diff --git "a/notebooks/Normalizing_flows_TEST.ipynb" "b/notebooks/Normalizing_flows_TEST.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/Normalizing_flows_TEST.ipynb"
@@ -0,0 +1,1684 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0253319c-408f-4d3e-a9ed-3a14c13c651f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from astropy.io import fits\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "from astropy.table import Table\n",
+    "from scipy.spatial import KDTree\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "72c3ff70-7ca2-47dd-b9b0-a5af920acf66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.utils.data import DataLoader, dataset, TensorDataset\n",
+    "from torch import nn, optim\n",
+    "from torch.optim import lr_scheduler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2d185382-9ec6-4f0e-ab83-2540ad6c23c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import flowtorch.bijectors as bij\n",
+    "import flowtorch.distributions as dist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2ff32452-a5af-4de6-b379-1d3dd8c84e8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../insight')\n",
+    "from archive import archive \n",
+    "from insight_arch import Photoz_network\n",
+    "from insight import Insight_module\n",
+    "from utils import sigma68, nmad, plot_photoz_estimates\n",
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "318c81b7-e736-4283-bb18-f4ae391dbff9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import sklearn.datasets as datasets\n",
+    "\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch import optim\n",
+    "\n",
+    "import torch.nn.functional as F\n",
+    "from torch.distributions.multivariate_normal import MultivariateNormal\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "805831cc-5188-44b3-8b7b-88b8b6bcaf39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import rcParams\n",
+    "rcParams[\"mathtext.fontset\"] = \"stix\"\n",
+    "rcParams[\"font.family\"] = \"STIXGeneral\"\n",
+    "parent_dir = '/data/astro/scratch/lcabayol/Euclid/NNphotozs/Euclid_EXT_MER_PHZ_DC2_v1.5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f38092a4-3a04-47fc-bf67-8f5c335d8983",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "photoz_archive = archive(path = parent_dir, Qz_cut=0.5)\n",
+    "f, ferr, specz, specqz = photoz_archive.get_training_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d31ab0bb-fa69-40f6-ae3a-f903a4e7191b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dset = TensorDataset(torch.Tensor(f), torch.Tensor(specz))\n",
+    "loader = DataLoader(dset, batch_size=64, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dd0cfbd0-6e0c-4f05-8e36-ebd7e32e3b70",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Define the base distribution (standard Gaussian)\n",
+    "base_distribution = torch.distributions.Normal(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "696d1a11-ba4d-492e-a5e0-6158071799e9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class SimpleAffine(nn.Module):\n",
+    "    def __init__(self, dim=2):\n",
+    "        super().__init__()\n",
+    "        self.dim = dim\n",
+    "        self.a = nn.Parameter(torch.zeros(self.dim))  # log_scale\n",
+    "        self.b = nn.Parameter(torch.zeros(self.dim))  # shift\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        y = torch.exp(self.a) * x + self.b\n",
+    "\n",
+    "        det_jac = torch.exp(self.a.sum())\n",
+    "        log_det_jac = torch.ones(y.shape[0]) * torch.log(det_jac)\n",
+    "\n",
+    "        return y, log_det_jac\n",
+    "\n",
+    "    def inverse(self, y):\n",
+    "        x = (y - self.b) / torch.exp(self.a)\n",
+    "\n",
+    "        det_jac = 1 / torch.exp(self.a.sum())\n",
+    "        inv_log_det_jac = torch.ones(y.shape[0]) * torch.log(det_jac)\n",
+    "\n",
+    "        return x, inv_log_det_jac\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "00a5e648-3bab-4e61-b8ef-3504e5865b48",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class StackSimpleAffine(nn.Module):\n",
+    "    def __init__(self, transforms, dim=2):\n",
+    "        super().__init__()\n",
+    "        self.dim = dim\n",
+    "        self.transforms = nn.ModuleList(transforms)\n",
+    "        self.distribution = MultivariateNormal(torch.zeros(self.dim), torch.eye(self.dim))\n",
+    "\n",
+    "    def log_probability(self, x):\n",
+    "        log_prob = torch.zeros(x.shape[0])\n",
+    "        for transform in reversed(self.transforms):\n",
+    "            x, inv_log_det_jac = transform.inverse(x)\n",
+    "            log_prob += inv_log_det_jac\n",
+    "\n",
+    "        log_prob += self.distribution.log_prob(x)\n",
+    "\n",
+    "        return log_prob\n",
+    "\n",
+    "    def rsample(self, num_samples):\n",
+    "        x = self.distribution.sample((num_samples,))\n",
+    "        log_prob = self.distribution.log_prob(x)\n",
+    "\n",
+    "        for transform in self.transforms:\n",
+    "            x, log_det_jac = transform.forward(x)\n",
+    "            log_prob += log_det_jac\n",
+    "\n",
+    "        return x, log_prob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "71402de3-2ffa-4b6a-bf8b-72483bf9f4c7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class RealNVPNode(nn.Module):\n",
+    "    def __init__(self, mask, hidden_size):\n",
+    "        super(RealNVPNode, self).__init__()\n",
+    "        self.dim = len(mask)\n",
+    "        self.mask = nn.Parameter(mask, requires_grad=False)\n",
+    "\n",
+    "        self.s_func = nn.Sequential(nn.Linear(in_features=self.dim, out_features=hidden_size), nn.LeakyReLU(),\n",
+    "                                    nn.Linear(in_features=hidden_size, out_features=hidden_size), nn.LeakyReLU(),\n",
+    "                                    nn.Linear(in_features=hidden_size, out_features=self.dim))\n",
+    "\n",
+    "        self.scale = nn.Parameter(torch.Tensor(self.dim))\n",
+    "\n",
+    "        self.t_func = nn.Sequential(nn.Linear(in_features=self.dim, out_features=hidden_size), nn.LeakyReLU(),\n",
+    "                                    nn.Linear(in_features=hidden_size, out_features=hidden_size), nn.LeakyReLU(),\n",
+    "                                    nn.Linear(in_features=hidden_size, out_features=self.dim))\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x_mask = x*self.mask\n",
+    "        s = self.s_func(x_mask) * self.scale\n",
+    "        t = self.t_func(x_mask)\n",
+    "\n",
+    "        y = x_mask + (1 - self.mask) * (x*torch.exp(s) + t)\n",
+    "\n",
+    "        # Sum for -1, since for every batch, and 1-mask, since the log_det_jac is 1 for y1:d = x1:d.\n",
+    "        log_det_jac = ((1 - self.mask) * s).sum(-1)\n",
+    "        return y, log_det_jac\n",
+    "\n",
+    "    def inverse(self, y):\n",
+    "        print('1',y[12],self.mask)\n",
+    "        y_mask = y * self.mask\n",
+    "        print('2',y_mask[12])\n",
+    "        s = self.s_func(y_mask) * self.scale\n",
+    "        print('3',s[12])\n",
+    "        print('3a',self.s_func(y_mask)[12])\n",
+    "        print('3b',self.scale)\n",
+    "        t = self.t_func(y_mask)\n",
+    "        print('4',t[12])\n",
+    "\n",
+    "        x = y_mask + (1-self.mask)*(y - t)*torch.exp(-s)\n",
+    "        print('5',x[12])\n",
+    "\n",
+    "        inv_log_det_jac = ((1 - self.mask) * -s).sum(-1)\n",
+    "\n",
+    "        return x, inv_log_det_jac\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b24a7301-114a-4c4c-94bb-fbd9ed8c5368",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    def inverse(self, y):\n",
+    "        print('1',y[12],self.mask)\n",
+    "        y_mask = y * self.mask\n",
+    "        print('2',y_mask[12])\n",
+    "        s = self.s_func(y_mask) * self.scale\n",
+    "        print('3',s[12])\n",
+    "        print('3a',self.s_func(y_mask)[12])\n",
+    "        print('3b',self.scale)\n",
+    "        t = self.t_func(y_mask)\n",
+    "        print('4',t[12])\n",
+    "\n",
+    "        x = y_mask + (1-self.mask)*(y - t)*torch.exp(-s)\n",
+    "        print('5',x[12])\n",
+    "\n",
+    "        inv_log_det_jac = ((1 - self.mask) * -s).sum(-1)\n",
+    "\n",
+    "        return x, inv_log_det_jac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "30bc7d3a-ad7a-491b-a474-ab8c775c7adf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class RealNVP(nn.Module):\n",
+    "    def __init__(self, masks, hidden_size):\n",
+    "        super(RealNVP, self).__init__()\n",
+    "\n",
+    "        self.dim = len(masks[0])\n",
+    "        self.hidden_size = hidden_size\n",
+    "\n",
+    "        self.masks = nn.ParameterList([nn.Parameter(torch.Tensor(mask), requires_grad=False) for mask in masks])\n",
+    "        self.layers = nn.ModuleList([RealNVPNode(mask, self.hidden_size) for mask in self.masks])\n",
+    "\n",
+    "        self.distribution = MultivariateNormal(torch.zeros(self.dim), torch.eye(self.dim))\n",
+    "\n",
+    "    def log_probability(self, x):\n",
+    "        log_prob = torch.zeros(x.shape[0])\n",
+    "        for layer in reversed(self.layers):\n",
+    "            x, inv_log_det_jac = layer.inverse(x)\n",
+    "            log_prob += inv_log_det_jac\n",
+    "        log_prob += self.distribution.log_prob(x)\n",
+    "\n",
+    "        return log_prob\n",
+    "\n",
+    "    def rsample(self, num_samples):\n",
+    "        x = self.distribution.sample((num_samples,))\n",
+    "        log_prob = self.distribution.log_prob(x)\n",
+    "\n",
+    "        for layer in self.layers:\n",
+    "            x, log_det_jac = layer.forward(x)\n",
+    "            log_prob += log_det_jac\n",
+    "\n",
+    "        return x, log_prob\n",
+    "\n",
+    "    def sample_each_step(self, num_samples):\n",
+    "        samples = []\n",
+    "\n",
+    "        x = self.distribution.sample((num_samples,))\n",
+    "        samples.append(x.detach().numpy())\n",
+    "\n",
+    "        for layer in self.layers:\n",
+    "            x, _ = layer.forward(x)\n",
+    "            samples.append(x.detach().numpy())\n",
+    "\n",
+    "        return samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "7ab5da84-c3f6-4e35-bfee-2c2849e69c49",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "torch.manual_seed(2)\n",
+    "np.random.seed(0)\n",
+    "ninput=6\n",
+    "nlayers= 4\n",
+    "\n",
+    "masks = torch.randint(0, 2, size=(nlayers,ninput), dtype=torch.float32)\n",
+    "hidden_size = 32\n",
+    "\n",
+    "NVP_model = RealNVP(masks, hidden_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "b76103e4-7d77-4fbc-bdec-95d4070e4b9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(model, data, epochs = 100, batch_size = 64):\n",
+    "    train_loader = torch.utils.data.DataLoader(data, batch_size=batch_size)\n",
+    "    optimizer = torch.optim.Adam(model.parameters())\n",
+    "    \n",
+    "    losses = []\n",
+    "    with tqdm.tqdm(range(epochs), unit=' Epoch') as tepoch:\n",
+    "        epoch_loss = 0\n",
+    "        for epoch in tepoch:\n",
+    "            for training_sample, training_label in train_loader:\n",
+    "                log_prob = model.log_probability(training_sample)\n",
+    "                loss = - log_prob.mean(0)\n",
+    "\n",
+    "                optimizer.zero_grad()\n",
+    "                loss.backward()\n",
+    "                optimizer.step()\n",
+    "\n",
+    "                epoch_loss += loss\n",
+    "            epoch_loss /= len(train_loader)\n",
+    "            losses.append(np.copy(epoch_loss.detach().numpy()))\n",
+    "            tepoch.set_postfix(loss=epoch_loss.detach().numpy())\n",
+    "\n",
+    "    return model, losses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "9e909036-7fcf-436c-9b73-8664c19a9081",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/1000 [00:00<?, ? Epoch/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 tensor([0.5588, 0.5051, 0.8231, 0.8420, 0.8778, 0.7652]) Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 1., 1.])\n",
+      "2 tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.8778, 0.7652])\n",
+      "3 tensor([ 1.7198e+00, -1.7909e-42,  1.1632e-19, -4.0638e-43, -7.0065e-45,\n",
+      "        -1.6816e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([-0.0042, -0.1158,  0.0854, -0.0132, -0.0431, -0.1054],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41,  1.3620e-18,  3.0681e-41,  1.6395e-43,\n",
+      "         1.5975e-43], requires_grad=True)\n",
+      "4 tensor([ 0.0516,  0.2844, -0.0888,  0.0503, -0.1146,  0.2456],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([0.0908, 0.2207, 0.9120, 0.7917, 0.8778, 0.7652],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([0.0908, 0.2207, 0.9120, 0.7917, 0.8778, 0.7652],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([1., 1., 1., 1., 1., 1.])\n",
+      "2 tensor([0.0908, 0.2207, 0.9120, 0.7917, 0.8778, 0.7652],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 2.8026e-45,  5.6052e-44, -1.8217e-44, -1.4013e-45,  4.2039e-45,\n",
+      "         2.5223e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0154,  0.3602, -0.1164, -0.0254,  0.0323,  0.1755],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([1.4293e-43, 1.5554e-43, 1.5975e-43, 4.4842e-44, 1.4013e-43, 1.4153e-43],\n",
+      "       requires_grad=True)\n",
+      "4 tensor([-0.1686,  0.0381,  0.0629,  0.0570,  0.1637, -0.1467],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([0.0908, 0.2207, 0.9120, 0.7917, 0.8778, 0.7652],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([0.0908, 0.2207, 0.9120, 0.7917, 0.8778, 0.7652],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 0., 1., 0., 1.])\n",
+      "2 tensor([0.0000, 0.2207, 0.0000, 0.7917, 0.0000, 0.7652],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([-1.7079e+01, -2.4074e-42, -9.7591e-25, -2.7465e-43, -1.4013e-45,\n",
+      "         0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0419, -0.1555, -0.1058, -0.0089, -0.0292,  0.1085],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41,  9.2245e-24,  3.0681e-41,  4.4842e-44,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.1332, -0.1503,  0.1129, -0.0973, -0.0010,  0.0215],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([-1.1077e+06,  2.2067e-01,  7.9908e-01,  7.9167e-01,  8.7877e-01,\n",
+      "         7.6520e-01], grad_fn=<SelectBackward0>)\n",
+      "1 tensor([-1.1077e+06,  2.2067e-01,  7.9908e-01,  7.9167e-01,  8.7877e-01,\n",
+      "         7.6520e-01], grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 1., 0., 0., 1.])\n",
+      "2 tensor([-0.0000, 0.2207, 0.7991, 0.0000, 0.0000, 0.7652],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 8.5253e+01,  5.1988e-43, -1.5444e-23, -6.7795e-42,  4.2039e-45,\n",
+      "        -0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([-0.2090,  0.0336, -0.1061, -0.2210,  0.1072, -0.1044],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41,  1.4549e-22,  3.0681e-41,  4.4842e-44,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.0386, -0.0513, -0.0110, -0.1049, -0.0297, -0.1083],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([-1.0460e-31,  2.2067e-01,  7.9908e-01,  8.9654e-01,  9.0845e-01,\n",
+      "         7.6520e-01], grad_fn=<SelectBackward0>)\n",
+      "1 tensor([0.4438, 0.7342, 0.8857, 0.8182, 1.0118, 0.8227]) Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 1., 1.])\n",
+      "2 tensor([0.0000, 0.0000, 0.0000, 0.0000, 1.0118, 0.8227])\n",
+      "3 tensor([-5.9213e+00,  1.2348e-04,  8.4177e-05,  9.7108e-06, -9.8091e-45,\n",
+      "        -1.5414e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0145, -0.1235,  0.0842, -0.0097, -0.0561, -0.0953],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02, -1.0000e-03,  1.0000e-03, -1.0000e-03,  1.6395e-43,\n",
+      "         1.5975e-43], requires_grad=True)\n",
+      "4 tensor([ 0.0540,  0.2895, -0.0752,  0.0601, -0.1159,  0.2518],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([145.3751,   0.4447,   0.9609,   0.7580,   1.0118,   0.8227],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([145.3751,   0.4447,   0.9609,   0.7580,   1.0118,   0.8227],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([1., 1., 1., 1., 1., 1.])\n",
+      "2 tensor([145.3751,   0.4447,   0.9609,   0.7580,   1.0118,   0.8227],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 1.0930e-42,  9.8651e-43, -1.2948e-42, -1.1631e-43, -1.0159e-42,\n",
+      "         3.6854e-43], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 7.6440,  6.3394, -8.1033, -2.5874, -7.2520,  2.6020],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([1.4293e-43, 1.5554e-43, 1.5975e-43, 4.4842e-44, 1.4013e-43, 1.4153e-43],\n",
+      "       requires_grad=True)\n",
+      "4 tensor([-0.8677,  3.3569, -2.8468, -3.5257, -0.1022, -4.8004],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([145.3751,   0.4447,   0.9609,   0.7580,   1.0118,   0.8227],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([145.3751,   0.4447,   0.9609,   0.7580,   1.0118,   0.8227],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 0., 1., 0., 1.])\n",
+      "2 tensor([0.0000, 0.4447, 0.0000, 0.7580, 0.0000, 0.8227],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([-1.8761e+01, -2.1720e-42,  1.0339e-04, -8.7301e-43, -3.4766e-05,\n",
+      "         0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0460, -0.1404, -0.1034, -0.0284, -0.0348,  0.0987],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41, -1.0000e-03,  3.0681e-41,  1.0000e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.1274, -0.1601,  0.1239, -0.0925,  0.0108,  0.0282],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([2.0404e+10, 4.4467e-01, 8.3686e-01, 7.5802e-01, 1.0010e+00, 8.2265e-01],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([2.0404e+10, 4.4467e-01, 8.3686e-01, 7.5802e-01, 1.0010e+00, 8.2265e-01],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 1., 0., 0., 1.])\n",
+      "2 tensor([0.0000, 0.4447, 0.8369, 0.0000, 0.0000, 0.8227],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 8.1560e+01,  3.6994e-43, -1.3359e-23, -2.1037e-04, -1.1212e-04,\n",
+      "        -0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([-0.1999,  0.0239, -0.0918, -0.2104,  0.1121, -0.1003],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41,  1.4549e-22,  1.0000e-03, -1.0000e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.0333, -0.0449, -0.0201, -0.0945, -0.0219, -0.1029],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([7.7397e-26, 4.4467e-01, 8.3686e-01, 8.5274e-01, 1.0229e+00, 8.2265e-01],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([0.5945, 0.6673, 1.0082, 1.0630, 0.6298, 1.0632]) Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 1., 1.])\n",
+      "2 tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.6298, 1.0632])\n",
+      "3 tensor([-1.4275e+00,  2.2543e-04,  1.5257e-04,  2.9404e-05, -9.8091e-45,\n",
+      "        -1.8217e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0035, -0.1137,  0.0782, -0.0148, -0.0614, -0.1098],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02, -1.9834e-03,  1.9510e-03, -1.9921e-03,  1.6395e-43,\n",
+      "         1.5975e-43], requires_grad=True)\n",
+      "4 tensor([ 0.0428,  0.3087, -0.0951,  0.0673, -0.1168,  0.2564],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([2.2998, 0.3586, 1.1031, 0.9957, 0.6298, 1.0632],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([2.2998, 0.3586, 1.1031, 0.9957, 0.6298, 1.0632],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([1., 1., 1., 1., 1., 1.])\n",
+      "2 tensor([2.2998, 0.3586, 1.1031, 0.9957, 0.6298, 1.0632],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 2.8026e-45,  6.1657e-44, -3.0829e-44,  1.4013e-45, -1.4013e-44,\n",
+      "         2.6625e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0150,  0.4005, -0.1945,  0.0333, -0.0999,  0.1882],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([1.4293e-43, 1.5554e-43, 1.5975e-43, 4.4842e-44, 1.4013e-43, 1.4153e-43],\n",
+      "       requires_grad=True)\n",
+      "4 tensor([-0.1687,  0.1566,  0.0153, -0.0972,  0.2347, -0.1636],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([2.2998, 0.3586, 1.1031, 0.9957, 0.6298, 1.0632],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([2.2998, 0.3586, 1.1031, 0.9957, 0.6298, 1.0632],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 0., 1., 0., 1.])\n",
+      "2 tensor([0.0000, 0.3586, 0.0000, 0.9957, 0.0000, 1.0632],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([-1.3367e+01, -2.4453e-42,  1.8307e-04, -8.7581e-43, -6.4590e-05,\n",
+      "         0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0328, -0.1580, -0.1104, -0.0286, -0.0339,  0.0970],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41, -1.6588e-03,  3.0681e-41,  1.9027e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.1458, -0.1714,  0.1541, -0.1088,  0.0262,  0.0556],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([1.3758e+06, 3.5860e-01, 9.4885e-01, 9.9569e-01, 6.0358e-01, 1.0632e+00],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([1.3758e+06, 3.5860e-01, 9.4885e-01, 9.9569e-01, 6.0358e-01, 1.0632e+00],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 1., 0., 0., 1.])\n",
+      "2 tensor([0.0000, 0.3586, 0.9489, 0.0000, 0.0000, 1.0632],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 8.9259e+01,  4.2039e-43, -1.4470e-23, -8.3781e-05, -2.1851e-04,\n",
+      "        -0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([-0.2188,  0.0272, -0.0995, -0.2110,  0.1293, -0.1077],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41,  1.4549e-22,  3.9711e-04, -1.6900e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.0407, -0.0533, -0.0131, -0.0925, -0.0134, -0.1099],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([2.3654e-33, 3.5860e-01, 9.4885e-01, 1.0883e+00, 6.1713e-01, 1.0632e+00],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([0.3671, 0.6055, 0.8315, 0.6872, 0.8595, 0.6406]) Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 1., 1.])\n",
+      "2 tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.8595, 0.6406])\n",
+      "3 tensor([-2.2511e+00,  3.5428e-04,  2.5944e-04,  6.4027e-05, -5.6052e-45,\n",
+      "        -1.6816e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0055, -0.1211,  0.0903, -0.0216, -0.0372, -0.1062],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02, -2.9244e-03,  2.8734e-03, -2.9608e-03,  1.6395e-43,\n",
+      "         1.5975e-43], requires_grad=True)\n",
+      "4 tensor([ 0.0506,  0.2929, -0.0717,  0.0664, -0.1106,  0.2497],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([3.0063, 0.3126, 0.9030, 0.6207, 0.8595, 0.6406],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([3.0063, 0.3126, 0.9030, 0.6207, 0.8595, 0.6406],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([1., 1., 1., 1., 1., 1.])\n",
+      "2 tensor([3.0063, 0.3126, 0.9030, 0.6207, 0.8595, 0.6406],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 0.0000e+00,  5.6052e-44, -4.0638e-44,  0.0000e+00, -2.1019e-44,\n",
+      "         1.8217e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0037,  0.3620, -0.2533,  0.0088, -0.1496,  0.1268],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([1.4293e-43, 1.5554e-43, 1.5975e-43, 4.4842e-44, 1.4013e-43, 1.4153e-43],\n",
+      "       requires_grad=True)\n",
+      "4 tensor([-0.1916,  0.1869, -0.0529, -0.1140,  0.2124, -0.2159],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([3.0063, 0.3126, 0.9030, 0.6207, 0.8595, 0.6406],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([3.0063, 0.3126, 0.9030, 0.6207, 0.8595, 0.6406],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 0., 1., 0., 1.])\n",
+      "2 tensor([0.0000, 0.3126, 0.0000, 0.6207, 0.0000, 0.6406],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([-2.6548e+01, -2.2547e-42,  2.2858e-04, -2.9427e-43, -7.2068e-05,\n",
+      "         0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0651, -0.1457, -0.0926, -0.0096, -0.0253,  0.0991],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41, -2.4675e-03,  3.0681e-41,  2.8481e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.1164, -0.1397,  0.1269, -0.0896,  0.0158,  0.0032],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([9.7882e+11, 3.1256e-01, 7.7596e-01, 6.2075e-01, 8.4374e-01, 6.4059e-01],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([9.7882e+11, 3.1256e-01, 7.7596e-01, 6.2075e-01, 8.4374e-01, 6.4059e-01],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 1., 0., 0., 1.])\n",
+      "2 tensor([0.0000, 0.3126, 0.7760, 0.0000, 0.0000, 0.6406],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 7.3881e+01,  4.1478e-43, -1.4058e-23, -1.4430e-05, -2.3971e-04,\n",
+      "        -0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([-0.1811,  0.0268, -0.0966, -0.2122,  0.0955, -0.1013],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41,  1.4549e-22,  6.8008e-05, -2.5105e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.0324, -0.0501, -0.0138, -0.0781, -0.0187, -0.1029],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([8.0267e-21, 3.1256e-01, 7.7596e-01, 6.9887e-01, 8.6262e-01, 6.4059e-01],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([0.3599, 0.4452, 0.6363, 0.6568, 0.8217, 0.5831]) Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 1., 1.])\n",
+      "2 tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.8217, 0.5831])\n",
+      "3 tensor([-2.7510e+00,  4.6408e-04,  3.5757e-04,  1.0549e-04, -5.6052e-45,\n",
+      "        -1.6816e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0067, -0.1220,  0.0946, -0.0271, -0.0324, -0.1089],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02, -3.8024e-03,  3.7790e-03, -3.8909e-03,  1.6395e-43,\n",
+      "         1.5975e-43], requires_grad=True)\n",
+      "4 tensor([ 0.0502,  0.2949, -0.0671,  0.0713, -0.1096,  0.2505],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([4.8498, 0.1503, 0.7032, 0.5854, 0.8217, 0.5831],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([4.8498, 0.1503, 0.7032, 0.5854, 0.8217, 0.5831],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([1., 1., 1., 1., 1., 1.])\n",
+      "2 tensor([4.8498, 0.1503, 0.7032, 0.5854, 0.8217, 0.5831],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 1.6816e-44,  6.3058e-44, -6.0256e-44, -2.8026e-45, -3.7835e-44,\n",
+      "         2.3822e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.1170,  0.4072, -0.3790, -0.0501, -0.2706,  0.1699],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([1.4293e-43, 1.5554e-43, 1.5975e-43, 4.4842e-44, 1.4013e-43, 1.4153e-43],\n",
+      "       requires_grad=True)\n",
+      "4 tensor([-0.2488,  0.2633, -0.0972, -0.1550,  0.2050, -0.2836],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([4.8498, 0.1503, 0.7032, 0.5854, 0.8217, 0.5831],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([4.8498, 0.1503, 0.7032, 0.5854, 0.8217, 0.5831],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 0., 1., 0., 1.])\n",
+      "2 tensor([0.0000, 0.1503, 0.0000, 0.5854, 0.0000, 0.5831],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([-3.0039e+01, -2.3514e-42,  2.9381e-04,  2.9427e-44, -9.0328e-05,\n",
+      "         0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0736, -0.1519, -0.0883,  0.0010, -0.0237,  0.1050],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41, -3.3290e-03,  3.0681e-41,  3.8152e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.1173, -0.1291,  0.1340, -0.0913,  0.0184, -0.0068],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([5.2589e+13, 1.5031e-01, 5.6904e-01, 5.8540e-01, 8.0341e-01, 5.8313e-01],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([5.2589e+13, 1.5031e-01, 5.6904e-01, 5.8540e-01, 8.0341e-01, 5.8313e-01],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 1., 0., 0., 1.])\n",
+      "2 tensor([0.0000, 0.1503, 0.5690, 0.0000, 0.0000, 0.5831],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 6.4794e+01,  5.7874e-43, -1.6851e-23,  4.1644e-06, -2.5950e-04,\n",
+      "        -0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([-0.1588,  0.0374, -0.1158, -0.2219,  0.0764, -0.1046],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41,  1.4549e-22, -1.8769e-05, -3.3987e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.0303, -0.0575, -0.0114, -0.0870, -0.0141, -0.1069],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([3.8119e-15, 1.5031e-01, 5.6904e-01, 6.7237e-01, 8.1776e-01, 5.8313e-01],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([0.4056, 0.6462, 1.1727, 0.6615, 0.8440, 0.8692]) Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 1., 1.])\n",
+      "2 tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.8440, 0.8692])\n",
+      "3 tensor([-6.2239e+00,  6.2491e-04,  4.2841e-04,  1.2205e-04, -8.4078e-45,\n",
+      "        -1.5414e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0153, -0.1327,  0.0917, -0.0253, -0.0556, -0.1005],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02, -4.7106e-03,  4.6738e-03, -4.8189e-03,  1.6395e-43,\n",
+      "         1.5975e-43], requires_grad=True)\n",
+      "4 tensor([ 0.0595,  0.3030, -0.0656,  0.0827, -0.1075,  0.2563],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([174.6882,   0.3430,   1.2378,   0.5787,   0.8440,   0.8692],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([174.6882,   0.3430,   1.2378,   0.5787,   0.8440,   0.8692],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([1., 1., 1., 1., 1., 1.])\n",
+      "2 tensor([174.6882,   0.3430,   1.2378,   0.5787,   0.8440,   0.8692],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 1.3214e-42,  1.1785e-42, -1.5512e-42, -1.4153e-43, -1.2233e-42,\n",
+      "         4.4281e-43], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 9.2428,  7.5791, -9.7105, -3.1466, -8.7261,  3.1238],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([1.4293e-43, 1.5554e-43, 1.5975e-43, 4.4842e-44, 1.4013e-43, 1.4153e-43],\n",
+      "       requires_grad=True)\n",
+      "4 tensor([-0.9876,  3.9710, -3.4243, -4.2180, -0.1421, -5.7226],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([174.6882,   0.3430,   1.2378,   0.5787,   0.8440,   0.8692],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([174.6882,   0.3430,   1.2378,   0.5787,   0.8440,   0.8692],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 0., 1., 0., 1.])\n",
+      "2 tensor([0.0000, 0.3430, 0.0000, 0.5787, 0.0000, 0.8692],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([-3.0317e+01, -2.1146e-42,  3.9720e-04, -5.4791e-43, -1.1751e-04,\n",
+      "         0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0743, -0.1366, -0.0942, -0.0179, -0.0245,  0.0877],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41, -4.2157e-03,  3.0681e-41,  4.7937e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.1220, -0.1520,  0.1610, -0.0913,  0.0317,  0.0079],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([2.5601e+15, 3.4296e-01, 1.0764e+00, 5.7872e-01, 8.1232e-01, 8.6921e-01],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([2.5601e+15, 3.4296e-01, 1.0764e+00, 5.7872e-01, 8.1232e-01, 8.6921e-01],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 1., 0., 0., 1.])\n",
+      "2 tensor([0.0000, 0.3430, 1.0764, 0.0000, 0.0000, 0.8692],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 7.6851e+01,  2.8586e-43, -1.2641e-23,  6.1521e-06, -5.4445e-04,\n",
+      "        -0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([-0.1884,  0.0185, -0.0869, -0.2044,  0.1258, -0.1059],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41,  1.4549e-22, -3.0105e-05, -4.3263e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 0.0393, -0.0543, -0.0076, -0.0562, -0.0051, -0.1046],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([1.0773e-18, 3.4296e-01, 1.0764e+00, 6.3493e-01, 8.1790e-01, 8.6921e-01],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([ 4.9432e-01, -3.3693e+02, -2.6266e-03,  8.0530e-01,  1.1569e+00,\n",
+      "         4.9910e-01]) Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 1., 1.])\n",
+      "2 tensor([0.0000, -0.0000, -0.0000, 0.0000, 1.1569, 0.4991])\n",
+      "3 tensor([-1.1122e+01,  7.6076e-04,  5.1235e-04,  1.8960e-04, -5.6052e-45,\n",
+      "        -1.5414e-44], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 0.0273, -0.1355,  0.0921, -0.0330, -0.0376, -0.1006],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02, -5.6132e-03,  5.5629e-03, -5.7538e-03,  1.6395e-43,\n",
+      "         1.5975e-43], requires_grad=True)\n",
+      "4 tensor([ 0.0527,  0.2966, -0.0330,  0.0852, -0.1149,  0.2606],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([ 2.9858e+04, -3.3697e+02,  3.0377e-02,  7.1999e-01,  1.1569e+00,\n",
+      "         4.9910e-01], grad_fn=<SelectBackward0>)\n",
+      "1 tensor([ 2.9858e+04, -3.3697e+02,  3.0377e-02,  7.1999e-01,  1.1569e+00,\n",
+      "         4.9910e-01], grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([1., 1., 1., 1., 1., 1.])\n",
+      "2 tensor([ 2.9858e+04, -3.3697e+02,  3.0377e-02,  7.1999e-01,  1.1569e+00,\n",
+      "         4.9910e-01], grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 2.3406e-40,  1.9776e-40, -2.6723e-40, -2.5654e-41, -2.1221e-40,\n",
+      "         7.6214e-41], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 1637.5739,  1271.4023, -1672.8444,  -572.1075, -1514.4027,   538.4979],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([1.4293e-43, 1.5554e-43, 1.5975e-43, 4.4842e-44, 1.4013e-43, 1.4153e-43],\n",
+      "       requires_grad=True)\n",
+      "4 tensor([-121.6049,  637.7076, -592.6364, -720.3204,  -24.3376, -932.0830],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([ 2.9858e+04, -3.3697e+02,  3.0377e-02,  7.1999e-01,  1.1569e+00,\n",
+      "         4.9910e-01], grad_fn=<SelectBackward0>)\n",
+      "1 tensor([ 2.9858e+04, -3.3697e+02,  3.0377e-02,  7.1999e-01,  1.1569e+00,\n",
+      "         4.9910e-01], grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 0., 1., 0., 1.])\n",
+      "2 tensor([   0.0000, -336.9679,    0.0000,    0.7200,    0.0000,    0.4991],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([ 9.5781e+02,  3.8436e-41,  2.9154e-02, -2.2716e-40,  1.8815e-02,\n",
+      "         0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([-2.3479,  2.4838, -5.6998, -7.4039,  3.2533,  4.4941],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0794e+02,  1.5475e-41, -5.1150e-03,  3.0681e-41,  5.7835e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([ 4.1454, -8.8374, 10.5911, -3.9252,  2.3440, -0.2137],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([   0.0000, -336.9679,  -10.2573,    0.7200,   -1.1650,    0.4991],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([   0.0000, -336.9679,  -10.2573,    0.7200,   -1.1650,    0.4991],\n",
+      "       grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 1., 0., 0., 1.])\n",
+      "2 tensor([   0.0000, -336.9679,  -10.2573,    0.0000,   -0.0000,    0.4991],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3 tensor([-2.3509e+03,  3.0007e-40, -7.0704e-22, -2.2626e-04, -2.0016e-03,\n",
+      "        -0.0000e+00], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([ 5.7628, 19.3911, -4.8596, -3.7468,  0.3790, -8.8993],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([-4.0793e+02,  1.5475e-41,  1.4549e-22,  6.0388e-05, -5.2811e-03,\n",
+      "         0.0000e+00], requires_grad=True)\n",
+      "4 tensor([29.4003,  5.9710, 10.3656, 13.7828,  5.3600, -5.4054],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "5 tensor([     -inf, -336.9679,  -10.2573,  -13.0658,   -6.5381,    0.4991],\n",
+      "       grad_fn=<SelectBackward0>)\n",
+      "1 tensor([0.3204, 0.6881, 0.7270, 0.8821, 0.7997, 0.8430]) Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 1., 1.])\n",
+      "2 tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.7997, 0.8430])\n",
+      "3 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([nan, nan, nan, nan, nan, nan], requires_grad=True)\n",
+      "4 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "5 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "1 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([1., 1., 1., 1., 1., 1.])\n",
+      "2 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([nan, nan, nan, nan, nan, nan], requires_grad=True)\n",
+      "4 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "5 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "1 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 0., 1., 0., 1.])\n",
+      "2 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([nan, nan, nan, nan, nan, nan], requires_grad=True)\n",
+      "4 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "5 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "1 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>) Parameter containing:\n",
+      "tensor([0., 1., 1., 0., 0., 1.])\n",
+      "2 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3a tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "3b Parameter containing:\n",
+      "tensor([nan, nan, nan, nan, nan, nan], requires_grad=True)\n",
+      "4 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n",
+      "5 tensor([nan, nan, nan, nan, nan, nan], grad_fn=<SelectBackward0>)\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Expected value argument (Tensor of shape (64, 6)) to be within the support (IndependentConstraint(Real(), 1)) of the distribution MultivariateNormal(loc: torch.Size([6]), covariance_matrix: torch.Size([6, 6])), but found invalid values:\ntensor([[nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan]], grad_fn=<AddBackward0>)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[155], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model, loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mNVP_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[154], line 10\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, data, epochs, batch_size)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m tepoch:\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m training_sample, training_label \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m---> 10\u001b[0m         log_prob \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_probability\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining_sample\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m         loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m log_prob\u001b[38;5;241m.\u001b[39mmean(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     13\u001b[0m         optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n",
+      "Cell \u001b[0;32mIn[140], line 18\u001b[0m, in \u001b[0;36mRealNVP.log_probability\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     16\u001b[0m     x, inv_log_det_jac \u001b[38;5;241m=\u001b[39m layer\u001b[38;5;241m.\u001b[39minverse(x)\n\u001b[1;32m     17\u001b[0m     log_prob \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m inv_log_det_jac\n\u001b[0;32m---> 18\u001b[0m log_prob \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m log_prob\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/torch/distributions/multivariate_normal.py:206\u001b[0m, in \u001b[0;36mMultivariateNormal.log_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlog_prob\u001b[39m(\u001b[38;5;28mself\u001b[39m, value):\n\u001b[1;32m    205\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_args:\n\u001b[0;32m--> 206\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    207\u001b[0m     diff \u001b[38;5;241m=\u001b[39m value \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc\n\u001b[1;32m    208\u001b[0m     M \u001b[38;5;241m=\u001b[39m _batch_mahalanobis(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_unbroadcasted_scale_tril, diff)\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/torch/distributions/distribution.py:286\u001b[0m, in \u001b[0;36mDistribution._validate_sample\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    284\u001b[0m valid \u001b[38;5;241m=\u001b[39m support\u001b[38;5;241m.\u001b[39mcheck(value)\n\u001b[1;32m    285\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m valid\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 286\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    287\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected value argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    288\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(value)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtuple\u001b[39m(value\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    289\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto be within the support (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(support)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    290\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof the distribution \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    291\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut found invalid values:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    292\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: Expected value argument (Tensor of shape (64, 6)) to be within the support (IndependentConstraint(Real(), 1)) of the distribution MultivariateNormal(loc: torch.Size([6]), covariance_matrix: torch.Size([6, 6])), but found invalid values:\ntensor([[nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan],\n        [nan, nan, nan, nan, nan, nan]], grad_fn=<AddBackward0>)"
+     ]
+    }
+   ],
+   "source": [
+    "model, loss = train(NVP_model, dset, epochs = 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1744cff-3b04-432f-a776-ee0bc66e6fc5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69fc34aa-721f-4c1d-a6c9-96678ca47e74",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08a91b95-2ae0-4d7b-891b-a92c95124f40",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "649f9039-2579-49eb-b8e6-e7c9b6614cce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.manual_seed(42)\n",
+    "\n",
+    "# Create the dataset\n",
+    "n_samples = 1000\n",
+    "dataset = create_dataset(n_samples)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Training the model\n",
+    "\n",
+    "\n",
+    "# Generate samples from the model\n",
+    "n_generated_samples = 1000\n",
+    "with torch.no_grad():\n",
+    "    z_samples = base_distribution.sample((n_generated_samples,))\n",
+    "    generated_samples, _ = flow_model(z_samples)\n",
+    "\n",
+    "# Plot the generated samples and the original dataset\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.scatter(dataset[:, 0], dataset[:, 1], label=\"Original Data\", alpha=0.5)\n",
+    "plt.scatter(generated_samples[:, 0], generated_samples[:, 1], label=\"Generated Data\", alpha=0.5)\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "5a59d279-c2f0-473b-b5b3-a26524105b5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "shape '[64, 6]' is invalid for input of size 128",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[88], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ff,zs \u001b[38;5;129;01min\u001b[39;00m loader:\n\u001b[1;32m      4\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m----> 5\u001b[0m     loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[43mflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mff\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mzs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mmean()\n\u001b[1;32m      6\u001b[0m     loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m      7\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mstep()\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/distributions/base.py:40\u001b[0m, in \u001b[0;36mDistribution.log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inputs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m context\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     38\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of input items must be equal to number of context items.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     39\u001b[0m         )\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/flows/base.py:40\u001b[0m, in \u001b[0;36mFlow._log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     38\u001b[0m embedded_context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_embedding_net(context)\n\u001b[1;32m     39\u001b[0m noise, logabsdet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform(inputs, context\u001b[38;5;241m=\u001b[39membedded_context)\n\u001b[0;32m---> 40\u001b[0m log_prob \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_distribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnoise\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membedded_context\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m log_prob \u001b[38;5;241m+\u001b[39m logabsdet\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/distributions/base.py:40\u001b[0m, in \u001b[0;36mDistribution.log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inputs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m context\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     38\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of input items must be equal to number of context items.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     39\u001b[0m         )\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/distributions/normal.py:104\u001b[0m, in \u001b[0;36mConditionalDiagonalNormal._log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     98\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected input of shape \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, got \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m     99\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shape, inputs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m    100\u001b[0m         )\n\u001b[1;32m    101\u001b[0m     )\n\u001b[1;32m    103\u001b[0m \u001b[38;5;66;03m# Compute parameters.\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m means, log_stds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m means\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m inputs\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;129;01mand\u001b[39;00m log_stds\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m inputs\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    107\u001b[0m \u001b[38;5;66;03m# Compute log prob.\u001b[39;00m\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/distributions/normal.py:91\u001b[0m, in \u001b[0;36mConditionalDiagonalNormal._compute_params\u001b[0;34m(self, context)\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m     87\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe batch dimension of the parameters is inconsistent with the input.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     88\u001b[0m     )\n\u001b[1;32m     90\u001b[0m split \u001b[38;5;241m=\u001b[39m params\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m---> 91\u001b[0m means \u001b[38;5;241m=\u001b[39m \u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43msplit\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_shape\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     92\u001b[0m log_stds \u001b[38;5;241m=\u001b[39m params[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, split:]\u001b[38;5;241m.\u001b[39mreshape(params\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shape)\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m means, log_stds\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: shape '[64, 6]' is invalid for input of size 128"
+     ]
+    }
+   ],
+   "source": [
+    "num_iter = 5000\n",
+    "for i in range(num_iter):\n",
+    "    for ff,zs in loader:\n",
+    "        optimizer.zero_grad()\n",
+    "        loss = -flow.log_prob(inputs=ff, context=zs.reshape(-1, 1)).mean()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "    assert False\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44951507-a9b9-4e82-af75-b85077b8e0ca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84429cb5-1b27-4e39-b929-e5786170ecc2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dd35aa4-6014-40be-92d4-8261b13d8f79",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7585a7c8-dd92-4c4f-b912-a15e24529aad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    if (i + 1) % 500 == 0:\n",
+    "        fig, ax = plt.subplots(1, 2)\n",
+    "        xline = torch.linspace(-1.5, 2.5,100)\n",
+    "        yline = torch.linspace(-.75, 1.25,100)\n",
+    "        xgrid, ygrid = torch.meshgrid(xline, yline)\n",
+    "        xyinput = torch.cat([xgrid.reshape(-1, 1), ygrid.reshape(-1, 1)], dim=1)\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            zgrid0 = flow.log_prob(xyinput, torch.zeros(10000, 1)).exp().reshape(100, 100)\n",
+    "            zgrid1 = flow.log_prob(xyinput, torch.ones(10000, 1)).exp().reshape(100, 100)\n",
+    "\n",
+    "        ax[0].contourf(xgrid.numpy(), ygrid.numpy(), zgrid0.numpy())\n",
+    "        ax[1].contourf(xgrid.numpy(), ygrid.numpy(), zgrid1.numpy())\n",
+    "        plt.title('iteration {}'.format(i + 1))\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f87baaeb-f79f-4f2e-aae9-b214e43c956d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a667868-cd33-4f85-859d-b442f1ae2147",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5171aded-051a-449e-9366-1ed475055e8a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbffa505-39ee-4110-8e30-98d0cb120070",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff3d3c2c-de69-41da-bae9-be3c23c870d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "088610df-de04-4d12-adbb-e3d90dece471",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88d558dc-0470-43dc-a174-b8c99841c616",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45f3e343-5fac-4049-99c5-e9ca2945f54a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "3b21662d-3825-4d10-ad34-8989c760abe9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "819d2620-38ca-4e2e-b0f8-6a0ef56089ec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "base_dist = torch.distributions.Independent(\n",
+    "  torch.distributions.Normal(torch.zeros(2), torch.ones(2)), \n",
+    "  1\n",
+    ")\n",
+    "target_dist = torch.distributions.Independent(\n",
+    "  torch.distributions.Normal(torch.zeros(2)+5, torch.ones(2)*0.5),\n",
+    "  1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "aabbaa36-feeb-40c6-929e-c26354438606",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_samples = 1000\n",
+    "samples_base = base_dist.sample((num_samples,))\n",
+    "samples_target = target_dist.sample((num_samples,))\n",
+    "\n",
+    "# Extract x and y coordinates for plotting\n",
+    "x_values_base = samples_base[:, 0].numpy()\n",
+    "y_values_base = samples_base[:, 1].numpy()\n",
+    "\n",
+    "x_values_target = samples_target[:, 0].numpy()\n",
+    "y_values_target = samples_target[:, 1].numpy()\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(x_values_base, y_values_base, alpha=0.5)\n",
+    "plt.scatter(x_values_target, y_values_target, alpha=0.5)\n",
+    "\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Samples from Base Distribution')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "164b546c-d46a-4f1e-81cc-a3cf2f72b7d0",
+   "metadata": {},
+   "source": [
+    "##### Normalizing flow with a single transformation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "edb22fa1-e69a-4742-b902-5e18189a6758",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "bijectors = bij.AffineAutoregressive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6fa8f7bd-956e-45d5-a242-68d36ab2ff9e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "flow = dist.Flow(base_dist, bijectors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ea37537e-1463-41ba-b8d2-c94aa0aa1fa0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 0 loss tensor(23.8129, grad_fn=<NegBackward0>)\n",
+      "epoch 500 loss tensor(3.6865, grad_fn=<NegBackward0>)\n",
+      "epoch 1000 loss tensor(3.2616, grad_fn=<NegBackward0>)\n",
+      "epoch 1500 loss tensor(3.0639, grad_fn=<NegBackward0>)\n",
+      "epoch 2000 loss tensor(2.5082, grad_fn=<NegBackward0>)\n",
+      "epoch 2500 loss tensor(1.6517, grad_fn=<NegBackward0>)\n",
+      "epoch 3000 loss tensor(1.4682, grad_fn=<NegBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Training loop\n",
+    "opt = torch.optim.Adam(flow.parameters(), lr=5e-3)\n",
+    "for idx in range(3001):\n",
+    "    opt.zero_grad()\n",
+    "\n",
+    "    # Minimize KL(p || q)\n",
+    "    y = target_dist.sample((1000,))\n",
+    "    loss = -flow.log_prob(y).mean()\n",
+    "\n",
+    "    if idx % 500 == 0:\n",
+    "        print('epoch', idx, 'loss', loss)\n",
+    "\n",
+    "    loss.backward()\n",
+    "    opt.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c6a98ae5-64b5-4b49-87cf-c6c4b0fc3e83",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dist_y = dist.Flow(base_dist, bijectors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "24208fa8-63a9-439f-9946-56d96fb33409",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "num_samples = 1000\n",
+    "samples_pred = dist_y.sample((num_samples,))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "37a2ff47-30db-45d3-849a-0e7c81ed7364",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_samples = 1000\n",
+    "samples_base = base_dist.sample((num_samples,))\n",
+    "samples_target = target_dist.sample((num_samples,))\n",
+    "\n",
+    "# Extract x and y coordinates for plotting\n",
+    "x_values_base = samples_base[:, 0].numpy()\n",
+    "y_values_base = samples_base[:, 1].numpy()\n",
+    "\n",
+    "x_values_target = samples_target[:, 0].numpy()\n",
+    "y_values_target = samples_target[:, 1].numpy()\n",
+    "\n",
+    "# Extract x and y coordinates for plotting\n",
+    "x_values_pred = samples_pred[:, 0].numpy()\n",
+    "y_values_pred = samples_pred[:, 1].numpy()\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(x_values_base, y_values_base, alpha=0.5)\n",
+    "plt.scatter(x_values_target, y_values_target, alpha=0.5)\n",
+    "plt.scatter(x_values_pred, y_values_pred, alpha=0.5)\n",
+    "\n",
+    "plt.xlabel('X')\n",
+    "plt.ylabel('Y')\n",
+    "plt.title('Samples from Base Distribution')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "db52a20f-f3df-4774-b2b7-2d7566ceb242",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from pylab import rcParams\n",
+    "rcParams['figure.figsize'] = 10, 8\n",
+    "rcParams['figure.dpi'] = 300\n",
+    "\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch import distributions\n",
+    "from torch.nn.parameter import Parameter\n",
+    "\n",
+    "from sklearn import cluster, datasets, mixture\n",
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "729a79a5-495d-432e-803f-5b3f130ecf45",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class RealNVP(nn.Module):\n",
+    "    def __init__(self, nets, nett, mask, prior):\n",
+    "        super(RealNVP, self).__init__()\n",
+    "        \n",
+    "        self.prior = prior\n",
+    "        self.mask = nn.Parameter(mask, requires_grad=False)\n",
+    "        self.t = torch.nn.ModuleList([nett() for _ in range(len(masks))])\n",
+    "        self.s = torch.nn.ModuleList([nets() for _ in range(len(masks))])\n",
+    "        \n",
+    "    def g(self, z):\n",
+    "        x = z\n",
+    "        for i in range(len(self.t)):\n",
+    "            x_ = x*self.mask[i]\n",
+    "            s = self.s[i](x_)*(1 - self.mask[i])\n",
+    "            t = self.t[i](x_)*(1 - self.mask[i])\n",
+    "            x = x_ + (1 - self.mask[i]) * (x * torch.exp(s) + t)\n",
+    "        return x\n",
+    "\n",
+    "    def f(self, x):\n",
+    "        log_det_J, z = x.new_zeros(x.shape[0]), x\n",
+    "        for i in reversed(range(len(self.t))):\n",
+    "            z_ = self.mask[i] * z\n",
+    "            s = self.s[i](z_) * (1-self.mask[i])\n",
+    "            t = self.t[i](z_) * (1-self.mask[i])\n",
+    "            z = (1 - self.mask[i]) * (z - t) * torch.exp(-s) + z_\n",
+    "            log_det_J -= s.sum(dim=1)\n",
+    "        return z, log_det_J\n",
+    "    \n",
+    "    def log_prob(self,x):\n",
+    "        z, logp = self.f(x)\n",
+    "        return self.prior.log_prob(z) + logp\n",
+    "        \n",
+    "    def sample(self, batchSize): \n",
+    "        z = self.prior.sample((batchSize, 1))\n",
+    "        logp = self.prior.log_prob(z)\n",
+    "        x = self.g(z)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "2f634ec2-7033-4ab5-bb8d-aeade4eba08c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "nets = lambda: nn.Sequential(nn.Linear(2, 256), nn.LeakyReLU(), nn.Linear(256, 256), nn.LeakyReLU(), nn.Linear(256, 2), nn.Tanh())\n",
+    "nett = lambda: nn.Sequential(nn.Linear(2, 256), nn.LeakyReLU(), nn.Linear(256, 256), nn.LeakyReLU(), nn.Linear(256, 2))\n",
+    "masks = torch.from_numpy(np.array([[0, 1], [1, 0]] * 3).astype(np.float32))\n",
+    "prior = distributions.MultivariateNormal(torch.zeros(2), torch.eye(2))\n",
+    "flow = RealNVP(nets, nett, masks, prior)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "702a29ed-3636-42ea-bead-2974c3af3f56",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0: loss = 2.715\n",
+      "iter 500: loss = 0.835\n",
+      "iter 1000: loss = 0.493\n",
+      "iter 1500: loss = 0.592\n",
+      "iter 2000: loss = 0.467\n",
+      "iter 2500: loss = 0.298\n",
+      "iter 3000: loss = 0.396\n",
+      "iter 3500: loss = 0.368\n",
+      "iter 4000: loss = 0.306\n",
+      "iter 4500: loss = 0.486\n",
+      "iter 5000: loss = 0.458\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer = torch.optim.Adam([p for p in flow.parameters() if p.requires_grad==True], lr=1e-4)\n",
+    "for t in range(5001):    \n",
+    "    noisy_moons = datasets.make_moons(n_samples=100, noise=.05)[0].astype(np.float32)\n",
+    "    loss = -flow.log_prob(torch.from_numpy(noisy_moons)).mean()\n",
+    "    \n",
+    "    optimizer.zero_grad()\n",
+    "    loss.backward(retain_graph=True)\n",
+    "    optimizer.step()\n",
+    "    \n",
+    "    if t % 500 == 0:\n",
+    "        print('iter %s:' % t, 'loss = %.3f' % loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "e81130c9-35b0-414d-b4cd-4d61bd768093",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "noisy_moons = datasets.make_moons(n_samples=1000, noise=.05)[0].astype(np.float32)\n",
+    "z = flow.f(torch.from_numpy(noisy_moons))[0].detach().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "c37ec61f-dddb-454f-b05e-93c1c9a38087",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.8747659e-01,  3.8629249e-01],\n",
+       "       [-4.5098123e-01,  1.1165810e+00],\n",
+       "       [ 2.7200544e-01,  4.9045309e-01],\n",
+       "       ...,\n",
+       "       [-1.9135862e-03, -1.3994721e+00],\n",
+       "       [ 1.8429850e+00,  4.9667689e-01],\n",
+       "       [ 2.3594198e+00, -1.4860994e-01]], dtype=float32)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "8e464e0d-449e-464c-b819-be28cdd29db0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '$X = g(z)$')"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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kX+zVGTasbveVl0e8+WZqY6mLAw6I6NMns3P27fu/pDIAAABAQ5DJYzw3l81jWCdOjHjwwYihQ7Mzf6ZtviA5Yst/5w1lsfLnbrghveMvXpx4hgYPrv2zXNedEDeX70cZO9EEAKgDRXsAUB8XXJA/O+xVNm9exNlnZzuK+jvxxETRW10SsStX5kbR4re/nfgYMinT8wEAAACkQzqO8axt8V+2j/QcNizisssiHn88setZQ7d0acSBByaK97b0d/6Tn+RG3i9VXnoptUfRbm7KlMTOevV9jmu7E+LmbrmlfnNnkxNNAIA6UrQHAHU1enT2E3L19dxz2Y6gfgoKIv75z7onYtevT3+M2/L50b7f/nZm5830fAAAAACplOpjPOtT/JftY1jHjUt8fNdfnyjgmzIlYocdshtTuq1Zkyhk29Lf+U03ZSeudEpHLnrKlIgBA1K3ML3yTog1jeGvf03N/NngRBMAoI4U7QFAXV1/fbYjoPIq59okYiMiiorSG19NfH60b8+eEYcfnpk5Py8UzBXZOroGAAAAyD+pPsazvsV/uXQM6+cf3y9/GbF2bbajIdVSXRy6eHHEMcekfkfCpUsjBg2q2VG5ixcnrt24MbUxZJITTQCAOlK0BwB1MXVq7ZOCZF51idjNtWiROL4gW4YOjTj22P+1L7kkM/N+XiiYTek4ugYAAABo2FJ5jOfeeyfyD/Ut/pszJ/eOYf3nP7MdAekwZkzEl7+cupzZBRekboe9yubNi7jwwprFMH9+emLIFCeaAAB1pGgPAOoi34/F3d6MHJlI6FY+lqGgIKJPn+zE1LFjxK23Jhevffe76Z+3cqFgpqX66BoAAABg+5DqYzwXLkzkH+pj5MiIgw9OTTywLeXlEc8/n5qc2ejR6c9xjxyZmCebMaRbrp1oAgDkFUV7AFAXqT6KgPSbNy+if/+qhXvZOL6gtDRRqPe1r229eC3VPi8UzIZUH10DAAAAbD/SdYxnKixYkO0I2F7VJ2d2/fXpiamyG27IfgzplAsnmgAAeUvRHgDUVnl5xKRJ2Y6Culi6NGLQoOQkVqaPL2jTJuKggyJ+8IPMHrFcWhrx1FOJ+TMtlUfXVLdjIgAAANCwpfMYT1Kje/f/nSbRuHG2o9m+1DZnNnVq5vKSL71U/VG+mYwhXbJ9ogkAkPcU7QFAba1cmZureqmZefMiLrzwf+3y8ohGGfqWqHXriCZNIp58MjPzfa5jx4ixYxPHZmRaqo+u2dKOiQAAAEDD1BCO0NweTJ8eMXt24u+qefNsR7P9qWnObOrUiHPPzUxMn6vu32++/5vO5okmAECDoWgPAGpr/fpsR0B9jRyZSPhOmRJx6KERmzZlZt716zN/ZMrQoYmPMxsFe+k6uqa6HRMBAACAhulXv8p2BNTU5zu+LVuW7Ui2T1vLmY0eHdGvX+Lv57XXMhvXhAk168sX2TzRBABoUBTtAUBtFRVlOwJS4ZprEgVlq1ZlZr5GjTI3V0QiCTd6dMR992UvgZTOo2sq75gIAAAANDz/+lfE+PHZjoLamD8/2xFs3yrnzBYvTizqHTw4e8fRTpyYOO3kc+XlEZMmZSeW+srmiSYAQIPTONsBAEDeadEi8bZyZbYjoT4yvaI03bv5lZZG9O0bccABEd/+dsQ++6R3vm3JxNE1I0cmko7HHZfeeQAAIE0mTZoU77//fsydOzciIjp16hR77bVX9O7dO6XzLFu2LMaNGxdz586NRYsWxU477RSdOnWKQw45JFq1apXSuQBSZsqUiFNPzXYUkH8+z5ntumti0XK6FtXW1NKlicXMLVok2itXpv5kjkwYOjRxJK4d9gCAFFG0BwC1tWSJI3LJPffem1gxmyuuvz4z89xwg6I9AACq+PDDD+ONN96I//znP/HGG2/EpEmTYuVmC686d+4cs2fPzkpsGzZsiN/85jdx5513xgcffFDtNd26dYuzzjorfvzjH0eTJk3qPNebb74Zv/jFL+KJJ56I9dX8HFtcXBzHHHNMXHnlldGrV686zwOQcosXJ4qN1q7NdiSwZaWlEV265OaucVdeGfHhh7lTHLdu3f+K9rKdW99nn4hp02p+fb9+EcOGRRx7bPpiAgC2S4r2AKC2LrggkWSAXPLrX9e/aK+8PLHSdf36xDHQLVpEFBTUfpypUzN33MZLLyWSbNneWRAAgKx78cUXY8SIEfGf//wnlixZku1wqvX+++/HySefHJO28cv9mTNnxqWXXhr//Oc/44EHHohu3brVeq7rrrsufv7zn8eGDRu2eM26deti1KhR8cQTT8Qvf/nLuOSSS2o9D0BaXHBB9ncHg63p2DHiqaciPvootxbSfm7ixGxHkKy4+H9/LirKXhxDh0bcd18in3j//RETJiQ+V5sXN+baiSYAQIOlaA8AaiMTR35CXdS1eG3q1P8lqCZNqpqg6tMnkaAaOrTmY2f638jdd0fcdFNm5wQAIOe89dZb8cwzz2Q7jC1asGBBDBw4MObMmZPU361bt9h7772jvLw83n777aTd9yZOnBhHH310jB8/Ptq1a1fjua699tq4/PLLk/qaNm0a+++/f+y8884xb968eOONN2Lt/9/Bav369TFs2LAoKCiIn/70p/X4KAFSQP6NXLf5Mak9eyYKuzyzW1ZaGtG8+f/aLVok+jK9C2DHjom/t4hEnvOaaxJ/Li9PHN+7bl2iuLB587otZAYAqKVG2Q4AAPJKpo78hLqoTXJw9OjE0Q5f+lLEiBERY8ZUTZQtXZroHzEikYDs1y/iiSe2PfaECbWLu74kRQEA2Iri4uLo2rVrVmPYtGlTnHDCCUkFezvvvHM8/fTT8f7778eoUaPi0UcfjZkzZ8aTTz4ZHTp0qLhu1qxZ8bWvfS3Ky8trNNfjjz8eV1xxRVLfOeecEx999FGMHTs2HnjggXjppZfio48+irPOOivpumHDhsVTTz1Vj48UIAXk38hV/folcmr33Zco2PvcbbclCsKoXt++yUVwBQWJhcKZ1LhxYmfEzf/eNo+nRYuInXaq+8kjAAB1oGgPAGoqk0d+Ql3UpFhu8eLEauDBg2v/PL/8csRxx0WcckpinOqUlyd27MukBQsiHn88s3MCAJCTmjRpEr169Yqzzjorbr/99pg4cWKsXLky7rzzzqzGdd9998Xrr79e0W7dunWMGzcujj766CrXDho0KMaNGxelpaUVfePGjYsHH3xwm/OUlZXFT37yk6QCvx/96Edx++23x0477ZR0bdu2beOOO+6IH/7whxV95eXlcfHFF0dZWVltPjyA1JF/I5eUlkYcdVTE8OGJZ3Ps2Ihjj616XZs2iYKwzf7vZjMHHFCzvnT63vcSi5IBAHKI43EbiEmTJsX7778fc+fOjYiITp06xV577RW9e/dO6TzLli2LcePGxdy5c2PRokWx0047RadOneKQQw6JVq1apXQugJxjNy9y3cSJiaK5La0GnTIl4phjIubNq988I0dGvPhiIhlZOdm1cmXmj7aISOwGOHhw5ucFACBnnHbaafH9738/dthhh2yHkqSsrCyuvPLKpL6bbropdt999y3e06VLl7jpppvi9NNPr+i74oor4lvf+lY0arTlddj33ntvTJ8+vaLdvXv3GDFixFbju+666+LJJ5+suO+dd96J++67L7773e9u9T6AlCgvT+QS1q+PKCpK5Bwgmw49NGLUqNofk9qzZ6Kob9Cg+ufeGppvf7tqX9++mY3hoosyOx8AQA0o2tuGDz/8MN544434z3/+E2+88UZMmjQpVq5cWfF6586dY/bs2VmJbcOGDfGb3/wm7rzzzvjggw+qvaZbt25x1llnxY9//ONo0qRJned688034xe/+EU88cQTsX79+iqvFxcXxzHHHBNXXnll9OrVq87zAOS0TB/5WVSUSFhCTS1dGrFqVeIYh8qmTIkYMCB1BXXz5kX0759IRm5euPfWW6kZv7bGjYuYNi1in32yMz8AAFlXmqO727zyyisxa9asinanTp3i1FNP3eZ93/nOd+KKK66oWKT7wQcfxLhx4+Kwww7b4j333ntvUvtHP/pRFBcXb3We4uLiuOiii+L8889PGkfRHpA2U6cmFsdOmJDYrX/zXEVjv7Yiy159NXGqQ11yTD17RjzwQMT3vx/xzjupjy0f9etX9XM5ZUrE2WdnNwYAgBzgeNxqvPjii/GVr3wl2rRpE127do2TTz45brzxxhg7dmxSwV42vf/++3HQQQfFZZddtsWCvYiImTNnxqWXXhoHH3xwzJw5s05zXXfddXHggQfGqFGjqi3Yi4hYt25djBo1Kg488MC44YYb6jQPQE7LxpGfmx1nBDX25ptV+xYvTuywl+od8JYuTaweXrw4YvToRAKsf//UzlEbdsMEACAHPfLII0nt7373u1FYWLjN+woLC6sU9z388MNbvH7x4sXx8mZHShYVFcXQoUNrFOMpp5yStOB37NixsWTJkhrdC1Bjn+cOvvSlxI75Y8ZUzVVs3Jid2GBzd91V+3s+f7779VOwt7lLLklupytPuTUXXBCxaFHEihVy7gBATlG0V4233nornnnmmZxNTC1YsCAGDhwYkyoVj3Tr1i2GDBkSxx9/fHTt2jXptYkTJ8bRRx8dn376aa3muvbaa+Oyyy6LDRs2VPQ1bdo0+vXrFyeddFIcfvjhSUeOrF+/PoYNGxa//vWv6/CRAeSwhx7K/JGfm33thRobMiSxYn1zF1yQvmM55s1LHGcxeHDEZr8gzIpM74YJAAA18NRTTyW1BwwYUON7K1/75JNPbvHaZ599NsrKyiraffv2jRbV7cJdjZYtW0afPn0q2hs3boxnn322xnECbNXixRFDh+ZG7gBq4oEHan6t53vr+vVLbqczT1mdoqKIE0+MaNs2oqQkok2biKOOihg+PHFqBwBAFinaq4Xi4uIqxXCZtmnTpjjhhBNizpw5FX0777xzPP300/H+++/HqFGj4tFHH42ZM2fGk08+GR06dKi4btasWfG1r30tymu4iuTxxx+PK664IqnvnHPOiY8++ijGjh0bDzzwQLz00kvx0UcfxVlnnZV03bBhw6okJAHy0udJl299K9uRQM0sW/a/3e8iEqt8070D3Wbfl2TVxIlWywIAkFPWrVtX5fSLgw46qMb3H3LIIUnt999/f4snYUyr9Ivngw8+uMbzVDfX22+/Xav7Aao1ZUpiZz2745NPFiyIePzx5L7y8sRObZvv2Ob53rZ16/7350zkKSur/H3T0qWJXT5HjEgcZ9yvX8QTT2Q2JgCA/0/R3hY0adIkevXqFWeddVbcfvvtMXHixFi5cmXceeedWY3rvvvui9dff72i3bp16xg3blwcffTRVa4dNGhQjBs3LkpLSyv6xo0bFw8++OA25ykrK4uf/OQnSQV+P/rRj+L222+PnXbaKenatm3bxh133BE//OEPK/rKy8vj4osvTlrdC5B3JF3IV/PmRVx4YeLP11+f3VgyaenSiFWrsh0FAABUmD59elJ+rF27dtGyZcsa39+yZcukXFxZWVnMmDGj2mvfqXQUX7du3WoVa+XFypXHA6i1KVMiBgzI7K5akCrXXZc4zWL48MTObG3aJHZq+3zHtlatEqdPeL63rrj4f3/OxTzlyy9HHHdcxCmn/G8RNABAhijaq8Zpp50WK1asiDfffDPuuOOOOOecc6JPnz7RpEmTrMZVVlYWV155ZVLfTTfdFLvvvvsW7+nSpUvcdNNNSX1XXHFFbNq0aatz3XvvvTF9+vSKdvfu3WPEiBFbvee6666L7t27V7TfeeeduO+++7Z6D0DOklQk340cGfH7329/x3JsvnoXAACyrPIue7vttlutx6h8z/vvv5+WuWo6D0CNLF4cccwxiQV2kI9efTWxoHvEiMTObJWf5RUrIjZuzE5s+aK0NKJ588Sfp07N7TzlyJGJv++pU7MdCQCwHVG0V43S0tLYYYcdsh1GFa+88krMmjWrot2pU6c49dRTt3nfd77znejUqVNF+4MPPohx48Zt9Z577703qf2jH/0oijdfDVON4uLiuOiii7Y6DkDGVXdswbZsr0nFfv0ibrstonHjbEdCquTi6tV028b3KwAAkEnLli1Lardr167WY1S+Z/ny5WmZq6bzANTIBRdYDAvbu759IwoKEn/Oh9Ns5s2L6N9f4R4AkDF+K59HHnnkkaT2d7/73SgsLNzmfYWFhXHqqafG9Zv94v7hhx+Oww47rNrrFy9eHC9vttqlqKgohg4dWqMYTznllLjoootiw4YNERExduzYWLJkSbRu3bpG9wOkxNSpiSTAhAkRkyYlF9+Vlkb06RNxwAERQ4dG7LNP1fu316Ti4MGJlaNWiDYcH3+c7Qgya/PVuwAAkANWrVqV1G7atGmtx6h8z8qVK9MyV03nqa1PP/00/vvf/9bqnsq7BgJ5ZvTo/CjQAdLrgAP+9+cJE7IXR20sXRoxaFDiJJ42bbIdDQDQwCnayyNPPfVUUnvAgAE1vnfAgAFJRXtPPvlklWNzP/fss89GWVlZRbtv377RokWLGs3TsmXL6NOnT7z++usREbFx48Z49tln46STTqpxrAB1Nnp0YmexrW2zv3Rp4jiDMWMSBWqHHRZx2WURxx77vzG216TiJZdkOwKon81X7wIAQA6oXEhXl9M9KhfTVR4zVXPVdJ7a+sMf/hBXX311SsYC8sT2uPM/UNW3v514X16eWFyfL+bNi7jwwoj77st2JABAA+d43Dyxbt26KitMDzrooBrff8ghhyS133///Vi/fn21106bNi2pffDBB9d4nurmevvtt2t1P0CtLV6c2DVv8OCtF+xV55VXIo47LmLvvRN/llSE/LX56l0AAMhBBXVYZFKXe+pyX13nAUgydWrt83NAw3PwwYliveHDI444Ivk0nHwwcmRigT8AQBrZaS9PTJ8+PWn3u3bt2kXLli1rfH/Lli1jp512ikWLFkVERFlZWcyYMSP2qeZYyHfeeSep3a1bt1rF2rVr162OB5BSU6ZEHHNM/Y+zfeediMMPT01MQHb07ZvtCAAAIEnz5s2T2mvWrKn1GJXvqTzm5v1LN/uFeG3nquk8AFu1vZ5gASR7++2IL30p21HUzw03JBb8AwCkiaK9PFF5l73ddtut1mPstttuFUV7EYnd9qor2qvvXJWvf//992t1P0CNTZkSMWBA/q3SA9LjrLMi9twzomfPbEcCAAARoWgvIuL888+PE088sVb3zJw5M0444YSUzA9k2IQJ2Y4AyAUrVmQ7gvp76aWIadMiqvldKgBAKijayxPLli1Lardr167WY1S+Z/ny5WmZq6bzANTL4sWJHfYU7AGfW7o0YtCgREFvmzbZjgYAAKKkpCSp/d///rfWY3z66adJ7VatWm1xro8//rjOc9V0ntpq165dnXKZQB4qL4+YNCnbUQCkzv33R1xzTbajAAAaKEV7eWLVqlVJ7aZNm9Z6jMr3rFy5Mi1z1XSeuvj0009rnXCsvHMg0EBccEH9j8QFGp558yIuvDDivvuyHQkAAMSee+6Z1J4zZ06tx6h8T+UxN++fNm1aneeq6TwAW7RypQW2QMNi91AAII0U7eWJyoV0O+ywQ63HqFxMV3nMVM1V03nq4g9/+ENcffXVKRsPyFOjRydWuAFUZ+TIiKFDI447LtuRAACwnevevXsUFhZGWVlZRCQWpK5cuTJatGhRo/tXrFgRixYtqmgXFhZusZjuC1/4QjzyyCMV7douZP3www+rjAdQK+vXZzsCgNSaODGxi2hBQbYjAQAaoEbZDoC6KajDN4d1uacu99V1HoAau/76bEcA5Lobbsh2BAAAEMXFxdG1a9ekvtdee63G948bNy6pveeee0ZxcXG11+6zzz51nici4tVXX93qeADbVFSU7QgAUmvp0ogUbk4CALA5RXt5onnz5kntNWvW1HqMyvdUHjNVc9V0HoA6mTo14uWXsx0FkOteeilis6PBAAAgWwYNGpTUfvHFF2t8b+VrjznmmC1eO3DgwCgsLKxoT5w4MVauXFmjeVauXBmTJk2qaDdu3DgGDhxY4zgBIiKiRYuI0tJsRwGQWuvWZTsCAKCBUrSXJxTtJZx//vkxbdq0Wr2NGjUqZfMDOcCxuEBN+XoBAEAO+NrXvpbU/tvf/lZxXO7WlJWVxd///vetjrW5nXbaKQ477LCK9vr162PkyJE1ivG+++6LDRs2VLT79esXrVu3rtG9ABUKCiL69Ml2FACptYVdjgEA6qtxtgOgZkpKSpLa//3vf2s9xqeffprUbtWq1Rbn+vjjj+s8V03nqYt27dpFu3btUjYekCfKyyNWroxYvz7imWeyHQ2QLyZMyHYEAAAQhx9+eHTp0iVmzZoVERGffPJJ/P3vf4/TTjttq/f9/e9/j7lz51a0u3btGoceeuhW7/nud78bY8eOrWjffPPN8b3vfW+LR+pGRKxbty5++9vfJvVtKzaALTrggIgxY7IdBUBqlJZGOFEMAEgTO+3liT333DOpPWfOnFqPUfmeymOmaq6azgOwVVOnRgwfHnHUURFt2kSUlES0bRsxcWK2IwPyxRtvJIp+AQAghQoKCpLetnXcbWFhYVx99dVJfT/+8Y9j9uzZW7xn9uzZ8aMf/Sip71e/+lU0arT1dO5pp50W3bt3r2hPnz49hg8fvtV7Lrvsspg+fXpF+4tf/GKccsopW70HYIu+/e1sRwCQOl/6UmJDATlGACANFO3lie7du0dhYWFF+9NPP42VK1fW+P4VK1bEokWLKtqFhYVbLKb7whe+kNSeOXNmrWL98MMPtzoewFaNHh3Rr1/ih+ERIxIrc5cuzXZUQD5avjzi9dezHQUAABn0ySefxOzZs6u8LViwIOm6jRs3Vnvd7Nmzk3JoqXLKKafEgQceWNFesmRJHHLIIfFMNbvJP/3003HwwQfH0s1+Fj7kkEPipJNO2uY8hYWFceONN0ZBQUFF30033RTnnntuLF68OOnaRYsWxTnnnBM333xzRV9BQUH85je/ScpDAtRKz54Rhx+e7SjYls3+nwC2YuzYxIYCbdokNhgYPjxi2rRsRwUANBCOx80TxcXF0bVr15gxY0ZF32uvvRZHH310je4fN25cUnvPPffc4rEY++yzT1L7tddeq1Wsr7766lbHA6jW4sURF1wQcf/92Y4EaEiOOSbipZcSvzTIRZsf/11UFNGihcQ5AEA9HHbYYTU6NWLu3LnRpUuXal877bTT4p577klpXI0aNYpHHnkkDjrooPjoo48iImL+/Pnxla98Jfbcc8/Ye++9o7y8PN5+++0qC2h33333ePjhh5MK8bZm8ODB8atf/Souv/zyir4///nP8be//S0OPPDA6NChQ8yfPz8mTJgQa9asSbr3uuuui0GDBtXzowW2e8OGRbz8crajYGsOPDBi0qREPgLYtqVLExsMjBmT2Gzg8MMjLr004thjsx0ZAJDH7LSXRyonzLZ19MbWrj3mmGO2eO3AgQOTVtNOnDixxrv6rVy5MiZNmlTRbty4cQwcOLDGcQLbqSlTEjvrKdgDUm3ZsohBgxKFwbliS8d/W7ULANCg7bzzzvHss89G7969k/rff//9GDVqVDz66KNVCvb69OkTzz77bLRv375Wcw0fPjyuvfbaaNKkSUXfmjVr4sUXX4wHHnggxo4dm1Sw16RJk7juuuvikksuqcNHBlDJccc5JjfXHXlkxPnnZzsKyF8vv5z4WnfKKbmVdwQA8oqivTzyta99Lan9t7/9LcrKyrZ5X1lZWfz973/f6lib22mnneKwww6raK9fvz5GjhxZoxjvu+++2LBhQ0W7X79+0bp16xrdC2ynpkyJGDAgYt68bEcCNFTz5kVceGG2o6jZ8d+fr9odMSKxO2C/fhFPPJGdeAEASLm99torXn/99RgxYkTsscceW7yua9euMWLEiBg/fnx069atTnNddtll8frrr8eQIUOiqKio2muKiopiyJAhMWHChBg2bFid5gGo1m23RXTsmO0o2JJvfzvijDOyHQXkv5EjE7m+qVOzHQkAkIccj5tHDj/88OjSpUvMmjUrIiI++eST+Pvf/x6nnXbaVu/7+9//HnPnzq1od+3aNQ499NCt3vPd7343xo4dW9G++eab43vf+94Wj9SNiFi3bl389re/TerbVmzAdm7x4sTRlZWLVgBSbeTIiKFDEytgU6E2x9rW5/jvl19OvA0dGnHrrYmd+AAA2KLZs2enfY7y8vJ63d+kSZO49NJL49JLL42JEyfGjBkzYt7/X8jWsWPH2GuvvaJv376pCDV69+4do0aNiqVLl8a4ceNi7ty5sXjx4mjTpk106tQpDjnkkCgtLU3JXABJ2rSJeOqpiP795f5yTb9+Efvsk/jz4Yc7ypj8cvbZicW5116bOyf3zJuX+Fo3dmxiES4AQA0p2suigkq/2H3hhRdiwIABW7y+sLAwrr766vjud79b0ffjH/84+vfvH7vvvnu198yePTt+9KMfJfX96le/ikaNtr7J4mmnnRY33HBDTJ8+PSIipk+fHsOHD4/f/OY3W7znsssuq7g+IuKLX/xinHLKKVudB8hjtSlY2ZILLrDDHpA5N9xQv6K9qVMTycAJEyImTUr+pUNpaUSfPhEHHJAosPs8+T1lSqI4ub5f60aOjHjxxcQvPCT/AAAajL59+6asQG9rSktL47hULWABqKmePRNFLIMGyQHmks13Vj3vPEV75JeWLSM6dEgU7Y0dmztfW5YuTXytmzLFolsAoMYcj7sFn3zyScyePbvK24IFC5Ku27hxY7XXzZ49OxYtWpTyuE455ZQ48MADK9pLliyJQw45JJ555pkq1z799NNx8MEHx9LNfqF8yCGHxEknnbTNeQoLC+PGG29MKiy86aab4txzz43FixcnXbto0aI455xz4uabb67oKygoiN/85jdRWFhYq48PyHFTp0YMHx5x1FGJHzxLSiLatk28b9Mm0T98eMS0adsea/To3FkJB2wfXnqpZl+fKqvrsba//31qj//+fNWu4zYAAADIFz17JopYhg7NdiREJP4ejj028ecpUyJ+8pPsxgO19ZvfJH4n0aVLxKpVEY1zaH+aefMSuwACANRQQXl9z3NooHbfffeYM2dOvcY47bTT4p577tni67Xdae9z8+fPj4MOOig++uijpP4999wz9t577ygvL4+33347Zs6cmfT67rvvHuPHj4/27dvX+GO49tpr4/LLL0/qa9q0aRx44IHRoUOHmD9/fkyYMCHWrPl/7N19fFT1nf/99xRIRBNgHKAQ2xVvqlUzrIaaKpYBK22BYNFKlzI8utXquldtwfpru9D0d13WthShW7cFbbvbyxa7V6IWq7ZLEEtZgbRQ0w3aTKTe1ZtWowJjEKKQAM31x/eXkJnczc0553tmzuv5eOQxnmHmnA8jmZnzPZ+bwymPWb16tf7lX/4l4+O46amnnlJlT7cbSa2trbrgggssRgQUoIYGafXq7KouZ8yQVqw4sQiULhajihOA92prpZUrM3tsPmNt3VRRQdUuAAA+xRoE4Ax+l4Ai1dBguuDv2GE7kmDqu57Q0mIKDRldDDhv48b8pn0AAABP2VyD8FH5ATI1efJkbdmyRZ/61Kf0xBNP9N7/3HPP6bnnnhvwOVVVVbr//vuzStiTpNraWoVCId166606evSoJOnw4cPatm3bgI8fNWqUvvnNb/omYQ9AnvJJWGlsND/xuLR2bWpySSJBwh4AO5qaMnucU2Nt3dBTtVtXZzsSAAAAAAAyV1NjflpbzXpjU5P5OXjQdmTFb9w4afNms0abTJo1DxL2AHesWUPSHgAAyAjjcQvUOeeco8cff1yrVq3SmWeeOejjzjrrLK1atUq///3vdfbZZ+d0rK9+9at6/PHHtWDBApWUlAz4mJKSEi1YsEBNTU1avnx5TscB4DMtLWYUZL4dpurrzX76jnP0W9cqAMHR3CwN12i6p9rcjwl7PerrTYcCAAAAAAAKTWWl6YK/ZYt04IC0a5c0aZLtqIpXKCRt325GFUumSNvPax5AoduxwyQnAwAADINOe4N46aWXXD9GvpOJR40apRUrVmjFihVqbm7Ws88+q7b/c6JVUVGhc845R9OmTXMiVF100UV6+OGH1d7erp07d+rVV19VMplUJBLRaaedpunTpyscDjtyLAA+4PR4hLY2aebME4tDmXa6AgCntbdLHR1SefnAf15I1eZU7QIAAAAACl0oJF1yiUlwWbbMFKnBWRdcYIqqJVMASEE14L577zXJyQAAAEMgaa9ITJs2zbEEvaGEw2HVcHEYKG5uJay0t0tz5khZBCbHAAEAAElEQVR//KO0e7ez+waAbHR2Dp60V0jV5j1Vu5WVtiMBAAAAACA/kYhUVyfF46ZIbccO2xEVj7feOvHfq1fbiwPFKRQafqpFENG4AAAAZIDxuACAVG4mrLS1STfdVBgdrAAUr9LSge8vxGrzQosXAAAAAICh1NSYaR2JhFRbK114oe2ICt9f/2qK/hIJqbHRdjQoFu99r/k3NWOG7Uj8qbmZZEYAADAskvYAACd4kbCyYYO7+weAoYwZI5WVDfxnhVhtTtUuAAAAAKAYVVaa0ZJPPCEtXmw7msJ3770U/sFZf/2ruZ0+3W4cftXeLnV02I4CAAD4HONxAQAnFGLCCgBk48gR6c03zdidvgq12rynajcUsh0JAAAAAADuWLfOdN9zazpIEDQ10fULzrvySrPOhoF1dkrl5bajAAAAPkanPQCAUagJKwCQja4uadmy/vcXarU5VbsAAAAAgGIXiUibN0vhsO1ICldzs/Q//2M7ChSbl16SDh60HYV/lZbajgAAAPgcSXsAAKNQE1YAIFv19WYceF+FPGa2s9N2BAAAAAAAuKuiQrrkEttRFK72dumtt2xH4Z3Jk21HgKALh6WyMttRAAAAnyNpDwBgFHLCCgBka/VqUwm8f79ZtN6923ZEuaNqFwAAAABQzH7xC2nKFOmRR2xHgkIwbhydGWHftGlSKGQ7CgAA4HMk7QEApO7uwk5YAYBsNTZKY8dKEyaYxdz2dtsR5YaqXQAAAABAtrq7TxSyHTxotv2oocEkvixcKHV02I4GheIDH5CmTpW2bzcdGgEbqqttRwAAAAoASXsAAOnQocJNWAGAIKNqFwAAAACQiURCqq2VZs+WIpEThWxjx5rt2bPNn7e22o5USialeFyaP59CY2SvJ1kqGpVaWsy/JcBrixfbjgAAABQAkvYAAFJXl+0IAAC5oGoXAAAAADCUhgYpFjOdx1atkrZu7V+8295u7l+1yiQ6xWLSpk124m1pMbHee6+d46Pw9U2WikSkujpp40apqspeTAiWWEyqrLQdBQAAKAAk7QEApJISO8dduNDOcQGgWGzd6p9OCAAAAAAA/+jbra6xMbvnNjZKNTXSkiVmP15paZFmzZLa2rw7JorLYMlSl1wivf669/EgmJYvtx0BAAAoECTtAQCk8nIpHPb2mOGw9MMfShUV3h4XAIrJ44/7oxMCAAAAAMA/nOpWV19v9pNIOBPXUJJJae7c/l0AgWwMliy1dCnJoPBGPC7Nm2c7CgAAUCBI2gMASKGQ9+MBpk2Txo83HaIAAPmz1QkBAAAAAOAfTnera2uTZs50P3GPpCrka7BkqYYGxi3DGxUV0tq1tqMAAAAFhKQ9AIBRXW3nePff7+1xAaDYedkJAQAAAADgH251q2tvl+bMca9AjKQq5Ovd7x48WWr1am9jQTCFw9LmzVIkYjsSAABQQEjaAwAYixd7f7xEwnSGAgA4y6tOCAAAAAAA/3CzW11bm3TjjdLBg9Lf/mZu9+8feLu7O7t9k1SFfIwYIW3ZMnCyFOvP8EJFhbRtm3T66bm/DwIAgEAiaQ8AYESj0owZ3hwrFpMqK6mghX+ceqp06aW2owCc5XYnBAAAAACAf3jRre7BB6WxY6WRI83thAkDb0ci0uzZUm2t1No69D5/8QuSqpCf664za9sDYf0Zbjv/fOnss81Y8lzfBwEAQGCRtAcAOGH5cm+P09TkzfGAoYTDphJy505TfVtbaxZZgGLQ1iYtW2Y7CgAAAACA27zsVpfeQSp9u71d2rpVWrXKJFPFYtKmTamPaWgw9y9c6G6sKH433zz4n7H+DLeMGGFu9+yRduzoP5Y8k/dBAAAQeCTtAQBOqKlxf0xuPC7Nm2cW83bvdvdYwHAqKqTt209U41ZWSitXShs22I0LcFJ9vbkYAgAAAAAoTn4fAdrYaNYdlyyRnnvOrA/On+/vmFEYeia6DIT1Z7jp+PHsHt/3fTDXqRjd3fmNIgcAAL5D0h4AINW6dSaRyQ0VFdLatea/Dx3qX30GeCkel1paBh6fUVLifTwIhpEj7Rx3zRo7xwUAAAAAuK9QRoDW10vvf3/hxAv/G2pyDOvP8KP6emnqVJNsnYmeyTCzZ5uRu4zgBQCgqJC0BwBIFYlImzebkaFOGjnSnDyOGmUqwXKtJgPyFYuZrmN1debf+0DKy53/HQAk6dgxO8fdsYPFOwAAAAAoVo8+ajuCzP3tb7YjQLHomegymK4u72IBstHWJs2cOXTiXs8I8alTzYjdrVsZwQsAQBEiaQ8A0F80akaGOtlx79gx6QtfOFEJduaZzu0bwTJmjDRlivkZMyb1z0aO7N/JLBw+UXGYSJh/20Mt6ElSKCRVVTkZNWAfnQwAAAAAoLj0JHUwAhRB03eiy2CYpAE/a2+X5szp39wgmcx9hLgTI3gBAICnLM3nAgD4XjRqRocuW2ZatgM2jRsn/fKX0kUXSWVlJqlOkrq7pY4OqbNTKi01fyb1v6/n8dmorjaVikCxaGqyHQEAAAAAwAnJpLR0KcVZCKZw2EyKGWyCRo+eSRqMyIVftbWZ6y91dWa7pUWaO9fcn4/6emnbNvN7Eo3mHSYAAHAPnfYAAIOLRMwJ47p1/buXAV6pqDCjPWMxs9jWNwEvFDL3jR9/4s8Gui8Xixc7Ez/gF83NJtEVAAAAAFC4WlrMuEQS9hBEFRVmikYmiUhM0kAhqK83XVNbWqRZs/JP2OuRyQheAABgHUl7AIChJZPSqlVmvC3sG26sa7GJx82ChY2KwGhUmjHD++OiMIwZI02aZDuK7LS3my6UAAAAAIDC5HRSB9wRi5kkHApCnZXLOmF1tXvxAE5ZudJ02HO6K+RgI3gBAIBvkLQHABja0qUsBPpFRYX0vvfZjsIbPYubdXXDj7pw0/Ll9o4Nfxs9Wnr9ddtRZK+z03YEAAAAAIBcJJPuJHUgf+GwNHu2VFtrulpt324Kb9etM+t5yE8+64QkTqIQ7Nrl3jWYnhG8AADAl5h1CAAYXEMDozb8IhyWNm+WbrnFdiS5GzNG+od/kF56yYzp7LvIHA5L06aZ6tfFi6XKSmthpqipMfHwe4B0b7xhO4LclJbajgAAAAAAkAsKa/1p1izpv//bjGJNF4mY9byZM4OXbFlWJl10kdTYmP1znVwn7JmkkUscQLGorzedKmtqbEcCAADSkLQHAIWqu1s6dEjq6pJKSqTy8oEXh/KxerWz+0NuKirMAl9lpbR7t+1octPzd+gZX9HdbcZ0dnaaJKKyMuf//Tpl3TpTIe3GwnhZGeNK4Z1w2PybAwAAAAAUFgpr/euPfxz8z7q7pdNPlx5+WFq0qDA79ueqo8P8u335ZfNvt6lp8CLeiy+WFiyQzjrLnXXC5ctJ2gPWrCFpDwAAHyJpDwAKSSJxYpFj9+7+ixxVVaYCMR7Pv1NZIsFihh/E49LataYy9+DBwqzK7ft36BEKmUTT8nJ7cWXKrarocFj68Y+lhQud2ycwlGnT/JscCwAAAAAYHIW1/tXebhLUeta4hlq/LSkxBdhB0dlp1qhXrjTbtop4maQBSDt2SK2t/plwAwAAJEnvsh0AACADDQ1SLCZNnSqtWiVt3do/eai93dy/apXpZhaLSZs25X5MFjHs+/rXpbq6E8luhbaoF4uZf7t9/w6FKho13fYqKpzZX0WF2d8115gRHYAXqqttRwAAAAAAyBaFtf7X2ZnZ+m2hre3lq7Q0dbuniHf8eHemxgxl3Trn1vWAQsU1HwAAfIekPQDws2TSdCmbPz/7xbnGRlNFuGSJ2U+2mpqyfw6cE49Lt96ael9JiZ1YBjNuXOp2OCzNni3V1poF5e3bpXnzrITmimhUamkx/2/yEY+b/fSMCl6+PP/YMjF6tDfHgX8tXmw7AgAAAABAtkiy8L/PfS639dse6Wt+xdAlPxw2nfT8omeSRjhsOxLAHq75AADgOyTtAYBftbSYysx8F+bq681+EonMn9PdbcY3wI6KCjNONl15ub8Wlp591ozs3bfP3CaT0pYtZuRFsbbZj0RM58CNG031dDYG6zzYM6LDTfG49Pjj/vr3A2/FYsX7ewkAAAAAxYwkC38LhaQHHshvH11d0uTJpgD24EHpyScLvyvctGn+Sz50epIGUGiam821HwAA4Bsk7QGAH7W0SLNmSW1tzuyvrU2aOTPzxL1Dh/qPb4A3wmFT9TnQONlQSKqq8j6mwZx0kr2RFrbV1JhFvkTCdBacPbt/Qlw2nQfdHNHRkwTKwmSwedXREQAAAADgHApr/c+pBJjXXpOuukp66SVTgO3EtAebqqttRzAwpyZpAIWovV3q6LAdBQAA6IOkPQDwm2RSmjvX+aS59nZpzpzMRuV2dTl7bGSmosIkVfWMTR2IXxa8/DbiwpbKStNZcMsW87uVa+dBt0Z0pCeBsjAZTPF4cY2qBgAAAICgoLA2WPqu3+Yz7cEP3J4qkY98XttLL5VOPdWduAAvdHbajgAAAPRB0h4A+M3Spc512EvX1iYtWzb840pK3Dk+BnfNNSaZaqiEPUk67TRv4hmOH0dc2BYK5dd50OlOeJMmSdu29f83VeiLvsjOYOO2AQAAAAD+R2Ft8KSv3/ZMe2hpMVMvCkEsNnwBqx/kMklj506z3uZ04S3gldJS2xEAAIA+SNoDAD9paJDuvdfdY9TXm+MMpbychQevjBwp3XWX9MADA4/E7ZFMmm5ZX/iCd7ENxS8d/4qNk53wXn/djNnuWVhsbU3984EWJumeWFyGGredie5u0zVy/35z69TIHwAAAABAZiisDaaB1m9PP106csROPNlavtx2BNnJdpKG04W3gFeYngMAgO+QtAcAfrJ6tTfHWbNm6D8PhaSqKm9iCbrmZummm4Z+TEuLNHWq+wmd2fDziItCMVhClJOd8Nrbpa1bpVWrzIJiLCZt2pT6mL4LkwcPmp8dO6Qvf5lEvkKWybjtgfRN4oxEpLFjpQkTzG0kMngSKAAAAADAeRTWBlf6+m2hdF2Mx6V582xHkbtMJ2k4VXhbyK8VCg/TcwAA8B2S9gDALxIJqbHRm2Pt2DF8wgWd1LxxxhlD/3lLi+mW5tbI5FwUyogLP8omISqTER3Zamw0+12yxFQKp+tZmJwxQ/rOd/on8n3pS9IVVww+KuT661n48YN58zIbt91XQ4P53Z461SR5bt1qkj77yiQJFAAAAADgHAprgyt9/bYQui5WVEhr19qOwjv5FN7GYmYtpqHBrMMBXuCaDwAAvkPSHgD4hddd1IY7Hp3UvNHZOfifJZPS3Ln9E2dsK7QRF36QT0JU3054jz0mTZ6cfzz19SaWRGL4x/ZN5PvXf5V+85vBR4XccQcjVG2rrDT/3jIdidszenv+/OwTx4dLAgUAAAAA5I8ki+Dqu37r966L4bC0eXPm6xHFJJPC256C19pa87jt20902WOtFV7hmg8AAL5D0h4A+EVTk7+OV1kpXXyxN7EEWWnp4H+2dKm/OuxJhT/iwmtOJkS1tEiXXy699pozsbW1STNnZpa4l26wUSGFMqqlmGUzZt2p0dvZJIECAAAAALJDkkVw/fd/n/hvP3ddrKgwSWjZdPwvRn0LbwcreF25sv8Ek5oafs/hPqbnAADgSyTtAYAfdHdLu3d7e8zm5v4dsRIJ6YYbzMjWkhLpD3/wNqagCYelsrKB/6yhwfvui8MJ2oiLfDmZEFVZKX3kI853XWxvl+bMca5LWiGMailm11yTeVKt06O380kCBQAAAAAMLhplfGZQPflk6vqtH7suxuNmjSHoCXvpBit4Hcy6dWbtFXALHR0BAPAlkvYAwA8OHfJ+BGp7u9TRYf67ocEkBU2dKt19t/TSS9KxY97GE0TTpg2+YJNNtywvBHnERS6cToh6/XVp715n9pWurU1atsyZffl9VEux+93vMkuac2v0ttNJoAAAAAAAg2SLYDpy5MT6reSvbmyxmFlTrqtzfr2wu9t0p9u/39ymF54Xo0jErL2yrgY3MD0HAADfImkPAPzA1kjJ114znZnmz5eeespODEE2WHVsIpH9KFU3MeIiO24lRLmpvt4stObLz6NaguD11zPrdufm6G0nk0ABAAAAAAbjM4PryJET/+1l18WRI1O3w2Fp9mypttasO2zf7mwSUCJh9j17tklgGztWmjDB3EYiJ47d2urcMf0mGpV+/GPbUaDYMD0HAABfI2kPAPzA1kjJ6dOlBx+0c2wMvtjqp7G4jLjInpsJUW5as8aZ/fhxVEuQDNftzovR204lgQIAAAAATmB8ZjAdPZq67VXXxYcfNl3u9u0zt8mktGWLtHKlmdjilIYG07Vv6lRp1Spp69b+hbDt7eb+VavMGmUsJm3a5FwMftLcbDsCFBOm5wAA4Hsk7QGAH9gaKckIQ3tiscEXuJqavI1lIG6OuChmXiREuWXHDmeqlan8t2+obndejd52KgkUAAAAAGAwPjOYRo1K3fai62I8bo5TXi6NH29uQyFnj5FMmuPMn5/9xJHGRhPfkiXFt77th3VhFAem5wAAUBBI2gMAP2CkZPAMVhXb3S3t3u1tLJI0bpy7Iy6CwquEKLc4kXDo5agWDG6gbndejt52KgkUAAAAAHBCNCr98pfSu7i0ExgnndT/Pje7LnoxSrOlxXTWy3cdqr7e7CeRcCYu22ytC6P4LF7M9BwAAAoEZ3YA4BeMlAyOeHzwhLhDh/qPgPDCyy+7M+IiSLxMiHKLU9W8Xo1qwdDSu9153QWyULtOAgAAAICf/fCH0t/+ZjsKeOGkk6Sysv73u9V10YtRmi0t0qxZZkqAE9rapJkziyNxz9a6MIrLhReahFam5wAAUBBI2gMAv2CkZDAMV63a1eVdLH44bjEphgSl5mZT1ZsvL0a1YHjp3e68HrHCSBcAAAAAcFZDQ3GsPyAz55wz+FjaaNRMynCq454XozSTSWnuXOcT09rbpTlzCn9ULuuzcALTcwAAKCgk7QGAXzBSsvhlUq1aUuJdPH2Vlto5bjEphgSl9napo8OZfbk5qgWZ67mYY2PEilNJoAAAAAAAY/Vq2xHAS88+O3QHuWjUdK6Lx/M7TjzuzSjNpUud67CXrq1NWrbMnX17xda6MIoLhdQAABQUkvYAwE8YKVm8Mq1WLS93frTFcMLhgUdtIHM2EqLc0tnpzH7cGtWC7PQkk9oYseJkEigAAAAABF0iITU22o4CXjpyZPgOcpGIVFcnbdwoxWLZ7T8WM90b6+rcH6XpRZfI+npznEJlY10YxSUWkyorbUcBAACyQNIeAPgJIyWLUzbVqqGQVFXlfkx9TZs2+KgNZMZGQpRbnOy66PSoFmSvp9udrRErTiWBAgAAAEDQMRY3mDLtIFdTY9ZgEgmptlaaPbt/Alg4bO6vrTWP277du1GaXnWJXLPGm+O4wca6MIoLTSEAACg4JO0BgN8wUrK43HVX9tWq1dXuxeOH4xUjWwlRTnOj66JTo1qQm55ud4zeBgAAAIDC1tNJHcGTTQe5ykpp5UppyxbToe/gQWnfPnObTJr7V670thuXl10id+yQWlu9OZYbvF6nveACb48H95x/vvR3f2c7CgAAkCWS9gDAbxgpWTxiMemmm7J/ntfdFunumD9bCVFOc6vrYj6jWpC/zk5GbwMAAABAIevulnbvth0FbMqlg1woZNYDxo83t7YmbXjdJTLX43V3m+TG/fvNbXe3s3Flwut12vvuY72uWOzZY4qnYzFp0ybb0QAAgAyRtAcAftQzUnLCBNuRIB+5tqOPRqUZM5yNZTCxmLeVtcXKRkKUG9yu5s12VMsvflEcr6ttpaWM3gYAAACAQnbokOmkjuAq5A5yXneJzOZ4fdeoIhFp7FizJj92rNnuWaPy6rW3sS480HrdmDHexADnNTaa/6dLlpjumgAAwNdI2gMAv+rulo4etR0FchWPS/Pm5f78XBP+/HqcYmcjIcoNXlXzZjqq5ROfMIuGjAzPXd9ud4zeBgAAAIDC1NVlOwL4gdcd65xgo0tkc/PwXfIaGkzS2tSp0qpV0tat/RNj29vN/atWedvBzNa6cN/1ugMHpF27pEmTvIkFzquvN/++EwnbkQAAgCGQtAcAfpRMSnPnmpNjFJ6KCmnt2vz2UVPjfgJVvomFSFXoCUq2ui4ON6qlslL63e9MAh+y17fbHaO3AQAAAKAwlZTYjgB+4HXHOifY6BLZ3i51dAz8Z8mkWROdP990JMuGVx3M/LAuHApJl1xiOgzG4+7GAve0tUkzZ5K4BwCAj5G0BwB+tHSpOaFC4QmHpc2bzfiEfK1b516HMScSC5Gq0BOU/NR1MX00yRlnSA8+aP5sxAi7sRWavsmkjN4GAAAAgMJUXm7WnBBsmXSQ8xtbXSI7O/vf19JiOo/l27HQiw5mflkXjkSkujpp40az1oPC094uzZnDqFwAAHyKpD0A8JuGhsIcdQCz4LF9u0lUOXhQ2r/f3Oa6mBaJmARApxdlnUwsxAleJkQ5zS9dFzMZTXL8uJ3YClV6MimjtwEAAACg8IRCUlWV7Shg21Ad5PzKVpfI0tLU7ZYWadYs5wrl3e5g5tW6cHd3ZuvYNTVm3TuRkKZPdzYmuK+tTVq2zHYUAABgACTtAYDfrF5tOwLkYt486R/+QbrlFrPoMXasNGGCuY1ETMew2lozUiAb0ahZEHGqsrInsTAadWZ/SFWIiUp+6LqYz2gSDO6yy6QLLki9zw8jVgAAAAAA2evbSR3BNVAHOT+z0SUyHJbKyk5sJ5PS3LnOj+l1u4OZW+vCUuqEi2zWsSsrpR/9yJl44K36elMwDQAAfIWkPQDwi0RCuuEGElYKzQUXmJ9Nm6TvfW/gzmDt7eb+VavMYkssZh6fqWjUVIPG4/nFGo+b/ZCw5x4vEqKc5Ieui06NJkF/v/vdwIutfhmxAgAAAADIXCGtN8A96R3k/M5Gl8hp08xxeyxd6lyHvXRudzBzcl34X/9V+vznh55wkek69nvfm188+Rg1yt6xC93KlbYjAAAAaUjaAwDb+o6DvPtu29Ggx5e+ZBJd0itBw2Fz/xe/aCo0n3rK/GSjsdEkdy1ZknklZiQi1dVJGzeafy/ZiMXMv7O6OkbiesHNhKgJE6RJk5zZlx+6Ljo9mgT9DbTY+vjjjN4GAAAAgEITjfbvpo5gSe8gVyi87hLZ93gNDe4XirrdwSzfdeF77zVjb+Px7BsG9F3HvvfeE9cx/vrX7PbjpKNHpXdxeTsnu3ZlPwkIAAC4aqTtAAAgsJJJU+VHdyl/WrFCGj/eLGh0dJjRE6WlZmEskTAJe/kmGtXXS9u2mSSXTBOnamrMT2ur+bfT1CQ1N6dWRYbDpqK0utpUYVdW5hcnshOJmP+nM2c6O3YjHDbJVxUVpoK3vj73fcXjphuazeQqt0aTYGiNjeYnHpd++UvpU59yJmmyoiK79zIAAAAAQOZ61hGzLRxFcUnvIFcoFi82xYReHq/H6tXeHHPNGrNm66Zc1oX/9jfn1rHzWYt02t/+ZjuCwvXNb0r335/fPrq7pUOHpK4uqaTEjMEuxPcmAAB8gKQ9ALChpcWZk+ViFwqZE0AbekZNhELmpLO83Gz3dAZzKtGorc0kd2Xb8ayy8kQ7+4ESCzlJtisaNf9P58xxJyGqrs4kXa1ZI+3Ykfl+YjFp+XJp3rz8Y8qXm6NJMLyepOH77pN+9KPCTwIFAAAAgGLFOiJ6eN2xzinRqDRjRvZd3nIRi50oYE4kvDmmZNbnWlu9KZ7OdF3Y6XVsFIdf/zq35yUSJ5JFd+/unyxaVWXeo+JxmggAAJAF+gcDgNcYB5m57m5p3LgTCXNeGWzUhFudwdrbTXJXpqNy0/UkFo4fT1Wbn0Sj5vc9Hs9vP/G42U96UmdNjUkMTCSk2tqhxznX1prHbd/uj4Q9L0aTYHhtbdLHP246i+YyYmX6dJPs98MfSqee6k6MAAAAABBkrCOir74d5ArN8uXeH8frtScba12DrQsz4QKDOXDArBNnqqHhxFjkVavMJJj0f1ft7eb+VavMGnYsJm3a5GjYAAAUK5L2AMBLnCxn78AB02bdS4ONmnCzM1hbmxl5iuISiZiueLkkRMViZlGkrm7oDmY91bVbtpj3mIMHpX37zG0yae5fudJfFY5ejSbB8A4ckD7wAek3v5HuumvoJNAxY6QpU8zPmDHSzp0mqXTsWPNvtCdBtLXVwl8EAAAAAIoM64j+NWGCOS/2Ut8OcoWopsb9pMN4PLVYtanJ3eOl8/p4Q2HCBYZy+eXSb3879GOSSfM7NX9+9h0rGxvN7/ySJbk3KgAAICBI2gMAL3GynJvOTm+PN9CoCS86g9XXm+Og+HjVFa8Qui56OZoEmenqkr73PVMJe9NN0mWXpSaB1tWZrnoHD0ovvWR+Dh5M3QcVtQAAAADgLNYR/Skel/70J5PwUlHh3XG96lTnpnXr3HvNKiqktWtPbHd3mxGeXmpuNse1jQkXGE4yaUZWT5s28PpdS4vprJfvv6P6erOfbDr7AQAQMCTtAYBXOFkuHANVfXrVGWzNGm+OAzsKsSue03gf9Le+lbDPPy/98z+b/965M/f9UFELAAAAANlhHdF/0iciRKMmsSUed//Y6R3kClUkIm3e3L+QNV/hsNlv30kVhw5536WyvV3q6PD2mANhwgUytXt3//U7p8eyt7VJM2eSuAcAwCBI2gMAr3CyXBgGGjXhZWewHTsYLRkUhdAVzw1+GhWCwdXXS+9/PxW1AAAAAOC17m5T0Af7pk4deiJCJGKS+OrrpZISd2JI7yBX6KJR81o61XGvosLsLxpNvb+ry5n9Z8vriTHpmHCBXPSs3zU2ujOWvb1dmjOHwl4AAAZA0h4AeIGT5cIx0KgJryubqaRGsbIxmgS5+9vfnNkPFbUAAAAAMLREwiSHzZ4tjRsn7dplO6JgO+kkkwj2xz9mNhFh8WLpf/5HGjPG2TgG6iBXDJzqUhiPm/2kJ+xJ7iVRDqe01M5xe7CujFy1tUlXXOHeWPa2NmnZMnf2DQBAASNpDwC8wMlyYRhs1ITXncHoRIZiZWM0CfyBiloAAAAA6K+hwUx9mDpVWrVK2rpVOnjQdlTBNmuW9Mor5v9LNqJR6be/db+DXLHo6VK4cWP2r3X6qOKBlJc7P4Z3OOGwVFbm7THTsa6MfBw96u7+6+vN7y4AAOg10nYAABAInCz732CjJmx0BmtuNscNyrhUBIet0STwh56K2ro625EAAICAefHFF/Xkk0+qra1NHR0dmjx5sk4//XRNnz5do0aNsh0egCBKJqWlSyn09ZNp06RvfGPggt5M9XSQW7bMJKfkKh4365TF1mFvIDU15qe11fw+NDWZtdG+RZ/hsPn/U11tuhoO1/lQMuuqVVUmEdYr06bZXc9lwgUKwZo15nceAABIImkPANzHybL/DTVqwkZnsPZ2qaPDVIQCxcTWaBL4R329ufjA4hwAAPDAAw88oDvuuEO7Bhkzeeqpp2rRokX6xje+ofHjx7sWx5QpU/Tyyy87sq/PfOYzWr9+/aB/vm3bNl1++eU57//000/XSy+9lPPzAWSgpUWaO9e9EYTIzJgxJgksm0SwTPR0kIvHTXLKjh2ZPzcWk5Yvzy9xsFBVVppRxJJZT+/okDo7zbjZsrLckuGqq71N2quu9u5YA2HCBQrBjh0mSdep91wAAAoc43EBwG2cLPvbcKMmbHUG6+y0c1zATTZGk8B/1qyxHQEAAChyHR0dWrx4sT75yU8OmrAnSW+++aZ++MMfqrKyUo8++qiHEeZu9OjRtkMAkI+WFjN+lYQ9+z7/eWnLFpMo5kbySE2NWXNMJKTaWmn27P5rIuGwub+21jxu+/ZgJuylC4XMGtL48eY21+51ixc7G5ffjpeOCRcoFHSZBQCgF532AMBtnCz7VyajJmx1BisttXNcwE02RpPAf6ioBQAALjp+/LgWLVqkTZs2pdw/YcIEXXTRRRo7dqz+/Oc/64knnlB3d7ck6Y033tCCBQv0m9/8Rh/60IdshJ2xa665xnYIAHKVTJoOexT3+sMf/uDNcdzoIIfMRKPSjBlSY6P7x4rF7K9zMOEChaKpyXYEAAD4Bkl7AOA2Tpb9J5tREz2dwbxcUA2HzaIdUIy8Hk0Cf7r33hMXLQAAABy0YsWKlIS9UaNG6Y477tCNN96okj7n53v27NENN9zQ24mvs7NTV111lRKJhCZPnuxoTL/97W917NixrJ9355136rvf/W7v9pQpU3TFFVdktY+bb75ZX/ziFzN+/MiRLBcDrlm6lA57ftLcbJLovEya6+kgV17u3TGDbvlyb5L2li93/xjDsbGODeTCxvsvAAA+xSoMALiNk2X7wmFp2jSTLLR4cXZVjzY6g02bxgkritfixdKqVbajgG1U1AIAABe88MIL+v73v59y34YNG7RgwYJ+jz3//PO1detWXXHFFb2Je8lkUrfddpt+9KMfORrXe97znpye19DQkLL92c9+VqEszxXHjRunKVOm5HR8AA5qaGAcoN+0t5uudyTQFbeaGrMW5ebvXzzuj7HGTLhAoeD9FwCAXu+yHQAAFL2ek2XY8fOfm/EjW7aYrk65jCmornY+Lj8dD/BSz2gSBFtPRS0AAICDbrvtNh09erR3+9prrx0wYa/H6NGjtX79+pQOfHfffbdeeOEFV+PMxO9+9zs9/fTTvdvvete7dO2119oLCEB+Vq+2HQEG0tlpOwJ4Yd06qaLCnX1XVEhr17qz71ywroxCwfsvAACSSNoDAG9wsmzPnXfm37Vu8WJnYvHr8QCv+WFkCOzqqagFAABwyOHDh/XAAw+k3Lc8g++d55xzjq666qre7WPHjqm+vt7p8LL2k5/8JGX7ox/9qN773vdaigZAxrq7pYMHpf37zW13t5RIeDOe07ayMikWMxMv+vLz2O3SUtsRwAuRiLR5c/9/m/kKh81+IxFn95sP1pVRKHj/BQBAEkl7AOANTpbt2bFDam3Nbx9edgaLxXLrBggUkp7RJAg2KmoBAICDHn30Ub3zzju925deeqne//73Z/Tc6667LmX7wQcfdDS2bHV0dOjnP/95yn3XX3+9pWgADCuRkGprpdmzTfLO2LHShAnmNhKRPv5x2xG6LxyWdu6Utm83Ey8OHpT27TO3XV2pr1F64lQ4LF1xhXTSSd7HXFbm7TFhTzRq/n061XGvosLsLxp1Zn9OYcIFCgHvvwAA9CJpDwC8EI1K06fbjiK47r03/3141RmMDmQICjdHk2TissvsHRsGFbUAAMBBmzdvTtmeNWtWxs+dMWOGRvbpBPXEE0/ojTfecCq0rN1///3q6NOVeMKECUOO+QVgSUODKb6cOlVatUrautV0Fe+rvV166SUr4XkmPXkpFJLKy6Xx481tKGQKVFeulLZs6Z/Ul0xKv/mN9+fpVVXSoUOpXRFR3KJRqaVFisfz2088bvbjt4S9Hqwvw++mTct/OhIAAEWCpD0A8MrnP287guBqasp/H150BovHpXnz3D0G4Bc9o0nGjbNz/IcfHrrSn4Ujd1FRCwAAHNaa1mH90ksvzfi5p5xyiqJpF96feuopR+LKRfpo3E9/+tMaNWqUpWgA9JNMmjWc+fODMfZ2KLkkLw2U1CdJ1dXuxDiY3/2uf1fE2bPNOkG+UzvgX5GIVFcnbdxokm6zEYuZZN26On+NxE3HhAv4ndfv9wAA+NjI4R+Cvl588UU9+eSTamtrU0dHhyZPnqzTTz9d06dPZ/EMwNDmz7cdQXA1N5tq2XyTcNatM5XDbW3OxNVXRYW0dq3z+wX8LBqVNm2y04m0tPREpb9k3iM6OszI1s5O6T3v8T6mIKGiFgAAOOxPf/pTyvbZZ5+d1fPPOussPfHEE73be/bs0Yc//GFHYsvG008/rZ07d6bcl89o3Mcee0wtLS168skntXfvXh0/flynnnqqJk2apEsvvVSXX365Pv7xj7OuCWSqpUWaO9edtSGbQqHsOs3FYqabl5PFp4sXm46FXjlyJHW7vd10S9y61cQxY4a0YgUFtsWqpsb8tLaaKS1NTWYNuW+3zHDYrF9UV5t/n5WV9uLNlpvr2EC+SCoFAKAXSXsZeuCBB3THHXdo165dA/75qaeeqkWLFukb3/iGxo8f71ocU6ZM0csvv+zIvj7zmc9o/fr1g/75tm3bdPnll+e8/9NPP10vFXvrfyAb5eWmavOtt2xHEjzt7SYZp7w8v/30dAabObP/uJN8hMNmv36u0ATccskl3r83DtTlrafSv7zcjMaBuy6+2HYEAACgiLz55pt68803U+77u7/7u6z2kf745557Lu+4cnH33XenbF9yySU6//zzc97fjh07+t3X1tamtrY27d69W3fddZfe8573aMWKFbrpppsUorACGFxLizRrlrNrQn7xxz+a82KbyUvRqEmU80v3wsZG8xOPm0Jb1u2K02AFnaWlZu2oUD8X3VrHBvIVixVWAiwAAC5jPO4wOjo6tHjxYn3yk58cNGFPMouDP/zhD1VZWalHH33UwwhzN3r0aNshAMESCkkf+IDtKIKrs9OZ/USjpkqxosKZ/VVUmP1lM0YEKCY23huH6/JWUuJdLEG1YIHtCAAAQBE5cOBAyvbJJ5+sU045Jat9TJw4MWX7LQsFd8eOHdN//ud/ptx3ww03uH7cV155RV/4whd05ZVX9nstAfwfyaTpsFeMyS+xmFmX6kle2rLF/H0PHpT27TO3yaS5f+VKd5Mtli93b9+5qq+Xpk6VEgnbkcBtg41uLlROr2M7YexYulcGnR/f5wEAsIhOe0M4fvy4Fi1apE2bNqXcP2HCBF100UUaO3as/vznP+uJJ55Q9/9pHf/GG29owYIF+s1vfqMPfehDNsLO2DXXXGM7BCA4EglTqfrnP9uOJLhKS53bVzRqqquXLTMLd7miUhcwqqvN+BkvjzeU8nJp3DiJC5buOess2xEAAIAi0tHRkbKdS6Fq+nMOHTqUV0y52Lhxo954443e7VNOOUWLFi3KaV9jxozR7NmzNXPmTF1wwQWaOHGiRo8erfb2dj377LPasmWL7r//fh3pMx6yoaFBV111lX7961+rxMFClr1792rfvn1ZPef555937PhARrq7pUOHpK4uU8iVnrCzdGnxjpkcKIGibzd6L9XUmE5+997r7XGH09ZmOpZReItC49Q6thMqKkz3v2hUamiQ1qyRBugIjCIWj5O0CQBAGpL2hrBixYqUhL1Ro0bpjjvu0I033piycLVnzx7dcMMNvZ34Ojs7ddVVVymRSGjy5MmOxvTb3/5Wx44dy/p5d955p7773e/2bk+ZMkVXXHFFVvu4+eab9cUvfjHjx48cyT8vQA0N0urV/hnrEFQDjcLMVyQi1dWZE81sFxhiMbMgygkqYCxeLK1a5e3xhhIKSX//92YxHu5wMpEaAAAEXnrS3kknnZT1PtKT9tL36YX00biLFi1SWZbnspMmTdJPf/pTfepTnxr0dbj44ou1ZMkS3X777frsZz+rRx55pPfPtm/frhUrVuiOO+7I/i8wiB/84Ae67bbbHNsf4JieItumJmn37v7jYKuqTNHXe97jvyQyp1xwgf/Wp9atM+fjfkuSbG+X5swxCVAU4KKQ5LuO/bnPSf/1X84Wr9fUmHHYY8fmvk83hMPSxz4m3Xef7UiKT0WF+TcAAABSkFU1iBdeeEHf//73U+7bsGGDFgwwyuv888/X1q1bdcUVV/Qm7iWTSd1222360Y9+5Ghc73nPe3J6XkNDQ8r2Zz/7WYWybO09btw4TZkyJafjA4GTTJoK3GJd0Cs0w43CzEdNjflpbT2x0Nvc3H+hd9o0s9C7eLG7Y0SAQhSNmoU6LxKcY7HMfgcvvJCkPbe4kUgNAADQR7ZrXrk+x0mvvfaaNm/enHLf9ddfn/V+3v/+9+v9739/Ro+dNGmSGhoatGjRIm3YsKH3/rvuuktLly7VGWeckfXxgYKQSZFte7vpCO9lV3gbHL5+4YhIxHTjmjnTf+OI29pMx7K6OtuRANnLZx37U59yvni9qyu/v48bpk2T7rzT/B39ljhcyMaNM+/rJDwDANDPu2wH4Fe33Xabjh492rt97bXXDpiw12P06NFav359Sge+u+++Wy+88IKrcWbid7/7nZ5++une7Xe961269tpr7QUEFLuWFmnqVBL2/GS4UZhOqKyUVq6UtmwxSZsHD0r79pnbZNLcv3IlCXvAYAYax2PzODmOIUMG3EykBgAAgZTeje7w4cNZ7yP9Odl2uMvXPffckzJd47zzztP06dNdP24oFNL69etTpoV0dXX16/oHFIVk0iSdzJ/PVAzJnJt96EO2oxhYNGoK6SoqbEfSX329SfwEClWu69g1Neb3MpGQamul2bNNkl9f4bC5v7bWPG779sG7efa5nuob1dUnEofT/27I3fTpjBYHAGAQJO0N4PDhw3rggQdS7luewQXec845R1dddVXv9rFjx1SfT7toh/zkJz9J2f7oRz+q9773vZaiAYpcS4s0axZVWH4z3ChMp4VCUnm5NH68uSU5BRheTY37v6vxeOZjfy65RBpJU2pXeJFIDQAAAqUYkvbS1+9y6bKXq5NPPlnLli1LuS+9618+brrpJrW2tmb18/DDDzt2fEASRbYD+cY3bEcwtGjU/H+Lx21H0t+aNbYjAJyRyzq2U8Xr5eX+S4zrWZv0c+JwIdq0Sdq40fwb2b/f3HZ3244KAABfIGlvAI8++qjeeeed3u1LL70047ES1113Xcr2gw8+6Ghs2ero6NDPf/7zlPu8XPQDAiWZlObO9d/YhqDLdBQmAPvWrXNvMayiQlq7NvPHh0LSBRe4E0vQeZ1IDQAAit7YsWNTtt955x29/fbbWe1j7969Kdvjxo3LN6yMNTY26rnnnuvdHjVqlD796U97dnxJmjNnTsp2IpFwbN8TJ07UBRdckNXP2Wef7djxAYpsB5BNUZtNkYgZRbtxo1nj84sdO8yIUSDo8ileD4Wkqir3YstW+nUEPycOF6Krr5bGjpUmTDC3kciJroy8nwIAAoykvQGkV5LOmjUr4+fOmDFDI/t0ZXniiSf0xhtvOBVa1u6//351dHT0bk+YMGHIMb8A8rB0KYt/fuTVyE0A+XNr/EQ4bPYbiWT3vEK4gFFoSKQGAAAuiEQiCqd9h/zLX/6S1T5efvnllO33ve99eceVqfRRtFdeeaUmTpzo2fElacqUKSnbXV1deuuttzyNAXAFRbb9ZVvU5geZjOU86SRvY6JrI5A/P01jGOg6QiRikvYo7M3fsWOp2+3t0tat0qpVJkEyFjMd+QAACBiS9gbQmpbRf+mll2b83FNOOUXRaDTlvqeeesqRuHKRPlrj05/+tEaNGmUpGqCINTSwUONHhVI1DOAEp8dPVFSY/aV9P8sIHeGcRyI1AABwyXnnnZey/fzzz2f1/BdeeGHI/bnl0KFD2rBhQ8p9NqZkjB49ut99uYwZBnyHIttUuRa1+cVQYzkvu8zbWJqavD0eUIz8svY20HWEZNLcP3++ZPE6b2A0NpoE7SVLzGsPAEBAkLQ3gD/96U8p29mOYzjrrLNStvfs2ZN3TLl4+umntXPnzpT78ln0e+yxx/SJT3xCZ555psrKyjR69GiddtppmjZtmr7whS/oF7/4hY4ePZpv2EBhWr3adgRIV4hVwwAMp8ZPxONmP7kk7PXE8YEP5BcDThgskbq721xg2b/f3HZ3ex8bAAAoeJVp3Xx37dqV8XPffvtttbS0DLk/t9x333165513erdPO+00fexjH/Pk2H3t37+/332RQk3qAXpQZJtq0qTci9r8qO9YzrIyafdub4/f3Mz5K5CvaFSaMcNuDANdR2hpkaZO5TPEhvp689onErYjAQDAEyTtpXnzzTf15ptvptz3d3/3d1ntI/3xzz33XN5x5SJ9tMYll1yi888/P+f97dixQw899JBefPFFvf322zpy5Ija2tq0e/du3XXXXVq4cKHOPPNM3XXXXermZBVBkkiYKiD4R6FXDQMwv791ddLGjWY8QjZiMXNxpq4uv/eBlhbpz3/O/fk4IX0BtO9Io0hEGjtWmjDB3EYi5v7aWimtAzYAAMBg5syZk7K9bdu2jJ/b2NioY31Gdl100UV697vf7VRoQ0pfv7vuuus0YsQIT47d1+OPP56yPWHCBKZ1oPBRZHtCSYk5DyuWhL10hw55PwK5vV3q6PD2mEAxsjmVYaDrCC0t0qxZdGm1qa1NmjmTxD0AQCCQtJfmwIEDKdsnn3yyTjnllKz2MXHixJTtt956K9+wsnbs2DH953/+Z8p9N9xwg+vHfeWVV/SFL3xBV155Zb/XEihYw3UAotrKX/IZhQnAf2pqzO903ySvcDj1MeHwiSSvRMI8Pt/R2D0LdF4v+hejvgugDQ0mqXLqVGnVKmnr1v6vcXu7uX/VKvNeHotJmzbZiR0AABSMj33sYykjXnft2qWnn346o+euX78+Zfvqq692MrRB7dmzJyVZLhQK6brrrvPk2Onq6+tTtmfNmmUlDsAxjY0U2fYVi5mOdMWqq8vOcTs77RwXKCY1NXbG5A50HSGZlObOZT3QD9rbpTlzGJULACh6I20H4DcdaZVRfRf7MpX+nEOHDuUVUy42btyoN954o3f7lFNO0aJFi3La15gxYzR79mzNnDlTF1xwgSZOnKjRo0ervb1dzz77rLZs2aL7779fR44c6X1OQ0ODrrrqKv36179WSUlJ3n+fHnv37tW+ffuyes7zzz/v2PERIImEScZrajKjFfqepIXDUlWVVF1tRv01NdmLE6nicdPJiQ57QPGprJRWrjT/3d1tqtk7O6XSUjMGJxRy7lgs0Dln/HjpV7+SJk8279G5JLr3XGzjPR4AAAzh5JNP1sKFC1OKWFevXq2f/vSnQz7v2Wef1UMPPdS7PXLkSMXjcdfi7Cu9y97ll1+uM88805Nj97Vt2zY9+OCDKfctWLDA8zgAx7S05F/IVWyqq21H4C4Hr0FkpbTUznGBYrNunUmg86q73WBrTEuX0mHPT9rapGXLzDQVAACKFEl7adKT9k466aSs95GetJe+Ty+kL/otWrRIZWVlWe1j0qRJ+ulPf6pPfepTg74OF198sZYsWaLbb79dn/3sZ/XII4/0/tn27du1YsUK3XHHHdn/BQbxgx/8QLfddptj+wP6aWgwozOGqsTt6QDU0wVoJG+l1sVipo0+C7JAMIRCUnm5+XEDC3TO2b9fmj7d/D9L71Sbrfp6ads207WPbqoAAGAAX//613Xffffp6NGjkkwHvauvvlof//jHB3z8kSNHdN1116mrT4em66+/XmedddaQxwmlFYw89thjWXemO3r0aL8pGddff31W+0j361//Wu9+97v193//9xk/5/HHH9c111yj7j7f1c4999yci38B63q6pjO2NJWNLlZeKi83hdZeFt+Fw6aIEED+IhGz3jNzpru/x0NdR2hoYKqSH9XXmyTLmhrbkQAA4ArG4w4jfRHOrec46bXXXtPmzZtT7stl0e/973+/rr322owSFydNmqSGhgZ98pOfTLn/rrvu0osvvpj1sQHPJZPmi//8+dmPzjh2zJ2YYEQi3ozCBACJBTq35Juw16OtzSzgJhLO7A8AABSVM888UzfffHPKfQsXLtSdd96ZkpgnSX/60590xRVXaOfOnb33RSIR3XrrrZ7E+qtf/SplmkQ4HNYnPvGJvPa5c+dOXXTRRZozZ47Wr1+vvXv3DvrYv/71r/rKV76iGTNm6M033+y9f9SoUfrBD36gkRQoohDRNX1gsZjpXl/MQiEzGcVL06Y52/UfCLpo1KzzV1Q4t88xYzK/jrB6tXPHhbPWrLEdAQAArmH1JU16N7rDhw9nvY/052Tb4S5f99xzj471SSI677zzNH36dNePGwqFtH79ev32t7/Va6+9Jknq6urS3XffrW9961uuHx/IWUuLWdCjq5I/jRplTqhLS90bhQkAPVig87/2dmnOHPP5zahcAACQ5vbbb9dTTz3VOw3i6NGjWrp0qb75zW+qqqpK5eXleuGFF7R79+6U7nIlJSV66KGHNHnyZE/i/MlPfpKyvWTJkpwmfqTr7u7Wo48+qkcffVSSdNppp+ncc8/VuHHjNHr0aL311lt69tln9eyzz/Z77ogRI/STn/xEH/7wh/OOA7CCrukDW77cdgTD6+6WDh2SurrMqNvy8uzX/qqrzWQUrxT7yGHAhmjUrPcsW2Y6rOXqmmukf/1X6fTTM3svSSSyb+YA7+zYIbW2Fn8COgAgkEjaS1MMSXvpi375jtbIxsknn6xly5bpq1/9au99mzdvdixp76abburXzW84zz//vK666ipHjo8i1DMygwpc/3r9denmm6W6OvdGYQKAxAJdIWlrMwu4dXW2IwEAAD4zYsQI/fznP9cNN9yg+++/v/f+vXv39ptM0WPixIm65557NGPGDE9ifPXVV3uT6nq4tX736quv6tVXXx32cWeeeaZ+9rOf6bLLLnMlDsB1dE0fWDzu3+kUiYT5f9bUJO3enbo+Gw6bznnV1ebvkEmixuLF0qpV7sU70PEAOC8SMes98bjpsLZjR+bPHWr87VD4/PC/e++VVq60HQUAAI4jaS/N2LFjU7bfeecdvf322zrllFMy3kf66Ilx48Y5EVpGGhsb9dxzz/Vujxo1Sp/+9Kc9O74kzZkzJyVpL+Hg+LKJEydq4sSJju0PAcfIjMJRX29O0mtqbEcCoJixQFdY+GwAAACDKCsr03333aeFCxfqu9/9rn7/+98P+LhTTz1VixYt0m233aYJEyZ4Ft/69et1/Pjx3u2qqipdeOGFee/34x//uPbt26fGxkbt2bMn5RgDGTlypKqrq/XP//zPWrRokUpLS/OOAbDm9tttR+A/FRXS2rW2o+ivocF0uR+qaK693XTN27rVJOLNmCGtWDF0Ik40ah7nRTFeEEYOA7bV1Jif1tYTCb7Nzf0TfKdNMwm+ixfn/nvZ1ORMzHAP/48AAEWKpL00kUhE4XBY7X2+9P3lL3/Reeedl/E+Xn755ZTt973vfY7FN5y77747ZfvKK6/0PMltypQpKdtdXV166623+iVEAtYxMqOwrFlDYgYAd7H4U3j4bAAAAENYuHChFi5cqBdffFG7d+9WW1ub3n77bU2aNEmnn366LrvsMpWUlGS9375jdXPxta99TV/72tfy2sdAqqqqVFVVJUk6cuSI9uzZo5dfflmvvfaaDh06pKNHj6qsrEzhcFhnnHGGPvCBD+jkk092PA7AMz2d2v77v6XHH7cdjb+Ew9LmzaZjlV8kk2Y9NpeCucZG8xOPm0TEwf5ey5d7k7RXCCOHgWJRWXmiw1p3t9TRIXV2SqWlUllZ9qO003V3m26f8LfmZvP/Kt//3wAA+AxJewM477zztHPnzt7t559/PqukvRdeeKHf/rxw6NAhbdiwIeU+L0fj9hg9enS/+w4fPkzSHvyFkRmFZ8cOU1VHFSsAN7BAV5j4bAAAABk444wzdMYZZ9gOw1MnnXRSShIfUFQy6dQWZBUVJmEvGrUdyQktLWbiSb4F1PX10rZtg//9ampMty031339PHIYKHahkFRebn6ccugQ05gKQXu7Sdh08v89AAA+8C7bAfhRZdpFv127dmX83LffflstLS1D7s8t9913n955553e7dNOO00f+9jHPDl2X/v37+93X8RPFX2AZBb2UHhItATgFhboChefDQAAAEAwJJMmYWr+fBL2BhOPmwQ5vyXszZrl3MSTtjZp5kzTaXEg69aZxEU3+HXkMIDcdXXZjgCZ6uy0HQEAAI4jaW8Ac+bMSdnetm1bxs9tbGzUsWPHercvuugivfvd73YqtCGlj8a97rrrNGLECE+O3dfjaaMIJkyYoFGjRnkeBzCoRIKFvULF6EoAbmGBrnDx2QAAAAAUv5YWaepUinYGE4uZDoR1df4biTt3rvNFcu3t0pw5Zv/pIhHTiS8cdvaYfhw5DCB/JSW2I0CmSkttRwAAgONI2hvAxz72sZQRr7t27dLTTz+d0XPXr1+fsn311Vc7Gdqg9uzZk5IsFwqFdN1113ly7HT19fUp27NmzbISBzCgZFL61KdsR4Fc/c//mBGWAOA0FugK1+9/z2cDAAAAUMyc7tRWbB58UNq+3Z8jW5cude//W1ubtGzZwH8WjZrXxKmOexUVZn9+6mAIwBnl5c4n+cJ54bBUVmY7CgAAHEfS3gBOPvlkLVy4MOW+1RmM0nz22Wf10EMP9W6PHDlS8Xjc8fgGkt5l7/LLL9eZZ57pybH72rZtmx588MGU+xYsWOB5HMCAeipy9+yxHQlydeCAlNbNEwAcwQJd4erokBYtGrjDAgAAAIDC5lantmIRi0keNQ7IWkOD+50R6+vNcQYSjZr14Hyv0fhx5DAA54RCUlWV7SgwnGnTzP8rAACKDEl7g/j617+eMtJ1/fr1+tWvfjXo448cOaLrrrtOXX1Gq11//fU666yzhjxOKBRK+clmFG+Po0eP6j//8z9T7rv++uuz3k9fv/71r/XHP/4xq+c8/vjjuuaaa9Tdp9PJueeeq0WLFuUVC+AIKnKLx5w5ZsQxADiJBbrCtmGDSczn8wEAAAAoLm52aisGy5fbjmBwGTRCcMSaNYP/WSRiRgZv3GgSHLPh15HDAJxXXW07AgyH/0cAgCJF0t4gzjzzTN18880p9y1cuFB33nlnSmKeJP3pT3/SFVdcoZ07d/beF4lEdOutt3oS669+9Svt27evdzscDusTn/hEXvvcuXOnLrroIs2ZM0fr16/X3r17B33sX//6V33lK1/RjBkz9Oabb/beP2rUKP3gBz/QyJEj84oFyBsVucXlrbdM4h4dlQA4jcWfwtbWJs2cSeIeAAAAUCy86NRWyOJxf47Elcx5WWOjN8fasUNqbR36MTU1ZrxtIiHV1kqzZ/fvth8Om/tra83j/DpyGIDzFi+2HUFufv/71Pe1Yr4eW6j/jwAAGEYRf3rn7/bbb9dTTz2lRx55RJLpaLd06VJ985vfVFVVlcrLy/XCCy9o9+7dKd3lSkpK9NBDD2ny5MmexPmTn/wkZXvJkiU66aST8t5vd3e3Hn30UT366KOSpNNOO03nnnuuxo0bp9GjR+utt97Ss88+q2effbbfc0eMGKGf/OQn+vCHP5x3HEDeqMgtPm1t0rJlptIVAJyyeLG0apXtKJCP9naT2N3SQicEAAAAoNB51amtEJWUSGvX2o5icF4nW957r7Ry5fCPq6w88bjubqmjQ+rslEpLpbIyRi8CQRWNSjNmeJds7IRw2BQgh0LmfS2RMFMoilEsZt6/AQAoQiTtDWHEiBH6+c9/rhtuuEH3339/7/179+7V5s2bB3zOxIkTdc8992jGjBmexPjqq6/2JtX1yHc07lDHevXVV4d93Jlnnqmf/exnuuyyy1yJA8gKFbnFq77eVBTX1NiOBECxKMQFOvRHYjcAAABQ+Lzs1FaIurqk117zb7FSU5P/jxcKSeXl5gcAli8vrM+dadNSE42L+TqYn0fBAwCQJ8bjDqOsrEz33XefNmzYoEsuuWTQx5166qn63Oc+p9bWVs2ZM8ez+NavX6/jx4/3bldVVenCCy/Me78f//jHddNNNykajWrEiBHDPn7kyJGaPn267rnnHu3Zs4eEPfgHFbnFbc0a2xEAKDYsAhWH+nppwwbTOQEAAABA4Snm5AOn+PU16u6Wdu/29pjNzZz/AchPTU1hjWCtrk7d9jpZ2it+HgUPAIAD6LSXoYULF2rhwoV68cUXtXv3brW1tentt9/WpEmTdPrpp+uyyy5TSUlJ1vvtzvNE8mtf+5q+9rWv5bWPgVRVVamqqkqSdOTIEe3Zs0cvv/yyXnvtNR06dEhHjx5VWVmZwuGwzjjjDH3gAx/QySef7HgcQF6oyC1+O3ZIra20RgfgnJ4FOr9e/EDm/uEfzKiQqiqzkBmP83kBAAAAuK27Wzp0yHSCKykxXcxyGTlarMkHTvLra3TokNTe7u0x29vNqFu65gHIx7p10vbtZoqD3/VNMLSRLO2Figp/j4IHAMABJO1l6YwzztAZZ5xhOwxPnXTSSSlJfEDBIOEiGO69V1q50nYUAIpJoSzQlZZKnZ22o/C39nZp61bzs2qVGX+8YgUVugAAAICTEgmzPtPUZJIG+iZs5VJIU6zJB07r6S6XS1Kkm7q67By3s5OkPQD5iUSkRx6R/v7vbUcytFgs9fPURrK028JhafNm/46BBwDAIYzHBVC8/FptCmfx/xmA0yIRsygUDtuOZGg33SRt3GgW6pCZxkbTTXHJEimZtB0NAAAAUNgaGsz5yNSppkhm69b+SQM9hTSrVknRqHn8pk1D77cYkw/c0NNdzm9ymEjkiNJSO8cFUFymTLEdwfCWL0/dtpUs7ZaKClNQHY3ajgQAMJDubungQWn/fnOb53TRoCNpD0BxoiI3OHqqigHASdGoWRyqqLAdyeASCZOAtn27+e/aWmn27P7JhuGwuf+kk+zE6Uf19ebCYiJhOxIAAACg8CSTpmve/PmmMCYbmRTSFFvygZv82H29vNz7IrhwWCor8/aYAIqT3z+D4vHUCRLd3dKTT1oLx3HxuNTSQsIeAPhN32tQkYg0dqw0YYK5jUTM/bW1Umur7UgLDkl7AIoTFbnB4deqYgCFLxo1i0TxuO1IBtY3abmy0owK37LFXPg6eFDat8/cJpPm/ssusxuv37S1STNnkrgHAAAAZKOlxRTA3HtvfvsZqpDGVqe2QnT77f67MBYKmZHIXpo2zX9jggEUJj9/BlVUSGvX9k+c+MhHbEeWv1jMdPCtq2MkLgD4iVvd1dGLpD0Axcnv1VBwlh+rigEUh0jELBb5cQztYEnLoZDpbDB+vLntuXBRXe1tfIWgvV2aM4dRuQAAAEAmWlqkWbNMAYwTBiuksdGprVB997vZXxjzYpyV1+efnO8CcIpfP4PCYZOod/XVQydO+M3JJ0uXXDL4ZJDaWvM9YPt200GQkYsA4A9ud1dHL5L2ABQnP1dDwXmlpbYjAFDsMhlDa0M2ScuLF7sXRyFra5OWLbMdBQAAAOBvyaQ0d67zyQEDFdLY6NRW6Ia6MNbdLe3aJX3xiybp0otxVl6ff3K+C8ApfvwMmjTJJL594QvZJ07Y9s470jPPSNu2DTwZZOVK8znFyEUA8A8vuqujF0l7AIrTiy8yEiEowmGprMx2FACCYrAxtK+8YieeVasyX7CKRqUZM9yNp1DV15s27wAAAAAGtnSpcx320g1USEPntNz0XBj7xS9MgsOFF5ri5unTpe9/3xSjeTHOysvzz1jMnKsDgFP89Bk0d665feQRu3Hko73d/D26ulIngzByEQC8N1xHU6+6q6MXSXsAik8yeaKNNtzlh8TIadP8EQeA4Ok7hraiwk7nvTvuyG7Bavly92MqVGvW2I4AAAAA8KeGhvy7LAwnvZCGzmm5a2uTFi40CQ5//KN07Fh2z3dqnJVX55+c5wJwmh8+g2Ix6c47pd//Xnr9ddvR5K9vgj4jFwHAW30nSA3V0fS3v/Wuuzp6kbQHoPi4WfmLVFOn2o7AX1VvAILL9uiMTBesamr8sfDoRzt2MGYDAAAAGMjq1d4cp28hDZ3C7ct3nJUX55/xuCneBgAn2fgMCodPJE0kEtKDD0rf/rbziRM21debRERGLgKAN7LtaDpjhrfd1SGJpD0AxcaLyl8YCxZIV1xhOwqSTwD4hx+SiIdbsEompc5Ob2MqJHyHAAAAAFIlEtl3wclVeiENHdTsy3ec1bp1pjO9GyoqpLVr3dk3AHj1GVRfb8YTJpPSli3SypVm5HexNqe45RZGLgKA2/LpaOqm9O7qkETSHoBi41XlL6Rf/tKMZbQpFjMnsADgB35JIh5swaqlxST0PfignbgKQVOT7QgAAAAAf/G6sKXv8egU7g/5jLOKRKTNm00HKSeFw2a/kYiz+wWAHl51C128WCovN1M8ehRzc4psR7YPp71d+uhHpf37nd0vABSqnutAfv0c6dtdHZJI2gNQTLys/IU/UHENwE/8NL4p/aJKS4s0a1ZxVug6qblZ6u62HQUAAADgH14XtqQfz81ObchcPuOsolFp+3bn/j9WVJj9RaPO7A8ABmOrWyjNKbLz+uvSaaedGC/ct2svAARJIVwHSu+uDpL2ABQRv2aMwx3xuDRvnu0oACCVn5KJey6qJJPS3LkmkQ9Da2+XOjpsRwEAAAD4Q3e3tHu3t8dML6Rxq1MbspfPOKto1FxEjMfziyEeN/shYQ+AF2x0C6U5RW66uqStW6VVq8xnRCwmbdpkOyoA8E4hXQcipyMFSXsAigcj7YJjqCo0ALDJb+Ob6uula67xd2WV33R22o4AAAAA8IdDh7y/6DNQIY3TndqQu3zGWUUiUl2dtHGjSabIRixmEgbr6hiJC8BbXncLJZHBGY2NZp12yZLcxrsDQKFZurRwrgOR05GCpD0AxcFG5S/sGKoKDQD8wG/jm7Zvtx1BYSkttR0BAAAA4A9dXXaOO1AhjVOd2pAfJ8ZZ1dSY89REwowxnD27fxercPjEmMNEwjyeiRsAbPGyWyiJDM6qr5emTjWfJQBQrBoaCivpO727esCRtAegONio/IX3hqtCAwA/YHxT4QqHpbIy21EAAAAA/lBSYue4gxXS5NOpDc5x6oJgZaW0cqW0ZYvpgnTwoLRvn7lNJs39K1eaxwGAbV50C6U5hTva2qSZM0ncA1C8Vq+2HUF2BuquHmAk7QEoDrYqf+GdTKrQAMAvGN9UmKZNk0Ih21EAAAAA/lBe7n0xUiaFNAN1ahs50pv44E4XqFDI/HsbP97ccl4GwK/c7BZKcwr3tLdLc+YwKhdA8UkkzEjwQjNQd/WA4kwWQHGwVfkL98Vi0vLljMAAUHh6RmcsW2ZGMcD/qqttRwAAAAD4RygkVVVJW7d6d8xsCml6OrVJ0le/Kt1+u3tx4YSecVYk1gEIsr6fQd3dpmNQZ6fpFltWltt7JM0p3NXWZtZp6+psRwIAzimksbh9DdZdPYDotAegONio/IU7yspyq0IDAD9ifFNhWbzYdgQAAACAv3hd2JLr8eJxZ+PA4BhnBQCpnOoWSnMK99XXm1HFAFAs3OiC7bZMuqsHCEl7AIpDT+UvCktPm/gvf1nasUM6eND8bNliqtQqK21HCADO6Ds640tfsh0NBhKL8bkDAAAApPO6sCXX40Wj0owZzsaCwTHOCgCcR3MKb6xZYzsCAHBGd7e0e7ftKLKXTXf1ACBpD0DxYKRdYfjSl6R9+0xyXjJpEvS+8x2zsJpPFRoAFILKSmnFCttRYCDLl9uOAAAAAPAfL5Ph8i2k4Tu9dxhnBQDOozmFN3bskFpbbUcBAPk7dMh0wS405HSkIGkPQGHq7jZJX/v3m9vubkbaFYo//jH/NvEAUMgYdeE/8Tij2AEAAIDBeJUMl+9xampYH/QC46wAwD0kMnjj3nttRwAA+evqsh1BbjhnS0HSHoDCkUhItbVmnGokIo0dK02YYG4jEemWW6T3vtd2lBhOc7NJsgSAoGLUhb9UVEhr19qOAgAAAPAvL5LhnCqkWbfOfMeHexhnBQDu+dSnbEcQDE1NtiMAgPwVYoOIfLurFyGS9gD4X0ODeQOfOlVatUraurV/q9f2dnP/X/9qJ8ZCtGGD9MIL3h+3vV3q6PD+uADgF4y68I9wWNq82ST/AwAAABicm8lwThbSRCKm6HfkSGf2h/7oAgUA7pkyxXYEwUBzCQDFoBAbRHjVxb2AkLQHwL+SSVNlO3++1NhoO5riEo9LCxeaD3MbOjvtHBcA/IKLHPZVVEjbt0vRqO1IAAAAAP+LREzBi9MXhZwspOlZS/zCF6Rjx/LfHwbGOCsAcE+hjjosNDSXAFAMCq1BhFPd1YsMSXsA/KmlxXTWu/de25EUn77Vy7ba5paW2jkuAPgFFznsisfNdw0S9gAAAIDMRaOm8MWpjntOFtKwlugNxlkBgLsKcdRhoaK5BIBiUCgNIpzsrl5kSNoD4D8tLdKsWVJbm+1Iis+YManVyzba5obDUlmZt8cEAL+JRqUZM2xHETyxmNTQINXVMRIXAAAAyEU0atbu4vH89uNkIQ1rid5hnBUAuOuFF2xHEBw0lwBQDAqhQYST3dWLEEl7APwlmZTmzjWtqeGskhLpt79NXQy10TZ32jRzXAAIOi52uC8clmbPlmprpUTCdPGg/ToAAACQn0jEFMJs3GgKY7LhdCENa4neYZwVALirpUW6/HLbUQQDzSUAFAu/N4hwsrt6kRppOwAASLF0KVWxbrnnnoE/EKurpa1bvYujUNr0AoDbampMFZSb45tmzjQnREGzY4d04YVm8Y1EcQAAAMAdNTXmp7XVnNc0NUnNzakJdOGwKeCsrjbnP06PVmUt0RuMswIAd/UkoR84YDuSYKC5BIBisny51NhoO4r+4nFzDkGHvSHRaQ+AfzQ0uJu4EGTxuPSpTw38Z163zS2ENr0A4JV168zFDzdUVEi/+EXw3ndjMVNZVl7O4hsAAADghcpKaeVKacsWk3Rw8KC0b5+5TSbN/StXOp+wd++9rCV6gXFWAOA+ktC9RXMJAMWkp0GEXzjdXb3IkbQHwD9Wr7YdgV0nn+zOfoerhPWybW4s5vwCLQAUskjEXPwIh53db9+LKuvWSZMmObt/P2PsMAAAAGBPKGQKaMaPd7eQpqVFuvZad/aNExhnBQDuo6GF9/yU3AIATnCzQcRwwmFp9myptlZKJMz5w7x5dmIpQCTtAfCHRMKfbVu9FApJY8Y4u89MK2G9SnAgkQIA+otGzUmMUydU6RdVIhHp/vud2bffxeOcDAIAAADFrqXFFKB2ddmOpLjF4+a1JmEPANwV9IYWXispkSZPth0FADjLrQYRQ7nmGve7qwcASXsA/IEqIuntt6WTTpLGjXNmf9lUwnrRNpdECgAYXDRqLobE4/ntZ7CLKhdemN9+C8GECUN3lgUAAABQ+JJJae5cc3EI7mCcFQB4h4YW3uvqkpYtsx0FADjP6QYRQ6mokP79393trh4QJO0B8IemJtsR+MPevdKBA/nvJ5dKWDfb5g43ohcAYC6G1NVJGzeaiyTZGO6iSnm5NHKkM3H6FSeGAAAAQPFbulRqa7MdRXEZOdIUejHOCgC8R0MLO+rrzVoqABSbngYRbn6fz3TSHzJS5FfuABSE7m5p927bURSPSERasSL7D8qetrkzZ0rt7c7Fwwc3AGSnpsb8tLaahbumJqm5OfW9ORyWpk2TqqtNp9ThWo63tkrHjrkbt21795oq2bo625EAAAAAcENDA8kNThg3ziTpXXih9KlPmfNKiqAAwA4aWtizZo1ZgwWAYtLQYMauu9XFtaLCXPfPpnEQhkTSHgD7Dh1yNkks6JJJk3iX6Wjcvnra5s6Z40zVMh/cAJC7ykpp5Urz393dUkeH1NkplZZKZWXZXVQJyoWt+nrTbZYFNwAAAKD4rF5tO4LCt2GDdM01JOkBgB/Q0MKuHTtMofNwxdAAUAiSSdOV3M1rQfG4maxHox5HMR4XgH1dXbYjKD7t7SbxLpnM/rk9bXPj8fxiyGVELwBgYKGQGXE7fry5zfYCS5CqdtessR0BAAAAAKclEu51iwiKeFxauJCEPQDwCxpa2BeUQmcAxa2lRZo61b33tFjMdPCrqyNhzwUk7QGwr6TEdgTFqa3NjAns7pYOHpT27ze33d3DPzcSMR+8GzeaD+Js8MENAP4StKrdnipZAAAAAMWDi+r5qagwXTEAAP5BQwv7glToDKA4tbRIs2Y5M0GvRygkXXKJVFtriqe2b5fmzXNu/0hB0h4A+8rLpXHjbEdRnOrrpTFjpLFjpQkTzG0kIs2ebT5oh0tqqKkxH8SJhHn87NlSOJz6mHD4xP744AYA/wli1S4X9AAAAIDiwkX13IXD0ubNFNcCgN/Q0MK+5ubMGl0AgB8lk9Lcuc5f/+nulv7yF+l//S9GiHtgpO0AAEChkFRaajuK4tXRkbrd3i5t3Wp+Vq2SZsyQVqwYOtGuslJaudL8d3e32Wdnp/n/VlbGWA0A8LMgVu1yQQ8AAAAoHkHrHu6kigqTsBeN2o4EAJCuvNwkVget2NZP2tvN9a7yctuRAED2li51tsNeXz0T/erq3Nk/etFpD4B9DQ3SG2/YjiK4GhtNR70lS0xG/nBCIXMCM368uSVhDwD8LYhVu01N2VfJ5jJOHgAAAID7gtg93AnxuBmXRcIeAPhTKCRVVdmOAp2dtiMAgOw1NLg/cai+3hwHriJpD4B9q1fbjgCS+eCdOtWMuAUAFI+eqt0gOXhQevnl4R/Xd/x7JJL7OHkAAAAA7gli9/B8xGLm4lpdHSNxAcDvqqttRwAmgQEoRF7lV6xZ481xAoykPQB2JRKm0xv8oa1NmjmTxD0AKCZBrdr90pcG75zX0GAuZE2dakbFb93av3NHzzj5VatMZ4pYTNq0ydu/AwAAABB03d3SkSO2o/C/U081BUeJhLR9uzRvnu2IEHR0swcys3ix7QiCLRyWyspsRwEA2fEyv2LHDpoauIykPQB2ud22Fdlrb5fmzMlsVC4AoDAEsWr3wQf7d86bOVO64AJp/vzsT2qzHScPAAAAIDfpHbHf+17bEfnf//V/SStXSpWVtiNBkNHNHsheNCrNmGE7iuCaNs0UPANAIfE6v4J8DleRtAfArqYm2xFgIG1t0rJltqMAADiFql2TlL5jh7RnT377YZw8AAAA4I5MOmJjYJzzwSa62QP5Wb7cdgTBFcRCZwCFz+v8CvI5XEXSHgB7urul3bttR4HB1NebBRcAQOGjatdZjJMHAAAAnJNMSvF4bh2xYZKf6LAHG/L53aWbPXBCTQ3J17bwugMoNDbyK5qbzXHhCpL2ANhz6BDVsn63Zo3tCAAATqFq11mMkwcAAADy19JiunMxcil3nOvBBqd+d+lmDxjr1kkVFbajCBaS3gEUIhv5Fe3tUkeHt8cMEJL2ANjT1WU7Agxnxw6ptdV2FAAAJ1C16zzGyQMAAAC5a2mRZs0y36uRm3hcmjfPdhQIGqd/d+lmD0iRiLR5sxQO244kOC6/3HYEAJA9W/kVnZ12jhsAJO0BsKekxHYEyASVzgBQPKjadR7j5AEAAIDsJZPS3LlM4chHRYW0dq3tKBA0bv3u0s0ekKJRaft21u688thjtiMAgOzZyq8oLbVz3AAgaQ+APeXlVA0VgqYm2xEAAJwSiXBRxw2MkwcAAACyc+ONdNjLRzhsOjJFIrYjQdAsXere7y7d7AGTuNfSYjqpwl1MmgJQiGzkV4TDUlmZt8cMEJL2ANgTCklVVbajwHCam6XubttRAACc0txsO4LiwyIfAAAAkLk775QefNB2FIWrosJ0YopGbUeCoGlocH8qC93sAZOQXVcnffKTtiMpfkyaAlBobORXTJtmjgtXkLQHwK7qatsRYDjt7VJHh+0oAABOoYOqO1jkAwAAAIbX0iLdcovtKApXPG5eQxL2YMPq1d4ch272gPF//9+2Iyh+rJMCKERe51eQz+EqkvYA2LV4se0IkInOTtsRAACc0N0t7d5tO4rixCIfAAAAMLRkUvrIR6Rjx2xHUnhiMdN9rK6OkbiwI5GQGhu9OZZT3ey7u6WDB6X9+80t02RQaKJRacYM21EUNyZNAShEXudXkM/hKpL2ANjFSUdhKC21HQEwOBbggMwdOmQ6qMJ5LPIBAAAAQ1u6VNq713YUhef3vzfjcOfNsx0Jgszr7vK5Hi+RkGprpdmzTYLr2LHShAnmNhIx99fWOpMUCHhh+XLbERQ3Jk0BKERe5lfEYlJlpTfHCiiS9gDYx0mHv4XDUlmZ7SiAVCzAAbnp6rIdQfFikQ8AAAAYXEOD90k/xSAcZhwV/MHr7vLZHq+hwVxUnjpVWrVK2rq1f9Fie7u5f9Uqc7E7FpM2bXIuZsANNTV0OHIbk6YAuMmtxiNe5VeQx+E6kvYA2MdJh79NmyaFQrajAAwW4ID8lJTYjqC4scgHAAAADGz1atsRFCbW5eAH3d3S7t3eHjPTbvbJpBSPS/PnZz++t7HRXJtYssTsB/CrdeukigrbURQvJk0BcJoXjUe8yK+Ix+n27QGS9gD4Aycd/kU1L/yABTjAGeXlplMD3MEiHwC/cauaFwCAbCQS2Z/Lw2BdDn5w6FD/olm3ZdLNvqXFFPbm28Wzvt7sJ5HIbz+AWyIRafNm1vTcwKQpAE7yuvGIm/kVEydKa9e6s2+kIGkPgDeGu1jESYd/0QURtrEABzgnFJKqqmxHUZxY5APgF15U8wIAkA3G4uaOdTn4QVeXneMO1c2+pUWaNUtqa3PmWG1t0syZrBvCv6JRaft2ml84jY62AJxgq/FIT37FmDHZPS8TR4449z0LQyJpD4B7sr1YxEmH/8RiUmWl7SgQZCzAAc6jU4M7WOQDYJvX1bwAAGSqqcl2BIWJdTn4RUmJneMO1s0+mZTmznW++197uzRnDpM64F/RqFkvj8dtR1I8WCcFkC/bjUcqKtyZAHTwIN+LPELSHgDn5XOxKBqVtm2TRo+2EjrSLF9uOwIEGQtwgDvo1OAOFvkA2GKrmhcAgEx0d0u7d9uOojCxLge/KC/3fkLOUN3sly51r/NLW5u0bJk7+wacEIlIdXXSxo3muhrywzopgHz4ofHI0qXSvn3OHH+gePhe5DqS9gA4x6mLRStWSIcPuxMjMhePS/Pm2Y4CQcYCHOCOaFSaMcN2FMWHRT4ANtiu5gUAYDiHDjlfjBcErMvBT0IhqarK22MO1s2+ocH9kdv19eY4gJ/V1JjJVYmE9NWvmqSRceNSHxMOm+SPwRJgg46OtgDy4VXjke5u0/Vu/35z29194rF8LyoKI20HAKBItLSYD6Z8E2zq652JJyjKyqSODuf3W1EhrV3r/H6BTHn1RTMeNwscQNAsX559gj0GxyIfABt6qnmdWhzsqebdvt0keAMA4ISuLtsRFB7W5eBH1dVmco6XxxvI6tXeHH/NGtYM4W+JhFk/b2oyHW37nheOHStdeKF06aXSH//ozjWkYkBHWwD5cLvxyPvfL511lvTss6nv8eGwKaaorpYefdSd46fje5Gr6LQHIH9Ot35F5iornR9NEA5LmzebNuuALV4uwAFBVFNDZzgnscgHwGteVfMCAJCvkhLbERQW1uXgV16vIQx0vETCuwLEHTuk1lZvjgVko6HBFI9OnSqtWmWSadPPC996yxRj3X679MgjduL0OzraAsiHF41H9u+XHn+8/3t8e7t571+1yiRte4HvRa4iaQ9Afty6WITMPPOMtG2bqcB1QkUFnTVgHwtwgDfWrXPu8yPIWOQDYIPb1bzLlrmzbwBA8JSXO19wWqxYl4OfRaPSjBneHGuwbvZuXxy3fTxgKMmkWYOaP5/pGfkqKZGuvNJ2FAAKmVeNR/yE70WuYTwugPy4ebEIw2tvl844w3Q7XLYsv/HC8bgZvUElL2yzsQC3cqW3xwT8IBIxHRxmziT5PleMrQJggxfVvPX15vyA0RcoEi+++KKefPJJtbW1qaOjQ5MnT9bpp5+u6dOna9SoUbbD88SBAwe0c+dOvfrqq9q/f7/Gjx+v0047TdOnT9e4ceNsh4diFgqZ8U1ejtUsRKzLoRAsX+5NstBg3eybmtw/ts3jAYNpaTHNM7gW54yuLtPN87/+i89eANnzsvGIn/C9yDUk7QHInRcXizC8zk5p/Hiprs4s8K1ZY7qHZSoWMwshdAmCX7AAB3gnGjWdHObMYeEvW4ytAmCLV9W8a9aQtIeC98ADD+iOO+7Qrl27BvzzU089VYsWLdI3vvENjR8/3tVYZs2ape3bt+f8/J/+9Ke69tprs37eE088oW984xvatGmTurq6+v15aWmp5s6dq1tvvVUXXnhhzvEBQ6quDk7S3tixZixhpliXQyGpqTGJLm5eExism/3+/d5fIG9ulrq7TfIxYEtLizRrFgW3bqivN5OsNm+myy2AzAU1N4LvRa5hPC6A3AWx9asflZae+O+aGpN8kUhItbXS7Nn9R5CEw+b+2lrzuO3bWRiEf3R3S7t3e3vMni+aQFBFo2YBMB63HUnhYGwVAFu8rObdsUNqbfXmWIDDOjo6tHjxYn3yk58cNGFPkt5880398Ic/VGVlpR599FEPI/TG7bffrg9+8IN6+OGHB0zYk6TOzk49/PDD+uAHP6g1a9Z4HCECY/Fi2xG474ILzOf0gQOsy6G4rVtnzondMFg3+5YWc/49yGeZa9rbpY4Ob48J9JVMmg57JOy5p63NTCFJJGxHAqBQBLURCN+LXEOnPQC5CWrrV78Jh6Wysv73V1aeGPfZ3W0+RDs7TYJfWRlZ8PCvQ4e8X4To+aJZXu7tcQE/iURy79gaNIytAmCT19W899574rwCKBDHjx/XokWLtGnTppT7J0yYoIsuukhjx47Vn//8Zz3xxBPq/j/FO2+88YYWLFig3/zmN/rQhz5kI2zHffvb39bXvva1lPtGjx6tiy++WJMnT1ZbW5v+8Ic/6MiRI5Kkrq4uLV++XKFQSF/5yldshIxiFo1KM2YU71piKGQ69fR07GRdDsUsEjFdqWbOdHYNb7Bu9ra7jHV2smYIe5YuZTKGF9rbzRSSlhbW+wAMzUbjET/he5ErSNoDkJugtn71m2nThl/oC4XMBygfoigEXlfM9uCLJmDU1Jif1lbzWd/UZLpRUtHL2CoA/uB1NW9Qq4dR0FasWJGSsDdq1CjdcccduvHGG1VSUtJ7/549e3TDDTf0duLr7OzUVVddpUQiocmTJ7se54svvpjV47MZ37tx40b97//9v1Puu/HGG7Vy5cqU/ezbt0+1tbX6f//f/7f3vuXLlysajWrOnDlZxQcM63OfK96kve7u1EkYfbEuh2IUjUq//KX0sY9Jhw/nv7+KioHHU/qhy9hgv9uA2xoauA7npbY2adkyU9QMAIOx0XjET/he5AqS9gDkhos3/lBdbTsCwFl9LqJ5ii+aQKqBOkPs3y+deabduGyZNs2MrQIAm2xU8zY3m+PSEQgF4oUXXtD3v//9lPs2bNigBQsW9Hvs+eefr61bt+qKK67oTdxLJpO67bbb9KMf/cj1WKdMmeLKfo8fP64vf/nLvV0EJemWW27RHXfc0e+xEyZM0I9//GOVlZXpe9/7niSpu7tbX/rSl/SRj3xEI0aMcCVGBFBLi/TlL9uOwl0UAyJIWlqkT33KmYS90aOl++7rn7An2e8yNtiUG8ALq1fbjiB46uvNhI2aGtuRAPArW41H/IDvRa55l+0AABSgoLd+9ZPFi21HADirvNx88fMSXzSBodEZwiSttLbajgJA0Nmo5m1vN4nbQIG47bbbdPTo0d7ta6+9dsCEvR6jR4/W+vXrUzrw3X333XrhhRdcjdNNP/vZz/TMM8/0bp977rlatWrVkM+5/fbbde655/Zu79mzR3V0GYFTekZbFvt4P4oBERRO/04fPiwtWCAlEqn3+6HLWCZTbgA3JBLF253W79assR0BAD+z1XjED/he5BqS9gBkL+itX/0iFjOdkIBiEgpJVVXeHpMvmkBmgnxCKtm/WAAAtqp5OzvtHBfI0uHDh/XAAw+k3Ld8+fJhn3fOOefoqquu6t0+duyY6uvrnQ7PMz/72c9Stm+55RaVDpNMVFpaqptvvnnI/QA58cNoSy9QDIigcOt3ur1dmjPH7L+HH7qMMeUGtrAGZc+OHRTuAhicjcYjfsH3IteQtAcge0Fu/eonGVx8AAqS11/8+KIJZCbIJ6SS1NRkOwIAQWcreZrOQSgQjz76qN55553e7UsvvVTvf//7M3ruddddl7L94IMPOhqbV5LJpBr7dGUpKSlRPB7P6LlLlizRqFGjere3b9+uN9980/EYETC2R1t6hWJABIWbv9NtbdKyZea//dJljCk3sIU1KLtImgQwGBuNR/yC70WuIWkPQPaC3mnHD+Jxad4821EA7vD6ix9fNIHMBPmEVDIjcru7bUcBIMhsJE/TOQgFZPPmzSnbs2bNyvi5M2bM0MiRI3u3n3jiCb3xxhtOheaZLVu26Pjx473b06ZNU3l5eUbPHTNmjKr6fNc7duyYtmzZ4niMCBA/jLb0ysUX244AcJ8Xv9P19f5572DKDWzp7pZ277YdRbCRNAlgKEFsBML3IleRtAcge0HvtGNbRYW0dq3tKAD3RKPSjBneHIsvmkB2gnhC2qO9XerosB0FgCCzkTzd3S098oi3xwRy1Jo2xurSSy/N+LmnnHKKotFoyn1PPfWUI3F5KZ/XQJKmT5+esl2IrwF8xA+jLb2yYIHtCAD3efU7vWaNPxJmmHIDWw4dKv6x8n63bZtJIAaAgQSxEQjfi1xF0h6A7AW9045N4bC0ebMUidiOBHCXV18A+aIJZCeIJ6R9dXbajgBA0HmdPH3ggFRTIy1ZIiWT3h4byNKf/vSnlO2zzz47q+efddZZKdt79uzJO6ah3HzzzaqurtbEiRNVUlKiU089Ve973/t05ZVXas2aNXr22Wez3md6zH5/DVDE/DLa0itpvztA0fHyd3rHDukPf/DmWINhyg1s6uqyHQGOHZPmz+c8GMDAvGw84gd8L3IdSXsAchPkTju2VFRI27ebLwNAsaupcT85iC+aQPaCdkKarrTUdgQAgs5W8nR9vTR1qrlgC/jQm2++qTfffDPlvr/7u7/Lah/pj3/uuefyjmsoa9eu1R/+8Aft27dPR48eVXt7u55//nlt3LhRy5cv13nnnadPfOIT+vOf/5zxPp9//vmUbb+/Bihifhht6SXOE1DsvP6dPnjQ2+P1xZQb2FZSYjsC9OA8GMBggtIQhO9FniBpD0Bugt5px2vxuNTSQsIegmXdOvOF0A180QRyF5QT0nThsFRWZjsKAEFnM3m6rU2aOZMLFvClAwcOpGyffPLJOuWUU7Lax8SJE1O233rrrXzDysvf/vY3PfTQQ6qqqtIvfvGLjJ6T/jqk/52G47fXAAXMD6MtvcJ5AoIgKL/TTLmBH5SXm3+L8AfOgwEMxIvGI7bxvcgzI20HAKBA9VwsCtKoCxtiMZMcQTcwBFEkYr4Qzpwptbc7t1++aAL56TkhDVr3jHPPlUIh21EAgDk/sHUe1t4uzZljCor4LgUf6ejoSNkePXp01vtIf86hQ4fyimkw0WhUc+fO1YUXXqizzz5b48aNU2dnp/bu3atdu3bp/vvvV6LPRcGDBw9q0aJF+tWvfqV5w6wN5Ps6uPUa7N27V/v27cvqOeldA1FAurul3bttR+GdadM4T0BxC8rv9OTJ0qOPUjQP+0IhqapK2rrVdiTowXkwgIGsWyc99pj0+uu2I3FeRYW5jsr3Ik+QtAcgdzYvFhW6L39ZevJJqbk5NRkpHDaLfdXVJiGistJaiIAvRKNmLPScOaaqLV980QScsW6d+d104veyULyLJuUAfMJ28nRbm7RsmVRXZ+f4wADSk9VOOumkrPeRnrCWvs98xeNx3XXXXbrgggsGfcyHP/xhfe1rX1NdXZ0+97nP9SbNHT9+XIsWLdLTTz+t0047bdDn5/s6uPUa/OAHP9Btt93myL5QAA4dcrbwzu+qq21HALgrCL/TJSUmGWf8eNuRAEZ1NUl7fsN5MIB0kYh00UXSI4/YjsRZ8biZVEaSsmdI2svSiy++qCeffFJtbW3q6OjQ5MmTdfrpp2v69OkaNWqU7fA8ceDAAe3cuVOvvvqq9u/fr/Hjx+u0007T9OnTNW7cONvhwUteXCy65hqptFSqr3fvGDYsX24WAbq7pY4OqbPT/D3LyqjOBdJFo2bhbNmy/N4L+KIJOKenE2YsJqWNYStaf/ub7QgA4ATbydP19dLRo9L/8/9QaARfCuVwXp3Lc7Jx4403ZvzYJUuW6JxzztGsWbP0zjvvSDIJdLfddpv+4z/+I+P9ZPt3cvs1QEB0ddmOwFvFPhYLCMLvdCxGwh78ZfFiadUq21EgXX29ucZQU2M7EgB+0NBQXAl7TP+zhnYRGXrggQc0ffp0nXnmmfrEJz6hL3zhC1qxYoU+85nPaNasWZo0aZJuuukm7d+/3/VYZs2apVAolPPP+vXrczruE088oauvvlrvfve7VVNToxtvvFG1tbW68cYbVVNTo0mTJunqq6/Wk08+6ejfFz63bp3pXOWGigrp3//dVK5s3Gg+LIpFaam5DYWk8nKzKFBeTsIeMJhI5MR7wbRp2T03FpM2bJC+/31p1CiTLAsgf9GotGOHqUgPgqef5v0DgH/0JE+Hw/Zi2LDBfBbEYtKmTfbiACSVlZWlbB8+fDjrfaQ/J32fXrv44ov1rW99K+W+e+65R2+//fagz8n3dfDba4ACFZTzA8l8BpK8jmIXhN9pOmbCb6JRacYM21FgIGvW2I4AgF+sXm07guFNmybV1kqzZ/dfQwyHzf21tVIiYYqDSdizgqS9YXR0dGjx4sX65Cc/qV27dg36uDfffFM//OEPVVlZqUcffdTDCL1x++2364Mf/KAefvhhdQ1SWdXZ2amHH35YH/zgB7WGLy3B4dbFonDY7LenI1ZNjfmwSCSkf/gHZ4/ltXDYdNQDkJ2GBvMluLk5u+f97nfSJz8pTZggjR1r3ld6voi2troTKxAU0ah00022o/DGgQPShz/MewcA/4hGzTmSW0VUmWpsNOdr11wjvfQSCc6wohiT9iTppptu0pgxY3q3u7q69Nhjjw36eL8m7d10001qbW3N6ufhhx925NiwoLw8GEk+kulEARS78nLvC0X6fPZ5go6Z8CM+Y/xpxw7WBQGYfIXGRttRDO8b35BWrpS2bJGSSengQWnfPnObTJr7V66kEMkykvaGcPz4cS1atEj33Xdfyv0TJkzQRz/6UX3yk59UVVVVyuiIN954QwsWLNBvf/tbr8N1zbe//W199atf1dGjR3vvGz16tGKxmBYtWqQZM2bopJNO6v2zrq4uLV++XN/5zndshAsbnL5YVFFh9heN9v+zykrpH//RJN8UqmnT6KgHZCOZNG3n58/P7Uvw8eOp2+3t0tatZsRANCpdeqn0wANcYAZy9dnP2o7AO9u2nXjvoLMUAD+IRqWWFvNdybYHH5TOOEMaN44CCXhu7NixKdvvvPPOkB3pBrJ3796U7XHjxuUbVt5KS0t1+eWXp9zX0tIy6OPTX4d9+/ZldTy3XoOJEyfqggsuyOrn7LPPduTYsGDTpmCM04zH6USBYAiFpKoqb49ZXe1dlzE6ZsKvampIKPWre++1HQEA2wrhfSD9fIXpf75F0t4QVqxYoU19LsSNGjVK69at0yuvvKJHH31UP//5z9Xc3KzW1lZdeumlvY/r7OzUVVddpddee82TOF988cWsfhYuXJjxvjdu3Kj//b//d8p9N954o/7yl79o+/btuu+++7Rjxw795S9/0Q033JDyuOXLl2vz5s2O/B1RAJy6WBSPm/0MlLDXN3Eny4VnX6HdPpC5lhZp6lR3vwD//vemE99JJ0kzZ3KBGchWUEd29HSWWrLEfEcBAFsiEamuTtq40STM2XbwYGqBBEnO8EAkElE4rQvQX/7yl6z28fLLL6dsv+9978s7LidMmTIlZXuoRLz0mNP/TsPx62uAAlMIY6LyVVEhrV1rOwrAO16vZ1dXe9dljG5m8LN16+x3Vkd/TU22IwBgm9/fBzhfKSgk7Q3ihRde0Pe///2U+zZs2KAvfOELKklr73/++edr69atKYl7yWRSt912myexTpkyJaufTEdbHD9+XF/+8pfV3afz0C233KJ///d/1/jx41MeO2HCBP34xz/WF7/4xd77uru79aUvfUnH0zscoXj1vVgUi2X33FjMjL6sqzsxErcvLxJ3vEJ1FJCZlhZp1iyprc2b43V1mfb2XGAGshfkRe76evMdJZGwHQmAoJs3z59VsiQ5wyPnnXdeyvbzzz+f1fNfeOGFIfdny+jRo1O2hxp5W6yvAQpIoYyJykc4LG3ePPD6JVCsvF7PXrzYmy5jdMyE30Ui5jPH6xHVGFpzM1N7gCDr7pZ277YdxeA4Xyk4JO0N4rbbbksZB3vttddqwYIFgz5+9OjRWr9+fUpC3913391vsauQ/OxnP9MzzzzTu33uuedq1apVQz7n9ttv17nnntu7vWfPHtXV1bkWI3yqpsaMt00kTMeq2bP7n1SEwydGJiUS5vGDnSB7nbjjJtrtA5lJJqW5c80oW1u4wAxkLugjO9raTKdOEvcA2HTokN3vTsMhyRkuq0w71961a1fGz3377bf7jZ1N358t+/fvT9lOL6TtK5/XQJJ+97vfDbk/YFjFUGw7lIoKs4Y50IQQoJhVVEhpzSxc03f93M0uY3SgQaGIRs1nDx33/KO9XerosB0FAFv8vP7G+UpBImlvAIcPH9YDDzyQct/yDLqHnHPOObrqqqt6t48dO6b6+nqnw/PMz372s5TtW265RaWlpUM+p7S0VDfffPOQ+0GAVFZKK1dKW7aYhJeDB81Y24MHzfaWLebPh1oE9kPijpOC3IkIkEwFysGD0v795nawirSlS/2TqDvQBeZM/x5AkAR9ZEd7uzRnDkm+AOzp6rIdwfBIcoaL5syZk7K9bdu2jJ/b2NioY8eO9W5fdNFFeve73+1UaHl5/PHHU7Yrhvi+9ZGPfEQjRozo3W5ubtahQ4cyOs6hQ4e0u0+3gJEjR+ojH/lIltEi8Pw+Jiof8bgpLOYCGIJo6VLvvmv2XT93q8sYHWhQaKJR8xkUj+e3H6+Sb4Ogs9N2BABs8ev6G+crBYukvQE8+uijeuedd3q3L730Ur3//e/P6LnXXXddyvaDDz7oaGxeSSaTauwzyqCkpETxDL8MLlmyRKNGjerd3r59u958803HY0SBCYWk8nJp/Hhzm+nYJj8l7uSLdvsIqr5dNyMRaexYacIEcxuJnOi62dpqHt/Q4L/q/LY26UMfkm64IfO/BxA0jOww7xXLltmOAkBQFcoFmPZ26aMfJckZjvvYxz6WMkp2165devrppzN67vr161O2r776aidDy1kikVAiLcl11qxZgz5+/Pjx+tCHPtS73dXVlXFBcV1dXcrUkVgsplNPPTW7gBFsfh8TlatYzKxT1NWR4INg8nKdbqD1c6e7jNGBBoUqEjGfRRs3ms+mbMRi0oYN/k00KUTDNLkBUMT8tv7G+UrBI2lvAJs3b07ZHmoxLN2MGTM0cuTI3u0nnnhCb7zxhlOheWbLli06fvx47/a0adNUXl6e0XPHjBmjqqqq3u1jx45py5YtjseIAPBj4k6uaLePIGpoMF8Wp06VVq2Stm7t3zWzvd3cv2qVWSyLxfzbkfLgQenuuzP/e2zaZCdOwCZGdpjunA0NtqMAEETl5YWTOP3669I//qPtKFBkTj75ZC1cuDDlvtWrVw/7vGeffVYPPfRQ7/bIkSMzLlx10/Hjx3XLLbek3Hf22Wfr/PPPH/J5/5j2u/Vv//Zv6hymE0hnZ6e+973vpdz3mc98JvNgAcnfY6JycdJJplPF9u0U4SK4Ghry7+yVqZKSwdfPneoyRgcaFIOaGvPZ1LdQPv08MBw+UWCeSJjHZ3GtG8MIh6WyMttRALDF9vrbQO/xnK8UNJL2BtCa1iHn0ksvzfi5p5xyiqJpX/ifeuopR+LyUj6vgSRNnz49ZbsQXwP4QAaL6wWBdvsImmTSLILNny/16dqakcZGqRg+MxobzQLKkiV0kUHw9CymD3NBuaitWWM7AgDFpLvbFA/s329uu7sHflwoJPUpoPO9TZuku+6yHQWKzNe//vWU6Q/r16/Xr371q0Eff+TIEV133XXq6tN15Prrr9dZZ5015HFCoVDKz3CjeNetW6cjR45k9peQ6ZD3T//0T9q6dWvK/bfeeuuwz/3MZz6jc889t3f7mWeeUW1t7ZDP+epXv6pnnnmmd/v888/XkiVLMo4XkFR83XuOHJGmTLEdBWBH37W9gwe9OWZXl+lef/CgtG+f9Mor5rbn+2++XcboQINiU1kprVwpbdlifmd7fncOHjTbW7aYP6+sNI/3W2eoQjZtWubTxAAUH1vrb9dcI7344sDv8ShoJO0N4E9/+lPK9tlnn53V89MX9vbs2ZN3TEO5+eabVV1drYkTJ6qkpESnnnqq3ve+9+nKK6/UmjVr9Oyzz2a9z/SY/f4aoAglEtkn+/jR5MnStm1U7yE4WlpMZ71i6ZKZr/p683qkjbQCil4kYj4Dg2rHDkZlA8hP364JkYg0dqw0YYK5jUROVNSmv9dUV9uJN1f/639R4ABHnXnmmbr55ptT7lu4cKHuvPPOlMQ8yaz/XXHFFdq5c2fvfZFIJKPEuGwtW7ZMZ5xxhr7yla/o8ccf17FjxwZ83LFjx/TLX/5SH/zgB/XTn/405c9mz56dUSLdiBEj9K//+q8K9bmQeMcdd+if//mflUz7fdu/f79uvPFG/du//VvvfaFQSN/97nc1YsSIbP6KQHEmAwzTpRIoSjbX9i66yHzfnThReu97ze3YsdLJJ0uXXGK+/55+em5dxuhAg2IWCpnOT+PHm9uBEspsd4YqJoV23g3AeTbeB37xC+myy7juUIRC3d2DlWgH05tvvqlIWqVNR0eHTjnllIz38aUvfUl33HFH7/ayZcv0/e9/37EYZ82ape3bt2f8+He9611asGCBvvOd7wxbKdxj6tSpSvRJMNi4caNqamoyPuZ//dd/6eMf/3jK/v74xz9m/HwnPfXUU6rsk2Xc2tqqCy64wEosyEJtrRkzWQzCYZNxX11tKhTJekexamkxbfaLaRyOU8Jhs0BIAi+CoqcKPsjvB7W1ptoNALLR0GA6jmdTwDRjhrRihbkQmUiYi6yFZN68ohorzhqEfcePH9eVV16pRx55JOX+iRMnqqqqSuXl5XrhhRe0e/du9V0WLSkp0W9+8xvNmDFj2GOE0i6EPvbYY5o1xMix9MeXlpbqggsu0OTJkzV27FgdPXpUe/fuVXNzszo6Ovo9/wMf+ID++7//W+Xl5cPG1uPb3/62vva1r6XcN3r0aH3wgx/UpEmT9Nprr6mpqUmHDx9Oeczq1av1L//yLxkfxy38LhWgYjwHOHjQJDkAQVEoa3t9v/9K5v2no8Mk2paWmrGVdMEC+ps9W0rr5IwcJBJc5wOCzub627hxZnrFJZfwfcdBNtcg6LSX5sCBAynbJ598clYJe5JZBOzrrbfeyjesvPztb3/TQw89pKqqKv3iF7/I6Dnpr0P632k4fnsNUICammxH4Jz2dnMitGqVSdiJxcyHKVBMkklp7lz/L+rZ0t4uzZlDJxkEx6FDvB8U03cZAO7rO4Is247jjY1STY20ZIlUUWEuYhaSTZuKKmkP9o0YMUI///nPtWjRopT79+7dq82bN2vDhg1qbm5OSdibOHGifvnLX2aUsOeEzs5O7d69Ww0NDaqvr9eGDRu0ffv2fgl7oVBIy5YtU2NjY1YJe5JUW1urb3/72ynjgg8fPqxt27bpvvvu0/bt21MS9kaNGqXbb7/dFwl7KFCFNqZ9OOGwSfwBgqKQ1vb6fv9NJjPrMgaADnFOiMVI2ANgrvfbWn87cECaPj21szDd9woaSXtp0hfHRo8enfU+0p9z6NChvGIaTDQa1b/8y7+ovr5eTU1NevbZZ5VIJLR161Z961vfUjStm8/Bgwe1aNEibcogWSjf18Gt12Dv3r166qmnsvp5/vnnHTk2PNTdLe3ebTuKgY0cmX8L8Z5FhWuukV56yfx9gUK3dKnU1mY7Cn9ra5OWLbMdBeCNtPFzgdTczGc8gMw4NYKsvt7sJy1RqSCsWWM7AhSZsrIy3XfffdqwYYMuueSSQR936qmn6nOf+5xaW1s1Z84c1+L5zne+o3nz5vWb7jGYCRMm6POf/7z27Nmj73//+zrppJNyOu5Xv/pVPf7441qwYIFKBhldWlJSogULFqipqUnLly/P6ThAr2JKBpg2jcQfBEshru31fP/tMzUKwBAWL7YdQeHj+zKAHrbfD956i6ZBRYLxuGl27typyy67rHf7tNNO0yuvvJLVPn784x/rxhtv7N3+6Ec/qkcffdSxGP/jP/5Dl112WUbtGOvq6vS5z30uJWmurKxMTz/9tE477bRBn1dSUqKjR4/2bj/33HM6++yzM47xueee0znnnJOyv87OzoyfP5ivf/3ruu222/LaB+M0CsDBg9LYsbajGNzvfmcS7l5/3Zn9jRkjXXwx43NRuBoaTFcYZGbjRpO4CxQzv3+We4VxWgCG48YIsnHjpMOHzXiwQlIkI4YY6elPL774onbv3q22tja9/fbbmjRpkk4//XRddtllgyazueWVV17RM888o1deeUXJZFKHDx/WiBEjFA6HNX78eF144YU666yzHD9ue3u7du7cqVdffVXJZFKRSESnnXaapk+frnC+xYku4HepQBXimPbB1NZKK1fajgLwRqGv7TEmDshcLJZ9d3cY8bhUV2c7CgB+Eo/nX4TrtHhcWrtWyrBoEIbNNYiRnhylgIVy+IKfy3Oy0TchcDhLlizROeeco1mzZumdd96RZLro3XbbbfqP//iPjPeT7d/J7dcARc7v3XnmzTPZ6045eNBkwvdkw8+YIa1YYY4DFILVq21HUFjWrCFpD8XvpZdsR+APnZ0k7QEYnFsjyA4ccHZ/Xrn3XpIj4JozzjhDZ5xxhu0wJEnvec979J73vMfz44bDYdVwHgK39YyJKoZkALoRIUgKfW2vZ0zc2LHSBz5AcTwwlOXLi+Nz2msVFSYJBgD6WrdO2r7dX92K6+ulbdukzZvN+Rl8j/G4acrKylK2Dx8+nPU+0p+Tvk+vXXzxxfrWt76Vct8999yjt99+e9Dn5Ps6+O01QIHxuMo9a04m7A2kZ3zukiXmQh7gZ4kEJ/nZ2rFDam21HQXgrvvusx2BP5SW2o4AgJ8V4ggyNzU12Y4AAOAE22OinBCLkeyD4CimtT3GxAHDq6khMT1b4bBJfqFrFYB0kYh5f/Bb9/q2NmnmTPM9Lxvd3abZ0P795pahrZ4gaS9NMSbtSdJNN92kMWPG9G53dXXpscceG/Txfk3au+mmm9Ta2prVz8MPP+zIseGh8nL/fbjZUF9vRopk+4EKeMlvbZ8LBa8bih2JF+a7jA/OAwD4VEMD3wfSNTezGAgAxaAYkgH6Jh5y4QrFrpi/k1IcDwxs3TrTOQ7Dq6gwXbToVgVgMNGoeZ/w2/tqe7s0Z87w34ESCam2Vpo92yQhjh0rTZhgbiMRc39tLc1IXETSXpqxY8embL/zzjtDdqQbyN69e1O2x40bl29YeSstLdXll1+ecl9LS8ugj09/Hfbt25fV8dx6DSZOnKgLLrggq5+zzz7bkWPDQ6GQVFVlOwp/yDUTHvAKiTm54XVDMevulnbvth2Ffeeea77TAMBACn0EmRva26WODttRAACc8LnPFW4BSzwuvfe9XLhCcARhjYrieCCVXztD+U08LrW0kLAHYHjRqHm/iMdtR5KqrU1atmzgP2toMF2Jp041XYq3bjVrc321t9PF2AMk7aWJRCIKp31J+ctf/pLVPl5++eWU7fe97315x+WEKVOmpGwPlYiXHnP632k4fn0NUECqq21H4B+ZZsIDXiMxJ3d0kkExO3So/8ldELW28tkNYGDFNILMaZ2dtiMAAOSj58JPLFaYidinniq98AIXrhAcQVrbozgeSOXXzlB+EIuZ7zR1dYzEBZC5SET6//4//xUv1deb97QeyaRJLpw/P/v1SboYu4KkvQGcd955KdvPP/98Vs9/4YUXhtyfLaNHj07ZHmrkbbG+Bigg06bZjsBfhsqEB2whMSd3dJJBMevqsh2BP3R08NkNYGDFPIIsX6WltiMAAOQinws/fjFypPTmm9Lvf5/d87hwhUIWtLU9iuOBVH7tDOWGSy450UU3vcNgOHyii24iYZIZ582zEyeAwnbokD+v/a1ZY25bWkyBUr5rk3QxdhRJewOorKxM2d61a1fGz3377bf7jZ1N358t+/fvT9keP378oI/N5zWQpN/97ndD7g8YVnOz7Qj8Jz0THrCNxJz80EkGxaqkxHYE/sFnN4CBBGEEWS7CYf9VIwMAhufUhR/bjh3L7/lcuEIhCuLaHsXxQKpIxHSU27jRdJgrVnv2SN/6lrRli0ncPXhQ2rfP3CaT5v6VKyWuZwPIh1+/W+3YYboAzpxpvgs5gS7GjiFpbwBz5sxJ2d62bVvGz21sbNSxPif4F110kd797nc7FVpeHn/88ZTtiiFaHn/kIx/RiBEjerebm5t16NChjI5z6NAh7e7TUn3kyJH6yEc+kmW0CDwuZA2sJxMe8AMSc/JDJxkUq/Ly/hWrQcZnN4C+gjSCLFvTpkmhkO0oAADZaGmRZs1y7sJPoePCFQpNUNf2KLAD+qupMR3mEonMOtLNnWsnzlwdPCh96ENmpH0oZNYvx483t5yHAnCKn79bffrT0oEDzu6TLsaOIGlvAB/72MdSRsnu2rVLTz/9dEbPXb9+fcr21Vdf7WRoOUskEkqkLRbMmjVr0MePHz9eH/rQh3q3u7q6VF9fn9Gx6urqdPTo0d7tWCymU089NbuAEWxcyBrcjh1Sa6vtKACDxJzc0UkGxSwUkqqqbEfhH3x2A+graCPIslFdbTsCAEA2kklzwZ7PtVRcuEIhCfLa3sqVtiMA/Kmy0vx+DNeR7j//UxqiOYwv7dzJSHsA7iov93finhvoYpw3kvYGcPLJJ2vhwoUp961evXrY5z377LN66KGHerdHjhypeDzueHzZOn78uG655ZaU+84++2ydf/75Qz7vH//xH1O2/+3f/k2dw4zy6+zs1Pe+972U+z7zmc9kHiwgSb//PQt+Qyn0cSMoHiTm5I5OMih2JF6k4rMbQA+/jsnwg8WLbUcAABhMd7e5WL9/v7nt7paWLqXD3mC4cIVCEeS1vV27pAcftB0F4G9DdaT7/e+lSZPsxZYPRtoDcMumTcFc+6OLcV5I2hvE17/+dY0aNap3e/369frVr3416OOPHDmi6667Tl19fgmvv/56nXXWWUMeJxQKpfwMN4p33bp1OnLkSGZ/CZkOef/0T/+krVu3ptx/6623Dvvcz3zmMzr33HN7t5955hnV1tYO+ZyvfvWreuaZZ3q3zz//fC1ZsiTjeBFwDQ1SLCZNn247En9jdDD8hMSc3PC6odiReJGKz24APYJWbZupWMx0dAAA+Eff8XiRiDR2rDRhgrkdM4bClOFw4QqFIshrVEuWkLQDZCuZlOJxaf78wp6Y1dZmzkN37UotSgCAfGTQCKxorVljO4KCRdLeIM4880zdfPPNKfctXLhQd955Z0piniT96U9/0hVXXKGdO3f23heJRDJKjMvWsmXLdMYZZ+grX/mKHn/8cR07dmzAxx07dky//OUv9cEPflA//elPU/5s9uzZGSXSjRgxQv/6r/+qUJ/KiTvuuEP//M//rGRa2+D9+/frxhtv1L/927/13hcKhfTd735XI0aMyOaviGI0UDVuX32/5Dc22omxkDQ3c/IA/yAxJze8bih20Whwq/UHwmc3gB5BHkE2lOXLbUcAAOjRU1Q7daq0apW0dWv/iRgdHXZiKzRcuEIhCPIa1ZEjjLMGstHSYr4fFEvi/oEDpolIT1FCJGKKFWprpdZW29EBKDSJRLDzHHbs4L0zR6Hubq4eDeb48eO68sor9cgjj6TcP3HiRFVVVam8vFwvvPCCdu/erb4vY0lJiX7zm99oxowZwx4jlDYa77HHHtOsWbMyfnxpaakuuOACTZ48WWPHjtXRo0e1d+9eNTc3q2OAxZMPfOAD+u///m+Vl5cPG1uPb3/72/ra176Wct/o0aP1wQ9+UJMmTdJrr72mpqYmHT58OOUxq1ev1r/8y79kfBy3PPXUU6rsU7Hf2tqqCy64wGJEAZFImC/uTU2m2qbv4l44bC7kV1ebEZHLljFOI1sHD5oLfoAfxGLB/iKarVhM2r7ddhSAu1papMsu42JeX3x2A+gxe7ZJgIARj0t1dbajcAxrEIAz+F2yIJk0I2+L5UK8XzQ2Sh/6kO0ogKEFfW2vyL6PAq5oaZFmzeqfyF/MZsyQVqyQ5s2zHQmAQlBba4qegqy2Vlq50nYUObG5BjHSk6MUqBEjRujnP/+5brjhBt1///299+/du1ebN28e8DkTJ07UPffck1HCnhM6Ozu1O4P2w6FQSEuXLtXq1at10kknZXWM2tpahUIh3XrrrTp69Kgk6fDhw4OO8h01apS++c1v+iJhDxY0NJjWr0Od5Le3m4tUXKjKXWcnF/7hH8uXB3thL1t0kkGxSyaluXNJ2Eu3cWOwOxgAOKG6mnOhHu9+t1nM6+6W0ooUAQAeamkx3+EpqnXe3LnSzp2mGzngV0Ff26uvN4l7NTW2IwH8qWetL0gJe5J5X2xsNO8Pa9eaTnwAMJimJtsR2MdrkBPG4w6jrKxM9913nzZs2KBLLrlk0Medeuqp+tznPqfW1lbNmTPHtXi+853vaN68eYpk+MVgwoQJ+vznP689e/bo+9//ftYJez2++tWv6vHHH9eCBQtUUlIy4GNKSkq0YMECNTU1aTkJCcHDiFtvlZbajgA4oaaGRJRMxeNU5qH4LV3Kxb6BxOPSkiWM3QHA96a+3nhDOuMMxhABgE09nXP4Du+Ojg5p5kwzlQTwK9b2GGcNDCXoa3319WYscEuLmaSxf7+5ZZghgB7d3WbyYNA1N/PemAPG42bpxRdf1O7du9XW1qa3335bkyZN0umnn67LLrts0GQ2t7zyyit65pln9MorryiZTOrw4cMaMWKEwuGwxo8frwsvvFBnnXWW48dtb2/Xzp079eqrryqZTCoSiei0007T9OnTFQ6HHT9evhin4QGqcb0VDpsL/nSigJ8kk9J550n79tmOxL8qKsz7JRV5KGYNDSaBH4OrqJA2b6bTBhB0QR9BNpwCHkPEGgTgDH6XPJJMmovQrOm5jzUB+B3vBya5ts9nDwCx1tdXKJSajBIOS1VVppt+PM77BxBkBw9KY8fajsIfXnxRmjLFdhRZYzxuATnjjDN0xhln2A5DkvSe97xH73nPezw/bjgcVg1twtGjpxo3aG2xbZo2jYQ9+M+rr5qxzcXgH/9Revhh8yXbKeGwSdJhcR7FbvVq2xH4X1ub6bSxfTuJe0CQBX0E2XAYQwQA3gh65xwvtbVJy5ZJdXW2IwEGFomYtauZM4O71n/vvdLKlbajAPyFtb4T0vsgtbdLW7ean1WrCrr4DECeurpsR+Afl14q/frXXPvIAuNxAeQumTQd9oJ6Em9LdbXtCIBUPe8FTia52RIKSV/6kvTb35oqeCdUVJCcg2BIJEhAyVR7uzRnDqNygSBjBFlmesYQMVIQAJzX0GASVOCd+nrzugN+FY2aNSyn1sQKzW9/azsCwF9Y68tOY6M511+yhDU/IGg8nsjpa6+/bopAWMvLGEl7AHJHNa4dXNyD3xTTe0F3t0lA7BlbE4/nt7943OyHhD0EARf8stPTaQNAcK1b594F0YkTi2csR0+HUhb7AMBZdM6xY80a2xEAQ4tGnVkTK0SNjebvDsBgrS83FJ8BwVNebiZuwaBpQVZI2gOQG6px7YjFpD7z1AHrivG9oCeRJhIxY2s2bjS/e9mIxcxrU1fHODcER1OT7QgKD502gGDrGUHm9KJeOCz95jfmomOxdElhsQ8AnEXnHHt27JBaW21HAQwtnzWxQtbdTbEI0Bdrfbmj+AwIllBIqqqyHYW/0LQgYyTtAcgN1bh2LF9uOwIgVbG+F/RNpKmpMaNBEgmptlaaPbv/xfVw2NxfW2set327NG+e93EDtnR3S7t3246iMNFpAwg2p0eQVVSY/UWjxdclhcU+AHBOsRXfFRpefxSKnjWxO++0HYl3DhygWASQWOtzAsVnQLBUV9uOwH9oWpARkvYAZI9qXDvicZKA4C/F/l6QnkhTWSmtXClt2WJOtA8elPbtM7fJpLl/5Uq6YSKYDh0yC1HIHp02ADiVXBePm/1Eoyfu6+mScuut0pgx+e3fD1jsAwBn0DnHLl5/FJr777cdgbcoFgFY63MK7ydAcCxebDsCf6JpwbBI2gOQPapBvVdRIa1dazsKIFWxvxcMlUgTCknl5dL48eY2FPI2NsBvurpsR1DYiv39FMDw8hlBFouZRLa6OrOfvpJJk8x3222m0KAYsNgHAPmhc459zc3m/wNQCIq9aHcwFIsg6Fjrcw7vJ0AwRKPSjBm2o/AfmhYMi6Q9ANmjGtRb4bC0eXP/C3CAbUF4LyCRBshMSYntCApbEN5PAWSmZwRZIiHV1kqzZ5vzgb7CYXN/ba153PbtA3fkbmmRpk4tvu8zLPYBQH7onGNfe7vU0WE7CiAzxfZdMhsUiyDIWOtzFu8nQDAsX247An8K8vfJDJC0ByA7VOMObsoUc/HMybFTFRXmIlzfEVeAHwTlvYBEGiAz5eX9k0qQOTptAEhXWSmtXClt2WK65R08KO3bZ26TSXP/ypXmcQNpaTHVvW1t3sbtFRb7ACB3dM7xh85O2xEAmQny2hjFIggy1vqcxfsJEAw1NYzJHUiQv09mgKQ9ANmhGndw8bi5eHbggPTii9I11+S/v5YWEvbgT0F5LyCRBshMKCRVVdmOonDRaQPAUEIhc8Fk/HhzGwoN/fh775Uuvrh4xuEOhMU+AMgdnXP8obTUdgTA8IJStDsUikUQVKz1OY/3EyAY1q0zTXlwAtdah0TSHoDsPPGE7Qj8qydzPhQyXfceeEDauFGKxbLbTywmNTRIdXWMxIV/BaUyn0QaIHPV1bYjKGx02gCQr2TSFP7E48X/XY3FPgDIHZ1z7AuHpbIy21EAwwtK0e5QKBZBkLHW5yzeT4BgiESkzZs55+qLa61DImkPQOaSSelTn7IdhT/FYgOPpqqpMeNtEwmpttaMz03/kA6Hzf21teZx27dL8+Z5EzeQqyBV5pNIA2SGtu/5odMGgHy0tEhTpwancp/FPgDIHZ1z7Js2bfjOuYAfFHshSCYoFkGQsdbnLN5PgOCIRs31fjruncC11kGNtB0AgAKydKn0+uu2o/Cn5cuH/vPKSmnlSvPf3d3mAlNnp7lAX1bGQh0KT09lfhCqbUmkATITjUozZkiNjbYjKTx02gCQj5YWadasYHwv66uz03wnBQBkr7pa2rrVdhTBReciFIogFe0OpqdYhO+dCCLW+pzF+wkQLNGoWbNbtkyqr7cdjX1cax0UnfYAZKahIThdG7IVj2fXGS8UMl/Kx483tyTsoRAFpTKfRBogO8MlsWNgdNoAkKtkUpo7N3gJexKLfQCQDzrn2MXrj0LBOG2DzjAIMtb6nMX7CRAskYhUVydt3Gim9gUV11qHRNIegMysXm07An+qqJDWrrUdBWBHECrD//7vbUcAFJaaGi5A5SII76cA3LF0qdTWZjsK77HYBwD56emcgxO86igWi5mJHEAhCErR7nAoFkGQsdbnLN5PgGCqqTHjchMJ6YYbpJEBG4hK04IhkbQHYHiJBO2vBxIOS5s3myx5IIiCcLK+bZv5HZ89W6qtlVpbbUcE+N+6dSapHZkLwvspAOcFuRs6i30AkD8655wQj0vr13tzLF53FJqgF5lRLAKw1ucU3k8ATJ4sbdokHTtmOxJvBf375DBI2gMwvKBeCBpKKCT9+MemMhkIqqBU5re3S1u3SqtWmb9zLGa+VAMYWCRiktoZoZMZOm0AyFWQu6Gz2AcA+aNzjvku3tBgRlYtXuz+6xGPS/PmuXsMwGlBf5+gWARgrc8pvJ8ACOrEjKB/nxwGSXsAhtfUZDsC/+nulpYtk5JJ25EAdgWxQryx0VzcWLJk8PeA7m7p4EFp/35z293tbYyAbdGoafdOFe7wgvg+CiB/Qe+GzmIfADgjqJ1zpk83n6Xbt6cm0bn5elRUSGvXurNvwE1BKdodDMUigBGNmkYWyB3vJ0CwBXViBk0LhkXSHoChdXdLu3fbjsKf2tpM4h4QZEGuzK+vl6ZONQv9krmtrTWjdCMRaexYacIEc8uIXQRRNCq1tJhuEhjY/Pl02gCQmyAu8vVgsQ8AnBPUzjknnzzwZ4lbr0c4bPYbiTi7X8ArQS42C+q6JzCQ5mbbERQ23k+AYAvqxIwgf4/MEEl7AIZ26JAZDYmB1debzHggyNatC+7Cc1ubqdCfNs0k8K1aZUbppr9vMmIXQRWJmFFTGzeaf/dI1dR0IvEXALIR5G7oLPYBgLOC2CW7uXnwjvhOvx4VFWZ/0agz+wNsCGrRLsUiQKpCOg8tKZFOOcV2FCfwfgIEW1AnZsTjNC3IAEl7AIbW1WU7Av9bs8Z2BIC7hhv1GolIc+bYic0POjqy70iayYhdoJjU1JgLVX07Ugatm8dA9u6VZs4kcQ/A0NK/i/3tb8Hths5iHwC4I2hdstvbzbn8YJx6PeJxsx8S9lAMgjhOm2IR4IRCm8rV1SW9/bZJ3vMD3k+AYPPrxIxJk6SJE93Zd0WFtHatO/suMiTtARhcQ4P08Y/bjsL/duxg3CWKT7ajXl95xW68hSp9xC5Q7CorpZUrpS1bTMLq9Om2I7Kvvd0kPpPAC6Cv4b6LBbEbOot9AOCuvl2yp02zHY37OjuH/vN8uobHYmZdta4uuJMJUHyCNk6bYhEgVaFO5fJDYxLeTwD4tVPplCnSrbc6//0uHDbfGzkXyghJewBSdXdLL70kXXONNH++tGuX7YgKg18z5IFsNTSYxeVsR73+z//YibcYtLXRaQvB1Noq7dxpOwp/aGuTli2zHQUAP8jku9iBA1ZCs4rFPgDwTk2NOcevqrIdibuefz6zx2XSNTwcPlHcmEiYx3NxHsWoZ3z0hAm2I3EXxSJAf35IfsuXja57vJ8A8HOn0t//Xvr856VLLzVd95xQUWG+L9JtPGMjbQcAwAcSCZN01tQk/eEPZuQSsuPXDHkgU8mktHRpbgmojY3OxxM0PZ22Wlq4GI3gIOE9VX29qbytqbEdCQAb8vkuVuwqKkzCHot9AOCu7m7TRaery1zUvu026corbUflnrlzzfSMTD9ferqGS+a16ugw3fpKS6WyMikUci9WwE+6u6WjR21H4R6KRYCB+WXMbD66uqTycunq/5+9e4+Pqr7zP/5OuUSUAMMASmzrBW+tGaqkjYISqFIbCBXdapGhrbXby9YW3Ha3i/Lb3da2FKFbdyu13d/215buNvFaL12CUIpyUVC6QZ2gVqSiVqMCQxBQTIDm98d3J2Ryncs553sur+fjwSPOMHPOh2lz5nu+38/387nSbKBtbMzeJFdSYq5xTuF6AkAKRqXSlStN0t6MGea/C5VMmkRlrnt5odIeEGU9VXEgYa8wjY3ODuYBL6VS5jrAIrFdVNpC1JDw3t3SpbYjAGADY7HeJZPm8yFhDwDc0Vc79s9+VjrxRNsRumffPrN5Lp3O/70lJWbRf9Qo85OEPURFOm0SXsNa+ZnKMEDvysrC0R77wAHpP//TFDA5/3zpG98wSfz790tPPWWuA07gegIgIyiVSt94w3Rg/PGPTf5IPqqrTd5JXR0JewUgaQ+IonTaLH7MnEmFLKe0tJgdtkDQpFLS1KkmYQz21debgS0Qdn4uCW/Thg2mbTCA6GAs1jMm+wDAXbm0Y29pkd580058XmHzHJCfefPCO25lswjQt5ISacIE21E45623pIcflm691YyJamulV18114Fksrhjcz0B0FmQKpW2tEjf/750333ZG7y6Jm3HYub5hQvN69avN1X6UBCS9oCooYqDe1pbbUcA5CezO9bvZZmjhkpbiIIglIS3hTEaEB2MxXp2331M9gGAW9jI2x2b54DcNDSE9351yhQ2iwC5qKqyHYF7Nm40iXvz55vWjitWUGkKgDOCVqk0s7GpokJatEhas8bcR+7fL+3ebX6m0+b5RYvM61AUkvaAKKGKg7tKS21HAOQnzLtjg4xKW4iCoJSEt4G2wUB0MBbr2f/8j+0IACCc2MjbOzbPAf1bssR2BO5Zv57kXSAXlZW2I3Bffb0ZL73//ebaQKUpAMXatk0aPtx2FPnpurGppMQkH44aZX6WlNiLLYRI2gOigioO7orFpKFDbUcB5C7Mu2PDgP9tEHZBKgnvtcZG0z4YQLgxFusdycsA4Dw28vaNzXNA35qawl+dk+RdoG+plPTFL9qOwhvNzaYCZ1MTlaYAFK6hwVTgHD9eeukl29Hkj7GRZ0jaA6KCKg7uqqwkqxzBEubdsWHAYjXCLmgl4b3U0iIdPGg7CgBuYyzWO5KXAcBZbOTNDcn0QO+i8PtB8i7QuyiOJVpapJoa82/PoNIUgFyk01IyKc2cGexND4yNPEPSHhAFVHFwX1WV7QiA3EVhd2zQsViNsCspkSZMsB2Ff7W22o4AgJsYi/WN5GUAcBYbeXPD5jmgd1H5/WANBehZVMcSzc3S/Pm2owAQJKmUqawXljFFWP4dPkfSHhAFVHFw35w5tiMAcscgy/9YrEYUkPDeu9JS2xEAcBNjsf6RvAwAzmAjb+7YPAf0rL1d2rrVdhTe2LRJ2rPHtL3kegAYUR9L1NebzwAA+pNKSVOnhivJOSobNywjaQ8IO6o4uK+6WqqosB0FkLsgD7IGDsx+fNxxduLwAovVCDsS3nsWi0lDh9qOAoCbgjwW8wrJywDgDDby5o7Nc0DPDhyITkvMdeuk0aOl4cOleFyaNk1auJDWcIg2xhLS0qW2IwDgd2FtI87GJk+QtAeEXZR3wHhlwQLbEQC5C/ru2FGjpJ07pd27za7XSZNsR+QeFqsRdomENHmy7Sj8p7LStA8GEE5BH4t5geRlAHAGG3nzx+Y5oLu2NtsR2NHSIq1dKy1ebOYvqqullSttRwV4i7GEsWEDybsA+hbWNuJsbPIESXtA2FHFwV3JpDRjhu0ogNwFfXfsG29I/+f/mOS9oUOlJ5+0HZE7WKxGVJD43h1tg4FwC/pYzAskLwOAM9jImz82zwHdvfCC7Qj8YeNGqbZWmjvXVNMBooCxxDF8FgB6E/Y24rlubGpvN8VW9uwxP6nQlzOS9oAwo4qDu8rLpdtusx0FkJ8w7I6trzeD4DAvep93HovViIbaWtrkdsXnAYRbGMZibhs50nYEABAObOTND5vngJ799re2I/CX+npp/HhTgQwIO8YSx/BZAOhN2NuI97WxqalJWrhQmjZNisel4cOl0aPNz3jcPL9wIdVK+0HSHhBmYU5osW34cGnVKvOFAwTJ4MG2I3DG0qXhXvT+0IdsRwB4Z9kykwgP026nosJ2FADcFJaxmJv+9CfbEQBA8LGRN39UegV69oc/2I7Af5qbpSlTSNxDuDGWyNbYSNUoAN2FvY34wIE9b2xqaDBrGePHS4sXS2vXds9JaWkxzy9eLCUS5vUrV3oTd8CQtAeEWZgTWmwaPdp8AScStiMB8ldWZnaPB92GDdKOHbajcM8119iOAPBOPG4S4cNwbSrWP/yD7QgAuC0sYzE3NTayAxcAisVG3vxVVdmOAPAfknZ619Ii1dTQKhfhxVgiW0uLdPCg7SgA+E2Y2+L2JJ2Wkklp5sz8kxU3bjSdl+bOZfzUBUl7QJhRxcF5M2ZIzz1Hwh6Cq6REmjDBdhTO+O1vw7noPXAgiwWInkRCWr+einuf+Qwl44GwC9NYzE1Rm/QEAKexkTd/c+bYjgDwH5J2+tbcLM2fbzsKwB2MJbprbbUdAQC/CXvr7CNHjiUsp1Kmsl6xc3b19eY4VCzuQNIeEGZUcXDO6NHmS6ihgZa4CL6wJIT94Q/hXPSuqKAlD6IpkTA3fsmk7UjsoWQ8EA1hGYu5KeyTngDgNjby5qe62tyLA8hG0k7/6uvNmgEQNowluisttR0BAD+JSkXi1lazbjN1qtmw4ITmZmnKFBL3/hdJe0CYUcWheO97n3T77dKuXbSrRHiEZfd4Y6P0kY/YjsJ5M2bYjgCwJx6X6uqkFSvMwlk+Bg1yJyabKBkPhFNYxmJuamw0k58AgMKwkTc/CxbYjgDwJ5J2crN0qe0IAOcxlsgWi0lDh9qOAoCfRKUi8cGD0vTpzv9bW1qkmhrWPUTSHhB+VHHIXyIh3XSTye5+5RXp+uttRwQ4K5GQJk+2HUXxWlqkWbNsR+E8FvIBk6i2fr35Ll640LSM7TpROHCg+ZNx+LC3MXqJkvFAuIRlLOamlhbp5Zel/ftJ3gOAQrCRN3fJJJvngN6QtJObDRukbdtsRwE4i7FEtspKuuMAyBaVisSf/axzFfa6am6W5s9359gBQtIeEHYkf+QvFpO+/33aYiDcwrKL/IwzwrXoTUseIFtFhbRokbRmjfSf/ylNnHjs744cMX+igpLxQLiEZSzmptNOk4YPN1VYp00zSdwshgJA7tjI27/ycum222xHAfgXSTu5u+MO2xEAzmMscQyfBYCuolKReONGd49fXy81NLh7Dp8jaQ8IO6o45I+dcYiC2tpwJPWWloZr0TtM/xbAKem0qX7xiU9ImzfbjsYuSsYD4RGWsZgXWlqktWulxYvN/W11tbRype2oAMD/+J7pWywmrVplksMB9I5Eldxs2WI7AsB5jCWOqamhEjyAbFQkds7SpbYjsIqkPSAKSALJHzvjEAXLlpld5UEVi0lDh4Zn0ZuWPEB3qZRpC8v38jGUjAfCI+hjMVs2bjTjv7lzSWIGgL6wkbdvp50m/fnPtqMA/C8Mc25eaGwkmQfhw1jimOpqKsEDyEZFYudEvKASSXtAFNTWStdcYzuKYGFnHKIgHje7yoO6E6Sy0gyKpeAvetOSB+gulZKmTjVJashGyXggHII+FrOtvt4kdtM2HAB6x0be3m3dShI4kAuSdnLT0iIdPGg7CsB5jCWyUQkeQGdUJHZOhAs3kLQHhFlTk9ntMW2aWQxC7tgZh6hIJKT164OZ8NZ5MBzkRe+SEumhh2jJA3SWTkvTp5uJMPQs4iXjgdDIjMXGjLEdSTA1N0tTppC4BwC9CUtlejeRBA70j6Sd3LS22o4AcB5jib5RCR6INq6PzolwQSWS9oAwamgwuzvGjze7Pdaulfbtsx1VsLAzDlGSSJiKVsmk7Ujy03UwHNQExPZ205YHwDHz5lFhrz8RLxkPhEoiwSRfMVpapJoaFkgAoDdBr0zvBZLAgb6RtJOb0lLbEQDuYCzRPzYBANFERWLnRLigEkl7QJik0ybpZuZMs7sDxWFnHKIkHpfq6qQVK0zSbz7e9z53YupLdbVUUdH9+aAmID75pO0IAP9oaIh0KfS88DkB4UESbnGam6X5821HAQD+FOTK9F4iCRzo21e+YjsCf4vFpKFDbUcBuCMzluD/431rbjbrFps3RzbxBIgkKhI7I8IFlUjaA8IilTK7OFi8dQ474xBFtbWmWl3n9tpdJ/ZjMfP8woXmdWed5X2cfQ2Ci0lAtOWaa1gYADKWLLEdQXBEuGQ8ECrt7dLWrbajCL76epP4DQDoLqiV6b1GEjjQu4cesh2Bv1VWSiUltqMA3JNISJs2SUOG2I7E3/btkyZNkkaMMFV8/+7vqL4HhB0ViZ0T0YJKJO0BYZBKSVOn0kbOSeyMQ9RVVEiLFklr1phksv37pd27zc902jy/aJF07rneLzIPHixNn97/6zonINqoBpiP119nYQCQzO8r1YJzF+GS8UCoHDhgdpOieEuX2o4AAPwrqJXpvUYSONAzNo31rarKdgSA+xIJ6emnSdzLxf790oYN0q23moIrsZj0qU9RZR8Iq2XLyCtwQkQLKpG0BwRdOm2SV1jkcRY744BjSkqksjJp1Cjzs/Pvho1F5ra2/Eokt7dLf/6ze/E4hYUBgIrB+YpwyXggVNrabEcQHhs2sAgCAH0JYmV6G0gCB7JRGbp/VNhBVJx5pvTEEySn5GvfPumee0zi48SJ0sqVtiMC4KR43BQ5QeEiXFCJpD0g6ObNo8KeG9gZB+TG1iJzPiWSg5QExMIAoo6d+/mLaMl4IFQGD7YdQbgEaewHADY0NUmPPSYNGiQNG2Y7Gn8iCRzIRmVoAJ1lWuWWl9uOJJgef9x0CZo71xRmARB87e3S9u22owi2CBdUImkPCLKGBhYk3MLOOCA3thaZW1ulPXtMmfn+WkMGKQmIhQFEGTv3CxPRkvFAqJSVmd2kcEaQxn4A4KWGBlNdb/x4afFiae1ac0+NnjHnChxDZej+cc1A1CQSUiolJZO2Iwmu+nozLmtqsh0JgGKxwaF4ES6oRNIeEGRLltiOIJyqq6WKCttRAMFgY5G5pER673ul0aOl4cNN2elp06SFC7MT3pqapJtuktav9za+YjHJh6jixjZ/ES4ZD4RKSYk0YYLtKMKjsbH/TR0AECXptFlQnzlT2rjRdjTBQRI4cAyVofvHNQNRFI9LdXXSihVmXQ35a26WpkwhcQ8IOjY4FC/CBZVI2gOCqqmJiTa3LFhgOwIgOGwsMnddhG1pMRUCFi82O/wqKsyf8eOlW26RjhzxNr5iMcmHqOLGNn8RLhkPhE6Ed5M6rqVFOnjQdhQA4A+plLk3ZnNY/kgCB46hMnT/uGYgymprzcb5jRvZXFqIlhappoZWuUCQscGhOBEvqETSHhBUTLa5I5mUZsywHQUQLH5bZH7mGfMnqJjkQ1RxY5s/v11/ARQuwrtJXdHaajsCALAvlZKmTjUVXJA/ksCBY6gM3T+uGYB08cXSpk0k+RaiuVmaP992FAAKxQaH4kS8oBJJe0BQUYnJeeXl0m232Y4CCB4WmZ3FJB+iihvb/HH9BYKvqUlauFD6+telgQNtRxMepaW2IwAAu9Jpafp0c3+JwpEEDhhNTdI779iOwv+4ZgCmE8769Wa9Dfmpr5caGmxHAaAQbHAoHAWVSNoDAqm9Xdq61XYU4RKLSatWSfG47UiA4EkkpMmTbUcRLkzyIYq4sc1PxEvGA4HX0GB+j8ePlxYvltaulY4csR1VOMRitGQCgHnzqLDnBJLAEXWdx6ybN9uOxv+4ZgBGImEq/iaTtiMJnqVLbUcAoFB0xckfBZUkSWzjBoLowAF2yjqpvNwk7CUStiMBgmvBAmnjRttRhAeTfIiqqiqTuIL+RbxkPBBY6bRJpLjjDtuRhFdlpUkEBzrZuXOnnnrqKTU3N+vgwYMaO3asTjnlFE2aNEmDBg3yPJ5Dhw7pueee0x//+Eft3r1bBw8e1NChQzVy5EhVVFQokUhoINU3UaiGBr5nnEASOKKMMWv+uGYA2eJxqa7OJO4tXSpt2GA7omDYsEHato2NukAQzZljNuYiNxRU6sDsDxBEbW22IwiPU06RGhv5QgCKVVtrBqRM5hWPST5EGTe2uaFkPBBMqZRpVUjlI3exsxmd3Hvvvbr11lu1uZfqQCNHjtTs2bP1ne98R6NGjXI1lq1bt+qBBx7Qww8/rC1btujw4cO9vvaEE07Q7NmzdcMNN2j8+PF5nWfdunX66Ec/WnCcp5xyil566aWC3w+L2tvNRt/vfc92JOFAEjiiijFrYbhmAD2rrTV/tm0zawdbtpjENNY5e3fHHdKiRbajAJCvTFcyCpz0j4JKWWiPCwTR4MG2IwiPl1+WHn/cdhRAOCxbZgZaKA6TfIgy2m33b/BgSsYDQZRKSVOnsvjphTlzbEcAHzh48KDmzJmjq6++uteEPUnau3evfvrTn6qiokKrV692JZZ3331X48aNU2Vlpb773e/qscce6zNhT5Lefvtt/eIXv9CECRN044039vt6RFhTk7RwoTRtmtmQOnw481xOIQkcUcSYtXBcM4C+VVSYRLQ1a6TXXmOdsy9bttiOAECh6I7Tv2TSjDlJ2OtApT0giMrKTCUmWuQ6Y+lSs9MHQHHicbMzYsoUrk/FYJIPUUe77b4dd5w0cqTtKADkI5021UoYH7mvupo2QtDRo0c1e/ZsrVy5Muv50aNH6/zzz9fw4cP1pz/9SU8++aTa29slSW+++aZmzZql3//+97r44osdjefIkSN68cUXuz1fUlKis88+W+9///s1atQoHTx4UNu2bct67dGjR7VkyRK98MILuuuuu2iZi2MaGqQlSxg3u4kkcEQNY9bicM0AchePm/ktqu31rLHRVFBmYz8QPLW10oknSm++aTsS/6muNms/dBDqhpkeIIhKSqQJE6S1a21HEg4bNpjS3CzuAMVLJKT166WaGnblFopJPkQd7bb7tn+/dPCg2cQBIBjmzWNc5BV2NEPSjTfemJWwN2jQIN1666360pe+pMGdKno8++yz+sIXvtBRia+1tVVXXHGFmpqaNHbsWFdiGzBggC677DJde+21uvTSS3tsydvY2KhvfOMb2rBhQ8dz9913n7797W/rewW0Pb3hhhv0t3/7tzm/nsRAn0unzfcKY2V3kQSOKGLMWjiuGUB+Dhww81voWUsLc39AULW3S+++azsK73zxi9Lo0aZCaGNj9uaPWMx0FquqMus9jJV6xSwMEFRVVSTtOemOO0xpbgDFSyRMaeP586X6etvRBAuTfICxbJlJAGbBoGetrUzcAUHR0EBihVeSSXbrQi+++KJ+9KMfZT13zz33aNasWd1e+8EPflBr167VpZde2pG4l06ndfPNN+vf//3fHY2rtLRUX/jCF3TjjTfqve99b5+vrays1MMPP6zPfOYzuqPT9eMHP/iBvvjFL+qUU07J69wjRozQqaeeWkjY8JtUylTBYozsPpLAETWMWYvDNQPIDxX2+sfcHxBMBw5Ib71lOwrv7Nwp/cd/mP9ubzcJx62tUmmpNHQoFUNz9B7bAQAoEJWYnLVli+0IgHCJx6W6OmnFCpOIhtwwyQcYmXbbsZjtSPyptNR2BABytWSJ7Qiiobxcuu0221HAB26++WYdPny44/HnPve5HhP2MoYMGaLly5dnVeD7+c9/3mM720Idd9xx2rFjh3784x/3m7CXMWDAAP385z/X+973vo7n2tradPfddzsWFwImlZKmTiVhzwskgSOKGLMWjmsGoq693VTN27PH/Gxv7/89ncbe6AVzf0AwRS0pOdPOWzIJemVl0qhR5icJezkjaQ8IqkRCmjzZdhTh0flLBYBzamtNtaymJmnhQmnatO5JOAzcDCb5gGyZdtsutacLrFjM7FID4H9NTdLGjbajCL9YzCR6x+O2I4Flhw4d0r333pv13IIcNsWcddZZuuKKKzoeHzlyRPUOVgwfOHBgzsl6nQ0ZMkTXXXdd1nOPPPKIU2EhSNJpU2Gvc6shuIMkcEQRY9bCcc1AVHWe64/HpeHDTXvE4cPN42nTzN9v29bz+8vK2KjbF+b+gOCKWlJypp03ikLSHhBkVGRyDl8qgLsqKkwL6jVrzILD/v3S7t3Sq6+SMCsxyQf0JpEwE4Ennmg7Ev+orCTZGQgKWoy5r7zcJHgnErYjgQ+sXr1a77zzTsfjiRMn6pxzzsnpvV2T4+677z5HYyvU+eefn/W4mSpr0TRvXjgr7A0aZDuCbCSBI6oYsxZu2TKuGYiWhgbTVWf8eGnxYmnt2u6bClpazPOLF5v7tOpqaeXK7NeUlEgTJngXd9Aw9wcEVxSTkltbbUcQeCTtAUFWW0ubXCfxpQJ4o3OJZMq8szAA9Ccelz7/edtR+Md559mOAECutmyxHUG4JZOmXSQJe/hfq1atyno8derUnN87efJkDRw4sOPxk08+qTfffNOp0ArWOSbJtMhFxDQ0hDehplMra+tIAkeUMWYtXGOj7QgAb6TT5v5r5sz8K3Nu3GjWMufONcfJqKpyNsYw4bMBgiuKScms8xaNpD0g6JYtMxNLKB5fKoD3olYquisWBoDcsEnhmMsvtx0BgFy0t0tbt9qOIpyqq00SS10dmx6QZVuX9lsTJ07M+b0nnHCCEl3G5M8884wjcRVjx44dWY/Hjh1rKRJYQ5cN95EEjihjzFocEh4RBamUqaxX7CaC+npznKYm85i5vt7x2QDBFqXEW9p5O4KkPSDo4nFToSlqpVadxpcKYEcUS0VnsDAA5C6RkCZPth2FP0Q92RkIigMHurcJQt/+7u+kadO6jw1jMfP8woVmgWf9emnGDDsxwteee+65rMdnnHFGXu8fN25c1uNnn3226JiKde+992Y9ripg8v+RRx7RX/3VX+n000/X0KFDNWTIEJ188smqrKzU1772Nf3mN7/RYT9VPIORTptrnQ+SR0OLJHCAMWuxGhtN4iMQVqmUNHWq1NzszPGam6UpU8x9HXN9PauulioqbEcBoBhRSrylnbcjBvb/EgC+l0iYhYuaGucGz1HDlwpgR6ZU9Nq1tiPx1u23S9dfbzsKIFgWLMi/BUcY/exn0gUX2I4CQH9oYZm/G2+URo0yC58HD0qtraYa+tCh3KuhX3v37tXevXuznnv/+9+f1zG6vv6FF14oOq5i/OEPf9Bjjz2W9dyVV16Z93E2bNjQ7bnm5mY1Nzdr69atuv322/Xe975XN954o66//nqV8PtmXyolTZ/OHJ/TRoyQPvxhU/lizhwWxAGJMWuxWlrMuLWszHYkgPPSaTMecTqxt6XFrGWmUsz19YQqy0DwZZKSo3B9i1JVQRdRaQ8Ii0TCDHKTSduRBBNfKoA9Ufz9+8xnbEcABE9tbbR2qfXmV7861koEgH9RFTN/paXmZ0mJWfgcNcr8JIEIOdi3b1/W4+OPP14nnHBCXscYM2ZM1uO33nqr2LAKdvjwYX35y1/Oem7y5MkFVdrLxauvvqqvfe1r+sQnPtHts4THnK5og2Nefllas0ZatIiEPSCDMWvxWlttRwC4Y94898Yjzc3S/PnM9XWVTFJVHgiLqCTgcg13BJX2gDCJx01Lh2RSWrpU6mE3da9Gj5Z273YvNr/jSwWwZ84cafFi21F4q7WVXbhAIZYtM9WFo7yIeeTIsR3JtPEC/KuszLR1pd1YbmIxU1EPKNDBgwezHg8ZMiTvY3R9z4EDB4qKqRjf/OY39eSTT3Y8HjRokG677ba8jjFs2DBNmzZNU6ZM0bnnnqsxY8ZoyJAhamlp0fbt27VmzRrdddddevfddzve09DQoCuuuEK/+93vNNjBRI5du3Zpd55zTjt27HDs/IHhVkUbGFQUA7pjzFq8zMYTIEwaGqQ77nD3HPX1Zi2TuT6jvFzKc7wPwMcyScluX0ttop23Y0jaA8Kottb82bbNfBls2SI1NmbffMdipiVsVZUZDH7ta/bitY0vFcCuKJWKzmBCDyhMPC6tWiVNmRLtRYXMjuS6OtuRAOhNSYk0YYK0dq3tSIKhspKKeihK16S94447Lu9jdE3a63pMr/ziF7/Qj370o6znvv3tb+u8887L6f0nnXSSfvnLX+qaa67p9XP4yEc+orlz5+qWW27R5z//eT300EMdf7d+/XrdeOONuvXWWwv+N3T1k5/8RDfffLNjxwstNyvagPtwoCeMWYszbBgbTxBOS5Z4c56lS03CXtTn+kaMMJ8Bm3OBcAl7UnJUqgl6gPa4QJhVVJiWD2vWmN26+/ebanr795vHmZYQd91lO1K7+FIB7Iva7+H69bYjAIIrkTC/Q+XltiOxq77e7HwG4F8utbEMJT4rOKykgCTQQt7jtFWrVulv/uZvsp6bOXOmbrrpppyPcc455+hzn/tcTomLJ510khoaGnT11VdnPX/77bdr586dOZ8TDvCiok2UUdEV6B3jsMK1tkp799qOAnBWU5N3m+s3bDDFR6I+1/fQQ+YzABAumQIEsZjtSJxHO29HkbQHREVJiSl3P2qU+ZmZjPZyAO5HfKkA/pApFR0VP/iB7QiAYEskTHvYZLK448yYYXazBtXSpbYjANCXKI1tisVnhSIN7ZKMc+jQobyP0fU9XY/ptscee0yf/OQndfjw4Y7nLr74Yt11112uJhSWlJRo+fLlGjt2bMdzbW1t+vnPf+7aOdEDryraRBUVXYHeMQ4rXGurqYIPhInXmwgy53Nqri9ohg+XLrjAdhQA3BLGpGTaeTuOpD0g6qK8i5cvFcBfli0L18C1L5ldhAAKF4+b9rArVphW9/kYMUKaNUtqb5f27XMjOm9wLQH8LZGQJk+2HYX/VVebKvFAEYKetNfY2Kja2lq98847Hc9VVVWpoaFBxx9/vOvnP/744zW/S9LBqlWrHDv+9ddfr23btuX154EHHnDs/L4X9Q21XqCSGNA7xqzFoQo+wmbLFnvnK2auL6g+8hE2FgBhF6ak5FiMdt4uIGkPiDqvB+B+wZcK4D9hLhXdkygnTQNOqq01u9WamqRPfSq3ynn79kkPPmjaTwQd1xLA3xYssB2B//EZwQHDhw/PevzOO+/o7bffzusYu3btyno8wqNqvKlUSpdddpneeuutjufOP/98rV69WsOGDfMkBkmqqanJetzU1OTYsceMGaNzzz03rz9nnHGGY+f3PcZz7qOSGNA3xmPF+da3bEcAOKO9Xdq61dtzNjaa83bWea5v4UJp2rTwrhmwsQCIhjAkJZeXm2sz7bwdR9IeEGU2BuB+wJcK4F9hLBXdm6gmTQNuSKel739fuvvuYFfOKwTXEsDfamtJFOjLjBnmD1CkeDyuWJeFvFdeeSWvY7z88stZj88888yi4+rPs88+q2nTpmnv3r0dz1VUVOh3v/udZ0mDGaeeemrW47a2tqxEQriI8Zy7qOgK9M+LMevgwe4e36bGRum++2xHARTvwAGppcXbc7a0SAcP9vx3FRXSokXSmjVm7m//funCC72Nz23MFwDRkktS8qBBdmLrSzJpqgWSW+GKgbYDCJqdO3fqqaeeUnNzsw4ePKixY8fqlFNO0aRJkzTIwi/QoUOH9Nxzz+mPf/yjdu/erYMHD2ro0KEaOXKkKioqlEgkNHAg/zOjFzYG4LYlk6YlLhX2AP/KlIqeP9+0mAirzC5Cyt8DxUmlpOnTpeZm25HYwbUE8L9ly8yEXFSvU31ZudIkU9x4I8l7KNoHPvABbdq0qePxjh079IEPfCDn97/44ovdjuem559/Xpdeeql2797d8dw555yj3//+9xo1apSr5+7JkCFDuj136NChblUM4bCobqj1EhXEgNy4OWYtL5fOOEPasMH5Y/vFtddKU6aw7oBga2uzc97WVqmsrO/XlJRIL70kPf64JyF5go0FQHRlkpIlc0948KC5Fj7yiOkm5BfV1eZ+ijk7V5HNlaN7771Xt956qzZv3tzj348cOVKzZ8/Wd77zHdcn1rZu3aoHHnhADz/8sLZs2aLDhw/3+toTTjhBs2fP1g033KDx48fndZ5169bpox/9aMFxnnLKKXrppZcKfj88YGsAbgNfKkCwZEpFz5kjXXmldOSI7Yicl9lF2N+EBIDepVLS1KnR24TQGdcSwP/icWnVKmnChHCOaYq1caP5wwYrFKmioiIraW/z5s36xCc+kdN73377baVSqW7Hc8uOHTt0ySWX6I033uh47swzz9TDDz+sE0880bXz9mXPnj3dnovz++i+KG6o9VIyyVwgkKvMmHXKFGevS7GYOe4dd4Q7ae/gQbMBua7OdiRA4WxVxCwtze11d9zhbhxeY2MBEC3t7eb+r63NXG/LykxCckmJ+e+yMrOJwg/+7u+kz32OxGKP0B63HwcPHtScOXN09dVX95qwJ0l79+7VT3/6U1VUVGj16tWuxPLuu+9q3Lhxqqys1He/+1099thjfSbsSWbS8Re/+IUmTJigG2+8sd/XI2LCUpJ+4MDupWNjMVNSduFCU2J2/Xom6YAgmjnTTBaGVWur7QiA4EqnTYU9Fjm5lgBBUFGR+0JEVNXXS+PHm/s3oAA1NTVZj9etW5fzezdu3KgjnZJqzz//fNeS53bu3KlLLrlEzZ0qGZ1++ul6+OGHNXbsWFfOmYsnnngi6/Ho0aOtdBWJnChtqPVaeblJBgeQu0TCzKOXlztzvPJyc7xEIhotIOvrpYYG21EAhSsr677W5rZYTBo6NLfXbtnibixeYmMBEA2d2+DG49Lw4dLo0eZnPH4sl2HbNvPajRttR2zceCMJex4iaa8PR48e1ezZs3XnnXdmPT969GhddtlluvrqqzVhwgSVdGpF9eabb2rWrFl69NFHHY/nyJEj3Vp1SFJJSYnOOeccXXbZZUomk7r88st1+umnd/u3LFmyRNdcc03WJCQizsYA3A1Hjpiy2Pv3S7t3m5/ptLRmjSkty5cKEGxVVbYjcA+L90Dh5s2j1WQG1xLA3xoapIsvlt5+23Yk/tfcbDZskLiHAnz84x/PavG6efNm/fGPf8zpvcuXL896fOWVVzoZWodXXnlFl1xyif785z93PHfKKafo4Ycf1nvf+15Xzpmr+vr6rMdTp061E0jUhGVDrd9kKntRLRLIXyJhqtonk8UdJ5k0x0kkjh138uTi4/O7pUtNJZ39+6U9e8zP9nbbUQG5KSkxFeK9VFlpztuf9nZp61b34/HC2LFsLACCKtfv+IYG0wVw/Hhp8WJp7druxQdaWszzixebcVJtrfvx54r1Bk+RtNeHG2+8UStXrux4PGjQIC1btkyvvvqqVq9erbvvvluNjY3atm2bJk6c2PG61tZWXXHFFXr99dddi23AgAGaPn267rzzTu3atUvPPfecVq9erbq6Oj344IP605/+pP/5n/9RdXV11vvuu+8+ffvb3y7onDfccIN27tyZ8x83EhfhMBsDcLe0tZkkxFGjjpWTBRAOYd2Jm88uQgDZGhrC1xKjUFxLAP9Kp81i5cyZUqeWnehHS4tUU2M+PyAPxx9/vK666qqs55YsWdLv+7Zv367777+/4/HAgQOVLDZRoQfNzc269NJL9dJLL3U8d/LJJ+vhhx/WKaec4vj58rFu3Trdd999Wc/NmjXLUjQRE5YNtX7SubIXgMLE46bN64oVZsE5H9XV5p69rq574mwUWkFu2CCNGNF3JR3Az7zeQJ/r+Q4cCE+3jfvuY2MBECR9VcuLxaSpU6WbbjLf8Z3nAvOtmtdpc59Vw4ez3uAxkvZ68eKLL+pHP/pR1nP33HOPvva1r2lwlx2QH/zgB7V27dqsxL10Oq2bb77Z8bhKS0v11a9+VS+99JJWrlyp2bNna9SoUT2+trKyUg8//LDmdEl2+MEPfqCXX34573OPGDFCp556as5/bO8QRo7CUsGKjG8gvMrLw1l9INddhAC6y2EBPjK4lgD+lEqZ3bQkGBemuVmaP992FAigb3/721ktXZcvX67f/va3vb7+3Xff1XXXXae2Ti1K//qv/1rjxo3r8zwlJSVZf/prxbtr1y5deuml2rFjR8dzY8eO1SOPPNKtW0Yxfve73+npp5/O6z1PPPGEPvnJT6q9U4WAs88+W7Nnz3YsLvQhTBtq/aBrZS8AxamtNUmwnReruyYax2LHEtKamszre2v5WFsb3s25ne3fn/24ayWd6mqpU8EQwFe8/h3N9XydxuuBd8YZtiMAkItcquW99ZYZ+9xyi/mOHzMm+HOBQ4aw3uAxkvZ6cfPNN+vw4cMdjz/3uc/1ucN0yJAhWr58eVZC389//vMe29kW6rjjjtOOHTv04x//OOeEuAEDBujnP/+53ve+93U819bWprvvvtuxuBBwYbhJpsIMEG7z5oXrpjwjLEnTgNeamvLfpRZmXEsA/0mlzC5bWngXp77eTJACeTj99NN1ww03ZD131VVX6cc//nFWYp4kPffcc7r00ku1qVMlzHg8rm9961uOxrRv3z597GMfy2rVe8IJJ+jnP/+5Bg0apJdeeimvP33ZtGmTzj//fNXU1Gj58uXatWtXr6/985//rG9+85uaPHmy9u7d2/H8oEGD9JOf/EQDBw4s+t+OHDGeK15flb0AFK+iQlq0SFqzxlSQ2b9f2r3b/EynzfOLFpnX9WfZMrNBN8o2bjQJjHPnUl0a/uNlK+vq6tyuG1K4NvVThATwt2Kq5f3lL+7E5KU33mA+zmPMvvTg0KFDuvfee7OeW5BD2e6zzjpLV1xxRUdC3JEjR1RfX69//Md/dCSugQMHFlS9bsiQIbruuuv0ne98p+O5Rx55RN/85jcdiQsBlxmAB3nxu7RU2ruXSTkgjMLcAjMMSdOADWG9JhSKawngL+m0NH16eNr22LZ0qVnQBPJwyy236JlnntFDDz0kSTp8+LDmzZun7373u5owYYLKysr04osvauvWrVnV5QYPHqz7779fY8eOdTSep556SqlUKuu5t99+WzN6q0LUj84x9/b3q1ev1urVqyWZFrxnn322RowYoSFDhuitt97S9u3btX379m7vHTBggH7xi1/okksuKSg2FGjOHFO5wSsDB0pHjnh3PjfEYqbidFWV+fxyXfAHULySEtPau6yssPfH49KqVdKUKYyZ6+uldevM50GFUPjJggXerBnm0zK7rMx8/wf9ukEREsDfUikzrxf1jbjMx3mKSns9WL16td55552OxxMnTtQ555yT03uvu+66rMf33Xefo7EV6vzzz8963Bz1Cw2y5TMw9qM33jClaZuabEcCwGlhbYGZzy5CANm2bLEdgX9wLQH8Z948JvactGGDtG2b7SgQMAMGDNDdd9/drb3rrl27tGrVKt1zzz1qbGzMSn4bM2aMHnzwQU32qqqIh1577TU9/PDDuu+++1RXV6cVK1b0mLB3+umna/369fr0pz9tIcqI87KizQc+EOyEvZ07C6vsBcBfEgnTSi7qFfckc+8wZQprG/AXL1pZJ5O9t9LuSUmJNGGCe/F4pbKStpOAX9E54xjm4zxF0l4PVq1alfV46tSpOb938uTJWe0jnnzySb355ptOhVawri0turYEQcR5MQB3Gze3QPhs3BjsKqB9CXqyNGBLe7u0davtKPyDawngL2GuEGwTnykKMHToUN1555265557dOGFF/b6upEjR+orX/mKtm3bppqaGg8jdMfll1+u66+/XolEQgMGDOj39QMHDtSkSZP0q1/9Ss8++6wuuugiD6JEj7wa1/3TP3lzHjfEYtIpp5hKOyx2A8GXSJjF8WTSdiT2tbRINTW0yoW/uNnKurxcuu22/N9XVeV8LF4Lw78BCCM6Z3THfJxnaI/bg21dskYnTpyY83tPOOEEJRIJPfnkkx3PPfPMMzrxxBMdi68QO3bsyHrsdKsPhMCyZWZ3W5CzxzM3t6kUrXKBoEul8ttpFyT57iIEcMyBA9w4Z3AtAfwnrBWCbaPCKopw1VVX6aqrrtLOnTu1detWNTc36+2339ZJJ52kU045RRdddJEGDx6c93H7a1Hb2dSpU/N6fTEmTJigCf9bgeTdd9/Vs88+q5dfflmvv/66Dhw4oMOHD2vo0KGKxWI67bTT9OEPf1jHH3+8J7GhH5kNtW4ujCSTwW5xRGUaIHzicamuzlyfli41VV2iqrlZmj/ffB6AH7jVyjoWM8ctZA1vzhxp8WLnYrEh6AVUgLCic0Z3zMd5hqS9Hjz33HNZj88444y83j9u3LispL1nn31Wl1xyiSOxFeree+/NelxVQCb/I488olQqpaeeekq7du3S0aNHNXLkSJ100kmaOHGiPvrRj+ryyy/XoEGDnAobXnJrAO41bm6B4MuUoD540HYk7mhtNbt2SC4G8ke1aKPQHckA3NPUFN4KwbY1NppKqyRqoAinnXaaTjvtNNtheOq4447LSuJDALi5oTYzfiwrM4vlQZz7ozINEF61tebPtm1mc9qf/2w7Ijvq64OfYI1wybSyrqlxZnxSXm7WIROJwuOZPDm4997V1VJFhe0oAHRF54yeMR/nGdrjdrF3717t3bs367n3v//9eR2j6+tfeOGFouMqxh/+8Ac99thjWc9deeWVeR9nw4YNuv/++7Vz5069/fbbevfdd9Xc3KytW7fq9ttv11VXXaXTTz9dt99+u2c7iOGwzAC8gF3mvlJfb75gAQRPFEpQ/+Y30vjxtPMGChH0MYoTitmRDMA9TO65p6UlvJs5AKCzzIbaWMzZ43YeP5aUSEFN5KQyDRB+7e3RTdjLWLrUdgRANqdaWSeT5jiFJuxlLFhQ3PttCnLsQJjROaNnzMd5hqS9Lvbt25f1+Pjjj9cJJ5yQ1zHGjBmT9fitt94qNqyCHT58WF/+8peznps8eXJBlfZy8eqrr+prX/uaPvGJT3T7LBEQFRVSGFqjcHMLBFNUSlA3N5vKpiTuAfnJVAaJqvJys8Gi2AlOAM6jZYS7WlttRwAA3shsqC0vd+Z4PY0fg1ixjso0QDSwEca0CN62zXYUQLZMK+sVK8x3cj6qq02Rjbo6Zzag1tYGM5E/mTSVRAH4C50z+sZ8nCdoj9vFwS7ZokOGDMn7GF3fc+DAgaJiKsY3v/nNrFa9gwYN0m15ttIaNmyYpk2bpilTpujcc8/VmDFjNGTIELW0tGj79u1as2aN7rrrLr377rsd72loaNAVV1yh3/3udxrsYEWUXbt2affu3Xm9Z8eOHY6dPxIOHJDCkHCZubllQg8IjqiVoG5pMa0FUikqZgG5ylQGWbvWdiTeSyZNSzOuF4D/tLdLW7fajiLcSkttRwAA3slUtJk/33STKFRv48fKyuLis4HKNEA4tbeb9Yi2NlNZn40wxh13SIsW2Y4C6K5zK+s77jC/s42N2V1zYjEz1qiqMsl1bqzRLVtmNiUEZfN/ebkZkwHwnyitSRaC+ThPkLTXRdekveOOOy7vY3RN2ut6TK/84he/0I9+9KOs57797W/rvPPOy+n9J510kn75y1/qmmuu6fVz+MhHPqK5c+fqlltu0ec//3k99NBDHX+3fv163Xjjjbr11lsL/jd09ZOf/EQ333yzY8dDD9rabEfgHG5ugWCJYgnq5mazEFNXZzsSIDiqqqKVtFddbRYp2Y0L+NeBA9mLFHBWLCYNHWo7CgDwVqaiTTJpukls2JD7e/sbPzY2OhOjV6hMA4RLU9OxZJ+tW7PH0SUl9uLyE5IX4XcVFcfW3trbTfvE1laT3DF0qPu/y/G4tGqV6WTj93vxWMzEyiZcwJ/4zu0d83GeoT1uP0oKGFgU8h6nrVq1Sn/zN3+T9dzMmTN100035XyMc845R5/73OdySlw86aST1NDQoKuvvjrr+dtvv107d+7M+ZzwAQcrI1rHFy0QHFEuQV1fb6oMAshNEFtg5GvECGnhQnNtXL+eRUrA78K08cmPKitZwAUQXbW1ZjzY1GTGh9OmmcWTzmIx83yu48cgzZdRmQYIj4YGk1Q8fry0eLHZjNc12aa93U5sftPYyGeB4CgpkcrKpFGjzE+v7t0SCTPmKS/35nyFKC83MSYStiMB0BM6Z/SN+TjPkLTXxdAu2aKHDh3K+xhd39P1mG577LHH9MlPflKHDx/ueO7iiy/WXXfd5WpCYUlJiZYvX66xY8d2PNfW1qaf//znrp0TLigr6z75F1Tc3ALBEfUS1EuX2o4ACI5EQpo82XYU7qqrMzuW3WghAsB5Ydr45EdVVbYjAAD7MhVt1qyR0mlp/35p927zM502z+cyfgzSwhSVaYBwSKdNxcyZM6O7YTdfLS2mchmAviUS0p132o6iZ8mklEqRsAf4GZ0z+sZ8nGdoj9tF0JP2GhsbVVtbq3feeafjuaqqKjU0NOj44493/fzHH3+85s+fn1XRb9WqVfre977nyPGvv/76btX8+rNjxw5dccUVjpw/EkpKpAkTwtF2LnNzW1ZmOxIA/QnSTn83bNggbdtGgg6QqwULwjvZT/svIHgyG5+Y6HNHFCqsAkA+MhVtCpnvCsrCVHm5SdhjoRsItlRKmj5dam62HUnwtLayrgHk4qGHbEeQrbrazFsytwf4H50z+sZ8nGdI2uti+PDhWY/feecdvf322zrhhBNyPsauXbuyHo8YMcKJ0PqVSqV02WWX6a233up47vzzz9fq1as1bNgwT2KQpJqamqykvaamJseOPWbMGI0ZM8ax46EXVVXhSNqTuLkFgiBIO/3ddMcdpjICgP7V1pqbxrBV6SwpkV591bQ2SyZJ5AWCIkwbn/ymupprIQA4KQgLU0OGmKo5JOwBwZZKSVOnBiNR2I9KS21HAASDn4oBDBsmrVtHO0kgKOic0Tvm4zxFe9wu4vG4Yl1ag77yyit5HePll1/OenzmmWcWHVd/nn32WU2bNk179+7teK6iokK/+93vPEsazDj11FOzHre1tWUlEiIAwpQ5zc0t4H9B2envNj9NMABBsGyZqcARJu3tpvLm4sVmkbK6Wlq50vlz7N8v7dljfra3O3t8IKpoGeGOBQtsRwAA4RKEhalDh6RZsyQHN4ID8Fg6bSrsMd9XmFhM8rCDFxBYfisGsH8/ra2BIMl0zkB3zMd5iqS9HnzgAx/Ierxjx4683v/iiy/2eTynPf/887r00ku1e/fujufOOecc/f73v9eoUaNcPXdPhgwZ0u25QtoMw6JEQpo82XYUxePmFgiGIOz090JjI8kzQD7icdMyK8w31hs3mqqCc+eaRY9CNTWZ6n3TppnPbfhwafRo8zMeN88vXGjadAMoTJg2PvkF7cIBwHlBWZhqaZFqaoobAwOwZ948WuIWo7KSSl1ALvxYDKC11XYEAHKV6ZyBbMzHeY6kvR5UdCn1uHnz5pzf+/bbbyuVSvV5PCft2LFDl1xyid54442O584880w9/PDDOvHEE107b1/27NnT7bl4PG4hEhQlDBnU3NwCwRCEnf5eaGlhJx6Qr0RCWr8+fBX3uqqvl8aPz7/aSEODqdY3fryp3rd2bffJzJYW87yb1f2AKAjLxie/KC+XbrvNdhQAED5BWphqbpbmz7cdBYB8NTRId9xhO4pgo4o3kBs/FgOg+xcQLHznZmM+zgqS9npQU1OT9XjdunU5v3fjxo06cuRIx+Pzzz/fteS5nTt36pJLLlFzpx1Lp59+uh5++GGNHTvWlXPm4oknnsh6PHr0aA0aNMhSNChYbW3wq0XwRQsEQ1B2+nuBnXhA/hIJKZUyO8DCrLlZuvji3BL30mnzecycaar15cOp6n5AFIVh45MfxGKmkiqb/wDAHS5uMHdcfb1JAAIQHEuW2I4g+IK+LgN4xW/FAOj+BQQP37nHMB9nDUl7Pfj4xz+e1eJ18+bN+uMf/5jTe5cvX571+Morr3QytA6vvPKKLrnkEv35z3/ueO6UU07Rww8/rPe+972unDNX9fX1WY+nTp1qJxAUb9myYFeuqay0HQGAXARpp7/b2IkHFCYel+rqpBUrpGHDbEfjnv37pQ9/WLrzzt5fk0qZynrFVjYotLofEGVh2PhkW3m5qaCaSNiOBADCa/t22xHkZ+lS2xEAyFVTU/4bx5CtujpYydWATX4rBkD3LyB46JxhMB9nFUl7PTj++ON11VVXZT23JIfdQdu3b9f999/f8XjgwIFKulDxo7m5WZdeeqleeumljudOPvlkPfzwwzrllFMcP18+1q1bp/vuuy/ruVmzZlmKBkWLx01GtZ8GvfmYN48KMUBQUBmTnXiAE97/fpPYFmZtbSYpqKdKeKmUNHWqqcrnhOZmacoUEveAfAR945NNyaS5jjFBCADuaWiQHnrIdhT52bBB2rbNdhQAckFb3OJRvRvInd+KATi1xtHebuY39+wxP9vbnTkugJ5F/buX+TjrSNrrxbe//e2slq7Lly/Xb3/7215f/+677+q6665TW1tbx3N//dd/rXHjxvV5npKSkqw//bXi3bVrly699FLt2LGj47mxY8fqkUce0emnn97Pvyp3v/vd7/T000/n9Z4nnnhCn/zkJ9XeafBw9tlna/bs2Y7FBQsSCZNZHcSFp+Zmaf5821EAyAVVadiJBzghSgsEXSvhpdPS9OlSS4uz52lpkWpq2AgB5CroG59sqK42SSR1dbTgAAC3/fM/246gMFEa5wNBtmWL7QiCLZmUZsywHQUQLH4qBlDMGkdTk7RwoTRtmrkvHj5cGj3a/IzHzfMLF7KRAXBDVDtnMB/nGyTt9eL000/XDTfckPXcVVddpR//+MdZiXmS9Nxzz+nSSy/Vpk2bOp6Lx+P61re+5WhM+/bt08c+9rGsVr0nnHCCfv7zn2vQoEF66aWX8vrTl02bNun8889XTU2Nli9frl27dvX62j//+c/65je/qcmTJ2vv3r0dzw8aNEg/+clPNHDgwKL/7bAskTAZ1i5UjnRdfb35wgHgb5Sg9tcEAxBUUVsg6FwJb9485yrs9XQeNkIAuQvyxievnHSSWXBoajKfFYuTAOCuhgazUWzrVtuRFCZq43wgiNrbpf/5H9tRBFd5uXTbbbajAILHL4k2hba2bmgw7x0/Xlq8WFq7tvuG3JYW8/zixWa+obpaWrnSmbgBGFHonDF06LEEYObjfIVsqj7ccssteuaZZ/TQ/7YMOHz4sObNm6fvfve7mjBhgsrKyvTiiy9q69atWdXlBg8erPvvv19jx451NJ6nnnpKqVQq67m3335bMwr8ZWrvp5xue3u7Vq9erdWrV0syLXjPPvtsjRgxQkOGDNFbb72l7du3a/v27d3eO2DAAP3iF7/QJZdcUlBs8KF43GRaP/+81NhoO5r8LF1qsuQB+NuCBdLGjbajsMcvEwxAULW3B3cRshgtLaYlbqfNM66orzcbOBhTAbnJbHyaP9/8/uCYsWPN5CC7eAHAfem02dwR9Ep1jY1mvE91esC/Nm+W3nrLdhTBFIuZat2Mj4H8ZYoB2F5XyLe9ZjFjtI0bzZ9k0iT7cu0AipfpnDF5cjjHM1dfLd11F/dTPkWlvT4MGDBAd999d7f2rrt27dKqVat0zz33qLGxMSv5bcyYMXrwwQc1OYTVgl577TU9/PDDuu+++1RXV6cVK1b0mLB3+umna/369fr0pz9tIUq46o47gpewJ0kbNlAyGgiCqJaglgrfiQfgmAMHnG8NGxRuJ+xlLF3qzXmAsMhsfFqxwnzX52PiRHdisi0Wk1avZlEBALyQSpmqLUFP2JPMOP/gQdtRAOhNKsUGr0KVl5tKN4mE7UiA4Mo3Yc5p+ba2dmqMVl9vjtPUVNxxABiJhEmIHT3adiTOKi+XfvpTEvZ8jKS9fgwdOlR33nmn7rnnHl144YW9vm7kyJH6yle+om3btqmmpsbDCN1x+eWX6/rrr1cikdCAAQP6ff3AgQM1adIk/epXv9Kzzz6riy66yIMo4bkuLaMDJQwTlEAURKEEdU9sTywAYdDWZjuC8GMjBFCY2lqzENfUZFpQTJtmktc6i8WyW1SsWmUnVjexIAkA3kmlTDXm5mbbkTintdV2BAB6kk5L06dL+/bZjiR4kklzvWZ8DBTHZjGAfFtbOz1Ga26WpkwhcQ9wSiIhPfdceNrGUs03EGiPm6OrrrpKV111lXbu3KmtW7equblZb7/9tk466SSdcsopuuiiizR48OC8j9tfi9rOpk6dmtfrizFhwgRNmDBBkvTuu+/q2Wef1csvv6zXX39dBw4c0OHDhzV06FDFYjGddtpp+vCHP6zjjz/ek9hgye23S7t3246icFu22I4AQC4yJainTIlOxax8d+IB6FkBY3EU4I47pEWLbEcBBFNFxbHfn/Z2UzGotVUqLZWGDs3e8drebibWwjIeom0PAHgnk0ATlu+QjNJS2xEA6Mm8eeFKEPZCdbXZwMt8IOCcZcvMJjEvr0f5JsO4NUZraZFqakxCIPfcQPHicamhwcyD33BDcPMjysvNNYrNAb5H0l6eTjvtNJ122mm2w/DUcccdl5XEh4hassR2BMVpbDQLX5R+BfwvkTA32DU14Z/0y3cnHoDelZWFK8HFr9gIATijpMRct8rKev/7CROktWu9jcsN551n2gQDALwRxgSaWMwkuAPwl8yCNkxl+lWrzD1zY2P23EQsJlVWSlVVphpYRYW9OIGw8roYQCHJMG6O0ZqbpfnzufcGnDRnjvnzk59It9wi/fnPtiPKHZtnA4X2uAD619QUrC+inrS0mEoWAIIhkTA7w5JJ25G4h7LUgLMyCS5wV2YjBAD3VVXZjsAZL7/MdQMAvBLWBJrKSjbiAn4U9I3+TjrvPFNVe80aU01r/35TmWf/fvN4zRrz9yTsAe7JFAMoL3f3PEOGSHfemV/CnhdjtPp6cx4Azrr+eumVV6R77/X/RqLqanMdqKtj7TFASNoD0L/6etsROKO11XYEAPIRj5uB5YoVZqAZJuXlZgKBstSAs8KS4OJnbIQAvDNnju0InMF1AwC8E9YEGsb5gP80NUkbN9qOwh+6VgPNVNUeNcr8JOkY8I4XxQAOHZJmzTLXwVx5NUZbutSb8wBRk0pJX/yif+e3Bg82Ma5fL82YYTsa5ImkPQD927zZdgTOKC21HQGAQtTWmoFmU5O0cKE0bZqZDOts8GA7sRUimTSDZxL2AOeFJcHF79gIAXgjkZAmT7YdhTO4bgCA+8KcQMM4H/CfMFb1LBTVQAF/6VwMYOJEd87R0iLV1JhKmv3xcoy2YYO0bZs35wKiIp2Wpk/3pvV2of7yF9sRoAgk7QHoW3u79NRTtqMoXtfdbgCCp6Ki9zYT777r7k24EyhLDbgvTAkufsZGCMA7CxbYjsAZXDcAwH1hTaCprqadJOBHW7bYjsA/qAYK+FNtrXTqqe4dv7lZmj+//9d5PUYL65gQsGXePPP77mdHjkhTp+aWSAzfIWkPQN8OHJDeest2FMU7+2x2uwFh0lObidpaadMms2vt+ONtRygNGmSqAi5caHbTUZYa8EZYElz8io0QgLdqa4NfXYjrBgB4I6wJNIzvAf9pb5e2brUdhX8EfbwOhFVDg/sJbPX15jx98XqMFtYxIWCDF9cRp+zda6r/krgXOCTtAehbW5vtCJzxHi53QCC0t5vKeXv2mJ/t7fm9P5WSZs+W3nnHnfjycfiw9Ld/a6oDUhUA8E4YElz8jLY/gPf+5m+CfT/DdQMA3BfWBJpkks1vgB8dOODvFnFemjiReT/Ar5Ys8eY83/pW739nY4zW2Jj/ugqAnnl1HXHKyy9LZ51lCon0pNg1WLgiwLO+ADwxeLDtCJxBL3fAv5qaTDW6adNM29jhw6XRo83PePxYtbpt2/o+Tiplyj/7qUz10qW2IwCiadkyqbzcdhThRNsfwFuplHTFFcG+n+G6AQDuC2MCTXm5dNtttqMA0JOwbPR3wvbtvS+MA7Cnqcl05PFCY6P06KM9/52NMVpLi3TwoLfnBMLIy+uIk/bulaZMOTY+cWoNFq4haQ9A38rKpBEjbEdRvKee4uYZ8JuGBqm6Who/Xlq8WFq7tvsNbEuLeX7xYimRMK9fubL7sdJpafp0/y1SbNjAQBewIR6XVq0yLRnhLKoYAt7x6/gmX1w3AMB9YUugicXMeD4etx0JgJ6EZaO/E9Lp7IVxAP7wox95e76/+Zuen7c1RmtttXNeIEyC0ha3Jy0tpsjJxInOrMHCVSTtAejbtm3hmPh7913zpcSXDWBfOm1a3Mycmf8ulY0bTevLuXPNcTLmzfNXhb3OvJ4gAGAkEtLPfmY7inCprqbtD+AlP49vcsV1AwC8EaYEmvJyaf16M54H4E9lZWyS66ylRaqpyZ6rBGBPOi391395e85nnjFFCrqyNUYrLbVzXiBMtmyxHUFx9u6VHn88v/f0tgYLV5G0B6BnnStgvfOO7Wicw5cNYFcqZa4rxe5Qqa83x2lqMtcrP+94+dWvuN4AtjQ22o4gXBYssB0BEB1+H9/kiusGAHgjLAk0yaSZNyBhD/C3khJpwgTbUfhLc7M0f77tKABI0te+ZqcYydKl3Z+zMUaLxaShQ709JxA27e3S1q22o7Cn8xosXEfSHoBsxVTAChK+bADvpVKmHLNTFWOam037iX/+Z2eO55bDh6XKShL3ABuCvhvOT5JJacYM21EA0bFkie0Iisd1AwC8E/QEmupqk7BeV0dLXCAoqqpsR+A/9fU9V9oC4J2GBunOO+2ce8MG072sMxtjtMpKc14AhTtwoHsr2ajJrMGSS+E6kvYAHONUBayg4MsG8E46LU2f7vwgt6UlGLtdXn5ZOussrjeAl9rbpT/8wXYU4VBeLt12m+0ogOhoagr+BiquGwDgvaAm0Hzxi6YdLoneQLDMmWM7An/qqdIWAO/Y3gDX0/qq12O0oI4JAT+xUa3Tj1papJoaipK4jKQ9AIbTFbCCgi8bwBvz5kXv+tLV3r0kCgNeOnBA2r/fdhTBF4tJq1ZR8QTwUtA3UXHdAAA7gppAs3On7QgAFCKRkCZPth2F//RUaQuAN/ywAa6nrh9ej9GCOiYE/GTwYNsR+EdzszR/vu0oQo2kPQDuVcAKCr5sAHc1NAR/8dkpJAoD3mE3XPHKy03Vk0TCdiRAtDz2mO0ICsd1AwDsKS8P5uJSY6Opkg0geBYssB2BPzEPCtjhh9+9nsY1XiY5V1dLFRXenAsIs7IyaeBA21H4R329WeuFK0jaA0AFLIkvG8BNtkvS+w2JwoA3Bg2yHUGwJZOmEjOJN4C3nn7afmWAQnHdAAC75s0L5saVlhbp4EHbUQAoRG0tFZ16snKl7QiAaOqpyp3XehvXeJXkTDI14Ixt26QjR2xH4S9Ll9qOILRI2gOijgpYx/BlAzjPDyXp/YhEYQB+VV1trk91dbS2BLyWSklTpwav2hDXDQCwL+jze62ttiMAUKhly0ylTxzz1FNmQwudNgDvtLdLW7fajsLoaVzjRZJzMinNmOHuOYCoCPK9lVs2bDDJjHAcSXtAlDU0mEEcDL5sAOcxsO0dicKAuw4fth2BP3Ut6x+LSdOmSQsXmkTr9euZ4ANsSKel6dOlfftsR9I/rhsA4D9Br3BfWmo7AgCFiselVaukESNsR+Ivd9whjR9vxssA3HfggKly5we9jWvcTHIuL5duu82dYwNR5IfKnX7Emq8rSNoDoiidNsl6M2dK+/fbjsZf+LIBnMXAtncbNkiPPy7t2WOuxUGrqgP43eDBtiPwp29+01xzdu82P9Npac0aadEiqaLCdnRAdM2bJzU3246if//xH9Lzz0u/+Y30ve9x3QAAPwh6hftYTBo61HYUAIqRSJh5rjFjbEfiL83N0pQpJO4BXmhrsx2B0de4JpPkHIs5f85Vq6h8DzjFT5U7/YY1X1eQtAdETSpldniRnNYzvmwA5zCw7d/EidLo0dLw4eamOlO1hqqfQPHKyqRhw2xH4T/JpPlsRo0yP0tKbEcEIEgtDb/0JbMYy9gFAPwjKN8hvamsZEwKhEEiIT37LBWYu2ppkWpqaJULuM0vm3f7G9ckEqZavVMV98rLzfESCWeOB8BflTv9prGRAiQuIGkPiJJUSpo6NRgVHGzhywZwDgPb/LS0SGvXSosXm5vs6mpp5UrbUQHBVVIifeQjtqPwl+pqqmIBfhTUloaMXQDAH4K+AbWqynYEAJwSj5sNKfX1VN3rrLlZmj/fdhRAuJWVOV/BrhC5jGsSCbNem0wWd67Ro6Uf/pCEPcBpfqnc6UctLdLBg7ajCB2S9oCoSKel6dNJoOkPXzaAcxjYFmfjRqm2Vpo7l924QKFYAMy2YIHtCAB0FfSWhp0xdgEA74Whwv2HP2w7AgBOmzNHevNN6cc/lt73PtvR+EN9vUloBOCOkhJpwgTbUZjrXy7icamuzlwbRo8u7Fy7d5vzcQ8OOMsvlTv9qrXVdgShQ9IeEBXz5lFhL1d82QDOYGDrjPp609a8qcl2JEDw5DpRFgXJJG2KAD8KekvDnjB2AQDvhKHC/f/8j+0IALjlq1+VXnnFjAvf+17b0di3dKntCIBws715N98k5VRK+vu/N8l3xeAeHHBWWZk0dKjtKPyrtNR2BKFD0h4QBQ0N4VwMcgtfNoAz/FKSPgyam6UpU7jxBvKVSEiTJ9uOwr7ycum222xHAaAnQW9p2BvGLgDgjTBUuA/rdyGAYyoqpH//d9tR2Ldhg7Rtm+0ogPCyvXn3z382c5HV1dLKlX2/NpWSpk51rtgK9+CAc0pKpIEDbUfhT7EYCY0uIGkPiIIlS2xHEBx82QDO8UtJ+rBoaZFqaih1D+Qr6i1hYzFp1SrTdgOAv4ShpWFfGLsAgPvCUOG+sdF8JwIIt9pa+wk1fkBxBcA9ftm8u3Gjueb11rY2nZamT3e+WjL34IAzmpqkfftsR+FPlZVm7ReOImkPCLumJjNARG74sgGcZbskfdg0N0uf/aztKIBgifLCwNix0vr1ZtISgP+EoaVhf5qbpfnzbUcBAOG1c2fw57FaWqSDB21HAcALy5aZSvBRRnVRwF1+2rzbW9vaefOcq7DXFffgQPFIsO8da76uIGkPCDu+WPLDlw3grKgmyrhp5UrTVqS/EvsAjonqwsC6dSTsAX4WhpaGuaivlxoabEcBAOGTTkszZoSjSl1rq+0IAHghHjeV4MvKbEdiD9VFAXf5bfNuc7M0adKxxL2GBvfXbbkHB4pDgn3v/HR9DRGS9oCw44slP3zZAM7yS0n6sHnmmb5L7APIllkYiMVsR+KtsWNtRwCgL1GqKrR0qe0IACB83KzS4rXSUtsRAPBKebk0ZIjtKOyhuijgPr9t3j14ULrgAumFF6QlS7w5J/fgQGHa26WtW21H4U/V1aagCBxH0h4QZnyx5IcvG8AdfipJHza9ldgH0F0iYVrF+mnSzk2xmDR0qO0oAPQmlTK77aNiwwZp2zbbUQBAeHhRpcUrjFuBaJk3T9q1y3YUdlFdFHCXHzfvHjpk5iY3bvTmfNyDA4U5cMAk2KM71npdQ9IeEGZ8seSHLxvAHX4rSR82zc3SlCkk7gG5SCRMokwyaTsS91VWSiUltqMA0JNUSpo6VXr9dduReCssySUA4Aff+57tCJzDuBWIjjAlHBeD6qKAN/7qr/z1++Z1wi7XWyA37e3S/v3Snj10tupNMinNmGE7itAiaQ8Is7Y22xEEB182gLv8VpI+bFpapJoabiiAXMTjUl2dtGKFqbIbVlQPBvwpnZamT4/m5qq1a49Ngu7fbyZFAQD5+81vpMcftx2Fc6qqbEcAwCtetYX0M6qLAu5qaDDzfePHSz//ebQrW27ZYjsCwL+amqSFC6Vp08x6wfDh0ujR0umn246sMBMnSiNHunPs8nLpttvcOTYkkbQHhNvgwbYjCAa+bAD3+bEkfdg0N0vz59uOAgiO2lpp3Tpp2DDbkbijvp4KnIBNnXfpdk5QmzfPfGdH0RNPHJsEHT7cjA+nTTOTpLTtAYDcpFLSpz9tOwpnUZkfiIamJu/aQvpZ1+qivd03AMhPOm2Kc8ycybUmo7GRawrQVefE3sWLzQbTIG6sHThQOu88M6fW1CRt2mTWOpxeg43FzNpuPO7scZGFpD0gzMrKSJDpD182gHcSCWn9eiruuam+3tx0AMjNgQNmUjyMdu2idTbgtd526WYS1M47j/Y0nbW0mMnRxYvNOLG6Wlq50nZUAOBfmWqt775rOxLnVFdTIRqICsbBRlVV//cNbGwB8pNKmQQcrjPZWlqkgwdtRwH4g58Te487Tjr11L4r8Q4bJn30o9Lf/q2put7WJj35pLRo0bH7KafXYMvLzfESCWeOh16RtAeEWUmJNGGC7Sj8iy8bwHuJhLmJph21e5YutR0BEBxtbbYjcBetswFv5LJLt6VFevppO/EFxcaNpgrq3LlctwCgJ2Gs1rpgge0IAHiFNo3GqlX93zewsQXIXSolTZ0avjGSU6LcHhjI8Hti7ze+Ie3caYoL7N8v7d4tvfWW+bN7t3lu3z7p4Yelf/1X6YILsqv2dpZZg00mi4spmTTHIYfCEyTtAWFXVWU7An/iywawJx43i9vnnms7knDasIGduECuBg+2HYH7aJ0NuMfPu3SDrL7eTKZSKRQAjmlo8O8iU6GSSTb0AVHR3i5t3Wo7Cn/I93NgYwvQu0wV4iC2tvRKaantCAC7gpDYO2eO+VlSYroojhplKusNG2b+u6ys9yS9nsTjUl2dtGKFSf7PR3W1ufesq6NLoYdI2gPCLnOhh8GXDeAfS5bYjiC8wraYA7ilrEyKxWxH4T5aZwPO8/su3aBrbqbFNwB0Frb75/Jy6bbbbEcBwCsHDpBUUyw2tgDdhbEKsZNisb7bbQJhF4TE3urqY+1tnVZbazoONjVJCxdK06Z1XwuJxczzCxea161fz8YqCwbaDgCAyxIJafLkaFR+GDjQLL53/vKNxaTKSlNxcM4c9774AOSvttb8XrLY7TxajgC5KSmRJkwwrWfCbulSc90FULzMLl0/T/qFQabFdyrFhisA0dbUFK55vZISk7DHtR2IjrY22xGEQ2Zjy/r1dBACwliF2GmVlflV5wLCJgiJvQsWuH+Oigpp0SLz3+3t0sGDpnV2aalJ7OU6YR2V9oAo8OKC7wdTp5qs+Uy/9/37zeM1a8yXEQl7gP8sW2Z22MNZjY1m8A2gf1VVtiPwBq2zAWcEYZdumNDiGwDCtyDd3m6u7bR5BKJj8GDbEYRHZmML11BEXdiqELshKnOeQE+CkNibTHpf1a5zC9582+7CNSTtAVGQqWYVdlVVfNkAQROPS6tWRaM9pZdaWsxuGQD9u+Ya2xF4x+8TFUAQBGGXbtjQ4htA1IWxkjpJ2UC0lJUx9+ckrqGIurBVIXZLFNaFgd74PbG3vNxUHwdE0h4QHVGoZsUAFAimRMK0dQj7Ncprra22IwCC4dRTbUfgnTAu+AJeCsIu3bBautR2BABgR3u7tHWr7SjcQVI2EB0lJdKECbajCBeuoYgy7sv7V11N9zFEl98TewcNMsVM4nHbkcAnSNoDoiLs1awYgALBlkhIqZQpBw1nlJbajgAIhrY22xF4h9bZQHH8vks3zGjxDSBq2tul/fvNXF6YW7KTlA1EB20ancc1FFHFptT+LVhgOwLAHr8n9h4+bOa5Osvc/+3ZY34yhx8pJO0BUZJISA8+KL0nhL/6DECB4IvHpbo6acUKk4iLwg0fLv3lLwzsgVwMHmw7Au/QOhsonN936UaB3yddAaBYTU3SwoXStGnSsGHmvm7GDNtRuYukbCA66JLjPK6hiKIwVyF2SjIZ/jEk0JcgJPbecIP0m98cu/+Lx8393+jR5mc8bp5fuJDv+ggIYeYOgD799KcmkSNMGIAC4VJba9rldl2wQO7eeksaMYKBPZCLsrLwViLuCa2zgcKQMGZfECZdAaAQDQ1m49r48dLixdLatdHaaMF3LBANiYQ0ebLtKMKHayii5sCBcFchLlZ5uXTbbbajAOxpb5eeeMJ2FP07elS66qpj939dr2stLeb5xYvNGKq6Wlq50k6scB1Je0CUNDSE7yaOASgQXhUV0qJF0po10vXX244mmBjYA/0rKZEmTLAdhXeitAAMOImEMfto8Q0gbNJpsxF15sxoV3PlOxaIDrrlOI9rKKKmrc12BP4Vi0mrVpmN/EAUpdPSpz4VzvnvjRtNwZO5c82/sxi04vUdkvaAKFmyxHYEzmIACkTHH/5gO4JwcHJgD4RJVZXtCLyzcKHtCIDgof2OP9DiG0CYpFKmsl7YNtcWgqRsIDpqa2mT6zSuoYiawYNtR+BP5eWme1EiYTsSwI7M/dW999qOxF319ebf2dSU3/s6dzajFa/vkLQHREVTU7h27TIABaKDhXLnFTqwB8IqSosGd9xhqi8DyB3td/yDFt8AwiCVkqZOlZqbbUfiDyRlA9GybJmZ24czuIYiasrKTEEPHJNMmvEl66WIqqjdXzU3S1Om5La+19BgOnCNH08rXh8jaQ+IijDt3GUACkQLC+XuyGdgD4RdIhGtFrlLl9qOAAgW2u/4R2mp7QgAoDjptDR9Ove4XZGUDURHPG6655B04xyuoYiSkpJozeH1pbraJOTU1dGRDNEV1furlhappqb3jlrptMmnmDkz/6JOdOzyHEl7QFRs2WI7guIxAAWiiYVy9/Q3sAeiIp2WXnrJdhTe2bCBUvdAPmi/4w+xmDR0qO0oAKA48+ZFpwJEPq6+mnZMQJQkEqaLDhX3nMHGFkTNqafajsCe884zY6amJnMdnTHDdkSAXVG+v2pulubP7/58plVwsQWd6NjlGZL2gCgIamvJgQOP9VBnAApEFwvl7uptYA9Eybx50t69tqPwVpiqMANuo/2OP1RWmqoKABBUDQ2MwXqzbh3tmICoSSTMonIyaTuSYGNjCxAtw4ZJixZJFRW2IwHs4/7KJNY1NBx77HSrYDp2eYKkPSAKgtpacuhQ6Xe/YwAKRB0L5e7rOrAHoiSqN/dhqMIMeIX2O/5QVWU7AgAozpIltiPom182zNGOCYiOeNx01fnnf7YdSXCxsQVRFKVuGV3RPQM4xu/3V15ZutT8dKtVMB27XEfSHhAFQW0tuW+fdPCg7SgA2MZCuTcyA3sgaqJ6c9/YaKoxA8gNCWP2zZljOwIAKFxTk0lG86vycunVV6URI2xHcgztmIDwS6dNpb3vfMd2JMHFfQqiJqidxZwUxc3HQFd+v7/yUiaZ181WwXTschVJe0AU+GWnbCFaW21HAMAPmIByH7v0EEVRvrlvaTE323v2SPv3k8AH9IeEMbuqq6m+DiDY/Ly4GotJq1ZJpaVmA62f0I4JCK9UyiTm+vn6GATcpyBqgtpZzEl0zwAYP3T13e+6/5nQscs1JO0BURDk1pKlpbYjAOAHTEB5gxsdRE3U/z//3vdKo0dLw4ebtkTTpkkLF5LAi2hrbzeJrF0TWhMJafJku7FF2YIFtiMAgOL4dXG1vFxav958z/m1UwftmIDwSaWkqVPdqwYTFWxsQRT5dbziJbpnAP69v7Lld7/z5jx07HIFSXtAUPW2mNSTvXuDWW0vFpOGDrUdBQA/YKHcG9zoIGr4//wxLS3S2rXS4sXmmltdLa1caTsqwBtNTSZhddo0k8A6fHjPCa3XXGM70mhKJqUZM2xHAQCF82sbt2RSevpp6ZRTzPziu+/ajqh3tGMCwiOdlqZPp1KWE9jYgijy21rnoEHen7OlRTp40PvzAn7h1/srm7yqmE7HLlcMtB0AgDw0NZmKMFu2mC+jzje2sZg0YYJpIZlMHtthlUqZm+A337QTczEmTJBKSmxHAcAvFiyIVhvLUaPMwomXMrv0uPYiCri579vGjeZPMinddptJXALCpqFBWrKk7/FFJqF17VrzePRoafdub+KDqQB12222o0AI7Ny5U0899ZSam5t18OBBjR07VqeccoomTZqkQTYW2jrZunWrXnjhBb322muSpJNPPllnnXWWzj//fEfPs2/fPm3atEmvvfaa9uzZo1GjRunkk0/WpEmTNGLECEfPhS781satslIaN87MFZ51lr9i60t9vRmb1tbajgRAMebNo8KeE9jYgqjKdBbzy/jl8GE7521tNZ8FEEV+u7+KmjvukBYtsh1FqJC0BwRBvotJixebilSzZ0v/9E/B/eI67zzbEQDwk9pa0yY3Ku0svU7Yk47t0uOGH1HAzX1u6uuldeukVatMBT4gDNJps1hYyJiChD3vxGLm2kPSMIpw77336tZbb9XmzZt7/PuRI0dq9uzZ+s53vqNRo0Z5Ftfhw4f1wx/+UP/v//0//elPf+rxNWeccYa+8IUv6Bvf+EZRiYVPPvmkvvOd72jlypVq66GdWGlpqaZPn65vfetbOo95GHf4qY3beeeZzVqNjbYjKczSpSTtAUHW0BCdeT03sbEFUVZSYgp+ZDbWRVVpqe0IAHv8dH8VRXQvchztcQE/S6fNjqmZM/OvLrVxo/S1rwV7MXrWLNsRAPCbZcvMxBTc09pqOwLAG9zc5665WZoyxVRw3r/fJBXv32+qFQJBk0pJ48ezWOiW9zg0zVReLq1fT7IwCnbw4EHNmTNHV199da8Je5K0d+9e/fSnP1VFRYVWr17tSWwvvPCCLrzwQt100029JuxJ0o4dO3TjjTdq4sSJ2rFjR0HnuuWWW3TBBRfogQce6DFhT5JaW1v1wAMP6IILLtDSpUsLOg/64ac2bk89ZTuC4tCOCQi2JUtsRxB8bGxBFLW3Z89HfeQjtiOyKxaThg61HQVgj5/ur6Io07ELjiFpD/ArFpOotAegu3jcTEzFYrYjCS926SEquLnPT0uLGZsNH27agw4fbq7J06ZJCxeyeIpgSKWkqVNpx+WWZFL64x/Nz2KPk0qRsIeCHT16VLNnz9add96Z9fzo0aN12WWX6eqrr9aECRNUUlLS8XdvvvmmZs2apUcffdTV2N544w197GMf09atW7OeP+OMMzRr1ixdfvnlGjduXNbfNTY26rLLLtOuXbvyOtf3v/993XTTTTrcqWXXkCFDVF1drdmzZ2vy5Mk67rjjOv6ura1NCxYs0A9+8IMC/mXoU6aNG5wR5blSIMiamvIvTIBsbGxBlDQ1mfmmadPM/FPn+aif/MR2dHZVVpqKg0BUcX9lV6ZjFxxD0h7gRywmmV0i7BQB0JNEwkxQUXHPeezSQ5Rwc5+/rjvoWlpMO5LFi821ubpaWrnSTmxAf9Jpafr0YFci90q+Y4HqatPqrK5OOvNM83PFCvN8ocehcgiKcOONN2plp++jQYMGadmyZXr11Ve1evVq3X333WpsbNS2bds0ceLEjte1trbqiiuu0Ouvv+5KXH/5y190xRVX6OWXX+54buzYsVq9erVeeOEFPfDAA3rwwQe1Y8cOPfTQQzrppJM6Xrdz505deeWVas9xN/uKFSv0j//4j1nPfelLX9Irr7yi9evX684779SGDRv0yiuv6Atf+ELW6xYsWKBVq1YV8S9FN5k2bnAG7ZiAYCLhtjhsbEFUNDSYe8Px481809q13e/j9++3E5tfVFXZjgDwRtcqm5n7Ye6v7KNjl6NI2gP8hsUk48ILzU9asAHoSSJhJqqKreSCbOzSQ5Rwc++8jRul2lpp7lwzpgX8ZN48dzdFjRnj3rFt+M1vjlU16JrgHIsdq7LZ1GQ2U8yYkf2a2lrzfOfqCIUcB8jTiy++qB/96EdZz91zzz362te+psFdqux+8IMf1Nq1a7MS99LptG6++WZXYqurq9MTTzzR8XjkyJHatGmTLrvssm6vramp0aZNmxTr9HuzadMm3XXXXf2e5+jRo/r7v//7rAS/r3/96/q///f/atSoUVmvHT16tH72s5/pb//2bzuea29v19/93d/p6NGj+fzz0B8WV51DOyYgmEi4LczAgWZDDBtbEHbptJnrnzmTqpz9mTPHdgSAe/qqstm568tpp9mONNro2OWokvZct2gCAfXMM8+ooqKi4/G2bdt07rnnWoyoH8kku84k6dRTpbfeyk5ejMXM4npVlfmcOv3vCiDCGhqkpUulDRtsRxJ8CxdKixbZjgLwzsKFZtcunFdebtqZUwUAftDQYCb+3fbjH5vEwC1bGsmhNAABAABJREFUTEJBkDdilZebDRLxuEmMOHjQ7KItLTWV+ApJ8nfqOD4XuDmIkLn22mv1n//5nx2PP/e5z+mXv/xln+/Zvn27EomE2traJEkDBw7U888/r9NPP92xuI4ePaozzzxTO3fu7Hhu+fLluvbaa/t83/Lly3Xdddd1PB43bpy2b9+u97yn933Yv/zlL/X5z3++4/HZZ5+tp59+WqV9TKq3trbqQx/6kJ5//vmO5371q1/ps5/9bJ/xuSl0v0tNTaZiDJyxf7+pnA0gGNrbzbgyyONjW6ZNk9assR0F4K5UyhQziXL3sVxVV5sNb0DYNDRIS5aQtBsEsZhJtA7ZnJ7NOQgq7QF+0tBAwl7GSy91v4mnBRuAnnSt5DJihO2Igotdeoga/j/vnuZmacoUc20GbFuyxJvz3H23SX5fs8ZMXu3fL/3VX3lzbqc1N0vz55v/LikxiRGjRpmfnSflemtV0pO+jgM44NChQ7r33nuznluwYEG/7zvrrLN0xRVXdDw+cuSI6uvrHY3t0UcfzUrYO/nkk/XpT3+63/d95jOf0cknn9zx+E9/+pM2bdrU53s6Jy1KpspeXwl7klRaWqobbrihz+OgSInEsa4SKB7tmIBgOXCAhL1CUakVYZdKSVOnkrCXqxzub4BAocpm8NCxy3Ek7QF+4tViUljQgg1AZxUVZqH817+2HUkwVVdTwRTRk0hIkyd7c67qatPSprram/P5QUuLVFPDOA12NTV5N+m3YYO0bZv570yC2n/8h6laF0T19WZjWVe5tirJfBaAR1avXq133nmn4/HEiRN1zjnn5PTeztXsJOm+++5zNLb7778/6/FnP/tZDRgwoN/3DRgwoFtyX1+xpdNpbex0zRs8eLCSyWROMc6dO1eDBg3qeLx+/Xrt3bs3p/ciR9/4hu0IwoN2TEBwpNNSl+9Z5OHDH7YdAeCedNpU2AtLUu9VV7l7/GRSmjHD3XMAXkqlTDVyChoFCxsKHEfSHuAXXi4mhU19vflSp5ILAMkk81I9K3/s0kNUefX//QULulcGnTbNlJPvLBYzz192mTRwoDexualztS7ABq8n/no634c+5G0MTlq69Nh/NzSYxOPx403187VrqY4OX1m1alXW46lTp+b83smTJ2tgp+/dJ598Um+++aZToRUVW9fXPvTQQ72+ds2aNTp69GjH48rKSpXl2EJ02LBhmjBhQsfjI0eOaA3t+Jz1xhu2IwiHWMy0WAfgf5nFeIeT4SPlr/6KMTXCa9688FTYi8Wkn/7UvU175eXSbbe5c2zAhqBU2TzxRNsR+A/rr44jaQ/wC7LIi0MLNgCdLVsW3Ko2NrBLD1HmRaJv19+xTGXQzi00d+82P9Np8/zq1WZhNwy/m71V6wK8sGWLvfNlFin7SLDxvQ0bpEcfLbxVCdXR4aFtXao7Tpw4Mef3nnDCCUokElnPPfPMM47E1draqh07dmQ9d2EebVInTZqU9fiFF15QW1tbj68t5jPo6VxOfQaQuQb+/d/bjiIcaMcEBENQFuODgDE1wqihIVzropWV0qhR0qpV3TfoFisWM8eNx509LmBLUKpslpdL775rOwp/oWOXK0jaA/zC68WkMKIFG4CMeNydG+QwYpce4G6ib3+/Y5kWmqNGmZ+dFyDjcTOJGYa2up2rdQFeaW+Xtm719pyNjea8YVqknD69+MUUqqPDA88991zW4zPOOCOv948bNy7r8bPPPlt0TJL0/PPPZ1W/GzNmjIYNG5bz+4cNG6ZRo0Z1PD569Ki2b9/e42u7xuyXzwCSPvMZqZdkS+SJdkyA/wVlMT5oGFMjTJYssR2BszLjk0TCdNlwap6xvNwcr8sGIyDQglBlMxaT7r1Xeust25H4Cx27XEHSHuAHNhaTwooWbAAynL5BDqMRI9ilB0juJfo6tRM2l7a6frdhg9Sl+g/gugMHvF8obGmRXn45XIuUBw86cxyqo8NFe/fu1d69e7Oee//735/XMbq+/oUXXig6LkndquzlG1dP7+kttmLP5dZnEHkNDcGuuuo3tGMC/C8Ii/FBxZgaYdDUlH8Vd7/rPD5JJMxGvmSyuGMmk+Y4JOwhTIJQZTOTLHvmmbYj8Rc6drlmoO0AAMjOYlKY1debL47aWtuRALAtc4M8f765NuCYkhJ26QGdZRJ9a2qcWVwYPFj63vec/R3LtNWVzKaPgwel1lbz573vde48brnjjmPxA16wVdHo7/6ORcreZKqjp1JsGoCj9u3bl/X4+OOP1wknnJDXMcaMGZP1+C2HdtR3ja3reXKRa2zFnsutz2DXrl3avXt3Xu/pmoAYaGGrJGMT7ZgA/wvCYnzQMaZG0IXtGtHT+CQel+rqzFrl0qVmM2s+x1uwgOQYhJPf742SSdM1Jx6X9u+3HY1/0LHLVSTtAX5AewznLV1K0h4Ao5gb5DCrrjYtNQAc42Sib1ub9NWvSo89duxG30mZtrplZSaBLxbz/yaQLVtsR4CoGTzYznnvu8/OeYMiUx29rs52JAiRg10qQg4ZMiTvY3R9z4EDB4qKKcPL2Io9l1ufwU9+8hPdfPPNjhwrcMJYScYm2jEB/uf3xfiwYEyNIAvb/FBf45PaWvNn2zaTrLhli9TYmD2HF4tJlZWmxe6cOWxQQHj5+d6op2TZsrJgzLm7zaluQugV7XEBP7C1mBRmtGAD0FVtrbRunTRsmO1I/OGii2xHAPhTJtF32TJnxmj19SZB1s3WNSUl0oQJ7h3fKY2NJsEQ8Epmcs1LA9kbmZP6elOFBXBI12S14447Lu9jdE1Y63rMQnkZW7HncusziLSwVZKxiXZMgP/5eTE+jBhTI4ja26WtW21H4ZxcxyeZ7hlr1kjptKngtXu3+ZlOm+cXLSJhD+Hmx3ujc881Y5f167v/Lgdlzt1NmVbBdOxyFUl7gB/YWEyKAj9++QOw68ABSlpnzJljOwLAv1Ip6Z//2blqyM3N0pQp7ibuVVW5d2yntLSYlr6AV2xMrh054u35gmzpUtsRIMRKSko8eU8hvIwt3/d59RlEStgqydhCOyYgGJiP9x5jagTNgQPhqVpV6Pgk0z1j1CjzkzE4osKP90bPPCPNnt37vH2Qk9WKrYyXTJp1kiB/BgHBFnDADzKLSWvX2o4kXPz45Q/ALtqRG9XV7NoDepNOS9OnOz+B2NIi1dSYG103SsnPmSMtXuz8cZ3W2momJAGvVFVxn+VXmerojEnggKFDh2Y9PnToUN7H6PqerscslJexDR06VC2dxjD5nsutz+D666/X1Vdfndd7duzYoSuuuMKR81sTtkoyttCOCQgO5uONgQO928zDmBpBE5b5+REjGJ8A+fDzvVFmw33XinKpVLDb0H/5y9KkSSbBf8OG3N/XU6tguIqkPcAvWExyXqYFG7tUAGTQjtxYsMB2BIB/zZtnbtTd0NwszZ/vzs1+IiFNnuz/VkSlpbYjQNQEJaE1qu64w7QAAopE0t6x5/2YtDdmzBiNGTPGkWMFSpgqydhSXm4WxKnuAPifnxfjveZ19e1f/EK69VZvzwkUKgzz84MHmwQYxidA7vx+b9R1w30qJU2d6u+Y+zNnjknqr601Cf533GE2WDQ2Zv+7YjGpstLkqmTeA0/RHhfwC9oUOo8WbAC6oh25KWnNDhmgZw0N7rfzqa8353GD3xNyYzHJocV/IGeZhFYvDGRfZN6oxgKHDB8+POvxO++8o7fffjuvY+zatSvr8YgRI4oNS1L32Hbv3p33MXKNrdhzufUZRFZYKsnYQjsmIFj8vhgfZrff3ntbP8BvwjA//9WvMj4B8hWEe6PMhnu3OvF4qWu3rYoKs2l2zRrz79u/X9q92/xMp83zixaRsGcJSXuAX3i5mBQlra22IwDgJ5l25FFVXi7ddpvtKAD/WrLEm/MsWmRuiNvb839ve7t575493Y9RW+vvjSCVlVRAhh1eJbR6XVEjDDLV0YEixeNxxbos/r3yyit5HePll1/OenzmmWcWHVdPx+l6nlzkGlux53LrM4isMFSSKdbFF0sTJ+b3nupqs8mlro6Wc0CQBGExPqza2sy1k8Q9BEEY5ucvvrj7c33N1wEIzr1Rfb10+eXudeLxSl9zoSUlJoF61Cjzk/l660jaA/zkK1+xHUH40IINQFdVVbYjsCMWM22FWPQAetbU5F1r2c2bpeHDze/jtGnSwoWmRH1fsS1caF4bj5v3jh7d/Rj33SeNGePf8U9Ur7+wr7ZW+qu/cv8cyB/V0eGgD3zgA1mPd+zYkdf7X3zxxT6PV6izzz5bAwYM6Hi8a9cuHThwIOf379+/X3v27Ol4PGDAgF6T6fz6GURWGCrJFOvRR6U//lG64AJp0iTpwgu7fyax2LHxbFOTtH491eGBIArKYnxY7dtn2vql07YjAfoX9PmhefPM71qu83V9zfkBURGke6NNm2xHUBy6bQUOSXuAn/z3f9uOIFxowQagJ36uQuWW8nKz8EHZfqB3brfF7UlLi7R2rbR4sfn9rK6WVq489vcNDea58ePNa9au7V6Wv/MxPvlJ6Uc/8m+l4ShefxEdtMYtnF+vWQicii5tXDZv3pzze99++22lUqk+j1eo0tJSjRs3ruDYNnVZMDjzzDNV2kuCfjGfgSQ99thjfR4PeQpDJRkntLRITzxhFr8ef1w691zpnntoxwSETZAW48Mq09YP8Lugzw81N0sf+EDu83U9zfkBUcO9kTfothVIJO0BftHQYGexOMxowQagJ1FrR55MSqnUsYQ9SvUDPduyxXYEptJfba101VUmAW/mTO+q/7mtuppFWNjT0GAqUbrpwQfZMFQov1YHReDU1NRkPV63bl3O7924caOOdGpxff755+vEE090KrSiYuv62unTp/f62o997GNZVf0aGxtzrup34MABbd26tePxwIED9bGPfSznONGLoFeSccOjj0pXXy3dcINp6cjcHRAOLMb7Q329uf8B/CwM8/O7d+f3+syc39y5VMREdHFv5C66bQUWSXuAXyxZYjuC8OHLH0BvFiywHYH7qqvNJF1dndn9R6l+oHft7VKnRWrrfvMb9xOMvBaF6y78y6t7rU6JMsgR1dHhoI9//OMaMmRIx+PNmzfrj3/8Y07vXb58edbjK6+80snQuh3vv/7rv3T06NF+33f06FH9+te/zjm2UaNG6eKLL+543NbWpvr6+pxirKur0+HDhzseV1dXa+TIkTm9Fz3ItEt7+GHbkfhXfb2pUNPUZDsSAE5hPt4fli61HQHQv6jOEzH+QZQFvcqmn9FtK9BI2gP8oKkpPFVU/IQvfwC9qa0N5zXiwgvNwlBTkxmgt7fn11qTUv2IqgMHuv9uwDnJpDRjhu0oEFVe3mu99ZY35wkTqqPDQccff7yuuuqqrOeW5JC0u337dt1///0djwcOHKhkMulobJMnT9Zpp53W8fjVV1/tlozXk1//+td67bXXOh6PGzdOF110UZ/v+exnP5v1+F//9V/V2k8b6tbWVv3bv/1b1nPXXnttv/GhBw0N2fdgTzxhOyJ/a26Wpkxh4RoIizDOtQXRhg1szIX/hXV+PheMfxBVYaiy6Uddu20hcEjay9POnTt1//336/bbb9eSJUv0n//5n1q/fn3WTlRbtm7dqrvuuku33nqrbr31Vt1111168sknHT/Pvn37tHLlSv3sZz/T4sWL9bOf/UwrV67Uvn37HD9XZNAW13m0YAPQn2XLzO6TMLnkEmnRImnsWDNQL6S1JqX6EUVtbbYjCK/ycum222xHgSjjXis/xx/v7fmoxgKHffvb39agQYM6Hi9fvly//e1ve339u+++q+uuu05tncYCf/3Xf61x48b1eZ6SkpKsP/21ux0wYIBuvvnmrOe+8Y1v6KWXXur1PS+99JK+/vWvZz33ve99T+95T9/Tuddee63OPvvsjsfPP/+8Fi5c2Od7brrpJj3//PMdjz/4wQ9q7ty5fb4HXaTThd+DRV1Li1RTw/0nEAYsxvsH90EIgjDOz+eK8Q+iKqpVNt3QudsWLXEDbaDtAILi3nvv1a233qrNmzf3+PcjR47U7Nmz9Z3vfEejRo3yLK7Dhw/rhz/8of7f//t/+tOf/tTja8444wx94Qtf0De+8Y2sict8Pfnkk/rOd76jlStXZk1mZpSWlmr69On61re+pfPOO6/g80TSli22IwgfvvQB9Ccel1atMrvawlJha8sWs6Nm+nSzY68Y9fXSunXmM2KHDsJu8GDbEYRTLGauIUwawCbutfIzZIj0zjvenS+qlRXgmtNPP1033HCD/uVf/qXjuauuukq33nqrvvSlL2lwp+/85557Tl/4whe0adOmjufi8bi+9a1vuRLb3Llzdfvtt+uJ/628tnfvXk2aNEnLly/XZZddlvXa1atX63Of+5xaOt2nTJo0SbNnz+73PAMGDNC//Mu/6PLLL1d7e7sk6dZbb9XBgwf1/e9/X/FO38t79uzRwoUL9bOf/azjuZKSEv3whz/UAFp+586pe7Aoa26W5s83C04Agm3BApKX/YD7IARBGOfn88H4B1GUqbJJcnnfTjpJam3NvjbGYqZjRVWV+QwpXhQaJe2Z2Rv06ODBg/riF7+oO++8M6fXn3jiifrVr36lj3/84y5HJr3wwgu65pprtHXr1pxeX1lZqTvvvFNnnHFG3ue65ZZb9M///M85VRQcPHiwvvvd7+of/uEf8j6PG5555hlVdLpobdu2Teeee67FiLpob5dGjpSoVOicZJJBLoDcNTWZXW1hWGAZMEAaNEh6913njhmLmVa7JO4hzNrbzURhFCcI3VJeTtIv7ON3uzCjR0u7d7t/nupqM8YIGd/PQUTA0aNH9YlPfEIPPfRQ1vNjxozRhAkTVFZWphdffFFbt25V52nRwYMH6/e//70m51AhqKRLW+dHHnlEU6dO7fd9r7/+ui688EK98sorWc+feeaZOvfcc9Xe3q5nnnlGO3bsyPr7U089VY8//rhOPPHEfs+R8f3vf1//5//8n6znhgwZogsuuEAnnXSSXn/9dW3ZskWHDh3Kes2SJUt8MacXmN+lVEqaOtXud83gweGpHL1ihVnIAxBsySSL8bbFYqaCV5cxC+BLTs7Pl5SYuYAgYfyDqEmnpfHjw7Em56b//m+T1NzaKpWWSkOH8r3uIptzELTH7cPRo0c1e/bsbgl7o0eP1mWXXaarr75aEyZMyJqoe/PNNzVr1iw9+uijrsb2xhtv6GMf+1i3hL0zzjhDs2bN0uWXX96tlUdjY6Muu+wy7dq1K69zff/739dNN92UlbA3ZMgQVVdXa/bs2Zo8ebKOO+64jr9ra2vTggUL9IMf/KCAf1kEbd5Mwp6TaMEGIF+JhFloSSZtR1K8o0edTdiTKNWPaCgpkSZMsB2Ff4weXdz7k0lzXSVhD7YdOEDCXiG8SNiTqI4O1wwYMEB33313t6p0u3bt0qpVq3TPPfeosbExK2FvzJgxevDBB3NK2CvG2LFjtWbNGp1//vlZz7/wwgt64IEH9OCDD3ZL2JswYYLWrFmTV8KeJC1cuFDf//73s7puHDp0SOvWrdOdd96p9evXZyXsDRo0SLfccosvEvYCI502FfZsftfEYtJnPmPv/E5butR2BACcEOWWl37R0iIdPGg7CiA3Ts3PX3118BL2JMY/iJ5Mlc1YzHYk/vaDH0hlZdKoUeYnCXuhRdJeH2688UatXLmy4/GgQYO0bNkyvfrqq1q9erXuvvtuNTY2atu2bZo4cWLH61pbW3XFFVfo9ddfdyWuv/zlL7riiiv08ssvdzw3duxYrV69utsk30MPPaSTTjqp43U7d+7UlVdeqVwLLK5YsUL/+I//mPXcl770Jb3yyitav3697rzzTm3YsEGvvPKKvvCFL2S9bsGCBVq1alUR/9IISKWkGTNsRxEetGADUKh43FToXLHCVH1BtkypfiDMqqpsR+Afu3dLt9+e//WwulpqaDDXU8Zj8IOwVB2yodjk3f4kk9wLw1VDhw7VnXfeqXvuuUcXXnhhr68bOXKkvvKVr2jbtm2qqanxJLazzjpLTzzxhBYvXqzTTz+919eNGzdOixcv1uOPP15Q1wxJuummm/TEE09o1qxZWa2BOxs8eLBmzZqlLVu2aAHJtPmZN89udYjyclOx9IYb7MXgtA0bpG3bbEcBoFgsxvtDa6vtCIDcFTM/n5mP+slP3InNbYx/EHbt7dL+/dKePeZne7tJ1l2/niT/vnBtiAza4/bixRdf1DnnnJNVXe6BBx7QrFmzenz9oUOHdOmll2rz5s0dz335y1/Wv//7vzse23/913/ps5/9bMfjkSNHqrGxUaeeemqPr9+5c6cqKyvV0mnX5x133KFrrrmmz/McPXpU5557rp5//vmO577+9a/r1ltv7fU9X//61/Vv//ZvHY8/+MEPKpVKacCAAf38q9zj23YalH51Fi3YADjpwgulJ56wHYX/UKofYdbUZMZmMBYulBYtMhMDd9whbdkiNTZmV5KJxaTKSpPwOGeO1GnMDfjC/v3S8OG2owiuMWOkPCv156S83GxgC2lyr2/nICJu586d2rp1q5qbm/X222/rpJNO0imnnKKLLrqo12Q2rzQ2Nmr79u1q/t/5ofLycp111lmqrKx09DwtLS3atGmTXnvtNaXTacXjcZ188smaNGmSYj5MqvD971JDgzRzpr3zJ5Om00TmWlpdLW3caC8eJ2XGoQCCz8mWl8jf/v2mMg8QRIXMRwV5DoDxD8KmqenY7/DWrd1/hydMML/DM2ZIP/2pVF9vL1Y/49rgGZtzECTt9eLaa6/Vf/7nf3Y8/tznPqdf/vKXfb5n+/btSiQSavvf3fwDBw7U888/3+eu2XwdPXpUZ555pnbu3Nnx3PLly3Xttdf2+b7ly5fruuuu63g8btw4bd++Xe95T+/FFn/5y1/q85//fMfjs88+W08//bRKS0t7fU9ra6s+9KEPZSX6/epXv8pKMvSabyf5kknzZQVnbNwoXXyx7SgAhEF7u1n4oJ1ed9XVZvcTEFaVlWYSAdK0adKaNdnPtbeb9jqtrVJpqTR0KGX54W98pxfnC1+QfvMbZz+/WMyMJUK82cq3cxBAwPj+d8lWklx1tWkv3rVaqe0kQif1NA4FEFzptOnewGK8t2Ix89lzz44wyHU+KshzACedZBIVQ7q5DRHS0CAtWZLfvdLkydIll0iPPGKqy+EY7o08Y3MOgva4PTh06JDuvfferOdyaQ9x1lln6Yorruh4fOTIEdU7fCPy6KOPZiXsnXzyyfr0pz/d7/s+85nP6OSTT+54/Kc//UmbNm3q8z2dkxYlU0Wvr4Q9SSotLdUNXVoydD0OZL6wSNhz1kMP2Y4AQFgcOBDMG3svUI4bYTdunO0I/KOx0Ux2dlZSYnbpjxplfjL5Dz9rbzff6SFODnPdSy8526ok08aR/00ABF1Tk7cJexdeaCosNDWZ62hP7cVra02lmTDoaRwKILgyLS/r66XRo21HEx2VldyzIzxynY8qKTHVu4LojTdMB5CmJtuRAIVJp03Bopkz879X2rhRuvlm6b3vNf+9cKFJVutaEX7gQOfiDQrujSKBpL0erF69Wu+8807H44kTJ+qcc87J6b2dq9lJ0n333edobPfff3/W489+9rM5tZ4dMGBAt+S+vmJLp9Pa2OmCOnjwYCWTyZxinDt3rgYNGtTxeP369dq7d29O742MJUtsRxA+W7bYjgBAWPxvxVz0gqRzhBlj1mNaWswuZiBImpqOTezF46YtDjt0C9fYaNoMpVJm4rUYyaQ5Dgl7AMLA63uiSy4xLZE67frv0bJlziVa28Q4FAifVEr6+7+Xdu+2HUl0VFXZjgCwI8j/329ulqZMIXEPwZNKmaTTYu+T6uul2bOla64x1eXSadP2evdu87NLnkwkcG8UCSTt9WDVqlVZj6dOnZrzeydPnqyBnbJ8n3zySb355ptOhVZUbF1f+1AflcnWrFmjo0ePdjyurKxUWVlZTucZNmyYJnTayXDkyBGtoWznMV7vxo0KMs0BOOWRR2xH4G8kSSOs2ttpjdtVa6vtCIDcNDSYdoHjx0uLF0tr11I11wmZicFMdZQVK8znnI/qavO/T10dbX4AhIfX90S5ni8el1at6l6RIogYhwLhkUpJU6eaZBR4p1PnLSBSgl55uKVFqqkxyUpAEDj9Pd85ebVrlc3aWmnwYGfOEyTcG4UeSXs92Nal7dvEiRNzfu8JJ5ygRJed488884wjcbW2tmrHjh1Zz1144YU5v3/SpElZj1944QW19VJNqJjPoKdzOfUZhAIVitxBpjkApyxbZjsCfyNJGmFFa+zuSkttRwD0rZjWG8hN54nB2lrTlrFzRcOuiSGxmHm+vzaOABBUNjZ65HMPlkg429rcFsahQDik09L06dxr2/DVr0pz55L4g+hJJKTJk21HUZzmZmn+fNtRAP1z63u+t+TVAwei2SmLe6PQI2mvB88991zW4zPOOCOv948bNy7r8bPPPlt0TJL0/PPPZ1W/GzNmjIYNG5bz+4cNG6ZRo0Z1PD569Ki2b9/e42u7xuyXzyAUqFDkHjLNARSLaqj9I0kaYRXFG/6+xGLS0KG2owB651TrDfStp4nBigrTprGnViXptHk+lzaOABBENjZ65HsPlkg409rcFsahQHjMm0eFPZvq6809E602ETULFtiOoHj19aZqPeBnbn7P95S8GsX5e+6NIoGkvS727t2rvXv3Zj33/ve/P69jdH39Cy+8UHRckrpV2cs3rp7e01tsxZ7Lrc8g8MLQdq1r2dmulRRWrJAuushObGSaAygWC/+5IUkaYRTF0vp9qaw0LQgAP6LFljdymRjs2qqE6waAsLO1UJTvPVimtfmHPuROPG5iHAqEQ0MD82x+0LnNIBAVtbXBb5MrSUuX2o4A6J0X3/Ndk1ejOH/PvVEkkLTXxb59+7IeH3/88TrhhBPyOsaYMWOyHr/11lvFhiWpe2xdz5OLXGMr9lxufQaBF+S2a5WV5ovx3Xf7rqRQW2uqVOVRBdIRZJoDcALVUHNDkjTCqKyMsURnVVW2IwB65scWW+Xl0oUX2o7CeUwMAkB3thaKumywzkl7u/TKK87H4jbGoUA4LFliOwJk9NZmEAizZcvMvXqQbdggbdtmOwqgZ159z3dOXi0rM/kAUcK9USQMtB2A3xzs0mpgyJAheR+j63sOHDhQVEwZXsZW7Lnc+gx27dql3bt35/WerlUDrQpy2dbXX5cuuOBYJYWyst5fW1IifeQj0tq13sXHghKAYoWhGqpX1q+XZs60HQXgnHTalPOn9fMxYdiRjHDyW4utWExatcokRYTtu5GJQQDoLrNQ5HXy+IwZ5j4skcj9PUHdPMw4FAi+piazsR+9Kykxc5FeybQZrKvz7pyATfG4uVefMiWY46GMO+4wRVMAP/Hyez6TvFpRYb47J0zwNv/ANu6NIoFKe110TVY77rjj8j5G14S1rscslJexFXsutz6Dn/zkJ6qoqMjrzxVXXOHIuR0R5LKtPfWO78upp7oWSo9YUAJQrKAuaNjwgx/YjgBwTioljR9P257OqqvNRAjgN35rsVVefiyBIiztdzoL278HAJyQWSjyWiFVkoK4eZhxKBAOfhqz+1EsJt1zj/dVwLq2GcxobzddlfbsMT+9TCYE3JRImHv2k06yHUnh6AwEP/L6e77z+aKUD8C9UWSQtNePkgIqdxXynkJ4GVu+7/PqMwicoJdt7e2mrqt0Wvrv/3Y/ns5YUAJQrCAuaNhCaX6ERSolTZ3qr6pdfrBgge0IgJ75qcVWMmmuIZ0rHoWh/U4GE4MA0DtbC0X5bqgN4uZhxqFAOJBk0rvMxp9PftLcTyST3p4/02awqUlauFCaNs1UJBs+XBo92vyMx83zCxcy/4fgSySkSZNsR1G4xkYSaeE/Xn/Pdz5flPIBuDeKDJL2uhg6dGjW40OHDuV9jK7v6XrMQnkZW7HncuszCDxbu3Gd1Ll3fG/mzZN27XI/lgwWlAA4IYgLGjaxaxpBl05L06dTYbOrZNK0XwP8xi8ttqqrzUamujqzmNVZpv1OkDdqZTAxCAC9s7lQlOuGWil4m4cZhwLh0N4ubd1qOwp/uvhi6emnj238icfNfcWPf+xdDBs2SJWVpuPA4sWmxWDXeZGWFvP84sUm1smTpZUrvYsRcFJDg3TffbajKFxLi+RQNzvAETa+5zPJq+m0+W6KAu6NImWg7QD8hqS9Y8+3dBqo+yVp7/rrr9fVV1+d13t27Njhrxa5VVXB7rXeuXd8T2y0rGJBCYATMgsaJPDkhl3TCLp586iw11V5uXTbbbajAHpmK1k8FjOLWlVVJkmjv81CmfY7H/+49Prr3sTotClTmBgEgL5kEhhsJZMvXWrasvcns3k4CPOQjEOB8DhwgLm13jz6qHTyydKFF0oXXWQW5CsqpNde8zaOfJMtHn3UfO988IPS//2/JvkQCAo/VewvVGurNHSoub62tZniA2VlZqwHeM3G93xLi/T449JVV0VjPp97o8ghaa+L4cOHZz1+55139Pbbb+uEE07I+Ri7ulQYGzFihBOhdYtt9+7deR8j19iGDx+uP//5zwWfy63PYMyYMRozZowjx7JmzpzgZ4HfcYe0aFHPf+f1AJhMcwBOCdKChh9kdjcxOYAgsrHJwO9iMVMhrGvlMMAvvE4Wr66WVqwwE+P5ftclEqYy4LnnSm++6U58bmprsx0BAPjfggX2kvb621DbWRA2DzMOBcKFsWTf2trMdXzDBrNONHmytH+/7ahy8+yzJt4JE6Tvfpd1GfifXyr2F+uqq0w77c6JUrGY+V2sqjqWAAx4wdb3/IwZ0r59ds7tJe6NIon2uF3E43HFurQNeOWVV/I6xssvv5z1+Mwzzyw6rp6O0/U8ucg1tmLP5dZnEAqZ3bhB1tuCmdcD4NGjyTQH4KyqKtsRBAel+RFkYdhl66TyclMZLNOiB/AbG603nn5a+stfCn9/PC59/vPOxeOlzZtNMggAoHe1tXbb5Oa6AcVmjLlgHAqEz+DBtiMIlo0bzb1HkGzdar4H5841rQoBvwrLht316/tvY11dTRtreMPW93wUEva4N4oskvZ68IEPfCDr8Y4dO/J6/4svvtjn8Qp19tlna8CAAR2Pd+3apQMHDuT8/v3792vPnj0djwcMGNBrMp1fP4PQCHo713XrTIWarrweAM+aRaY5AGf5fUHDb1pbbUcA5C8su2ydkkya3bpMBsDPbLTeeOstacQIc78xbZq0cGH+iWxBHleEZXEDANy0bJlZWLEh1wq0ft48zDgUCKeyMlMlBuFXXy+NH2/mWQA/8rpiv00bN5JMC2/wPe8O7o0ijaS9HlR0KSG7efPmnN/79ttvK5VK9Xm8QpWWlmrcuHEFx7Zp06asx2eeeaZKS0t7fG0xn4EkPfbYY30eL/Js78Yt1pEj0syZ3Qd/Xg+AX3rJ2/MBCD8/L2j4US/jCMDXSEQxqqvNJoy6OjZBwP9sttjqbfd6e7tpY7Vnj/n5l79kP25vN68fPdpe7MWI0uIGABQqHjeti0aM8P7cW7aY75pc+G3zMONQINxKSkzLRkRDc7M0ZQqJe/AfGxX7/YBkWriN73lncW8EkbTXo5qamqzH69aty/m9Gzdu1JEjRzoen3/++TrxxBOdCq2o2Lq+dvr06b2+9mMf+1hWVb/Gxsacq/odOHBAWzsNhAYOHKiPfexjOccZGTZ34zql8+DPxgC4sTH3CUoAyJXfFjT8KhaThg61HQWQvygnoowcaaqFNTWZUvszZtiOCMiNn1psZXavH3ecNHy4ScobPlwaODD7cTwunXeetHu37YgLw70WAOQmkbDTimz/fumii3I7tx82Dw8cKN10E+NQICqqqmxHAC+1tEg1NVT3gr/YqNjvFyTTwm18zxcuFjvW0YN7I/wvkvZ68PGPf1xDhgzpeLx582b98Y9/zOm9y5cvz3p85ZVXOhlat+P913/9l44ePdrv+44ePapf//rXOcc2atQoXXzxxR2P29raVF9fn1OMdXV1Onz4cMfj6upqjRw5Mqf3RkpmN27QS8hmBn+PP+79ALilRTp40NtzAgg/PyxoBEFlpdlVBQRJVHfZZvzN30iLFklUwUbQ+LH1Rtfqf10T3FpapKef9i4ep3GvBQC5O/NMO+fdvDn3Nmi2Nw9PnSp9//uMQ4GoYF4tepqbpfnzbUcBHGOzYr8fkEwLN/E9X7j166U1a5ijRxaS9npw/PHH66qrrsp6bsmSJf2+b/v27br//vs7Hg8cOFDJZNLR2CZPnqzTTjut4/Grr77aLRmvJ7/+9a/12muvdTweN26cLrrooj7f89nPfjbr8b/+67+qtbW1z/e0trbq3/7t37Keu/baa/uNL7ISCXNxDnrFvZYW6a/+ys65+/n/JAAUZNmy4Laz8wq7qRBEUd5lKzGhguCi9YYd3GsBQG5sV4TNpQ1aPG6qOdjC/SMQLYmEdOGFtqOA1+rrTYs/wA9sj8/8gGRauCWRkCZPth1FMN15p+0I4EMk7fXi29/+tgYNGtTxePny5frtb3/b6+vfffddXXfddWrrlLn/13/91xo3blyf5ykpKcn601+72wEDBujmm2/Oeu4b3/iGXnrppV7f89JLL+nrX/961nPf+9739J739P0//7XXXquzzz674/Hzzz+vhf1M7tx00016/vnnOx5/8IMf1Ny5c/t8T+QlElIqJTmc4Om5N96wc97SUjvnBRBu8bh0+eW2o/A3kn8QRFHeZVtdze49BBuL/d7jXgsAcuOHirC5tEG76y7v4umK+0cgWlIpaft2uzEMH067ORuWLrUdAWD4YXzmByTTwi0LFtiOIJi2bLEdAXyIpL1enH766brhhhuynrvqqqv04x//OCsxT5Kee+45XXrppdq0aVPHc/F4XN/61rdciW3u3Lm64IILOh7v3btXkyZN0u9+97tur129erUmTpyolk4VRSZNmqTZs2f3e54BAwboX/7lX1TSqfXdrbfeqi9/+ctKdymnu2fPHn3pS1/Sv/7rv3Y8V1JSoh/+8IcaMGBAXv++SIrHpbo6acUKs6Caj+pq6VOfcicuv4vFpKFDbUcBIKz6SIiPPJJ/EFRR3mXLRAqCjsV+b3GvBQC580tF2L7aoDU1SRs3eh+TxP0jEDWplGmJvXevvRjKy801r6GhsDUXFG7DBmnbNttRAP4Zn/kBybRwQ22tvS58QdbYKLW3244CPkPSXh9uueUWTZ8+vePx4cOHNW/ePL3vfe/T9OnT9alPfUof/vCHde6552Yl7A0ePFj333+/xo4d60pc73nPe3T//ffr/e9/f8dzr7/+uj7+8Y/rrLPO0pVXXqkrrrhCZ555pmpqavRGpwpop556qu67776sRLy+zJw5U9/73veynvuP//gPve9979NHP/pRzZkzR1OnTtX73/9+/exnP8t63S233KKampoi/qURVFtr2uU2NZmWFdOmdd8JEouZ5xcuNK9bv176x3+0E69tlZVm4A0ATmtvl7ZutR2Ff+WQ/A/4UlR32SaTVBhA8NF6w1vcawFAfvxSEba3Nmh33OF9LBlsHgGiI52Wpk83ScS2JJMmcTCRMI+7rrlMmWIvtqiw+Z0DdOaX8ZltJNPCDamU1Ck/BjlqaZEOHrQdBXyGpL0+DBgwQHfffXe3qnS7du3SqlWrdM8996ixsVHtnbJhx4wZowcffFCTXV5MGDt2rNasWaPzzz8/6/kXXnhBDzzwgB588EHt2LEj6+8mTJigNWvW6MQTT8zrXAsXLtT3v//9rHbBhw4d0rp163TnnXdq/fr1OnToUMffDRo0SLfccov+4R/+oYB/GSSZ3aeLFklr1pgb3f37pd27zc902jy/aNGxXapRXcBiwA3ALQcO2J1g9LtFi3qu3gD4XRR32ZaXS7fdZjsKwBks+nuHey0AyI+fKsL21AbNVhsmNo8A0TJvnkketqG62lz76upMZ6OuMmsuv/2t97FFDa3/4Bd+Gp/ZRjItnJSpqtupcJSjysrcOa5frFhhOwL4DEl7/Rg6dKjuvPNO3XPPPbrwwgt7fd3IkSP1la98Rdu2bfOsutxZZ52lJ554QosXL9bpp5/e6+vGjRunxYsX6/HHH9cZZ5xR0LluuukmPfHEE5o1a5YG99JWbPDgwZo1a5a2bNmiBSymOKekxHw5jRplfvZW7SCKnzkDbgBuaWuzHYG/9Va9AQiCKCWixGLSqlU9L1gAQVRbyz2AV/icASA/r7xiO4Jsndug2aokz+YRIFoaGrxNCjnuOOnCC7M7EuWSJBzVCvxeovUf/CKqBU96QjItnOJ2Vd2TTpIuuMCdY/tFMinNnUthDHQoaW9n5JSPnTt3auvWrWpubtbbb7+tk046SaeccoouuuiiXpPZvNLY2Kjt27er+X93MpWXl+uss85SZWWlo+dpaWnRpk2b9NprrymdTisej+vkk0/WpEmTFPPhzc4zzzyjikxFOknbtm3TueeeazEiFyWT0dktUV1tbsQBwA3790vDh9uOwv9WrDAJFECQNDVJ48fbjqJnY8aYie3du4s/Vnm5SdjLtAQCwiKdNr/Dtip4RAH3WkWJ1BwE4KLA/S5VV0sbN9qOIltTk6ksZev+dvNmk1ADIBq8ug5WVkqPPCINHdp7gYP+TJsmrV3rbFzItn9/+CslwT3t7aYTTlubNHhw3wVN+tPQIM2c6Wx8QRSLmfmUQj9HIMPtXIRzzpH++Ef3ju8nzN/7is05iIGenCVETjvtNJ122mm2w+hRZWWl4wl6PYnFYqplgd6fli0ziytRWMCKYmVBAN7J7LqlRW7fli4laQ/Bk9ll67dF1WTyWCWS+fNNW7Nij0WFPYRRPG4mtKZM4XvaLdxrAUB+mpr8N7aUzGLaokX2KskX2PEFQAB5eR1sbJReftkkJReqqoqkPbe1tpK0h/w0NZmxy5YtpkJw5/v9WEyaMMH87iaT+f3+Zyr2R6XgSW9aWqSDB/m9RHG8qKoblYQ9yeRzTJlicjtI3Is02uMCYZJZwPJhxUNHJZO5lboHgEKVlJiJAPRtwwZp2zbbUQD581NCSnW1mfCoqzNjuXjc/PeKFebvijkWEFaJhJnQKi+3HUn4cK8FAPnz6yJwpg2are4wpaV2zgvAe15fB4s935w5zsSB3vEdgFw1NJj5rPHjpcWLTUJt1w16LS3m+cWLzXxAdbW0cmXu51i2jPkDySTTAsVYssR2BOHT0iLV1NAqN+JI2gPCxi8LWCee6M5xy8uPVaEBADdVVdmOIBj8ukAF9CWzy9ZN555rWu503UwRi5nnFy40u4jXr+85Qaa21vxdU5N5bTHHAsIokZBSKZNkBmdwrwUAhckkx/lNY6NpL5epJO+lWMy0rgQQDV5fB4s9X6YCP9zBdwBykU6b+/mZM/Ov1Llxo5k3mzs3t0SXqBQ86Q/JtCiGX6uLh0Fzs+m8g8giaQ8IIz8sYD3wgPMD4FjMDKypHAPAC+y6zY1fF6iA/ri5y7a83CTQrVljJg/375d27zY/02nz/KJFubXzqKgwr3XiWEDYZCpT/vjHtiMJPu61AKAw7e2mhZsfZdqg2agkX1lpzgsg/GxcBzNJyblobzf3z3v2mJ+Z9/mpAn/Y8B2A/qRSprJesZvB6+vNcZqa+n+tXwqe2EIyLYpF8QZ31debyqOIJJL2gLDKLGD9939LAwd6e+5YTLrgAmcHwJnFb3q6A/AKu25zk89EKeAnbu2y7Zr4UlJiqpuMGmV+FjNx7eSxgDB57TXbEQQb91oAULgDB7q3cPOTTBs0ryvJU7keiA4b18FMUnJvOlesj8el4cOl0aPNz3jcPP/YY9L06d7FHCV8B6AvqZQ0daqpLOWE5mZpypTcE/dsFzyxhWRaFIviDe5butR2BLCEpD0g7GbONANWL2UGf04NgJNJcxwWkQB4jV23/etvohTwM6d32ZL4AtjBxGHhuNcCgOK0tdmOoG+ZNmheV5Kncj0QHbaug5mk5M4aGqTqalN5a/Fiae3a7gmFLS3m+cWLpYcekgYP9ibeKOE7AL1Jp02yrNOJvi0tUk1N7q1y6+qkFSvM9SIqSKZFMfxcXTxMNmyQtm2zHQUsIGkPiAKbu2mLGQBXV5sb7bo62jQBsKO2lommXPQ0UQoEBZsMgGCL+sTh4MGmPTD3WgBgh5+TPTq3QXvlFe/OW10tVVR4dz4Adtm6DmaSkiWTqJNMmgIGGzfmdxy/J18HzcSJfAegd/PmOVdhr6vmZmn+/NxfX1trNt5mKnNeemm4K9GxxoFi+L26eJjQhjiSSNoDosAPu2m7DoCnTeveji4WM88vXGhet369NGOGNzEDQG+WLcueCER3/X0+7e3S/v3Snj3mJ+104TdsMgCCK8oTh+95j/T730tf/Sr3WgBgS1lZ92uuX3Rug7ZkiXfnpWI9EC02roOdk5JTKVNZj0Vuf3j++dzalCJ6Ghrc/z2trzfnyUdFhbRokbm3vuQSd+LqzYgR3pyHDRUoFgnu3qGbSCQNtB0AAA8kEtLkyfnvMitEf4O/zABYMkkbBw+aCk2lpeZGO8w7WQAE08iR5hpFNbmedZ4o7aypyUzEbNliKiB1TqiIxaQJE0xl1mSSSQP4R22t+bNt27H//zY2dv//b2Wl+f/vnDn8/xewLaoTh4MHm0WFyZOPPce9FgB4r6TE3NusXWs7ku4ynTCamryZE8x4//u9OxcA+2xcBzNJyamUNHVqdDfx+NHevdKUKWaTEJ0I0JlXGwiWLjVze4WoqvL2WnbZZdLdd7t/HjZUoFh+ri4eNo2NZk6PObxIodIeEBVeDcryOU9JidmJN2qU+ckXEAA/OnDAVIdDzzpXb5DMbsbqarPLefFiM9HRdfK0pcU8v3ixmcCrrpZWrvQ2bqAvmcSXNWtMm539+6Xdu83PdNo8v2gRCXuAH0Rx4jAel/7nf7IT9rriXgsAvJNJjvObTCcMr6tPUe0KiB6vr4NVVebefPp0dxL2Bg1y/phR0tIi1dSY/40AydsNBBs2mM24hfC6a9k//ZP750wmqbKP4vm5unjYtLSYTbiIFJL2gKiorWXwBwCFiGoFn1x96ENm5086bb4HZs7MfxJm40bzPTV3LhN68B8SXwB/i9rEYTJpWk5RtQIA/MPrBd5cdO6E4XWLJVo6AdHj9XVwzhxp3jypudmd4x8+bNY5PvUp79pXhk1zszR/vu0o4BdB2UCQ6VrmhcxYbdkyqbzcnXOUl0u33ebOsREtmaq68AZdvyKHpD0gShj8AUD+oljBJx8//KGZwCwvL34Cpr7eVOhranIkNABABERp4nDoUOnGG02lPQCAf3i5wJurTCeM9nZp61Zvz51p6QQgOrxOdHn5ZfeTgFaulD77WVPxJpWS/v7vTdtXkvhyV19vOnIAQdpA4HXXsnhcWrXK+c2IsZg5LvMHcIpfq4uHUWmp7QjgMZL2gChh8AcA+YtaBZ9C7N/vXEXC5mYzCUriHgAgV1GZODx4kDZTAOBXXi3w5qJzJ4wDB9xpHdkXWjoB0eRlosuSJd6ca+lS8zORkH7wA2ndOmnvXukb3/Dm/GGQ+QwRXUHbQGCja1kiIa1f71zRlfJyczwq9MNJfqwuHkaxmNm0i0ghaQ+IGgZ/AJCfKFXw8YuWFpISAAC5Saelp56yHYV3aDMFAP7kxQJvLrp2wnBqc1W+aOkERI9XiS7ve5+0caO758nYsEHati37uZIS6emnvTl/GPT0GSJagriBwEbXskTCVPVMJos7fjJpjsOaLZzmx+riYVRZacYaiBSS9oAoYvAHAPmJSgUfPyEpAQDQn1TKtFV/6CHbkXiLNlMA4E9uLvDmoqdOGIMH24mFlk5ANHmR6OJ2W9yuup7PRtWwoPP6fzP4SxA3ENjqWhaPS3V10ooVphV4PqqrzTxBXR1d0eAeP1UXDyvWIiOJpD0gqhj8AUDu/FAxIYpISgAA9CaVkqZONUneUUSbKQDwH7cWeHPRWyeMsjLv46GlExBdXiS6bNni7LH70/V8NqqGBZ3X/5vBX4K6gcBm17LaWvPapiZp4UJp2rTu19VYzDy/cKF53fr12S13ATf4pbp4mPH5RhJJe0DUMfgDgP5R+tsekhIAAF2l09L06dFeLKPNFAD4k9MLvLnoqxNGSYk0YYJ3sUi0dAKizs1EFxtV7hobzXkzbFUNC7KunyGiJcgbCGx3LauokBYtktasMfMg+/dLu3ebn+m0eX7RIvM6wCu2q4uHWXU1v88RRdIeAIPBHwD0jdLfdpCUAADoat686FbY64w2UwDgT04t8PYn104YXrdYoqUTALcSXWxUuWtpkQ4ePPbYVtWwIOv6GSJagr6BwC9dy0pKTALkqFHmJxskYIvN6uJhxxpkZJG0B6A7Bn8A0B2lv+0hKQEAkNHQwPdCBm2mAMC/ilngfd/7pPPOc64Thtf3sdw3A5DcSXSxVeWutfXYf5eVSSNG2IkjyDp/hoieMGwgoGsZcEwiIf3sZ9J7SDVyTDLJ9SLCBtoOAAAAIDCWLTM321T38RZJCQCAjCVLbEfgH5k2U2yyAgD/qq01f7ZtM0nnW7aY63fnSlGxmKkIU1VlEt4yXS7a201lotZWqbTUtHkr5JqfSEiTJ0sbNzrzb+oLLZ0AdFXMdbArW1XuSkuP/XdJiTR+vOkMgdx1/gwRPXPmSIsXe3s+t2S6lknOjdWAoEmlpC9+UfrLX2xHEg7l5dJtt9mOAhaRtAcAAJCrTOnvKVO8b8cRZSQlAAAks1Pdi4SDoMi0mSorsx0JAKA/hSzwZjphOHGdX7DAm+9QWjoB6I0TiS5lZSbBz8s5uVjMxNfZhz9M0l4+evoMES1h3UDg5FgNCIp0Wpo+PZrrY8OGmeqC+/Y5d8xYzKw5FttCG4FGzUoAAIB8JBKm2l55ue1IoiOTlAAAiDba4nZHmykACJ7MAu+oUeanG5uT2tul/fulPXvMzxkz3G9bS0snALkq9DpYUiJNmOBubF1VVnaP79prvY0h6Hr6DBE9XiX2s4EAcNe8edHtRFVVZZL2nVobLC83a42JhDPHQ2CRtAcAAJCvRMKUAE8mbUcSHSQlAABol94dbaYAABlNTdLChdK0aaZSw/Dh0ujR5mc8Lr32mnuVjmjpBMArVVX2zzd+vDRihLdxBJnX/5vBn2pr2UAABF1DQ7Q31FZVObc2mEya45CwB5G0BwAAUJh4XKqrk1asMGX34S6SEgAg2trbpa1bbUfhL7SZAgBIZvGsutokkSxeLK1d271dVUuLqQrhRgVzWjoB8JLbST+5nu9jH/M2jiDz+n8z+NeyZe51r3FrA0HX6sXt7c6fAwiKJUtsR2BX5vusmLXB6mpz/1ZXx/0TOpC0BwAAUIzaWlPCuqlJ+sIXpIEDbUcUPiQlAAAOHOiegBB1tJkCgGhLp02FhpkzpY0b7cRASycAXkskpMmTvTlXdbVUUdHz3/3TP3kTQ9D19RkieuJxk+gfizl7XKc3EPRXvXjaNPP327Y5cz4gCJqa7N1z+EFP32ed1wYz14yu17dY7Ng1o6nJvJ6KoOiCVWUAAAAnjB0rrVwpHTliO5LwISkBANDWZjsC/6HNFABEVyolTZ8uNTfbiyGZNBVtqBABwGsLFniTOLBgQe9/l0hIF14oPf64+3EEWV+fIaIpkTBJKzU1zoxjystNwp4TGwgaGkwlsb6uLy0tpqrx2rWmwvHkydKNN5KEg/CLcltcqe/vs4oKadEi89/t7aa6eWur6R41dChrW+gXlfYAAACcMG+e3QWTMCMpAQAweLDtCPyHNlMAEE2plDR1qr37T1o6AbCtttb9sXAy2X8Szj/+o7sxBF0unyGiKZEw45lksrjjJJPmOMUm7BVTvXjjRnNNmjvXHAcIqy1bbEdgTz7fZyUlUlmZNGqU+UnCHnJA0h4AAECxGhrYaeQmkhIAAGVlzrfQCTLaTAFANKXTpsKeGy3jhw413y+0dAIQBMuWmQpbbigvN5VE++NF8mBQ5foZIrricbMBYMUKM/7Ih5MbCFIpafz44uf26+vNcZqaijsO4Eft7dLWrbajsIPvM3iA9rgAAADFWrLEdgThRVICAEAyO1MnTDAtaECbKQCIKjcrvB88KL33vdK6dbR0AuB/8bhpiTllirOJzLGYOW4uiUDptLlWIls+nyFQW2v+bNtmEue2bJEaG7N/r2MxqbLSdGOZM8e5ueJM9WKnriHNzeaatH69M+16AT9ob5dee82dTUN+x/cZPELSHgAAQDGamvIvm4/ckZQAAMioqiJpT6LNFABElRcV3uvrzfdMba2pcgsAfpZImOSYmhpnEprLy83ifC7JNqmUqXxqq1W5X+XzGQKdVVRIixaZ/25vd38DgVvVi1tazDUplSLRB8HV1HQsiXbr1mgm7PF9Bg/RHhcAAKAYtMV1D0kJAIDOaD1FWw4AiDKvKrwvXerNeQDACYmESY5JJos7TjJpjpNrwt7UqSTsdZXPZwj0paTEbB4YNcr8dKPir5vVi5ubpfnz3Tk24KaGBtP5aPx4afFis3E2igl7Q4aY6uN8n8EjJO0BAAAUY8sW2xGEE0kJAICuEglp8mTbUdhDWw4AiC4vK7xv2GBa1AFAUMTjUl2dtGKFSTbIR3W1SVKoq8u9Ja4b1bmC7lOfyv0zBGzzqnpxQ4O75wCckk6bxOuZM+kqJUmHDknf/rbtKBAhJO0BAAAUqr3dlAeHs0hKAAD0Jqpt08vLTesvdvkCQDR5XeGdivIAgqi21oyZm5qkhQuladPMHFNnsZh5fuFC87r16/Pr8uBmda4g27vXdgRA7qheDByTSpnKeoz/s5F4Cw8NtB0AAABAYB04wM5apw0eLH3veyQlAAB6Vltr2uRGaTIxmTTVZ0lmB4Do8rrCOxXlAQRZRYW0aJH57/Z26eBBqbVVKi2Vhg4tvNWmF9W5gqqx0XzWbrQxBZxko3pxRYU35wPylWn3zhpXz5YuNfOQgMuotAcAAFCotjbbEYRPW5v01a9Kc+easuwAAHS1bJmpPBd2+bbqAvD/2bv38KjLO///r0GaCCSEMZHq1ANCPWeCBRoQJUkVFQgVbbWWwXrer7tWsN1eV8Om7nroRRF2v/4qrG7d2q6tJbQWtF9LMBVQSbQWFFySQD1RQEs8hCGQBDGpMr8/7k7IDDnM4XOYw/NxXbmGzyfzue93hslh7nnf7zeQmdyo8B5OvgCAdOfxSPn5UlGRuU0mqcyp6lzpqK3NJEcCqY7qxYBBu/fBhRNvAZuRtAcAAJConBy3I4jfuHHSt77Vd4uQUaOkSZPMzmO31dSYsuxNTW5HAgBIBaGQ1N4u7dsnfe5z0rPPHvt7LN1E/x2RbKsuAEBmcqPCO8kXABDJyepc6aqry+0IgMFRvRgwaPceGxJv4QDa4wIAACQqP9+8wZ5Ou5F27pQOH5bq6kxp/k2bpF//Wtq2zXy89prbER7V0iKVl5ukBdrlAkD2aWoyi2ObN5sKQ71/344caVpbtbdLn33mXozJeO01acwYa1p1AQAyl1sV3ru6zGteAID0s5+5HUHqS4VNwMBA3KxezGt9pBLavceOxFs4gKQ9AACARHk80plnpt8f7i0t0tSp0llnOb9QEa+2NmnGDKmxkdaAAJAtamtN66mBKlm0t5uPdFVWdjQhnYQIAMBA3KrwTvIFABiNjdJPfuJ2FKktvAkJSGVuVi/mdT9SCe3eY0fiLRxAe1wAAIBEBYPSjh1uR5GYzs7UT9gLa2mRFixwOwoAQLx6t7Rtb5eOHIk8DoUi7x8MSoGANHt25reeCgaltWvdjgIAkA7CFd6d5PWSfAEAkknYq6ig9etgurulyy6Tqqul5ma3owH65mb1YiBV0O49PuHEW8BGJO0BAAAkav58/mB3Sk2NqbwEAEhtTU3mjZrp002F1IIC6cQTze3QoZHHhYXmftXV0urVUklJ9rTn2L5dqqyU5s0zCXwAAPTH45EmTHB2zokTqSYBAMGgNHOm85W50lEoJG3YIC1ebCqKl5WxSQmph+rFSCfRG2GjN74mKlvW3axE4i1sRntcAACARNTW8gLHaUuXmgQHAEBqCYWkVaukBx+U/vSnge/XW1ubeWNnwwZ740tlNTXSiy9KdXVH2+UCABCttNTZ35elpc7NBQCpav580/0B8WtoMB+BgLRsmdmwBbgtXL3YyURcqhcjHk1N5j2nzZtNl6Tez1Wv12zkKS01P1uLixObY/Nma2LNJiTewmZU2gMAAEjEkiVuR5B96utpsQEAqSJcUa+8XDr+eOkb3xg4YQ/9a2kxj2NTk9uRAABS1dy5mT0fAKQaNutao6bGVFTntQ5SAdWLkapqa02F0pISU7F0w4Zjk0vDG1+TqWgaCplkQMSOxFs4gKQ9AACAeDU1md2icB4LpgDgruiFxPp6qbvb7ajSX1ubNGMGrXIBAH3z+6Vp05yZq6ws8codAJAp2KxrHTYpIZU4XU2Y6sUYSDBoqubNnh3/+00NDaYr0bx5sa8ldXTQ8j1eJN7CASTtAQAAxIvEMfdQvh0A3JHMQiJi09IiLVjgdhQAgFRVVZVZ8wBAqmKzrvXYpIRUQfVipIrGRrMhNtn3muKpaMqm2/iReAsHkLQHAAAQLxLH3LNliynjDgBwjlULiRhcTY2pZggAQLTKSvvf+A0EpFmz7J0DAFIdr3vswSYlpAKqFyMVNDZKFRXm56IVYq1ompNjzXzZhMRbOICkPQAAgHiEQtLWrW5Hkb3a2qTOTrejAIDsYfVCIga3dKnbEQAAUtXy5ZLPZ8/YPp+0bJk9YwNAOmGzrn3YpIRUQPViuCkYlGbOtL5N7WAVTZuapMWLpaFDrZ03k5F4C4eQtAcAABCPjg7rX1AhPl1dbkcAANnBroVEDKy+XmpudjsKAEAqKiyU6uokr9facb1eM25hobXjAkC6YbOu/dikBLc5Ub1Ykh54QFq71v55kF7mz7dvY2xfFU1ra03yWUmJeU5++qk9c2ciEm/hEJL2AAAA4tHd7XYEyM11OwIAyA52LiRiYLTkAgD0x++XNm60ruKez2fG8/utGQ8A0hmbde3HJiWkAjurF4c1NJgEwXnz+q9+huxSW2v/ek+4omkwKAUC0uzZ5rmI+AQC0qxZbkeBLEH9SwAAgHjk5LgdQXbzeqW8PLejAIDM58RCIvpHSy7AMn/+85+1fft27d27V93d3fL5fBo7dqwmT56sIUPc2c/c0dGhHTt26M0331QwGNTHH3+skSNHqrCwUBdccIHOPfdceTweV2JDmvD7TQv7BQvMG3OJCgRMS1wq7AGAwWZdZ6xcKS1a5HYUyGbh6sXl5fYn6tbUSC++aOZjk0R2W7LEmXn+7d+kDz5gI26ifD7zGglwCEl7AAAA8cjPN4lj7Lp1x8SJEm9gAoD9nFpIRN+2bDGtufidByQkFArppz/9qR5++GE1Njb2eR+fz6cbbrhBd999t0aMGGFrPEeOHNHLL7+sZ555Ri+88IJef/11HTlypN/7n3DCCbrhhhs0f/58jR07Nq65Hn/8cd18880Jx1peXq4XX3wx4evhoMJCacUKk3i3dKmpXBSrsjLT7onqEQAQic26zmCTElJBuHrxjBn2Jze1tJgEQaobZ6+mJucq3tHmPXFer0mwZVMTHER7XAAAgHh4PNKECW5Hkb1KS92OAAAyn5MLiehbW5vU2el2FEBa+vDDD3X55Zfr9ttv7zdhT5JaWlr0wAMPaPz48Xrttddsi2fPnj065ZRTVFZWpv/4j//Qli1bBkzYk6T9+/frxz/+sc4//3w9+OCDtsWGDFFZad4AbmqSqqul6dPNm029eb3mfHW1ud/GjSTsAUBfwpt1Ya8XX5T+5V9okwv3hasXBwL2z9XWZhIEaZWbnehmkfp8PhJr4QqS9gAAAOJF4ph75s51OwIAyHwsJKaGri63IwDSzqFDhzRr1iytX78+4vwpp5yiWbNm6Wtf+5rOP//8iM/t3LlTl19+ud58801bYuro6ND7779/zPnjjjtOJSUlmjFjhgKBgCorK+Xz+SLu88knn+h73/ue7rrrLltiQ4YpLjatBtetM28Gt7dLra3mNhg05xctMvcDAPSNzbrO+PRT6YEHTGJEWZm0dq3bESGbhasXr1ljno92ammRFiywdw6kJiqMprZAwCTwkrAHF9AeFwAAIF5z50qLF7sdRfYpK+MNJgBwAguJqSE31+0IgLRz0003aWuvVkD5+fl69NFHdd1112nIkKN7lzdt2qQbb7yxJ1Gvra1NlZWVampq0rBhw2yLLzc3V3PmzNH111+v8vJyjRw58pj7PP/88/rOd76jpqamnnPLli2T3+/XbbfdFvec//7v/65rrrkm5vsff/zxcc+BFOTxmGpR+fluRwIA6ae0VNqwwe0oskdDg/kIBKRly2hJCPdUVprb+np756mpMc/38HzIfKEQLWtT2cMPS3fc4XYUyGJU2gMAAIiX3y9Nm+Z2FNmnqsrtCAAg87GQmBq8Xikvz+0ogLTy0ksvadWqVT3HOTk5ev755zV37tyIhD1Jmjx5sl5++WWNGzeu59zOnTv10EMP2RJbfn6+7r77brW0tOg3v/mNvvrVr/aZsCdJl1xyiTZt2qTy8vKI8wsXLlRnAm2zi4qKNGbMmJg/TjrppIS+RkQJhUyFu337zG0o5HZEAIBY0eXBHTU1UkmJaeMOuGXJEmfmWbrUmXmQGjo6THtkpKa9e92OAFmOpD0AAIBEkEDmrBNPdDsCAMgOLCSmhokTTZUkADH7wQ9+EHFcXV2tSZMm9Xv/wsJCPfbYYxHnlixZovb2dkvjOv3007V792798Ic/1AknnBDTNcOGDdPKlSuV1yt5NxgM6tlnn7U0NlisqUmqrpamTzdVggoKzOuYggJzPH26+Xxzs9uRAgAGwmZd97S0SOXlJO7BHU1NpuqjE+rr+Zswm3R3ux0BBkLHEbiMpD0AAIBEVFay89ZJra3mMZ83TwoG3Y4GADIXC4mpobTU7QiAtLJnzx7V92pjNWzYMC1YsGDQ6yoqKlTa6/vtwIEDeuaZZyyNLT8/P+Zkvd5OPvlkfe1rX4s498ILL1gVFqxUWyuVlZnqQIsXm5aK0QnwbW3m/OLFJhmkrExau9adeAEAg2Ozrnva2qQZMyLX/6hgCyesXJnZ88E9OTluR4CBbNnC7xW4iqQ9AACARC1bRhUcp9EqAwDsxUJiamBjABCXp59+OuL4qquuktfrjenam2++OeL4qaeesiyuZH3pS1+KOG5paXEpEvQpGJQCAWn27PirsjQ0sCkJAFLZqadKn/uc21Fkr5YW6YYbqGALZzldbYvqXtkjP1+K8fUpXNDWJnV2uh0FshhJewAAAInKyWEHjhtolQEA9snPl0aNcjuK7FZWJhUXux0FkFbq6uoijisqKmK+Nvq+zz33nI4cOWJBVMkbOnRoxHE31VBTR2Oj2UyUbIUUNiUBQOoJBqWZM6W//c3tSLLb2rVUsIVzQiFp61Zn56S6V/bweKQJE9yOAgPp6nI7AmQxkvYAAAASxZtm7umrVQYAIHkej3nTA+6hFRcQt+aoCisXXnhhzNeec845Ee1rDx06pN27d1sVWlLeeeediOOTTz7ZpUgQobFRqqgwm4mswKYkAEgt8+db9zMezqCCLZLV0XFscqjdqO6VXUpL3Y4AA8nNdTsCZDGS9gAAABJFC0F3tbRICxa4HQUAZJ4vf9ntCLJXICDNmuV2FEBaaW9v1969eyPOjRs3Lq4xxo4dG3G8Y8eOpONK1pEjR45p+1uawBs9q1evVmVlpU4//XQNHz5cI0aM0GmnnaYLL7xQ3/ve91RXV6cQFT5iF66+ZPWbumxKAoDUUFubfBVVuIcKtkiUW5vzqe6VPebOdTsC9MfrlfLy3I4CWYykPQAAgETl55s/6OGemhqzoAoAsM6NN7odQXby+aRly9yOAkg70dXoioqKNHz48LjGOO200yKO33777aTjStbq1av17rvv9hwfd9xx+upXvxr3OGvWrNHatWv17rvv6vDhw/r444/13nvv6U9/+pMefPBBzZw5U+eee65+85vfWBl+5rKz+hKbkgAgPqGQ1N4u7dtnbq1IQl+yJPkx4C4q2CIRbm3Op7pX9vD7pWnT3I4CfZk40XQeAVxC0h4AAECiPB5pwgS3o8DSpW5HAACZpaREGjXK7Siyi9cr1dVJhYVuRwKknQMHDkQcjx49Ou4xoq85ePBgMiEl7eDBg/rnf/7niHPz5s2Tz+ezZb4333xT3/zmN3XLLbeoi2of/XOi+hKbkgBgYE1NUnW1NH26+du5oEA68URzW1hozldXS83NiY3d0GB9zHAeFWwRLzc251PdK/tUVbkdAfpC62K4bKjbAQAAAKS10lJpwwa3o8hu9fVmMba42O1IACBzXHaZ9Nvfuh1FdvD5TMKe3+92JEBa6uzsjDgeNmxY3GNEX9PR0ZFUTMkIhUK66aab9Ne//rXnXEFBgRYvXhzXOEVFRZoxY4amTp2q8847T0VFRcrJydH+/fvV1NSkP/zhD3r66af12Wef9VzzP//zP/rkk0+0YsUKeSysNPDRRx+ptbU1rmuiKyimBKeqLy1dKlVWOjMXAKSL2lrzc3igpLq2NrNGt2GDtHixqWi0cKE0a1Zsc9AWN7OEK9iuWOF2JEgH4c35Tq7zU90ruzQ1SS+/LH3+89KHH7odDXqjdTFcRtIeAABAMiZOdDsCSGZhddEit6MAgMzxr/9K0p4TAgHTEpcKe0DCopP2jj/++LjHiE7aix7TSffcc49+97vfRZx75JFHYq6yd9ZZZ2nVqlWaM2eOhg7te+l38uTJuu222/T2229r3rx5evXVV3s+t3LlSk2ZMkULLGzT+sgjj+i+++6zbDxXOFl9iU1JAHBUMGhakyeSUNfQYD5i/Zt78+bEYkTqqqkx//8kwyMWTm/Op7pXdogl6RzuKSvjdRdcR3tcAACARDU2Sv/wD25HAYmFVQCwmt8vTZnidhSZq6zMLNyuWEHCHtLenXfeKY/HY/vHvffeG1M8iVSIs7KqXDJ+9rOf6Yc//GHEuTvuuEOBQCDmMaZOnaqvf/3r/Sbs9XbmmWeqoaFBF110UcT5+++/X+3t7THPmRWcrr5EtScAMOtuJSXJ/0ysqTHjNDX1f59QSNq6Nbl5kJqWLnU7AqQLp6ttUd0rswWDJml49mwS9lIZLYuRAkjaAwAASEQwKM2caVpvwH2bN5sFVgCAde6+2+0IMtOUKdLGjbG36QIwoLy8vIjjw4cPxz1G9DXRYzph9erVuv322yPOXXvttVq2bJmt8+bm5urJJ5+MqDYYDAb15JNP2jpv2nF6kxCbkgBku8ZGqaLCtDi1QkuLVF7ef+JeRwdrfJkqXMEWiBYKSe3t0r595ra42LTVdgLVvTKbVUnnsFcgwNocUgLtcQEAABIxf751C4dIXnu7tGkTVaEAwEqVlWbnN4uM1nrzTfPmQIpU9gLSXSYk7dXW1ioQCOizzz7rOffVr35VK1as0HHHHWf7/D6fTzfccIMeffTRnnN1dXW67bbbLBn/jjvu0LXXXhvXNe+8846uuuoqS+ZPmhvVl7Zs4XcFgOxl10bZtjZpxgyTTBFd7bq729q5kFpWrpQWLXI7CqSCpibzfNi82fx91/vnjNcrnXaaM3FQ3StzhZPOSQRPbT6fZPMGOSBWJO0BAADEq7aWBIZUdPXVZucsbQYBwDrLl5uqcCSqW6etTerslPLz3Y4EsMScOXN0yimn2D7PxRdf3Of5goKCiOPW1ta4x/7oo48ijkeNGhX3GIlav369rrnmGnX3Sha44oor9Nvf/laf+9znHItjxowZEUl7jY2Nlo09evRojR492rLxHOdG9SV+VwDIZnZulG1pkRYskFasiDyfk2PPfEgNVLBFba20ZMnAbUrb2pz5m4/qXpmL7kzpweuV6up4Hwkpg6Q9AACAeC1Z4nYE6MsHH/S98AoASFxhoVnIKi9n0dFKXV0kYiBjXHbZZbrssstcm//MM8+MOG5tbdXHH3+s4cOHxzzGnj17BhzTLhs3btScOXP0ySef9Jz7yle+oqefflq5ubmOxBA2ZsyYiONEkh8zllvVl/hdASAbObFRtqbGJM1UVh49l59v3sTnNU9mooJt9goGTSJwqmzAp7pXZqM7U+rz+cw6p9/vdiRAjyFuBwAAAJBWmpoG3pEHd9XUmAVeAMh2oZBpHb5vn7kNhRIfy+831fZ8Puviy3YOJ+MAmWzkyJHyRf182rlzZ1xj7Nq1K+L43HPPTTquwbz88suaPXu2Pv74455zF198sX7/+99r2LBhts8fLXrORNoMZyy3qi/xuwJANnJqo+zSpZHHHo80YYIzc8N54Qq2yC6NjVJJSeok7FHdK7PRnSn1BQLm5wIJe0gxJO0BAADEgxdeqS964RUAskVTk1RdLU2fbhaBCwqkE080t4WF5nx1tWklHi+/3yxsBQLWx51tvF4pL8/tKICMUlxcHHH8yiuvxHztG2+8oWAw2HM8fPhwnXHGGZbF1pdNmzZp5syZ6uz15vGUKVO0du1ajRgxwta5+7Nv376I46KiIlfiSEnh6ktO4ncFgGzk5EbZ+vpjXxeVljozN9zR1eV2BHBSY6NUUZE6Vc98PrMZkmShzEV3ptRVVmaSKlesIGkWKYmkPQAAgHhs3ux2BBhMfb300ktuRwEAzqmtNQtQJSXS4sXShg3HtnVqazPnFy82i8RlZdLatfHNU1hoFrjWrDHXx6OsTLrnnvivy0QTJ9KWCbDYjBkzIo5ffPHFmK+Nvu8VV1yhIUPsWzLdsmWLrrjiCnV0dPScmzRpkurq6pTvYivUTZs2RRxHVy/Mam5UX+J3BYBs5PRG2ej55s51dn44iwq22SMYlGbOTJ1211T3ynx0Z0pNY8aY/5uNG6VZs9yOBugXSXsAAACxCoWkrVvdjgKxmDnTvCADgEwWDJrF39mz418cbGiQKiulefPMOPGorDQLXr0r+0VXIPJ6j1b2Cy+Q3Xuvuf3P/4xvvkwTVREMQPKuvvrqiOPf/e53OnDgQEzXPv744wOOZaVt27bp8ssv18GDB3vOXXDBBXruuedUUFBg27yxWBmVuFBRUeFOIKnK6epLVHsCkI2c3igbPZ/fL02b5mwMcEZ0BdtQSGpvl/btM7ehkHuxwXrz56dGhT2qe2UPujOlpkCANTikBZL2AAAAYtXRkTo79DCwzk6pvJzEPQCZq7HRVNZLdmGwpsaMk8jPy+JiadEiad06k/jX3i61tprbYNCcX7To2AWy3/wmuZjTXU0Nv58Ai40ZM0bTer3JfvjwYT300EODXrdx48aICnOjRo3SlVdeaUuMO3bs0GWXXab9+/f3nPP7/Vq/fr28TrdejfKLX/xCr732WsS5OXPmuBRNinK6+hLVngBkGzc2ym7ZcmyyVlWVszHAGRMnmnbI4U1nJ5wgFRRIJ55obk844eims+i2yUgvtbXOJFBdcEFsmxep7pUd6M6UmnhNhTRB0h4AAECsurvdjgDxaGuTZsyIv4IUAKS6xkaposK6neMtLcknOns8Un6+VFRkbvtr6UfLEOmjj0gsB2zwox/96Jjj6ES03vbv369bb7014lxVVdWgFe92794tj8cT8bF79+4Br3n77bd16aWXqrW1tefcueeeq/Xr16vQwqobTz31lP7yl7/Edc2aNWt0++23R5y79NJLddFFF1kWV0ZwsvpSWRkVIQBkHzc2yra1mU2fvVVW8iZ/JnrzTbNZbfFiacMGKboi84ED5vzixeZ3flmZtHatG5EiWUuWODPPyJHxbV5E5qI7U2riNRXSCEl7AAAAscrJcTsCxKulRVqwwO0oAMA6waBpAW71G1pOJTrTMsQgsRyw3MUXX6xrrrmm57i7u1uXXnqpfv3rX+vIkSMR9920aZOmTp2qnTt39pwbN26cFtjwd+O7776rSy+9VB988EHPuc9//vP6+c9/ro8//li7d++O+eOvf/3rgHM988wzOvvss3XNNdfoySefjGjDG+2NN97QbbfdpiuvvFJdXV0950eOHBlTlcKs5FT1Jao8AchGbm2U7fU7sMfy5ZLP53wssM9778V3/4YGk8A5bx6v2dKJk5sE6+ul7dtj27yIzEZ3ptTEayqkkaFuBwAAAJA28vNNmXtehKWXmhopEDCLbQCQ7ubPt67CXrRwovOKFfaML9EypDcnHm8gyzz++OPauXOnXn/9dUlSe3u75s6dq+9///saP368cnJy9NZbb6k5qu2Z1+tVbW2thg8fbnlMzz//vN6LeqP4ww8/1IUXXhj3WKeffvqgVf0+/fRTrV69WqtXr5YknXHGGfriF7+ogoIC5eTkqK2tTdu3b9e77757zLXDhg3T008/rfPPPz/u2LJCuPqSnQnogQBt1ABkJ7c2yubmHnuusFCqqzPVsVkDzG41NdLzz0vPPWcq8CG1Ob1JcOVKU1UvWihkErm6u83PNhL6MhvdmVIPr6mQZkjaAwAAiJXHI02YYNolIL0sXUrSHoD0V1tr/yK0HYnO4QXrri5pyxbrxs0EJJYDlhoxYoTWrl2r66+/Xht6/c3+3nvvHZM4FzZu3DitXLlSZ599tlNhOmrXrl3atWvXoPf70pe+pF/96lc677zzHIgqjS1fLm3caE8Cvc8nLVtm/bgAkA7c2Cjr9Up5eX1/zu83P+9nzLBv0xTSwwcfSBdfLL30Eol7qc7pTYK952tqMus1mzebVqm9f5Z5veY9hdJS8/qflp2Zhe5MqYXXVEhDtMcFAACIR2mp2xEgEfX1UlRFFQBIO0uWODPP0qXJj9HUJFVXS9Onm0oVBQXS6NHSgQPJj51prHi8AfQ46aSTtG7dOv3kJz+Rf4A3Vk8++WRVVVVp27Zt+vKXv+xghPa58cYbdcstt+iss86SJ4ZqHrm5ubrsssv01FNP6dVXXyVhLxbh6kter7Xjer1m3MJCa8cFgHQR3ijrpIkTB65+5fdLjY0myQbZrb1duvRSWuWmslDIJMs5acsWac0aqaxMKimRFi82m/2jk4/b2sz5xYvNz5WyMmntWmdjhX3CSedwH6+pkKY8oVAo5HYQgJ22b9+u4l67Fpqbm2nzAQBIXFOTeRGO9FNd3XfLAgBIB07//mlqSmz3d22tSS5saLA+pkyW6OONlMMaROrZsWOHmpub1dLSou7ubvl8Po0dO1ZTpkzRkCGZu5+5s7NTO3bs0J49e/TBBx/o0KFD+vTTTzVy5Eh5vV6deeaZmjBhgnJStDJEyn8vNTVZV33J5zNvLlG9B0C2q642SS1OzhfrOlFtrdlsU19vb0xIbbNnS7//vdtRoC/t7WazYDoJBExFMBKM0t/06XRnchuvqZAkN9cgaI8boz//+c/avn279u7dG7HAN3nyZNcW+Do6OrRjxw69+eabCgaD+vjjjzVy5EgVFhbqggsu0LnnnhvTrloAABAHv1+aNs2ZZISyMunhh4+W1t+yJXKn3qhRVCyKh9MtEgDASna3xe1rvngSnYNBaf585+PMFPE+3gBidt5552VlBbm8vDyVlpaqlErh9ghXX1qwwLQ6TxRv1gLAUXPnOpu0N3du7PetrDQfzc3mb/eaGmn3bttCQ4pas8as1X77225Hgmjd3W5HEL+aGunFF0k0ygSlpSTtxSIvT+rsPHrs9ZoNyvv2Sdu3Jz4ur6mQ5kjaG0AoFNJPf/pTPfzww2psbOzzPj6fTzfccIPuvvtujRgxwtZ4jhw5opdfflnPPPOMXnjhBb3++us6cuRIv/c/4YQTdMMNN2j+/PkaO3ZsXHM9/vjjuvnmmxOOtby8XC+++GLC1wMAkNKqqpxJ2quqMlV3wm/ih0LmRU1Xl5SbKx0+LH3+8/bHkSm2bDGPIZsaAKQjpxOP45mvsVGaOdOaakPZisRyAEg/hYXSihXmTaJ4qy+VlZnXe7Nm2RcfAKQbpzfKJlLpOrxOt2kTSXvZasEC8/whySq1pGj16EG1tEjl5dLGjTyn0pnTSefp6KSTzPO99/tbeXlH36tJpKItr6mQITK3B0SSPvzwQ11++eW6/fbb+03Yk6SWlhY98MADGj9+vF577TXb4tmzZ49OOeUUlZWV6T/+4z+0ZcuWARP2JGn//v368Y9/rPPPP18PPvigbbEBAJB1Kivj242biEDg2BcbHo+Uny8VFZnbdNxB6Ka2tsidXACQLkIhaetWZ+cMJzoPprFRqqggYS9ZsT7eAIDUU1lp3mhtajKtFqdPN1UjevN6zfnqanO/jRt5cwkA+lJVlfrzuPH6DKnjyBHpy1+W3n7b7UjQW37+sX9/pYu2NmnGDNPBAOkpnHSO/n3zm8e+v9W7uAKvqZDFqLTXh0OHDmnWrFnaGvVH9ymnnKKSkhIdf/zxevPNN7W9V5nOnTt36vLLL9crr7yis88+2/KYOjo69P777x9z/rjjjtP5558vn8+nE044QQcPHtTrr7+ull5vmHzyySf63ve+pz179uihhx6yPDYAALLS8uXmRYEdSQo+nynnPZjcXOvnznRdXeYFIQCkk46OyPboTggnOg/0MzMYNBX2nI4tE8XyeAMAUttAVdJ7V5EAAPQvvFF25Ur75uhro2w83Hh9lk2GDpU+/dTtKAbW1SWNH28qLlIdLTV4PNKECenborSlxVRxXLHC7UiQKKe6M6WrW2+N7X68pkIWImmvDzfddFNEwl5+fr4effRRXXfddRoy5Ghxwk2bNunGG2/Um2++KUlqa2tTZWWlmpqaNGzYMNviy83N1Zw5c3T99dervLxcI0eOPOY+zz//vL7zne+oqamp59yyZcvk9/t12223xT3nv//7v+uaa66J+f7HH3983HMAAJBWCgulujpTvt7KhTqv14xbWDj4fUnaix+PGYB05FZl1cESnefPp8KelUgsB4DMEa4iwc91AIhfKmyUHQidL+wzZEjqJ+yFHT4sTZ0q/fGPJO6litLS9E3ak6SaGpNUXFnpdiRIhBNJ5+kq0Zb0vKZClqA9bpSXXnpJq1at6jnOycnR888/r7lz50Yk7EnS5MmT9fLLL2vcuHE953bu3GlbNbv8/Hzdfffdamlp0W9+8xt99atf7TNhT5IuueQSbdq0SeXl5RHnFy5cqM4E2sIVFRVpzJgxMX+cdNJJCX2NAACkFb/fLCL6fNaM5/OZ8WJd6MnPl0iUj11BgdmNBQDpJifHnXkHSnR++GEWIq1GYjkAAABwdKOs1a0u49koOxC3Xp9lupwc03o2nXR20tY0lcyd63YEyVu61O0IkIzly617ryiTJNOSHsgCJO1F+cEPfhBxXF1drUmTJvV7/8LCQj322GMR55YsWaL29nZL4zr99NO1e/du/fCHP9QJJ5wQ0zXDhg3TypUrldfrzelgMKhnn33W0tgAAMhqfr/U2Gh2wSUjEDDjxLMz0+MxrRgQm08+kZqb3Y4CAOKXn2/9G1aD8Xr7TnSurTU7ZO+809l4Ml1/jzcAAACQjdzeKDsQN16fZbKhQ83jma4VDMNtTeE+v1+aNs3tKJJTX8/6dTqzK+k8nSXbkh7IAiTt9bJnzx7V19f3HA8bNkwLYvhDq6KiQqWlpT3HBw4c0DPPPGNpbPn5+TEn6/V28skn62tf+1rEuRdeeMGqsAAAgGRejK1YIa1ZYxIZ4lFWZhIgVqxIbKfvJZfEf0226uoy7YybmtyOBADi4/FIEyY4O+fEiWbesGDQLLTNni01NDgbSzaIfrwBAACAbOfmRtmBuPH6LJN9+qnU1uZ2FMmpqTHru3BfJlT0oqtBegsnnSeQ15FxrGhJD2QBkvZ6efrppyOOr7rqKnljzIS++eabI46feuopy+JK1pe+9KWI45aWFpciAQAgw1VWmhdkTU1SdbU0ffqxu6q8XnO+utrcb+PG5HYaZULZfye1tdG2AkB66rVRzPH5GhulkhIWju3k9P8vAAAAkA7c3Cg7EP5+RzTamqaGysr0Xy/fvNntCJAsv1966y3p9NPdjsQ9VrWkB7IASXu91NXVRRxXVFTEfG30fZ977jkdOXLEgqiSN3To0Ijj7nQtMQ0AQLooLpYWLZLWrTPJYe3tUmuruQ0GzflFi8z9kpUJZf+dRtsKAOnI6UXn8HyNjVJFhfnZCfuk+5sKAAAAgJ3c2Cg7EP5+RzTamqaO5cuta63thi1bpFDI7SiQrMJC83+ZjRX3rGxJD2QBkvZ6aY76Y+rCCy+M+dpzzjknon3toUOHtHv3bqtCS8o777wTcXzyySe7FAkAAFnI45Hy86WiInNrR+s7J8v+e73Sgw86N59daFsBIN04maRdVmYSy4NBaebM9G9VlOouvNCaRH4AAAAg0zm5UXYgbKJFX6hOnxoKC02Frxi76aWctjaps9PtKBAtFJIOHpR27TIfBw8OnlxZWGgqxWabCy9M78RZwGEk7f1de3u79u7dG3Fu3LhxcY0xduzYiOMdO3YkHVeyjhw5ckzb39IEynavXr1alZWVOv300zV8+HCNGDFCp512mi688EJ973vfU11dnUJk/QMA4A6nyv6Hd0i1tto/lxNoWwEg3TiVpB2eZ/58Kuw54c03TSUQAAAAALFzYqPsQL7xDWfnQ+qjrWnq8PvNOna6Jg51dbkdASSzVnPbbdIZZ0g5OdKoUdLYseZj1ChzbswY6R/+of9Km2ee6WDAKWL1aqmkhLUuIEYk7f1ddDW6oqIiDR8+PK4xTjvttIjjt99+O+m4krV69Wq9++67PcfHHXecvvrVr8Y9zpo1a7R27Vq9++67Onz4sD7++GO99957+tOf/qQHH3xQM2fO1Lnnnqvf/OY3VoYPAABiZXfZ/69/3bRI9PszZwHKybYVoZDZ9b1vn7llswOQuez8fnciSTsQMC2kamupEuCU/ful8nIWMwEAAIB0EQxKixe7HQVSDW1NU4vfb9azAwG3I4lfbq7bEWS32lpTsbWkRPrZz6Tdu6VPPz32fp9+Ku3ZIz32mHm+FRdLa9dG3icnx5GQU05LC2tdQIxI2vu7AwcORByPHj067jGirzl48GAyISXt4MGD+ud//ueIc/PmzZPPpjf033zzTX3zm9/ULbfcoi52AAAA4Cy7yv4PHSo9/LC0apWZIxSStm61dg432ZmQ0tQkVVdL06ebx66gQDrxRHNbWGjOV1c7lzgIwD5Ofr/bmaTt80nLlpl/L1lizxzoW1ubNGOGefMPAAAAQGqjKjn6QlvT1FNYKK1YYVqUlpXFd21ZmbnO6Ta7Xq+Ul+fsnDCCQVO8YPZsafv2+K/fvt1suL3mmqPrO7t3S8cfb2mYaYO1LiAmQ90OIFV0Rv0RNWzYsLjHiL6mo6MjqZiSEQqFdNNNN+mvf/1rz7mCggItjnPnT1FRkWbMmKGpU6fqvPPOU1FRkXJycrR//341NTXpD3/4g55++ml99tlnPdf8z//8jz755BOtWLFCHovLkX/00UdqjbMlX3QVRQAAMla47P+MGdYsHI4eLa1fb8YN6+gwL7YyhR1VA2trTbJLQ0P/92lrkzZsMB+LF0vTpkkLF5rqVgDShxvf7+Ek7fJya38ee71m3MJCk4Q40NcEe7S0SAsWmDcUAABIFaGQeR3Y3W0qhbjRghIAUglVyTGQri7zuxKppbLSfDQ3m+/fzZtNZcTe6yperzRxolRaarocFBeb8xMmmDUdp0ycyN9abmhsNJtu48xD6NPq1dK6ddIXv5hZBRASwVoXMCiS9v4uOmnv+AQynqOT9qLHdNI999yj3/3udxHnHnnkkZir7J111llatWqV5syZo6FD+36aTJ48WbfddpvefvttzZs3T6+++mrP51auXKkpU6ZowYIFCX8NfXnkkUd03333WTomAAAZJVz2f8ECqaYm8XECAVNtqbAw8nx3d3LxpZpw2worFkKCQbPTOpGF24YG89Hf4w4gtbj9/W51krbPZxL2wknavAHlnpoa89yorHQ7EgBANmtqOvqG9tatx76hPWGCeUM7EDj6hjYAZAuqkmMgtDVNbcXF0qJF5t+hkKmM2NVl/t/y8vpeIy4tdTZpr7TUublgNDaaTbbt7daN2d5Owl4Ya13AgFKmPe6dd94pj8dj+8e9994bUzyJVIizuqpcon72s5/phz/8YcS5O+64Q4FAIOYxpk6dqq9//ev9Juz1duaZZ6qhoUEXXXRRxPn7779f7Vb+cgMAALFJtux/ba25vq9Ekpwca2JMFVa1rWhslEpKkk90qakx4zQ1JR8TAHukyvd7OEk7jtd5fQoEzDi9q6raUYUUsVu61O0IAADZqrbWvCYsKTEVgjdsOLayb7iK8OLF5u+HsjJp7Vp34gUAp1GVHAOhrWl68XhMVcTCQpPAFwyaRKtQKPJ+c+c6G5fT82W7YFC64gprE/ZwLNa6gH6lTNKe2/Ki/og6fPhw3GNEXxM9phNWr16t22+/PeLctddeq2XLltk6b25urp588smIaoPBYFBPPvmkrfMCAIABVFaaSkxNTVJ1tSnv7vVG3sfrNeerq839Nm4cuGVjfv6xY6S7rq7krm9slCoqrKl2JZlxystJ3ANSUap9v9uRpB0KsRPYbfX1pmUPAABOCQZNIv/s2fEnozQ0mNee8+aZcQAgk1GVHAOJta1pKGQShPbt6ztJDPbrvV5eWCgVFEgnnmhuvV6z9vMv/2Jem/v9pgqbE8rKqGJshXi+x+bPlz74wLnYshVrXUC/aI/7d5mQtFdbW6tAIKDPPvus59xXv/pVrVixQscdd5zt8/t8Pt1www169NFHe87V1dXptttus2yOO+64Q9dee21c17zzzju66qqrLIsBAIC0k0jZ//54PKYVkpMtAeyWTNuKYFCaOfPY6hPJamszbS8bG2mVC6SKVP5+r6w0H83NR1vZbdlybCu7iROl8eOlOXPMbX7+sWN1dFj/NSJ+K1ce/d0NAICdGhvN3zjJbkqoqZFefFGqq4us4AsAmYSq5BjIQG1NY209P3eudPrpUne36XiSnz/42m0oZF7Lx3NNtqqtNS2uB9qkcPCg2di+caP0wAPSqacmtrEhEVVV9s+RqWL9HgsEjiZG1taSjO0k1rqAPqVM0t6cOXN0yimn2D7PxRdf3Of5goKCiOPW1ta4x/7oo48ijkeNGhX3GIlav369rrnmGnV3d/ecu+KKK/Tb3/5Wn/vc5xyLY8aMGRFJe42NjZaOP3r0aI0ePdrSMQEAyCrhsv99JWrEorZWeusta2NyU7JtK+bPt67iVrSWFmnBAlMFC4D70uH7va8k7ddfl/7f/5P+939NIt/69dL//b/mPn0tWvZ6TQkX8WYgAMAJ4SrCViXsh6sIb9xI4h6AzENVcgymr7amsSSJhVvPh9vP99ZfslEiCUrJSPfEwGDQrOskkqD13nvSf/2X2fidbMeWgQQCA3fAQd8S+R6bNk365jdNUiacw1oX0KeUSdq77LLLdNlll7k2/5lnnhlx3Nraqo8//ljDhw+PeYw9e/YMOKZdNm7cqDlz5uiTTz7pOfeVr3xFTz/9tHKTqRyTgDFjxkQcJ5L8CAAAUlAyCxupLNa2FX1xYideTY1ZsKmstHceAANLx+/3tWsTW7RcsMCa+ZGcLVvMmyLp9CYIACC9pHIVYQBIRVQlx0Ci25patZYa/br9/PPN+e3bY79m2jRp4cL4E8KcTgy0i1VVhe1M2PP5pGXL7Bs/EyXzPdbQ4EzlRERirQvo0xC3A0gVI0eOlM/nizi3c+fOuMbYtWtXxPG5556bdFyDefnllzV79mx9/PHHPecuvvhi/f73v9ewYcNsnz9a9JyJtBkGAAApprFRKinJvIQ96WjbilBIam+X9u0zt6HQ4NcuWWJvbGFLlzozD4D+pdP3ezBoFswTad3S0CBde63ZNQ93tbWZSokAANjFiSrCAJBJ3KpK/thj7syL+PRua2rnWur27QMn7PWlocFsEJw3z6wZDKa21iQhlpSYpL8NG45NWA0nBi5ebKrrlpWZzYOpJlxV2K6/eazg9Up1dWx2iEcmv1+RyVjrAvpE0l4vxVG7AF555ZWYr33jjTcU7PWHzvDhw3XGGWdYFltfNm3apJkzZ6qz1w+3KVOmaO3atRoxYoStc/dn3759EcdFRUWuxAEAACySDgsbyfjoI2n6dLMoUlAgnXiiuS0sNOerq6Xm5mOva2pybjdefX3fMQBwRjp9v1u1aEmL3NRg5y5+AEB2c6qKcG2tvXMAgJPc2tz0jW/03XYVqaN3W9NUXkutqTFrBk1NfX8+2U2A8SQGOsGuqsJW8vmkjRtN4iNik8rfYxgca13AMUja62XGjBkRxy+++GLM10bf94orrtCQIfY9vFu2bNEVV1yhjo6OnnOTJk1SXV2d8vPzbZt3MJs2bYo4jq5eCAAA0kg6LGwk67HHEtst6vQuvvB8iVQEBJAct77f48WiZebJzXU7AgBApkqnKsIAkCry801FLCd5vVJenrR8uUnuQerp3dY0HdZSW1qk8vJjE/es2gQ4WGKgk+ysKmyFQMA87iTsxS4dvscwMNa6gGOQtNfL1VdfHXH8u9/9TgcOHIjp2scff3zAsay0bds2XX755Tp48GDPuQsuuEDPPfecCgoKbJs3Fiuj/pirqKhwJxAAAJC8VF/YcFJ4t+jXvy7t3i1FbVSwXU1NYhUBASRv8+bUn49FS/t5PM7OF35zDgAAq6VTFWEASCUejzRhgrNzTpxo5i0sNO0znU4axMA8HpMIf8IJ5jhd1lLb2qQZM45WxLN6E2B/iYFOcqKqcNiJJ8Z3/7IyE9+KFbTEjVe6fI+hb6x1AX0iaa+XMWPGaNq0aT3Hhw8f1kMPPTTodRs3boyoMDdq1ChdeeWVtsS4Y8cOXXbZZdq/f3/POb/fr/Xr18vr8h/rv/jFL/Taa69FnJszZ45L0QAAgKQ4ubCRTp56SjrjDOn5552dd/fuxCoCAkhOKCRt3ersnFu2xF9Fk0VLe/l8Ummps3OG35wDAMBq6VJFGABSkdOvC3rP5/ebNppU3EsdoZD0rW+ZpD0rqtQ5qaVFWrDAvk2A0YmBTnOqqrAktbZKDz9sNlVPn35scq3Xe3TTdVOT+T4Ot1NG7Hi/Iv2x1gX0iaS9KD/60Y+OOY5OROtt//79uvXWWyPOVVVVDVrxbvfu3fJ4PBEfu3fvHvCat99+W5deeqlaW1t7zp177rlav369Ci3MxH/qqaf0l7/8Ja5r1qxZo9tvvz3i3KWXXqqLLrrIsrgAAICDnFzYgHXCFQHnzXNvUQzIJB0dzleva2uTOjtjvz+LlvYKt6u55BJn53X6zUAAQPZIhyrCAJCq5s51dz6/37w+CQScjQMDO3AgNdrBxqumRvra1+zbBBhODHSak1WFw37zG2nRImndOrMm295ukvna283xunXm88XFzsaVSXi/Iv2x1gX0iaS9KBdffLGuueaanuPu7m5deuml+vWvf60jR45E3HfTpk2aOnWqdu7c2XNu3LhxWmDDHyDvvvuuLr30Un3wwQc95z7/+c/r5z//uT7++GPt3r075o+//vWvA871zDPP6Oyzz9Y111yjJ598MqINb7Q33nhDt912m6688kp1dXX1nB85cmRMVQoBAEAKcmNhA9aqqTE7fNNxwRBIJd3d7szb67XVoFi0tEd0uxq335wDAMAK6VJFGABSld8v9erYZauysr4TfAoLzeuUNWvMfYBk1NfbO35NjXlt7SQ3NjbW10vNzebfHo+Uny8VFZlbKoslj/crMgNrXUCfhrodQCp6/PHHtXPnTr3++uuSpPb2ds2dO1ff//73NX78eOXk5Oitt95Sc/iX7995vV7V1tZq+PDhlsf0/PPP67333os49+GHH+rCCy+Me6zTTz990Kp+n376qVavXq3Vq1dLks444wx98YtfVEFBgXJyctTW1qbt27fr3XffPebaYcOG6emnn9b5558fd2wAACAFULEpM7S0SOXlpuWC3+92NEB6yslxZ97c3Njux6Kl9W67TbrrrmPfHAu/OefE493fm3MAACTLzSrC+fnOzgsAdqmqcuZ1QVXVwJ+vrDQfzc1mLW/zZpMo3fvnvNcrFRRIg7wnCNhq6VLzXHXK8887N1dvK1eaanqwHu9XpD/WuoB+kbTXhxEjRmjt2rW6/vrrtWHDhp7z77333jGJc2Hjxo3TypUrdfbZZzsVpqN27dqlXbt2DXq/L33pS/rVr36l8847z4GoAACALWhflDna2qQZM0zrlMJCt6MB0k9+vnmTw8k3t71eKS8vtvuyaGm90aP7X0RMlTfnAABIlJtVhEnaA5ApKitNtSA7X48FAtKsWbHdt7j4aKJQKGQSpbu6zGawvDyT1FdSYl+swGDCVejsTtiprZUeeEDatMneefrDmrp93ErEhHVY6wL6RXvcfpx00klat26dfvKTn8g/QGWSk08+WVVVVdq2bZu+/OUvOxihfW688UbdcsstOuuss+SJoWRvbm6uLrvsMj311FN69dVXSdgDACCdudEuCfZqaZEWLHA7CiA9eTzShAnOzjlxYuytU1gQtt5Aj2n4zTk7xfPmHAAA8Ur1KsIAkC6WL5d8PnvG9vmkZcsSu7avtpxOtvQF+tNfkmsoJLW3S/v2mdtQKP6xg0HzWnr2bOmll5KLMxlbtiQWPwYWCknbtrkdBZLBWhcwICrtDcDj8ej222/X7bffrh07dqi5uVktLS3q7u6Wz+fT2LFjNWXKFA0ZEn/u45gxYxSK4xf3TTfdpJtuuinueRLxla98RV/5ylckSZ2dndqxY4f27NmjDz74QIcOHdKnn36qkSNHyuv16swzz9SECROU49aCDwAAsJYb7ZJgv5oa8+LYyVYUQKYoLZV6VWB3ZL5YkGRtj/Aie3+Jk8uXm7bjLS3Wz53Mm3MAAMQi1asIA0C6KCyU6uqk8nJrf6Z6vWZcq7slOFU1HOhP7w1yTU1HWzpv3XpsS+cJE8zaSCAweHW+xkZp5kx7XqPHq63NVLqkurC1OjqkTz5xOwokirUuYFAk7cXovPPOy8oKcnl5eSotLVVprG8cAQCA9OZWuyTYb+lSkvaARMydKy1e7Ox8sSDJ2h6DLbKn25tzAAD0Fq4i7OSGhHiqCANAOvH7zYaeGTOsSRjy+cxrggG6fyXMiZa+wEC2bJHWrDHrkwMlkLa1mb9TNmwwazHTpkkLF/ZdpauxUaqoSK21ka4ukvas1tXldgRIFGtdQExojwsAAICjqJ6buerrpeZmt6MA0o+TrYTKygbfRR5GkrV9BlsQDr85Z1U7LJ/PjGfHm3MAAERzenM2m8EBZDK/3yQOBQLJjRMImHHsfE1gZ0tfYDBtbdJXvxp/xceGBpN0Om+eaYMbFgyaCnuplLAnSbm5bkeQeUjaS08nncRaFxAjkvYAAABwVLhdEjITO6qBxFRVpd48JFnbJ5ZF9nR6cw4AgN5ireqbrvMBgNMKC6UVK0wVsbKy+K4tK5Nqa831dlciKiw0LQqpfop0VFMjlZSY1rqSNH9+arTE7c3rlfLy3I4i85AImZ5eeYW1LiBGJO0BAADgqHC7JGSmzZvdjgBIT+FWQnYKBPpu99IfkqztEc8ie7q8OQcAQG+pWkUYANJdZaWpKtTUJFVXS9OnH/uazes156urzf02bozvdWAyGhulf/gHKRRyZj7Aai0tUnm59PDDqbkxeeJE+5JiQyGpvV3at8/cZtP3MUl76cfrlU4/3e0ogLQx1O0AAAAAkGJKS6UNG9yOAnbYssUs6rCrGojf8uXmDRU7dnL7fKbiQTzCSdb8vLZWIovslZXmo7nZvHGwebP5edu7TY/Xa8YuLTUJoCQwAADcVFUVf3u6ROcBgGxTXCwtWmT+HQpJnZ2mvWNurtkg5MaaTKq2EgXi1dYm/fM/ux1F30pLrR2vqenoGsPWrceuMUyYYOYMBDJ7jSE/Xzr+eOmTT9yOBLGyM4EVyEBU2gMAAEAk2hdlrrY2s1gMpJJ02S1cWCjV1Vlf3c7rNeMmUm3N6gVhJPeYht+cW7fOvCnW3i61tprbYNCcX7QosxfTAQDpIRWrCANAJvJ4TMJJUZG5dSuJIRVbiQKJ6u52O4K+WfW3VW2tqVZcUiItXmw2a0Yn3La1mfOLF5sqymVl0tq11syfajweafx4t6NAPFivBOJC0h4AAAAiOdkuCc7r6nI7AiCyXVBhoVRQIJ14orktLDzaLqi52e1II/n9ptqez2fNeD6fGc/vT+x6kqytZ9VjmipvzgEA0J/ly637myZaIlWEAQD2qK1NzVaiQCYpK0t+g14waDY9zJ4df0XkhgazKWPePDNOprnkErcjQDxYrwTiQtIeAAAAjkUbo8yVm+t2BMhmmbBb2O+XGhvNQmoyAgEzTqIJe+FYSLK2jhWL7AAApItUrCIMALDekiVuRwBkvmTX0hsbpXPPTT7BtqbGrLk1NSU3jl0S7bZBElj6YG0NiBtJewAAADiWE+2S4DyvV8rLczsKZKNM2y1cWCitWCGtWWMWo+JRVmaSF1esMOMk2x6YJGvr8FgCALJNqlURBgBYq6kp/tfgAOITCEizZiV+/X/+pzRxotTaak08LS1SeXnqJO5Z0W2DTavpg7U1IG4k7QEAAKBvdrZLgjsmTqRFI5zX2Gh2+WbibuHKSvPGdO8FyOhqNV7v0QXIpiZz/1NPta49cGWldOml9nx92STZRXYAANJVKlURBgBYi7a4GEh5udsRpD+fT1q2LLFrg0Hp61+X5s+XPv3U2rja2qQZM9zd/Gp1tw2SwVIfa2tAQkjaAwAAQN/sapcE95SWuh0Bsk1jo1RRYXb5WiHVdguHFRdLixZJ69aZBdH2drNDur3dHK9bZz6/Z4/17YGDwdR7PNJNMovsAABkAiurCAMAUsfmzW5HgFTl80mrV9NpJRler1k7T+Tvn/AG16eesj6usJYWacEC+8bvj13dNugMlNpYWwMSRtIeAAAA+hdul3TyyW5HAiuwsAEnBYPSzJnHJqUlK3q3cLLtZa3m8Uj5+VJRkbn1eKxZsKys7HuH9Pz50kcfWRN7Nho5MvFFdgAAMk2iVYSpqAEAqScUkrZudTsKpKLeyWZ0WkmMz2f+BoquMBzLGpXVG1wHUlNjNlc4xe5uGzxfU1MyCawANNTtAAAAAJDiwu2SvvAFqbvb7WiQqLIyUw0MsFMoJHV0mJ8V//RP9i1AtrSY5/TJJ5s3IXonBnq90oQJprJkIOD+876x0SQvJvtYrF1rFib/v/9PuuMOc+7ee2l3lKzcXBZ8AQCIFq4iLJm/7zo7pa4u83szL89sSgAApLaODus30SH9+XwmuSacbBbutFJezvMlVoGAqSgWTlBqajJrM5s3D75GdfLJ9mxwHcjSpWZjht3CyYhWfW3hbhu9kyMLC83GkTvvtGYOJC/6ZwqAuFFpDwAAAIMrKpKmTXM7CiSjqsrtCJCpeldiKSyUCgqkE0+UVq2yd94dO6xpL2snq3dPd3dL3/62dOGF0te/Lt13nzXjZrPWVnfaxQAAkC76qiIMAEh9bLxFtEBAevllkzjWuwpcuNMKG9oGVlZmqtatWGHWv2przbmSErMGFcsa1bnnOlNhr7f6eqm52d45nOq2IUm/+Y21c2Sb4483rbEbGqTzz09urEDArH2SsAckhaQ9AAAAxKa01O0IkKhAgJZVsF4si5OpINxedt68vtvL2sWuBUtJ+tOfpKeesn7cbOV0uxgAAAAAsFtOjtsRIJUMHWpe955xhtloWVBgEs/CLe89HpN8Ewi4HWlqGTPGPD5NTSaxcdYss94TCEizZ5s1p3i0ttoS5qDs7tLwrW/Z220jvNmyqSn+xxxH+XymIuTXviZdfLFJ5lyzxqzvxiM6gRVAUkjaAwAAQGwmTnQ7AiTC5zMtGwCrJLM46aaaGpNg2NTkzHzz5zu/exqJW7rU7QgAAAAAwDr5+aY1JyBJn34qHTwYeS66CtzVV5sNj2vWmJaukH7/e2nRIqm42Bw3Npq1JbuT4Ky2ebM949bWmsfm2WftGT8svNky3R73VJKTI23bdmxVvMpKk5Dau5NK9O8Or/dogm/vBFYAlhjqdgAAAABIA8Eg7QPTkdcr1dWx4w3WaWw01ePSNRmtpUUqLzeLS3a2bmAhMf2E28WEF+IBAAAAIJ15PCbxasMGtyNBumhoMB9f/3r6rvtYqawsco2gsVGqqEjNLhOD2bzZtEP2eKwZLxg0m1WdXPtaulT63Oecmy/TdHdLubn9f7642CSoSua50tkpdXWZa/LyrHvuADgGlfYAAAAwOCpGpR+fz/7EJGSX8OJkuv8saGuTZsywt1XukiX2jQ37kGgJAAAAIJOUlrodAdLR6tXSBx+4HYX7qqqO/jsYNJtY0zFhT5La26VNm6wZy61qg/X10quvOjtnpunqiu1+Ho+p1lpUZG5J2ANsRdIeAAAABkbFqPQTCJgFFBL2YJV0X5yM1tJiX/XQpqb0ahuMo+xqFwMAAAAAbpg71+0IgPQUCES2/8yEDe1XX538Bla3N/S2t7szb6YYqNIeANeQtAcAAICBUTEqfZSVmSTLFStoiQtrZcLiZLSaGvP9YjWSnNPXli2mBQgAAAAAZAK/37TIBRA7n09atuzocaZsaP/gg2M3sIZCJhFu3z5zO9CaSKZt6M02Xq9pcwsg5Qx1OwAAAACkMCpGpbaCAunLXzbtTubOlYqL3Y4ImShTFif7snSpVFlp7ZhUa0tfbW1SZ6dp/QEAAAAA6S4YlHbvdjsKIH14vVJdXeRm6Eza0F5TI02dKu3da9avtm6NTMLzek2ib2mpqTbYe605Ezf0ZpOJE2lzC6QokvYAAADQv0xN1MkU774rjRzpdhTIdJm0OBmtvl5qbrYu4TUUMgueSF9r1tBCCgAAAEBmmD9f2r/f7SiA9ODzmYQ9v//ouUzc0H7nnf1/rq1N2rDBfCxeLE2bJi1caNa7eJ8gvZWWuh0BgH7QHhcAAAD9o2JU6vJ6qQYF+2Xi4mQ0KxcdOzpoE5LuAgFp3jxTkQIAAAAA0lUmV80HrBYISI2NkQl7Et9DDQ2mQ8XNN7sdCZLFBlUgZZG0BwAAgL5RMSq1UdIeTsiGxUkrk5O7u60bC+6pqZFKSkzSKgAAAACko0yumg9YqaJCWrEisiVuGBvajdZWtyNAMsrKrOsyAsByJO0BAACgb1SMSm2UtIcTsmFxcssWk6ScrGBQ+qd/Sn4cpIaWFqm8nMQ9AAAAAOknG6rmA1Z58UVTmTIaG9qRKaqq3I4AwABI2gMAAEDfqBiV2ihpD7tly+JkW1vyyYmNjaYy26pV1sSE1NDWJs2YQatcAAAAAOklG6rmA1ZauvTYc2xoRyYIBKRZs9yOAsAASNoDAABA33Jy3I4A/aGkPZyQTYuTU6aY76u1a+O/trHRtFJpabE8LKSAlhZpwQK3owAAAACA2GVD1XzASvX1UnNz5Dk2tCPd+XzSsmVuRwFgECTtAQAAoG/5+ZLX63YU6EvvkvahkNTeLu3bZ26taPMJSNm3ONnQIFVWSvPmxV5ZLRiUZs7MnuTGbFVT03erHAAAAABINdlSNR+wWnSFSja0I515vVJdnVRY6HYkAAZB0h4AAAD65vFIEya4HQWiBQLSqadK1dXS9OnmhXdBgXTiiea2sNCcr64+docoEI9sXZysqTGtbpuajp7rLzl2/nwq7GWLvlrlAAAAAECqyaaq+YCVoitUsqEd6crnkzZulPx+tyMBEAOS9gAAANC/0lK3I0BvJ5wg/eUvJqFo8WJpw4ZjF2Lb2sz5xYvNC/NEW34C2bw42dIiXXyxdNtt/SfHXnDBsbuwkbn6apUDAAAAAKkm26rmA1bZsiWygwkb2pGOAgGpsZGEPSCNkLQHAACA/s2d63YECMvJkfbvl/70p/iuS6TlJyCxONneLv3sZ/0nx27b5k5ccA9JmgAAAABSXbZWzQeS1dYmdXZGnmNDO9JFWZlUWyutWEFLXCDNkLQHAACA/vn90rRpbkeBIUOS3yndV8tPYDAsTgJHRbfKAQAAAIBUk81V84FkdXVFHrOh3X0nneR2BKlp1CjTHaS62qz3b9wozZrldlQAEkDSHgAAAAZWVeV2BNnN45GOHLFmrJYWaepUEvcQOxYngaOiW+UAAAAAQKrJ9qr5QDJycyOP2dDurrw86amn3I4i9YwaZTrqrFsnLVokFRe7HRGAJJC0BwAAgIFVVpK44yarE0Q6O6XJk6W337Z2XGQmFieBo/pqlQMAAAAAqYaq+UD8vF6TJBaNDe3uGTdOmjKF6qHRJk0ynXkAZAS+mwEAADC45csln8/tKGCVw4el8eOpuIfYOL046fWa9g7nnefsvEAsolvlAAAAAECqYfMtEL+JE02lymhsaHfPtm3S9u3SWWe5HUlqITEbyCgk7QEAAGBwhYVSXR272jLJ4cO0ykVsnFicvPZaqbVVam8/2t6hvp5kYaSe6FY5AAAAAJBqqJoPxG+gRKj77qOymVu+8hVp0ya3o0gtJJECGYXfLgAAAIiN3y9t3Nh3mwCkp85OacYMkyQFDMTOaps+n/Rf/yUVFUn5+Ud3NZMsbD8S0OLTX6scAAAAAEg1tPQE4jNQItQ990hHjjgXC47at8/tCFJLWZlUXOx2FAAsRNIeAAAAYuf3S8uWuR0FrNTSIi1Y4HYUkUIhU3Ft3z5zGwq5HRHsSqDzes24hYV9fz6cLEzFPXvMnk31hXj01yoHAAAAAFINLT2B2A2UCFVbK61c6Ww8QH9IyAYyDkl7AAAAiF0wKN19t9tRwGo1NWYByk1NTVJ1tTR9ukngKiiQTjzR3BYWmvPV1VJzs7txZopEEiOtTqDz+cx4fv/g8zY2SoGANfPiqNWrpddfdzuK9DFQqxwAAAAASDV2Vs0HMslAiVBLljgXBzCQQECaNcvtKABYjKQ9AAAAxG7+fFOZDZlnyRJ3qtvV1prdrCUl0uLF0oYNUltb5H3a2sz5xYtNAldZmbR2rTPxZRIrEiOtSqALBMw4gyXshRUWSitWSGvWmP9/WKez0+0I0gdVKgAAAACkE7uq5gOZZKBEqKYmqaHB2XiAvvh8dEACMhRJewAAAIgNrQAyW0ODs9XtgkGzKDZ7dvyLXw0Nps3LvHlmnERlSxteKxMjQyHpc5+THnpIevLJ+BPoyspMPCtW9N8SdyCVlaY63yuvxH8tMtPQoc7MM1CrHAAAAABIVVZXzQcyyWCJUKyFIxEej7Xjeb0mATuRtVQAKY+kPQAAAMSGVgDZxc7qdo2NJoEs2YWvmhozTlNT7NdkShveWBIOrUqMbGjo+zH7xjfM4zl5sjR1qjRlyrG7973eo49pU5N5o8CKNg6TJ1MpAIZTiXQDtcoBAAAAgFRmVdV8IJN4PCZhb6BEqM2bnYsHmcHnk377W+sSpX0+s54aa7cSAGnHoS3pAAAASGu0AkBDg/m49lrpkUekoqLExmlslCoqjq30lqiWFqm8fPDFi9pak3g60PM4nKgYTlacNk1auNCaJDMrNDWZRMfNm6WtWyMfQ69XmjBBKi01i/BHjkgzZybfzrqmxnz0p61N2rTp6PHFF0t33WX+j3Nzpbw863eXSmbMCRPM/xWy26xZ0rnn2rv7faBWOQAAAACQDgoLTdX7QEBatIgK9kAoJP3DP0hnndX3mmIoZNbfgFgFAkcTQSsqpAULBl5XjWc8ABmLpD0AAAAMjlYACPvtb6VVq0xy2CWXmMWDWCtdBYMmkcyqhL2wtjZpxgyTEBi9iBEMSvPnJ/YcDicqur1AkkjC4dCh0qefOhdj2EsvmY/wY2ZHwl5YaSlJe5DmzpVOPtkk7iabpNqXwVrlAEh5f/7zn7V9+3bt3btX3d3d8vl8Gjt2rCZPnqwhQ7KnCckHH3ygV199VXv37tWBAwf0+c9/XqeccoouuugiDR8+3O3wAACAUyorzUdTkzRxovS3v7kdEeCegdYUOzqsX8NE6jvtNNNhZMuW2K8pKzMdGnpv+OydKL10qVRfn9x4ADIWSXsAAAAYHK0A0FsoZKqrbdoUX0W6+fPtSaqRzLgLFpjFkLDGRuuqzb34olRXd3TnbShkFu+6u6WcHCk/3/oEtWQSDt1I2Outr8fManPnmucfsldZ2dGk4bo6U3XTygV1r9eMy45mIO2EQiH99Kc/1cMPP6zGxsY+7+Pz+XTDDTfo7rvv1ogRI2yPacyYMdqzZ0/C17/wwguqqKiI+7rnn39eixcv1gsvvKDPPvvsmM/n5eXpqquu0v33368zzjgj4fgAAECa8fvNayo2wyHb9bWmKJk1P2Sfd981XWZOP/1ox5MtW47teDJxotlQPHfuwBvaw4nSzc3WjAcg42TPdlIAAAAkhlYAGExDg1l8mDfPJJr1pbbW/oqNNTVmHuloG16rkgRbWkzr19tuk6ZPN0k8BQVm52VBgTmePl2qrjaLMMlqbJRKStK7ymW4dXFTkz3j+/0mYRTWy8tzO4LYVFUd/bffb6rt+XzWjO3zDd52G0BK+vDDD3X55Zfr9ttv7zdhT5JaWlr0wAMPaPz48XrttdccjNAZn376qb7zne9o+vTpWr9+fZ8Je5LU2dmpX/3qVxo/fryeeOIJh6MEAACuKi11OwIgNfReU5TMevgnn7gXD9y1dKlJnFu0SFq3zqx3t7dLra3mNhg05xctij3BzurxAGQMkvYAAAAwsFRvBVBWZhKbrErUQOJqakyiWV9JWkuWOBPD0qX2teFtb5d+9jOzCz167HB72sWLj+5Wr6011+zbZ25DodjmsTrh0E3hNiP9JXMmq3fSFqwzcaJp35HKAoFjq3v6/eb7J9nYAwEzDgl7QNo5dOiQZs2apfXr10ecP+WUUzRr1ix97Wtf0/nnnx/xuZ07d+ryyy/Xm2++6WSotvv2t7+thx56SKFef38UFBTokksu0Te+8Q1NmTJFxx13XM/nOjo6dOONN+rJJ590I1wAAOCGuXPdjgBIHffcYzbjhjfrnnqq2xHBLfX1kZuyPR7TZaWoyJpuK1aPByCt0R4XAAAAA3OrFcB3vmNeHMdaLv78861vjYj4haur9a5Q1dRkqvE5ob5e+ta33E94a2g49mv2eqUJE8xzNxDoe+ekXQmHbuqvzYgVKivNz4F0rkiYihobpRdeMM/TpUvN91Uq8fmkZcv6/lxhoXmuJRJ7WZlJBB2s1TeAlHXTTTdpa68K0fn5+Xr00Ud13XXXaciQo3uXN23apBtvvLEnUa+trU2VlZVqamrSsGHDbI/zC1/4gl566aW4rjnppJNivu/DDz+s//7v/+459ng8uvvuu/X9739feb2qqe7Zs0cLFizQM888I8m0Fb7xxht1zjnnqKSkJK74AABAGgpXsHdqzQZIZVu2mA9AMmuNixa5HQWALOAJhWIt9wCkp+3bt6u41xuizc3Nx+yqBgAAA2hvN+0/3Zg3P99UJ+vslLq6pNxc07axv91nTU2mqpfbCVswSTWNjSaBprraVKBDpGnTpIULIxOEAoHMTUBbs8Yk2Vmlqck8Vi+/bN5g4KWttcI/gyWTQP3DH0rPPScdOOBqWBo61LRsj7UKXnOzeZ5s3hx7EjiQBNYg3PPSSy9pWq+26Tk5OXr55Zc1adKkPu8fDAY1efJk7dy5s+fc4sWLtXDhQlviGzNmjPbs2SNJOv3007V7925b5jl48KDGjh2r/fv395x76KGHtGDBgj7vf+TIEX3jG9/Q6tWre85dccUVqqursyW+WPG9BACAQ2prpdmz3Y4CAFLL9OmmZS2ArODmGgTtcQEAADCw/HyT2OAkr9ck50nxlYu3qjUikheuriZJa9e6G0uqamgwSWzz5pkKe7W1mZuwJ5mqZ1aorTUV0UpKTDJofT0Je3bo6jK3waD0ox9JTz7pfsKeJN10U3xta4uLzc7odevM19LeLrW2mttg0JxftIiEPSAD/OAHP4g4rq6u7jdhT5IKCwv12GOPRZxbsmSJ2tvbbYnPKQ8++GBEwt5XvvKVfhP2JGnIkCH6yU9+osLCwp5zf/jDH1SfalVWAQCAPcIV7AEAR23ZwnojAEeQtAcAAICBeTympaeTJk4cODlvIOHWiGvWmMQeuKemxrTK3bbN7UhSW02NSUD7t39zOxJ71debqmeJCgZNQu7s2bTucUJurkmCLilJrWTSu+5K/Np4ksABpJU9e/ZEJJkNGzZswES1sIqKCpWWlvYcHzhwoKdVbLp64oknIo6rqqoGvaaoqEi33nprxLlf/vKXlsYFAABS2PLlpmMCAMBoazPdfwDAZiTtAQAAYHC93sxMm/kqK6WNG6Verb7gAqq0xKalxbT8zHSJJn+lYvJYJvN6pb/8RaqoSK1242VlVMQD0Kenn3464viqq66SN8ZK0TfffHPE8VNPPWVZXE773//9X+3atavn2Ofz6fLLL4/p2ujH4ZlnntFnn31maXwAACBFFRZKdXXOd9oAgFQW7kIBADYiaQ8AAACDc7pNhpXzfe1r0rRp1o0HIHGbN8d/TWNj6iWPZbqSEmnWLLOrOJXEUC0KQHaqq6uLOK6oqIj52uj7Pvfcczpy5IgFUTkv+nEoLy+XJ8aqouecc45OOumknuPW1la99tprlsYHAABSmN9vNr9aVXFvCG9BA0hzubluRwAgC/AXEwAAAAbn9zuX+GZ1JaXaWmn/fuvGA5C4LVukUCj2+weD0syZ9iSPeTzm581tt0kjR1o/fjprbU29JMlAwCQSAkAfmqPar1944YUxX3vOOefohBNO6Dk+dOiQdu/ebVVojkrmcejr/tu3b086JgAAkEb8frNxLhBIbpxAQHrjjeTHAQC3eL1SXp7bUQDIAiTtAQAAIDZOVTiyap5g0CwOzp4t8YYjkBra2qTOztjvP3++fcljoZB0yinST38qNTSYJD4YO3a4HUEkn09atsztKACkqPb2du3duzfi3Lhx4+IaY+zYsRHHO2z+Odje3q5//Md/1AUXXKCioiLl5OSoqKhIZ599tq699lotW7bsmK8pFtFxf/GLX4zr+ujHze7HAQAApKDCQmnFCmnNGrPRLR5lZWbz7IoV0plnJj5OJsvJcTsCALGYOJG1QgCOIGkPAAAAsamstL9NrlWVlBobTXvHlSuTHwuAtSorpepqKaoa0DFqa+3/Hq6pMfOMGRNfBcBMlmpVB71eqa7OvHEEAH145513Io6Lioo0fPjwuMY47bTTIo7ffvvtpOMaSFtbmx599FFt27ZNwWBQf/vb3xQMBvXWW29p1apVuuuuuzR27FjdfPPN+uijj2IeN/qxiP66BuP04wAAAFJYZaVpl9vUZF7DT59uXp/15vWa89XV5n4bNx67rhc9zgUXOPYlpJyrr5a6u92OAkAsSkvdjgBAliBpDwAAALFbvtxUPLKDVZWUGhuliorUa+0IwGhokBYvPtp2e9Uqad8+qb09MnFuyRJn4lm6lEXz3trb3Y7gKJ/PvLnj97sdCYAUduDAgYjj0aNHxz1G9DUHDx5MJiRLdHd36/HHH9cFF1yg+vr6Qe9/5MgRdXR0RJyL97FIxccBAAC4rLhYWrRIWrfOdLVob5daW81tMGjOL1pk7hfLOPn5zsSdivbvdzsCALGyu3gBAPzdULcDAAAAQBopLDQVj8rLTZtLq1hVSSkYlGbOtDY2APZ56SXzEeb1ShMmmMp3DQ3OxFBfL0VVJspa552XOq1xAwGTyE2FPQCD6Ixquz5s2LC4x4i+Jjr5zSpDhgzRpEmTdMUVV2j8+PE644wzNHLkSH388cdqaWnRH//4Rz3xxBPavXt3zzXvv/++Zs2apfr6ek2YMKHfsaMfByn+x8Kux+Gjjz5Sa2trXNdEVw0EAAApwOMxSXeJJt41NTn3Wj8VbdvmdgQAYlFWNngiMgBYhKQ9AAAAxMfvN5WPZsywppqdz2cS9qyopDR/PhX2gHTW1iZt2OD8vM88YxIGsznh1+eTiorcjsIsjFZVWdMqHUBWiE5WO/744+MeIzpZra8EuGTddddduvrqqzVmzJg+P19SUqIZM2bonnvu0bJly1RVVaW//e1vkqRDhw7pyiuv1FtvvdVv69++Yo73sbDrcXjkkUd03333WTIWAABIYytXuh2Buw4ckAoKJKoZA6mtqsrtCABkEdrjAgAAIH5+v2lDGwgkN04gYMaxImGvtpbFPwCJeeEFaeRIt6Nwj9crPfusqXrgtFGjpOnTpepqM//GjSTsAWnmzjvvlMfjsf3j3nvvjSkej8cT99eQyDXx+u53v9tvwl5vxx13nL773e9q1apVGjLk6NLt3r179eMf/ziuOeP9upx4HAAAQBbbvNntCNx37rluRwBgIIEA61IAHEXSHgAAABJTWCitWCGtWWMqI8WjrMwk2a1YYV3rwyVLrBkHQPb505+kPXvcjsIdPp9JlBszxp1Kg3v2SOvWSYsW0XoEQELy8vIijg8fPhz3GNHXRI/phiuvvFJ33nlnxLn/+q//6vf+fcUc72ORio8DAADIEKGQtHWr21G4b+JEtyMA0B+fT1q2zO0oEhcKSe3t0r595jYUcjsiADGgPS4AAACSU1lpPpqbTaW7zZulLVsikz+8XrMoVVoqzZ1rfWJGU5PU0GDtmACQ6QIBsxhZWGgW9NzQ3e3OvAAyRqYm7UnSwoULtXz5coX+/mbLX//6VzU3N6u4j7+l+0vai+drsetxuOOOO3TttdfGdc0777yjq666ypL5AQBACujocGejWCrxeqV/+Afp4YfdjgRANK9XqquzrsCAU5qajr4ns3Xrse/JTJhg3pMJBNgsC6QokvYAAABgjeJiUylJMru4Ojulri4pN1fKy5PsbLdFW1wAiF1ZmVRVFdnuIyfHnVhyc92ZF4Bl5syZo1NOOcX2eS6++OI+zxcUFEQct7a2xj32Rx99FHE8atSouMeww8knn6ySkhJt27at51xjY2OfSXtDhgxRXl6eOjs7e861trbqxBNPjHk+ux6H0aNHa/To0ZaMBQAA0hQbtsyG5vHjpVGjpAMH3I4GQJjPZxL2/H63I4ldba3pPDRQIYO2NmnDBvOxeLE0bZq0cCHtf4EUQ9IeAAAArOfxSPn55sMJmzc7Mw8AZIKHHz52d21+vtmB62TlA6/XJHUDSGuXXXaZLrvsMtfmP/PMMyOOW1tb9fHHH2v48OExj7EnqkV69JhuGjNmTETS3kBJiWeeeaZef/31nuM9e/bovPPOi3muVH4cAABAmnNro1gqOeMMc3vZZdJvf+tuLACM3l0o0kEwKM2fn1gRg4YG85FuXzOQ4Ya4HQAAAACQlFDIlH4HAMSmr4U9j8e0zHDSxIn2VmEFkBVGjhwpn88XcW7nzp1xjbFr166I43PPPTfpuKwybNiwiOOB2v9Gx/3OO+/ENddf/vKXAccDAABIWHijWDYLhcztv/6ru3EAMF0oamulFSvSJ3mtsVEqKUm+61BNjRmnqcmauAAkhaQ9AAAApLeODmcrQwFAuuuvOmlpqbNxOD0fgIwV3S72lVdeifnaN954Q8FgsOd4+PDhOiNcBSUF7Nu3L+K4qKio3/sm8zhI0h//+McBxwMAAEiYGxvFUs3u3ebW75emTHE1FCCrPfywtHFjerWJbWyUKiqklhZrxmtpkcrLSdwDUgBJewAAAEhv3d1uRwAA6WXLlqM7/HubO9fZOJyeD0DGmjFjRsTxiy++GPO10fe94oorNGRIaiyZfvbZZ9qyZUvEueiqgr1FPw719fUK9fXzvg9vvPGGPvjgg57joqIiTZo0KY5oAQAABpHtG7d6vxa/+253YwGyVSAg3XGH21HEJxiUZs60vnBBW5s0Y4YZH4BrUmMFCgAAAEhUTo7bEQBAemlrkzo7jz3v90vTpjkTQ1mZRAUnABa5+uqrI45/97vf6cCBAzFd+/jjjw84lpueffZZtfV6Y2bo0KG6+OKL+73/l770JY0ZM6bneO/evXruuedimiv6cbjyyit13HHHxRUvAADAgLJ941bv1+KVlSYJB4BzfD5p2TK3o4jf/PnWVdiL1tIiLVhgz9gAYkLSHgAAANJbfr7k9bodBQCkl66uvs9XVTkzv1PzAMgKY8aM0bReSceHDx/WQw89NOh1Gzdu1KZNm3qOR40apSuvvNKWGON16NAhLVy4MOJcRUWFRo4cOeB13/rWtyKOlyxZMuhcwWBQjz32WMS5G264IcZIAQAAYuTkRrFU1fu1+BNPsBkZcIrXK9XVSYWFbkcSn9paaeVKe+eoqTHzAHAFSXsAAABIbx6PNGGC21EAQHrJze37fGWl/dUPAgFp1ix75wCQdX70ox8dc/zaa6/1e//9+/fr1ltvjThXVVWlgoKCAefZvXu3PB5PxMfu3bv7vf++ffv0xBNP6LPPPhv8i/i7jo4OXXvttdq+fXvE+XvuuWfQa7/3ve/phBNO6Dl+4YUXtHz58n7vf+TIEf3jP/6jgr1aIl1xxRUqLy+POV4AAICYZfsGrt6vxQsLpe99z71YgGzh80kbN5rE4ViEQlJ7u7Rvn7kNt7V2QwybsCyxdKkz8wA4Bkl7AAAASH+lpW5HAADpw+uV8vL6//zy5WZB0w7p2ooEQMq7+OKLdc011/Qcd3d369JLL9Wvf/1rHTlyJOK+mzZt0tSpU7Vz586ec+PGjdMCG9oCdXZ26oYbbtBZZ52le++9V42NjcfEE/bJJ5/ol7/8pS644AI9++yzEZ+75ZZbBmyNG1ZQUKD7778/4txdd92le+65R51RrdHfffddXX311Vq1alXPudzcXC3lDRsAAGCXykrp8593Owp3DPZaHID1AgGpsXHwhL2mJqm6Wpo+3STUFhRIJ55obgsLzfnqaqm52Zm4wzE1NDgzV329s18bgB5D3Q4AAAAASNrcudLixW5HAQDpYeJEU6W0P4WFpmVIebnU1mbdvOnaigRA2nj88ce1c+dOvf7665Kk9vZ2zZ07V9///vc1fvx45eTk6K233lJz1JsRXq9XtbW1Gj58uG2x/eUvf9F9992n++67TyNGjFBxcbFGjx6tkSNH6vDhw3r//fe1detWdfXRvryyslKPPvpozHN9+9vf1rZt2/TTn/5UkhQKhXT//fdr2bJlmjRpkgoLC/Xee+9p8+bN+vTTT3uu83g8+sUvfqGSkpLkv2AAAIC+hEKRLWKzSV+vxTdvdicWINOVlZnKnoN1eqitNdXsBkqOa2uTNmwwH4sXmzbfCxfa30XC7ra4fc23aJGzcwIgaQ8AAAAZwO+Xpk6V/vhHtyMBgNQXS3VSv9+0DpkxQ2ppSX5On88k7MXaigQAEjBixAitXbtW119/vTZs2NBz/r333tN7773X5zXjxo3TypUrdfbZZzsVpg4dOqRNmzYNer/Pfe5zuueee7Rw4UIdd9xxcc3xyCOPaNiwYVq+fLlCf2/ndODAAa1fv77P++fl5ek///M/dd1118U1DwAAQFw6OqQDB9yOwh3Rr8VDIWnrVndisZLXK40fL734otuRIJsVFEhf/rL5Pps7VyouHvj+waA0f35iiXENDeYjEDDdJOzanOp0Ui9JxIAraI8LAACAzPDtb7sdAQCkh7lzY7uf329aiAQCyc0XaysSALDASSedpHXr1uknP/mJ/AP83Dn55JNVVVWlbdu26ctf/rJt8Zx44om6//77demllyo/Pz+ma0499VQtXLhQ77zzjn7wgx/EnbAnSUOHDtVDDz2k9evX69JLL9WQIX0vA48YMULXX3+9GhsbdeONN8Y9DwAAQFy6u92OwD3Rr8U7Oqytbu+0z39eeuUVk/z0/PMmeQ9w0kUXSTU1Unu7+V5at85UihssYa+xUSopSb6SXU2NGaepKblx+uJGUu+WLWZeAI7yhEJ85yGzbd++XcW9fjk3Nzfr/PPPdzEiAABgi/Z2s6MOANC/sjJTQS9etbXS0qVSfX18c8XSigTIIKxBpJ4dO3aoublZLS0t6u7uls/n09ixYzVlypR+E9nsEgqFtGvXLr399tvau3ev2tradPjwYeXk5Mjr9Wr06NGaNGmSvvCFL1g+9/vvv6/Nmzdr7969OnjwoEaPHq1TTz1VF110kUaMGGH5fMniewkAgAyVCut3Q4ZIR444O2dfr8X37ZNOPNHZOKzm9Zqvy++Xpk837UMBJ/zpT9LkyfFf19goVVRYmzDb+/vAKm79rGxvl2LcbAZkEjfXIGiPCwAAgMyQn29eyB486HYkAJC6qqoSu66y0nw0N5udyJs3mx24vRc5vV5p4sTYW5EAgAPOO+88nXfeeW6HIUnyeDwaO3asxo4d6/jcJ598subMmeP4vAAAABHy881rR7cqzHk8zifsSX2/Fs/JcT4Oq7W1STNmmESo0lKS9uCccePivyYYlGbOtP7nT+/vA6ta5bpVlbSri6Q9wGG0xwUAAEBm8HikSZPcjgIAUlcgkHzVu+Ji02pk3Tqz2NneLrW2mttgMPZWJAAAAACA7OPxSBMmuDe3Gw3o+nstHk5gTHctLdKCBce2/wXslJsb/zXz55vnqx3C3wdWcSupN5HHFUBSSNoDAABA5igtdTsCAEhNOTnSsmXWjunxmDcZiorMrcdj7fgAAAAAgMzjxvrdsGHuJOzl5vb/WtzNBEar1dRI774rTZvmdiTIBl6vlJcX3zW1taZzhJ1qasw8VnAjqTeRxxVA0kjaAwAAQOZgRycA9K27W3r/fbejAAAAAABkO6fX7yZOlA4fdnbOsNxc6YQT+v98Jm1AXrq07zbAgNUmTox/4+iSJfbEEm3pUmvGcSOpN5HHFUDSSNoDAABA5vD72dEJ53i90vTp0m23SZ/7nNvRAIOze0cxAAAAAACDcXL97vzzpS1bnJmrL+3tUmdn/5/PpA3I9fXS6adn1teE1BRvsmtTk9TQYE8s0errpeZma8ZyOqk3k5KIgTQy1O0A0sWf//xnbd++XXv37lV3d7d8Pp/Gjh2ryZMna8iQ7Ml9/OCDD/Tqq69q7969OnDggD7/+c/rlFNO0UUXXaThw4e7HR4AAIDZ0enUi3Bkl5EjpZ/9TKqoMDul8/KO7j7My5N+/GM3owMGt3mz2xEAAAAAAJBd63ddXabVZV/CCYyZ8lisXCktXy5t2CB99JHb0SBTxZsY6vQm1pUrpUWLkh9n7lxp8eLkx4lnPgCOI2lvAKFQSD/96U/18MMPq7Gxsc/7+Hw+3XDDDbr77rs1YsQI22MaM2aM9uzZk/D1L7zwgioqKuK+7vnnn9fixYv1wgsv6LPPPjvm83l5ebrqqqt0//3364wzzkg4PgAAgKRVVpoXmFSUgtXa26Vrr5UCAWnZssh2AbfcQtIeUt+WLVIoRKsLAAAAAIC7nFi/mzVLWrvWvvFjlZs78OczKYFx82apsFD66lfNxlfAamVlUnFxfNds2GBPLP2xatOsk0m9iTyuACyRPSXi4vThhx/q8ssv1+23395vwp4ktbS06IEHHtD48eP12muvORihMz799FN95zvf0fTp07V+/fo+E/YkqbOzU7/61a80fvx4PfHEEw5HCQAAEGX5csnns2fsk0+WRo2yZ2ykh5oaqaTEtFYI8/ulqVPdiwmIRVvbwG15AAAAAABwip3rdz6fdNZZ9owdD6/XdGcYSDiBMROENwvu3u12JMhUVVXx3X/bNunVV+2JpT/h7wMrxPv1pvo8AI5B0l4fDh06pFmzZmn9+vUR50855RTNmjVLX/va13T++edHfG7nzp26/PLL9eabbzoZqu2+/e1v66GHHlKo1y+WgoICXXLJJfrGN76hKVOm6Ljjjuv5XEdHh2688UY9+eSTboQLAABgFBZKdXVmYcxKXq/0hz9IEydaOy7ST0uLVF4embj37W+7Fw8Qq64utyMAAAAAAMDe9bu6usg1G7dMnBhbtXs7Exid1NYmdXRIGVjoBikgEDAVNGMVDEozZ1qXQBcrKzfNOpHUG+/jCsBStMftw0033aStW7f2HOfn5+vRRx/VddddpyFDjuY5btq0STfeeGNPol5bW5sqKyvV1NSkYcOG2R7nF77wBb300ktxXXPSSSfFfN+HH35Y//3f/91z7PF4dPfdd+v73/++8nrtCtmzZ48WLFigZ555RpJpK3zjjTfqnHPOUUlJSVzxAQAAWMbvlzZulGbMMAlWyfL5zIKf3y+VljpfVh+pp63NPL8aG81C8+zZbkcEDG6wtjwAAAAAADjFrvW74mKp13u9riktje1+4QTG8nKz3pTOgkHp4EG3o0Cm8fmkZcviu2b+fOn99+2JZzBdXVJ+vjVjLV9ufk5a8TMyWiKPKwBLUWkvyksvvaRVq1b1HOfk5Oj555/X3LlzIxL2JGny5Ml6+eWXNW7cuJ5zO3fu1EMPPeRIrEOHDtWYMWPi+jj++ONjGvvgwYP6t3/7t4hzP/7xj3X//fdHJOxJ0umnn66nn35aX//613vOffLJJ/r+97+f/BcJAACQDL/fJFQFAsmNEwiYcfx+c5wpLSuQvJYWacEC8+/8fKmgwN14gIHE0pYHAAAAAAAn2bF+19GRGslv8awhhhMY073i3gsvuB0BMk24emZhYezX1NZKK1faF9NgrNw0a3dV0ngeVwCWI2kvyg9+8IOI4+rqak2aNKnf+xcWFuqxxx6LOLdkyRK1t7fbEp9THnzwQe3fv7/n+Ctf+YoWhN+M7MOQIUP0k5/8RIW9fqj/4Q9/UH19va1xAgAADKqwUFqxQlqzRiori+/asjLzAn/FisgXr36/NG2atXEifdXUmOeJxyMN8NoBcF2sbXkAAAAAAHCS1et33d3WxxivsjJT8S8e4QTG886zJya7eb1S1PvmQFJ8PpPMGt5MH6slS+yJJxZ2bJq1Oqk30ccVgOVI2utlz549EUlmw4YNGzBRLayiokKlvcobHzhwoKdVbLp64oknIo6rqqoGvaaoqEi33nprxLlf/vKXlsYFAACQsMpK80K0qUmqrpamTz92d5rXa85XV5v7bdwozZrV93gx/H2ELLJ0qbmNte0J4AaenwAAAACAVGbV+l1OjnMx9yfRtcPCQunXv7Y2Fqecfbb0yituR4FMEd39JlZNTVJDgz0xxcKuTbN2dRUC4CqS9np5+umnI46vuuoqeWMsM3rzzTdHHD/11FOWxeW0//3f/9WuXbt6jn0+ny6//PKYro1+HJ555hl99tlnlsYHAACQlOJiadEiad06KRiU2tul1lZzGwya84sWDb4TtrKSNrk4qr5eam7mOYHUxvMTAAAAAJAOkl2/y8+3vpVkPAKB/jcCxyJdu3wMIfUAFjj11L6738TKzba4kr2bZu3oKgTAVfzm7KWuri7iuKKiIuZro+/73HPP6ciRIxZE5bzox6G8vFyeGLPBzznnHJ100kk9x62trXrttdcsjQ8AAMAyHo9ZxCsqMrfx7oBbvlzKzbUnNqSflSvTd1EVmS+RtjwAAAAAALgtkfU7j0eaMMH+2Pri80nLliU/Tjp2+aCQC6xw9tnJJb1u3mxdLIlwYtOs1V2FALiGpL1empubI44vvPDCmK8955xzdMIJJ/QcHzp0SLt377YqNEcl8zj0df/t27cnHRMAAEBKKiyUrr/e7SiQKtauNbfpuKiKzMfzEgAAAACQTeysdtUfr1eqq7OmilW6dfmYNk166y23o0Am2LJFCoUSuzYUkrZutTaeeDi9adaqrkIAXEPS3t+1t7dr7969EefGjRsX1xhjx46NON6xY0fScQ2kvb1d//iP/6gLLrhARUVFysnJUVFRkc4++2xde+21WrZs2TFfUyyi4/7iF78Y1/XRj5vdjwMAAICr7rrL7QiQKv73f037kylT0mtRFZkv2bY8AAAAAACkG6fXZk480VSy8vutG3P5clO5Lx0sWCC1tbkdBTJBW5vU2ZnYtR0d7j4P3dw0m2xXIQCuIGnv7955552I46KiIg0fPjyuMU477bSI47fffjvpuAbS1tamRx99VNu2bVMwGNTf/vY3BYNBvfXWW1q1apXuuusujR07VjfffLM++uijmMeNfiyiv67BOP04AAAAuIp2qOht5UqppET6p39Kn0VVZDar2vIAAAAAAJBOnFyzGz1a+vOfrU3Yk0zFvrq6Y9tepppAQKqocDsKZJKursSu6+62No54sGkWQAJI2vu7AwcORByPHj067jGirzl48GAyIVmiu7tbjz/+uC644ALV19cPev8jR46oo6Mj4ly8j0UqPg4AAAC2ou0kemtpkebMMYlSqb6oisxmZVseAAAAAADSjVNrdv/zP/a99vb7TQW/VN0cGt4smJPjdiToj8cj1ddL1dXS9OnHrlcOHWo+UklubmLXufU8POkkNs0CSEiK/fR1T2dUidVhw4bFPUb0NdHJb1YZMmSIJk2apCuuuELjx4/XGWecoZEjR+rjjz9WS0uL/vjHP+qJJ57Q7t27e655//33NWvWLNXX12vChAn9jh39OEjxPxZ2Pg4fffSRWltb47omunIgAACA5SorTcuNlSvdjgSpoq3NtAX5f/9PuuIK6fBhtyNCtvH5TMKe1bv8AQAAAABIF06s2TlRXcvvlxobzVpTTY29c8XD45F+/WuTsBgKmWQwWuSmnsmTTdXJcOXJUMi0n+3qMslxeXnmfGen9K//Kj30kHuxSuZ5FI4pXvn5zj8PPR7pD39g0yyAhJC093fRyWrHH3983GNEJ6v1lQCXrLvuuktXX321xowZ0+fnS0pKNGPGDN1zzz1atmyZqqqq9Le//U2SdOjQIV155ZV66623+m3921fM8T4Wdj4OjzzyiO677z7LxgMAALDM8uVm12tLi9uRIFW0tJiFLhL24LRAwOzuZbEQAAAAAJDt7FyzC1eZc0JhobRihXnNv3SpqZzmtlBI+slPTDKYxyNNmCBt2OB2VIh2ySWRxx6PSW7Lz488n58v3Xqr+0l7EyeaGBPhxvNw8mSppMS5+QBklJRpj3vnnXfK4/HY/nHvvffGFI8ngV8EiVwTr+9+97v9Juz1dtxxx+m73/2uVq1apSFDjv437927Vz/+8Y/jmjPer8uJxwEAACDlFBaaqla0Q0VvGze6HQGySVmZVFtrFvFJ2AMAAAAAwL41O6/XjOv06+/KSrPe1NTUf7tTr9ec/8Y37I+npsasRUhSaan98yF+c+fGfl+//2hFPrck+zxy+nkYnRQJAHFImaQ9t+VFlVg9nEA1jOhrosd0w5VXXqk777wz4tx//dd/9Xv/vmKO97FIxccBAADAEX6/WTTz+dyOBEA2CC/CV1ebxfqNG+1vyQMAAAAAQLqxes3O5zPj+f3WjJeI4mJp0SJp3TopGJTa26XWVnMbDJrz77/vTCxLl5rbeJLDspnPJ02Z4sxcZWXmuRKPqip7YolVss8jp5+HPO8BJIH2uH+XqUl7krRw4UItX75coVBIkvTXv/5Vzc3NKu7jF3R/SXvxfC12Pg533HGHrr322riueeedd3TVVVdZFgMAAMCA/H6psVFasMDsNAUAO1RUSM8/n3i7EAAAAAAAsolVa3aBgGmJm0oV7vtqd9rUJDU0ODN/fb3U3Hy0SptT86ajcIXGd9+VZs+2f75EEvAqK00i2sqV1sczmESSDKM5+Ty0Il4AWS1lkvbmzJmjU045xfZ5Lr744j7PFxQURBy3trbGPfZHH30UcTxq1Ki4x7DDySefrJKSEm3btq3nXGNjY59Je0OGDFFeXp46Ozt7zrW2turEE0+MeT47H4fRo0dr9OjRlo0HAABgi8JC054yEDA7Tevr3Y4oNl6vNHGiNGaM9NhjbkcDYCBTp5KwBwAAAABAPJJZsysrMwlQ6VLh3umEq5UrTeW/qiqS9vrj85mEPb/ffNidGBcIJP58Xb7cVJNsabE2psFYVeXPqeeh21UJAaS9lEnau+yyy3TZZZe5Nv+ZZ54Zcdza2qqPP/5Yw4cPj3mMPXv2DDimm8aMGRORtDdQUuKZZ56p119/ved4z549Ou+882KeK5UfBwAAAEdVVpqP5mazALN5s7Rli9TWdvQ+4US58eOl//t/nY9x714pJ0fKzZXy8kwSUHW183EAiA+tNwAAAAAASEw8a3alpeY1eLpV09q82Z353KzSlsr6qtBoZ2Kcz2fmS1RhoUkwLC+P/L6wUzJJhtGceB5aGS+ArJUySXtuGzlypHw+n1p6/VLcuXOn/H5/zGPs2rUr4vjcc8+1LL5kDRs2LOJ4oPa/5557bkTS3jvvvBPXXH/5y1+OGQ8AACCrFRebnaaSFApJnZ1SV1dkolwoJP38584tgkhm8fHkk4+t1uX0oh6A+NB6AwAAAACA5MWyZpeOQiFp61Zn59yyxczr8bhXpc0JxcUm2TNWA1VotCsxLtyCN9kWzn6/+X+cMcP+/8tkkwz7kspJkQDwd0PcDiCVRLeLfeWVV2K+9o033lAwGOw5Hj58uM444wzLYkvWvn37Io6Lior6vW8yj4Mk/fGPfxxwPAAAgKzm8Uj5+VJRkbkNL/55PNKECc7GMnHisYuPbizqAYgPrTcAAAAAALBWf2t26aijw9mNwZKZr7PT/DucjOb1OhuD3QIBqanJfFRXS9OnH/s1er3mfHW1ud/GjQNXYwsnxvl81sTo85nx4ihMNCC/X2psNF+7XaxKMoxm1/PQrngBZCWS9nqZMWNGxPGLL74Y87XR973iiis0ZEhqPLyfffaZtmzZEnHON8Av/ujHob6+XqFQKKa53njjDX3wwQc9x0VFRZo0aVIc0QIAAGSx0lL353NjUQ9A7Gi9AQAAAAAABtLd7c68XV1H/211MprbeldWC1doXLdOCgal9naptdXcBoPm/KJFsXdJsCoxLhAw41iVsBdWWCitWCGtWWMqB1rJ6iTDaKmeFAkg66VGVlmKuPrqqyOOf/e73+nAgQMxXfv4448POJabnn32WbX1euN16NChuvjii/u9/5e+9CWNGTOm53jv3r167rnnYpor+nG48sorddxxx8UVLwAAQNaaO9f9+dxa1AMwOFpvAAAAAACAweTkuDNvbm7ksRNV2pwwUGU1qyo0JpMYV1Ym1daa6+2s/lZZaRLWmpqk735XOumk5MazK8kwWqonRQLIaiTt9TJmzBhNmzat5/jw4cN66KGHBr1u48aN2rRpU8/xqFGjdOWVV9oSY7wOHTqkhQsXRpyrqKjQyJEjB7zuW9/6VsTxkiVLBp0rGAzqscceizh3ww03xBgpAAAA5PdLvf4etVVZWd+7Pd1a1AOclm7V6mi9AQAAAAAAYpGf73xrWq9Xyss79rydVdqc4HRltd6JcVa14LVacbH04IPS++9Lv/+9NHVqfNc7lWTYWzokRQLISiTtRfnRj350zPFrr73W7/3379+vW2+9NeJcVVWVCgoKBpxn9+7d8ng8ER+7d+/u9/779u3TE088oc8++2zwL+LvOjo6dO2112r79u0R5++5555Br/3e976nE044oef4hRde0PLly/u9/5EjR/SP//iPCgaDPeeuuOIKlZeXxxwvAAAAJFVVuTuPG4t6gNPCi23psmBM6w0AAAAAABArj0eaMMHZOSdOHLjKXCzJaEOH2htjvNysrGZ1C167zJ4tvfxyaicZ9pYOSZEAsgpJe1EuvvhiXXPNNT3H3d3duvTSS/XrX/9aR44cibjvpk2bNHXqVO3cubPn3Lhx47RgwQLL4+rs7NQNN9ygs846S/fee68aGxuPiSfsk08+0S9/+UtdcMEFevbZZyM+d8sttwzYGjesoKBA999/f8S5u+66S/fcc486Ozsjzr/77ru6+uqrtWrVqp5zubm5Wrp0aaxfHgAAAMIqK+1vkxsI9L/Q4MaiHuC0cNJqXwt1xx3nbmzRaL0BAAAAAADiVVqamvMNlIz29NP2xhirVKusZlULXjulS5JhWLrFCyBjeUKhUMjtIFLNoUOHNG3aNL3++usR50899VSNHz9eOTk5euutt9Tc3Bzxea/Xq1deeUVnn332oHPs3r1bZ5xxRsS5Xbt2acyYMTHff8SIESouLtbo0aM1cuRIHT58WO+//762bt2qrq6uY8aorKzU7373Ow2NY5fC//k//0c//elPI86NGjVKkyZNUmFhod577z1t3rxZn376ac/nPR6PVq5cqeuuuy7meey0fft2Fff6hdrc3Kzzzz/fxYgAAAAGEQxKJSVSS4v1Y/t8JgFooAWn6mpp8WLr5wZSQSBgFl378y//Ij3wgHPx9KeszCQXspMXSCusQQDW4HsJAAAgSU1NZn3RyfnOP1/q6JC6u6WcnMQSzAIBaeVKe2KUTFxDhkiffHL0nNdrKgWWlprN1CRqAUBWcXMNIsVqzKaGESNGaO3atbr++uu1YcOGnvPvvfee3nvvvT6vGTdunFauXBlTwp5VDh06pE2bNg16v8997nO65557tHDhQh0XZ9WIRx55RMOGDdPy5csVzu88cOCA1q9f3+f98/Ly9J//+Z8pk7AHAACQlgoLpbo6qbxcamuzblyv14w72A7RuXNJ2kNm8vmkZcsGvk8g4E7SHgvEAAAAAADAKn6/NG2a1NDgzHzTp5tEuIMHj57zek1Hj9JSs94Sy1rH8uWmK4Idm5lPPtkkF55wgtTZKXV1Sbm5Ul5ealavAwBkPNrj9uOkk07SunXr9JOf/ET+AdoQnXzyyaqqqtK2bdv05S9/2bZ4TjzxRN1///269NJLlZ+fH9M1p556qhYuXKh33nlHP/jBD+JO2JOkoUOH6qGHHtL69et16aWXasiQvp8yI0aM0PXXX6/GxkbdeOONcc8DAACAKH6/WaDy+awZz+cz48XSYjO8qAdkkliTVp18/l90Ea03AAAAAACAPaqqnJvrww8jE/Yksxl5wwazOdjvN10F1q4deJzwZmav19r4vF7pD38w46dDu1kAQFagPW6MduzYoebmZrW0tKi7u1s+n09jx47VlClT+k1ks0soFNKuXbv09ttva+/evWpra9Phw4eVk5Mjr9er0aNHa9KkSfrCF75g+dzvv/++Nm/erL179+rgwYMaPXq0Tj31VF100UUaMWKE5fNZgXYaAAAgrQWD0oIFUk1N4mMEAqa62GDJSr3V1kqzZyc+J5BKfD6z4BtL0qrk3PO/tpb2t0CGYQ0CsAbfSwAAABaxu91sImJZq2xqkmbMsKbinsdjqv1dcknsFf8AAFnDzTUIkvaQ8VjkAwAAGaG2Vlq6VKqvj/2asjKzozbRpKDy8vjmA1JRIkmr4evsXNQOBKQVK+wbH4ArWIMArMH3EgAAgEWCQamkxJ52s8mIZYOlFZuZ+zJtmrRwIRspAQCS3F2DoD0uAAAAkA4qK01726Ymqbpamj792DYRXq85X11t7rdxY3KLT93dycUMuKmszCS7rlgRf8JeKCT96EfSSSfZE5vPZxIJAQAAAAAA7GRXu9lktbSYDcNNTf3fp7DQrOusWWPWeazS0GDWWufNM4mBAAC4ZKjbAQAAAACIQ3GxtGiR+XcoJHV2Sl1dUm6ulJdn2j1YoalJ+tOfrBkLcNpTT0lXXx3fNU1NprLe5s3S1q1SW5s9sXm9ZrE83kRCAAAAAACARPj9ZnOvVe1mrdLWZmJqbBx4naSy0nw0N5u1mw0bzPpNsg0Fa2qkF18cvOIfAAA2odIeAAAAkK48Hik/XyoqMrdWJexJ9rYFBexUVhZfwl5trbmmpERavNgs/NqVsOfzmUVyFoIBAAAAAICT/H6THBcIuB1JpJYW0wI3FsXF0nXXSW+9lXzCXu/5B6v4BwCATUjaAwAAABApGJR+/nO3o8DIkdYmYmaLqqrY7hcMmoXq2bNNWxS7BQJmcZyEPQAAAAAA4IbCQmnhQrejOFZNjdlUOZhgUJo50/rNluGKf7TKBQA4jKQ9AAAAAEc1NpqKYx9+6HYk2WnUKOmjj6T2drNgmJ/vdkTpJRCQZs0a/H7h57kTFSXLyszC84oVtMQFAAAAAADuStXuGkuXDn6f+fPta+8bT8U/AAAsMtTtAAAAAACkiMZGqaLCvtagGNyBA9Lxx5tkvfZ284HY+HzSsmWD38/u57nXK02cKJWWSnPnmtYtAAAAAAAAqWDzZrcj6Ft9vdTc3P86Sm2t/QmHNTVmQ2hlpb3zAADwd1TaAwAAAGBfewnEr6vL3HZ3uxtHOvF6pbq6wSvZ2fk8//znpV27zBzr1kmLFpGwBwAAAAAAUkcoJG3d6nYU/RsoKW/JEmdiiKXiHwAAFiFpDwAAAIC97SUQn9xcc5uT424c6cLnkzZulPz+we9r5/P8ww+lH/xA8njsGR8AAAAAACAZHR2pvWG3vyqATU1SQ4MzMYQr/gEA4ACS9gAAAIBs50R7CcTG65Xy8sy/8/PNMfoXCJh2t7Ek7DnVRqW21t45AAAAAAAAEpHqXR22bDHVAKM5vW7JOikAwCEk7QEAAADZzqn2EhjcxIlHK7V5PNKECe7Gk6rKykxy3IoVg7fEDaONCgAAAAAAyGap3tWhrU3q7Dz2fH8V+Ozi9HwAgKxF0h4AAACQzZxsL4HBlZYOfJytvF5p+nSputo8ZzdulGbNiv162qgAAAAAAIBslw5dHbq6Io/XrDHrQE7qr+IfAAAWI2kPAAAAyGa0e0gtc+cOfJxtTjpJ2rVLCgaldeukRYuk4uL4x6GNCgAAAAAAyHbp0NUhN9fcBoNSICB99avSp586G0N/Ff8AALAYSXsAAABANkuVdg+p3p7DCWVlxyak+f3StGnuxOM2r1d67jlpzJijLYMTRRsVAAAAAACA1O7q4PVKeXlSY6NUUuLupsjoin8AANiApD0AAAAgW4VC0tatbkchnXCCtH695PO5HYm7qqriO5/JfD7T+sTvT34sN57ntFEBAAAAAACpKJW7OkycKDU1SRUVUkuLu7GEK/4BAGAjkvYAAACAbNXRYdo9uCknR3rxRVNNrrHRtL3IRoGANGtW35+rrEztBVWrBQLmuWBFwp7kzvOcNioAAAAAACAVpXJXB79fmjnT/fXKcMU/AABsRtIeAAAAkK26u92df8gQU2EvnJxVWCitWCGtWWNaxWYLn09atmzg+yxfnvmVCMvKpNpa8xwoLLRuXLee57RRAQAAAAAAqShVuzq88Yb7FfYkU/HP43E7CgBAFiBpDwAAAMhWOTnuzh2usBetstK0Rm1qkqqrpenTzQ7X3rxek+Tl5K7X0aOlkSOtHdPrlerqBk9SKyw094t+HFLdU08N/H84fbr5fFOT+T/vr9pgMtx6ntNGBQAAAAAApKJU7OpQXCw9+6zbURilpW5HAADIEkPdDgAAAACAS/LzTeKU0y0nTjjBJOwN1v60uFhatMj8OxQy7Ua7ukwyVF6e2fHa1CSVl9v/NQQCphpeS4s0Y4Y1u359PpOIF2sbWL/fJLZNmuR+lcRYlJVJV19tPqT+/w/t5sbznDYqAAAAAAAglS1fbtaZUqGynWTWjVJFqiU0AgAyFpX2AAAAgGzl8UgTJjg750knSW+9FXuiWpjHY5KviorMbTjZK5zIZlfr2OiWrX6/1NhokviSEQiYceJ9HKT0SNiTjm210t//od3ceJ7TRgUAAAAAAKSyVOrqMGuWtH2721EYZWVmIzEAAA4gaQ8AAADIZk63e7jllsFbwcbLqkQ6SRo1avCWrYWFJolvzRqzkBeP6CTAeK1cGf81bggE7Gl1myinn+e0UQEAAAAAAKnO7s2wsfD5pDPPdG/+aNGbUAEAsBFJewAAAEA2c7rdg13zJZNIN3WqVFMjtbdL+/dL69aZtryD7aqtrDQLm01NJslv+vRjdyd7vYMnAcZj8+bEr3WKz2daCaeSTHmeAwAAAAAAWMnKzbDx8npNtb/mZufn7kuqbUIFAGS8oW4HAAAAAMBFfr80bZrU0GD/XE60l6isNB/NzaYq3ebN0pYtUlvb0ft4vaZ9aWmpSa5KNqbiYpPkJ0mhkNTZKXV1Sbm5Ul6edW1SQyFp61ZrxrJLeLHV6mqKycq05zkAAAAAAIBVwpthAwFp6VKpvt7+OX0+s4ZUXJwa612puAkVAJDxSNoDAAAAsl1VlTPJTE62l3AqkS6axyPl55sPq3V0RCYfpprwYqvf73YkfcvE5zkAAAAAAIBVYtkMO2qUdPzx0gcfJD5PIGAS5AoLTecLt9e7UnUTKgAg49EeFwA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\n",
+      "text/plain": [
+       "<Figure size 3000x2400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "noisy_moons = datasets.make_moons(n_samples=1000, noise=.05)[0].astype(np.float32)\n",
+    "z = flow.f(torch.from_numpy(noisy_moons))[0].detach().numpy()\n",
+    "plt.subplot(221)\n",
+    "plt.scatter(z[:, 0], z[:, 1])\n",
+    "plt.title(r'$z = f(X)$')\n",
+    "\n",
+    "z = np.random.multivariate_normal(np.zeros(2), np.eye(2), 1000)\n",
+    "plt.subplot(222)\n",
+    "plt.scatter(z[:, 0], z[:, 1])\n",
+    "plt.title(r'$z \\sim p(z)$')\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "x = datasets.make_moons(n_samples=1000, noise=.05)[0].astype(np.float32)\n",
+    "plt.scatter(x[:, 0], x[:, 1], c='r')\n",
+    "plt.title(r'$X \\sim p(X)$')\n",
+    "\n",
+    "plt.subplot(224)\n",
+    "x = flow.sample(1000).detach().numpy()\n",
+    "plt.scatter(x[:, 0, 0], x[:, 0, 1], c='r')\n",
+    "plt.title(r'$X = g(z)$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27e2c49d-7b84-49ae-ad86-8fd1114021f9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## WITH NFLOWS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "e7b79c85-a90e-4c04-b4b6-201febee2113",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'nflow' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[92], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnflow\u001b[49m\u001b[38;5;241m.\u001b[39mbijectors\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'nflow' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "nflow.bijectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "3e85d649-e33d-4d49-9e4b-dff44fbed770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_layers = 5\n",
+    "base_dist = ConditionalDiagonalNormal(shape=[6], \n",
+    "                                      context_encoder=nn.Linear(1, 4))\n",
+    "\n",
+    "transforms = []\n",
+    "for _ in range(num_layers):\n",
+    "    transforms.append(ReversePermutation(features=6))\n",
+    "    transforms.append(MaskedAffineAutoregressiveTransform(features=6, \n",
+    "                                                          hidden_features=4, \n",
+    "                                                          context_features=1))\n",
+    "transform = CompositeTransform(transforms)\n",
+    "\n",
+    "flow = Flow(transform, base_dist)\n",
+    "optimizer = optim.Adam(flow.parameters())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "e097405a-851f-4b5d-bff3-6081bc2d6357",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 6]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "shape '[64, 6]' is invalid for input of size 128",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[60], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(ff\u001b[38;5;241m.\u001b[39mshape, zs\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m      5\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m----> 6\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[43mflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mff\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mzs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mmean()\n\u001b[1;32m      7\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m      8\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/distributions/base.py:40\u001b[0m, in \u001b[0;36mDistribution.log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inputs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m context\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     38\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of input items must be equal to number of context items.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     39\u001b[0m         )\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/flows/base.py:40\u001b[0m, in \u001b[0;36mFlow._log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     38\u001b[0m embedded_context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_embedding_net(context)\n\u001b[1;32m     39\u001b[0m noise, logabsdet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform(inputs, context\u001b[38;5;241m=\u001b[39membedded_context)\n\u001b[0;32m---> 40\u001b[0m log_prob \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_distribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnoise\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membedded_context\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m log_prob \u001b[38;5;241m+\u001b[39m logabsdet\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/distributions/base.py:40\u001b[0m, in \u001b[0;36mDistribution.log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inputs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m context\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     38\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of input items must be equal to number of context items.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     39\u001b[0m         )\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/distributions/normal.py:104\u001b[0m, in \u001b[0;36mConditionalDiagonalNormal._log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     98\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected input of shape \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, got \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m     99\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shape, inputs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m    100\u001b[0m         )\n\u001b[1;32m    101\u001b[0m     )\n\u001b[1;32m    103\u001b[0m \u001b[38;5;66;03m# Compute parameters.\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m means, log_stds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m means\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m inputs\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;129;01mand\u001b[39;00m log_stds\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m inputs\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    107\u001b[0m \u001b[38;5;66;03m# Compute log prob.\u001b[39;00m\n",
+      "File \u001b[0;32m/data/astro/scratch/lcabayol/anaconda3/envs/DLenv2/lib/python3.9/site-packages/nflows/distributions/normal.py:91\u001b[0m, in \u001b[0;36mConditionalDiagonalNormal._compute_params\u001b[0;34m(self, context)\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m     87\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe batch dimension of the parameters is inconsistent with the input.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     88\u001b[0m     )\n\u001b[1;32m     90\u001b[0m split \u001b[38;5;241m=\u001b[39m params\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m---> 91\u001b[0m means \u001b[38;5;241m=\u001b[39m \u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43msplit\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_shape\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     92\u001b[0m log_stds \u001b[38;5;241m=\u001b[39m params[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, split:]\u001b[38;5;241m.\u001b[39mreshape(params\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shape)\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m means, log_stds\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: shape '[64, 6]' is invalid for input of size 128"
+     ]
+    }
+   ],
+   "source": [
+    "num_iter = 50\n",
+    "for i in range(num_iter):\n",
+    "    for ff, zs in loader:\n",
+    "        print(ff.shape, zs.shape)\n",
+    "        optimizer.zero_grad()\n",
+    "        loss = -flow.log_prob(inputs=ff, context=zs.reshape(-1, 1)).mean()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9cfcdee-479e-4be2-aa5c-3755f52649a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    if (i + 1) % 500 == 0:\n",
+    "        fig, ax = plt.subplots(1, 2)\n",
+    "        xline = torch.linspace(-1.5, 2.5,100)\n",
+    "        yline = torch.linspace(-.75, 1.25,100)\n",
+    "        xgrid, ygrid = torch.meshgrid(xline, yline)\n",
+    "        xyinput = torch.cat([xgrid.reshape(-1, 1), ygrid.reshape(-1, 1)], dim=1)\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            zgrid0 = flow.log_prob(xyinput, torch.zeros(10000, 1)).exp().reshape(100, 100)\n",
+    "            zgrid1 = flow.log_prob(xyinput, torch.ones(10000, 1)).exp().reshape(100, 100)\n",
+    "\n",
+    "        ax[0].contourf(xgrid.numpy(), ygrid.numpy(), zgrid0.numpy())\n",
+    "        ax[1].contourf(xgrid.numpy(), ygrid.numpy(), zgrid1.numpy())\n",
+    "        plt.title('iteration {}'.format(i + 1))\n",
+    "        plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "DLenv2",
+   "language": "python",
+   "name": "dlenv2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}