diff --git a/_toc.yml b/_toc.yml
index 77570724ca12de95e9f8ca400e114d7c4d345085..f98b9dd7601051000b6e4dcf15af3314903e08a2 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -1,2 +1,22 @@
 - file: README
-- file: 01-federated-averaging
\ No newline at end of file
+  title: Welcome
+
+- part: Federated Learning
+  chapters:
+  - file: federated_learning/introduction.md
+
+  - file: federated_learning/FedAvg_FedProx_MNISt_iid_and_nidd.ipynb
+    title: FedAVG and FedProx
+
+  - file: federated_learning/federated_mcvae.ipynb
+    title: Federated VAEs
+
+- part: Heterogeneous Data
+  chapters:
+  - file: heterogeneous_data/introduction.md
+  - file: heterogeneous_data/multivariate_models_v1_7.ipynb
+    title: "Example: Latent analysis"
+
+# - part: federated Learning
+# chapters:
+# - file: federated_learning/introduction.md
\ No newline at end of file
diff --git a/federated_learning/FedAvg_FedProx_MNISt_iid_and_nidd.ipynb b/federated_learning/FedAvg_FedProx_MNISt_iid_and_nidd.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3834babaf8c47f542bece784a94ab9134ec9d856
--- /dev/null
+++ b/federated_learning/FedAvg_FedProx_MNISt_iid_and_nidd.ipynb
@@ -0,0 +1,798 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example of FedAvg and FedProx for two datasets: MNIST iid and MNIST non-iid\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. MNIST iid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"UPLOADING THE DATASETS\"\"\"\n",
+    "import torchvision.datasets as datasets\n",
+    "import torchvision.transforms as transforms\n",
+    "\n",
+    "mnist_trainset=datasets.MNIST(root='./data', train=True, download=True, \n",
+    "    transform=transforms.ToTensor())\n",
+    "mnist_train_split = torch.utils.data.random_split(mnist_trainset, \n",
+    "    [200, 200, 200, 60000 -3*200])[:-1]\n",
+    "mnist_train_dls =[torch.utils.data.DataLoader(ds, batch_size=10, \n",
+    "    shuffle=True) for ds in mnist_train_split]\n",
+    "\n",
+    "mnist_testset=datasets.MNIST(root='./data', train=False, download=True, \n",
+    "    transform=transforms.ToTensor()) \n",
+    "mnist_test_split = torch.utils.data.random_split(mnist_testset, \n",
+    "    [100, 100, 100, 10000 -3*100])[:-1]\n",
+    "mnist_test_dls =[torch.utils.data.DataLoader(ds, batch_size=10, \n",
+    "    shuffle=True) for ds in mnist_test_split]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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NI/fOId/MTTQYQRCMIQJGEARj5GYiCYIgBIxoMIIgGEMEjCAIxhABIwiCMUTACIJgDBEwgiAYQwSMIAjGEAEjCIIxRMAIgmAMETCCIBhDBIwgCMYQASMIgjFEwAiCYAwRMIIgGEMEjCAIxhABIwiCMUTACIJgDBEwgiAYQwSMIAjGEAEjCIIxRMAIgmAMETCCIBhDBIwgCMbIbW3qSFzTRFYHdMjPc4P8Pb98MzfRYARBMIYIGEEQjCECRhAEY4iAEQTBGCJgBEEwRm5RJEEQIpiJEycCMGbMGI4cOQLAyJEjAXjiiScoW7as0euLBiMIgjEs284x5J5v4vHZkJ/nl6e5jRgxAoBVq1axcuVKvX348OHp9ruM3DuHPM3t6NGjAHTp0oXly5dne1yXLl14//3383IpbyQPRhCE4GLEB3P48GEAHn74YQCmTp1K1apVTVxKyCMrV65Mp6GsWrVKb88KZb/nhiENR/CBX3/9FYDly5dTo0YNwPG3JCYmArB69WoAjh07ZnwsRkykq6++GoBixYoBsGHDBooUKeLXOQ4ePMjjjz8OwEsvvUStWrV8/aio2Q45zk0JkNjY2DwOJ2eGDx9OixYtAPTPHAjavVu3bh0AycnJpKWlAfD3v/8980Vsm86dOwPw2GOPAXDnnXcGetmgmEjnzp0DYO/evZQvXx6AqKgomjdvDsDmzZsBZx5Lly7Ny6W8ERNJEITg4roGk5yczI033gjAvn37AChRooTfA/vss8+46667AJgyZYrWZnwgJBrMtm3bAPjpp5/47LPPAPj000/1tosXLwLOmwSgb9++jB49GoCSJUv6cylX34KW5c+fKzCU5pKUlJTboa7duwsXLgBw8uRJUlJSABg6dKjev3XrVsDzjPpC6dKlAfj3v/9NXFycH0PVBL3YcdasWYATrv7f//4HQMGCjmdkxowZdOvWza1LZTk3130wb7zxhv4SBSJY1IMxZ84cGjVqBEDTpk3dG2CAJCcnA84DuWfPHsB50BTKd5HVF/Yvf/kLDRo0SHfclClTOH78OACzZ882Nu7cGD58eI5+lxYtWmjVOjty89sEi7NnzzJ9+nTAE0nx1WfkC8pnoe5/uDNv3jweffTRTNtr164NQPXq1Y2PQUwkQRCM4ZoGc/bsWQAWL17ME088EfB51Fti1qxZPPXUUwDUqVMn7wP0kePHjzNt2jQA/vjjDwDmzp3LL7/8AsD58+c5f/48QDrHtXKmtWvXjipVqgDon127dtXHKqfqb7/9xuWXX256OrniRrQnN0exyp0xzfjx4wO6VsWKFQFo06YNPXr0SLdv4MCBfPnll+m2TZw4UUdIixcvHuBozdOsWTPtoLZtm2+++QZAm0pxcXH07dsXcFfT80Y0GEEQjOGaBvPuu+8CjgYwYMCAgM/j7dfo2LFjXoflN08++WSm7MYqVaro0GSdOnW0RnXttdfqY5SPJTuUE/irr74CIDo6mtatW7s27mCh/CzqZ8ZMX4Vy7HqHqU2TnUZYqVIlgEzaiaJLly6AxzfhzbRp06hbt266bbt372bBggUAPPLIIwGP1zRVqlRh7ty5+v+7d+8GPNrKwoULda1S165dAfxJB/EJVwTMyZMnGTJkCAB9+vShTJkyAZ/rtddeA+D666/XTt5g8tZbb1G5cmXAUTEBbrrpJv2QBsp3332X7v8xMTG5Ok/DjREjRvikSg8fPjwkiXZbtmyhYcOGmba//vrrANx6662uXUslrYWzgMmIyk9TL/HTp0/zwQcfAJ6Ip9sCRkwkQRDMYdt2Tv98YvTo0XbhwoXtwoUL28nJyb5+LB1Hjx61jx49akdHR9vR0dH2hAkTAjqPnfN8AppfXjly5Ihdp04du06dOnZUVJQdFRVlr1ixItDTBWVuSUlJdlJSkj18+HAbJy/D5395IOzu3eHDh+1WrVrZrVq10vcuKirKjouLs+Pi4vw9XVjN7ZNPPrEty7Ity7JjY2Pt2NhY+/z584GeLsu5uGIiJScnU6BAAcDxYSg/g0q4q1ChAldccQXgmB0tW7YE4LLLLtPnmDJlCgCFCxcGPHVM+YG5c+eyfft2AK688krA87cJB1QUyK08Fsuy/EmuC2vKli1L48aNAXKsTI50VC7TiRMnXO0RIyaSIAjGcEWDqV+/PjExMYCTgq0iKnv37gVg586d+vfx48frY5UT9+677+aVV15xBnQpjfn333/XuSWRiio6U4V14NEW/CwPiDiUNmRZltZighVNcpP9+/ezePHiUA/DCDNmzMC+VCrUrl07ANc73IkGIwiCMVzRYP7xj3/kuP/MmTNagwG0P+KLL74AYPLkybp2RHHjjTeya9cugDyFvUPJ/PnzAXjvvfcoWrQogM5ODidUuDw7H0xW2bHeLRgyhqQzhrKV1ma4G54Rjh8/rjNfvVF5X6Hkjz/+0L7P6OjogM6haucy5vq4RVi0zJw+fTq9evUCPMKnWrVq6ZzAfhA2/WBUAteCBQvo1KkTgBstCo1V5I4YMcKf3i3ZsnLlyhzLB3J45sLm3qkeMX379iU+Pj7dvujoaA4dOgRAqVKl/Dmtq/du7NixOm/lvvvu83kQqtTljjvuYP369YDnZX/zzTf7fJ4MSD8YQRCCS1gsWzJr1iyuuuoqwFNCrpy9kcipU6cA2LRpE+Bk7Q4ePDiUQ/IJ06ZLJDl5VUZ5Ru0FnGJHPzUXI1x22WU6KzcuLk6bS7mxZs0aANauXcs111wD5ElzyRHRYARBMEZI1YSTJ08CsH79el33E8mai0J1qlPFZX369DHmRAsHlHNYOXf9cRYLgfPAAw/Qv39/ABISEnKti1ILr3lr0w8++KC5ARJiAbN27VrA6SXTu3fvUA7FVbwrwoFAndVGyFgN7U2LFi18NmO8hUpuGcC5BBKEAClWrJh28g4aNEhX+WeVJX7s2DHuvvtuADZu3Ag4z2VWHe/cREwkQRCMEVINZubMmfr3rMrsI5Fff/1VhwHV20W1sggHcgofZ9WKoUWLFgHVKHn3g8kv3HDDDQC6S1yoKVq0qO4pNHHiRNq0aQN46vo6dOig0yImTZqkNReVMzNs2DDdwsEUYeHwaN++fb5ZmG3VqlWcOHECcPIUILLLAgIRLklJSREVMfIV1R6zXLlyIR6Jh3HjxgFOac2cOXMAdM5V4cKFddtXQBcc9+vXD3DMKtOIiSQIgjFCqsGoLMKxY8fmi+iRQjk11To64YQqPMwY+QmUFi1aaDMoP2gtatlVpX16E4xlPvxF5b5MmTKFe++9F/BoJt5dFNu1a8fAgQMBuOWWW4I2PtFgBEEwRkjUBpUfogog89rvNtwIxmqJgZKx1ii77F213Z/QdX5ALfyX1cLww4YNC/Zw/EI5fMOpmXxIBIxaL0h1gf/22291ol1+QPXUuOeee0I8ksCJpIpn09SoUQMIr3ymSEFMJEEQjBESDUb13VWmUn6ifPnyOpNSzVOIHFS3xQceeABwevk89NBDgGcFSMF3wqIfjMuEvKeIqjvaunWridMb6wcTBoT83hnmT3fvxEQSBMEYuWkwgiAIASMajCAIxhABIwiCMUTACIJgDBEwgiAYQwSMIAjGEAEjCIIxRMAIgmAMETCCIBhDBIwgCMYQASMIgjFEwAiCYAwRMIIgGEMEjCAIxhABIwiCMUTACIJgDBEwgiAYQwSMIAjGEAEjCIIxRMAIgmAMETCCIBhDBIwgCMbIbeG1SFxyQNbWccjPc4P8Pb98MzfRYARBMIYIGEEQjCECRhAEY4iAEQTBGCJgBEEwRm5RJMFHtm7dCsDHH3/MNddcA8CmTZsAePnll+nVqxcA48ePB6BEiRIhGKUgBBfRYARBMEbQNJhjx44B8J///IfExMR0+woUKMC5c+cAuP/++wGoUqUKcXFxwRqeX2zbtg2A+fPnA/DOO+9w4sQJAP74448sPzN9+nQAKlasCMDIkSNND1P4E7Bo0SIAZsyYwYoVKwD4/fffAbBtG8ty0lMqVqxI06ZNAejXrx8ATZs2pUCBAkbHJxqMIAjGsGw7x6RBVzIKV61axT333ANAamqqT58pVaoUu3btAqBcuXL+XM5oNuiAAQN46623AEhLS/Oc6NLfsVChQpQpUwaA48ePA45Wo94k9evXB2Djxo3+Xlrxp8sGzQa/59ehQwcWLlwIQMOGDQE4efIkP/74IwDNmzfXx/72228AbN++XW9r06YNABcvXmTJkiX+Xh4M3Lt7770XgL1792rtuEGDBgBER0fr4w4ePMjSpUsB2LdvHwAtW7Zk7ty5APqZzQNZzi0oJtLgwYO1YGnevDkdO3YEPGZT1apVKVq0KACrV68G4M0332Tq1KkADBkyJBjDzJGffvoJgNdee00LC29uu+02ALp3787f//53wHHuAjz77LP6uJYtW5oeqk988cUXALz//vt625QpU1w596hRo8LinmXkwQcf1IJ9y5Ytevt9990HOMKkRo0aAFx11VUAHDlyhF9//RWATz75BIBbb701aGPOjQ8//NDnY48cOQLAiy++CDhm++DBgwH0d81txEQSBMEYQdFgUlJS9Bth0aJFlCxZMt3+06dP89lnnwGwYMECAIYNG5buzR9qVOi5cuXKHDhwAIBbbrkFgP79+2vntDfe5qf6PReT1Dg7duwAHE0LHNVaEagG85///AeAl156CYCdO3fyww8/ADB79uyAx+o2HTt21IGDmTNnAo7J/sgjjwBO6oBykB4+fBhw/l5KgylWrBgAw4cPD+q43aJs2bIAvPLKK4Cjla9bt87oNYMWRVJ+lIzCBeCxxx5j3rx5ANq8GDJkCAULhl+azvbt2/WXtFGjRgCZxjlq1CgARo8erbcps6p06dLBGGa2KBPh559/du2c6su4efNmvS0hIQFwvrSTJ0927Vp5pXDhwgA6L8mbo0eP0qFDB8Bjql955ZUMHToU8NzvcDFz/eXQoUMA/Otf/wIgKSnJeDRTTCRBEIwRlCjS5Zdfrr3yO3fu5OTJkwDa2ZuWlsa4ceMAaN++PZDeA+4nIe0p8v7779OjRw/AkxPjnY+gtJ22bdtq08JP8hSJUNePisr8bpk7d65+g/vD22+/DXjyK8CJtADcfvvtWoO57rrrcjtVSO7dwYMHAXjhhRcoVKgQ4Iwb4Prrr6dmzZpuXSpkEcAvvviCLl26AJ48mbfeeouHHnrIrUtIPxhBEIJLUJwc3bt3Z8yYMQDcfffdnDp1CvA4TufMmUPVqlWDMRRjKA1sxIgROis5K1TuzKJFi3ToW/0dQk3fvn1ZtmwZ4IR0AZ39GShr1qxhzZo1gE8aTNA4f/48AM888wwzZswAnOf01VdfDeWwXEHNbdmyZaxcuRKAadOm0bhxYwDi4+MBx79kmqCYSFu3buWmm24C4Ny5czohSBUI+plIlxtBV7P79OnDtGnTMm3v1KkTAD179uR///sfAE8//bTe36pVKwA+/fRTfy5nzES6ePGi3q4evjJlymjB0L59ex0pUtEh27Z1ZMY7IqVMpKioKG0iKdMxB4J27/r37w/ApEmTdBRJCRqDGDWRVAKdeins2bNH77vrrrt0omCzZs0A58WWVdAlQMREEgQhuATFREpLS+PChQv6/+p3722RSM+ePQFPSBagaNGiOn9n0KBBgFM+oEzAYcOGAXDmzBlWrVoFOGnp5cuXD8qYq1evDqDLMLxRWgd4wtg///yzDm17Z/3WqVNHfyY7bSjc2LZtm75XSkMrX768ftMfOHCAypUrA5CcnAzA5MmTddqByjYPV2JiYgBHowbHPE1KSgIcLTmjplyvXj1eeOEFAF3K4zZGBYwyCwYNGqTNoKNHj+q8iTvvvBNwJl+pUiWTQzHChg0bAMfsK1WqFOCkXCtvvTdXX301ANWqVQOcBC7lqwnml1HVo6hEO+UfUWQlLHLbF8hnQkFiYiITJ04EPOn+GzZs0GZjkSJF9LHKrFuyZAnffPMN4EnOq1atmvEq5EAoXrw44PiV1E9VHpCWlsbZs2cBdFLrihUrdNRW5cYMHTpUn8cNwufuC4KQ7zDq5FXq+I8//sjixYsBx8mo1DHl7X7ooYf028EFguYoVA7brVu36mzP7KIu343gEG0AAA/8SURBVH77LeCp4gVP2cCBAwe049sHXHEUZpe7kpO5k5tjOLvPhJOTV2VhK2rVqpXj8Z06dcqUrzRkyJBAM2DDrhL+gw8+AKBbt26Ak0mvugX4qX2Kk1cQhOBixAdz5swZwPG3gFMUqOo3ChUqxHPPPQd4iuPmzZun7cbatWubGJIRJkyY4PdnvDVG1YNDZY8GE5Wx+/nnn/Pf//43035Vxr9gwYJMb/1IJjeNJSPTpk3TzlGVAZufUKkUKuDSvXt32rZtC+BKR0kjAkYlaal+L0OGDEn3JRoxYgTg6bGSkJCge6f8+9//NjGkkKOqxL17yaiCu1AUQCqnu2r7mR0jRozQAqZNmzbpcl0gewd1OEaRAqFAgQKZ+v+EumDVBF27dgWc8oGdO3e6dl4xkQRBMIYRJ2/GlpH79u2jSpUqmY5TptQNN9yg34wq9KvyLAIg7BZQT0lJoUWLFkD6bFcVrvfzjRgyR+Hq1avT5cJktR/ghx9+SOfkfeCBBwBPaDirVgmXCJt7p5q4d+zYUeeSqIzfKVOm6LYPfhJ2Tt6MDBw4UOcFqRYqPiJOXkEQgktQMnl37dqVpQZz2WWXAU6nMJV05mtT8GCjwroLFizQzmnvkHNWKB/Ts88+m8l3cc0110ScLd+sWTNdx5IVKoNUdbNTvPfee4AnyTASUMlm3svQtG7dGiBQ7eVPiREBo26OMpHGjx+v80OyyoCsWrWqzvoNRw4ePMjjjz8OOFGgunXrAujMSFUdrlCd6NUqjqrFJqAzflXT7fyEig6uWbMmy8iTaq4dCbRr1w6A77//Xm/78ssvAU/kJT+SlJTkatmAmEiCIBjDiAajQs0q92XJkiW6eM6794l6s2/ZskU3VK5QoYKJIeUZ71DlG2+8ke5nWlqazm/JakkT8BTKqbefKqrLT6iCzvr167N7927AKSZ8/vnnAc96PeHKunXrdPN29WyWKlVKj1/lBoU7almSQYMG+WzOKWvjwIEDOgPfDYwImHr16gGeXIvDhw/r5J1PPvlEmxYq2Wvv3r061T5cmi95o74sCu8F13yhZs2ajB07FogsMyFQZs+erf1UTZs2zVbohgNbt25l0qRJgJN06G3OAvz3v//liiuuANIXQ4YzypQrU6aMLs944okn9JpP3iifpyp7qVq1Kp07d3ZtLGIiCYJgDKPFjqpUfOTIkdqcyOpt1rlzZ72ynOppkQdcz6U4e/asNvv69OmTaQ4tW7bU7SYsy9Lp6CrKVKdOHTdNorDPpcgDRvNgLl68yOnTpwF0C4b27dvrt3hUVJRe2nfOnDmA0zdGRTtdICj3LiUlBXAy5FWh6cGDB3WTfdXo+7fffuOdd94BPMW4H374YaDLskgejCAIwSUoPXmPHDmibcFVq1bpxsrqra4aT7lE2GSDGkI0GAef56c06fXr1+u2BKpODtAN6evWrav71hoi6PdOzX3JkiVaK1MNp8ATjn/qqacAz3ItAZDl3IIiYIKMCBiH/Dw38GN+iYmJgOP8/Mc//gF4Out36tRJOz+D4MT90907MZEEQTCGaDCRx5/uLZgN+Xl++WZuosEIgmAMETCCIBgjNxNJEAQhYESDEQTBGCJgBEEwhggYQRCMIQJGEARjiIARBMEYImAEQTCGCBhBEIwhAkYQBGOIgBEEwRgiYARBMIYIGEEQjCECRhAEY4iAEQTBGCJgBEEwhggYQRCMIQJGEARjiIARBMEYImAEQTCGCBhBEIwhAkYQBGOIgBEEwRgFc9kfiUsOyOJdDvl5bpC/55dv5iYajCAIxhABIwiCMUTACIJgDBEwgiAYQwSMIAjGyC2KJLjAgQMHaNmyJQDlypUDYNWqVaEckiBky/Tp0wEYM2YM99xzDwCTJk0K6FyiwQiCYAzLtnMMuecpHn/kyBEAnnvuOeLj4wG4ePEiUVGOXOvSpQsAQ4cOpVatWnm5lDdhk0tx8uRJAG677TZ27NgBwFVXXQXAypUrufLKKwM5bUhzKVJTUwH49ddf9ba9e/cC0LFjRwCOHj1KpUqVAFi6dCk33HCDr6cPm3tniLDPg5k+fTr9+vUD4MKFC/oZ3b17d24flTwYQRCCi+sazP79+3nqqacA+PrrrwH45ZdfPCe0bSwrs7BTx954443+XjIjYfMWfOeddwB4/PHH9bamTZsCjgYTICF7C6ampvLoo48CsHDhQuci2dxPRZcuXXjvvfd8vURI793WrVu1jywlJQWAypUrU61aNbcuEbYajJpvbGys1kiBPGswrjl5p06dCsC4cePYs2ePc8UsHry2bduyZMmSTNvVF+/jjz8G4I477nBraEHnzJkzAIwfPz7TvooVKwZ7OHlmy5YtAIwcOZIPP/zQp8+UL18egAEDBhgbVyAos/39998H4IsvvtDPaVJSkv5CffvttwD85S9/oV69egA8+eSTADRs2JCiRYsGddym+PHHHwGIi4sDYM+ePfrvUaFCBXr16pWn84uJJAiCMVwxkfbv389tt90GOA4/dU4lFdu0aUPbtm0BqFSpEgcPHgTgxRdfBCA+Pl5/pk6dOoDjHKxcubJ/s3EIuYmkHNq9e/fW21q0aAF43pzqDR8AQVWz58+fz8MPPwzA+fPnM18kCxOpfPnyjBs3DoBHHnnEn8sZuXdKA0tISNAa8q5du5yT5GLiZbX/wQcfZObMmX4MVRNWJlJKSgqtW7cGsv57rF27lsaNG/t6OnMm0rZt27RZBGi1asqUKVkef8UVVwAwbdo0/bNkyZL6XACzZs3imWeecWN4QScre1X5YfIgWELCxx9/nE6wqBdFmTJlAOc+Kf72t78BMHjwYGrXrh3EUWaPZVk6apkV3lFNX/fnwX8WFuzcuROAu+66K933Fpz5quiuH8IlW8REEgTBGK5oMPHx8Vqtqly5craaS0706NED8GQMbt682Y2hhQRlIimKFy+utbZI4fDhwwB8+eWXFChQAIA+ffpos/auu+7SxyrH9UsvvQQQaH6PEaKiovSzeccdd1C2bNl0+zOaQF999RUA+/bty/R59VmV3RppKM1FaaF79+7NZP5dd911+j66gWgwgiAYw/VapIYNG7p9yoiib9++OstVvR0qVapEkyZNQjksv/npp58AxxF42WWXAfD6669rDWbDhg2AM8c33ngDCC/NRbF27Vp9H66//nqKFy+e7bGLFy9m+fLl2e5v1aoVAG+++aa7gwwSkydPBrL2Eapcn6VLl+psczdwRcA0bNiQBQsWAI5TcOLEiYAnilSjRo1cz5HRrIhUvv/++0zbVOp1JHHzzTcD0KhRIzZt2gTA3LlzdQ7TxYsXAefeqxKBcMQXwd6nTx8AFixYwNGjRzPtV5GW119/3d3BBQGV99O3b18SExOB9Plp1157LeAIV8BV4QJiIgmCYBBXNJhnnnmGs2fPAjBq1CiefvppAAYOHAg46eKq+M2b5ORkwNF6VB6Mkq5btmzRWaAjR46kRIkSbgzVGOvXrwdg48aNmfblpJaHO+3bt+ebb74BnPyPjAwbNizYQ3KVAQMG6NwkVcjpzSuvvKIDEJF4H9XclIXhTbVq1bTmUr16dSPXd80Hox40y7JYunQp4EnemT9/vj4uq8Ql7/+r35OTk3VEqWDBgrz88stuDdUIKrEsNTVVmw8q+lKwYOS23WnZsqUuD1ARFvAk0EVaRCUhIQHwjN87z6Vy5co6hyvSBSfA2LFjef755/X/1XOp5tu5c2djgkUhJpIgCMYw2g9Gxd2PHj3KCy+8AMCnn36qtRSVG9KkSROtflapUgVwiieVwy06OtqfIsiglQqoosZx48Zpx/bp06e1uadKHVROhUsENd08NTWVdu3aAbBixQo9N1Ux/+qrr7pxGYXRezdjxgz++c9/Ap57Z9s2Q4YMARxnvOFM66Dcu1GjRgFOR7pz5855Tnrp3qn5Pv/88xQqVCgvl/JG+sEIghBcjDoHVHg6Y36ByiQcPXo04Clw9KZfv346bv/iiy/Sv39/AO3fCbAQ0lVmz54NeN4Y+ZGPPvqIpKQkwPGPKe3Tu2dIuDNjxgzAcegqzUXlfSQkJOjcLV/f5jt37tThX/AU7Z44cSLTsWvXrg143P4yduxYwNFcgHTaC6Br+5RfxkXtJVuMChjVxKZ3796kpaUB8M033/jUVKpChQpcfvnlgKPaeRdBAiEthBw+fDiQPuFKqdYFCxZk//79IRmXCebMmaN/L126NEWKFAE8DacSEhLo1q1bSMbmCwkJCenMIpX3oZy9lSpV4tChQ9l+ftWqVXz00Ufptn399dfa7M2uGjsmJsaV8eeG+l5NnTpVCw7v8ahAQ5UqVbTgCSZiIgmCYAyjTt6hQ4cCThGcMmkCUa0LFCigpfL9998POFml2WDUUbht2zZiY2MBOHbsGOAU+y1atAiAZcuWaSdaJDt5X3vtNcDJZVJvwTVr1uhObs2aNQOcZVh++OGHvFzKG9fvnfezAx7zXGmZmzZt8rsfTHb7ldnVr18/br/9diBT6Yzr905p0U899VSmXDLwq+VlXhEnryAIwcWoD0aFqQG6du3qyjndOk9eUI2h1Vt83LhxOmFp2bJlIRuXm6js3YsXL+o3c6NGjfR+1ZTo008/DfrY/CFjwyiVuZrd/tw+r2jevDkA9evX1z65UqVKuTFkn8ktkW7t2rUhL7INWoppgwYN/P6MKkLz/nybNm1cG1Mg1KlTJ1NHs0hs5J0dKvfIW1AqU9ebmjVrAvDuu++yevVqwCNww4kuXbpok+HIkSOsWLEi3f6oqCidjayqxr1p1qwZ9957b6btyokbivIB5WaYOXNmOnNICRZloodDZwMxkQRBMIZRDUa90RITE3XG5wMPPJDjZ1R+wQcffMDbb78NOJJZvUWCEbvPjfyksWRE9d/1blugHJaLFi3iu+++Azz9ls+ePcvx48eDPErf8Q4GnDp1iu3bt6fbb9u233kwoUI5pjt16gR4av0UymwNZp5LbhgVMKqTfokSJXQLzDfffFPnwagEO+/EpZ49ewLOYm1K/Rs2bFiWanq4oxpPzZs3T9/8SETljmRFuXLl9LpB4U6JEiVcaWQdKlS5jIoIxcTEaOEeExOjc8PCQbAoxEQSBMEYRjUYpaG8++67Op3+n//8Z7oWhuCoeqqfjHchZN++fQEYNGiQyWEaQzkNTZfEu0nhwoUBT9FpdlnJf/3rXwEYMWIEVatWDc7g/uQo7V5FsFJTU3XvpQkTJoSlJikajCAIxjCayevNqVOnAHj66aez7L9brFgxwNPo59FHH820xISPhHxlR8MEJZNXOePnzZun35xxcXFa61StNqKjo/NymYzIvXPIN3MLmoAJIvKQOuTnuUH+nl++mZuYSIIgGEMEjCAIxhABIwiCMUTACIJgjNycvIIgCAEjGowgCMYQASMIgjFEwAiCYAwRMIIgGEMEjCAIxhABIwiCMf4PzfFcUQ9UM+IAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In[2]: \n",
+    "\"\"\"PLOT THE DISTRIBUTION OF A CLIENT\"\"\"\n",
+    "def plot_samples(dataset, title=None, plot_name=\"\", n_examples=20):\n",
+    "    \n",
+    "    n_rows = abs(n_examples / 5)\n",
+    "    plt.figure(figsize=(1* n_rows, 1*n_rows))\n",
+    "    if title: plt.suptitle(title)\n",
+    "    \n",
+    "    for idx,(X,y) in enumerate(dataset.dataset):\n",
+    "        \n",
+    "        if idx<n_examples:\n",
+    "    \n",
+    "            ax = plt.subplot(n_rows, 5, idx + 1)\n",
+    "            #ax.set_title(f\"{y}\")\n",
+    "            \n",
+    "            image = 255 - X.view((28,28))\n",
+    "            ax.imshow(image, cmap='gist_gray')\n",
+    "            ax.axis(\"off\")\n",
+    "\n",
+    "    if plot_name!=\"\":plt.savefig(f\"plots/\"+plot_name+\".png\")\n",
+    "        \n",
+    "    plt.tight_layout()\n",
+    "        \n",
+    "plot_samples(mnist_train_dls[0],\"Client 1\")\n",
+    "plot_samples(mnist_train_dls[1],\"Client 2\")\n",
+    "plot_samples(mnist_train_dls[2],\"Client 3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NN(nn.Module):\n",
+    "    \n",
+    "    def __init__(self,layer_1,layer_2):\n",
+    "        super(NN, self).__init__()\n",
+    "        self.fc1 = nn.Linear(784,layer_1)\n",
+    "        self.fc3 = nn.Linear(layer_1,10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.fc1(x.view(-1,784)))\n",
+    "        x=self.fc3(x)\n",
+    "        return x\n",
+    "    \n",
+    "model_0=NN(50,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loss_classifier(predictions,labels):\n",
+    "    \n",
+    "    m = nn.LogSoftmax(dim=1)\n",
+    "    loss = nn.NLLLoss(reduction=\"mean\")\n",
+    "    \n",
+    "    return loss(m(predictions) ,labels.view(-1))\n",
+    "\n",
+    "\n",
+    "def loss_dataset(model, train_data, loss_f):\n",
+    "    \"\"\"Compute the loss of `model` on `test_data`\"\"\"\n",
+    "    loss=0\n",
+    "    \n",
+    "    for idx,(features,labels) in enumerate(train_data):\n",
+    "        \n",
+    "        predictions= model(features)\n",
+    "        loss+=loss_f(predictions,labels)\n",
+    "    \n",
+    "    loss/=idx+1\n",
+    "    return loss\n",
+    "\n",
+    "\n",
+    "def accuracy_dataset(model,dataset):\n",
+    "    \"\"\"Compute the accuracy of `model` on `test_data`\"\"\"\n",
+    "    \n",
+    "    correct=0\n",
+    "    \n",
+    "    for features,labels in iter(dataset):\n",
+    "        \n",
+    "        predictions= model(features)\n",
+    "        \n",
+    "        _,predicted=predictions.max(1,keepdim=True)\n",
+    "        \n",
+    "        correct+=torch.sum(predicted.view(-1,1)==labels.view(-1, 1)).item()\n",
+    "        \n",
+    "    accuracy = 100*correct/len(dataset.dataset)\n",
+    "        \n",
+    "    return accuracy\n",
+    "\n",
+    "\n",
+    "def train_step(model, model_0, mu:int, optimizer, train_data, loss_f):\n",
+    "    \"\"\"Train `model` on one epoch of `train_data`\"\"\"\n",
+    "    \n",
+    "    total_loss=0\n",
+    "    \n",
+    "    for idx, (features,labels) in enumerate(train_data):\n",
+    "        \n",
+    "        optimizer.zero_grad()\n",
+    "        \n",
+    "        predictions= model(features)\n",
+    "        \n",
+    "        loss=loss_f(predictions,labels)\n",
+    "        loss+=mu/2*difference_models_norm_2(model,model_0)\n",
+    "        total_loss+=loss\n",
+    "        \n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "    return total_loss/(idx+1)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def local_learning(model, mu:float, optimizer, train_data, epochs:int, loss_f):\n",
+    "    \n",
+    "    model_0=deepcopy(model)\n",
+    "    \n",
+    "    for e in range(epochs):\n",
+    "        local_loss=train_step(model,model_0,mu,optimizer,train_data,loss_f)\n",
+    "        \n",
+    "    return float(local_loss.detach().numpy())\n",
+    "\n",
+    "\n",
+    "def difference_models_norm_2(model_1, model_2):\n",
+    "    \"\"\"Return the norm 2 difference between the two model parameters\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    tensor_1=list(model_1.parameters())\n",
+    "    tensor_2=list(model_2.parameters())\n",
+    "    \n",
+    "    norm=sum([torch.sum((tensor_1[i]-tensor_2[i])**2) \n",
+    "        for i in range(len(tensor_1))])\n",
+    "    \n",
+    "    return norm\n",
+    "\n",
+    "\n",
+    "def set_to_zero_model_weights(model):\n",
+    "    \"\"\"Set all the parameters of a model to 0\"\"\"\n",
+    "\n",
+    "    for layer_weigths in model.parameters():\n",
+    "        layer_weigths.data.sub_(layer_weigths.data)\n",
+    "        \n",
+    "        \n",
+    "def FedAvg_agregation_process(model, clients_models_hist:list , weights:list):\n",
+    "    \"\"\"Creates the new model of a given iteration with the models of the other\n",
+    "    clients\"\"\"\n",
+    "    \n",
+    "    new_model=deepcopy(model)\n",
+    "    set_to_zero_model_weights(new_model)\n",
+    "\n",
+    "    for k,client_hist in enumerate(clients_models_hist):\n",
+    "        \n",
+    "        for idx, layer_weights in enumerate(new_model.parameters()):\n",
+    "\n",
+    "            contribution=client_hist[idx].data*weights[k]\n",
+    "            layer_weights.data.add_(contribution)\n",
+    "            \n",
+    "    return new_model\n",
+    "\n",
+    "\n",
+    "\n",
+    "def FedProx(model, training_sets:list, n_iter:int, testing_sets:list, mu=0, \n",
+    "    file_name=\"test\", epochs=5, lr=10**-2, decay=1):\n",
+    "    \"\"\" all the clients are considered in this implementation of FedProx\n",
+    "    Parameters:\n",
+    "        - `model`: common structure used by the clients and the server\n",
+    "        - `training_sets`: list of the training sets. At each index is the \n",
+    "            training set of client \"index\"\n",
+    "        - `n_iter`: number of iterations the server will run\n",
+    "        - `testing_set`: list of the testing sets. If [], then the testing\n",
+    "            accuracy is not computed\n",
+    "        - `mu`: regularixation term for FedProx. mu=0 for FedAvg\n",
+    "        - `epochs`: number of epochs each client is running\n",
+    "        - `lr`: learning rate of the optimizer\n",
+    "        - `decay`: to change the learning rate at each iteration\n",
+    "    \n",
+    "    returns :\n",
+    "        - `model`: the final global model \n",
+    "    \"\"\"\n",
+    "        \n",
+    "    loss_f=loss_classifier\n",
+    "    \n",
+    "    #Variables initialization\n",
+    "    K=len(training_sets) #number of clients\n",
+    "    n_samples=sum([len(db.dataset) for db in training_sets])\n",
+    "    weights=([len(db.dataset)/n_samples for db in training_sets])\n",
+    "    print(\"Clients' weights:\",weights)\n",
+    "    \n",
+    "    \n",
+    "    loss_hist=[[float(loss_dataset(model, dl, loss_f).detach()) \n",
+    "        for dl in training_sets]]\n",
+    "    acc_hist=[[accuracy_dataset(model, dl) for dl in testing_sets]]\n",
+    "    server_hist=[[tens_param.detach().numpy() \n",
+    "        for tens_param in list(model.parameters())]]\n",
+    "    models_hist = []\n",
+    "    \n",
+    "    \n",
+    "    server_loss=sum([weights[i]*loss_hist[-1][i] for i in range(len(weights))])\n",
+    "    server_acc=sum([weights[i]*acc_hist[-1][i] for i in range(len(weights))])\n",
+    "    print(f'====> i: 0 Loss: {server_loss} Server Test Accuracy: {server_acc}')\n",
+    "    \n",
+    "    for i in range(n_iter):\n",
+    "        \n",
+    "        clients_params=[]\n",
+    "        clients_models=[]\n",
+    "        clients_losses=[]\n",
+    "        \n",
+    "        for k in range(K):\n",
+    "            \n",
+    "            local_model=deepcopy(model)\n",
+    "            local_optimizer=optim.SGD(local_model.parameters(),lr=lr)\n",
+    "            \n",
+    "            local_loss=local_learning(local_model,mu,local_optimizer,\n",
+    "                training_sets[k],epochs,loss_f)\n",
+    "            \n",
+    "            clients_losses.append(local_loss)\n",
+    "                \n",
+    "            #GET THE PARAMETER TENSORS OF THE MODEL\n",
+    "            list_params=list(local_model.parameters())\n",
+    "            list_params=[tens_param.detach() for tens_param in list_params]\n",
+    "            clients_params.append(list_params)    \n",
+    "            clients_models.append(deepcopy(local_model))\n",
+    "        \n",
+    "        \n",
+    "        #CREATE THE NEW GLOBAL MODEL\n",
+    "        model = FedAvg_agregation_process(deepcopy(model), clients_params, \n",
+    "            weights=weights)\n",
+    "        models_hist.append(clients_models)\n",
+    "        \n",
+    "        #COMPUTE THE LOSS/ACCURACY OF THE DIFFERENT CLIENTS WITH THE NEW MODEL\n",
+    "        loss_hist+=[[float(loss_dataset(model, dl, loss_f).detach()) \n",
+    "            for dl in training_sets]]\n",
+    "        acc_hist+=[[accuracy_dataset(model, dl) for dl in testing_sets]]\n",
+    "\n",
+    "        server_loss=sum([weights[i]*loss_hist[-1][i] for i in range(len(weights))])\n",
+    "        server_acc=sum([weights[i]*acc_hist[-1][i] for i in range(len(weights))])\n",
+    "\n",
+    "        print(f'====> i: {i+1} Loss: {server_loss} Server Test Accuracy: {server_acc}')\n",
+    "        \n",
+    "\n",
+    "        server_hist.append([tens_param.detach().cpu().numpy() \n",
+    "            for tens_param in list(model.parameters())])\n",
+    "        \n",
+    "        #DECREASING THE LEARNING RATE AT EACH SERVER ITERATION\n",
+    "        lr*=decay\n",
+    "            \n",
+    "    return model, loss_hist, acc_hist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
+      "====> i: 0 Loss: 2.3152280648549395 Server Test Accuracy: 9.666666666666664\n",
+      "====> i: 1 Loss: 2.077920834223429 Server Test Accuracy: 32.33333333333333\n",
+      "====> i: 2 Loss: 1.7763458887736001 Server Test Accuracy: 46.666666666666664\n",
+      "====> i: 3 Loss: 1.451335589090983 Server Test Accuracy: 57.33333333333333\n",
+      "====> i: 4 Loss: 1.1880313158035278 Server Test Accuracy: 66.66666666666666\n",
+      "====> i: 5 Loss: 0.9950338204701741 Server Test Accuracy: 71.0\n",
+      "====> i: 6 Loss: 0.8533134261767068 Server Test Accuracy: 76.0\n",
+      "====> i: 7 Loss: 0.7462861935297647 Server Test Accuracy: 78.66666666666666\n",
+      "====> i: 8 Loss: 0.6632819771766663 Server Test Accuracy: 80.66666666666666\n",
+      "====> i: 9 Loss: 0.5970219771067301 Server Test Accuracy: 82.33333333333333\n",
+      "====> i: 10 Loss: 0.5427657763163248 Server Test Accuracy: 82.33333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"RUN FEDAVG\"\"\" \n",
+    "n_iter=10\n",
+    "\n",
+    "model_f, loss_hist_FA_iid, acc_hist_FA_iid = FedProx( model_0, \n",
+    "    mnist_train_dls, n_iter, mnist_test_dls)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"PLOT THE LOSS AND ACC HISTORY FOR THE DIFFERENT CLIENTS FOR FEDAVG\"\"\"\n",
+    "\n",
+    "def plot_acc_loss(title:str, loss_hist:list, acc_hist:list):\n",
+    "    plt.figure()\n",
+    "    \n",
+    "    plt.suptitle(title)\n",
+    "\n",
+    "    plt.subplot(1,2,1)\n",
+    "    lines=plt.plot(loss_hist)\n",
+    "    plt.title(\"Loss\")\n",
+    "    plt.legend(lines,[\"C1\", \"C2\", \"C3\"])\n",
+    "\n",
+    "    plt.subplot(1,2,2)\n",
+    "    lines=plt.plot(acc_hist )\n",
+    "    plt.title(\"Accuracy\")\n",
+    "    plt.legend(lines, [\"C1\", \"C2\", \"C3\"])\n",
+    "    \n",
+    "\n",
+    "plot_acc_loss(\"FedAvg MNIST-iid\", loss_hist_FA_iid, acc_hist_FA_iid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
+      "====> i: 0 Loss: 2.3152278264363604 Server Test Accuracy: 9.666666666666664\n",
+      "====> i: 1 Loss: 2.160791953404744 Server Test Accuracy: 21.666666666666664\n",
+      "====> i: 2 Loss: 1.9843504826227822 Server Test Accuracy: 39.33333333333333\n",
+      "====> i: 3 Loss: 1.7798415819803872 Server Test Accuracy: 46.33333333333333\n",
+      "====> i: 4 Loss: 1.5679299434026082 Server Test Accuracy: 54.0\n",
+      "====> i: 5 Loss: 1.3742664655049641 Server Test Accuracy: 60.0\n",
+      "====> i: 6 Loss: 1.2120484908421834 Server Test Accuracy: 65.0\n",
+      "====> i: 7 Loss: 1.0791771014531453 Server Test Accuracy: 69.0\n",
+      "====> i: 8 Loss: 0.9699301520983377 Server Test Accuracy: 72.33333333333333\n",
+      "====> i: 9 Loss: 0.8801418542861938 Server Test Accuracy: 74.33333333333333\n",
+      "====> i: 10 Loss: 0.804727832476298 Server Test Accuracy: 76.66666666666666\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"RUN FEDPROx, mu=1\"\"\" \n",
+    "n_iter=10\n",
+    "\n",
+    "model_f, loss_hist_FP_iid, acc_hist_FP_iid = FedProx( model_0, mnist_train_dls, \n",
+    "    n_iter, mnist_test_dls, mu =1.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_acc_loss(\"FedProx mu=1. MNIST-iid\", loss_hist_FP_iid, acc_hist_FP_iid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. MNIST non-iid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fonts = ['InconsolataN']#,'jsMath-cmti10']\n",
+    "all_digits=[i for i in range(10)]\n",
+    "\n",
+    "C1= {\n",
+    "     'n_samples_train': 200,\n",
+    "     'font':'InconsolataN',\n",
+    "     'numbers': all_digits,\n",
+    "     'tilt':0,\n",
+    "     'std_tilt': 10, #std on the tilt,\n",
+    "     'seed':0\n",
+    "     }\n",
+    "C1['n_samples']= int(1.5 * C1['n_samples_train']) #20% more for the testing set\n",
+    "\n",
+    "\n",
+    "\n",
+    "C2=deepcopy(C1)\n",
+    "C2['tilt'] = 45\n",
+    "\n",
+    "C3=deepcopy(C1)\n",
+    "C3['tilt']=90\n",
+    "\n",
+    "clients = [C1, C2, C3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 %\n",
+      "3 %\n",
+      "7 %\n",
+      "10 %\n",
+      "13 %\n",
+      "17 %\n",
+      "20 %\n",
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+      "87 %\n",
+      "90 %\n",
+      "93 %\n",
+      "97 %\n",
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+      "3 %\n",
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+      "10 %\n",
+      "13 %\n",
+      "17 %\n",
+      "20 %\n",
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+      "27 %\n",
+      "30 %\n",
+      "33 %\n",
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+      "40 %\n",
+      "43 %\n",
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+      "50 %\n",
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+      "60 %\n",
+      "63 %\n",
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+      "0 %\n",
+      "3 %\n",
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+      "10 %\n",
+      "13 %\n",
+      "17 %\n",
+      "20 %\n",
+      "23 %\n",
+      "27 %\n",
+      "30 %\n",
+      "33 %\n",
+      "37 %\n",
+      "40 %\n",
+      "43 %\n",
+      "47 %\n",
+      "50 %\n",
+      "53 %\n",
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+      "60 %\n",
+      "63 %\n",
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+      "80 %\n",
+      "83 %\n",
+      "87 %\n",
+      "90 %\n",
+      "93 %\n",
+      "97 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "from create_custom_MNIST import MNIST_custom_train_test_sets\n",
+    "custom_mnist_train, custom_mnist_test = MNIST_custom_train_test_sets(clients)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAAETCAYAAADtSRlPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAOC0lEQVR4nO3da+gsdR3H8c9HPd4xq2OSgV20CIyiB6WW3cAHmUE+6IHZBbOI6EIlRUUSUSkYesrQINLAC0gkFRkUPuqiWRgERtGFspN5OUfNYxxN8fLtwc5f5qyzOzO7852dmfN+gfg//53dne/+Zj/z/f328ndECAAyHLDpHQAwXQQMgDQEDIA0BAyANAQMgDQEDIA0BMxA2f6S7euKn4+3vdf2gZveL6ANAmaDbJ9j+3dFeNxj+6e2T5vfLiL+FRFHRsSTHdznz21/cMnl223fYvsB23ts32r79eveb4v9e5HtsH1QX/eJPATMhtg+X9I3JF0k6VhJx0v6lqR3bHK/JO2VdJ6kYyQ9W9LFkm7kCY9VEDAbYPtZkr4s6aMR8YOIeDgiHo+IGyPiMxXb73NWt/0s21cVXc9dtr+6NX2yfa7tm21fYvtB23fYPqO47EJJb5B0edE1XT5/XxHxaET8JSKekmRJT2oWNM9ZUMthti+1vdP2Q8V9H2b7zbb/PbftP22fXvz82qJ7+6/tXbZ3FJv9svj/nmIfT7V9gO0LivvYbfua4jEsPzbvt31nUfOHbb/G9u1FF/aMOtEPzkqbcaqkQyX9cMXrXy1pl6QTJR0h6SeS7pT07eLyk4tttkv6kKSrbL8gIr5QTHeui4grl92B7dslvVzSNklXRsTuBZteIukkSa+TdG9x3081qOEySZdFxLW2j5T0iuL3b5R0h6SjI+KJYl/Ok3SupLdI2i3pGkmXS3pv6fZOlvTS4vo/lvQzSacX+/9729+PiF802C90iA5mM54r6f6tJ1Abto+VdIakTxadz25JX5d0dmmznRHxnWLN5mpJz9dsGtZYRLxS0lGSzpF084J9OUCz6dQnIuKuiHgyIn4dEY81uIvHJZ1oe3tE7I2I3yzZ9t2SdkTEPyJir6TPSzp7btr2laL7uknSw5Kuj4jdEXGXpF9JenWDfULHCJjNeEDS9hXXNV6o2Vn5nqL936NZ5/K80jb3bv0QEY8UPx7Z9o6KJ+z1kj5n+1UVm2zXrBP7e9vblvQBSS+T9Gfbt9l++5Jtj5O0s/TvnZp13+XQ3FX6+X8V/25dP9ZHwGzGrZIelXTWCte9U9JjkrZHxNHFf0dFxEkNr7/Kx+e3SXpJxe/v16yOEyoue1jS4Vv/KNaIjnl6JyL+FhHv0iwYL5Z0g+0jFuzf3ZoF65bjJT2hfUMEA0TAbEBEPCTpi5KusH2W7cNtb7N9hu2v1Vz3Hkk3SbrU9lHFAugJtt/U8O53qTosJEm2T7F9mu2Di8Xaz2rWKfy2Yl+ekvRdSTtsH2f7wGJR9hBJf5V0qO0zbW+TdIGkQ0r38x7bxxS3saf49ZOS7tNsDae8j9dL+pTtFxfrNRdJ+t4qU0z0i4DZkIjYIel8zZ5492nWmXxM0o8aXP19kg6W9CdJD0q6QbN1liYuk/TO4tWWb1ZcfoikKzSbxt0l6W2SzoyIuxfc3qcl/UHSbZL+o1k3ckARoh+RdGVxOw9LKr+q9FZJf7S9t9ins4sp2SOSLpR0SzEFPEWzELtWs1eY7tCsa/p4w3qxQeYLpwBkoYMBkIaAAZCGgAGQhoABkKbujV5jXAF2i22nXN+Ua5OmXd9kaqODAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaA6YHd5jN86JttxigJAQMgzUb+suPW2WKq3wdcdTas+t0Y65+vY4w1SIvHaKz1VCnX2LSuVa6zDB0MgDQpHcyy+WxEPCMZx3R2r5urt9nvIXdyTdckxjR2Un1d82PS9Rm9D3UddLm27Jp6nyK1OSCH9ARc5wknVR+wQ9TF/o1tqlE1Nk2fpEPS9hjtowamSADSbGSRd17dWX+sxrb/q3QvEbH0bD+kx6DL7nFo9dXNDDa1qN1bwNQVW2XoLWnTmsY2ZSirWy+rqm3o08CyunFZ5bgdkqogrJoKZh2fTJEApOmtg6nrRsZ4dqhTt2iIfi2azi3aVtq3QxvLQn1Z0zqyaqKDAZBmMGswY+xq1t2/MazLtHnfz9DHq6zqsa8bjzHVJ1V3LWVtOrpVpQRMXSvZ9Ik1pCdg0/a4j0HLMuZ9b2rZMVW1YD2Wx2SV/exjgZ4pEoA0rukShtNCNNfb3zdu8zJ6hy8H9vL3jdf5SMQaL8sP8m9Td/hxCP42NQB0ZRDv5B2rNmexIa0nNbHO/o6t1ramXl+XCBigBoGyOqZIANIQMADSEDAA0hAwANIQMADSEDAA0hAwANIQMADSEDAA0hAwANIQMADSEDAA0tR9HwwArIwOBkAaAgZAGgIGQBoCBkAaAgZAGgIGQBoCBkAaAgZAGgIGQBoCBkAaAgZAGgIGQBoCBkAaAgZAGgIGQBoCBkAaAgZAGgIGQBoCBkAaAgZAGgIGQBoCBkCag2ouH+PfNHGLbadc35Rrk6Zd32Rqo4MBkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAQSu2Zbf50PO4VdW6P9W/LgIGQJq674NZybKEjxjjV10sV3dGm0rNtp+uparmKdTZpGOZQp19oYMBkCalg6lL+K0zQtXZcAxnh7Zz8Kmc7ZuOa5vr9GnRcbbOeA6pvnnljnP+94t0XU/vU6Sx66q2IR2kUx6vVbV9Yg5BFwvSi0JpVUyRAKTpvINpk5h1ibvpM3tTEVHbdg797Idq++O4zS9hrKPzgNkfn0zzoThff1XbOfXHaCwnhyaWvXI2FIvWv5qut2TVxhQJQJrUV5EWpeIYzgiL1J0VFl025FrbnMmm8j6YIY9Hl5aNV9cLulXoYACkSX2ZetGZcexnvzF3YMs0WSuaWs2LlI/hsR+vi/RRQ29TpKZPyi5XsLOt816JIdZXdULoYyFwiMp1D3Gsmtr084kpEoA0KR3MlkVnxCl/GHLs+7+l3HFu/Tz2tyBMZWyqNJnKNe1meCcvgFFI7WDKqua0Yz4b7i/mz4D72xsGx2SdziSru+stYKpMuWWdoqrxYgyxDFMkAGkIGABpCBgAaQgYAGkIGABpCBgAaQgYAGkIGABpCBgAaQgYAGkIGABpCBgAacyH1QBkoYMBkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQhoABkIaAAZCGgAGQ5qCay8f4JwfcYtsp1zfl2qRp1zeZ2uhgAKQhYACkIWAApCFgAKQhYACkIWAApCFgAKQhYACkIWAApCFgAKQhYACkIWAApCFgAKQhYACkIWAApKn7PpjW7PqvvIiIhduWL9v6GeMyhbFbdhxHxNOXD6nOun1ue90uaqODAZCm8w6mq0Qf0plhf9ekKy0b69g1rXOoHdoQ96mzgGl7ELa5naE9cHW1Dm1/2+hiHMu3MaTHYtF+dXXsblrTOvqsnSkSgDSdT5Ew3ilFm/0eUwewyv5VjUn5doa2yNumxvK+z+9/12M56IAZyuDhmZZNg4YWOFWvWi4KiCkdc+VXu6r0sZbEFAlAms46mDYtc5P3wQxVOfWnvGhYN0Vo8vsxmu9sxl5b06leFjoYAGlS3slbNycf81mhSYc19C6siXXHaOyPwVTeilC11tRnd5b6Rruq4sYcLvPGcpA1Vbco2OZ2xqKrV5iGqG4Rt6vxXoYpEoA0qS9TV6VnH6mJ1bV9yXksZ3Np9U56DDWu+rzKfosBHQyANBt5o11Vao7hLLE/Gdo7Vbs2xbq6WLzt+nEZxDt5pzjYY8eYjNeQxo4pEoA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQe0gejAEwLHQyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0B9VcPsY/OeAW2065vinXJk27vsnURgcDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0Bg33Ylt3mQ82Ykq7HnoABkKbu+2BSzKdkxBi//mK1tB96rVv7V1Xbon3f2nbotVWxvc9+NxnTMdXZ9hjtujY6GABpXJNYa8XZOvO5iFj1zJj+rWhdzVNXPFukfitaXW3LOpyq7drefYttW99BuVtZdGw1Hdsxjl1bXTzvUgKm6yfgfBtbd/dt7qLtPmUsgCYF6JRrkwZybLa9+6Y3v9KNb3b8+MpMAP3qfJF31YXPqusNffGwbtGzatuxvQTcZAF0LLW12b+qsS1ff2jHZpMpa5OF+67HsJdXkZoUMZaDdF1VdbacAg5K1VrZ0Maw7ok1tP3tSnlsqtaf+sAUCUCajbwPpsrQz4JVqtrkMex3G1N4X8gqx9TQpkCr2vSxSQcDIE0vHUybs+AYO4A27wlp8w7Zvi1aCFxmjOMlNd/vuoXtIRvC2HTewYzhgV/HuvVFxKAfo2X7N+T97kqT8Rni47DKPvVRJ1MkAGk2+lGBZfe9xku3vf8B9XKdPZzdBvEH1Mf4UQFp+X5PZezafNyjw5p5Jy+AfqV2MBvSewfTs0F0MEkYu5nJ1EYHAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyANAQMgDQEDIA0BAyBN3aepAWBldDAA0hAwANIQMADSEDAA0hAwANIQMADS/B+6tT9KZoBf2gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_samples(custom_mnist_train[0], \"Client 1 custom\")\n",
+    "plot_samples(custom_mnist_train[1], \"Client 2 custom\")\n",
+    "plot_samples(custom_mnist_train[2], \"Client 3 custom\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CNN(nn.Module):\n",
+    "\n",
+    "    \"\"\"ConvNet -> Max_Pool -> RELU -> ConvNet -> Max_Pool -> RELU -> FC -> RELU -> FC -> SOFTMAX\"\"\"\n",
+    "    def __init__(self):\n",
+    "        super(CNN, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(1, 20, 5, 1)\n",
+    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
+    "        self.fc1 = nn.Linear(4*4*50, 500)\n",
+    "        self.fc2 = nn.Linear(500, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.conv1(x))\n",
+    "        x = F.max_pool2d(x, 2, 2)\n",
+    "        x = F.relu(self.conv2(x))\n",
+    "        x = F.max_pool2d(x, 2, 2)\n",
+    "        x = x.view(-1, 4*4*50)\n",
+    "        x = F.relu(self.fc1(x))\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n",
+    "    \n",
+    "model_0 = CNN()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
+      "====> i: 0 Loss: 2.304858684539795 Server Test Accuracy: 7.666666666666666\n",
+      "====> i: 1 Loss: 2.2495672702789307 Server Test Accuracy: 14.333333333333332\n",
+      "====> i: 2 Loss: 2.147202094395955 Server Test Accuracy: 17.0\n",
+      "====> i: 3 Loss: 1.8952369689941406 Server Test Accuracy: 36.666666666666664\n",
+      "====> i: 4 Loss: 1.6778336763381956 Server Test Accuracy: 33.33333333333333\n",
+      "====> i: 5 Loss: 1.3170110384623208 Server Test Accuracy: 58.99999999999999\n",
+      "====> i: 6 Loss: 0.9794035752614338 Server Test Accuracy: 62.0\n",
+      "====> i: 7 Loss: 0.7040616075197855 Server Test Accuracy: 73.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_iter=10\n",
+    "\n",
+    "model_f, loss_hist_FA_niid, acc_hist_FA_niid = FedProx( model_0, custom_mnist_train, \n",
+    "    n_iter, custom_mnist_test, epochs=5, lr=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_acc_loss(\"FedProx MNIST non-iid\", loss_hist_FA_niid, acc_hist_FA_niid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_iter=10\n",
+    "\n",
+    "model_f, loss_hist_FP_niid, acc_hist_FP_niid = FedProx( model_0, custom_mnist_train, \n",
+    "    n_iter, custom_mnist_test, epochs=5, lr=0.1, mu=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_acc_loss(\"FedProx mu=0.5 MNIST non-iid\", loss_hist_FP_niid, acc_hist_FP_niid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/01-federated-averaging.ipynb b/federated_learning/federated_mcvae.ipynb
similarity index 60%
rename from 01-federated-averaging.ipynb
rename to federated_learning/federated_mcvae.ipynb
index 0d3f6ef1df9315bdec1dbc91416693815ddebe7e..c7ab1a3c20ea48736d114d9d088fb4c2632dceb4 100644
--- a/01-federated-averaging.ipynb
+++ b/federated_learning/federated_mcvae.ipynb
@@ -1,26 +1,5 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Federated Aveaging"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -31,9 +10,34 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.7.8-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
-}
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Example: Federated learning using variational autoencoders"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from copy import deepcopy"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/federated_learning/introduction.md b/federated_learning/introduction.md
new file mode 100644
index 0000000000000000000000000000000000000000..df8989c72bb56fe5a68bf83f98ce4b5b93650b5d
--- /dev/null
+++ b/federated_learning/introduction.md
@@ -0,0 +1,2 @@
+# Introduction
+Introduction to federated learning...
\ No newline at end of file
diff --git a/heterogeneous_data/introduction.md b/heterogeneous_data/introduction.md
new file mode 100644
index 0000000000000000000000000000000000000000..ce48ba57c4770630d9f9dca4ce8154c9a1e2a18b
--- /dev/null
+++ b/heterogeneous_data/introduction.md
@@ -0,0 +1 @@
+# Introduction to heterogeneous data
\ No newline at end of file
diff --git a/heterogeneous_data/multivariate_models_v1_7.ipynb b/heterogeneous_data/multivariate_models_v1_7.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..005299bb795ed9751a1065ad76d47a8dea7febef
--- /dev/null
+++ b/heterogeneous_data/multivariate_models_v1_7.ipynb
@@ -0,0 +1,6591 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Lorenzi_multivariate_models_v1-7.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.5"
+    }
+  },
+  "cells": [
+    {
+      "source": [
+        "# Example: Statistical analysis on heterogeneous data"
+      ],
+      "cell_type": "markdown",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tPXXCyu2bqbd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9a0f1741-5d3b-46da-e8b9-061933e30f8d"
+      },
+      "source": [
+        "## Uncomment the following lines to set up the environment (una-tantum operation)\n",
+        "#!pip install torch\n",
+        "#!pip install nilearn\n",
+        "#!pip install nibabel\n",
+        "#!pip install pandas\n",
+        "#!pip install sklearn\n",
+        "#!pip install matplotlib\n",
+        "#!pip install numpy\n",
+        "#!pip install torch torchvision\n",
+        " \n",
+        "!git clone https://gitlab.inria.fr/epione_ML/mcvae.git"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "fatal: destination path 'mcvae' already exists and is not an empty directory.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "u20pHm5vcDiv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "df9ec1ad-be71-428f-cca4-9b285bee8fc8"
+      },
+      "source": [
+        "%cd mcvae\n",
+        "!python ./setup.py install"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/content/mcvae\n",
+            "WARNING: '' not a valid package name; please use only .-separated package names in setup.py\n",
+            "running install\n",
+            "running bdist_egg\n",
+            "running egg_info\n",
+            "writing src/mcvae.egg-info/PKG-INFO\n",
+            "writing dependency_links to src/mcvae.egg-info/dependency_links.txt\n",
+            "writing top-level names to src/mcvae.egg-info/top_level.txt\n",
+            "writing manifest file 'src/mcvae.egg-info/SOURCES.txt'\n",
+            "installing library code to build/bdist.linux-x86_64/egg\n",
+            "running install_lib\n",
+            "running build_py\n",
+            "creating build/bdist.linux-x86_64/egg\n",
+            "copying build/lib/__init__.py -> build/bdist.linux-x86_64/egg\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae/datasets\n",
+            "copying build/lib/mcvae/datasets/__init__.py -> build/bdist.linux-x86_64/egg/mcvae/datasets\n",
+            "copying build/lib/mcvae/datasets/synthetic.py -> build/bdist.linux-x86_64/egg/mcvae/datasets\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae/os\n",
+            "copying build/lib/mcvae/os/__init__.py -> build/bdist.linux-x86_64/egg/mcvae/os\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae/distributions\n",
+            "copying build/lib/mcvae/distributions/utilities.py -> build/bdist.linux-x86_64/egg/mcvae/distributions\n",
+            "copying build/lib/mcvae/distributions/__init__.py -> build/bdist.linux-x86_64/egg/mcvae/distributions\n",
+            "copying build/lib/mcvae/distributions/kl_utilities.py -> build/bdist.linux-x86_64/egg/mcvae/distributions\n",
+            "copying build/lib/mcvae/distributions/normal.py -> build/bdist.linux-x86_64/egg/mcvae/distributions\n",
+            "copying build/lib/mcvae/__init__.py -> build/bdist.linux-x86_64/egg/mcvae\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae/plot\n",
+            "copying build/lib/mcvae/plot/__init__.py -> build/bdist.linux-x86_64/egg/mcvae/plot\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae/gpu\n",
+            "copying build/lib/mcvae/gpu/__init__.py -> build/bdist.linux-x86_64/egg/mcvae/gpu\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae/diagnostics\n",
+            "copying build/lib/mcvae/diagnostics/__init__.py -> build/bdist.linux-x86_64/egg/mcvae/diagnostics\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae/models\n",
+            "copying build/lib/mcvae/models/vae.py -> build/bdist.linux-x86_64/egg/mcvae/models\n",
+            "copying build/lib/mcvae/models/utils.py -> build/bdist.linux-x86_64/egg/mcvae/models\n",
+            "copying build/lib/mcvae/models/__init__.py -> build/bdist.linux-x86_64/egg/mcvae/models\n",
+            "copying build/lib/mcvae/models/mcvae.py -> build/bdist.linux-x86_64/egg/mcvae/models\n",
+            "creating build/bdist.linux-x86_64/egg/mcvae/utilities\n",
+            "copying build/lib/mcvae/utilities/__init__.py -> build/bdist.linux-x86_64/egg/mcvae/utilities\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/datasets/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/datasets/synthetic.py to synthetic.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/os/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/distributions/utilities.py to utilities.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/distributions/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/distributions/kl_utilities.py to kl_utilities.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/distributions/normal.py to normal.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/plot/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/gpu/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/diagnostics/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/models/vae.py to vae.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/models/utils.py to utils.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/models/__init__.py to __init__.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/models/mcvae.py to mcvae.cpython-36.pyc\n",
+            "byte-compiling build/bdist.linux-x86_64/egg/mcvae/utilities/__init__.py to __init__.cpython-36.pyc\n",
+            "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
+            "copying src/mcvae.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+            "copying src/mcvae.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+            "copying src/mcvae.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+            "copying src/mcvae.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+            "zip_safe flag not set; analyzing archive contents...\n",
+            "creating 'dist/mcvae-2.0.0-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
+            "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
+            "Processing mcvae-2.0.0-py3.6.egg\n",
+            "Removing /usr/local/lib/python3.6/dist-packages/mcvae-2.0.0-py3.6.egg\n",
+            "Copying mcvae-2.0.0-py3.6.egg to /usr/local/lib/python3.6/dist-packages\n",
+            "mcvae 2.0.0 is already the active version in easy-install.pth\n",
+            "\n",
+            "Installed /usr/local/lib/python3.6/dist-packages/mcvae-2.0.0-py3.6.egg\n",
+            "Processing dependencies for mcvae==2.0.0\n",
+            "Finished processing dependencies for mcvae==2.0.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0CPmqTRF6zuG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b175b369-1a36-474e-a565-e5ed8d7de96b"
+      },
+      "source": [
+        "#Before running this cell, got to Runtime and select \"Restart runtime [Ctrl+M]\"\n",
+        "\n",
+        "import mcvae\n",
+        "print(mcvae.__version__)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "2.0.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mMBgdh3wbE5f"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "from sklearn.cross_decomposition import PLSCanonical, CCA"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rVQtzHTfbE5k"
+      },
+      "source": [
+        "## Random data generation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2AAaOAR8LMg_"
+      },
+      "source": [
+        "Our journey across multivariate model starts with the generation of synthetic data. We first need to generate multivarite correlated random variables X and Y. To do so, we rely on the generative model we have seen during lesson:\n",
+        "\n",
+        "$$ z\\sim\\mathcal{N}(0,1),$$\n",
+        "$$ X = z w_x,$$\n",
+        "$$ Y = z w_y.$$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bG3wSWgxbE5l",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "738523c9-7289-4701-8b58-def1af34e7b0"
+      },
+      "source": [
+        "# #############################################################################\n",
+        "\n",
+        "# N subjects\n",
+        "n = 500\n",
+        "# here we define 2 Gaussian latents variables z = (l_1, l_2)\n",
+        "l1 = np.random.normal(size=n)\n",
+        "l2 = np.random.normal(size=n)\n",
+        "\n",
+        "latents = np.array([l1, l2]).T\n",
+        "\n",
+        "# We define two random transformations from the latent space to the space of X and Y respectively\n",
+        "transform_x = np.random.randint(-8,8, size = 10).reshape([2,5])\n",
+        "transform_y = np.random.randint(-8,8, size = 10).reshape([2,5])\n",
+        "\n",
+        "# We compute data X = z w_x, and Y = z w_y\n",
+        "X = latents.dot(transform_x) \n",
+        "Y = latents.dot(transform_y) \n",
+        "\n",
+        "# We we add some random Gaussian noise\n",
+        "X = X + 2*np.random.normal(size = n*5).reshape((n, 5))\n",
+        "Y = Y + 2*np.random.normal(size = n*5).reshape((n, 5))\n",
+        "\n",
+        "\n",
+        "print('The latent space has dimension ' + str(latents.shape))\n",
+        "print('The transformation for X has dimension ' + str(transform_x.shape))\n",
+        "print('The transformation for Y has dimension ' + str(transform_y.shape))\n",
+        "\n",
+        "print('X has dimension ' + str(X.shape))\n",
+        "print('Y has dimension ' + str(Y.shape))\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "The latent space has dimension (500, 2)\n",
+            "The transformation for X has dimension (2, 5)\n",
+            "The transformation for Y has dimension (2, 5)\n",
+            "X has dimension (500, 5)\n",
+            "Y has dimension (500, 5)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pcRdsd30bE5p",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "7f0a7df1-1216-4dd0-b1ce-2d2ef46ae3ce"
+      },
+      "source": [
+        "dimension_to_plot = 0\n",
+        "\n",
+        "plt.scatter(X[:,1], Y[:,2])\n",
+        "plt.xlabel('dimension X' + str(dimension_to_plot))\n",
+        "plt.ylabel('dimension Y' + str(dimension_to_plot))\n",
+        "plt.title('Generated data')\n",
+        "plt.plot()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZNaARJBCbE5t"
+      },
+      "source": [
+        "## PLS and scikit-learn: basic use\n",
+        "\n",
+        "\n",
+        "Our newly generated data can be already used to test the PLS and CCA provided by standard machie learning packages, such as scikit-learn."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "16_ywJcXbE5u"
+      },
+      "source": [
+        "##########################################################\n",
+        "# We first split the data in trainig and validation sets\n",
+        "\n",
+        "# The training set is composed by a random sample of dimension N/2 \n",
+        "train_idx = np.random.choice(range(X.shape[0]), size = int(X.shape[0]/2), replace = False)\n",
+        "X_train = X[train_idx, :]\n",
+        "\n",
+        "# The testing set is composed by the remaining subjects\n",
+        "test_idx = np.where(np.in1d(range(X.shape[0]), train_idx, assume_unique=True, invert = True))[0]\n",
+        "X_test = X[test_idx, :]\n",
+        "\n",
+        "# We reuse the same indices to split the data Y\n",
+        "Y_train = Y[train_idx, :]\n",
+        "Y_test = Y[test_idx, :]\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "t345T9XybE5w"
+      },
+      "source": [
+        "#######################################\n",
+        "# We fit PLS as provided by scikit-learn\n",
+        "\n",
+        "#Defining PLS object\n",
+        "plsca = PLSCanonical(n_components=2)\n",
+        "\n",
+        "#Fitting on train data\n",
+        "plsca.fit(X_train, Y_train)\n",
+        "\n",
+        "#We project the training data in the latent dimension\n",
+        "X_train_r, Y_train_r = plsca.transform(X_train, Y_train)\n",
+        "#We project the testing data in the latent dimension\n",
+        "X_test_r, Y_test_r = plsca.transform(X_test, Y_test)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Dno-YefGU1cK"
+      },
+      "source": [
+        "We note that the projections in the latent space retrieved by PLS are indeed correlated. The different dimensions of the projections are however uncorrelated. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5AUG_6iKbE50",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 513
+        },
+        "outputId": "b82671b9-c2d2-42e2-dd90-1c6c930f7131"
+      },
+      "source": [
+        "# Scatter plot of scores\n",
+        "# ~~~~~~~~~~~~~~~~~~~~~~\n",
+        "# 1) On diagonal plot X vs Y scores on each components\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "plt.subplot(221)\n",
+        "plt.scatter(X_train_r[:, 0], Y_train_r[:, 0], label=\"train\",\n",
+        "            marker=\"o\", c=\"b\", s=25)\n",
+        "plt.scatter(X_test_r[:, 0], Y_test_r[:, 0], label=\"test\",\n",
+        "            marker=\"o\", c=\"r\", s=25)\n",
+        "plt.xlabel(\"x scores\")\n",
+        "plt.ylabel(\"y scores\")\n",
+        "plt.title('Comp. 1: X vs Y (test corr = %.2f)' %\n",
+        "          np.corrcoef(X_test_r[:, 0], Y_test_r[:, 0])[0, 1])\n",
+        "\n",
+        "plt.legend(loc=\"best\")\n",
+        "\n",
+        "plt.subplot(224)\n",
+        "plt.scatter(X_train_r[:, 1], Y_train_r[:, 1], label=\"train\",\n",
+        "            marker=\"o\", c=\"b\", s=25)\n",
+        "plt.scatter(X_test_r[:, 1], Y_test_r[:, 1], label=\"test\",\n",
+        "            marker=\"o\", c=\"r\", s=25)\n",
+        "plt.xlabel(\"x scores\")\n",
+        "plt.ylabel(\"y scores\")\n",
+        "plt.title('Comp. 2: X vs Y (test corr = %.2f)' %\n",
+        "          np.corrcoef(X_test_r[:, 1], Y_test_r[:, 1])[0, 1])\n",
+        "\n",
+        "plt.legend(loc=\"best\")\n",
+        "\n",
+        "# 2) Off diagonal plot components 1 vs 2 for X and Y\n",
+        "plt.subplot(222)\n",
+        "plt.scatter(X_train_r[:, 0], X_train_r[:, 1], label=\"train\",\n",
+        "            marker=\"*\", c=\"b\", s=50)\n",
+        "plt.scatter(X_test_r[:, 0], X_test_r[:, 1], label=\"test\",\n",
+        "            marker=\"*\", c=\"r\", s=50)\n",
+        "plt.xlabel(\"X comp. 1\")\n",
+        "plt.ylabel(\"X comp. 2\")\n",
+        "plt.title('X comp. 1 vs X comp. 2 (test corr = %.2f)'\n",
+        "          % np.corrcoef(X_test_r[:, 0], X_test_r[:, 1])[0, 1])\n",
+        "plt.legend(loc=\"best\")\n",
+        "\n",
+        "\n",
+        "\n",
+        "plt.subplot(223)\n",
+        "plt.scatter(Y_train_r[:, 0], Y_train_r[:, 1], label=\"train\",\n",
+        "            marker=\"*\", c=\"b\", s=50)\n",
+        "plt.scatter(Y_test_r[:, 0], Y_test_r[:, 1], label=\"test\",\n",
+        "            marker=\"*\", c=\"r\", s=50)\n",
+        "plt.xlabel(\"Y comp. 1\")\n",
+        "plt.ylabel(\"Y comp. 2\")\n",
+        "plt.title('Y comp. 1 vs Y comp. 2 , (test corr = %.2f)'\n",
+        "          % np.corrcoef(Y_test_r[:, 0], Y_test_r[:, 1])[0, 1])\n",
+        "plt.legend(loc=\"best\")\n",
+        "\n",
+        "plt.show()\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x576 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CjqjEnG8XDwU",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "933eb084-fbe6-468f-faaa-b5cd45e59dee"
+      },
+      "source": [
+        "#We can check the estimated projections\n",
+        "print('X projections: \\n' + str(plsca.x_weights_))\n",
+        "\n",
+        "print('Y projections: \\n' + str(plsca.y_weights_))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "X projections: \n",
+            "[[-0.16451212  0.68571364]\n",
+            " [ 0.54390907  0.06696435]\n",
+            " [ 0.35175957  0.68878177]\n",
+            " [-0.52665514  0.13389056]\n",
+            " [-0.52535536  0.18156422]]\n",
+            "Y projections: \n",
+            "[[-0.46578528 -0.35502995]\n",
+            " [ 0.51468494  0.19333414]\n",
+            " [-0.04788634  0.78072669]\n",
+            " [-0.47967689 -0.05341369]\n",
+            " [ 0.53456567 -0.47348545]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cl0QGQPWXFcJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "outputId": "a886504b-f2ab-4091-e4ff-4bf4e4424772"
+      },
+      "source": [
+        "# We can also predict Y from X\n",
+        "\n",
+        "predicted_Y_test = plsca.predict(X_test)\n",
+        "\n",
+        "plt.scatter(predicted_Y_test[:,dimension_to_plot], Y_test[:,dimension_to_plot])\n",
+        "plt.ylabel('target Y' + str(dimension_to_plot))\n",
+        "plt.xlabel('predicted Y' + str(dimension_to_plot))\n",
+        "\n",
+        "plt.show()\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZnyDB4o_TPk8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "cbf2c214-b11a-40f4-b2c0-6f7775bbd6a2"
+      },
+      "source": [
+        "# We can also predict Y from X \n",
+        "\n",
+        "predicted_Y_train = plsca.predict(X_train)\n",
+        "\n",
+        "plt.scatter(predicted_Y_train[:,dimension_to_plot], Y_train[:,dimension_to_plot])\n",
+        "plt.ylabel('target Y' + str(dimension_to_plot))\n",
+        "plt.xlabel('predicted Y' + str(dimension_to_plot))\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "j-QCTnYPTjbr",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fda49934-e0d1-408c-86f0-6381e7f4e188"
+      },
+      "source": [
+        "plsca_4 = PLSCanonical(n_components=4)\n",
+        "plsca_4.fit(X_train, Y_train)\n",
+        "\n",
+        "predicted_Y_train = plsca_4.predict(X_train)\n",
+        "\n",
+        "print('X projections with 2 components: \\n' + str(plsca.x_weights_))\n",
+        "\n",
+        "print('Y projections with 2 components: \\n' + str(plsca.y_weights_))\n",
+        "\n",
+        "print('X projections with 4 components: \\n' + str(plsca_4.x_weights_))\n",
+        "\n",
+        "print('Y projections with 4 components: \\n' + str(plsca_4.y_weights_))\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "X projections with 2 components: \n",
+            "[[-0.16451212  0.68571364]\n",
+            " [ 0.54390907  0.06696435]\n",
+            " [ 0.35175957  0.68878177]\n",
+            " [-0.52665514  0.13389056]\n",
+            " [-0.52535536  0.18156422]]\n",
+            "Y projections with 2 components: \n",
+            "[[-0.46578528 -0.35502995]\n",
+            " [ 0.51468494  0.19333414]\n",
+            " [-0.04788634  0.78072669]\n",
+            " [-0.47967689 -0.05341369]\n",
+            " [ 0.53456567 -0.47348545]]\n",
+            "X projections with 4 components: \n",
+            "[[-0.16451212  0.68571364  0.67228022 -0.20039767]\n",
+            " [ 0.54390907  0.06696435 -0.02234212  0.1321209 ]\n",
+            " [ 0.35175957  0.68878177 -0.51792753  0.16218841]\n",
+            " [-0.52665514  0.13389056 -0.05465677  0.8123139 ]\n",
+            " [-0.52535536  0.18156422 -0.52564613 -0.5061876 ]]\n",
+            "Y projections with 4 components: \n",
+            "[[-0.46578528 -0.35502995 -0.60683096 -0.48987443]\n",
+            " [ 0.51468494  0.19333414 -0.09330121 -0.14547852]\n",
+            " [-0.04788634  0.78072669 -0.06147003 -0.56209311]\n",
+            " [-0.47967689 -0.05341369  0.75236698 -0.27896003]\n",
+            " [ 0.53456567 -0.47348545  0.23068686 -0.58744505]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B3ZbgFhibE53"
+      },
+      "source": [
+        "## Into the guts of latent variable models\n",
+        "\n",
+        "After playing with the builti-in implementation of PLS in scikit-learn, we are going to implement our own version based on the NIPALS method we saw during lesson.\n",
+        "\n",
+        "### NIPALS for PLS"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Wi2jt1O0bE54"
+      },
+      "source": [
+        "# Nipals method for PLS\n",
+        "\n",
+        "n_components = 3\n",
+        "\n",
+        "\n",
+        "# Defining empty arrays where to store results\n",
+        "\n",
+        "# Reconstruction from latent space to data\n",
+        "loading_x = np.ndarray([X.shape[1],n_components])\n",
+        "loading_y = np.ndarray([Y.shape[1],n_components])\n",
+        "\n",
+        "# Projections into the latent space\n",
+        "weight_x = np.ndarray([X.shape[1],n_components])\n",
+        "weight_y = np.ndarray([Y.shape[1],n_components])\n",
+        "\n",
+        "# Latent variables\n",
+        "scores_x = np.ndarray([X.shape[0],n_components])\n",
+        "scores_y = np.ndarray([Y.shape[0],n_components])\n",
+        "\n",
+        "\n",
+        "# Initialization of data matrices\n",
+        "current_X = X\n",
+        "current_Y = Y\n",
+        "\n",
+        "for i in range(n_components):\n",
+        "    # Initialization of current latent variables as a data column\n",
+        "    t_x = current_X[:,0]\n",
+        "\n",
+        "    # NIPALS iterations\n",
+        "    for _ in range(100):\n",
+        "        # estimating Y weights given data Y and latent variables from X\n",
+        "        w_y = current_Y.transpose().dot(t_x)/(t_x.transpose().dot(t_x))\n",
+        "        # normalizing Y weights\n",
+        "        w_y = w_y/np.sqrt(np.sum(w_y**2))\n",
+        "\n",
+        "        # estimating latent variables from Y given data Y and Y weights\n",
+        "        t_y = current_Y.dot(w_y)\n",
+        "        # estimating X weights given data X and latent variables from Y\n",
+        "        w_x = current_X.transpose().dot(t_y)/(t_y.transpose().dot(t_y))\n",
+        "        # normalizing X weights\n",
+        "        w_x = w_x/np.sqrt(np.sum(w_x**2))\n",
+        "\n",
+        "        # estimating latent variables from X given data X and X weights\n",
+        "        t_x = current_X.dot(w_x)\n",
+        "\n",
+        "    # Weights are such that X * weights = t\n",
+        "    weight_x[:,i] = w_x\n",
+        "    weight_y[:,i] = w_y\n",
+        "    \n",
+        "    # Latent variables\n",
+        "    scores_x[:,i] = t_x\n",
+        "    scores_x[:,i] = t_y\n",
+        "    \n",
+        "    # Loadings obtained by regressing X on t (X = t * loadings)\n",
+        "    \n",
+        "    loading_x[:,i] = np.dot(current_X.T, t_x)/t_x.transpose().dot(t_x) \n",
+        "    loading_y[:,i] = np.dot(current_Y.T, t_y)/t_y.transpose().dot(t_y)\n",
+        "    \n",
+        "    # Deflation = current_data - current_reconstruction\n",
+        "    \n",
+        "    current_X = current_X - t_x.reshape(len(t_x),1).dot(w_x.reshape(1,len(w_x)))\n",
+        "    current_Y = current_Y - t_y.reshape(len(t_y),1).dot(w_y.reshape(1,len(w_y)))\n",
+        "    "
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HuothRrBbE57",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "36bbbbbc-5034-4484-a1fe-8e4429719f32"
+      },
+      "source": [
+        "print('The estimated projections functions of of X are: \\n' + str(weight_x))\n",
+        "\n",
+        "print('\\n The estimated projections functions of of Y are: \\n' + str(weight_y))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "The estimated projections functions of of X are: \n",
+            "[[ 0.17329009  0.5336278   0.51808589]\n",
+            " [-0.39082094  0.0616058   0.6000854 ]\n",
+            " [-0.30968135  0.81479162 -0.37827723]\n",
+            " [ 0.47546915  0.09994223  0.41327064]\n",
+            " [ 0.70374433  0.19383566 -0.23999633]]\n",
+            "\n",
+            " The estimated projections functions of of Y are: \n",
+            "[[ 0.45869999 -0.51053537 -0.56646878]\n",
+            " [-0.44037568  0.33125328 -0.10969391]\n",
+            " [ 0.03974949  0.55626222 -0.28369485]\n",
+            " [ 0.28309562 -0.13013815  0.76585086]\n",
+            " [-0.71689638 -0.55069166 -0.00836996]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6Hs_o8NIbE59",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 714
+        },
+        "outputId": "bd6043b4-e909-45b6-d4f5-ed890b1cdd73"
+      },
+      "source": [
+        "plt.figure(figsize=(12, 12))\n",
+        "plt.subplot(3,2,1)\n",
+        "plt.scatter(scores_x[:,0], latents[:,0])\n",
+        "plt.xlabel('PLS 0')\n",
+        "plt.ylabel('ground truth 0')\n",
+        "plt.subplot(3,2,2)\n",
+        "plt.scatter(scores_x[:,0], latents[:,1])\n",
+        "plt.xlabel('PLS 0')\n",
+        "plt.ylabel('ground truth 1')\n",
+        "plt.subplot(3,2,3)\n",
+        "plt.scatter(scores_x[:,1], latents[:,0])\n",
+        "plt.xlabel('PLS 1')\n",
+        "plt.ylabel('ground truth 0')\n",
+        "plt.subplot(3,2,4)\n",
+        "plt.scatter(scores_x[:,1], latents[:,1])\n",
+        "plt.xlabel('PLS 1')\n",
+        "plt.ylabel('ground truth 1')\n",
+        "plt.subplot(3,2,5)\n",
+        "plt.scatter(scores_x[:,2], latents[:,0])\n",
+        "plt.xlabel('PLS 2')\n",
+        "plt.ylabel('ground truth 0')\n",
+        "plt.subplot(3,2,6)\n",
+        "plt.scatter(scores_x[:,2], latents[:,1])\n",
+        "plt.xlabel('PLS 2')\n",
+        "plt.ylabel('ground truth 1')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x864 with 6 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4uQVTVL8bNyt"
+      },
+      "source": [
+        "Once that the PLS parameters are estimated, we can solve the regression problem for predicting Y from X. We adopt the scheme used in scikit-learn, where a rotation matrix is first estimated to accoung for non-cummutativity between projection (weights) and reconstruction (loadings).\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gV0N6AqKbE5_"
+      },
+      "source": [
+        "# Identifying rotation from X to t\n",
+        "# t_x * loadings_x = X\n",
+        "# t_x * loadings_x.T * weight = X * weight \n",
+        "# t_x =  X * weight * (loadings_x.T * weight)^-1 = X * rotations_x\n",
+        "\n",
+        "rotations_x = weight_x.dot(np.linalg.pinv(loading_x.T.dot(weight_x)))\n",
+        "\n",
+        "# Solving the regression from X to Y\n",
+        "# Y = t_x * loadings_y.T\n",
+        "# Y = X * rotations_x * loadings_y.T\n",
+        "\n",
+        "regression_coef = np.dot(rotations_x, loading_y.T)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZKD-C0UobE6D",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "05aa2ee9-6169-44cc-de32-8a259635d9b7"
+      },
+      "source": [
+        "plt.figure(figsize=(12, 18))\n",
+        "for i in range(Y.shape[1]):\n",
+        "  plt.subplot(Y.shape[1], 1, i+1)\n",
+        "  plt.scatter(X.dot(regression_coef)[:,i], Y[:,i])\n",
+        "  plt.xlabel('predicted dimension ' + str(i))\n",
+        "  plt.ylabel('target dimension ' + str(i))\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x1296 with 5 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2t7WMrrHbE6F"
+      },
+      "source": [
+        "# Comparing with SVD of covariance matrix\n",
+        "\n",
+        "eig_val_x, eig_vect, eig_val_y = np.linalg.svd(X.transpose().dot(Y))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "R30NyLjebE6G",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "15bc7063-fb94-4d50-9b4a-d888215563f2"
+      },
+      "source": [
+        "print('Eigenvalues for X \\n' + str(np.real(eig_val_x[:,:3])))\n",
+        "print('Estimated weights for X\\n' + str(np.real(weight_x[:,:3])))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Eigenvalues for X \n",
+            "[[ 0.17329009  0.5336278   0.51808589]\n",
+            " [-0.39082094  0.0616058   0.6000854 ]\n",
+            " [-0.30968135  0.81479162 -0.37827723]\n",
+            " [ 0.47546915  0.09994223  0.41327064]\n",
+            " [ 0.70374433  0.19383566 -0.23999633]]\n",
+            "Estimated weights for X\n",
+            "[[ 0.17329009  0.5336278   0.51808589]\n",
+            " [-0.39082094  0.0616058   0.6000854 ]\n",
+            " [-0.30968135  0.81479162 -0.37827723]\n",
+            " [ 0.47546915  0.09994223  0.41327064]\n",
+            " [ 0.70374433  0.19383566 -0.23999633]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MlmTRN6lbE6I",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9639572d-3547-495b-c8ec-97ae5beb3a45"
+      },
+      "source": [
+        "print('Eigenvalues for Y \\n' + str(np.real(eig_val_y.T[:,:3])))\n",
+        "print('Estimated weights for Y\\n' + str(np.real(weight_y[:,:3])))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Eigenvalues for Y \n",
+            "[[ 0.45869999 -0.51053537 -0.56646878]\n",
+            " [-0.44037568  0.33125328 -0.10969391]\n",
+            " [ 0.03974949  0.55626222 -0.28369485]\n",
+            " [ 0.28309562 -0.13013815  0.76585086]\n",
+            " [-0.71689638 -0.55069166 -0.00836996]]\n",
+            "Estimated weights for Y\n",
+            "[[ 0.45869999 -0.51053537 -0.56646878]\n",
+            " [-0.44037568  0.33125328 -0.10969391]\n",
+            " [ 0.03974949  0.55626222 -0.28369485]\n",
+            " [ 0.28309562 -0.13013815  0.76585086]\n",
+            " [-0.71689638 -0.55069166 -0.00836996]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FfYndKcgbE6J",
+        "scrolled": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5c0dd4ad-eb0d-4da1-924e-30e487151dd0"
+      },
+      "source": [
+        "# PLS in scikit-learn \n",
+        "\n",
+        "plsca = PLSCanonical(n_components=3, scale = False)\n",
+        "plsca.fit(X, Y)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PLSCanonical(algorithm='nipals', copy=True, max_iter=500, n_components=3,\n",
+              "             scale=False, tol=1e-06)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G6e2k9PGbE6L",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f8f43bb9-1b70-4e24-af08-20a0fe3c410a"
+      },
+      "source": [
+        "print(plsca.x_weights_)\n",
+        "print(plsca.y_weights_)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 0.17383805  0.53364999  0.52130621]\n",
+            " [-0.39080614  0.06233964  0.59563819]\n",
+            " [-0.30862178  0.81501795 -0.38007785]\n",
+            " [ 0.47529345  0.09999907  0.41226941]\n",
+            " [ 0.70420141  0.1925549  -0.24296038]]\n",
+            "[[ 0.45833158 -0.51171805 -0.54355424]\n",
+            " [-0.43956112  0.33169686 -0.08774437]\n",
+            " [ 0.04100715  0.55561051 -0.25599346]\n",
+            " [ 0.28301917 -0.13075296  0.79453687]\n",
+            " [-0.71759093 -0.54983857  0.00531297]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uAsqNC29bE6N"
+      },
+      "source": [
+        "### NIPALS for CCA"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1CjCX7FtbE6N"
+      },
+      "source": [
+        "# Nipals method for CCA\n",
+        "\n",
+        "# Defining empty arrays where to store results\n",
+        "\n",
+        "# Reconstruction from latent space to data\n",
+        "loading_x_cca = np.ndarray([X.shape[1],n_components])\n",
+        "loading_y_cca = np.ndarray([Y.shape[1],n_components])\n",
+        "\n",
+        "# Projections into the latent space\n",
+        "scores_x_cca = np.ndarray([X.shape[0],n_components])\n",
+        "scores_y_cca = np.ndarray([Y.shape[0],n_components])\n",
+        "\n",
+        "# Latent variables\n",
+        "weight_x_cca = np.ndarray([X.shape[1],n_components])\n",
+        "weight_y_cca = np.ndarray([Y.shape[1],n_components])\n",
+        "\n",
+        "# Initialization of data matrices\n",
+        "current_X = X\n",
+        "current_Y = Y\n",
+        "\n",
+        "for i in range(n_components):\n",
+        "    # Initialization of current latent variables as a data column\n",
+        "    t_x = current_X[:,0]\n",
+        "\n",
+        "    # NIPALS iterations\n",
+        "    for _ in range(500):\n",
+        "        ## CCA variant\n",
+        "        # estimating Y weights given data Y and latent variables from X\n",
+        "        Y_pinv = np.linalg.pinv(current_Y)\n",
+        "        w_y = Y_pinv.dot(t_x)\n",
+        "\n",
+        "        # normalizing Y weights\n",
+        "        w_y = w_y/np.sqrt(np.sum(w_y**2))\n",
+        "        # estimating latent variables from Y given data Y and Y weights\n",
+        "        t_y = current_Y.dot(w_y)\n",
+        "\n",
+        "        ## CCA variant\n",
+        "        # estimating X weights given data X and latent variables from Y\n",
+        "        X_pinv = np.linalg.pinv(current_X)\n",
+        "        w_x = X_pinv.dot(t_y)\n",
+        "\n",
+        "        # normalizing X weights\n",
+        "        w_x = w_x/np.sqrt(np.sum(w_x**2))\n",
+        "        # estimating latent variables from X given data X and X weights\n",
+        "        t_x = current_X.dot(w_x)\n",
+        "\n",
+        "        \n",
+        "    # Weights are such that X * weights = t\n",
+        "    weight_x_cca[:,i] = w_x\n",
+        "    weight_y_cca[:,i] = w_y\n",
+        "    \n",
+        "    # Latent dimensions\n",
+        "    scores_x_cca[:,i] = t_x\n",
+        "    scores_x_cca[:,i] = t_y\n",
+        "    \n",
+        "    # Loadings obtained by regressing X on t (X = t * loadings)\n",
+        "    \n",
+        "    loading_x_cca[:,i] = np.dot(current_X.T, t_x)/t_x.transpose().dot(t_x) \n",
+        "    loading_y_cca[:,i] = np.dot(current_Y.T, t_y)/t_y.transpose().dot(t_y)\n",
+        "    \n",
+        "    # Deflation = current_data - current_reconstruction\n",
+        "\n",
+        "    current_X = current_X - t_x.reshape(len(t_x),1).dot(w_y.reshape(1,len(w_x)))\n",
+        "    current_Y = current_Y - t_y.reshape(len(t_y),1).dot(w_x.reshape(1,len(w_y)))\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OlAUjY4fbE6O",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "38080ddc-1ad2-4cc1-ad1d-435ed0867242"
+      },
+      "source": [
+        "print(weight_x_cca)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 0.02390038 -0.02390038 -0.02390038]\n",
+            " [-0.30777485  0.30777485  0.30777485]\n",
+            " [-0.51891638  0.51891638  0.51891638]\n",
+            " [ 0.46316222 -0.46316222 -0.46316222]\n",
+            " [ 0.64877574 -0.64877574 -0.64877574]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pTWftzESfx0C",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "661cee39-f95b-4f5d-ede9-f09ddcfa2b99"
+      },
+      "source": [
+        "cca = CCA(n_components=3, scale = False)\n",
+        "cca.fit(X,Y)\n",
+        "cca.x_weights_"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.0216039 ,  0.58191248,  0.53816073],\n",
+              "       [-0.30452577, -0.05050608,  0.6203162 ],\n",
+              "       [-0.52804241,  0.67464905, -0.30296943],\n",
+              "       [ 0.47287615,  0.10355333,  0.40063206],\n",
+              "       [ 0.63589047,  0.43926343, -0.27072924]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lI89d2n8bE6X"
+      },
+      "source": [
+        "### Reduced Rank Regression\n",
+        "\n",
+        "We finally review reduced rank regression through the eigen-decomposition seen during lesson."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0YO1GUEjbE6Y"
+      },
+      "source": [
+        "# Reduced Rank Regression\n",
+        "\n",
+        "n_components = 2\n",
+        "Gamma = np.eye(n_components)\n",
+        "\n",
+        "SYX = np.dot(Y.T,X)\n",
+        "\n",
+        "SXX = np.dot(X.T,X)\n",
+        "\n",
+        "U, S, V = np.linalg.svd(np.dot(SYX, np.dot(np.linalg.pinv(SXX), SYX.T)))\n",
+        "\n",
+        "A = V[0:n_components, :].T\n",
+        "\n",
+        "B = np.dot(np.dot(A.T,SYX), np.linalg.pinv(SXX))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2MxP4xP2bE6Z",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c7611c17-c8a2-4eee-8764-32ac8b27a387"
+      },
+      "source": [
+        "A"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[-0.49146741,  0.47883842],\n",
+              "       [ 0.46146201, -0.30124517],\n",
+              "       [-0.00265136, -0.55764906],\n",
+              "       [-0.29119127,  0.1114257 ],\n",
+              "       [ 0.67875858,  0.59714065]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Y-oxIQWGbE6b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "337bf7b7-10a8-4ee6-f400-d713c8940408"
+      },
+      "source": [
+        "B"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[-0.06045402,  0.22588251,  0.31396256, -0.33470603, -0.49448429],\n",
+              "       [-0.35097886, -0.02289009, -0.48029001,  0.00442613, -0.16565336]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "I3_rCSO6bE6c"
+      },
+      "source": [
+        "regression_coef_rrr = np.dot(A,B)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ckhYTr9ObE6d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "outputId": "59d5216f-347d-4663-c3aa-08fe0b8401a3"
+      },
+      "source": [
+        "plt.scatter(np.dot(X,regression_coef)[:,0],Y[:,0])\n",
+        "plt.xlabel('predicted dimension 0')\n",
+        "plt.ylabel('target dimension 0')\n",
+        "plt.title('RRR prediction')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a8Mrev32bE6f"
+      },
+      "source": [
+        "### Sparsity in latent variable models\n",
+        "\n",
+        "We now focus on the effect of spurious variables in mutivariate models. To explore this new setting, we are going add spurious random features to our data matrices X and Y.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jzXPHX5IbE6f"
+      },
+      "source": [
+        "## Adding 3 random dimensions\n",
+        "## No association is expected from these features\n",
+        "\n",
+        "X_ext = np.hstack([X,np.random.randn(n*3).reshape([n,3])])\n",
+        "Y_ext = np.hstack([Y,np.random.randn(n*3).reshape([n,3])])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "s30IL5ypbE6g",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "outputId": "04f79603-b0c5-49a7-cc96-c022df33e769"
+      },
+      "source": [
+        "plt.scatter(X_ext[:,0], Y_ext[:,-1])\n",
+        "plt.xlabel('X dimension 0')\n",
+        "plt.ylabel('Y random dimension')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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8HwBvA7Ceme/IWDYAADPfBuA2oLYG0IhjBsl7WupigNJoHZhn7PXGB/bX+cenubbdVaadB0ad3lfqrI06VeROGH5j6/qbz+sq4ZUjk6EdtNR3M1X9VO9Tv28Sn78OXTb3xgf2R857cBmhByOmarkM07OOf9OaZbhpzbKWdrcWCZc8gPO9hd+LADwL4K0ABrIUqmjkPS0NM0BBt8TYRBWHx6uF6Trkgimc0TXMEYhgkD2tHFa1UuF3cbj+5ofHq9Zw0OB+bLkV/t83SyaqU9j0YH3NIIUp78F1hB78HsGzo44vyr9xuBgANUu4EMA97VgCIu8yrmEGKK3WgUXGZQ0krMOVwh966E+OsvnjlXFxNRo2gp3PTH1l1fvCfl8TBODWdcsjfcZWokLlPcRNKHP5HofHq4VqmdjquOQBPEREB1BzAf0+Ec0HcCSNgxPRXQD+G4DXE9EPAFzPzJ9JY99pkrd/PMwv6to6sMiRTPO6SlrlM6+r5LQGMjRSwauvTTofr+JzwbiEc/qN8NxSR2wffLBblEkp+t+33tIUxoZaQ+gulyLNpEz48x7iXDdxXKaNXGtrR0JnAMy8AcA7APQxcxXAqwDem8bBmflSZj6FmUvM/KYiKn9Ff28Pvr7hXXhm84X4+oZ3NfSCDBt1ubglurtKGLhn76zRVdKm5Wly/XuW1kWJlDoJ179nqVPkiSnRyNgOEfoIm7BSFVfft8+pkJuOcqkTN3/gbKf+A9PMkX5fEz8cm8DGi5fWRVh1AHXnu1zqNJaXmNdVSnzNx/0eWay1NVPP6SxxbQq/BMA6IvotAO8HcH52Igk6VGOPU73olcHtB60N1v2US504Up2q80lH7VCVFrqbr7+3B4PvP3uWkRt8/9nWhVf/dmMOAB8Lu5y1HdC6xWzGNq4rRnH8nPrbzWV9aWD1Yu13cEG5bQYvmX1uP7lued35vmnNMmy8eKmx/lBSdNepPyS2q6RXR2Gdy6JiCuW9dmif02dbyXC4RAF9HsBbAOzBsZ7AjCZI3molojRY10UBmaJM0nANROHaoX2zOlwFv4dulNltcA/5FYOtQbcpnLQyNqGt8W+SI+lIdGyiWvebuYQ99vf2WHsDmwiWqzBVPtVhq+mTpASIbd/LN+2o6yAG1Ix4mphCee/cdQh9p51s/D6tWKbCZQ2gD8CZ7FozQsiEuA3WFXEUSBDXpuo25RFsbxj8HjqOGEbd/u06RQqE5xIoo5FG83cXdJ3UXMIeexyO7U8485eySGvtx3UtxnYs23Vqalpj2h4XW8a47TrMOx8oC1wMwFMA3gjghYxlESwkzUWwLbIqbDdv2Mhdfd6mIAa3HwyNi9dhKtfg3x6s9x+HsJvZZGSiEoy9v2nNMmu5i6GRCsYdFrj93/qId26ijlpt7w9TgElHyGn2FLBdyzZDbrsO884HygKXNYDXA/g2EW0nogfUI2vBhNkkzUWwLbIC9nIXYSN39fmr7t5rXay13ShJcyqC9f7jojJ/daj1AVu4aFTCQnRNC8/lUofRZ672u37LHly5ZU+k0g02JR+mAG29o1185WmFW4eVbrGtqdiuw7zzgbLAZQawMWshhHCSpsi71Ja3KQrbyN0Wy67eA5hHXgRov4caxZnoIGDRhm1WP38cBu7di40P7MfLE/WZ1Gomo5tNEcXzVwfLTfjRZeUCwMknHA8AGLcYVZsoprBgm5IPG6Hbeke7zATSCre2ZTIrF9Twcy/VDWrC7qdWLFPh0hT+K0R0GoAzmPlRIuoCkCwTRoiM7eZw9fHGqS0fNr09tbscGh2jFITuBiIAl523ULuWEOZuUUFNaSp/oNZMXS2O69wYxnPCwLObLwwtra1DpyCHRirWrNwknFQuad01pgV3dV3ZFKDNtWJyr+mu3SRln13P2Y39y9B32smRjE3e+UBZ4BIF9LuoFWM7GbVooB4AnwLwK9mKJgTRKfC0IhPCRne2kbstUUnXOMXlBkoacpkmQeUVdq7iLBbrFKRt9mP7XcIolzpBBO0oGWCUS51aJR/2+4WtkQRltV27tuPYcDlnijgJbXnWy8oClzWAPwCwEsCPAYCZ/x3AG7IUSnAnbnneIDb/qyl+W43cbT7QYON114S6NBbW4sbO6/DLE1a6Om65iOB3tp0D0+/iwk1rlhmT2Saq03j7wpO0eRAuET43rVlmrGsUTL4zXbubHtwfu/y67ZypsN9mj91PE5c1gKPM/JpqBENEc+De00LImLQiE1xG57aRn6k2vWuFziBphFymeZHWGbmgjvM9V+flqrv3at1TpjWL4DFM5+CE4zpn5XzMLXVgbLxqdN/4ufy8hTPims7PN55+CbesW17njnKZaar/dddDMMzSdI3qvkPS/s2KoNxFLo/SCFxmAF8hoo8DKBPRuwHcA+DBbMUSXEkzMsE2Og97LU54p41GLqyVOggnHGceSROOjR6vHdqHq+6ur99fneJZs67+3h5ceu6COjtRLnXi0nMXOEW76Eb4pU7Ca5PTsyq/HqlO45Z1yzFy3fnWCKXLz1uIG/uXWcNxAX2WdJSZpuv1EPUadbmWXGZFSu4iNHrKGxcDsAHAKIB9AH4PwMMArs1SKMGdvCuVKnpSDpHr7+0x1qVJk57uMtads6CuFwH5/vpzH+7YdSg02gk4Fpbqfyeh5hK7sX8Z1q7omXGVdBLVucoAfVmKE46bU1fSQ7lNVm5+DIfHq1qjc+u65bixf1mdnCZc3VGm7S7XQ1QXlmvPAf85M1EZmwgNW24HXKKApgF82nsIBaMIkQlDIxW8elSfqDT+2qQxxDGMjRcvTbX7lR8CZtwcKzc/ZuzyFcWN5FdQpnIDOw+M1uUsTDFj6+7KTBkCm1vi9A3btMc+PF6dcZ2otpOq81jwenBxr7m6o1S/gqC8riUuALOrLMiqJfND36P2q/Ztqu5KMEePNXNiV1QorMIDEV0E4BMATkPNYBAAZuafyl682fT19fHw8HCjD9uypOH/dAnXLJc6Y/cV9nfCSjPeX7lDAMQK2wwS/I5x9tnTXcaqJfO18elq3yaFZtqfLqQy7DdTxwOODSy6vQ5n/tlHudSJtSt6sHV3pU7RBz9vu75cz5Xp+9jQfVfb+kfc42RJGvcpEe1m5r667Q4G4HsA1gDYl3c9IDEA6aG7MeIoaleFlMZNNTRSca5pNK+rhKPVKW1xse5yCXuuP1bQNopSNXH5eQux88DozE366tHJWIX2TMrJ30PYdVZEAJ7ZfKH2tbAWogDqehV3dhBOPH7OrAQ5U5vKKL+36/m3fR8bQQVqO1aSwUoWpHWfmgyASxTQ8wCeylv5C+mSVmEr1+lyGtNqV9+sKoQGQHvzbLx4dmnjpDV+yqWOWaNgpWA6CHVrCzZsMxx/5u5EdWrmvT0WY6PcOKYRpO137r1hR91C99Q0g2i2EjblgET5vV3Pf1olF0x1sYD6sOW8yboAncsi8McAPExEVxPRH6tH4iMLuZJW+KjrTZnGzesq2+HxY2WXXdoXqoXDOHkDpQ7C3EDilCKK8gfsGc3dXaVZ/XSnmGd86qYa/v4mNlEjXUwKMrg9jSi04MJtd7mkbVYTJ7BB9/1fOTJZt3/F1t2VQkUBZV2AzsUA/DmAcQBzAZzoewhNTFrhoy7NStKKSooim3+U5JJ41t/bY+weZmPwkrMxFhJ778K8rpIxcqa26KbP3NX1NvYbOltdnCCq2ckiw0KzDlMU2qol8yM1TvH/TnuuP1/brCbOiFc3gq5OM044bo42Ya1oUUBZF6BzcQGdysxnpXI0oTCkVdiqv9ferMTf2zbpYlZUV03UUVLUEXuP6rZl8IO74u+4ZaqVdOeuQ9rPqu9oKhPiWkvIdW0hGJqri0JbtWR+nUssr8YppmvA1mOgSFFAWRegczEADxPR+cy8I5Uj+iCiCwD8NWrF5f6ZmTenfQxBT7B+vikW3QVbsxLV2zYsk9TFOPiVjYvCjTpKiuKz99+EtkzoMHRhmrrzsPPAaORa+VHq4rjWXgqunwD1xkcXVhvFb51m5604Na6KVN456zBvlyignwA4AcBRAFWkFAZKRJ0Avgvg3QB+AOBbAC5l5m+bPiNRQOmRVnSB2pdNAdoWKpUCjCpLmJvCFAdvMjRDIxX88ZY90Lee0cvsly3YMMeFUifN9D0OI87vZQuvvHXdcgDHFIur3LrvHuW4zzpE8ZiigtIKA/WHqepmPf5uaq1C7CggZs7K338OgO8x8/cBgIi+AOC9AIwGoJVpdE2SNKMLTPXVFbbRumpwn3akg65zGQDjyHJw+0En5U+AVgn5ywtXxiZCY82BY+UjgjV3Nj6wv85YzusqYe2KnlmhpmHXiGn0q8pFxIl8Mo3G/ddvhyGaSRWDC/tN01z4dBlBB8+3P4iglYyADuMMgIiWMPMBInq77nVmfiLRgYneD+ACZv4f3vPfBHAuM3808L4rUCtHjYULF6547rnnkhy2kKQ5GnfFNEqLG2sNzE7acqWnu2wcgdpkOfPPvqSN8bcdB9AbI5sMuve6jEKjnAs1Kh4aqWDgnr11pR78RBmd6q6rUgfhdXPnhBaNA+wJU/7zECU3weX8pTkDcMGWh+Ay4zFRpEJzphmALQroKu/vzZrHX6UuoQFmvo2Z+5i5b/58t1TwZiOtks5RyCK6QEVyuAbTKD+6TRYVmeKPJhkaqdTFqAP28s8/HJsI7XYVhr8onGtUy7ObL7TK5S+RPLj9oFX5A7XR6cA9e9F7w47QCBtdeCXIHOIZJKyjmCJK74a4Bd2yrG8VVkI6ToG4Zik0Z3QBMfPven9XZXTsCoAFvudv8ra1HXk0m84yusDmeug6bo52RKSTZdWS+VqXzfFzOrSK0qawTu0uY/y1yUjdroKjZZ1byWVEZ8s+ZQBXer17XalO84xMYbIE6+LEyU7W4TeYUa5T14JuQPSFz6D7zHW2FJYdHMcdmXUCV1oYDQARrbF9kJnvS3jsbwE4g4hOR03xfxDAbyTcZ1MSFqmQhLAs0CymqCbjYroZTbKYbqKofmtCrZDYlm8+X/daqZOs3a4AfW17VYHT5fwNrF5cV1YhTfyKRVc7Sbkx0hpQBAcKtl7PYT13bddnlGtR5z47PF7FwL17AdgNtUt4cdRzl8egLg62ReD3eH/fAOAdAB7znq8C8A0AiQwAM08S0UcBbEctDPR2Zt6fZJ/NSlaj8bBwuqg3mStxjItOFlurySioKpy6WcOkb5tOhpWbHzPOLPwVOG0jcZWQldboW4cqFeH/vdVCrJLtpHIpkQwEaH9L0/UbtmidZrinyX2mW2gP4hJeHHUwluWgLk1sLqAPAwAR7QBwJjO/4D0/BcBn0zg4Mz+MWn+Btiar0Xie09A0jEsaXcGAmu/bNPJi1jdkB2oKKsrxbefWlniUBqd2l7HpwfqsX79sc0sddf1+Sx0EEJxmJy75GVGu37DrM8oiqm1k7TLqVterKSAj6mAs6wSutHBJBFuglL/HjwAszEietiWL0Xje09CoURDB9wczSuPy6muT1tGvTnErRRAV07lNy5iZcNn34fEqVr7lZOz6/uGZ5L915yyYFb4adgzbLCfq9Wu7PqPODmznN2pdIiD5YCzrBK60cDEAXyai7QDu8p6vA/BodiIJaZHnNDTqDax7/9bdFaxd0YOH9r6QyHVRnapVsQyOfv0ElVGUyBY//nPrN2jdXSWUOmiWm8KUeRy1imgUvv70SzP/TzHPlJjwh3RuenC/pWl8be0jDUVmuj67u0raJjG2GdbA6sXaEFq1xhOFtAZjWblY0yS0GJwXl/8pAGd7j9uY+Q+zFkxITpJwOl34ZRSihraa3v/Q3hdwdNIt3t/WD3dsvIqb1izTFgAD6o1inFlSqZPw6tFJnL5hG5Zv2oGBe/fOhAEeHq8CVHNHqZDMuXPqb7/ODsJvnLvQWBgubRjAnbsOzfy+/b09GLnufGv46uHxKhYFros414up5/ErRyYjd+vq7+3B4CVnz6pVNK+r5Jxp3a64zADAzPcDuD9jWTKhSMkYjSZJOF3Sxbmo7ifT9igjf2Zz8pJS8MfNIUxU698RbDcY1WUzz+uYpeTVyV2dYpxw/BxcdPYpxqzpqWnGtidfwMh156fSqDGW2qAAAByHSURBVMYF1QQ++NuGnYPK2ASu3LIH19y/D69NTs+Mvl2vF931GdZIxzZ7LcqIu5l0jks56KalWZIxskQlJIWVQ/aTRmJa1ESzNNxSYxNVY0bxsZwC/Wxi54HRWc+1o9MO0tapv3XdcnRpmrXrqIxNhNYMOjxerYU1RmyangSdAXZ1nbz62pS2Ub3L9RK8Pm2L5UVcRA3SbDqnpQ1AHhm2zUhw+m4a9UXt8hTF/TSwenEtIiUDGMAduw5FivMOZtH2dJcxeMnZxjr1ruemk8ip5ISpzv/Kt5wcq3FNGDoD3N/bU1f+OQpx3GimgYC/rHiRaTad4+QCalbyjoIpErYqmEF3T5gbxYUw95NOHlONGpfCakkxLd6e2l3GLeuWz1I8UaNQFLZF6CBhdf6T9iAIYjLMGy9eGrtdZpxZnW4xt9RBGLzEvWpqnu6XZtM5oQaAiC4C8AkAp3nvT6UcdCNolmQMRVYXr82nrxuxMNyyOMMw+WRN8piUDMPexzUp/u8WLOvs6s+2lZLwN1vXRbfocPF1p7VGMK+rZPxuLklSOoLXS6RrOzjFcZzypJlYFnYc03dpNp3j4gK6FcBvA/hpZv4pZj6xGZQ/0PiiUknI0ndom5YaE6SAVFryRZHHRCcRRq47H7euWz4jkymaR73f1U0yr6s0q2OZzj8f1kLx9A3bMLj9INau6KlzGY1cd/6s9RcX5e96jaYxqvR3IzOh/PSukUnB6yXKtT24/WBdUprK5rUxNFLBVXfvzdz9EvZdmknnAG4uoOcBPMVhnWMKSLMkYwDZZu2aRm7+ejFB0iq9qxstRVVcU8xYufkxDKxejK9veBeGRirW4mk3f+Ds0BGyqROX6SJXMvvdL/5Zkn9xd15XCa8encT6LXuw8YH9IMLMLCBsJkOAsTNbWIy+K6r8ddT7weV30103Ua7tOC4UpZSjho7GIey7NJPOAdwMwMdQawv5FdS6ggEAmPmTmUmVIkUJDQsjS9+hSckD0G4vdURPntFhmpKbsnJtclbGJrDeq5ppG9373RmmImzq+7kqH+BYaWr/9wlKqp77FbT/e1bGJlDqIGuiFwPYuruCvtNOrivulgYmw+7ioglb5zCNdKNc2zYXiknGsKS9NN0vLt+lWXQO4OYC+nMA4wDmAjjR9xBSJMvwSBe3wyxSCjMxjZZUVq6fcqkTl567wBr2yIG/QYLujP7eHpxwXP0YpzqtdymYzjUBxuqkUalOM8J+DjWi9Lsb0sCkoF1cNEMjFYy/Nln3WXWp2NyEUa5tnQuFACz66bJRRpvhTtv9kuV9mgcuBuBUZl7DzNcz8yb1yFyyNiNN32EwrDNqKJ+Lz9UFY3KXl5UbXGO4sX/ZzPY46BSQKa7cFPeuUz6XnbcwUqhnGC7m2NQqMwkmBR0WuqgMRND11F0u4ZZ1y/FsSI5JlGu7v7cHa1f0zBqDMIBvPP2SUcZGho42m48/DBcX0MNEdD4z78hcmjYmLd+hzu1S6qS6OjRhpKHsTNP5Dm8BN+iK8E/xbe4gHT3d5Ujhmaa4d8D8G2Rd0C0oX5rHutwzYjrC3BomQ3TC8XOcrk/Xa9vm7rKtzdyybnnDWqo2m48/DBcD8PsA/oSIXgOghgBNEQbabPhL0g5uP4j1W/ZgcPvBSBeY7matTvFMNy7XhuVpTGlNjTammDFw715sfGA/Xp6oait/RlH+thHYqiXzcYdX8Cy4XeEaoujSOMRFVgJb+xmr7+MaMurCjf3LjK+FGckk61NhORX+98U5t6f6DH+jlHIz+fjDCDUAzCz+/gaSNJbZ5nYZue78mWOom+WkcgmvvjY5a6E0rSmtklenyKpTPLNAaiuPoGYCQaOlnoc17Q6WeAhuj3K+bd/Hhe5yCRsvrq1RmJq/q/f09/aEtokkQuh6AoBQl1pY7fq4se1Rzq2Lu8uWm9JKSrmROGUCE9HFAN7pPf03Zn4oO5Ham6ThoC43a/BmyTJ7sr+3x6mzl0mPTTPj2c0XxpYxjnsjeL6Dx7703AWR+hR0Es2EpvrRdQnzVz7tMfyWwUgeW7hrMKLLdh5N23UGQtVXspFG+KfCpcOYEB2XTODNAH4ewJ3epj8iopXMfHWmkrUpScNB49ysWY+ekvizleHSyXjt0D7c9fjzM81NLj13QZ2rI657ozI2MaNYg/H+qk+BOnYY08za2cTg9oN1BsCvIF27Sg2sXqztWwwAr5s7Z5Yh84fFVsYmcNU9s11xOhdNf28Php97adYsLRiuqiPs3PoVue0aCZvlCfFxiQL6dQDvZubbmfl2ABcAuDBbsfIjaR38pPtIGmZmiqLYuruSW0VC16qWwehTmyvq2qF9uGPXoRkFPMWMO3YdwrVDs7t4hUVt2EI/lULSZQbvPDCKaUc3kDpG1KJ7umJwuoXN/t4e4wxqzBe5s+nB/XU5EVPTPFNF1Zahu/PAqHOGdPB76wiGc65aMl/7O926brk2wiiN+1Rwrwba7fv/pKQHJaJLiGg/EU0TUV/S/aVFGuUYku7DNFIPm277iXOzZklQkc3zumP5KZc6cdl5C53LT9z1+PPa7Xc+fmzBV7k7JqpTM6Ujgvs1hX6GqXY1eg1D5RDorgtTukXQXff1De/CLeuWAwDWb9kzo/D8StBUGeMkXwiwSwax6TqJMzN1rfCqDKqLsQOar+RykXFZA7gJwAgR7UTten4ngA0Jj/sUgDUA/inhflIljXIMSfcRtmjpQhErEiZZd9C91+R6YcaMIvC7T6aYZ0b+usqe/v27uKuUHGGRK6rZiim0Ube4rVwktoqtA/fsnd3M3WCxLCWTjETN0LXiePwfjk04uyKzLJvSbrhEAd1FRP+G2joAAPwpM/9HkoMy83cAgOJcnRmShuJMuo80ZGiGioSuN7spksQWAaNGsK5KIihLWJXNoCEJK9Vge01FMunWGmwVW11zOvyj/m5DCY4gpgxdl/UIP7rCblGOaaKIA5xmxegCIqK3qweAUwD8wHuc6m1rCER0BRENE9Hw6Kj7KDgOaaR5J91HGjK0UraiabRX1vTTVfxwbCKRkjC5hYB614Ry0dy6bnldtzAXVERPT3fZ6LZLotgIx2ZEGy9eijCPjC1D19VFo3CVW7nJXGm1cgx5YpsB3Oz9nQugD8Be1H6rtwEYBvALth0T0aMA3qh56Rpm/qKrgMx8G4DbAKCvry/TiqRxRjlp7yMNGfLMVkw7pNSkRCaq0+gqdWgTqpQiiDsLinP++nt7tGGdNvy/q81gJYmi8vf7DZPRVokUiB4t5iK3v9SGK2ncI0INowFg5lUAQET3AXg7M+/znp8FYGPYjpn5V1OSsWGkoTiT7iMt5Z1HYkwWDTls7iyTIli1ZD62PflC3WeiKIk458/WzzZIMLTRVjYjmCUN1OL7Z60BWPAbF5uMDLe1piiZ07ZkNpXkt/PAKIZGKg2/RwS3ReDFSvkDADM/RUQ/l6FMuZKG4ky6j6yVd7CuvD/7NAlZLM7ZRns6RaBTlkB631GHUogu01NThJGtbMaduw7hHW85Gc/+18QshQfM/u6vHp3Uju79s56wUXmY2yZq5rRpxkE4Vu4jzkBBMn/TwcUAPElE/wzgDu/5ZQCeTHJQInofgL8FMB/ANiLaw8yrk+xTcCOYDATUatYP3LMXQLLWeVkszoWN9nQLuEkKl9nQjXwBhEYC+TG1mrSVmVDVME1JWn75wlwjYZFLYS6yqEZ+48VLtT0ZTOsdotQbi4sB+DBqBeH+yHv+VQD/mOSgzHw/gPuT7EOIhykyQ9XIT3IDZhV9FGW0l1WEiGnke/ycjtjF4YJKz1Y2w+/LN6Eydv3Z0UGfvvpfNzJ3cZFFOb9qpukaCSRRPI0nNBGMmY8w8y3M/D7vcQszH2mEcEL62G6ypDdgEaKP0o4QUclWV27Zox35Rln01RE85zY5XdwzW3dXZmVH6zLA+3t7sOf62T2WXfs+u55fUw+BOPsWssOlFtBK1BZ9T/O/n5nfnJ1YQlbYfMC6mzhqJAyQ7+KcS4SI6/eKW6I4CsFzbqvrk7Z7Jo4f3TUCJ2ozmzgDhSyLGLYLLi6gzwBYD2A3gOzuBKEhhPXJVcSN6AlTKlnftGFGKO0SxfO6SjhSna6L0JliNvb9VeiUnq7wmum9QRqRIOVq5F2O6VrSW0cWEWftiIsBeJmZv5S5JEJDUDdHWBRQFhE9jbppbUYo7RLFqgexbnHY1jfApvRu7F+GvtNOjmwoG5UB7jJzcMkBUMpf16Q+DCkHkQ4uBmAnEQ0CuA/AUbWRmZ/ITCohU1xu4CxGk0W4aaN8ryglinXy2/ogqJ6/us/GnSUVKUHKNNMMEvd6knIQ6eBSDfRc1DKB/xdq2cE3A/irLIUS8sc0auwgil11sQg3bZRFYtOi9uXnLQQwuzJnlGMBMFaxTFLpMk65hkxxCP6JOzuRchDp4BIFtErziD5nE5oKUw3/KebYpXeLcNNGiVTSKdS1K3qwdXfFSUGvWjI/tBhmsPyybZbkgqpN9MzmC7V19BvF4PaDoQXrksxOihBx1gq4toS8EMBS1OoCAQCY+YashBLyx5aYNFGdwpUxGtYXwUURNVLJJdFM58ZSIZkuEfD+GVARZklpYJOXgMQBAEWIOGsFXMJAPwWgC8AqAP8M4P0AvpmxXEIBsCUmAdEXcYty08YtIzA0Ugnt4qWIEgbpUqqh2Vwbpu8Rd9FXh5SDSI7LGsA7mPm3ABxm5k2oVQH92WzFEopCmOKJ2mmsKC6KqCjfvInurtKs564jdl2phqBro9RBGH9tsqnaH4qLpjlwMQAq63eciE4FUEWtP4DQBrj0820290Qcwkb0wWhPk+HsLpe0i7Qq43j9lj2YW+pAd7kE8t4PqjV2abb2h3NLx9RLd7mU74K0oMVlDeBBIuoGMAjgCdTW9j+dqVRtTNGyG/1uG9cM4lYkzMiNTVRx+oZtM7+Zab1DV5E0mB9xeLyKcqkTt6xbjsHtB+vKTRQ93l2XQX10sr5vg5A/1hkAEXUA+DIzjzHzVtTKQSxh5usaIl2bUdRm18ptc+u65W07rXcxcv7fDIBzSKYt8qcZF4WTRjIJjcM6A2DmaSL6ewC93vOj8CWDCelShEQpG0VZxM0DlwbwionqFK66ey9u/sDZTguecbqB+Q1S2rPGpPtrRqPVrri4gL5MRGsB3MdsasMtpEEz3DjNEnmRtlJ0cYX5UfkS/s+aiNP1TM260i6vYdrf8HMvYeeBUafz2SqRTO2AyyLw7wG4B8BRIvoxEf2EiH6csVxtSRESpVqBrFxpyhXW4/h7uLo9TE3oK165iLUreoyupLTdLab93bnrkPP5TCsCSC2MN1P0U7MROgNg5hMbIYhQjESpZiBsdJ+1Ky2KO8hl9hacXfjbRlbGJrB1d8W4fmDaf2VsAis3PxZ5BmTaX5QOXmm4CqXaZ2NwygQWGkM7+9hdcVEMWbvSgr/TSeUSXj5SrQsFBdxnb8q1tnLzY3XuE5uyNblb1AwCiKY8Xap4KmznM6mrsOjrYa2CGICC0Sw+9rxwUQyN8EEHfyeXfrwuRDVeutmIrvG8q/J03R+QrWuyGdbDWgHjGgARPUxEi7I4KBENEtEBInqSiO738gwEIRQXxZBHFmpalThPKpcibdcd1xSp4eqOCu7vsvMWNvx8ynpYY7DNAP4FwA4i+hyAv2TmZM1PZ/MIgKuZeZKI/gLA1QD+NMX9Cy2Ky+g+L1daGrM3MpQPNW3XHVfnRgKiu6P8xGlQkwRZD2sMRgPAzPcQ0ZcA/BmAYSL6PIBp3+ufjHtQZt7he7oLtQJzghCKq2JoVlfa2Lh+nGXariML5dno82kz4kXLlm9mwtYAXgPwKoDjAZwInwFIkd8BsMX0IhFdAeAKAFi4cGEGhxfypOiN5xupbIZGKugg0raRjOL6aJVgAp3RkeigdCFTbhcRXQDgkwAeAHADM49H2jHRowDeqHnpGmb+oveea1DrNrbGJcmsr6+Ph4eHo4ghFBjTwmlRioY1Uj7dsbI+ZjNicm+lWWa6FSGi3czcF9xumwFcA+ASZt4f54DM/KshAn0IwEUAfkUyjNuToof6NVI+U7XRTiJR/j4kOihdbGsAv5TVQb3ZxccA/HLUmYXQOhT9Zk5bPps7ybTPaWZR/j6kzES6uJSCyIK/Q21N4REi2uN1HRPajKKH+qUpX1h5iqKfi6IgjWbSJRcDwMxvZeYFzLzce3wkDzmEfCn6zZymfGE1e4p+LopCWvkWQg3JBBZyo+jRKmnKF+ZOKvq5KBLNGuJbRIxRQEVEooCEZkWiV4Q8MUUB5bUGIAhNRdLSxOLiEYqIuIAEIYQ0ko/ExSMUETEAghBCWvkA4rsWioa4gAQhhKLnKwhCXMQACEIIEqMvtCpiAAQhBFnAFVoVWQMQhBAatYArZY6FRiMGQBAcyHoBV8ocC3kgLiBBKABhpSIEIQvEAAhCAZBIIyEPxAAIQgGQSCMhD8QACEIBCIs0SlqKQhB0yCKwIBSAsCboskAsZIEYAEEoCKZIo6K3zhSaF3EBCULBkQViISvEAAhCwZEFYiErcjEARPQJInrS6we8g4hOzUMOQWgGpBSFkBV5zQAGmfltzLwcwEMArstJDkEoPNIHV8iKXBaBmfnHvqcnAGievpSCkAPSS0DIgtyigIjozwH8FoCXAazKSw5BEIR2JTMXEBE9SkRPaR7vBQBmvoaZFwC4E8BHLfu5goiGiWh4dHQ0K3EFQRDaDmLO1/tCRAsBPMzMZ4W9t6+vj4eHhxsglSAIQutARLuZuS+4Pa8ooDN8T98L4EAecgiCILQzea0BbCaixQCmATwH4CM5ySEIgtC25BUFtDaP4wpCEqRjl9BqSC0gQXBACrIJrYgYAEFwoFULssmspr0RAyAIDrRiQTaZ1QhSDE4QHGjFgmzSh1gQAyAIDrRiQbZWnNUI0RADIAgOtGJBtlac1QjRkDUAQXCk1QqyDaxePGsNAGj+WY0QDTEAgtCm2PoQC+2BGABBaGNabVYjREPWAARBENoUMQCCIAhtihgAQRCENkUMgCAIQpsiBkAQBKFNkSggQRAyQQrNFR8xAIIgpI4UmmsOxAUkCELqSKG55kAMgCAIqSOF5poDMQCCIKSOFJprDnI1AER0FRExEb0+TzkEQUiXViyf3YrktghMRAsAnA/gUF4yCIKQDVJorjnIMwroFgAfA/DFHGUQBCEjpNBc8cnFBURE7wVQYea9Du+9goiGiWh4dHS0AdIJgiC0B5nNAIjoUQBv1Lx0DYCPo+b+CYWZbwNwGwD09fVxagIKgiC0OZkZAGb+Vd12IloG4HQAe4kIAN4E4AkiOoeZ/yMreQRBEITZNHwNgJn3AXiDek5EzwLoY+b/bLQsgiAI7YzkAQiCILQpxNw8bnUiGgXwXIJdvB5A0WcaRZdR5EuGyJcMkS8epzHz/ODGpjIASSGiYWbuy1sOG0WXUeRLhsiXDJEvXcQFJAiC0KaIARAEQWhT2s0A3Ja3AA4UXUaRLxkiXzJEvhRpqzUAQRAE4RjtNgMQBEEQPMQACIIgtCltYQCIaJCIDhDRk0R0PxF1+167moi+R0QHiWh1TvJdQkT7iWiaiPp82xcR0QQR7fEenyqSfN5ruZ+/IES0kYgqvvP263nLBABEdIF3nr5HRBvylicIET1LRPu8czZcAHluJ6IXiegp37aTiegRIvp37++8gslXyGvPRFsYAACPADiLmd8G4LsArgYAIjoTwAcBLAVwAYB/IKJO416y4ykAawB8VfPa08y83Ht8pMFyKbTyFej86bjFd94ezlsY77z8PYBfA3AmgEu981c0VnnnrAix7J9F7bryswHAl5n5DABf9p7nxWdRLx9QsGvPRlsYAGbewcyT3tNdqBWgA4D3AvgCMx9l5mcAfA/AOTnI9x1mLmy3bIt8hTh/TcI5AL7HzN9n5tcAfAG18ycYYOavAngpsPm9AD7n/f85AP0NFcqHQb6moi0MQIDfAfAl7/8eAM/7XvuBt61InE5EI0T0FSL6pbyFCVDk8/dRz+V3e55uAh9FPlcKBrCDiHYT0RV5C2PgZ5j5Be///wDwM3kKY6Bo156RPDuCpYqt/wAzf9F7zzUAJgHc2UjZvGOHyqfhBQALmfm/iGgFgCEiWsrMPy6IfLkR0m/iHwF8AjWF9gkAN6Nm+AU7v8jMFSJ6A4BHiOiAN8otJMzMRFS0OPamuvZaxgCY+g8oiOhDAC4C8Ct8LPmhAmCB721v8rY1XD7DZ44COOr9v5uIngbwswBSX6CLIx8aeP6CuMpLRJ8G8FDG4riQ27lyhZkr3t8Xieh+1NxWRTMAPyKiU5j5BSI6BcCLeQvkh5l/pP4v0LVnpC1cQER0AWr9hy9m5nHfSw8A+CARHU9EpwM4A8A385BRBxHNV4uqRPRm1OT7fr5SzaKQ589TDIr3obaInTffAnAGEZ1ORMehtnj+QM4yzUBEJxDRiep/1Dr2FeG8BXkAwG97//82CtZTvKDXnhlmbvkHaouTzwPY4z0+5XvtGgBPAzgI4Ndyku99qPmEjwL4EYDt3va1APZ7Mj8B4D1Fkq8o508j7+cB7APwJGoK45S8ZfLk+nXUotCeRs21lrtMPtneDGCv99hfBPkA3IWaG7TqXX//HcBPoxb98+8AHgVwcsHkK+S1Z3pIKQhBEIQ2pS1cQIIgCEI9YgAEQRDaFDEAgiAIbYoYAEEQhDZFDIAgCEKbIgZAaFqIaAERPUNEJ3vP53nPF4V8biMR/Yn3/w1EFCcJLhFEdHFaFUGLXmVUKC4SBio0NUT0MQBvZeYriOifADzLzDeFfGYjgFeY+a8aIWOWeImC3wXwbtRi0b8F4FJm/naugglNgcwAhGbnFgDnEdGVAH4RgFapE9E1RPRdIvoagMW+7Z8lovd7/z9LRDepevhE9HYi2k5ETxPRR3yfGSCib3kFvzZ52xYR0XeI6NNU652wg4jK3mv/k4i+7b3/C962DxHR3/k++5j3+peJaKFPtr8hom8Q0feVnAGkyqgQGzEAQlPDzFUAA6gZgiu957PwCul9EMBy1LJxf96yy0PMvBzA/0Wt3vv7AZwHQCn681EreXGOt78VRPRO77NnAPh7Zl4KYAy1TG6gVrO+l2v9KHQ9Hf4WwOe81+8E8De+105BzbBdBGCz5rPNUGVUKChiAIRW4NdQS8k/y/D6LwG4n5nHuVZJ1VaDR722D8DjzPwTZh4FcJRqneTO9x4jqJXnWIKa4geAZ5h5j/f/bgCLvP+fBHAnEV2OWjXaIL8A4H97/38eNYWvGGLmac+lU8TSx0ITIwZAaGqIaDlq/u/zAKwPFOOKw1Hv77Tvf/V8DgACcBMf6/j0Vmb+TOCzADCFY9V2L0StG9jbAXyLiKJU4fXvkzSvF77KqFBcxAAITQsREWr1169k5kMABqFfA/gqgH4iKnsVL9+T4LDbAfwOEb3Ok6HHq59vkrEDwAJm3gngTwGcBOB1gbd9AzUXFQBchpr7yZVCVxkVik3L9AMQ2pLfRc1n/4j3/B8AfJiIfpmZv6LexMxPENEW1Cpdvoia0owFM+8gop8D8P9q9gevALgctRG/jk4AdxDRSaiN4P+Gmce8zyr+EMC/ENEAgFEAH44gzyQRfRQ1w9QJ4HZm3h/xawltioSBCoIgtCniAhIEQWhTxAAIgiC0KWIABEEQ2hQxAIIgCG2KGABBEIQ2RQyAIAhCmyIGQBAEoU35/6XaWoVD+DwTAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-uJuKUSLbE6h"
+      },
+      "source": [
+        "from sklearn import linear_model\n",
+        "\n",
+        "n_components = 3\n",
+        "\n",
+        "#### Sparse PLS via regularization in NIPALS [Waaijenborg, et al 2007]\n",
+        "# Everything is as for the standard NIPALS algorithm, with the added sparse estimation step   \n",
+        "\n",
+        "loading_x_sparse = np.ndarray([X_ext.shape[1],n_components])\n",
+        "loading_y_sparse = np.ndarray([Y_ext.shape[1],n_components])\n",
+        "\n",
+        "scores_x_sparse = np.ndarray([X_ext.shape[0],n_components])\n",
+        "scores_y_sparse = np.ndarray([Y_ext.shape[0],n_components])\n",
+        "\n",
+        "weight_x_sparse = np.ndarray([X_ext.shape[1],n_components])\n",
+        "weight_y_sparse = np.ndarray([Y_ext.shape[1],n_components])\n",
+        "\n",
+        "current_X = X_ext\n",
+        "current_Y = Y_ext\n",
+        "\n",
+        "## Penalty parameter for regularization\n",
+        "penalty = 10\n",
+        "\n",
+        "eps = 1e-4\n",
+        "\n",
+        "for i in range(n_components):\n",
+        "    t_x = current_X[:,0]\n",
+        "    for _ in range(100):\n",
+        "        w_y = current_Y.transpose().dot(t_x)/(t_x.transpose().dot(t_x))\n",
+        "        w_y = w_y/np.sqrt(np.sum(w_y**2))\n",
+        "        t_y = current_Y.dot(w_y)\n",
+        "        w_x = current_X.transpose().dot(t_y)/(t_y.transpose().dot(t_y))\n",
+        "        w_x = w_x/np.sqrt(np.sum(w_x**2))\n",
+        "        t_x = current_X.dot(w_x)\n",
+        "        \n",
+        "        ## Estimating sparse model for the weights of X\n",
+        "        lasso_x = linear_model.Lasso(alpha = penalty)\n",
+        "        lasso_x.fit(t_x.reshape(-1, 1), current_X)\n",
+        "        \n",
+        "        ## Estimating sparse model for the weights of Y\n",
+        "        lasso_y = linear_model.Lasso(alpha = penalty)\n",
+        "        lasso_y.fit(t_y.reshape(-1, 1), current_Y)\n",
+        "        \n",
+        "        # Replacing the original weights with the sparse estimation\n",
+        "        w_x = (lasso_x.coef_ / (np.sqrt(np.sum(lasso_x.coef_**2) + eps))).reshape([X_ext.shape[1]]) \n",
+        "        w_y = (lasso_y.coef_ / (np.sqrt(np.sum(lasso_y.coef_**2) + eps))).reshape([Y_ext.shape[1]])\n",
+        "\n",
+        "    # Weights are such that X * weights = t\n",
+        "    weight_x_sparse[:,i] = w_x\n",
+        "    weight_y_sparse[:,i] = w_y\n",
+        "    \n",
+        "    # Latent dimensions\n",
+        "    scores_x_sparse[:,i] = t_x\n",
+        "    scores_x_sparse[:,i] = t_y\n",
+        "    \n",
+        "    # Loadings obtained by regressing X on t (X = t * loadings)\n",
+        "    \n",
+        "    loading_x_sparse[:,i] = np.dot(current_X.T, t_x)/t_x.transpose().dot(t_x) \n",
+        "    loading_y_sparse[:,i] = np.dot(current_Y.T, t_y)/t_y.transpose().dot(t_y)\n",
+        "    \n",
+        "    # Deflation\n",
+        "    \n",
+        "    current_X = current_X - t_x.reshape(len(t_x),1).dot(w_x.reshape(1,len(w_x)))\n",
+        "    current_Y = current_Y - t_y.reshape(len(t_y),1).dot(w_y.reshape(1,len(w_y)))\n",
+        "    "
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DI4Eth2MiFui"
+      },
+      "source": [
+        "We observe that the new weights are similar to the ones estimated before. However the parameters associated with the spurious dimension are entirely set to zero. This indicates that the model does not find these features necessary to explain the common variability between X and Y. There are also other weights which are set to zero, corresponding to the third latent dimension. This makes sense, as our synthetic data was indeed created with only two latent dimensions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CC8kmd_dbE6i",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "281a2a83-d218-4492-9b78-3469fc4a37d3"
+      },
+      "source": [
+        "weight_x_sparse"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.1688485 ,  0.51581891,  0.        ],\n",
+              "       [-0.37835468,  0.        , -0.        ],\n",
+              "       [-0.24701734,  0.84857151,  0.        ],\n",
+              "       [ 0.47800973,  0.01459515, -0.        ],\n",
+              "       [ 0.73396599,  0.11621768, -0.        ],\n",
+              "       [-0.        ,  0.        , -0.        ],\n",
+              "       [-0.        ,  0.        ,  0.        ],\n",
+              "       [ 0.        ,  0.        ,  0.        ]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "I9wR7mBibE6j",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "07dee1b5-5b88-46c9-f374-88d0fc65b64e"
+      },
+      "source": [
+        "weight_y_sparse"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.46798509, -0.52387449, -0.        ],\n",
+              "       [-0.43466388,  0.19485361, -0.        ],\n",
+              "       [ 0.        ,  0.59007812,  0.        ],\n",
+              "       [ 0.2402143 , -0.        ,  0.        ],\n",
+              "       [-0.73089749, -0.58232098,  0.        ],\n",
+              "       [ 0.        , -0.        ,  0.        ],\n",
+              "       [ 0.        , -0.        ,  0.        ],\n",
+              "       [ 0.        , -0.        , -0.        ]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SPZWT6wHbE6k",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8abffce0-9df0-4643-c431-cd0172fa4beb"
+      },
+      "source": [
+        "## Non-sparse parameters previously estimated\n",
+        "\n",
+        "weight_x"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.17329009,  0.5336278 ,  0.51808589],\n",
+              "       [-0.39082094,  0.0616058 ,  0.6000854 ],\n",
+              "       [-0.30968135,  0.81479162, -0.37827723],\n",
+              "       [ 0.47546915,  0.09994223,  0.41327064],\n",
+              "       [ 0.70374433,  0.19383566, -0.23999633]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KB3sbQdXbE6l",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f8951897-67be-4339-a20c-13ae7039d366"
+      },
+      "source": [
+        "weight_y"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.45869999, -0.51053537, -0.56646878],\n",
+              "       [-0.44037568,  0.33125328, -0.10969391],\n",
+              "       [ 0.03974949,  0.55626222, -0.28369485],\n",
+              "       [ 0.28309562, -0.13013815,  0.76585086],\n",
+              "       [-0.71689638, -0.55069166, -0.00836996]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HxfPTMZrbE6n",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "56385209-b6ee-4a04-8d89-05da1dbbc4b1"
+      },
+      "source": [
+        "## PLS result from scikit-learn PLS on the data augmented with spurious dimensions \n",
+        "\n",
+        "plsca = PLSCanonical(n_components=3, scale = False)\n",
+        "plsca.fit(X_ext, Y_ext)\n",
+        "print(plsca.x_weights_)\n",
+        "print(plsca.y_weights_)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 0.17382382  0.53365034  0.51670561]\n",
+            " [-0.39079893  0.06234366  0.52243092]\n",
+            " [-0.30862518  0.8149948  -0.36564531]\n",
+            " [ 0.47528934  0.10001824  0.4127567 ]\n",
+            " [ 0.7042006   0.19256528 -0.27561772]\n",
+            " [-0.00161774  0.0046724   0.20489303]\n",
+            " [-0.00313618  0.00190836  0.1297317 ]\n",
+            " [ 0.00104285  0.00188645  0.14573972]]\n",
+            "[[ 4.58314551e-01 -5.11680097e-01 -5.05400766e-01]\n",
+            " [-4.39540082e-01  3.31669925e-01 -3.78149864e-02]\n",
+            " [ 4.10017360e-02  5.55569740e-01 -2.65089918e-01]\n",
+            " [ 2.83005518e-01 -1.30730000e-01  7.90515077e-01]\n",
+            " [-7.17547197e-01 -5.49823824e-01 -5.32497302e-03]\n",
+            " [ 1.17814612e-03 -8.80064617e-03 -1.63385012e-01]\n",
+            " [ 1.20331546e-03 -6.82391627e-03 -3.78147301e-02]\n",
+            " [ 1.01097675e-02  4.63226667e-04 -1.40713787e-01]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xVAxtnQ4bE6o"
+      },
+      "source": [
+        "## Cross-validating components\n",
+        "\n",
+        "In addition to sparsity, the optimal number of components in latent variable models can be identified by cross-validation. \n",
+        "A common strategy is to train the model on a subset of the data and to quantify the *predicted residual error sum of squares* (PRESS) in non-overlapping testing data. We can finally choose the number of latent dimensions leading to the lowest average PRESS. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dZUKi6jQffAQ"
+      },
+      "source": [
+        "n_cross_valid_run = 200\n",
+        "\n",
+        "# number of components to test\n",
+        "n_components = 5\n",
+        "\n",
+        "rep_results = [] \n",
+        "for i in range(n_components):\n",
+        "  rep_results.append([])\n",
+        "\n",
+        "for k in range(n_components):\n",
+        "  for i in range(n_cross_valid_run):\n",
+        "\n",
+        "    # Sampling disjoint set of indices for splitting the data\n",
+        "    batch1_idx = np.random.choice(range(X_ext.shape[0]), size = int(X_ext.shape[0]/2), replace = False)\n",
+        "    batch2_idx = np.where(np.in1d(range(X_ext.shape[0]), batch1_idx, assume_unique=True, invert = True))[0]\n",
+        "\n",
+        "    # Creating independent data batches for X\n",
+        "    X_1 = X_ext[batch1_idx, :]\n",
+        "    X_2 = X_ext[batch2_idx, :]\n",
+        "\n",
+        "    # Creating independent data batches for Y\n",
+        "    Y_1 = Y_ext[batch1_idx, :]\n",
+        "    Y_2 = Y_ext[batch2_idx, :]\n",
+        "\n",
+        "    # Creating a model for each data batch\n",
+        "    plsca1 = PLSCanonical(n_components = k+1, scale = False)\n",
+        "    plsca2 = PLSCanonical(n_components = k+1, scale = False)\n",
+        "\n",
+        "    # Fitting a model for each data batch\n",
+        "    plsca1.fit(X_1,Y_1)\n",
+        "    plsca2.fit(X_2,Y_2)\n",
+        "\n",
+        "    # Quantifying the prediction error on the unseen data batch\n",
+        "    err1 = np.sum((plsca1.predict(X_2) - Y_2)**2)\n",
+        "    err2 = np.sum((plsca2.predict(X_1) - Y_1)**2)\n",
+        "\n",
+        "    rep_results[k].append(np.mean([err1,err2]))\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ENEZZISdvKa1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "outputId": "49ac367a-103f-4162-a004-36e7aac805ef"
+      },
+      "source": [
+        "plt.plot(range(1,n_components+1),np.mean(rep_results, 1))\n",
+        "plt.xlabel('# components')\n",
+        "plt.ylabel('prediction error')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bfKHxkXWqxmY"
+      },
+      "source": [
+        "## Application on ADNI data\n",
+        "\n",
+        "We are going to load volumetric and cognitive data for a sample from the ADNI dataset.\n",
+        "The exercise consists in applying the methods seen so far to understand the relationship between this kind of variables."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-LzoZTAWq0Tf",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "59037126-e498-4a7e-d703-8130d6e55b0e"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# adni = pd.read_csv('https://marcolorenzi.github.io/material/winter_school/volumes.csv')\n",
+        "adni = pd.read_csv('https://gist.githubusercontent.com/ggbioing/14c27963370417ed454b7475e828f15e/raw/ef511da656a58c65341500949203f2d03034f615/pseudo_adni.csv')\n",
+        "\n",
+        "brain_volume_cols = ['WholeBrain.bl', 'Ventricles.bl', 'Hippocampus.bl', 'MidTemp.bl', 'Entorhinal.bl']\n",
+        "cognition_cols = ['CDRSB.bl', 'ADAS11.bl', 'MMSE.bl', 'RAVLT.immediate.bl', 'RAVLT.learning.bl', 'RAVLT.forgetting.bl', 'FAQ.bl']\n",
+        "\n",
+        "adni[brain_volume_cols]\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>WholeBrain.bl</th>\n",
+              "      <th>Ventricles.bl</th>\n",
+              "      <th>Hippocampus.bl</th>\n",
+              "      <th>MidTemp.bl</th>\n",
+              "      <th>Entorhinal.bl</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.785447</td>\n",
+              "      <td>0.009243</td>\n",
+              "      <td>0.005398</td>\n",
+              "      <td>0.016662</td>\n",
+              "      <td>0.003236</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.657061</td>\n",
+              "      <td>-0.000411</td>\n",
+              "      <td>0.004769</td>\n",
+              "      <td>0.012169</td>\n",
+              "      <td>0.001333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.760612</td>\n",
+              "      <td>0.026070</td>\n",
+              "      <td>0.004417</td>\n",
+              "      <td>0.013270</td>\n",
+              "      <td>0.001722</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.693180</td>\n",
+              "      <td>0.021099</td>\n",
+              "      <td>0.004813</td>\n",
+              "      <td>0.012030</td>\n",
+              "      <td>0.002338</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.651476</td>\n",
+              "      <td>0.037908</td>\n",
+              "      <td>0.004810</td>\n",
+              "      <td>0.011593</td>\n",
+              "      <td>0.002221</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>995</th>\n",
+              "      <td>0.615301</td>\n",
+              "      <td>0.033753</td>\n",
+              "      <td>0.003653</td>\n",
+              "      <td>0.010539</td>\n",
+              "      <td>0.002116</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>996</th>\n",
+              "      <td>0.724784</td>\n",
+              "      <td>0.026887</td>\n",
+              "      <td>0.005394</td>\n",
+              "      <td>0.013648</td>\n",
+              "      <td>0.002266</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>997</th>\n",
+              "      <td>0.684429</td>\n",
+              "      <td>0.023915</td>\n",
+              "      <td>0.004808</td>\n",
+              "      <td>0.014075</td>\n",
+              "      <td>0.002257</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>998</th>\n",
+              "      <td>0.653331</td>\n",
+              "      <td>0.010033</td>\n",
+              "      <td>0.005044</td>\n",
+              "      <td>0.012203</td>\n",
+              "      <td>0.002678</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>999</th>\n",
+              "      <td>0.672998</td>\n",
+              "      <td>0.029385</td>\n",
+              "      <td>0.004873</td>\n",
+              "      <td>0.011489</td>\n",
+              "      <td>0.002068</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1000 rows × 5 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     WholeBrain.bl  Ventricles.bl  Hippocampus.bl  MidTemp.bl  Entorhinal.bl\n",
+              "0         0.785447       0.009243        0.005398    0.016662       0.003236\n",
+              "1         0.657061      -0.000411        0.004769    0.012169       0.001333\n",
+              "2         0.760612       0.026070        0.004417    0.013270       0.001722\n",
+              "3         0.693180       0.021099        0.004813    0.012030       0.002338\n",
+              "4         0.651476       0.037908        0.004810    0.011593       0.002221\n",
+              "..             ...            ...             ...         ...            ...\n",
+              "995       0.615301       0.033753        0.003653    0.010539       0.002116\n",
+              "996       0.724784       0.026887        0.005394    0.013648       0.002266\n",
+              "997       0.684429       0.023915        0.004808    0.014075       0.002257\n",
+              "998       0.653331       0.010033        0.005044    0.012203       0.002678\n",
+              "999       0.672998       0.029385        0.004873    0.011489       0.002068\n",
+              "\n",
+              "[1000 rows x 5 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 91
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KOQLTtfmq-St"
+      },
+      "source": [
+        "volumes_value = adni[brain_volume_cols].values\n",
+        "\n",
+        "# Standardization of volumetric measures\n",
+        "for i in range(volumes_value.shape[1]):\n",
+        "    volumes_value[:,i] = (volumes_value[:,i] - np.mean(volumes_value[:,i]))/np.std(volumes_value[:,i])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mYzM_EbDrECI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "3d6cb88d-0268-407f-94b8-9af7ea231e23"
+      },
+      "source": [
+        "# cognition = pd.read_csv('https://marcolorenzi.github.io/material/winter_school/cognition.csv')\n",
+        "\n",
+        "adni[cognition_cols]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>CDRSB.bl</th>\n",
+              "      <th>ADAS11.bl</th>\n",
+              "      <th>MMSE.bl</th>\n",
+              "      <th>RAVLT.immediate.bl</th>\n",
+              "      <th>RAVLT.learning.bl</th>\n",
+              "      <th>RAVLT.forgetting.bl</th>\n",
+              "      <th>FAQ.bl</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.962499</td>\n",
+              "      <td>12.121430</td>\n",
+              "      <td>26.512484</td>\n",
+              "      <td>32.384281</td>\n",
+              "      <td>1.317685</td>\n",
+              "      <td>2.760228</td>\n",
+              "      <td>4.107597</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.010088</td>\n",
+              "      <td>15.963385</td>\n",
+              "      <td>25.011142</td>\n",
+              "      <td>19.155085</td>\n",
+              "      <td>-0.308747</td>\n",
+              "      <td>0.973699</td>\n",
+              "      <td>6.284147</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2.096965</td>\n",
+              "      <td>19.763477</td>\n",
+              "      <td>25.762473</td>\n",
+              "      <td>41.318980</td>\n",
+              "      <td>2.949965</td>\n",
+              "      <td>5.056548</td>\n",
+              "      <td>7.455946</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.229865</td>\n",
+              "      <td>10.550063</td>\n",
+              "      <td>25.403987</td>\n",
+              "      <td>36.643902</td>\n",
+              "      <td>5.699475</td>\n",
+              "      <td>4.267712</td>\n",
+              "      <td>1.022553</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4.554087</td>\n",
+              "      <td>22.872831</td>\n",
+              "      <td>23.741273</td>\n",
+              "      <td>14.177173</td>\n",
+              "      <td>0.806626</td>\n",
+              "      <td>-0.835271</td>\n",
+              "      <td>16.870301</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>995</th>\n",
+              "      <td>2.560101</td>\n",
+              "      <td>17.780861</td>\n",
+              "      <td>25.367873</td>\n",
+              "      <td>23.780462</td>\n",
+              "      <td>5.336351</td>\n",
+              "      <td>3.830480</td>\n",
+              "      <td>8.311540</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>996</th>\n",
+              "      <td>-0.456435</td>\n",
+              "      <td>6.919416</td>\n",
+              "      <td>29.027886</td>\n",
+              "      <td>39.746836</td>\n",
+              "      <td>1.909160</td>\n",
+              "      <td>4.509488</td>\n",
+              "      <td>-2.959273</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>997</th>\n",
+              "      <td>1.377666</td>\n",
+              "      <td>3.147824</td>\n",
+              "      <td>28.654013</td>\n",
+              "      <td>44.377188</td>\n",
+              "      <td>5.682925</td>\n",
+              "      <td>8.224721</td>\n",
+              "      <td>-4.682819</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>998</th>\n",
+              "      <td>0.239798</td>\n",
+              "      <td>13.587954</td>\n",
+              "      <td>25.974968</td>\n",
+              "      <td>30.610099</td>\n",
+              "      <td>1.801188</td>\n",
+              "      <td>4.609666</td>\n",
+              "      <td>5.923102</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>999</th>\n",
+              "      <td>3.087345</td>\n",
+              "      <td>17.028103</td>\n",
+              "      <td>26.176852</td>\n",
+              "      <td>36.530510</td>\n",
+              "      <td>3.669550</td>\n",
+              "      <td>3.072860</td>\n",
+              "      <td>2.173231</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1000 rows × 7 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     CDRSB.bl  ADAS11.bl  ...  RAVLT.forgetting.bl     FAQ.bl\n",
+              "0    0.962499  12.121430  ...             2.760228   4.107597\n",
+              "1    2.010088  15.963385  ...             0.973699   6.284147\n",
+              "2    2.096965  19.763477  ...             5.056548   7.455946\n",
+              "3    1.229865  10.550063  ...             4.267712   1.022553\n",
+              "4    4.554087  22.872831  ...            -0.835271  16.870301\n",
+              "..        ...        ...  ...                  ...        ...\n",
+              "995  2.560101  17.780861  ...             3.830480   8.311540\n",
+              "996 -0.456435   6.919416  ...             4.509488  -2.959273\n",
+              "997  1.377666   3.147824  ...             8.224721  -4.682819\n",
+              "998  0.239798  13.587954  ...             4.609666   5.923102\n",
+              "999  3.087345  17.028103  ...             3.072860   2.173231\n",
+              "\n",
+              "[1000 rows x 7 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 93
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BIfCi6iMdUNe"
+      },
+      "source": [],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ra-oAnC_rJBg"
+      },
+      "source": [
+        "cognition_value = adni[cognition_cols].values\n",
+        "\n",
+        "# Standardization of cognitive measures\n",
+        "\n",
+        "for i in range(cognition_value.shape[1]):\n",
+        "    cognition_value[:,i] = (cognition_value[:,i] - np.mean(cognition_value[:,i]))/np.std(cognition_value[:,i])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AprwvjSJrTeF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c2ddd476-4ab8-4e13-fb01-c76fea30b7df"
+      },
+      "source": [
+        "plsca_adni = PLSCanonical(n_components=3, scale = False)\n",
+        "plsca_adni.fit(cognition_value,volumes_value)\n",
+        "print(plsca_adni.x_weights_)\n",
+        "print(plsca_adni.y_weights_)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 0.41838492  0.14007211  0.28951362]\n",
+            " [ 0.4623727   0.03618432  0.38666811]\n",
+            " [-0.40799927  0.09422857 -0.27439862]\n",
+            " [-0.41271527  0.26979564  0.51080117]\n",
+            " [-0.32595524 -0.1648086   0.65226865]\n",
+            " [ 0.08115131  0.92307874 -0.05800773]\n",
+            " [ 0.40191527 -0.13477967  0.04047091]]\n",
+            "[[-0.38627487  0.49962384  0.37949554]\n",
+            " [ 0.29805494 -0.25873494 -0.33115407]\n",
+            " [-0.4885044  -0.21558137  0.41990958]\n",
+            " [-0.48905293  0.4096158  -0.73556108]\n",
+            " [-0.53305315 -0.68496118 -0.17013515]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-8g91KHyrej5"
+      },
+      "source": [
+        "n_cross_valid_run = 200\n",
+        "\n",
+        "# number of components to test\n",
+        "n_components = 5\n",
+        "\n",
+        "rep_results = [] \n",
+        "for i in range(n_components):\n",
+        "  rep_results.append([])\n",
+        "\n",
+        "for k in range(n_components):\n",
+        "  for i in range(n_cross_valid_run):\n",
+        "\n",
+        "    # Sampling disjoint set of indices for splitting the data\n",
+        "    batch1_idx = np.random.choice(range(cognition_value.shape[0]), size = int(cognition_value.shape[0]/2), replace = False)\n",
+        "    batch2_idx = np.where(np.in1d(range(cognition_value.shape[0]), batch1_idx, assume_unique=True, invert = True))[0]\n",
+        "\n",
+        "    # Creating independent data batches for X\n",
+        "    X_1 = cognition_value[batch1_idx, :]\n",
+        "    X_2 = cognition_value[batch2_idx, :]\n",
+        "\n",
+        "    # Creating independent data batches for Y\n",
+        "    Y_1 = volumes_value[batch1_idx, :]\n",
+        "    Y_2 = volumes_value[batch2_idx, :]\n",
+        "\n",
+        "    # Creating a model for each data batch\n",
+        "    cca_adni1 = CCA(n_components = k+1, scale = False)\n",
+        "    cca_adni2 = CCA(n_components = k+1, scale = False)\n",
+        "\n",
+        "    # Fitting a model for each data batch\n",
+        "    cca_adni1.fit(X_1,Y_1)\n",
+        "    cca_adni2.fit(X_2,Y_2)\n",
+        "\n",
+        "    # Quantifying the prediction error on the unseen data batch\n",
+        "    err1 = np.sum((cca_adni1.predict(X_2) - Y_2)**2)\n",
+        "    err2 = np.sum((cca_adni2.predict(X_1) - Y_1)**2)\n",
+        "\n",
+        "    rep_results[k].append(np.mean([err1,err2]))\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eHnWUduIsYj_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "bb1492dd-0c24-4867-ce96-9cb8854c2656"
+      },
+      "source": [
+        "plt.plot(range(1,n_components+1),np.mean(rep_results, 1))\n",
+        "plt.xlabel('# components')\n",
+        "plt.ylabel('prediction error')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qodzauhuwh2x",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 281
+        },
+        "outputId": "f8991c9d-7aff-40f0-8f8a-fb464d3a00f7"
+      },
+      "source": [
+        "plsca_adni = PLSCanonical(n_components=1, scale = False)\n",
+        "plsca_adni.fit(cognition_value,volumes_value)\n",
+        "\n",
+        "\n",
+        "\n",
+        "plt.subplot(2,1,1)\n",
+        "plt.bar(np.arange(len(plsca_adni.x_weights_[:,0])), plsca_adni.x_weights_[:,0], tick_label = cognition_cols)\n",
+        "plt.title('Component x')\n",
+        "plt.subplot(2,1,2)\n",
+        "plt.bar(np.arange(len(plsca_adni.y_weights_[:,0])), plsca_adni.y_weights_[:,0], tick_label = brain_volume_cols)\n",
+        "plt.title('Component y')\n",
+        "plt.show()\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4YuOQLqpiO1f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "cbe87bec-b5b7-4fd1-c861-81b278254694"
+      },
+      "source": [
+        "plt.scatter(plsca_adni.x_scores_[:,0], plsca_adni.y_scores_[:,0], c = cognition_value[:,1])\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rKKYh5jobE6o"
+      },
+      "source": [
+        "## Multi-channel Variational Autoencoder\n",
+        "\n",
+        "The last part of this tutorial concerns the use of the *multi-channel variational autoencoder* (https://gitlab.inria.fr/epione_ML/mcvae), a more advanced  methods for the joint analysis and prediction of several modalities.\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jBGL07pTbE6o",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a0b18f3a-6b97-49d7-977a-98824c1e57ea"
+      },
+      "source": [
+        "import torch\n",
+        "from mcvae.models import Mcvae\n",
+        "from mcvae.models.utils import DEVICE, load_or_fit\n",
+        "from mcvae.diagnostics import *\n",
+        "from mcvae.plot import lsplom\n",
+        "\n",
+        "print(f\"Running on {DEVICE}\")\n",
+        "\n",
+        "FORCE_REFIT = False"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Running on cpu\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ku7IxhURbE6q"
+      },
+      "source": [
+        "### Data in mcvae is specified by: \n",
+        "# 1 - a dictionary with the data characteristics\n",
+        "\n",
+        "init_dict = {\n",
+        "    'n_channels': 2, # X and Y\n",
+        "    'lat_dim': n_components,  \n",
+        "    'n_feats': tuple([X.shape[1], Y.shape[1]]),\n",
+        "}\n",
+        "\n",
+        "# 2 - a list with the different modalities\n",
+        "\n",
+        "data = []\n",
+        "data.append(torch.FloatTensor(X)) #warning: data matrices must be converted to type torch.FLoatTensor\n",
+        "data.append(torch.FloatTensor(Y)) #warning: data matrices must be converted to type torch.FLoatTensor"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "h0u_fQlvbE6r",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "40382153-6352-4d87-bf44-4ddb3bc2c62b"
+      },
+      "source": [
+        "# Here we create an instance of the model\n",
+        "\n",
+        "adam_lr = 1e-2\n",
+        "n_epochs = 4000\n",
+        "\n",
+        "# Multi-Channel VAE\n",
+        "torch.manual_seed(24)\n",
+        "model = Mcvae(**init_dict)\n",
+        "model.to(DEVICE)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Mcvae(\n",
+              "  (vae): ModuleList(\n",
+              "    (0): VAE(\n",
+              "      (W_mu): Linear(in_features=5, out_features=5, bias=True)\n",
+              "      (W_logvar): Linear(in_features=5, out_features=5, bias=True)\n",
+              "      (W_out): Linear(in_features=5, out_features=5, bias=True)\n",
+              "    )\n",
+              "    (1): VAE(\n",
+              "      (W_mu): Linear(in_features=5, out_features=5, bias=True)\n",
+              "      (W_logvar): Linear(in_features=5, out_features=5, bias=True)\n",
+              "      (W_out): Linear(in_features=5, out_features=5, bias=True)\n",
+              "    )\n",
+              "  )\n",
+              ")"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "P3WSnv6kbE6s",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dda0fe81-b47d-431f-a9de-53671623993e"
+      },
+      "source": [
+        "###################\n",
+        "## Model Fitting ##\n",
+        "###################\n",
+        "model.optimizer = torch.optim.Adam(model.parameters(), lr=adam_lr)\n",
+        "load_or_fit(model=model, data=data, epochs=n_epochs, ptfile='model.pt', force_fit=FORCE_REFIT)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\tCreating model.pt.running\n",
+            "\tCreated: 2020-12-14 16:14:00.939603\n",
+            "Start fitting: 2020-12-14 16:14:00.940069\n",
+            "\tModel destination: model.pt\n",
+            "====> Epoch:    0/4000 (0%)\tLoss: 170909.8438\tLL: -154308.3281\tKL: 16601.5156\tLL/KL: -9.2948\n",
+            "====> Epoch:   10/4000 (0%)\tLoss: 11180.3018\tLL: -10654.2305\tKL: 526.0714\tLL/KL: -20.2524\n",
+            "====> Epoch:   20/4000 (0%)\tLoss: 6269.6274\tLL: -6037.3447\tKL: 232.2827\tLL/KL: -25.9914\n",
+            "====> Epoch:   30/4000 (1%)\tLoss: 3919.8779\tLL: -3652.1843\tKL: 267.6935\tLL/KL: -13.6432\n",
+            "====> Epoch:   40/4000 (1%)\tLoss: 3152.9185\tLL: -2838.6370\tKL: 314.2814\tLL/KL: -9.0322\n",
+            "====> Epoch:   50/4000 (1%)\tLoss: 2384.3752\tLL: -2069.5769\tKL: 314.7984\tLL/KL: -6.5743\n",
+            "====> Epoch:   60/4000 (2%)\tLoss: 1938.2260\tLL: -1618.1376\tKL: 320.0883\tLL/KL: -5.0553\n",
+            "====> Epoch:   70/4000 (2%)\tLoss: 1660.2758\tLL: -1318.9084\tKL: 341.3673\tLL/KL: -3.8636\n",
+            "====> Epoch:   80/4000 (2%)\tLoss: 1397.3420\tLL: -1045.2446\tKL: 352.0974\tLL/KL: -2.9686\n",
+            "====> Epoch:   90/4000 (2%)\tLoss: 1313.5752\tLL: -962.2386\tKL: 351.3366\tLL/KL: -2.7388\n",
+            "====> Epoch:  100/4000 (2%)\tLoss: 1242.9863\tLL: -897.2018\tKL: 345.7845\tLL/KL: -2.5947\n",
+            "====> Epoch:  110/4000 (3%)\tLoss: 1205.2605\tLL: -869.6089\tKL: 335.6516\tLL/KL: -2.5908\n",
+            "====> Epoch:  120/4000 (3%)\tLoss: 1204.5037\tLL: -881.4944\tKL: 323.0093\tLL/KL: -2.7290\n",
+            "====> Epoch:  130/4000 (3%)\tLoss: 1230.7664\tLL: -920.3049\tKL: 310.4614\tLL/KL: -2.9643\n",
+            "====> Epoch:  140/4000 (4%)\tLoss: 1196.2932\tLL: -896.9962\tKL: 299.2970\tLL/KL: -2.9970\n",
+            "====> Epoch:  150/4000 (4%)\tLoss: 1083.9316\tLL: -795.5340\tKL: 288.3977\tLL/KL: -2.7585\n",
+            "====> Epoch:  160/4000 (4%)\tLoss: 1075.6467\tLL: -797.2339\tKL: 278.4128\tLL/KL: -2.8635\n",
+            "====> Epoch:  170/4000 (4%)\tLoss: 1036.0605\tLL: -767.2588\tKL: 268.8018\tLL/KL: -2.8544\n",
+            "====> Epoch:  180/4000 (4%)\tLoss: 1069.1871\tLL: -808.5513\tKL: 260.6358\tLL/KL: -3.1022\n",
+            "====> Epoch:  190/4000 (5%)\tLoss: 1000.6576\tLL: -748.0992\tKL: 252.5584\tLL/KL: -2.9621\n",
+            "====> Epoch:  200/4000 (5%)\tLoss: 960.3487\tLL: -714.8709\tKL: 245.4778\tLL/KL: -2.9122\n",
+            "====> Epoch:  210/4000 (5%)\tLoss: 971.4668\tLL: -733.1089\tKL: 238.3579\tLL/KL: -3.0757\n",
+            "====> Epoch:  220/4000 (6%)\tLoss: 937.3365\tLL: -706.4905\tKL: 230.8461\tLL/KL: -3.0604\n",
+            "====> Epoch:  230/4000 (6%)\tLoss: 913.8390\tLL: -689.6444\tKL: 224.1946\tLL/KL: -3.0761\n",
+            "====> Epoch:  240/4000 (6%)\tLoss: 961.3102\tLL: -742.5258\tKL: 218.7844\tLL/KL: -3.3939\n",
+            "====> Epoch:  250/4000 (6%)\tLoss: 909.4338\tLL: -697.0640\tKL: 212.3698\tLL/KL: -3.2823\n",
+            "====> Epoch:  260/4000 (6%)\tLoss: 900.5679\tLL: -693.4589\tKL: 207.1090\tLL/KL: -3.3483\n",
+            "====> Epoch:  270/4000 (7%)\tLoss: 849.9542\tLL: -648.0632\tKL: 201.8910\tLL/KL: -3.2100\n",
+            "====> Epoch:  280/4000 (7%)\tLoss: 874.0905\tLL: -677.1689\tKL: 196.9215\tLL/KL: -3.4388\n",
+            "====> Epoch:  290/4000 (7%)\tLoss: 838.8462\tLL: -647.2023\tKL: 191.6440\tLL/KL: -3.3771\n",
+            "====> Epoch:  300/4000 (8%)\tLoss: 868.7034\tLL: -681.2524\tKL: 187.4509\tLL/KL: -3.6343\n",
+            "====> Epoch:  310/4000 (8%)\tLoss: 817.2480\tLL: -634.0419\tKL: 183.2061\tLL/KL: -3.4608\n",
+            "====> Epoch:  320/4000 (8%)\tLoss: 809.9817\tLL: -631.3971\tKL: 178.5846\tLL/KL: -3.5356\n",
+            "====> Epoch:  330/4000 (8%)\tLoss: 783.9614\tLL: -608.6271\tKL: 175.3343\tLL/KL: -3.4712\n",
+            "====> Epoch:  340/4000 (8%)\tLoss: 764.7062\tLL: -593.5272\tKL: 171.1790\tLL/KL: -3.4673\n",
+            "====> Epoch:  350/4000 (9%)\tLoss: 811.2693\tLL: -643.4791\tKL: 167.7902\tLL/KL: -3.8350\n",
+            "====> Epoch:  360/4000 (9%)\tLoss: 749.0129\tLL: -584.9716\tKL: 164.0414\tLL/KL: -3.5660\n",
+            "====> Epoch:  370/4000 (9%)\tLoss: 730.7030\tLL: -569.2465\tKL: 161.4565\tLL/KL: -3.5257\n",
+            "====> Epoch:  380/4000 (10%)\tLoss: 741.1149\tLL: -583.2612\tKL: 157.8537\tLL/KL: -3.6949\n",
+            "====> Epoch:  390/4000 (10%)\tLoss: 728.7142\tLL: -574.2626\tKL: 154.4516\tLL/KL: -3.7181\n",
+            "====> Epoch:  400/4000 (10%)\tLoss: 756.2413\tLL: -604.1705\tKL: 152.0709\tLL/KL: -3.9730\n",
+            "====> Epoch:  410/4000 (10%)\tLoss: 725.6537\tLL: -576.0381\tKL: 149.6155\tLL/KL: -3.8501\n",
+            "====> Epoch:  420/4000 (10%)\tLoss: 684.6050\tLL: -538.0845\tKL: 146.5204\tLL/KL: -3.6724\n",
+            "====> Epoch:  430/4000 (11%)\tLoss: 781.7255\tLL: -637.2776\tKL: 144.4479\tLL/KL: -4.4118\n",
+            "====> Epoch:  440/4000 (11%)\tLoss: 687.6153\tLL: -545.4189\tKL: 142.1964\tLL/KL: -3.8357\n",
+            "====> Epoch:  450/4000 (11%)\tLoss: 660.9287\tLL: -521.3130\tKL: 139.6157\tLL/KL: -3.7339\n",
+            "====> Epoch:  460/4000 (12%)\tLoss: 690.0142\tLL: -553.0217\tKL: 136.9926\tLL/KL: -4.0369\n",
+            "====> Epoch:  470/4000 (12%)\tLoss: 671.2242\tLL: -536.2076\tKL: 135.0166\tLL/KL: -3.9714\n",
+            "====> Epoch:  480/4000 (12%)\tLoss: 646.6749\tLL: -513.6363\tKL: 133.0386\tLL/KL: -3.8608\n",
+            "====> Epoch:  490/4000 (12%)\tLoss: 646.5170\tLL: -515.5278\tKL: 130.9891\tLL/KL: -3.9357\n",
+            "====> Epoch:  500/4000 (12%)\tLoss: 645.1477\tLL: -516.1998\tKL: 128.9479\tLL/KL: -4.0032\n",
+            "====> Epoch:  510/4000 (13%)\tLoss: 639.6024\tLL: -512.0939\tKL: 127.5084\tLL/KL: -4.0162\n",
+            "====> Epoch:  520/4000 (13%)\tLoss: 643.5860\tLL: -517.9759\tKL: 125.6101\tLL/KL: -4.1237\n",
+            "====> Epoch:  530/4000 (13%)\tLoss: 619.0413\tLL: -495.1470\tKL: 123.8942\tLL/KL: -3.9965\n",
+            "====> Epoch:  540/4000 (14%)\tLoss: 595.7733\tLL: -473.4763\tKL: 122.2970\tLL/KL: -3.8715\n",
+            "====> Epoch:  550/4000 (14%)\tLoss: 599.3822\tLL: -479.2128\tKL: 120.1694\tLL/KL: -3.9878\n",
+            "====> Epoch:  560/4000 (14%)\tLoss: 584.8198\tLL: -465.9992\tKL: 118.8206\tLL/KL: -3.9219\n",
+            "====> Epoch:  570/4000 (14%)\tLoss: 581.8887\tLL: -464.2617\tKL: 117.6270\tLL/KL: -3.9469\n",
+            "====> Epoch:  580/4000 (14%)\tLoss: 571.8738\tLL: -456.1504\tKL: 115.7234\tLL/KL: -3.9417\n",
+            "====> Epoch:  590/4000 (15%)\tLoss: 577.0997\tLL: -462.5333\tKL: 114.5664\tLL/KL: -4.0373\n",
+            "====> Epoch:  600/4000 (15%)\tLoss: 581.1548\tLL: -467.7261\tKL: 113.4287\tLL/KL: -4.1235\n",
+            "====> Epoch:  610/4000 (15%)\tLoss: 562.5135\tLL: -450.7373\tKL: 111.7762\tLL/KL: -4.0325\n",
+            "====> Epoch:  620/4000 (16%)\tLoss: 542.3489\tLL: -432.0107\tKL: 110.3382\tLL/KL: -3.9153\n",
+            "====> Epoch:  630/4000 (16%)\tLoss: 541.3604\tLL: -432.1180\tKL: 109.2424\tLL/KL: -3.9556\n",
+            "====> Epoch:  640/4000 (16%)\tLoss: 545.5951\tLL: -437.6672\tKL: 107.9279\tLL/KL: -4.0552\n",
+            "====> Epoch:  650/4000 (16%)\tLoss: 536.6854\tLL: -430.0718\tKL: 106.6135\tLL/KL: -4.0339\n",
+            "====> Epoch:  660/4000 (16%)\tLoss: 539.4613\tLL: -433.9598\tKL: 105.5015\tLL/KL: -4.1133\n",
+            "====> Epoch:  670/4000 (17%)\tLoss: 536.5095\tLL: -432.2298\tKL: 104.2797\tLL/KL: -4.1449\n",
+            "====> Epoch:  680/4000 (17%)\tLoss: 517.2606\tLL: -414.0600\tKL: 103.2007\tLL/KL: -4.0122\n",
+            "====> Epoch:  690/4000 (17%)\tLoss: 521.0253\tLL: -419.0925\tKL: 101.9328\tLL/KL: -4.1115\n",
+            "====> Epoch:  700/4000 (18%)\tLoss: 504.3251\tLL: -403.0199\tKL: 101.3052\tLL/KL: -3.9783\n",
+            "====> Epoch:  710/4000 (18%)\tLoss: 511.3374\tLL: -411.2439\tKL: 100.0935\tLL/KL: -4.1086\n",
+            "====> Epoch:  720/4000 (18%)\tLoss: 518.8746\tLL: -419.6909\tKL: 99.1837\tLL/KL: -4.2315\n",
+            "====> Epoch:  730/4000 (18%)\tLoss: 524.4761\tLL: -426.5222\tKL: 97.9538\tLL/KL: -4.3543\n",
+            "====> Epoch:  740/4000 (18%)\tLoss: 493.4583\tLL: -396.6490\tKL: 96.8092\tLL/KL: -4.0972\n",
+            "====> Epoch:  750/4000 (19%)\tLoss: 489.4124\tLL: -393.1375\tKL: 96.2749\tLL/KL: -4.0835\n",
+            "====> Epoch:  760/4000 (19%)\tLoss: 502.6629\tLL: -407.2720\tKL: 95.3909\tLL/KL: -4.2695\n",
+            "====> Epoch:  770/4000 (19%)\tLoss: 468.3705\tLL: -373.8737\tKL: 94.4968\tLL/KL: -3.9565\n",
+            "====> Epoch:  780/4000 (20%)\tLoss: 475.2756\tLL: -381.9084\tKL: 93.3672\tLL/KL: -4.0904\n",
+            "====> Epoch:  790/4000 (20%)\tLoss: 472.5278\tLL: -379.8351\tKL: 92.6928\tLL/KL: -4.0978\n",
+            "====> Epoch:  800/4000 (20%)\tLoss: 462.0844\tLL: -370.4140\tKL: 91.6704\tLL/KL: -4.0407\n",
+            "====> Epoch:  810/4000 (20%)\tLoss: 462.0068\tLL: -371.0010\tKL: 91.0058\tLL/KL: -4.0767\n",
+            "====> Epoch:  820/4000 (20%)\tLoss: 466.1634\tLL: -376.5029\tKL: 89.6606\tLL/KL: -4.1992\n",
+            "====> Epoch:  830/4000 (21%)\tLoss: 475.7635\tLL: -386.6195\tKL: 89.1440\tLL/KL: -4.3370\n",
+            "====> Epoch:  840/4000 (21%)\tLoss: 448.9477\tLL: -360.5697\tKL: 88.3779\tLL/KL: -4.0799\n",
+            "====> Epoch:  850/4000 (21%)\tLoss: 445.8538\tLL: -358.1939\tKL: 87.6600\tLL/KL: -4.0862\n",
+            "====> Epoch:  860/4000 (22%)\tLoss: 443.7466\tLL: -356.9370\tKL: 86.8097\tLL/KL: -4.1117\n",
+            "====> Epoch:  870/4000 (22%)\tLoss: 448.6539\tLL: -362.8943\tKL: 85.7596\tLL/KL: -4.2315\n",
+            "====> Epoch:  880/4000 (22%)\tLoss: 439.5746\tLL: -354.1956\tKL: 85.3790\tLL/KL: -4.1485\n",
+            "====> Epoch:  890/4000 (22%)\tLoss: 433.3827\tLL: -348.9973\tKL: 84.3854\tLL/KL: -4.1358\n",
+            "====> Epoch:  900/4000 (22%)\tLoss: 429.6326\tLL: -345.7849\tKL: 83.8476\tLL/KL: -4.1240\n",
+            "====> Epoch:  910/4000 (23%)\tLoss: 428.5811\tLL: -345.5276\tKL: 83.0535\tLL/KL: -4.1603\n",
+            "====> Epoch:  920/4000 (23%)\tLoss: 420.9882\tLL: -338.5907\tKL: 82.3974\tLL/KL: -4.1092\n",
+            "====> Epoch:  930/4000 (23%)\tLoss: 418.4583\tLL: -336.6154\tKL: 81.8429\tLL/KL: -4.1129\n",
+            "====> Epoch:  940/4000 (24%)\tLoss: 423.4271\tLL: -342.0346\tKL: 81.3924\tLL/KL: -4.2023\n",
+            "====> Epoch:  950/4000 (24%)\tLoss: 416.1346\tLL: -335.6628\tKL: 80.4719\tLL/KL: -4.1712\n",
+            "====> Epoch:  960/4000 (24%)\tLoss: 405.9783\tLL: -326.0879\tKL: 79.8904\tLL/KL: -4.0817\n",
+            "====> Epoch:  970/4000 (24%)\tLoss: 404.7733\tLL: -325.7485\tKL: 79.0248\tLL/KL: -4.1221\n",
+            "====> Epoch:  980/4000 (24%)\tLoss: 395.9296\tLL: -317.5970\tKL: 78.3326\tLL/KL: -4.0545\n",
+            "====> Epoch:  990/4000 (25%)\tLoss: 402.3820\tLL: -324.5630\tKL: 77.8190\tLL/KL: -4.1707\n",
+            "====> Epoch: 1000/4000 (25%)\tLoss: 391.3369\tLL: -313.9251\tKL: 77.4118\tLL/KL: -4.0553\n",
+            "====> Epoch: 1010/4000 (25%)\tLoss: 390.4985\tLL: -313.9651\tKL: 76.5335\tLL/KL: -4.1023\n",
+            "====> Epoch: 1020/4000 (26%)\tLoss: 390.8318\tLL: -314.5568\tKL: 76.2750\tLL/KL: -4.1240\n",
+            "====> Epoch: 1030/4000 (26%)\tLoss: 383.5078\tLL: -307.9846\tKL: 75.5232\tLL/KL: -4.0780\n",
+            "====> Epoch: 1040/4000 (26%)\tLoss: 381.1388\tLL: -306.4159\tKL: 74.7229\tLL/KL: -4.1007\n",
+            "====> Epoch: 1050/4000 (26%)\tLoss: 374.6802\tLL: -300.3320\tKL: 74.3482\tLL/KL: -4.0395\n",
+            "====> Epoch: 1060/4000 (26%)\tLoss: 379.9408\tLL: -306.4099\tKL: 73.5309\tLL/KL: -4.1671\n",
+            "====> Epoch: 1070/4000 (27%)\tLoss: 376.6357\tLL: -303.1474\tKL: 73.4883\tLL/KL: -4.1251\n",
+            "====> Epoch: 1080/4000 (27%)\tLoss: 370.9841\tLL: -298.6210\tKL: 72.3631\tLL/KL: -4.1267\n",
+            "====> Epoch: 1090/4000 (27%)\tLoss: 372.7411\tLL: -300.6603\tKL: 72.0809\tLL/KL: -4.1712\n",
+            "====> Epoch: 1100/4000 (28%)\tLoss: 366.0524\tLL: -294.3176\tKL: 71.7347\tLL/KL: -4.1029\n",
+            "====> Epoch: 1110/4000 (28%)\tLoss: 366.2048\tLL: -295.3002\tKL: 70.9046\tLL/KL: -4.1648\n",
+            "====> Epoch: 1120/4000 (28%)\tLoss: 363.3614\tLL: -292.6458\tKL: 70.7156\tLL/KL: -4.1383\n",
+            "====> Epoch: 1130/4000 (28%)\tLoss: 361.9388\tLL: -292.0863\tKL: 69.8524\tLL/KL: -4.1815\n",
+            "====> Epoch: 1140/4000 (28%)\tLoss: 360.7772\tLL: -291.1350\tKL: 69.6422\tLL/KL: -4.1804\n",
+            "====> Epoch: 1150/4000 (29%)\tLoss: 353.8964\tLL: -284.9950\tKL: 68.9015\tLL/KL: -4.1363\n",
+            "====> Epoch: 1160/4000 (29%)\tLoss: 350.6387\tLL: -282.0695\tKL: 68.5693\tLL/KL: -4.1136\n",
+            "====> Epoch: 1170/4000 (29%)\tLoss: 344.4034\tLL: -276.3727\tKL: 68.0308\tLL/KL: -4.0625\n",
+            "====> Epoch: 1180/4000 (30%)\tLoss: 343.3341\tLL: -275.5831\tKL: 67.7510\tLL/KL: -4.0676\n",
+            "====> Epoch: 1190/4000 (30%)\tLoss: 344.5306\tLL: -277.5760\tKL: 66.9545\tLL/KL: -4.1457\n",
+            "====> Epoch: 1200/4000 (30%)\tLoss: 340.0209\tLL: -273.3618\tKL: 66.6591\tLL/KL: -4.1009\n",
+            "====> Epoch: 1210/4000 (30%)\tLoss: 336.3910\tLL: -270.0388\tKL: 66.3522\tLL/KL: -4.0698\n",
+            "====> Epoch: 1220/4000 (30%)\tLoss: 337.8081\tLL: -272.0444\tKL: 65.7637\tLL/KL: -4.1367\n",
+            "====> Epoch: 1230/4000 (31%)\tLoss: 338.5134\tLL: -273.4595\tKL: 65.0539\tLL/KL: -4.2036\n",
+            "====> Epoch: 1240/4000 (31%)\tLoss: 332.4761\tLL: -267.6093\tKL: 64.8668\tLL/KL: -4.1255\n",
+            "====> Epoch: 1250/4000 (31%)\tLoss: 332.0515\tLL: -267.6746\tKL: 64.3768\tLL/KL: -4.1579\n",
+            "====> Epoch: 1260/4000 (32%)\tLoss: 333.7927\tLL: -269.5978\tKL: 64.1949\tLL/KL: -4.1997\n",
+            "====> Epoch: 1270/4000 (32%)\tLoss: 325.6506\tLL: -262.2859\tKL: 63.3646\tLL/KL: -4.1393\n",
+            "====> Epoch: 1280/4000 (32%)\tLoss: 328.7067\tLL: -265.3806\tKL: 63.3261\tLL/KL: -4.1907\n",
+            "====> Epoch: 1290/4000 (32%)\tLoss: 319.6376\tLL: -256.9427\tKL: 62.6949\tLL/KL: -4.0983\n",
+            "====> Epoch: 1300/4000 (32%)\tLoss: 315.0149\tLL: -252.8406\tKL: 62.1742\tLL/KL: -4.0666\n",
+            "====> Epoch: 1310/4000 (33%)\tLoss: 314.1542\tLL: -252.0621\tKL: 62.0921\tLL/KL: -4.0595\n",
+            "====> Epoch: 1320/4000 (33%)\tLoss: 314.8731\tLL: -253.4567\tKL: 61.4164\tLL/KL: -4.1269\n",
+            "====> Epoch: 1330/4000 (33%)\tLoss: 315.7991\tLL: -254.6251\tKL: 61.1740\tLL/KL: -4.1623\n",
+            "====> Epoch: 1340/4000 (34%)\tLoss: 310.1622\tLL: -249.4646\tKL: 60.6976\tLL/KL: -4.1100\n",
+            "====> Epoch: 1350/4000 (34%)\tLoss: 307.2725\tLL: -247.0135\tKL: 60.2590\tLL/KL: -4.0992\n",
+            "====> Epoch: 1360/4000 (34%)\tLoss: 303.4719\tLL: -243.4266\tKL: 60.0452\tLL/KL: -4.0541\n",
+            "====> Epoch: 1370/4000 (34%)\tLoss: 305.1471\tLL: -245.7212\tKL: 59.4259\tLL/KL: -4.1349\n",
+            "====> Epoch: 1380/4000 (34%)\tLoss: 299.9376\tLL: -240.8087\tKL: 59.1289\tLL/KL: -4.0726\n",
+            "====> Epoch: 1390/4000 (35%)\tLoss: 297.8383\tLL: -239.1393\tKL: 58.6989\tLL/KL: -4.0740\n",
+            "====> Epoch: 1400/4000 (35%)\tLoss: 297.3672\tLL: -238.8164\tKL: 58.5508\tLL/KL: -4.0788\n",
+            "====> Epoch: 1410/4000 (35%)\tLoss: 299.1420\tLL: -241.0703\tKL: 58.0717\tLL/KL: -4.1513\n",
+            "====> Epoch: 1420/4000 (36%)\tLoss: 293.4668\tLL: -235.6494\tKL: 57.8174\tLL/KL: -4.0758\n",
+            "====> Epoch: 1430/4000 (36%)\tLoss: 287.8819\tLL: -230.6924\tKL: 57.1895\tLL/KL: -4.0338\n",
+            "====> Epoch: 1440/4000 (36%)\tLoss: 291.5509\tLL: -234.4564\tKL: 57.0945\tLL/KL: -4.1065\n",
+            "====> Epoch: 1450/4000 (36%)\tLoss: 297.8324\tLL: -241.1624\tKL: 56.6700\tLL/KL: -4.2556\n",
+            "====> Epoch: 1460/4000 (36%)\tLoss: 283.6096\tLL: -227.1350\tKL: 56.4746\tLL/KL: -4.0219\n",
+            "====> Epoch: 1470/4000 (37%)\tLoss: 284.9940\tLL: -228.8226\tKL: 56.1714\tLL/KL: -4.0736\n",
+            "====> Epoch: 1480/4000 (37%)\tLoss: 281.6870\tLL: -226.0425\tKL: 55.6445\tLL/KL: -4.0623\n",
+            "====> Epoch: 1490/4000 (37%)\tLoss: 281.2645\tLL: -225.8806\tKL: 55.3839\tLL/KL: -4.0785\n",
+            "====> Epoch: 1500/4000 (38%)\tLoss: 279.4028\tLL: -224.2945\tKL: 55.1083\tLL/KL: -4.0701\n",
+            "====> Epoch: 1510/4000 (38%)\tLoss: 279.1047\tLL: -224.4628\tKL: 54.6419\tLL/KL: -4.1079\n",
+            "====> Epoch: 1520/4000 (38%)\tLoss: 278.8433\tLL: -224.4806\tKL: 54.3627\tLL/KL: -4.1293\n",
+            "====> Epoch: 1530/4000 (38%)\tLoss: 274.3933\tLL: -220.1894\tKL: 54.2040\tLL/KL: -4.0622\n",
+            "====> Epoch: 1540/4000 (38%)\tLoss: 276.2929\tLL: -222.5862\tKL: 53.7067\tLL/KL: -4.1445\n",
+            "====> Epoch: 1550/4000 (39%)\tLoss: 272.5593\tLL: -219.1822\tKL: 53.3770\tLL/KL: -4.1063\n",
+            "====> Epoch: 1560/4000 (39%)\tLoss: 267.7464\tLL: -214.4970\tKL: 53.2494\tLL/KL: -4.0282\n",
+            "====> Epoch: 1570/4000 (39%)\tLoss: 266.0259\tLL: -213.2013\tKL: 52.8245\tLL/KL: -4.0360\n",
+            "====> Epoch: 1580/4000 (40%)\tLoss: 266.0210\tLL: -213.3041\tKL: 52.7168\tLL/KL: -4.0462\n",
+            "====> Epoch: 1590/4000 (40%)\tLoss: 268.8331\tLL: -216.4891\tKL: 52.3441\tLL/KL: -4.1359\n",
+            "====> Epoch: 1600/4000 (40%)\tLoss: 266.5642\tLL: -214.8027\tKL: 51.7615\tLL/KL: -4.1499\n",
+            "====> Epoch: 1610/4000 (40%)\tLoss: 261.4364\tLL: -209.8704\tKL: 51.5659\tLL/KL: -4.0699\n",
+            "====> Epoch: 1620/4000 (40%)\tLoss: 255.8784\tLL: -204.6318\tKL: 51.2467\tLL/KL: -3.9931\n",
+            "====> Epoch: 1630/4000 (41%)\tLoss: 261.1717\tLL: -209.9436\tKL: 51.2281\tLL/KL: -4.0982\n",
+            "====> Epoch: 1640/4000 (41%)\tLoss: 256.5820\tLL: -205.7258\tKL: 50.8562\tLL/KL: -4.0452\n",
+            "====> Epoch: 1650/4000 (41%)\tLoss: 258.2456\tLL: -207.5983\tKL: 50.6472\tLL/KL: -4.0989\n",
+            "====> Epoch: 1660/4000 (42%)\tLoss: 250.9438\tLL: -200.9973\tKL: 49.9465\tLL/KL: -4.0242\n",
+            "====> Epoch: 1670/4000 (42%)\tLoss: 257.7567\tLL: -207.6725\tKL: 50.0842\tLL/KL: -4.1465\n",
+            "====> Epoch: 1680/4000 (42%)\tLoss: 253.0654\tLL: -203.4021\tKL: 49.6633\tLL/KL: -4.0956\n",
+            "====> Epoch: 1690/4000 (42%)\tLoss: 248.6044\tLL: -199.4632\tKL: 49.1412\tLL/KL: -4.0590\n",
+            "====> Epoch: 1700/4000 (42%)\tLoss: 251.4095\tLL: -202.2285\tKL: 49.1810\tLL/KL: -4.1119\n",
+            "====> Epoch: 1710/4000 (43%)\tLoss: 247.9875\tLL: -199.0341\tKL: 48.9534\tLL/KL: -4.0658\n",
+            "====> Epoch: 1720/4000 (43%)\tLoss: 248.4358\tLL: -199.8878\tKL: 48.5480\tLL/KL: -4.1173\n",
+            "====> Epoch: 1730/4000 (43%)\tLoss: 246.2700\tLL: -197.9882\tKL: 48.2818\tLL/KL: -4.1007\n",
+            "====> Epoch: 1740/4000 (44%)\tLoss: 238.2170\tLL: -190.1734\tKL: 48.0436\tLL/KL: -3.9584\n",
+            "====> Epoch: 1750/4000 (44%)\tLoss: 242.6404\tLL: -194.7694\tKL: 47.8710\tLL/KL: -4.0686\n",
+            "====> Epoch: 1760/4000 (44%)\tLoss: 238.6333\tLL: -191.1691\tKL: 47.4642\tLL/KL: -4.0276\n",
+            "====> Epoch: 1770/4000 (44%)\tLoss: 241.5175\tLL: -193.9612\tKL: 47.5563\tLL/KL: -4.0786\n",
+            "====> Epoch: 1780/4000 (44%)\tLoss: 232.3736\tLL: -185.2306\tKL: 47.1430\tLL/KL: -3.9291\n",
+            "====> Epoch: 1790/4000 (45%)\tLoss: 238.4180\tLL: -191.4399\tKL: 46.9781\tLL/KL: -4.0751\n",
+            "====> Epoch: 1800/4000 (45%)\tLoss: 235.2049\tLL: -188.5982\tKL: 46.6067\tLL/KL: -4.0466\n",
+            "====> Epoch: 1810/4000 (45%)\tLoss: 236.8595\tLL: -190.4775\tKL: 46.3820\tLL/KL: -4.1067\n",
+            "====> Epoch: 1820/4000 (46%)\tLoss: 230.3833\tLL: -184.1979\tKL: 46.1854\tLL/KL: -3.9882\n",
+            "====> Epoch: 1830/4000 (46%)\tLoss: 232.4081\tLL: -186.4487\tKL: 45.9594\tLL/KL: -4.0568\n",
+            "====> Epoch: 1840/4000 (46%)\tLoss: 227.7962\tLL: -182.0385\tKL: 45.7577\tLL/KL: -3.9783\n",
+            "====> Epoch: 1850/4000 (46%)\tLoss: 227.6385\tLL: -182.2903\tKL: 45.3482\tLL/KL: -4.0198\n",
+            "====> Epoch: 1860/4000 (46%)\tLoss: 225.4294\tLL: -180.1594\tKL: 45.2701\tLL/KL: -3.9797\n",
+            "====> Epoch: 1870/4000 (47%)\tLoss: 224.4816\tLL: -179.4981\tKL: 44.9834\tLL/KL: -3.9903\n",
+            "====> Epoch: 1880/4000 (47%)\tLoss: 224.9229\tLL: -180.2484\tKL: 44.6744\tLL/KL: -4.0347\n",
+            "====> Epoch: 1890/4000 (47%)\tLoss: 222.6879\tLL: -178.0705\tKL: 44.6174\tLL/KL: -3.9911\n",
+            "====> Epoch: 1900/4000 (48%)\tLoss: 222.2804\tLL: -177.9992\tKL: 44.2812\tLL/KL: -4.0197\n",
+            "====> Epoch: 1910/4000 (48%)\tLoss: 222.2084\tLL: -178.3111\tKL: 43.8973\tLL/KL: -4.0620\n",
+            "====> Epoch: 1920/4000 (48%)\tLoss: 219.3706\tLL: -175.3994\tKL: 43.9712\tLL/KL: -3.9890\n",
+            "====> Epoch: 1930/4000 (48%)\tLoss: 219.3019\tLL: -175.4492\tKL: 43.8526\tLL/KL: -4.0009\n",
+            "====> Epoch: 1940/4000 (48%)\tLoss: 219.4677\tLL: -176.0341\tKL: 43.4336\tLL/KL: -4.0529\n",
+            "====> Epoch: 1950/4000 (49%)\tLoss: 218.8352\tLL: -175.7168\tKL: 43.1185\tLL/KL: -4.0752\n",
+            "====> Epoch: 1960/4000 (49%)\tLoss: 213.7763\tLL: -170.9102\tKL: 42.8661\tLL/KL: -3.9871\n",
+            "====> Epoch: 1970/4000 (49%)\tLoss: 215.4075\tLL: -172.5686\tKL: 42.8390\tLL/KL: -4.0283\n",
+            "====> Epoch: 1980/4000 (50%)\tLoss: 213.7078\tLL: -171.0346\tKL: 42.6732\tLL/KL: -4.0080\n",
+            "====> Epoch: 1990/4000 (50%)\tLoss: 208.7895\tLL: -166.4720\tKL: 42.3176\tLL/KL: -3.9339\n",
+            "====> Epoch: 2000/4000 (50%)\tLoss: 208.3035\tLL: -166.1162\tKL: 42.1873\tLL/KL: -3.9376\n",
+            "====> Epoch: 2010/4000 (50%)\tLoss: 208.8476\tLL: -166.7295\tKL: 42.1181\tLL/KL: -3.9586\n",
+            "====> Epoch: 2020/4000 (50%)\tLoss: 208.2242\tLL: -166.4443\tKL: 41.7799\tLL/KL: -3.9838\n",
+            "====> Epoch: 2030/4000 (51%)\tLoss: 206.4779\tLL: -164.8934\tKL: 41.5846\tLL/KL: -3.9653\n",
+            "====> Epoch: 2040/4000 (51%)\tLoss: 206.8612\tLL: -165.3743\tKL: 41.4869\tLL/KL: -3.9862\n",
+            "====> Epoch: 2050/4000 (51%)\tLoss: 207.9006\tLL: -166.8422\tKL: 41.0584\tLL/KL: -4.0635\n",
+            "====> Epoch: 2060/4000 (52%)\tLoss: 205.4056\tLL: -164.4089\tKL: 40.9967\tLL/KL: -4.0103\n",
+            "====> Epoch: 2070/4000 (52%)\tLoss: 201.6513\tLL: -160.7163\tKL: 40.9350\tLL/KL: -3.9261\n",
+            "====> Epoch: 2080/4000 (52%)\tLoss: 204.3775\tLL: -163.6977\tKL: 40.6798\tLL/KL: -4.0241\n",
+            "====> Epoch: 2090/4000 (52%)\tLoss: 201.2880\tLL: -160.7778\tKL: 40.5101\tLL/KL: -3.9688\n",
+            "====> Epoch: 2100/4000 (52%)\tLoss: 200.4215\tLL: -160.0020\tKL: 40.4195\tLL/KL: -3.9585\n",
+            "====> Epoch: 2110/4000 (53%)\tLoss: 197.1440\tLL: -157.0526\tKL: 40.0914\tLL/KL: -3.9174\n",
+            "====> Epoch: 2120/4000 (53%)\tLoss: 197.7890\tLL: -157.8464\tKL: 39.9426\tLL/KL: -3.9518\n",
+            "====> Epoch: 2130/4000 (53%)\tLoss: 196.0482\tLL: -156.1544\tKL: 39.8937\tLL/KL: -3.9143\n",
+            "====> Epoch: 2140/4000 (54%)\tLoss: 194.3117\tLL: -154.7170\tKL: 39.5947\tLL/KL: -3.9075\n",
+            "====> Epoch: 2150/4000 (54%)\tLoss: 194.4441\tLL: -155.0650\tKL: 39.3791\tLL/KL: -3.9377\n",
+            "====> Epoch: 2160/4000 (54%)\tLoss: 195.6700\tLL: -156.4967\tKL: 39.1733\tLL/KL: -3.9950\n",
+            "====> Epoch: 2170/4000 (54%)\tLoss: 191.7182\tLL: -152.8356\tKL: 38.8826\tLL/KL: -3.9307\n",
+            "====> Epoch: 2180/4000 (54%)\tLoss: 192.9207\tLL: -154.0037\tKL: 38.9170\tLL/KL: -3.9572\n",
+            "====> Epoch: 2190/4000 (55%)\tLoss: 192.9933\tLL: -154.1486\tKL: 38.8447\tLL/KL: -3.9683\n",
+            "====> Epoch: 2200/4000 (55%)\tLoss: 188.1343\tLL: -149.6622\tKL: 38.4721\tLL/KL: -3.8901\n",
+            "====> Epoch: 2210/4000 (55%)\tLoss: 188.9056\tLL: -150.4432\tKL: 38.4623\tLL/KL: -3.9114\n",
+            "====> Epoch: 2220/4000 (56%)\tLoss: 185.9356\tLL: -147.6000\tKL: 38.3356\tLL/KL: -3.8502\n",
+            "====> Epoch: 2230/4000 (56%)\tLoss: 187.5179\tLL: -149.5352\tKL: 37.9827\tLL/KL: -3.9369\n",
+            "====> Epoch: 2240/4000 (56%)\tLoss: 186.7010\tLL: -148.7210\tKL: 37.9800\tLL/KL: -3.9158\n",
+            "====> Epoch: 2250/4000 (56%)\tLoss: 184.5382\tLL: -146.7887\tKL: 37.7496\tLL/KL: -3.8885\n",
+            "====> Epoch: 2260/4000 (56%)\tLoss: 184.7657\tLL: -147.1111\tKL: 37.6546\tLL/KL: -3.9069\n",
+            "====> Epoch: 2270/4000 (57%)\tLoss: 184.4920\tLL: -147.1245\tKL: 37.3675\tLL/KL: -3.9372\n",
+            "====> Epoch: 2280/4000 (57%)\tLoss: 183.0076\tLL: -145.6996\tKL: 37.3080\tLL/KL: -3.9053\n",
+            "====> Epoch: 2290/4000 (57%)\tLoss: 181.0506\tLL: -143.9672\tKL: 37.0834\tLL/KL: -3.8823\n",
+            "====> Epoch: 2300/4000 (58%)\tLoss: 181.9475\tLL: -144.8984\tKL: 37.0491\tLL/KL: -3.9110\n",
+            "====> Epoch: 2310/4000 (58%)\tLoss: 177.7639\tLL: -141.0707\tKL: 36.6933\tLL/KL: -3.8446\n",
+            "====> Epoch: 2320/4000 (58%)\tLoss: 180.6996\tLL: -143.9831\tKL: 36.7165\tLL/KL: -3.9215\n",
+            "====> Epoch: 2330/4000 (58%)\tLoss: 178.7113\tLL: -142.4135\tKL: 36.2979\tLL/KL: -3.9235\n",
+            "====> Epoch: 2340/4000 (58%)\tLoss: 176.1638\tLL: -139.9124\tKL: 36.2514\tLL/KL: -3.8595\n",
+            "====> Epoch: 2350/4000 (59%)\tLoss: 176.2311\tLL: -140.1522\tKL: 36.0789\tLL/KL: -3.8846\n",
+            "====> Epoch: 2360/4000 (59%)\tLoss: 175.7780\tLL: -139.8168\tKL: 35.9612\tLL/KL: -3.8880\n",
+            "====> Epoch: 2370/4000 (59%)\tLoss: 175.9680\tLL: -140.1202\tKL: 35.8477\tLL/KL: -3.9088\n",
+            "====> Epoch: 2380/4000 (60%)\tLoss: 174.9419\tLL: -139.1963\tKL: 35.7456\tLL/KL: -3.8941\n",
+            "====> Epoch: 2390/4000 (60%)\tLoss: 174.3522\tLL: -138.7987\tKL: 35.5535\tLL/KL: -3.9039\n",
+            "====> Epoch: 2400/4000 (60%)\tLoss: 171.7534\tLL: -136.2845\tKL: 35.4689\tLL/KL: -3.8424\n",
+            "====> Epoch: 2410/4000 (60%)\tLoss: 172.7780\tLL: -137.5026\tKL: 35.2754\tLL/KL: -3.8980\n",
+            "====> Epoch: 2420/4000 (60%)\tLoss: 170.6770\tLL: -135.5900\tKL: 35.0870\tLL/KL: -3.8644\n",
+            "====> Epoch: 2430/4000 (61%)\tLoss: 172.3362\tLL: -137.4439\tKL: 34.8923\tLL/KL: -3.9391\n",
+            "====> Epoch: 2440/4000 (61%)\tLoss: 168.1519\tLL: -133.3562\tKL: 34.7956\tLL/KL: -3.8326\n",
+            "====> Epoch: 2450/4000 (61%)\tLoss: 167.5183\tLL: -132.8088\tKL: 34.7095\tLL/KL: -3.8263\n",
+            "====> Epoch: 2460/4000 (62%)\tLoss: 168.4088\tLL: -133.9812\tKL: 34.4276\tLL/KL: -3.8917\n",
+            "====> Epoch: 2470/4000 (62%)\tLoss: 166.8286\tLL: -132.3030\tKL: 34.5256\tLL/KL: -3.8320\n",
+            "====> Epoch: 2480/4000 (62%)\tLoss: 167.4358\tLL: -133.2620\tKL: 34.1737\tLL/KL: -3.8995\n",
+            "====> Epoch: 2490/4000 (62%)\tLoss: 166.4434\tLL: -132.2448\tKL: 34.1986\tLL/KL: -3.8670\n",
+            "====> Epoch: 2500/4000 (62%)\tLoss: 162.5269\tLL: -128.4176\tKL: 34.1094\tLL/KL: -3.7649\n",
+            "====> Epoch: 2510/4000 (63%)\tLoss: 164.1618\tLL: -130.1830\tKL: 33.9788\tLL/KL: -3.8313\n",
+            "====> Epoch: 2520/4000 (63%)\tLoss: 163.8493\tLL: -130.0457\tKL: 33.8036\tLL/KL: -3.8471\n",
+            "====> Epoch: 2530/4000 (63%)\tLoss: 162.5131\tLL: -128.8660\tKL: 33.6471\tLL/KL: -3.8299\n",
+            "====> Epoch: 2540/4000 (64%)\tLoss: 161.8123\tLL: -128.2363\tKL: 33.5760\tLL/KL: -3.8193\n",
+            "====> Epoch: 2550/4000 (64%)\tLoss: 162.3587\tLL: -128.9804\tKL: 33.3783\tLL/KL: -3.8642\n",
+            "====> Epoch: 2560/4000 (64%)\tLoss: 160.3283\tLL: -127.0450\tKL: 33.2832\tLL/KL: -3.8171\n",
+            "====> Epoch: 2570/4000 (64%)\tLoss: 161.3521\tLL: -128.0960\tKL: 33.2561\tLL/KL: -3.8518\n",
+            "====> Epoch: 2580/4000 (64%)\tLoss: 158.7046\tLL: -125.5792\tKL: 33.1254\tLL/KL: -3.7910\n",
+            "====> Epoch: 2590/4000 (65%)\tLoss: 157.8142\tLL: -125.0226\tKL: 32.7916\tLL/KL: -3.8126\n",
+            "====> Epoch: 2600/4000 (65%)\tLoss: 159.1425\tLL: -126.3389\tKL: 32.8036\tLL/KL: -3.8514\n",
+            "====> Epoch: 2610/4000 (65%)\tLoss: 156.8453\tLL: -124.2011\tKL: 32.6442\tLL/KL: -3.8047\n",
+            "====> Epoch: 2620/4000 (66%)\tLoss: 155.3814\tLL: -122.9291\tKL: 32.4523\tLL/KL: -3.7880\n",
+            "====> Epoch: 2630/4000 (66%)\tLoss: 155.1056\tLL: -122.7118\tKL: 32.3938\tLL/KL: -3.7881\n",
+            "====> Epoch: 2640/4000 (66%)\tLoss: 154.9363\tLL: -122.7096\tKL: 32.2267\tLL/KL: -3.8077\n",
+            "====> Epoch: 2650/4000 (66%)\tLoss: 155.2481\tLL: -123.1156\tKL: 32.1326\tLL/KL: -3.8315\n",
+            "====> Epoch: 2660/4000 (66%)\tLoss: 154.2651\tLL: -122.4258\tKL: 31.8393\tLL/KL: -3.8451\n",
+            "====> Epoch: 2670/4000 (67%)\tLoss: 153.0162\tLL: -121.1808\tKL: 31.8354\tLL/KL: -3.8065\n",
+            "====> Epoch: 2680/4000 (67%)\tLoss: 153.3540\tLL: -121.6958\tKL: 31.6582\tLL/KL: -3.8440\n",
+            "====> Epoch: 2690/4000 (67%)\tLoss: 151.0189\tLL: -119.3795\tKL: 31.6394\tLL/KL: -3.7731\n",
+            "====> Epoch: 2700/4000 (68%)\tLoss: 149.0005\tLL: -117.4972\tKL: 31.5033\tLL/KL: -3.7297\n",
+            "====> Epoch: 2710/4000 (68%)\tLoss: 150.6892\tLL: -119.1635\tKL: 31.5257\tLL/KL: -3.7799\n",
+            "====> Epoch: 2720/4000 (68%)\tLoss: 150.5474\tLL: -119.0929\tKL: 31.4545\tLL/KL: -3.7862\n",
+            "====> Epoch: 2730/4000 (68%)\tLoss: 150.1251\tLL: -118.9845\tKL: 31.1406\tLL/KL: -3.8209\n",
+            "====> Epoch: 2740/4000 (68%)\tLoss: 149.3145\tLL: -118.2364\tKL: 31.0781\tLL/KL: -3.8045\n",
+            "====> Epoch: 2750/4000 (69%)\tLoss: 147.9406\tLL: -116.9808\tKL: 30.9598\tLL/KL: -3.7785\n",
+            "====> Epoch: 2760/4000 (69%)\tLoss: 146.1526\tLL: -115.3108\tKL: 30.8418\tLL/KL: -3.7388\n",
+            "====> Epoch: 2770/4000 (69%)\tLoss: 147.0824\tLL: -116.2015\tKL: 30.8809\tLL/KL: -3.7629\n",
+            "====> Epoch: 2780/4000 (70%)\tLoss: 145.6000\tLL: -114.9982\tKL: 30.6017\tLL/KL: -3.7579\n",
+            "====> Epoch: 2790/4000 (70%)\tLoss: 146.5214\tLL: -116.0340\tKL: 30.4874\tLL/KL: -3.8060\n",
+            "====> Epoch: 2800/4000 (70%)\tLoss: 144.4466\tLL: -114.0276\tKL: 30.4190\tLL/KL: -3.7486\n",
+            "====> Epoch: 2810/4000 (70%)\tLoss: 143.0165\tLL: -112.6974\tKL: 30.3191\tLL/KL: -3.7170\n",
+            "====> Epoch: 2820/4000 (70%)\tLoss: 143.7614\tLL: -113.5596\tKL: 30.2018\tLL/KL: -3.7600\n",
+            "====> Epoch: 2830/4000 (71%)\tLoss: 145.0206\tLL: -114.8435\tKL: 30.1771\tLL/KL: -3.8056\n",
+            "====> Epoch: 2840/4000 (71%)\tLoss: 142.1198\tLL: -112.0627\tKL: 30.0571\tLL/KL: -3.7283\n",
+            "====> Epoch: 2850/4000 (71%)\tLoss: 143.7500\tLL: -113.7803\tKL: 29.9697\tLL/KL: -3.7965\n",
+            "====> Epoch: 2860/4000 (72%)\tLoss: 141.2687\tLL: -111.3841\tKL: 29.8847\tLL/KL: -3.7271\n",
+            "====> Epoch: 2870/4000 (72%)\tLoss: 139.4670\tLL: -109.6832\tKL: 29.7839\tLL/KL: -3.6826\n",
+            "====> Epoch: 2880/4000 (72%)\tLoss: 140.6563\tLL: -111.0928\tKL: 29.5635\tLL/KL: -3.7578\n",
+            "====> Epoch: 2890/4000 (72%)\tLoss: 138.2082\tLL: -108.6934\tKL: 29.5148\tLL/KL: -3.6827\n",
+            "====> Epoch: 2900/4000 (72%)\tLoss: 137.4865\tLL: -108.0920\tKL: 29.3945\tLL/KL: -3.6773\n",
+            "====> Epoch: 2910/4000 (73%)\tLoss: 139.0881\tLL: -109.5772\tKL: 29.5109\tLL/KL: -3.7131\n",
+            "====> Epoch: 2920/4000 (73%)\tLoss: 137.7102\tLL: -108.4637\tKL: 29.2465\tLL/KL: -3.7086\n",
+            "====> Epoch: 2930/4000 (73%)\tLoss: 137.3731\tLL: -108.2462\tKL: 29.1269\tLL/KL: -3.7164\n",
+            "====> Epoch: 2940/4000 (74%)\tLoss: 136.3458\tLL: -107.3031\tKL: 29.0428\tLL/KL: -3.6947\n",
+            "====> Epoch: 2950/4000 (74%)\tLoss: 136.5561\tLL: -107.6778\tKL: 28.8783\tLL/KL: -3.7287\n",
+            "====> Epoch: 2960/4000 (74%)\tLoss: 137.0980\tLL: -108.2901\tKL: 28.8079\tLL/KL: -3.7590\n",
+            "====> Epoch: 2970/4000 (74%)\tLoss: 136.3618\tLL: -107.6070\tKL: 28.7548\tLL/KL: -3.7422\n",
+            "====> Epoch: 2980/4000 (74%)\tLoss: 134.3330\tLL: -105.8375\tKL: 28.4955\tLL/KL: -3.7142\n",
+            "====> Epoch: 2990/4000 (75%)\tLoss: 134.2286\tLL: -105.6968\tKL: 28.5318\tLL/KL: -3.7045\n",
+            "====> Epoch: 3000/4000 (75%)\tLoss: 133.8124\tLL: -105.3488\tKL: 28.4636\tLL/KL: -3.7012\n",
+            "====> Epoch: 3010/4000 (75%)\tLoss: 133.7874\tLL: -105.3757\tKL: 28.4116\tLL/KL: -3.7089\n",
+            "====> Epoch: 3020/4000 (76%)\tLoss: 132.3541\tLL: -104.1892\tKL: 28.1648\tLL/KL: -3.6993\n",
+            "====> Epoch: 3030/4000 (76%)\tLoss: 133.6310\tLL: -105.5238\tKL: 28.1072\tLL/KL: -3.7543\n",
+            "====> Epoch: 3040/4000 (76%)\tLoss: 132.7172\tLL: -104.6281\tKL: 28.0891\tLL/KL: -3.7249\n",
+            "====> Epoch: 3050/4000 (76%)\tLoss: 131.2343\tLL: -103.2491\tKL: 27.9852\tLL/KL: -3.6894\n",
+            "====> Epoch: 3060/4000 (76%)\tLoss: 132.7934\tLL: -104.8030\tKL: 27.9904\tLL/KL: -3.7443\n",
+            "====> Epoch: 3070/4000 (77%)\tLoss: 130.4426\tLL: -102.5695\tKL: 27.8731\tLL/KL: -3.6799\n",
+            "====> Epoch: 3080/4000 (77%)\tLoss: 130.1777\tLL: -102.3544\tKL: 27.8234\tLL/KL: -3.6787\n",
+            "====> Epoch: 3090/4000 (77%)\tLoss: 128.4621\tLL: -100.9622\tKL: 27.4998\tLL/KL: -3.6714\n",
+            "====> Epoch: 3100/4000 (78%)\tLoss: 128.0448\tLL: -100.5523\tKL: 27.4925\tLL/KL: -3.6575\n",
+            "====> Epoch: 3110/4000 (78%)\tLoss: 128.5619\tLL: -101.1273\tKL: 27.4346\tLL/KL: -3.6861\n",
+            "====> Epoch: 3120/4000 (78%)\tLoss: 127.6141\tLL: -100.2471\tKL: 27.3669\tLL/KL: -3.6631\n",
+            "====> Epoch: 3130/4000 (78%)\tLoss: 126.9544\tLL: -99.7138\tKL: 27.2406\tLL/KL: -3.6605\n",
+            "====> Epoch: 3140/4000 (78%)\tLoss: 126.5619\tLL: -99.3246\tKL: 27.2372\tLL/KL: -3.6467\n",
+            "====> Epoch: 3150/4000 (79%)\tLoss: 125.9857\tLL: -98.7766\tKL: 27.2091\tLL/KL: -3.6303\n",
+            "====> Epoch: 3160/4000 (79%)\tLoss: 126.0026\tLL: -98.7972\tKL: 27.2053\tLL/KL: -3.6315\n",
+            "====> Epoch: 3170/4000 (79%)\tLoss: 125.2106\tLL: -98.2065\tKL: 27.0041\tLL/KL: -3.6367\n",
+            "====> Epoch: 3180/4000 (80%)\tLoss: 124.7438\tLL: -97.8680\tKL: 26.8758\tLL/KL: -3.6415\n",
+            "====> Epoch: 3190/4000 (80%)\tLoss: 124.1098\tLL: -97.2355\tKL: 26.8744\tLL/KL: -3.6182\n",
+            "====> Epoch: 3200/4000 (80%)\tLoss: 125.5547\tLL: -98.8218\tKL: 26.7329\tLL/KL: -3.6966\n",
+            "====> Epoch: 3210/4000 (80%)\tLoss: 122.3780\tLL: -95.7655\tKL: 26.6125\tLL/KL: -3.5985\n",
+            "====> Epoch: 3220/4000 (80%)\tLoss: 123.0664\tLL: -96.5172\tKL: 26.5492\tLL/KL: -3.6354\n",
+            "====> Epoch: 3230/4000 (81%)\tLoss: 122.0410\tLL: -95.6268\tKL: 26.4142\tLL/KL: -3.6203\n",
+            "====> Epoch: 3240/4000 (81%)\tLoss: 121.9902\tLL: -95.6098\tKL: 26.3804\tLL/KL: -3.6243\n",
+            "====> Epoch: 3250/4000 (81%)\tLoss: 120.6468\tLL: -94.2750\tKL: 26.3718\tLL/KL: -3.5748\n",
+            "====> Epoch: 3260/4000 (82%)\tLoss: 121.9353\tLL: -95.6465\tKL: 26.2889\tLL/KL: -3.6383\n",
+            "====> Epoch: 3270/4000 (82%)\tLoss: 120.9320\tLL: -94.8618\tKL: 26.0702\tLL/KL: -3.6387\n",
+            "====> Epoch: 3280/4000 (82%)\tLoss: 120.9243\tLL: -94.8953\tKL: 26.0290\tLL/KL: -3.6458\n",
+            "====> Epoch: 3290/4000 (82%)\tLoss: 119.1000\tLL: -93.0940\tKL: 26.0060\tLL/KL: -3.5797\n",
+            "====> Epoch: 3300/4000 (82%)\tLoss: 119.3683\tLL: -93.4896\tKL: 25.8787\tLL/KL: -3.6126\n",
+            "====> Epoch: 3310/4000 (83%)\tLoss: 119.7491\tLL: -93.9433\tKL: 25.8058\tLL/KL: -3.6404\n",
+            "====> Epoch: 3320/4000 (83%)\tLoss: 119.1131\tLL: -93.4268\tKL: 25.6862\tLL/KL: -3.6372\n",
+            "====> Epoch: 3330/4000 (83%)\tLoss: 118.0468\tLL: -92.3385\tKL: 25.7083\tLL/KL: -3.5918\n",
+            "====> Epoch: 3340/4000 (84%)\tLoss: 116.8066\tLL: -91.1478\tKL: 25.6588\tLL/KL: -3.5523\n",
+            "====> Epoch: 3350/4000 (84%)\tLoss: 119.0381\tLL: -93.4970\tKL: 25.5411\tLL/KL: -3.6606\n",
+            "====> Epoch: 3360/4000 (84%)\tLoss: 116.4149\tLL: -90.9258\tKL: 25.4892\tLL/KL: -3.5672\n",
+            "====> Epoch: 3370/4000 (84%)\tLoss: 116.9100\tLL: -91.5174\tKL: 25.3926\tLL/KL: -3.6041\n",
+            "====> Epoch: 3380/4000 (84%)\tLoss: 115.7693\tLL: -90.4147\tKL: 25.3547\tLL/KL: -3.5660\n",
+            "====> Epoch: 3390/4000 (85%)\tLoss: 115.2962\tLL: -90.0464\tKL: 25.2498\tLL/KL: -3.5662\n",
+            "====> Epoch: 3400/4000 (85%)\tLoss: 116.0509\tLL: -90.9281\tKL: 25.1228\tLL/KL: -3.6193\n",
+            "====> Epoch: 3410/4000 (85%)\tLoss: 115.9565\tLL: -90.9516\tKL: 25.0049\tLL/KL: -3.6374\n",
+            "====> Epoch: 3420/4000 (86%)\tLoss: 115.2048\tLL: -90.1896\tKL: 25.0152\tLL/KL: -3.6054\n",
+            "====> Epoch: 3430/4000 (86%)\tLoss: 113.9982\tLL: -89.0042\tKL: 24.9940\tLL/KL: -3.5610\n",
+            "====> Epoch: 3440/4000 (86%)\tLoss: 113.9281\tLL: -88.9887\tKL: 24.9393\tLL/KL: -3.5682\n",
+            "====> Epoch: 3450/4000 (86%)\tLoss: 114.7952\tLL: -89.9374\tKL: 24.8578\tLL/KL: -3.6181\n",
+            "====> Epoch: 3460/4000 (86%)\tLoss: 113.5692\tLL: -88.8686\tKL: 24.7006\tLL/KL: -3.5978\n",
+            "====> Epoch: 3470/4000 (87%)\tLoss: 112.2384\tLL: -87.5223\tKL: 24.7161\tLL/KL: -3.5411\n",
+            "====> Epoch: 3480/4000 (87%)\tLoss: 112.6518\tLL: -88.0059\tKL: 24.6458\tLL/KL: -3.5708\n",
+            "====> Epoch: 3490/4000 (87%)\tLoss: 111.2180\tLL: -86.6682\tKL: 24.5497\tLL/KL: -3.5303\n",
+            "====> Epoch: 3500/4000 (88%)\tLoss: 112.5038\tLL: -88.0061\tKL: 24.4977\tLL/KL: -3.5924\n",
+            "====> Epoch: 3510/4000 (88%)\tLoss: 110.4317\tLL: -86.0196\tKL: 24.4121\tLL/KL: -3.5236\n",
+            "====> Epoch: 3520/4000 (88%)\tLoss: 111.0925\tLL: -86.8583\tKL: 24.2342\tLL/KL: -3.5841\n",
+            "====> Epoch: 3530/4000 (88%)\tLoss: 109.0868\tLL: -84.8094\tKL: 24.2775\tLL/KL: -3.4933\n",
+            "====> Epoch: 3540/4000 (88%)\tLoss: 110.6314\tLL: -86.4181\tKL: 24.2133\tLL/KL: -3.5690\n",
+            "====> Epoch: 3550/4000 (89%)\tLoss: 109.4872\tLL: -85.2209\tKL: 24.2663\tLL/KL: -3.5119\n",
+            "====> Epoch: 3560/4000 (89%)\tLoss: 110.0296\tLL: -85.9655\tKL: 24.0641\tLL/KL: -3.5724\n",
+            "====> Epoch: 3570/4000 (89%)\tLoss: 109.7715\tLL: -85.7112\tKL: 24.0603\tLL/KL: -3.5623\n",
+            "====> Epoch: 3580/4000 (90%)\tLoss: 108.6098\tLL: -84.5638\tKL: 24.0460\tLL/KL: -3.5168\n",
+            "====> Epoch: 3590/4000 (90%)\tLoss: 107.8562\tLL: -83.8779\tKL: 23.9783\tLL/KL: -3.4981\n",
+            "====> Epoch: 3600/4000 (90%)\tLoss: 108.2910\tLL: -84.4757\tKL: 23.8153\tLL/KL: -3.5471\n",
+            "====> Epoch: 3610/4000 (90%)\tLoss: 108.3264\tLL: -84.6559\tKL: 23.6704\tLL/KL: -3.5764\n",
+            "====> Epoch: 3620/4000 (90%)\tLoss: 108.0917\tLL: -84.3043\tKL: 23.7874\tLL/KL: -3.5441\n",
+            "====> Epoch: 3630/4000 (91%)\tLoss: 107.0209\tLL: -83.3084\tKL: 23.7125\tLL/KL: -3.5133\n",
+            "====> Epoch: 3640/4000 (91%)\tLoss: 106.3009\tLL: -82.7879\tKL: 23.5130\tLL/KL: -3.5209\n",
+            "====> Epoch: 3650/4000 (91%)\tLoss: 106.6769\tLL: -83.1717\tKL: 23.5052\tLL/KL: -3.5384\n",
+            "====> Epoch: 3660/4000 (92%)\tLoss: 106.7767\tLL: -83.2491\tKL: 23.5276\tLL/KL: -3.5384\n",
+            "====> Epoch: 3670/4000 (92%)\tLoss: 105.1499\tLL: -81.8193\tKL: 23.3306\tLL/KL: -3.5069\n",
+            "====> Epoch: 3680/4000 (92%)\tLoss: 104.8569\tLL: -81.5281\tKL: 23.3289\tLL/KL: -3.4947\n",
+            "====> Epoch: 3690/4000 (92%)\tLoss: 104.7087\tLL: -81.5053\tKL: 23.2035\tLL/KL: -3.5126\n",
+            "====> Epoch: 3700/4000 (92%)\tLoss: 104.0246\tLL: -80.8311\tKL: 23.1936\tLL/KL: -3.4851\n",
+            "====> Epoch: 3710/4000 (93%)\tLoss: 104.4147\tLL: -81.3060\tKL: 23.1087\tLL/KL: -3.5184\n",
+            "====> Epoch: 3720/4000 (93%)\tLoss: 104.1592\tLL: -80.9390\tKL: 23.2202\tLL/KL: -3.4857\n",
+            "====> Epoch: 3730/4000 (93%)\tLoss: 104.6355\tLL: -81.7948\tKL: 22.8408\tLL/KL: -3.5811\n",
+            "====> Epoch: 3740/4000 (94%)\tLoss: 102.8473\tLL: -79.8862\tKL: 22.9611\tLL/KL: -3.4792\n",
+            "====> Epoch: 3750/4000 (94%)\tLoss: 103.1497\tLL: -80.1671\tKL: 22.9825\tLL/KL: -3.4882\n",
+            "====> Epoch: 3760/4000 (94%)\tLoss: 103.1550\tLL: -80.2264\tKL: 22.9286\tLL/KL: -3.4990\n",
+            "====> Epoch: 3770/4000 (94%)\tLoss: 102.8670\tLL: -80.0533\tKL: 22.8137\tLL/KL: -3.5090\n",
+            "====> Epoch: 3780/4000 (94%)\tLoss: 102.7208\tLL: -79.9663\tKL: 22.7545\tLL/KL: -3.5143\n",
+            "====> Epoch: 3790/4000 (95%)\tLoss: 100.7119\tLL: -78.1148\tKL: 22.5971\tLL/KL: -3.4569\n",
+            "====> Epoch: 3800/4000 (95%)\tLoss: 101.4521\tLL: -78.8268\tKL: 22.6253\tLL/KL: -3.4840\n",
+            "====> Epoch: 3810/4000 (95%)\tLoss: 101.3162\tLL: -78.7015\tKL: 22.6147\tLL/KL: -3.4801\n",
+            "====> Epoch: 3820/4000 (96%)\tLoss: 100.8753\tLL: -78.2744\tKL: 22.6008\tLL/KL: -3.4633\n",
+            "====> Epoch: 3830/4000 (96%)\tLoss: 99.7866\tLL: -77.2873\tKL: 22.4993\tLL/KL: -3.4351\n",
+            "====> Epoch: 3840/4000 (96%)\tLoss: 99.9234\tLL: -77.5922\tKL: 22.3312\tLL/KL: -3.4746\n",
+            "====> Epoch: 3850/4000 (96%)\tLoss: 99.8955\tLL: -77.5364\tKL: 22.3591\tLL/KL: -3.4678\n",
+            "====> Epoch: 3860/4000 (96%)\tLoss: 99.6857\tLL: -77.3915\tKL: 22.2942\tLL/KL: -3.4714\n",
+            "====> Epoch: 3870/4000 (97%)\tLoss: 98.8920\tLL: -76.6695\tKL: 22.2224\tLL/KL: -3.4501\n",
+            "====> Epoch: 3880/4000 (97%)\tLoss: 99.0695\tLL: -76.9643\tKL: 22.1052\tLL/KL: -3.4817\n",
+            "====> Epoch: 3890/4000 (97%)\tLoss: 99.6109\tLL: -77.5066\tKL: 22.1043\tLL/KL: -3.5064\n",
+            "====> Epoch: 3900/4000 (98%)\tLoss: 98.8328\tLL: -76.8672\tKL: 21.9657\tLL/KL: -3.4994\n",
+            "====> Epoch: 3910/4000 (98%)\tLoss: 98.2027\tLL: -76.2475\tKL: 21.9552\tLL/KL: -3.4729\n",
+            "====> Epoch: 3920/4000 (98%)\tLoss: 97.8600\tLL: -75.8453\tKL: 22.0147\tLL/KL: -3.4452\n",
+            "====> Epoch: 3930/4000 (98%)\tLoss: 98.1271\tLL: -76.2246\tKL: 21.9025\tLL/KL: -3.4802\n",
+            "====> Epoch: 3940/4000 (98%)\tLoss: 96.9685\tLL: -75.1975\tKL: 21.7710\tLL/KL: -3.4540\n",
+            "====> Epoch: 3950/4000 (99%)\tLoss: 98.0648\tLL: -76.3929\tKL: 21.6720\tLL/KL: -3.5250\n",
+            "====> Epoch: 3960/4000 (99%)\tLoss: 96.0229\tLL: -74.2633\tKL: 21.7596\tLL/KL: -3.4129\n",
+            "====> Epoch: 3970/4000 (99%)\tLoss: 96.9208\tLL: -75.2309\tKL: 21.6899\tLL/KL: -3.4685\n",
+            "====> Epoch: 3980/4000 (100%)\tLoss: 96.1310\tLL: -74.5489\tKL: 21.5821\tLL/KL: -3.4542\n",
+            "====> Epoch: 3990/4000 (100%)\tLoss: 96.5808\tLL: -75.0211\tKL: 21.5597\tLL/KL: -3.4797\n",
+            "End fitting: 2020-12-14 16:14:27.904408\n",
+            "\tElapsed: 0:00:26.964339\n",
+            "\tDeleting model.pt.running\n",
+            "\tDeleted: 2020-12-14 16:14:27.904915\n",
+            "\t\tElapsed: 0:00:26.965312\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Eri50IsIbE6s",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "outputId": "077a46e1-8ed1-498d-83d3-6b662f1ba2e0"
+      },
+      "source": [
+        "## Plotting model convergence\n",
+        "plot_loss(model)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xKBJt6pjx0L9"
+      },
+      "source": [
+        "The plot above indicates that the model converged smoothly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3dp0VGV2bE6t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "outputId": "407086b1-241b-4b63-ffd3-d049b591b3ac"
+      },
+      "source": [
+        "# We can plot the original data, dimension x dimension\n",
+        "lsplom(data, title = 'Original data')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 50 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qjBw8yhjbE6u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "outputId": "d88a7b07-2066-4f36-950b-a1587e6cb6b8"
+      },
+      "source": [
+        "# We can estimate the reconstructed data (decoding from the latent space)\n",
+        "x_hat = model.reconstruct(data)\n",
+        "\n",
+        "# Plotting the reconstructed data, dimension x dimension\n",
+        "lsplom(x_hat, title = 'Reconstructed data')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 50 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gFoHUBq4bE6v",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "outputId": "f3b51141-8676-4c5e-97fc-5bafa2db121b"
+      },
+      "source": [
+        "## Plotting the weights of the encoder\n",
+        "plot_weights(model, side = 'encoder')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "n7dqcz9MbE6w",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "outputId": "7031342f-3afd-4a05-b60b-37c2d3a2fb90"
+      },
+      "source": [
+        "## Plotting the weights of the decoder\n",
+        "plot_weights(model, side = 'decoder')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RDEiUUuRX1LP1SDlbj5Sz5XA5P5sWgq+kFgBHgJVBwQGdTsynOTlaMDQazYDA4zPILKhia7N1H45WNQAQYrcyOSWCUIeNYdEhXUrM1x/pCe/idNCCodFo+i3VLg9//+IQXx4oZWdeBS6PGX9Iighi5vAopg6J4JwhEaTGhWG1CLt37ybU0fH1HTSdQwuGRqPpl3yWXcz9b+2isMrFxKRBfGvGEKYMiWDKkAhiwxx9bd5ZiRYMjUbTr6is8/Dov7N4Y2seIwc7efOHszgnJaKvzdKgBUOj0fQjPsos4oGVGZTVurnz/JHctWAkgbauDwfVdA9nTlRIo9EMWEprGrjrX9v53itbiXYG8s6PZvPzS8ZosThNSktLOf/883E6ndx5553ddl3tYWg0mj5DKcV7Owt5cFUm1S4P/33RaH5w3oh+uXjQQMLhcPDoo4+SkZFBRkZGt11X/1U0Gk2fUFzl4vuvbOWuf20nOSKI9+6ay10LRg14sVi6dCkTJ05k0qRJ3HTTTQAcPnyYCy64gIkTJ7JgwQJycnIAWLJkCT/84Q+ZOXMmw4cP57PPPuPWW29l7NixLFmypOmaTqeTu+++m7S0NBYsWHDKtbhDQkKYM2cODkf3Dg4YMB6GiCQDS4FYzGzDf1VK/W/fWqXRaDqLUoo3t+XzyLuZNHgN7r88lVtnD8PW3ULxwS+gaFf3XjNuAlz2xEmLMzMz+c1vftMivTnQ6+nNe4qBJOVe4L+VUuOAmcCPRGRcH9uk0Wg6QX5FPUte+oqfv/41Y+JC+eAnc/nevBHdLxZ9xCeffKLTm/cHlFKFQKF/u1pEdgOJQFafGqbRaE6JYShe25zD4+/vRgEPX5XGTTOHdPs61i1oxxPoT/REevOeYkDKuogMBSYDm/rWEo1GcyqOlNbyrRc28cuVGaSnDOLDn87j5llDe1Ys+oj+kt68p+g1D0NELEopo43jojqRMldEnMCbwE+VUlWtyr4HfA8gJSWlixZrNJqu4DMU//jyME99mI3NIjxx7QQWT0s+oxMA9pf05gBDhw6lqqoKt9vNypUr+eijjxg3rmu9+L2S3rxRLPyewRxgMLAe2KmUqj+ZmLRxnQDgPeBDpdQf2qur05trNH3H/uIa7n1zJ1uPlHP+mBgeu3YC8eFBPX7fttJ1nwmcNenN/R6EISITgE+BDGAikA/kisiNSqkKEbEqpXztXQf4O7D7VGKh0Wj6Bq/P4G/rDvHMx3sJCrDyzOJJXJOeeEZ7FWcTPS4YSiklIiHA/wOWAT/DjJ0sAn4EbBaRGUqp8lN4GrOBm4BdIrLDf+x+pdT7PfwRNBpNB9hTVMX/vL6TXfmVXJoWxyPXpDE4VCcJ7A7OtvTmIUA0sEYp5QYQkdeAfcCfgE9EZH7rmERzlFJfAPoxRaPpZ7i9Bs9/tp/nPt1PmCOA5791DpdPiO9rszQ9QG+NkqoEajHjFwAopbzARuAXgAHcL9pv1WgGFDvzKrjq2S/448f7+MaEeFb/bL4WizOY3hIML7AOmCMiTePJlMka4EtMMenzYb4FFfX8+p0MKus8fW2KRtNvcXl8PPHBHq55bj3ldW5e+M5U/njDZCJD7H1tmqYH6ZUG2h/MfgwIBx4TkSki0vze64AoYFBv2NMeGw+W8s9NOSz4w2e8syOf3hhFptEMJLYeKePyP63jz58f4PopyXx093wuHBfb12ZpeoFeEQz/CKhiYAEwFPgjcJ2IWPzCMQc4Brh6w572uPacJFbdOZvEiGB+smwH33lxM0dKa/vaLI2mz6lze3n43Uyu+/MGGjwGr9w2nSevm0h4kF4Stb+xevVqpkyZwoQJE5gyZQqffPJJt1y3V4LeSimfiNiUUvkiMh14DfgV8Axmao9pwHlKqX7RMqclhPPWD2fxz01H+N1/srn4mbX8eMEobp87HLutz3vNNJpe58sDJfzizV3klNXxnXOHcM+lqTgDB0xmobOO6Oho3n33XRISEsjIyOCSSy4hPz+/y9fttdZPKeX1i0YhcDXwE+AvwOvANKXUjnYv0MtYLcJ3zh3Kxz+bzwWpg3nqw2yu+L91bDlc1temaTS9RrXLwwNv7+LGv23CIrD8ezN55OrxWizaoT+kN588eTIJCQmAOfu8vr6ehoaGLn+2bvurN5vN7QRQStW0Krf6RUP8w2c/8b/6NXHhDv7ft6fwcdZRHlxluuP/NT2FX1yaSniwdsU1Zy6fZRdz/1u7KKpycfvcYfzsojEE2QfOCnhPbn6SPWV7uvWaqZGp3Dv93pOW98f05m+++SbnnHNOi8SGp0uXPAwRmS0iUQB+sbgKeAP4QkR+KiJDGus2m8W9SERGdOW+fcGF42L56O553D53GCu25OqguOaMpaLOzX+v+JolL31FcKCNN344iwe+MW5AiUVf0d/Sm2dmZnLvvffyl7/8pVs+32l5GP75ElMwRzc9LCJPAucArwIvA0eAR4HxIvK/Sqld/vOuAv4FPCcid/vnYgwYQgJtPPCNcVydnsgDb+/iJ8t28MbWPH5zzXiGRIX0tXkaTZf5MLOIX67MoKzWzZ3nj+SuBSMH7Lra7XkC/YmeSm+el5fHwoULWbp0KSNGdM8zeqc9jMbsskqpLcCPgQf979OAh5RSP1ZKfR+4EbgIuNufRwql1Crgt8CzA00smjM+MZy37pjNw1elsT2ngoufWctzn+7H7T1l/kSNpl9SWtPAna9t4/uvbCXaGcg7P5rNzy8ZM2DFoq/oL+nNKyoq+MY3vsETTzzB7NmzO/sxTkqnPIxmcYp4YBhmmvFS4J9AAdCUFFAp9a7fE3kO8IrI35RSXymlftVt1vchVotw86yhXJIWx8PvZvLUh9m8syOfxxZOYOrQyL42T6PpEEop3t1ZyEOrMql2efjvi0bzg/NGDPh1tfuK/pLe/Nlnn2X//v088sgjPPLIIwB89NFHDB48uEufr8PpzZuJxTjMrLE1gFsp9Q0RuR1zxNNSzGVUS5uddwWwHHgR+LlSquuh+g7Q2+nNG4Pi+RX1OiiuGRAUV7l4YGUGq7OOMikpnKeun8To2NC+NqtL6PTmnaNH0ps3S1GeBnwBPA88C5QAKKX+5l+r4llgv4g8p5Qq95e9JyLXAft7Syz6ggvHxXLuiCj++PFeXlx/mNVZRfzqinFcNSlBp3bW9CuUUryxNY9H38uiwWtw/+Wp3Dp7WL9cV/tg5UH+ffDf1LjNxlKhUEqhOP6g23z/oqCLKKgpaKrbghN2239YbrdcdfF8aHfATOtzFYpDlYfavm4bnyvSEUmEI6Ld+58OHRIMf4rySMwU5a8qpR5oLPPPrfAqpZ4XERvmLG5aicYH3W55P+SMC4orBe4aqK+A+nJwVbS97amD4GgIjYOwBAiNh7B48z2g5xfN0XSc/Ip67ntrF2v3HmPa0Aie/OZEhsc4+9qsFngMD5/mfMry7OVsLtqMVawEBwQDIAgigvgTVzfuN27PHTGXand1m9eV1smuT9ht/8Guqfwk1do7vyv3FoTM/Mw264pIm/b01ENqZ2IYsUAi8GDzdSv8cyss/u0/iYiBKRpOEXlCKVXR7Vb3cxqD4q9uPMJTH/aDmeKe+lM3+m1tuyrAaGdsgsUGQRGmKNSWmMLRmqAICE3wi0m8ud36PTgKLP3v6fZMwjAUr23O4fH3d6OAh69K46aZQ/rVutpFtUW8ue9N3tz7JsfqjxEfEs9PzvkJ14y8huig6A5dY/fu3YyJHNPDlp69dEYwJgNDgM/93VPNRcMAEJFg4C3MVOZ/AJ7uZnsHDN0eFPd5jjfi9eUd364vB197PYECjnCzYQ8aBI5BMCjl+Hbj8aAI//4glD0MAwfKZ8FwuVBuNxanE6tDsDSUQlUBVBdBdQFUFUJ1oXnsaAbUFHOCD20JMAWlyTNpJibNvRZ7cOe/Nw1HSmu5982dbDxYxuyRUTxx7USSI/vHd2kog02Fm1iRvYJPcz/FUAazE2fz4JgHmZM4B6tFj9LqT3RGMA5jpim/FnjjJCvj3Qb8l1JqloisbOyS6i5E5FLgfwEr8IJS6onuvH5P0Hym+MPv7OS2P6/m25PC+eH0SJyqup1Gv7LlcfcpAl72ULMxd4SDfRBGyHCMMCeGhKAkBEMCMZQDpQIwVACG14oyLBhuhdHgQtXVY5S6MOrrUa56jLpqDNcxjPo6VL3/eH29+e52n9QMCQrCOmiQ+QoP97+PwTpoBtaoQViHOrE6wGpzY7XUYZVqrL4ypK7YFJijWbB/Tduf1xHehpi0EpiQGO2t+PEZipe/PMxTH+4hwGLhiWsnsHhacr+IqVU2VPLO/nd4fe/rHK46zKDAQXwn7TtcP/p6kkOT+9q8nkMpzABI4zut9tt770RduxMCu7+rsTOCcQSoAL4jIl8ppY7A8XkZ/jrJwFciYvXX7Tb813wOc25Hnv8+q5RSWd15n1OiFDRUd65rp76cC10VLGiogkCFyhSMrwWPTzC8guF/VyoQw+LEsISgcGDgwFApKDUCw2fDMCwor2B4wfAYKLcPo8Fjvlz1/oa9EnydzHdlsWAJCkKCg7A4gsztIAeWoGACQmOxBAch/uOWIAcS5K/nPy4BARg11fgqKvBVVJrvleZ7Q3a2uV1ZCb6TLtmOJTTULzDDsIanYw0NwRpswxoIVrsPq82FVeqw1ldhrSnB6tuNxX0UodVzi8UGzrj2u8BC43rkx9Sf2F9cwz1vfM22nAouSB3MbxeOJz687+NJmSWZLM9ezgeHPsDlczEpZhKPzXmMi4deTKC1jdQVhgHeevC5wecFw2N2k/qav3vA8JnbXge4qmi7IaXt4201zM33T1rvNBr+3qKH/sc7LBj+TLN3YGaafVREnlRKZfoD4k7gl8ANwMXN0oB0J9MxR1odBBCRZZhJDLtVMFTeDoyv38aoKkVVl2NUV6BqqjBqqzDqajHqalFeheG1YPjE34DL8W2fFUMFoAybue2zoLwWDE8EhicM5e7IV1Pnf5mI3W420kFBWByO4w37oCCs/obd4nC03bD7X+Jv4C2OVseDzAa/p586lWFg1Na2FJVmwtL07t925+biq6zEqKw8yRUFLAlYQ51YncFYQ+xYgqzYAhXWAA8WqwurZGBV67FaarHaDfMVaGCxKcQR1kYXWHzLY87BMMC6RLw+g7+uO8gfP95HsN3KHxenc3V6L4zU87pNr9BTB+66FtsuVzn/ObaN5SVbyHAVEyRWrnAkstg2mNR6K2x+Db74G3jqUA01qPpa08utr0M1uFA+adbuyvG2G8CQprYaBca3/4mvqO3PampBq7IWbbi0fBf/tVsfP+m2AJbjl2zrfi2Q45/jpDY1Gd6iXLW901R3W8Yu7rjnMv8hxUMPPcTChQvbsaVjdHgeBphzMYDbMYfPHsBcYrUBiMec6X25Ump7l61q+97XAZcqpb7r378JmKGUurOt+vHx8eqWW25pcWzOnDlcfvnlNDQ08PDDD59wzoIFC5iS8TH7/7KKVxNOXGZyZkUlk2pqqLDZWB7XuGCMgEXAYuF8n8F4u51jDgcrDB9YrYjFAhYLYrHwjaFDGR8XT57HzWtZWVQ1+Cip82IgRIY5uGPhlaSlprK3qJB/vfde03n4f+zf/e53GT58ODt27GDFihUn2HfHHXeQlJTE5s2bmxKbNefuu+8mJiaGdevW8cEHJw5c+8UvfkFYWBhr1qxhzZo1J5Q/+OCDBAYG8v7777eZy+axxx4D4O233+arr75qUWa323nooYcAWLZsGTt37mxRHhoayn333QfAP/7xD7Kzs80CpVA+H5EhIfzoqqvwVVbyj//8hyPFxSivF7xelNdLjFIs9hn4KipYLnCsVQMZ39DAVcdKzPvHx1FpD0AsmC8xGK5quC4gH6vd4HnvMGrEhgiIzYYE2JmYFMR/zRkGYfE89PZe3MoKNjtYA8FiZdq0aU0/yPvvv/+E76Yj/3sLFiygqqqKJ544saf1sssuY+7cuRw7doxnnnnmhPJrrrmG0ORU7vnHp9TueJ/IEDvDokOaJuAtWrSI9IkTObg3kxde+of5RK4M/7uPmy6ewtiEUHYfzOGVjzPA8KEMr+kVGj6WjBaG2F18XeTmrVyr+eRvGAi0GPUAACAASURBVCjDAKVYEniYOBrY5h7Eh95YlAIfQj1CA8IUKSbB46bSG8xOawRCMxFA+E5xIcFug69CnGwNCzvh892SX4BdKTaEh7Mz9MQn5+/nmam7q//f88T5s7Q2IkC0x1xBs8pqo6FVoN8CRPnLK2023K3+d6xApL+8wmbD06rLM0ApInwGCJRbrXjwj2SS4+WNK8OVidD6kdEuwiARQChRRmufGYfFQpjVigDFXu8JehJktRJmM5/9j/q7i11uNwEOBzabjdraWubOnUteXh7FxcUtzs3JyaG+vr7F/97jjz/etXkYjfjjFn8Rke3A/wCTMCfwfQH8TCm1vzPX625E5HvA9wCioqJO6xpBl95MtD0F+9atZl+4xYJYrWCxEHPuLIbPmE5ZfT1BS5ceb9D9xF9zDSOmTycwLw/H88+fcO2IRYuISU+n+uBBAkpLiQJCvQaHS2s5VOvmge21/DY9muDBPiRAT/oDQASx2bA4nQRPngxA4L59BLT60ToTEhh2p/nsEP7ss9Tm5aF8PpRfUEIjIoifNAlfRQX2DRuw1daivGa54fPicVkpz6tHNbhxxdtxt5iT4KbkQCV7VmVitRvUDU7CaxVEFGIBLELtwbVUrPsj1shofAUWJCAQsQcigUFgCzQb5o6gVLOG3ADDi/J58R3ejkfy8BQfxSjJBZ8PZZiNufL5OPK3r4mpr+QOn2K1JxRrBagyA7dSYChKv36XfG8VR40gXAEJND3d+lufvA3rsbpc5NiCqIuIOeEpN2/rMYwGN0eDg2iIbD5ow/yeSo6GIh4HlSHBeCKsKBEMUVgFQiwW0n1RJASHsSPEjvh8SONvx2JBRAidOZvwUCfOklICiorAIk3liJDw07txOAIZtGsXgfv2Nf1vNL6nPPYYYrGQ5TOwBLXsehMRAmNiALBVVeFplebbarXiiIkBEWrLy/G2LrfZcMSaD4i2khJ8rcvtdhz+GdTW4mKWv/Yaf/nLXxARxo4dy5///GeKamu59dZbKSoqIjIykt///vckJiZy991343Q62b17N8XFxTz11FOsWLGCbdu2kZ6ezjPPPIM1OJiolBRuv/123n//fWJiYnjuueea2jhrSAj2CHPOheTlARBkP75Ursvl6jYPs1MeRosTzXTlPdH1dLL7nYuZq+oS//59AEqpx9uq39szvbvKx1lH+fU7GRRUurhxRgr3XtI/Z4rXeerIq8kjvzqf/BrzVd5QzuSYycxLmke880TPbCBhuFzHu8jKT+wq85Udw1d6DF9Zidm1Vl2Dr8YFxsl/RxabgTVQYXVYsIYEYgkJMsXM40W5vRheH8pj+Ls6FcoHymd2c6K68EMXEJsFS4AVCbAi9gAsATYk0I7Y7Wb3ZGCg2V3pCEKCgpGgYCzBTiTIaXZZ2gMRRyCWwECzbqB/2+FA7IFYHOaxcqOGd/M+5I0j75LvPkpsSBzXjb6Ob476JjHBMaf/GTpJ85nLRY89RsPu7k1vHjg2lbg2PMhGMjMzWbhwYYv05pGRkVx55ZVcd911TenNV61axcqVK1myZAkul4t//etfrFq1iptuuon169eTlpbGtGnT+Pvf/056ejoiwquvvsq3vvUtHnnkEYqLi0+Z3nzTpk3ceuutHDlyhFdeeaXNLqkemel9Epo8p1aB757iK2CUiAwD8jHjJTf28D17jcaZ4s+s3suL6w/xUeZRfn3lOK6cGN+ro1o8Pg+FtYWmKNTktxCG/Jp8ylzNAuoKYj0pxDcMYXXQ0/zG9htGR4xmXtI85ifNZ0L0hAE3LNLicGCJiyMgLq7D5yilMGrr/KJSga+8HN/RPHzFufiOFWKUFeMrK8NXVYWvug5PUQ1iFSTAiiUgEGuQ2Yhb7HazMQ8MNBvwQIfZgDuCkeAQJCgES3AoEhyK1+Fk5d5q3sgqJyQ0hB9dksascYlNDbgEBvZ4bEopxZajW1i2Zxmf5HyCV3mZlTCL/xlzP/OT5mOznH2LLLWX3vytt94CzPTm99xzT9M5baU3B5rSm6enp5+Q3vzaa689pS0zZswgMzOT3bt3c/PNN3PZZZfhcDi69PlO+y/aXCB6QSwaJwjeCXyI2a34olIq8xSnDShCAm388opxXDM5kfvf3sWP/7XdnCl+9XhSorpn3LyhDI7VHWsSgNbewtG6oxjNRkzbxEa8M55EZyLnJ59Poj2FiJJEJC+UqgM+6srNvt3Z9uuRsZVsC/2YlzJe4oVdLzAocBBzE+cyL2kesxJnEWY/sW/6TEBEsDpDsDpDICmxx++35XAZ97y5k4PHrCy+eAL3f2Nsr66rXe2uZtWBVazIXsHByoOE2cO4ceyNLBqziCFhQ059gV6iPU+gP9FT6c0bGTt2LE6nk4yMDKZObdNx6DAD6hFAKfU+8H5f29HTjE8M5+07ZvPKhsM89WE2Fz3zOT+50JwpfqosokopqtxVJwhB435BTQFuo+U8isFBg0kMTWRK7BQSnYkkhSaZ784koh0xlOXWkZNVSu76MooOVXHUUAQ4PCSNiSDlsigGDQ4ia30h+7fCpF3f5Mr02/COL2aT7zPW5q/l3YPvYhUr58Sew/yk+cxNmsuwsGH9Yj7AQKLO7eWpD7N5+cvDJIQH8cpt05k7qve6e/aU7WHZnmW8f+h96r31jI8az6OzH+XSoZfisHXtyfVM4YILLmDhwoX87Gc/IyoqqqlLqjG9+U033dSl9OY33HBDh9KbHzp0iOTkZGw2G0eOHGHPnj0MHTq0C5/MZEAJxtmE1SIsmT2MS8bH8fCqLH73n2ze2V7AY9eOZ1xiEAU1BaYQVOe16DLKq86jxtNy0luYPYxEZyKjIkZxXvJ5JDmTSAxNJNGZSIIz4YTx77UVDeRklbIzq5Tc3ftoqPWCwOCUUM65JIWUcVHEDg/D2ky8klIjOXfhCHZ+kkvmFwV4tgUwfdT13H7h3VTG5rO2YC2f533O01ue5uktT5Mcmsz8pPnMS5rH1NipBFj7X7ymP/Hl/hLufWsnuWX13HzuEO65NJWQXlhXu8HXwEeHP2JZ9jJ2HtuJw+rgsmGXsXjMYtKi03r8/gON/pLe/IsvvuCJJ54gICAAi8XC888/39RN1hVOO+jd3xloQe/meA0vRbVFLURga/4BdhQdxGspwWJrKQgOq4MEZwKJzsQmD6G5KITa209Z7fX4KNxfSU5mKTlZZZQV1AIQHGYnZVwkyWmRJKdGEhRqb/c6jTTUe8n6ooCdn+RSU95ARFwwkxYkM2ZmHMcailmbZ4rH5qLNNPgaCLYFMythFvOS5jE3aW6H8wadDVS7PDz+wR5e25TD0KhgnvzmRGYMP70RgJ0htzqX17Nf5+39b1PRUMHQsKEsGrOIq0ZcRXhgeI/f/3TR6c07R2eD3low+gClFKWu0hO8g/xqs+uoqLYIX7MBaFaxEhcSR1xwAmWVTrLzAgi2DOa2mVNYnD6J6KDoTnXvKKWoOFpHTmYZOVllFOwtx+sxsNiE+BGDSBkXSUpaJFGJzi51G/l8Bge2FrN9dQ4luTUEhQYw4bwkxs9PJMhpp95bz+bCzXye9zmf531OcZ05Rnx81HjmJZuB87GRY8/arqtPs4u5/61dHK1y8d25w7n7wtE9uq62z/CxLn8dy7OXsz5/PRaxcH7y+SxOXcyMuBkD4u+gBaNzaMHw09eCUe2ubiECzb2FgpoCXD5Xi/pRjqgmjyDJmdQUR0h0JhIXEtdixElGfiX3v72LnXmVzBsd06GgeEOdh7w95eRklZGTVUpNmTmWfFBsMMnjIkkZF0ni6AgCAru/QVJKkb+3gh2rcziSUYotwMKYc+NJX5DMoNjgpjrZ5dlN3seuY7tQKGKCYpiXNI95SfOYGT+zKc31mUxFnZtH39vNm9vyGDXYye+um8jklO5f26CRkvoS3t73Nm/sfYOC2gJigmKahsTGhsSe+gL9iDNVMHoKLRh+elowGnwNTXGExuByXk1ek9dQ5a5qUd8Z4GzRZdT8PcGZQJCtc3l+fIZqCop7DXVCUNwwFMeOVJvB6iwzWK0MRYDDagar06JIGRdJWHTv5hcqK6hlx5ocsjcVYfgUwyZGk35RCvEjwls8wZa5yvgi/ws+z/2cLwu+pMZTQ4AlgOlx05sEJCk0qVdt7w0+zCzilyszKKt1c8d5I7jzgpE9sq62UoptxdtYnr2c1UdW4zW8zIibweLUxZyXfB4Blr6NKSlDUbCvgj2biji04xgAgSEBOEICcITYCAwOwOEMwBFsw+EMMPdDAqhWRaSmpmKxCGKRAeEV9SVaMPx0VTB8ho/iuuIW3kGTt1CdT3F9yyn2AZaAEwQh0ZlIYqjpMYTZw3rkn7ewsp6HVmXyYeZRJkY5+WFqApajDeTuLmsRrDa9iBOD1X1FbWUDGZ/ns+vzPBpqvcQOCyP9whSGp0djaWWfx+dhW/E21uatZW3eWg5XHQZgRPiIpq6rSTGTBvS4/9KaBh5clcl7OwsZGx/GU9dNZHxi98cKaj21vHfgPZZlL2N/xX5CA0K5euTVXD/meoaHD+/2+3WW8qJasjcVkb2piJqyBgICrQxPj8EeZMNV66Gh1oPL/2qo89JQ13LY6bRvRzA0eUTTvlikSTws1mbbzY612LdwVgmNFgw/pysYa46s4fdbf09hbSHeZosHCUJsSOyJgWX/fkxwDBbp3Ya4ebA6a3sx7lKzm8lntzBqYjQjJkV3KljdF3gafOzZUMiONblUHasnLNrBxAuSGTsrHrujbQE4UnWkqetqa9FWvMpLmD2M2YmzmZ80nzmJc/p1YLY5Sine3VnIQ6syqXZ5+PEFo/jBeSNOOXy6s+wt38uK7BW8e+Bd6rx1jI0cyw2pN3Dp0Ev7vJvPVeNh35ajZG8q4uihKkQgeWwkY2bGMSw9hoB24jaGz6Ch3ktDrRdXrYdj1bmMHDHazKpiKJRPme9Gs3efand5VDguNCcKiiCthKfxmEjPrXTXU2jB8HO6grHt6DZe2/Nai1FGSc4k4kPi+3zo56mC1XFjBvFJRRUv7Mwj0hnYJzPFTxfDUBz+uoTtq3MoOlhJYLCNtLmJTDw/iZBBbaS99lPtrmZDwQbW5q1lXf46ylxlWMRCekx604zzEYNG9Mvv4GiVi1+uzGB11lEmJQ/iqesmMjq2/RFtncHtc/PxkY9Znr2cbcXbsFvsXDrsUhaPWcyE6Al9+p34vAZHMkrJ3ljE4V0lGD5FZEIIqTPjGT09tt2/eXt0NIah1HERMXytBMUw07M032+sc6r2si1BOZV305dCowXDT18HvbuLkwWrwwcHNcUhWgerM/Irue+tXezK73hQvD9RdLCSHatzOLjjGGIRRk+LJf2iFKIS28/vbyiDjJKMpq6r3WW7AUh0JjI3cS7zk+czLW5a2+su9CJKKd7Ymsej72XR4DX4+cVjuHXOMKzdtFxqQU0Br+99nbf2vUWZq4zk0GQWjV7ENSOvYZBj0Kkv0EMopSg+Uk32xiL2fXUUV62HoNAARk+PY8zMOKKTujYqD3o+6K2Uat9zMRSGQeeFpg1Ppk3xabbfEaHJyclh3LhxPPTQQ/z85z8/oVwLhp+BKhjdFaz2GYqlGw7z9EmC4gOBymN1fL0mj91fFuB1G6SMiyT9whSSxkZ0qGE5WnuUdfnr+DzvczYWbMTlcxFkC2Jm/MymwPng4MG98EmgtsHL7sIqMvIrWb37KOv3lzJ9aCRPXjeRYdEhXb6+oQzW569nefZy1uatRUSYnzSfxWMWc27Cub3eXdqc6jIXezcXkb2xiPKiOqw2C8PSoxkzI46UcZEnxKy6Qn8dJXWCB2O0FJ6TeTftts/CiQLj91zsDhv2IBvXXXcdIsKMGTO0YLTHQBKMxpnVOVll3R6sbh4UHxMbymPXTmDKkJ4botkTuGo9ZKzNZ+enedRXuYlKdJJ+UTKjpsZitXXsO3F5XXxV9FWT91FQWwDA2MixTV1XadFp3dKwltW6ySyoJLPAFIisgioOldY2pRKPdgZy1wUjuWnmECxd9CrKXeW8vf9tVmSvIL8mnyhHFNeOupbrR1/fp5mD3S4vB7cfY8/GIvL3loOC+JHhpM6MZ8Q5MQT2UCbm/iAYS5cu5emnn0ZEmDhxIq+88gqHDx/m1ltvpaSkpGmmd0pKCkuWLCEoKIjt27dTXFzMiy++yNKlS9mwYQMzZszgpRdfwjAU4YPCuPWW2/h4zWpiB8fy0t9fIToyuk3vxjAUIeF2Vn/6AevXryckJASn06kFoz36s2A0BauzysjJLG0xszrZP2muu4PVH2UW8eCqTAorXXxrRgr3XJraqwnrugOfx2DvV0VsX51LeWEtIeF2Jl6QTNrchE41QEop9lfsbxKPHcd2YCiDSEdkk+dxbvy5OO3td4EppSiodJGZb4qD+aqksPL4HJvEQUGkJYSRlhDO+ETzPTYssEtdL0opvj72Ncuzl/PR4Y9wG26mxk5l8ZjFLEhZ0GexNsNQ5O0pI3tTEQe3H8PrNgiLCSJ1Zhyjp8cRHtPzQ7ibN4DrVuylJLd7J7tFJzuZu2j0Scv7S3rz6upqLr74YlavXs3TTz/dbYIxcMchDiBOFaw+d2Fct8ysbo+L0+KYNTKaZ1bv5aX1h/gw8ygPXjmOKwZIUBzAGmBh7KwEUs+NJyerjB2rc9jw9gG2vH+YcbMTmHhBUofmlYgIoyJGMSpiFLdNuI0KVwVfFHzB2ry1rMlZw8r9K7FZbEyNndrkfSQ5kzlUWmuKQpNAVFJe5/FfE0bEOJk+LLJJINISwhgU3H2iX+ep49+H/s3yPcvJLs8mJCCEb47+JotGL2JkxMhuu09nKc2vIXtjEXs3F1Fb6SYw2MaYGXGMmRlP3PCeGU7eX+kv6c0ffvjhpsWZuhMtGD1EQ72XvD1lfpFoGaweOyehR2dWnwxnoI1fXTGOhZMTue+tXdzVmD79mvEkRw6coLiIMCQtiiFpURzLrWbHxzns+iyPnZ/mMmLKYNIvTCF2aMdTqQ9yDOKK4VdwxfAr8BpethRu5519H7Ox6As2Fv6O3331O5Q7Bk91Kt6aVKwNwxkdF87F4+IYnxjGuIRwxsaHEmzvmZ/TgYoDLM9ezrsH3qXGU8PoiNH8auavuGL4FX02JLauys2+r8yhsMdyqrFYhJTxUcyZEcfQiVHYAvp+HZT2PIH+RE+kN9+0aRNvvPEG99xzDxUVFVgsFhwOB3fe2eaK1h1GC0Y3capg9ZRLh/bJzOq2GJ8YzsofzW4Kil/0zOf8ZMFovjt32IAKigPEJIdy0S1pnHvNCHZ+kkfmunz2bykmYdQg0i9KYej4KKSdOEFjMLrRY8gsqGLv0Wo8vonAREKCK4iLP4QE76EkcAO+qHU4A0JJTZzFHP+cjwhH98eEPD4Pa3LXsCJ7BV8VfUWAJYCLh17MDWNuYFLMpD55avd6fBzeWUr2xkKOZJahDEVMSihzFo1i1NRYgsP673yf3qK/pDdft25d0/ZDDz2E0+nssliAFowu0V6w+mRpwPsLVotwy+xhXDo+jodWZfLkf/awcnv+gAyKAzgjHMz65kimXj6UrPUFfP1JLu8/v5NBsWam3NSZcVR7fGYguuB4l9KhkuPB6MgQO2kJYdw2Z7i/WymMoVEhTYHpOk8dGwo3NMU+Pjz8IYIwMWZiU6r20RGju9SYF9UW8cbeN3hz35uU1JeQ6Ezkp+f8lIWjFhLpiDz1BboZpRRFByrZs6mI/VuKcdd7CRkUyOSLkhk9I46ohO7t8hjo9Jf05j3FgAh6i8hTwJWAGzgA3KKUqmjvnJ4IejcPVudmlVKa3ypYPS6S5LH9e2b1yWgMihdVubhx+sAMijeilCK/rJ5N6/Io2HQUS7kHl0WxNcDL9kAv9RYzGD3OLwrjE8JJSwwjLszR4cbeUAa7y3azNtcUj4zSDABig2OZnzS/ac5HR3KEGcpgY8FGlmcv57O8z1BKMTdpLovHLGZ2wuw+Wea28li9maJjYyFVJS5sdgsjJg9mzMw4EsdEdHl0V0/RH0ZJ9QQ6W20nEJGLgU/8y7Q+CaCUure9c7pDMHorDXh/oabB2xQUjwwJHBBBccNQHC6tJcPvMWT5RyuV1ZqrCgowPSSYc+ptOMs8iE0YMS2W6ZcMISKu6/MfGimpL2FdnjnnY0PBBuq8dQRaA5kRP6PJ+4gLablOeGVDJSv3r2RF9gpyqnOIdESycORCrht9XZ8kVmyo87B/azHZm4oo3F8JAkljIhgzM87M53SSVC39CS0YneOMFIzmiMhC4Dql1Lfaq3e6gtEUrM4qIzezjOoyc4hk+OAgUsZFkZLW+8Hq3qb5TPH5o2P6TVDc7TXYV1zdYqTS7sIqat3m2iEBVmF0bKjpNSSao5RS48KaVqYrK6zl6zW5ZG8swuc1GDoxmskXJRM/clC3iqLb52bL0S1mvqvcz8mryQNgdMRo5ifNZ0L0BNbkrOE/h/9Dg6+ByYMns3jMYi4achF2a+96pz6fQW5WGdkbizj0dQk+r0FEXDBj/ENhQyMH1tKrZ6pg9BRng2C8CyxXSr3aXr3TFYzMdfl89s/sPk8D3tc0nynuU6rXg+J17mbB6Hwz7rDvaA1unwFAsN3KuPiw40NYE8MYNTgUewcm8tVVudn1eR4Zn+XjqvUweEgo6RelMGJyTLfOOgbTSz1UdYi1uWayxO3F2/EpH0G2IK4cfiWLxixiTOSYbr1nR2wqyfUPhf2qiPpqDw5nAKOmxZI6M46YlNB+7VW2x+7du0lNTR2w9vcmSin27NkzMAVDRD4G4tooekAp9Y6/zgPAVOBa1YbhIvI94HsAKSkpU44cOdJpO+qr3ZQX1fXbYHVvU1BhzhT/KOsoqXGh/HZh9wfFK+rcTbOiG4PRB5sFoyOCA5pEoXF+w9CokC7nXvK4fWRvLGLHxzlUFtcTGulg0oJkxs4+eabcrlLlriKzJJMJ0RNOOTGwu6mtaCDbn6KjrKAWi00YNiGaMTPjSEmL6vCs+f7MoUOHCA0NJSoqSotGOyilKC0tpbq6mmHDhrUoGxCCcSpEZAnwfWCBUqruVPX780zvgciHmUU81MWguFKKoioXmflVTaOVsgqqyK+ob6qTEO5gnF8UGruV4sM7How+HQxDcXhnCTs+zqFwfyX2IBvj5yUw4bxknBF9m6iwq3gafBzccYzsTUXk7S5DKYgbHsaYmfGMnDIYR8jAHNhwMjweD3l5ebhcrlNXPstxOBwkJSURENDyf2DAC4aIXAr8AZivlDrWkXO0YHQ/NQ1e/vDRXl7+8hBRzkB+fcXJg+KNwejmKTNaBKMFhkWFkOYXhcaupciQvh1hVnSokh2rczm4vRixCKOmxZJ+YQrRSQNn+KgyFPn7KsjeWMiBbcfwNPgIjXKYs69nxDUti6vRtMWZIBj7gUCg1H9oo1Kq3YHHWjB6jl15ldz39k4y8quYPzqGB68ch8tjNIlC42il5sHoUYNbBqPHxh8PRvdHqkrq+XpNLllfFuJt8JE8NoL0i1JIHhvZb7s6yotqyd7oX62uvIEAh5WRUwaTOjOO+BGD2p3AqNE0MuAF43TQgtGzeH0GSzcc4fcfZTcJA0BQgLVpfkOj1zAq1tkj61L3Bq5aD5nrzEy5dZVuohJDSL8whVHTOp4pt0ft869Wt2djEcWH/avVjYsidWYcwyZFY2tntTqNpi20YGh6jIKKelbuyPdnZQ1nWHTXg9H9EZ/HYN+Wo2xfnUNZQS3B4XYmnp9E2tzEXo8D+DzmanV7NhZyJKMUw6eISnKSOjOOUdNiCQkf2HEXTd+iBUOj6SaUUuRmlbHj4xxyd5djC7QyblY8kxYk9+jQa6UURw9XmavVbTlKQ62X4DA7o/1xiYEUY9H0b3R6c42mmxARc25OWhQledXs+DiXjM/z2fVZHsMnDyb9omTihoV32/2qSuvZu8nMCltxtA5rgIXh6TGMmRlHcmpEt88b0WjaQ3sYGk0XqSlvYNdnuWSsLcBd7yV+ZDjpF6YwbGL0aQWa3fVeDmwvJntjEfl7zZRpCaMGMWZmHCPPGYw9SD/naXoO3SWl0fQCbpeX3esL+XpNLtVlLsIHB5F+YQpjZsYRcIrgs2Eo8naXsWdjEYd2HMPrMQgffHy1urMt04Cm79CCodH0IobP4MD2Y+xYnUPxkWocIQGMPy+RCfOTTlgzojS/hj3+1erq/KvVjZoWy5iZccQOPbtWq9P0D7RgaDR9gFKKwv0VbF+dy+GdJVhtFsbMjGPsrHiKDlaSvamIktwaLBZhyIQoUmfGM2R8FNYAHZfQ9B066K3R9AEiQsKoCBJGRVBeZGbK3bOxiKwvCgAYPCSUeTeMZuTUwQQ5B94aKpqzDy0YGk0vEBEXwnnfSmXGVcM5vKuU2GFhRMZ333ocGk1voAVDo+lFgkLtjJ0V39dmaDSnhe4s1Wg0Gk2H0IKh0Wg0mg5xxo6SEpFjQOdXUDKJBkq60ZzuQtvVefqrbdquzqHt6hxdsWuIUiqmrYIzVjC6gohsOdmwsr5E29V5+qtt2q7Ooe3qHD1ll+6S0mg0Gk2H0IKh0Wg0mg6hBaNt/trXBpwEbVfn6a+2abs6h7arc/SIXTqGodFoNJoOoT0MjUaj0XQILRgajUaj6RBntWCIyKUiki0i+0XkF22UB4rIcn/5JhEZ2k/sWiIix0Rkh//13V6y60URKRaRjJOUi4j8yW/3ThE5p5/YdZ6IVDb7vn7dCzYli8inIpIlIpki8pM26vT699VBu3r9+/Lf1yEim0Xka79tD7dRp1d/kx20qU9+j/57W0Vku4i810ZZ939XSqmz8gVYgQPAcMAOfA2Ma1XnDuDP/u0bgOX9xK4lwLN98J3NA84BMk5SfjnwASDATGBTP7HrPOC9Xv6u4oFz/NuhwN42/o69/n110K5e/7789xXA6d8OADYBWywSfQAAIABJREFUM1vV6dXfZAdt6pPfo//ePwNea+vv1RPf1dnsYUwH9iulDiql3MAy4OpWda4G/uHffgNYID2/ok1H7OoTlFJrgbJ2qlwNLFUmG4FBItLjmfY6YFevo5QqVEpt829XA7v/P3tnHh9Vdff/97mzJzPZ94SQsJOwhCWACkVxqVqlYqkigoJVW1u11d9T28c+j1XrU/VRq3WvIlBFxaWK1G74aBeh7JuQsEsSkpB1kkwyk9nP7487GZKQQAJZ4b5fr5uZuffMvd85ufd+7vd8z/keIL1dsT6vry7a1S+E6qEp9NEQWtr3yunTa7KLNvULQogM4FvAsk6K9Hhdnc+CkQ4ca/W5lJMvnHAZKaUfaADiB4BdAN8JNWN8KIQY0ss2dZWu2t4fXBBqVviLECK3Lw8cagqYhPp02pp+ra9T2AX9VF+hJpZdQBXwmZSy0zrrq2uyCzZB/1yPzwEPAMFOtvd4XZ3PgjGY+SOQJaWcAHzGiacIjY7ZgZofZyLwArCmrw4shLACfwB+IqV09NVxT8dp7Oq3+pJSBqSUeUAGME0IMa6vjn0WNvX59SiEuAaoklJu7+1jteZ8FowyoPWTQEZoXYdlhBB6IBqo7W+7pJS1UkpP6OMyYEov29RVulKnfY6U0tHSrCCl/DNgEEIk9PZxhRAG1Jvy21LKjzoo0i/1dTq7+qu+2tlQD/wduLLdpv64Jk9pUz9djxcBc4UQRajN1nOEEKvalenxujqfBWMrMFIIkS2EMKIGhda2K7MWuDX0fj7whQxFkPrTrnbt3HNR26EHAmuBW0K9f2YADVLK4/1tlBAipaXtVggxDfW879WbTOh4bwD7pJS/6aRYn9dXV+zqj/oKHStRCBETem8BLgf2tyvWp9dkV2zqj+tRSvmfUsoMKWUW6j3iCynlonbFeryuztsZ96SUfiHE3cDfUHsmLZdSFgghHgW2SSnXol5YbwkhDqMGVRcMELvuFULMBfwhu5b0tl0AQoh3UXvQJAghSoFfogYBkVK+CvwZtefPYcAFLB0gds0H7hJC+IFmYEEfCP9FwGJgT6j9G+BBILOVXf1RX12xqz/qC9QeXL8XQuhQRep9KeWn/XxNdsWmfrkeO6K360pLDaKhoaGh0SXO5yYpDQ0NDY1uoAmGhoaGhkaX0ARDQ0NDQ6NLDJqgd2gwzJtAMupIy9eklL/trHxCQoLMysrqI+s0NDQ0zg22b99eIzuZ03vQCAZqD4T/J6XcIYSwAduFEJ9JKQs7KpyVlcW2bdv61kINDQ2NQY4QorizbYOmSWog58DR0NDQOB8YNILRmtPkwDkr3L4Af9xdjtbdWENDQ6Mtg04wTpUDRwhxpxBimxBiW3V19Rnt/90tJdzz7k4e+PAr3L5AD1isoaGhcW4wqASjCzlwXpNSTpVSTk1M7DBmc1puuSCLe+eM4IPtpXz31Y2U1rnO0moNDQ2Nc4NBIxhdzM1z1ugUwf1XjOb1W6ZSVOPk2hfWs/5QTW8dTkNDQ2PQMGgEgxM5cOa0mgrx6t462OU5yXxy90UkWE3csnwzr/7ziBbX0NDQOK8ZNN1qpZTrUadL7DOGJVpZ86OLeODDr3jiL/vZfayep747Eatp0FSbhoaGRo8xmDyMPsHb7Kf8cH3Ym4g06Xlx4SQevHoMfyuo4LqXNnCkuuk0e9HQ0NA499AEox0HNlfw8dM7eO+xrRR8WYbPE0AIwZ3fGM6q703H7vRy3YsbWFdQ0d+mamhoaPQpmmC0Y8wFqVx882gA/vH2AVb+fAPr3z9EfaWLC0ck8Md7ZpKdGMmdb23nmXUHCAS1uIaGhsbAoLnRy8EtFdSUNvbK/s/Z+TCmTp0qzyY1iJSS40ca2PuPUo7sqCYYlAzJiWP87HSSx8Ty8B8LeH9bKbNHJfLbBXnERBh70HoNDQ2N0xMIBKn82kFJYS3HCu1UlTSChElXZHLh9SPOaJ9CiO1SyqkdbtME4/Q4GzwUri+n4MtynPUebHFmcr+Rxj6L5JF1+0iJNvO7RVPJSYvqkeNpaGhodIajppmSQjvHCu2U7rfjdQcQiiAlO4ohOXFk5sSTONSGopxZHyFNMHqIQCDI0V017P1nKWUH69HpFeLGxrCyqoavgz4ev3488yZl9OgxNTQ0zm98ngBlB+vCIlFfqQ4mtsaZyMyJJzMnjowxsZgiDD1yPE0weoHa8ib2/rOMA5sq8HkCOCIE6/GQPyuDX8zNwaDTwkMaGhrdR0qJvdxJcYHazFR+uJ6gX6I3KKSNiiEzJ54hOXHEpkSgjmfuWU4lGOfVgAKfz0dpaSlut7tH9peUB4kTE/F5AvjcAS4NSIL42LplNzarEb1+8IqG2WwmIyMDg6Fnnlo0NDQ6x93k49g+ezgW4WzwAhCXFsmEizPIzIkndWQ0eoOuX+08rwSjtLQUm81GVlZWjyuzlBKfO4Cj3kPAG0AAOpMOa7QJo1nXK08CvYWUktraWkpLS8nOzu5vczQ0zjmCgSCVRx2UFNopKagNB6tNEXqGjI0LxSLisMaa+9vUNpxXguF2u3tFLACEEBgtehIselzNPqprmzF5AjRUudDpFSw2A+ZIA8ogaKoSQhAfH8+ZZvzV0DhT3L4ABysbKSh3UFDeQEG5g2N2F3ddPILvzRzcDy+O2maOFdopKbRTur8Ob7MfISA5O4r8b2WTmRNHUlbUGQer+4LzSjCAPnnSj7AYSE/VcczuwucOECUlgToPznovpkg9FpsRg7F/XcvTMZg8Io3BicPto7DcERaHwnIHh6qawmObbGY9OalRDEuw8qtPC/EHgnx/9vB+trrr+LwByg/Wh5uZ6ipCwepYE8MnJ5KZE0/GmFjMkYOn2fe8E4y+Qq9TyEqIpLLRQ5XDjdWkI06nw+P0427yYTDpsNiMmCL0/XJzfvzxx3njjTfQ6XQ8//zzfPOb3+xzGzTOH6oc7jZeQ0G5gxL7iakDkmwmctOiuGxsMrlpUeSmRTMkzoIQAn8gyH3v7+bxv+wnKOGuiwemaLQEq9XeTLWUH2og4A+iMyikjYwhZ2YamTnxxKb2TrC6L9AEoxcRQpASZSbCoHob5cEgQxIi0PmDNDf6cNQ0o+gEZqsRi9WAro+C5IWFhaxevZqCggLKy8u57LLLOHjwIDrdwPZ6NAY+waCkxO46SRxqmjzhMlnxEYxPj+bG/CFhcUi0mTrdp16n8OwNExHAk3/dT1BKfnTJmQ1K62nczpZgtdrl1Vmv/s7Y1EjGzU4nMyeOtJEx6Ad4i0JX0QSjD4iyGBiRZKXY7uL5197g7WUvYdAp5OaO4+XnXmf/3oP85IEfUVdvJyk5iZUrVzB06FCWLFmCxWJh586dVFVVsXz5ct588002btzI9OnTWblyJQBWq5U77riDdevWkZKSwurVqznVBFKffPIJCxYswGQykZ2dzYgRI9iyZQsXXHBBH9WIxrmALxDkUGVTWBgKyx0UHnfQ5PEDoFcEI5NtXDw6MSwMY1Nt2Mzdb4LR6xR+c8NEFAFP/e0AUkrunjOyp3/SaQkGglQWNZ4YWV3kQIaC1RljYsNdXm1xAytY3VOct4LxyB8LKCx3nL5gN8hJi+KX1+Z2uM1k0OGuKuaNF55hxcd/JSs9hQjpJiYhgoeX/ieLF93C9dfeyNur3+Su7/+IDz/4CCmhrq6OjRs3snbtWubOncuGDRtYtmwZ+fn57Nq1i7y8PJxOJ1OnTuXZZ5/l0Ucf5ZFHHuHFF1/s1M6ysjJmzJgR/pyRkUFZWVmP1oXGuYXT42d/RSjeUOag4HgDByua8AaCAEQYdYxNjeL6yelhcRiZbMWk77kna71O4Zkb8lCE4Ol1B5ES7rm090Wj0e4OBatrKd1fh8flBwHJWVFMuTqLobnxJA21DYoOLWfLeSsY/cE///F3Ftz4XXKy06locOPWG7H5AmzavImP13yMXqfnttuX8KvHH6LJ7sbj8nHZpd8k4Asyfvx4kpOTGT9+PAC5ubkUFRWRl5eHoijceOONACxatIjrr7++P3+mxiDH7vS2aU4qKG/gaI2TljG+cZFGctOiWDozi9y0aHLTosiKj0TXB717dIrgqe9OBAHPfHaQoIQfX9azouH3Big/VB/u8toSrI6MNpKdl0hmThxDxsYNqmB1T3HeCkZnnkBvI4Qg0WbGYtBRYm/mSFUTLWPthSKwWI0IRRCbEoFOpyCkDvtxJ421HowGI1JKhBAoioLf7+/0GKciPT2dY8eOhT+XlpaSnp7eUz9RY5AgpaSsvjksDIUhkTjecGJga3qMhdy0KL49MeQ5pEeREmXu16CtThE8NX8iihA8+38HCUrJfZePOuP9SSmpO+6ipLCWkkI75YfqCfiC6PQKaSOjGXtRGpk5ccSlRQ7aYHVPcd4KRn8wZ84c5s2bx/333098fDxx+gYaMTF+Uj6vLn+Lu+9cyttvv82sWbMwmPQYzDps8WYiY0wEyoMEApLaMicWm6HNdLHBYJAPP/yQBQsW8M477zBz5sxT2jF37lwWLlzI/fffT3l5OYcOHWLatGm9/fM1+pFAUPJ1ddNJweiGZh8AioDhiVamZ8eFvYactKgBm4VZpwj+9zsTEMBvPz+EBO67bGSXb+hup4/S/XXhWERTXShYnRJB7iy1N1PaqJgB3/29r9EEow/Jzc3lF7/4BbNnz0an0zFp0iSWL1/BE08/y70/vJOXnv8NacnJrFy5IvwdRVGIjDaFPA6B3qDgrPfgcfpxObx43X4iIyPZsmULjz32GElJSbz33nsAvPrqqwD84Ac/OMmOG264gZycHPR6PS+99JLWQ+ocwu0LcKCi7eC3/RUO3D413mDUK4xNsXH1+NRQvCGKMSlRWAbZzVFRBE9+ZwKKEDz/+SGklNx/+agORSMYlFQVOcJdXiuPqsFqo0UNVk+9Wh1dHRVv6YdfMng4r5IP7tu3j7Fjx57ye1JKfEEfRl3fPVlJKbE7vZQ3uDHoBEPjIrAYO9dyvy9Ac6MPd5MPKSXDctKorrBjijT06CjRrtSXRv/S0Nwy+K0hPAjucHXbwW8tQeiW1+GJkejPoQBtMCh58OM9rN56jB9dMpz/uGI0Qgia6jxqM1OBmga8JVidlGkjM1fN8pqcHXVeBKu7g5Z8sBvUe+o53nScGHMMiZZEDLreD2wJIYi3mjAbdJTYXRypdpIeayG2k+YAvUGHLU5HZIwJj1NtUmi0u2mq92CONGCxGfo9SZlGzyKlpKrRo3oMZaFg9PEGjtmbw2WSo0zkpkVzRe6JwW8ZsZZzvt1dUQS/njceEYQ/rTuK/qsGUlxgP+4EICLaSPbEBHVk9dhYLNaB2cw2GNAEox1Wg5VYcyx1njrqPfXEmeNIsCSgV3q/qiJNekYkWSmxuzhmd9HsDZASbUbp5IJXFIHFZqSxqRGfR/U6mhu9NDd6MZr1WGwGjJb+GUmuceYEg5Jiu6tNrKGwvIGaJm+4THZCJBMyYrhpWia5adHkpEadcvDbuYiUkroKV7jLa9bBejJ8JvxHmqhKMDFr3nAyc+OJT9eC1T1FnwmGEELIUPtX6/cDDYPOQKo1lXhLPNWuamqba6lz1xFviSfeHI9O6d0nd4NOITshkooGNzVNHpq9ATLjI045v4YQAqNZj9GsJ+A34W7y0dzkpaHaj06vYLYasFgHR+LD8w2vP8ihqsbwwLeC8gb2HW8MD34z6AQjk2xcMjop1EspmrGpUVhN5/azXnlTOdsrt7Ojagd7a/aiCIUoYxQxxBFnH4K1IhldWTSyUa0HU7wgZaqFjLExfFjcwLvbq3EF4vl5eqYmFj1In5x1QghFShkUQsQAQSmlYyCLBoBRZyTdlk6CJYGq5iqqXdXY3XYSLAnEmeNQRO/dfBUhSIuxEGHUUVrXzOGqJjLjIojswk1Cp1eIjDEREW3E4/LT3OjFWe/B2eDFHBFKfGjSmqv6izqnlz/tOc5XpfUUlDs4VNl28FtOahTfmZyueg1pUT0++G0gIqXkaMNRtldtV0WicgfHnccBsOqtTDfMJroiHfPxBKz1iShSwatzUxxVSMmwfZRG76fRbFd3tk99sY0VvH3cwtpVUWTFJhBliiLKGFpMUUQboztdF6EfvLmeepteF4xWYjEWWAV8JIR4UUrZMNBFA8CkNzHENoRmSzNVrioqnZXUNteSYEkg1hzbq8IRE2HEpNdRbHfydY2TtGgzcZHGLp3MQgjMkWpKdb83FCR3qoveqCPCZsAUYUAM4FTK5wpSSnaU1LFqUwl/2nMcrz/YZvDbuFaD3wZyauueIhAMcKDuANsrVYHYWbUTu1u94ceb45mcPJnFI5aQUjaamu1+7OXOcLB6yAx1zuqkbBteeTEOr4MGTwMOr0NdPI7wus8PFnOgqhKrTiCiGylrKgtvD8hAp/bphR6b0RYWEZvJdkJYjFFEm6LDItN+nUV/bseMel0wQmKRCbwLpAPzAZcQYvlgEQ0Ai97C0KihOH1OqlxVVDgrqHXXkmRJItoU3WsnicWoY0SilWN1zZTVN+PyBkiPsXTrxqI36rDF64iMDTVXNXpx1LoRdR4sVgMWm7HPEh+eTzR5/KzZWcaqTcXsr2jEatKzIH8IC6dnMjrZdk7fWFrjCXjYW7OXHZU72F65nV3Vu3D61IB0ujWdmekzmZI8hUmJkzDXxlK4vpzDf6jioK+BxEwbsxeOZvikRCy2tsFqPRFEGCJIiUzp8Lj3TJI8+mkhKzYUMfOiLB66LgchBFJKXH4XDo+DBm9DWERaBKf9ugZ3AyWOEhxeB43eRoIy2Olv1Sv6U3sypxAcs37g55/qEw8DuAooAxYC9wJ3hLZ1SzSEEFcCvwV0wDIp5RO9Z3nHRBoiyYrKwulzUumqpKypjOrmapIikogyRvXKTUCvU8iKj6Cq0UOlw43bF2BofATGbjZVKIogIsqIy+vghoXz2bZtGzfOX8jjjz6NySSwREgMhiBCBkEGwdMI/3gSfE7wOsHrAm8T+Fyhz6El4IOoNIjLhtgsiA29xmVDZCKcJzfGFvZXOFi1qZg1O8tp8vjJSY3i1/PG8+28tC41Kw52nD4nu6p2hT2IvTV78QbVgP3w6OF8K/tbTE6ezJTkKaREpuBx+Ti4pZLNq8uoLTuKwaRj1IwUcmemkTQkEpzVYPSANHTrXBJC8NA1OQgEyzccRUr45bWqaEQaIok0RJJKard+W1AGcfqcnYpL+3W17lqONhylwdtAk7cJSee3OaNi7NBr6UpzmknXNx0e+srD+CdQIaUsBH4ghPgdcDuAEGKFlLL+dEFxIYQOeAm4HCgFtgoh1ob22WMEfT6k2w2Kot78WxZFASHC6yL1EQyLHkajt5EqVxWljaWY9WaSIpKwGqw9IxxSqjduGUTIAMnmIFYhqHE0UVnVSGKkAbMetUwwGC6LDHS+TgaxOJ08dt8t7N1/EXv2f02EUofbE4XHo0NHAIvSgFlphOY6+MevQW8GQwQYrWCMAGOk+jkqXf2s6KGhDI7+C3avhtYXhSEyJCJZJwtKTCboz40ujh5/gL/sqWDVpmK2Fddh0itcMyGNm2dkMmlITPjJVnq9SJ/v5HMq9D583g0i6tx1qvcQikHst+8nKIPohI6xcWNZMGaB6kEkTSLWHAuozXSVRx18/q8CDm+vwu+TJCb4uDi/jJExuzE2fQ1rSqChFIKhFDh6C1iTwJocem31PrLdeoM6AE8IwX9fMxZFwLL1RwlKySNzc8+4jhWhYDPasBltpFu7l04nKIM0ehtPeC4tTWntBKfltcpVxeH6wzg8Dhp9jafct1lnbiMu14+8nm+P+PYZ/cZT0ScD9zoRgFeBWcAyoMXTuAn4RErp6mAfFwAPSym/Gfr8nwBSysc7OuaZDtzz19Xh607mVqFe4EEhCaIuQhHoFb0a3xCtHooErProY559/Q2EEIwfM5qVz/4vRaWl3PnAL6ipqyMxLpZl//soQ9NTuO0//guL2cTOggNU19pZ9tTDvPXRp2zasZtpeeNZ+ewjIMA26iLuWDiPdf/aREpSAqtffZrExAQQCgid+qoobT8LhZXvvM+2nbt58dmnkSh4PAKXC/w+iRBQWnmUzOQs4jOju14fPjfUl0BdEdQdVV/tode6IvCfGDeAUFTR6UxQIuK6ftwQUkrw+Qh6fUifF+n1IX2+8I1afW1Z7+1gW+g19D7o9Yb2176M+r7Z5aaixoG9wYXweYlQJHFGgU0JIvz+k/bdZU4nKB28V8uACP+/239XqNtavqsI4BTb2+xbIEJlvdKHw9eIw9dEg8+By9+MFKrNUaZooiwxxJhjiTZHo1cMEPAg/C7wNeP1BygVwygy5OHQp6ELuhni2kR249+J9ReDAAwRCJMVzDYwRyFMVmTAp3q4Phd4m8HrQvqawe8+8XwiQ28loDOqDzqhRerMVLqgvEkSY7ORlRynltGZ1DqQUl2QyKA88Tm0SCS0X49Uz7dgB2UloQe2duukDK9vs65lfcs+25eVkqAMEAwGCQYDBIMBpAy2eg2qDyTBAEiJ87uXM/uBZ7p9/ainXj8P3GstFkIInZQyIKX8QUg0bgcUIUQOMBdYD5wkGKjxj2OtPpcC0zs7ZllZGQ8++GCbdfPnzwfU3EvOP9yDsfZAm+06vQ6dECgBH75Aa31T3+sAnZBICV7Z9glFAXSxI/BNvZdmBE4EQgYRQfUaACjbv5/HX3iZdW+vQpeYSH19Pcc9cNd/P8a1c6/je9fO5d2P/sA9Dz3JKy+/jCugo67Oxfvvvs9nX3zBdd/7CZ+/+SbP/PcoZt94I3/dVkzu2LE4Xc1kjZ7Epvv+i6deeZkHf/0qDz388AnbRRAIEq/ToVckTilpCgSwNzTT5PJy7LgacEyKiMBm0OEI+mjy+mhqbuT++3+O0QAWC/z3zTcQGWVl3c6dbCosbHNjkVLyyI/uRvq8rP38C7bvK2x1IcRi0CXw08v/E9lUy5pt2yioqlGbsgI+ZMCPNVjAD4KfIIOCj2QqX4tI1LufDolCDHCL8COlnj9IHWWtLnIZDJLo8/Gdqmqkz8cfkpKoMbYdcJnq8TC3ugaA1SnJNOjbnvqZbjdX1dQC8FZqKq5wF2T1RjrC6+MKrxdhMPC6NRI3Ar8EfxCkEAy3GbkqNom4mEieq6466Yadn5TEnKxsvDqF51o/yIQujYvS07koPZ1Gt4dXdu2ClsaL0J+LU1PJT0jE7naz7OCB8Pda9nFZcjITo6KpcLl4u/RY6GsnylwVF8/YiAiONTfzQXVVq9NaLTM3KpphRiNfu9184miAUM8tpCQog1yqeLH6Gjng87JdsaFKjR6dSETRCeYHPAwJSAp8kjXNAZDVICvD+7/Wo8MRP4sdlhiKXYXoAl9g8jsw+Z0ckEFuafTTSAZbjWa2mkzQHLJdegA337PbMSL4d0QEuy3RQHTo/FCL/bC+DiEkfzdb2Gc0of5z1N9nCAa5036MKBlkky2Bz53A143hf29kMMjSpiqEouePEXEUGUIiEhLMGCG4Ra8HnYGP/H7K2jz7CpJ0CgssFgSCd5ubqQq2uuiBDL2B+TYbCMHvHQ3UiZYEoWqhbJOJb8fGIoTgtepqnIG23x8TEcHV8QkgBC+UleJr95A/PiqKKxITEYrCM4cPAwJdUTN/C93/Zs6cydVXX43H4+GRRx6hPZdeeimXXnopDoeDJ544dSt/nzeoSikD7UTjFeApoAm4WEp57DS76BQhxJ3AnQDx8fFntg+CIP2oEtAOBYSiICWIAG3+qQCK2Yw1PR1DIIjT7sAjVVdaJxSMipENBw9xw8KFpE3Np7q6mriISAB27N7N68uXY0xMZOn99/Nfzz2HYrGAXs/lF1+MLiKCsePHk5SUxKQ5c2j2ehk9ejRlVVXkTJiAoihcfc21YLVy88KFzL/9dvWG1ULLCRYMIgOBE4kLQzdbGcp666+pUX+1TodQFJSgH0tzDZ5gNA0+Ax++VUNmxadUNe7AbTk5fvL11VcDYI+NwRMZ2WZbQErK/+9zABrj4vBFtM7ZI/BKHQ0NyQi9Dp/NSlBvAKE+xQkC6lOquxRFkejMKSg6S8hOHSh6jGYLcdNGIaISMJdUY3B7Wj0lK1gTE0iffTHCYCBi3d9wNzW1eYqOHTaMEd/5DsJoxPrKK0inE9Hy5A0kTJhAzNXXsXrrMb76/fN4vV6MOoWkKBNJNjOjL5jOxHnzADC1e1ABsM2cSWLoojV04MHa5swh8dJLMTkcGDq4aKOvuorkWbNQqqsxPvvsSdvjrruO1GnTCJSWYnz55ZO2J9xwA+l5eXi+/hrTsmUnbU9avJghY8fiKCwguOJ1Gr2NNPoaafI24Qv6+HKiHWuCnrG1CQS/0mELBrH4PQh/MyAZkXmQDHMztfXxWGqHgd5MUGem2W/D6Taxw3YVNksUackVuGptGM2xwIlg9fCf/5yoqCiOff45EZ9/fpJ9o375S0wmE0f+/Gci1q8/efuvfw3A3o8/pmjr1jbbjEYjIx5+GHxutr39exo2bKOh0Um8RRBvEUQaAgy/MB2aqojZ5cFid6vNtyGsBg/Z2ftBKEQdz6XWbQOdIeShGLAmxpI1/xKwJmH96Esc1Q3q9pb/bXY2mXfcAUDEM8/QXFvbxj7b6NEMufVWACyPP46/sW3zk23CBNIXLADA/PDDKF5vm+3R+fmkhc49YwfnXk/Sb7mkWkRDCPFbYBEw61TxiL5qkjpxorRuSzozAsEAte5aaptrCcogH634CKfdyROPt70hJCQkcPz4cQwGAz6fj9TUVGpqaliyZAnXXHMN8+fPp6ioiGuuuYa9e/cCtNmm0+nYebQanU6Hv6GCJTcvYOfOnae1b+XKlWzbti082ZJs54bv27+fEVYrAY+HYwebKPzKSdkxP0JAZlqQUUN9JEa6IeBD6A0IoxFhMCCMBoTBGHo9sV6+72vJAAAgAElEQVQxGluVOfGKTnf6NuVgABxl7Zq4WjV5uevblo9I6DgIH5sF1pS2gnoKpJRsPFLLqs3FrCuoxB+UzBqZwM3Th3LZ2KRBn5PJ63VRUPJPtpetZ3vtXnY1HaNJqk1naQGY0uxisruZKW4PWT6/2iwVlQbRQ9QYVLtFRqVTWeqh4MtyDm+txO8LkjDESu6sdEblJ2O09H/QX0rJ0+sO8NLfj3DTtCH8z3XjT+516GkCZxU0VUFTZeg19N5Z3XZdwHPyQRRDKLaSePqYi8k2oDqG9HuTVEeExGIxcA8wtQvB663ASCFENmqPqwWova56lh4cV6FTdCRFJBFnjqOmuYaJF07k3lvuZdEPFjE6YzSNDY3ExcVx4YUXsnr1ahYvXhxOb94dgsEgX335V2Zcfi2vrHiLyfkzwvNmdIdwm3XLZ50OY1YWAKNGw6hroaHaxd5/lrHv38cpLlOIS4tj/Ox0svMSiYzuxZ4aiu7EjSn7Gydvb647ESexHz0hJiWbYe8f2jwxojdDzNCOBSUmEwwWGlw+PtxRytubi/m62klMhIGlF2WxcPpQshMiTz7+QCXgU4W2vgTqS3DZj7DLvo/tzmPs8DvYo5N4QjfLYV4fV7k9TBYRTIlIJTVmGGS1E4WojA47Knia/RzcXEHBl19RW9aE3qRj1PQUcmelkTQ0qq9/9SkRQvAfV4xGEYIXvjhMMAiPX99ONExWdYkbduqdSQnuhlZi0lpkQsLSeBwqvlLXdzT+Q28Ba4uwJJ9aZIwRPVsZ3aTHBKPVAD0rgJSyqd12nZQn1dYfgH9KKUtOt38ppV8IcTfwN9RwwnIpZUEPmd+r6BU9KZEpXDXjKn7ywE+Yd+U8FJ1CXl4eb7/5Ni+88AJLly7lqaeeIjExkRUrVpx+p62IjIxkx/ZtPPH4r4mOjed/XnyDoloXf37/9+gU5aT05gBZWVk4HA68Xi9r1qxh3bp15OTknPZY0YkRXDR/JNPmDuPQ1kr2/KOUf757kH++e5CEIVYyc+IZOi6O5GHR6Pry6dsSqy5pk07e5vdCwzFVRFoH4OuK4OiXarfhVtTrEznkiycqkMTt0ZmMuGA8EyfmYUpMgoj+vWBPIuBTexKFBIGGYyfe15dQ33ScHSYD280mdphN7DMaCQiBAowxRXJDRAZT4sYwKXU6cUm5nQpCR0gpqSxyUPhlOYe2VeL3qt7E7IWjB4w30RlCiHAq9Oc/P0RQSjVVencHTgoBlhh1STzNJE7BIDTb23otztYeTKV6fpZsAlctbXobtmC0tRKSkJhEthaWxBPreqEH4lk1SQkhLgL2SylrQ5/nAj9EbZxcCXwspSxu950bgW1SyiNnfOAucMZNUn2AN+Cl2lVNvaceRShnnafKarXS1HRCn2ubPF1OlX4qupoOvrasieK9ahrp40cakEGJ0awjY2wcQ3PjycyNwxo7QAclSYmrvoINW7aye89ulLoisvXVTLLWkS4rMbgq25Y32kJeSQceSkxmm7brHsHvbeMhnLQ0lrfxnir0BnbEprA9wsoOfZDDQbVXmlHoGRc7iimpFzAlNZ+JiROxGq1nZNIJb6L8hDcxNYncb6STmDn4BiQ++9lBfvv5IeZPyeDJ70zok6lmT0vAD66ak5vDwiLT6nP75liAb/wU5vzXGR26x5ukhHpGTAG+BB4RQjwJTEZN/bESKAZ+BYwTQvxWSrkn9L25qCO+XxJC3Cel7HiO0XOcljxV8ZZ4qpurezxPVbzVhMWgo7gLqdLPFiEECRk2EjJsTLkyC0+zn9L9dkr21lJcYOfrndUAxKVFhsUjdUTMgBhZfriqkVWbSvjDjlIa3TpGJ89m0TWLuWxSOjZz6MbvdYW6CbfrIlxzEA591rb9WigQnaEKSEfNXeYOuif7veAo7VwQHOW0edJs6Yock4nMmkmxNZYdugDbvXVsbyqiLCRwkQbIS5zM1clTmJw8mXEJ485qcNdg9iZOx32Xj0IIeO7/VE/jqfkT+180dHqwpajL6fB7ThaSlPG9Yla3PYx2A+zuBp4Hfg54AJ2U8jehbdcCLwKfA8+2Eo1fAauklAc62n9PMZA9jPY0+9U8VU3eJvSKnkRLIjHmmLMWDl8gSIndhdPjJ8FqOmWq9I442/qSUmIvd1JcEPI+DtcTDEgMJh3po2MZOk4VkL6c5czrD/K3AnWA3eajdow6havGp7BoxlCmDo3t3tNxMKi2T3c45uRoqFmhFZZYVUCi0tRtnQpCRgcB5SEEotM5FGhme83ucJK+Wrd6jFhTbHj09OTkyYyOHd0jKfnD3sT6cmpLB783cSqe//wQv/nsINdPSuep7w4A0egneszDaBWnSAWyUWMQtcDbQDnwm5ayUso/hjyRlwC/EOJ1KeVWKeV/n+kPOVdpn6fquPM4Ne6as85TdSap0nsSIQTx6Vbi061MvmIoXrefsgN1FBeoHkjRV+q4iNiUCDJz4xmaG0/qyOhemfyptM7Fu1tKeG9rKTVNHobEWfjZlWO4YWoG8dYzfPJWFIhOV5esi07e7na0ipe0ip3UHFIDm9mzTxaGqLRws5Yv4KOgtkBNsXFkNbuqdoVH/KZEpjAjbQZTkqcwJWkK2dHZPXbzllJSVdRIwZdl55w3cSruvXQkioCn1x0kKCXP3JB33opGZ3T5P99KLHKAN1DHTXillN8KBbp/B0wQQsS3xDSklGuFEEHgPcAjhPhKStlBHzQNOJGnqsnXRJWrirKmMmqaa0iKSMJmPLOnubNJld7TGM16sicmkj0xESkl9ZUuNfZRaGfvP8vY/fkx9EaF9NGx4eB5dOKZB5kDQcm/DlazalMxfz+gDlabMyaJm2cMZfbIxN7PDGuOgtQJ6tIFXD4Xuyu3saNKTdK3p3oP7oAbgOzobK7IukIViOQppFnTetxcT7OfQ1sq2PtlK28iP5mcWekkDT23vInOuHvOSIQQPPW3A0jgme9OHPRdp3uSLt01Qs1QQSFELupI7JdRm5tqAKSUrwshDKF1h4UQL0kp60LbPhVCzAcOa2JxeoQQ2Iw2rAZrOE/VscZjZ52n6mxSpfcGQghiUyKJTYkk77JMfJ4AZQfrKCmwU1xQS/GeWr58D6KTLGHvI21UDAbj6b2PmiYP7209xrtbSiitaybRZuJHl4xgwbRM0mP6rvnrdDR4GthRuSMsEPtq9+GXfhShMDp2NPNHzQ/nYIq3nNlA1NNxvnoTp+JHl4xAEYIn/7qfoIRnb9BEo4UunQ1SSimEiANeQY0//KJlmxBCL6X0SylfFkLogedC61uLxl96wfZzGiEEUaYobEYbDZ4GqpqrKHGUEGGIICkiiUhD98cC9ESq9N7CYNKRNT6BrPEJANRXuSgpqKV4r53C9eXs+XspOoNC+sgYMkPB85jkExPdSCnZctTOqs0l/HXvcXwByQXD4vnPq8ZyRW5ynzXDnQq7287Wiq1srdjK9srtHK4/DIBBMTA+YTxLxi1hSvIU8hLzzrgHU1fRvIlTc9fFw1EEPP6X/QSl5Lc35mmiQfdiGMmo+Zx+2dI8BeHxEUro/fOhJqjnAKsQ4gkpZQd9vjS6ihCCGHMMUaYo6t31VDdXU9RQhNVoJSkiCYu+e0/Mep3CoR0b+OkDP8fldmMymXjm6f/lm5df3ku/4MyISYogJimCCZcMwe8NUH6oPhw8X//BIfgAohLMpIyJpcQQ5MPSavZVO7GZ9SyaMZSbpw9lRFLv3nRPR527ju2V29lSsYWtFVvDAmHRW5iUNIkrs65kSvIUxieO75P01GFvYn0Zh7Zq3sTp+P7s4QgBv/7zfpDw3IK8AfHg0Z905wyZBAxFHWgXbCcaQQAhRATwEeBEDYA/3cP2nrcoQiHOEkeMOQa7205Ncw1f139NlCmKJEsSJn3XbziJiYn85c+fYo1N5IuN27jl5u9w+Gjxia6kAwy9URfyKtRmGUdNM5s2lPHVtgpq15djQHC1gHmpceRNS2XEhERizyL2caY0eBrYVrmNbRXb2FKxhYN1B4ETAvGtYd8iPyWfnPgcDErf1bW32c/BLWpPp5pjTeiNCiPzk8nVvInTcuc3hqMIwWN/2kdQSp6/adJ5LRrdEYwi1PyP1wMftohEO74H3CSlvFAIsaalSUrjBG+++SZPP/00QggmTJjAW2+9RVFREbfddhs1NTXhkd6ZmZksWbIEi8XCzp07qaqqYvny5bz55pts3LiRadOm8dQrT1HbXMvolNHcvORmNvxjA6kpqaxevZrExMRObZg06cRo6Ctn5uNxN3OwzM6QpGgSraYBewNx+wJ8+tVxVm0qZtexeswGhblzUvlWahz6Sg8lhXa2rvmarWu+xhpnCsc+MkbH9srTs8PrYEfljrAHccB+AInEpDORl5THPZPuYVrKNHLjczH09IC+0yClpKo4FJsIeRPxGVZm3zSKUdNSNG+iG9w+axhCCH71aSH3vLOTFxaev6LRnbOmGKgHbhFCbG0Zwd1uroshqBMb6UJlByxPbnmS/fb9PbrPMXFj+Nm0n3W6vaCggMcee4x///vfJCQkYLeracXvuecebr31Vm699VaWL1/Ovffey5o1awCoq6tj48aNrF27lrlz57JhwwaWLVtGfn4+5QfLGTdhHM2uZkaOH8l9j97HimdX8MuHf8nLL52csbQjPl27hilTppAYY6WiwU2zN0BGbMSA6k74dXUT72wu4YPtpTQ0+xieGMlD1+TwnckZREecuBFfBDTa3ZSEmq4Oba2k8MtyFEWQOiI67KXEp0eekSg2eZvYUbWDrRVb2VKxJTxRkFExkpeUx115dzEtZRrjE8Zj1PXPxFCaN9E7fG9mNoqAR/5YyN3v7OCFmyZjHACDT/uaLguGlLJMCPFD4B3gV0KIJ6WUBaGAuBX4L9SEgFd0kDNKA/jiiy/47ne/S0KCGtiNi1MnCNq4cSMfffQRAIsXL+aBBx4If+faa69VJ1saP57k5GTGj1dHcObm5lJUVEReXh6KonDP0nuo89Vx2XWX8eOlP6bCWUGCJeGUg7cKCgr42c9+xrp16xgSF4GlyUtFg5vDviaGxvdvziRfIMj/FVayanMxGw7XolcE38xN4eYZmVwwLL7TG58tzkzurHRyZ6UT8AepONJASaEaPN/48RE2fnyEyGhjWDyGjI3FFNHx07/T52Rn1U7Vgzi+lUJ7IUEZxKAYmJA4ge9P+D75KflMSJzQZ1NkdsSpvImR01Iwad5Ej7D0omwUIfjl2gJ+9M4OXlp4/olGd8+kNcCPUbvPThNCbEId4Z0K5ANXSyl79rG9lziVJzCQMJnUG5GiKOH3LZ/9/hOZVQw6A2mmNBzRDvSKntrmWurcdZ3mqSotLWXevHm8+eabDB8+HIBEmwmLQaHE3syRqiY8vr7X/eMNzby75RjvbS2h0uEhLdrMf1wxihvyh5Bk614+Kp1eHdORPjqWC+aBs94TCpzXcmRnNfv+fRyhCFKGRZGZG0/S6EiOGQ+yrUqNQRTUFBCQAfSKngkJE7h9/O1MS5nGxMSJmPX9nxvL2+zn4NZKCr4sa+tNzEwnKUvzJnqDWy/MQgh46JMCfvj2dl66eTImfc8PNB2odEswQnGL3wkhdgI/BSaiDuBbD9wvpTzc8yaeO8yZM4d58+Zx//33Ex8fj91u77H05h9++CELFizgw/c+5JJvXMLwmOFUuzrOU1VfX8+3vvUtnnjiCS66qO0IZavZwIgkHSV2J+VNXp76237uv3x0rzZRBYOS9YdrWLWpmM/3VxGUktmjEvmf64ZyyZikHjt2ZIyJnIvSyLkojWAgSPHhanZsO0D5vgqOf9IAgMvgoDTGTULmMG7LvZD8rMlMTJxIhGFgZKlt8SYKvyzj4LYq/J6A5k30MbdckKXOFb5mLz9ctYOXF50/onFGZ5eUcosQYoHW9NQ9cnNz+cUvfsHs2bPR6XRMmjSJlStX9kh68y1btvDYY4+RlJTEe++9h1lv5k/v/AlvwMu3F3+bSmcltc21JEYk8tILL3H48GEeffRRHn30UQDWrVtHUlISAEa9wrAEK1XHdLy09ghflTbw/IJJxEb2bLt8ndPLB9uP8fbmEoprXcRFGrlj1jAWTssksxeaxDwBD7urdrO1citbjm9hT80efEEfulE68iZNZZJ3Jmk1WcQevQDPdj9iB5RnG9DnVqoeSKYtNA9239OhNzE1FJvQvIk+Z/GMoSgCfvHxXn7w1nZeWTQFcy+ktBlonHF683ZJCFsHvgcEgyn54NnSPr15R7TkqXL5XBh0hi7lqdq3bx+7GiP55ScFJEWZeHXRFMald5BxtRtIKdlRUs/bm4r5dM9xvP4g+VmxLJoxlCvHpfTok5o34OWr6q/UwXKVW9ldtRtv0IsiFMbGjWVayjSmpkxlctLkNgPlgkFJVZEjPO6jqtgBEsxWA5k5cWr8IycOi613A9udeRPjZqVp3sQA4Z3NJTz48R4uHp3Iq+eIaJwq+WC/TdHa22iCcTJSynCeKrffjUlnOmWeqpb62llSx12rdlDn8vLEd8Yzb1JGt21s8vhZs7OMtzeXsO+4A6tJz7xJ6dw8I5MxKT0zI5sv4GNPzZ7waOpd1bvwBDwIBGPixpCfks+0lGlMTp6MzWjr8n6bG72UFNopKazlWKGd5kYfCEjKtJE5Tu26m5QV1WMj5jVvYnCxeksJP/9oD7NHJfK7xYNfNDTBCHGuCkZ3kVLi8DqoclXhDXgx680kRyQTaWjb3bR1fVU3erj7nR1sPmpnyYVZPHj12C71ENlf4WDVpmLW7CynyeNnbGoUi2Zkcl1e+lknQPQFfRTUFLQRiGa/OmHQ6NjRbQQi2nR2nlELMiipPtYYmjCqlsqjDqQEU6SezLGq9zEkJ67b09VKKakuaaTgX628iXQrubPSGDVd8yYGOu9vPcbPPvqKmSMSeP2WqYNaNDTBCKEJRluklNR76ql2VeML+k7KU9W+vvyBIE/8ZT/L1h8lPyuWlxZOJinq5N5CHn+Av+xR55zYVlyHUa9wzYRUFs0YyqQhMWf8hOwP+imsLQwLxI6qHWGBGBk7kmkp08hPzmdK8hRizDFndIzu4nb6OLbPHh774XJ4AUgYYlUnjBoXT0p2FEonA728bj8Ht5zsTeTMSiM5K0rzJgYR7287xs/+MPhFQxOMEJpgdExQBqlz11HTXIM/6A/nqSo6VNRhfa3dXc7PPvwKm1nPK4smM2WoOp6kpNbF21uK+WBbKXanl6z4CG6ePpT5UzLOKGAeCAbYb98fHkm9o2oHztD828Ojh6seROo0piRPIc4cd3aV0APIoKSmtCk07qOWiq8d6nS1Fj1DxsaGYh/xWGNNVBU7KPiynINbKzVv4hziw+2l/PTD3Vw0XBUNSxeyKw80NMEIoQnGqQnKoJqnylVDQAZoONZAyrAUhkUPO6ns/goH339rO+X1zXz/G8PZU9bAvw5VowjBZWOTWDRjKBcNT+hWu34gGOBA3YE2GV2bfGpsJjs6m/zkfPJT85maPJUES0KP/e7ewuPyUbq/Lhw8d9ar2f0jo404G7yaN3GO8tGOUv7fB7u5YFg8b9yaP+hEQxOMEJpgdI1AMECtu5a9hXu5r/A+rh12LXfl3UW6Nb1NuYZmH/e9t4sv9leRHGViQX4mN03LJCW6a4PagjLIobpDYQ9iW+U2Gr3qjHJDo4aGYxBTk6eSGNF5bqzBQOvpaquOOkgfHat5E+cwH+8s5f+9v5vp2fG8sWQqEcbB83/WBCOEJhgqW7Zs4c477wTUG9nDDz/MvHnzTipXUFjAn51/ZvX+1QQJMn/kfO6ccGebm3cwKDlS3URWQuRpE7IFZZAj9UfaCESDRx0wN8Q2hPyUfHVJzic5MrkHf7GGRt/zya4y7ntvF/lZcaxYmj9oREMTjBCaYKi4XC6MRiN6vZ7jx48zceJEysvL0evbntAt9VXhrOC1r17j40Mfo1f03DT2Jm7Lve20gWUpJV83fH1CICq2UedRExinW9PDHkR+Sj4pkSm99ns1NPqLFtGYmhXHiiX5/TI1cnc5lWAMfOvPMXoqvfn06dNZuXIloI7DuOOOO1i3bh0pKSmnTW8eEXFiFLXb7T5t23lKZAoPXfAQS3OX8vLul1m5dyUfHPiAW3NvZXHO4nCvKiklRY6icDbXrRVbsbvt4X3MypgV9iLaN29paJyLfDsvHUUIfvLeLpau2MqKpYNDNDrjvPUwKn79azz7ejZPomnsGFIefLDT7QUFBcybN69NevO4uDiuvfZa5s+fH05vvnbtWtasWcOSJUtwu928++67rF27lsWLF7NhwwZyc3PJz8/njTfeIC8vDyEEq1at4uabb+bRRx+lqqqKF1988ZS2bt68mdtuu43i4mLeeuutDpukOvPIDtUd4qVdL/F5yefEmmKZP2o+pU2lbKvYRnVzNQBJEUlMS5kWHk2dYc3Qgroa5y2fflXOj1fvYnJmDCuWTsM6gEVD8zAGCL2Z3vzGG28EYNGiRVx//fWntWX69OkUFBSwb98+br31Vq666irM5q4Fq0fGjuS5S55jb81eXtj5Aq/veZ1ES2LYe5iWMo0htiGaQGhohLhmQhqKENzz7k5uXb6FlUvzB+wMl6diUAiGEOIp4FrACxwBlp7tXOGn8gQGEl1Nb96a7tyox44di9VqZe/evUyd2uFDRaeMSxjH7y7/HQ2eBqKMWrdQDY1TcfX4VASEReP3t00bdKIxWGb/+AwYJ6WcABwE/rOf7Tkj5syZwwcffEBtbS1AeMa9lvTmwFmlNwd45513mDlz5inLHz16NCw2xcXF7N+/n6ysrG4dszWnS2KooaGhctX4VF5cOJmvShu4ZfkWHG5ff5vULQaFYEgp10kpWx6nNwHdz343AGid3nzixIncf//9ALzwwgusWLEiHAT/7W9/2639tqQ3HzduHF988QUPPfQQAK+++iqvvvrqSeXXr1/PxIkTycvLY968ebz88svhZjINDY3e5cpxKbx882T2ljWw+I0tNDQPHtEYdEFvIcQfgfeklKtOVe586lbb1Wy13eVcrS8NjYHAZ4WV/PDt7eSkRvHm96YTbRkYzVOnCnoPGA9DCPF/Qoi9HSzfblXmF4AfeLuTfdwphNgmhNhWXV3dV6ZraGhodJvLc5J55eYpFB53sPiNzTS4Br6nMWAEQ0p5mZRyXAfLJwBCiCXANcDNnU3WJKV8TUo5VUo59VTjEM41esO70NDQ6H0uy0nm1UVT2H+8kUWDQDQGjGCcCiHElcADwFwppau/7dHQ0NDoKS4dm8zvFk/hQEUjN7+xiXqXt79N6pRBIRjAi4AN+EwIsUsIcXIkV0NDQ2OQcsmYJF67ZQoHK5tY+Ppm6pwDUzQGhWBIKUdIKYdIKfNCyw/62yYNDQ2NnuTi0Um8fstUDlc3sXDZZuwDUDQGhWBoaGhonA/MHpXIslum8nV1Ewtf3zTgREMTjPOYkpISrFYrTz/9dH+boqGhEeIboxJ549Z8jtY4Wfj6JmqbPP1tUhhNMM5j7r//fq666qr+NkNDQ6MdM0cmsHxJPkW1Tha+vpmaASIammD0MW+++SYTJkxg4sSJLF68GICioiLmzJnDhAkTuPTSSykpKQFgyZIl3HXXXcyYMYNhw4bxj3/8g9tuu42xY8eyZMmS8D6tViv33Xcfubm5XHrppXRlDMqaNWvIzs4mNze3V36nhobG2XHRiASW35pPsV31NAaCaAyK5IO9wZfvH6TmWM+OX0gYYmXWDaM63V5QUMBjjz3WJr05wD333MOtt94aTm9+7733smbNGgDq6urYuHEja9euZe7cuWzYsIFly5aRn5/Prl27yMvLw+l0MnXqVJ599lkeffRRHnnkkVOmN29qauLJJ5/ks88+05qjNDQGMBeOSGDFkmnctnIrN722iXfumEGizXT6L/YSmofRh5wqvfnChQsBNb35+vXrw9/pKL25oijh9ObASenNW3+/Ix5++GHuu+8+rFZrT/9EDQ2NHuaC4fGsWJpPaV0zN72+iapGd7/Zct56GKfyBAYSvZHefPPmzXz44Yc88MAD1NfXoygKZrOZu+++u+cM19DQ6DFmDItn5dJ8loY8jXfvmEFSVNfmr+lJNA+jDxko6c2//PJLioqKKCoq4ic/+QkPPvigJhYaGgOc6cPi+f1t06hocLPgtU1UOvre09AEow8ZKOnNNTQ0Bif5WXH8/rZpVDpU0aho6FvRGHTpzbuKlt787DlX60tDY7CzvdjOrcu3kmA18u6dM0iNtvTYvgdFenMNDQ0Nja4xZajqadQ0eVnw2iaONzT3yXE1wTgH0NKba2icf0wZGsub35uGPSQa5fW9LxqaYGhoaGgMUiZnxvLW7dOxO1XRKOtl0TjvBONcjdn0NFo9aWgMDvKGxLDqe9Opc3lZ8NpGSut6b8qg80owzGYztbW12s3wNEgpqa2txWzu+37eGhoa3WfikBjevn06DS4fC17bxDF774jGeTVwLyMjg9LS0i7lWjrfMZvNZGRk9LcZGhoaXWRCRgzv3DGDm5dtZvXWEn76zTE9fozzSjAMBgPZ2dn9bYaGhoZGrzAuPZpP75lJekzPdbNtzXklGBoaGhrnOkPiInpt3+dVDENDQ0ND48zRBENDQ0NDo0ucs6lBhBDVQPEZfj0BqOlBc3oKza7uM1Bt0+zqHppd3eNs7BoqpUzsaMM5KxhngxBiW2e5VPoTza7uM1Bt0+zqHppd3aO37NKapDQ0NDQ0uoQmGBoaGhoaXUITjI55rb8N6ATNru4zUG3T7Ooeml3do1fs0mIYGhoaGhpdQvMwNDQ0NDS6xHktGEKIK4UQB4QQh4UQP+9gu0kI8V5o+2YhRNYAsWuJEKJaCLErtNzeR3YtF0JUCSH2drJdCCGeD9n9lRBi8gCx62IhREOr+nqoD0NtsDQAAAPISURBVGwaIoT4uxCiUAhRIIT4cQdl+ry+umhXn9dX6LhmIcQWIcTukG2PdFCmT6/JLtrUL9dj6Ng6IcROIcSnHWzr+bqSUp6XC6ADjgDDACOwG8hpV+aHwKuh9wuA9waIXUuAF/uhzr4B/P/2zufFyiqM458vNUFgJKSQOIUuXBWlBsOEmyiCqJhZ6MJFWi2DCGkR1CbwDwihTQsL7DdhEZMoIii0SjQJNIwYIsgQBCUNimLy2+KcscvbvTNHmnvOjft8YODceR94vnzvPO/zznPOzN0KnBtw/UngCCBgGjg5IroeAQ5V9modsDWv7wC+7/M+VverUFd1v3JeAavyegI4CUx3YqrWZKGmJvWYc78MfNjv/RqGV+P8G8YUMG/7B9t/Ah8Ds52YWeBAXh8EHpOkEdDVBNtfAleWCJkF3nXiK2C1pHUjoKs6ti/aPpPXvwLngfWdsOp+FepqQvZh8eMjJ/JXd5O1ak0WamqCpEngKWD/gJAV92qcG8Z64Kee1xf4d+HciLG9AFwF7hoBXQDb8xjjoKR7hqyplFLtLXg4jxWOSLqvZuI8CthCejrtpalfS+iCRn7lEcs3wCXgmO2BntWqyQJN0KYe9wGvANcHXF9xr8a5Yfyf+QLYYPsB4Bj/PEUE/TlD+ncHDwJvAp/XSixpFfApsMf2tVp5l2MZXc38sv2X7c3AJDAl6f5auf+Dpur1KOlp4JLtr4edq5dxbhg/A71PApP5e31jJN0K3Alcbq3L9mXbf+SX+4GHhqyplBJPq2P72uJYwfZhYELSmmHnlTRBuil/YPuzPiFN/FpOVyu/Ohp+AU4AT3QutajJJTU1qsdtwIykH0lj60clvd+JWXGvxrlhnAI2Sdoo6TbSptBcJ2YOeDavdwDHnXeQWurqzLlnSHPoUWAO2J1P/0wDV21fbC1K0t2Ls1tJU6Sf+6HeZHK+t4Hztt8YEFbdrxJdLfzKudZKWp3XtwOPA991wqrWZImmFvVo+1Xbk7Y3kO4Rx20/0wlbca/G9gOUbC9IehE4SjqZ9I7tbyXtBU7bniMV1nuS5kmbqjtHRNdLkmaAhazruWHrApD0EekEzRpJF4DXSZuA2H4LOEw6+TMP/AY8PyK6dgAvSFoAfgd2Vmj824BdwNk8/wZ4Dbi3R1cLv0p0tfAL0gmuA5JuITWpT2wfalyTJZqa1GM/hu1V/KV3EARBUMQ4j6SCIAiCmyAaRhAEQVBENIwgCIKgiGgYQRAEQRHRMIIgCIIiomEEQRAERUTDCIIgCIqIhhEEQRAU8TfnSkJ5gDkGiwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eR48izbIbE6y"
+      },
+      "source": [
+        "# Inspecting model parameters\n",
+        "\n",
+        "# Decoding parameters\n",
+        "weights_decoding_X = model.vae[0].W_out\n",
+        "weights_decoding_Y = model.vae[1].W_out"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ncsPtFjmbE6y",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "84393bc0-659f-4a40-b115-91cf5e2f59a0"
+      },
+      "source": [
+        "weights_decoding_X.weight"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Parameter containing:\n",
+              "tensor([[ 1.4053e+00,  3.7175e-02, -9.7960e-01,  1.0956e-03, -1.9893e+00],\n",
+              "        [-2.4138e+00,  4.5134e-01, -1.8913e-01,  2.5171e-01, -2.9227e-01],\n",
+              "        [-1.2381e+00, -1.8943e-01, -1.9962e+00, -1.9159e-01, -3.0498e+00],\n",
+              "        [ 2.7702e+00,  8.1172e-02,  5.2416e-01,  1.0625e-02, -8.0672e-01],\n",
+              "        [ 4.1638e+00,  1.9567e-01,  5.4233e-01,  6.9451e-02, -1.2456e+00]],\n",
+              "       requires_grad=True)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1xggx9EwbE60",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "828a19bd-caf1-4575-825b-c8c7a79b3d69"
+      },
+      "source": [
+        "weights_decoding_Y.weight"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Parameter containing:\n",
+              "tensor([[ 1.9637, -0.0292,  0.5208,  0.0742,  1.6159],\n",
+              "        [-1.4734,  0.1668, -1.7519, -0.0504, -0.2075],\n",
+              "        [ 0.3112, -0.4200, -0.6527,  0.0611, -1.4427],\n",
+              "        [ 1.1446,  0.1228,  0.3933,  0.0329,  0.3000],\n",
+              "        [-3.1780, -0.2801,  0.1007,  0.0530,  1.5110]], requires_grad=True)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cgSa843tbE60"
+      },
+      "source": [
+        "# Encoding parameters\n",
+        "weights_encoding_X = model.vae[0].W_mu\n",
+        "weights_encoding_Y = model.vae[1].W_mu"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cw_EqUiMbE61",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "60004580-80b7-4084-ce21-4563233313c2"
+      },
+      "source": [
+        "weights_encoding_X.weight"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Parameter containing:\n",
+              "tensor([[ 0.0327, -0.1610, -0.0302,  0.0404,  0.0831],\n",
+              "        [ 0.0779,  0.5635, -0.1575,  0.0789,  0.1736],\n",
+              "        [-0.0349,  0.1877, -0.1555,  0.0720,  0.0630],\n",
+              "        [ 0.0546,  0.3447, -0.1048,  0.0379,  0.1079],\n",
+              "        [-0.0420, -0.1349, -0.1639, -0.0608, -0.0898]], requires_grad=True)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 68
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DJ5yxHlrbE62",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bb8a54fb-681a-4338-c3d0-6951e709845e"
+      },
+      "source": [
+        "weights_encoding_Y.weight"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Parameter containing:\n",
+              "tensor([[ 0.1302, -0.0212,  0.0050,  0.0447, -0.1890],\n",
+              "        [-0.0278,  0.1890, -0.2728,  0.0296, -0.1521],\n",
+              "        [-0.1212, -0.4067, -0.0618,  0.0275,  0.0937],\n",
+              "        [ 0.0881,  0.0248,  0.1012,  0.0185,  0.0474],\n",
+              "        [ 0.2407,  0.0546, -0.1416,  0.0596,  0.1504]], requires_grad=True)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 69
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xxdwtuCybE63"
+      },
+      "source": [
+        "# Here we compute the encoding and plot the latent dimensions against our original ground truth for the syntetic data\n",
+        "\n",
+        "encoding = model.encode(data)\n",
+        "\n",
+        "encoding_x = encoding[0].loc.detach().numpy()\n",
+        "encoding_y = encoding[1].loc.detach().numpy()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wmmv6Gegy5Zr"
+      },
+      "source": [
+        "We note that the estimated encoding is correlated with the original latent dimensions. There seems to be however some redundancy. This motivates the use of a *sparse model*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Y_bHczQZbE64",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 714
+        },
+        "outputId": "02da4b0a-fd11-4a66-8abb-0075778d4955"
+      },
+      "source": [
+        "plt.figure(figsize=(12, 12))\n",
+        "for idx,k in enumerate(range(n_components)):\n",
+        "  plt.subplot(n_components,2,2*idx+1)\n",
+        "  plt.scatter(encoding_x[:,k], latents[:,0])\n",
+        "  plt.xlabel(str('mcvae dim ') + str(k))\n",
+        "  plt.ylabel('ground truth 0')\n",
+        "  plt.subplot(n_components,2,2*idx+2)\n",
+        "  plt.scatter(encoding_x[:,k], latents[:,1])\n",
+        "  plt.xlabel(str('mcvae dim ') + str(k))\n",
+        "  plt.ylabel('ground truth 1')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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U173ft0lZZyeDn3HspwNsDdjFg07rOzpjq1QsdIxfXRiTKmY5CipdVq3Vfb3aNriNyKg7Vjpk6Us/L7DNPBPUex13EiLHUKdPzvDeCQAnARwA8G8B/FXz75PN9ximq5g8Bm5st8K8yrLcjJ+T7Wb9JrytY5NYu+sVnDh3UfuZh+66zUqWbhF0q1OF+whOjrB40AGhMTlJr5jOo5Inws4HVmPLZ29rbaMSAMoRpGh1IbD/ZBnjE+bFi5eRoRKObb8bb+6+F7OaSbvPt0lZZyfAyFAJTz+4BqVioWOcuz8jx96x7Xe3vWcz5gpOHjvuv9GgaHTTShScfMdnVMbWyFAJm9eV4N1IqlRrsXWzIUD7TMXBxlVLATR0cRJncVylL3W6yRQy451nbOciic0YCkLQ8zPxY+ow+McAQERfFUJ80/0eEX01acEYxott3JhtFyypLFUl3WwVeKVa6wgJGX1pCjsPnMblaiNT/JMfWYj/7++vWB6xt6jNCgzOG8DE1+9pe13nDasL0YpZlF4pgc5Qkahe4rlYq5V1dnJESSDz00eqJiJur3W5Um3FGau6iI5PlLH/ZFlZf1kgHk+ufG6iVtDRcfTMBQCwMv6cPAUO3ajNitax/bzAunmmXKm28idsqqB4iTMJkWOo08fkeZb8nOK1n49ZDobxxc9jIFF5bbxIZRl3STegEZNXqdZaXmyv4ZxIb+MUUSls6WnJK+Kqa3Vh5QGPMhEE8dz1IayzfRifKGPD7iNYsf0QNuw+EniXIwi6sfjslrV4S+Gplsj6ywUn3xG775bXLyxEAMrnUFIqFvDI+mWtsAbvJ+VzY6NXdeSJjHqvXKlal6Dc8tlwu3nu0AaTF9i0wJY6Xfdb5HeTHl+2cyGTHKaY54cA/AyAFUR0wPXWzQD0+9TMnCfuRAZ3qSWvF0VlEKliEL3VNjauWhqpvXQUshMBHQ8qhS3vWZR47ygTQZYbbiQF62w7uh0vGmUs2iS3+S0yFzcbjZjYe/w8bi0W8OyWtb6ybjXkM6goOPmWcaozkAl2Xu3Fg06kCkby2ukWLEAjhERVhciNyqPvdsh4x9fWsUnsOngaO+5fHcsY6+VunP0CCc3kRkS3A1gB4GkA211vvQ/gdSHETPLitTM8PCxOnODQvSyjqnrgVp5xHE8qLdV2p+4Y7uL712fqrbJKcxGbJEbbREcC8MyWtVaVL4IQZcxkFSI6KYQYTvD4rLMt0BlwYbvjAdEdBrrvr9h+SLvYfmv3vQD8mwYVCw4Wzh+wdhQUCw52PqA38nTnyxNhVggscHK4NjPbSv51N5Xy1s0PgpOnUCXqdOjmD1sPuDyG956Zvh907jLB1Ta6g05vm2KezwE4B+BzSQrG9Be2BfttH3hTzU8bvIZc3CEavYifUSwnBBs+f8eSjvvnt42smgSdHOGmBQNWHcUYNayz7Yg7XjSqJ/uJ8VPYe/x8Wy1j+X1dfC3hRrUfv6ZBl6s17HxgtfWCtlKtBa4cAQA3LxjAfXd+DPtPljuSf4dvb+gJGdsclJKm42EUdPfJdhyoFlumrpAAlPc4rJ7TxVCzUd0dbDoMvo8b93weGvVDrwghPpSkYExvEqRgv1Qgqu5WNluSquQ8b/cpVcmyrJJrlmpLG3kfbLwvb73X+RnTPVN1CGMFHy+ss83EnUwapXvc+ES5zXD2fl+WffS+L3Ajuc5vsXqrq7ydbaiaSX752q6Dp9uanniTp1XHCrtA8QulCJNE6JVNYqP7VCESfl0hbc7tJaghnGRIEhvl7fgmDAohbhZCfKipeAsANgP4g8QlY3oSUyKDbpLZe/x8W5k4d0KM34TmTc772tgkRl+aah0vqOFcCjmBxkEQwznJpEOpGJ28/1lUk6HunklPjfSY6Ep7MdFgnW0m7mTSKJ5sUzOhdypVjAyVtO9LXWky9Lz1qI9tv9tad/hVjhicZ9Nj7YasG3YfQXHQsf6OG5PhnCNgYQBZvHh/p804kIZvkMRNm3O7UZVQ9SaLekmqhF0YWfodm2obLUSDcQCbEpKH6QFUmcTyNVUmslTgOkWh8ro89uIUVmw/hOnrM3By9qbiLMJ3oFo86ODY9rtTNaBtScpBLe/VyFAJe754Jxb7THYqQ3mOV7rIFKyzbyB11LaxSSxwcigWOuuThyFK5QOT8SS/r9NHsnyd6nXT77L1sC8qOMaKEUG9yOVKFZema8gH0Oc25IkihXMUB522OW3P4bNYOM+/qohsY/7EeMOzG8arbroXYQxhU5m9KFU/uK50JzZhGw+6/swBGAZwNTGJmEyj2hYa3TcFuFq0ujOR3YkRQapbSI+x3BYsODlj2+k4uDRdC5Qs0m8MOjn8L57JdnBeIw55geL6m5o2AByWkRasszvx6q1L0zUUnHxHsqvfMdx1l+tCoOTqRhqm8oEppll+X1dZQeflnBUCbzaTCVWMblqJ0X1TxsQ7J0e4cv1GjLFq+982tMuLt6Z7VGqzIlIn18vVWtv1KFeq1g4bgUalkuHblwS+Hn5jJMyOhkmGKCEcXFe6ExvP8/2uf5vQyNz+6SSFYrKLagVamxUd3l5pOLu35FUeSVsfxNXaLB5Zvyx0nVFb5qrhDACLF87vaD8rt+mqtdlWN0EAbU0bVN6MqGEZ3azD24ewzvYQ1XPmfh4AtNVd3n+yjM3rSqG6x+l04sPrl7W+r+tOp90hI2DoyVeUz45cAJgM51KxgJsWDHTodO/1Gt20MjM16+tChJ4bZhUVPGqzAo7lvryMPw9SB9tmjITZ0fCTIay3mOtKd2L0PBNRHo0SR890SR4m4wRZaXo/q6u/7PXaqBBodKF6+sE1re9TRhLs+gV3B62LV651eJprswJCtHu9kqiR2+06vP0E62w1UT1npnjWaq2Oo2cuhCpzZ7tLo6usoKp6IcSNHTv3s6P7vJd3mgtm3XtumfadOI9jP0y/hPjiQQc77l8duAa1iSAbnTKEoxHTLYy7pHkiq7ESppazTWJoGG8x15XuxGg8CyHqzcL7rIgZAMG26lSrUtUkMHz7ktbkAYKyzSxwI4HG7R0dfWkqdIwz04lMBtGhii2M2krbS5TqBXMd1tlqolbY8DM4omxfh23bLL/z2ItTxpAFt7fRJqHNpE3d12t8oozXzl+2EzZh5M+PEr4RWQagrfqIDlv5vAurRQUHRMC2scmWp1tXDcVUbzqMt5hD8TqxSVE9RkS/B2AMQKvPsBDitcSkYlqkUR5mfKKMnQdOtwwluaoHgCvXOvssODlqi3kGIqxKDXrF+9CPDJVw4txFvPDq2z1Tji4JvJ2u0iDO2DeOr4sM62wPUT1nfk6DtLavR4ZK2GbhbY0jHE12Adyw+whGN63EroOnIzVCMvHJjyzEDy9csd5ZrFRrypJ+WWTxYCMR02ZOl4awMtdIUabVfZy4vcVhF3n9io3xvLb53yddrwkA4VoxuSCiLwD4JoA8gD8UQuyOesx+Io3ta1UHqEvTNTy2bwo5dMaGuQ3rMEZ+kG50qrqa+0+W57ThXCw4mNxxD+54/DtduQ6LBx2ldyVO4yHuOrxzkMR0dq8S1XNmakSSxva126nSjVr27gW6TBKPq9Ofirfem8bnPrEEf/3GRWsDuhdmgXyO8MHVGWVYTdBmYbJMq+447C1OFhvj+ReFEG+4XyCiT0Q9cTM27/cB/HMAPwLwN0R0QAjx/ajH7he6vX0tC/arqM8KqMzbwXkDHQ9rEGxrYxYLTuBOdnMBambsPHTXbcpaqAvn5TF9vR7LxJIj4N5Pfyx0ZQFbOL4uMono7F4niufMG0vqrrbRbYPE63CIYjgXnHwrj0TXshzo9FyHMZwXDzq4Wpu10tm1ushELLUfQXf9VPOozZxus+umOk5S3mJumGJnPL8E4DOe1/YBWBfx3D8B4O+kkieiP0UjI5yN5ybd3r42FezXEVUWm+3EgpPHzgdWdzywc7kyhqQyXcP4RLmj7W2eCA/ddRueGlkDALGU4JsVaFUW0HWEjAP2mEQmKZ09p8nKtrXOaZAnwqwQrdhYU/wtAR3PlW7BahMW4kfBybd2KN0hgb3O4Lw8rlyP7sDxm0eLmh2/IMeJy+DlhO4GWuOZiFYBWA1gkadu6IcALIjh3CUAb7v+/hGAuxRyPArgUQBYtmxZDKftHbq9fR13kXc/xifKvit3dztnd3IgG84NioOOctLzlkEybTur2HDHEqXnJ0plgSBkxVDpJbqgs5ku4Gfk6PS0t76zbsEsS4i6MS1Yg9Tn19GhjxIO++gWV67Xkc9R5NrVfvOo7eaC7jhxGryc0N3A5HleCeA+AEU06oVK3gfwr5IUyo0Q4jkAzwHA8PBw7z9tAej29rXJm5vPUUfMs40susYCMuHEz3CWSn7oyVf6sqrGs1vW4sS5i8b2syZU3gjZoXHb2GTH5GszEZaKBbz1nv4znLiXWTKhs5nguPWkN77YtjmJ13AKOn94dcRjL05h69gkFg86cHIU2tj19hvxqzPda8TR9OXKtRmMT5TbKkm5FzI2nnrTvQ1r8LrlKA46EEJdcQmYe/OC1ngWQnwbwLeJ6HNCiL9O4NxlALe5/v548zWmSbe3r3XeyUbnuU8rZQGgzRzWxebZJJx4FYHNllWa6Dy1JuYP5Foe1qNnLsTqTXdfa/fkayphBNht1Ubd+eB4uWTogs4GwInecePVk16t6DVybI3ioPOHTl9fmq51GMBBmBWNXUMp01wzsmyoVGvaetzeBZUKv9j7MCGgqq6cJpJM6M7inOEb85ygEv4bAJ8kohVoGM1fAfAzCZ2rZ+nm9rWNsnX/v99WkCmhz8/zMH8g1zpHmI5I3cTPU6vj2sxsy9sQNKwiCN7J16Qw5faqzkPtbhscBo6XS56EDWdO9I4Zm8Rnb3MS+T0/YyLI/GGSI6pztVYX2HXwNEaGSpyvosFUj9vv8vuF0YUJAQ2SkJ/kjnhW5wybhMFEEELMENGvADiMhgfjj4QQp9OSh2kQVdlWa3XsOti4jVEUZKVaw+i+qY760Vkkyu/cdfB027ZYUtVDypVqayGiu5qlYsHo3fK2DQ4Dx8v1PJzoHTM2nlhVjfu4n5ekPcKXpmtYsf0QihHDQPqZMHOJtk27izAhoLbjIemKM1mdM1IzngFACPEdAN9JUwbGjGm7RPdwXZquxdImdS4o10vTtdZ22KXpWmINT3Jkbs3rVaRJhQxxA5SexzfRey4neYfBzxPbrTKNthUdoiC78NlGgRScHGZmRewOFCcHzBuIp1JGmtiOjTD63GaHQJV8GjdZnTNM1Ta+ZvqiEOIb8YvDZAm/7ZIo22+qroRMY3LRGdA5Mm+fPrtlrXbRMiv0rXl1noMkvFvcACU5sqKz53KSdxh0uzwC+mczSAyo7WdNFR3i1tc2R5FVgwC7ROcg1GaBmR43nPNEHVVMTATV56ObVhpzk5Je1PntlKY9Z+QM793c/DcM4JfR8DiUAPwSOmuIMn3E+EQZG3YfwdaxSe12CQBsXLU01PFLxQL2fOlO7PninSgVCyA0FEGQ79tsVaVJnggfvXleqO8KNJrCdBwzR9hwxxLldx5phlMEvS6ERrxct7a/RjetRMHJt73GDVBioxs6mxO9Y2ZkqISnH1zT0oWlYgHPbFmLt3bfq3w2pVOjXKlC4IZTY3yi8zYE+exlQ0UHqa8XD3bqJUmUpEIVsmoQ4B/TG4akV3WlYsF4vaIyK0TyeltzT0vFQiDDPSjucasiC3OGqdrGLgAgor8E8BkhxPvNv3cCONQV6ZiuY9Mu+51m/Oz+k8HmTLnF4/WEPLx+GQ69/q71luHGVUtx6PV3A52729SFwN+/fz3Udwk3Oge6qdUF3nqvime3rNV6knSxbfMHcsoSQ91evXMDlOToks7mRO8EiCPXRBUDGuSzul0hmQsxPlHG1dqsVq4kouzqQmDb2GQsYYC2xBE6J50S4xNlbBubTMRQj1t3e+fl6eszyp2GboRqmJIV0+jqqcIm5vmjANxWwPXma0wGiLuEi02G7aKCg8denArcFnZ000plKEjQGsdj33s78/HQsqZ1GGRcoIp3KlXjRKszTgF9B7Fuww1QEicxnc2J3ukTJAY0yGd14SPlShUbdh/BlWsziWffXG4AACAASURBVCU0m0hS03sN5YKTx+Z1Jfz51LuRuiAuau4cjgyVcOLcRew9fj7W32HS3WFsAtW8rKMbsca6c8hFSRawMZ7/BMD3iOjPmn+PAPjj5ERibEmihIvfg+HkCFeuzwQ2DDfcsaRVYziqAs664QzAeH3yRPhQYSBUco6Nt8FknLLHd06QqM7mRO900XmIi4NOR8193WcXFTo/622S4m3W0m8UnDw+s2wRjr9xCXUhkCfC5nUlPDWyBkfPXIhkPFeqNTwxfgpPjazBUyNrMHz7klAOJxXFZvv1bWOT2HP4LDauWoqjZy7gnUoViwoOrrg8xrY2QZCydDkirNh+KNE5pBdyY0hY3EwiWgfgv2n++ZdCiIlEpdIwPDwsTpw4kcapM0mQ9qtRjymPO319xmj0lYoFbFy1FC+8+nZLIT101214aqSR+LFi+6HEY83SZvGgg8F5A9o6yc9sWQvAXP2iWHBwbWbWt+22JItF5Jl2iOikEGK4S+dind2nqELrnDwBorMD7OZ1Jew/WW7/rCL5z6tbTPOAlyi7bGmycF4e12dm265ZnNWOpK6X1zSuuS9MmT+dTeDubBkG05wUBdUYT+pcfuj0tm2pukkA78rPE9EyIUS4fsJMYHSGURIlXHQxs3LQrtiuD52UW0kjQ6WWseyl3wvkF5w8dty/GkCncayqk7zr4OmOxUjByWPnA41j2BjEWS0iz6QK6+w+RRWadeXaTIentFqr4+iZC3j6wTUdsaxenROkkZIbaaDbht4lVYozDKoydXHKJgBsbXqHTbsAQSAKt/Oqup82+U3Sy61zmAWptxzEwdMLuTG+xjMR/SqAHQD+C4A6boz/TycrGgOYDaMktja8W3d5orYKG7pz2pbNSbKTXtqoEhn8ujXKRBzd52yURVaLyDPpwDq7v9DpB/ezrXNqqHIkTJ+V6PT8wnl5XK3NdoQ52MYIJ2E4O3nCQI5QNSQzpkm5UsXWsUk4uUbFpHrIsMOCkw89b6psAr9QDSdH2PnAauw5fNa422yz0Arj4Ml6boyN5/mrAFYKId5LWhimE5NhFKZrkA1ywKoGu2obMMh2impFKatnJF2g34awnhFVIoPtwx9VSWS1iDyTGqyz+wRboyOII8Xms6q5xckTrs/MtkI06kJg/8kyhm9fgp0PrMboS1O+daDjDvHwOizGJ8pWcqRBbRbIQWDhvODNWeTvDFN1RGcT+M4PZPc5G2ddPzp4bIzntwFcTloQRo3JMPLzEifRRvnPp97FAifXeq9YcFohBroEFC9eY3F8ooyx772t/Gw3cDckCLut5lUgScUgq47bC8kVTFdhnd0n2BodQRwpNp8NEhry2ItT+J0vN+pAq8LQ3OfYvK4Ua+UJr8Niz+GzSsPZlIfSTWYBXK3NgtDonjjt4y33OqZM11eF3B1Q7XD6dZSs1QUee3HKeK9snXX96OCxMZ7fAPB/E9EhANfki9xhUE3cRpOfYWTyErvfDyqfblB7leflamcr7nKlitF9Uzhx7mIrC9h0rj2Hz6ZWQUMa/2ESZSReBaLyFslapVFqVOq8ULrdgLSLyDOpwTq7T7A1OoLEiNp+1jbcoy4EHn/5FJ5+cA0mvn5P63XdXBO0NKkO2VjLfR7dLHJpugYhGt7ztL3S0vM+XZtFrpHnqezuqJor7v30xwJdv7oQ2Hv8PJ4/fj5U9RTTLsHiQQc77l/tG3oI9Eb1jKDYGM/nm//mNf/NCeKqlRglcWt8oowr12Y6XvcaRrbeCRv5/FpietF9rjYr2h5y07nS9AZcm7mx8tddbxO6OGfv/XArrbBjQnefVUlBWUuuYLrKnNTZ/UgQoyNI+FeYUDFTwptqvtGdY7HG46l7XUddCKukN0mU0nNJMSv0lZW8OlzVmIwADPqEgQjPf6OiCpXxsyuSCjFNE1/jWXatmkuENYLjjOvRKQX3ak9i653wky+IIgpDN88VVKaghexN5QD9FgNhx4RfCA8bywwwN3V2v5Ilo8Mv2ds2ceyDq50OCidP2HH/6kAxvaViIVB9Yj/SqgSiMuq9c6XOySQAOPkcCg66Mpeqcnts7J5eqJ4RFJtqG0ehGFNCiGy0eUmAIEawzZZRmLgenVIYnDdg3VbV653wM7LjVEQ6ypUqhp58BZXpWmZKFpUr1UCGs9/kZZMUE2ZM9OPWFxM/c1Fn9ytZMjrkOXXNPmwTx1Qhegub89rOA6etPMROjjC6aSW2xdS2W8ZjHz1zIfW4aMk7lWojAXLflDGs8XK1hme2rI2tCYsJ9z322zlWhRb1srHsxSZs439y/f8CAJsBBNvb7jFsPbm23tMwxo2NDO7Bq2oz6jXwFhUcpWKS8nUreD9qVY0oJXtUBMkAt4lZtjlWmDGRJS8Uk2nmnM7uZ7JkdKhybIDoiWOXm/PSfXfaxfTetKBhbMcR9kdAq+Qe0JhXw1S1iBtZJ9qPW4sFjAyVYltI6HDy1LrHNrZPvzt1cn4fEEKcdP07JoT4GoB/mrxo6aG76d7XbTy1YY0bPxnk4JWKQ6BVWQalYqGjdNz4RBlXriu2y3I3HoheGOyynnRJI6vMYg6CjbGbQyNk5p1KFXsOn8X4RFn7WZ1skrBjYmSo1PrtBPV9Zpi5qLOZ7hFFD5nmNVVMr45K0wEzumlla94LiwBw9MwFADfmVRtkwmJQQn5NiXsuCTt/O3lqdJ30Ycbl/baxfcqVKu54/Dt4YtzuevYavlYGES1x/buFiDYBWNQF2VJjdNNKFJx822sqg8fkqY1q3PjJoEtKWzzoAGj0vd+w+0jLyNOV8JEreHlOm4dIsnjQCa1AwlIXAnsOn8XGVUs7ro/s4Pf0g5/2H9gBmUUzYxs3YuB1BrTq3pkWNkEYGSrh2Pa78ebue3Fs+91sODMdzEWdzXSXsHrINK8FCRt0V5v6/B1LggmvIGjoYqlYwGzIEIk4IysWuBxFYRYSpWIBe754J/Z86c7WYkiHEGjNe7a71HXRKBzQjwa0TdjGSdxwbM4AeBPALyYpVNrYxpnpYlBNyWQ2yHCMaq3eCinwhgvoBu+l6VorLEIaeSfOXdRubblDKEaGSoHqSF6aruGR9cs6yqQlTblSxf6TZWxeV2rrbFUcdDB8+5LAvyMMpqS/LMUpMnOSOaezmd7ApBttww7cTqTxiTK+99alyHIFCV0koGXspx0ffWm61lbMwDbcRNXYzKZca7VWx66DpwNnV77w6tutsJh+wabaxopuCJI1bOLMwsagmsrgeWOJ6kIoy9aYyga5qdbq2GuIIaPmOeWxKwENTmnEynrOiwoOrlyfSbyWZrVWx6HX320rNedWJEF/RxhMijZLcYrM3GKu6mymN9DpRt2cNujkcG1GoC4EiACCwLaxSew62EgujOrJ9YY++M2r8wZymYiJlrgdObpGX8WCg4XzB9q6+u45fBbbxiY7bJCNq5Ya4851TilTDe2kExnTwCZswyGiXyOil5r/foWInG4Il3XCxH65Y5VVIQCmSh9uVNtfOkzDVjTPOT5RxobdRwJXwJB1huUW3uSOe7Dni3f6hnNQ85/uc6ViAc9uWWuMH740XdNeqyjxX7b0Qow4M/dgnc24kbp9xfZDbaF8WUM1p0mDTBpfQjSaiwjcaHwSBl0Ync286nbYqCg4eTyyfllr7upGZKN05CivYY5AhDbDef/JstYGkfHfQVk4b0A7n3c7vNNNUuPfJjT0fwOwDsAfNP+ta77GIHjsl59xHKSj1NMPxrMNIh8e3YpbKgMdKtn84sEEgDd334vf+fKd2hg4eW39EvBU8gRZXEhk/JeMGzfBVS6YDMM6mwHg76zJEipn1MJ5A7F1n3UbzM9sWYu3FHO2lCGosZej9jynp0bW4Nj2u/HW7ntx66LknSzuGHD3NSwWHIDa83X2Hj8fygbx43K1hofuuk35nu71pEly/NsYz58VQvycEOJI898vAPhs5DPPUfyMY9tKHwBa2zRRyRNpY5bdykB3LpVsfl5Zd7jI5nWllrLKE2HzuvZtPV2SyaCmqkZx0OlQIosHHWMypNtgn/j6Pb6JF1zlgskwrLMZAPY7mVnB64y6HGNXQIEb+Ugm3T0yVMLvfPnOQMnzswJ4Zsta5bGTLgHrdeTIa/jMlrV4/2pnCKVfPwrT3G26IrcWC3hqZA0eWb+sbT5/ZP2y1OKdkxz/NsZznYjukH8Q0ScApNsWrofxM45tK31IwnhYvcfWxSPJbkKmLS2dbH5yucNF9p8st2SoC4H9J8ttK0NdeMy8AfXx5c9xK+KJr9/TllFcLDhYPOhow21MCqTUrKvJMBmFdTYDwH4nMy7i3iKPOzQu0O8OGGmgM8iKFjuZUVA5cqTHNUissSwXeOWauiS8kyM8vH6ZclHhrgH91Mga/PDpn8KzW9bixxYtwN7j51MLF0py/Ns2STlKRG+gMZxuB/ALkc88R/FLMgxaqcH7edOjQmg0SiFqJNTJY+uyhr2KK4hs7s+aOhDZdnNUJZnosrN13oogSXyjm1Zi9KWpjlW7uy42w2QU1tkMgO52JfUmu8stcgChnQ1+LcHdODnCTQsGUJmuIadpfGX63X4d8/xQGWS6duReCk4eC5ycMhlv8aBjrBylurZ+JfdUTdWWf7iAbWOTShuiWHBw350fw9EzF1CbFR3fv2l+uymZxFgIQ5Lj32g8E1EewJ0APglAWgxnhRDXIp95jmJjgAat1OD+vK7MjF/5PNuqIUFkk5/VyXRrsRBpZZjkgyF/o7vkXbHgYOcDq9nrzGQW1tmMm252JbV1hATBO1+6nT/FQQdCNJwlch6Vn7Xtuiux7RYMNBIAVQ5d1byja0decHJYsnA+3qlUW7/j0nRNKfOO+1drjVope5BQEXcrcncS4d7j55XnkGVy3dfH+7lL0zWMvjQFAK3Oj3GPhTAkOf6NxrMQok5EDwkhngHweuSzMQCSLWMWdLDY1JROUiZbr3fQ48aBvE/u0oJya44NaCaLsM5m3HSz5nxSW+S286XXAJaFzmWss+l3B2mOEmTe0f32am229XkbmU2l8VQGqanknrsVucRUaUu3Q+ylVhfYdfA0RoZK2t9drlSxYvsh33FoKucbhCTHv03YxjEi+j0AYwCuyBeFEK9FPjsTO0EGi21N6aRlCmsAd2NiyMr2E8MEgHU206JbNee7GSKiQtd116ZpmU2ohndutJl3TEbs4y+fwvyBnFLmPFHbMXX1m4EbBrrb4FxU0MdZv/Dq261mYt5jqDDtEHuRu7Sm3+2uegF0zqNxz7lJjX8b43lt879Pul4TAEK30COiPQDuB3AdwA8B/IIQohL2eHMNv1WZ7WDp5taKTqaoBnDSE0NWtp8YJgCx62yG8aObISIqoni+85oYaff77sQ823nHFLNdrdW13ty6EG0G4+imldrQDZno5z5PxVClxHtseQyVsRu2m6JNrLpuHu2VOdemw+DGBM77XQCPCyFmiOi3ADwO4H9O4Dx9R9BVmcnQ7nYmtp9sz2xZm6mHA0j/GjFMUBLS2QxjpJshIiqieL5NhrOqlbUt8jthOhK6DcaRoRJOnLvYEZfsDn+0CTtRHRtQG7sE4OH1y7Q7xCqKTY+3bSED1TzaK3Our/FMRF9TvHwZwEkhRKgelUKIV1x/HgfwxTDHmYsEWZX5GdppbrOF2Zrxbkt5q4YkoaTT3opkmKAkobMZxgavR1aWruuGztYZgBtXLfX9ri4swutxDsPIUAk7D5xWeoMXDzq4WpvVGqVug/GpkTUYvn1JxxxoSiY04T6238JHlbj5j1drcOdCOjnCzgdWtx3Tr5CBrkeE6rOLCk5rLHV7YabCJmxjuPnvYPPv+9BIRPklItonhPjtiDL8SzRi85QQ0aMAHgWAZcv0Xe7mCkFWZX6GdlzbbGGC+/1k8x5TthRVbUslGYec9lYkw4QgaZ3NML6YQgmS0Nkq76wAsP9kuSPG14vO8K4LEShJXDUXAsCV653l6pwcYcf9q3Hi3EU8f/y88ni6crHuKlBhUR3b9BtNlU/85v0g86jqs06OcOX6TGsMZSH3yMZ4/jiAzwghPgAAItoB4BCAfwLgJAClIiai/wDgxxRv/YYQ4tvNz/wGgBkAe3UnF0I8B+A5ABgeHo6nT2fGMRmjNp5Qv3qV0tCOY5stbHC/aRGgOqaujI4kyVhtIL2tSIYJQSidzTBx4hdKkITOPnrmQsc8YXMet573lrmzndN0c+H8gVxHvwAAuGnBQKusmwoZb2w6hy0FJx+Lk8y7GCo4eatwS908CqDNm7xx1VIcPXOho/rX9PWZjsVC2nHQNsbzRwC4a4TWAHxUCFElIm3tUCHEPzMdlIh+Hg2PyE8KEaANTp/jZ4z6reBsHi63oa1bbdp6k8MG95sWAbqsaT+SionqVrY6w8REKJ3tByd6M0Gw0cdx6+wo8bKmvgQ2c5puLtTNxZWmMaiTTaDTWA8a2wzcCD2J6gCKmsinCunx2jpuD7y7+peuIVqacdA2xvNeAK8S0bebf98P4N8T0UIA3w9zUiL6AoB/A+C/E0JMhzlGv+I3QP08oX4Pl82K02TAe8/t593WYVoE6B4UPzgOmWEAJKCzm3CiN2ONaX5wf6Yb5wxynrAGeFBDTsqkk7mkkDmMsSiTIf3K9fkRdyKfzUJA2j5ZzD3K+X1ACPFv0Yg5rjT//ZIQ4kkhxBUhxMMhz/t7AG4G8F0imiSi/z3kcYzIZIUV2w+l1ls9KDYDdGSohGPb78abu+/Fse13W9drLBULVskPOgN+54HTePzlUyg3s2fl9pYKv0E9MlTC0w+uQalYAHlk031Xdy6A45AZRpKQzoYQ4hUhhAzePI5GeAjDKBndtBIFJ699PwmdrTpn0PPo5h+/OU33/uJBxyhTEJlNMpjmx8dfPhXZ/gl7XXTYGt3vVKqx3Ne4sfE8QwhxAsCJuE4qhPiv4jqWjqw3t9CFRURdYZlWsbqVp1cWnbdAlS3s7ogkCdLkRHUvdF5pd0vRblXbYJheJG6drUCb6M1J3gwQLcEs6jnd1S0WOL4+wjbCJonrvrfj/kYFCttKFqZro6ufXCw4uO/Oj7Ul1buJIz447uR5m50J+bks5h5ZGc+9SJYLbZsM+6gDNEx7bq8sXmPYD9nFKa5BncUHhWHmAnEkes/FJG9GTVr5ItdmZlv/f2m6FshxFnb+MSXF+R3L9jr5yTZ8+xJtTek42qSbzh0Um0Yqbtsla7lH1Eu5esPDw+LECTtnyorth5QGIAF4c/e9scoVFF3NQ+kdjtrXPcj3dbKovMkLnJyyPI5N+1OGmesQ0UkhxHDackShmej9P6KR6O2brxJEZzNMHPjNr91ElcAvm64AyTiIsvT7/VCVpJW7y1lxmun0dt96nrMYYC7xi2uOusIyfd82REPlTQY6uwylHXfEMEx34ERvphfIUoc6U/7QtZnZRMJKe6k3Qda8yUHoW+M5ywMoLcM+SIiGaZXK4RQMMyf5PQDz0Uj0BoDjQohfSlckhmknS44zncGuyh+KK6yUwx67Q98az1keQLpYn+nrMxifKCcmo65+cpCEv15eKTIME55uJHozTFSy5DizTYqTeI1t905xkIRLnqeTp2+NZyC7A0iVEQwET2wIiqkYe5wJfwzDMAyTBllynOkMeV3+kLdTcDfbmzPB6GvjOcvItpze7ZskK4KEKWPHMAzDML1EVhxnpgocft7xNNqbRyFqoYNeg43nGAg7aLqd2JCl7SyGYRiG6XdMhrzJbkijvXlYst5XIwnYeI5IlEHT7cSGLG1nMQzDMMxcxc87nkZ787Bkua9GUrDxHBLpbVYNbttBk4YnOCvbWQzDMAzDqPFrItLNXWO/3fUslQfsFmw8h0BV+NyLzaBhTzDDMAzDMF7SaG+uwmZ3PUvlAbsFG88h8AvkB+wHDXuCGYZhGIbxkgX7wCYkYy7mU7HxHAI/r3K/DxqGYRiGYfofm5CMubiLzsZzCEyB/KU5MGgYhmEYhul/bEMysuAl7ya5tAXoRUY3rUTBybe9VnDyeHbLWhzbfvecGkAMwzAMw/QnOntnru+us+c5BHNxi4JhGIZhmLkF2ztq2HgOyVzbomAYhmEYZu7B9k4nJIRIWwZriOgCgHNpy+HDLQD+IW0hfGAZ44FljIe5IuPtQoilcQjTK7DOjg2WMR5YxniYSzIq9XZPGc+9ABGdEEIMpy2HCZYxHljGeGAZmTTphXvLMsYDyxgPLCMnDDIMwzAMwzCMNWw8MwzDMAzDMIwlbDzHz3NpC2AByxgPLGM8sIxMmvTCvWUZ44FljIc5LyPHPDMMwzAMwzCMJex5ZhiGYRiGYRhL2HhmGIZhGIZhGEvYeE4IIvpVIjpDRKeJ6LfTlkcHET1GRIKIbklbFi9EtKd5DV8noj8jomLaMgEAEX2BiM4S0d8R0fa05fFCRLcR0VEi+n5z/H01bZl0EFGeiCaI6M/TlkUFERWJ6KXmOPwBEX0ubZmYZGCdHZ2s6myA9XZcZF1nA93R22w8JwARbQTw0wDuFEKsBvC/piySEiK6DcA9AM6nLYuG7wL4r4UQnwbwtwAeT1keEFEewO8D+O8BfArAQ0T0qXSl6mAGwGNCiE8BWA/gX2dQRslXAfwgbSEMfBPA/yWEWAXgTmRbViYkrLNjI3M6G2C9HTNZ19lAF/Q2G8/J8MsAdgshrgGAEOLvU5ZHxzMA/g2ATGaNCiFeEULMNP88DuDjacrT5CcA/J0Q4g0hxHUAf4rGpJsZhBDvCiFea/7/+2gojsz1ViWijwO4F8Afpi2LCiJaBOCfAPgWAAghrgshKulKxSQE6+wYyKjOBlhvx0LWdTbQPb3NxnMy/DiA/5aIXiWi/4eIPpu2QF6I6KcBlIUQU2nLYsm/BPAXaQuBhjJ72/X3j5AxBeeGiJYDGALwarqSKHkWDUNgNm1BNKwAcAHA/9ncpvxDIlqYtlBMIrDOjp+s6GyA9XZcZF1nA13S2wNxH3CuQET/AcCPKd76DTSu6xI0tl4+C+BFIvqE6HJdQB8Zfx2N7b9UMckohPh28zO/gcaW1t5uytbrENFNAPYD2CqE+Me05XFDRPcB+HshxEki+qdpy6NhAMBnAPyqEOJVIvomgO0AfjNdsZgwsM6OB9bZyZJVvd0jOhvokt5m4zkkQoh/pnuPiH4ZwMtNxfs9IpoFcAsaq6GuoZORiNagsTqbIiKgsbX2GhH9hBDiP3dRRON1BAAi+nkA9wH4yW5PZBrKAG5z/f3x5muZgogcNBTwXiHEy2nLo2ADgAeI6KcALADwISJ6XgjxSMpyufkRgB8JIaT35yU0lDDTg7DOjoce1NkA6+046AWdDXRJb3PYRjKMA9gIAET04wDmAfiHVCVyIYQ4JYT4iBBiuRBiORqD7TPdVsJ+ENEX0NgiekAIMZ22PE3+BsAniWgFEc0D8BUAB1KWqQ1qzK7fAvADIcQ30pZHhRDicSHEx5vj7ysAjmRNCTefh7eJaGXzpZ8E8P0URWKSg3V2DGRUZwOstyPTCzob6J7eZs9zMvwRgD8iov8XwHUAP5ehFXgv8XsA5gP4btPbclwI8UtpCiSEmCGiXwFwGEAewB8JIU6nKZOCDQB+FsApIppsvvbrQojvpChTr/KrAPY2J9w3APxCyvIwycA6Ox4yp7MB1ttzkMT1NrfnZhiGYRiGYRhLOGyDYRiGYRiGYSxh45lhGIZhGIZhLGHjmWEYhmEYhmEsYeOZYRiGYRiGYSxh45lhGIZhGIZhLOmpUnW33HKLWL58edpiMAzDBObkyZP/IIRYmrYc3YR1NsMwvYxOb/eU8bx8+XKcOHEibTEYhmECQ0Tn0pah27DOZhiml9HpbQ7bYBiGYRiGYRhLesrzzDDjE2XsOXwW71SquLVYwOimlRgZKvX9uRmGYeYyrH+ZLMHGM5M6tkpxfKKMx18+hWqtDgAoV6p4/OVTAJC4Ek3z3AzDMHMZ1r9M1mDjmekKOgM5iFLcc/hs63OSaq2OPYfPJq5Akz43e1UYhmHUpKn7ew2eS7oDG89M4pgM5CBK8Z1KVXl83etxEuTcQZWX7QKClSLDMHORNHV/L8Ee+u7BCYNM4pgM5CBK8dZiQflZ3et+jE+UsWH3EazYfggbdh/B+ERZ+1nbc0vlVa5UIXBDeZmObbo+UY7LMAzTS+h0cty6v1+xmUuYeGDjmYmMnxFqMpCDKMXRTStRcPJtrxWcPEY3rQwlcxBj1PbcYZSXzQKClSLDMP2MSSfHqfvjkNPW6dJt2EPfPdh4ZiJhY4SaDOQgSnFkqISnH1yDUrEAAlAqFvD0g2tCbUcFNUZtzx1GedksIFgpMgzTz/iF8MWl+6OQ9R1A9tB3D455ZiJhE7M8umllWxwWcMNAlp+xjeUdGSrFojDDGKM25761WEA5QMgJYL4+UY7LMAzTK/jp5Lh0fxSSSlyMK5/FZi5h4oGNZyYSNkaon4GchlJMyhgNo7x01wcANuw+gncqVRQHHTg5Qm1WWB+XYRimV+gFB0ESO4BxJvkFdUYx4WHjmYmErcLLgtfATVIr9LDKy3t9vAr10nQNTp5QLDi4XK2xUmQYJhWSqvrTC17TJAz8uL3Zci6R92nb2CT2HD7L80XMsPGcEXq1DFkvKDwVJiM36r2IY6GgUqi1usDC+QOY3HFPpGMzDMOEIclSaL3gNU1ivsu6N5tRw8ZzBujlgd4LCk+HysjNyr3o1wTBXl0kMgyTfLOSMI6HbuqUJOa7XvBmM52kZjwT0W0A/gTARwEIAM8JIb6Zljxp0usDPWshGVHIyr3ohfg/NzYTWFYWJgzDhCPMoj5J4zZpnTI+UcbOA6dRqdYAAIsHHey4fzWObb878rElveLNZtpJs1TdDIDHhBCfArAewL8mok+lKE9q8EBPliB1ObNyL7JU1xQwX0Pb8k1cq5phepugpdCSY3z1yAAAIABJREFULu2WpE4ZnyhjdN9Uy3AGGrknoy9NxVqaLokyfFyyLnlSM56FEO8KIV5r/v/7AH4AYE66n3igJ0dQ5Z2Ve5FUXdMwBf79rqHtBJaVhQnDMOEIuqhPesGcpE7Zc/hsW3UjSa0uYl/wjwyVcGz73Xhz9704tv3uyHo+a86XfiQTMc9EtBzAEIBXFe89CuBRAFi2bFlX5eoWvZp01wsEDcNI+l4E2cKMOxwm7Ban3zW0ncB6LRSFYZh2gsb8RjVu/fRlkjrFJGPWF/y9nIvUK6RuPBPRTQD2A9gqhPhH7/tCiOcAPAcAw8PDncvAPoAHenIEVd5JVuFIO+Y3bDy33zW0ncB4kcgwvU+QRX0U49ZGXyapU3Syy/fSxm8+SjoXaa4nf6dqPBORg4bhvFcI8XKasqRNPyXdZYkwyjupKhxpJyOG9QL5XUPbCYwXib0PJ3oztoxPlHHl2kzH67bGrY2+TFKnjG5aidF9Ux2hG06eur7g9xqqG1ctxf6T5dQcMWk7grJAmtU2CMC3APxACPGNtORgOumnFWVcngk/RW5zzdKO+Q3rBfK7hkEmMF4k9jwy0fs1IroZwEki+q4Q4vtpC8ZkB69xJZHVKmx0gE4vlitVjE+UE+9QK4+pqrbRTR2mMlT3Hj8P7zZ8tVbHroOnuzJ3p+0IygJpep43APhZAKeIaLL52q8LIb6TokyZpJvGbFIryrQM8rg8EyZFPvTkK/jg6kzLQ6G7ZknG5z0xfgovvPo26kIgT4SH7roNT42saftM2IWEzTVko3huIIR4F8C7zf9/n4hkojcbz0wLlXEFAIPzBiKHfABIzMupmqfSbkqlupa6+NVL0zVcmm4Y+kl6g9N2BGUBrfFMRAMAfhHAvwBwa/PlMoBvA/iWEKKm+64NQoj/BICiHGMu0O3tEd2KcueB06HPl/YWTxyG3aKC01ayyI1UVm5Uq/Ck4vOeGD+F54+fb/1dF6L1t9eAnj+Qa50/iBeFjePehoieE0I8GvMxl0OT6M3MbUzOhhXbD1k5MVT6UpKElzPteUpHFIM0KW8wJ3+bS9X9OwBrAewE8FPNf7sA3Ang+cQlYwB0vzau7kGtVGuha1v2en3f8YkyrlzvjN3zw3stveXnFg86mD+Qw7axSeuycSpeePVt4+vjE2UMPfkKto5Nti0ArtZmQ52PySZEtETz78No6O84z6VN9CaiR4noBBGduHDhQpynZSwIU44ybkxGlG29Z6kvdcTt5czqPKW7lraexyS8wVwKzxy2sU4I8eOe134E4DgR/W2CMjEuur09YtoqC7uC1R2vXKliw+4jKFeqyBEg8zKKBQc7H7DziEYJB7H97p7DZ1GrBy/04lZ63nM9vH5ZbAkfdaGWrS6ENvYQmHsxanOACwDOoX1eFc2/PxLXSfwSvedChaSsEpf3NGqYnclrLLHRPyNDJew5fFY5hxQHHWt5bMha90SJbsdy87oSjp650Dr3lWszyt3RRQUHG3YfiVVGTv42G88XiehLAPYLIWYBgIhyAL4E4FI3hGPCbY9EeaBHN63E1rFJ5XthDPbxiTII6hgtwg3D2p3QXKnWMLpvCkC7wo8z4zjIJKMz/v3YuGqp9ly6hA/TZKK7r3kipQGdJ9LGHkb9bVHop4TUjPEGgJ8UQpz3vkFE6u2JgHCid7bReU93HbQPu4vDAPcaV7oVlM2cMrppJUZfmupwYHxwdaYtcVBFEF0TdK5NMszDK7fXUFb9DpWjxMkRrly/YVTHKeNcD+UzhW18BcAXAfwXIvrbprf5PwN4sPke0wWCbo9EbYc6MlTCYs2KPkw8057DZ7WK0+SSqs22d3FS/a69x8+H3mYLskWXp3Ch+UfPXNCeK+hkYrqvD911m/I7D911m+/kFPa3hSXpdr1znGcBLNa899sxnUMmet9NRJPNf7GGhDDh0T3vl6btw+7iCl9wd80rhejcKsNPto1NYkax8+edI1TfD6JrstI9USX3/pNljG5aaexAqOpKe9OCgY5FRxZCUfoBredZCPEWgC0A0IyZgxDive6IxUiCbo/EUUJmx/2rO1ewecKVazMdyR4qb7B7hRzFs+meCOIwQG0+o3pdFxZhe44gHnvdZGK6r8e23w0AymobR89cMN6DsL8tLGmUOJornm4hxO8b3vvdmM7Bid4ZJo6wO1OyX9jt/6DJ0qZwMxtZgeC6ptvdE+OS243XG7xi+6FEZOwmWdXfVqXq2GiOl6CDIcj2SBwPtFeJFAcdfHC1c+vnxLmLHWET7qoP5UpVG7KhCzVw4zYkoxqg3muuq56h+q5OVgKwwMlrFbw8lu0iouDksXHVUuUE5XdfnxpZg6dG1rR+597j53H0zIWO0BYvOo9QUnQ7hj+rGfQMkwRxhN3p9JU7zC7ocxSHE0gnq24+DaNr4uyeGNboi1NH9npVjCzrb1PYRs+ThaxjlUxJblvrHoooD8s/Vmc6uixVa3W88OrbvgpOZiu5KTh5rP+Ebne5gZNr7+Jkm3Gs8maornmlWusY/DpPiM7IF4Dx929ctVTbZQsActRIjpRbbJ9Ztgh7j5/vGBtPjJ9CThNe4U1KVG33bV5XQrHQGYqTRnZ0EuPTRFYz6BkmCUaG1M86YP+MqcIXVE4QGUsdRDYZxqELPZDYGIoFJ4/lHy5g29hkm87bOjaJtbtewaKI18EPU5iHShdvHZvE0JOv+M71cerIXq+KkWX93bfGc1ZjK5MeDHE8LN5rZ6rmYIMA2uKwnn5wDd56T68ciwUHe750ZyssRFbkUBnKD69f1nFsr1LWeTFmccP4JjSMWVXZuLDe2UOvv4vHXz6lrQ89KxrJkTLc5a9+eFE5Qe09fl55rb33VTe2jp65gMkd9+DZLWt9r1XSdFuZ+9WbzcqimmHiYucDqyM9YzJ21m2Em5pyhHl+/BxbOkMxT9TSX5vXlZQ6E2jo1SvXZ+Dk2meNOHWNKsZY6lTdnHNpuuZrh8Q1h8t48fkDOSwedHz1vveePDF+KnXnY5absViFbRBRCcDt7s8LIf4yKaHiIKvtI5MeDHGUkLHdMrMJvQAaD6yMy5Vs02wtAsD7V2ewdWwSuw6ebuvcJ73YAk2PLQF7j59HcdDBooKDdyrV1iLE/XtN11a4/nvl+o2toW1jkzhx7iKeGllj3Ao1oWqeokJXfcMro5s8UYcS9BtbWciO7naJI1PIjHtR7ZatH+hFnc3EQ1zP2LUZuzrwQedU3Vb8iXMXW/kyiwoOnDy1JbsVnHybztuw+4g56bwusHjQweC8gdDXwS/0QqdTTXOOX/WTqPfPe30r1RqcHKE4qJ8jVffEG4KZhp7MctiJr/FMRL+FRuLg9wFIi0oAyLQizuqKpRuDIaqRZLtltnldyRhPK1GtmE1GjTTIVcanNJyvzcy2zuv+nOohD5O4KAA8f/w8hm9fEuh7YQmatjcrRMc9DlNqKa2W6d2qORtXvdleold1NhMfUecAWwcKELzcpc6x5XYgSINv8aCDynRN+dzbzFOV6Romvn6jvfb4RBmf+s2/wHSzQRQR8PBdy9ryReIoheo350iPfRI6Z+eB0x3XtzYr2tp2bx2bxM4Dp1v9FGzudxp6MqmuvHFg43keAbBSCHEtaWHipJsrliCTe5YHg0R37fJEmBWi7TcO375EW8ReoroWNkaNDl0YhMT7kI9uWoltY5OBDVQA2HXwNAbnWW3QdFBwcqgm1MVPNY6DjK0wiRhpGNtRE0birDfbQ/SkznaT1Qz7fkR1rYM8DwTgifFTvnWIJbpje5/N2qzA4LyBNuPXjY1TxJsT8rUXJ9t6CgjRcJK8eeEDvHb+cmy1+E3J8hLdcYLoPJXB7zc/SirVWuu4tve723oyy81YbKyCNwA4AHpKEXfLSA06uWd5MEhU187JEW5aMICKxxssPRwyLtmLKl5YPvDVWt069CMo7od8ZKiEE+cuGkMjdFyarnX8ZlsWOHkA1HYd/RSqGwLw+TuWtCl1QD+Og4wtnfdn69gk9hw+2/G9tLKe4wi/cnvhdOM0C9uAMdKTOluS5Qz7XsfWu1ocdKzDzuQunaRcqbY1uXKfc1HBCaQEy5Wq1kPr54BR5YTMas577IcXlb9LhV8tfimP30/UHcdW5+mabwVBHtd2dzYNPZmFcEMVWuOZiH4Xjfs/DWCSiP4jXMpYCPFryYsXnm4ZqWEm96wMBp13x33t5Arau+3jncxsFyveB74uRCCD0hbvQz58+xIcev1d6wnBe6ww9arlueQCodScrJ63UHA5Ar7x5bWtyWfXwdOt480f0Of52o4tkwdBdX9txnkS3sK4w696YecnLL2usyVZzVfpdYJ0Op0/kEPBUIbTj9qswM4DjUoc3vhbFaY5QLdwUpVUFQK4XO0M8xifKMfWTTVILX4TumogtjpPFZ4RZh59p1LFM1vW+u4E94uejAuT5/lE878nARzwvNfdzgoh6YaRmtXYaj/8vDt+3lrvZCY/727UsXld5/XXNTuJ04AmoFUuyC+kxI9iwYkUYgI0FgiyhvP+k3YZy7MCbRVYrrrCPyrVGraOTWLr2CRKIY1UP8+S9/76jXPVeNoWUUYg/vCrXtj5iUDP62ygd3Vq1gnSaOpytYZntqzFzgOnrcMAvFSqNSuDUjZ10uXP+DU28Xt2pW6KA28JVTdBx2etrg7ps9F54xPl0PdFdVyVXvQ2POsjPRkLpg6DfwwARPRVIcQ33e8R0VeTFqxXyHI2qAk/7874RNk3zMGtLMYnyth/stwKwagLgf0nyxi+fYlVkoeAffUOPwQQOkzDy313fkypWJZ/uKDc6tMh62IH+X1yQTN/IKedgMJsaY9PlHHZwgPvvld+49w0MQeR0W9bGYjuAcnKzk/c9IvO7lWdGpRux3UHbTTV2lXcN9VR5z/Oc84KgadG1mD49iWRG7yoCOoRNmLorRl0h/LK9boyJMVmd8xU2tbriJLhf99/9/0Oh4n7uP2qF5PCps7zzyle+/mY5ehZerUIuZ93Z8/hs76Gp3syMxnj7vqRumYfhPa60QUnj4Xz8srP+rF40MHzMRjOQCOW71O/+RfYeeB02yT3peFlHTVE/QizMKjW6tYJkrbsPHAaNmmMsnuXrs627DimiyMOKqOpyUva9al7jJ7W2b2qU4Ng24cgzkZfto2m3M/1roOnlYZzXqPH3SwedKwWPPIzI0P6Bi+LCk6oaxFnuAbQKH+n02NhxqfqWKb60RLTYuLzdyxpu6cCwGvnL2PH/avxyPplrXun2x1m7DDFPD8E4GcArCAi9xbgzQDsXW59Tq9uA/t5d/xW+t7JzNSMwl3pQtfmWhV3Vyw4cPKzbbU+bfjgajzbWZLp2myrtJHbGxzWG5MEQTwzNtt9hEaHRG8CDKEzzMYms1x+zoSpyYu3TjjTSb/o7F7VqUGwzSGIM3FS59HcvK6Eo2cudDzHpud1VjRqKOtCv5w8Ycf9qwHAGPLm5NvDIHQ2ebVWD1UdyBSuETZUUKVr5S5CHMcC/L3Auvl78aCDt97rrCoka0tfrc367g6HYS5WxzHFPP8VgHcB3ALgd1yvvw/g9SSF6jV6cbvDb2vItAW1eNDBjvtXt/1mv2YUXtxl73Tfu1yt4eH1ywKHXyRUHa5FtVaPbxvQAlnr1HQNTB4er2KzYSAH/PtXz3dkp+vCa2zi1qkpi+5Z0Y2DOD1HfU7f6Oxe1KlBsInrjjtx0m9RYrODJLm1WMCVazPK9wjAni/e2SajLvek3kws3DY2iVuLBa0xrmrYUq3VsfOAvtmIKqFO4l40vFOpouDkWg4S9+9Q6TOvDvUucrw4ecJAjpRlS3NEWLH9UCz16wtOHjvuX61tQKa6tt5QzTAGcNRFnuq8QPYXz6aY53MAzgH4XPfEYbqFnyJVPZwE4OH1jYLyXkY3reyooWliVgi8uftejE+UtTWYby0WcPTMhd7JdEqQz9+xRNuK1rSlrVJsNpgWILrwE9mG3bSIMk38uph3my1ihnV2t4nibbOJ6w6aOOktCUeEjgYjpkWJ7e6V1DemrqteY6w4qA7HmBU3dsLCLJIrVXWzEb+EOlXol02+hTukRV5T35hqAXxmWVGZIyP1XdT69VJem3BLL+80ywGGNYB1i7zHXpxqLYp0z4bqvKP7pgBCa8c5q6UqbToMvo8bC7B5aNQPvSKE+FCSgjHJY1KkJuNaN2mYWm57EWh4Oq5cm1E+7LJiRpBj+iFNsFxCtaXj4JH1y/DnU++2Kf5L0zX81Q8v4vN3LMFb71XbJhm/uLVYk2V8kG3YTR4s0wStuyeq16N4SaK0vc26NwRgnd0NdJP+roOntR3x3NgkhfkZ2F7j9IOrM61QMrf+sDU+bBPepMfXdBzv9QlTItQW1YLcL4RC1aLaOx+OT5Tx51Pvtt0jVRK036KjNitw/I1Lvr8jSv16X+93jrBw/oByQXFrsRBpl0P3+20WBqrzqsIhs1iq0jdhUAhxsxDiQ03FWwCwGcAfJC4ZExthk05Ghko4tv1uvLn7XhzbfnfLcNYlugQ1R8uVqtY7II8VNsNelcxXHHTwzJa1eOiu20xJ06mxeNDBUyNrsHB+55pWAPirH17ExlVL25Kp6kLg+ePnMfTkK6376r7fpslQJqQs1niFguCe+E2Tiel+qhrqqF63TbbyEvZ7Ub/bbVhnx4dOd+om/UvN8Cq/8WGTFGZKnPSOx0vTNWMOhk3Crup8Okz1mkc3rezqol02UnHjZ9D63R95fU3ea9lUSpcE78bWWRO2qojf9b5pwQB2PrC64/46OcL09ZlQzg6JzRytG39Bfm/WSlXaVNtoIRqMA9iUkDxMzMQ96ZtWqCbCGKuPv3yqw1i0YfGgg5sWdBqgl6ZrGH1pCmPfezuToSBCNO6XqZzf3lfPK5XkpelGq9Unxk9h9KWp1v3WIb3Ez2xZ21ZDOiiqid+kTDeuWqp9z7bKQtgxGPZ7Ub+bJqyzw2PSnTYTud/4UDknvO/rDOwwxqku0U0uDvYcPovN6/QVL2yQoVndzlPwzmm6BiRuTPcnyPWNcxfTXeEoiLPLbzxWpmsd46nY7PZo2hWwMYxtF10qGYM4x2R8eNSqM3FhE7bxoOvPHIBhAFcTk4iJlahJJ96t6rBJXc9sWauNbdZRrdXx/PHzGHRyIGoYl/K/Ji5P17Sl2IJW7ugmlWrNtzWu6bdXa3XsffW87/UBGorsifFTOHrmQmgPkTTAvZiaypiyu22rLIRtohGl+UYvNe5gnR0PJt1pG+IQdXzoQuvCHNcv0U2Whnz6wTXYd+J8oDr2EhkT3G28c5ptmkQUj2vcyApHoy9NtcX7jr50o9W5Dr/x6C4H6E4QNXnWbctDevW2LixSZSir5gonR20xz5Kw8eFJ4Ws8A7jf9f8zAN4C8NOJSMPETpRJX6VcdRnIfg1OpLckjGJ1Z0HbGIZxF9twcvYVPGwqY5iQrXHDYusEEYBvm3BZikp1z02KVSq0x16c6hgTcbSuD9tEI0rzjR5r3ME6OwZMutOmnTGQ3PgI2pAjyA7OzgOnldUt/LAp/VZwcsqqE3Hgvh6VADHWstqFu6Net/NiZDL+odff7TAaa3WBXQf1VUUAs8NCp6tNNkDQrrB+8dc6GXQOE/drqnuRhRhoo/FMRHkArwshnumSPEwMuL3FQVaBXmxbaRecvHESKRULeGL8VCa9dDYE0fVxJMZUqjXkEP8iIAgL5+Ux8fV7Wn8HTZYzJZBGHQc2yVZxfi/qd7sJ6+z4MC2YvJP+ooKDK9dn2gyfMOPD5jkbnyijMn3d+piq0qKA/jkM0/bZVGWnnWSzTWTljSCLCxmS43YmqObMHMG6mpQN8nhuQ1Xn0PCbV9zjsVyptpxZJiNYd410O4q2BK3T7v38nsNnMbppZUuGFdsPKb+Xtj1hNJ6FEPVm4X1WxBlHKl2vd1ilBGyVuin2tlQstD0YOq8yAVj+4YKvl7MXiauduIo0DWegs3VsmLq7Yb21fgZE2CYaUZpv9Erjjn7X2d2seOK3YFJVZ4gim025sPGJctu2vg26nIag3msd0kCzCcvz89SHbVwikdfLr5ReUGQceJiFhY6PLWo3Um0Snv30YpDxFqdDQCWbrQHuN+6j7volpTNI+Ez+RPQMGqWOxgBcka8LIV6LfPaADA8PixMnTnT7tL6kXcLKr0wN0N6UxFY+XckxQiOG2Ttx6OpCB21y0g1sYqf7BSdHobohysWBjRdDhW77zlRjVRUapPtOr0FEJ4UQw104T1/q7CDjKS66qdt1+tbtCQzSyMRNngi/8+U7fXV2wcljgZMLtIP2yPplrQ6FUSgWHNx358c6aiuHPdb1mXpH45OwSGdRnFMGAXhz972tv4PeWydPHc1oghLH+I76XPqN+yjHj0Nn6PS2Tczz2uZ/n3S9JgBE7pdLRF8A8E0AeQB/KITYHfWY3SbuFqphsMkMlk1JgqDzJqiaXZhil7LodRYiyFZjb7PnS3fiN/7sFK5cDzYhudu4AvEU8tdtQ3vbgLsJGt+W9mI2AySms9Mk7o57NoTZcbFBNUZt8lPCblXXheh4dk06O0hyt4wTjoKTI+x8oBFaMnz7ksgVO2ToW1yhFn4J82Hwek6DXkObWGg/4hjfUZ9Lv3EfZdcvSZ1hYzz/ohDiDfcLRPSJSGdFKzbv9wH8cwA/AvA3RHRACPH9qMfuJkFuTlKTethajDbbQLqtL9U5VQ/iht1HfGVLiyQN54KTw/UZ0bWkE10ISckVn/nYvinUI84kUQr567BZ/NlOLFlYzGaARHR22vRSxRMTujGqq7Lj1t0mA07WQ9e9b2rF7N5JDFqz3x1jG5barGiVjZNyRQ3hmIX6AI+sXwYgmFMnRxTrfKEKjzBVWdKRZPMZW6I+lzZhGWGN/CR1ho3x/BKAz3he2wdgXcRz/wSAv5NKnoj+FI2M8J4ynm1vTpKTut+K2F1YX5fgopNH5521jTfqtYktLmZm9YZzwcnham0WxUEHV2v1yNnnBSePzetKyu3Oi1eu4VO/+RexbV8C8d/TuArxA+l4JzNIUjo7VXqs4okW3RidP5DrSL72Glmjm1YqY55zBFy5NoNKVV0dR+LXihlA2//botJ1Ug5bw1qWZUu6nOjRMxcwfX0m0HfidoJcm2k0WJHJcQDwwdVgMmUF2+dS56yzib0O63hMUmdojWciWgVgNYBFnrqhHwKwIPKZgRKAt11//wjAXQo5HgXwKAAsW7YshtPeIA5PsO3NSXJSVw0+qbhKrq0492dUiQ8qeaImFcS91ZUWQT0r5gmA8MyWxs56mIkKuBGz7b6/h15/VzHG4k89jNtYsV382dAv3skwdEFnp0qvVDzxQzcWL1dreGbLWqtk2V0HT7e8joNODrW6aOl0k+bxa8Us/19FkBCIYsFphWE8MX7K2svbjTr8QWKX466wIZHHLFeqgfsfuCE0qlEsKjggglVr+LixNX79nIe6cR/F8ZikzjB5nlcCuA9AEe11Q98H8K8in9kSIcRzAJ4DGskncR03Lk+w7c1JclJXKdRFLuUFNMInbJIwvPIEiVu1XVX2IrPNhLk4FgI2E5UfQrSPs25d4ySMFZvFn+0z2S/eyZBkQmcnRa9UPPHDrwSeTdUY92dMid3exFtTFQo/3fahBY51tYkr12fw+Muvx1rxIi4WFex/RxKGs5cop5Dfdf8eacucOHexFY+e5LNi81z6OQ9N4z6K4zFJnaE1noUQ3wbwbSL6nBDiryOfqZMygNtcf3+8+VpXiMsTbHtzujGpu0sSyW51UkZbI12goYzdv8FPoYdZVWZRqZqQRfTjSn7MihEehKCGrC1xKrioW4C9nGzYBZ0NIN1E76QS+LpJ3N4wm5Ki0jOpq73uhzyOLbW6SMSLnCPgZ+5aFqkiR5zl5rJKtVZvq3KVdO6H33OZZnfXpHSGb8xzgkr4bwB8kohWoGE0fwXAzyR0rg7i9ATb3Jyktxz9FgNBwieCPmj9Hmfq5KlVyzpOoibEAN1rhRu1cL4fcSm4KFuAAGLZjUqbhA3nvkj0TpO4vWF+zS5sSpmakPNU1AoYUXGXZhu+fQl2HjidWUM4rvJ9APCsJ5Rn+vqMVaJg1KpFcdKP3V1tEgYTQQgxQ0S/AuAwGh6MPxJCnO7W+bt9Q5LectQ9pHIxoOshf9OCAeWD6PZq+slsWoiojJXRl6aC/0AFshU2YqrZrDNmF84bMFYeCYuqW2NWiTtmeHyi3Db56bqghSHsFqD8f9V7vWQ8J0xfJHoD6e4y+C0Wnxg/hRdefbtVZ/2hu27DUyNrlJ/VOWY2rloaui400NBN3usyum8qVM14FRvuWILXzl+2NupnXOeV1298opypXUz3vQoS662jpAjl0XXcsyGt3I9+7O6amvEMAEKI7wD4ThrnTuOGJFkzVGeEycWAyXhfsf2Q8rvS86bz0tm0AFcZK3Ft5w3OG8DE1+/B8gjKxI1OqsvVmvEaJ3HOrBGm1KGO8YlyxyR8abrWWlQlacCE2XGaC8mGAfBN9E4yyTsuslzS0Gt01YVo/a0yoFW6feOqpZFCG/JE+OHTP9X5RozdtV87fxmb15VaiwQ/hEDHPRoZKqXuEQca4STf+HJ747CjZy5EOqbKHhmfKEfyuNg4B5MIa4viPMxqroO2wyARfc30RSHENxKRyEDcHQazGt8YVK4gnQCDfF9XYaJYcHBtZtaomGUXnyiZxH7I35fkOQD/+qm9yOJBB4PzBpQd/byoOjLpOjdtXlfyTVIxecOSDg8xdbMC1Pc4LpmS7jDYDZ1NRF8E8AUhxP/Q/PtnAdwlhPgV1eez2hXWpptfWtzx+HeUeldr0Cqw8TibKghtuGMJ3nqv2maM2xq5QSiFyCXx3qOoYSlxUSw4mNxxT9uuQRCIGsfQVczw+51h9LgX3Tl03R/7pfuriTAdBm+mp7o1AAAgAElEQVRu/nclgM8COND8+34A34tXvHTIYvJJGI+IKWHE5vepaoc6edJ6iHVxZqoW4El6BXJEiW/ZydV/2ESbtHFygLdaXcHJt4VIeBdrG1ctbRnAxUEHQjQSjWRNUnlfVSEOqiQVb9a3aTwk7eX123HK4vZgALqhs1NN9I6LLJQ0VDlJAH1NYfm6jXPF73e4jZ6H/4+/xrEfXmy9lwPwvbcutfUASKpL7DuVamAPraoi1L4T59t+wyc/shDvVK4G6qr60Zvn4b+8fz2QLG4q1VqkmvpCABNfv0f7vl8zKZkgqnOE2Ri5unNU/n/23j/Ijeu68/0eYJoihnIIKlIcCxZF2uulygxNjkXbynLrZaVkTa1lyRPKNqPIqcTJPpVfxRtTUSZLxaqIdOQiE5Yip9bJplSbrZc8Mw71K2PJTJZyHrnlWm4om/SQYmiTebFkSYaUhDE5ssQBSQzmvD+Ai2k07r19u9GNbgDnUzUSATS6D/rHueeee37U6h26XTHKYW22ahs7AYCIvg7gvcz8Ruv1DgDJrJMLXcRJvrMljACOnmxND+4VETse6VqA33zDNdqHLgnS6N7n98gWidrnPkp5ozzgjyE2DdKbdh+03hMPTJ80ZmzbJmx+dAa1zUOSdhKIa1mlvK1GudAnnZ1pondSZJ2EpM0FefyENSyiSKT93rZ9x7HzmVMdE+KwToTqvp6eqeJvXzjX8fkCgIWEQusK1HzWTar62ohVPNR3/DwwfbLDcAaA/++fL2DcK0Taby+Gs6KXZlSE5n0R1Unm/74uXMfV4xzm6LI13hlFXGKe3wrAf1ddbr0npEBUj8j0TFXbLcnfVTDMk73j6VNdSSD1BW7XEg4+iEu9QmgbWSXbk8eitXoN0s+EOq9AePD2tQC6qy6Ytk8qeSZJli0pdngwgissLvfE9EzV6mmI0kpWMy/ToqqapI1txSmPq1ExSE1nZ53onRRZJyFpc0FCdMldH7jO6Bk8P9dZmtT0+5QRNT1T7SmZMAomw5kArPrREv7x9YvOjhDdNdr7nN4rnmRX1X7AQCwnmf/7h06fxa4t6yI5AHoNe8m66kVWuBjPfwbgG0T0l63XkwD+ND2RRpsoHhFbfNKOO5pG4H2PnehSTH5P9vRM1ehRNXW8AtyWt8OWmVxI0zRdtqTYXtYLO2dB1Pb3P/V8Kl381DHCYst1zIUsVbqsbuw5cMbqaVhe8iLJpKPs8+aHecqHwKDtJ6nq7CwTvZMiyySk6ZlqZKP1EzetxEOT66yVFoJNJwD970syRphCKh3Z5gMMdHmMbQTrzE/PVLHzmVOJVFrKCzYvrssq7quzNWcHgIu3OYip8c4o4lLn+fNE9D8A/NvWW59k5pl0xRpdonhETMbpsiual/X+p04ajUD1kNpqF4d1vOo17i5LVox7XfFlalBx8YI0m9A8j8sptZP1CoQdd6zF0ZfORU4+CfMEuKxu2K5dlKVW08qBKTErz1UQBgXR2W5kscqg7u8oVMqldpWNKDkDpt+XhFND0S/D1V+zWnnMB6XMZxR0q7fKwHX5va5e4DgTKHV8lWhaiRACOIy4lqo7DuA1tT0RrWTmdDIIRpwoHhGbERSmINVDZjOCbDNKl4EnSmOWqPHVOoJhFCWviPeuXI7//d1zXbNlFZ7hJ+qgkpbHGWiGMPjbrZuI4wlwWd0wbUOAtWFCueRh2RVj1pJZNhmHveFOHxGdnUOi6hj1vCl0zhU/LsZTnp0aJm6+4ZquHIxBNJyV4akzhHWdUP3XOuz3uuj+KN5mU0WmBrMxyXqUnB2hxjMR/ScADwL4JwANLI7X70lXtNHF1SNiM4JsD4dfIZu2XTHuhcpgWl6POltWXuDpmaoxZEJV8rDta8/H1hvl8RuiV4zpE0nyNKjM1RdCY/bU5ODIC+fbzRTuvDGZbpe6bQjA3TetbO9ft48dd3Q3Otl4/VXOy+N5qIIw6IjOzi9R7+NgxSR/nkow3M4rEOYuz2P19v3W58zFqRG1gUnapJV0biOuQ0dX4UgR9NiGJS67nH9dMxsdUbzN/opMutj4sMZS9z2Wfs3+rHHxPH8GwBpm/kHawgjRsHWWsikbv0LWbWvyzPoxLa8ffelch6fR30WvXPLwxqV5NAKBcG9enG9nGZtKwimlY8sgt006Lvq0WTPkont2HMVTnjUVn1dXTTYazHjyWBUbr7/KqrRcVjfCtomyQhJleTzrKghDgujsnBJVx1Q0972/u556/paXPFzwtW02eQBNCeaKIhEe/vj6LidI1iRhONvqWgchIPZKaNiCpLo2u7ass9YUd5lo6cIPTbga48HY8jgOjQbz0HugXYznVwC8nrYgQnRMBowt2QvoLGEXrIZBgJP30rS8rovPVfUnD2+/BRt2PtvlMakvMLa16gibSsIR7E1KXnu9hgemT2o7cLmGAujqXSsKZE9+yYL9z78WO8TBxaAN2yaNmNGsqyAMCaKzc0pY2EWQC5cWHQtB/M/fpt0Hu/RmUBe4eB4bzNY8mEFFNXBy7bqYtqoPJu3rnBAuEy2/4ymMKLW//YQ5NEwyDnu4nYvx/AKA/0lE+wFcUm9m0WFQ6EZnwNgaevgNEZ1RqcrdhGF6EMMSFF+31EpWD6HOUA1TZgsMYwtb28w5qLhMGTBXjBVwucFdXvOsSKLRSJSqFv2qgJFlFYQhQnR2xpieF//97eLRna25ta03PfPVlo4zNTYyfUfVnDY1yoqDX6/3M9nPHyahczhkhRp/TDHDLhOt+gI7G6iutb+DRG0sFfyNw4qL8fxy629J60/IObaHxO9V7iW+1HQM09JYoVXg32U23Yt9+uXnXmnH2KpGJ7bd+TsU2uSq1RfgFQj5ULt2XEIcXGs962LX004KGZJay1kiOjtDwp4t9bd6+34nA7LeYOx85pT1mbDp1bDGRtpjpuAk8O+S0YzR9oqUaj1mf35PHlp4+7m2XLKuiqqQDjUJ67VJSVjtbxMuDg1TrtIwh9u5lKrb2Q9BhOSwzVj9MbGmRhfl8fAavqYH0bQ0pmKgoiydxaHB3GEQ22Lcog4PeWyIEsQU4hD0hM1dnreGfEzPVDtCWKQt6+AgOjtbXMPEosQ/h8Xf2nS+OnZSOR0q3+LQ6bM9lYyrLzCWXTEGBmnHkShJgl4BmF/QN2Ta+cwp/LA2n0pH2rgoPW1aJa7O1jrKv6l+C73kg/SyqhfWWApw6/0wTLhU2zgEzbPBzOZIdyFT1M0c1iDFpEtm5+qhGdvB5UfVyvrQ6bO488aKNvZZfa46IGWViBIlcSRJKiExYknsX1d3M1guziXkY+czp0KXbId5SW6QEZ2dPFHCllxX9KLGP9tQsmwzGGOvztbwyNYNPR/PX2/50OmzIADjvmZTQcIM69drddx908oOQ/livdEOwXNFZzgrei2BmjT+EAnTOOjP8VErFzrHU1QDNa1VvVEMt3MJ2/gN37+XArgTgDldV8gFtsoVYfHHtuX54CCiM8z8FSB0x/Zni5uUfVoQgIWIhrNXJCxbMmbsxKgIM8qTGsB0ENAe1IJLxlE8OMqL4TLgDPOS3IAjOjtBojbuca0YY3NyBCk7dPS0GWOq4RWwaOAULPrKK1BXzLMy1ILnQ2c4t76OsJDp5SUP+77xSod+iuPWWF7y8Hqt3pc46l7itT9x00ocOn0W97YS5HV18HX79zue/GUKl3r6sqtZMGrhdqFnnpmP+f4OM/OvA/h36Ysm9IrJuFHvuxg//nqOSmlWW/FXyjDTLVEWiZxkKug3S41ryyXr7y55RXzippWolEsgNL0Eez66HjvuWIuSV7R+7+GPr8cXtm6A6SepAWzXlnXaElS9oH6TKQnUhSheDK9AQ70kN8iIzk4WWxiGjqnNa7p0henZmpyo4OGPr7fqFtVt1IWwY09OVHB4+y14cfdtePjj6+EV9dpq6/uvw56Pru/Qg7u2NBOx73vsROjk/0eWeqGGs1cgEEUPhwvKXPKKzTbhkfbSTYHQ1v024h5n3CvgyWNV7fipxstKuRQa23xpfjFG/Pxcs+zq9Ew1plSLqO6Nq7fvx6bdBxPZ5zATajwT0VW+v6uJaDOA5X2QTXDAdsOHKVLd5zr8rbxdDTPVhch0bLW/pMKIXWxwlTxi+t0rxj3s2rIOD02uaw8wh7ff0p5RK6OX0PQErRj3OgYWtd3dN63skkc3gCVpQM9dbpYsihJKUS55XYOj8hyEebquXDo2Ul6GQUJ0drJETawO6orgsxW2/YpxD+XSom7Z87H1zs+a7th33tj0SAfHiMmJCpYt0S8+Hzp9tsPQVslr9z910inkLWyVrlzysOdj62OFVOiM+tkEQjMWGPjqidcS181AU/8vGSsax09/1z7TscMSDHtB5xi7d99xPDAdrZX8KOEStnEMi70u5gG8COBX0hRKcMMlqxtwb3RhWsZzaeUdRMV12WKgkoqZVQksYeEJwW5dNtnCSk3ZeGhyXVdXvZtvuAZ7DpzBvfuOY3nJA1G8WDwC8MjWDV0dxpQHwpQEqmvjresGqNhxx1pMPX7C6BU67xAXr+hXmbt+HyvHiM5OkDiNe6IuYSe55O3fV9gYYQrd0+nmqK3FTSxbUsSyK8ZiheypZlhKnldna9b+AFeMFTo8tWHM1up4YPpkonkp5ZKHHXestZaQBRaNYFtpuLBQzLiYHGN7j7wc2nRrVHGptrG6H4II0XHJ6o7S6EJXRN/vMTUNIjrDzMXYTCL7mwhtr0hYkol/Rh/83cqwNcVxRy3NZjuvYV4ZGyr0Y8+BM9qmCFeMFeAVqSNW0SsStr7vOhw6fdbZqHSJx1QeCltcvOp8puRJs8xd1NjUYUV0drIMcuOesDEiysTA1UAreUUs9QpG50DtcgMXLkfX+16RcOHSPFZt399VOtMrErwCdUz2vSJFMpwVYeOIKbelSIS7PmDWsy5J8ion6OhL5zqS7tU1MzlH1PWK6zwwXVtuyW1r5jKquIRteET0a0T0ROvv00QUnr0gpE4vdZp1hC03msJA7g7ECIfVjVS4ho3Y8Osw21Kbv96nnyhx3HGXxuJ6bGyhH6ZrPFura+s1bbz+qq5QlDBc4jEBe1z8bK3eVbUjiWVGHWktaQ4aorOTJWoYRp4IGyOixGe75MioDrUP3r7WGE8dp6rzsiVFgBcdD0EVV28wrlw61nGNTCEpveAVmway7pw9/PH12pA/hct4d2251O78GzTQq7M1vHlxHsVAopBXpI5ETv9YNvX4CUx87tnQOGbbtfU3c/HvO6lY60HF5e76rwA8AH/Uev0Lrff+Y1pCCW7EWU4Mw6WeY1Kzz7DySi74DeapzWuM+1JqyF/CTYWVuMZxx52URPWuq8L1gP5cT89UjSE2RaKuUIsoXaiCBK952LlxnSikUeYu6cnkACM6O2EGtZJA2BgRRaersIGw0LhDp8/iocl1XaFlvXCxvhAaaz07V8fMb3+w/Xr19v3W7XUrdLbynCvGPTx4ezPULRiW5zIOBj3QwRVbVZ7OttqnDaNrvaXTvfUFbnuqbStxtmsbFms9iM9FErgYz+9j5vW+1weJ6ERaAgnuZLGcmPQgYiuvBDQV1uxcXVtLNPhbJycqRoW9YtzTLulH8QibJiVhy1lR6koH26QGz7XyAOj2V/K6E1IUvRiQ/mu+afdB62Dsepw0ytylMZkcUERnCwDcxgh/6VAVvqZib/36R4UThIU1qGfQFE8dBxf9GXzObWGByhAO6m2T4Vok6jDM446DunDBoCEdtQeBco646F6TwauubTBvKO1Y6yTIKpzEpUhgg4jeqV4Q0TuAgehSPPQM8nKiH91yFqFZNmjmtz+IF3ffhlOfuxVf2Loh9LfqSsqVvCKYEamkni1kwo/LclYUwzkspMLk2S0SWUvgJWVAhi3zuhwnrQlelCXoIUd0tgDAfYzQLvk/cQIbdnYu+T80uQ4rQjrQKp2a5KTVpKcVymvrD02Y2rymWa86uK8CtT3IU5vX4NpyqZ14eNM7Vmj3f9cHruv5NwTxV13qteiUMhxdt9Xx0OQ6PGIYY8PK3mZFluEkrk1SDhHRC2jeo9cD+GSqUgnODOpyoh/XpUOX32ral2nmrEoE6drDuiTYuSxnVRwTI11m8aZtFpjbx0tzNSLsWuk8XV6BcOXSMczO1VP1DCQdVjTAiM4eUuJ42Vz0pnbJv8HtVTz/kv+Dt6+1rto1mDHxuWe1iW1egbAAoBGhRqlLu25bYy//aqQ/9EKXYHzuwmVseudVOPLCeTSY20mAD02uw/RM1bivXogS1mdq0KLuBZfV1DhVYvKaNJtlOInVeCaiIoD1AN4FQJ2lM8x8KVWphJEjrVJNClNoiEtJPRsucbZJKDX/NknFMMaln3HxSco2CojOHl7SrCYTZclfVTeyxebqDGdVsg0Iz6FQrBj3wNwsmVbyCpirh6cb+o0nmz4wGV7f+0EN3931oY73p2eqXaU7z8/VMfVEMxqql/MfFtanPlclWXUdCW++4RoAzY6D6rOSV8D8Amu7REYla71uIss8F6vxzMwNIrqLmR8B8Hzq0gh9Y9TKzthmzq4xfzpc4myDiidYvs0vSy+/w3+8LK9l1scfZURnDy9Jedl0ut+1bKgySkyrXDaWXbHYWCkshwJAVy38ufoCvCJhrECohRjRvaziqfCPYJk5XbJevcHYFmG80GEznL+3+zbt+34vPAPY941XsO+brwQSHglb3/f2SCVKbeRRr2eZ5+IStnGYiL4IYB+AC+pNZv5WalIJqTKK9XDDZs5xz4nrclZQ8cSdvOTVAyDkCtHZA4aLPkjCy2bSc3feWOnyaOrQOQVcq2ro5LRWSNLYlPUGY8Gh1t3yktdVWSl4Pm0ThqD+DzvHvYyhprA+U/7KodNnu0v1aQz7Wr2BQ6fPtlcKhpEsw0lcjOcNrf9/zvceA4h9RYhoD4DbAVwG8F0An2Tm2bj7C2PUvKxhjGrZmThLeGHnJK4x28ssPo8eACFXJK6zhfRwnbgn4WUz6blDp89i15Z1bT1WHvfw5sX5DqPM5BTQNWzSoZNzcqKCnc+citRtNSwB2ysQLlye18Zr+89nWDidSzMZ0/ZRiGoARpks5aEaRppk6Uxy6TB4cwrH/RqA+5l5noh+F8D9AP5zCscZSS9rGEnHCSU1OclyktPLORFjVsgTKelsISXCmvv4w72CtYijetlsei7u6piLjvQKZJQzLAExiC1GuFIuYe7yfJcxrjNs/YaXyTD2N5MJxjzbto9CVAMwSmferKth9IOsxt9Q45mIfl3z9usAjjFzrO4WzPys7+URAB+Nsx8XRtXLaiPJOKGkJidZT3LyWCPYXwfUnzQy6isngp00dLaQHrbYW79OnK3V4RWoXfs+joMhip5zNUrCjDmVKOiaZGxqAAUsVt4IhpioxlKTExVjcxTdeVa/0RR7XSDCA9Mncej0WdQX2FjtQqHCRVx19gPTJ9ttuAvUTPhTZfP858aPqaIRCIkkBwpuuIRtbGz9PdN6/WE0E1E+RUSPM/Pv9SjDL6MZm6eFiO4BcA8ArFy5MvLOR7XrmM1rkGScUFKTkzSTYVy+n7dSPMHJhBpMZOVEcCBtnT3w5CmUz2R8Fom0HePGl4x1NOyIQhp6zrTPKD0H/EnbpjAOU4e/cqsih0r0Xl7ytGEkNkeIKYSjwdzRFIZhNuCD4SJhOvuB6ZMd+15gtBMh/d8BOlcfiBZ7FPiNc/92Wd/To4CL8fx2AO9l5jcBgIgeBLAfwP8B4BgArSImor8B8OOajz7LzF9pbfNZAPMA9poOzsyPAngUADZu3Bi5lngePYppE+bFTTJOKKnJiet+bINeL97rvCXi2dpc6yYVeTIGhMyJpbPzQD/u46xXuYKYjM+0uoUCyeq5pPYZvC4Knefab2wHr6VXJHgFCo3X1v0GW/k9hS5G/FpDuIj/O0Gd/eXnXgk9zo6nT+HS/ELH6oNC9SiwdaQV0sPFeP4xAP4aoXUAb2XmGhEZa4cy88/YdkpEv4SmR+SnmSP2o4xA3jyK/cDFi5tUnFBSkxOX/YQNer16r5M4J0kN/mEDpP/zvBkDQubE0tlhpJ3o3a/7OG+hfCbj0xSL26vjJ40Y0ST2aXIY+EvcuXyn3mCsGPcwvmQschK3qZlWEF2MuClcxP8dPy6dZ8MSMUc9BDVLXIznvQCeI6KvtF7fDuDPiWgZgG/HOSgR3QrgNwH8FDPPxdmHK3nzKPaDfoaqJDU5cdlP2KBnix3sB0kO/mFxhP4BNG/GgJA5ievsFqkmevfrPs5jKJ/J+Bwlx0+c62L6bHauHjm0ZXqmao239qObwETR2UB4cxRXhj0ENa+4VNv4HSL6awCbWm99ipmPtv59d8zjfhHAFQC+Rs2e9UeY+VMx9xXKqFVD6GeoSlKTE5f9hClX0+8mNBVj2vdAr4O/32uty6xXBAfQPBoDQnakpLNTT/Tu1308KKF8o+b4iXNdkrqWyvHhYsyaJjC20ne679z1ges6Yp5131nqFULL+OXtvh0VXDzPaCneo6EbOsLM/yqpfQnd9DtUJWxy4s8oLhLhrg9ch4cm10XeT5iinNq8BvfuO96VDc1AX7ywvQz+Qa+1P7P+/Fzdmrk9KMaA0D+S1tkajInecZO8+3UfD1Ion18n+jugDqMhHee6JHUtbTkmfmyVQ4Kl78Kqbagx0F9t44qxAi7WF9rXF7B3cczrfTsKOBnPwmCRJ49FMKPYn72sM6BthCnKyYmKsVtVP7ywvQz+2tg9x8z6QTIGhHyTRKJ33CTvft3HedKProxCXkOc65LUtbSNDwSE7jeY6/KFrRucZHhocp3TOBisthG3VKGQHGI8Dyl5CVUxZRR/+blXIhvPLorS1Oq0H17YXgb/Xpu0AINlDAj5JMtE737ex3nRj67kPa8hqUTpONcliWtpcnxUyqXQ9tZpT2wG7V4dFcR4FlLFFEMWN1EiTJFk6YXtZfCP4rU2DVSiYIU06Ueit9zHevKc1zAMXvFexo28T2yEdBDjWUgVU0ZxsZkomjhZe2HjDv6uynsYBiphYOlrorewSJ7zGobBeOxl3MjzxEZIDzGehVQxZRQv9QqpVcAYRO+Vq/IehoFKGEwk0Ts78pzXMCzGY9xxwzaxGabmVcP0W5JAjGchVVRc894jL3dUwbhwuSEe0wAuyntYBipBENzJekXNRp694v3ANLG5+YZrhmaVUFY8uylkLYAw/Dw0uU6rSJXHVHDHNCCNykAlCKPK5EQFh7ffghd334bD22/JjdEytXkNSl6x4728eMX7weREBbu2rEOlXAKhmWR4540VfPm5V4yrhIOGbcVzVBHP8wiR5bKLeEyTIc/Lt4IgjB559or3i2A9blvDlUEc82T87kaM5xEh62WXUV/aSwoZqARByBuDmGeSFmENVwZxzJPxuxsxnkeErBPNsvCYDmuCgwxUgiAMq34bdGze2EFdJZQVz27EeB4Rsl526bfHNGtPuyAIQlqIfssvJi9tkQi7tqwbyOsjK57diPE8IuRh2aWfHtOsPe2CIAhpIfotv5i8tINqOCtkxbMTqbYxIoxaRnTWnnZBEIS0EP2WX3TVNwbdcBa6Ec/ziDBqyy558LQLgiCkgei3fCNe2uFHjOcRYpQeaElwEARhWBH9JgjZIsazMJSMmqddEITRQfSbIGSLGM/C0DJKnnZBEEYL0W+CkB3Ehi44eYSIzgJ4yXHzqwH8S4rixEFkckNkckNkciMvMl3PzNdkLUQ/EZ2dCiKTGyKTGyKTHa3eHijjOQpEdJSZN2Ythx+RyQ2RyQ2RyY08yiR0k8frJDK5ITK5ITK5kUeZgkipOkEQBEEQBEFwRIxnQRAEQRAEQXBkmI3nR7MWQIPI5IbI5IbI5EYeZRK6yeN1EpncEJncEJncyKNMHQxtzLMgCIIgCIIgJM0we54FQRAEQRAEIVHEeBYEQRAEQRAER0bCeCai+4iIiejqHMjyO0T0PBEdJ6JniejaHMi0h4hOt+T6SyIq50CmjxHRKSJaIKJMS9YQ0a1EdIaI/oGItmcpS0ue/05E/0xEf5e1LAoiuo6IDhHRt1vX7TM5kGkpEX2DiE60ZNqZtUyCG6KzQ2USnW2XRXR2CKKze2PojWciug7ABwG8nLUsLfYw83uYeQOArwL47awFAvA1AD/BzO8B8PcA7s9YHgD4OwBbAHw9SyGIqAjgDwH8BwDvBnAXEb07S5kA/N8Abs1YhiDzAO5j5ncDuAnAr+bgPF0CcAszrwewAcCtRHRTxjIJIYjOdkJ0tgHR2c6Izu6BoTeeATwC4DcB5CIzkpl/6Hu5DDmQi5mfZeb51ssjAN6epTwAwMzfYeYzWcsB4P0A/oGZX2DmywD+AsBHshSImb8O4FyWMgRh5teY+Vutf78B4DsAMu0dzE3ebL30Wn+ZP29CKKKzQxCdbUV0tgOis3tjqI1nIvoIgCozn8haFj9E9HkiegXA3ciHF8PPLwP466yFyBEVAK/4Xn8fGSuYvENEqwBMAHguW0maXigiOg7gnwF8jZkzl0kwIzo7FqKzOxGdHRHR2dEZy1qAXiGivwHw45qPPgvgt9Bc/usrNpmY+SvM/FkAnyWi+wF8GsCDWcvU2uazaC7l7E1bHleZhMGCiK4E8CSAbQGPXSYwcwPAhlZM6F8S0U8wc27iDkcR0dnJyNTaRnS20BOis+Mx8MYzM/+M7n0iWgdgNYATRAQ0l7W+RUTvZ+Z/zEImDXsB/BX6oIjDZCKiXwLwYQA/zX0q/h3hPGVJFcB1vtdvb70nBCAiD00lvJeZn8paHj/MPEtEh9CMO8ydIh4lRGe7ITo7NqKzHRGdHZ+hDdtg5pPM/GPMvIqZV6G5dPPetJVwGET0Lt/LjwA4nZUsCiK6Fc0YwzuYeS5reXLGNwG8i4hWE9ESAD8H4OmMZcod1LR2/nzU2+MAACAASURBVATAd5j597OWBwCI6BpVhYCISgD+PXLwvAl6RGe7IzrbiuhsB0Rn98bQGs85ZjcR/R0RPY/m8mTm5WEAfBHAWwB8rVWO6Y+zFoiIfpaIvg/gJwHsJ6IDWcjRSsr5NIADaCZUPMbMp7KQRUFEXwbwtwDWENH3iehXspSnxSYAvwDgltY9dJyIPpSxTG8DcKj1rH0Tzfi5r2YskzB4iM52QHS2GdHZzgyMzpb23IIgCIIgCILgiHieBUEQBEEQBMERMZ4FQRAEQRAEwRExngVBEARBEATBETGeBUEQBEEQBMERMZ4FQRAEQRAEwZGBapJy9dVX86pVq7IWQxAEITLHjh37F2a+Jms5+onobEEQBhmT3h4o43nVqlU4evRo1mIIgiBEhoheylqGfiM6WxCEQcaktyVsQxAEQRAEQRAcGSjPsyAIw8n0TBV7DpzBq7M1XFsuYWrzGkxOVLIWq4NBkFEQBh15zoRBQIxnQRASI87ANz1Txf1PnUSt3gAAVGdruP+pkwCQm0FzEGQUhEFHnjNhUJCwDUEQEkENfNXZGhiLA9/0TLVjm027D2L19v3YtPtg29hWg6WiVm9g277j7W2yxiTjngNnMpJIEIYPec7c0elSoX+I51kQhESwDXyTExWjVyn4HT9xPU9+D/jykgciYHauHnsZ+NXZWqT3BUGIjjxnboiHPnvE8ywIQptevBlhA5/JuC4SWfcb1fMU9IDP1uo4P1c3esNduLZcivS+IAjRGabnLE3PsHjos0eMZ0EQAOjDLqYeP4GJzz3rNACEDXwm47rBjJJXtMoWxfOkG1j8xBlkpjav6ZKx5BUxtXlNpP0IgmBmWJ4zlxC2XjDpw+psTUI5+oQYz4IgANAbnfUFdvbahg18JuO6Ui5h15Z1qFi8S1E8Ty6GdtRl4MmJSltGwqLMskQqCMkxLM9Z2p5hkz4kIDWDXehEYp4FQQDgZlD6Y5iDqPdM1TamNq/pinFWxvXkREUbF+3fxpVryyVUQ35LnGVgJaMgCOmR5HOWVdm7tGO3dbqUAHBgO5u+FnpDjGdBEAC4GZ0ArNvYBr4w49p1mzB0A4ufQVwGFgQhGlkm1Zl0aVKx2zo9adLLkmyZDmI8C4IAINzoVBCaA1OcAcjFq9Sr5yk4sCRRbUMQhMEirPqPjV491lOb12DqiROoNxZ9wV6REp20B/Xkpt0HUzXYhU7EeBaEnNOvpUed0Tlbq3dtxwC27TuOPQfO5NYQlRALQRht4oZOJOaxDsZQBF8njC0sTkgeMZ4FIcf0e+kxaHSu2r7fuG0askhrXkEQkiBu6EQvHmv/PuoLndZyfYGx4+lTqem3JELeBHcyM56J6DoAfwbgrWjOyR5l5j/ISh5ByCNJKPJeqITEQceRRRnI1dkaikRoMKNSLuHmG67Bk8eqUvhfEIacfkyS44ZOJJHsZ9p2tlZvr+alod9kxa2btO61LEvVzQO4j5nfDeAmAL9KRO/OUB5BSI24BfOz7rilKz/Xiyz++qdAs8Yz0BxI9h55WQr/C8KQk3YN5A5ihE4k0ajFdVvRb+mS5r2WmfHMzK8x87da/34DwHcAyJRJGDp6eYCz7rjlr7tqIoostgYmpnFNFf6XeqWCMPj0qzueKXQi7DhJNGpxcToopBpGeqR5r+WiSQoRrQIwAeA5zWf3ENFRIjp69uzZfosmCD3TywOsU8IE4OYbrklSRCuTExUc3n4LvrB1Q8+DStyBQgr+C8Jw0K/VtLDjmFYDk2jUotvHinFPu61Uw0iPNO+1zBMGiehKAE8C2MbMPwx+zsyPAngUADZu3JhyvqogJE8vD/DkRAVHXzqHvUdebntmGcCTx6rYeP1Vqce3BePF7ryxgkOnz8aOHwurJa0r9K+Qgv+CMPikXQPZ5ThhidhJxA4H92FrAJW3ROm8yROXNO+1TD3PROShaTjvZeanspRFENKi19CLQ6fPGjtHpYku3OTJY1VMbV6DF3ffhsPbb4msUG3LmSWviLtvWmkNEZElzmwhouuI6BARfZuIThHRZ7KWSRgskgiLUNhySWzH6VfoiB+TRxtAl57dtu84Jj73bCYrbX2NSU+ZJO+1IFlW2yAAfwLgO8z8+1nJIYwe/Z5V2+pvusiSVdJgGpU+/OWUgtU2/L9dCv7nFpXo/S0ieguAY0T0NWb+dtaCCYNBLyXV/PqyPO7hzYvz7bhmnfdYHUfpGqW/surGp/Nob9p9UJsHcn6unkm1oawrPCVJmuX7sgzb2ATgFwCcJKLjrfd+i5n/KkOZhCEni5atpgcYgJMspmYly0uLMXRxJwSm703PVCMPMK4yuCyJSsH/fMLMrwF4rfXvN4hIJXqL8Sw4EycsIqi7z89168Sgkaf+H9SzpvCwfk7O/SU7TWRhtGZd4Slp0irfZzSeiWgMwK8A+FkA17bergL4CoA/YebuOzcCzPy/0AxxFIS+kdWs2tXjoJOFDE+Jet91QhA0bk11lY++dA77vvmK8bfoBpikJyVS8D9ZiOhRZr4n4X2ugiHRWxCSxlapx0/QyNN9j9GdX9FL6EhUPaWLfzbRb6O1XzHpg47N8/z/AJgFsAPA91vvvR3ALwL4EoCtqUomCCmQ1axap2BNHofg+7MaD4v//bAJwfRMFTuePtXhvVZ1lXWx1H/+3MtYMGTtmQaYsBjCOEawFPyPBhFdZfoIwIcSPpYx0ZuI7gFwDwCsXLkyycMKI4yrjg4aeabvMWAMG3MlrtPAdSIA9N9olVU/N2zG843M/K8D730fwBEi+vsUZRKE1EhyVu3qcTApWNPSYZGoY9+FloLvgpr7tk0IpmeqmHr8RFe9UxiODcBoOAOdBrH/t5pkUL9Vugb2hbMAXkLnip5ysv1YUgcJS/SWCknxGZYqB2kQVqkH0Bt5tu81mNvfiXOe465kuk4EkjRao4TVAbLqF4at2sY5IvoYEbW3IaICEW0FcD590QQheZLIvp2eqWLDzmexbd9xp4xkk4I1WRUN5o5sZ63hDIAZmHriREfss5/yuIcdT5/SGs69oPutpsmHStLxI121UuMFAP+OmVf7/t7BzKsB/FMSB5BE7/QYpioHUXDtvqrT3V6RUC551nrMYTXxe9FHUVcy1W81aeRlS4o91Zc2EfXempyoYGrzGlxbLuHV2Rr2HDgz9PdhVGye558D8LsA/oiIlLFcBnCo9ZkgDBy9zqptsWomj0PUkBCdwWmi3mAQNQeReqNTJb8+V8dCpCM39zNWINTq9m8Gf6tpqc/0OwY1+STnfAHACgAvaz77vYSOIYneKTFMVQ5ciRL2EFd3Hzod3lzNRR/pPLdRVjJd4py9YgGHt98SKktUdj5zKtK9lUVi/aBhNJ6Z+XtoxTUT0Y+23vtBf8QShEWSXsrsJZZ2x9PdSshPUAlPz1TNYRcGomwLNGOfl3qFLuM5quEMAMuWjGHHHWuxbd/x0G39v9VUFqpo+O2SfJI8zPyHls/+S0LHkETvlBi2KgcuRJ0wxNHdLucvTB+ZjMk7b6x0JF0DnSuZTuF3Pl7XVFXqlemZqrYyCWA+N6M4kYuKU6k6MZqFrLDNgIHk4rJcDPQHpk9qS8b58SthJXtUYzgqBQr3FLvyeq2OyYkKfuup5zEXss/ggKMrC6X77ZJ8IgjdDEOVA5se1X3WjwlDWKy0iz4yGZOHTp/Fri3rtGVIJz73bIfR6jIOpHGtbSEppuON4kQuKpm35xaGg7QSXUxKa8fTp3BpfiGRZSWdgb5t33HsfOYUHrx9bbtixd4jutXwRYJKOEpGdS+EKWVdSIeJ5SUPG3Y+G2o4R6m6ATRDURaYJflEEAzktcpBr4nRCt1nphr25XEPm3YfTGQ80Z1XlaztWmXDZky6tOF2wXStex1bbQav6d4aholc2ojxLPRMmvFRpgdfp3DjLiuZDD5/h6c9B84YkzwAYMW4hwdvXwsAbaWfhzIDSq5gRz8dBQAXLs8bDW1qjTg2BW66XqoclEo+ASR2ThD85LHKQRTdHlauUvfZUq/QlRvhFQlvXpxve22rszXcu+84tu07HquknK1J1Z4DZ3DvvuPYc+CMdVJgCrnQGZNRnCZhRrzu/E89fgI7nzmF2bm60z1iMoTLJc/4vbxO5PKEk/FMRBUA1/u3Z+avpyWUMFikGR/lUp7IT5xlJZcOT7b9rhj3MPPbH4ztcUgLr0htz3lw6bQ6W0OBFsvSlUseiPRdu9ow8OLu26zHNF0vwuJ5luST9BGdPZjE7byXlsEdRbfHWeqfnavjka0bOuS/cGm+yzmizNa4ukOdV3Wutu073lEq1NZYyhR6ZzImw8agAjUrJYV58U3dB+sL3DGxCDsfJkN4xx1rjTLmcSKXN0KNZyL6XTQTB78NQJ19BiCKWACQbnyU6cFf6hW0hp7LslJwsPEbkTrUdiajUHmc+xWm4YLOkxE07he4eS5VOaTV2/db92k7t35lH6xfratnLckn6SE6e3SIs+oXxdiOotvL455WJ5fHPTDrVwuXt7yf/uOH6aFavYH7HjsBIJoBHTxXLjrJptPfu3J523Pt92ZTyzg28SNLPRx/8IPOcoZRqzew85lTxmsa1xCWJlV2XDzPkwDWMPOltIURBhOTYWmqPxwF25JbnGUl3WAThjqmLm7u7ptWtmV08TgUiRKvuxykWCCt4XzfYye6PCj+AcPm5Q+eW/8AXB738ObF+fbv8re+rVj2KcknqSE6OwaD2KAk6qpfVGM7SuyryWBkboV8adC977LaqGrhA24GtEn/BQnqJJuOOvzdc+1/q3CKBdgdMUB4RY04Tpjzc3WrN1oM4eRxMZ5fAOABEEUsaJnavEbbxe7C5XlMz1R7fmhtD77OqLYlmkRVTMHuU/7j3XzDNTh0+ixWb9/f9K6E7GuBgeWlMXtoRAI0Fhg7nznVEaphq/qhBgjdBAFYjJsO7k9tp/s9ynA+vP0WbNj5rNbrJMknqSE6OyKDWtfW1tlTpwejGttRYl9NRqHNWJzV6I6bb7gGXwpJzlZy2zyuiihVj4I6KUrYoKtThNEco0yVSJJwrQz6yt4gTGSNxjMR/Rc0r/McgONE9P/Cp4yZ+dfSF08YBCYnKtj5zKkuI6re4FQf4DhxbFG8nSWv0NHhKRg77J8wuBrEaRvO/uOs3r6/HUNomzCoAcN1ec91AqLag1+4PN/1mdfyjgvJITo7PoNa1zZqjkHUELsoS/5hXmpXD7ZLUxOFyePql9m1zr5OJ5kcCr2iKjrtePoUPrz+bV21opNgUFf2BmUia/M8H239/xiApwOf5aGQgJAjdB4EIJ0H2D8rXV7yOipE6OLY7nvsRDsuzVQaScdVy67oMM5ViIIpfi9vqDasNoJeJJflPddrem25hD0Hzmird1y5dCxXinBIEJ0dk0Gta2srw+ZHTQTilCBzXfI3yXLzDddg4/VXGT3YQS9jlATxILoypq519nU6Sb12CfmIw2ytjr1HXg59OHXXNIxry6WB8OAGGZSJbMH0ATP/KTP/KYCy+rfvvRX9E1EYBEzKN+mleTUrrbaWt2Zr9dAaxg3mtiF54fI8vIJbg7TqbA0PTJ/sON75ufpAGM4uFIk6POuuuFxTNTAaSw32yQM/SojOjk+/9FfSTE5UsGvLOlTKJRCaoVImbfjqbA1Tm9eg5BU73k+qBNnkRAV33ljpaD/JAJ48VgWALjnvvLG5Yrlt3/G2frUZzq5tLWdr9VheXJNOmpyo4OGPr+86b0G8AqHoOLb4sY1e6lzdfdNK6z6CRy15Rdx8wzUdY5fy4E7PVCPL2E8GZSJrNJ59/KLmvV9KWA5hwElTKfvptaJFvcG4cukYyr5kRpu623vk5dxU0EiSklfEwx9fD6AZf7d6+35s2n3QSbHqrrVXpGa5OzSVvTLKB9UoGXBGXmdPz1Qj3df90l/9YMW4PlH72nJJa2zHmUCbOHT6rLWKxeHtt+DF3bdhavMaPHnM3DY6SMkr4u6bVrblToMCkfF+UefNxp6PrcfDH1ufmDyVcgkv7r4Nh7ffgocm13WMWUH856Zc8rDUK+BLmrHLX3c7rwzKmGGLeb4LwM8DWE1E/iXAtwA4p/+WMKrEKYcTZ0kpidnn+bl6x0Bpm/kPy1r3inEP40vGOs710ZfOdSwZusaWRbnWUmy/f4jObhInZnJQ69rqfqtXoK6uov5nLs3KC66VdaI4QXRlNzftPhg5vEN1OS15BW0HVRWWESfGtkjUcQ9FlS0YlqE8x/6kzw+vfxv+/MjLCEpeLBA2Xn8VHppc51TmLm8e3CCDMmbYYp7/N4DXAFwN4GHf+28AeD5NoYTBJIpSjpsU0GtMHNBUdMPoTbZx23vehocmFz0nD0yf1Ga0u8aWuV7rQTVKBhTR2YgfMzmI5bx0v7W+0DQQFxaaBmGRCHfe2JlcHeVZjNKe20TQa+hqwBGAw9tv6ZJjecnrmiAQgCVjBVya7zaMAeCuD1yHQ6fPNnNXWk2hZufq2oRCU81nEw3mdgUNU5LhsiVFMHOX4e4VCEvGCrhwubl9ueR1JRFWZ2vGCiSNhcXEfJdJic6Dm6fY6EEZM4zGMzO/BOAlAD/ZP3GEUSHuAOdaxshEsBVs2hQLhEbKdZ1d8GewT89Urecwac/EIBolg4jo7CaDEjOZBKbfVPMZaA3mdtxx0CBzaaji4uSYnqni3n3HjXIGvYauTpDlJa/tZfZ7Z1XeybhXQK2+0Dawtllk8Os89f1P3LQSew26MErNZ2Cx1vOVS8c6xpgiEe76wHXYeP1V2HPgDOZmayi2DPZyK+FdGc4AcGl+Afuffy3SOKVkC5PR78E1NbbKQ3WLQRgzQmOeiegNIvph6+8iETWI6If9EE4YXuIOcFHKGAVR8X2VCLFTXoGw6Z1XxY6zy4PhDDQVoorn2/nMKeu2YbFlUeNJhf4y6jp7UGImk8D1N9XqDex9LnoMrMnJsW3f8fazrwxsm6YLGkJTm9eE6lSvQLhweb5tZOv2X6svtJPpbIaziS8dednYzEtX8zkMf+tsRYMZ+77xCqaeONH+LQ3mduhgMOG9Vm9ELmmqZLPJ6I9v9yfeA+bqLK6M4pgQajwz81uY+UeY+UcAlADcCeCPUpdMGGrCBjjTw5hUy++wzGnFlUvH8LGNK1E2JOEMEirjOkwx22LLgtVOBiWDe5QYdZ0dNflvkAf+KLrMVGnNplNt3mH17O94+lTk1bzJiYrV2K6US7hy6VhoJSVG0wDuJZRvtlbXVqvQ1Xx2PddB6gusNZKTqNxEWNTZpnv/C1s34PD2WzpCIsKumetYO6pjgkuHwTbMzACmiehBANvTEUkYBWxJAbalwl5inlVh+hXjHu68sYKvnngtVHmdn6vH8mgMKoRFL5EuDm5QanAKTUZRZ0eJmcxLQ4a4Mae63zp3eT6S59LvsAh2UA2rL1yrN0KNsGD1D3UcExWHEIykYSwm7ekSFBVXjBVylS9DaFba8Mez1+qNdliI6be4GMaudaKTGhPyFHftQqjxTERbfC8LADYCuJiaRMJIYBvgNu0+aHwYk+j4dH6u3lPcdBKsGPfweq2OnER1tFHimIwK03kfxnjSQUV0tnvMpG3gV5/79ZPuvV4H+F4N+OBvdam44MfksHBp3hGGVyQ8ePtaZ9m8IrXrE/cbRjM+2dbiO2nDedxQ+cMFv2EclE+FhUTtBulHXYew+zKJHIO8TGKj4OJ5vt3373kA3wPwkVSkEYYa3cxSZVL7tzE91Or9XVvWtfcDMi9H5pVioTmgpOlZKXnF9nnStu81nDcVD24yKoqGVrfDGE86wIjOdsQ0wAcni9XZGqaeOAFwcwnevw3Q2wBvetZ2PnMq1n51jonZucsdSWmKcskzOixc1eqKcQ8X6wtaw3LZkk4TIyxcYKxAOHT6bGbe3QYztu07jt984gQut8IsVrS6yvYik1cggDrjm70Cte+lqPirkADRvb9hTqhyydNeB90+43StDDKIK5pW45mIigCeZ+ZH+iSPkFN6XVLRzSynHj+Bnc+cwuxcvb1MqLLCTWzbd7wjw7rX6htZkFYSoWnZURcec+eNlY7Me/W+8q6ZjArl0ch7Dc5RRXR2NEwDv66cpS7+NokB3vSsnZ+rY3qmaq2EsePpU+3QsxXjHh68fW3bE+0Pv5p6/ETX970iYccda60yhFHyim3Psq5yw2yt3qHnwzRfrb6Qi1Wsy75rHTV5L4jSx0dfOocvP/dKu3ygvzxdVFxL/5neV/fGzmdOdf2+klfEjjvWGqunBPeZRF3mQayQYzWembnRKrwviniESWJJxVSPVD24tjqWQdQyV3W2hn3ffMXpO3ljz4EzKJc8bcz1uFfApXnWenht/Jt3XoXv/aCGV2dr7WVnW3iMKp2kmxCZjAo1EAxSbNooITo7GqaBP06ZsLjYltB3PK33PiuD2O+5PD9Xx9QTJ3D0pXPtesbXlku4cGle6+FctmQs9HnXxTyrVavgJF15sIP70VWgsNFLXouq39yrwZskN99wTZeR2mC2Gs5ekTpWOYKfXbg0j9Xb97f1bxzvr5pgmRxjplXL4D6TqMuchPe63xCHDNBE9AgAD8A+ABfU+8z8rXRF62bjxo189OjRfh925DF1c6qUS11hF4DeS33vvuND060vCQjAI1s3dA2AXoGwp9XiNfiZyz67BjqEJ8Horheg91gn2cp31CCiY8y8sQ/HEZ0dAVNirKsBZ9KDUY5vC+H63u7but6zddgLS/Lz84WtG7Qxs0DTSBsrUEfN6HLJw4471hp1wOrt+3vW8yvGPaPxW6DmqkBQLy5bUoRXLOD1Wh3LDU6JQeITN61sOzeqvrrQK8Y9vHmxczJkW0nsRV/r7omwfcZdoY5zrH5h0tsuMc8bWv//nO89BhBfWywKdSuAPwBQBPDfmHl3r/sUkifKkorJSz0MCi1JCkS4d99xlFvxdK/X6h3KZnqmiqjFpXWDVljhe9P12rVlXUdsuXiYB4rUdPYwYkou1BmTQW+g6/K0zaiYnKhEzn+webujGK9BnaBkLLeMtFogmU2FYfi/4yeJDrDn5+ooELqSqZUx5ZfTH+6nxpe0xpkrxgqYb0RfEYzDodNn8dBkt+G4affBrolFrd7AodNnsWvLug4P9xVjoZWIrUT1KPeyQj0oXQX9uBjPv8LML/jfIKJ39HrgVmzeHwL49wC+D+CbRPQ0M3+7130LyRJlSWXnM901P2v1Blr5EuJ9bqEU8Pm5OkpeEY+0PECKPQfOhNY4jYqp5awpUcNfF1QYKFLR2aOEaTDXvefS3jrMqDB5W4Nl3hRJGKlAp07wTyJ0RpqivsC497HjHfIrkqiGBDQN52VLiiiPL8GrszUs9Qq4NN9szqI69j00uQ7TM1Xc99iJvhi0prbfaRA1Bli9f9E32Zmt1XHvvuM4+tI5PDS5LpYcUTr99Zr0NwhdBf24GM9PAHhv4L3HAdzY47HfD+AflJInor9AMyNcjOc+EGV5xTUhYHqmalS4cRMjRgGdgkkrUcK15WyeEzWEUNLS2SOFaTCPOsC7GBUP3r4WU0+c6KzGECjz5mdq85rIYV0mdM962PPPvOi1BvTJgr1y4XIDn//ZNfitp57vKOfWYMaXjryMF8++iW+9/HpfDGeFqdpQHLxCc1+6S2hrImZyZOma1TCAvUdexsbrr0rdMB21scRoPBPRDQDWAlgeqBv6IwCWJnDsCgB/ttf3AXxAI8c9AO4BgJUrVyZwWMHFE+Lve68URljh9SjtPEeRisVbFFQwSXmWguhazg5aooagpw86e2R5YPpkR6UE5fl0wcWoiLpsrd4PVtu47T1v64p9DUP3rLvon1q9gR1Pn8Kl+cUydUmbsc1xSu/xPfzdcwkfLZwGc08l5hQlr4D5BcaC5qfZQoFMjixb1SkG+lLybdTGEpvneQ2ADwMoo7Nu6BsA/s80hfLDzI8CeBRoJp/067jDTJgnRFdwXf3fX3g96L1Ow9gbBvyJD6ZEn6CCibP8GZZpbmo522uZISE35EJnDxsPTJ/sMEyU5xOAkwFt0o3LSx427T5orXtvI1iObs+BM9h75GUsL3lY6hUwO1dH2ZJ8p6jO1rBq+/6OZEBX/ZN2HkueuvkBzdDDJWMF1HtcSb1sqKZUJLImyZkmWWGOq+psraM6R1RD2mWletTGEqPxzMxfAfAVIvpJZv7bFI5dBXCd7/XbW+8JLdJqVxnmCbEVsvd33wp6r9OmSEDCYcCJUWrVng4uWxKAO29cHORcFYwugUclFi4vebhweb5jiVfV5vRPavyrBqbVgkFM1BD09EFnAxi9RO8vP6cvh/nl515xMp51z7xXIFy4PN82PntpuBJ0dszWFvModjx9ynk/umRAv2dbaOr2JEIQTaEfC8xOyXXBbUw1mf0w7InjpjHANRFw1MaS0JjnFJXwNwG8i4hWo2k0/xyAn0/pWANHmu0qw5ZXwmKUVB3hfnsF8mo4A2gvLQZFZDQzpxVRFIwtgSIsez/KPTJoiRqCnZQN55FL9DYZOq6xr7pnfu7yvLZqgqktuO35tK0kRjV86wvclUAYbMYySHhFSjzxOk3ihjhEWfkNxtuH2RpREgFHaSxxSRhMBWaeJ6JPAziApgfjvzOz+zR5yEmzXWWY9zPsQby2XBraJIA0CJ6rJBTMKCkpIVeMXKK3KUmsSO61JIPP6+rt+7Xb6dqC+w0Z3aQ56USt6mwND0yf7Gi0ojoRRql/nTVFImx933WJd6BVq4xJ4xLiYHKaRA3z898bYbbGqCUCutJbIcAeYea/YuZ/zczvZObPZylL3kjzhp2cqGDXlnWolEsgNBPZ/HFWYQ+w6mgkuBE8V9MzVWzafRCrt+/Hpt0HmzWdBWEw0CV6d8ziiOgeIjpKREfPnj2LQeeuD1wX6X0XTPpT1xZcGTLKQ1idrXUswS8vmcvZtzf8HQAAIABJREFUmUrdhfGlIy93HQdo6v4ok4Ysefjj6ztW/aJg+o0EYKlXjLy/AsxlB4Fm18awhiCm669auAfH9C9s3YCKpWqHIszWsFX+GGVs1TZ+3fZFZv795MURFGlnruqSTe7dd7w9mx33Ch3lgRTjXqH9PekaGE7Qm5BmOI4w2uRFZ2eZ5J1GnoiKa45bbUNH1LbgplC5Wr2BpV6h67t+vRMsgReHWr2BqcePAwmWakubyYmKUyxwENt1YACzIQmY3ftrVtawJW6OFcInJGEeYt2YrisfGByTwmwNW4WPYMLrKI1hNs/zW1p/GwH8X2h6FyoAPoXuGqJCwkxtXoNSYIabRubqA9Mnce++412z2XpDvyylHsLJicpIGc4lr4iywcNjIujRVwX9TQpQEHqkHzo7t4neNs9crzw0uQ7f3fUhfG/3bfjurg9FNpyDq00AtKt/Nk+hyUN4fq6OWr3R9pYG9c6yJYs+smVLinCw07TUFzBQ8cNAuLOJgLaH1uU6EICyoze/Ui7he7tvw1XLrgg9b/UGh44BrqvR/ucAaI7Z6pIH7w0g3NbQebVVO/A0nrVBwVZtYycAENHXAbyXmd9ovd4BQB+wJSRGPzJXgyWYFLa4KX+sl61u8TCh6qfuf/61SN/xl51SCs3ktRn1+DGhd/qks3Ob6J1mnkgvmFabdm1Zpy1NZ8pHCYs3Vrpl7vK89rgAcLmxMFJtXm31j4FmuUBT/ohuZZXRbBBj804DTa/k3OV5rN6+3/l0+8cA3QqK62q07jlgNMdr3f3mYmsEz9Gm3Qdz+az1E5eEwbcCuOx7fbn1npAyaSaFTc9UsTdmIoV/SSgPJNn1ScebF+ex75uvdHkPSl5z4UaXPBIUJ6w6yajHjwmJkprOznOid14Tm0xG/X2PnegIlfPre78hs+pHS5FC5M7P1TH1xAnMN7jrO4PmOY6Lii8Oi3m+cHm+HTPsZ3Kigm2GkI/Xa3U8snWDtS34Asz19k2oMcA02VLe3rAyp3Geg6i2Rl6ftX7iYjz/GYBvENFftl5PAvjT9EQS+sGeA2diOSCWLSlGbt6RNqp5S1oymbpJXawvGM/h64GyTjalMsyF5IVMSFVnM/NfAfirpPaXFEnkiaQRM2169pXhVZ2tYerxEx1tqFeMe3hk6wYcfelcrGoRo2Ik61BtzadnqqEOnnqDsfOZU9prXC552vJ8DBgN67j4xwDTZOvQ6bPYtWVd6P2ZZr6Uej5Md9coOYFc6jx/noj+B4B/23rrk8w8k65YgitRlL1/27iqNYkC8Wnw3pXLceSF87E80GVN0xEXGGavt2sb7LCOUoIQlVHV2aYl+ptvuMbp+2kl87rU4K0vcMck3e89FqKx56PrAaB97cI4P1fXep/TKCpCaN4PN99wTUcpQP+4bfPquniIb77hGuw98rI1STAOujCgIHMGT/4w4lrn+TiA19T2RLSSmZMtnihEJoqyd7nxB5nD3z0Xuo0u61gZrnFDUXRe7yhtsMVwFlJi5HS2aYnetVxZWjHTUWvwKkbZexyXSrmEyYmKNibXhu4aR62qEcaKcQ8zv/3B0O168RxPz1Tx5LGqtcttVKKMjefn6iNTPSrUeCai/wTgQQD/BKCBRRvkPemKJoRhU/bqczWzvXBpfmgNZxdKXhF33lgxzvb93bR0rXRNoRuq7XWY93/UWpcK2TGqOjtqHGZw1c5kHKjvxw3pCD77hYRyNNSqV9ApkAa9HqtAgEGFJgZhsUdB1Nhb3fZRuvYpVox7uFRvaMu8ul7ysCZmNkzJgnHrXcdxuo1K4qCL5/kzANYw8w/SFkaIhklB6LpUjSpqmSzuQKe+e/Slc8alMNdkC+kKKPSJkdTZUTx2ulU7k2F4bbnUc0hHsAavq0FikqlYIDz8sfXaur46em1TvcAcu7pSpRWmoEu6TpJ/886r2ucjquHLaFaQ8I8TUVYMyiUPxx9sepVN3SODeTAmenG0JJ3IF5boHlWOYcLFeH4FwOtpCyJExxZHO8peZoWpNE8YOiN3cqKCjddfJZ5jYRAYSZ0dxWNn8tCZGkq4hnTYvNP+z5q1gjm0zbPO1Fy2pIjP/2xnuFdY4ypV61mXAOeCrc60iQIBv//xDe0wirTDUL73g0X5pjavidzESzchumKs4DbJ8cVHm8blApFzPHBcR0vSyYJh19w24Rx2XIznFwD8TyLaD+CSelM6DGaPaWY8KB2golIsEBqOa3/+JbykiKvQ0sjgFwQLI6mzo3jsTEaBqocb/L6pU12wNq/JOw101m8+P1eHLR/NFOawbEkRpz53q/Y7tioIr9fqzs09grjWmQ6ywItJe/3wRPpls5Was1GrN7Bt33HsfOYU3rw4bwzXCzLbSjo0dfUDmuNy2vHAvYR86LB58NWKgkv5vGHExXh+ufW3pPUnZIj/AW3HoZF7PNUg01jgSLFzSgkyNwePpAzXqBVOpB230GdGVme7TnBNRoFptao87mnr9voN0rAcFJ2nWwfBrM8vXG7ggemT2g6HNgM1jucYWMzpaIcyRGz1rX5/nPjhqBDQ4dntpYmXqUazydNaHvc69LzpDKUdD5x0bo1Lovuorsi6lKrb2Q9BhHCChpjyMI+C4axwNZzVZn4lmIThGtUYzmvXM2F4EZ0dTlQPnUnH+t9PKt60QIS3LB0zhlh8+blXtMazyUBVq3BRPMdegbDHF1PdJsZY8+psDY9s3ZB6tScGOpLl0zDWdT/fKxKY7Z15/aTthU8ytyZO98FRwaXaxiFo7hlmjh5MKvRE3OD9MHpNJkmaFQYvj8JUvN6FXgzX6ZmqtquUbZ/SiUnoN6Kzw4nqoTMle/nfD4s3NRm2uuX9C60W2zpMYXm6CQEBuPumle3f5WLA+r3NSXSTLY977ePvfOZU5M57UQgmy/eDZUvGnJMBgcGLBx5V4zgMl7CN3/D9eymAOwGYn2whNdIwuEpeAXfe+PauShJZUfKKePD2tdjx9Cmjgfzh9W/rirOKQpzzqDzOpoHLtM80uz0JggHR2Q5EMQpcnuMwb7busztvrODLz73SpVdszgwVnmAy/P3vq2Ycq7fvx7XlUrtcp8kYJqAdtpJUb4A3LzYbZwDNrqy6YzKQSPhhgdw9wEkxW6s7h4iMSjzwKOAStnEs8NZhIvpGSvIIMCvGNOLGdm15D3Y8fSpzw1lXUs6U8KFrU3rh0ryzNzqO4Rrm9TftM+kEDkEIQ3R28rg8xy7ebN1ne2O03/bHHgdDx0wl8aqzNTx5rIo7b6wYnSV+PZbUSmd9gY1x30DTcA42mopLL7WkgzJ4RWp7la8tl/Da6zXj/k3dLf2USx523LFWW31llGKFhwWXsI2rfC8LAG4EsDw1iUYcW0xtnPI7Nj5x00oA8csXmSiXPCy7YqzD+/HVE68ZjxNM0lFKxYSuTamrlySu4WrzVtv2Kc1RhH4jOjt5XJ/jOEvcpmTESrmE2bnLuHC5O9Ew6JnWNcfSNWOp1Rv48nOvaMeQYIWiJFc6w5w+tXqjnQCfBmFebZdmV6sM9ZsB4MljVSxbUuy6Vn4uzS963SWRfPBxCds4hsUSmPMAXgTwK2kKNcrYEswOb78lVvmdIEUiPPzx9e36m2FEjYn2z66BxZah2n0XqENhuxjBqmlBcHlyqWevyRnMHI+CraZ2WIttiRkT+ozo7BTo5Tk2GUtHXzqHNy92R9R4xaZejKLvg/G+JkPU9D6j03DrR4UMPw3mnj3QRMDSsWJ33PcHVlpDE03NrqZnqtYQQkWt3kC55KHkmcNG/Lkxkkg++BTCNmDm1cz8jtb/38XMH2Tm/9UP4UYRW9fATbsPYkXMWp1+GtxcRpueqbp5Fxjt49pqkwLN7YIPv3X5L7DDsKVCr0C4+YZrcP9TJ1GdrYHRPDdfOvKyNRHF793etPsgVm/fj027D7Zj8cKY2rwGJa/Y8V7JK7YnIYKQF0Rn95/pmapVr5iMpT9/7mVtLWHV1CRM3/pxbY5VMOy02GriodDpPIVXIHjFKNKFo+TvZa/MwHtXLu/YB6PpGV5e0o+d5VL3mAU0r+nU4yecV2Zfr9Wxa8s6VCxhgWq8tY3zwmAQajwTkUdEv0ZET7T+Pk1EvVtwghZbPG51toY3L84norSUl8KlcH59gTG+ZAzf230bHtm6wWjAq2S/IDYDvd7gjhCNMGP+yqVjOHT6bCTvhAqrUN4fv9F9/1MnnQzoyYlKWzESmsZ4mMdZELJgVHV2mAGb5nHD9IpJr5liaGdrdWvTEx0uIQ9ekYzl5lQTDyX35EQFd95Y6TJmCcDW91+HPR9d39aH5ZKHZUuKHdtEpV16NcZ3/Rz+7rmufdTqDRBB6wDZcUf3mAU0JzyuTVKA5tg9OVHB4e23GA1oNb6bxnmVDBqFrO77UcclbOO/AvAA/FHr9S+03vuPaQk17NgSBUxdAxX1Be6KKY47W63VG7hYbziFZfiVvy5jukDAnTfqlzXDZPTvO2zb2bk6ZiOUOvKHamzafbCnpTIJvxAGhJHT2VnGkLoswUfV04ToXkhTzHCRCAvMTonVQbkPnT7bZYhy6/2HJsOdB5t2H9T+Dv8YpovNToPZuToe2brBOf8kasy3P/wwLMHUlL+kalW73rMSO50doZ5nAO9j5l9k5oOtv08CeF/agg0rYV4Kv4fTxOu1Og5vvwUv7r7NOst1gVv/WTHugdBUtDrUTNkUVrHAzaUx3azXtvzn37frtq7VMlSohlIiUnNZGBFGTmeHdfdLExe9EqbXgjDMutiEihn2o0LL1FjhUo/YL3dYGGGYl9MU7rbjjrXtMWwhhuFc8orthHdX/J5hdT5sBmaUqkzB0I+wlcrJiYrRwx5lPMryvh91XIznBhG9U70goncA6G8hxSHC5WafnKhgavMa49JX8KEOU8yEZj1nEyos48Xdt+Hhj6/XKjs1Y7Y92KaHVikSXbiHrtxT2LYuA5GuAoZJGUrNZWHIGDmdbfLS9iOG1EWv6IypsiEGV2Eyhm0xy2GhZS66zr9NWBjhvfuOY5UlXMAl3M1FpnGv0J5MFIlw540VbZdFE/7xwDXMYWrzGnimkx3Yty70I8xQDwvtcEEcQtnh2iTlEBG9gKYddj2AT6Yq1RDjerOb4t2C5YSA7jJK5XEPzGjXpzQV6dcdP6wkU5QQjKCM/o5VLuWewrbVNQOw7VdqLgsjwsjpbFvIQtq46pU45TWXegVcMVbo0OWmChwN5tDQsrCwwKDcN99wjbVKhXrfFi6g+90bdj7bDh8Z9wrW0EH1mbq+DWY8eayKjddfFdqNVqEMdl2Yw9QTJ7Dj6VMd59gvs63ahku1JRNJjEfShCs7rMYzERUBrAfwLgDqip5h5ktpC5YEeSxC7nqzm4zQYDkhhWs8rq69dPD4tn2FKd+whzZK3LBt2zjxx1JzWRh2Bl1nxyVqWbYkCeqV5SUPRMC9+45jz4EzRh3jYpydn6uj5BXxyNYNHcfRjSEu4XsmWWfn6l36UJUYdT2DLvkjqoKFPxFvrpVDs2xJEXOXG10y6eK01bEevH0t7nv8BBqWxL5KK1wDaLYHD45d9Qa3969rOKMzuoGmodtL0ngS45E4hLLDajwzc4OI7mLmRwA83yeZEiGvgfSuN7vJyC6XPGzafTDWw6a26+Vhsyn8QXhoJelPGGYGWWf3gqk9ci/5IFEwGVlh405wlU33G4JGaRSDyeRAMulAFdIQN5FPOX1Mx7VVsJi73OiYJChWG5qTqGMVYI5J8q/UTs9UnbzUuoYz/tbmSTpeeh2PxCGUHcQhDwcRPYJm5vY+ABfU+8z8rXRF62bjxo189OhRp21NWb7BbnZZ4OIR1810vQIB1NldKs7sNymPfB49+4KQV4joGDNv7MNxBlJn90IansE49DrurN6+3xiu9+Lu29qvXXTvA9Mnu0IubOfEtUurDdWC2nQtwjrk6s6T7ZwC5rh2AnD3TSvbsdGm/ZgINmzJ4n4Sssekt11inje0/v8533sMILYFSkR7ANwO4DKA7wL4JDPPxt2fjiwD6cMUm8tsUzejnLs83zVzjtOVKCnvq3hxBSGXJK6z805ePHC9jjuuYX1hund6pqqNVbaNF2ENqlwgsifFx8mZsXna77V0YAx6saOM/bqGM9IBUPATajwz880pHPdrAO5n5nki+l0A9wP4z0keoN+B9P5lN4I9kcLVYxtUkGHLV4IgCCnp7NyTh8l8r+OOzVCMstJna7CSpmPJVof/1dkaHtm6oSvm2Y/uPNkmRrb47+C5ca21bWsRLmOtoAg1nono1zVvvw7gGDObp30WmPlZ38sjAD4aZz82+hlIH1zu0s3273vsBO7ddxxLvQJqviYjUWKxJbNWEIQw0tDZo4bfULUl1QXpddwxVU7atu94qFPGj83Is5XWczEuCUDZUOXCFiddIMK9+46jPO7hzYt1BHtt2c6TaWIU5XybttXFMpuM8ihjrYQ1DjcuYRsbW3/PtF5/GM1ElE8R0ePM/Hs9yvDLaMbmaSGiewDcAwArV7oXRe9lGW96ptqRELdi3MODt6+1zvLDlruUQqlpuvO5LgdJZq0gCA6krbOHmqAzxJ8Y7ZIACPQWPmJKPnQJwVAGm8nrrCt1alo1NVEgwm3veRuePFbtGvdsCYbqs/NzdRCATe+8Ct9+7Y22EX7FmEvbiU6inO+o16aXsTavBQuE5HBJGPw6gA8x85ut11cC2A/gVjQ9Ge82fO9vAPy45qPPMvNXWtt8Fk0lv4XDBEF/kk90pXSAZq3JPR9dr73xTUkeUQgmhNjk6/dsVmbQgtA7fUwYjKWz06BfCYNJ4pJY1o/Ecxc5/OOGS8LfJ3wJdIA+qdCFoMfW5nE21eAGmknw/rE2T0l5vYx7eS5YIESjl4TBHwPgrxFaB/BWZq4RkbF2KDP/TIhAv4SmR+SnXQznfmEqpVNvsNE77LrcZcN1OajfcX0ygxaEgSOWzg6jH4neecAlrrUfsa8ux/CPG2EroCvGvQ7D2ZRU6EKt3sCh02fbhqApH4cAa/vt4Fibp6S8XsZa6fw3/Lisk+wF8BwRPUhEDwI4DODPiWgZgG/HOSgR3QrgNwHcwcxzcfaRFrab2/SZS7voMPIaeuHSTlwQhFyRuM5u8TUAP8HM7wHw92gmeg8dUVtYZyVHMIwgzDALJvLZwjtc8B/PFkcd9VwNg4Hp0rJdGGxCjWdm/h00Y45nW3+fYubPMfMFZr475nG/COAtAL5GRMeJ6I9j7idxbDd3edzTvj85UcGuLetQKZdAiNcSNo2Ztip4v3r7fmzafRDTM9XI+5AZtCAMFinpbDDzs8w833p5BMDbe5c2f4Q5Q/qVZ6KTQ40slXKpK7whzDBz7WLrin9/OlnVeZravAZRRsRhMDBt50MYDlzCNsDMRwEkFrjGzP8qqX0lzdTmNdhmqB15ybIk5l/iMS1h2di0+2CiscRJhVtIhQ9BGDyS1tkajInecZO8gXzkV0RpYd1POeJU+lBE6WKrKBJhgblVHWO+KzbZv78wWY++dK4rRMQrEsCw7ndQyUvdcSE9QhMG80S/kk9WWYzfL2jahwYxJQuUSx4uzS8Y49KSTJZIKmEhL527BGHQ6VfCYC8knegdRWeLromHrqze+bl6O1GvEqGLrZ9gMmKvhqBuH4AYmEK+6SVhcOSoWGbkvZSU23HH2vY+dPtPMlkiqXALmUELwuiQZaK3Lb9imPRNkt51XVm9kld0cvKoz+977IS2GoZ/dTGJRHXTPobp2gqjgxjPGmyhGy7Gp4vB2cv+XUgy3CIPnbsEQcgWX6L3T6WR6D0K+RVJVy/qdcKhtpH+AYIQDTGeNUxOVDqapPjptaScUp4mkoolloYqgiAkzBcBXIFmojcAHGHmTyW181HIr0jau57EhENWFwUhOmI8G9hxx9pEjU9/FycTSRq3ohAFQUiStBO9R2HCn7R33TThKBBheqbqrO/jrC7mIblTELJCjGcDSRqfLp2fACSeGCPhFoIgDAqjMOFP2rtuqrDRYE61mZU0zxJGHTGeLSjjU82w7913HHsOnIms0MM6PwHNJEVROoIgjDLDPuFP2rtuS/pLM9nSFH5y32MnOuQShGHFpcPgSKNm2NXZGhiLM+woDUfCluSGbWlSEARB6CbYUEvX7CTOPk0tsNNKtjTtV3m84zTkEoRBQjzPISSR4GErRm+qwSkIgiAMH2l41/udbGkb04axvKAgBBHPcwhREzx0LbFNrTq/sHUDDm+/RZSMIAjCEKDT//2g3+2gw1qYD1N5QUHQIcZzCKaZu+59U4gHgMSX6gRBEIT8kESIX1zSCAdxOV6xWbKwi2EqLygIOiRsI4QoCR62EA/xMAuCIAwvWXdI7HeypTRYEUYZMZ5DiFI+aRQ6ZAmCIAjdjKL+H4XygoKgQ4xnB1xn9KPQIUsQBEHoZlT1/7CXFxQEHRLznCD9TtoQBEEQ8oHof0EYHcTznCCyhCUIgjCaiP4XhNFBjOeEkSUsQRCE0UT0vyCMBsSGzkR5hIjOAngpxlevBvAvCYsziMh5WETORRM5D036cR6uZ+ZrUj5GrhCdnQhyLprIeWgi56FJv86DVm8PlPEcFyI6yswbs5Yja+Q8LCLnoomchyZyHvKFXI9F5Fw0kfPQRM5Dk6zPgyQMCoIgCIIgCIIjYjwLgiAIgiAIgiOjYjw/mrUAOUHOwyJyLprIeWgi5yFfyPVYRM5FEzkPTeQ8NMn0PIxEzLMgCIIgCIIgJMGoeJ4FQRAEQRAEoWfEeBYEQRAEQRAER0bOeCai+4iIiejqrGXJAiLaQ0Snieh5IvpLIipnLVM/IaJbiegMEf0DEW3PWp4sIKLriOgQEX2biE4R0WeylilLiKhIRDNE9NWsZRG6EZ0tOnvUdTYgejtI1np7pIxnIroOwAcBvJy1LBnyNQA/wczvAfD3AO7PWJ6+QURFAH8I4D8AeDeAu4jo3dlKlQnzAO5j5ncDuAnAr47oeVB8BsB3shZC6EZ0NgDR2aKzm4je7iRTvT1SxjOARwD8JoCRzZJk5meZeb718giAt2cpT595P4B/YOYXmPkygL8A8JGMZeo7zPwaM3+r9e830FRAI9lTmIjeDuA2AP8ta1kELaKzRWePvM4GRG/7yYPeHhnjmYg+AqDKzCeyliVH/DKAv85aiD5SAfCK7/X3MaLKR0FEqwBMAHguW0ky4wtoGmcLWQsidCI6W4vo7BHX2YDobeRAb49ldeA0IKK/AfDjmo8+C+C30Fz+G3ps54GZv9La5rNoLgPt7adsQn4goisBPAlgGzP/MGt5+g0RfRjAPzPzMSL6d1nLM4qIzm4iOltwRfR2PvT2UBnPzPwzuveJaB2A1QBOEBHQXPb6FhG9n5n/sY8i9gXTeVAQ0S8B+DCAn+bRKvRdBXCd7/XbW++NHETkoamA9zLzU1nLkxGbANxBRB8CsBTAjxDRl5j5ExnLNTKIzm4iOtuI6GwforcB5ERvj2STFCL6HoCNzPwvWcvSb4joVgC/D+CnmPls1vL0EyIaQzPh5qfRVMDfBPDzzHwqU8H6DDWtkT8FcI6Zt2UtTx5oeTB+g5k/nLUsQjeis0VnY4R1NiB6W0eWentkYp6FNl8E8BYAXyOi40T0x1kL1C9aSTefBnAAzWSLx0ZRCaM5c/8FALe07oHjrVm8IAj5Q3S26GxA9HauGEnPsyAIgiAIgiDEQTzPgiAIgiAIguCIGM+CIAiCIAiC4IgYz4IgCIIgCILgiBjPgiAIgiAIguCIGM+CIAiCIAiC4MhANUm5+uqredWqVVmLIQiCEJljx479CzNfk7Uc/UR0tiAIg4xJbw+U8bxq1SocPXo0azEEQRAiQ0QvZS1DvxGdLQjCIGPS2wNlPAtCXpieqWLPgTN4dbaGa8slTG1eg8mJStZiCYIgjAyih4WsEONZGErSVKrTM1Xc/9RJ1OoNAEB1tob7nzoJAKK4BUEQ+oDoYSFLJGFQyD3TM1Vs2n0Qq7fvx6bdBzE9Uw3d/v6nTqI6WwNjUamGfc+VPQfOtBW2olZvYM+BM4nsXxAEQbAjeljIEvE8C7nA5CmO412wKdUkPBKvztYivR8HWY4UBEEw0w89PMjIGJIuYjwLmWMzkOMYwmkr1WvLJVQ1+7q2XNJuH1WJ9bIcKQpTEIRRIKoeHiUkpCV9JGxDyBybgRzHEDYpT5txGyUsZGrzGpS8Ysd7Ja+Iqc1rtPuOGkISdzky7XAVQRCErFH6ujpbAwU+M+nhUUNCWtJHjGehL9gMVJuBHNUQBtI3bicnKti1ZR0q5RIIQKVcwq4t67Qz+jhKLK7nXBSmIAjDjF9fAwADbQPapof7SVRnTBpISEv6SNiGkDphS0i25bepzWs6vguEexeU8nQJX4gbHz05UXFS0nE953GWI0VhCoIwzOj0NaNpOB/efks2QvnIS7iEhLSkj3iehdQJ84jaPMVRvLx+JicqOLz9Fry4+zYc3n5LpvHRUd4HonnOez2WIAjCoJB3B0ESq39JeK7jjiGCO+J5FlInTOGFeYpdvbxxSHuGnqTnHAA27T5o9KbHOZYgCMKgkHePaq/GfVKe6yirr0I8xHgWUsdF4aVpINtI2+CMq8SC58NFqYrCFAQhC/pV5SfvDoJejfsky6yaxlSpyJQMYjwPCIN8w+dZ4ekMzptvuAZ7DpzBvfuOJ3Kuk5gYuCrVrCYhgiCMJv2M8827g6DXsS7tsJS8xGQPA2I8DwCDfsPnXeH5Dc68nuu8x/rFYZAnhIIgNEm7KVUQVwdBFvql17Eu7bCUfl+rYSYz45mIrgPwZwDeimbC7KPM/AdZyZNnhuGGHxSPaF7Pdd5i/VwHpiQ7RwqCkD+SnNgnZfCmqV+mZ6rY8fQpzNbqAIAV4x4evH1tIjk6aa/SDqMTJiuyrLYxD+A+Zn43gJsA/CoRvTvActlVAAAgAElEQVRDeXKL3PDJEZbJnNdznafsadfa2LbtpCa1IAwHSVX5SbLJU1r6ZXqmiqnHT7QNZwA4P1fH1BMnEqnnHLe6lCtSkSk5MjOemfk1Zv5W699vAPgOAHE5aZAbPhlclHNez3XSSrWXckiuA1PSnSMFQcgfSU3skzR409Ivew6cQX2Bu96vNzixib9rmdU45MkJM+jkIuaZiFYBmADwnOazewDcAwArV67sq1x5Ic8Jd4OES0hGmue61yXJpEJfel3SdB2YwjpH5ikMRRCEeCSV0xLV4LXp07T0i834HoSJf97zjwaJzI1nIroSwJMAtjHzD4OfM/OjAB4FgI0bN3ZP+UYAueGTwUU5RznXUYzhPMX49hrX7TowJd05UhCEfJLExD6KwRumT9PSLyYZTXJmQdi4lGb+0SglgWdqPBORh6bhvJeZn8pSlrwzKAl3ecZVObuc66jGcJ4SEXtd0nQdmGzbyYTw/2fv/aOkOs87z+9T1ReoRjLV2DiRyiBkjQMnLIa2sIVDzuxKnhHeyFI6kmUiyzmTTDY6mTOZNVjTsyjRsRqvckTC2pInyeyMdpKzyYhRGoTSRkazyLNijndIkA3ubjFtg2P9AKmkiYmhZYkuoLr62T+q3uLWrfd973t/1b1V9X7O4UhdXX3vW7fufd7nfd7n+T7diS30tsSJ29laVnDg5AnV2tUYmcrh9bOnSdmX0a1rMLp/ui11w8lTRxf+WSzEzlKAqBOkqbZBAP4UwA+Y+WtpjaMbSXt1l/b5wxJnNMLPeHuvkSpakcZWX9QtTdOJKc3OkZbEEIXe3yOiawGcIKJvMfP30x6YpbvwOluzlSqcHGFo0MHsXFU7t5juIialM61T20ganZOqmpd2PTeT+PiyFCDqBGlGnrcA+DUAJ4loqvHa7zLz8ymOKfOkvbqL8/yddsLjjEaojHd5toLhr7yA9y7NN6MT5dkKCPUwnZe4t/omJsvY9dwMLszVDXux4GDsrlbDHsciwnRisg5yb8HMbwN4u/H/7xKRKPS2zrMlEDJnq7rAGFw0gMkv367922UFp0Xxwv16UnjnK69d7RRhCrEvzFUxMVlOdLz9VgSudJ6JaADAbwL4FQDXN14uA/gGgD9l5vY7NwDM/F8BUJRj9CNpru4mJst4cN80atzqBoY5f1qLgDicuYnJMnJEbddBIBxXNwy0OdBx5/hOTJYx+sx0y7bnbKWK0f3TANrbeLud7MUDaapWWpKGiJ5k5gdiPuZqSAq9bZG3xYQozhYpPAfV61FJO2jlJkwhNoDEfYR+KwLXzZj/AcBGAGMAfqnxbxeADQCeSnxkFilpre6E8VA5jEHPH7cOZxTptaDn0V0HHQy0SM3dc3MJew6fjm3Mew6fbnGcBdWFVhklb3QaqDvZYTVVLdmAiJYr/r0fdfsd57mUhd7M/CQzb2LmTStWrIjztJYIdMpGmlIclEeJVa+7mZUEKHSvRyVLuvQ6OVVdMCZpH6HfZPB0aRs3M/PPeV57E8AxIvphgmOyaAi6uosrNUJmPEzOrxqLyvUsz1aweuchEAH337IKj46sNzpu1KiA6XXyuw46SsUCju68DQDw8MRJ7D12tnkd4ohkmMgoec/rppfz0/qEcwDOoHVHT2x6fDCuk9hC7+4ji+l2qviDSVyiOOhId/iSinLq0vS27H6xo/U/foXY7nxsN9cXC4mmSvZbEbjOeT5PRPcCOMDMCwBARDkA9wK40InBWdoJkq8ap8HUOWYmq0vvWPxgBp46dhYAlA60MASyxUQQRzDIdVJtifnhvkYTk2WpAxs2/UUYK10qiTCcKsdZ0In8tG4tOO0CXgXwKWY+6/0FEb0RxwlsoXd3suu5mVjS/eKcU96ROHi6191jeO/SfNvrpooXYeyPKmhFuDonJJ3K4VUmWeLkpIWVY3etk/oIt65dkXjqST/VuOjSNn4VwGcB/B0R/bARbf7vAO5u/M6SAkE6zcW51aRa0eeJjDrdhY3YPv2SfM53dwtUYeoIBrlOQXLqigVH+h3tOXxa6cAGcV69HRNVjrOTq08quvMKks5Pi7MFr6WNJwAMKX73hzGdQxR630ZEU41/saaEWOJlYrIsjdIC6abbhe3mquryNy9JWfMS1v7IUhJkReBJpXJ4xz1bqeJSdQGPb9vY1oVQ5SMcOXUuM6knYclS6pEy8szMrwPYBgCNnDkw8086MyyLDtPVXZz50bKIN6HusD24bxrbx6dQ8uhNqraPgqByCE2ccVNH0PQ6TUyWjbYUBapqbN31D+K8qq4B0dWtT7faxo7xqbb3uulEflpW5Ix6MfrNzH+i+d0fxXQOW+jdZeico6CLZb/0hSDPUVjVH9UYGPCNpIa1P7KUhE7KjwYdt8xHUNn/blHDyFLRJmAoVWed5mRIegKPs/rVbTy80mvCwRU38/Ez5zH+nTek0QEvpcZYVIYorwj1+j3wulQW7zU3vU5BV+jbx6ewY3yqWSgovl/dFqBq4pCNWzmJcP1Ypi1rAbmkXRJkQc4oa0bYYkkS3bMVdLGssyFBn6OwObK6MVSqNYwdnFEeM4r98TqkW3a/GHh+NZESDTK+IHaz29UwshJ4EfS0PlWWQvxeOrF9HWf1q9t5yxMpt/8r1RqefsnMcRZjuXWtuiL/vltWSl/XPfCqVBYh5ea+5qPPTOPWtSuMrlOYfGdvQeDEZFn5eRcp5OJU49ZpmsruKdXW4xc2r8LUI7d3xACF3aqNkyxVzlssSaN6tooFJ/AzL7MhboI+RyPDJRzdeRte231HW/pB2DHMVqottnLH+BQenqg79XHan6Dzq7DjXpWj0f3TvvN+HOPudjWMLARe3PSs85z13MpOTOBB8qN1mObWCvx+7x3LkVPnpO8rODlpseDEZBkXL7cXjBScPJ6Q5IAJdj030yblVq0xDr38dst1Ghp0ADC2j09h9c5DGP7KC5iYLCuj4KaIqIgqj/vy/IL0HlWNu1pb0E4i4pzinpLdD49v22ikaBIXWTDgflvPWbERFkscqJ65sbvWhTreEkfvNkRxZkwCXsKOmdpjBrD32Nm68xqj/Qk6v5pKicoIO2739dxz+DTuubmkHa/s+mclCJmFwIsbo7QNIioBuMH9fmb+dlKDioOshfi9dGoVFUf1axR5Ni9uuTaB6jNfqi60/DwxWcbvPvsy5jyvA60tUt0qHPmGAkWpWFAWzVyYqzav08RkGaP7p1si5xfmqtjuky9sil8OuOweVY374pUanti20Vf+L+mWtUHIgpxRnFvPWaUbbbYlGeJ65kwVk8I6M7J0qh3jUzh+5nzbAl+M3VTBiVH//GLuMb0WfqmVQeypbm4vz1a0HQDDfIey6/nUsbMYGnTw+LaN0p1Z7/tH908DhKbTn6Z9jKMzbpz4Os9E9AeoFw5+H4AYNQPItCHOQohf9+B1U/5RkGumakMNqKWETK6FzKl1M7hooOn8uh8wdz62Dp3sXacJUnzjNt5hcvAEnSygi8uBDztmmRF2k6VFdhi61WZbkqOTQRRdGl7Q44uo8aYblreN31uH44eYx7yO6J7Dp7H/+Fkce/UCaszIEzXTBePU4tct2gF9sWMQW+c3l12YqzZrk46cOtc85sXL89J26V7Sso9ZCLy4MYk8jwBYw8yXkx5MnKTtnPoVJWVtFaVDdS1FVNcd3dUZhz2f3SC90U2uhUqeSCBW7mGi5E4OzcK+rCDymoF6bqIsYl305DyHvafCFtClqVgRpejPZNLtlgp0BV1ps730oiJKt2HS2MrLXh99fhU6FQ0/VQlV4MCNmPtltsP9tzXmZo8BL0H7B7jv31vXrsD4d9+Qpm7ojh3E1om8atU53OfyLgyCkJZ9THvn1I1JzvOrAPz7ZWaMtHMr/XKa48pH7gS3rl3Rpk0lZOpKxQK++rkNeL1R8FFSLE5KxYJ2S+qxu9e3OIPevDqTh3V0/3RgI5ADUF1QR8vjolQsNHKpzanWGLuem8HYXevg5Fq/ASdHbTmLYe8pXQMFVb5b2jUFUWsGRKGS6n7N4g5QALrSZrtJ+/7qF3T5rN7vwBR3jrH3PKt3HsJNDz2P1RJ7ktPkMPvVI/gVEbrn/qhpiCZzkez+PXCijG0fX6mdB2THDmLrZPUxKqLMeV1uH2NBGXkmoj9C/frOAZgiov8XQDOSwcz/a/LDC0/aIX6TtJEsraJUUZ6JyTIOnCi3PWiqrawoEfXL81dzmcXWkjjuEieHiiTX2Y2ZwkcOV+a5GTFfPEDSHOq4IACv7b6jqXsdFJGPDaBF4mjpYvmjG/Se0jVQEN+tLOJhWlOQVPQwrrSsbtoB8qPbbbabrNes9AJ+Ec0oTiYDeHDfdPNnVSqdSB84cKLsW2iui7h65/tlBQdEaOnAB6hT24Jg4jiq7t+nX3oD992yUtnpVXbsILZOZcvD4uSoJecZ6F77GDe6tI3jjf+eAHDQ87ss7XArSdM5TTttJAg6I2piQL2T2uKBXPNv3IV8OvxW136OswlOjjC/wE0jXWPGXDXZWzlHhIcnTuLAiXLoiUjILM1KJI52PTcjbdFqwsRkuWWCk6H6TkwMuuy+2j4+hbGDM5E1peN6vtJeZMdM19tsQRZqVnodvwVK1GtdY8ZDz55smQ+8CIfSz3GWjc+Lbr43LXY0waQmRXXtdCkhKp3/JHwJVW3S0KCDwUUDLbYQ6Bn7GCu6DoN/DgBE9EVm/rr7d0T0xaQH1u10U0RLZ0RNDajIOfZ+Zq9ihgqdfNiu54JHbGXUmLEQwAf/wuZV0hy1QSdXl4rzRLpzBHiD3zpjaYrq76sL3Iw0BC1mEd+V6aTlRtdhy23QVQuv2UpVO1aTaHWcz1eWdoCi0Es2u5uCD3HTqVxvvwWKrpmTqdWoVGu+DmtQGxQmchynYpQYg86G+RUHymDFsYLYOt13465NunXtiraATsHJKwNdvWAf48Yk5/mfSF779ZjH0XN0U06zzoiaTlZ5okC5Wd5cu6ImDyyurSiDrI4Wnn5JXtxxaX4B2z6xsuW7fWLbRnztcxuRiyYFHYkgOb8mk4kqBTFH5NtYZmKyrJ08dPeFrCGMN9exm56vFOh6m512zUpayHJlR5+ZxsZdL8Sus6vTzVVp6QdxnE1R6TWrXifUd+OCaA/rgkDFgoMtNy03Hq/AbcO885nMPvqhqr8IYut0302NufkMPTqyvu2Y99xcT9VJW8+5WyBWrPqI6D4AnwfwiwD+P9evrgWwwMyfSn54rWzatImPHz/u/0ZLIFR5YKKltOl2l86wuttFA+36nLLcKhNUShRJk88Rvnpvu3rI6p2HOj4WNyLH2o8bdx7ynQQHnRwYJP3uC04e99xcapE6EhGyhydOKnP6/MY6/JUXpIuloUEHk1++3eeI2YaITjDzpgSP31M2ux/VNkxycgtOPpaFomynUDzXsjQz2c6aGxHZDIKYM7xzhxiHiR0R79ddE911FX8LmOtGu8f/+LaNyutompIS13dqcv/Iei2o7gXTMXXqWU3DJqjsts55vgHAjQAeA7DT9at3AbzMzO3L0oSxznMy+D047hs2pzCQYtXs9+DqHOwgjjABuH/zKmy6YTke3DcdKv0gKtT4MEKG6Mipc6nrRMsMoxeR62zSCfIXblqOo6+cNz7XxGTZWPZP9ve6xcfrBouCLNMB59na7C7HZFELmD3nJsickTB690EDLYB6Lii5nKKgwYiSwqHyW9CL6+nWSPZbLIi/A+Tz3tCgA2Z1YywRV5cVN4Z1CE1yu2VBC5XTnSfCArN2XFEdb/dxdI5xXOcJispu63KezwA4A+CTiY3Kkgn8iqbcOaGqG1gVUfais0XvVKpGDnSeCF/93Ibm+YI6zsIgFAcdvDNXRdhSRHFa0bmp0+RzhJrLugfRdDaLhuSUjjOgllUyjRT1+hZ8p7E2O1uEiZKZ5sr61aJMTJZb1HmKBUdapCvL998RsJtqwcnj1rUrmqlgJhFonZ2fnbuCHeNT2HP4NJYuyuPiFfNIsCwXWaUY5cbdQEWmFlSerUij46Nb1yivl1+6oTjWO5VqbI1YRoZLOH7mvDbiHUTRw6uMIhtXEGUcnaqXn5Z11hR4TDoMvour3/Mi1PVDLzLz+5IcmKWzmBZNqRxt8VqUogwG8NNL/pHnBeamMH7Q83lXqkJCLo3Uj7DkCPj8LfWou/geio0oh5h0VBO16XdEACrz+mXFskJ7nrrfpO5O35GNz7QhjCDKNl7Qv+2WNAJrs9PH2xG12eoYeqfINHrr7b7q14xDqPP4nV8c2zTyTFR3YNzBA5Fbu3ggp3aQNfZWOMthd/G8DpWJzcsRSdtjexsquYvuxPMftTOt18WN4hCKhYLKcXZywbr8mozLVBknqKqX93xZU+DxdZ6Z+Vrx/0REAH4ZwOYkB2WJnzgnftnqPC4ZIJOiPjFxBH1oio3tMZmD2SkHWmzVyT6maTHOAtebEDx17CxKxQLu37yqJUfRbZSA1oWOqZFn1SDd45XU8/idwy8fe+yudW1t2GUNYYBoXQaD/m2Uc3Uaa7OTx8+ejh2caVPjqS4wxg7OaO8Xb3CiOOjgvUvzLcfyFuZ670tVekJ1gY2csiDpF6oAc6VawxInh4KTj1XlwhT33GAyTwhZPQDauc1ddCciprLCyqiEdQj9FgrXLBkwVvQwHZepMk4YVS/361lT4DFR22jCdSYAbE1oPJYESLpbV9wyQDrEFuGW3S8GrvqerVRxYa7aUsV+///1N9gxPqV1nMMqaMj+jKH2SRl1eTyTKm33Nt/eY2elRmn7+BR2jE+1fO+mH8WkG6Jbd1owunWN8hyq6LGbkeES9ty7oaUKfI+kMBOI1mUw6N9G7WiYFtZmx4+JPQ0TcRWI7pev7b4Dk1++ve15cO+cye5Lk/QE3WcTx4wqHHRhrop7bk6vz4LA261Whex51j334j5IIugiFE+CKIoA/t+vzGYD8i6/qnF5MVXGCaPq5X5d1UXy4uX5VJRBTNI27nb9mAOwCcClxEZkiZ0kcoXckZewpXphohI5Asa/84ZRN0E/qjXW5vQKFrhVPF4WDfJScPL42KplOPbqhUA52QdOlJsqFoGixIa/M81HNhmyzOCJnDtZDvjFK/PSrVHZMUzuyyjbeEH/NmtbhjqszU6WTude6p6HoPefyknx5kgD8UjSjX/nDeXvFg/kWrrKxsmta1cAqH+uIA22vNdT99wnGTQaXJRrKbw23eny2/nTRWlFCopqMaCqUzFtNKWLHJtoWcs67QL+fQOSwmRJdqfr31bUK7d/OclBWeIl7onfG3kJQp6oRVdSpeWp4uKVWiyOc1AuzFUxO3cFywoOZuequGaJft35sVXL8NevnA9czCjyB+eudFYYQXwPIrL1jk80RVfw9+jIemnkulrjWCO1JtGKuP42yrlSwNrsBDGxp6qdG5MdnSAEuf9U+a7CnvsVuAlLXSoWjHaRAChtNQG4d9OH8IXNq4yOE5QDJ8rNAE8QGHXFn5seeh4PT5zUPvdJLpz/9scXlbnQOlTRWcCsSFv3mXSqFu7dkqM7b1Omhqgi1KZa1iPDJQwuap9709gF1DrPRJRHXeLoNxr/fouZf5+Zf9yh8VlC4N3ukRV2AeEn/igr7lpD5UJ0OEpDYi4sF6/UMNuojL4wV1Wmc3zkg0tx9JXzkSI3cTWGMUV8DyJ/T3dvmDQkUW0PxjnhRGmkEfRvu6Vph7XZyWOykHrkznVw8q0GwskTHrmzPXffTdCtep2z1HLuXD3fdcf4VNtxTe0546qk29hd6+ra/CFh1B3cTTcEb05iQtAOuV5EZ9i/f+9y2+/Ec69r7KUjygLK7/O4nVCgPShiUiwqo1QsRI7qqhxkoC6VJ1RLHt+2UemAA9nZBdSGz5i51hDef7xD47FERFZAIiPIxO8tjomqZXxhrqqVdnNyQICdttRYYGBRnlBbqBvbPBE2f3gIf22QCpJVxBaYrFFCEE3NsMUdQQpbTbcL4/jbKOfqJP1qszuphBJkizmomkvQolSvGoSK6sLVBbn3uEGcjvJsBTfuPITriwVs+8RKfHP67dA5v8LBHRp0EgkWxDFfedNK3JJ/YwdnAh9PpP+F/bzuQJjqnjdNe5MRpBW43zhk+IkNmNzzQeeWpGyDsklK8w1Ej6MudTQO4KJ4nZm/F/nsAekmwf20ZK1MOgypND9lyJQ0/MTtVbqYSeHkKXBnwjh5YtvG5rU0uf7dgPgu3SokRPXqelUjAjdhBO1195rJObNO0k1SXOfpK5udRvOEJOy7rtOrSUMUVXdOFcWCg6lHbg9ts3TdCOMgak60CGaY1LWY4v4uTBvaNMeTI1y7eMB3seE3bwq5PJnudFydJ03v7ajPXph7Psg547ANgZukuNjY+O9XXK8xgMjtjYjo0wC+DiAP4N8z8+6ox8wCacpamUQRli6Wy9XIGDs4I63mVgnGexuqbA8ouB8GJ5eu87xj3xTGDs60iN0njZ+BLTVy8oqDDt6pVI0kAN0Ig+aevNxNYYJExEwdDJ1ygOkz1C1azAmTmM3OImk0T4gS3QPk92nU7WhVqpTy/ZVqXY86YGdAQaVaM24/HYb5iDa9xvqCcCcHzC/Uo7mm0XN35D3I3+WoniPr9/5SsYDV7y9oxy2ud5z60G6C3NtRn70w93yQuSVJ22DiPP8mM7/qfoGIPhzprGjm5v0JgH8M4E0A3yWig8z8/ajHTpugX1icE77JNpWpMZ6YLCsfdhEN1I05DgF5E+ZSzvHQtWBNAkI9L0y1MMkTNXcAhAN9uVoLdJ3yRNpcSBMDFNTB8Lsv/c7ZTVrMCZOIzc4qWcmBNEV1nxYV6QumtSlhUhT2HD7djPB5G60cOPGmr1JFkjUrSdfDDOTz+MSNywKn2Ql5QtOUb7+GMeI9Iv/3wX3TgcbjptP3fNRnL2x6XycUmfwwUdt4RvLa/shnBj4B4EfM/CozXwHwl+iRivAgX1jcGswmBSSim5IfuupVsa2iq64V44lSWNIrxHkJrm8Ub6gq1Td/eKjlnrowV8VcdQGDTg6Eq81idNSYfQ1M3IbaxEnQnbNbtZgTICmbnUm6TAlFeZ8yI1JRqmnxoBt3W2phz0e3rsH4d94wknjzM2tuhY6sUanWIhV2m+7mVao13+BKjoDfffZlbB+firRo6PQ9H+bZcxfFXrw831ZY6+7lEETjOq7xmaJ0noloLRHdA2AZEd3t+vfrAJZEPjNQAuAWgXyz8Zp3HA8Q0XEiOn7u3LkYTutPGHFyN0G+sLgnfG+1rcy4iW5Kfp9L56iYGnTR9MJU2ijrBPWB80T4wuZVbZJ8Tq7+epiFhRCFf3Rkfcuxxblm3npXGjGeqy6AUY+S+9nnUrHga2DiNtQmk7/unN0WgYybDtjsTNItSigC1f34TqVqJNelQqW0oFN3kClG7Dl82lgOVPcuAjC4KA8COi692W1cvBJsZ1CGk6fYnE5Tgj573mDhbKUKNPoouCVsD5woxxJQTNI26NI21gD4DIAi6nqhgncB/FbkMxvCzE8CeBKoF58kfb44tn6DVKwmocEstt9Kje03WV6ayba7aktlaNCRJuarUk+8edDifcsKDn56ySwf9yMfXIq//fFF/zcmSCnEtmiNWXr9qwuMI6fO4ZolwSuvZytVjO6vb+09OrIej47Ut/smJsuxtBkn1O/h42fOK1v9JuGceJUDVHn1KrLWvjUFMmGzO023KKEIdPdp1Fxq2d9v2f2i0sa8d2keD0+cxJFT55opXib2yKQYnFF3CoHOS2/GSRi7nwa1GmP8u28063+8vksS9SBBnz1ZsLC6wBhcNIDJL98OoH6/xpWnnKRtUDrPzPwNAN8gok8y899EPlM7ZQArXT9/qPFaqsSRYB7kC4tzwpc5/jot5bdmK9oHSrUI8GqVBllwyKRq3J2UVPz9e1ew5abl+OtXzxt1v4sbQr1rVRhpJtX1D9Iu20t1gTF2cKZ5LR+eOKl0dIPyCzfVtVcPnChrVVWS7qYW1Nir7lcRjZEdp5cKDDtgswFks9A7qtPZScLIgUVBF4ipLnCLbKifkyui2llwJocGHVyqLiTW5U+Qhc9KABb5qI8sAFioyYNkABKrBwny7JkEC+MOKCZlG3wLBhM0wt8F8BEiuhF1p/lXAXw+oXMZE9cXZ/qFxWlIVY6/kLbxUhx0tA+U6SIgSoHksoJj5PBdmKvie2ffwbIl5hXOcSJE/ePMXSbAONIjQ1yHhydOanWzg/L6T9StZ01ls+IgqNGT3a+iGY/sHgeSm1DSJGHHuWcLvTtFpyPlcWjzA/W0gNGta5rNLNLEHcTpRFF6FvjANYtDfc7ybAUP7psOtfscNybBwm7ZQTRR20gEZp4not8BcBj1CMafMXNw1fGY6fQXF6chVTn4NWYUnHybg84MX6dX5sCYNk3xjkeWVhDEEa5Ua4lHGfzOHycMNIuEwh57YrIcq+MM6BeKUfKHVVHeOKO/3vtVtwUo/l/2u252nhOmWegNAEQkCr273nnu5C6EbmHoHkdx0AFzPR86yJi8x3ByZJzHLGNo0MEjd67rmIKSjjwR7rm51DJHbdz1QipBFRnFgoN1118rlZvL5wi1EN9D1Hbgut3nMIR9VkyChZ3emQlLas4zADDz8wCeT3MMXtL44uLaVlA5sgTgnptLzbw2cbOrIgi6B8q0g6EYj+rvLHVmK1U8sW1j0xAFMatDg04iShLXa7Zmw3ZxUqX2HD9zXhkZjuOZCLOT1C8FhiGRFXrf4n4DET0A4AEAWLVKrgiTNbIic+gdh3tXKojWufcYTp5QcHJGChpevLtNt65dEfuCfdDJGRfM1Zibrb3FdRi7ax1G909HWiDExbuX5jH1xjvS3+UJWLIo38wFdyM666pqPfwWLTkA+YANw2T2PKwtB/yfFZNgYbfUMCidZyL6ku4Pmflr8Q8nfZbGGwYAACAASURBVLL6xZms9IRD7H10GMCRU+fatttVD6Muyq7T/nXjXXCY/p0fSbVyNaVYcGJvhiK+s1KxgLkr88af746PXoe9IScxIXPnzZN2f2+mi0iZMR3dP41dz81gdq4eMbt4eV4a5Q1bzGqK305SN2wPmpIVm93pIu84SKPRiuk4go5JWpRVY3zw2iVGTpiXt2YrWL3zULPW4cip+FWvgjr1sh1S4Oq8vazg4OKV+VSaZ9WYpc4xAFypMa7U5L+bZ+D13Xdo53pdfVA+T9j28ZU4cuqc0fcrs+cTk2WMPjPdUnQ4+ky9ON19jaM8KybBwm6oYdDpPF/b+LcJwD9DPeJQAvDbAD6W/NDSw615qdMw7hSmWtAjwyXlgyWLpslkXKhxfJXMjS4qlydSSizFFc1jBpYuCqZlGhavjFzByWPsrnWxdxF0d9F771K77qWKI6fO+Tp6su/3C5tXNVU6Ht+2USqN5Za9cksI7Tl8uk0GSVVBfWGu2rxfVVuqcW8netFJFXWbxJkBnbDZmSz0jkraModCHtXE6RFd7oLa6LdmKxgZLmF065pA0qHeLp9hUzZ05wxjU72fU3y264sFzFaqLR0KFw+YtLRIF2Zg9c5D2HP4NEa3rmnzP3TzO1BfIIkgmUpXWzdHA8Cu52baFhzVGmPXc1czatN+VrKCTm1jFwAQ0bcBfIyZ3238PAbgUEdGZwEQbKWnktVh1PM/vRJy4vheaTDVVowux3mBGa/tvqPt9YnJMnKKosWgzFaqRp19okKoO4Ci2NKtMJFkzl91gZuTjF8O31uzFdy/eZVyC1XkKe56bqYZzV5WcLDphuXN9+hW+O7feYsS3RGJJIxmXNFfk52krO0yhaVDNjuThd5RSbNIKUxKmzuIcvzM+ZaUPFXb6OuLhcjpc7oCdB3CFgW1nTpJPO934/1s7r+7olGpyBq6NAg/2TzxO1X6qU43fGKyrNz1dL/eLQV9SWPih/wMgCuun680XrN0CL+Vnrupy9yVeWXjDVnEWkTZS8VCm5GqVGsYO9hawzm6dY1SXk2VP/XQsydjbbXaCTMoRltjborPi6ir7hrHwTuVKqYeuR1PNKLCKpYVHO0WqqhGv+TaEp2tVAMLzquKEkVEIm6j6eQo1uivbicpa7tMMZGYzWbmeQCi0PsHAPZlodA7KmnuQkRJaatUa9h77GzLruRFiX1y581GTZ8TBeje439h8yoUHLlLcWGuiu3jUzh/8bLxzhpQt8PFguP73UxMlvHgvmnlZ+uK3CEXqkZpfo2kRGMc2c6hn+PsViHS0YM7dqEwcZ7/AsB3iGisEcF4CcCfJzoqSwu6joXelI4Lc1WA1FtklWqtZQtGoHLQZyvVNmf7/s2r2hxo1cMTV65zkmy5aXnTyMjaVldrdS1U1TX2dg/UYdQ+l+rGTDh2qg5hRPqor4iSR+1gqXvvhblqqLbAWmw396gkarOZ+Xlm/jlmvomZfz+u46ZJUGcjCiLYsXrnIdz00PORd7G8jmG1xrhmyYD0s8SxY0YAPrZqWUtn03tuLuHRkfVYvnSx9m8r1QUgYOrdrE/nxSQCNFlAZduXKBYoQGsaXJDAgN887fYnOvmsZBkTneffJ6L/B8AvNl76DWaeTHZYFjc6BRBVccjSxQPKbf8Lc9WmcybQpWN400MeHVmPTTcsN9ru1jl3Jl2qomBy/FKxgL2/9cnmz6t3mu1uV2vcvL5BjPY9N5dw6OW3tUWBzPXCkO3jUygVC8r3imI82fcmItZx5Kf5vdekWKfg5LHEyRkVQ1ZrbOXiImBtdjg6UaTkTS0IYjuCpEvMzlWbHdvCHkMFA/jrV8637M4J9QsTu1JdYCwELBA8fqZd9k3QDQGaMIigmSgglHVd9aLbqdTh9719ZsN1zf+XFTQCUDai6lVMpeqmALwt3k9Eq5g5Xq0aixJd3qZKbs7vQfM6J6Nb12B7AOk6E/1nUbyhcu5MoiBh5HeAurPmludTOXSd3mp6+qU38NXPbQAA5fUGWnPPVYhrrFPFiCM/TbewEhEJ7/2gMrCm+Zb9VnySANZmZ5Cwjp6wZ25ZR0AdIFA933FFZ2UpfnsOnzZuyBJ0HN56i+3jU9j13AweuXNd7LaCANy/eZWxaoUKP2nAYsEBUT2YpZKn0+Vxt59PrYbkF+jy+95EeqBKWQkEZVvwXsXXeSaifwHgEQB/B6CGq8/rR5MdmsWNKiqiuun9IgyySmV3YZn3HCpUq2LxAKkMfnm2YhQFWeYqNPFryiKOrTIQD0+cbEqjeYX2BUH0RsNQY25eFyKEbjUu2oWLyVhW2AjEo1s+unVNi3yRIIe6vqoMXRTP/V2qpPn6rfgkTqzNzi4mjl6p0RnTq8s/Mlxq2/HzdtAEWp9vr+NUVBQTxkF5toJiwYETItgRhgtz9foNVYFkWIS0qy6gZILOcSYAU49c3RlQObiyJk8yvHbffVwTTWbZPOFG3LcqZSUv/dBsyiTy/EUAa5j5J0kPxhIclXPk98DJnJNH7lwnfYAuXp5vS/MA9NXNQP0BOnLqHB67e73UwTaJPlyYq7YZFVXFtq519MRkGQdOlJvnlAntT0yWlUY/zglBFPlEOZpoF+7e/hWTZtyC8+K97sVVseBg7K51gY2jLELdDd2kugxrszOKaWR20w3L8ejI+rbXZYtSVQqdzHHK+xQ654mwwIxlmqiojk53+atUa0iidls4mUn1FfA2EFPZZ5PF1tCgo5z3TJW6xP/L2ni7xxtnul+3Y+I8vwFA3i7Hkjoq50j1EABq50TmJAFXFRrc7xHn9HPShbaoWEXLJo4cAbrGUOJvhDyaTOnCyRHmrszjxp2HlHJkfkZkz+HT0lV0wclh8UA+9uhGFPJExvKFceRyJpUPmtWmRF2OtdkZxS/CB1xNSxg7OGO0QFU9mzKb59causYMArB08UAz+m26S2jKlpuW4/WfVGKT+1Q1JHHj5OvbfEE2FSvVWiK51N6dAV1k2GSxpftagtS8iHsoTBqgjF7fPTRxnl8F8F+I6BCAy+LFXu0w2I3IjKduu0lXGSsUGryrbZljZrKydD9AqvcH6aharXFbBLjg5DDfaMoByLemVOcWDWFGt65RvqdSXQjV1jYpnBwp29DqDJtJ7ltQ4jhmN3ST6jKszc4Y7udkWcFpFs/qnNLZSl3e7fiZ89IotB9hI39C8s6dZyyc6jjc52OvXsDmDw8lppXvJefKx80C7vnXL6hjsth6RxPUCVrz4hfMMBkP0B+7hybO89nGv0WNf5YuQFWQVyoWmlt6QbeKvK/7rULdRQ97Dp9OTFnj8vxCmwPulmPzO7dwtpf4FHh0CicHzC+oJyq/EZqk2JRnK9hhODGr7hXTfDpLx7E2O0N4n5PZShUFJ48ntm3EyHAJN+48pLVPTx0725JeZkqQKKEJjHgUkmrMOPrKeTg5SCPBQ4MOBhcNGKlL6BgadHCpupApJQ4x/wr85lpvIzMZughvmJoXv8ZZgDq9A1DnX/caJlJ1uzoxEEu83Lp2hbSxxa1rV4TeKvI+pLIHUxg78QAB5goLYVFFrsXnMjl3lgzs/ALw+LaNSoPpt/UqS92QRTgYwF7PxOx1lL0FSe57JUjnS0vnsDY7W/g9JyZObphnyjRKGARh201TOXTvmef2+pyCk8cjd67TpvmZUHDyYM6WXQfq868b1XefI2pJQTy687ZQ9SFJpMXp0jv6Se/ZRG3jCCSLP2aWZ6hbMoGq89yRU+dw5NS5wFtFQt3BzchwCcfPnG9RsLjvlpUtkUxdtXCpWMDFy/OJFplkzXiakCPCjvGp0Dlj7miGWw1FBuPqxCxbVMkKG8W9EoeGtCV+rM1OjjBpSn7PiYmTG+aZEuPSpfCZFJe7cRdlT0yWlQpN4tgfW7UMR1+RazQzo62YvFKtYXtD4z4sInCjknFNE++8rPruxYJDtpsX9P5LIi3O1qqYpW38S9f/LwFwD4D5ZIZjiYswjo17q+j4mfMtjpOIUj517GxLVNlPwcLP6M9WglVzdzPFgoPL8/7biG7DGQa3uL5J5EknQ6T6XoQslWzh0+uFIl2AtdkJEDZNyW8nT/ztjn1TyuKvsM+UqGFRyZm6nVc/dIVuXnJUd4RVjrM4fzOXdv+0so7DCwHKFLti4aryhOnnco+nxoyhQQfvXZo3Hk8Q3DU2bqdWOKE5SaTeHdjyvl+kJqbhtPZ7rYpve25mPuH6d5SZvwTgf0p+aJYo6Fp6634nOHLqXJvj5NVwHjs449v6WXUuofUsjkuu15Om4OTa2kmrzut9nVCvFvf+fcHJK1uii99/ZsN12taqceCe4EwbMoSRIQKAi1fm25RP+qFQJOtYm50MYVvdy9rXe5+TkeESHv/cRumE7OQp0jOlOv9XP7ehudOosn95ImkLZj/bYuJ33nfLyuaxgjiqjHqdiwxyfRDZ51ZRcPK475aVKBULmJ2r4polA0jKVIv5c2Ky3PY7VYqLsM1i0VKerTQLO1XHCopoG3/jzkPYsvvFWI7Zy/jeHkS03PXvA0S0FcCyDozN4oPuZtcZbBNj7udIVao1ZbqF+29l55JFmhl1Q92JCPSSRm5WqVhoTgz3b14lvSb3b17V8r7Ht23E3t/6ZNvfP3b3eozdtU5qrIsFp9ksJqxmqJjEhgYdqVSfOI97gjNxhr0yRDJUE2u1xrhmyUDbdfDqOFuD3FmszU6GsGlKI8Mlqb2QSUp+bdvGlkX40KCDPZ/dEDrCJ9JMRCMlSM4/MlxSFycz47XddzSjueJZjlqISKjvZIbNa1b52rMu+yquu4kOtNDeF07phblqIFm7oIhFl9cZViFsc9gFnB8Tk2WM7p9uccpH909be63BJG3jBK4GB+cBvAbgN5MclMUfvy1Ek5wk3e+iVGq7nTDZOFTHjUtH1I/Zuapvs4HioAPmuoG/vuE0e0XlTTromXaKKjh5LB7IKRckYhID6t/92MGZtvd6ozG67pMLzEYyRKItsKz4FKhPMqLAx0taahxJSPJ1GdZmJ0CUVvemW9xxboV7nz9VIyVArc4UNAXMFPcuZhh0bcndz39x0DGWQo0y+1Djn9sC5wh43xJ190ORdhFE9i2pOpOxgzNt0f/qAmPs4Ey/2U5jTNQ2buzEQCzBMFE68JOc0T0UJkUsMikg2ba991xRqqjjwK+7E4DQTp/quuo+71CjBTkA7BifUk4K7nPsOXy6zSh75flkUk+6imjdgks0S5AhuzYTk2WpnFHSahxWPs/a7KSIo9V9JwmihuP32UxTwDqFzEY6OcKta1e0fI4kugN6IdTVkQB14ES1MPFzet2ybxOTZWlONNCuzhHU1qkcfPG6DUi0Y6K24QD4ZwD+YeOl/wLg3zFzZ/twWlpIWunAqy8pc8KEwxf0oYpTQskvYuuFGucH1I7W4oFc7BJsOsmmwUUDzeN6CzUB+QSta/ribZnulQ/UfQaV86/7zrzXRlxXv/y9JLDyedZmJ0W3KQyE6S4XpU20Crf9CRM0GXRyICLfToLXLBmQKkkFGWNQvMEIU9spbLquqNG9S+BnU2XqHEA896oNSMgxSdv4PwE4AP5N4+dfa7z2vyQ1KIs/UbYQTXE7UrqVZ9AHyET4XUcULWlGPbq75/BpzF2ZlzpaquNEmUB0KSnuCuxHR9a3pI+ojN4yhdKFrG23uFYibzEMfrJX7mvjF6VKUo3DyucBsDY7MbpJYSBMdznVZwubxpcnahYmAsBNDz1vlJ7ntVdbdr+Ii1f0578wV23JeTY5h1vPXpWaJsgTYfOHh/D6TyqBpeIAtSNrEpQIEvmvVGsYOzjTouzk5/AODTrSKP3QoGMDEgpMnOePM/MG188vEtF0UgOymNHpLcS4Jw1xPJMUjqWL8nDyObxTqWoNlts46fSjRUFEUNz5f0FX9H5RF1XOuoyJyTIuXmlXHtO17Y7DedTJXpm0YQeS3+buxKKyC7A22xJ6jpDZt9Gta0JpLy8wt9gy07oWrw0xsV95IvzssiVGtn1o0GkLJhw48aZS/m7qkdt9j6lDZdP9ghLiswS137K5T+fwPnLnOow+M93SxtzJEx65c51SLzutgERWUkhMnOcaEd3EzK8AABF9GEB2kp/6lG7bQlShashy/+ZVvm2j3XiNU5QCF10ud9gtLJPJx3Q1v+fw6RYjJ7hmyUCzra2XuJxHkwlZV6iYdAeqbstLTQhrsy2h5giZfRt9ZhpLF5m4Cu1460tM0yO89sok8l1jNkoJdPKEOz56HbbsfrHlujx290fb9KadHGHsrnUGI04GoZASV6t1lcOru1dMAiadIkspJKZNUo4Q0auo+zU3APiNREdlMaKbthBVJLUI8B7XNJ/NL5dbppph4vSODJekChleTFbzqvfMNpQvknQeTb4vlQPbidatvbKojIi12X2ASQQu6Bwh26Kv1jh0F1h3Stqew6eN7LC7LkVg4hQLRTrR+MWdknHk1LmWnw+cKLc5YI/dvR577t0gvaZehSNR5B3VrghnUIWI1AepEyo4eSxxctI0DJ3DG6TeJa2ARJZSSLTOMxHlAWwA8BEA4kqdZubLSQ/MEh9Z2eZQkdQiwH1cVXpIseBg6eIB41zuKDm1Y3e1O7ZeTFbzutSETjiPft9X2g5sLywqw2Jtdn+QVAQuyFb8UEPO08+x9hYx+3H/5lVtu4hurWpV6gejbnOO7rxNew10ARDZ3woNZHdE+sJcFaPP1DOholxvv1zmkqcLpV+dkK4OKKzDm7Y9d5Olmhat88zMNSK6j5kfB/Byh8ZkiZEsbXOkiWr1PHbXumZkYc/h081iQpVxiKr1CugVTEyMm18kIAvOYxbG0I9Ym90fRI3AqQIqQdIDZufqNSif2XBdm0KQFz/HV1AsOC3pehOT5ZZcXL+/j7JzV56tYGKy3Hb9VB0Qq7XoOshB6kP8CgxlGt5xObxZsedZqmkxSds4SkR/DGAcwEXxIjN/L7FRWWIjS9scaaJbPQdZYETdwjJVMAn7WSwWWJvddQS1BVEicDp7FyQ9QBReHzhRNkrHEE1adMd+xxPF3vXcjLS+Q4W3SUrQBmAyu6+7prOVqtThDjLeIPUhukh1kD4P3UqWUkhMnOeNjf9+xfUaAwite0VEewDcCeAKgFcA/AYzz4Y9nglZT11Iiixtc6SNypgEWWDE6bhGMW69aBgtsRG7zbYkR5jdwSgROJ29EwoU7g59712aV6r4iL81iSqLlAJd6oF3/EGanBScfFuTFNm1DKJZL8aki8ZHCUQFrQ/xm7d7fV7PUuDIpMPgrQmc91sAHmLmeSL6AwAPAfjfEjgPgP5OXUhymyPuBUlaC5ygCwzruFqyTEI225IQOmdW/N5rE6NE4PzsnUy5yK/w2i+q7E4pkO32BRm/DBGpNe28C5hp1gN1B9eb86x7fxCCOoN+jnw/SHJmZf416TD4JcnL7wA4wczBhR8BMPMLrh+PAfhsmOOY0s+pC0ltc8S9IElzgZOVPCoxSZVnK81IjklXQIvFTRI225Icpt1CZTYxTLAhSvMUVeG1O6osItbMUGrzm46/qGgG5cYdqTXVJNZJsDHqzVzuu2Vls2GVLvKeI8LqnYeMbbbKzt+/eRWOnDqnrbvRRc37UJIzVYh9tlqI6D8C2ATgucZLn0G9EGU1gP3M/IeRBkD0HIBxZn5K8fsHADwAAKtWrbr5zJkzgc9x485D0hUzAXht9x2Bj5dFdFHbJCK6OiMappNdmOPF9blUUZBOSKvpxpDWWCzJQEQnmHlTB86TqM0OwqZNm/j48eOdOl1k0tj9Utk+VSpE1G6hUexdUrZSdd1lShcAkCNggevX6L5bVjaLDIPMIya9AHIA2tum+KO6JkH6Dzg5gpMnzDUatwh5PAA2yNJBVHbbJOf5QwA+xszvNQ70CIBDAP4hgBMApIaYiP4zgJ+V/Or3mPkbjff8HoB5AHtVJ2fmJwE8CdQNscF428hKZDEp/KK2SWxzxJ1L7Xc8r3FVaXUC0dqFp5VHFaQQREe/5vZbWghls/3oRK1KmvdvWrtfqt1BlT2Imtcaxd4lYStNrrs437KCg4tX5lvUNw6cKGPTDcsDp7OYyL/JHOc8ERaYkdPkeatsdpA229UFlsrj7fnshkiLJ0s8mDjPHwTg1gitAvgZZq4QkVI7lJn/ke6gRPTrqEdEPsV+4e+IZKlCMwnSSEuJe0GiO57MuMqkkaJ85jALjDgn+jgKQfo5t9/SQiibbUCitSpp379ppfepHNIkO7tlqVjZ77p700a8aRze94pjmthl8Terdx4yHu8CM17bfQdu9Pkbmc2OuvCp1rgv0k27ARPneS+Al4joG42f7wTwH4loKYDvhzkpEX0awL8C8D8y81yYYwQhC5HFJElDUSPuBYnueDLjqlptqbQ64ybuiT6OQpB+zu23tBC7zQaSr1VJ+/5NU5lI5ZD2ctBHEOS6m7w3iHMvAiBBELY4jM2Oo812rytqdAs5vzcw8/+Oes7xbOPfbzPzV5j5IjPfH/K8fwzgWgDfIqIpIvq3IY9jzMhwCUd33obXdt/h24Go21A5VkmmpYwMl/DY3etRKhZAqOeUqXK8tux+ETfuPIQtu1/ExGQ58PGCGouHnj2pPE9c+FXIB2V06xo4OZL+Lq4qekt/kJDN9vJPAfynmI4FIP37Nw07qsPUxnY7Qa57nN+RCIDonFmvg+S2xaNb16Dg5KV/p7LZur8xpVfSTbsdk8gzmPk4gNiqPpj5H8R1LEt6aSmqFb67mtjdRc8vOqs6nmq17u3QJ+hEtCqRiV7iO4sikSSq6C29S1ibHUetiqfI2/jcad+/WUrv86aEPb5tY885zYIg1z3O70iXfywKEYXahmzH2pszbVK8p/ubW9euwJFT55q53T+9VIVX5MPJU8/tPHQrRs6zJdtkKS3Fm84QR16yymDec3MJTx07K/2bpKNVcU/0ew6flnbSGlw0YHytsjT5W7qTOGpVwhZ5p33/ZsWOpp373WmCXPc4vyPVHEEAXnnsl9rOKYha62KaVjIxWcbYwZlmjneQQIoleazz3CNkRTjcpJo4qGOrM5hHTp1LJVoV90QfRyQ7K5O/pTdJulYlC/dvFuxo2rnfQYiraDrIdY/rOwoTAOnkwiYL96JFjXWeLbFi4uyFcWxVhiTNlBUgvoleZciLgw627H7R+BzW4FoS5I8BLEa9VgUAjjHzb8d5Anv/pp/7bUq3R8jDzB3dtLCxJIt1ni2x4ldNHLdjm2a0Ks6JXmbInTzhvUvzuDBX37brtsnJ0lvYWpXOkHbutynd7kiGmTu6ZWFjSR7rPFtiReYEisK+PFGLIkVcBrYXolUyQ37x8rxW09RisfQeaed+m9ILjmTQucNvYdNrTap67fPEiXWeLbEicwLj7AbYy3gNuUqEv5smJ4vFEows5H6b0C0R8jjRLWy6PY3FS699nrixznOf0YmVpNcJ3LL7xa7e3kuLfpycLBZLd+ymdUuEPE68C5tlBQdEwI7xKWm77m6e57o9LSdpfJukWHoHtyg84+pKMumGIr2wvZcGMkH9Xp+cLBZLd9AvTVy8iIZrj2/biMvzC7gwVwUDbY6zoFvnOTtv67GR5z4irZWkjaCGo1u2by0WS3/SDRHypDCRZQW6d56z87Ye6zz3EWmtJNPe3uvmood+npwsFks73WzPegmTebObdwrTnrezjnWe+4i0VpJpRlBt0YPFYukVrD3LDqr5NE+EBeauX9jYnU891nnuI9JcSaYVQbVFDxaLpVew9iw7qObTXsr7tjufaqzz3Ef040rSFj1YLJZewdqz7NCP86nlKtZ57jP6bSVpix4sFkuvYO1Ztui3+dRyFStVZ+lprNybxWLpFaw9s1iygY08W3oau7VmsVh6BWvPLJZsYJ1nS89jt9YsFkuvYO2ZxZI+xIquOFmEiM4BOJP2OAz4AIC/T3sQHaKfPivQX5/XftZ4uYGZVyR8jkxhbXZm6afPaz9r75Ka3e4q57lbIKLjzLwp7XF0gn76rEB/fV77WS39Qr99//30ee1n7V3S/Ly2YNBisVgsFovFYjHEOs8Wi8VisVgsFosh1nlOhifTHkAH6afPCvTX57Wf1dIv9Nv330+f137W3iW1z2tzni0Wi8VisVgsFkNs5NlisVgsFovFYjHEOs8Wi8VisVgsFosh1nlOCCLaQ0SniOhlIvorIiqmPaakIKJ7iWiGiBaIqCdlcojo00R0moh+REQ70x5PkhDRnxHRj4nov6U9lqQhopVEdISIvt+4h7+Y9pgs6WBtdm9hbXZvkhWbbZ3n5PgWgP+BmT8K4IcAHkp5PEny3wDcDeDbaQ8kCYgoD+BPAPzPAH4ewH1E9PPpjipR/m8An057EB1iHsCDzPzzADYD+Oc9/t1a1Fib3SNYm93TZMJmW+c5IZj5BWaeb/x4DMCH0hxPkjDzD5j5dNrjSJBPAPgRM7/KzFcA/CWAX055TInBzN8GcD7tcXQCZn6bmb/X+P93AfwAgO193IdYm91TWJvdo2TFZlvnuTP8UwD/Ke1BWEJTAvCG6+c3YR2snoOIVgMYBvBSuiOxZABrs7sba7P7gDRt9kCnT9hLENF/BvCzkl/9HjN/o/Ge30N9m2FvJ8cWNyaf1WLpVojoGgAHAGxn5p+mPR5LMlibDcDabEsPkLbNts5zBJj5H+l+T0S/DuAzAD7FXS6o7fdZe5wygJWunz/UeM3SAxCRg7oR3svMz6Y9HktyWJvdN1ib3cNkwWbbtI2EIKJPA/hXAO5i5rm0x2OJxHcBfISIbiSiRQB+FcDBlMdkiQEiIgB/CuAHzPy1tMdjSQ9rs3sKa7N7lKzYbOs8J8cfA7gWwLeIaIqI/m3aA0oKIvoVInoTwCcBHCKiw2mPKU4aRUS/A+Aw6sUJ+5h5Jt1RJQcRPQ3gbwCsIaI3ieg30x5T5qtX2gAAIABJREFUgmwB8GsAbms8p1NE9EtpD8qSCtZm9wjWZlubnTS2PbfFYrFYLBaLxWKIjTxbLBaLxWKxWCyGWOfZYrFYLBaLxWIxxDrPFovFYrFYLBaLIdZ5tlgsFovFYrFYDLHOs8VisVgsFovFYkhXNUn5wAc+wKtXr057GBaLxRKYEydO/D0zr0h7HJ3E2myLxdLNqOx2VznPq1evxvHjx9MehsVisQSGiM6kPYZOY222xWLpZlR2u6ucZ4ulF5mYLGPP4dN4a7aC64sFjG5dg5HhUtrDslgsFktCWLvf3Vjn2WJJkYnJMh569iQq1RoAoDxbwUPPngQAa0gzhp3sLBZLHFi73/3YgkGLJUX2HD7dNKCCSrWGPYdPhzrexGQZW3a/iBt3HsKW3S9iYrIcxzD7HjHZlWcrYFyd7Oz1tVgsQYnb7ls6j408WywJ4hetfGu2Iv071et+50o7mtGr0VndZNcLn89isXSOIHa/V21qt2OdZ4slIUyc2euLBZQlBvP6YiHw+ZJ28IQRL89WkCdCjRkllzHPgvOeFHEuciwWS39javd72aZ2OzZtw9JXdDKtwWRrbnTrGhScfMt7Ck4eo1vXBD5fkg6eO20BAGrMAFrTF3p5K1K1mAmzyLFYLP2Nqd3vhE21qX7hsM6zpW/odN6qiTM7MlzCY3evR6lYAAEoFQt47O71oaIKpg5eGGMpM+ICYcx7OTob5yLHYrH0N6Z2P2mbKpsTd4xP4eGJk7Ecv5exaRuWvqHTeaumW3Mjw6VYzj+6dU3LFh/Q7uCF3Qb0M9YiHy+uFJSsIa6NzT20WCxxYGL3k7apsjmRAew9dhabblhu7ZsGG3m29A2djox2OlppEs0Iuw3oZ6yFM9nL0dmR4RKO7rwNr+2+A0d33mYnFoulz0k65SFpm6qa+xjoiXS7JLGRZ0vfEHYVH7baOY1opV80I+wCQhbVFghjbqOzFoulX+hEMV/SNlU1JwK9kW6XJNZ5tvQNJmkNXqIayLhSMuIi7ALCbcRVahvifVn6vBaLxZIEQdIAo8jNxWVTZWMY3boGO8anwJL390K6XZJY59nSN4RZxfulOXRblPXWtSuw99jZFmNpug1oHWOLxWKpY7qLlwW5OdUYHrt7Pe7fvCr0nNDPWOfZ0lcEdQBVBlJUJbPr56zrb05MlnHgRLnFSBKAe262TrHFYrEEwXQXr1OF6rrotm4MR3fehk03LO+6QFDapOY8E9FKAH8B4GdQz09/kpm/ntZ4LBYZupww71ZXpVrD9vEpbB+fQp4I992yEo+OrE9+kIaoKquPnDqXzoAsFoulAyTRpU+XBug+nywlAog3p9gvuu0XJe/lXcWkOjSmqbYxD+BBZv55AJsB/HMi+vkUx2PJCFkSbZdVO5PB39WY8dSxs5nSy+xlHWaLxWKRkZS+v0rdCEDL+VTEmVPsl17Yr02ekuztkJrzzMxvM/P3Gv//LoAfAOjNpY/FmE43MvFDZiB1BtHL0y+9EfrccS8i+tWAWiyW/iXJLn0y+UpdQylB3DnFfoGRXpcRVZHkd5+JnGciWg1gGMBLkt89AOABAFi1alVHx2WJRpjtkk43MjHBu6W1ZfeLylQOL6KNdVCSKDIJozZisVgs3Uynd9x0xyWgbS6MI63AL/+6X2VEk/zuU3eeiegaAAcAbGfmn3p/z8xPAngSADZt2hTOE7F0nLg72WUptUCneewlTyZJHu2YLCKCGt1+NaAWi6V/6VTnU2GPVU5KqVjA0Z23tf1NHEESExWlXs5rVpHkd59qh0EiclB3nPcy87NpjsUSL3F3sstSaoEslWPLTcul773vlpWhzuG3iAib3mK75FmiQEQriegIEX2fiGaI6Itpj8li0dGJlAW3PZahOt/YwZnIaQVWRUlNkt99mmobBOBPAfyAmb+W1jgsyRBnJ7swN7ssKgvEF3WVreIfnjiJp196AzXm0GobftELsYjIYnqLpS8Qhd7fI6JrAZwgom8x8/fTHpjFIkPW4MntoKrsZZCdPV2es7eRlPv4s5Wq9G9M0wJV57YqSnWS3G1NM21jC4BfA3CSiKYar/0uMz+f4pgsMRFHJztxs9+6dgX2HD6NHeNTRje/bCtsdP80QEC1xs3XwmyP6QzqoyPrI0nTecftxb2I0OlPBz2nTeOwmMLMbwN4u/H/7xKRKPS2zrMlswibZpoiETSdQmWPCWhL1RDoosuydD+Vre6GVMc0SSpdRek8E9EAgN8E8CsArm+8XAbwDQB/yszyJZMhzPxfYab6ZelCokSQ3Td7mJww2Uq8utAeyw0aqZWNZcf4FI6fOd90mk2dUdn7gkQvVIsTahzb5DNlofOVpTMQ0ZPM/EDMx1wNSaG3LfK2ZJEgu3VBd/bCBIt0zm2NuWWOWFZwcPHKvDT406mcbksrupzn/wBgI4AxAL/U+LcLwAYATyU+MktXo9LADOqUmeROeyXdgkRfg6zOVdtje4+dxcRk2TgPWfU+1bhF9MJ97Ua3rpGuPBnAg/umjeTtkpTxsXQeIlqu+Pd+1O13nOdSFnoz85PMvImZN61YsSLO01osoQkSoQ0azQ2TW6tzbpcuymPH+FRzjpitVJuOs0DY6n6VoUsbXdrGzcz8c57X3gRwjIh+mOCYLBkh6pZ+HNslpoVz7uhpEBh16TmTnGjVWLjxd3NX5o0UMh7cN90mYVep1pAnkkrbCSMrvg+Rt6fKixbHCLvVaLf7upZzAM6gdUePGz9/MK6T2ELv/qMX0ruCRGiDRnN1ubXea3fr2hU4cuqccq7K5wgXr/grOQF1W21VlNJB5zyfJ6J7ARxg5gUAIKIcgHsBXOjE4CzpEXVLPy5j62fETATp/SjPVjD6zDTAV9M7ZJ9X16pb57R7HX2V9nONGQUnr2z36v4+TPWjK9Uadj03E9tWoyXTvArgU8x81vsLIgrfraf1OLbQu8/olfSuIKmEsvcS6p9dBFu8n10WLJJdu6eOtT2eLedYnCfMSdIMZbh1nLvpu+gFdGkbvwrgswD+joh+2Ig2/3cAdzd+Z+lhomzpx9kl0G9LKq4oabXGbXnR3s+rSpXww9TRzxMpU12iLBIuzFWl195u9/UcTwAYUvzuD2M6hyj0vo2Iphr/Yk0JsWSLXknvCpJK6H6vQMwOIthiMp8FtdsMYK66YPRea6vTRRl5ZubXAWwDgEbOHJj5J50ZlqXTeCPFqkiqibMap4yaaksKqKdbJN01x/15R4ZLOH7mfJsYvR+r31/AjTsP+f5NjVkZQTC57qq0DwDSa2+3+3oLZv4Tze/+KKZz2ELvPqOX0ruCRGjFe4e/8gIuzLXqI1RrrNzRc5PUNRoadPDIneusrU4RI6k66zT3NrKtJQKkzp7Jlr7O2Kryv9zO2/Ez59v0koUaRXm2gh37phCy63VglhWclp+FqoZu682NkwOOvnLe6L0lzbVdVnCUmqDAVWO6fXxK+nvVd2K3+ywWi45Opnep0v3SzLn2Os7u1/3GpQtERWFw0YC12ymTentuS/qoVCS8DrRsm2hisoyxgzNNx25o0FE6essKjjb/qzxbwZf2TcGdPVFjxlPHzuLp77yBWuMXOseZAPzCTcsx89a7bWNw8tSS12yCV25TdHMyoeDkcXnebMtOdW3FgsGP9y7NAwCKimtv85gtFksY4mpc5YdKCnT/8bP43tl3Es25Duuc++WCy65dHHRj1L/XSLU9tyUb6FQkdPlhD0+cxPbxqRZn7cJcFe9enoeTa/U6C04eRPA1Iiq/tmbo8DKA139SwdQjt+OJbRtbxr/nsxuw594NzQivEKLXRXy9UQfTHLahQQeP3b1e+XncyK6tX7tXL9UFxp7DpzF21zqbx2yxWGIjLtlRP1RBnKOvnJemAZpIcnplTGXv86vRKXp2HwWy+cybC+69dkODDpwYvK4ckZEcqSU5jCLPRFQCcIP7/cz87aQGZeksqq2lUrGg7I40MVnGXkXqQm2B8b5BB4OLBlpW8jsUKQVxIxYDqpQE2Ws6feiHJ0420zVMVvxf2Lyq+X6ZLJ0bJ0fSKEeYAsGyR7ZIfB4x0bibuVh6G2uzLXGiUpKQ1aIEieC6jxE0E89PktNUJcSvRmfsrnUY3T/dsmPp5Ei5g6kLRs3OVds+56CTw2Inr0wPkWEqR2pJDt81EBH9AYCjAB4GMNr49y8THpelg8hUFwDg4uV55ap2z+HTWmM3O1fF6NY1uL5YwFuzFew5fLotfzgp3LrIJlEHv8YqogmK+9g6jpw61/z/+25ZqX1vdYExdnCm7fUw23Lu7oK3rm1tTiHSXx6eOBn4uJbuwtpsS9LIorWjz0xjdP+0scqS9xhRkKl/qJziB/dNN5ta6Wx/uVGjAwBLF1+NMw4NOtj2iZXKqtkcUfPvvDuIss85tHQxJr9c3ynVQZC37e5G5ZNewCTyPAJgDTNfTnowlnQQK9Zdz820rH5nK1XlqtbPuZPlNzv55Iv0VbrIshW69z0qRBOUkeGSUQ6btzDSD1WOcpiGL2MH6xXgT78kl/V9+qU3bPS597E225IoMsfU2wEPaI3geiPVsqZSUfBqMKvsZ40Zo/unAZKP2Y1X/x8ALlUXcOjlt7VNqsRcY7KD6N4pddcPuRG7wDfuPKQ9hqVzmGTfvAqgMyFDS2qMDJcwuKh9LaVa1eoisE6eUK0tGBnXtr/NEbbctNxgxO3kiZrj3fXcjG8+WpDUiLdmK3h44iQe3Dft+zfFQSdyREW1G+DHbKVeAa5rxOKHScTekmmszbYkShBnTQQTvJHqIGkKg06umTcsi74KRJDEb4etusBG85FK/99v7CLCbRIAub5YaNpcmeMsUvvEe1XHUGHteTIonWci+iMi+tcA5gBMEdG/I6J/Lf51boiWThFEz1Pl3C1dlMe2j680bi8KXBWNLRYcLBrIGUu7eXHngamMmzDkfqkaXhh1eTo/55NQVwMJGlGRGbQlrsoSzXzRxp7Dp5Xv9ztOnA1uLJ3F2mxLpwii3nN9sRC5E+zdN38IR3fehtd234H7blGnTAB126vaeeskpl1gb127Ql8c7vqwQRtbWXueHLq0jeON/54AcNDzuw6p7FrCEFZ2J4iep67BxpbdLwYaL6O+un738ryxqkZYioMORp+ZNoo6hIEBvKPRY1Yh8p5FoZ9XJjCIrvVbsxUUnJy0U1VhQL/ZFGeDG0vHsTbb0hFk6WsyKdCCk8eta1cY6+KrEHUkQirUpOFUNzA06ODIqXPahUW1xk37G7SxlbXnyaHrMPjnAEBEX2Tmr7t/R0RfTHpglnCYVhjLCKrnqVKzCCMK76e9rGraEpQgW4VhELJ3smug0mAG6ukWbqc+ymcVRZoyKj6tX3upm1i/YW22pVPoOr96m2DpdPGJgGVLHLxTqWobQQn7YxrBjmu+kJEDYNZAW4+TJzxy5zojFSpvp1tTx9fa8+QwKRj8JwC+7nnt1yWvWTJAlJVmXO2adW2iw9INcQRC3Wmua3lSWwTmMxuu00ZggkbDVZEedzdGL37brZ3sJmZJDGuzLaEx3bk0kQLdsvtFrbPLDFyeX8Dj2zZiZLiEjbtekDrQQtfYxEI6OcICzHsDBGUB8Tjni/L1XUCT4vCw9tfa8+RQOs9EdB+AzwO4kYjcW4DXAgiXlGpJnKgrTbdBFEZ0x/iUtJW2rLX2yHCpa7bM4sRtTC/MVeHkCcVCPaIirk0cckJiYVIy0FX17iII595dke5FtvtAQJv0nSV7WJttiUqUnUsZJruQXk1lmZqRUa0J6vYxSAdZN7pdQy9xzHAXr9Qw+sw0tn18JQ6cKCsXGVGaXHWqO2Q/oos8/zWAtwF8AMBXXa+/C+DlJAdlCY9qpRlUY1lmRL2ttL0/CyM7NOhI0yOS3EqLgzDjc/KEpYsG2qIlIors1rkOk87ipcbcdIL3HD6N0a1rpI1svM1S3J9NNyGODJdw/Mx57D12tvl+BnDgRBmbblhu8+SyjbXZlkB4o8wXL7fLxwnlCMDcgRbHNcUt1wZcDQgQqbvOuiECBnIUupZF5GZ/c/rtUH8flmqNceTUOTx29/rmZ15WcEBU75UQdvdXENdusqUd4i6KEm7atImPHz/u/8Y+ZmKy3NYNCag7eds+vlIaKZYRVI1CMDTo4L1L823nz+cIOahzm3OGRlJGnGkiQR1oXaepqMc2oeDkfVvlqr7LPBEWmNvuBdX7dR0nLf4Q0Qlm3pT2ODqJtdnZxVTnXiCzNaoug0GOC9TnjcFFAyjPVpr2XBWESYKli/KoXKnFkssscHIAyN+hJwCv7b5D+56wIgCW6KjstkmHwXeJ6KeNf5eIqEZEP01mmJaojAyXcM2S9g2Fao2x99hZY8masAUFF+aqUmdyYaFdL1NQcPL4/C2rtPJDKkrFAr76uQ1GguUmiK0/U4JsETIQ6jPqqFRr2PXcjFbHU/Vd1pil94ItMulurM22mBBUPs7dnQ9Qy6CNHWzX2Nfh5AnvXZpvLthFIKRTjjNQT6GI03EGgOoCjKIl7o6EMpKWm7M60OHw9TmY+Vpmfh8zvw9AAcA9AP5N4iOzhGZWYXS8z7GqAcrEZBm5IMLCBuhsyGN3r8ejI+sDR2VF7tb+42d9DV+OGpEAA2rMcHLJdENMYp/nwlxVa1hNikPc90IYIX5LdrA2uz8J6gSFWQyL7nkiEipL8VApZgicHGFo0AGhHvxYumggdJ5y1qkusG8wxn1NZehEAKJidaDDEyhgx3UmAGxNaDyWGAji5HgNqHiYwqRBFJw8igFzq0vFQrN1a5CILwDcc3N928qkqQoz8MH3BXD+qC4tR0BTPaNb8BpW026F4l4IKsTvZmKyjI27XsDqnYeweuchDH/lBWuIU8Ta7P4gjBMUdjEs7EsY57tULGDPvRsw+eXb8druO3B0522hdPG7CVGnokPnDMe1EyhbXCXpmPc6vlJ1RHS368ccgE0ALiU2IksLYXKdVIoJMnfYa0DDdoLKE+Gxu9cDMM93Ew5ZWId9/LtvGBd4mMgBuanWGEsXD2DqkduxZfeLHd1C9BImH9xrWJc4ueZ3QiRvuiLuhbBFJrJ8+wtzdf1q93EtyWJtdv+hcoK+tG+qqZbkfYZNG53IKM9WUApoU7+weRUeHVnfdOKEbdHpO3cDTo4A0kuNipQ93VVVOcNxyM2pVFRU87RN0fPHROf5Ttf/zwN4HcAvJzKajJJWsr7sht8xPoXjZ843jZBsXDLnR4jV+0nWhH1oasxNI33PzSUcOXVOa1iLBQdjd61rdiQM47BXa2xkdMN2uRLjj0MlIwoLrC6K9FsUyYqCBoiAXKux994LQYT4BXsOn5ZOuu4OWZaO0Pc2u99Q2W3xOIq5Y/v4VFPmUijrPP3SG6hxPb1g28dXYtMNy43UgWTOt44jp85J5zQnT7E1HkmDPfduAADsem5GG2Rh6OXwVM5wHHJzqsWVal6xKXr+aJ1nIsoDeJmZH+/QeDJH3LqXQc774L7pthubAextOIFuZ9g7LpnzI4yi26F26ziPbl0TOELrHVt5toIDJ8p47O712DE+pVxpz1aqTWOeJCWDLlc6Vu88FPOIwlFjRsHJtxnQe24uaRdFMqNZXWAUCw6WLh6IdUGoW3jZSEZnsDa7PzGx216pyuNnzuPAiXJzjqkxt9hJv0ipTNZSh5DXbLNHNf+0hiwj7KZIP9QtPN6areDxbRsDd/IFosnN6YrGZfOK1YH2R+s8M3OtIbzft4Y4jd7wfmkMDDSjBUHG5W2A4m4HXZ6tNB3ZqJJqYhx+Bj2uEhHvww8Aiwdy+IN7Phopst0pFuUJV3zkjES0SGZAvYsit2FVGc13KlVMPXJ7rJ9D933bSEZnsDa7PxnduiZQIKJSrSnnEBNnWDi7R06dM7bjQp9eRreWC3prfMQcq5L7vL5R4wMEc4bD7AR6z6uSH1XNKxY9JmkbR4nojwGMA7goXmTm7yU2qgyRhmyXSd6xyrE2Hdeu52aUOVpxGLLybAVf2LwqcKpEUET6x9jBmZYUjsvzC81IfNajnn6Os+jw59cOd9dzM81F0NjBGYzdtU5pNBnQdhr0Q5YyNLp1jVJj3EYyOkpf2+x+ZGS41GYD/dAFZ/wQtd1BbGu3Osg6PrPhurbXJibLuHh5vu11d0Q3qjMcFF3qR6fH0iuYqG1sBLAOwFdQ71r1VQD/RxwnJ6JPE9FpIvoREe2M45hxk4Zsl4lBUilTmI6rEwVwTx07m/h23Gc2XIeR4RKWLm5fBwoN5G6PeooOf6rKebGL4P5OZytVbB+fwur3F5RKG2FliVSV/UA9/88djRkadLDnsxusce4sidlsS3r4SdGN3bUukCpQUHUjNwsMPDxxsutta1SOnDrX8rOwjd5FzNCg49vMKklGhkt47O71KBULTYnANMfTC/h2GCSiDzPzq36vBT5xPTfvhwD+MYA3AXwXwH3M/H3V36TRrUpWcGXS1c3990G3RPy6+zk5wrZPrGzLdRXpFiWf80xMlhPPNe4Uokue7i7+yAeX4kc/vtjxyEex4OCdStU3faVoWG2u6vCnu18IwP2bV+Gb028rzxG0c6DtQBiOTnUYTMpmh8F2GIwH03lo+CsvGAVGVPUSYUiic2qaBOlY6+0OmJRttB0G0yN0h0EAz0he2x99SPgEgB8x86vMfAXAXyKDFeFRVmxhBcj9dHmvWTKAR0fWN8cFtBowsXW/cVe7xq4YU69Q83GcAeBvU3CcAWDp4oGmlmlJEaEZGqynnQTRYTZ9HajfE0dOnZNG5k3+Pq5xWDpKUjbbkhImerwTk2Wt4yzmsGLBwRInh73HzmKJk2vq2ZeKBQyadpJy0UuOM6BOZ5FxfbHQsiOgKxQMi21kkk2UMyoRrUV962+ZRzf0fQCWxHDuEoA3XD+/CeAWyTgeAPAAAKxatSqG06rRSb+FWeWFLTYUv1NFhy/MVZv5qkd33qZc7c5Wqm3KIGF1nC3BKc9Wmt+TKufskTvXtRWQ5ALKB/lFtv0Md9Ct1zh0Ry3x0wGbbUkIv8ii6hkuz1aaTpQuKCIin94I9oW5KgpOHo9v24iR4RJuzIi6UJqYRp6FBKqJXF8U25iGaIHFH90ycw2AzwAooq4bKv59DMBvJT+0Osz8JDNvYuZNK1asSOw8SazuokToRoZLymglPOPTOU7e6ETYFfDSRfmWKHe/syhvdhXc+cC6HYyR4RKO7rwNr+2+A1/93IZAHf5Gt66pNzdQcH2xoDTe1Pj7IETpQGhJlEzYbEswTOYenfP10LMnMXZwRunAuZtRPbhvWuqIbR+fwpbdL2JZwA6xvYiQb5Mh8sSF/T5y6pyv4xzVNtqdvmyijDwz8zcAfIOIPsnMf5PAucsAVrp+/lDjtVRQre4e3Det7NDkR9QInV9jj0q1hh0GucvuhyyKjrOQtEm7aUgWuFJjLF2Ux8Ur/lH8SrWGsYMzxrrKQaWMxOu/++zLmKu2thpwG25Z18n7N68KHL2IQ3fUEj8dsNkA6oXeAL4OIA/g3zPz7qTO1Q+YRBZVSjbivToH7p6b68fw6+Janq0gH6DgMGnSyqUeGnRwx0eva5Psk+WY6+ZfAgJ1Z1XZU7vTl018peoSNMLfBfARIroRdaf5VwF8PqFz+aITEQfCNUeJ2hnIW8krw8S4uB8yaUvWHOGaJQOYnasXt61+fwFHXznfcoyLV2otutAWYO5KTaoxLWO2Um0W7JncS0FThcT7/bZ/43J4rbxRdknYcc4D+BO4Cr2J6KCu0NuixySyODJc8u1gp+Kb028bRUgBoObTlruTMIBBJ9cWEEj8vCzXr5alSuj0k00LBP0ascXRYdASPyY6z4nAzPNE9DsADqMewfgzZp5JazwmEVl3CoSJExI1QhfHtozsIVvi5JoPortNtnC8vI6zoFrjQJXIvQ6jnoohovFBro3YKt1z+HTHorbW4bXEQLPQGwCISBR6W+c5JKaRxVmN4zw06Cgda/fCvZsYGnRwqcOOM1BvIPWO4np55+QkW2cLR93u9GWT1JxnAGDm5wE8n+YYBLKHQIZYFZq2647isERJsRC4t5lkckeX5xeUv5NRY27bTusWqSInT5iv+atzBMX03pERZ7v3tFrJW/oK30LvThZ59wKmDphqPiAAd3z0Om2KXzcGPaL0IsgBANX1qIMiFi0mC5okW2d7dx6sDc8WOrWNL+n+kJm/Fv9w0sNU8SBPZFz5GlWbcXTrGuwYn4rk7D24bxrbx6dQKhYwd2VeK3dk4vx1q+MMAGD/sco+3z/44FL87Y8vSt+/Y98UdHOSuO66iSCuymlbld3fZMVmM/OTAJ4E6jrPnThnlvGbB0wcsInJMuautHetE3ULfil+NWY4eeqbtLt8yM+qqxFx/y5O3WWb09yd6CLP1zb+uwbAxwEcbPx8J4DvJDmotHCv7lSi9CoH07t6jCMKODJcwvEz56URhZzhqtqds60iSovVJEyxmBDibu0tK7bxUhx0wIxmcxNhFFcrJJx0jjMBUnkoGXGk6Niq7L6nEzY7U4XeWcd0HtBFFicmy3hw/3RbPrJIuQNgZCtrNdamd/QKeZI7ziYNtbwFgTIHOe4dPpvT3J3o1DZ2AQARfRvAx5j53cbPYwB6XgxSFQ1QqU14V4lxRQEfHVmPTTcsbykWEUbz+JnzbRXBYWBkY1vPW5185NS5jit7eHVPoyDuCfe9pPo8cUQZbASjv+mQzc5UoXfWiWMe+L2/Oikt5KvW6il3pk2vFlDPm87nKFOFgXHi5EgZJFlgxmu779B2ATRZ0MS9w2dzmrsTk5znnwFwxfXzlcZrPY/q4TFZJcYZBZQpKYhCs003LI9FPi5txzlHwNc+1+qwjm5dE0jhY8tNy/H6TyqRr4XMEJpG+gXee8L9HQaJMgTZHrQRDEuDxGx21gq9s47fPCCeb3fBcanxnAPPZCHzAAAdtElEQVR1h0olh3nxSl1KNYjtZmRLUSN2SF08mSPCxGQ5sp1MYofP5jR3Hya9OP8CwHeIaKwRwXgJwJ8nOqoMY9quW9cNLgwqIX0A2vbPQREi8J1mgSFtShPE0L/+k4pva3NTvIYwyHyja+EepN170MY9UVrJW3qKRG02Mz/PzD/HzDcx8+/Hddw0cLdW3rL7xVhbHk9MlpFT2FPR1lk830Brit3o/mmMPjPtGwhIO+iRNao1xqVqTToH1JjbGlYBV+uY9hw+bfT9xz23W7oTYoOHj4huBvCLjR+/zcyTiY5KwaZNm/j48eNpnDowqjxXtzRcEHRbTaNb12Ds4EwsckQE4LXdd2DjrhdSkTdy62MOf+WFwPl5T2zbCEDd2tyLKl3Fq9Opuv5uZCL6UdB956YaopbsQEQnmHlTh85lbbYPql2gOJ5hXZ2DOIdtOGVGmKL0L2xehadfekO5uCgVC7h17QocOFEO/P0ned9YsofKbptEngFgCsB+AH8F4CdEZPWHfBBRwKHB1nans5VqqLbfqi0hEY30OrphG0WJ1fPYXetiieAGxf05wxS2iMiCSSS+4ORx3y0rjVpN+23pJRHltQWAlghYm+2DLnc1iWMD9cW6sBP2OfanVCzg/s2r4ASc0I6cOocFn26Ke4+dlX7/YwdntLsRdofPAhg4z0T0LwD8HYBvAfgm6oUn30x4XD3ByHAJg4va08rDGGjVlpBMOg8ArltWwOu77wiUzkGoG5Utu18EUN/a6nQah8hL80M1KnFtR7eugZNXj10YvEdH1hsZwpHhUttCyD2WJAo87PagJQzWZusRqRqqqG8cTq3q2O5IqH2O1RScPJ7YthFHd96GR0fW45olwVpSiBoRHSrXerZS9U2VGxku4ejO2/Da7jtwdOdt1nHuQ0wiz18EsIaZ1zHzR5l5PTN/NOmB9QpxRQ9lubwEdc6bOH4YGTp3PnWnc+pEXtrEZBnFgtxZLRYcPN5Iz5BRnq1g+/iUstBQpD0Ig2dqCB+5c53UaWcglmiVF9l3bgsALQZYm63Am2csI6pTOzFZVi7ugau1HX4L/H5FFsDQdVeUIYqr49g9jWs3wtJbmCzn3gDwTtIDySpRxdD95MNMj++VO/PLA8sR4cadh5TNXvwQBiMNCTtx7rG71mF0/3SL9JCTo2bOeNicQRFdF9f81rUrcOTUuf+/vbsPsqus7wD+/e7dG7IbanaRUOuSkAiWDDQmIVsaJ44KMqDlbStKiuIMUscy4xsYYyNoiTQzTc0g2sG242itU1ONgEQ0FcEmtk5qsAl5wUjwjRdZdRoLS2uyZN9+/ePes7l79zznPOfec+69597vZ2aHvZd7z/3l7u7vPPc5v+f3eP0MXLXUWVyCVQsjqVFH5+wornKKQLFAHDsxgSUbdtT897bl209E5ubKq2P52WEqe1F1w67zaF9PEScmpkI7Z/i0CPWlEhupFrtgkOTnUWq6vwPAieD+Zuww2OjFJ1ELAwC/QU3cMcJasfX3FnH7le5FhT6L1/IuWLgY9eHCd0vxsGNH/dZHJfFWWMSX5u5W0jiNWjDYyTk7zpINO5x/+/29Rfz2xYkZH9Yrc4Hv313UawSCnvbtnsd9VW74UvkeX7R0AXYc+lXo+peeYgHXrBrANw/+anrNj+vcmXTTs2papN25XHnbZ+b5mfLXnPJXx3AtKNn4wOEZn3ajdhiKmj1cecdDoaUFzx8fj9yxqBU/Ba85+zT84MnnvXbx89HXW5wxOxxsWvLR7Y9N9zYtkFj9in489T+j+GW5Rs1H3OOiGt6H9QgFgGMnJrB9/3Dmg1jX7lZ7n37Oa/ZcOkLH5uw4rgFrsDakepBWecned1c5n0Hxy/t6WjKPN1qBxJ3XLnfu3OfaObG/t4jLX/V7s7plvDg+Ffr4pJueVVKpnITxalXXKho9i+Ezg1ApyafT7fuHY9upuY7XijPPtbQTcikWCNjs7bTnFIixkA8b169ehE1Dy1J9X4KZ7zDb9w/P2PEx0Ih2Ra5/Y/X7r9ZJraeRrepaRavNPEddCbxl24HQHBY1SxyWo+OuiLVSq7rrVy9yzuw2QmWeTZK/gw879V4FDPtZFQvEvDndeGF0XBMRUvvMM8ldCBkXmVnbX8NIelnNdyYh+IOt9Xiu2c9mqnfgXCAxZYa+3iJGjo+HHi9s4AwAX37kF9g0tCzV9yVq0VBQb+2apaol0fpeEnb9TlS/M/XEIvnWyTk7TtSVQNdgNmqWOOz+6teY31MEWVr0Vv233ew87prZbZTKPJtkJj7qsUmOo3UlUiufso0PVXw/F8A1ACayCae1uLbxnFvsCv2k7rtKO27RSmB+z8zSheCPOiw5N2JDk2IX0F3wrxNL4s5rlwMonUySDsQnzaYX+FyzagC7jhx1fugJW2BSzecyXZo9mF2lGEBtl4TriUXaQsfmbB+urZDD8n3QvtO1cNqV88NeI/iAfMu2A/j4Nw7jxfFJjDrKDDpBdZ5Nch57ecTMc9JuKdoaW2oRO3g2s31Vd+0m+YOM4mkprk+lwOwZg+pEUD2TWNnRwWdwWOwijo1NTCeT6gFV8OU7ix2HiE9e41PA2gsHsPWRZxBW7VNdOpCklGNo5QDWbN5Z88A86Ml5377h6cWYYT8j16KUpPXCcV1UkojarKE6DtcJPux9Vh/ZztTJObseUR2NwgbOSWphqz8gN7pMolggJqYsNG83S3VZ2fik3weJYhe9z8MiWfEp2zit4mYXgFUA5mcWUYuJ+lTq2wUiauFDmIG+Hhwfm/AqC/CdxY7z9tWLMHjWabGXEe9/dBjdXZy10LHYRay9cOH0rG8tLe7SqP8L3qOg5s31M6p3psF1VaKWxF3PJeFg8B+2zaxOIp2p03N2PYJ876q/DcrLkl7eTytP18rV894l6xalxMwcvH3/MI6Nxb8/QVeO6nOgSi6k0XzKNvah9AGcKF36exLAn2UZVB7EDaqTJsrqBV5LNuwIfVz1gCqtS/P3PzqMwbNOw1+/edl0N4swrgR36txubBpaVlP7OAJYecdDiR4fldaHR0YTb3+eVJq1cklnscN+9wbPOk0nEQkoZ9fJlVenzHDX2hXT5RdBv+a4v7W8lVC9pKfba3a8QGJusctr4FupupvS8TF3VVHUAkCVXEiz+JRtLGlEIO0kSaIMVnJXJ2DfAVVavUKPjU1i/b0HseUty3HntctjO4FUC3aAquWDQ1cXvS9j9vcWsf8vLwUQvTp7/T0HAZ6ccRkeGcUt2w5g79PPYdPQskTxuaSVuNOYxdZJRALK2bWpLLVzbS7VO6cwoytH1PqESrXm6f7eYmhuTLO7URjffDxphrGJZHXbxQLx2xdPXlmNe190BU1aUez23CSLJN9P8t7y13tJhu+bLACS1ZretXZF6JbQvlszp7UFKVAaaNbaoSH4NyedYTmluwuTnr2he4oF3H7l+dO3o/7t41M261KlAdi655nMZ6WTGlo5gL9+8zIM9PWACN+eVsSXcra/7fuHsWbzTizesAO3bDuA4fKalLCBc7FAHBubdHa2qTzekg07sGbzzulcs/6ycyO37HYxK5XEzbq/hmNVS2tj8PEpiz1W8P8H+nowb063934AfT3F6bU9Ye+rSLP4lG38PYAigL8r335H+b53ZRVU3iVpmXaz49Kfb1lA2OMWv7QH//mz52Yk2C4AIBCXs3wGv9U7MwWD+u37hxNvB34iZtYiqsYw+D7JLLkBLdnCTTPHkiLlbA/VJWZhWasy/xw7MeFcUP3LcqlYVNecvU8/h617nkk08HW9XtzMc39vEWbu5wOlCY/dGy5OpT++YfZ5IYhxoCp3u0oSqwULvJN0IxJpFJ/B8x+a2fKK2ztJHswqoHYQ/EFvfOCwV+sdVzLwHVBFtUUKWtkdG5vwWjQSzCAPRGwKEOzMFNaBJM1FJj4bfUT1Z3XJW/2hSEJtm7PT3Jrep8Rsymx6E4+oQd/L+3qcXXPWfbX01m8aWobBs05LXBIXJmywGmxXHXQO6p0TfUVyeGQ0tY2lXOeFsJ+Nq4Slr6eIead0z3p+WBcm9bGXZvMZPE+SPNvMfgYAJF8BoHV252hhcbOqldJOBpUD6jWbd3oN4ouFky2AoupwwwbrrjZzXSzN3iTdtjtsVbVLWKzFLvdrqoWbtLm2zNlpz0D6fIiuzBWuQR9RykG3OAbFk2a4ZdsB3LztAAb6epx1zElV7lJYIDE6PjljZttnEV91S74w1YP0sP/vOi+EcZ1bXPk+zZ76ImmJrXlGqeH+LpLfJfnvAHYCWJdtWPlXy8K5rJKBz3H7e4vY8pblM1q5JanDde58Z8CWty6f3k41TF9PccbrfGrtChy4/VLvE2JYrFveuhzXr140qxZPLdykA7Rlzo7qh16LuA/R1bnCtcYiKAXr63WXlVcuMPztixMoFuqvON7y7Sdw0dIF6CkWpq/41XLdL678ozq3Xr960fTtvp4i5ha7cMu2A961yEnPLa6fkyZBpJkiZ55JFgAsB/BKAEEWecLMTmQdWJrSvNTnq5aBcFbJwDVjEtezNEkdblR3kOoNXXxnHJIIi3Vo5YBauElHaZecHSbtGcioDYcGyv3Tg5Z0Qe6onO2tnLEdHhlFsYsoFmb3wK8WXBELeinX2jljeGQ0cQ11EsUCcfuV5zvPA/VcCUhybkmzp75IWiJnns1sEsB1ZnbCzA6Vv3KVhIM/8GAVdfAHnvVqXddAOJhZ9emkkRbXjMmkWWrviU93kGZ0lRhaOYDdGy7Gk5svD+1qItJO2iFnu6Q9AxmWj+5auwJPbb4c6y87F/ftG5513gCA3RsuLi3Iqzre+JRh3pxuFOg3qxzMFpNAMeYasOuIaQ6c+3tnXgGsvBIZJqrGO83zq7oRSSvyqXneTfJuANsAHAvuNLNHM4sqRUm2Pk5TXM1wEFsjZkSrXy+sI0a970mS7iBKeiKZynXOdsliBtKVj+JKRFw1yy+MjuOutSuw/t6D3rv6TVl0F6RgMV5lf+mkfGa3R46PT/fQ9+Ga8Z80S70bhs4b0mp8Bs8ryv+9o+I+AxC+5Y8HklsAXAlgDMDPALzTzEZqPV6UZi02iBtMJkkGaZSdVL6e7+6FSTUywTWjFEckJ1LP2a2gkZMOUeeNqBrr6VnwFKeEg931fLs3VQt26Fvx8YdiW9clEbXxi7phSLvz2WHwogxe92EAHzGzCZJ/A+AjAP4ig9dJvPVxmtIYTGbR49L1nszvycc+Cur7KeKWUc5uCY36gB513oiaZAjatSXtLuTSX7EI0bMaZIbKmfmNV52P9fccDI2tlhn8uP0M1A1D2lns4JnkB0PufgHAPjOrqWGlmT1UcXMPgLfUcpw42/cP49iJiVn3RyWKVpvRzKLsZP1l54Ym0WNjE9i+f7jlB6DNKsURyYMscnaj1Jp/08rbwXHCWrgRwEVLF2DXkaOhA+veYldsz/moFpqzHltesBcY8Whv5+qVDMycuQ/a202azdjEJMn7GNy/7qsHQ/v7qxuGtDOfso3B8tc3yrevAHAIwE0k7zGzT9QZw40o1eaFIvluAO8GgEWLFnkfNKyzA1D6JB+sIK5OFBctXYD79g231IxmFmUnQysH8PFvHJ5Vt1fP9tyNpL6fIpGyztmZqPWKku/z4gaGcTsOGoD79g3jmlUDM84TAKa7bMRuOFJeHDgeswVAFzFrwV5UmQTg17koauZ++/7hGZMqwyOjWH/PwennuY4HQN0wpOP49Hk+E8AFZrbOzNYBWAXgDACvBXCD60kkv0PyhyFfV1c85jYAEwC2uo5jZp81s0EzG1ywYIHnP8vdZ7l3TveMtmmVq6m37nkm1T6iaXCVUkSVWGzfP4w1m3diyYYdzt6brlmMtAegPrEkpb6fIpFqytlxSG4heYTkIZL3k+xLJ9ySWvs4+zzPp+uST2/+0fFJ7DpydFb3h3lzur1mlMcnDWe8pNQrOaorR9ihwjoaBUdI0oHClZM3PnB41r9hfMqw8YHDkcdTNwzpRD4zz2cAqGx1NA7gd81slKSzBZKZXRJ1UJI3oDQj8gazFPd0LoubnQxLlK4gfAeUWZR8uPKr637fWZhG1IJnVZusvp8ikWrK2R4yXatS6xUln+f5lHr55vlfjozOmsGN2ro77PmbhpZh09CyyO2xq68CprFgMionuxYT+ixSVDcM6TQ+g+etAB4h+fXy7SsB/AvJeQB+VMuLknwjgA8DeJ2ZHa/lGHHiBodJZlh9BpS1XPLy4Zohdt3vWw/ciAFoVrXJjW71J5IzqedsIPu1KrV+oPd5ns8AO64sIioe3+dWP3/9ZefiZse23mEx1ztITXuXRpFOFVu2YWZ/hVLN8Uj56yYzu8PMjpnZ22t83bsB/A6Ah0keIPkPNR7HKW7TDldCrnU751ovecVxxWlAaBmE7+xNIy61uWIZHhmtu4xDm5+IhMsoZ1e7EcC3UjoWAL+Nlmp9nk+p1/rLzo3dNtsVj2sjqrjnD60cQJ+jBC+LMrSo80O/Y3tx1/0incxn5hlmthfA3rRe1MzOSetYLnGzk66Z12tWDWDXkaOJZzTrueQVJaodUFgZRJLZm7jFI/XO7EbNxlTv2qXBr0h6as3ZJL8D4GUh/+s2M/t6+TGRa1VqXeQd1g2iclY0btFaVL7yudI2tHIgspfyQEQeDO679WuHcNyxGtD1/I1Xnd+wMrSo88P6y86dtblLdccPESlhBuXGmRkcHLS9e1Mbw6dao7w4oubtqc2X1xoigJntk8IETfCDx4Yl4iSzymkcw3WcuPhF2hXJfWY22Ow46lFeq/LnKK1ViS25qyVn15t/XHndJ98v2bAjdO0LATzpmcc/uv0xfPmRX2DSDAUS1/3RQmwaWlZTzGmLe29brVWrSLO58nZHD57TtPKOh0K3bO3vLSba8jSKb2KvNwG6FrHUMsitjMX1m5bkxCSSV3kfPJfXqnwSpbUqR32eU0vOrif/1Dvwdr12gcSd1y5vi4GkBsgi/lx526tsQ9yCRBQ2cE77kpdvSUa9i0rS7KNcGYvrxKQWcyK5cDeAU1BaqwIAe8zsprRfpJ78U+8iZVeZ3KRZ25SYqTOGSP18+jyLQ2Xv0EBl383qJvf1qnVBTVJZ9VFuVPwikj4zO8fMFprZivJX6gNnoL78U+8H/2AhdVgPZnWlEJGABs91cPWKDi4vpv3pvlHN6MMGucUu4vjYRF1dMtRMX0TiJPmQXb3hR9SmUr6bNQ2tHMCUo5xRu5iKCKCyjbo0Y5voRlxyq169Pr+niGNjE9OlKfV0ydAlQxGJ4tvHPWzDj2KBKHZxRtvQYhdxbGxiuouGT/5qxCZSIpJfGjzXoZ0TbHWtcnX7Jt86Qi1OEZGkfD5kh135G5809PcWYXayTeikGaaqusfF5S/tYioiUVS2UYdOqeGtdYa9sia8sq9zrRujiIgEXPnn+ePjODFxcrQ85WjzE5W/VGImIlE081yHTtkmutYZ9qy25xYRceWlYHMVn+dHUYmZiLho8Fyndk2wleUW83uKKBY4Y+cpnxn2ZtSEi0hnCCutqM5TLu14hVBEGkdlGzJLdbnFyOg4YKUNX5Jcwsyq5Z2ISHVpRX9vEc6dmFCakVYJhoikQTPPMkvoQpwpQ++c7kS7JWrRjYhkqXphc9hmVUCyXQZFROJo8CyzpFVu0Sk14SLSfFH5SQNnEUmTBs8yS5ot+Nq1JlxEWosrbw309SgHiUiqVPMss3RKCz4RaR/KWyLSKJp5lllUbiEieaO8JSKNosGzhFK5hYjkjfKWiDQCzeJ7YrYKkkcBPN3sODycDuA3zQ4iAcWbLcWbrbzEe5aZLWh2EI2knJ0ZxZstxZutPMUbmrdzNXjOC5J7zWyw2XH4UrzZUrzZylu80nry9jukeLOleLOVt3jDaMGgiIiIiIgnDZ5FRERERDxp8JyNzzY7gIQUb7YUb7byFq+0nrz9DinebCnebOUt3llU8ywiIiIi4kkzzyIiIiIinjR4FhERERHxpMFzhkiuI2kkT292LFFIbiF5hOQhkveT7Gt2TGFIvpHkEyR/SnJDs+OJQ3IhyV0kf0TyMMkPNDumOCQLJPeT/GazY/FBso/kveXf38dJvrrZMUm+KW+nK095O485G8hX3m6XnK3Bc0ZILgRwKYBnmh2Lh4cB/IGZvQrAjwF8pMnxzEKyAOAzAN4E4DwA15E8r7lRxZoAsM7MzgOwGsB7chDzBwA83uwgEvg0gAfNbCmA5chX7NJilLfTlcO8ncecDeQrb7dFztbgOTt3AfgwgJZfkWlmD5nZRPnmHgBnNjMehwsB/NTMfm5mYwC+AuDqJscUycx+ZWaPlr//P5SSRMvuHUzyTACXA/hcs2PxQXI+gNcC+DwAmNmYmY00NyrJOeXtdOUqb+ctZwP5ytvtlLM1eM4AyasBDJvZwWbHUoMbAXyr2UGEGADwi4rbz6LFk1olkosBrATwSHMjifQplAYOU80OxNMSAEcBfKF8yfJzJOc1OyjJJ+XtTOQ2b+ckZwP5ytttk7O7mx1AXpH8DoCXhfyv2wDcitKlv5YRFa+Zfb38mNtQumy1tZGxtTuSpwK4D8DNZva/zY4nDMkrAPy3me0j+fpmx+OpG8AFAN5nZo+Q/DSADQA+1tywpFUpb4uPPORsIJd5u21ytgbPNTKzS8LuJ7kMpU9XB0kCpUtpj5K80Mx+3cAQZ3DFGyB5A4ArALzBWrP59zCAhRW3zyzf19JIFlFKwlvN7GvNjifCGgBXkfxjAHMBvITkl8zs+ibHFeVZAM+aWTAzdC9KiVgklPJ2w+Uub+coZwP5y9ttk7O1SUrGSD4FYNDMftPsWFxIvhHAJwG8zsyONjueMCS7UVoU8waUku9/AXibmR1uamARWDoLfxHAc2Z2c7Pj8VWewfiQmV3R7FjikPwegHeZ2RMkNwKYZ2brmxyW5JzydjrylrfzmrOB/OTtdsnZmnkWALgbwCkAHi7Puuwxs5uaG9JMZjZB8r0Avg2gAOAfWzUBV1gD4B0AHiN5oHzfrWb2r02Mqd28D8BWknMA/BzAO5scj0ijKG+nTzk7e22RszXzLCIiIiLiSd02REREREQ8afAsIiIiIuJJg2cREREREU8aPIuIiIiIeNLgWURERETEkwbPIp5ILib5w/L3gyT/NoVjriNpJE+vP0IREQkoZ0tW1OdZpAZmthfA3nqOQXIhStsBP5NKUCIiEko5W9KkmWdpGeVZgiMk/4nkj0luJXkJyd0kf0LywvLjTiX5BZKPkTxE8hqSN5HcUnGsG0jeXf5+O8l9JA+TfHfFYy4l+X2Sj5K8h+SpITGtInmQ5EEA76m4//Ukv1n+fiPJL5L8HsmnSb6Z5CfK8T1Y3u41zF0APgxAzdZFJHeUs6VTafAsreYcAHcCWFr+ehuA1wD4EIBby4/5GIAXzGyZmb0KwE4A9wH4k4rjrAXwlfL3N5rZKgCDAN5P8qXlS24fBXCJmV2A0ozEB0Pi+QKA95nZ8pi4zwZwMYCrAHwJwC4zWwZgFMDl1Q8meTWAYTM7GHNcEZFWppwtHUdlG9JqnjSzxwCA5GEA/2ZmRvIxAIvLj7kEwJ8GTzCz58uP/znJ1QB+glIS311+yPtJBkl6IYBXAjgdwHkAdpe3tp0D4PuVgZDsA9BnZv9RvuufAbzJEfe3zGy8HGcBwIPl+yvjDo7bi9JJ5dK4N0NEpMUpZ0vH0eBZWs2Jiu+nKm5PIf739SsArgVwBMD95QT+epQS96vN7DjJ7wKYC4AAHjaz69KM28ymSI7byX3vw+I+G8ASAAfLJ4EzATxK8kIz+3VK8YiINIJytnQclW1IHj2MmbVs/eVv7wdwNYDrcPLy33wAz5eT8FIAq8v37wGwhuQ55WPMI/n7lS9iZiMARki+pnzX29MI3sweM7MzzGyxmS0G8CyAC5SERaRNKWdLW9HgWfJoE4B+kj8sLwq5CJi+FPg4gLPM7Aflxz4IoJvk4wA2o5SAYWZHAdwA4MskD6F0+W9pyGu9E8BnSB5AaeZDRESSUc6WtsKTVypERERERCSKZp5FRERERDxp8CwiIiIi4kmDZxERERERTxo8i4iIiIh40uBZRERERMSTBs8iIiIiIp40eBYRERER8fT/3v5p7Jb2M9AAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 864x864 with 10 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sZ2f3QyQbE64",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8a938305-719b-491b-fbb7-87b26a4ae790"
+      },
+      "source": [
+        "# Calling sparse mcvae\n",
+        "\n",
+        "adam_lr = 1e-2\n",
+        "n_epochs = 4000\n",
+        "\n",
+        "model_sparse1 = Mcvae(sparse=True, **init_dict)\n",
+        "model_sparse1.to(DEVICE)\n",
+        "print(model_sparse1)\n",
+        "\n",
+        "model_sparse1.optimizer = torch.optim.Adam(model_sparse1.parameters(), lr=adam_lr)\n",
+        "load_or_fit(model=model_sparse1, data=data, epochs=n_epochs, ptfile='model_sparse1.pt', force_fit=FORCE_REFIT)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mcvae(\n",
+            "  (vae): ModuleList(\n",
+            "    (0): VAE(\n",
+            "      (W_mu): Linear(in_features=5, out_features=5, bias=True)\n",
+            "      (W_out): Linear(in_features=5, out_features=5, bias=True)\n",
+            "    )\n",
+            "    (1): VAE(\n",
+            "      (W_mu): Linear(in_features=5, out_features=5, bias=True)\n",
+            "      (W_out): Linear(in_features=5, out_features=5, bias=True)\n",
+            "    )\n",
+            "  )\n",
+            ")\n",
+            "\tCreating model_sparse1.pt.running\n",
+            "\tCreated: 2020-12-14 16:16:35.634436\n",
+            "Start fitting: 2020-12-14 16:16:35.635008\n",
+            "\tModel destination: model_sparse1.pt\n",
+            "====> Epoch:    0/4000 (0%)\tLoss: 12423.5400\tLL: -12419.2295\tKL: 4.3107\tLL/KL: -2881.0420\n",
+            "====> Epoch:   10/4000 (0%)\tLoss: 7944.5845\tLL: -7939.9292\tKL: 4.6553\tLL/KL: -1705.5748\n",
+            "====> Epoch:   20/4000 (0%)\tLoss: 4998.4390\tLL: -4993.4243\tKL: 5.0145\tLL/KL: -995.7948\n",
+            "====> Epoch:   30/4000 (1%)\tLoss: 3416.5762\tLL: -3411.1050\tKL: 5.4712\tLL/KL: -623.4674\n",
+            "====> Epoch:   40/4000 (1%)\tLoss: 2442.5486\tLL: -2436.5430\tKL: 6.0056\tLL/KL: -405.7127\n",
+            "====> Epoch:   50/4000 (1%)\tLoss: 2226.7312\tLL: -2220.1890\tKL: 6.5422\tLL/KL: -339.3632\n",
+            "====> Epoch:   60/4000 (2%)\tLoss: 1922.8663\tLL: -1915.8074\tKL: 7.0589\tLL/KL: -271.4028\n",
+            "====> Epoch:   70/4000 (2%)\tLoss: 1613.8423\tLL: -1606.2744\tKL: 7.5679\tLL/KL: -212.2483\n",
+            "====> Epoch:   80/4000 (2%)\tLoss: 1445.1316\tLL: -1437.0812\tKL: 8.0505\tLL/KL: -178.5089\n",
+            "====> Epoch:   90/4000 (2%)\tLoss: 1329.9011\tLL: -1321.3967\tKL: 8.5044\tLL/KL: -155.3780\n",
+            "====> Epoch:  100/4000 (2%)\tLoss: 1187.6051\tLL: -1178.6711\tKL: 8.9339\tLL/KL: -131.9320\n",
+            "====> Epoch:  110/4000 (3%)\tLoss: 1147.4551\tLL: -1138.1144\tKL: 9.3408\tLL/KL: -121.8439\n",
+            "====> Epoch:  120/4000 (3%)\tLoss: 1078.8278\tLL: -1069.0986\tKL: 9.7291\tLL/KL: -109.8870\n",
+            "====> Epoch:  130/4000 (3%)\tLoss: 1019.6946\tLL: -1009.5899\tKL: 10.1047\tLL/KL: -99.9127\n",
+            "====> Epoch:  140/4000 (4%)\tLoss: 936.4872\tLL: -926.0184\tKL: 10.4688\tLL/KL: -88.4554\n",
+            "====> Epoch:  150/4000 (4%)\tLoss: 871.5378\tLL: -860.7164\tKL: 10.8213\tLL/KL: -79.5388\n",
+            "====> Epoch:  160/4000 (4%)\tLoss: 870.3144\tLL: -859.1536\tKL: 11.1608\tLL/KL: -76.9794\n",
+            "====> Epoch:  170/4000 (4%)\tLoss: 818.3040\tLL: -806.8207\tKL: 11.4834\tLL/KL: -70.2600\n",
+            "====> Epoch:  180/4000 (4%)\tLoss: 781.9640\tLL: -770.1710\tKL: 11.7930\tLL/KL: -65.3073\n",
+            "====> Epoch:  190/4000 (5%)\tLoss: 734.5228\tLL: -722.4339\tKL: 12.0889\tLL/KL: -59.7599\n",
+            "====> Epoch:  200/4000 (5%)\tLoss: 723.7692\tLL: -711.3951\tKL: 12.3741\tLL/KL: -57.4908\n",
+            "====> Epoch:  210/4000 (5%)\tLoss: 694.9260\tLL: -682.2795\tKL: 12.6465\tLL/KL: -53.9502\n",
+            "====> Epoch:  220/4000 (6%)\tLoss: 628.5433\tLL: -615.6322\tKL: 12.9111\tLL/KL: -47.6822\n",
+            "====> Epoch:  230/4000 (6%)\tLoss: 623.1614\tLL: -610.0018\tKL: 13.1596\tLL/KL: -46.3541\n",
+            "====> Epoch:  240/4000 (6%)\tLoss: 595.4843\tLL: -582.0864\tKL: 13.3979\tLL/KL: -43.4462\n",
+            "====> Epoch:  250/4000 (6%)\tLoss: 575.3743\tLL: -561.7458\tKL: 13.6284\tLL/KL: -41.2187\n",
+            "====> Epoch:  260/4000 (6%)\tLoss: 564.2142\tLL: -550.3665\tKL: 13.8477\tLL/KL: -39.7441\n",
+            "====> Epoch:  270/4000 (7%)\tLoss: 551.3114\tLL: -537.2495\tKL: 14.0619\tLL/KL: -38.2060\n",
+            "====> Epoch:  280/4000 (7%)\tLoss: 529.0936\tLL: -514.8255\tKL: 14.2681\tLL/KL: -36.0824\n",
+            "====> Epoch:  290/4000 (7%)\tLoss: 502.3279\tLL: -487.8610\tKL: 14.4669\tLL/KL: -33.7226\n",
+            "====> Epoch:  300/4000 (8%)\tLoss: 483.8651\tLL: -469.2090\tKL: 14.6561\tLL/KL: -32.0147\n",
+            "====> Epoch:  310/4000 (8%)\tLoss: 493.2451\tLL: -478.4055\tKL: 14.8396\tLL/KL: -32.2384\n",
+            "====> Epoch:  320/4000 (8%)\tLoss: 463.6119\tLL: -448.5949\tKL: 15.0170\tLL/KL: -29.8725\n",
+            "====> Epoch:  330/4000 (8%)\tLoss: 465.1217\tLL: -449.9359\tKL: 15.1858\tLL/KL: -29.6288\n",
+            "====> Epoch:  340/4000 (8%)\tLoss: 441.9619\tLL: -426.6108\tKL: 15.3511\tLL/KL: -27.7903\n",
+            "====> Epoch:  350/4000 (9%)\tLoss: 439.2622\tLL: -423.7549\tKL: 15.5072\tLL/KL: -27.3263\n",
+            "====> Epoch:  360/4000 (9%)\tLoss: 397.4768\tLL: -381.8171\tKL: 15.6597\tLL/KL: -24.3822\n",
+            "====> Epoch:  370/4000 (9%)\tLoss: 405.6262\tLL: -389.8177\tKL: 15.8084\tLL/KL: -24.6588\n",
+            "====> Epoch:  380/4000 (10%)\tLoss: 411.8439\tLL: -395.8909\tKL: 15.9530\tLL/KL: -24.8161\n",
+            "====> Epoch:  390/4000 (10%)\tLoss: 383.7861\tLL: -367.6917\tKL: 16.0944\tLL/KL: -22.8459\n",
+            "====> Epoch:  400/4000 (10%)\tLoss: 369.3396\tLL: -353.1097\tKL: 16.2299\tLL/KL: -21.7568\n",
+            "====> Epoch:  410/4000 (10%)\tLoss: 363.7650\tLL: -347.4035\tKL: 16.3615\tLL/KL: -21.2330\n",
+            "====> Epoch:  420/4000 (10%)\tLoss: 361.6829\tLL: -345.1950\tKL: 16.4879\tLL/KL: -20.9363\n",
+            "====> Epoch:  430/4000 (11%)\tLoss: 340.4852\tLL: -323.8745\tKL: 16.6107\tLL/KL: -19.4979\n",
+            "====> Epoch:  440/4000 (11%)\tLoss: 353.9610\tLL: -337.2321\tKL: 16.7289\tLL/KL: -20.1587\n",
+            "====> Epoch:  450/4000 (11%)\tLoss: 339.4053\tLL: -322.5589\tKL: 16.8464\tLL/KL: -19.1471\n",
+            "====> Epoch:  460/4000 (12%)\tLoss: 326.4094\tLL: -309.4482\tKL: 16.9612\tLL/KL: -18.2445\n",
+            "====> Epoch:  470/4000 (12%)\tLoss: 329.6633\tLL: -312.5918\tKL: 17.0714\tLL/KL: -18.3108\n",
+            "====> Epoch:  480/4000 (12%)\tLoss: 309.1764\tLL: -291.9992\tKL: 17.1772\tLL/KL: -16.9992\n",
+            "====> Epoch:  490/4000 (12%)\tLoss: 307.1112\tLL: -289.8323\tKL: 17.2789\tLL/KL: -16.7738\n",
+            "====> Epoch:  500/4000 (12%)\tLoss: 305.7955\tLL: -288.4167\tKL: 17.3788\tLL/KL: -16.5959\n",
+            "====> Epoch:  510/4000 (13%)\tLoss: 289.1095\tLL: -271.6322\tKL: 17.4773\tLL/KL: -15.5420\n",
+            "====> Epoch:  520/4000 (13%)\tLoss: 289.5198\tLL: -271.9472\tKL: 17.5726\tLL/KL: -15.4756\n",
+            "====> Epoch:  530/4000 (13%)\tLoss: 294.7266\tLL: -277.0612\tKL: 17.6653\tLL/KL: -15.6839\n",
+            "====> Epoch:  540/4000 (14%)\tLoss: 279.7219\tLL: -261.9659\tKL: 17.7560\tLL/KL: -14.7536\n",
+            "====> Epoch:  550/4000 (14%)\tLoss: 275.7182\tLL: -257.8739\tKL: 17.8443\tLL/KL: -14.4513\n",
+            "====> Epoch:  560/4000 (14%)\tLoss: 274.3112\tLL: -256.3805\tKL: 17.9307\tLL/KL: -14.2984\n",
+            "====> Epoch:  570/4000 (14%)\tLoss: 268.1662\tLL: -250.1523\tKL: 18.0139\tLL/KL: -13.8867\n",
+            "====> Epoch:  580/4000 (14%)\tLoss: 261.6724\tLL: -243.5778\tKL: 18.0946\tLL/KL: -13.4613\n",
+            "====> Epoch:  590/4000 (15%)\tLoss: 256.2175\tLL: -238.0433\tKL: 18.1742\tLL/KL: -13.0979\n",
+            "====> Epoch:  600/4000 (15%)\tLoss: 254.8964\tLL: -236.6440\tKL: 18.2524\tLL/KL: -12.9651\n",
+            "====> Epoch:  610/4000 (15%)\tLoss: 250.0432\tLL: -231.7136\tKL: 18.3296\tLL/KL: -12.6415\n",
+            "====> Epoch:  620/4000 (16%)\tLoss: 242.5408\tLL: -224.1366\tKL: 18.4042\tLL/KL: -12.1785\n",
+            "====> Epoch:  630/4000 (16%)\tLoss: 241.7367\tLL: -223.2590\tKL: 18.4776\tLL/KL: -12.0827\n",
+            "====> Epoch:  640/4000 (16%)\tLoss: 237.3556\tLL: -218.8067\tKL: 18.5490\tLL/KL: -11.7962\n",
+            "====> Epoch:  650/4000 (16%)\tLoss: 229.9377\tLL: -211.3200\tKL: 18.6178\tLL/KL: -11.3504\n",
+            "====> Epoch:  660/4000 (16%)\tLoss: 229.2484\tLL: -210.5630\tKL: 18.6853\tLL/KL: -11.2689\n",
+            "====> Epoch:  670/4000 (17%)\tLoss: 231.1909\tLL: -212.4388\tKL: 18.7521\tLL/KL: -11.3288\n",
+            "====> Epoch:  680/4000 (17%)\tLoss: 224.6940\tLL: -205.8764\tKL: 18.8176\tLL/KL: -10.9406\n",
+            "====> Epoch:  690/4000 (17%)\tLoss: 221.4319\tLL: -202.5499\tKL: 18.8820\tLL/KL: -10.7272\n",
+            "====> Epoch:  700/4000 (18%)\tLoss: 223.7523\tLL: -204.8069\tKL: 18.9453\tLL/KL: -10.8104\n",
+            "====> Epoch:  710/4000 (18%)\tLoss: 215.6302\tLL: -196.6227\tKL: 19.0075\tLL/KL: -10.3445\n",
+            "====> Epoch:  720/4000 (18%)\tLoss: 212.7308\tLL: -193.6640\tKL: 19.0668\tLL/KL: -10.1572\n",
+            "====> Epoch:  730/4000 (18%)\tLoss: 211.0002\tLL: -191.8756\tKL: 19.1246\tLL/KL: -10.0329\n",
+            "====> Epoch:  740/4000 (18%)\tLoss: 203.4833\tLL: -184.3020\tKL: 19.1813\tLL/KL: -9.6084\n",
+            "====> Epoch:  750/4000 (19%)\tLoss: 209.7755\tLL: -190.5383\tKL: 19.2372\tLL/KL: -9.9047\n",
+            "====> Epoch:  760/4000 (19%)\tLoss: 201.5991\tLL: -182.3061\tKL: 19.2930\tLL/KL: -9.4493\n",
+            "====> Epoch:  770/4000 (19%)\tLoss: 197.7950\tLL: -178.4484\tKL: 19.3466\tLL/KL: -9.2238\n",
+            "====> Epoch:  780/4000 (20%)\tLoss: 199.1005\tLL: -179.7010\tKL: 19.3995\tLL/KL: -9.2632\n",
+            "====> Epoch:  790/4000 (20%)\tLoss: 192.1174\tLL: -172.6661\tKL: 19.4513\tLL/KL: -8.8769\n",
+            "====> Epoch:  800/4000 (20%)\tLoss: 191.2913\tLL: -171.7898\tKL: 19.5015\tLL/KL: -8.8091\n",
+            "====> Epoch:  810/4000 (20%)\tLoss: 187.7220\tLL: -168.1705\tKL: 19.5514\tLL/KL: -8.6014\n",
+            "====> Epoch:  820/4000 (20%)\tLoss: 187.6203\tLL: -168.0189\tKL: 19.6014\tLL/KL: -8.5718\n",
+            "====> Epoch:  830/4000 (21%)\tLoss: 186.7848\tLL: -167.1350\tKL: 19.6498\tLL/KL: -8.5057\n",
+            "====> Epoch:  840/4000 (21%)\tLoss: 183.8981\tLL: -164.2005\tKL: 19.6976\tLL/KL: -8.3361\n",
+            "====> Epoch:  850/4000 (21%)\tLoss: 181.0941\tLL: -161.3493\tKL: 19.7448\tLL/KL: -8.1717\n",
+            "====> Epoch:  860/4000 (22%)\tLoss: 175.6559\tLL: -155.8660\tKL: 19.7899\tLL/KL: -7.8760\n",
+            "====> Epoch:  870/4000 (22%)\tLoss: 178.5542\tLL: -158.7211\tKL: 19.8332\tLL/KL: -8.0028\n",
+            "====> Epoch:  880/4000 (22%)\tLoss: 174.1442\tLL: -154.2673\tKL: 19.8769\tLL/KL: -7.7611\n",
+            "====> Epoch:  890/4000 (22%)\tLoss: 172.3531\tLL: -152.4335\tKL: 19.9195\tLL/KL: -7.6525\n",
+            "====> Epoch:  900/4000 (22%)\tLoss: 175.4139\tLL: -155.4525\tKL: 19.9614\tLL/KL: -7.7876\n",
+            "====> Epoch:  910/4000 (23%)\tLoss: 168.9361\tLL: -148.9328\tKL: 20.0032\tLL/KL: -7.4454\n",
+            "====> Epoch:  920/4000 (23%)\tLoss: 167.2159\tLL: -147.1715\tKL: 20.0444\tLL/KL: -7.3423\n",
+            "====> Epoch:  930/4000 (23%)\tLoss: 165.2206\tLL: -145.1360\tKL: 20.0846\tLL/KL: -7.2262\n",
+            "====> Epoch:  940/4000 (24%)\tLoss: 162.9974\tLL: -142.8737\tKL: 20.1237\tLL/KL: -7.0998\n",
+            "====> Epoch:  950/4000 (24%)\tLoss: 162.1551\tLL: -141.9932\tKL: 20.1619\tLL/KL: -7.0427\n",
+            "====> Epoch:  960/4000 (24%)\tLoss: 159.9750\tLL: -139.7760\tKL: 20.1990\tLL/KL: -6.9200\n",
+            "====> Epoch:  970/4000 (24%)\tLoss: 160.9397\tLL: -140.7044\tKL: 20.2353\tLL/KL: -6.9534\n",
+            "====> Epoch:  980/4000 (24%)\tLoss: 159.4130\tLL: -139.1409\tKL: 20.2721\tLL/KL: -6.8637\n",
+            "====> Epoch:  990/4000 (25%)\tLoss: 152.2758\tLL: -131.9678\tKL: 20.3080\tLL/KL: -6.4983\n",
+            "====> Epoch: 1000/4000 (25%)\tLoss: 155.1800\tLL: -134.8373\tKL: 20.3427\tLL/KL: -6.6283\n",
+            "====> Epoch: 1010/4000 (25%)\tLoss: 152.0665\tLL: -131.6891\tKL: 20.3774\tLL/KL: -6.4625\n",
+            "====> Epoch: 1020/4000 (26%)\tLoss: 148.3357\tLL: -127.9241\tKL: 20.4116\tLL/KL: -6.2672\n",
+            "====> Epoch: 1030/4000 (26%)\tLoss: 149.1388\tLL: -128.6942\tKL: 20.4446\tLL/KL: -6.2948\n",
+            "====> Epoch: 1040/4000 (26%)\tLoss: 146.2431\tLL: -125.7657\tKL: 20.4773\tLL/KL: -6.1417\n",
+            "====> Epoch: 1050/4000 (26%)\tLoss: 147.8855\tLL: -127.3763\tKL: 20.5092\tLL/KL: -6.2107\n",
+            "====> Epoch: 1060/4000 (26%)\tLoss: 145.1004\tLL: -124.5595\tKL: 20.5409\tLL/KL: -6.0640\n",
+            "====> Epoch: 1070/4000 (27%)\tLoss: 146.4259\tLL: -125.8540\tKL: 20.5720\tLL/KL: -6.1177\n",
+            "====> Epoch: 1080/4000 (27%)\tLoss: 145.0835\tLL: -124.4805\tKL: 20.6030\tLL/KL: -6.0419\n",
+            "====> Epoch: 1090/4000 (27%)\tLoss: 143.2339\tLL: -122.6000\tKL: 20.6338\tLL/KL: -5.9417\n",
+            "====> Epoch: 1100/4000 (28%)\tLoss: 143.8524\tLL: -123.1883\tKL: 20.6641\tLL/KL: -5.9615\n",
+            "====> Epoch: 1110/4000 (28%)\tLoss: 142.3581\tLL: -121.6641\tKL: 20.6940\tLL/KL: -5.8792\n",
+            "====> Epoch: 1120/4000 (28%)\tLoss: 140.4158\tLL: -119.6927\tKL: 20.7232\tLL/KL: -5.7758\n",
+            "====> Epoch: 1130/4000 (28%)\tLoss: 138.0195\tLL: -117.2674\tKL: 20.7521\tLL/KL: -5.6509\n",
+            "====> Epoch: 1140/4000 (28%)\tLoss: 138.3788\tLL: -117.5987\tKL: 20.7800\tLL/KL: -5.6592\n",
+            "====> Epoch: 1150/4000 (29%)\tLoss: 138.9796\tLL: -118.1720\tKL: 20.8076\tLL/KL: -5.6793\n",
+            "====> Epoch: 1160/4000 (29%)\tLoss: 137.6203\tLL: -116.7854\tKL: 20.8349\tLL/KL: -5.6053\n",
+            "====> Epoch: 1170/4000 (29%)\tLoss: 134.1320\tLL: -113.2702\tKL: 20.8618\tLL/KL: -5.4296\n",
+            "====> Epoch: 1180/4000 (30%)\tLoss: 133.9718\tLL: -113.0837\tKL: 20.8881\tLL/KL: -5.4138\n",
+            "====> Epoch: 1190/4000 (30%)\tLoss: 132.7921\tLL: -111.8775\tKL: 20.9146\tLL/KL: -5.3492\n",
+            "====> Epoch: 1200/4000 (30%)\tLoss: 132.1943\tLL: -111.2541\tKL: 20.9401\tLL/KL: -5.3130\n",
+            "====> Epoch: 1210/4000 (30%)\tLoss: 130.8284\tLL: -109.8635\tKL: 20.9649\tLL/KL: -5.2404\n",
+            "====> Epoch: 1220/4000 (30%)\tLoss: 129.6894\tLL: -108.7002\tKL: 20.9893\tLL/KL: -5.1788\n",
+            "====> Epoch: 1230/4000 (31%)\tLoss: 131.0708\tLL: -110.0578\tKL: 21.0129\tLL/KL: -5.2376\n",
+            "====> Epoch: 1240/4000 (31%)\tLoss: 128.0935\tLL: -107.0565\tKL: 21.0370\tLL/KL: -5.0890\n",
+            "====> Epoch: 1250/4000 (31%)\tLoss: 129.2543\tLL: -108.1931\tKL: 21.0613\tLL/KL: -5.1371\n",
+            "====> Epoch: 1260/4000 (32%)\tLoss: 127.0436\tLL: -105.9588\tKL: 21.0848\tLL/KL: -5.0254\n",
+            "====> Epoch: 1270/4000 (32%)\tLoss: 126.5558\tLL: -105.4478\tKL: 21.1080\tLL/KL: -4.9956\n",
+            "====> Epoch: 1280/4000 (32%)\tLoss: 124.3838\tLL: -103.2535\tKL: 21.1303\tLL/KL: -4.8865\n",
+            "====> Epoch: 1290/4000 (32%)\tLoss: 123.9809\tLL: -102.8287\tKL: 21.1523\tLL/KL: -4.8614\n",
+            "====> Epoch: 1300/4000 (32%)\tLoss: 124.6560\tLL: -103.4819\tKL: 21.1741\tLL/KL: -4.8872\n",
+            "====> Epoch: 1310/4000 (33%)\tLoss: 121.6643\tLL: -100.4683\tKL: 21.1960\tLL/KL: -4.7400\n",
+            "====> Epoch: 1320/4000 (33%)\tLoss: 120.9442\tLL: -99.7271\tKL: 21.2171\tLL/KL: -4.7003\n",
+            "====> Epoch: 1330/4000 (33%)\tLoss: 120.4077\tLL: -99.1701\tKL: 21.2376\tLL/KL: -4.6696\n",
+            "====> Epoch: 1340/4000 (34%)\tLoss: 118.6612\tLL: -97.4033\tKL: 21.2579\tLL/KL: -4.5820\n",
+            "====> Epoch: 1350/4000 (34%)\tLoss: 118.3661\tLL: -97.0887\tKL: 21.2774\tLL/KL: -4.5630\n",
+            "====> Epoch: 1360/4000 (34%)\tLoss: 118.2281\tLL: -96.9310\tKL: 21.2971\tLL/KL: -4.5514\n",
+            "====> Epoch: 1370/4000 (34%)\tLoss: 118.3729\tLL: -97.0566\tKL: 21.3163\tLL/KL: -4.5532\n",
+            "====> Epoch: 1380/4000 (34%)\tLoss: 116.8274\tLL: -95.4923\tKL: 21.3351\tLL/KL: -4.4758\n",
+            "====> Epoch: 1390/4000 (35%)\tLoss: 116.9835\tLL: -95.6298\tKL: 21.3536\tLL/KL: -4.4784\n",
+            "====> Epoch: 1400/4000 (35%)\tLoss: 116.9503\tLL: -95.5778\tKL: 21.3725\tLL/KL: -4.4720\n",
+            "====> Epoch: 1410/4000 (35%)\tLoss: 114.0119\tLL: -92.6209\tKL: 21.3910\tLL/KL: -4.3299\n",
+            "====> Epoch: 1420/4000 (36%)\tLoss: 113.8360\tLL: -92.4269\tKL: 21.4091\tLL/KL: -4.3172\n",
+            "====> Epoch: 1430/4000 (36%)\tLoss: 113.6112\tLL: -92.1846\tKL: 21.4266\tLL/KL: -4.3023\n",
+            "====> Epoch: 1440/4000 (36%)\tLoss: 112.7382\tLL: -91.2950\tKL: 21.4432\tLL/KL: -4.2575\n",
+            "====> Epoch: 1450/4000 (36%)\tLoss: 113.5708\tLL: -92.1112\tKL: 21.4596\tLL/KL: -4.2923\n",
+            "====> Epoch: 1460/4000 (36%)\tLoss: 111.4707\tLL: -89.9944\tKL: 21.4763\tLL/KL: -4.1904\n",
+            "====> Epoch: 1470/4000 (37%)\tLoss: 111.0704\tLL: -89.5775\tKL: 21.4929\tLL/KL: -4.1678\n",
+            "====> Epoch: 1480/4000 (37%)\tLoss: 110.6339\tLL: -89.1250\tKL: 21.5089\tLL/KL: -4.1436\n",
+            "====> Epoch: 1490/4000 (37%)\tLoss: 109.3774\tLL: -87.8525\tKL: 21.5249\tLL/KL: -4.0814\n",
+            "====> Epoch: 1500/4000 (38%)\tLoss: 111.8936\tLL: -90.3531\tKL: 21.5405\tLL/KL: -4.1946\n",
+            "====> Epoch: 1510/4000 (38%)\tLoss: 109.4554\tLL: -87.8988\tKL: 21.5565\tLL/KL: -4.0776\n",
+            "====> Epoch: 1520/4000 (38%)\tLoss: 108.2782\tLL: -86.7058\tKL: 21.5724\tLL/KL: -4.0193\n",
+            "====> Epoch: 1530/4000 (38%)\tLoss: 108.4012\tLL: -86.8137\tKL: 21.5875\tLL/KL: -4.0215\n",
+            "====> Epoch: 1540/4000 (38%)\tLoss: 106.5217\tLL: -84.9196\tKL: 21.6021\tLL/KL: -3.9311\n",
+            "====> Epoch: 1550/4000 (39%)\tLoss: 107.5553\tLL: -85.9388\tKL: 21.6165\tLL/KL: -3.9756\n",
+            "====> Epoch: 1560/4000 (39%)\tLoss: 105.8241\tLL: -84.1933\tKL: 21.6308\tLL/KL: -3.8923\n",
+            "====> Epoch: 1570/4000 (39%)\tLoss: 106.5788\tLL: -84.9339\tKL: 21.6449\tLL/KL: -3.9240\n",
+            "====> Epoch: 1580/4000 (40%)\tLoss: 105.4466\tLL: -83.7876\tKL: 21.6590\tLL/KL: -3.8685\n",
+            "====> Epoch: 1590/4000 (40%)\tLoss: 103.5972\tLL: -81.9243\tKL: 21.6729\tLL/KL: -3.7800\n",
+            "====> Epoch: 1600/4000 (40%)\tLoss: 105.0901\tLL: -83.4040\tKL: 21.6861\tLL/KL: -3.8460\n",
+            "====> Epoch: 1610/4000 (40%)\tLoss: 105.1188\tLL: -83.4201\tKL: 21.6987\tLL/KL: -3.8445\n",
+            "====> Epoch: 1620/4000 (40%)\tLoss: 103.6114\tLL: -81.9004\tKL: 21.7110\tLL/KL: -3.7723\n",
+            "====> Epoch: 1630/4000 (41%)\tLoss: 103.2754\tLL: -81.5527\tKL: 21.7227\tLL/KL: -3.7543\n",
+            "====> Epoch: 1640/4000 (41%)\tLoss: 102.1724\tLL: -80.4380\tKL: 21.7344\tLL/KL: -3.7010\n",
+            "====> Epoch: 1650/4000 (41%)\tLoss: 101.0151\tLL: -79.2689\tKL: 21.7462\tLL/KL: -3.6452\n",
+            "====> Epoch: 1660/4000 (42%)\tLoss: 99.9949\tLL: -78.2374\tKL: 21.7575\tLL/KL: -3.5959\n",
+            "====> Epoch: 1670/4000 (42%)\tLoss: 100.7004\tLL: -78.9319\tKL: 21.7685\tLL/KL: -3.6260\n",
+            "====> Epoch: 1680/4000 (42%)\tLoss: 101.4615\tLL: -79.6823\tKL: 21.7792\tLL/KL: -3.6586\n",
+            "====> Epoch: 1690/4000 (42%)\tLoss: 100.3219\tLL: -78.5321\tKL: 21.7898\tLL/KL: -3.6041\n",
+            "====> Epoch: 1700/4000 (42%)\tLoss: 100.4852\tLL: -78.6845\tKL: 21.8007\tLL/KL: -3.6093\n",
+            "====> Epoch: 1710/4000 (43%)\tLoss: 99.8109\tLL: -77.9992\tKL: 21.8117\tLL/KL: -3.5760\n",
+            "====> Epoch: 1720/4000 (43%)\tLoss: 98.8221\tLL: -76.9997\tKL: 21.8224\tLL/KL: -3.5285\n",
+            "====> Epoch: 1730/4000 (43%)\tLoss: 98.7124\tLL: -76.8800\tKL: 21.8325\tLL/KL: -3.5214\n",
+            "====> Epoch: 1740/4000 (44%)\tLoss: 98.3687\tLL: -76.5266\tKL: 21.8421\tLL/KL: -3.5036\n",
+            "====> Epoch: 1750/4000 (44%)\tLoss: 97.4476\tLL: -75.5958\tKL: 21.8518\tLL/KL: -3.4595\n",
+            "====> Epoch: 1760/4000 (44%)\tLoss: 97.1276\tLL: -75.2664\tKL: 21.8612\tLL/KL: -3.4429\n",
+            "====> Epoch: 1770/4000 (44%)\tLoss: 95.7363\tLL: -73.8658\tKL: 21.8704\tLL/KL: -3.3774\n",
+            "====> Epoch: 1780/4000 (44%)\tLoss: 95.9629\tLL: -74.0834\tKL: 21.8795\tLL/KL: -3.3860\n",
+            "====> Epoch: 1790/4000 (45%)\tLoss: 95.7357\tLL: -73.8471\tKL: 21.8886\tLL/KL: -3.3738\n",
+            "====> Epoch: 1800/4000 (45%)\tLoss: 95.2536\tLL: -73.3563\tKL: 21.8972\tLL/KL: -3.3500\n",
+            "====> Epoch: 1810/4000 (45%)\tLoss: 96.6184\tLL: -74.7132\tKL: 21.9052\tLL/KL: -3.4108\n",
+            "====> Epoch: 1820/4000 (46%)\tLoss: 93.5353\tLL: -71.6225\tKL: 21.9128\tLL/KL: -3.2685\n",
+            "====> Epoch: 1830/4000 (46%)\tLoss: 95.1430\tLL: -73.2231\tKL: 21.9199\tLL/KL: -3.3405\n",
+            "====> Epoch: 1840/4000 (46%)\tLoss: 93.4482\tLL: -71.5211\tKL: 21.9272\tLL/KL: -3.2618\n",
+            "====> Epoch: 1850/4000 (46%)\tLoss: 93.9535\tLL: -72.0191\tKL: 21.9344\tLL/KL: -3.2834\n",
+            "====> Epoch: 1860/4000 (46%)\tLoss: 94.0292\tLL: -72.0878\tKL: 21.9415\tLL/KL: -3.2855\n",
+            "====> Epoch: 1870/4000 (47%)\tLoss: 93.8426\tLL: -71.8938\tKL: 21.9488\tLL/KL: -3.2755\n",
+            "====> Epoch: 1880/4000 (47%)\tLoss: 92.8330\tLL: -70.8767\tKL: 21.9563\tLL/KL: -3.2281\n",
+            "====> Epoch: 1890/4000 (47%)\tLoss: 91.6431\tLL: -69.6800\tKL: 21.9631\tLL/KL: -3.1726\n",
+            "====> Epoch: 1900/4000 (48%)\tLoss: 92.2573\tLL: -70.2878\tKL: 21.9695\tLL/KL: -3.1993\n",
+            "====> Epoch: 1910/4000 (48%)\tLoss: 91.5864\tLL: -69.6107\tKL: 21.9757\tLL/KL: -3.1676\n",
+            "====> Epoch: 1920/4000 (48%)\tLoss: 91.0896\tLL: -69.1076\tKL: 21.9820\tLL/KL: -3.1438\n",
+            "====> Epoch: 1930/4000 (48%)\tLoss: 91.5397\tLL: -69.5515\tKL: 21.9881\tLL/KL: -3.1631\n",
+            "====> Epoch: 1940/4000 (48%)\tLoss: 90.8522\tLL: -68.8578\tKL: 21.9944\tLL/KL: -3.1307\n",
+            "====> Epoch: 1950/4000 (49%)\tLoss: 91.3805\tLL: -69.3804\tKL: 22.0001\tLL/KL: -3.1536\n",
+            "====> Epoch: 1960/4000 (49%)\tLoss: 89.8116\tLL: -67.8063\tKL: 22.0054\tLL/KL: -3.0814\n",
+            "====> Epoch: 1970/4000 (49%)\tLoss: 89.7915\tLL: -67.7812\tKL: 22.0103\tLL/KL: -3.0795\n",
+            "====> Epoch: 1980/4000 (50%)\tLoss: 89.5096\tLL: -67.4942\tKL: 22.0154\tLL/KL: -3.0658\n",
+            "====> Epoch: 1990/4000 (50%)\tLoss: 89.6088\tLL: -67.5889\tKL: 22.0199\tLL/KL: -3.0694\n",
+            "====> Epoch: 2000/4000 (50%)\tLoss: 89.0191\tLL: -66.9949\tKL: 22.0242\tLL/KL: -3.0419\n",
+            "====> Epoch: 2010/4000 (50%)\tLoss: 89.4416\tLL: -67.4130\tKL: 22.0286\tLL/KL: -3.0603\n",
+            "====> Epoch: 2020/4000 (50%)\tLoss: 89.3153\tLL: -67.2827\tKL: 22.0326\tLL/KL: -3.0538\n",
+            "====> Epoch: 2030/4000 (51%)\tLoss: 88.0524\tLL: -66.0155\tKL: 22.0369\tLL/KL: -2.9957\n",
+            "====> Epoch: 2040/4000 (51%)\tLoss: 88.4071\tLL: -66.3662\tKL: 22.0409\tLL/KL: -3.0110\n",
+            "====> Epoch: 2050/4000 (51%)\tLoss: 88.3576\tLL: -66.3132\tKL: 22.0444\tLL/KL: -3.0082\n",
+            "====> Epoch: 2060/4000 (52%)\tLoss: 87.4461\tLL: -65.3984\tKL: 22.0477\tLL/KL: -2.9662\n",
+            "====> Epoch: 2070/4000 (52%)\tLoss: 87.3637\tLL: -65.3124\tKL: 22.0513\tLL/KL: -2.9618\n",
+            "====> Epoch: 2080/4000 (52%)\tLoss: 87.4161\tLL: -65.3609\tKL: 22.0552\tLL/KL: -2.9635\n",
+            "====> Epoch: 2090/4000 (52%)\tLoss: 86.0846\tLL: -64.0257\tKL: 22.0589\tLL/KL: -2.9025\n",
+            "====> Epoch: 2100/4000 (52%)\tLoss: 86.8085\tLL: -64.7466\tKL: 22.0619\tLL/KL: -2.9348\n",
+            "====> Epoch: 2110/4000 (53%)\tLoss: 86.3615\tLL: -64.2968\tKL: 22.0647\tLL/KL: -2.9140\n",
+            "====> Epoch: 2120/4000 (53%)\tLoss: 85.1346\tLL: -63.0674\tKL: 22.0672\tLL/KL: -2.8580\n",
+            "====> Epoch: 2130/4000 (53%)\tLoss: 84.8839\tLL: -62.8148\tKL: 22.0691\tLL/KL: -2.8463\n",
+            "====> Epoch: 2140/4000 (54%)\tLoss: 84.7866\tLL: -62.7157\tKL: 22.0709\tLL/KL: -2.8415\n",
+            "====> Epoch: 2150/4000 (54%)\tLoss: 84.5500\tLL: -62.4778\tKL: 22.0722\tLL/KL: -2.8306\n",
+            "====> Epoch: 2160/4000 (54%)\tLoss: 85.3553\tLL: -63.2818\tKL: 22.0734\tLL/KL: -2.8669\n",
+            "====> Epoch: 2170/4000 (54%)\tLoss: 84.8897\tLL: -62.8149\tKL: 22.0748\tLL/KL: -2.8455\n",
+            "====> Epoch: 2180/4000 (54%)\tLoss: 84.7676\tLL: -62.6913\tKL: 22.0763\tLL/KL: -2.8398\n",
+            "====> Epoch: 2190/4000 (55%)\tLoss: 84.0322\tLL: -61.9550\tKL: 22.0772\tLL/KL: -2.8063\n",
+            "====> Epoch: 2200/4000 (55%)\tLoss: 83.9393\tLL: -61.8618\tKL: 22.0775\tLL/KL: -2.8020\n",
+            "====> Epoch: 2210/4000 (55%)\tLoss: 84.2459\tLL: -62.1680\tKL: 22.0780\tLL/KL: -2.8158\n",
+            "====> Epoch: 2220/4000 (56%)\tLoss: 83.3849\tLL: -61.3066\tKL: 22.0783\tLL/KL: -2.7768\n",
+            "====> Epoch: 2230/4000 (56%)\tLoss: 83.8465\tLL: -61.7677\tKL: 22.0787\tLL/KL: -2.7976\n",
+            "====> Epoch: 2240/4000 (56%)\tLoss: 83.7424\tLL: -61.6631\tKL: 22.0793\tLL/KL: -2.7928\n",
+            "====> Epoch: 2250/4000 (56%)\tLoss: 83.1482\tLL: -61.0685\tKL: 22.0796\tLL/KL: -2.7658\n",
+            "====> Epoch: 2260/4000 (56%)\tLoss: 82.9097\tLL: -60.8304\tKL: 22.0793\tLL/KL: -2.7551\n",
+            "====> Epoch: 2270/4000 (57%)\tLoss: 83.2008\tLL: -61.1224\tKL: 22.0784\tLL/KL: -2.7684\n",
+            "====> Epoch: 2280/4000 (57%)\tLoss: 82.6833\tLL: -60.6055\tKL: 22.0778\tLL/KL: -2.7451\n",
+            "====> Epoch: 2290/4000 (57%)\tLoss: 81.7527\tLL: -59.6755\tKL: 22.0772\tLL/KL: -2.7030\n",
+            "====> Epoch: 2300/4000 (58%)\tLoss: 81.5454\tLL: -59.4691\tKL: 22.0763\tLL/KL: -2.6938\n",
+            "====> Epoch: 2310/4000 (58%)\tLoss: 82.2121\tLL: -60.1369\tKL: 22.0752\tLL/KL: -2.7242\n",
+            "====> Epoch: 2320/4000 (58%)\tLoss: 81.7167\tLL: -59.6425\tKL: 22.0742\tLL/KL: -2.7019\n",
+            "====> Epoch: 2330/4000 (58%)\tLoss: 81.7063\tLL: -59.6332\tKL: 22.0732\tLL/KL: -2.7016\n",
+            "====> Epoch: 2340/4000 (58%)\tLoss: 80.6279\tLL: -58.5563\tKL: 22.0716\tLL/KL: -2.6530\n",
+            "====> Epoch: 2350/4000 (59%)\tLoss: 81.5703\tLL: -59.5007\tKL: 22.0696\tLL/KL: -2.6960\n",
+            "====> Epoch: 2360/4000 (59%)\tLoss: 80.2639\tLL: -58.1965\tKL: 22.0674\tLL/KL: -2.6372\n",
+            "====> Epoch: 2370/4000 (59%)\tLoss: 81.1737\tLL: -59.1089\tKL: 22.0648\tLL/KL: -2.6789\n",
+            "====> Epoch: 2380/4000 (60%)\tLoss: 81.1867\tLL: -59.1245\tKL: 22.0622\tLL/KL: -2.6799\n",
+            "====> Epoch: 2390/4000 (60%)\tLoss: 79.9681\tLL: -57.9084\tKL: 22.0598\tLL/KL: -2.6251\n",
+            "====> Epoch: 2400/4000 (60%)\tLoss: 80.1336\tLL: -58.0764\tKL: 22.0572\tLL/KL: -2.6330\n",
+            "====> Epoch: 2410/4000 (60%)\tLoss: 80.1307\tLL: -58.0761\tKL: 22.0546\tLL/KL: -2.6333\n",
+            "====> Epoch: 2420/4000 (60%)\tLoss: 80.0038\tLL: -57.9520\tKL: 22.0518\tLL/KL: -2.6280\n",
+            "====> Epoch: 2430/4000 (61%)\tLoss: 80.1970\tLL: -58.1485\tKL: 22.0485\tLL/KL: -2.6373\n",
+            "====> Epoch: 2440/4000 (61%)\tLoss: 79.3923\tLL: -57.3467\tKL: 22.0455\tLL/KL: -2.6013\n",
+            "====> Epoch: 2450/4000 (61%)\tLoss: 79.1181\tLL: -57.0759\tKL: 22.0422\tLL/KL: -2.5894\n",
+            "====> Epoch: 2460/4000 (62%)\tLoss: 79.5042\tLL: -57.4656\tKL: 22.0385\tLL/KL: -2.6075\n",
+            "====> Epoch: 2470/4000 (62%)\tLoss: 78.5193\tLL: -56.4849\tKL: 22.0344\tLL/KL: -2.5635\n",
+            "====> Epoch: 2480/4000 (62%)\tLoss: 78.9486\tLL: -56.9184\tKL: 22.0302\tLL/KL: -2.5836\n",
+            "====> Epoch: 2490/4000 (62%)\tLoss: 78.9531\tLL: -56.9272\tKL: 22.0259\tLL/KL: -2.5846\n",
+            "====> Epoch: 2500/4000 (62%)\tLoss: 78.8070\tLL: -56.7855\tKL: 22.0215\tLL/KL: -2.5786\n",
+            "====> Epoch: 2510/4000 (63%)\tLoss: 77.7453\tLL: -55.7282\tKL: 22.0172\tLL/KL: -2.5311\n",
+            "====> Epoch: 2520/4000 (63%)\tLoss: 78.1940\tLL: -56.1813\tKL: 22.0127\tLL/KL: -2.5522\n",
+            "====> Epoch: 2530/4000 (63%)\tLoss: 78.3882\tLL: -56.3804\tKL: 22.0078\tLL/KL: -2.5618\n",
+            "====> Epoch: 2540/4000 (64%)\tLoss: 77.8477\tLL: -55.8448\tKL: 22.0029\tLL/KL: -2.5381\n",
+            "====> Epoch: 2550/4000 (64%)\tLoss: 77.0804\tLL: -55.0823\tKL: 21.9981\tLL/KL: -2.5040\n",
+            "====> Epoch: 2560/4000 (64%)\tLoss: 77.9788\tLL: -55.9862\tKL: 21.9926\tLL/KL: -2.5457\n",
+            "====> Epoch: 2570/4000 (64%)\tLoss: 77.8110\tLL: -55.8237\tKL: 21.9873\tLL/KL: -2.5389\n",
+            "====> Epoch: 2580/4000 (64%)\tLoss: 77.4661\tLL: -55.4840\tKL: 21.9821\tLL/KL: -2.5240\n",
+            "====> Epoch: 2590/4000 (65%)\tLoss: 77.5157\tLL: -55.5391\tKL: 21.9766\tLL/KL: -2.5272\n",
+            "====> Epoch: 2600/4000 (65%)\tLoss: 76.4047\tLL: -54.4341\tKL: 21.9706\tLL/KL: -2.4776\n",
+            "====> Epoch: 2610/4000 (65%)\tLoss: 76.7175\tLL: -54.7532\tKL: 21.9644\tLL/KL: -2.4928\n",
+            "====> Epoch: 2620/4000 (66%)\tLoss: 77.6347\tLL: -55.6765\tKL: 21.9582\tLL/KL: -2.5356\n",
+            "====> Epoch: 2630/4000 (66%)\tLoss: 76.4835\tLL: -54.5314\tKL: 21.9521\tLL/KL: -2.4841\n",
+            "====> Epoch: 2640/4000 (66%)\tLoss: 76.8306\tLL: -54.8851\tKL: 21.9456\tLL/KL: -2.5010\n",
+            "====> Epoch: 2650/4000 (66%)\tLoss: 75.8135\tLL: -53.8746\tKL: 21.9389\tLL/KL: -2.4557\n",
+            "====> Epoch: 2660/4000 (66%)\tLoss: 76.4996\tLL: -54.5680\tKL: 21.9316\tLL/KL: -2.4881\n",
+            "====> Epoch: 2670/4000 (67%)\tLoss: 76.4238\tLL: -54.4999\tKL: 21.9239\tLL/KL: -2.4859\n",
+            "====> Epoch: 2680/4000 (67%)\tLoss: 75.6667\tLL: -53.7507\tKL: 21.9160\tLL/KL: -2.4526\n",
+            "====> Epoch: 2690/4000 (67%)\tLoss: 76.0121\tLL: -54.1039\tKL: 21.9082\tLL/KL: -2.4696\n",
+            "====> Epoch: 2700/4000 (68%)\tLoss: 75.3327\tLL: -53.4323\tKL: 21.9004\tLL/KL: -2.4398\n",
+            "====> Epoch: 2710/4000 (68%)\tLoss: 75.4457\tLL: -53.5534\tKL: 21.8923\tLL/KL: -2.4462\n",
+            "====> Epoch: 2720/4000 (68%)\tLoss: 75.2132\tLL: -53.3287\tKL: 21.8845\tLL/KL: -2.4368\n",
+            "====> Epoch: 2730/4000 (68%)\tLoss: 75.6619\tLL: -53.7853\tKL: 21.8766\tLL/KL: -2.4586\n",
+            "====> Epoch: 2740/4000 (68%)\tLoss: 74.6821\tLL: -52.8141\tKL: 21.8679\tLL/KL: -2.4151\n",
+            "====> Epoch: 2750/4000 (69%)\tLoss: 75.5872\tLL: -53.7281\tKL: 21.8591\tLL/KL: -2.4579\n",
+            "====> Epoch: 2760/4000 (69%)\tLoss: 74.8228\tLL: -52.9721\tKL: 21.8507\tLL/KL: -2.4243\n",
+            "====> Epoch: 2770/4000 (69%)\tLoss: 75.0963\tLL: -53.2547\tKL: 21.8416\tLL/KL: -2.4382\n",
+            "====> Epoch: 2780/4000 (70%)\tLoss: 74.9037\tLL: -53.0711\tKL: 21.8327\tLL/KL: -2.4308\n",
+            "====> Epoch: 2790/4000 (70%)\tLoss: 74.8087\tLL: -52.9850\tKL: 21.8237\tLL/KL: -2.4279\n",
+            "====> Epoch: 2800/4000 (70%)\tLoss: 74.3998\tLL: -52.5853\tKL: 21.8145\tLL/KL: -2.4106\n",
+            "====> Epoch: 2810/4000 (70%)\tLoss: 74.0430\tLL: -52.2377\tKL: 21.8053\tLL/KL: -2.3956\n",
+            "====> Epoch: 2820/4000 (70%)\tLoss: 74.5605\tLL: -52.7650\tKL: 21.7955\tLL/KL: -2.4209\n",
+            "====> Epoch: 2830/4000 (71%)\tLoss: 74.5309\tLL: -52.7457\tKL: 21.7852\tLL/KL: -2.4212\n",
+            "====> Epoch: 2840/4000 (71%)\tLoss: 73.6960\tLL: -51.9212\tKL: 21.7748\tLL/KL: -2.3845\n",
+            "====> Epoch: 2850/4000 (71%)\tLoss: 73.7033\tLL: -51.9391\tKL: 21.7642\tLL/KL: -2.3864\n",
+            "====> Epoch: 2860/4000 (72%)\tLoss: 73.8974\tLL: -52.1438\tKL: 21.7537\tLL/KL: -2.3970\n",
+            "====> Epoch: 2870/4000 (72%)\tLoss: 73.2483\tLL: -51.5053\tKL: 21.7430\tLL/KL: -2.3688\n",
+            "====> Epoch: 2880/4000 (72%)\tLoss: 73.5280\tLL: -51.7959\tKL: 21.7321\tLL/KL: -2.3834\n",
+            "====> Epoch: 2890/4000 (72%)\tLoss: 73.1691\tLL: -51.4481\tKL: 21.7210\tLL/KL: -2.3686\n",
+            "====> Epoch: 2900/4000 (72%)\tLoss: 73.6298\tLL: -51.9201\tKL: 21.7097\tLL/KL: -2.3916\n",
+            "====> Epoch: 2910/4000 (73%)\tLoss: 73.4722\tLL: -51.7739\tKL: 21.6983\tLL/KL: -2.3861\n",
+            "====> Epoch: 2920/4000 (73%)\tLoss: 72.8499\tLL: -51.1629\tKL: 21.6870\tLL/KL: -2.3591\n",
+            "====> Epoch: 2930/4000 (73%)\tLoss: 72.8760\tLL: -51.2007\tKL: 21.6753\tLL/KL: -2.3622\n",
+            "====> Epoch: 2940/4000 (74%)\tLoss: 72.5990\tLL: -50.9355\tKL: 21.6635\tLL/KL: -2.3512\n",
+            "====> Epoch: 2950/4000 (74%)\tLoss: 72.7050\tLL: -51.0538\tKL: 21.6512\tLL/KL: -2.3580\n",
+            "====> Epoch: 2960/4000 (74%)\tLoss: 72.6825\tLL: -51.0436\tKL: 21.6389\tLL/KL: -2.3589\n",
+            "====> Epoch: 2970/4000 (74%)\tLoss: 72.9854\tLL: -51.3592\tKL: 21.6262\tLL/KL: -2.3749\n",
+            "====> Epoch: 2980/4000 (74%)\tLoss: 72.5960\tLL: -50.9825\tKL: 21.6135\tLL/KL: -2.3588\n",
+            "====> Epoch: 2990/4000 (75%)\tLoss: 72.5377\tLL: -50.9374\tKL: 21.6003\tLL/KL: -2.3582\n",
+            "====> Epoch: 3000/4000 (75%)\tLoss: 72.2184\tLL: -50.6315\tKL: 21.5869\tLL/KL: -2.3455\n",
+            "====> Epoch: 3010/4000 (75%)\tLoss: 72.3681\tLL: -50.7945\tKL: 21.5736\tLL/KL: -2.3545\n",
+            "====> Epoch: 3020/4000 (76%)\tLoss: 72.4859\tLL: -50.9256\tKL: 21.5603\tLL/KL: -2.3620\n",
+            "====> Epoch: 3030/4000 (76%)\tLoss: 72.3132\tLL: -50.7665\tKL: 21.5467\tLL/KL: -2.3561\n",
+            "====> Epoch: 3040/4000 (76%)\tLoss: 72.0343\tLL: -50.5014\tKL: 21.5329\tLL/KL: -2.3453\n",
+            "====> Epoch: 3050/4000 (76%)\tLoss: 71.9953\tLL: -50.4762\tKL: 21.5192\tLL/KL: -2.3456\n",
+            "====> Epoch: 3060/4000 (76%)\tLoss: 71.7729\tLL: -50.2677\tKL: 21.5052\tLL/KL: -2.3375\n",
+            "====> Epoch: 3070/4000 (77%)\tLoss: 71.5431\tLL: -50.0523\tKL: 21.4908\tLL/KL: -2.3290\n",
+            "====> Epoch: 3080/4000 (77%)\tLoss: 70.8481\tLL: -49.3721\tKL: 21.4759\tLL/KL: -2.2990\n",
+            "====> Epoch: 3090/4000 (77%)\tLoss: 71.3631\tLL: -49.9023\tKL: 21.4608\tLL/KL: -2.3253\n",
+            "====> Epoch: 3100/4000 (78%)\tLoss: 71.0774\tLL: -49.6317\tKL: 21.4457\tLL/KL: -2.3143\n",
+            "====> Epoch: 3110/4000 (78%)\tLoss: 71.2992\tLL: -49.8689\tKL: 21.4303\tLL/KL: -2.3270\n",
+            "====> Epoch: 3120/4000 (78%)\tLoss: 71.4146\tLL: -49.9996\tKL: 21.4149\tLL/KL: -2.3348\n",
+            "====> Epoch: 3130/4000 (78%)\tLoss: 70.9178\tLL: -49.5183\tKL: 21.3995\tLL/KL: -2.3140\n",
+            "====> Epoch: 3140/4000 (78%)\tLoss: 70.9787\tLL: -49.5950\tKL: 21.3837\tLL/KL: -2.3193\n",
+            "====> Epoch: 3150/4000 (79%)\tLoss: 71.0008\tLL: -49.6334\tKL: 21.3674\tLL/KL: -2.3229\n",
+            "====> Epoch: 3160/4000 (79%)\tLoss: 71.4288\tLL: -50.0778\tKL: 21.3510\tLL/KL: -2.3455\n",
+            "====> Epoch: 3170/4000 (79%)\tLoss: 70.9065\tLL: -49.5716\tKL: 21.3349\tLL/KL: -2.3235\n",
+            "====> Epoch: 3180/4000 (80%)\tLoss: 70.2600\tLL: -48.9415\tKL: 21.3185\tLL/KL: -2.2957\n",
+            "====> Epoch: 3190/4000 (80%)\tLoss: 70.6021\tLL: -49.3002\tKL: 21.3019\tLL/KL: -2.3144\n",
+            "====> Epoch: 3200/4000 (80%)\tLoss: 70.9358\tLL: -49.6501\tKL: 21.2857\tLL/KL: -2.3326\n",
+            "====> Epoch: 3210/4000 (80%)\tLoss: 70.3896\tLL: -49.1203\tKL: 21.2693\tLL/KL: -2.3094\n",
+            "====> Epoch: 3220/4000 (80%)\tLoss: 70.4486\tLL: -49.1962\tKL: 21.2524\tLL/KL: -2.3149\n",
+            "====> Epoch: 3230/4000 (81%)\tLoss: 70.2740\tLL: -49.0390\tKL: 21.2350\tLL/KL: -2.3093\n",
+            "====> Epoch: 3240/4000 (81%)\tLoss: 69.6882\tLL: -48.4710\tKL: 21.2172\tLL/KL: -2.2845\n",
+            "====> Epoch: 3250/4000 (81%)\tLoss: 70.1564\tLL: -48.9572\tKL: 21.1992\tLL/KL: -2.3094\n",
+            "====> Epoch: 3260/4000 (82%)\tLoss: 69.7269\tLL: -48.5456\tKL: 21.1813\tLL/KL: -2.2919\n",
+            "====> Epoch: 3270/4000 (82%)\tLoss: 69.8484\tLL: -48.6857\tKL: 21.1627\tLL/KL: -2.3005\n",
+            "====> Epoch: 3280/4000 (82%)\tLoss: 69.9044\tLL: -48.7605\tKL: 21.1439\tLL/KL: -2.3061\n",
+            "====> Epoch: 3290/4000 (82%)\tLoss: 69.9250\tLL: -48.7999\tKL: 21.1251\tLL/KL: -2.3100\n",
+            "====> Epoch: 3300/4000 (82%)\tLoss: 69.0768\tLL: -47.9708\tKL: 21.1060\tLL/KL: -2.2728\n",
+            "====> Epoch: 3310/4000 (83%)\tLoss: 70.1713\tLL: -49.0849\tKL: 21.0864\tLL/KL: -2.3278\n",
+            "====> Epoch: 3320/4000 (83%)\tLoss: 69.3757\tLL: -48.3089\tKL: 21.0667\tLL/KL: -2.2931\n",
+            "====> Epoch: 3330/4000 (83%)\tLoss: 69.2374\tLL: -48.1907\tKL: 21.0467\tLL/KL: -2.2897\n",
+            "====> Epoch: 3340/4000 (84%)\tLoss: 69.3250\tLL: -48.2984\tKL: 21.0266\tLL/KL: -2.2970\n",
+            "====> Epoch: 3350/4000 (84%)\tLoss: 68.8976\tLL: -47.8911\tKL: 21.0065\tLL/KL: -2.2798\n",
+            "====> Epoch: 3360/4000 (84%)\tLoss: 69.4133\tLL: -48.4272\tKL: 20.9861\tLL/KL: -2.3076\n",
+            "====> Epoch: 3370/4000 (84%)\tLoss: 69.2607\tLL: -48.2953\tKL: 20.9654\tLL/KL: -2.3036\n",
+            "====> Epoch: 3380/4000 (84%)\tLoss: 68.7325\tLL: -47.7877\tKL: 20.9448\tLL/KL: -2.2816\n",
+            "====> Epoch: 3390/4000 (85%)\tLoss: 69.0423\tLL: -48.1185\tKL: 20.9238\tLL/KL: -2.2997\n",
+            "====> Epoch: 3400/4000 (85%)\tLoss: 68.8982\tLL: -47.9958\tKL: 20.9024\tLL/KL: -2.2962\n",
+            "====> Epoch: 3410/4000 (85%)\tLoss: 68.7225\tLL: -47.8413\tKL: 20.8812\tLL/KL: -2.2911\n",
+            "====> Epoch: 3420/4000 (86%)\tLoss: 68.5909\tLL: -47.7310\tKL: 20.8599\tLL/KL: -2.2882\n",
+            "====> Epoch: 3430/4000 (86%)\tLoss: 68.8153\tLL: -47.9765\tKL: 20.8388\tLL/KL: -2.3023\n",
+            "====> Epoch: 3440/4000 (86%)\tLoss: 68.0047\tLL: -47.1875\tKL: 20.8173\tLL/KL: -2.2667\n",
+            "====> Epoch: 3450/4000 (86%)\tLoss: 68.5611\tLL: -47.7659\tKL: 20.7951\tLL/KL: -2.2970\n",
+            "====> Epoch: 3460/4000 (86%)\tLoss: 68.1901\tLL: -47.4171\tKL: 20.7730\tLL/KL: -2.2826\n",
+            "====> Epoch: 3470/4000 (87%)\tLoss: 68.7156\tLL: -47.9653\tKL: 20.7503\tLL/KL: -2.3115\n",
+            "====> Epoch: 3480/4000 (87%)\tLoss: 68.2088\tLL: -47.4813\tKL: 20.7275\tLL/KL: -2.2907\n",
+            "====> Epoch: 3490/4000 (87%)\tLoss: 68.0395\tLL: -47.3350\tKL: 20.7045\tLL/KL: -2.2862\n",
+            "====> Epoch: 3500/4000 (88%)\tLoss: 68.1401\tLL: -47.4583\tKL: 20.6818\tLL/KL: -2.2947\n",
+            "====> Epoch: 3510/4000 (88%)\tLoss: 68.1058\tLL: -47.4472\tKL: 20.6587\tLL/KL: -2.2967\n",
+            "====> Epoch: 3520/4000 (88%)\tLoss: 67.7327\tLL: -47.0971\tKL: 20.6356\tLL/KL: -2.2823\n",
+            "====> Epoch: 3530/4000 (88%)\tLoss: 67.3659\tLL: -46.7536\tKL: 20.6123\tLL/KL: -2.2682\n",
+            "====> Epoch: 3540/4000 (88%)\tLoss: 67.8407\tLL: -47.2525\tKL: 20.5882\tLL/KL: -2.2951\n",
+            "====> Epoch: 3550/4000 (89%)\tLoss: 67.9747\tLL: -47.4111\tKL: 20.5636\tLL/KL: -2.3056\n",
+            "====> Epoch: 3560/4000 (89%)\tLoss: 67.4340\tLL: -46.8949\tKL: 20.5391\tLL/KL: -2.2832\n",
+            "====> Epoch: 3570/4000 (89%)\tLoss: 67.8157\tLL: -47.3014\tKL: 20.5143\tLL/KL: -2.3058\n",
+            "====> Epoch: 3580/4000 (90%)\tLoss: 67.3112\tLL: -46.8214\tKL: 20.4898\tLL/KL: -2.2851\n",
+            "====> Epoch: 3590/4000 (90%)\tLoss: 67.5411\tLL: -47.0763\tKL: 20.4648\tLL/KL: -2.3003\n",
+            "====> Epoch: 3600/4000 (90%)\tLoss: 67.4868\tLL: -47.0472\tKL: 20.4396\tLL/KL: -2.3018\n",
+            "====> Epoch: 3610/4000 (90%)\tLoss: 67.4476\tLL: -47.0332\tKL: 20.4143\tLL/KL: -2.3039\n",
+            "====> Epoch: 3620/4000 (90%)\tLoss: 67.3863\tLL: -46.9973\tKL: 20.3890\tLL/KL: -2.3050\n",
+            "====> Epoch: 3630/4000 (91%)\tLoss: 66.9737\tLL: -46.6101\tKL: 20.3637\tLL/KL: -2.2889\n",
+            "====> Epoch: 3640/4000 (91%)\tLoss: 67.2449\tLL: -46.9067\tKL: 20.3382\tLL/KL: -2.3063\n",
+            "====> Epoch: 3650/4000 (91%)\tLoss: 67.0054\tLL: -46.6931\tKL: 20.3123\tLL/KL: -2.2988\n",
+            "====> Epoch: 3660/4000 (92%)\tLoss: 67.3673\tLL: -47.0817\tKL: 20.2856\tLL/KL: -2.3209\n",
+            "====> Epoch: 3670/4000 (92%)\tLoss: 67.1712\tLL: -46.9122\tKL: 20.2590\tLL/KL: -2.3156\n",
+            "====> Epoch: 3680/4000 (92%)\tLoss: 67.1752\tLL: -46.9429\tKL: 20.2323\tLL/KL: -2.3202\n",
+            "====> Epoch: 3690/4000 (92%)\tLoss: 66.9096\tLL: -46.7045\tKL: 20.2051\tLL/KL: -2.3115\n",
+            "====> Epoch: 3700/4000 (92%)\tLoss: 66.7524\tLL: -46.5749\tKL: 20.1775\tLL/KL: -2.3083\n",
+            "====> Epoch: 3710/4000 (93%)\tLoss: 66.6810\tLL: -46.5312\tKL: 20.1498\tLL/KL: -2.3093\n",
+            "====> Epoch: 3720/4000 (93%)\tLoss: 66.6713\tLL: -46.5497\tKL: 20.1216\tLL/KL: -2.3134\n",
+            "====> Epoch: 3730/4000 (93%)\tLoss: 66.5894\tLL: -46.4958\tKL: 20.0936\tLL/KL: -2.3140\n",
+            "====> Epoch: 3740/4000 (94%)\tLoss: 66.4523\tLL: -46.3867\tKL: 20.0656\tLL/KL: -2.3118\n",
+            "====> Epoch: 3750/4000 (94%)\tLoss: 66.4713\tLL: -46.4339\tKL: 20.0374\tLL/KL: -2.3174\n",
+            "====> Epoch: 3760/4000 (94%)\tLoss: 65.8991\tLL: -45.8901\tKL: 20.0089\tLL/KL: -2.2935\n",
+            "====> Epoch: 3770/4000 (94%)\tLoss: 66.5019\tLL: -46.5220\tKL: 19.9798\tLL/KL: -2.3284\n",
+            "====> Epoch: 3780/4000 (94%)\tLoss: 66.4632\tLL: -46.5125\tKL: 19.9507\tLL/KL: -2.3314\n",
+            "====> Epoch: 3790/4000 (95%)\tLoss: 66.2932\tLL: -46.3720\tKL: 19.9211\tLL/KL: -2.3278\n",
+            "====> Epoch: 3800/4000 (95%)\tLoss: 66.2186\tLL: -46.3270\tKL: 19.8916\tLL/KL: -2.3290\n",
+            "====> Epoch: 3810/4000 (95%)\tLoss: 65.9540\tLL: -46.0918\tKL: 19.8622\tLL/KL: -2.3206\n",
+            "====> Epoch: 3820/4000 (96%)\tLoss: 65.9319\tLL: -46.0995\tKL: 19.8325\tLL/KL: -2.3244\n",
+            "====> Epoch: 3830/4000 (96%)\tLoss: 66.2473\tLL: -46.4447\tKL: 19.8026\tLL/KL: -2.3454\n",
+            "====> Epoch: 3840/4000 (96%)\tLoss: 65.8039\tLL: -46.0314\tKL: 19.7725\tLL/KL: -2.3281\n",
+            "====> Epoch: 3850/4000 (96%)\tLoss: 65.6412\tLL: -45.8993\tKL: 19.7419\tLL/KL: -2.3250\n",
+            "====> Epoch: 3860/4000 (96%)\tLoss: 65.7826\tLL: -46.0718\tKL: 19.7108\tLL/KL: -2.3374\n",
+            "====> Epoch: 3870/4000 (97%)\tLoss: 65.6844\tLL: -46.0051\tKL: 19.6794\tLL/KL: -2.3377\n",
+            "====> Epoch: 3880/4000 (97%)\tLoss: 65.4906\tLL: -45.8428\tKL: 19.6478\tLL/KL: -2.3332\n",
+            "====> Epoch: 3890/4000 (97%)\tLoss: 65.9075\tLL: -46.2913\tKL: 19.6162\tLL/KL: -2.3598\n",
+            "====> Epoch: 3900/4000 (98%)\tLoss: 65.4051\tLL: -45.8203\tKL: 19.5848\tLL/KL: -2.3396\n",
+            "====> Epoch: 3910/4000 (98%)\tLoss: 65.9463\tLL: -46.3929\tKL: 19.5534\tLL/KL: -2.3726\n",
+            "====> Epoch: 3920/4000 (98%)\tLoss: 65.3626\tLL: -45.8406\tKL: 19.5220\tLL/KL: -2.3482\n",
+            "====> Epoch: 3930/4000 (98%)\tLoss: 65.8228\tLL: -46.3327\tKL: 19.4900\tLL/KL: -2.3773\n",
+            "====> Epoch: 3940/4000 (98%)\tLoss: 65.5647\tLL: -46.1066\tKL: 19.4581\tLL/KL: -2.3695\n",
+            "====> Epoch: 3950/4000 (99%)\tLoss: 65.6882\tLL: -46.2625\tKL: 19.4257\tLL/KL: -2.3815\n",
+            "====> Epoch: 3960/4000 (99%)\tLoss: 65.3756\tLL: -45.9827\tKL: 19.3928\tLL/KL: -2.3711\n",
+            "====> Epoch: 3970/4000 (99%)\tLoss: 65.5233\tLL: -46.1635\tKL: 19.3598\tLL/KL: -2.3845\n",
+            "====> Epoch: 3980/4000 (100%)\tLoss: 65.0315\tLL: -45.7044\tKL: 19.3271\tLL/KL: -2.3648\n",
+            "====> Epoch: 3990/4000 (100%)\tLoss: 65.0678\tLL: -45.7739\tKL: 19.2938\tLL/KL: -2.3725\n",
+            "End fitting: 2020-12-14 16:17:02.623970\n",
+            "\tElapsed: 0:00:26.988962\n",
+            "\tDeleting model_sparse1.pt.running\n",
+            "\tDeleted: 2020-12-14 16:17:02.624361\n",
+            "\t\tElapsed: 0:00:26.989925\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bvj0NwnDzgXZ"
+      },
+      "source": [
+        "The sparse model estimates a probability of redundancy associated to each dimension. This means that we can retain only the dimensions with low probability of redundancy. In this case the model correctly identifies only 2 meaningful latent dimensions. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bSMmJoWebE66",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 311
+        },
+        "outputId": "a5ec1768-0878-44d1-c64b-2b421abdb38e"
+      },
+      "source": [
+        "print('Probability of redundancy: ', model_sparse1.dropout.detach().numpy())\n",
+        "plot_dropout(model_sparse1, sort=False)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Probability of redundancy:  [[0.06632785 0.18452074 0.20528041 0.00532892 0.04781039]]\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JgstDg9bg6Pf"
+      },
+      "source": [
+        "# We fix a redundancy threshold\n",
+        "dropout_threshold = 0.10"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8cIbxTfjg-wP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 814
+        },
+        "outputId": "c7454d0d-a3a0-461e-89f9-3281936d0e2e"
+      },
+      "source": [
+        "# We plot the remaining latent dimensions\n",
+        "keep = (model_sparse1.dropout.squeeze() < dropout_threshold).tolist()\n",
+        "kept_comps = [i for i, kept in enumerate(keep) if kept]\n",
+        "print(f'kept components: {kept_comps}')\n",
+        "\n",
+        "plot_latent_space(model_sparse1, data=data, comp=kept_comps);"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "kept components: [0, 3, 4]\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fR0DdsfsbE67",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4279d0fd-18bb-4caa-f588-c31e76c9c780"
+      },
+      "source": [
+        "# We repeate the same exercise with the synthetic data with redundant dimensions\n",
+        "\n",
+        "init_dict = {\n",
+        "    'n_channels': 2, # X and Y\n",
+        "    'lat_dim': n_components + 3,\n",
+        "    'n_feats': tuple([X_ext.shape[1], Y_ext.shape[1]]),\n",
+        "}\n",
+        "\n",
+        "\n",
+        "data_sparse = []\n",
+        "data_sparse.append(torch.FloatTensor(X_ext))\n",
+        "data_sparse.append(torch.FloatTensor(Y_ext))\n",
+        "\n",
+        "model_sparse = Mcvae(sparse=True, **init_dict)\n",
+        "model_sparse.to(DEVICE)\n",
+        "print(model_sparse)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mcvae(\n",
+            "  (vae): ModuleList(\n",
+            "    (0): VAE(\n",
+            "      (W_mu): Linear(in_features=8, out_features=8, bias=True)\n",
+            "      (W_out): Linear(in_features=8, out_features=8, bias=True)\n",
+            "    )\n",
+            "    (1): VAE(\n",
+            "      (W_mu): Linear(in_features=8, out_features=8, bias=True)\n",
+            "      (W_out): Linear(in_features=8, out_features=8, bias=True)\n",
+            "    )\n",
+            "  )\n",
+            ")\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X_QEBhcjtbna",
+        "outputId": "67eaa943-ecd3-4d2e-d49b-df190cefee93"
+      },
+      "source": [
+        "# Fit (or load model if existing)\n",
+        "model_sparse.optimizer = torch.optim.Adam(model_sparse.parameters(), lr=adam_lr)\n",
+        "load_or_fit(model=model_sparse, data=data_sparse, epochs=n_epochs, ptfile='model_sparse.pt', force_fit=FORCE_REFIT)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\tCreating model_sparse.pt.running\n",
+            "\tCreated: 2020-12-14 16:19:06.030093\n",
+            "Start fitting: 2020-12-14 16:19:06.030988\n",
+            "\tModel destination: model_sparse.pt\n",
+            "====> Epoch:    0/4000 (0%)\tLoss: 13984.0703\tLL: -13977.1387\tKL: 6.9314\tLL/KL: -2016.4900\n",
+            "====> Epoch:   10/4000 (0%)\tLoss: 7683.1577\tLL: -7675.6602\tKL: 7.4974\tLL/KL: -1023.7820\n",
+            "====> Epoch:   20/4000 (0%)\tLoss: 4887.7144\tLL: -4879.6938\tKL: 8.0207\tLL/KL: -608.3887\n",
+            "====> Epoch:   30/4000 (1%)\tLoss: 3365.6387\tLL: -3357.0969\tKL: 8.5417\tLL/KL: -393.0247\n",
+            "====> Epoch:   40/4000 (1%)\tLoss: 2555.3811\tLL: -2546.2922\tKL: 9.0890\tLL/KL: -280.1523\n",
+            "====> Epoch:   50/4000 (1%)\tLoss: 1975.2694\tLL: -1965.6050\tKL: 9.6644\tLL/KL: -203.3868\n",
+            "====> Epoch:   60/4000 (2%)\tLoss: 1778.6443\tLL: -1768.3644\tKL: 10.2799\tLL/KL: -172.0218\n",
+            "====> Epoch:   70/4000 (2%)\tLoss: 1414.2970\tLL: -1403.3685\tKL: 10.9285\tLL/KL: -128.4137\n",
+            "====> Epoch:   80/4000 (2%)\tLoss: 1348.1387\tLL: -1336.5682\tKL: 11.5705\tLL/KL: -115.5153\n",
+            "====> Epoch:   90/4000 (2%)\tLoss: 1232.6722\tLL: -1220.4846\tKL: 12.1876\tLL/KL: -100.1417\n",
+            "====> Epoch:  100/4000 (2%)\tLoss: 1170.8824\tLL: -1158.0793\tKL: 12.8031\tLL/KL: -90.4528\n",
+            "====> Epoch:  110/4000 (3%)\tLoss: 1108.2974\tLL: -1094.8934\tKL: 13.4040\tLL/KL: -81.6841\n",
+            "====> Epoch:  120/4000 (3%)\tLoss: 1020.8096\tLL: -1006.8198\tKL: 13.9897\tLL/KL: -71.9684\n",
+            "====> Epoch:  130/4000 (3%)\tLoss: 1023.7580\tLL: -1009.2080\tKL: 14.5500\tLL/KL: -69.3615\n",
+            "====> Epoch:  140/4000 (4%)\tLoss: 898.5973\tLL: -883.5008\tKL: 15.0965\tLL/KL: -58.5236\n",
+            "====> Epoch:  150/4000 (4%)\tLoss: 853.0538\tLL: -837.4263\tKL: 15.6275\tLL/KL: -53.5866\n",
+            "====> Epoch:  160/4000 (4%)\tLoss: 825.3513\tLL: -809.2095\tKL: 16.1417\tLL/KL: -50.1315\n",
+            "====> Epoch:  170/4000 (4%)\tLoss: 782.7322\tLL: -766.0916\tKL: 16.6406\tLL/KL: -46.0376\n",
+            "====> Epoch:  180/4000 (4%)\tLoss: 789.7529\tLL: -772.6270\tKL: 17.1260\tLL/KL: -45.1144\n",
+            "====> Epoch:  190/4000 (5%)\tLoss: 752.9468\tLL: -735.3498\tKL: 17.5970\tLL/KL: -41.7884\n",
+            "====> Epoch:  200/4000 (5%)\tLoss: 682.6172\tLL: -664.5613\tKL: 18.0559\tLL/KL: -36.8057\n",
+            "====> Epoch:  210/4000 (5%)\tLoss: 688.6638\tLL: -670.1644\tKL: 18.4994\tLL/KL: -36.2262\n",
+            "====> Epoch:  220/4000 (6%)\tLoss: 634.9907\tLL: -616.0682\tKL: 18.9226\tLL/KL: -32.5573\n",
+            "====> Epoch:  230/4000 (6%)\tLoss: 630.5760\tLL: -611.2480\tKL: 19.3280\tLL/KL: -31.6250\n",
+            "====> Epoch:  240/4000 (6%)\tLoss: 595.3467\tLL: -575.6279\tKL: 19.7189\tLL/KL: -29.1917\n",
+            "====> Epoch:  250/4000 (6%)\tLoss: 574.0090\tLL: -553.9197\tKL: 20.0893\tLL/KL: -27.5729\n",
+            "====> Epoch:  260/4000 (6%)\tLoss: 559.9808\tLL: -539.5347\tKL: 20.4461\tLL/KL: -26.3882\n",
+            "====> Epoch:  270/4000 (7%)\tLoss: 545.8663\tLL: -525.0755\tKL: 20.7908\tLL/KL: -25.2552\n",
+            "====> Epoch:  280/4000 (7%)\tLoss: 546.4919\tLL: -525.3721\tKL: 21.1198\tLL/KL: -24.8758\n",
+            "====> Epoch:  290/4000 (7%)\tLoss: 510.1153\tLL: -488.6801\tKL: 21.4352\tLL/KL: -22.7980\n",
+            "====> Epoch:  300/4000 (8%)\tLoss: 500.3357\tLL: -478.5957\tKL: 21.7400\tLL/KL: -22.0145\n",
+            "====> Epoch:  310/4000 (8%)\tLoss: 499.3078\tLL: -477.2734\tKL: 22.0344\tLL/KL: -21.6604\n",
+            "====> Epoch:  320/4000 (8%)\tLoss: 463.7399\tLL: -441.4229\tKL: 22.3170\tLL/KL: -19.7797\n",
+            "====> Epoch:  330/4000 (8%)\tLoss: 463.5752\tLL: -440.9851\tKL: 22.5901\tLL/KL: -19.5211\n",
+            "====> Epoch:  340/4000 (8%)\tLoss: 458.4768\tLL: -435.6202\tKL: 22.8565\tLL/KL: -19.0589\n",
+            "====> Epoch:  350/4000 (9%)\tLoss: 434.3099\tLL: -411.1974\tKL: 23.1125\tLL/KL: -17.7911\n",
+            "====> Epoch:  360/4000 (9%)\tLoss: 429.2077\tLL: -405.8496\tKL: 23.3581\tLL/KL: -17.3751\n",
+            "====> Epoch:  370/4000 (9%)\tLoss: 412.4832\tLL: -388.8886\tKL: 23.5946\tLL/KL: -16.4821\n",
+            "====> Epoch:  380/4000 (10%)\tLoss: 398.0058\tLL: -374.1815\tKL: 23.8243\tLL/KL: -15.7059\n",
+            "====> Epoch:  390/4000 (10%)\tLoss: 393.4560\tLL: -369.4090\tKL: 24.0470\tLL/KL: -15.3620\n",
+            "====> Epoch:  400/4000 (10%)\tLoss: 392.5750\tLL: -368.3130\tKL: 24.2620\tLL/KL: -15.1807\n",
+            "====> Epoch:  410/4000 (10%)\tLoss: 378.7209\tLL: -354.2501\tKL: 24.4709\tLL/KL: -14.4764\n",
+            "====> Epoch:  420/4000 (10%)\tLoss: 367.9682\tLL: -343.2985\tKL: 24.6697\tLL/KL: -13.9158\n",
+            "====> Epoch:  430/4000 (11%)\tLoss: 361.7554\tLL: -336.8932\tKL: 24.8622\tLL/KL: -13.5504\n",
+            "====> Epoch:  440/4000 (11%)\tLoss: 357.3630\tLL: -332.3181\tKL: 25.0448\tLL/KL: -13.2689\n",
+            "====> Epoch:  450/4000 (11%)\tLoss: 348.8848\tLL: -323.6605\tKL: 25.2242\tLL/KL: -12.8313\n",
+            "====> Epoch:  460/4000 (12%)\tLoss: 335.6483\tLL: -310.2470\tKL: 25.4013\tLL/KL: -12.2138\n",
+            "====> Epoch:  470/4000 (12%)\tLoss: 331.5128\tLL: -305.9395\tKL: 25.5733\tLL/KL: -11.9632\n",
+            "====> Epoch:  480/4000 (12%)\tLoss: 334.0102\tLL: -308.2699\tKL: 25.7402\tLL/KL: -11.9762\n",
+            "====> Epoch:  490/4000 (12%)\tLoss: 324.4068\tLL: -298.5001\tKL: 25.9066\tLL/KL: -11.5221\n",
+            "====> Epoch:  500/4000 (12%)\tLoss: 311.6939\tLL: -285.6309\tKL: 26.0630\tLL/KL: -10.9592\n",
+            "====> Epoch:  510/4000 (13%)\tLoss: 304.1358\tLL: -277.9218\tKL: 26.2141\tLL/KL: -10.6020\n",
+            "====> Epoch:  520/4000 (13%)\tLoss: 299.2650\tLL: -272.9048\tKL: 26.3601\tLL/KL: -10.3529\n",
+            "====> Epoch:  530/4000 (13%)\tLoss: 294.5582\tLL: -268.0558\tKL: 26.5024\tLL/KL: -10.1144\n",
+            "====> Epoch:  540/4000 (14%)\tLoss: 294.6752\tLL: -268.0354\tKL: 26.6398\tLL/KL: -10.0615\n",
+            "====> Epoch:  550/4000 (14%)\tLoss: 293.1478\tLL: -266.3736\tKL: 26.7743\tLL/KL: -9.9489\n",
+            "====> Epoch:  560/4000 (14%)\tLoss: 281.9582\tLL: -255.0537\tKL: 26.9045\tLL/KL: -9.4800\n",
+            "====> Epoch:  570/4000 (14%)\tLoss: 281.8732\tLL: -254.8418\tKL: 27.0314\tLL/KL: -9.4276\n",
+            "====> Epoch:  580/4000 (14%)\tLoss: 276.1241\tLL: -248.9697\tKL: 27.1544\tLL/KL: -9.1687\n",
+            "====> Epoch:  590/4000 (15%)\tLoss: 276.0783\tLL: -248.8074\tKL: 27.2709\tLL/KL: -9.1236\n",
+            "====> Epoch:  600/4000 (15%)\tLoss: 266.6712\tLL: -239.2841\tKL: 27.3871\tLL/KL: -8.7371\n",
+            "====> Epoch:  610/4000 (15%)\tLoss: 264.8676\tLL: -237.3665\tKL: 27.5011\tLL/KL: -8.6312\n",
+            "====> Epoch:  620/4000 (16%)\tLoss: 263.0833\tLL: -235.4697\tKL: 27.6136\tLL/KL: -8.5273\n",
+            "====> Epoch:  630/4000 (16%)\tLoss: 251.1515\tLL: -223.4301\tKL: 27.7215\tLL/KL: -8.0598\n",
+            "====> Epoch:  640/4000 (16%)\tLoss: 251.7767\tLL: -223.9522\tKL: 27.8245\tLL/KL: -8.0488\n",
+            "====> Epoch:  650/4000 (16%)\tLoss: 246.3300\tLL: -218.4038\tKL: 27.9262\tLL/KL: -7.8208\n",
+            "====> Epoch:  660/4000 (16%)\tLoss: 245.8501\tLL: -217.8259\tKL: 28.0242\tLL/KL: -7.7728\n",
+            "====> Epoch:  670/4000 (17%)\tLoss: 245.3094\tLL: -217.1899\tKL: 28.1195\tLL/KL: -7.7238\n",
+            "====> Epoch:  680/4000 (17%)\tLoss: 236.6945\tLL: -208.4813\tKL: 28.2132\tLL/KL: -7.3895\n",
+            "====> Epoch:  690/4000 (17%)\tLoss: 237.0758\tLL: -208.7699\tKL: 28.3059\tLL/KL: -7.3755\n",
+            "====> Epoch:  700/4000 (18%)\tLoss: 231.6397\tLL: -203.2452\tKL: 28.3945\tLL/KL: -7.1579\n",
+            "====> Epoch:  710/4000 (18%)\tLoss: 227.0637\tLL: -198.5839\tKL: 28.4798\tLL/KL: -6.9728\n",
+            "====> Epoch:  720/4000 (18%)\tLoss: 223.9821\tLL: -195.4181\tKL: 28.5641\tLL/KL: -6.8414\n",
+            "====> Epoch:  730/4000 (18%)\tLoss: 223.5738\tLL: -194.9274\tKL: 28.6463\tLL/KL: -6.8046\n",
+            "====> Epoch:  740/4000 (18%)\tLoss: 221.3486\tLL: -192.6204\tKL: 28.7283\tLL/KL: -6.7049\n",
+            "====> Epoch:  750/4000 (19%)\tLoss: 219.0152\tLL: -190.2068\tKL: 28.8084\tLL/KL: -6.6025\n",
+            "====> Epoch:  760/4000 (19%)\tLoss: 214.9406\tLL: -186.0567\tKL: 28.8839\tLL/KL: -6.4415\n",
+            "====> Epoch:  770/4000 (19%)\tLoss: 213.2012\tLL: -184.2440\tKL: 28.9572\tLL/KL: -6.3626\n",
+            "====> Epoch:  780/4000 (20%)\tLoss: 211.0520\tLL: -182.0237\tKL: 29.0282\tLL/KL: -6.2706\n",
+            "====> Epoch:  790/4000 (20%)\tLoss: 207.7386\tLL: -178.6399\tKL: 29.0986\tLL/KL: -6.1391\n",
+            "====> Epoch:  800/4000 (20%)\tLoss: 207.3290\tLL: -178.1631\tKL: 29.1659\tLL/KL: -6.1086\n",
+            "====> Epoch:  810/4000 (20%)\tLoss: 203.9268\tLL: -174.6934\tKL: 29.2334\tLL/KL: -5.9758\n",
+            "====> Epoch:  820/4000 (20%)\tLoss: 200.9620\tLL: -171.6631\tKL: 29.2989\tLL/KL: -5.8590\n",
+            "====> Epoch:  830/4000 (21%)\tLoss: 199.0520\tLL: -169.6889\tKL: 29.3630\tLL/KL: -5.7790\n",
+            "====> Epoch:  840/4000 (21%)\tLoss: 198.4690\tLL: -169.0436\tKL: 29.4255\tLL/KL: -5.7448\n",
+            "====> Epoch:  850/4000 (21%)\tLoss: 195.9314\tLL: -166.4465\tKL: 29.4849\tLL/KL: -5.6452\n",
+            "====> Epoch:  860/4000 (22%)\tLoss: 195.1087\tLL: -165.5657\tKL: 29.5430\tLL/KL: -5.6042\n",
+            "====> Epoch:  870/4000 (22%)\tLoss: 190.1465\tLL: -160.5462\tKL: 29.6003\tLL/KL: -5.4238\n",
+            "====> Epoch:  880/4000 (22%)\tLoss: 191.0585\tLL: -161.4023\tKL: 29.6562\tLL/KL: -5.4425\n",
+            "====> Epoch:  890/4000 (22%)\tLoss: 187.9181\tLL: -158.2053\tKL: 29.7127\tLL/KL: -5.3245\n",
+            "====> Epoch:  900/4000 (22%)\tLoss: 187.8090\tLL: -158.0425\tKL: 29.7665\tLL/KL: -5.3094\n",
+            "====> Epoch:  910/4000 (23%)\tLoss: 183.1152\tLL: -153.2968\tKL: 29.8184\tLL/KL: -5.1410\n",
+            "====> Epoch:  920/4000 (23%)\tLoss: 184.7666\tLL: -154.8980\tKL: 29.8686\tLL/KL: -5.1860\n",
+            "====> Epoch:  930/4000 (23%)\tLoss: 182.0757\tLL: -152.1576\tKL: 29.9181\tLL/KL: -5.0858\n",
+            "====> Epoch:  940/4000 (24%)\tLoss: 180.2840\tLL: -150.3191\tKL: 29.9649\tLL/KL: -5.0165\n",
+            "====> Epoch:  950/4000 (24%)\tLoss: 179.1542\tLL: -149.1447\tKL: 30.0094\tLL/KL: -4.9699\n",
+            "====> Epoch:  960/4000 (24%)\tLoss: 176.6705\tLL: -146.6174\tKL: 30.0531\tLL/KL: -4.8786\n",
+            "====> Epoch:  970/4000 (24%)\tLoss: 174.1113\tLL: -144.0163\tKL: 30.0950\tLL/KL: -4.7854\n",
+            "====> Epoch:  980/4000 (24%)\tLoss: 174.5362\tLL: -144.3990\tKL: 30.1371\tLL/KL: -4.7914\n",
+            "====> Epoch:  990/4000 (25%)\tLoss: 173.4974\tLL: -143.3190\tKL: 30.1784\tLL/KL: -4.7491\n",
+            "====> Epoch: 1000/4000 (25%)\tLoss: 172.4671\tLL: -142.2473\tKL: 30.2198\tLL/KL: -4.7071\n",
+            "====> Epoch: 1010/4000 (25%)\tLoss: 168.5215\tLL: -138.2631\tKL: 30.2584\tLL/KL: -4.5694\n",
+            "====> Epoch: 1020/4000 (26%)\tLoss: 168.8979\tLL: -138.6027\tKL: 30.2952\tLL/KL: -4.5751\n",
+            "====> Epoch: 1030/4000 (26%)\tLoss: 169.1301\tLL: -138.7994\tKL: 30.3307\tLL/KL: -4.5762\n",
+            "====> Epoch: 1040/4000 (26%)\tLoss: 165.3543\tLL: -134.9891\tKL: 30.3652\tLL/KL: -4.4455\n",
+            "====> Epoch: 1050/4000 (26%)\tLoss: 166.6760\tLL: -136.2785\tKL: 30.3975\tLL/KL: -4.4832\n",
+            "====> Epoch: 1060/4000 (26%)\tLoss: 163.4576\tLL: -133.0274\tKL: 30.4302\tLL/KL: -4.3716\n",
+            "====> Epoch: 1070/4000 (27%)\tLoss: 162.1066\tLL: -131.6454\tKL: 30.4612\tLL/KL: -4.3217\n",
+            "====> Epoch: 1080/4000 (27%)\tLoss: 161.9474\tLL: -131.4554\tKL: 30.4920\tLL/KL: -4.3111\n",
+            "====> Epoch: 1090/4000 (27%)\tLoss: 161.1549\tLL: -130.6333\tKL: 30.5217\tLL/KL: -4.2800\n",
+            "====> Epoch: 1100/4000 (28%)\tLoss: 158.8929\tLL: -128.3419\tKL: 30.5510\tLL/KL: -4.2009\n",
+            "====> Epoch: 1110/4000 (28%)\tLoss: 158.8573\tLL: -128.2784\tKL: 30.5789\tLL/KL: -4.1950\n",
+            "====> Epoch: 1120/4000 (28%)\tLoss: 157.2661\tLL: -126.6604\tKL: 30.6057\tLL/KL: -4.1385\n",
+            "====> Epoch: 1130/4000 (28%)\tLoss: 156.6613\tLL: -126.0293\tKL: 30.6320\tLL/KL: -4.1143\n",
+            "====> Epoch: 1140/4000 (28%)\tLoss: 153.7078\tLL: -123.0503\tKL: 30.6575\tLL/KL: -4.0137\n",
+            "====> Epoch: 1150/4000 (29%)\tLoss: 153.5472\tLL: -122.8657\tKL: 30.6815\tLL/KL: -4.0046\n",
+            "====> Epoch: 1160/4000 (29%)\tLoss: 153.6827\tLL: -122.9786\tKL: 30.7042\tLL/KL: -4.0053\n",
+            "====> Epoch: 1170/4000 (29%)\tLoss: 153.4385\tLL: -122.7126\tKL: 30.7258\tLL/KL: -3.9938\n",
+            "====> Epoch: 1180/4000 (30%)\tLoss: 151.0386\tLL: -120.2924\tKL: 30.7462\tLL/KL: -3.9124\n",
+            "====> Epoch: 1190/4000 (30%)\tLoss: 150.2672\tLL: -119.5029\tKL: 30.7643\tLL/KL: -3.8845\n",
+            "====> Epoch: 1200/4000 (30%)\tLoss: 150.5638\tLL: -119.7826\tKL: 30.7812\tLL/KL: -3.8914\n",
+            "====> Epoch: 1210/4000 (30%)\tLoss: 149.4304\tLL: -118.6317\tKL: 30.7986\tLL/KL: -3.8519\n",
+            "====> Epoch: 1220/4000 (30%)\tLoss: 146.7223\tLL: -115.9066\tKL: 30.8156\tLL/KL: -3.7613\n",
+            "====> Epoch: 1230/4000 (31%)\tLoss: 147.2367\tLL: -116.4042\tKL: 30.8325\tLL/KL: -3.7754\n",
+            "====> Epoch: 1240/4000 (31%)\tLoss: 146.7947\tLL: -115.9468\tKL: 30.8479\tLL/KL: -3.7587\n",
+            "====> Epoch: 1250/4000 (31%)\tLoss: 144.7565\tLL: -113.8939\tKL: 30.8626\tLL/KL: -3.6904\n",
+            "====> Epoch: 1260/4000 (32%)\tLoss: 145.1058\tLL: -114.2298\tKL: 30.8760\tLL/KL: -3.6996\n",
+            "====> Epoch: 1270/4000 (32%)\tLoss: 143.7189\tLL: -112.8299\tKL: 30.8890\tLL/KL: -3.6528\n",
+            "====> Epoch: 1280/4000 (32%)\tLoss: 143.6399\tLL: -112.7386\tKL: 30.9013\tLL/KL: -3.6484\n",
+            "====> Epoch: 1290/4000 (32%)\tLoss: 141.5525\tLL: -110.6395\tKL: 30.9131\tLL/KL: -3.5791\n",
+            "====> Epoch: 1300/4000 (32%)\tLoss: 141.9317\tLL: -111.0081\tKL: 30.9235\tLL/KL: -3.5898\n",
+            "====> Epoch: 1310/4000 (33%)\tLoss: 141.4617\tLL: -110.5276\tKL: 30.9341\tLL/KL: -3.5730\n",
+            "====> Epoch: 1320/4000 (33%)\tLoss: 140.3811\tLL: -109.4366\tKL: 30.9445\tLL/KL: -3.5365\n",
+            "====> Epoch: 1330/4000 (33%)\tLoss: 139.6463\tLL: -108.6926\tKL: 30.9537\tLL/KL: -3.5115\n",
+            "====> Epoch: 1340/4000 (34%)\tLoss: 138.2954\tLL: -107.3335\tKL: 30.9618\tLL/KL: -3.4666\n",
+            "====> Epoch: 1350/4000 (34%)\tLoss: 138.9049\tLL: -107.9368\tKL: 30.9681\tLL/KL: -3.4854\n",
+            "====> Epoch: 1360/4000 (34%)\tLoss: 138.6879\tLL: -107.7132\tKL: 30.9747\tLL/KL: -3.4775\n",
+            "====> Epoch: 1370/4000 (34%)\tLoss: 138.5089\tLL: -107.5286\tKL: 30.9803\tLL/KL: -3.4709\n",
+            "====> Epoch: 1380/4000 (34%)\tLoss: 136.7854\tLL: -105.8001\tKL: 30.9852\tLL/KL: -3.4145\n",
+            "====> Epoch: 1390/4000 (35%)\tLoss: 135.4976\tLL: -104.5080\tKL: 30.9896\tLL/KL: -3.3724\n",
+            "====> Epoch: 1400/4000 (35%)\tLoss: 135.0643\tLL: -104.0716\tKL: 30.9928\tLL/KL: -3.3579\n",
+            "====> Epoch: 1410/4000 (35%)\tLoss: 134.9902\tLL: -103.9954\tKL: 30.9947\tLL/KL: -3.3553\n",
+            "====> Epoch: 1420/4000 (36%)\tLoss: 132.2818\tLL: -101.2859\tKL: 30.9959\tLL/KL: -3.2677\n",
+            "====> Epoch: 1430/4000 (36%)\tLoss: 133.5187\tLL: -102.5225\tKL: 30.9963\tLL/KL: -3.3076\n",
+            "====> Epoch: 1440/4000 (36%)\tLoss: 133.1371\tLL: -102.1412\tKL: 30.9959\tLL/KL: -3.2953\n",
+            "====> Epoch: 1450/4000 (36%)\tLoss: 131.2534\tLL: -100.2581\tKL: 30.9953\tLL/KL: -3.2346\n",
+            "====> Epoch: 1460/4000 (36%)\tLoss: 131.7203\tLL: -100.7265\tKL: 30.9938\tLL/KL: -3.2499\n",
+            "====> Epoch: 1470/4000 (37%)\tLoss: 131.6152\tLL: -100.6233\tKL: 30.9920\tLL/KL: -3.2468\n",
+            "====> Epoch: 1480/4000 (37%)\tLoss: 130.5289\tLL: -99.5396\tKL: 30.9893\tLL/KL: -3.2121\n",
+            "====> Epoch: 1490/4000 (37%)\tLoss: 130.0383\tLL: -99.0530\tKL: 30.9854\tLL/KL: -3.1968\n",
+            "====> Epoch: 1500/4000 (38%)\tLoss: 129.4489\tLL: -98.4675\tKL: 30.9814\tLL/KL: -3.1783\n",
+            "====> Epoch: 1510/4000 (38%)\tLoss: 129.1524\tLL: -98.1759\tKL: 30.9765\tLL/KL: -3.1694\n",
+            "====> Epoch: 1520/4000 (38%)\tLoss: 128.6468\tLL: -97.6755\tKL: 30.9713\tLL/KL: -3.1537\n",
+            "====> Epoch: 1530/4000 (38%)\tLoss: 126.9510\tLL: -95.9852\tKL: 30.9658\tLL/KL: -3.0997\n",
+            "====> Epoch: 1540/4000 (38%)\tLoss: 126.9411\tLL: -95.9812\tKL: 30.9598\tLL/KL: -3.1002\n",
+            "====> Epoch: 1550/4000 (39%)\tLoss: 126.2609\tLL: -95.3080\tKL: 30.9530\tLL/KL: -3.0791\n",
+            "====> Epoch: 1560/4000 (39%)\tLoss: 126.6997\tLL: -95.7539\tKL: 30.9459\tLL/KL: -3.0942\n",
+            "====> Epoch: 1570/4000 (39%)\tLoss: 126.0221\tLL: -95.0835\tKL: 30.9387\tLL/KL: -3.0733\n",
+            "====> Epoch: 1580/4000 (40%)\tLoss: 125.6849\tLL: -94.7550\tKL: 30.9299\tLL/KL: -3.0635\n",
+            "====> Epoch: 1590/4000 (40%)\tLoss: 125.1534\tLL: -94.2337\tKL: 30.9197\tLL/KL: -3.0477\n",
+            "====> Epoch: 1600/4000 (40%)\tLoss: 124.8732\tLL: -93.9645\tKL: 30.9087\tLL/KL: -3.0401\n",
+            "====> Epoch: 1610/4000 (40%)\tLoss: 124.5181\tLL: -93.6207\tKL: 30.8974\tLL/KL: -3.0301\n",
+            "====> Epoch: 1620/4000 (40%)\tLoss: 124.0309\tLL: -93.1451\tKL: 30.8858\tLL/KL: -3.0158\n",
+            "====> Epoch: 1630/4000 (41%)\tLoss: 123.1747\tLL: -92.3018\tKL: 30.8729\tLL/KL: -2.9897\n",
+            "====> Epoch: 1640/4000 (41%)\tLoss: 123.4157\tLL: -92.5558\tKL: 30.8599\tLL/KL: -2.9992\n",
+            "====> Epoch: 1650/4000 (41%)\tLoss: 121.8925\tLL: -91.0457\tKL: 30.8468\tLL/KL: -2.9515\n",
+            "====> Epoch: 1660/4000 (42%)\tLoss: 122.4098\tLL: -91.5773\tKL: 30.8326\tLL/KL: -2.9701\n",
+            "====> Epoch: 1670/4000 (42%)\tLoss: 122.3709\tLL: -91.5529\tKL: 30.8180\tLL/KL: -2.9708\n",
+            "====> Epoch: 1680/4000 (42%)\tLoss: 121.2519\tLL: -90.4488\tKL: 30.8031\tLL/KL: -2.9363\n",
+            "====> Epoch: 1690/4000 (42%)\tLoss: 121.9796\tLL: -91.1922\tKL: 30.7874\tLL/KL: -2.9620\n",
+            "====> Epoch: 1700/4000 (42%)\tLoss: 120.3036\tLL: -89.5318\tKL: 30.7718\tLL/KL: -2.9095\n",
+            "====> Epoch: 1710/4000 (43%)\tLoss: 119.5227\tLL: -88.7679\tKL: 30.7548\tLL/KL: -2.8863\n",
+            "====> Epoch: 1720/4000 (43%)\tLoss: 119.0011\tLL: -88.2642\tKL: 30.7369\tLL/KL: -2.8716\n",
+            "====> Epoch: 1730/4000 (43%)\tLoss: 120.3862\tLL: -89.6674\tKL: 30.7188\tLL/KL: -2.9190\n",
+            "====> Epoch: 1740/4000 (44%)\tLoss: 119.4758\tLL: -88.7758\tKL: 30.7000\tLL/KL: -2.8917\n",
+            "====> Epoch: 1750/4000 (44%)\tLoss: 118.2723\tLL: -87.5913\tKL: 30.6810\tLL/KL: -2.8549\n",
+            "====> Epoch: 1760/4000 (44%)\tLoss: 118.1901\tLL: -87.5287\tKL: 30.6615\tLL/KL: -2.8547\n",
+            "====> Epoch: 1770/4000 (44%)\tLoss: 116.7912\tLL: -86.1504\tKL: 30.6408\tLL/KL: -2.8116\n",
+            "====> Epoch: 1780/4000 (44%)\tLoss: 117.1384\tLL: -86.5186\tKL: 30.6198\tLL/KL: -2.8256\n",
+            "====> Epoch: 1790/4000 (45%)\tLoss: 116.5916\tLL: -85.9929\tKL: 30.5988\tLL/KL: -2.8103\n",
+            "====> Epoch: 1800/4000 (45%)\tLoss: 116.3022\tLL: -85.7254\tKL: 30.5768\tLL/KL: -2.8036\n",
+            "====> Epoch: 1810/4000 (45%)\tLoss: 116.5209\tLL: -85.9671\tKL: 30.5538\tLL/KL: -2.8136\n",
+            "====> Epoch: 1820/4000 (46%)\tLoss: 116.1897\tLL: -85.6591\tKL: 30.5306\tLL/KL: -2.8057\n",
+            "====> Epoch: 1830/4000 (46%)\tLoss: 115.0434\tLL: -84.5361\tKL: 30.5073\tLL/KL: -2.7710\n",
+            "====> Epoch: 1840/4000 (46%)\tLoss: 115.0255\tLL: -84.5430\tKL: 30.4825\tLL/KL: -2.7735\n",
+            "====> Epoch: 1850/4000 (46%)\tLoss: 115.2710\tLL: -84.8145\tKL: 30.4566\tLL/KL: -2.7848\n",
+            "====> Epoch: 1860/4000 (46%)\tLoss: 114.5987\tLL: -84.1686\tKL: 30.4301\tLL/KL: -2.7660\n",
+            "====> Epoch: 1870/4000 (47%)\tLoss: 114.2191\tLL: -83.8153\tKL: 30.4038\tLL/KL: -2.7567\n",
+            "====> Epoch: 1880/4000 (47%)\tLoss: 113.9020\tLL: -83.5255\tKL: 30.3765\tLL/KL: -2.7497\n",
+            "====> Epoch: 1890/4000 (47%)\tLoss: 114.1575\tLL: -83.8086\tKL: 30.3490\tLL/KL: -2.7615\n",
+            "====> Epoch: 1900/4000 (48%)\tLoss: 113.6494\tLL: -83.3287\tKL: 30.3207\tLL/KL: -2.7482\n",
+            "====> Epoch: 1910/4000 (48%)\tLoss: 112.7187\tLL: -82.4266\tKL: 30.2921\tLL/KL: -2.7211\n",
+            "====> Epoch: 1920/4000 (48%)\tLoss: 112.8725\tLL: -82.6098\tKL: 30.2627\tLL/KL: -2.7298\n",
+            "====> Epoch: 1930/4000 (48%)\tLoss: 112.6450\tLL: -82.4123\tKL: 30.2327\tLL/KL: -2.7259\n",
+            "====> Epoch: 1940/4000 (48%)\tLoss: 112.1951\tLL: -81.9939\tKL: 30.2012\tLL/KL: -2.7149\n",
+            "====> Epoch: 1950/4000 (49%)\tLoss: 111.3912\tLL: -81.2213\tKL: 30.1699\tLL/KL: -2.6921\n",
+            "====> Epoch: 1960/4000 (49%)\tLoss: 112.4142\tLL: -82.2758\tKL: 30.1384\tLL/KL: -2.7299\n",
+            "====> Epoch: 1970/4000 (49%)\tLoss: 110.9165\tLL: -80.8093\tKL: 30.1071\tLL/KL: -2.6841\n",
+            "====> Epoch: 1980/4000 (50%)\tLoss: 111.2946\tLL: -81.2199\tKL: 30.0747\tLL/KL: -2.7006\n",
+            "====> Epoch: 1990/4000 (50%)\tLoss: 111.0928\tLL: -81.0520\tKL: 30.0408\tLL/KL: -2.6981\n",
+            "====> Epoch: 2000/4000 (50%)\tLoss: 110.0879\tLL: -80.0814\tKL: 30.0065\tLL/KL: -2.6688\n",
+            "====> Epoch: 2010/4000 (50%)\tLoss: 110.1007\tLL: -80.1289\tKL: 29.9717\tLL/KL: -2.6735\n",
+            "====> Epoch: 2020/4000 (50%)\tLoss: 110.1946\tLL: -80.2585\tKL: 29.9361\tLL/KL: -2.6810\n",
+            "====> Epoch: 2030/4000 (51%)\tLoss: 109.1378\tLL: -79.2379\tKL: 29.8999\tLL/KL: -2.6501\n",
+            "====> Epoch: 2040/4000 (51%)\tLoss: 109.1932\tLL: -79.3297\tKL: 29.8636\tLL/KL: -2.6564\n",
+            "====> Epoch: 2050/4000 (51%)\tLoss: 108.8520\tLL: -79.0253\tKL: 29.8267\tLL/KL: -2.6495\n",
+            "====> Epoch: 2060/4000 (52%)\tLoss: 108.7456\tLL: -78.9567\tKL: 29.7889\tLL/KL: -2.6505\n",
+            "====> Epoch: 2070/4000 (52%)\tLoss: 107.9749\tLL: -78.2239\tKL: 29.7510\tLL/KL: -2.6293\n",
+            "====> Epoch: 2080/4000 (52%)\tLoss: 108.5042\tLL: -78.7921\tKL: 29.7121\tLL/KL: -2.6518\n",
+            "====> Epoch: 2090/4000 (52%)\tLoss: 107.8152\tLL: -78.1427\tKL: 29.6725\tLL/KL: -2.6335\n",
+            "====> Epoch: 2100/4000 (52%)\tLoss: 107.2682\tLL: -77.6357\tKL: 29.6325\tLL/KL: -2.6200\n",
+            "====> Epoch: 2110/4000 (53%)\tLoss: 107.4031\tLL: -77.8108\tKL: 29.5923\tLL/KL: -2.6294\n",
+            "====> Epoch: 2120/4000 (53%)\tLoss: 106.8915\tLL: -77.3395\tKL: 29.5519\tLL/KL: -2.6171\n",
+            "====> Epoch: 2130/4000 (53%)\tLoss: 106.5973\tLL: -77.0860\tKL: 29.5113\tLL/KL: -2.6121\n",
+            "====> Epoch: 2140/4000 (54%)\tLoss: 106.6751\tLL: -77.2055\tKL: 29.4696\tLL/KL: -2.6198\n",
+            "====> Epoch: 2150/4000 (54%)\tLoss: 106.3242\tLL: -76.8966\tKL: 29.4276\tLL/KL: -2.6131\n",
+            "====> Epoch: 2160/4000 (54%)\tLoss: 106.0420\tLL: -76.6573\tKL: 29.3846\tLL/KL: -2.6088\n",
+            "====> Epoch: 2170/4000 (54%)\tLoss: 106.2133\tLL: -76.8724\tKL: 29.3408\tLL/KL: -2.6200\n",
+            "====> Epoch: 2180/4000 (54%)\tLoss: 106.2968\tLL: -77.0005\tKL: 29.2964\tLL/KL: -2.6283\n",
+            "====> Epoch: 2190/4000 (55%)\tLoss: 105.3442\tLL: -76.0925\tKL: 29.2517\tLL/KL: -2.6013\n",
+            "====> Epoch: 2200/4000 (55%)\tLoss: 104.8452\tLL: -75.6389\tKL: 29.2062\tLL/KL: -2.5898\n",
+            "====> Epoch: 2210/4000 (55%)\tLoss: 105.5631\tLL: -76.4038\tKL: 29.1593\tLL/KL: -2.6202\n",
+            "====> Epoch: 2220/4000 (56%)\tLoss: 104.4840\tLL: -75.3712\tKL: 29.1128\tLL/KL: -2.5889\n",
+            "====> Epoch: 2230/4000 (56%)\tLoss: 104.6136\tLL: -75.5478\tKL: 29.0657\tLL/KL: -2.5992\n",
+            "====> Epoch: 2240/4000 (56%)\tLoss: 103.9667\tLL: -74.9480\tKL: 29.0187\tLL/KL: -2.5827\n",
+            "====> Epoch: 2250/4000 (56%)\tLoss: 103.3397\tLL: -74.3695\tKL: 28.9702\tLL/KL: -2.5671\n",
+            "====> Epoch: 2260/4000 (56%)\tLoss: 103.5129\tLL: -74.5916\tKL: 28.9213\tLL/KL: -2.5791\n",
+            "====> Epoch: 2270/4000 (57%)\tLoss: 104.3382\tLL: -75.4660\tKL: 28.8722\tLL/KL: -2.6138\n",
+            "====> Epoch: 2280/4000 (57%)\tLoss: 103.1101\tLL: -74.2871\tKL: 28.8230\tLL/KL: -2.5774\n",
+            "====> Epoch: 2290/4000 (57%)\tLoss: 102.8944\tLL: -74.1213\tKL: 28.7730\tLL/KL: -2.5761\n",
+            "====> Epoch: 2300/4000 (58%)\tLoss: 102.6628\tLL: -73.9409\tKL: 28.7219\tLL/KL: -2.5744\n",
+            "====> Epoch: 2310/4000 (58%)\tLoss: 103.0186\tLL: -74.3483\tKL: 28.6702\tLL/KL: -2.5932\n",
+            "====> Epoch: 2320/4000 (58%)\tLoss: 102.3465\tLL: -73.7283\tKL: 28.6182\tLL/KL: -2.5763\n",
+            "====> Epoch: 2330/4000 (58%)\tLoss: 101.9786\tLL: -73.4132\tKL: 28.5654\tLL/KL: -2.5700\n",
+            "====> Epoch: 2340/4000 (58%)\tLoss: 101.4500\tLL: -72.9381\tKL: 28.5119\tLL/KL: -2.5582\n",
+            "====> Epoch: 2350/4000 (59%)\tLoss: 101.5689\tLL: -73.1111\tKL: 28.4578\tLL/KL: -2.5691\n",
+            "====> Epoch: 2360/4000 (59%)\tLoss: 101.4989\tLL: -73.0955\tKL: 28.4034\tLL/KL: -2.5735\n",
+            "====> Epoch: 2370/4000 (59%)\tLoss: 101.7111\tLL: -73.3631\tKL: 28.3480\tLL/KL: -2.5880\n",
+            "====> Epoch: 2380/4000 (60%)\tLoss: 100.3236\tLL: -72.0310\tKL: 28.2926\tLL/KL: -2.5459\n",
+            "====> Epoch: 2390/4000 (60%)\tLoss: 101.0817\tLL: -72.8457\tKL: 28.2360\tLL/KL: -2.5799\n",
+            "====> Epoch: 2400/4000 (60%)\tLoss: 100.2342\tLL: -72.0545\tKL: 28.1797\tLL/KL: -2.5570\n",
+            "====> Epoch: 2410/4000 (60%)\tLoss: 100.2808\tLL: -72.1583\tKL: 28.1226\tLL/KL: -2.5658\n",
+            "====> Epoch: 2420/4000 (60%)\tLoss: 99.8616\tLL: -71.7969\tKL: 28.0647\tLL/KL: -2.5583\n",
+            "====> Epoch: 2430/4000 (61%)\tLoss: 99.8383\tLL: -71.8324\tKL: 28.0059\tLL/KL: -2.5649\n",
+            "====> Epoch: 2440/4000 (61%)\tLoss: 99.6338\tLL: -71.6860\tKL: 27.9477\tLL/KL: -2.5650\n",
+            "====> Epoch: 2450/4000 (61%)\tLoss: 99.4741\tLL: -71.5851\tKL: 27.8891\tLL/KL: -2.5668\n",
+            "====> Epoch: 2460/4000 (62%)\tLoss: 100.0250\tLL: -72.1951\tKL: 27.8298\tLL/KL: -2.5942\n",
+            "====> Epoch: 2470/4000 (62%)\tLoss: 98.9745\tLL: -71.2041\tKL: 27.7704\tLL/KL: -2.5640\n",
+            "====> Epoch: 2480/4000 (62%)\tLoss: 98.4756\tLL: -70.7653\tKL: 27.7104\tLL/KL: -2.5537\n",
+            "====> Epoch: 2490/4000 (62%)\tLoss: 98.6267\tLL: -70.9780\tKL: 27.6487\tLL/KL: -2.5671\n",
+            "====> Epoch: 2500/4000 (62%)\tLoss: 98.0346\tLL: -70.4483\tKL: 27.5863\tLL/KL: -2.5537\n",
+            "====> Epoch: 2510/4000 (63%)\tLoss: 98.4276\tLL: -70.9042\tKL: 27.5234\tLL/KL: -2.5761\n",
+            "====> Epoch: 2520/4000 (63%)\tLoss: 98.1849\tLL: -70.7244\tKL: 27.4605\tLL/KL: -2.5755\n",
+            "====> Epoch: 2530/4000 (63%)\tLoss: 97.6992\tLL: -70.3018\tKL: 27.3974\tLL/KL: -2.5660\n",
+            "====> Epoch: 2540/4000 (64%)\tLoss: 97.5508\tLL: -70.2164\tKL: 27.3344\tLL/KL: -2.5688\n",
+            "====> Epoch: 2550/4000 (64%)\tLoss: 97.8494\tLL: -70.5784\tKL: 27.2710\tLL/KL: -2.5880\n",
+            "====> Epoch: 2560/4000 (64%)\tLoss: 97.2034\tLL: -69.9969\tKL: 27.2065\tLL/KL: -2.5728\n",
+            "====> Epoch: 2570/4000 (64%)\tLoss: 97.5908\tLL: -70.4494\tKL: 27.1414\tLL/KL: -2.5956\n",
+            "====> Epoch: 2580/4000 (64%)\tLoss: 96.8615\tLL: -69.7853\tKL: 27.0762\tLL/KL: -2.5774\n",
+            "====> Epoch: 2590/4000 (65%)\tLoss: 96.7472\tLL: -69.7369\tKL: 27.0103\tLL/KL: -2.5819\n",
+            "====> Epoch: 2600/4000 (65%)\tLoss: 96.0204\tLL: -69.0766\tKL: 26.9438\tLL/KL: -2.5637\n",
+            "====> Epoch: 2610/4000 (65%)\tLoss: 96.9956\tLL: -70.1181\tKL: 26.8774\tLL/KL: -2.6088\n",
+            "====> Epoch: 2620/4000 (66%)\tLoss: 96.4353\tLL: -69.6241\tKL: 26.8112\tLL/KL: -2.5968\n",
+            "====> Epoch: 2630/4000 (66%)\tLoss: 95.9664\tLL: -69.2221\tKL: 26.7443\tLL/KL: -2.5883\n",
+            "====> Epoch: 2640/4000 (66%)\tLoss: 95.7654\tLL: -69.0891\tKL: 26.6762\tLL/KL: -2.5899\n",
+            "====> Epoch: 2650/4000 (66%)\tLoss: 95.4350\tLL: -68.8269\tKL: 26.6081\tLL/KL: -2.5867\n",
+            "====> Epoch: 2660/4000 (66%)\tLoss: 95.1923\tLL: -68.6523\tKL: 26.5400\tLL/KL: -2.5867\n",
+            "====> Epoch: 2670/4000 (67%)\tLoss: 94.9227\tLL: -68.4515\tKL: 26.4712\tLL/KL: -2.5859\n",
+            "====> Epoch: 2680/4000 (67%)\tLoss: 95.1568\tLL: -68.7551\tKL: 26.4017\tLL/KL: -2.6042\n",
+            "====> Epoch: 2690/4000 (67%)\tLoss: 95.1969\tLL: -68.8649\tKL: 26.3319\tLL/KL: -2.6153\n",
+            "====> Epoch: 2700/4000 (68%)\tLoss: 95.0173\tLL: -68.7554\tKL: 26.2619\tLL/KL: -2.6181\n",
+            "====> Epoch: 2710/4000 (68%)\tLoss: 94.8561\tLL: -68.6641\tKL: 26.1920\tLL/KL: -2.6216\n",
+            "====> Epoch: 2720/4000 (68%)\tLoss: 94.8266\tLL: -68.7049\tKL: 26.1217\tLL/KL: -2.6302\n",
+            "====> Epoch: 2730/4000 (68%)\tLoss: 94.5142\tLL: -68.4631\tKL: 26.0511\tLL/KL: -2.6280\n",
+            "====> Epoch: 2740/4000 (68%)\tLoss: 94.0154\tLL: -68.0347\tKL: 25.9807\tLL/KL: -2.6187\n",
+            "====> Epoch: 2750/4000 (69%)\tLoss: 93.7879\tLL: -67.8786\tKL: 25.9093\tLL/KL: -2.6199\n",
+            "====> Epoch: 2760/4000 (69%)\tLoss: 93.5266\tLL: -67.6884\tKL: 25.8382\tLL/KL: -2.6197\n",
+            "====> Epoch: 2770/4000 (69%)\tLoss: 93.4782\tLL: -67.7109\tKL: 25.7673\tLL/KL: -2.6278\n",
+            "====> Epoch: 2780/4000 (70%)\tLoss: 93.2745\tLL: -67.5792\tKL: 25.6953\tLL/KL: -2.6300\n",
+            "====> Epoch: 2790/4000 (70%)\tLoss: 92.9669\tLL: -67.3433\tKL: 25.6236\tLL/KL: -2.6282\n",
+            "====> Epoch: 2800/4000 (70%)\tLoss: 93.6935\tLL: -68.1418\tKL: 25.5517\tLL/KL: -2.6668\n",
+            "====> Epoch: 2810/4000 (70%)\tLoss: 92.9768\tLL: -67.4972\tKL: 25.4797\tLL/KL: -2.6491\n",
+            "====> Epoch: 2820/4000 (70%)\tLoss: 92.8659\tLL: -67.4588\tKL: 25.4071\tLL/KL: -2.6551\n",
+            "====> Epoch: 2830/4000 (71%)\tLoss: 92.8922\tLL: -67.5585\tKL: 25.3337\tLL/KL: -2.6667\n",
+            "====> Epoch: 2840/4000 (71%)\tLoss: 92.5307\tLL: -67.2701\tKL: 25.2605\tLL/KL: -2.6631\n",
+            "====> Epoch: 2850/4000 (71%)\tLoss: 92.0877\tLL: -66.8994\tKL: 25.1883\tLL/KL: -2.6560\n",
+            "====> Epoch: 2860/4000 (72%)\tLoss: 92.1591\tLL: -67.0442\tKL: 25.1149\tLL/KL: -2.6695\n",
+            "====> Epoch: 2870/4000 (72%)\tLoss: 91.6305\tLL: -66.5891\tKL: 25.0414\tLL/KL: -2.6592\n",
+            "====> Epoch: 2880/4000 (72%)\tLoss: 91.5860\tLL: -66.6177\tKL: 24.9683\tLL/KL: -2.6681\n",
+            "====> Epoch: 2890/4000 (72%)\tLoss: 91.6784\tLL: -66.7827\tKL: 24.8957\tLL/KL: -2.6825\n",
+            "====> Epoch: 2900/4000 (72%)\tLoss: 91.4959\tLL: -66.6736\tKL: 24.8224\tLL/KL: -2.6860\n",
+            "====> Epoch: 2910/4000 (73%)\tLoss: 91.6736\tLL: -66.9244\tKL: 24.7493\tLL/KL: -2.7041\n",
+            "====> Epoch: 2920/4000 (73%)\tLoss: 91.0729\tLL: -66.3964\tKL: 24.6765\tLL/KL: -2.6907\n",
+            "====> Epoch: 2930/4000 (73%)\tLoss: 90.9630\tLL: -66.3593\tKL: 24.6038\tLL/KL: -2.6971\n",
+            "====> Epoch: 2940/4000 (74%)\tLoss: 91.3166\tLL: -66.7850\tKL: 24.5316\tLL/KL: -2.7224\n",
+            "====> Epoch: 2950/4000 (74%)\tLoss: 90.9104\tLL: -66.4514\tKL: 24.4590\tLL/KL: -2.7168\n",
+            "====> Epoch: 2960/4000 (74%)\tLoss: 90.3585\tLL: -65.9717\tKL: 24.3867\tLL/KL: -2.7052\n",
+            "====> Epoch: 2970/4000 (74%)\tLoss: 90.5635\tLL: -66.2495\tKL: 24.3140\tLL/KL: -2.7247\n",
+            "====> Epoch: 2980/4000 (74%)\tLoss: 90.1193\tLL: -65.8782\tKL: 24.2411\tLL/KL: -2.7176\n",
+            "====> Epoch: 2990/4000 (75%)\tLoss: 90.0103\tLL: -65.8426\tKL: 24.1678\tLL/KL: -2.7244\n",
+            "====> Epoch: 3000/4000 (75%)\tLoss: 89.8190\tLL: -65.7243\tKL: 24.0948\tLL/KL: -2.7277\n",
+            "====> Epoch: 3010/4000 (75%)\tLoss: 89.8121\tLL: -65.7894\tKL: 24.0227\tLL/KL: -2.7386\n",
+            "====> Epoch: 3020/4000 (76%)\tLoss: 89.6767\tLL: -65.7264\tKL: 23.9503\tLL/KL: -2.7443\n",
+            "====> Epoch: 3030/4000 (76%)\tLoss: 89.3854\tLL: -65.5077\tKL: 23.8778\tLL/KL: -2.7435\n",
+            "====> Epoch: 3040/4000 (76%)\tLoss: 89.5572\tLL: -65.7528\tKL: 23.8044\tLL/KL: -2.7622\n",
+            "====> Epoch: 3050/4000 (76%)\tLoss: 89.0642\tLL: -65.3328\tKL: 23.7314\tLL/KL: -2.7530\n",
+            "====> Epoch: 3060/4000 (76%)\tLoss: 88.9482\tLL: -65.2887\tKL: 23.6595\tLL/KL: -2.7595\n",
+            "====> Epoch: 3070/4000 (77%)\tLoss: 89.0676\tLL: -65.4795\tKL: 23.5881\tLL/KL: -2.7759\n",
+            "====> Epoch: 3080/4000 (77%)\tLoss: 88.6159\tLL: -65.0990\tKL: 23.5168\tLL/KL: -2.7682\n",
+            "====> Epoch: 3090/4000 (77%)\tLoss: 88.4032\tLL: -64.9583\tKL: 23.4449\tLL/KL: -2.7707\n",
+            "====> Epoch: 3100/4000 (78%)\tLoss: 88.3004\tLL: -64.9270\tKL: 23.3734\tLL/KL: -2.7778\n",
+            "====> Epoch: 3110/4000 (78%)\tLoss: 88.8734\tLL: -65.5708\tKL: 23.3026\tLL/KL: -2.8139\n",
+            "====> Epoch: 3120/4000 (78%)\tLoss: 88.3635\tLL: -65.1310\tKL: 23.2325\tLL/KL: -2.8034\n",
+            "====> Epoch: 3130/4000 (78%)\tLoss: 88.3539\tLL: -65.1913\tKL: 23.1626\tLL/KL: -2.8145\n",
+            "====> Epoch: 3140/4000 (78%)\tLoss: 87.8807\tLL: -64.7871\tKL: 23.0936\tLL/KL: -2.8054\n",
+            "====> Epoch: 3150/4000 (79%)\tLoss: 87.9172\tLL: -64.8934\tKL: 23.0238\tLL/KL: -2.8185\n",
+            "====> Epoch: 3160/4000 (79%)\tLoss: 87.6789\tLL: -64.7245\tKL: 22.9544\tLL/KL: -2.8197\n",
+            "====> Epoch: 3170/4000 (79%)\tLoss: 87.7996\tLL: -64.9146\tKL: 22.8850\tLL/KL: -2.8366\n",
+            "====> Epoch: 3180/4000 (80%)\tLoss: 87.8042\tLL: -64.9880\tKL: 22.8161\tLL/KL: -2.8483\n",
+            "====> Epoch: 3190/4000 (80%)\tLoss: 87.3867\tLL: -64.6387\tKL: 22.7479\tLL/KL: -2.8415\n",
+            "====> Epoch: 3200/4000 (80%)\tLoss: 87.1189\tLL: -64.4397\tKL: 22.6792\tLL/KL: -2.8414\n",
+            "====> Epoch: 3210/4000 (80%)\tLoss: 86.9710\tLL: -64.3606\tKL: 22.6104\tLL/KL: -2.8465\n",
+            "====> Epoch: 3220/4000 (80%)\tLoss: 87.3506\tLL: -64.8091\tKL: 22.5414\tLL/KL: -2.8751\n",
+            "====> Epoch: 3230/4000 (81%)\tLoss: 86.5046\tLL: -64.0322\tKL: 22.4725\tLL/KL: -2.8494\n",
+            "====> Epoch: 3240/4000 (81%)\tLoss: 86.6836\tLL: -64.2798\tKL: 22.4038\tLL/KL: -2.8692\n",
+            "====> Epoch: 3250/4000 (81%)\tLoss: 86.6839\tLL: -64.3479\tKL: 22.3360\tLL/KL: -2.8809\n",
+            "====> Epoch: 3260/4000 (82%)\tLoss: 86.6879\tLL: -64.4199\tKL: 22.2679\tLL/KL: -2.8929\n",
+            "====> Epoch: 3270/4000 (82%)\tLoss: 86.2511\tLL: -64.0505\tKL: 22.2006\tLL/KL: -2.8851\n",
+            "====> Epoch: 3280/4000 (82%)\tLoss: 86.1050\tLL: -63.9719\tKL: 22.1332\tLL/KL: -2.8903\n",
+            "====> Epoch: 3290/4000 (82%)\tLoss: 86.0897\tLL: -64.0235\tKL: 22.0662\tLL/KL: -2.9014\n",
+            "====> Epoch: 3300/4000 (82%)\tLoss: 86.2958\tLL: -64.2965\tKL: 21.9993\tLL/KL: -2.9227\n",
+            "====> Epoch: 3310/4000 (83%)\tLoss: 86.2684\tLL: -64.3344\tKL: 21.9340\tLL/KL: -2.9331\n",
+            "====> Epoch: 3320/4000 (83%)\tLoss: 86.1643\tLL: -64.2960\tKL: 21.8683\tLL/KL: -2.9401\n",
+            "====> Epoch: 3330/4000 (83%)\tLoss: 85.8047\tLL: -64.0011\tKL: 21.8036\tLL/KL: -2.9353\n",
+            "====> Epoch: 3340/4000 (84%)\tLoss: 86.3398\tLL: -64.6006\tKL: 21.7393\tLL/KL: -2.9716\n",
+            "====> Epoch: 3350/4000 (84%)\tLoss: 85.4060\tLL: -63.7312\tKL: 21.6748\tLL/KL: -2.9403\n",
+            "====> Epoch: 3360/4000 (84%)\tLoss: 85.3514\tLL: -63.7419\tKL: 21.6095\tLL/KL: -2.9497\n",
+            "====> Epoch: 3370/4000 (84%)\tLoss: 85.3147\tLL: -63.7701\tKL: 21.5446\tLL/KL: -2.9599\n",
+            "====> Epoch: 3380/4000 (84%)\tLoss: 84.7695\tLL: -63.2895\tKL: 21.4800\tLL/KL: -2.9464\n",
+            "====> Epoch: 3390/4000 (85%)\tLoss: 84.7476\tLL: -63.3326\tKL: 21.4150\tLL/KL: -2.9574\n",
+            "====> Epoch: 3400/4000 (85%)\tLoss: 84.7559\tLL: -63.4052\tKL: 21.3507\tLL/KL: -2.9697\n",
+            "====> Epoch: 3410/4000 (85%)\tLoss: 85.0421\tLL: -63.7556\tKL: 21.2865\tLL/KL: -2.9951\n",
+            "====> Epoch: 3420/4000 (86%)\tLoss: 84.3974\tLL: -63.1741\tKL: 21.2233\tLL/KL: -2.9766\n",
+            "====> Epoch: 3430/4000 (86%)\tLoss: 84.4073\tLL: -63.2467\tKL: 21.1606\tLL/KL: -2.9889\n",
+            "====> Epoch: 3440/4000 (86%)\tLoss: 84.4917\tLL: -63.3934\tKL: 21.0983\tLL/KL: -3.0047\n",
+            "====> Epoch: 3450/4000 (86%)\tLoss: 84.3608\tLL: -63.3241\tKL: 21.0367\tLL/KL: -3.0102\n",
+            "====> Epoch: 3460/4000 (86%)\tLoss: 83.9466\tLL: -62.9714\tKL: 20.9753\tLL/KL: -3.0022\n",
+            "====> Epoch: 3470/4000 (87%)\tLoss: 84.1358\tLL: -63.2229\tKL: 20.9130\tLL/KL: -3.0231\n",
+            "====> Epoch: 3480/4000 (87%)\tLoss: 84.0468\tLL: -63.1959\tKL: 20.8508\tLL/KL: -3.0309\n",
+            "====> Epoch: 3490/4000 (87%)\tLoss: 83.9879\tLL: -63.1992\tKL: 20.7888\tLL/KL: -3.0401\n",
+            "====> Epoch: 3500/4000 (88%)\tLoss: 83.6544\tLL: -62.9275\tKL: 20.7270\tLL/KL: -3.0360\n",
+            "====> Epoch: 3510/4000 (88%)\tLoss: 83.8493\tLL: -63.1840\tKL: 20.6653\tLL/KL: -3.0575\n",
+            "====> Epoch: 3520/4000 (88%)\tLoss: 83.9265\tLL: -63.3224\tKL: 20.6040\tLL/KL: -3.0733\n",
+            "====> Epoch: 3530/4000 (88%)\tLoss: 83.4202\tLL: -62.8766\tKL: 20.5436\tLL/KL: -3.0606\n",
+            "====> Epoch: 3540/4000 (88%)\tLoss: 83.4843\tLL: -63.0015\tKL: 20.4829\tLL/KL: -3.0758\n",
+            "====> Epoch: 3550/4000 (89%)\tLoss: 83.2805\tLL: -62.8573\tKL: 20.4232\tLL/KL: -3.0777\n",
+            "====> Epoch: 3560/4000 (89%)\tLoss: 83.4515\tLL: -63.0891\tKL: 20.3624\tLL/KL: -3.0983\n",
+            "====> Epoch: 3570/4000 (89%)\tLoss: 83.1420\tLL: -62.8398\tKL: 20.3023\tLL/KL: -3.0952\n",
+            "====> Epoch: 3580/4000 (90%)\tLoss: 83.1346\tLL: -62.8913\tKL: 20.2433\tLL/KL: -3.1068\n",
+            "====> Epoch: 3590/4000 (90%)\tLoss: 83.2231\tLL: -63.0382\tKL: 20.1849\tLL/KL: -3.1230\n",
+            "====> Epoch: 3600/4000 (90%)\tLoss: 82.6401\tLL: -62.5135\tKL: 20.1265\tLL/KL: -3.1060\n",
+            "====> Epoch: 3610/4000 (90%)\tLoss: 82.8429\tLL: -62.7744\tKL: 20.0685\tLL/KL: -3.1280\n",
+            "====> Epoch: 3620/4000 (90%)\tLoss: 82.5110\tLL: -62.5018\tKL: 20.0092\tLL/KL: -3.1236\n",
+            "====> Epoch: 3630/4000 (91%)\tLoss: 82.7717\tLL: -62.8207\tKL: 19.9510\tLL/KL: -3.1487\n",
+            "====> Epoch: 3640/4000 (91%)\tLoss: 82.2600\tLL: -62.3677\tKL: 19.8922\tLL/KL: -3.1353\n",
+            "====> Epoch: 3650/4000 (91%)\tLoss: 82.2248\tLL: -62.3922\tKL: 19.8326\tLL/KL: -3.1459\n",
+            "====> Epoch: 3660/4000 (92%)\tLoss: 82.6233\tLL: -62.8494\tKL: 19.7739\tLL/KL: -3.1784\n",
+            "====> Epoch: 3670/4000 (92%)\tLoss: 82.2699\tLL: -62.5523\tKL: 19.7176\tLL/KL: -3.1724\n",
+            "====> Epoch: 3680/4000 (92%)\tLoss: 82.3075\tLL: -62.6461\tKL: 19.6614\tLL/KL: -3.1862\n",
+            "====> Epoch: 3690/4000 (92%)\tLoss: 82.1828\tLL: -62.5786\tKL: 19.6042\tLL/KL: -3.1921\n",
+            "====> Epoch: 3700/4000 (92%)\tLoss: 82.0798\tLL: -62.5315\tKL: 19.5482\tLL/KL: -3.1988\n",
+            "====> Epoch: 3710/4000 (93%)\tLoss: 82.0010\tLL: -62.5087\tKL: 19.4923\tLL/KL: -3.2068\n",
+            "====> Epoch: 3720/4000 (93%)\tLoss: 81.9678\tLL: -62.5310\tKL: 19.4368\tLL/KL: -3.2171\n",
+            "====> Epoch: 3730/4000 (93%)\tLoss: 81.7141\tLL: -62.3324\tKL: 19.3817\tLL/KL: -3.2160\n",
+            "====> Epoch: 3740/4000 (94%)\tLoss: 82.0993\tLL: -62.7725\tKL: 19.3268\tLL/KL: -3.2479\n",
+            "====> Epoch: 3750/4000 (94%)\tLoss: 81.4743\tLL: -62.2018\tKL: 19.2725\tLL/KL: -3.2275\n",
+            "====> Epoch: 3760/4000 (94%)\tLoss: 81.6056\tLL: -62.3883\tKL: 19.2174\tLL/KL: -3.2465\n",
+            "====> Epoch: 3770/4000 (94%)\tLoss: 81.3737\tLL: -62.2111\tKL: 19.1627\tLL/KL: -3.2465\n",
+            "====> Epoch: 3780/4000 (94%)\tLoss: 81.4818\tLL: -62.3737\tKL: 19.1081\tLL/KL: -3.2642\n",
+            "====> Epoch: 3790/4000 (95%)\tLoss: 81.2036\tLL: -62.1503\tKL: 19.0533\tLL/KL: -3.2619\n",
+            "====> Epoch: 3800/4000 (95%)\tLoss: 81.6255\tLL: -62.6267\tKL: 18.9988\tLL/KL: -3.2964\n",
+            "====> Epoch: 3810/4000 (95%)\tLoss: 81.2949\tLL: -62.3494\tKL: 18.9454\tLL/KL: -3.2910\n",
+            "====> Epoch: 3820/4000 (96%)\tLoss: 81.0136\tLL: -62.1221\tKL: 18.8915\tLL/KL: -3.2884\n",
+            "====> Epoch: 3830/4000 (96%)\tLoss: 80.7962\tLL: -61.9589\tKL: 18.8373\tLL/KL: -3.2892\n",
+            "====> Epoch: 3840/4000 (96%)\tLoss: 80.6964\tLL: -61.9131\tKL: 18.7833\tLL/KL: -3.2962\n",
+            "====> Epoch: 3850/4000 (96%)\tLoss: 81.0618\tLL: -62.3325\tKL: 18.7293\tLL/KL: -3.3281\n",
+            "====> Epoch: 3860/4000 (96%)\tLoss: 80.8021\tLL: -62.1260\tKL: 18.6761\tLL/KL: -3.3265\n",
+            "====> Epoch: 3870/4000 (97%)\tLoss: 80.7582\tLL: -62.1350\tKL: 18.6231\tLL/KL: -3.3364\n",
+            "====> Epoch: 3880/4000 (97%)\tLoss: 80.5951\tLL: -62.0237\tKL: 18.5713\tLL/KL: -3.3398\n",
+            "====> Epoch: 3890/4000 (97%)\tLoss: 80.5728\tLL: -62.0530\tKL: 18.5197\tLL/KL: -3.3506\n",
+            "====> Epoch: 3900/4000 (98%)\tLoss: 80.1718\tLL: -61.7031\tKL: 18.4687\tLL/KL: -3.3410\n",
+            "====> Epoch: 3910/4000 (98%)\tLoss: 80.3315\tLL: -61.9125\tKL: 18.4191\tLL/KL: -3.3613\n",
+            "====> Epoch: 3920/4000 (98%)\tLoss: 80.5162\tLL: -62.1461\tKL: 18.3701\tLL/KL: -3.3830\n",
+            "====> Epoch: 3930/4000 (98%)\tLoss: 80.3840\tLL: -62.0640\tKL: 18.3201\tLL/KL: -3.3878\n",
+            "====> Epoch: 3940/4000 (98%)\tLoss: 80.1318\tLL: -61.8610\tKL: 18.2707\tLL/KL: -3.3858\n",
+            "====> Epoch: 3950/4000 (99%)\tLoss: 79.9984\tLL: -61.7779\tKL: 18.2204\tLL/KL: -3.3906\n",
+            "====> Epoch: 3960/4000 (99%)\tLoss: 79.9384\tLL: -61.7683\tKL: 18.1701\tLL/KL: -3.3994\n",
+            "====> Epoch: 3970/4000 (99%)\tLoss: 80.0093\tLL: -61.8892\tKL: 18.1201\tLL/KL: -3.4155\n",
+            "====> Epoch: 3980/4000 (100%)\tLoss: 79.9254\tLL: -61.8547\tKL: 18.0707\tLL/KL: -3.4229\n",
+            "====> Epoch: 3990/4000 (100%)\tLoss: 80.0126\tLL: -61.9907\tKL: 18.0220\tLL/KL: -3.4397\n",
+            "End fitting: 2020-12-14 16:19:33.463493\n",
+            "\tElapsed: 0:00:27.432505\n",
+            "\tDeleting model_sparse.pt.running\n",
+            "\tDeleted: 2020-12-14 16:19:33.464009\n",
+            "\t\tElapsed: 0:00:27.433916\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7L_4azNO0T6i"
+      },
+      "source": [
+        "We see that the model recognises again only two meaningful latent dimensions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LC2yKkfKbE68",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "6d761283-152c-4de3-8780-198c083eb29d"
+      },
+      "source": [
+        "print('Probability of redundancy: ', model_sparse.dropout.detach().numpy())\n",
+        "indices = np.where(model_sparse.dropout.detach().numpy().flatten() < 0.2)[0]\n",
+        "non_redundant_comps = indices.tolist()\n",
+        "print(f'Non-redundant components: {non_redundant_comps}')\n",
+        "plot_dropout(model_sparse, sort=False)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Probability of redundancy:  [[0.4360267  0.01331541 0.44695997 0.6023492  0.5960179  0.44789484\n",
+            "  0.00492412 0.33416668]]\n",
+            "Non-redundant components: [1, 6]\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dtSyJ-hFbE69",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "outputId": "49d49df2-3a7c-43a4-d00c-2ca2caa3c515"
+      },
+      "source": [
+        "x_hat = model_sparse.reconstruct(data_sparse, dropout_threshold=0.2)\n",
+        "lsplom(x_hat, title = 'Reconstructed data')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 128 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vsGPs9xjbE6-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 311
+        },
+        "outputId": "0a519fcd-6a73-4203-eea5-d4fcfb4acd0b"
+      },
+      "source": [
+        "## Plotting the weights of the decoder\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "plot_weights(model_sparse, side = 'decoder')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x576 with 0 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3Y8vF4OSbE6_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 311
+        },
+        "outputId": "50032f99-9451-40df-9e85-53f850a4a95d"
+      },
+      "source": [
+        "plt.figure(figsize=(12, 8))\n",
+        "plot_weights(model_sparse, side = 'encoder')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x576 with 0 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8anRUBdObE7A",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 714
+        },
+        "outputId": "1c02e054-d3a4-4d9b-88ad-d9a91cf99032"
+      },
+      "source": [
+        "# PLotting estimated encoding vs ground truth\n",
+        "\n",
+        "encoding = model_sparse.encode(data_sparse)\n",
+        "\n",
+        "encoding_x = encoding[0].loc.detach().numpy()\n",
+        "encoding_y = encoding[1].loc.detach().numpy()\n",
+        "\n",
+        "plt.figure(figsize=(12, 12))\n",
+        "for idx,k in enumerate(indices):\n",
+        "  plt.subplot(len(indices),2,2*idx+1)\n",
+        "  plt.scatter(encoding_x[:,k], latents[:,0])\n",
+        "  plt.xlabel(str('mcvae dim ') + str(k))\n",
+        "  plt.ylabel('ground truth 0')\n",
+        "  plt.subplot(len(indices),2,2*idx+2)\n",
+        "  plt.scatter(encoding_x[:,k], latents[:,1])\n",
+        "  plt.xlabel(str('mcvae dim ') + str(k))\n",
+        "  plt.ylabel('ground truth 1')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x864 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "R3_M5YlaE71B",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 537
+        },
+        "outputId": "d7983331-7858-4260-b3cc-aceb2ae07727"
+      },
+      "source": [
+        "plot_latent_space(model_sparse, data_sparse, comp=non_redundant_comps);"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kieWlo3leVAt"
+      },
+      "source": [
+        "### Increasing the number of channels\n",
+        "\n",
+        "In this section we explore the use of the model on data with multiple modalities (or channels)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AoR7yJsqeXvb"
+      },
+      "source": [
+        "# generating a new modality z\n",
+        "# This modality has meaningful as well as redundant dimensions\n",
+        "\n",
+        "size_z = 3\n",
+        "size_z_redundant = 4\n",
+        "\n",
+        "transform_z = np.random.randint(-8,8, size = 2*size_z).reshape([2,size_z])\n",
+        "\n",
+        "Z = latents.dot(transform_z) + 2*np.random.normal(size = n*size_z).reshape((n, size_z))\n",
+        "Z = np.hstack([Z,np.random.randn(n*size_z_redundant).reshape([n,size_z_redundant])])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CVDIt3ssec1I",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1aa1889d-e7c6-468f-eda8-d08bb17f9646"
+      },
+      "source": [
+        "init_dict = {\n",
+        "    'n_channels': 3, # X and Y and Z\n",
+        "    'lat_dim': n_components + 3,\n",
+        "    'n_feats': tuple([X_ext.shape[1], Y_ext.shape[1], Z.shape[1]]),\n",
+        "}\n",
+        "\n",
+        "data_multi = []\n",
+        "data_multi.append(torch.FloatTensor(X_ext))\n",
+        "data_multi.append(torch.FloatTensor(Y_ext))\n",
+        "data_multi.append(torch.FloatTensor(Z))\n",
+        "\n",
+        "adam_lr = 1e-2\n",
+        "n_epochs = 4000\n",
+        "\n",
+        "model_multi = Mcvae(sparse=True, **init_dict)\n",
+        "model_multi.to(DEVICE)\n",
+        "print(model_multi)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mcvae(\n",
+            "  (vae): ModuleList(\n",
+            "    (0): VAE(\n",
+            "      (W_mu): Linear(in_features=8, out_features=8, bias=True)\n",
+            "      (W_out): Linear(in_features=8, out_features=8, bias=True)\n",
+            "    )\n",
+            "    (1): VAE(\n",
+            "      (W_mu): Linear(in_features=8, out_features=8, bias=True)\n",
+            "      (W_out): Linear(in_features=8, out_features=8, bias=True)\n",
+            "    )\n",
+            "    (2): VAE(\n",
+            "      (W_mu): Linear(in_features=7, out_features=8, bias=True)\n",
+            "      (W_out): Linear(in_features=8, out_features=7, bias=True)\n",
+            "    )\n",
+            "  )\n",
+            ")\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "p_2L3wm5uV7s",
+        "outputId": "22a82afe-04a8-4228-f9d1-cad59f10cbd2"
+      },
+      "source": [
+        "# Fit (or load)\n",
+        "model_multi.optimizer = torch.optim.Adam(model_multi.parameters(), lr=adam_lr)\n",
+        "load_or_fit(model=model_multi, data=data_multi, epochs=n_epochs, ptfile='model_multi.pt', force_fit=FORCE_REFIT)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\tCreating model_multi.pt.running\n",
+            "\tCreated: 2020-12-14 16:20:40.804657\n",
+            "Start fitting: 2020-12-14 16:20:40.805750\n",
+            "\tModel destination: model_multi.pt\n",
+            "====> Epoch:    0/4000 (0%)\tLoss: 23179.3867\tLL: -23169.0723\tKL: 10.3139\tLL/KL: -2246.3830\n",
+            "====> Epoch:   10/4000 (0%)\tLoss: 14202.3711\tLL: -14191.2461\tKL: 11.1255\tLL/KL: -1275.5631\n",
+            "====> Epoch:   20/4000 (0%)\tLoss: 10286.3857\tLL: -10274.6289\tKL: 11.7568\tLL/KL: -873.9325\n",
+            "====> Epoch:   30/4000 (1%)\tLoss: 7187.7417\tLL: -7175.3794\tKL: 12.3623\tLL/KL: -580.4223\n",
+            "====> Epoch:   40/4000 (1%)\tLoss: 5085.6094\tLL: -5072.4561\tKL: 13.1533\tLL/KL: -385.6420\n",
+            "====> Epoch:   50/4000 (1%)\tLoss: 3828.0840\tLL: -3813.9509\tKL: 14.1331\tLL/KL: -269.8593\n",
+            "====> Epoch:   60/4000 (2%)\tLoss: 3254.8677\tLL: -3239.6702\tKL: 15.1976\tLL/KL: -213.1697\n",
+            "====> Epoch:   70/4000 (2%)\tLoss: 2942.2786\tLL: -2925.9700\tKL: 16.3086\tLL/KL: -179.4132\n",
+            "====> Epoch:   80/4000 (2%)\tLoss: 2733.5825\tLL: -2716.1785\tKL: 17.4041\tLL/KL: -156.0651\n",
+            "====> Epoch:   90/4000 (2%)\tLoss: 2498.7783\tLL: -2480.3118\tKL: 18.4665\tLL/KL: -134.3144\n",
+            "====> Epoch:  100/4000 (2%)\tLoss: 2348.8967\tLL: -2329.4004\tKL: 19.4962\tLL/KL: -119.4796\n",
+            "====> Epoch:  110/4000 (3%)\tLoss: 2165.9702\tLL: -2145.4854\tKL: 20.4850\tLL/KL: -104.7346\n",
+            "====> Epoch:  120/4000 (3%)\tLoss: 2068.2927\tLL: -2046.8735\tKL: 21.4192\tLL/KL: -95.5625\n",
+            "====> Epoch:  130/4000 (3%)\tLoss: 1990.3497\tLL: -1968.0311\tKL: 22.3186\tLL/KL: -88.1789\n",
+            "====> Epoch:  140/4000 (4%)\tLoss: 1876.8773\tLL: -1853.6923\tKL: 23.1850\tLL/KL: -79.9521\n",
+            "====> Epoch:  150/4000 (4%)\tLoss: 1820.3872\tLL: -1796.3715\tKL: 24.0157\tLL/KL: -74.7999\n",
+            "====> Epoch:  160/4000 (4%)\tLoss: 1732.8452\tLL: -1708.0211\tKL: 24.8241\tLL/KL: -68.8049\n",
+            "====> Epoch:  170/4000 (4%)\tLoss: 1636.6565\tLL: -1611.0582\tKL: 25.5982\tLL/KL: -62.9363\n",
+            "====> Epoch:  180/4000 (4%)\tLoss: 1570.5535\tLL: -1544.2238\tKL: 26.3297\tLL/KL: -58.6495\n",
+            "====> Epoch:  190/4000 (5%)\tLoss: 1528.4508\tLL: -1501.4221\tKL: 27.0287\tLL/KL: -55.5492\n",
+            "====> Epoch:  200/4000 (5%)\tLoss: 1440.3572\tLL: -1412.6450\tKL: 27.7122\tLL/KL: -50.9756\n",
+            "====> Epoch:  210/4000 (5%)\tLoss: 1392.6108\tLL: -1364.2522\tKL: 28.3586\tLL/KL: -48.1072\n",
+            "====> Epoch:  220/4000 (6%)\tLoss: 1392.3856\tLL: -1363.4095\tKL: 28.9760\tLL/KL: -47.0530\n",
+            "====> Epoch:  230/4000 (6%)\tLoss: 1343.0719\tLL: -1313.4943\tKL: 29.5776\tLL/KL: -44.4084\n",
+            "====> Epoch:  240/4000 (6%)\tLoss: 1281.7622\tLL: -1251.6025\tKL: 30.1596\tLL/KL: -41.4993\n",
+            "====> Epoch:  250/4000 (6%)\tLoss: 1232.7887\tLL: -1202.0726\tKL: 30.7160\tLL/KL: -39.1350\n",
+            "====> Epoch:  260/4000 (6%)\tLoss: 1199.6124\tLL: -1168.3619\tKL: 31.2505\tLL/KL: -37.3870\n",
+            "====> Epoch:  270/4000 (7%)\tLoss: 1185.1733\tLL: -1153.4005\tKL: 31.7728\tLL/KL: -36.3015\n",
+            "====> Epoch:  280/4000 (7%)\tLoss: 1121.8638\tLL: -1089.6036\tKL: 32.2601\tLL/KL: -33.7756\n",
+            "====> Epoch:  290/4000 (7%)\tLoss: 1091.6443\tLL: -1058.9108\tKL: 32.7335\tLL/KL: -32.3494\n",
+            "====> Epoch:  300/4000 (8%)\tLoss: 1095.7848\tLL: -1062.5999\tKL: 33.1849\tLL/KL: -32.0206\n",
+            "====> Epoch:  310/4000 (8%)\tLoss: 1045.0228\tLL: -1011.3966\tKL: 33.6262\tLL/KL: -30.0776\n",
+            "====> Epoch:  320/4000 (8%)\tLoss: 1021.8412\tLL: -987.7934\tKL: 34.0478\tLL/KL: -29.0119\n",
+            "====> Epoch:  330/4000 (8%)\tLoss: 974.3214\tLL: -939.8766\tKL: 34.4448\tLL/KL: -27.2865\n",
+            "====> Epoch:  340/4000 (8%)\tLoss: 964.3118\tLL: -929.4898\tKL: 34.8219\tLL/KL: -26.6927\n",
+            "====> Epoch:  350/4000 (9%)\tLoss: 926.3442\tLL: -891.1453\tKL: 35.1989\tLL/KL: -25.3174\n",
+            "====> Epoch:  360/4000 (9%)\tLoss: 930.1298\tLL: -894.5657\tKL: 35.5640\tLL/KL: -25.1537\n",
+            "====> Epoch:  370/4000 (9%)\tLoss: 884.7361\tLL: -848.8184\tKL: 35.9178\tLL/KL: -23.6323\n",
+            "====> Epoch:  380/4000 (10%)\tLoss: 882.8637\tLL: -846.6050\tKL: 36.2587\tLL/KL: -23.3490\n",
+            "====> Epoch:  390/4000 (10%)\tLoss: 862.3376\tLL: -825.7488\tKL: 36.5888\tLL/KL: -22.5683\n",
+            "====> Epoch:  400/4000 (10%)\tLoss: 838.1866\tLL: -801.2783\tKL: 36.9084\tLL/KL: -21.7099\n",
+            "====> Epoch:  410/4000 (10%)\tLoss: 819.2541\tLL: -782.0418\tKL: 37.2123\tLL/KL: -21.0157\n",
+            "====> Epoch:  420/4000 (10%)\tLoss: 797.7609\tLL: -760.2545\tKL: 37.5065\tLL/KL: -20.2699\n",
+            "====> Epoch:  430/4000 (11%)\tLoss: 783.9937\tLL: -746.2067\tKL: 37.7869\tLL/KL: -19.7478\n",
+            "====> Epoch:  440/4000 (11%)\tLoss: 790.8916\tLL: -752.8274\tKL: 38.0642\tLL/KL: -19.7778\n",
+            "====> Epoch:  450/4000 (11%)\tLoss: 759.4716\tLL: -721.1357\tKL: 38.3359\tLL/KL: -18.8110\n",
+            "====> Epoch:  460/4000 (12%)\tLoss: 744.1223\tLL: -705.5258\tKL: 38.5966\tLL/KL: -18.2795\n",
+            "====> Epoch:  470/4000 (12%)\tLoss: 733.6872\tLL: -694.8367\tKL: 38.8505\tLL/KL: -17.8849\n",
+            "====> Epoch:  480/4000 (12%)\tLoss: 716.8749\tLL: -677.7791\tKL: 39.0958\tLL/KL: -17.3364\n",
+            "====> Epoch:  490/4000 (12%)\tLoss: 697.6487\tLL: -658.3191\tKL: 39.3296\tLL/KL: -16.7385\n",
+            "====> Epoch:  500/4000 (12%)\tLoss: 688.1102\tLL: -648.5572\tKL: 39.5530\tLL/KL: -16.3972\n",
+            "====> Epoch:  510/4000 (13%)\tLoss: 686.1336\tLL: -646.3629\tKL: 39.7707\tLL/KL: -16.2522\n",
+            "====> Epoch:  520/4000 (13%)\tLoss: 673.4418\tLL: -633.4590\tKL: 39.9828\tLL/KL: -15.8433\n",
+            "====> Epoch:  530/4000 (13%)\tLoss: 665.9103\tLL: -625.7220\tKL: 40.1883\tLL/KL: -15.5698\n",
+            "====> Epoch:  540/4000 (14%)\tLoss: 649.1750\tLL: -608.7802\tKL: 40.3948\tLL/KL: -15.0708\n",
+            "====> Epoch:  550/4000 (14%)\tLoss: 633.4230\tLL: -592.8297\tKL: 40.5933\tLL/KL: -14.6041\n",
+            "====> Epoch:  560/4000 (14%)\tLoss: 626.0389\tLL: -585.2566\tKL: 40.7823\tLL/KL: -14.3507\n",
+            "====> Epoch:  570/4000 (14%)\tLoss: 612.5331\tLL: -571.5681\tKL: 40.9650\tLL/KL: -13.9526\n",
+            "====> Epoch:  580/4000 (14%)\tLoss: 593.7220\tLL: -552.5775\tKL: 41.1445\tLL/KL: -13.4302\n",
+            "====> Epoch:  590/4000 (15%)\tLoss: 587.9482\tLL: -546.6285\tKL: 41.3197\tLL/KL: -13.2293\n",
+            "====> Epoch:  600/4000 (15%)\tLoss: 587.0996\tLL: -545.6094\tKL: 41.4902\tLL/KL: -13.1503\n",
+            "====> Epoch:  610/4000 (15%)\tLoss: 577.8549\tLL: -536.1976\tKL: 41.6573\tLL/KL: -12.8716\n",
+            "====> Epoch:  620/4000 (16%)\tLoss: 566.7219\tLL: -524.9022\tKL: 41.8196\tLL/KL: -12.5516\n",
+            "====> Epoch:  630/4000 (16%)\tLoss: 566.4370\tLL: -524.4570\tKL: 41.9800\tLL/KL: -12.4930\n",
+            "====> Epoch:  640/4000 (16%)\tLoss: 555.8317\tLL: -513.6934\tKL: 42.1383\tLL/KL: -12.1907\n",
+            "====> Epoch:  650/4000 (16%)\tLoss: 545.9163\tLL: -503.6234\tKL: 42.2929\tLL/KL: -11.9080\n",
+            "====> Epoch:  660/4000 (16%)\tLoss: 527.4069\tLL: -484.9644\tKL: 42.4425\tLL/KL: -11.4264\n",
+            "====> Epoch:  670/4000 (17%)\tLoss: 521.9754\tLL: -479.3908\tKL: 42.5846\tLL/KL: -11.2574\n",
+            "====> Epoch:  680/4000 (17%)\tLoss: 523.1740\tLL: -480.4539\tKL: 42.7201\tLL/KL: -11.2466\n",
+            "====> Epoch:  690/4000 (17%)\tLoss: 513.1467\tLL: -470.2939\tKL: 42.8527\tLL/KL: -10.9747\n",
+            "====> Epoch:  700/4000 (18%)\tLoss: 512.8859\tLL: -469.9010\tKL: 42.9848\tLL/KL: -10.9318\n",
+            "====> Epoch:  710/4000 (18%)\tLoss: 500.4602\tLL: -457.3428\tKL: 43.1174\tLL/KL: -10.6069\n",
+            "====> Epoch:  720/4000 (18%)\tLoss: 496.7839\tLL: -453.5382\tKL: 43.2457\tLL/KL: -10.4875\n",
+            "====> Epoch:  730/4000 (18%)\tLoss: 484.1201\tLL: -440.7484\tKL: 43.3717\tLL/KL: -10.1621\n",
+            "====> Epoch:  740/4000 (18%)\tLoss: 488.5350\tLL: -445.0437\tKL: 43.4913\tLL/KL: -10.2329\n",
+            "====> Epoch:  750/4000 (19%)\tLoss: 480.9297\tLL: -437.3199\tKL: 43.6097\tLL/KL: -10.0280\n",
+            "====> Epoch:  760/4000 (19%)\tLoss: 472.9252\tLL: -429.2013\tKL: 43.7239\tLL/KL: -9.8162\n",
+            "====> Epoch:  770/4000 (19%)\tLoss: 468.0021\tLL: -424.1680\tKL: 43.8341\tLL/KL: -9.6767\n",
+            "====> Epoch:  780/4000 (20%)\tLoss: 458.2108\tLL: -414.2685\tKL: 43.9423\tLL/KL: -9.4275\n",
+            "====> Epoch:  790/4000 (20%)\tLoss: 453.0122\tLL: -408.9642\tKL: 44.0480\tLL/KL: -9.2845\n",
+            "====> Epoch:  800/4000 (20%)\tLoss: 451.2137\tLL: -407.0624\tKL: 44.1512\tLL/KL: -9.2197\n",
+            "====> Epoch:  810/4000 (20%)\tLoss: 445.7707\tLL: -401.5200\tKL: 44.2507\tLL/KL: -9.0738\n",
+            "====> Epoch:  820/4000 (20%)\tLoss: 440.4721\tLL: -396.1263\tKL: 44.3458\tLL/KL: -8.9327\n",
+            "====> Epoch:  830/4000 (21%)\tLoss: 435.7615\tLL: -391.3216\tKL: 44.4398\tLL/KL: -8.8056\n",
+            "====> Epoch:  840/4000 (21%)\tLoss: 433.9913\tLL: -389.4612\tKL: 44.5301\tLL/KL: -8.7460\n",
+            "====> Epoch:  850/4000 (21%)\tLoss: 430.6368\tLL: -386.0183\tKL: 44.6185\tLL/KL: -8.6515\n",
+            "====> Epoch:  860/4000 (22%)\tLoss: 425.5091\tLL: -380.8025\tKL: 44.7066\tLL/KL: -8.5178\n",
+            "====> Epoch:  870/4000 (22%)\tLoss: 419.0846\tLL: -374.2923\tKL: 44.7923\tLL/KL: -8.3562\n",
+            "====> Epoch:  880/4000 (22%)\tLoss: 408.4190\tLL: -363.5446\tKL: 44.8744\tLL/KL: -8.1014\n",
+            "====> Epoch:  890/4000 (22%)\tLoss: 407.3178\tLL: -362.3629\tKL: 44.9549\tLL/KL: -8.0606\n",
+            "====> Epoch:  900/4000 (22%)\tLoss: 402.3154\tLL: -357.2812\tKL: 45.0341\tLL/KL: -7.9336\n",
+            "====> Epoch:  910/4000 (23%)\tLoss: 409.1215\tLL: -364.0097\tKL: 45.1118\tLL/KL: -8.0691\n",
+            "====> Epoch:  920/4000 (23%)\tLoss: 396.1076\tLL: -350.9192\tKL: 45.1884\tLL/KL: -7.7657\n",
+            "====> Epoch:  930/4000 (23%)\tLoss: 395.8462\tLL: -350.5850\tKL: 45.2612\tLL/KL: -7.7458\n",
+            "====> Epoch:  940/4000 (24%)\tLoss: 388.5185\tLL: -343.1840\tKL: 45.3345\tLL/KL: -7.5700\n",
+            "====> Epoch:  950/4000 (24%)\tLoss: 386.4948\tLL: -341.0910\tKL: 45.4038\tLL/KL: -7.5124\n",
+            "====> Epoch:  960/4000 (24%)\tLoss: 383.8800\tLL: -338.4093\tKL: 45.4707\tLL/KL: -7.4424\n",
+            "====> Epoch:  970/4000 (24%)\tLoss: 380.2651\tLL: -334.7290\tKL: 45.5361\tLL/KL: -7.3509\n",
+            "====> Epoch:  980/4000 (24%)\tLoss: 378.4908\tLL: -332.8901\tKL: 45.6007\tLL/KL: -7.3001\n",
+            "====> Epoch:  990/4000 (25%)\tLoss: 374.0636\tLL: -328.3989\tKL: 45.6647\tLL/KL: -7.1915\n",
+            "====> Epoch: 1000/4000 (25%)\tLoss: 369.7365\tLL: -324.0118\tKL: 45.7246\tLL/KL: -7.0862\n",
+            "====> Epoch: 1010/4000 (25%)\tLoss: 366.1321\tLL: -320.3518\tKL: 45.7803\tLL/KL: -6.9976\n",
+            "====> Epoch: 1020/4000 (26%)\tLoss: 363.1796\tLL: -317.3452\tKL: 45.8344\tLL/KL: -6.9237\n",
+            "====> Epoch: 1030/4000 (26%)\tLoss: 358.9219\tLL: -313.0340\tKL: 45.8878\tLL/KL: -6.8217\n",
+            "====> Epoch: 1040/4000 (26%)\tLoss: 358.6765\tLL: -312.7351\tKL: 45.9414\tLL/KL: -6.8073\n",
+            "====> Epoch: 1050/4000 (26%)\tLoss: 356.7256\tLL: -310.7305\tKL: 45.9951\tLL/KL: -6.7557\n",
+            "====> Epoch: 1060/4000 (26%)\tLoss: 351.5028\tLL: -305.4524\tKL: 46.0504\tLL/KL: -6.6330\n",
+            "====> Epoch: 1070/4000 (27%)\tLoss: 352.4602\tLL: -306.3572\tKL: 46.1030\tLL/KL: -6.6451\n",
+            "====> Epoch: 1080/4000 (27%)\tLoss: 342.6107\tLL: -296.4569\tKL: 46.1538\tLL/KL: -6.4232\n",
+            "====> Epoch: 1090/4000 (27%)\tLoss: 342.6695\tLL: -296.4703\tKL: 46.1992\tLL/KL: -6.4172\n",
+            "====> Epoch: 1100/4000 (28%)\tLoss: 340.0652\tLL: -293.8231\tKL: 46.2421\tLL/KL: -6.3540\n",
+            "====> Epoch: 1110/4000 (28%)\tLoss: 341.8078\tLL: -295.5216\tKL: 46.2862\tLL/KL: -6.3847\n",
+            "====> Epoch: 1120/4000 (28%)\tLoss: 335.3220\tLL: -288.9926\tKL: 46.3294\tLL/KL: -6.2378\n",
+            "====> Epoch: 1130/4000 (28%)\tLoss: 329.2775\tLL: -282.9061\tKL: 46.3714\tLL/KL: -6.1009\n",
+            "====> Epoch: 1140/4000 (28%)\tLoss: 331.3678\tLL: -284.9579\tKL: 46.4100\tLL/KL: -6.1400\n",
+            "====> Epoch: 1150/4000 (29%)\tLoss: 328.9242\tLL: -282.4753\tKL: 46.4489\tLL/KL: -6.0814\n",
+            "====> Epoch: 1160/4000 (29%)\tLoss: 328.5851\tLL: -282.0984\tKL: 46.4866\tLL/KL: -6.0684\n",
+            "====> Epoch: 1170/4000 (29%)\tLoss: 326.2185\tLL: -279.6953\tKL: 46.5232\tLL/KL: -6.0119\n",
+            "====> Epoch: 1180/4000 (30%)\tLoss: 320.2354\tLL: -273.6756\tKL: 46.5598\tLL/KL: -5.8779\n",
+            "====> Epoch: 1190/4000 (30%)\tLoss: 320.4562\tLL: -273.8608\tKL: 46.5954\tLL/KL: -5.8774\n",
+            "====> Epoch: 1200/4000 (30%)\tLoss: 320.6399\tLL: -274.0102\tKL: 46.6297\tLL/KL: -5.8763\n",
+            "====> Epoch: 1210/4000 (30%)\tLoss: 315.7383\tLL: -269.0775\tKL: 46.6608\tLL/KL: -5.7667\n",
+            "====> Epoch: 1220/4000 (30%)\tLoss: 311.9279\tLL: -265.2383\tKL: 46.6897\tLL/KL: -5.6809\n",
+            "====> Epoch: 1230/4000 (31%)\tLoss: 310.3593\tLL: -263.6434\tKL: 46.7159\tLL/KL: -5.6435\n",
+            "====> Epoch: 1240/4000 (31%)\tLoss: 308.3669\tLL: -261.6270\tKL: 46.7399\tLL/KL: -5.5975\n",
+            "====> Epoch: 1250/4000 (31%)\tLoss: 308.3204\tLL: -261.5565\tKL: 46.7640\tLL/KL: -5.5931\n",
+            "====> Epoch: 1260/4000 (32%)\tLoss: 305.6326\tLL: -258.8446\tKL: 46.7881\tLL/KL: -5.5323\n",
+            "====> Epoch: 1270/4000 (32%)\tLoss: 304.1394\tLL: -257.3273\tKL: 46.8121\tLL/KL: -5.4970\n",
+            "====> Epoch: 1280/4000 (32%)\tLoss: 301.6221\tLL: -254.7892\tKL: 46.8329\tLL/KL: -5.4404\n",
+            "====> Epoch: 1290/4000 (32%)\tLoss: 300.9247\tLL: -254.0715\tKL: 46.8532\tLL/KL: -5.4227\n",
+            "====> Epoch: 1300/4000 (32%)\tLoss: 300.3797\tLL: -253.5077\tKL: 46.8720\tLL/KL: -5.4085\n",
+            "====> Epoch: 1310/4000 (33%)\tLoss: 299.8310\tLL: -252.9418\tKL: 46.8892\tLL/KL: -5.3945\n",
+            "====> Epoch: 1320/4000 (33%)\tLoss: 296.6628\tLL: -249.7560\tKL: 46.9068\tLL/KL: -5.3245\n",
+            "====> Epoch: 1330/4000 (33%)\tLoss: 295.4937\tLL: -248.5691\tKL: 46.9246\tLL/KL: -5.2972\n",
+            "====> Epoch: 1340/4000 (34%)\tLoss: 292.8611\tLL: -245.9188\tKL: 46.9423\tLL/KL: -5.2387\n",
+            "====> Epoch: 1350/4000 (34%)\tLoss: 290.0535\tLL: -243.0961\tKL: 46.9573\tLL/KL: -5.1770\n",
+            "====> Epoch: 1360/4000 (34%)\tLoss: 290.6156\tLL: -243.6433\tKL: 46.9723\tLL/KL: -5.1870\n",
+            "====> Epoch: 1370/4000 (34%)\tLoss: 289.4982\tLL: -242.5111\tKL: 46.9871\tLL/KL: -5.1612\n",
+            "====> Epoch: 1380/4000 (34%)\tLoss: 287.6275\tLL: -240.6274\tKL: 47.0002\tLL/KL: -5.1197\n",
+            "====> Epoch: 1390/4000 (35%)\tLoss: 284.6978\tLL: -237.6878\tKL: 47.0100\tLL/KL: -5.0561\n",
+            "====> Epoch: 1400/4000 (35%)\tLoss: 282.6115\tLL: -235.5924\tKL: 47.0192\tLL/KL: -5.0106\n",
+            "====> Epoch: 1410/4000 (35%)\tLoss: 280.6945\tLL: -233.6679\tKL: 47.0266\tLL/KL: -4.9688\n",
+            "====> Epoch: 1420/4000 (36%)\tLoss: 281.6543\tLL: -234.6192\tKL: 47.0351\tLL/KL: -4.9882\n",
+            "====> Epoch: 1430/4000 (36%)\tLoss: 279.2562\tLL: -232.2135\tKL: 47.0427\tLL/KL: -4.9362\n",
+            "====> Epoch: 1440/4000 (36%)\tLoss: 276.2132\tLL: -229.1667\tKL: 47.0464\tLL/KL: -4.8711\n",
+            "====> Epoch: 1450/4000 (36%)\tLoss: 275.0312\tLL: -227.9817\tKL: 47.0495\tLL/KL: -4.8456\n",
+            "====> Epoch: 1460/4000 (36%)\tLoss: 273.4490\tLL: -226.3952\tKL: 47.0538\tLL/KL: -4.8114\n",
+            "====> Epoch: 1470/4000 (37%)\tLoss: 273.5178\tLL: -226.4604\tKL: 47.0574\tLL/KL: -4.8124\n",
+            "====> Epoch: 1480/4000 (37%)\tLoss: 271.5527\tLL: -224.4925\tKL: 47.0602\tLL/KL: -4.7703\n",
+            "====> Epoch: 1490/4000 (37%)\tLoss: 271.8683\tLL: -224.8047\tKL: 47.0636\tLL/KL: -4.7766\n",
+            "====> Epoch: 1500/4000 (38%)\tLoss: 269.1200\tLL: -222.0537\tKL: 47.0664\tLL/KL: -4.7179\n",
+            "====> Epoch: 1510/4000 (38%)\tLoss: 267.7680\tLL: -220.7012\tKL: 47.0668\tLL/KL: -4.6891\n",
+            "====> Epoch: 1520/4000 (38%)\tLoss: 266.6580\tLL: -219.5915\tKL: 47.0665\tLL/KL: -4.6656\n",
+            "====> Epoch: 1530/4000 (38%)\tLoss: 265.0479\tLL: -217.9838\tKL: 47.0642\tLL/KL: -4.6316\n",
+            "====> Epoch: 1540/4000 (38%)\tLoss: 265.5561\tLL: -218.4953\tKL: 47.0608\tLL/KL: -4.6428\n",
+            "====> Epoch: 1550/4000 (39%)\tLoss: 262.9431\tLL: -215.8864\tKL: 47.0568\tLL/KL: -4.5878\n",
+            "====> Epoch: 1560/4000 (39%)\tLoss: 262.7262\tLL: -215.6752\tKL: 47.0510\tLL/KL: -4.5839\n",
+            "====> Epoch: 1570/4000 (39%)\tLoss: 259.9336\tLL: -212.8884\tKL: 47.0452\tLL/KL: -4.5252\n",
+            "====> Epoch: 1580/4000 (40%)\tLoss: 260.2950\tLL: -213.2567\tKL: 47.0382\tLL/KL: -4.5337\n",
+            "====> Epoch: 1590/4000 (40%)\tLoss: 257.6018\tLL: -210.5697\tKL: 47.0321\tLL/KL: -4.4771\n",
+            "====> Epoch: 1600/4000 (40%)\tLoss: 257.5687\tLL: -210.5460\tKL: 47.0227\tLL/KL: -4.4775\n",
+            "====> Epoch: 1610/4000 (40%)\tLoss: 257.5729\tLL: -210.5599\tKL: 47.0130\tLL/KL: -4.4788\n",
+            "====> Epoch: 1620/4000 (40%)\tLoss: 255.6943\tLL: -208.6920\tKL: 47.0023\tLL/KL: -4.4400\n",
+            "====> Epoch: 1630/4000 (41%)\tLoss: 254.7098\tLL: -207.7181\tKL: 46.9917\tLL/KL: -4.4203\n",
+            "====> Epoch: 1640/4000 (41%)\tLoss: 252.4693\tLL: -205.4875\tKL: 46.9818\tLL/KL: -4.3738\n",
+            "====> Epoch: 1650/4000 (41%)\tLoss: 252.2289\tLL: -205.2585\tKL: 46.9704\tLL/KL: -4.3700\n",
+            "====> Epoch: 1660/4000 (42%)\tLoss: 250.8939\tLL: -203.9374\tKL: 46.9565\tLL/KL: -4.3431\n",
+            "====> Epoch: 1670/4000 (42%)\tLoss: 249.9224\tLL: -202.9805\tKL: 46.9418\tLL/KL: -4.3241\n",
+            "====> Epoch: 1680/4000 (42%)\tLoss: 250.3661\tLL: -203.4383\tKL: 46.9278\tLL/KL: -4.3351\n",
+            "====> Epoch: 1690/4000 (42%)\tLoss: 250.4978\tLL: -203.5842\tKL: 46.9135\tLL/KL: -4.3396\n",
+            "====> Epoch: 1700/4000 (42%)\tLoss: 248.9303\tLL: -202.0323\tKL: 46.8980\tLL/KL: -4.3079\n",
+            "====> Epoch: 1710/4000 (43%)\tLoss: 246.7902\tLL: -199.9095\tKL: 46.8807\tLL/KL: -4.2642\n",
+            "====> Epoch: 1720/4000 (43%)\tLoss: 245.8177\tLL: -198.9552\tKL: 46.8625\tLL/KL: -4.2455\n",
+            "====> Epoch: 1730/4000 (43%)\tLoss: 244.4876\tLL: -197.6438\tKL: 46.8438\tLL/KL: -4.2192\n",
+            "====> Epoch: 1740/4000 (44%)\tLoss: 243.4603\tLL: -196.6357\tKL: 46.8245\tLL/KL: -4.1994\n",
+            "====> Epoch: 1750/4000 (44%)\tLoss: 242.9744\tLL: -196.1695\tKL: 46.8048\tLL/KL: -4.1912\n",
+            "====> Epoch: 1760/4000 (44%)\tLoss: 242.6434\tLL: -195.8596\tKL: 46.7838\tLL/KL: -4.1865\n",
+            "====> Epoch: 1770/4000 (44%)\tLoss: 242.2506\tLL: -195.4875\tKL: 46.7632\tLL/KL: -4.1804\n",
+            "====> Epoch: 1780/4000 (44%)\tLoss: 242.1641\tLL: -195.4220\tKL: 46.7421\tLL/KL: -4.1809\n",
+            "====> Epoch: 1790/4000 (45%)\tLoss: 238.9897\tLL: -192.2678\tKL: 46.7219\tLL/KL: -4.1152\n",
+            "====> Epoch: 1800/4000 (45%)\tLoss: 239.6071\tLL: -192.9061\tKL: 46.7009\tLL/KL: -4.1307\n",
+            "====> Epoch: 1810/4000 (45%)\tLoss: 237.2984\tLL: -190.6201\tKL: 46.6784\tLL/KL: -4.0837\n",
+            "====> Epoch: 1820/4000 (46%)\tLoss: 238.8152\tLL: -192.1618\tKL: 46.6534\tLL/KL: -4.1189\n",
+            "====> Epoch: 1830/4000 (46%)\tLoss: 237.6759\tLL: -191.0476\tKL: 46.6283\tLL/KL: -4.0972\n",
+            "====> Epoch: 1840/4000 (46%)\tLoss: 236.2029\tLL: -189.6013\tKL: 46.6017\tLL/KL: -4.0686\n",
+            "====> Epoch: 1850/4000 (46%)\tLoss: 235.9433\tLL: -189.3703\tKL: 46.5730\tLL/KL: -4.0661\n",
+            "====> Epoch: 1860/4000 (46%)\tLoss: 235.7486\tLL: -189.2058\tKL: 46.5428\tLL/KL: -4.0652\n",
+            "====> Epoch: 1870/4000 (47%)\tLoss: 234.1151\tLL: -187.6012\tKL: 46.5138\tLL/KL: -4.0332\n",
+            "====> Epoch: 1880/4000 (47%)\tLoss: 234.6786\tLL: -188.1929\tKL: 46.4858\tLL/KL: -4.0484\n",
+            "====> Epoch: 1890/4000 (47%)\tLoss: 232.9378\tLL: -186.4810\tKL: 46.4568\tLL/KL: -4.0141\n",
+            "====> Epoch: 1900/4000 (48%)\tLoss: 231.3737\tLL: -184.9473\tKL: 46.4264\tLL/KL: -3.9837\n",
+            "====> Epoch: 1910/4000 (48%)\tLoss: 231.3686\tLL: -184.9734\tKL: 46.3952\tLL/KL: -3.9869\n",
+            "====> Epoch: 1920/4000 (48%)\tLoss: 230.0107\tLL: -183.6464\tKL: 46.3643\tLL/KL: -3.9609\n",
+            "====> Epoch: 1930/4000 (48%)\tLoss: 230.2941\tLL: -183.9611\tKL: 46.3330\tLL/KL: -3.9704\n",
+            "====> Epoch: 1940/4000 (48%)\tLoss: 228.9888\tLL: -182.6885\tKL: 46.3003\tLL/KL: -3.9457\n",
+            "====> Epoch: 1950/4000 (49%)\tLoss: 228.9804\tLL: -182.7132\tKL: 46.2672\tLL/KL: -3.9491\n",
+            "====> Epoch: 1960/4000 (49%)\tLoss: 227.8192\tLL: -181.5870\tKL: 46.2322\tLL/KL: -3.9277\n",
+            "====> Epoch: 1970/4000 (49%)\tLoss: 227.1206\tLL: -180.9238\tKL: 46.1968\tLL/KL: -3.9164\n",
+            "====> Epoch: 1980/4000 (50%)\tLoss: 225.9960\tLL: -179.8347\tKL: 46.1613\tLL/KL: -3.8958\n",
+            "====> Epoch: 1990/4000 (50%)\tLoss: 224.6021\tLL: -178.4772\tKL: 46.1249\tLL/KL: -3.8694\n",
+            "====> Epoch: 2000/4000 (50%)\tLoss: 225.9287\tLL: -179.8409\tKL: 46.0878\tLL/KL: -3.9021\n",
+            "====> Epoch: 2010/4000 (50%)\tLoss: 225.0101\tLL: -178.9595\tKL: 46.0506\tLL/KL: -3.8862\n",
+            "====> Epoch: 2020/4000 (50%)\tLoss: 223.9444\tLL: -177.9312\tKL: 46.0132\tLL/KL: -3.8670\n",
+            "====> Epoch: 2030/4000 (51%)\tLoss: 222.9843\tLL: -177.0114\tKL: 45.9729\tLL/KL: -3.8503\n",
+            "====> Epoch: 2040/4000 (51%)\tLoss: 223.6223\tLL: -177.6918\tKL: 45.9305\tLL/KL: -3.8687\n",
+            "====> Epoch: 2050/4000 (51%)\tLoss: 222.0344\tLL: -176.1460\tKL: 45.8884\tLL/KL: -3.8386\n",
+            "====> Epoch: 2060/4000 (52%)\tLoss: 222.4404\tLL: -176.5944\tKL: 45.8460\tLL/KL: -3.8519\n",
+            "====> Epoch: 2070/4000 (52%)\tLoss: 222.2237\tLL: -176.4209\tKL: 45.8028\tLL/KL: -3.8517\n",
+            "====> Epoch: 2080/4000 (52%)\tLoss: 221.4802\tLL: -175.7200\tKL: 45.7602\tLL/KL: -3.8400\n",
+            "====> Epoch: 2090/4000 (52%)\tLoss: 219.8707\tLL: -174.1523\tKL: 45.7184\tLL/KL: -3.8092\n",
+            "====> Epoch: 2100/4000 (52%)\tLoss: 219.5230\tLL: -173.8484\tKL: 45.6746\tLL/KL: -3.8062\n",
+            "====> Epoch: 2110/4000 (53%)\tLoss: 219.0513\tLL: -173.4219\tKL: 45.6295\tLL/KL: -3.8007\n",
+            "====> Epoch: 2120/4000 (53%)\tLoss: 217.9831\tLL: -172.3979\tKL: 45.5852\tLL/KL: -3.7819\n",
+            "====> Epoch: 2130/4000 (53%)\tLoss: 218.7363\tLL: -173.1948\tKL: 45.5415\tLL/KL: -3.8030\n",
+            "====> Epoch: 2140/4000 (54%)\tLoss: 218.0720\tLL: -172.5767\tKL: 45.4953\tLL/KL: -3.7933\n",
+            "====> Epoch: 2150/4000 (54%)\tLoss: 216.5800\tLL: -171.1303\tKL: 45.4497\tLL/KL: -3.7653\n",
+            "====> Epoch: 2160/4000 (54%)\tLoss: 215.6718\tLL: -170.2688\tKL: 45.4029\tLL/KL: -3.7502\n",
+            "====> Epoch: 2170/4000 (54%)\tLoss: 215.0435\tLL: -169.6881\tKL: 45.3554\tLL/KL: -3.7413\n",
+            "====> Epoch: 2180/4000 (54%)\tLoss: 215.1854\tLL: -169.8782\tKL: 45.3072\tLL/KL: -3.7495\n",
+            "====> Epoch: 2190/4000 (55%)\tLoss: 214.4789\tLL: -169.2226\tKL: 45.2563\tLL/KL: -3.7392\n",
+            "====> Epoch: 2200/4000 (55%)\tLoss: 214.0253\tLL: -168.8229\tKL: 45.2024\tLL/KL: -3.7348\n",
+            "====> Epoch: 2210/4000 (55%)\tLoss: 213.3435\tLL: -168.1946\tKL: 45.1489\tLL/KL: -3.7253\n",
+            "====> Epoch: 2220/4000 (56%)\tLoss: 213.3725\tLL: -168.2765\tKL: 45.0959\tLL/KL: -3.7315\n",
+            "====> Epoch: 2230/4000 (56%)\tLoss: 213.2803\tLL: -168.2366\tKL: 45.0437\tLL/KL: -3.7350\n",
+            "====> Epoch: 2240/4000 (56%)\tLoss: 211.9398\tLL: -166.9478\tKL: 44.9920\tLL/KL: -3.7106\n",
+            "====> Epoch: 2250/4000 (56%)\tLoss: 211.2930\tLL: -166.3545\tKL: 44.9384\tLL/KL: -3.7018\n",
+            "====> Epoch: 2260/4000 (56%)\tLoss: 211.5419\tLL: -166.6579\tKL: 44.8840\tLL/KL: -3.7131\n",
+            "====> Epoch: 2270/4000 (57%)\tLoss: 210.0051\tLL: -165.1765\tKL: 44.8286\tLL/KL: -3.6846\n",
+            "====> Epoch: 2280/4000 (57%)\tLoss: 210.5142\tLL: -165.7428\tKL: 44.7714\tLL/KL: -3.7020\n",
+            "====> Epoch: 2290/4000 (57%)\tLoss: 210.9710\tLL: -166.2585\tKL: 44.7126\tLL/KL: -3.7184\n",
+            "====> Epoch: 2300/4000 (58%)\tLoss: 209.4807\tLL: -164.8256\tKL: 44.6550\tLL/KL: -3.6911\n",
+            "====> Epoch: 2310/4000 (58%)\tLoss: 208.5322\tLL: -163.9345\tKL: 44.5977\tLL/KL: -3.6759\n",
+            "====> Epoch: 2320/4000 (58%)\tLoss: 208.9807\tLL: -164.4395\tKL: 44.5412\tLL/KL: -3.6918\n",
+            "====> Epoch: 2330/4000 (58%)\tLoss: 207.8009\tLL: -163.3157\tKL: 44.4852\tLL/KL: -3.6712\n",
+            "====> Epoch: 2340/4000 (58%)\tLoss: 208.4664\tLL: -164.0385\tKL: 44.4278\tLL/KL: -3.6922\n",
+            "====> Epoch: 2350/4000 (59%)\tLoss: 206.8394\tLL: -162.4692\tKL: 44.3702\tLL/KL: -3.6617\n",
+            "====> Epoch: 2360/4000 (59%)\tLoss: 206.3355\tLL: -162.0241\tKL: 44.3114\tLL/KL: -3.6565\n",
+            "====> Epoch: 2370/4000 (59%)\tLoss: 207.0103\tLL: -162.7591\tKL: 44.2512\tLL/KL: -3.6781\n",
+            "====> Epoch: 2380/4000 (60%)\tLoss: 206.0454\tLL: -161.8539\tKL: 44.1915\tLL/KL: -3.6626\n",
+            "====> Epoch: 2390/4000 (60%)\tLoss: 206.0241\tLL: -161.8949\tKL: 44.1292\tLL/KL: -3.6687\n",
+            "====> Epoch: 2400/4000 (60%)\tLoss: 206.1860\tLL: -162.1201\tKL: 44.0659\tLL/KL: -3.6790\n",
+            "====> Epoch: 2410/4000 (60%)\tLoss: 204.5608\tLL: -160.5570\tKL: 44.0039\tLL/KL: -3.6487\n",
+            "====> Epoch: 2420/4000 (60%)\tLoss: 205.0431\tLL: -161.1019\tKL: 43.9412\tLL/KL: -3.6663\n",
+            "====> Epoch: 2430/4000 (61%)\tLoss: 203.6675\tLL: -159.7890\tKL: 43.8785\tLL/KL: -3.6416\n",
+            "====> Epoch: 2440/4000 (61%)\tLoss: 203.7712\tLL: -159.9566\tKL: 43.8145\tLL/KL: -3.6508\n",
+            "====> Epoch: 2450/4000 (61%)\tLoss: 203.3167\tLL: -159.5673\tKL: 43.7494\tLL/KL: -3.6473\n",
+            "====> Epoch: 2460/4000 (62%)\tLoss: 203.2091\tLL: -159.5252\tKL: 43.6839\tLL/KL: -3.6518\n",
+            "====> Epoch: 2470/4000 (62%)\tLoss: 203.1322\tLL: -159.5142\tKL: 43.6179\tLL/KL: -3.6571\n",
+            "====> Epoch: 2480/4000 (62%)\tLoss: 202.7020\tLL: -159.1497\tKL: 43.5524\tLL/KL: -3.6542\n",
+            "====> Epoch: 2490/4000 (62%)\tLoss: 201.8122\tLL: -158.3243\tKL: 43.4879\tLL/KL: -3.6407\n",
+            "====> Epoch: 2500/4000 (62%)\tLoss: 201.6334\tLL: -158.2107\tKL: 43.4227\tLL/KL: -3.6435\n",
+            "====> Epoch: 2510/4000 (63%)\tLoss: 201.8340\tLL: -158.4792\tKL: 43.3548\tLL/KL: -3.6554\n",
+            "====> Epoch: 2520/4000 (63%)\tLoss: 201.5960\tLL: -158.3093\tKL: 43.2867\tLL/KL: -3.6572\n",
+            "====> Epoch: 2530/4000 (63%)\tLoss: 200.3200\tLL: -157.1003\tKL: 43.2197\tLL/KL: -3.6349\n",
+            "====> Epoch: 2540/4000 (64%)\tLoss: 199.9251\tLL: -156.7742\tKL: 43.1509\tLL/KL: -3.6332\n",
+            "====> Epoch: 2550/4000 (64%)\tLoss: 199.1660\tLL: -156.0844\tKL: 43.0816\tLL/KL: -3.6230\n",
+            "====> Epoch: 2560/4000 (64%)\tLoss: 199.2379\tLL: -156.2267\tKL: 43.0111\tLL/KL: -3.6322\n",
+            "====> Epoch: 2570/4000 (64%)\tLoss: 198.6312\tLL: -155.6910\tKL: 42.9402\tLL/KL: -3.6258\n",
+            "====> Epoch: 2580/4000 (64%)\tLoss: 198.4452\tLL: -155.5756\tKL: 42.8696\tLL/KL: -3.6290\n",
+            "====> Epoch: 2590/4000 (65%)\tLoss: 199.1094\tLL: -156.3106\tKL: 42.7987\tLL/KL: -3.6522\n",
+            "====> Epoch: 2600/4000 (65%)\tLoss: 198.7424\tLL: -156.0153\tKL: 42.7271\tLL/KL: -3.6514\n",
+            "====> Epoch: 2610/4000 (65%)\tLoss: 197.8763\tLL: -155.2210\tKL: 42.6554\tLL/KL: -3.6390\n",
+            "====> Epoch: 2620/4000 (66%)\tLoss: 197.6120\tLL: -155.0284\tKL: 42.5836\tLL/KL: -3.6406\n",
+            "====> Epoch: 2630/4000 (66%)\tLoss: 197.2280\tLL: -154.7166\tKL: 42.5113\tLL/KL: -3.6394\n",
+            "====> Epoch: 2640/4000 (66%)\tLoss: 195.7013\tLL: -153.2627\tKL: 42.4386\tLL/KL: -3.6114\n",
+            "====> Epoch: 2650/4000 (66%)\tLoss: 195.4587\tLL: -153.0942\tKL: 42.3645\tLL/KL: -3.6137\n",
+            "====> Epoch: 2660/4000 (66%)\tLoss: 196.7354\tLL: -154.4467\tKL: 42.2887\tLL/KL: -3.6522\n",
+            "====> Epoch: 2670/4000 (67%)\tLoss: 196.1044\tLL: -153.8927\tKL: 42.2117\tLL/KL: -3.6457\n",
+            "====> Epoch: 2680/4000 (67%)\tLoss: 196.4494\tLL: -154.3132\tKL: 42.1362\tLL/KL: -3.6622\n",
+            "====> Epoch: 2690/4000 (67%)\tLoss: 194.4806\tLL: -152.4212\tKL: 42.0595\tLL/KL: -3.6239\n",
+            "====> Epoch: 2700/4000 (68%)\tLoss: 195.0426\tLL: -153.0632\tKL: 41.9795\tLL/KL: -3.6461\n",
+            "====> Epoch: 2710/4000 (68%)\tLoss: 194.4960\tLL: -152.5956\tKL: 41.9005\tLL/KL: -3.6419\n",
+            "====> Epoch: 2720/4000 (68%)\tLoss: 194.0250\tLL: -152.2034\tKL: 41.8216\tLL/KL: -3.6393\n",
+            "====> Epoch: 2730/4000 (68%)\tLoss: 193.5186\tLL: -151.7754\tKL: 41.7433\tLL/KL: -3.6359\n",
+            "====> Epoch: 2740/4000 (68%)\tLoss: 193.6770\tLL: -152.0128\tKL: 41.6642\tLL/KL: -3.6485\n",
+            "====> Epoch: 2750/4000 (69%)\tLoss: 193.3441\tLL: -151.7611\tKL: 41.5830\tLL/KL: -3.6496\n",
+            "====> Epoch: 2760/4000 (69%)\tLoss: 193.1457\tLL: -151.6443\tKL: 41.5014\tLL/KL: -3.6540\n",
+            "====> Epoch: 2770/4000 (69%)\tLoss: 192.0457\tLL: -150.6275\tKL: 41.4182\tLL/KL: -3.6367\n",
+            "====> Epoch: 2780/4000 (70%)\tLoss: 191.8735\tLL: -150.5371\tKL: 41.3364\tLL/KL: -3.6418\n",
+            "====> Epoch: 2790/4000 (70%)\tLoss: 191.8836\tLL: -150.6268\tKL: 41.2568\tLL/KL: -3.6510\n",
+            "====> Epoch: 2800/4000 (70%)\tLoss: 191.6745\tLL: -150.4999\tKL: 41.1746\tLL/KL: -3.6552\n",
+            "====> Epoch: 2810/4000 (70%)\tLoss: 192.2465\tLL: -151.1534\tKL: 41.0931\tLL/KL: -3.6783\n",
+            "====> Epoch: 2820/4000 (70%)\tLoss: 191.3649\tLL: -150.3547\tKL: 41.0102\tLL/KL: -3.6663\n",
+            "====> Epoch: 2830/4000 (71%)\tLoss: 190.5342\tLL: -149.6092\tKL: 40.9251\tLL/KL: -3.6557\n",
+            "====> Epoch: 2840/4000 (71%)\tLoss: 190.4260\tLL: -149.5863\tKL: 40.8397\tLL/KL: -3.6628\n",
+            "====> Epoch: 2850/4000 (71%)\tLoss: 190.6738\tLL: -149.9187\tKL: 40.7552\tLL/KL: -3.6785\n",
+            "====> Epoch: 2860/4000 (72%)\tLoss: 190.4191\tLL: -149.7488\tKL: 40.6703\tLL/KL: -3.6820\n",
+            "====> Epoch: 2870/4000 (72%)\tLoss: 189.1614\tLL: -148.5760\tKL: 40.5854\tLL/KL: -3.6608\n",
+            "====> Epoch: 2880/4000 (72%)\tLoss: 188.5985\tLL: -148.1003\tKL: 40.4982\tLL/KL: -3.6570\n",
+            "====> Epoch: 2890/4000 (72%)\tLoss: 189.5092\tLL: -149.0997\tKL: 40.4095\tLL/KL: -3.6897\n",
+            "====> Epoch: 2900/4000 (72%)\tLoss: 189.1243\tLL: -148.8023\tKL: 40.3220\tLL/KL: -3.6904\n",
+            "====> Epoch: 2910/4000 (73%)\tLoss: 188.2083\tLL: -147.9729\tKL: 40.2354\tLL/KL: -3.6777\n",
+            "====> Epoch: 2920/4000 (73%)\tLoss: 187.8262\tLL: -147.6782\tKL: 40.1480\tLL/KL: -3.6783\n",
+            "====> Epoch: 2930/4000 (73%)\tLoss: 187.9603\tLL: -147.8991\tKL: 40.0612\tLL/KL: -3.6918\n",
+            "====> Epoch: 2940/4000 (74%)\tLoss: 187.1119\tLL: -147.1365\tKL: 39.9755\tLL/KL: -3.6807\n",
+            "====> Epoch: 2950/4000 (74%)\tLoss: 187.3116\tLL: -147.4233\tKL: 39.8882\tLL/KL: -3.6959\n",
+            "====> Epoch: 2960/4000 (74%)\tLoss: 187.9205\tLL: -148.1208\tKL: 39.7998\tLL/KL: -3.7216\n",
+            "====> Epoch: 2970/4000 (74%)\tLoss: 186.8928\tLL: -147.1829\tKL: 39.7098\tLL/KL: -3.7065\n",
+            "====> Epoch: 2980/4000 (74%)\tLoss: 187.4350\tLL: -147.8149\tKL: 39.6201\tLL/KL: -3.7308\n",
+            "====> Epoch: 2990/4000 (75%)\tLoss: 186.4205\tLL: -146.8906\tKL: 39.5299\tLL/KL: -3.7159\n",
+            "====> Epoch: 3000/4000 (75%)\tLoss: 186.6955\tLL: -147.2554\tKL: 39.4401\tLL/KL: -3.7336\n",
+            "====> Epoch: 3010/4000 (75%)\tLoss: 186.1712\tLL: -146.8191\tKL: 39.3522\tLL/KL: -3.7309\n",
+            "====> Epoch: 3020/4000 (76%)\tLoss: 186.7852\tLL: -147.5225\tKL: 39.2628\tLL/KL: -3.7573\n",
+            "====> Epoch: 3030/4000 (76%)\tLoss: 185.3695\tLL: -146.1969\tKL: 39.1726\tLL/KL: -3.7321\n",
+            "====> Epoch: 3040/4000 (76%)\tLoss: 185.0630\tLL: -145.9812\tKL: 39.0818\tLL/KL: -3.7353\n",
+            "====> Epoch: 3050/4000 (76%)\tLoss: 185.1331\tLL: -146.1443\tKL: 38.9888\tLL/KL: -3.7484\n",
+            "====> Epoch: 3060/4000 (76%)\tLoss: 184.8444\tLL: -145.9489\tKL: 38.8955\tLL/KL: -3.7523\n",
+            "====> Epoch: 3070/4000 (77%)\tLoss: 184.3987\tLL: -145.5956\tKL: 38.8031\tLL/KL: -3.7522\n",
+            "====> Epoch: 3080/4000 (77%)\tLoss: 183.8382\tLL: -145.1264\tKL: 38.7118\tLL/KL: -3.7489\n",
+            "====> Epoch: 3090/4000 (77%)\tLoss: 184.2164\tLL: -145.5978\tKL: 38.6186\tLL/KL: -3.7701\n",
+            "====> Epoch: 3100/4000 (78%)\tLoss: 185.0666\tLL: -146.5378\tKL: 38.5289\tLL/KL: -3.8033\n",
+            "====> Epoch: 3110/4000 (78%)\tLoss: 183.9802\tLL: -145.5426\tKL: 38.4375\tLL/KL: -3.7865\n",
+            "====> Epoch: 3120/4000 (78%)\tLoss: 183.1161\tLL: -144.7709\tKL: 38.3452\tLL/KL: -3.7755\n",
+            "====> Epoch: 3130/4000 (78%)\tLoss: 182.8914\tLL: -144.6420\tKL: 38.2494\tLL/KL: -3.7815\n",
+            "====> Epoch: 3140/4000 (78%)\tLoss: 183.2693\tLL: -145.1154\tKL: 38.1540\tLL/KL: -3.8034\n",
+            "====> Epoch: 3150/4000 (79%)\tLoss: 182.9395\tLL: -144.8800\tKL: 38.0595\tLL/KL: -3.8067\n",
+            "====> Epoch: 3160/4000 (79%)\tLoss: 182.4435\tLL: -144.4792\tKL: 37.9642\tLL/KL: -3.8057\n",
+            "====> Epoch: 3170/4000 (79%)\tLoss: 182.3324\tLL: -144.4647\tKL: 37.8677\tLL/KL: -3.8150\n",
+            "====> Epoch: 3180/4000 (80%)\tLoss: 181.6046\tLL: -143.8326\tKL: 37.7720\tLL/KL: -3.8079\n",
+            "====> Epoch: 3190/4000 (80%)\tLoss: 181.9798\tLL: -144.3048\tKL: 37.6750\tLL/KL: -3.8303\n",
+            "====> Epoch: 3200/4000 (80%)\tLoss: 181.9239\tLL: -144.3431\tKL: 37.5808\tLL/KL: -3.8409\n",
+            "====> Epoch: 3210/4000 (80%)\tLoss: 181.6115\tLL: -144.1256\tKL: 37.4859\tLL/KL: -3.8448\n",
+            "====> Epoch: 3220/4000 (80%)\tLoss: 182.0205\tLL: -144.6290\tKL: 37.3915\tLL/KL: -3.8680\n",
+            "====> Epoch: 3230/4000 (81%)\tLoss: 180.6194\tLL: -143.3225\tKL: 37.2968\tLL/KL: -3.8428\n",
+            "====> Epoch: 3240/4000 (81%)\tLoss: 181.2858\tLL: -144.0844\tKL: 37.2014\tLL/KL: -3.8731\n",
+            "====> Epoch: 3250/4000 (81%)\tLoss: 181.0525\tLL: -143.9477\tKL: 37.1048\tLL/KL: -3.8795\n",
+            "====> Epoch: 3260/4000 (82%)\tLoss: 180.6334\tLL: -143.6262\tKL: 37.0072\tLL/KL: -3.8810\n",
+            "====> Epoch: 3270/4000 (82%)\tLoss: 180.0044\tLL: -143.0962\tKL: 36.9082\tLL/KL: -3.8771\n",
+            "====> Epoch: 3280/4000 (82%)\tLoss: 180.1004\tLL: -143.2934\tKL: 36.8070\tLL/KL: -3.8931\n",
+            "====> Epoch: 3290/4000 (82%)\tLoss: 179.9590\tLL: -143.2512\tKL: 36.7078\tLL/KL: -3.9025\n",
+            "====> Epoch: 3300/4000 (82%)\tLoss: 179.3436\tLL: -142.7338\tKL: 36.6098\tLL/KL: -3.8988\n",
+            "====> Epoch: 3310/4000 (83%)\tLoss: 178.7243\tLL: -142.2133\tKL: 36.5111\tLL/KL: -3.8951\n",
+            "====> Epoch: 3320/4000 (83%)\tLoss: 180.0766\tLL: -143.6657\tKL: 36.4109\tLL/KL: -3.9457\n",
+            "====> Epoch: 3330/4000 (83%)\tLoss: 178.7730\tLL: -142.4600\tKL: 36.3130\tLL/KL: -3.9231\n",
+            "====> Epoch: 3340/4000 (84%)\tLoss: 178.8894\tLL: -142.6744\tKL: 36.2150\tLL/KL: -3.9397\n",
+            "====> Epoch: 3350/4000 (84%)\tLoss: 178.9565\tLL: -142.8399\tKL: 36.1166\tLL/KL: -3.9550\n",
+            "====> Epoch: 3360/4000 (84%)\tLoss: 177.8551\tLL: -141.8409\tKL: 36.0142\tLL/KL: -3.9385\n",
+            "====> Epoch: 3370/4000 (84%)\tLoss: 178.5703\tLL: -142.6572\tKL: 35.9131\tLL/KL: -3.9723\n",
+            "====> Epoch: 3380/4000 (84%)\tLoss: 177.5569\tLL: -141.7432\tKL: 35.8137\tLL/KL: -3.9578\n",
+            "====> Epoch: 3390/4000 (85%)\tLoss: 177.8621\tLL: -142.1484\tKL: 35.7137\tLL/KL: -3.9802\n",
+            "====> Epoch: 3400/4000 (85%)\tLoss: 177.1562\tLL: -141.5425\tKL: 35.6137\tLL/KL: -3.9744\n",
+            "====> Epoch: 3410/4000 (85%)\tLoss: 176.9366\tLL: -141.4255\tKL: 35.5111\tLL/KL: -3.9826\n",
+            "====> Epoch: 3420/4000 (86%)\tLoss: 176.9569\tLL: -141.5480\tKL: 35.4089\tLL/KL: -3.9975\n",
+            "====> Epoch: 3430/4000 (86%)\tLoss: 176.2266\tLL: -140.9223\tKL: 35.3043\tLL/KL: -3.9916\n",
+            "====> Epoch: 3440/4000 (86%)\tLoss: 176.9983\tLL: -141.7966\tKL: 35.2017\tLL/KL: -4.0281\n",
+            "====> Epoch: 3450/4000 (86%)\tLoss: 176.9343\tLL: -141.8347\tKL: 35.0996\tLL/KL: -4.0409\n",
+            "====> Epoch: 3460/4000 (86%)\tLoss: 176.0161\tLL: -141.0156\tKL: 35.0004\tLL/KL: -4.0290\n",
+            "====> Epoch: 3470/4000 (87%)\tLoss: 176.7653\tLL: -141.8664\tKL: 34.8989\tLL/KL: -4.0651\n",
+            "====> Epoch: 3480/4000 (87%)\tLoss: 175.5563\tLL: -140.7572\tKL: 34.7991\tLL/KL: -4.0449\n",
+            "====> Epoch: 3490/4000 (87%)\tLoss: 175.3006\tLL: -140.6010\tKL: 34.6996\tLL/KL: -4.0520\n",
+            "====> Epoch: 3500/4000 (88%)\tLoss: 175.2206\tLL: -140.6210\tKL: 34.5996\tLL/KL: -4.0642\n",
+            "====> Epoch: 3510/4000 (88%)\tLoss: 174.5045\tLL: -140.0060\tKL: 34.4984\tLL/KL: -4.0583\n",
+            "====> Epoch: 3520/4000 (88%)\tLoss: 175.1497\tLL: -140.7520\tKL: 34.3977\tLL/KL: -4.0919\n",
+            "====> Epoch: 3530/4000 (88%)\tLoss: 174.7250\tLL: -140.4303\tKL: 34.2947\tLL/KL: -4.0948\n",
+            "====> Epoch: 3540/4000 (88%)\tLoss: 175.2608\tLL: -141.0703\tKL: 34.1905\tLL/KL: -4.1260\n",
+            "====> Epoch: 3550/4000 (89%)\tLoss: 174.6609\tLL: -140.5726\tKL: 34.0884\tLL/KL: -4.1238\n",
+            "====> Epoch: 3560/4000 (89%)\tLoss: 174.3106\tLL: -140.3266\tKL: 33.9840\tLL/KL: -4.1292\n",
+            "====> Epoch: 3570/4000 (89%)\tLoss: 174.6531\tLL: -140.7730\tKL: 33.8801\tLL/KL: -4.1550\n",
+            "====> Epoch: 3580/4000 (90%)\tLoss: 174.1357\tLL: -140.3559\tKL: 33.7798\tLL/KL: -4.1550\n",
+            "====> Epoch: 3590/4000 (90%)\tLoss: 173.7440\tLL: -140.0654\tKL: 33.6786\tLL/KL: -4.1589\n",
+            "====> Epoch: 3600/4000 (90%)\tLoss: 173.3671\tLL: -139.7910\tKL: 33.5761\tLL/KL: -4.1634\n",
+            "====> Epoch: 3610/4000 (90%)\tLoss: 173.2915\tLL: -139.8185\tKL: 33.4730\tLL/KL: -4.1771\n",
+            "====> Epoch: 3620/4000 (90%)\tLoss: 172.8276\tLL: -139.4575\tKL: 33.3701\tLL/KL: -4.1791\n",
+            "====> Epoch: 3630/4000 (91%)\tLoss: 172.9147\tLL: -139.6464\tKL: 33.2683\tLL/KL: -4.1976\n",
+            "====> Epoch: 3640/4000 (91%)\tLoss: 173.0478\tLL: -139.8802\tKL: 33.1676\tLL/KL: -4.2174\n",
+            "====> Epoch: 3650/4000 (91%)\tLoss: 173.2998\tLL: -140.2311\tKL: 33.0688\tLL/KL: -4.2406\n",
+            "====> Epoch: 3660/4000 (92%)\tLoss: 172.2287\tLL: -139.2616\tKL: 32.9671\tLL/KL: -4.2243\n",
+            "====> Epoch: 3670/4000 (92%)\tLoss: 171.8722\tLL: -139.0071\tKL: 32.8650\tLL/KL: -4.2296\n",
+            "====> Epoch: 3680/4000 (92%)\tLoss: 172.0429\tLL: -139.2784\tKL: 32.7646\tLL/KL: -4.2509\n",
+            "====> Epoch: 3690/4000 (92%)\tLoss: 172.1229\tLL: -139.4595\tKL: 32.6635\tLL/KL: -4.2696\n",
+            "====> Epoch: 3700/4000 (92%)\tLoss: 172.2275\tLL: -139.6653\tKL: 32.5622\tLL/KL: -4.2892\n",
+            "====> Epoch: 3710/4000 (93%)\tLoss: 171.7850\tLL: -139.3249\tKL: 32.4600\tLL/KL: -4.2922\n",
+            "====> Epoch: 3720/4000 (93%)\tLoss: 171.9042\tLL: -139.5498\tKL: 32.3544\tLL/KL: -4.3132\n",
+            "====> Epoch: 3730/4000 (93%)\tLoss: 172.0135\tLL: -139.7638\tKL: 32.2498\tLL/KL: -4.3338\n",
+            "====> Epoch: 3740/4000 (94%)\tLoss: 171.3583\tLL: -139.2136\tKL: 32.1447\tLL/KL: -4.3308\n",
+            "====> Epoch: 3750/4000 (94%)\tLoss: 170.3373\tLL: -138.2997\tKL: 32.0376\tLL/KL: -4.3168\n",
+            "====> Epoch: 3760/4000 (94%)\tLoss: 171.0468\tLL: -139.1173\tKL: 31.9295\tLL/KL: -4.3570\n",
+            "====> Epoch: 3770/4000 (94%)\tLoss: 171.0766\tLL: -139.2559\tKL: 31.8208\tLL/KL: -4.3763\n",
+            "====> Epoch: 3780/4000 (94%)\tLoss: 170.5388\tLL: -138.8249\tKL: 31.7139\tLL/KL: -4.3774\n",
+            "====> Epoch: 3790/4000 (95%)\tLoss: 170.1219\tLL: -138.5132\tKL: 31.6087\tLL/KL: -4.3821\n",
+            "====> Epoch: 3800/4000 (95%)\tLoss: 170.4101\tLL: -138.9035\tKL: 31.5066\tLL/KL: -4.4087\n",
+            "====> Epoch: 3810/4000 (95%)\tLoss: 170.7061\tLL: -139.3013\tKL: 31.4048\tLL/KL: -4.4357\n",
+            "====> Epoch: 3820/4000 (96%)\tLoss: 168.9147\tLL: -137.6141\tKL: 31.3006\tLL/KL: -4.3965\n",
+            "====> Epoch: 3830/4000 (96%)\tLoss: 169.8200\tLL: -138.6273\tKL: 31.1927\tLL/KL: -4.4442\n",
+            "====> Epoch: 3840/4000 (96%)\tLoss: 170.5949\tLL: -139.5070\tKL: 31.0878\tLL/KL: -4.4875\n",
+            "====> Epoch: 3850/4000 (96%)\tLoss: 169.3627\tLL: -138.3782\tKL: 30.9846\tLL/KL: -4.4660\n",
+            "====> Epoch: 3860/4000 (96%)\tLoss: 169.6400\tLL: -138.7620\tKL: 30.8780\tLL/KL: -4.4939\n",
+            "====> Epoch: 3870/4000 (97%)\tLoss: 169.5833\tLL: -138.8114\tKL: 30.7719\tLL/KL: -4.5110\n",
+            "====> Epoch: 3880/4000 (97%)\tLoss: 169.2347\tLL: -138.5701\tKL: 30.6647\tLL/KL: -4.5189\n",
+            "====> Epoch: 3890/4000 (97%)\tLoss: 169.0941\tLL: -138.5356\tKL: 30.5585\tLL/KL: -4.5335\n",
+            "====> Epoch: 3900/4000 (98%)\tLoss: 168.8915\tLL: -138.4390\tKL: 30.4525\tLL/KL: -4.5461\n",
+            "====> Epoch: 3910/4000 (98%)\tLoss: 168.5398\tLL: -138.1954\tKL: 30.3444\tLL/KL: -4.5542\n",
+            "====> Epoch: 3920/4000 (98%)\tLoss: 168.1529\tLL: -137.9168\tKL: 30.2361\tLL/KL: -4.5613\n",
+            "====> Epoch: 3930/4000 (98%)\tLoss: 168.2688\tLL: -138.1414\tKL: 30.1274\tLL/KL: -4.5852\n",
+            "====> Epoch: 3940/4000 (98%)\tLoss: 168.2566\tLL: -138.2386\tKL: 30.0180\tLL/KL: -4.6052\n",
+            "====> Epoch: 3950/4000 (99%)\tLoss: 167.6752\tLL: -137.7677\tKL: 29.9076\tLL/KL: -4.6064\n",
+            "====> Epoch: 3960/4000 (99%)\tLoss: 167.5804\tLL: -137.7800\tKL: 29.8004\tLL/KL: -4.6234\n",
+            "====> Epoch: 3970/4000 (99%)\tLoss: 167.8829\tLL: -138.1908\tKL: 29.6921\tLL/KL: -4.6541\n",
+            "====> Epoch: 3980/4000 (100%)\tLoss: 167.7373\tLL: -138.1533\tKL: 29.5840\tLL/KL: -4.6699\n",
+            "====> Epoch: 3990/4000 (100%)\tLoss: 167.3663\tLL: -137.8931\tKL: 29.4732\tLL/KL: -4.6786\n",
+            "End fitting: 2020-12-14 16:21:28.542977\n",
+            "\tElapsed: 0:00:47.737227\n",
+            "\tDeleting model_multi.pt.running\n",
+            "\tDeleted: 2020-12-14 16:21:28.543145\n",
+            "\t\tElapsed: 0:00:47.738488\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QM9z8KI9ei02",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "3d769cac-1d7d-433b-ae44-b49c341106ed"
+      },
+      "source": [
+        "print('Probability of redundancy: ', model_multi.dropout.detach().numpy())\n",
+        "indices = np.where(model_multi.dropout.detach().numpy().flatten() < 0.2)[0]\n",
+        "print('Non-redundant components: ', indices)\n",
+        "plot_dropout(model_multi, sort=False)\n",
+        "\n",
+        "encoding = model_multi.encode(data_multi)\n",
+        "\n",
+        "encoding_x = encoding[0].loc.detach().numpy()\n",
+        "encoding_y = encoding[1].loc.detach().numpy()\n",
+        "encoding_z = encoding[2].loc.detach().numpy()\n",
+        "\n",
+        "plt.figure(figsize=(12, 12))\n",
+        "for idx,k in enumerate(indices):\n",
+        "    plt.subplot(len(indices),2,2*idx+1)\n",
+        "    plt.scatter(encoding_z[:,k], latents[:,0])\n",
+        "    plt.xlabel(str('mcvae dim ') + str(k))\n",
+        "    plt.ylabel('ground truth 0')\n",
+        "    plt.subplot(len(indices),2,2*idx+2)\n",
+        "    plt.scatter(encoding_z[:,k], latents[:,1])\n",
+        "    plt.xlabel(str('mcvae dim ') + str(k))\n",
+        "    plt.ylabel('ground truth 1')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Probability of redundancy:  [[0.5039417  0.41527027 0.15137723 0.10085765 0.56430954 0.01477021\n",
+            "  0.04940293 0.40078884]]\n",
+            "Non-redundant components:  [2 3 5 6]\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x864 with 8 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6AkpD8cL09_d"
+      },
+      "source": [
+        "The multi-channel variational autoencoder allows to predict any modality from any other."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "67PCF2GGcKsn"
+      },
+      "source": [
+        "# We compute the reconstruction of the data from the encoding\n",
+        "z = [_.sample() for _ in encoding]\n",
+        "reconstruction = model_multi.decode(z)\n",
+        "\n",
+        "# This variable has several dimensions over two indices:\n",
+        "# the first index indicates the modality from which the encoding is done (0:x, 1:y, 3:z, ...) \n",
+        "# the second index indicates the modality to decode(0:x, 1:y, 3:z, ...)\n",
+        "\n",
+        "decoding_x_from_x = reconstruction[0][0].loc.detach().numpy()\n",
+        "decoding_x_from_y = reconstruction[1][0].loc.detach().numpy()\n",
+        "decoding_x_from_z = reconstruction[2][0].loc.detach().numpy()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0y0yGMQhcNXl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 617
+        },
+        "outputId": "a3127a15-de5f-47bd-f844-cc0120d6a1ad"
+      },
+      "source": [
+        "plt.figure(figsize=(18, 10))\n",
+        "plt.subplot(2,3,1)\n",
+        "plt.scatter(X_ext[:,0], decoding_x_from_x[:,0])\n",
+        "plt.title('decoding X from X')\n",
+        "plt.xlabel('X0')\n",
+        "plt.ylabel('X0 from latent X')\n",
+        "plt.subplot(2,3,2)\n",
+        "plt.scatter(X_ext[:,0], decoding_x_from_y[:,0])\n",
+        "plt.title('decoding X from Y')\n",
+        "plt.ylabel('X0 from latent Y')\n",
+        "plt.xlabel('X0')\n",
+        "plt.subplot(2,3,3)\n",
+        "plt.scatter(X_ext[:,0], decoding_x_from_z[:,0])\n",
+        "plt.title('decoding X from Z')\n",
+        "plt.xlabel('X0')\n",
+        "plt.ylabel('X0 from latent Z')\n",
+        "plt.subplot(2,3,4)\n",
+        "plt.scatter(decoding_x_from_x[:,0], decoding_x_from_y[:,0])\n",
+        "plt.title('decoding X vs decoding Y')\n",
+        "plt.xlabel('X0 from latent X')\n",
+        "plt.ylabel('X0 from latent Y')\n",
+        "plt.subplot(2,3,5)\n",
+        "plt.scatter(decoding_x_from_x[:,0], decoding_x_from_z[:,0])\n",
+        "plt.title('decoding Y vs decoding Z')\n",
+        "plt.xlabel('X0 from latent X')\n",
+        "plt.ylabel('X0 from latent Z')\n",
+        "plt.subplot(2,3,6)\n",
+        "plt.scatter(decoding_x_from_y[:,0], decoding_x_from_z[:,0])\n",
+        "plt.title('decoding Y vs decoding Z')\n",
+        "plt.xlabel('X0 from latent Y')\n",
+        "plt.ylabel('X0 from latent Z')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1296x720 with 6 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HtG7Ta5vKp1W",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "64152bbc-a952-4215-bfe0-44d7c354f9a9"
+      },
+      "source": [
+        "plot_latent_space(model_multi, data_multi);"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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WFIgVO3EcGwYoe0tyOAU6ieL+Z6c49MQXafs1dr7+J9g5spPjjZjtAyEDJZ9OnCFYkZXhcsAPX7edY42IiaES4wMlZtspI1U7450ZQzNKkTWT0i4veuI2ae7hPjPdpJ0omnnebpQpUq2JYmVDC0nGc9MdGnEGxlDyXVJlmI8W5wOqgabkip0IM5AoQ2PFfIEAjtG0vv9FnCwjeOm7CXcdJBKHubku852UA2NVDu2ooXQujrRKTMFz1x8yKgxvwaYlW+IR9tJ2VtLOwwoL3ZQozTg812GuldCIU5rdjEjZWHCUatJM4zrWo40yhQBxZih7DuXQxc09l0w5/YvJHH+C46XdZE7IU/t/lDgYRPQALw49Brf7OGIzIqzXTC8VgqGKz1DF6jJ04gzHcZhpGbbVA3zPBcOGVdK2Olmel5vkBldpgwBznZTZTsJsK0aMje92kozjzZhOmjG5EHG0kfRvW9pkrKwITzKDi8HN4w5RttzoDndeoDw2ge+7dK95MwRlYr+E7qZEmebR4y20gUePNdGMc+2OAXwvXNWzDb31T5QWhrdg07LULiVK8/Bkg+8fWeCGiUEOba+TaoVSmuONiCPzXQ7PtDl8om3zPZWmm2bEqbEXbmr6e800iCg6qV0yD4wbn0rgohEcLdCdh0e+iJl5jvKh2xFvP3E4jCNQDR0acUYpz+NdrHCycUPXlWVygb2wQjvRSBMGKv56iykuOTKlme0kmFxXoRdCOt7oMtuKme2ktGNFN8lwHeG7hxd4fq7DQMmjm6pl3404s//3pRgDiQJXK1INSW6YXRWx89g3GFl4nFZyC/G+H8ALBgBBZxpjUprxYjhCGXhmusNIJaQS+NRXnDfXEYI1inlOxeV99gs2NUt1S773/Dwf+usHyZTGcxz+99sPMVoLmGpEPHeiw7FGh6lmQjvK0BhSpekmmlRrVqZVpopls9AGaKca3xWrGnX0fvRTX0fEITv0gzzl7utf6PtHywxVAis7aAy+6+B5Dm4vbmtAZQbBlhT73nLpyERr4kzhuQ7uGqlylwONKOuf33a8GEI60Yw41ojym5ahm2ienG7x8LEmAM1YEa6wc5kB15y8TOf/3p4zPNB4monJr+OpmLnxm2ltvwU3VaSOzW5wRFDaqs2J2O+fIzBQ9nFwmG7GOAIj1ZDQdwk8oeafnQktDG/BpqX/6A589/l5MmUFrzOtuf+FBUquwyPHGnkYwgrfZMrOoHXSjG7KMi3dHsqAs2Khiy0trT7xRdwTj6FHD+Be9yamIx/TSvvrdRLFQjfl2EIEwO7hMqO1kMAV3CUz29YIG1Rq05AC13rA1vu1Oq7ZZTq5lqpFvdxM2RzdbqpIUsWRuYh2nGEQkkzRTlKePtFetv1q9Qmr3cKW/ne3nfguO6e+Rac0xjP73kxaHqOkhUA0ytjqQ1ccDDZtcO9QSKJgtBZQD1xKgc2I6SaKBTehajwy7VIrecvyxM+UwvAWbCqMMaTKYDDL5BdvnBjCcxwybeO0nTjl7x8/gbYpugyVhDSXhEz16hdn/xjYC7VXCOyJAp2hTZn2tuswo/txdl6L67uYKFq2bZoZvnt4oW/MJxsxL9s/zGg1QGR14RylDbE2BL6Tq2htXEB9K7O0ErGTZMx3EjJlON6MePR4g2dPdBmp+gxXfB452qC7Qrz+jEPjxuDoFO0GzA8cBITp0ReD2Am3dmoLX8qea59U0LjGeryB51AruVQDF3EgSQ21kpBhxZTiVDFUCejEijTT1Er+us5pYXgLNgXGGNqJsuW9+ZW1kLuslcDjqvE6v3z7Ib7/wgKeY3j0WKsfh7Oauyaf6Frd+1kNDVS60+w++lXa1QmO73wlgb+NBWcH9W7GsOswUg04kXu8vXLhpRe+MTDTThipBihlcLzVLz6NTYerhS6VM6jlv5RJ+robhtl2wveeX+DhYw2iOOOrTyzeTHfVA6bayVkdw08a7J78GiA8s/fNpEGd6bGbTlovykBpRclz8BxB5YL4vmuF1jMDJc/FAFFqKAf2qcVz3X60KlWGVpQyUD65E8VaFIa34KJjjGGuky5r12J1VTNaiaLRzWjHKSPVgKvGKzx4ZIEkz0roGcHUsA5XCERnbJ++j20zD5B5ZZrVXWQGsgxA0UwUiGGkGjIxGLIQZQyUXCYXlhsCmz5mL6OV3QlW4jqC57rLWsdcjvT+T3Gmuf/wAr/9T0/YEJEsxvUNcKyZnPFNtI/RjM4+xM6pb2FEmBx/+Wk3STWkiabqQei56Dx0YMMOQui7uK7kFY9CPfDw3fwOnFvfVBnSTOOf4bktDG/BBUVr+6hmMEhe2tlJsr7RNcbQiFKSzJAqKzgy3Y14fKpFK0p59GiDuU5MlCrKHnSzddlbAMrdafYe+RJh0mBm6Gomt78C7YYnrdfoWu/7WDPNhXmWz6bXQ5erttcYXqXn1kpcR6iX/GU5wpcb/Ri90ogIzSjj4ckGmcolZlacyPUaXT9psvfIl6l2j9Oo7eHIzleT+vXTb5iTGgiwnq7RBtcTyvnTSS+Htxy4VpUuz/teil7HN7EwvAXnHZV3ZbCFDMu/nNbbTfBdq33bjpd3hpjvJBxbiJhtdHl6tsNsJ6LRzZjvZkRnmRSgHB8jLk/t+xHa1Yk113McoZUsXk49B8eWocKVYxUGyz7i9CQjVzeoniPUSx7lwD0jycBLgUxpokzTiTMaUWaV2bD/i26qEIGZZsIV28o2d/ocJDUrN8DRKYcn3mBjuuud8HJAxMFxHHzPpRZ6GJMX8mihXnGpBdZkrtRpFmFdwkeF4S04r7QiW8Lbe7wUpN+mBezjptVM1Uy3ItpdxTMzLR493mLfcAXXMSx0M6bbMdONKO+VtX6jW28ept46zNGdryIJh3j8ineteWE6QODaajacRWMrwK7BEAMMlj1KgYvJ08YEm7+7krLvUiu5lAK3r+EL1kiHa8SDtzLGGJqxrSaMUtsNZLHQwdBNFDOtmLlOwpHZDokyvPLKYe59Zo5utn7jW+5OMzbzAC9MvA7thjxxxT9bl8F1sXNtrthS8pLnUAnsxFoYuPmTikfgO+h83bJ/crioGnpWmMdxqJ6BBsemNbx5J4VPGmP+1Tnc313GmNedi/0VrI4xhjjvc7YQJTS62arriVjvx3b31cy0Io4udHn0WJPP3HukXw125XiVHbWQIwsRkw1bm7+e69PNuuw6fg/DC08ShcO4Kka54SkvTk3P2BoqrkOpHpAZQzX0GCh5tj+bK3m7GY341ttZ6vGGnkM5sF1qQ89+Vgk8Asfmfwa+S3AJSkM2IivRqLSh2U2x2X1WZa6RNxXtxIrjjS7PzrSZbiccm+us2+O1Mfpvs23me2RemSBpEIfD6zK6VirSerqB61IJXQbKPpXAo+Q51HyXoWrAcDXA9xxC1yFYcZ59VxipBozUQuqhl+s2n34Mm9bwng4R2QP8HPBDwBVADZgDHgA+D3zCGLNwHo8/AnwYeDuwE5gBvgB82Bjzwvk67mYmShXNKMv7l9ly0LV4+KitQrtqvEqqNDPNmG6mefhIs38RKgOPH2/z+PH2mvtZE2MYbDzFxLG7cVTC8bFbmBp7CcY5s0d9AQSDiL3get5Pvuu+125yV9jqv4LvWX3gUuD2jXHJdamU7MUc5CGV4ZrPaPXkuPJWJsls14+Fbsp0K7adQDLFw5MNHp1sMlwJyDAcn4/wHc1cJ6PRSZjr2uqyM6XaPsruya8RJgvMDl3N0TVi9KdDA4ED9bJP2XcZKAXUyx7l0KXsuYwPhGyrhXiuy1DFZ7Ac4DlQCjzG6yFDZZ+BcrCuXms9tqThFZF/A/wuEALfAz6FNbqjwKuAjwL/BzB2no4/CtwDHAL+Cfg0cDXwr4G3iMhtxpinz8exNytRqvL0L0umzLLKs0eWlPsC/So01xFed9UYw9WATGvq5XMTA3V0wsSxu0n8Oi/sey1RaXRd2xvA5B0nSvmEitVjWPR8PA/KgUsl8GzowXcIXQ/fsyXDge9QCzyqoU/oOwyUfAbLHrXQx3Wdk8pctzpH5zs8Mtlgtp0wvRAz1415ZqbL159cTBGDxTk0J/99Xb6uMew6fg8YzdN730KrtntdY7SKZYu4rstAOWCg5DFU9SkHPlXPY9dQyLZ6SL0SMFzxCTzXnnffYaQSMloLNjRJuuUMr4i8F/hDrKH9MWPM362yzg8Av3ceh/HrWKP7W8aYX1py3J8Hfhv4feCO83j8TYXSxnZ+zYsfMq1pxxkLXdsl4onjTf7TPzxGpm0H31dcMUqai1IbZTgyH1EJXJ6b6fDsbOfsB2IMg82nWagfQLuLojbI+h/pjQiB41ENHbbXA2qlAN+3RrcaepQ8j3Ig1MoBJc9mLHjiEPhCPfTzR1aXsu/iezas0CucOBsPabPz3EyL//HsDJPzXRod6/XOd1IePrqwLN96Keup26u1DtMpb0e7Ic/ufhOZV8Y4/hlv75DHc93F4hoB9gyX2TlUYajiUfE9hso+owMhldCz4kl5d5OeHGQt9BipblzE/oIaXhF5Fth3ilVOGdMVkTrwO/nb9xhj/nG19Ywxd4vIqgl8IjKGNZxvBUaAJ4H/bIz5k9P+AXb7GvA/AW3gIys+/l3gF4HbReSKy8Xr7eZltInSi3mYeWxPacNDkw1SpXPpP0M7Fz5Rebt2pRXffGaG4830lMc5FUGywO6jX6PWOcrhiR9kfvCgjfmdJfXAZazmc/WuOtsHyuwequJ5Qi3w8FyHauBRLXmUfYfRWkgpn3DxHGfd2qxbnblOwvdfWGC+lTDfSmnGKVGqeexYkxPtsz+nsDxGf3zsZo6Pv5Q0GFjXPjzA6RncJZOyAuwZqXHNrhqDFZ+Bkk/F9whWpPxFiaZW86mVPFzHYaadMlo9uxDD0jFdSD4KDK2y/K3AzcDp3J1/hjWW31zL6PYwxsSrLB4C7gYS4C+woYp3AR8XEW2M+eRpjg/wCqAM/KMxprnimFpE7sS2UX89cMkaXmNsPm4rypjvxMx3E+K820PPk4mSjNl2SpzqvuiI6wgHRiuMV0OOLHQ40Up4bGojXq5mbPZBdkzdixGHF3a+hvmBK9e9Gx9wXUCgFrhMDJd50Y46B7bVqIc+V++sU8/byoeeg+PYybJqcGaTKZcqWhvm2gnNKGOundCKU+a7CZPzMS8srHYJniHGMNh4moljX8ddEqNfL4FANXDyLJiTS49To5kYKuPkOhudVNFNdZ7Z4OO7tj9e6Dv9TAZtDNrYhppnywU1vMaYj65cJiI/BHwI63l++DS7eFX++uWzHMKNwB8D/7MxRuXH/yh2Qu4DwJkY3hflr4+v8fkT+euhsxzjpkVrY1utpJo4VbRiO4liwwvWCPfSxmzHV82zM23+4cFjNsYncPOeQaaaEUfmuiSZ5uhGLk5gz9GvMLzwJI3aPl7Y+Woy/+TuvqdDAMexmQjV0GVbLWDvSJkdg2U812HfSJXdI1UEW9kU+g7hBSqEEJE7sOErF/gjY8xvnPeDroLWhjizcptpLiyfKU2mFIdPtDky3+H52S5znZhGN2GqtTFPd9vM/bmozbazitH3SAyk8eqlDZ4jXDlmRc4roYPrOLmcp0338xw7Eeq7tnAC7Hd4oOT30yHPlosa4xWR67Ge5wLwZmPMidNssjN/PdusgQ7wiz2jC2CMeVhE7gZeIyI1Y0zrNPvotRhdK2Oit3w1z/68cb4v0ChVNKLU6qcmilackWlbXWaMjaGVA4dWXo308NEGgevwxFRrsTeawaYQtdIN9V4QYw9qHI+Z4eto1PaxMHDluhPme9QCCPOZ66FqyHDFx3VcOoli51CZq3fVGK6uLoJ9PhERFztX8UPY7/y9IvI5Y8zDF+L4SmlmOgmtvOtDlLdeSlJrgLUxPHRkgQeOLKCUjevPtiLaqaITn4Xy2lJRm8GDGHE5MXL9WcXol+12ye+7h0rcuGeIONNct2uAA2P2Rh147kltfQIvL3zx3b7XWz7DdLHTcdEMr4jsBP4O+7j/FmPME6fZ5FzwhDGmscry5/PXYeB0hnfTcSEu0EY3zYVCFM1cP9W20HGYXOgSK0OcKB6eXOBTeR7uasy0N2Z0y90pdh+9i1Z1gskdr6RT2Vh3vXQAACAASURBVLGBvYEnsH2gTC30KIUevtiLsBq6iEAlcM6qp9Y54mXAk725AhH5NPA24KzOqzEGbRYflU3/d/uZYVGmcaGb8OxMlzjJaMYpja4VJo+0Ik0MqVIcmevylcdn+uez5AlG28aiaycSrk6QNJg4eheIY0Vt/DonRl98Nn/mMvrCRnmY69WHxji4rcZA2c/T/WzxQ8l1cFzbBboSuIxWQ2olr98Z+lw/3VwUwysiVeBvgT3Ae40xXz/DTSfz17XrPE/N/BrLe9+TM8ll6nm0g2t83lu+1rHOB+f0Al2NwHNsX7NssU2L1oaFbkqcaU40Y56f6/Dd5+fXNLpgc3PPBtEpO6buY2z2+6RehdYpSn3XQ5Ybn2o+cVINPKqBR+C77BosMVINmW4lKAOD5cB2mHDO/YW4BhMsOgVgb6onTRqLyPuw8wrs3bsXsKGBJNe9Vdpmm6wm4mOMsaEjpUgzk2tSZByeaTHfSekmihPtmFZkS77j1PZAa8cpj011lt1Eo7OoPFseoxcmt79i/ftYhX3DIYOVkANjFSqBx7FGxHU7B7hhzxDDFZ+S79nQUuDiuzZ85Ls2hLTRMMKZcMENb+6dfRo7mfYhY8yn1rH514GfAn4Qm6d7MXgsf10rhntV/rpWDPh8cEYX6HoxxtBJFJ1EoY2xaTXKUCt5tKKMTGniTHO8GXF0rsOTU23m2knfwzhXlLtT7H3hy4Rpg5nha5gcf/lZJcyvRaat9F/Jt57ueD1k/1iV8YES1dAnVYZGlC3rkeY6gp/n8/Ym2y4WxpiPAR8DuOWWW81sO1mm9LYWSaZpxZm9ieY97b793CzfPTxPveTiOQ6z7YSpZkScaeY7Ma1I4TlWTrGdnEU4YQlW1OZLVLtTNGp7c1Gb2ob2GbrCi3cP8JI9Q+werTBcDqmFLrWSz96RCtXQw3ME111d6OZCcTE83o8CPwJ83Bjz6+vc9i+A/wzcJiJvNMZ8aa0VRSRcI7Nho3wT6AI/ICL1pZkNIuIAb8rffuU8HPusWc0rOh1LpRq1tqlh9JT6NXSTDDA0OglPHG9z3+H5vnEaLHlgDO1UndSIcL0oJ0A7Hk/teyvt6q6N7WwFjsC1OweYGC5R8X3aqW2quHukykg1xHOESugSruirpbRBaatH0CuyqAXeub6Qj2CfCnvszpetiY27r5i9z9P6+mGGvKvvVCOim2Y2hJBlPHqsxSfufo4sL9e+adcAIpq5TkorVky1FwMI5+KvVG6AY5RN/9tAjH4pN+4Z4A2HxnnplaPsGCjnUpwOg3loYbNwofN4fwF4P/Al4H9Z7/bGmGZepPCnwGdE5CeNMXeucpxXYIsYbt7geK/Oj/vokjG0ROS/Yo3YR4BfWrLJ+4H9wJ0XOIf3tBfoUq/o1ltvPa0/qrQhShTNyNbXd9OMRFldhdl2QjNSNLsJC52U2U7EVDNa5hFGacZGWorVm89Sbz3P0Z2vJgmH1i1+spSK7xB4wnx3cUB7hkvsHq5w1XiNa3cNMFT2eX4u4g/ufIZMaz73vUn+y0+8hBv3nH6O1GBzmZUyDFfPXAz7DLgXuEpEDmDP53uAnzzlWIwV5c56hjY3sivXmc/bF3US++TSiFLuPzzfnwjVBp6d61DxHKIsY657ll0gVlDuTrFt5gGen3i9FbU58GMbNrhDZZddgxX2b6tycKxKpeTjOw4DefrfZtQ/vmCGV0R2AP839pw9CHxolTjZ/caYvz7Vfowx/01EythihS+IyP3Y8t1eyfBt2LSx02VInAmP9Ia/Yvl/AF4H/KKI3AR8C7gGG1edwmpIXEjWfYGuRpLpfrpYqjRznZij8xGNKLWde1NlPcL5mPkopptoOklq25+sCN6erZfrZl0mjt3NUOMpuuEIjoptWGEDF+dILaDkOTS6HTRWieqN12znlr1DVPOWLbWSxz1Pz5LpRc3Y7z4/f0aGF/LS4nMs+WiMyUTk/cCd2PmHjxtjHjrlNljFt7XC7EmmOLrQzWO2irlWwuRCl7l2SpYpHKFf3psqxUKW0U3Wr427kqUx+rMVtVkLpeGHb9jOYMlnfKBEObBiNfXSmVe2XWgupMdbwmYdAfzCGut8Ejil4QUwxvxRXqjwfuxM/nuBKnZC60Hg3wEf3+iAT3H8GRG5DfgVrEjOq7EiOX/CRRDJOZsLdCk6L/ltxKk1vqmi0U2ZnO/m/aU0s62IE52E+bad5W5GVgBlrpPQjPRJQd11T6IZw1DjSXYduwdHJRzbdivTYzdhZOPG7Oh8ZFPeHOHWPUPctGeIF22voaAvctNNFbfuG+Yz9z5vOxm7DrfsPX3lmyNC6NtJufOR+WCM+Xvg7890fUeE0VrYbyiZKkOSaSvTmClmWwnTrYiFTkorSWl1Mo42Io4tRFQDlytHK0w1Y7TRgCFK1p+hsJJq+yi7j951zmL0oSvES75gN+0Z5MBolXreeifwhLH65hYgumCG1xjzLOcmNNTb3/PYoocPnOH6ax47L1P+V+vcZhb4t/nPRWe9F+iS7XhhrpN3fdA0uynHGhHdXEu1m1qvaLaT0E4ytNZ2wi1KmY0U0xtMlO/h6IRdx+4hDgZ4YedriUsjG9rf0rZAPe9Pa8OuoRK37h/CyXNDA1eolfx+X7ff+8mb+fbhOW7ZO8wNu22CSi+h3pWe1gL9iqbNWh7cG1umrNks+Q6Jymh0E9pxRrubMttJONFK+MYzs30vd+dAQDkQml1DmsGGz64x7Dr+DYDTCs+fDkfs+HYNVYhSxdGFmBv3DPCjN06QGdsuqhp6HBi18fnNzJYTySk4N6RKk2SaYwsdnpnuMNdJ6aQprchKOyZKkWWadqKYbcW0E4XGdlftRCmtVLGwhtbuGZNLNy4MXJGL2ryNOBjYcMI8rB6D9F3h1VdtY/fwYnVbNXCphh7DFVt7//qrx3n91ePAYg7oViwJjjPFQielm2ZEqe0EMd/NmOskNpRkxTQ4viQ2b7ANRrUxKLUxT7fePEynsh3VF7UprUvUZtV9hi7VwKPRTXnxniHe+4phW1nm2OKG0VrA1TsGKAeb36xt/hEWnHMybTjRjJlciJhtx0w1Ixa6Kd1UEaUZnWSx/1mmNIj1NtpxxkI7pZspuok6ZQv10xHEC+yevItaZ5LDkIvarK/Yb6lXu3QZ5G14xPS1c1991TZ+6NrtXLNzUWCl7LuMD5SoXEJ6C0prppsRU804j70rMqWZ79qYbifKmO2mNKOM2XZk+5+x+H80eWGFMutTD+vhZl12HbuH4cZSUZsz73t2KjxHeGqmi9aGp2c6XD8xwK37hyn5NkVMRE7KPtmsFIb3MqTX1XemFTHZiOzMt4Ess2lHSllPN0413SSjm2mUUkSJXR5l6uwzFoxm28wDbJ++DyMuz+987VmJ2sDqXu01O+uMlANu2DNIPfR46kSbG/cM8uKJocX2LK5QCz2GKhvTVN2MGAPTjYTpZkSqDCpPJeumtpddpDRppljoxjSjDEdgW92nm2jEQKaVbcV0FgceajzFrlx4vhejP5fMdhbbCClteHq6wx3Xn9MskgvGZja8/xG4/2IP4lJEacNc23bqzTLTVxZLtE2iV2ZRxNxzBZNpWpGimyoyrdlIhGHPka8w3HiShfp+jux41VmJ2qyF6wgv2z/MVdsHGB+wrVhuv34HldAjcG2/M9+5+AUP55NeNkq0JK3EGEOUaFKtUUrTSjLasTVirutQ9Q2+CKmy5zax82rrYvzEd9kxfS/t8vg5idGDjec6IhxZIqTUO22e43DzislP390653XTGl5jzEcu9hguVVKlmY9SokRhMKSZoZta0ZNEWeUpPzdOTgZBpnG9DElBm9Ue8E+NaAVojOMzM3Idjfp+FgauOKtUIldWz5i4ekedN107zqHxAbYPhuwZLjNcC7fMo+e5QgSrQ+A5NKOMNLWxfN8TWm1NK9FkyuA5LmmmMMZ2mHNccI1tYX7GZYfG4OgE7YbMDR1C5ed3PTF6AVzn5PRDR+DgeA3PcTjWTNDa4LnCP3/5Xlpxxi37RvqTnz2q4dY515vW8BacP4yBNNN4noNEtqljyXfoJgqjbc5fmj+eJqltvz7bzvDEesDrmequdI5ZUZvabo7u+IENidoIcP2uOtrAg5PNfvz2LTfs4LYrx2z4oOpzxbYaQ5Vg02YcnF+kn3FR9lyONyNCcVHGUAtcOkmG4wqe51AW8JVtt65TRWqgES8aXTe/x67mAPeE540Iz+x9C6lfY2b0hnWNNBColhxGyy6jg1VSZSj7Lkob9o9W2DtSpRZ63LxvmOdmO1yzY4Brdw1QyVP3luZNV0NvS91kC8N7GSJik/3bcYaXC3t3U4URCH3HtvDJFcjmuwmPHW/3043K/pl5MzZh/luMzT5I6tVoVvecfqMzoBZ6XL97kFdeuY3pVszB7VUOjFYZLPtUQo+D26rUSlsz7neu6WaKauihoxTfcciMsXoUmWszVrTge3nDTgPNFYF717E3tnhpaMloxma+z47pezHibkjUJjFQQciMyxWjVa7ZNWilGQW2D4Rsq5XoZgrfcXjlQfCdxVBCyXf6ehnVVUq6NzuF4b0sEcq+S5QqHBEGKwF0ErK8JDhS9nHUEVkmDmPI06tY3rBw5ex3uXOcfUe+TJA2OTF8LcfGX452z40xdB2Xq8ZrDFdCQt8FA/Wyx0DJ9jkrjK6lJ+/Y02goBS5e16EcgNI+BoPGZjyYXFe5EjjMdZZ6t0K2pOTYT5rse+GLVKJpFmr7OHKWwvNLme8q5ruKF75zlJ8bLPOSPUOkeem16zrsqPiUA8+qrWUaZQyB5zBSCQh9d8s+1RSG9zLEloUaRIRayUXpXksTSJQVu/YcAQ0jVZ9nZxbLSOuhR+gpZjvWO1ot5Ui5JZQb8OSuH6VT3bnKGqfHEyvZuGyZK1w3McBIrcRIJWAgb8vtOILnCMOVwuj2zFCa3y2XCebkhSC+51CXAGNSokzjO/Zu6roOjuOw0ElZiBTJimC6ckPA8NzEG886Rr8W2hiONyKGKgEl32GwHOC70o/nO47027D3ukFsZQrDexniiBB6NqQANhtgoGTjZu0os56wY9PLdtTLvOKAw7FGjI0yCNOtk83tQOMZ6u3nObLj1STh4IbFT7I8fmvylkE37h7ijuu387L9I1RLPtXAdn91xApXX0q5uBvBcWx1XY+ldjdwha42eI6gte2uQJQhYg21MUItgE682BWy3DnOttkHeH7iDWg34MkD79ywwa0HLuXAtrt/ZqaLwUqOvv7qcXYOlQEYqQb4rtMXb5f8b7tUKAzv5YhAreQTZZpOlJHlWruCwgC1khWJVtrgxRklv8xg2aebKtJMkWnDXJ5T6WUddh+7m4HG03TD0f4s93ovTt+BdIU9PzBaYbQWct2uAa6fGOTgeJ1a6DFQ9tlWDzG5116wiAgMVmyFWJSqZUX6Fd+lmz/NpCK4jkMt9HAShQ5MHkYy1EsuM80OO6buzYXnq4RJA10Zoey5aGOIlV6zgMbPb5iak5+IBNg2EDAxWGKsXuLG3cM4Ltx25Rg37Rnur+PlRlZE7CTfJUZheC9THLFxXldsm/U40/iuLQv2XCFTgucKldCjE2eUAw9XHFLfZb/vUXKF5nMPMHD4vyMqZWbHS5keuxFPXNvaPT/O6VTKPAcqnkMldEmUZq5jjb8rcPXOAa7bOcBQJWCw7CPYThi9tKHC6K6OI8JQxUdpgyMJC10rdB4GHpXMam2EnkOcKeY6Vh5yqOpTDly0NtTbk1z7zBdw4wYnhq/l+PjLGRmsEuQtcHpPSu1I0cn0spBEybUiNRiD59psioVo8Uuwd6TElWM1hqs2VDRSDXj5FaOMLJHT9N0L00j0YlIY3ssQNze6niPMtmPrVThCpjxcgVaicExKnBkcVzjcajO5EDNS86mFPoHvsrPm4L1wD7o6SueKN5D5g9RyOUKDtbyZNqsa3l6ILsw7+5bDgMAX9lV8KqHPbCflqrEaB8arbBsI+5qqJd+lFnpUtkAt/sVGRKiXPfKzkXcGNoxWQwSrvTHfSfnSo9MobXAd4bVXjVIJXLpP3oXrusQ3vZss2M4uX/BF+t5rnu1LqeYwqKz32040bt4WSWuN61pvupUs93sHSgEjtYB62SdwHcbrJaqBu+yc+ptQP/dcU3yDL0Mk158Fm//YyIspUqUJfYdayVAPPea6Kc/PtPnq4zO59wS3VY6z/dCLMX4d943/msSrM9fN8FLbjSHTix0PWoktnOjhClR8OxHmubZ0N/BcAlco+TYjYd9YmWt8j52DFXYMlEiURgTKvseOwRID5c2rsbrZCD2XLDCk2tA2GZ4L+LbQoNFNeejoAjqfhKsuPMvcQsjVu0dRt72LVEIq4lPHFmAorVHmZHlBzwMv05Q8W3xjBALHoxw4VHyPwYphtpOijT3/V2yrsK1WwnVtD7uhim+zanIEGxK51CkM72VO4DkMVXyOp6rfGiX0rEEeqQZ8+7k5lDYE8Ty7J++i0TnGWD2ktu8GGB6npDWZAclbrERJhtZCIoayZ5b15Ros23xL1xU87ASf64DvugyUXbbXy2yrVtgxGLKtHqKMzSveMVBmYqhMtVR8XddLT5+i1x8P7I13sBJw4+4h7n38KNuPfp2hxlMMDnWpHryDiR3jNGN7I1ZK5+JJhkSpZQ0zO4miHStqoUet7KC0gyc2TBD4DmXPoRz6jNZDmlHGtTvqXDlewxGbrTBWCxgfLC2bDKyG57x90qak+CYX4OWTLEpDlKm+F+S5DjdNDPC9r93J2JQVtdnzqney75qbifKc3wSHwUpAlEZoVzAitNKMkm8zJQLPIco0oetQCaxegucInuPg5YY3cF221UMO7qgyXgvZPVrFc+yM9sAllEJ0sSj5LjsHy8y0Ypp5NYQxBjn2MNc9++dkScyel76JG1/9JrqZxhMIfc+WHGcKz3MIPI1SDrE2uVxoxrMzXbSB6VbCrXuHqJU9dKYhVwor+y7j9YChPJ67vVYi9F0bw8XGcgeXdImohh7V8PIwSZfHX1lwWrSBcuBS8h3izD5eZtrw1Fc+w/jxeynvvoYXv/HHODCx3T52asNgybMtgTJF4ApPTbdselCeAnZwtMxILcAX8HyvL3Di591dqyWfgZKLI8LEUJUdAyV2DFasUXbsxF7ouVQuEy/ofOK5DrOdlO88N8eekTJT3/sq3/mnzzO+5wCvets/Z2ibzbfuxIpOkJFmmsGKR5zaYpo41URZXkxjDA9PthYF5g1EmeaKWojjCEopQBiplRgueQzWAkLPpRx6uGJDTINln4GKz3A5gDy9cTM1ozzfFIa3AKD/CCkiuChKriEoh7zqDbdzxfU3s/+al6D6HWrt7HfouUw2Ghye6bBnpEIp8Pr6KsaA57nsH62SaY3Jo4OO2Go3RxzKoctg2WP3cIWD2+tsr5Vw85nzXh1+kE/SFGyMRyYbfOivvo9OI5ygzC+/5lYG6lUO3fIaTJ7ZAjbLxPfsk0klNw+Z0iRKE6c2XCECRhwen2r1J+aunxhk52Boq+R8h7LvEaeKcrBYzuuKTVWsBh71ku2LVtvEfdHOJ8U3ugBYTP967qnH+cs//WMOXn0db/3xf8GBg4cY3rX/5PWN4cmpFp++93mrduUK73jJLr7z3IJtD+4Ie0Yq1MoemTYoZRPhPUdwHaiXfKqBx94Ra3QPjdeJMt2vtBLso2elCDGcE+59+Cn2PPXXaHF5bt9beKHr8+473kKmNJnWJJkiSjWR60CcsbQ3sfhuPy1ba0OUasbqAUPlvTw32+XgthoHtlVwROjmqWoDlYDQc/AEjNj0sh2DZUq+27+pXs7ZKZfvX16wjDiKuPOvP8s37/oSg8OjXH3DSwCbHrQ0d7OHiPDo8SaZspeo0oZ2rPhfX38lT0y12DNUZrjq04xTVC60rvP83sGyy45amVrZZ7QWMjFYoRJ6VEJb4qqNwXM2bz+zc8ywiDyE7VL9MmPMfb0PROTfAz+NLSP7eWPMnevduVKKu7/8Dzzx+b+kZITJ7bfhuQ43TFhJRc918FyHku8xULZpZ3OdBLU0GRtsSbEIzpJWSPtHarxcm/7TkiNCvWQoeYt5uNWSDRf5rjC0JHvBc+SyjtsXhncLICLvAj7Cebo4n3rycf6f3/koC3MzvOK1b+RNb3s3YanU/9zOip+s0XrNjgF89yhZ/rh5cLzGgbEqB8YWhVPGVIlMabS2Mq3lwGG4stiIsBZ6eb6p5XKK8+V0gR8D/mDpQhG5FngPcB2wC/iSiBwyxpxxc4jZE1N86g9/lyOHn+Ham27lmte/k6cacMPE4LIWSEvphRnctfrDL8F1ZNnNMfBsWKgZpaSqF7qw59Nbcl4FGLzM0wILw7s1eBB4J+fh4gSo1waoVqv8+E/9LPuuPHTS574rZKvs8eB4jQ/ccTWPHGtwaLzOrsES2QrP2HMFz7WeTei7/RxNASqhy2CeSH8ZExljHlulUuttwKeNMTHwjIg8CbwM+MaZ7rhSrSMi/OTP/DzX3/xSAG45g+0C1yHSZ/4VEuzEbK/JZC30aScpWtM3zGFeFOE6dmLNu7zPeWF4twLGmEdg1RLZDV+cAOM7dvBLH/71Ze1ilhJ6Lt01CvMPjtc4OF7rjZM4sxMwS/M9HYFy4PUNbOAJZd/Dcx0qgVtkLKzOBPDNJe9fyJedMaVymZ/9wEfWXX4beI7VeTjDdXvC5D0cR6iXAjxHcBzQ2sZzA8+hWogZAYXh3eps+OLs4ef5tqvRE5xeJjG4Cr1shNBz7ISasaljpcAKWPuOjSf2LlI3Txm71HnjG9/IsWPHTlr+a7/2a+dk/yLyPuB9ALt270GWdO85GyPn53HfbI3zLWINbslz1/RcHbGerTXCRZn3Sor/xiZBRL4ErNYX50PGmL85B/vvX5xAa8dg+TFgDDix0X1fQLbaeOEUY377298OsG+N7Y4AS9t27M6XnYQx5mPAxwBEZPr6iaHnTnXcTc5WHPdqY17rvAKF4d00GGPeeBabndXF2UNE7jPG3HoWx70obLXxwobG/Dngz0Tkt7Dx+6uAb51uI2PMtg0e96KyFcd9NmO+vCPcW5/PAe8RkVBEDnCGF2fB5kFE3iEiLwC3AX8nIncCGGMeAj4LPAx8Afi59U6aFmxeCo93CyAi7wD+C7ANe3Heb4y53RjzkIj0Ls6M4uLcchhj/gr4qzU++zXg3ASCCzYVheHdApzHi/Njp19lU7HVxgsXb8xb8X8FW3Pc6x6zrKxIKigoKCg4vxQx3oKCgoILTGF4CwoKCi4wheG9zBCRd4nIQyKiReTWFZ/9exF5UkQeE5HbL9YYV0NE7sjH9aSIfPBij2c1ROTjIjIlIg8uWTYiIl8UkSfy1+HzPIbi/J5HztU5Lgzv5UdP9+FrSxeu0H24A/h9EdkU8lH5OH4P+GHgWuAn8vFuNj6B/d8t5YPAl40xVwFfzt+fT4rze375BOfgHBeG9zLDGPOIMeaxVT7q6z4YY54BeroPm4GXAU8aY542xiTAp7Hj3VQYY74GzK5Y/Dbgk/nvnwTefp7HUJzf88i5OseF4S3oMQE8v+T9Wes+nAc289hOx3ZjzGT++zFg+0Uax2b+H27msZ0J6z7HRR7vJcj51n0oODuMMUZENpy/WZzfzcuZnuMij/cyRUS+CvxyT1Q9F1THGPOf8vd3Ah8xxqxLYvJ8ICK35WO5PX+/bKybCRHZD/ytMeb6/P1jwOuMMZMishP4qjHmRefr+GNjY2b//v3na/ebAm0MqbL9/zKtURoMpt+IcyUrlzgiiFgdYRGxnTUcYT1CbgK4ztoBgwfu/86Jnm7GahQeb0GPsxJluUDcC1yV61EcwU4S/eTFHdIZ8zngXwK/kb+eV490//793HfffadfcQsz0445Mttlph2z0E1JM003VX0DmypNnCprnA2AQWSxjZVg2xjVyz4lz6Fe8qkELvWy3++YcTpcRxipBmt+vmOw/Nypti8M72XGVtR9MMZkIvJ+4E7ABT6ei8hsKkTkU8DrgLFc+OZXsAb3syLy08BzwLtX2e7jwI8AU0s85RHgM8D+/7+9Ow+y/CwLPf59futZeu+epadnyUwWEhISEwZI2GQLIIqgEC8KVaBSaF0oLnq9hVbU0nvrWujVW7mWWCVqbmKJoFK3EAGJBllMQpQEQhJmApNtJpNZe3o7fZbf+tw/fqe3md575syS51PVdaZ/fX7v7+2pqWfe877P+7zAs8DPqOr4uf8tLny1ZspkM2aqVRxD30gyVJVGnFGPUqI0I8uLAuzzP9ELxfFTQft4o2aaUfJcxuoJg10B9Tijp+ThiFOMiKUYHbuOzL7OyNtHIz35wycIgpCdl+1e0+9gUw3GnGci8lpgGvireYH3D4AxVf1EO6+1X1U/vlJbe/fu1Ut5xBunOY8/P8GRiRatJKUeZ7TijIlmQjPOyHMlnze5oKpkuZJTBMtc4VQ9ZrQWsa0v5LKhKj3lgNBxqIQefdWA7iWK888V8xfyJOb+r/w/vn7vPVx3w4189NcWZpBt7S0/vFypSBvxGnOeqeo323PD872DYvQMRYrS14EVA++lrhGlTDQSkiyjHmdMNWMmGglxOhduVZWsHXBPP0TjVD3mvgOjZApPHKvxmivhyi3QXQoIVRmrR8UJyP6ZKc55rkR5RpTCA//8jzx47z285g1v5l3vWfuslwVeYy5Mq05Rmn+6yM6dOzvQtfNDVRlrxDTjlEacMVGPGKsnxVH0zI1uk1wXW2MDYLQWMXNgdqZwshaxqStAEELXoRS4jNdjnK6A0FsYfJuNaepTkwxtHeFlr3sr113/I+y+4ioaGeStlHLgLpiOWI7l8RpzgdNiPnDJOUFV/ZSq7lXVvZs2LbmQftGL0oypVkKqyth0xNj0XNDN8uKg1ThbOugCDHWHuO3YAPqPlQAAIABJREFU6ErxfaZQjzKmmglJlpPmOY0oXXDm3A8ffYi7/tdv8cW//jM0z/GDkJ17rgSKRIpmkjHeiKlH6ap+FxvxGnNhOi4iw/PS0E6c7w6dT6rKVKOYx52OEsYaKTN5DEmWk2SLR1tVZf7/Wv0Vn1ddMcip6Zih7pDBdmZCkuU00oxykuGIkORKPc5w4km+9vnPcODx77B5ZCdvue0DSDvz4fSxrSo04ox0ib7MZ4HXmAtTR9PQLlR5rkzHKa0448hki5NTEYdHG7TSFAfaI9R571dF23O7SrGodrpq6NEVeghCkuZFDi9KmuYkWU4rzigHLtPHj/Dlv/wj0jTmNW97F3tf+2Yct5h+EIrFtsXEK5zGDRZ4jTnv1puGdimK0gzV4oj5NM+ZbCazUwdxltNMM6ailEaUkSRKLjl53l5My8BxFEfAcRwmmynjjYSBqk9/ZWHObTEKbuf5ZkqagesU+cAOyoHjEccmYNOLbuLWN7+Fgc3DC+5fKuiulgVeY84zVf3ZJX70xo525DzKc2WymcyOFvNcSbKccF52QZTmnKq1qLcS6lFGK51LM88X/EE4Plnn6dEGSjE6ffG2bnYOVJZ+PhAlKQe/+yAnH3+A7+94B7FTwnOv44a8ysBp73c3uDpmgdcYc17NZCtk+dzc6FQrIcmUbooND2mWM1aLOFWPiTKlmRZzqZkqruvMLpgB1FrJbNCFYnS770iNqu/SV/GLizKzdbi4MZk6yehDXyQafQ4Z2k2eZahTLNrtP1rj6q09C/rsrGV/8SIs8BpjzqvpKF0QdKO02O6b5cqJqRalwKXeyjgx1WJ0KiZJc9I0J20vnJHm4AqaF+PbsXp8RgqIAvuO1bhqSxe9ZX/2qqjSPHA/9f33Ia4P172N8q7r0YMTSK64jnDFpq4z+uxtcMhrgdcYc96oKs144c70uL3INdFIaMZpURQnV8aaMc20uFaL0vYWYEEV3LSoeiMC1cBF5Mx6OdNRxnefm+TGHb2zwVdFSMaPI5uvYF/vy0nyCu7BCV51+SCOwIu2dLN7U/WMfvurrOmwFAu8xpjzZqqZMN6IUS2CpucUI9ax6Xh2DrcVp0xFKSenWhwfbzKdFLvWVMFpF7/xXYfQc/Dar7sHy5yoxUxHC4O6KoxNNfCeepjSrutxu4fo2vsODk3EJKMNoJhfbiYZt16zmdB3SU7LUvBdscU1Y8zFaTpKGZ2OaLaL26S5kmU5B081aCYp5dAlSXKeG29Sa8X84Ng0h8dbCEpOTpYWwVqcIgA7FAHYdYXAdegOXOpRtmDaoVo/Qtcz/0arOUHqlShd3o/rOPSXPRyBvB3Mt3SHxO3FvTQ/PfBufN+ZBV5jTEcURWoUR4RMlXqUkmTKdJTSjDOOTjUYq8Ucn2yR5jlTzYRWmtGIM45NRRyeiGaDqMfcpoiZGnqCgmS4Ar5TZB50BUKUKWkcs/XEvzM0vo8s7CG/8d2kQ5dRjzNcETxxeOnOXmqtjJH+EsN9JdJMiZOMarBw67AFXmPMBS/N8jPSv1pxhjgwHafUo4RDp5q0sozvHZ7kyZPTlAOXwC1KQNbjlOkoXzByXXJjrhY1GLK8CLyOFFuIN516lMHxfZwceAnTI69goFLBjxI818WRYrGs4rsMdQVUfHc24MZZ3p4vVkSEoD2dsVEWeI0x50yUZkzUY+JsbrQbeA71OCVXGJuOOVGLaCQJTxyt8Y0Do+Ra5N5uqfpEWUorVpKVN4MtkAFO3MJN62TlQU4M3cBU906a5c30uA6tNCNKHXxXCT3Bcx1SVfI0oxY5+G6KitBf9slznU07W6xq2XpY4DXGnBNJmnN4vEEryRdkGKgWi1eB63Bsssl/PDPGUyenGavHzGSVKTARJWgO0RqDLqr0Tj3NyLH7SN0ST17+M6jj0yxvnm17ZpdbnCqqQuDnSAol3wGURpLhOkLJcxhvxgx1lyj5Z2e0CxZ4jTHnQJYrz082acZnRs0ky2nGGRNZxMPPjvHlx4/NjnLni1MWrbWwHC+pM3LsPnprz9IoDXF42+vIT9vs0EpzAs+ZnT7I28VtusMiH23mdAkViFIlTWGyHtM7dGZa2XpZ4DXGnHWTzYRGdObJUfuPTvHQs2OM9JVRzXn8yOSCUe58aw26YWuMK579B0Qzjmy+mdHBlxQpD6dJcmjGKaHn4HsuCKSpkvvFVIjjCHle1MydOZ8tF4gzJVhFxCwHK09HWOA1xpxVSZYTpxn5vPmF/Uen+NcnTvAv+46TtXeEvXLPAFAcPrmRA8hEM1RcorCP8b6rGO2/jjjsXfaeKAPHyXFyQXAQV0jznDTNqPgurlv0q+K7OC6UPJc4zSFcuk3XEbpCj8BbeTrCCqGbjhIRFZG7znJ7Xz9b7ZmNayXZgjnd/UenuP3zj/NPjx8jzYttvlmuPDfRoOTBYNU9Y5phVTRn6NSjvOjAZ3HTJojDka2vWjHoznClOBATVYpYWdThbcYZeab4rkM5FCq+h+86LLVnwnWE7pLHQDUg9Bwb8ZqLj4jsAD4M3ArsAbqAceBR4B+Bu1R18hw9+1bgrcCPtL8GgPtV9dXn4nmXqiTNidKMRpyiCg8fGidJF04ciBTB9+hkUuxCW+MzwtYY249+g2rzBFNdOxFd28RE4ALi4DkC7Z1vgecUObrtk4hLgYtKcT3wHErzMhocgdBzZxfcfNeh5DuUfXc2A2I5FnjNBUNEPgj8CcUHuu8Bn6EIuoPAq4E7gN8Chs5RFz5McchkC3gSzqgGaJbRjIvjc8YaEVPNonRjlOYMlH1cR8hyRQSuGKpS8h0eO1Jb+xSDKptHv8Pmk98hdwMOjbyRiZ7Li0i+CkKxuaL4cnCc4th2EXAdBweo+l472HpUPBdxQEToLfv4bnHKcMl3i/e4xddatxBb4DUXBBF5L/DnFIH2Xar6pUXe8yrgk+ewG78P3A48AewAnjmHz7qknKy1GJ0uSjsmacZUM6UWJRydaDBWj3jl7n6OTEWUPIdalHBwrLG+eV0RStEYkz17OLL1lWReeU23KxDn0OO57U0aQuC5DHYF+K5Dd+hTLbls6grZ1l+itxzQXfKo+C7V0KUr9KmG3qoPtVyKBV6zYSLyLLBrmbfcraofWOb+buCP29++R1X/ebH3qer9IvKKJdoYAn4PeDvFSPVJ4A9V9f+u+AvMtf+tee2t9rYXvJO1JgdHG9Si4vTfJM14fqLJA0+eYv/xGj2hx5aeEt2BcGSqxfFasqb2JU/YcvJhxvteRBT289zIG1DZ2EaGKAVxcsqBz87+Ml2lgK6Sy6aeMpu6Arb1lamG/uxCmSOCI0IzyWgmGSXfpWsDAdgCrzkb7gD6Frn+duAmoLHC/e+mCJYPLhV0Z6hqtMjlPuB+IAY+RzFVcRtwp4jkqnr3Cs8366CqjNVj9h+Z5mStRabF7rSx6ZhvPXWK+58eA2CsnjDZiokSpbnGLWjV+hG2H/0GYTxF6lWIwv41BV2n/eW50JqX3TYdZ9TijFP1hGu39XD9zh4GKyW6yz497SmFGV578Wz+f8atJCPJcgarwbr+k7bAazZMVe84/Vp7oep2ipHnb6/QxMzi1VfX2YUbgL8Efkm1KJkiIndQLMh9HLDAe5ZEaUYrKTZANOKU45MtjkzWqbWK49DrccboVIvvHV64/jneODOndzlOFjN84t8ZHN9H5Pfw1K6foF4dWVMbXrFuRjUo5mc91yPNc7IcDk+0Zt+X5MLm7gpl32m/rwi6riOEnkMlWHzBbLlj5Ffs2/pvNWZxInIdxchzEnibqo6ucMvMSYKH1/nIBvCrM0EXQFX3icj9wGtFpEtVp9fZ9gueqhIlGSdqEfW4yM9VhXqUMB0nTDRSfnCsxjOj0/SUPKaaCbVFNk+sxdDYowyM7+fkwPUc27wXdfyVb5rHAdL2ue5JS9kxGLK1t8SmngBR4W+//TxpO5/4xh197eAKSZ7TU/bnMhyWUPJcuk4bBa+FBV5zVonIMPAlio/7P66qBzrw2AOqOrXI9efar/3ARRl4ReStwP8BXOAvVPUTZ6vtrH2gZNY+pbco21gckZ6rFkEry5lqpUw04tmUMBHBkeLwyZO1mEcOTXDPvuPFwZICveH65l/dtImfNmiVBjk5eAO1rl00y5vW1db8CY3iQCDhys3dXLm5i9B3Ge6tcODENFcPd3Pjzr4iT7c9pRB6i/d/JuUsXCEor4YFXnPWiEgV+CJFRsB7VfW+Vd56tP26ts+ScyaWuD5TPfDslJTqMBFxKbI4bqX4NPBtEfmCqu5bb5tZrjTilFaSL9hZtpSxesy+o1M8fHCcyzdV2bOpiqqQqzI+HfHg06dmgy4UH7/XPNpVpW/qKbYdu5/UK/PDPbe1i9qsL+ieznWEG3f1c81wDyXfxXcdrhvp5bqRXgLXIfRdAtehWnIJXBe3vZDmOMW9nuPgu3JWF1wt8Jqzoh0kPkuxmHa7qn5mDbffB/wCxXHmv3UOunexejnwpKo+DSAin6XIM15X4E2ynPHFDoJUJcmK0W+c5WR5TppBmud89+A4v3/PD0gzxXOEn33ZDrb1l2i2En54Ypp9R8/Mxc3WMPfpJ9OMHP03eqYPUS9v5vDwj646J3e+0mmLZ1DM7167rYdb9gyyZ6hKkucEmYPjwUA1oK8SMFAJCLxitNvJTBYLvOZsuQP4CeBOVf29Nd77OeAPgVtE5E2qeu9SbxSRcInMhkvRCHPTJVCMes9IpxORDwEfAti5c+eSjTlSbBZIc52dt51oFPm2cZaTJBlpnpMXswwkmfIfz5wizYptvkmu/NuBUa7cUuHoZItaM6URry01bL65ojY5R7bcwujAdYsWtVmN04PuroEyb7h6M1dt7ib0i3q7riNUSy7V0Ke7vYg2HWcEmc5OIWz0LLXVssBrNkxEPgZ8BLgX+OW13q+qNRH5KPDXwN+KyM+p6j2LPOdm4E8pRtUb6e/V7ec+sZF2LhSq+ingUwB79+7VedfbI9mMZpITpTlJmtOMU05MtXh+skUzTojTYo5XKY7nmW5vfmilGeONhYH1mbEGrSRFNacepTTXEXclz1DHJQr7Geu7mlMD1xIHPRv6O5jPEfix64bZe1k/B081ePi5Ka7Z2sNVW7qohkW6WNCeo81VaaVzp2NUgiI/91yPfi3wmg0Rka3AH1EMkh4Hbl/kH+0jqvr55dpR1U+LSJliy/BXROQR4AHmtgzfQpE2tlKGxGrsn+n+/Isi8mrgg+1vu9qvV84v6rPcRpBz4HmK+fIZ29vXltVKMiYbCfUoJc5y0nzhZ/+JRsxkO/3LdRxCH+pRylQz5uR0RCspAnSS5ShKJXCoz6ure2I6Lo7YWetvozlDpx5jaOwxntzz06RehaNbb1lrK2cYrLiMN7PZgypve+kIL98zwDMn6/z+V54gzRXPOcof3nY9l2/uXnbTQyPOSDOlvxpsuF/LscBrNqrEXJW7jy3xnruBZQMvgKr+hYjcQzF6vhV4L1ClWDx7HPgV4M6NdngZVwDvP+3a5tOufeAcPv9036YI/LspAu57gJ9b7oY4y3lmtE6WLz7RqqrUWglpXpwlNt1KmWwk1FoJzSTjwIk6Rydb9Jc9KqHLeLOYhphvLXO4M8LWGDuOfJ1K6yRTXcttcly9wIUd/WW2dIVUQo9UhZt29fH6F23GcxzuOzBK2s7UyPKc/cdqvGLP4IrtrmbRcaMs8JoNUdVnOfPwgI209xzFpoePr/L9Sz67PTr9wGrvUdW7gLtW89xOUNVURD4C3EORmXGnqn5/uXuyXJcMugBZe/phopEw3UpoxBlTUUKtlfL0yWmeOF4HYLyR0OULiJJvJCVXlS0nH2bT6HfJ3YCDI29kcg1FbZYzVA24fEsXfaHPlcPd7B7sYqS/wkB7tPrKy4f49L8fIs1yPNfhpTv7V2yz5Ll0l859WLTAa8wFTFW/DHx5I23kqsRpTiNOGa9HNOKMWithtNYiSotj1qM448jkwjXLRqII65hSmE+EMB5nsncPR7asvajNks0CL97Ww1AlpBx6bKqGiAhd83KIX7K9l0/+3E08fGicl+7s5yXbz6zTKyzMzz1bZ6qtxAKvMZeQmZzTOC02Rkw1Y+pJRhxnHK/FTDZi4jwny4qDHvMsJ89z6klCd+gwNbeTtlhsW0cfJE/YeuIhxvqvJgr7OTTyBthgUZszngEEvsvIQJndg1W291eoljzKgUvoucVxQgovu2yAl102AMVxakVmx0yOrsyUhOx8QSQLvMZcYgSh1ko5UWtSa2U044zJVsJEI0bbu9LyXImSnFqUMt0sqoqVPIfuwKGR5OtbPAOq9efZfuSbhMkUiV8lCvvPetCFdrpbqlw/0sdQV0h/u1hNJfCorOZgtPPswu+hudT8LvDI+e7EpSrNi00QuSqVwKMV50xnOaJKxXdoxBl5Xmz3jbOcRpQy0YpI2yllgQtRAmvNEnOyiOHjDzI48QRR0MtTu95Ovbptzf0PXSFaYvVu/rjUd4Wb9wzSU/Zng660T4W4GFjgNR2lqr9zvvtwSVOYjhLG6xHjjYQ4zUlRwsAtvrycqWaCasap6YQozyn5HplT5PfGmRKvY1F/aOwxBiZ+wInBGzi+aS/qrC20lDyHbX0lekKXR4/UWGp98LqRHoZ7S9y8Z5DrRnrpr8yVZewO/Q0XKO8UC7zGXEKy9mGNzSSb3X02+7MsJ8lzHBfGJoqNE64IjiukaUacKWspl3s2i9ps7ytxxaYKV2/t5cUjfdx3YJRD482Fz3OEvZf1c9OOfrYPVCj5LqHv4ojQU166uM2FyAKvMZcQpVhAKvsuzSTHc6C9KYsoVaIkZayekCl4nqAqJO2pCVXFd4TWSofyqNI3eYBtxx8g9SpnpajNQNVla1+Zge6Q/u6QSuBy1wMHSdvntF2zpZvXXLmJq4aL6mKqyqaugO72LrSL7cQQO97dmEtQV8mnO/QIPBcXQdtbYxtJUchc2qv7aabUWimj9ZQohdBzWK6qo59Mc9lzX2Hnka8RBb0c3H7rWcnJnY6VXQNVyoFLJXDZOVjhx68fLo5UVzhwcpqBLp+hasjmnpDhvjJD3SVCb3Wn+l5obMRrzCXIaZ+K6wiEvsOp6Yg4zYqqYzpXV3c6SvnhifrsaQo9gUvJc4CcOGPB2LcoavN5RJXnt7ySUwPXrruozcK+wjVbu9jWX6Gv4uO5QpTmfO+5Sdplgcly5emTdV6xe4gt3eFFNa2wGAu8xlyiPNehpxzgxxlZpjx9cponT9QJPZfu0Cnyd6N0wRE2SV5kNvhuMUqOc5A8RR2vXdTmGkYHriU5C0VtegOHga4SV2ypsveyIXrLHp7rEHouoQc37xnkHx89Orvz7GW7B9jUHeA4RSH2i5kFXmMuYa4jdJU8njg2xT987xhZrjgCe3f1Uw2LY80PT7TItUjXKnlSzBMDnmT0nXqUwVOPcWDPuzZU1EYA3ynOQcsQAtdheCDkxl0DbKqGjPSXKPkefWWfRrtI+zXDPfzPd17HY89PcuOOPl62e2B2Z1mndpidKxZ4jbnEpXnO0+3COQrkClOthKHuAN8LuX6km1P1hK7QJU0zppopWjtJ37Nfo9QaZbL7shWfMRMG5ydFuBTB1nehFBTZB/U4pxHnRFnGgeMNbtoxwKaukJ6STzX0KIce5bCosJblyt5d/dxy+eAZR+2UPAu8xpgLmcIVm7vw3GIxzXWEHf1lSp6L4jLcJwxUQ9I8pxmlBAcfxDn0bTI35OD2W5ns3r3iAloOBE4xH6sKrlOcCuF7Lp7rEKU5E810wZxxrnCsFnHz5UVR8oFqMFvCsuQvPYfrOmIjXmPMBU5g91CV//y6y9l/tMa2vhK9JY/xRkIjTnFF8D0HTYuAl0STJFtexMnhVzIZzYWIklsEvaXyfUWg7IG2pxKqoUvgeTw12lh0Q4TrCNdu62agGtJX9osTilvpGfWDT9dTWtuJwxciC7zGXEIWG5h6joMg7B6qsmugwuh0TJRlDHYFTJ6IOXRiEv/ZB+i94iakMkBw00+iSU5vnOK2Mhpxhgjtc9eKlLNWktFIFz6nEriUfYfAdQgCodv3mWhlZwRd1xFedlkfe3f186orhugKg9mRcV8lYKweL1kTt7vkEVzk0wxggdeYS4rrCL0Vf7Y62cyX7xYjVccRyr5DnOUcm2ry9QcfYvj5b0JSo9ndz+A1mxFxEEmLTRbtameHJ6L2Memwucunu+TRSnNqrQxHoDt0KQcuviNUSwF9ZZ9qyWNYlWfHmuS54jjw8ssGuGXPIFcP9xC4QjXw8RzBa1cJcwWGugLqcUYjmpuaCD2Hauht+Fj1C4UFXmMuMZ7j4AVzAUpV6Qo9xhsxaV6cJhy16hz418+x69D3aAV9PHXZT3Lt9hezqbtE2U+ZioTAdYmzjFqUzR3fTpH/21Py6RFhU3eRaFuUV4SSX1QH29obUA09esOA/orPZCvjhu19XLG5a7ZfMyUZfdfBnzeKLerqenSFHnl759rFuEliORZ4jbnEiQih79JXCahHKSg8/sj9JM89xuimGzk2eBOO53Ptth4GugL6qwGTzYRTtYhG4pDnytHJ1uyZZlt7Q3orAY4IaJHz67mCo4I6UPFdhqolEBjoCtjWX2awKzyjX/5sapjMHj55uk6d+ttpFniNeYFIGjUmT52iNDDMS159Kzuvvp4pf4AnT0xz2WCFbb0V4nZmQ0/ZJ88hiBNKnsvNux1G6wkDVZ/BSlDk5XoOoetSCoqTG5Si3kNv2aevEuI5QiX02NQdki5S6tFz50o5LpfFcCmywGvMJU5V+e6D9/Glz32a7t4+Pvjf/jvNJGRweAeDFBkPMyrqFPO3cU7gOJycBk9SKmGF3UOCg4PnFdMEjsyd3uCIUG5Pb4SeS9l324ttLr3lgOkoITotFcJziuN2+ioXf5bCWlngNeY8EpHbgN8BrgFerqoPzfvZbwC/SHEYxEdV9Z4V26PIbJhJChg/Ncrn/+ZODux7jF2XX8VPve8X6SoHRJky3UpJ2qcNC8XJFY7rFGUWS8Jwb4mdAyknpyPG6zFxpnPFG9pH6bhOMU1QCYqshDjL6C+HeK7QU/bZ1lumleZUA488T2bLVAaeg+cKg13hC260CxZ4jTnfHgd+Gviz+RdF5MUUx7lfC2wD7hWRq1R12RN5XMdhqD2f+tyhg/zx//hNAP7T+z7Aa974ZhzHQYDQExpxjuY5zTQnyXIWy+Dqcn1ctxgF16OceislLyYVcHEIgmK6wXWKYFoNQuJMqYQuuwYrlHwPbSZEaU5POaARp8RpTnfJY6SvTF8l2PBf4MXIAq8x55Gq7odFV+3fAXxWVSPgGRF5Eng58K3Vtj2yfQevu/UtvP5Nb2FwaGGt3K6ST5LF5I5Lt1fUt00zJcmLNLQ819lc2mrgtke1Re5v8fN8NsNhRtl36asW8799ZZ+SX4SXnrJPM85oxCmVwGO412dLT+mSyMddLwu8xlyYRoAH531/uH3tDCLyIeBDANt37Ji97jgO737P+xZt3BGhGnrUWulMG/ie4C9TojvLi5q+cVJsishUZ0fJlaCYy60EDpWgOO23Ec8NzstBkedbbaeJvdDZ34Ax55iI3AtsXeRHt6vqP2y0fVX9FPApgBtufOmqT0wr+S5RUhx6uRquI1QDj2rgkWY5WbtYblepCKYz9RN6Kz6h5xJ4Dq04J81zPMehFDgXfR3ds8UCrzHnmKq+aR23PQ/smPf99va1ZT36yHdGt/aWDwJDwOg6nnu+XGr93bXczRZ4jbkwfQH4GxH53xSLa1cC/7HSTaq6CUBEHlLVvee2i2fPC62/L9zZbWMuACLyUyJyGLgF+JKI3AOgqt8H/g7YB3wF+PBKGQ3m4iG6RBUgY8zF64U2guw0G/EaYxbzqfPdgTV6QfXXRrzGGNNhNuI1xpgOs8BrjDEdZoHXmEuIiNwmIt8XkVxE9p72s98QkSdF5Aci8pbz1cf5ROSt7f48KSK/fr77sxgRuVNETojI4/OuDYjIv4jIgfZr/1ratMBrzKVlpujON+dfPK3ozluBPxWR87qNrP38TwI/BrwY+Nl2Py80d1H8nc3368BXVfVK4Kvt71fNAq8xlxBV3a+qP1jkR7NFd1T1GWCm6M759HLgSVV9WlVj4LMU/bygqOo3gbHTLr8DuLv957uBd66lTQu8xrwwjADPzft+yaI7HXQh9mm1tqjq0fafjwFb1nKzbRk25iJzrovumLVRVRWRNeXlWuA15iLTyaI759iF2KfVOi4iw6p6VESGgRNrudmmGox5YfgC8B4RCUVkN6ssunOOfRu4UkR2i0hAsfj3hfPcp9X6AvD+9p/fD6zpk4YFXmMuIRdT0R1VTYGPAPcA+4G/a/fzgiIin6E4+eNFInJYRH4R+ARwq4gcAN7U/n71bdqWYWOM6Swb8RpjTIdZ4DXGmA6zwGuMMR1mgdcYYzrMAq8xxnSYBV5jjOkwC7zGGNNhFniNMabDLPAaY0yHWeA1xpgOs8BrjDEdZoHXGGM6zAKvMcZ0mAVeY4zpMAu8xhjTYRZ4jTGmwyzwGmNMh1ngNcaYDrPAa4wxHWaB1xizYSKiInLXWW7v62ervQuNBV5jzDknIjtE5BMi8rCIjItIIiInROReEfkvItJ7jp5bFZH3isjfiMgTIlIXkZqIPCQi/7V9rHzH2SnDxpgNExEF7lbVDyzysw8CfwKEwPeAB4BxYBB4NXAtcEpVh05r7xuq+roN9uutwD8BY8DXgCeBfuAnga3tvrxRVVsbec5aeZ18mDHmhUVE3gv8OUWgfZeqfmmR97wK+OQ56sIx4H3A36tqPO+ZvwZ8HXgl8GHgj87R8xdlUw3GmEWJyLPtudalvu5a4f5u4I/b375nsaALoKr3A69Yoo0hEfmUiBwVkUhEvi8iP7/a30FVH1HVT89UhmshAAADHUlEQVQPuu3rNeaC7etW297ZYiNeY8xS7gD6Frn+duAmoLHC/e8GBoAHVfWfl3ujqkaLXO4D7gdi4HMUUxW3AXeKSK6qd6/w/JUk7dd0g+2smQVeY8yiVPWO06+JyK3A7RRzpb+9QhOvbr9+dZ1duAH4S+CXVDVrP/8O4FHg48BGA+8vtF+/ssF21symGowxqyIi11GMPCeBt6nq6Aq3DLdfD6/zkQ3gV2eCLoCq7qMYBV8jIl3rbBcR+QjwVuAR4M71trNeFniNMSsSkWHgSxQf99+pqgc68NgDqjq1yPXn2q/962lURH6aYhrlGMWCX7LCLWedTTUYY5YlIlXgi8AO4L2qet8qbz3afh1Z56Mnlrg+MyfrrrVBEXkn8FngBPB6VX16nX3bEBvxGmOWJCIuRaC6CfhNVf3MGm6fCdBvPOsdWwcRuQ34e+A48KOq+oPz1RcLvMaY5dwB/ARwp6r+3hrv/RzFxoVbRORNy71RRMJ19m9V2vnEnwGOUATdTkyVLMkCrzFmUSLyMeAjwL3AL6/1/nau7Efb3/6tiLxliefcDHxrvf2c187VInL1ItffD/wVcAh47fmaXpjP5niNMWcQka0UGwwUeBy4XUROf9sjqvr55dpR1U+LSJliy/BXROQRFm4ZvoUibWylDInV2D/T/Xm/x+spshYcii3DP7/I7zGxWOrcuWSB1xizmBJzn4g/tsR77gaWDbwAqvoXInIPxej5VuC9QJVi8exx4Fc4dyldu5j7PX5hifccpJhS6RgrkmOM2bDliuSYM9kcrzHGdJgFXmOM6TALvMYY02G2uGaMORt+l6LugVkFW1wzxpgOs6kGY4zpMAu8xhjTYRZ4jTGmwyzwGmNMh1ngNcaYDrPAa4wxHWaB1xhjOswCrzHGdJgFXmOM6TALvMYY02EWeI0xpsMs8BpjTIdZ4DXGmA6zwGuMMR1mgdcYYzrMAq8xxnSYBV5jjOkwC7zGGNNhFniNMabDLPAaY0yHWeA1xpgOs8BrjDEdZoHXGGM6zAKvMcZ0mAVeY4zpMAu8xhjTYRZ4jTGmw/4/bZvFkKcBE/QAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jxyKQjV3I4bj"
+      },
+      "source": [
+        "### Example on ADNI data\n",
+        "\n",
+        "In this last application we apply the multichannel autoencoder to the ADNI data, for jointly modeling different modalities across individuals. We wil focus on the joint analysis of:\n",
+        "\n",
+        "- brain volumes;\n",
+        "- sociodemographic information (e.g. age, sex, scholarity); \n",
+        "- cognition; \n",
+        "- apoe genotype;\n",
+        "- fluid biomarkers (abeta and tay concentrations in the CSF)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r8WKeAH4W3SA"
+      },
+      "source": [
+        "We first import and standardize the different data modalities."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ItOleDI9I4bk"
+      },
+      "source": [
+        "# volumes = pd.read_csv('https://marcolorenzi.github.io/material/winter_school/volumes.csv')\n",
+        "adni = pd.read_csv('https://gist.githubusercontent.com/ggbioing/14c27963370417ed454b7475e828f15e/raw/ff1b58bb548506f96f21a3dcc4eb2d086efc09a2/pseudo_adni.csv')\n",
+        "\n",
+        "volume_cols = ['WholeBrain.bl', 'Ventricles.bl', 'Hippocampus.bl', 'MidTemp.bl', 'Entorhinal.bl']\n",
+        "volumes_value = adni[volume_cols].values\n",
+        "\n",
+        "for i in range(volumes_value.shape[1]):\n",
+        "    volumes_value[:,i] = (volumes_value[:,i] - np.mean(volumes_value[:,i]))/np.std(volumes_value[:,i])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7C_9veF0I4bl"
+      },
+      "source": [
+        "# demog = pd.read_csv('https://marcolorenzi.github.io/material/winter_school/demog.csv')\n",
+        "\n",
+        "demog_cols = ['SEX', 'AGE', 'PTEDUCAT']\n",
+        "demog_value = adni[demog_cols].values\n",
+        "\n",
+        "for i in range(demog_value.shape[1]):\n",
+        "    demog_value[:,i] = (demog_value[:,i] - np.mean(demog_value[:,i]))/np.std(demog_value[:,i])\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wk1Z3OzjI4bl"
+      },
+      "source": [
+        "# cognition = pd.read_csv('https://marcolorenzi.github.io/material/winter_school/cognition.csv')\n",
+        "\n",
+        "cognition_cols = ['CDRSB.bl', 'ADAS11.bl', 'MMSE.bl', 'RAVLT.immediate.bl', 'RAVLT.learning.bl', 'RAVLT.forgetting.bl', 'FAQ.bl']\n",
+        "cognition_value = adni[cognition_cols].values\n",
+        "\n",
+        "for i in range(cognition_value.shape[1]):\n",
+        "    cognition_value[:,i] = (cognition_value[:,i] - np.mean(cognition_value[:,i]))/np.std(cognition_value[:,i])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MtypILJjI4bm"
+      },
+      "source": [
+        "# apoe = pd.read_csv('https://marcolorenzi.github.io/material/winter_school/apoe.csv')\n",
+        "\n",
+        "apoe_cols = ['APOE4']\n",
+        "apoe_value = adni[apoe_cols].values\n",
+        "\n",
+        "for i in range(apoe_value.shape[1]):\n",
+        "    apoe_value[:,i] = (apoe_value[:,i] - np.mean(apoe_value[:,i]))/np.std(apoe_value[:,i])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oe1i7vNDI4bn"
+      },
+      "source": [
+        "# fluid = pd.read_csv('https://marcolorenzi.github.io/material/winter_school/fluid.csv')\n",
+        "\n",
+        "fluid_cols = ['ABETA.MEDIAN.bl', 'PTAU.MEDIAN.bl', 'TAU.MEDIAN.bl']\n",
+        "fluid_value = adni[fluid_cols].values\n",
+        "\n",
+        "for i in range(fluid_value.shape[1]):\n",
+        "    fluid_value[:,i] = (fluid_value[:,i] - np.mean(fluid_value[:,i]))/np.std(fluid_value[:,i])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SbaJIVfoXPbc"
+      },
+      "source": [
+        "We have 5 channels in total as an input for the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DpEqM6FKI4bo"
+      },
+      "source": [
+        "# Defining data characteristics\n",
+        "\n",
+        "init_dict = {\n",
+        "    'n_channels': 5, \n",
+        "    'lat_dim': 5, # We fit 5 latent dimensions\n",
+        "    'n_feats': tuple([volumes_value.shape[1], demog_value.shape[1], cognition_value.shape[1], apoe_value.shape[1], fluid_value.shape[1]]),\n",
+        "}"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Be5TUj75I4bp"
+      },
+      "source": [
+        "# Creating a list with multimodal data\n",
+        "\n",
+        "data_adni = []\n",
+        "data_adni.append(torch.FloatTensor(volumes_value))\n",
+        "data_adni.append(torch.FloatTensor(np.array(demog_value)))\n",
+        "data_adni.append(torch.FloatTensor(cognition_value))\n",
+        "data_adni.append(torch.FloatTensor(apoe_value))\n",
+        "data_adni.append(torch.FloatTensor(fluid_value))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5bpD6qh3I4bp",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1205b04e-8ccc-4101-b072-01c5f72e8b05"
+      },
+      "source": [
+        "# Running the model\n",
+        "\n",
+        "adam_lr = 1e-2\n",
+        "n_epochs = 6000\n",
+        "\n",
+        "model_adni = Mcvae(sparse=True, **init_dict)\n",
+        "model_adni.to(DEVICE)\n",
+        "print(model_adni)\n",
+        "\n",
+        "model_adni.optimizer = torch.optim.Adam(model_adni.parameters(), lr=adam_lr)\n",
+        "load_or_fit(model=model_adni, data=data_adni, epochs=n_epochs, ptfile='model_adni.pt', force_fit=FORCE_REFIT)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mcvae(\n",
+            "  (vae): ModuleList(\n",
+            "    (0): VAE(\n",
+            "      (W_mu): Linear(in_features=5, out_features=5, bias=True)\n",
+            "      (W_out): Linear(in_features=5, out_features=5, bias=True)\n",
+            "    )\n",
+            "    (1): VAE(\n",
+            "      (W_mu): Linear(in_features=3, out_features=5, bias=True)\n",
+            "      (W_out): Linear(in_features=5, out_features=3, bias=True)\n",
+            "    )\n",
+            "    (2): VAE(\n",
+            "      (W_mu): Linear(in_features=7, out_features=5, bias=True)\n",
+            "      (W_out): Linear(in_features=5, out_features=7, bias=True)\n",
+            "    )\n",
+            "    (3): VAE(\n",
+            "      (W_mu): Linear(in_features=1, out_features=5, bias=True)\n",
+            "      (W_out): Linear(in_features=5, out_features=1, bias=True)\n",
+            "    )\n",
+            "    (4): VAE(\n",
+            "      (W_mu): Linear(in_features=3, out_features=5, bias=True)\n",
+            "      (W_out): Linear(in_features=5, out_features=3, bias=True)\n",
+            "    )\n",
+            "  )\n",
+            ")\n",
+            "\tCreating model_adni.pt.running\n",
+            "\tCreated: 2020-12-14 17:04:24.621344\n",
+            "Start fitting: 2020-12-14 17:04:24.624218\n",
+            "\tModel destination: model_adni.pt\n",
+            "====> Epoch:    0/6000 (0%)\tLoss: 1345.6525\tLL: -1334.8462\tKL: 10.8063\tLL/KL: -123.5247\n",
+            "====> Epoch:   10/6000 (0%)\tLoss: 915.5555\tLL: -903.8951\tKL: 11.6603\tLL/KL: -77.5188\n",
+            "====> Epoch:   20/6000 (0%)\tLoss: 749.2255\tLL: -736.9542\tKL: 12.2713\tLL/KL: -60.0550\n",
+            "====> Epoch:   30/6000 (0%)\tLoss: 644.4911\tLL: -631.8514\tKL: 12.6397\tLL/KL: -49.9896\n",
+            "====> Epoch:   40/6000 (1%)\tLoss: 568.1503\tLL: -555.2150\tKL: 12.9354\tLL/KL: -42.9223\n",
+            "====> Epoch:   50/6000 (1%)\tLoss: 518.6566\tLL: -505.3234\tKL: 13.3332\tLL/KL: -37.8997\n",
+            "====> Epoch:   60/6000 (1%)\tLoss: 478.9616\tLL: -465.1817\tKL: 13.7799\tLL/KL: -33.7579\n",
+            "====> Epoch:   70/6000 (1%)\tLoss: 448.5258\tLL: -434.3348\tKL: 14.1910\tLL/KL: -30.6064\n",
+            "====> Epoch:   80/6000 (1%)\tLoss: 416.1818\tLL: -401.6276\tKL: 14.5541\tLL/KL: -27.5954\n",
+            "====> Epoch:   90/6000 (2%)\tLoss: 393.7797\tLL: -378.9197\tKL: 14.8600\tLL/KL: -25.4993\n",
+            "====> Epoch:  100/6000 (2%)\tLoss: 373.0923\tLL: -357.9672\tKL: 15.1251\tLL/KL: -23.6670\n",
+            "====> Epoch:  110/6000 (2%)\tLoss: 354.4394\tLL: -339.0795\tKL: 15.3600\tLL/KL: -22.0756\n",
+            "====> Epoch:  120/6000 (2%)\tLoss: 337.3403\tLL: -321.7726\tKL: 15.5677\tLL/KL: -20.6692\n",
+            "====> Epoch:  130/6000 (2%)\tLoss: 322.1905\tLL: -306.4597\tKL: 15.7308\tLL/KL: -19.4815\n",
+            "====> Epoch:  140/6000 (2%)\tLoss: 308.8236\tLL: -292.9715\tKL: 15.8520\tLL/KL: -18.4816\n",
+            "====> Epoch:  150/6000 (2%)\tLoss: 295.2274\tLL: -279.2910\tKL: 15.9365\tLL/KL: -17.5253\n",
+            "====> Epoch:  160/6000 (3%)\tLoss: 283.4903\tLL: -267.5052\tKL: 15.9851\tLL/KL: -16.7347\n",
+            "====> Epoch:  170/6000 (3%)\tLoss: 273.4962\tLL: -257.4915\tKL: 16.0047\tLL/KL: -16.0885\n",
+            "====> Epoch:  180/6000 (3%)\tLoss: 263.3515\tLL: -247.3505\tKL: 16.0010\tLL/KL: -15.4584\n",
+            "====> Epoch:  190/6000 (3%)\tLoss: 255.7631\tLL: -239.8005\tKL: 15.9626\tLL/KL: -15.0226\n",
+            "====> Epoch:  200/6000 (3%)\tLoss: 248.9181\tLL: -233.0216\tKL: 15.8965\tLL/KL: -14.6587\n",
+            "====> Epoch:  210/6000 (4%)\tLoss: 241.4419\tLL: -225.6372\tKL: 15.8047\tLL/KL: -14.2766\n",
+            "====> Epoch:  220/6000 (4%)\tLoss: 233.9519\tLL: -218.2603\tKL: 15.6916\tLL/KL: -13.9094\n",
+            "====> Epoch:  230/6000 (4%)\tLoss: 227.1712\tLL: -211.6035\tKL: 15.5677\tLL/KL: -13.5925\n",
+            "====> Epoch:  240/6000 (4%)\tLoss: 221.2449\tLL: -205.8161\tKL: 15.4288\tLL/KL: -13.3397\n",
+            "====> Epoch:  250/6000 (4%)\tLoss: 215.7415\tLL: -200.4682\tKL: 15.2733\tLL/KL: -13.1254\n",
+            "====> Epoch:  260/6000 (4%)\tLoss: 211.7324\tLL: -196.6297\tKL: 15.1026\tLL/KL: -13.0196\n",
+            "====> Epoch:  270/6000 (4%)\tLoss: 206.9455\tLL: -192.0201\tKL: 14.9253\tLL/KL: -12.8654\n",
+            "====> Epoch:  280/6000 (5%)\tLoss: 203.0456\tLL: -188.3085\tKL: 14.7371\tLL/KL: -12.7778\n",
+            "====> Epoch:  290/6000 (5%)\tLoss: 198.7751\tLL: -184.2323\tKL: 14.5428\tLL/KL: -12.6683\n",
+            "====> Epoch:  300/6000 (5%)\tLoss: 195.2762\tLL: -180.9350\tKL: 14.3412\tLL/KL: -12.6164\n",
+            "====> Epoch:  310/6000 (5%)\tLoss: 190.7639\tLL: -176.6380\tKL: 14.1259\tLL/KL: -12.5046\n",
+            "====> Epoch:  320/6000 (5%)\tLoss: 188.5488\tLL: -174.6402\tKL: 13.9086\tLL/KL: -12.5563\n",
+            "====> Epoch:  330/6000 (6%)\tLoss: 185.2334\tLL: -171.5423\tKL: 13.6911\tLL/KL: -12.5295\n",
+            "====> Epoch:  340/6000 (6%)\tLoss: 182.9316\tLL: -169.4684\tKL: 13.4633\tLL/KL: -12.5875\n",
+            "====> Epoch:  350/6000 (6%)\tLoss: 179.5972\tLL: -166.3612\tKL: 13.2360\tLL/KL: -12.5689\n",
+            "====> Epoch:  360/6000 (6%)\tLoss: 176.8132\tLL: -163.8034\tKL: 13.0098\tLL/KL: -12.5908\n",
+            "====> Epoch:  370/6000 (6%)\tLoss: 174.9482\tLL: -162.1653\tKL: 12.7829\tLL/KL: -12.6861\n",
+            "====> Epoch:  380/6000 (6%)\tLoss: 172.5576\tLL: -160.0012\tKL: 12.5564\tLL/KL: -12.7426\n",
+            "====> Epoch:  390/6000 (6%)\tLoss: 170.7862\tLL: -158.4548\tKL: 12.3314\tLL/KL: -12.8497\n",
+            "====> Epoch:  400/6000 (7%)\tLoss: 169.4274\tLL: -157.3134\tKL: 12.1140\tLL/KL: -12.9861\n",
+            "====> Epoch:  410/6000 (7%)\tLoss: 166.5901\tLL: -154.6922\tKL: 11.8979\tLL/KL: -13.0016\n",
+            "====> Epoch:  420/6000 (7%)\tLoss: 164.6044\tLL: -152.9200\tKL: 11.6843\tLL/KL: -13.0876\n",
+            "====> Epoch:  430/6000 (7%)\tLoss: 163.0498\tLL: -151.5741\tKL: 11.4757\tLL/KL: -13.2083\n",
+            "====> Epoch:  440/6000 (7%)\tLoss: 161.5838\tLL: -150.3221\tKL: 11.2617\tLL/KL: -13.3481\n",
+            "====> Epoch:  450/6000 (8%)\tLoss: 160.5118\tLL: -149.4658\tKL: 11.0460\tLL/KL: -13.5313\n",
+            "====> Epoch:  460/6000 (8%)\tLoss: 158.6908\tLL: -147.8564\tKL: 10.8343\tLL/KL: -13.6471\n",
+            "====> Epoch:  470/6000 (8%)\tLoss: 157.3979\tLL: -146.7707\tKL: 10.6272\tLL/KL: -13.8108\n",
+            "====> Epoch:  480/6000 (8%)\tLoss: 156.5752\tLL: -146.1470\tKL: 10.4282\tLL/KL: -14.0146\n",
+            "====> Epoch:  490/6000 (8%)\tLoss: 155.4386\tLL: -145.2035\tKL: 10.2350\tLL/KL: -14.1869\n",
+            "====> Epoch:  500/6000 (8%)\tLoss: 154.0549\tLL: -144.0060\tKL: 10.0489\tLL/KL: -14.3305\n",
+            "====> Epoch:  510/6000 (8%)\tLoss: 152.7079\tLL: -142.8439\tKL: 9.8640\tLL/KL: -14.4814\n",
+            "====> Epoch:  520/6000 (9%)\tLoss: 151.9882\tLL: -142.3049\tKL: 9.6833\tLL/KL: -14.6960\n",
+            "====> Epoch:  530/6000 (9%)\tLoss: 151.8842\tLL: -142.3763\tKL: 9.5079\tLL/KL: -14.9745\n",
+            "====> Epoch:  540/6000 (9%)\tLoss: 150.6297\tLL: -141.2982\tKL: 9.3315\tLL/KL: -15.1421\n",
+            "====> Epoch:  550/6000 (9%)\tLoss: 149.2990\tLL: -140.1416\tKL: 9.1574\tLL/KL: -15.3036\n",
+            "====> Epoch:  560/6000 (9%)\tLoss: 148.5943\tLL: -139.6078\tKL: 8.9866\tLL/KL: -15.5351\n",
+            "====> Epoch:  570/6000 (10%)\tLoss: 147.4403\tLL: -138.6157\tKL: 8.8247\tLL/KL: -15.7078\n",
+            "====> Epoch:  580/6000 (10%)\tLoss: 147.2960\tLL: -138.6320\tKL: 8.6640\tLL/KL: -16.0008\n",
+            "====> Epoch:  590/6000 (10%)\tLoss: 146.5151\tLL: -138.0039\tKL: 8.5112\tLL/KL: -16.2144\n",
+            "====> Epoch:  600/6000 (10%)\tLoss: 145.6822\tLL: -137.3213\tKL: 8.3609\tLL/KL: -16.4242\n",
+            "====> Epoch:  610/6000 (10%)\tLoss: 144.9745\tLL: -136.7597\tKL: 8.2148\tLL/KL: -16.6479\n",
+            "====> Epoch:  620/6000 (10%)\tLoss: 144.5797\tLL: -136.5105\tKL: 8.0692\tLL/KL: -16.9175\n",
+            "====> Epoch:  630/6000 (10%)\tLoss: 144.2861\tLL: -136.3598\tKL: 7.9262\tLL/KL: -17.2036\n",
+            "====> Epoch:  640/6000 (11%)\tLoss: 143.7982\tLL: -136.0089\tKL: 7.7894\tLL/KL: -17.4609\n",
+            "====> Epoch:  650/6000 (11%)\tLoss: 143.1487\tLL: -135.4922\tKL: 7.6564\tLL/KL: -17.6965\n",
+            "====> Epoch:  660/6000 (11%)\tLoss: 142.3267\tLL: -134.8008\tKL: 7.5259\tLL/KL: -17.9116\n",
+            "====> Epoch:  670/6000 (11%)\tLoss: 142.1928\tLL: -134.7951\tKL: 7.3977\tLL/KL: -18.2212\n",
+            "====> Epoch:  680/6000 (11%)\tLoss: 142.0003\tLL: -134.7272\tKL: 7.2731\tLL/KL: -18.5241\n",
+            "====> Epoch:  690/6000 (12%)\tLoss: 140.5825\tLL: -133.4309\tKL: 7.1516\tLL/KL: -18.6575\n",
+            "====> Epoch:  700/6000 (12%)\tLoss: 140.4937\tLL: -133.4592\tKL: 7.0346\tLL/KL: -18.9719\n",
+            "====> Epoch:  710/6000 (12%)\tLoss: 140.3624\tLL: -133.4413\tKL: 6.9210\tLL/KL: -19.2805\n",
+            "====> Epoch:  720/6000 (12%)\tLoss: 140.2886\tLL: -133.4781\tKL: 6.8105\tLL/KL: -19.5990\n",
+            "====> Epoch:  730/6000 (12%)\tLoss: 139.1761\tLL: -132.4718\tKL: 6.7044\tLL/KL: -19.7590\n",
+            "====> Epoch:  740/6000 (12%)\tLoss: 139.4532\tLL: -132.8597\tKL: 6.5935\tLL/KL: -20.1501\n",
+            "====> Epoch:  750/6000 (12%)\tLoss: 139.5505\tLL: -133.0623\tKL: 6.4882\tLL/KL: -20.5084\n",
+            "====> Epoch:  760/6000 (13%)\tLoss: 138.9370\tLL: -132.5498\tKL: 6.3872\tLL/KL: -20.7523\n",
+            "====> Epoch:  770/6000 (13%)\tLoss: 138.6171\tLL: -132.3291\tKL: 6.2880\tLL/KL: -21.0448\n",
+            "====> Epoch:  780/6000 (13%)\tLoss: 138.0430\tLL: -131.8521\tKL: 6.1909\tLL/KL: -21.2977\n",
+            "====> Epoch:  790/6000 (13%)\tLoss: 138.2258\tLL: -132.1286\tKL: 6.0971\tLL/KL: -21.6706\n",
+            "====> Epoch:  800/6000 (13%)\tLoss: 137.2843\tLL: -131.2763\tKL: 6.0080\tLL/KL: -21.8502\n",
+            "====> Epoch:  810/6000 (14%)\tLoss: 137.1843\tLL: -131.2668\tKL: 5.9175\tLL/KL: -22.1827\n",
+            "====> Epoch:  820/6000 (14%)\tLoss: 137.3829\tLL: -131.5564\tKL: 5.8264\tLL/KL: -22.5792\n",
+            "====> Epoch:  830/6000 (14%)\tLoss: 137.0387\tLL: -131.2991\tKL: 5.7396\tLL/KL: -22.8760\n",
+            "====> Epoch:  840/6000 (14%)\tLoss: 136.9263\tLL: -131.2707\tKL: 5.6556\tLL/KL: -23.2108\n",
+            "====> Epoch:  850/6000 (14%)\tLoss: 136.5428\tLL: -130.9675\tKL: 5.5753\tLL/KL: -23.4908\n",
+            "====> Epoch:  860/6000 (14%)\tLoss: 136.6886\tLL: -131.1910\tKL: 5.4976\tLL/KL: -23.8634\n",
+            "====> Epoch:  870/6000 (14%)\tLoss: 136.3994\tLL: -130.9758\tKL: 5.4236\tLL/KL: -24.1492\n",
+            "====> Epoch:  880/6000 (15%)\tLoss: 136.6653\tLL: -131.3140\tKL: 5.3512\tLL/KL: -24.5390\n",
+            "====> Epoch:  890/6000 (15%)\tLoss: 136.0517\tLL: -130.7696\tKL: 5.2821\tLL/KL: -24.7570\n",
+            "====> Epoch:  900/6000 (15%)\tLoss: 135.7922\tLL: -130.5793\tKL: 5.2128\tLL/KL: -25.0497\n",
+            "====> Epoch:  910/6000 (15%)\tLoss: 136.1264\tLL: -130.9814\tKL: 5.1449\tLL/KL: -25.4584\n",
+            "====> Epoch:  920/6000 (15%)\tLoss: 135.5970\tLL: -130.5174\tKL: 5.0796\tLL/KL: -25.6946\n",
+            "====> Epoch:  930/6000 (16%)\tLoss: 135.7482\tLL: -130.7356\tKL: 5.0126\tLL/KL: -26.0816\n",
+            "====> Epoch:  940/6000 (16%)\tLoss: 135.7020\tLL: -130.7532\tKL: 4.9488\tLL/KL: -26.4214\n",
+            "====> Epoch:  950/6000 (16%)\tLoss: 135.1311\tLL: -130.2434\tKL: 4.8877\tLL/KL: -26.6469\n",
+            "====> Epoch:  960/6000 (16%)\tLoss: 135.0137\tLL: -130.1850\tKL: 4.8287\tLL/KL: -26.9606\n",
+            "====> Epoch:  970/6000 (16%)\tLoss: 134.8850\tLL: -130.1152\tKL: 4.7698\tLL/KL: -27.2788\n",
+            "====> Epoch:  980/6000 (16%)\tLoss: 134.8445\tLL: -130.1320\tKL: 4.7124\tLL/KL: -27.6146\n",
+            "====> Epoch:  990/6000 (16%)\tLoss: 135.2212\tLL: -130.5649\tKL: 4.6563\tLL/KL: -28.0407\n",
+            "====> Epoch: 1000/6000 (17%)\tLoss: 134.6010\tLL: -129.9993\tKL: 4.6017\tLL/KL: -28.2503\n",
+            "====> Epoch: 1010/6000 (17%)\tLoss: 135.0942\tLL: -130.5461\tKL: 4.5481\tLL/KL: -28.7035\n",
+            "====> Epoch: 1020/6000 (17%)\tLoss: 134.5320\tLL: -130.0364\tKL: 4.4956\tLL/KL: -28.9255\n",
+            "====> Epoch: 1030/6000 (17%)\tLoss: 134.2009\tLL: -129.7564\tKL: 4.4444\tLL/KL: -29.1953\n",
+            "====> Epoch: 1040/6000 (17%)\tLoss: 134.3440\tLL: -129.9494\tKL: 4.3945\tLL/KL: -29.5706\n",
+            "====> Epoch: 1050/6000 (18%)\tLoss: 134.4231\tLL: -130.0779\tKL: 4.3452\tLL/KL: -29.9358\n",
+            "====> Epoch: 1060/6000 (18%)\tLoss: 134.0270\tLL: -129.7283\tKL: 4.2987\tLL/KL: -30.1783\n",
+            "====> Epoch: 1070/6000 (18%)\tLoss: 134.1636\tLL: -129.9095\tKL: 4.2541\tLL/KL: -30.5371\n",
+            "====> Epoch: 1080/6000 (18%)\tLoss: 133.8709\tLL: -129.6608\tKL: 4.2101\tLL/KL: -30.7979\n",
+            "====> Epoch: 1090/6000 (18%)\tLoss: 133.8601\tLL: -129.6939\tKL: 4.1662\tLL/KL: -31.1300\n",
+            "====> Epoch: 1100/6000 (18%)\tLoss: 134.2823\tLL: -130.1590\tKL: 4.1234\tLL/KL: -31.5661\n",
+            "====> Epoch: 1110/6000 (18%)\tLoss: 134.1490\tLL: -130.0662\tKL: 4.0828\tLL/KL: -31.8570\n",
+            "====> Epoch: 1120/6000 (19%)\tLoss: 134.0510\tLL: -130.0087\tKL: 4.0423\tLL/KL: -32.1619\n",
+            "====> Epoch: 1130/6000 (19%)\tLoss: 134.1721\tLL: -130.1703\tKL: 4.0019\tLL/KL: -32.5275\n",
+            "====> Epoch: 1140/6000 (19%)\tLoss: 133.8402\tLL: -129.8770\tKL: 3.9633\tLL/KL: -32.7703\n",
+            "====> Epoch: 1150/6000 (19%)\tLoss: 134.0624\tLL: -130.1379\tKL: 3.9245\tLL/KL: -33.1606\n",
+            "====> Epoch: 1160/6000 (19%)\tLoss: 133.7955\tLL: -129.9090\tKL: 3.8865\tLL/KL: -33.4256\n",
+            "====> Epoch: 1170/6000 (20%)\tLoss: 133.7247\tLL: -129.8760\tKL: 3.8487\tLL/KL: -33.7454\n",
+            "====> Epoch: 1180/6000 (20%)\tLoss: 133.6103\tLL: -129.7982\tKL: 3.8121\tLL/KL: -34.0489\n",
+            "====> Epoch: 1190/6000 (20%)\tLoss: 133.5762\tLL: -129.8000\tKL: 3.7762\tLL/KL: -34.3733\n",
+            "====> Epoch: 1200/6000 (20%)\tLoss: 133.5866\tLL: -129.8452\tKL: 3.7414\tLL/KL: -34.7046\n",
+            "====> Epoch: 1210/6000 (20%)\tLoss: 133.5418\tLL: -129.8322\tKL: 3.7096\tLL/KL: -34.9988\n",
+            "====> Epoch: 1220/6000 (20%)\tLoss: 133.3302\tLL: -129.6534\tKL: 3.6768\tLL/KL: -35.2623\n",
+            "====> Epoch: 1230/6000 (20%)\tLoss: 133.7652\tLL: -130.1208\tKL: 3.6444\tLL/KL: -35.7043\n",
+            "====> Epoch: 1240/6000 (21%)\tLoss: 133.6321\tLL: -130.0177\tKL: 3.6144\tLL/KL: -35.9719\n",
+            "====> Epoch: 1250/6000 (21%)\tLoss: 133.6247\tLL: -130.0410\tKL: 3.5837\tLL/KL: -36.2872\n",
+            "====> Epoch: 1260/6000 (21%)\tLoss: 133.5499\tLL: -129.9947\tKL: 3.5552\tLL/KL: -36.5645\n",
+            "====> Epoch: 1270/6000 (21%)\tLoss: 133.3774\tLL: -129.8494\tKL: 3.5280\tLL/KL: -36.8058\n",
+            "====> Epoch: 1280/6000 (21%)\tLoss: 133.6184\tLL: -130.1176\tKL: 3.5008\tLL/KL: -37.1682\n",
+            "====> Epoch: 1290/6000 (22%)\tLoss: 133.3666\tLL: -129.8930\tKL: 3.4736\tLL/KL: -37.3941\n",
+            "====> Epoch: 1300/6000 (22%)\tLoss: 133.5076\tLL: -130.0608\tKL: 3.4467\tLL/KL: -37.7346\n",
+            "====> Epoch: 1310/6000 (22%)\tLoss: 133.0075\tLL: -129.5872\tKL: 3.4203\tLL/KL: -37.8877\n",
+            "====> Epoch: 1320/6000 (22%)\tLoss: 133.2026\tLL: -129.8093\tKL: 3.3933\tLL/KL: -38.2543\n",
+            "====> Epoch: 1330/6000 (22%)\tLoss: 133.6542\tLL: -130.2849\tKL: 3.3693\tLL/KL: -38.6687\n",
+            "====> Epoch: 1340/6000 (22%)\tLoss: 133.2303\tLL: -129.8838\tKL: 3.3465\tLL/KL: -38.8119\n",
+            "====> Epoch: 1350/6000 (22%)\tLoss: 133.4958\tLL: -130.1742\tKL: 3.3215\tLL/KL: -39.1911\n",
+            "====> Epoch: 1360/6000 (23%)\tLoss: 133.2251\tLL: -129.9277\tKL: 3.2973\tLL/KL: -39.4037\n",
+            "====> Epoch: 1370/6000 (23%)\tLoss: 133.6619\tLL: -130.3885\tKL: 3.2734\tLL/KL: -39.8325\n",
+            "====> Epoch: 1380/6000 (23%)\tLoss: 133.5643\tLL: -130.3129\tKL: 3.2514\tLL/KL: -40.0784\n",
+            "====> Epoch: 1390/6000 (23%)\tLoss: 133.1822\tLL: -129.9523\tKL: 3.2299\tLL/KL: -40.2343\n",
+            "====> Epoch: 1400/6000 (23%)\tLoss: 133.1276\tLL: -129.9198\tKL: 3.2079\tLL/KL: -40.5004\n",
+            "====> Epoch: 1410/6000 (24%)\tLoss: 133.0490\tLL: -129.8628\tKL: 3.1862\tLL/KL: -40.7577\n",
+            "====> Epoch: 1420/6000 (24%)\tLoss: 133.4692\tLL: -130.3034\tKL: 3.1658\tLL/KL: -41.1601\n",
+            "====> Epoch: 1430/6000 (24%)\tLoss: 133.3459\tLL: -130.1988\tKL: 3.1471\tLL/KL: -41.3711\n",
+            "====> Epoch: 1440/6000 (24%)\tLoss: 133.5201\tLL: -130.3917\tKL: 3.1284\tLL/KL: -41.6798\n",
+            "====> Epoch: 1450/6000 (24%)\tLoss: 133.3412\tLL: -130.2302\tKL: 3.1110\tLL/KL: -41.8607\n",
+            "====> Epoch: 1460/6000 (24%)\tLoss: 133.0881\tLL: -129.9964\tKL: 3.0917\tLL/KL: -42.0470\n",
+            "====> Epoch: 1470/6000 (24%)\tLoss: 133.2387\tLL: -130.1670\tKL: 3.0718\tLL/KL: -42.3753\n",
+            "====> Epoch: 1480/6000 (25%)\tLoss: 133.6417\tLL: -130.5866\tKL: 3.0551\tLL/KL: -42.7442\n",
+            "====> Epoch: 1490/6000 (25%)\tLoss: 133.0963\tLL: -130.0589\tKL: 3.0375\tLL/KL: -42.8184\n",
+            "====> Epoch: 1500/6000 (25%)\tLoss: 133.2616\tLL: -130.2435\tKL: 3.0180\tLL/KL: -43.1554\n",
+            "====> Epoch: 1510/6000 (25%)\tLoss: 132.9644\tLL: -129.9644\tKL: 3.0000\tLL/KL: -43.3219\n",
+            "====> Epoch: 1520/6000 (25%)\tLoss: 133.0117\tLL: -130.0275\tKL: 2.9841\tLL/KL: -43.5729\n",
+            "====> Epoch: 1530/6000 (26%)\tLoss: 133.2170\tLL: -130.2487\tKL: 2.9683\tLL/KL: -43.8794\n",
+            "====> Epoch: 1540/6000 (26%)\tLoss: 133.2410\tLL: -130.2881\tKL: 2.9529\tLL/KL: -44.1215\n",
+            "====> Epoch: 1550/6000 (26%)\tLoss: 133.0870\tLL: -130.1491\tKL: 2.9379\tLL/KL: -44.3005\n",
+            "====> Epoch: 1560/6000 (26%)\tLoss: 133.3240\tLL: -130.4016\tKL: 2.9224\tLL/KL: -44.6210\n",
+            "====> Epoch: 1570/6000 (26%)\tLoss: 133.2569\tLL: -130.3479\tKL: 2.9090\tLL/KL: -44.8079\n",
+            "====> Epoch: 1580/6000 (26%)\tLoss: 132.7808\tLL: -129.8867\tKL: 2.8941\tLL/KL: -44.8799\n",
+            "====> Epoch: 1590/6000 (26%)\tLoss: 133.4772\tLL: -130.5977\tKL: 2.8795\tLL/KL: -45.3541\n",
+            "====> Epoch: 1600/6000 (27%)\tLoss: 132.9013\tLL: -130.0341\tKL: 2.8672\tLL/KL: -45.3519\n",
+            "====> Epoch: 1610/6000 (27%)\tLoss: 133.4101\tLL: -130.5571\tKL: 2.8529\tLL/KL: -45.7622\n",
+            "====> Epoch: 1620/6000 (27%)\tLoss: 133.0717\tLL: -130.2307\tKL: 2.8410\tLL/KL: -45.8398\n",
+            "====> Epoch: 1630/6000 (27%)\tLoss: 133.0259\tLL: -130.1975\tKL: 2.8284\tLL/KL: -46.0319\n",
+            "====> Epoch: 1640/6000 (27%)\tLoss: 132.9216\tLL: -130.1056\tKL: 2.8160\tLL/KL: -46.2015\n",
+            "====> Epoch: 1650/6000 (28%)\tLoss: 132.7462\tLL: -129.9426\tKL: 2.8036\tLL/KL: -46.3487\n",
+            "====> Epoch: 1660/6000 (28%)\tLoss: 133.4447\tLL: -130.6534\tKL: 2.7914\tLL/KL: -46.8059\n",
+            "====> Epoch: 1670/6000 (28%)\tLoss: 133.2197\tLL: -130.4403\tKL: 2.7794\tLL/KL: -46.9305\n",
+            "====> Epoch: 1680/6000 (28%)\tLoss: 133.2234\tLL: -130.4553\tKL: 2.7681\tLL/KL: -47.1279\n",
+            "====> Epoch: 1690/6000 (28%)\tLoss: 132.9040\tLL: -130.1463\tKL: 2.7577\tLL/KL: -47.1940\n",
+            "====> Epoch: 1700/6000 (28%)\tLoss: 133.1376\tLL: -130.3923\tKL: 2.7453\tLL/KL: -47.4966\n",
+            "====> Epoch: 1710/6000 (28%)\tLoss: 133.2285\tLL: -130.4943\tKL: 2.7342\tLL/KL: -47.7269\n",
+            "====> Epoch: 1720/6000 (29%)\tLoss: 132.9272\tLL: -130.2038\tKL: 2.7234\tLL/KL: -47.8097\n",
+            "====> Epoch: 1730/6000 (29%)\tLoss: 133.0045\tLL: -130.2923\tKL: 2.7123\tLL/KL: -48.0378\n",
+            "====> Epoch: 1740/6000 (29%)\tLoss: 133.0678\tLL: -130.3637\tKL: 2.7041\tLL/KL: -48.2096\n",
+            "====> Epoch: 1750/6000 (29%)\tLoss: 133.1398\tLL: -130.4446\tKL: 2.6952\tLL/KL: -48.3993\n",
+            "====> Epoch: 1760/6000 (29%)\tLoss: 133.0225\tLL: -130.3358\tKL: 2.6867\tLL/KL: -48.5110\n",
+            "====> Epoch: 1770/6000 (30%)\tLoss: 133.2934\tLL: -130.6165\tKL: 2.6770\tLL/KL: -48.7929\n",
+            "====> Epoch: 1780/6000 (30%)\tLoss: 133.0797\tLL: -130.4102\tKL: 2.6694\tLL/KL: -48.8535\n",
+            "====> Epoch: 1790/6000 (30%)\tLoss: 133.3351\tLL: -130.6742\tKL: 2.6609\tLL/KL: -49.1096\n",
+            "====> Epoch: 1800/6000 (30%)\tLoss: 133.2311\tLL: -130.5781\tKL: 2.6530\tLL/KL: -49.2192\n",
+            "====> Epoch: 1810/6000 (30%)\tLoss: 133.3567\tLL: -130.7126\tKL: 2.6441\tLL/KL: -49.4360\n",
+            "====> Epoch: 1820/6000 (30%)\tLoss: 132.8771\tLL: -130.2398\tKL: 2.6372\tLL/KL: -49.3849\n",
+            "====> Epoch: 1830/6000 (30%)\tLoss: 133.0965\tLL: -130.4673\tKL: 2.6291\tLL/KL: -49.6235\n",
+            "====> Epoch: 1840/6000 (31%)\tLoss: 132.8690\tLL: -130.2458\tKL: 2.6231\tLL/KL: -49.6527\n",
+            "====> Epoch: 1850/6000 (31%)\tLoss: 132.9219\tLL: -130.3062\tKL: 2.6157\tLL/KL: -49.8165\n",
+            "====> Epoch: 1860/6000 (31%)\tLoss: 133.0343\tLL: -130.4257\tKL: 2.6085\tLL/KL: -49.9996\n",
+            "====> Epoch: 1870/6000 (31%)\tLoss: 133.2347\tLL: -130.6358\tKL: 2.5989\tLL/KL: -50.2659\n",
+            "====> Epoch: 1880/6000 (31%)\tLoss: 133.1526\tLL: -130.5600\tKL: 2.5926\tLL/KL: -50.3591\n",
+            "====> Epoch: 1890/6000 (32%)\tLoss: 133.4113\tLL: -130.8252\tKL: 2.5861\tLL/KL: -50.5870\n",
+            "====> Epoch: 1900/6000 (32%)\tLoss: 132.7154\tLL: -130.1356\tKL: 2.5798\tLL/KL: -50.4436\n",
+            "====> Epoch: 1910/6000 (32%)\tLoss: 133.2498\tLL: -130.6783\tKL: 2.5715\tLL/KL: -50.8173\n",
+            "====> Epoch: 1920/6000 (32%)\tLoss: 133.0033\tLL: -130.4383\tKL: 2.5649\tLL/KL: -50.8547\n",
+            "====> Epoch: 1930/6000 (32%)\tLoss: 133.2121\tLL: -130.6530\tKL: 2.5591\tLL/KL: -51.0540\n",
+            "====> Epoch: 1940/6000 (32%)\tLoss: 133.1826\tLL: -130.6289\tKL: 2.5537\tLL/KL: -51.1521\n",
+            "====> Epoch: 1950/6000 (32%)\tLoss: 132.9814\tLL: -130.4342\tKL: 2.5472\tLL/KL: -51.2064\n",
+            "====> Epoch: 1960/6000 (33%)\tLoss: 132.9916\tLL: -130.4488\tKL: 2.5427\tLL/KL: -51.3028\n",
+            "====> Epoch: 1970/6000 (33%)\tLoss: 132.8761\tLL: -130.3372\tKL: 2.5390\tLL/KL: -51.3349\n",
+            "====> Epoch: 1980/6000 (33%)\tLoss: 133.1187\tLL: -130.5837\tKL: 2.5350\tLL/KL: -51.5125\n",
+            "====> Epoch: 1990/6000 (33%)\tLoss: 133.3297\tLL: -130.7972\tKL: 2.5325\tLL/KL: -51.6477\n",
+            "====> Epoch: 2000/6000 (33%)\tLoss: 133.3724\tLL: -130.8452\tKL: 2.5272\tLL/KL: -51.7744\n",
+            "====> Epoch: 2010/6000 (34%)\tLoss: 133.0419\tLL: -130.5176\tKL: 2.5242\tLL/KL: -51.7057\n",
+            "====> Epoch: 2020/6000 (34%)\tLoss: 133.3109\tLL: -130.7912\tKL: 2.5197\tLL/KL: -51.9079\n",
+            "====> Epoch: 2030/6000 (34%)\tLoss: 133.0333\tLL: -130.5178\tKL: 2.5156\tLL/KL: -51.8842\n",
+            "====> Epoch: 2040/6000 (34%)\tLoss: 132.8567\tLL: -130.3445\tKL: 2.5122\tLL/KL: -51.8842\n",
+            "====> Epoch: 2050/6000 (34%)\tLoss: 133.0848\tLL: -130.5761\tKL: 2.5087\tLL/KL: -52.0502\n",
+            "====> Epoch: 2060/6000 (34%)\tLoss: 133.2782\tLL: -130.7747\tKL: 2.5035\tLL/KL: -52.2377\n",
+            "====> Epoch: 2070/6000 (34%)\tLoss: 133.2804\tLL: -130.7812\tKL: 2.4992\tLL/KL: -52.3301\n",
+            "====> Epoch: 2080/6000 (35%)\tLoss: 132.9846\tLL: -130.4895\tKL: 2.4951\tLL/KL: -52.2984\n",
+            "====> Epoch: 2090/6000 (35%)\tLoss: 133.2616\tLL: -130.7721\tKL: 2.4895\tLL/KL: -52.5289\n",
+            "====> Epoch: 2100/6000 (35%)\tLoss: 132.9297\tLL: -130.4434\tKL: 2.4863\tLL/KL: -52.4650\n",
+            "====> Epoch: 2110/6000 (35%)\tLoss: 132.8555\tLL: -130.3718\tKL: 2.4837\tLL/KL: -52.4910\n",
+            "====> Epoch: 2120/6000 (35%)\tLoss: 133.2130\tLL: -130.7323\tKL: 2.4807\tLL/KL: -52.7002\n",
+            "====> Epoch: 2130/6000 (36%)\tLoss: 133.0687\tLL: -130.5914\tKL: 2.4773\tLL/KL: -52.7142\n",
+            "====> Epoch: 2140/6000 (36%)\tLoss: 133.2422\tLL: -130.7684\tKL: 2.4737\tLL/KL: -52.8627\n",
+            "====> Epoch: 2150/6000 (36%)\tLoss: 133.2630\tLL: -130.7942\tKL: 2.4688\tLL/KL: -52.9778\n",
+            "====> Epoch: 2160/6000 (36%)\tLoss: 133.0494\tLL: -130.5821\tKL: 2.4674\tLL/KL: -52.9239\n",
+            "====> Epoch: 2170/6000 (36%)\tLoss: 132.8594\tLL: -130.3952\tKL: 2.4643\tLL/KL: -52.9147\n",
+            "====> Epoch: 2180/6000 (36%)\tLoss: 133.4201\tLL: -130.9575\tKL: 2.4626\tLL/KL: -53.1781\n",
+            "====> Epoch: 2190/6000 (36%)\tLoss: 133.0845\tLL: -130.6251\tKL: 2.4594\tLL/KL: -53.1128\n",
+            "====> Epoch: 2200/6000 (37%)\tLoss: 133.1610\tLL: -130.7043\tKL: 2.4567\tLL/KL: -53.2031\n",
+            "====> Epoch: 2210/6000 (37%)\tLoss: 133.2481\tLL: -130.7946\tKL: 2.4536\tLL/KL: -53.3082\n",
+            "====> Epoch: 2220/6000 (37%)\tLoss: 133.0073\tLL: -130.5566\tKL: 2.4507\tLL/KL: -53.2741\n",
+            "====> Epoch: 2230/6000 (37%)\tLoss: 132.8275\tLL: -130.3802\tKL: 2.4473\tLL/KL: -53.2744\n",
+            "====> Epoch: 2240/6000 (37%)\tLoss: 133.0184\tLL: -130.5738\tKL: 2.4446\tLL/KL: -53.4128\n",
+            "====> Epoch: 2250/6000 (38%)\tLoss: 133.0421\tLL: -130.6032\tKL: 2.4389\tLL/KL: -53.5507\n",
+            "====> Epoch: 2260/6000 (38%)\tLoss: 133.2583\tLL: -130.8198\tKL: 2.4385\tLL/KL: -53.6480\n",
+            "====> Epoch: 2270/6000 (38%)\tLoss: 133.0016\tLL: -130.5635\tKL: 2.4381\tLL/KL: -53.5517\n",
+            "====> Epoch: 2280/6000 (38%)\tLoss: 133.2627\tLL: -130.8264\tKL: 2.4363\tLL/KL: -53.6994\n",
+            "====> Epoch: 2290/6000 (38%)\tLoss: 133.0726\tLL: -130.6395\tKL: 2.4331\tLL/KL: -53.6921\n",
+            "====> Epoch: 2300/6000 (38%)\tLoss: 133.0894\tLL: -130.6596\tKL: 2.4298\tLL/KL: -53.7735\n",
+            "====> Epoch: 2310/6000 (38%)\tLoss: 132.7506\tLL: -130.3236\tKL: 2.4270\tLL/KL: -53.6976\n",
+            "====> Epoch: 2320/6000 (39%)\tLoss: 133.4447\tLL: -131.0178\tKL: 2.4269\tLL/KL: -53.9854\n",
+            "====> Epoch: 2330/6000 (39%)\tLoss: 132.9429\tLL: -130.5174\tKL: 2.4256\tLL/KL: -53.8091\n",
+            "====> Epoch: 2340/6000 (39%)\tLoss: 132.9248\tLL: -130.5011\tKL: 2.4237\tLL/KL: -53.8427\n",
+            "====> Epoch: 2350/6000 (39%)\tLoss: 133.0614\tLL: -130.6382\tKL: 2.4232\tLL/KL: -53.9119\n",
+            "====> Epoch: 2360/6000 (39%)\tLoss: 132.9443\tLL: -130.5228\tKL: 2.4215\tLL/KL: -53.9027\n",
+            "====> Epoch: 2370/6000 (40%)\tLoss: 132.9579\tLL: -130.5386\tKL: 2.4193\tLL/KL: -53.9574\n",
+            "====> Epoch: 2380/6000 (40%)\tLoss: 132.7638\tLL: -130.3464\tKL: 2.4174\tLL/KL: -53.9193\n",
+            "====> Epoch: 2390/6000 (40%)\tLoss: 132.9938\tLL: -130.5774\tKL: 2.4163\tLL/KL: -54.0392\n",
+            "====> Epoch: 2400/6000 (40%)\tLoss: 132.9811\tLL: -130.5670\tKL: 2.4141\tLL/KL: -54.0862\n",
+            "====> Epoch: 2410/6000 (40%)\tLoss: 132.7823\tLL: -130.3684\tKL: 2.4139\tLL/KL: -54.0075\n",
+            "====> Epoch: 2420/6000 (40%)\tLoss: 133.1912\tLL: -130.7796\tKL: 2.4116\tLL/KL: -54.2288\n",
+            "====> Epoch: 2430/6000 (40%)\tLoss: 133.0645\tLL: -130.6535\tKL: 2.4110\tLL/KL: -54.1908\n",
+            "====> Epoch: 2440/6000 (41%)\tLoss: 133.0467\tLL: -130.6376\tKL: 2.4091\tLL/KL: -54.2273\n",
+            "====> Epoch: 2450/6000 (41%)\tLoss: 133.1540\tLL: -130.7442\tKL: 2.4098\tLL/KL: -54.2552\n",
+            "====> Epoch: 2460/6000 (41%)\tLoss: 133.1761\tLL: -130.7675\tKL: 2.4086\tLL/KL: -54.2926\n",
+            "====> Epoch: 2470/6000 (41%)\tLoss: 133.0616\tLL: -130.6519\tKL: 2.4097\tLL/KL: -54.2192\n",
+            "====> Epoch: 2480/6000 (41%)\tLoss: 133.0708\tLL: -130.6597\tKL: 2.4111\tLL/KL: -54.1917\n",
+            "====> Epoch: 2490/6000 (42%)\tLoss: 133.0471\tLL: -130.6367\tKL: 2.4104\tLL/KL: -54.1980\n",
+            "====> Epoch: 2500/6000 (42%)\tLoss: 133.4211\tLL: -131.0127\tKL: 2.4084\tLL/KL: -54.3982\n",
+            "====> Epoch: 2510/6000 (42%)\tLoss: 133.1596\tLL: -130.7519\tKL: 2.4077\tLL/KL: -54.3047\n",
+            "====> Epoch: 2520/6000 (42%)\tLoss: 133.0044\tLL: -130.5991\tKL: 2.4052\tLL/KL: -54.2976\n",
+            "====> Epoch: 2530/6000 (42%)\tLoss: 133.4255\tLL: -131.0217\tKL: 2.4039\tLL/KL: -54.5040\n",
+            "====> Epoch: 2540/6000 (42%)\tLoss: 133.2812\tLL: -130.8796\tKL: 2.4016\tLL/KL: -54.4961\n",
+            "====> Epoch: 2550/6000 (42%)\tLoss: 133.0337\tLL: -130.6318\tKL: 2.4020\tLL/KL: -54.3853\n",
+            "====> Epoch: 2560/6000 (43%)\tLoss: 133.2414\tLL: -130.8380\tKL: 2.4034\tLL/KL: -54.4381\n",
+            "====> Epoch: 2570/6000 (43%)\tLoss: 132.8521\tLL: -130.4496\tKL: 2.4025\tLL/KL: -54.2963\n",
+            "====> Epoch: 2580/6000 (43%)\tLoss: 133.0305\tLL: -130.6284\tKL: 2.4022\tLL/KL: -54.3796\n",
+            "====> Epoch: 2590/6000 (43%)\tLoss: 133.1159\tLL: -130.7124\tKL: 2.4035\tLL/KL: -54.3841\n",
+            "====> Epoch: 2600/6000 (43%)\tLoss: 133.0533\tLL: -130.6479\tKL: 2.4054\tLL/KL: -54.3141\n",
+            "====> Epoch: 2610/6000 (44%)\tLoss: 132.8950\tLL: -130.4895\tKL: 2.4055\tLL/KL: -54.2463\n",
+            "====> Epoch: 2620/6000 (44%)\tLoss: 133.1395\tLL: -130.7347\tKL: 2.4048\tLL/KL: -54.3642\n",
+            "====> Epoch: 2630/6000 (44%)\tLoss: 133.1315\tLL: -130.7249\tKL: 2.4067\tLL/KL: -54.3177\n",
+            "====> Epoch: 2640/6000 (44%)\tLoss: 133.0746\tLL: -130.6661\tKL: 2.4085\tLL/KL: -54.2531\n",
+            "====> Epoch: 2650/6000 (44%)\tLoss: 133.2337\tLL: -130.8225\tKL: 2.4111\tLL/KL: -54.2579\n",
+            "====> Epoch: 2660/6000 (44%)\tLoss: 133.3108\tLL: -130.8986\tKL: 2.4122\tLL/KL: -54.2661\n",
+            "====> Epoch: 2670/6000 (44%)\tLoss: 132.9371\tLL: -130.5230\tKL: 2.4142\tLL/KL: -54.0656\n",
+            "====> Epoch: 2680/6000 (45%)\tLoss: 133.0764\tLL: -130.6629\tKL: 2.4135\tLL/KL: -54.1381\n",
+            "====> Epoch: 2690/6000 (45%)\tLoss: 133.0312\tLL: -130.6157\tKL: 2.4155\tLL/KL: -54.0729\n",
+            "====> Epoch: 2700/6000 (45%)\tLoss: 133.0921\tLL: -130.6734\tKL: 2.4186\tLL/KL: -54.0285\n",
+            "====> Epoch: 2710/6000 (45%)\tLoss: 132.8516\tLL: -130.4294\tKL: 2.4222\tLL/KL: -53.8468\n",
+            "====> Epoch: 2720/6000 (45%)\tLoss: 133.0340\tLL: -130.6127\tKL: 2.4213\tLL/KL: -53.9421\n",
+            "====> Epoch: 2730/6000 (46%)\tLoss: 133.1593\tLL: -130.7355\tKL: 2.4237\tLL/KL: -53.9397\n",
+            "====> Epoch: 2740/6000 (46%)\tLoss: 133.1793\tLL: -130.7567\tKL: 2.4226\tLL/KL: -53.9748\n",
+            "====> Epoch: 2750/6000 (46%)\tLoss: 132.9713\tLL: -130.5496\tKL: 2.4217\tLL/KL: -53.9086\n",
+            "====> Epoch: 2760/6000 (46%)\tLoss: 133.0917\tLL: -130.6715\tKL: 2.4202\tLL/KL: -53.9922\n",
+            "====> Epoch: 2770/6000 (46%)\tLoss: 133.0875\tLL: -130.6654\tKL: 2.4221\tLL/KL: -53.9479\n",
+            "====> Epoch: 2780/6000 (46%)\tLoss: 133.0194\tLL: -130.5938\tKL: 2.4257\tLL/KL: -53.8383\n",
+            "====> Epoch: 2790/6000 (46%)\tLoss: 133.1053\tLL: -130.6790\tKL: 2.4262\tLL/KL: -53.8609\n",
+            "====> Epoch: 2800/6000 (47%)\tLoss: 133.0050\tLL: -130.5798\tKL: 2.4252\tLL/KL: -53.8440\n",
+            "====> Epoch: 2810/6000 (47%)\tLoss: 133.1673\tLL: -130.7399\tKL: 2.4273\tLL/KL: -53.8612\n",
+            "====> Epoch: 2820/6000 (47%)\tLoss: 133.0424\tLL: -130.6159\tKL: 2.4265\tLL/KL: -53.8295\n",
+            "====> Epoch: 2830/6000 (47%)\tLoss: 133.0934\tLL: -130.6669\tKL: 2.4264\tLL/KL: -53.8518\n",
+            "====> Epoch: 2840/6000 (47%)\tLoss: 133.1055\tLL: -130.6742\tKL: 2.4312\tLL/KL: -53.7483\n",
+            "====> Epoch: 2850/6000 (48%)\tLoss: 133.2407\tLL: -130.8089\tKL: 2.4318\tLL/KL: -53.7911\n",
+            "====> Epoch: 2860/6000 (48%)\tLoss: 133.4170\tLL: -130.9836\tKL: 2.4334\tLL/KL: -53.8273\n",
+            "====> Epoch: 2870/6000 (48%)\tLoss: 132.9359\tLL: -130.5025\tKL: 2.4333\tLL/KL: -53.6310\n",
+            "====> Epoch: 2880/6000 (48%)\tLoss: 132.7656\tLL: -130.3304\tKL: 2.4352\tLL/KL: -53.5199\n",
+            "====> Epoch: 2890/6000 (48%)\tLoss: 132.7310\tLL: -130.2950\tKL: 2.4360\tLL/KL: -53.4865\n",
+            "====> Epoch: 2900/6000 (48%)\tLoss: 132.8404\tLL: -130.4007\tKL: 2.4397\tLL/KL: -53.4493\n",
+            "====> Epoch: 2910/6000 (48%)\tLoss: 132.8821\tLL: -130.4407\tKL: 2.4415\tLL/KL: -53.4268\n",
+            "====> Epoch: 2920/6000 (49%)\tLoss: 133.2326\tLL: -130.7879\tKL: 2.4447\tLL/KL: -53.4992\n",
+            "====> Epoch: 2930/6000 (49%)\tLoss: 133.2066\tLL: -130.7608\tKL: 2.4457\tLL/KL: -53.4647\n",
+            "====> Epoch: 2940/6000 (49%)\tLoss: 133.0302\tLL: -130.5776\tKL: 2.4527\tLL/KL: -53.2393\n",
+            "====> Epoch: 2950/6000 (49%)\tLoss: 133.3830\tLL: -130.9254\tKL: 2.4576\tLL/KL: -53.2727\n",
+            "====> Epoch: 2960/6000 (49%)\tLoss: 132.9934\tLL: -130.5330\tKL: 2.4604\tLL/KL: -53.0540\n",
+            "====> Epoch: 2970/6000 (50%)\tLoss: 133.1093\tLL: -130.6442\tKL: 2.4652\tLL/KL: -52.9963\n",
+            "====> Epoch: 2980/6000 (50%)\tLoss: 132.8110\tLL: -130.3451\tKL: 2.4659\tLL/KL: -52.8588\n",
+            "====> Epoch: 2990/6000 (50%)\tLoss: 132.7879\tLL: -130.3187\tKL: 2.4692\tLL/KL: -52.7777\n",
+            "====> Epoch: 3000/6000 (50%)\tLoss: 132.4734\tLL: -129.9987\tKL: 2.4747\tLL/KL: -52.5310\n",
+            "====> Epoch: 3010/6000 (50%)\tLoss: 133.0942\tLL: -130.6167\tKL: 2.4775\tLL/KL: -52.7220\n",
+            "====> Epoch: 3020/6000 (50%)\tLoss: 133.1514\tLL: -130.6714\tKL: 2.4801\tLL/KL: -52.6890\n",
+            "====> Epoch: 3030/6000 (50%)\tLoss: 132.9188\tLL: -130.4355\tKL: 2.4834\tLL/KL: -52.5238\n",
+            "====> Epoch: 3040/6000 (51%)\tLoss: 132.8970\tLL: -130.4154\tKL: 2.4816\tLL/KL: -52.5528\n",
+            "====> Epoch: 3050/6000 (51%)\tLoss: 133.2276\tLL: -130.7453\tKL: 2.4823\tLL/KL: -52.6702\n",
+            "====> Epoch: 3060/6000 (51%)\tLoss: 133.3316\tLL: -130.8484\tKL: 2.4832\tLL/KL: -52.6934\n",
+            "====> Epoch: 3070/6000 (51%)\tLoss: 133.1800\tLL: -130.6923\tKL: 2.4878\tLL/KL: -52.5342\n",
+            "====> Epoch: 3080/6000 (51%)\tLoss: 132.8947\tLL: -130.4029\tKL: 2.4918\tLL/KL: -52.3319\n",
+            "====> Epoch: 3090/6000 (52%)\tLoss: 132.8816\tLL: -130.3926\tKL: 2.4890\tLL/KL: -52.3876\n",
+            "====> Epoch: 3100/6000 (52%)\tLoss: 133.2460\tLL: -130.7567\tKL: 2.4892\tLL/KL: -52.5288\n",
+            "====> Epoch: 3110/6000 (52%)\tLoss: 133.1471\tLL: -130.6561\tKL: 2.4910\tLL/KL: -52.4515\n",
+            "====> Epoch: 3120/6000 (52%)\tLoss: 133.1018\tLL: -130.6025\tKL: 2.4993\tLL/KL: -52.2560\n",
+            "====> Epoch: 3130/6000 (52%)\tLoss: 133.0217\tLL: -130.5170\tKL: 2.5048\tLL/KL: -52.1076\n",
+            "====> Epoch: 3140/6000 (52%)\tLoss: 133.0530\tLL: -130.5435\tKL: 2.5095\tLL/KL: -52.0204\n",
+            "====> Epoch: 3150/6000 (52%)\tLoss: 133.2551\tLL: -130.7436\tKL: 2.5115\tLL/KL: -52.0590\n",
+            "====> Epoch: 3160/6000 (53%)\tLoss: 132.9467\tLL: -130.4299\tKL: 2.5168\tLL/KL: -51.8235\n",
+            "====> Epoch: 3170/6000 (53%)\tLoss: 133.2999\tLL: -130.7759\tKL: 2.5240\tLL/KL: -51.8138\n",
+            "====> Epoch: 3180/6000 (53%)\tLoss: 133.1075\tLL: -130.5795\tKL: 2.5280\tLL/KL: -51.6524\n",
+            "====> Epoch: 3190/6000 (53%)\tLoss: 132.7889\tLL: -130.2554\tKL: 2.5335\tLL/KL: -51.4142\n",
+            "====> Epoch: 3200/6000 (53%)\tLoss: 132.9803\tLL: -130.4489\tKL: 2.5313\tLL/KL: -51.5335\n",
+            "====> Epoch: 3210/6000 (54%)\tLoss: 132.7364\tLL: -130.2030\tKL: 2.5334\tLL/KL: -51.3943\n",
+            "====> Epoch: 3220/6000 (54%)\tLoss: 133.1233\tLL: -130.5878\tKL: 2.5355\tLL/KL: -51.5034\n",
+            "====> Epoch: 3230/6000 (54%)\tLoss: 133.1990\tLL: -130.6592\tKL: 2.5398\tLL/KL: -51.4440\n",
+            "====> Epoch: 3240/6000 (54%)\tLoss: 132.8175\tLL: -130.2697\tKL: 2.5479\tLL/KL: -51.1290\n",
+            "====> Epoch: 3250/6000 (54%)\tLoss: 132.9335\tLL: -130.3844\tKL: 2.5491\tLL/KL: -51.1493\n",
+            "====> Epoch: 3260/6000 (54%)\tLoss: 132.6599\tLL: -130.1087\tKL: 2.5512\tLL/KL: -50.9987\n",
+            "====> Epoch: 3270/6000 (54%)\tLoss: 132.9718\tLL: -130.4216\tKL: 2.5502\tLL/KL: -51.1419\n",
+            "====> Epoch: 3280/6000 (55%)\tLoss: 133.2064\tLL: -130.6491\tKL: 2.5574\tLL/KL: -51.0875\n",
+            "====> Epoch: 3290/6000 (55%)\tLoss: 132.9621\tLL: -130.3984\tKL: 2.5636\tLL/KL: -50.8644\n",
+            "====> Epoch: 3300/6000 (55%)\tLoss: 133.1794\tLL: -130.6102\tKL: 2.5692\tLL/KL: -50.8372\n",
+            "====> Epoch: 3310/6000 (55%)\tLoss: 133.0049\tLL: -130.4284\tKL: 2.5766\tLL/KL: -50.6206\n",
+            "====> Epoch: 3320/6000 (55%)\tLoss: 132.8761\tLL: -130.2953\tKL: 2.5808\tLL/KL: -50.4860\n",
+            "====> Epoch: 3330/6000 (56%)\tLoss: 132.7804\tLL: -130.2067\tKL: 2.5737\tLL/KL: -50.5904\n",
+            "====> Epoch: 3340/6000 (56%)\tLoss: 132.8623\tLL: -130.2882\tKL: 2.5742\tLL/KL: -50.6140\n",
+            "====> Epoch: 3350/6000 (56%)\tLoss: 132.9168\tLL: -130.3422\tKL: 2.5746\tLL/KL: -50.6262\n",
+            "====> Epoch: 3360/6000 (56%)\tLoss: 133.0719\tLL: -130.4901\tKL: 2.5818\tLL/KL: -50.5426\n",
+            "====> Epoch: 3370/6000 (56%)\tLoss: 132.8375\tLL: -130.2490\tKL: 2.5886\tLL/KL: -50.3171\n",
+            "====> Epoch: 3380/6000 (56%)\tLoss: 133.1550\tLL: -130.5556\tKL: 2.5995\tLL/KL: -50.2238\n",
+            "====> Epoch: 3390/6000 (56%)\tLoss: 132.9676\tLL: -130.3616\tKL: 2.6060\tLL/KL: -50.0241\n",
+            "====> Epoch: 3400/6000 (57%)\tLoss: 133.1567\tLL: -130.5522\tKL: 2.6045\tLL/KL: -50.1257\n",
+            "====> Epoch: 3410/6000 (57%)\tLoss: 133.1066\tLL: -130.4986\tKL: 2.6081\tLL/KL: -50.0365\n",
+            "====> Epoch: 3420/6000 (57%)\tLoss: 133.0832\tLL: -130.4754\tKL: 2.6078\tLL/KL: -50.0331\n",
+            "====> Epoch: 3430/6000 (57%)\tLoss: 133.3232\tLL: -130.7150\tKL: 2.6082\tLL/KL: -50.1177\n",
+            "====> Epoch: 3440/6000 (57%)\tLoss: 133.1112\tLL: -130.4928\tKL: 2.6185\tLL/KL: -49.8356\n",
+            "====> Epoch: 3450/6000 (58%)\tLoss: 132.8518\tLL: -130.2321\tKL: 2.6197\tLL/KL: -49.7127\n",
+            "====> Epoch: 3460/6000 (58%)\tLoss: 133.1351\tLL: -130.5130\tKL: 2.6221\tLL/KL: -49.7742\n",
+            "====> Epoch: 3470/6000 (58%)\tLoss: 133.2712\tLL: -130.6371\tKL: 2.6341\tLL/KL: -49.5941\n",
+            "====> Epoch: 3480/6000 (58%)\tLoss: 133.1468\tLL: -130.5014\tKL: 2.6454\tLL/KL: -49.3314\n",
+            "====> Epoch: 3490/6000 (58%)\tLoss: 132.9723\tLL: -130.3239\tKL: 2.6484\tLL/KL: -49.2078\n",
+            "====> Epoch: 3500/6000 (58%)\tLoss: 133.0173\tLL: -130.3695\tKL: 2.6478\tLL/KL: -49.2369\n",
+            "====> Epoch: 3510/6000 (58%)\tLoss: 132.7194\tLL: -130.0740\tKL: 2.6454\tLL/KL: -49.1702\n",
+            "====> Epoch: 3520/6000 (59%)\tLoss: 133.1600\tLL: -130.5221\tKL: 2.6379\tLL/KL: -49.4790\n",
+            "====> Epoch: 3530/6000 (59%)\tLoss: 132.8696\tLL: -130.2223\tKL: 2.6473\tLL/KL: -49.1902\n",
+            "====> Epoch: 3540/6000 (59%)\tLoss: 133.0505\tLL: -130.3945\tKL: 2.6560\tLL/KL: -49.0938\n",
+            "====> Epoch: 3550/6000 (59%)\tLoss: 133.1022\tLL: -130.4435\tKL: 2.6587\tLL/KL: -49.0637\n",
+            "====> Epoch: 3560/6000 (59%)\tLoss: 132.8370\tLL: -130.1733\tKL: 2.6637\tLL/KL: -48.8685\n",
+            "====> Epoch: 3570/6000 (60%)\tLoss: 132.8783\tLL: -130.2143\tKL: 2.6640\tLL/KL: -48.8788\n",
+            "====> Epoch: 3580/6000 (60%)\tLoss: 132.9259\tLL: -130.2585\tKL: 2.6675\tLL/KL: -48.8325\n",
+            "====> Epoch: 3590/6000 (60%)\tLoss: 133.0668\tLL: -130.3975\tKL: 2.6693\tLL/KL: -48.8510\n",
+            "====> Epoch: 3600/6000 (60%)\tLoss: 132.6639\tLL: -129.9828\tKL: 2.6811\tLL/KL: -48.4813\n",
+            "====> Epoch: 3610/6000 (60%)\tLoss: 132.7976\tLL: -130.1172\tKL: 2.6804\tLL/KL: -48.5439\n",
+            "====> Epoch: 3620/6000 (60%)\tLoss: 132.7588\tLL: -130.0745\tKL: 2.6843\tLL/KL: -48.4582\n",
+            "====> Epoch: 3630/6000 (60%)\tLoss: 132.7233\tLL: -130.0350\tKL: 2.6883\tLL/KL: -48.3700\n",
+            "====> Epoch: 3640/6000 (61%)\tLoss: 133.1408\tLL: -130.4516\tKL: 2.6892\tLL/KL: -48.5103\n",
+            "====> Epoch: 3650/6000 (61%)\tLoss: 133.0945\tLL: -130.3991\tKL: 2.6953\tLL/KL: -48.3797\n",
+            "====> Epoch: 3660/6000 (61%)\tLoss: 132.8982\tLL: -130.1935\tKL: 2.7047\tLL/KL: -48.1359\n",
+            "====> Epoch: 3670/6000 (61%)\tLoss: 132.7764\tLL: -130.0604\tKL: 2.7160\tLL/KL: -47.8876\n",
+            "====> Epoch: 3680/6000 (61%)\tLoss: 132.8489\tLL: -130.1382\tKL: 2.7107\tLL/KL: -48.0092\n",
+            "====> Epoch: 3690/6000 (62%)\tLoss: 133.0299\tLL: -130.3205\tKL: 2.7094\tLL/KL: -48.0998\n",
+            "====> Epoch: 3700/6000 (62%)\tLoss: 133.1541\tLL: -130.4468\tKL: 2.7073\tLL/KL: -48.1840\n",
+            "====> Epoch: 3710/6000 (62%)\tLoss: 132.8282\tLL: -130.1184\tKL: 2.7098\tLL/KL: -48.0179\n",
+            "====> Epoch: 3720/6000 (62%)\tLoss: 132.7177\tLL: -129.9965\tKL: 2.7212\tLL/KL: -47.7724\n",
+            "====> Epoch: 3730/6000 (62%)\tLoss: 132.8555\tLL: -130.1451\tKL: 2.7104\tLL/KL: -48.0163\n",
+            "====> Epoch: 3740/6000 (62%)\tLoss: 132.9526\tLL: -130.2432\tKL: 2.7095\tLL/KL: -48.0697\n",
+            "====> Epoch: 3750/6000 (62%)\tLoss: 132.7786\tLL: -130.0687\tKL: 2.7099\tLL/KL: -47.9974\n",
+            "====> Epoch: 3760/6000 (63%)\tLoss: 132.6053\tLL: -129.8963\tKL: 2.7090\tLL/KL: -47.9496\n",
+            "====> Epoch: 3770/6000 (63%)\tLoss: 132.7544\tLL: -130.0323\tKL: 2.7221\tLL/KL: -47.7699\n",
+            "====> Epoch: 3780/6000 (63%)\tLoss: 133.0918\tLL: -130.3685\tKL: 2.7232\tLL/KL: -47.8728\n",
+            "====> Epoch: 3790/6000 (63%)\tLoss: 132.8009\tLL: -130.0816\tKL: 2.7193\tLL/KL: -47.8361\n",
+            "====> Epoch: 3800/6000 (63%)\tLoss: 132.7959\tLL: -130.0875\tKL: 2.7084\tLL/KL: -48.0307\n",
+            "====> Epoch: 3810/6000 (64%)\tLoss: 132.7718\tLL: -130.0647\tKL: 2.7071\tLL/KL: -48.0455\n",
+            "====> Epoch: 3820/6000 (64%)\tLoss: 132.7485\tLL: -130.0360\tKL: 2.7125\tLL/KL: -47.9392\n",
+            "====> Epoch: 3830/6000 (64%)\tLoss: 133.0300\tLL: -130.3152\tKL: 2.7148\tLL/KL: -48.0018\n",
+            "====> Epoch: 3840/6000 (64%)\tLoss: 132.9101\tLL: -130.1836\tKL: 2.7265\tLL/KL: -47.7478\n",
+            "====> Epoch: 3850/6000 (64%)\tLoss: 132.7282\tLL: -130.0041\tKL: 2.7241\tLL/KL: -47.7235\n",
+            "====> Epoch: 3860/6000 (64%)\tLoss: 133.1301\tLL: -130.4064\tKL: 2.7237\tLL/KL: -47.8790\n",
+            "====> Epoch: 3870/6000 (64%)\tLoss: 132.6597\tLL: -129.9342\tKL: 2.7255\tLL/KL: -47.6737\n",
+            "====> Epoch: 3880/6000 (65%)\tLoss: 132.9106\tLL: -130.1756\tKL: 2.7350\tLL/KL: -47.5961\n",
+            "====> Epoch: 3890/6000 (65%)\tLoss: 133.1604\tLL: -130.4199\tKL: 2.7405\tLL/KL: -47.5895\n",
+            "====> Epoch: 3900/6000 (65%)\tLoss: 133.0052\tLL: -130.2522\tKL: 2.7530\tLL/KL: -47.3127\n",
+            "====> Epoch: 3910/6000 (65%)\tLoss: 133.1069\tLL: -130.3567\tKL: 2.7502\tLL/KL: -47.3994\n",
+            "====> Epoch: 3920/6000 (65%)\tLoss: 132.5744\tLL: -129.8139\tKL: 2.7605\tLL/KL: -47.0255\n",
+            "====> Epoch: 3930/6000 (66%)\tLoss: 132.8980\tLL: -130.1413\tKL: 2.7568\tLL/KL: -47.2080\n",
+            "====> Epoch: 3940/6000 (66%)\tLoss: 133.0177\tLL: -130.2707\tKL: 2.7470\tLL/KL: -47.4226\n",
+            "====> Epoch: 3950/6000 (66%)\tLoss: 133.0444\tLL: -130.2795\tKL: 2.7649\tLL/KL: -47.1184\n",
+            "====> Epoch: 3960/6000 (66%)\tLoss: 132.7923\tLL: -130.0274\tKL: 2.7649\tLL/KL: -47.0277\n",
+            "====> Epoch: 3970/6000 (66%)\tLoss: 132.9112\tLL: -130.1460\tKL: 2.7653\tLL/KL: -47.0642\n",
+            "====> Epoch: 3980/6000 (66%)\tLoss: 132.8152\tLL: -130.0516\tKL: 2.7635\tLL/KL: -47.0599\n",
+            "====> Epoch: 3990/6000 (66%)\tLoss: 132.6129\tLL: -129.8452\tKL: 2.7677\tLL/KL: -46.9147\n",
+            "====> Epoch: 4000/6000 (67%)\tLoss: 133.3761\tLL: -130.6190\tKL: 2.7571\tLL/KL: -47.3759\n",
+            "====> Epoch: 4010/6000 (67%)\tLoss: 133.0267\tLL: -130.2596\tKL: 2.7670\tLL/KL: -47.0759\n",
+            "====> Epoch: 4020/6000 (67%)\tLoss: 132.9866\tLL: -130.2220\tKL: 2.7646\tLL/KL: -47.1037\n",
+            "====> Epoch: 4030/6000 (67%)\tLoss: 133.0004\tLL: -130.2446\tKL: 2.7558\tLL/KL: -47.2619\n",
+            "====> Epoch: 4040/6000 (67%)\tLoss: 132.9068\tLL: -130.1413\tKL: 2.7655\tLL/KL: -47.0593\n",
+            "====> Epoch: 4050/6000 (68%)\tLoss: 132.9856\tLL: -130.2278\tKL: 2.7578\tLL/KL: -47.2223\n",
+            "====> Epoch: 4060/6000 (68%)\tLoss: 133.0827\tLL: -130.3223\tKL: 2.7604\tLL/KL: -47.2111\n",
+            "====> Epoch: 4070/6000 (68%)\tLoss: 132.9832\tLL: -130.2239\tKL: 2.7593\tLL/KL: -47.1943\n",
+            "====> Epoch: 4080/6000 (68%)\tLoss: 132.6705\tLL: -129.8996\tKL: 2.7710\tLL/KL: -46.8789\n",
+            "====> Epoch: 4090/6000 (68%)\tLoss: 133.0363\tLL: -130.2708\tKL: 2.7655\tLL/KL: -47.1053\n",
+            "====> Epoch: 4100/6000 (68%)\tLoss: 132.8695\tLL: -130.1009\tKL: 2.7685\tLL/KL: -46.9929\n",
+            "====> Epoch: 4110/6000 (68%)\tLoss: 132.8064\tLL: -130.0417\tKL: 2.7647\tLL/KL: -47.0370\n",
+            "====> Epoch: 4120/6000 (69%)\tLoss: 133.2127\tLL: -130.4418\tKL: 2.7709\tLL/KL: -47.0762\n",
+            "====> Epoch: 4130/6000 (69%)\tLoss: 132.7499\tLL: -129.9653\tKL: 2.7846\tLL/KL: -46.6726\n",
+            "====> Epoch: 4140/6000 (69%)\tLoss: 133.1228\tLL: -130.3397\tKL: 2.7831\tLL/KL: -46.8331\n",
+            "====> Epoch: 4150/6000 (69%)\tLoss: 132.6748\tLL: -129.8926\tKL: 2.7822\tLL/KL: -46.6870\n",
+            "====> Epoch: 4160/6000 (69%)\tLoss: 132.7256\tLL: -129.9524\tKL: 2.7732\tLL/KL: -46.8601\n",
+            "====> Epoch: 4170/6000 (70%)\tLoss: 132.9082\tLL: -130.1362\tKL: 2.7721\tLL/KL: -46.9454\n",
+            "====> Epoch: 4180/6000 (70%)\tLoss: 132.8381\tLL: -130.0647\tKL: 2.7734\tLL/KL: -46.8971\n",
+            "====> Epoch: 4190/6000 (70%)\tLoss: 132.6916\tLL: -129.9201\tKL: 2.7715\tLL/KL: -46.8767\n",
+            "====> Epoch: 4200/6000 (70%)\tLoss: 133.0600\tLL: -130.2944\tKL: 2.7657\tLL/KL: -47.1116\n",
+            "====> Epoch: 4210/6000 (70%)\tLoss: 132.8080\tLL: -130.0390\tKL: 2.7690\tLL/KL: -46.9617\n",
+            "====> Epoch: 4220/6000 (70%)\tLoss: 132.4969\tLL: -129.7293\tKL: 2.7676\tLL/KL: -46.8745\n",
+            "====> Epoch: 4230/6000 (70%)\tLoss: 132.5535\tLL: -129.7871\tKL: 2.7664\tLL/KL: -46.9163\n",
+            "====> Epoch: 4240/6000 (71%)\tLoss: 132.8436\tLL: -130.0786\tKL: 2.7650\tLL/KL: -47.0447\n",
+            "====> Epoch: 4250/6000 (71%)\tLoss: 132.9412\tLL: -130.1795\tKL: 2.7617\tLL/KL: -47.1369\n",
+            "====> Epoch: 4260/6000 (71%)\tLoss: 132.9167\tLL: -130.1594\tKL: 2.7573\tLL/KL: -47.2055\n",
+            "====> Epoch: 4270/6000 (71%)\tLoss: 132.9050\tLL: -130.1511\tKL: 2.7539\tLL/KL: -47.2614\n",
+            "====> Epoch: 4280/6000 (71%)\tLoss: 133.1364\tLL: -130.3734\tKL: 2.7630\tLL/KL: -47.1857\n",
+            "====> Epoch: 4290/6000 (72%)\tLoss: 132.4992\tLL: -129.7166\tKL: 2.7827\tLL/KL: -46.6158\n",
+            "====> Epoch: 4300/6000 (72%)\tLoss: 133.0435\tLL: -130.2559\tKL: 2.7876\tLL/KL: -46.7267\n",
+            "====> Epoch: 4310/6000 (72%)\tLoss: 133.1124\tLL: -130.3153\tKL: 2.7971\tLL/KL: -46.5901\n",
+            "====> Epoch: 4320/6000 (72%)\tLoss: 132.9398\tLL: -130.1419\tKL: 2.7979\tLL/KL: -46.5145\n",
+            "====> Epoch: 4330/6000 (72%)\tLoss: 132.9838\tLL: -130.1870\tKL: 2.7969\tLL/KL: -46.5476\n",
+            "====> Epoch: 4340/6000 (72%)\tLoss: 132.8500\tLL: -130.0556\tKL: 2.7944\tLL/KL: -46.5423\n",
+            "====> Epoch: 4350/6000 (72%)\tLoss: 132.7545\tLL: -129.9532\tKL: 2.8014\tLL/KL: -46.3893\n",
+            "====> Epoch: 4360/6000 (73%)\tLoss: 132.8241\tLL: -130.0269\tKL: 2.7972\tLL/KL: -46.4847\n",
+            "====> Epoch: 4370/6000 (73%)\tLoss: 132.7082\tLL: -129.9120\tKL: 2.7961\tLL/KL: -46.4610\n",
+            "====> Epoch: 4380/6000 (73%)\tLoss: 132.5949\tLL: -129.7965\tKL: 2.7984\tLL/KL: -46.3825\n",
+            "====> Epoch: 4390/6000 (73%)\tLoss: 132.9887\tLL: -130.1935\tKL: 2.7952\tLL/KL: -46.5777\n",
+            "====> Epoch: 4400/6000 (73%)\tLoss: 132.7753\tLL: -129.9756\tKL: 2.7997\tLL/KL: -46.4253\n",
+            "====> Epoch: 4410/6000 (74%)\tLoss: 132.9688\tLL: -130.1859\tKL: 2.7829\tLL/KL: -46.7805\n",
+            "====> Epoch: 4420/6000 (74%)\tLoss: 132.7004\tLL: -129.9181\tKL: 2.7823\tLL/KL: -46.6937\n",
+            "====> Epoch: 4430/6000 (74%)\tLoss: 132.6711\tLL: -129.8848\tKL: 2.7863\tLL/KL: -46.6151\n",
+            "====> Epoch: 4440/6000 (74%)\tLoss: 132.9563\tLL: -130.1668\tKL: 2.7895\tLL/KL: -46.6627\n",
+            "====> Epoch: 4450/6000 (74%)\tLoss: 133.0492\tLL: -130.2547\tKL: 2.7945\tLL/KL: -46.6114\n",
+            "====> Epoch: 4460/6000 (74%)\tLoss: 132.8522\tLL: -130.0505\tKL: 2.8017\tLL/KL: -46.4193\n",
+            "====> Epoch: 4470/6000 (74%)\tLoss: 132.9205\tLL: -130.1141\tKL: 2.8064\tLL/KL: -46.3638\n",
+            "====> Epoch: 4480/6000 (75%)\tLoss: 132.9040\tLL: -130.1115\tKL: 2.7924\tLL/KL: -46.5941\n",
+            "====> Epoch: 4490/6000 (75%)\tLoss: 132.3934\tLL: -129.5897\tKL: 2.8037\tLL/KL: -46.2209\n",
+            "====> Epoch: 4500/6000 (75%)\tLoss: 132.5826\tLL: -129.7888\tKL: 2.7938\tLL/KL: -46.4554\n",
+            "====> Epoch: 4510/6000 (75%)\tLoss: 132.7670\tLL: -129.9764\tKL: 2.7906\tLL/KL: -46.5761\n",
+            "====> Epoch: 4520/6000 (75%)\tLoss: 132.8040\tLL: -130.0044\tKL: 2.7997\tLL/KL: -46.4358\n",
+            "====> Epoch: 4530/6000 (76%)\tLoss: 132.8082\tLL: -130.0042\tKL: 2.8040\tLL/KL: -46.3645\n",
+            "====> Epoch: 4540/6000 (76%)\tLoss: 133.0613\tLL: -130.2540\tKL: 2.8074\tLL/KL: -46.3972\n",
+            "====> Epoch: 4550/6000 (76%)\tLoss: 132.8281\tLL: -130.0192\tKL: 2.8089\tLL/KL: -46.2884\n",
+            "====> Epoch: 4560/6000 (76%)\tLoss: 132.7771\tLL: -129.9726\tKL: 2.8045\tLL/KL: -46.3446\n",
+            "====> Epoch: 4570/6000 (76%)\tLoss: 133.0129\tLL: -130.2133\tKL: 2.7996\tLL/KL: -46.5109\n",
+            "====> Epoch: 4580/6000 (76%)\tLoss: 132.5505\tLL: -129.7539\tKL: 2.7967\tLL/KL: -46.3962\n",
+            "====> Epoch: 4590/6000 (76%)\tLoss: 132.6401\tLL: -129.8494\tKL: 2.7907\tLL/KL: -46.5300\n",
+            "====> Epoch: 4600/6000 (77%)\tLoss: 132.6519\tLL: -129.8515\tKL: 2.8004\tLL/KL: -46.3686\n",
+            "====> Epoch: 4610/6000 (77%)\tLoss: 132.9786\tLL: -130.1933\tKL: 2.7853\tLL/KL: -46.7438\n",
+            "====> Epoch: 4620/6000 (77%)\tLoss: 132.8759\tLL: -130.0835\tKL: 2.7924\tLL/KL: -46.5840\n",
+            "====> Epoch: 4630/6000 (77%)\tLoss: 132.7694\tLL: -129.9769\tKL: 2.7925\tLL/KL: -46.5453\n",
+            "====> Epoch: 4640/6000 (77%)\tLoss: 132.8194\tLL: -130.0186\tKL: 2.8009\tLL/KL: -46.4205\n",
+            "====> Epoch: 4650/6000 (78%)\tLoss: 132.8016\tLL: -129.9968\tKL: 2.8047\tLL/KL: -46.3489\n",
+            "====> Epoch: 4660/6000 (78%)\tLoss: 132.5880\tLL: -129.7830\tKL: 2.8050\tLL/KL: -46.2688\n",
+            "====> Epoch: 4670/6000 (78%)\tLoss: 132.9885\tLL: -130.1640\tKL: 2.8245\tLL/KL: -46.0844\n",
+            "====> Epoch: 4680/6000 (78%)\tLoss: 132.7997\tLL: -129.9556\tKL: 2.8442\tLL/KL: -45.6921\n",
+            "====> Epoch: 4690/6000 (78%)\tLoss: 133.0685\tLL: -130.2254\tKL: 2.8430\tLL/KL: -45.8054\n",
+            "====> Epoch: 4700/6000 (78%)\tLoss: 132.8090\tLL: -129.9732\tKL: 2.8359\tLL/KL: -45.8319\n",
+            "====> Epoch: 4710/6000 (78%)\tLoss: 132.7294\tLL: -129.8934\tKL: 2.8361\tLL/KL: -45.8007\n",
+            "====> Epoch: 4720/6000 (79%)\tLoss: 132.8013\tLL: -129.9618\tKL: 2.8395\tLL/KL: -45.7695\n",
+            "====> Epoch: 4730/6000 (79%)\tLoss: 133.0408\tLL: -130.2069\tKL: 2.8339\tLL/KL: -45.9460\n",
+            "====> Epoch: 4740/6000 (79%)\tLoss: 132.8850\tLL: -130.0603\tKL: 2.8247\tLL/KL: -46.0435\n",
+            "====> Epoch: 4750/6000 (79%)\tLoss: 132.8476\tLL: -130.0286\tKL: 2.8190\tLL/KL: -46.1255\n",
+            "====> Epoch: 4760/6000 (79%)\tLoss: 132.6246\tLL: -129.8091\tKL: 2.8154\tLL/KL: -46.1061\n",
+            "====> Epoch: 4770/6000 (80%)\tLoss: 132.7387\tLL: -129.9137\tKL: 2.8250\tLL/KL: -45.9869\n",
+            "====> Epoch: 4780/6000 (80%)\tLoss: 132.9017\tLL: -130.0813\tKL: 2.8204\tLL/KL: -46.1222\n",
+            "====> Epoch: 4790/6000 (80%)\tLoss: 133.1325\tLL: -130.3131\tKL: 2.8194\tLL/KL: -46.2203\n",
+            "====> Epoch: 4800/6000 (80%)\tLoss: 132.9159\tLL: -130.0909\tKL: 2.8249\tLL/KL: -46.0507\n",
+            "====> Epoch: 4810/6000 (80%)\tLoss: 133.1520\tLL: -130.3292\tKL: 2.8228\tLL/KL: -46.1708\n",
+            "====> Epoch: 4820/6000 (80%)\tLoss: 132.5668\tLL: -129.7437\tKL: 2.8231\tLL/KL: -45.9576\n",
+            "====> Epoch: 4830/6000 (80%)\tLoss: 132.7628\tLL: -129.9397\tKL: 2.8231\tLL/KL: -46.0275\n",
+            "====> Epoch: 4840/6000 (81%)\tLoss: 132.7586\tLL: -129.9449\tKL: 2.8137\tLL/KL: -46.1829\n",
+            "====> Epoch: 4850/6000 (81%)\tLoss: 132.6937\tLL: -129.8635\tKL: 2.8303\tLL/KL: -45.8840\n",
+            "====> Epoch: 4860/6000 (81%)\tLoss: 132.6427\tLL: -129.8119\tKL: 2.8309\tLL/KL: -45.8556\n",
+            "====> Epoch: 4870/6000 (81%)\tLoss: 133.0462\tLL: -130.2061\tKL: 2.8401\tLL/KL: -45.8462\n",
+            "====> Epoch: 4880/6000 (81%)\tLoss: 132.9483\tLL: -130.1120\tKL: 2.8363\tLL/KL: -45.8740\n",
+            "====> Epoch: 4890/6000 (82%)\tLoss: 133.0773\tLL: -130.2337\tKL: 2.8436\tLL/KL: -45.7991\n",
+            "====> Epoch: 4900/6000 (82%)\tLoss: 132.7478\tLL: -129.8973\tKL: 2.8505\tLL/KL: -45.5708\n",
+            "====> Epoch: 4910/6000 (82%)\tLoss: 132.5815\tLL: -129.7355\tKL: 2.8460\tLL/KL: -45.5853\n",
+            "====> Epoch: 4920/6000 (82%)\tLoss: 132.8449\tLL: -129.9871\tKL: 2.8579\tLL/KL: -45.4841\n",
+            "====> Epoch: 4930/6000 (82%)\tLoss: 132.9024\tLL: -130.0588\tKL: 2.8436\tLL/KL: -45.7370\n",
+            "====> Epoch: 4940/6000 (82%)\tLoss: 132.3429\tLL: -129.4983\tKL: 2.8446\tLL/KL: -45.5244\n",
+            "====> Epoch: 4950/6000 (82%)\tLoss: 132.7079\tLL: -129.8738\tKL: 2.8341\tLL/KL: -45.8255\n",
+            "====> Epoch: 4960/6000 (83%)\tLoss: 132.9509\tLL: -130.1013\tKL: 2.8496\tLL/KL: -45.6565\n",
+            "====> Epoch: 4970/6000 (83%)\tLoss: 132.9920\tLL: -130.1495\tKL: 2.8425\tLL/KL: -45.7865\n",
+            "====> Epoch: 4980/6000 (83%)\tLoss: 132.7372\tLL: -129.8960\tKL: 2.8412\tLL/KL: -45.7184\n",
+            "====> Epoch: 4990/6000 (83%)\tLoss: 132.8835\tLL: -130.0501\tKL: 2.8334\tLL/KL: -45.8996\n",
+            "====> Epoch: 5000/6000 (83%)\tLoss: 132.5691\tLL: -129.7131\tKL: 2.8560\tLL/KL: -45.4177\n",
+            "====> Epoch: 5010/6000 (84%)\tLoss: 132.6718\tLL: -129.7914\tKL: 2.8805\tLL/KL: -45.0590\n",
+            "====> Epoch: 5020/6000 (84%)\tLoss: 132.6803\tLL: -129.7952\tKL: 2.8851\tLL/KL: -44.9884\n",
+            "====> Epoch: 5030/6000 (84%)\tLoss: 132.8172\tLL: -129.9127\tKL: 2.9045\tLL/KL: -44.7277\n",
+            "====> Epoch: 5040/6000 (84%)\tLoss: 132.9444\tLL: -130.0385\tKL: 2.9059\tLL/KL: -44.7495\n",
+            "====> Epoch: 5050/6000 (84%)\tLoss: 132.8668\tLL: -129.9652\tKL: 2.9016\tLL/KL: -44.7913\n",
+            "====> Epoch: 5060/6000 (84%)\tLoss: 133.0774\tLL: -130.1822\tKL: 2.8952\tLL/KL: -44.9648\n",
+            "====> Epoch: 5070/6000 (84%)\tLoss: 132.2549\tLL: -129.3336\tKL: 2.9213\tLL/KL: -44.2723\n",
+            "====> Epoch: 5080/6000 (85%)\tLoss: 132.6404\tLL: -129.7415\tKL: 2.8990\tLL/KL: -44.7545\n",
+            "====> Epoch: 5090/6000 (85%)\tLoss: 132.6378\tLL: -129.7471\tKL: 2.8907\tLL/KL: -44.8837\n",
+            "====> Epoch: 5100/6000 (85%)\tLoss: 132.8712\tLL: -129.9832\tKL: 2.8880\tLL/KL: -45.0081\n",
+            "====> Epoch: 5110/6000 (85%)\tLoss: 133.0341\tLL: -130.1619\tKL: 2.8722\tLL/KL: -45.3174\n",
+            "====> Epoch: 5120/6000 (85%)\tLoss: 133.0057\tLL: -130.1246\tKL: 2.8811\tLL/KL: -45.1646\n",
+            "====> Epoch: 5130/6000 (86%)\tLoss: 132.7161\tLL: -129.8238\tKL: 2.8923\tLL/KL: -44.8868\n",
+            "====> Epoch: 5140/6000 (86%)\tLoss: 132.5223\tLL: -129.6197\tKL: 2.9025\tLL/KL: -44.6573\n",
+            "====> Epoch: 5150/6000 (86%)\tLoss: 132.7162\tLL: -129.8188\tKL: 2.8975\tLL/KL: -44.8044\n",
+            "====> Epoch: 5160/6000 (86%)\tLoss: 132.5467\tLL: -129.6740\tKL: 2.8728\tLL/KL: -45.1390\n",
+            "====> Epoch: 5170/6000 (86%)\tLoss: 132.9732\tLL: -130.1069\tKL: 2.8662\tLL/KL: -45.3931\n",
+            "====> Epoch: 5180/6000 (86%)\tLoss: 132.6291\tLL: -129.7444\tKL: 2.8847\tLL/KL: -44.9770\n",
+            "====> Epoch: 5190/6000 (86%)\tLoss: 132.5494\tLL: -129.6681\tKL: 2.8814\tLL/KL: -45.0025\n",
+            "====> Epoch: 5200/6000 (87%)\tLoss: 132.5635\tLL: -129.6766\tKL: 2.8869\tLL/KL: -44.9183\n",
+            "====> Epoch: 5210/6000 (87%)\tLoss: 132.4743\tLL: -129.5937\tKL: 2.8807\tLL/KL: -44.9875\n",
+            "====> Epoch: 5220/6000 (87%)\tLoss: 132.6496\tLL: -129.7489\tKL: 2.9008\tLL/KL: -44.7293\n",
+            "====> Epoch: 5230/6000 (87%)\tLoss: 132.4879\tLL: -129.5843\tKL: 2.9037\tLL/KL: -44.6281\n",
+            "====> Epoch: 5240/6000 (87%)\tLoss: 132.7154\tLL: -129.8126\tKL: 2.9028\tLL/KL: -44.7198\n",
+            "====> Epoch: 5250/6000 (88%)\tLoss: 132.9190\tLL: -130.0127\tKL: 2.9064\tLL/KL: -44.7338\n",
+            "====> Epoch: 5260/6000 (88%)\tLoss: 132.7044\tLL: -129.8106\tKL: 2.8937\tLL/KL: -44.8592\n",
+            "====> Epoch: 5270/6000 (88%)\tLoss: 132.8033\tLL: -129.9080\tKL: 2.8954\tLL/KL: -44.8677\n",
+            "====> Epoch: 5280/6000 (88%)\tLoss: 132.3667\tLL: -129.4792\tKL: 2.8875\tLL/KL: -44.8411\n",
+            "====> Epoch: 5290/6000 (88%)\tLoss: 132.7787\tLL: -129.9026\tKL: 2.8761\tLL/KL: -45.1656\n",
+            "====> Epoch: 5300/6000 (88%)\tLoss: 132.7093\tLL: -129.8394\tKL: 2.8699\tLL/KL: -45.2426\n",
+            "====> Epoch: 5310/6000 (88%)\tLoss: 133.0990\tLL: -130.2224\tKL: 2.8766\tLL/KL: -45.2695\n",
+            "====> Epoch: 5320/6000 (89%)\tLoss: 132.7236\tLL: -129.8341\tKL: 2.8895\tLL/KL: -44.9329\n",
+            "====> Epoch: 5330/6000 (89%)\tLoss: 132.7927\tLL: -129.8953\tKL: 2.8974\tLL/KL: -44.8320\n",
+            "====> Epoch: 5340/6000 (89%)\tLoss: 133.0317\tLL: -130.1523\tKL: 2.8794\tLL/KL: -45.2010\n",
+            "====> Epoch: 5350/6000 (89%)\tLoss: 132.5191\tLL: -129.6163\tKL: 2.9028\tLL/KL: -44.6522\n",
+            "====> Epoch: 5360/6000 (89%)\tLoss: 132.7944\tLL: -129.8606\tKL: 2.9338\tLL/KL: -44.2642\n",
+            "====> Epoch: 5370/6000 (90%)\tLoss: 132.8943\tLL: -129.9661\tKL: 2.9282\tLL/KL: -44.3845\n",
+            "====> Epoch: 5380/6000 (90%)\tLoss: 132.7417\tLL: -129.8088\tKL: 2.9329\tLL/KL: -44.2601\n",
+            "====> Epoch: 5390/6000 (90%)\tLoss: 132.7566\tLL: -129.8360\tKL: 2.9206\tLL/KL: -44.4556\n",
+            "====> Epoch: 5400/6000 (90%)\tLoss: 132.5002\tLL: -129.5855\tKL: 2.9147\tLL/KL: -44.4592\n",
+            "====> Epoch: 5410/6000 (90%)\tLoss: 132.7074\tLL: -129.8005\tKL: 2.9068\tLL/KL: -44.6535\n",
+            "====> Epoch: 5420/6000 (90%)\tLoss: 132.9982\tLL: -130.0970\tKL: 2.9012\tLL/KL: -44.8427\n",
+            "====> Epoch: 5430/6000 (90%)\tLoss: 132.6676\tLL: -129.7584\tKL: 2.9093\tLL/KL: -44.6018\n",
+            "====> Epoch: 5440/6000 (91%)\tLoss: 132.8497\tLL: -129.9624\tKL: 2.8873\tLL/KL: -45.0120\n",
+            "====> Epoch: 5450/6000 (91%)\tLoss: 133.0029\tLL: -130.1063\tKL: 2.8966\tLL/KL: -44.9171\n",
+            "====> Epoch: 5460/6000 (91%)\tLoss: 132.8508\tLL: -129.9467\tKL: 2.9040\tLL/KL: -44.7471\n",
+            "====> Epoch: 5470/6000 (91%)\tLoss: 132.4013\tLL: -129.4973\tKL: 2.9040\tLL/KL: -44.5926\n",
+            "====> Epoch: 5480/6000 (91%)\tLoss: 132.8036\tLL: -129.8888\tKL: 2.9148\tLL/KL: -44.5620\n",
+            "====> Epoch: 5490/6000 (92%)\tLoss: 132.8675\tLL: -129.9473\tKL: 2.9202\tLL/KL: -44.4997\n",
+            "====> Epoch: 5500/6000 (92%)\tLoss: 132.6931\tLL: -129.7630\tKL: 2.9301\tLL/KL: -44.2869\n",
+            "====> Epoch: 5510/6000 (92%)\tLoss: 133.2456\tLL: -130.3277\tKL: 2.9178\tLL/KL: -44.6662\n",
+            "====> Epoch: 5520/6000 (92%)\tLoss: 132.8721\tLL: -129.9531\tKL: 2.9191\tLL/KL: -44.5187\n",
+            "====> Epoch: 5530/6000 (92%)\tLoss: 132.6889\tLL: -129.7849\tKL: 2.9040\tLL/KL: -44.6913\n",
+            "====> Epoch: 5540/6000 (92%)\tLoss: 132.4883\tLL: -129.5870\tKL: 2.9012\tLL/KL: -44.6661\n",
+            "====> Epoch: 5550/6000 (92%)\tLoss: 132.3532\tLL: -129.4500\tKL: 2.9032\tLL/KL: -44.5895\n",
+            "====> Epoch: 5560/6000 (93%)\tLoss: 132.8233\tLL: -129.9168\tKL: 2.9065\tLL/KL: -44.6981\n",
+            "====> Epoch: 5570/6000 (93%)\tLoss: 132.5022\tLL: -129.6145\tKL: 2.8877\tLL/KL: -44.8852\n",
+            "====> Epoch: 5580/6000 (93%)\tLoss: 132.9653\tLL: -130.0665\tKL: 2.8988\tLL/KL: -44.8691\n",
+            "====> Epoch: 5590/6000 (93%)\tLoss: 132.5952\tLL: -129.6929\tKL: 2.9022\tLL/KL: -44.6878\n",
+            "====> Epoch: 5600/6000 (93%)\tLoss: 132.9174\tLL: -130.0269\tKL: 2.8905\tLL/KL: -44.9838\n",
+            "====> Epoch: 5610/6000 (94%)\tLoss: 133.0182\tLL: -130.1452\tKL: 2.8730\tLL/KL: -45.2998\n",
+            "====> Epoch: 5620/6000 (94%)\tLoss: 132.6458\tLL: -129.7465\tKL: 2.8993\tLL/KL: -44.7510\n",
+            "====> Epoch: 5630/6000 (94%)\tLoss: 133.0311\tLL: -130.1233\tKL: 2.9079\tLL/KL: -44.7488\n",
+            "====> Epoch: 5640/6000 (94%)\tLoss: 132.5444\tLL: -129.6500\tKL: 2.8944\tLL/KL: -44.7932\n",
+            "====> Epoch: 5650/6000 (94%)\tLoss: 132.5383\tLL: -129.6442\tKL: 2.8941\tLL/KL: -44.7960\n",
+            "====> Epoch: 5660/6000 (94%)\tLoss: 132.7017\tLL: -129.8093\tKL: 2.8924\tLL/KL: -44.8789\n",
+            "====> Epoch: 5670/6000 (94%)\tLoss: 132.6780\tLL: -129.7931\tKL: 2.8848\tLL/KL: -44.9914\n",
+            "====> Epoch: 5680/6000 (95%)\tLoss: 132.7517\tLL: -129.8755\tKL: 2.8762\tLL/KL: -45.1554\n",
+            "====> Epoch: 5690/6000 (95%)\tLoss: 132.6578\tLL: -129.7703\tKL: 2.8875\tLL/KL: -44.9421\n",
+            "====> Epoch: 5700/6000 (95%)\tLoss: 132.9001\tLL: -130.0247\tKL: 2.8754\tLL/KL: -45.2201\n",
+            "====> Epoch: 5710/6000 (95%)\tLoss: 132.8434\tLL: -129.9542\tKL: 2.8892\tLL/KL: -44.9794\n",
+            "====> Epoch: 5720/6000 (95%)\tLoss: 132.9444\tLL: -130.0432\tKL: 2.9012\tLL/KL: -44.8244\n",
+            "====> Epoch: 5730/6000 (96%)\tLoss: 132.7201\tLL: -129.8176\tKL: 2.9025\tLL/KL: -44.7257\n",
+            "====> Epoch: 5740/6000 (96%)\tLoss: 132.9171\tLL: -130.0218\tKL: 2.8953\tLL/KL: -44.9076\n",
+            "====> Epoch: 5750/6000 (96%)\tLoss: 132.7321\tLL: -129.8423\tKL: 2.8898\tLL/KL: -44.9311\n",
+            "====> Epoch: 5760/6000 (96%)\tLoss: 133.0464\tLL: -130.1611\tKL: 2.8853\tLL/KL: -45.1124\n",
+            "====> Epoch: 5770/6000 (96%)\tLoss: 132.7669\tLL: -129.8725\tKL: 2.8944\tLL/KL: -44.8697\n",
+            "====> Epoch: 5780/6000 (96%)\tLoss: 132.8380\tLL: -129.9413\tKL: 2.8967\tLL/KL: -44.8589\n",
+            "====> Epoch: 5790/6000 (96%)\tLoss: 132.6664\tLL: -129.7552\tKL: 2.9112\tLL/KL: -44.5712\n",
+            "====> Epoch: 5800/6000 (97%)\tLoss: 132.9152\tLL: -129.9990\tKL: 2.9162\tLL/KL: -44.5784\n",
+            "====> Epoch: 5810/6000 (97%)\tLoss: 132.8696\tLL: -129.9778\tKL: 2.8918\tLL/KL: -44.9467\n",
+            "====> Epoch: 5820/6000 (97%)\tLoss: 132.5691\tLL: -129.6764\tKL: 2.8928\tLL/KL: -44.8280\n",
+            "====> Epoch: 5830/6000 (97%)\tLoss: 133.0714\tLL: -130.2142\tKL: 2.8572\tLL/KL: -45.5742\n",
+            "====> Epoch: 5840/6000 (97%)\tLoss: 132.7568\tLL: -129.8886\tKL: 2.8682\tLL/KL: -45.2858\n",
+            "====> Epoch: 5850/6000 (98%)\tLoss: 132.9365\tLL: -130.0742\tKL: 2.8623\tLL/KL: -45.4443\n",
+            "====> Epoch: 5860/6000 (98%)\tLoss: 132.7600\tLL: -129.8571\tKL: 2.9029\tLL/KL: -44.7343\n",
+            "====> Epoch: 5870/6000 (98%)\tLoss: 132.8735\tLL: -129.9600\tKL: 2.9135\tLL/KL: -44.6063\n",
+            "====> Epoch: 5880/6000 (98%)\tLoss: 132.5275\tLL: -129.6046\tKL: 2.9229\tLL/KL: -44.3414\n",
+            "====> Epoch: 5890/6000 (98%)\tLoss: 132.6602\tLL: -129.7378\tKL: 2.9224\tLL/KL: -44.3937\n",
+            "====> Epoch: 5900/6000 (98%)\tLoss: 132.7798\tLL: -129.8752\tKL: 2.9046\tLL/KL: -44.7143\n",
+            "====> Epoch: 5910/6000 (98%)\tLoss: 133.0587\tLL: -130.1693\tKL: 2.8894\tLL/KL: -45.0503\n",
+            "====> Epoch: 5920/6000 (99%)\tLoss: 132.8335\tLL: -129.9535\tKL: 2.8800\tLL/KL: -45.1230\n",
+            "====> Epoch: 5930/6000 (99%)\tLoss: 132.7608\tLL: -129.9020\tKL: 2.8588\tLL/KL: -45.4397\n",
+            "====> Epoch: 5940/6000 (99%)\tLoss: 132.8587\tLL: -130.0178\tKL: 2.8409\tLL/KL: -45.7663\n",
+            "====> Epoch: 5950/6000 (99%)\tLoss: 132.7397\tLL: -129.8638\tKL: 2.8759\tLL/KL: -45.1563\n",
+            "====> Epoch: 5960/6000 (99%)\tLoss: 132.8424\tLL: -129.9492\tKL: 2.8933\tLL/KL: -44.9143\n",
+            "====> Epoch: 5970/6000 (100%)\tLoss: 132.9998\tLL: -130.1031\tKL: 2.8967\tLL/KL: -44.9147\n",
+            "====> Epoch: 5980/6000 (100%)\tLoss: 132.7002\tLL: -129.7899\tKL: 2.9103\tLL/KL: -44.5965\n",
+            "====> Epoch: 5990/6000 (100%)\tLoss: 132.9829\tLL: -130.0721\tKL: 2.9108\tLL/KL: -44.6864\n",
+            "End fitting: 2020-12-14 17:07:22.845118\n",
+            "\tElapsed: 0:02:58.220900\n",
+            "\tDeleting model_adni.pt.running\n",
+            "\tDeleted: 2020-12-14 17:07:22.845617\n",
+            "\t\tElapsed: 0:02:58.224273\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UaiNyAx_rJOt"
+      },
+      "source": [
+        "The model identified only a significant dimension, that we are going to store and analyze:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "d_YhoXFPI4bq",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 329
+        },
+        "outputId": "1dfa1bc7-5cb8-4d19-e5c8-7803078ed0b7"
+      },
+      "source": [
+        "print('Rendundancy: ', model_adni.dropout.detach().numpy())\n",
+        "significant_dim = np.where(model_adni.dropout.detach().numpy()<0.5)[1]\n",
+        "print('Significant dimensions: ', significant_dim)\n",
+        "plot_dropout(model_adni, sort=False)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Rendundancy:  [[0.9895346  0.995257   0.41938987 0.99138963 0.989567  ]]\n",
+            "Significant dimensions:  [2]\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pTeBTJbUI4bq",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b359bccb-516c-4f38-e0b9-c73e190959f6"
+      },
+      "source": [
+        "# Here we store in a list the deconding weights estimated for each modality. \n",
+        "# We are interested in the decoding weights corresponding to the non-redundant dimension\n",
+        "\n",
+        "decoding_weights = []\n",
+        "\n",
+        "for i in range(model_adni.n_channels):\n",
+        "    decoding_weights.append(model_adni.vae[i].W_out.weight.detach().numpy()[:, significant_dim[0]])\n",
+        "\n",
+        "decoding_weights[0]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([-0.24356948,  0.18002225, -0.3040631 , -0.26428506, -0.29517686],\n",
+              "      dtype=float32)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 112
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dF0OLWlVro_E"
+      },
+      "source": [
+        "The weights give us a nice way to interpret how the different modalities interact together."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xsDNGhapI4br",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 720
+        },
+        "outputId": "aaa3d429-690c-44a1-d030-a0c38ec1a474"
+      },
+      "source": [
+        "plt.figure(figsize=(12, 12))\n",
+        "plt.subplot(5,1,1)\n",
+        "plt.bar(np.arange(len(decoding_weights[0])), decoding_weights[0], tick_label = volume_cols)\n",
+        "plt.subplot(5,1,2)\n",
+        "plt.bar(np.arange(len(decoding_weights[1])), decoding_weights[1], tick_label = demog_cols)\n",
+        "plt.subplot(5,1,3)\n",
+        "plt.bar(np.arange(len(decoding_weights[2])), decoding_weights[2], tick_label = cognition_cols)\n",
+        "plt.subplot(5,1,4)\n",
+        "plt.bar(np.arange(len(decoding_weights[3])), decoding_weights[3], tick_label = apoe_cols)\n",
+        "plt.subplot(5,1,5)\n",
+        "plt.bar(np.arange(len(decoding_weights[4])), decoding_weights[4], tick_label = fluid_cols)\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<BarContainer object of 3 artists>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 114
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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j685xE/AnwH8C/9zK3DtQ/gE2fD8FOKeqlk9XqKrWtBPlucCNwI8nT8LWIvKPwLKquinJ24BRLYqT9ZyujsNfRO4Xk6/1aeD4JM8CfpYuuE63/zZke0PXVWdjBPi7qnr/w2Z0dfx/gL9Jcm5VTdXC6r7uZ9T2mo3t+EHglVV1WZLD6PpYTpo8jh5k3WvNg6z/PL6ftY0vD10Xquqj7Zby/sBZSX6vqr4wzXqGhzdlK4G/p9tHj5+m3IZsr/V9BmzIvt/Q48/zfXZs6HV9lOHr/eB+zXpe86HXTbIz3R3l51TV99N179qQu4mb9HFgS/Tc+wrdbZY7W6vSncDj6P7SnO4hgmuAiYF+hK+ha40adAFd14+nwEN9WX9xeEXp+kfuCVw3xetMnjS3t750M30YYa/Wn++n6G4f/ccM1/eIUVV307U0nkT31/wG7b8hPwQes4EveS6tC1CSzZI8dmj+2cD/GOhDuX2Sn2/dCX5UVR8GjqPrCjKVVyX5qSRPpuuGdM0G1mtTdRBAkhfQdXOYbOE/IMnPJHk8XbC6mO72+0Ftvy2luwt1EXAO8NtZ2+d567aOxwC3Jvlpupbovn6Ktef+b7L2vL0BeHYbfujakOQXgOur6t10fxyOug180MDv8zeiXo9EJwFvbw8KjnIea+9aTbc/N+YaMmxDr9mjjl/N3PB1/Susu/+//LAlZm5LukB+V5JtWdvVcH026ePAED33rqD7Vo4Lhqbd1W7fTqnddvlt4OPtFu2DdP2mBsusAQ4DTm19n89n3VvqH2m3+C4BPlhVl0zxOj8A/g9dK/nZdB/gGyzJsgx8bV9b/j10/TK/BXyqz/o2AafS9XM9dQP231T+BfiNyQdQ1lP2DXRdda6gOwbWuVVXVZ+ju2V4fitzBt2FfDfgonbsHA38DTz0lYevGFjFt+mC3WeA17VjVqP9OMnX6M7jwT7Ll9P9cXUB3bML36E7by4HLgO+APxpVX23qj5L15K5qu2fyWcT/oquf+15wDc2om7/RRemrqTrBjR55+Hvgd9v9R78urpXA1e2OjwD+BBAkrPaH2GTtmrH9hvo+mRv8lrXvXevp9gbgD9s5+X206xrY64hw6a8Zif5QNb9OrNRx68ebrhP9Ki+ypOGr+t/RPfH8uV0DWhvmO0KVtVldN04vkH3OXDeVOU8Dtblv/2WpDmW5Et0DxGtGpr+NuDuqvr7+ajXQD3urqqpvtlHj2BJ9qY7Lv12HWkD2BItSZIk9WRLtCRJktSTLdGSJElST4ZoSZIkqSdDtCRJktTTovxnK9tss01NTEzMdzUkSZL0CHbJJZfcXlVLp5q3KEP0xMQEq1atWn9BSZIkaSMluXHUPLtzSJIkST0ZoiVJkqSeDNGSJElST4ZoSZIkqadF+WChpEeuiRVnzncVpF5uOHb/+a6CpHlgS7QkSZLUkyFakiRJ6skQLUmSJPVkiJYkSZJ6MkRLkiRJPRmiJUmSpJ4M0ZIkSVJPhmhJkiSpp1kJ0Un2S3JNktVJVkwx/0VJvprk/iQHDs07NMm17efQ2aiPJEmSNE4zDtFJNgNOAF4O7AosT7LrULFvA4cBHx1admvgaOC5wF7A0Um2mmmdJEmSpHGajZbovYDVVXV9Vd0HnAYcMFigqm6oqsuBB4eWfRlwTlXdWVXfB84B9puFOkmSJEljMxshenvgpoHxm9u0WV02yRFJViVZtWbNmo2qqCRJkjQbFs2DhVV1YlUtq6plS5cune/qSJIkaRM2GyH6FmDHgfEd2rRxLytJkiTNi9kI0RcDuyTZOcnmwMHAyg1c9mxg3yRbtQcK923TJEmSpAVrxiG6qu4HXk8Xfr8OfKyqrkpyTJJXACR5TpKbgVcB709yVVv2TuCv6YL4xcAxbZokSZK0YC2ZjZVU1VnAWUPTjhoYvpiuq8ZUy54EnDQb9ZAkSZLmwqJ5sFCSJElaKAzRkiRJUk+GaEmSJKknQ7QkSZLUkyFakiRJ6skQLUmSJPVkiJYkSZJ6MkRLkiRJPRmiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1JMhWpIkSerJEC1JkiT1ZIiWJEmSepqVEJ1kvyTXJFmdZMUU8x+V5PQ2/8IkE236RJJ7klzaft43G/WRJEmSxmnJTFeQZDPgBOClwM3AxUlWVtXVA8UOB75fVU9JcjDwDuCgNu+6qtpjpvWQJEmS5spstETvBayuquur6j7gNOCAoTIHACe34TOAlyTJLLy2JEmSNOdmI0RvD9w0MH5zmzZlmaq6H7gLeHybt3OSryX5tyQvHPUiSY5IsirJqjVr1sxCtSVJkqSNM98PFt4K7FRVewJvBj6aZMupClbViVW1rKqWLV26dE4rKUmSJA2ajRB9C7DjwPgObdqUZZIsAR4L3FFV91bVHQBVdQlwHfCLs1AnSZIkaWxmI0RfDOySZOckmwMHAyuHyqwEDm3DBwJfqKpKsrQ9mEiSXwB2Aa6fhTpJkiRJYzPjb+eoqvuTvB44G9gMOKmqrkpyDLCqqlYC/wSckmQ1cCdd0AZ4EXBMkp8ADwKvq6o7Z1onSZIkaZxmHKIBquos4KyhaUcNDP8YeNUUy30C+MRs1EGSJEmaK/P9YKEkSZK06BiiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1JMhWpIkSerJEC1JkiT1ZIiWJEmSejJES5IkST0ZoiVJkqSeDNGSJElST4ZoSZIkqSdDtCRJktSTIVqSJEnqaVZCdJL9klyTZHWSFVPMf1SS09v8C5NMDMz78zb9miQvm436SJIkSeM04xCdZDPgBODlwK7A8iS7DhU7HPh+VT0FOB54R1t2V+Bg4OnAfsA/tvVJkiRJC9ZstETvBayuquur6j7gNOCAoTIHACe34TOAlyRJm35aVd1bVd8CVrf1SZIkSQvWkllYx/bATQPjNwPPHVWmqu5Pchfw+Db9gqFlt5/qRZIcARwBsNNOO81CtfubWHHmvLyuNBM3HLv/fFehl8VWX2mx8bNMi9FC/GxYNA8WVtWJVbWsqpYtXbp0vqsjSZKkTdhshOhbgB0Hxndo06Ysk2QJ8Fjgjg1cVpIkSVpQZiNEXwzskmTnJJvTPSi4cqjMSuDQNnwg8IWqqjb94PbtHTsDuwAXzUKdJEmSpLGZcZ/o1sf59cDZwGbASVV1VZJjgFVVtRL4J+CUJKuBO+mCNq3cx4CrgfuBP6yqB2ZaJ0mSJGmcZuPBQqrqLOCsoWlHDQz/GHjViGX/Fvjb2aiHJEmSNBcWzYOFkiRJ0kJhiJYkSZJ6MkRLkiRJPRmiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1JMhWpIkSerJEC1JkiT1ZIiWJEmSejJES5IkST0ZoiVJkqSeDNGSJElST4ZoSZIkqSdDtCRJktTTjEJ0kq2TnJPk2vZ7qxHlDm1lrk1y6MD0LyW5Jsml7efnZ1IfSZIkaS7MtCV6BXBuVe0CnNvG15Fka+Bo4LnAXsDRQ2H7kKrao/3cNsP6SJIkSWM30xB9AHByGz4ZeOUUZV4GnFNVd1bV94FzgP1m+LqSJEnSvJlpiN62qm5tw98Ftp2izPbATQPjN7dpk/65deX4qyQZ9UJJjkiyKsmqNWvWzLDakiRJ0sZbsr4CST4PPGGKWUcOjlRVJamer39IVd2S5DHAJ4DXAB+aqmBVnQicCLBs2bK+ryNJkiTNmvWG6KraZ9S8JN9Lsl1V3ZpkO2CqPs23AHsPjO8AfKmt+5b2+4dJPkrXZ3rKEC1JkiQtFDPtzrESmPy2jUOBT09R5mxg3yRbtQcK9wXOTrIkyTYASX4a+HXgyhnWR5IkSRq7mYboY4GXJrkW2KeNk2RZkg8AVNWdwF8DF7efY9q0R9GF6cuBS+larP/PDOsjSZIkjd16u3NMp6ruAF4yxfRVwO8MjJ8EnDRU5r+AZ8/k9SVJkqT54H8slCRJknoyREuSJEk9GaIlSZKkngzRkiRJUk+GaEmSJKknQ7QkSZLUkyFakiRJ6skQLUmSJPVkiJYkSZJ6MkRLkiRJPRmiJUmSpJ6WzHcFJEnS3Lnh2P3nuwrSI4It0ZIkSVJPtkT34F/vkiRJghm2RCfZOsk5Sa5tv7caUe6zSX6Q5F+Hpu+c5MIkq5OcnmTzmdRHkiRJmgsz7c6xAji3qnYBzm3jUzkOeM0U098BHF9VTwG+Dxw+w/pIkiRJYzfTEH0AcHIbPhl45VSFqupc4IeD05IEeDFwxvqWlyRJkhaSmYbobavq1jb8XWDbHss+HvhBVd3fxm8Gth9VOMkRSVYlWbVmzZqNq60kSZI0C9b7YGGSzwNPmGLWkYMjVVVJarYqNqyqTgRObHVak+TGcb2W5sU2wO3zXQnpEc7zTBovz7FHnieNmrHeEF1V+4yal+R7SbarqluTbAfc1qNSdwCPS7KktUbvANyyIQtW1dIer6NFIMmqqlo23/WQHsk8z6Tx8hzbtMy0O8dK4NA2fCjw6Q1dsKoK+CJw4MYsL0mSJM2XmYboY4GXJrkW2KeNk2RZkg9MFkryZeDjwEuS3JzkZW3WnwFvTrKaro/0P82wPpIkSdLYpWsQluZXkiNav3dJY+J5Jo2X59imxRAtSZIk9TTT7hySJEnSJscQLUmSJPVkiNacSHJkkquSXJ7k0iTPTfKlJNe08UuTnNHKvjvJUUPLnjB/tZcWviSvTFJJnjYwba92nl2b5KtJzkyyW5v3tiS3DJx/lyZ53Py9A2m8kjzQjvMrk3w8yfYDx/53h86HzQfKT/6saOuZ/Oy6PMk3krxn8txJMpHkyqHXfVuStwyMv6Utd2mSi5O8dmDeNkl+kuR1bfyEVu7qJPcM1OVANO/W+z3R0kwleR7w68CzqureJNsAm7fZh1TVqqFF/hK4NMmH2/jvAHvOTW2lRWs58B/t99FJtgU+BvxmVX0FIMkLgCcDV7Rljq+qv5+Pykrz4J6q2gMgyUeAgwbG3wbcPXg+JHmo/BQOqapVSTYH/o7uK3p/dX0VaOH4pcBeVfWfSbYEfmOgyKuAC+jO4/dV1R+25SaAf52mPpoHhmjNhe2A26vqXoCquh0gyZSF24XlSOA9bdJRVfWDuaiotBgl2QJ4AfBrwL8ARwOvB06eDNAAVfUf81NDacH5MrD7TFdSVfcl+VNgdZJnAnetZ5G/APauqv9sy/8ncPLA/OXAnwAfTbJDVd080zpqfOzOobnwOWDHJN9M8o9JBv9a/8jA7anjJidW1anAVsCWVXXKXFdYWmQOAD5bVd8E7kjybODpwFfXs9ybBs6/L469ltICkGQJ8HLW3pEZ5dFD3TkOmqpQVT0AXAY8bar5A6+7JfCYqrp+xPwdge2q6iK6u0hTvp4WDluiNXZVdXf7UH8hXUvZ6ZN9y5i6OwdJdqBrwX4wyRZVdffc1VhadJYD72rDp7XxdSS5ENgS+FxVvaFNtjuHNiWPTnJpG/4y6/8Hb9N15xg2eWt11PcGb8j3CR9EF56hO49PAv7XBr6+5oEhWnOi/aX+JeBLSa5g7b+LH+VddLekf6n9futYKygtUkm2Bl4M7JakgM3oPrBPBp5F11eTqnpuexjp1+errtI86xOKN1iSzYDdgK8Dd9DdRR20NfCt1lXx7iS/MKI1ejnwhCSHtPEnJtmlqq6d7TprdtidQ2OX5KlJdhmYtAdw4zTlXw78PPAh4K+B/55k1/HWUlq0DgROqaonVdVEVe0IfAs4BzgsyfMHyv7svNRQeoRK8tN0DxbeVFWXt7umtyZ5cZu/NbAf3UO/tLIntK4dJNkiyWuT/CKwRVVt387jiVb2YXeVtHDYEq25sAXwv9tXAN0PrAaOAM6g6xN9Tyt3O10r2T8AB1b37zT/K8lb6R4yfPGc11xa+JYD7xia9ok2/SDgHUm2B26jO8eOGSj3piS/NTD+yqq6YYx1lRaTwe4f0D13MNkV8SNJ7gUeBXye7rmESa+lC8r/Xxt/e1Vd14bfS/eZeHGSnwA/oeuysRz41NDrfwI4nXXPWS0g/ttvSZIkqSe7c0iSJEk9GaIlSZKkngzRkiRJUk+GaEmSJKknQ7QkSZLU06L8irttttmmJiYm5rsakiRJegS75JJLbq+qpTFXrwgAACAASURBVFPNW5QhemJiglWrHvafoiVJkqRZk2TkP4ezO4ckSZLUkyFakiRJ6skQLUmSJPVkiJYkSZJ6WpQPFuqRbWLFmfNdhQXrhmP3n+8qSJIkbImWJEmSejNES5IkST0ZoiVJkqSeDNGSJElST4ZoSZIkqSe/naMHvzVien5zhCRJ2lTYEi1JkiT1ZIiWJEmSerI7hyRJ0jyzy+j0FmKXUVuiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1JMhWpIkSerJEC1JkiT1ZIiWJEmSehpriE6yX5JrkqxOsmKK+W9OcnWSy5Ocm+RJ46yPJEmSNBvGFqKTbAacALwc2BVYnmTXoWJfA5ZV1e7AGcA7x1UfSZIkabaMsyV6L2B1VV1fVfcBpwEHDBaoqi9W1Y/a6AXADmOsjyRJkjQrxhmitwduGhi/uU0b5XDgM6NmJjkiyaokq9asWTNLVZQkSZL6WxAPFib5LWAZcNyoMlV1YlUtq6plS5cunbvKSZIkSUOWjHHdtwA7Dozv0KatI8k+wJHAr1bVvWOsjyRJkjQrxtkSfTGwS5Kdk2wOHAysHCyQZE/g/cArquq2MdZFkiRJmjVjC9FVdT/weuBs4OvAx6rqqiTHJHlFK3YcsAXw8SSXJlk5YnWSJEnSgjHO7hxU1VnAWUPTjhoY3mecry9JkiSNw4J4sFCSJElaTAzRkiRJUk+GaEmSJKknQ7QkSZLUkyFakiRJ6skQLUmSJPVkiJYkSZJ6MkRLkiRJPRmiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1JMhWpIkSerJEC1JkiT1ZIiWJEmSejJES5IkST0ZoiVJkqSeDNGSJElST0vmuwKSJGlhm1hx5nxXYcG64dj957sKmie2REuSJEk9GaIlSZKkngzRkiRJUk+GaEmSJKmnsYfoJPsluSbJ6iQrppj/qCSnt/kXJpkYd50kSZKkmRhriE6yGXAC8HJgV2B5kl2Hih0OfL+qngIcD7xjnHWSJEmSZmrcLdF7Aaur6vqqug84DThgqMwBwMlt+AzgJUky5npJkiRJGy1VNb6VJwcC+1XV77Tx1wDPrarXD5S5spW5uY1f18rcPrSuI4AjAHbaaadn33jjjWOrt/RI53e+Tm+2vvfV7Tya23hu+B3G0swkuaSqlk01b9E8WFhVJ1bVsqpatnTp0vmujiRJkjZh4w7RtwA7Dozv0KZNWSbJEuCxwB1jrpckSZK00cYdoi8Gdkmyc5LNgYOBlUNlVgKHtuEDgS/UOPuYSJIkSTO0ZJwrr6r7k7weOBvYDDipqq5KcgywqqpWAv8EnJJkNXAnXdCWJEmSFqyxhmiAqjoLOGto2lEDwz8GXjXuekiSJEmzZdE8WChJkiQtFIZoSZIkqSdDtCRJktSTIVqSJEnqyRAtSZIk9WSIliRJknoyREuSJEk9GaIlSZKkngzRkiRJUk+GaEmSJKknQ7QkSZLUkyFakiRJ6skQLUmSJPVkiJYkSZJ6MkRLkiRJPRmiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1JMhWpIkSerJEC1JkiT1ZIiWJEmSejJES5IkST2NLUQn2TrJOUmubb+3mqLMHknOT3JVksuTHDSu+kiSJEmzZZwt0SuAc6tqF+DcNj7sR8Brq+rpwH7APyR53BjrJEmSJM3YOEP0AcDJbfhk4JXDBarqm1V1bRv+DnAbsHSMdZIkSZJmbJwhetuqurUNfxfYdrrCSfYCNgeuGzH/iCSrkqxas2bN7NZUkiRJ6mHJTBZO8nngCVPMOnJwpKoqSU2znu2AU4BDq+rBqcpU1YnAiQDLli0buS5JkiRp3GYUoqtqn1HzknwvyXZVdWsLybeNKLclcCZwZFVdMJP6SJIkSXNhnN05VgKHtuFDgU8PF0iyOfAp4ENVdcYY6yJJkiTNmnGG6GOBlya5FtinjZNkWZIPtDKvBl4EHJbk0vazxxjrJEmSJM3YjLpzTKeq7gBeMsX0VcDvtOEPAx8eVx0kSZKkcfA/FkqSJEk9GaIlSZKknsbWnUOSpHG74dj957sKkjZRtkRLkiRJPRmiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1JMhWpIkSerJr7iTNkF+LZgkSTNjS7QkSZLUkyFakiRJ6snuHJI0JnabkaRHLluiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1FOqar7r0FuSNcCN812PBWAb4Pb5rsQjnNt4brid54bbefzcxnPD7Tx+buPOk6pq6VQzFmWIVifJqqpaNt/1eCRzG88Nt/PccDuPn9t4bridx89tvH5255AkSZJ6MkRLkiRJPRmiF7cT57sCmwC38dxwO88Nt/P4uY3nhtt5/NzG62GfaEmSJKknW6IlSZKkngzRkiRJUk+G6DmQ5AlJTktyXZJLkpyV5BeT3JPka0m+nuSiJIcNLHNYkjVJLk3yjSRvGpj31CRfavO+nuTENn3vJHe16Zcn+XySn5+iPnsn+dcRdb0hyTZj2AxjkeSVSSrJ09r4xHTbdWC5S5OcNjTtl5NcOLBd39amPy3J+UnuTfKWoWVOSnJbkiunqePbhpcbqOvI5Rayts0/PDC+pB2v/9rGD2tl9hkoM7mvDmzjv97202VJrk7ye23625Lc0vbD5M/jhl5/5LZr58ZYvpYpyQOtPlcm+Zcp6vXQcZXkZ5PckWTLoTL/N8lBbRu9p0172cB7vTvJNW34QwPLPTHJGeN4XxtiqL6vS/La9ZR/ZZJdN/Y1pphX87jt5+RcTXLM4Dkzw3VNeS0fvh7N5zHdyh2X5Kokx83G+55Kkscl+YOB8YkkvzkwvizJu2fptRbU9X5g/07+TLTpb0zy4ySPHSr/ynT54RvtmDhwxHoX1PucL4boMUsS4FPAl6rqyVX1bODPgW2B66pqz6r6JeBg4I1Jfntg8dOrag/gV4Ajk+zYpr8bOL6q9mjL/u+BZb7cpu8OXAz84Xjf4bxbDvxH+z1p2u2a5JeAzYAXJvm5geVOBo5o2/wZwMfa9DuBPwb+forX/yCw3yy9l8Xkv4BnJHl0G38pcMtQmSvotv+k5cBlAEl+mu6hlf9WVc8E9gS+NFB28vie/PnBGN7Dxrin1ecZdMfFQ+fX8HFVVT8CzgZ+Y6DMY4EXAP8yuNKqOnvyvQKrgEPa+GsHynynqqb8QJtrVfW+qvrQeoq9EugVojfgdedl28+WdEZ+7lbVUVX1+dl+3fWYt2O6OQLYvareuiGVTbKk/1vkccAfDIxPAA+F6KpaVVV/vBHrXQwm9+/kzw1t+nK6jPDfJwsmeSbd59wBVfU04L8B70jy7Lmu9GJhiB6/XwN+UlXvm5xQVZcBNw0WqqrrgTfThTWG5t0BrAa2a5O2A24emH/F8DItvD8G+P6Iem2Z5MzWOvC+6S7sC1WSLegu3oezblh7yIjtuhw4BfgccMDA9J8Hbm3LPVBVV7fh26rqYuAnU6z/3+k+eNbnmelas69N8rsbUH4xOAvYvw0vB04dmv9lYK8kP9321VOAS9u8xwBLgDsAqureqrqm5+svSfKRdHcNzkjysxv1Ljbe+cD2A+NTHVensu6x+RvA2S2M9DLYwtNa/P5vknNai+Prk7w5Xcv+BUm2buW+lOT4JKvadnpOkk+24/BvBtb9W+nu2lya5P1JNmvTfzvJN5NcRPfH/GT5h1qhkvxukovT3VH4RGutfD7wCuC4ts4nt5/Pprsb9+W0u0dT2LHV+9okR48oM6fbflCSzVrr6cWtxW7yDsoWSc5N8tUkVyQ5oE2faNfZDwFX0gXSryf5P+laYD83+cdokg9m7Z2aG5K8fWB9k3fblrb9flWSDyS5MaPvHv5pW/aiJE/ZgLc318f0SmAL4JJ0LdkTSb7Qtuu5SXZq5T7YPqcuBN7ZjqUL2nv7myR3D6zzrQP75u1t8rHAk9uxeFwbf2Ebf1MG7s62Y/ukdgxen+SPB9b9V21f/keSUzNFS2yzoK/3SZ5Mt93/knUboN4C/M+q+hZA+/0/gT8ZsaoF/T7nwqILTovQM4BLNrDsV4GHfbC0C8nPAJe3SccDX0jymXYBGLz99sIklwLfBvYBThrxWnsBf0TXUvRkBv4aXUQOAD5bVd8E7sjov5aHt+tBwGl0HwaDF5DjgWuSfCrJ7yX5mVms6+7Ai4HnAUcleeIsrnu+nAYc3LbT7sCFQ/ML+DzwMrp9tfKhGVV3tvEb24fRIVn3D7k3Ze3txy+OeP2nAv/Y7jj8J+u2NI1VC5kvYeA9MfVxdTbwrCSPb+MH8/A/NjbWM+jO2+cAfwv8qKr2pAtCg61997X/OvY+4NN0LY3PAA5L8vh0rY0HAb/SWg0fAA5Jsh3wdrrw/AJGtyp/sqqe0+4ofB04vKq+Qrdt3tpav66ju/PwR+1u3FuAfxyxvr2A/5fumHpVhrrmLIBtfzhwV1U9h27b/26SnYEfA79RVc+iazz5X0nSltmF7lh9OnBjGz+hjf+gvd+p3N7W9166bQZwNPCFtuwZwE7T1PWuqtoNeA/wD9O9qfnYrlX1Cta2lJ5Od1f15HYn9SN0d10n7QA8v6reDLwLeFd7bw81KCXZl27b7gXsATw7yYuAFXR3KPdoLd4rWHvX9vgpqvY0uuvWXsDR6RoCnkO3n54JvByYrsvYQrreP3rgWvqpNu1guv36ZeCpSbZt05/Ow/PKKkaf+wvpfc4LQ/TCkqHxg5JcTtcK/Y9V9WOAqvpn4JeAjwN7AxckeVRbZvLCsCPwz8A7R7zWRVV1fVU9QHcBfMHsvpU5sZzuQkD7vXxEuYe2a/tAvr2qvg2cC+yZ1mpXVcfQXRg/R3er77OzWNdPV9U9VXU78EW6i/OiVlWX090WXU7XKj2V0+gu2A/7oK2q36H70L6ILiAM/sE32J3j10as+6aqOq8Nf5i5OYYf3f5I/S5dl6xzYPRxVVX30YWSA1tr4Z50IWQ2fLGqflhVa4C7WHs7/Qq6/TJp5cD0q6rq1qq6F7ge2JFuHzwbuLi9t5cAvwA8l64b2pr2Pk4fUY9npGtZvgI4hO6DeB3p7kQ8H/h4e433s/bO2rBzquqOqroH+CQD+3WBbPt9gde2ulwIPJ4uuAX4n+2a/Xm6Ft3JcHJjVV0wsI5vVdXkXZlLWHd/DfrkFGVeQLvuVdVnGX23Edaec6fSBZ2pLKRj+nnAR9vwKax7Tn+8fV5Nlvt4G/7oQJl928/XWNt4sstG1OPMdnfsduA2uu3yK3TX8R9X1Q8Z6r4yZCFd7we7c0x2w1kOnFZVDwKfAF61keteSO9zXhiix+8qug+oDbEnXUvOpNPbX+TPB45N8oTJGa1/5ElVdQBwP13L0rCVwItGvNbwF4Qvqi8Mb8H3xcAHktwAvBV4NQ//QwTW3a7Lgae1Za4DtmSgFaiqrquq99IFiWcOtLbM1KLe3tNYSdeHbsqWqKq6CNgN2KbdMRief0VrCXopo1vjRpmPbXpPa619Et2xNtl/dLrjavL294F0HzoP6xa0ke4dGH5wYPxBuq4yw+UenGKZJXTv4+SBD9qnVtXbetTjg8DrW6vg2+numg37KeAHQ30zf2nE+kbu1wWy7UPXoj75Pnauqs/R/QGxFHh2q+f3WLst/mtoHYP74QHW3V9TlZuuzHRqxPCghXRMT2d4G04lwN8N7JunVNU/bcRrbej+GWXBXu+T7Eb3h8U5bd8ezNoGqKt5eF55Nl1r9FQW7PucK4bo8fsC8KgkR0xOSLI7XQsQA9Mm6MLI4EOCQPfQA91f5W9oZfdL92AWLVg/noc/1AXdX/HXjajXXkl2brfQD6J7OG8xORA4paqeVFUTreX9W0yzXdt7fTWwW1tmgq6bwfJWdv+h268P0N1qnQ0HJPmZFsr3pnug45HgJODtNUW//AErgL8YnJCu/+jeA5P2oLvN3cdOSSZb136TOTyGW//PPwb+JMnmTHNc0T0wuQtdOJmtrhyz6Vy6VsWfh+4P1CRPomtl/dXW5eOnGd1a9Rjg1lbmkIHpP2zzqKr/BL6V5FXtNZLuIaapvLTV4dF0DyeeNzhzAWz7s4HfH7gG/2K6B5QfC9xWVT9J8mt0oXQczqN7z5PdF7aapuxBA7/Pn26lC2C7AnyFtf2tD6HrbjCVC1gb6Af7Z58N/I9254Mk27fj+qFjsRke3xDnAf+tXce3AH59mrIL+Xq/HHjb5H6tqicCT2zn/N8Df5613+AxAbwRGPXNKQv5fc4JQ/SYVVXRPXixT7qvuLsK+Du6W2dPTvsqNrpvgnh366oxlXcAv53kMXS3q65MchndReOtVfXdVm7yYYnLgNfQHghI9xU+HxhY38V0/eS+Thc+P8XispyH1/kTdN98Mmq7vhC4paq+M7DMvwO7tv6fr6HrE30p3R8th1TVA+m+ovBmugcU/zLJzWlf8ZTkVLoPp6e26Ye36a9L8rqB17mc7nbXBcBfD9Vh0aqqm6tq2q+GqqrPVNVwv+bQPfQ0ub3fDhw2MH+wT/Sl6R44emKSwW4j1wB/2PbzVnT9RudMVX2Nbr/+OdMcV+2W6Rl0f+z+29BqDmvHzeTPDoMzpzhvZ111D9D+JfC51hXhHGC7qroVeBvd8X0e694lG/RXdIH7POAbA9NPA97azsUn04Wiw9u16Srag2pJXpHkmIHlLqI7ly8HPtEaEYbrPJ/b/gN0LXZfTfeg5/vpWio/AixL163ltUPbYja9Hdi3vfar6D5LftjqfFbW7Ze6VdunbwDe9LA1DVkAx/Qf0X3OXU53PX7DiHJvBN7cyj2FrjsT7Y7AR4Hz2344A3hMdQ/nn5fuK9uOa+/xgXQPw653u7R1X0x35+1y4DN03aPuau9pMV3vD+bhn52fAg5uXYz+DPiXJN8Evgn8frWHvhfZ+5wT/ttvSZIWiXTPvzxQVfe3OzHvbd0xNhnpvonnnqqqJAcDy1vXxnG/7hZVdXd7/X+n+0rUr477dedLkmPpno14WesLryEb08dKkiTNj52Aj6XrnnYfsCl+tdizgfe07nc/AP7HHL3uien+gdDP0D1H8IgN0ABVtWK+67DQ2RItSZIk9WSfaEmSJKknQ7QkSZLUkyFakiRJ6mlRPli4zTbb1MTExHxXQ5IkSY9gl1xyye1VtXSqeYsyRE9MTLBq1ah/oCNJkiTNXJKR/wjM7hySJElST4ZoSZIkqSdDtCRJktSTIVqSJEnqaVE+WDhfJlacOd9VkCRJ2uTccOz+812Fh7ElWpIkSerJEC1JkiT1ZIiWJEmSejJES5IkST0ZoiVJkqSeDNGSJElST4ZoSZIkqSdDtCRJktTTrIToJPsluSbJ6iQrppj/5iRXJ7k8yblJnjQw79Ak17afQ2ejPpIkSdI4zThEJ9kMOAF4ObArsDzJrkPFvgYsq6rdgTOAd7ZltwaOBp4L7AUcnWSrmdZJkiRJGqfZaIneC1hdVddX1X3AacABgwWq6otV9aM2egGwQxt+GXBOVd1ZVd8HzgH2m4U6SZIkSWMzGyF6e+CmgfGb27RRDgc+03fZJEckWZVk1Zo1a2ZQXUmSJGlm5vTBwiS/BSwDjuu7bFWdWFXLqmrZ0qVLZ79ykiRJ0gaajRB9C7DjwPgObdo6kuwDHAm8oqru7bOsJEmStJDMRoi+GNglyc5JNgcOBlYOFkiyJ/B+ugB928Css4F9k2zVHijct02TJEmSFqwlM11BVd2f5PV04Xcz4KSquirJMcCqqlpJ131jC+DjSQC+XVWvqKo7k/w1XRAHOKaq7pxpnSRJkqRxmnGIBqiqs4CzhqYdNTC8zzTLngScNBv1kCRJkuaC/7FQkiRJ6skQLUmSJPVkiJYkSZJ6MkRLkiRJPRmiJUmSpJ4M0ZIkSVJPhmhJkiSpJ0O0JEmS1JMhWpIkSerJEC1JkiT1ZIiWJEmSejJES5IkST0ZoiVJkqSeDNGSJElST7MSopPsl+SaJKuTrJhi/ouSfDXJ/UkOHJr3QJJL28/K2aiPJEmSNE5LZrqCJJsBJwAvBW4GLk6ysqquHij2beAw4C1TrOKeqtpjpvWQJEmS5sqMQzSwF7C6qq4HSHIa/3979x9kV3nfd/z9iVTZeCi2MRvHEYKVQYkjagfqjdwSjNOAY1FPEUkgSE0aPIVRPS795aYTXLekQzotNnGcZkxqq4YakzgCQ+xqghziGpI4odhabBwQHkACbETtWvwoGWpsLPj2j3vkHC67Kz3ae7Va8X7N3NlzzvOcc767o2f1uWefcy6sA74foqvqoa7tuRGcT5IkSVpQo5jOsRx4uLe+q9u2v16aZDrJ7UnOGUE9kiRJ0liN4kr0fB1fVY8keS1wS5K7qmrncKckG4GNAMcdd9zBrlGSJEn6vlFciX4EWNFbP7bbtl+q6pHu6wPAnwCnzNJvU1VNVdXUxMTEgVcrSZIkzdMoQvQ2YFWSlUmWAeuB/XrKRpJXJnlJt3wM8JP05lJLkiRJh6J5h+iq2gNcDNwMfBW4vqq2J7ksydkASX4iyS7gPOAjSbZ3u/8YMJ3kK8CtwOVDT/WQJEmSDjkjmRNdVVuBrUPbLu0tb2MwzWN4v9uA14+iBkmSJOlg8RMLJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqNJIQnWRtknuT7EhyyQztpyf5UpI9Sc4darsgyf3d64JR1CNJkiSN07xDdJIlwJXAWcBqYEOS1UPdvg68A/jE0L5HA78GvAlYA/xaklfOtyZJkiRpnEZxJXoNsKOqHqiqZ4DNwLp+h6p6qKr+EnhuaN+3AZ+tqser6gngs8DaEdQkSZIkjc0oQvRy4OHe+q5u20j3TbIxyXSS6d27dx9QoZIkSdIoLJobC6tqU1VNVdXUxMTEQpcjSZKkF7FRhOhHgBW99WO7bePeV5IkSVoQowjR24BVSVYmWQasB7bs5743Az+T5JXdDYU/022TJEmSDlnzDtFVtQe4mEH4/SpwfVVtT3JZkrMBkvxEkl3AecBHkmzv9n0c+HUGQXwbcFm3TZIkSTpkLR3FQapqK7B1aNulveVtDKZqzLTv1cDVo6hDkiRJOhgWzY2FkiRJ0qHCEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjUYSopOsTXJvkh1JLpmh/SVJruvav5Bksts+meTpJHd2rw+Poh5JkiRpnJbO9wBJlgBXAm8FdgHbkmypqnt63S4EnqiqE5OsB94HnN+17ayqk+dbhyRJknSwjOJK9BpgR1U9UFXPAJuBdUN91gHXdMs3AGckyQjOLUmSJB10owjRy4GHe+u7um0z9qmqPcCTwKu6tpVJvpzkT5O8ebaTJNmYZDrJ9O7du0dQtiRJknRgFvrGwm8Ax1XVKcC7gU8kOWqmjlW1qaqmqmpqYmLioBYpSZIk9Y0iRD8CrOitH9ttm7FPkqXAy4HHquq7VfUYQFXdAewEfmQENUmSJEljM4oQvQ1YlWRlkmXAemDLUJ8twAXd8rnALVVVSSa6GxNJ8lpgFfDACGqSJEmSxmbeT+eoqj1JLgZuBpYAV1fV9iSXAdNVtQW4Crg2yQ7gcQZBG+B04LIk3wOeA95ZVY/PtyZJkiRpnOYdogGqaiuwdWjbpb3l7wDnzbDfjcCNo6hBkiRJOlgW+sZCSZIkadExREuSJEmNDNGSJElSI0O0JEmS1MgQLUmSJDUyREuSJEmNDNGSJElSI0O0JEmS1MgQLUmSJDUyREuSJEmNDNGSJElSI0O0JEmS1MgQLUmSJDUyREuSJEmNDNGSJElSo5GE6CRrk9ybZEeSS2Zof0mS67r2LySZ7LW9p9t+b5K3jaIeSZIkaZzmHaKTLAGuBM4CVgMbkqwe6nYh8ERVnQh8EHhft+9qYD1wErAW+J3ueJIkSdIhaxRXotcAO6rqgap6BtgMrBvqsw64plu+ATgjSbrtm6vqu1X1ILCjO54kSZJ0yFo6gmMsBx7ure8C3jRbn6rak+RJ4FXd9tuH9l0+00mSbAQ2dqtPJbl3/qVL0ovKMcCjC12EJLXK+xbs1MfP1jCKEH1QVNUmYNNC1yFJi1WS6aqaWug6JOlwMIrpHI8AK3rrx3bbZuyTZCnwcuCx/dxXkiRJOqSMIkRvA1YlWZlkGYMbBbcM9dkCXNAtnwvcUlXVbV/fPb1jJbAK+OIIapIkSZLGZt7TObo5zhcDNwNLgKuranuSy4DpqtoCXAVcm2QH8DiDoE3X73rgHmAP8E+r6tn51iRJmpFT4iRpRDK4ICxJkiRpf/mJhZIkSVIjQ7QkSZLUyBAtSYtIknOSVJLXdeuTSZ5OcmeSe5J8OMkPdG0nJbklyb1J7k/y77sPuiLJO5Ls7vbb+1rdO89RSXYl+dDCfKeSdGgzREvS4rIB+PPu6147q+pk4A3AauCcJEcweALS5VX1o8CPA6cC7+rtd11Vndx73dNr+3Xgz8b5jUjSYmaIlqRFIsmRwGnAhXRPOeqrqj3AbcCJwD8E/qKq/rhr+zZwMXDJfpznjcCrgT8eWfGSdJgxREvS4rEO+KOqug94rAu735fkZcAZwF3AScAd/faq2gkcmeSobtP5Q9M5juimgnwA+JVxfzOStJgZoiVp8dgAbO6WN/PXUzpOSHIn8BfATVX1mf083vB0jqcZTPfYWlW7Rlq5JB1m5v1hK5Kk8UtyNPDTwOuTFIMPtyrgSv56TnTfPcDpQ8d4LfBUVf1Vd3/hTP4u8OYk7wKOBJYleaqq9jkNRJJeTLwSLUmLw7nAxvWxEwAADQNJREFUtVV1fFVNVtUK4EFgxSz9fw84LcmZAN2Nhr8NvH+uk1TVL1bVcVU1yWBKx8cN0JL0QoZoSVocNgCfGtp2I/CemTp3UzPWAf8uyb0M5klvA/qPrBueE33qGOqWpMOSH/stSZIkNfJKtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktRoUX7YyjHHHFOTk5MLXYYkSZIOY3fcccejVTUxU9uiDNGTk5NMT08vdBmSJEk6jCX52mxtTueQJEmSGhmiJUmSpEaGaEmSJKnRWOdEJ1kL/BdgCfDRqrp8qP3dwEXAHmA38I+rata5J5IkaX4mL7lpoUuQmj10+dsXuoQXGNuV6CRLgCuBs4DVwIYkq4e6fRmYqqo3ADcA7x9XPZIkSdKojHM6xxpgR1U9UFXPAJuBdf0OVXVrVX27W70dOHaM9UiSJEkjMc4QvRx4uLe+q9s2mwuBz8zWmGRjkukk07t37x5RiZIkSVK7Q+LGwiS/BEwBV8zWp6o2VdVUVU1NTMz4zGtJkiTpoBjnjYWPACt668d2254nyZnAe4G3VNV3x1iPJEmSNBLjvBK9DViVZGWSZcB6YEu/Q5JTgI8AZ1fVt8ZYiyRJkjQyYwvRVbUHuBi4GfgqcH1VbU9yWZKzu25XAEcCn0xyZ5ItsxxOkiRJOmSM9TnRVbUV2Dq07dLe8pnjPL8kSZI0DofEjYWSJEnSYjLWK9GS1MpPU9Nicyh+kpqk8fNKtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjQzRkiRJUiNDtCRJktTIEC1JkiQ1MkRLkiRJjcYeopOsTXJvkh1JLpmh/SVJruvav5Bkctw1SZIkSfMx1hCdZAlwJXAWsBrYkGT1ULcLgSeq6kTgg8D7xlmTJEmSNF/jvhK9BthRVQ9U1TPAZmDdUJ91wDXd8g3AGUky5rokSZKkA7Z0zMdfDjzcW98FvGm2PlW1J8mTwKuAR/udkmwENgIcd9xx46p3TpOX3LQg55Xm46HL377QJTRZbPVKi41jTBqNRXNjYVVtqqqpqpqamJhY6HIkSZL0IjbuEP0IsKK3fmy3bcY+SZYCLwceG3NdkiRJ0gEbd4jeBqxKsjLJMmA9sGWozxbggm75XOCWqqox1yVJkiQdsLHOie7mOF8M3AwsAa6uqu1JLgOmq2oLcBVwbZIdwOMMgrYkSZJ0yBr3jYVU1VZg69C2S3vL3wHOG3cdkiRJ0qgsmhsLJUmSpEOFIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEZjC9FJjk7y2ST3d19fOUOfk5P8ryTbk/xlkvPHVY8kSZI0KuO8En0J8LmqWgV8rlsf9m3gl6vqJGAt8FtJXjHGmiRJkqR5G2eIXgdc0y1fA5wz3KGq7quq+7vl/w18C5gYY02SJEnSvI0zRL+6qr7RLX8TePVcnZOsAZYBO2dp35hkOsn07t27R1upJEmS1GDpfHZO8j+BH5qh6b39laqqJDXHcV4DXAtcUFXPzdSnqjYBmwCmpqZmPZYkSZI0bvMK0VV15mxtSf5PktdU1Te6kPytWfodBdwEvLeqbp9PPZIkSdLBMM7pHFuAC7rlC4D/MdwhyTLgU8DHq+qGMdYiSZIkjcw4Q/TlwFuT3A+c2a2TZCrJR7s+vwCcDrwjyZ3d6+Qx1iRJkiTN27ymc8ylqh4Dzphh+zRwUbf8u8DvjqsGSZIkaRz8xEJJkiSpkSFakiRJamSIliRJkhoZoiVJkqRGhmhJkiSpkSFakiRJamSIliRJkhoZoiVJkqRGhmhJkiSpkSFakiRJamSIliRJkhotXegCFpOHLn/7QpcgSZKkQ4BXoiVJkqRGhmhJkiSpkSFakiRJamSIliRJkhqlqha6hmZJdgNfW+g6NFLHAI8udBHSYc5xJo2XY+zwc3xVTczUsChDtA4/Saaramqh65AOZ44zabwcYy8uTueQJEmSGhmiJUmSpEaGaB0qNi10AdKLgONMGi/H2IuIc6IlSZKkRl6JliRJkhoZoiVJkqRGhujDXJJzklSS13Xrk0meTnJnkq8kuS3Jj3ZtP5Xkya5t7+v83vI3kzzSW1+W5Jgk30vyzjlq+JMkX0+S3rZPJ3lqhpr2vn65a3soyV3d654k/zHJS3v73T10rt/qavyB3rZ3JHkuyRt62+5OMjlLrS94PFF3jA/t789dL05Jnu3+/d6d5JNJlh/I+Nk7NnrrM/7768ZsJbmot+3kbtuvdOsfS/Jg77y39Y65O8mXk9yf5OYkp/aO87Ek5/bWZ6v1oSQ39tbPTfKxWWr9w1l+bg8lOWbWH6w0JMmr5hhbP+i4clwdDIbow98G4M+7r3vtrKqTq+rHgWuAf9tr+3zXtvd13d5l4MPAB3ttzwDnAbcPHX8m/xf4SYAkrwBeM9S+c+i8H++1/b2qej2wBngt8JGZTtAF558FHgbeMtS8C3jvPmqU5uvp7t/v3wKeAc4f0fiZy93AL/TWNwBfGerzb3rnPbW3/bqqOqWqVgGXA3+Q5MdmOc9ctb4xyeoDrF9qVlWPzTa2gJ/HcaWDwBB9GEtyJHAacCGwfpZuRwFPzOM0G4B/DSxPcuwc/Tb3avg54A9aT1RVTwHvBM5JcvQMXX4K2A78V174C+kPgZPSXXXfh3/Uu5q4prVOqfN54MR99Nnf8TOXrwEvTfLqJAHWAp9pPUhV3crgyQIbD6DWD7B/b1KPSnJTknuTfLj/FyNphBxXOij8QR/e1gF/VFX3AY8leWO3/YQuJO4E3g38Zm+fN+f50ypOmO3gSVYAr6mqLwLXA+fPUcvngNOTLGEQpq8baj9h6LxvnukgVfVXwIPAqhmaNwC/D3wKeHuSv9Frew54P8+/6j6bl3VXM94FXL0f/aXnSbIUOAu4a44+LeNnX25gcEXrVOBLwHeH2q/oja3fm+M4XwJedwC1Xg/87ST7etOwBvhnwGrgBAZvqKWRcVw5rg4mQ/ThbQODK8B0X/dend07deIE4F/y/OdaDk/n2DnH8c9nMMiHjz+TZxlMK1kPHFFVDw21D0/n+Pwcx8oLNiTLgL8PfLoL2l8A3jbU7RPA30myco5jwyCIU1V/xuAd/iv20V/a64gkdwLTwNeBq+bo2zJ+AOZ6Hun1DP6z3/tGclj/z86/OMdxXjC29rPWZ4ErgPfMcWyAL1bVA1X1bFfnafvoL7VyXDmuDpqlC12AxqOb7vDTwOuTFLCEwS+LK4e6bgH++wGeZgPwQ0n2/vL44SSrqur+WfpvZnCV+D8c4PlI8jeBSeA+4OW9prcBrwDuGvzljZcBTzOYxgFAVe1J8gHgV/dxmuFfqj5MXfvr6e6vGPtjrvHzdJJl3bxpgKOBR2c7UFV9M8n3gLcC/4LBlbMDcQrw1cZa97qWwX/2d79g716p+1iX5stx5bg6aLwSffg6F7i2qo6vqsmqWsFgGsSKoX6nAXNdbZ5Rkh8Bjqyq5d3xJ4H/zNzv+j/f9ZnpHf3+nPNI4HcYXG0ense9AbioV8tK4K1JXjbU72PAmcDEHKc6vzvfacCTVfXkgdQrzWY/xs+fAr/U9T2CwQ1Ot+7jsJcCv9pdjTqQmt7CYN7mf2usFYCq+h7wQeBfzXGaNUlWdnM2z2fw1ylpJBxXjquDzRB9+NrA4Kpv340M3tHunX/8FeA/ARf1+gzPiT6Xmc12/A0ASbYm+eF+Yw38RlXN9M5/eE70P++13ZrBo+y+yOBP5P+kv2MXlNcCN/XO9f8Y/CL5B0M1PAP8NvCDvf0/muc/1u47Sb7M4I7vC2f5/qX5mHP8MLjq9XPd1JDbgU9204tIcnaSy4YPWFW3VdWnZznfFUPja1m3fe8jLO9jcL/Az1fV8BWzfdXadxW9v3AmmUry0V77NuBDDK7KPTjDcaX5cFw5rg4qP/ZbkiRJauSVaEmSJKmRIVqSJElqZIiWJEmSGhmiJUmSpEaGaEmSJKmRIVqSJElqZIiWJEmSGv1/0gGB+LAlk5oAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 864x864 with 5 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2ZxnI4qTr1Lz"
+      },
+      "source": [
+        "Once the model is learnt we can use it for prediction. For example we can predict brain volumes from the cognitive data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3IyEdFrcI4bs"
+      },
+      "source": [
+        "# Predicting volumes (channel 0) from cognition (channel 2)\n",
+        "predictions = model_adni.reconstruct(data_adni, reconstruct_from=[2])\n",
+        "decoding_volume_from_cognition = predictions[0].detach().numpy()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1Iqcds0lI4bt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "ad17bdd5-b08a-407b-b1b2-f5d822481b95"
+      },
+      "source": [
+        "plt.figure(figsize=(12, 28))\n",
+        "\n",
+        "# Plotting predictions for each volumetric featurez\n",
+        "for i in range(5):\n",
+        "    plt.subplot(5,1,i+1)\n",
+        "    plt.scatter(decoding_volume_from_cognition[:,i], volumes_value[:,i])\n",
+        "    plt.title('reconstruction ' + volume_cols[i])\n",
+        "    plt.xlabel('predicted')\n",
+        "    plt.ylabel('target')\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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E3gbgpIj8gVLqz3JsEyGEdIWoSFkRI39Z0+3FSFn3cS8UezBR1EVhUf3ZyxHRXrpWiu7B3A1yE89KqdcBvN7872+LyJ8DGAVA8UwI6Xtc07T9mNLhI1S7OfXerT7uRaGR5XnoZMDi+m0viU8bvXitDCqFyHkWkQ0AxgC8lG9LCCGkO7hyB9OM/PmKlSyjsL5CtZt5n0WNrpro9ixEVuehkwGLz28pPkm3yN1tQ0RuAHAQwINKqb8z/P2jInJCRE5cvHix+w0khOROP7pSuHIH04r8+eauZp3j6pvf3c28z15ZYJZH/nHS82C7T/XnDx6YSZznzzUCpEjkGnkWkTIawvkppdRnTd9RSn0KwKcAYHx8XHWxeYSQAtCPKQwaW6Qsrcifb3Q16yisr1Dt5tR7r7gb5BEh9z0PwYj4yHAZb741j/pi4zWt79MT5y/h4Mlq2zEE8Rmw9MpghwwGuYlnEREAvw7gz5VSv5xXOwghxaaXptfTIi07KF/BkbUwiSNUuzX13iuWW3mJxqjzEB7UXp6rt32nVl/A0y+9igXljnv5DFh6ZbCTF4O4wDhP8kzb2ArgXwK4S0Rmmv/70RzbQwgpIIMYcUrLDsrXvitrm68i2nD1iuVWUS3YTINaE1HC2fc6KOI1VBSKai3Yz+TptvEnACSv/RNCeoMkEad+iMKkEYE1RVfLJcGVq/O4Zc+hpb7JOgpbVCeEXlhgtm3TOnz6+AXj53mSxuB1NMZ1UNRrqAgM4uxc3hTCbYMQQmzEFXa9kiPdDYEfFhw6L3W21phi133z+H2b8fh9mzP3PM6q//thsGTj6FnzQnnb593CNqj1oVIuJYry5z3YKep1Noizc3lD8UwIKTRxI069EIXppsAPCo6tk0faclN13xzbc1dh+icOvTJYSkpRhZFtVmN+QcGUqFESwaJShRKdJmwCucjXGfPBuw/FMyGk8MSJOBVVbATJS+D3Qt/EpYiDpTQjlDZhtLpS7rSZHWEb1O46MGP8/qJSeHlyRzebGBuXQC7idaYxDWQAYO7aPKamq7m3rx+heCakCxR1uq8f6YUoTF4ithf6Ji5FGxCkHaHcvX0jdj97askCTnOlAMLINKjdf/hcIcW+Dy6BXLTrLIg+B3ufP7OUkgU0HFCKEh3vN3IvkkJIv8OV0N2lF1bl24RE1iK2F/omLkVzo0irmIcuLLLrwAzmDY4V9QVVyAIhu7dvRHmo3QtAi/0i4xLIRbvOwkyMjWLVivZ4KAvJZAMjz4RkTJGn+/qRoq/Kn5qu4sq1+bbPy0MSS8Qmmc0oet8E8T0+3wWl3Zr9cQmwOKXSW47J4vZWhKin6ZhuWLmsLbdei/0iXmsa18xML/iCZx0d5wzqdSieCcmYIk/39St5r8p3sf/wOdQX2tXQDSuXebe5k9QAW98U6cUY5/h8BgTdXOzlylH2bYOvh3LeUU9bv9raXvRnnksg98LAM8u0rCIvmMwDimdCMqYf80xJcmwCYtZQoc1G2rMZRXsx7nvhTKzjixosdXP2xybARODdBh+RWYSop61fSyLG4ijrRyqFGqSFiRLIRR6UA9lWzeQMaisUz4RkTC9M95HukXQwFRQdtpptSSN7RXoxTk1XjaWegeTH183Zn7guFKY22K6RrOzekgpaW/8tKIVKudT2zNu2aV2hBmkmii6QXWQZHecMaisUz4QkIM7LxndauajRGJIeU9NVXLnanu8cNZhqy4G1kHQ2o0gvRtfipqTHl9bsj+99qgWY/v6uAzMYckRjw9gG3KbCIq42+bS3k1kHW7/qyoHhfRdpkJYmRXp+ZyX+OYPaCsUzITFJ8rJxPdCKNmVOssEmgNcMl/HoPbfFTjswkbRks+vF2G1h4BLsSWdr0pj9iXufhr9vEs62NvhGEG1tOnH+Ej536vUW2zJbezsRtFE5wuHf26LvSSsVJiV4TY8Ml6EU8Eatnuj6HpTnN2dQW6FVHSExScuKKqvtkWJiE8DDy6MXCvpGgJOWbLZZ2Olp9rRtFrUN2y17DmHr5JGW7dkiWSOVcmIxMjE2isfv24zRkQoEjcho3PLQce9T2/kuiSRuQ5i9z5tzw586fqFFOLva28msQ9x+tZ1bASKvKdc1E4ewdejluTpma/XE1/egPL/TuIf6CUaeCYlJ2lPcRZoyJ9nRyXm2RYaTbMuELdKZxTR7VKTOFuHae+9tifan6XQ629a31dkatk4eaYsQ277vU2nPJ5o5NV01CmTA6mxnPI5Op+N9+lVHem3XsAKc11Ra0d2p6SoeeuaUcRZAE/f6HqTndy/ng6cNxTMhMUk794u5ZH4UKa8wCZ2cZ73gzCWKfLdlI840eyfCIEqQF9USzHb+BNfTDoKirpPz7TNoSRrZDO8/i+n4cFrEm2/Nt1VIDOO6ptIYxGkB7hLOPm0Jw+f3YMK0DUJiknaVtn6s+pY2/VClsZPzPDE2GimctYjbsu9FjH3ixY6nt4Ho6n1JptJ9InUTY6M4tucuvDy5A8f23JW7cAbM50/QHuWt1Rew74UzHZ1vW4S22iy0AiQbwJj2n/Z0vCktIko4A8CQiPX6SSO667tuAIgnfPn8HkwYeSYkJmlHxooaaSsScX1/i0in53nUkboRFHE+i8R8cUUlp6ar2P3cqaWCL9XZGnY/dypyX70aqTOdP9v50FZ7j9+3ue18AzCmeWimpqtGUa6JimyvWl7Comr3lA4vTM1qJieOSA2yoJT1Wk3jmvEV2nGFL5/fg4kojymMojA+Pq5OnDiRdzMIIV1karqKBy3pAwJE5o/6bL8XXnwmt45KuYQVy4asua+a0ZEKju25K/F+Tf0z9okXjX7Ma4bLmP743bGPoxcXH22dPGIV0KY+9zl21zaD23bZ2QHxKi6a2pGUW/YcipwlcZG036Kw9euQAN+xspzYbYP0NyJyUik1Hv6ckWdCSKHJwvdX00s2U3GLbwTpJEfZtkjIVsjE9nlwe0D8SF3Wg5wk29+2aR0+ffyC8W+vNVMsgtu8cnU+cgbF51xVZ2vYf/gc7r9jFEfPXrRWw7Mdo0lE2mZy4vaLz+LWcAGVIKbjTyO6G8c7m5AoKJ4JIV0h7SpmQHLfX02vFW0wCVmXi4GmaCkRcVftZz3ISbp9lzXg6kq5bZs2gte4r7NKdbaGgyer3uLPp9BO+F5L0i8mkVouCVYtX9YS3bVdt7ZrtVOnB6ZXkDSheCaEZE4WVcw68f3V9IPNlEmsmL6TNiOVsjFdZKRSTn1fUYOcqekq9r1wZinqPVIpY++97sIzcbZvw3WdiLTnHdsICkaf8xmnjRqfXOSwcE3SL74VVeeuxa+02SmDbLXWK+lpvQLFMyEkc7KoYtap7y/Qu4vXguj+s/nXrhlOPshwvXD33nsbdj97qsVJoTwkqZyXMK5BTnjhItBYNLn72ejFiz7bN6H7xZbbu2a4jNmI9BVNWDCGxWelPIS5+mLstsf9nkm4Jh1cxqmoqok74DFtl+LQTC+lp/UKtKojhGRON6uYxaFfbKYmxkbxyQ/dbjyWR+9JJmaj7AEnxkax/4O3t5yX/R+8PZOXscsyb//hcy3CWVNfVF5eyFPTVQyJeO832C8mdJ/b2rxmuBx5LQet+tasWuFsf5RtoP7ctYjP1o4oq8Ik2CLgq1ZEV9q00Q9WllkyKFUQuwkjz4QMIN2O0nSjilkS+ikPMu1jsb1w971wpuv95bLMcy2YjBqcuQpnhAdRUVXygOsuGLo/TG0O2sX54DqGoG2gKbJ44vwlHDxZtaZrRC2Yy6KAShapUr22dqHb9EN6WtGgeCZkwMhjCi+Ll3BaZJ0H2c2Biu1YkrTB9mK9PFdfyi0OXjtAdoMQ18DAJWijBme2KGhJpEVU+iy2E6DFYi2twYxt4Bls49bJI0bx+PRLr1or6mmhD9h9p7MYXGaRKuUqnd5Nipo60g/paUWDPs+EDBg2v9NOvIB9KOqLJUuK4GmctA0+fsOaNcNlvFVfzOU4TTnPQcLR4CA2T+Kwf7iv93IW94/P+YvrrayPL4/r07VPIJlQt50fAfDEzi1dudeKcK/bKHLbig59ngkhAPKbwhuEle7hAcLctWhf36xJOqUdx/XB5O2cxnH6DLj0v4NuG0FcMyu+Ebmoe0PQ8HyO23YffKK/rui0KfKsjy+tdIc4x2o7HgCJZ8R0+k74SFVzP92414qcOtJP6WlFgeKZkAGDU3jZYEqHsdHNXMNOHBOA1hfulavzkdUM4+xDYxJfgL+YCg7MTFFIm4ixDRCuXJ3H1HR16ftR3ssKwMGTVYzfvHbJOi/c9t3PncLe588kqmQXNfC0pUXdf8doW85zMF0qjYF0kjQw0/HYUk98xOfE2Ki1Cmm37rWi5xUPQvCim+QqnkXkNwC8H8DfKKW+L8+2EDIoFDn/uJfx8dHVdHOg0slgKfzCjVsi3GcfJvG168AMVpaHUAtZtNXqC3jomVN48MDMUlQ1nJYRR8TYotaztToePDCDfS+cwaP33OYVha/VF7D3+TNLedjh79YX1FIf6WN88MCMNa0kjWjuxNgoxm9ea91OGgPptCKunYrP0ZyDAgxKDBZ5R55/C8CvAvh/cm4HIQMDp/DS4ZGp00sLsmzT4yaCDglR5yDo8GATi1GkOViyXTsnzl/CU8cvtEyb++7DJL4U0CacNbqf9f+HI51xRYwWu6aUj8tzdTz82dO4/47Rpph3D45ma3VMTVe9BJ/uK1OkNs1Fva6IYxrXRloR15HhsvEcjAz7Fd3JOyiQ9/5Jd8lVPCulvigiG/JsAyGDCKfw/LAJ3EemTuPTxy8sfc8lnEcqZaxasSx2SkJYQNnEYlRb0x4smaLRB09WW4SzALj/Dr9rLI1pbR2RPnH+Eq5cjV+5ztWGWn2hbWDgYv/hc94ltoP7CEZq40ZzTWI7GDlPErH2Ja2Iq+0W8vU06GZQwDXwZVBiMMg78kwI6WH62UHDFf17+qVXvbYhAN5/+zvx2MTmls998jtdaSDh70ZFKk2DpXDk/MPvubGtnT7YIsdHz170+r0t4hiXBaVaBjSaNcPlSG9ln5xmX16breGJnVu8F1sGf2f6b9t3gtiuFR05B+LlH8chrYjrG5ZcetvnJroRFPC510j/U/gKgyLyURE5ISInLl70exgTQrKn36t6uaJ/rkhzsFadXkgW7hMfcRQVka3O1pa2G7eCmI6cB1MgPn38Ah6ZOt32XVvlOp9jifot4I4sBqvxlSxVAKMYXr5saRGfrS2mSpNJWT9SwcRYa1XMVcujtx2M1Mat7BcVOX/wwIy1/zslfKxJK4BmUc0wC4pSrc/n3iLZkbvPczNt43M+Cwbp80xIceiWX3Re0W2XB/BQjBxnoL1PbH1XEsGiUt7OFtqr1WTTpdsa9CvW3Prw563tfzLgizs1XcXuZ0+hvnj9u+UhaSnDbTuWkUoZV+fN3s/A9eltVy+G2xI3mgtc9/q1/Vbnkje8qhfacq0F/pFnk3euT7vDv7MtzLz/jlEcPXux7V7w9eT28fYN32/bNq0z7jNtsvQi7vQZEvy97Vqw3WtZQN/m7mHzeaZ4JmRA6fSF4ltgotM25vWScA0Otm1aZ0wRsBHuEx9BVS4JoNAiXE2MNiNzUWI8eH437Dlk3V6wf7fse9Eo4EcqZcw8erf1WCrlElaWh4zpGKaCKibWDJcx/fG7Wz7zKZEdxtU/YbRAPfS115faXikPYX5RWYuwaHFtW8jpM1DycdvYtmmd0XZOD0Z8Bxau/cYV+mkMbIPbGBkuQ6nGwsuoBbK+i2k7fYb4DtqiggZpBgHyKnQ1iNjEc65pGyLyNIAvA9goIn8tIj+TZ3sIGRTSSLnoxjSrbYo0y2lozYa3m49j26Z1GL95LUpD/mkE4T4JT3WbUhLqCwo3rFyGNRFuA6/N1qxpBwtKGc+vKwUiOAVti3wHP7dN289a8pgvz9UjhUi52bfhKemJsVEc23PXkiCOQufe+i5KrNUXcOhrr+OtQPS5Vl8EVEPMCxr/P1IpLx3rEzu34JXJHTi25y6jGLLte1EpvOz4nT5W/Z2jZy868+Qfv28zRirRzhS2awLws1vUizMfmTrd8TMk/By6PFfHlavzKJekbYFscLvB3+t0b3YAACAASURBVOljsn230zQLnz6JyvFOO8Wt6J7Sg0Cu4lkp9WGl1DuVUmWl1LuUUr+eZ3sIGRTSyNszCba0rZlcL4Msc6ynpqv40tcvGf929OzFRt5zRERYY+uToDhatMwAzs7VMbzcva7blGNrEsfB8/vh99zo3GZ1toatk0ec3wkSFnraMi4ugkZUexENIaWFxu7nTkXmKAuArbeuNebexmmLSdzXFxWGly/Dy5M7MP3xuzHz6N1O4Ruk00Gmzm21Rc6D52rm0bvx5M4t3oOL8D3vK74WlMJTxy8YnyF7nz/jnYtr9MM2RPnD7fRZTBt1TL7H6vqeb4532nnSvZIf3s8UfsEgISR90ohcpLVQyEXUyyCrhTr7D5+z5ja+NluL7KeSSKw+cb0MXfsKCnMfMa639djEZnzkzpucbXKlOURFwwH74MoWHR0dqeDlyR0QQdvApL6gsO+FM0v/Nl17T+zcgg+Om49p9/aNSLbc8DpJo3qdDDLDEVYbwYGkvg6e3LnFaxFk8LjiiC/b/TFbq3tHWOP0aZzFtD7H5Hustu/p69VnAJV2pLgbgQvihlZ1hPQZPrl1aXmzZm3N5FPZLYupStc2dR/ZBE2SnGyX3Zctx7ckYt2Pz/l9bGIzxm9em2gRnlJoKV9twuZ7C7Tn5wZf/DbbuvDnUdUPwxZipkIucVhdKWPr5JG2Y4m61zrx/937/BnvcxO2Lwzv17bINXhN+NxvcXH5U8fxww67kbh+F3VMcYRmGlZ8aVcfpKd0/lA8E9JH+FYmK1I1LJfYD74kbC/LLKYqbS87AawCEGikHOy91+0pbCLqZRh3wVPU+Q0v0rKV17YxW4v2D9Z/s/3dJKrjpIoArcdhEodB4aYHC+FFZuH/N1EeEly5Nt9SXnv3c6daFnS6qgAmGWROTVdjnROgfdAX3K9t4duVq/NLAyGf+y2IrxOJbTBquk7LQwIIWlI3ws8ml8gPf7dToakHXkFPdN8CQK72dvq8pad0vuTuthEHum0Q4ibOKuwiFDiJsxI+TecN27EHV/CHhYEAeN+ta/HKtxppG6srZYg08pKz7r8kBU1cx2jqx7gCGkjvuvJxNAg6fPj+BvB3f7G5xwCNNJU4hVw+cudNiQrOBPG1nwtSEsEnP3S7tb8fmTqNz7x0AeF0/bD9oM/+TdZ5c9fmjf3kcoEwXS9AtNh1uW34/N6XtJ47RXjekvgU1qouDhTPhLjphn1cmsS1XErLGsskuobLQ22LlYI2ZC6rMB/f3OpsDUOCJeHiG6VO267P1udxBaJmtBml1wImPOjwaWuUUEsi7oLt87Hvsm0vjs9zkCQC2sdPOIo4g88g4cGJazABtHpwu/bRbf9hUxui7ARd0BZusLGJZ6ZtENJHpJ1blzVxF9KkMVVpW6k/FyqOAVx/4R7bc5dXSe0w4Rd5MOI3W6tj97OnALhTH2wr9fc+fybRQMLWt7NzdYxUyrGiz4Lrud865SEsuIJ9ZBv8uHLMbYLHJ9fdlqpiKv6xbdM6Y060ApwpHTY+ffwCnjp+IdXoe5DgQCyI7ZqMslwLn3dXXvFo0+ElTBFycW3l4gF3ao0N2sIRExTPhFjoxWm2NHLrunnceYj9uC89/f0kL9EowVJfVE7x7dr+bK3ekoPrKwpsfa4FRrkk1oIgtt9EoUt12/LxbW1yRfeiFo0JsJSbatp3sMhNdbaGgyer1uNZUAqVcqk1N9ejiE3QcQKIP0hy4XJKNPVL3Ot+9/aN2P3cqbZroTwkxudJ+LnxhCEy7Usnz6Co44wa8IbptYAE6Q60qiPEQNqm9t2iU/u4bh93HpZLcV96+vtJLK98BIv26bX54vq219e2z1ZQBWhGH1UjhSVN1o9UjM4Rus0232bdN6brz3UcQEO4Pv3Sq0tCzKf4hw19HwXvq/0fuB37P3i7c5vBbUedmzQjmQLEvo7C9oMTY6PY/4HbWz4fqZTb0meAdJ8bnW7L536J09e+zyjtx+3jb016H+Y8E2JgUPPcbOWYszzuOFGmLHOeTQTzNR+ZOt02rR+Vz+mTlxuVIxynvb657VPTVex74Yw1xzmOE0UUemGZrZy5brNrsaZpcVpwYVhUBLqTt1zUOX5k6rR3qXZbnnDckuM+hO9Z13VULgn2f8C+0DDYTtO9l7QEuQnbtsI52a52Rt0vcZ9nUc+dIuR6k2xgzjMhMRjEPDeXNVaWx+2bx+xrw+ezP6DhoRs+3vKQ4IaVy9ocNKamq23T+sG0ABs+vrmuHOGo9oZZP1LxGmBMjI1i/+FzVvGshbIpXSEOOl/ZFXU1RQpNfRJOs9h1YAYP3HnTUj66TXwmEc7BQUMwamw613pRoHZDcRG+Xk0DMt+2RWGyrQOuDzbCDhWmY7MNaIL3nv63iXDZ7GA7gtsPXquuNKUob3HTcZoGYnFntkye4kHP77lr87HXQ5DehpFnQgy4IikuK6hexiVAihBx72Q2IMqaLiqSnca+TW4bNjFsiiBHRbG1SAiLhXJJsGr5MrxRax0QRLkpmLbtSziS6drXkzu3ADD7Zvu064nm7035uZ1gGjSE3VfCkXCbFWAQfc3EiVhrykOCnT9wo9fvOr1nfSK4Io2COb4E25TEMjHJMaW9hiOLmSBSXBh5JiQGtojhglKJop29gCu6XISyr0lnA6Ii1sH0iP2Hz2HXgZm2l2wnMxGuyLpNEJuisVH7UqH/19QXVMvCwgcPzGDv82cwEsOaLq4k1eW09XHbFl2tGS5jYmzU6GTi2y6dMx23kZXyEK7OLxoX3umIs2l/gHnBYfi58OCBGeN+9eLJp2IKZwCAAOM3r8Whr73uPHdprBvwyROPG3sLXsM2F5mVjnz7JDNgaRcTibOwk4sK+xcuGCTEgF54VxJp+5vvwqxew/ag1wKnW9gW3tjaNyTiXKRje0kHz2HUIqUkiwV9iFqMFOyLIcO1mJTZWh1vvjXfcIzIiMtzjWn2qekqrlydb/t7pVzCo/fcBqCztKDXZmvYf/ic0/XCRK2+iJJIWx9UyqVE+d3h9I5RxzWz//C5ROkk9YWGO8uj99xmXGAJxF8kbCOLVK3g/eKyTAwvXjT9Pg+mpqveuel5VWwl3YHimRALE2OjWLS8RPsx99km5LTA6QYuEWtzV1hQyrkq3/ayq3pEwbQYsu17w9uTvcy1KN51YAYry0MYqZTb3FHCfZF0wZ4NXRBGDxBHRyr4yJ03Ga8Bm5iJ4t8+M4Pdz55qm4ZfM1xuEXirK8m2DzQEVdL7sb6osGr5sjZ3GpvwjSLYDtfAqNPBgslV54mdW/DK5A4c23OXcVFiXCeIToTqSKUc6VDhGpCaBgd5i1F9P9oYqZQTuxwVmTxdRIrsYMK0DUIcdMvjM2mJ2jQxFTjYtmmdNZUhC1wiVuc66vYNGRZOmRbp2BZYBWcVbGJGn/uJsVE8e+ICjn39Usvfj339Eh6ZOh2rklw4jeTyXB2VcgkP3HkTjp69iF0HZrD/8Dlcudq+CCkL9KJAfW7Hb15rvBZ3HZiJHS1dVDAOQIeXL2s5R0mD6pVyCds2rfNarGfjjVq9zcXhxPlLsfORgUZax9bJI0v50LX6gnFhno+7hi3PXA80slhoG3wOVRzpE65Fi5VyCXvvbQy4Xc8vlyd9EYqthHGla+hjdi0qtOXIF5m0Fmn32r59oHgmxEEaRUeiMD0kdj93qqUAQ7ceHOEc4G4/vHxEbHARms82bC/54Oe2QZL2y50YG8Xxb1w2bufpl16NJZ5tA4Sg60LatmVRBAcdJmeBpGkGNqrNvF+9n1nP3Osga4bL2PHud+LgyWpHUXlT1Pvo2YuJtxfOhw4PTgBg26Z1TnGuFyQe+MqrbekoV67Ne7lOaFwDUpfdmqnipm6bbU1IuOS8q41RAjntXGVN0gWErtmCcJR5arrasoDVJ0e+iPheO/22bx8onsnA43qYdiMCYnpImFwDuv3g6MbDK9z3qy0OFEERq4maFdDbthGcmt+9faMxsqoXpE2MjXqJcB9sL+GsnC98CbdrarrqZY+XtF27DszgxPlLeGxic2S1QBNv1RfxuVOvG6OBJRHc+T1rcPwbl5cs54ZEwaQHTVHvtNOywveNS5wHHSVMCwN13nOU5Vvcxa6+C+F02oj+TSfPxTjR8zSewZ0EBFxVMMO/3ffCmUjnlyIJQRt5WrYW3S42UjyLyC1KqZejPiOkF/F5mGYVAdHEeRh088GR9cPL1Pe2BWxBEatxzQpE2Unp6f7gtKrtVaeP1yf9w4ckQlETLBZi8rDthGAqUlT/2YqYfOalC86y0UEUgKeOX8D4zWu9/LDD1OoL1u8vKoWnfva9LZ/ZZipMUW/fcxTHA7s6W8Mtew5Fbjs4q2WLyAfvwbBXdPgZ5pt65ntfx00b6ZQ0Z8A6CQjEmYX0dbEpihC0kWdp8qKXRfdZMHjQ8NlzaTeEkDzwcWLImjgPg24+OLJymND4Rtw14QepadGUnj51RdEEwD+6aTUOnqy2LEy0SWB9vB9+z43Gv9s+txFVVjpMeDHhYxObcWzPXXhlckdq5yIsAkyltINor+Ngu8ZvXht7IBEcFAXPZaeY+iWOW4vtHK1Y1r64M87iQn2t2Qg729jaPDJcxtbJI9iw5xA+bSiyUqsvYO/zZ6zHYhJ9vtdSiqYvXqT5jLb1va/lpO15k5SiCEEbvtdOv+3bB2vkWUQ2AbgNwGoRuS/wp+8AsDLrhhHSDYowNWSKaJRL0pLzDKT34AgW7XBVGcs63ztuH5uEmS365dq2Apam88Ofu6qRhSvJlUTw4ffc2JLv7Fvd78T5S96V5VatWGYtS9zJdRos+BFsp6vSpMZUrGLr5JHYdnHA9WMInkufsuYa3wpyLu92oLnW4NlT2PfCGczO1TG8vF08Ly6qtoVhQLICLyZ2vPudkW0ulwRvvjUfGd0MV+SLui59o/+X5+rYOnmkawve0npGT01XrTM1viLWN9ruKoCkKZIQtJHnws0iLhoN4krb2Ajg/QBGANwT+PzbAH42y0YR0i1shSJGElpzJcH2kDB91umDIzwF6iqfm/XDK276Qpzc4qht27alxaTteB+b2GxdHBhnevno2Yve6RYukeA6zvKQOMWsPtawCPaJ6G3btC5WO10MibTls0ctqAsSHPS4Sk2Hr2eTW0t9US09D65caxeR9UWFvc+fadm+/u+HnjnVsZ3gwZNVjN+81nkPXrk6752HblsEasKnfzSdpE7EzV9Oa/retuhVgLYUrk6fc3vvvQ27nz3Vcv8NAVg9XMbsXL1wQtBFt9JzirbvKKziWSn1ewB+T0Teq5T6chfbREjXsL3rul213vaQSPvB4UpnCE6FZiGYwy/NbZvW4eDJqnd53qjp8eD2V1fKKJfEmgZiy18Oi8k4dlNx8injCE2XSNi2aZ0xgl0pD2HeIwpsaodP255+6VU8dfxCS58kvWVMVTvjul3YBgJhfNxaojBdmxNjo9hlqSgYB9P1En42xGm361zaRKzNbcenrVH7GRku48235o0uQoD5uZPWDJhroW7wOZSGE0bRo6akc3zcNr4lIv8vgO9SSn2fiLwbwL1KqccybhshmfOGJYJj+7zXiRJG+sWRtj2daVHTwZPVpcVv4Yi7z8vS9VKerdVRHhIMl4faLLf04jaTcA/uwxRJdtlNRdnsBXFZ45lSEGw+4AdPVlu+L8CSX7RPVD+8SNDXki44Y5HEDzlMrb6AB5v+1kmLiMT9TScLN7PcXtRxxNlP2HnGNnCNmnlKmits8jQPo/Ozr84vtrXnxPlLS37ZQ4KlxagrlsWv72brN1Mp9jScMNKOmqblOELSwecK/DUADwOoA4BS6msAfjzLRhHSLbJeFFc0oo7L9SJJyiNTp62Lmo6evYhje+7Cy4HKaD4Lc8LV9y7P1dtSFOqLCmtWrcCTO7e0bEsLdl3AApZ9+Fh3BfvGluqjbfaC2BakrSwPYc1w64I0AMaqi6ZFfQrA50697i0kdfpFsD/zROcd2xgdqTjLXpuwVSmLu3BTY6u2mHR7YaLuUd/9hJ1ngtfPU8cveN3nE2OjOLbnLmuf64WLtgpwvvZ3s7W61ftcX5PB23u2VjdWE3VhW4BmS00pkhOGq/IqyQefyPOwUuor0rpYZz6j9hDSVUzT3kVbyJGmx+ncNfut67Ld8nmRmKJbnzv1ujM/07Rdn+ON60lrm4o2FbCIc8z6e1PTVbz5lrlvTTZ7+r/3vXCmJRpXqy8CEDyxc0vL4jmTsLAd/2yt7rVgCbieHhHlTtJpFpOrIl0YW552eUisMxOChqAIL2TzyUP3qfa31IaSWMvVJ9leGJ9nz9KC05cuONPLdHTWdG6jbBnD+CxcNPVtpwLUdcXEjQ7bUils56tIAZSiFwwZRHzE89+KyK1oXsci8gEAr2faKkK6wNR01Tjtff8d+S1SSDK96rtdU/5ieKGV60ViaptOuVhdKePKtXlrRS0b4ReU76I735dyePtxqq35sn6kgv2HzzkX55naqy31wlPZOoVh7/NnIOLvGRtExM+DWLcryp0kjvg1sdh0dOkksn3DysbrSp9D3aaguA9fL1HnW//Px93DtRhRo/8Wp7hMnO0DgedWxOnQ0dk4LiA2wei7cFGnYPgsOtRUyiUMiXmBZhRxxbktlSLrKrKdUgRXKNKKj3j+3wB8CsAmEakCeBnARzJtFSFdwBaR6aQ0byeYhKNpMViSiIMtsrjesMjK9CLZtmmdM/83rlAAGsI9nM9retmajtcn79P0AvR5CU1NV7H72VNe0Va9j6jFYjZR4nr5JelTzeW5RvR5ZXkIs3N1q4jR7fJxJ+kkAj0yXE5UCCXI5bl626yBqU3B68V1voOWjUMO7+JKubSU0hNeQBoWu1GL7BrbG2rOMLTuw3dGyXfWBUDLICOKKMHou3BxtlZfunZN+y2XBKuWL8MbteuuEw1P6vjXRRrR4aIv7puarkbev6T7ROY8K6W+oZT6YQDrAGxSSv1jpdQraexcRH5ERM6JyF+JyJ40tkmIL90azdtyLsOkMb1qw1c0BqN6wPW8W50jnCYP3HkTgNZ8Xt/8Q1P+YrkkbQUswi9Anxx3VxR51fKSMRfb9RIrl8QqSrJ8+c3W6pidq0MBeNvKZW3VG4NiySePtpPUDaXMRSY+cudN3nnCpnz8qPtDV8MLs7I81JLjHT7dOksxeI598k6jhO3oSAVrV61o+zzOuoK49/6CUsbKnUPSXoAnjmCMc+2WRJb2s/8Dt2Pm0bvxxM4tABpl2pMMFNOMDuvc7uDaiyKgrznTc7Fo0fFBw6c8978N/RsA3gBwUimV2JtHREoA/huAfw7grwF8VUSeV0r9WdJtEhKHbpT/jOP9m5Z9md5vMJJi87MOrsZ35QKnYcOl0Y4Qj01sNubzmggfb9JokSn6qX1eNa7zMHdtAWc+0W6H5oqqLiwo7HvhDHYdmGlrZ6fR2Cj0KzcsTirlIawsD2FXwOHi8fs2L/Vn2k6N2r0mfN6Onr3Y4riyulLGt6/OYyGkZqM8q8PoNKMrlhz/cOS37ferzf7XUSk/Ufew6+/VZjQ86hqO6+oxakmxWFTtBXimpqstKSdrhst49J72ojBAvGt3USm8PLmjZT8+v62Uh/BWfbHtenS1q5+wDcZKIh1XNySd4ZO2Md783wvNf78fwNcAfExEnlVK/eeE+/4BAH+llPoGAIjI/wDwYwAonklXyLqCHhBvoYfvSzGqjSbBXh6SNt/j4Hai2pmWDVf4peczYLAtBktiBbW02CqQDqN9XnVxCtex2hw1XIUyFgHroqo0FpkloVZfXBKQ1dkadh2YwftuXbv09zQWCQZZXSkv5RWHc5QPnqy2CIGp6WrLQsqRShl7770tVh9t27SuMYPgKPfuIo7/dfDzqPtEDwJt3/FZzxBHtOp7/EHL4NeUrhQcpFyeq2P3c6eW2hQelAcHPq6e9ll7YGq7dpspakpF1tiuuUWlBqYPioqPVd27APwjpdRDSqmHANwB4DsB/BCAn+xg36MAXg38+6+bn7UgIh8VkRMicuLixXxyUUlvY0ub8LFE65Q4qSG7t29E2ZB8OSRosy9ztdH0YqovKqxavsx6rFHt9JnWLw9JSzs/cudNLft7cucWTH/87rbcZRM6bSQstHYdmMEjU6fbvj81XcWWfS9iw55D2LDnEMY+8aI1PcZU0CM4be4amLhSRyfGRrHokVsanqLXU8ZP7tySitVZEhSAY1+/tJSSkHbk+cq1+SXB6Op7oNEf0x+/G69M7sArkzuWIqMmpxhbqvLTL73a0WDENEjySflx3SdayLq+45O+MTE2ivvvGIWhWr0xdQmw95NPulJ9QWH/4XPGtJWDJ6vYvX0jXp7c4SxipAe/+p50DZrDzydTSoVvKlyvM2hWqr2ET+T5OwFcDfy7jkbBlJqIXLX8JjWUUp9CY8EixsfHu1z3jfQ6UWkTWZf/jJMaMjE22mZdBjSmV4eXL8P0x+9u+40J24vpjVq9ZYo2SFRahyliC/iVRXZhi/7rFIJw3ykATx2/0FLC2CdiFsRVzERHt21EFc/xjdAHF6wFI2rB1InVlTJE4Fzw1ytERYCjKuGZIq0uO75O+8r0c5etZbhgj66SqRfrjTTPpU7duf+OUasbTbgvbA484TbaUhm2Th6xlqUOXuuuc/DabC1ydmr39o3Y/dwp67kOPntt94lPlcg4qXC9TjdmR0kyfCLPTwF4SUQeFZFHARwD8BkRWYXOUiyqAG4M/Ptdzc8ISQ3XA79THpk6jVsf/jw27DmEWx/+vDEiajPmtz38Zi22ZGnkQ7uKSJg8isML3UwR22BZ5CQvLlf031VON3j+XBGzh5451RadckVt9It4xLLQLK0CFgDw4IGZtsVnAHBsz114YucWrFqxDLNzDUeCXhbOPrj61TbFv2rFssiS7TYc5hoAWgdJU9NVjH3ixbZCP9rWEmhd9Hp5ro6r84t4cucWfP3xH8WTO7fg6vwiLjcXb+qIra3YSrjqo0+BE6AxwDbdg677yGcWSP8tanZqYmwUq5a743H62Rv3uRgky2d60ejG7ChJhvNKl8bqwN8C8PsAtjY//phS6kTzvx/oYN9fBfD3ROQWNETzjwP4iQ62R0gbWTlq6Kp5mgWllv792MTmpc/jLmxLYxFj3GiFTXyuCr2M0+xLn0IorihucJ+u/QfLSGtxGpUzWqsvYGV5qM0nOapEuD4OV1RRY5LCtfoCHnrmFB48MNOWrmKjU//lIhAlmmzHX52t4cmdWxItuHzgzpuc58i2kDaIrub4mZcutLl1BMWcKQ++Vl/AimXR11gaDjyuKG+Q3ds3ts3gANcH0bac8yER3LLnENaPVLxcM3ThIiBZLvOgeR5nPTtKkuEUz0opJSKfV0ptBnDC9d24KKXmReTnABwGUALwG0qpM2nug5CsHDWefulV6+dB8QzEe/ilMU0X98XkSvPQpOk16jvtqv2TTWIhuE/fVAktaPS0sGsB2uW5OtY0p9+DfrQuT9/qbK0hfKPCmg50//rIYQHwyQ/dbu0joDGV/8ZcHW5viWgq5dJSKoKJuCI+TrqPbdslkaXf7XpmJrJoiGakUsZjE5ud4tm1kDaISyzq69rWL2/U6nhi5xbnfZrGjJPvM8VU4CWcCmIaSAQHqHHaaXsuRg2su+GSREgUPjnPfyoi36+U+mraO1dKfR7A59PeLiGarHLGbC/EOALC9ZLwEb6u38cR7FEvo7S9Rn0dSGx51uF92iJmJoJTzFGV5S7P1VEpl1pKZWumpqvGiCLgXlSYJnra3TYI0Ok0weskmEe9ulJGfWExsrKbFlAnzl+yCs6oCoJDct1LWbtn+F6fPvfasiFpy7UdAtoWP+pxja3Ah25rGuWlTb7UrftptMaV42u7N8NuKK77MM4zxfXcCG8nSR6+y/Mc8BtYMw+YFAEf8fweAA+IyHkAV9C8b5VS7860ZYSkQFbVo1zRMB98FjJ28vs4RL2MOvUaDYt8n1QM/TudZ6372xSpNEXMRMwiNhyd8knhMJXudkUUu4Wedt+2aZ1R1FZna9iw51BkdNcmrvW9AkSXm3blxAKtRUiuzl+Pg/uk79hEuT5+myVdZXkJ1+YXWwZVCtGVG3/iPTe1HFdS146o62NBqch71nZvBi3ifJ5paU39B7fjGoDYmI8Y4PoMrIteEZAMBj7ieXvmrSAkQ7LIGbvze9bg2NcvtX3+4ffcaPh2O3H8n7P4fZCol1Ecr1GbM0BQ5Ls8hHXuZPh34aItpmMI+tCa9uOaqnalcISPP0555KwIHktUOfmogVW474LXAGCeqg8SlRMbJpgP7Ju+4xrc2fYZFVE3MQRg/Obrfte2wZV2zzA51Oi/r1qxLLI/ou7ZIgtF36h4EKUajiMPHpgxDup885mZB0zyJlI8K6XOA4CIfCeAlZm3iJCCMzVdxZ9eeKPt8623rm3Ld7YRZ9GLSdQkWTSTNM3DN8fQFA0Pp1wAjhdr8/9tv4sSGuH9K9hzax+ZOo2nX3oVC0qhJIIPv+dGHD170XicI8ONAh9RkfNuEY74+6QWxO07LWRXLBuKHCgEF5b6Lt5zWZ/tff6M0cIvGP1eWb5uFJXmoslFwDvKOTVdNVqzlYcEe++9DYBff4QLlZj2FZ75CF6PeYlpV1Rc31smgvd5eLDEfGbSK/iU574XwCcBrAfwNwBuBvDnAG7LtmmEFBNb5PGVb8Vb3JNUkD782dNYbfG4ddnRJU3zcEX+gi97Uw5kUkkT11EAsDsThL1jbU4pW29di0tXrrVso1wSvPnWfEuFwDjV97RntS0/Okklv3DEP463tA2bkPURwqbS29XZmlPUjgyXre2ZrdWXrm19nd5/x2hLusflufrS9Zt2+oxvlFN/ZqqGGPx+VI6wzdnDdI8WyePYNbAYv3mt18AhPKhjPjPpdsuOQgAAIABJREFUFURFPHhE5BSAuwB8QSk1JiLbAHxEKfUz3WhgkPHxcXXiRKqmH8QDn7zEQeKWPYesRQdentzRkjpgy9U1WWBpoRXsW9uCtjXDZbxVX4z8fdR2AD/Hg6RT+mnjKqJgOy/6d/oYb33489Z89U9+6PaW47xydd7Lfiuqzba85KTbC/aBy07N9jvfPPSs2lMeEiwryVKJ8ChsQnyomdtuOu/D5SHMeW4/iE+hjiRE3fO2ezTYHp/vFAXXoloT4YIywbz7QX7fkHwRkZNKqfHw5z45z3Wl1LdEZEhEhpRSR0XkyQzaSApIkSIdRcEVNQ73l8lnOBjJihqUWKNzc9E2V0Fc4sjnnJqib1snj3gJ5yTRVdPvoiJQLhFYna1h97ONioMu94bweXG12/e4qrO1pXQePZ2dtE8AYHbuGqamq22pBabqlMG26r4z3dM2hgRYsaxkPc+mc+KTE15fVLEixrbv2taflUuC2nx84VwecrtBdELStQXBz13Xd5ZMTVedFnYm4qby6HM8W7O73BBSFHwiz18AMAHgcQDvQCN1Y1wptdX5wwxg5Ln79FKko1u4IkhRi6bi9lsa/T81XXV6ASdtmyvSqzE5A8xdm7eKPNfvfKLjUS/qkUoZ335r3p5OUCnjyrX5yHLSOpIdFEK24xKgRQj4ng8X5ZJg/wdub1vo59rmK5M7ALhnIUw8GRikjQyXoRSs3teA33URl24Vg1kzXMb0x80l7LMiOFNloiSCRaWWHE1MvVASwdcf/9HM2mcrnqKvQdNvwg4ul+fqsQaNg/yOIcWhk8jzKQBzAHahUVFwNYAb0m0eKSqDVs3JB1cEadeBGedvbf1mS41JIwdw/+FzXi+suOfUFukNvuy3bVrXJoABdzRquDyE5cuG8NTxC1g/UvGOQPk4Z8zW6viIo7qcb4rGlavzOHG+1W3lH77zbUYHFoVGlTndxqjz4SMU6wvK6FhhI1hNLs55Hh2pxHY28E0D8RXEgoaLTdB9pRPWDJetg7fZiEFd2vgM+HyKkGQ5sLBVINXXoMlxJ3hMOpL85M4tS9uLGugBg/2OIcXHRzxvU0otorEQ+bcBQES+lmmrSGHg6mczNkERJRxM/eaTGhM359w3GhnVNhc2Ya9zOG3H9fh9m1ui9OFo1Fx9cSlXNW6akD4vGxwetEfPXsTWW9fi+DcuJxYds7V6iwCvztYixY0+DpcoiCMUbY4VYcKDrTg5zts2rfP6XjjSWC61Fy0J4nucgkYp7ccmNmP85rWxcmhNlIbEKZCTVMvsZD2I6/zFjbZvnTySSX6w63o1/c1lo3lsz11L7YuaARn0dwwpNlbxLCL/CsC/BnBrSCy/DcCxrBtGigFXP8fDVXTD1m+2l81Dz5zCrgMz1peyq3KcT+qBT9tcRAl7n5eoTwpBEv9qV3SxOlvDpSvXIktbp40+Dpd41UIRgNGuL0hUYRJpfmfbpnXYf/jc0rUU9tAGGrm+C0q15RAf+MqrS77H4UWw+v+D1QOBxsCiPCRYtbxk9VpWwJIgds0UBPsjbg6tqT8WHEU64t4DaawHcZ2/uIOE6mxtyUPZVVQoLq7r1SRwfWcrkzwrCSkKrsjzZwD8Phq5znsCn39bKdU+N0n6kiKb9BcRm12X6yVme9nYFhsC5qlRjU/qQXlIcMPKZc4V7T5RNdeUvs9L1HdqNu4U7qP33Gb04NX4CNkseG22hid2bmkTDcEIK4ClyoouLl25imGLQA2W5g4LvIMnq9556PVFhX0vnGlxdtHXpf5/kx6tLyrUHUVKdBqJvn7GPvGicf+fPn4BR89ebPM7tuVgu/LObVUndXvSiBr7DPSi7B2jWDNcxvBycwEWvSXXsyMuu7dvtOY8mwSu72xlkmclIUXBKp6VUm8AeAPAh7vXHFJEWM0pHlnkiIZfykmr3OloZNzFdzYB7xLXtuMaElmqJDjiiBAHcU3hutoRVTnQJGSzZH0zh/jE+UttRVqCBXb8Cp+Y3SSCosYm8I6evdiyGMtVatnn/MTBFFV07cPkVBO+BrXX9I53v7Mtqq4HJi6rwCQL05IWKjK58fhSKZeWXC58F2bGnbkx3U/7P3i7020jKm3HFklO691CO1XSbXxyngnpK4r2oN29faMzSqpJErENEmf1elRUzUdc26Zlg1Gx8pBE5se6pnCj2uFKDdFCFkCLMAgzYilIk4QrV+fxyNRpHDxZbYngHjxZxfjNayMrrflQHhIv+7NOI6BJsXmRuzAJwKioejCaefTsRWvkuSSS6Dhcg8OglWCQTku7B/suzjXi+7xwrVOYedTsQmKaBSsPCdYMl7vi1Uw7VZIHQ9FfIaR/0A/aanMhnX7QTk1X822Yh24JRl/jLqaJm0MYFVVziWvNxNgoHr9vM0ZHKhCYRUp9UWHV8mVL3xkdqeAjd9609O+RShkry0PYdWAGWyePtJ0nV764/u7u7RtRKZdavhPsj4mxUaxaYY4jjFTKmHn07ha3ChPhdut/rxkut3xPLzSM6rvd2zcimaRDS2EQ23UyMlxuuQ+6JZxLIkuDr62TR3DLnkPYOnkElXL0qyh8Tbqi6vqcBwdqtv788HtuNH4ebmP42jNdV8D1xaGmZ0onDhKjgcGea/8mfJ8XPve1z2/qiwrDy5fh5ckdLYsEfYnq+07bnAVx2kx6H0aeyUCRRp5i0kiKbRs2K6ggYfHrWmwD+OU0u4jKW/Sdsg5Oy9pSA96o1Y1RLZ+IkitffNeBGZw4f2kpHcJ1/mzb0ekAPoubTP179OxF77SHYH/r1I6oRYM2dOTTtuBXKXS1MqRmQSmMfeJFvDFXh5b41dkahqQRyXGVNQkLQNc1aLrPF1XDBvHqvLKmy2hM155ejBfOyTU5gNgW/CadUTANfsOpSTYP5TgD5yRFWNK2M40bSbbtp9qcXelG9JnR78GD4pkMFGnkKSZ5MLq24eOYEBZn4YVTq1MuaWsTXds2rcPWySNWQeeKcMW1PbQNdPY+f8Yr3UCh4Vih0yHC+ZlBYRPVtiihZBt8xRUQwZd92I0i7GrhwmV1mGap8CC6DHmU/ZxpMKGF7ZpVK4xC0CQAXefM1u+1+iJebhaLcWG69nR7wve/zdvdtGjPdF9FFQ5xLaAzXdedLL6z2eO5UlvStjONG+BwDUi6JWCTBmVI70LxTAaKJA/6NB6Mrm3Y2hTOUTZFrrOqwGUTXS5hFBXhimt7aC1NXqsv5SBHpRuo5jHYXEqqszU8eGAGq5aXUB6SlhmAcNtcQkm3NXyOVsfMl973wpm2QVLw349MnfYSvsHrMyywtKBIk+C1On7zWvzi75622tTZmKsv4s+a2/CZ6XFdT7aFor6CLmrQE+zfOAt+dR8Fj83121c8hL4mjcV3rtL1NtK2M40b4HDNCnVLwLKY2OBB8UwGiiQP+jQejK5tmBwfwm3KY1ow/DLeOnnEKpx9IlxxbQ/TspEL9r1twdaVawsolwQjlTLeqNWXIvm7Dsxg/+FzkdPu60cqxnNULsXLXI5K8dDR6H0vnIn8rm8BizQI78s3Qm7DRwhGXU+dCDqfa686W8Mtew5hZLjcNvAyofvIdF/ZBs/dZtQxkLeRtp1p3ACH3s+DEQPbLGExscGD4pkMFEke9Gk8GG3b0JHRsO9uuE2dRL/TchexvYQEdquvTqLlUXndvgTPk+tFWl9QWLViGfbee5t1oBIV7WxbOLXQXkSkU3zFl0J71bmshESwj5MK9PDiSh9sIrtTQed77Sm0D3hsaRi258WGt5ufDb7VHZNiujeTRpHTtDNN0ga9diQvActiYoMHxTMZOOI+6NN4MLpextpiy2XhlTT6nWbE2mcQEXwhjwyX8eZb80sRuSSltoFWAWQrggE0Si+HK8jFLU1tW2zmmnbXosyW0rGoGu3wEZQjFbeA9BU8mnCfZ1EUJtzHSQR6uSR49J7blv5tG/DFGQh2Iuj073wi/GGWlQRQcKYAaaamq/jS1801x46evRhrv1PTVacXc/i7Nku6x+/bnKuVZ9KBT54CNu3oOyk+orpkUZQG4+Pj6sSJE3k3gwwgabpt2MSLyYc5yW+CuKaEdQU63+MKv3CBxstJi37T35O02UXUPnT1NZvoihKOo83FZranoqvIjKuvd2/f6CXEnty5JVb/A41j3vHudy55G9uOy1RxsFNM6To+JdeHAKy2+AA/MnW6zWWkUi7h/jtG23Lug9efL3GueZ9jMWG7DuNu33fh39R01VoFcP8Hbo91rWa1jqIbFM3Dn/Q+InJSKTUe/pyRZ0I8SGNaUm/DVhksHLGLEjo+UZWoIhlxotJR0RXf6fpOUgei8hsvz9Ux/fFW2ztfwSiAc7EZgBZv8GB7AHfkS08rR4ln1zVm69/Lc/WlmYtdB2ac11b4HCYNnQiAJwJCPzzj4MIlBKemq0Z7vlp9YakiY/jzh545BcBvNiMtG7QoZg3XoYmo7fuW2bbZXdYXlDG1q18XuKWZPkKICxZJIaTL2HLwwp+7xOjoSMUr4ubaV5LiAhNjozi25y5j8QPfF2+nOYgTY6NW6yzT576iXjW37VN8wtRPE2OtRWHC5yiqf6Iq3bkilFpERlkIhiNzUWkiNnRf6W0GC65cnqtjyHAolXIJT+7c4iyasf/wOesxuJwgfAsd+V7zuuBF0sGF7zUe515w3Zuua8v0N99nECHEDMUzIV0mquKdJmqBnk+ExbWvtKNPPi9eve9Oq3G5hFR4W77Hox0FwiLYhmm7psGFrxCLst2LEte23wf7PFxd88q1eedLwLbPoPuCrSDJSKVsHUjYcJ0r1/H7VJSbmq5aByDB/Qb7yYVuT7hVcfJs41QJDLczSJS/us9+ucCNEH8ongnpMlERSk0a0SHXvtKOPpleyNr+LbhvAB2XSHdZZ+06MIMNAVHuezxhT2ctgm378tmurxADoq3JkpTQDp5vmxuIbYQwOlLBJz90e6TIclVntM1S2LD1qaBRRtslNH0KHfns13emYlEpvDK5A0/s3BJ7kKAJ359RAyRb/+zevhFlQ7i/XBKjIPZ9BhFCzDDnmZAc8MnNS2v1uG1faa9Od+VEB6v6maoCxi1m4HKYCFaC23VgBu+7dS0uXbnmFEQjlbJ13530k68Q89mezYPXRthC0CYubTZ6r83WjOd026Z1LRUaR4bLxlzuJIMwWwW+B+68acnj2lTlMWp/rvOQ1C1kdaWMrZNHlvrlCcdiTxemIjauMvC2bQDwdtsI75cQEo9cxLOIfBDAXgD/AMAPKKVooUFIiKBw0SV3g9PTaSxg1NtPa3V6WAjsP3wODx6YafG+tUVQ46SLRC0c1CgAX/r6JTxw501LbhSm0s97773NsoV4/WQqkWxDO3uYBKlvRT0AEAFMuwmLSZtNna2dQyK4Zc+htkFQeMGdiaSDsKi+TloAxXVthSOuPnZ+5SHBlWvzS0I1rg2jzRXCdM/7ltmmGCake+RiVSci/wDAIoD/DuDnfcUzrerIIBJlEZdke1nbOSWxREtik+VrJRbcdlbHb7JYs7FmuLzkxhDn/IZdLd6qL6BWX2zbvun3tv2YLODC6AGHa0Cgv+Mj9Hxwnae45zCONZvN9i34G5vfuM81nPb9TAjJjkJZ1Sml/hwAJCK/i5B+Iqlo66S6oKkNcay60myzi6SRSt9KcNXZWssUe1xxF9UPNos1G0H9Gef86uiiS+DZputdUd3xm9cufW5Kq4maNdDfScsnOOo6jRtljZN6MzE2avXk1sd3y55Dxv34zJ6keT8TQvKBOc+EdIFOKv2l6Yphe3Hve+FMm6gCkHqbTZREEkfdwtPcLvTffY8jmIIRTPUw/d5lsWbijdp1YZbk/Np8fTWuCK1J3AbF6AaLMPQh7FqRNMKftsCMm6I0a/Hj1sfnU23TRr96LBMySGQmnkXkCwC+2/CnX1RK/V6M7XwUwEcB4KabbkqpdYR0l07EQCcv6jC2F/TlufpSpE0vtKuUh9pSAmxtDgql1ZUy2hKLHSwqlVqetW/qRFTfhwc7pqIdwd/HFT7Bc5fk/Lr2p89jkgHb1HQ1zqmztrnTsvBZCMw40eqoc9LJItI07+duwKp9hLSTmVWdUuqHlVLfZ/ift3BubudTSqlxpdT4unXrsmouIZnSiRhI05PV9wWtAMwZcmkBeyVEbT03W6sbF7DZiKpIF9xPlDf0YxObW6zDXNZfUZHdqFQQ/fup6SqGYqSghc9dkvPrcx6TFMGJG0EPEmxzkn0HsR3fkEhsT/AkRJ2TTqzeeslj2eQNHtdWkpB+hGkbhHSBTqJNabpi+OYIu4hTCVGjpaVJmCnll1PsG8kMRhhtuamm4wjiM6hZP1JZyj125QKvWl5CuTSEN2p147ElOb+7t2+0Oo3oqoFJBmxJI7vhRYKdRo5t16muJgh07jbjwuecJHW3yMLlJiuYn02Imbys6v4nAP8VwDoAh0RkRim1PY+2ENINOvVUTsuGyvTivnL1uuVWFHEqIQZxRTNna/VIYZz0JW4btAgQGdl15VDrftj7/Bln7jHQ8FHee6/dbxdoPy8uO0I90LBRX1jELXsOGRf+AdHV6OJ4SQPtftKu7fjOfOjjNnk6d0u8ZWn91iu2cszPJsRMLhUGlVK/q5R6l1JqhVLquyicSTfotCR0JxSpotfEWGsJ6b333uYsQ60RAPff0f7S7zRXU/tXBwlP8Sd9iZumyHXRjajIrq1PggscfQYdvqWjfabHfSoWXrm2AAWzM0bUgC1uuWggu/LPE2OjWEzBE5wkJ+0qpIT0C0zbIANBeCFZ3AVMaVDUaNPE2ChOnL8UudBOATh69mLb552kglTKJevvggLJFslUaHj42qa9bRXyjp692FYAJPw7W1pEkgWOUWLPN7Ie1wIQaIj9RaW80gPC/WWLXmtcdm/B7cRNTdDRddueV1f88+TjtKGIi+PybFPaVUgJ6RconknfY/PgLULuXlFe1uM3r8XnTr0eGUk1icCwUFpdKUOkYfe13lFQQkdwgyWFgwSjWy6B7hoIhft326Z1LQVBXL+1lcMOtmuNpTS16zcmXJH14DEkWcy3qBRentzh/I7pOgTcFRyjiqEkHSz6FNjxWZ+ZxNO8E4eQLEjqmJLWM6WX8rMJ6SYUz6TvcUWw8pz+Nb0Ydz97CvteOLMkPNN+UdlEUlis2OzK9CI5V2nhqOMErldUA4BvX503/m7bpuvuOlF+zqaBkKl/4wyifKJuj95zG3Y/dwr1hfjR2SC2yPrqSjn1BZ5hbNehK5cnrWIoJnyi6zYf5qjtuAbMRVwcF7dNWQwAijpjRkie5JLzTEg3cQnkPHP3TC/G+qLC5bn6Ut7rrgMz2JBSjrYtr3bv82fa2qHQrp0k0KY41lWufO/9h89hwbLgLpwionO1bZoufJ5N/WuTuNVmhNe33cHv7P/A7S3f+cidN8XObbflCIvA28lkpFJGudTaOz7C3XYd2gYEWU/b+zqdJN2O7XNbHnmeA+y4x9CpRSAhxA9Gnknfk9RxIWviuFSkEUGyvVht4kyXWw5X2EuS/mKLXrn6wCZmfJ0c4ooeU//6RN3SiMzZpsd3OdImBGibnUgyZR+3n4KDgSzSjnydTpJuxzR7sm3TOudsS17EdS2hOwYh3YHimfQ9pul3H8eFrIlrC9bpFHJcCzI9Nb918kjkb5O+nF19IGiIsyTpFFHbNpH3FL1JhNvSVGxpE6ZtRAncOP00OlJpEc5ppAhE5aUD19OIovKsg9iuk22b1nml8+j95jnAjrtgr9eqFxLSqzBtg/Q9pun3J3ZuwWMTm3NtVxJbsKQiVZddNrFmuOy0FUtrGt3E7u0b21INNAowTjfb0ikAtFgRbnh7euXL45CWJeLUdBVz19rzweOkTfhY4MURh8E89H0vtKf7xE0RMLXv4Mkq7r9jtO1+faVpregrzG3XydGzF73TeRTyWywIxLe47KXqhYT0Mow8k4FAR+R0lGvXgRnsP3wu15XjJpeKK9fmnYvPkopU26JJQWPRW7Ad4ehkWtPoJvQ+bK4ONjEbjrCaoqBJhHDS/tXXVTjFpZNorGmh4EilHFlwJYjPgjOXLV8YnYc+NV21uozE6Xdb+46evZjKgkRTJN6VChNmtAAR2zhpQXTHMFMUVyPSP1A8k4GhiFZUJhFoEmFAu0iN80KwCZpgZM0VzbK5ccSZRrehFw7a3Ca2Th6JPMY4iwNtJB0EhK+rNCwRbY4Tq1Ysi2VR5psD62u5p3/nii7HGYCknaMb7IOwZaK+dlxrIFz3m88+iyDM6I7RShGf+6T3YdoGGRh6YSW6dpR4ZXIHnti5xTpd61uRTmMTND6RNVvaS9xpdBem6ebykODKtXmvY4wrtkoiiVwxTPhYq8VtX1xRabsebMVEwtfDo/fcZk2fMf3OdTxxBiBpVrAL98Fsrd7iXKOvHVtqwwMJroe49yHpPr3w3Ce9ByPPZGDotZXorghSXP/XOAuPgtHvUrPCXBoRZhem6WZTcZXgMQYjflGV8MIsKpVazrvPYru4YjDuwi/b9bCyPNRWxdF03nX/P/TMKWs/aqvCrZNHsLpSNha2GamUY10jaVawixrE6GtHp4OkES0uojc0aaXXnvukN6B4JgNDP61Ej/tC8M2FDE9xaiHVjanO8GDhlj2HjN/TVfdM7QxSKZcgUJirL7b9zeec+0zH64WYLtmeRAzu3r6xrfhKuSTW7djO++xcHU/s3OIlFCfGRp35wME87nJJUB4S1AMe3ZVyCXvvvS1WGkOaObo+Ykh/J63UBgqz4tNPz31SHCieHRQtl410RppRrrxJ8kLwWTTpit51O6LmOkZbO0siWFRqyfLs0NdebxPPwQiq7Z72zZN0Va8E2nPCYz1Twht27MjWVyPD5VhC0de2rr6gsGa4jOHly5zVKn0GXWkJWZ+2py2YKMyKTz8990lxYM6zBeay9R9xbZ+KgM32LKklVdR1HRUxyzKiFj7WbZvWWY/R1o5FpfDy5A7s3r4RB0+aHSGCEdQHD8xg7BMvtt3XvnmSrv4I54THeabsP3yuJaoLNKr+2fI0bZZ/b741H+uZFcc+cXaujmN77sLLgePMM780qu1ZCCZawxWfXnzuk+LDyLMF5rL1J1msRM9qhsIn+hl3v1HXdVT0LquImulYtd/v0bMX247R5s6h2+eziE9zea6OBw/MYN8LZ/DoPQ0bOFcJ56D7x4jFpcK0EDPOMyVJWs7e58+05SFrwd2J1Zkp9xwwXwt5pjGYrB9NbhtZ7pMzlMWEDiQkbSieLTCXjfiQpQ1SlNhK8kKIuq5NU5yaTiNqtkHG1HTVuFDN5fcbNRWb5D69PFdfOncuOzP9eXW2hvKQoFySltxkWz/FeaYkSQd4w7CAz7VfG1Ee2oD9GNNIY+hkMJqHSKIwI2TwYNqGhTQtlIiZtCqx5UmW09RZDOCiruvgFCfQyCEGOp/qtKUsPDJ1Gg9/9rTV4cEVaXVNxSa9T/W5M03HmxYG1hcVVi1f5jUlHOeZkiQdIKtnVpxp707TGJKmy/XDs4QQ0jsw8myBiwyyJS/j+rRTLLKcochiMZLPdR0nkubbn7ZBxtMvveq0mPNZAGli26Z1eOr4hRaxWy4JoNCWSxzmtdmacTrels4yW6tj1YpleGLnFme/xXmmJEkHyPKZ5XtNdJrGkCRdjkUwCCHdhuLZAnPZsiWPnPI0XrJhsWjLeU1jhiILMZTmdW3qz10HZnDi/KU2D2XbYMIlnIPHGmfQMzVdxcGT1RbhLAB2fv+NGL95rTE3OEgwCh/cx9bJI1YB7esqAfj3fdx0gKI8szpJY0gyGOX6lGyg2xQhdiieHTCXLTvyyCnv9CVrEosm0oz2AemLobSua1tJ7KeOX8D4zWtb9mGL3JYsxU1KIkupAXEHPbZ2HT17cUnUhz2UNa5z58oHB/yupayfKVltv1tCKslsC9enpA+j+YS4oXgmuZCVP6rrJd/pS9bHwWHNcHnJsSENkoqhbogdW78poE1E2qLo998xioMnq22fB3Nq4w56os7z/sPnjMI5KNhN6M/3vXDGONvg2ncv000hlWS2pZe8lnslmstoPiFuuGCQ5EIW/qhRi406XVDlI4yGly/L/eXSLY/yONFA26KzxyY2Ry5GizvoiTrPrhSS/YfPORedTYyNYni5PeZQRMHWKd30bk7iydsrXsu9VDuA0XxC3DDyTHIhi5SEqGhJpznEPhXMivBy6VbUaPf2jdh1YMZY+M4kIm1R9KjoetzIYtR59rWhs0VXXee4aIItDbotpHo11zuKXorm9lI0n5A8oHgmuZF2fmbUS77Tl2xUzitQjJdLt8TOxNgoTpy/1OZqkXbUL+6gJ+o8m7ZnsqGr1Rew9/kzbdeHtRR2pVw4EZQGvSCkemF9Si9Fc+k2RYgbimfSN/i85Dt5yQZFWXW21ia4ivJySSp2kuRjPjaxGeM3r8006pdk0OM6z3Ft6B6ZOt3iHmITFnvvvS32sZkoWl5sJ0KqaMeSJ70wCNH0SjSfkLwQ5bCKKhrj4+PqxIkTeTeDFBRbJbROintE7S/Nl0ta20vSD93uu6LhsqEToM3DuVsl2YFinIckx1vUY8kL9gchvYeInFRKjbd9TvFM+olejXSl/WKN2w828Tg6UjGWx+43pqarePDAjPXv3eqHfjoP/XQsadGrzydCBhWbeM4lbUNE9gO4B8A1AF8H8FNKqdk82kL6i17IfTSR9mKiuP3QS/mYWTAxNloIC7p+Og/9dCxp0avPJ0JIK3lZ1f0BgO9TSr0bwF8AeDindhBSCPIWGp3a+PUDj95zG8Tyt271Q57nYWq6iq2TR5xWfXHgNUUI6VdyEc9KqReVUvPNfx4H8K482kFIUchbaPSKV26WTIyN4n23rjX+bdumdV1pQ17nIQsP4qhjSVusE0JItyiC28ZPAzhg+6PIIG50AAAgAElEQVSIfBTARwHgpptu6labCOkqeVtDDfLq+mAe6pCYY89Hz17sSlvyOg9ZeBC7joXlnwkhvUxmCwZF5AsAvtvwp19USv1e8zu/CGAcwH3KoyFcMEj6GS4m6j6mhZomBMDLkzu606gcuGXPIWOxm6yOm4sJCSG9QNcXDCqlfjiiQT8J4P0A/pmPcCak3+FionikMdgwRVxN9Huebrc9iG22gIO8mJAQ0jvkkvMsIj8C4BcA3KuUmsujDYTEgfmZxSKtHF0fsTYIud/dzLWemq7mvjCTEEI6Ia+c518FsALAH0gjx/C4UupjObWFECfMzyweaeXo2iKuJREsKjUw6TNp5Vr7zAbsP3zOmiLS74MUQkh/kIt4Vkp9bx77JSQJWSymIp2RlrWfbaHmIFZ96zRtyHeQaTtHChyMEkJ6gyK4bRBSaPL2YO4Fur3YMa0c3U4irlzg2YrvINN27kaZskEI6REongmJoNuLqbpJGgIwj7SWNK39kkRcmcrTju8gM29bRkII6ZS8KgwS0jP0awGRtBbduSKOWTExNorH79uM0ZEKBI2oZTdTLfI45qJgWzzrW+gn73NHCCGdwsgzIRH0awGRtHK580prydPab1BTeVwR9zgRZdoyEkJ6GYpnQjzox5d9WgKwn9NabAziMQPuAZcubtJvg0xCCAlD8UzIgOISgHFyoQcxh7XIx+w6d53muEcNuPpxkEkIIWEongkZUGwCcNumdbEWw/VrWouLoh6zK60CQMeLHAc14k4IIUGklypjj4+PqxMnTuTdDEL6BlMkcv/hc1YrMT01T4rJ1skjThu4Ts9rWJwDg+uLTQjpf0TkpFJqPPw5I8+E9ABZeQqbptl3HZgxfrffF8P1A0ny2OOc16JG3AkhpJtQPBNScLrtKcyp+d4l6tylVViGYpkQMsjQ55mQgtNtT+F+9bVOE5vXcd64zh3PKyGEpAMjz4R4klc55m57CnNq3k2Rqwv6nDueV0II6QwuGCTEgzwXSrkWgXEBX/fh+SCEkMHAtmCQaRuEeJBnOWZOtxeLQa0uSAghpAHFMyEe5CmYJsZG8fh9mzE6UoGgEeGkNVh+2BbYcUElIYQMBsx5JsSDvB0o6HBQHIpcXZAQQkj2MPJMiAdMnciGorpWuOBMACGEDDaMPBPiAR0o0qfIrhVRcCaAEEIGF4rnHiUv27RBhoIpXVyLMNnPhBBCigrFcw/SyxE7QjR0rSCEENKLMOe5B8nTNo2QtKBrBSGEkF6E4rkHYcSO9ANchEkIIaQXoXjuQRixI/0AXSsIIYT0Isx57kHoM0v6BS7CJIQQ0mtQPPcgtE0jhBBCCMkHiucehRE7QgghhJDuw5xnQgghhBBCPMkl8iwivwTgxwAs4v9n7/3D5LjKO9/vOz0tqUcmGgkUggfLMoRIwQhLeAIi2rsbO1lEMDYTG1Ackyz55Zu9myexQyYrEt9YJsq1Ei0xZJObG29CAsE44x9kIltk5RA74cFg45FHQgikgDGSaJsgkEbBmpbVM/PeP7prVF19zqlT1dXd1TPfz/P4saa7uurUqVNV3/Oe9wfwbQDvVdXnutEWQhYqLKRDCCGEZE+3LM+7VfX1qroRwMMAfrdL7SBkQRIU0ilPVaC4UEhnfLLc7aYRQgghPU1XxLOq/nvoz+UAtBvtIGShwkI6hBBCSHvoWsCgiPw+gJ8DcAbAVY7tbgZwMwCsWbOmM40jpMfpRiEduokQQghZDLTN8iwinxaRLxn+ewcAqOrvqOolAO4B8Ku2/ajq3ao6rKrDq1evbldzCVlQdLqQDt1ECCGELBbaJp5V9SdU9XWG//4+suk9AG5oVzsIWYx0uvQ13UQIIYQsFrqVbeM1qvrV+p/vAHCkG+0gZKHS6UI63XATIYQQQrpBt3yed4nIOtRS1R0D8CtdagchC5Y0hXTS+i1fPFhC2SCU2+UmQgghhHSLbmXbuKHuwvF6Vb1WVekYSUiXacVvudNuIoQQQki3YIVBQgiA1vyWRzYN4c7rN2BosAQBMDRYwp3Xb2C2DUIIIQuOrqWqI4Tki1b9ltO4iRBCCCG9Bi3PhBAAnU9vRwghhPQiFM+E5ITxyTK27HoUl23fiy27Hu14jmT6LRNCCCHx0G2DkBwQBOsFPsdBsB6AjrlCdDq9HSGEENKLUDwTkgNcwXqdFK/0WyaEEELc0G2DkBzAIiOEEEJIb0DxTEgOYLAeIYQQ0htQPBOSAxisRwghhPQG9HkmJAcwWI8QQgjpDSieCckJDNYjhBBC8g/dNgghhBBCCPGE4pkQQgghhBBPKJ4JIYQQQgjxhOKZEEIIIYQQTyieCSGEEEII8YTimRBCCCGEEE8ongkhhBBCCPFEVLXbbfBGRE4CONbtdnSYlwH4TrcbQRYMHE8kKziWSJZwPJGsyHIsXaqqq6Mf9pR4XoyIyISqDne7HWRhwPFEsoJjiWQJxxPJik6MJbptEEIIIYQQ4gnFMyGEEEIIIZ5QPOefu7vdALKg4HgiWcGxRLKE44lkRdvHEn2eCSGEEEII8YSWZ0IIIYQQQjyheCaEEEIIIcQTiuecISLvEpHDIjInItZUKyLyVhE5KiJfE5HtnWwj6R1EZJWI/KOIfLX+/5WW7WZF5ED9vz2dbifJL3HPGhFZKiJj9e+fFJG1nW8l6QU8xtJ7ReRk6Fn0S91oJ8k/IvIREfm2iHzJ8r2IyB/Xx9oXReQNWR6f4jl/fAnA9QA+Y9tARAoA/hTATwJ4LYAbReS1nWke6TG2A/gnVX0NgH+q/22ioqob6/9d17nmkTzj+az5RQCnVfUHAdwF4A8620rSCyR4b42FnkV/0dFGkl7irwG81fH9TwJ4Tf2/mwH8WZYHp3jOGar6FVU9GrPZGwF8TVW/rqrnAfwtgHe0v3WkB3kHgI/W//1RACNdbAvpPXyeNeEx9gCAHxcR6WAbSW/A9xbJDFX9DIBTjk3eAeBjWuMJAIMi8oqsjk/x3JsMATgR+vub9c8IifJyVX2+/u9vAXi5ZbtlIjIhIk+ICAU2CfB51sxvo6ozAM4AeGlHWkd6Cd/31g31ZfYHROSSzjSNLEDaqpP6s9oR8UdEPg3gBwxf/Y6q/n2n20N6G9d4Cv+hqioittyUl6pqWUReBeBRETmkqs9k3VZCCHHwEIB7VfVFEfk/UVvRuLrLbSKkCYrnLqCqP9HiLsoAwjPyV9Y/I4sQ13gSkX8TkVeo6vP1JatvW/ZRrv//6yLyzwA2AaB4Jj7PmmCbb4pIP4AVAL7bmeaRHiJ2LKlqeNz8BYA/7EC7yMKkrTqJbhu9yVMAXiMil4nIEgA/DYAZEoiJPQD+S/3f/wVA08qGiKwUkaX1f78MwBYAX+5YC0me8XnWhMfYOwE8qqy+RZqJHUsRn9TrAHylg+0jC4s9AH6unnVjM4AzIRfGlqHlOWeIyE8B+J8AVgPYKyIHVHWriFwM4C9U9W2qOiMivwpgH4ACgI+o6uEuNpvkl10A7hORXwRwDMC7AaCeBvFXVPWXAPwwgD8XkTnUJtS7VJXimcD2rBGRDwCYUNU9AP4SwN+IyNdQC+D56e61mOQVz7H0ayJyHYAZ1MbSe7vWYJJrROReAD8G4GUi8k0AtwMoAoCq/n8APgXgbQC+BmAawM9nenwaCAghhBBCCPGDbhuEEEIIIYR4QvFMCCGEEEKIJxTPhBBCCCGEeELxTAghhBBCiCcUz4QQQgghhHhC8UwIIQsMEfkxEXm4/u/rRGS7Y9tBEfm/Uhxjh4j8ZivtJISQXoTimRBCegQRKST9jaruUdVdjk0GASQWz4QQsliheCaEkBwgImtF5IiI3CMiXxGRB0RkQES+ISJ/ICJPA3iXiLxFRD4vIk+LyP0iclH992+t//5pANeH9vteEfmT+r9fLiJ/JyIH6//9KGqFdF4tIgdEZHd9u1EReUpEvigid4T29Tsi8q8i8lkA6zrYPYQQkhtYYZAQQvLDOgC/qKqPi8hHcMEi/F1VfUO9fPonAfyEqp4Vkf8O4DdE5A8B/C8AV6NWUWvMsv8/BvAvqvpTdSv2RQC2A3idqm4EABF5C4DXAHgjAAGwR0T+I4CzqFUP3Ijau+NpAPszPn9CCMk9FM+EEJIfTqjq4/V/fxzAr9X/HYjhzQBeC+BxEQGAJQA+D2A9gGdV9asAICIfB3CzYf9XA/g5AFDVWQBnRGRlZJu31P+brP99EWpi+iUA/k5Vp+vH2JP+NAkhpHeheCaEkPyglr/P1v8vAP5RVW8MbyQiGzNsgwC4U1X/PHKMWzI8BiGE9Cz0eSaEkPywRkTeXP/3zwD4bOT7JwBsEZEfBAARWS4iPwTgCIC1IvLq+nY3wsw/Afiv9d8WRGQFgO+hZlUO2AfgF0K+1EMi8v0APgNgRERKIvISANe2cqKEENKrUDwTQkh+OArgv4nIVwCsBPBn4S9V9SSA9wK4V0S+iLrLhqqeQ81NY289YPDblv3/OoCrROQQav7Kr1XV76LmBvIlEdmtqo8A+ASAz9e3ewDAS1T1adTcRw4C+AcAT2V54oQQ0iuIanSVkBBCSKcRkbUAHlbV13W5KYQQQhzQ8kwIIYQQQogntDwTQgghhBDiCS3PhBBCCCGEeELxTAghhBBCiCcUz4QQQgghhHhC8UwIIYQQQognFM+EENJDiMhNIvJIt9uRNSKyRkReEJFCzHY/JiLfzPjY7xWRaEGa8Pf/LCK/lOUxCSG9C8UzIYS0gIhoUPGvDfteW99/f/CZqt6jqm/J+DhDIjITqlAY/u7vROR/tLj/b4jIT7i2UdXjqnqRqs62cixCCGk3FM+EkJ4hLCJ7hV5os6qWUSvd/bPhz0VkFYC3AfhoO4/fC31ECCEBFM+EkFxTt1r+93o56rMi0i8im0XkcyIyJSIHReTHQtuvEpG/EpHnROS0iIyHvvtlEfmaiJwSkT0icnHoOxWRXxGRr9b3+6ciIvXvflBE/kVEzojId0RkrP75Z+o/P1h3OdgWuBXU2/wtAH9lcgsIW6xFpCQiHxSRY/VjfFZESgCC/U/V9//m6L5E5EdF5Kn6754SkR8NfffPIvJ7IvK4iHxPRB4RkZdZuvqjiIhnAD8N4MuqekhELhaRB0XkpIg8KyK/FjrODhG5T0Q+Vj/OYREZrn/3NwDWAHiofg6/FbKo/6KIHAfwaNTK7rqOkX50teuNIjIhIv8uIv8mIn9kOff65vIn9X48IiI/7tiWELKIoXgmhPQCNwK4BsAggJcD2AtgJ4BVAH4TwIMisrq+7d8AGABwOYDvB3AXAIjI1QDuBPBuAK8AcAzA30aO83YAPwLg9fXtttY//z0AjwBYCeCVAP4nAKjqf6x/f0Xd5WCs/vcP1Nt2KYCbPc7vfwC4EsCP1n/3WwDmAAT7H6zv//PhH9Utw3sB/DGAlwL4IwB7ReSloc1+BsDP1/tiCWr9ZeLvALxMRP5D6LOfBfBREekD8BCAgwCGAPw4gFtEZGto2+tQ689BAHsA/AkAqOrPAjgO4Nr6Ofxh6Df/CcAP40I/hzFex8j5x7XrwwA+rKrfB+DVAO6znDsAvAnAMwBeBuB2AJ+s9y8hhDRA8UwI6QX+WFVPqGoFwHsAfEpVP6Wqc6r6jwAmALxNRF4B4CcB/IqqnlbVqqr+S30fNwH4iKo+raovAng/gDeLyNrQcXap6pSqHgfwGICN9c+rqAnhi1X1nKpag8vqzAG4XVVfrLfZSl0A/gKAX1fVsqrOqurn6m2M4xoAX1XVv1HVGVW9F8ARANeGtvkrVf3XejvuC51TA/Xv7wfwc/V2vQY1Qf8J1CYUq1X1A6p6XlW/DuB/oWaZDvhs/ZrMoiZ8r/Bo/w5VPRvto5jrGCauXVUAPygiL1PVF1T1CUdbvg3gQ/VjjQE4ilr/EkJIAxTPhJBe4ETo35cCeFfdtWJKRKYA/AfUrMmXADilqqcN+7gYNWszAEBVXwDwXdQslgHfCv17GsBF9X//FgAB8IW6S8IvxLT3pKqe8zgvoGbpXIaa1TMpDedU5xj8zsnER1Hr22WoWZ33qeq3UZ84RPr8t1FbBbAdZ5nE+zKfsHzuuo5h4tr1iwB+CMCRukvL2x37Kquqhv4+hlr/EkJIAwzSIIT0AmFRcwLA36jqL0c3qlssV4nIoKpORb5+DjWxFWy7HDVXh3LswVW/BeCX67/7DwA+LSKfUdWvebQXAM6i5oIQHPsHQt99B8A51NwKDsbsJ0rDOdVZA+B/x/zOxmcBnALwDtQs/L9V//wEgGdV9TUp92s7D9vnJ2C/jtHtrO1S1a8CuLFu3b8ewAMi8lJVPWvYfEhEJCSg16DmfkIIIQ3Q8kwI6TU+DuBaEdkqIgURWVYP0nulqj4P4B8A/L8islJEiiIS+A3fC+DnRWSjiCwF8P8AeFJVvxF3QBF5l4i8sv7nadRE31z9738D8KqYXRwEcHn92MsA7Ai+UNU5AB8B8Ef14LdCPTBwKYCT9ePY9v8pAD8kIj8jtUDKbQBeC+DhuHMyUReOHwPwB6j5Lj9U/+oLAL4ntSDIUr2NrxORH/HctU8fhdvhuo5hnO0SkfeIyOp6HwcifM6wH6DmV/1r9WO9CzVf7E/5tpkQsnigeCaE9BSqegI1y+hvoyYuTwAYxYXn2c+i5ut6BDU/1lvqv/s0gP8bwIMAnkfN0hv22XXxIwCeFJEXULNG/nrdvxaoCeGP1t0G3m1p878C+ACATwP4KmoW3jC/CeAQgKdQs/z+AYA+VZ0G8PsAHq/vf3Nkv99FLcjxfai5oPwWgLer6nc8z8vEx1Czuo4Fftd1P+a3o+Yv/Sxq1vK/ALDCc593Aritfg62gMUoxusYxqNdbwVwuH7dPgzgpwP/6nrmj/8jtLsnAbymvo/fB/DOev8SQkgD0ujiRQghhBBCCLFByzMhhBBCCCGeUDwTQgghhBDiCcUzIYQQQgghnlA8E0IIIYQQ4gnFMyGEEEIIIZ70VJGUl73sZbp27dpuN4MQQgghhCxw9u/f/x1VXR39vKfE89q1azExMdHtZhBCCCGEkAWOiBwzfU63DUIIIYQQQjyheCaEEEIIIcQTimdCCCGEEEI8oXgmhBBCCCHEE4pnQgghhBBCPKF4JoQQQgghxBOKZ0IIIYQQQjzpqTzPhBBCFi/jk2Xs3ncUz01VcPFgCaNb12Fk01C3m9UxFvv5E5IXKJ4JIYTknvHJMt7/yUOoVGcBAOWpCt7/yUMAsCgE5GI/f0LyBN02CCGE5J7d+47OC8eASnUWu/cd7VKLOstiP39C8gTFMyGEkNzz3FQl0ecLjcV+/oTkCYpnQgghuefiwVKizxcai/38CckTFM+EEEJyz+jWdSgVCw2flYoFjG5d16UWdZbFfv6E5AkGDBJCCMk9QVDcYs02sdjPn5A8Iara7TZ4Mzw8rBMTE91uBiGEEEIIWeCIyH5VHY5+TrcNQgghhBBCPKF4JoQQQgghxBOKZ0IIIYQQQjyheCaEEEIIIcQTimdCCCGEEEI8oXgmhBBCCCHEE4pnQgghhBBCPKF4JoQQQgghxBOKZ0IIIYQQQjyheCaEEEIIIcQTimdCCCGEEEI8oXgmhBBCCCHEk66JZxFZJiJfEJGDInJYRO7oVlsIIYQQQgjxob+Lx34RwNWq+oKIFAF8VkT+QVWf6GKbCCGEEEIIsdI18ayqCuCF+p/F+n/arfYQQgghhBASR1d9nkWkICIHAHwbwD+q6pOGbW4WkQkRmTh58mTnG0kIIYQQQkidropnVZ1V1Y0AXgngjSLyOsM2d6vqsKoOr169uvONJIQQQgghpE43fZ7nUdUpEXkMwFsBfKnb7SGEEEJIZxmfLGP3vqN4bqqCiwdLGN26DiObhrrdLEKa6Ga2jdUiMlj/dwnAfwZwpFvtIYQQQkh3GJ8s4/2fPITyVAUKoDxVwfs/eQjjk+VuN42QJrppeX4FgI+KSAE1EX+fqj7cxfYQQgjpILQ0koDd+46iUp1t+KxSncXufUc5Jkju6Ga2jS8C2NSt4xNCCOkegaUxEEyBpREAxdIi5LmpSqLPCekmrDBICCGk47gsjWTxcfFgKdHnhHQTimdCCCEdh5ZGEmZ06zqUioWGz0rFAka3rutSiwixQ/FMCCGk49DSSMKMbBrCnddvwNBgCQJgaLCEO6/fQBcekktykaqOEELI4mJ067oGn2eAlsZeoJ1BniObhiiWSU9A8UwIIaTjBCKJ2TZ6BwZ5ElKD4pkQQkhXoKWxt2A6OUJqUDwTQsgig/mVSRoY5ElIDYpnQghZRORx6Z1ivje4eLCEskEoM8iTLDYongnJGRQSpJ3kbek9j2K+XfT6vd2uIM9e7xey+KB4JiRHLCYhQfzJUlzkbek9b2K+XSyEe7sdQZ4LoV/I4oPimZAcsViEBPEna3GRt6X3Ton5bls3F8q9nXWQ50LpF7K4YJEUQnJE3qyCpPtkXcY6b5XcOlEsJZiAlKcqUFyYgIxPljM7Rhy8t82wX0gvQsszITkib1ZB0n2yFhd5y6+cxI82rfU4bgLSib7gvW2G/UJ6EYpnQnIEq66RKO0QF3nKr+wr5ltxX7FNNIJ9dMLflve2GfYL6UUongnJEXmzCpLu001x0Sk/YR8x34pvrG0CUhDpmL8t720z7BfSi4iqdrsN3gwPD+vExES3m0EIIR2lHSI2bp9RSy9QE+13Xr+hK8Lmsu17YXpbCYBnd13j/K3tXKLCOck+CSELHxHZr6rD0c9peSaEkJyTtZuFjwtE3rIgDA4UcXq62vS5j/uKzbq5e99R+tsSQhJD8UwIIYsMH2GcpywI45NlvHBupunzYkGa3FdsFnXbBIT+toSQpFA8E0LIIsNHGOcpC8LufUdRnWt22li+pN/pahIXAEh/W0JIGiieCSE9SbeLXvQyPsI4T1kQbGL/TKXRjSONq0meMo/Y4FgnJF+wSAohpOfIQ9GLXsanUMrIpiHcef0GDA2WIACGBktdCxb0LaSSJ1eTrOBYJyR/0PJMCOk58hbM1iuELZiDA0Us7e/DmUrVas3Mi1XW1wqextUk71ZdjnVC8gfFMyGk51iIFsZ2E/UHPj1dRalYwF3bNuZehPn6Jid1NWml8Eqn4FgnJH9QPBNCeo48BbP1Cr1uwfSxgicNAOyFPuFYJyR/UDwTQnqOPAWz9QqLxYKZxNWkF/qEY52Q/EHxTAjpOZhiLDkLxYKZpY9yL/TJYhzrefdDJ4TluQkhJKdkKSLaVW67k0In63NIuz+Ku/aRt7LwZHGTu/LcInIJgI8BeDkABXC3qn64W+0hZKHAF/vCIOtgtnZYMDsdcGfzUX7ffQdTHTNNn/RCkGEv0wt+6IR0021jBsD7VPVpEXkJgP0i8o+q+uUutomQnoYv9oVDO0RE1qnnOi10bL7Is6qpx3nSPqG4ay+94IdOSNfEs6o+D+D5+r+/JyJfATAEgOKZkJR088VOi3c6bP3WCyKi0220+SgDnRvnrnPmPdA6veCHTkguKgyKyFoAmwA82d2WENLbdEtwsQpaOlz95ltVr5t0uo2myohhOjGxsJ3b4ECR90AG+FS/JKTbdF08i8hFAB4EcIuq/rvh+5tFZEJEJk6ePNn5BhLSQ3RLcLks3sSOq996QURk1cbxyTK27HoUl23fiy27HrUKzqBkeEHE+H0nJha2c1ZFR+8B3z7rNfJUFp4QG11NVSciRdSE8z2q+knTNqp6N4C7gVq2jQ42j5Ceo1s5YXvBxSAPRJf1bS4Iz01VeiJFWRZtTOqnH3zWrdzHtnO+deyAcft23AMLPbYhL2XhCbHRzWwbAuAvAXxFVf+oW+0gZCHRLcGVFz/FPPucmgSPoJZqKErQb1mKiHb1TattTOOn3+2Jhemcd+872rF7gEGLhHSXblqetwD4WQCHRCSYsv+2qn6qi20ipOfphtUmD1XQ2mWNy0p0mgSPAk0Cuh39lmdLZdpVi7xZJzt5D3Clh5Du0s1sG59F7b1BCOlxum0JDI6dtTUuS9FpEzaKml9nO/ut25ZK1wTEtmoxOFBse7uypJP3QF5WeghZrLA8NyEkE7ptCWyHNS5L0WkTPEODJTy+/erUbfShG5bKQDBH3VOiE5DRresw+sBBVGcbHVheODeD8clyrqzLcXTqHjBZuYt9gunzM7hs+97cuSwRstDoerYNQgjJgnZkGslSdCbNTJFlNoVOZ2EJp+ADmv26g6qAgThevqTZjlOd057M1hJ33bK6rkv7L7y+B4p9gACnp6tMk0dIB6B4JiQlCzVVVK/SjtRuvqLTZywkScGVdd7sTqe9M1nsowRVAccnyzhTqRq36TUf3rjrlsV1DfYxFeqzSnWuyXLPVJGEtA+6bRCSgjwHYC1W2uFz6hMElmQs+C7rZ+2j3GmfdF/RG5yTy4c3LmAzTxlW4q5bFtfVFnhqotcmH4T0ChTPhKSg2wFYxEzWPqc+orMdY6EdPsrt9MeNCtjBgSJOT5utyVGem6rgrm0bjZOUq9avdk5M8jaJjbtuWVzXJNsygJCQ9kDxTEgKejFVVJ4sdL1EnOhsx1hoxRLbaW4bP4R7njjeEBBY7BMUC9LkSmDi4sGSdZLSCUtulsRlwcgiS4ZtH51IeUgIqUGfZ0JS0K0y2GnJ2oeWXKAdY8HmoxxYYvNyHccnyw3COaA6p1i+pL/Bv/s9m9c4/a5HNg3h8e1X49ld1+Dx7VdjZNOQdQJSjrHklqcq877nnYxNiPMtz8L3/Kr1q5tyvJaKBdy0eQ1LWhPSISieCUlBpwOwWsVloSOt0Y6xYAsufOzIyVxdx937jlr9bc9Uqg1ieOfIBu+AyQDbBERQE+6uCUp5qoLRBw5i9P6DHZtsxAWFJgkaNTE+WcbYUyea+vyGK4cwfOmqTM+FEGJHVOOX1fLC8PCwTkxMdLsZhADoLTeIy7bvNYocAfDsrg33PSwAACAASURBVGs63ZwFR6fGQruuY9r229oDZJO/enyyjFvHDhiPMVRvZ9RX2odO5NZuB5s+8IjRl3z5kgLmFE0+47Q+E9IaIrJfVYejn9PnmZCUdLsoSBJYkay9dGostOM6thJ05/K/jVre0wj0kU1DuGXsgPG756YqDb7SpnbYyHNsggtbEObZ882Th0p1Fjv2HO6ZCT4hvQTdNghpE3nKA91rbibETDuuYysuPab2CICbNq9pSieXxld7fLLc5N8bEEwYAl/poYRBd4uBqUo1N/7xhCwkKJ4JaQN5C9Br1ddyMdONSZDtmO24jq1kCzG1565tG7FzZEPDdmkFus2n2mTZNgl5E8WC9OykcbBUNH5um2BEYZwDIdlAtw1C2kDeUmgBveVmkhe6kUfYdMxbxw7glrED836+WfrrtuoK4jOu0gp02/cKcwEa4EK6uz4RzBpiepYv6e/Z+2DHdZdj9P6DqM5dOK9in2DbGy/Bg/vLXr7fveqy0iq9FKNC8g/FMyFtoBfzQJNmspgEJX1puyrIlacquCUipFsVAD5VFFslrUC3/c7mohEW8pdt32vcxlYK3EX4Gq4oFSECTE1XOy7CXEV7hi9d1fD59PkZo4/0YnFZCZO3Yjqk96F4JqRFTOKo1wL0aJUx0+okKO6lbep3331nJQBcgiyrcZFWoJt+J7iQx9nVnqzuweg1nAqJ726IMJulP/p5tN3A4o1zyONKIOltKJ4JaQGbOLrhyqGmZdQ0L65OiNrFYJVJ24+tCrA4X19TvycpbZ2VADAJsizHhU+Z87jflacqDVX04tqTlUXddA3D5FWEpe3zhQhXAknWMM8zIS2wZdej1mXloLxw2heXzXKUdaCf6xx6MRdulFb60fVbIF6YuPIy24T5YKmIF2fmvHMXm3I8ZzHpytu48GlP9LyvWr8ajx056d0Ppn6z5ZkOw3zp+SZvY5n0DszzTEgbcFk0Wg3Q69RS40K3ytj68X33HQTgtqLarHeA2Woc3Z/Lcm3r36lKFR/attE7d3HUCu5jMfYR10nHhc8+47ZxfR/XHtN5P7i/7D3ZtPWbz0qA6RosBIvvQjmPTvj1k8UFxTMhLdBO3+ZOidpe889Oiq2/ZlW93BBMk6Atux71mti4Xto2cRykHQssYreNH8I9Txw3Wj9NAiBu0uXrjuE7LsYny7jjocMNAtMm2OP8v13fx7Wn1cmm7fdL+/tQKhasKwHFvsbUd510g2qnuE1yHnkX2XRhIVnDPM+EtEA7i4/YxGvWoraXC6j45GB29VeSvLfhY9kswlGh7srLPLp1nTE/rwINbdo5sgF3bds4n2GiILVf2XI8x026fHMu+4yLQGCZLLPRfcYdN+57W3uuWr/auiwfPu84bNudqVTnr6GRyEVspehMEky55EcfOIiNdzySSU5y3/PIW057G0ExnWd3XYPHt19N4UxagpZnQlogS4uGyV8zi6DDOHrVKuObySIaaBbFR1yZfJ9NmIS6KzuCq/S0zz5sbXBZaH1XNHzGRVwwXXifccd1fR9cy0p1FoV6/uYhyz0SxXey6eq3oP9NIr06qw3W7U6sGI1PlvG++w425bGuzup8NpBWLd6+55GnTBZ5t4CThQPFMyEtErxYgwf3rWMHsHvf0UQPbpu/5g1XDiUKeGr1HHoJ20v7jocOA2j0SXYFfPmIqziRCKSb2Ay1wWUmzr8ziZtO3LiIE4ThfcYd1/b9ilKx4XxmVRtcX+Kuy1XrVzu/D/Dxi/URlO12gwqeFaYCMFFaEbG+55GXmInFkDWI5Ae6bRCSAa0uXdqE4GNHTnKp0YLt5Xx6uoo7HjpsFFVRNwlfwesSAq2UyY5zjTC5pcS5qthcRYALWQei/SDwF5lhXIIw2rdx52r7XgTWgE+fgMoH95ed92HQn7eOHcCyYh8GS0XrNfVxpWq3G5TPhCFMWhHrex6dci+Lo1PuMoQAtDwTkgmtLl3mxXrTS9gsYwCs2REUNVEUrhQXt1IwPlm2lnpuNdVV1DVicKAI1Vqbduw5jLPnZ1CdrR23PFXB6P0HAUHDZ4F1Lbyf6CpF1CoXPRNFTWQOX7oq0QTAZK0Faun2dlx3ecO+4txAbN/fanFt8bG8Au77MNovp6erKBULuGvbRu/c0QAwfX4G45PlBkt9u9wHkj4T0opY3/OwFbJJMxlrBT5DSSeheCYkA1p9cC+EjBed9jcc3brO6jPs4qr1qzF86SqvJV7XEnlSa6Ktf8JuP7ZKdgHVueZ2BK4q56pz1vPxsVamWeJPKhTj3EBM3/um7HNhuw+TTnqDz3bsOdxwfU5PVxv6O6kbVJJ7x/as6JNaMGl4jLRq8fY5j5FNQ5g4dqohI0zayVgrLIRnKOkd6LZBSAa0unTZrYwXPtkqfPfT6Yj7kU1DGCwVE//u3idPeC/x2kRnQSSRm4apf24dO4DbxhutxkmW48Ocnq46zydpxokk4yJNFoMk+x/dug7FgikviT+2+zDNpHdk0xCWL222O6V1EUh679ieFX/07o3Y/a4rrO46vvd4mmfCY0dONq1mdNplopezBnWLrJ7/ixGKZ0IyoNUHtyulWbvIUvB2y99wx3WXN/V7HLOq3qLJtt1c3RLt8+IJMiNE+0cB3PPE8fnftWN5Odin7yRucKBoFfprM3rBJh13I5uGsHxJ+kXScDq76LVKO+nN0kUg7t6JChwA1mdFdCIDIFFfp30m5MFlohvP0F6mV1IM5hWW5yYkI3otTVKWJWttZaiD/bWzT8Ip6XwoiOAHViyzlsZevrR/3v/Y5jtdKvYBkNiS3z4p7oL+duUqDij2SYPPc3Dcpf19RjePYN++qfaKfYLlS/uN+wofL6nVPXxfTJ+fMfara9y5xpeJggjmVJ0pHwOrbJrS7Z24dwTAXds2pmpf3D1ha2fa82L5696D18wPW3nurlqeReQjIvJtEflSN9tBSBZ0Kwl/2qW3LK1FNkudAG23bAT9/qFtG72s0De+6RLjSkGxT3D2/Mx8e10lmV+cmWvJ7SNM0N+2oikBQ4Ml7H7XFdj9zualeZMFPrzyYbLKDRSbH//VOXUKZ9t52jBZt2z96hp3Sf1W51Tn78PHjpx0+jW7rJW2e+uq9atTZ27xPbeLB0upVnTCfW4j6b0f90ygy0TvkYfVgl6m2wGDfw3gTwB8rMvtIKQnaSW3aZYBNraIe5sfZLtyVQONwWtrX1rCE18/jVlVFERw45suwc6RDfO/8bGImjDE7QHwd/sIE/S3q2iKoLFc97fOnIMC+NaZc5g4dmr+nFwrH9Hgr8u2741tmw3fF2wSP27XuLPlYLZZ3MP7srU1GPu2oDjbvTVx7BQe3F9uGNsC4IYr3cF1YQt8kOllarqKwYEiin3mQD9bphFX//v0uUuwp3km9GqhpVbptdXGMAywbI2uimdV/YyIrO1mGwjpBWwP6VZS5PkUhfDF9PJstVxyGpJkOWhFTBYsqeuiLx6X6wfQ3N8rLdsH+71t/BA+/sTx+c9nVef/3jmSzL/Tdo1WDhQbMnfYfusiqStN3LiziTOg2e0imibNdp5Sb2fUzSY4hik9YaU6i3ufPNH0uaIWNGfDlUnl9HQVxYJgsFTEmUq16f5OKnDi7i9XX7fyTOjFQkut0OtFWbJ8/i9Gum15JoSEMIlkANaHdCtLb1lbi6IvT5tPXV4tGy7BH6ZULOCGK4e8Sqe7QkqGDLmYXzg307RdsSDz+733yRPGfd3zxPHElShtL8/br70cwIUUcdEVhLgXrK9/dUC0H2zYxFlcmrTAgmvKbb1jz+EGa3A4r7Ytj7Tt81aswdVZxfKl/Thw+1saPk8jcFzjOK6vF6sFOQ15KkueBl7r1si9eBaRmwHcDABr1qzpcmtIJ+jlpbBWsFkylvb3WR/SrS69tdNa1GuWDVsBjDArB4q4/dpa8Y/hS1cZx2l4/Nq0s9SPF5RzD9xGTHmcZ0LBgTbhFvgTA24LWPTecpV/D4v6JPdjElcNW3BSkmO60qQF49vmDjNVqc5bgeN8vQN8Vx3C+ExmTdukETi2+843yHOxWZDTshB8hnmt05N78ayqdwO4G6hl2+hyc0ib6fWlsFawWTJsQuS5qYo1Gj8PAjWvlg1XsZJwe4Nqf9Gl9ADTi8fX6rqiVGwa5zYUmPe19SUcVBY+nxfOXRDo5akKHtxfjhVVvi/YrFw1kj4DXD7NW3Y9itGt6zDkuarg02bfVYcwPqsagfg2jc8k2Q/yet8tNOgzvLjpeqq6us/zw6r6urhtmapu4bOY0+ckTccVTkPW6osyj9b+LNsUFnYmNwSXBTYJvunmLlrW7x2cGGAKwIyjVCzECvks7i2fSUM4DaCrj5M+A+L63CZ4fQmnvQuvLtzx0OGGaxhelYiOXVO6vGgbW0md1y5s92AenxedxjTmu3mtSHuwparrquVZRO4F8GMAXiYi3wRwu6r+ZTfbRLrLQlgKS0uSAK5oGrJWHtZ5tPZn2abxyTJGHzg478tqWuIP+8y2ciyfcXrRsn5MJRTOgF04L+3vw4szc02fF0S8xGLSe8sknOJcNUrFAnZcd7lXfyZ9BsS521Sqs3jsyEncef2GxNlVbGJo4tippt+enq5i9IGD89k4wmP3wf3lhglaONtGWHxu2fVobvxo47KNhD+/dexAQ9aXxQAt/IubbmfbuLGbxyf5YzEvhfkEcLXjIW1zF7njocOpC2G02r4sg3HueOhwQ1ERE0lT6tnO12d5PhBMtiIt3zs3Y/VtNlEqFtAnzQVbskgRBzSeq8n1I87iHIj4wI0krk9tZz44YC7FHhYxrgwv0Unm+GTZ6gsN2IPrxifLuCeU8SRMdVaN2TgCAR9n4be1PwuXExOu+9Z2D4YnmgFBtcwgSLPbdMoyTp/hxUvufZ7J4mF8sozp883ZBvLiw+tL2gd3nCWj1Ye0rV02i97p6WpTKi/bfrO2XGe5ApHUPSLuWK7z9Qk6DPreNFHacV1touSTMzvgTKWKu7ZtNFqD40RX0swZpr6sVGetQXSCC0GO5akKbhk7gB17Ds+fp02Um3DNJwIRkyTDy8imoSbXiwCXK4tL4APpsnEE2PqxIK7yOemIu29t7bWduwK5yDSRx5U0svCgeCYdwyUqbT6Tg6Wi93JvHmj1wR22ZAT9FWRjaNXnN9quW8YO4I6HDmNFqWjNNODzMvSxEiedULSyAhE9Vhw2YWr7ret8A8EVl+YtbqI0cezUvAWzIILNr1qJzz1zytpOk1XVNBENUxDBnddvwMSxU3jffQcbRNuQpztGgCsLSJSpShWj9x9sKDPuM8E545ENwzfDSzBGTk9XjdfoqvWrsWXXo8ZrEyeC+8RcRKdPJHYymjQ9XivEVS805bmOIwsXoFaf9b2eQo70BhTPpCPEiUrbS3r50v6eeuAlfXC7AnJ8RbjPC8jWv0GBBhvBy9AUABX4b9per+HfJp1QpE1zZzqWC5GaRTNJLmNXdofLtu/1DqyyLfmOT5bx4P7yvHCZVcXjz5zCkoLgfMT9xCYMfTJ+zKli4tiphqIr4XNJkqvZZjG14bIw24hOZuJcDmz3Q7R/FBeu/5AhuC86XuNcc2ynNqsaO+5tWUGG2uC25hrH7//koVSCPYl7XbssxIs5bmahkefA1L5uN4AsDuKsHAvlged7HuOTZWz6wCO4ZewAynUBGrw8ggeGqb9uGTuALbsexfhkeX4/7//koYZ93Dp2ALeNH/JqF1Cz/tlWhftEsHb7XtwaaefHnzg+/7eN4EUad+1NjGwawp3Xb8DQYAmCmnjwiWJPkmMYuOAKEAgoeBzLJRCi13Fk0xAe3341nt11zbxVesuuR3HZ9r0N19HnHM7PKop9gpUDRWef+PbBxYMla9EVAN79WCoW2mIZjR4jPEkwjfugz+Mw9U8gnB/ffjUeO3LSOV5Ht65DqVhIdR5x496073a5rdnGsS3QtCC1sWcjaTvTPBd8cJUeJ71DK/d4J6DlmXSEOFG5UAIFfc7DZRkMXh4usRu20NiEQDR4J85apmoOMgtEUZoUacGLNO3EKE0wjmufcdbRsIByMbp1XUP2DhMmt5Udew43uMfYLG3Oic6cYmBJPyZ/9y3WbXwmnIFrgsnqHEexIFi+pL8h/3WS/M6+BNZgU8ChK8g1nJnG1Mdx4zHue58ARReu69PJDA621R3bpGlOFbdfe7nx2RVO0+dLu4Ije604EzGTd/cbimfSEeJE5UJ54PmcR5xlMHhpul4icSI7GrwTF8gW9nF9bqqSyt8RqAme6Au/kxMj27GGBkteojIorBErXDy6xua2Esb0Moi79qbvwsubNifucL7iwDXBF1Ou4yimcQ8oKtXmNHomoqLc5T7hCnKNEu3juPHoM17jAhRdxI37TmVwsAl126Qg8K03/SZNe9sVHMkUcguDvK9GUzyTjhAnKhfKA8/nPOJufls2hijlqQpWDhStwVbh4wTHj1o/gcYgtmC7y7bvdbbRhM1q28mJkelYxYLg7IszXtZzQXyZ6937jnr57LrcVsKEK+GNbBqKvfYCNASeNYlzQ9OKBcHud14x/xtTPmEXc6p4dtc11u9HNg01BTnecOWQNaVbFFNaOFfOY5+UgGHC90LceDR9H4yL8HWybeui2CeYPj/T5BvfLWxCPe5ZnUWb2xkcyRRyvU/eV6NjxbOIXKaqz8Z9RogLH1G5UB54cefhevGbsjG4RMIL5+wZFaIPmaBdPkEYScWJAFYx3MmJUfRYQQo0WzaRMCaDrcky7Gv5mD4/g/HJsrfFOyrUTRMd1Nv4vvsOzmdhmT4/Eyveli/pT3UOAQNLCk6LvCnI8cH9ZWcmlwDbpMtlebKVpV/a32c8XtRqDMSnhDRlTIlep+i+VpSKOHt+xujSIwDmcME6ntcUaq7+yTKAq5PBkaT3yPtqdGx5bhF5WlXfEPlsv6pe2daWGWB5brIQSJqWLy57QqnYh3PVuaZsEa6gt7iXoOmYceWhv+GwTNpodzS1a1l9MFLlzbadAA1WV9s+Tf3jEnQmwkIyrohHEnzPYdAh/sKEx9f4ZLkp1V2AqTqmbT9R4sp0m8YOkG156ySlwsMl4AOXBJ+S6r7l0budeSDrctSt7q/b/UHaTx6uceLy3CKyHsDlAFaIyPWhr74PwLLsm0jI4iCpJTb43CakzlXnjEUyXMI5LkVUUn/INNYin3bE5QaPO2eb9VIAHLi9MejOVWAjWmWv2CcNrhulYgHLin1NLjSV6iz6ErhwhtvbataBMNFVCFeRFpvFO0xD9on7zcIZqE1MwmPTVpbahI+rl+23Wb1wk2TPCbfVVzi7juHafzes1q4gzXYUhXJh6o/R+w/ijocOe40t0hvkeTXaankWkXcAGAFwHYA9oa++B+BvVfVz7W9eI7Q8k8VMEitYmv0E+0oivIH01icfy6Itq4XJQmpqR1LLoencbrhyCGNPnWg4VqFP8JKljRknbh07kDgria1dWVqdo/1ispCGr/tl2/d6+4fHuWUkHZtRum158h0/aQIHbftK0o6VA0Vn5pU02Prcd1y0Yo32xae/O9EOsvBJbHlW1b8H8Pci8mZV/XxbW0cIiSUrHzDfNHiml06W/stxVr07HjpsdR8wCTaTf3KSPrOdm6kds3O13NhhV4hW07UF7QpEfBZEJ0MmC2nUz97X3z1uO9fY9BXF3bY8uQJQw0F/aTMA2AIRo7iyi8RVLUyCy8LtOy5s6cSynAj59Hee0pqRhYePz/MPAfgzAC9X1deJyOsBXKeqOzvRwDC0PJPFThYvIB+rTasWw1baERx7bYqMH+F9ZBXk5GpH2M/bZrkWKKY90rV9aNvG2PRnxYJgdk6tVezCrBwoYmBJ/7yriap50gE0+1rHZZAIrHpx1vGgYl9QjdKUgi68v6jfvctCHt2uXdbpqMvOC+dmmlx2kvi1L+3vw4szzeMhjf830Nq9Gu27sy+ag2sLIrjxTZc0XTcX4bSVQGd80U1tcGWJISQOm+XZRzz/C4BRAH+uqpvqn31JVV/XlpY6oHgm3aLby8dZctv4IdzzxPHYJdg0AYBJiHMBaUU8R/fVCr7iGTCXMR/7wonY1Hbh5XfX8vh7Nq/Bwwef9xZqvkRFhqsce3j8b/rAI9ZUia5jmc7PV8BHgxXjRFmW967LfWJqupqZ204UlxtPWoHoW8Y9IHBhCo8Dm9gOUywILlrabxwnaYW/b9s7YQRoBwvpfdPrJHbbCDGgql+QxsTl9vxYhOSUtA+kbgXrtOMBGqQUi3vJt1qowKftcS4ggx5pzlxUqrPYsedwqmC1MLZ2DJYulCqOnu9d2zYCgDULRZhiQXD7tZfP/+1aHk9i+UuEABvveKTBhztcNOexIyeN/XX7tZfHVluMYtsyGixpO8/wcnxcFbKs712bu0AWwtm1/5FNQ9ZAzrR5b5OWsq9UZ/HYkZMNYtRHxFZn1SsXfVKW9vc5j+tKn5ln8hAcSuLxEc/fEZFXo/7ME5F3Ani+ra0iJGNaeSB1o0xoux6gvi/MJIUKTFZKW2U4k4C2nc+O6y7H6P0HrVbbaMYLE1OV6rzg8CmN7duOYp9gx3U1wWu6Vj7BfsGy9lXrV2P3vqPzeZuvWr+6KUAxoC3CGWhw6ShPVZoEcXmqgt+on1M0NztQ809PaoGOEhaBcaLKt5S2694Nvk8yOY0r3NBqaWmXEN5xXXNp7Fby3qYRrtHfRCfASScQwfkmMRT4Wp0VvSk2816WmtTwEc//DcDdANaLSBnAswDe09ZWEZIxrTyQulEmtF0PUN82D3m+1EzC0eQS4htEFHUP2PbGS+b/Dvx2w9ZRH8uujUp1tqHYiCtoDbALraQWPKAxq0a0/8aeOoHZBJbcgOVLCpg+P+u9nO7CJNznALz/k1809tE5zxLcNgp9jUF4cVk84kpp94k43V+CyVPSyWlcAGrS3Ohh4oRw1sWGkhZCCn5jalfQBpeLU6lYMPZbUkOB7/3Wq8VW8l6WmtSIFc+q+nUAPyEiywH0qer32t8sQrKllQdSN8qEtusB6vPCTPJSM73IfJblAbPw/nionHN5qoIH95cb/Ft37zuKMyFR1Wop3+D3PllGkmZCsBHuX5P4T+ICATQXe7EJuSyoGERymslDlNk5bbB8FwtiXVmIK6UN+I2LNJNTHwHrWoWxsXKgiNuvbS6QZDp+VtbHq9avbproFguC2VmFaSpU7JNYK7fLxWnHdZcb+81Vht10rj73W54q0SUl72WpSQ2f8ty/EfkbAM4A2K+q2SQiJaTNtPJAclmbki43+m7brgeoMfVWn+CiZf1NvsA+L7UkwjHadh/RFfgtR/09y1MV3Dp2AKVin1HQpSFpiq3g8yRStyCCO6/fAKAmblsV/wBw9sUL2R+CScCd12/AnddvaMgSEbbaT5+fSe1mEe0P12RsaLCE02df9Mo2EqY6q/PZQlzZNqJitq++XVrKU5XY1G8uAWv6bvjSVV7Bl53EFPsgALb9yCXY+8XnjWPjomX9se10uTjZ+i2pocA25goimFPt+QC7vJelJjV8sm18AsAwgIfqH70dwBcBrAVwv6r+YTsbGIbZNkhakhT3sJX9baUUcNLiIlmXwo07P9M+bUuw4eh+31LVprb7Fl3oJMG5hdOkmc7lhiuHEgfvhfuglaIaPsRlGfDNuBJFACyLLL/HtSFpVocwQfo+X7IYU71YXCNpcLErTaTLdzmuiFKWbQmnWoxOWtv1bMwLzLaRH1pJVfcZAG9T1Rfqf18EYC+At6JmfX5tG9prhOKZtILPAynJgzlJ9bqNdzxiXM50iZxuPkDHJ8vWinlxKcVMKa1MvtK/cd8Br3zFcSTxK+2TWnCczUIZiIM4sVdIYeF8z+Y12DlSszp3YuJgEzo2MTtQt+JfPFjC2peW8Pgzp5r2mcTSHz7ftBUTfUVReLKTBe1OcZbm3natgCQVk7bxFwSxxhW/yVKomtpfLAigaMqnHXXhorgk7aaVVHXfD+DF0N9V1AqmVETkRctvCMkdPv6CSQL1fJcbxyfL1uAnl9tDN6ur2dwRoumf0gQxBS/LLIQz4C+cgZpwDizLtqVRH3cSm3AWAIMDReOy92NHTs7/O02wVlJsfty281u5fCm+HBKMt40fwr1PnsCs6nyRjLBPehzh8x3ZNJQquDOcGSOc0SPwobWJx1ZpZ3BWmkw6rt/Ynlnvu++gdZ8ut7C4yWM0BWSr4tX0DDEFvIafw918NhIC+InnewA8KSJ/X//7WgCfqAcQfrltLSOkCyTxv/P1Sw5e/iaCrAB5s57Y+sGU/in68gvO13YuPuI0sIBNTZ/H2fPZiaLg2rhE/60eFlKb5XlFySycgQtlmANXkDQktXibJn6+Y3znyIZ5yzFQE3BJXD2i+0sqvgNMqfOmKlWM3l8Th1kELEZZUSpiy65H22LZTJNJx/Ub2/WcVbWKcpdfbfjesE3wwikgs0ijGRXDl1lcxphxguSFPteXUosO/GsANwOYqv/3K6r6AVU9q6o3tb+JZKExPlnGll2P4rLte7Fl16MYnyx3u0nz2ALyTJ+Pbl2HUrHQ8Fk4sCM4T5eFcVYVigsvoLz0ha0fTOmfAqtYue4rGXcucS/AocESnt11DUa3rsN5QxljX1zXBqi9sB/ffjWe3XUNHt9+9fzLOy4os1Qs4MY3XdK0/2Kf4Ox5e/0owYU8wGmM7rbjxhHt7yRjPEzS4Mjo/naObMB7Nq9JXICnT8wZSKpzivfdd7AlC/7KgaL1OvqO56SkyaTj+o3ruoUt92FGNg3hzus3YGiwBEHtngu7YgT3hm+6N9tx0pJ2jBLSKZziWWsO0Z9S1QlV/XD9Pzodk9QkFVqdJk4Qh3G9gMLn6UvWL6BWSNIPcYUoorhegOFj7N53NLYIio3gWtjEgQvTuQcURHDDlUPYObKhaf8XLet3pplrqtz9NwAAIABJREFU1UslelwAXhbscCEKm+XbJ5o/idWv2Ce4av3qpknyzpENeObOt+Ebu67xtr67hkCr2Uquef0rvK5jlvdmGmHo+o1rvALuqoWmyWOYuH37HCcNSZ4/hHQDH7eNp0XkR1T1qba3hix48l49KakPr833Lu1ScruWJccnyw3p3kx5ZaNBOK6gv/C2vnmdA2w+lSI1gZgmDV6YUrEwX7UvzbJ7dNk6HJA4q4oH95cxfOkq76XmrPjEE8ex94vPz6cU9ClfbsvZrbgQaFkQaRCHtoAsm5tSkGc67I/89iteEVtlst1+31IPDnXx2JGT2DnSnAXGRFb3ZppUZD5uFjaf8lastabnoS3NYZZW4awLwhCSNT7i+U0AbhKRYwDOov7MVdXXt7VlZEHSC9WTsghGcZ1PQQQvWdZvFD4rSsWWjmtifLLclHv19HQVow9cCCgyBSSFC5RE9+cToGV7mQb7i+ZuVgXGnjqBhw8+jzOVqjNnr0sYvWHNCu/y4MH5mF7StpRytsleu8XgHC4I1LjjBD7jrpzdgYCOFoqZOHbK2H+m9HylYmE+cC+MT45wn6wmrTDo8D8PMN2ntoDPwYFs7s3o5Mw0eXH9xiYmv6/U39TuLKy10eehK9g2SxgUSPKMj3je2vZWkEVDHqondSLNkUtIzakaiwkAwNnzM7FFGpJic3+oztZ8RieOnZrPqhDGJhJ9rOo+pYZ37zvaNIGozl6oNGcSzgLgps1rMHzpKqvw+twzpxKVB3dlPkgy2WtFDK4cKOKa17/CeB2SYkqz5goADVOpzlrHwsefOI7BUhHLin1N1QyjwXU+/eabZQFotm7HIfDb1vTcsXV/BvVs5gnO3TX2TM8pU/o822Q2nJEkS2gVJsSvPPcxABCR7wewLMuDi8hbAXwYQAHAX6jqriz3T/JHt6snpU0TlfRFMbp1nTVP8sWDJYxsGmpIvRVQndXMXVhcVvBZVWcGBNMEwLW/qMUzbbtsKDDvNnHn9RuMS9VJ3Eji3IiSTPaiosJlObdVzBu+dJV13PgQBCVu2fVow36TWMVd4n2qUkWpWMBd9eIlpvvplrEDEEvy7SC7TLTioW1/QKN121a4J4pP/9meO2csrjC2z9MSFyvg+5yyTWaXL42vCOiLr5AnZLHgU577OgAfBHAxgG8DuBTAVwBc3sqBRaQA4E8B/GcA3wTwlIjsUVWmv1vAdNtqkdTnOo3YDr6bOHaqKbVX+IU9ZbGMZe3C0oo7gSkzgmt/N4UKY7SrXUGe2eVL+xNZaU2C13b88lQFr37/pzCraqwwaJvshZea0xSvCMZNkpRuy5cUcPb8bEM7o+PUNGlNUlwmTPh+sQk322UJrld40hiUWp84dgrDl65CX2jICRr94Icyco2xWWXHJ8vWSU/Wq2Mu67zvc2p8smztj6yeI2mfgYQsZJzZNur8HoDNAP5VVS8D8OMAnsjg2G8E8DVV/bqqngfwtwDekcF+SY4wpaXzifJuF7YXSnmqYkydlzSTRJidIxtw17aN1owPnUrHNLp1HYp9vrkNGjGJCNcqwb1PnrB+Fx0LV61fnTjtWsBUpeoUUb7ZJFxp04JzD3yDgWRZO8LZWIJjBWPHlV1m58gGfGjbRqz09LGdPj+LlQNFq6tKtC3BWLxp85rU/R/cR74CLS49nQL4+BPH8b77Dzbk9VYAY184Md9fSbI/uDBZZQORaBrz7Vgdc93/Pm4vQXuT7j8priIsecmSlOf0p2Rh4uPzXFXV74pIn4j0qepjIvKhDI49BCD8pv0masGJDYjIzajlmcaaNWsyOCzpFHm0WLisneHUeUByn1cTrqCXTrmw2AL0fDDleR3ZNGQttWwSHuOT5SYXlSAgMcjokWWgXVAe/OGDz8+f77Ji33xbwqsevpZrRbqSzS7f1oljpxraGM6AEvznE5ypsPv3Rn2MTWMxSeGTgKCIiK97hK8f+KzJN3/ugitTdOUqrXuLrwsPUBP+WZajDnDd/7YCJWFB7Io9yPI5kqYISyskdZMzvWdG7z+IOx463OCbTys5yRIf8TwlIhcB+AyAe0Tk2wBeaG+zLqCqdwO4GwCGh4czDNkg7aYbaeniHrw+QV1pfV6TtqmTLixJxFiA6wVsq3QXtTC6jlepzuKxIycxunWdMXgyIIl7QeBDDNR8owPms4so5o8TTUMXx3NTlVT+77b7IOqaEc6AEvzuuakKVpSKqTNSKNDk/xzmsSMnEwvQPtQCW30nYpXqbOLKiFFsk4C4IkQ2TPevTSTOqSa+J33GSdz9Hzexdk3glxX7cOvYAezed7TlZ4rL4JD18zyNwcV0f1XntCEzTbeNNmTh4SOeDwKYBnArgJsArABwUQbHLgO4JPT3K+ufkQVCp9PS+Tx4fS1XQRtbtQ7HtclkDWxnNhDTC/uq9avnrb+2QLYotlLLN77pkoa/73josFP4lacq1vy0AeGcxEOOPLNhy7ApVZqpgEl433Eo0GBx98mO4Fq9MFGdVdzx0GGcq87Nt3+qUo1toyt1X+BTfP/EcXzjuxWvrBgu5gDMOYrBmJhVTWSBjmKbrI5uXWddBQGAD23b6Lx/g2vmEuDhIjM+92USAWhbDfBJZ2cTteFMI1kIR1fwM5Dt8zyNwcXn+HmqJUAWBj7i+SpVnUPtmflRABCRL2Zw7KcAvEZELkNNNP80gJ/JYL8kJ3Q6LZ3vg9fHchW0sVXrcKcCFJPgkz81EAq3jh0wnnMQFBikNSuI4MY3XdIQLDg+WfZKF+ZjkQy7TfjkmU3yQg/2ndQSDVy4ltHg0PB1SxoYaeqzuDbFdaECePyZU/N/B+2z5TTOmqH6JC2Ni0ixT1K5IBRErPcvAGy845HURWZcEydTwGFS8RYW9aZc3IB5Ym8av60KR5ebFtD8PE8yyYhul8bg4nt/5amWAOl9rAGDIvJfReQQgPUi8sXQf88CaFk8q+oMgF8FsA+17B33qerhVvdL8kOnS6ymefD6tLGVAMekbWolQDErfEuo7xzZgA+++woMDZYwp4rHjpxsCrbMkqDPTMFvUZ/UJBO0gtTE2Td2XTMf4JmE8lTFKAqD62YaY+nCN7OlUp2FKqwBeFm1Mbif0riIiAC733WFc8neRrAKEr1/gZpLRJxwDvs6u+7L6P1imwz6irfw/gC3GI7eB+2yDtvuCUFjALHvs8O2na1IlOt+9g0i7WQtAbLwcVmePwHgHwDcCWB76PPvqeop80+SoaqfAvCpLPZFWqMdrgKdTkuXxtLt20af8tZZtMklttvpzpHGcuayxgV/Z0nUfzds8Yv6d5qscsWCNPg8B0QDn4JzvGz7Xm+x5xItNleZsS+caGpLoU8wN6epA+GSMlWpzhc+OT1dbXLbsbk0xFnoB0vF+RzOwTW51WG9tKLuFReXKLSlTPQp8gPUxkUwtmznWraklTPhK9589heeSIb7J24lLS02K/dNm9c0HN93pc223bJiX5N7j0/BpWCfQYzA2fMzDW5anawlQBYHVvGsqmcAnAFwY+eaQ7pBO10FkrgItCoK0/onx7XRp7x1Vm2yie3BgWLbirvcNn6owX/Z13JmewEGPrtpWTlQxAvnZowBhFGBHtcnpuV6k4+16QWfhUtD2P0n2vfDl65qmJAtX1LA+Zk5mGRT2pzMPgSFTz5UL1QSxTR+b7hyyFoNcbBUxIHb39L0eZq83kFRFdv4te3TtXqQxArrUwbd55ySiDef9rl8wNuRwcfXyOC70mbbbmq6iru2bUz8PojeX52oIksWNz4+z4uWxXIDdiMrRkCWwr1dlm5Xeeu4PkraJtvLTxWJrtFt44esPrjRl4xvQY7wkqqrOEMrgrMggtuvvdxYfTEg7Mbi6hPbpMhmAQ1b97OwmkeXtKP4Wg2BZMK5VCzgDWtWGMuU27CNJVeAqa2AzI7rzPWz0pQvN/n6xmXPiROLrRQNiqJwZ56ZU038HPJp31XrVxs/93nepH2v+RhCfFfaXNv5HCeLthLSCqItpA/qNMPDwzoxMdGRY6WpDtar2JanBcCzu65p67FtgiFNTt12MD5ZdgbLpO0j1wss/F1Qxtjmn2k6/vhk2brUHLUKJk319Z7NazB86arEIigJPlkZAn/cNOPWds4rB4oNWS6yYGWkDLVLqCRxEwko9gm2vfESPHbkZNNYiq4oJMFVgc917be8ehXu+eU3W/cb5x4Uh+m5kEQMmnKOB/QJMKfpqhiaXA3Svit80kmm3X+732u++19M71fS24jIflUdjn5Oy7OFblpjO02ns2KE6XQ6uyTEVfAC0vWRb/o6n5eo6fi79x21irCpSnW+yiOQvJ/veeI4Hj74fNuEc5CSK47gvE3jtk+k4RyjJLHuu/BxpYgWhnGtqvhaRIfqFejihOJjR07G7svGVKWKW8YO4JaxAw1pC+P8cR9/5hRuGz9k9TeO+pQnxTReXVbGsLA2+cIC5onC2gRtC/uHh63zrmw1LoLtfFZfkr6L2v1e811py2qVcLGsDpP8QfFsIc+iLms6VenORDeFexxxQqFYSJdCq5WgmjC2axQ3RsPHSbqErbBbwZPQh1ruyzC+eYDD522aXMyq4paxA7jjocPGoE7biztpQNtNm9fgwf3l2FRhYYKyxkE7wm4iPhXUk6zIZPWsCot+n33e++QJDF+6yuhvHv5sRaloHEsCWL9LWpwoPD5s49ZUqntlAn/3sy/OAMD8dcnCFS08gbatfKW5vp14r/m6TLTqWpHHCrZk8WBNVbfYsT2k8yDqssYn9Ve76HQ6uyS4XigrB4rY/U57Cq00+/UNqgHc18iW7im63/HJ8vyLPyt80psJgEJBmj674cqh2DRx4fRhwbiNVjUMOD1dNabJAszpB5Pc20ODJewc2eCdKixMkN3jtvFDDSnJLAUW50lanKfP0i9pCCZ3Pn0UnF84DdnoAwcxev/Bhs/Onp9Bsa95HNy0eQ12XHd5y88F3ywYpvvs9mvNfttBG8NMVaoYvf/g/DjLMt3kyCb7PRGssCRhIb3X8pDWkyxeaHm20E1rbDfoZIBFdKnthiuHjD6bWR8n6X5dkfxJ/bF9fD19g2pcxx+fLOPsebcgvniwZMwgkgU/+upVePr4GadoUTRX+1NgvlS3zVWlWBAsX9LflJbOZTGOK0jjkz4uSvg5kCToL9ouW7YKE8HkIjhGXFrF0fvdVRvT8NxUBXdt2+iMAQjwqe5YnVWsHChiYEm/8Xwmjp3CPU8eny8AIwk9wn2tqSbhOLJpqKnwDVAbg8ZzmVPs2HMYI5vsFSXTWndt90Q0xWLaffXqe20xrQ6T/EHxbKFdmRsWO6altgf3lzO3dGexpJfViyaa+cIkakz7TXP83fuOGl/u0d/v2HM4c+EMAN/4bgV3Xr8hVcaKICfyxLFTTcIySF8XLL0nqeBnepmaxsfYU3YxGy4PnjRbio0k4lYBPHzw+QY3EduYbte1DURmoU8wm9H+p6armPzd5rR245NljD11oqFy4nR1DqP3x6eHDLc3bgy67qedIxswfOmqBt/jGce9FYzNOFe0pJP64DvfFIsuFtJ7Lc8uf2ThQ/HsgOlusqdTgZhZHCeLF834ZNlaktgnldXS/r758/ApzBLn6hEcx8d6mIZAAI9sGkoUdAVcsIg/uL/cIBKCYL6oIAxX8HMJ1mXFZu800/hwTTrC5cFduIROq5h8dk1jOq1PepBtwkR40mUTzgURbH7VSjzx9dPe5x4O7vRZnanOxaeHDLhq/WpjtpGBYh8q1Tnv+zmcs9znrFyT3rjCQrZnjWuFxcfSuhAD6xaSFZ30HhTPpKO0c6kt/ILIqkxtmglUVATY2jKnak2pZsq04VN4JEtXkyg+2SXCVh9byi9TSrjgpWeb9NiEcbiCn21C8OJMc79lGWwVJWjP6AMHnYI8K7LKWTynwDfq49EktgB3ysQPvvsKvP+Th4yiN66648SxUw1WdZf49j1fW7aRlcuX4sue94Kv3zRQG9eAe9K9ZdejxvG9Y89hvDgz1yCqbx07gIljp+Yzl6S1tPZyYJ1L9C8kKzrpPRgwSDpKuwJWghdE2SGcszhO0na4RICrLWmDYUa3rmsKwir2NWcFCV70UVYOFPGezWuagqJKxQJu2rzGGpwXbBM+jikYFABULwQHRgNUk4raoGS3C5OhNM04sP1mfLKMLbsexWXb92LLrkfnLanLl2Rrm7D1fPSa2K6tz/6DADRTQKVr7PWJ4JaxA0ahWRDB7ndegd3vusI4fgL/7yRpAjfe8UhssFwWE3XfbQt90hBkaOo/1/6mKtWm81fUUkMG55k2uLpXA+uiz9JA9Ievu62fCWk3FM+ko7Qru4aPhagTS3pJLFW2KmFAiy/+qD4xqK7br728Zg0MUSzUBMDOkQ24a9vGJnG7c2QDPvjuK4yCeLBUbPJbD7JhLF/SuP1UpYoH95cxunVd00vPJlBXDhSNxwUuvFRdqd4CURtgGofFglgfiKYJCOB+wZ/JIKVfGNs0LDpBM11b3/27BJVr7LkmiXOq8ys4c5btkrq4TFXsmVQCspio27YN9+7KgSI++C6/zDtJJ23ha+LKimSawAX0amBdr4p+sjig2wbpKO1aaot7EYRTnLWTJC8kVxGLtEu0poBBUxnxtNchze+mzzdPJmz+5zY/xsCqZwtErFRnUar7spowFaIxnYetMMVFy5pzAQe/t73gsywD7SKayqwVv+twGsNo3wwmyH0cJjxmbX1iK3HtIpozO0oWPrG2fcQ9S2zuBmlKlIefKSY3sji3jF4NrOtV0U8WBxTPpOO0IxDTVlQhILB+tZskgsn1Ekj74k/ywrFdB98KiMG2rkpqrmqHtjYFv7P5OdrKWJ+rzuE9m9dYU8AFYivc1qgfuC0oayoiGsPFTWzndte2jZmWMR8sFRv8YgH7mIhL4WcjCNqMXv/33X8wVYYNkyuPaVzfcOVQU8GZUrGAZcU+p2CfVcWthkqIQDYT9TT78PExTjKx8Zkwu4KjezWwrldFP1kcUDyTBUFcPYjBgWJsftxWsRUdsQXauV4CaV/8rVZmG58se6fEsomEiWOn5vN2u+TBipL5msRNrlwTlHueOO78PjgvW9CUzwvbt2x6+Br6TKi2vHoVPvfMKWOflYoF7LjugvXdNCai1s6klmJX0KZNOLuCSE1p/Vzj2laVMK6vg+ObrmkWE/Wk+4gTs0kmNllMmHs1sK5XRT9ZHFA8kwVB1DIYplgQvHBuZl5IJIk2903xZBNUKweKuOb1rzBa1eJeAklf2rYCKTZ/XdPvbdkSgOaXtE0k2FLzRTl73py3Oe6cR7euMxZ4CYson8wgpgmBzws7zq9d6m149fs/hVlVDA2WMFDsw7QjW8rQYAn3/PKbGyzagSuDzaIaxjSRKfaJM/1c9Pg+RWeiKJrLqse5NdjGtWu829xporQj7WVSfFZ/bJO0wVIRy5eai8bYsO4rFDjai2lXe1X0k8UBxfMiYiHm+gxw+VIuX9LfZI31eckmSfFkE1QDS/rniy20u+9tBVJs/rqm38dZ+NZu34vBUhE7rrvcKhJ8hLOgOa+yr/AZ2TQUK6YUfqn1oiJ3dOs63HDl0LzrR0EEN1zZKDxc7jbhY4at3MWCXci6KhbG4XIfqc5p7IoMULtHwuMxietR0GftHNtBn9hWRaJ0ws/chc/qhW2StuM6dx53E6Nb1xnTIr5wbmY+80uv0ouinywOKJ4XCb2c69MHV2BP2uICNsvqLZHy0K59hZdOuxWs6LLK+/y+aX+VKkbvP5g6gAywi1pf4eNzToqaJS+uaEhY5I7efxCQC5/NqmLsCyew94vPY2q6iosHS1bXGFfQW7gUtcuqnAQf9xEft9pomWffoDYBrCsa7ZioB7+Pa5srnWIn8Fm9yNKqOrJpCDv2HG4ak0kKyoTptJFlIRt1yMKF4nmR0KnKft3C9TKyWebi/IBdYjIoYhAEKrXqa9wKwcvHppMGPfP+JrE4VucUqs1L9j7WXsAtNNdu3xsrKn3aGmShSFJxz1TWujqnDS4/BUNOvGg/mLCVok6LT1pE3ywWUZ/cYP/PTVUwWC+NHu4bAXDT5jUA0DQpDyYggSU0y4m6jx953Pm2W6z5CuMsJ9S2tIhJM1N02siy0I06ZOFC8bxIWAxpf2wvI5sl6Kr1q61BhOOTZWuJ4ICwj22xICj2SYPAMPk1Z/3izsr6CJj7ycWZShV3bdvYcD5XrV/d5N9t4sY3XeL0jY57ica1Nej7NBkn4ogGzwlqRV8eO3IyVtBf/rv/G2frqfsC95e01z/u3rVlsbBRnqrgtvFD8+dRqFfHHFjSj2te/4r5INC4inmmCUiWE/W4jBXR1H1hOiXWshLGvs+LrDJTdNrIstCNOmThwiIpi4R2VfbLGley/7SMbGouLhCIClNxi7jAORPVWcVFy/qNBQzC5xYtqHHr2AGsbeFcfayPvsU6TP3kqlYXZJR4fPvVuGvbRgC1bBfLin0YLNl/1wdg+NJV3gF9Pm1dOVDEYKnY1PedGN+KWs5uW0XF8HZnQzmvA/eXtGPcdW4rB4rzhW3uvH6DtyvDx584Pi/Cwu4stqI2SSbf5alKJve06/6MC8TtpcIbPhX2ArIqPpWlkcXnWb4YjDpkYULLcw7ohM9XL6T9aadVKGoJMlnMwi/RNLl545blTS/uaJqtiWOn8PDB5xtcDVwWSp+XTBIBGe2n8cmyMRgpnMEjet1OT1dRKhbwoW0bjb6YcwB+4z4/i3D4/Ez3STRPc5Sk1vQ+AIWCGAMv49qZNDUd4PZLjXsuBJZ1U0sHllwIEvX1FXZhswYmLQSTxT1tmzD6FELqJbF2x0OHva2yWflQZ2XB9n2WM5cz6VVoee4ySawLrRBY6sKWxKX9+br8nbQK2V6W5alK6mj9Vnyogdq5fvyJ401i02WhjDtmqxOkkU1D2P3OKxrGzWCpiN31csRBBgTbdbNZvX3rbQTnZ7tPbhs/5LRuJbWmrxgoYvc7r5jffrBU9Cp1HbQzsMR/Y9c1pqroRkzjwue5MLJpyLsAjclSnxRTO00Wz7inSqv3tO0+CgohuSyevbQCZwvGtZ1/MPaiqwNJyMqC7fssz+p4hHQaWp67TKd9vs6Fcs1OVaq5Cs7opFUoTelkEbv/sCvzQCvHDLBZKE2W1SBozxR0l2aVw+a/6ZMXutUS1UGf+uSUtlm3TNb0WxyVBE3bu6zJxYI5j7bvuZuEm+9zYSiB5c60+pLk2tj2GbQ3uN7T52dis7D43tOm8eqyVsZZPHthBQ6Ac3LRTqGflQXb91nOXM6kV6F47jKdFIx5D87o5BJe0uX8IPhq7KkTxiX9H331KmeZ6jTHjBKMiaigCILVkhZyaXUJPc7fOmiLTajGEVhHXSIveiV8xrMrT7RLdG684xFj5o7+PkkV1Ag0u78E19XXotyKGEwyHl37jIryy7bvjd3fCodPfIBtvNpKeduqI7oyieRVrLme/+0W+lkEOyZ5ljOXM+lF8rVuvwjp5DJi3v39OrmEF17GdhEOQNs5ssHowvCezWvw9PEzsa430WNGl/XjlvnDlrXwsWzBXFGydotxjZsgm0nafZeKBbz2FS/BrWMHEluuy1OV2IDT26+93DjWggwspt/bUt5VLJUDo64Sg6Uili+5cMyo+0v4utqIPhdMbilxfr/R38aRZJ+mNpo4e34m1jXNNl4fO3LSes4+z7gs3Bvaja0PB0vFXLY3Ct0xyEKHlucu08llxLwHZ3TaKuST8ioakGaykriCD125XW8bP9RQyW7zq1bi818/ZfQH7hN4WdZcJJ08xbl4uKo6JkmRFv7dnOp8yru4Mt+unNLhSQzQbFkf2TSEiWOnGvr/DWtWNLQ5i+A2X6uaT9YUAXDV+tWpj2Frn6tqX3gS5FpVCeNj0a7OxhfwcI1X2znn/Rnni6sCYS/QKxZ+QtJC8dxlOvmQ6QV/v2h/BJbLdvSHb8qrOBHpCj50HfvB/eWGSnZPHz+Dpf19Rkvm9y2rWZxc1RLTil2TsPBx8XBVdfQRg1HmVPHsrmsA1CYkLuE85JlTOmxZN+WjDvf/5545ZXUDAWAtr50m+C4gzp86jAJ4cH8Zw5euyvR+2PyqlXj8mVPG73z9ysNE72FfF5QoaYRwLzzjfFgI4pPuGGQhI5ogl21mBxV5F4AdAH4YwBtVdcLnd8PDwzox4bUpsZD3Uqimoh+BIIu2s9VzsfnSFkTwwXc3Lqe72mPbjwC4a9tGY5uSBmsJgGd3XWP93WCpiBdn5pztHJ8sY/T+gw1FLIp9Mu864NO+qDXedg0u277XKJoEdlEU3rft99HtfPyEg75IUwnR9vuAYkGw+53N/eeDT4EbE6YVkVZIOhaTtsE1Zpcv7bfev0meBWHSPhfy/mwkhHQeEdmvqsPRz7tlef4SgOsB/HmXjr9o6YQ1oJWXkK9bQhbBb3Epr3zbY8u3q/Xfm9qT1M88sLbZLGsizbmpje4cno7WLmv6+GR5fp/BeAqu+a1jB7B731FnuXKXdTCu1Hg0q0l4PLsmQ7b82r7Y8gqnFc6An6uGieg1MJHkHkwT85DkN6brXewTnD0/Mz9GTPdvWutrmmdc3POEwrp9sG9JL9IV8ayqXwEA8ax6RXqHVkWtr19uK76/cQItvCzs256kS9M26+vKgSLOVeeazm26HmBlExQud46A3fuONmUKsfmeutKsRa+n6ZoXC4I+1AqiBASZJWznEOzbJigFwE2b1zQcN640uM1ibCOJRTo8yTKR1t3HB9c9lfQedF1rW3+sKBWtpe2j+KazM92/nVr6jwumzSJLDUViM50ql05I1uTe51lEbgZwMwCsWbOmK23gQ8+fVtPh+fo5ps0cErdUHvWPjGtPsD8b4XaHx9GKevGNsJgtFQu4/dpaQFC0Mt/p6cac3NG+tPnN+kwETJamKWt2AAAgAElEQVRMV9BXpTqLHXsOO311jRX6QnNl38DLgGjOatNL98H9ZWPaPl+f4iAdYfD7PhFniXaX762PKGglB7brnoq7B30mHQCwfEkBP/WG5sBPH6txlOj1tqWz61bmH9fzJIsUn+0SidGg4xvfdAl2jjRmUMnz+yvv6VMJsdG2VHUi8mkR+ZLhv3ck2Y+q3q2qw6o6vHp1c6R5u+lUBcCFQqvp8GwpjqIpxAYtQVpxUfWupXJTSq64lEuu/UWDDsPjaKpSBbRmaY6m2xrZNITlS5vnta60cqZ2FguCsy/OzPfZwJKC8bcAjNXrXGnMpirVxMIvsHLbsI0RAZpSirnSmAVpyMLCOW6NKyjtvHNkw/zv5xzCOS4IzSctYKtBbEnvtSCo1JTq8A1rVjT10dnzs3j44P/P3tvHyXGVd76/Z3pa1owMHhmUgAdkeXmRFiMk4QmYaLPBDrHZGDsTG2yISUJe8OVudm/kOErk4GCJmLWILjE3S/beJZuEJDZEfoFBIPhYBCvh4iDHEjOyI5AD2Pil7QSBNQJrxlLP6Nk/umtUXX3OqVMv3VXd8/t+Pv5Y0y9Vp06drvrVc37neZ7BVReMtqSFiz70AclTHhZR6S9t5cE8Unx2onrqTRMP4/Z9T7Qser193xO4aeL0w3zZ719lT59KiI2OiWdVfYuqvtbw32c7tc9O0M2S0VFcF/uykvWmaMpbG6Q9C98Annt+rq1sss+q+iQCzdaesMB2XeTDnzONo/opxQ9n53DbNevb9p30phJt5/LhKqANkRv02fGTflkpwtuMy4OdlEDAmca1bYwEFoHw523CPXj9pomHW3JEK9x5tE0WDFt7BMDS6gCu3zll/V365hvOkqnD1V+2z9uuZ/sePWq0Z0zP1lvyiG++dDVmLHmtk/qgu5kHOE5EutqTh9DvhEj81ANPxr5e5P3Lh14pl05IFBZJiaGoJ+M0EYMyiO08borjG1qLGOw9fMQoPJctGTSK2rQRpoDo9wFYiyrYtjc6MuQlhudVjec1603lh7NzLVk1fDC10ZRXOAtnDVWt49oYPW9aBKKfH7Ao4YoIJiZrxhzR2nzfhKlfbe0ZrAiOztSNv8tg7Pj46QFzsRYfbL+pickajp+ca3s98Ju7xqGNaLo/G0kET9xDad7Eichwe4DTi0x33PsILlqzIvM1rRMi0XbOwq+XPbLLYiqkVylEPIvIL4jIUwDeBGC3iNxbRDt8KOrJOGnEoCzTc524Kdou9Mdm622iNkuECUjejz7bi8tZbDqvSW8q0Xa7xJCNqBVmYrJmjW7FMVwdMLbflRUEaER0F9ozVMWZSweNFgHbc8G8qnMx6Lyqd7+axrKtPTvufaTlHJgw7Scq2nxw/aZMi0IB4MylgxjfMGq9btkeKgKC32CeZaOjD8md9Lj6zgQEv7vg9xP20me5pnVCJNrOWfj1skd2u/0QRUheFJVt4zMAPlPEvpNSVNL9pBGDMi28yHuFfJJiCXH9EJf+Kkk/BgtxZuvzqDQXl4UXtiXJ4xs9r0nTdKVNexYmrLddBWTiqFYE/+3K1y20yycrSPCQEj6GaN5qH0YdHtXg/c2Xrsa2zx1ayPZwxqA9hpBkoVucn952/oJ9+ORbjsuzbfv+dPNYbdezuIqQwW/NlSWmE9eZvBa7+V5D4rz0aUmbds/Fu974cty+7wnj6wG9UDSmWxlVCMmT0mfbKJpOXPR8SFpdq+zTcz7YbpRJbgC+EaakOW+jr0eFcRDRjBPiNkznNclNJY/zfCyU3SOLGA/nPvbNCmLKxRx+KDERTaMW9L9tH+Ec0c+HfLvTs62ZTFy4fpdxfnoXE5M1HD/RbreIbic85k0ZHGyp5YLxFb2ejQxXoQrcse8JjAxXIdA2T3P4t2Yqmx7OEpMneWao8L2GdPI6mrdIDLJquLJtFHX/IqTfoXj2oIgn46QRgzSlbDtBlupecTdKn+1m7YesEapwhNr3hptHJMjW7ooITqkupCQLbrS2bQS42u7KhRz1ekexjWubUA8eSkzvB4sAFe2R3eg+wjmiTSnxfGdpXL9Ln3SBJnxmKKI5rgHzGAz3SbR9AcH1LLrfozN1DFUrePeFK9vS/QWfv+dArWXbAuCqCzpzfcxzNs33GlKW66gvt4yvbUtNF4WRXULyh+K5pCSNGJRhei5LpMjHbuFzA8jaD3lGqHzy+C4fruLmy8/PfHOztTvqH7zDMM0b3gbQOI+uHMc24ezTz7ZxbROeJptFtC1RK0PcbydLdNG07YvWrGhJiecSriZsUf7og8/ew0dw3pbdC8dja6+iMa7ibClJLQo2sb738BHn8aUl7yiwzzWkDNdRQkj5oXguMUkiBmWYnssSKcrrRpm1H/KMULkKjQQMLxnM5RxlbXfgWXV5neMizr79HG1rkNHAVB0w2OaOex8ximfAPEZcv52s0cXwtqMPjK5ouA1XmfjHtl9mfSi1lUAfGap62VLS5IlO8npWiogCp71+lLkQCSEkfyie+4iip+ey3FzzvFHm2Q8zJ+ewddchXL9zKrEPO3wjtkWgkwiPuBt0lsha4Fl1RUFd3mPfxVQTk7W2KLKrOqCPBSbpGHGdu3AfB37gY7N1qyCyRWOj0XAXcWPf9lC6tJnRJHoctowmm3ZO4YY7Dy4sbLWJb1vp7W6L2aKiwEmvHywxTcjig3meSW5kSYtUlnyf0ZRvR2fqLYVGgrR1vimWgnRctmIYvsIjr1SEce12RUFt6dSSHoMpghytDuibTzu6iM4HWx8A8Dr3YfKIxsaNfdu2pmfqLccxMlTF0uqANUIPoCUF2/GTc6hGEmbb8mrb8nB38jfaK2nMyl6IhBCSP4w8EyNppiGzRIrKYDsJ9u+yWaTxYU9M1loyWQRUK+ItPDq5eCpcKMIVXcwaCYzr27gcwq5FgGF8xq7p3JkWEoYx9Xce0di4se/ah23xnw/1ecXy4SqGlwwu7Hfm5Fyb+A6OO4ikm/ze0ZmZvCh6Ns2Hfsh0RAhJBsVziSiLby7tNKRNBAAwTgObvh8IgeCGvOPeR7raDz43vKQ3xW2fO2Qs6lEdkMyp6NLcoF3n1yZSa9Oz2LrrUEtlv5GhKrZe4b/YMa6tLsHp+3BlOrbNdx/E1l2HnPaL4LNJjyEva4FLJPrsI21qwemZOiY/cMnC36481tF20q7QwGV/IYT0JxTPJaFMN6IsUc6oCEh6XEX3g0+GjKQeT9s0ejSfbpp2pfGbus5vOLoYzR4RFQgn5vzbH7Q1SfW9KD5RSNOx1ed1oe2u8eTydQeMDLf7gW+9cm1HH3p9HhzSRjmj4yfPgkSLBVtxxh8+X1+weBFC+gt6nktCmXxzeUY5kx5Xp/shKJd93pbd2Lj9vjYPq8nXGaaotFV5+k3jzm/g0x4dGUpcVtyFrW9Hhqq5eVl9xqit3XHCuVoRPPd8ux8YQMfLTAfnxLYP20PU8uGqdTybxk+ScUa7QoNpy8PxKUWqdQmdIO66RwhJBiPPJaFMN6I8o5xlSoflE9WORvl8Mi7EMeJIKeZLnp5w3/ObxsbgwucYAstObXrWWPI8Dp+ZA1u7Rx3frYhg2ZLBtvOYNdKal1XLJ4uKT58mGWedzL5RFgubD64xV4ZIfNGzeYT0IxTPJaFMla3yTBGV9Lg62Q++08x5L1LaesX52HzXQdRDxufqgGDrFclKGufVLp/zOzFZc+Z1Dkh6XlzHYCp5Dphv9klKufu2e/Olq9vOE9CIOO94+zpcv3PKuK20D3Z5ipo40evanqkvfdLsdSqVXK+JvbgxV3QknvYaQvKHto2SUJZUbUC+KaKSHlcn+6Go6P74hlHseMe6lv7c8Y51hd24fM7vjnsfiRXOpvOSZXrYtegtbLWwpe27aeLhhW1UmkbU5cPVtnRstvEUnKfwjMDy4Sp2vH3dQiYSE2kf7PK2KMVZO0xkSYHYqVRyZbKw+RD0Q8Vifi66tHeZZjUJ6RcYeS4JZUjV1omp0qTH1cl+KDK6X7aUW3Htcd1YBbBaLrJEDONu5rXp2YUxahJXd+x7YkHwz6u22RZ8x5/tvYvWrMDthvLmF61ZEXNkZsogarJGJTsxrsvQL0kJ+qCMpb3LNKtJSL9A8VwiihRYSYRPUpGd9Lg61Q9FVSzrRWw3XFflvKxCzMev7Joej0bKZ+vz2LrrEJadMZjLg9jew0cSvR5HGURNGYVqGfolDWUIgJjgdY+Q/KFtgwDwnyrNq9JdEXRqmrkfSWOfySrE4jKdAGixZPgwPVvPbazmLTTLYNXK24qSB2Xol7Sksc50o0287hGSL4w8EwD+wqDXF5+UzT5RVtJE0bJGDMP7dEWgA0tGtJBLnEcbyDZW846IliFSGReVLCLrRRn6pd/gdY+QfKF4JgD8hUEZp3lJZ0h6w00yPWwTZeF9btx+n9U6svnS1W1lou85UPOqspclUpz39HfRosYlVIvMetHpfumlVHiEkPJB8UwA+AuDXvUjxsGbaXZMQuyiNSsWSq2Hy7VHRdn1O6ew//Fnccv42oXtucakSVyNnXt2y75nTs4Zqzt2K1LczTGVZV82odrrs0w2ypIKj9ccQnoX0ZiqWmVibGxM9+/fX3Qz+hafi3n0xgM0BE0ve+h6+Ziy3IA7ffO29evS6oBR1AqA265Zbyyakvb4ijqv3dx3p/Z13pbdRiuMAHhs+2Wpt1s0rhkNn/zWedDL1xyAwp8sHkTkgKqORV9n5Jks4DNV2o9+xF6KsIVvWiPDVTz3/NxCUY8kEbRuRN9s/erKlpFnwZoix2o3x1Sn9tWvs0xlsJ710jUnSlki94QUCcUzSUzRPs28KcPN1EQ0uhP19Zqit7434G7cvNP0X959XtRY7eaY6tS++jXFWRkeCsp6zfGhl4U/IXnBVHVk0ZN3uq4sVfbC24imBLxj3xO5LYjrxs3b1n8jQ1XYks31elQzoJsp4EaGq4le96VfU5yVIRVeGVME+tLLwp+QvKB4JouePG+meeXBNkV3fFcn+NyAu3HztvXr1ivOx7UXrmwT0P0Q1QzotEALP6BNz7bPQABAHstZypi3OCtleCgog4BPSy8Lf0LygrYN0lOUoYS4q00DIpiPqJY0U5ppozi+N+BuTMm7+nV8w2hbdoxe986H6aTfum2xmUUkT8/WsXH7fX3Zv1kp2nrWy2tH+tXOQ0gSmG2D9AxlXKFuapOJpBkKbBkBosVAqhXBsiWDODZbL122DdIZbGMjSnSsFP1bIf0Drx1kscBsG4uUfrrIlXGhiqlNJpJOadqiO1ddMIq9h4/kcj6Ljr6RdPjMSpgqLhb9WyH9A68dZLFTiHgWkR0ALgdwEsB3APyqqk4X0ZZ+pt9SCpVxoYrPvtNMafbytC7pLLZsERURnFK1vg9wURchhORBUZHnLwG4UVXnROTDAG4E8HsFtaVvKWOkNiBNRLwMKaZM+44TMmlF72KJ7vTT7IgPWY/XNisRtmTYrB1c1EUIIdkpJNuGqu5R1bnmn/sAvKyIdvQ7ZYzUAukzUpRxhbqtTR+5el1fZSjoFHFjIY+0f2UiazaWQHjP1udRkUa+ElO2iDL+VgghpF8oQ6q6XwPwxaIb0Y+UNaWQKyLuogwppnqhTb2EayzklfavTKQd+0Cr8AaAedUFQRwdbxyXhBDSOTpm2xCRvwPwEsNb71fVzzY/834AcwDucGznOgDXAcDKlSs70NL+pawphbJExMtmZVhsloO8cY2FMtuO0pJl7Cftj7L9VgghpF/omHhW1be43heR9wB4G4CfUUe+PFX9OICPA41UdXm2sd8p66KzMnqX09BvCzKLwDUWOmU7KvKBJ8vYz9offNAjhJB8KMS2ISJvBfC7AK5Q1Zki2rBYKGOFsH7xY2aZgicNXGOhE7ajoq0gWcZ+lv4o+rgJIaSfKCrbxscAnAHgS9JY9LJPVd9XUFtIlylrRDwp3V6QmTVyWMbIY9xYyNt2VLQVJMvYz2LD8j3uMo4RQggpG4WIZ1V9ZRH7JeWhH/yY3bSfZLWIlNliYhsLnXjIKkMGmrRjP0t/+Bx3mccIIYSUCVYYJCQl3VyQmTViWnTENS15P2T1ut8+bX/4HHevjhFCCOk2ZUhVR0hP0s10YFkjpmWIuJaBfvHbJ8XnuDlGCCHED0aeCclAt+wnWSOmvR5xzYt+8dsnxee4OUYIIcQPimdCeoCsFpGy5vwugn7w26ch7rg5RgghxA+KZ9LzLIYMAVkjpos14kr84RghhBA/xFGfpHSMjY3p/v37i24GKRHRDAFAI1rGUsSEEEIIyYKIHFDVsejrXDBIehoWKiGEEEJIN6F4Jj0NMwQQQgghpJtQPJOephMlnAkhhBBCbFA8k54mad7eickaNm6/D+dt2Y2N2+/DxGStG80khBBCSJ/AbBukp0mSIYDlhwkhhBCSFYpn0vP45u1l+WFCCCGEZIXimSwaemlx4WLIXU0IIYT0IhTPZNHQK+WHaS9ZnPCBiRBCegMuGCSLhqSLC4vCZi+54c6DXODYpwQPTLXpWShOPzDxfBNCSPlg5JksGoooP5wmmmizkcyrMgLdp9CPTwghvQPFM1lU+C4uzIO09gubvQSgoOpXbA9MtnFACCGkOGjbIKRDpC0dbrKXhCnjAkeSDZvvXgBaNwghpGRQPBPSIVzRRFeRlvENo7j1yrWoiBi/X7YFjiQ7my9dDdPZViD2YYsQQkh3oXgmpEO4RG7corDxDaP4yNXremKBI8nO+IZRqOU9zjQQQki5oHgmpEPE2S8At40jiECPjgxBAIyODOHWK9fS79ynjFoetjjTQAgh5YILBgnpENHsHmkii91c4EiKZfOlq1sWmAKcaSCEkDJC8UxIBwmL343b7+uJIi2kGIpIpUgIISQ5FM+kUBZTVTVGFkkcnGkghJDyQ/FMCmOxlaFmZJEQQgjpfSieSWEsxqpqjCwSQgghvQ3FMykM20I5puYi/cRisiYRQshigKnqSGHYFspxAR3pFwJrUq2ZbcWV25sQQkhvUIh4FpE/FJGHRGRKRPaIyDlFtIMUiykPMhfQkX4ibYl2Qggh5aWoyPMOVX2dqq4H8HkAHyioHaRAWASE9Du0JhFCSP9RiOdZVX8Y+nMZYK0fQfocLqAj/cw5I0PM7U0IIX1GYZ5nEfmQiDwJ4Fo4Is8icp2I7BeR/UeOHOleAwkhJCO0JhFCSP8hqp0J+orI3wF4ieGt96vqZ0OfuxHAUlW9OW6bY2Njun///hxbSQghnYXZNgghpDcRkQOqOtb2eqfEsy8ishLAF1T1tXGfpXgmhBBCCCHdwCaei8q28arQnz8P4HAR7SCEEEIIISQJRRVJ2S4iqwGcAvA4gPcV1A5CCCGEEEK8KSrbxlVF7JcQQgghhJAssMIgIYQQQgghnlA8E0IIIYQQ4gnFMyGEEEIIIZ5QPBNCCCGEEOJJ4XmekyAiR9DIzkHsvBjA94tuRA/D/ssG+y8b7L9ssP+ywf7LDvswG2Xrv3NVdUX0xZ4SzyQeEdlvSuhN/GD/ZYP9lw32XzbYf9lg/2WHfZiNXuk/2jYIIYQQQgjxhOKZEEIIIYQQTyie+4+PF92AHof9lw32XzbYf9lg/2WD/Zcd9mE2eqL/6HkmhBBCCCHEE0aeCSGEEEII8YTimRBCCCGEEE8onnscETlbRL4kIt9q/n+55XMrRWSPiHxTRL4hIqu629Jy4tt/zc++UESeEpGPdbONZcan/0RkvYh8TUQOichDInJNEW0tEyLyVhF5RES+LSJbDO+fISI7m+8/wN9rKx7999vN69xDIvJlETm3iHaWlbj+C33uKhFRESl96rBu4tN/InJ1cwweEpFPdruNZcbj97tSRPaKyGTzN/xzRbTTBcVz77MFwJdV9VUAvtz828RfA9ihqv8ewBsAfK9L7Ss7vv0HAH8I4CtdaVXv4NN/MwB+WVXPB/BWAB8VkZEutrFUiEgFwJ8C+E8AXgPgXSLymsjHfh3AUVV9JYDbAHy4u60sL579NwlgTFVfB+BuAH/U3VaWF8/+g4i8AMBvAXiguy0sNz79JyKvAnAjgI3N696mrje0pHiOv5sA3KmqGwC8E8D/6G4r46F47n1+HsBfNf/9VwDGox9oDsxBVf0SAKjqc6o6070mlprY/gMAEbkAwI8D2NOldvUKsf2nqv+iqt9q/vtpNB7c2io2LSLeAODbqvqoqp4E8Ldo9GOYcL/eDeBnRES62MYyE9t/qro3dI3bB+BlXW5jmfEZf0AjWPBhAM93s3E9gE//vRfAn6rqUQBQVQarTuPTfwrghc1/nwXg6S62zwuK597nx1X1mea//xUNgRfl1QCmReTTzWmQHc2nP+LRfyIyAOAjAH6nmw3rEXzG3wIi8gYASwB8p9MNKzGjAJ4M/f1U8zXjZ1R1DsAxAC/qSuvKj0//hfl1AF/saIt6i9j+E5HXA3i5qu7uZsN6BJ/x92oArxaR+0Vkn4i8tWutKz8+/bcVwLtF5CkAXwDwX7vTNH8Gi24AiUdE/g7ASwxvvT/8h6qqiJhyDw4C+CkAGwA8AWAngPcA+PN8W1pOcui//wzgC6r61GIM/uXQf8F2XgrgbwD8iqqeyreVhLQjIu8GMAbgp4tuS6/QDBb8MRr3CJKOQQCvAvBmNGY9viIia1V1utBW9Q7vAvAJVf2IiLwJwN+IyGvLdN+geO4BVPUttvdE5N9E5KWq+kxTnJimh54CMKWqjza/MwHgQiwS8ZxD/70JwE+JyH8GcCaAJSLynKq6/NF9Qw79BxF5IYDdAN6vqvs61NReoQbg5aG/X9Z8zfSZp0RkEI2pyx90p3mlx6f/ICJvQeMB76dV9USX2tYLxPXfCwC8FsDfN4MFLwGwS0SuUNX9XWtlefEZf08BeEBV6wAeE5F/QUNMP9idJpYan/77dTTWx0BVvyYiSwG8GCVaq0XbRu+zC8CvNP/9KwA+a/jMgwBGRCTwmV4M4BtdaFsvENt/qnqtqq5U1VVoWDf+erEIZw9i+09ElgD4DBr9dncX21ZWHgTwKhE5r9k370SjH8OE+/XtAO5TVrQKiO0/EdkA4H8CuIJ+0zac/aeqx1T1xaq6qnnN24dGP1I4N/D5/U6gEXWGiLwYDRvHo91sZInx6b8nAPwMAIjIvwewFMCRrrYyBorn3mc7gJ8VkW8BeEvzb4jImIj8LwBQ1Xk0RN+XReRhAALgzwpqb9mI7T/ixKf/rgbwHwG8R0Smmv+tL6a5xdP0MP8XAPcC+CYaq8oPicgHReSK5sf+HMCLROTbAH4b7iwwiwrP/tuBxizRXc3xFr05L1o8+49Y8Oy/ewH8QES+AWAvgM2qypkjePffDQDeKyIHAXwKwHvKFjxgeW5CCCGEEEI8YeSZEEIIIYQQTyieCSGEEEII8YTimRBCCCGEEE8ongkhhBBCCPGE4pkQQgghhBBPKJ4JIaTPEJE3i8jnm/++QkSsqe5EZKRZACjpPraKCEvWE0IWHRTPhBDSI4hIJel3VHWXqm53fGQEjRL0hBBCPKB4JoSQEiAiq0TksIjcISLfFJG7RWRYRL4rIh8Wka8DeIeIXCIiXxORr4vIXSJyZvP7b21+/+sArgxt9z0i8rHmv39cRD4jIgeb//0kGoVtXtEsJrKj+bnNIvKgiDwkIttC23q/iPyLiHwVwOoudg8hhJSGwaIbQAghZIHVAH5dVe8Xkb/A6YjwD1T19c1Sv58G8BZVPS4ivwfgt0Xkj9CoGnoxgG8D2GnZ/p8A+AdV/YVmFPtMNKoXvlZV1wOAiFwC4FUA3oBGNdJdIvIfARxHo5TuejTuHV8HcCDn4yeEkNJD8UwIIeXhSVW9v/nv2wH8X81/B2L4QgCvAXC/iADAEgBfA7AGwGOq+i0AEJHbAVxn2P7FAH4ZAFR1HsAxEVke+cwlzf8mm3+fiYaYfgGAz6jqTHMfLHlNCFmUUDwTQkh5UMvfx5v/FwBfUtV3hT8kIutzbIMAuFVV/2dkH5ty3AchhPQs9DwTQkh5WCkib2r++xcBfDXy/j4AG0XklQAgIstE5NUADgNYJSKvaH7uXTDzZQD/Z/O7FRE5C8CP0IgqB9wL4NdCXupREfkxAF8BMC4iQyLyAgCXZzlQQgjpVSieCSGkPDwC4DdF5JsAlgP4f8NvquoRAO8B8CkReQhNy4aqPo+GTWN3c8Hg9yzb/y0AF4nIw2j4lV+jqj9AwwbyzyKyQ1X3APgkgK81P3c3gBeo6tfRsI8cBPBFAA/meeCEENIriGp0lpAQQki3EZFVAD6vqq8tuCmEEEIcMPJMCCGEEEKIJ4w8E0IIIYQQ4gkjz4QQQgghhHhC8UwIIYQQQognFM+EEEIIIYR4QvFMCCGEEEKIJxTPhBBSICJyrYjsKWjfPyUijxSx715FRN4sIk853v+EiNzSzTYRQroLxTMhhIQQEQ0q+HVg26ua2x8MXlPVO1T1kg7syyjyROTvReQ3mvv+/1V1dd77JoSQfobimRBSGGER2Sv0YpsJIYTkB8UzIaSriMh3ReT3muWlj4vIoIhcKCL/KCLTInJQRN4c+vzZIvKXIvK0iBwVkYnQe+8VkW+LyLMisktEzgm9pyLyPhH5VnO7fyoi0nzvlSLyDyJyTES+LyI7m69/pfn1gyLynIhcE0Rwm23+VwB/KSLvEZGvRo5rIWItIkMi8hEReby5j6+KyBCAYPvTze2/KbotEflJEXmw+b0HReQnQ+/9vYj8oYjcLyI/EpE9IvLiDOeiJTrdPDc3isg3mn39lyKyNPxZEfn9Zp99V0SuDX33LBH5axE50jzum0RkIPT+e0Xkm812f0NEXt98fYuIfCf0+i+EvvOe5rHe1jyHjzb75z0i8qSIfE9EfiX0+U+IyP8nIl9qbu8fROTc5nttUf9wFN42Jhx9Z+wHQkj/Q/FMCCmCdwG4DMAIgB8HsLlIWQ0AACAASURBVBvALQDOBvA7AO4RkRXNz/4NgGEA5wP4MQC3AYCIXAzgVgBXA3gpgMcB/G1kP28D8BMAXtf83KXN1/8QwB4AywG8DMB/BwBV/Y/N99ep6pmqGgiolzTbdi6A6zyO7/8GcAGAn2x+73cBnAIQbH+kuf2vhb8kImc3++JPALwIwB8D2C0iLwp97BcB/GqzL5ag0V95ci0a/fQKAK8GcFPovZcAeDGAUQC/AuDjIhLYPv47gLMA/DsAPw3gl5vthIi8A8DW5msvBHAFgB80v/cdAD/V/O42ALeLyEtD+3wjgIfQ6I9PonGOfwLAKwG8G8DHROTMSPv/sNnOKQB3eB63cUxYcPUDIaTPoXgmhBTBn6jqk6o6i4YA+oKqfkFVT6nqlwDsB/BzTRH1nwC8T1WPqmpdVf+huY1rAfyFqn5dVU8AuBHAm0RkVWg/21V1WlWfALAXwPrm63U0hPA5qvq8qrZEkQ2cAnCzqp5ottlKM9r6awB+S1Vrqjqvqv/YbGMclwH4lqr+jarOqeqnABwGcHnoM3+pqv/SbMedoWMycU4zYrvwH4D/ENOGjzXPzbMAPoTGg06YP2j2wz+gIfSvFpEKgHcCuFFVf6Sq3wXwEQC/1PzObwD4I1V9UBt8W1UfBwBVvUtVn26e+50AvgXgDaH9Paaqf6mq8wB2Ang5gA8227AHwEk0hHTAblX9SrO/34/GmHh5zDEDycdEWz947IMQ0gdQPBNCiuDJ0L/PBfAOg8B7KRpC6VlVPWrYxjloRJsBAKr6HBrRzNHQZ/419O8ZAEGE8ncBCIB/EpFDIvJrMe09oqrPexwX0IhILkUjopqUlmNq8jj8jsnE06o6Ev4PQJwoDJ+bx5ttCjiqqscN778YQDXS9nC7Xw5Lf4jIL4vIVOjcv7a5vYB/C/17FgBUNfpauA8W2t8cE89GjsFGkjFh6wdCyCKA4pkQUgQa+veTAP4mIvKWqer25ntni8iIYRtPoyG8AQAisgyNqf1a7M5V/1VV36uq5wD4PwD8D3Fn2NDI38fRsJIE+35J6L3vA3geDdtD3HaitBxTk5XwOKYcCUdpV6LRpoDlzX6Ovv99nI7cht8L2v0kDP3R9CP/GYD/AuBFTXH/z2iI2Mztb9o5zm62MRC7w6HPLpy3hGPC1g+EkEUAxTMhpGhuB3C5iFwqIhURWdpcnPYyVX0GwBfREDLLRaQqIoFv+FMAflVE1ovIGQD+G4AHmpYBJyLyDhF5WfPPo2iI2lPNv/8NDd+ui4MAzm/ueykafl4AgKqeAvAXAP5YRM5pHtObmm080tyPbftfAPBqEflFaSykvAbAawB8Pu6YcuQ3ReRlTf/1+9GwSoTZJiJLROSn0PCU39W0VNwJ4EMi8oKmKP5tNM4tAPwvAL8jIhdIg1c2P7MMjb4/AgAi8qtoRJ6z8HMi8h9EZAkaPuZ9TRvKETTE/Lub5+TXEBL0MWPCRFs/ZGw3IaRHoHgmhBSKqj4J4OcB/D4aIupJAJtx+vr0S2hENQ8D+B6ATc3v/R2APwBwD4Bn0BBC7/Tc7U8AeEBEngOwCw1/8qPN97YC+KumjcDoY1XVfwHwQQB/h4ZHN2qF+B0ADwN4EA3bwIcBDKjqDBo+4vub278wst0foCHEbkDDgvK7AN6mqt/3PK48+CQaC+ceRcNqES748a9oCMun0ViI9z5VPdx877+iEd19FI3++CQaDxFQ1bvQOO5PAvgRgAkAZ6vqN9DwRn8NjYeWtQDuz6H9N6PR7xeg4akPeC8aY+sHaCxA/cfQe9Yx0bRxhDNquPqBENLniGrcLCIhhJDFgIh8F8BvNB9Mou+9GcDtqvqy6HtlQUQ+AeApVb0p7rOEEJIWRp4JIYQQQgjxhOKZEEIIIYQQT2jbIIQQQgghxBNGngkhhBBCCPGE4pkQQgghhBBPBotuQBJe/OIX66pVq4puBiGEEEII6XMOHDjwfVVdEX29p8TzqlWrsH///qKbQQghhBBC+hwRedz0Om0bhBBCCCGEeELxTAghhBBCiCeFiWcRWSoi/yQiB5ulT7cV1RZCCCGEEEJ8KNLzfALAxar6nIhUAXxVRL6oqvsKbBMhhBBCCCFWChPP2qjO8lzzz2rzP1ZsIYQQQgghpaVQz7OIVERkCsD3AHxJVR8wfOY6EdkvIvuPHDnS/UYSQgghhBDSpNBUdao6D2C9iIwA+IyIvFZV/znymY8D+DgAjI2NMTJNCCGEkI4wMVnDjnsfwdPTszhnZAibL12N8Q2jRTeLlIxSZNtQ1WkAewG8tei2EEIIIWTxMTFZw42ffhi16VkogNr0LG789MOYmKwV3TRSMorMtrGiGXGGiAwB+FkAh4tqDyGEEEIWLzvufQSz9fmW12br89hx7yMFtYiUlSJtGy8F8FciUkFDxN+pqp8vsD2EEEIIWaQ8PT2b6HWyeCky28ZDADYUtX9CCCGEkIBzRoZQMwjlc0aGCmgNKTOl8DwTQgghhBTJ5ktXY6haaXltqFrB5ktXF9QiUlYKzbZBCCGEEFIGgqwazLZB4qB4JoQQQghBQ0BTLJM4aNsghBBCCCHEE4pnQgghhBBCPKF4JoQQQgghxBN6ngkhhJA+gKWlST9R5vFM8UwIIYT0OEFp6aBCXlBaGkBpBAchvpR9PNO2QQghhPQ4LC1N+omyj2eKZ0IIIaTHYWlp0k+UfTxTPBNCCCE9jq2ENEtLk16k7OOZ4pkQQgjpcVhamvQTZR/PXDBICCGE9DgsLU36ibKPZ1HVotvgzdjYmO7fv7/oZhBCCCHEQplTjBGSBBE5oKpj0dcZeSaEEEJILpQ9xRgheUDPMyGEEEJyoewpxgjJA4pnQgghhORC2VOMEZIHFM+EEEIIyYWypxgjJA8ongkhhPQ9E5M1bNx+H87bshsbt9+Hicla0U3qS8qeYoyQPOCCQUIIIX0NF7F1j7KnGCMkDyieCSGE9DWuRWwUdfkzvmGU/Ur6GopnQgghfQ0XsfU+zB1NygQ9z4QQQvoaLmLrbQLbTW16ForTthv61klRUDwTQgjpa8q8iI0LGeMpW+5onjNC2wYhhJC+pqyL2LiQ0Y8y2W54zghA8UwIIWQR0IlFbFl9uFzI6Mc5I0OoGYRyEbYbnjMCUDwTQgghickjAlmmiGqZ2Xzp6pa+Boqz3ZT9nHVyYSUXbZ6G4pkQQghJSB4RyDJFVLtJUhFWJttNmc9ZJy0ltKu0wgWDhBBCSELyiECWeSFjp0ibOWN8wyju33IxHtt+Ge7fcnFhgq3M56yTCyvLtmizaBh5JoQQQhKSRwSyTBHVbtHrnmHbOQOAjdvvK/Q8dtJSUna7SreheCaEEEISkpcPd7FV4yuLCMvi342es7JYGmwPdGcNVTu27TLYVYqgMNuGiLxcRPaKyDdE5JCI/FZRbSGEENLf5J2bd3zDKG69ci1GR4YgAEZHhnDrlWs7ktGjn3IKl6FgTd5FV8piadh86WpUB6Tt9eMn53DTxMOZxlGZ7SpFUGTkeQ7ADar6dRF5AYADIvIlVf1GgW0ihBDSZ3QqMtipqHEQFa1Nz0IAaPP1Tkc0u5FNoQyZM5JaR+L6pSzR9PENo9j2uUM4OlNveb0+r7hj3xOZxtFitBi5KEw8q+ozAJ5p/vtHIvJNAKMAKJ4JISQlvZhOqtNt7iWfbVToa+T9TrW7W9aDMoiwJGLXp1/KZGmYjgjngDzG0WKzGLkohedZRFYB2ADgAcN71wG4DgBWrlzZ1XYRQkgvURbvZRJMbb5+5xQ27ZzCaE7CqiyRQR9MQj9KJ9rdzQeMokVYErHr0y9liKYH2I7NRBnHf69QeKo6ETkTwD0ANqnqD6Pvq+rHVXVMVcdWrFjR/QYSQkiPUBbvZRJMbY5OL2f1+ZbBZ+uLj6AxLQDL6o0u4gEjTz93km0l8e/69Eu3/O8+mI6t3QXdoIzjv1coNPIsIlU0hPMdqvrpIttCCCG9TpkjrDZrRlzb8oh+likyGIdP5PD4yTlMTNYW+iSPGYduWw982uxr50l6/EmsI7790u1ouq1vTMd20ZoVuOdArSfGf69QZLYNAfDnAL6pqn9cVDsIIaRfKGuE1ZXdwKdtWcV/mSKDcZgih1Hq89oym5DHjEO3synEtTlJRow0xx8UXbntmvUAgOt3Thkj1mXMMhHXN9GCMreMr+2Z8d8rFBl53gjglwA8LCJTzdd+X1W/UGCbCCGkZylrhNUlbkxtjpKH+C/aZ+tLNHIYXegVEH6gyGPGodsL+eLanMSDnfb4fSLWZVjgGCWNPz3L+O/FRcidpshsG1+F3YpDCCEkIWW80QNucRNuczQ1G1AO8d9twkJn4/b7Ym0DeVkuOp16Lzwm49qcRBCnPX5fEerbL90Smd20Z/XiIuRuUPiCQUIIIfkRnbItww0uzk4StPm72y/Dbdes5/RyCB/bQBmtBQE2i8FFa1Y425zEgpT2+PMUoXkXXjFtP1gQOSDmuGMn7Fm9uAi5G5QiVR0hhJDexxZ5S2In6RV7hY28o4+22QSgEZUOXrvqglHsPXykVDMOgF187T18BLdeudbaV0nHTLCvJMef5yLJTqb6i0Z/57XdzNOph6UyL0IuEopnQgghRpIIQZ/p3bLZSfKmW5UMTfu550AtNkpfhHc1zrLj8ugC/mMmzUNXnmsEOikybbm/KyI4pdrRc1mmAjBlguKZEEJKQNkW5SQVgnGRt16PKAPx56hbhUbS7Cfp+cxrPGYRX50eM3k+1HVSZNoE+ClVPLb9sszbd1HWRchFQ/FMCCEFU8ZFOUkFmu0GX5uexXlbdpfigSALPucoLvqYlyBNE+VMcj7zGI/BsZZ9EWheAr2TIrPI6O9imTVKCsUzIYQUTDdLI/uSVKC5inuEF1ABrQKsbBF3Gz7nyNYHCmD9tj04fnIO9fmGjMzygJRGTCU5n0nHY/QcRotyKLAgoPMquV42Oikyi47+9sOsUd5QPBNCSIFMTNasotMVSey06Ewq0HzyNUcFWBkj7jZ8xKerD6Zn622vJX1AyhLNTXI+kwht0zm8fd8TbZ8LhPP9Wy62trHX6ZTIZPS3fFA8E1IwvRJ5I/kTCA8bNqHaDdGZNNqVprhHGSPuNnzEZzRntQ++C8qi5zxpNDfJ+UwitG2L2UwUlaGhH66xjP6WC4pnQgqklyJvJH9cwsMlVLshOtNEu5IW9+ilNFi+4jPog/O27LY+QIRxPSCF+37m5FzbOU8SzU1yPpMI7STnqogMDbzGkk5A8UxIgfRS5I3kj0t4uNKOdUt0Zol2+QiwXkqD5Ss+A9HrI5xtgtQk+GyEz3lchNX3fCYR2i6ve5QiFgnyGks6AcUzIQXSS5E3kj824TE6MuS8sfeC6PQRYEUvhEpKnPiMil4Ty5ZUMHNy3ilIk1ghgnOed4TVV2j7eN0BYPlwtRCxmncVwV63f5B8oHgmpEB6QQSRzpFWPPaK6IwTYGVaCJWHMPIRvSPDS3Dog26bha+wC5/zoiKs0XM4MlzFsdk6ToVC79WK4ObLz+9YG1zkdY2l/YOEoXgmpEB6RQSRzpBWPJZJdGal2wuhTCIZQC7CyEf0+nzGJvhGhqpYdsag8Zz7RFg7FTkNn8ObJh5uz7bh42HpEHldY2n/IGEongkpkH4SQSQdacVjP62+twnaJL8LH2Foix6eMThgFEZbdx3C+IZRb9Hp4//1iXjaBN/WK8639kFchLUbkVOjcAZQP6VtKQq7dc3L6xpbdosdLSXdheKZkILpJxFE8qffb4omUbf57oOANkRX8FpcKWkfYWiLHtqsFtOzddw08XBLwQ9XW3z8vz4Rz6SCb2KyhuMn5tpe76atY2KyhjsMwjkgXGWx2/aHPK6xaewf3frt0lLSfSieCSGkpPTbTdEkJkyiLqjCF8Yk9MJFQ3w+nyZK+KkHnsS8trbHJjqDv2+482Dbd4Bki+Z8BZ9tkeKAAFddcHobnYqcus5BmEBk9qr9Ian9o5u/3V7t016G4pkQQrpAmiiU66YYvN8rEWmbmPDNKgG0+3fjvh8Vhrbo4fLhKo7OtFcABGAUwaZtBwTnwCS0ki6a8xkztkWKpxS450ANY+eejfENox1ZnOxzDoBGMZdAZHba/tBJXzfg/5vrpqAtu6WkH6F4JoSQDpM2CmW7+UWFZzci0llFiU1MVESsAjVKWOj5ZLaICkNb9PDmy8/Hts8dMgpoW/tcojMPn63vmHEJpLBY68TiZN+UetdeuHKhzZ3MMNTpaG8S+0c3BS2zNnWfgaIbQAgh/U5cBNmG7eZXEUm1vbQEoqTWLLsdiJKJyZr3NmyiYV4VQ9VKy2vViqA6IC2vRYVenAixVf+79cq1GB0ZgqCRTzsoRnPz5ee3tWOoWsG73vhy4+txonN8wyju33IxHtt+Ge7fcnEuae9M5zhOIAX95Dr2tPgIwaHqAMbOPXvh782Xrk7Vnz6k/Z11Att56YSg7WSfEjOMPBNCSIdJG4WyRQtt0b5OTdPmMQXtKggTeJ+TZNtwZbYYdUR6bdFDV7R47NyzE0eRfSP1ts/5jpm4RYphsZb34mSf7CKz9VMt0d9OZhgqk32hm2lImbWp+4h6TpeVgbGxMd2/f3/RzSCEkERs3H6fVTjev8VdMMO2yM5ne3n5P8/bstuaqlcA71RyJjHhE/30yc2cZHudJu5Yw4vsBK1pkIeqFVx1wahxoSJgHjMTkzVs3XUI07OttpNO94fpOKPHE+Az1rOS5XfWCfL4/fV7tp2yIyIHVHWs7XWKZ0JIv1P0DSiLcEy7vTzF6szJOeuCuoBANNmivlGBt3y4ipsvt+ct9jlWoJzRNpuIAxqFTo6fnDNmFIkj7vwVMc6j+7QdtwB4bPtlHW9LNx6oikpBB5TnAXGxQPFMCFmUlOUGlPcNN257aaNwpv6qDggg5hRyNkaGqgtFPbKcg7JFE31wRerTUhHBR65eV3rRlPcsS9kitd28nvTi2O83bOKZnmdCSF9TlhyoUV9isIgpbRvi/Ktp/Z/GvMuntKU0tI8wnJ6tL3hds5yDMvlYffHxAiflVDPQtXH7fV17AEvDRWtWGKsMXrRmRWxb8siU0emiU2nHcpq+7sWxv1igeCaE9DVluQF1q2hCcJO2CVzf7AxRjs3WMXXzJQDctoQws/V5bNo5lXhfYfJMw5WXWIzbjk+lwaQsrQ7kOn46NR73Hj6S6PWAborSLKS5nqTta6agKy9MVUcI6Wu6mTLKRTfSaIVTypnwWe3v01+m1FhpiDsHPmWnfckj3Z5tO5t2TmH9tj0L2wqnhcuL2fqpXMdP0vE4MVnDxu334bwtu7Fx+33WfrONvbgHpSyiNOs5TUKa60na3z5T0JUXimdCSKnxvWnbKMsNqBsRcFfRCt+8vhetWQGJvBbtr6g4jH7eB4F7Kj8QRtEMEsuHq4n8pcH42bRzKhfxaevjwKYSFtD3b7kYI0NV43bS9JmJtOMnyXj0FakTkzXrccU9KLlEqe0aUERe5zTXk6R9HRzrjnsfwVUXjOaam5vkA20bhJDSksfUcllyoOY1BeuaprbdpAXwWmA0MVnDPQdqbZaPsCAJ9hX2lk5M1qwV+mwogDua3thbxte2vW8TqcNLBlvSvbnOaZoS3nH4VvQLODZr75N3X7jSmpLOF9P48embJOPRt0z8gIjRLhQuz23Dlhf5ojUr2q4Bm3ZOGVPzBeT5QGrqy1uvXJvoeuLb16br3T0HahTMJYTimRBSWvJa7NfpRUQ+5FE0Ie5hIqtAd0WuXQ8uQf+u2rLb+1iA0wJ67Nyz27bpitb5PlT5lI8+yxIZthG3GLA2PYvztuxeEFW2z581VMU9B2qZhDPQHr039c31O6ewaedUSxrBJOPRdrzRMvG2Y1HEP+zaHnJdkX5bTunweA+L37OGqhABpmfqqXKTB8d765Vrjbm2bYLat6/LsrjZRdFpP8sCbRuEkNJSlsV+eZBHeeS4CKDNi1ybnvWyvMT162x9HjfcedBqoUnj8VXAOM3umsb3na73GSfHT84lsgL5POyErQ0XrVlhnOYXgVPY+9o6PvXAky3nw9Q3gcAM2y18x6PLimEqE2/Cd1yYSpq7zqGivZ/CojRqN5merePoTN3bH+07zuJsLb59XfbrXREe87JSaORZRP4CwNsAfE9VX1tkWwgh2ck7KtFvq82zRsDjbq7jG0ax//Fncce+J9oicj6WF58Ua0F0MZg+3/a5QwvFTmxZJgLfb5JpdtO2qgPiLNgS3Y7P8dTn1SuyF64K6MtsfR57Dx8xTvNf78hCEkSIfaww4fPhk+EjHMn0GY+2zC0Ce6Q5jGt2JXq9uGjNCuw9fKSln+LOYVCYx3TNiZt5iIvqxs1+hO0q0b6Ibtunr8t+veuFyHi3KNq28QkAHwPw1wW3gxCSkU6kvsrD6tBP+Nxc9x4+Yk1TF3ejS5Ni7ehMve08mx6gJiZruH7nVKJp9pHhKs4YHMCx2TrOalbmc4nJqMjwPZ6oSDKJunsO1FKlnnt6etYonHyE+M2Xn4/Ndx/0Lk7j2z7fSObEZM3axkC0mt6viOCUqvMB2nS9COeHDq4fV10w6ux7V8EQn+N0nQPb721kuOplV0kaMS779a7skfFuUqhtQ1W/AuDZIttACMmHTqx8z8PqUAZM2QImJmtYv20PVm3ZjVVbdmPDB/fETn/6rPSPE2SuG13aLBrRBYXRqffg9WsvXJlomv3oTB0n5k7htmvWA3BXODSJjOj4qYj5iKLiPTo1fce+J1LnbLZFDV3p/sIPnjvevi5TVpMkbQoT9IONIDperbS2qlppVEKMnv8oPn70cOR++XC7Nz1OWPocpwDW353t96bq96CSNGJc9utdWdJ+loGiI8+EkD6hU1GJNFaHMi1qMUXYbrjrIOZPtQrBozN1bL77IID2SH100dPS6kDboqfgM3HE3eiiWTRcU9NhfM7zLeNrMXbu2dZzY3sAc2VWANCyEC7ueOIiey7PcBzRBWwucRc+ZtMDT/BAEhagaawjUYIMFnGVCl3ituW4op1j6CzT79H3uhCO3Cf9XfvMPASee9vYAdpnUlyWm4C0EeMyLG62UfbIeDcpvXgWkesAXAcAK1euLLg1hBAbZfHrdauSny8mERIVzgEm/+3EZA2b7zqIevM707N1VAcEt12z3ikKTYRThvkIkTjhGcb3PLvEgU1QxQln3zR80XNREcFVF7S2J8vDXvisugS9L8FCz9r0LCrNh5eRoSqqFfG2cgRtsVlQbL8Pl0APoqEbt9+3MC4D6qcUm3ZOYce9jyyMNdPvcWS46pXacMQQcfYlKn5tPRY3G+NrufGxq/QyZUn7WQZKL55V9eMAPg4AY2Nj2XL6EEI6RlmiEmVb1JI1j/DWXYeMAmXrrkPeC6MCgpRhaR4wgtdNUeC8zrPPAr8oPlFUm/CfV8U9B2otqfLStCHK8uFqrKD3eeARnBaxQdTf9SBhIvxwMTFZww13Hoxd3AZgQaxHqYjE5hUHTqfIM920Z+vzOGNwAEPVSuy4fe7509lQ0jwUh8Wvrax80gd827WuTBaLTlHmyHg3Yao6QkgulMWvV7ZFLUlvzNHP28RS+HXfYwv8nWn96eMbRjF18yX46DXrO3KeTR5TATBcNd+qli2p4J4DtdjUWa6Hi+hx51F63CeiGvfAY8thnITKgOD4iTmct2U31m/bg813twvngGAMBf582+fCr8eNbVf7j83WvUqY1081ZmPyWFORV7XRslzrSHEUnaruUwDeDODFIvIUgJtV9c+LbBMhJD1liEp00z7iY31IksGiWpGWG7lv/lTfaGng78z6gJH0PPt6VU2p9hQNAVUdkJYI/FC1gmploO3hwhRFjTuu6PtLqwOpFwj64mqTLYtFUuZP6UL/xEWsgzLYcWM1LHbTZGcJ7y/sZXZtx9VXSR6Kg/EVVHU02XaSbKvoax0pjkLFs6q+q8j9E0L6j27ZR3ytD1Gf4MhwFc89P9dmxVi2pIIP/cLp6FVctoNw9oEkIiYQsL7lgsO5hkeGqnjbupe25eJNU6UNME+3m1Lt1ecVy4erUD0tApdWB3LL9xwct693HGhEhoeqA5ipn2p7b8SjaqGtTYHNwmYx6ATB7yMuGh79HcUterRRHRDjdkyWEuD0+cn6UByUnw/2YbLtEOIDbRuEkL6iW1OqrswQpjYF6dsmP3AJdrxjXUv7PnrNehz64Ftb2hgnZC573Utbth89ZlNqLwALgjdu+npisobNdx9sEajTs3Xcvu+JRBXGbP0UrVQY2AVsIuzoTL0lenp0pm5N3WbK92yzYoSP29c7DjQi4ksGK6gOtLfi/HNe0JaaMErcOcjDPuKDSONB5PqdU04BbPodteTkbi5k9OHMpYPGxakfuXqdtU/ysFx0Ip0mWZyUfsEgIYQkpRtTqq7MEEH5Yxs+7Yubjt57+Ihzm660bD6r5nfc+4hXRgfXYkxXkY1wZbzNdx0ExJ3H2YTp09GoJtAeIQ0Ww402s0/suPcR6+I2F8dm67j2wpVtFR3v/87p8gW+MxLRc2Brsy8+nulqRQCN92ibMppEx1fwYLOkIjgZcx6nLfvzHZdpMz2UbT0E6V0ongkhJAUuK8ANd5rzNee1fSD+hu8jzlztSyIoTJ+Ns52EiVpYMmEJfpoeLrbuOtRS1S4p54wMOSs6BgSR9ut3TuGsoSpE0Jan26fN523Zbd3X8uEqhpcMOqsiVgcEZy4dXNj3s8dPYNZgOwlji+7aovRxwhkABkRw3pbdxuN3jcvomA4X5vGhLOk0Se9D8UwIISnYfOlqbLIUS5hX7Uhp8jA+N/wsEfgkKdtMbUligcgTU67sKEm8zTYCUelTMAMwp5qrTc9i084pbN11CFuvON9YnCYsMF3nZHqmjskPXNLymqsgzU0TDzsfHARwivss0drwrEOS30nWVO97IAAAIABJREFUHO5lSadJeh+KZ0IIScH4htGWxXRR8sgtfcagOeuD7YZvEl1AuqnuzZeuxua7D8ZaKWxtKXIqPG7fPsI+EI8XrVmBvYePtNk9gn7MWvEPaAjqcJTeJhADsW46I6YHGNvD08RkDXc4hLNP4Zmk+bAFMFapTPI7yZrDnUU+SF5QPBNCFiVZSngH3w0WraWpXBa3fVdk9IzB9rXepqjcpp1TGAAQTMwnidQF76fNtmETVyNDVZyYO9VmJ0jqeQ4WRJoeXuKi8nHnJVpYZO/hIxAALzlracvxTkzWcPzEnHebXYQXrtkE4v1bLsZd+59o8VQHTM+cjPXaB+y49xGn1cQnEusS8lGC/jxvy27j++Ec067fZB6eZaaYI3lA8UwI6XuiN2XfEsW2bYVFqks8+FgrTIIhLjIajlTGVRmMOlp9I3VBu6Zn6qlKTZumyKsDApFGG6JRXADOSH5AuC22RZFxVQddUdNwJN1lEwCQ2foRxVVCOmjvd39gbvfxk/NGC4htPzaWD1e9o7jRnNwmwv1p6/cBEazasrvlQdT0m6RnmZQFpqojhPQ1gQAKp1e7Y98TXunTTPh6eU1ZH3zaFvwdRzTFVtYFfnHt2rRzCuu37WnrlyDFXLTfounzRoaqgJyOFM+rtmT/GN8wiuEl7nhOEMEML3qMpui76oLR2KqDmy9dbU2rFi6a4bIJJPF0+yVwa4jAipg/Hbwed+6CBytX+kCb2BQAN19+fstrtvMbROQ11LbRkSG8+8KV1jSRtvR7gZUjKsJ9qj/Ss0yKQDRB6puiGRsb0/379xfdDEJID5G22MRQtWLMD+3KeBCmWhHsePs6ZxTP1rakackqIlhaHcDxk35iLs7T6uqzcL/YIr+mfrNtc2SoimVnDDqjrkBD2N12zXqrhzeI3pt8tUD7Ma/ftsdYdS/8Odu5DuRtkrvn6MgQnp6exVlDVdTnT7Wdq6DfbItQAeC72y/zHs+uc2w6bwLg2gtX4pbxtQuv3TTxcFtkeahaWXhAsZ13l/3C51yZjiU8axS2DUX/poe5f8hircsLETmgqmPR12nbIIT0NWl9xzZ7g+9Cqfq8YtPOKey495HEGQuCqKxvZHNe1Vs4VwcEMyfnrKnCAHMlt4AgQg8kW8DlyosdVzoaAH7yFeYqcFEhaBNj0f0fs+wz/DmXTWDm5FysxSTAlifZJAxsCxCDsti+1SRd495n4VywqNAUDQ7KW0dfD6LErowYYc+xzQMdRnB6PNamZ3HPgZr14S1p9g1SXsp+bimeCVmElOGJvlvYBJBPEQmTAElSChtwX/RdQvyMwQEsrQ5geqaOs4aqOH5yzmtBXfS4qhXBsiWDODZ7ejuB6LO1LS7yPa/qzMRh6rek2RmihL2+aaKXYavCxGTN+r3w52zn+ujxE3jekB95QIDKgLT0i81WYFu4FpdOLfjO1l2HnA8drlzKrv0HuBYVuh5QkjxQxY0J0280LNJN5bzzyHJDiidrZpVOQ88zIYsMm8/W5ZHsZWw+yWtD3kybz9SW/ivqsx0ZMpfCDrCVAHaVYJ6ereP5+incds16TN18CXa8/XRJbxe3XbO+pW073r4OUzdfgse2X4ZlZwy2CV5T23zEqEvIm/ota7npcEaG8Pj1aatpEaDpe1GhG5zraKnzmfqptoWYAPDCpdWW85SmNHx4fAGNsRmco7CffOrmS/DRa9Zby7DPq2b6faeZsTmnaa/w3Z5pTATje3RkyLl40nYObfsivUXZq0Ey8kzIIqPsT/R54ztFnaR4gk8p7Ci16Vls3H6fswRzlPB5Ce/zFTd+wSkag4wd0SpsthtP0Lagf0aGql5WChOuSCvQeh6SWB8GRBYizj5R/wEBVNsLfdi+XxExCt3ASuHTzmOzdWdu5bgx6JMRZv/jz7Z4fIMFfq5IfPT37TPzlHSmIDjvtrEcjfwH+z9rqLowwxJti2tNgGsMMPtG71P2zCoUz4QsMsr+RG8ji9Ukboo6a/GEOBEcELVJhI/JRvDeTRMPL3hNXdHnzXcfBPR0yevwPl0WlrCvtFoRVAckVdnsly1f6l1u2vTQUa1IS/sD5lW98woDjSjw1M2XtL1u6+tTqtZ2+/42bDf2OP+m6X2b3zj8erCdW69c27LI0XUMvl5SVwXNMKZKhK4H0ej+p2ftudJtFhaXcGb2jf6g7NUgKZ4J6TBl8xeX/YneRDcWj2QtnhB8Py4KbVtYZUMBrIoIIpeANNkpgkV+gfAOf8IkXOrziuXDVQwvGVyIDvp6rr/1vePY8ME9uPlyd65hwP7QApj9rEmkvG1BYJrx7xOFNd3Yg99+3KyCKRpuO1abB9g3F7LvzFNcBU3AvBAy7kHUdaymxYWmbdn61DZ7UAbKdh8oO2WvBknxTEgHKeOKYdMTvQC4aM0K63eKvvAnsZoU3VafKLRtYVUnCefSDQSzy54xPVPH5AdOR24nJmtekUigkcs5SSVD02eud+zLZ7GnSQzbKgLGRbSMBV9CCzHDoj+wv/g8cATR4KyzPuHvx0Xsksw83Xz5+dYHvCS2prj9hIn+rm3b8k2RWAbKeB/oBbIGNDoJFwwS0kFcoq8oxjeM4qoLRlum/hXAPQdqxkVFeS0wtBVb8PmM7w2/LIshxzeM4v4tFy8s+oriWljVDQLhfGLOtOytQVR8jm8YtR6Piazj3BUJVqCl+Eq04IktCnzjpx9ue1hYPly1iq5gPF6/cwpLqwMYGaoaF2IG0dfw2JuercdG6oNjdBUtcf0doMDC78W0oDV8fLZ92V4Pl4IP1tWmWQgZt58wgQffdq2IO8ayUcb7AMkGI8+EdJCy+ouDymBhZuvz2Pa5Q16lopMuMPSJvLg+4zvVnudiyDwi2LYo4EVrVhhz5QKnC0J0unyVa0FgVHyG7Qc+Ud+AsM82aV9uvnS11eMctQuYtg+gZRHk8RNzxgjq8JJBr/zRR2fqGKpWrIVaks4kBD7zjdvva1scCJwuRrL7oWcWrBNLqwOYO6VGUW6zPETx9ZKa7EdLB7NHd31SPUY9+KYobZmjklHKeh8g6WHkmZAOkjTK0ymiEV2bneDoTN27VHSSC79P5MX1GZulZHrmZEtUKq6tPtHv4HN5RLBNEbKgOpsrVVrR/vNwiepwXwCnbR9Aa1TSxDkjQ6n7cnzDKK69cGVbtNUk9IJIvy0KXJuetT4s2MaxbTzecOdBY9uT/B7CDyBB4Y+rLhhti6SOnXt2Sz7p2fopQGFNTxc8ALvwjdp2KloaTcVniq7HlenuNcpyHyD5wcgzIR2kDCuGTRFdX2br89aCGWcNVVsie6ZoomvBFNAqOFzRmb2HjxjfO35yfiEqBdi9sGER5+M7jItgJ4mkRiNkG7ffZ4y6iTQii9fvnMLIcDV1tgtfpJnKzUS4v20LvCoi+PBVrwNgLtgRTl2WdjbglvGGgEwatU4SBbYJGFf1R9O48VlUKGj8bqJ9NVufx+6HnsHwktZbsuk46qcUw0sGMT1TN471ozP1BfuGDZ+obV7RUttvxZY2L4+H9bzJOgtVhvsAyReKZ0I6SBlWDGddmGYqFV0dEBw/ObcgAnxsGCbCwsVlzXDdOMNRKZOYECCxiHPlQ16/bU+L+PFZ/BO++VqzKCgWpuePztRRrQiGqwOYMVSyywNXbRGfh5p5VWy+6yB2vKPh/bUJDNvCP9N244SWDxOTNe8HxOqAWAWMS8iZxo2PHUFhzwJydKbeVvnRtq04oRltWxrxNzJcNWbaEEGsOA/vN+6B1fRwWaZsQHks9ivDfYDkC8UzIR0mqzcva9Qja8RmNOR9dhW3iAqKONEejbzYxMfMyTljtC5M4MU1oUBiEecSJqZ2uCKpPg8RJurzijmP1HA2hqoVvH7lWfjH7zwbm5ouis9DDdCIgv7+px9aGOOm4/fxq09M1tqi12lEyk0TD+P2fU94fTZo/6adU9hx7yNY9aIh7Hv0KOZVURHBhf9uOZ49ftIpYMP4ZFkR2EVpFNesT1BExXas4baZxN/muw9i665DLZlCon1se7g6pTA+KJuuUWlmHcoWpc1rHUUvebRJPPQ8ExKDr0+2U/vO6r31XckfFMYIE9y0op7SacvN3ydiCZh9loEXMlrq+uhMHcdPzrW1LXosIxYfaOCtTOI7XPWi5FGuJN5ZX0z6pSISW6I7yHd7x3vf1FauO044mx5qXGW1Z+qnMDFZs/5ObOXRo0UzXA8lPkxM1nBHAuEcpjY9i/u/8+yCUJ1Xxf3feRavX3lW4tLtmy9d3Zb9I0DREKW+ZcqDWZ8wQd/ZrEzRthmtH/OK6dm685pii5ADreflpomHcf3OKeM1Ko31o2yZNLjYj5igeCbEQdGpz/JYtGMTL9deuLLlBrXj7euw4x3rvG5aPkLU9pkgU4Jpu+MbRrHsjPYJsfq84sylg23COsAmSsIiLU7Ehdn36FHjflwk9c4G2MSZjVOqeGz7Zda0cQLgI1eva5kWDz/4uNLNuR5qXNz46Yesv5M4MRT3cOErUnbc+0juGUr2PXoUH7l6nfe4CdrhSlE3PVtvWxxoG9dBX5n6ztUvMyfnYtM8hgmuKeEHoIGYcfn09OzCA4ttgV+ahXJpZ9o6FeTwOYYiAyykGGjbIMRBnqnP0pBH1COp387nuHymVm3Tyq5iLID92I7O1DE6MmS1bxybreO2a9ZbjzP4f2vVNMW2zx3C9TunWj5vmiqPIxAs0f6z2RaWLalg5uS8dV+uxY+AvdjNtReudHqvZ062FwkB0PBXn5zD9U0bQ9AXgZhxMWvwZYd/J1mKZvh6XTsRCZxXXbgGBBaK0Zjfj0877jlQa3mAMFl74qwKLjtNUKRm/+PPYsBi/YgS9VnHfeeckSHnA8vT07O47Zr1iY4rrb+4k0VI4q51LICyOIkVzyJynqo+FvcaIf1IGvEajpyMDFehCqe30EVepbSjAjoQQ2kv7j6C3Dat7JpuBtyiwJVn+JyRIS9fYTT1VyD8wjc9m9fUha2qnq063fGT9mjraNPTasr9e9GaFS1V7JZWBzA9Ez++4rzXM/VTC4sTA1/s/sefbWtDEnx+J65ejooU13jzyXaRhmCbgYUi7jfs0w5TFT3AXKZ8890HFyLZwXkB4hcoztbnjVFhGxURcxYYtP/egn5wVYEMfo+m47L1X9pgRSeDHHHHUHSAhRSDaMwNQkS+rqqvj7x2QFUv6GjLDIyNjen+/fu7vVuySJmYrOGGOw9aC1mEizSEv+O6oSUtIWuLSCX1APpuJ8/S1udt2W29cbuidz4L7KI39Lg+iUuZF23bqhcN4f7vPBv7Wdv3o2Mj2q/PHj9hjNYCjWN7bPtlxu/ZBLXPeHDl97YxII0FYmmx/U5umng4VtgtH67i5svPX4h+x43ftAszk2I7poAk7QgK4th+a6/5gy8as60sH65i8gONDCetMynxDEhDKIfTIEaz6YQRwDqjYxtTwXeSXjts14zwbyLJ9wD3tSYP0raZ9AZNvTsWfd0aeRaRNQDOB3CWiFwZeuuFAJbm30RCykNwA3QVsjAR599MGpHIK8WRT3Qk7+nHuAiybds+WQuC8sw+fZJUVNWmZ/Hs8ZNen7V9/7wtu3HWUBUiaIsKT0zWsCkmYhcQjaSv37YndZQrja0hi3AOUgRGsflkA0YjfWUTaNHj3v/4szgxl0w4h3NdVweAeW0cs2vmodb0+sbZnoLfrM02EVdFb2KyZk1TGIjlIKNFEvGsCuy4ep2xkqhtlss2o5PWOmS7nqWdaUt7rcmDvGYHSW/hsm2sBvA2ACMALg+9/iMA7+1kowgpGpsIDrIYZPE7JhUxeaQ48rGfbN11yFpRLWhHEnymlW2iLzhmm3CKi/6FSZrtwjZ9nQQFrGnXXP5hm+AEGqIjaZW8MJ2yNdgIUgRGxdLxE3NW4SzAwnn1eegJjjtpijqgsVq+MiALloj6qdZotitSHyfGwr9Z03G4quiFxbcPSa8nwUJA0+8nqe866cN93AN62jR1Wa41WSlbaj3SHaziWVU/C+CzIvImVf1aF9tESOHYbkinVDP7HTsRkUjrBw3a4hJmtopqcfhEkONu/HncmJKIC9f0dVaCG7irPddeuBIAjJUbXWIqbky5FgomxSdPNNB4wEla3TIuxZrt80lT1A1VB7C0WnHmKncJstn6PLbuOtRimRgZqmLrFec7Z1KSVNFzjZNwdg7b9oaqAwDaHwRtv+e0s1xJHu7jZsCytCH4XrcrFOY1O0h6C59sGz8QkS8D+HFVfa2IvA7AFap6S4fbRkhhuMSmS6jGRUCSCD9f/7GP3SJOhMZnU0gXuYmLIMeJvjxuTL4R18AuYCo1nRcu8bR8uIqxc8+2nkvXzd+VwcQWwR0ZquJt616KvYePxPZPYGMI/h8noF2luW1Eo+5xYic8fn3dJYGl4JbxtThvy27jZ4L9BmPMZrGJjpHp2To232WepYlGoq/fOeXMpBL823Zetl5x/sK/bb/tIL2gad2G7fecxyyXC58ZsLRtyHqtyUKn+42UD588z38G4EYAdQBQ1YcAvDOPnYvIW0XkERH5tohsyWObhOSBLSfwRWtWOPM+j29ozWm7fLiKkaFq4mT/SfJL++SCjrYr2hafqEzg5U2Tx/SiNSvaCnv4PkiMb2jNU5zGPhJXlCKcezph2mVUK4KPNguRxBGIf1N7VBtp9GzWmbMsuYCBRuoz2zmxCdhlZwzilvG1uH/Lxfju9svw0WvWW7cfZJoIRJhLrI4MVb1yEYcx+WRtRW+AZL+l8Ji/7Zr1uGW8ISp98veObxj1Oq8B9VOKG+486Px92DKMRB8eTONEALw70k+u3/b4hlGcsni3beemkzmL0+R9TkqSfO6EpMUn8jysqv8krXeUzPN/IlIB8KcAfhbAUwAeFJFdqvqNrNsmJAmuCK9pUU3cgq08ohBJ0h/5ptNztcs3OhsW8sE245iYrOGeA7W2EtFXXdCdaE10SteUqSN8Y7VVT7QxOCCxUcrofs4YHGg7v65o97wqfvi8/f3ww1J0zCYZH7Zpb5sPfPlwFcNLBluygew9fGQhV7StFHX4e8HCytv3PYFPPfAk5lUxMlQ1Hm+1Itjx9nVt42bZkoox9d+yJRWrN97XEhQ3mxQlzuZkOx/BmAxbdq66YBR7Dx+JnXUJftvBtSycqzvJgraki4aTZuex9eXxE+Yc6WmgjYJ0Ax/x/H0ReQWav20ReTuAZ3LY9xsAfFtVH21u928B/DwAimfSNeJuFtELri2vad5+uiT5pfNY7b350tVO4RcliY3D9CCgiM/3nCfRaXPbjXVisuZdVCJgtlmeGmgsQJs3pKgI0q4B7YuyfInLfBEtchH8bROwAyJtguWiNSuMmTBs/TE9U8fkBy4BYP4tVQcE1Yq0VNwbqlasKeiC/dgeJJYtGWxp700TDy8I7iiVAcGHfsFeGdFXZCXxLAe4fh8u2060/6LFVFzYrmVXXTBqTG9oisQmeWhPk50neD2aXm961pwjPQl5ptkkJA4f8fybAD4OYI2I1AA8BuDdOex7FMCTob+fAvDGHLZLiDdJE9x3Ky2Rz37CuYvjoqlxjG8YTez19X1gyFIlsRM3RFsEPi494RmDA9b+CaK+NuEcCMyN2+/raB5i01g+Y3DAuBByXhWbdk5h665DCx7anQ8+majEddwCv/qpRhR52RmD1pmdJP1xLNT/rgwbPrl9w8K7IoKL1qxo+bxr7K3ftif2t2Ib37aIt6r5/GV5SJ2tz2Pv4SO49cq1Xr+jJL/VtMVBghkO12LNpLDKH+k2seK5GRl+i4gsAzCgqj/qfLNOIyLXAbgOAFauXNnNXZNFQFJh1620RElLwipOZ0FIWxRg6xXnG/dpE42+DwxpHzi6fUOMS08I2G0ZrgeBsEjweWAYrg5Y8/umIShbbiv4E0T9BgQtEeI4ouPedmzHZuuYuvkS43tJZ2zCY+ZTDzxp/ExFpMWqYRLB+x9/tkV4z6su/H3L+NrYsbf1ivOx+a6DLYVGXG0NY4t4Z53Vcl3LfK1ktlkKk/88y0Nxlu+aYJU/0m18ynP/duRvADgG4ICq+s/ztlMD8PLQ3y9rvtaCqn4cjcg3xsbGMqTsJ6SdpMKuW366uP3YrBBBCquw59G3bbZ9Asnzv4ZJ+8DR7RuiT3pCWzW3YLzYqq0F9oi4Kf+hagVLBvMVz0GRC1cp5TTR8KidIMlsSTC+hhI8KETHjM1KEn7dJoKftxzvHQ88gVvG1yZKqZZm5sckZl1FSnzIY1bM5lYyvZ5lf77f9Z15yluM20iSAYkWkv7Gx7Yx1vzvc82/3wbgIQDvE5G7VPWPUu77QQCvEpHz0BDN7wTwiym3RUgq0gi7bqUlcu3HdlOYnq0vRInTRGpd+0x7M0j6wBFXSjt67ElvVBOTtRaLSuBH9rmh33y5OTofjBdTZFqbr++49xFjee2AoB2+3vPqgDgjn9G25VkkZbQpyMMknS1J0hZTcSJbFcBKc3H7xGTNmqbNhmrjez5jz9dH70vSa5Fv6fYks2LHLFYU0+tZZuF8vptk5qkbdjrf9tBCsjgQjVkYIyJfAfBzqvpc8+8zAewG8FY0os+vSb1zkZ8D8FEAFQB/oaofcn1+bGxM9+/fn3Z3hBjpxSiBq/pZlCTV+LpFWCAHImjUIgCihI/HlMM4XCXOtF/TdHu1IrjmJ15uFB/RbdnGS1zZ7WB7V10wit0PPdMWwQ7ec5WuDjMg7kWEUftOXMU+3+Intu0H+7D9lpKM2TDVAcGOd6xrE6q2bb37wpUYO/fsWFuFdX+RBY5hOvFbCveZraS76Tumce+bncPGhg/uMc6s2I47y7Uz7rtJqosmvQ6kwbc9eVRFJeVBRA6o6lj0dZ/I848BOBH6u45GwZRZETlh+Y4XqvoFAF/Isg1CstKtSHKeJEmf1anKWmmxZVioTc/GCsdodCqptWPHvY8YBVV9XvH5g89gafV0CjlXxTjbtuMIFnANLxk0Lpj61AP+C/bidGH0Ru2yngxVK3j9yrNw/3ee9dx7e0QtTgylHoehLKlxDwAbX3E2bhlfi/Xb9qQSzoDb920rRpNWREaPZ3q2jqFqBbdds975fdfiwLQCbWKyhueeb89CW62INZqc5doZ990kVoxu2Ol829MtCwkpFh/xfAeAB0Tks82/LwfwyeYCQqaVI6QATDeLmZNzTj+ui7yi7z7bcWVYiCu+IdLq5056o3LdwKKLIk/MJfMd+0ZVXW1IkiLPxXJLgZFoPuDolH+UuGh0OL/05rsPLgjP2vQsNu2cwqadUwsR6rS2kfq8LjwMxWXn+PoTx5yl5oF0DwoBpvSKWXIjm9Ii+vj6OyHQbA+WQXrAbs/QpVmPUob2dCsjEykWp3iWxurATwD4IoCNzZffp6qBd+LazjWNkMVHkhtU9GZhm7qM8yDm5dHz3U6aG/zIUBUn5k555zB2VTLzFXBJFidOTNa8bQ+uxYUuH28SYT09U1/IPW0aT9GxY0uh57PHp6dnse1zh6wRW1e+YV+CMRM3doJqjC6CtG1pMO1/6y5zVUjT2Llp4uGW2RXbOY07zk4INFe2lCJ8vN3KbJR3e8rWbtIZnOW5tWGI/oKq7lfV/6f5H03HhHSAJCW5TYxvcJfgtuFT3tsH3+3E3eBNZbxFzDlwVZGoFO/mS1e7L3oRfIW+reRylKBtthLC73rjy42vf+TqdYnKRCuAzXcdxOa7D7aMp007p7B+2x7cNPFwSwnmLAsJzxkZMj7AhAnnG05yHOF9hP/vwvWQsXy4ivEN/mXDowSFZQJcUe7wPiYma9jwwT243dPPHnecnShB7XrgzOsakYS017Oi21O2dpPO4GPb+LqI/ISqPtjx1hCyiMkjNVt0Wt4nZV1eU8C+23H5tYOFe9FFT7Y0a0EO4yTTyZWK4FQkSmor7+wbyXP11chQdUFgLa2elu42f/XYuWe3WSpcC+Rs2Dy/07P1lhzHWYRzINh8MoSE8w3HeZdN+wCSl8oOU63IQpXHtBaSeVVcH7KjzJxs9wgHKBoRfZ9FsGF8RHAnPL6uiGm3KqtG6bQVI6kVxbc9vbiOhiTDRzy/EcC1IvI4gONo2uBU9XUdbRkhi4wkIjauxHSSKda8poB9t+NauBZM/UcXPdnyK480I4lxqelcHlMAqFYGMFRF6qlW27EPVQdavNNHZ+rYfPdBQFvFbfgz0RRoacVipwkLfp/qlOFxEBV/I8NVqDYehsL/jo7tuPzKUUZHhoy/kSwiPNifj/iuTc9aqyD+b/bePkquqzzzfd6uLknVcqKWQQHUIEsBIg+OYinqIQZlZmINYyVjbDpWwENgEia58WXuZM3YcZS0A8Fy4oyVUYgzmeTOHWbyAbHjkT+gsRGMgcgJCyVyaNGShYIcCMY2ZScIrHawuiVVd+/7R9VpnTq19z57nzpfVfX81vKyuurUOft8P/vdz37fMBURLCnlJYLTFmg2Qd5tDuoywpRypBtcxPOuzFtByIAzNVM3Cjtd4QDbQ98UwQ68oL75eV3xWU/QTp0gvvfIMxi/7NK2dvoUbwhjyuwRJUkEO8yeXZu1qdHmNQVAdN5g0wiDrephILauvnzdcpnpPFm9cni5vXuvt+en1l0HScVftHNhqpxoSw0WFYqmey8PBMAH33Gl87HIcuKe6Zxcffm6jkw4ve7jZVVC0g0u5bmfBgAR+R4AqzJvESEDQDS369kLC9qXt+4FFffQN0WwF5XSRlbSGgL2XY+pnaq1jvDvfIo3hInLzhAQVOHrJu2WKTruiu542KoePrXv2uW/xy+7tC3bRR5EC4bc9tEntJ0FoN0f65oX2kUkBn8n6fyVIcIvAN511QYv4Zx3tHRqpo6HjtbbhLMA2L29t60JTClHusGlPPf1AD4IYD2AbwK4DMCXAVyRbdMI6U90uV11VESwe/uFNBVzAAAgAElEQVTYsnc5EBBxD32bn9MUWdEJxyQRrqggibY9/HtbO6P7mNRa4voiPHt+YbmEdlJmuxDOQHMy2qbJg23HKm6/w+eoY6ZlC5H4CH0Sosd+VbViFM9AU+jdcuAYpp9+AXdObLEKQQDOIjGNzl/UDuLDaK2K1SuHvSPYgeXksVOnna89U8f5Zoe5DUnRbTNody/DlHKkG1xsG78O4CoAn1VKbRORqwG8O9tmEdK/uEZDF5Vqm2jkmp4tzs/pIii7jXC5/D6YiKSTGtEXWFJriekFGa3ONzvfMO6fayfCtC3XFHbhYjFBW2z7HU17ZtyIalbdc61c6Eq4YMjUTN0p6q4A3HPkGW2FReCiENSl5rMNqafh/w3W4ZN9pFattBXScYlgrx2p4qVzC8sWH597y3bvZhWF7tcILVPKkW5wydrUUEp9G8CQiAwppR4D0FGqkBDihutLpyKSKD1bkCqpIvpQpEtkxSU11dRMvS3dWTiFl8vvJ7aN4V1XbdCmptP5Y5OkfzKl9PruVZ1FRHSpt3zSB5q29ebXXmo8FybCQlG33wCcxfD60RoeO3U6sXA2tfyho3VMzdSXj5EPcUI7af7jKME1unHyIF572yexUXOtRtGdRx2CZsaUWw4cW15ncL5MRWqqFYFSnZlQXNO+xd27WaSPs6Ww62WYUo50g0vkeVZELgHwOQD3isg3AbyUbbMI6V9c0mTVqhVj9Mplcls3XlAgPtoUF1l2jVbdObEF45dd2uYXXjk8tLyN6D7aSg+/f+rE8sS5igje+UOvwZ0TTaEZTf1myoAQbV9cJyDavqAAR7RyX5LJaEFbdFHVHfsOOYvh2bkL2jR8rrz5tZdqq/GFj0NeXuH1ozXjSICpaqKuDHxchHbl8FDsPilc7ARE1zmxbQzbfu3T2mwyLnmhTbhkCUk7ItzPEVqmlCNJcRHPxwHMAbgFzYqCawBckmWjCOlndC+j6pDgklXDmJ27mJ7Llh7K5aHfjRfUJPDX1KrL67RNWvT1E54LeWVn5xvY88BxQNBW7tkmeN4/daJNEC8qtfz3nRNbOobVTYxGIoYmIRK0J9p52L29vW0Hn3g+VoSZqgfqPNBx7dLRjXAGgK9/27ytPIfuq0OCqy9f13HcbzlwDA9MP4MvPvNi2+e2yLzJAqKzXbjabqLr9PXAu0RyXbzZ4fWkkZmjm+dIVplB8i4VTkgUF/F8tVJqCcASgA8DgIg8kWmrCOljfF5G3UZ8kkRWpmbqOHteX/zh7IXmxLq4yLJPtEonxHUFPmye1/sef1bbnvsef3Y5+mzaVpgzcw28f+rE8m9snmld5yEs2Fx8s7VqxVi22hYpTVrkw5fRWtUqkG2lxtPmklXDeOzUae3kNV1kPE7w6vbLNDnOVUCH12k6R2tHqjjXWOoQ6GEPuY3gntYJ/fA9lmZmjqTPkSwygzA/MykDRs+ziPx7ETkB4HIReSL031MAKJ4J8STsEQ5mxj+171ocntxpnASVtycveDGZhpYbiwo3HziGoRg/tU/bfaKXtjR8Lp+7bOveI88se2J1/tdqRWAo3uflKw6OyZ0TW9qOlc4fHfWyugqtbtl7/RXWiOjVl69z9gibqFUrePdVG2LXcWaukapI1+2XLX1i+Fo2eZrD6zR54G+/7grs3j7W5iVXuOghdyXuHiuipHaYrLZf9H4RAtgjz38K4FMA7gIwGfr8O0qpzm4+IcRI0mhJ3p48n0wgUaKRZde2+0RRg5LH0Ui9yfoQFaIu2wrnmdaNEpw9vxBbTc+GLooZ3tamyYPa39Vn57Fj36HllGiu6+4Gk3c+4KGjdYxfdmmb1zvIW+6aczoQfEFZ8jQFsul4mEZBTNdHRcRaxTPYVnCOwsuaKvZF25WkQIftHis6S0ZW2y96vwgBLOJZKfUigBcBvDO/5hDSn8R5hF0nQWXt7fN9ASUpKxzF5AEPe57DBB2P6adfwGOnTuO52Xmsqg5hvtG57FXfu7ZjW7ZKeOFtbJw8iDHNREWTuLURiLiwmDN1oGwp74LPTZH2IEJ68ZiY8y4HjFnsBYFYX1Or4vzCYkfEPbiGo6Mn4et2dKSK2bmGVsSOtfz7gN2O4EJUKNeqFfzghjU48rUzbcdrrDWZUJeD3DQhL1pgKOo9tp1Xn9GWNAVgmnmMkzyHssqjzPzMpAy4eJ4JIV1ie1maotLTT7+gzfMMJPf2RUWNUs3sHcEL0ddLG610lwRThC74TNeeqL/YJBL/8u9eaBPBE9vGnMRzQFp+YwV9dFwXbdQJONeIcrgktUu+4iCi2tF5qQheOrewnC3CFmkPrmFbZpQ4f26YJAVLAu940JkyZTqpVSsdWTiinTGTaI+eK1te6Lgoch4CMK0sGUlHzXy37yrQ+zn7B+kdKJ7JwFHETG3by9IUlQ7SrkU/9x3aDYi+BMNptMLZIqKT12zCzfay16UOC4ubuOMeiJNNkwe123cRk7pooC/RY+6SLkyHa+5iXWfCdfJhWEC4RDEXlerY3ppaFS+eazhXJRwdqWozYASVBE37ZDv/PgVLRmvVtkIlATv2HfK6r1zyZvuUUA/bbKL7m4cA7CZLRpi4UbM0tu8j0NPaL0K6QVQWdVszYnx8XE1PTxfdDNLDmCJgeU3E023XVGXPxliCF4aLEAnWq4sC+xw3l6H34Pdx6/ap+BbHaK2ayK8sQFuEPegYpNGucLTYhOkY6GwzPm1bO1LFzAeuWf47iWWiZrCHCIC7b9zadWaFPQ8et/qng+MX7axlkQFEd65M50ZnIwnfL72Sbs3UeY3eE91gu8eTPOsISQsROaqU6igMyMgzGSiSRlG6JW7ykEkYmaKVSSwcLpHIIGq4frSmFT6uL3uXiYe2Ihvhc9KNjSFK0ol+0Qj7cgQxRtjF4RptNEUqo50XX/H70rmF5cp4gPuE0TAmy0x44mVXxBxek/XJdI3Y7isbpnPlen2abB9lJw+LSRFlxwnpBopnMlAUOVPb9LI0CSNT/t8AX9HvGo0Ll6EO2m1rvw7X42lbLlxhD+isEmg7Nj4E1QhN67Plp04qnAXwija6DlX7it/Gkmq7htK+D8IVKZNEWfc/+qQ253cYk/VJl585uK8OfOFZr3Nni3762Gx6MSNE2hYT3bUQ92zKI8BBiA8Uz2SgKONMbZswikvf5fMy9vXqdvPCchXqtiIb4XOiE+5ppTZbUmq5THiwviA6aRNNSYVQeOg/yP3t4wO2keRYhPdjTUJbi4mgnHbSohZxxzgQcbcYJoGGs4+Ej+8njj/vvJ8CxNpqoufGZEPoxYwQaXqMTddCXKAA6M2OB+lfKJ7JQFHWmdomYRQ3ccrnZRx9CYazbZhicElfWK5C3eanjjsncRMKXQkXdjEJAp9oma6CXIAA2Piy2vL5dEld58rUTD2RnSV8DRnSRyciqJrXjVXK1gkLd2zueORk2wTYgLUjVa3wfdGjg5BE8Bb5nMnCS52WxcR0LTx26vRyrnCXsuOEFA3FMxkoenWmdlovY9NLMK1IWTQV3srhIWOEb/WKSltbkp6TbiaHuRxDn2hZUEEu2J9ou6KlpH0LZdiEka7wRhzR/dcJ0DCjtWZlPZeobVA1z9SBcumYuXq9TRZm0+eu10y1Ijh7fgGbJg9mYrNJW+iWvXS1zTZny/NdhgAHIWEonsnA0SsTdcJkLfrTEOe6VHhB6eWox7RaEfzGj29Z/jt8TgJBES1goduerkiFjVp1CJeuXumUOis41kOG/MzhaJlufUkzhZgERpww8h0lCEdup2bquOORk9blq0OCt175Kjx01L2E9Hxj0ThBz6Vj5nrdmyLJps8Dq4ftmlk7UsVL5y5Wk/QVonHPmSyEblETol1xsc31aoCDDBYUz4T0CFmK/qQvLFeRuf8nrkw132t0OdeI63xjKda/+v6pE205f235mePOSRLby5BIWwYMoLm/t95/3Jr321aZ0JYyLbq/RgQ4+MTzifJb16qVxB0zl+vedy7DxDZ7sZxatQKl0DFZMU0hahK6t95/fLmNvpS9dLVrJ70XAxxksKB4JiQnui0akjW+L6yogLWl1QPiJ10B7pGzvQ+fNHqKbSJQWu222SKchCTcIqdJLCXRUtDBcY4rsmLL2mK6znz2t7GorLYO07E35Q5Peq3rrA4mj/3chQXj+TaVJQea11w3dhMXTOuJnn8fyjghOgyjyqRfoHgmJAd0EdV7jjyz/H3ZvIku+KRF6za7QvjzqZm60XMbZFcwiaK43MOuvuFu8jO7EO4wxB3n8IRHoNmxCI7PquoQxi+7dLnSX5QkPmkdpnR/waTBtCKJppGJu27Ygrtu2NK270DTOmS69pKem6gQTepbtnWskka4yzohOgyjyqQfGCq6AYT0IkGKsU2TB7Fj3yFMzdh9oL5FQ3oBnwic676ZImThz23rCdLAfd1S+azeKqqhw2WfxkZrsRUpg+ujmUKtU54GSS3GLBHBoC22NumE0fmFi0VLAvEY3d+gfWlU4atVK/jgO67EnRNbsHv7GMIJO4JJg3H3hytxIxOrV3bGg0zX3sS2Mdx1wxZUDClG1o5UUatW2j6LHu9AzNdn59vyo7vs755dmzvWHyZJhDvYp7HRGgRu1yohxB9GngnxJMlEnzSKhiQlqzLAvpaEbrIrhAWLbT3h5WwRaNP5itsnl1La0etDV4EvLKdNJcODDoOpTRUR7N4+1ja5cu7CQqztJUkJbhNrW+kObzlwDPsffRJzFxa8M4j4YDr39dl5a2fA9LugTbprLpw1xXTvdDNBL/he52UHklstGNklJHsongnxJMkL07doSFpkmbrKJWNBmLSyK5iO5eoVlTYhaatCaDpfcUP59dl5bJw8iNFaFXuvv0J7DH3sLPXZeVQrguqQtE1OC3cYXCtQ2q6vsHg0+cXDDAkQU9gPo7X2fNau24/D1tmz3Ue2rCu2ay/umksyIdR1f23ivUxWC0JIO4WIZxF5O4C9AP4JgDcqpaaLaAchSUjywnTxV2bxwuw2dZVNyMRlLIgSHe62rdfWNt2xrFYEFxaWloVVfXYeB77wLKpD5qofuvMVFw0MmJ1vYM8D+qwIvqMHjUWFtSNVjKwYtoq36PHyEemBeLT5xYFmZP3M2fOY00TLo/hUInTtFMZ19uLuI1NJbteCO76kMUGPk+gI6T2Kijx/CcANAP5HQdsnJDFJXpi6F2Qe2Ta6iYy5RK1t1ogwI9Wh5ajwmloVZy8sLOd9rs/OY8+Dx7H34ZN4cb7hVKYaaD+WZ88vdIi5xqJqyy0dZf1oDe+fOoH7Hn8Wi0otT3oLJtfFRdUbS0rbCUmSYWN2roGZD1xj/F4n7kwlqaNUK7IsHuP84nt2bfbqELkggHOnMK6zFz73tkmhupLcWZB18SJCSDkpRDwrpb4MAJJmLVhCciLpC7OIF2Q3kTGTkLn5wDHcfOAYxmKsEQHViqCxqJbboYtYNhaVVzGKqIBOUllv48tqbRlPFpVa/vvOiS1OIlLXCdFGxocEl6waNqZ6W9Oq3BclqYUhzOoVw8u/iRsdyWLCqoK7RcilsxfcRyaPs4svPS16KWqc1dwHQgYRZtsgxJNemtGum9EfpA+LIy46XZ+dx0NH69i9faztWLz7qg1tf69eMdxRbCKOuOwc0SwHPgTn68jXzmi//9PHn8GOfYec1mXqhKyqXny0jtaq2P/2KzHzgWvwOzdu1VpJvnN+QZsRw5bJIS5bQ0C4yp6pvWtHqomqFLpgyygSxSXbSoBu/4vwCk9sG8PhyZ14at+1ODy5s5TPgW6yghBCOsks8iwinwXwSs1X71NKfdxjPTcBuAkANmzYkFLrCOmOcBTZtZy0L2lEiia2jWH66RfaCmEE6cPGL7vUuj6XyGZQQdAW6ds0edCrzQE2Iefj9w0TjkqaIstLyj75LaA6JB1CTZfJIpw6bmLbGN73sRNoXGhv++KSwh2PnPTK5BCNeuoqPALtwtM0ahJklkhiOQGaHbKoHSdYt4+Y9RnV6aWob9GUvWw3Ib1GZuJZKfWWlNbzIQAfAoDx8fE08vkT4oVNxKaVzUJXfTCaSSFplozHTp1OlD7MtYhEXLQyqSCzWUtcIqRDAMLT3qIirGIQm8b1hTJQmLJtuIiUsxf0xzNq6fCxMAB64R7d5zjBmaRwSLhD0m2Hz1cQ0yvsRtnLdhPSazBVHSEWdOI4PLlNF+3zjejEVR9Mut6ApC9O18wTcf7pOEG2ekUFFxaWjKnaTNuME+RrLBksAOCdP/Qa7XE28bW72guvBIVGwutPU6SY9nFIBJsmDy5vE7goNtfUqlhVHcLsnHnipU1wBp+7ThoMn6e0PLUUxOlT9rLdhPQahXieReTHReQbAN4E4KCIPFpEOwiJQxdJDCa3KcAoKn3Ekm9eYJeKhmFML0gFxK5rYtsYPviOK63e2rkLnX7d6DoCjzgARB2/Swq48Y2v8fKQu/h9Z+ca2LNrM9a3Mi/sf/TJtnbeObEF775qw3KFuYoIqoYnogBtv9V5SPc8cBymOdDhczBqmBwY/dy0j4tKtW1zz4PHl9sxO9/AucYS7r5xa2L/7cS2MSef8thobblIy8bJg7jlwDF6aktKWfzhhPQLojyGLYtmfHxcTU8zJTRJj7ho2abJg94T0gC/Gf9JtlGrVpwnKcZVlHNZV3Cc6rPzEAGijw3X9pgyJFRE8MF3XJnI6mKKQK8daS/iEdfOqZk69jx43JjeLnxOfUpbR7c5NVPHngeOt0Xaq0OC/W/v3P/w9WnyNOsYrVVx7HZz6rs44q6ZIK1dnMWjIoIlpbwj0bYUgiQZZc+2Ufb2kcFERI4qpcajn9O2QQYWF79yEr+ub0QnyTZ8LBxxuXFd1hUeStcJx7h1xAndRaW8Pd1Bm0xeX6XgNUlq/6NPWvNCh0cTXEcWKiIdYt3H1+uS11jH7HwDUzP1xOIj+N3eh092pBYMJkq6jJgEYt/Hs//+qRPWFIIkGWW2w2RZCZWQLKB4JgOLy+Qu1wlU4Qjb1Zev88q+4VvmOsDHGhK8OE1Rbp91+fp646KYASZhGxeRMolRUxGRoJ3R9caJ07D1wrXDs6SUURQ7R/qhLzkdR7eZFJbvgWg0vmVN8fVxu3b47nv8WePnruKZUczegtlASK9B8UwGFtdsBsBFYTY6UsVL5xY6JrcF0cUkERRdOjkXkkz2SWPi0JpaVVvoxLQOH0939Jy4Hk+dGDVFa9eP1rTrtYnU6GiCayW+JOco2rakxrrwsUwqJnXR+MZis7JikhETF8Ftsqa4WlbKHMWkqNfDbCCk12CRFDKwuBZkCBdBmPnANdj/9iuNk9tsERQbd05swd03bnUuKJF0so9p4tDVl6/Djn2HsGnyoHUS4dRMHWcvLHR8rst5HCzvI7Cixz7p8QSahWCi8/eC46Zbr0LnZEag6Z2OepZdtp/0HCXNYR0lOJbdFMiwiRpTAR4Ay5MwTW2yYfqt6fMoPtdMkDEl7rpPg6TnIc82FoVPcRxCygDFMxlYks5An9g2Zszi0E0EJRDpJgEtrf/CWQ58X6i66oi7t4/hoaN1p5e6yRd8yaphrd0iiPiZ9if6d7TyYdLjOTVTx0NH621RWwGwe/uYtZKeAtqOze/cuBUzH7imI5eyS4fANjHRJoZcrpU4HRm+jpN0QII2mmK9QyK45cAxrBwewtqR6vLxuvvGrfj6vmu1GVpcOxPv/KHXeH0exfWaybvqXtLzMAiVAZkNhPQatG2QgSVphTLbsHAatog9uzZ3ZGMAgOGKYP9PXAkAXQ1LRy0OO/YdcvYbmoTJ7FynjcMWQa1VK/jBDWtw+O9eWP5MAbjnyDN46Og3sHv7q7XFXQLijqcpsvzYqdPLv9edp7gsKa5R4bHRmlE4dztJtVatWNswFrmOs/CoBxaK2fkGatUK7r5xa+JJkVECX3M428ZV37sWj5063Zbf2rQu13swb59tko7goHiBWS2S9BoUz2SgSTID3fZC8ykvbGvTHY+c7Kg4F3hNg+3ptp/kZePzUjcJkyBntItoA5pR2b0Pn9R+N99YshYvcTmecfuU9Dy5RIVt60k6STXwYwfC2OTn1ol/3w6dr23EdO11k93hzoktyyLa18Psem7z9tkm6VgPkhe4zNlACIlC2wYhntheaDpbhGs+5jC6SG6wjbRfqD5+Q1txkuiQsmm9QVRWN+kwjuB4ArBaH0zbDqrz7X/0SezePuZ9nkzrrYg4rccUUQ5/rruGAjtEUPjEZ5jbd0g8yXWUpZjztTu43oN5+2yTWBOK8gIPgs+akG5g5Jn0FXnMZo+LIKURQYnbRlwEy+c4+ERhfXJGpxGFDyMADk/udIpEmlIMhvMO33PkGaxeYa9SGMW0T64dpIqh0El0MlzcNZQ0V7TL9ZAki0aWYi5JZ9HlHkz7+owjiTUh7zYC5c5WQkhZoHgmfUNeD/08Xmhx27B953scfF/qrjmj49a7dqTaYU2xEQg0F+tDdNum6nxnL/hdK6Z9ApqR8LjjZ0vD5lvUJFwkJsgrHliHurFQuOY2D8hSzE3N1I3nrlvBXoTP1rdjXUQbB8VnTUg3sDw36RtMJZNNk8C6iVLnEeG2bcP2ne9xSEq3pbanZuq49YHjWFyKfwaFo7sm0S4Antp3rfb3riXQkxwjU4VDXTTaVtbbJ4KdZNs+2Iq0VCuC1SuG8eJ8I1MxZ5u4mMY+Ej1J7i9C+hWW5yalIgvx6TO8222UOg1rhkvlvLjor45u0rt1O6QMuJfaDr6LTo4crVXx1itfhcdOnda2JcnEK1crQhLvrk+kzhbVTRLdyypKGL6+iirsYZq4qCt5roMFSZKRRsYgQvodimeSO1nZK2wP/eiL9Oz5hUKHJrO0mJiOw+hI1WgtSFoZEQBuvf94x7C667FM0gnxtc1MzdRx9nxnYRcdumME2IfNfTorwe9M1Ql9xXse2Ri66Sh2I2BN+2AqeR7dLn27ySjCZ01Ir8FsGyR3uqkaZ8NWPS9aaMCU6SGvFFBZHQNAfxyqFcFL5xaMxRaStmdi2xiWDNavrI6lT0aTQES5ZPbQHaM9DxzHngePW4tU+GZEmNg2ZiyE4xvdSysbQxbZFWwFPly2182+ZXl/9TtpZQwipJ9h5JnkTlbRMtPkGp+8tXkNTWYZMdQdh7PnFzoEZDg63E17ihjmdY2G2s79aK0KkWZaQNMxihaqAdzyMsdF6tKK7qWxHt8orWs02SRg73jkJM41lmK3182+DVJ+5CxgzmVC7FA8k9zJUmzpHvqmIfIoeQ5NZi04o8dh0+RB7XKBmOimPWUe5jWJJQFw7PZr2j4zHaO49SbJiJBWFoU01uPjm9YJ7VsOHMP00y+0FTUxpTIEoM2wotue776FRX1WGToIIQSgeCYFkKfYmpqpd2QLCFg7UsXIiuFCJhTlLTjjxHE37UlDwGU1ucunU+CT3zj6+ySRurSie92uxydKayp7fu+RZzB+2aUAOtModtMO132LinqdcC5Lh44Q0vtQPJPcyTN36f5HnzSmXbr9uisKE3h552+NE8cT28Yw/fQLuO/xZ7GoFCoi2L3dXZR1O6ksq8ldPp0C3bLVIQGkWRo97ve9yqgh3/boSLXjM5PQVoCxdHyYWrWClcNDWg96N1FhW2aOJaUyT6nHrB6EDBYUz6QQ8vLU2V723eTTTUPg5ekrjBPrUzN1PHS0vhyxW1QKDx2tY/yySzNvYxrp1kwCxqeTYlrW9fe9iinVv+5zW3Q+zk88FjqeaY+62DJzJMlN7CqImdWDkMGE4pn0NaaXvSnbgYl+qLplE+tF7p9J+NRn552q9sUJGJ9OimnZXjnHSXjRkIlE9/meXZtxy4Fj2tEcW+l4XfGZNDskvp79uAJEroK4H54LhBB/mKqO9DW6tG0C4OrL13mtp99n7xe5fyaBI4A1RVyAScDcfOBYamnX+hmflHAT28bwrqs2QCKfB5FjU7rIaFR5YtsYDk/uxFP7rsXhyZ2p2HNctgvYU+gBfmnu+v25QAjRQ/FM+pqJbWPYvX2s7WWvADx0tO4lqtLKp2sji1y7rpj2QwGZt8XUwYlGN30FDGAX3S4UeU7ywkd4AsCdE1tw941btXmAi8oR7LPdOHHsI4jzeC4QQsoHbRuk73ns1GmjEHN9qWedHaNo76StbHTWbdF5jX18taYJbwFJh9GLPid5kTTVnq3yZBHHx3W7ceLYxwJS5jSNpAkndJIsoHgmfY/NU+tK1tkxivZOhvdPd1yybktU+OzYd8hZwJgmvIVJMoxe9DnJk0EqipFm2sa8s+YQPwalA0zyh+KZ9D2ml6Wg+XDNIx1bHGXwTgb7t2nyoHZCmE9no1v27NqMPQ8cb6vwVx0SrYAxTXgLk2QYvQznhKSPS9pGwF0QD1LHo9cYpA4wyReKZ9L3mDIEBLlpy/AQzbPEddwwZlqdja6JzkqL/t0irrhJ0mH0IsqOk+xxEccUxP0BO8AkKzhh0MIgTBYaBCa2jWkjqUB5HqK+k7aSEpdpIGiLTqeGC2Fkzf5Hn2wrTAI0C5Xotm+acAh0N2Etr3MyaJThuZp2tg9STjihk2QFxbMBF5FBegdTXueyPETzylLgkoarDJ0Nn4iR7tjdfeNWfL1LYVRU5oh+hs9VkifsAJOsoG3DAL1S/UXZZ8XnNSPcVZSOFWxZ8LVMZDXMzuH7dOFzleQJJ3SSrKB4NkCvVH9R5odonjPCXUWpb2cjbfFf9s4OSUbaz1WmISNxsANMsoDi2QAnCzXpp5dTWR+ieUbjXEWpT2cjC/Ff5s4OSU6az1WmISOEFEUhnmcR2S8ip0TkCRH5mIiMFtEOG/RK0Z+YF3mOcvj4eF0nVfmUM/Zt6+HJnbj7xq0AgFsGrNx2GSbWpU2az9WsrjtCCImjqMjzZwDcppRaEJHfBHAbgF8uqC1aGPmiPzEvTNG4IZFEqUYeoc8AACAASURBVOHiRgvSjsBnKf4HNbrYr/ud5nOV1jpCSFEUIp6VUp8O/XkEwE8U0Y44yjrMnxd8OeWDqTT2olLegilv0TU1U8eQCBY1Zf7SsDiVqQOXp4Up7/3Oc9/Seq7SWkcIKYoypKr7GQCfMn0pIjeJyLSITJ8+fTrHZhHmyMyHwEpRkc7syr7D0HkOZQdCXSec44biXS0JZenA5W1hynO/e9WeRWtdvvSjjYiQpGQmnkXksyLyJc1/bwst8z4ACwDuNa1HKfUhpdS4Ump83bp1WTWXaODLKT8mto1hSSNCAT/BlKfo0gl1AKiIWPMh+4i1snTg8uiUhMXJkKYjBWSz373qHWYe7vzo1Q4WIVmRmW1DKfUW2/ci8h4AbwXwL5UyqAZSKPR950saw9B5DmWbBPmSUsasHPsffVLbPpMlQWdpqVYEZ88vYNPkwdyuyaw7JVG7TZJoflJMpc1tJc/LwqBb6/KiTPYpQspAIZ5nEflRAL8E4F8opeaKaANxgy8nM2XMbZxnfmQfoR4VhzpM1QOBix240ZEqXjq3gNn5BoD0Pd2mc2qb1JmGiLdF8ZeUyrSTUDF41nU2on6gn9Jv5kVZ7FOElIWism38HoCVAD4jzQf0EaXUewtqCyHelDW3cZ6jBT5C3SQOw7hUD9yx7xDOzDXavk8rAmY7p7ZJndFl08wcsaQUntp3rff6fNAJZ9vnvUy/ZjHJGk7OJKSdorJtvK6I7RKSFlkNY6YR6c9rtMBHqMdFqFyj41lGwGzn9PDkzuVlnpud12YY6eb8FylOTKXYx/pQGNF+kAxW/CSkHVYYJCQBHMZs4irUTeIQaIo01+h4liIz7pyG93XT5EGvdcRRpDjJY9tlsUrwvk0G578Q0g7FMyEJyDtSWBbxkRSTQPPNjpCl0PM5p2mf/yLFSdbbLpNVgvaD5HD+CyEXoXgmJAF5RgrLJD6SkpZAy1Lo+ZzTLM5/keIky22XySpB+wEhJA0onglJQJ6RwjKJj25IS6BlJfR8zmmS89/rowdJKZNVgvYDQkgaUDwTkpC8IoVlEh/9SlTY3n3j1lSFcD+MHiSlbFYJ2g8IId1ShvLchBALZamy16/4Vk9LUm2tV6v4pQErlRJC+g2KZ0JKDsVHtvgK2yRCeJBHD1hGmxDSb9C2QUjJSdunOajeWxO+wjaJEC6bdSFvaJUghPQTFM+E9ABpiY9B9t6a8BW2SYQwszwQQkj/QNsGIQPEIHtvTfjaYpLYaGhdIISQ/oGRZ0IGiEH23prwtcUktdHQukAIIf2BKKWKboMz4+Pjanp6uuhmENKz7Nh3KJUy2YQQQki/IyJHlVLj0c9p2yBkgNBZDgJcUq4RQgghgw5tGz0IsyWQpIQtB7oIdC9WLiSEEELyhOK5x2C2hGLph45L4L3dNHkQOtPWIPufCSGEkDho2+gxmC0hOVMzdezYdwibJg9ix75D3vaEJJXlygwrFxJCCCH+UDz3GMyWkIw0hG+/dVxYubCdbjtXhBBCBgOK5x6D0cJkpCF8+63jwtzDF+m3UQVCCCHZQc9zj8FKZclIQ/j2WollF382cw83sXWueHwIIYSEYeS5x2C0MBlpROx7yebASKof/TaqQAghJDsYee5BGC30J42IfdLKckXASKofvTaqQAghpDgonslAkJbw7ZWOCyOpftAORQghxBWKZzIw9IrwTQNGUv3opVEFQgghxULxTEgfwkiqP4PUuSKEEJIcimdCYujFqoKMpBJCCCHZQPFMiIVeLofOSCohhBCSPkxVR4iFfqsqSAghhJDuoHgmxAKzVhBCCCEkDMUzIRZYDp0QQgghYSieCbHQS1UFCSGEEJI9nDBIiAVmrSCEEEJImELEs4j8OoC3AVgC8E0A71FKPVdEWwiJg1krCCGEEBJQlG1jv1LqB5RSWwF8AsAHCmoHIYQQQgghzhQinpVS/xj6czUAVUQ7CCGEEEII8aEwz7OI/AaAnwLwIoCrLcvdBOAmANiwYUM+jSOEEEIIIUSDKJVN0FdEPgvglZqv3qeU+nhoudsArFJK3R63zvHxcTU9PZ1iKwkhYXqxFDkhhBCSBSJyVCk1Hv08s8izUuotjoveC+CTAGLFMyEkO3q5FDkhhBCSF4V4nkXk9aE/3wbgVBHtIIRchKXICSGEkHiK8jzvE5HNaKaqexrAewtqByGkBUuRE0IIIfEUIp6VUruL2C4hxMz60RrqGqHMUuSEEELIRViemxACgKXICSGEEBdYnpsQAoClyAkhhBAXKJ4JIcuwFDkhhBBih7YNQgghhBBCHKF4JoQQQgghxBGKZ0IIIYQQQhyheCaEEEIIIcQRimdCCCGEEEIcoXgmhBBCCCHEEYpnQgghhBBCHBGlVNFtcEZETgM4C+BbRbeFpM7LwfPar/Dc9ic8r/0Jz2t/wvOajMuUUuuiH/aUeAYAEZlWSo0X3Q6SLjyv/QvPbX/C89qf8Lz2Jzyv6ULbBiGEEEIIIY5QPBNCCCGEEOJIL4rnDxXdAJIJPK/9C89tf8Lz2p/wvPYnPK8p0nOeZ0IIIYQQQoqiFyPPhBBCCCGEFELpxbOIvF1ETorIkogYZ4qKyNdF5ISIHBOR6TzbSPzxOK8/KiJPishXRWQyzzaSZIjIpSLyGRH5Suv/aw3LLbbu12Mi8nDe7STxxN1/IrJSRA60vn9cRDbm30rii8N5fY+InA7dn/9XEe0kfojIH4rIN0XkS4bvRUR+t3XenxCRH8y7jf1C6cUzgC8BuAHA5xyWvVoptZXpWHqC2PMqIhUAvw/gxwC8AcA7ReQN+TSPdMEkgD9TSr0ewJ+1/tYx37pftyqlrs+vecQFx/vvZwGcUUq9DsDdAH4z31YSXzyeqwdC9+f/yrWRJCl/DOBHLd//GIDXt/67CcB/z6FNfUnpxbNS6stKqSeLbgdJF8fz+kYAX1VKfU0pdQHA/wbwtuxbR7rkbQA+3Pr3hwFMFNgWkhyX+y98rh8E8C9FRHJsI/GHz9U+RSn1OQAvWBZ5G4CPqCZHAIyKyKvyaV1/UXrx7IEC8GkROSoiNxXdGJIKYwCeDf39jdZnpNy8Qin1fOvffw/gFYblVonItIgcEREK7PLhcv8tL6OUWgDwIoCX5dI6khTX5+ru1tD+gyLymnyaRjKG79SUGC66AQAgIp8F8ErNV+9TSn3ccTU/rJSqi8j3APiMiJxq9cJIQaR0XkkJsZ3b8B9KKSUippQ+l7Xu2e8FcEhETiil/i7tthJCvHkEwH1KqfMi8n+jObqws+A2EVIaSiGelVJvSWEd9db/vykiH0NzaIriuUBSOK91AOGIx6tbn5GCsZ1bEfkHEXmVUur51pDgNw3rCO7Zr4nInwPYBoDiuTy43H/BMt8QkWEAawB8O5/mkYTEnlelVPgc/i8A/yWHdpHs4Ts1JfrCtiEiq0Xku4J/A7gGzQlppLf5AoDXi8gmEVkB4N8AYFaG8vMwgJ9u/funAXSMMojIWhFZ2fr3ywHsAPA3ubWQuOBy/4XP9U8AOKRYPKDsxJ7XiA/2egBfzrF9JDseBvBTrawbVwF4MWSxIx6UXjyLyI+LyDcAvAnAQRF5tPX5ehH5ZGuxVwD4vIgcB/DXAA4qpf5PMS0mLric15aH8ucBPIrmw/t+pdTJotpMnNkH4F+JyFcAvKX1N0RkXESCWfv/BMB06559DMA+pRTFc4kw3X8i8msiEmRH+QMALxORrwL4BZgzq5CS4Hhe/2MrlehxAP8RwHuKaS3xQUTuA/BXADaLyDdE5GdF5L0i8t7WIp8E8DUAXwXwPwH8PwU1tedhhUFCCCGEEEIcKX3kmRBCCCGEkLJA8UwIIYQQQogjFM+EEEIIIYQ4QvFMCCGEEEKIIxTPhBBCCCGEOELxTAghfYaI/IiIfKL17+tFxJhCTkRGRcQ7ZZWI7BWRX+ymnYQQ0otQPBNCSI8gIhXf3yilHlZK7bMsMgrmeyWEEGcongkhpASIyEYROSUi94rIl0XkQREZEZGvi8hvisgXAbxdRK4Rkb8SkS+KyAMicknr9z/a+v0XAdwQWu97ROT3Wv9+hYh8TESOt/57M5pFbF4rIsdEZH9ruT0i8gUReUJE7git630i8rci8nkAm3M8PIQQUhqGi24AIYSQZTYD+Fml1GER+UNcjAh/Wyn1g61S5h8F8Bal1FkR+WUAvyAi/wXNimE70awedsCw/t8F8BdKqR9vRbEvQbMq4PcrpbYCgIhcA+D1AN4IQAA8LCL/HMBZNEs5b0Xz3fFFAEdT3n9CCCk9FM+EEFIenlVKHW79+x40SyMDF8XwVQDeAOCwiADACjTL8V4O4Cml1FcAQETuAXCTZv07AfwUACilFgG8KCJrI8tc0/pvpvX3JWiK6e8C8DGl1FxrGw8n301CCOldKJ4JIaQ8KMPfZ1v/FwCfUUq9M7yQiGxNsQ0C4C6l1P+IbOPmFLdBCCE9Cz3PhBBSHjaIyJta//5JAJ+PfH8EwA4ReR0AiMhqEfk+AKcAbBSR17aWeyf0/BmAf9/6bUVE1gD4DppR5YBHAfxMyEs9JiLfA+BzACZEpCYi3wXgum52lBBCehWKZ0IIKQ9PAvgPIvJlAGsB/Pfwl0qp0wDeA+A+EXkCLcuGUuocmjaNg60Jg980rP8/AbhaRE6g6Vd+g1Lq22jaQL4kIvuVUp8G8KcA/qq13IMAvksp9UU07SPHAXwKwBfS3HFCCOkVRKnoKCEhhJC8EZGNAD6hlPr+gptCCCHEAiPPhBBCCCGEOMLIMyGEEEIIIY4w8kwIIYQQQogjFM+EEEIIIYQ4QvFMCCGEEEKIIxTPhBBCCCGEOELxTAghhBBCiCMUz4QQ0oOIyLtE5NMFt+ElEflew3fvEZFohcRSICIbRUSJyLDh+70ick/e7SKE9AYUz4QQkgItMfa6jNbdIfaUUvcqpa7JYFs/0trWxyKfX9n6/M9DbbhEKfW1mPVtaIns4D8lImdDf/+ztPeBEEKyRNvrJoSQMiMiw0qphaLb4UOPtfk0gDeJyMta5bsB4KcB/K3vipRSzwC4JPhbRBSAK5VSX02lpYQQkjOMPBNCegIR+bqI/LKIPAHgrIgMi8hVIvKXIjIrIsdF5EdCy18qIn8kIs+JyBkRmQp993Mi8lUReUFEHhaR9aHvlIi8V0S+0lrv74uItL57nYj8hYi8KCLfEpEDrc8/1/r58VY09cZWBPcbrTb/PYA/0lkZwhFrEamJyAdF5OnWNj4vIjUAwfpnW+t/U3RdIvJmEflC63dfEJE3h777cxH5dRE5LCLfEZFPi8jLLYf7AoApAP+m9fsKgBsB3Gtp+8tax/IfReSvAbzWsv7g9ytF5LdE5BkR+QcR+f9a+xtEwL8hIr8kIt8UkedFZEJE/rWI/G3r3P1KaF17ReRBETnQ2scvisiVMU34mdb18byI/GJcewkhBKB4JoT0Fu8EcC2AUQCvAHAQwJ0ALgXwiwAeEpF1rWX/BMAIgCsAfA+AuwFARHYCuAvAOwC8CsDTAP53ZDtvBfBPAfxAa7ldrc9/HcCnAawF8GoA/w0AlFL/vPX9lS0rw4HW369ste0yADc57N9vAdgO4M2t3/0SgCUAwfpHW+v/q/CPROTS1rH4XQAvA/DbAA6KyMtCi/0kgH/XOhYr0DxeNj4C4Kda/94F4EsAnrMs//sAzqF5TH+m9V8c+wB8H4CtAF4HYAzAB0LfvxLAqtDn/xPAu9E8Rv8MwK+KyKbQ8m8D8ACax+5PAUyJSNWy/asBvB7ANQB+WUTe4tBmQsiAQ/FMCOklflcp9axSah5NEfVJpdQnlVJLSqnPAJgG8K9F5FUAfgzAe5VSZ5RSDaXUX7TW8S4Af6iU+qJS6jyA29C0KGwMbWefUmq2ZTl4DE1xBwANNIXweqXUOaVU3IS4JQC3K6XOt9psRESG0BSc/0kpVVdKLSql/rLVxjiuBfAVpdSfKKUWlFL3ATgF4LrQMn+klPrbVjvuD+2TFqXUXwK4VEQ2oymiP2JpewXAbgAfUEqdVUp9CcCHbetvRfNvAnCLUuoFpdR3APxntKLdLRoAfkMp1UCzg/NyAP9VKfUdpdRJAH8DIBxdPqqUerC1/G+jKbyvsjTjjlZ7TwD4IzQ7Z4QQYoXimRDSSzwb+vdlAN7eslbMisgsgB9GM/L5GgAvKKXOaNaxHs1oMwBAKfUSgG+jGd0M+PvQv+dw0bP7SwAEwF+LyEkRiYuunlZKnXPYL6ApDFcB+DvH5cO07VOLp+G2Tzb+BMDPoxmh/ZhluXVozqEJn59oe3S/GQFwNHT+/k/r84BvK6UWW/8OOh//EPp+Hu37sbx9pdQSgG+geWxMRNtrW5YQQgBwwiAhpLdQoX8/C+BPlFI/F12oFXm+VERGlVKzka+fQ1N4B8uuRtPqUI/duFJ/D+DnWr/7YQCfFZHPWSa/qcjfZ9EUjMG2Xxn67lto2h5eC+B4zHqitO1Tiw1oitFu+BMAXwXwEaXUXMv6reM0gAU0Oy2nQtu38S00xe8VSqnYY+/Ia4J/tCL5r4bdahJtr21ZQggBwMgzIaR3uQfAdSKyS0QqIrKqNcns1Uqp5wF8CsD/KyJrRaQqIoFv+D4A/05EtorISjStAo8rpb4et0ERebuIvLr15xk0Re1S6+9/AKDNeRziOIArWtteBWBv8EUrUvqHAH5bRNa39ulNrTaebm3HtP5PAvg+EflJaU6kvBHAGwB8Im6fbCilngLwLwC8L2a5RQAfBbBXREZE5A1oZuew/WYJTQ/z3SLyPQAgImMissv2uxi2i8gN0kzpdzOA8wCOWJb/1VZ7r0DTD37AsiwhhACgeCaE9ChKqWfRnCD2K2iKy2cB7MHF59q/RdMzewrAN9EUU1BKfRbArwJ4CMDzaEZ6wz5bG/8UwOMi8hKAh9H0Jwd5jvcC+HDLgvAOQ5v/FsCvAfgsgK8AiHqmfxHACQBfAPACgN8EMKSUmgPwGwAOt9bf5uNtpZN7K4Bb0bSg/BKAtyqlvuW4X0aUUp9XSrlEZH8eTQvF3wP4YzQ9xHH8MpqR7SMi8o9oHpfNCZsKAB9HMyvIGTTP/w0t/zNE5FPh7Bwt/qK1/T8D8FtKqUKLzhBCegNRKm40kBBCCCk3IrIXwOuUUu8uui2EkP6GkWdCCCGEEEIcoXgmhBBCCCHEEdo2CCGEEEIIcYSRZ0IIIYQQQhzpqTzPL3/5y9XGjRuLbgYhhBBCCOlzjh49+i2l1Lro5z0lnjdu3Ijp6emim0EIIYQQQvocEdFWSqVtgxBCCCGEEEcongkhhBBCCHGE4pkQQgghhBBHKJ4JIYQQQghxhOKZEEIIIYQQRyieCSGEEEIIcYTimRBCCCGEEEd6Ks8zIYQQkpSpmTr2P/oknpudx/rRGvbs2oyJbWNFN4sQ0mNQPBNCCOl7pmbquO2jJzDfWAQA1GfncdtHTwAABTQhxAvaNgghhPQ9+x99clk4B8w3FrH/0ScLahEhpFeheCaEENL3PDc77/U5IYSYoHgmhBDS96wfrXl9TgghJiieCSGE9D17dm1GrVpp+6xWrWDPrs0FtYgQPVMzdezYdwibJg9ix75DmJqpF90kEoETBgkhhPQ9waRAZtsgZYYTW3sDimdCCCEDwcS2MQoQUmpsE1t57ZYH2jYIIYQQQkoAJ7b2BhTPhBBCCCElgBNbewOKZ0IIIYSQEsCJrb0BPc+EEEIIISWAE1t7A4pnQgghhJCSwImt5acw24aIrBKRvxaR4yJyUkTuKKothBBCCCGEuFBk5Pk8gJ1KqZdEpArg8yLyKaXUkQLbRAghhBBCiJHCxLNSSgF4qfVntfWfKqo9hBBCCCGExFFotg0RqYjIMQDfBPAZpdTjRbaHEEIIIYQQG4VOGFRKLQLYKiKjAD4mIt+vlPpSeBkRuQnATQCwYcOGAlpJCCGEkDBTM3VmhCADSynyPCulZgE8BuBHNd99SCk1rpQaX7duXf6NI4QQQsgyUzN13PbRE6jPzkMBqM/O47aPnsDUTL3ophGSC0Vm21jXijhDRGoA/hWAU0W1hxBCCCHx7H/0Scw3Fts+m28sYv+jTxbUIkLypUjbxqsAfFhEKmiK+PuVUp8osD2EEEIIieG52XmvzwnpN4rMtvEEgG1FbZ8QQggh/qwfraGuEcrrR2sFtIaQ/CmF55kQQgghvcGeXZtRq1baPqtVK9iza3NBLSIkX1iemxBCCCHOBFk1mG2DDCoUz4QQQgjxYmLbGMUyGVho2yCEEEIIIcQRRp4JIYSQEsOCJISUC4pnQgghpKQEBUmCvMpBQRIAFNCEFATFMyGEkL6l16O2toIkvbQfhPQTFM+EEEL6kn6I2rIgCSHlgxMGCSGE9CX9UEbaVHiEBUkIKQ6KZ0IIIX1JEVHbqZk6duw7hE2TB7Fj3yFMzdS7Wh8LkhBSPmjbIIQQ0pfkXUZ6aqaOPQ8eR2NRAWjaRPY8eBxAcptINwVJet3vTUhZoXgmhBDSl+zZtbnN8wxkG7W945GTy8I5oLGocMcjJ7sSrS4FSaJC+erL1+Gho/Vc/N5xIn1QRPyg7CeheCaEENKn5F1G+sxcw+vztNBNjLz3yDNQkeWyyNIRNymzHyZtuojiftjPslHmzgjFMyGEkL5lEMpI6yZGRoVzQNp+77hUer2eas9VFPf6fpaNsndGOGGQEEIISYHRWtXr87TwEcRp+73jJmX2eqo914wtvb6fZaPsmXIongkhhJAU2Hv9FagOSdtn1SHB3uuvyHS7JkEskb+T+r1tGUTiUun1eqo9V1Hc6/tZNsreGaF4JoQQQlJgYtsY9r/9SoyN1iAAxkZr2P/2KzMfZjals3vXVRva2nLXDVu82xIMn9dn56Fwcfg8ENBxqfR6PdWeqyju9f0sG2XvjNDzTAghhKREER7rLCdGxnl547bt07YyThBzzdiS9+TUfifvTDm+iFKmaQXlY3x8XE1PTxfdDEIIIWQg2DR5UDv5UAA8te/a1LYTnSAGNMVSkmh52pRR1A8CZTjuInJUKTXe8TnFMyGEEEJ07Nh3SFtoZmy0hsOTOzPfTkUES0pRtJJCMIlnep4JIYQQoiUvL69pItiiUlqvNSFFQvFMCCGEEC0T28Zw1w1bup54GIfLRLAsUpXZMokQYoITBgkhpKSUwfNHSB6TIHUTxHSkmaqs7IU4SHmheCaEkBLCF3u+sKNSLNFsFUMiWNTMyUozVRmrApKkUDwTQkgJGdQXexEilh2VchCOcJuyb6TptS57IQ5SXiieCSGkhAzii70oEZtVR8XWEejnSHca+5ZH3uT1ozVtho+yFOIg5aUw8SwirwHwEQCvAKAAfEgp9V+Lag8hhJSJQXyxFxVtz6KjYusIAOjbSHeaHaCsvdZpFuLo584Q6aTIbBsLAG5VSr0BwFUA/oOIvKHA9hBCSGkYxHK/RUXbsygFbOsI2L7rdbLct7QzY6SVSSSuhDnpPwqLPCulngfwfOvf3xGRLwMYA/A3RbWJEELKwiCW+y0q2p5FKWDdfgSfi+E3eVpysoqUZtUBmpqpY88Dx9FYak4irM/OY88DxwF0F61PI7o9qPMTBplSeJ5FZCOAbQAeL7YlhBBSHvJIEVYmshCxLmTRUakYskVURPDKNasKteRk6S3PqgO09+GTy8I5oLGksPfhk4XfI4M4P2HQKVw8i8glAB4CcLNS6h81398E4CYA2LBhQ86tI4QQkhdFRtvT7qjohHPweVGdhIAsI6U+++YT/Z6db3h9nieDOD9h0ClUPItIFU3hfK9S6qO6ZZRSHwLwIQAYHx/XP40IIYT0Bf0SbR8zCKqx0VrhlpwsI6Wu+9ZP6QGL7gyF4cTFfChswqCICIA/APBlpdRvF9UOQgghJG3iJnxObBvD4cmduPvGrQCAWw4cy608dBYTJIGLE/puOXAMAHD3jVtxeHKnVrz5TixcO1L1+jxP8iphHgcnLuZHkZHnHQD+LYATInKs9dmvKKU+WWCbCCGEWGBkyw2XCGxR0dcsIqW+++Ib/b79uiuw58HjaCxeHICuVgS3X3dF4janSRlGTDhxMT+KzLbxecA46ZgQQgBQrOVJ3LHup6H2PIgTVEWJnSxsI3GR5GBba2pViDSLO+gYHalix75DHe0q2urSC3DiYn4UPmGQEEJMUKzlh8uxZmQrXYoUO2lHSk1tDq6j4LqxTfCrVgQvnVvAmblG22/D7eV1ZoYTF/OjyCIphBBipZ+LSZQNl2Odl9hLuxhGWVlT0/t1TZ/HkfZx81mfSaBVRDquKx1jozWsXjHckY6O97s7g1hYqSgongkhpYXDkPlhK+oRkNVEszD9MunJRXiKwbho+jxue2keN9/1mYSbKWVfGAFweHInXjREpfvhfs+jQ1iWiYuDAG0bhJDSwmFIf8K+5cBfOjvXiPWI2op6BOSRkqsfrCGudqPZOUPuYsPnNk962sfNdX3hNo2OVLFyeAgvzl+83vY/+qSxYxYQ3M/9er/naT+jtSUfGHkmhJQWDkP6EY0Wzs43cGau4RQ5tBX1CMgjstUPow2udiOfSH5cJDjt4+ayvmibzsw1cH5hqS1Fne4eDhO+n/v1fu8n+9mgWKrioHgmhJQWDkP6oXtJh7G9sMcMQi76eZCf+Kl91xpz+HZDHtaQrHEVsj5iMU6ApX3cXNbnIgqj9/BorYq1I1Xt/dyv93s/dAiB/rFUpQFtG4SQUsNhSHdcXsamZbq1ZKSVUjCvam1ZpkB0tR/4pF+zZbOYmqmnftxc1ucqCn3uYdOyaZ2vIlJf9osdpR8sVWlB8UwIIX2C6SUdXUZHN3l03z91AvceeWY5d283ns488vlm7UH1EbKuwtJ2bm/76AncdcMW3HXDltSOm8t5cBGFaYjVtM5XPxWlKYJ+iaCnAcUzIYT0CbqXdJi4F3aSKP/UTL1NOAd0t5NxZwAAIABJREFUE5HKerQh6whatx0AneC0ndug7WmLsbjzECcKXcSqi7hO63ylsZ4knYG8C7xkFV3vlwh6GohySCNTFsbHx9X09HTRzSCE9ACDWpkwabaNpOzYd8gYERUAT+27NtXtpcGmyYPaCndlaG9UcAJNQXrXDVsAADcfOGb8ba1a0f4uy+ted58BsGbZWDtSxciKYdRn5yForzaoa3Na56ub9UzN1LH34ZMdRV7yOMY+2K6fbtuY5brLiogcVUqNRz9n5JkQ0ncMcmXCvD3itiFbl4hUL3tQs2i7LTp6eHKnUZTqipHkVerbVsJdx5m5xnIVQZcRi7TOV9L12PapbJ7fLEdVWCL9IhTPhJC+gxNb0hV2tnWZBIkAsTaCXvagZtV2m690aqaOuQsLHd9FI84u68uKuIwvLuiykqThGU66nrh9KpPnN2tfMidwN2GqOkJI31H0xJaic6GmmVIqbl26dGsC4F1XbYh9yRaV/1aXEm339jHsf/RJ53OWVdtNUdA1tSpu++iJ5YhtwGiturwvPuvLirgJqy7ospKkkcIu6Xrinhtl8vz2Q6rHXoCRZ0JI31HkxJYyWEbSjLzHraubodwiOznhtic5Z1m1XRcdFaDDaxuweuXwchvLkNHBVKkSaHqdlTLvC9B9VpI4kqzHlumkbFkz+iWzR9mheCaE9B1FvkDKYBlJU9i5rCupsCnL7P0k56ybtttsMOHOiG5CXZTgPJTFj2oSzgAwsmLY2DlQaEaCs0hLGJ7ot3akituvu8JrG6ZMJ0nWlTWm6wBoTu4ddK9yWlA8E0JKS1LfbpGpoUzSIU9fZJoT4oYMkcQ0BG5ZomRJOhtJ2+4S5Q46I7ZMJgHh85A01WCa94kt8vzc7Hxm96Yp68eeB46jsXSxPWfmGtjz4HFMP/0CHjt12qkNZemYuBI3iXOQJlBnBcUzIaSUdPvAz2tii0t2ASDfaGqaE+J0QigtgRucn3BkcFU1v6k4geAydXhs52xi2ximn34B9z3+LBaVQkUEu7fHX3OuUe6pmXqscO72PGQhqmyR5+B4pn1vmvZjVXWoTTgHNBaVd1GfXp4oV4bRsH6D4pkQA4OaJ7gs9MoD3yW7gE7kZHl9pREpM+1XRaSrvK7R/b768nU4v7C0/P2ZuYaTgOv2+MV1euKE6dRMHQ8drS+LxUWl8NDROsYvu9TaDpcod9A2G4F/+JYDx5YLpKTtZ0/CWBfZV5Ji2g/bfZlmUZ+yU/QE6qSU+R1M8UyIBg5zFU+vPPBt7RFA+9D3ub66sa5kkTJtSamuhPOeB4+jsdiULvXZedxz5JmO5eKETBr3p63T4+K9TSo8XSw1trbVqhXs3j6Gh47Wu34+ZXGPmTzNLtlXkpLWM6Fsz5a0KMvcAh/K/g5mqjpCNBSVQotcpFdSLpnaMzZaw1P7rsXhyZ1e5YbDpJlyzpcsjv8dj5xcFs5x2IRMGvdnt0IpLh+zKVWhLrVfNMpta9tdN2zBY6dOa/f/1vuPe6VHNJ1LBSROsahLB3f3jVtx58QW73W5ktYzYXSkWmiKyaxwuebKRtnfwYw8E6Kh7FHPMg9npUVZJpPFYZqJf/b8AqZm6trz4np9mV4gt97vN+EpCVkc/2iOYhs2QWQ6fvXZeezYd8jpWNjSj7lEuUy/D/IxmyJmLpYa07orIgDM+x9YSOqz87jlwDHcfOCYNYpuunZdjwFgfhbl+Tzas2tz24hGEqoVwUvnFpav0fD+A+iwGmV576VNr014BMr/DhZlMfeXjfHxcTU9PV10M8gAYJrlPjZaw+HJnQW06CI6r2atWmnzofaLuC7jfphm9d/xyMkOcRg9LwGu19emyYPWNGW6bQH6NFVJjmPax3/j5EHnZW1pwOKyUJiOexiXiZ7R8xE+HqMjVbx0bqFtQlqtWsGq6pC2k+Dz7LC1zbYNE7bjEeyT6Xja2q1rZ3VIcMmqYZyZayxn3sgiBV20HdGsGlECC5WpU/Jdq4a1+afXjlRxrrFkvU5crjfiR1newSJyVCk13vE5xTMhnbgI1KKIe6iUue29ju3YmgSI7mHveo5cUpWF0b3oq0MCCNqickVdD1vv+LRWoIxUh7BiuNLxXbUiWL1iGC/ON9rEexLhqyNOOAqAp/Zdu7xsh1DUtO/mA8es23QVklMzddx6/3Ft9orRWhXnF+yCTrdd2/EwddTCxyCKz/UZd81101FzacfYaM2YSlJa/+9GDQ0JoJR+jkO/kGcwoyzvMZN4pueZEA1plYPNgrjhrLJ7xZLQbbnrtMpl22wUppe37ny5Xl86r6KNM3ONjvY1llTHcLbJX52133Pv9Vc0xXyI6pDgP9/wA1i9stNF2FhUmJ1vdPi9J7aNYff2MUjHLy7iMrw7sW0Mhyd3OpW21p37xqLC6pXDy952ANY2Ae6+9YltY1gyBLdenG+0XT+BncNG0hLTSewzOmzPoG69/XHtCOxGtn3s1je9pGBtex73V5bkPf+izO9ggJ5nQoyUNa9n3MzpsnvFfOl21nWas7bjvKY6TC9ll+sr+N4UgewGXWq0bo9RXGTK5r28JSZiC7Rns3js1GlrpNBHDLn4u13uK1vO6DCuadFs93r4+nGJxMcdjyQe99GRqpd9xHQMu02ZZ/OvRyP9tn3UfbdyeMhaTlxHtO1lzxzhQhGpQ8v6DgYongnpOeJecr2YlshGtw/tNB/6tpe0jjQmOJpe+rpt+bzo46KqvsfIVSCYXoiuxzYQYLbOoG9e7W4m8YWPo08HNW7ZqZk65i4sdHwu6JwYGW6/rqS3y3WYZFKZb38uOFbRc+EzaqPD9EyMRipd9lE3X8ClCJKt7b2Ss95GvwVluqVQ8SwifwjgrQC+qZT6/iLbQkivEPcC6JUsFa50+9BO86Fvy04QJc1JUrpzrpvxD3S+6IcALEXWlySqGodJIOx9+GSsIJuaqePs+U6hqGNIBJsmDxrLhgPAyuF2R6JPSWwTLveVT+fK1pm1RZJNVfGikeg8coO/6BGRDY6V7lyYcO3w+wh/2z7avvMd/XHpVPWS8Oy3oEy3FB15/mMAvwfgIwW3g5CeIu4FAPRWWiIbPg9tnWhI86EfPbYmAZfFjPDgnAf7eO+RZ7C+lUNXl0f6udl5rKlVcfbCApZCnmcBOspIp3GMTEJgdr6xHA3XiVaTUFy9ooILC0sdGRTCFf1MzM432lK1zV1Y6Dry53JfuXau4jqzLlUrbfsQfT4EfttuqjHq9tuWvSIoWR7NtrFj3yGnffPt8Gc5xO86+hPg2qnqJeHZb0GZbik824aIbATwCZfIM7NtEDJ4uM66Ni0XrcZm+n2wDp9OR14zwsNZIXRD8qbtuaZ7SmM/fDIvhLdva+OeXZtjOyoVSwTatS1pdjLD19CaWhUi8E7b5pOi0JYNI2iPz7nVlU833T+A3idsu25c9i3r1HZJiaYqVKoZfQ/O8+xcQ3sdlSVzRLeUMXVo1piybRQdeSaEECuukXSTbeCxU6eXU8lFLQ7haFxUJLhM6skjyh998UaFhy2C6jpcnMZ++Fhawtu3tTEcTdxkyBG9pFRHh8KHQLhHz3e4w+IjfNOIgKZl/wD8/LY6S8W9R54xXnNBB8jnuonbNwEKz6VvIum57ZfRwDJP4Mub0otnEbkJwE0AsGHDhoJbQwgpApeHtq3qXNwkoDiRkLVYsuEyhG/ad1M2BJ3g6nY/dAJh7sJC7PZdh7TjlvOZyGkinE4tfH1EK/dNP/1CbLnpbqJ0e3Ztxi0HjsV2CFyGzX38trprzdSGoAy57z7GdbJ6ycrgA4Vnf1F68ayU+hCADwFN20bBzSGElBSTuAqyEwAXo4urqkNeIqFIXLZv8n+/dK5zEl61Il4+RZtA0n3nYgcJb19XWlnXxjjPZVx55tFaFd85txBr8Xiu1dkyiTsF4J4jzyx3tsZCHTJT9UFbqWed4JzYNobpp1/APUeeMbbT1drg47f1udZHR+xlyE0E3+19+KS2KM7Z8wvYNHmwZ6OzZDAovXgmhBAXdCIM0NscfNJO+UbC0vYFxg1zm6KP+x99UluuePWK4VTSzwGd0XudQBwdqWLl8FBHlcA2os3U6NtoZDvwnN5y4BjWj9YwPCRG8VyrVrD3+iucckmvb3mg4whnvdjz4HFAYfl466LtQeaRcGVAm+C8c2KLUTz7WBt8Jnq52kVq1QqUQuJJmNHJr+EOh21yKSFloehUdfcB+BEALxeRbwC4XSn1B0W2iZAiGcQJGanS5dhUkvy4YbIohqATP0E7bdFHkwB8cb7hfJ3FVas0paYLC8Qzcw3UqhVtVpBgG1GR31hSuPX+47jlwLGOyVhXX74OB594vk2gxgm+YGKWrRQ30DyuwSRFHxuILeIdRpeD2yY4x1LI0uDjt3X1rd91wxZjR8Qneh22MuzYd6ij01GGXMh8JhMdhZbnVkq9Uyn1KqVUVSn1agpnMsjkXf60TKRRutYUadUxWqt2lL2uVSt411UbuioHm0Vp9IltnWVq775xK77eKgltap9JYAXD7eHr7OYDx7Dt1z7dcdxtPnKTuJyd7ywRHpQw151XW9VG1VrfmbmLJbrvOfKMV1W7sVY1PlPRkTAKzePtWxa9W0zHQNeOJOnBJrY1y5DffeNWAM1ove4+C19rJoLjmaSct40y5kIe5GcysUPbBiEloR+qUCUhrWit60s2GMIH0p/9nqYAcIl42ZYxDdfrhtuBZoQ4etx9KyraWFRKe17T3EYUU2EOE4FoDNp3s4PNIw1sJdyBdK5T3wqQcX71tPP+ljEX8qA+k0k8FM+ElIQyRl7yIK0XlOnlu3akipEVw9ayzGnhIgBcRXGc0IlbxiS8bL7f6HG/+vJ11klrvsw3FnHHI+0VB3V5hJMSPteB3eOWA8es1QgDosLPxeYRUK1Im+fZhzjBacrS4GsnMN1nNx84hv2PPtnx+zjhrpv4t6qafDDbRYznbaEo0zOZ9pFyQfFMSEkoY+QlC6IvAZM48X1BmV6+t193RW4vmTgB4Br9c+lQuCyjE15xgjB83B87ddptxz04M9dYtl3UZ+fx0NE6dm8fWy41ntS2Hj7X0eNsE84CGMWIzW8e5H5e25q4qPMzx5GkGMjUTL0jU4XLaI3tfgp+P/30Cx0l38OFagL7UXgb5xcuFn/XjV64EifWs5hPEEdZnslF7DuxQ/FMSEkYhPKnupeACd8XVBkKEcS1wTXK7hLxStrpiJsUNiSCqZk6JraN5RJhCwrZHJ7ciamZeiKrxGitir3XX2E9zjriyqgHKePue/zZ5XLT7/yh1yzneHa1g5jwLQZi214wWdN07cXZY+Ybi225zuuz89jzwHFALk6IjIq2tG0NtlzIRVgoyvJMdtl3RqbzheKZkJJQBvHnSxpDxzqSvqDKUIjA1gbXYeC4iNfUTN1YVS+u02HLswu0e5Oz9COHCfbfdWJlkIUiiP6uXnnxVTY1U3du89WXr7N+//6pE22CclEpPHS0jvHLLjWKxyim81QRactnDCSrohlmdr5hTPXmkkkj2k6dDSUs2vK0NRRhoSjLMzlu3xmZzh+KZ0JKRBnEnytJHtguL7okQ9m9guswsC3iNTVTx633H9cKsiDVWhzhSWG33n+8w9YQCKQ0PM8VESwphfWjNZw9v6AV7MH++1wfumtv+ukXcOALzzq3zWZLmZqpx1adjGtvIO51AjpctTAuwhvgKxQDj3mwjiHx+rmRoB1Z2Rp0nXKXDmUWIrcMz+S4fefExvyheCaEJML0wA7y8+peYHGRTJ/iD2XA94XtOgxsingBzcIkJg+vQnykKdpm07qem53v2vNcq1ba0v3FZXBwLQhjuvYCe4UrNjG6/9Enjf7r+uw8duw7ZPVnCy4K5PByFc3kxbgIb0CSkYAzcw28f+oEDnzhWWs+alOEXEcg2rKY5GfqlO/ePtYxsdSUTaXoyGuwz+HRkW6CAnHHuUwTGwcFUR4PmqIZHx9X09PTRTeDEC/61Yu2afJg7MvWRTyFifOglgmTEIzLDd3N9bBj36HYzgdgngCna7NJNI3WqnhxvtFV3Zl3X7Vh2R8cboPrpLBw+8IiJC0rSTgqHj1etuvbR2h2+7uxVsXDbjKT6AR7dBu6dVeHpC0iDujvaZ/zWa0IVq8YNlacNF3jgfjUbcv2m7yfJ7ZnnMvzwbZe03Eu0/73GyJyVCk13vE5xTMh2eEjsHpNZMcJuYDoA3xqpo47HjnZUeiimxdLERTxwnLpsATojqfrOQMuipwkWSRsbYgjeh/oRJ3NR+wTeba11edY+SACJH3tVocEb9y0Fof/7oX02gPgqX3XAtA/g4Dknl+XY1gdEux/+5XL6zRd4+F2Rknym6yI2+csng9JO/K+2+il91NamMQzbRuEZIirF61sw44uuJbyjQ4dhv22ZX4Y60RcOI1XWin2TNuLHo+pmbpTvuIAnyweOhqLCiLNl3D0pbxyeMhJVCfxXUY9pjv2Heq4xnRHoFoRDA8J5hvJlGm0ra7Xtw5bhFl3+oYEqAxJbJnvxpJKJJxtnYqwP9nk7016X7pcb40lhb0PX/RlJ/FRlyWlHBC/z2laKcLPjNGRKlYODxkj+t1up9feT1lD8UxIhrh60XpxwkfUl2sSdutHa0ZhWNZ9070swhPnuk2xFxddjb6cgvb4RlVds3iYODPXwNqR6nK7gpRw00+/4DyRsFuxYPv92pEqZucaGB2p4qVzC5hvLBmXdaE+O9+WAeOuG7ZoR0nieNdVG7STDU0sKeC7VwxDBJida6Sa5aRWrWD39jGt57k6JJmmXVtTqzp1ssLLJEkPV5aUckD8PZaWoI8+o87MNVCrVnD3jVtTf6724vspayieCckQ14hIr074CAvgqZk69jxwvG3yU3VIcPXl6wqPWmSVUi+K7oXtIpRNWR1uvf94V+1xyeJhQ4A24Xh+YWk577FvG5JMHLNN2gOAkRXDmPnANdj2a5/WTrobqQ5hzlNQK1y8Ru+6YQtGVgx7ieex0dqyz9tHQM/Ot4ufjZMHvdodYKqoOX7ZpW0dAUEz6qsrfJIWkiC7R5L0cGVJKQfY77E0BX2egrZX309ZQvFMSIa4RkTKNOzYFdGXpQCfOP6890M+TUtHVin1AmyTzuIi2AEmgbWolFPREEFTqIT1oymLR7joR9w6dYLeRxBWK4Kz5xc6hGDcOXAtPlKfncfUTN0obn2Fc5jgGvW5FsKR3DsntmD8sku9ir6E74u1I1XviDfQjFzPfOCajs/DdqluO7Ou9+esY/vXjlS1bfWhLCNZYSGfVrYNHXkK2r55P6UIxTMhGeIaESnTsGNS9j/6ZMewcGNRGYdtn2sJH1M6trQi1UkiND7D5otK4XcMQ6VJI8a+KADDIlhTG14e9jdl23joaD1WONs8sj7GEdv5t50D1+M2JFiOzmdBnL99KNJhiXYeg0IqPhaMQPzcft0V2PPg8VgfdJQ4QdNtxDJOfIfvaVeP/rU/8KrYZXqJPIR8noK2H95PaUPxTEjGuDxIix52TCPS6xvxGB2pal/Cq6pDqQ5HJonQ+Nob9jzYFHDR9uU5rNlYUss2BhMuorRaEez/iSu9RV8S6oYOlOtxawrX7jNGmToLQXtM0eOoU6SxqDqu0z27NnfYmWysqTWjsLpnwsaX1WInDMZVTew2YmkT30B7x1d3TIcARMcDwlUbw5R9UnGR5Cloi34/lRGKZ0JKQlHDjmnNpDZFQtaOVHGusdTxkFcK2pewSdz5CtE4z6wtQhMdeo2jsajwC/cfa/ttsI08ylsHpDHTv7GolqsOJs1n7IpAP8rgOtEsLRaV0mYVSSJEdNllgPZy6GtHqjjfWNTaSsI+4eicguC+tBFX2MYnYunTsXludt7YOQtbm+YuLHTYUZJkIErqoe8X8Ze3oC2LLaYsUDwTMuCkNfHEFAm5/borlrcTnjTnW/Y5iMi5EOeZdRFGJo+oiSWFjk5HN+nOkjA6Ul3OM6vzWrqKeV1lvCxQ0HegWrU5Mt9+gKkAB+BvC9GJUJ3w2GSYEGjyCbtaWeI6SK4RS514vfnAsY5pDQHrW8VcdCwptZxv2bTfrhmI7njkJAA/a1e/plqjoC2OobgFRGSTy2eEkO6Ymqljx75D2DR5EDv2HcLUTD2X7aY18WRi2xjuumELxkZrEDQFSZCkf2LbGA5P7sRT+67Fnl2b8dBR876ZZuj7zNy3CY1wu1yI7peN8PB19LdA53zKWrXi1AYXqhXBS+cWlsVxIIADoTA1U8eeXZsTbXPtSHV5H/Lg7IVFRK3EO157adu1NerYmTId+zCBMJy7sIC7b9y6XMQiSXrAwDYRdz+bRj5Mn7vej3Ge14ltY9i9fQyV1g1VEcHu7Z0izHQP6Y5GIL5d9ilumeC4mTp5Z+YauOORk1brSJQ4qwkhvrhEnh8C8IORzx4EsD395hAymBQZGUlz4olLJMQmbKND52FcZ+4DZqEhQKLqXuH92nrHp62WAlNRGEA/dGyyhgQpx+qz8x1R2CB3b7hoy9nzC7GT84J99/Uzn5lrxGZ+qA4BXaZZNqIAfP3b8x2VKl2i+ocndzpXCzwz11j2r8dFek2VAh87ddrpfvb1rLqMGriMqEQnjS4qpfUcx4l1U5aZuH3S7beg2elwPaema9E3EDDIqdZIdxjFs4hcDuAKAGtE5IbQV98NYFXWDSNkkCgyCX3eM6ltL6y7bthiFHY+Yj6NDoHJI7n3+iusE8DivNS682myu9hEt6sNICA47mE7ik8atTiyEs4B0QImLr70ILrqW1kxLkVdtWKuBhhEsaOE83aHrwNXz+rVl6/rSBNYHRJcssqeYSWK67MmTqwvKoWvR0pfu+xTkC4xvC8KzUmDB5/oTGvpgy2qzVRrJE1skefNAN4KYBTAdaHPvwPg57JsFCGDRpGRkbwnnpheZGOjNefoVRzddgimZuptacLqs/MdGTXCE8Bs2wiEr8mHHHf8bcLZJy1YWCi4Tj7Lm7HRGl44e95YKTBcwMSFVdUhbJo86FXWHK1t2NL1QTWrLfpOalxUCrccOIabDxxbvgZcRkKCaHHUynLjG1+zXJDFFddnjYtf//1TJzq27zL69Nip09oc4q7CebRWxfmFzknIpvubqdZI2hjFs1Lq4wA+LiJv+v/Ze/s4Oarzzvf3TKsRPeAwwlYcGPMW4kUxxkhhYhOzd9civsg2BitgUIjZxHkj3rt7ExSiRMTESI5zUaI4OG93b9jEWScQIjBkIgz5iDiw9ifEEEaMhKxYil8wwm0Sy0ZDgmaEemae+0d3taqrzzl16qW7qnt+389npJnu6qpTVaerfvWc33keVf1CH9tEyECTZlZ30ZGRfk48ibuRBe0IV0Nbvix2ekYHWR8Itj6035izeutD+9vHymfGf3QYOuxD9hFRruF/ID4tWECtWsHaVStx2bbHEuXfTYIpq0oSguMQPKS4mGssYMvO/V0CKkAAjIwIjh6PPzY2XJ9pLCpEmpFf3xR0AcHSNmuWzdoT3U9FfGYNE3HXmvD2x0arEKi12My9T72QWLwD2YICtWoF7734jI7iSytGqx0jNVGYao3kjY/n+Tsi8ncAXq+qbxaRtwC4WlU/1uO2EdIXiq5mB+QXGelHOqas2/C9kR0L3bBn5hqJPeBZHghsnsojsw1MTtfb643bhss3GxZRN+/Ygy0792PL1Z0CIG6ik49QHTeUBHcJw9HqCJZXK5iZbXiL7GpFOrKqJE3PJ0BbJPoWBbFFfYMJgq425JHJY2a2gbGUVQADonYJ0/Vj44491ramEaGua010+0dmG84Jpqa+MTld73jwHatVu/p1khSO1YrglJOW4eW5hrG8PdB5rbDBzBQkT0TjSrSKfA7AJgB/pKprWq99UVXf3If2dTAxMaFTU1P93iwZYkwTVGrVSqJsDGFsE5PGx2qxw7NZRWke+5I0kppmGz5kOY5pCe+766ro2t/o8UsjIhUnorBZPclBW00WExthS4nvBK7qiGD7dRd39ZUP/9W+dvQ3jq9vuxLnbX44s6gNsmr0Os2d09aRAAHaadx8JzYGBN+HvHIeJ91+RQRfveM9Hes1VUWM9g/ffgUAn9iwGgBi7Um9vDaQpYuI7FbViejrPpHnUVX9R+nMEzWfW8sIKYjJ6Xq7GESYflezC8gaGel12d08thHdnu2G3w8PeHj7p9WqOHp83ivqGY78Rqu//cNXX+qIKCeNcEaH9LNGSOcaC9h43x5jVggbpvMeJ74bi4qN9+1pi/0g2jg2ehKOHo8/Z0G02PeBo1at4OTqiDHqe6ZH5DkPfCPyUDitHYqmaI6rrGjKuLJp3QW4bXJfx+Q7n9Eu27Um6ffrhred1fG3beSgsdhZeTE6+mQ7OsGETx97EjNnkH7iI56/LSLno/W9FZH3A3ixp60ipMcEQjHvC7Ht5h8Ur+ilnSJOcMZFp3yEcV6iNk6o99oDHt1+0olfQXvD7Te1V5HeIpBXYRWXxrNFT+caC/jVB5/t6C9zjQW8Om8fHg+vZmaugV/csaerDLOJsD3JZ5JaRQR3XNP02bqsTqYIaB74RpyDCD5wwspi6wv1mTlsun+vs58omg8lgX1h07oLMPX8S8ZiQ2kfaF2TedeuWol7n3oBC6qoiOCGt3VPVnRdB1wpHM+1ZIpZUPUuDlN05oxhq2BI3PiI5/8G4C4Aq0SkDuA5ADfmsXEReReA3wVQAfDHqrotj/USEkfcBTnthdh08w+KVwRRsl7lcHYJTp+osi1SF349L1EbJ9R7PTve94ZsQ8Rf3ParSl4aPn79xVZP7WxjEbOtc12fmUN1RFAZESx4TpDzEc6BEI5GJE0jQgGBoAomtAHazs5xcjU0sTTnAy8A7tywGhsdVppxi2iKZkcxfYd8Jh6+Or+IOzesbltqXFU60/qhow8d1Yq098k0OdA344vGA+tOAAAgAElEQVTtGjE5Xbc+kIw7qhaGKTpzxrBWMCR2Yqewq+rXVPWdAFYCWKWq/1FVv551wyJSAfCHAN4N4E0AbhCRN2VdLyE+uC7IWS7Epip7p5y0rOvG2IvqVqbqccG++FTYqlhK+IVfd20jCXERbFe1wqSYKr1lGeKtViSRDaKsnHJSpR3l96GxqHjNcp94ix/VEcHHr7/YKDQXHQdY0BQniuaEtnBauyOzzYmlW3buT5wFI44PXHq283gFnlubF/6ybY85hbcPUcuQizQBgKnnX+qO1jsOYyAag/NhE87VETFeI1wjgHFVCysima8NecEKhkuP2CuhiPxi5G8AeBnAblXNciV4K4CvqOrXWuv9SwDvA/BPGdZJiBe2CGo0EmbDNUQX9RPailfECbikw4CuLBa2m3a4Da5In8824vCJUIVvlNHjGAiQJNu1RYROS5GjF2imxEpS6TALtWoFy5eNpGqnD9VKM3aSZGJirm1x1Mt2eZ/jJHGSfMG+rBittqOuSUdFTH0wC8F3Nu76kSZTzz2GSHbUrxzGNoIzIkDw7GLKthH3+eh1uB+TlLPACoZLD58wwkTr56HW3+8F8CyAD4nI/ar6Wym3PQ7ghdDf3wDwtpTrIiQRthugr3BOMkSXxuqQdhjQNhHIpw3jjgeKJOnZfPbHFWmyfT5c1S/wiAbtsWGLCI1YhNvyZSNOX++xxmJfbBguX29ezMw12g8jtlLTvaSxoLh5xx5s33Ww60HIVsI5zyb6TOYLCD8wJX2AzGoRihJ8Z10PGMuXjST+jm7fdTBxSjzb66roqj6Y5POLqtbJhWX0Exedp5/0H5/KA28A8AOqeouq3gLgEgDfDeA/AfhgD9sGABCRm0RkSkSmDh9OnhCeEBNZbAFJh+jSWB3yHgb0aYNpGaApdG99cB8mp+uptg24I0w+x980DN9YVGzZud+5XdvN2ZY+7XWnLseK0arxvYpIogpop5xkz48bR1g8hAvEBKLf1sYkhO0PWYTz+FgNN156dkebatWRpjj1IHgwDPcv0/fzzg2r21k54lgxWu3qy9URwYrRant9299/MbZfd3HHNmzHNSqC1q8ZxxObL8dz2660WjUC8ow+CpplugF3ZNmWm9lkYfJpp6vsdZLX034+yfEugrzsbGRw8Ik8fzeAV0N/N9AsmDInIq9aPuNDHUA4z80bWq91oKp3oTlhERMTE0PgNCRlIW1quKRRmDSRk7yHAX3a4JqsFVR0Sxv9cUWY7tywGtt3HcRGSxQSsNsF4mwESXMtf3NmDnduWG2cNJUkc8Mpy5fh6KvzANJFHE2TPAFg+bIToyNrPvpopgIdeVxMBWjn1o1OJktSOnyusYBb7tuLm3fsMZYwDxMXia9VKx0FW3xKnocn4Zki3oFgTUOafN82FMADu+uYOOd0rF8zbrXbvGz4XqTNcBMUsDGRdWKvLbvK0VfnO0a7ys4gRMdJvvgUSfk1AD8C4K9bL10FYCeAjwO4S1U/kGrDIssA/DOAH0ZTND8N4MdU1RpKYpEUUjS23NBAvkn6iygSEuBbqCKwuQCdN421q1bi8QOHu8oLm/bHVNLZZJ+xpbIC3MPDSYoxAPY0ZNURwSnLl3l7frMW6vhE64HC1Qcmp+vO6nP9wLc/Jj0PAaa+cNvkPmeWiWhRDZNwDluAAoISz1PPv9SRN9nWDt85Cb77nsSWEjxY2M6/qXhKXHER24PDBy4921mCO4/iTuGKhAFl8zWTpYmtSIpTPEtzduAbALwewGWtl59Q1VwUrIi8B8An0ExV90lV/Q3X8hTPpEhcN8G8L/S9rOQXd7NLUmXMJH6j1KoVXHvJeFdJXdeEuKgos0VZV4xWMf2RK5xtdKUHS4LPvgb4lIi2MVarYs/tV1gfYsIV6VwPFf3gsvNPxz0/+0Ney6Y9D+G+4CNEP7FhtfO7s3rro9aHIFfxlbh2+FaePK1Wxb8dayBqtb7s/NPx9e+Y84VHEdgj2kFKPcDPLx/uT7ZrQ69zGBcZLCDERaoKg6qqIvKIql4EIHfVqqqPAHgk7/US0gt8Z4bnQa+GAW1Dt1PPv9SOFp9Wq3rbFHxsA3ONBTx+4DDuuOairv3xyQICALdfdaHRShEMz9v2NSxYsnJktoEbLz27KyoZpVatYO2qlfjM3nS1pLZc3dynuElIrvy4/eLJrx3pes0mtIKfpFHo+swcbpvch8cPHI4VluNjtdgc4q7RA1emjnCfTFppM2wRm5yu45b793YZzf/xuSPYft3FXg8YZzryH2tre5dteyxxcRGTla0fOYyZrYIMGj6e52dE5AdV9emet4aQEuMzMzwJcdGcrOW6Tdhu+uFh8Jm5Rnty1cxss5rZ7PH5TP7ab87MGffHJhTCN/TgODUWNNYPG/5MlgqCJgTAZ/a+aBTOFREsqrZtK9Eouy83tnIJA24/aVyFTB9WjFYxetIyL0+yjehnfIRW9MHQZ9sum0aYo6/OW891ViEW7pO2ddVn5nD+rY84++j2XQeNhWaClHBxVRbDudtt0dqgLXH4+JOTPiikgdkqyKDhk23jbQC+ICJfFZFnRWSfiDzb64YRUjayziwPEy0uYMo40At8BURjUfFvc/O4c8NqPLH5ctx+1YXWGfw+2I5R3Cz18HECmmIteD9pirqsKOwifFG1nQng8QOHY7ddrQhGQ9XwxmpVfGLD6g5vaZBxYiwUNZ9rLGDrQ/ux9aH91lGQGy89uy2gbPkuBMCVbzmjncHg49dfnOr8Rgvr+GaJCWdPuOFtZ7lSPifC9ZAU9MG4TCUCdGUKiYpM13c+eBCwfadd38H6zFz7GAbHdqxW7cgSEoxyxX13bEWPgn30yTA0OV23ivA8o8KmfQmywUSzghBSBnwiz+t63gpCCsTXz5dnyeh+RHNMJJn5H6SoA9JFCwNcxyjOnmI7Trfc587x3O/hXp+oZMApJ1VwfH4Rs6HKeK7c0s2sHSdwjQAsqLYFuMu7Hs3aED7eSTzJN7ztrI6/kwy/T07XsWXn/p4VgQkTFmJXvuUMZyRbW/+ER1588lCbMH2nXd/BoJ3AiQdFW4GRuO+O6/v5nEcO5uDB1cZYDukSA6L9Lzx5kqWuSRnxKc/9vKo+D2AOzf4c/BAy8CSJAOdZMrpfHr9oXte1q1YmijCGo4bhaKGrfHIQ+QwfIwDW/LJBFC3wcW7fdbD9vu14xOWe7tVwryl/cPThIM5ffayx6F2uffuug4nLTPuWII9uMzgPvtxoyMLgOzpz2+Q+bNyxp2/COSzE7n7yEKoxd77GomL0pGXWvMLBtcAV3Q2IRk83rbvAmgM7eqbjcru7vju2nNi+ubLjRm9eOTafa0Q4uL6Mj9USHwdC+o1Pee6r0UxLdyaAbwE4B8CXANhn6hAyIKSd+BNEq125iV300uMXzmpgEg5Ji3eYRJit/eNjNWO+X5cP1vW+K0rnOk+myGB1RHDqycswM9tI/fR/5VvOwMQ5pzvToB09Pu9chy0iaDrOaR6m6jNzuHnHHi8rRHj9t03uM5ZnNmE6z4B9dGbtqpXtaoZpS6OHCTJb+ETKTUe7sXiiP9gi+XHHfv0ae9n7KKboqSk9W9J2uL47WUfK4vbfVbY7C5w82EmvM52QdPjYNn4dwKUAPquqa0RkLYAbe9ssQvpDmgu17+xz10XP58aW5qIZbZtJONiq69kwCfokN2aX9WLjjj1GC0ggjOOGx785M+c8Tra0W7Zc3QG2LBaPHziMj623pyOLW68L03HOIjR9WhFsMy53cpg4AbZ82Uj7fK0YreLKt5yBHf/4QjuCnmZ/qiPAd39XzXiO05YvD6LLoyctS/0gm8QGFX7YS9J2Vztc362PX3+xMcONr/Dy2TfbdTKL4EsaWBhmcdmPTCckHT7iuaGq3xGREREZUdXHReQTPW8ZIX0gTQTYJ1odd9GL8yumvWhmmShXGRGMAB02AZtQSpJKz2W9CP9v+pyr6iHQFJdxqfeiwjkuS8W4Iw1YtK/Yovw2bMtEq7gF6+21rWFm9njiXNHXXmLOAmNKQXessYgHd38jsfUkSmMRxv4V/G3yTrtyNgcEFSXTRmh9vc/h7QX4fFfj2hFna7rjmotS50n22TfTdTKr4EvyYD7s4rKouTEkHp9sGzMiciqAzwO4R0R+F8ArvW0WIf0hbsa6CZ9otU/WgbCHOOqr9M1a4Ns2G2Fv8sevuxjbr7s4F093mLRWlBGRdoleUzaIWrUCERiP0z1PHjL62G1ZKsIEgtvGmo8+isnpelcmEB95aFsmyM0LdGcY6SVJRyGA5kRDk9fV1mfDEyOzYOv769eMY8/tV+ATG1a3/bwVkXZbqiN2A8uZY7VMcxlMnw23w7S9ANd31bcdrn6a1Scc3regTWGSjjT5tiXJ+ci6reickLJl9aCFpbz4RJ73ApgFsBHABwCcBuDUXjaKkH6RphiJT7Q660Uv7eeTDCPbqnfFVUgz5TJ2RXySRucCXNk+4oqsmCYcbbxvT7QuhZHwuk2LH5lt4NYH9+Hk6kjqKH+UsNjqRZq9PAlbbsLfl17f0H08yECnFeLIbAPViqBWHcGcQcTPHs8+6c2Wjz0ueuqaN+AbLfaxNZnwtTpEi7tkGWlK0j98c9xn2dYgRK2Z/7q8+Ijntaq6CGARwKcAgHmeyTCRtBiJz7Bi1ote2s8nEaprV630akswkSw88dBUZc82nJglzV3UJ+pbZMWEzybDOaRvdkwGc1WiS0P4XAxCVCmayxhI7s8ekeY5OXOs5ixuEuDz3TE9eDQWFIuLzewgn9n7Ysd2jsw2sOn+vYCgXb3SV0TFicmo9/v2qzpTzuWR+jLO1pTFVmHaPx9R30/Bl2Vbg2CJyDM9KskXq21DRP6riOwDsKpVHCX4eQ4AxTNZsvgMK6axg4RJ+3lT28YsqdMeP3A4th2T03WjULbpUJvwC1tUbBYMGy4xuWndBc5h+SRURHDtJePYvusgztv8sFcasrwIn4s8SomHkcj/eTPXWMCWnftjs4yEqVYEv3P96rZlacvV7iI8vt8dlwf4gd11mE5pY1G7StFHh/6jw/u3Te6zprgMxGlYpB8zRL3zSn1pszXZioz4WB2yFHGyPZT7PqwnIct1dhAsEXmmRyX54oo8/wWAvwFwB4DNodf/XVVf6mmrCMmZvGdku6LVwbaCKmE+5aRN6weS2UlsbTvPMiHM5yaxfdfBRGndfGfEX3vJeNeEPp9S3aZ1Zp2MFhAIrEBYxEXHx2pVvDq/mEsEOnwu8tDs0pqZGJ0sGZyD02pVHD0+3yUa05J0YmNjQTvyh0f7+2m1KkRgLVJiIy61YZJzFZwTU6TWNfIS/G56zzQqk4cQSlJkJOucjbj22h7KfR7Wk5LlOjkoloi8+gjJF6t4VtWXAbwM4Ib+NYeQ/MnqbUsivKPb8i0nbSKvi2aWm0TcpKawgEgyI/6B3XVjBCXtLPs8CE8yCyPSbfkIKr8B8daRakViRaqiWdxk07oLMOOR+9dFdUSw/bqLY4VaOFNIHBURLKomstzEYcpAYxL5s8fnvfOpp/XXmwi+H1t2dk8ytR0B17FMG9FM6k82VZYMC99ez9nod0Q37XWSlgiSBZ9sG4QMNFlmZCcdvsw6+7sXZBnatAlsAfABQyXBLDPi44Yow0PnG3fsiRVIpmqA1RHBqKG8XK1asYtCRTuDQrRdgR3Fll1hxWgV299/IoPJitGq1WYS9K2sZY9PPXmZ8ZiZKjs+sflyfGLD6th1Lqjizg2rrZabFSnbHGcXmJlr4Mhsw2iNMO1X0IdslpuxmrlPRCv+Bd+Pyel6bukCo98ln0wPSa8/k9N1q4APxKvP9cBmHYq+btoH3yqTRUNLBMmCz4RBQgaaLJGQpMOXeUVd8rSZZBnaNEVnAuFsqjBnIskxsUWR0kSaXdUATcfXZRuJi27ZIp6BHg9PtHJFfOcaC1i+bATVEemwo1RHBBveehYefvbF2Kp0QeTaFPHfuGMPpp5/qeu42LJRdOzjp/di+/vNhTeA9MVKkuQ+nmssYOtD+3Gssdg1khTO7W2ypURHC6LtN/WTy7Y9lnh/TJgKIPmMhiW5/gTrtBGIV5/rgc06FH7dtg/XXjLeYX8y7X9ZoCWCpIXimQw9vbAt2F7Pw0fXixRKaW8SWYR3QNqKYfWZubZn3Fbxz4WrGmDa9GI2gnVFSy7PzDW6zl2w7fM2P2wc/p+Za3RFQhuLis/sfRFbrj6RscE0PA+cOK4m4aUA7n7yUEfFv/rMHKoV6RLsURoLiq0P7cfoScusfcG35LSpvYDfQ6Zp/UFu76D1M3MNVEcEK0arRt+0rU9EyWo1EMB4nHxFcZLrj+vBI9qP464HNuvQkdkGztv8MM4cq2H2+LxxHx4/cDhTZcOkDHOFQVJeKJ7J0JPF25ZU+OXho3OV3AX6n4M0a3QmS8WwuCqELnxzvcZNZEyy7/82151xwhYptPWtipg90lEhbjqu1Yrg6KvzsVUDoyK5saBYMVq1lqoOODLbaItX00Pd6EnLcGS20X7Y8am6GM7CkCRPeZTodoLy29MfuSLV+rK2x5Wv2VcUJ7n+uPp72B6TdeJlYB+xEVQG7cd1yhRo2HT/Xmx9aH/iyaaEJIGeZzL0ZPG2JfUL5+Gjiyu5W7YqWHEkOSZ5FgiJi/ab/KQP7K5j07oLjFUffdblKjUeXjaIGkdHxwXuB4WoCAof11NOqqCxoKk9ujOzDad/29WeaFXEYKJs4It3Ec7CYPq+hamOiDX1ooksk/Rs58iHuAdmX19wkutPXH9Pkm4u7jy46Ke32ZjXe1GNPnlC8oSRZ7IkyBIJCVeSG6tVO4bO894WEJ9uy+Z3TJIRpKgh1Ts3rHZuK68Z+T7R/jyLJMSJ/kBQRCNlihNZS3yitEDnMQpnqLBVW/QlaOOmdRc4C8REqc/MGZcPhvCf2Hy51WICdO8PYC/6cdKykUSp/NIIuWhRIJ9zU6uO4ORqxTvaaRw1GBHMHp9v2yLC6/D5vvpkGvHt39Ht+o779Nvb7HO9sFXEJCQLFM9kSZBGMJomqb06755UlQdrV63E3U8esr4fvWEk8Uj3syStbcLazTv2WPNe+w6Tj0XyAK9dtbJttxgbrUIV7fRm4fd8ykmnEfCuz9SqFaxdtdIqIBVI5Ok2CcKk+bhNbQxEz/o149iyc38uWSbqM3OxFpLo/qxfM259EDh6fAFHj3eKQ1MqQaApeJMKOVdRIFde7+ZkS4l9OAxYv2YcU8+/hHufeqF93oOIKWBO4+ezTiBe8Pr27/B2bX13rFbFKcvtHvhe43u9MFXEpIAmWaBtgww9aatlFZF2bnK6jnufesG5TFRsJGlnP/fJNmENOBGtXL310Y7z4DNcHGRNmP7IFW17xcfWX4QnNl+OOzesxrHGImbmTgzb3v3kIeO5zzOllusz8wsL2PH0C86bvK9wtkX2skTsKyJdNhpTxT9TSres2ARuknPgyDCYagTBdiZenmu0bTImknyPJqfreGB33dui40uQfvC5bVda2xk+tj7p8gC7fWTL1Re2t5fE5pQXaewlRacOJcMBI89k6Ek7PJ93sv9w9DuIjr48d2KYF4DTNxsQLXObpJ1J9ymLxcPnOEUnwZkiclFc1hXbcL/p82tXreyKMqYddt607gJs3LHHKLyaGeDcbbJFnuMie8H5yRJ1XlQ1FlQB3CndspZKCVIeprUgxJHEux3g6rPhlIW2TCm+1wZfb3+WhyLbMZw9Pt8Wyb6jUHlk3ekF0Xb5Vs4sUwluMphQPJOhJ60IzrN8a9TCEE63Fdy0wt5qF9Eyt77tnJyuWyvEmfYpq8XDd0g1LIZ9InKA3briG8ENJgeGlxYA114SX3bdJB7WrxlP5BMOIwAu/d4VeObQy10ZSVz++ryqLEaLstw2ua/98FIR6crpHbRnzUcfTZyWDrCnbwtjEmtHX503WklMdoq0D0Gn1arGbUQj5FmvDb7iLcvku+AYRi04R2Yb1uuNK6hQ1pzI0XaFv6dJrneEJIG2DTL0pB2ez1KZL4pP8QdfIRK98fq00yUubfuU1eKRZEg12CffiJyPdcWFqRS3ovvBJMDH+pMm0hls95lDL+PaS8Y7MpJce8k4tu86aB1Sd+1zkraEu8Rtk/tw95OHOlIE3v3kIdw22V18I02V7vGxmvcQf9iC8MTmy41WEgHw3ovPSJzhxmRXmJyu4+jx7lSDQHeEPOu1wUe8pfFsR1m/ZhynLO+OkbmuN4MelQ33G1tFzDIWbCGDBSPPZOhJm3s5z6HKPG9IIyKYnK53FXxwtdMmtEx+17g2J5lwBMArIhuICZ91m85dkuPrSgdXn5kz2mtM0cholC6L1WCusYCHn32xnZN4crqOTZ/e2x5+rs/M4Zb792LLzv1tq48rqp8kN/HLoX2z+e3vfeqFroqSLyecUJhVtASWnmgmjAd21zFxzunWnMpRbCMqJ1dHjMP9K0arXfue9doQ11dclpakJL32DFNUtqx2EzL4UDyToSfLBTSvocosxRaiBPmege6qdTZsN1CT3xVoCgxbfi6f/MnhYz1mGQoPCIsqV+GQRdV2Zo3tuw52pJ7yPb6BKLF5qkVgtdeYCMR2+PinzVRxZLbRXtfWh/Z3CbmFxRM5nIP8w1l9x0Dn+bQ9VNiGvk3HPFxsJfBy27KrRDFZYwB0DMNHW5I0vaBtRMUmZG3V9rJcG0xe3XD2mDwFnu085Wl3KTNltZuQwYbimSwJir6Apo1K3mgRekkFQxKP5uR0HZvu32sclq9WxHlzNUX1TKWfA+EXFVW2UYIgOm6LGl57ybgzvV/UZ2tbVhWJz1F0wmPQzqDEeBK2PrQf23cd9LLwJMkNDTRF7SvH5jvOQ1Qs2SYuVkLJlcOe6Ci1agW3X+XOgx4mLJajk73qM3PY9Om9gJ6oiOhThCaOskRi+3VNsn2ntlx9IQBGZQlJQyHiWUSuA7AFwPcDeKuqThXRDkL6RTTS5LIDBIyP1fCx9RfhHovQSyICklhXtu862FW+OeCUk5Y5b67Gil8LCpGmeIuLrMWNEtiiho8fOGyNcJvKJI/nOBJgKp0eCCNXcRAT4fLXPiiaxzXuMwJg+iNXxGZPueFtZxkfLG5421kATniibdtwTbiMEn0QMp27uKwJAabJseH9DOf6tk0iG9ZIbNx3imKZkOQUFXn+IoBrAPxRQdsnpO+YIk22lFcAYq0MSSJiSawrLlE+M9dwCjDbZ1WBY41FryISroicTYjWZ+Zw46Vn44HddS/xY3uYWL5sJJXlwmSlsW0nT1aMVnGsEV+4J+grcdHOwNsbzrZxw9vO6njdhmvCZRTftII+2CbHhkcnwoLfFjEf5khs0SNvhAwbhYhnVf0SAEiSOquEDCEuP2KclSFpRMz3BhrnH45OZAuLxjSlxZPgqsT3wO46rr1k3FhNMIorj3HalHOm/QtvJ69Id0CtWvGymcRZbaJ8bP1FXRPkApKmEDSRNK2gibAH3ndyrO86KDIJIXGU3vMsIjcBuAkAzj777L5uO0uBCEJ8iPMjAv2fMb5p3QXYdP9eq3UjOpQeFo1xkdasxVjiKrI9fuCwd9YF28NEltLUJoEcbOf8Wx+xtn/ckcc4eN9UZtxWxjpgxWg1kQc5jrgy4j6jIUnSClYr0uF5Bjo98CZ87UyLqnhu25Vey/Yb3nsIKTc9E88i8lkA32N468Oq+te+61HVuwDcBQATExN5TC73ImuBCEJ88BXG/Rh2jU7eSiIg6zNzuGzbY9i07gLccc1F1iF5k7i6bXJfR/ox13ctzqscCKcs4mPL1RemtlpIa9tJhf8Tmy83Fj2xCcW4yoImn3f4c2lFmc0THbTVJ8LtErfVEcGpJy/r8MYDyR4cfTOvlDUlG+89hJSfnolnVX1nr9bdD9KWdCYkKWXwI5ombyVNhRbc5O+45iJ8/PqLvawmk9P1rhLZgP27FhfZPnOslll8ZLFaaOtzpu3YhP94q83BNceV3m1yuo6tD+13ThC0idg8RFnUEx3eB18h7kpHuP26i502Gx98fOZlngjIew8h5af0to2iyFoggpA4yjQ0a7phm1KhVUcEEHsWhOAmH0Q9oxkPovmZXdFT03ctOD4mARkIojzER/BA45rQacMW/V67aqVxQuO5r61h44497e0sqLb3JSqcw35zEy4Rm+W4RPfl49ebRa4PcekIs2IazTFZXtKk0+vH97RM954yXaMIKRNFpar7EQC/D2AlgIdFZI+qriuiLTbyyHBAiI2yDc1as2SgKchMRStsUdlgXeGIum1/XdFB23fNJKDHalVsubrp7bX5gNOID9t1ICh/bbtGmPbXNKFx7aqV3pF3U+GUMAI4/d5pRVnefbUfHn7XaE4gCMMPca5l+/09Lcu9p2zXKELKRFHZNv4KwF8VsW1f8spwQAYfV87YtDf+MgzNhvfLlvvW5p115TA23eRt+2ubgCaA9btm8ga/On8iXVue4iPuOmB7z5WPOnw8L9v2mHfkPS6Xc9z+pT0uveirRVmVkgrCIr6nZbn3lOEaRUhZGSm6AWVl/Zpx3HHNRRgfq0HQFBF5DSuSwSG42dZn5qA4kTM2/PetD+5rlrNOQNFDs9H9suW+dd2wN627ALVqpev12ePz7eMxOV13FgoJLApR3n7+6dbvmu2mvvWh/dZ2CU5MakxyrlzXAdd7vufXdb6TiH0fcWU6LsHngvN07uaHcf6tj+DczQ+3j1XcCEOvCdp2XqhNaXEJQhNFfE/Lcu8p+hpFSJmh59lBGSZykWLxSauVJhpT9NCsbb+kZXKORtRd3sdoarcjsw3c+uA+TD3/EnY8/UKsR9dkXXjm0MvWrBW2m/eR2QZum9yHxw8c7ohqh33bQcnnLTv34+U5d7XDANd1wPae7/m1LWeKvNsqKALAydX4OIgrt3U40hk8SLXLY9p2NFMAACAASURBVFvoR1/N2zqQVBAW9T0tw72n6GsUIWWGkWdCHPhGWeozc4kiY3lGR9PgqgR454bVeGLz5V1+ZVO0ff2acZyyvPsZfK6xgL946pBTOAdRz8cPHLZ6fk24bt73tEYFAHQJ54DGgmJmruEcOUgS7TQt64ryhrH1gw9cenbH8b9s22PO1IHBA0tcv1m/ZhxPbL4cz227sn2OXQ+ItvPnstXkSdJIcRy2vmN73fc8loU8o/SDtu+E9BOKZ0IcJImyJLFxhIdmAXRFR9NYQZLg2q+oMIkTMLZhfUuNFQDpLA4Brpt3dJM+mTLmGgu45b69bcFx2+Q+68NCFNODxcYdezD1/EteQ++mIfo7N6xup4QLrz/AVpc1rahMMwyv6M+ksbytA0kFYVksFD64HnLTMEj7Tki/Ec1QIrXfTExM6NTUVNHNIEsI0+Q0H2wT7UzYPMFJ1hHGJ73U5HTdWoZagI7Ka7Z0bcFyrsp5Nr4eWr9t/8dqVZyyfJlxP1ZvfTR1FcA4bPmtTefD1nZBM4KfVWi4/OImoufOhs9kURdxfTOvFGdZvxumdgD9q9bZT/K+jhBCABHZraoT0dcZeSbEgSn6cuOlZ7f/tpEkMpZndM03+rR+zTjGalXjOky+XNdySYVXdLumaGB1RHD0+Lx1P7ZcfaHR7mDCdZ5MJMk77Urxl9Za4LN+Gz4jJT6TRQOqFWnm9g4RN3SfZwQ0i3XA1g4AXdaVYYAT/AjpHxTPhMQQ9Yl+bP1F7b/HE3ookyybZmJOEo+oSYD6+nLDy9mOgY0tV1/Y8bfpAeXUk5d1+W3D+2H6zAcuPdvYzg+EHnZWjFa7xKAvpvPhOkd5iBbb+sdq1dSi0uZxroh0/D8+VsP291+M7dddnGjoPk+fchbrQJZ25Okd7hd5XkcIIW6YbYOQDOSRkzXPvK5Jok++xSriljO131XaOyyAw9sI/33e5odj98OUkWDinNO9LCtxloVo+23nY9O6CzqqA4bJQ7TY+kbwAJLGfmDrI4uqHXaaMHEFR8JtyDsCmjbzRFmKwvSLvK4jrCpISDwUz4RkII9qab7r8LmpJU0v5StM4tK1RdtvKkUd4CNG0qbJ8tkfV+VDoCk4opUAbed0/Zpx3D91CE989aWO1/PKSmA7tuG/k3qrfY6tr28+KjJv3rHHapPpdwS0yKIwRQjQPK5Fg/rgQEi/4YRBQgYAm8iLDmGblquOCE49eRlmZt15jfO+4Qfrs014c01k8t3fJO0PtyfIAR3kmU5bMdLUziDVXJAxI0/SHJek6/DdRpLJjEnbmAdpj1XcBNlebbcMcNIhIZ3YJgwy8kzIAOAbDYtGn06rVXH0+Hy7tLMtktSLiFMQ4bWJEdfwuSuKZsug4Gp/dP/ChUAe2F1PLWxM50UBfGbvi5lLuPtuL2lUNC5C6buNOPtDRQSLqn2LvJr6xR3XXJT4gTBrcZBBLmvNSYeE+EHxTMgAkNTLHNykTcU1TDfyXt7w87Rg2ET+ydURZ/tdhUCy7KftvMzMNdrHPc+h77zEjcveYltXUAgoEKG28xqwqOoVqY2SZgTE1i/uuOYiY8TUtY2s3uFBFqCsKkiIH8y2QYgnRc7ATzuT3vdG3ssbfp6VymwiP4isRwnaH7cfSXIph/EVFVmq4vlsL09x41pXOOXb2lUru85r1jalTXOXJLNG3DayFgcZ5KwXrCpIiB8Uz8TJIKZs6gV5V+9KytpVK7smYvnc1Hxv5KdZcj4DyHzu86xUllTMj4jgvM0PY0Tc6ekqMe/bMIkNG2V7EEmyjShzjQU8fuAw7rjmIqwYNfed2ePziftM2vRySR7+fLZhKmPuyyALUFYVJMQP2jaIFc68PkGRPsbJ6Toe2F3v8A0LgGsvic8s4TsEbdOO0ZLhQLpznzbdWBTbsPJYrYpX5xe7zlHgbY4r5JK00EuAyT88G/KYR9uelTwyKiTdhu3IBOdh+iNXYHK6ji0793dYhI7MNhL3mbQjIEnsBr22VfTjHPWSvL6rhAwzFM/EyiBPfMmbIn2Mtklpjx84HPtZ3xv5jMX2EKaX597X5+qb9zhpyWlbtcUkbQ5SxtmyLeQVeeyFuDEd/8Ar7MqqERbH23cd9PLXu0jruU3iU+6Hr5cClJDhhuKZWBnkiS95U+REmqznwedGHjf5K+k2k5BkhCPuYSD431ZkxcbLcw2s3vooZuYaHWnsXGn94tpsys28ccee0kUi4/bFJEwDwuI4j+tF2sl6SaK9eRYlIoQsTSieiRXOvD5BUTfcyem6NYqa53lwCaRebTMg6QhHloeBiuVYKtCOmobT2NlEfFybXYVY8rY/Zc3P7bMvAHDzjj3GzwfiOI/rRRbLQ5KCP2m3QQghAMUzccAIzQmKuOEGossk9nzOQxJRFbx+y317rXaHXp37Xoxw2Ppu3MNBlLnGAm7esQdbH9oP1WaE2hWlTzpBLWv/yUOY+xz/wJbhEsd5XS/6YXmgrYIQkgWKZ2KFEZpO+n3DteUmrojEzoB3iapg3dFzGqzPFIFeMVrF7Vdd2JP978UIh63vuioeughPAKzPzEEA40S6fk9Qy0OY+x7/OHGc5npRRBnrQYPHiJDyQfFMnAxDhMZUltnH11o0NnG1qBrbXpuo2rJzf0dWimikspcPTDYRkFfE0jXpLcymT+9FYyFddo0ABboEdL8nqE1O1xNFwG34Hv9w3wi+S+EUb1G7ik/7mc3HDY8RIeWE4pkMNbayzD6+1qLJIrpcle+iRCOVvcrmkGSCXVLBftvkPtzz5KGu1HpTz7/UVSZ7+/svxtaH9rejyaPVETQWFI3FZIJa0cyDW8QEteB42oj2EVf0MsnxN41OpP0OMZtPPDxGhJQTimcy1LjKMgeU9WaURXT5Zs8I6HUGlSQT7JIyOV3vEM7h9ZsE9R3XXITpj1zRtY7o6EQc42M1Y2Q7Si+i+a5+He0jPg8uSY5/XoKO2Xzi4TEipJxQPJOhxvcmU8abkUt0xfkgbcL75OpIzwp4uOi179cmdU2C2iTyouLRlKs5TNLIcd7RfNdxi/rh845e5nUumc0nHh4jQsoJxTMZanwjsGW9GZlEl28kEegW3kD3hMAkQjDt5KVeioCkos1n+ejxGxutdmTbKNonbzue42O1rnbl/eCS17lkNp94eIwIKScUz2So8clfPGg3I99IoivaaRLVl217zCmKs0xesomAtatWxm43jqQWFV+RV+bJskVW1MszJR3AbD4ueIwIKSeiCUrY5rZRke0ArgJwHMBXAfykqs7EfW5iYkKnpqZ63TwyZAxqtg0b5zqq531925WJ12crJx0d/reVafb1/kaj1mtXrcQDu+ux203TfgHw9vNPxzOHXvZef5KoehnSh/m2wef8utZleg+goCOEDD8isltVJ7peL0g8XwHgMVWdF5HfBABV/ZW4z1E8kyyUQfDkwfm3PmKc0FYRwVfveE/i9fmK4vM2P2z0FguA51KI9qxiPIzt3OYpMNMsm4Ze9NM4cWzbH8Bs88lrXwkhpMzYxHMhtg1VfTT055MA3l9EO8jSIe98qUUKcVsmCJ8MESZ8PbF5D//n6cW1WSx8rRdJJtWVvWKgiTgLj21/gt9N71E8J2dYHuAJWeqMFN0AAD8F4G9sb4rITSIyJSJThw8f7mOzyDARJxCSEAic+swcFCcEzuR0PafWuhm3iFXb63HYxK+pwlytWul4LYtf3He7/SCJkC+qYmCvcO0PU6XlR9HXDUJIfvRMPIvIZ0Xki4af94WW+TCAeQD32Najqnep6oSqTqxcubJXzSVDTp4ioAiBEyZvEeu7vvVrxnHHNRdhfKwGQVOsZxm+z3s/spBEyPdS9BchVl37U6YHnEGn6OsGISQ/embbUNV3ut4XkQ8CeC+AH9YijNdkSZGn5aDoaFzeM/CTVpjLa5i5TJkEkmSQ6GX6sCLy+sbtD1Ol5UPR1w1CSH4U4nkWkXcB+GUA/1lVZ4toA1la5Cl4ylC4IGtFPpNgLUK0liUdXJoS1b0Q/UXk9fXZnzI84Aw6ZbhuEELyoahsG18BsBzAd1ovPamqH4r7HLNtkCzkNVmn19kWeskgZopYavAYDieDfN0gZKlSqlR1aaF4JmVhUAVOnunhgM7jMDZaxSvH5tFYPHFNoTgg5ASDet0gZKlSqlR1ZGkzDDeQstgNkpKn7zIaSTsy2+hahmnNCDnBoF43CCGdUDyTvtKrPLYknsnpOkZalRWjpPFdmrIHmOCEKEIIIcNEGfI8kyUE0zUVQ/DQYhLOaSek+YpiTogihBAyTDDyTPpKr9M1DYMlpBfYosQVkdSeZFv2gDBMa0YIIWTYYOSZ9JVeFl1gBS87toeTRdVci5xUK4KxWjWXIiqEEEJIGWHkmfSVXuaxdVlClrqA60WO2TIVOSGEEEL6BcUz6Su9FFys4GWnVw8tzB5ACCFkqUHxTPpOrwQXK3jZYZSYEEIIyQeKZzI0FFHaeJBglDgbnIxKCCEEoHgmQwSjq6RXMD85IYSQAIpnMlQwupodRli74WRUQgghARTPhJA2jLCa4WRUQgghAczzTAhpwwqQZnqZn5wQQshgQfFMCGnDCKsZU0EYTkYlhJClCcUzIaQNI6xm1q8Zxx3XXITxsRqrJxJCyBKHnmdCSBum+7PDyaiEEEIAimdCSAim+yOEEELcUDwTQjpghJUQQgixQ/FMSElgfmVCCCGk/FA8E1ICmF+ZEEIIGQwongkpAaxgZ4cReUIIIWWC4pmQEsD8ymYYkSeEEFI2mOeZkBLA/MpmWPGQEEJI2aB4JqQEsIKdGUbkCSGElA2KZ0JKACvYmWFEnhBCSNmg55mQBPRy8hrzK3fDioeEEELKRiHiWUR+HcD7ACwC+BaAD6rqN4toCyG+cPJa/2HFQ0IIIWVDVLX/GxX5LlX9t9bvPw/gTar6objPTUxM6NTUVM/bR4iJy7Y9hrrBazs+VsMTmy8voEWEEEII6RUisltVJ6KvF+J5DoRzi1MA9F/BE5IQTl4jhBBCSGGeZxH5DQA/DuBlAGsdy90E4CYAOPvss/vTOEIMnDlWM0aeOXmNEEIIWTr0LPIsIp8VkS8aft4HAKr6YVU9C8A9AP67bT2qepeqTqjqxMqVK3vVXEJiYTo5QgghhPQs8qyq7/Rc9B4AjwC4vVdtISQPOHktX1h2mxBCyCBSVLaNN6rql1t/vg/AgSLaQUhSmE4uH5i5hBBCyKBSVJGUbS0Lx7MArgDwCwW1gxBSACy7TQghZFApJPKsqtcWsV1CyspSszAwcwkhhJBBheW5CSmYwMJQn5mD4oSFYXK6XnTTegbLbhNCCBlUKJ4JKZilaGFg5hJCCCGDSmF5ngkhTZaihYGZSwghhAwqFM+EFMxSLb7CzCWEEEIGEdo2CCkYWhgIIYSQwYGRZ0IKhhYGQgghZHCgeB5Allpas6UALQyEEELIYEDxPGCwMhshhBBCSHHQ8zxgLMW0ZoQQQgghZYHiecBYimnNCCGEEELKAsXzgMHKbIQQQgghxUHxPGAwrRkhhBBCSHFwwuCAwbRmhBBCCCHFQfE8gDCtGSGEEEJIMdC2QQghhBBCiCcUz4QQQgghhHhC8UwIIYQQQognFM+EEEIIIYR4QvFMCCGEEEKIJxTPhBBCCCGEeELxTAghhBBCiCeiqkW3wRsROQzg+aLbMUS8DsC3i24EKS3sH8QF+wexwb5BXAxS/zhHVVdGXxwo8UzyRUSmVHWi6HaQcsL+QVywfxAb7BvExTD0D9o2CCGEEEII8YTimRBCCCGEEE8onpc2dxXdAFJq2D+IC/YPYoN9g7gY+P5BzzMhhBBCCCGeMPJMCCGEEEKIJxTPSwgRuU5E9ovIoohYZ7qKyLtE5KCIfEVENvezjaQ4ROR0EflbEfly6/8VluUWRGRP62dnv9tJ+kfctUBElovIjtb7T4nIuf1vJSkKj/7xQRE5HLpe/EwR7ST9R0Q+KSLfEpEvWt4XEfm9Vt95VkR+oN9tzALF89LiiwCuAfB52wIiUgHwhwDeDeBNAG4QkTf1p3mkYDYD+DtVfSOAv2v9bWJOVVe3fq7uX/NIP/G8Fvw0gCOq+n0A7gTwm/1tJSmKBPeKHaHrxR/3tZGkSP4XgHc53n83gDe2fm4C8D/60KbcoHheQqjql1T1YMxibwXwFVX9mqoeB/CXAN7X+9aREvA+AJ9q/f4pAOsLbAspHp9rQbjPfBrAD4uI9LGNpDh4ryBWVPXzAF5yLPI+AH+mTZ4EMCYiZ/SnddmheCZRxgG8EPr7G63XyPDzelV9sfX7vwB4vWW5k0VkSkSeFBEK7OHF51rQXkZV5wG8DOC1fWkdKRrfe8W1rWH5T4vIWf1pGhkABlprLCu6ASRfROSzAL7H8NaHVfWv+90eUi5c/SP8h6qqiNhS8ZyjqnUR+V4Aj4nIPlX9at5tJYQMPA8BuFdVXxWRn0NzlOLygttESGYonocMVX1nxlXUAYSjA29ovUaGAFf/EJF/FZEzVPXF1vDZtyzrqLf+/5qI/G8AawBQPA8fPteCYJlviMgyAKcB+E5/mkcKJrZ/qGq4L/wxgN/qQ7vIYDDQWoO2DRLlaQBvFJHzROQkAD8KgBkVlgY7AfxE6/efANA1UiEiK0Rkeev31wG4DMA/9a2FpJ/4XAvCfeb9AB5TFg9YKsT2j4iH9WoAX+pj+0i52Qngx1tZNy4F8HLINlh6GHleQojIjwD4fQArATwsIntUdZ2InAngj1X1Pao6LyL/HcAuABUAn1TV/QU2m/SPbQDuE5GfBvA8gOsBoJXW8EOq+jMAvh/AH4nIIpoP39tUleJ5CLFdC0TkowCmVHUngD8B8Oci8hU0Jwf9aHEtJv3Es3/8vIhcDWAezf7xwcIaTPqKiNwL4B0AXici3wBwO4AqAKjq/wfgEQDvAfAVALMAfrKYlqaDFQYJIYQQQgjxhLYNQgghhBBCPKF4JoQQQgghxBOKZ0IIIYQQQjyheCaEEEIIIcQTimdCCCGEEEI8oXgmhJAhQ0TeISKfaf1+tYhsdiw7JiL/V4ptbBGRX8rSTkIIGUQongkhZEAQkUrSz6jqTlXd5lhkDEBi8UwIIUsVimdCCCkBInKuiBwQkXtE5Esi8mkRGRWRr4vIb4rIMwCuE5ErROQLIvKMiNwvIqe2Pv+u1uefAXBNaL0fFJE/aP3+ehH5KxHZ2/p5O5rFcc4XkT0isr213CYReVpEnhWRraF1fVhE/llE/h7ABX08PIQQUhpYYZAQQsrDBQB+WlWfEJFP4kRE+Duq+gOtkugPAninqh4VkV8B8Isi8lsA/ieAy9Gs2LXDsv7fA/A5Vf2RVhT7VACbAbxZVVcDgIhcAeCNAN4KQADsFJH/BOAomhUEV6N573gGwO6c958QQkoPxTMhhJSHF1T1idbvdwP4+dbvgRi+FMCbADwhIgBwEoAvAFgF4DlV/TIAiMjdAG4yrP9yAD8OAKq6AOBlEVkRWeaK1s906+9T0RTTrwHwV6o629rGzvS7SQghgwvFMyGElAe1/H209b8A+FtVvSG8kIiszrENAuAOVf2jyDZuznEbhBAysNDzTAgh5eFsEfmh1u8/BuDvI+8/CeAyEfk+ABCRU0TkPwA4AOBcETm/tdwNMPN3AP5r67MVETkNwL+jGVUO2AXgp0Je6nER+W4AnwewXkRqIvIaAFdl2VFCCBlUKJ4JIaQ8HATw30TkSwBWAPgf4TdV9TCADwK4V0SeRcuyoarH0LRpPNyaMPgty/p/AcBaEdmHpl/5Tar6HTRtIF8Uke2q+iiAvwDwhdZynwbwGlV9Bk37yF4AfwPg6Tx3nBBCBgVRjY4SEkII6Tcici6Az6jqmwtuCiGEEAeMPBNCCCGEEOIJI8+EEEIIIYR4wsgzIYQQQgghnlA8E0IIIYQQ4gnFMyGEEEIIIZ5QPBNCCCGEEOIJxTMhhBBCCCGeUDwTQsgAICIfEJFHi25H3ojIB0UkWkkx/P7fiMhP5LCdd4jINyzvnSsiKiLLLO9vEZG7s7aBEDIcUDwTQkgKWmLr+3q07i4xp6r3qOoVPdjWO0RkUUReifz8kMdnnaIzD1T13ar6qV6tnxBCktKzCx4hhOSFiCxT1fmi25GEAWvzN1X1Df3eaC9FNyGE9ApGngkhpUREvi4ivyIizwI4KiLLRORSEfkHEZkRkb0i8o7Q8qeLyJ+KyDdF5IiITIbe+1kR+YqIvCQiO0XkzNB7KiIfEpEvt9b7hyIirfe+T0Q+JyIvi8i3RWRH6/XPtz6+txWl3RDYAlpt/hcAf2qyJIQj1iJSE5GPi8jzrW38vYjUAATrnwmiwNF1icjbReTp1ueeFpG3h9773yLy6yLyhIj8u4g8KiKvS3keXOsytXNERG5r7dO3ROTPROS01rqCSPVPi8ghAI+FtvPbrfP2nIi8O7L9n2n9/sHWMbIt+5Mi8qVWO78mIj+XcHd/qtV/XhSRX0pzvAghww/FMyGkzNwA4EoAYwBeD+BhAB8DcDqAXwLwgIisbC375wBGAVwI4LsB3AkAInI5gDsAXA/gDADPA/jLyHbeC+AHAbyltdy61uu/DuBRACsAvAHA7wOAqv6n1vsXq+qpqrqj9ff3tNp2DoCbPPbvtwFcAuDtrc/9MoBFAMH6x1rr/0L4QyJyeutY/B6A1wL4HQAPi8hrQ4v9GICfbB2Lk9A8XmmxrcvUzg+2ftYC+F4ApwL4g8j6/jOA78eJ4/w2AAcBvA7AbwH4k+ABxoBr2W+heS6/q9XeO0XkBxLs51oAbwRwBYBfEZF3JvgsIWSJQPFMCCkzv6eqL6jqHIAbATyiqo+o6qKq/i2AKQDvEZEzALwbwIdU9YiqNlT1c611fADAJ1X1GVV9FcCtAH5IRM4NbWebqs6o6iEAjwNY3Xq9gaYQPlNVj6mqdWJbi0UAt6vqq602WxGREQA/BeAXVLWuqguq+g+tNsZxJYAvq+qfq+q8qt4L4ACAq0LL/Kmq/nOrHfeF9snEma2oe/jnlJTr+gCA31HVr6nqK2ge7x+NWDS2qOrR0DF6XlX/p6ouAPgUmg85r7es37qsqj6sql/VJp9D88Hn/3C0NcrWVrv2AfhTNB/eCCGkA4pnQkiZeSH0+zkArgsLPAD/EU3xdBaAl1T1iGEdZ6IZbQYAtATddwCMh5b5l9Dvs2hGS4FmJFgA/KOI7BeRn4pp72FVPeaxX0AzcnoygK96Lh+mY59aPA+/fTLxTVUdi/wcTbmuaNueR3N+TVgMv4BO2utX1dnWr7ZtWJcVkXeLyJMte84MgPegeZx9CbfreTT3hRBCOqB4JoSUGQ39/gKAP48IvFNUdVvrvdNFZMywjm+iKbwBAK2I6msB1GM3rvovqvqzqnomgJ8D8P+KO8OGRv4+iqaVJNj294Te+zaAYwDO91hPlI59anE2PPYpZ0ztjLbtbADzAP415nOZEJHlAB5A0wrzelUdA/AImg8/vpwV+v1sNPeFEEI6oHgmhAwKdwO4SkTWiUhFRE5uTdJ7g6q+COBv0BS3K0SkKiKBH/deAD8pIqtbAuv/AfCUqn49boMicp2IBFkojqAp+hZbf/8rmp5eF3sBXNja9skAtgRvqOoigE8C+B0RObO1Tz/UauPh1nZs638EwH8QkR+T5kTKDQDeBOAzcfuUM6Z23gtgo4icJyKnonm8d/Qh88hJAIJjN9+aSJg0td+vicioiFyIpmd6R9wHCCFLD4pnQshAoKovAHgfgF9FUyC9AGATTlzH/guaHuUDaE4cu7n1uc8C+DU0o5Ivohnp/VHPzf4ggKdE5BUAO9H0J3+t9d4WAJ9qWUiut7T5nwF8FMBnAXwZQNQz/UsA9gF4GsBLAH4TwEjLjvAbAJ5orf/SyHq/g+bEuFvQtKD8MoD3quq3PfcrypnSnef52rgPWdr5STQnb34ewHNoRtf/75Tt8kZV/x3Az6PpyT6C5iTHnbblpVl85VcjL38OwFcA/B2A31bVoStKQwjJjqjmPnpGCCGEEELIUMLIMyGEEEIIIZ5QPBNCCCGEEOIJxTMhhBBCCCGeUDwTQgghhBDiybL4RcrD6173Oj333HOLbgYhhBBCCBlydu/e/W1VXRl9faDE87nnnoupqamim0EIIYQQQoYcEYlWcgVA2wYhhBBCCCHeUDwTQgghhBDiCcUzIYQQQgghnlA8E0IIIYQQ4gnFMyGEEEIIIZ5QPBNCCCGEEOIJxTMhhBBCCCGeDFSeZ0IIIYQQMvxMTtexfddBfHNmDmeO1bBp3QVYv2a86GYBoHgmhBBCCCElYnK6jlsf3Ie5xgIAoD4zh1sf3AcApRDQtG0QQgghhJDSsH3XwbZwDphrLGD7roMFtagTimdCCCGEEFIavjkzl+j1fkPxTAghhBBCSsOZY7VEr/cbimdCCCGEEFIaNq27ALVqpeO1WrWCTesuKKhFnXDCICGEEEIIKQ3BpEBm2yCEEEIIIcSD9WvGSyOWo9C2QQghhBBCiCeMPBNCCCGEkFLBIikGRORkAJ8HsLzVjk+r6u1FtYcQQgghhBQPi6TYeRXA5ap6MYDVAN4lIpcW2B5CCCGEEFIwZS+SUljkWVUVwCutP6utHy2qPYQQQsigUuYhbkKSwiIpDkSkIiJ7AHwLwN+q6lOGZW4SkSkRmTp8+HD/G0kIIYSUmGCIuz4zB8WJIe7J6XrRTSMkFSyS4kBVF1R1NYA3AHiriLzZsMxdqjqhqhMrV67sfyMJIYSQElP2IW5CklL2IimlSFWnqjMAHgfwrqLbQgghhAwSZR/iJiQp69eM445rLsL4WA0CYHyshjuuuag0VqQis22sBNBQ1RkRqQH4PwH8ZlHtIYQQQgaRM8dqqBuEclmGuAlJA4ukmDkDwOMi8iyAp9H0PH+mwPYQQgghA0fZh7gJGTaKzLbxLIA1iu1CogAAIABJREFURW2fEEIIGQaC6ByzbRDSH1hhkBBCCBlwyjzETciwUYoJg4QQQgghhAwCjDwTQgghhAwYLIxTHBTPhBBCCCEDRFAYJ8jvHRTGAUAB3QcongkhhJCSwugiMeEqjMP+0XsongkhhJASwugiscHCOMXCCYOEEEJICWHZbWLDVgCHhXH6A8UzIYQQUkIYXSQ2WBinWGjbIIQQQkpIL8tuB17q+swcKiJYUMU4PdUDAwvjFAvFMyGEEFJCNq27oMPzDLiji76TC6Ne6gVVAPRUDxosjFMcFM+EEDKEMEvD4JMkuphkcqHJSx3AjA2ExEPxTAghQwazNAwPvtHFJKnL4jzT9FQT4obimRBChoylngN2KUbdk0wutHmpw++TcrAU+/IgwGwbhBAyZCzlLA1B1L0+MwfFiaj75HS96Kb1lCSpy0yZGgKYsaE8LNW+PAhQPBNCyJCxlHPALtXcyElSl61fM447rrkI463+UBEBAIyP1XDHNRcljmxOTtdx2bbHcN7mh3HZtsco7nJiqfblQYC2DUIIGTKSZmkYJpZq1D1p6rK8MjXQX987lmpfHgQongkhZMhYyjlge5kbOUwZvahFpC5b6v76MHn3iX71ZZIcimdCCBlClmoO2H5E3RltPYFPdLSMDxp504s+sZRHkMoOPc+EEEKGhrCfV5Dex+uCXtQTxPnrl8qkty079+feJ/rRl0k6GHkmhBAyVPQ66k4v6gnioqNLwdYxOV3HzFzD+F7WPrFUR5DKDsUzIYQQkgB6UU8Q56/P40Gj7LYPV3R5KfaJpQDFMyGEEJKAYfWiphWpruho1geNQfCXux4EBr1PEDP0PBNCCCEJGEYvaq+8yUnyT5sYBH+57UFgxWh1oPsEscPIMyGEEJKQYfOi9sqbbLJ1rF21Ett3HcTGHXtiI9yD4C+3jUTcftWFBbYqX8punek3FM+EEELIEqeXIjX8oJHUhpGXv7yX4m/Y86oPgnWm3xQmnkXkLAB/BuD1ABTAXar6u0W1hxBCCFmq9GsSZNIIdx7+8n6IvzKNROT9oLAUMqYkpcjI8zyAW1T1GRF5DYDdIvK3qvpPBbaJEEJKDYdP+08/jnnR57VfkyCTRrjziOouJfHXiweFQbDO9JvCxLOqvgjgxdbv/y4iXwIwDoDimRBCDHD4tP/045jbtjH1/Et4/MDhvgjqqEgdG61CFdi4Yw+27zqY27bTRLizRnWXkvjrxYMCUzN2U4psGyJyLoA1AJ4yvHeTiEyJyNThw4f73TRCCCkNthvjLfftxXmbH8Zl2x4rfeW2yek6Ltv22MC0N022h6T7aNvGPU8e6mtlvvVrxvHE5stx54bVeOXYPGbmGu1tb7p/by7bTpp9I4/+YhN5IyID0w996cWDQtaMKcNI4RMGReRUAA8AuFlV/y36vqreBeAuAJiYmNA+N48QQkqD7Qa4oM1LYxERyyQMYuQ8qRhJs4+2dUVveP2yGmzZuR+Nxc6tNxYVW3buz7ztJDaMvPqLyZICdH9voust2kqThl5EiYd9QmQaChXPIlJFUzjfo6oPFtkWQggpO7YbY5ggYhlInzIJ1EH0niYVI2n20ee8BoSFdq/Ena3UtO31pPjaMPLqL1HxNyLSFs629Q7igx7QO+96mSZEloHCbBsiIgD+BMCXVPV3imoHIYQMCqbhUxO2iGXRDKL3NOmQdZp9NG1DLMsGor1XRU3KRJ79JbCkPLftSiyqeRA7vF6XRSrJMe63TWkYC/iUkSIjz5cB+C8A9onIntZrv6qqjxTYJkIIKS0+ETQbZRCo/Z54lEdkNlh+60P7cWS2GXldvswed0o7IQ7oLiTywO66NYLYyyj+itFqe1+jr/eTXvUXn/W6LFK+EeiioteMEveewiLPqvr3qiqq+hZVXd36oXAmhBAH4Qjax6+/OHHEskj6OfEo78jsscZi+/eZuYZ1XWknxG3c0Ywh3blhNZ7YfDk+tv4iZwSxl1H826+6ENVKZ0+qVqTvFfN61V981uv6vviO5AxCaXGSjsInDBJCCElHmohlkfRz4lGekdkk60qyj7dN7nP6010RxF5G8csyQSxtO+JGHHzWa5tkGODzkDKINiXiB8UzIYQMMCaBNXHO6YULHxv9GlLOU7ikKezhI/DCwjnAV+D3uqhJWYb+k7bD1yoRt97gvVvu22u0Rvk8pDA/8vBC8UwIIUNGWYRPkeQhXIIIps1VnkUEudbrI/DLEh3OCx9/us8yeY44BMunfUjpV9VG0n8ongkhhAw8UWGV1b4SjWBGEQBrV630aktU5E1O152p6U6rVXHZtsdiRfGwPCT5RIt9I8p5WyWyPKQM2wMOOYGo50ztMjAxMaFTU1NFN4MQQpYEg1IkwiR0a9UKrr1kPHWxmMu2PRabe7lWrXSlAbO1JVguTpQDwIgA4Rolpu2koaznc81HHzVm9xgfq+GJzZcDsJ+P8DJJliPEBxHZraoT0dcZeSaEENLFoBSJmJyuG32pc40FPH7gcGrB5BOpNNkB4mwDpvejRIr75ZKCrpfnM4son5yuG4Uz0HkOfCPKtEqQflBYqjpCCCHlZRDSbAWC0JbrOktWA18/c3QbcSIvbZuyZmjo1fnMmhLQtf3wObCdj+jrZS0S0u9iKaS3MPJMCCGki36l2coStYyL4maZ0BeXqizgtFpn4ZC4iYpJSnGbPp+WXp3PrBP04iovhn/3jSiXzQs+KKM4xB9GngkhJCFLIYrkG+kD0h8PU9Ry4449ONdzPS7hVR2RTEP10QjmWK2KEUMFmqPH5zvauWndBcYCI0FbfEusR8lqO0hyPpOQRZRPTtcxIuayPmO1aldqubQR5aK/r4MwikOSwcgzIYQkYKlEkXwjfVmOh0lU2AqGmHBFcasVwfZdB7Fxx56OiHaSSHc0gmma2NZY0O4oa8RFsrCo2LJzf7stwURG3wj0itFq5r7VKy9w2pSALstNrVrBlqu7qxmmiSjn9X3NMkLCYinDByPPhBCSgKUSRfKN9GU5HnHiIW49LuE321js8uHeNrkvkz93xmNi2/ZdB9GIzPhb1GZJ72CbD+yuY9O6C/CJDatjo9C1aiWXsti98gKnLaFts9xURDK3KxxpvuW+vZm/r1l93Umj/kVHykk8jDwTQkgCGEXqJMvx8PH/utazfs04tj6035qtIcxcYwH3PvWCMSuHrz/XJ8qaJEtHkAlk+66DqM/MoSKCBdX2/+M5p5PrhRc4bS5j23FaVM01q0jcZNIshVduuW8vgPgIdpKo/1IZ2Rp0GHkmhJAE9Mo7WjZ8o21ZjoetyEiS9dx+1YXeHuKsWTl8oqxJs3SsXzPeXm/QvgXV9nrLLpjS2hnGRquJXvfFJxUg0DxPvn3c1j8WVL0i0Emi/ktlZGvQYeSZEEISkDSKVGRRirwzWZiitGtXrcTdTx7q+ryPMH78wGHn+z7D/6bI5+zxeWM0Olp8JMBX8LqirMGxrs/MQdBle3ZuM8+S0v3EFiWdev6l2OI0tvpsWeu2+TwIBf3K97i7Rkh8z5Nv1L+sI1tFX8vKBsUzIYQkwHeYuujh16zb972J2wRwnDB2bQNAIstCVJiYqvhVK4KFhW5lFs6E4YNJBEW3p0BbQK8YreKVY/MdPujoQ0FSwVSEkDFt0yY+73nyUOzEz5fnzFab8Otp9tMmdCsiWFTtWM/GHXuM6/ApvOJaPgtpJ2D2kqKvZWWE4pkQQhLiE0XKK5qYVihl3b7vTbwXnuespZRNDzhHX53HjEGwnXLSsswCwJY1JNiPuHOYRDD5Cpk8BbZpmxt37LFG16OvJ4nmBvucVrDZRobCNolgQp6t/abCKwCMlSxNy2ehjBUSB3VkpJdQPBNCSA/IY/h1crqOTZ/ei0YrYlqfmcOmT/tNUrJtpz4zh8npem6TnLJEynopFKIPOOdtfti4nC0C6iIqTG1D+mFfcxrBZ0oLaCtFHhYyNrE79fxL+Nj6ixLvryuloC9Jy2inFWxxI0OmUYkwrsIrAHoubNNOwOwlZbWSFAnFMyGE9IA8hl+3PrS/LZwDGguKrQ/tTz18DcArgud7E88igPspFPIaDjcJU5vHObxuVyTY5zjcNrmvww4RJZouzyR273nyECbOOT3x8U0iknyOBRC/z1kEm+thxTWhMM4q1K/+WrYKiWW0khQNxTMhpFQMy8QUm6hcu2olLtv2mNf+2VKwuVKz+UxcSzPJKVhvtOhIVkHRL6FgOh8Cv4mNYWzCNHqsw+c6ei5MFgSblzo4l3H4pMvTVvuTHm/fkuLjYzWsXbUSD+yupy6jHeyzr6UiKbZjI4CXVahswrYflNFKUjQUz4SQ0jBME1NMojIqLPLeP9PENRtJ7SOu8xInKHr5QOS77vVrxjH1/Esd0VsF8MDueqJorEuYjo/V2u0497W1rm2FiXuAibMXhDGly4uzkiQhbsIc0OlTnzjn9FTnO62lIgmMoianjFaSoqF4JoSUhmGbmBIVlWs++mii/RurVY2T3MZq5ly4vjlugWRiIe15mZyudxUxyfOBIenD1uMHDicWscF2AuEw0ipgEiUsHien684JdQEuIet7LkcEWL5sBBt37MGWnfsh4h6ZSCMSw+LJNKIRFbVpo7NZLBW+MIqajqUYcXfBIimEkNIwzBNTJqfrVlET3b8gG4BJOAOACIyFGXyPU1KxkOa8BMLWtM95FX1IWlAiy34EhTRMwjl6PF22gzAuIet7LsOlv2fmGk7hnIdIFDQLmYzVqrmW+QbiLRV5bCNJwRJCbDDyTAjpCz7D664h1UH3QrvEYnRiWdwQ+ZHZBjbd3511w3b8xmpVnLJ8Wepjl2aoe8vO/c59yCOHcRIxPDldt0aNXfthi4aa8gbHtStMdcSdX9rXZ+yLT+TWduyjffLIbAO1agV3blhtXF/a72q/LBWMopKsUDwTQnqO7/C6a5LdoHuhXYJq07oLEk0OA4DGomLLzv1ex2/L1RdmOk5Jh7onp+vWqHlAlhzG4XX4iK1gvT5R4yi287aoiue2XWl8z0f4Lmgza0p0AmaAj8/Yl4qIl3C2Hfsktp0s8xaGxVIx6A/6JB7aNgghPcd3eN02pPr4gcOJhufLiC16FviXA2tAEqICtVdD0knXG3debIIoqQ1j07oLUKtWYtftih7HHR/beXNFQ33E3qI2I7iKE3mYb5vc134/eszHalVUKxK7XhMLqrj1wX1Gq0+A69gnifDb1rP1of2x7Qz2ecXoCU//8mXZZUpggzpv88O4bNtjzuOQx7bCNp/g4aGX2yT9p9DIs4h8EsB7AXxLVd9cZFsIIb0jyc3XNKTqW0a3nySNLrmiwkkm+sXRqyFpV1qx6DFwnZexWtUaCXcVdjGl9/PNAuCKHvciGrp+zThutvRZGwrg7icP4TN7X8TLc432vgSTEF0eeB/iJka6vqNJ7BS2B8Ajsw2v4jwAcKyx2P59Zq6RaZSp3xl8hm3SMzFTtG3jfwH4AwB/VnA7CCk9gzwUmNXLWLb0UmluyC6hZ3s4iCMcoes3tip2N+/Yg4rFW7xitIrpj1xhXaftPAtOiLI0afLSeJ0D4gS67Xs5ntKzHAjk6H7m8aDoWofrO5akAqIttzjQLG8NuEVr3uKz32J2mCc9kxMUKp5V9fMicm6RbSBkEBj0/MdZvYxFeyGjAmn2+Hzq0sFJvLvAicITO55+oaPaYLUiuP2qC1PukR+uBzZXyWabtziuvbZCJrb0ckE7bA+ULq9zteKesBfGdt5c38s8PMvhPpXHBMI4q4ntO+Yb4Y/LMhLYRwD7dStv8Rk3mpF3EKJsD/qkNxQdeY5FRG4CcBMAnH322QW3hpBiGPShwDyq0GX5fBZMAslG2hu8TbiE/bhpC0+kxRVZ9o2qhjNSrF210lihMIzpPNu2EwhV1wOlyw6zsOiTTM6N63sZ2C2STAI1EfSpODEezqgyNlrFK8fm0Qjto4/VJGivrYR4nG3HZz/jrlt5i0+fPgTkF4Qo+kGf9AdRwxN5XxvQjDx/xsfzPDExoVNTUz1vEyFl47zNDxsjOgJYZ/yTfAjKK/sQLpSRlGhBEZc3uNcWnsnpOm65b68xYpuEoH+a0u9FHw5MbXAJT5s1JHwOzt38sLN9cTYSF5PTdaevWXDC8pBFQI9H1jEizcmGYUzHsh99xGeUwITrupWmryRtZ5Qs31vbNgfVYkc6EZHdqjoRfb30kWdCCIcCi8Q3miwA1q5amWlb0YlSm+7fi60P7cfM7IkJZAB6auFxWR2SMtbyZCcdOfEp02x7L3y+bAI7IFpQxFf0BO1zEc60ECfcXMK6PjPXIdIXFaiMCF6zfFnHxEJTFD9vsRw+Nkdf7bYu+fYY13Ur7SiT7dxFqyOayNuPzDzSww/FMyEDAIcCi8N3OFoBPLC7jolzTs9tYlNjUdsCLxBiy5eN9NTCk2fmj1eOzWNyup7Yx+pTpjla9jsgLMx8HwAmp+vYsnN/RzYL10NJkmM011iIFfFJWVhUiCQbdQqLy7HRKlThFN/hz0T7fxYbis91K6n4jJsTEvzYRpEYhCBJKTpV3b0A3gHgdSLyDQC3q+qfFNkmQspIkZ7fpc6mdRdg4449XlG1LCLWJ/o111jwiria8I2q5hmFaywqtu86aH0AGRFp57/19c4GwuuVY/Nd70UnAcZFdcdqVWeUO8hPHD1uSY/Rgqo1Wl6fmfO2O4QJHhx8zqupQmB4+6aHBB+7QxLCNhZTphJfQW/Cd2SDQQiSF4V7npNAzzMhpAjivLNh4vycNqGTxFttwuTbvG1yH+596gVr9guTjzRrO6IIgDs3rLYKseqIAIKOTCIuMTneihLaypDvuf2Eh9klAEcAnDZaNUavXdSqFSxfNmLMuezyYcd5n9MI6E8YjqvpvPqc02j/ybMfmPqmjzXH1+ecZE5IVj9yEX5meqiLw+Z5ZoVBQnpIPytbkd4xnmBY1zYEbKo8Fq4qZ6qWZ2LFaLVrOZPf+rbJfbj7yUNWu4Ctcp9vO8LUqpV2pcQoZ47V2pXjKtJdIa+xqB3CGXCLyG/OzFkjvy87Ki52Id2eZx/mGgsQQVe1v2pFcMPbzmo+DIRfHzlRGvuJzZfDViNQgXY1QdNxijJWq3pXZPSJlEeX8Y2uB212EY7sBtfEm3fscUa1k1QQTVIFMjgPz227Ek9svjyVPaSf1QNZsbCcUDwT0iPKftHrhbAf1ocFk6CsjkiXgHINAdvyIt/95CGs3vooAHSVY65EhVgrt/O1l4x3CJbAbx0+3vc8eSh2v2wVHsOCMyqMatUKbrz07K5S3VuuvtBZKnv9mnEs5jDSeeZYLbFYMk3kzJKp7shso0vwtxV/9IC1/g6+G66IeiDqPn79xc4HmOqI4L0Xn+E9Ac7H03vmWK3j+zviIeAD7tyw2mu58DXRB18B71umPStJy8cXtc1eXYeH9fqeBk4YJKRH9DM3c9JhvV4UXRn0Qi4ubJ5z02tJiz8AJ0oQ33HNRe3h7cnpOjbdvxcdPailvB4/cNhaOGT9mnFMTte9LACBYAr24bRaFSJoZ/f4REsUJelbrmWTFPpYMVrFscZi13do9vg8rnzLGXhgd93bu3rvUy94bTMLjUU1WmQaC4pfffBZzDUWreck2vZof4v6gc99bQ13Ox6OomI5Lkd0rVrB2lUrO5bxneAYfM9XOCwwQb9MOhnVdyJfv+aEFFE9MOk2e3UdHubrexoongnpEf260Ka5qPVC2P//7b19lBzXed75vN3TIHpALQeMYEscASQta4EVBBFjQiK9OCcbyA4piyE1BkXBCLWOEju0kvhkCSnwghHXAGwmQDxHS6+TnMQf8Yl3RTOQCGoECvKClonEJ4wga8AZEIIFOKIokG7KK0TAIBKmCfTM3P2j+zaqq++9deurq3vm+Z3DQ0x3V9WtW1W3nvve92PQC7mY8JmU+J7bTdWK0U9WE+6riWPnOopcAE2B5sq/rO8tH0uYSTCZsk08eGezPQrAX11+E48emsHEsXPO9Gi633aFfmsScSaf52A1wnAWjEtzDTx14jUoXPcxHo0QS1lmunD5JtuOMxdIQWjihqHuRWBXVUNXfmnTJCJKjGt/bJOwFQGiuq/eWDCeg0ZPmOKMfb6W4/Az+uSOzbmNN0WkDPU9pisvehbj8FIc39NA8UxITvRqoE0yqOUh7POcLBQVpJOlpcVnFTzYV7Z+cwnBkohXcONohGDS1BsLbaEaPLarL3z6LcqCr6sRvjFbN7oPBNsTLiFtIm2qOC2YfSsrxkWvPADR91bUxMgWZBeV/m2XRZArBXzs7nU4fvYC3mi5oJm4XG9gxDJBFDTvi6iVh5FqJVa2jV5bQ4vI1uFzTJ/MKGnH4SKs7v0MxTMhOdGrgTbJoJaHsM9rslDUcmHWlpZZj+C0YF/FcXHQRAlELQK1BdkH2x7rjQV86nOn2n8HxW64HcF+s4k4bbHe/9yZDpeEqHPyuSY771rrdHNwsXq4gr33b2y3L6ryYgmA285sxvfeihIrSZ8J1/12+GStLcpduZJtaR0VENlvSar89doaWkTKUJ9j+rjDpB2HWairE4pnQnKiVwNtkkEtD2Gf12Qh6QsyrbU6a0tLlBjWWRk0UX6qSQhbkNOyoBR2f/5Uh9tFlEuJjTR5hd+YrTt9t3W/ap/kqLRwtpzEPpUXg8JZZyBxueuEzyMK131UkmY7k4wxrvst+Ly5nvPxsVHrpMzVb0nHiSKsoS53mrzG+qhVg6jzzWIcZo7sTiieCcmRXpRpjRrUXIN6loN9XpOFJC/ILKzVI5YAKF1yOg6T0zVcudpd1CPIjSuHuopHZCmc8yLsl20jykKV5nxFgN2fP9Vui8l3+8D2TXhifFOkSLdZQJO07+r8Ih68c7QruNGGqY/Cz++2DWtw6M9eN/b7ogIePTSDx559GSsr5Y7JQ9R9r7+3iV/9vEU959VKCfUIH2+g6UqzqJR3gLPpeLZnNKk1NKkALjqYzjWhiooH8IWFujqheCZkwHENaj5la7NuSz9Y1rNYzrUZykyfu166vhZV7dYxOV3D7mdOdadCG2B8LFRprIWLCs40eMFr7xLBrnYmaV+9sYDjZy/gwPZNXQLYJ1uI6fk9fLKGHe9fiy+d+q7Vol1vLLYFbBwhp/vH9LxJwKrtes6vzvs5rSwq5VVe/PHJ0x1+9/p8ps5f9Koy6UsaAVx0MJ3NgOJbZMaXXhiDBgWKZ0KWALZBzTaoa1/VQRgIkywXZrGcGy64Yfvc9NLd/cwp7DtyBpfrDaMPsAk9Gdj/3JlMhXO1UsbKSslZEGR1K/uCr3tBHEYDbhNbD75gtVq5spEkqb4XRl971z1gEht6YpT0+G/M1o3P55Zbb4604tme3+NnL2Bm7z3Wynph4gi53feuN07eFhWw+5noccM3f7aPdXhyutYhnDX1xoK1cuaqFUOJxrU0ArjoYDpahXsPxTMhSxhXxoZBydHp+2IIWn9tgjXOcq6vxdv00m0sqLYQ9BHOwcmAS+SOjlTbPr0uoRtO4QbAaf1OUmnPl7lr85g6f7HD0mqy6rmykSikF9D6utmu62irEmKQqFUDn+BA2z3nY8WLEmVxgkqD+3KtlIyPjXalB9Q0FhT2P3cmdWYTX19Z16TFdgzbpDeKNAK4H4LpXPcTy3tnD8UzIUsY18t1EHJ0+uZwDYsc04u1UhLMXZvH7XuOer1AfC3eaa1LcXwSg764Y7/2vFH0urIW6CV5LXCysOhGcWmuYcx0Eb7/XNlISpKuImDwum3bsMbYntpsHe/+P/4I9cZiZO5joCkSd9611moBDR83CVGiLE5Qqd4m7BakV0qA6xMZlwC9NNfoCEoMP6N3/9hqvPjKxa7thlu+0HHEW5JnqySSKGgyjQDu52C6ov2xlyosz03IEkSXUa3N1ruqBQdxVakqugyrqbz5o4dmsHn/813tsYmcski71DWk+eL3LZU+PjbaUS5bl6EOv3DSWJcETUEc3KfO0GAieC3ue+/bjaWzbS/s8bFRvLjnA/jOwfvwyoEPYXSkmrtwjqLWypIBuPsxrnCuVkpYPVwxXrfjZy9Yt5trVQHU94fLqrugFJ468ZrTyprE5zT47M1dm28WkQkQnAROHDuHB+8cxeqIINbgfWFyC2osKDx6aKZ9f/kEd+q2hp/Rl167jHf9yKqO3299583481//mXYZ712BY7lI8mzpVbW4Y1aaEt++Y0URFFFSfDlAyzMhS4ywpcGlO2zR/f1gqbAJYlNBCdskQAclbT34QtcytI/l3WdpPU1KOVP/73tgY0fmiCDBYKnDJztLcAuAB+/0D+jJwh9TAKwYKnkHiZnYdWgGU+cvZpyaT9q5mcP4nne9sRDpguB6trQbSJwl8/Czp1cWqpUS3mws4qZqBVeuzbc/1wGEB7Zvwv7nzhhXIsoiHULO5aITrCppy+gBdFayNAmzb33vSsdnL712GY9Pno503Qljuid8VkuSrKql9Rvu12C6ov2xlyq0PBOyxLCW2Q39bbOq9IulwjW4h9tjs1DdMlLF5HTNakGMSnfnY30PW51WD1e6rIUmdHns8DHGx0Yx8dAdGLWckw6WCl8jBbdVNUwW/pgKwLUUwlnv46mWG8WB7ZtStwlw369xzjtpPmz9bJkssy6rqO3ZrTcW2ykSw1Zjfa42t5dFpWKJOh2QOPHQHdZVK92HtufHFuDnM64EnzttWQ9adJ/csdn6bARJIg716syrB+/rWhEaVFxjI0kOxTMhSwzXC81nWbFfLBVR+ZSD7bEtuW7bsKZt3TIR9AMNitjHJ0/HEj26eMQtI1XMzjVw48ohp7vM6Ei1nf/X9xhBkhYiCWLqszCrVpTb94yNLFw/FNC2FPoIIx9qs3Xj5MfnvNOiVwDiTkRd1+/SXMMaJKotpSbCn7vcgoL7Gx8bxZM7NjtdGbIl+U5dAAAgAElEQVSYiIQDGcPP3eGTNey+d32HoPW5hhSHTdK4oxA7dNsgpEDyiIJ2ZRPwKX/bD5HjgD3PsibYHtuSq09OX5Obii09lm0p2LbcbsPWtnpjAfufO4M3G4tO9wURc//EuUb6PGyZFQDg2vz1NvhkUUiDFlG2gL64CNC+j00uAv/02Zcx51HMIwmHT9aw5dabY01EJ6dr3mkNw+jc0eH71iSSXG5Bwf0B0a4MSd0qTMfSx/FJFxdsl47riDrv5QrT2OUDxTMhBZGXb3HayO9+iRx3Rfyb2mPyOdxlqZYGAAJlraZme/nbxFDc6nMuweqTNk4ADJWkQwAluUbaOmprS2PxugCNI+q0mFk9XMEP35z3qkKoXWwOn8wmONU0+QnmNx8fG8Xjk6czEeph9CTIJoZHhisdOa910ZQkwlmvsPj6wMcVni5fXpMwMxWAsSFoTpY0cSYbwXYxFZubfvXHHmRE5WhJyJotW7aoqampoptBSCbobBhhfC3ELtK+TPrhZWTrn7IIPvPRO7zaY0vnlgZTajnfYhVZsnq4guEVQx3XCIhvYcq67cEc01euznsVXqmUBBMP3WGtbpcl4cprcc+/UhZMfOQOTJ2/aFyh8NkeqrOseRxrrem62/rNZywJPusjrWI5l+udZb3jjgeT0zXrxDRM8HrkOSYSkgQROamU2hL+nJZnQgoiT9/itJaGfrBUZFFyNo1twCZoTCsENlcXm3tFFszONTD9q/e0/066kjEyXMl0gqGtp3FE8I0rm1XhXCsFQUrSFOk+Fu0wQZ/jRJUDWxs8Mb4JW269GZ/63Clvi3FZBKtWDHVNKOK04b73vh1PjHcGVtr6zWcs0c+67f5xFbgBzJM1V5nvMEG3jH5Z9SIkCgYMEpIRcXMjR2WIKDrPctGMj43iwTtHUW6VniuL4CfW3YSJY+e8+yVptbFqpYyH717nzHgRDPqyBeU8fNe63ILTwsGOjx6aSZQlpR8WH3WmCB+f7WqljP/zo5udGUmi8MnjbAuSbCw2q+wBzXv0Mx+9w/saLyqV+J7UmDKq3GQJAtSf+4wnNn9jW5aM/c+dcQbVxgnM1CK/n/MlExKElmdCMiCJ1c9mZdEZItL6QtuWWvvBJcMH7f+qrXoLSnVULvPplzjlizXhvLi2Zf2gVc8VlLPl1ps7Pp8L5On1weQ3bAt2NBF1/nHFXNpqfyZ8K+YJOguPuJb6XZRFvH1yTacarLJnuvY2dxV9nmlcU0zWZFtp89l6A2O/9nzH/WN7bmxWaptV3XQPB63Ipn6x3fvh4N9+HI8ICRIpnkXkdqXUq1GfETKoZCEmfaPEg8TJEBE36X+SJdg8X1hJ+tgnCC+qX+IW3tD+rMFJhk0nhq2kpgCmXYdmus738cnT3r6y2tdzcrqGT3/hNK5ca57Hm40FTJ2/iONnL0Sem7Tak9UEI2vhHMx3rf1uAYW6IRPGULlbJe6+d31Huekw5ZJgITTxiFPO2tY3+46c6RDxADp8hyuOgE7TpNn3Hi2JdJWYd5U2jxK5wfO1xRjECWQMTyqDxzBN9uiWQQYRH7eNw4bPnsm6IYQUQdwiBjZsVptg+WETpqT8WfhCx12CzbMAStI+9j1f1+/0MnDZZpoLsGpFGatWDGFXqwT47mdOWYWT64XvOt/J6RoO/dnrXsI5eIyp8xfbwhloWkM/e+I1L9Grcyjb6EXeYxurVpQBqPa5KDTF3puWFHKNBdV1LuNjo5j4yB3WYywsqnb5al+Ls+57l6gLWpbD1/zSXAOQZk5lk/vBysr1V+9ItdJ2VfBhQamu+ypJGsnwc2NzPdp511rj57Z80a62BN0ygOvXY+LYuWXpmkYGF6vlWUQ2ANgI4CYR2R746n8AsDLvhhHSC7Kw8gJu611cy24WeZbjLsHmWQAlaR/7WkSj+kUfw2SBXj1cwd77N7a/14LIlSHClG0jiOt8r1z1S9tWEuCGoRJ2HZppWjMvp7s+b7QmcbbgLsCdPi8vghOCIK4e0sVPwufhyu4wO9dApSxW63SQ8PWNyhoxOV0zBg02FhRW3TCEmb32oE4A7dLmScqTa9/jJH7rppUTwO56FLw/VlZKuO+9b+9KSedjRR4fG21nKgkGl/qMk4PickaWPi63jfUA/haAEQD3Bz7/AYC/n8XBReSDAP4vAGUAv6eUOpjFfgnxJauMF64XX1wxnkXEedwl2DwLoCTtYx8x4dsvUYUCth58wdsHNipllmsVwgedykwLlSxSt62slJx+9FH5nvsJU/GTqfMXndsodJe1NvGdg/d1fbbako1k9XClLYZ9J6WuiZW+r/Q96lswJWmmlLlr813uPC5/46uBMuyX5ho4fLKGB+8cxfGzF2KJ2cnpWuwiRHq7PPLiE5IEq3hWSn0RwBdF5CeVUl/N+sAiUgbwrwH8TQB/CeDrInJEKfXnWR+LEBs+Vl4fa0eUlSqOAMqiIpRNgOuS0L30ObT1cUmk/fJ29XG4AEPcl7XGJQx8J0sjwxVs3v98W2Rqy3XQ1zlplTgBnMFmaTD5D4fFSq/LryfFJLqeyqDQic1tYu/9G7t8qitlwd77N0b65YcnpVETyeA9evueo7HaH5dLcw1v8WkT/cfPXoidf9kVR+C6B21tCBa+IaRX+GTb+L6I/AmAH1VKvUdE3gvgAaXUEymP/X4A31JKfRsAROQ/APgwAIpn0jOirLxxrB2u3KZRQVthssjTDLizP9Rm6x0+h6ZzioNNANvKLS8o5RXEmHUVMdN+fFxEKmXB5bkGgjL00lwDu5851f7bZYWMQrfFN9dxFtRm6+3gs6zzPfeStDGMAlgnkK5nyXWtTJPSOC5Zaa+HT+EVH2uvK19zkgmXaxvXCpjLFY0WaNJrIisMish/ArAbwG8rpcZan31DKfWeVAcW+QiADyqlfrH19/8K4C6l1C+HfvcIgEcAYN26dXeeP38+zWEJ6cIlyuJWvJqcrmHXoRnjS6ufqmTZot7j5lQNvlxNZX4PbN8UWSzB5koS7K8s27v786c6/I4rJcGO96/tsshXSoIbVw5hdq7hZRGOm5XAhACoVkqYswTM5UkJQO+P2j+YXDaiiFsF01YO/GN3r+sqfBJc4QiiQ19vGanijVaAoun4rxz4UMfYZrszBcCrhnP3SYOo/cNdk9rw+GpLVycAntyx2fo8R6Uk7Kfx1QT9tQeTNBUGh5VSfyad0erzmbUsAqXU7wD4HaBZnrtXxyXLhyTL+bbPXYFL/bQsblsCfbQVoObruxh8udp8GKMsuj7+olkFdu47cqYrYK+xqPClU99tC33byy1qGT2tcAaafTjXWPQObotDlCUyS+E8Uq3gyrX5rnMYrpRQbyxmWg48TmlrG0mLrcStgmkqcGL73JV/W4vd2yz3pL4Xg2ObTXzarL1RLik+OelNK3eVknTd3wLg4bvXOZ9l2wqWpsjxNUoY01976eEjnv+biLwTrfGpZTH+bgbHrgFYG/j7Ha3PCOkbkmS+GM0gW4YmL2uF60XjO7D75GE2WaTD+AQxZhXYabMcz9Ybka4ySQquJGXViiGsumEos+NVyoId71vb9hfP2wqx74FmBhPTvZuFL29ZBItKtf3gTX78AmW04JtWSJL6/MeNT4hzH7tiBdquNtWK8Z42TQbiBiK7nq2gxdk1qTV931hUGKlWsOqGoVjjmm3iobmpWjFmYckbH2Gc1eSf9A8+4vkfoWn53SAiNQCvAvhYBsf+OoB3icjtaIrmnwPwtzPYLyGZkSTzRRbZMoB8rRVRQtDHCu0rXF1CzTeIMYv0fWnZfe96fPLQTE9cGy7XG5jZe0+i6nkmdrxvbYdbQFb7tfHYs6fx4J3ZVX0Ms6hUh6tBuIqjrRhJpSwYKklHAGUw5zLQPWG97a9VceLbl7CgFMoi2HlXZ1+aCoEERdy2DWtw9OXvOv2XTfexLdtMML0b0O1uYxtr4gp923UKukdErbLZxgh9f8fBNd5USoIr1+Y7MtT0yrLrI4yzmvyT/iFSPLcC+n5aRFYBKCmlfpDFgZVS8yLyywCOoZmq7veVUmey2DchWZEk80UW2TL09nlZK3xzyrpeQmlFUDCfrkn8BI+X1YTElXYsTFhEbduwJrWPgHZ+i9pFsFx1VJ5hH8JWu6gl8LToDBj6PIP3UZJ8xmFc1R2DTJ2/iKe/9joWlEJJgIUF1eVKcmmugUcPzeCxZ19GvbHYcYlrs/WOe3xBqXa/hX2UAfOEN6qffcWuLYvLIppuMpfrjcixJk4gsk8wte1x0Ncny0mvK/3mjSuHup7rXll2fYRxP0z+Sbb4lOf+ZOhvALgM4KRSKtWorpT6MoAvp9kHIXmTJPNF2mwZQL7WiuCLOUoA1xsL2HfkTJe4TSOCgmWng1Y6W8BQVhOSvfdvxCc/N9NRYrokaBdK0YQDC31EkA9RJZ+BToEyPjaaSfGS4D0zOV3D4ZPRHnKC5lJ40mPbfOCD+YyTTL5MYtPk3gQAh0/W2oIzqjaNtkb7zI2e/trrRvHs48oUpCyCB++0jxW+qevCxViCJHX9inrmbCnngplLspr0uvZ1YPsma9aTXlh2fYRxlv1A+gOf8txbAHwCwGjrv18C8EEAvysiv5Jj2whZ1tisEllZK8bHmqXBf3PH5sjyzLP1Rle5aQDtUru6BLEP+qXhW7ZbC2z9gnxyx+Z2KfMklEvi/BswBxamJVjyOdzfugXhMs5A0384bfnskYBl3VfgqYyOHSRc6TAupv6x3Uf7nzuTyrrtIqtKnQtK4fDJmldp6iRp3Ex9s+vQDG7bcxRbD74QeVw9Rrx68L6uZ852TIXrwnt8bLRrjIibISfYFtu+8h4rXdjKmgeFcZb9QPoDH5/ndwD4CaXUDwFARPYCOArgrwM4CeA38mseIUsTH2tQr6wVcazQmqAVMdhuV+ouHeClz3Xz/ucj3VKy9vueOHaua9m+saC6lnezLlJSFul6WQZzbWt/2tpsvSvndvj6JPEcCWq9OAJv/3NnOqrIjQxX8MM3o0uM29o4MlxJvFphq+5oc2/KSzgDzetpIokrk697gc4B7nKTCGPqG5MrTZJnyeUTHSSLVbioffVqrPQt5mQrpEWxvHTwEc8/AuBq4O8GmgVT6iJy1bINIcSCryBM6qqQZJlWfx/HRcAkwnxTd01O16zHySNFnavNrs+zYlEp47UN9lUwECx8P4SLxcT1g56tN9ruMXHU96W5Bg59/XVMfKSZs3jrwResgW96EjDqyIChFBKLWptALCLoauddazv+duU798FHcI+PjWLq/MWu0tZJM2YA5mfJd/zoJ1eErNy6XESN2xTGywsf8fwUgK+JyBdbf98P4A9bAYSsBkj6iiIS0cc9ZhxBaBuUbcd0DfD62KZ2+hRECGMSM74vsf3P2WODs0hRZ+sfm7UsnOZqOONCJQpNq3zYUmXr7yhRE7cgiyAg0GIqu8aCwv7nzjhXJgTAKwc+1PGZKQjUVZFPlye3CW+bKLNd05FqBVfnFzv2Uy4JFlK445iybUTlO/fBtwLpE+ObIoNrg/hYwmstV5qo8cM0NgHZCda0Y3feApbp5kgQp3iWZnTgvwfwRwC2tj7+hFJqqvXvh/NrGiHxKCIRfZJjprV+uo5pG+D3HTnTISTC7Ywb6OQSM1EvscnpmjNtl2+KuiQTCJO1zJTmKi62fLtBwn0edb3DQX4mK7Uvab23L801YqdaM90HNgEerg635dabO1ZBwunkgtgsoPse2NiRbaMsghVlQT2heDZNEPQ5xXl2TJZp1dqPbyBfmowZJqLGD1vbshKsg1BEhOnmSBCneFZKKRH5slJqE4Ap128JKZoiLANJjulT/CAoBMMC0XVM20BuEnbBdsZ5AZj8d208Pnm6Q7zsvGttZLGDIDZh5Kps5uqfYKaHqHLBvgiAmb33WKu9hduhrbhREi54P1y5Op+rD28aKiXxXqqPs9R/df665f/SXCO2BRTozLaxoBTqjWjh7FO0J0icZ8dWQCm4nyxXz3z95aPGj7wF4iBYdZlujgTxcdt4SUTep5T6eu6tISQFRQz8SY7pU/zgsWdPY+r8xY7la/25TUTpl20cy6lup29KMlfp4TCPT57uSO8WzJHrIvjCtAmjJBMI3S/hfaa1yuqXp0sYBYmy4mrCxTBsjI5U8cZsHcMryrhyrfveyNoFJcyNK4diCZyVlVL72o1UK9j3wEajhdp0fT/1uVMAzAI6/NnWgy8kmnAsKIVqpZzabcRWydBmfderKVlbYH395V3jR94CMeqZTUqWE5F+8vEmxeOTqu4uAF8VkVdE5GUROS0iL+fdMELiUkS6oiTHDKctMkXu1xsLePprrxsFhCvS35Y2yVQERG8zOV3DD67OW9u7erhiTK+kU8jdbkl79fTXXrfu00Vttt6xP1O6LNfLtmTpH+1XGk7flYbgy9PU93HQ19WQOc+Kdnd49eB9GBleYfzNXKvwR5BKWTBSrXR9HsanKbOeVnvd78GJQ9C6HMR2fReUMqYzDB8nTQVFvbLim1bM9sw9fPc64z5cqc1ck8IsGB8btaaUtI0f2l/eJ7VdUmzjpX5mk+CbCtMXppsjQXwsz/fm3gpCMqAIy0DSYwatQbblfptfq8sy5lrCtrVz4tg5ZxDV8IohTP9qZwEGHwuZyy833P4wURY3l4XddlztVwokz/gQZDRkyQr3vW9aN6DTn9bH/QPovs9cYjHYgnC7XcfzmVxo4WMrVOKqkGezJruub9D1Jcrf3cTq4QqGVww57584vrxxA+dcv7cFVGrxmrcF1eXmkacfsi0NXxxf8DB5uIIwqwbR+JTnPg8AIvIjAFbm3iJCEhLnJZZ2OS+4/U3VClZWSpidiy6PayJu5gQtfmztdw3wcV7YGpMV0OfFZDsvbdmLSotXbyzg0UMzmDh2zqtctw9ZufCEA9w04b4P32dXrs4bz9l3dSSYEzpcmU6kM5+zjW0b1nRsZytZ7oO2So792vMdE4XabB27P38KELTzarsmg2FRtvve9R0VHsMEXV+i/N2DVCtl7L1/Yzvtnk+eYhtpxhDbM+pyAdGfpxWxUeOkbpupf/LyQx4fG3W6kySBAX4kT3zKcz8A4DMAbgHwPQC3AvgmgI2u7QgpAh/LQFq/wvD2s/UGqpWytbR0FC7hXClLR1GPoIUo7rHivrCD34fxeTHtvGut0cd5511r2215fPJ0V97aMLa8x0D8Es8uAeuLa2XBJKiCIttmGX3jch2PT57GE+ObnJk7gsFvh0/WsOXWm9sWV9/512dPvNbeDmiWJt/9zKmu4jE+6C1M4jtOhcawKBsfG8X+5854i/oof3eg2+KeZqUqr+wQpjaZgvx6YUFNIz6TTCxsMQNJ3e4Y4EfyxMfn+dcB3A3gL5RStwP4KQAncm0VITmS1K9Q+1I+emgmU79Ep6VL2X2Os2L3vetRKZu9W22ZFFy+3rqfnjrxGlatKLf9Zssi+Njd6zpy5B4/e8HLNcDUv9oX2tZ/4TPSwsjiEu2F6xrYfCwfnzzd9g2fOHYOD945iuFQ6jWlmqL24d/9KvY9sBEVD8fnYJ/EvfeCvp/jY6OY+MgdbV9Ofb/1mrAo8/Wl1tRaQs2EXikIX7dgCryRasX7+craNzlYgn5lpdT2Rx8dqVqfD13q3BV3kKYttuOOWOIngtsn8TX2KXMdh6z3ZyKP/ieDgY94biilvg+gJCIlpdRxAFtybhchuRHXojI5XcPm/c/j0UMzTitn0uVAV6BZY1FheMVQR7BcFgQH/Ylj57DjfWu7ggpHqhVMPHSH8Zi2F5NOIadfnFeuLWBlpYzf3LEZrxz4UIdwBuL1me23cQO2XKJMxB4kZxNgGpugeurEax1CoplBxRwo9+IrF5v7euiOjrbb0AIqaUlojQ5kG2m5cKQNpExCWPjGtRAKmi4pPgFvUcGLUaLIFbAaV0CFxealuQauzi/iyR2bnZPDlZUSdrXGpCwC4sJtsfHDN+edx0g6scg6IC/vAL+sAxLJYCEqYq1PRL4CYBzAAQBvRdN1Y4tSaqtzwxzYsmWLmppiummSDpevY9iPNU7lPZsfrA+uFFIC4NWD9yXar+1YPiW0ffZjSiHn27eA/VqYcPVvOJ/03T+2Gt/5fr3tky6Ctk960rzOUdfh9j1HMxGdpvN09VOSctDBY8UNbswDWwl307NXEsDWzNHQfWhKF3dg+ybnfWrzpw+m1XNdj7jPUtR4ZOqHSkms1yrNOOT7PLqOYXsOsh7HiibOe4QMLiJyUinVZTD2sTyfAjAHYBeA/xfAKwDOZts8QnpHnOU83+phaZcDo1JIZUlWS85xUsjFsRpXytLlthDlZxwuhvHiKxfbFqHZeqNtTa3N1vHDN+1p+UZHqomvQ1bXKVxZUL+kbRZxm9ytVsrY+s6brcfRFllt7SxKOAMwis3xsVE8eOdo13m7mvnGbL3DncfmK+y6T23P/Gy90bYsulaL4j5LUc+MyYJ640p7uFKagDjfbV2/KyJlaBEwIHF54yOetymlFpVS80qpP1BK/RaA9+XdMELyIs5yns9AmNVyYC989IB8B/24L87wtVg9XMGqFUNoLKp23uOo/o1bHrmxqFCtlKw+0bZct9s2rHHu17adiVUr7Pmgg6nfgkvoyrE/Ewe2b8JTf/8n8Zs7NhvbFVcqp/EV18c0MTpStV5bX594TfA+c93nrvvU9RwEA/UObN9k/V2cZ8nnmQlPVF2uR2lEqu+2rt/1ahzrJSY3nuUySSBmrOJZRP6BiJwGsKFVHEX/9yoAFkkhA43JamrCNRBWW768Wfkiu0R9loEpeQ76rhen7Rz0tXhyx2a82VhsZ5rQ+ayjIvWTiP56YxEKMAp0k8VToVnq2dXv+voFfcdXVkpdwZiVsqBSNg+9QZGx/7kzXZMCXyGpBal2rQkW13EFobmoDvnYWjrRxyyLGMV/lKiKc23D+7IFtrmKCe2+d33kcxC0CPuuUrie3yRi01VUJI1I9Sn0E9W2pVZMxObbbPKvH/RJAvHHlaruDwH8EZq+znsCn/9AKXUx11YR0ifY/B9XD1fa+WKzxJRCKuu0WHkWk/Et0mI6h6RFDeKWJA+yoFTbfSG41P70116PlR5Mi9Swe0W9sYhKSbB6uILZuUbbtziYik5bgYOp1Cana4lzLwNoT1aCfR6cjMRN8afPJS7hgj5aQOuJS9DFIUkaRU24zPfkdM3onlMpX88e4yoT7opzCApXn2cp6vmNW2TFdlwB8PDd61KNSaa2bNuwBsfPXoiVdm4pFROxjUvHz15o+89nUbyGDBZW8ayUugzgMoCdvWsOIf1Fkhdb1mRdKSvvczK9OLcefCHyHJK6kyQtmKIJVlDb/cwpQNlzb5sqvQGdYiu8pc6YMv2r92DrwRe6RLHpSC6f2eFKCQpiPV/tLm67bz71uVPYedfaVtaPQBBaWTC/oIzt0RZWm5B1FcQxWc8F1/vYNRk0XdtKSXDjyiFnUaKJY+eMPtyrVjRfeeF9BjNt6H2Z8kyHhbHPs+Tz/MYVm3k+w0tJ+GaBa1xiXy1fIrNt9BPMtkGWI/0UvZ60qporE8V3WueQJnrdp9hKFpiyN9wwVIosuqKvVVRGDp2pwVSquL2vViVBV9aJqPLnzd+UsLJS7hChgLmMu/bv/dTnTxlLuW9958146bXLXdvFndCEC5kAfvdc+DeuzCS2723ZdtIK1H56fkl8mFVjeWPLthFZYZAQUiz9UinLtPz86KEZ7H/ujNWFRYsPqxBs/UbnGU7qThI3sCwJtkpvPgJRX6soNwRtkXT9Tts7FpXdWqx9nF3VK+uNRbzZWMTDocI1wPXKjUHXit33rsdbbhgyThS+8/26cQk7rnuIrZqkj6tMcAXBFhDpCgg0fZ7Gshh17zOwbDDI082NDC7xI0AIIblgCyrql+h1W1aLS3MNY3EAn4ILCujweU0aaJR3eigd8JaE4LXyCch6Y7berITose+Gxc0CaLpFRFUqVACeOvFax7UbHxvFtg1rjK4VNgt7MEVcMAg3TgYSjXYtcQXHmjKRhM/LxLYNa3qSJSHq3qf4GhyWWgAkyQZangnpA3yCAnvtd+27FA6YfbB9U8jVZuu4fc/R9nklWQpNEzQYRVRhjdXDFbzZWOwK3goHAQKd19LW3ltamTKmzl9M5YoiAFYMldC45r4GegITDLYzHddlzVZoLm+H70tbAFrY3zpMlD903PSEmuNnL/TEkuhqn8k1JSlZuJWQaOjbTMJQPBPSB0QFFfV68DaJ+SjC1t841uBgCiiNKWOHTShs27AGnz3xmvfx4rCyUsLU+Yu4crU7e0O1Usbe+zc62xZGX0tbpUd9rk+Mb8KWW29u77dkEa46k4fJ+nolQjhrgtfK5WoQzp4RxCZ0TfeuPi+f+8o0MUu60qAt5EC+k1Fb+wTIzE826yw8hBB/KJ4J6QP6rVpVEsteeNl7ZLgSO91avbGAT35uBlCAzn9Qm63jk4dmUC4LGgtmi+TxsxdiHScOl+YaRmEeTldoEyw266CPiAv+7uHf/SpefKU7S+i73/4W4+dx8CkuAnSXvw7jmwXGNYEwEW5T1EqDy+c5ePy86EWcQtZZeAgh/hTi8ywiD4nIGRFZFJGuKEZClhv9UK0q6HMd1wXClNvWVQbbxWJAOLc/A9rCWRPMEVzEJGN4xVCkSLEVWHh88jS2HnwBuw7NAACe9Ci2c+Lbl6yf24p1mCophgkX1ogqwKF9m237jXMtwv6kZUsZw3CbXL7UoyNVPHz3usziBJIUKOpFnEK/TbgJWU4UZXn+BoDtAH67oOMT0lcUHdHtawE0URbpCqCx5dmVlknwlpEqrlydj0zxFoUWCnn6PEcd24XNOhj0Ka7N1rHr0Aymzl/synoRxJY5Y0Epaz7k+UV7QCFgLqzhW4AjK+tq0Aoc5coS3AZwW1ZJThEAAB3kSURBVO2DLi9JXTNsrhFT5y86C4f0wjWkX7LwpIV+22QQKUQ8K6W+CQBisTIQkjX9PkD3KijQ1g9JA7B0MF24nVZhqa7ntk0j2AO7w217jmK4VQY7aJ2ulAVQMIr4LPARKbZ+MPknP3XiNWy59WbrNXcVIjHdP3PX5o1uM2URLCplvcd878U8JnxxnoMo14ssXDN8Jz82QZ1nHuCiJ9xZQL9tMqj0vc+ziDwC4BEAWLduXcGtIYPIoAzQefthuvohyopq8iEVAA/eaW6zj1VMb/doy3UhDXONRZTkevCczurwpVPfjW3dXu3hq+0rUuJYxMNZL8LsvGut0fd6511rAXTfP7fvOWrcz6JSkcU5fO7FvCZ8/ZTZwHfyYxPUQH5jTFFZeLKEfttkUMmtwqCIfAXA2wxffVop9cXWb/4jgH+ilPIqG8gKgyQJWVaI6ncLtgtXPwDmjBq6jyana9ZyxSbLs2353fTb2ywiL0ilLHj/bavxX1656HRDKIvgMx+9A0B3pTwfbIFmQeKkGotrXY+qOvf45Gk8/bXXsaAUyiLYeddaq6tH1pXRsr73++VZcrXD1oe+RFn5lzuuipu/uWMz+4sUTs8rDCqlfjqvfRMSh6SBNeGXajg/bVrrUq/Fg6sfntyx2egzO3dtHrftOWp1GdAFLYDu9GSAn1Vs1GKdDQsPVwo1zYJSztLWQcJCOUo428Q/EC+jhkuM3TJSdd4XT4xvcvpFB0m7rB9sx03VCq5cm7dmO4nqB9O++2E1KKodNv9vX5NTVL7q5Y7reWB/kX6m7902CElLksAa00vVVjgiyRLj45One77M6+qHsMjTYklbml1lnheUcuZnjjofm8gLC9Vdnu4dvsJmZaWElZVy283DJWq1tRloWiPD+addAizshmCzZgqa+aqzEpVRExiX0A3f/ybXl/C9H0cQ98tyvU9+df07V5EXH0FNd4RuTM++hv1F+plCxLOI/CyAfwlgDYCjIjKjlLq3iLaQpU8SC5zppWp7Ofos64ateD5iJGui+iEoFOIuVdcbC9h35Ayuzi8axZPer0mojY81q+kF3RFMvtRZZ9SoNxYBCJ5sLQ9HLdFPnb9oXHlYWSnFEoKubBbHz17IVFTa/IejhK5vAGm4uIqt7fr7KOt7r9Os+axKuYq8xKma6DreciUq7oH9RfqVorJtfAHAF4o4Nll+JAmsiTNoC5pixFUkI8qKp6nN1rssm1mJaR9LZJrsF7YJwf7nznSUrw4LtcnpGg6frLWt2wtK4fDJWlfmCZeVKilBYerav2vlwdYe2z3kug62IL+sRISexEUVOPE9XrAst20bfb2D1z+qiEmvSJruLUpQ26pBDloauV6gJ2tLIe0eWT7kFjCYBwwYJL0ibqCQKwgrbdDRSLWCfQ9szHX5cnK6hk997pTTPSNrdJ/FCWyL8kNPQjBIzyUu45IkMC/rIL8gYVchE7ov4t6z1UoZNwyVjBMom798WEC7fMrzwhXYCiTPZOETMNsvAZP9QJwAY0J6Sc8DBgkZZGwuDnGtjFHf+TBbb+TqD61fXL7CuVISQDor/lUrZayslGKV49b94rJYmqzwNoufyaLp44saTp+nLcC+04iRaqXDXQVIlm93crqGK1e7qzJmkbt3croWKZyB630R18pfbyzg6vwCKiXpyKvtemYUmpOCIsWjbRVg6vzFVDEJcVd5lntA4VJIu0eWFxTPhBiwDeZJlhez8NXN0x86ToGUYOBcuG+A7vRwLouk7jNb/wiu+5ObXD1ML9q4lmmbMHW1KSzOZ+sNrB6u4IahEi7Xr+eYnjh2DrsOzXgJAZvLzOrhCvbe373qENdque/ImUjhnNb/fVEB5VJzMqH7wfXMZGFNz4LwhMw20Yj7DLryVfdLwGQ/0U/5vQmJguKZEAu2wTxu8KGtdPKNK4e8Mj1o9G/iCCef3/pYxk35h13CwEdU6+9804EFg8+islsECfqijgxXoBQ6xJ1vUF+1UsaDd47i+NkLXVbuS3MNVCtlPHz3Onzp1Hc7ipn4WBVtE5j/Xu+2RMe1Wk5O1yILxZhyV+t/f/LQDBadW1+nsaCw6oYhzOy9p+PzQaqE50qJmJXvedL0mYSQ/oDimZAYJFle9NnGx8dU0PRbdeWaTpKb10e8+wbumKx4Whhq39ewUDOlybOJvTdm67GtdkksWrYUZbr8ssmPN1xlzrd9+rxMBNMABtsU5/z1hMOEAO1sIyb2HTnjLZw1tdk6bt9ztOs+H5QleZeAzSqALWmgIiGkP6B4JiQmScWYaxsfH1MFtNO5BbFZZH3T4UUdO6mVMGwhXVCqva9wX+j+0dvYuKXlI2tC+0gnFWbBiUfYQh12/7D5h7tcI6JEmW0CE75mca2WruM+fPc6Z1/FLW2uUYheEehXXC47WVnL0xawIYQUS6noBhBCmuLiwPZNGB2pQhy/s4k2m0XW9lvXsUeqFawerkDQXM73iXifnK5h68EXcPueo9h68IUOi3OQoNA34ToHLS5c1jkt2Cana872mtr/2LOnUZutQ6HphjFbb7RF4FMnXkudIs/V7t33rke1UrZ+H7xmtv3E/Xz1cMW7WqGJkWrF2Wageb0fPTTTvicGAdO10Hm4s0wbGXzmTM+Z6ZkihPQHtDyTZUfWPsNZEbTO2dw4bGm/XBZZ029dx46LzQc368wkQXHh2n+SwKv9z52JtPqnIcqqqNtqSxcYvGZxrZa23++9f2Nku1cPV6wZVPY90NxePx+uPhqkbBK9cjNxPXPMxkFIf0PLM1lWhC2MLktlnN9mjcn6Va2UsfOutcbPoyyy4d9mic3CXBazDT0qM4mJ0UAJ8aDVzkacwKvJ6VqsFHtxWT1c8bLej4+N4jMfvcN6fYO/i7Jahvcb5/dB9t6/EZVy93X8WMsKOz42ihf3fACvHrzPeT2A6FWHfiJ4Xi/u+UDPBWuSVRtCSO+g5ZksK+IEW/U6nVTYyq0zO4StX+HSwEGrmCurh/bj3XVoBhPHzllTzsU9N1ewWzjPb5LMJKZttHCzWehvqlasxwj389y17owWJnxyRgcxZbCIwtfqGXelIOnKQhwrrI/fvu1eYcGQTpiNg5D+huKZLCvivJR6+QIzLdMePlkzWghtQsgldEz73/35Ux3FTpIuDdsCrLR4TJOZ5KZqBSKdgj8c7Lj786c6CnMAwJVr88aS6aZ+8CGcps5FpSyY+MgdicVf0cF1JiHrk4/ZJze0aWWBLgrdMBsHIf0NxbMDWkOWHnFeSr18gWVl5bYJL9P+w4Iz6TG3bVjTlaItmFUjaWYSl6jS5/TGbB0m75DGgvJeTbChdxt+9l1pBYcrJfzz7e8d2HEirZC1XTvAvurAgiHdMBsHIf0NxbMFWkOWJnFeSr18geVt5Y6zn7j+wodP1rqq7j14Z7dojjsZtYmqfUfOdJTDtlUVj7OaEKZaKVv9gl3uCcqZK6X/yXISp/cXdb3zvvcH0QgyaLmxCVluUDxboDVkaRLnpdTLF5itMIjLdzcOcUqEl0SMLg8mTM+JAnD87IX235PTNew7cqbj/Hwmozbx5Jt7eGS4u+9s/TDS6me975UVeyy1KzPGoI0RYWFpu0eSCFnfVQfbcRWQKm83MNhGkKLddwghdiieLTBgY+kS56XUqxeYJTGF9fO42EqEB32eNaaqdjainhPT8r0mSmjGEfwmfvhmt9+zbTXhb93xdhw+eT2LyqW5hrMPxsdGsevQjPG4vRgjsrCmmoSlLSgy7KqUpTXXZclPK3ZpBCGE5AFT1VmIW3CADC79UIxg1pIqzfZ5HMIlsoFmMN/EQ3dg4iN3GFPK+abFinpOonyMXULTlq5vtcGibKKxqLrOwZa27fjZC7FTgxU1RmSVQtG2ahC+G8KuSlmncIxKPZgmRRuNIMRGP4z7ZHCh5dkCAzaWB3GWdfP0ncwrONGnRHYaC2rUcxK1D9f52dxmAHeRlKhzMK0mJOkD3zEi7X0T3v7K1flMrKm2c1NoTips7c3Dmquvye17jhot30lXIJi1gpgYZHce0h9QPFtgwMbSwiZgfIVA3oNtXpM1n/NLIzCinhOX64XP+bncZvQxR4YrmJ1reLkb2EjSBz5jRNr7Jk5qvbjWVFeKQVdqujytubY2CeD0w7c93zSCEBN05yFpoXh2wICNwYxUD+MSML5CIO/BNq/Jms/5pRUYrufE5s+6eriCvfdvTG2p1NfWJJzjnEORk5e429uIa01Nes55WnO3bViDz554retzBVj7zGeCMuhjGMkWuvOQtFA8EytLZWnLJWB8hUAvBts8Jms+55enwMhbvNjEZVnEuwR10nb6PB9p75s4qfXiCv2k1yaviYZOe2jD1hdRExQaQUgYuvOQtFA8Dxi9tAQvlaUtl4B5csdmLyEwaIOtvk9MGRRc5a7zwLTvrO5j27VdVCpxcRZf8naJcW2/eriC4RVDqfsvaREbIPsJUZSV3dZntCKSuNCdh6SF4nmA6LUleKm8lFwCxlcI9NNgGyU8w/eJzqCgA8GKXrY23ce7Ds1g6vxFPDG+Kda+ipzU9MIlxrZ9GpeXLMhjsuUaV1x9NmgTW81ScIkbVOjOQ9JC8TxA9NoSnMdLqYgXRpSA8RECaQfbJOdt2gaAl3+nKQVZVCBYr7C176kTr2HLrTfHuh+KnNT0wiWmFy/54H12U7UCkWaKxF4LClt/Rrng9NPE1pel4hI3yNCdh6SB4nmA6LUlOOuXUpEvjJWVUvu4I9UK9j0Q33KXdLBNct62bYLnoQlPoHwKlxRpcXGlSMuzDHTW5+37fKR9Sef5kg/fZ3GrQGaJrT+jfNcH0Yq4VFziCFmuUDwPEL1ensz6pVTEC8NU4e7q/GIux7KR5Lxt29h8QoOC1HWf9IPFa2S4gktz5uIveZWBzuO8fZ+PoicrLqL8jHsp6NKMN4NmRVwqLnGELFcKEc8iMgHgfgDXALwC4O8qpWaLaMsgUcTyZJYvpSJeGP1g4Uly3kly9voECRbdH5PTNfzwzXnr93lNBPM676jnox8mKy587rNeCrq4400/T0xcDKqfNiGkSVHluf8YwHuUUu8F8BcAHiuoHQPF+Ji5tPAgvCyAYsoZ24pKJK1YloQk5237bqRaMZas3rZhTbtkMtBZZjl4nxRt8Zo4dg6NRVNW5nwngkWdt0u09wM+z16/Crqsy4T3Elvp+X720yaEXKcQ8ayUel4ppc1PJwC8o4h2DCLjY6N4cc8H8OrB+/Ding8MjHAGinlhlEVifZ4HSc7bts2+BzYaJ1DHz15wBgkGU6eZ6JVAconVPCeCRZ130ZOVKEz3WZB+FnT9PjFxMeiGEEKWO/3g8/z3ABwquhEkf4oI7FlQZiun7fM8iHveeim63lhAWQQLSnWlmAtvu+vQjHFfYZEW1/XHJy1enOvpKgmd531QVEaGfl+eD9+bRWbbiEu/T0yiGDQ/bULIdXITzyLyFQBvM3z1aaXUF1u/+TSAeQBPOfbzCIBHAGDdunU5tJT0kl6/MEYdYq2X+J532Ed2Qam2yEsiSsMiLW52Cpe/bhJ/Xh8Rm4cfa1EZGQYhjdqgirh+n5gQQpYuonpoges4sMjHAfwSgJ9SSs35bLNlyxY1NTWVa7vI0sKUbcMn/VVRbD34glXsu3I053GeUW1J01abiE17Hv0YQNaPbVoKDNqzTQgZPETkpFJqS/jzorJtfBDArwD4X3yFMyFJGLQcsEmXovM4z6i2pGlr3BR9Plkx+jWzxaBadvudQXu2CSFLh6J8nv8VgBsA/LE0A7dOKKU+UVBbyBJnkMRLmqXorM8zqi15LJun8WMtOg0f6T2D9GwTQpYORWXb+HGl1Fql1ObWfxTOZGCYnK5h68EXcPueo9h68IVMU2P1UwqrqLbk0dY0WTEGPYCMEELIYNAP2TYIGRjydg2wLUUDTR/kXi5PRy2L57FsnibAjgFkhBBCekFhAYNJYMAgKZqkQXJpWG6BUUkD7JZbP5HsYXAnISRIXwUMEjKoLNcS470kqR8rA8hIGvo14JQQ0n9QPBMSgyJcA7IU7GmsuoMgShlARpKy3CaphJDkFBIwSMigUkRAX1alpbVlrTZbh8J1y1pUwGPS7cjgkGcQ7KDAgFNCiC8Uz4TEYHxsFAe2b8LoSBWCpq9z3j61WQl2l2Utj+3IYMDJUZOsJqkkPZzMkX6HbhuExKTXrgFZ+fImtazRIre0obtCk0Eopb4coO85GQQongkZALIQ7En9tZkCbmnDyVETBpz2B5zMkUGA4pmQZUJSyxotcksbTo6uw4DT4uFkjgwC9HkmZJmQ1F+7CD9vG/SFzJ5+qmpJCH3PySDAIimEkIGARVDyY1BSEZKlD59z0k+wSAohZKChL2R+0F2B9Av0PSeDAMUzIWQgiPKFpPWUkKUBJ3Ok36HPMyFkIHD5QjJXMSGEkF5B8UwISUSvg/dcgW0s5EIIIaRXUDwTQmJThKXXlfWD6a0IIYT0Cvo8E0JiU1Twns0XkrmKCSGE9Apangkhsek3Sy9zFRNCCOkVFM+EkNj0WyGDfirkQgghZGlDtw1CSGz6sWQ301sRQgjpBRTPhJDYsJABIYSQ5QrFMyEkEbT0EkIIWY7Q55kQQgghhBBPKJ4JIYQQQgjxhOKZEEIIIYQQTwoRzyLy6yLysojMiMjzInJLEe0ghBBCCCEkDkVZnieUUu9VSm0G8CUAv1pQOwghhBBCCPGmEPGslPrvgT9XAVBFtIMQQgghhJA4FJaqTkT+GYCfB3AZwLai2kEIIYQQQogvolQ+Rl8R+QqAtxm++rRS6ouB3z0GYKVSaq9lP48AeAQA1q1bd+f58+fzaC4hpMXkdI3FTwghhCx7ROSkUmpL1+d5iWdfRGQdgC8rpd4T9dstW7aoqampHrSKkOXJ5HTNWHb7wPZNFNCEEEKWFTbxXFS2jXcF/vwwgLNFtIMQ0snEsXMdwhkA6o0FTBw7V1CLCCGEkP6iKJ/ngyKyHsAigPMAPlFQOwghAd6Yrcf6nBBCCFluFCKelVIPFnFcQoibW0aqqBmE8i0j1QJaQwghhPQfrDBICGmz+971qFbKHZ9VK2Xsvnd9QS0ihBBC+ovCUtURQvoPHRTIbBuEEEKIGYpnQhKwlNO5jY+NLplzIYQQQrKG4pmQmITTudVm63js2dMAQNFJCCGELHHo80xITJjOjRBCCFm+UDwTEhOmcyOEEEKWLxTPhMTElraN6dwIIYSQpQ/FMyExYTo3QgghZPnCgEFCYsJ0boQQQsjyheKZkAQwnRshhBCyPKHbBiGEEEIIIZ5QPBNCCCGEEOIJxTMhhBBCCCGeUDwTQgghhBDiCcUzIYQQQgghnlA8E0IIIYQQ4glT1ZFlweR0jXmZCSGEEJIaimey5JmcruGxZ0+j3lgAANRm63js2dMAQAFNCCGEkFjQbYMseSaOnWsLZ029sYCJY+cKahEhhBBCBhWKZ7LkeWO2HutzQgghhBAbFM9kyXPLSDXW54QQQgghNiieyZJn973rUa2UOz6rVsrYfe/6glpECCGEkEGFAYNkyaODApltgxBCCCFpoXgmy4LxsVGKZUIIIYSkhm4bhBBCCCGEeFKoeBaRT4mIEpG3FtkOQgghhBBCfChMPIvIWgD3AHitqDYQQgghhBAShyItz08C+BUAqsA2EEIIIYQQ4k0h4llEPgygppQ6VcTxCSGEEEIISUJu2TZE5CsA3mb46tMA/imaLhs++3kEwCMAsG7duszaRwghhBBCSFxEqd56TYjIJgB/AmCu9dE7ALwB4P1Kqb9ybbtlyxY1NTWVcwsJIYQQQshyR0ROKqW2dH3ea/Hc1QCR7wDYopT6bx6/vQDgfO6NInnxVgCR15kMPLzOywNe56UPr/HygNfZzq1KqTXhDweqSIrpBMjgICJTphkcWVrwOi8PeJ2XPrzGywNe5/gULp6VUrcV3QZCCCGEEEJ8YIVBQgghhBBCPKF4Jr3kd4puAOkJvM7LA17npQ+v8fKA1zkmhQcMEkIIIYQQMijQ8kwIIYQQQognFM8kN0TkIRE5IyKLImKN5BWRD4rIORH5lojs6WUbSXpE5GYR+WMR+a+t/6+2/G5BRGZa/x3pdTtJfKKeTRG5QUQOtb7/mojc1vtWkrR4XOePi8iFwPP7i0W0kyRHRH5fRL4nIt+wfC8i8lute+BlEfmJXrdxkKB4JnnyDQDbAfyp7QciUgbwrwH8DIB3A9gpIu/uTfNIRuwB8CdKqXehWQDJNgGqK6U2t/57oHfNI0nwfDZ/AcAlpdSPA3gSwL/obStJWmKMwYcCz+/v9bSRJAv+PYAPOr7/GQDvav33CIB/04M2DSwUzyQ3lFLfVEqdi/jZ+wF8Syn1baXUNQD/AcCH828dyZAPA/iD1r//AMB4gW0h2eHzbAav/TMAfkpEpIdtJOnhGLwMUEr9KYCLjp98GMD/rZqcADAiIm/vTesGD4pnUjSjAF4P/P2Xrc/I4PCjSqnvtv79VwB+1PK7lSIyJSInRIQCu//xeTbbv1FKzQO4DOCv9aR1JCt8x+AHW8v5z4jI2t40jfQQvotjUHiRFDLYiMhXALzN8NWnlVJf7HV7SD64rnPwD6WUEhFbCp9blVI1EfkxAC+IyGml1CtZt5UQkjnPAXhaKXVVRH4JzdWGDxTcJkIKg+KZpEIp9dMpd1EDELRivKP1GekjXNdZRP4/EXm7Uuq7rWW+71n2UWv9/9si8h8BjAGgeO5ffJ5N/Zu/FJEhADcB+H5vmkcyIvI6K6WC1/T3APxGD9pFegvfxTGg2wYpmq8DeJeI3C4iKwD8HABmYhgsjgD4O61//x0AXSsOIrJaRG5o/futALYC+POetZAkwefZDF77jwB4QbF4wKAReZ1Dvq8PAPhmD9tHesMRAD/fyrpxN4DLAXc8EoKWZ5IbIvKzAP4lgDUAjorIjFLqXhG5BcDvKaU+pJSaF5FfBnAMQBnA7yulzhTYbBKfgwA+JyK/AOA8gI8CQCs94SeUUr8I4H8C8NsisojmpP2gUoriuY+xPZsi8msAppRSRwD8OwD/j4h8C81gpJ8rrsUkCZ7X+R+LyAMA5tG8zh8vrMEkESLyNIC/AeCtIvKXAPYCqACAUurfAvgygA8B+BaAOQB/t5iWDgasMEgIIYQQQogndNsghBBCCCHEE4pnQgghhBBCPKF4JoQQQgghxBOKZ0IIIYQQQjyheCaEEEIIIcQTimdCCFliiMjfEJEvtf79gIjscfx2RET+YYJj7BORf5KmnYQQMohQPBNCyIAgIuW42yiljiilDjp+MgIgtngmhJDlCsUzIYT0ASJym4icFZGnROSbIvKMiAyLyHdE5F+IyEsAHhKRe0TkqyLykoh8XkRubG3/wdb2LwHYHtjvx0XkX7X+/aMi8gUROdX6739Gs8jNO0VkRkQmWr/bLSJfF5GXRWR/YF+fFpG/EJH/DGB9D7uHEEL6BlYYJISQ/mE9gF9QSr0oIr+P6xbh7yulfqJV2vxZAD+tlLoiIv87gE+KyG8A+F0AH0CzQtghy/5/C8B/Ukr9bMuKfSOAPQDeo5TaDAAicg+AdwF4PwABcERE/jqAK2hWENyM5rvjJQAnMz5/QgjpeyieCSGkf3hdKfVi69+fBfCPW//WYvhuAO8G8KKIAMAKAF8FsAHAq0qp/woAIvJZAI8Y9v8BAD8PAEqpBQCXRWR16Df3tP6bbv19I5pi+i0AvqCUmmsd40jy0ySEkMGF4pkQQvoHZfn7Suv/AuCPlVI7gz8Skc0ZtkEAHFBK/XboGI9meAxCCBlY6PNMCCH9wzoR+cnWv/82gP8c+v4EgK0i8uMAICKrROR/BHAWwG0i8s7W73bCzJ8A+AetbcsichOAH6BpVdYcA/D3Ar7UoyLyIwD+FMC4iFRF5C0A7k9zooQQMqhQPBNCSP9wDsA/EpFvAlgN4N8Ev1RKXQDwcQBPi8jLaLlsKKXeRNNN42grYPB7lv3/bwC2ichpNP2V362U+j6abiDfEJEJpdTzAP4QwFdbv3sGwFuUUi+h6T5yCsAfAfh6lidOCCGDgigVXiUkhBDSa0TkNgBfUkq9p+CmEEIIcUDLMyGEEEIIIZ7Q8kwIIYQQQogntDwTQgghhBDiCcUzIYQQQgghnlA8E0IIIYQQ4gnFMyGEEEIIIZ5QPBNCCCGEEOIJxTMhhBBCCCGe/P8COQ5y9uz92wAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 864x2016 with 5 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhSMWX6bsU7x"
+      },
+      "source": [
+        "We are finally going to compare the multichannel model with the standard PLS modeling cognition and brain volumes jointly:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BDunWuRVI4bt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "63a56ccf-a988-4fd3-97e3-c0b808429b2c"
+      },
+      "source": [
+        "plsca = PLSCanonical(n_components=1, scale = False)\n",
+        "plsca.fit(cognition_value,volumes_value)\n",
+        "print(plsca.x_weights_)\n",
+        "print(plsca.y_weights_)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 0.40598888]\n",
+            " [ 0.46651653]\n",
+            " [-0.40601934]\n",
+            " [-0.4190897 ]\n",
+            " [-0.34498463]\n",
+            " [ 0.05629512]\n",
+            " [ 0.39352756]]\n",
+            "[[-0.40713321]\n",
+            " [ 0.31733175]\n",
+            " [-0.50182736]\n",
+            " [-0.47646877]\n",
+            " [-0.50466814]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ua7rHRiksjsx"
+      },
+      "source": [
+        "We obtain predictions from the PLS model and compare them with the predictions of mcvae. We observe a strong correlation between predictions. However, as we increase the number of dimensions of CCA, the difference between prediction increases, as CCA tends to overfit. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C9NRFaX5ssyN"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "O4SFSHCgI4bv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "outputId": "ddd10aec-86b0-46cd-aa5c-080a6e7acd8f"
+      },
+      "source": [
+        "predicted_plsca = plsca.predict(cognition_value)\n",
+        "\n",
+        "plt.figure(figsize=(16, 18))\n",
+        "\n",
+        "for i in range(5):\n",
+        "    plt.subplot(10,2,2*i+1)\n",
+        "    plt.scatter(predicted_plsca[:,i], volumes_value[:,i])\n",
+        "    plt.title('reconstruction' + volume_cols[i])\n",
+        "    plt.xlabel('predicted')\n",
+        "    plt.ylabel('target')\n",
+        "       \n",
+        "for i in range(5):\n",
+        "    plt.subplot(10,2,2*i+2)\n",
+        "    plt.scatter(predicted_plsca[:,i], decoding_volume_from_cognition[:,i])\n",
+        "    plt.title('reconstruction' + volume_cols[i])\n",
+        "    plt.xlabel('cca')\n",
+        "    plt.ylabel('mcvae')\n",
+        "    \n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1152x1296 with 10 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "M5DYp-_hI4bw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bc5248e1-16f9-472d-e88a-eb3a0d5219b2"
+      },
+      "source": [
+        "# Comparing average reconstruction errors between models\n",
+        "\n",
+        "print('Reconstruction error:')\n",
+        "print('CCA: ' + str(np.sum((predicted_plsca-volumes_value)**2)))\n",
+        "print('mvae: ' + str(np.sum((decoding_volume_from_cognition-volumes_value)**2)))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Reconstruction error:\n",
+            "CCA: 4682.308721283598\n",
+            "mvae: 3854.593484495343\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "33w3KIjHtGyl"
+      },
+      "source": [
+        "It is interesting to notice that mcvae leads to a lower reconstruction error than CCA. This may be due to the fact that the prediction of mcvae benefits from training from other modalities as well."
+      ]
+    }
+  ]
+}
\ No newline at end of file