxIfz9bHpmYQN7L4ZDgXJD4iJvMa5HIFrFZHlwQf7dDzlcLH/WjzQlCYNscr3QBev74Lg0C2MZlnQo4tc/6rlWfswsek+/Rh5GLwkS0kCs6LtDqkFjVSd/7Vx3y0uL4YjMyM7hZK2s7U6OmLEHk1fcRiG8Cmved1qeUASlJO9wPgNeAuH8WNtgoAToFd3okLDXYf6HMPkGufmZmZhnPK5L3fD7aBmQvvmW/bs3t3jESG7mV4cNC5ApbUdrp/WgHjYdq18PrdYIe/UDjY4MBAbHsbxD1vSeH309PTdYb6zvAZa0RPRDBB2087xtuVYJP3dQBg14YN9fN5w2+FwfEoGINDLgqMVqs4NTWVOP5dYFYpl7E7l6vfLyd84yuVam13uV/myVkSSdN3rvJvSVUL+Pggdn4XRs7OzqbGx/sA8Bcgqvrgw8hhMISXPnyUc0Yje4l7yd0eT95PPs+OXZ5LKnGAJnwzrmgNDg7j8ePHLQLD6elpViZMy0335WPPst94GOIuNArVEbRJiALctesGsa0NTaimrYBJiUITq+FxVIrslOhrIiijtroU5CtRW8jo7u7BXI6XQEr2ktVqNcEy/lmU5HB9ff149OhR1k+6cqpJho+rX1qBjwit0SEpPS05Fe1JBDjOMMO3+FRBE6IdL+/E5cyZMwIj0mBfa/ARUQs9Xwl8lPnYEh8nWX/L9rXh0aNHlUW2+OJBEmO4wcggPKYTzSKAJLmT+OjGJBdGbtjQxc7ZKD6eVe49aFqHnJubw2u2bsUNEOdO6gUWlh6eZzEYmRndLZS0nUn147QHhuA3YCxiDBaqDRAZpidOnFAVRSJIC8CED3eCMU6r4rxkaDUSPkEKjS9kvTMIYmV4tH0KXV2J+RJJK5i1Wg0HBwawEwTlP0RkR9SPPV1dsfu+9ZZbYl5TaRiS57wKdig7PUe6hyEwiykyTHtGOW8FIrb4SrmM+SDA28CUF7ofFNZ6xwIEhd7I87vy6XvBzmHlz5narRkG/PlpZUrk8+J9oLLIt7XVjY20q5Q8f/0QayM/pjN87qfEc5DnJm97K+Z5Jrak6TsNH/PhfEuDj9rY1zDy+PHjXnw8FM7hLtBL7LWFc1Mbc0n4eAH8inFnENS98S5lxoT/6flvPnycmprCQmETGqXqNjSlow4hhSlzplvp/fF9N8fvQNsLQzmGpIwVMAp/5B7uGbQ9LENI3ularYblciW8/7sR4DcQYFtsf5eCHXlxtPzDUdRJnJIUZGqzzSJuE7PZnnudkZzvG3++5XJF7Gcrp1w4PtJij8TxDB9Xh6TpO1d5zCp7Xq3QIWdmZpz4uCY27mmcPor2AhkZWpw3wT1WEREHByl3OTlk3cz9ZN6HZvER0WDkwMAg2vhIHvhOJIxsPT5yg3UIDUZyfHSxxV9f71cTHp4P2/7LmBYfEXn4tsRIjdG8FwFuTsSkZIxsFh/pY4z+j0JzOqRcxHLpkGsgisxcDEZmRncLJU1nesMewu9FBezyEFcmhwYH8eZSyWmYkoKnGV9HxDbaPgtR/qIs0eXzsCJGkzVNeNOz7H9tn6TjZa6kNEDn5+etms4BRC+bIkR1pvMQJ5HpALCV/iDAocFB3FsqxYxfrW85e3knRAR4RTBG5kNgFiWuD3+/C6LwVA4YRXFu+s6ZODUhT3ce7JU4zbvcDyafW1tAGIJkw4A/I61MCT2Xubm5GIN7mtx637irp2eMjOBGQVxHz4LCggpiYcV1P0ODgxk77xKKr+/S4iOtNPPxUQCbpT4JIyXpZBp8XIB4ib294bkbxcc00SF3g1kkS1Zm9Jqv5XIl1nZNwbrlllvR9j4EqOUTRh6VC+HfPGrG4PT0NJZKezGqra0ZtrzGNiBACeOhk3k0OYslNN7rKN/dVsB0pfPkyZOJ47C7u8e6h4hRWBoO4wiwGXVlkwwEl4fnQfH8FlCGZo6NjePMzIxgJ072+pTLtvdMeq54+hrxe8hFI/qe4ePqkFbhI4KuQ1oEr4vCxwCN4SnHaQ0p57hcrmC1Ouodq/L+zCfZ022w71n2f2vxcX5+3uLWiPCRsOcsNoePHex8Gj7ye+hEwjUbH3vCforST1qBj5GnW8PIzWi3mdKWtAVLH0Y2g4/2eSSvik+H5PojpZsl6ZA3iOjTZjEyM7pbKGk6k3I8XA/syXBwdAPgZWIAdIDxtnBDMAmAh0PGbABj1PHVG7refogbZdxLOz09nVirU1spJIOPQjKk55NAvwTxeytCxPDbC35CpE6RBy4V3Uq5rBItBZCQzwTpa30TKdkEGA91J8SZ1+laP8GO+1/hdn7fbeL7+NgYTk1N1ftQGs0BJOfTIdo1wbV7oGdP93E/RAY2V8gOePrjeuUZd4JZ3BgeGlLLzhTBpCkkPd8bBgasHG0ad1p43fWgLzIU6dmGRCmcSEsdAynyvjKlsnnx9Z0PH+8K/z8CduRHGwAWBC74MLIZfOS/J5XJ8+HjU6BHyxRyOYELWtgmhSc27uFBJOU2QLuUTQGTvbonMEkRJh6MOAP5MBrFMWIVNsqYNG4Bo5I09GmLfSflOFIK455g8konYaS/bjW1/TY0BgTtv0W0rYpRXniSYaDlpXfixo3d+NxzzznC/ZNZkgcGbrDCR0dGKnV8lOe8BgLMgZtYK8PH1SG+vvNV/iB8pEXIfjG/GtUhffgY1I1Gd6qDltJC81hiZDz1gzzK8bkTMZVL/CiiMYIXj4+IGBrm0uh0lSxrFB+PsX3vQWOYujD+J9g9DmH8nluPj4h+ojIdH+9X2pcGI6+3njFAJ65f3+nAx+sxWpiIziPHpkuH1HTSY2D0+DwsvQ6ZGd0tlEaMbh/IBWDye2fBhBdrx5C3Jckw9bFPa2Hj9XxklpsyOTlZ97BqRg+FI0mqfrlyRN7mfHidJyEqWUL7jgPg055235Mw4EmpcoXFu3KXp8Ef8gkQEZGRN5lCzfl9FsX3Nra/DAvbMzQUe/FIo5nCAem5pFulTF6JoxC1dynPSFsY0DzyPd3dal56CQyxWncuh5t7etR88Ks9zzcfBM60CR5e9yT7PclrTjI/P19ntUzrnWxmnmeiS1qj24ePhCV7IeJLaAYjf8AzdtS0GrDxJg0+Liws1Oc2nzNy/hF/A+HjmwDQ9ojswWRF5h51O2FMRPCjh32685eTvQ2RN6KK0WKBlpcdlbcCAOzo2IC6Z8UonjK6J64UzqEMWUzCSH/d6o3Y19cfeuc+jAADou094juRBMUJzyJv+hG0vUNbEKAzZGHWiO2uTny+xJJcLldix46NjTPPIiOWCnNrfYpiho8rK4v1dEt8HIXF6ZA+fLynfj13qgMZ1idPnqzrOdrC0NjYOD799NPh90cx8nhK7CNvM4WXR3Wno30Xh4/RwtxS4ePZ8P6SMLIovmvs4AVsNT4i+jGyq6vAohceUZ6RhpHxKh1B0BNuL6DkrjCh9BuwUNiEQVBQ+qKAtMASsCoMCH4dkqJ3uf54yjPOSYfUiIAbxcjM6G6hpA0vD8D2buTBhPhykpM8GI/vqAMYfQNlAkw4ZL8yQPIQzwFCiBvr42NjTuWRD1oyHnnYUhB+J6XxbjAldNZAZDhypZN/Pxm2ZTo8j/Sikrf3SUfbp6am6mFR9TZDnFVwPztuDqIXzyREnt+kl1oxbIeW4zkAxjuch3jIuq98m/SGAbhZxo8ePZo4Dn0v5tnZ2brxqi0E9ALg1nDf973vfRapWwCG8Oy5556LGQzvhohgju/r8q6owAVxMooYoUVI9sLvjdc8PwX6avzU1FS9b4jZX44/yVy/2HmeiS5plEoNH3sgYtGXGLmFPcdGMXIWGsfHu8KxeOstt6TGR6rpSXMiD5FRTSUY28FgpYaP5jOBkfLEPRla/vGTGG9+5Hman58PS+Pwc1NYIymPVMqGFLa7w+2PY5IyGy9LpuV4bkGA38XIk1MNz6WFqdrKMEncI8bPX63fp0t8nu5SaS+eP38+NApcIZYVBHgbAgA++OCDoYEdtaOvrx8/8YlPYFwZvR/t+sRatAF5uLQIh97wWUXGd5y1OZ94b8ccGEn9leHjykqavqMoPvneLECcOJXwcQgAf3IJ8DEIv49BgG1inAZBD95yy61OD3ecEMuM3ciDzcf9aTSG2kY05QQnEOCTaBt5fBHvJC4WHwcH5Tx14SNhJJXwOpg4/yJPdxHdefADaIz3PEYh68mh9o3iI9eJNPFh5PPPP6+wiEuM3IqkQ5rc+DjumTB7wChXfQJNhE9F7Et9xc8fLbC0Q1usOlGSDikjO0h/JH4pnw7JiYCl7p+mxHHaOU6SGd0eaYRITXr/AoizT86DbpguQBwYVQU1CGK1GBdAN4b49eh8APFa1RZhhycc6XR4Xtf9cWNaC52m8CiaBFof0ESiScO9wJSfIc89DlHZrg0A+GE2aaSnqR3cTOEaoyfP8XQ9K9+KMlcSea112U95MHmrUniYltNbIQgeiCU0aYGBFlCkATFardaN9nXhviUwL30Cudsg+YX+A696Vb2eex24wC5ho7WL9+NNyjPkebly9ZraI9nb04RONjLPM7ElLZGaFh3Bn9086AtSGqZpizt5iIzqRvGRXrCace3Dxwkwi2kS13z42G4ZYFr5GP6dhz9GSqHNACsNSVIeN7JzSQW3gC6mcF1Zq2G8rqxUZJH97g5T5eIOjzcGq1YmjEcTmdBsadDmsVS6uX5MxKKcFGJp+kfWzG5r6w3JkwIEWIeRwpvHyDt3W+L5X/WqH2C1itMo//5+TMLIDB9XXtL03cLCguXBI96YZvFRcwL58JGutwDG8Oa/j4xU1IgLk9u8zzOvfhc1VmpjoCKahSvNyCMD80mMCNaWCh870UTB0DUlRrqZwvX71zCyiBE+JnueG8XHcrli7c/xcX5+PsQvuZgSD8OPIhOSMNLGxyDg+LiB3du+sG8nMCLSc5//mWee8TrZuA6p6eHjYBa9fTqk5sziuv9S4GNmdHskbWdqedLaQNAMriLEV3RGwQZXF3MfvTzvBrcienOphIhub6nXeIS4knnHHXfgwYMH68cQeZrP0AOIPFvcI0QeI201vi8kUUoTekUhnEWlnwsAFgkbrWL5ShG80tE/pzz3LScs5eO79qfwIC0aYXNPD64FG0Qu6+3FmZmZOrgOhWDluhdXSBoP37kfTKi4ZEMdB8DDnrECAPgrv/IrCOBOBTjpOI63aRRsbz0xUPNFBl/YvS/PqdF5noktaZVKOabJm03PzoWPHWB7gEbBbeQm4aOmiPZ0d+Ps7Oyi8ZFf7y1veYsXHyPiIn4fFG4YEenkcgXs7b2MhUbHPR1+hdd8crk1aAzrImpehjVr1sf2j+cSupVDgFeHf2V45qnEdkl89OUccnyU3rZbbrk1ZG23FeVS6aY6PkYsyq572e9tt/FuD4T/58T1dnjOH+GjO8z1ow31I/f6SIzM8HHlpREdUpLJLgYf0xjoHB8BAK8FoyvR+Q6BiWjcWyql4E1IGvfT4Xfj3SV8TEMwGJ9ntFDZenw0XvccRt5otyeW4yMi1nVI9/2/UvndX06LS5r+TwrzHxl5HRoyzDhG5vO9sdK6foyURJJJ+LhV9HHFO1buuOOOegnFNDrkKbDfrwvhOM5Dsg65EvgIqc74MpZGXza1Wg2np6frBhYNGo3Mi4dB04dWdBCi2tk0mchLLQG2GA6uTaDXXh6tVhHRPcC0QcsHeA3MKqsMveCenKSBe83VV2NPaFAXwFaW+/v68Pz583VPLp8kxNDqOjcnRcqDXcbLmqwsB4nEF7p9LOGc5MGKRSXkcrhr+3YLNO+9997Eezlw4AAiIu4LmWknwmezH0yaAvXN6XBbN9ikbbmEtgKYcPukNmwR5ytCxAjfC4D7lPNrERxtEH958zQCzdNN0QxPAeAnPfcwOzub+tllnpyll0b6jvCxUi5jZ84oUhOQHh85RvIFm5MnT3rxcRvYJERFMERtvDJEM/hI80BiZBH8BINXX3VVfb6PAHnAo3O0Qxu+bmTEGRptCISSlKQnMVIaeYkaW2FKwke3knUs4Xc7HDQIenDr1musHEQfky3hY7m8L7znCYzCNNeyfjmNxqDNo51XmEtoK2AU5p/Un5JYqIhxxmN5/nnUczkLGPfKU5is9HSbfrTrAudxJ8t7RAUjM3xceWlGhxwaHKzjIydHlc/xodiYStYh+TtWw8cCAL4O3Dqkf/HNN6/mUc9xTjZYOzo2hLnCxxBgBDW+hJGR17UIHwP0hX1rlWb8JGUujCyiFp1z5ZWvtfAxTf9PT0+zsmIUfXMo7APuJSeMpO3m+HK5kuJZTnrakYSPvQjQ5zl/4zpkABAb27/smC+rAR8h1RlXiczPz+NP//RPY1dXF+bzeXzb296G3/nOdxKPqVQqYgAA/vzP/3zqazbzspmfn8fXjYzErjkAkeLlChO63/HgaTJp3iLyfFKNxWOg1152hVLwQZsXA7wHImZfrXB8IZfDDoiAP8kbLcuNyTA3youR59jvObe2Cpak4Mq8lyQSBZnTLn/Pgyn/oy1G0P97S6X6KqKPgfz48ePWSjc/F89Z4X13G8TzvTaDnjc/Dn4m97x4xnQc3+ca0RdF5bg8GMDk91JkY6HerjDEf2FhoR7m73uGcvVRNbYaIAlqdp6vNlkJfERsvO80fNzKnrkLH28H93z34WMHAO4O/58AvWanC4OSwtmLbD8NI/PgXwik3/jc1DA8zpLLT7XfsZ0rvFKp0RUmDR/jyqzGOAzq7+b7DrQV7UjJKxT68PHHH4/ho+s+jh8/bpGMmRqzmpLIczJvwzgx02a080LzYTvp2KT+jCv18Tq8tNDA+6KoHJNHe0GgiACU0x0P89cI1gACPCvGlIaRGT4auVjwERHx3LlzanlUet7N6JBaVQbCxyKYcNzm8IfmhYYBPWxujKMdGp1H30Jg9Bufl3Zt6NbhI3ly/RgZ9yo3g5HdCg5E7OUcH9N4uk1ouIz24d/pWOL1oAWGBxFgIlzcCDBimte4JxaLjxDetzZWgpgum1aHpNRIOVfT6JDLjY8XldH9Qz/0QzgwMIBf+MIX8LOf/SxeddVV+MY3vjHxmEqlgu94xzvw7/7u7+qfRsCvGcAcHxtTjZAg/L+RMCEaZDKHdT+YEAsCxrT5xdoA6wHjYaFr0acDTAjkKfArjS6llPJ7Kaw5ycvLf+cvFde5q45zJbWVs17z/nCRKMzOzuL09DTOzs7aId9i/wBM/vMx0HM8x8fG8LLeXvVe+vv6sFIuW7nWPRAt2JwF/UVbPz9E+SoVsV8RotVvrR4yjU++H++3Guvfjbkcrl+zJnZ+V3/fDXYageyTT3/60xaxW9I55epjUgm8pZznq01WAh8RG++7lcDHNAty09PTKj7mQV9co3SWp8CPkT58PAVRKT8fRkbK4DzG2YClEsOVnUhpTFKYyuVK7FlFuZA2OdDY2HgMH+0yMHF277a2tWjyyo+h8XwUrfOZGrq28d7X14/lcsXKIzT7bmH9wvtEKp2cmMk2YKP8Um4gSMM8wHheplTeL4T3Kr1xLgX1buTs6NXqqNWPpdLNFk9GIxiZ4aORiwUfEfXyqHmIFvkWg5FknN4D8cXHNDpkhAdyXhTVOR3lQZ/yzAN90S7iP7iAAI+x/21juHX4mMzLUC5X6s8pniuuY1oyRkpG8DVocEg/V7U6GmKgNFY7sFzeh1u3XoN6tQjSr86iGx+BXfN+ZT+Oe4vDR5Pz3W6NlSrkll2HXG58vGiM7q985SsIEA8t/f3f/33M5XL4N3/zN87jKpUKvuc972n6uo0Cpo/IaqNnMPBPEaLQRyJFmJw0oR0Nh0CGg0wdYBAZWuQ93Qi20SiveRbiRl8g9u/v68NPf/rTqrdWtk96mfhLRTNgA3CTIml1ppMU9EZIFI4fP45Xb92q3i9vk/ZS7G0zYaISNDb39uIeT743jQd5zh4wxgB5pckwuAuM9/suMFEQhVyu3h8TYIjlYm0AwA9A3LONEM9b5QsX58+fj3nlXS9qDcgoxHhmZsbJtt4BdmhRHgCHBwetZyLLl5w4ccIKkW31PF9tslL4iNhY360UPqZZkKvVaio+FiHCR44P94NtiPPrynDzNPiYBiMjZZBqResKn1v50Wqo9taPt0vs6ORALnzcupVKY2keFzJseduNctjW1osjI68LyXii43p7N+Pu3b5yQfSbfd54bWBaOLgLiUU5CPKsnNhTYV/uUNo/ErbfpaxP19vz/PPPC890UkhupFSToler1XBqagor5bKzLKUW0ZRPwEgqgZfho5HViI/z8/MqsSvHAV/0n4aRlXK5/sybTqMJ8VGvQc9xhryn70E7ykXOg7MY9wRH+/b19ePMzAy7Bp/nNga0Dh9pESEZI92eZ3P/Wgmvubk5PHz4MF577XUKvvjxsVodDQkj+XG5sExhcv9E9yzxkVec6EWAIAxH50zza5ixv3h8LJX2WvdRgSDm8FluHXK58PGiMbqPHj2KBcHw/H//7//FtrY2/PjHP+48rlKp4KZNm7Cvrw+3b9+O9957L/7TP/1T6us2+rK5IVxtcSl9pGC5ft+xbVtskN1cKlnF4F3AqHlTXGESpJweA51On0pw1QDwMcc1i8pA7wzbwdnSidzM1z7yMrnC1WlB4B6IjFo5odaLfq5PVjDGO0Dk9W+EREGSm7kUoSr4Q7grIrS2r1DAjWH+lqstOz3nlN48/hmtVmNjiNo+AVFNdTK2Zdg+fT8Epkb3DpGrnhSSCwA4vHt3jKSDy/jYmJMZ+ojyDAMw4fqkmGqEcz3d3SpIt3qerzZZKXxEbKzvfER/S4mPfOz7MLJWq9U9J6fAUZIJIox8m3JdLdw8CR/TtK9croQhgMR0rit8hg1Y80gRWZpUmMbRKFM2PqZh17XJe8izJL3SVfSFJ46MVGJtKxT6MJfbmNiWiP04Sek8hHKcABhjNyonJtvO88NliKQMSz2EuVw3Dg4OWV7FJEV4cHAoZgySJOFj/V0i7iUNRsaiozJ8XFX4OD425uWxoU9ajJSL/EmErs3go588rYZUis+d00w4MYEAG7C9vaM+LuN1722PdZxFfLH4GGCUspKMkSuFjzMzMxZGGg/57Z7nsTPxvDxUf/fueHm1uLG/GHzswe3bd9XxsVar4WOPPeYcjyuhQy41Pl40Rvf73/9+vOaaa6ztmzdvxt/4jd9wHvfoo4/is88+i2fPnsVjx47hq171KvzxH/9x5/7f/e538Vvf+lb98/Wvfz11Z0YrXwkDyPP7zMyMldfbAcaDmrSC09vWhntLJZtqf2wsxm5NcubMmUQ6/dOifSUwZbkol8KXx33y5Ml6jh55npPq4M3Pz+NotRprk+ulsi6XwwNgh3cXw34ZDL/L8Cnp9W+ERIGXWjvlufcksrIAAPO5nOW57/ac87CnT6bZ/0ODg/WVQH4PaQCOiFeehGghIwfKIgZ7bmops/B5uBZ9eBhw0n09CMazmFfOp5a/g3gt9bS5ORe7Urlc+IjYPEYuJz5a4zEcB7OzsxZGVspl9aXuw0ieX34hnCeduRweSoERGj4mpaWcO3eOKQdcGXQpWP8WjVLJz1VEkz9I938PRnmRkXKU7Om290OUYZanEo9LJuIJMJfLo+2V6vaccxYjAh9Xn5hyNWQUa94Mv5FcE+cjZT2HUkkfGxvHc+fOqWV6uHeJGw1yrvjwkUc05SEdRsaiozJ8XHX4KPlvJHb4MNSHj3kAXL9mjZrHqumQSfjonyt8DuYwl+tEY/heQLdhHJE6GozMhfPfHcJ97tw5sWiWBh9LAmOqGHlqX4M+jFw6fDyY2PbBwWEMgoLVDwCbPOc97OmTaeQLBhpGNo+PdjQDf6+piz0KnmnzpdU65FLj44ob3ffcc48YOPbnxRdfbBo0pTz//PMIAPjVr35V/f2+++5T25CmM8mgqYLuhS2G36/XBlhbG1bKZRwaHMR8EFiDgIf8ais4fEVGhl5o+42Pjan5wx3huQ+BMeRmIDmP2DXQKQSbPkXQQzRpQvPBfwzSvXQA3KRIa8DOWy7kcrFQ1KNHj+LVW7diJwu91lZ2pXHuYyO+wtF+WqjYArqXfJMybggAfIsc3NP9/PPPq+Nzfn7e62m8TfRvBQBfq7SX+mh8bEwt9dYfPm9XHjatEp9K+azHxfl8HnbXQotLVqtSudrwEbF5jKRnvtL4iBgP33Xtl4SRRYgIDWdAT39pFh8RIsWbQhSlcnBP/VhfGZwJ1JRG4xmJ5+TlcoV6XW7Cx61brw4VZd2zhKgZ577yOVc42k5K+HUY1XXlXqBNqHuniphGiScvVxI++svlTCp9XEGA16L0XFFdb11B7scotNVW0hvFx2YxMsPH1YePF0CP5CNd4BBE1UwkRvZ0d3vxkZ75prAcq4Z9afGxHdowUAkUixiRdc0o499vGEdpKvShevZYn8sHDx5ERGnUXkBjLKfBR0B36b5kjHzsscfwqquuxlxuA0YLCWnwMQ0LPA8119q+BXVPuYaRtMDnw8fI063hQTp8PIB2RFEFAd6NAPHyhpSf7eRUgpXXIZcCH1fc6P77v/97fPHFFxM/3/ve95oOD5Lyj//4jwgA+Oyzz6q/L8bTTUb3o2Abqm3iuwz5scgBIFLC+CCYA+OFJMBJyj3QVnF629q8OUO9+Xy9Ha4waqpd7TqHBH4KX6b9eAi3NGrnwRhteXAr5r7VrduVZ5ADE46qPQ/+XSroMgxdK5vF770Tonrk1P4Pg3lR+p5xRezDjYd+sBcS6EVM/8s61nI8+MIVAYzXfWPYX1S/M2l/+k0y0nPyNck4zp+5K1WA7r0S9hMf+69//esTn/+0/O6pt7halcrVho+Ii/fkLBc+AujlXUhc+EgLc0njfg3E56jEyAJExHBLgY+m/+xQy0jhfRKTlaPb0SbKyWGpdHP4f5v4LfrOc48RtTB0X13cTozqkXOFdiPqIZ0L7NiK+F168zcrfUK5mIZwSHqVSaJSOz5lvQMBrkVDBjSAfmV2IvxOjMuklJKib4ejNoKPAwA4C9EiEC1av/Od70QfRmb4uPrw8ViIbUnODpkyJn9Pi49aiUCSNPj4KACOqbnJHeK7ZiBeljhvzFx0sWCbOTM5OakYtUSgloSPFzBiNW8MI4eG9qCej21SBP346GP/ngjb2RFrexAUsLtbEq/xhQgXRvK2avhIRnlEWqlJenwENJEJG8M+OYRJ5Q0n2LaYQ07iVZM6ZLfAR4rmTKNDLgU+rrjRnVaICOOLX/xifduJEye8RBhSPve5zyEA4J/92Z+l2r+Rzpybm8MAIsOIwr66w4cvAY7XrLXADSIljB48hV7SZ9f27c58A5/SmDTYOkOF8BQkG1w50A3jJGVT80BKo3Yc9Hre14HxMrvqnvPzUng8GYKnw3b1BAEWQF9IyG/cqL58eF/SdYuOSV5hbRgeiuoVaoq59owHwLAYd4Lx+AGYcPUZABwFPUeFv2jroa/iPnwAJQnZZN+7xooPtJJWCumlfgT0+sZnITJgZH1luu9L3dOdVlYKHxEb6zti5eX4mGfjjRvJi8XHAOwSWCQ+fPQRsm0Mj/VhZCcsDT72glF4K5byV0SjhPkUu9PhdzIET6Mh0OlBE4JuK8kbN+YT8TFe1qeIule6Um/Djh3ETGw+hcIm5bqkaJMiPIDGm9IZO5fxQM+gTvBD3yP2chc+xsnY9HBw0z+jaCuaSWHt2nbKedW9S2nxMQ9ufPRhZIaPqxcfL4BZwI7pAmIRkWp6Jy3iLQc+cj0rbmSfYvNKw6Ec6oZxkHBM3CNrG7U0f+2KC8ZDPBvu1xxGGmNY4pRZ0Bsa2uPsy3i5rgBt9m8q11VDG1sgjJrpRd9ChLnHLjTpNrTAlwYfCbebwUe+mMHJK6NrnBVjicblk47tUxKvGtQhq2DekflVho8XjdGNaEo+3HDDDXjmzBn83Oc+h1dffXWs5MNf//Vf47XXXotnzpxBRMSvfvWreP/99+MXv/hF/NrXvoa/8zu/g1u2bMF9+/alvmajL5vRajXmBQEw3sfRalXdX4KbrME8Gf5Pxry26qiJjySMrknX4wOMVp6mIdmoegSUMmNtxiNyCnTyIcoV5m3nfXBGTIQamFBKK1wUTJmzPJjQa15SQFNqKTzb57kd3r1bXcigyU2rcGdBX4meYvdKCyy+PGre90S0VASzWEN10AfY8z8NgD8Z7n/a0c9yZY6PB20VfRjslXEAwDvvvDOx7Um/0fNwjVGNKZqPP/oUQV+wKEByNISWKuCSi12pRFwZfERsrO8WFhYsr3URIhZyKYvBxzwY0kJNfPjI5+xiMHIL2BiRA4Mf/Lxp8VFbbJwBwMvr1+CGn60cmRBMTaElb+3dmKSI7t49rOJjFN55V3j8WdRL00yhljfozxUk5bETozqyRzCq8/skGlZ2Uoap1u5HMR5eb3uV+Xgwvy8obR9G26N0GgEAH3jgAU/bJxzbT9efieZ9Xyw+FkHnf+HRURk+ZvioSbP4+FC93WlDqW/HuFecl9s7pc5dgLswlytgtToa6wO7ljddawYBXiHmEXmIqyGmUDWDNBiZNNdNSTGJkfFSaxSFJBcE6HuUWz05OZkSH7mh3onGo034WAz7civa+Hga4zXPm8XHragzwft1SIlntL2UAqM0jNwPcb4VDR8JA+m35cTHi8ronp+fxze+8Y3Y2dmJ3d3d+HM/93P4ne98p/771772NQQA/MxnPoOIiBcuXMB9+/Zhb28vdnR04FVXXYX79+9fsjrdRAYmlazRahUXFhbquXJa7pY04jSPpqtElhZC6Vup3FsqYYe4RgcYhZCA1udNpoHNWcV7gsAKFeUe2Po2EcJNRi0pqRzsK8qk6QWz+quFpb5uZMQiCdkQ/v5e5fwI0ctkXS6HwyERGRf5MqSXzSRELzbqE/lcfC+wu8BmD4fwWbSz7/w5eJ9NivGghfLwdtF5KuUy9gSBRXJxPUAssoODFo1fH/sjL9Xw5je/2eon331eAfHnb429lwk7L+LK4CNi6zBSI3xcKXys1Wp46y23tAQjCRs+CpGyWxDnLXrGrVz043NkPDynrZQtoMyl3NR3GY6MvE6ptbsh3Oe9GClXtpKcy63DcrliPaeFhQVR6ovacQKNZ5qM+Ug55M/FxwRsFMlxtD1AMgyee5PcSmqyJ4f2jcqj2aRQUP/txIkTODhI7OnSc9Vubc/FiIXscFTZrsXg45sccyXDxwwflwIfI30lbSg1NxYfRICJMNpGzusoSgXAsGnzcWsv+snFxx60PcS3YsToTZ+gztTtxsgknAIMgk4cHBy28NEutXYMI3w8qfaJ1CHd170LI683x8gA457spcLH0+jqjyQdshvcBGppMYow8o477sBG8fEwRFEky4WPF5XRvRLSaLkHLQdmtFp1EprRYC5CxGBbBX1VssoGjawBS+fkwEztkWQb42NjOFqtWiRBebBDH6nsjZwUWv7hJMRXr/iq0howBtKGXA4r5bLVdwsLC/Vccz5JkiZNADrBl9bfpfCvz9N9N+gTjp7TBADeAKAq4yNgcrflb74ceoAoB4v68sHw/grgzl+vgg1YhVwOby6V1DwteuaaZ1hr156hoXo/apEGRCgnx+EAjYfJSXWezM3NqSQtWq1ynyeRPleDCcun70ODg2oZnlbM80zismiMDAI7Z3uF8RERW4aRHB/5/OIY2Qmm3GESPnJM0/BxDAJsU0I1r4FcVBowxEdbASQm32RPd/R73GCMlLIJNF7hAtrkSZvD3zusc0T1eF3XraBZROCEPSWMPDdaDrvt6ScPmbYAXq2Oslq0MmzSblMQ5HFTH+WlkoIrDYVft7aXIcD7EjByKfBxJxi8pgWgHdu2Zfi4TPJywseu+vn4PCajV/IsyLDoSTHnOZFiDxpis8sxl+u0IkNsozaNcWnnmlPUib2ICGgiarT7420G5ERifEEtwsj9CLAXJQ6a7yMI8GHrt3LZx5NBeMMx8sHwHguo56+3Ch/dqQBDQ3sSdcijyvYKAP5GAj5SX2oYKReX0uJjDgDvXSZ8zIxuj6TtTN/KoJVzw8CLG2VpvctaDdiCGMAa2UZlZKSu4KTJQzwCthFZhDjTLjceCciJyGoedGZfbUBr7MZE+iEnzSlPP1E4Dg81oT4rgB6W3M8m437xjLi3+mbQc8KD8CNfRJylUV5zK0Q56jwsjAztpDGhsTSvBQMg8uWMaF6S2sp0t9IXfYUC3jAwEBu3E+HYqICt8M8A4JA4d2VkJLZKqNVELEKUm9jb1obr29tjz8bH2n4aooWdDgA1SqGV8zwTW1qFkZLZtNX4SHPUh49cmW0WIzUiI9p+FiKMjEjR/PiIiDg5OYkB6Pi4ADap0U4IVKyOwhU5cy8pxwXU8yz7MZ6nHCmpcU/MTHieuEJrvreFiput6MZDMOm6BTQGNzEhk0Irwy015dr29AN0hHV/o22kGJs6tFIRbkdDCmQrmt3dBewOsT4ipzqNWkjs28EYvRXxrDlGLjU+FsFEorlCilsxxzOx5VLCx+FwMTu5rWTM0pw5oswrjQCMtp8N/yfOg3llHgcqRppc8wAjY9JFKHlKwYuoLRE+yuoPRYxyumVUSwfG+Sf2x1JH4hh5M+r4aDzTGkb29fUr+JhHE9pdQxsjydB2hd23Ah/N/lq7urt7cG+pFFtEcumQEynwEdGPkYVcDjuawMc8GI/3cuBjZnR7JG1n+kKI97tAMCzNAGAMSfLWJYUin0o5kHrb2vDWW26xVoN8ZaO40QZsIuwP/yeFj4cTVyF6uQcQB3dageXgPjw4aPVhEruxvFctzJLfw4EDB6zfFyBaFLDC0gHwPMRfTvwZUduu8/R90m+VkZHYNdvBANBW0ZYCGKIm3n4XO+MO0BnPPwXxlzN/SRLpSQ0Afxlsw90qJQI6EypClBOzA3TvP+cyUFfxw/PPsWfaJ66v1RR1lUF55plnlnSeZ2JLqzByWnmeS4GPvwvJ+Dg+Nla/ZlqMDADwfjALT3lw42MvRIuQNWgMHxEx5tly4WMNAH/C00+Ej3FllHIcAe3wzn4EOI/xEEhNSeVKXGM5zrOzs4oHfg1GNXPpsxmpBI3Pc2OU0j5xfIAA9yNXaMvlCmszz3E8gtJT3d1t4+OM557zQeDFyOXAx+GhoVThks3O8UxsWS58vACAj0GkA2rnaBQfyfs9JGp3+3RIO+qD5tcWTKpZHyfg4ouBvSgN1MHBYasPI29yUVxfzkstDB2RDGYdHxHNgkBOub8ADbkir7QQJ3yL2kYe8ySvtf7byEhFXLcdTerNVrG9gIY9nN/DUuCjadf27buUc5j/i6A76ZZSh+SfNtB5LTR83Nmgh7vROY6YGd1eadUqpQsEp6en8cyZM5bh4xqo/OMD53oIdhDgfmCrOinKRr0dTDiknBgaSyA3ymhl6SQ0nneMiLGQJmI37gCwwqK7c7nEc8safHNgVtJk/kYHmJDypLBQIpXo7+ur54YnhaskKbonT57Ehx9+2Exw0EPk82E7ePtdJURGwKG0i/6QbKOah21ocBBvFiuTHNT4vbwXjFe+EATYVyjUSzPwcVZfgGEvnKQxR5/hwUF85pln8MCBA/grv/IreO+99+IexgQPYMYgnx+8/9Pm4TQzzzOxpVUYqfEKLAU+DrFjNHxMU1ZRYiR5jtLi41ZoDh8RI4w8BMbDqtU192G8n5iHPh1oQsq1sNBISZ2eng7DMfPoL8Wjs3kfOHAAa7Ua1mo1PHjwIEbKbR51j5Bsv+a52Yyawm5C4LV7dudqDg0OWp4bjo9aeH9bWy9Wq6O4r1xOxEhfzVgXPj7++OP48MMP467t22P7ZPi4emSp8dG1EOeKtmkEHwEMuWxnEMQ87Wl0SN1L3K3MUe71JrKyreH3xnKPEXl+9yGMEyzy8lv5xPPq+DiHAO9C2+Beh7xOtxY6TzqkwUjKDXdjjes3yvEmHdLkpLswslfcg0aA1jp8BAAsdHV5y2GeCvd9MBwvS6FD7tq+HQ8fPowPPPAA/sIv/EJD+NgMRmZGdwulmXwcmQMTJAwUyp0pgG04FcV5xsfG6sZT0uA7GX7/JOh5FBSWrLVV5g5Vw/0LQWB5QAGixQTNiBuAZHCnFwbPHdHYCF01KQOwjXEedlUpl7ELopeRNHAfhXRhoXzCU/h3M55u+vR0d9fbkrR/DuwVuk4wwHb8+HHv9TgrsmRQVz1sjhcpGQrHwX6x9/f14S/+4i86xxkA4Jvf/GaV5IKPhf2sHb1tbfi6kRFrLAYAWPbcM+WtpmGcbHaeZxKXxWIkYV0r8LFWq+Htt9/unYc1SMZHABOZkhYjfx1sLPHhI0W4NIKPiDpGanOFMMaFj+VyBbUw8nYI6jhnK8hF1Fhq40pqmjq02nbzGRsbx87OAhoPTtJ5cmg8OvH2Fwp9ePjwYXbOJBInqTjq+06A39D4FQDcKBTyTX2X4fnz5/Hhhx9OHGs33nhj4liQ+DhareKtt9xindP3TsjwcfllqfEREfGy3l4nQ3Oz+HgWdHxcgGjB0o2PMsTc1Jk2ZQH5OYmAi2prS6z5jfB/3dh77LHHVHyUETN2fnagtpGHg5fLhI8fxigCSBq4j2Ka0Pm4p5vCvxv3dBM+7tpVZG3xYaTE+C689tptKa6VHh9Pg183Pgx6VOZotYoHDx5MxMcf+ZEfCRdiU2JkEODm3t6G8ZEvvDeCkZnR3UJptNyDRpg2Wq06CSnSrt5I4owA7HDj3nA7ebq3gs76TZ6YYREy1N/XhwWxSpUPz8lDLp8EwPeLwesKk0xacNDCOml1iRgbOZhWyuXYiqtqNLNzTE1NYVvYDh8g7IBkBZWHfql1rsNJ3q6cJw+RgXAMjOFMIJEEIocPH04sH+IL8ZJ1sumF7spzeRv4DYWOsN8vhH3aGQTYtX69Os5GwH5pFyE51Ihv61TGUxtEBHOyj/shUgi4QtLqeZ5JXBaLkYQ7rcBHROMxUg1OiHNO+PBxamrK2VYXRpKxJ/Gm1fiIaGOkrNl7FpJZ0Q2e22GSj0JEQGMULgonrKBWgszO6UZ0lStrb1+Her54HsnTEnmhkj3mhw8fVpTpIgZBAcfGxnFwcCjxeFknu1yuWLmJbZDHMQhUfCSMlJEN14Mpp0mG+tDgIF571VXWWCsA4GZxbCP4uAb0vFx652X4uDpkKfER0e8hbxYfi6Dj4zhEOoZLh3wUACsCW8bGxus6ZFSyiow4PYRcj2ixF+ro/D58HBwcCvHlGJpQ8aLzHBE+knHrM5Z3WNjmzum28TEIerC3dzNqFQ/MdxOCbzAqh2kw8tprr0ctD7taHW0JPgYMH7XUBg0fixDlYDeKjwG4Wfg1jOxuAh9j50yJkZnR3UJp5mVz4sSJeigxogFSzVO77brr6kqay3ACgPp5SCj0USpU9P00+AkEaEDJOqlJHnkO7tMQGf6+a3HSIXphXNbbaxGOFXI5tZ753Nxc3VMra/Dx8Gk5QZ599tlYe5P6eQpsA1NjLz8G7lDvHEQGNv+tDQBn2TUp7+SUp9/omUxAvBY3/U7eblefHID4y1kyxFNfaMb1OACOgm0o9ITbfTn39dBdsEGvCHGwqzqeiYsH4T0QL6VGY78AcYVA1pps9TzPxEijfcfLINVqNSc+bn3Na/C//bf/1jA+IiLeVColrpz7qhjwF26zGNkTju/lwMfp6Wk8evRoHS/4dWgBQPYT4SPhyyTDBQp994Vv2+zl7lDGngItIGqsvTmMPCuUb3lKaUOkBLrJjp6K4aNbSY7XyV5YWGC5i+H9QYDnQMdHeg9Iw7cX0mFkERaHj0ljqltcO8PHlZOlxMdnnnmmrhu5MPLgwYPWNRaLj/USqR4dMlaWNObtPYYmhDmPUUi5Pk87O/MYBDIXuRDiRmSk89rdsj85PtqRNsaYlvgYtZXa5ivbNYUS83T28mOoh3oHGHmmi+K3NgSYFfjlx8joNzsPO8LHQ6jhJ8AAAhyJ4aOMIKisAD5yorRmdcgkfOTHp8XIzOhuoTRaY9FVGmx8bMzOrYaIkCdJ+aNcNxKqGZ0Pwe/J8G8BbM+iC4hvGBiItd1L4hEOPuktLaa4liThqHjyJeleeX+6wpzoGlqYEb8nX/7kaZrA4cobnYeHd8rQr0NgCM8GAPA2cX4iK6OX0yS75im2r4sgLYBoFZn6lRjOT4fHy1XmKpiXJjF5t0FUI97F+rgAuheOcmlc/UVg2Cy7PFceXCuXD4Kex3Z7+Ne1GMEVgqWY55nEJW3fLRU+ukIM+/v6sBOM0lef3xDHkrT4iNg4Rt6b8lpLiY/8OrKf5P1IjHTlKJfL8TrdhJEjI7YnBKATr4EcToAph5akBEYlg7gi6SL/CVi/6Urw1NRUWNIrrx5fx83qKJ47d84am9dDMj6WIHlsJmGk732UBh/pvaWNqQwfV48sJT4GANi1cWPiWHrggQdajo+dYbQdl7T4GOVc/zLGI23c89gmWAzQhHbbhibHx+g4GdHDQ8Ddoepxb7AvbcYs4gVBHgcHh1QdslyuhHXICY8OoSE8G0CA28T5CSPJwz4Za68PI7u7e0S/Erv56Xq/apFCUbnHPAK01Wuiu3RIl3NmOfDRhZFkn3zUMRaT8FGSKLdyjiNmRrdXWlGn20fKswbscIcuiMKQJQAjGtCUbNhDIcHK9PQ0Pv3004nX3FsqxdruJfFQcq7Jk+lbFeWrobVazbsyS7X5xsfGsBAE9RJfcrVsGIw311I4RSi+DPHk/VzI5WLH07EawGzu6bGuRd93KsARuyfRLwG4iZdK4X11BgHmwn3kqmAbmLwVNcwVWF5qWPNXG5t5ANzieXYupY5WEM84jqfVbVd/XA1xAip6Jlqdc55HBgB40HNuTSFo5TzPJC5p+66V+NgbjiHX3EfUMXJ4aAinpqYaxkfE5jGSyBdXAh9r4ZzS+kkrHcnn41kwJcf4cdxzIzHSLJDY4eo0h99U3+byFPE6vRTSeQSjfErz2QMBHgBpxNtK8N5SCQtBYN3DpvB4AMDOXA7Hx8aaxsek57QfTBTRPuUcvsirZvAxbbsyfFxeWWp83ACRPiF1yKXCx75CwSKaagQf48YwEYslk6URRj7wwAOJOEL4GBn3O9EYkjx0vReNp72GJmdbxznCSDsMPl7bmh9Px8aNfo6NOkaadqbFR0Dj6e9BDSMBtmEu143Gc34ANa96qXRz2D8ypL8fOQt7uVxxjk+yUZrFRwRdh/Th4xUQGczynVWUYx4aw8e7IMvpXlFpFfNk0kN+N8RLWAWgh+bKPJ7p6Wk8fvy45fEcDllWO8AG4jwblHIVx0UEpw0+KlVxc6lUb3NeXMt1rCT1kn01OTnZUIkunmcs+4nfkzohx8ZwdnbWWuGUAFNkz+M6MC+6CTAe3f3gz888JJ5Bb0imxttCXj25vV2MBV9+OidQA0jHjOsDRnkMGePjYf/LcZYGjE+DXeM4AKXOOev/fvCvglbK5YyddxklTd+1Eh8BoqgeF0YSPs7MzNiKZZP4iNg4Rh49ehQ3hcysK4WP7WDnttG15f1oNccr5TJOTU053xfHwCwcUhghx0fCq06IiDV9niKAPA4N3eggP5L4qBA2QR7bw5Jnrsgjiha6i52rmbGZdNxpiBZ6ixBf8E2T/pUWHzlhVgF0Iz/Dx5WTpcbHu8DoDnKcNIuPlXIZi7t2OfGRKsC0Ah+npqawVLpZncc8H5oL4aMLRzg+piMta0fDqh6vi03Xjoz3p9CVB67pkNFxx9AYxT3sGlegWWyYQBPZsx+jCBw/PpZKN4d1s6UBTwuRcnsHmsgAMq67PP1SQ+5Rd+mQPueKDx8RdB0yDT6STs0xUhvzjeIj2QUZe/kKSatqLPoGH4CpEReA32DhBDvaSzgfbt8BJvSDT74ATA1nADtfwUUElzT4KOzpdojCSZKOnZ+fr5dPkYDeA+blQflM/FxJRqGr5t6JEydwampKfaloSiSJfAFyI4/+17zPAZhwc/mS6lX2o+tPTk7ia668sk4axpVX/jyL7P58q4CcQA1Ar1medmzmlfuhxQXqC62uegCgksr1glJSKVz48BkbXQD4ufC7VkauJwiwMjKScmbHJVMqm5c0fdcKfHzNFVfUx1aaRZ1W4yNi4xhJSuj9wI3O5vExAKhzhqTFR4kfvJ+0RVvXQiQXjpEaPh4DnStCU65luDdAgFNTU4iIePLkSXzl5ZfjBtDx8VEAbJdeKgjw3Z5+IWXxNPj7MPFdDO70IL44qvGAtEFr8RHARFzR4rI8d4aPKyPLhY/XbN3aUnwEMIvcS4mPiAYju3M5fI3C/K/hY7m8L8QLHqZN9b4DgY9E2ObyHpOxWxS/R7wRU1NTVn3scrmSSofUqznQ9yOo53VvTI2PtVotrAxDueCagX8s/M5LPSbXKTdh6NHChEuHPOUZZ9eDGx/T6JASwzR8pDFGOerN4mNvWxsOs9TSRiQzulsorfJ079m9Wx1A42wfWk3ysVp3hmHFvgGfB6NUVsQA3Rr+JWILqVxpzOFp71kSZ0ghJXQH2B6VdmXS+V44NfE/7yf+8RnaXOQLkBu59H8VdDbijrY29R4qYMjaZD6dS3l13Sum2O8QRN4VPq5c+5dAX9HuADsyoL+vD/eWStgTBNYqJ3mTSAl4E9heex76TkQxNAZlLXE57vmnt7vbIpdppv4sSaZUNi+t8ORURkawRzL0QhwfT548mRoj98PS4SNiOoxsNT6uBcDLentVfGkEH11zamhwEGdnZ1M9c46RGj5eAD0XuhsAC/lece0Ab4DF4yPVTMeUz56PrWbxUUsPqoyM4A27dmFnGP7Ox2gNohzGVuPja8S5ZOWLDB9XRi5WfJwIr9EnxhXHR2p7q3RIGZEij4+8xzvQJmRci729l4ltV4Z/kzy6skQWNz6jT7mcbGhziTOVSwI2+l5FjbG9rU3eV4AAN6Ahajtk9QsRYqYr10j3eMqz36GwbUX06ZDaAnEeADcVClgIAkuH3FQo4A27dqXSIZPw8eTJk/UxNjc353UuSXyUJZCXCx8zo9sjrajTTQbfuvb22EOuhgMoHz5wmqhpwBDB7/GkAZ0H2zjc3Nvb8IqklLTEGSQcYM9BfBVVCwvJh9uLoJdHs5gGRT9RHz8K/hwN/uJI4+lOej7yGT8KJqxlJ9jhXVyRSuPBpm1F0L3QFIZbhKi0GCLiaLVqez4gzjLJ2z04MGAZtTu3bcPZ2VlrNdsXXpmHOFlLT9guLW8+bb+2AeDzzz9fz4GVCuqJEydSvfRJMqWyeWk0Z1ELPZyZmcGe7m4nPvb39SEipsZIenlfKvgIALg2CLBH4XEoQGP42GqM1PDRlwJDn1J4bboPwqzF4iMpgxpGBmxs0T03io8BAN6wa1ds29Vbt+IzzzyDiPFKERk+vrylVfjoIk7NA+D6NWsQsbX4yCPmKFWEMKe/ry+R+C2tNIKRNvv3rRiFU5Nxmkc7R7kNba94L0aeX1kiixufZBw/6gx35+2T+Jjs6U4yevmniCb8vVAvh8ivF0WFaga+XETgZcB8UUdR+cX5+XncVCioGKkxkQcAePToUWt8NKpDVhLwEVEnH1zt+JgZ3R5pRY1F/n0jRLm7fIDOzs7GFBlXLeiAAVQaRm4Ad15uZy4XZ8MUzN2I+iom/41fn9i1SeGSuWOy1nUB4p5U131shCh3kT7DEJXh4vdLQFCFKB+YrwbL+3C9OGRt9SJECtwAJL+s9gPLoRLX33vjjZYxS8ec8PQDzwvvBjvPtRhup9VB/gIcrVZxrdi/Ckah1MJAKyMjdSIRya5M552dncWe7m47giMkgNsfPiPXuTViDi1sPB9ut4yNri7r2eVAWR1NoQhkSmXzkrbvNHwcrVat+SDHKb0gEdGLkXmIyocsFh/zQVCvDsFrLksMcWGk5r2ag8jY9+FjL9ie1KQwPh8+cvxoNUZWIfJ2VCEKb3Vh5JOOa/fk8y3Bx0Iuh21gRwt0QPz9S9iQhI8IBiNl+lRlZASfe+45Jz4iIt5cKjkN/0NgvPsV5bwZPl460mp8lBGBBTZvW4mPnBtGW7CrlMs2o/oS6pBx7zGiMZoLGM+xdhmxawSmVNB4jrmn+xDGQ9WrGJGujdf30/BRsquPjY1jtTrKcsEp5JsM3C3iXqRxvB/jpG/mHguFPjx37pzC5k7HUBlFf144QM5KzXERyo2PjeFGQTosdUgXjjWjQ+bB6PxJ+Ijo5l5azfiYGd0eaeZlQ6E1lXLZZqMMB9Fk+CHFT5abUcPWlJVzyW6qhR2dZmBWA5PTIAeW9KpoYK8NPmLQLYrjCxD3tCJGAEuAWgzb6guFyoclMejD216EuKEJYFbeJFuh7Gfefo0xdLRatSakNHKTXlby5UHX37FtW/16Z8EO394Meh6LfD6kOLrKHvCak7LfbxPt18JAJdGS9huizoAqF5qqELGxE5u6K5zuiDI2A3CXzekU+WgdkExA2Mp5nomRRvuOhx6q4wuiygQfVeZtEkbKsdIMPhLubFXOzbFQKzMlMZLa+mGlrR0Asbrbcp5yxdmHkdqc4fgosUtjdF0sRvI2+Eq9nQAdHwGM4t4qfDwGkVePh9qTZ0Prd4mPfBzJPujv6/Pioxwjt95yi5UmcD0AbszlMny8BGUp8fEkw4A0OmQj+OjSaVyhv0utQ+re4yIao3R/+F03Ytev74qdO25cdqBNPDaKUUkxbrDa+BgnTIuI2KrVUQd7Of8khYFTia9DsWPK5Qq7nk3sBrAZbQ92AWVeeLP4OAGAj8HS6pDr18QXSSQ+8nZxjFwA+52x2vAxM7o90uzLxls+QX5Xys3Qh+fapWGbpbCj3rY2zIHtBSkoAysfDlb6XsjlrNUhGRpdq9VwYWGhHnYiXww0+CUreGcQTX5frl4QXpefm3tp8uH9PAqAPwnGw7IPbAX6LqUtaUpc0AuQQnh+FwCHwB26OAJ6qM272fdDYfsqoOeFSwWZnietInOSOZcSTmUznn32WXzTm95U3/fWcLxQ+9OwRLp+I8N+bm6uTn5XGRlRS+8EEH/h+sLKJicn8bHHHsPLNm1K3O8n2Tbvyn1CqFCmVDYvS4mR2rNLwshW4GMRojw1F+4kGVz7yuUYPlZCYjSJkUSM5sLHU2w8+zBShp0T/hI+ngVT2rEDTKSOtsiwGIykfLrTEHknNIwsgE7OxPHxNlg+fJybm8Pbb7+93naJj09B+nSvNPjoMqTyYBYrM3y89GQ16ZBp8LEIBi9Ip9kBtrexDZrXITk+UnvT6pCRgXsXa48vlzlQSmNxT3YeTbmuYwjwkwjQiQD70Bi8nMX7Lic+JpU74/hoDPcpNJ52V3j3COoEa/z7obB9FdTywgHaxP5VBDiAQdCJ27btrG9vFB95hGqrdEiyJyhH26VDcnxE9GPkgQMH8JWXX564z0rgY2Z0e6RZwPQNiCcZqFG+IhcXEYWLHfK3f/u3sdAVX83b3NuL69rbGy43pX1H0Ou9+vLWAOIrgzzXjfePGgoVBN62ynwN2T4C7bxSm7SRfCIiqyCD+Rkwq83y2tvAgJIEjLWgh6647q0zl7OeJxmtEdC7j3//+99veZyLAPg6iBYp+Cq4qw98v8lrJK0qaqunrvbLlU/Xfh9l27w5agrb6mLneSZLh5F3gRsfEXWMXCw+HgM/8y/HHW5wzYNekvDhhx9OPB8pONR+wkfp3W4GI+thmmKOthojZY43YaSMFiiAbki3g1n8SIuP94CdqtUIPh4+fBhvveUW63qXgYkMkPjoe5enxUctWo23ixvqGT5eGrLUOmQhrDUvpRF8nJ2dxaNHj2KfIJda197eVMlS7bsLH6kWdhodMl7jmz483DxeQzsI8uE+aYjU6CMN3CICAAZB3srptkPe6XNBxcdogWAaAZ5BgK3KtbehXVc8j1GZL9k+/d5yuU7s6oo/z7GxcVZ7vHF8HIUo2qgVOqQ8f1p85H2aBiNXEz6Cd4+XuSzVKqVUeNLmwJBIQKUVzAmIchDXKAMuLRmN/I7hBMuDUFBDpS9pwmksl8O7d8fappHV3DAw4G0r/b8f3OW22kAPa9IUReld4/mM0sNSBMCuXK6ew+RbgMix/iMG2ySDg0BGGwubCgVnHc0AAPOdnU5iOt4+X55k0m/blPGQhyhnTBsL9KLlOYux0DbFg1gAN2kcb1vmyVkZWUqMXG58RPDXHuW4ww0uV4jdjm3bEs/HjW66t+GhIewO2a+TMJJy5ZIUcwDwliT0YSQtNmreNcJIWSsYwu97SyVzTwLz5fPm3jMfPnKSJ76YR+LDx2uuuirRW0fXq4HxhiS12+Xp1vBR8rJoGJnh46UlS61DBgCxqgPN4CPi0uuQLnz0YZimQ548eZJ5bMnoXEDpIR4cHAr/TyIW43nUWrmtPAK01fObtWcUN6YjQ57w0SwU5NBmXO/AUmlv/VmYcmhJiwScLO5Jz70Zz7ymQ/rwcee2bbhGwS/6nY+Hox5ccf0WgBJN1gA+8jGbhJFaStJK4mNmdHtkMS8bdUCA8TrI0L7p6emm2SDJC6sNIAlmactSye+u43yhJVdv3YqHDx+2VuZHq1Xc3NOjToYtYLwuPiOWG8g+41GSFpHcesstqqJIuZb0DIvhs4sRK0E85OXw4cNWf0vAoPalJXnSVtcI6F21DX3G8unwOxGoBaDXLKR8nHxY2oGI6nrBhPCnGUfyusfYubVVd+2Zn4eILIY+l/X24s2lkkUq1BHuK8eUrz5tplQ2L0uBkcOwMviIkI75Vxpcvvmc9NsDDzygYmR/X199oY73TzcYz3ClXPYq5jwPz9dGDSPn5+fViBmea0nPcGc49zhGFnK5OpZOTU158TFtf8qxwSUNPgYpzs8JJl0YScrdQ+E9U7lGHz66DPUJaA0+rl+zBm8cGsrwcRXIUuqQXE9oFh/n5ubUGvCt1CEXg49JOqQpEZZH7t3OQScGEMTwMZ2nO5mEzKVD3nLLragZ09XqKCLykPidaDzYh9AYzBOYyxXq+yFGOqTbkObt892bnoOeBh99nBynIR0+unTIvOf8afARUY/ckBh5HuyUpvVr1uDNpZI1t5YDHzOj2yOLAUxtQBQhIrCJAVOthpVy2c7NS0jilyDLgXGaXVMO7KIyQfIQ5ePQ947w/1NgwkpcCpM24fIQV3DyEF/RKuRyeMu+fbgn9H7UXxJh/1C/7Ni+HfOiPiXlVtJ1quBffe1UQicRDat3QZApkKKoEb/JZ0nPDhHVFxd/xrJ9WrhoL7iZhF31WmVtwx5PX9wmrq+FUhJRlCRCCcKxMOW5xtsgUkJpLMhnS/fGV919ivnNYELvx8fGsFIuW0yubWA8THybzAVq9Tx/uUurMVIj+VpOfKRVeIlpEnc2s/lL3vHToI/bZjFyYMeOxFqlhw8frhN2ShwpsmtcgOYw0pd/3AhGpvHcaZEDGj7KsYGYHh97AXCDpy+mIe6ZO6vc2/jYGP7Jn/yJtSjRD4CPe86/IZezlNB+WDw+/jiY0Pvetjbsy+czfFwFslz46COskqIZ6a3UIQsQ6ZAPwtLg441DQ2gxbkMQw8eRkQpjEZfs5JRHXUVfuS2XDlmtjmIuV0DuHSdjOjL6iQytGGsrfZc6ZHIpMd4+O6Q+iW1dq2ct8fFy8OPjbZAOH1065A2e83cGQWwhk8YCf/e56sK7MJJIWV8PERHoZkFoSYvcS4mPmdHtkVa8bCSbuVwNuvWWW3DIE3qnhTYQyGo5NrSyWARbcekGwI62ONGCRm6j5dgVQV80kOVUOsCwaxLYuu6LFKQnIZ77I43AIOH/R8G/kkp91AhJEG9bAEpoVLid5+0EYIfsEGGSvJbGtFiFeP1YRP3l6FNek35fB3qEQg3iZEraS5xq16ZZuabPZgD8AEQKs8tDhYgNRTdQX/NwOAJnurZrdVpKplQ2L63CyKHBQWuBbSXwMQ+A+a4uJyM4/f8BMB6nNrGfphR/SpnrawHwdoiUiKS5dBqi6CgNHzVuBQCTk0nnbhQj05CoUU5jWoyslMsq+zj1DfdwaPhYhIjkiTCyGXz05aXud/xOx1FuoboQBH5Pt3xWO8AYKa3AR/qe4ePqkKXGx/GxMXz66aebxsdjoEf3LFaH3AkRoWIz+JhWh0yDj5v6LhPzTjJ5P4o+z3GSDuk6JiJ1fDK8jiQ960WAQOR+B2jXFe9hbebXWkDbkK8iwJFYXfFG61n78HGt4/c0+NgDfnyU3AJch6Rn68qxblSH7AaIl72DyNO/FPiYGd0eaeXLxlU+ZFOhUH/IzpV3R4gIDSzNK9ARDigJZgUAXB/+/yBEL+oTAPheto9Gn5+H+GomGWEIcaNNMgK77uvOO++0JoirPMtwmDtNyt7s7Cz29/XVlbgquMtfaP3IiUo0Nl9adfz3HoA4ePBgHYhHq1U1XH2H4xn1trXh3lIpsdZr2nqtvRCtIFaVa+XBgMzWFM/FR/50GvTVbo2xNA+G8V2W5HHVNdZe8nkwL3DexqT23XHHHYk5OFIypbJ5aVXfpSmvtNz4+FGGCxwf10LEDC4xknvEOT5ypYR/fNifFh8rIyMxFlhZXaJRjPTh4/T0dL2fXRhJi3tPPPFE/Rlr4erdEJGsyXk/PDSEU1NTdZIdiZFp8TEPgNtZX7vqGHeya7ieydTUlFe5ux6aw8e7Ia64yjHtw0cEf4pEho/LJ0uJj3tvvDHmSWwWHzmupMHILoj4G7jDhGNkHiIdoRl8lNdcND4y1nSuQ/b19WMUoi7raT+FbZDHMQgSdUiXdzzyXN8d/tWNc8JHROM518LVAXag5tlua+vFUmkvy183H56DnraetQ8fC2B0yMs9zyQNPpaU8aZxVhwCg8kVdqyGj3xct0KHPHjwYOq5mRndLZTFAKYktNBWm4jQoNszADhRBqLNbDkD5sXNzz1areLNpVIcpCEKFa/vp2zb5WkPfaoQ93zTYCbinqc853nLW96CuXD/Q+BXFiRpjqzx51pV1VZ8+QSVSiAAWEoev9czYErvSCXw/Pnz1jPu7+vDQmBCnmREADeuNXIT+XIk5feAcq9bIDK6Xfk6ufAZ+XLgfeRP9Onp7rauQezl86IN1rMR+WY0ps862j6ljEFX+zg5VRrJlMrmZTFEQWnwMQ+A13nG63LjYy8YT44PI134uCE87hTb13WeV15+OW6AdPh44sQJlbG4GYz04ePMzIz1vOh+z4Bd3YHmO5VRkxjZnctZ15KhfRIjNeNhDkwJMnmfOQB8Nes/jZiuDQz+pOEISUMiukl4bJLwka7vwsi0+IjgJwPM8HH5pFX4iGgwUs7lNPgoPXYaM/oM2DqNhpGbFDw4p4zJgqdNAEYf0vAxB1HUjE+HvGzTptT4ePz4cRUfR0YqrO12uLpLhzxz5ky4nzsP3JxvQ7ifO1eb46NkaO/r68cgKCDAETQLA6x9zLhOq0NOge0YS4OPa8Mx5+vntPgoMZqzl2v42Cu+rxYdMjO6WyjNAKaL0GK0WrVWcOj3YxDVwJOhyR3h8VxoIh1xDK5nnnkGEaNBSKuRGoNkPrwG30ar/XJAnhLXck06Iu4h49Dy8ELEIisnvm+iapONgw1N+EMQX0GjPuQvM/KUW/3R1qaGVt8KcfIxuZpK1+DtWVhYUMsv7C4WE0NXeP7NJ8E2GgIwpcGGhZe8wMbRaTAKWCdEK9NJ3r88QCzP3vV8d23fHrvm0OCgRSTHxxp52Fz9xcc0XZNyjWie1IncwvrKSe1rxIuDmCmVi5FG+65RfOT5wqsFH3sB8GrwY2QafARIjkxpBh9bhZFa/dw8mMU2Zz1V8OOjbI8LI0erVSdGcnw8BVF9cH789ddei4NhFQwNH2mcbQCbqdmHj74QzD1Dcc+TXMiUY63o6TMfPvJn6S17l+Hjskmr8FFGdZxiz53wUdOzisrc42NJM252bd9eX8jkGFkBHR8LyvY0OuR+x/iktqXRIVcSHysjI9gObRgo9bZ7CsZj+ygADkMuvK5unE+EfUikk7I9CwsLoQec30MO95ZKifjIOS4eARsfAQCveu1rU+FjDqIFF7JXFoOPUoeslMuxfOxG8VGOaw0jOYfGFk/7lioSKDO6PdLMy8ZFaBGIB/wYG3CnAPBhBURcxAF0nTVgwG2CXYvXbZRlsRIHGdsmc341YN5UKGC3mHSFcHJWxb4F8b0Y/s2LfA+fx18yYGviqkV57ty5hnL/NAbFIEUbnflT4l7zEPfkEEhKD1JOtLcKxluSB8D8xo32WAN7NZpW+Qi4ngKd/KK/r68OfFr4Tx6Mwq2Nbx5OxMdaI6UYNLbWQi4Xmxf5jRvr95RX2jc4MJB6rpJkSmXz0mjfNYqPVD7qPlg9+CiVQxdGdoTt8+HjDrC96QFEEUPU9i5PG/l9tgojkzxV8rciNG/wjVarKlkSV0Tn5uZMqDnz9kl8LILBtmOgp0i1Ch8vgFFiLQ6PILBCJI9BvKa6HGtpMTKpRI68nwwfV4e0Ch9lDWMiOiN8/A0A3CPGa1KkH7FKb9HmiLLY4zOipL6URodcC3F87IEoXzitDskxbyXw8VEw3vC4XhlY7RiBAHPCOKfQ9bT42AXx/OM0+Cjfmc3i43+DCOPz4XOTz2gx+Mh1SDnWWq1DUkSRCyN5n6aRzOhuoTQKmD7ymf1gwniSwm45KQSCnpczPz9vreC7AJYG4V3hdtcq4LTYxgek6qkMAiscrgJ6/b08mHAWyUZICgivkU3HS48Wz/1JCqckcdXqpXYRWY6rP54U20+xZ5jYjynyp/g97C2VYkBO/X4EDIGEBMx82M8aGRo/L30ojOhuANyYy2FHSK4klVYKQTtz5oxVWkfu6/Tu7N6NPWF5COojH2vygQMH6s9Ie9kNDw7iM888g1NTU9jHPG+fhGi1kvedDDVeinmeSSSN9J1vLkyA7bGUYXCrBR8BTOmuJIwknEuDj0WIR6Y0go90vNanWh1rkiSM9NXKlr+RYtQoPqYZF3wh0oePBTCh7b4Q8WbxUYbdS4xMqkceAFj4iJAeI134ODs7izMzM1jo6qov1GT4uDqklfhIeskMRGRUEh9pH75IKOfe/Py8xSrtYkVHNLjgCxeW+lIaHVLi4zgA/jKk1yGLoOMjXTMNPjarQ0p8lCzgsq8WAHDYEbrO+1ELa24GH48B4AiAyjF0AzSOj6fB6JwbczlcK0j0cmAiexaLjwDGAy7HWit0yI985CM4NDhYZyh3YWRlZCQVeVqzczwzuj3S6MtGy5XhgwPArLIUwoe+mQ2AU55JwHMftbDoXogT4rhe1K7znwgH9wmIcsI4qYzrOE42lJbVmhRiuVJG37cq23nuzynxO0ByTUpX7l9SW+XKLfVJ2ufUyJggjz8/9zCYsJ7Y6ixEYHYtJAPRkxCt/srFkW1gVsbvAVOeYXj37vrLZXxsLFZGbSIcB2sA6oRvSffCQTaNB1F7fjMzMxa5HOVHukKGN4IdZrxU8zyTSBrpu7T4SNi2GvGxBtGC10aIIl+Sjp2ExvBx+3XXNYSPARjvBT+nxEhfzV4t98/XVv4bKUaN4iNiVCrHNS52bNtmMdH78JE+rcZHRLTG10TYlr5CwTvGJT6m7Wv+DDN8vHik1fgYhJ8C2ESOPm80J3y1SqWCibbh1+MY6SPGopQZqUNSGk4afOS45ZsLOTBGpYaP0tBLg48+jFwsPiLrE+ov7bjDhw9b124GH58N+6gb4u9BXk0nDT7mIRpzEiPvA8C3g9Ff9zGiusXgIz3bxeAjoq5D0gKECyNvaCIKqNE5nhndHmm1p/vtbMI9qwwkjUEyD/EwZB/4SYZcGoiTk5NqbkweQK3nmQRacxCt9G2ByCD2rUi96U1vwpMnT+Lw7t2x1cxTYDwk3RCFVR6AyLBMClGqggnxSQoXck30Ktiror1g8hK1fqK2yHyWh8CASk9XV8NjAsAwek+DefkAKIRrEPfSAbjLNshw2CLoIUQV0Ak1AojIfuQ5jx8/nnhNUoAPQGQ88VVubfW53ib2/Fz1gQOI8l/lbz3d3Q2vUDY7zzOJpJWenJXEx1qthtds3WozToONjznwK3YcI4nAxYePt99+O05OTuLO7du9+HgITJ4erdQnYWQRAAuOOrMkWn1TteJCuL0CcezkkTeN4CMi4gMPPODFyEMQ5cCnwcf/Txk/jeDjOBhCOLnAIcN85XlPnDiR+PtpsPHxAsRD230YmeHjxSOtjgQiRwix3Mt9XeOIMNJbClDMI46Ru7ZvV/WlPrA9qo3okEMQ6ZC+iKM3velNFj7SgkMnGGwmct53QUQOtxQ6ZBI+ut5VhMfyuE4w75nhoSHr2mR0p8VHuVirRTGUHeNH4iOPHmiFDpkGH49BVCGkGXxE1HXIAiRjZDOpN43O8czo9shicrql4qbVwpaTeEGZMFxhiuoCukFpg2MguthouyC+GqYpIfnwHjSWyrVgchKl4kXtmleuSffuWnGiv10QreY6w9zDc2ieFMqTdk30I2Arz+MAeB4U9l0wK8E9EM9nkcfz8BQiRSHQkuAs+4XOV3Dc41lxPdd5KRTft0LIQ234s66CPramp6fV8U3gx19GtE0+Vz6utZrvPlAGMAYI9zzKUFqN+bXV8zwTI83mLK42fEQ0q+NynF4GtrfAVU7RhZHd4dx1pYR8WJkfAQD+unIuvl8ubAvHARdG0lzU8LFWq6mLFVqtbFLgppQ290Nz+OjCMvLK8LztlcbHniDAAJLD57UxzlOkNHwEsGsfuzDSh4+HwHAikAdR4qN89q2e45lE0gp87IWIxFViwVkxDuQckBjp85reBm5jRsPHcTALU3yeFJV5k4SPa8F4rZ8CPS/bpUM+CroRTZix1DpkEj4ugL1gRzqknPs+jKyUyw3ho+v+ZA1zHz4i+CMPWqVDyhTSHWAv5MjFbxc+ptEhb4N4hAW9m6kU2VLhY2Z0e6SZl42WU0CeUy3MI03IDQ2IgwcPel+61zsGIoUOUxjKkxDl36UN4+hKuA9qVxuYfG+5QpVmVZ6YEl/BJlUB4gCVtEJGioXGACo9C6TsB+FEGxocxDxrN/X5fvYcJJASaEmljAyA4cHBOoOlZix0Q5wZmTPR8rxV/mz59R4FG5g4EPk8a0n9qRFHufIK6eU/BxEBFp13Bmzm9RzYygK1iRiJZZs1ZYK+y9xOvp8vrLbZeZ6JkUb7bjXjI2JEMHRXeA55rmYwkvBOw8enIKorLY/ZDDo+XgEmXJgULIkvScrGgQMH1HJfARhFpxdsb8Lw0JATHycgwqpm8fEY6MRlHQDY291tpRisBnx0hYe6MLIaXldGcGj4ePXWrd52uX6TGJkD44nk22SpRx9GZvjYvLQCH8lY03Cl6BiH94g5QvP1zjvv9GIEnyNybHN8PA32QuJidcgAAG8ulWJGmUuHrEKc5foURBFB5H1dah2yJ5xjO7Zvj+HjUxCRJzajQ1JdcaoSoemQLnw8BnauO0BEbJYWHxH8kQet0iEJH6UOWQO9MkWrdMgcmIV1vk2WemwlPmZGt0eaCS+n1RFabbnHMzDlJCYwQdBX8WhiaMqRbwVeG+x8cC5GCbkDItCSZaySAP6Y4z4DcewOT9veJiaNFn7nMs6GBwfVGtuk1PJ+phzO21Lcm/y9BvG8nhwA3s9+17xH5MHx9eWDEJX0eAoMAL/Nc0xSf97F7lkrl3Hy5MnYCjB/qfHz8pdiGmXBpRRoL95esPPauJFTBH9YbTPzPJNIGg2fXM34iKgrvfyYxWDkR0HHx6RjJhz3+UrlOB9GctzRjPwR0FnUb9i1a1nwESEe/k+kO2TgthIfL4C96NwIPnYKBduFkeRZlFFdSfiosfnLdrmqa0gsdEVlBJAurLbROZ5JXJrVIR9//PH6c/YZsry0lYzEcOlWGlktza2V0iFpPk5NTTVURSEpYpIf2wodUi5gBWDw9OjRo1ab94Ue6qXSIXeGZbcIC6jvpXGedkG2BHZa0LoUxy1Gh3Thow8j89AaHTIJH0mX8OmQmdHdQknbma7aintLpfpKkmtgypInAUT5ENpg6wgnbVEcR6vXzlUnRjTESwrQwKJVpmYn2KS41szMTOoVe9ekopfHHNvfB1CyD+V+w2CXEdJqbFN9QwlgA+Ez68zlEu/tJx39pa22UUhSBXTDkj9r1/X2h+e4VZyfFDHp4b+5gf6sjIyo5TJoBZATZ5xi502rLHwYdJZNKifiCst1bafzd7K+SwoTypTK5iVN3yXV5t4QzqPTjnHdKnyk1es0+CjbSviIKca0Twnh+MiJXlzHPOm4zwLEFxEaxUh+T3wfwiBemmY58ZF+l3VcxwFwLyweH0mxC9hfiY958OMj5Xbz8ezCyNFqFTvAKN+c7Mg3lrjnTMPIIpj3yVOgl2rznb8T3GG1jc7xTHRZjA7Z39eHXbkc/oRnbPPPzrAePD1zDTvWKmOpAyKC2ySM1NrJMXkx+HgbxMdhWh2yqtxjHuKLCK3UITshjo95MDoSYpzxnPLAm8XI9zr6y6VDzoAeXp5mQQXC+5X4CKB7+Hsh8j6n6c+0+LicOqTv3PeAOzWrmTmOiJnR7ZO0nSmT9o8oA8Cl5EyAURJzYMKc6Vwug0LLQ+4LFUquQJwCo+TlxSqNi4SlGH6/HvQJdr2jPXzwSyIDXpvUNSnT1H/kK3h8JY4UJBmivQHMqp0LXFzGG+VzIMZJTWbAzvHu6epKbLerJEMR3KQUSefb4Pm9M/y/CnHQ1XJjOsB4tFQikLY2HK1WLaXSV1uRt4tIRMjI8CkLAeieNznO5XnSlDWic2ilihqd55nYkqbvVFKTIMDLenvjL2CwCV9agY97SyUcrVZbgo9PgU3ykhYj+bV4qk/SMS7CJBnmnBYjT4FRGPMQz6FbLfhIHlsLayA53cBXzpDwkb/jjoG7rI4LH/Ng6ri6lEcNI/eWSrG20Xl9+MgXZXwYuUU5VxqvYwCAn4JkjMzwsXlZDh3yHohIxHgaYSMYSbXeCSMfgqg8l5egKpfDDmV+LQYf6VppdEjf78egcXzsBHde8n6xXcNHxNWnQyb9RrnZT4HRnQvhvlXQ+Sf6IVrE1HRIGk/N4CNh5FLrkGnwkd93K/AxM7o9kqYzNTZIGjCa4lZX9iBSevhKogzfcQ2IyclJk/NRLsfy4nYoA21vqZSKuZI+beJ7EaIwXgmmfGCT55Nfx6W4BOF1yJh03ecGiECNr8TxyeYi1KiArcTza2nH8PwNesEUQQ8B3FQoYI/M44EIrOW9+zyzSf2wMZfD68EOVepta8PBgYFY9AKdn6/k1cDO8ZkFG0wrIyMWOPoMA9luSXjlOo6HF7kU/Z9IOI+vP++HaKxlnu6lEV/fuTBHUxx6wLzwW4mPXAl9FIyRtVh8zEFzGKnho4YTT4FRINLiIy0iJGHkBxzbZ5V7XCl8fArAq2S7IiJuGBioe0vk9SQ+0vnT4KPWZ3vDnNPY+z2FcUD9uiDOm+RZrNVqKkbOQTxV6ZRyLp8n53owC1pb2fWameOZuKXVOuR1wIxhaL0O+VkwOMbHfKGrC59//vnU+NisDsmxJa0OSYttrnvkiwiLwUcNg3z4iNgcRvLn2kodsh10fBweHMTZ2VmrdKbED8JI0geJx0PqkJsKBSyEZXCbwUfCyFbpkPRbM/hYgEhfyDzdyyBpOlOWEnA9SI2hdjQcXDQp7rjjjvqDPXr0aGzwS4WgVqvhs88+iwDx/K5xcKxM9fWpZWH4pAQweYdTEK/RTaC5BnSvQA7ijIucIVMqGBCeh+o3XucZ+ASqBJ5PgQG9uyCukLpCMKsQKbE3OzwOEghptTVNfUqpzNEiAAGHlnOa1P/O64TtkGOIgzyFfNL5pzzXI+OmD0z5IQDAt7zlLVY7FpOntWv7dpuNlfWxnD+uHDSN5KkX4syktL0HzLhMEzqZdp5noouv77RSKxpGagy1afGRzhlTCEIWe/lCHgebWKcRfDwNEblLIxjZy0o2yT7RcKId0uEjxwMNIyknrwp6COYARMqXDMFfCXzcEYbHup6B9DLVDeTZ2Xo0Q2wMVav1fq/VajGSHR+uPQlR6HYBAH+XnVfmVKfxyMg+OhT2h6V0ixxrPl5c+EjGRAHiWEg5ixI3i+wvvV+aneOZuKVVOqSGj/1gKq3wcXbw4EFENEYrL8WXVoekOc8rENCHIi9cYzwHpr79SVgcPiI2pkMm4Q9AVF61GXzcyuZNHuLRNj58RGwOI4sQOYs0o7ZZXWxvqZSIj4hxHTItPuYhrkO2Ch9bqUMWwegTzeLj8O7di5rjJJnR7ZFmVildA7UKekgINwzoQ8oPlYSRAHVzqWSTNgDgYTbANAAhYpokA/dYOGj3gp1PCWAUhUkAfCD8S0puPgjw1ltusVbL6BqcAOJ6iAD5AkSsijHDKQjw2quuqk9gjcEwYG3yAW8HRGGBSaFXMlTIVeObwILn73Cv2lOgM/L6+t8CACWfkkjMpCEpx+K+FP1SAVPGQ7azBOnzWHtCBk25gkq1QV254IhYf+mTYjAEOrHFWqWN9Pzlqvo4GKUBICL3y8LLl0aa8XRrGKkqMJCMjwEYNlw5Lta3t+PrhCJTBcCnIcKfxeAjgh8jHwBDLnmSHV8pl3F+ft6phHESsTT4uIsR6UyD7pldw/5PwoEimJB/SiNZDny8AHouXlJbeZi4ZqDWajWcnJyMlcdyjcc0+adJGFmBCCNPec61RnmGhVzOGarOPX584Wgf2JwkeYjyF0dEG3MA2JvPW/go65pPTU01PcczcUurdMgkfJRjlrMvN6JDUvjzhON6Pm8lcagsFh8b1SHJYJb6x5ZXv7p+DloorYi2pMVH6rfNPT0N4SNiYxhJUQfN4mMaHXI14uNasA3iQi5Xx8JW6JA7YeXxMTO6PdJoPg4P82skjIHA6gJEOcnXg1k90gbPuvb2xFIrAcRZsfm19uze7VzZAYhIiAhwT4PxLqyHOPmMvN67w7/5IMD9YIw2TVHUDGVtJbMyMqJ6nmpgvDPk5S5CXEHVQI2TvMkwmlNgXnIzWhvKZZyZmUl8do8//njMAOYAQSur/Pl1gA0uBIqzs7MWs7GvXAFnhK6X4QmCOvjTs5QvpDUQkVAUQTdeKG8HIQoHlu3mBhA/ls5F/cIVb0SbOKZdHK/l9/JPO5iQUlqZPQ3xEin07O8S7VjMPM/ElkZyul0hcs3i41lws4+2s21afuQ20PMjdxeLifiocSdwjKT6sPxa/EU/vHs3dgZB3fCX84mIkJrFR4RooYmOHwI/PvI+4HwOF8LnoymsafDx5MmTMYySClQjGHlzqdQwPiLaGDk4MFAPOQ3AzeS8AdJj5FMQ5aJLrE16b87OziKiHx8B4gaCSzmkzxXheKTSQwDGM0X4yJ9/ho9LJ63QIdOQSfWAwaRjYDBqS4gDeWV+5aF5HTIAJTID4oujh6B5fKyMjDSsQ7pCxg8fPtwSfDwAovRWSnwcCsO2fWH5XIdcDD62SodcTnzsDYK60yQJHxFbq0OuFD5eNEb3wYMH8aabbsL169djPp9PdcxLL72E73vf+/Dyyy/HdevW4Q/+4A+mKnTOJW1nLiws4Gi1ak0UGly+0IotYFaKtFBk10T99+EAUVckwzaMgzEmpyECmnvvvddbdgXADg8hcoyi43o7lUnTI75LsJL5KofAMDpSqJsGVvQCovISZ8Hv0a2xviaP59NPP221d214HzHQ6Our5zhLZcrysDJwo5JIsk0aiYkERQkumkjA4efk/59VxlUA9gq067mWwvslLxg/pi+fx252zASYF35F6W8p42NjWAgCC6SrEJaxgWgVnxsHMkTO1c/0uyRnWcw8X82ymjFS4iOEc4cUBx8hngsfaa66nv1BiDBSy4/sBxsft7zmNYn4WAjJb1wY2eG4VqDMewsfR0bw4YcfXhQ+IsRz2S6E9+jDR97fSaUAC+J7Ej5q90xY58pVToORafARMT1GVsW16LtU4lwYeT1EBJTSUO4Ag2d8/yHwR+C48LEYjkXu6aTndhvo9ct9GJkUWp52jq92Wc34iJisQ6YhDB2H5dMhrwkZxfm1eRpQs/go5yUA4CbxPUmHpJDx7tBDuhL4uANsbyonEUurQy4GHxFbq0O68FG2oRl8LHR1qTpkifW1S5rRIWkBZaXw8aIxun/pl34JP/CBD+B73/ve1ID5a7/2a5jP5/G3f/u38c/+7M/wR37kR/C1r30t/su//Evq6zbSmcS0uB8MG6gcCEkP9TTYwEdGZRLQ+s4rV8YDAPzQhz5UB0T+gtZyzglMydD1hXF3KhOOFK/Dhw8jIsZCK10eHI2IQi5gnBJtqYC+IqcRjdB5JdshrSDKe9BCsQrKvjyMh+ckac/v4MGDqRTHpPFGK9V8VZnaslH0jyTB0D5JYURU8kGymietvvP+5kIvQw2kCSj5C0++/Hg/Tk9PW2OEK/1pPGGXglK52jGS4+NpMC9F6X1uBB/5GF8MRkp8pP8lPtZfwCJsXWJkM/g4wMYyVxSbwcenwNSQlm2RBvxTYPCL8FHDSF4KkLwZ/HsSPhbBKJ8Sl1YTRvIyW4SPHG8awchKuayWxFkqfNTO5SKZS8JISgFKkgwfV4cO6RpLHxVzfLl0yP0Qfyen0SGTrkWh0RJfesPfJycnEbExHXI58dGlQzoNTse+Lyd89L2bXfe2XDpkq/HxojG6SZ544olUgPnSSy/h5ZdfjocOHapv+4d/+Afs6OjA3/zN30x9vbSd6VpRo5chL3WjhcpoxqwvpIhCdpImpabk7dy2TQ0Z0cI0afBSjpFvxdVFctMJUR1DXtc55sEJAnVFScvnoH6pQLQafBbslxSFoPLwG9fz8vV3rVarr4j5yvgkrVLSPvQCaUbS5t3w1XJ6zr0QX7l8kP3veq7UVg7SD3qO6UzwMFM4j298A0Rkb8WEvtbGCDFyppFLQakkWY0Y6Quvm5ycxMrISEP4iOBnck2DkfshwrsCRIuE2gJeG+ilrDhGNoOP3RBn2B+tVrGQy8XwcSMYIhwp2tgnpZSX7tHwcS1EdUx9GNkIPk6k3H+1YKRVEQIi4+E0AD6W4tlyxW058LEmzhV4+lobJ3IBxyUZPhpZSR1yz9CQEx9dY3w16pBJ10pqK5VDQ2xMh1wufGwUI19uOqTExwsQ1WV3HbNj+3Zn+5dDh1wKfISGntIqkLSAee7cOQQAfOGFF2Lb9+3bh3fccYfzuO9+97v4rW99q/75+te/nqoz0xAluMgA9pZKzrIwVWXAc++tb5LL8B6+gsQnjW/wEjD7PN2ulXbyTnPQtDxGHo+kRsZzBAA3i3u4HkyuSQ8LA5Xn156Xj6lxeno6lvvh23dubi4xH6bZ1UnZfl+7tZXnIxB/5t7w/DDXh+/zmOeYndu2OZ8nnSup3XexNvb39WHBw/Arx0gj8nJUKpcTI5cKHy+ArhQ0gpE1ZZuGjyXPefZDfE4tFh+TiGM0kWOfQu8K4j4IHwcHBhKvIZ9ZI/iYBpdWE0ZuFX1UVJ6lDyNliOJS4+M0O1elXFbDV1uFkRk+RrJSOuTU1JQTH/PgTmFcbTpk0j5J98/xEbFxHbIRfNyYy+G2665LPH+mQzamQ2oh3D6MpOhYTVaTDpkZ3Yj4+c9/HgEA//Zv/za2/fWvfz2+4Q1vcB533333WSCSpjN9nhz+AOVDXVhYwD5RqoU+Wu5GFeIEQC5QDRIGI12LQkYmPYMXwJRWyINOzNXjWWl35a81ayRJpVTmJ/O8mDQs3wjpVinpON/iA/dYacyho9VqQ/crpRGGyRIYT9pd4XPgYCMXL+Q4SirLMAe6l6gA6V4IvjIa+SDA4cFB5ypkmrDxtPJyVCqXEyOXCh/p+HXt7Q1jZB6ietE+fOSsuUkY2Q5RzqK81nLiI/UbzRkXPiZdoxlPdyO4tJowshsMptfrHwt8JE9YQRlHtO9y4+MhMO9dHkG2VBiZ4WMkK61DavjIuVZWuw7pwseKZ7xr+Kj1R1ppNT42ipEXkw5JTPGL0SFdpUMXs6iwWnTIi8bovueee1Rw4p8XX3wxdsxSA2azq5SIeu6ItpKiycLCAm4qFGzgC1+qJ0+exNtvvz01qPq8MrIsgS9M8yQA/jqAk2XQRRTRC+lrJTcjHBAbBV/teak1+8QzpOOKoLMP832XcqLz9lcV4Crkck5yEmqDxpTpaqv2khkFvRxJmheCFiLGDRStnxZjhCTJalUqLyWMXEp8RMSGMTKAqF60Dx+fAn+5nDvAKG8aO+9otboi+IjoLzOYJPKZFRvARxcurSaMJKxJi48A8XJMfN/lxkdXPy0FRmb4GMlq1CEXFhawUi7rocDh8atFh9Tm2y379tXvwWo/2JVQWimtxMdLTYfkWCNJdBvVIV2LSjuWECNXIz6uqNH993//9/jiiy8mfr73ve/Fjlnq0CApjXTmYidHmnCZJFCenZ3FIVYqIAmAtbZSfVa54lRVwPvAgQP4xBNPOEtl0acaAnpa5Xo5RWvvaLWKN5dKic9AlgRL87yXYqKnAbvZ2dlUixJpFy/k+NNKMS12zDeSi90qWa1K5aWEkcuBj4hujBytVmMkgAFALG/ah49JC4tFdizVQT148GCsFurFho+IeptdRqfrmNWMkVR2rRF8TGrrcuAjb/NySYaPkaxmHTJNuajVokPecccdePDgQStkXBp3RTCVUy4WfNxbKuHgwMAloUNKrFmsDqmNPa0qzsWmQ140Rncz0igJxsTERH3bt771rSUjweCy2MmRdHwaUKbjZT3qpH1d4RdBExNBAnejk2i5RevvNM9wMSukrZTFePsbFdf448Z9M7LU7fbJalUqm5HVjpFLiY+IfoxsFh9d507yOmpyseEjYnqj03XMSs/v5WpLho+rX1Y7PiK+vHVI8nhfzPjo2uY67uWiQyaNvaXWC5ZSGpnjOUREuAjkwoULsLCwAJ/85Cfh0KFD8NnPfhYAAK666iro7OwEAIDrrrsOfvVXfxV+/Md/HAAAHnzwQfi1X/s1ePLJJ+G1r30tvO9974OzZ8/CV77yFVi3bl2q637729+GfD4P3/rWt6C7u3tpbq4J+cu//Ev46le/CldddRVcffXVLdtX7g8ADR27mOtmcvHIpfZsV+s8b0QyjIxL2jHazFimY9rb2+H//b//l+FjJjG51J7tap3jjUiGj3FZ7TrkpTaHMonkUnu2Dc3xJV8CaJG89a1vja2O0Oczn/lMfR8AwCeeeKL+/aWXXsL3ve992N/fjx0dHfiDP/iDODc319B1L6UV3kwyyUSXS2GeZxiZSSaZLIVcCnM8w8dMMslkKeSS9HSvlKzWVcpMMsmkdZLN8+Yl67tMMrm0JZvjzUvWd5lkcmlLI3M8WKY2ZZJJJplkkkkmmWSSSSaZZJLJy07aV7oBq10oEODb3/72Crckk0wyWSqh+Z0F/jQuGUZmksmlLRk+Ni8ZPmaSyaUtjeBjZnR75Dvf+Q4AAFxxxRUr3JJMMslkqeU73/kO5PP5lW7GRSUZRmaSyctDMnxsXDJ8zCSTl4ekwccsp9sjL730Evzt3/4tdHV1QS6X8+7/7W9/G6644gr4+te/fknm71zq9weQ3eOlII3eHyLCd77zHXjlK18JQZBl3TQijWDkpT7uAC79e7zU7w8gu0cpGT42Lxk+xiW7x4tfLvX7A1g6fMw83R4JggB+4Ad+oOHjuru7L9nBCHDp3x9Ado+XgjRyf5kHpzlpBiMv9XEHcOnf46V+fwDZPXLJ8LE5yfBRl+weL3651O8PoPX4mC1ZZpJJJplkkkkmmWSSSSaZZJLJEklmdGeSSSaZZJJJJplkkkkmmWSSyRJJZnS3WDo6OuC+++6Djo6OlW7Kksilfn8A2T1eCnKp39/FKi+H53Kp3+Olfn8A2T1msjLycngm2T1e/HKp3x/A0t1jRqSWSSaZZJJJJplkkkkmmWSSSSZLJJmnO5NMMskkk0wyySSTTDLJJJNMlkgyozuTTDLJJJNMMskkk0wyySSTTJZIMqM7k0wyySSTTDLJJJNMMskkk0yWSDKjO5NMMskkk0wyySSTTDLJJJNMlkgyo3uR8v73vx/27t0LGzZsgEKhkOoYRIRf+qVfgle84hWwfv16uPXWW+Ev//Ivl7ahi5CFhQX4mZ/5Geju7oZCoQC33XYb/OM//mPiMa973esgl8vFPv/xP/7HZWqxXx555BF4zWteA+vWrYMbb7wRZmZmEvd/+umn4brrroN169bBzp07YXp6epla2rw0co8f+chHrOe1bt26ZWxtY/KHf/iH8O/+3b+DV77ylZDL5eC3f/u3vcecOnUKBgcHoaOjA6666ir4yEc+suTtzOTSx8gMHzN8XG2S4ePFI5c6PgJkGAlw8WHkpYyPACuHkZnRvUj513/9V3j9618P/+k//afUxzz00EPwwQ9+EI4cOQJnzpyBjRs3wtjYGHz3u99dwpY2Lz/zMz8DX/7yl+HTn/40fOpTn4I//MM/hHe+853e497xjnfA3/3d39U/Dz300DK01i9TU1Pw3ve+F+677z74kz/5ExgYGICxsTH4+7//e3X/P/qjP4I3vvGNcNttt8ELL7wAP/ZjPwY/9mM/Bn/xF3+xzC1PL43eIwBAd3d37Hn91V/91TK2uDH5p3/6JxgYGIBHHnkk1f5f+9rX4Id/+IfhlltugT/90z+FO++8E97+9rfDiRMnlrilmVzqGJnhY4aPq00yfLx45FLHR4AMIy82jLzU8RFgBTESM2mJPPHEE5jP5737vfTSS3j55ZfjoUOH6tv+4R/+ATs6OvA3f/M3l7CFzclXvvIVBACcnZ2tb/v93/99zOVy+Dd/8zfO4yqVCr7nPe9ZhhY2Lnv27MF3vetd9e/f//738ZWvfCX+6q/+qrr/G97wBvzhH/7h2LYbb7wRf/7nf35J27kYafQe047f1SgAgJ/4xCcS97n77rtx+/btsW3/4T/8BxwbG1vClmXC5VLEyAwfM3xc7ZLh48UhlyI+ImYYiXjxYeTLCR8RlxcjM0/3MsvXvvY1+MY3vgG33nprfVs+n4cbb7wR/viP/3gFW6bLH//xH0OhUIChoaH6tltvvRWCIIAzZ84kHvvRj34UNm3aBDt27ID/+l//K/zzP//zUjfXK//6r/8KX/rSl2L9HwQB3Hrrrc7+/+M//uPY/gAAY2Njq/J5ATR3jwAA//iP/wivfvWr4YorroAf/dEfhS9/+cvL0dxlkYvtGb6c5WLCyAwfL765leGjLRfbM3w5y8WEjwAZRgJcXPMrw0ddWvUM21vZqEz88o1vfAMAAPr7+2Pb+/v767+tJvnGN74Bl112WWxbe3s79Pb2Jrb3p3/6p+HVr341vPKVr4SzZ8/CPffcA3Nzc/Dxj398qZucKP/n//wf+P73v6/2///+3/9bPeYb3/jGRfO8AJq7x2uvvRYef/xx2LVrF3zrW9+CiYkJ2Lt3L3z5y1+GH/iBH1iOZi+puJ7ht7/9bfiXf/kXWL9+/Qq1LBMpFxNGZviY4WOGj5ksp1xM+AiQYSTAxYWRGT7q0iqMzDzditx7770WKYD8uAbfxSJLfY/vfOc7YWxsDHbu3Ak/8zM/A//zf/5P+MQnPgHnzp1r4V1k0iq56aab4C1veQsUi0WoVCrw8Y9/HDZv3gyPPvroSjctk1UolzpGZviYCZcMHzNpRC51fATIMDKTSDJ8TC+Zp1uR//Jf/gv87M/+bOI+W7Zsaercl19+OQAAfPOb34RXvOIV9e3f/OY3oVgsNnXOZiTtPV5++eUWecL/+3//DxYWFur3kkZuvPFGAAD46le/Clu3bm24va2STZs2QVtbG3zzm9+Mbf/mN7/pvJ/LL7+8of1XWpq5Rylr1qyBG264Ab761a8uRROXXVzPsLu7O/PiNCGXOkZm+JjhY5Jk+JhJklzq+AiQYeSlipEZPurSKozMjG5FNm/eDJs3b16Sc7/2ta+Fyy+/HJ5//vk6QH7729+GM2fONMReuVhJe4833XQT/MM//AN86Utfgt27dwMAwB/8wR/ASy+9VAfBNPKnf/qnAACxl8RKyNq1a2H37t3w/PPPw4/92I8BAMBLL70Ezz//PLz73e9Wj7npppvg+eefhzvvvLO+7dOf/jTcdNNNy9DixqWZe5Ty/e9/H/78z/8cxsfHl7Clyyc33XSTVaJjNT/D1S6XOkZm+JjhY5Jk+JhJklzq+AiQYeSlipEZPurSMoxslOUtk7j81V/9Fb7wwgt44MAB7OzsxBdeeAFfeOEF/M53vlPf59prr8WPf/zj9e+/9mu/hoVCAX/nd34Hz549iz/6oz+Kr33ta/Ff/uVfVuIWvPJDP/RDeMMNN+CZM2fwc5/7HF599dX4xje+sf77X//1X+O1116LZ86cQUTEr371q3j//ffjF7/4Rfza176Gv/M7v4NbtmzBffv2rdQtxORjH/sYdnR04Ec+8hH8yle+gu985zuxUCjgN77xDUREfPOb34z33ntvff/Pf/7z2N7ejhMTE/jiiy/ifffdh2vWrME///M/X6lb8Eqj93jgwAE8ceIEnjt3Dr/0pS/hT/3UT+G6devwy1/+8krdQqJ85zvfqc81AMAPfOAD+MILL+Bf/dVfISLivffei29+85vr+58/fx43bNiA+/fvxxdffBEfeeQRbGtrw2effXalbuFlI5c6Rmb4mOHjapMMHy8eudTxETHDyIsNIy91fERcOYzMjO5Fylvf+lYEAOvzmc98pr4PAOATTzxR//7SSy/h+973Puzv78eOjg78wR/8QZybm1v+xqeU+fl5fOMb34idnZ3Y3d2NP/dzPxd7IXzta1+L3fOFCxdw37592Nvbix0dHXjVVVfh/v378Vvf+tYK3YEtH/rQh/DKK6/EtWvX4p49e/ALX/hC/bdKpYJvfetbY/v/1m/9Fl5zzTW4du1a3L59O/7e7/3eMre4cWnkHu+88876vv39/Tg+Po5/8id/sgKtTief+cxn1HlH9/TWt74VK5WKdUyxWMS1a9fili1bYnMyk6WTSx0jM3zM8HG1SYaPF49c6viImGEk4sWHkZcyPiKuHEbmEBEb841nkkkmmWSSSSaZZJJJJplkkkkmaSRjL88kk0wyySSTTDLJJJNMMskkkyWSzOjOJJNMMskkk0wy+f/buUOVWLcACsBLuMVgmaJg8wFEpsgUFcagGEQQwehbyICYfILJCiaDVkFMAxbTgEGYaLMJIkaZ2+ReDv/hzIF99oH5vrj/stKCxWb/AFCI0Q0AAACFGN0AAABQiNENAAAAhRjdAAAAUIjRDQAAAIUY3QAAAFCI0Q0AAACFGN1Mtaurq8zOzub19fX77OjoKMvLy3l/f6+YDKAu/QjQTEcyiZnxeDyuHQJqGY/HWVlZydraWvr9fk5PT3NxcZHHx8csLi7WjgdQjX4EaKYjmcQ/tQNATTMzMzk7O8v+/n4WFhbS7/fz8PDwXZZ7e3sZDAbpdru5ubmpnBbgz9GPAM10JJNw0w1J2u12np+fc39/n/X19e/zwWCQj4+PXF5eKkxgKulHgGY6kl/hTTdT7+7uLqPRKF9fX5mfn//ft42NjczNzVVKBlCXfgRopiP5VUY3U204HObg4CDn5+fpdrs5OTmpHQngr6AfAZrpSCbhTTdT6+XlJTs7O+n1ejk8PMzS0lI6nU6Gw2Ha7XbteADV6EeAZjqSSbnpZiq9vb1la2sru7u7OT4+TpKsrq5me3s7vV6vcjqAevQjQDMdye9w081UarVaGY1GP5zf3t5WSAPw99CPAM10JL/D38vhJzY3N/P09JTPz8+0Wq1cX1+n0+nUjgVQnX4EaKYj+S+jGwAAAArxphsAAAAKMboBAACgEKMbAAAACjG6AQAAoBCjGwAAAAoxugEAAKAQoxsAAAAKMboBAACgEKMbAAAACjG6AQAAoBCjGwAAAAoxugEAAKCQfwEEnzRsySds8gAAAABJRU5ErkJggg==\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": "0209a1bf-83f5-42fa-b549-b820967f8cac"
},
"execution_count": 48,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1696870264.7928553\n",
"Mon Oct 9 16:51:04 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
}