diff --git a/smartmeters-ANP-RNN-mcdropout.ipynb b/smartmeters-ANP-RNN-mcdropout.ipynb
index 8cb958e..2c6e7da 100644
--- a/smartmeters-ANP-RNN-mcdropout.ipynb
+++ b/smartmeters-ANP-RNN-mcdropout.ipynb
@@ -14,8 +14,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T06:52:03.606115Z",
-     "start_time": "2020-02-16T06:52:01.153439Z"
+     "end_time": "2020-02-16T09:00:30.092975Z",
+     "start_time": "2020-02-16T09:00:27.666631Z"
     }
    },
    "outputs": [],
@@ -44,8 +44,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T06:52:03.654891Z",
-     "start_time": "2020-02-16T06:52:03.609144Z"
+     "end_time": "2020-02-16T09:00:30.146529Z",
+     "start_time": "2020-02-16T09:00:30.096030Z"
     }
    },
    "outputs": [],
@@ -60,8 +60,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T06:52:03.704343Z",
-     "start_time": "2020-02-16T06:52:03.657808Z"
+     "end_time": "2020-02-16T09:00:30.199286Z",
+     "start_time": "2020-02-16T09:00:30.150700Z"
     }
    },
    "outputs": [],
@@ -76,8 +76,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T06:52:03.821105Z",
-     "start_time": "2020-02-16T06:52:03.707186Z"
+     "end_time": "2020-02-16T09:00:30.310063Z",
+     "start_time": "2020-02-16T09:00:30.202401Z"
     }
    },
    "outputs": [],
@@ -94,8 +94,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T06:52:03.884144Z",
-     "start_time": "2020-02-16T06:52:03.826825Z"
+     "end_time": "2020-02-16T09:00:30.360614Z",
+     "start_time": "2020-02-16T09:00:30.312650Z"
     }
    },
    "outputs": [],
@@ -117,8 +117,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T06:52:12.710878Z",
-     "start_time": "2020-02-16T06:52:03.886598Z"
+     "end_time": "2020-02-16T09:00:39.230592Z",
+     "start_time": "2020-02-16T09:00:30.363005Z"
     }
    },
    "outputs": [],
@@ -131,15 +131,15 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T06:52:13.433355Z",
-     "start_time": "2020-02-16T06:52:12.713575Z"
+     "end_time": "2020-02-16T09:00:39.842350Z",
+     "start_time": "2020-02-16T09:00:39.232526Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f327ab4b470>"
+       "<matplotlib.legend.Legend at 0x7f54f3a23a90>"
       ]
      },
      "execution_count": 7,
@@ -191,8 +191,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T06:52:19.284380Z",
-     "start_time": "2020-02-16T06:52:19.220689Z"
+     "end_time": "2020-02-16T09:00:39.896626Z",
+     "start_time": "2020-02-16T09:00:39.845286Z"
     }
    },
    "outputs": [
@@ -223,11 +223,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T07:16:27.209738Z",
-     "start_time": "2020-02-16T07:16:27.136390Z"
+     "end_time": "2020-02-16T09:00:40.074950Z",
+     "start_time": "2020-02-16T09:00:39.899504Z"
     }
    },
    "outputs": [],
@@ -331,11 +331,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T07:16:27.430570Z",
-     "start_time": "2020-02-16T07:16:27.377936Z"
+     "end_time": "2020-02-16T09:01:17.405062Z",
+     "start_time": "2020-02-16T09:01:17.348144Z"
     }
    },
    "outputs": [],
@@ -418,11 +418,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 28,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T07:27:29.632597Z",
-     "start_time": "2020-02-16T07:16:32.547803Z"
+     "end_time": "2020-02-16T11:15:25.833579Z",
+     "start_time": "2020-02-16T10:55:26.906110Z"
     },
     "scrolled": true
    },
@@ -480,14 +480,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "step 0, {'val_loss': '1.5792388916015625', 'val/kl': '0.500720202922821', 'val/mse': '0.27271074056625366', 'val/std': '1.0320733785629272'}\n",
+      "step 0, {'val_loss': '1.547433614730835', 'val/kl': '0.5002708435058594', 'val/mse': '0.258502334356308', 'val/std': '0.9986149072647095'}\n",
       "\r"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "608b592f59e6418f8cca7486a1557fc2",
+       "model_id": "62d9177407374e1d9157858532ab1736",
        "version_major": 2,
        "version_minor": 0
       },
@@ -514,7 +514,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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1a+f0eCklt956KxUVFRw6dIjY2K5eXXRnNBoJCXHPDHs8riw+rqmsDCaf8SVacDRvE0gdl7cIPoeKAQMGkJOTw9q1a+s74JKSEvbv328ztEN1dTWjR49m5MiRREebhcDq1T/xn//cyJdfftik+kyadDnDh3dh3bpV9dtWrvyGjIy5VFTY9ksNqoZaELY8UnzF8dhxOUOv9137Ho/t2ZyCIJDaMywsjE6dOjXY1rVr13pX0tatWzfYFxMTw5dffklpqVpvonHo0D6WLPmQyMgorrrqBo/rc+TIQXJzc2jduk39thdfnMK+fbsYNuwsevfu3+icoCBoIdTU+D5UgaYOadUqsF40fyAl5OX5zu3xeGzP5nRYCPT2TElJobKykoqKikaCAFTbWQoBgOHDR/LyywtIS2vcUbvDN9/8xbFj2XTo0Ll+24UXXklRUX6TDdHeJhhiwg3q6tRyfV8b5Q4dUsvaBwxQS/htPL/HLWVl3vEQskfnzhAR4bvy/U1trVpk11zExKiFfYHAF198wU8//cTVV1/NBRdc4PT48vIKCgoiESLcD7VzjYgI9Yz6gmCICS9RUOAfz4yiIjUrOHYs8Edc3kRK30TItL7G8YQnaqG1a3/hiiuGs3+/a8lSWgqrV69m3rx5bN++3aXjH374SVJTo1i06C0f18x1gsbiAKeszD+62Koq81S/rOz467gcUVbme0F7PLWnTufZorHJky/n77//4LXXnmpyHQKpPcePH89bb73FqFGjnB5rNEJRURlSStq2behhVFtby48/LmX58k89rsu2bZu4++5reffdVxtsNxgM5OfnUlRk2wgWFAQBjMHQWI9ozdKlGaSnd6dnzxDS07uzdGmGW9c4dkx7OM3bpAysF82X+CpevjUtvT2NRqUO0uk8t6V8+ukq3nrrCx555KUm1yeQ2nP48OHceeedDBw4sH7byy+/zMknn8xHH33U4NiqKpgx4z127arhggvGNtin09Vy++1X8NhjkzyuS1bWPr777nM2blzTYPvChbM57bQk3nzzeY/L9gVBY7ELlJQ4fuCXLs3g8ccnU12tApcfOZLF449PBmDs2Ak2zykqUg9jcjJkZ6uPtSroRIkfL6VaEOWP3xtIHZcnlJc7H5Q4o3//U+jf/xSv1CfQ27OwsJBt27Zx+PDhBtsrKtTfCBsGo9jYOEaPHkt8fFuP1gIAnHLKcN5441MSEzs22J6Y2JGEhETCwmzbJYL5CFzE38ZivV4FN3NEenp3jhzJarS9S5cUMjMP2jxn9271Qick2DeOtm4Nw4f7Lj55oODNmO/OaN9eeWO1RKRUAwZvqc8MBgM1NdXExrqXUECrR9euKq6Rlcdms/HNN98QGhrKueeeS3R0NACHDh2iuLiYrl27kmB6kQwGVf9AIj9fPZshIdCtm2+uETQWNwFXOqicnENubdfpzKM6Rx4yRmPgj7iaSm2t/4QAtOz2rKxsuhDQ6/XMnTuDiRMv4JRTEjxSUVRWqsFRoNmwbrvtNsaMGUOxxZSpW7duDBo0qF4IgJrhHzq0nyuuGM60afc1R1UbUFMD+/bBP/80nxYgqBpyQHm5eQrpiM6du9mcEXTu3FC0798PXbq4vlgqkF4yX3CiRgn1FG8kScnPP8aMGY/W/5+V5X4aR81AvW+fcm8OFEaPHk1ubm6DTh+U8KyqgvBwNeiorlaC4O+//yAyMspmWXV1dZSVlRAT08ruMY5Yt24Vx45lM3RoOsnJKfX1CA1tfGx+vvpbWAjdu7t9Ka/g0xmBEOIiIcRuIcReIcSjNvZ3E0L8IoT4SwixRQhhP1asnykvd33h2EMPTW+0QCQiIpKHHjKnYC4oUMa9rVtVlEhXON5nBCUl5uxh/qKltqdmJG4q4eHh3Hrrv7n11n/z+++HefvtL9wuQxMEOl1gteeHH37IDz/8QFSUueM+fDibhx56kpkzX+PYMbPn3+DBp/P555k8/PCLNsuaPHksQ4Yk8ttvP3pUl4yMt3nwwYn89NMapFR5MzZsgD17arn88hFccMFA6urM9jGN425GIIQIBeYAFwDZwHohxDIppWU+uCdQSe3fFkL0A74DuvuqTq5iMLi3qEkzCM+aNbV+ZjBmzNVcdNEE8vMhMVE9CFrZmZkZLF48lYKCQyQmdmPcuOmkpzc2Kh/PXkM1Nf5VCWm01Pa0TsHpKe3bJ/HEE6+4fV5lpVo8BmZB0BLact++Iv773+fp02cAt976YP322Ng4hg490+55bdu2o3XrNuh0nknfoUPTKSkRRESksXGjOXdGYWEEO3duQq+vZf36KpKTYxrcW23w5+/AiL5UDZ0G7JVS7gcQQnwKXA5YCgIJaOtm4wEfptl2HWdeQrYYO3YCY8dOYNGit5g7dwZff/0xa9dmcvXV07ngggn1o7nMzAzmz5+MTqc8jAoKspg/X3kYWQuDlvCiOUOna7iSV6dTI1tfBJRzhZbapr6KLlpXV8fUqXewe/dWPvtsNeHhtr1ZcnPVfYyPbzhqDRTPNr1ej16vJzo6uj4lZGEhtGmTzAMPTGukpnXGyy8vaFJqyTFj7qFPn3uAhgmUhBA89dQqoqPjMRojGxmttcGfvwWBL1VDXQBLn61s0zZLpgEThRDZqNnAvbYKEkJMFkJsEEJsyLecR3mIpdHNYGh4o3Q61+wCtnjttaeZPv0/5OQcQkpJbq7q5H/6ybymYPHiqfVCwHzNKhYvntqovJY+I9DiBlWZfq5er8IhFBY2T+x8rU4tEW/NCHJyDpOVtY/aWrVqMTQ0lLVrf+bvv/9g375dds8rK1OeNtbhLJrrPlrz+++/06pVq/rFZEVFSr3bpk0C99//NNdcc3P9sVJKpky5mc8+e59aOxLWHSGwbt0qbr31EmbPns7mzSpEjCNPw9TU0+nSpS8hIY0NBs0lWJvba2g8sFBKmQyMARYJIRrVSUo5T0o5VEo5tH379k26YG2t0tcfOaI6pCNHlP9+XZ3qqPLyPC979uxn618wDetOvqDAtidRYWHj7S3dRlBaqoSsNvq3zDncXDT39T3FWzOCN954jnPPTW2QN/fpp2ezePFvdpOm6PXm1e7WKtNAEQRVVVVERkYSFxfnNE3nli0b+OKLhcyY8YhXEsqXlZXw88/fsnbtWqqrVTyynJwcamoqbYa/dkRzDf58qRo6AnS1+D/ZtM2SW4GLAKSUa4UQUUAi0ITu2D5GoxICWmNrXhiWaf588WBbdvKJid0oKGjsYdSuXeOpa0ueEVRVmW0AOp0yiAVCWs+W2J51dfbzM7tLbGxrunRJoVMn86s5apTjjFyOOtVAac+LL76YmpoaysoMjZw88vOPsXnzelJTT6J791TS0voxc+b71NbW2FxQBvDHH78xa9ZUBg8+nalTZ9m97oYNW0lMPJ1XX/0KgyENUDOO++5Loa7OwIcf1hAeHtngnE2bvmHXrtUMG3YFaWnDG+xrrnfelzOC9UCaEKKHECICuA5YZnXMIeA8ACHESUAU0HTdjw3KytTo356Xik7XdCHQpUuKze2Wnfy4cdOJiLD2MIph3Ljp1qcFjP7VXYqK1MzK8oH2RzY3VwiUjssdvKUWApg6dRaZmQeddv6WOHJbDZRn1GhUM/uiorBG93jOnBeYNOkyvv9eeUjFxLTimmtuZuLEO+2WV1NTzYYNmezcudnuMdnZBxk3bjCPPnoFHTpcQteuKmy1Xl9DXFx7YmMTGgkBgC1bfmT58pns3fuHzd9xXM0IpJQGIcQ9wA9AKPC+lHK7EOJZYIOUchnwH2C+EOJBlOH4JumDpc46nW9DG2s89NB0Hn10MrW1ZhuAdSefnj6Bw4e3s2bNRxQVZdOu3fHlNWQweBYIzV+0tPYE36ehNBqNfPLJPHbv3sa0abMbhVRwNiMIhPSflZX2Z5xDh6bzzz/bSUpKdrm8wYNP45NPfqVjR/sxoffv30Pr1ol06NCL0FBzVxoREc3bb9v3exky5BLatOlE794jGu0LhphwEU9CTBQV+a9zev31DD76aCqFhYdsdvJGo5EJE5SRSAjB00+vpk8f+25sZ52lFqG1FEpKms8jyBVatVJL+VsK/sqBMXx4F3Jzc/j55z306JFGYaGy6URFqevbY+BA6NtXhUZoTp54YiarVv2P2277N+ecc5Hd45599gESEtozYcIdjaKOuovRCL//XktVVRk7d67i6NE9jBo1idatPX/A0tLg5JN9kzPDUYiJ435lsZT+UUtkZ2cxalRvwsKief99+z1hbu4+QkPDqavTI6WkqMhxIKMWJqc99rjyFy2hPQ0G1fGHh5ttV94gP/8YF188iD59BpCRsZLqahXWoG1buOKK/1BdLTh4MJ7ycrOnlzMCQTWk08HWrX+TmfkTV111o93jSkuLWbRoDkajkeuu8zyyqEZFBYSFRdK6dXuWL3+Zffv+5KSTzmmSIDgejcUBQVWVv5LJ5KPX6zCYrHqvvXYV27f/wvXXz2TUqNvqj+vUKY2PPtKxbt0XRERE0bfv2Q7LDRSvDFeorvaeUdNXBLogqKtTum6DQalbvFnfvLyjFBbmUViYj5QqRERVlfqcc86/649zVQhAYKgvy8vhvvue5sorb6Rv35PtHldRUc5LL71LXt5REhOdp1VbsOB1Cgvzuf/+pxutr/jttx9JSRkJqO0jRoynX79zad26A9u2reTjjx9h8OCLufba5xqVW1tbxaFDW5DS2Eg9FBQEPsJfI9TU1H7MmrWc7Gyl0N29+3cqK4uZP38SX331fL2dwHpFcUyM4zyUgTDicpVAMQg7ork7LXtUVKjOv7LSLEy9Xde+fQeyatURCgsrycryzrvR3ILAaFRt1qtXH3r16mP3uBdemML8+bN47LGXufPORtFubPLmm89TVFTAjTfeQ/v2SfXby8pKuPHGC+nUqRevvPIPQgjGjHmgfv+ePb9z4MBGunQ5yWa5ubn7eOqpM+jS5SRmzdrRYF8web0P0On8k1UMICYmhsGDL6FLF7V6uLLSbJ0uKMhi7tybEUJgMOjqt9lbUWxJSxEEUro3kmwuAlEQ1NX5Z41FaGgoFRWdqa1Vsw5LpJQcOLCJQ4e2cM45N7nsX9/cgqCiwrV3pGfPPsTFxVNT43qHcPPND2A0GomIMHv+6HSwfftRunbtTVRUW5vtdOqpl/Lcc38QHW17kNemTRI9ew6lU6fejfY1l9fQcW0sLijwr85661Y1Orn33u421wrYIjY2gfnz7Ue3O/VUZUAKdKqrXQ+m5wlLl2Ywa9ZUcnIO0blzNx56aLrdpD+2KC5WL1hSku+Sg3uKv5wZyspgxw7b+6SUTJ6cSEVFEW++eYh27braPtCKPn2gf3/fGDddITtbzaDmz3+FyMgoxo27jcjIxi6ber2e0NBQj5LMVFaqZ9tobCiw6+oM9d5Cen0tBQVZ1NUZSE7u5/Hv6dYNBg82x3XyJieksbiuzr+qil9//Z7nn3+abt0G2V09bIuKCsd+rS1lRuDL2YAnGeAsKSiAvXuVB0xSktPD/YrR6L/Byrvvzmfnzr8455ybSE09rcE+IQTDhl1JbW0ler3r/qrN+XxqarS6ujpmzHiEuro6u0ZgezGUXOHYsYYRQjUsXUZ3785k+vTz6dv3bJ5+epXH1wraCLxMRYV/G3TdurXs27ee/Pwsu6uHbREb69iFLSgIVFRXTQhoVFdXMWvWVKeCQAsBrH0PtAlwaanv73FhoerI1q37nvXrv6J//5GNBAHA5Mnz3S67udpUSrObsl6v4777nqKgIM/uSmFPKCoqICvrAEePdqSo6CglJbn07DmU+PgODYQAQLt2XenQoScJCV1Ys+YTcnP3cfrpV9m1E6jfIJFSNpilBG0EXsafswGDAXr0OItBgy6iU6fe9Op1WoMIo/aIiIjhxhtfd3hMSxAE1dW+9W5yNwOcJXl55gVZgSYI/BGKu7ZWuYgCXHjhvfTrN5KePW1qBzyiudq0rMwcJSAqKpr77nvK69dYsOB13nzzea66ahp5eftYvXoRY8c+ztKlL5CWNpxp09bUd+KdOvXm9ddVkp+XX76MTZuW063byXYFwWuvXcXGjct59NEVDBgwqn57cEbgRQwG7y7Ld0RNjUbJHoYAACAASURBVLINpKRcwKOPXtBg3/vv30V1tW3lb0hIKJMmzXNoKIbAFQR6vfJzB9/rt+1lgAsJCWHp0oz6WUFpqVIDJSerXLq1tebZAARWED8t7pWvsYy707//SPr3H+nweINBR07Obrp1s++GaUlzdFyWgQx9SUpKL3r1GkLr1onExLRm9epFLF36AqBcQO3ZG0aMGO9QCAAIEUpdnZ6Kiob2waCx2EVcMRaXlfknpASoVZeHHAxMMzMzmDv3ZurqzEGOIiJiuOaaZ4mIiCY+viOnn36V3fP79VMrDZt7Cb8ler0ynnXpogSudWhib2NtI7AkKiqGMWNuZPXq78jPV265N9wwnTvumMCuXQ1nhiEhMHy475KDu0NxsX8S82zZ4rraThmM21NRUcjcubnExzv3te/eHQYNAlOueL9g7QSyY8dmQkJC6NEjzaO0krYoKVGGaMvrbN78A6+8cjnp6RO59NKHbXr96HQqyJwzr6uqqlLCwiKJiGhY36QkGDIEWjv2KveIEy55vT/dGLWRya5dmfz441vs3bu+wf709AmcdtqVgHowEhNTmDRpHoWF2SxYcDcffni/w/IDUU5rPu8lJf7pzMaOnUC3bv3Q2tCSmpoqliyZS35+FiApKMjizTcn8+abGY3Ug4EyI/BXPCZtsRhATU0lmZkZ7Nq12u7xQgiSk/uTlJTGO+/cwhNPnG4zMJol/m5TW/lCZsx4hIsvHuhxWklr6upgz57G1xkw4DzeeSefyZPftSkE5s+fzE03xTBtWrrTa8TExDcSAnDi5iPwOt7K7eoKli/0F188zYIFd/PRR/9ucMznn0/jr7++A2iQlrJTpzSiouJITLQdsVQjUDovDSnNL0hpqe+FblER/PFHBbt3b0DFJbRZqwb/6XRVfPxx40Q/EBiqNk8y4LmCpZrGaGw4U8vPP8icOROZP99xaIUnnljJa6/tISIimn37/iQ//6DL1/QHxcWNt3Xu3I0ePXrTs6f9BWXuUFRknYWtjrKyAkJCQomOjrN73oAB5xMXl8iwYVd6fO2gjcBL+Ools4XlaLhdu65ERcXRsaM5uUdmZgZLljyL1lEVFByqX0Q2evRdjB59l9NrBIqBs7ra/HL4K+xFVRUsWJBBRsYjbp9rK9EPNF9OWFAqDV8Z1o1G2L1bqcLi4lTYBctwH2FhEZxxxnW0aePYf1bzhrniiicYM+ZBOne2r+cG/z6ftbW2F4i++OI8r15HcxUtLc3j8ceH1McD69ChB6+/vt/ueWeccS3Dh1/TQA1sj4MH/2bp0hfo3LlPgzAUQUHgIo5GdDqdf0MgWxqs7rxzIXfeubDBfpWZrPFodfHiqU6NxBqBMCOQUhkd/RlHyGiEd9/NYN48Z95XAlszBVuJfrRym0MQVFX5dr3Arl3mZ9/WqLlTpzTuu+8Tl8tLSRnk0nH+fDb9oYasqTG3Y3R063ohEBMTT1xcotPzhRCEhTl3Ya2pKeePPz43xRoyC4KAzUdgSh05COgMVAPbpJQ+ySDmClpkxlCrdJ9aZ+UvKipsv3CWuJOW0h7NLQRAvRj+FAJSqgVgixY1zu8MyuNKSiPt2nXjlFPGsGrVBw2Os5foB5rvRXP2rDQFfw+ALPFXe+r1ttWQtbW1hIeHe7Ri2BqdDg4eNP8fERHFG29kEReXSGRkDHV13nsJkpP7c++9nzRSDQfcOgIhRC/gEeB84B9U5rAooLcQogp4B/hASul3rWtJCbSzWIel16vpnL9cRnNz1QPj7KY5Skt5+PB2Hn74ZEJCQsjIsP+ANddUsa5O+eCHh/s/htCBA0pPa0+QSmnk44/Nj13v3meycOF9VFYWERvbjhtvfN3ujKs52rO01H5mPG+V74yKimIiIqKIiHDNvaewMJs1az6mVau2nHeefbuCvwSBPUH3wQdv8NprT3HvvU9y112PeVx+UZEafFhrHBITzTNL60VkTSE2NoERI65rtL25BiqOxOjzwEdALynlhVLKiVLKq6WUA4HLgHjgX/6opDXl5cpts6xMdVY5Of4TAmVlqqOyvll3392N8eND+OKLZ+q3OUpLGRfXDpAYjY4Vxv7suCwX6RQVKZ2sq0G9vEVpqbqn0PAltERT+2gB/MLDo+qD/KWmnu40iJ877VlTo3T7nuZbLi/37WwAXJsNvP/+ndx4Ywxr1y52qczi4hw++eQRVq6c6/A4fzyf1dX202Xm5h6hpqaaVq3sG3FdKX/fvsBwJAg4G4GUcryDfXnAf50VLoS4CHgdlaryXSnlS1b7XwO0FS4xQAcpZRsX6u239JOW1NWpB8Z2faoASUiIWWeldUiLFzfOWGY0Gnn11T3ExbW3KgemTVPJQh56yPcPhaZmq61V7RkaCm3aNF9I6SMWeXrGjZveaIV2REQMJ598PhMmhBET04b58wvYvHlF/f5+/c51WL4rL1ptrepcLQ3kFRWQkOC6f7cWAsEfem1XBIHRWEdoaHij580eHTr04P/+7z8kJTmOeOjr51Ob7dvjySdf4/77p7kcLdUazVXUngF/5cp5vPvu7YSGhvPoo98zYMB5Hl3HFhs2LKOg4CDp6f8iNrYtEICCwBIhxAigu+XxUsoPnZwTCswBLgCygfVCiGVSyvr4h1LKBy2Ovxc4xZ3K+5sDB+y7pr722l7Ky/OJj+/YYHt6+gSbI9SQkBA6dWr8km3bpq5z4ID6npLiuwejpkapueLizOofLSRyc1BW1rBTsydI4+IS+eWX96iuVr3sgAHnk5e3nx49TuXSS6c4vIajGYG22teeKqyoCMLCnEeG1Bbc+cOuUlvrmrv0Aw98jtFoxL4LbkNat27PxImznB7n644rP9/5SL1163iPyz940HGo+n/+WQtAXZ2+QS5yb/DFF0+RlbWZPn3S6wVBc81KXDEWLwJ6AX8DmtyUgENBAJwG7JVS7jeV8ylwOWAnEC7jgaddqHOzcPiw45AAsbFtiI11aTLjEMtF08uWwZgxvnvRiopU2YGQbN4yOJwltgSpTlfDE0+sJC1NZXcaMWIcI0aMc/k6ttDplErKWeddUACdOpnDa1hTV+c/IQDu3TtvGFStcaZqq6lRbSulEqDuBAEtK/Otyjc/3/FsA+Dcc2+hffsetGnTibS0M7x6/dNOu5revc8kJsYsyAJ5RjAU6Cfdj0XRBbB8tbOB020dKIRIAXoAP7t5Db9QWtpQZeENjEZ46qkzKS7O4sEHvyI5eRgREbBxo9ofGqpmBDt2qJwE3qKuTo1Ya2v9Z1dxhX377OuBrYmIiKK4+Cj/+U/f+mxvV1/9DElJqZSVFTBs2OV2z7XVcel0KtSwK6MxbaFWmzaN1URSuiZMvIWUzjuyppCfn0VRUTZduw5o0FlZYk8QGAxqZmk52i4uVjGgYmPVx5E2x9V4Qg88MAGDwcALL8xza2Zgb+BhTd++Z9G371kul+sOV175RKNtgWgs1tgG+DqK+3XAF1JKm5o6IcRkIcQGIcSGoiIfPvl2yM52fsyUKSdz//2pHD36j9Njq6vh/vth//5XKCoqYOnSKG67Dd56S42C2reHiy9Wxy5b5t0Ho6REdXq+NmC6g6PZ1k8/vc348YK77kqu35aZmcH8+ZNNHlnSlO3tNqZNS+e11xyv6rR+0bQcwe5MyY1GNZs6erRhp68Z2P2BtoDMlRlBfn4Wjz8+lLffvsmta7z99o1Mm5bO/v0b7R5j69nU4k/ZUrnU1ioBceSIfRWcXu/6Pfnxx6V8++1nbs92fD3b8BRnMwJPZwzOznHkProcpQKKA3YIIf4E6h9zKeVlTq59BLBMc5Rs2maL64C77RUkpZwHzAMYOHCoX+VlaalrI9UjR7YjpcRgcN4TrFundXzDadduCxs3qrgla9ao/UOHqsxP33xjzqxlCynViyWlmkE4C8UuZeDlFa6qcjzbKixUwzaj0dzjLl7ceH2B5uMdGelYgW8tCMrKPNfL1tYqj7WoKGU7cHVG0xQKCtTsSQjX611aeowDBza6bVBNSRmEwVDr0G3SumMyGl3rxA0GNXuKiFAzK83uUlGhBiuu/rY33/yMiooyYmJauXaCCVdnUjk5u5k58/9ISRnEgw9+6dY1nGEw6CgtzUOIEBISVNo8R6rLkhKzcA0PV7Or6Gj1cXRrXbH7OVINObcUOWY9kCaE6IESANcB11sfJIToC7QF1jbxel5HStdVQpdcMoXy8kK7K1rz8lRHf8EF8Msv5u2FhUoIDBqkIkVKqQSBNlrR620/HMXFquOxfGHCwpTKIjbWdh2rqgLDRc4SywU8mZkZLF48tV7dM27cdK6++lnOO+92amrMy3LtZ4ATLFjguDe27Ljq6ppuHzEa/bfOQko1O3V3VJicPIDnnnMcPM4W9nJlGAxK9x8b27gu7nTioJ5zbTYohPuj3VGj/s+9E1D33VWPw7y8A+Tm7iM31467YBNYv/4rZs++joEDL+Sxx5Tnmy3VUFlZ4wGhTqc+5eWq3WJilNNHZGRDoVBb65rQc+Q+6nm+NXW+QQhxD/ADyn30fSnldiHEs8AGKeUy06HXAZ96YIPwKXq9Sujhakdx/fUzGm0rKVE3JipKqX1274Y//oCsLCXFx4yBL7+Evn3h4Yfh77+V4DnpJNi+3VwPS4xGdWNtTbsNBvVSVVYq91PrGYI/8ze7Qm6uuX01dY820lfqHhWXydpYbG+hnr11B5ZYvmhlZYGxcttVPF3PEBXVymZGMk954w3YvBmef17NXLU2NBiaNivy170oLHRdWA0adCH/+tdrpKUN93o9+vY9m+joOIzGk1m8GC65RM2OLNuhqsq50NJm+pWVSghERKiPEOp+SKkGiY5wxWvoSmAG0AEV2EUAUkrp1KNaSvkd8J3Vtqes/p/mrJzmYOfOpo30Kirg3/+GVq3gyiuVEAAlBADOOAOSkjIYNiyLM88cSEjIJQwZomKRg/nGaR4XGtYGOFtUV6uP9jCAUh05O8+faAvzNGype3S6Kt5++0agoTCwt77AXlgJSywFQaAJRmf4Ou+DPaSU9WolnQ42bVKd/mefwVRTkFfN9dafgrWoqIAVK74kKSnZ4cxASvXsh4crtZU7jh9CCMaMecCj+lVWqkGgdTgcjbZtOzF/fhHTpoWxdCns3w+PPNJQsLqbvEhTF7trq3LFwjITuExKGS+lbC2ljHNFCLRkCgvdEwIVFSW8++6dfPbZk/Xbtm9XD19BAcwzBUc891w1Q9C+f/PNy6xfP5XMzPcalakJAoPB/GCUl7un49fpzA+Fv8NEaFjXV9MN79nTcLs9dY/RWMecOf9i3rzb6relp09g0qR5JCamIISoz/Hw7bevMmFCGJ9++rjd+miCQKfzXxRVb2Av1o4rbNy4nK++ms7Bg3+7dd7Onb9x++0dmTnzkvpt+/ebDeTr16sBjuYy6+nqa0/JytrH1Kl38Prrz9g9RotbtWWL8sjztvefPXJz4e67laAsKYGvv4ann1aegA2PC2PvXvV9yxbIyFDfa2vdd2JoCq64j+ZKKXf6vCYBgqaHdYe8vL2sXDkXIULqQ8pqN1zTe7ZtCzffDKNHK9VOWhqkpY2gqqqUlJTBjcrUBIFerz6ayqcloderqJgpKZCYqB7uLVtsd8D21D0Kybp1nzN58rv1W9LTJ5Cbu4+NG5dx4MBG5s27jfbtu2M01pGXd8BOOWaddiDNjlyhKfd+/folrFq1kPj4jnTv3vhZs0dERAxlZXkUF+eg06kR9a5dap+WCvSTT+Daaz2vW1OIj2/LdddNonNn+yrBgwebZ4HkN9+o9snKggceMI/QX3gBRoxQmciGDYO1Jstoz546Dh6M4Pvv1btuzzboKxx5DWl+eBuEEIuBpTT0Glri47r5BCnVqCYyUhlWi4rU9K2DKSvfsWPudxLh4dG0b9+dyEiz54Km47/zTuUldP75SlXTo4f6APTpcyZ///0dX375DKtWLagPPwENBUEgLPjyhP37Vf0PH1aCcN8++6NwW+oeS6qrGyufv/321frVxUZjHXffvQi9vpauXe3n222pgqApaqxTT72c+PgkevRwb0FKSspA5sw5QllZe265RTk6aOqpcePgww+VCrW56Nmzt8NcBNnZamTuL0pK1Hvfpw+sMllYk5JUn9KqlVIHr1xp9g5ctgzCwyUgyM6+jPj47ykuFpSV+SZVpSMczQgutfheBYy2+F8CLVIQHD5stqJbjvw1NYonC3S6du3P7NnmUWhhoXphoqOV9D/LxnoUZ8ZRbQWmL6NW+gpN2GprFWpr1QviSLWRnj6BZctmcvjwFpv7bRmCTz75PHJz93PhhffQq9fpHDq02eR1lEVISChGYx2JiSkNBKzRqISRv/z9vUVTBMGwYWMZNmys2+eFhUWQkNCZX39VbfbDD+YBytChShAE6vOZl+f+zL4pGI0wY4aagUREqHYZMkQNBH/7TbVXhw5qQLhzp1KNrl0Ler0gNPQw4eEbCQkxAOHN8mw6EgQ/AT9IKZsp8oz3yc5Wft/29rmLtbvjWWdN5+DBCbQ3xfU66ST7hiJ7xlEtaY2ljaClsXt341Whrui3S0uPARASEtZg3YA9Q7ClX7e1YNWiuloLWH+6e3qT5lQL/mNaIyml6uA6dFCqPm0tg638IP6grKyUmpoq4uPbNkhaX1ysBiK+RqeDN9+Ebt3UIlDNFVpz/b70UuViO2aM+ZyUFPW58EJ13uefw5VXtmbs2FwefliZbANNEHQFPhdChAMrge+BPwPNzdMVpFS6TW9GgrQ1ov/qq4nA/aiAqxPo39/++c6S1mgvVksTBGVlroUGsMWUKd/w11/fEhERzfLlM6isLCYkJJQBA853mtHNlmDVsBSwWsfVkqitbdrIe9euTGJjE+jcua/bK3D/+msFW7emA7GEh6t69OmjhEBYmPpfp1OzX3+zaNEcZs2ayl13PcaUKS8ASghYOyL4im3blMF8/XrQmnXyZGU0F0K1kz2EgLFjlZCIiFChMbTBX0AJAinlDGCGECIOlZzmFmCuEGInsAI1W/CjBs5ziou9Hw7YfsdTCKgR6Mkn2++8HCWtAVqsasjejMsVUlOHkZo6DIBt2/7Htm3/w2isY9OmZTaPNxgMzJ59LZs2feM0T6wmYFveMKZpvvl6fS3PPHMWISGhLFrkfkyFrCw9dXWxhIcXcO21iWRkwHCTS70mGPT65hEEYWHhtGvXgTZt2tWrI30Ze8kabaYEambUvTucc45ZKLiC5VofS5dxf+PUa0hKWQ58ZfoghOgHXIyKPnqhT2vnJY4d836Z9le3AlQRFzeVrl3tCwJnvvCWxuKWQnW157MBa8455yaqqko4dGgrHTr0sHnMt9++wvr1X7lUniZgA21ltSs0xT5QW1tFnz7pSGn0KPpoSMjZAAwYEMUll8CoUeZwENozWlPjf+MmwO23T+H221XY8YIC/woBoN7t89prldPJRRe5JwQ0fvvtQ1av/pCqqgygY2AKAgAhRBcgxeL49VLKV3xWKy9SVeV9r5vMzAxCQkIcZherqHCcl9hR0hqgRdoIPBW4mZkZLFhwN1VVpbRqlcBNN822m8fB8pwlS551qXxLAdvSBEF+ftMGMrGxbZk2bbXNfVrmL81jzhLNHpCdrdQWAwequCWWuRiaU5Vhjb8X2xmNZkEwcqTyQPSUgoIstm1bSfv2FUBHv6/HANdWFs8AxqHyCFjmI/jNh/XyGt5+QDTbgLMUk/ZiDlniqLMLDW1+Y5w7SOmZv7a1raWysqjeuAs0ij1kKUDtJba3vDfqvBcaeA0FOloa1rAw91eWuopOpxY4HT6sOrLu3VXYiORkZcz87DPleaN19rb03Zr6sjk6LkvKyvxvTM/OVr+7ffumCQGAESPGk5o6nK+/7kR+fvNoAVyZEYwF+kgpA0Duu4dlQCtv4cgoqeFquAOAu+9Opry8kBdf3ESXLic12KcZ4/T6wBcEpaWezV7seU998MH91NSU1+cltvb+cZTY/pNPJOPHhwCShx/+jq5dHVjtA4jqaiUEfKni0OnUc7VkiTkev2UQxE2bGh6v9P91rF8/nZycVM480xw3sjl12gBTptzMnj3bmTRpLomJQ/x6bc0+kJra9LKSklJJSkrlf/9T/wfkjADYD4RjsZispZCb633joGPbgCAurhs33DDdqZeLRllZPgaDjpycPXYFgU6nFr0FMp6u3rTXnhUVjQu09P5xZmyfNGkeHTv2pFOnNNavV54kCQkqtEe/fp7V1VdICVu3+sat9ccf5/DZZ09y0UX3MXDgNJ57Trk0lpWpGeftt8NffykhfsopyvX34EE4+2xIT1eBEIuKVvH550/TpUs/zjjjunpbQ3Orhvbs2caWLRsoKTGQmOifaxqNyg6mxQ7r3dt7ZQeksVgI8QZKBVQF/C2EWEnDlcX3+b56nmM0+mZVob0OKCQkBaPxIE8+CV272jjRDrfeOpewsAhOOumcRvuae8TlKlqiFk9wHFqiMZr3jzNj+6hRKjZRcTG8/rrZZXTTJqUKCSTy8ny3tqG0NI/KymKklCxZ0jDz10UXKS+XcywevfOscrOfey4YDOkcOTKeK654ooHBWVMNNZcgeP31T9i6tYC4ON9JdqMRvvtOGYE7dFAqM8vMZt6YEeTnH2TjxmWUlFwOpASWIAC07LkbAdv+ewFMQYFvDK32OiCdbjqhoSqfrTuce+7Ndve1FBfSwkLPffPttWdERLTNWYE24ndmbNf46SdVtw4dVIcbCJnZ5s9/hVmzHuessy7kpZcWkJ3dzmfXuuqqp7jwwnvIyYlkyRIVWuXhh5UwOM3FyNRhYRHce+/HNrarv80lCDp1SuXYMS/0xA7YtMkcCE5Di7OUlKRsK00lJ2c3H3xwPwkJqQSiICgFfpdS5vmrMt4kz0e1Tk+fQG1tNe++OwmAxMQUhg6dzooVE0hKch732x0C2YVUi4diMMAhxw5SDtE67jlzJgLQtm2X+twOtgTE5ZdPb3CuPRXcH398ydKl/yU7+1ugNVdeCXPnBoYX1rp1v6LT6Vi5cjmrV2+mU6dRPruW0Qjl5e35/nv1f1NVY1JK8vMP0qFDj/qBSnPNWP0RSVQLCpeSop6d00+Hyy9X6rzwcM/cRa3p0KEHo0ffw+HD3fya7tQSR93WRGCOEKIK+B1YgxIM2xycExDU1Pg21vzIkbcwbNhYCgsLmD27LytUciG6OXcUasTPP79LZuZHDBhwfqNk1oEqCEpK1ErtPn2Ukbip9RsxYjwffHA/BkMts2fvJyzMvMpGG/EnJHRDp5vOwoUT2LpV5XhISWlcVlGRytR06NBmDh7sC7SmRw8YMEDtDwQ120svzeeuu64mKiqempomupw4oKQklwcf3EFNjdKFhYTA/7mf0KseKSWPPTaErKy/efPNw4SFqTzSzdFxFRYa+e9/HyMmJp6xY+2HHW8KNTUqdDWo3CK23Gy9QadOvbn55jdYtEjFIQqoGYGU8moAIUR3YITpc7sQohtqHcEYe+c2N75eWBISEkLr1ons359YH1lw2DA1UnCXjRuXs3PnKqqqSu0KgkDw07ZEWz28Z493jPEhISHMn9/YvctyxL9okdLVAvz5J+zYAdOnq5dTSqUC+t//lP42JQUuueRqQkNTqKvTlvGrcwNBELRvn0RGRiZbtvh2hvL994upqbkHMJCWZiQ9PaI+DpYnCCFo3z6FwsLDHD26h/BwJQj83aZ6PezYUcHy5TOJior1mSD46y/17qWm+k4IWNKcqmBXVhYfFEJEAdGmj/bdKUKIi1CBd0KBd6WUL9k45lpgGsowvVlK2Sivsbv4yvfamq1b1d/zz4frrvOsjKFDL6OqqpiBAxsv0g4kY7Hmf19RYV6g549wDeXlKnz1ihXKy2XKFBUFc/NmeOUVmDhRCYA//zSfk5UFc+ZsBS4DDrF4cTf0+unAhICZXR086FshkJu7j9jYW4AQOnTYzrRp/b2ixpg06V1iY9sSEhJa73bq74HK4cNQVxfKuHEv+KR8KZW686ef1P9nnOGTy9RjNNZRWHiY6uo4oF1gzQiEEI8DZwDtgd3AOuBNYLKU0qlpUAgRCswBLgCygfVCiGVSyh0Wx6QBjwFnSimLhRBNlrv+0LFt2vQN7713F1VVvwC96tUOnjBy5K2MHHmrzX2BNCPYu1e1rWVibG9hNNoOf5CZqfT6miH6gguUi2Pv3vDEE+plfcHUF0RHw623qtHbE09kUFExGeXwptYgLFyo1iDo9ROQ0je/wxU+/3wBW7fupH//iXTs2JPCwsON3Ia9wbRp6ZSUfACM5sorT/aKEABo3drsp9kcNoKaGmX/i4pqxdixj/nkGh9+SL26VwhzbCVfUVVVyn339SA8/BngqYCzEdwAVALLUTaCP6SU7oRuOw3YK6XcDyCE+BS4HLVCWWMSMEdKWQzQVMN0XZ05J7AvOXx4G0VF1UAvwsO960tsSaAIgoICs3uoL2YBq1YtYN6824iNTWD+fOUpVFQECxaoe9q1q8rops26WrVSuV0//1zZKFq1Uvs0j63w8KloQkBDGZ2nIuUEdDpzylB/YjTCkiWLWbfuB+64oz+PPjqI8PAoFi6s9CgOkD2klMTE9KCkZCQhIZJTT/WN1GuO59PSddMX6HTmpDJnnqlmAwkJvr1mREQMiYkp1NXFUFwcYKohKWVfIUQCyjZwLvCoECIW2IwyGi9wUnYXwPK2ZQOnWx3TG0AIsQalPpompVzh1i+w4MgR/zyUgwePYceOjmzZogymlhEE3UWnq2HjxmWUlxcwevRdDXIchId3A6aj07m2OM3b5OUp9UVTIoq6Qnm56vwtI5x/8IHyrR8yBB56qPEIPikJ7r3XdnnFxfbcmNT2mhr/CwIpVZrO0067hV69zmfw4ItJTEwhMrIVlZXFxMU1zYW0pkaNYmtq4KSTBAMHiKOUIgAAIABJREFU/k5OjvIQio310o8wkZExhTVrPiYl5Tegl99mBJWV5oWL5eWFHD26m/j4JDp27Om1a2zerFZ49+gB99zjtWIdEhERxRtvHOSHH2DhwgAzFgNIKYuAb4QQK4BTgbOB21EhqZ0JAlevn4YSNMnAb0KIk6WUDWJYCiEmY4rt3LFjN4zGhm5bBoMSAv4KPJWSMpDExIEADnMOuEJFRRGzZ48DICYmvoHLpF6fBUxmzRo4/3zfCwO93jzdP3LE96MvjUsueYhRoyZTU1OG0agMw3/+qdQ9t9zivhrH3iI1Ibohpeos4+O9VHkXOXpUXfeMM8wJfmfPPoDwgo7q4EG1aE4LTvf11+Z9vlBr1NZWUVycQ+fO6jX1V8dl6S66a9dvvPrqlQwdejn/+c9Sr11Dcxf1tV3AFs1pE7Q7HxVCXCaEeEkIsRrIA2YB7YD/AEkulH0EldxGI9m0zZJsYJmUUi+lPADsQQmGBkgp50kph0oph7Zq1Z7du83uoXl5ahm8v4RASQm89ZY5PsvJ9tPjukSbNkmEh0cTExPPp58+ZiOOURVffz21aRdxAaNRua7t2KG8rvwlBEB5DcXGtiExsRtz56qRbWioCn/QzoOB8rhx04mIiGmwLSIihuhotQbB3/mKdTrbPu/eEAKgsmQdO6ZUaBdfDL16qRSpd92l1g14m7FjH2f27IP06jUY8I8qo6qq4er1yMhY0tKG06WL91YV19SYYy352i5gi0D1GroJtXbgYWCjlNJdObUeSBNC9EAJgOsAa4+gpcB4YIEQIhGlKnKaZK60VH1CQ/2bbcpohP/+V8UZEcLAaaftp2fPphkIQkJC+PDDKjIzM+oXVVljX9XRdGprlZrk8GFzmANvh+22xDq9p+Vq4D17YPVqVZ8pUzyfbdlbdfz55xOoqvK/zeXAAS1PchVbtvxImzZJpKV5p6cpKlJCJjoann9eqSnXrPmEJUueITn5X4SGen8QkZDQBTCrRP3RntapZAcOvICBAy/wWvn//APLl6vfkpZGk9xsPWHGjDHs358GvB5YqiEp5ZUAQoiLrYWAEOIOKeVcRwVLKQ1CiHuAH1D6//ellNuFEM8CG6SUy0z7RgshtBDXU9zJkezvlIO//aaEQGhoEXV1QykvT0GIX5yf6IDMzAw++OB+m+EUNOLjPVip5gLV1UonqmWasoejztsdbKX3nD9/MqtXLyI7ezvh4d8DA7j44qar3LQ1CAaDgYqKAtq0SWKpSYPgz+iOR4+aw1oUFBzi1VevoFOn3rz66m62bVvJggX30Lv3CG6//T2PyteCn6WlmTvm/PyD5OTspqrKy2n5rPCX11BRkeexrFxh0yaYNYt6b7JLLvHdtexRWHiYsjJluAq0GYHGk0KIWinlzwBCiIeBkYBDQQAgpfwO+M5q21MW3yXwb9MnoCkpMccc6d//O3JyjE0e1Vl3jLaJYeRI10Jau4tmBHYmBGx13oBdYWBPcNgLOb1jxy8YDD2AfoSHq8Te3mDFijf44IP7iIlpw3vvFdd3lP4SBAUFDb3YQkPDGDLkUtq2Ve5NISGh5OTsIi7O89CZu3apv5b5Ai688B6GDr2c6Og4j8t1REVFMV9+OY39+88CrvZpx6XXqxmVNVJKr6jWSkqUi7KUKvvaZZdBx45NLtZtHnxwCTt3tmL+/MAVBJehDMZTgIuAvig30BOGw4fh5ZeVXaJfP3j00YkIYVuN4w6u5DaAefTu7X1DcU2Nayuw7XXeWjhoaxwJDnshpw0GPYmJX1FQEMLZZzc90YdGmzaqwzUa1cotzRjnaxuBlErVYD2KTUpKZcoUc/zGnj2HMXPmVtq27ezxtbQZQd++5m3R0XEkJ/suImdoaBgrVswmJMQIXO3TGUFWlu2O8Y03xrN9+8/cccdCTjnFsyAHRiO8/bZatDhggFqH4kUvXrfo1Cmt3iMqIAWBlLJACHEZ8D9UJNKrpWw5KcCbqtYoLYVnnlGua716KZdFby1GcpzbACIiOqPTeX81bGlpY52rPezVUQsHbY0jwWHPmyc0tBsFBScREwOXXupavVxh6NDLWLCgnKgo5T/pL1XG0aOuqTKiolrRtavnqxGrqtSiutBQ74RDdpXo6DhuuOG/ZGefwc8/+67jqqiwHyWgqOgIZWX5REa28rj8jAzl0hsbC3fe2XxCQCNQvYbKhRBlQogyYC/KkHsNoG0LeLTRqep8ZP3oNDMzw+m5Gt99p4TASSfBk0+q0Wpu7gGKinIwNDFGQGKifd1/ZGQMAwbMBLz7ouXkKO+g8nLXjrdXR3upOB0JjnHjpjcIKAcQEhJDXd10kpLg2We9Oy0PC4uoFwKAX1RDOp19IWsw6DB6MVemFuupZ8+Ga1k++eQxFi+eSkWF72JuX3zx/fTtq+JY+6rjchTV9qmnfmXOnCOkplovTXKNVavUux0aCg8+6PtFY85Yu3YxP/74GtA8MwK7gkBKGSelbG3xiZJSxmrb/VlJT3E0OnWFigpzvJHrrzcvQnr88VO4++4u/PDD7CbVz5abI0B8fDseeWQePXqomYs3vTLcNbqNGzedkJCGeTIdpeJ0JDjS0ydw001z6oWBEDEYjfMIDb2G1NTHEWK3e5VzE3/k2D182H5e5GXLZjBxYhhLljxXv+3771/nnXdupbDQPX/dmhrzs2mpFgL46ae3WLrUN3F4LPHlDKuoyLH3WkhIKAkJnYmIcD91n5QqVSfAzTcHRsa6zZt/YO3ahYBvBIEzHY6jGUF3RycKRbInlfIX7qo1rPn+e6VPHjCg4dRb6xhjY5u2GjQ9fQKTJs0jMTEFIQSJiSncffdH/PFHAddfP8HrYahra90Pz52ePoE77lhY/39CQjKTJs2zq16z58OvCY7zzruNSZPmAyGEhv4JTABeIjPzRfbu/cO9yrnAI48M4pZb4tm+/VefZ9SqrnZsd6mtrUJKSUSEOWbjn39+ya+/vk9u7j6n5RuN8Mkn8OKLarX1pk1qJnDmmeZjpJRMmDCLa655llatfBfi+ujRPezbtxrwfsdVU6MCDfqKf/5R648SEgInW93w4ddy6aUPAr4RBM5C7ziyEbwshAgBvkbZBvJRkUdTUV5D5wFPoxaFBSTO8trao6wM3n8f/jD1S2PHNtyvxcNpCgkJakW05uYYH6909zExcM89l7Nu3S/07PkpMMZrIy5P8wqfddZE3n77RqQ08uCDXzicjruSOWz48OvZsWMiq1aFkJAAiYmrKSpKoXNn7wdfy8vbT01NBdnZ2wgPPxfwnSrDWaKU8eNf5Nprn0NK85ThoovuIz19IklJDddRVlWV8fXXL5Ce/i+6dlW+tGvWwDKLXIHdusF990GXLg2vM3DgBbRv370pP8Upq1cv4ptv1gMrvNqeBoMygDtyDc/J2c3HHz9MaurpHoWgzsxUf0eMaH67gMbgwRfRqZNay+BtQZCd7VwT4GgdwTVCiH6oIdstQCdUJK+dKJfQ6VJKP3pku4+zvLb2eOcd82jr2mub7tOuIYR5ita+vZpab9sGbdsq979du1QAtcOHD1BRUU5VlfLv9NaD0RRf7FmzdpCYmOLSVHzt2k/p2/dsCguz2b17I3Pm/IvFi6cybtx0NmxYxa5dl1JaWgZMpajoECEhSlikpg7zvIJ2GD9+Bnp9Daeeelm97t4XqqHqatfCn4eGNnzlTj/9apvHffPNyyxbNoPvv3+dDz+sprYWPv1U7bv6ajVL7dXLbGDMytpMeXkhX331HFlZfzNr1i7atPGdH2SPHqfSt28Iu3Z57/msqVHvgLP7c+zYXjZuXIZeX+u2IDAYzGEk0tM9rKiP8MXK4sJCJQhiGmugG+As1tAOwPfxDXyENgpduPBeKiuLiYlpw803v+nQa+jAASUEIiNh5symJaSIjFTTeb1eGaX69lUhHMLDldFZCFV+sknBlpqqHtRHH53BwYN7yc6+iL17mzaClVI9DKWlnmVty8rawmOPDaZVqwSbyWOsMRgMbNr0jem/cEA91QUFWbzzzmQMhteAMlToKNfXJnjK6NF31X/3VDVkMKiOXkp1Ty0D1un1Sh3k7RAnQ4ZcyvbtP9O371kAfPutEuQ9esAVVzQeyS5f/jJr1igniG7dBnLgwEaP3SpdYdiwscTHj+Xpp5vecRkMkJur2tAV/4uePYfy4INfEh3tnqnSYFAzqooK9c55klHQVxQXH2Xv3n1AutcEQWWl6yo2L2bYDUzS0yfw66/vs337z3TpchLp6ROoqFASMiREddRr1mTw2WfKxTQiQkX8PO+8CTaFgNFo5JZbWhMaGs477+Tz/+ydd3hUVfrHP2dmMpn0CiEJJPSO0kRco4jiqrgqCtKyggWwoqviWvhZVoW1u6IoxYaK2Bc7KAhKLCgignSk9xogCenn98eZmymZmtw7M2HzfZ48mbn1zLnnnvc9b/m+Fh9FipOSVNnEP/9UFMkJCWoZX13tCEFt7UScaLGov3POuQhQvOhQ97j3igpVR+BoPRJMN236CSklFRWBNaK8vITu3Qfy++/f485aXllZAkxGiHKkDDw3QS8EKwjKypSWv3u3q6nCbFarxerq4ITKtGnXUlx8mNGjp9Q41Y8fP8SmTUuJjo6lc+dzao5t27YP//rX94CjAhtAfr5nc0aTJrlkZ3dm+PDJLF78aq2VhxEw22MIyssdWbkapFR9U1Kixm95ueovTTGSUo318nJ1TDAB6cnJGfTpc0VAxxYVKS6mjRvVPbR36fzzw1eTwhOWLfuYV1/9J3AsIEFQWek6j4AaF8XFKpO9qEh9DjRI7aQXBLff3p7Dh3eRk3MKffsO5csvFbtldDSkp8OePbOpqnJop+XlivFTcY3UnpSKig5TVlYM4FMIgBIEaWlKk2tmp+nLzg5cg9JCAouKVKhgdrZ6ucrKXF8s7bMmSEAdU1xcfxqOfv2upU2bPrzzzn3ceGMWPXpczLhxM12OceRqbMNkMlNd7eum272+9IE68YPB8uWf8c03L5ObewpRUQ8D3ldYUir/0MGDKuPU23OqqqqbcF616msOH97JqFH/qdm2ZctynnjiYjp3PsdFEDhDa09cnPcIl2HDJtWYPHv3Nj7fUwgQohywUlamiB9TU1W/lJRERlW9wkJVuMiZQDErS5l7+/QJX7s8IT09hw4dTmf9ejXJr16t+rOqSvVlRYX67PxdT5zUgqCk5Dh7924EYOLEhRQUpNdo2aWlsHPnbGA0iubI5Uw+/3wiF15YWxDYbPGMG/cypaX+7SyJ9pVr+/YOyS2E//oFixd/yRtvvEB19TBgFJWVxvOteIPFYqFly+5YrTYKC/fUiuxxzyT2LQQc4aV1ceLXBcuXf8avv37M1q2/cf75ShBoWnxRkcqn0DTW48eN5a8aP/5tjh07QFKSg7y3adNWdOt2Pq1b93Y59uWXb+DIkd0MGfIvdu/uBlho0yZytNjMzGqGDz8FWEdFhaSsTISMAfjHH9+jsrKMU0+9yKVimjNKS+Gxx5QQyMpSUVaxseqdDFUfBkOKecYZAzn11IHk5ytBcOxY4Lk+esCvIBBCfAS8AnwpncMdGgBMJhNXXHE/u3dvYMmSdN56S20fMwaqqmbz1lvjqKjw/KS8aadWq81raUlnxMQ4TBHBDryfflrMokVf2IuVjDK0tm2gGDDgRpo2bUXnzq7xdoHRZChERdlo1aoXGRlt+OqrqUE78euCLl3OZePGH2nf/sya51FUpDJKSwJrtm7Q7P3OaNasLffd9xUAO3euobBwD127nseaNYvYs2cDy5d/St++24EWXrOHCwv3ERubVKeY+rogLg4yM00kJiZw7BhUVFSjeCVDg//+91F27FjF5Mm/ehQEGnXEtm1qJf7AA6GvPwHKD1FVpZy1ZrMyDRcW1j7ObFYrvVWrHMKjstIxf4QCgawIXgSuAaYIId4HXpNSGpv5owOcqSXi4lrw00+zgXzGjIHzzoPx4ydSUeF9JqirdpqerpbyifVIuTvttLP44YeFJCX1pKDA2CLnnuBs6tGQlJTB3//+ND17ulIz+qPJ0BAXl0KXLv35+eePiI9PZezYGT5DTPXCGWcMrSkGo9nZS0pCLwT8QUrJAw/05cSJ40ydupObb36Lhx7Ko7KynO3bVd6BN0Hw+uu3sGzZXG677X1OO22Q54N0REKCmqRmzPiZ4cNVIflQok+fwTRv3oX09Nxa+6qrVXW7n39WK4AJE8IjBEwmFRlosaiJPSMDbDaVLe1e8S8tTVkJWrZU/RqRgkBKuQBYIIRIQtUOWCCE2AHMBN6SUoYhIdo33M0VxcXbUVEqr3DmmZ8BsT4nMF/a6bp1BXzwwUPk5HRj1Khna+3PzFQD0FYP5ey88/7Geef9jcWLVcxzKFPOvTGiHj26z2Nkj7dcDQ0WSzRxcSkMHfoI5eUnWLPm2xqnfVxcCjNmjMFisRrmJHZWCOLjVSBARUXoS38eO3aQgoI3SU/PreXolFJSUnKU7OzObNq0lAMHttKhw5nMnHkYkymGsWOVd7hNG8/XPn78ENXV1WRkeDlAB1itDru/VvqyRQu11A21ojJkyIMet1dVwcyZij7CYoHbbqudY2EkhFDBH1u2qGhAzV+X6ySvcnLUvmPH1Iqlqkopj2vW/M7QoXmUl+8AkqmoUFaFUCGgdAohRBqqUM0Y4DfgOaAn8LVhLasHPJsrShDiW44fV6mf3qgQTCazz8zZtWu/ZfXqhSxZ8qbH/TExyiaZklLn5tdAC1MM5Yvmy9RTXl7CzJljXbZ5o8kAJVCvv/4Vpk3bg9Uaw+efP01R0WEOHdpBQcFsYmISKSzcw969GxkxQjB+fMugeKD8wZ1rqqhIBQLs3q3fPQLF/v1/8uabd3ikfnjkkf6MGZPC5Zf/H7NnV9Ghg0oVttni2LXLRHk5NG0qsVqLPV77/vu/YdasEpo31ynhxQOaNXNMTAl2dmtt1RsObhx3HD+uMq6//VYJrbvuglNOMf6+zibgjAy1CujSRSmE3mCzKWHQoYP6nJgIVquV4uIiQEnbUAtXv4JACPFfYAkQC1wipbxUSvmulHI8oHNZbH3gXduXNGmixLM3KoTzzhvHu+9OZMQIk8eJqWXLHrRv/5daJhJQE7cW2lcfh1R5eTmLF3/Jr78uBEI7KPyZesrLT3DsmCOfIC8vn2uumYFWlVQlo0N6em6NQPVG/qfFvTvuHTwpoC94Uwi2bw99akx8fBoXXngrffteWWtfampzOnY8G4vFislk4sCBrXzyyeMsX/55TRy4xfIbt97akn37PBfwi4qKxmRgmmx8vBIGVqtDQXnvvRcAzUcQGpSUHGPfvj8pK3M818pKxRC8erUyA02cGBohACofqH17pf1n2dnE4+Jcc028ITHRkazaqlV7Vq48Snq6Ko0WakEQiI9gipTSYxkuKWVvT9vDjUCoJdypEOLiUjlxoogFC6ahsWx7SnTq0WOg10SdQB5+INi/fw/XXDMQ6AasDOmg8GfqMZujqKhwTf2Mjs5Hhdr+wu237+S00y532f/OO/d5JP/75hvXMFRtu175BN6EWnm5caU/vaFZs7aMHv2cx329el3C/Pkv0L79XwBYv/4P5sx5g/T0wRw5kgck0bZtNatXx/Daazdzzz1fhrDlCvHxaoIrdlqUHDy4B6hGShPV1aGha/j993lMmTKMPn0Gc/vtHwBqFbBrl9LI77+/bnWu64q4OLVC6tbNfzSgJ2irCbPZTEJCou78YoEikEeXIoS4wu3vPCFEPXJujYU3bX/o0EcpL3dMYnl5+Tz//FbefruaUaP+Q1VVGe6lFoJhK9XLpte0aSYWSxRWq3LChVIQDBs2yUcykqCqqoKHHspz0dq/+07979x5J+3aneFyRmVlpdcILG+hpnrlE3in+TbpaoKqL844YxgPPbSEwsJC7rzzDaZOPQ9YzcGDD1NVlYTJ9A1XXNGCpk1bM3iwq3183rznue++Xnz77SzD2hcXpyZ5s9k1G3f48OvCMHFJ0tNzaxzFlZXUlCAdOjS0QgAcZjK9lEBNMETiiuA64AxAWxWcgyKhayWEeFhK6dlYDgghLkT5E8zAy1LKx9z2Xw08iSpuD/CClPLlYH6AJ+Tl5fPLL/DzzxMBVZAmLi6NF1+8ikWLXuaBBxYDsHnzMj7//Blyck6hf/8xXq/nPDFt3ryMAwe2kZNzCpmZrkRhegkCq9XKxo3lbN8O/fqFdlDk5eVz4MA2Pv54MmVlxU4JYgKovVLq0CGf339Xk8Rtt11eK1rqp5/e9Xovb8lneuUTeOKaUqgyjNLCGwoL91FeXkJSUgbR0a5KSmkpzJ8P8+dnceTIKAAyMyVxcdCq1e/06SNo2rRpzbh1xvbtv7Nly/KaJEcjEO9kAHbOoczJaY3VqsZnRYV+k6EvnHHGMM44Y1jN90WLVJRedjb0rV/l2KBhsej7m++4YxQHD94PtIs8HwGKMKaTlHKwlHIw0Bk1I5wO3O3tJCGEGZgKXGQ/Z4SdxM4d70opu9v/6i0EQEU3rFo1GNjKvfeW8fzzW2s48Neu/bbG9r9ixTx++GEOn332JImJ6R7D0cAxMRUUzObBB/P4z3+GcPfdp9TSKvX28mtLzVAvEy+//D5ef72IOXMks2dX2vul9kpp9uyJPPusysjt08dzyKz31ZTgvPPG+aSsri80mm/3egoQ3EpPD3z++VPcdltr5s9/3mW7lPDcc4pQ7sgRE2lpxxgz5k+efhoeeUQwaFBTHnvsAp588pJaq1WAkSOf5OGHf6R37+DDRoUILMw53ocnMFwaLKj34uOP1efBg0PPJBpX9+JoHjF//kcUF+8HItM01FxKuc/p+36ghZTyMBqjmGf0ATZJKTdLKcuBdwhRrePvv4cTJ2zAcjZs+DcFBbPZseP3mv2aRnvs2D6ysjrSoYNK9FG2f1cvrzYxaQ7PykqVllpRUVrLsVmfkFFPCPVLVlAwm/HjW9ZylHuztRcWbmPLltmkpZVTUnIVb79dWy/w5bi/9toXa9Vj8BWxVRfk5eXjLQ/SCEoLb4iOjic9Pdclqxjgm28UPUNcHNx9N0yZkkjHjuXs2/cnlZXlbN68DJCYzRaEEKxc+RWzZv2jJsM7Pj6Fdu36kpoafN3j7GxVeS81VWm2GRmOMRcd7aja5U0QHDlyiIoKVT0mHIJg8WJFqNiiBZxet0Jl9YJmFtILTz75Orm57YHINA0tFkJ8Brxv/z7Yvi0O8JAnV4NswLns0k7UKsIdg4UQZwMbgNullLVKNQkhxqESAXyWdwSlYb333mwUaeo2FixoRlVVBeXlruQw5eUl/Prrpzz//FZATYLffPMK7ppvv36jycvLZ/z4lj6LuJtM+i4T+/bNZv/+EuCIobQHGnwVnfftQB5H27bLWLr0LdaujWHkyMdd9no7V1t9afUYjETTpjns2xcaSgt3SCkRQjBkyIO14t/37aMm2/3aa6F7d/V5+vTr2LjxRx58cAm9e1/KtGn7KS5WZSd/++0L5s17jsTEJnUq05iRoZKuiouVIBAC2rVzRLm1aKGoTNLTlYZ9/Lj3lW5xcRElJRVAYsg02EmTBnD8+CHGj/+AuXNV3kQ4VgOg/4pg4MAhvPaayi+IRNPQzcBrQHf73xvAzVLKYillfev7fAq0lFKegspJ8OjxklLOkFL2llL2Tkxs4vOCc+bMprBwHKBe/KNH91JU5Lkii7NG+O67E6mqqs2UtWyZqgTir9qZ3quBkpJipFTCKxSDYtas27wKusGDJ2E2eyM0L2HdurfJzT2Vjh3PrrXXX8WyUODOOycRExP6Nqxbt4QHHjiD9eu/r7WvvBz+8x/lHzj9dDjD7mPftu13Nm5UhPkpKSoYPT4+hYwMRVN72mmXM3Too3TvfhG7dq3j5ZdvCMrxnZWlEtO6dnXlv9JgsagYd21i9aX1pqc3JSFBLRdCNXFt376SbdtWsGhRUw4fVs7r0/QvY+GCmBjHpJ+YqFZR2dm+TWZ1hRE1CQKBzxWB3c6/wD7hfxjktXehBZcrNMfhFAZASuk8Q78MPBHkPWph3ryJaEyi/pCcnMnWrSto1qyt14n+yBGVD+4rJDU1VX/tYNas+UhpYvBgB22vUWRZBQWzfQrLNWvy7auSv3s85tixA0ybts/jvkAqlgUKIZRTOphJJz4errwyn6goePzxiezdux0hchg71hhKC2d8+ukTbNq0lFWrFtQkiWl47TXYulVp6GPHOp7tli3LATjzzHyPmcKdO/ejc+d+ACxZ8hYLF06nqOhQQL8lLc2xatVDg7bZYmjSJIbjx0MnCP797xV8910p776bgBCqlrjRq4GmTVUOxbFjDroKI2grtm37k4MHBdA6skxDUsoqIUS1ECJJupPL+8cvQDshRCuUABgOjHQ+QAiRKaXUOAsvRVU/qzM2bICKisDsvkpLNXHvvT04/fQhPkwYynwwbNgkZswY4xJDb7XGcu21k2jfvj6t9owePdSyX6uZUFXlGrGhJ3w5TWNicliyROUKWK0TOX48eBOLXuafrCz1Um7cGHiRHa2mxKBB+fTvn0/37hAVJcnLM56Ccvz4Ocyb9zwXXjjeZfs33yj7ttUKt9/uqkS0adOHSy75Z01OgS+0bduH0aOfo0mTVgG1J8OAgmWh1mBLSrJqCs/n58Oppxp/z9RUJaiN5iz6+efv2LjRArSOSGdxEbBKCPGKEGKK9ufvJCllJXALMB81wb8npVwthHhYCHGp/bBbhRCrhRC/A7eiaCzqhMpKeO652Xj7SfHxadhsap2bltaCsWNnkJycgRCCpKRmfk0YeXn5nH32qJp9mmNz0CBjtcpQOIx9ZROXlKjfP24cjBrluY/OPXcca9d+R1GRcTzZ0dFqOR4drZgaPdmtExMV46PWZ0lJ2OtKKFxxRVcAystlUIVQ6gqbLZ5Bg+7ffG7pAAAgAElEQVTFZnPYEP78E15/XX2+7jpXHhqAFi26MHLk4z5rChw4sJWCgtlUVlZw4YW30qvXJV6PFQJ69lSZtvUhQvQOpRgdDxFn8rvvKqHTrx8MNK4AWw1iY0MTFgvQpUsPsrMd+RGhRCA65kf2v6AhpfwCVd/YedsDTp/vBe6ty7WdoQmBw4fHUbu2gJqsRo9+rpZW6klLdTZhDBnyCG3aOCpYjBkznSuvfIS9ezfWLPX1jhzQ8OyzD/Hll+9TXb0MiDF0YHh3BqfRsWM+/fqpQt9aoR6t9Gd0dDxjxkzjk08e5733JnLaaZdzxx11Gipe0by50pxjYhwmAJNJOThXrXJUtmrRwkEwlpamJty2bV3NaUJUoQLdogxdYXmClCrx7s031UQ2YACcXdulEhC++OJZ5s2bwpVXPlxT2N4bFI9N3bJeA8Hu3auBXmzbtp5evYwlGli0aC3LlnXCYqlk+HBLSOoKaJFToUDnzt3p3VtlSUecIJBSzhJCxAA5kUg//d138PrrszlxwlOBGf8kcs5wNmG8/vptTJumVgDp6bk1du2kpKYkJTW1X1t/34CGn3/+lo0b12A2n8BoQTB06CRefPE6wLnuYiyXX/4cQ4e6HuvJzPPVV1MxmcykpbVAT8TFOSJb3BEbq0i7DhxQk5wzy2RMjHKGuuOVVz7jr38111R4MlIQ7N69nkWLXqF58y706zead95R9XIBevWCUaN8n+8LnTv359tvXyMnxz+hTnJy3e8TCOLibBw/DkIYJGnskBI++UT9mKysL0lO9r4K0hN6kEcGg3DlDgVSmOYS4CnAisom7g48LKW81PeZxmDzZvjnPxXd6/79s1m79jbAs6MTQMrqmolr4cIZrFmzmDPPHOmRNE6DCiV18OB4K66ekGCcA7dVq/asWPEzpaXpQA4//jiJiy7S1wxVWam0jyNHtOveB+zAbM7h4osnMXRoYPd7+OEfdG2Xhtxc3/2bnBzcRJeb24boaEepPyNpfvfs2cBnnz1Jjx4Xk5Mzmk8/Vb/l+uvVSqA+46Z378u47bb3a9F5eILRE1nbtl3YuxeysoxleVuxAvbuzSQ6+gSDBxukfTmhe3f9IwEDQxkQzYkTpUDoGhCITvQQKjlsMYCUcoUQorWvE4zGjh2wY8dsVGqB7wghZ0fmF188y+7d6xBC0LPn37juuhSqqip4+OGfyMlxqJDvvjuxVrH28vISXnppNB988CB33/0lmZntDDMLzZ07m7lz36K0VPtt25gzZxwJCfpSIjz7LCxfrn3L5+ab88nLC+zc0tISNm78kdzcU72WC6wrhFCmHiNs2qFybmZldWT48H+Tnt6aV15RGu1FFynbdn0hhODUUy/we1x0tPGTWSj6s7paZV4DDBkSQ58+5xp3M5SCEB4hAN9//wGQz54924AOIbtvIM7iCg8RQ2ErWdmqFTzyCMTF+Q8TdY8V79y5P5mZHWjVqhcAJSWFlJUVExPjOqN7c55WV1exb9+fPProuRQUzDbI+QZPPTWREydcf1tFhb6UCDt3KiFgsSgTS34+nHmm//M03HRTJpMnD2DJkjd0axMoc1vXrg5KXz3x00+LOXpU+UKMFgSZme247LJ7KC4eyp9/KlvzlbUZqHWF2ewaShkKs4bDlGGc9/2HH1Rlr9RU+OtfDbtNDUJtDnJGaqpa4lZWhrY4dSArgtVCiJGAWQjRDhXdY4wtIAAIoZyAquqYd3jyDVx33Ysux/zzn59z5MhuUlJcyxj5o2I+fHgnr7wyjrZtMSRqaPdu38lremD+fPW/Xz9Vw/m++3qzeHEJt9zyNi1bdvd7fmJiE8rKiikpOcZVV9kQQjBlyhaSk5v5PdcXmjQxzu9SXFxEVZWKbgmFDbaw0KHJjh5trCmqeXNVDEXLBrZYQqPVFhZuB3L46quZ9O8/Tvfrl5WpSCGA00//gw0b9tO27enYbMaZh8IpCM4552L++AOaNDEgJt0HAlkRjAe6oIxXc4BjwD+MbFQg8EU1YbXGcuONs/yaUXr0GMi5547B4uY19FV1S0NpaQlPPWUMaVlWluffphclQkkJLFmiPl9gtzBs3fobu3atreFS8odnntnA7NmVXHLJP6msLKOiopT4+PqbiHxVdqovTj21T00mbHntJHJdsX//NqZOPcCJE9Cjh7HZr2azcpabzQ4iudjY0NAuWK3qJiUlpX6OrBs+/VSxi+bmwi+/XMakSedRWLjH/4l1RFSUcZGAgUALVY24PAIpZYmUcqKU8jQ7zcNEKaUxTz0IeJus4+PTvEYJVVaWs23byhrCLm/QWCvj432Tm3vT3OuLCRMmYbW6Bi9HRelDiVBZqfhtyspUdaQW9kCfiy++k759h5GV1Smg6/zwwxzGj2/JNdfE1VQlu/32tvXi+U9ONlaLTU9vSnZ2S8DY8LySEnjyySL++KMJZnM511xjXFABqBVUKEIpPaFZM7UC7N//Zt2vffCgEgSgVlSdOuXRsePZJCcboy1YLMYk3QUDzdQWceGjQoj2wASgpfPxUkpjPTZ+oE30U6cq2oPk5Ezy85/0uQpYtOgVXn31Jmy2eCZOXMBrr40nO7sTN91Um+IoLy+f7t0vZsGCl5g//3mPWog3zb2+0MxNTz01kV271NL7oovqT4lQVqbquq5fr7THK5xqqOfnB87u4U5Qp7F7eouuCgTR0cq8YTSMDs+TEiZPhp07uyBEMf37/0iTJgOMuZkd4dRgY2LUlFBdXZvqu7744AO1cjvjDMXv06mTccV3mjcPzfjzh4qKIiCeH374iLFjr/B7vF4IxEfwPjANxQUUAh7MwJGXl8+mTUspLNzDDTe85pLB6QlNm7ZGCIHFEs2mTT+zefMv7NmzDi9cd8THJzNo0L2YTJI5c1zNQDExsUyYYBxp2aBB+QwalM+wYfDzz9CxY/2v+f33SgikpChqg3bt/J/jCf4K3AdbarJpU2jZMjSmjCNHtgCt2L59I1271rEDfOD331UyW3IyPPhgHM2aGSsEILyCwCgNdv9+Zb40mWDYMP/H1wexsa55KOFEYqJaEpeWllFdXeWxloYRCEQQVEopXzK8JXXE1Vf7ZbuowamnXsDbbyvtdf367znllAtITfWvBgwZks+6dZ+yZs0KysvLyMrKYcKESYbTSwAIoTJh9XjRtFDRwYNdhcD27X/w9dcvkpt7KgMGXO/3Ov4K3Afj1I6KUvbfUNEI79mzGmjFhg3LGThQf0Hw+efq/0UXKaKyUMAIFsxAoQmCH3+cy8UXn0NcnD4ZbJ98osJGzzpLmWuqqiqRsrqmwJReEEKxsYbLtOaO2Fg1JffoMbjG5KoH1vphcQvkTp8KIW4SQmQKIVK1P11aF0Z06HAm9947j+uv910U7fXXb2b48Hb89ttPpKdn8Mwzb1JQsNVQIaANynbtrCxd+l+g/hqXqtqmPvfo4bpv+fJPWbDgJd55JzC2D381IYJxamtOzlAhOnop0JKlS0e4FN/RA1u3wh9/KD/HeefpdlmfsNlCS5XhDk0Q7N27jaNHPTPQBosDBxQpnxAwyF54bf36Aq66KppJk87X5R6grt++vXFRanWBlpcBVoRO0unAAXjoId/HBCIIRgN3oUJGf7X/Latn23TBvn1/MmvWbSxYMN2Q6xcUzGbBghlU2A3Ku3Zt4777xjF3rrGFz2Ni1IBQy0IV3lJfQfDHH0oYtGpVmz8lNTWbtLScmvwKf/AVVRUMz7/NFlrn3Ny5szlx4hlUrQpZ49PQSxjMm6f+n3MOvPba3xkzJpVff/1Ul2t7QzjNQuAQBF27/q3eocMa3nxTse3+5S+OfJLS0iJMJnOtes/1QevW4Q0V9QS9o4akhJkzVd0LXwiEaygwjtswYOXKr5g3bwo2W3xAJg2Am2/O4ciRXcTEJFBSctSrk9lshg8+mEhVlesMfOKEChs1ckWgCYJly/Zxzz0JfPFF/QeGZhbq2bP2vrPPHuXCrOoPzjUGDh7cVlOE3pmTyR/i45U2Fsol+VNPTaS62nuVufqgtBR++kl9/utf4Y03jlBcfEQ3rc4ToqJUJbFwQpu40tLaEKvDHL1iBfzyi7ruSCfS+p49/8Zbb1VQWalP3K/FEv6+8wStP3fsWM+aNXtrak/UFYsXK0tAQoLKL/EGr4JACPFPKeUT9s9XSinfd9o3WUp5X71aqAMSEprUaLOB4ujRfUhZTUmJSpYuLNzjMdKla1fYv9+zrVuPsNG4ODW5e4pnj41V+xISEmsGRn3i3vfuhV9/VZ89CYK6INAaA7GxKpzSGU2aKOdwKE1CYGyi3tKlKiqrQweVC3H77R9SVlZMdLS+doeoKNV/NptiWQ11H7pDT2dxSYkq2AMwZEjtlasQgqgofTih09Iixy/gDO19P3r0CNu2raiXIDh2DGbbF7tjxihKGW/wZRoa7vTZ3Xh8YR3bpiv69h3C1Kk7gyI9i4urXV1C0wo1JCcrrdxbeGigYaMxMa5c5s6UFPHxju8mk6OAitWqXm6bTf3X+Op/+ok6cegvXKhI+goLVXhcy5a1jzlwYBvHjh0M/uJ+kJysBKpm/jGZVFZ4mzbhmcCMTNT77jv1X6OWtlptJCSkYbXqmxjRqpUqz9i0afiFADjG95492/jzz1/qfJ3qapg6VUUL5eTAhQbPMGm+U4TCBscKqy3dutXPH/L++6o2ddeu/jmufAkC4eWzp+8NBt4mPGetULNLTphQu9ZtMGGjNpujqlFsrNJwoqLUC6zVQTWb1YQZG+vICNXwf/93A6++2hKLpZj16/17/t2xbZvSsCoqVPTF/fd7js7517/O4vrrm/Daa+Nr76wjkpKUdmwyKWGWkKBiwcO5HJ8wYRImk+vErEft4v37Yc0aJcT79q3XpXwiNTW0/PiBQKPN2Lz5AN9//06dr/Phh8p8GRcHd9xR2wH+2WdP8fjjA1m58qt6tFbBajWqSE/9ofVneXk6qamd63ydzZuVEmgyqWQ8f6sfX4JAevns6XtYcOzYQUpKjgV1Tny85zdJ0woTEhyDZNCgfCZPnkF2di5CCLKzc5k8OfCqZDabmtijohx0ycnJjnA/IZRmojn8kpNdIxi2bt3AsWPbSExUZCtz5wb+O6uqYPp09X/AALjpJv+DPyVFH6a3uDhX+7/JpLKYw+3YVOUqXwZyAVFTZa4+/oHKStXPoGgkNEE+a9ZtvPTS1Rw9ur/e7dbgXs0sEtCzJ8TElAO9qa6uGzP9smXw0UdqvIwf7zmAYPPmX1mx4kuOHTtQr/ZaLJGROOYNrVurv+PH4e23gz+/vBzmzFFRQlIqf1Ugv9eXs/hUIcQxlPYfY/+M/XtA610hxIXAc4AZeFlK+ZiX4wYDHwCnSSkDjkj6978vYOvW5fTqdRkTJvifJQsKZlNcfKTW9qgoK7ffPonOnWtPVlpiV7AQwmE/bdrUERYWF+dKo+C8AjCZXDX2O++8m44dzyEhoT1Tpyqnz6JF0L+/73tXVamVwJYtSgMfOdL7sQUFs+3xyoIFC6aRnp5Tr4kxKkqtBCLBbOEJPXrks3BhPn36qKS6+kCLyFizRgnxESMc+5Yu/YAjR3YzdOij9buJHVZr6EomBoOkJBg+3Mprr8GKFf2oqHAOgfSP7dvhRTsX5PDh3msQX375/5GXl0/Llj08H+ADiYnKb2M2KyUsVDkrdYHFAtdcU8xDD9lYuNBMr161w729obAQnn4aNm1S3888M/BkPK+CQEpZr1dZCGEGpgLnAzuBX4QQn0gp17gdlwDcBvgmAPKA6mrloQo0ieWDDybW0CE4Iy4ugZEj9YkC0pyj0dEOjdj9xQh0kjz//Ato3vwCNmxQk/mrr6qJx2r1Thl9+DC89JIKF7VYVCEUb6yX7lQRhw5trzNFhIY2bYwri6gHzjpLvSy//gpHj9avIPnHHyvfQHQ03HWXq935mmumUlJSSEKCPsbocCaN+YIQKmfiq69UkaOPP1aO3kCwYgVMmQInTiiT2iU+io61aNHFb1lOT2jaVPlVItEx7AkmE8TH76Gq6mXgMZ5/Xpl0W3mJ3ayuVhP/0qVQUKAcxOnpcOutwTEHGJmK0gfYJKXcDCCEeAe4DFjjdtwjwOOoXIWg8Pjjv1NdXV0jEHwhLc17FNDRo/oVXU9OVpqiHtqbEA7t5fzz1UP+4AN44QX10C+5RJlgdu9W/oDt22HBAhXKmJgId96p9nuDJ6qI+oRTZmQYXxqxvmjWDEym+VRVXcAXXxxixIjgJ+rqarUye/dd9YxuuUUt551x2mmDdGqxQqQJApNJ9YNaxUry80t58kkbH30k6NrVNyXKunWKTE4LaT79dLjxRv0n6+Tk2s8llLBY1Oo8mCAPISApKYNTT13F3r2/sG/faTz2GFx9teonk0mZf376SQnSlSuVQ1hDhw7wj38E/x4aKQiygR1O33cCpzsfIIToCbSQUn4uhAhaEACYTCZMJt8qaHS00lSzsnLYtat2nQE9yeMsFiV0qnRgZdq4cT333PMQZWVxXH/9y1xxhdLuP/hADYIVKxwvpDNOO005iPxFRnijiqhrOKWRFNJ6QQiQchpwAQUFNoYPD24CWrpUJTwdsldHzc+H3sbWbAciSxBoVNeFhepzSclRnngiBYvlSSorJ/D880oJcZ+Ed+xQkSy/2IOLoqLg0ksV+aE/c82XXz6H1RpDv35XB0QzER2tItTCieRk5UMqLFTfPb2rGsxmx5wRG5vAPfd8TmUlPPGEMglPmaI0/U6d1Gr/iJOFu2lTZT76y1/UKqAuAjVsyelCGaafAa4O4NhxqLqUfukNPEEjNJswYRL33TfOpfqXnuRxWoUok0mftP9NmzYyf/47WCxWrr/+ZYSAgQOVWeiLLxQZ3d69aoC0aaMm4i5dPBdu9wRvBXjqEk6ZmBi+8n7BwGSCG24YxqxZJzh8OI4VKwK3wS5cSE3ZyYwMuPhi5Yh3R2VlBT/++C4xMYn07q1Pae9IEgRWq2pPYaHqz9jYJGJiEoiJeZH09DvYsMHE/fdDXp6ajAsLYfVqRXgIapIeOFDVwgjENFddXc2bb96BlNX063eN3+NjYpRmHA7qDW1CN5kcgR8nTijfo80Ge/bUVhKFUONp92713WRSx1gsKvR78WLFvXTggKOOSG6u8hWeeqo+nFZGdtUuoIXT9+b2bRoSgK7AYnv2ZTPgEyHEpe4OYynlDGAGQJs2vWsWWjfemEVVVQUPPvgd2dmeefRTUhxp5M70zrt3b9edPE7vgdelSxdatepIfHwLl+1JScoxOXy4MgPVpfJVQcFsSkuLam2vazillgcR6TCZ4KyzhlNYqKIyXn8dOnf2bcqTUoU3fvih+j5smNJkvWmxJSVHefHFq4iPT6N37/rnZ8TFRZaDMzraUQGtslIler36qoolKS2F995TdBvffefIrwB1fF4eXH55cGGw1dWVXHzxnZSVFWOx+PZEJyUpc2ioghWczT8Wi1LG9u5VvkJNM3deKWdkqHBj5wS82FglXLXsX5MJKislJSVHiYqyMWCAjXPPVcEf69erib9HD31NaUYKgl+AdkKIVigBMByoiV+x10GuiSoXQiwGJgQTNaTVCPBVTSzHTbmtaxRQINBbELRu3YpPP11bQxbnDiHqLgScncQa4uPTGD36uaD9AxZL5MW3e4M2oV50EcybN5v9+ydy9dXbSU/P8UiPceKEctD/+KPq76uv9l8312QyceaZI/1WuQsUkbAacM4Q14RmfHztrHGbDUaNUtrqypXKd5WUpMxEPXrUbdVosVgDqpeRmqpWIKEUmjExavwfOaJMQWazb/4sq1WFc544oTT86mrHyiE5GYqKVPufeuoyli//lLvu+pSePf+GyaRW/W3aGPM7DBMEUspKIcQtwHxU+OirUsrVQoiHgWVSyk/qe48xY6Zz5MieWjWHNWgZwqFCMGFzgUBzFhcUzLbz+nifsIKBt3oCNlt8nQrKtGsXWRqrLwgBn3/+DF999SKFhTvQSP2ci+r85S/57N+vJrE5c2DfPjWObrklMIqO+PhUbrlFHyK7xMTIWG3FxqpJq7TUERXmqxxmixaO6nehQFxc3e3j9UF0tLp3aaljQg9EIYyJUSbdgwcdc5RmWgZISEgjJiaBsjLPdT/0hpB14S0II9q06S0nTQps0dCpU/3CA4NFerq+2lt1Ndx11+NMmfKgSy1hqzW2XolQI0aY8JQTKISoqdfgCxaLo4ZAUlJ4aZCDRVERDBt2LmvWLPK432TKxWze6kLyl5urEp1CUbzEZFKabVKSw64cCcjOVlxKhw87VtlSKmG5dCl8+OGj/PzzhwwZ8i/d/CIaSkqOcuTIHpKSmnpNCO3aNTwrp6wsJRilrJsQKi93DbfeuVOFNhcX61uUJjYWLrtM/Cql9Bja0ED0uOARGxtaIQD6rwgAnnnmnloF5d25kYKFN4d7IE5is1mFBjZpoqKSGooQ0DQtkwn69r3S63HV1dupqFCTcdeucOWV8PDDwQmBysoKSkqOUlkZHGVsZib06qXMGxqxXCTAZFJjOzbWtU3OmeNHjuxm27YVHDy4Vff7r1q1gAkTOjFjxhiP+zMywiMEhHC883Vdibjn3GhWgFBVJtPQYAXBpk2/8O9/X8hbb3mOOg1HGrnek+KcOd7NC/VhzPRUT8Cfk1gjxguX5lUfaHbZpCT1O84//0bS0z3zNaSk5PDqq4oAbeJEFdoYbILc2rXfct11yTz2WGDMaSaTcljn5kZmRrb2+02m2vz9Qqi/AQNuYNKkZZx77jjd728ymWnWrJ3HZ2Y2h9YE5QyrVX9TlHPuUCjRQPS52vjii2dYuXI+K1fOZ+nS913s5omJoXdemkz6vsSzZ89m3DjvL1V9GDOd6wkcOrSdtDTffgezWZnZGpoA0KDZsuPjVcQGKGHo7jC3WmMZOXJSvf1KVVWV2GzxxMQERq7Uvn14SNAsFtU3x/zQdTlHVLmverWJKzf3FP0baMdppw3ymqCXmRm+VakRGfSaYD1+/BAvvJBPcfERHn00aNKFoNEgBUFBwWx++eW/Nd+dHX15eflhIefSezBOnDiREveQDDv0YMwMtJ6A1aqccA1VCIDDiadNWiUlx6ioKKVv3ytZs2ZxQMIwGHTvfiGvveajCogTWrUKbTa2zabs0tXVyrQXE6PYaU+cUM/aU90LXxOeNnE5Y9u235kz5x769BnMued6NufoAS1cM1wwUhDExSWzdu23VFSUUlJylNhYY+3cDVIQvPvuRK9288svzw9pDVKLxZhJcvt276af+jJmAuzYsZpXX72JzMz2jBs3s9Z+zSGcnh45PC1asl4wRVCiolztuCYT7Nixqsbe3LlzfwYPfpAOHfLIzNS/mL0vNGkS2lKdoEw7JpOKV9dWPk2aKMFgsShn+uHDaoUSFaUyqH3lWDibMr799nX++GMhMTGJ/P77PJKTMw0VBKEypVmtqn8qK7UEOhU2a5Qg0HwE//zn5zRpkovNZjxtb4P0EfiiRgj1asBmUxqd3lpdjnsChB1Wa6wuWuuff/7MunXf8cMPDq5bi0Vpz0lJ0K2bmiAiRQiAmsQyMoKzoTorBdpL1qxZuxofyZo1i5g+/VqvjkijkJjonUjMKGgMplFRrqZT50z4+Hhlc9co0f2ZXpwFwbJlcykoeItmzdpxyy1v063bAH74oe41CjS8/PIN3HlnR1au/Lrmnu3aqfEZCsTFqSSumBj1Pz1d+ZyMFAQAXbueS0ZGG0whcBo0SEHgLeolIyMn5PZCo6iBJ02aRGxsbYfu2LEzdLl+ZmZ7OnY8mx49Lq7Z1qGDEgCdOkUO5bHVql6M6GgVThkVFXhcvdnsSiuuLbuTkpoya1Yx5547tmafnlEaBQWz+de/+rFwoednlZjoKNoTSgS6cnUW/v4i4ZxNQ/37j+Haa1/itNMG0bv3ZUyffi0vvDCy3jUZEhLSKC8/UcMcnJ0d2gpjWtJYRoar49wIJanRWRwEhg2bxNSpf3fZZrPFcvfd+nAGBQOjJsz8fKX13323osPQ04YN0KHDmTz44Lc135s3D3/hGHdoHCyKKM6x3WZTk6mzk1MrRm61Km6bY8eUxuhsOtBe3O+/n80772gJerm69ivA/v2bWbfuOzp2PKvWvpSU0CfgZWQohkojTJjOE1fPnn9z2de79+WApLT0OElJwWfFLVw4g0OHdtCu3Rl8//3btGzZA4tFH26dQGGxhJZW3Vm4FBbuZf78F5CymuHDJxt63wYpCPLy8nnrrQkcPboXEGRn68sZFChMJmMHSX5+Pv375/PTT/UrXu8LcXFqAg3lyxUoEhO924BTUpSDU0v8Sk93xLinpqpJz9Oz+fjj2cyY4YgWcg800AP9+l1Nx45nkZrqGsPcpImiWgiluS06Wmm0RmXYuzuLly79kO3bV9K371BuvXVOva79/fdvs3btt0ycuJApU7YAoY8SCiUzAbgK1urqaubOnURcXArDhk1CGDhwGpwgMJsV497y5XuorAxvEZRQ3VsIWL36G5544m9YrbHMnFl/IrN16wo4enQXl13Wh8zMEBurA4CWtewN2mrh2DH12T35ytuzefJJfWsweEJaWgvS0lyD22NjQy8EwPikNHdTxpdf/of16wuIj0+tUyEZZ5x//o107nwOWVkdAPVMQ62whFMQpKRkMnjwQ2RldUDKalStL2PQ4ASBzeZ4OOGuhBWKzE9N44qOTqC8/AQVFWX+TwoAs2bdytatv/HHH5cyc+bHulxTLyQlqdWAP/NJXcjudu/2HGjgiY5bT4RDCIDxE5m7IGjdujeHDm3nL39RdTurq6vYtWst6em57N27iWXL5tKqVa8aGoqVK78mLa0F2dm1K9mccYZrncWcnNAl3DVposyRoc7udu5PIQRDhjwYkvs2OEEAaskUCk+6P4TCoaoNjJycbtx44/7p0PEAAB+9SURBVCwyMvShH0xIaILVaiMnxyA6wzoiJqZ29qqe8FacCKC0tAibrf6G9GXLPmbv3k307Pk3srI6kJkZPgoEo8eou2lo1KhnGTXq2ZrvTzzxN37/fR7nn38TX3+tihOfddYoeve+FCklM2Zcx6FDO5g8+VdatfLO6JeYqMx/oYDZTEhD0J3hKS8jFAj/bFoHzJz5NK1amTjnnNDGfWtITlZmiVCtCJQvwsbZZ4+iQwcvxYqDxMMPz2f9+hPcf/8zulxPLxjtsL7nnklER7tGY1ksVi699F4sluAeaEHBbMaPb8mIESbGj29JQYGiBFmy5E1mz57Ajh2rMJsVMVk44Fw32yj4m7hyck6hSZOWrFr1Vc22M89UbPSlpUV06XIuLVp0Ize3u8t5JSXHWLFiHjt3qsq2LVvq3nSvCLU5yBnu/VlScoxlyz7hxx/fM/S+DXJFsGXLBkBSVRVEZpFOsFpDmwlqlIYQic5hjfLASFxxRT5bt8KsWYHRa3iDe00HZ6dzr16Xkp6eQ2amWg0YQUbojvh4NYFVVysnenV1aFYh/sIdhw+fzMiRj1NScpQ///yF1NTsmiJSMTEJ3Hjj63z22VO88EI+48bNrFmR7dz5B48/fhFt2vRh2rSlho8LZ4RbEDj354EDW3n66cto2rQ1Z5wx1LD7NkhBMHnydMaOnUBZ2YmQ3zvUxdmdBcFLL13Dtm0ryM9/im7dzqvzNSM1SigU4atCwLnn5tO7d/0cw55qOmhO5+ef3wqMIioqNP0shIqr18ZJKMOA/QkCLT8jNjaJbt081PUEvvvuDXbsWMXAgf+gbVtV1txsttK16wCaN+8cEvpvZ0SSIMjO7sQpp1xAbu6phprEG6QgMJlMtGnTIeT3tdmM11jd4Twwli//lKKiQ6xfX1BnQaBFr7RrZ0XKaubNW0Xbtp7LfIYKWjZzKJz/nlZY77//AL/99gX9+1/H+effGNB1fGW3g/pN7duHJtTRCBbMQBHoirWqqhIpqzl+/BCbNy/DYrFis8XTrt0ZXHLJXVRWVpCe3rLm+DZtejNx4tc0aRLaiTk6OvxFlpzvb7FEce+98wy/Z4MUBKFGdLSKIggHy6Hzi5aXdxUHDmyhU6faiUqBQmVkVtdw5TdtGkbWLtSgT0sL3cvnSYP99ddP2bZtBSkp2QELgvT0HI+RRmlpOezdu55u3UwIkQsYL93CnQXu79ndcUcH9uzZwIUX3orNFs/cuY7kqIEDb+eqq7z7qUJFKhcdrUxpoTDj+UK4BLqhU5sQ4kLgOVSpypellI+57b8BuBmoAoqAcVLKNf6ue8EFXamsrGLq1Pfp2LGrAS13RTircDlPXKNHP+v74ACgmbaWLNnKrl3bSEwMsa3LDYGEieoJT4LgzDNHkpycyemnD651vKcyoaCqZrlDY4V9/PEL2bt3K998s4FWrYwPaAinIAhkRTBgwA28+eYdtGzZg/j4NLp2PY/jxw9RVHSIiy++0+M5lZXlpKZGERtr/MwohDKXhlsIaG1xH59SSo4c2cPRo/to1apHwNeSUgachGbYKyhU9sNU4CKgMzBCCNHZ7bC3pZTdpJTdgScAvyEshYWH2bBhNZs3r2P06L8yd64+tWG9QavMFC7oyT3y44+zueCClrRta2H48H7s2bNDnwvXESZT6Hn4PfVnSkoWu3at4aWXrnaJ/tEcwkrzlxw8uI1p065h+vRrKSkpdLlGfHxaDSvsddf9E5PJFDIhG25B4G98XnjhbTzzzAbOPns0vXpdwsSJC3jssd947rnNpKZmI6Vk1aoFfPnlc2ilc5955goGD45nyZKvDf8NSUmRIQTAc39u2/Y7N9+czQsvjPB6XlVVJQsWTHfZ9uyzg9my5beA7mukLtYH2CSl3CylLAfeAS5zPkBK6VwSIw5PhXTdsHOnYzm+f/8e7rtvnKHCIBwFQ7xhz56NfPHFf1iy5K2gzy0omM306ePYtWsbUkp27dpmeN/5gtWqlv2htse6a7CeJvupU//O1VfH8+KLo2o5hKuqKqisrM33YbPFk5eXj9UKY8bcyIcf/khamvH0mM7MoeFAIILAZDKRmdmulnZqNjsa/vzzI3jjjX9w+PBOAE6cOEJpaQkJCcby8MfEhL6krS946s/s7E4kJjYlNbU51dVVHs/76KOHee21mykuVgrK1q0r2LFjFQsXTgP85+YYOYSyAWeVcydwuvtBQoibgTtQxtRz/V1UYyDUcOJECU89NVF3nqHEROXwC7f9VQhHNuXChdP4/PNnSEnJ4qyz/u77RDe8++5EyspcJzWj+s4ZnoqdxMe7RrmEEu4vmqfoH4CysuKgrnvo0HaklKSnqx/VvXuferUzUETC+KzvcxRC0KfPYMzmKIRQD2fWrAJiYo5jsxnnKU5ICC2LaSDw1J9RUdFMm7bXq5lHSsn+/VuQUrJnzwbatu2DECZKS4tJTs7EbPZfujfsCWVSyqlSyjbA3cD/eTpGCDFOCLFMCLHM035vtAF1hdmsJGi4XzJQg0JblZSWFiGE4MiR3S4mjEDgLcpF775zRkqKSqZy1riSk8Nb7MZdEHjrl2AhpeTGGzOZP3+qLtcLFOEeo3rluYwZM41rrnme1NRse3CGICEhkSiDbDZCGJvBXld4609nIbBv358uSp0QgptueoNJk5bRtq1SQHJyujF16g6GDHmIrCz/pi8jBcEuwJl5q7l9mze8A3gsTCqlnCGl7C2l7O1pf1ZW3ev3ekJCQuQUZFH8+Yo6ecmSt2psqFoCU6DCICPDcx8lJRlT3DklxSEAUlJUn2Zmhj4Pwx3ugsBbbYu64OjRfUyZcp/h5rboaKUcZGSE36xhBH9+drbx719MTPjDRD3BX39u2bKcp5++nGuvTeSnn953Ok+4OJKFEJhMZsxmyeefP8Pu3b79gUZ2xS9AOyFEKyGEFRgOfOJ8gBDCOaTiYmBjsDeJiYllwoS61SHwLHkji5dfMw299573BCZv0CgQRo40UVbmuYZucfFx3ScuLS/AGWlp4ddeobbGNWzYpJpqZXW/poMJrajomKG+l9RUJVBTU9VkFm6FRQj9fBSlpcWsXj2PLVuW8Pe/n8+TT96nz4U9IFxcQv4ghHftXUrJDz/MwWQyc//9izj99CHs2rWWnTvX1CiI7igtXcuLLz7KmDEedewaGOYjkFJWCiFuAeajwkdflVKuFkI8DCyTUn4C3CKEGABUAEeA0f6uGxMTR3l5KdXV1WRlBV+HQNOw4+JUh5eXQ2kplNlJPW220DEcBgLtRT9wwHcCkzvcKRAKCw97PK6iolxXP0FMTOjIweoC9xdNo5aYNes2iooOBX09k8lcy4FnlO8lNjayghfAoTiZTIrWoj6YNu1qli79gLPO+ivff78Ai0FecCHCmz3sC0Iov1pSEhw96r5PkJ//pMu2uXMnU1DwFtde+2KtHBgh4I03JlJdXcU//vEg113nEqvjAkPjDaSUXwBfuG17wOnzbcFes23bjnzyiUdXgV9YrarMofP4slrDT2ftC5ogyMzMYffu2glMUkrGjk1n9OjnXPhyvDlBPUEvP4HNpvo33FqqL3iaBPLy8snLy2fECO8NF8KMlK4TvsVi9RhBBMb4XiLNsQkOU0Zysip6Xx/06TOAEye2MmTI1QwfPpa0tOCrmgWC2NjINAuB491p0qS2IPCEhIR0kpOb0aFDXq196enw0kvvU15eRmKi7yVQhHaH/hAifNnB9YE2MO66qzZrpoaiokNMn36ti78gGCeoXj6W1NTIFgKg2uctLyQ9Pdfr9ptumkV8vGMmjo9P4/rrX/V6jt5+q6ioyFqpatCetx6O15tuup6PP/6FSy8dwcCBQzj99LPrf1FUvyUlKUGalRW6ovd1gdafqam+56r9+7fw1FOXsXv3Ol58cTctWrgm1sbGqvoNFouF2Fj/drAGJwhWrfqVM8/MCdoGm5ISOUkjwUAbGIMG5TNx4gyvRdYrK8td/AWBOkHr42Nxhs0W2SsrDZrPxROFuCd/gZYtnJeXz8yZB5kzRzJnjmTmzIPk5eV7PEevPnVGJPhXPMFZENRHCWjSxBha99hY5XzWAhYifYxqfWgy+TaxxsensmLFl/zxxwJKSo66RBXFxUHnzsHNdw1OEADs3r0jKIdcJNpW64L8/PxaeRTOcPYX5OdPwuxHhczOzmXy5Bm62LIbSv9q74unVUFeXj5jx84gPT0XIQTp6bk12cLekJeXz623ziA7W52jZ586I9IFgcVSvzHgL869LoiOVqbKSDUDeYKzMG3qwzIWG5vE7bd/yJQp24iLc4TiWSx1IzsU3rzNkQohRE2Ds7NzKSjY6vN4i0UtBxvSYHBGaSns3ev43rdvS/bt81xhKz09106BDB07Qt++MZSVlXo8NpC+CxRWa/iKrwSLkhLYvx927lR/eqBzZ+MFYVZWZGqzlZWOfjxwAP78M/hrNG2qGHH1hMmk+qyhmYIBtm1TZTIB1qxRdbkDRfv2nsu3WizQooX41VsIfgOdHhUCcciFktnSCLgvt++6axJms+c1X2lpEa++ehPjxqXTo4egrKwUIUy1knL0Ml3Ex6tQxoYiBMD3iqAuSEw0XghokSSRCOfxWRc/QVSUsmXrCbO5dlBIQ4JznwZaz8JkgjZtgq/hraGBdpWCP4ec1Rq5YWKBwl0QDB6cT3k5TJ5cO9yxqOgQX3/9kss2KaupqhKkpKRRWHi4TiG37oiNbfg+F73iyI0wabgjUs1C4Do+LRYVPVRY6P14d7Rure+EHROj/A0ni/KnMRxo4e2ekJio+rE+PpYGKwgC0Wobit3aFzw54EaMyKd//3wuuaSlR058d1RXVxEbG8/y5Qfr1ZaoKLXCCkWtZqOg9Wd0tNIcqzxzeAWEUKwGoOEIAlDjI1BB0KyZvjQPmk8g0iPX/MG5/UIoTX/tWoe5SEN0tFJE9IiCapCCIDs7169WazZHbvZgMPA2qJs1855M5gn1jWtPSGgY4aH+4Nz+2Fg47jnhOiAYWULRYnGQHkay4HUfD1r0kD/XY7Nm+hakt1gU5UZDH59Q+zckJkKHDrB+vaNfW7TQl723wQmCbt16BZRQVt9wtkiBr9+QlZXDrl3+VwTasXWBVkHsZBCq4NqfiYl1FwStWunP8xMVpcatxRK5PgFPcJ74LRalle/b5/14vYWAEA0vOihYJCdD9+6wY4dSyOrqC/CGk7Lr4uPV38kAX4JgwoRJxMT493pGRVnr5By22ZQj+GQRAuDan3UlwGvdWmmfeiImRml4sbENSwhA7TGam+t9FZORoa8QACU8G1qf+YK3dz46Gtq21V8IwEkoCDQ79skCX4Jg0KB8Jk9WMexATd6Ac3JJSkoaTzzxalDOYY0qolmzhht54Q3O/ZmQELzDOybGd3x3XZCUpCbIk0WjNZmgXbvaYyc3V62k9ITVenL4Ap0RDkvGSfaah6/giVHw91u0Cf6++8Zx4oTiFpJSEhMTG3RiU3y80q4ikcpAL7j3Z3Kyin8PFHr7BdLTG/7q1dMYjYtTpoxduxz1PYxYWUZiTYH6Ihzz10migyjEx0e2Y80oPPXUxBohoEFjwAwU2krqZBYC4Nm5GShsNn1Xm0lJDV8IgPeJy2JRq4DmzY0RAjZbww8P94TGFUEdoRWfDmeReSPhLwrDW0RQoJFCGiHfybSS8gb335iUFFiUC6jEJ736KDHx5NFmwzFuEhJOnv5zR+OKoA5ISlIDIj7+5LGxusPfwPAWERRIpJDmED6ZnG3+4NyfZnNg5p7kZH2cdEIof4ARDr9wIdQTl8Yk+r/6vhuBBtmVFoua+E8mrcoX/A0MT9FDgSTcaWF3DTFDuD5w78+sLN9JW0Lo4+TU+vtkM2eEcuIymU4+57A7GgVBgGjSRDnZTiatyhcCcRhr0UPBMGBGat1Wo+HenxpPi6e+MJuhUyd9snu18pInG0I5cSUk/O/5sUKBBucjsFgiO+XeCAQyMAYNyg+aP+hkyg8IBp76MzFRRbns3q1oJ0wmNeGkp+vje4qLi6xa2HoiVBOXyaR/El8k4qQTBEKIC4HnUDWLX5ZSPua2/w5gDFAJHACulVL6TJVt1GD1u+bJqJ0GAm/9abXqn+wESnk5mXJb3BGqiSs19X/j/T+pTENCCDMwFbgI6AyMEEJ0djvsN6C3lPIU4APgCaPa05BhxMD4XzULQehftOTkk7uvQ9GfsbEnR6htIDipBAHQB9gkpdwspSwH3gEucz5ASrlISqkFwP8EhIDUt+HBiIHxv7DE9oZQvmgnC/mhLxjdn0L87/gD4eQTBNnADqfvO+3bvOE64EtPO4QQ44QQy4QQyw4EkwbaCI9ISPjf87M4I5QvWmLiyZ+fYfTvi4s7+ahOfOGk8xEECiHE34HeQD9P+6WUM4AZ9mMPCCECo9z0j3SgfiT9oUFjO/VHQ2lrYzv1xf9yO3O97TBSEOwCWjh9b27f5gIhxABgItBPSumjDo+ClFKHMgw1917mrYZnJKGxnfqjobS1sZ36orGdnmGkaegXoJ0QopUQwgoMBz5xPkAI0QOYDlwqpdxvYFsa0YhGNKIRXmCYIJBSVgK3APOBtcB7UsrVQoiHhRCX2g97EogH3hdCrBBCfOLlco1oRCMa0QiDYKiPQEr5BfCF27YHnD4PMPL+AWBGmO8fKBrbqT8aSlsb26kvGtvpAUIGQrvYiEY0ohGNOGlxEqe5NKIRjWhEIwLBSS0IhBA6V5Y1DkKIBsGjKoRoEGQJjf2pL4QQukXrGYmG8s5HWn+elIJACBEvhPgP8KUQYroQ4opwt8kbhBCxQoipwDwhxHh7JBVCiIh6NvY+fRb4XAjxqBCif7jb5AmN/akvhBA2IcRLwCJ7oMe59u2R2J8R/85Han9G1MPUA0KIbOBNQAADgW+JbA6jO4A0YDRgQ4XTIqWsDmejnCGEaAf8F6gCrkURBN4X1kZ5R2N/6otrgaaoZM8twKtCCFuE9WdDeucjsj9POkEAlKKYTm+TUu4F3gNWCCFOCXO7aiCEsNn/WwAr8LaUcp2U8knggF1TDLuW4IRiYIaUcoKUcg0qEmyPECIiuKEa+1NfCCGc6d0E8KOU8pCU8jXgR2Cy/bhIIc+I6He+IfRnpLwYdYYQooMQYpoQIgZASnkIWOx0SAugNbA+DM1zgRCivRBiNvC8EKK3PdciHjjD6bAbgKuEEM3DpSXY+7RGo5JS7saVByoW6Cil3BnyxjmhsT/1hRCirRDiPeB1IcTFQgiNkaqp02F3AZcLIdpIKWU4Jq+G8s43lP6EBi4IhBB5qCXhOJRJACGEkFIWOx1mBbYGQl9hJOyDdjrwO7ASuFkIcR3wOHCDECIdQEq5A3gLGBumdl4MfARMEELcY99mkVIWOR2WSvhfssb+1BH21dJ/gD9Q79QlwAPAG8BAIURXALuw+hi7KUuGOP68obzzDaU/NTRoQQAcQtnc2gPXCCFyPXRkD+BPACHE2DAuF9sAxVLKJ6SUzwMvA5cDMcBLuCaQbECxtYZjubgPyEf16d1CiHgpZaUQwuTUls7Aanv7Rgoh2oe4jdDYn3ojEygEJkkpPwYeAc4H2qEE7kThiMiZB+hF/BgsGso731D6E2jggkBKuRZV82AT8DXwMNSyBZ8HpAkhPgRGouyJIYeU8g+gpRDibPumlcBC4J8o0r1UIcSDQoihqKptJ+znhVRDkFIuA9bZ+3QeMM2+Szi1JQ9oIoT4L2qSqwhlG+3tbOzPesJZKEopd6EYgM93+v4SMNkuaIuBfwkhxqBWXYdD0UYP7YzYd965DZHcnx4hpYz4P9TSOdHpu3D/DCQAm4Dz3M79EqVtDQlRW9OBDOf2ASb75/HAW077ugOv2M9pD4wCvgLyw9FOD32aiNJqTnPaF42adH8FhoagnZnAX9y2RWJ/1mpnBPfndW7bzPb/VwMFTtuTUY7X7kAKyrzxTgj78zov+yLmnUfVWHkKsEZyf/r9HeFuQAAdfT+KtO4d4CH7NpPbMVrH/wP4zP55hH0SPieEbf0/YB2q7OZjzm2zf24LfAiMtn9PQ5HyNQtxn3pqp7c+vR9YZP98of3/oBC2dTVKk+5p/+4ssCKiP/21M1L6037vX4E7vOw3Ad8A/3Da9gbQNcR96bOdbv0ZtnceFYjwB/ACKkjB/R2KiP4M6LeEuwE+OjkOxU76LpCB0vCOAi3t+2tpsPbPR+zHvQLYQtRWG/AYSto3sbe3BEjVBoTTsX9F2S97AkOBRUBOJLTT7VjnPq0EjgPPAVEhaqvJ/nJ9iVo63w7ERVJ/BtBOb2M0HP35lP2ete7n1rZeqPj2QcDf7RNy5xD2p9d2+mhzSN95HCvS571N6jhWLWHtz4B/U7gb4KEDk+3/LcA5gMVp30zgWi/nJaEEx0rgzFC21f450+nzOcAcoIuXwXEjMMU+KAxva7DtdNqfbu/z30LdTqdtLwAT7JPm2ZHWn4G0MxL6E+iEqguegFKqrgX6uh2vTXCXAf8CvgPyIq2dTseG9J13a6cJWIVSprrY23E1TkoVjlVLSPuzTr8t3A1w6rQ0+8v0FXAr0NZpn0CFhC0Cuns53wScEqa2drRvt6A01K3A08DPqOWqNiCcNVlzpLbT6XxLiF4w53aOBzrZt7cFXrd/vgOlcf8DaK6NizD2Z8DtDHN/3urUzodQjugfgAdRkSqjnManMLpterTT6fyQvPMenns3+/bJwLP27dejEgMnO71nJqPbptdfREQNCSH6opbWB4FHUQ6YW50OMaEGRjkeyl2CohCQUq40uKne2nqTvQ2VwHKgtZTyTpQWcIe9/UinhCYpZVWkttOpjZVSyu9D3M7mTu3cBBwTQpiBDsBtwOnSnngl7W+b/XOo+zPgdjq1MRz9mY1aMYFarTwMDJBS/gtVOOomp/ZJQoT6tFNDKN55L8/9OvtuzST5vpRyOnA30Ax7wpiMIBoOf4iI4vWoSIpnpJTvQA1z5Hn2TLwKKWWVEKK1/fMBoQilrNrxEdJWG1AmpawpOC2l/FwIMQE1yLc2tjOodkah4uu7oZb+f6Lsx1II0U5KubGxnQG3c4D9uR+VUj6iHSil/FQIcQdqcgt1HHtDbuf59n3forLYe9rbucqeyNhgBICGkAsCexagi+YhpVwnhNjhtK8CZRpyzgw8D4gRQryB0rrujrC2ljqfI4TohNIgtgG7G9sZdDsrgN+FEAXAEinlV0KIVqja14bGhZ+E7WyjPXenczujnvtWIue5N5R2trbv2ySEeAG4Ryg+q9aoVfUWI9tpBEJqGhJCRDl3tFuiSLHTvpYo7coZ6UBXVFzu6VLKxRHa1mghxFWoaKdvpJRXSynLG9tZt3ZKKe+XUn5l/7xFSvlvqWgjGttZh3YKISxCiMtR4dgLpZTX2IVZYzvr9txXoMxWBcAnUsq/SZU81qAQMkEghLgFWCAUB/cloGySztl4Tp9bA0vt264QQiShaHtbSikNr+VZn7aiCMTmo2zEUxvbWe9n38zItv2vtRMVafMt0CeSn3tDaacQIktKeVhK+aGU8hUj22kkDBcEQohUocw5FwD3opwuo+3L5xqHihDiFOlwrnQG2gshvgSuRPkD1kkpTzSAtv5/e/cTYlUZxnH8+zj+ITTcWFBQiTBWROYiCFyEBAlGRJBtgoI2QWFtat/GhRAIgkXtzBbRom3kqkUURRAatHYpWlik0gjeeVq8Z+gydL1NnT/3+H4/cJi5555z+Z0zAw/3vP+WMvNyl1kryvkCPTxvrSjnEUpPlivrH8GY8z//3W92la9X2X3XqyXKTIFrfb73AKdp+q5TWtnPAF8D9wL3A39QppXtbQTrmLKa05zmrCdnL/eig5u7mTKw5r6pfTumft9EGTCy3Lx+Gnhj3We82tM/wiiymtOc5qwn5xBb2zf6UUr/9EvApzOOeRj4YsZ7W9vMcztkNac5zVlPzqG2ttsIfqUM9X+IMkXwIYCIWJpqhb+HskYrEfFENIt2R0Rkh71WRpzVnOY0Zz05B9FqIcjMi8Bnmfkb5Vnb2qo7E8o0EQCPAVsj4j3K8Oy1c3sb1TimrOY0pznryTmU1nsN5d+9UM4AKxHxVrN/tam8TwJPAVcy80BmftV2hn9rLFnNaU5z1pNzEF0+d6J0y/q++X1f8/M5phprFmUbS1ZzmtOc9eTs7X70cMO/BG5QZubbNfQF3w5ZzWlOc9aTs4+tswFlURbnPkZpiT+amc/k1ERni2QsWc3ZLnO2y5zjtTaQopsPjzhMmcfmxtyDBzaWrOZslznbZc5x6rQQSJIW30IsTCNJGo6FQJIqZyGQpMpZCCSpchYCSaqchUCaIyImEXEuIn6OiPMR8XZMrV4145zdEfFSXxml/8NCIM33Z2buz8xHKHPUHwbenXPObsBCoFFwHIE0R0Rcy8wdU6/3AD8Au4AHgE+A7c3bRzPz24j4jjJy9QLwMWUK5OPAQWAb8H5mftTbRUi3YCGQ5lhfCJp9vwMPAleB1cxciYhlyqInj0fEQeCdzHy2Of414O7MPBYR24BvgBcz80KvFyP9g81DB5BGbgtwKiL2AxNg74zjDgH7IuJI83onsEz5xiANykIgbVDzaGgCXKa0FVyiLGqyCViZdRrwZmae7SWktAE2FksbEBF3AR8Cp7I8V90JXMzMVeBlYKk59Cpw59SpZ4HXI2JL8zl7I2I70gLwG4E03x0RcY7yGOgmpXH4RPPeB8DnEfEKZX77683+n4BJRJynLI14ktKT6MdmNaxfgOf7ugDpVmwslqTK+WhIkipnIZCkylkIJKlyFgJJqpyFQJIqZyGQpMpZCCSpchYCSarcX/I3uiehRV9kAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -528,7 +528,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "step 2194, {'val_loss': '-0.8957019448280334', 'val/kl': '0.002843243535608053', 'val/mse': '0.010960877873003483', 'val/std': '0.12809321284294128'}\n"
+      "step 2194, {'val_loss': '-0.7450954914093018', 'val/kl': '0.003384896321222186', 'val/mse': '0.013488266617059708', 'val/std': '0.12421879172325134'}\n"
      ]
     },
     {
@@ -547,7 +547,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -561,7 +561,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "step 4389, {'val_loss': '-1.1492339372634888', 'val/kl': '0.0010864834766834974', 'val/mse': '0.009058449417352676', 'val/std': '0.08994901180267334'}\n"
+      "step 4389, {'val_loss': '-1.314606785774231', 'val/kl': '0.0010940443025901914', 'val/mse': '0.006733658257871866', 'val/std': '0.06799031049013138'}\n"
      ]
     },
     {
@@ -580,7 +580,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -594,28 +594,157 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "step 6584, {'val_loss': '-1.084285020828247', 'val/kl': '0.00045751998550258577', 'val/mse': '0.01010067667812109', 'val/std': '0.07211266458034515'}\n"
+      "step 6584, {'val_loss': '-1.2472330331802368', 'val/kl': '0.00047912346781231463', 'val/mse': '0.007364457473158836', 'val/std': '0.06327439844608307'}\n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-a695fbb7ccb7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLatentModelPL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;31m# plot, main metric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m         \u001b[0;31m# ON CPU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py\u001b[0m in \u001b[0;36msingle_gpu_train\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_pretrain_routine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdp_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mrun_pretrain_routine\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    839\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    840\u001b[0m         \u001b[0;31m# CORE TRAINING LOOP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    331\u001b[0m             \u001b[0;31m# RUN TNG EPOCH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m             \u001b[0;31m# -----------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m             \u001b[0;31m# update LR schedulers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;31m# RUN TRAIN STEP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0;31m# ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m             \u001b[0mbatch_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_norm_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_step_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_batch\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m                 \u001b[0;31m# calculate loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer_closure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m                 \u001b[0;31m# nan grads\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36moptimizer_closure\u001b[0;34m()\u001b[0m\n\u001b[1;32m    492\u001b[0m                     \u001b[0;31m# backward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m                     \u001b[0mmodel_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 494\u001b[0;31m                     \u001b[0mmodel_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_amp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    495\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    496\u001b[0m                     \u001b[0;31m# track metrics for callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/core/hooks.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, use_amp, loss, optimizer, optimizer_idx)\u001b[0m\n\u001b[1;32m    153\u001b[0m                 \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    148\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m         \"\"\"\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     97\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     98\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 8779, {'val_loss': '-0.7295200228691101', 'val/kl': '0.00029361346969380975', 'val/mse': '0.013804015703499317', 'val/std': '0.06340480595827103'}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+ACQmJpKZmUloaCitWrVycwvdi24srmUSExOrtF+n+uTmgosiBDdKSkpKWLhwIddeey2tWrXilVdecXeTXMbUqVO54YYbyMzMLN8XFhbGJZdcYiUETp48ydChQ3n00Ufrupn1El015ALCwsJszgjCwsLc0JqLl5IS0GxsHTuCl373Vplz587x9NNPl392ZxpHVzN69GhSUlKcGvmfPHmS7du34+vra/N4WVkZWVlZNG3a1G4ZR2zcuJGkpCSioqIIDw+v8vfrmlqdEQghxgghjgghjgshnrZxPEwIsV4I8ZcQYp8Qwn6s2HrMvHnzrBaI+Pj4MG+enoLZlRQXm97n5LivHQ0Zb29vZsyYwYwZMzh9+jRffvmlu5vkMlasWMHPP/9s0XEnJSXx/PPPs2TJEouyQ4YM4ffff2fBggU264qOjiYoKIhffvmlWm3573//y8SJE9myZYvF/qKiIoYPH06/fv2qVW+tYc+KXNMN8AROAJ2BJsBeoFeFMrHAP43vewHxldVb372GAAnI6OgYmZPjuKzuYVQ1zp+X8tQptcXHS1lW5u4WXRxcuCBlaam7W1E77N27VwKyT58+VfrevffeK1u2bClXrVpVrfO+9dZbcvz48XLHjh0W+w0Gg/Tx8ZGAzMvLq1bd1QUHXkO1KQiGAT+bfX4GeKZCmXeBWWbl/6is3voqCDQWLFgmg4PDpBBChoSEyxUrLDv5jz/+WPr7+5cLDED6+/vrwsAJzp0zCYJTp6TMynJzgy4CiotL5fjxU+XAgUNkcXGxu5tTbYqLi2VeXp40GAwW+zMyMuQLL7wgP/zwwyrVV7EeV7Jt2zb5999/y9I6lr7uEgS3A++bfb4b+E+FMh2A/UASkAlcZqeu6cCfwJ9hYWG1ea1qxJw5c6SPj69FJ+/nZ9nJm88azLfw8HD3NbyBkJhoKQgSEi7ekWxtkZiYKI8fPy4LCgqklGqWFRbWWQJy3759bm5d9dmwYYME5MiRIystazAY5KRJk+QHH3xgtYaguue+8cYb5SuvvFLjumoTR4LA3V5DE4CPpJQdgRuAlUIIqzZJKWOllAOllAOdSTbhLl566SWKiixdWgoK8pk9e3b5Z93DqHoYDFBWZr3PzEFExwlefvllunbtykcffYSUcOECzJ37Jp9/volOnbq6u3nVJj8/Hx8fH6dcYf/8808++ugjZs2a5VRy+8rIysri+++/Z+vWreX7kpOTycvL0way9Z7a9Ls4A5hneuho3GfOFGAMgJRyqxDCFwgCUmuxXXWOeSevexhVD3NDsTm5udC0KRjdxnUqISAggPDwcEJDQykqUsL0qqtuBKCB9Fk2uf766yksLKS0tNTq2Llz59i5cyc9e/aka9eu9OrViw8//JDCwkKbC8oANm3axOzZsxkyZAiLFy+2e979+/czZMgQvvnmG7p16wYoLUt4eDilpaUUFhaWp87UWLt2LZs3b+bWW29l6NChNfjVLsTeVKGmG0rInAQ6YTIW965Q5kdgkvF9TyAZ4yI3e1t9thGEhtpW+3TsGF5eRrcRVI/sbEu1UMUtM9ONjWugZGZaXsOzZ93coFrikUcekYBcsGCB09/56aefJCCvueYau2VOnTolPTw85NChQ2VJSUn5/vz8fNmhQwfZqlUrh+154403nP8RLgB3hJiQUpYKIR4GfkZ5EH0opTwohHjJ2KA1wJPAe0KIJ4yd4iRjgxskzz03j8cfn05BQX75Pj8/f2bNMrmRxsTEcPDgQT7++GOSkpIICwtj3rx5eiiKSrA3I9DIygJvbzU70HGOggL1ajAY+PTTWI4cOUBs7Jv4+rpbY+xaoqKiOHjwIB07dnT6O4MHD2bDhg0WuQQqcvToUVq1CqJTpy54mS1q8fPzIzk52e73brrpJjp06MDw4cOdbk9to4eYcCGpqfDJJ3EsXjyb5OREgoPDmDlzHtHRMYSFgYeHeug8PT0BEEKwefNmLr/8cje3vP6TnFy5MPD2hpCQumlPQ8dgAHOz1NChIaSkJLNt21GGDOnmvoZVk9dee43/+7//Y8aMGYwZM8Zuuccff5w2bdrwwAMPOJXX2BGlpXDiRBHe3jns3r2Ro0ePMm3aNOqrHVNPXl8HJCQk0K1bJL6+fuzbl2V1vLAQ/P3VSk5vb29KSkqQUnLmTEWziY4tSkqcK5ObC82a1X57GiLnzp3jkksuoU+fPnz33TqLY1OnPokQAi+vFpSWNrxV23v27CnPNWyPzMxMli1bhsFgYNq0aTU+Z06OWjjq69uGRYsWsWPHDkaMGFFvBYEjGtjfXX9JTU2jpKSYsjJlrHrggdvYtm09Tz/9GuPHT6WoSAmCbt26UVxczJdffomvry9XXnmlm1te/ykpcd6QmZmpZg5+froBuSJnz54lNTWVtLQ0q3hNU6fOKH+flwctWtRx42rI3Llzuffee+nbt6/dMhcuXOD999/n7NmztG3bttI6ly5dSlpaGnPnzrWKFvrjj78QGTkKT09viopg/PgJjBw5krZt27Ju3TpmzZrF9ddfz8svv2xVb35+Pvv27cNgMNQb9ZCuGnIRWVn5fPXVbxQXF3H99bcxaFAH0tPPARASEs4zz8yjZUuYPXs2iYmJum2gCuTnK7VbVfDwUGoioxZOBxU/JyUlhby8PAICupXbCCrStCk0wEGtQ5566ikWL17MokWLmDlzplPfadOmDenp6Zw9e5b27duX7z9/PovWrQMJD+/C+vXHEELQpo3JPvW///2PSZMmMXHiRFauXGlV7/79++nXrx89e/bk0KFDLvl9zqCrhuoALy9/rr76JgBWr44jO/t8+bEzZxJ44on7EEJQbFR06zkLnKcy24AtDAbIzoZWrZQu18NDbY0ZT0/PcuNnUpLlMSklBw7s5vDhfUyYMAmouX99faJ79+60aNGCAnvSzwaPP/44BoPByv3z0KGzdOoUSYsWgeXrEDIz1ay1aVO4+eab2b59OwEBATbrbd++PQMHDiQyMrL6P8jF6DMCF5GVpTaAqKgIzpyxXitgi1atWpGRkVGLLWv4pKWp5D+2jPCOEAJat4bz5yEoSKnmdFSHlZhoqW6TUjJgQBBZWef5449Ehg4NxQVrreqM119/HV9fX6ZOnWrVcYMKv+3p6VmtJDPmlJWBluOmtLS03FuoqKiIM2cSaNKklKioXjU6R22hzwjqgJ9//pH58+fSs+clJCc7v0r4/PnzlRdq5Hz+eRzPPmtyyz1zJoFnn1WzKUfCQEpT2OqCAl0QvPfee/z1119MnDiJ4ODBFseEEFx33TgKCvIoKiqipATsrLWqd5SVlTFr1izKysrsGoFdlREsN9f03txl9M8/f2fixGsYPPhKfvttI02bQlER2JBJ9RJdELiI7du3sm/fTs6cSSA4OMzpGUFNXdgaA6++OttibQao0B2LF8+udFZgKl8bLWtY/Pjjj3zzzTdcfvkoK0EA8Oqr75W/b0iCoLi4mDlz5pCammp3pXB1SE9P59SpU7Rr146zZ8+SkpJCSMhAWrRoayEEADp0CCUsrDPt24fw4YefcubMCa655jauuKKnXWGgLeaq6SzFFbi/BRcJAwdewYgRY7j55vHMnDkPP7/Kh5/+/v4sXbq0DlrXcCkpwe4Mqyozr9JStTVmHnnkEd5880369rWpHbCgOnYZd+Hn58ecOXP4z3/+49J6ly5dyuDBg1m+fDnLli1j7NixfPjhMrp182bcuGEYDIbysp07R7Jx4wmWLv2Eb7/9lIULn+fkyaN282bcdttt+Pj4sGHDBpe2ubroMwIXcfnl1zJ8+LUW+55//kFyc23fCZ6ensTGxuqG4kooKcHuDMvDw4PVq+OqNCu4SNLzVotRo0YxatQozp+3n9inuLiYkyeP0L+/fTfMxkKXLl0YMGAAQUFBBAQEsHLlSt5+ez6gZqT2RvK33DKB7t370rVrT/LzlV2hoveap6cnJSUl9cY+qBuLXYC5Aakiq1fH8dRT91FaaloR5efnz8svv4Sfnx/t2rXjtttuq6OWNjyysuCjjyxtBOb4+fkzbty9bNjwQ6WG5IvRLbI6pKTYVpVJKbnssjZkZmbw118pXHpp5b729YG9e/fi4eFBt27dqpVW0lk+/fRnJk0aS3T0RO6//1907mzt9VNUVEiTJj4WUU0DApT3mjnZ2dnGxWi1196K6Mnra5nSUti583dWrnybvXt3WhyLjo5hzJhx5TdGSEg4//53LElJSTz00EM89thj7mhyg6G4WF3Drl172QwZXFCQzyefvMOZMwnGldrKkLx6dZxVWWdWJ1+s5OXlERcXx+bNm+2qyIQQdOvWm4iIbjz55GSGDBnC9u3b67ah1WDWrFn069ev2mklncFggEGDrmbXrjQWLnzfphB45pnp9Ozpzz/+EWWxPyfH0sgM0KJFizoVApWhCwIXUFoKS5bMZc6ch3jllRkWx5YseYH1638AKB+tXn99DF26dKN58+YNIrG1OykuhtzcXPbv/9NubPeK+zVDsnW5WmligyA+Pp6JEycybdo0h7aSuLh1rF9/FB8fP3bs2EF8fHydtbG6hIWFERkZSffu3Wul/rKyMpKS0vH09KRZM/u6xcsvv4ZWrYK47rpxVscyMpQXUX1FtxG4gNJSCA4OpWnT5kREmJJ7rF4dx1tvvVTeUSUnJ5a7PU6e/CAPP/ygW9rbUDAY4Msv41i4cFaVv2vLkNyYBUGTJk0YP348bdu2d3gdNG+Yhx9+jkceeYKhQ3vWUQurT2xsbK3Um5qayoABA8rjgYWGdmLTppN2y9900x3ceOM/KLEx9ZRSzQo0D6I9e/Ywf/58unfvbjMMRV2j2whcQEaGyvRUEXsLy0JCwtmxIx4HEW51ULaBBx+0bRvQEELYnCmEhITz++/xFvs8PSE01Kpoo6KwEM6dc65sY79ehYWF+BkDVgUEtCAiIpJvv91R7frMo+Nu3ryZK6+8kuHDh7NlyxZXNLdSarSgzJg68hIgGCgADkgpL6oMYjWl4lQ7J0ethnXk9lhcrHTWLlrnclEyZ471+gEADw9PpDQQHBzGyJE38PXX/7PKATFz5jyr7zWwMU+tUBW30LIyJTjqkSrbiqKiIry9vWvFF9/X15eEhAQCA4PIyPC3mf2sKpSUUB7ZtXfv3nz66af1RjVs9+oJIboIIWKB48CrqPzCDwL/J4TYJoS4z1Z+4caI+f2RlwdHjihB0Lq17fSTwcFhHD16EB8fD6uFKTomkpJsC1IpDXzyiYEffojnlVfeZv78WFq0UG4ZgYGtmT8/1mmX0sZCZmYmBQUFNgVBaipW0UjPnk3inXde45133rP+Qj3irbfeolmzZixYsKBW6g8LC8PTU60JcsWzqnlrtWrVivHjxzNs2LAa1+kKHHXkrwAfA12klNdJKSdKKW+XUvYDbgFaAHfXRSPrO6WlMHx4GJ07e/Dqqy+WJ1m/8855+PhYLizTRquBga2RUlJWMSO7TjnBwbYFqSZgjx4txmAAHx/f8iB/l146xK4QaMwzgn/+85/4+/vzxRefW+wvKVFxh1JSLMunpCSzcOEsPvzwnTpsZdU5c+YMBQUFTiWtry6uXJVeUeDWF+wKAinlBCnlJlupI6WUqVLKN6SU/3NUuRBijBDiiBDiuBDiaRvHlwgh9hi3o0II64wu9ZyyMtXBFBTkGzt208qRqKgYHnggluDgcIQQhISEl49WW7duy2+/HSUjI9ONra+/SInNFdpNmvjTt+81xMR4cd99wSQlwcaNP5UfHzJkpMM6GytlZWV4e3vTooXlQorERDWQSUtTxnmN0NBOTJ36JOPHT6c+j1WWLFlCVlYW99xzT63UHxsbS1CQIDKyCVu2rKv8C5VgLlTWrFnDm2++SWam+/sAp+Y6QojhQIR5eSnlikq+4wksA64FkoCdQog1UsryANxSyifMyj8C9K9K4+sDmlpo48bjpKSkcfp0O4vjQ4fGEBMTY7WQycPDg06dujWYeC51jbZ+ACiPOtq6dRh33jmP5s2DWL/+AwoKsklOhn79riEx8SR9+17G/fc/5bBeKWlQUTVdxRdffEFBgYHkZJM0LCszBeUrLVVOD9p92rp1G2bPXgyoWUN9zuvQothBrbUAACAASURBVBaz6Pzxx1ZARS915LTgLAaDKRjdnDlz2Lt3L1FRUQQGBta47prgjLF4JdAF2ANoYwMJOBQEwGDguJTypLGez4CxgL1MDBOAuU60uV6hCYKAgJZAS2ytGE9Ls7+itaBAT61oC+26RkfHEB0dQ04OaDk8iosLee65dXTrprI7de58J3Pm3EnXrnYq0wGgpMTDokPPybGcJZ09a/s+LS6u3wbj2uSuuybTqlUn2rTpwIAB1vr8rCyVza0qg4vCQiUIbr/9di6//PJaFWTO4syMYCDQy5aKqBJCAPPAC0nAEFsFhRDhQCfgtyqew+2YG4rz7QwYcnJsxxsZN24YZ8+eZs2abxg0aFDtNbIBUtFBIy/P9L5JE18yM8/y5JM9SE9PJCgojNtvf5Fevbri45PO6NFj7dbbWGcEYO0xlFVBEZufr2YIQUHqc1JSAufOJTFgQB8CAtzfWdkiJiaG0tJSYmNjq9ShSqkSF3l7q/Dk9u6Jyy67gsjIK+zWc/q0GuVXDCHhiMJCJTyee+45579Uyzjj9XMAaF9pqZoxHvhSSmlTGymEmC6E+FMI8WdaWlotN6VqaB3Wddf1Zdy4rpw9e8xmuexs631//72Xc+fOcPDgkVpsYcNEu64ff/xfOnUS3Hxzx/Jjv/8ex3vvTSc9PQGQpKcn8N57U3n00Sj++U/rVZ3mNEY7QUJCAgMHDuTBBydZ7K8oCEB1bNo1mjnzXv7xjyh27dpV+42sJqtXr2bVqlVVdh/VEkmlpamZkD07iCPjbk6OGqBo6jVnKSysf/eh3RmBEOI7lAqoOXBICLEDKF8kLaW8pZK6zwDmy1E6GvfZYjzwkL2KpJSxQCyoBWWVnLdO0TqsY8cOIqWktNT2OvKsLOtRw/Tp/+LChWwuvbR+uJDVJ7TrmpysJpVlZaYpwuefz6a42HL6pR1v0sSf1FQVYE7LIWtOfXsA64Jz586xa9cuSkpMw97CQtshD4qKVMfWpg307HkJxcVFSFl/XZxXrVpFTk4OTW392XYoLLQcmBUXK2EQHGxKZ2owKJvJ4cNHmDLlRnr2vIT//vcri3rOnlWvmZmm9QHOIKW6zh4exaSmpuLh4VGeQtRdOGr64hrWvRPoJoTohBIA44G7KhYSQvQAAoGtNTyfW9A6rOnTn+Lo0Qy7awdszQieeOIFAIyLFxs9cXFxzJ49m8REUxTRGTNeYsKE+9m2zRS1Kz3dXh4CwfLlFzh5Uj2Ul15q/XA2RkHQp08ftmzZTqrZMlBHjio5OUoQzJ1rypVRlY6uLrnxxhurVF7zkLK1XzOWl5SotRUlJZCUdIqEhBMkJJywKJ+ZabqGWia89lXQmxQWws8/f8P48eO57rrr+Omnnyr/Ui1i96+VUm6sScVSylIhxMPAz4An8KGU8qAQ4iXgTynlGmPR8cBn1bBB1As0QfD44wvZs8d+uaIiZRi21elrU8XGqrsGJQSmT59Ofr51Osprr42xCHUQFBRmVAtZEhRkEsKlpXDmDISHqxGfp2f99nypTZo2bUr//oMt1gpUjIZpjq1jxcX1UxBUBSlVB29PDZSXp37jhQsmV9orr7yO559fQv/+Qy3KHaugAY6PV2sxOnZUebIro7AQrrzySpo3b243yX1dUmmsISHEOGAh0BYQxk1KKd3S+voUa6i0FJKS1Pvz5+HoUcflO3ZUm8bq1XHs2LGZq666gYkTb2m0nhkAERERJCRYd+6enp7Mnfs/unUzLRLTbATm6qEmTfyZNi2WqChTOSGUIEhKgk6d1AMaHNxwUjC6kgsXsPBo27fPvnMDwKBBJsEppSQwUNCyZe22saqkp6fz1Vdf0bFjx0pnBiUlaibgisxre/faX2QmBPTurTwBbTmImJcLD4fS0tI6iy5Q03wErwG3SClbSCkDpJTN3SUE6hvabCAnJ4uXXvonq1Y977B8Sorlop3Y2EV8+um7fPHF8nodorYuSEy0re4pKytj7ty7iY2dWr4vKiqGadNiCQpSC/WCgsKZNi2W77//NzExXnz22bOAGgHGx6v/STOMNsx5Z8347rvvWLhwHocOmaasla1wzcuD7ds3MXBgO6ZMuale5nI4ceIEDzzwAC+++KLDcsXFkJzsGiGQm+t4pbGUarYQHw+7dtkvK6U2y6of0yxnBEGKlPLvWm9JA0QTBPHxx/n223dYvXq+w/IlJZYeBpddNpyOHSPo1evSRp9PNyzMtm0F1Ih027YvLPZFRcUwcuRkIiIGkJ6eQGzsVIqL8zEYykhNPWVVR1aWevgaoyD4+uuvmTfvOfbtUzPpoiLLAYkt8vJUOJSMjFRSUpLrpSAIDAxk2rRp3HKLY7+VjAzX/e/OOC0WFakIrwaDEkD20ASTlJJzzoaErSUceQ1pfnh/CiE+B1Zj6TX0dS23rd6jdd6+vn60bRtBkyaVey4kJ0NbYwbAyy67nPXrf2Dp0hf58svlvPrqvEabw3jevHkWNoKKFBRYx/n+/vt/U1CgrPAGQxkPPbSSkpIiQkOt8+2WlKjRXGMUBGPHjqVZs/b07XsZ4Fy8m7w86NGjH9u2naFVqzb1cqASGRlZaS6CnBzXJYQxGKruKpqWptTBWh4Cc4qLIScnh969e5OVlUVmZqbbZgiOznqz2ft8YLTZZwnogsD4cERG9uadd045dcMVFqrR6YYNlnl4k5ISmD5dGUcbozCIiYnhtddeY9++fTaPmxuCNfr2vZqUlJNcd93DdOkyhMTEvXz++WzS0xPw8PDEYCgjKCicO++cR1RUjE2/+cZAdHQ0gwZFl4/qnQmilpurktm0a6fcGg0GtdVCtOdao6zM9lqJ6pKZad/Q7IikJOjSxXp/URG0ahWAr68vPj4+JCQk0MVWwTrAkSD4FfhZSmkjaIIOwKpVccyfbxkHx9xYaY9z51T8nIqxS/Lz85k9e3ajFARA+fTYy8vLIvZ7kyb+3HmndX6BJ54w+XVXNCAbDOqJVYvNlIAdMyamUc4IwHKltjOCoLDQ2thZUmJ7ZOsusrOzyc/PJzAw0Gb+38zMylVgVaG6seHS0tR1M3cUAZNqaNOmTbRr165Wcio4i6MzhwJfCCE2CyFeEEIMEbayhzdS4uLimDlzennS9PT0BJYtm8i0aUH8/rt14nRzsrLsJ62xZzRtDKxdu5a5c+fyr3+9QkCACsLl4eFJnz7XVCpgbS0y0yguzufzz2dTWto4VUMbN/7O0aOHMBh7RWfDKufkqMiuU6bcxIoVy+qdemjZsmUEBwfz0ksvWR1Tua5dez5ba4GcJSlJeRaaoxmMO3To4FYhAI7XESwEFgohmgPXAJOBd4QQfwM/oWYLKfa+f7Hz7LO2s2fl5maUj0AddV5t24aRkmLtLunIaHqxM2jQIAYNGkRiImzc+H9s2fJ/GAxl7N69xmb50tJS3nzzDnbvXktZmWNrZkZGYqM0FhcVFTFy5BV4enpy9KgagjobE//8eTh/Pp3ffvseb+8mPPaY3cX/bsHb25u2bdvS2objfk06bVvk5VFjg3lGhnV0geLi+uHOXKkYklJekFJ+I6W8X0rZH5Wwpg2VRx+9qDl92v7IXRuBOuKOO+bh62udtGbePGsVSGOirExN52+/fRL9+g3Ey8uH4OAeNst+//3r7Nz5TaVCAFQym8YoCPLz8xk+PIpLLx2Ch4cHZWXOu1GePw/Dhl3NW299xuzZr9e7GcFTTz1FSkoKTz75pMV+lR/Etedyha0hO9v6/isqghUrVnDNNdewYoX7ulRn8xGEAOFm5XdKKV+vtVbVc+Li4owPlX3LUUaGYxXP8OExtG8Pb7+tbAzBwWHMmdM4vYbi4uJ46KGHyM7OJjCwFXPmvEl0dAw33hiDrXhn58/Dvn1xfP21tUrAFpqNwZX64oZCYGAgP/64uVwtUZUOsqwMmjTpwE033QnUfERcVxQWutY2AK6ZYZSWqoV95guJ8/NVUMB169YxdOhQ+1+uZZzJR7AQuBOVR8A8H8GmWmxXvUULhVBZislWrSpX8QwZEsNdd5k6fn9/B4UvUiqGlsjMPF8eWiI/H5YsmV0eanrAgHkcOxbDqVPg5TWb0lLbie01QzEob6M775xPVFQMBkPjmxGApadLVUfK5uqM+jYjsIejFdPVoaxMdeCuIDvbUhCUlcG4cRMYOnQoXd2YUMOZEBNHgH5Synqx9tXdISbshUIwRwh/IJaBA2O45RbsJkzx8oJHHulIZmYGa9fupmfPnoSEuL7N9Rl717Nly9bk5V2gpMRcj6GuK8SgtJrW964Qgk8+MTBhgjr+2msHCA3tbTwGY8aoWPCNibQ0Uz6HU6es8xM7wsMDWrY8xI8/fkFERDceffSuehMT67777uPgwYO88847DBgwoHz/6dPVc/O0R1YWHD7smrr8/aFfP8t9AQFVy2dQXWoaYuIk4O3aJjVcHHn1CCHw8QlHylikjGHnTpg3z9pbQENFPEyjqKiQkyePNpgRlyuxdz2zsjIqCAGAfDw8ZvPoowCOE9tPmxbLc8+to0OHbuXHGqONYNmyZURGtuKNN14ArD1pcnPhySfVfWpLnWIwwMGDB3njjRdYtmwexcX1R7924MABdu7caeFqXFTkWiEA1XcbtUV+vvUCN/OkS+7CriAQQrwlhHgTtZhsjxDiXSHEm9pWd02sX9jz6gkKCuehhwwUFcXj7R3D008ryV9YCJ98Yr++xx9/hyVLPmbo0BFI2XCm366iql5SBkMiw4ZBly7zUDMEE+brDa66aiq9e1+Fl5elS0Z9TsReG6SmppKVlYmUEoPButP57DO12v3AAfj9d9t1DB06lltumcCyZV9gMNSfFWWffvopW7dupVevXuX7XG0kBtcuSgPllmtOQkI8r7/+Jl999ZXtL9QBjv7VP4FdwBrgZeAP42dta5TMmzcPf3/bHdCWLerzXXfBJZfAlCkqFd6WLaZ8uxUZNuw+oqNjjDmPG45BzlXYup5+fv60bGk7lm/TpkpwzJoVQ0BALMqHQdCqVbhV9FFb1AeD8euvv46Pjw+33HILGbaSXLuQ2bPn8OefqUye/LjVbODwYVi3zvT5889th2MoKWnC0qWf0K1br3o1UOnatStDhw6lmVnSb2ddY53FXgIfjeLiqg8uKhqeT5w4wsyZj1UaLqM2cSQIsoEfpZT/s7XVVQPrGzExMSxaZErY0b69qQM6ZYx1duml6rVtWxhrTJ/77be269PyFGi4IkJiQyImJsbiAWjXLoT582N56qmlNGlS0Xruz3XXqRF/8+bw4osxNGsWDxgoKIgnPd20cnj79q+YPXswy5bdY1FDfZgRbNiwgeLiYr777jv27t1bq+cqKYHWrdvQokWglSD4+GP1euutEBGhVJjff29dh/ksoqREcuqUdVC/+oCW+cuVOJoNpKbCgw/Cm1XUj1ScEYSGdmLy5Ie59dZbq95AF+FIEEwE/hJCHBNC/M+YN7hPXTWsPnPXXZPZtSuNX389xBdfxBMVFUNmprpp/PxMQeUArr1WGdwOHrS90vG3394nJmYkb731CtD4BAHAhAkTaN26NU2bNmPTppNER8dw1VWmUNMqBUY4EMvNN5tG/O3bw3PPqfjvBQVqRLthgzqWmLiXkyd3smfPDxbnqg8zgvfee4/LL7+cG264gZa1GOQ/JSWFgQP78csvahRifv9lZMCJEyr0QXQ03H232r96tSkFo4bmhSOlZMSIAXTu3JkkLRGHmzAYDMyaNYv5800Rf4uKqmYDKipSQs7R4MCRIPjhB/X9HTvg5Ennz1tcbOnZ1LlzJC+88BYPPPCA85W4GLuCQEp5u5QyBLgWlWWsH/A/IUSaEOIHe99rDJSVedCqVRBdu/YsH4HEx6vXiAjLwFwBAaqjKiuDnTut69q16zv++msjP/2k9IONMS+Bh4cHqanpHD58gSbGZZaFhWpl9ltvxfPyywYgno4dY6yS94SHK2Fw//3q80cfwZ490LHjbfTufQ2jRz9sUb4+zAjat2/P77//zvfff2/h7eJqPvzwQw4fPsSnn75LenqqhQvk7t3qtW9ftbK1Vy+44go1g/jgA8sOtaRE3ZdCCEJCwmndujVHK8vCVMvk5uby2muvsWDBgvJ9VbEPXLgAf/0F+/er+8VWFOjsbOvRu+n8pkEHwBrbi9/tUlE9VFbmXrVwpesIpJTxQghfwM+4ae8rRQgxBliKSlX5vpTyVRtl7gBeQPkC7pVSWuU1rk9IaSnNNZ2kNlvu1Mn6O0OGqBtu+3YYNcry2MCBt5Cfn8mYMdcByljc0KI8uoL0dEtDublA1EZbtq6txsiRyg6zeTMsXAhwAD+/Yxw8uI5Nmz4qDwhYHwRBXXDixAmmTZuGlAGcOHGcZs2CLDoaTRCYy6GJE1WnePCgGrQMHmw6lp+vZg8LF75Pnz6BeLo576enp6fFbACctw+UlsLx46bPJSVqIGcwqAx2paXqeXZkvvntN3WPduoEiYlqVrBjh4oy6kyqyuxs6NBBvS8rK+Ps2dNkZRVz6aWRzv0IF+MoH8GzwDBUOIkjwDbgP8B0KWWlj5MQwhNYhppRJAE7hRBrpJSHzMp0A54BLpdSZgoh2tqurf6Qnw+//LKW559/kC5devDYY78AJkEQEWH9nUGD4MMPlWdGbq5KY6cxatQURo2aQg+zKArFxVy0aSvz8y0XzhkMkJlpID/fUvKZP9SaIOjc2XHd992n1mYcOBBHWpopxLdKXKMWqQ0Z4t6V28uXL+fvv/9m4sSJdO7cmdOnT9OzZ0+XnycqKopz586xfXsSd90VYjGyLSxUnb0Q0L+/aX9AANx0E3z6qRII5oIgLw8CA6FFi6B6sY6gadOmPPPMM+WftQBulSGlUonZmnknJqr7MS3N8czc3JZyxx2wbRts3AhLlqhrev/9MGKE43aYz84uXMjmiis60bJlIJmZdnzNaxlH4857gGBUgLk44BMp5V/OCAEjg4HjUsqTUspi4DNgbIUy04BlUspMACllapVa7wZycuDo0QOcPXua/fv/NMtSpl5tdVYBAWrqXVYGf/xhu15z/e3FaicoKFAGtuRk9TClpqrFP++/v5xOnQT9+5uGUuaCQBOylQkCPz+YPh2knI3yejZRUpLPp5/OdruN4PPPP2fRokX89ddfNG/enAEDBpRHBXUVUkpat25NYGAggYFqbGXesR04oEbBXbpglYdY88T8u0JOQvNZcH3yHNJwNrLsqVOO1wUkJTkWAkVFsHix6gd69lTegXfcoTr+Hj1UG2JjYflytT4jzk4g4rIykyrLz8+fkJBwgoPdF3DSkY2gB2o0/ycwEvhGCLFDCPGeEOI+J+oOAU6bfU4y7jMnEogUQmwRQmwzqpLqLUVFahs58gbuvfcRJk16ClA3RXq6mjpr072KXH21ev32W0tdYHFxIVu3rmLVqrcBldC+d+8IPDw8iIiIIM7endRAyMgwdeqa4U1lZlKdi5SQmanm4Noqd3OjX3Gxeji1ZN/OkJ5ue5Ha+fOJblcNTZ48mUWLFnH99dcTHh5O586dyXTliiWULv/AgQOcOXMeb2+1FtS8c9u/X72azwY0IiLUfXzunKWh1Fww/+tfTxESEsLq1atd2u6qkJGRwR9//MFJ43SxMv16SYmaCaTWcKgZF6eESdu28Pjj6r5s1QoeeADmzlVeggYD/PKLGvCsXQv2AhFogz8fH19+/z2eH37Y47YFjw5tBFLK88BaIcRPwGXAlcD9qJDUy110/m4oQdMR2CSE6CultLDVCyGmA9PBfWGai4pMN1HPnv144YU3SU9XukZtNhAebl+3P3gwhIaqEfD69TDamO8tN/c8b76pgnp17tzCImtZQkLDzlqWk6OmwHl5KqyDvZHW9Okzueuu6Vy4oPQX5uUSEtSDFRrqfFKUoKAw0tOtnz5v7zC3C4I77rij/P2pU6eozRQf5jNL82uqLea2FfrEywu6dVOzhiNHlH0LLAVBXl4+ycnJbs2dsWnTJsaNG8fYsWNZvXq1Q0GQn6/sRzWdyeTnwyZjhLUZMyxjBmnceafqA1JTlfDZsQNWrYKnnrIum5sLbdqYPkupvuOOsNSOVhbfIoR4VQixGUgFFgOtgSeB9k7UfQaV3Eajo3GfOUnAGilliZTyFHAUJRgskFLGSikHSikHtjG/cnVEcbEaIVXsRLSHQ/OkcySjPDzg9tvV+9WrTQ9py5bt8fb2w9+/Ba+99ozdrGUNCYNBi2Vv+uxo0Ovh4UFAQEtCQtQFrI59wJw775xncw1Caek8hwbAuqa28zyZd46aIJBSDUbAOmOWhmavMo+vYzCY6njssWeJj493q7tjs2bNGDp0aPmqYnuCwGBQgzVXqLO2blXXoGdP+7NTIZSq6OGHYfJkNXjZvVsJ1YrYcid3l9rNkY1gEpAG/AtoL6W8Qkr5tJTyWyllmhN17wS6CSE6CSGaAONRq5TNWY2aDSCECEKpiqrgkVs3nD9vqX/87rvPef75B/ntN+Wfrbme2VMLaQwcqG6gzEzl3QKqE1yxIp/77lvG2bOnbX6vIWUty89XgtGe293q1XFERUXQubMHUVERrF5trfqqjn3AnKgo0xoEIQRBQeGEhqr4T7/9VoUf42Ly8/NZvXo127Ztq9XzfPrpp/To0YNFi0y5LbROPDNTzdCaNlXGX1togqCinUCro23bEMLDw8tdfd3Btddey9atW8s9h+wJgsRE10UjXb9evVb0/LNHixZw/fXq/RtvKFWROXl5pnUt9913AyNGdOHYMfcs1nNkIxhnzDnQ0mjsLUcIUelQQEpZCjyMWoPwN7BKSnlQCPGSEOIWY7GfgQwhxCFgPfBUbedILixUf4iz9rn8fGu3tJUrl/Hxx//lyy/fAEyCoH0l8yQPD7jF+MvXrlVt+P33OKZNC2LZsol2v+furGVxcXFERFRut8jOVlNie9d29eo4nn3WlN7zzJkEnn12OvfcM4Zhw0JZsOBfQNVdR22hrUFYsaKYl1/expgxSrX2yy9Vq8eVJCYmcuutt3LvvfcCsG7dOnr27MmUKVNcep74+HiOHDlCtpmzujYD1WavoaHY9f7p2lXlKq7YiWrPQX00FtsSBDk5ttcHVIfERGVj8POz9KaqjFtvVeuIsrLglVesF6hps4KzZ0+TmHiS7GwXxbuuIs54qz8vhLhK+yCE+BfW3j82kVL+IKWMlFJ2kVLOM+6bI6VcY3wvpZQzpJS9pJR9pZSfVedHOEN+vjLopqSoh8IZ+1xpqe1yffpcRkhIOF26qEQS2krMymYEoHSubduqG3TlSpVwPTfXvuzz93dv1jItX0BCguq8NbtFRWFgMJgWydgb9S9ebJ3es6Agn23b1nPuXBJ//63CLWgdTmGh6rg8PJw3FJvz009vcffd3jz5ZM9yfbirOobq4OXlxc0338zIkSMB5Qt/+PBhly/Oevjhh9mz5yD33PMIYGl8r0wtBEqd0a2b+o55YiDtf8nIyOSxxx7joYfcl7rSPHy+ltXOnLIy1XG7Cs3bb/hw521VoPT9M2eq65mZabIxaGjhO/77369Zv/4Y4eHdXdPgKuKMILgFmC+EuEIIMQ8YgpOCoL6Qm6tGqrm5pgfiwgUlndPSrKeOJSVKaJw5Y3ukMWfOEtati+fOOxdQXKw8Yzw8LA0/9vD0hBtvVO//7//sJ1zXiI2NdauhePbs2eVJYzRs2S1yctTDaG/Uv3p1HMnJtlVcJSUlTJr0KBMnPgiYOpzERPV/hYZWz4DWsqWSzAZDafn33blyu2vXrqxZs4Z3330XUDma9+/fz7f2AlFVk+bNm9OtWy+Cg5WJzvw3m88IHDF8uHo1j0iq/S9CePHmm2/ywQcfWHTIdcmECRNo27YtP/zwg81nNDnZtf/1nj3qtSqzAQ1fX7U+A6BiKhVt8NSpUzciIrri4VEFKeNCnMlZnI4SBstQ6wpur6gqqs+sXBlHjx62ddJZWUoiZ2SoEYSU6v2ZM5ZCwxaaD3BqqirXpo3yuHCGESPUFLO01LHuv02bNm73FrJnnzDfbzCYbAL2Rv2LF8+26ycdEhLG3LlLGT16LIWFJqN8ddVCGgMH3sLy5RdYvvwCRi/KehXCo2nTpvTp04dWtZCVxJ7HkDMzAoBhw9T9vH+/yeivCYJmzZqzePEbfPTRRy5fA+EsZ86cIS0tjaZNm1oJAilr7iZqTkaG8l7z8VGG4urQr5+KRHzsmKWWISvLUtWm9UN1jSOvoQtCiBwhRA5wHGXI/Qeg7av3xMXFcf/9tken5pSVqT87JcW5lHSnT58iISGZ0tLSKqmFNHx8tJGFfd2/n58/S5Yscb7SWsKefcJ8f2amaWpub9SfnJzIzJnz8Pa2HNr7+fkzc6ZJ9WV+/Y8dU69dulSj4YCXVxN8fdUybk0QuHOxXnFxcZ10nM888wwvvjib7GzV42iCwGAwzQgqEwTNmqnwE1Ka1CLmtrL773+M8ePHuy3UxIYNGzhz5gxDhgyxsllkZbk2bo82G+jTx3QfVRVfXxXXCbDKw33+PKxd+zkvv/wEe/bscEvMIUfG4uZSygCzzVdK2UzbX5eNrC7PPGN/dFoRW0Zhe9x4Y3+io0P4+ec3nTYUV0RNva2TqwAEBrZm/vxYxo93/9qBefPmWT3s5naL3FzLztveqD84OIzo6BhefHFZuTBQ8WJiOXr0IM8//yAnThyxcKnTBEE3K4fiqqOphtwpCBYuXIiXlxcvv/xy+b6lS5cyZcoUTp+27TFWHd5++23eeMMyKicodWdRkVpNbMsHviJRUepV83AzGEzXz915Mzw9PQkODsbX19eqLa6cDYBJENhagFcVBg1SrxXVQ+npsGnTz3z44RscObLfLdfWUayhCCllvIPjAgiRUro3Hq0d1IpU+6PTmqB1jM2atUaz81VVEPTuDQEBMeTkQMuWs8nOTqR1Fm8F3AAAIABJREFU6zCefnoet92mBEBJibIpuJOYmBiklNxtjFPcsWNHXn31Ve66S7W9ojF95sx5FoviwHLUP2HCVJo0acLMmffSsWME0dEx9OzpT2FhAZdeOpTISGUs0+w3vr6V67MdMWvWJaSlxfP442uAEW4VBPn5+Ugp8fMzxWz86quv2Lx5M3fffTehlfzQggKlRmjWzL7Hj5SSRYsWc+TIufJkR5og0LR5lc0GNPr3VyPgxERTjKzCQiVUDx8+SmLiX/Ts2ZN+FZPw1jGWK/Vdm1qypMS0ElvLM1JdBgxQ/9uBA5Yxt3JyYPToO4iM7MMllwx2i1eWIxvBIiHEV0KIe4QQvYUQbYUQYUKIq4QQLwNbANdHy3IRFy44Hp3WhN27M/jsM8mIEfdWSzUEqoMfNgwghuHD4/nkEwNvvRXPtdfGMG3aWPr2DeC5556tUTtdxcSJE/EwLpn+8ssvGTcupjxeUEV9ZnR0DPPnxxISEl4etnj+/Fiio02zm7Fj7+LYsRI2bFAhIPv2HUhISDidO/cs96IwVwvVJBJraupJCgpySE7ejxBKDegu98cFCxZQUlLCoyrpMgCPPvoo7777Lt0qTHtycnJ4+umnOXjwIKA685QUpcK0FyM/O1t12CNHXssjjzxfvmBNEwTaoiZnVW1eXqYgitp6Dq2uzz9fyfjx4/n666+dq8yFHDlyhLFjx5avITD/P10crYODB9VvDg93LqqoIwIC1Oy2rMw6Y2G/fmOYOnUGPXr0rXeqoX8AzwPdUYbizcC3wFRUNNKrpJS/1kUjq0NhoRqd+vlZp0E010lXt26tA6yuaghgqPI+ZdcuU325ucoGkZt7gSO2liO6iUOHDlFQUECXLkNISXGsGli79jMGD76SIUNGkpKSzIwZd5cb6h9/fCITJoxi0aLZ5S6mmv2gS5dB5XU4UgtVZUHuhAkLmTjxdQYOvKVcPeTqdIZVwcvLy2Ih1u2338706dMJCbEMw7Vo0SIWLlzIwIEDAcsFejk5liqukhJYv34v33//G6NHX8Xgwf1JS0sBVCepdd7aArGqGDw1Q71muNeuXa9el3HbbbfRwzxsbh1x/Phx1qxZw6ZNm6yMq+ddHLxzxw71avwbaoxmJ9BmGRrm6lV3DFQqizV0CGhY8Q1QF7KkhPJR6AsvPEJ2diYBAS158cX/WIxOq4PmTVlYqEYgXl4QFGRZJjBQtcHWMnKNyEg13U5JUWsRgoOVF9PTTy8kPv44I0dWEsu2Dti3bx+XXnoprVq14tixdIe/B6C0tJR169Za7dcM9QaDpKiogD17tlNaWmJx7F//gh491H9jTxC0batURfHxjuPFa4we/WD5e29vU2pQ81Dg7kTzuCosVJ5nnp6qY7v55pv57bffuOKKKygttUwXKaW6Z7y81PdLSmDp0kV8+61ygujRox8HDuxi1Kgbyr9XUKBG9R4e6r5zFm1Fd0VBMHp0NPfdF11t42lNGDhwIF999RUBAQFWHjf2VrRXh7Iykz6/Om6jtujXD776yloQxMefJTX1MEFB7ejevZdrTlYFLsr0J+YjvujoGHr1Ulaerl172hQCzoQ90DAYDFxxRTOmTAkkKUndhe3aWaov2reH7t2Vl4EjfayHh0nvqCUKUVP765k06RF69HCv7rW4GH79dRtSSvLzC6yyKtmioCCfUaNuwMuGL21BQb5xROxTLgTMj/33v2rMUVpq6njMA6M1aaI6Jm9vpd5o2rRqv0frtKqSycqVTJ48mejoaBITE8nKUi6Je/dm8M03P7BhwwaSk5XhMDERunYdzJYtW5g//1Wb6o6yMiXUtJlZSEg43br14t13V9OxYwSenur6a4L76FElNDp1Uq7LzqKpkbT/w9wV1V0G43bt2jFu3DiuueYaC0GQleVa18sjR9RIvX37mtmpzOnSRdkGzp5VNjCNP/74lrvuuoqPPnoTg6HuZwUXvSAYNSqSPXu20aNHP2688Q6rso4WQNkiK+s8BQV55Odnce6cetjMZ/W+vpbB5zp2tMxhXBEtQ5QmCMwfbnfFzr9wQbkZJifD2LGT+f77vxgyZARDhgTz9NPTrMprglTlFGjF+vU/UGrnTs7Pz6WkxLbFNi1NWTNPn1ZCqH17S+8W81G8h4dScTRv7vi37N69lsWLo/niizluFwS//vor3377Lbm5hvJO68CB3UyefCNLl75IWZlp/YoWHiEpyXI2YI+nnprHL78cZPTosbz33rdceaUKb6t9VwsgZ0st1L69/cWQwcHK3Tk9XdkgzJ+t3NwizlZMcFzHmAsjR/mF7ZGXZ502UmP7dvU6eHDV1JGO8PRUjiIA+/aZ9gcFhTFgwAjCw9XIp66dGpxcAtWw0B703NwLxMcrHUNc3DpatbLU36xeHcfMmfdSViGsqOZiamv2kJfXjOnT36ewMJczxliq5oKgY0dr42ZoqJL+tkYr/fqpm+PIEZNnxi+//MhXX/2HyMhevPPOoqr9+BoipXqgtEvi5eVFr16X4uPjS2rqWfbs2W5RXhOkmpdQxWtZkdatwxAC0tKsw0S3bq0kqObrXjGsREV1jpeX6thOn1ZqIlsPz+7da9m161vi4//C1/clwH2C4JNPPiEpKQ1PT5NBKTS0E1FR19Kvn6USevbsB0hJSeaJJ14kMrJPeV6BqqIJAnv2AR8fdX96eKhBSEXVioeHmkUcPqxUSy1aqHtDCAMdOvhjMBgoLi6udvuqw6pVqygqKuL6668HTM90VQzFBw6o8NDHj6t7PihIhYa/6Sal+v3xR5V1DByrhby8qj5679tXpQLdv9+Up6R//xsYM+aGcptMcbFlJr/aplJBIIT4GvgA+FFK6eb8TpVTXGzqxITw4JFHnufUqaM2hcCzz06323HZcjGVEtLTfRk1SgUJ09Z7mQsCWyNUb2/lcZCebn2saVOlRjp0SN0Yw4bBH39sYP36HzhwYHedC4KCAtsJ3mNi/knHjp0YNswy9KKtlcT28Pb2pVOny2jXrgu//LLMIrxGkyb+3HmnMuJrgqCC/dSmXl+LQxQeruwGFWMJ9e59FceObSUy8vLy2DPuMhYPGnQFYWGWA4KIiK6sXKki4R07dojU1LNcfvnVbN26nlOnjrJu3Xd89tlGhgy50m69aWkpBAS0wMfHMr+plnS+uFh1eEKoe82czp1NLsqRkeoerLj6unNnJQhOnFCqzMJCaNrUgzZt2gEGcnJyaF1Tl5oq8Morr7B//3527dpFcLB6rouKnO+Qz56F119Xv8PTUz2f6enwySfqdx4/bhKIQ4c6jnwbGqpm0LaebXtoBuNDh9S9oM02zA3GdT0jcEY19DZwF3DMmJ/APVGRnEQbAa1eHcd11/XmP/95hd27tzJnziO8885r5eUq68Aqupjm5FjnOtVmBMHB6tXb235AKkfqIc3xQstk1L37FfTtexlXX32z/S/VEnl5lqoebZsx42769BnA1VffZFHe2TUZTZsG0r//Dezc+TUbNnxgFSZ62rRYoqJijHWq7zgjCMyJiFAqDg8P08M1bNgdLFy4lylT3i5XDblDEEhpf1aojkvGjRvKxInXcO7cGZYs+bjcuygz03EvM3fuw/T5f/auPC6qqn8/ZzaYYV9EBVnczUyzMCu1XpdKK8uytKL0LY3MMvu1WIqVb0Vl+VZmi2GLLZi2mplZmZVi1ivuqZmmgCAgILIvs5zfH1/O3Htn7sAMzKAgz+czH5iZO3funHvO+e7Pd0AQfvhB2TFMJDWIXhpduijjKgYDafgCOh0JCkeLVmyEogGTGL8//shDQUFBqwoBAJg4cSJuueUWxMfH2zd/d9xnAG2wixfTb7j4YuCdd4B336VuYwYDuWjLy2kcnnwSmD3btVtIp6P5Fh/vPr0MQDHF0FDa+OWetepqSQlrbSqUJi+fc74BwAbGWAiAWxv+PwZgGYCPOeenucZQicpKZ3fF8eM5+Oij1wEAd9xxP+rqTI1uYI4ppseOSZv+X39l4PPPF6Bbt0EoKPgvGJMEQWMbVXAwxQ/UNiHxeTEpBgy4FlOm0IZrs7Usj94T2GzAihXpTgVhAFBcXIh586hbmtxlFh0dh7w8F734AOh0fggICMOkSc+gvr4G+/f/ipiYczB8eBICAsKQljYdOp3BLgQAqLrcTCb3xqFnT3rU1QG7dwObNqVj1aoUFBfnQK+PA5CKmprWr9jOyirGe+99hJiYeIwde6PiPc45KirK0KtXf+za9Qdyc7OQmDgMO3eehL+/0V7D4QqnTpXAZrMhLk5ZICACxYWUSYrOnZWfU6M4MploPubKykRFwoMQ0FKTGwazufm0C83FU0891fD9kvLkTs8BiwVYsoQ+06ULcPfdtCYBYgWOiADWrKH/L7206biASBLRaEhwWCy0vl21phQQltkff5BLODoayM7ejQULhqNnz35Yu3YbrFYSCq1VUOrWFsMYiwA1qpkOYCeAxQAuAHBG1REIt4YrbZ8xDf74owh//w107qxeVCZoD8RmV14ubUwAcODAr9i37yds3vwbrFbyLQoroCmN1VHDFRCCQCy0+nrJzG3NgHFtLfDSS64tpZqaasydqwwWq9VqCBgMJtxzz7tYujQfBoMR3377X1RWnkRJyTFkZKTDaAzGqVP5KCg4hFtvZZg1KwG//pqOggJaLPIiPU/TPf38gP37ieab2lZymM3ZAJLxww+t2wfaZgP27v0Hzz77EN588zmn92+9dSQGDQrD/ffPxz//WJGYOAwAYDIF2IWAxWJBdbW62rtixUbs31+NPn3OVbwutGRPBAHg3LBG1MgUFtL6kiszp7NSW+7CbCruU1lJQiAzk6yiBx909sH36kUtKIcNa1oI6HTK8QwKonHr2pUUkeDgxlvXihReUSqk0xlQW1tpb9cKtO7YNikIGGNfgYrJTADGc86v45yv4pzPAnCGZGMThAbkWtvnMJkoAjlpUir8/Z2LzW65JRmLFqWgRw8NLr44Ae++q9w0EhIGo0+fS5GQcBuApuMDcnTqpJ7yKDa8ggI0pI7V44cfvsO6dZ+3qiCw2Zp29dTW1uDkSclVISqJdTpSCxmjKSV392RkKDfk4uJsLFuWjC1blGNbXJyNd99NBufpiIpSUk83NbZqWLZMjea7Gh9/3LqlMRUVQHBwBP797wdw9dU3O73fpUs3XHTRZdDrDdBoNMjNzcLSpQuxceO39mMWLnwcI0YkICdHvYGfn5+fk+UgfM5qgkCncz2mAQHKsffzI4XHaiUeH2ERLF++BCNGXIQVK1Y0PgBeRHl5Of755x9UV1crMoYaswjWrgUeeICKw4xGYO7c5vW3kKN3b9fU6J06Af3707oWSp4jRKxGCIKuXfvg3XfL8O67++zHnFGCAMBrDY1jnuecK3LFOOdeqrfzDoRW4IpCQmSlAMAllyRh2jTJTx0WFgGLxYoVK5baU0kLC7OxdGkyMjKkDWvw4Kvxn/9swYABsxq+Szq/O3ntahPQ35+0M4uF/Milpfm4776rcd99N7dqs3WrtWn6Db1ej7o6pX+LrCdSof7v/z7HJ59wLFmSZXf3rFw5z2lDrq+vxsaNy5zObzZXA0jxOD6ghvx8daFWXNx6rT9Fw56EhF546qnFmDHjMadjxowZD5vNhgsvpCYA+/fvwsKFj+OVV57EtGnXYsaMGzFgwAXw9zfiySfdawYjT0NWEwRhYY1rvfLYASBZBfn5kkVQVFSAnTu34R9vdoBpAuvXr0evXr0wZcoUhdXsyiL4+WcgPZ3eP+884Kmnms9mKxAf7zw+riDSbx2RkECvFxTQ/NBotDCZglFXJ3nrzzRBEMYYu9HhMZox1kj4s/Uh71Kk5q4wGEyYNOlZ1NdLm5hoZ7hihQ333fcqzOY6p0Yb9fXVWLXKWYMU7iLhPzUa3fPnBQer94qVu4fCwrpCq9XDaDTBYmk9k8Bmo7FTKwYDqNm62WzGzTcPV9RZFBcDU6a8imuueQi9e1+i+IzFYkFJifrGa7O5knI5CkGg1XpWBCXgSqhpNBqX7Ta9DdGwpzFce+1kfPbZZpSXn8K0aeORmvowpk9/GOPH34qNG79FRsaPuPTS0YiN7YHZs59SfHb58iUYP/5CfPHFB4rX5RkoaoKgqY3McY7K56foeDZ58jR8+eVW3HPPPY2fzIvgnCM+Pl4RKHYlBA4fBt57j/6/+25g3ryWWwKOLqGmoNGQ4HEUulqtVCwpb1Ant2xas2DPnVj3NACXgHoKA9RsfjuA7oyxpznnH7n6IGNsLCieoAXwDuf8BYf3/w3gJQDCC/865/wdT36AgHzQhH//xRdTUFBArJ4BARF488078PPP7+DJJ38BABw5kolvv30ZcXEDMXLkdJfnlm9kR45koqgoG9nZ1wDwt29Ynmis3bo55zx37Uq5zcePA4MHG7B2bT1am8bFZqOxy8vLxptvPofq6ipotVpYrVYwxuxCUhTdAcCYMUk4cgS44op7Vc/5+++rXH6fRqN1IQziFIKguXQQakyoAAmg5GS6fl82/pE37CkqKkRtbTUiIzu7jKmYTAHYuHEtTKYAzJtHacMJCb0RFBSMyMgorFz5i9NnDhzYjT//3OEUPxBuUouFBDVjysy1pqzXkBD6jNCL5O5LgLTVuLgeiIvroarY+AqTJ0/G5MmTAUgCTs0tdOoUpXdbLMAVVwCjRjkf0xxERnqevBEcTJmBBw8qlYK+fYnUbt8+oqh+880pqKmpwPLlnyEgQNeq1cXu/CQ9gHM45xM55xMB9AfAQS0rne3cBjDGtCCyunENn7mVMaZGorGKc35+w6NZQgBQk5463HXXUnz4YS2WLMmCTkcOvQMHfsWsWQnIyEjHrl3r8dtvn2Dt2pcQHByJyEh1dUG4lDIy0vHUU8Px6qs3ITe3L4B0hUXgLgICnAN1jplDQstpzRiBcEPdd9887NtXiaNHOQ4ftiAmJt7JUqqpqcaLL6bYqQtcQc2aIjCMHp0Mg0G5KTJmApDqUezFFUT8Qq15ilq7TW/DbJbGZtmyRbjssh744IMlLo8PCQnD22+vxmefbQFAFtigQUMwdepVmD59PGpquFPjpHnzXsKXX27FlVdOULwuBEFJCV1DeLiU3aPRND1ftVql1SAEgUhokGvhp6vrm1jzjhaBxUIpoidP0mY7ZYr3vtMTa0COkBBnhmLR3+CPP+ge/e9/XyIzczVKS+kH2WzqNT2+gDuCoBvnvFD2/ASAWM75SQCNGS8XATjMOT/S0NpyJXzY69hRes6ffw9SU8fh669fQEZGOo4d221/TwQry8sLER3dD337jgBA/n/h6xYQhU4i4GmxiFmfA8aSsWMHuRg8dV04cpcIQSBcTnV1NBFaUxB8/rk655KrAHJ+fjY2bkxHfv4hLFo0AStWOOsFrv3xHHfd9aainiAiIh6MpQFI8jj24goTJiS57Armqg2ntyCfkwEBgYiJiUenTo3T1F555fUwGAzIzv4H9fX12LMns0EI67B7N8Mnn/yAxx57EDt3UoV3SEgYBg++GJ07SwNms0kZQ0KDl29g7o6n3IJQU1RKS0vw1lsv4NVXX3bvhF6EnI/H0SJYt44Kw0JDqQ7Akxz/xhAc3DwXpYCj8tezJ43xqVNU+X3vvcvx0ENfwmKRggqt5R5yZ4h+YYytBfBZw/OJDa8FAGiM3SMGgLztUi7IinDERMbYZQD+BvB/nHOnVk2MsWQAyYDr1onyAVu9Oh01NaQS/fjjW7BazaivV6oN9fXV2L79GyxZkgWAtP2NG98FGTsSLr98KoYPT8KsWQlOAU/OKX4wfHiSx+XgRiNpukLDE9qCWGgzZ8agrKwAb7+9AtOnT/bs5M1Aeno6HnlEcqPI3T+N1QosW5aMESNux/btX2PvXiNuu22h4v3IyLiGbCE4vE7W1/DhSfag8v79wDPPkJCUj2dLmUJdXb+rueQtEEUyB2MMs2c/5eTfd4XHHpuGHTu24tNPN+OKK65DZuYJ7NlDvsSdO9dh/frF6NKlEwYPpuUkaEHy82lehYZKLh21+IC7czUsjKwIs5ly7PV6+p7qalH8VIkXX5yLrl27Yf78h9w7aQsxZswYlJSUID39c5hMFPWVWwS1tZQlBAAzZqjH45oLR4ZhTxEQoKwlYozqFVavBrZsAZKTbwKgVCDMZqnWwZdwxyK4D8D7AM5veHwI4D7OeRXnfGSjn2wa3wBI4JwPBNUkfKB2EOc8jXOeyDlP7OSCHUsIgtWr0zF3brLd91xWVoDKSnW+Yrnvf9WqFFitzmH6zMw1AFxrtiUlOWDMdSpZY5BrXBERdI6yMtLmamurYLPZcPx4nusTeBGzZ8922dazsVqB+vpqZGauRnz8IPTr50yDMHlyqpP7R04nIUdDDxY7KRdAmRUtLVhSu355u01fYfPmzbjxxkuQmbnF5TEWC1mBf/1FVuD+/buxY8dWAEBUFGkHfn5h0OupvHfIkBswadKziI0dhx9++AszZ87Ayy+n4+BBikcUFkopiYC6IHBXsDImkdFpNMrMoZoaIDIyCsnJj+Luux9274RewJ49e7Br1y77nOJc6ZrauJGUq169iMfLmwgNbfk5HIXJMCoZwf/+J+1h8irpM8IiaPDzb2jY8L/w8Nx5AOQOkG6QgsIAAM65fId+B8CLaCaEFF20KAW1te5x34SGdkVW1i506dLL5UZfWkpOUVeabUREHIzG5rEThocTkZeoHo6OpjL+nBxg3rzv0bmzBqNG+b4JXHp6OkpckPsfP56D7t2TcP/9wEsv3a56THl5EZYuLVR9T2j7q1aloKSEAveTJ6cqKokF1ARBc+MDckh9KaglaGBgHJYuTcUNN/i2wvi1117Erl1/YMuWDfYiMYHcXBIA8tDLn38CW7cSDe3o0UmIi+sJs1lZqdq//+Xo35/6VGze/DG+++5tDB1agmHD1H+L6N/bHIsAIGVFxAW6dqUq+/x8yqP39zdi7lxasq1VYbxr1y7k5+cjJCSqQWGS3jObJWtgwgTvMYYCNA+bo+w5IjzcuWo7Pp7u8ebN+eB8LTp1SsCAAVdAqz1DBAHn3MoYszHGQjjnbrDRK7ANQG/GWHeQALgFxFlkB2Osq6w24ToABzz8DgAkBMSCcpf7hjQKDebOHYyhQ29qxIVB7oPJk1ORljYdZnOt4hyTJ6c222+o1ZIlIHjJe/YkQXD4MDB+/FBERLQOA2FjQdOIiDjU1AAXXJCEyMgUl8KwMcjdP65QWysRo/WXpRR4q4HMhAlJqKlJwrx5wPXXc9x0E0NNjW/H9403PkFa2hJMnTpL8fqJE8rNQMBsBjp3vgjjx89Bnz6XYu9eGhdXcaJevS7C1KmL0alTd5fXIDZxIQgY8+w3+/uTVVZXp3Rf2mz0msiRr69vHUEQHR2N6Ohou6Ujdwv973+UjRcbK9G7ewvecjGZTM5UMxdeSIJg69aT+PPPZAwbloRbbrkCwcGtJwjccQ1VAtjLGHuXMfaaeDT1Ic65BcD9AL4HbfCfcs73McaeZoxd13DYA4yxfYyx3QAeANFYeAy5W4i5UAMCAyPg70/qZURELO6+Ow2hoZ3BGENISJcmXRjDhyfhssumAPADwKDVSpWzLQkgyU1FkVd8+LD0u1ojWNxY0FTuwnE1RqNGJePAgU2orGx+n8C//iKfeo8eyo1K3o+gpXj1VQpmb9u2Q0Hw5Sv4+wdi5sy5CAiQpFlRkdTkRQ2xsefittsWIjHxelRXq9//oqIsZGSkw2IxY+zYB3DhherkhOXlJAj0eik5wWTyXFMWwlhONQHQJpybm4XMzC04ccLLzYKbgCi2km+ogjZ6zBjvWgOAazqO5sDRxTRgAP0tKuqByy6bigEDRivSf73ZbMcV3AkWf9nw8Bic83UA1jm89qTs/7kA5jbn3HJYLBLRnFqGiMFgwtSpi520UjUtVe7CuOmmZ9Czp0RGPn362wgNXYQvvgjCv/4FDB9Or7dEEAQHS5zmoi3j4cPAZ58twLZtn2HixJvxyisLmv8FbiAuLg7ZKkxZgYERijES/y9fPgtVVaXw8wvE9OlLsWbNQnz6aQqGDLkBDz1EU4VzyoTYto0a9QwdCnz2Gbl/YmJICxKcLmVlwC+/0HeIRQGQxeRNjZ0xciZXVppRXe29bBJXcBQ0RUWAN4pw1617BevXv4abb34asbHnujxOxAp695Z+a3MsrMBASkN1FATV1cDcucnIyPgRK1Z8h+7dx3p+cg+QmZmJ9957D8OGjcCwYbcCkCyCkhJyrel0FID1JoxG7wZsQ0OVdOm9epHbqbDQiAULliM0VBknsFh8b225wz76AWPMCCCOc37mdFOX4ZNP1BvMAFS0JKc4BlwzespdGMuXz8bSpZSAHBkZj8mTUzFkSBK2biWrQt5CsSWCgDEyO4uKyPQ2mSj/ec+efTh2bD+2bPkJwILmf4EbSE1NxZQpUxRCVAhPR6i5eX744Q1oNFpERMSithbYtAn4/nvJLQEA778vWW7HjgG//w7s2kWb/ZYt0qYpD/B5Iz4gx+zZj2HePKBfv6Ewm72vNcqxf/9BLF78Lvr0ORcTJ06F2UzxIG+gf/+R+PXX9xEX13g0VHQlkxcmNifgKYSHcC+JTaymBujb9zxUV1dCo3HBv+5F7Nq1C2+99RaqqmrtgkAEijdvJuUjMdH7/ahbmi3kiOBgZbGeXk/3aM8eypy79FJlr/PWcLu505hmPIBFAAygauLzATzNOb+u8U/6Hunp6Zg9e7bLQCcAcG6zb1w//PAOfvihE06cuAYTJ+pwvYuqBkollXhwiouz8fbbydi4ETh+PAkxMUqtoyWCAJAEgShH37sX8PfvA4PBhG3bMhAfn4Dnnkv1WRVsUlISysrqsGDBfBQVFSAy0nVAVw1PP/0bamqAzz8HZs6UtLSwMOJ8z8yk3xcbC9x2G/mYV63NgkHzAAAgAElEQVQiAQDQ777gAuBf/1J20PKmWwgAunUjJ3dNDUkAX7qG/vrrb6SlvYRRo67BxIlTkZfnPTdfYuL1mD37Myc6D0c4diVjrHljGhhInw0NpZhAZSU9AgKA+fP/az/O17TJl156KRYvXozYWEmy1dTQhrppEz2//HLvf6+3BYFGQ/dB3iLz3HNJEOzeXY+YmL/QqVM8zOYQ6PWiEZB3r8ER7hjHC0DFYb8AAOd8F2OskZ49rYP09HQkJyejugki8oiIOOTnE/nUunVXwWolZ+mqVcBXX40H8DOefvp3xMVJPolVq1JgNivrDiyWahw4kALgZtxySz4MBsqD9/dvuWYZGkqTw2YjS2Pv3nQcOPAqrFb6bTk52T6nRLjjjrswdOhdOHTIveNra6tx6NBWxMcPQnBwJJYvlxZj377A2LGknel0wC23kLurd2/SbM4/n1xAH3xA7obx49UrNl1ZBHJtyhOIwKbwK1Oev28sg169+mHOnOcRG9sddXWSO8UboIrjqxo9prqaEg/knDZBQc3bqBkjYVBRQfcpJ4d+T1CQ0rqur2+5UtQY+vfvj/79+6OoiFwnVitZmaWlpFyYTFL3L28hJMR1s6mWIDRUKQiES/T330uwadMgPProNxg8+FqEhbVOIyV3gsVmlYyh096yMiUlpUkhYDCYMG5cKp54AvjmG8BqjYVWexQ9euxryD/+AHV1B/HKK33sN6W+vrFq2GwAfnj//eF2RlI1SS3yr2NiKCuoqcWn0UgmOy3aFLsQEPA1JYK8UtMdzJzZFc89NwabN3/YkPpGv3PBAnpcfLHklzYYKBNIbt7GxgLz5wPTp6sLAY1G3cTX6ynNtjlZHIcObQcAHDggUf36yipISOiNe+99HNdeOxm5ua0T8JPj77/pO3v0kPzb7jJmqkEtYMw5uTEBKpxrLaoJ8T3C8hTBd3nbTW/BRdlSi+HooktIoL2kvr4roqJGwmaz2uMEZrPnfZE9hTuCYB9j7DYAWsZYb8bYEgC/+faymkZT9AAajRbTpqXhf/9LQlUVbUQpKcDy5d3x9NPnNvCBhwOIQUGBAW+8Qa6KWbMAoPF0yJMnc7FsGdFTqwUzQ0Pppur1pDV17dq01ikECgkC9d/mS0qEf/0rEbfc0h9ZWbvcOj44uBM0Gi2qq8sxd+534BwYMaLKqSducyHcEXKIZjV6PW1qUVGeLXyLhRyvtbXSruwrQSDOW10tpQe3JoRbqKXxAQFXcYLvvluPxMQo3HffJFRU+Fbgff/999iwYSPKy2mHFJqyXBA0ByaT5LcX8PMjRc6b2UJyGI1KxUijkZrVTJq0EYmJ1ysCxk013mkp3HENzQKQAqAOwCegdNBnfHlR7sBVpgtAlsDdd6chNzcJBw+S9jh7ttI/OmcOBWaCg6mR9d699ACAyMhUlJYmO2nlcgh66mnTlK4avd7ZD6vT0UYvDwA5QpjUNCHjwHnrUiLs2bMTNptNxqXUOF5++W9oNBps3FgHzv0AVOCmm7znF1CztPz8lEF+kZNdXk6WnPAXu8J555H9HRIi8fL4ShAcPZqNf/45hrq6eOh0sU1/wMvY1SDPhctBzEE1hITQ/CstdU0gJxQeIQiEq8ts9kdJSRGKiwthtZLLxtvBWoEZM2YgKysLP/98CAkJveyCQGRieSoIQkOpmEusvZoaSnAIDSUB4I7L0GCAva2kpwgOpmwngT59gJ07yZobNkzJoVRT4/3kCTmatAg459Wc8xTO+ZAGmocUzvlpaP+tRGpqKkwq6nhgYATuvjsNen0Svv6abub999OgWyz1yM7eg8OH/4DJRD7sPn2A++6j4/z8yFXx2mtJmDEjDYGBjTflLinJcbIIBH2vI0JDG59Y8vNER6eCGsLJ3/ctJcKMGQ/j8ssnIzravUrm3377BPfck4Bly/xB+kQwFizopWji0xKoWVpqvlrhVouKImuhsZTQLl3ofvr7S2qerwTBqlUfYdKkEfjii6VeO6egU2hK6y4qIj++v78UKHbluzcYaPz8/cnt42oTF+4lxxTSXr0uxpo1+UhP/wmA0u/tbVx22WW49NLL7NQbVVU0Fs2xCIKDKZYlHxejkZI1IiLcjxuFhdGYaLWkBHoST3B01Tm2rxTEkwBZP760ttzJGuoD4BEACfLjOedeYvhuHkTQ9PbbifYgIqIrbrnlJQwfnoT8fGpHR8dJlao///wu3ntvJvz9A5GSsgHvvz8LMTHnYObMD7BwoZIeevjwJJx//jXYsOEtfP/9Epw6le94CYiIiHO68a4WnE4nBdzU4OcnBUEHDEhCXh5gMqWgpiYH3brF4fnnfZc1BADz57+InTsl/vzG8OWX6fjii2TYbEJlod1UsLoC6jUankBNe20ql9tgoPiBoO8tKFAuHnGv5FqvrwRBp05dMXDgJY1W/XqCZcuoYMpqpQ1s/nzXQm87hUIwaJDkfnA1dvJNjzHKkOFcmccu3jMaJUEgXEMGgz+02i727zGbSXv1RdD4gw8+QGGh5CaprqZeC5WVpC27m92j15MLtqVJAnq99Du7dZPOd/IkjZ+/v/M4yuHoOejZE9BoOLKzbZg9+0IsXrwL1dW0b9hsouVpy67ZFdxxDX0GYCmIC6gVGyc2jUmTkvDjj3/gxIl8zJjxPqqrSZ3ZuJEW+9ChwNVXS8dHRfUAYww6nR8OH/4fjhzZhvz8vwB84EQLDQCBgaGYMGEuOOf49FNloNZgMOHf/1Zq6Dpd4z7rsDC4rGgVC626WnQ9S8KFFyYhNZXMe183/7DZmi5nLyqieoCdO1MAqLvNhMusJYLAFV++O9qWRkMPnY40XXkDIPH5iooa7NmzDwMHJvokCMc5cPPN0zBixDSv1A78/TfNaYGDB4EVK5x59i0Wmn87iK5IQbOgNp4Gg/qYhoSob2BGo8RIWlYmbfiOLs/yct9lD4mKYouF1rjcLeTOxi5y9r3BGyTX6OXfHR4uKZRarWvlyt+frkP8Jj8/ICGB48gRLU6c6ASbzYqaGq3dSistdY4teAvuCAIL5/wt7391y1FXByxYQGwXwr8PkJ8NoM5E8hs0aNBVWLGCbK2DB7dg4MCrEB7ercnvGT48CTt2fIPs7F2wWOrsxGnXXKPc7JrSWDUa0sAEEZgjlIKA+GhKS0+hri4Q7t2q5uHPP//EwoVvwmQahJEj1dsOlpYCzz4rrr3xoLWr1pTuQo0GQa/3vDOUcNOJIj2x4KxWHX76aS0GDkz0iUVgNjuzYrpCSQnw7be0ud98M6XWlpVJHD8A1WcAwPXXkztzwQLgu+/os35+lCYqmspHRdHrjNG5BNQ2ZldV2waDRD/teLxGQ9+Rl0cpmz16AB988DDeeScLixa9i+DgUNTUeJ+EzmKxoL7eBquVdnDhP/fELWQykfvFG1XCBoN7uf1C+XOlcAQHk1Uj0LevBkeOAOPHfw3GNIo4AefKCm9vwp3d5RvG2EwAX4ECxg0XxZtPLOMlyPNrhbl44gRNUqMRjWaw9O07DHPnrm/yO9577z5s3LgMVqvZXmEstF3HieCOxmoy0UMt81VuZgLA0aPVuP76cKxZsxnjxw9z/oCX8M033+Djj99CQECYXRBYLMDWrVQhXF5Oz0tLacGdOhWHkyfVA/VA0yR0TUFtg2ru4hWmtKTlfgxgPl57LRtffLEcjz+eivvv967LTWygrpqPl5ZSB60jR5Sb7cKFQPfuVIEcGQk88ghtEnv30ty49lpyE9x2G/DRR0SyJgdjkpLRt6/SjaAmCBrbyAIDndupinvQrRutsWPHaD7s2LEGBQWHUVT0HIKDKTWpvJyUHm8hIyMDI0eOxLBhY/Dxxz/aLRZhcTUmCPz9ye3irWCrVkvC0B0LRLQILShQLygMCXEUBCTks7JMYMx5nxCxAm/XvrgjCKY2/H1U9hoHcNqLyv766x+8++5rSEjoj759aQMT1sDAgS3nkqEK4zRYrSTOHX3gjovL3c0qPFw9w0WcLzCQ3BqnTpkAdEeBnJjEB+jSJQbR0XEID++DkyeB5cthZ76UIzoaeOwxYM+eVCxbluzUqAdw3WvAE6gFLFta1OPnB6xblw7gHgi3Vl5eNubMSUZYmHcL9cxm4MEHb8dPP63DjBkfKEjhKiuB55+nTRQgrTkxkbJxvv5a2tiKiyndWVgs48ZJ43L11bRh5OeTFRAbS6mORiNlC23fruzRKxIh5BBavyuoCQIxP+Pjqb2iyGa+5ZYXEBZmU3Rfq6wkge4tF1FlZSW0Wq29r4TYIMU1uGpKbzBQwNybRWGRkZ7tLQYDJTIUFDi7heVU9ICSb8xmU08bra/3fpGbO1xD3ol2eRlmM/DLLz9g+fLXYDIF4t13lYJA9AN1xH33xaG0NA9GYxCqq8sQGtoVSUkvuSSgE0JAQPjAL7ssSaFRiawBd6DTOZeYA0pNODaWukE9/vghXHWVhz4RDzFx4hQMHToFe/YAr75KRHEAkcWNHUvmdEkJaV2BgcoeA8XF2fYm9I4WU3PhTYtAjoULnWMbNTXVmDcvxeuCoLy8FJWVpQo23MpK4IUXSAhER1PAV55NlphI2TgDBgAffkh1LUYjuThvuEH5HT170sMRF1zgTMEsEhHkaMqtIQj/5Bqp2NRFFrPYhIcOnYiQEKUFwjn9ltBQ7zR0ufbaa5GXZ8apU2RmVVXR+ikvp7mhFijW6bwvBOQBYk8/FxIiFeAJaLXkPhJppOHhQFBQJSoqAvHLL5kYNYriWHLBI6f/9hZcCgLG2BzO+YsN/9/MOf9M9t5znPN53r0Uz1BbC4SHd0KXLjH2ngG1tVQb4OgflaOsrBCc21BdTbvwqVP5LjNdGutKFhTkXIDiCUJCKAtAbi4KugrOyfzeuxfIydHAZvMdFQJAWofFQgsrM5O+Z+FCZV9leTN5wL0eA82BRuMsCLRa7zCF5uaq389jx7xbqFdfD7z55hfYvLkKfn6045aWSpZAp07AvHnOCQDyzf2++8gKEESELYHaxuWOYA0KUgoCjYbmuVwQiHnpqsi/stI7ggAAzGYGPz8/2Gz0faKnQ2ys89oQQsDbQeuWuJcCAmgeOHoCIiOV9QSBgQdQUTEEu3dXYNQo+q1yIevK5dgSNKZq3iL735Eq2rd8s26gtha4+uqbsHVrLl57jQqd//iDtLHevV2X0wcEOL8htHxHCAHjiIiIOKfze5qFoNE4a2UicwiQ4gRC6/JVmiPnwKFD2SgqKsamTfQ9gwdDNYuqNSB4l+TwFgVwbKz6/YyO9l6hHuciMOiPoKAIGAx08UuXkhCIiaFgb1P+c8ZIKHiDhttxM3S3tapahorJRBuX0UjaeFkZcPz4QWzc+BEyM7c5ncNi8Q7ZnlqzeuFe66aS79Grl/eJ2gTnUnOh1arP5dBQ5TgPHkwagtlMpp1jBldrCwLm4n+1560Ouf9aDNRPVNOCkY10Ui4vL1Z9XS3TpbFmNY75vM1JR1PLCRYTTXCcbN2agfnz7/OZIKitBSZPHoExYzph9WqKNI4e7ZvvcgdqabLeMoOffTYVgHJXNBpNeOSRVJcaracQjUTki3XPHnqYTOQO8hVtgSs4CgKDwX3r0nGOirascqtg+/Y1ePPNKfj665Wq5/DGxrVw4SLceefV2LTpB3stjtwikKNTJ+9ZIXIEBnqeuaZ2DkcIKnqByy4jlsDcXNI2HWM19fXeLy5r7GdxF/+rPW9VmM2kuZ48WYyysnJUVVGrt0OHaLFd0gg7b2Cg+ipUy3QZPjwJd9+dhsjIeDDGEBlJXckuvzzJSdtojiDQ6501BGF6iolstUZi//7dPhUEhKGoro5CeDgVInkDnrqyHBeEgLcsgttvT0KXLssAxAOg+/ncc2mYMCHJaxWxYtN77rnZeOutf6O09ATSG4qtr7/eNxtUU3AcP08Ea0CA8j6Kc8kFQXz8IAwbdhvi49UDc95ot7h9+3b88st3OHmyyJ6XLywCuSDQ66Vr8za8YWGIFFxHyOd9bCwJ3KIiiimIrD05vG0VNOZ5HcQYKwdp/8aG/9Hw3K2lyRgbC2AxAC2AdzjnL7g4biKAzwEM4ZxnNnVeMShTplyFfft24MILr0d4+GoA1DXM1UTPyEhHVZVzSz2dzuAy00XNF+4YHxAFTM1BUJDSunEUBHp9Au6//1mfsQ+uWJEOxjQA/gcgAeHhqdBqm+/7F9XZkZHSfaislALjGo0USHTMsxfd2uTQaLxT/AOIVL4kFBQkYcECyrxJTKT3hJbV0jiM2PR++ulzFBcfR1zcy8jJofEYexocqmouSE8EgRh/ca+EIBBZOjk5wLXXXomBA6+ETqfek8Abm9aDD87HuHFJ6N9/sJ3NVc01FB3tm4IrV24dT8EYncfRApU3q6mrq0DnzjXIyorC338Tk29ZmdKd6O2Ascvti3PeIkJXxpgWwBsArgCQC2AbY2wN53y/w3FBAGYD+MPdc4tNUWT0mEyh+P13ek2eNieHTgd89lkKOHd2WPr7B3kU+HTU6loy8UTxlDD1/P3pfIGBNPnMZn+cc86/fGIRpKen49FHk1FTI2ZlNo4cSUZGRvMoIvr1U9d4AwOdTeKQEGpbabPRRsW5euaHt7MjxPnEhl1XR3ODc3qtpUJHnHfWrDeQk1OBL78kVW/SJO8JNE8QEuKsgXo6pv7+zoJAaN2HDtGmZjLRujxxQmpyL+ANQZCQcC66dTsXlZXCGyARsYl4nV6vTmnuDXgz3qBWR6TVSpmEZWWFyMr6CMB/7ILg1ClnQeBN+DIv8SIAhznnRzjn9QBWAlDrCfYMgIUA3CayE5vid9/txtq1VkyalIaKCrpZamZhcDD1yC0qUs8Oqapyro0zGmnhBgZKRFQA+R8dJ1tLFriofJVDWBxiUy0s9E2weN68FJkQINhs6oHzphAW5pnbIyCA0lL796dmIgMHqnO/e7NXrPx8zz9/PQoL/1EsKG9sWOI+JSZOQGXlHSgtZUhIIDbJ0wFHV1tzMrDk98BgoHPExtI9LCgAZs/m2LGjBiUlucjPd/Zft9Q1JLLaAKi6hYQV17Vry334ruDN3tmuMpnE+gkJ6YwePUhhFXUljtlG1dXe63gH+FYQxAA4Jnue2/CaHYyxCwDEcs6/9eTEYlJwDlRXa5CfTzuxnPhJwM+PsogYc50d0rVrnF2rj4qi1NNBgygfe8AA2qB696Z0tJ49nb+jpZqeo7Yh3EMBAVRN8tJLz/tEELhKm/SUIoIx1wU9jSE0tGkSLV8JAptNi8LCIwpB4A1ftrhPp04Ba9bQ/0lJvtugmoKjcG6OheX4GX9/esydS+61ykqGl17agUcf7Y/6euf+C542PXJERQXw/vuLsWJFGkpKSFqLbDrhFmLMd9aAt9xC8vOp7RlCaBuNQXj0UWL6z8qSxk/e5U6NGLAlOE3TE2DkmH4ZwMNuHJvMGMtkjGUWFRUpmn5YrVL2gGMaGWNSe0QAeOSRVHtlooDRaMKjj6aid2/qGyrv6OQIVympLRUERqNyoxBuFIOBHOu//77TJ4KgWzfX6bGeoHt372/YgJS37k2I840bNw99+lyisAK8YRFYLEBtrRmff74eNTVkoYqeAK2NwEDnudkczdYxTiM02p49gYceEq8OgtEYDovFjLw8Z6uguWNrtQKVlTY8++xDSEm5BxUVpIUJjqGEBPrb3Dac7sAXfQBcFU2KdSR6ItTWSgIgO1vpUmqsv4mn8KUgyAMgT+zq1vCaQBCAAQB+YYxlAbgYwBrGWKLjiTjnaQ29EBI7depk1y4uvzwaycmdcPAgBYAdBUHPnkrf9IQJSXjuuTTExFAWUEyMlDUSHNz8G97S4JS8fgCQLISoKJot0dEjvC4I0tPTUVXlPJP0es8oIrp3JyvKF/CFcBHnjI1NhL9/oFddQ6Lwr7CwDF99RalCjoV4rQlHt5CaG9JdyO+F/P/gYOE2DURKShZ0Oj3q6pT8OUDz++5WVhLh3PTpD2Py5JnQaGixid7agpLBV/TMjPlGELhyDwkXNOccsbFkogr3EOcS2ypAcQJvWLGAbwXBNgC9GWPdGWMGUIHaGvEm57yMcx7JOU/gnCcA+B3Ade5kDYlNsaQkHxUVxSgspFGVC4KoKPXg44QJScjIyMKRIzZkZGRhwoSWVcfqdN4x++War6Bh7tqVZvcll8yyN1r3BtLT05GcnIyTJ0sc3onAlClpTQaKdTra4AYP9p05DviGylic02ymLLLbb09Ajx4aDB+egC++SG+RwBWfranRIDaWOCG8NT46HVmsAwe6F7gULKFyOHZ48wTyzd/RShNauZxyOy9PGdeqqGhenKuiAjAYDJg790U8+OAbAChQfPIk3cvohmZzvhIEImnD23B1L4TwXrToeuzeTQqZfFyrqpSBYlf9TTyFzwQB59wC4H5Qa8sDAD7lnO9jjD3NGLuuJecWPrPp09/GjTcuQGEhzUy5IHC3SUVL4S3XhZoJL/y7+/alY/jwBGi1GiQkJCBdJKY3EykpKahWraAKxOjRjQuB6Gip8tjbbhtH+NIi+PTTO7B06Z0oKsoG5xx5edmYNy8ZH33U/LEVG51WG464uBsBeM9aiokhzVRQKTdlhXbu7HxMSzJf1FxDAkIQZGVJr9XWArt3y5vbe969TB4kBqRNT2jF1Mil5RW/riAyeXwFNUUnMJDWFVWl/wVAOa6Asr9BZaV3FETfkdwD4JyvA7DO4bUnXRz7L/fOSX9LS4HRo5NRUgJ8+SUNoPDh63S+7e8ph7c2Q8dFazQKQZCOAwfuBucUOM7OzkZyMnEjNZcoLSfHVTA4p9E8esFy2RrwhMTPE4jFV1W1BoDSrq6pqcaTT6bg3/9u3riKTauyUqKDboo7XqulZISgIPIB19fT/JVvgH5+SsvCz4+yrcrK6Dg/P1KOSkulzdYxhRNoWeaLTielOTsKaCEINmz4A337FiIxkfS8+nraxEQlteiw5W7Wktj4y8vLUFSUj+PHo+DnF253C/WiAtwWV/wajTT+FN+h8TSZfDP/HL9XLeAbHg4kJ7+Dm2/W4v77aQzlNS7l5VKGnc3mnT7RPhUEvoBcEADKQLEYqLAw3xG0OcJbueFaLT2EVhkQIMzEFLsQEKiurkZKSvMZM+Pi4pCd7dxPQKdTBolDQ2lD8fenReJt7pbG4AtrAJBrYeo2dW5uTrMLy6xWGqeqKjMKCrQANC5dQ7GxUq9bAdEzITiY5vc//9D8Skhw3uiMRmeNsnNn2hhqa53npV7fcheHKCzT6ZSNa7o38BPX1vZGUZGyHKi+nq7H35/W7qlT7lnrVqtEwbxlywbMnHkThgy5AQ899CUOH6bXWxof0GjoWuQC0pcWgCNcuT7DwoD8fG0DEykJxOJiafN37HhWUdFyQXDasoZaApsNyMzchuefH4uvv/4GgLLM3NdtHQXUeN5bArkGYjIBhw6lA1BvAONaq28aqampMDmphybExZFPMiaGKm779aPNyc+vdYUA4DtBIN0v1wR0joFOd2G1UlbHrl0ZqKjQgLE61dqKbt1ojB03Zp1O6qoWHk4uuEGDXGerqUGNvRXwjsIin5/yTSw8HAgIsAAIx8CBzh3u5BtXVZV7Ac7iYknp02q1iIvrjcjIeFitUsaQsAiaY/1rNCSIvVkf4ClcWb0iA4oxScgK4QeQMJYH3+vqWh4raHOCgHMyvdeufRl79nyPAweuA5CAujry7Wo0rcfnotd71/KQL9bvvkvHJ58kuzw2rgWEKklJSVi6VMqeCgiIB5CGXr2SoNVSHMAbtM8tga8EgaQwpAJwTiV+5JFUVFU1L0ebUh2BkhK6kX5+hU6afEKCOlumGrx5D7zh5pDPT7liQEypdLF5ec6akVwQCKugMYiqYYErr5yA5cv/xpQpr+DYMaklp9Dem6MNR0ScnkpvR6hZBeSCK8Hzz49Fbu4SAEQPL4djvKWkhNyRzS0ya5OC4Ouv07Ft21eyV7OxdWsyMjLSERHResU73g6WyifmokUpqh3AAMBkMiE1tWVdwCZNouyplSttGD06C0ASQkPJVPZVPra78FV8AAAmTgSeeaYSl18eCyANBoNEKJiammbPImtOCb+wCEJDqYz43HOVwrpHD9/0m3UH3tj05Odw1KSFdv7770B29m688MI4bNz4DgBnV0ZVFRWdOY6xoKhwPF4uPHbsoL/nnEN//fw8F5ihoa1v4bqCq+vo2jUUBw78ipMnXwVADbfk6c2ODW4Amnt5eepdzZrK2GqTMYI33kiBxaKcRWYzUSPMmOH9Zimu4G1BIN/8jh937fpJS0trcUetPXv2YebMmQgK6gOLZRkAWiCna6OSw5fZSBoNoNXuxq+/zgKwG8BwJCdvRN++wzF6dG/7cc2pKRDBRtFZVB4fCA72Xb2FO/CGYJWfw3EDGzUKWL3aiq1bGRhbjd271yM0tCtGjZquiBMICKtLq6X7bbGQy0gtA+bECUloiD7NF11Efz2xBhgjS8AXGUbNhZ+fMt4iEB6uxZw536JTp3i8+ipHVhbD3r1ElQNI3dkcYxpWKxWgaTR0XtEfoynluM1ZBABw4oRraoTWvMm+tAhc0WEYjSavtFXctu1/yMzchN9+W2HXtqKjfZO77yl85RYCaEF069Yben0eABvq66Px9tvJSEubrvCzNkcQWK20YYlKULkgaA4Fh7fAmHcEgUhoAJyr4SMigPDwnwBocOLERNx//wqcd94Y/PYb9ShwlToqrChXHPtWK/DEEzPw8MP98OuvGcjOpu8W1drurHe9ngKtsbFnlhAQULMKAgKA888fhc6de+Kii8j/LISgwLFjzp8TsNloLtbXu+cuanOCgFgq1TfJzp19RESuApE54U3IF+wjj6TC39/Zh/3ii2le+a64uD4YPPgyDB58jV0QeNMaYKz58RNfCgIKxEbhww+L4e9/EoAeQE9oNFqFIPCUHwOJ/SkAACAASURBVMdmo03LbAb+/ptMgvx8ypyOijq9rghvzlNxLrUq5QkT9GDMhqNHz0V8/AS8/fZdeP3121BWdgJ5ec2rgs3PBwICIlBfX4ODBynd6PzzJaWpqXE1GGheBwScPr6npuBKOAltf+hQ+rt9u5JioqKi6XiLuzhDh6Zx3Hqrs3/cYDBhzpzG/eaM0eDGxLQ8EOerhS2sjAkTkvD002kwGqmJitFIPuxrr01qMRWC1QoMGDAMr7zyK2bP/tSeiquWe+4J/PzIvRQVRRw7MTGU8WIyuW89uSLk8hYYA375JR2zZiWgtjYKQALGjXsCTzyxERUVSq3Uk3G2WiWttqSEdkiL5S87U+fphDfHUy1OIP6OHj0Sw4ZpYLUyfPmlEYmJN+DiiyehtrYC9fVSto+7+OijNLzyyhPo3fsSMKZBdnYPAJJbCGh8HZpMzim6ZyJ0OnXlx2wuwKpV87Fp0zz06kWutLffVs7R7GzvFJS1OUHAOXDJJUkICLgE8k5TM2em4YYb1F0mGo1kGoaHS7zl5C9unubqq7Qz+YSYODEJd96ZBcCGgQOzMHYs/b6WpoqJz9fUkIZRWalOSeAJtFoa09BQqceCTkepvFFRJGSioui9kBDXi9PXhYCffpqOJUuSUVycDWq0l40ffqBEA+GmEPBUENTVkT+7tjYYRqMZ48Zdh5gY3xcmNQVfWAQAabI6HVU6C/Tpsw4ajRlbtwJjx36CBx5Yic6dewKg2gg5g2ZT+OKLFfjyy2dhMJjwzDNHcfSoAXo9WQQAuYhczaOQEJpvZ6oV4Ag1gWYy2bB6dSo2bFiKe+/lMBrJPfStjKu5psazMXWFNjJMEoT0GzbsNwBZGDfOhiVLsnD11epCgLpSOZuGoqVdbKySB1+jkQJ7wcG0MTtqVDqd7wKackGg0Ui+5aNHKzF4sAmDB0eisrJlXOSVlcC2bRnYsGEV1q+npPl+/VoWHwgPb3rRmUw0rmFh6rUeviL4kuOpp1JQV6fMxrJapR4MzY0TWCwkCP78k56fd54e8fG9zojguzctAsdal9hYmrNiPWzZ8jxstoUASHv96y/l57Oz1bNaHFFXB1x++b2YOPEpREf3xe7dtPb795fWiCuXSkhI69USeQtqimV8fFfcdNMC3HXXm+jSxYZ776XXv/xSOTdzc1veqKbNZQ2JIMjmzfT88svprysNPSKiaZ+zyUQTx2ZTdnSSn7OkRNokfFmEotMpKQZEQcnJkyZYLHUwm+vstRTNqYKsrqZzL1jwAPbv3wmT6W8AkRg5svnCzWTy3FUWGEjjaTbT/5WVjWt43kJurnqiAVkIlPETFkZj4YkgqKykeblvHz0fMODM8Ev7sugxIEC67wEB9Pt79EhEcXE6NJrHcfy4Dv/5DzB2bCEmTTKhoOAwMjNXY8+eCzF16nXQ64HNm39EdHQsevbsZz9vdTVw8CBwySWT7a/t2kV/hTUAqAuCwMC2JwQAqeeBvFBMo2G4886n7Om0Q4bQfnD0KKWTitiBxQLs2UNKY3Ot+jYnCDgHfvvNhpoaDXr3ljRmtc05JMT9LIGmqjfDw2nA9XrfF6yJdDpA0p5LSzW4/fbPcN11lIpSVka/2dNYhwguhYV1gk53EaqreyMggPyuzdEcAwKaT/DXqRNtlKII0Jsdl1whNjYOOTnq1dq1tZUAArF/P2megHoPXkdUVdECrq2VBEFR0XsoKhqGfv36eu/imwFvCyOtls5nsyldqgEBlNs+ZcormDLlFdTUAGvXkva6fr0JdXUL8PPPLwMARoyYgoEDr0NkJMecOdNQUHAM33yzHf36XYATJ5yZS61W2ugApSBwVIT0emU7x7YGk8mZrjsoSFlXMXw4CYLNmyVBAMBecX3yJNWreLqW25xryGYDvviCsjIKCx+zv+4oCIxG72oGogOSOy6QlsKR+11Q7cbE3IjevalYyWol7dUTat/KSknLXbLke4waRbwww4Z5tmEItseoKNrMm5sdJKfw1mhap5r52WdT4efnTK9x4YXPQ6ejga+rk7pslTgydcuQnp6O+PgEBAURjXV6ejrKywG9vgTffDMNubl7vXbdjJHAjYz0bJx84WpTizk4WoRGI3DzzUB4+BEAQcjMlC5k2LDbYLMB2dmV6NNnFOLizkNd3fnYsYM6j1Gsphy7dq1Hbu5+HD5MwrZLFymzzWBwdmVGRrYex5gvoGZVM1aOzMw12Lr1UwDApZfSb9y1y7nwDiBFb9cu4MABcsP9/TcJ0d27G//uNicI6uqAkpJoAKdgMFB7AzWffWuSR3kbjtzvIpvn+HFlupjFQq+5Q4dgNis/K2iCAWDECPc1iOBgokhwJOtqK7jttiTMmZOGyEglvcbffz+OqipphxXdn6qr1YPzoqdDTo5EY/3xx8kA0tG9+ylcffX/oU8f71kDwroNDJQSHZqCweCbWJY7gkAgKSkBABAY+ATmzv0Rixbtx6BBVwGgloz33rscI0ZMwX//m4TqaqlRUm7un1i4cByWLr1T1S3kKOD8/HxPi+5raLXOa+rUqSz897/XY+XKuQDIch44kIRlRob6eWw28hjk55OFIDo5NoY2JwiE6dS/vw4PP/wJAOfB02h8m4vua+j10kL385Oonzds+Bl33jkYW7b8ZD/WaiXtNS+PtNeqKtib2FgstPnn5tL78rz448cp20CnI/4bdxZRWBhZRGd6Ol5j0GiAK69MwpIlWVixwoZly7Jw7rlJqKgAPv5YOk7eBvDkSSJBk8cM5s517ulgtVYDSMHIkT1xxx0vY+DA89y6nuBgsqyEpu+KjE5AryfNOCyMNkSR1OAIT8jqPIGaINDr1ZWJiy7SIDSU2DTr6sYgJuYcp2M2bfoQW7euRG7uPvtrWq0BAwaMQefON+L77+m1wYOlzzj+traolKjBkTm5b99zMHjwVRg69CbYGnynI0fSeytXKjuWtQRtLkYgouN9+gQiIYFUBMdJINIX2zIMBokbXbiGCgsNsNl2YdOmDAwbNlpxvNlMD3dSS6uqgEmTRgP4CV261ECnMzotYtE+MySEFrnVevrTIL0Bx+ApY0C3bq9g374H8NtvwNSpWgQGktCsqaExEMH5ykopmO8q6AzkIC6u6YLDsDDawOXz1Gike+jnR99dXCyl4DrOZ4PBeeMtL5cUgYgI31WJu/pdAQHOAXadDrjqKmDVKmDxYo7bbitD586bodMZ4O8fiN69L8H48Y/CYjEjMjLB/rmePRNxzz0/4oknaCyGDlX2fna0+NuLINDrpUQKeq7Hyy+vt9OWABTPGzkS+PlnYNEi4OmnlZmPzUGbEwRiosnJNx3N0jOFUKol0OslXnnhGtLrz8OAAdcjJmZEsznzASAnxwabbSAAoHt3Mj3km2NoqESHLHC6s1+8CZFZcfQomdF//fUhgPNgs43Btm2SxlVR4byZWiz0iI6OQ16eWtA5DsDfOHmSob4+HgYVNTk8XN11qdFI98FoVPbYcAfBwa3jEnUlCEJDpT4hcvz66zkApsFqfQQffRQKYDOAlwAAV1/9f7jjjpedPlNfD7z8Mlm055wDzJwpzUGDQWkB+aLK/3QiNFSp0EVEQCEIGAPuuoss+v37gWefBZ56SmoA1Bz4dHkzxsYyxg4yxg4zxh5XeX8GY2wvY2wXYyyDMda/qXMKQfDJJ1cjJ4eStuVmYlt3CwmI/YMxsgioKUgw7r13Nfr0GWUPZnqKykparEOGPAMA6N3bT/F9Wq2zEGhvEAWGQosaNuw2dOtGc+m336TjKiqA1aupTajoa7x6dTpWr05HRYUaeY4JAQGpePXVq3DPPX2cBIWoaXF3sz5T74HoVuaITp3U3UNXXJEM4FGMGLEVVMT3IoKCPkZY2CBcc83DTsdzDixfbsXRo3TOhx5Sntdxw2sv1oCAY3V9YCBHVdVxHD260/6aTgc8/DClk544ATzzDAkFObgHJcc+EwSMMS2ANwCMA9AfwK0qG/0Kzvl5nPPzAbwIwFk1cIDFchJAHIqKvsPzz1+J7dvTFYMWFHTmLiBP4NgjVtAUiIKl7GylH9sdrF6djlGjEpCUpMOOHQNAgU16T2iigYHtY/wag/h9IrU4LCwa1dWvAtDgzz8T8MMP1Nti7dp0zJuXjLw8KSA8Z86dmDPnLpSXK0lejMYIAGno3j0J48fPgUajQXCwMs+4c+f2sWmJqnFHaDTqNCVjx87Gyy//jXvvvRgzZzIwBlRUJKGycie++CIGx45x7NmzAWvXLkFmJscTTwA//6wFUItrr/3dKQXcsUivPYypI+SW6IEDuzF9egxef/1WxTEmEzB3LnlHCgpIGLz+upRN9MorExXCozH40jV0EYDDnPMjAMAYWwngegB2ucU5lydABYDUhSaQDYCCJqdO5eP115MREwM7j3xr9Sr2NeSCwM+P8of/+QdYuTIH9fWbMGLE7ThwAOjb1z0Nc/XqdDz+eLK9qtZqzQaQjNxcoFevJBgMEhdTe4cQBEFBQEZGOpYtS5b1fsjG++/fjhUr7kF9fQ04VxY3mF0wp2k0gQCS0LUrcMUV92LkyAsRESE5bgMD24elKmAwqJPIde5MiQjy9zQaDbp2JYrvESPIKvr6a2DnToaNG4GNGxmARABDANDN0WpPwmqdhh495irOHxqqHEdvF8ydKTCZJMbWnj3PQUREFMLDu8Fms0KjkbIJgoJIALzwwk84cOASbNliwt69wJAhBTh0KAZLlxYhKqrp4kjmifngCRhjNwEYyzmf3vD8DgBDOef3Oxx3H4CHABgAjOKcH2rivE4XHBMTj4yMLAQEtDxociZBMDbm55PZd889dbBa/RAcPAJvv73ZflxoqKRtOi6Kujrys153XQJOnHD2aUdGxuPNN7MwZAhpIa567LYn5OdLSQdDhiTYq4pbBgbAhqlTgbFjKcNF3AuNRr01ZVtGebnUHEXeaxug7LVDja5iQl4esH49ueNEAlZMjBn/+pceo0dzcF4Bg8EInU4KAJxzjtIV7O9/ZvTQ8AVycqQiS845jhxhqi5hzjnefHMKMjJ+R0LCdmRludLm2HbOeaLaO6c9WMw5fwPAG4yx2wDMBzDV8RjGWDIAl30bRROX9qbNCq0rMJAC4FFRryM/fwnKy7Mxa1YCJk9OxfDhSTh1SqoR0Osls1I0BAEa7+EgFlJ7Crg1Brnrq6Sk+b2flaAIdH7+GgQEXKcQyGFh7UsIAHByx8prVCIiSEg0VowHkHCcNo0Cn2VlIttJTEIGQLmg/f2d00bbk5XlCKNRqhFijKFHD1JgDh36B6GhXe2FkYwxzJz5Ia6+ehfi44OxaxewezdHfj7QsydDQgLNwaeecv1dvgwW5wGQE/B2a3jNFVYCmKD2Buc8jXOe6EqaRUfHKTIu2gvknOtbtqSjuPhJiGb2xcXZWLaMWDPlMJtJWysvV5aru+rhEBAQflYLgqgob/WwqAegwaZNU7B9u3RPDIb2466UQ77WAgKcYwYJCe5nsTBGVm1T9BBqPDpnQiMlX8HxtzEG1NfvwMsv34C77grG779/JnuPoXv3wdBogAsuAO68k2HePIZJkziKi19GTEwjXWzgW0GwDUBvxlh3xpgBwC0A1sgPYIz1lj29BoAbBqXykkXD8TOhEbW3IX6TRgOsWpUCs1lZwFRfL7FmqiEjg3j3b71Vg9pa9QKD2toKfPstbVxniyCQp8I+8EAqDIaWRRspL6IUAEdtbRkWLkzG6tU0pi1J6TuTwRjNT9Ff2nH96fVETz1woPubdW1tFXbtWo+//tqM1NQrsHLlPMX7jpxW4hraKxzHjXOOdes+gb+/Fk888TOGDr0JeXkHkJu732WGUF7eAXz11bN4/nlVHdsOnwkCzrkFwP0AvgdwAMCnnPN9jLGnGWPXNRx2P2NsH2NsFyhO4OQWcoRWG9EQLGGIiYnHc89Rw/H2Zg0AykleXOyKNVP9dREEFbz7lZUq3a4BWCz1WLSIhElrcP2cCZBbBJMmJeHuu9MQGNg8tjKNRgvOlfX7tbXVWLQoBX5+7dt1Ic/nd7X+TCYqBOvcuen5tXTpv7Fw4Th89dWz+PPPDcjKkjJewsKcN31///ad4eaYRsoYw7x5L+H773fiootGgDGG1aufw6OPnosNG5aqnmPVqhTYbFZMntyIXwg+jhFwztcBWOfw2pOy/2d7es74+DgsXnxCUWUItE/NQPSItVqpDWdBgVpQk2PatEjceediDB8u9WRYtSpFlgnTOI4fz3GZEtgeId88dDrghhuSMHx4Em691fWuwpjzhq/TGWCxqKdjHD+e0y7npBx+ftJYNvZbtVrKd09IoBqW48fVU58HDBiDoqIsXHbZvzFq1N0IDiZfkE6nnpbanoWsgL+/esZP796UMhoSEonQ0C7o23e46ucffPAzmM11CA9vvMq2TS59NRro9rroDAYqsX/ooVTMn5+surlXV5fgjTfuwoYNwLnnJiEiwrWloIbo6Lizxi0EOFdJd+9OWSuRkfGqGUSRkfGYPDkVH3wwG5WVFAENDIzA1KmL8fHHKSgrc/5MdHRcu52TAn5+Sk6spkD9oulRVkZZQ3IGzTFj7sGYMffYn2u1lCMv6Mod0R7rBxxhNKqzjBYUHMXTTz8Is7keGzceR2Ghev9irVYHrbbpbb7NCYIjR7bj5pvj8Nhjz9trBzSa9uvfFoLg5puTkJsLvP76VNhsalSC9Th4MAUHDwqrIA4isNwYRIzlbLEGAGd3gkZD/uxbb03F2287ClsTLr2UsrPkFhdAmTGUNpkMQPpMe45bySH/fYJGXE5s2BhCQuhRUSGx6graFJOJHt26uRYw7Y1WwhWE+8sxBBASEo5ff/0OnHNotWXo1y8UFRVAVpZ7bMSO8Fkdga8g6giMRpM9PtCec4mrqiRu/Px84NJLNXBdd8dwww02FBcDpaXp2LfvLnDuupIkOjoejz6aigkTktpke7/moqxMnROnsBD46KN0rFqVgpKSHBiNcaiuTkVMTBJeeEHpOjt5kgp5CgqAqKh0WK0pOHkyB9HRcXjkERrTuLj2xdHUFIqKmrcJAVLPZ6PRPb9/cHD7DcQ7oqDAuWENAGzY8A3OO+9CdO4cbX+Nc5rHxcWkQIaFUVaXXg8MGeK6jqDNCgJAKiRrz5PCbCYTWuCiixJQVKSu6UdGxmPJkiz78ylTjDCbVWYQgKioePzxh3RsRET7THNUQ10dCVU17N8vmeJmMzBnDi3Eq64Cxo2jFMYjR4BXX6XFFh8PpKTQ2HXrRg+AhIb4/2yBXGnxNTp3bt+po3JUVtJcawl0OiA29gwuKGsJRCFZe8wYEtDrlabhnDmpePzxO2G1Otf319ZW4r33ZmLr1k/tvmzGNNBotIrjDQYTHngg1el7zhaIbB41LatvX9Kojh+n53fcAbz0EvD99/QwmUiQWK1Ar17AY49JnEVyTpz27hZSQ0AAuXhcsHB4De2FWNJdiDagvmzl2qYNV1FI1t6DRvJN5aabkrBw4fsICnJOd6ysLMGPP75lFwIAwLkNNpsNgYERYIwhMjIeTzyRhqQkpb/7bBIEgOumLVotsb0Kbf6CC4jlcehQ2uhFt6crrwSefFK5+cvpz89GQQD4rhmOHGcDMaIcjPmeWr/NWgQiIHc2TAqioJaeT5yYhBtuSMKllyagsLDpgDDnVvj7B+KDD4rRs6fzYtXp2h8FQlMwGmlcXZFxRUYSwyvnQGIiPTiXUyEoj/fzUwrTs1UQBAbSGPnKKtDrz55YlhxBQe41nWou2qRFIC8kOxv82moWj0bjmj9IDSUlOTjvPHWNranS/vYKR3pjOXQ653FpjApBznPVXhkx3UVUlHqQ3GAgzVaNksJdtPUG9c2FwdD4fG0p2pxFcN55F2LNmkwA5Cc8G1waRqMzwyPQWJcsZ7iqFQgKOnuCbo4QvldXiIpyL0jn76/smBcUdPZZWHLo9ZT7X1REfm2DgcZSvvlzTsHl0tKmG6sLBAWd3QI2IoLGs1qlTlQtxdQTtEmLAKCF5sg90p6h5iN85JFUGI1NB0j0egMeeSTV6XWN5uw0swW02saDjsHBTQtJnQ7o109SSESP4bMdotVmp05UFexoATBGGm63bu6xs2o06oWkZxMYo/EMD6fx9PensYuNpXFsSWfBNmcRAFLLv7OpCCow0LnCUBTULVqUgry8bGi1WlitVjDG7CRUYWERePLJxfZj5QgJObvy3NUQEKCePSQQFwccPKj+nkZDQkAuTNQazZ+t0GiaDnIyRvMwOJjiYBoNab0ixsAYve7nd3ZbWQKieZQa5X5YGFlNpaVkNXBO4+aOFdUmt9Lg4LPPRBT+VceCHbHBz5uXjJoashk554qCOzVotWdP3UBjEO4hV2a1WFyOgTqNhlJN5X5bURXbAc/BmFKgyv/3pW+8vUGnI6vBZqMqb3eTFtqkPtgaKWpnIjp1omYeISFKa2jRohS7EBCoqam2s4o6Qqslf+PZbg0ANAZRUY1r8fHxpHwIjdRgAM4913kemkwd1kAHzgxoNJ5lrrU5i0CrPbs3MJE+FxZGrqJTp6TCOkeI1xmTMllE1kbHhiVBtOgsKyM3hGPhTmAg0L8//S8Cm2puCl/nenegA75CmxQEHSAEB5PbIjY2Djk5ztlDsbFxSEho/etqi/D3l9wRZjMJWDXeHFfzT6M5e7OvOtD2cRbr1u0DjAHPPZcKk4Nz2mQy4bnnnDOFOtA09HpynXmidHS4hTrQltEhCNoBkpKSkJaWhvj4eDDGEB8fj7Q0ZxqJDrgPT1JrjcaztyivA+0DbY59NDExkWdmZp7uy+jAWYL8fCW9hyOMxqaDzR3owJkAxlyzj/rUImCMjWWMHWSMHWaMPa7y/kOMsf2MsT2MsZ8YY/G+vJ4OdMBTNJahJipoO4RAB9o6fCYIGGNaAG8AGAegP4BbGWP9HQ7bCSCRcz4QwOcAXvTV9XSgA82ByaRes6LTUabR2ZzB1oH2A19O44sAHOacH+HUJmslgOvlB3DOf+aciwT43wGcZa08OtAWEBoq9YUAKD9bjTahAx1oq/DlVI4BcEz2PBfA0EaOnwbgO7U3GGPJoMawiJOze3WgA60Ao5EK+QCpr24HOtCecEboNIyx2wEkArhc7X3OeRqAtIZjixhj7lFuNo1IAC1sAtcq6LhO76OtXGvHdXoXZ/N1uozB+lIQ5AGIlT3v1vCaAoyxMQBSAFzOOW8kP4PAOe/krQtkjGW6iqKfSei4Tu+jrVxrx3V6Fx3XqQ5fxgi2AejNGOvOGDMAuAXAGvkBjLHBAN4GcB3n/IQPr6UDHehABzrgAj4TBJxzC4D7AXwP4ACATznn+xhjTzPGrms47CUAgQA+Y4ztYoytcXG6DnSgAx3ogI/g0xgB53wdgHUOrz0p+3+ML7/fDaSd5u93Fx3X6X20lWvtuE7vouM6VdDmKos70IEOdKAD3kVHOUwHOtCBDpzlaNeCgDHW+XRfg7tgjLWJTreMsTZBr9Yxnt4FY8xr2Xq+RFtZ82faeLZLQcAYC2SMvQrgO8bY24yxG0/3NbkCY8zEGHsDwHrG2KyGTCowxs6oe9Mwpq8A+JYx9ixjbOTpviY1dIynd8EY82eMvQXg54ZEj1ENr5+J43nGr/kzdTz/v70zj7aqquP45/veYwoUAxxwpCc8BZXILM1cLlaI5VRa6lppTqCmpmhI2WSOkaVZJqaWlqHl0CKXTSqlaE5ZaYBaVLigZWmK4kii8Pj1x94XNpd3eQ+49wzc32etu+65Z3jvc3/n7Lv32WcPhTqZ9UDSNsCNgIADgfsp9hhGk4HBwHFAX0JzWsxsxdoOyhJJI4DbgU5gArAI+FKuUrXxeNaXCcAWhM6eC4AfSupbsHiWKc0XMp4bXUYALAWuM7Mzzey/wG3AbEmjc/ZaiaS+8b0N6A381MzmmdmlwKJYUsy9lJCwBPi+mU0xs78SWoI9J6kQY0N5POuLpHS6eAGPmNlLZvYj4BFgatyvKINtFDrNlyGeRUkY642knSRdI6kfgJm9BNyX7LId0A78PQe91ZDUIeknwJWS9oh9LQYAH0h2OwU4RtK2eZUSYkxXlqjM7FlWHwfqHcDOZvbvzOUSPJ71RdJwSbcBN0g6SFJl3NUtkt0+BxwmaUczszx+vMqS5ssSTyh5RiBpH8It4cmEKgEkyczS2WZ7Awt7MnxFI4kX7bXAHGAu8BlJE4FvAKdIGgJgZs8ANwEn5eR5EPBzYIriHBKS2szsjWS3QeSfyDyedSTeLX0HeJKQpg4BvgpMBw6UtCtAzKzuIFZlWcbtz8uS5ssSzwqlzgiAlwh1bh3ACZJ26CKQ7wGeBpB0Uo63izsCS8zsm2Z2JXAdcBjQD7ia1TuQ/IMwWmset4vPA0cTYnqOpAFmtlxSS+IyCngq+h0lqSNjR/B41puhwCvA18zsDuAiYDwwgpDhflmrWuTcBdRr4Md1pSxpvizxBEqeEZjZ3whzHswHfgtcCGvUBY8DBkuaARxFqE/MHDN7Ehgmad+4ai5wD/B5wqB7gySdJ+lI4ETgzXhcpiUEM/szMC/G9C7gmrhJics+wOaSbif8yC3L0jF6ejw3kDRTNLP/EEYAHp98vhqYGjPaJcAFkk4k3HUtzsKxC8/CpvnUocjx7BIzK/yLcOu8afJZ1cvAJsB8YFzVsXcSSluHZ+Q6BNgy9QNa4vIZwE3JtjHA9fGYDuBYYCZwdB6eXcR0U0Kp5n3Jtj6EH93HgCMz8BwK7F21rojxXMOzwPGcWLWuNb4fDzyYrN+M8OB1DPBOQvXGLRnGc2KNbYVJ84R5Vy4Dehc5nt1+j7wFehDocwmD1t0CnB/XtVTtUwn8WcCv4vIn44/w2AxdvwLMI0y7eUnqFpeHAzOA4+LnwYRB+bbKOKZdedaK6bnArLj8kfh+aIauTxFK0rvHz2mGVYh4nj8yaQAAB5hJREFUdudZlHjG//0YMLnG9hbgXuCsZN10YNeMY7lWz6p45pbmCQ0RngSmERopVKehQsSzR98lb4G1BLk/YXTSW4EtCSW8V4FhcfsaJdi4/HLc73qgb0aufYFLCLn95tH3f8CgygWR7Ls/of5yd+BIYBawfRE8q/ZNY7oceB24AuiVkWtLTFx3Em6dPwv0L1I8e+BZ6xrNI56Xxf+5xv+rcnsvoX37ocCn4g/yqAzjWdNzLc6ZpnlW3ZFeWetHnVV3LbnGs8ffKW+BLgK4WXxvA8YCbcm2HwATahw3kJBxzAU+mKVrXB6aLI8FbgZ2qXFxnAp8N14UDXddV89k+5AY879k7ZmsmwZMiT+a+xYtnj3xLEI8gZGEecE3IRSqJgB7Ve1f+YH7GHAB8Htgn6J5JvtmmuarPFuAJwiFqV2ix/EkhSpW3bVkGs/1+m55CyRBGxwT00xgEjA82SZCk7BZwJgax7cAo3Ny3TmubyOUUBcC3wL+SLhdrVwQaUm2taieyfFtGSWw1PMMYGRcPxy4IS5PJpS4zwK2rVwXOcazx545x3NS4nk+4UH0w8B5hJYqxybXpxrtVg/P5PhM0nwX5323uH4q8O24/tOEjoFTk3TW0mi3er0K0WpI0l6EW+sXgYsJD2AmJbu0EC6Mt+liuksIQwiY2dwGq9ZyPS06LAceB9rN7GxCKWBy9MeSDk1m1llUz8RxuZk9lLHntonnfOA1Sa3ATsCZwJ4WO15ZTG1xOet49tgzccwjntsQ7pgg3K1cCOxnZhcQJo46LfEzMmJDPCtkkeZrnPeJcXOlSvJnZnYtcA6wFbHDmBVoGI7uKMTk9YSWFJeb2S2wcuTIcbEn3jIz65TUHpcXKQwo1buyf0Fc+wJvmdnKCafN7NeSphAu8oXuuU6evQjt63cj3Po/Tag/NkkjzOyf7tljz/3ieX/VzC6q7Ghmv5Q0mfDjlnU79jJ7jo/b7if0Yt89ej4ROzKWJgOokHlGEHsBrlbyMLN5kp5Jti0jVA2lPQPHAf0kTSeUus4pmOvS9BhJIwkliH8Bz7rnOnsuA+ZIehB4wMxmSnoXYe7rhrYL3wg9d6yc9+TYUYTzvpDinPeyeLbHbfMlTQO+oDCeVTvhrnpBIz0bQaZVQ5J6pYGu6iiyJNk2jFC6ShkC7Epol7unmd1XUNc+ko4htHa618yON7O33XP9PM3sXDObGZcXmNnXLQwb4Z7r4SmpTdJhhObY95jZCTEzc8/1O++zCdVWDwK/MLODLXQeKxWZZQSSTgd+pzAG9yEQ6iTT3njJcjvwaFz3cUkDCcP2DjOzhs/luSGuhAHE7ibUEV/lnht87rdqpFuzeRJa2twPvL/I570snpK2NrPFZjbDzK5vpGcjaXhGIGmQQnXOh4EvEh66HBdvn1c+UJE02lY9XBkFdEi6EziC8Dxgnpm9WQLXVjN7oZGuTeT5CTKob20iz8MJLVkWV1fBuOd6n/fljfLLFGt806tWwkiBlTbf7cANxLbrhKfs04EHgK2B7YHXCMPKZtaDtUyu7ume7tk8npnEogHBbSN0rNkuWTcgWW4hdBgZET+PB06r+hsnZHQhlMLVPd3TPZvHM49XvQO9G6F9+vPAzTX2GQn8psa23vX02Rhc3dM93bN5PPN61fsZwYuErv47E4YI3h9AUmvyFH4oYY5WJO2pOGm3JFkDW62U2NU93dM9m8czF+qaEZjZc8CtZvYyoa6tMutOJ2GYCIB3A70lXUronl05NrNejWVydU/3dM/m8cyLurcaslWtUKYDSyVNiutXxJx3X+BDwGIz29vMZtXboaeUxdU93dM9m8czFxpZ70RolvVoXB4d3z9K8rCmKK+yuLqne7pn83hmFo8MAn4X8BZhZL4heX/hjcHVPd3TPZvHM4tXwzqUKUzOfTHhSfzpZnagJQOdFYmyuLpnfXHP+uKe5aXSkaIxf1w6gDCOzVvd7pwzZXF1z/rinvXFPctJQzMCx3Ecp/gUYmIax3EcJz88I3Acx2lyPCNwHMdpcjwjcBzHaXI8I3Acx2lyPCNwnG6Q1ClptqSnJM2RdLaS2atqHDNM0lFZOTrOhuAZgeN0z5tmNsbMdiGMUX8AcF43xwwDPCNwSoH3I3CcbpD0hpkNSD63A38ChgA7ADcC/ePm083sYUl/IPRcXQD8mDAE8iXAWKAPcJWZXZvZl3CcteAZgeN0Q3VGENe9AuwEvA6sMLOlkkYQJj3ZQ9JYYIqZHRz3PxnYwswultQHeAg4wswWZPplHKcL2vIWcJyS0wuYJmkM0Al01Nhvf2C0pMPj54HACMIdg+PkimcEjrOOxKqhTuAFwrOC5wmTmrQAS2sdBpxhZndnIuk464A/LHacdUDS5sA1wDQL9aoDgefMbAVwDNAad30d2CQ59G7gVEm94t/pkNQfxykAfkfgON3TT9JsQjXQcsLD4cvjtu8BMyQdSxjffklcPxfolDSHMDXiFYSWRI/H2bAWAYdm9QUcZ234w2LHcZwmx6uGHMdxmhzPCBzHcZoczwgcx3GaHM8IHMdxmhzPCBzHcZoczwgcx3GaHM8IHMdxmhzPCBzHcZqc/wPvI/M2XEER9gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 10974, {'val_loss': '-0.6619825959205627', 'val/kl': '0.00017249824304599315', 'val/mse': '0.012649530544877052', 'val/std': '0.05861051008105278'}\n",
+      "Epoch     4: reducing learning rate of group 0 to 2.0000e-04.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Epoch 00005: early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:\n",
+      "                                  Name           Type Params\n",
+      "0                                model    LatentModel    3 M\n",
+      "1                          model._lstm           LSTM  807 K\n",
+      "2                model._latent_encoder  LatentEncoder  394 K\n",
+      "3   model._latent_encoder._input_layer         Linear   66 K\n",
+      "4       model._latent_encoder._encoder     ModuleList  131 K\n",
+      "..                                 ...            ...    ...\n",
+      "70    model._decoder._decoder.3.linear         Linear  262 K\n",
+      "71       model._decoder._decoder.3.act           ReLU    0  \n",
+      "72   model._decoder._decoder.3.dropout      Dropout2d    0  \n",
+      "73                model._decoder._mean         Linear  513  \n",
+      "74                 model._decoder._std         Linear  513  \n",
+      "\n",
+      "[75 rows x 3 columns]\n",
+      "INFO:root:model and trainer restored from checkpoint: /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/anp-rnn-mcdropout/anp-rnn-mcdropout/version_31/chk/_ckpt_epoch_1.ckpt\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "519efcd9fa694a2eaa2f3485c60686fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Testing', layout=Layout(flex='2'), max=234.0, style=Progr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+ACQmJpKZmUloaCitWrVycwvdi24srmUSExOrtF+n+uTmgosiBDdKSkpKWLhwIddeey2tWrXilVdecXeTXMbUqVO54YYbyMzMLN8XFhbGJZdcYiUETp48ydChQ3n00Ufrupn1El015ALCwsJszgjCwsLc0JqLl5IS0GxsHTuCl373Vplz587x9NNPl392ZxpHVzN69GhSUlKcGvmfPHmS7du34+vra/N4WVkZWVlZNG3a1G4ZR2zcuJGkpCSioqIIDw+v8vfrmlqdEQghxgghjgghjgshnrZxPEwIsV4I8ZcQYp8Qwn6s2HrMvHnzrBaI+Pj4MG+enoLZlRQXm97n5LivHQ0Zb29vZsyYwYwZMzh9+jRffvmlu5vkMlasWMHPP/9s0XEnJSXx/PPPs2TJEouyQ4YM4ffff2fBggU264qOjiYoKIhffvmlWm3573//y8SJE9myZYvF/qKiIoYPH06/fv2qVW+tYc+KXNMN8AROAJ2BJsBeoFeFMrHAP43vewHxldVb372GAAnI6OgYmZPjuKzuYVQ1zp+X8tQptcXHS1lW5u4WXRxcuCBlaam7W1E77N27VwKyT58+VfrevffeK1u2bClXrVpVrfO+9dZbcvz48XLHjh0W+w0Gg/Tx8ZGAzMvLq1bd1QUHXkO1KQiGAT+bfX4GeKZCmXeBWWbl/6is3voqCDQWLFgmg4PDpBBChoSEyxUrLDv5jz/+WPr7+5cLDED6+/vrwsAJzp0zCYJTp6TMynJzgy4CiotL5fjxU+XAgUNkcXGxu5tTbYqLi2VeXp40GAwW+zMyMuQLL7wgP/zwwyrVV7EeV7Jt2zb5999/y9I6lr7uEgS3A++bfb4b+E+FMh2A/UASkAlcZqeu6cCfwJ9hYWG1ea1qxJw5c6SPj69FJ+/nZ9nJm88azLfw8HD3NbyBkJhoKQgSEi7ekWxtkZiYKI8fPy4LCgqklGqWFRbWWQJy3759bm5d9dmwYYME5MiRIystazAY5KRJk+QHH3xgtYaguue+8cYb5SuvvFLjumoTR4LA3V5DE4CPpJQdgRuAlUIIqzZJKWOllAOllAOdSTbhLl566SWKiixdWgoK8pk9e3b5Z93DqHoYDFBWZr3PzEFExwlefvllunbtykcffYSUcOECzJ37Jp9/volOnbq6u3nVJj8/Hx8fH6dcYf/8808++ugjZs2a5VRy+8rIysri+++/Z+vWreX7kpOTycvL0way9Z7a9Ls4A5hneuho3GfOFGAMgJRyqxDCFwgCUmuxXXWOeSevexhVD3NDsTm5udC0KRjdxnUqISAggPDwcEJDQykqUsL0qqtuBKCB9Fk2uf766yksLKS0tNTq2Llz59i5cyc9e/aka9eu9OrViw8//JDCwkKbC8oANm3axOzZsxkyZAiLFy+2e979+/czZMgQvvnmG7p16wYoLUt4eDilpaUUFhaWp87UWLt2LZs3b+bWW29l6NChNfjVLsTeVKGmG0rInAQ6YTIW965Q5kdgkvF9TyAZ4yI3e1t9thGEhtpW+3TsGF5eRrcRVI/sbEu1UMUtM9ONjWugZGZaXsOzZ93coFrikUcekYBcsGCB09/56aefJCCvueYau2VOnTolPTw85NChQ2VJSUn5/vz8fNmhQwfZqlUrh+154403nP8RLgB3hJiQUpYKIR4GfkZ5EH0opTwohHjJ2KA1wJPAe0KIJ4yd4iRjgxskzz03j8cfn05BQX75Pj8/f2bNMrmRxsTEcPDgQT7++GOSkpIICwtj3rx5eiiKSrA3I9DIygJvbzU70HGOggL1ajAY+PTTWI4cOUBs7Jv4+rpbY+xaoqKiOHjwIB07dnT6O4MHD2bDhg0WuQQqcvToUVq1CqJTpy54mS1q8fPzIzk52e73brrpJjp06MDw4cOdbk9to4eYcCGpqfDJJ3EsXjyb5OREgoPDmDlzHtHRMYSFgYeHeug8PT0BEEKwefNmLr/8cje3vP6TnFy5MPD2hpCQumlPQ8dgAHOz1NChIaSkJLNt21GGDOnmvoZVk9dee43/+7//Y8aMGYwZM8Zuuccff5w2bdrwwAMPOJXX2BGlpXDiRBHe3jns3r2Ro0ePMm3aNOqrHVNPXl8HJCQk0K1bJL6+fuzbl2V1vLAQ/P3VSk5vb29KSkqQUnLmTEWziY4tSkqcK5ObC82a1X57GiLnzp3jkksuoU+fPnz33TqLY1OnPokQAi+vFpSWNrxV23v27CnPNWyPzMxMli1bhsFgYNq0aTU+Z06OWjjq69uGRYsWsWPHDkaMGFFvBYEjGtjfXX9JTU2jpKSYsjJlrHrggdvYtm09Tz/9GuPHT6WoSAmCbt26UVxczJdffomvry9XXnmlm1te/ykpcd6QmZmpZg5+froBuSJnz54lNTWVtLQ0q3hNU6fOKH+flwctWtRx42rI3Llzuffee+nbt6/dMhcuXOD999/n7NmztG3bttI6ly5dSlpaGnPnzrWKFvrjj78QGTkKT09viopg/PgJjBw5krZt27Ju3TpmzZrF9ddfz8svv2xVb35+Pvv27cNgMNQb9ZCuGnIRWVn5fPXVbxQXF3H99bcxaFAH0tPPARASEs4zz8yjZUuYPXs2iYmJum2gCuTnK7VbVfDwUGoioxZOBxU/JyUlhby8PAICupXbCCrStCk0wEGtQ5566ikWL17MokWLmDlzplPfadOmDenp6Zw9e5b27duX7z9/PovWrQMJD+/C+vXHEELQpo3JPvW///2PSZMmMXHiRFauXGlV7/79++nXrx89e/bk0KFDLvl9zqCrhuoALy9/rr76JgBWr44jO/t8+bEzZxJ44on7EEJQbFR06zkLnKcy24AtDAbIzoZWrZQu18NDbY0ZT0/PcuNnUpLlMSklBw7s5vDhfUyYMAmouX99faJ79+60aNGCAnvSzwaPP/44BoPByv3z0KGzdOoUSYsWgeXrEDIz1ay1aVO4+eab2b59OwEBATbrbd++PQMHDiQyMrL6P8jF6DMCF5GVpTaAqKgIzpyxXitgi1atWpGRkVGLLWv4pKWp5D+2jPCOEAJat4bz5yEoSKnmdFSHlZhoqW6TUjJgQBBZWef5449Ehg4NxQVrreqM119/HV9fX6ZOnWrVcYMKv+3p6VmtJDPmlJWBluOmtLS03FuoqKiIM2cSaNKklKioXjU6R22hzwjqgJ9//pH58+fSs+clJCc7v0r4/PnzlRdq5Hz+eRzPPmtyyz1zJoFnn1WzKUfCQEpT2OqCAl0QvPfee/z1119MnDiJ4ODBFseEEFx33TgKCvIoKiqipATsrLWqd5SVlTFr1izKysrsGoFdlREsN9f03txl9M8/f2fixGsYPPhKfvttI02bQlER2JBJ9RJdELiI7du3sm/fTs6cSSA4OMzpGUFNXdgaA6++OttibQao0B2LF8+udFZgKl8bLWtY/Pjjj3zzzTdcfvkoK0EA8Oqr75W/b0iCoLi4mDlz5pCammp3pXB1SE9P59SpU7Rr146zZ8+SkpJCSMhAWrRoayEEADp0CCUsrDPt24fw4YefcubMCa655jauuKKnXWGgLeaq6SzFFbi/BRcJAwdewYgRY7j55vHMnDkPP7/Kh5/+/v4sXbq0DlrXcCkpwe4Mqyozr9JStTVmHnnkEd5880369rWpHbCgOnYZd+Hn58ecOXP4z3/+49J6ly5dyuDBg1m+fDnLli1j7NixfPjhMrp182bcuGEYDIbysp07R7Jx4wmWLv2Eb7/9lIULn+fkyaN282bcdttt+Pj4sGHDBpe2ubroMwIXcfnl1zJ8+LUW+55//kFyc23fCZ6ensTGxuqG4kooKcHuDMvDw4PVq+OqNCu4SNLzVotRo0YxatQozp+3n9inuLiYkyeP0L+/fTfMxkKXLl0YMGAAQUFBBAQEsHLlSt5+ez6gZqT2RvK33DKB7t370rVrT/LzlV2hoveap6cnJSUl9cY+qBuLXYC5Aakiq1fH8dRT91FaaloR5efnz8svv4Sfnx/t2rXjtttuq6OWNjyysuCjjyxtBOb4+fkzbty9bNjwQ6WG5IvRLbI6pKTYVpVJKbnssjZkZmbw118pXHpp5b729YG9e/fi4eFBt27dqpVW0lk+/fRnJk0aS3T0RO6//1907mzt9VNUVEiTJj4WUU0DApT3mjnZ2dnGxWi1196K6Mnra5nSUti583dWrnybvXt3WhyLjo5hzJhx5TdGSEg4//53LElJSTz00EM89thj7mhyg6G4WF3Drl172QwZXFCQzyefvMOZMwnGldrKkLx6dZxVWWdWJ1+s5OXlERcXx+bNm+2qyIQQdOvWm4iIbjz55GSGDBnC9u3b67ah1WDWrFn069ev2mklncFggEGDrmbXrjQWLnzfphB45pnp9Ozpzz/+EWWxPyfH0sgM0KJFizoVApWhCwIXUFoKS5bMZc6ch3jllRkWx5YseYH1638AKB+tXn99DF26dKN58+YNIrG1OykuhtzcXPbv/9NubPeK+zVDsnW5WmligyA+Pp6JEycybdo0h7aSuLh1rF9/FB8fP3bs2EF8fHydtbG6hIWFERkZSffu3Wul/rKyMpKS0vH09KRZM/u6xcsvv4ZWrYK47rpxVscyMpQXUX1FtxG4gNJSCA4OpWnT5kREmJJ7rF4dx1tvvVTeUSUnJ5a7PU6e/CAPP/ygW9rbUDAY4Msv41i4cFaVv2vLkNyYBUGTJk0YP348bdu2d3gdNG+Yhx9+jkceeYKhQ3vWUQurT2xsbK3Um5qayoABA8rjgYWGdmLTppN2y9900x3ceOM/KLEx9ZRSzQo0D6I9e/Ywf/58unfvbjMMRV2j2whcQEaGyvRUEXsLy0JCwtmxIx4HEW51ULaBBx+0bRvQEELYnCmEhITz++/xFvs8PSE01Kpoo6KwEM6dc65sY79ehYWF+BkDVgUEtCAiIpJvv91R7frMo+Nu3ryZK6+8kuHDh7NlyxZXNLdSarSgzJg68hIgGCgADkgpL6oMYjWl4lQ7J0ethnXk9lhcrHTWLlrnclEyZ471+gEADw9PpDQQHBzGyJE38PXX/7PKATFz5jyr7zWwMU+tUBW30LIyJTjqkSrbiqKiIry9vWvFF9/X15eEhAQCA4PIyPC3mf2sKpSUUB7ZtXfv3nz66af1RjVs9+oJIboIIWKB48CrqPzCDwL/J4TYJoS4z1Z+4caI+f2RlwdHjihB0Lq17fSTwcFhHD16EB8fD6uFKTomkpJsC1IpDXzyiYEffojnlVfeZv78WFq0UG4ZgYGtmT8/1mmX0sZCZmYmBQUFNgVBaipW0UjPnk3inXde45133rP+Qj3irbfeolmzZixYsKBW6g8LC8PTU60JcsWzqnlrtWrVivHjxzNs2LAa1+kKHHXkrwAfA12klNdJKSdKKW+XUvYDbgFaAHfXRSPrO6WlMHx4GJ07e/Dqqy+WJ1m/8855+PhYLizTRquBga2RUlJWMSO7TjnBwbYFqSZgjx4txmAAHx/f8iB/l146xK4QaMwzgn/+85/4+/vzxRefW+wvKVFxh1JSLMunpCSzcOEsPvzwnTpsZdU5c+YMBQUFTiWtry6uXJVeUeDWF+wKAinlBCnlJlupI6WUqVLKN6SU/3NUuRBijBDiiBDiuBDiaRvHlwgh9hi3o0II64wu9ZyyMtXBFBTkGzt208qRqKgYHnggluDgcIQQhISEl49WW7duy2+/HSUjI9ONra+/SInNFdpNmvjTt+81xMR4cd99wSQlwcaNP5UfHzJkpMM6GytlZWV4e3vTooXlQorERDWQSUtTxnmN0NBOTJ36JOPHT6c+j1WWLFlCVlYW99xzT63UHxsbS1CQIDKyCVu2rKv8C5VgLlTWrFnDm2++SWam+/sAp+Y6QojhQIR5eSnlikq+4wksA64FkoCdQog1UsryANxSyifMyj8C9K9K4+sDmlpo48bjpKSkcfp0O4vjQ4fGEBMTY7WQycPDg06dujWYeC51jbZ+ACiPOtq6dRh33jmP5s2DWL/+AwoKsklOhn79riEx8SR9+17G/fc/5bBeKWlQUTVdxRdffEFBgYHkZJM0LCszBeUrLVVOD9p92rp1G2bPXgyoWUN9zuvQothBrbUAACAASURBVBaz6Pzxx1ZARS915LTgLAaDKRjdnDlz2Lt3L1FRUQQGBta47prgjLF4JdAF2ANoYwMJOBQEwGDguJTypLGez4CxgL1MDBOAuU60uV6hCYKAgJZAS2ytGE9Ls7+itaBAT61oC+26RkfHEB0dQ04OaDk8iosLee65dXTrprI7de58J3Pm3EnXrnYq0wGgpMTDokPPybGcJZ09a/s+LS6u3wbj2uSuuybTqlUn2rTpwIAB1vr8rCyVza0qg4vCQiUIbr/9di6//PJaFWTO4syMYCDQy5aKqBJCAPPAC0nAEFsFhRDhQCfgtyqew+2YG4rz7QwYcnJsxxsZN24YZ8+eZs2abxg0aFDtNbIBUtFBIy/P9L5JE18yM8/y5JM9SE9PJCgojNtvf5Fevbri45PO6NFj7dbbWGcEYO0xlFVBEZufr2YIQUHqc1JSAufOJTFgQB8CAtzfWdkiJiaG0tJSYmNjq9ShSqkSF3l7q/Dk9u6Jyy67gsjIK+zWc/q0GuVXDCHhiMJCJTyee+45579Uyzjj9XMAaF9pqZoxHvhSSmlTGymEmC6E+FMI8WdaWlotN6VqaB3Wddf1Zdy4rpw9e8xmuexs631//72Xc+fOcPDgkVpsYcNEu64ff/xfOnUS3Hxzx/Jjv/8ex3vvTSc9PQGQpKcn8N57U3n00Sj++U/rVZ3mNEY7QUJCAgMHDuTBBydZ7K8oCEB1bNo1mjnzXv7xjyh27dpV+42sJqtXr2bVqlVVdh/VEkmlpamZkD07iCPjbk6OGqBo6jVnKSysf/eh3RmBEOI7lAqoOXBICLEDKF8kLaW8pZK6zwDmy1E6GvfZYjzwkL2KpJSxQCyoBWWVnLdO0TqsY8cOIqWktNT2OvKsLOtRw/Tp/+LChWwuvbR+uJDVJ7TrmpysJpVlZaYpwuefz6a42HL6pR1v0sSf1FQVYE7LIWtOfXsA64Jz586xa9cuSkpMw97CQtshD4qKVMfWpg307HkJxcVFSFl/XZxXrVpFTk4OTW392XYoLLQcmBUXK2EQHGxKZ2owKJvJ4cNHmDLlRnr2vIT//vcri3rOnlWvmZmm9QHOIKW6zh4exaSmpuLh4VGeQtRdOGr64hrWvRPoJoTohBIA44G7KhYSQvQAAoGtNTyfW9A6rOnTn+Lo0Qy7awdszQieeOIFAIyLFxs9cXFxzJ49m8REUxTRGTNeYsKE+9m2zRS1Kz3dXh4CwfLlFzh5Uj2Ul15q/XA2RkHQp08ftmzZTqrZMlBHjio5OUoQzJ1rypVRlY6uLrnxxhurVF7zkLK1XzOWl5SotRUlJZCUdIqEhBMkJJywKJ+ZabqGWia89lXQmxQWws8/f8P48eO57rrr+Omnnyr/Ui1i96+VUm6sScVSylIhxMPAz4An8KGU8qAQ4iXgTynlGmPR8cBn1bBB1As0QfD44wvZs8d+uaIiZRi21elrU8XGqrsGJQSmT59Ofr51Osprr42xCHUQFBRmVAtZEhRkEsKlpXDmDISHqxGfp2f99nypTZo2bUr//oMt1gpUjIZpjq1jxcX1UxBUBSlVB29PDZSXp37jhQsmV9orr7yO559fQv/+Qy3KHaugAY6PV2sxOnZUebIro7AQrrzySpo3b243yX1dUmmsISHEOGAh0BYQxk1KKd3S+voUa6i0FJKS1Pvz5+HoUcflO3ZUm8bq1XHs2LGZq666gYkTb2m0nhkAERERJCRYd+6enp7Mnfs/unUzLRLTbATm6qEmTfyZNi2WqChTOSGUIEhKgk6d1AMaHNxwUjC6kgsXsPBo27fPvnMDwKBBJsEppSQwUNCyZe22saqkp6fz1Vdf0bFjx0pnBiUlaibgisxre/faX2QmBPTurTwBbTmImJcLD4fS0tI6iy5Q03wErwG3SClbSCkDpJTN3SUE6hvabCAnJ4uXXvonq1Y977B8Sorlop3Y2EV8+um7fPHF8nodorYuSEy0re4pKytj7ty7iY2dWr4vKiqGadNiCQpSC/WCgsKZNi2W77//NzExXnz22bOAGgHGx6v/STOMNsx5Z8347rvvWLhwHocOmaasla1wzcuD7ds3MXBgO6ZMuale5nI4ceIEDzzwAC+++KLDcsXFkJzsGiGQm+t4pbGUarYQHw+7dtkvK6U2y6of0yxnBEGKlPLvWm9JA0QTBPHxx/n223dYvXq+w/IlJZYeBpddNpyOHSPo1evSRp9PNyzMtm0F1Ih027YvLPZFRcUwcuRkIiIGkJ6eQGzsVIqL8zEYykhNPWVVR1aWevgaoyD4+uuvmTfvOfbtUzPpoiLLAYkt8vJUOJSMjFRSUpLrpSAIDAxk2rRp3HKLY7+VjAzX/e/OOC0WFakIrwaDEkD20ASTlJJzzoaErSUceQ1pfnh/CiE+B1Zj6TX0dS23rd6jdd6+vn60bRtBkyaVey4kJ0NbYwbAyy67nPXrf2Dp0hf58svlvPrqvEabw3jevHkWNoKKFBRYx/n+/vt/U1CgrPAGQxkPPbSSkpIiQkOt8+2WlKjRXGMUBGPHjqVZs/b07XsZ4Fy8m7w86NGjH9u2naFVqzb1cqASGRlZaS6CnBzXJYQxGKruKpqWptTBWh4Cc4qLIScnh969e5OVlUVmZqbbZgiOznqz2ft8YLTZZwnogsD4cERG9uadd045dcMVFqrR6YYNlnl4k5ISmD5dGUcbozCIiYnhtddeY9++fTaPmxuCNfr2vZqUlJNcd93DdOkyhMTEvXz++WzS0xPw8PDEYCgjKCicO++cR1RUjE2/+cZAdHQ0gwZFl4/qnQmilpurktm0a6fcGg0GtdVCtOdao6zM9lqJ6pKZad/Q7IikJOjSxXp/URG0ahWAr68vPj4+JCQk0MVWwTrAkSD4FfhZSmkjaIIOwKpVccyfbxkHx9xYaY9z51T8nIqxS/Lz85k9e3ajFARA+fTYy8vLIvZ7kyb+3HmndX6BJ54w+XVXNCAbDOqJVYvNlIAdMyamUc4IwHKltjOCoLDQ2thZUmJ7ZOsusrOzyc/PJzAw0Gb+38zMylVgVaG6seHS0tR1M3cUAZNqaNOmTbRr165Wcio4i6MzhwJfCCE2CyFeEEIMEbayhzdS4uLimDlzennS9PT0BJYtm8i0aUH8/rt14nRzsrLsJ62xZzRtDKxdu5a5c+fyr3+9QkCACsLl4eFJnz7XVCpgbS0y0yguzufzz2dTWto4VUMbN/7O0aOHMBh7RWfDKufkqMiuU6bcxIoVy+qdemjZsmUEBwfz0ksvWR1Tua5dez5ba4GcJSlJeRaaoxmMO3To4FYhAI7XESwEFgohmgPXAJOBd4QQfwM/oWYLKfa+f7Hz7LO2s2fl5maUj0AddV5t24aRkmLtLunIaHqxM2jQIAYNGkRiImzc+H9s2fJ/GAxl7N69xmb50tJS3nzzDnbvXktZmWNrZkZGYqM0FhcVFTFy5BV4enpy9KgagjobE//8eTh/Pp3ffvseb+8mPPaY3cX/bsHb25u2bdvS2objfk06bVvk5VFjg3lGhnV0geLi+uHOXKkYklJekFJ+I6W8X0rZH5Wwpg2VRx+9qDl92v7IXRuBOuKOO+bh62udtGbePGsVSGOirExN52+/fRL9+g3Ey8uH4OAeNst+//3r7Nz5TaVCAFQym8YoCPLz8xk+PIpLLx2Ch4cHZWXOu1GePw/Dhl3NW299xuzZr9e7GcFTTz1FSkoKTz75pMV+lR/Etedyha0hO9v6/isqghUrVnDNNdewYoX7ulRn8xGEAOFm5XdKKV+vtVbVc+Li4owPlX3LUUaGYxXP8OExtG8Pb7+tbAzBwWHMmdM4vYbi4uJ46KGHyM7OJjCwFXPmvEl0dAw33hiDrXhn58/Dvn1xfP21tUrAFpqNwZX64oZCYGAgP/64uVwtUZUOsqwMmjTpwE033QnUfERcVxQWutY2AK6ZYZSWqoV95guJ8/NVUMB169YxdOhQ+1+uZZzJR7AQuBOVR8A8H8GmWmxXvUULhVBZislWrSpX8QwZEsNdd5k6fn9/B4UvUiqGlsjMPF8eWiI/H5YsmV0eanrAgHkcOxbDqVPg5TWb0lLbie01QzEob6M775xPVFQMBkPjmxGApadLVUfK5uqM+jYjsIejFdPVoaxMdeCuIDvbUhCUlcG4cRMYOnQoXd2YUMOZEBNHgH5Synqx9tXdISbshUIwRwh/IJaBA2O45RbsJkzx8oJHHulIZmYGa9fupmfPnoSEuL7N9Rl717Nly9bk5V2gpMRcj6GuK8SgtJrW964Qgk8+MTBhgjr+2msHCA3tbTwGY8aoWPCNibQ0Uz6HU6es8xM7wsMDWrY8xI8/fkFERDceffSuehMT67777uPgwYO88847DBgwoHz/6dPVc/O0R1YWHD7smrr8/aFfP8t9AQFVy2dQXWoaYuIk4O3aJjVcHHn1CCHw8QlHylikjGHnTpg3z9pbQENFPEyjqKiQkyePNpgRlyuxdz2zsjIqCAGAfDw8ZvPoowCOE9tPmxbLc8+to0OHbuXHGqONYNmyZURGtuKNN14ArD1pcnPhySfVfWpLnWIwwMGDB3njjRdYtmwexcX1R7924MABdu7caeFqXFTkWiEA1XcbtUV+vvUCN/OkS+7CriAQQrwlhHgTtZhsjxDiXSHEm9pWd02sX9jz6gkKCuehhwwUFcXj7R3D008ryV9YCJ98Yr++xx9/hyVLPmbo0BFI2XCm366iql5SBkMiw4ZBly7zUDMEE+brDa66aiq9e1+Fl5elS0Z9TsReG6SmppKVlYmUEoPButP57DO12v3AAfj9d9t1DB06lltumcCyZV9gMNSfFWWffvopW7dupVevXuX7XG0kBtcuSgPllmtOQkI8r7/+Jl999ZXtL9QBjv7VP4FdwBrgZeAP42dta5TMmzcPf3/bHdCWLerzXXfBJZfAlCkqFd6WLaZ8uxUZNuw+oqNjjDmPG45BzlXYup5+fv60bGk7lm/TpkpwzJoVQ0BALMqHQdCqVbhV9FFb1AeD8euvv46Pjw+33HILGbaSXLuQ2bPn8OefqUye/LjVbODwYVi3zvT5889th2MoKWnC0qWf0K1br3o1UOnatStDhw6lmVnSb2ddY53FXgIfjeLiqg8uKhqeT5w4wsyZj1UaLqM2cSQIsoEfpZT/s7XVVQPrGzExMSxaZErY0b69qQM6ZYx1duml6rVtWxhrTJ/77be269PyFGi4IkJiQyImJsbiAWjXLoT582N56qmlNGlS0Xruz3XXqRF/8+bw4osxNGsWDxgoKIgnPd20cnj79q+YPXswy5bdY1FDfZgRbNiwgeLiYr777jv27t1bq+cqKYHWrdvQokWglSD4+GP1euutEBGhVJjff29dh/ksoqREcuqUdVC/+oCW+cuVOJoNpKbCgw/Cm1XUj1ScEYSGdmLy5Ie59dZbq95AF+FIEEwE/hJCHBNC/M+YN7hPXTWsPnPXXZPZtSuNX389xBdfxBMVFUNmprpp/PxMQeUArr1WGdwOHrS90vG3394nJmYkb731CtD4BAHAhAkTaN26NU2bNmPTppNER8dw1VWmUNMqBUY4EMvNN5tG/O3bw3PPqfjvBQVqRLthgzqWmLiXkyd3smfPDxbnqg8zgvfee4/LL7+cG264gZa1GOQ/JSWFgQP78csvahRifv9lZMCJEyr0QXQ03H232r96tSkFo4bmhSOlZMSIAXTu3JkkLRGHmzAYDMyaNYv5800Rf4uKqmYDKipSQs7R4MCRIPjhB/X9HTvg5Ennz1tcbOnZ1LlzJC+88BYPPPCA85W4GLuCQEp5u5QyBLgWlWWsH/A/IUSaEOIHe99rDJSVedCqVRBdu/YsH4HEx6vXiAjLwFwBAaqjKiuDnTut69q16zv++msjP/2k9IONMS+Bh4cHqanpHD58gSbGZZaFhWpl9ltvxfPyywYgno4dY6yS94SHK2Fw//3q80cfwZ490LHjbfTufQ2jRz9sUb4+zAjat2/P77//zvfff2/h7eJqPvzwQw4fPsSnn75LenqqhQvk7t3qtW9ftbK1Vy+44go1g/jgA8sOtaRE3ZdCCEJCwmndujVHK8vCVMvk5uby2muvsWDBgvJ9VbEPXLgAf/0F+/er+8VWFOjsbOvRu+n8pkEHwBrbi9/tUlE9VFbmXrVwpesIpJTxQghfwM+4ae8rRQgxBliKSlX5vpTyVRtl7gBeQPkC7pVSWuU1rk9IaSnNNZ2kNlvu1Mn6O0OGqBtu+3YYNcry2MCBt5Cfn8mYMdcByljc0KI8uoL0dEtDublA1EZbtq6txsiRyg6zeTMsXAhwAD+/Yxw8uI5Nmz4qDwhYHwRBXXDixAmmTZuGlAGcOHGcZs2CLDoaTRCYy6GJE1WnePCgGrQMHmw6lp+vZg8LF75Pnz6BeLo576enp6fFbACctw+UlsLx46bPJSVqIGcwqAx2paXqeXZkvvntN3WPduoEiYlqVrBjh4oy6kyqyuxs6NBBvS8rK+Ps2dNkZRVz6aWRzv0IF+MoH8GzwDBUOIkjwDbgP8B0KWWlj5MQwhNYhppRJAE7hRBrpJSHzMp0A54BLpdSZgoh2tqurf6Qnw+//LKW559/kC5devDYY78AJkEQEWH9nUGD4MMPlWdGbq5KY6cxatQURo2aQg+zKArFxVy0aSvz8y0XzhkMkJlpID/fUvKZP9SaIOjc2XHd992n1mYcOBBHWpopxLdKXKMWqQ0Z4t6V28uXL+fvv/9m4sSJdO7cmdOnT9OzZ0+XnycqKopz586xfXsSd90VYjGyLSxUnb0Q0L+/aX9AANx0E3z6qRII5oIgLw8CA6FFi6B6sY6gadOmPPPMM+WftQBulSGlUonZmnknJqr7MS3N8czc3JZyxx2wbRts3AhLlqhrev/9MGKE43aYz84uXMjmiis60bJlIJmZdnzNaxlH4857gGBUgLk44BMp5V/OCAEjg4HjUsqTUspi4DNgbIUy04BlUspMACllapVa7wZycuDo0QOcPXua/fv/NMtSpl5tdVYBAWrqXVYGf/xhu15z/e3FaicoKFAGtuRk9TClpqrFP++/v5xOnQT9+5uGUuaCQBOylQkCPz+YPh2knI3yejZRUpLPp5/OdruN4PPPP2fRokX89ddfNG/enAEDBpRHBXUVUkpat25NYGAggYFqbGXesR04oEbBXbpglYdY88T8u0JOQvNZcH3yHNJwNrLsqVOO1wUkJTkWAkVFsHix6gd69lTegXfcoTr+Hj1UG2JjYflytT4jzk4g4rIykyrLz8+fkJBwgoPdF3DSkY2gB2o0/ycwEvhGCLFDCPGeEOI+J+oOAU6bfU4y7jMnEogUQmwRQmwzqpLqLUVFahs58gbuvfcRJk16ClA3RXq6mjpr072KXH21ev32W0tdYHFxIVu3rmLVqrcBldC+d+8IPDw8iIiIIM7endRAyMgwdeqa4U1lZlKdi5SQmanm4Noqd3OjX3Gxeji1ZN/OkJ5ue5Ha+fOJblcNTZ48mUWLFnH99dcTHh5O586dyXTliiWULv/AgQOcOXMeb2+1FtS8c9u/X72azwY0IiLUfXzunKWh1Fww/+tfTxESEsLq1atd2u6qkJGRwR9//MFJ43SxMv16SYmaCaTWcKgZF6eESdu28Pjj6r5s1QoeeADmzlVeggYD/PKLGvCsXQv2AhFogz8fH19+/z2eH37Y47YFjw5tBFLK88BaIcRPwGXAlcD9qJDUy110/m4oQdMR2CSE6CultLDVCyGmA9PBfWGai4pMN1HPnv144YU3SU9XukZtNhAebl+3P3gwhIaqEfD69TDamO8tN/c8b76pgnp17tzCImtZQkLDzlqWk6OmwHl5KqyDvZHW9Okzueuu6Vy4oPQX5uUSEtSDFRrqfFKUoKAw0tOtnz5v7zC3C4I77rij/P2pU6eozRQf5jNL82uqLea2FfrEywu6dVOzhiNHlH0LLAVBXl4+ycnJbs2dsWnTJsaNG8fYsWNZvXq1Q0GQn6/sRzWdyeTnwyZjhLUZMyxjBmnceafqA1JTlfDZsQNWrYKnnrIum5sLbdqYPkupvuOOsNSOVhbfIoR4VQixGUgFFgOtgSeB9k7UfQaV3Eajo3GfOUnAGilliZTyFHAUJRgskFLGSikHSikHtjG/cnVEcbEaIVXsRLSHQ/OkcySjPDzg9tvV+9WrTQ9py5bt8fb2w9+/Ba+99ozdrGUNCYNBi2Vv+uxo0Ovh4UFAQEtCQtQFrI59wJw775xncw1Caek8hwbAuqa28zyZd46aIJBSDUbAOmOWhmavMo+vYzCY6njssWeJj493q7tjs2bNGDp0aPmqYnuCwGBQgzVXqLO2blXXoGdP+7NTIZSq6OGHYfJkNXjZvVsJ1YrYcid3l9rNkY1gEpAG/AtoL6W8Qkr5tJTyWyllmhN17wS6CSE6CSGaAONRq5TNWY2aDSCECEKpiqrgkVs3nD9vqX/87rvPef75B/ntN+Wfrbme2VMLaQwcqG6gzEzl3QKqE1yxIp/77lvG2bOnbX6vIWUty89XgtGe293q1XFERUXQubMHUVERrF5trfqqjn3AnKgo0xoEIQRBQeGEhqr4T7/9VoUf42Ly8/NZvXo127Ztq9XzfPrpp/To0YNFi0y5LbROPDNTzdCaNlXGX1togqCinUCro23bEMLDw8tdfd3Btddey9atW8s9h+wJgsRE10UjXb9evVb0/LNHixZw/fXq/RtvKFWROXl5pnUt9913AyNGdOHYMfcs1nNkIxhnzDnQ0mjsLUcIUelQQEpZCjyMWoPwN7BKSnlQCPGSEOIWY7GfgQwhxCFgPfBUbedILixUf4iz9rn8fGu3tJUrl/Hxx//lyy/fAEyCoH0l8yQPD7jF+MvXrlVt+P33OKZNC2LZsol2v+furGVxcXFERFRut8jOVlNie9d29eo4nn3WlN7zzJkEnn12OvfcM4Zhw0JZsOBfQNVdR22hrUFYsaKYl1/expgxSrX2yy9Vq8eVJCYmcuutt3LvvfcCsG7dOnr27MmUKVNcep74+HiOHDlCtpmzujYD1WavoaHY9f7p2lXlKq7YiWrPQX00FtsSBDk5ttcHVIfERGVj8POz9KaqjFtvVeuIsrLglVesF6hps4KzZ0+TmHiS7GwXxbuuIs54qz8vhLhK+yCE+BfW3j82kVL+IKWMlFJ2kVLOM+6bI6VcY3wvpZQzpJS9pJR9pZSfVedHOEN+vjLopqSoh8IZ+1xpqe1yffpcRkhIOF26qEQS2krMymYEoHSubduqG3TlSpVwPTfXvuzz93dv1jItX0BCguq8NbtFRWFgMJgWydgb9S9ebJ3es6Agn23b1nPuXBJ//63CLWgdTmGh6rg8PJw3FJvz009vcffd3jz5ZM9yfbirOobq4OXlxc0338zIkSMB5Qt/+PBhly/Oevjhh9mz5yD33PMIYGl8r0wtBEqd0a2b+o55YiDtf8nIyOSxxx7joYfcl7rSPHy+ltXOnLIy1XG7Cs3bb/hw521VoPT9M2eq65mZabIxaGjhO/77369Zv/4Y4eHdXdPgKuKMILgFmC+EuEIIMQ8YgpOCoL6Qm6tGqrm5pgfiwgUlndPSrKeOJSVKaJw5Y3ukMWfOEtati+fOOxdQXKw8Yzw8LA0/9vD0hBtvVO//7//sJ1zXiI2NdauhePbs2eVJYzRs2S1yctTDaG/Uv3p1HMnJtlVcJSUlTJr0KBMnPgiYOpzERPV/hYZWz4DWsqWSzAZDafn33blyu2vXrqxZs4Z3330XUDma9+/fz7f2AlFVk+bNm9OtWy+Cg5WJzvw3m88IHDF8uHo1j0iq/S9CePHmm2/ywQcfWHTIdcmECRNo27YtP/zwg81nNDnZtf/1nj3qtSqzAQ1fX7U+A6BiKhVt8NSpUzciIrri4VEFKeNCnMlZnI4SBstQ6wpur6gqqs+sXBlHjx62ddJZWUoiZ2SoEYSU6v2ZM5ZCwxaaD3BqqirXpo3yuHCGESPUFLO01LHuv02bNm73FrJnnzDfbzCYbAL2Rv2LF8+26ycdEhLG3LlLGT16LIWFJqN8ddVCGgMH3sLy5RdYvvwCRi/KehXCo2nTpvTp04dWtZCVxJ7HkDMzAoBhw9T9vH+/yeivCYJmzZqzePEbfPTRRy5fA+EsZ86cIS0tjaZNm1oJAilr7iZqTkaG8l7z8VGG4urQr5+KRHzsmKWWISvLUtWm9UN1jSOvoQtCiBwhRA5wHGXI/Qeg7av3xMXFcf/9tken5pSVqT87JcW5lHSnT58iISGZ0tLSKqmFNHx8tJGFfd2/n58/S5Yscb7SWsKefcJ8f2amaWpub9SfnJzIzJnz8Pa2HNr7+fkzc6ZJ9WV+/Y8dU69dulSj4YCXVxN8fdUybk0QuHOxXnFxcZ10nM888wwvvjib7GzV42iCwGAwzQgqEwTNmqnwE1Ka1CLmtrL773+M8ePHuy3UxIYNGzhz5gxDhgyxsllkZbk2bo82G+jTx3QfVRVfXxXXCbDKw33+PKxd+zkvv/wEe/bscEvMIUfG4uZSygCzzVdK2UzbX5eNrC7PPGN/dFoRW0Zhe9x4Y3+io0P4+ec3nTYUV0RNva2TqwAEBrZm/vxYxo93/9qBefPmWT3s5naL3FzLztveqD84OIzo6BhefHFZuTBQ8WJiOXr0IM8//yAnThyxcKnTBEE3K4fiqqOphtwpCBYuXIiXlxcvv/xy+b6lS5cyZcoUTp+27TFWHd5++23eeMMyKicodWdRkVpNbMsHviJRUepV83AzGEzXz915Mzw9PQkODsbX19eqLa6cDYBJENhagFcVBg1SrxXVQ+npsGnTz3z44RscObLfLdfWUayhCCllvIPjAgiRUro3Hq0d1IpU+6PTmqB1jM2atUaz81VVEPTuDQEBMeTkQMuWs8nOTqR1Fm8F3AAAIABJREFU6zCefnoet92mBEBJibIpuJOYmBiklNxtjFPcsWNHXn31Ve66S7W9ojF95sx5FoviwHLUP2HCVJo0acLMmffSsWME0dEx9OzpT2FhAZdeOpTISGUs0+w3vr6V67MdMWvWJaSlxfP442uAEW4VBPn5+Ugp8fMzxWz86quv2Lx5M3fffTehlfzQggKlRmjWzL7Hj5SSRYsWc+TIufJkR5og0LR5lc0GNPr3VyPgxERTjKzCQiVUDx8+SmLiX/Ts2ZN+FZPw1jGWK/Vdm1qypMS0ElvLM1JdBgxQ/9uBA5Yxt3JyYPToO4iM7MMllwx2i1eWIxvBIiHEV0KIe4QQvYUQbYUQYUKIq4QQLwNbANdHy3IRFy44Hp3WhN27M/jsM8mIEfdWSzUEqoMfNgwghuHD4/nkEwNvvRXPtdfGMG3aWPr2DeC5556tUTtdxcSJE/EwLpn+8ssvGTcupjxeUEV9ZnR0DPPnxxISEl4etnj+/Fiio02zm7Fj7+LYsRI2bFAhIPv2HUhISDidO/cs96IwVwvVJBJraupJCgpySE7ejxBKDegu98cFCxZQUlLCoyrpMgCPPvoo7777Lt0qTHtycnJ4+umnOXjwIKA685QUpcK0FyM/O1t12CNHXssjjzxfvmBNEwTaoiZnVW1eXqYgitp6Dq2uzz9fyfjx4/n666+dq8yFHDlyhLFjx5avITD/P10crYODB9VvDg93LqqoIwIC1Oy2rMw6Y2G/fmOYOnUGPXr0rXeqoX8AzwPdUYbizcC3wFRUNNKrpJS/1kUjq0NhoRqd+vlZp0E010lXt26tA6yuaghgqPI+ZdcuU325ucoGkZt7gSO2liO6iUOHDlFQUECXLkNISXGsGli79jMGD76SIUNGkpKSzIwZd5cb6h9/fCITJoxi0aLZ5S6mmv2gS5dB5XU4UgtVZUHuhAkLmTjxdQYOvKVcPeTqdIZVwcvLy2Ih1u2338706dMJCbEMw7Vo0SIWLlzIwIEDAcsFejk5liqukhJYv34v33//G6NHX8Xgwf1JS0sBVCepdd7aArGqGDw1Q71muNeuXa9el3HbbbfRwzxsbh1x/Phx1qxZw6ZNm6yMq+ddHLxzxw71avwbaoxmJ9BmGRrm6lV3DFQqizV0CGhY8Q1QF7KkhPJR6AsvPEJ2diYBAS158cX/WIxOq4PmTVlYqEYgXl4QFGRZJjBQtcHWMnKNyEg13U5JUWsRgoOVF9PTTy8kPv44I0dWEsu2Dti3bx+XXnoprVq14tixdIe/B6C0tJR169Za7dcM9QaDpKiogD17tlNaWmJx7F//gh491H9jTxC0batURfHxjuPFa4we/WD5e29vU2pQ81Dg7kTzuCosVJ5nnp6qY7v55pv57bffuOKKKygttUwXKaW6Z7y81PdLSmDp0kV8+61ygujRox8HDuxi1Kgbyr9XUKBG9R4e6r5zFm1Fd0VBMHp0NPfdF11t42lNGDhwIF999RUBAQFWHjf2VrRXh7Iykz6/Om6jtujXD776yloQxMefJTX1MEFB7ejevZdrTlYFLsr0J+YjvujoGHr1Ulaerl172hQCzoQ90DAYDFxxRTOmTAkkKUndhe3aWaov2reH7t2Vl4EjfayHh0nvqCUKUVP765k06RF69HCv7rW4GH79dRtSSvLzC6yyKtmioCCfUaNuwMuGL21BQb5xROxTLgTMj/33v2rMUVpq6njMA6M1aaI6Jm9vpd5o2rRqv0frtKqSycqVTJ48mejoaBITE8nKUi6Je/dm8M03P7BhwwaSk5XhMDERunYdzJYtW5g//1Wb6o6yMiXUtJlZSEg43br14t13V9OxYwSenur6a4L76FElNDp1Uq7LzqKpkbT/w9wV1V0G43bt2jFu3DiuueYaC0GQleVa18sjR9RIvX37mtmpzOnSRdkGzp5VNjCNP/74lrvuuoqPPnoTg6HuZwUXvSAYNSqSPXu20aNHP2688Q6rso4WQNkiK+s8BQV55Odnce6cetjMZ/W+vpbB5zp2tMxhXBEtQ5QmCMwfbnfFzr9wQbkZJifD2LGT+f77vxgyZARDhgTz9NPTrMprglTlFGjF+vU/UGrnTs7Pz6WkxLbFNi1NWTNPn1ZCqH17S+8W81G8h4dScTRv7vi37N69lsWLo/niizluFwS//vor3377Lbm5hvJO68CB3UyefCNLl75IWZlp/YoWHiEpyXI2YI+nnprHL78cZPTosbz33rdceaUKb6t9VwsgZ0st1L69/cWQwcHK3Tk9XdkgzJ+t3NwizlZMcFzHmAsjR/mF7ZGXZ502UmP7dvU6eHDV1JGO8PRUjiIA+/aZ9gcFhTFgwAjCw9XIp66dGpxcAtWw0B703NwLxMcrHUNc3DpatbLU36xeHcfMmfdSViGsqOZiamv2kJfXjOnT36ewMJczxliq5oKgY0dr42ZoqJL+tkYr/fqpm+PIEZNnxi+//MhXX/2HyMhevPPOoqr9+BoipXqgtEvi5eVFr16X4uPjS2rqWfbs2W5RXhOkmpdQxWtZkdatwxAC0tKsw0S3bq0kqObrXjGsREV1jpeX6thOn1ZqIlsPz+7da9m161vi4//C1/clwH2C4JNPPiEpKQ1PT5NBKTS0E1FR19Kvn6USevbsB0hJSeaJJ14kMrJPeV6BqqIJAnv2AR8fdX96eKhBSEXVioeHmkUcPqxUSy1aqHtDCAMdOvhjMBgoLi6udvuqw6pVqygqKuL6668HTM90VQzFBw6o8NDHj6t7PihIhYa/6Sal+v3xR5V1DByrhby8qj5679tXpQLdv9+Up6R//xsYM+aGcptMcbFlJr/aplJBIIT4GvgA+FFK6eb8TpVTXGzqxITw4JFHnufUqaM2hcCzz06323HZcjGVEtLTfRk1SgUJ09Z7mQsCWyNUb2/lcZCebn2saVOlRjp0SN0Yw4bBH39sYP36HzhwYHedC4KCAtsJ3mNi/knHjp0YNswy9KKtlcT28Pb2pVOny2jXrgu//LLMIrxGkyb+3HmnMuJrgqCC/dSmXl+LQxQeruwGFWMJ9e59FceObSUy8vLy2DPuMhYPGnQFYWGWA4KIiK6sXKki4R07dojU1LNcfvnVbN26nlOnjrJu3Xd89tlGhgy50m69aWkpBAS0wMfHMr+plnS+uFh1eEKoe82czp1NLsqRkeoerLj6unNnJQhOnFCqzMJCaNrUgzZt2gEGcnJyaF1Tl5oq8Morr7B//3527dpFcLB6rouKnO+Qz56F119Xv8PTUz2f6enwySfqdx4/bhKIQ4c6jnwbGqpm0LaebXtoBuNDh9S9oM02zA3GdT0jcEY19DZwF3DMmJ/APVGRnEQbAa1eHcd11/XmP/95hd27tzJnziO8885r5eUq68Aqupjm5FjnOtVmBMHB6tXb235AKkfqIc3xQstk1L37FfTtexlXX32z/S/VEnl5lqoebZsx42769BnA1VffZFHe2TUZTZsG0r//Dezc+TUbNnxgFSZ62rRYoqJijHWq7zgjCMyJiFAqDg8P08M1bNgdLFy4lylT3i5XDblDEEhpf1aojkvGjRvKxInXcO7cGZYs+bjcuygz03EvM3fuw/T5f/auPC6qqn8/ZzaYYV9EBVnczUyzMCu1XpdKK8uytKL0LY3MMvu1WIqVb0Vl+VZmi2GLLZi2mplZmZVi1ivuqZmmgCAgILIvs5zfH1/O3Htn7sAMzKAgz+czH5iZO3funHvO+e7Pd0AQfvhB2TFMJDWIXhpduijjKgYDafgCOh0JCkeLVmyEogGTGL8//shDQUFBqwoBAJg4cSJuueUWxMfH2zd/d9xnAG2wixfTb7j4YuCdd4B336VuYwYDuWjLy2kcnnwSmD3btVtIp6P5Fh/vPr0MQDHF0FDa+OWetepqSQlrbSqUJi+fc74BwAbGWAiAWxv+PwZgGYCPOeenucZQicpKZ3fF8eM5+Oij1wEAd9xxP+rqTI1uYI4ppseOSZv+X39l4PPPF6Bbt0EoKPgvGJMEQWMbVXAwxQ/UNiHxeTEpBgy4FlOm0IZrs7Usj94T2GzAihXpTgVhAFBcXIh586hbmtxlFh0dh7w8F734AOh0fggICMOkSc+gvr4G+/f/ipiYczB8eBICAsKQljYdOp3BLgQAqLrcTCb3xqFnT3rU1QG7dwObNqVj1aoUFBfnQK+PA5CKmprWr9jOyirGe+99hJiYeIwde6PiPc45KirK0KtXf+za9Qdyc7OQmDgMO3eehL+/0V7D4QqnTpXAZrMhLk5ZICACxYWUSYrOnZWfU6M4MploPubKykRFwoMQ0FKTGwazufm0C83FU0891fD9kvLkTs8BiwVYsoQ+06ULcPfdtCYBYgWOiADWrKH/L7206biASBLRaEhwWCy0vl21phQQltkff5BLODoayM7ejQULhqNnz35Yu3YbrFYSCq1VUOrWFsMYiwA1qpkOYCeAxQAuAHBG1REIt4YrbZ8xDf74owh//w107qxeVCZoD8RmV14ubUwAcODAr9i37yds3vwbrFbyLQoroCmN1VHDFRCCQCy0+nrJzG3NgHFtLfDSS64tpZqaasydqwwWq9VqCBgMJtxzz7tYujQfBoMR3377X1RWnkRJyTFkZKTDaAzGqVP5KCg4hFtvZZg1KwG//pqOggJaLPIiPU/TPf38gP37ieab2lZymM3ZAJLxww+t2wfaZgP27v0Hzz77EN588zmn92+9dSQGDQrD/ffPxz//WJGYOAwAYDIF2IWAxWJBdbW62rtixUbs31+NPn3OVbwutGRPBAHg3LBG1MgUFtL6kiszp7NSW+7CbCruU1lJQiAzk6yiBx909sH36kUtKIcNa1oI6HTK8QwKonHr2pUUkeDgxlvXihReUSqk0xlQW1tpb9cKtO7YNikIGGNfgYrJTADGc86v45yv4pzPAnCGZGMThAbkWtvnMJkoAjlpUir8/Z2LzW65JRmLFqWgRw8NLr44Ae++q9w0EhIGo0+fS5GQcBuApuMDcnTqpJ7yKDa8ggI0pI7V44cfvsO6dZ+3qiCw2Zp29dTW1uDkSclVISqJdTpSCxmjKSV392RkKDfk4uJsLFuWjC1blGNbXJyNd99NBufpiIpSUk83NbZqWLZMjea7Gh9/3LqlMRUVQHBwBP797wdw9dU3O73fpUs3XHTRZdDrDdBoNMjNzcLSpQuxceO39mMWLnwcI0YkICdHvYGfn5+fk+UgfM5qgkCncz2mAQHKsffzI4XHaiUeH2ERLF++BCNGXIQVK1Y0PgBeRHl5Of755x9UV1crMoYaswjWrgUeeICKw4xGYO7c5vW3kKN3b9fU6J06Af3707oWSp4jRKxGCIKuXfvg3XfL8O67++zHnFGCAMBrDY1jnuecK3LFOOdeqrfzDoRW4IpCQmSlAMAllyRh2jTJTx0WFgGLxYoVK5baU0kLC7OxdGkyMjKkDWvw4Kvxn/9swYABsxq+Szq/O3ntahPQ35+0M4uF/Milpfm4776rcd99N7dqs3WrtWn6Db1ej7o6pX+LrCdSof7v/z7HJ59wLFmSZXf3rFw5z2lDrq+vxsaNy5zObzZXA0jxOD6ghvx8daFWXNx6rT9Fw56EhF546qnFmDHjMadjxowZD5vNhgsvpCYA+/fvwsKFj+OVV57EtGnXYsaMGzFgwAXw9zfiySfdawYjT0NWEwRhYY1rvfLYASBZBfn5kkVQVFSAnTu34R9vdoBpAuvXr0evXr0wZcoUhdXsyiL4+WcgPZ3eP+884Kmnms9mKxAf7zw+riDSbx2RkECvFxTQ/NBotDCZglFXJ3nrzzRBEMYYu9HhMZox1kj4s/Uh71Kk5q4wGEyYNOlZ1NdLm5hoZ7hihQ333fcqzOY6p0Yb9fXVWLXKWYMU7iLhPzUa3fPnBQer94qVu4fCwrpCq9XDaDTBYmk9k8Bmo7FTKwYDqNm62WzGzTcPV9RZFBcDU6a8imuueQi9e1+i+IzFYkFJifrGa7O5knI5CkGg1XpWBCXgSqhpNBqX7Ta9DdGwpzFce+1kfPbZZpSXn8K0aeORmvowpk9/GOPH34qNG79FRsaPuPTS0YiN7YHZs59SfHb58iUYP/5CfPHFB4rX5RkoaoKgqY3McY7K56foeDZ58jR8+eVW3HPPPY2fzIvgnCM+Pl4RKHYlBA4fBt57j/6/+25g3ryWWwKOLqGmoNGQ4HEUulqtVCwpb1Ant2xas2DPnVj3NACXgHoKA9RsfjuA7oyxpznnH7n6IGNsLCieoAXwDuf8BYf3/w3gJQDCC/865/wdT36AgHzQhH//xRdTUFBArJ4BARF488078PPP7+DJJ38BABw5kolvv30ZcXEDMXLkdJfnlm9kR45koqgoG9nZ1wDwt29Ynmis3bo55zx37Uq5zcePA4MHG7B2bT1am8bFZqOxy8vLxptvPofq6ipotVpYrVYwxuxCUhTdAcCYMUk4cgS44op7Vc/5+++rXH6fRqN1IQziFIKguXQQakyoAAmg5GS6fl82/pE37CkqKkRtbTUiIzu7jKmYTAHYuHEtTKYAzJtHacMJCb0RFBSMyMgorFz5i9NnDhzYjT//3OEUPxBuUouFBDVjysy1pqzXkBD6jNCL5O5LgLTVuLgeiIvroarY+AqTJ0/G5MmTAUgCTs0tdOoUpXdbLMAVVwCjRjkf0xxERnqevBEcTJmBBw8qlYK+fYnUbt8+oqh+880pqKmpwPLlnyEgQNeq1cXu/CQ9gHM45xM55xMB9AfAQS0rne3cBjDGtCCyunENn7mVMaZGorGKc35+w6NZQgBQk5463HXXUnz4YS2WLMmCTkcOvQMHfsWsWQnIyEjHrl3r8dtvn2Dt2pcQHByJyEh1dUG4lDIy0vHUU8Px6qs3ITe3L4B0hUXgLgICnAN1jplDQstpzRiBcEPdd9887NtXiaNHOQ4ftiAmJt7JUqqpqcaLL6bYqQtcQc2aIjCMHp0Mg0G5KTJmApDqUezFFUT8Qq15ilq7TW/DbJbGZtmyRbjssh744IMlLo8PCQnD22+vxmefbQFAFtigQUMwdepVmD59PGpquFPjpHnzXsKXX27FlVdOULwuBEFJCV1DeLiU3aPRND1ftVql1SAEgUhokGvhp6vrm1jzjhaBxUIpoidP0mY7ZYr3vtMTa0COkBBnhmLR3+CPP+ge/e9/XyIzczVKS+kH2WzqNT2+gDuCoBvnvFD2/ASAWM75SQCNGS8XATjMOT/S0NpyJXzY69hRes6ffw9SU8fh669fQEZGOo4d221/TwQry8sLER3dD337jgBA/n/h6xYQhU4i4GmxiFmfA8aSsWMHuRg8dV04cpcIQSBcTnV1NBFaUxB8/rk655KrAHJ+fjY2bkxHfv4hLFo0AStWOOsFrv3xHHfd9aainiAiIh6MpQFI8jj24goTJiS57Armqg2ntyCfkwEBgYiJiUenTo3T1F555fUwGAzIzv4H9fX12LMns0EI67B7N8Mnn/yAxx57EDt3UoV3SEgYBg++GJ07SwNms0kZQ0KDl29g7o6n3IJQU1RKS0vw1lsv4NVXX3bvhF6EnI/H0SJYt44Kw0JDqQ7Akxz/xhAc3DwXpYCj8tezJ43xqVNU+X3vvcvx0ENfwmKRggqt5R5yZ4h+YYytBfBZw/OJDa8FAGiM3SMGgLztUi7IinDERMbYZQD+BvB/nHOnVk2MsWQAyYDr1onyAVu9Oh01NaQS/fjjW7BazaivV6oN9fXV2L79GyxZkgWAtP2NG98FGTsSLr98KoYPT8KsWQlOAU/OKX4wfHiSx+XgRiNpukLDE9qCWGgzZ8agrKwAb7+9AtOnT/bs5M1Aeno6HnlEcqPI3T+N1QosW5aMESNux/btX2PvXiNuu22h4v3IyLiGbCE4vE7W1/DhSfag8v79wDPPkJCUj2dLmUJdXb+rueQtEEUyB2MMs2c/5eTfd4XHHpuGHTu24tNPN+OKK65DZuYJ7NlDvsSdO9dh/frF6NKlEwYPpuUkaEHy82lehYZKLh21+IC7czUsjKwIs5ly7PV6+p7qalH8VIkXX5yLrl27Yf78h9w7aQsxZswYlJSUID39c5hMFPWVWwS1tZQlBAAzZqjH45oLR4ZhTxEQoKwlYozqFVavBrZsAZKTbwKgVCDMZqnWwZdwxyK4D8D7AM5veHwI4D7OeRXnfGSjn2wa3wBI4JwPBNUkfKB2EOc8jXOeyDlP7OSCHUsIgtWr0zF3brLd91xWVoDKSnW+Yrnvf9WqFFitzmH6zMw1AFxrtiUlOWDMdSpZY5BrXBERdI6yMtLmamurYLPZcPx4nusTeBGzZ8922dazsVqB+vpqZGauRnz8IPTr50yDMHlyqpP7R04nIUdDDxY7KRdAmRUtLVhSu355u01fYfPmzbjxxkuQmbnF5TEWC1mBf/1FVuD+/buxY8dWAEBUFGkHfn5h0OupvHfIkBswadKziI0dhx9++AszZ87Ayy+n4+BBikcUFkopiYC6IHBXsDImkdFpNMrMoZoaIDIyCsnJj+Luux9274RewJ49e7Br1y77nOJc6ZrauJGUq169iMfLmwgNbfk5HIXJMCoZwf/+J+1h8irpM8IiaPDzb2jY8L/w8Nx5AOQOkG6QgsIAAM65fId+B8CLaCaEFF20KAW1te5x34SGdkVW1i506dLL5UZfWkpOUVeabUREHIzG5rEThocTkZeoHo6OpjL+nBxg3rzv0bmzBqNG+b4JXHp6OkpckPsfP56D7t2TcP/9wEsv3a56THl5EZYuLVR9T2j7q1aloKSEAveTJ6cqKokF1ARBc+MDckh9KaglaGBgHJYuTcUNN/i2wvi1117Erl1/YMuWDfYiMYHcXBIA8tDLn38CW7cSDe3o0UmIi+sJs1lZqdq//+Xo35/6VGze/DG+++5tDB1agmHD1H+L6N/bHIsAIGVFxAW6dqUq+/x8yqP39zdi7lxasq1VYbxr1y7k5+cjJCSqQWGS3jObJWtgwgTvMYYCNA+bo+w5IjzcuWo7Pp7u8ebN+eB8LTp1SsCAAVdAqz1DBAHn3MoYszHGQjjnbrDRK7ANQG/GWHeQALgFxFlkB2Osq6w24ToABzz8DgAkBMSCcpf7hjQKDebOHYyhQ29qxIVB7oPJk1ORljYdZnOt4hyTJ6c222+o1ZIlIHjJe/YkQXD4MDB+/FBERLQOA2FjQdOIiDjU1AAXXJCEyMgUl8KwMcjdP65QWysRo/WXpRR4q4HMhAlJqKlJwrx5wPXXc9x0E0NNjW/H9403PkFa2hJMnTpL8fqJE8rNQMBsBjp3vgjjx89Bnz6XYu9eGhdXcaJevS7C1KmL0alTd5fXIDZxIQgY8+w3+/uTVVZXp3Rf2mz0msiRr69vHUEQHR2N6Ohou6Ujdwv973+UjRcbK9G7ewvecjGZTM5UMxdeSIJg69aT+PPPZAwbloRbbrkCwcGtJwjccQ1VAtjLGHuXMfaaeDT1Ic65BcD9AL4HbfCfcs73McaeZoxd13DYA4yxfYyx3QAeANFYeAy5W4i5UAMCAyPg70/qZURELO6+Ow2hoZ3BGENISJcmXRjDhyfhssumAPADwKDVSpWzLQkgyU1FkVd8+LD0u1ojWNxY0FTuwnE1RqNGJePAgU2orGx+n8C//iKfeo8eyo1K3o+gpXj1VQpmb9u2Q0Hw5Sv4+wdi5sy5CAiQpFlRkdTkRQ2xsefittsWIjHxelRXq9//oqIsZGSkw2IxY+zYB3DhherkhOXlJAj0eik5wWTyXFMWwlhONQHQJpybm4XMzC04ccLLzYKbgCi2km+ogjZ6zBjvWgOAazqO5sDRxTRgAP0tKuqByy6bigEDRivSf73ZbMcV3AkWf9nw8Bic83UA1jm89qTs/7kA5jbn3HJYLBLRnFqGiMFgwtSpi520UjUtVe7CuOmmZ9Czp0RGPn362wgNXYQvvgjCv/4FDB9Or7dEEAQHS5zmoi3j4cPAZ58twLZtn2HixJvxyisLmv8FbiAuLg7ZKkxZgYERijES/y9fPgtVVaXw8wvE9OlLsWbNQnz6aQqGDLkBDz1EU4VzyoTYto0a9QwdCnz2Gbl/YmJICxKcLmVlwC+/0HeIRQGQxeRNjZ0xciZXVppRXe29bBJXcBQ0RUWAN4pw1617BevXv4abb34asbHnujxOxAp695Z+a3MsrMBASkN1FATV1cDcucnIyPgRK1Z8h+7dx3p+cg+QmZmJ9957D8OGjcCwYbcCkCyCkhJyrel0FID1JoxG7wZsQ0OVdOm9epHbqbDQiAULliM0VBknsFh8b225wz76AWPMCCCOc37mdFOX4ZNP1BvMAFS0JKc4BlwzespdGMuXz8bSpZSAHBkZj8mTUzFkSBK2biWrQt5CsSWCgDEyO4uKyPQ2mSj/ec+efTh2bD+2bPkJwILmf4EbSE1NxZQpUxRCVAhPR6i5eX744Q1oNFpERMSithbYtAn4/nvJLQEA778vWW7HjgG//w7s2kWb/ZYt0qYpD/B5Iz4gx+zZj2HePKBfv6Ewm72vNcqxf/9BLF78Lvr0ORcTJ06F2UzxIG+gf/+R+PXX9xEX13g0VHQlkxcmNifgKYSHcC+JTaymBujb9zxUV1dCo3HBv+5F7Nq1C2+99RaqqmrtgkAEijdvJuUjMdH7/ahbmi3kiOBgZbGeXk/3aM8eypy79FJlr/PWcLu505hmPIBFAAygauLzATzNOb+u8U/6Hunp6Zg9e7bLQCcAcG6zb1w//PAOfvihE06cuAYTJ+pwvYuqBkollXhwiouz8fbbydi4ETh+PAkxMUqtoyWCAJAEgShH37sX8PfvA4PBhG3bMhAfn4Dnnkv1WRVsUlISysrqsGDBfBQVFSAy0nVAVw1PP/0bamqAzz8HZs6UtLSwMOJ8z8yk3xcbC9x2G/mYV63NgkHzAAAgAElEQVQiAQDQ777gAuBf/1J20PKmWwgAunUjJ3dNDUkAX7qG/vrrb6SlvYRRo67BxIlTkZfnPTdfYuL1mD37Myc6D0c4diVjrHljGhhInw0NpZhAZSU9AgKA+fP/az/O17TJl156KRYvXozYWEmy1dTQhrppEz2//HLvf6+3BYFGQ/dB3iLz3HNJEOzeXY+YmL/QqVM8zOYQ6PWiEZB3r8ER7hjHC0DFYb8AAOd8F2OskZ49rYP09HQkJyejugki8oiIOOTnE/nUunVXwWolZ+mqVcBXX40H8DOefvp3xMVJPolVq1JgNivrDiyWahw4kALgZtxySz4MBsqD9/dvuWYZGkqTw2YjS2Pv3nQcOPAqrFb6bTk52T6nRLjjjrswdOhdOHTIveNra6tx6NBWxMcPQnBwJJYvlxZj377A2LGknel0wC23kLurd2/SbM4/n1xAH3xA7obx49UrNl1ZBHJtyhOIwKbwK1Oev28sg169+mHOnOcRG9sddXWSO8UboIrjqxo9prqaEg/knDZBQc3bqBkjYVBRQfcpJ4d+T1CQ0rqur2+5UtQY+vfvj/79+6OoiFwnVitZmaWlpFyYTFL3L28hJMR1s6mWIDRUKQiES/T330uwadMgPProNxg8+FqEhbVOIyV3gsVmlYyh096yMiUlpUkhYDCYMG5cKp54AvjmG8BqjYVWexQ9euxryD/+AHV1B/HKK33sN6W+vrFq2GwAfnj//eF2RlI1SS3yr2NiKCuoqcWn0UgmOy3aFLsQEPA1JYK8UtMdzJzZFc89NwabN3/YkPpGv3PBAnpcfLHklzYYKBNIbt7GxgLz5wPTp6sLAY1G3cTX6ynNtjlZHIcObQcAHDggUf36yipISOiNe+99HNdeOxm5ua0T8JPj77/pO3v0kPzb7jJmqkEtYMw5uTEBKpxrLaoJ8T3C8hTBd3nbTW/BRdlSi+HooktIoL2kvr4roqJGwmaz2uMEZrPnfZE9hTuCYB9j7DYAWsZYb8bYEgC/+faymkZT9AAajRbTpqXhf/9LQlUVbUQpKcDy5d3x9NPnNvCBhwOIQUGBAW+8Qa6KWbMAoPF0yJMnc7FsGdFTqwUzQ0Pppur1pDV17dq01ikECgkC9d/mS0qEf/0rEbfc0h9ZWbvcOj44uBM0Gi2qq8sxd+534BwYMaLKqSducyHcEXKIZjV6PW1qUVGeLXyLhRyvtbXSruwrQSDOW10tpQe3JoRbqKXxAQFXcYLvvluPxMQo3HffJFRU+Fbgff/999iwYSPKy2mHFJqyXBA0ByaT5LcX8PMjRc6b2UJyGI1KxUijkZrVTJq0EYmJ1ysCxk013mkp3HENzQKQAqAOwCegdNBnfHlR7sBVpgtAlsDdd6chNzcJBw+S9jh7ttI/OmcOBWaCg6mR9d699ACAyMhUlJYmO2nlcgh66mnTlK4avd7ZD6vT0UYvDwA5QpjUNCHjwHnrUiLs2bMTNptNxqXUOF5++W9oNBps3FgHzv0AVOCmm7znF1CztPz8lEF+kZNdXk6WnPAXu8J555H9HRIi8fL4ShAcPZqNf/45hrq6eOh0sU1/wMvY1SDPhctBzEE1hITQ/CstdU0gJxQeIQiEq8ts9kdJSRGKiwthtZLLxtvBWoEZM2YgKysLP/98CAkJveyCQGRieSoIQkOpmEusvZoaSnAIDSUB4I7L0GCAva2kpwgOpmwngT59gJ07yZobNkzJoVRT4/3kCTmatAg459Wc8xTO+ZAGmocUzvlpaP+tRGpqKkwq6nhgYATuvjsNen0Svv6abub999OgWyz1yM7eg8OH/4DJRD7sPn2A++6j4/z8yFXx2mtJmDEjDYGBjTflLinJcbIIBH2vI0JDG59Y8vNER6eCGsLJ3/ctJcKMGQ/j8ssnIzravUrm3377BPfck4Bly/xB+kQwFizopWji0xKoWVpqvlrhVouKImuhsZTQLl3ofvr7S2qerwTBqlUfYdKkEfjii6VeO6egU2hK6y4qIj++v78UKHbluzcYaPz8/cnt42oTF+4lxxTSXr0uxpo1+UhP/wmA0u/tbVx22WW49NLL7NQbVVU0Fs2xCIKDKZYlHxejkZI1IiLcjxuFhdGYaLWkBHoST3B01Tm2rxTEkwBZP760ttzJGuoD4BEACfLjOedeYvhuHkTQ9PbbifYgIqIrbrnlJQwfnoT8fGpHR8dJlao///wu3ntvJvz9A5GSsgHvvz8LMTHnYObMD7BwoZIeevjwJJx//jXYsOEtfP/9Epw6le94CYiIiHO68a4WnE4nBdzU4OcnBUEHDEhCXh5gMqWgpiYH3brF4fnnfZc1BADz57+InTsl/vzG8OWX6fjii2TYbEJlod1UsLoC6jUankBNe20ql9tgoPiBoO8tKFAuHnGv5FqvrwRBp05dMXDgJY1W/XqCZcuoYMpqpQ1s/nzXQm87hUIwaJDkfnA1dvJNjzHKkOFcmccu3jMaJUEgXEMGgz+02i727zGbSXv1RdD4gw8+QGGh5CaprqZeC5WVpC27m92j15MLtqVJAnq99Du7dZPOd/IkjZ+/v/M4yuHoOejZE9BoOLKzbZg9+0IsXrwL1dW0b9hsouVpy67ZFdxxDX0GYCmIC6gVGyc2jUmTkvDjj3/gxIl8zJjxPqqrSZ3ZuJEW+9ChwNVXS8dHRfUAYww6nR8OH/4fjhzZhvz8vwB84EQLDQCBgaGYMGEuOOf49FNloNZgMOHf/1Zq6Dpd4z7rsDC4rGgVC626WnQ9S8KFFyYhNZXMe183/7DZmi5nLyqieoCdO1MAqLvNhMusJYLAFV++O9qWRkMPnY40XXkDIPH5iooa7NmzDwMHJvokCMc5cPPN0zBixDSv1A78/TfNaYGDB4EVK5x59i0Wmn87iK5IQbOgNp4Gg/qYhoSob2BGo8RIWlYmbfiOLs/yct9lD4mKYouF1rjcLeTOxi5y9r3BGyTX6OXfHR4uKZRarWvlyt+frkP8Jj8/ICGB48gRLU6c6ASbzYqaGq3dSistdY4teAvuCAIL5/wt7391y1FXByxYQGwXwr8PkJ8NoM5E8hs0aNBVWLGCbK2DB7dg4MCrEB7ercnvGT48CTt2fIPs7F2wWOrsxGnXXKPc7JrSWDUa0sAEEZgjlIKA+GhKS0+hri4Q7t2q5uHPP//EwoVvwmQahJEj1dsOlpYCzz4rrr3xoLWr1pTuQo0GQa/3vDOUcNOJIj2x4KxWHX76aS0GDkz0iUVgNjuzYrpCSQnw7be0ud98M6XWlpVJHD8A1WcAwPXXkztzwQLgu+/os35+lCYqmspHRdHrjNG5BNQ2ZldV2waDRD/teLxGQ9+Rl0cpmz16AB988DDeeScLixa9i+DgUNTUeJ+EzmKxoL7eBquVdnDhP/fELWQykfvFG1XCBoN7uf1C+XOlcAQHk1Uj0LevBkeOAOPHfw3GNIo4AefKCm9vwp3d5RvG2EwAX4ECxg0XxZtPLOMlyPNrhbl44gRNUqMRjWaw9O07DHPnrm/yO9577z5s3LgMVqvZXmEstF3HieCOxmoy0UMt81VuZgLA0aPVuP76cKxZsxnjxw9z/oCX8M033+Djj99CQECYXRBYLMDWrVQhXF5Oz0tLacGdOhWHkyfVA/VA0yR0TUFtg2ru4hWmtKTlfgxgPl57LRtffLEcjz+eivvv967LTWygrpqPl5ZSB60jR5Sb7cKFQPfuVIEcGQk88ghtEnv30ty49lpyE9x2G/DRR0SyJgdjkpLRt6/SjaAmCBrbyAIDndupinvQrRutsWPHaD7s2LEGBQWHUVT0HIKDKTWpvJyUHm8hIyMDI0eOxLBhY/Dxxz/aLRZhcTUmCPz9ye3irWCrVkvC0B0LRLQILShQLygMCXEUBCTks7JMYMx5nxCxAm/XvrgjCKY2/H1U9hoHcNqLyv766x+8++5rSEjoj759aQMT1sDAgS3nkqEK4zRYrSTOHX3gjovL3c0qPFw9w0WcLzCQ3BqnTpkAdEeBnJjEB+jSJQbR0XEID++DkyeB5cthZ76UIzoaeOwxYM+eVCxbluzUqAdw3WvAE6gFLFta1OPnB6xblw7gHgi3Vl5eNubMSUZYmHcL9cxm4MEHb8dPP63DjBkfKEjhKiuB55+nTRQgrTkxkbJxvv5a2tiKiyndWVgs48ZJ43L11bRh5OeTFRAbS6mORiNlC23fruzRKxIh5BBavyuoCQIxP+Pjqb2iyGa+5ZYXEBZmU3Rfq6wkge4tF1FlZSW0Wq29r4TYIMU1uGpKbzBQwNybRWGRkZ7tLQYDJTIUFDi7heVU9ICSb8xmU08bra/3fpGbO1xD3ol2eRlmM/DLLz9g+fLXYDIF4t13lYJA9AN1xH33xaG0NA9GYxCqq8sQGtoVSUkvuSSgE0JAQPjAL7ssSaFRiawBd6DTOZeYA0pNODaWukE9/vghXHWVhz4RDzFx4hQMHToFe/YAr75KRHEAkcWNHUvmdEkJaV2BgcoeA8XF2fYm9I4WU3PhTYtAjoULnWMbNTXVmDcvxeuCoLy8FJWVpQo23MpK4IUXSAhER1PAV55NlphI2TgDBgAffkh1LUYjuThvuEH5HT170sMRF1zgTMEsEhHkaMqtIQj/5Bqp2NRFFrPYhIcOnYiQEKUFwjn9ltBQ7zR0ufbaa5GXZ8apU2RmVVXR+ikvp7mhFijW6bwvBOQBYk8/FxIiFeAJaLXkPhJppOHhQFBQJSoqAvHLL5kYNYriWHLBI6f/9hZcCgLG2BzO+YsN/9/MOf9M9t5znPN53r0Uz1BbC4SHd0KXLjH2ngG1tVQb4OgflaOsrBCc21BdTbvwqVP5LjNdGutKFhTkXIDiCUJCKAtAbi4KugrOyfzeuxfIydHAZvMdFQJAWofFQgsrM5O+Z+FCZV9leTN5wL0eA82BRuMsCLRa7zCF5uaq389jx7xbqFdfD7z55hfYvLkKfn6045aWSpZAp07AvHnOCQDyzf2++8gKEESELYHaxuWOYA0KUgoCjYbmuVwQiHnpqsi/stI7ggAAzGYGPz8/2Gz0faKnQ2ys89oQQsDbQeuWuJcCAmgeOHoCIiOV9QSBgQdQUTEEu3dXYNQo+q1yIevK5dgSNKZq3iL735Eq2rd8s26gtha4+uqbsHVrLl57jQqd//iDtLHevV2X0wcEOL8htHxHCAHjiIiIOKfze5qFoNE4a2UicwiQ4gRC6/JVmiPnwKFD2SgqKsamTfQ9gwdDNYuqNSB4l+TwFgVwbKz6/YyO9l6hHuciMOiPoKAIGAx08UuXkhCIiaFgb1P+c8ZIKHiDhttxM3S3tapahorJRBuX0UjaeFkZcPz4QWzc+BEyM7c5ncNi8Q7ZnlqzeuFe66aS79Grl/eJ2gTnUnOh1arP5dBQ5TgPHkwagtlMpp1jBldrCwLm4n+1560Ouf9aDNRPVNOCkY10Ui4vL1Z9XS3TpbFmNY75vM1JR1PLCRYTTXCcbN2agfnz7/OZIKitBSZPHoExYzph9WqKNI4e7ZvvcgdqabLeMoOffTYVgHJXNBpNeOSRVJcaracQjUTki3XPHnqYTOQO8hVtgSs4CgKDwX3r0nGOirascqtg+/Y1ePPNKfj665Wq5/DGxrVw4SLceefV2LTpB3stjtwikKNTJ+9ZIXIEBnqeuaZ2DkcIKnqByy4jlsDcXNI2HWM19fXeLy5r7GdxF/+rPW9VmM2kuZ48WYyysnJUVVGrt0OHaLFd0gg7b2Cg+ipUy3QZPjwJd9+dhsjIeDDGEBlJXckuvzzJSdtojiDQ6501BGF6iolstUZi//7dPhUEhKGoro5CeDgVInkDnrqyHBeEgLcsgttvT0KXLssAxAOg+/ncc2mYMCHJaxWxYtN77rnZeOutf6O09ATSG4qtr7/eNxtUU3AcP08Ea0CA8j6Kc8kFQXz8IAwbdhvi49UDc95ot7h9+3b88st3OHmyyJ6XLywCuSDQ66Vr8za8YWGIFFxHyOd9bCwJ3KIiiimIrD05vG0VNOZ5HcQYKwdp/8aG/9Hw3K2lyRgbC2AxAC2AdzjnL7g4biKAzwEM4ZxnNnVeMShTplyFfft24MILr0d4+GoA1DXM1UTPyEhHVZVzSz2dzuAy00XNF+4YHxAFTM1BUJDSunEUBHp9Au6//1mfsQ+uWJEOxjQA/gcgAeHhqdBqm+/7F9XZkZHSfaislALjGo0USHTMsxfd2uTQaLxT/AOIVL4kFBQkYcECyrxJTKT3hJbV0jiM2PR++ulzFBcfR1zcy8jJofEYexocqmouSE8EgRh/ca+EIBBZOjk5wLXXXomBA6+ETqfek8Abm9aDD87HuHFJ6N9/sJ3NVc01FB3tm4IrV24dT8EYncfRApU3q6mrq0DnzjXIyorC338Tk29ZmdKd6O2Ascvti3PeIkJXxpgWwBsArgCQC2AbY2wN53y/w3FBAGYD+MPdc4tNUWT0mEyh+P13ek2eNieHTgd89lkKOHd2WPr7B3kU+HTU6loy8UTxlDD1/P3pfIGBNPnMZn+cc86/fGIRpKen49FHk1FTI2ZlNo4cSUZGRvMoIvr1U9d4AwOdTeKQEGpbabPRRsW5euaHt7MjxPnEhl1XR3ODc3qtpUJHnHfWrDeQk1OBL78kVW/SJO8JNE8QEuKsgXo6pv7+zoJAaN2HDtGmZjLRujxxQmpyL+ANQZCQcC66dTsXlZXCGyARsYl4nV6vTmnuDXgz3qBWR6TVSpmEZWWFyMr6CMB/7ILg1ClnQeBN+DIv8SIAhznnRzjn9QBWAlDrCfYMgIUA3CayE5vid9/txtq1VkyalIaKCrpZamZhcDD1yC0qUs8Oqapyro0zGmnhBgZKRFQA+R8dJ1tLFriofJVDWBxiUy0s9E2weN68FJkQINhs6oHzphAW5pnbIyCA0lL796dmIgMHqnO/e7NXrPx8zz9/PQoL/1EsKG9sWOI+JSZOQGXlHSgtZUhIIDbJ0wFHV1tzMrDk98BgoHPExtI9LCgAZs/m2LGjBiUlucjPd/Zft9Q1JLLaAKi6hYQV17Vry334ruDN3tmuMpnE+gkJ6YwePUhhFXUljtlG1dXe63gH+FYQxAA4Jnue2/CaHYyxCwDEcs6/9eTEYlJwDlRXa5CfTzuxnPhJwM+PsogYc50d0rVrnF2rj4qi1NNBgygfe8AA2qB696Z0tJ49nb+jpZqeo7Yh3EMBAVRN8tJLz/tEELhKm/SUIoIx1wU9jSE0tGkSLV8JAptNi8LCIwpB4A1ftrhPp04Ba9bQ/0lJvtugmoKjcG6OheX4GX9/esydS+61ykqGl17agUcf7Y/6euf+C542PXJERQXw/vuLsWJFGkpKSFqLbDrhFmLMd9aAt9xC8vOp7RlCaBuNQXj0UWL6z8qSxk/e5U6NGLAlOE3TE2DkmH4ZwMNuHJvMGMtkjGUWFRUpmn5YrVL2gGMaGWNSe0QAeOSRVHtlooDRaMKjj6aid2/qGyrv6OQIVympLRUERqNyoxBuFIOBHOu//77TJ4KgWzfX6bGeoHt372/YgJS37k2I840bNw99+lyisAK8YRFYLEBtrRmff74eNTVkoYqeAK2NwEDnudkczdYxTiM02p49gYceEq8OgtEYDovFjLw8Z6uguWNrtQKVlTY8++xDSEm5BxUVpIUJjqGEBPrb3Dac7sAXfQBcFU2KdSR6ItTWSgIgO1vpUmqsv4mn8KUgyAMgT+zq1vCaQBCAAQB+YYxlAbgYwBrGWKLjiTjnaQ29EBI7depk1y4uvzwaycmdcPAgBYAdBUHPnkrf9IQJSXjuuTTExFAWUEyMlDUSHNz8G97S4JS8fgCQLISoKJot0dEjvC4I0tPTUVXlPJP0es8oIrp3JyvKF/CFcBHnjI1NhL9/oFddQ6Lwr7CwDF99RalCjoV4rQlHt5CaG9JdyO+F/P/gYOE2DURKShZ0Oj3q6pT8OUDz++5WVhLh3PTpD2Py5JnQaGixid7agpLBV/TMjPlGELhyDwkXNOccsbFkogr3EOcS2ypAcQJvWLGAbwXBNgC9GWPdGWMGUIHaGvEm57yMcx7JOU/gnCcA+B3Ade5kDYlNsaQkHxUVxSgspFGVC4KoKPXg44QJScjIyMKRIzZkZGRhwoSWVcfqdN4x++War6Bh7tqVZvcll8yyN1r3BtLT05GcnIyTJ0sc3onAlClpTQaKdTra4AYP9p05DviGylic02ymLLLbb09Ajx4aDB+egC++SG+RwBWfranRIDaWOCG8NT46HVmsAwe6F7gULKFyOHZ48wTyzd/RShNauZxyOy9PGdeqqGhenKuiAjAYDJg790U8+OAbAChQfPIk3cvohmZzvhIEImnD23B1L4TwXrToeuzeTQqZfFyrqpSBYlf9TTyFzwQB59wC4H5Qa8sDAD7lnO9jjD3NGLuuJecWPrPp09/GjTcuQGEhzUy5IHC3SUVL4S3XhZoJL/y7+/alY/jwBGi1GiQkJCBdJKY3EykpKahWraAKxOjRjQuB6Gip8tjbbhtH+NIi+PTTO7B06Z0oKsoG5xx5edmYNy8ZH33U/LEVG51WG464uBsBeM9aiokhzVRQKTdlhXbu7HxMSzJf1FxDAkIQZGVJr9XWArt3y5vbe969TB4kBqRNT2jF1Mil5RW/riAyeXwFNUUnMJDWFVWl/wVAOa6Asr9BZaV3FETfkdwD4JyvA7DO4bUnXRz7L/fOSX9LS4HRo5NRUgJ8+SUNoPDh63S+7e8ph7c2Q8dFazQKQZCOAwfuBucUOM7OzkZyMnEjNZcoLSfHVTA4p9E8esFy2RrwhMTPE4jFV1W1BoDSrq6pqcaTT6bg3/9u3riKTauyUqKDboo7XqulZISgIPIB19fT/JVvgH5+SsvCz4+yrcrK6Dg/P1KOSkulzdYxhRNoWeaLTielOTsKaCEINmz4A337FiIxkfS8+nraxEQlteiw5W7Wktj4y8vLUFSUj+PHo+DnF253C/WiAtwWV/wajTT+FN+h8TSZfDP/HL9XLeAbHg4kJ7+Dm2/W4v77aQzlNS7l5VKGnc3mnT7RPhUEvoBcEADKQLEYqLAw3xG0OcJbueFaLT2EVhkQIMzEFLsQEKiurkZKSvMZM+Pi4pCd7dxPQKdTBolDQ2lD8fenReJt7pbG4AtrAJBrYeo2dW5uTrMLy6xWGqeqKjMKCrQANC5dQ7GxUq9bAdEzITiY5vc//9D8Skhw3uiMRmeNsnNn2hhqa53npV7fcheHKCzT6ZSNa7o38BPX1vZGUZGyHKi+nq7H35/W7qlT7lnrVqtEwbxlywbMnHkThgy5AQ899CUOH6bXWxof0GjoWuQC0pcWgCNcuT7DwoD8fG0DEykJxOJiafN37HhWUdFyQXDasoZaApsNyMzchuefH4uvv/4GgLLM3NdtHQXUeN5bArkGYjIBhw6lA1BvAONaq28aqampMDmphybExZFPMiaGKm779aPNyc+vdYUA4DtBIN0v1wR0joFOd2G1UlbHrl0ZqKjQgLE61dqKbt1ojB03Zp1O6qoWHk4uuEGDXGerqUGNvRXwjsIin5/yTSw8HAgIsAAIx8CBzh3u5BtXVZV7Ac7iYknp02q1iIvrjcjIeFitUsaQsAiaY/1rNCSIvVkf4ClcWb0iA4oxScgK4QeQMJYH3+vqWh4raHOCgHMyvdeufRl79nyPAweuA5CAujry7Wo0rcfnotd71/KQL9bvvkvHJ58kuzw2rgWEKklJSVi6VMqeCgiIB5CGXr2SoNVSHMAbtM8tga8EgaQwpAJwTiV+5JFUVFU1L0ebUh2BkhK6kX5+hU6afEKCOlumGrx5D7zh5pDPT7liQEypdLF5ec6akVwQCKugMYiqYYErr5yA5cv/xpQpr+DYMaklp9Dem6MNR0ScnkpvR6hZBeSCK8Hzz49Fbu4SAEQPL4djvKWkhNyRzS0ya5OC4Ouv07Ft21eyV7OxdWsyMjLSERHResU73g6WyifmokUpqh3AAMBkMiE1tWVdwCZNouyplSttGD06C0ASQkPJVPZVPra78FV8AAAmTgSeeaYSl18eCyANBoNEKJiammbPImtOCb+wCEJDqYz43HOVwrpHD9/0m3UH3tj05Odw1KSFdv7770B29m688MI4bNz4DgBnV0ZVFRWdOY6xoKhwPF4uPHbsoL/nnEN//fw8F5ihoa1v4bqCq+vo2jUUBw78ipMnXwVADbfk6c2ODW4Amnt5eepdzZrK2GqTMYI33kiBxaKcRWYzUSPMmOH9Zimu4G1BIN/8jh937fpJS0trcUetPXv2YebMmQgK6gOLZRkAWiCna6OSw5fZSBoNoNXuxq+/zgKwG8BwJCdvRN++wzF6dG/7cc2pKRDBRtFZVB4fCA72Xb2FO/CGYJWfw3EDGzUKWL3aiq1bGRhbjd271yM0tCtGjZquiBMICKtLq6X7bbGQy0gtA+bECUloiD7NF11Efz2xBhgjS8AXGUbNhZ+fMt4iEB6uxZw536JTp3i8+ipHVhbD3r1ElQNI3dkcYxpWKxWgaTR0XtEfoynluM1ZBABw4oRraoTWvMm+tAhc0WEYjSavtFXctu1/yMzchN9+W2HXtqKjfZO77yl85RYCaEF069Yben0eABvq66Px9tvJSEubrvCzNkcQWK20YYlKULkgaA4Fh7fAmHcEgUhoAJyr4SMigPDwnwBocOLERNx//wqcd94Y/PYb9ShwlToqrChXHPtWK/DEEzPw8MP98OuvGcjOpu8W1drurHe9ngKtsbFnlhAQULMKAgKA888fhc6de+Kii8j/LISgwLFjzp8TsNloLtbXu+cuanOCgFgq1TfJzp19RESuApE54U3IF+wjj6TC39/Zh/3ii2le+a64uD4YPPgyDB58jV0QeNMaYKz58RNfCgIKxEbhww+L4e9/EoAeQE9oNFqFIPCUHwOJ/SkAACAASURBVMdmo03LbAb+/ptMgvx8ypyOijq9rghvzlNxLrUq5QkT9GDMhqNHz0V8/AS8/fZdeP3121BWdgJ5ec2rgs3PBwICIlBfX4ODBynd6PzzJaWpqXE1GGheBwScPr6npuBKOAltf+hQ+rt9u5JioqKi6XiLuzhDh6Zx3Hqrs3/cYDBhzpzG/eaM0eDGxLQ8EOerhS2sjAkTkvD002kwGqmJitFIPuxrr01qMRWC1QoMGDAMr7zyK2bP/tSeiquWe+4J/PzIvRQVRRw7MTGU8WIyuW89uSLk8hYYA375JR2zZiWgtjYKQALGjXsCTzyxERUVSq3Uk3G2WiWttqSEdkiL5S87U+fphDfHUy1OIP6OHj0Sw4ZpYLUyfPmlEYmJN+DiiyehtrYC9fVSto+7+OijNLzyyhPo3fsSMKZBdnYPAJJbCGh8HZpMzim6ZyJ0OnXlx2wuwKpV87Fp0zz06kWutLffVs7R7GzvFJS1OUHAOXDJJUkICLgE8k5TM2em4YYb1F0mGo1kGoaHS7zl5C9unubqq7Qz+YSYODEJd96ZBcCGgQOzMHYs/b6WpoqJz9fUkIZRWalOSeAJtFoa09BQqceCTkepvFFRJGSioui9kBDXi9PXhYCffpqOJUuSUVycDWq0l40ffqBEA+GmEPBUENTVkT+7tjYYRqMZ48Zdh5gY3xcmNQVfWAQAabI6HVU6C/Tpsw4ajRlbtwJjx36CBx5Yic6dewKg2gg5g2ZT+OKLFfjyy2dhMJjwzDNHcfSoAXo9WQQAuYhczaOQEJpvZ6oV4Ag1gWYy2bB6dSo2bFiKe+/lMBrJPfStjKu5psazMXWFNjJMEoT0GzbsNwBZGDfOhiVLsnD11epCgLpSOZuGoqVdbKySB1+jkQJ7wcG0MTtqVDqd7wKackGg0Ui+5aNHKzF4sAmDB0eisrJlXOSVlcC2bRnYsGEV1q+npPl+/VoWHwgPb3rRmUw0rmFh6rUeviL4kuOpp1JQV6fMxrJapR4MzY0TWCwkCP78k56fd54e8fG9zojguzctAsdal9hYmrNiPWzZ8jxstoUASHv96y/l57Oz1bNaHFFXB1x++b2YOPEpREf3xe7dtPb795fWiCuXSkhI69USeQtqimV8fFfcdNMC3HXXm+jSxYZ776XXv/xSOTdzc1veqKbNZQ2JIMjmzfT88svprysNPSKiaZ+zyUQTx2ZTdnSSn7OkRNokfFmEotMpKQZEQcnJkyZYLHUwm+vstRTNqYKsrqZzL1jwAPbv3wmT6W8AkRg5svnCzWTy3FUWGEjjaTbT/5WVjWt43kJurnqiAVkIlPETFkZj4YkgqKykeblvHz0fMODM8Ev7sugxIEC67wEB9Pt79EhEcXE6NJrHcfy4Dv/5DzB2bCEmTTKhoOAwMjNXY8+eCzF16nXQ64HNm39EdHQsevbsZz9vdTVw8CBwySWT7a/t2kV/hTUAqAuCwMC2JwQAqeeBvFBMo2G4886n7Om0Q4bQfnD0KKWTitiBxQLs2UNKY3Ot+jYnCDgHfvvNhpoaDXr3ljRmtc05JMT9LIGmqjfDw2nA9XrfF6yJdDpA0p5LSzW4/fbPcN11lIpSVka/2dNYhwguhYV1gk53EaqreyMggPyuzdEcAwKaT/DXqRNtlKII0Jsdl1whNjYOOTnq1dq1tZUAArF/P2megHoPXkdUVdECrq2VBEFR0XsoKhqGfv36eu/imwFvCyOtls5nsyldqgEBlNs+ZcormDLlFdTUAGvXkva6fr0JdXUL8PPPLwMARoyYgoEDr0NkJMecOdNQUHAM33yzHf36XYATJ5yZS61W2ugApSBwVIT0emU7x7YGk8mZrjsoSFlXMXw4CYLNmyVBAMBecX3yJNWreLqW25xryGYDvviCsjIKCx+zv+4oCIxG72oGogOSOy6QlsKR+11Q7cbE3IjevalYyWol7dUTat/KSknLXbLke4waRbwww4Z5tmEItseoKNrMm5sdJKfw1mhap5r52WdT4efnTK9x4YXPQ6ejga+rk7pslTgydcuQnp6O+PgEBAURjXV6ejrKywG9vgTffDMNubl7vXbdjJHAjYz0bJx84WpTizk4WoRGI3DzzUB4+BEAQcjMlC5k2LDbYLMB2dmV6NNnFOLizkNd3fnYsYM6j1Gsphy7dq1Hbu5+HD5MwrZLFymzzWBwdmVGRrYex5gvoGZVM1aOzMw12Lr1UwDApZfSb9y1y7nwDiBFb9cu4MABcsP9/TcJ0d27G//uNicI6uqAkpJoAKdgMFB7AzWffWuSR3kbjtzvIpvn+HFlupjFQq+5Q4dgNis/K2iCAWDECPc1iOBgokhwJOtqK7jttiTMmZOGyEglvcbffz+OqipphxXdn6qr1YPzoqdDTo5EY/3xx8kA0tG9+ylcffX/oU8f71kDwroNDJQSHZqCweCbWJY7gkAgKSkBABAY+ATmzv0Rixbtx6BBVwGgloz33rscI0ZMwX//m4TqaqlRUm7un1i4cByWLr1T1S3kKOD8/HxPi+5raLXOa+rUqSz897/XY+XKuQDIch44kIRlRob6eWw28hjk55OFIDo5NoY2JwiE6dS/vw4PP/wJAOfB02h8m4vua+j10kL385Oonzds+Bl33jkYW7b8ZD/WaiXtNS+PtNeqKtib2FgstPnn5tL78rz448cp20CnI/4bdxZRWBhZRGd6Ol5j0GiAK69MwpIlWVixwoZly7Jw7rlJqKgAPv5YOk7eBvDkSSJBk8cM5s517ulgtVYDSMHIkT1xxx0vY+DA89y6nuBgsqyEpu+KjE5AryfNOCyMNkSR1OAIT8jqPIGaINDr1ZWJiy7SIDSU2DTr6sYgJuYcp2M2bfoQW7euRG7uPvtrWq0BAwaMQefON+L77+m1wYOlzzj+traolKjBkTm5b99zMHjwVRg69CbYGnynI0fSeytXKjuWtQRtLkYgouN9+gQiIYFUBMdJINIX2zIMBokbXbiGCgsNsNl2YdOmDAwbNlpxvNlMD3dSS6uqgEmTRgP4CV261ECnMzotYtE+MySEFrnVevrTIL0Bx+ApY0C3bq9g374H8NtvwNSpWgQGktCsqaExEMH5ykopmO8q6AzkIC6u6YLDsDDawOXz1Gike+jnR99dXCyl4DrOZ4PBeeMtL5cUgYgI31WJu/pdAQHOAXadDrjqKmDVKmDxYo7bbitD586bodMZ4O8fiN69L8H48Y/CYjEjMjLB/rmePRNxzz0/4oknaCyGDlX2fna0+NuLINDrpUQKeq7Hyy+vt9OWABTPGzkS+PlnYNEi4OmnlZmPzUGbEwRiosnJNx3N0jOFUKol0OslXnnhGtLrz8OAAdcjJmZEsznzASAnxwabbSAAoHt3Mj3km2NoqESHLHC6s1+8CZFZcfQomdF//fUhgPNgs43Btm2SxlVR4byZWiz0iI6OQ16eWtA5DsDfOHmSob4+HgYVNTk8XN11qdFI98FoVPbYcAfBwa3jEnUlCEJDpT4hcvz66zkApsFqfQQffRQKYDOAlwAAV1/9f7jjjpedPlNfD7z8Mlm055wDzJwpzUGDQWkB+aLK/3QiNFSp0EVEQCEIGAPuuoss+v37gWefBZ56SmoA1Bz4dHkzxsYyxg4yxg4zxh5XeX8GY2wvY2wXYyyDMda/qXMKQfDJJ1cjJ4eStuVmYlt3CwmI/YMxsgioKUgw7r13Nfr0GWUPZnqKykparEOGPAMA6N3bT/F9Wq2zEGhvEAWGQosaNuw2dOtGc+m336TjKiqA1aupTajoa7x6dTpWr05HRYUaeY4JAQGpePXVq3DPPX2cBIWoaXF3sz5T74HoVuaITp3U3UNXXJEM4FGMGLEVVMT3IoKCPkZY2CBcc83DTsdzDixfbsXRo3TOhx5Sntdxw2sv1oCAY3V9YCBHVdVxHD260/6aTgc8/DClk544ATzzDAkFObgHJcc+EwSMMS2ANwCMA9AfwK0qG/0Kzvl5nPPzAbwIwFk1cIDFchJAHIqKvsPzz1+J7dvTFYMWFHTmLiBP4NgjVtAUiIKl7GylH9sdrF6djlGjEpCUpMOOHQNAgU16T2iigYHtY/wag/h9IrU4LCwa1dWvAtDgzz8T8MMP1Nti7dp0zJuXjLw8KSA8Z86dmDPnLpSXK0lejMYIAGno3j0J48fPgUajQXCwMs+4c+f2sWmJqnFHaDTqNCVjx87Gyy//jXvvvRgzZzIwBlRUJKGycie++CIGx45x7NmzAWvXLkFmJscTTwA//6wFUItrr/3dKQXcsUivPYypI+SW6IEDuzF9egxef/1WxTEmEzB3LnlHCgpIGLz+upRN9MorExXCozH40jV0EYDDnPMjAMAYWwngegB2ucU5lydABYDUhSaQDYCCJqdO5eP115MREwM7j3xr9Sr2NeSCwM+P8of/+QdYuTIH9fWbMGLE7ThwAOjb1z0Nc/XqdDz+eLK9qtZqzQaQjNxcoFevJBgMEhdTe4cQBEFBQEZGOpYtS5b1fsjG++/fjhUr7kF9fQ04VxY3mF0wp2k0gQCS0LUrcMUV92LkyAsRESE5bgMD24elKmAwqJPIde5MiQjy9zQaDbp2JYrvESPIKvr6a2DnToaNG4GNGxmARABDANDN0WpPwmqdhh495irOHxqqHEdvF8ydKTCZJMbWnj3PQUREFMLDu8Fms0KjkbIJgoJIALzwwk84cOASbNliwt69wJAhBTh0KAZLlxYhKqrp4kjmifngCRhjNwEYyzmf3vD8DgBDOef3Oxx3H4CHABgAjOKcH2rivE4XHBMTj4yMLAQEtDxociZBMDbm55PZd889dbBa/RAcPAJvv73ZflxoqKRtOi6Kujrys153XQJOnHD2aUdGxuPNN7MwZAhpIa567LYn5OdLSQdDhiTYq4pbBgbAhqlTgbFjKcNF3AuNRr01ZVtGebnUHEXeaxug7LVDja5iQl4esH49ueNEAlZMjBn/+pceo0dzcF4Bg8EInU4KAJxzjtIV7O9/ZvTQ8AVycqQiS845jhxhqi5hzjnefHMKMjJ+R0LCdmRludLm2HbOeaLaO6c9WMw5fwPAG4yx2wDMBzDV8RjGWDIAl30bRROX9qbNCq0rMJAC4FFRryM/fwnKy7Mxa1YCJk9OxfDhSTh1SqoR0Osls1I0BAEa7+EgFlJ7Crg1Brnrq6Sk+b2flaAIdH7+GgQEXKcQyGFh7UsIAHByx8prVCIiSEg0VowHkHCcNo0Cn2VlIttJTEIGQLmg/f2d00bbk5XlCKNRqhFijKFHD1JgDh36B6GhXe2FkYwxzJz5Ia6+ehfi44OxaxewezdHfj7QsydDQgLNwaeecv1dvgwW5wGQE/B2a3jNFVYCmKD2Buc8jXOe6EqaRUfHKTIu2gvknOtbtqSjuPhJiGb2xcXZWLaMWDPlMJtJWysvV5aru+rhEBAQflYLgqgob/WwqAegwaZNU7B9u3RPDIb2466UQ77WAgKcYwYJCe5nsTBGVm1T9BBqPDpnQiMlX8HxtzEG1NfvwMsv34C77grG779/JnuPoXv3wdBogAsuAO68k2HePIZJkziKi19GTEwjXWzgW0GwDUBvxlh3xpgBwC0A1sgPYIz1lj29BoAbBqXykkXD8TOhEbW3IX6TRgOsWpUCs1lZwFRfL7FmqiEjg3j3b71Vg9pa9QKD2toKfPstbVxniyCQp8I+8EAqDIaWRRspL6IUAEdtbRkWLkzG6tU0pi1J6TuTwRjNT9Ff2nH96fVETz1woPubdW1tFXbtWo+//tqM1NQrsHLlPMX7jpxW4hraKxzHjXOOdes+gb+/Fk888TOGDr0JeXkHkJu732WGUF7eAXz11bN4/nlVHdsOnwkCzrkFwP0AvgdwAMCnnPN9jLGnGWPXNRx2P2NsH2NsFyhO4OQWcoRWG9EQLGGIiYnHc89Rw/H2Zg0AykleXOyKNVP9dREEFbz7lZUq3a4BWCz1WLSIhElrcP2cCZBbBJMmJeHuu9MQGNg8tjKNRgvOlfX7tbXVWLQoBX5+7dt1Ic/nd7X+TCYqBOvcuen5tXTpv7Fw4Th89dWz+PPPDcjKkjJewsKcN31///ad4eaYRsoYw7x5L+H773fiootGgDGG1aufw6OPnosNG5aqnmPVqhTYbFZMntyIXwg+jhFwztcBWOfw2pOy/2d7es74+DgsXnxCUWUItE/NQPSItVqpDWdBgVpQk2PatEjceediDB8u9WRYtSpFlgnTOI4fz3GZEtgeId88dDrghhuSMHx4Em691fWuwpjzhq/TGWCxqKdjHD+e0y7npBx+ftJYNvZbtVrKd09IoBqW48fVU58HDBiDoqIsXHbZvzFq1N0IDiZfkE6nnpbanoWsgL+/esZP796UMhoSEonQ0C7o23e46ucffPAzmM11CA9vvMq2TS59NRro9rroDAYqsX/ooVTMn5+surlXV5fgjTfuwoYNwLnnJiEiwrWloIbo6Lizxi0EOFdJd+9OWSuRkfGqGUSRkfGYPDkVH3wwG5WVFAENDIzA1KmL8fHHKSgrc/5MdHRcu52TAn5+Sk6spkD9oulRVkZZQ3IGzTFj7sGYMffYn2u1lCMv6Mod0R7rBxxhNKqzjBYUHMXTTz8Is7keGzceR2Ghev9irVYHrbbpbb7NCYIjR7bj5pvj8Nhjz9trBzSa9uvfFoLg5puTkJsLvP76VNhsalSC9Th4MAUHDwqrIA4isNwYRIzlbLEGAGd3gkZD/uxbb03F2287ClsTLr2UsrPkFhdAmTGUNpkMQPpMe45bySH/fYJGXE5s2BhCQuhRUSGx6graFJOJHt26uRYw7Y1WwhWE+8sxBBASEo5ff/0OnHNotWXo1y8UFRVAVpZ7bMSO8Fkdga8g6giMRpM9PtCec4mrqiRu/Px84NJLNXBdd8dwww02FBcDpaXp2LfvLnDuupIkOjoejz6aigkTktpke7/moqxMnROnsBD46KN0rFqVgpKSHBiNcaiuTkVMTBJeeEHpOjt5kgp5CgqAqKh0WK0pOHkyB9HRcXjkERrTuLj2xdHUFIqKmrcJAVLPZ6PRPb9/cHD7DcQ7oqDAuWENAGzY8A3OO+9CdO4cbX+Nc5rHxcWkQIaFUVaXXg8MGeK6jqDNCgJAKiRrz5PCbCYTWuCiixJQVKSu6UdGxmPJkiz78ylTjDCbVWYQgKioePzxh3RsRET7THNUQ10dCVU17N8vmeJmMzBnDi3Eq64Cxo2jFMYjR4BXX6XFFh8PpKTQ2HXrRg+AhIb4/2yBXGnxNTp3bt+po3JUVtJcawl0OiA29gwuKGsJRCFZe8wYEtDrlabhnDmpePzxO2G1Otf319ZW4r33ZmLr1k/tvmzGNNBotIrjDQYTHngg1el7zhaIbB41LatvX9Kojh+n53fcAbz0EvD99/QwmUiQWK1Ar17AY49JnEVyTpz27hZSQ0AAuXhcsHB4De2FWNJdiDagvmzl2qYNV1FI1t6DRvJN5aabkrBw4fsICnJOd6ysLMGPP75lFwIAwLkNNpsNgYERYIwhMjIeTzyRhqQkpb/7bBIEgOumLVotsb0Kbf6CC4jlcehQ2uhFt6crrwSefFK5+cvpz89GQQD4rhmOHGcDMaIcjPmeWr/NWgQiIHc2TAqioJaeT5yYhBtuSMKllyagsLDpgDDnVvj7B+KDD4rRs6fzYtXp2h8FQlMwGmlcXZFxRUYSwyvnQGIiPTiXUyEoj/fzUwrTs1UQBAbSGPnKKtDrz55YlhxBQe41nWou2qRFIC8kOxv82moWj0bjmj9IDSUlOTjvPHWNranS/vYKR3pjOXQ653FpjApBznPVXhkx3UVUlHqQ3GAgzVaNksJdtPUG9c2FwdD4fG0p2pxFcN55F2LNmkwA5Cc8G1waRqMzwyPQWJcsZ7iqFQgKOnuCbo4QvldXiIpyL0jn76/smBcUdPZZWHLo9ZT7X1REfm2DgcZSvvlzTsHl0tKmG6sLBAWd3QI2IoLGs1qlTlQtxdQTtEmLAKCF5sg90p6h5iN85JFUGI1NB0j0egMeeSTV6XWN5uw0swW02saDjsHBTQtJnQ7o109SSESP4bMdotVmp05UFexoATBGGm63bu6xs2o06oWkZxMYo/EMD6fx9PensYuNpXFsSWfBNmcRAFLLv7OpCCow0LnCUBTULVqUgry8bGi1WlitVjDG7CRUYWERePLJxfZj5QgJObvy3NUQEKCePSQQFwccPKj+nkZDQkAuTNQazZ+t0GiaDnIyRvMwOJjiYBoNab0ixsAYve7nd3ZbWQKieZQa5X5YGFlNpaVkNXBO4+aOFdUmt9Lg4LPPRBT+VceCHbHBz5uXjJoashk554qCOzVotWdP3UBjEO4hV2a1WFyOgTqNhlJN5X5bURXbAc/BmFKgyv/3pW+8vUGnI6vBZqMqb3eTFtqkPtgaKWpnIjp1omYeISFKa2jRohS7EBCoqam2s4o6Qqslf+PZbg0ANAZRUY1r8fHxpHwIjdRgAM4913kemkwd1kAHzgxoNJ5lrrU5i0CrPbs3MJE+FxZGrqJTp6TCOkeI1xmTMllE1kbHhiVBtOgsKyM3hGPhTmAg0L8//S8Cm2puCl/nenegA75CmxQEHSAEB5PbIjY2Djk5ztlDsbFxSEho/etqi/D3l9wRZjMJWDXeHFfzT6M5e7OvOtD2cRbr1u0DjAHPPZcKk4Nz2mQy4bnnnDOFOtA09HpynXmidHS4hTrQltEhCNoBkpKSkJaWhvj4eDDGEB8fj7Q0ZxqJDrgPT1JrjcaztyivA+0DbY59NDExkWdmZp7uy+jAWYL8fCW9hyOMxqaDzR3owJkAxlyzj/rUImCMjWWMHWSMHWaMPa7y/kOMsf2MsT2MsZ8YY/G+vJ4OdMBTNJahJipoO4RAB9o6fCYIGGNaAG8AGAegP4BbGWP9HQ7bCSCRcz4QwOcAXvTV9XSgA82ByaRes6LTUabR2ZzB1oH2A19O44sAHOacH+HUJmslgOvlB3DOf+aciwT43wGcZa08OtAWEBoq9YUAKD9bjTahAx1oq/DlVI4BcEz2PBfA0EaOnwbgO7U3GGPJoMawiJOze3WgA60Ao5EK+QCpr24HOtCecEboNIyx2wEkArhc7X3OeRqAtIZjixhj7lFuNo1IAC1sAtcq6LhO76OtXGvHdXoXZ/N1uozB+lIQ5AGIlT3v1vCaAoyxMQBSAFzOOW8kP4PAOe/krQtkjGW6iqKfSei4Tu+jrVxrx3V6Fx3XqQ5fxgi2AejNGOvOGDMAuAXAGvkBjLHBAN4GcB3n/IQPr6UDHehABzrgAj4TBJxzC4D7AXwP4ACATznn+xhjTzPGrms47CUAgQA+Y4ztYoytcXG6DnSgAx3ogI/g0xgB53wdgHUOrz0p+3+ML7/fDaSd5u93Fx3X6X20lWvtuE7vouM6VdDmKos70IEOdKAD3kVHOUwHOtCBDpzlaNeCgDHW+XRfg7tgjLWJTreMsTZBr9Yxnt4FY8xr2Xq+RFtZ82faeLZLQcAYC2SMvQrgO8bY24yxG0/3NbkCY8zEGHsDwHrG2KyGTCowxs6oe9Mwpq8A+JYx9ixjbOTpviY1dIynd8EY82eMvQXg54ZEj1ENr5+J43nGr/kzdTz/v70zj7aqquP45/veYwoUAxxwpCc8BZXILM1cLlaI5VRa6lppTqCmpmhI2WSOkaVZJqaWlqHl0CKXTSqlaE5ZaYBaVLigZWmK4kii8Pj1x94XNpd3eQ+49wzc32etu+65Z3jvc3/n7Lv32WcPhTqZ9UDSNsCNgIADgfsp9hhGk4HBwHFAX0JzWsxsxdoOyhJJI4DbgU5gArAI+FKuUrXxeNaXCcAWhM6eC4AfSupbsHiWKc0XMp4bXUYALAWuM7Mzzey/wG3AbEmjc/ZaiaS+8b0N6A381MzmmdmlwKJYUsy9lJCwBPi+mU0xs78SWoI9J6kQY0N5POuLpHS6eAGPmNlLZvYj4BFgatyvKINtFDrNlyGeRUkY642knSRdI6kfgJm9BNyX7LId0A78PQe91ZDUIeknwJWS9oh9LQYAH0h2OwU4RtK2eZUSYkxXlqjM7FlWHwfqHcDOZvbvzOUSPJ71RdJwSbcBN0g6SFJl3NUtkt0+BxwmaUczszx+vMqS5ssSTyh5RiBpH8It4cmEKgEkyczS2WZ7Awt7MnxFI4kX7bXAHGAu8BlJE4FvAKdIGgJgZs8ANwEn5eR5EPBzYIriHBKS2szsjWS3QeSfyDyedSTeLX0HeJKQpg4BvgpMBw6UtCtAzKzuIFZlWcbtz8uS5ssSzwqlzgiAlwh1bh3ACZJ26CKQ7wGeBpB0Uo63izsCS8zsm2Z2JXAdcBjQD7ia1TuQ/IMwWmset4vPA0cTYnqOpAFmtlxSS+IyCngq+h0lqSNjR/B41puhwCvA18zsDuAiYDwwgpDhflmrWuTcBdRr4Md1pSxpvizxBEqeEZjZ3whzHswHfgtcCGvUBY8DBkuaARxFqE/MHDN7Ehgmad+4ai5wD/B5wqB7gySdJ+lI4ETgzXhcpiUEM/szMC/G9C7gmrhJics+wOaSbif8yC3L0jF6ejw3kDRTNLP/EEYAHp98vhqYGjPaJcAFkk4k3HUtzsKxC8/CpvnUocjx7BIzK/yLcOu8afJZ1cvAJsB8YFzVsXcSSluHZ+Q6BNgy9QNa4vIZwE3JtjHA9fGYDuBYYCZwdB6eXcR0U0Kp5n3Jtj6EH93HgCMz8BwK7F21rojxXMOzwPGcWLWuNb4fDzyYrN+M8OB1DPBOQvXGLRnGc2KNbYVJ84R5Vy4Dehc5nt1+j7wFehDocwmD1t0CnB/XtVTtUwn8WcCv4vIn44/w2AxdvwLMI0y7eUnqFpeHAzOA4+LnwYRB+bbKOKZdedaK6bnArLj8kfh+aIauTxFK0rvHz2mGVYh4nj8yaQAAB5hJREFUdudZlHjG//0YMLnG9hbgXuCsZN10YNeMY7lWz6p45pbmCQ0RngSmERopVKehQsSzR98lb4G1BLk/YXTSW4EtCSW8V4FhcfsaJdi4/HLc73qgb0aufYFLCLn95tH3f8CgygWR7Ls/of5yd+BIYBawfRE8q/ZNY7oceB24AuiVkWtLTFx3Em6dPwv0L1I8e+BZ6xrNI56Xxf+5xv+rcnsvoX37ocCn4g/yqAzjWdNzLc6ZpnlW3ZFeWetHnVV3LbnGs8ffKW+BLgK4WXxvA8YCbcm2HwATahw3kJBxzAU+mKVrXB6aLI8FbgZ2qXFxnAp8N14UDXddV89k+5AY879k7ZmsmwZMiT+a+xYtnj3xLEI8gZGEecE3IRSqJgB7Ve1f+YH7GHAB8Htgn6J5JvtmmuarPFuAJwiFqV2ix/EkhSpW3bVkGs/1+m55CyRBGxwT00xgEjA82SZCk7BZwJgax7cAo3Ny3TmubyOUUBcC3wL+SLhdrVwQaUm2taieyfFtGSWw1PMMYGRcPxy4IS5PJpS4zwK2rVwXOcazx545x3NS4nk+4UH0w8B5hJYqxybXpxrtVg/P5PhM0nwX5323uH4q8O24/tOEjoFTk3TW0mi3er0K0WpI0l6EW+sXgYsJD2AmJbu0EC6Mt+liuksIQwiY2dwGq9ZyPS06LAceB9rN7GxCKWBy9MeSDk1m1llUz8RxuZk9lLHntonnfOA1Sa3ATsCZwJ4WO15ZTG1xOet49tgzccwjntsQ7pgg3K1cCOxnZhcQJo46LfEzMmJDPCtkkeZrnPeJcXOlSvJnZnYtcA6wFbHDmBVoGI7uKMTk9YSWFJeb2S2wcuTIcbEn3jIz65TUHpcXKQwo1buyf0Fc+wJvmdnKCafN7NeSphAu8oXuuU6evQjt63cj3Po/Tag/NkkjzOyf7tljz/3ieX/VzC6q7Ghmv5Q0mfDjlnU79jJ7jo/b7if0Yt89ej4ROzKWJgOokHlGEHsBrlbyMLN5kp5Jti0jVA2lPQPHAf0kTSeUus4pmOvS9BhJIwkliH8Bz7rnOnsuA+ZIehB4wMxmSnoXYe7rhrYL3wg9d6yc9+TYUYTzvpDinPeyeLbHbfMlTQO+oDCeVTvhrnpBIz0bQaZVQ5J6pYGu6iiyJNk2jFC6ShkC7Epol7unmd1XUNc+ko4htHa618yON7O33XP9PM3sXDObGZcXmNnXLQwb4Z7r4SmpTdJhhObY95jZCTEzc8/1O++zCdVWDwK/MLODLXQeKxWZZQSSTgd+pzAG9yEQ6iTT3njJcjvwaFz3cUkDCcP2DjOzhs/luSGuhAHE7ibUEV/lnht87rdqpFuzeRJa2twPvL/I570snpK2NrPFZjbDzK5vpGcjaXhGIGmQQnXOh4EvEh66HBdvn1c+UJE02lY9XBkFdEi6EziC8Dxgnpm9WQLXVjN7oZGuTeT5CTKob20iz8MJLVkWV1fBuOd6n/fljfLLFGt806tWwkiBlTbf7cANxLbrhKfs04EHgK2B7YHXCMPKZtaDtUyu7ume7tk8npnEogHBbSN0rNkuWTcgWW4hdBgZET+PB06r+hsnZHQhlMLVPd3TPZvHM49XvQO9G6F9+vPAzTX2GQn8psa23vX02Rhc3dM93bN5PPN61fsZwYuErv47E4YI3h9AUmvyFH4oYY5WJO2pOGm3JFkDW62U2NU93dM9m8czF+qaEZjZc8CtZvYyoa6tMutOJ2GYCIB3A70lXUronl05NrNejWVydU/3dM/m8cyLurcaslWtUKYDSyVNiutXxJx3X+BDwGIz29vMZtXboaeUxdU93dM9m8czFxpZ70RolvVoXB4d3z9K8rCmKK+yuLqne7pn83hmFo8MAn4X8BZhZL4heX/hjcHVPd3TPZvHM4tXwzqUKUzOfTHhSfzpZnagJQOdFYmyuLpnfXHP+uKe5aXSkaIxf1w6gDCOzVvd7pwzZXF1z/rinvXFPctJQzMCx3Ecp/gUYmIax3EcJz88I3Acx2lyPCNwHMdpcjwjcBzHaXI8I3Acx2lyPCNwnG6Q1ClptqSnJM2RdLaS2atqHDNM0lFZOTrOhuAZgeN0z5tmNsbMdiGMUX8AcF43xwwDPCNwSoH3I3CcbpD0hpkNSD63A38ChgA7ADcC/ePm083sYUl/IPRcXQD8mDAE8iXAWKAPcJWZXZvZl3CcteAZgeN0Q3VGENe9AuwEvA6sMLOlkkYQJj3ZQ9JYYIqZHRz3PxnYwswultQHeAg4wswWZPplHKcL2vIWcJyS0wuYJmkM0Al01Nhvf2C0pMPj54HACMIdg+PkimcEjrOOxKqhTuAFwrOC5wmTmrQAS2sdBpxhZndnIuk464A/LHacdUDS5sA1wDQL9aoDgefMbAVwDNAad30d2CQ59G7gVEm94t/pkNQfxykAfkfgON3TT9JsQjXQcsLD4cvjtu8BMyQdSxjffklcPxfolDSHMDXiFYSWRI/H2bAWAYdm9QUcZ234w2LHcZwmx6uGHMdxmhzPCBzHcZoczwgcx3GaHM8IHMdxmhzPCBzHcZoczwgcx3GaHM8IHMdxmhzPCBzHcZqc/wPvI/M2XEER9gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 4389, {'val_loss': '-1.314606785774231', 'val/kl': '0.0010940443025901914', 'val/mse': '0.006733658257871866', 'val/std': '0.06799031049013138'}\n",
+      "\n",
+      "None\n"
      ]
     }
    ],
@@ -629,14 +758,13 @@
     " 'latent_enc_self_attn_type': 'uniform',\n",
     " 'dropout': 0.2,\n",
     " 'hidden_dim': 128*2,\n",
-    " 'latent_dim': 128*2,\n",
-    " 'attention_dropout': 0.2,\n",
+    " 'latent_dim': 128*2, \n",
     " 'use_deterministic_path': False,\n",
-    " 'use_rnn': True\n",
+    " 'use_rnn': True,\n",
     "})\n",
     "trial = optuna.trial.FixedTrial(params)\n",
     "trial = add_sugg(trial)\n",
-    "trial.number = 3\n",
+    "trial.number = 31\n",
     "\n",
     "checkpoint_callback = pl.callbacks.ModelCheckpoint(\n",
     "    os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode=\"min\")\n",
@@ -676,11 +804,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 29,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-15T09:29:14.568668Z",
-     "start_time": "2020-02-15T09:29:14.505166Z"
+     "end_time": "2020-02-16T11:15:25.889159Z",
+     "start_time": "2020-02-16T11:15:25.836460Z"
     },
     "scrolled": true
    },
@@ -714,11 +842,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 30,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:04:40.304317Z",
-     "start_time": "2020-02-16T08:04:40.252530Z"
+     "end_time": "2020-02-16T11:15:25.941789Z",
+     "start_time": "2020-02-16T11:15:25.891856Z"
     }
    },
    "outputs": [],
@@ -740,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-16T08:09:11.284347Z",
@@ -748,27 +876,16 @@
     },
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LSTM(17, 256, num_layers=2, batch_first=True, dropout=0.2)"
-      ]
-     },
-     "execution_count": 171,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 31,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:12:10.678207Z",
-     "start_time": "2020-02-16T08:12:10.593955Z"
+     "end_time": "2020-02-16T11:15:26.016952Z",
+     "start_time": "2020-02-16T11:15:25.944670Z"
     }
    },
    "outputs": [],
@@ -796,11 +913,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 32,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:12:11.095623Z",
-     "start_time": "2020-02-16T08:12:10.841864Z"
+     "end_time": "2020-02-16T11:15:26.343443Z",
+     "start_time": "2020-02-16T11:15:26.019286Z"
     }
    },
    "outputs": [],
@@ -824,11 +941,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 33,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:12:29.839499Z",
-     "start_time": "2020-02-16T08:12:29.783715Z"
+     "end_time": "2020-02-16T11:15:26.396973Z",
+     "start_time": "2020-02-16T11:15:26.345706Z"
     }
    },
    "outputs": [
@@ -836,9 +953,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "predicted std 0.08 \n",
-      "std of std 0.01 \n",
-      "mcdropout of mean 0.04\n"
+      "predicted std 0.09 \n",
+      "std of std 0.02 \n",
+      "mcdropout of mean 0.03\n"
      ]
     }
    ],
@@ -854,17 +971,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 34,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:12:11.518700Z",
-     "start_time": "2020-02-16T08:12:11.157247Z"
+     "end_time": "2020-02-16T11:15:26.745949Z",
+     "start_time": "2020-02-16T11:15:26.399277Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -890,17 +1007,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 35,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:12:11.961152Z",
-     "start_time": "2020-02-16T08:12:11.521075Z"
+     "end_time": "2020-02-16T11:15:27.190055Z",
+     "start_time": "2020-02-16T11:15:26.749414Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -940,11 +1057,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 253,
+   "execution_count": 36,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:22:41.740593Z",
-     "start_time": "2020-02-16T08:22:41.686313Z"
+     "end_time": "2020-02-16T11:15:27.240181Z",
+     "start_time": "2020-02-16T11:15:27.192477Z"
     }
    },
    "outputs": [],
@@ -954,11 +1071,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": 37,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:22:41.979140Z",
-     "start_time": "2020-02-16T08:22:41.918436Z"
+     "end_time": "2020-02-16T11:15:27.302421Z",
+     "start_time": "2020-02-16T11:15:27.242730Z"
     }
    },
    "outputs": [],
@@ -1013,33 +1130,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T11:15:27.352098Z",
+     "start_time": "2020-02-16T11:15:27.304762Z"
+    }
+   },
    "outputs": [],
    "source": [
     "n_mcdropout=10\n",
-    "n_steps=100"
+    "n_steps=300"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 39,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:23:05.222197Z",
-     "start_time": "2020-02-16T08:22:42.120345Z"
+     "end_time": "2020-02-16T11:17:04.923091Z",
+     "start_time": "2020-02-16T11:15:27.354467Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f05a4efde45043ce911ba6600c305b9e",
+       "model_id": "9134c8055b234ae6a9a6351ebf1b605b",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
+       "HBox(children=(FloatProgress(value=0.0, max=300.0), HTML(value='')))"
       ]
      },
      "metadata": {},
@@ -1068,11 +1190,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 40,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-16T08:23:05.279301Z",
-     "start_time": "2020-02-16T08:23:05.224402Z"
+     "end_time": "2020-02-16T11:17:04.986099Z",
+     "start_time": "2020-02-16T11:17:04.925659Z"
     }
    },
    "outputs": [
@@ -1081,8 +1203,8 @@
      "output_type": "stream",
      "text": [
       "Lower is better, validation loss\n",
-      "MCDropout: -1.40\n",
-      "Inference: -0.64\n"
+      "MCDropout: -1.48\n",
+      "Inference: -1.31\n"
      ]
     }
    ],
@@ -1094,14 +1216,28 @@
     "print(f\"Inference: {loss:2.2f}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that we get a better (lower) loss when we not only use the models estimate of the loss, but we take a step back and add an estimate of how unsure the model behaves. \n",
+    "\n",
+    "There are remaining sources of uncertainty that are not reflected in how the model behaviour varies with dropout.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "We can see that we get a better (lower) loss when we not only use the models estimate of the loss, but we take a step back and add an "
-   ]
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/smartmeters-ANP-RNN.ipynb b/smartmeters-ANP-RNN.ipynb
index c51da5c..a9b1cec 100644
--- a/smartmeters-ANP-RNN.ipynb
+++ b/smartmeters-ANP-RNN.ipynb
@@ -2777,6 +2777,3138 @@
      "text": [
       "step 15364, {'val_loss': '-1.293749213218689', 'val/kl': '0.0004639705002773553', 'val/mse': '0.00423781294375658', 'val/std': '0.04431043565273285'}\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 17559, {'val_loss': '-1.3339027166366577', 'val/kl': '0.00040979773621074855', 'val/mse': '0.0038757165893912315', 'val/std': '0.04089698940515518'}\n",
+      "Epoch     7: reducing learning rate of group 0 to 4.6758e-04.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 19754, {'val_loss': '-1.4316049814224243', 'val/kl': '0.00032472642487846315', 'val/mse': '0.003270149929448962', 'val/std': '0.0393795520067215'}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 21949, {'val_loss': '-1.4260458946228027', 'val/kl': '0.0002844082482624799', 'val/mse': '0.0033221307676285505', 'val/std': '0.03993909806013107'}\n",
+      "\n",
+      "logger.metrics [{'val_loss': -1.4260458946228027, 'val/kl': 0.0002844082482624799, 'val/mse': 0.0033221307676285505, 'val/std': 0.03993909806013107, 'epoch': 9}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 13:49:52,848] Finished trial#42 resulted in value: -1.4260458946228027. Current best value is -1.5311908721923828 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.009979117958707866, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 43 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004368391916372206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                                 Name                  Type  \\\n",
+      "0                                               model           LatentModel   \n",
+      "1                               model._latent_encoder         LatentEncoder   \n",
+      "2                  model._latent_encoder._input_layer                Linear   \n",
+      "3                      model._latent_encoder._encoder            ModuleList   \n",
+      "4                    model._latent_encoder._encoder.0         NPBlockRelu2d   \n",
+      "5             model._latent_encoder._encoder.0.linear                Linear   \n",
+      "6                model._latent_encoder._encoder.0.act                  ReLU   \n",
+      "7            model._latent_encoder._encoder.0.dropout             Dropout2d   \n",
+      "8               model._latent_encoder._encoder.0.norm           BatchNorm2d   \n",
+      "9            model._latent_encoder._penultimate_layer                Linear   \n",
+      "10                        model._latent_encoder._mean                Linear   \n",
+      "11                     model._latent_encoder._log_var                Linear   \n",
+      "12                       model._deterministic_encoder  DeterministicEncoder   \n",
+      "13          model._deterministic_encoder._input_layer                Linear   \n",
+      "14            model._deterministic_encoder._d_encoder            ModuleList   \n",
+      "15          model._deterministic_encoder._d_encoder.0         NPBlockRelu2d   \n",
+      "16   model._deterministic_encoder._d_encoder.0.linear                Linear   \n",
+      "17      model._deterministic_encoder._d_encoder.0.act                  ReLU   \n",
+      "18  model._deterministic_encoder._d_encoder.0.dropout             Dropout2d   \n",
+      "19     model._deterministic_encoder._d_encoder.0.norm           BatchNorm2d   \n",
+      "20          model._deterministic_encoder._d_encoder.1         NPBlockRelu2d   \n",
+      "21   model._deterministic_encoder._d_encoder.1.linear                Linear   \n",
+      "22      model._deterministic_encoder._d_encoder.1.act                  ReLU   \n",
+      "23  model._deterministic_encoder._d_encoder.1.dropout             Dropout2d   \n",
+      "24     model._deterministic_encoder._d_encoder.1.norm           BatchNorm2d   \n",
+      "25      model._deterministic_encoder._cross_attention             Attention   \n",
+      "26  model._deterministic_encoder._cross_attention....              BatchMLP   \n",
+      "27  model._deterministic_encoder._cross_attention....         NPBlockRelu2d   \n",
+      "28  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "29  model._deterministic_encoder._cross_attention....                  ReLU   \n",
+      "30  model._deterministic_encoder._cross_attention....             Dropout2d   \n",
+      "31  model._deterministic_encoder._cross_attention....            Sequential   \n",
+      "32  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "33  model._deterministic_encoder._cross_attention....              BatchMLP   \n",
+      "34  model._deterministic_encoder._cross_attention....         NPBlockRelu2d   \n",
+      "35  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "36  model._deterministic_encoder._cross_attention....                  ReLU   \n",
+      "37  model._deterministic_encoder._cross_attention....             Dropout2d   \n",
+      "38  model._deterministic_encoder._cross_attention....            Sequential   \n",
+      "39  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "40   model._deterministic_encoder._cross_attention._W    MultiheadAttention   \n",
+      "41  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "42                                     model._decoder               Decoder   \n",
+      "43                   model._decoder._target_transform                Linear   \n",
+      "44                            model._decoder._decoder            ModuleList   \n",
+      "45                          model._decoder._decoder.0         NPBlockRelu2d   \n",
+      "46                   model._decoder._decoder.0.linear                Linear   \n",
+      "47                      model._decoder._decoder.0.act                  ReLU   \n",
+      "48                  model._decoder._decoder.0.dropout             Dropout2d   \n",
+      "49                     model._decoder._decoder.0.norm           BatchNorm2d   \n",
+      "50                               model._decoder._mean                Linear   \n",
+      "51                                model._decoder._std                Linear   \n",
+      "\n",
+      "   Params  \n",
+      "0    27 K  \n",
+      "1     6 K  \n",
+      "2   608    \n",
+      "3     1 K  \n",
+      "4     1 K  \n",
+      "5     1 K  \n",
+      "6     0    \n",
+      "7     0    \n",
+      "8    64    \n",
+      "9     1 K  \n",
+      "10    2 K  \n",
+      "11    2 K  \n",
+      "12   10 K  \n",
+      "13  608    \n",
+      "14    2 K  \n",
+      "15    1 K  \n",
+      "16    1 K  \n",
+      "17    0    \n",
+      "18    0    \n",
+      "19   64    \n",
+      "20    1 K  \n",
+      "21    1 K  \n",
+      "22    0    \n",
+      "23    0    \n",
+      "24   64    \n",
+      "25    7 K  \n",
+      "26    1 K  \n",
+      "27  544    \n",
+      "28  544    \n",
+      "29    0    \n",
+      "30    0    \n",
+      "31    0    \n",
+      "32    1 K  \n",
+      "33    1 K  \n",
+      "34  544    \n",
+      "35  544    \n",
+      "36    0    \n",
+      "37    0    \n",
+      "38    0    \n",
+      "39    1 K  \n",
+      "40    4 K  \n",
+      "41    1 K  \n",
+      "42   10 K  \n",
+      "43  576    \n",
+      "44    9 K  \n",
+      "45    9 K  \n",
+      "46    9 K  \n",
+      "47    0    \n",
+      "48    0    \n",
+      "49  192    \n",
+      "50   97    \n",
+      "51   97    \n"
+     ]
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+    {
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 0, {'val_loss': '1.6595051288604736', 'val/kl': '0.5012072920799255', 'val/mse': '0.2947925627231598', 'val/std': '1.1311085224151611'}\n",
+      "\r"
+     ]
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+    {
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+       "HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), max=1.0), HTML(value='')), …"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 2194, {'val_loss': '-1.0242871046066284', 'val/kl': '0.0017947274027392268', 'val/mse': '0.007185660302639008', 'val/std': '0.09871241450309753'}\n"
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+    {
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+     "output_type": "stream",
+     "text": [
+      "step 4389, {'val_loss': '-1.2936391830444336', 'val/kl': '0.0012684506364166737', 'val/mse': '0.006113472394645214', 'val/std': '0.0684652253985405'}\n"
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+      ]
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 6584, {'val_loss': '-1.3037265539169312', 'val/kl': '0.0007404569769278169', 'val/mse': '0.004260512068867683', 'val/std': '0.06514627486467361'}\n"
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 8779, {'val_loss': '-1.4981956481933594', 'val/kl': '0.001371236750856042', 'val/mse': '0.0036893989890813828', 'val/std': '0.06575974076986313'}\n"
+     ]
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+    {
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 10974, {'val_loss': '-1.1481499671936035', 'val/kl': '0.004259718116372824', 'val/mse': '0.0038308214861899614', 'val/std': '0.046711280941963196'}\n"
+     ]
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+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 13169, {'val_loss': '-1.4938817024230957', 'val/kl': '0.0014279276365414262', 'val/mse': '0.0036505504976958036', 'val/std': '0.05014738067984581'}\n"
+     ]
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+    {
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 15364, {'val_loss': '-1.3433947563171387', 'val/kl': '0.001294697285629809', 'val/mse': '0.004627563524991274', 'val/std': '0.060922276228666306'}\n",
+      "Epoch     6: reducing learning rate of group 0 to 4.3684e-04.\n"
+     ]
+    },
+    {
+     "data": {
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 17559, {'val_loss': '-1.573096513748169', 'val/kl': '0.0014650896191596985', 'val/mse': '0.0032621892169117928', 'val/std': '0.04872477054595947'}\n"
+     ]
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+    {
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 19754, {'val_loss': '-1.568063497543335', 'val/kl': '0.0015724594704806805', 'val/mse': '0.003314936999231577', 'val/std': '0.04916192963719368'}\n"
+     ]
+    },
+    {
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+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 21949, {'val_loss': '-1.5752214193344116', 'val/kl': '0.001221427577547729', 'val/mse': '0.0032312502153217793', 'val/std': '0.04751383513212204'}\n",
+      "\n",
+      "logger.metrics [{'val_loss': -1.5752214193344116, 'val/kl': 0.001221427577547729, 'val/mse': 0.0032312502153217793, 'val/std': 0.04751383513212204, 'epoch': 9}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 14:10:01,348] Finished trial#43 resulted in value: -1.5752214193344116. Current best value is -1.5752214193344116 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004368391916372206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 44 params {'attention_dropout': 0, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'ptmultihead', 'dropout': 0.5, 'grad_clip': 40, 'hidden_dim': 64, 'latent_dim': 32, 'latent_enc_self_attn_type': 'multihead', 'learning_rate': 0.0039055042332039043, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                                 Name                  Type  \\\n",
+      "0                                               model           LatentModel   \n",
+      "1                               model._latent_encoder         LatentEncoder   \n",
+      "2                  model._latent_encoder._input_layer                Linear   \n",
+      "3                      model._latent_encoder._encoder            ModuleList   \n",
+      "4                    model._latent_encoder._encoder.0         NPBlockRelu2d   \n",
+      "5             model._latent_encoder._encoder.0.linear                Linear   \n",
+      "6                model._latent_encoder._encoder.0.act                  ReLU   \n",
+      "7            model._latent_encoder._encoder.0.dropout             Dropout2d   \n",
+      "8               model._latent_encoder._encoder.0.norm           BatchNorm2d   \n",
+      "9            model._latent_encoder._penultimate_layer                Linear   \n",
+      "10                        model._latent_encoder._mean                Linear   \n",
+      "11                     model._latent_encoder._log_var                Linear   \n",
+      "12                       model._deterministic_encoder  DeterministicEncoder   \n",
+      "13          model._deterministic_encoder._input_layer                Linear   \n",
+      "14            model._deterministic_encoder._d_encoder            ModuleList   \n",
+      "15          model._deterministic_encoder._d_encoder.0         NPBlockRelu2d   \n",
+      "16   model._deterministic_encoder._d_encoder.0.linear                Linear   \n",
+      "17      model._deterministic_encoder._d_encoder.0.act                  ReLU   \n",
+      "18  model._deterministic_encoder._d_encoder.0.dropout             Dropout2d   \n",
+      "19     model._deterministic_encoder._d_encoder.0.norm           BatchNorm2d   \n",
+      "20          model._deterministic_encoder._d_encoder.1         NPBlockRelu2d   \n",
+      "21   model._deterministic_encoder._d_encoder.1.linear                Linear   \n",
+      "22      model._deterministic_encoder._d_encoder.1.act                  ReLU   \n",
+      "23  model._deterministic_encoder._d_encoder.1.dropout             Dropout2d   \n",
+      "24     model._deterministic_encoder._d_encoder.1.norm           BatchNorm2d   \n",
+      "25      model._deterministic_encoder._cross_attention             Attention   \n",
+      "26  model._deterministic_encoder._cross_attention....              BatchMLP   \n",
+      "27  model._deterministic_encoder._cross_attention....         NPBlockRelu2d   \n",
+      "28  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "29  model._deterministic_encoder._cross_attention....                  ReLU   \n",
+      "30  model._deterministic_encoder._cross_attention....             Dropout2d   \n",
+      "31  model._deterministic_encoder._cross_attention....            Sequential   \n",
+      "32  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "33  model._deterministic_encoder._cross_attention....              BatchMLP   \n",
+      "34  model._deterministic_encoder._cross_attention....         NPBlockRelu2d   \n",
+      "35  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "36  model._deterministic_encoder._cross_attention....                  ReLU   \n",
+      "37  model._deterministic_encoder._cross_attention....             Dropout2d   \n",
+      "38  model._deterministic_encoder._cross_attention....            Sequential   \n",
+      "39  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "40   model._deterministic_encoder._cross_attention._W    MultiheadAttention   \n",
+      "41  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "42                                     model._decoder               Decoder   \n",
+      "43                   model._decoder._target_transform                Linear   \n",
+      "44                            model._decoder._decoder            ModuleList   \n",
+      "45                          model._decoder._decoder.0         NPBlockRelu2d   \n",
+      "46                   model._decoder._decoder.0.linear                Linear   \n",
+      "47                      model._decoder._decoder.0.act                  ReLU   \n",
+      "48                  model._decoder._decoder.0.dropout             Dropout2d   \n",
+      "49                     model._decoder._decoder.0.norm           BatchNorm2d   \n",
+      "50                               model._decoder._mean                Linear   \n",
+      "51                                model._decoder._std                Linear   \n",
+      "\n",
+      "   Params  \n",
+      "0    61 K  \n",
+      "1    13 K  \n",
+      "2     1 K  \n",
+      "3     4 K  \n",
+      "4     4 K  \n",
+      "5     4 K  \n",
+      "6     0    \n",
+      "7     0    \n",
+      "8   128    \n",
+      "9     4 K  \n",
+      "10    2 K  \n",
+      "11    2 K  \n",
+      "12   36 K  \n",
+      "13    1 K  \n",
+      "14    8 K  \n",
+      "15    4 K  \n",
+      "16    4 K  \n",
+      "17    0    \n",
+      "18    0    \n",
+      "19  128    \n",
+      "20    4 K  \n",
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+      "23    0    \n",
+      "24  128    \n",
+      "25   26 K  \n",
+      "26    5 K  \n",
+      "27    1 K  \n",
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+      "29    0    \n",
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+      "31    0    \n",
+      "32    4 K  \n",
+      "33    5 K  \n",
+      "34    1 K  \n",
+      "35    1 K  \n",
+      "36    0    \n",
+      "37    0    \n",
+      "38    0    \n",
+      "39    4 K  \n",
+      "40   16 K  \n",
+      "41    4 K  \n",
+      "42   10 K  \n",
+      "43    1 K  \n",
+      "44    9 K  \n",
+      "45    9 K  \n",
+      "46    9 K  \n",
+      "47    0    \n",
+      "48    0    \n",
+      "49  192    \n",
+      "50   97    \n",
+      "51   97    \n"
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+      "step 0, {'val_loss': '1.720916748046875', 'val/kl': '0.49156513810157776', 'val/mse': '0.2860490083694458', 'val/std': '1.2384977340698242'}\n",
+      "\r"
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+    {
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+     "text": [
+      "step 2194, {'val_loss': '-0.958224356174469', 'val/kl': '0.0005997503758408129', 'val/mse': '0.008417556993663311', 'val/std': '0.11981521546840668'}\n"
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+    {
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+     "output_type": "stream",
+     "text": [
+      "step 4389, {'val_loss': '-1.047938585281372', 'val/kl': '0.0007705810130573809', 'val/mse': '0.00699149863794446', 'val/std': '0.1092245802283287'}\n"
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 6584, {'val_loss': '-0.970568835735321', 'val/kl': '0.001158003113232553', 'val/mse': '0.0072646429762244225', 'val/std': '0.11499355733394623'}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 14:16:11,306] Setting status of trial#44 as TrialState.PRUNED. Trial was pruned at epoch 2.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 45 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'multihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.0019041317749200822, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 4, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                   Name           Type Params\n",
+      "0                                 model    LatentModel   58 K\n",
+      "1                 model._latent_encoder  LatentEncoder    6 K\n",
+      "2    model._latent_encoder._input_layer         Linear  608  \n",
+      "3        model._latent_encoder._encoder     ModuleList    1 K\n",
+      "4      model._latent_encoder._encoder.0  NPBlockRelu2d    1 K\n",
+      "..                                  ...            ...    ...\n",
+      "108       model._decoder._decoder.0.act           ReLU    0  \n",
+      "109   model._decoder._decoder.0.dropout      Dropout2d    0  \n",
+      "110      model._decoder._decoder.0.norm    BatchNorm2d  192  \n",
+      "111                model._decoder._mean         Linear   97  \n",
+      "112                 model._decoder._std         Linear   97  \n",
+      "\n",
+      "[113 rows x 3 columns]\n"
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+      "step 0, {'val_loss': '1.687723994255066', 'val/kl': '0.5016866326332092', 'val/mse': '0.2728389501571655', 'val/std': '1.18678879737854'}\n",
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+     "text": [
+      "step 2194, {'val_loss': '-1.02152419090271', 'val/kl': '0.010310388170182705', 'val/mse': '0.007663198281079531', 'val/std': '0.11175099015235901'}\n"
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+      "step 4389, {'val_loss': '-1.4133368730545044', 'val/kl': '0.0045659178867936134', 'val/mse': '0.004236111883074045', 'val/std': '0.0618896484375'}\n"
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+      "step 6584, {'val_loss': '-1.3207731246948242', 'val/kl': '0.004010752309113741', 'val/mse': '0.004357465542852879', 'val/std': '0.057210419327020645'}\n"
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+     "text": [
+      "step 8779, {'val_loss': '-1.3673999309539795', 'val/kl': '0.001945743802934885', 'val/mse': '0.00422749063000083', 'val/std': '0.06024956330657005'}\n"
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+     "output_type": "stream",
+     "text": [
+      "step 10974, {'val_loss': '-1.1022073030471802', 'val/kl': '0.0028823784086853266', 'val/mse': '0.004391775466501713', 'val/std': '0.055085066705942154'}\n",
+      "Epoch     4: reducing learning rate of group 0 to 1.9041e-04.\n"
+     ]
+    },
+    {
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+      "step 13169, {'val_loss': '-0.7505188584327698', 'val/kl': '0.003391333855688572', 'val/mse': '0.004430678673088551', 'val/std': '0.051771990954875946'}\n"
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+      "step 15364, {'val_loss': '-1.0067957639694214', 'val/kl': '0.003156122751533985', 'val/mse': '0.004495357163250446', 'val/std': '0.05695757642388344'}\n"
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+     "text": [
+      "step 17559, {'val_loss': '-1.3625431060791016', 'val/kl': '0.0014195841504260898', 'val/mse': '0.0037416103295981884', 'val/std': '0.0520796924829483'}\n",
+      "Epoch     7: reducing learning rate of group 0 to 1.9041e-05.\n"
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+    {
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+     "text": [
+      "step 19754, {'val_loss': '49.18473815917969', 'val/kl': '0.032739244401454926', 'val/mse': '0.020993946120142937', 'val/std': '0.04838518053293228'}\n"
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+     "text": [
+      "step 21949, {'val_loss': '-1.3830543756484985', 'val/kl': '0.0012432830408215523', 'val/mse': '0.003595026209950447', 'val/std': '0.05097007006406784'}\n",
+      "\n",
+      "logger.metrics [{'val_loss': -1.3830543756484985, 'val/kl': 0.0012432830408215523, 'val/mse': 0.003595026209950447, 'val/std': 0.05097007006406784, 'epoch': 9}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 14:36:15,872] Finished trial#45 resulted in value: -1.3830543756484985. Current best value is -1.5752214193344116 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004368391916372206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 46 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 8, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.00976440374071206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': False, 'use_rnn': True, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                                 Name                  Type  \\\n",
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+      "1                                         model._lstm                  LSTM   \n",
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+      "5                    model._latent_encoder._encoder.0         NPBlockRelu2d   \n",
+      "6             model._latent_encoder._encoder.0.linear                Linear   \n",
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+      "12                     model._latent_encoder._log_var                Linear   \n",
+      "13                       model._deterministic_encoder  DeterministicEncoder   \n",
+      "14          model._deterministic_encoder._input_layer                Linear   \n",
+      "15            model._deterministic_encoder._d_encoder            ModuleList   \n",
+      "16          model._deterministic_encoder._d_encoder.0         NPBlockRelu2d   \n",
+      "17   model._deterministic_encoder._d_encoder.0.linear                Linear   \n",
+      "18      model._deterministic_encoder._d_encoder.0.act                  ReLU   \n",
+      "19  model._deterministic_encoder._d_encoder.0.dropout             Dropout2d   \n",
+      "20     model._deterministic_encoder._d_encoder.0.norm           BatchNorm2d   \n",
+      "21          model._deterministic_encoder._d_encoder.1         NPBlockRelu2d   \n",
+      "22   model._deterministic_encoder._d_encoder.1.linear                Linear   \n",
+      "23      model._deterministic_encoder._d_encoder.1.act                  ReLU   \n",
+      "24  model._deterministic_encoder._d_encoder.1.dropout             Dropout2d   \n",
+      "25     model._deterministic_encoder._d_encoder.1.norm           BatchNorm2d   \n",
+      "26      model._deterministic_encoder._cross_attention             Attention   \n",
+      "27  model._deterministic_encoder._cross_attention....              BatchMLP   \n",
+      "28  model._deterministic_encoder._cross_attention....         NPBlockRelu2d   \n",
+      "29  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "30  model._deterministic_encoder._cross_attention....                  ReLU   \n",
+      "31  model._deterministic_encoder._cross_attention....             Dropout2d   \n",
+      "32  model._deterministic_encoder._cross_attention....            Sequential   \n",
+      "33  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "34  model._deterministic_encoder._cross_attention....              BatchMLP   \n",
+      "35  model._deterministic_encoder._cross_attention....         NPBlockRelu2d   \n",
+      "36  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "37  model._deterministic_encoder._cross_attention....                  ReLU   \n",
+      "38  model._deterministic_encoder._cross_attention....             Dropout2d   \n",
+      "39  model._deterministic_encoder._cross_attention....            Sequential   \n",
+      "40  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "41   model._deterministic_encoder._cross_attention._W    MultiheadAttention   \n",
+      "42  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "43                                     model._decoder               Decoder   \n",
+      "44                   model._decoder._target_transform                Linear   \n",
+      "45                            model._decoder._decoder            ModuleList   \n",
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+      "47                   model._decoder._decoder.0.linear                Linear   \n",
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+      "49                  model._decoder._decoder.0.dropout             Dropout2d   \n",
+      "50                     model._decoder._decoder.0.norm           BatchNorm2d   \n",
+      "51                               model._decoder._mean                Linear   \n",
+      "52                                model._decoder._std                Linear   \n",
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+      "Epoch     7: reducing learning rate of group 0 to 9.7644e-05.\n"
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+      "step 21949, {'val_loss': '-1.116992712020874', 'val/kl': '0.0013356534764170647', 'val/mse': '0.00445883022621274', 'val/std': '0.045566070824861526'}\n",
+      "\n",
+      "logger.metrics [{'val_loss': -1.116992712020874, 'val/kl': 0.0013356534764170647, 'val/mse': 0.00445883022621274, 'val/std': 0.045566070824861526, 'epoch': 9}]\n"
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+     "text": [
+      "[I 2020-02-16 14:57:35,834] Finished trial#46 resulted in value: -1.116992712020874. Current best value is -1.5752214193344116 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004368391916372206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n"
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+     "text": [
+      "trial 47 params {'attention_dropout': 0, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 64, 'latent_dim': 64, 'latent_enc_self_attn_type': 'multihead', 'learning_rate': 0.005578860461082355, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 4, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
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+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                  Name           Type Params\n",
+      "0                                model    LatentModel  122 K\n",
+      "1                model._latent_encoder  LatentEncoder   17 K\n",
+      "2   model._latent_encoder._input_layer         Linear    1 K\n",
+      "3       model._latent_encoder._encoder     ModuleList    4 K\n",
+      "4     model._latent_encoder._encoder.0  NPBlockRelu2d    4 K\n",
+      "..                                 ...            ...    ...\n",
+      "62       model._decoder._decoder.3.act           ReLU    0  \n",
+      "63   model._decoder._decoder.3.dropout      Dropout2d    0  \n",
+      "64      model._decoder._decoder.3.norm    BatchNorm2d  256  \n",
+      "65                model._decoder._mean         Linear  129  \n",
+      "66                 model._decoder._std         Linear  129  \n",
+      "\n",
+      "[67 rows x 3 columns]\n"
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+      "step 21949, {'val_loss': '-1.0883088111877441', 'val/kl': '0.0007551736780442297', 'val/mse': '0.00359739875420928', 'val/std': '0.034629255533218384'}\n",
+      "\n",
+      "logger.metrics [{'val_loss': -1.0883088111877441, 'val/kl': 0.0007551736780442297, 'val/mse': 0.00359739875420928, 'val/std': 0.034629255533218384, 'epoch': 9}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 15:18:42,703] Finished trial#47 resulted in value: -1.0883088111877441. Current best value is -1.5752214193344116 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004368391916372206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 48 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'multihead', 'det_enc_self_attn_type': 'ptmultihead', 'dropout': 0.2, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 16, 'latent_enc_self_attn_type': 'uniform', 'learning_rate': 0.0007489973570107983, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 8, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
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+      "INFO:root:\n",
+      "                                   Name           Type Params\n",
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+     "text": [
+      "step 2194, {'val_loss': '-0.8427137732505798', 'val/kl': '0.0019351966911926866', 'val/mse': '0.011691011488437653', 'val/std': '0.12049313634634018'}\n"
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+    },
+    {
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+     "output_type": "stream",
+     "text": [
+      "step 4389, {'val_loss': '-0.8563151955604553', 'val/kl': '0.001034051994793117', 'val/mse': '0.010801645927131176', 'val/std': '0.12607762217521667'}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 15:23:24,637] Setting status of trial#48 as TrialState.PRUNED. Trial was pruned at epoch 1.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 49 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0.5, 'grad_clip': 40, 'hidden_dim': 16, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.003667694327569568, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 8, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
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+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                  Name           Type Params\n",
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+      "1                model._latent_encoder  LatentEncoder    5 K\n",
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+      "step 0, {'val_loss': '1.5659270286560059', 'val/kl': '0.5043103098869324', 'val/mse': '0.2756112515926361', 'val/std': '1.0073413848876953'}\n",
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+      "step 2194, {'val_loss': '-0.5222434997558594', 'val/kl': '0.0002722125791478902', 'val/mse': '0.018109584227204323', 'val/std': '0.19142760336399078'}\n"
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+     "text": [
+      "step 4389, {'val_loss': '-0.42250725626945496', 'val/kl': '5.115848398418166e-05', 'val/mse': '0.02268870547413826', 'val/std': '0.20704622566699982'}\n"
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 15:29:34,381] Setting status of trial#49 as TrialState.PRUNED. Trial was pruned at epoch 1.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 50 params {'attention_dropout': 0.2, 'attention_layers': 3, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 3.1088917887340893e-05, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 4, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                  Name           Type Params\n",
+      "0                                model    LatentModel   58 K\n",
+      "1                model._latent_encoder  LatentEncoder    8 K\n",
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+      "step 0, {'val_loss': '1.4470834732055664', 'val/kl': '0.49991416931152344', 'val/mse': '0.18826858699321747', 'val/std': '0.9204845428466797'}\n",
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+      "step 2194, {'val_loss': '0.7972294092178345', 'val/kl': '0.3388958275318146', 'val/mse': '0.06819967180490494', 'val/std': '0.39780858159065247'}\n"
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+      "step 4389, {'val_loss': '0.12696629762649536', 'val/kl': '0.14482451975345612', 'val/mse': '0.044183190912008286', 'val/std': '0.2921614944934845'}\n"
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+     "text": [
+      "[I 2020-02-16 15:33:53,417] Setting status of trial#50 as TrialState.PRUNED. Trial was pruned at epoch 1.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 51 params {'attention_dropout': 0.2, 'attention_layers': 2, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.007193614773934935, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
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+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                  Name           Type Params\n",
+      "0                                model    LatentModel   93 K\n",
+      "1                model._latent_encoder  LatentEncoder    6 K\n",
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+      "step 10974, {'val_loss': '-1.4931892156600952', 'val/kl': '0.00041607089224271476', 'val/mse': '0.004085092805325985', 'val/std': '0.06542228907346725'}\n"
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+      "step 13169, {'val_loss': '-1.4815890789031982', 'val/kl': '0.0005998624837957323', 'val/mse': '0.0038775834254920483', 'val/std': '0.05528929457068443'}\n",
+      "Epoch     5: reducing learning rate of group 0 to 7.1936e-04.\n"
+     ]
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+    {
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+      "step 15364, {'val_loss': '-1.5160049200057983', 'val/kl': '0.0004562124377116561', 'val/mse': '0.003399617737159133', 'val/std': '0.048332419246435165'}\n"
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+     "text": [
+      "step 17559, {'val_loss': '-1.4910939931869507', 'val/kl': '0.00036651312257163227', 'val/mse': '0.0037599860224872828', 'val/std': '0.04610859602689743'}\n"
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+      "step 19754, {'val_loss': '-1.4462004899978638', 'val/kl': '0.0003672659513540566', 'val/mse': '0.003462857799604535', 'val/std': '0.04291912913322449'}\n",
+      "Epoch     8: reducing learning rate of group 0 to 7.1936e-05.\n"
+     ]
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+    {
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+      "step 21949, {'val_loss': '-1.3219516277313232', 'val/kl': '0.00033365757553838193', 'val/mse': '0.00348706915974617', 'val/std': '0.04272124543786049'}\n",
+      "\n",
+      "logger.metrics [{'val_loss': -1.3219516277313232, 'val/kl': 0.00033365757553838193, 'val/mse': 0.00348706915974617, 'val/std': 0.04272124543786049, 'epoch': 9}]\n"
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+    },
+    {
+     "name": "stderr",
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+     "text": [
+      "[I 2020-02-16 15:56:42,878] Finished trial#51 resulted in value: -1.3219516277313232. Current best value is -1.5752214193344116 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004368391916372206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 52 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.006974889906878952, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                  Name           Type Params\n",
+      "0                                model    LatentModel   94 K\n",
+      "1                model._latent_encoder  LatentEncoder    8 K\n",
+      "2   model._latent_encoder._input_layer         Linear  608  \n",
+      "3       model._latent_encoder._encoder     ModuleList    2 K\n",
+      "4     model._latent_encoder._encoder.0  NPBlockRelu2d    1 K\n",
+      "..                                 ...            ...    ...\n",
+      "87       model._decoder._decoder.7.act           ReLU    0  \n",
+      "88   model._decoder._decoder.7.dropout      Dropout2d    0  \n",
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+      "90                model._decoder._mean         Linear   97  \n",
+      "91                 model._decoder._std         Linear   97  \n",
+      "\n",
+      "[92 rows x 3 columns]\n"
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+      "step 2194, {'val_loss': '-1.3835784196853638', 'val/kl': '0.0008006279822438955', 'val/mse': '0.004557789769023657', 'val/std': '0.063215471804142'}\n"
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+      "step 4389, {'val_loss': '-1.5923731327056885', 'val/kl': '0.000711232831235975', 'val/mse': '0.0032738428562879562', 'val/std': '0.057123053818941116'}\n"
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+      "step 8779, {'val_loss': '-1.3843765258789062', 'val/kl': '0.0006477347924374044', 'val/mse': '0.00456392765045166', 'val/std': '0.053285516798496246'}\n"
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+      "step 10974, {'val_loss': '-1.4790021181106567', 'val/kl': '0.0006617918843403459', 'val/mse': '0.0036568765062838793', 'val/std': '0.054161760956048965'}\n",
+      "Epoch     4: reducing learning rate of group 0 to 6.9749e-04.\n"
+     ]
+    },
+    {
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+      "step 13169, {'val_loss': '-1.6128934621810913', 'val/kl': '0.0004931865842081606', 'val/mse': '0.0029674884863197803', 'val/std': '0.04655424878001213'}\n"
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+      "step 15364, {'val_loss': '-1.5709235668182373', 'val/kl': '0.0004162131517659873', 'val/mse': '0.00316846021451056', 'val/std': '0.04433130845427513'}\n"
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+     "text": [
+      "step 17559, {'val_loss': '-1.5288468599319458', 'val/kl': '0.0004013367579318583', 'val/mse': '0.0031520789489150047', 'val/std': '0.04289095103740692'}\n"
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+     "text": [
+      "step 19754, {'val_loss': '-1.4257675409317017', 'val/kl': '0.0003335531509947032', 'val/mse': '0.0034472807310521603', 'val/std': '0.04128710553050041'}\n",
+      "Epoch     8: reducing learning rate of group 0 to 6.9749e-05.\n"
+     ]
+    },
+    {
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+     "text": [
+      "step 21949, {'val_loss': '-1.5207812786102295', 'val/kl': '0.00031931945704855025', 'val/mse': '0.0032018099445849657', 'val/std': '0.04250483587384224'}\n",
+      "\n",
+      "logger.metrics [{'val_loss': -1.5207812786102295, 'val/kl': 0.00031931945704855025, 'val/mse': 0.0032018099445849657, 'val/std': 0.04250483587384224, 'epoch': 9}]\n"
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+    {
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+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 16:21:09,177] Finished trial#52 resulted in value: -1.5207812786102295. Current best value is -1.5752214193344116 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004368391916372206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 53 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004945595289855884, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                                 Name                  Type  \\\n",
+      "0                                               model           LatentModel   \n",
+      "1                               model._latent_encoder         LatentEncoder   \n",
+      "2                  model._latent_encoder._input_layer                Linear   \n",
+      "3                      model._latent_encoder._encoder            ModuleList   \n",
+      "4                    model._latent_encoder._encoder.0         NPBlockRelu2d   \n",
+      "5             model._latent_encoder._encoder.0.linear                Linear   \n",
+      "6                model._latent_encoder._encoder.0.act                  ReLU   \n",
+      "7            model._latent_encoder._encoder.0.dropout             Dropout2d   \n",
+      "8               model._latent_encoder._encoder.0.norm           BatchNorm2d   \n",
+      "9                    model._latent_encoder._encoder.1         NPBlockRelu2d   \n",
+      "10            model._latent_encoder._encoder.1.linear                Linear   \n",
+      "11               model._latent_encoder._encoder.1.act                  ReLU   \n",
+      "12           model._latent_encoder._encoder.1.dropout             Dropout2d   \n",
+      "13              model._latent_encoder._encoder.1.norm           BatchNorm2d   \n",
+      "14           model._latent_encoder._penultimate_layer                Linear   \n",
+      "15                        model._latent_encoder._mean                Linear   \n",
+      "16                     model._latent_encoder._log_var                Linear   \n",
+      "17                       model._deterministic_encoder  DeterministicEncoder   \n",
+      "18          model._deterministic_encoder._input_layer                Linear   \n",
+      "19            model._deterministic_encoder._d_encoder            ModuleList   \n",
+      "20          model._deterministic_encoder._d_encoder.0         NPBlockRelu2d   \n",
+      "21   model._deterministic_encoder._d_encoder.0.linear                Linear   \n",
+      "22      model._deterministic_encoder._d_encoder.0.act                  ReLU   \n",
+      "23  model._deterministic_encoder._d_encoder.0.dropout             Dropout2d   \n",
+      "24     model._deterministic_encoder._d_encoder.0.norm           BatchNorm2d   \n",
+      "25          model._deterministic_encoder._d_encoder.1         NPBlockRelu2d   \n",
+      "26   model._deterministic_encoder._d_encoder.1.linear                Linear   \n",
+      "27      model._deterministic_encoder._d_encoder.1.act                  ReLU   \n",
+      "28  model._deterministic_encoder._d_encoder.1.dropout             Dropout2d   \n",
+      "29     model._deterministic_encoder._d_encoder.1.norm           BatchNorm2d   \n",
+      "30      model._deterministic_encoder._cross_attention             Attention   \n",
+      "31  model._deterministic_encoder._cross_attention....              BatchMLP   \n",
+      "32  model._deterministic_encoder._cross_attention....         NPBlockRelu2d   \n",
+      "33  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "34  model._deterministic_encoder._cross_attention....                  ReLU   \n",
+      "35  model._deterministic_encoder._cross_attention....             Dropout2d   \n",
+      "36  model._deterministic_encoder._cross_attention....            Sequential   \n",
+      "37  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "38  model._deterministic_encoder._cross_attention....              BatchMLP   \n",
+      "39  model._deterministic_encoder._cross_attention....         NPBlockRelu2d   \n",
+      "40  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "41  model._deterministic_encoder._cross_attention....                  ReLU   \n",
+      "42  model._deterministic_encoder._cross_attention....             Dropout2d   \n",
+      "43  model._deterministic_encoder._cross_attention....            Sequential   \n",
+      "44  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "45   model._deterministic_encoder._cross_attention._W    MultiheadAttention   \n",
+      "46  model._deterministic_encoder._cross_attention....                Linear   \n",
+      "47                                     model._decoder               Decoder   \n",
+      "48                   model._decoder._target_transform                Linear   \n",
+      "49                            model._decoder._decoder            ModuleList   \n",
+      "50                          model._decoder._decoder.0         NPBlockRelu2d   \n",
+      "51                   model._decoder._decoder.0.linear                Linear   \n",
+      "52                      model._decoder._decoder.0.act                  ReLU   \n",
+      "53                  model._decoder._decoder.0.dropout             Dropout2d   \n",
+      "54                     model._decoder._decoder.0.norm           BatchNorm2d   \n",
+      "55                               model._decoder._mean                Linear   \n",
+      "56                                model._decoder._std                Linear   \n",
+      "\n",
+      "   Params  \n",
+      "0    28 K  \n",
+      "1     8 K  \n",
+      "2   608    \n",
+      "3     2 K  \n",
+      "4     1 K  \n",
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+      "6     0    \n",
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+      "16    2 K  \n",
+      "17   10 K  \n",
+      "18  608    \n",
+      "19    2 K  \n",
+      "20    1 K  \n",
+      "21    1 K  \n",
+      "22    0    \n",
+      "23    0    \n",
+      "24   64    \n",
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+      "29   64    \n",
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+      "31    1 K  \n",
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+      "33  544    \n",
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+      "36    0    \n",
+      "37    1 K  \n",
+      "38    1 K  \n",
+      "39  544    \n",
+      "40  544    \n",
+      "41    0    \n",
+      "42    0    \n",
+      "43    0    \n",
+      "44    1 K  \n",
+      "45    4 K  \n",
+      "46    1 K  \n",
+      "47   10 K  \n",
+      "48  576    \n",
+      "49    9 K  \n",
+      "50    9 K  \n",
+      "51    9 K  \n",
+      "52    0    \n",
+      "53    0    \n",
+      "54  192    \n",
+      "55   97    \n",
+      "56   97    \n"
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+      "\r"
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+     "text": [
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+     "text": [
+      "step 8779, {'val_loss': '-1.477478265762329', 'val/kl': '0.0007185991271398962', 'val/mse': '0.003651161678135395', 'val/std': '0.054432306438684464'}\n"
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 10974, {'val_loss': '-1.4673835039138794', 'val/kl': '0.0007735468680039048', 'val/mse': '0.003901753108948469', 'val/std': '0.05738849192857742'}\n"
+     ]
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+    {
+     "data": {
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 13169, {'val_loss': '-1.4404581785202026', 'val/kl': '0.001055828994140029', 'val/mse': '0.004138254560530186', 'val/std': '0.07071433961391449'}\n",
+      "Epoch     5: reducing learning rate of group 0 to 4.9456e-04.\n"
+     ]
+    },
+    {
+     "data": {
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+    {
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+     "output_type": "stream",
+     "text": [
+      "step 15364, {'val_loss': '-1.6021909713745117', 'val/kl': '0.0007687730831094086', 'val/mse': '0.003044766141101718', 'val/std': '0.05109384283423424'}\n"
+     ]
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 17559, {'val_loss': '-1.5778357982635498', 'val/kl': '0.0007317148265428841', 'val/mse': '0.0031571618746966124', 'val/std': '0.05000822991132736'}\n"
+     ]
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+    {
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 19754, {'val_loss': '-1.5999406576156616', 'val/kl': '0.0005693243583664298', 'val/mse': '0.0031292077619582415', 'val/std': '0.05022908002138138'}\n"
+     ]
+    },
+    {
+     "data": {
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+       "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 21949, {'val_loss': '-1.56947660446167', 'val/kl': '0.0005845829145982862', 'val/mse': '0.003213117830455303', 'val/std': '0.05039476230740547'}\n",
+      "Epoch     9: reducing learning rate of group 0 to 4.9456e-05.\n",
+      "\n",
+      "logger.metrics [{'val_loss': -1.56947660446167, 'val/kl': 0.0005845829145982862, 'val/mse': 0.003213117830455303, 'val/std': 0.05039476230740547, 'epoch': 9}]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-16 16:41:27,996] Finished trial#53 resulted in value: -1.56947660446167. Current best value is -1.5752214193344116 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004368391916372206, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 1, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trial 54 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 128, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.005270631834316311, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 1, 'n_det_encoder_layers': 8, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:gpu available: True, used: True\n",
+      "INFO:root:VISIBLE GPUS: 0\n",
+      "INFO:root:\n",
+      "                                  Name           Type Params\n",
+      "0                                model    LatentModel  347 K\n",
+      "1                model._latent_encoder  LatentEncoder   68 K\n",
+      "2   model._latent_encoder._input_layer         Linear    2 K\n",
+      "3       model._latent_encoder._encoder     ModuleList   33 K\n",
+      "4     model._latent_encoder._encoder.0  NPBlockRelu2d   16 K\n",
+      "..                                 ...            ...    ...\n",
+      "82       model._decoder._decoder.0.act           ReLU    0  \n",
+      "83   model._decoder._decoder.0.dropout      Dropout2d    0  \n",
+      "84      model._decoder._decoder.0.norm    BatchNorm2d  384  \n",
+      "85                model._decoder._mean         Linear  193  \n",
+      "86                 model._decoder._std         Linear  193  \n",
+      "\n",
+      "[87 rows x 3 columns]\n"
+     ]
+    },
+    {
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+    },
+    {
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+     "output_type": "stream",
+     "text": [
+      "step 0, {'val_loss': '1.522579550743103', 'val/kl': '0.5025149583816528', 'val/mse': '0.31865665316581726', 'val/std': '0.9117485284805298'}\n",
+      "\r"
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+    },
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+     "text": [
+      "step 2194, {'val_loss': '-1.3701834678649902', 'val/kl': '0.0027001516427844763', 'val/mse': '0.00422711344435811', 'val/std': '0.06300579011440277'}\n"
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+     "text": [
+      "step 4389, {'val_loss': '-1.4492722749710083', 'val/kl': '0.002561358967795968', 'val/mse': '0.004086161497980356', 'val/std': '0.060740407556295395'}\n"
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+      "step 6584, {'val_loss': '-1.3625338077545166', 'val/kl': '0.002082530641928315', 'val/mse': '0.004729835782200098', 'val/std': '0.057794589549303055'}\n"
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+     "output_type": "stream",
+     "text": [
+      "step 8779, {'val_loss': '-1.2864140272140503', 'val/kl': '0.003959427587687969', 'val/mse': '0.004717061761766672', 'val/std': '0.06390085816383362'}\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 10974, {'val_loss': '-1.2884516716003418', 'val/kl': '0.003093295730650425', 'val/mse': '0.005015104543417692', 'val/std': '0.04962388053536415'}\n",
+      "Epoch     4: reducing learning rate of group 0 to 5.2706e-04.\n"
+     ]
+    },
+    {
+     "data": {
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    "source": [
diff --git a/src/models/lstm_std.py b/src/models/lstm_std.py
index 8e9e004..b73c22f 100644
--- a/src/models/lstm_std.py
+++ b/src/models/lstm_std.py
@@ -272,7 +272,7 @@ class LSTM_PL(pl.LightningModule):
         return parser
 
 
-def plot_from_loader(loader, model, vis_i=670, n=1):
+def plot_from_loader(loader, model, vis_i=670, n=1, window_len=0):
     dset_test = loader.dataset
     label_names = dset_test.label_names
     y_trues = []