Some minor changes.

9 files changed, 227 insertions, 1482 deletions

diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/src/notebooks/04a-look-at-iam-lines.ipynb
index 576dd5e..0187518 100644
--- a/src/notebooks/04a-look-at-iam-lines.ipynb
+++ b/src/notebooks/04a-look-at-iam-lines.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -66,7 +66,7 @@
        "(28, 952)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -86,7 +86,7 @@
        "(97, 80)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +106,7 @@
        "'He rose from his breakfast-nook bench____________________________________________________________'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +141,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAABCCAYAAADt2ys3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAxDUlEQVR4nO3deXAc53ng/+/bcx+474s4SIAASZASRYqkRNIUqUhyFPnYOCXLthxvHP+Sqp/LtdlNNrvZw8nWZje75XV2E9evUnH9Kq5YluUjssuSbJWsWJZk8RApHgJBEgQIEMR9Ywb3XL1/YLrdM5gZYADwsPV8qlgEpnu63367B1XvM8/7vErXdYQQQgghhBBCCCHEvUW72w0QQgghhBBCCCGEECtJ0EYIIYQQQgghhBDiHiRBGyGEEEIIIYQQQoh7kARthBBCCCGEEEIIIe5BErQRQgghhBBCCCGEuAdJ0EYIIYQQQgghhBDiHiRBGyHEbaWU+oZS6r/egfO0K6WO/aod+05SStUppXSllP3X7fxKqT9XSj232ccVQgghhBDibpKgjRDi14Ku6zt1Xf/5r9qxN5NS6udKqd+/2+0wKKVuKqUevQ3HPaaU6t/E431OKfWLzTqeEEIIIYQQm0WCNkKIu+ZuZXzcq+61/rjX2iOEEEIIIcQHjQRthBCbSil1v1LqvFJqRin1HcBt2XZMKdWvlPpTpdQw8A9KqQKl1MtKqTGl1FT85+r4/o8opdos7/+pUuqs5fe3lVIfi/9sZnXEp8p8Vyn1j/F2tCul9lnet1cpdSG+7XtKqe9kmsKVzbFTvPcxpVSHUiqglPr/lFJvGtkw8QyPd5RSf62UmgD+XCnlUkp9RSl1Syk1opT6O6WUJ75/pr76S+AI8DWl1KxS6msZbtPvKaUGlVJDSqk/trT1z5VS31dKPaeUCgKfU0rlKaX+//i+A0qp/6qUssX336qU+plSakIpNa6U+pZSKj++7ZvAFuCleHv+reX8n45f37hS6j9Yzv+gUuqcUioYv/avpuhPH/AToDJ+3FmlVGV8szPDPf93Sqkb8W1XlFIfj7/eAvwdcCh+rOkM/SaEEEIIIcQdJUEbIcSmUUo5gR8C3wQKge8Bv520W3l8Wy3w/7D8d+gf4r9vARYAI+BwGmhUShUrpRzAbpYH6znxQMY+4O00zfkI8AKQD/zIOGa8jT8AvhFvx7eBj2d5qSmPnUwpVQx8H/j3QBHQATyUtNsBoBsoA/4S+CugCbgP2AZUAf85vm/avtJ1/T+w3Bdf1HXdr+v6FzO0/xGgEXgM+FOVOIXpo/E25wPfYrmfIvG23B9/jzEFSwH/HagEWoAa4M/j7XkWuAU8FW/P/7Sc4zCwHTgB/Od44ATg/wD/R9f1XGAr8N3khuu6Pgd8GBiMH9ev6/pgfHOm+3KD5aBWHvAXwHNKqQpd168Cfwicih8rP0O/CSGEEEIIcUdJ0EYIsZkOAg7gf+u6HtZ1/fvA2aR9YsCXdV1f0nV9Qdf1CV3X/0nX9Xld12dYDlx8CEDX9YX4+48CDwCXgHeAh+Pn6tR1fSJNW36h6/qPdV2PshxE2mNpox34m3gbXwTezfI60x072W8C7bquv6jregT4G2A4aZ9BXdf/Nr59keVA1h/puj4Z74//BnwSIFNfZekvdF2f03W9jeUg0DOWbad0Xf+hrusxIDd+Df8qvv8o8NeW9nTpuv7T+L0cA766xvb8RfzeX2L5nhr9Fwa2KaWKdV2f1XX9dJbXlfa+6Lr+PV3XB3Vdj+m6/h2gE3gwy+MLIYQQQghxR0m9AiHEZqoEBnRd1y2v9SbtM6br+qLxi1LKy3Ig4AmgIP5yjlLKFh98vwkcA/rjP0+xHBhYiv+ejjU4Mg+41XKNllRt7LO05ycsZ2QA/IGu699a67HjgRerSuuxdV3X1coCun2Wn0sAL/CeUspsEmBMR1qtr9bKes5eoDXNtlqWg3BDlvZoxj5KqTKWs2OOADnxbVNrOH9y//njP38e+C/ANaVUD8vBnZfXcLx0xzXvi1Lqs8C/Buri2/1AcRbHFkIIIYQQ4o6TTBshxGYaAqqUZYTP8jQeKz3p93/D8lSZA/FpMUfjrxvHMII2R+M/v8ly0OZDZA7aZNPGGrNxuv5hy7SbVAGbbM9VbfwSP2d10j7W/hhnecrTTl3X8+P/8nRdN4Iaq/VVct+mU2P5eQswaPk9OZi1BBRb2pOr6/rO+Pb/Ft+/Nd6ez1jakk17lnfW9U5d158BSoH/AXw/XsNmxa7ZHFcpVQt8HfgiUBSfAnWZ7PtNCCGEEEKIO0qCNkKIzXSK5fonX1JKOZRS/4LVp6DksByomFZKFQJfTtp+kuVAxYPAu7qut7OcAXIAeGudbYwCX1RK2ZVSH11DG9frFaBVKfWxeJbP/8tyTZ+U4lOSvg78tVKqFEApVaWUejy+y2p9NQI0rKFd/0kp5VVK7QT+JfCdNO0ZAl4D/pdSKlcppcWLDxtToHKAWSCglKoC/mSd7QFAKfUZpVRJvB+m4y/HUuw6AhQppfLWeGgfy4GZsfh5/iWwK+l41fF6R0IIIYQQQtwzJGgjhNg0uq6HgH8BfA6YBJ4GXlzlbf8b8LCcZXIaeDXpmHPAeZZrw4TiL58CeuM1Vtbbxs+zHBj4DPAyyxklm0rX9XHgd4D/CUwAO4Bzq5zrT4Eu4HR8BafXWQ5awSp9xfJUpU/EV5b6mwzneDN+jn8GvqLr+msZ9v0s4ASusDz16ftARXzbXwB7gQDLAarke/3fgf+olJq2rlKVwRNAu1JqNn4tn4zXNUqg6/o1lgtId8ePXZm8T9L+V4D/xfJzM8LydLB3LLv8DGgHhpVS42topxBCCCGEEHeESizrIIQQHzxKqTPA3+m6/g+3+Tway7V5Pq3r+hu381xCCCGEEEKIX32SaSOE+MBRSn1IKVUenx71uywvJZ6ctbJZ53pcKZWvlHIBf8ZyHZVsV0USQgghhBBCfADJ6lFCiA+i7cB3Wa510g18Il6/5XY4BDzPL6cYfSzVlB9x71BKGXWTko2xvMKXvC6vy+vyurwur6/n9XSrUgohRFoyPUoIIYQQQgghhBDiHiTTo4QQQgghhBBCCCHuQVlNj1JK6Uqp29WWe46u62Rzvcb+2b5vM9vwq+bX5fqcTie6rhOLxYhGo3e7OR8YLpeLUChEqoxBpRQej4dwOEw4HL6t7ZCMRSGEEEIIIcQGjeu6vmJqZbZBG+x2uxmYSN6WbuCiaRolJSXU1dUxPz9PW1sb8MuBTraD9kznWq9UwYO1vrbZ57b+bvysadqKa7b+bu2T29E/qdr562616zX6+ZOf/CSVlZW4XC46Ozt5+eWXWVxczPp42bA+H3dLpjbcrmfQoGkaFRUV/Nmf/Rlf/epX6e3tJRKJJGx/+umnaWlpoaurizNnztDR0ZHxmMltVkqZryV/Jq37a5rG0tKmrxYuhBBCCCGE+GDpTfXiuqZHpRqMZRqg6bpOeXk5jz76KAcOHMBuX44VGYOidO9Jd8w7EbBJ53YELayDxeS2rPV81j65XYPl9V77nbqPm20t11tYWIjf7+fUqVOcOnWK+fl5du7cmfK9v2oBLyNTxeFwrHgm8/LyaGpqoqCgwPwcG/ts5Doz/U2w0jSN+vp6otGoGayxnt/lclFfX8+ZM2cIhULU1tauelxrMCZdgCb5c7bW9gohhBBCCCHEemyops1aByy6rpOTk0NZWRklJSU4HI5NO/ZmSHeetZzfyILZaFvTDXitWTapBpMGl8uFzWa7JweQG+nf20HTtA3fMyPr7NChQ3R3d3P16lWuXLlCIBCgsbFxE1ubWqag5mZQSlFRUcHx48epqKhIeD41TePQoUMcOnSIsrIyNE0zt90JRt/v2rWLoaEhlpaWiMViCfvk5+cTi8Xo7Oxkfn4en8+3qe2TYI0QQgghhBDiTlhX0CbVgGW1AYzX6wUgGo2uKWhzL1lr8GatVhtsJw/IU33Ln6y0tBSv12sOoNfLmvFztxnXnE1bkqeZpVJeXk5paSk+n2/dbdM0jby8PB544AEuXbrEzMwMMzMzhEIhSktL74n+26jW1lY+/OEPU1VVlfC6x+Ph0KFDtLS0UFBQYD5z1mdnvde/1vc6nU6am5sZHh42gzbWrJiKigqmpqaYm5tLmDaVTRuS/xlSTZkUQgghhBBCiNthQyN862BmtW+eCwsL8fl82Gy2jZxy1Xastt96uN3utFksRvHZ5G/6M1ktwJOuL41zpbqOXbt2kZ+fv+bMp4228U5InnZjla6Oylra/Zu/+ZscO3aMmpqadbfN7XbT3NxMd3c3s7Oz5v1XSv3KBSWTKaWw2WwcOHAAIKGwsqZpNDQ0EAwGmZycJBKJYLfbb3vmj5WmabhcLioqKhgbG1sRlDHaOD09TTQaTZtVZWQNrSfzymazmfW9svnsCyGEEEIIIUQ21l3TJlUh4rVMFQqHwywsLGQ8drYyFUTN9A35Wn32s5+lurp6TVkcd8vJkycZHh42B7CZ2reeftjo9SY/F6vVQMq0LV0NIiOoZUi1j6ZpHDx4kHA4zMTExFqbv4LP5+Pw4cO88cYbhMNhdF3H6XRis9kIBAJZH28j/bvZz6JSivz8fLZs2cLMzAyLi4vmOex2O0ePHuWnP/0pIyMjKKVwOp0JGV6bMV3QaEeqzDGHw0F5eTmNjY1cuXIloeizEXBqbGykv78fm82GzWYjGo2uCK64XC4KCwupqKigtLQ0bZZaqnZs2bKF/fv3b/gahRBCCCGEECKTrFaPAsz6Krqu43A4KCwsZP/+/Wzfvt1cSSUajRIMBunr6+Ps2bMEAgFCoRCDg4MMDw9nXBI5ebDncDjYsWMHe/bsobCwkFAoRCAQ4K233mJkZIRQKJT2vcZrqQI32axc9c1vfnNF3Yy1ZrRY97PZbDQ1NXHfffcxNjbGlStXGB4ezvhNfXIgIp1gMJjwe6b2ZSq8nC4YstFBePI1JK+OlbzNCAa4XC7sdjsLCwsJ9yDd+4znLxVd1/F6vdTW1jI3N8f09PS6rkXTNNxut5npYTxLhYWF5OTkMDY2hqZp686+yra/Nzsrygh6eDwe2tramJiYMNtkt9tpaGjghRde4L777jPba7zv0Ucf5cqVK4yOjqZdinut0mXvOJ1OSktLGRgYYHx8POF+G/uXlZURDAYpLy9nenqayclJ4JfFlZ9++mmz/Uam1MLCAi+99BIdHR1mIC5VO7xeLwcOHGDfvn1cuHCBUCh025cUF0IIIYQQQnwwZR20geXBWW1tLS0tLZSUlHD58mXef/99c+BiDIzq6up49tlnuXnzJpWVlQwMDHDz5s01D+S8Xi+PP/44NpuNjo4O+vv7iUaj5OXlsXfvXqLRKJ2dnXR1dRGNRtMOdlNl4mQz0M2UGQSQl5dHXV0d5eXljI6O0tbWRiQSSTiH2+2mqqqKQ4cOceHCBRoaGjhw4ABdXV3mEujpZDv1y8g2qKiooL+/P2VWVDZS1fBIXk3ICLLY7XYzKyESiRAOh1MOaNMtoWyz2SguLubIkSMUFhYyPDyM0+nE5/MxNzdHZ2cnly5dMt/rcDhoampiz5491NfXE4vFmJyc5Ac/+IE5fcfaztLSUrq7u5mcnMxqoG1to6Zp2O32hOwNpRT19fVUVFRw4cKFNR83leT+ttlseL1eSktLcbvd9PX1MTc3tyI45fV6KSkpwe/343K5cDgcxGIxBgYGzGlEqVY/Sj63w+Fg586dFBUVcfPmTTMgqJTC7XZTWFhIOBzG7/cDmJkuxqpNJ06c4NVXX2V0dHTdQRvrVLfk4JfX66W5uZk333wzIQvIeJ+maTgcDqanpzl8+DC9vb3cunULTdPw+Xx8/vOfZ2Zmhueff56xsTGWlpaw2Wz4/X5+67d+C7/fT3t7+4pAqHH8oqIi3G43gUBApkYJIYQQQgghbqusgzZKKZqamsx6IJcuXaKvr8+sH2EMBJ1OJwsLC8zOzrJv3z727NnDxYsXmZqaSjuQy83Npby8nEAgwNjYGEeOHAGgt7eX7u5upqen0XWdmZkZYrEYVVVVNDQ0kJeXx9mzZ1e0EzZnelSmgWdhYSGHDx/G7/cTCoWoqqoiPz+ft99+2+wPTdMoKiri6NGjXLp0iZ6eHoLBIPv376esrIz29vZNG/wppfD7/dTX1+PxeOjv79+U4yafw+BwOCgoKOC+++7D7/czPT1NKBTC6XTidDqJxWLcuHGDmzdvrigWm3wsu91OU1MTe/fuZXZ2lnPnzjEzM4Pdbsfj8VBQUEBtbS2Li4t0dHTg8Xg4duwY+fn5LC4ucubMGcLhMJWVlRw7dszMxrKe8+jRo/T29hIIBNZd4NgIKFiDIEbWmcfjYWBgYM3HtdlsuFwuNE1jdnY2YZvL5aK8vJyamhrC4TCxWAy/309zczOdnZ10d3ezuLhIcXExDz74IF6vl0gkYrbLqDWzbds2wuEwly5dYmBgYNUgpKZpVFRUMDAwwPDwMIuLi2YwJy8vj9nZWXNKlDULJRaLce3aNb74xS9y6tQpxsfHN/Rcp7o/mqaRm5tLa2srL7zwgvkZS6aUorKyEqUUs7OzzM3N4fV6OX78OLFYjHfffZfe3l7m5ubMv1kul4uTJ0+yY8cOgsEg3d3dK/rK+PuXk5OTVQBaCCGEEEIIIdYj65o2Pp+P6upq7HY7PT09tLW1MTk5aQ6ejOk8i4uLDA0Ncf78ecLhMBUVFdhsNvNb+VSDHY/HQ0VFBVVVVRQXF1NfX8/AwABdXV3mOWKxGHNzc3R1dXHjxg2UUtTV1VFUVGQex+l0Ul9fT2Vl5aqFjzeSCeByuXjwwQcpKSkxp4PNzs5y5MgRnE6nuZ/b7aaoqIiKigpzWeje3l4ikcialiLONtiUm5vL7t27mZ+fX9e1rZXH46GyspIdO3bg9/vNoqzRaNR8HpxOJy0tLTQ3N69aoLeyspLGxkby8/Pp6OjgwoULdHZ20tHRQVtbG9euXWN+fp7W1laUUrS2trJlyxYWFxdpb2/n5MmTnDx5kkuXLplZLx6PB/hl9tfjjz/O4ODgigBJNqw1ToxAVHl5OW63m5mZmawCQi6Xi5qaGrZv375iKt3WrVtpbm4mLy+PSCRCIBBgcnISh8NBfX0927dvp7a2lgMHDlBUVITNZmN+fp6pqSnGx8cZGRlhZGSEpaUl8vLy2LlzJ3V1dWafpGIEMEpLS3n//fcJBAJmRo/NZiMnJ4dgMEh9fT0LCwssLCwkTCPq6+sjEolQWVlJTk7OmvszU3us++Xk5FBdXW1mTKUL2MRiMXbu3Mno6Kg5vSsvL48DBw5w8eJFrl+/bgZ/jb9ZCwsL5nTOuro6KisrVyxn7na72bFjB16vl87OTgnaCCGEEEIIIW6rrDNtampqsNls3Lx5k2vXrq06aNE0jbm5OYLBIHNzcwlTqFLta0zryM/PZ3x8nN7eXjPDxkrXdfr7+8nNzWX79u3s2LGDt99+G4CSkhIOHjzI+Pj4ipoXxntTtcEI8KRboSlZdXU1hw4d4qc//SltbW3Mzs7S09PD8ePH8Xq9Zg2WnJwcCgoKGBkZYWFhwZwGZLQl0/QlI0CQadliY6qOkdWTn5/Ptm3beOGFFzZ9UGkM6m02G5WVlTQ3N1NcXMyrr77K2NhYwn4Afr+f3/u932Pv3r387d/+bUKtEENeXh5zc3Ps3r2b4uJiLly4wNWrV83txupcw8PDADz77LPY7XZ+4zd+g7Nnz3Lp0iVGRkbM/a9du8b+/fupqalhenqamzdvYrfbqamp4cCBA3zjG99gbm5uxTVlw3hWjZ937dpFJBKhp6cn4V4Zq46lW2HMmOpTXl7OhQsXEvrNKHT785//nL6+PvP9IyMjHD9+nCNHjjA1NUVtbS3PPfccAwMDCcFTo56TpmmUlZXxxBNPsGvXLpaWluju7k55XUbdmqKiIr797W8nTD8yMuh0XefgwYPcuHEjIWALEAqFOHnyJFu3bmV8fDzlZzf5fMl1p6zbkqfOGc+cUZMo3fui0ShVVVW8/fbbjI2N4Xa7KS0tpaCggNOnTyc8h9asvHA4zFtvvcWTTz5JNBplZGSEubk5s50VFRXU1dUxOjpKd3d3xvpcQgghhBBCCLFRWWfaHDp0iMHBQXp6elatI2PUJ3nggQd49913GRkZSZttoZQiNzfXXLHm2LFjXLhwIeOgT9M0JicnmZ6e5v777zdXrTl69CiRSISBgQGWlpbSXkty2wsKCsjNzTWzZIx2Ja+GYwxeP/nJT/Liiy9y8eJFgsGgmQV05swZamtrzekpeXl5lJSUmEENY+oGwNzcnDkY1zQNm82Gw+HA7Xbj8XjweDwZs3GUUpSVlZkZAR6PB6/Xy+LiYtqpKZuxPLpRgLq0tJTvfve7jIyMmIEJ62DYCAA89thj5OTkpKyh8tRTT1FQUMCWLVuw2Wy0t7envE5jKpHNZqOgoACfz0dXVxejo6MJ+8ZiMV5++WWamprYsWOH+Wx95jOfIRKJMDg4mDDtJduATTQaJRwO4/V6sdvt+Hw+9uzZw8zMDO+//37CvtY6M6m4XC6zcLf1fu3atYvFxUWuXbtGb29vwrbc3Fzcbjdbt27l/vvv52tf+xo3b95kaWmJSCRiZqQZ/xufhZdffplYLMbhw4fTXrPdbicnJ4dIJMKlS5fMqVFGv4ZCIUpKSsyVm6zTz4y+ee2116ioqKCmpsYMbKWTrtB2qpXA8vLyqK2tpaysjKtXrxKNRs2gWPJ7bTYbp06dMmv55Ofn09DQQFdX14oAaHLgdHx8nO7ublwul5kBpes6fr+fxx57DK/Xy+joKMFgMKH2jhBCCCGEEEJstqyDNkVFRebgDZYHPMbgMHmg73a7+d3f/V1+/OMf09PTg67ruN3uFcvnapqG0+mkvLyc5uZmuru7KS4uZnp6OmWxWCOIUlpaysMPP8zx48fNAZzNZjO/Ce/r60t5DUYgxqjJYaioqOCjH/0oTz75JHa7HaUUDQ0N7N+/P2Gqh9PpZNeuXUSj0YS6GLqus7i4yM9+9jPq6+vNAJXb7cblcpm1Tux2O08++SRTU1Nm0VqbzUZLSwtf+MIX+Ku/+iu+8pWv8OUvf5k/+IM/4NFHH8XhcKwYHGqaRk5ODr/zO7+D1+s1644AZhBlvVZbXepDH/oQU1NTvPbaawkBEONe5ufns337dj72sY/xqU99itOnTzM/P79iuovL5WLfvn243e6EqVXJnE4ne/bs4SMf+Qivv/46NTU1zM7OpszcAZiammJwcJBAIEBxcTGHDx/G4XBw+fJlJiYmNrTaj9FOl8tFXl4eTz31FN3d3XR2diYsPw3LRay3bt1KRUXFiuPYbDaOHDlCTk4O586dS9i2bds25ubmzJWorJqamlBKcfXqVc6dO8f09PSa7nUgEDDvVbpMt/Lycp566in6+/sJhUIpM4ROnDjBd77zHcbGxojFYiuONTs7y9mzZ3G5XLS0tKRcSns9wY6jR4/i9/t5+eWXzQwfa+BY0zQ8Hg8tLS243W7glwGZhYUFJiYmyM3NTbu0t1V3dzdLS0tUV1ebAcOPf/zjdHR0MDAwkDEoKoQQQgghhBCbJevpUcY3zh6Ph5mZmZT72Gw2ysvLeeyxxxgaGuLdd9+lsbGRLVu2kJubm3Kws23bNioqKrh8+TKLi4s4HA6z2Gny/g6Hg4cffpj9+/fT0NDAwsIC169fN6eChMNhlpaWUg7+lVLs3buXqqoqJicn+cUvfmFuu379Otu3b8fv9+Pz+ZiZmaGqqmpFto7b7ebEiRP84Ac/WFE3JhaLMTExkTA49ng8ZpFem83Gk08+yY0bN7h+/TqBQMB838DAAD/5yU945ZVXWFpaSsicSBeciMViCbV7jL6yZgulWu1po3JychgeHk6YlmScy+fzcf/999Pc3Iyu6/zzP/8zX//61xNqvRj98rnPfY4333zTrAm0a9cunn76ab7xjW8QiUTQNI3GxkYef/xxfD4fb775JhcvXsTj8bBt2za2b9/O3NxcygLXly9fxm63s3fvXo4cOcLXv/51/uRP/mTFKkrrMTc3x6lTp/jSl76EUornn3+e3t7eFceNRqNUVlYSjUYTpvtomsbhw4cB6Orq4ubNmwnvu3r1KidOnCAvL4+BgQFCoZA5DQuWp4DV1tbS2tqKw+HImFFmKCwsZHp6mqmpqZSfQZ/PR2VlJVVVVbzzzjsrVpoyVl+amZmhp6eHpaWllNP7dF3nvffe4/HHH2f37t20tbWtOF82/a9pGvv370fTNPr6+ujr6zP71Ho8IwPrvvvu40c/+hENDQ20tbURDAYJBAJcv36dY8eO8elPf5qXXnrJrNdjBJCsgeeamhrzM+v1ennggQfo7+9nbGyMqakpFhYWzP0l00YIIYQQQghxu2QdtDl79ixbt24lGo1y/vz5hKWtlVIUFxfT0tJCQ0MDExMTvPfee8zNzdHX10d5eTkul8scvFoHsVu3biU3N5c33niDSCTC+Pg45eXlzMzMmPVHjOkKx48fZ2hoiKtXrzI7O4vdbufy5cvmt/7BYJDc3Fzy8/OZn583v4XPzc3l+PHj7N27F03TuHjxYsK15ebm4nA4iEQiLC4uomkaO3bsoKOjI2GKlDGFZGBgIGUAYGlpidHR0RXTMOx2O4888gg2m40rV66YmQqG2dlZM9hjzThJN8A1ppBYC6qGQiGUUmzbto0tW7akzTbaCCPzoKamhtHRUXp7e3G73WZRYqfTycTEBG+99RahUIji4uIVgQK/309rays5OTm8+OKLzM3Nce7cOZRSPPjgg3zpS1/i3XffZdeuXei6zo0bN+jp6TEDGJFIhOeee44dO3bQ2NjI2NgYAwMDTE9PMzc3RyAQYGhoiL1791JdXc0Pf/hDFhcXmZ6ezlgfaK2Wlpbo6enhj//4j/mjP/ojRkdHU2bvGP1iBDiNvjt48CA7duzg3LlzXL9+fUWbOjo6aGpqoq6ujk984hO89tpr7Nmzh9LSUs6ePcvExIR5n5955hlee+01xsfHzecx1TMzPz9PT09PygCgzWajoaGBhoYGfvzjHzM6OpoQxDBWstq7dy+vvPIKW7duzdiXwWCQYDBIfn6+WZ9qPYw6Mq2trbS3t5vBWWNKnPVvz5YtW9i1axeXL19mfHychx56CK/XawZ+JyYm+Pa3v83hw4f5wz/8Q3p6erh16xbDw8NMTk4yMzNjBqUbGxuZnZ1lcHCQ5uZmQqEQHR0dbNu2jWg0mlDrR4oRCyGEEEIIIW6XrIM2V69epaioiKamJnJzc5mcnCQQCODz+SgtLTWnHwwODtLV1cXw8DC6rpvfdsPyANCaGWC324nFYgQCAbOY6jvvvMPDDz9MVVUV09PT2O128vLysNvtDA4Ocv36dXP6htvtTlheuK2tjZqaGg4fPmwOlr1eL263G5/PR3d3N4WFhdTW1uJ0OolGo1RXV7Nv3z6UUmYxWaOOxvz8vBlAMQJA0Wg0YeUcK6OAqTGg1TTNXLkG4MKFC4yOjpqZCrA88DOWa05eSjmTaDTK1NSU2b5wOMzIyAjt7e18+MMf5ty5c9y8eXPFVKKNDjTb2tqor6/n4MGDtLa2Eg6HsdvtLC0tMT4+Tn9/P0NDQ9jt9oQBLvwyALB7927OnDljTgGKRCIEg0EWFhZobGykqKiInJwcrly5wuDgILdu3TKnWBlBw1AoRGFhIbFYDK/Xa/Z3Tk4O+fn5uN1u+vr6uHz5Mj6fz+yDtV6/cb+tASev10tNTQ3Nzc1cvHiR6upqysrKWFpaMttnBCVbWlrMdrndbvLz82lubqaqqorOzk56e3vNYIFVMBjk/PnzZuBw69atuN1uTp48ycTEhLkktdvtpqqqikceeYSZmRnm5+fNoFY0GjVXyZqdnWVmZsZckj1ZdXU1JSUlBAIB2tvbE4KRNpuNLVu2sHPnTm7dusXZs2epr6+nvb3d/AwkZ5sYz6FSiqKionUFbYyV1w4dOsTAwAC3bt0iGAzidDoZHBxMCPqWlZWZS3zfuHGDUCjE7Ows+fn5jI2NMTc3RygUoru7G6/XS0NDA0tLS3g8Hmpra81VyCKRCKOjo4yPj6OUoqCggFgsRm9vL1NTUzgcDubn581AsmTZCCGEEEIIIW6nrIM2o6Oj3Lhxg+3bt9PU1MTs7CyTk5P4fD6KiooIhUJ0dnZy6dIlM0hjTFkaHR01687ALwMHdrudsbExJicnzWDOqVOn2LlzJw0NDebqLW63m+7ubk6fPk0wGKSoqIiCggLcbrc5GI/FYly5coXc3Fzq6+spLy9HKYXX6yUUCnH+/Hlu3LhBY2Mjhw4dor6+Hk3TzOWT33//fTo7O4nFYthsNjPrx5pFEYvFVmQiGIwB++TkJLFYDE3T8Hq9lJaWmoPujo6OjNke2dTKiEajXL9+3QzaRCIRhoaGeOONN/jUpz7Frl27yM/PZ2ZmhoWFBXNgOj8/by7ZbNQkWo01O8oouNva2kpRURGBQIDx8XEuXbrE2NiYeR/9fv+KIIFRaDkcDnPq1ClisRh2ux2Hw0EgEKCtrc2cmtbf38/w8DBOp5OqqiozG8kIAp46dYqcnBxyc3PxeDxmkMjv91NQUMD4+Dg3btwgGAyi67oZFMyG0+k0Ay8+n4+ysjLq6+vxer38/d//Pfv27WP37t3m+YLBIOFwmKKiIh566CFzxbCGhgYKCgqoq6tjbGyMs2fPmllSyf3j9/vNAFhdXR0tLS309/czPT1NXl4eSikCgQCnT59m69atPPTQQ+Tn57O0tGTe40gkwszMDA6Hg1AoxMTEBP39/SsKN+fl5dHY2IimaXR0dDA1NWVuU0qRn59PZWUlXq+X119/nampKUpKSlbUpbHb7RQUFDA9PU00GmV6epqcnByKi4vp6OjIqs+NItZNTU2UlJRw8uRJxsbGzBWexsbGEpbhrqiowOVy0d3dzeTkJJqmceHCBfPeWYN9Fy9e5OrVqxQWFlJQUEBBQQGFhYVmwNnpdDI2NobX68XlctHb28vQ0BC6rpurVllXH1tLjRwhhBBCCCGEWA+VZW0J3W63mwU/y8vLaWlpoaioiLGxMbq6uhgaGkpYESn+PiBx+WNr0MLj8VBSUmIOqg1er5eysjJcLhdzc3OMjIwQDofNQbfH46Gurg6/38+5c+cS6oUYSzJ7PJ6Eb8eNGhbGlIuqqirKy8tZWFjg9ddfp6enh9nZWbNGxjPPPMNLL71kDq6NWix79+5dUfcjoWPjA0q/389HP/pRjh8/zj/90z/xk5/8JKF462ZMrUiVmWMEuY4ePcqRI0fIy8tjaWmJqakpAoEAg4ODZjaMUYNnPW0xMlHSrQLk8/n49Kc/zfe+9z0CgYCZEeN2u82gUarjwXIQoLCwkIaGBmpqasw6SkNDQ1y8eHFFYeNkNpstoV1KKerq6ujr61vzFClj5an9+/ej6zpbt26lsrKS6elp/vEf/5Hp6Wm2bdvGb//2b1NeXs7ExIRZq+jIkSPk5uby6quv4nA4KC0tZXR0lFOnTjE6OpryntlsNkpLS2loaKC1tRWbzcb169e5ePGiuWpTJBJhbGyMvr4+hoeHzeCgEXCw2+1mcNQ4b319PTt27ODMmTO88cYbZp/YbDb27dtHXV0dXV1dnD9/PqHukFKK+++/PyF4EYvF8Pl8ZkHyaDRq/k04dOiQWZC5urqaiooKFhcXef3119fU39Z7V1BQwBe+8AWef/55hoaGCIfDKwJFuq5TU1NDWVkZgUDADLjCcvCrrq6O6elpJiYmzKCj0V9GANKo1wNQXl7OI488wvT0NN3d3fT395uFxmOxGIWFhTQ2NhIMBs1AlN1uTwjiCCGEEEIIIcQ6vKfr+r7kF9cVtIGVq78YgyFDtgGA5CBG8rfXqaa0GIEJY9CUKmhgbad18J5umfJ0wQcru91ORUUF/f39GZcj93g8PPDAA+zevRun08krr7ySdcbBRlkH826328xA8fv9VFdXc9999/HKK69w9uxZc7rWZk758Pl8PPPMM/zoRz9KmMK2XsYKWZFI5I6t3qNpGk888QQVFRX09fWZKwhZAz9Op5OioiKqq6upr6+ntLSU9vZ2Tp8+vWJ6WDpGZslHPvIROjo6eP/99xkaGjJXKkqepmUtoJuKdbumaTz22GNMTk7S3t5uZq+Vl5dz8OBBzp8/v+KakoOBazlfcXExv//7v099fT02m40LFy7wrW99KyF7Zy2Ki4s5cOCAmU1l/ezCLz/LxrNt1IBSSqUtQJ6p7dZrThUEtZ7PCAZapz+upRC0EEIIIYQQQmSweUGbVEGP5EGVNfhiDBqTB0KpVoYyjm2z2RIK8WbKaEkejGVTE2a9rPU0UtE0zVw2fMuWLTQ0NDA9Pc2ZM2dWPfbtWJHGCIIZWQXGOaqrq1lcXDRryayXkYFkZF4Yx/f5fDz77LP88Ic/ZHR0NOupSenOlak481r7Lptsp+TMjFQBRON/6/OX7XNoTAuyLrW91vatFpAwgg3Wmjtut9usf7MZQTCj/cbS85FIJGXh41TBVON1h8OBz+cjNzeX/v7+jM+M8Vwn38vkY1q3bYQ1mGy9txtZQl4IIYQQQgghSBO0ybqmjWGtAyBr4dtU9V/S7W8d+GfbjtsdsEl33uTtxnQOowBsTk7Omo59O4qbWu+DtRaPMd1lMwbsNTU1dHd3rzinUcNos1iDDsmy6btsAjzJQYDkIIm13o/x7K6HkcGR7fvXsn9yDSZd19Mu271e1oLaq7Ut3evhcJhgMMjs7GzG7Dnr9SQfyzol07g+Y/+1BLisgZ5UU7KEEEIIIYQQ4k5YV9Am06AlXWDGOkBON9BKdQzr1Iy1Dv7u1qDKen3WNiwtLREKhfD7/atm6NyuNllZz29kCGy0TUbWzq1btxKyDowgxmYss209pnXZ+I0ea7Pft9G+XM/71/KeVPvcjilm622L9XXrkveZnuHVzmUN7FgzpazHXS1jznpOI4CTfAwhhBBCCCGEuB3WtexJupowq23LRqbpUGtp22YNprI9TvL+1qCFx+O5pwZ5yYPYjcrJycHlcq2oRxSLxTaUfZLsTvdhqnt6tzK87mV36r4k97P1M2/97Fuza6zv2UhQKXnbZv6tEUIIIYQQQohkG16rNptBS6ZpLZn2z+ZcGxlEpXrveo6VPHg0sgZcLte62rVeq2UEbPaAc2FhgaKiIlwuV8JxbTbbpmd0bOZ0ntWO9UEdlK/ls2b9+Xb1U3LQxXou6/+p/mWqLbSWe28EfqznEkIIIYQQQog7ZV1BG2vwJVW9h0wDofUM7qyDrtUG/5sdHFjP8RwOB9aCzUYxVqfTCaxv8JdNloBxjlQZB1apXlsvXde5du0ara2t5Ofnm69rmobf709ZjHajUmXArFWqAEA6d2qVqnvNan2THCC5U/2UKmhj1GWy/lstI3A11r9lyce1FqYWQgghhBBCiNtlQ9OjjGBANoO1tQReNosxuFqr9U5vSa5zkZeXl1C/xhjoFRQUrHv6VvIgdTUb6eds+sxqaGiI7du3U1BQgM1mM/s/HA6ve9Wo9U6Ty/Tetb7/XpHtc7xZ0gX1jLYkb09u4+3IvknO1ssUnMkUlDSCLum2Zdqe/HfCCMYKIYQQQgghxGbbUKZNwoEyDHLuNGsdi40GiLLNbNF1nYWFBR544AFzOlQsFiMcDies1HOnv6FPN7A17pt1+3r6TNeXV8t65513qK6upr6+3gzcOByOdRcizuaZ2mggJ5O7+Xyv9hzf6WcpFosl9MedrvGT3B+Zgkvp7lmmKU9GINoIDhlBHOOY1n2ATVnGXgghhBBCCCFSWfeS31aapqWtV7MZqxJtxnHWe6y11tCxHnNhYYGpqSnsdru55LB1oHu7VuzZyLSrVFL1V6bz6LpOW1sbTzzxBE6nk0gkwsLCAouLi1m3K1upavckt38jmR/3cpHhuxFMSu6PO903ay0mnGmVqmzqY1mnJ1qfJ03TJGgjhBBCCCGEuG02Zc5Fco2be9lmr2CUXAgVlr+F7+/vp76+npKSkg3Vssm2PXf7PTMzM1y7do28vDxOnDjBjh07mJyczPo8m2kznst7NWBzt6TLrLG6U38P0p1nLUWGs9nHCM4mn+9e/5snhBBCCCGE+NW1aUGbdK9txmB5I3VmUrUp3XnW8ppx7NXONTExgdvtpr6+nu3bt1NZWcnCwsI9VdQ21Wo7Vuvp+1gsRmdnJyMjI+Tn57NlyxZu3LiRcN23IwCy2lSdtZDATHqrFfX9VQ5crPb8J2dqJRdeF0IIIYQQQojbZUPTozIFRawDnLs5fWCtK7ykGnRmmgaU6jVrUVRjNaUTJ05QWVnJ/Pw8AwMDd22Ql3xvkjMGNrNA9MLCAmfOnGFwcJCmpiYuXLiwYvC7Eenu6Uan0m124OFOri60mdMIjeNkW08o+dx36tqzqTuV/Aym+j35WtJl2AghhBBCCCHE7aayGVhpmqbb7ctxnkzfTCcvh3u3skuMNt7u82eaKmG323G73djtdoLB4LoL8m4Wa584HA6i0ah5rzarn6x9sNk1P7K5p+ut87NZrLWe7sS54O7U3rlTn7ONSG6jtb+M3621p5KnfMZiMbM+VfI2pdQdqdskhBBCCCGE+LX2nq7r+5JfzCpoo5QaA3o3s1VCCCGEEEIIIYQQH3C1uq6XJL+YVdBGCCGEEEIIIYQQQtwZm1KIWAghhBBCCCGEEEJsLgnaCCGEEEIIIYQQQtyDJGgjhBBCCCGEEEIIcQ+SoI0QQgghhBBCCCHEPUiCNkIIIYQQQgghhBD3IAnaCCGEEEIIIYQQQtyDJGgjhBBCCCGEEEIIcQ+SoI0QQgghhBBCCCHEPUiCNkIIIYQQQgghhBD3oP8LHeDa0Ls6z4YAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -151,7 +151,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -161,7 +161,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAABCCAYAAADt2ys3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAA4l0lEQVR4nO29eXicV3n3/zkzI41G+y5Zq+VFtiU7XmLHcWziOAnJ6/wCaUiTNIUQSuEtv5bywq99C2/hKqX0pXShb9rSXylLCaRAICG7IUuzLzZJLFte5V2LZe3bSJoZzXbeP2bOk0ePZmQpMbFI7s91+crM85znnPssz+Q6X933fZTWGkEQBEEQBEEQBEEQBGFh4brYBgiCIAiCIAiCIAiCIAgzEdFGEARBEARBEARBEARhASKijSAIgiAIgiAIgiAIwgJERBtBEARBEARBEARBEIQFiIg2giAIgiAIgiAIgiAICxARbQRBEARBEARBEARBEBYgItoIgnDRUEpppdSyd0s771aUUn+plPrPi2zDL5VSd6W5tzg5x5532q53GqXU80qpT1yktieUUksuQD0fVko9dSFsSlF3u1Lq2jT33qeUOvbraFcQBEEQBOHXhYg2giBcEJKbpcVKqXuUUh9TSv15cpM3oZQKKaVitu+HL7a971WcApZz3i6iabOitd6ptf7BxbbjnWQhiGV2tNa5WuvTF6CeH2mtr7sQNs2z3Ze01ive6XYFQRAEQRDeDiLaCILwa0Fr/bXkJi8X+BSw23zXWjdfbPuE9ybvpDfOu8Xz593SD0EQBEEQhN9ERLQRBOFic61S6oRSalQp9a9KKWVuKKU+rpQ6qpQaUUo9qZSqT17/V6XUN+yVKKUeVUp9br7tKKWWKqWeVUoNKaUGlVI/UkoVJu99Xin1gKOdf1JK/XPyc4FS6ntKqR6lVLdS6q+VUu5UjSul3Envo1NKqXGl1F6lVG3y3hVKqdeVUmPJ/15he+75ZL2vJr2UHlNKlSTt9CfLL7aVX6mUelopNayUOqaUum1Os3B+MpVSP0zaflgptdHWZpVS6udKqQGl1Bml1GfSVaKUukEpdSRZT7dS6k+T14uUUo8n6xhJfq5xjMMnbGP5D8n5Og38P7MZnvQm+rxS6gAwqZTyKKUuT47pqFKqVSl1VbLs7UqpNxzPf04p9WjyszfZdqdSqk8p9S2llC957yql1NlkW73A95VSLqXUF5LzPqSU+plSqjhZ3oR13ZWsb1Ap9cXkvf8G/Dlwe3LeWx02ZSbneI3tWrlSKqCUKpttPJJlS5Jryayhv1ZKvWy7r5VSf6SUOgGcsF1blvx8T7LvTyfn8gWVfD9tZT+jlDqd7NffK6VcyXsfS9HWp1Tq99OtlPpGso4zSqlPq/OHwq1TSh1Qiffpp0qpLPv82Nr9fHINjifflWvON26CIAiCIAjvNCLaCIJwQdBaL9Zat2utP6a1vmcej94IbAIuAW4DrgdQSt1EYtP6IaAMeAn4SfKZHwB32DaBpcC1wI/n2w6ggL8BqoBVQC3wl8l79wE3KKXyku24k8+adu4BosAyYD1wHZAu38j/B9wB3ADkAx8HAskN/C7gn4ES4B+BXUqpEtuzvwPcCVQDS4HdwPeBYuAo8OWkfTnA00n7ypPP/f9KqaZ0gzKPeftgcjwKgUeBbybbdAGPAa1J+64BPquUuj51NXwP+AOtdR6wGng2ed2V7FM9UAcETRsp+CSJ+VwPbAR+exa7DXeQEHcKgQoSY/7XJMbwT4GfJ8WOx4AVSqnltmd/lzfn/OtAI7COxLxXA39hK1uZrLMe+O/AHwO/BWwnscZGgH912LYNWEFi7P5CKbVKa/0E8DXgp0nvtLX2B7TWYRLz8RFHH5/RWg/MYTz+FZhM2ntX8p+T3wI2A+nWz4eBrwKlwH7gR477N5OYnw3ATSTWfDrSvZ+fBHaSGO8NSZvOx23AfwMakvV9zFlAKbUC+DSwKbkWrwfa51C3IAiCIAjCO4qINoIgXGy+rrUe1Vp3As+R2JxBIqTqb7TWR7XWURIb2HVKqXqt9WvAGIlNLiTEiee11n3zbUdrfVJr/bTWeiq52f1HEhtstNYdQAuJzSfA1UBAa71HKVVBQoD5rNZ6UmvdD/yfpC2p+ATwJa31MZ2gVWs9REJIOKG1vldrHdVa/wRoAz5ge/b7WutTWusx4JfAKa31fyXH5X4S4gUkNr7tWuvvJ+vaB/wcuHWWcZkrL2utf6G1jgH3AkZE2ASUaa3/SmsdTuY8+c4s4xABmpRS+VrrEa11C4DWekhr/XOtdUBrPQ78b5LzkILbgLu11l1a62ESotv5+Odk+SAJoeMXyf7EtdZPA28AN2itA8AjJAQQkuLNSuDRpPfHfwc+p7UeTtr5NUdf48CXk+spSGIdf1FrfVZrPUVCEPxth6fIV7TWQa11Kwnxa5pAMwtGvDTeaXeSmJtZSYqPtyTtDGitjyTrcvI3yX4G01S1S2v9YrJfXwS2qKT3WJK/TT7fCdxNckzTkO534Dbgn5LjN0JCNDsf/6y1PpdcG4/Z6rITA7wk1mJGUrg8NYe6BUEQBEEQ3lFEtBEE4WLTa/scAHKTn+uBf0qGS4wCwyS8YqqT93/Am14GH+H8m9WU7SilKpRS9yXDJPzAf5LwHDD8mDc3m3aPi3ogA+ix2fjvJDxcUlELpNoUVgEdjmsdvNlPALsYFUzx3T5mm409SZs+TMKb4u3iHL+spPBQD1Q52vxzEt4sqbiFhNjVkQyp2QKglMpWSv27UqojOQ8vAoUqdbhZFdBl++4cv1TYy9cDtzps3gYsSt53zvnDSTGnDMgG9tqeeyJ53TCgtQ452nrIVv4oCcHAPj7p3oFZ0Vr/Kln+KqXUShKeP4/O4dEywMP0MelKUS7VtZT3tdYTJN7RqjTPdzjuOUk3Bs65Pp9Ns9VlobU+CXyWhIjWn/wNmM0+QRAEQRCEi4KINoIgLFS6SITRFNr++bTWrybv/ydwk1JqLYmwpoffYjtfAzSwRmudT0IAUrb795PYFNeQ8Lgxok0XMAWU2uzLnyXJcheJ0CYn50hs7O3UAd1voS9dwAuOMcvVWv+/poDWWiU3rBeKLuCMo808rfUNqQprrV/XWt9EQtx6GPhZ8tafkAgR2pychyuT19WMSqCHhAhmqJuDndph870Om3O01saL42mgTCm1joR4Y+Z8kIRI1mx7rkAnkm2nase0tdPRVpbWei7z66wrFUa8vBN4wCEYpWOARFhfje1abYpy52vfekYplUsiLOxcmjrrHPfmSg/nt/MtobX+sdZ6G4n3TwN/e6HqFgRBEARBuFCIaCMIwkLlW8D/Uko1g5X01wrz0VqfBV4n4WHz81lCOM5HHjABjCmlqoH/ab+ZDJl6nkS+lTNa66PJ6z3AU8A3lFL5KpFwdqlSKl1Iz3eBryqllqsElyTz1vwCaFRK/a5KJMi9nUQOkcffQl8eT9Z1p1IqI/lvk1Jq1Vuoa668Bownk7r6koljVyulNjkLqkTy3A8rpQq01hHATyKcCBLzEARGk3l+vjxLmz8DPqOUqlFKFQFfmKfN/wl8QCl1fdLerGSS2hqApG33A39PQoh4Onk9TiL06/8opcqTfaqeJX8PJNbx/1ZvJtEuS+Zrmgt9wGKTu2mWvtxMQrj5of1GMmHvVc4HkiFuDwJ/mfRwWgl8dI422blBKbVNKZVJIrfNHq213RPmf6pEgula4H8AP30LbfwM+B/JcS4EPv8W6piBUmqFUupqpZQXCJFYe/HzPCYIgiAIgvCOI6KNIAgLEq31QyT+8n1fMlzmEImEpHZ+AKxhDnk8ZuErJBKcjpFITvtgijI/JnWi448CmcAREglmH+DNEBsn/0hiA/oUCbHie4AvmdfmRhKeJkPAnwE3aq0H59uRZI6V60jkWDlHIkzkb0nk7gBAJU4iWjLfumdpM0bC/nXAGRLeKN8FCtI8cifQnpzTT5EI34JEzhNf8vk9JMKO0vEd4EkS+V9aSD1ns9ncRSIx7p+T8DrpIiHW2f+faOb8/mTuIMPngZPAnmQf/ouEh1A6/olEyNJTSqlxEn3bPEdT70/+d0gp1TJLX1pIeIq8ZK4nhZJx4GCauj9NYo56Sbw/PyHhOTYffkxCXBsGLmV6UmRI5AbaSyJJ8S4Sa36+fIfEO3MA2EdC5IySCDF7O3hJ5McZJDEG5cD/ept1CoIgCIIgXHCU1nPxvhYEQVh4KKWuJOFpUK/lx0x4j6KU+g/gnNb6S7ZrHyERxjUnIUIp9bdApdY61SlSqcrfA5y1t+m4r4HlFzgUD6XUTuBbWmtnSKEgCIIgCMK7Es/5iwiCICw8lFIZJEIuviuCjfBeRSm1GPgQb54gBoDW+j/P89xKEl5iB0mcAPb7pD+u/qKhlPIBO0h421SQ8Ox56KIaJQiCIAiC8A4ioo0gCL9xJHO0vEEiPOb3LrI5gnBRUEp9FfgciaO5z8zz8TwSIVFVJHLnfINEONNCQ5EIYfwpibwzu4C/+LU1ptQvgfeluJUDTMp1uS7X5foFuP41rfXXUlwXBEFIiYRHCYIgCIIgCIIgCIIgLEAkEbEgCIIgCIIgCIIgCMICZF7hUcnjQ39dtgiCsMAx77/WGp/PB0A0GiUSiVxMs942Sim01lb/4nE5+VcQBEEQBEEQhHeUQa11mfPifEUbMjIyLpxJgiBcUIz4cKGeMSKGUsoqF4/HycjI4Morr6Snp4fOzk4CgQCpBF1Tr10U0Vpbn+0ikP2zEU1MGbuNSini8fgMocXZl3RtxeNxXC7XjHsul4t4PI7WmnA4LMKNIAiCIAiCIAjvJB2pLkoiYkF4F/FWclTN9oxTTNFa43K5KC4upquri6GhIUKh0HlFH3hTLLFfNwKMXUBxtmkXX5z3TL12Ycde3t6W+W6vay5jIAiCIAiCIAiCcLEQ0UYQhFlJJX5MTU3R09NDKBQiGo0CTPNeMeVTPWsXbJziiR274OOsx/58urqdHjXOPjk9g0S4EQRBEARBEARhoSGijSC8B3grYVPp0FozOjo6Q5RJJ9I47XDWZbx30tk3H7vt9aVqy3j6GFtmE5gEQRAEQRAEQRAuNiLaCMJ7gPkIH6kEl3QeNHZisVhK4cN+zZ43xin4ONu057VJ1Z9U3jL2Z+1CkD03TjqBxh5aJQiCIAiCIAiCsBCQI7+FdxXvdS+JC9H/uYQs2UOTUnncGGHGWW80GrXEG/t1I/ikyl1jyjjtMPdNXeYZ893kunG5XDM8b5zt271wUnnpCIIgCIIgCIIgXAx+ozxtXC4XhYWFVFZWcvz4cSuXxnuNZcuWUVxcTEdHB319fdPulZeXU1JSQjAYpL29/eIYeBERL4nppPMcmY9HiTMRsfOaweVyEYvFpt1PVZcpYy+XKj/NbOFSzgTH9jrMc3Zhxo7xwnHmx7HXJetIEARBEARBEISFwG+UaGOOHG9sbOTMmTPvWdEmMzOTiooKsrOzZ4g2ubm51NfXAzAxMUFxcTFnzpwhEolcDFOFd5jzeai81TrnW87uhWM8b5xl5mqn/dQn53W7x835yptrqRImA7jd7mkeP4IgCIIgCIIgCBebeYVHXeywgXg8TigUoqKiIu1f0d8LxONxMjIyKC8vn7Fpdbvd5OTkUFdXR3Z2Nh/+8IcpKChIO2/5+fnk5eXh9XrfKfOFOeDxeN7yu5bqSOtUZd4OqfLJGGEGZve0OV+9c00IbPfSSVW/M9wq1XHg9nLmXbrYv3OCIAiCIAiCIAiGeSkfXq+XRYsWkZubi8dzcZx0pqamKC4ufk+LNsbDqLCwcMa9UChEJBKhtLSUaDTK8uXLycvLw+12p6zL5/NRXV1NWVnZr9NkYR54vV5qamrIzc1NW8btdpORkUFGRsY7aNncsee6mU8YlvOfue68nwqv10tBQQFut9sSXmYrP5voJAiCIAiCIAiCsBCYl/Lh8/lYv349DQ0N5Ofn/7psSovWmng8TlFRUVoR4r1AOBwGoKCgwLpmNqd+v5+RkRFycnIYHx/n8OHDKKVSem4opYhGozQ0NFghVcLFJzs7m0svvZTS0tKU991uN2VlZdTX17No0aI51ZlKwHi73iTpjvNOVW+qtZeqjFNssScSTicAGS+ZvLw8KisrcbvdM7xm7Lly0gk5JteOhEYJgiAIgiAIgrBQmHd41IoVK7jyyitZtmzZrJu+t7ohnEv4RGFhIT6fb17CzWx/rf9NCoUw+UEikUhKb6NgMMj4+Li18dy1axfDw8NEIpGU+U6Gh4fJzs5O6bWz0DGb83cbU1NTNDY2pp2TwsJCbr31Vr7whS9wxx13zDmMyPn97YgTzvfHrMV0iYTteWacpzjZnzGnTsXj8ZSeL6nq11qTmZlJVlYWbrd72nVnYmLz/qT6PTDX341rShAEQRAEQRCE30zmJdp4PB6eeOIJRkdHrQ0SJBLj2jc6xcXFLF26lNLS0nnntkiVqDTV8b5er3faX9TTkS5R6WxJU1OFZSwkPB4PLpeLQCBARkaGFQ5iNqwdHR3cd999xONx2tramJycnJbPw47WmqysLLKzsxdcPzMzM2e9/8lPfpLa2tp3nddVNBplyZIlFBYWzhDmlFJcccUVBAIB6wQ1t9s9bQzSrfkLIdbY6wOorKxk8+bNXHPNNdOuz3ZkuF1MSReS5MzT5PF4rPfdiC9m3Zu1X1paSmNj44z+2uspLS0lIyMjpX1ut3uaoCMIgiAIgiAIgnCxmVdiGr/fTzweZ2RkhJKSEm688UZ27NjB6tWr2bdvH93d3XR3d+PxeCgoKGBwcJAHHnggZV2zbRxThTLYBZSpqSny8/NZv349zc3NRCIRfvKTn9DZ2TmntpzeJnYxY7YwjlSbXvtf73/dYRWmfSOSDQ8PT7tuxikQCHD69GnrxChzIo4pY7fT7XZbSYhTiToXC7fbzaZNmxgbG+PkyZOEQqEZZZqbm3njjTcYGBggGAxeBCt/PcRiMaLRqCVG2IWNkpISMjMzaWpqYmRkhEceecQSP9LlkDHf7Z4kF2KelVIMDQ1RXl5OY2MjlZWV9Pb2TrtvbzfVe+h2u4lGo7OKuOb+bKdBxWIxPB4PhYWFKcXWgoICVqxYwTXXXMN3vvMdBgcH5ZQoQRAEQRAEQRAWPPMSbaamplBKEQqFrPCFsbExotEoPT099Pf3o5QiEong9/upq6uzNlTpcG7G7N+deSyM18H4+DhNTU0UFBTQ2dlJMBhk586dfPvb355RhzOfRSqPA601GRkZKY/7TffZ7Xazdu1a1q1bR0lJCX19fezfv5+DBw+ilKKkpIRwOMzk5OQFO5pca215HZh5cHoUmA2+EZFMn9J5WdjrNJ4L6UJT3kni8ThHjhyhqKhoVq8N42nxbsK5Bu3zu3nzZlauXInL5aKnp4eOjg7rmXRcSA8bO2atnD17lkgkYnn7pDqlKV3bLpeLiooKhoaG0q4554lSZs0ajGgaj8dxuVxkZWVNE/G01gSDQbq6ujhx4gSlpaUMDw8Ti8XOK8oKgiAIgiAIgiBcTOYl2sTjcWKxGPF4nImJCSKRCMPDw9TV1aG15ujRo9YGq7CwkMbGxmnCi1P0yMvLs0SNdH9FN9g3V5FIhDVr1tDS0sKpU6fIz89n3bp1VnhQU1MTlZWVjI6OcurUKfx+Pz6fj2AwaAkodg+exsZGNmzYwODgIIcPH6a7u3tau07bvF4vTU1NVFRU0Nvby9jYGAUFBSxZsoRjx47R3NxMTU0NXq+X1tZWenp6rE3t5OTkece5uLiYZcuWUVhYyPDwMPv27bM2tFprKzxqdHR0zrlDZsPlcpGdnU1DQwObNm1iaGiIp59+eprYppQiJyeH5cuXEwqFOHv2LJOTk3MWd8wGf65orRkZGSEej6cVvd7q5trlclFdXU0sFmNkZORte+lkZWURDoen9c/tdrN+/XqOHj1KMBicd98jkciMZ5RSbN68mdraWg4cOMC+ffusOXg73jNKKbKysojH40xNTc2pvLETEiLq1NTUtBPl0tniXKsej4cNGzbQ2tpKf3//tL6kC+ez3zdCIyQSdIdCIYqLi+np6Zm2fs1JW4cOHWJ0dDTl2Jr/imgjCIIgCIIgCMJCYV6ijV0gCIfDxGIxYrEYXV1dLFq0iLNnzxIIBHC5XNTX15Obm0ttbS25ubn09/czOjrK1NSUdaRxWVkZQ0NDdHV1pdw4pzrpxWzU8vPz6erqorOzk2XLllk5bvLz82lubmbRokWMjo4CcODAAerr6wkEAnR3d6OUori4mIKCArq6utiyZQtLliyx8sT09/enTNwLic1fSUkJa9eupa2tjSNHjhCLxWhubqa0tJT8/Hw2bdpEKBSivLwcSIg8AwMDlJWVcfjwYRYvXszQ0BDRaJS8vDwKCwsJBAJ0dXXh8/lYunQpK1asoL6+nmg0ypEjRyyvGkiIBABDQ0OzegWlymvixJQpKipi9erVrF27lszMTF555RUmJiYsr4bc3Fzq6+tZsmQJxcXFvPzyy3R0dBAIBNKuFyMG1dbW4vP56O3tZWhoaJowcD7vhrGxsRl1mvIulyvlqVjno7KyklWrVuFyuejs7KSzs5OJiQmUUhQWFlohNhMTEwwMDMwIhysoKCAvL89a9zU1NZw9e5bh4WFCoZCVO2XTpk309/fT29uLz+ejoKCAeDxOf3+/dQJYKkpLSyksLKSiooKysjL8fj+Tk5N4vV42bNjA2bNnOXDgAMePH7c8qswYlJeXEwqFmJiYmCb0pQo5NP1atGiRlfR4ZGSEnp6eGTa5XC5ycnKorKyku7ubqakpq/5YLDZn4cvpBed2uykuLiY3N5fBwcFpYYbpBBtnP8y/UCjE+Pg4NTU1+P1+SkpKGBoaIhAIkJeXR01NDYcPH2Z8fNwShzIyMvB4PMRiMcLhsOSzEQRBEARBEARhQfGW40qMB0QkEmFwcJCCggIrFCoSiaCUory8nA984AP84R/+IVdddRWVlZV4vV7Ky8u56aab2LhxI7fccgulpaVWklGzUTObKefGzOTLOXz4MKOjo5aXTmZmJh6Ph40bN6KU4qWXXmJwcJANGzaQkZHB2rVrufnmmykpKaGyspIdO3bw0Y9+lPLycpqamvj3f/93wuEwRUVF+Hw+q5/OMKvCwkLWrFmDUoq9e/cSCASIRqMcPHiQ3bt3s2jRIgoKCnjooYf4+c9/Tm1tLZdeeilVVVVce+215OTkcOedd7JhwwaamprYsWMHH//4x7n11lvx+XwsW7aMpqYmurq6OHnyJMuXLycrK2vaGGRkZBCPx+np6Zm2AXd6NKULT3HmwIlEIpSXl7N48WLuuecevF4vRUVFZGRkoJQiNzeX5cuXs337dnp7e1m7di1LliwhPz9/1k1udnY2K1eu5Pd+7/f4oz/6I66//nrKysqseczJySE/P5+ioiK8Xu+sYU6ZmZnk5+dTUlJCaWkp2dnZVh3nS1js5H3vex+1tbUsX76cbdu2UVNTAyTynqxdu5abbrqJ22+/ne3bt+Pz+ab1MTs7m9WrV3PjjTfi8/nYuXMnn/jEJ7jqqqsskS4rK4uNGzcSj8cpLCwkJyfHeua6666jpqZmWp1GfDLvwMaNG1m/fj3btm1j8+bN1NXV4fF4aGhoYOnSpTz22GMcOHDAEr/s78327dtZuXLljDFxCnrmu9frZfv27WzdupX3v//9XHvttdPWvyEnJ4dVq1Zx1113UV1dbdnq8Xgszy9TZ3Z2Nrm5uWRlZVn3zJoqLi4mPz/fuhcOh9m9ezd9fX0zvF9mCxMznlvRaNQKAQwGg4yNjbF8+XKWLl3K7bffzurVqyksLCQvL4+qqiorCXFWVpZ1raGhgUWLFpGTk2OJgSLeCIIgCIIgCIKwEJiXpw3MTOIbj8cJhUIUFRVZmx3zF/TMzEyqqqr4u7/7O/74j/+YyclJXC4XixcvRmvN9773Pe6++27KysoYGBiwks26XC4uu+wyenp66O3ttbwSTL11dXU88MADVl4KSISjFBQUcOWVV/L973+foaEhiouLrZCNJ554gm9961u0tLRQVlZGVlYWv/rVr1i3bh3d3d0UFBRQUVGB2+227LBvhs1f9isqKrj00ku57777rOsmUWpeXh6LFi3iwIED+Hw+/H4/ra2tltfF6tWrufLKK6mpqeHTn/400WiU1157jV/84hfcfPPNXHfddaxZs4Ynn3ySNWvW0NjYyGuvvWZ5TZjxHRgYYHJykrGxsVnDUFIlnXXmDDLzZHL6dHZ2smfPHsrLyxkaGsLj8bBixQquuuoqhoaG+OIXv8hTTz3Fvn376OvrS9u+2+1myZIl3HLLLfzLv/wLo6Oj3HLLLWRmZuL1emloaGDnzp0UFBSQn5/PE088QUtLCwMDAzPsVEqxZcsWtm3bRkNDA1lZWTzyyCP4/X7KysooLCxkZGQkZaJbMxb2/jY3N/Pwww9z8OBBK0TH6/Xy+7//+xw6dIgHH3yQJUuWWG3u3r2bUChENBqlqqqKnJwczp07xyWXXALAwYMHLY+pc+fOkZ2dzcaNG7n77ruZnJxk3bp1VFdX09fXR1dXF3/1V3/Fpz71KSs3VHFxMaWlpcRiMdrb28nNzaWvr4+jR4/S1tZmeWndfffd7Nu3jxMnTjA2NjYtX5Hp66WXXorH4+H48eMp17FzbLZs2UI4HObJJ5+kvr6eyy+/nNtuu40f/OAH08qvXLmS7du3c+LECbZt20Z3dzfRaJRFixaRm5vL6Ogovb29ZGdnc9NNN1FfX8+ZM2fYv38/586dY8uWLdx5551kZWVx+vRpnn/+ed544w3Gxsbo7u6ekW/KOX8ej2eaV5E9F5O5FggEGB0dpba2lq1bt/Lss8+ydetWlFL4/X6KioqIxWK43W6uuuoqtm3bRk5OjuV1c+rUKb797W9f9HxOgiAIgiAIgiAIhnmLNgaTBNZsnMxmyGy0TMjECy+8wOjoKC0tLRQVFbFt2zaysrI4dOgQf/Inf0JRUZGVD8R4Wng8Hq6++mqeffZZRkZGCIfDlkBkTtYZGRmx8n5MTk7S29tLXV0dmZmZRKNRVq9ezebNm1m0aBGXXXYZv/rVr7jnnnt4//vfT1VVFXv37uW5555jx44d+Hw+vvjFL/LII4+wd+9eYrHYjBwspr+ZmZlWKAcw43QfpRT19fW88MILxGIxuru7LU+KU6dO8bnPfY7PfOYzfOlLX6Kvr4/Dhw9z8OBB4vE4v/M7v4PH4+HGG28kFovR0tLCrl27rA2q2Xh3dHRYYktxcTF9fX3A/PLYQMJbKiMjg4KCAsLhMCdPniQejzMwMGDZvG3bNnbu3MnixYt5+umn+YM/+ANGRkYIBAKz5v7weDxkZmaitaa3t5dYLMaPfvQjMjIy2LFjB+vXr+fRRx+ls7OTwsJCvvCFL3DFFVfw3HPP8fLLLwOwbt06Wlpa2Lx5M2vXrqWzs5N/+7d/w+PxsHPnTurq6ojFYpw5c4b29vaU4TQGn8/HDTfcQGtrK2fOnGF0dHSa4HfrrbfS1tbGnj17WLp0KZdccgler5c33niDNWvWsHz5cnbt2kVtbS3Z2dn09PTw4Q9/mFdeeYUrrriCb37zmxw/fnxaziETbnPZZZfR19dHb28vN910Ex0dHWzfvp1QKMTixYupq6ujvr6e6upq7rrrLp566ik2b97M8ePH6erqwuVy0dDQwCOPPEJjY6MlMvh8Pnw+H5FIxArvysnJweVyzTiRySmCmXdt+/bt/OhHP2J4eJjm5mYaGhpoaGjg/vvvt0LyysrKWLp0KTk5OTz00EOWN53L5aKwsJCGhgbC4TADAwO43W42bNjAnj17aGho4Oqrr7ZyYH3lK19hYmKC5uZmtm7dysaNG7n//vvZsmULP/vZz6zTzoqLi6mqqiIzM5P9+/dz++23s3PnTh566CGef/55a30qpaYlEza/QWvWrOHRRx/l9ddfZ/HixXi9XkuYU0rxoQ99iHXr1vHII49w6tQpy2PJ3BcvG0EQBEEQBEEQFgrzFm1Shdvk5ubS1dVlbVLNBnFiYsI6lvrUqVOsWLGCVatWsXjxYsbHxzl06BBf/epX6ezsJC8vjyVLlrBq1Sq6u7u55ppr+OlPfzrtqGeToPU//uM/6OvrsxLUmoTIQ0NDNDU18clPfpKzZ8+yd+9e2tvbLU+dlpYWbr/9doqLi3G5XIRCIQYHB/mzP/szvv71r/Paa69ZYozH46GiooLOzs5p4UfRaJRwOExjYyOtra3T8ob4/X5Onz7Nxz/+cQ4dOmSFXrW3t1t1X3755QwPD/PSSy8RjUYZHR217J+cnKS5uZmWlhZaWlro7Oy0BCR7smZzvLFJfmxEm3RzNds1k68mHo9b9Sil8Hq9lvfI0aNH+da3vkV/fz9+v39Op2FFIhG6u7s5ePAg3/jGN/iHf/gHenp6WL9+Pfn5+bz++uucPHmSSCRCNBplfHycSy+9lGAwSGtrK8FgkGuvvZbjx49z2WWXMTg4yL59+yxx4pe//CVaay6//HKKi4vPaw8kQqxuueUWOjo6rDVhPLRWrlzJrl27qK6uZt26dfT39/Poo48yOTnJ2bNnueOOO3jppZcoKyujqKjIOu0oJyeHb37zm5w5c8YSDfLy8ti8eTMul8vKgVRdXU13dzd9fX08/vjjNDY28rnPfY5z587xwgsv8OKLL3LDDTdw6aWX8sILL1gipUkO3NbWxsTEBDfeeCPl5eWMjIxQUFDA5s2bue2229izZw9PPfUUtbW1HDlyxAotTJWk2Kxlt9tNZWUl4XCYqqoqSktLGR8fZ3x8nMbGRktMLC4uxufzMT4+TjgctvI9AYyOjhIIBKw5CAaD+P1+Ojo62Lt3L/X19Vx33XVs3bqV+++/n+HhYfbv38/Y2BhNTU3ceuutFBcX86tf/YrOzk5Wr17N6tWraWhoIDc3l8zMTMLhMLt27aK+vp4tW7bwzDPPWF5Kpj8mPMuE87366qtMTEzg9/stUcblcpGbm0tRURFut5vs7GwyMjKsEEeTp0tEG0EQBEEQBEEQFgpvy9MGEt4akUiEffv2WRsppRTj4+McOHCAoaEhACspcF9fH6dOnaK6uppz585x7tw5pqam0FozOTnJ+Pg4kBAmJiYmZhwXHo1G2bt3L36/39qkTU1NMTAwwMTEBD/5yU8YGxuzQqtMu7FYjLGxMVpbW2lqaiIejxMOhzlx4gRtbW2sXr2avr4+ampqKCkpoaysjEOHDtHV1TWtv/39/bS0tPBbv/VbVuJZk0h4aGiIgYEBHnroIVasWEEkEqGlpYUzZ87Q19fH1NQU9957L5OTkzz//PNAItGu8Rp45ZVXWLRoERUVFVRVVeF2u8nKyqK4uJjHH3/cEku01oTDYUZHRzlx4sR5Q0tmm0MjMpg5Mt4LkMhN4na7GR0d5cyZM0QikZTHoqciHo8zMjLC7t27AfjoRz/Ks88+y9q1axkbG7M8HJRSNDc309/fT0lJCZFIhNzcXCKRCJWVlZaHRDQaJRAIWOtrbGyMvr4+CgoKKCgowOPxTBMTnEQiEVpbW/nIRz7Cvffea42lEVUikQjBYJCJiQn6+voIBAKMjY0Rj8fx+/0UFhZyySWX4PP5iMViFBUVMTExwe7du+nu7iYQCFgC29jYGE8++aQlKng8HoaGhti/f7+V3Dg/P5+lS5eyZ88eDh48SDgcpqOjg6qqKlwuF+3t7fj9fksEDYfDnDt3jocffpjGxkbC4TC9vb20trYSjUbJyMhgamrKSg5uf29mE+8yMzPJzs6mubmZzMxM9u3bRywWY8uWLRw5cgRIeCllZWVNC380fRsZGWFoaIjs7GxLPJmamrKExnA4bNm+ZcsWOjs78Xq9VFVVUVRUZJ0YtXTpUpRSrFq1inA4TGtrK1u3bmXlypUcP34cr9fLpk2bqKmpIR6P88QTT8xY70a4MWKN1prBwUErWbaZn927d+NyudixYwdXX301vb297N+/n3379r3rjo8XBEEQBEEQBOE3m7ec08Yu2gwNDVkeI5DYEI6Pj9PS0mLl3jCn5vT09NDZ2Ul1dTVDQ0NW6FM4HKa/vx+Xy0V+fr513LMzh45SyvKGMZvIQCDAiRMnCIVCPPfccwBMTk5O28QaW1999VX6+/utE6L6+vp46KGHaGxsZMWKFYRCISs5r9/vn9H/0dFRDh48yMqVK1m3bh3j4+OMjY0RDAYt4em5555jzZo1TExM0N7ebvVlcHCQl156iUgkQnt7u+UB4fF4LJHLJGutrq6mqKiIYDBIMBicIcLEYjECgYAVpjQXkSbdPJp8NmacjHBmxICCggKqq6stj6Dx8fE5tRMKhSwPrJqaGq666iry8vKIRqPk5uZSXFxMfX09S5cu5ciRI0xOTuL3+60wFSPq9Pb2kpmZSWlpKWfPngUSITTBYJBwOIzP5yM/P98SCFMRi8Xo6OiwcurYT6GampoiHA5z5ZVXEggE8Pv9DA4OWuF3oVCI48ePs3TpUnp7e+nu7sbj8VBcXExRURF+v59QKGQJWn6/3xJttNacPn3a8lyamJjA7XYzODhIa2srBw4coLe3l9zcXEs0isfj7N+/f1rOIK01oVCIZ599liVLlhAIBJiYmGBwcJDu7m4qKioIh8NMTk5atszmMWKEt/b2djZs2GDl4zECydatW8nIyACw5qGgoMAKBzR9C4VCDAwMWDlnXC4XkUiEJUuWUFJSgsfj4cyZM3R3d7N582YOHz5MVlYWBQUFVo6furo6S4wpLi7mxIkTnDx50soxVV1dTV5eHqOjo1RUVHD55Zfz/PPPW++cM7H28PCwFR5m+hkMBunt7SUajXL69Gl8Ph/r1q2jtLSU4uJitmzZQk1NDU888cS0900QBEEQBEEQBOFiMi/Rxmxk7BteI8Y4E6KGQiFOnDgBYAkCp06dsjwYuru7ZwgqY2NjhMNhKzwlXTJdpw3j4+O0tbXhcrkYHR21TrEyG0t7Lo/W1lba2toALM+RXbt20dnZyapVqyw7Dx48yNjY2IxkxFNTU3R3d/Ozn/2M6667Dq/Xy6lTp+jt7bVOzRoaGuKFF16YZq/x7DEJc+31xmIx+vr6cLvdPPzww6xdu5aGhgaUUvT09Fh5dpx9SeXxkmqsUmG8EkzeHbsI19/fT25uLmNjYwQCASorK1m/fj0jIyMcOnTIEnVmw8ybye3y6quvctddd9HZ2Ynb7Wbt2rXk5eVxySWXMDg4SEtLC2fPniUrK4toNEo8Hufo0aPEYjEOHTrEpk2bWLVqFefOnSMWi9HU1MTIyAinTp3C6/VSV1fH8PDwrImZQ6EQe/futUKDICFIGK+sLVu20N7ezoEDB6zE2CYk74knnuCyyy6ju7ubY8eOUVpaisvlYuPGjUxOTlonmQGEw2GOHTtmjfHu3bvZsGEDq1atwuv1cu7cOcbHx/nBD37AqVOnLOHS7/dbfT5w4ICVw8nep7Nnz9LR0WH1ycxhe3s7VVVVTE5OEgwGpyUpNqeNOeuKx+M8++yzXH311Rw7doy2tja6urrIz8/H5/NRVFTE8PAwIyMjDA8PU1ZWZs2tIRaLWR5n5vdgcnKSlStXMj4+zunTp9m7dy8TExNcf/31rF27llAoxMmTJzl27Jh1RHpNTY2V0Hh0dJTh4WHeeOMNiouLufzyy8nIyODll1+moaGB9evXU15eTldXl9UnI1K53W76+/utULWcnByysrLo6+vj9OnTVtLtQ4cOcezYMfLy8li1ahUf/OAH+e3f/m2OHDnC6dOnRbQRBEEQBEEQBGFBoObqmQHgcrm0z+ez/tpuwiGMx4vZCJvNZGZm5rRwCbs3iH3DaT8VpqioiA9+8IPk5ubywx/+kPHx8WmJfs1mbFonkuKAXVQy9ZvNqr2MSeprDxGBN5MKm2OEjQhjt8++mXO73dM2wmYs7KKM3VvI7hVgTrUy9tvvmYTOziON7WOXdkJt4ou93lTijsm5UlhYCIDf77cSzLrdbsLhMFlZWSxevJgVK1bQ29vLnj17ZoSsOTEhTZmZmeTk5LBjxw62bt3Kgw8+SFtbG7W1tWzcuJFoNMozzzxDZ2en5aVlt90Ic5mZmdx0002sXr2awcFB+vv7qaur47vf/S51dXVs3bqVYDDI9773vVnz7bhcLlasWEFGRgYdHR2WJ5Xb7Z4RFmNfo/Y+2ddCRkYGWVlZ004xs4eqmfVhjqM3a8OEcZkj1Y1t5ghsMwb2f8Y+4wVmt9P0oampiTvvvJMXX3yRJ5980srlUl9fTygUYnh42Krf1GX6bn+Ps7Oz+djHPsaJEyd45ZVXCAaD1hHePT09lqhmb9/ehx07dtDc3ExrayuHDx8mHo+TmZnJl7/8Ze6//35OnjzJ+Pg4sVgMr9dLdXU1S5cu5ejRo0SjUfx+P36/3zoZasWKFQQCAYaHh2lqamLLli10dHRw3333EYlErD4UFRWxfPlympubuffee3G73dx8882cOXOGU6dOUVRUxKpVq2hra7O8xbKzs6muruayyy7jd3/3d/n7v/97du3aZXniCYIgCIIgCIIgvEPs1VpvdF6cd3iUcxNp9xaxCxWAJTjYsW+EnUKE2QgvXryYXbt2zdiAm7pMbgq7QAFvhvqYTaVduHGeBmXfpBuhwp4PxJS1eyw4RRj7Bt6UNW2ZsqnEHrNxN9edngupPIzs42gXgeynVzmFGec4O583otDQ0NC0eTHjbo4/b2tr48iRI3M+Cnn79u2sXLkSr9drhcD86Z/+qRVm09/fz759+6y1ZE+ybMcuhDzyyCO8/vrrrFy5kkgkwmOPPUYwGGR8fJzCwkJycnLOa1c8Hre8rOztOXPA2MfJPg8mbMZ8D4VClvDj9IIy9dqFN3M9Go1aophZK852jb1mLZgcMXZh1N4vpRQlJSXTQuYMlZWV1NXV8cILL8xIWm0XNu3j8dhjj/HZz36WlpYW/H6/lXjZvgZSrbd4PM6JEydYtWoV8GZolcvlYs+ePfh8PutdNGNoRBUzXk6R9tixY9aYt7W1kZ2dzR133MEDDzwwzVNvamqK3t5eBgYGrJDLZ555hnA4bIVKjo6Octddd1FRUWGJMibE80tf+hKvvPLKNGFLEARBEARBEAThYvKWTo9yhg6YDZX9mGH7d7NhtW9CjReCed6IMJmZmSxZsoTOzk7i8fg0rxPjIeMUh+z2OP86bq/bbG6NHfCmV445+cp+z9k/+xjMNjZ20cVuqxFZ7CKV3aPEWZfTq8aZTwjSizmz4RQs0j1nF4Pm45H10ksvWcleTR0mBMgpSJmTsOybdft8GTuCwSAdHR309vZam30jNrz44otWXecjVT9SrWl7v50eS/br6eo3Xmj2emDmHKXzWLF7G9lFPvs7Y54zQs6yZctob2+3jlg3dR45coSlS5dOW2d2Ycn+HRJzZRIG5+bmMjw8bHntzDaOxla/308gELDCD83a7+npsUTVcDhsHTlvF+eMLeadNP0wnkB+v5/e3l68Xi/Lly+nra3N+p2ZmJhgcnJyWt4dczy4y+VicnKS1157jUOHDuH1eq2wMSNShkIhpqamZk1oLQiCIAiCIAiC8E7ylk6PSuUd4hRUnCFBds8XpyBhyvh8PkpKSsjMzLROCrJ7vdiFELvYYBc+7Bs/Z96XVKFCZsPnFHTmumlz2mD37HGKN8YGpweM3UvJfj0dThFgrs/NVpfTE+it1GffbBvxzClspBIwnN4VzvGzj5NJPmsXO+Y7Z6lsTiWOzVYmlZfJbGKeE3sOFueY2MUnu9CZ6t0xZGRkUFtby6uvvmolZDbPGLFibGwspU1ZWVkz3rVoNEpLSwsNDQ2WUHK+8bWH5PX19U1LWB2LxRgfH2dwcBC/32+VjUaj1rvr/I0ArPEx92KxGIODg7zxxhts2LCBkydPWvftIp9zPdvF2MnJScvLydmu04tPEARBEARBEAThYjJv0cYujqTyeHGG0NhFG/s1Z51moxQMBnn++een5c2w5/0wmzPnRtrumeAMW3J6d6Syw3nP+X0+OG1zemqkI5Vtzs1nKhFkNhvmyoXwLDifqJEqr1GqNWPuO+fPfE41v/DW58wpNM7Wt1Tilr2eVM+YtZlqLp2eO3bBwS5qpfLasa/rrKwsAoGAleTYblssFuPs2bMzvGUMGRkZVFRUkJmZaYVglZWV4XK5KC8vx+fzzWlszf2pqSmOHDli5a0xdHV1MTExYXld2ftn71uqduy/BYFAgDNnznDFFVfw4IMPWvXBm94+pj772Nk9uOyJve2CjlmngiAIgiAIgiAIC4G3fOS3UzCBmYKO+ey8lsqTwXgYDA8P88wzz0zbSDk3u/bNqrnmTF7rbDuVmDSXPs6VdN4v9nupxiKVPc7PcxVqFiIX0n6nB1A6weft2nc+7GLD+YSe8wmBqbyO7N43zj6nssO8C62trfT19TE1NTWjfDAYTGtnLBYjIyOD7OxsAHw+H/X19QSDQSs0ai7CmLFnamqKjo6OGfb19/dPmzOncJWuvvz8fCuMLjs7m0WLFpGdnT3NS8fejvN5p0BkPGqcYyzeNYIgCIIgCIIgLDTmfeR3Og8W8z3VM06cngdGVIlGo0xMTMw4McqUcebfcG7QtNaWp0C6zVgqr5tUG+m5eMbYyzvtsNdjtyHVX/TPN26zeQfZr6US1NKRqm9vx7totnYMc7HLXtZeh93TIp2o9XZst9c71817Og+qdJ47s11zetmYe/Z6jdeIff2adycQCPBf//Vf1nPzIRAIcOzYMTIyMqyjvkOhENFolI6ODiYmJqa9s+mEMqcA4/RacYok9lPd7PmrTBnzbFVVFVprCgoKqKmpoba2lry8PB5//HErnMnehhkr+wlt5howzRvPjhnb3yRRVBAEQRAEQRCEdzfzOvLb7XZrk//Cvtl0JhU2my6TYBZmbpTM5tSeC8e+YXWGTDg3jM6Noz0Hit0eU4czFMMagGR5ezjW+Ui1Sbe3k06EOZ/XRSqxIF0Zu3dSqucX0sZzruFh53ve/t1wIfqZTvQ5X7vnE4vmKv44w8acYYH2+p1hUs73xn5vNtvS2WveB5Pc2B5KZN6/83mp2cMdnXalElw9Hs+M99X0VWtNaWkpVVVVFBUVEQwG6e/vZ2xsjHA4bOVPsv8mOU+JM9fNsetG2LWLu+b3ySQlDgQCsx4fLwiCIAiCIAiCcIFJeeT3vEQbpdQA0HEhrRIEQRAEQRAEQRAEQXiPU6+1LnNenJdoIwiCIAiCIAiCIAiCILwzyDEpgiAIgiAIgiAIgiAICxARbQRBEARBEARBEARBEBYgItoIgiAIgiAIgiAIgiAsQES0EQRBEARBEARBEARBWICIaCMIgiAIgiAIgiAIgrAAEdFGEARBEARBEARBEARhASKijSAIgiAIgiAIgiAIwgJERBtBEARBEARBEARBEIQFiIg2giAIgiAIgiAIgiAIC5D/C76tBn5G10ghAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAABCCAYAAADt2ys3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAA4l0lEQVR4nO29eXicV3n3/zkzI41G+y5Zq+VFtiU7XmLHcWziOAnJ6/wCaUiTNIUQSuEtv5bywq99C2/hKqX0pXShb9rSXylLCaRAICG7IUuzLzZJLFte5V2LZe3bSJoZzXbeP2bOk0ePZmQpMbFI7s91+crM85znnPssz+Q6X933fZTWGkEQBEEQBEEQBEEQBGFh4brYBgiCIAiCIAiCIAiCIAgzEdFGEARBEARBEARBEARhASKijSAIgiAIgiAIgiAIwgJERBtBEARBEARBEARBEIQFiIg2giAIgiAIgiAIgiAICxARbQRBEARBEARBEARBEBYgItoIgnDRUEpppdSyd0s771aUUn+plPrPi2zDL5VSd6W5tzg5x5532q53GqXU80qpT1yktieUUksuQD0fVko9dSFsSlF3u1Lq2jT33qeUOvbraFcQBEEQBOHXhYg2giBcEJKbpcVKqXuUUh9TSv15cpM3oZQKKaVitu+HL7a971WcApZz3i6iabOitd6ptf7BxbbjnWQhiGV2tNa5WuvTF6CeH2mtr7sQNs2z3Ze01ive6XYFQRAEQRDeDiLaCILwa0Fr/bXkJi8X+BSw23zXWjdfbPuE9ybvpDfOu8Xz593SD0EQBEEQhN9ERLQRBOFic61S6oRSalQp9a9KKWVuKKU+rpQ6qpQaUUo9qZSqT17/V6XUN+yVKKUeVUp9br7tKKWWKqWeVUoNKaUGlVI/UkoVJu99Xin1gKOdf1JK/XPyc4FS6ntKqR6lVLdS6q+VUu5UjSul3Envo1NKqXGl1F6lVG3y3hVKqdeVUmPJ/15he+75ZL2vJr2UHlNKlSTt9CfLL7aVX6mUelopNayUOqaUum1Os3B+MpVSP0zaflgptdHWZpVS6udKqQGl1Bml1GfSVaKUukEpdSRZT7dS6k+T14uUUo8n6xhJfq5xjMMnbGP5D8n5Og38P7MZnvQm+rxS6gAwqZTyKKUuT47pqFKqVSl1VbLs7UqpNxzPf04p9WjyszfZdqdSqk8p9S2llC957yql1NlkW73A95VSLqXUF5LzPqSU+plSqjhZ3oR13ZWsb1Ap9cXkvf8G/Dlwe3LeWx02ZSbneI3tWrlSKqCUKpttPJJlS5Jryayhv1ZKvWy7r5VSf6SUOgGcsF1blvx8T7LvTyfn8gWVfD9tZT+jlDqd7NffK6VcyXsfS9HWp1Tq99OtlPpGso4zSqlPq/OHwq1TSh1Qiffpp0qpLPv82Nr9fHINjifflWvON26CIAiCIAjvNCLaCIJwQdBaL9Zat2utP6a1vmcej94IbAIuAW4DrgdQSt1EYtP6IaAMeAn4SfKZHwB32DaBpcC1wI/n2w6ggL8BqoBVQC3wl8l79wE3KKXyku24k8+adu4BosAyYD1wHZAu38j/B9wB3ADkAx8HAskN/C7gn4ES4B+BXUqpEtuzvwPcCVQDS4HdwPeBYuAo8OWkfTnA00n7ypPP/f9KqaZ0gzKPeftgcjwKgUeBbybbdAGPAa1J+64BPquUuj51NXwP+AOtdR6wGng2ed2V7FM9UAcETRsp+CSJ+VwPbAR+exa7DXeQEHcKgQoSY/7XJMbwT4GfJ8WOx4AVSqnltmd/lzfn/OtAI7COxLxXA39hK1uZrLMe+O/AHwO/BWwnscZGgH912LYNWEFi7P5CKbVKa/0E8DXgp0nvtLX2B7TWYRLz8RFHH5/RWg/MYTz+FZhM2ntX8p+T3wI2A+nWz4eBrwKlwH7gR477N5OYnw3ATSTWfDrSvZ+fBHaSGO8NSZvOx23AfwMakvV9zFlAKbUC+DSwKbkWrwfa51C3IAiCIAjCO4qINoIgXGy+rrUe1Vp3As+R2JxBIqTqb7TWR7XWURIb2HVKqXqt9WvAGIlNLiTEiee11n3zbUdrfVJr/bTWeiq52f1HEhtstNYdQAuJzSfA1UBAa71HKVVBQoD5rNZ6UmvdD/yfpC2p+ATwJa31MZ2gVWs9REJIOKG1vldrHdVa/wRoAz5ge/b7WutTWusx4JfAKa31fyXH5X4S4gUkNr7tWuvvJ+vaB/wcuHWWcZkrL2utf6G1jgH3AkZE2ASUaa3/SmsdTuY8+c4s4xABmpRS+VrrEa11C4DWekhr/XOtdUBrPQ78b5LzkILbgLu11l1a62ESotv5+Odk+SAJoeMXyf7EtdZPA28AN2itA8AjJAQQkuLNSuDRpPfHfwc+p7UeTtr5NUdf48CXk+spSGIdf1FrfVZrPUVCEPxth6fIV7TWQa11Kwnxa5pAMwtGvDTeaXeSmJtZSYqPtyTtDGitjyTrcvI3yX4G01S1S2v9YrJfXwS2qKT3WJK/TT7fCdxNckzTkO534Dbgn5LjN0JCNDsf/6y1PpdcG4/Z6rITA7wk1mJGUrg8NYe6BUEQBEEQ3lFEtBEE4WLTa/scAHKTn+uBf0qGS4wCwyS8YqqT93/Am14GH+H8m9WU7SilKpRS9yXDJPzAf5LwHDD8mDc3m3aPi3ogA+ix2fjvJDxcUlELpNoUVgEdjmsdvNlPALsYFUzx3T5mm409SZs+TMKb4u3iHL+spPBQD1Q52vxzEt4sqbiFhNjVkQyp2QKglMpWSv27UqojOQ8vAoUqdbhZFdBl++4cv1TYy9cDtzps3gYsSt53zvnDSTGnDMgG9tqeeyJ53TCgtQ452nrIVv4oCcHAPj7p3oFZ0Vr/Kln+KqXUShKeP4/O4dEywMP0MelKUS7VtZT3tdYTJN7RqjTPdzjuOUk3Bs65Pp9Ns9VlobU+CXyWhIjWn/wNmM0+QRAEQRCEi4KINoIgLFS6SITRFNr++bTWrybv/ydwk1JqLYmwpoffYjtfAzSwRmudT0IAUrb795PYFNeQ8Lgxok0XMAWU2uzLnyXJcheJ0CYn50hs7O3UAd1voS9dwAuOMcvVWv+/poDWWiU3rBeKLuCMo808rfUNqQprrV/XWt9EQtx6GPhZ8tafkAgR2pychyuT19WMSqCHhAhmqJuDndph870Om3O01saL42mgTCm1joR4Y+Z8kIRI1mx7rkAnkm2nase0tdPRVpbWei7z66wrFUa8vBN4wCEYpWOARFhfje1abYpy52vfekYplUsiLOxcmjrrHPfmSg/nt/MtobX+sdZ6G4n3TwN/e6HqFgRBEARBuFCIaCMIwkLlW8D/Uko1g5X01wrz0VqfBV4n4WHz81lCOM5HHjABjCmlqoH/ab+ZDJl6nkS+lTNa66PJ6z3AU8A3lFL5KpFwdqlSKl1Iz3eBryqllqsElyTz1vwCaFRK/a5KJMi9nUQOkcffQl8eT9Z1p1IqI/lvk1Jq1Vuoa668Bownk7r6koljVyulNjkLqkTy3A8rpQq01hHATyKcCBLzEARGk3l+vjxLmz8DPqOUqlFKFQFfmKfN/wl8QCl1fdLerGSS2hqApG33A39PQoh4Onk9TiL06/8opcqTfaqeJX8PJNbx/1ZvJtEuS+Zrmgt9wGKTu2mWvtxMQrj5of1GMmHvVc4HkiFuDwJ/mfRwWgl8dI422blBKbVNKZVJIrfNHq213RPmf6pEgula4H8AP30LbfwM+B/JcS4EPv8W6piBUmqFUupqpZQXCJFYe/HzPCYIgiAIgvCOI6KNIAgLEq31QyT+8n1fMlzmEImEpHZ+AKxhDnk8ZuErJBKcjpFITvtgijI/JnWi448CmcAREglmH+DNEBsn/0hiA/oUCbHie4AvmdfmRhKeJkPAnwE3aq0H59uRZI6V60jkWDlHIkzkb0nk7gBAJU4iWjLfumdpM0bC/nXAGRLeKN8FCtI8cifQnpzTT5EI34JEzhNf8vk9JMKO0vEd4EkS+V9aSD1ns9ncRSIx7p+T8DrpIiHW2f+faOb8/mTuIMPngZPAnmQf/ouEh1A6/olEyNJTSqlxEn3bPEdT70/+d0gp1TJLX1pIeIq8ZK4nhZJx4GCauj9NYo56Sbw/PyHhOTYffkxCXBsGLmV6UmRI5AbaSyJJ8S4Sa36+fIfEO3MA2EdC5IySCDF7O3hJ5McZJDEG5cD/ept1CoIgCIIgXHCU1nPxvhYEQVh4KKWuJOFpUK/lx0x4j6KU+g/gnNb6S7ZrHyERxjUnIUIp9bdApdY61SlSqcrfA5y1t+m4r4HlFzgUD6XUTuBbWmtnSKEgCIIgCMK7Es/5iwiCICw8lFIZJEIuviuCjfBeRSm1GPgQb54gBoDW+j/P89xKEl5iB0mcAPb7pD+u/qKhlPIBO0h421SQ8Ox56KIaJQiCIAiC8A4ioo0gCL9xJHO0vEEiPOb3LrI5gnBRUEp9FfgciaO5z8zz8TwSIVFVJHLnfINEONNCQ5EIYfwpibwzu4C/+LU1ptQvgfeluJUDTMp1uS7X5foFuP41rfXXUlwXBEFIiYRHCYIgCIIgCIIgCIIgLEAkEbEgCIIgCIIgCIIgCMICZF7hUcnjQ39dtgiCsMAx77/WGp/PB0A0GiUSiVxMs942Sim01lb/4nE5+VcQBEEQBEEQhHeUQa11mfPifEUbMjIyLpxJgiBcUIz4cKGeMSKGUsoqF4/HycjI4Morr6Snp4fOzk4CgQCpBF1Tr10U0Vpbn+0ikP2zEU1MGbuNSini8fgMocXZl3RtxeNxXC7XjHsul4t4PI7WmnA4LMKNIAiCIAiCIAjvJB2pLkoiYkF4F/FWclTN9oxTTNFa43K5KC4upquri6GhIUKh0HlFH3hTLLFfNwKMXUBxtmkXX5z3TL12Ycde3t6W+W6vay5jIAiCIAiCIAiCcLEQ0UYQhFlJJX5MTU3R09NDKBQiGo0CTPNeMeVTPWsXbJziiR274OOsx/58urqdHjXOPjk9g0S4EQRBEARBEARhoSGijSC8B3grYVPp0FozOjo6Q5RJJ9I47XDWZbx30tk3H7vt9aVqy3j6GFtmE5gEQRAEQRAEQRAuNiLaCMJ7gPkIH6kEl3QeNHZisVhK4cN+zZ43xin4ONu057VJ1Z9U3jL2Z+1CkD03TjqBxh5aJQiCIAiCIAiCsBCQI7+FdxXvdS+JC9H/uYQs2UOTUnncGGHGWW80GrXEG/t1I/ikyl1jyjjtMPdNXeYZ893kunG5XDM8b5zt271wUnnpCIIgCIIgCIIgXAx+ozxtXC4XhYWFVFZWcvz4cSuXxnuNZcuWUVxcTEdHB319fdPulZeXU1JSQjAYpL29/eIYeBERL4nppPMcmY9HiTMRsfOaweVyEYvFpt1PVZcpYy+XKj/NbOFSzgTH9jrMc3Zhxo7xwnHmx7HXJetIEARBEARBEISFwG+UaGOOHG9sbOTMmTPvWdEmMzOTiooKsrOzZ4g2ubm51NfXAzAxMUFxcTFnzpwhEolcDFOFd5jzeai81TrnW87uhWM8b5xl5mqn/dQn53W7x835yptrqRImA7jd7mkeP4IgCIIgCIIgCBebeYVHXeywgXg8TigUoqKiIu1f0d8LxONxMjIyKC8vn7Fpdbvd5OTkUFdXR3Z2Nh/+8IcpKChIO2/5+fnk5eXh9XrfKfOFOeDxeN7yu5bqSOtUZd4OqfLJGGEGZve0OV+9c00IbPfSSVW/M9wq1XHg9nLmXbrYv3OCIAiCIAiCIAiGeSkfXq+XRYsWkZubi8dzcZx0pqamKC4ufk+LNsbDqLCwcMa9UChEJBKhtLSUaDTK8uXLycvLw+12p6zL5/NRXV1NWVnZr9NkYR54vV5qamrIzc1NW8btdpORkUFGRsY7aNncsee6mU8YlvOfue68nwqv10tBQQFut9sSXmYrP5voJAiCIAiCIAiCsBCYl/Lh8/lYv349DQ0N5Ofn/7psSovWmng8TlFRUVoR4r1AOBwGoKCgwLpmNqd+v5+RkRFycnIYHx/n8OHDKKVSem4opYhGozQ0NFghVcLFJzs7m0svvZTS0tKU991uN2VlZdTX17No0aI51ZlKwHi73iTpjvNOVW+qtZeqjFNssScSTicAGS+ZvLw8KisrcbvdM7xm7Lly0gk5JteOhEYJgiAIgiAIgrBQmHd41IoVK7jyyitZtmzZrJu+t7ohnEv4RGFhIT6fb17CzWx/rf9NCoUw+UEikUhKb6NgMMj4+Li18dy1axfDw8NEIpGU+U6Gh4fJzs5O6bWz0DGb83cbU1NTNDY2pp2TwsJCbr31Vr7whS9wxx13zDmMyPn97YgTzvfHrMV0iYTteWacpzjZnzGnTsXj8ZSeL6nq11qTmZlJVlYWbrd72nVnYmLz/qT6PTDX341rShAEQRAEQRCE30zmJdp4PB6eeOIJRkdHrQ0SJBLj2jc6xcXFLF26lNLS0nnntkiVqDTV8b5er3faX9TTkS5R6WxJU1OFZSwkPB4PLpeLQCBARkaGFQ5iNqwdHR3cd999xONx2tramJycnJbPw47WmqysLLKzsxdcPzMzM2e9/8lPfpLa2tp3nddVNBplyZIlFBYWzhDmlFJcccUVBAIB6wQ1t9s9bQzSrfkLIdbY6wOorKxk8+bNXHPNNdOuz3ZkuF1MSReS5MzT5PF4rPfdiC9m3Zu1X1paSmNj44z+2uspLS0lIyMjpX1ut3uaoCMIgiAIgiAIgnCxmVdiGr/fTzweZ2RkhJKSEm688UZ27NjB6tWr2bdvH93d3XR3d+PxeCgoKGBwcJAHHnggZV2zbRxThTLYBZSpqSny8/NZv349zc3NRCIRfvKTn9DZ2TmntpzeJnYxY7YwjlSbXvtf73/dYRWmfSOSDQ8PT7tuxikQCHD69GnrxChzIo4pY7fT7XZbSYhTiToXC7fbzaZNmxgbG+PkyZOEQqEZZZqbm3njjTcYGBggGAxeBCt/PcRiMaLRqCVG2IWNkpISMjMzaWpqYmRkhEceecQSP9LlkDHf7Z4kF2KelVIMDQ1RXl5OY2MjlZWV9Pb2TrtvbzfVe+h2u4lGo7OKuOb+bKdBxWIxPB4PhYWFKcXWgoICVqxYwTXXXMN3vvMdBgcH5ZQoQRAEQRAEQRAWPPMSbaamplBKEQqFrPCFsbExotEoPT099Pf3o5QiEong9/upq6uzNlTpcG7G7N+deSyM18H4+DhNTU0UFBTQ2dlJMBhk586dfPvb355RhzOfRSqPA601GRkZKY/7TffZ7Xazdu1a1q1bR0lJCX19fezfv5+DBw+ilKKkpIRwOMzk5OQFO5pca215HZh5cHoUmA2+EZFMn9J5WdjrNJ4L6UJT3kni8ThHjhyhqKhoVq8N42nxbsK5Bu3zu3nzZlauXInL5aKnp4eOjg7rmXRcSA8bO2atnD17lkgkYnn7pDqlKV3bLpeLiooKhoaG0q4554lSZs0ajGgaj8dxuVxkZWVNE/G01gSDQbq6ujhx4gSlpaUMDw8Ti8XOK8oKgiAIgiAIgiBcTOYl2sTjcWKxGPF4nImJCSKRCMPDw9TV1aG15ujRo9YGq7CwkMbGxmnCi1P0yMvLs0SNdH9FN9g3V5FIhDVr1tDS0sKpU6fIz89n3bp1VnhQU1MTlZWVjI6OcurUKfx+Pz6fj2AwaAkodg+exsZGNmzYwODgIIcPH6a7u3tau07bvF4vTU1NVFRU0Nvby9jYGAUFBSxZsoRjx47R3NxMTU0NXq+X1tZWenp6rE3t5OTkece5uLiYZcuWUVhYyPDwMPv27bM2tFprKzxqdHR0zrlDZsPlcpGdnU1DQwObNm1iaGiIp59+eprYppQiJyeH5cuXEwqFOHv2LJOTk3MWd8wGf65orRkZGSEej6cVvd7q5trlclFdXU0sFmNkZORte+lkZWURDoen9c/tdrN+/XqOHj1KMBicd98jkciMZ5RSbN68mdraWg4cOMC+ffusOXg73jNKKbKysojH40xNTc2pvLETEiLq1NTUtBPl0tniXKsej4cNGzbQ2tpKf3//tL6kC+ez3zdCIyQSdIdCIYqLi+np6Zm2fs1JW4cOHWJ0dDTl2Jr/imgjCIIgCIIgCMJCYV6ijV0gCIfDxGIxYrEYXV1dLFq0iLNnzxIIBHC5XNTX15Obm0ttbS25ubn09/czOjrK1NSUdaRxWVkZQ0NDdHV1pdw4pzrpxWzU8vPz6erqorOzk2XLllk5bvLz82lubmbRokWMjo4CcODAAerr6wkEAnR3d6OUori4mIKCArq6utiyZQtLliyx8sT09/enTNwLic1fSUkJa9eupa2tjSNHjhCLxWhubqa0tJT8/Hw2bdpEKBSivLwcSIg8AwMDlJWVcfjwYRYvXszQ0BDRaJS8vDwKCwsJBAJ0dXXh8/lYunQpK1asoL6+nmg0ypEjRyyvGkiIBABDQ0OzegWlymvixJQpKipi9erVrF27lszMTF555RUmJiYsr4bc3Fzq6+tZsmQJxcXFvPzyy3R0dBAIBNKuFyMG1dbW4vP56O3tZWhoaJowcD7vhrGxsRl1mvIulyvlqVjno7KyklWrVuFyuejs7KSzs5OJiQmUUhQWFlohNhMTEwwMDMwIhysoKCAvL89a9zU1NZw9e5bh4WFCoZCVO2XTpk309/fT29uLz+ejoKCAeDxOf3+/dQJYKkpLSyksLKSiooKysjL8fj+Tk5N4vV42bNjA2bNnOXDgAMePH7c8qswYlJeXEwqFmJiYmCb0pQo5NP1atGiRlfR4ZGSEnp6eGTa5XC5ycnKorKyku7ubqakpq/5YLDZn4cvpBed2uykuLiY3N5fBwcFpYYbpBBtnP8y/UCjE+Pg4NTU1+P1+SkpKGBoaIhAIkJeXR01NDYcPH2Z8fNwShzIyMvB4PMRiMcLhsOSzEQRBEARBEARhQfGW40qMB0QkEmFwcJCCggIrFCoSiaCUory8nA984AP84R/+IVdddRWVlZV4vV7Ky8u56aab2LhxI7fccgulpaVWklGzUTObKefGzOTLOXz4MKOjo5aXTmZmJh6Ph40bN6KU4qWXXmJwcJANGzaQkZHB2rVrufnmmykpKaGyspIdO3bw0Y9+lPLycpqamvj3f/93wuEwRUVF+Hw+q5/OMKvCwkLWrFmDUoq9e/cSCASIRqMcPHiQ3bt3s2jRIgoKCnjooYf4+c9/Tm1tLZdeeilVVVVce+215OTkcOedd7JhwwaamprYsWMHH//4x7n11lvx+XwsW7aMpqYmurq6OHnyJMuXLycrK2vaGGRkZBCPx+np6Zm2AXd6NKULT3HmwIlEIpSXl7N48WLuuecevF4vRUVFZGRkoJQiNzeX5cuXs337dnp7e1m7di1LliwhPz9/1k1udnY2K1eu5Pd+7/f4oz/6I66//nrKysqseczJySE/P5+ioiK8Xu+sYU6ZmZnk5+dTUlJCaWkp2dnZVh3nS1js5H3vex+1tbUsX76cbdu2UVNTAyTynqxdu5abbrqJ22+/ne3bt+Pz+ab1MTs7m9WrV3PjjTfi8/nYuXMnn/jEJ7jqqqsskS4rK4uNGzcSj8cpLCwkJyfHeua6666jpqZmWp1GfDLvwMaNG1m/fj3btm1j8+bN1NXV4fF4aGhoYOnSpTz22GMcOHDAEr/s78327dtZuXLljDFxCnrmu9frZfv27WzdupX3v//9XHvttdPWvyEnJ4dVq1Zx1113UV1dbdnq8Xgszy9TZ3Z2Nrm5uWRlZVn3zJoqLi4mPz/fuhcOh9m9ezd9fX0zvF9mCxMznlvRaNQKAQwGg4yNjbF8+XKWLl3K7bffzurVqyksLCQvL4+qqiorCXFWVpZ1raGhgUWLFpGTk2OJgSLeCIIgCIIgCIKwEJiXpw3MTOIbj8cJhUIUFRVZmx3zF/TMzEyqqqr4u7/7O/74j/+YyclJXC4XixcvRmvN9773Pe6++27KysoYGBiwks26XC4uu+wyenp66O3ttbwSTL11dXU88MADVl4KSISjFBQUcOWVV/L973+foaEhiouLrZCNJ554gm9961u0tLRQVlZGVlYWv/rVr1i3bh3d3d0UFBRQUVGB2+227LBvhs1f9isqKrj00ku57777rOsmUWpeXh6LFi3iwIED+Hw+/H4/ra2tltfF6tWrufLKK6mpqeHTn/400WiU1157jV/84hfcfPPNXHfddaxZs4Ynn3ySNWvW0NjYyGuvvWZ5TZjxHRgYYHJykrGxsVnDUFIlnXXmDDLzZHL6dHZ2smfPHsrLyxkaGsLj8bBixQquuuoqhoaG+OIXv8hTTz3Fvn376OvrS9u+2+1myZIl3HLLLfzLv/wLo6Oj3HLLLWRmZuL1emloaGDnzp0UFBSQn5/PE088QUtLCwMDAzPsVEqxZcsWtm3bRkNDA1lZWTzyyCP4/X7KysooLCxkZGQkZaJbMxb2/jY3N/Pwww9z8OBBK0TH6/Xy+7//+xw6dIgHH3yQJUuWWG3u3r2bUChENBqlqqqKnJwczp07xyWXXALAwYMHLY+pc+fOkZ2dzcaNG7n77ruZnJxk3bp1VFdX09fXR1dXF3/1V3/Fpz71KSs3VHFxMaWlpcRiMdrb28nNzaWvr4+jR4/S1tZmeWndfffd7Nu3jxMnTjA2NjYtX5Hp66WXXorH4+H48eMp17FzbLZs2UI4HObJJ5+kvr6eyy+/nNtuu40f/OAH08qvXLmS7du3c+LECbZt20Z3dzfRaJRFixaRm5vL6Ogovb29ZGdnc9NNN1FfX8+ZM2fYv38/586dY8uWLdx5551kZWVx+vRpnn/+ed544w3Gxsbo7u6ekW/KOX8ej2eaV5E9F5O5FggEGB0dpba2lq1bt/Lss8+ydetWlFL4/X6KioqIxWK43W6uuuoqtm3bRk5OjuV1c+rUKb797W9f9HxOgiAIgiAIgiAIhnmLNgaTBNZsnMxmyGy0TMjECy+8wOjoKC0tLRQVFbFt2zaysrI4dOgQf/Inf0JRUZGVD8R4Wng8Hq6++mqeffZZRkZGCIfDlkBkTtYZGRmx8n5MTk7S29tLXV0dmZmZRKNRVq9ezebNm1m0aBGXXXYZv/rVr7jnnnt4//vfT1VVFXv37uW5555jx44d+Hw+vvjFL/LII4+wd+9eYrHYjBwspr+ZmZlWKAcw43QfpRT19fW88MILxGIxuru7LU+KU6dO8bnPfY7PfOYzfOlLX6Kvr4/Dhw9z8OBB4vE4v/M7v4PH4+HGG28kFovR0tLCrl27rA2q2Xh3dHRYYktxcTF9fX3A/PLYQMJbKiMjg4KCAsLhMCdPniQejzMwMGDZvG3bNnbu3MnixYt5+umn+YM/+ANGRkYIBAKz5v7weDxkZmaitaa3t5dYLMaPfvQjMjIy2LFjB+vXr+fRRx+ls7OTwsJCvvCFL3DFFVfw3HPP8fLLLwOwbt06Wlpa2Lx5M2vXrqWzs5N/+7d/w+PxsHPnTurq6ojFYpw5c4b29vaU4TQGn8/HDTfcQGtrK2fOnGF0dHSa4HfrrbfS1tbGnj17WLp0KZdccgler5c33niDNWvWsHz5cnbt2kVtbS3Z2dn09PTw4Q9/mFdeeYUrrriCb37zmxw/fnxaziETbnPZZZfR19dHb28vN910Ex0dHWzfvp1QKMTixYupq6ujvr6e6upq7rrrLp566ik2b97M8ePH6erqwuVy0dDQwCOPPEJjY6MlMvh8Pnw+H5FIxArvysnJweVyzTiRySmCmXdt+/bt/OhHP2J4eJjm5mYaGhpoaGjg/vvvt0LyysrKWLp0KTk5OTz00EOWN53L5aKwsJCGhgbC4TADAwO43W42bNjAnj17aGho4Oqrr7ZyYH3lK19hYmKC5uZmtm7dysaNG7n//vvZsmULP/vZz6zTzoqLi6mqqiIzM5P9+/dz++23s3PnTh566CGef/55a30qpaYlEza/QWvWrOHRRx/l9ddfZ/HixXi9XkuYU0rxoQ99iHXr1vHII49w6tQpy2PJ3BcvG0EQBEEQBEEQFgrzFm1Shdvk5ubS1dVlbVLNBnFiYsI6lvrUqVOsWLGCVatWsXjxYsbHxzl06BBf/epX6ezsJC8vjyVLlrBq1Sq6u7u55ppr+OlPfzrtqGeToPU//uM/6OvrsxLUmoTIQ0NDNDU18clPfpKzZ8+yd+9e2tvbLU+dlpYWbr/9doqLi3G5XIRCIQYHB/mzP/szvv71r/Paa69ZYozH46GiooLOzs5p4UfRaJRwOExjYyOtra3T8ob4/X5Onz7Nxz/+cQ4dOmSFXrW3t1t1X3755QwPD/PSSy8RjUYZHR217J+cnKS5uZmWlhZaWlro7Oy0BCR7smZzvLFJfmxEm3RzNds1k68mHo9b9Sil8Hq9lvfI0aNH+da3vkV/fz9+v39Op2FFIhG6u7s5ePAg3/jGN/iHf/gHenp6WL9+Pfn5+bz++uucPHmSSCRCNBplfHycSy+9lGAwSGtrK8FgkGuvvZbjx49z2WWXMTg4yL59+yxx4pe//CVaay6//HKKi4vPaw8kQqxuueUWOjo6rDVhPLRWrlzJrl27qK6uZt26dfT39/Poo48yOTnJ2bNnueOOO3jppZcoKyujqKjIOu0oJyeHb37zm5w5c8YSDfLy8ti8eTMul8vKgVRdXU13dzd9fX08/vjjNDY28rnPfY5z587xwgsv8OKLL3LDDTdw6aWX8sILL1gipUkO3NbWxsTEBDfeeCPl5eWMjIxQUFDA5s2bue2229izZw9PPfUUtbW1HDlyxAotTJWk2Kxlt9tNZWUl4XCYqqoqSktLGR8fZ3x8nMbGRktMLC4uxufzMT4+TjgctvI9AYyOjhIIBKw5CAaD+P1+Ojo62Lt3L/X19Vx33XVs3bqV+++/n+HhYfbv38/Y2BhNTU3ceuutFBcX86tf/YrOzk5Wr17N6tWraWhoIDc3l8zMTMLhMLt27aK+vp4tW7bwzDPPWF5Kpj8mPMuE87366qtMTEzg9/stUcblcpGbm0tRURFut5vs7GwyMjKsEEeTp0tEG0EQBEEQBEEQFgpvy9MGEt4akUiEffv2WRsppRTj4+McOHCAoaEhACspcF9fH6dOnaK6uppz585x7tw5pqam0FozOTnJ+Pg4kBAmJiYmZhwXHo1G2bt3L36/39qkTU1NMTAwwMTEBD/5yU8YGxuzQqtMu7FYjLGxMVpbW2lqaiIejxMOhzlx4gRtbW2sXr2avr4+ampqKCkpoaysjEOHDtHV1TWtv/39/bS0tPBbv/VbVuJZk0h4aGiIgYEBHnroIVasWEEkEqGlpYUzZ87Q19fH1NQU9957L5OTkzz//PNAItGu8Rp45ZVXWLRoERUVFVRVVeF2u8nKyqK4uJjHH3/cEku01oTDYUZHRzlx4sR5Q0tmm0MjMpg5Mt4LkMhN4na7GR0d5cyZM0QikZTHoqciHo8zMjLC7t27AfjoRz/Ks88+y9q1axkbG7M8HJRSNDc309/fT0lJCZFIhNzcXCKRCJWVlZaHRDQaJRAIWOtrbGyMvr4+CgoKKCgowOPxTBMTnEQiEVpbW/nIRz7Cvffea42lEVUikQjBYJCJiQn6+voIBAKMjY0Rj8fx+/0UFhZyySWX4PP5iMViFBUVMTExwe7du+nu7iYQCFgC29jYGE8++aQlKng8HoaGhti/f7+V3Dg/P5+lS5eyZ88eDh48SDgcpqOjg6qqKlwuF+3t7fj9fksEDYfDnDt3jocffpjGxkbC4TC9vb20trYSjUbJyMhgamrKSg5uf29mE+8yMzPJzs6mubmZzMxM9u3bRywWY8uWLRw5cgRIeCllZWVNC380fRsZGWFoaIjs7GxLPJmamrKExnA4bNm+ZcsWOjs78Xq9VFVVUVRUZJ0YtXTpUpRSrFq1inA4TGtrK1u3bmXlypUcP34cr9fLpk2bqKmpIR6P88QTT8xY70a4MWKN1prBwUErWbaZn927d+NyudixYwdXX301vb297N+/n3379r3rjo8XBEEQBEEQBOE3m7ec08Yu2gwNDVkeI5DYEI6Pj9PS0mLl3jCn5vT09NDZ2Ul1dTVDQ0NW6FM4HKa/vx+Xy0V+fr513LMzh45SyvKGMZvIQCDAiRMnCIVCPPfccwBMTk5O28QaW1999VX6+/utE6L6+vp46KGHaGxsZMWKFYRCISs5r9/vn9H/0dFRDh48yMqVK1m3bh3j4+OMjY0RDAYt4em5555jzZo1TExM0N7ebvVlcHCQl156iUgkQnt7u+UB4fF4LJHLJGutrq6mqKiIYDBIMBicIcLEYjECgYAVpjQXkSbdPJp8NmacjHBmxICCggKqq6stj6Dx8fE5tRMKhSwPrJqaGq666iry8vKIRqPk5uZSXFxMfX09S5cu5ciRI0xOTuL3+60wFSPq9Pb2kpmZSWlpKWfPngUSITTBYJBwOIzP5yM/P98SCFMRi8Xo6OiwcurYT6GampoiHA5z5ZVXEggE8Pv9DA4OWuF3oVCI48ePs3TpUnp7e+nu7sbj8VBcXExRURF+v59QKGQJWn6/3xJttNacPn3a8lyamJjA7XYzODhIa2srBw4coLe3l9zcXEs0isfj7N+/f1rOIK01oVCIZ599liVLlhAIBJiYmGBwcJDu7m4qKioIh8NMTk5atszmMWKEt/b2djZs2GDl4zECydatW8nIyACw5qGgoMAKBzR9C4VCDAwMWDlnXC4XkUiEJUuWUFJSgsfj4cyZM3R3d7N582YOHz5MVlYWBQUFVo6furo6S4wpLi7mxIkTnDx50soxVV1dTV5eHqOjo1RUVHD55Zfz/PPPW++cM7H28PCwFR5m+hkMBunt7SUajXL69Gl8Ph/r1q2jtLSU4uJitmzZQk1NDU888cS0900QBEEQBEEQBOFiMi/Rxmxk7BteI8Y4E6KGQiFOnDgBYAkCp06dsjwYuru7ZwgqY2NjhMNhKzwlXTJdpw3j4+O0tbXhcrkYHR21TrEyG0t7Lo/W1lba2toALM+RXbt20dnZyapVqyw7Dx48yNjY2IxkxFNTU3R3d/Ozn/2M6667Dq/Xy6lTp+jt7bVOzRoaGuKFF16YZq/x7DEJc+31xmIx+vr6cLvdPPzww6xdu5aGhgaUUvT09Fh5dpx9SeXxkmqsUmG8EkzeHbsI19/fT25uLmNjYwQCASorK1m/fj0jIyMcOnTIEnVmw8ybye3y6quvctddd9HZ2Ynb7Wbt2rXk5eVxySWXMDg4SEtLC2fPniUrK4toNEo8Hufo0aPEYjEOHTrEpk2bWLVqFefOnSMWi9HU1MTIyAinTp3C6/VSV1fH8PDwrImZQ6EQe/futUKDICFIGK+sLVu20N7ezoEDB6zE2CYk74knnuCyyy6ju7ubY8eOUVpaisvlYuPGjUxOTlonmQGEw2GOHTtmjfHu3bvZsGEDq1atwuv1cu7cOcbHx/nBD37AqVOnLOHS7/dbfT5w4ICVw8nep7Nnz9LR0WH1ycxhe3s7VVVVTE5OEgwGpyUpNqeNOeuKx+M8++yzXH311Rw7doy2tja6urrIz8/H5/NRVFTE8PAwIyMjDA8PU1ZWZs2tIRaLWR5n5vdgcnKSlStXMj4+zunTp9m7dy8TExNcf/31rF27llAoxMmTJzl27Jh1RHpNTY2V0Hh0dJTh4WHeeOMNiouLufzyy8nIyODll1+moaGB9evXU15eTldXl9UnI1K53W76+/utULWcnByysrLo6+vj9OnTVtLtQ4cOcezYMfLy8li1ahUf/OAH+e3f/m2OHDnC6dOnRbQRBEEQBEEQBGFBoObqmQHgcrm0z+ez/tpuwiGMx4vZCJvNZGZm5rRwCbs3iH3DaT8VpqioiA9+8IPk5ubywx/+kPHx8WmJfs1mbFonkuKAXVQy9ZvNqr2MSeprDxGBN5MKm2OEjQhjt8++mXO73dM2wmYs7KKM3VvI7hVgTrUy9tvvmYTOziON7WOXdkJt4ou93lTijsm5UlhYCIDf77cSzLrdbsLhMFlZWSxevJgVK1bQ29vLnj17ZoSsOTEhTZmZmeTk5LBjxw62bt3Kgw8+SFtbG7W1tWzcuJFoNMozzzxDZ2en5aVlt90Ic5mZmdx0002sXr2awcFB+vv7qaur47vf/S51dXVs3bqVYDDI9773vVnz7bhcLlasWEFGRgYdHR2WJ5Xb7Z4RFmNfo/Y+2ddCRkYGWVlZ004xs4eqmfVhjqM3a8OEcZkj1Y1t5ghsMwb2f8Y+4wVmt9P0oampiTvvvJMXX3yRJ5980srlUl9fTygUYnh42Krf1GX6bn+Ps7Oz+djHPsaJEyd45ZVXCAaD1hHePT09lqhmb9/ehx07dtDc3ExrayuHDx8mHo+TmZnJl7/8Ze6//35OnjzJ+Pg4sVgMr9dLdXU1S5cu5ejRo0SjUfx+P36/3zoZasWKFQQCAYaHh2lqamLLli10dHRw3333EYlErD4UFRWxfPlympubuffee3G73dx8882cOXOGU6dOUVRUxKpVq2hra7O8xbKzs6muruayyy7jd3/3d/n7v/97du3aZXniCYIgCIIgCIIgvEPs1VpvdF6cd3iUcxNp9xaxCxWAJTjYsW+EnUKE2QgvXryYXbt2zdiAm7pMbgq7QAFvhvqYTaVduHGeBmXfpBuhwp4PxJS1eyw4RRj7Bt6UNW2ZsqnEHrNxN9edngupPIzs42gXgeynVzmFGec4O583otDQ0NC0eTHjbo4/b2tr48iRI3M+Cnn79u2sXLkSr9drhcD86Z/+qRVm09/fz759+6y1ZE+ybMcuhDzyyCO8/vrrrFy5kkgkwmOPPUYwGGR8fJzCwkJycnLOa1c8Hre8rOztOXPA2MfJPg8mbMZ8D4VClvDj9IIy9dqFN3M9Go1aophZK852jb1mLZgcMXZh1N4vpRQlJSXTQuYMlZWV1NXV8cILL8xIWm0XNu3j8dhjj/HZz36WlpYW/H6/lXjZvgZSrbd4PM6JEydYtWoV8GZolcvlYs+ePfh8PutdNGNoRBUzXk6R9tixY9aYt7W1kZ2dzR133MEDDzwwzVNvamqK3t5eBgYGrJDLZ555hnA4bIVKjo6Octddd1FRUWGJMibE80tf+hKvvPLKNGFLEARBEARBEAThYvKWTo9yhg6YDZX9mGH7d7NhtW9CjReCed6IMJmZmSxZsoTOzk7i8fg0rxPjIeMUh+z2OP86bq/bbG6NHfCmV445+cp+z9k/+xjMNjZ20cVuqxFZ7CKV3aPEWZfTq8aZTwjSizmz4RQs0j1nF4Pm45H10ksvWcleTR0mBMgpSJmTsOybdft8GTuCwSAdHR309vZam30jNrz44otWXecjVT9SrWl7v50eS/br6eo3Xmj2emDmHKXzWLF7G9lFPvs7Y54zQs6yZctob2+3jlg3dR45coSlS5dOW2d2Ycn+HRJzZRIG5+bmMjw8bHntzDaOxla/308gELDCD83a7+npsUTVcDhsHTlvF+eMLeadNP0wnkB+v5/e3l68Xi/Lly+nra3N+p2ZmJhgcnJyWt4dczy4y+VicnKS1157jUOHDuH1eq2wMSNShkIhpqamZk1oLQiCIAiCIAiC8E7ylk6PSuUd4hRUnCFBds8XpyBhyvh8PkpKSsjMzLROCrJ7vdiFELvYYBc+7Bs/Z96XVKFCZsPnFHTmumlz2mD37HGKN8YGpweM3UvJfj0dThFgrs/NVpfTE+it1GffbBvxzClspBIwnN4VzvGzj5NJPmsXO+Y7Z6lsTiWOzVYmlZfJbGKeE3sOFueY2MUnu9CZ6t0xZGRkUFtby6uvvmolZDbPGLFibGwspU1ZWVkz3rVoNEpLSwsNDQ2WUHK+8bWH5PX19U1LWB2LxRgfH2dwcBC/32+VjUaj1rvr/I0ArPEx92KxGIODg7zxxhts2LCBkydPWvftIp9zPdvF2MnJScvLydmu04tPEARBEARBEAThYjJv0cYujqTyeHGG0NhFG/s1Z51moxQMBnn++een5c2w5/0wmzPnRtrumeAMW3J6d6Syw3nP+X0+OG1zemqkI5Vtzs1nKhFkNhvmyoXwLDifqJEqr1GqNWPuO+fPfE41v/DW58wpNM7Wt1Tilr2eVM+YtZlqLp2eO3bBwS5qpfLasa/rrKwsAoGAleTYblssFuPs2bMzvGUMGRkZVFRUkJmZaYVglZWV4XK5KC8vx+fzzWlszf2pqSmOHDli5a0xdHV1MTExYXld2ftn71uqduy/BYFAgDNnznDFFVfw4IMPWvXBm94+pj772Nk9uOyJve2CjlmngiAIgiAIgiAIC4G3fOS3UzCBmYKO+ey8lsqTwXgYDA8P88wzz0zbSDk3u/bNqrnmTF7rbDuVmDSXPs6VdN4v9nupxiKVPc7PcxVqFiIX0n6nB1A6weft2nc+7GLD+YSe8wmBqbyO7N43zj6nssO8C62trfT19TE1NTWjfDAYTGtnLBYjIyOD7OxsAHw+H/X19QSDQSs0ai7CmLFnamqKjo6OGfb19/dPmzOncJWuvvz8fCuMLjs7m0WLFpGdnT3NS8fejvN5p0BkPGqcYyzeNYIgCIIgCIIgLDTmfeR3Og8W8z3VM06cngdGVIlGo0xMTMw4McqUcebfcG7QtNaWp0C6zVgqr5tUG+m5eMbYyzvtsNdjtyHVX/TPN26zeQfZr6US1NKRqm9vx7totnYMc7HLXtZeh93TIp2o9XZst9c71817Og+qdJ47s11zetmYe/Z6jdeIff2adycQCPBf//Vf1nPzIRAIcOzYMTIyMqyjvkOhENFolI6ODiYmJqa9s+mEMqcA4/RacYok9lPd7PmrTBnzbFVVFVprCgoKqKmpoba2lry8PB5//HErnMnehhkr+wlt5howzRvPjhnb3yRRVBAEQRAEQRCEdzfzOvLb7XZrk//Cvtl0JhU2my6TYBZmbpTM5tSeC8e+YXWGTDg3jM6Noz0Hit0eU4czFMMagGR5ezjW+Ui1Sbe3k06EOZ/XRSqxIF0Zu3dSqucX0sZzruFh53ve/t1wIfqZTvQ5X7vnE4vmKv44w8acYYH2+p1hUs73xn5vNtvS2WveB5Pc2B5KZN6/83mp2cMdnXalElw9Hs+M99X0VWtNaWkpVVVVFBUVEQwG6e/vZ2xsjHA4bOVPsv8mOU+JM9fNsetG2LWLu+b3ySQlDgQCsx4fLwiCIAiCIAiCcIFJeeT3vEQbpdQA0HEhrRIEQRAEQRAEQRAEQXiPU6+1LnNenJdoIwiCIAiCIAiCIAiCILwzyDEpgiAIgiAIgiAIgiAICxARbQRBEARBEARBEARBEBYgItoIgiAIgiAIgiAIgiAsQES0EQRBEARBEARBEARBWICIaCMIgiAIgiAIgiAIgrAAEdFGEARBEARBEARBEARhASKijSAIgiAIgiAIgiAIwgJERBtBEARBEARBEARBEIQFiIg2giAIgiAIgiAIgiAIC5D/C76tBn5G10ghAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -171,7 +171,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -181,7 +181,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -191,7 +191,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -201,7 +201,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -211,7 +211,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -221,7 +221,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -231,7 +231,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -273,7 +273,7 @@
        "torch.Size([97])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -312,7 +312,7 @@
        "140"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
@@ -346,7 +346,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAACMkAAADiCAYAAABebgK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABF5klEQVR4nO3debxeVX0v/vWcKSchBCKEMJSiiDgAYgG5t1jrADi1ilLnal9MgqhU0F4Ex4BoRVsVJ4peEScs3rbg9EIcKCKliMggIijKGAOBJECGk5zp2b8/uN6++nvx/e5k7zyck8f3+08/Z629nr3XXnvttZehU1VVAQAAAAAAAACAfjYw0w0AAAAAAAAAAIBes0kGAAAAAAAAAIC+Z5MMAAAAAAAAAAB9zyYZAAAAAAAAAAD6nk0yAAAAAAAAAAD0vaFN+eNOp1P1qiFtVFXVeaT/fba2t5SyoqqqRY8UaPPmE/WLUrR5c3L/PSq2uDb3U18uZctr82xtb9GXHxXa/Ohw//VeP/WLUra8Ns/W9pYtsC+XLbDN/dSXS9ny2jxb21u2wL5ctsA291NfLmXLa/NsbW/Rlx8V2vzocP/1Xj/1i1K2vDbP1vaWLbAvly2wzf3Ul0uZuTZ3OmGTSlVV6f03MBD/+xBVFf+cxYsXp22anJwMs5UrV6ZlS01fzn7vTKmqyv33KOinNs/W9pbg/tukTTJsNnfOdAMa2BLbDI9kS+zLW2Kb4ZHoyzBz3H/0iy2xL2+JbYZHsiX25S2xzfBIZl1fzj5AdbvdR7El0HOz7v6jf9V9KM8+8G+EtC8PDcWfC6emphqVa2tqaqon91/2DGuj7fOvzbO16W+ayTZnfSerN9usMjAwUEZHR8N8eno6zI488sgwK6WUe++9N8y+9KUvpWWnp6fDvtzpdHp6HzU1OTmZ3n+Dg4M9OW7TPrkx42PWr3p1/2VlN+a39qpvZMfOfk/2PCgl7xdNj9m2bHT/+c8tAQAAAAAAAADQ92ySAQAAAAAAAACg79kkAwAAAAAAAABA37NJBgAAAAAAAACAvjc00w0AAAAAAGDL0e12Z7oJAH2nqqrGZTudTqu6p6amGh23abmZNJPPsIGB+N8uaNOu2fhczn5rnaxfZX252+2WsbGxMF+wYEGYvfCFL0zbdNRRR6X5H5qsz7Xp5726R9qo68u9Ohd12oy/s/E8N9W0vf4lGQAAAAAAAAAA+p5NMgAAAAAAAAAA9D2bZAAAAAAAAAAA6Hs2yQAAAAAAAAAA0PdskgEAAAAAAAAAoO/ZJAMAAAAAAAAAQN+zSQYAAAAAAAAAgL43NNMNAAAAAAAAAJqpqmqmm8BG6Ha7M92EzSr7PQMD+b/T0Itz0el0ysjISJgfdNBBYXbiiSemdd9+++1hNj09Xdu2SFVVrco3NTg42Kp83fWN1I1VTftU23OYnY/JycnG9U5NTTUuW0q7e6xpvUND8faRuvs2y7NrX3f9Op1OmjfhX5IBAAAAAAAAAKDv2SQDAAAAAAAAAEDfs0kGAAAAAAAAAIC+Z5MMAAAAAAAAAAB9zyYZAAAAAAAAAAD6nk0yAAAAAAAAAAD0vaGZbgAAAAAAAADwh2NoKP9EOTU19Si1hF7odrtpnl3/urKRqqrK+Ph4mH/3u98Ns8HBwbTu6enpRm2q0+l0yvDwcJhn56JNm9r+nqxdAwO9+Tc6mvaL38valZ2PuuNm9WbZxvyerHw2RtaNr5m25znS6XR6Um9df4uurX9JBgAAAAAAAACAvmeTDAAAAAAAAAAAfc8mGQAAAAAAAAAA+p5NMgAAAAAAAAAA9D2bZAAAAAAAAAAA6Hs2yQAAAAAAAAAA0PdskgEAAAAAAAAAoO8NzXQDAAAAAAAAgD8cU1NTM92EvjEw0OzfROh2u5u5JRuvV9d/cHAwzKanpx/1Y9Ydt6qqMjk5GeZtrtHw8HCjNm2MqqrCLGtzp9NJ6x0aircutO0zTc9l3f2V5W3vsax8dtw256rpeNJLWX9r2pdn368EAAAAAAAAAIDNzCYZAAAAAAAAAAD6nk0yAAAAAAAAAAD0PZtkAAAAAAAAAADoezbJAAAAAAAAAADQ92ySAQAAAAAAAACg7w3NdAMAAAAAgNlpYCD+/9h1u91HsSUAQKTT6aR5VVWPUkuYCf02J+vV/HNwcDDMpqene1a2jexc1JmcnOxJvW3UjUVZm4eHh8NsamqqcZvq1PW5LG97npv297rj9mrMyK5v3XMq06Zs1Cb/kgwAAAAAAAAAAH3PJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7NskAAAAAAAAAAND3bJIBAAAAAAAAAKDv2SQDAAAAAAAAAEDfG5rpBgAzb+eddw6zPfbYI8yuvfbaXjQHANhEZ555Zpq/4x3vCLN58+aVpzzlKWF+zTXXNG5Xr8yfP7/st99+YT42NhZms/H3AMBs1u12Z7oJAMAsNTSUf2acmpp6lFqy8QYG8n8/wNxn82hznttcg+np6TAbHBzsSb1tdDqdtF2Tk5ON6667BrNRp9MJszbnopS8X7U5V1nZtuNJ07q3tHGsqqo0z/pFU1ve3QEAAAAAAAAAAJvIJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7NskAAAAAAAAAAND3bJIBAAAAAAAAAKDvDc10A4CZNzIyEmbPfe5zw+zWW2/tRXMAgE20ww47NC67aNGi8qY3vSnMjzrqqMZ198rcuXPLU57ylDBfuHBhmK1atSqt+3GPe1yY/fCHP6xvHAAAADzKqqpqVX5oqNnnwqmpqVbHpX91u92ZbsIWoaqqMj093ZO6s2swMNDu39EYHBwMsza/p+1Ylmn7myP91td79Xt6dW2b1utfkgEAAAAAAAAAoO/ZJAMAAAAAAAAAQN+zSQYAAAAAAAAAgL5nkwwAAAAAAAAAAH3PJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7Q5vyxyMjI2WnnXYK8zvvvLN1g4BH3/333x9mj3/848NsdHS0F80BADbRypUrG5edN29e2WeffcJ8YKD5vvput9u4bKaqqjI5ORnmBxxwQJh961vfSuv+wQ9+EGZz584Ns/Hx8bTeLc3IyEjZeeedw3zdunVhls0tAQCALVPdWvCGDRsepZbQC1NTUzPdhEdNr9YqmP2mp6fDbHBwsHHZtnrVJ9us6dXpVZs7nU6YVVXVqu6szb08V200Pc91v2cmxsFeXdus3qzu2XnFAQAAAAAAAABgM7JJBgAAAAAAAACAvmeTDAAAAAAAAAAAfc8mGQAAAAAAAAAA+p5NMgAAAAAAAAAA9D2bZAAAAAAAAAAA6HtDm/LHixcvLn/3d38X5rfcckuYfec730nrvuOOOzalKcBmNDk5GWaPfexjw2xkZKQHrQEANtV9993XuOzcuXPLvvvuuxlb818GBprtye92u2m+du3actVVV4X5scceG2a/+93v0rovv/zyMNtxxx3DbNmyZWm9O+20UznuuOPCfMmSJWn5prJ6zznnnLRsdh1e/epXh9knP/nJ2nYBAACzz/bbbx9mL3zhC9OyX/7ylzd3c2bUggULwmzt2rVhNjg4WLbZZpswX7VqVat29ZOhofwT5dTUVJpnaw516wozUS+z2/T09Iwdu2mfq1t3GxwcDLNe/t5OpxNmVVX17Lh1mq5TtpGNc3VjXF35Nn0jk5Wta3PTa5+V6xX/kgwAAAAAAAAAAH3PJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7NskAAAAAAAAAAND3bJIBAAAAAAAAAKDv2SQDAAAAAAAAAEDfG9qUPx4eHi477LBDmD/taU8Ls0MOOSSt+6/+6q/CrNvt1rYNNqdOpxNmVVU9ii15dGT32C677BJmw8PDYTYwMFDmz58f5qtXr964xlFrYCDe75hd26xcnV6Oy39o9x/AxhocHAyzlStXNq630+mkz/TZaHJysixdujTM2/yeM844I8weeOCBMJuenk7rveeee8qSJUvCvGlWp2nZiYmJcvfdd4f5nnvu2bBFAADAbLV+/fow+9M//dO07Je//OXN3Zye23HHHcPsvPPOC7MTTjghzHbbbbfy93//92H+6le/Osz6ce1zaGiTPkNuNk3XzDcmZ8uVra3VrevMlDbfcWbqN7UZy7Kybb8dZfd2m7Eqq7fteNLLurckvXg++pdkAAAAAAAAAADoezbJAAAAAAAAAADQ92ySAQAAAAAAAACg79kkAwAAAAAAAABA37NJBgAAAAAAAACAvmeTDAAAAAAAAAAAfW9oU/54ZGSkPPaxjw3zE044Icw+9KEPpXWPjo6G2djYWG3bHkmn0ykjIyNhPjExEWZVVTU6Ziml7Lfffml+7bXXhtnIyEjZeeedw/yuu+4Ks263W9+4GTAwEO/Fmq1tbnP9M51Op2fHbHOes7JNDQ4Olvnz54f52rVrG9c9W/tNps05nqnfO1PHze6F7B7q1TE3Rq/uvy2xrwMzY9ttt21cdnJysixfvjzM586dG2bT09Np3Vk+ODgYZuPj42m93W43/Zu3v/3tadnMf/zHf4TZ1NRU43p32mmnctxxx4X5kiVL0vIzIXs+Zn2u7nndq7k20J/mzJkTZnXPi36z6667ps+4U089NczWr1/fiyYB0GcmJyfDbJ999knLZu94de+OvVL3bvKyl70szL7//e+H2erVq8Osqqr029PQUPxZru69spfnMWtX9i7cRtt6s3XVXrWZLVube+g1r3lNmB1//PFp2T//8z9vfNxsXKj77tSm7Ezp5XfUzJb4LaZX13B4eDjMsnuozb6MXvblqM2z8w4AAAAAAAAAAIDNyCYZAAAAAAAAAAD6nk0yAAAAAAAAAAD0PZtkAAAAAAAAAADoezbJAAAAAAAAAADQ92ySAQAAAAAAAACg79kkAwAAAAAAAABA3xvalD9etmxZWbJkSZj/5je/CbNjjz02rfvII48Ms0996lNhdsABB4TZfvvtV6655pow/+EPfxhmN998c5iVUsoNN9wQZnvttVda9tprrw2zxYsXl5NOOinMs2y26na7M92EzarT6TQuW1VVq2MPDMT72ppmdfm6devCLLu2IyMjZddddw3zj33sY2F2+OGHh1kppTzzmc8Ms6uuuiotOxu1vUd6dY/V9ZtI2/Zk91ibe6jNvdtG0/PYy+P2qs8MDeXTiqmpqcbl68pCPxgcHGxcdqeddmpcdsOGDbVz36ay35Rlbcfsn/zkJ2HWZjzJyrad52XvWVnWK51OJx2Xt9pqqzCre/ZNT083bhfwh+eFL3xhmN13331p2SuvvHJzN2dGzZ8/vzzrWc8K8+222y7Mli5d2osmAdBnsrl63bvUnDlzwmxsbKxxm9qoe1ceHh4Os8MOOyzMvv3tb4fZ8uXLy1lnnRXm2bpc3Zpdr9ZNS+nd2lsv1/SyumdibZQtW9263De+8Y0wO//88xsfd3R0tOyxxx5h/stf/rJx3Zm631t3n2RjTi/Hql7Jngfj4+Np2Zn6BtRGdn2z35tdv6qqtqjvOFveVQMAAAAAAAAAgE1kkwwAAAAAAAAAAH3PJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7NskAAAAAAAAAAND3hjblj9euXVt+9KMfhfnk5GSYbdiwIa37iiuuCLMnPelJYXbHHXeE2X333Vc+8YlPhPnKlSvD7Pbbbw+zUkpZuHBhmF111VVp2cxWW21VDjzwwDAfGIj3NXW73cbHna1m6vd2Op0wq6qqcb2Dg4NhNj09XVu+6W8eGtqkW/2/Wb58eZhNTU2F2XbbbVeOPPLIMM+u7Qte8IK0TV/84hfD7MlPfnJatu4cZu3qVX+suz7Zea4rn7Wrzbno5f2X3WOjo6Np2fHx8Ub19tJMjc29Gi+OPvroMDv22GPTsvvvv3+Y7bvvvuV73/temH/84x8PszPPPDM9btaX99hjjzDL5hillDI8PFy23377ML/nnnvS8vD/VzcXmDt3bqNsY467atWqRu3K5jZ1ZbOsbsyuqip9Pmb3fZ2JiYnGZdtYsmTJjBw3UneOs2dy3bMky+vmPRszZwb6y0UXXRRmT3ziEx+9hswCVVWlz6k27/4AUEq7tcQsb7venpXPvPzlL0/zpz/96WF26623hln2vWv9+vXlxhtvrG/cI5ip9cteajM/qXs/hM1pzpw5aZ59e9h7770bH7fT6TS+T9p848m+6be1JY5l2XrTU5/61LTsr371qzBre56bfqdrszbaRnYes2d5XV9u8yyJ2uRfkgEAAAAAAAAAoO/ZJAMAAAAAAAAAQN+zSQYAAAAAAAAAgL5nkwwAAAAAAAAAAH3PJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7Q5vyx1VVlcnJyUYHqit3xx13hNng4GCYTU1Nhdm6devK1VdfHeYLFiwIs4suuijMSillZGQkzAYGmu89GhwcTNs1Z86cxnV3u91GWVvZudpzzz3TsrfddluYjY2NNW7TTJmenm5VPutbWVZ3fbOy2T1WVVWYLVq0qBx33HFhvuuuu6ZtyixcuDDM6u6R9evXh1mn0ylDQ/Gw+O53vzvMnve856XHPfHEE8PsmmuuSctmRkZGyo477hjm9913X5ht2LCh1XEjG/Oc6HQ6YfbqV786zI4//vi03p/85Cdhdsopp4TZxtybTe+xumdCL8ffSNamD37wg2nZpUuXhtnBBx/cuE2/+MUvypOe9KQwP//888PsIx/5SFr36aefHmZ77713mL3tbW9L63384x9fvvjFL4b5G97whjDLzmMppWy99dZhduedd6Zl+S9/9md/luZZ3/nNb34TZu9973vDbGBgoMybNy/Ms+dQnYmJiTCrm1Nl1q1bV37605+GeTZGtp3b9Eo2f6kbl9vMqfpNdi6yd7S99torrffss88Os5tvvjkte8QRR6T5TNh5553TfNmyZWG2yy67lLe85S1hfu+994bZueeemx53zZo1ac7Gyea9peRjM733q1/9aqabAP/P8PBwmr/uda8Ls+9///tp2br3B2Zer+aQixcvDrOVK1c2rretbP0sewf/9Kc/nda57bbbhvmKFSs2qm30Tt16fHYfZOvIGyMrn72bHH744Wm9W221VZi9+MUvDrOsP1ZVlc4Rs9+SrZnWlZ2tsvfzbCzppZlcr81+8+677x5m2TpVKX946xWZbEzI1I1xWb233HJLo2OW8vB3mptuuqlx+cyW2C9mapzLztV73vOetOw555wTZpdeemnjNtVpsx+hV7LnWJvnXy+eJbPv7AEAAAAAAAAAwGZmkwwAAAAAAAAAAH3PJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7NskAAAAAAAAAAND3hjblj6uqKtPT040ONDg4mOZjY2NhNjw8nLYpMj4+Xm6//fYwf/7zn5+2KZO1d2hok07rfzNnzpzy+Mc/PszPPvvsMHvlK1+Z1v3GN74xzL72ta+lZcfHx8Os0+mUOXPmhHnW5qxcKSXtb0ceeWSYTU1NpfWW8nC7I1m/aqOXxxwYaL7nLbsOO+64Y5hl9+bKlSvLeeedF+bZNaobZ+bPn9+oTaWUsn79+jDbdtttG48LxxxzTJqfdtppYfbXf/3XadnsXG233XblqKOOCvMFCxaE2cknn5we92lPe1qYffWrXw2zww8/PK13eHi47LDDDmF+8MEHh9nLX/7ytO5PfOITYZYdc8WKFWm9bXS73cZls/u6Tb2veMUrwmzbbbdNy7773e9ufNxMVVVpX8+ecXvvvXda95Oe9KQwe/3rXx9ma9euTeu96667ypvf/OYw//KXvxxmL3nJS9K6s/N8wgknhFn2vC6llN1337186EMfCvMPf/jDYVY3vznrrLPC7MYbbwyz97///Wm9dcfOxqrs+paS96u77rorzD7+8Y+H2cjISPmjP/qjML/tttvCrO75l82nd9ttt7RsZvXq1eUHP/hBo+PWmZiYaFRuY+ZFTcfBunJtxtfMPffcU5YsWdKobF25LM+yc845J8w6nU76HMrGyOOOOy7MSinl6KOPDrO///u/T8tmhoeHy+LFi8N82bJlYVZ33bNnSfYsKCUft1evXl2+973vhfm5554bZnfeeWd63G9+85th1qt+PluNjo6Wxz72sWH++c9/Psz++I//OK37ox/9aJh97GMfq23b5lb3vM7u66Zj9h+iuXPnpnl2nrP5Wt1axsDAQJk3b16Y183l6a23vOUtaf6YxzwmzD74wQ+mZf/mb/4mzBYsWFCe8YxnhPnFF18cZnV9+U1velOYnXjiiWH2ohe9KK33j//4j8s73vGOMD/ppJPCbKbGqqGhobJw4cIwz57Zhx12WFr3GWecEWavetWrGte7cOHC8rznPS/ML7jggrR8JltXyNbtsvFx7ty5ZZ999gnzH//4x2H22c9+Nsx+X3fk1FNPTcvecccdad5vsvWKv/zLv0zLHn/88WH2k5/8JMyuv/762nZl6+o777xzmD3rWc9K691///3D7MEHHwyzpt/JSundN4JsXC2llDPPPDPM6t6lli5d2rhdmY35jtNUr9552qzX7rbbbuU973lPmG+zzTZhlq3zl9Lue+dM2XXXXcMs+/7whje8IcwWLFhQDjrooDC//PLLw6xuXTV712rTl+u+vWf9qu67YC/f/ZuOZVm5UvLf1GbsLaX59/t//Md/TPNsL0Lbbzy9uoZt+lWmzbXP9OL+8y/JAAAAAAAAAADQ92ySAQAAAAAAAACg79kkAwAAAAAAAABA37NJBgAAAAAAAACAvmeTDAAAAAAAAAAAfc8mGQAAAAAAAAAA+p5NMgAAAAAAAAAA9L2hTfnjTqdTBgcHe9WW0PT0dJhVVRVm3W63rF27Nsx33nnnxm0aGIj3Fw0NbdJp/W86nU6ZM2dOmO+9995hduCBB6Z1X3311WF22WWXpWXvuOOOMNt6663LM57xjDDPfs9xxx2XHveXv/xlmO24445htnz58rTeUvK+08YLX/jCMLvtttvCLDvHv5f1uzaGh4fDbN999w2zuXPnhtmqVavK17/+9TAfGxsLs6zPlJKfh7r7evXq1WG23Xbblde//vVhfvzxx4fZhg0b0uNm48LIyEhaNqv7/vvvL//0T/8U5j/72c/C7Pzzz0+P+zd/8zdh9tGPfjTM6u6/+fPnlz/7sz8L89tvvz3Mtt9++7TuXXfdNczWrFkTZtlzZmNkfbLb7Taut03ZrM+99a1vDbO3vOUtjY/Z9reuX78+zLNzvP/++6d1X3zxxWG2zTbbhFnWnlJKmZycLPfcc0+YZ/fY4Ycfntb9nOc8J8xe97rXhdlFF12U1js6Olqe9KQnhfl3vvOdMHvooYfSuo844ogw+8hHPpK2KbNo0aLyyle+Msyzc3XGGWekdWfX6Ctf+UqYrVq1KswmJibK0qVLwzwbb+rm2Vl+wQUXpGUz4+Pj5be//W2Ytx0jZ0I2HtXNp3o1ptdZsmRJo6zO+973vjD71re+lZbNniXZPLCuL2f97Xvf+15aNjM6Olqe8IQnhPmyZcvCrG6Oceqpp4bZb37zm/rGBfbcc89y6aWXhvmnPvWpMPv2t7+d1t3L/joTtt566zBbt25dWna33XYrn/vc58L8qKOOCrO6uW12jzU1PDyc9skPf/jDYVb3PrRo0aIwq/stF154YZo31el00rxX7+6l5GN+1mcOO+ywtN7tttsuzE4//fQwO+ecc9J6JyYmyl133RXmT33qU8Ps5z//eVr3bBwz6q59Xd9pKqs3ezY+97nPTet9+ctfHmZTU1P1DQusXbu2/PjHPw7z7Jn9zW9+M607my8/7WlPC7MHH3wwrXfu3Llpfz322GMbtamUkq4FT0xMpGUzW2+9dXn2s58d5pdffnmYPe95z2t83O9+97thlq1xlfLwec7WkrNxvW5MyMbBN73pTWGWvVeuWbMm7cvZ83p8fDzMSinl3HPPDbNs/bmUUubNmxdmnU4nfafN+lzdOX7iE5+Y5pG6NeZOp5OOZdk6SbZmV0opP/nJT8Ls1ltvDbO6ddXBwcGyYMGCMM/eay655JK07l//+tdhNjk5GWZ1z6gsz54zbZ5tr3jFK9L8zDPPDLO6d6lsnaNO1t/aPP/aqFsXyO7Po48+Oszq5st1a4knnHBCmNWt+X3hC18IsyOPPDItO1OyPpl9s8zW+Xfbbbdy9tlnh3n2vavuWZKtm990001p2ex5Xid7N8zOYSml7LfffmF21llnpWXPO++8MKt7lrz0pS8Ns1NOOSU97vz588PsiiuuCLPsPauUh8e53XffPczvvPPOMLvhhhvSunupV9+Fe7H+2el00u+d2byobm9F1qa6stHz3L8kAwAAAAAAAABA37NJBgAAAAAAAACAvmeTDAAAAAAAAAAAfc8mGQAAAAAAAAAA+p5NMgAAAAAAAAAA9D2bZAAAAAAAAAAA6HtDm/LHVVWV6enpMB8cHGzckOHh4Ub1Tk5Ohlmn0ykjIyNhPjU1FWZDQ/mpmZiYCLNut5uWzVRVVcbHx8P83HPPDbPly5endX/9618PsyOPPDIt+773vS/MFi5cWA4//PAw/853vhNm559/fnrcbbbZJsweeuihMMv66e9l/Sq7hgMD+d6yV77ylWH21a9+NcyWLVuW1tvpdNJjt+l32X2U3SdVVYXZxMREufvuuxu1J7sHSinl5z//eZg99alPTcvecsstYVY3ZmzYsCHMXv/616fHPeCAA8LspS99aVr2S1/6Uph1u90yNjYW5u9+97vD7B//8R/T4+68885hdsYZZ4TZgw8+mNY7NDRUFi1aFOZbbbVVmH3iE59I6z7ttNPCLOtXWV/+vV7df704Zt14sdNOO4XZXXfdtXGNewTZs7yUfDxpo+78P+1pTwuzL3/5y2GWjY2lPPx7Vq5cGebZWPWkJz0prfvf//3fw+xzn/tcmF133XVpvbfddlt57WtfG+Y33nhjmP3Lv/xLWnf2e9///veH2T333JPWu/3225cjjjgizLM52zve8Y607mzu9OY3vznM1q5dG2Z18+XsObMx85fIP/zDPzQuW/f823fffcPshhtuaHzcXo2d/HfZszGbfw4MDJQ5c+aE+eLFi8Psm9/8Ztqm7NrXjQmZTqeTPouyfn7WWWeldWdj59lnn13fuMAvfvGLsscee4R5dj569Vydrbbddtswy+bpv5fNjW6//fYwqzvP/+t//a8wy9q8Zs2aMNtzzz3Lv/3bv4X5G9/4xjCrmwvsuOOOYXbzzTenZTudTpjtsssu6bMzW1OoO8fPec5zwiybM9XZYYcdyqtf/eowf/rTnx5mT37yk9O6s/72zne+M8zq1tXmzp1bnvKUp4T5c5/73DDL1iNmq6zP9cqiRYvKy1/+8jC//PLLw+xnP/tZWnfW19vMi7rdbjoOZs+p7373u2nd2fvS3/3d34XZeeedl9Y7Z86c8oQnPCHM//mf/znMDj300LTugw46KMze/va3h1ndWLT11lun99g555wTZtl6bimlXHDBBWH2qle9Kszq5iDLly8vH/vYx8I8+82jo6Np3dk4l/XHbP2lbi3jxBNPDLNsbbqUvE/WPTszCxYsKM961rPCfP369WG23377pXU/7nGPC7Osn2fPtlIefofO5vIXXnhhmNWtYf7yl78Ms2zuU/cuPDo6Wp74xCeGefaMq1srzjRdlxseHi7bb799mGdrlNk3j7o2LVy4MC2bmZycLPfff3+Y130va6qu3l6989R948kcf/zxYXbFFVekZVesWFE+//nPh3k2F8zugVJKOeSQQ8LspJNOCrPs3vy9pudr7733TvP9998/zLJ3i9WrV4fZzTffXA488MAwz/pUtgZSSknrzdYMSsnnXHVrGVmWfY8spZSTTz45zL7whS+kZbNn55577ln+9//+32Ge9au6b2nZOPgXf/EXYbZu3bq03r322qtcc801Yf4//sf/CLM3vOENad3ZGnSb70p1a3PPfvazwyz7BldKSdccPvzhD4fZBz/4wTDbZ5990r7+kpe8JMzq1piz89h0jPIvyQAAAAAAAAAA0PdskgEAAAAAAAAAoO/ZJAMAAAAAAAAAQN+zSQYAAAAAAAAAgL5nkwwAAAAAAAAAAH3PJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPS9oU35406nUwYHB8N8enq6dYM2p8HBwbJw4cIwv+mmm8JsamoqrXtoKD51AwPN9x5NTk6W+++/P8zvvvvuxse9+OKLw+y1r31tfeMC22+/fTnmmGMa1f2Vr3wlrfspT3lKmE1OToZZVVVpvaOjo+Wxj31smB9//PFh9rOf/Syt+4gjjgizU045JczWr1+f1ttL4+PjYZZdv9tvvz3MpqamyvLly1u1K5LdB7/5zW8a17tgwYJyyCGHhPkFF1wQZldccUVa99Of/vT0uL3yne98J8zOPffctOxdd90VZt1ut3GbxsfHy29/+9sw/+QnPxlmhx9+eFr3lVde2bhdbRx44IFhdtVVV6Vls7G76Xmuqiotm92bBx98cFr3hRdeGGbZuFyn0+mk5yJ7LmfPt1Lyvv7Tn/40zLL7p5RSdtppp/R58ZKXvCTMXvCCF6R1P/WpTw2zl770pWFWN3+Zmpoq9957b5h/4hOfCLNszC8l76/33HNPmNX1m7q50fOf//wwO/vss9O6/+3f/i3Mmt5/AwMDZWRkJMyz532bufTExETjsiMjI2WXXXYJ88MOOyzMbrjhhsbHnSltnmG9tGTJkpluwn/T6XTK8PBwmO+www5h9qtf/arxcbP3rDoDAwNl3rx5YZ61eccdd0zr3nXXXcNsxYoV9Y0LTExMpHPbrL/Wvf/N1r7e1LJly8Ksbvy8//77yz/90z+FeTY3rXtm33zzzWH29re/PcyyZ1Rde3/0ox+lbcpkz93seV1nxx13LKeeemqYZ7936dKlad1HHXVUmF1zzTVp2TVr1oTZDjvsUE488cQw/9u//dswW716dXrczHve854wW7t2bVp2+fLl5aMf/WiYZ21m42Vrn9m6zqWXXtqL5tQaGRkpO++8c5gfeuihYXbSSSeldWfv4K95zWvC7Jvf/GZa780331z+5E/+JMyze6xuXS4bP9/5zneGWd1zc968eWXfffcN82233TbMNmzYkNa93XbbhVl2/erGz263W8bGxsI8m0uMjo6mdb/iFa8Is2ydOFM3l3viE58YZqeddlpad3auTjjhhPrGBaqqSp+tn/3sZ8PsoosuSut+73vfG2Ynn3xymNU9o7beeuvynOc8p3G7Mtl9lGV1a/ljY2PpvZ+tcxx33HFp3YsXLw6zbJ6fPbO33nrrdH3t05/+dJjVjZ/nnHNOmO2+++5p2czQ0FA6jmXvaXVjXFa2bh2rV+rG/Gx8zL5ZzZ07N613eno6nZ+++MUvDrOsXCn594nsm8cPf/jDtN462bnca6+90rLZGnX2zSobM6ampsqqVavS4zb13e9+N8zq1iDbyH7PHXfckZa9/PLLw2zRokVNm1TGxsbKtddeG+bZe0n2/aeUUq6++uowy77R1b1L3XnnneWNb3xjmH/uc58Ls7pz9a//+q9hln0fqhs/d9ppp3Se8hd/8Rdhtvfee6d1Z98lV65cGWbZuP273/2uvOtd7wrz008/PcyOPvroMCullAcffLBRmzL+JRkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7NskAAAAAAAAAAND3bJIBAAAAAAAAAKDv2SQDAAAAAAAAAEDfG9qUP66qqkxPT4f54OBgmGXlSillYCDer5OVraoqzLrdblmzZk2Y/5//83/Sspm6vKn777+/fOYznwnzW2+9tXHdy5YtC7Ntttmmcb2//vWvyyGHHBLmf/VXfxVmN954Y1r3+vXrw6zNNZiYmCi/+93vwvzHP/5xmK1duzate+nSpY3K9qpPbYypqakw+8Y3vhFmk5OTaZ0PPPBAmI+OjoZZNh6UUsr1118fZitXrkzLZm6++eby9Kc/Pcyz83TXXXeldWdlV6xYUd+4QFVVZWJiIsyz7Mwzz0zrPuyww8Ks7hpl1q1bV37yk5+E+XnnnRdmJ5xwQlr3vHnzwmzhwoVhdsEFF6T17r///uWaa64J87e+9a1hdvXVV6d1Z7Lz3GbMeOMb3xhm1113XVr2RS96UZhl93wppVx11VVhtt1226XPi4svvjjM7r333vS42X2d9anLL788rbfb7abj+rOe9aww27BhQ1p3do8ceuihYfbrX/86rbfb7ZaxsbEwf9/73peWzwwNxdPKbJ6RzR9LKWX58uXlIx/5SJjvsssuYXb44YendWeyMeOLX/ximD3taU9Lx4vtttsuzOrmGJlsvK+zfv36dE72y1/+Mszq5viZXo1xdXXXmck5WS9k9/W3vvWtMOt0Oul9/Ud/9Edhlr13lJLf99n8v05VVen1e8xjHhNmq1evTutuM1/L1LW5adaP2ow3q1atKl/96lfDPHvPvvTSS9O6TznllDDL3pOz9jz00EPp/ZmpG/+OP/74MPve977X6JillLJmzZpy2WWXhfnixYvD7L777kvr3nHHHcOszXhfVVX6TptlbWT1ZmtcpTz8XpmNR3vssUeYbb311mnd2Tj4hzTe1I0Xn/zkJ8Ps29/+dlp3r87j6OhoefKTnxzmd999d5jV3UM33HBDmH3ta18Ls1WrVqX1djqdMmfOnPRvInXP5G233TbMTj755DA755xz0nrHxsbS9+W99947zOrWqs4444wwy+b5deNU3ZpRpu7d5HWve12YZWtgmbpxOeuvH/jAB9K6ly9fHmbj4+P1jUvKZvOIxz72sWF29tlnp3Vn7+4333xzmNWtN4yMjKRz+ew8Z2vmpeTPseHh4TDbmPExe4fI1oWOOeaYtN63vOUtYfapT30qzLLz/OCDD5YLL7wwzLM51/777x9mpeTzpjZrCnVzjDaajgm9VNfnsvsge351Op203uHh4bLTTjuF+Tvf+c4w+/jHP57WnfXJU089NcxuuummtN66tYFM3XfH7BtR02MODAyUrbbaKszXrVvXqN5S8rleNpZsjKxPZn2ubg1lhx12CLP58+enZbMxodPppNfvkksuCbPjjjsuPe61114bZk996lPDLFuPLeXhd9KTTjopzLP13qc85Slp3c9//vPDbMGCBWH2/e9/P6132223LS9+8YvD/CUveUmYXXTRRWnd2TeGz372s2GW3UNVVaVj0Qte8IIwq5uLv+Y1rwmzpusC/iUZAAAAAAAAAAD6nk0yAAAAAAAAAAD0PZtkAAAAAAAAAADoezbJAAAAAAAAAADQ92ySAQAAAAAAAACg79kkAwAAAAAAAABA37NJBgAAAAAAAACAvje0KX/c6XTK8PBwmHe73TAbHR1N656ent6UpmyUsbGxcsMNN4R51t6Bgeb7h9qUXbFiRTn33HPDPGtz3TmcO3dumJ1++un1jQusX7++/PznPw/zLJuamkrrrsubqqqqbNiwIcy/8Y1vhNlBBx2U1n333XeH2dBQfMt1Op203rr7b3x8PMyyflPXrqbq2pv117q+fNZZZ4VZdsw64+Pj5bbbbgvz7DzNVF8upflY9qEPfSit9zOf+UyYZfdPXX+rqqpMTk6G+Tvf+c4we/azn53W/ZznPCfMbr/99jAbHBxM673pppvK3nvvHeZ33HFHWn4mZH3uF7/4RZj9z//5P9N63//+94fZ7373u7TsVVddFWYPPvhgOvauWrUqrTuTPYeOO+64MMvG1VJKWb58eToeZfdf3X0yNjYWZr/+9a/DrK7NVVWlx87u7TpZvddee22YZb/19/l1110X5i94wQvC7IgjjkjrfulLXxpmP/rRj9KykZ///Odl1113DfOJiYkw68V8eHPI2lwnG19n6+/dElVVFWannXZamC1btqzxMbPxZuutt07LZu3N5tJt3XXXXWF2wAEHpGVPOOGEMFuxYkVa9gtf+EKYjY6Olj322CPMs2c2myZ7Tl199dVh9rznPS+t9/rrrw+zr33ta2GW3UOLFy8ub3vb28L8S1/6UpidcsopYVZKft9nx6yzcuXKtF0jIyON67755pvDrM36y/33318+/elPh3n2XnLZZZc1Pu6hhx4aZnXzj6mpqfLggw+GeTbvPfDAA9O6f/CDH6T5H4rp6en0HF9xxRVhtttuu6V1t3k/yKxZsybtk6eeemqYPfTQQ2ndF198cZh94AMfSNuUmZycTMej7H127dq1ad3HHHNMmJ144olhdsEFF6T13nfffemYkb1LffKTn0zr/upXvxpm2TGvvPLKtN5Smo+TdX0yO1/ZMbN5YN160Wtf+9owu/DCC8OslFK+973vhdlHPvKRtGw2JkxNTZWVK1eGedYf26xzzJ8/P8zqrvmaNWvK5ZdfHubZNci+W9SVbfv+l5XPfnM2zyullP/8z/9s1J7sPbmqqjTPxrhLL700Pe4uu+wSZvfdd19aNlM3x8jUfVvo5bp4r2RjYDbfrlsb3Wuvvco111wT5u9+97vD7F//9V/TurP1l89//vNhln03LOXh+2urrbYK8/Xr14dZ9ltLKeXss88Os+w7T/YtbWBgoMyZMyfM161bl7Yps3r16jB717ve1bjeqqrSMS57d6xbV83G7bo1oWc84xlh9sADD5Svf/3rYX7nnXeGWTa/LCX/Xp29V2bff0p5eL3pb//2b8M8W3PPvjuVUsrrX//6NI/UzeUmJibK0qVLwzy7/tl3i1JKuhaVfRt80YteFGY77LBDeo4PPvjgMKt7/i1atCjM7r///rRsdA/5l2QAAAAAAAAAAOh7NskAAAAAAAAAAND3bJIBAAAAAAAAAKDv2SQDAAAAAAAAAEDfs0kGAAAAAAAAAIC+Z5MMAAAAAAAAAAB9r1NV1Ub/8cDAQDU0NJTlYTYyMpLWvWHDhjAbHh5Oy01PT3eC9lSjo6Nh2ay92e+sMzU1lebr1q37WVVVBzxSNjw8XG233XZh2excrF27Nj1uVrbO/fffH7Z5aGio2nbbbcOy3W63UVZKKZOTk43Kjo+Pl263+4j9opT6vpz115NOOinMSill0aJFYfbud787zNatWxf25VIebnOba5jJ+mx2niYnJ8PzPDAwUNXd9021OQ9r164N+/Lg4GA6ZmTnoq4vZ/nExERadmpqKmxzXb/IxrksKyX/vVmfycbl/3vcas6cOWH5LKuTnYvx8fEwq7v/BgcHq/nz54flx8bGwmxwcDDMSsnHuUy32y1VVT1imzudTjrGtZGd47rfkvXlTqdTNe2vdb81u/+yejfmWZKdj6bHbVM2G5f/b9m0zdm4XTfOZe3KytaNGYODg9W8efPSY0faPIeyuWmb+ef09HTjNrUs2/j+mwnZGFdK/TiXPad6df9tTJvTA7ewZMmSMHvf+94XZgcccEC55pprHrHNdfOiXXfdNczuueeeMCullPXr14dZ3Zi+fv36dC7X9Hm9/fbbp8c98sgjw+zWW29Ny/7Lv/xLev817cu9NFN9OZs3Ze/Jq1atKpOTk2Gbh4eHq8c85jFh+WOPPTbMXvWqV4VZKaW8973vDbP//M//DLMVK1aEbd59992rD3zgA2HZbL78sY99LMxKKeWGG24Is2y+XEopExMT6XtJ9txtMy63eUaNj4+n6y8LFy4My15//fVhdtNNN6XHvfHGG8PswAMPDLNjjjmm3HLLLWFfrlt/Ofjgg8PszW9+c5iVUsohhxwSZnXz/JkaM9rI3qU6nfDnlP333z/M6t5lr7zyyqw9adlSSuP7bybmeRs2bOjZu1Sd7Dpk84SVK1emz5K6eUZm7ty5ab711luH2dKlS8Os7p21bp6R9Y26OUjTd/SszXXtzc5/m7WkbI5YSilr1qxJ77/s2L1aG83m6XXzoqGhocZ9ue7ezN5Zsz61MWsZTa9x3RiYzX/q7pGsL9etB0bq3oey59A//MM/pGUPOuigsC+PjIxUixcvDsvee++9YVY3XrzsZS8Ls7vvvjste80116TvUmnhFrJrnz2/JiYmavtydv82XecvJX+XOv/888PsLW95S/n1r38dtnnRokXV4YcfHpb/53/+50ZtKqWUPffcM8yy+XTdulzT95I6bdblSjKXq1uXy/rM2972tvSgf/mXfxlmRxxxRFr2lltuSceMHXbYISybjWVf+cpX0uN+6EMfCrPsPWzZsmVlfHw87MsjIyNVtvaTPWf23XffMCullD/90z8Ns0suuSTMrrnmmrJ69eqwzQsWLKgOOOARL0EpJX+/r1tPz/rzs5/97DC75JJLysqVKx+xzfPnz6/22WefsOydd94ZZmeddVaYlVLKddddF2Yf//jH07LRWuLsWg0HAAAAAAAAAIAesEkGAAAAAAAAAIC+Z5MMAAAAAAAAAAB9zyYZAAAAAAAAAAD6nk0yAAAAAAAAAAD0PZtkAAAAAAAAAADoezbJAAAAAAAAAADQ94Y2Z2WTk5Nh1u1207IDA/F+nenp6TCrqqq+YYGpqalG7akrW/dbM1VVpedx/fr1jevO6m2jqqoyMTHRqGx2beu0Oc9t7LPPPmn+hS984VFqycbL+mubstn9V1VVen0HBwfDrE2/qLt36wwNxcNi1s/r+mOWtx0zmpZvMy73UjZW1bUp6zvZ9at7lnS73TI2Npb+TaRu7M2uQ5tr0OYZlxkfHw+zXo7LWd11z6Dsvm57/7UZX2dC3ZixYcOGMKvrN037Vd39V3ees6zuvm3aN+r6TdOxqI3suboxst80PDwcZjM5l+vV869XZdtYsmRJ47KnnXZamC1btizM6t5LbrvttjBrc56ycahOt9sta9euDfPsnl+1alVa90c+8pEwa/ss2NKeJb106aWXhtmf//mfh9kBBxyQ1rto0aJy9NFHh/mvfvWrMHvGM56R1t302ZmNn3fddVd505veFOZt1l6yuVwbbdYFZmpsnZ6eTseM/fbbL8yOOOKItO7sXHz4wx8Os5UrV6b11o1zF198cZg985nPTOvmv2Tz05/97Gc9qbeNumf2TGg7x8/Gz7r3jmxMeeCBB8JsY+a1TdeD6+Y3dfd+ZGP6VK/WozJN1xJLyduUPcPqnm9Zv2n7bGy6XtHpdNJ6s3OV1VvXl7vdbvqbe7UW1Uan00nn8r0aA7Nr22ZMb3Nf7rzzzmF2zz33NG5TL9e4nvOc54TZK17xirTsTjvttLmb01rb75XZNW66/lxKvi70/ve/P8zq+s0OO+xQTjjhhDA///zzw6zu/SCbVzW9T9r05TZrXL2Uteuyyy5Ly/71X/91mK1Zs6Zpk1q9/11yySVpvv3224dZNt63nWtnz8a6d4Crr746zLL+WPfNf2xsrFx33XVh3nTeVEp+LrN+lfWb8fHxcvvtt4d5di4+85nPhFkp+bf3urLRefYvyQAAAAAAAAAA0PdskgEAAAAAAAAAoO/ZJAMAAAAAAAAAQN+zSQYAAAAAAAAAgL5nkwwAAAAAAAAAAH3PJhkAAAAAAAAAAPre0Kb8cVVVZXp6uldt2ew6nU4ZGIj3AU1NTYXZxMREWndWbxtVVZXx8fEwHxwcDLPJycm07jlz5oRZdsyNkfWL4eHhRuXqtL0GQ0Ob1P3/nz/5kz9J85NPPjnMsjZ3Op203k6n0/hc1v3WbrcbZlmb6/pcJivbq/urTlVVtfd+U21+U3Z92oxzdW3KymaqqmpU7vey31vX57L7KLtH2ra5jew6ZOeijV7V20tNx6m2Zes0LV93DbI8G9M3pi83vf515Zo+VzfHsXtRb9NjtpkvZ/O8tmXr2tSr533TY27M+d8Sx7KmlixZ8qgfs6qqxvOI2Xptms5tSpm9v6nfHHrooWGWjXMbNmxI67333nvLhz/84TBvOp+uk5XNntndbreMjY2leZNjllLKyMhI47J1st+UzdPbzOXaqKoqfcatXLkyzM4+++zGx83WX+qe13Vjc3au3vrWt9Y3jloz+e6Y6eU7z0xoM0/v1bnodrvpmtGWeA1m63t2JBsj6/pFL9fymz6nZur516bumTzP2fMzO26vvj+0uT5t1k/23XffMPv3f//3xvVWVZXOqdvME7N5+AknnNC43jba9NU256JNX66T9cnrr78+zLL3jlIebtO8efPCfPvttw+ze++9N617JszGb9ydTid9382u7dKlS9O6H3rooUb11ul2u+mYkdX929/+Nq17dHQ0zJoes5SHr/26devCfO7cuY3rrjtuZGPeLZqOC3X3dtP3yrrfk12j7LfcdNNNYVZK/uys+25/2WWXPXJ70lIAAAAAAAAAANAHbJIBAAAAAAAAAKDv2SQDAAAAAAAAAEDfs0kGAAAAAAAAAIC+Z5MMAAAAAAAAAAB9zyYZAAAAAAAAAAD6nk0yAAAAAAAAAAD0vaFN+eNOp1MGBwfDfHp6OswGBvL9OE3rzVRVVSYmJsJ8ZGQkzLrdbqNjllL/W9toc44nJyc3d3NKKQ+f56mpqTBvcy4zbertdDrp+dpjjz3CbHR0NK177dq1YZadp6qq0nqrqkqvYXY+6vrG0NAmDQWbRZv7ZHh4eDO2ZPNo83t6dY+01at21fXl7Fy2aVPb35Pdvzys7j5ocw3aPLOza5eNf3Xjct2xs/PRZlyerX0xa1f2ezfm+der39y0zbNRNufdGE378kyOy/Redn9m7yWdTqcnx9wY+tXMa/Nu33Ysi/TyWdILdWsZbZ5RM/HuXkp+b9cdNyvbZrypO3Z2nsfHx9N6s76eHXNjxsCmY3Md4+fMquvLbZ6Pvbq2MzVfbvPe2fb+a3ou3V+bx2w9j9nY2+bebfNNpM3f9OpZ0qvzVHfcNv2mzVp+0+PWfXvYa6+9wuyiiy5qdMyN0Wad6t577w2zZz7zmY3b1MZMjSeDg4Nl2223DfOxsbEwq5t/ZrK1+Lq+vGrVqnL++eeH+cte9rIwO/vss9O6Z+u4/mhr872kzn/8x3+EWZvxvu4bazYurFq1Kq371ltvDbMNGzaEWV1f7nQ66ViW3WNtvkm26ed1axltruGcOXMal43UtTfrM3XrQV/60pfC7MUvfnFa9rLLLnvk9qSlAAAAAAAAAACgD9gkAwAAAAAAAABA37NJBgAAAAAAAACAvmeTDAAAAAAAAAAAfc8mGQAAAAAAAAAA+p5NMgAAAAAAAAAA9L2hzVnZ8PBwmHW73cb1Ni1bVVWZmppqVO/QUH5q2vyeTFVVad1ZNjCQ73lqU7ZO2/JNzJkzJ8yy6/57WZvXrl0bZsccc0xab9M+V1VVWm8peZuzLLs3SyllfHw8zJq2udPplMHBwTCfnp5udMxSStmwYUOYZces0+l00nt/YmKicd3Z9WkzntSNGb06bmZj+nJTbca5trK+sTFjDu20OcfZtetln8nUHbeX7erVfGAm5gJ1ZmL+2Ub2bCyl3TOujZm6T9qYiecf/6WXcwFmv7qxbKb0as5ct3bQtN6mz9WZnC/3SqfTmekmbLJenufsfLQZfz07Z1bbZ2fT6zcb5/CzWdPz3Ksxv61eHbdX72GzcS2/jTZtavNsrPtm0mYNJVuDnpycrG9coM36Z52Z6BtZexcsWJCWvemmm8LsV7/6VeM2dbvddM29jay/XXHFFT055mw1MDCQftc6+uijw+yhhx6qrTvyzGc+M8ze9a53pfWOjo6WJzzhCWG+dOnSRm3aEjX93tVL9913X5q/733v68lxu91u+r0se5ZceeWVad297DfZmN+rZ1jb78K96ltZvU3XoOue11mfGR0dTev+xS9+EWann356Wvbtb3/7I/7v/TVCAQAAAAAAAADAI7BJBgAAAAAAAACAvmeTDAAAAAAAAAAAfc8mGQAAAAAAAAAA+p5NMgAAAAAAAAAA9D2bZAAAAAAAAAAA6Hs2yQAAAAAAAAAA0PeGNrXAwEC8r6bb7TYqV0opk5OTYTY4OFjfsM0s+y0zqe48zlS9Tc9Xm+NOT083LltK3uYHH3wwzH760582rnemrF+/vnHZ7Bp1Op3G9Q4PD4dZNh6Uko8JbfpFVVVlYmIizIeG4iFzamoqrbuX/aJXdTcd79uajfdQKfk1nqlz1W969SyZqf7aZl7U9JhtZXVnY2Bd2dmq6XWo+61t5yiPdr2l9O5cND1m2/60JfbHNpYsWdIo65W6OWJVVY9SS+C/9OpduulcoE29/Wa2jgnZ/L/u+szU9Wsz//xD6nN/aHo15+rVuLql6tU73mxds+1lm5uo+52zsb/WPf96tf7ZSzPVrqZrdnV5ltWtBWeafpcopZQPfvCDjY/LzJucnCzLli0L849+9KNhts0226R1P+5xjwuz1atXh9lDDz2U1rtixYryxS9+McyvvvrqMMu+tdRpM3/Z0tblOp1Ouu6aHbeuTVney2/vbcbPpu/Ydc/VqqrSb4+92uMwZ86cMKtbP+t0Oum31F7Nx571rGeF2WWXXRZm8+bNK/vss0+Y77bbbmF2yCGHpG16/OMfH2ZnnHFGWjYy+2aHAAAAAAAAAACwmdkkAwAAAAAAAABA37NJBgAAAAAAAACAvmeTDAAAAAAAAAAAfc8mGQAAAAAAAAAA+p5NMgAAAAAAAAAA9L2hzVnZwEC852ZycnJzHmqjdDqdMjIyEuZTU1Nh1u1207qz35rV21ZduzJZm8fHxxvXW1VV2q7suHW/Z2go7qJZ2aqq0nrr2jw2NpaWz7S5Rr2qOzuPberNVFWV3vdt+sXg4GDjdvVKmzEjy+rq7nQ6tdc3UjdWZcftZT/Prm/ds6Tu3u+VXp6PXmhz/2XanofZ2q6ZMFNtnqn5Ta/Uja+Z2dhv2jz/pqenw6zT6bQae2dKr8aMLdGSJUtmugn/zUw9jyHT9J0VHkmb97829ZbSfIz9Q3s20nsztf7Vq7XRjdG0XXXHnY3z2tn4TtrGlngeO51Oo2xjbInv/jNxDbNz0at3nomJiZ7UW6eqqll77f+QZNfggQceSMuuXbs2zG655ZYwq/sWtnbt2vKjH/0ozNs8s2fj828m9PJb2vDwcON21cmOvWHDhkblSpm59/PsGsyZM6dxvdn39435lp21KzuXddc+Wyu+9dZbwyz7PYODg2XhwoVhPjo6Gmaf+9znwqyUUpYvXx5mWX/LWAkCAAAAAAAAAKDv2SQDAAAAAAAAAEDfs0kGAAAAAAAAAIC+Z5MMAAAAAAAAAAB9zyYZAAAAAAAAAAD6nk0yAAAAAAAAAAD0PZtkAAAAAAAAAADoe0Ob8sdVVa0YHx+/s1eNiXS73SzeLQp62d7p6ek2xdM2r1+//lE/xxshbHMpZcXU1FRP2jwxMdG0aNbe2Xqea9s8MTGxJbV5RbfbDdtbc1+nxsfHG5ctNfdfdo5b9Me2Gre5jRbXKO3LpZQV09PTYZtbjq9N1ba5lLJF3X8laW+b+6+lxmNGpoe/p2f9Yra2eYb6Rm2bm/aNHmp8/7XRy/nn5OTkFjPH/7+2tH5RSp89S2aQNvdev/XlUnr4/OvRnHk2nuNS+rDNvXqW9PJdyvNvs9CXe29W9uWZmn/O1ve/XmlxnmdjXy6lps1VVfWkzVNTU02LtlqXa6PFO2ttm5ue5x6uMzbuFzO09lnKDH3jaWlLe2bP2Fpi9s2k5ntK2uZut7tibGxsS5ozz8Z+UUqP1vLrzuHk5GRduzIz8v29hdq+vG7dukZtXr9+fbMW1ZuVbf7lL3+ZxWGbV69eveLiiy+ebf2ilKDNnaqqHu2GAAAAAAAAAADAo8p/bgkAAAAAAAAAgL5nkwwAAAAAAAAAAH3PJhkAAAAAAAAAAPqeTTIAAAAAAAAAAPQ9m2QAAAAAAAAAAOh7NskAAAAAAAAAAND3bJIBAAAAAAAAAKDv2SQDAAAAAAAAAEDfs0kGAAAAAAAAAIC+9/8B/DZx2GnogwAAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 2880x2880 with 50 Axes>"
       ]
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -379,7 +379,7 @@
        "torch.Size([190, 1, 28, 5])"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,7 +396,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fddb02b4f90>"
+       "<matplotlib.image.AxesImage at 0x7fabf098e9d0>"
       ]
      },
      "execution_count": 16,
@@ -405,7 +405,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -434,47 +434,48 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "__init__() missing 1 required positional argument: 'pad_token'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-388038927ee3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtarget_transform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCompose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAddTokens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"<sos>\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meos_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"<eos>\"\u001b[0m\u001b[0;34m)\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;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'pad_token'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", eos_token=\"<eos>\")])"
+    "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\")])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IAM Lines Dataset\n",
+      "Number classes: 82\n",
+      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_', 80: '<sos>', 81: '<eos>'}\n",
+      "Data: (7101, 28, 952)\n",
+      "Targets: (7101, 97)\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\", target_transform=target_transform)\n",
+    "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\")\n",
     "dataset.load_or_generate_data()\n",
     "print(dataset)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 342,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data, target = dataset[0]\n",
+    "data, target = dataset[181]\n",
     "sentence = convert_y_label_to_string(target, dataset) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 343,
    "metadata": {},
    "outputs": [
     {
@@ -483,15 +484,15 @@
        "([], [])"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 343,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 1440x1440 with 1 Axes>"
+       "<Figure size 5760x1440 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -499,7 +500,7 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(20, 20))\n",
+    "plt.figure(figsize=(80, 20))\n",
     "plt.title(sentence)\n",
     "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
     "plt.xticks([])\n",
@@ -508,1077 +509,187 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.networks import VisionTransformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tt = Transformer(3, 3, 512, 8, 2048, 0.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vt = VisionTransformer(6, 6, 256, 82, 8, 118, 512, 256, 0.1, 79, (28, 16), (1, 8), \"gelu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchsummary import summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 344,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "VisionTransformer(\n",
-       "  (slidning_window): Sequential(\n",
-       "    (0): Unfold(kernel_size=(28, 16), dilation=1, padding=0, stride=(1, 8))\n",
-       "    (1): Rearrange('b (c h w) t -> b t (c h w)', h=28, w=16, c=1)\n",
-       "  )\n",
-       "  (character_embedding): Embedding(82, 256)\n",
-       "  (position_encoding): PositionalEncoding(\n",
-       "    (dropout): Dropout(p=0.1, inplace=False)\n",
-       "  )\n",
-       "  (linear_projection): Linear(in_features=448, out_features=256, bias=True)\n",
-       "  (transformer): Transformer(\n",
-       "    (encoder): Encoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (decoder): Decoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (head): Sequential(\n",
-       "    (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "    (1): Linear(in_features=256, out_features=256, bias=True)\n",
-       "    (2): GELU()\n",
-       "    (3): Dropout(p=0.1, inplace=False)\n",
-       "    (4): Linear(in_features=256, out_features=82, bias=True)\n",
-       "  )\n",
-       ")"
+       "tensor(0.0631)"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 344,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "vt"
+    "data.mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 345,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==========================================================================================\n",
-      "Layer (type:depth-idx)                   Output Shape              Param #\n",
-      "==========================================================================================\n",
-      "├─Sequential: 1-1                        [-1, 118, 448]            --\n",
-      "|    └─Unfold: 2-1                       [-1, 448, 118]            --\n",
-      "|    └─Rearrange: 2-2                    [-1, 118, 448]            --\n",
-      "├─Linear: 1-2                            [-1, 118, 256]            114,944\n",
-      "├─PositionalEncoding: 1-3                [-1, 118, 256]            --\n",
-      "|    └─Dropout: 2-3                      [-1, 118, 256]            --\n",
-      "├─Embedding: 1-4                         [-1, 97, 256]             20,992\n",
-      "├─PositionalEncoding: 1-5                [-1, 97, 256]             --\n",
-      "|    └─Dropout: 2-4                      [-1, 97, 256]             --\n",
-      "├─Transformer: 1-6                       [-1, 97, 256]             --\n",
-      "|    └─Encoder: 2-5                      [-1, 118, 256]            --\n",
-      "|    └─Decoder: 2-6                      [-1, 97, 256]             --\n",
-      "├─Sequential: 1-7                        [-1, 97, 82]              --\n",
-      "|    └─LayerNorm: 2-7                    [-1, 97, 256]             512\n",
-      "|    └─Linear: 2-8                       [-1, 97, 256]             65,792\n",
-      "|    └─GELU: 2-9                         [-1, 97, 256]             --\n",
-      "|    └─Dropout: 2-10                     [-1, 97, 256]             --\n",
-      "|    └─Linear: 2-11                      [-1, 97, 82]              21,074\n",
-      "==========================================================================================\n",
-      "Total params: 223,314\n",
-      "Trainable params: 223,314\n",
-      "Non-trainable params: 0\n",
-      "Total mult-adds (M): 23.93\n",
-      "==========================================================================================\n",
-      "Input size (MB): 0.10\n",
-      "Forward/backward pass size (MB): 0.86\n",
-      "Params size (MB): 0.85\n",
-      "Estimated Total Size (MB): 1.81\n",
-      "==========================================================================================\n"
-     ]
-    },
-    {
      "data": {
       "text/plain": [
-       "==========================================================================================\n",
-       "Layer (type:depth-idx)                   Output Shape              Param #\n",
-       "==========================================================================================\n",
-       "├─Sequential: 1-1                        [-1, 118, 448]            --\n",
-       "|    └─Unfold: 2-1                       [-1, 448, 118]            --\n",
-       "|    └─Rearrange: 2-2                    [-1, 118, 448]            --\n",
-       "├─Linear: 1-2                            [-1, 118, 256]            114,944\n",
-       "├─PositionalEncoding: 1-3                [-1, 118, 256]            --\n",
-       "|    └─Dropout: 2-3                      [-1, 118, 256]            --\n",
-       "├─Embedding: 1-4                         [-1, 97, 256]             20,992\n",
-       "├─PositionalEncoding: 1-5                [-1, 97, 256]             --\n",
-       "|    └─Dropout: 2-4                      [-1, 97, 256]             --\n",
-       "├─Transformer: 1-6                       [-1, 97, 256]             --\n",
-       "|    └─Encoder: 2-5                      [-1, 118, 256]            --\n",
-       "|    └─Decoder: 2-6                      [-1, 97, 256]             --\n",
-       "├─Sequential: 1-7                        [-1, 97, 82]              --\n",
-       "|    └─LayerNorm: 2-7                    [-1, 97, 256]             512\n",
-       "|    └─Linear: 2-8                       [-1, 97, 256]             65,792\n",
-       "|    └─GELU: 2-9                         [-1, 97, 256]             --\n",
-       "|    └─Dropout: 2-10                     [-1, 97, 256]             --\n",
-       "|    └─Linear: 2-11                      [-1, 97, 82]              21,074\n",
-       "==========================================================================================\n",
-       "Total params: 223,314\n",
-       "Trainable params: 223,314\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (M): 23.93\n",
-       "==========================================================================================\n",
-       "Input size (MB): 0.10\n",
-       "Forward/backward pass size (MB): 0.86\n",
-       "Params size (MB): 0.85\n",
-       "Estimated Total Size (MB): 1.81\n",
-       "=========================================================================================="
+       "tensor(0.1638)"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 345,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "summary(vt, [(1, 28, 952), (97,)], device=\"cpu\")"
+    "data.std()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 346,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = vt.preprocess_input(data)"
+    "from torchvision import transforms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 118, 256])"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 347,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = vt.encoder(x)"
+    "n = transforms.Normalize((0.5,), (1.,))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 390,
    "metadata": {},
    "outputs": [],
    "source": [
-    "trg = torch.tensor([10, 62, 22, 24, 31, 14, 62, 55, 50, 62, 54, 55, 50, 51, 62, 22, 53, 74,\n",
-    "        62, 16, 36, 44, 55, 54, 46, 40, 47, 47, 62, 41, 53, 50, 48, 79, 79, 79,\n",
-    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-    "        79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
-    "        79, 79, 79, 79, 79, 79, 79])[None, :]"
+    "ra = transforms.RandomAffine((-1.1, 1.1), scale=(0.5, 1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 399,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t, tm = vt.preprocess_target(trg)"
+    "d = ra(data)\n",
+    "d = (d > 0.15) * d\n",
+    "d = (d - d.mean()) / d.std()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 400,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "VisionTransformer(\n",
-       "  (slidning_window): Sequential(\n",
-       "    (0): Unfold(kernel_size=(28, 16), dilation=1, padding=0, stride=(1, 8))\n",
-       "    (1): Rearrange('b (c h w) t -> b t (c h w)', h=28, w=16, c=1)\n",
-       "  )\n",
-       "  (character_embedding): Embedding(82, 256)\n",
-       "  (position_encoding): PositionalEncoding(\n",
-       "    (dropout): Dropout(p=0.1, inplace=False)\n",
-       "  )\n",
-       "  (linear_projection): Linear(in_features=448, out_features=256, bias=True)\n",
-       "  (transformer): Transformer(\n",
-       "    (encoder): Encoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): EncoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (decoder): Decoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (1): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (3): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (4): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (5): DecoderLayer(\n",
-       "          (self_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (multihead_attention): MultiHeadAttention(\n",
-       "            (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
-       "            (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (cnn): _ConvolutionalLayer(\n",
-       "            (layer): Sequential(\n",
-       "              (0): Linear(in_features=256, out_features=512, bias=True)\n",
-       "              (1): GELU()\n",
-       "              (2): Dropout(p=0.1, inplace=False)\n",
-       "              (3): Linear(in_features=512, out_features=256, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (block1): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block2): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "          (block3): _IntraLayerConnection(\n",
-       "            (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "            (dropout): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (head): Sequential(\n",
-       "    (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
-       "    (1): Linear(in_features=256, out_features=256, bias=True)\n",
-       "    (2): GELU()\n",
-       "    (3): Dropout(p=0.1, inplace=False)\n",
-       "    (4): Linear(in_features=256, out_features=82, bias=True)\n",
-       "  )\n",
-       ")"
+       "([], [])"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 400,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 4320x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "vt.eval()"
+    "plt.figure(figsize=(60, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(d.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 351,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t"
+    "import torchvision"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d  = torchvision.transforms.functional.gaussian_blur(d, 7, (0.75, 0.75))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([[[-0.4344,  0.4939,  0.0382,  ..., -0.0808, -0.0290,  0.4399],\n",
-       "         [-0.4350,  0.5146,  0.0356,  ..., -0.0634, -0.0289,  0.4271],\n",
-       "         [-0.4303,  0.5245,  0.0435,  ..., -0.0755, -0.0267,  0.4240],\n",
-       "         ...,\n",
-       "         [-0.4477,  0.5377,  0.0596,  ..., -0.0866, -0.0283,  0.4457],\n",
-       "         [-0.4475,  0.5435,  0.0606,  ..., -0.0900, -0.0293,  0.4440],\n",
-       "         [-0.4488,  0.5476,  0.0689,  ..., -0.0914, -0.0276,  0.4411]]],\n",
-       "       grad_fn=<AddBackward0>)"
+       "([], [])"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAEYEAAAClCAYAAAAU5YV7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAA+VElEQVR4nO3dd7hcZbk34OcNobdg6B1EBUREDoJUAYEYIIYWQ+9ViIcoVZqUEGmiQKRIiDFgCB0MBhIkAqKcgxT9QAHpIIgQekkguL4/9nDca95F9mSY7EnIfV8Xlzy//cy7nj11rdn6moqiCAAAAAAAAAAAAAAAAAAAAAAA2qNHuwcAAAAAAAAAAAAAAAAAAAAAAJid2QQGAAAAAAAAAAAAAAAAAAAAAKCNbAIDAAAAAAAAAAAAAAAAAAAAANBGNoEBAAAAAAAAAAAAAAAAAAAAAGgjm8AAAAAAAAAAAAAAAAAAAAAAALSRTWAAAAAAAAAAAAAAAAAAAAAAANrIJjAAAAAAAAAAzLRSShunlB6dwcdYMaVUpJR6zsjj8MmklPZOKf2+hesVKaVVWrXerCCltFtKaXw3HCellEaklF5LKf1vLTskpfRSSuntlFLv2n+uXPvZL1JKp9X+fYa/5gEAAAAAAAAAYGZkExgAAAAAAAAAZlpFUdxVFMUXGulNKW2aUvpd7d+LafQ9nVLaokUjTnMzkZRSz9pmF+t1ynar3aY+e6RVM/HJpZT6pJTuTCm9lVJ6OaV0R0rpW+2e65MoiuKKoii26qqv86YsTdooIraMiGWLolg3pTRnRPw4IrYqimKBoigm1f7zyYoZG37NAwAAAAAAAADAp4lNYAAAAAAAAABgBimKYmpE/DEiNukUbxIRj1Rkd3bjaB8rpdSz3TO0W0ppp4i4OiJ+GRHLRsQSEXFiRPRr51yzkBUi4umiKN6p1UtExDwR8XD7RgIAAAAAAAAAgJmbTWAAAAAAAAAAmC4ppbVTSg+klN5KKV2dUhqTUjqt088PSCk9nlJ6NaV0U0pp6VqeUkrnppT+lVJ6M6X0/1JKa9R+tnVK6a+1Nf+RUjqilm+aUnq+09pPp5SOrfW+llIakVKaZzpmHxURy0fEr1NKb6eUjur0491SSs+mlF5JKR3X6TbrppT+mFJ6PaX0YkrpgpTSXLWffbRxy59r6w2sOOydUd7wZeOIOKMiu7O25rYppQdrx/tDSmnNTrMck1J6onY//TWltH0tn7vWv0an3sVSSu+llBZvYN2nU0pHp5T+EhHvVG0Ek1LaIKV0b0rpjdp/btDpZ79LKZ2aUrq7Ntv4lNKinX6+Ue2Yr6eUnksp7d1p7rNr9/tLKaWLUkrzVtyHnec4u/bYP5VS6tsp3yel9Lfa8Z9MKR1Ud7sja4/fCymlfaexfoqIH0fEqUVRXFoUxRtFUfy7KIo7iqI4oNbTI6V0fErpmdrz+ZcppYVrP1sxpVTU5nmuNuvBKaWvppT+UrsPLuh0vL1r99u5tZ89Wbuv967d/l8ppb3q7uv9627/+051UTve32vrDav9TqXe1CF7PaaUDoyI3SLiqNpz+tcfcz/9tDbfmyml+1JKG9fy/SLi0ohYv3b70RHxaO1mr6eUbu805yoV69a/5iuf8wAAAAAAAAAA8GljExgAAAAAAAAAGpY6Nj+5PiJ+ERGfiYjREbF9p59vHhFDI+LbEbFURDwTEVfWfrxVdGx88vmIWLjWM6n2s+ERcVBRFAtGxBoRcfs0xtgtIvpExGdrax0fEVEUxe+Koti09u+p6oZFUewREc9GRL+iKBYoiuLMTj/eKCK+EBHfiIgTU0qr1fIPI2JwRCwaEevXfv6d2nofbeTy5dp6YyoOe2dEbFjbOGTRiJg/Iq6KiHU7ZatFxJ0ppa9ExGURcVBE9I6IiyPippTS3LW1noiODWMWjoiTI+LylNJSRVFMiYjrImKXTsf9dkTcURTFvxpYN2q33SYiehVFMbXzL5BS+kxE3BwR59Vu/+OIuDml1LtT264RsU9ELB4Rc0XERxv5rBAR4yLi/IhYLCLWiogHa7f5UXQ8hmtFxCoRsUxEnFhxH35kvejYUGTRiDgzIoZ/tMFJRPwrIraNiIVqc5ybUlq7NsM3a/NsGRGfi4gtpnGML0TEchFxzTR69q79s1lErBwRC0TEBXU969WONTAifhIRx9WO+8WI+HZK6et1vX+Jjvv2V9HxmvlqdNwnu0fEBSmlBaYxT71ta7dfMzqeB30qeipfj0VRXBIRV0TEmbXndL+POca90fG4faY289UppXmKohgeEQdHxB9rt9+l9jtHdDy3Np+O3yPiY57z07kGAAAAAAAAAADM9GwCAwAAAAAAAMD0+FpE9IyI84qi+KAoiusi4n87/Xy3iLisKIr7axuTHBsR66eUVoyIDyJiwYhYNSJSURR/K4rixdrtPoiI1VNKCxVF8VpRFPdPY4YLiqJ4riiKVyNiSJQ3PvkkTi6K4r2iKP4cEX+OiC9HRBRFcV9RFPcURTG1KIqno2MDla9PY516/xMR80XEl6JjM4vfF0XxbkQ81Sl7uiiKZyPiwIi4uCiK/ymK4sOiKEZGxJTouN+jKIqri6J4oSiKf9c2nPl7RKxbO86vImLnTsfdtZZFV+vWnFe7X9+r+B22iYi/F0UxqnY/jI6IRyKi8wYhI4qieKx2+6uiY4OQj+a4rSiK0bXnzKSiKB6sbd5yYEQMLori1aIo3oqI0+t+h3rPFEXx86IoPoyIkdGx0dAStfvm5qIonig63BER42v3bUTHBicjiqJ4qCiKdyLih9M4xkcb27w4jZ7dIuLHRVE8WRTF29HxPN85pdSzU8+pRVFMLopifES8ExGji6L4V1EU/4iIuyLiK516nyqKYkTt9xoTHZvQnFIUxZTa7d+Pjg1hGvWjoiherz2nJsZ/HovOpvV67FJRFJfXHsupRVGcExFzR8cGOi3VxXMeAAAAAAAAAAA+NWwCAwAAAAAAAMD0WDoi/lEURdEpe67u5898VNQ2yJgUEcsURXF7RFwQEcMi4l8ppUtSSgvVWneMiK0j4pmU0h0ppfWnMUPn4z1TO2Yr/LPTv78bEQtERKSUPp9SGptS+mdK6c3o2Khk0UYXLYpicnRslLNJ7Z+7aj/6fafszlq2QkR8P6X0+kf/RMeGIEvXZtkzpfRgp5+t0WmWiRExX0ppvdqmO2tFxPWNrFvT+X6tV3pca56JiGU61ZX3X+04T1SsuVh0bI5zX6eZbqnlH+f/jlHbSCfiP49T35TSPSmlV2trbR3/uW+Wjvx583Em1f5zqWn01N8fz0TH5khLdMpe6vTv71XUC0yjN4qimFZ/Vz7usfg/Xbweu5RSOiKl9LeU0hu1+3vhmI7XxXQc52Of8ymlh1NKb9f+2XiaCwEAAAAAAAAAwEzOJjAAAAAAAAAATI8XI2KZlFLqlC3X6d9fiI4NRyIiIqU0f0T0joh/REQURXFeURT/FRGrR8TnI+LIWn5vURT9I2LxiLghIq6axgydj7d87ZjTo+i6peTCiHgkIj5XFMVCEfGDiEjTvknmzujY7GXj+M8mMHd1yj7aBOa5iBhSFEWvTv/MVxTF6JTSChHx84g4LCJ6F0XRKyIe+miWoig+jI77bZfaP2OLonirq3U7zTit+6X0uNYsH7XHtQvPRcRnK/JXomNzky92mmnhoiimZ7OTiIhIKc0dEddGxNkRsUTtvvlN/OdxejHy583HebQ2847T6Km/P5aPiKlR3sxlRnknOjbP+ciSzS70ca/H6OI1Uttw5aiI+HZELFK7v9+I6X9dTFMDz/kvFkWxQO2fuz5+JQAAAAAAAAAAmPnZBAYAAAAAAACA6fHHiPgwIg5LKfVMKfWPiHU7/Xx0ROyTUlqrtjHH6RHxP0VRPJ1S+mpKab2U0pzRsZHF5Ij4d0pprpTSbimlhYui+CAi3oyIf09jhkNTSsumlD4TEcdFxJjp/B1eioiVp6N/wdpMb6eUVo2IQ5pY786I2Cw6NiL5ay27OyI2jYi14j+bwPw8Ig6u3U8ppTR/SmmblNKCETF/dGzO8XJEREppn4hYo+44v4qIgRGxW+3fo4F1G/GbiPh8SmnX2uM+MDo2DhnbwG2viIgtUkrfrt22d0ppraIo/l2b69yU0uK132mZlFKfBmfqbK6ImDs67pupKaW+EbFVp59fFRF7p5RWTynNFxEnfdxCRVEUEfG9iDghpbRPSmmhlFKPlNJGKaVLam2jI2JwSmmllNIC0fE8H1MUxdQmZp9eD0bEDiml+VJKq0TEfs0s8nGvx9qPu3pOLxgdm968HBE9U0onRsRCzczRhUae8wAAAAAAAAAA8KlgExgAAAAAAAAAGlYUxfsRsUN0bDzxekTsHh0bgUyp/fy2iDghIq6NiBcj4rMRsXPt5gtFx6Yfr0XEMxExKSLOqv1sj4h4OqX0ZkQcHB2bmHycX0XE+Ih4MiKeiIjTpvPXGBoRx6eUXk8pHdFA/xERsWtEvFWbv37TmR9GxMjaet/+mDX+EBELR8eGOEVERFEUr0TH5hb/Kori77XsTxFxQERcEB330+MRsXftZ3+NiHOiYyOelyLiS9Gxkcz/KYrif6JjQ4+lI2Jcp/xj121EURSTImLbiPh+dDxuR0XEtrXfoavbPhsRW9du+2p0bGLy5dqPj67Nck/tsb8tIr7Q6FydjvFWRHw3OjZ7eS06Hq+bOv18XET8JCJurx3v9i7WuyY6NtPZNyJeiI77+7SIuLHWcllEjIqOzXueio4NVAZN79xNOjci3q/NNDI6NtlpxrRej8MjYvXac/qGitveGhG3RMRjtdtOjojnmpzjYzXynAcAAAAAAAAAgE+LVPvvlgEAAAAAAABAU1JK/xMRFxVFMaIbjvV0ROxf22wGAAAAAAAAAAAAPhV6tHsAAAAAAAAAAGYtKaWvp5SWTCn1TCntFRFrRsQt7Z4LAAAAAAAAAAAAZlU92z0AAAAAAAAAALOcL0TEVRExf0Q8GRE7FUXxYntHAmZnKaWNI2JcxY/mjYj35HK5fCbKoyiKBapyAAAAAAAAAGZvqSiKds8AAAAAAAAAAAAAAAAAAAAAADDb6tHuAQAAAAAAAAAAAAAAAAAAAAAAZmc2gQEAAAAAAAAAAAAAAAAAAAAAaKOe09OcUipm1CAAAAAAAAAA8Gm10EILleo333yzTZMAAAAAAAAAAADQZq8URbFYfThdm8AAAAAAAAAAANPva1/7WqkeP358myYBAAAAAAAAAACgzZ6pCnt09xQAAAAAAAAAAAAAAAAAAAAAAPyHTWAAAAAAAAAAAAAAAAAAAAAAANooFUXReHNKjTcDAAAAAAAAwExol112ybJ99903y7bccsuWHfN3v/tdqd50001btjYAAAAAAAAAAACzlPuKolinPuzRjkkAAAAAAAAAAAAAAAAAAAAAAOhgExgAAAAAAAAAAAAAAAAAAAAAgDayCQwAAAAAAAAAAAAAAAAAAAAAQBv1bPcAAAAAAAAAANCd1llnnSybPHlylh1xxBFZdvbZZzd1zB49yv8fLdtss03Wc/PNNze1No076aSTsuzkk09uwyQAAAAAAAAAAABlPbpuAQAAAAAAAAAAAAAAAAAAAABgRrEJDAAAAAAAAAAAAAAAAAAAAABAG9kEBgAAAAAAAAAAAAAAAAAAAACgjWwCAwAAAAAAAAAAAAAAAAAAAADQRj3bPQDAp0GfPn2y7Pjjj8+yjTfeuDvGAQAAAADgU+qaa67JsksuuSTLxo8f3x3jfGI33nhjlvXv378NkzC7WWGFFbLsuuuuy7I11lijZceca665SvX777/fsrVnFZdeemmW7b///t06w+x4vwMAAAAAAAAAALOGHu0eAAAAAAAAAAAAAAAAAAAAAABgdmYTGAAAAAAAAAAAAAAAAAAAAACANrIJDAAAAAAAAAAAAAAAAAAAAABAG9kEBgAAAAAAAAAAAAAAAAAAAACgjXq2ewCAmcmAAQOybJVVVsmyoUOHluqtttoq61l00UVbNxgAAAAAAETETjvt1O4RmjZ48OAsW3DBBbPswgsvLNWHHHLIDJuJ2cd2221Xqp944omsZ8SIEVl28cUXt2yG9957r1RPmDChZWtXOfXUU0v1CSecMEOPV2XnnXcu1SuvvHK3z1DvT3/6U7tHAAAAAAAAAAAAqNSj3QMAAAAAAAAAAAAAAAAAAAAAAMzObAIDAAAAAAAAAAAAAAAAAAAAANBGNoEBAAAAAAAAAAAAAAAAAAAAAGgjm8AAAAAAAAAAAAAAAAAAAAAAALRRz3YPAFQ79NBDs2zYsGFtmGT28sorr2TZ1772tSwbNGhQqX700Uezns9//vNZdthhh5XqCy64YHpHBAAAAGAmt88++2TZFltsUaonTZqU9Tz00ENZdskll7RuMIA2+9KXvpRlV1xxRZYdc8wx3TEOs5n6v9u88847Dd1u8uTJLZthySWXbNlajZhrrrm69XhVFllkkVL95ptvtmmS/5gwYUK7RwAAAAAAAAAAAKjUo90DAAAAAAAAAAAAAAAAAAAAAADMzmwCAwAAAAAAAAAAAAAAAAAAAADQRjaBAQAAAAAAAAAAAAAAAAAAAABoo57tHgCo9sYbb2TZHnvskWWjRo3qjnFmGxMnTsyyQYMGZdniiy9eqqsem6233jrLvvKVr3yC6QAAAABot7322qtUb7zxxlnPfPPNl2W//e1vS/Xrr7+e9Xzzm9/Msl69emXZmWee2cWUALOO4cOHZ9kmm2xSqo8++uis54wzzphhM/HpNMccc5TqhRdeuKHbvfnmm00db9ttt82ynj2798/zd911V7cer8rkyZNLdaP3OwAAAAAAAAAAwOyoR7sHAAAAAAAAAAAAAAAAAAAAAACYndkEBgAAAAAAAAAAAAAAAAAAAACgjWwCAwAAAAAAAAAAAAAAAAAAAADQRjaBAQAAAAAAAAAAAAAAAAAAAABoo57tHgCodvnll7d7BGp22GGHpm533XXXZdmuu+76Scehk8GDB2fZueee24ZJmJG22WabLFtzzTVL9dChQ7trHAAAAGYjl112WZbNMcccpfqKK67IesaPH9/U8a699tosGzJkSFNrAcyMnn322Yb6brvttlLdr1+/GTEOs5kll1yyVC+88MIN3e6FF15o6njLLrtslk2dOrWptZo1duzYbj1elZdffrlU1z8OAAAAAAAAAAAA/EePdg8AAAAAAAAAAAAAAAAAAAAAADA7swkMAAAAAAAAAAAAAAAAAAAAAEAb2QQGAAAAAAAAAAAAAAAAAAAAAKCNbAIDAAAAAAAAAAAAAAAAAAAAANBGPds9AMCn1S9/+cuGMpq31lprtXsEukHv3r2zbL311mvDJAAA0Fqnn356qX766aeznksuuaSbpqEVhgwZkmVPPfVUqb700ku7axym08iRI7PsxRdfzLJjjjmmO8b5P8cdd1y3Hg+glfbaa69S/Yc//KGh240aNapUH3zwwS2bidnX/PPPX6o//PDDhm5XFEVTx5s6dWqWzTXXXE2tNSsbO3Zsqe7ua5yqc6mq8/ZWuuiii0p11d85BgwYkGVXXXVVqf75z3+e9UyYMOETTvcfm222WameOHFiy9YGAAAAAAAAAACa06PdAwAAAAAAAAAAAAAAAAAAAAAAzM5sAgMAAAAAAAAAAAAAAAAAAAAA0EY2gQEAAAAAAAAAAAAAAAAAAAAAaKOe7R4AABqxyy67ZNlKK63U1Fq77757li2zzDKl+owzzmhqbVrvrbfeyrIll1yyDZPQ3XbaaadS/f7772c9N910U3eN03KHH354qf7ggw+ynmHDhnXTNABAO9xzzz2leptttmnTJLTKAgsskGVf+MIX2jAJjRgyZEiprjonP+aYY7prnG5x/vnnl+qVV1456/n973+fZUOHDp1hMzVr++23z7Lrr7++DZPQKgMHDsyyjTbaKMsGDRrUHePM1PbZZ58sGzFiRBsm6dqKK65YqkeOHNnUOj175n/SPPPMM7PsqKOOamp9Zg8LL7xwqX7jjTcaut17773X1PEWWWSRLJs8eXJTa32avPvuu1lW9Xebyy+/vCXHa+XfEwYPHpxl/fv3z7K77767VN94441Zz4EHHphlc889d6meMGHC9I74sYYPH55l66yzTqmea665sp555pkny55//vlSffrpp2c948aNm94RAQAAAAAAAACAiOjR7gEAAAAAAAAAAAAAAAAAAAAAAGZnNoEBAAAAAAAAAAAAAAAAAAAAAGgjm8AAAAAAAAAAAAAAAAAAAAAAALSRTWAAAAAAAAAAAAAAAAAAAAAAANqoZ7sHAIBGrL766ln24IMPNrXWIosskmXbbrttqT7jjDOaWpvWe+eddxrK+PRZddVVS/UHH3yQ9dx0003dNU7LPffcc6X6gAMOyHqGDRvWXeMAMJPbYostsuy2225rwyS00lxzzVWqt9566zZNQqusvPLKWTbnnHO2YRIa0bdv31J99NFHt2mST27nnXfOssMPPzzLrrnmmlI9aNCgrGfAgAFZduqpp5bqE044YTon7LDjjjtm2bXXXtvUWieddFKWVX1fMH78+KbWP/PMM0v1GmuskfW08n27/vk4bty4hm5X9R3WEkssUar33nvvpueqV3V/Tpw4sVQPHTq0qbXXXnvtLJtnnnmaWqvKsccem2X33HNPqa7/XRpV9dxeZpllsuy8885rav3TTz+9VH/jG9/IekaMGNHU2jPau+++25J1Fl988Sw78sgjs2zgwIGl+o033sh63nvvvSybMmVKlm2yySbTMyJdqH+fi8ivc1577bWs57TTTmvZDG+99Vap7tGjsf+/lBVXXLGp46211lpZtvzyy5fq66+/Puupet6+9NJLpfof//hH1vPoo49m2a233trVmN3u//2//5dlX/7yl7Ps8ssvb2r9+vOWpZZaqql16v9uFBHRv3//LNt0002bWv+II47Iss985jOl+sQTT8x6TjnllKaOt99++zV1u8022yzLmv28BAAAAAAAAAAAutbYf7MNAAAAAAAAAAAAAAAAAAAAAIAZwiYwAAAAAAAAAAAAAAAAAAAAAABtZBMYAAAAAAAAAAAAAAAAAAAAAIA2sgkMAAAAAAAAAAAAAAAAAAAAAEAb9Wz3ANDdtt122yxbbbXVsmz55Zcv1W+++WbWc9xxx7VuMGCaFl100Sx79dVXZ9jxdt555yy78sorZ9jxZmUHHXRQll188cUtW3+OOebIsjnnnLNUH3vssVnP0KFDWzYD7bHEEkuU6ieffLKh22233Xaleo011sh6TjvttKbnapVrr722VH/rW9/KevbYY48sGzVq1AybCYD2OOqoo7Jsgw02KNVvvPFG1jNgwIAsqzo3a1b9Z9Oqq66a9fz1r3/NsrFjx7Zshk+7+se1V69eLVv7/PPP77Jn0KBBLTtelbvuuivLJk2aVKpvueWWrKfq+q+7z9/qv/MZMmRI1rPjjjtm2SOPPJJlK620Uqneb7/9sp7hw4dP74hMp6OPPjrLHn744VI9YcKE7hqn5Q4//PAsGzZsWJY1cj1x9dVXZ9m6665bqg899NCGjlevX79+WVZ/bfRx6j/jUkpZz/jx4xtaq97++++fZZtsskmprvp+oll9+vTJsvr3mXHjxmU9Ve/tSy21VJatuOKKzQ/XyciRI7Os/lr9kxg4cGCpnmeeebKeqqwRVZ9xVdfdL7zwQlPr11+vf/vb3856ll566Sw777zzmjrec889V6p79Gj//8dD1d9azjjjjCxbaKGFSvXcc8+d9VR9ztd/XladH/zhD3/Isg033LBUV72+q57HVX8DonH1389W/d1tkUUWybL6a4yq51Ar1T//6r9n/jhTp07tsmefffbJsilTpnR5uzvvvDPL3n///SxbbrnlSvXmm2+e9VS999Vfc1Tdx1Wf/TNS1e9c/5nQqKq/1R5wwAGlesyYMU2tvfHGG2fZxIkTm1qryoILLphld999d6necssts55TTjmlZTM0opW/MwAAAAAAAAAA0LX2/7dEAQAAAAAAAAAAAAAAAAAAAABmYzaBAQAAAAAAAAAAAAAAAAAAAABoI5vAAAAAAAAAAAAAAAAAAAAAAAC0kU1gAAAAAAAAAAAAAAAAAAAAAADaqGe7DrzddtuV6uWWWy7r+de//pVlY8aMme61IyJuuOGGRkeb6fXp0yfLbr311jZMUrb99ttnWa9evUr1iBEjZugMgwcPLtXf/OY3s56nn346yx5//PEse/jhh0v15z73uazn/vvvz7K11167qzEbsssuu2TZ6NGjW7L2xxk4cGCp3nTTTbOexRZbLMuWWWaZUv3KK69kPffcc0+WDRkyZDonZHa2yCKLZNljjz3W1Fovv/xyltW/nque/1deeWVD65966qmluv69MCJi0KBBDa01I/34xz8u1SuuuGLWs8MOO2RZ3759S3XV59LFF1/8yYbrpOr+q89OO+20rKfqfOCWW24p1SeddNInGY0ZbN111y3V1113XUO3m3feeUv1Agss0LKZZqTnn38+y5Zeeuk2TFK22WabZdmSSy5Zqmf0OcqMVPVeUf8cipg5f8cdd9wxy3r2zC/xGrmGasSPfvSjLHv11Vez7Mwzz2zJ8apUfX4uu+yypfroo4+eYcfnPw4++OBSXXWuNnny5Cyrv/ZPKWU99dd1VVnVtd7777+fZf369cuyZtVf826zzTZZz/7779/lOlWvkfr31Yjq96d6/fv3z7IBAwaU6quvvrrLdSIiDj/88Cz75z//WaqrZj///POzbPXVV+/ydnSYMGFCqS6KIuupP/+OiBg3blyp3mmnnbKequ8nnn322ekdsWG/+MUvsuzuu+/OskmTJpXqvffeO+t58MEHWzRVY84444wsW2mllUr1d7/73axn7rnnzrIjjzwyy4444ohSXfV5Nnz48C7n5JPZaKONsuzee+9twyStceONN5bqCy+8MOsZNWpUy45Xf545bNiwptZ56aWXmp5hzz33LNUvvvhiU+tUfe/69a9/PcsuvfTSUl313UOzqr7PP/3000v1RRddlPV8+ctfzrKq8/0111yzqbl++tOfluqq69T6z/mIiKeeeqrLtbfccsssqz+/evfdd7OeBRdcsMu1IyIOOeSQUl11LfvBBx9k2XrrrVeqR44cmfUce+yxWfbVr361VI8dOzbr+cEPfpBle+yxR6muuo8nTpyYZausskqpnjp1atbT3eacc84s++Mf/5hlDz30UKn+3ve+12VPRP5d5SOPPDKdE3aofy3zyZ1zzjlZVv85Uf+e/XHqP4urzodbqf41V/8e8HHq/2YYkZ+DV/3d4YEHHsiy+uu4c889t6EZmnXFFVeU6vr3y4jGrxtbpep3rnrPrP/uoXfv3lnPt771rSybMmVKqW72b0mvv/56llV9DlZ9R7fUUkuV6qr3zEb+PlH1N+yqv8lff/31Xa4FAAAAAAAAAADMGnq0ewAAAAAAAAAAAAAAAAAAAAAAgNmZTWAAAAAAAAAAAAAAAAAAAAAAANrIJjAAAAAAAAAAAAAAAAAAAAAAAG3UszsOcvTRR2dZjx7l/WfuvfferGeZZZbJsh/96EdZNnHixFK93377ZT033HBDV2POtH72s5+V6gUWWCDrqfqd//nPf2bZd7/73ZbMdPjhh2fZl770pSxbaKGFSvURRxyR9Vx88cVZdt555zU111tvvVWq+/Tp09Q6jfrTn/6UZQMHDizVY8aMaWrtTTfdNMtGjx7d1FpVBg8enGUHHXRQl8e78847s2y++eYr1csuu2zWs9tuu2VZ/eOzySabVA8Lkb+fREQst9xyTa0177zzZtl9991Xqg899NCm1q6y6qqrtmytZlV9fi666KKlerHFFmtorfXWW69UzzHHHFnPPvvsk2UjRoxoaP16VecDyy+/fKl+5ZVXsp5TTz01y9Zee+1Sffvtt2c9I0eObChjxnv99ddLdf0538dZeOGFS3X9eWdE9bnT8OHDGx9uBnj33Xez7J133unWGS688MIse+yxx7Lsww8/LNVXXHFF1nPddddl2bXXXvsJppu2M888M8uWXHLJLJs6dWqp7t27d9ZTdW0yI2233XZZ1sj1y+KLL55ljb6XN6L+nPz555/PelZaaaUsO+yww0r1BRdc0LKZzj///Cyrf7+v6hk0aFDLZvi0q/oM32abbbLs5ptvLtVDhw5t6ni77LJLllW9nlNKpfqSSy7JelZbbbUsq7+e/s53vjO9I/6ffv36NXW7+vu0as5m11599dWz7L333mtqrf79+2fZZptt1uXtql5fv/nNb0p11WNKtT//+c9ZtsMOO2TZuHHjSvUxxxyT9bz66qtZttZaazU/XCdV10uf/exns2zvvffucq0dd9wxy6quAVrlsssuy7Kq88z67zqqzitefvnlho559tlnl+rtt98+6/nLX/6SZWuuuWZD69OY+eefP8uqvmedGe2+++5ZVv8cbeV1a9X10muvvdaStau+N6z6DrcqW3DBBUv1Sy+91NAx+/btW6q33nrrrOfKK6/MsvrvTeqvgz6Jqs/Yz33uc6W66ruwQw45JMvqnwsREXPOOWeXM/zqV7/Ksvq/KRx33HENzVD/vXzV+9z++++fZfXfFVV911e1VtXfnOrPWU866aSsp+o79/rv82+55Zas54UXXsiyqs/neieeeGKW/eQnPynVVddLVZ9Lb775Zqmuf362w/XXX59lVd+D1v/O9fd5RPW15ZQpU0p1r169sp6qv9HQWlWfQUsvvXSWVV3b1as676tfq+pvm61U/z1X1TVi1fvjgw8+mGWNXOcMGDCg8eFmkFtvvbVUn3POOW2aZNpGjRqVZfWfQ/XvhRERp512WpZtscUWpbrZ64uq7zqqPgerPrMfeeSRUn3bbbc1NcNVV12VZd39nTEAAAAAAAAAANC98v9FLAAAAAAAAAAAAAAAAAAAAAAA3cYmMAAAAAAAAAAAAAAAAAAAAAAAbWQTGAAAAAAAAAAAAAAAAAAAAACANrIJDAAAAAAAAAAAAAAAAAAAAABAG/Vs9YK77LJLls0zzzxZdvLJJze1/u67755l//3f/12qF1544aynf//+WXbjjTc2NcOMdOSRR2bZ008/XarPPPPMhtbab7/9smz48OFd9jRi6aWXbuh49Y444ogs+8EPfpBl6623XqnebbfdGprr0ksvbaivVW6//fYs23LLLUv1mDFjmlp70qRJWXbUUUdlWaPPh3obb7xxll199dWlutnXaZWqtf7617+W6n322SfrGTFiRMtmYNby85//vFSvs846Wc/777/f5TpbbLFFlk2ePDnLRo8eXaqPPfbYrKfqM67+dhERAwYMKNWLLLJI1rPXXntl2ciRI7OsGTvttFOWbbbZZllW/157xRVXZD077rhjli277LKl+r777st6qu73Zl/PK6ywQpY9/vjjpbrqc2nllVfOslNOOWWadUTEQQcdNL0jMoPMOeecTd1uhx12KNXXXntt1lP/Oo3Iz5O627///e8se+yxx1q2/rbbbptlW221Vak+5JBDmlr7pz/9aZatttpqTa1VpWr2H/7wh6V6/PjxWc+ee+7Z5dq/+c1vsuzNN99sfLjptP/++2fZV7/61Sy74YYbulyr/johIuL1119vYqqIvn37Ztmjjz5aqocNG5b1HH/88VnW7Gu3WSeccEKpPvTQQ7Oe7bffPsuuv/76GTbTrKT+vGHrrbfusqeVqs6lqh6vl156qVRXvbdX+e1vf9vUXFXva/Xno42uXX+NOHbs2KZmqjq/2mCDDbKsX79+Ta3//PPPN3W7KvXXs9/61reynptuuqllx/s0Oe+887LsggsuyLJtttmmVNdfz0dUf5bUv/fVPz8jIiZMmNDVmLHuuutm2VVXXdXl7aqMGzcuy77whS9k2cCBA0t1o9+31L+HVX1Ovfvuu1lWdd1Y79xzz21ohnobbrhhlv3xj3/MsnvvvbdUV50z0Lhbb701y+qvzau+e5sZLLXUUlnWyHO0EaeeemqWvfXWW1lWdf3cjC9+8YtZ9p3vfCfLXn755Syr/+636nvlqu+oBw0aVKqrPudvvvnmLm/3yiuvZD3NmjhxYpbV/73iuuuuy3puu+22htav/+z405/+lPVUPc677rprl2tPnTo1y+qvsVddddWsp+p+v+aaa7o8XtXjvMYaa2RZ/XVA1X1cla2//vql+v77729ohkZU/X6rrLJKqT7ppJMaWuuNN94o1fXfz0VE7LHHHlk2atSohtZvlaFDh3bZU/X9R9XfEe++++5SXfX3wQceeGA6pqMZffr0ybKq72AaUf/8j4h48sknS3Uj3/l/Es8880ypnnfeebOeqvPhZr/bfvvtt7NsypQppbr++7mI5u/jKr179y7VL7zwQsvWbqXvfe97Wfa73/2uVDd6HVn1XUOrXHjhhTNs7SrNfgYBAAAAAAAAAACzrh7tHgAAAAAAAAAAAAAAAAAAAAAAYHZmExgAAAAAAAAAAAAAAAAAAAAAgDayCQwAAAAAAAAAAAAAAAAAAAAAQBvZBAYAAAAAAAAAAAAAAAAAAAAAoI16tnrBDTbYIMsGDRrUsvUvv/zyLDv66KNL9TvvvJP1bLjhhll24403tmyuVvnsZz+bZQcffHBTaw0fPjzLXnnllVK9yy67ZD2jR4/OssMOO6xUT5o0qamZzj777Cy77777uuy75pprsp6ddtqpqRlaaeGFF86yJ554oiVrT5kyJcv69OmTZWeeeWZT60+dOjXL/vnPf3Z5u9133z3Lql6X9aqea6+99lqp/ve//93lOsw+rrvuulK96aabZj3f+MY3smzUqFGl+p577sl6hg0b1uXxH3744SwbMGBAln3pS1/KsvrPoarjnXDCCVlW/946duzYLuessv3222dZ1ftovdtuuy3LNtpooyy74YYbSnXVnH/5y1+yrG/fvlk2bty4Lueaf/75u7zdDjvskPVUPV7nnXdel8e7+OKLu+yhezz//POluuqc6KKLLsqy+tdg1WO67bbbZln9uUUjr5tWWnrppbNs6NChLVu/6jN8xIgRTa1V//paffXVs54tt9yyqbWrHHDAAVl2zjnnlOqqc9hGLLjggllWdU3TyPtHI+add94sW3bZZZtaa/nll8+y999/v6m1VlhhhSx79913S/VWW22V9Xz961/PslY+9s2o+tw966yzsuz666/vjnFmev369SvVl1xySZsmmbaqc7NGrLLKKl32VD0/qq71nnrqqVK92mqrNTTDSiutVKp33XXXhm5Xr+pcrSprVq9evVq2Vv215U033dSytT/trr766oay+vfaCRMmNLT+W2+9Vaqr3v8bUfWdwpJLLpll++yzT5b17t27VJ988skNHfP8888v1WPGjGnodptvvnmp/t///d+sp+p7w/rrsyFDhjR0vGatv/76WVb/3nfvvfdmPT/4wQ+yrNHnw+zmjDPOyLJNNtmkVD/++ONZT9X3oPXfab3wwgtZz9/+9rcsq/9utup2t956a5Y98MADWXb44YeX6gsvvDDrqfo8q79eqr/Gj4i4+eabs6xZe+65Z6leeeWVs56q+2r//ffvcu2q71ZOOumkLDv99NNLdaPXQW+88UaprnrPvOyyy7Js3333bWj9envssUdTt6tSf063zjrrZD3bbbddU2tXvf/Wf29X9dhUPdcasdhii2XZjjvumGXXXnttU+tXXee0yn//93+3bK2f/OQnpfrvf/971tPK124jBg8enGXnnntul7erPxeIqP4uceLEiaV61VVXnY7paJW55547y3r06Pr/Y6TqO+r67xki8r8D1H82R7T23ObDDz8s1fXv9RGN/X6Nqvr+u36G8ePHt+x4Vepfl428TmcWriUBAAAAAAAAAIDZUev+W2wAAAAAAAAAAAAAAAAAAAAAAEw3m8AAAAAAAAAAAAAAAAAAAAAAALSRTWAAAAAAAAAAAAAAAAAAAAAAANqoZ6sXXHHFFVu9ZMngwYOzbPz48aX6L3/5S9bzve99L8uOOuqo1g3WIvPOO+8MXf/GG29s6nbrr79+qd5tt91aMU5EREycODHL/uu//qtUP/HEE1nPsccem2VDhw7t8nh9+/bNsg8++CDLFl100VK90korZT2TJk1qaoZGPPXUU1lW9fyvz84999yG1n/++eezbL755uvydmuuuWaW3XrrraW6Z8/8rWWhhRbKsl//+teleuTIkV0en9nHuHHjSvUdd9yR9fTr1y/L9thjj5Ycf8SIEVn2i1/8IstWXnnlLKv/fKn/nIqIePbZZ7Nsm222KdVf/vKXs54hQ4ZkWb2q19tDDz3U5e3eeuutLHv00UezbOzYsV2udc4552TZKaeckmVbbbVVqV5uueWynmuuuSbLrrzyylJd9VxYddVVu5yTmdvDDz9cqr/61a9mPVXZ3//+9y7Xvuqqq7LsBz/4Qamueu610hZbbFGqP//5z8/Q41Xp0aPrPSHrX6cREfvss0+pPvvss1s203bbbZdl7777bpaNHj26qfXr36Or3tOqfuf6c8j6z6lGvf3221lW9d7XiMUWWyzLqq6FGnHRRRdlWf252uuvv571XHDBBU0db0bq06dPls0111xtmGTW8JnPfKZUT5gwoVuPX3VdN3ny5Cyreo42ov4au+q9vep8bu21186yk08+uVTvu+++Dc0w//zzl+rtt98+67n++usbWmtGqnqN/+xnPyvV999/f9azzDLLZNldd93Vsrmo1uxrtf78atttt816Lr300i7XqTqXqj8/iKj+3Gv2e5NBgwY1dbv65+1ee+2V9VSdP1Z9l9jdvvjFL5bqO++8M+vZeuuts6y738tnZfXX4VUGDhyYZVOmTCnVN9xwQ6tGqnTbbbdl2Xe/+91S/bWvfS3rueeee7Ksu79/++Uvf1mqq757aPZzcP/998+yqmuHZl8T9bMfccQRWc9vfvObptae0U477bQZtnaz78etdO2117Z7hLa7+eab2z1CfPvb386yqtf4RhttVKqvvvrqrGdm+H2oVvXddtX7b/3fEV966aWs57jjjuvyePXvvRHN/z2wSv3fJP/xj39kPVXXiK1Udd4MAAAAAAAAAAAAH+n6f/UJAAAAAAAAAAAAAAAAAAAAAMAMYxMYAAAAAAAAAAAAAAAAAAAAAIA2sgkMAAAAAAAAAAAAAAAAAAAAAEAb2QQGAAAAAAAAAAAAAAAAAAAAAKCNUlEUjTen1GXznXfemWWnnnpqlk2YMKHL4x188MFZ9tprr2XZmDFjulzrb3/7W5adffbZpXr48OFdrjOjHX300VnWs2fPUj1kyJAZOsOPfvSjLHvggQdK9ZQpU7Ke1VZbLcsefPDBLo+30EILZVn9Y3r33XdnPWPHjs2y+sf5yCOPzHqeeeaZLJs8eXKWPfHEE6X63nvvzXrGjx+fZTPSY489lmW/+tWvSvUPf/jDhtY64ogjsmzttdcu1bvuumvjw8EM1qdPnyy77LLLsuzAAw8s1TfffPMMm2lmVfU+sMQSS2TZo48+Wqo/+9nPZj2DBg1q2VxVvvvd75bqZ599Nuu54YYbulynb9++Wbbzzjtn2V577dX4cLTdZpttVqpHjhyZ9cwxxxxZtswyyzR1vNGjR5fqlVZaKev52te+1tTaVerfw6rOc7///e+37HgHHXRQlu27776l+sorr8x6Pvjggyyrf/9o5Ny+UYceemiW9e/fP8u22mqrLtf61re+lWXLLbdcqR42bFjWc9hhh2XZwIEDS/XGG2/c5fGrbLHFFll23XXXZVnVOXK93/3ud1m26aabNjNWpauvvrpUL7300lnPySefnGXdfY5c/xnQr1+/rOc73/lOd40zy7n//vtLdf01QavVvxettdZaWc8hhxzSsuO9/PLLpfqhhx7Keuo/bxr1/vvvZ1nVuUb9e9ECCyyQ9VQ9b5u10047leqq127Ve8wKK6yQZfXn4FXX0yeeeGKWTZw4scs5aY/6z6Gq74D+/Oc/Z9l+++03w2YCAGZNVefRm2++eZY9/vjjpbrq+x2YltNPPz3LevXqVar/8Ic/ZD2XX355l2sff/zxWVZ13TNq1Kgu1wIAAAAAAAAAAIDpdF9RFOvUhz3aMQkAAAAAAAAAAAAAAAAAAAAAAB1sAgMAAAAAAAAAAAAAAAAAAAAA0EY2gQEAAAAAAAAAAAAAAAAAAAAAaCObwAAAAAAAAAAAAAAAAAAAAAAAtFEqiqLx5pS6bD7wwAOz7IADDsiyN998s1S//fbbWc8ll1ySZTfffHNXI1Q666yzsmyzzTYr1euss05Ta89oP/vZz0r1hx9+mPX89re/zbIbbrihy7UPO+ywLHvuueey7MYbbyzVgwcPznqq7r9XX321VC+00EJZz3LLLZdlt9xyS6l+4YUXsp7LL788y/hkrrnmmlK90047tWkSaMzOO++cZVdeeWUbJpn5HX/88Vn2mc98plT/+te/znomTpw4w2aC6VX1HO3Vq1eWbbzxxi05XtX5Y9Vn47hx40r1XXfdlfWstdZaWbbBBhuU6lbNPT0OPvjgUj1w4MCsZ5lllsmyP/7xj6W6/tw+IqJnz55Z9t5775XqqVOnZj2PPfZYlm211VZZNuecc5bqd999N+u56aabsmzMmDFZ1ojdd9+9VH/lK1/Jer7//e83tXbVufxDDz1Uqv/xj39kPVtssUWWVd1XrXLsscdmWd++fbOsftZHHnkk63n22WezbJ555plmHVH9Wnr66adL9UknnZT18PF+/OMfl+rVV18967nuuuuybNKkSaW6/jUZEbHSSitl2V//+tdSXX+t2Wp77rlnqf7lL3/ZsrWrzjvrn48REcccc0yprr/ejYhYeeWVs6z+e5IXX3wx67n33nuzrP79sOo9+qmnnsqyW2+9NcvqnX/++Vm26qqrZtkdd9xRqh944IGsp9nvdwAAAAAAAAAAAAAAAOBT4L6iKLJNOnq0YxIAAAAAAAAAAAAAAAAAAAAAADrYBAYAAAAAAAAAAAAAAAAAAAAAoI1sAgMAAAAAAAAAAAAAAAAAAAAA0EY2gQEAAAAAAAAAAAAAAAAAAAAAaKNUFEXjzSk13jwLuPvuu0v1Sy+9lPXssMMO3TVOw3bZZZcs+/rXv55lH3zwQZb9/e9/L9XnnXde6wZjlnfiiSeW6lNOOaVNkwDAzGngwIFZVn9uttRSS2U9jz32WJbtsccerRuMT51f//rXpbpXr15ZzznnnJNlN9xwwwyaqHF9+vQp1SuuuGLWM99882XZHHPMUaqrXjc33XTTJxuOLm277bZZtvnmm2dZ/ePzxBNPZD0TJkxo3WDMUg4//PBSvdpqq2U9c889d5ZNmTKlVC+99NJZz8svv5xlTz75ZKl+8MEHs56xY8dWjQoAAAAAAAAAAAAAAADtcF9RFOvUhz3aMQkAAAAAAAAAAAAAAAAAAAAAAB1sAgMAAAAAAAAAAAAAAAAAAAAA0EY2gQEAAAAAAAAAAAAAAAAAAAAAaKNUFEXjzSk13jwLuv3227PsnXfeybJ+/fp1xzjQ7QYMGFCqr7766jZNAgAAALn99tsvy9Zcc81Svfbaa2c9Y8aMybILLrigdYMBAAAAAAAAAAAAAABA4+4rimKd+rBHOyYBAAAAAAAAAAAAAAAAAAAAAKCDTWAAAAAAAAAAAAAAAAAAAAAAANrIJjAAAAAAAAAAAAAAAAAAAAAAAG1kExgAAAAAAAAAAAAAAAAAAAAAgDZKRVE03pxS482fEhMnTsyyF154oVTvtttu3TUOAAAAAAAAAAAAAAAAAAAAADDruq8oinXqwx7tmAQAAAAAAAAAAAAAAAAAAAAAgA42gQEAAAAAAAAAAAAAAAAAAAAAaCObwAAAAAAAAAAAAAAAAAAAAAAAtJFNYAAAAAAAAAAAAAAAAAAAAAAA2igVRdF4c0qNN7fRdtttl2VLLrlkll100UVNrX///feX6jvuuCPrGTx4cFNrAwAAAAAAAAAAAAAAAAAAAACfWvcVRbFOfdijHZMAAAAAAAAAAAAAAAAAAAAAANDBJjAAAAAAAAAAAAAAAAAAAAAAAG1kExgAAAAAAAAAAAAAAAAAAAAAgDbq2e4BZoQlllgiyw488MAs23DDDUv15ZdfnvXceuutWfb973+/VJ911llZz4knnphlp5xySj4sAAAAAAAAAAAAAAAAAAAAADBb69HuAQAAAAAAAAAAAAAAAAAAAAAAZmc2gQEAAAAAAAAAAAAAAAAAAAAAaCObwAAAAAAAAAAAAAAAAAAAAAAAtJFNYAAAAAAAAAAAAAAAAAAAAAAA2igVRdF4c0qNN88CfvrTn5bqDTfcMOu5/vrrs2zIkCFdrn3//fdn2VlnnVWqR48e3eU6AAAAAAAAAAAAAAAAAAAAAMCnxn1FUaxTH/ZoxyQAAAAAAAAAAAAAAAAAAAAAAHSwCQwAAAAAAAAAAAAAAAAAAAAAQBvZBAYAAAAAAAAAAAAAAAAAAAAAoI1sAgMAAAAAAAAAAAAAAAAAAAAA0EapKIrGm1NqvHkW1L9//4ay3r17l+oHH3ww6+nVq1eWrbvuuqV6/fXXn74BAQAAAAAAAAAAAAAAAAAAAIBZ2X1FUaxTH/ZoxyQAAAAAAAAAAAAAAAAAAAAAAHSwCQwAAAAAAAAAAAAAAAAAAAAAQBvZBAYAAAAAAAAAAAAAAAAAAAAAoI1sAgMAAAAAAAAAAAAAAAAAAAAA0EapKIrGm1NqvPlTrG/fvqV6vfXWy3p69+6dZcsvv3yp7t+/f2sHAwAAAAAAAAAAAAAAAAAAAABmZvcVRbFOfdijHZMAAAAAAAAAAAAAAAAAAAAAANDBJjAAAAAAAAAAAAAAAAAAAAAAAG1kExgAAAAAAAAAAAAAAAAAAAAAgDZKRVE03pxS480AAAAAAAAAAAAAAAAAAAAAAHR2X1EU69SHPdoxCQAAAAAAAAAAAAAAAAAAAAAAHWwCAwAAAAAAAAAAAAAAAAAAAADQRjaBAQAAAAAAAAAAAAAAAAAAAABoI5vAAAAAAAAAAAAAAAAAAAAAAAC0Uc/p7H8lIp6ZEYMAAAAAAAAAAAAAAAAAAAAAAHzKrVAVpqIounsQAAAAAAAAAAAAAAAAAAAAAABqerR7AAAAAAAAAAAAAAAAAAAAAACA2ZlNYAAAAAAAAAAAAAAAAAAAAAAA2sgmMAAAAAAAAAAAAAAAAAAAAAAAbWQTGAAAAAAAAAAAAAAAAAAAAACANrIJDAAAAAAAAAAAAAAAAAAAAABAG9kEBgAAAAAAAAAAAAAAAAAAAACgjWwCAwAAAAAAAAAAAAAAAAAAAADQRjaBAQAAAAAAAAAAAAAAAAAAAABoI5vAAAAAAAAAAAAAAAAAAAAAAAC00f8H6wQ5SWMpFEMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 5760x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "vt.decoder(t, x, tm)"
+    "plt.figure(figsize=(80, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(d.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "tensor([[[-0.4179,  0.4755,  0.0407,  ..., -0.0609, -0.0870,  0.4562],\n",
-       "         [-0.4262,  0.4845,  0.0361,  ..., -0.0497, -0.0847,  0.4459],\n",
-       "         [-0.4237,  0.4900,  0.0409,  ..., -0.0573, -0.0812,  0.4434],\n",
-       "         ...,\n",
-       "         [-0.4477,  0.5053,  0.0394,  ..., -0.0489, -0.0815,  0.4589],\n",
-       "         [-0.4469,  0.5069,  0.0407,  ..., -0.0500, -0.0808,  0.4573],\n",
-       "         [-0.4464,  0.5079,  0.0416,  ..., -0.0510, -0.0801,  0.4570]]],\n",
-       "       grad_fn=<AddBackward0>)"
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AttributeError",
+     "evalue": "module 'torchvision.transforms.functional' has no attribute 'gaussian_blur'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-37-2a6f5c80ce06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorchvision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgaussian_blur\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'torchvision.transforms.functional' has no attribute 'gaussian_blur'"
+     ]
     }
    ],
    "source": [
-    "vt(data, trg)"
+    "torchvision.transforms.functional.gaussian_blur"
    ]
   },
   {
diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb
index 208f098..ca0b848 100644
--- a/src/notebooks/Untitled.ipynb
+++ b/src/notebooks/Untitled.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +87,7 @@
        "'CNNTransformer'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,14 +98,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-11-16 20:07:51.973 | DEBUG    | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n"
+      "2020-11-18 20:31:23.104 | DEBUG    | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n"
      ]
     }
    ],
@@ -115,581 +115,206 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "checkpoint_path = \"../training/experiments/VisionTransformerModel_IamLinesDataset_CNNTransformer/1102_221553/model/last.pt\"\n",
-    "model.load_from_checkpoint(checkpoint_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.eval()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data, target = dataset[1006]\n",
-    "sentence = convert_y_label_to_string(target, dataset) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data1 = (data - data.mean()) / data.std()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([98])"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "target.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 2880x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(40, 20))\n",
-    "plt.title(sentence)\n",
-    "plt.imshow(data1.squeeze(0).numpy(), cmap='gray')\n",
-    "plt.xticks([])\n",
-    "plt.yticks([])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('Horbwargethers sis tHater alate Bate Bath Con Hats the Bateries.<eos>',\n",
-       " 0.2612667977809906)"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict_on_image(data1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data, target = dataset[110]\n",
-    "sentence = convert_y_label_to_string(target, dataset) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([], [])"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAABCCAYAAADt2ys3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABFb0lEQVR4nO29eXhb13nn/z3YCYAkQBLcqZ3aJWtfbEt2vEaRWzdJ48ZJOm4mTX5t4uY305lmJtNm0ixNp8l0SZNxm0ybxM1qR3VSJ64db7JlLZYtyZJoUhT3RSRFgiAAAiR2nPnj3nN8cHkBkrJsKcn7eR4/BO4992z33Gu9X7zvexjnHARBEARBEARBEARBEMT1heVad4AgCIIgCIIgCIIgCIKYC4k2BEEQBEEQBEEQBEEQ1yEk2hAEQRAEQRAEQRAEQVyHkGhDEARBEARBEARBEARxHUKiDUEQBEEQBEEQBEEQxHUIiTYEQRAEQRAEQRAEQRDXISTaEARx3cEYq2OMHWGMxRhjf72A8gOMsTvejr6V6MMLjLHfL3JuGWOMM8Zsb7KN/8EY+6cS56/ZPDDG/pwx9j398xLGWJwxZr0WfVH6dCtj7NK17MO1RL8HK95kHd9hjH1R//xrPZ8EQRAEQRDXgjdlQBAEQbxFfAzAJIAKzjm/1p25XuCcf+la92EhcM6HAHivdT/eThhjywD0A7BzzrPXoP0XAHyPcy5FPc75r9U9IAiCIAiC+FWEPG0IgrhuYBoWAEsBdPwyCDZKn39leLMeQQRRCsZY3S9z/QRBEARBEG8nv1KGBkEQi0cPqfk0Y6yDMRZmjH2bMeZSzn+UMdbDGJtijD3OGGvUj3+OMfY1/bOdMTbDGPuK/r2MMZZkjFXp3/cwxo4zxiKMsXOMsVuV+l9gjP0FY+wYgFkA/wLgAQCf0sM77lBDNPRrrihMgzH2YcbYz5Tv3YyxHyvfhxljW/TPNzLGXmWMRfW/N5boc0EICmPMyhj734yxScZYH4CD8/RrkDG2Xf/8QT2UaoP+/SOMsZ/qn2UIkv79d/VrQ4yxPzXUaWGM/XfGWK9+/lFxP0zav5Uxdokx9t8YY5cBfJsx5meM/ZwxFtTXxc8ZY83KNcsZYy8yLYTtGQA1yrmCcDDGWKO+dqb0tfTREnNxkDH2GmNsWr8ff25S7wOMsSF9fv9UOV+mr5UwY6wDwM4S7TDG2N8yxib0ttoYYxsZYzsZY+NMCe1ijL2HMXZO/7yLMXZKv2acMfY3erEj+t+Ivm736uX/I2Psgt6nXzDGlir1csbYx/V1GGOMfYExtlJ/Vqb1e+bQyxa9H4yxvwCwD8DX9ba/rtS/Spmbv9bXS5QxdpQxVqaf+zFj7LJ+/IhYe6VgjP0JY+xfDcf+njH21SLl3fp6fR7AYeV4JWPsnxljY4yxEcbYF8Xc62v4z/Q+TzDG/oUxVqmfczHGvqev7QjTnlEh1vw5095nf8IYq59vLARBEARBENczJNoQBAEAHwRwN4CVAFYD+DMAYIzdBuAvAdwHoAHAIIAf6de8COBW/fNOAJcB7Ne/7wVwkXM+xRhrAvAEgC8CqALwXwH8K2MsoLT/u9BCosoBfBjA9wF8mXPu5Zw/exXH+SKAfbox2AjAofcVTMv94QVwnmnixhMA/h5ANYC/AfAEY6y6SJ8HDe18FMA9ALYC2AHgtxfQr1v1z7cA6MMbc3mLfr4Axth6AP+g96NR72ezUuSPAPyWfn0jgDCA/1OiD/XQ7s9SfVwWAN/Wvy8BkADwdaX8DwCchibWfAGa0FaMHwG4pPfjtwF8SV9bZswA+A8AfNDErj9kjP2WoczNANYAuB3A/2SMrdOPfxbaGl4JbT2X6tNd0OZ4NYBKaGs8xDl/FUBIPy/4XWhiIgB8FcBXOecVejuP6sfF/fLp6/YEY+xeAP8DwHsABAC8BOCHhn7cDWA7gD0APgXgmwA+BKAFwEYA9+vlit4Pzvmf6nU/qLf9oMl4/7fezo3Q7vOnAOT1c08CaAVQC+AMtOdvPr4H4J2MMR8gvbPer8wT9ON7GWP/F8AItPv6z3o/BN8BkAWwCtrzchcAkRvq9/T/3gFNGPXijTX4ALT71gJt7f+BPicA8AkAnwSwGcBFXTB8N2PMvoBxEQRBEARBXFeQaEMQBAB8nXM+zDmfAvAXeMNQ/CCAb3HOz3DOUwA+DWAv0/J3nADQqgsZ+6EZY02MMS8KhYYPAfh3zvm/c87znPNnAJwC8C6l/e9wzts551nOeeatGiTnvA9ADMAWvc+/ADDKGFur9/klznkemljQzTn/rt6nHwLoBPAbC+zzfQD+TpnTv5ynay/q7QOax8RfKt9NRRto4sfPOedH9HvzGbxhhAOaEfunnPNL+vk/B/DbrHjoUx7AZznnKc55gnMe4pz/K+d8lnMeg7YubgG0RMPQhLrP6OWPAPiZWaWMsRYANwH4b5zzJOf8LIB/gmbAz4Fz/gLnvE1fK+ehiRy3GIp9Tu/jOQDnANygH78PwF9wzqc458PQRLdiZKAJbmsBMM75Bc75mH7uYWjrFrqAdzc0kUpct4oxVsM5j3POXy7Rxh8A+Eu97iyALwHYonrbQBMnpznn7QBeB/A057yPcx6FJqZs1eel6P2YD6aF7/1HAP8/53yEc57jnB/X1wU459/inMeUdXKD8Ggphj5XRwC8Tz/0TgCTnPPTepv3McY6oYky/QA2cc7v5Jx/n3Oe0MvUQXsP/CfO+QznfALA30ITfwDt/fM3+nzEob1/3q+v4Qw0sWaVPp7TnPNpvW95zvmznPPfhSZk/hTAfwYwwhj7wkLmjCAIgiAI4nqBRBuCIABgWPk8CM0jAvpf6UWiG04hAE264XUKmuG4H5qwcByaga4KDUsBvE8PYYgwxiLQPCUairT/ViO8WkSfX4DWX7XPBePWGQTQpHwv1edGzJ1TAABjbJ8ewhJnjLUrfdrHGGsAYIXmvXGTLo5VAjg7Xxuc8xlo90awFMBPlDm/ACAHoFi+jyDnPKn0080Y+4YemjINzUD36aErjQDCeptzxmjSzyldaFDLNpkVZoztZowd1sOAotCEjxpDscvK51m8kfS46Lwb4Zw/D81r4/8AmGCMfZMxVqGf/h6A32CMeaAJQS8pgs5HoHnndOohOfcUawPaPfiqcg+mADDD2MeVzwmT715g3vsxHzUAXAB6jSeYFsr3v5gWRjcNYEC5Zj6kuKX//a5yrhnaOM9CE9YuYy5LAdgBjClz9A1oHj/A3OdwENoGCnV6W78A8CPG2Chj7MtmnjT6ujuv98MOzUOLIAiCIAjilwYSbQiCALQQA8ESAKP651FohhUAQDdiq6GFOgCa2HAbNG+AV/XvdwPYhTdyfAwD+C7n3Kf85+Gc/y+lzfkSDs8AcCvf30yeCiHa7NM/Cy8XVbQpGLfOErwxbqB0n8cwd061izh/SQ9h8XLON+jHeqCJD38E4IjuMXAZWpjSUd37p2QbjDE3tHsjGAZwwDDvLs75iLGiIuP5L9AM3N16KJAI/2F62359PcwZo4FRAFWMsXJD2WL9+AGAxwG0cM4rAfyj3uZCKDrvZnDO/55zvh3AemhCzJ/ox0egeZK9B1po1HeVa7o55/dDExb+CsAhfR7M1sMwgP/PcA/KOOfHFzgelVL3A0XaF0wCSEIL5zLyAQD3ArgDmkC4zFBvKX4KYDNjbCO0cEAZVsU5/xtoos1zAP4UwCWm5RDaqlw/DCAFoEaZnwrxXGDuc7gEWijVOOc8wzn/HOd8PbSQr3ugeG8xxpqZltOpA1p4XhDADZzz+xYwLoIgCIIgiOsGEm0IggCAT+hGThU0A+sR/fgPAXyYMbaFMeaEFt5xknM+oJ9/EZqh1ME5T0PzWvl9AP2c86BeRngt3K3/qu9iWuJbNf/KfJwF8C7GWJWeWPQ/FSuo113KgH0RWo6MMs75JWi5QN4JTfB4TS/z7wBWM8Y+wBizMcZ+B5ph//MF9vdRAJ/U59QP4L8v4JoXATyIN4SjFwzfjRwCcA9j7GamJav9PArf6f8I4C9EKA5jLKDnWFko5dA8PSL6uvisOME5H4TmZfU5xpiDMXYzCkPHoJQdhuaB9Zf6vd8MzVvle2bl9XanOOdJxtguaKLCQnkUwKeZlrS3GZoIZgrTEg7v1r0zZqCJGqo49i/Q8r5sAvCYct2HGGMBXUiL6Ifz0ESBPAqTUv+j3h+RVLqSMfY+XBlF74fOuKFtid7XbwH4G6YlhbbquWacer0paF5abmjP+ILQPbMOQRPaXuHaVu/q+WnO+Tc55zdCE0WTAH7GGHtOPz8G4GkAf80Yq2BarqmVjDER9vVDAP+ZaUmvvXrfHuGcZxlj72CMbdI9jaahhUvlAS1hN4B2aCLXHwBo5Zx/wdg/giAIgiCIXwZItCEIAtCMrqehJcDthZY0GFxLAvwZAP8KzYthJd7INwFoxngZ3vCq6YBmmInvwmgXCVmD0H5d/xMs7v3zXWghFgN6Px8pUbZF75cpnPMuAHFoYg10r5Y+AMc45zn9WAjaL/f/BZox+ykA93DOJxfY3/8LLXTjHLTEro+VLg5AE2fK8cbcGb8bx9EOLeHqD6DdmzC0ZL+Cr0LzWHmaMRYD8DKA3QvsPwD8HbR7O6lf+5Th/Af0+qagCQj/guLcD82DYxTAT6DlzimWYPrjAD6v9/l/4o1Evwvhc9BCaPqhrZPvlihbAe0+hfVrQgC+opz/CfQQM875rHL8nQDaGWNxaHP8fj2/ziy0PDPH9FCfPZzzn0DzxvmRHnr0OoADixiPyt+h9P34KrScRWHGmFkun/8KoA2aR9yU3i8LtPs2CM3zqUOvezE8DE3YKjXX4Jxf5Jx/Gpq3zJ8pp/4DtITgHdDuxSG8ETr5Lb3eI9DuaRJvCHH1etlpaKF/Lyp9+CmARs75h7mW82k+Tz6CIAiCIIjrFkb/liGIX28YYwMAfr+EEf1LBWPsnwD8mHP+i2vdF+KXG8ZYL7Twpl+JZ+OtgGlJqTsB1ItEwARBEARBEMTVo9guIgRBEL+UcM5/f/5SBFEaxth7oeWJef5a9+V6hWm7Uv0xgB+RYEMQBEEQBPHWQKINQRAEQSgwxl6AlsPod4skgf61R0++PA4ttOqd17g7i0YPbzOjDFruIDpOx+k4Hb/ejx/gnL9kcpwgiF8xKDyKIAiCIAiCIAiCIAjiOoQSERMEQRAEQRAEQRAEQVyHLCo8ijHGLZbiOg/nHIwx+XkB9S2onFperVt8v5rYbDbYbNq0pFKpBffP2Dez6xY7XmJ+rFYrGGPI5/PI5ymKgbh+uNrPu1rf1Xj3qXUU6yc9UwRBEARBEATxtjHJOQ8YDy5KtLFYLHA6nQC0f/ALI0J8Fv/AVwUMcVw1NowGh1pHLpeT361WKywWC3K5HPL5fEGbNpsNnPOC/0r1w2q1FrSpnhNCFGMMt9xyC1auXImRkRE8++yzSKfT4JzLOi0WCywWi+yPaHflypWoqanBhQsXEI1Gkc1mTdsRfTUbv/Gz8btxbKJ9oyFnZtip86OWMf4VfTSjmBEq6hRzotZX6lrj/VosFosFH/7wh+FwOHD+/HmcOHHCdH4XwkIM7GJjuFrtmRnlxb5fSRvF6i/V7mLbKjWu+YSGhQq9alkz4UE8z2ZjKiYsq8+Y8bjxOTOOS7wPjHWJNszqFP00mxdjW6IPal+M/RBjFs+g+Kv2zfhdFeBzudycvgNAPF4s7QdBEARBEARBEFeZQbODVxQeZRRehKhhFFHEOWEsmBk/xmuEUCPqFCKOEXHeKAgVExXUcmq7xnJutxtOpxOzs7PweDyy71arVQo/qrAk6nA6naivr8fWrVtN50o1CoV3iHrMeI3oo/rXWLbY8WLU1tZiw4YNqK2tnXONmQhXVlYGh8Nhet/MDE31+EL6o4phdrsdpby4zLBarchms8hkMgAgPaSuhIUIBsXW4dVqz0zMUz9fiVdaqfquRjtmdZj1o9h6eLOeKGbihrFts7VpVk6UNX6fb07UZ8YoEBn7aFan+h5T+yoEYlUUNbZlHIsqQBvHo15vbNf4nlvss0gQBEEQBEEQxFvDov9lbiYwmIk4ZmXV86rBrh4zljN6zQgjw0wkms+4KvaLtrjGZrPB6/XC4/EUeBUZrzUzxqanp5HP57Fnzx5p9BQzII3GmXH+FsJ8hiBQaMAxxuBwOKRwsxBxpaWlBatXr0ZDQ0PRe1zqs5lnjxmMMaxfvx719fVwOBwlx61e43Q6kcvlkE6nkc1mYbfbF3TtQuq+llRXV8Pv98u5uJL+lBIz1DLz1b3Qto1r3WKxoKmpCatWrYLP5ytaT7H3SbF6za5dyDNf7J20kPG/mfWw2Oe7mIeN2lfVY8is3lLtGUX0Ul4+1/o5IAiCIAiCIAjiTYo2xl+DjSKB2bXzGRTGX5PNyqjl5uuj8bjxGjWkx+VyobKyEj6fDx6PR4Y4qe2q16vXTk1NIRKJYM2aNXC73XNEG/V6EYqgzsl8fRd1LBSzelOpFABg9+7dc+6VmZdAS0sLtm/fjrVr15p6By3UAC4lEIl6WlpaEAgECoSy+bDZbHC5XNIDyhgCV6wfCynzVhqsou6qqirU1tbC6/UW3INVq1ahubkZbre75HyXOl6s3bdiXMY5EwLhunXrsGPHDgQCgZLPqtmcz7fWzCgl2ho9X1wuF8rKygq8s+YTjMwEjvnaNgok6vdSdavnxDp3uVylhj+n30J49vl8sFgssNlsUlBW/1Of+1LvVYIgCIIgCIIg3n6uSLRR//Fvs9nmCDeqEaIeV8uJY0YjwfhdCC3qr8umA9HrVo0wYz3Ca0ccF+EA4pjT6YTL5YLT6YTFYkEkEikI1TL+Cq0eSyQSiEajSCQSqK+vNxVB1GvNxmz0QFKN1vnCIsyOGecyHo9jdnZWetqYGZHqMc45GhoasHTpUun1of5Sb1wX6rwXu1/Fftk/evQoRkdH595YE0QduVwOPp8PlZWVsNvtyGazBf0S/RQhHzabrahwaBQgS3kjGPsxnxipllefi1tvvRUHDhzA+vXrC9btmjVrpIBlXEfFRLZSqGKF8ZlYCGo5o+BgVi8AlJWVYdmyZVi2bNmc0DczTw71mSglgBjLq2vROC9mubTE95aWFixZsgQVFRUFbZo9Y2bvJHUcZs+EGg5qrN+Y26rYfRDnKysr0dTUhLq6ujnvWbG+zep1Op1oaGjAzTffDKvVCr/fD7/fD6fTKcuJNQ+8kYRdfVcSBEEQBEEQBHFtWXQSENUAFuEoIjzFzPAxJuw1GjyqaAK8YYwZE2iKMkZDxygMiD4Z8zWYXSe8XtQcDqlUCj09PfjFL34hBRR1DGp9qpHEGEM8Hkd/fz927tyJrq4uU1FKbds4ZjPjzUz8MdalGoxGI1FNdprJZJBKpWCz2VBZWYloNGpqyIvrstksPB6PNPRSqVRBwlNj/0U7xntq/GuEc45IJGJ6rhSpVApVVVUAAI/Hg3Q6PWe9lZWVwefzobGxER6PB6dPn0Y8Hjc1mOczVI1i20IxE9+EIMYYQ2NjI6LRKLq6ugAAjzzyCJxOJ5LJ5Jx1YVzPpTBeayY2LKQOFWNCcGO9Yi36/X7U1NTAZrNhcnISAOTzYCZazCcmqWvMWMasj8UQ6zeTyUixbGpqquD5MYoxajvFRCDje0/ti/G9oSb9FaKJeG8lk0kwxmSZbDYLxhjq6+uxfPlyPProo8hkMrKMqCOfz0vBRdSdz+fh8Xhw8OBBPP/889ixYwfy+Ty6u7sxMjIi+ygSvYu2xDxRXhuCIAiCIAiCuPYsWrTZtm0bDh48iCVLliCVSiEYDCKXy8Fms2FwcBAvv/wyBgYGMDMzM+eXZTWBr/prOGNMehqo3jBGUcS484kwAB0OB6qrq7F//37k83k4nU688sorGBgYQDqdBgAZWpBOp6VBpAo3jDGk02nZN5Hc1u/3IxKJyDJWqxUulwubNm1CMBjEyMiINHaSySTGxsawYsUKWK1W01/oRf/VcCO1jJm3QTHPEPV6UVZ4GzHGpMiiGrHpdBqhUAjl5eWYnp4GoAldom9C1AE0w9btdoNzLsU6ETJWyovGzJBVUftqJkIVq08tI4xWu92OTCaDbDZbMLe7du3C0qVLwRjD9PQ0ZmZmsGLFClRXV+Opp57C7OxsSW+HYn02Oybmx8zDw3hvVaN4amoK0WgUANDU1ISenh7k83mk02kZymZGKS+UUsLY1cD4HIq61eccAMrLy2G1WpFIJOQ6KyYWqfNSTAxS21c/G8+ZXW8UPS0WC6anp7FlyxYAQGdnp7xnxh2dir1/VBGmmKiqeumZzaMov3TpUqxcuRLpdBpHjhyZ8w4MhUI4deoU2tra5HtJvIuMIo3an1wuh2w2C5/Ph0AggGAwiDvvvBOJRAIjIyMFQo0q0og+X601QxAEQRAEQRDElbNo0cbhcOCJJ57A2rVr0djYiGeeeQZTU1Ow2Wxobm7Gnj17sHPnTly8eBGnT5+WIoDYulsYAuLXYovFAo/Hgw0bNsBiseD06dOynNmOT3a7HUuXLsXo6CiSySQaGxuxfv161NXV4eTJk4hGo7jrrrvg8XjgdDrhdruxbds2VFZWYnBwEH19fYjFYjKURvQLgDSW7XY7nE4n1q1bh/Xr1+OnP/0pYrGY7E8ikUBXVxfuuOMONDU1oaurC9FoFKlUCpcvX8aOHTvmeAqpXkdGQcBut6OyshKTk5NFxQHjVuNVVVVYuXIlotEoxsbGsGrVKmSzWeRyOfj9fqxZswbd3d2oq6tDX18fLl26hOnpaaTTacRiMQQCATDGcNNNNyGVSiGZTCKXy6G/vx/d3d1gjCEUCsFiscDlciGTyRRsJ2zsnyrQuFwutLS0wO124/z58wXeFWI+jNcLjN4LVqsVgUAAK1asgN/vRzQaxalTp5BOp2GxWJBKpRCJRBAOh8GYlkvl/vvvR39/P06ePIlEIgG/34/169ejrKwMTz31FFKpVEnhQO2TxWJBRUUFysvLMTk5KUVAtcyWLVuwceNGlJeXIxQK4dy5c+js7JR1er1etLS0YPny5aivr0dZWRmefvppzMzMwO/3w2q1YmJiAlardY6R73A4UF5eDp/PB5fLhbGxMUxPTxc8S2aeN6U8UdTzizXMVZGzVJnly5fD5XJhcnLSdNeiUv0Qa10dn9maU6831iMEDWNYoyCRSKCqqgo+nw8Oh0N6aameWiIEyZj8XLSnrnmze2H0SjIrwzmHzWZDQ0NDgeeMmveKc45MJiPPq946oj61DeHpxhhDNptFJBKBx+NBKBRCU1MTqqur5VoToqeZ8ESiDUEQBEEQBEFcexYt2gwMDCCZTKK6uhq1tbWw2+24fPkyAC1nSiQSQUNDA1paWtDQ0IDJyUm43e4CAyabzSIcDqO3txfRaBSbN29GIBDA5cuXC35ldjgc0nVfGBOcc8TjcaxYsQKRSASbNm1Ca2srjh8/jlAohM2bN8NmsyGVSsHhcKCpqQkrVqzA8ePHEQ6HpcGuGinCi0RN0im8bfbu3YtnnnlGemYI4ygWi6Grqwv79u1DKBRCLBZDMpnE+Pg46urqZN9V484sDwxjDD6fD7fffjt++tOfIpFImM57IBBAZWUl3G430uk0PB4PWltbkUwmEQgEMDExAYvFgkAggJUrV2LVqlVgjOHUqVOYnJxEIpGQv6xPTU2hpaUFt956K7q6ujA6OorZ2Vk4HA40NDQgFAohEokgHo9Ljw81KbMR1WgUoRz19fXzXmM0poWxqXo97Nq1Cy0tLdIrxel0oqWlBb29vSgvL0dZWRkSiQTsdjvcbjf27t2LXC6HRCKBxsZG+Hw+OJ1OhMNhdHV1IRQKFQ2fMQoBNpsNdXV12Lt3L2644Qb09PRIr51wOIxQKISamhps3LgRg4ODmJ2dhcvlwpIlS+Rzsnr1aqxfvx4WiwXRaBS9vb1YsWIF9u/fj9HRUZn/aHJyEg6HQ4auBYNB2O12rF69Gps2bZLb0N9333147bXXcO7cOYyOjs7xyFHXdTFhRdwvp9NZ4Hm2GMT98Xq98Hq9sNlsiMfjMvSspqYG+XxehuAZ+2X0whLilshRVF1djfr6eszMzMBms+H06dOYnJwsENw45ygvL8fatWsxMjKC6elpeL1eNDc3w+/3I5PJoK2tDZFIpOCeC/Ezl8vB5XLB4/FIDxY1P47L5UIgEEBzczM6OzsRDocL5k+ty2yOjfNt5kEkhEe32w2Hw1Eg2BiFFFVIMt5f4zOotpXL5VBWVoZQKASn0wmHwyG95kSdZgngSbQhCIIgCIIgiGvPokQbxhjGx8dhtVoxMzODVCoFn88n/8E/OTkpd1HasWOHFAWGh4eRTCbhdDphs9mQy+WkB8frr7+OdevWYXp6GuFwuCDEZe/evYjFYhgeHkYwGJS/vsdiMWzduhU+nw9NTU1wuVwIBoPYtGkTlixZgrGxMYTDYZSXl6OlpQWZTAbT09MYHx83DVdwu91oaWlBdXU1WlpaAADNzc3SQN+5cyfOnz+PiYkJJJNJOd7e3l68613vKsjtE4lE4Ha74XK5ZH4KMe5EIlFgcIoQh/LycmzevBk/+9nPYLFYsHz5cnDOEQ6HMTU1JecegBRrvF6vrJ8xhmAwKJONigS2DQ0N6OnpkV4EgObhNDU1hT179sDj8eCpp57C4OAg0uk0ysrK4HK5UFdXh+np6TleTk6nE7W1tdIwVz1scrmcnJtly5ahsrISly5dkn0vFuridDqRyWTAOZft5/N5RCIRtLa2YtOmTYjFYhgbG0M8HkdNTQ3sdjtsNhtaW1tlLpxAIICqqips374dr776qhQTXC4X4vE4BgYGMDAwUNDn+UKJbDYbKioqsHTpUuzbtw+RSATJZBLJZFJuD3/nnXeit7cX7e3tmJqaQiAQwMaNG7Fz50709vZix44dsNlsGBkZQX9/PyKRiBQqp6enUVVVJedHrIft27fjxIkTaG5uxtq1a1FeXo5wOIxoNCrXRzKZhN1ux+TkpPQyMhuHKooIgbC6uhoVFRVSaOnv75f3zmq1wuFwyHw6xd4Dfr8fzc3NKCsrk8fFGurs7ER5eTmSyWTRXEWq6OF0OrF+/XrpKVVeXo5AIIBly5ZhfHwcfr9feomp3miMMWzevBktLS2oqalBPB6H1WqFx+OR4kuxnZry+TzC4TBsNhuqq6tlmJq4pq6uDtXV1aipqZHjfPHFF6Wg4nK5UFFRgbKyMgwPD8Pr9SKRSMgwPeOaNwo9Qry2WCxIJpOyThFWaQwNNd5TM489o1ij5p8SibpzuRwcDocU7IqFgonvBEEQBEEQBEFcWxYl2gjBQ+SEEEKB6h2Tz+cxOzuLWCyGyspKlJeX4/nnn0cymURZWZk0uD0eD2677TZEIhHU19cjGo1KMUANc7l8+TKefPJJTE5OSmMrk8mgoqICLpcLdrsd+XweK1aswG233YYjR47g1KlTSCQS2Lx5M5qbmzExMYFNmzZhfHxcGirAGwac2+3Ghg0b0NTUJHNLrFq1CqdPn8ZTTz2FO+64A06nE52dnZicnJS/yttsNgSDQWnw5nI5ZDIZZDIZuQOVMHB9Ph/GxsYQi8XkfArDym63w+v1SuPqpptugsvlQnt7O44dOwYAmJycRDAYRDwex549e9DY2Ijvf//7SCaTuHTpkszHE4vFpMdTU1PTnFCKVCqFqakp3HPPPXjooYcwPj6O2dlZAMDs7CwuXryIzZs3o7e3F9XV1SgrK5NeS3a7Hdu3b0ckEkEmk5EJqPP5PJLJJCYnJxGLxdDc3AzGmAwlEvNcXl6OVCol74HdbkcgEJBCksfjQVNTE5xOJ86fP48DBw5gdnYWL7zwAoLBIMrKypDNZjE7O4tAIIANGzbgzJkzWL58OZYtW4ZgMIiqqiqMj4+jtbUVly5dwtDQEKampqTHlrqWSyEM4FQqhXA4jGQyiRMnTuD1119HIpGAzWZDY2Mjtm/fjm9/+9syDCgSiWB0dBQf+9jHcOLECSxbtgyHDh1Cf3+/bP/8+fMYHh6W4oLT6SzwfNizZw9GRkawf/9+5HI5PPPMM+jt7QUAHD16FPv27YPP58PGjRsxPj6OkydPmoqR6lgAyLC51atXo7q6GslkUt6/oaEhzM7OoqysDE1NTRgaGpoj3Ij16nK5sHXrVqxbtw6RSASXL1/GzMwM6urqsHPnThn2FY1GC9a7Wb+Eh829994rc0TFYjEEg0HMzs5ieHhY7vQUCoUwMzODmZkZANruSHfeeSfa2tqwefNm+P1+jIyM4MyZM+jo6EAqlZJhfYAmBPt8PjnXU1NTcDqdaG5uRn9/v/Syq66uxsaNG+H3+5HNZpFKpbB//34cP35czrPH48GKFSuwfPly/PznP8emTZswPDwsQ+isVivcbjfi8Tjy+bzcXlz853K5MDo6Cs65fI6Awt32gLmhZA6HAy6XS3rOqeKNKrqJZ16IPyJPVTqdll5p8XhcevYIb5tiIZwEQRAEQRAEQVwbFu1pI4STmZkZzM7Oorq6uiD8R3hAbNq0CS+99BKWLl0q8zGo3htWqxXPPPMMtmzZAr/fj+rqalRXVyMYDEqX/ampKdjtdrhcroLEw0LcuHTpEtavX4/bbrsNTqcT3/nOd6Qw43Q6pWF199134/Dhw3juuedMwxQikQieeOIJGapQXl6OI0eOIB6PY3JyEt3d3Th48CB27doFxpgUAWw2G1599VWEw2GZ0FMk/1TzSgQCAWzevBmxWAw///nPpWEkcDqdMnmxCNnYs2cPKisr8corrxQYYKItp9OJRCKBnp4eWY/wUqioqEAqlcLq1avneMRkMhmEw2GsWLFCGrZqnoyZmRmEQiFYrVbs3r0bfr8fw8PD8Pv9iMfjcLlc2Lt3LxKJBCKRCGKxGNLptFwP8XgcsVgMPp8PPp+vYO3ccccd6O/vR29vL+LxONxuN5YuXYpdu3bh+9//PqamprB27VrcdNNNmJiYwI033oiPfexj0vCfnZ1FMpmE1+vFwYMH8eKLLyIUCqGhoQGrVq1CX18fotEoenp60NnZCWDuTl2l1rYoL/5mMhlMTEzA7Xajt7cXq1evxvj4OIaGhuB0OrFmzRqcPXsW0WhU3lPhbeX3+/HAAw/g05/+NEZHR2UOGlF/OBzG9PQ0Ll26hObmZng8HnDOkc1mMTQ0hO3bt2P58uV4/fXXMTAwIL27ZmZm8Oyzz0qPnP3796O9vV16tBiNbdGew+HAb/7mb8Lv96OzsxOPPPII8vk8tmzZgjvvvBNPPfUU+vr6UFdXh/e973147LHHcPHixQLBw+FwwOv1Yv369Xj/+9+Phx56CD09PQUeX8PDw9i1axdaW1tx4cKFgrVudg+EyDswMIDly5cjl8shnU7LeQ6Hw+ju7saNN96ITZs2we1248yZM8jn81iyZAkCgQB6e3vh8/lQX18vPbGMOWjEjla/93u/B4fDgYGBAVRVVcHr9WJ6ehoOhwPZbBaNjY34jd/4DfT29uL8+fNIpVJYvny5DLES4ZQiCfbNN9+MsbExfPjDH8bx48fx4osvYmJiAvX19di6dSteeuklJBIJbNu2DbW1tXC73TKE66/+6q8Qj8flu0SIeGLntpqaGiQSCRkC5/F4sHTpUvluDYfDsFgs0kMtGAzC5XKhsbERwWAQsVhMzn1FRYV8d9tstoJQLPHeEveZIAiCIAiCIIjrh0XntBGGZyKRQCaTQUNDAywWCzKZjPwHf2NjI3bu3Im2tjb827/9m+lOJBaLBbt378bFixeRyWSwZcsW3HzzzRgcHEQ+n0dVVRWWLVsmQ3eMBpjIU/LEE09genpaesN0dHSAMYYlS5bA7/cDADo6OvDQQw9JA1I1TNQknwCkF4jD4ZC/PA8ODuIb3/gG7Ha7TFIMQO4qJerL5XKIRqP4oz/6I0xNTcnrg8EggsEgbrjhBjzxxBPSSBJG08zMDIaGhrBmzRp0dHTg6aefRmtrK1pbW7FmzRqZ1JZzDr/fL0M67rrrLgwNDckcNzfddBM8Hg+GhoZQU1Mjkw2rolo6nZa5UMSuUWpS23w+j/7+ftx1112Ix+M4e/YsGGNYu3YtDh8+jEOHDsly6j0V4+Gc4/z587j77ruxZ88evPzyy9Iz6c4778Tzzz+PsbExRKNRhMNhnDhxAhs3bpTJqsUY3/3ud2NsbKzAM8rtdqOurg7r1q3Dk08+ibGxMVgsFgSDQZSXl8NisWDVqlVYv349Lly4IMOu1HXjdrulOGexWGSon5o0Wp2LVCqFtrY2OJ1O7N+/H2NjYxgeHobD4ZC5a4yCjJjrZcuWIRQKmXqsiLUYjUbltuVClHzttdfw3ve+FxUVFQWeIgJ1XYncPiK8R21Hzbeyfft2NDY24vTp03j22Wfl/Xr99ddx4MABlJeXyxCaWCxWsG293W7Hhg0bcMcdd2DdunUIBAL4zGc+g56engKPD4vFgv7+fmzduhUTExPSy2Q+L6BkMokf/OAHYIzB4/HIxMvLly/HvffeC5vNhocffhibNm3Chg0bUFNTg+PHj+PjH/84Hn74YfT396OzsxNerxfLly/Hvn37cPDgQbS3t+PZZ5/FxMSETMj7ne98R4ZTtba2oqWlBfl8XnoeffnLX4bFYsHGjRuRTCYRj8dx8eJFfOtb3ypIUizeSa2trfjoRz+Kb3zjG3j3u98tQ+g2btyIvr4+fOlLX5JC2/nz5+F2u7Fjxw6k02kkk0n5bACQHjiinfvvvx9nzpzB2NgYli5dive85z3o6urC6dOnUVNTg3Xr1mHlypVYuXIl/H4/Xn75ZRw9ehTvfe978dxzz+G1115DJpNBJBKRnjuqACzEm9nZWZmYWE2+XGrrdIIgCIIgCIIg3h4WLdo0NDQgkUgU/IIrcro4nU6sWrVKeo2Ew2FcvHixILGm1WpFVVUV3vGOd6Cvrw+dnZ0y78rNN9+Mr3/965iZmcHp06fxz//8z7j99ttx2223wev14tVXX4XVasXNN9+My5cvIxKJIBKJYGhoCD/72c9QXV0Nl8uFaDSKI0eOyKS+In8EMHcLZhE6IAyWdDotw5uE90Aul5P/pVIpmZtGTZIs6hOhPqpxJMrV1NTIcmr4QzAYxCOPPIJPfvKTCAaDcDqdGBkZwejoKD74wQ8in8/j1KlTMrHt0NAQent7sW/fPnz+859HT08PQqEQxsfH0dHRgVAohObmZpkvRxUNhBAxMTGBj370oxgfH0d3d7cMm6itrcXdd9+NSCSCkydPory8HBs3bsSGDRtw5MgRGU5j9F4Rhh4AjIyMYHBwEDt37sT73vc+KfRwzmWC6mAwCAAyEa/YtntsbAzd3d1yy+79+/fLvCZ+vx+pVArHjh3D7Oys7Mfx48cxMDCAQCCAv/3bv8WBAwdw7733YnJyEtFoVHoBhUIhjI6OIhgMFoghqthiFBez2Sza2trwiU98Ak1NTQgEAjJMa3x8HDt37pQ7FVksFjQ1NWH79u34wQ9+gO3bt8vt19V2hIi2YcMG6aWybNkyOYeDg4Nwu92orq7G0qVLsXr1aincieds5cqVaGlpwczMDCYmJgrug7Edi8WCW2+9FX19fejt7ZXHxfbiTU1N8Hq9YIwhkUigt7cX27Ztk/mnbrjhBgQCAYyNjeHGG29ELBbDwMCAFGzUthsaGuByudDZ2YlMJgOPxyOfezUxt+ifCOkpKyuTYZViTL29veju7sZHPvIROBwOHD9+HC0tLdiwYQN+53d+B4lEAt3d3fJ5nJ6exuuvv46uri44HA7cc889OHDgAF5++WV0dHQgl8shHA7L3ZRyuVxBm1u2bEFnZyceffRRRKNRZLNZZLNZpNNpJBKJglCiTCaDmZkZZLNZDAwMYHBwEPX19fjQhz6Eo0eP4tChQ8jlcvjsZz+LL37xizh9+jQikQhqa2sxPT0t58HhcGD16tVYsmSJ9BYaHh4G5xxerxdLlizBtm3b4Pf7cejQIbS3tyOVSqG1tRXbtm1DMBjE1772Nfl+ESF2Ys5FWJQQFsWYOOeoqKjArbfeiu7ubrS1tSGfz8vwK5vNhsnJyVL/KyAIgiAIgiAI4m1g0TltMpkMGhsb0dTUhDVr1mDJkiW48cYbpRGSyWRw5swZTE1N4ZZbbkFvby86OzuRTqfh9XrR0NCA2tpajI6OorOzUxqtp06dQl9fH3w+Hzjn8pf6sbExrFy5EmvWrMGOHTswOzuLtrY2XLhwAVNTUzKHjBrWI8QVowcEMHdHFtUTpVguBzUpqDHcSNQlMG4LrB4TBqxxx5lkMom2tjY89NBDMkGoMBrdbjf8fj88Hg+SySRefPFFTE5OIpvNoq+vDxaLRSaFTiQS0gNqZGQEn/rUp6QHhhhnPp9HLBbDj3/8YzgcDjzwwAMIBoPS+8XlcmFwcBDt7e0IhUJyO3fGGOrq6qRBqRrexrnNZDI4deoUZmZmsHv3bjz44IPI5XJoa2tDXV0dDh48WLDV+NGjR2Xo3Pj4OI4dO4ZMJoPHH38cNTU1cDgcGBkZQW9vLxKJBKanpwt2mBLbrQsPosceewxWq7Ug547Yzl14N5h5fxULoYrH43j00Ufx4IMPwufzwe12Y2pqCmfOnMHBgwfx7ne/W4ptNpsNoVAIPT09OHfuHNauXSvFRdGG1WrFunXrsHz5cvT19QEAWlpa0NjYiMHBQUxPTyOVSuG5555DPp/HAw88gPHxcYyOjoIxBrvdjnA4jJGREYyMjEhvDWPCW3VcgUAAbW1tMtRMlGlpaZGiiUgKHY/HUVlZCavVihtuuAE1NTWYmprC9PQ07HZ7wbMl6hEC4Z49e3Dy5Ek4HA5s3boVjY2NBetDfebUZ6aqqgpr166V4ZButxtOpxMVFRU4cuQIIpEIEokEJicnMT09jT179uDhhx+WW7+rAqrILdXe3o53vetdqKmpkWWEmCqeE5F7KpfLIRgMYu3atTh48CC6u7sxOjqKUCgkExyLhMGinXg8jjNnzqCnpwczMzOIx+OIRqMyrMvtdgPQPP3E+vZ4PKitrZWeYWKHrWg0Kj2LOjo6cOzYMUxPT8PlcqG2thY2mw3d3d2YmZkBYwzbtm3D1NQULl68iEgkIj2Wmpub4fV64XA4Ct5PXq8XVqtVho6JMLSuri685z3vQSwWQyQSwbZt29DY2Ihz587J3bIIgiAIgiAIgrh2LNrTJhaLweFwwOPxyHwm4XBY5nIReUdGRkZgs9kQCATg8Xjk9tG5XA7Dw8MYGBiQhozIEyNyNIhfsi0WC2KxGKanpxEKhRAIBJBMJtHV1YVgMCh/MVbziRgFFqD09sfivED8Qq2KHMZy4pjwlilm7Is6nE4n3G63NLiMiLCq8+fPF+ymJOr1eDyoqqpCKpWSiU4ByN22zASU2dlZHD9+XIpi6hiSySR+8pOfwOPxoKWlRYaOCIFneHgY4+Pj0itncHAQyWSyIIxIbU+MU20/FAqho6MDFosFVVVVyOVy6Ovrk+tBrBexe5EIg5qdnZUhX+FwGC0tLUin0wgGgzIBsnGs2WxWJqhljKGvr2/OPRfG+ny5bczIZDI4ceIE6urq0NHRIRP4TkxM4JlnnpHeFzMzM5ienpbG/qFDh7BlyxYAkIl+hRFeVlaGkZERDA8Py12HNm/ejEuXLiGTyaCjo0N6qYndoUToDQBMTExgYmICU1NTcwRII4wxXLp0CfX19WhoaEA0GgVjDDU1Ndi0aRPa29thsVhkmFU6nUZtbS3Wrl2LjRs3or+/H8PDw3C73RgaGkJtbS2am5sxMDCATCYjw9aWL1+OsbExDA0NyVAfM2HTKGoyxjA9PS3DdVwuF2ZnZ2Gz2TAxMYHu7m4ZwlNfX4+mpia8/vrr6OnpAeccjY2N8Hg8sFqtchtysUPWyMgIQqGQbEe8K3K5nBRjRFjY2NgYnnnmGZmcvKWlBXV1dQUhTPl8XiZhTyaTeOqpp+R9efnll2Uy71QqBZfLhVwuh0AggHQ6jfLyclRVVSEYDGLVqlWorKyUeaFCoZDcfU/sGJdMJuFwOFBRUVGwGxbn2pbqQmhVc2iJRN5ia2+LxYLKykqZqNxms8lk7plMBpcuXUJfXx/27duHeDwud8EiwYYgCIIgCIIgrg8W7WkzOzsr/8FfUVGBaDSKrq4upFIpKaKIJMITExPYsmULamtrZYLWkZERjI2NFYTZqMa1MfRGiDzDw8MFnitmHjPqebXPajmBKG8UPEQdwpPALK+Dsc5ixrI45vV64ff7paeEsYwoJ3ZkUsfCOZe/xIv+qONVd4YyiigihEg1joXXyWuvvSa9NoQhJ+6JGLNoSwhqxcYpUHeeyefzCIVCOHbsmDQoRUJVYYACmBO2JnL89PT0SDHPbK7Ue2V2T4rdi/nGYAbnHJcvX8YjjzxSEB6XSqXw+OOPo6GhATabTQpf4h7+4he/wJIlS7BkyRKUlZVhZmYGdrsdFRUVGB4eRmdnJxKJhLxPW7duhc1mQyqVwiuvvCLDu9ra2uD1eqXQILxKjAmtS/HKK69gx44dWLt2LRhjSKVSMj/S0aNHUVVVBZ/Ph3A4jJmZGVRVVWHv3r1SgJ2YmEBlZSWOHj2KTZs2Yfv27fD5fDI5dXV1Nfx+P5588knMzs6Cc45z585JLxbjnBv7HQwGEQqF4HA4pGgqxAVRvqWlBWvWrEF5eTkef/xxGbLkcrng9/vlbnIVFRWoq6tDPp/H0aNHpXeY8Z5OTk5KMUoIlz/60Y/Q2NgoRR/h9SOEagCIRqPI5XLo7+/HK6+8Isfy0ksvIZVKIRaLIZvNIplM4uzZs1i9ejW8Xi/sdjvi8Tg6OjqkeBmNRpFMJuW29mfPnkVPT498x9psNiSTSTidTmzZskUm27ZarXJL8mAwCLvdjvr6ejgcDoyNjcHpdKKqqgo2mw1utxvBYFB6IAphXaynZ599Fp/61KeQTqdx7tw5nD17FqOjo4t6RgiCIAiCIAiCeGtY9O5RAAoMfPErtSrCAJoxHg6HcfjwYWlIq8ab6rWieqyUEnBEH4yeNOo5IQKomHnaGI+J+oShKLbxVvuqlhX9M55Xc7sIr52amhpUV1fL3XiMoVXiPzMvIcDco0edU1HGKFyYlVfnXOTwSaVSBQKUcf7V+TKKZMXuj0CEqqh1mIWtGee21Lwbj6mCTLEQHJXFCjeqCGU8nkgk0N/fP0c45FzLsfL1r38da9asQXNzM1wuFy5fvoznnnuuIC9UMplEX18fLl++LNfOhQsX5nhOFROpzLxZjHN08uRJuFwubN68Ga2trQgGg2hvb5eJsW+88UbY7XaZqDocDmPVqlX42te+Jr1/QqEQHnvsMRw+fBgPPvggdu/ejUQigcuXL6OzsxOPP/64FGw453JrbrP5VPum7mAkdjMT5cTa93q92L17N3w+H06fPo2hoSHk83nkcjl0dnbCbrfLbemFN5kIhyom6I2Pj+O5556T4+acy8TD4vlQ+yyS9zqdTrnNvdg1DoBcBwBk2N4XvvAFHDhwAPX19Th//rzMwSNEkUwmI9djNpuVYVvZbFaGb507dw5erxf3338/jh49iq6uLmQyGaxbtw5Op1PuOLdq1So8/fTT6O/vl15CwWAQly5dwtDQELLZLLq7u3H58uWC5O7xeByMMen1Njg4WFSwJgiCIAiCIAji7YUtxni1WCxc7L7j9XqxZcsW3HffffjjP/7jgl1+gMLtwcUOS6rRZCYGiOtUzIxSM+NVbddIqTGK+oVB9oEPfACVlZX44Q9/iMnJyTmJVs36ZBSdxDitVivcbjcOHjyI7du348tf/rIMZzD2Qa1H7OCiii/C+wBAgTdMMfFjPvFCFXlKzZPRI2m+LZyN81/ME8S4DszqnO/aYsJRKeYTOBZyrfq9VNtma1m9zijymLGQe7eQsajXqM+mOKeGA1qtVni9XqRSKSnoqePhnMud1MTzLf6K8+rYjWKfcY7UdWom4Ho8HnzoQx9CNpvFuXPncO7cuTkisXEtGEVMY3tqG8XugXHtq3Olzod4vxnXg9idTJwzevHZbDbpTSM8l44dOybDSH/rt34LTqcTnZ2d6O3tRWVlJZYuXYrZ2VkEg0H4fD60tLSgsrIS/f39MnQPeENQFm2Ld476DuFcS4R83333yR3ZLly4UODVp3q6EQRBEARBEATxlnKac77DeHDROW3UXZRELgY1B4tR0BBGoJq41Ch4GJPCGg2l+QxUszAns7LzGcj5fF7mnxEGl9kOQ8bvan2qlw1jDNu3b4fD4cALL7yAWCxm6r1iDOsyGrdiXlXPI9XQNRuX2g+z/qvbFxfzVDHOo2oMGw3ZYl5Jxe6N2Xkj8wk6peo1tmGs80owu3Y+QbBUnxYjNpnVu5hrzbYNF/1QQ+zEd5HfSG1TXRcifMesX2b9Vdd6KaFRFZJE6NPHP/5xXLp0CSdOnMDIyMgcwcasf2YhksX6pn42CjJGzzjhFaR6AYn3hBCxjAKNQPRJ3dEJ0LwSX331VTn3IqeXSEQt3p2Tk5OYmJiQ142OjqK9vX3OWFThS92O3dh/sXvUli1b8JWvfAWRSMR0bgmCIAiCIAiCuHYsWrQRoTWpVAqdnZ34h3/4hwIDSTUYxH/CUBAYRQegMCmpeszM08VYzuxX/FIGdjEhw+Vywev1wuVyIZVKFQgb6jXG72oYhVrnjh07UF1djd7eXrS3t8/J32IUUMz6Jo6pYR7C6FLLCsPRWEbFeM18xqxRUDF6iJiJOcZ5mC9psyrcGcdfSmRbjFFpJkpdLUp5aSxUjDI7Z6zfeG6xYyj1bBS7z2YYn7PFtG0Um8yeefFdJND9wAc+gK6uLpw8eRKRSKTgORBryiwZt9GzaCFzZ3wG1eNm3lFG4dMsd4+ZoCmuE+9SkZTb2GY8Hi/4Luoxbp1u9JwyjsEo+DLGUF1djQ0bNmDr1q345je/KUPZVI+rxd5ngiAIgiAIgiCuPosWbYA3jIBEIoFkMmn6y38xzwvBQoxno3BQzHg3E2EWYngaf/UXv4Ank0kp2izEi8PoxSBCrWZmZnDhwgVMTk4iHo/PEXzMhKz52iomeJn1Vf3VX63fKESp/TBeY5w3tYzxnLFOM0PZrN5SuTMWskYWytX2HjATthbb1mKfgcXUbVYPUNpLqZiwIjDWsRgvFmMdZucA7Tmsr6/Hrl27MDU1hddeew3hcFiGYBZ7BszeP/PNnfEZLHU/jaKnkWLhWkZR2zj/Ru88cS6bzcryakikcXyinlLjVMXTQCCAjRs3YsOGDTh8+LDMWaQKT6p3DkEQBEEQBEEQ144rFm1UY0P8YqwacqpRYvZLujinflfrnq99s7/G8/PVYfTqyGQyaGtrkwl6jUZQKW8F4/l8Po+xsTEkEgkpABmvK2ZAFqu7WPvFBBaz+ooJKWbXlhJaFlJ/sfKleLPixNvNm+nj1RJ23sr2SglSZuvX7Nxi+m+329HS0oLVq1fD5XLh3LlzGBkZKXivqOJhsTW8EIp5dZl5183n9WX2jjOKrGZtqv035vMxe6dybu7VZnz3qH0QZUQusrVr12LJkiWYmJjA2bNnkU6nTb0AfxmeP4IgCIIgCIL4VWfRoo1qbBgNHMaY3Pbb7DqguABwJf0wshhDw8zrYHZ2Fi+88AIAFN2FyuyY8RdyzrWdYEKhUMFcCWPLzLhdiHdQKU8As34u1AvEaFTO96v9fMdL9YtCLn65KSbYXA1sNhvq6uqwbt061NfX45VXXsGFCxdknhfjun6za7SU6GR2Xm3TKKIa61ERYowaVmWsX71GfDZLFm707FPfC2bCklqX2GGqtbUVyWQShw8fRiKRkHnK1P4KEZsgCIIgCIIgiGvLokWbYsaKMWTG7JdnM6Hnahp/pepaSIgJ5xzpdHrBdRYrZ2ZUic9moUrFwpvUfpvNVakQFdW4MxOEjAaemTdAsbJvBvr1/tqx2OdtseXnK1usPsYYrFYrampqcMsttyAej+PJJ5/E5OTkHKHvzayf+UTRxV5v9jyaecYYd48SYyoWiijeBequVOI6NXeVWqcxgbMoo+4i1dzcjAMHDqCjowMvv/wywuGwDIcy8+ohgZUgCIIgCIIgrj1XHB6lflaNAzOjZrH/+F+IsWgMLTDrW7E+Fzt/JQbhfL+4q+dUY8rMQCrlFWNWZqFeBKXEs2I5MczqulrG80L5ZQjRmK+Pb/UYFvusLIZiAovR22Mh7RQTH4Vg4/P5cPvtt+O1117D0NAQZmdnC9Z+qbxHZm2Z9X+++2RWxkxkNZ4zS76uks1mYbVaC3LEiJBSMTa1DiHMCAFGHFcTLxvff2q/jMmaLRYLGhsb8Yd/+Id4/PHH0d7ejnA4LIUhMxGomMckQRAEQRAEQRBvL29atAGKG3LCsDAzMszqKVb/YvpT6tf8t8IImc/TRfVeMeaNMNYzXx+LhVNcqTdEsX4XOz+fQb7Yc/Ohrpfr1YBciBh4tblSD5iFXlPq+TSu68XcI7MyFosFTqcTa9euxRNPPIGZmRkpGBjfGQvt/0LW4mLeE2bhjMIzRT1mfD7VZ99Yhxoqabfb54i3RqFKfBd1GIUeIfaIvqn1VVRU4JOf/CQOHTqECxcuIBaLyXAoxpgUk9X3tSoSEQRBEARBEARx7WCLMf4sFgsXOywVVHIVw2cWytUO9Xg7xJ5SXgrztfVWz/H1LIwQby/qNtULwUyUWMg1oi2PxwOr1YpYLGYaJng9rktV0DATeVREiJPRw41zDpvNVnSnK9GOWl5N/m5sR9w3IdqInaJuu+02ZDIZHD58uEAUE303E5RFHeq24wRBEARBEARBvKWc5pzvMB68Ik8bI+If/qV4K0SBxdapbp87n2FYKl/MlfJmvDJK/fp/NbgeDWPi2nAl4mWpZ6XUtfl8Hul0WobpvJVczWfGzOttIZ5rRm8acVz1yhHn1ETExrJG0cjMQ87n86GlpQUzMzNob2/HzMyMDJ0yetypYrJZnh2CIAiCIAiCIK4Nb0q0Mf66fj3/Q59zjhUrVsDtdmN0dBThcLikkXi9jgO4su2NCWKhFAvfK1XemOtloddwzpHJZK7rdwdgvj14sfNmIorRy8YsB5halzH3lTiuijvq9cZQLLfbjerqarhcLly4cAHDw8NzQqxEeePuXGqfCYIgCIIgCIK4tiw6aYEwCq61kXUlRsU73/lOHDx4EKtWrYLL5VrQNS6X623L7bAQb6WysjI4nU7KN0FcNxgFA2BhO0mJv5zzAg+Qt0owWGxOn2LnjOeFGCI8YIzvRmOiX1He6EmjljHz9GOMQYSn5vN506Tmog81NTUoKyvD2NgY+vr6kM1m5TVmY1SFpnw+L8sTBEEQBEEQBHFtuWLLfyG/xhsNs2IIQ0NN7nm1sVgsWLNmDc6cOYNsNotAIGDqHWC8Zu/evaisrJRJPt8siwmBMuP222/H/v370dzcXHCcvG+IYpgJDfOVN/tcDKPYspCcNMbwoLcjPFGt2yh6mr2rjCKU+E8VM0QZVYBRv4swJ/H+yOfzBUmAzZIVi1w3AOSuU8IjJpPJzGnH2Gen04l4PI7h4WHpYSPGoIpCap+Nu0hdrfcdQRAEQRAEQRBvjkUlImaMBQEMvnXdIQiCIAiCIAiCIAiC+LVjKec8YDy4KNGGIAiCIAiCIAiCIAiCeHugxCgEQRAEQRAEQRAEQRDXISTaEARBEARBEARBEARBXIeQaEMQBEEQBEEQBEEQBHEdQqINQRAEQRAEQRAEQRDEdQiJNgRBEARBEARBEARBENchJNoQBEEQBEEQBEEQBEFch5BoQxAEQRAEQRAEQRAEcR1Cog1BEARBEARBEARBEMR1CIk2BEEQBEEQBEEQBEEQ1yH/D2H6pkip88OuAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20, 20))\n",
-    "plt.title(sentence)\n",
-    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
-    "plt.xticks([])\n",
-    "plt.yticks([])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('Boyis cheed iitrincy- tarisaing one', 0.3990435302257538)"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict_on_image(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[1, 28, 952], [92]]"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "experiment_config[\"train_args\"][\"input_shape\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "=========================================================================================================\n",
-      "Layer (type:depth-idx)                                  Output Shape              Param #\n",
-      "=========================================================================================================\n",
-      "├─Sequential: 1-1                                       [-1, 158, 1, 28, 6]       --\n",
-      "|    └─Unfold: 2-1                                      [-1, 168, 158]            --\n",
-      "|    └─Rearrange: 2-2                                   [-1, 158, 1, 28, 6]       --\n",
-      "├─Linear: 1-2                                           [-1, 158, 512]            86,528\n",
-      "├─PositionalEncoding: 1-3                               [-1, 158, 512]            --\n",
-      "|    └─Dropout: 2-3                                     [-1, 158, 512]            --\n",
-      "├─Embedding: 1-4                                        [-1, 92, 512]             41,984\n",
-      "├─PositionalEncoding: 1-5                               [-1, 92, 512]             --\n",
-      "|    └─Dropout: 2-4                                     [-1, 92, 512]             --\n",
-      "├─Transformer: 1-6                                      [-1, 92, 512]             --\n",
-      "|    └─Encoder: 2-5                                     [-1, 158, 512]            --\n",
-      "|    |    └─ModuleList: 3                               []                        --\n",
-      "|    |    |    └─EncoderLayer: 4-1                      [-1, 158, 512]            3,150,848\n",
-      "|    |    |    └─EncoderLayer: 4-2                      [-1, 158, 512]            3,150,848\n",
-      "|    |    |    └─EncoderLayer: 4-3                      [-1, 158, 512]            3,150,848\n",
-      "|    |    |    └─EncoderLayer: 4-4                      [-1, 158, 512]            3,150,848\n",
-      "|    |    └─LayerNorm: 3-1                              [-1, 158, 512]            1,024\n",
-      "|    └─Decoder: 2-6                                     [-1, 92, 512]             --\n",
-      "|    |    └─ModuleList: 3                               []                        --\n",
-      "|    |    |    └─DecoderLayer: 4-5                      [-1, 92, 512]             4,200,960\n",
-      "|    |    |    └─DecoderLayer: 4-6                      [-1, 92, 512]             4,200,960\n",
-      "|    |    |    └─DecoderLayer: 4-7                      [-1, 92, 512]             4,200,960\n",
-      "|    |    |    └─DecoderLayer: 4-8                      [-1, 92, 512]             4,200,960\n",
-      "|    |    └─LayerNorm: 3-2                              [-1, 92, 512]             1,024\n",
-      "├─Sequential: 1-7                                       [-1, 92, 82]              --\n",
-      "|    └─LayerNorm: 2-7                                   [-1, 92, 512]             1,024\n",
-      "|    └─Linear: 2-8                                      [-1, 92, 512]             262,656\n",
-      "|    └─GELU: 2-9                                        [-1, 92, 512]             --\n",
-      "|    └─Dropout: 2-10                                    [-1, 92, 512]             --\n",
-      "|    └─Linear: 2-11                                     [-1, 92, 82]              42,066\n",
-      "=========================================================================================================\n",
-      "Total params: 29,843,538\n",
-      "Trainable params: 29,843,538\n",
-      "Non-trainable params: 0\n",
-      "Total mult-adds (M): 118.22\n",
-      "=========================================================================================================\n",
-      "Input size (MB): 0.10\n",
-      "Forward/backward pass size (MB): 2.73\n",
-      "Params size (MB): 113.84\n",
-      "Estimated Total Size (MB): 116.68\n",
-      "=========================================================================================================\n"
+      "2020-11-18 20:34:49.381 | DEBUG    | text_recognizer.models.base:load_from_checkpoint:379 - Loading checkpoint...\n"
      ]
     }
    ],
    "source": [
-    "model.summary(experiment_config[\"train_args\"][\"input_shape\"], 4)"
+    "ckpt_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1116_082932/model/best.pt\"\n",
+    "model.load_from_checkpoint(ckpt_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t=[12,1,1,1,1,1,4,4,4,4,4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t[t!=79]"
+    "model.eval()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = torch.arange(10)\n",
-    "value = 5\n",
-    "x = x[x!=value]"
+    "data, target = dataset[1]\n",
+    "sentence = convert_y_label_to_string(target, dataset) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([0, 1, 2, 3, 4, 6, 7, 8, 9])"
+       "torch.Size([98])"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x"
+    "target.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
-    "t = torch.rand(98)"
+    "data = data * (data > 0.1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(1.7656e-43)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t.cumprod(dim=0)[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pred_tokens = torch.Tensor([1,2,21,31, 89, 89])"
+    "from torchvision import transforms"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pred_tokens = torch.stack([pred_tokens, pred_tokens])"
+    "ra = transforms.RandomAffine((-1.1, 1.1), scale=(0.5, 1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.,  2., 21., 31., 89., 89.],\n",
-       "        [ 1.,  2., 21., 31., 89., 89.]])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pred_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
-    "eos_token_index = torch.nonzero(\n",
-    "            pred_tokens == 89, as_tuple=False,\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    }
-   ],
-   "source": [
-    "if eos_token_index.nelement():\n",
-    "    print(eos_token_index[0][0].item())"
+    "data = ra(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": "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\n",
       "text/plain": [
-       "tensor([[0, 4],\n",
-       "        [0, 5],\n",
-       "        [1, 4],\n",
-       "        [1, 5]])"
+       "<Figure size 4320x1440 with 1 Axes>"
       ]
      },
-     "execution_count": 32,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "eos_token_index"
+    "plt.figure(figsize=(60, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
+   "execution_count": 133,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8"
+       "('and Came came into Mr. I. I. \"Amering whin<eos>', 0.32183724641799927)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "eos_token_index.nelement()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from text_recognizer.models import accuracy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred = torch.Tensor([1,2,21,31, 80, 80]).unsqueeze(0)\n",
-    "target = torch.Tensor([1,2,1,31, 80, 80]).unsqueeze(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred = torch.stack([pred, pred])\n",
-    "target = torch.stack([target, target])"
+    "model.predict_on_image(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
-    "target = torch.tensor([0, 1, 2, 3])\n",
-    "pred = torch.tensor([0, 2, 1, 3])"
+    "data, target = dataset[110]\n",
+    "sentence = convert_y_label_to_string(target, dataset) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.5"
+       "([], [])"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy(pred, target)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "acc = (target.argmax(-1) == pred.argmax(-1)).float()\n",
-    "\n",
-    "# return float(100 * acc.sum() / len(acc))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAABCCAYAAADt2ys3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABFb0lEQVR4nO29eXhb13nn/z3YCYAkQBLcqZ3aJWtfbEt2vEaRWzdJ48ZJOm4mTX5t4uY305lmJtNm0ixNp8l0SZNxm0ybxM1qR3VSJ64db7JlLZYtyZJoUhT3RSRFgiAAAiR2nPnj3nN8cHkBkrJsKcn7eR4/BO4992z33Gu9X7zvexjnHARBEARBEARBEARBEMT1heVad4AgCIIgCIIgCIIgCIKYC4k2BEEQBEEQBEEQBEEQ1yEk2hAEQRAEQRAEQRAEQVyHkGhDEARBEARBEARBEARxHUKiDUEQBEEQBEEQBEEQxHUIiTYEQRAEQRAEQRAEQRDXISTaEARx3cEYq2OMHWGMxRhjf72A8gOMsTvejr6V6MMLjLHfL3JuGWOMM8Zsb7KN/8EY+6cS56/ZPDDG/pwx9j398xLGWJwxZr0WfVH6dCtj7NK17MO1RL8HK95kHd9hjH1R//xrPZ8EQRAEQRDXgjdlQBAEQbxFfAzAJIAKzjm/1p25XuCcf+la92EhcM6HAHivdT/eThhjywD0A7BzzrPXoP0XAHyPcy5FPc75r9U9IAiCIAiC+FWEPG0IgrhuYBoWAEsBdPwyCDZKn39leLMeQQRRCsZY3S9z/QRBEARBEG8nv1KGBkEQi0cPqfk0Y6yDMRZmjH2bMeZSzn+UMdbDGJtijD3OGGvUj3+OMfY1/bOdMTbDGPuK/r2MMZZkjFXp3/cwxo4zxiKMsXOMsVuV+l9gjP0FY+wYgFkA/wLgAQCf0sM77lBDNPRrrihMgzH2YcbYz5Tv3YyxHyvfhxljW/TPNzLGXmWMRfW/N5boc0EICmPMyhj734yxScZYH4CD8/RrkDG2Xf/8QT2UaoP+/SOMsZ/qn2UIkv79d/VrQ4yxPzXUaWGM/XfGWK9+/lFxP0zav5Uxdokx9t8YY5cBfJsx5meM/ZwxFtTXxc8ZY83KNcsZYy8yLYTtGQA1yrmCcDDGWKO+dqb0tfTREnNxkDH2GmNsWr8ff25S7wOMsSF9fv9UOV+mr5UwY6wDwM4S7TDG2N8yxib0ttoYYxsZYzsZY+NMCe1ijL2HMXZO/7yLMXZKv2acMfY3erEj+t+Ivm736uX/I2Psgt6nXzDGlir1csbYx/V1GGOMfYExtlJ/Vqb1e+bQyxa9H4yxvwCwD8DX9ba/rtS/Spmbv9bXS5QxdpQxVqaf+zFj7LJ+/IhYe6VgjP0JY+xfDcf+njH21SLl3fp6fR7AYeV4JWPsnxljY4yxEcbYF8Xc62v4z/Q+TzDG/oUxVqmfczHGvqev7QjTnlEh1vw5095nf8IYq59vLARBEARBENczJNoQBAEAHwRwN4CVAFYD+DMAYIzdBuAvAdwHoAHAIIAf6de8COBW/fNOAJcB7Ne/7wVwkXM+xRhrAvAEgC8CqALwXwH8K2MsoLT/u9BCosoBfBjA9wF8mXPu5Zw/exXH+SKAfbox2AjAofcVTMv94QVwnmnixhMA/h5ANYC/AfAEY6y6SJ8HDe18FMA9ALYC2AHgtxfQr1v1z7cA6MMbc3mLfr4Axth6AP+g96NR72ezUuSPAPyWfn0jgDCA/1OiD/XQ7s9SfVwWAN/Wvy8BkADwdaX8DwCchibWfAGa0FaMHwG4pPfjtwF8SV9bZswA+A8AfNDErj9kjP2WoczNANYAuB3A/2SMrdOPfxbaGl4JbT2X6tNd0OZ4NYBKaGs8xDl/FUBIPy/4XWhiIgB8FcBXOecVejuP6sfF/fLp6/YEY+xeAP8DwHsABAC8BOCHhn7cDWA7gD0APgXgmwA+BKAFwEYA9+vlit4Pzvmf6nU/qLf9oMl4/7fezo3Q7vOnAOT1c08CaAVQC+AMtOdvPr4H4J2MMR8gvbPer8wT9ON7GWP/F8AItPv6z3o/BN8BkAWwCtrzchcAkRvq9/T/3gFNGPXijTX4ALT71gJt7f+BPicA8AkAnwSwGcBFXTB8N2PMvoBxEQRBEARBXFeQaEMQBAB8nXM+zDmfAvAXeMNQ/CCAb3HOz3DOUwA+DWAv0/J3nADQqgsZ+6EZY02MMS8KhYYPAfh3zvm/c87znPNnAJwC8C6l/e9wzts551nOeeatGiTnvA9ADMAWvc+/ADDKGFur9/klznkemljQzTn/rt6nHwLoBPAbC+zzfQD+TpnTv5ynay/q7QOax8RfKt9NRRto4sfPOedH9HvzGbxhhAOaEfunnPNL+vk/B/DbrHjoUx7AZznnKc55gnMe4pz/K+d8lnMeg7YubgG0RMPQhLrP6OWPAPiZWaWMsRYANwH4b5zzJOf8LIB/gmbAz4Fz/gLnvE1fK+ehiRy3GIp9Tu/jOQDnANygH78PwF9wzqc458PQRLdiZKAJbmsBMM75Bc75mH7uYWjrFrqAdzc0kUpct4oxVsM5j3POXy7Rxh8A+Eu97iyALwHYonrbQBMnpznn7QBeB/A057yPcx6FJqZs1eel6P2YD6aF7/1HAP8/53yEc57jnB/X1wU459/inMeUdXKD8Ggphj5XRwC8Tz/0TgCTnPPTepv3McY6oYky/QA2cc7v5Jx/n3Oe0MvUQXsP/CfO+QznfALA30ITfwDt/fM3+nzEob1/3q+v4Qw0sWaVPp7TnPNpvW95zvmznPPfhSZk/hTAfwYwwhj7wkLmjCAIgiAI4nqBRBuCIABgWPk8CM0jAvpf6UWiG04hAE264XUKmuG4H5qwcByaga4KDUsBvE8PYYgwxiLQPCUairT/ViO8WkSfX4DWX7XPBePWGQTQpHwv1edGzJ1TAABjbJ8ewhJnjLUrfdrHGGsAYIXmvXGTLo5VAjg7Xxuc8xlo90awFMBPlDm/ACAHoFi+jyDnPKn0080Y+4YemjINzUD36aErjQDCeptzxmjSzyldaFDLNpkVZoztZowd1sOAotCEjxpDscvK51m8kfS46Lwb4Zw/D81r4/8AmGCMfZMxVqGf/h6A32CMeaAJQS8pgs5HoHnndOohOfcUawPaPfiqcg+mADDD2MeVzwmT715g3vsxHzUAXAB6jSeYFsr3v5gWRjcNYEC5Zj6kuKX//a5yrhnaOM9CE9YuYy5LAdgBjClz9A1oHj/A3OdwENoGCnV6W78A8CPG2Chj7MtmnjT6ujuv98MOzUOLIAiCIAjilwYSbQiCALQQA8ESAKP651FohhUAQDdiq6GFOgCa2HAbNG+AV/XvdwPYhTdyfAwD+C7n3Kf85+Gc/y+lzfkSDs8AcCvf30yeCiHa7NM/Cy8XVbQpGLfOErwxbqB0n8cwd061izh/SQ9h8XLON+jHeqCJD38E4IjuMXAZWpjSUd37p2QbjDE3tHsjGAZwwDDvLs75iLGiIuP5L9AM3N16KJAI/2F62359PcwZo4FRAFWMsXJD2WL9+AGAxwG0cM4rAfyj3uZCKDrvZnDO/55zvh3AemhCzJ/ox0egeZK9B1po1HeVa7o55/dDExb+CsAhfR7M1sMwgP/PcA/KOOfHFzgelVL3A0XaF0wCSEIL5zLyAQD3ArgDmkC4zFBvKX4KYDNjbCO0cEAZVsU5/xtoos1zAP4UwCWm5RDaqlw/DCAFoEaZnwrxXGDuc7gEWijVOOc8wzn/HOd8PbSQr3ugeG8xxpqZltOpA1p4XhDADZzz+xYwLoIgCIIgiOsGEm0IggCAT+hGThU0A+sR/fgPAXyYMbaFMeaEFt5xknM+oJ9/EZqh1ME5T0PzWvl9AP2c86BeRngt3K3/qu9iWuJbNf/KfJwF8C7GWJWeWPQ/FSuo113KgH0RWo6MMs75JWi5QN4JTfB4TS/z7wBWM8Y+wBizMcZ+B5ph//MF9vdRAJ/U59QP4L8v4JoXATyIN4SjFwzfjRwCcA9j7GamJav9PArf6f8I4C9EKA5jLKDnWFko5dA8PSL6uvisOME5H4TmZfU5xpiDMXYzCkPHoJQdhuaB9Zf6vd8MzVvle2bl9XanOOdJxtguaKLCQnkUwKeZlrS3GZoIZgrTEg7v1r0zZqCJGqo49i/Q8r5sAvCYct2HGGMBXUiL6Ifz0ESBPAqTUv+j3h+RVLqSMfY+XBlF74fOuKFtid7XbwH4G6YlhbbquWacer0paF5abmjP+ILQPbMOQRPaXuHaVu/q+WnO+Tc55zdCE0WTAH7GGHtOPz8G4GkAf80Yq2BarqmVjDER9vVDAP+ZaUmvvXrfHuGcZxlj72CMbdI9jaahhUvlAS1hN4B2aCLXHwBo5Zx/wdg/giAIgiCIXwZItCEIAtCMrqehJcDthZY0GFxLAvwZAP8KzYthJd7INwFoxngZ3vCq6YBmmInvwmgXCVmD0H5d/xMs7v3zXWghFgN6Px8pUbZF75cpnPMuAHFoYg10r5Y+AMc45zn9WAjaL/f/BZox+ykA93DOJxfY3/8LLXTjHLTEro+VLg5AE2fK8cbcGb8bx9EOLeHqD6DdmzC0ZL+Cr0LzWHmaMRYD8DKA3QvsPwD8HbR7O6lf+5Th/Af0+qagCQj/guLcD82DYxTAT6DlzimWYPrjAD6v9/l/4o1Evwvhc9BCaPqhrZPvlihbAe0+hfVrQgC+opz/CfQQM875rHL8nQDaGWNxaHP8fj2/ziy0PDPH9FCfPZzzn0DzxvmRHnr0OoADixiPyt+h9P34KrScRWHGmFkun/8KoA2aR9yU3i8LtPs2CM3zqUOvezE8DE3YKjXX4Jxf5Jx/Gpq3zJ8pp/4DtITgHdDuxSG8ETr5Lb3eI9DuaRJvCHH1etlpaKF/Lyp9+CmARs75h7mW82k+Tz6CIAiCIIjrFkb/liGIX28YYwMAfr+EEf1LBWPsnwD8mHP+i2vdF+KXG8ZYL7Twpl+JZ+OtgGlJqTsB1ItEwARBEARBEMTVo9guIgRBEL+UcM5/f/5SBFEaxth7oeWJef5a9+V6hWm7Uv0xgB+RYEMQBEEQBPHWQKINQRAEQSgwxl6AlsPod4skgf61R0++PA4ttOqd17g7i0YPbzOjDFruIDpOx+k4Hb/ejx/gnL9kcpwgiF8xKDyKIAiCIAiCIAiCIAjiOoQSERMEQRAEQRAEQRAEQVyHLCo8ijHGLZbiOg/nHIwx+XkB9S2onFperVt8v5rYbDbYbNq0pFKpBffP2Dez6xY7XmJ+rFYrGGPI5/PI5ymKgbh+uNrPu1rf1Xj3qXUU6yc9UwRBEARBEATxtjHJOQ8YDy5KtLFYLHA6nQC0f/ALI0J8Fv/AVwUMcVw1NowGh1pHLpeT361WKywWC3K5HPL5fEGbNpsNnPOC/0r1w2q1FrSpnhNCFGMMt9xyC1auXImRkRE8++yzSKfT4JzLOi0WCywWi+yPaHflypWoqanBhQsXEI1Gkc1mTdsRfTUbv/Gz8btxbKJ9oyFnZtip86OWMf4VfTSjmBEq6hRzotZX6lrj/VosFosFH/7wh+FwOHD+/HmcOHHCdH4XwkIM7GJjuFrtmRnlxb5fSRvF6i/V7mLbKjWu+YSGhQq9alkz4UE8z2ZjKiYsq8+Y8bjxOTOOS7wPjHWJNszqFP00mxdjW6IPal+M/RBjFs+g+Kv2zfhdFeBzudycvgNAPF4s7QdBEARBEARBEFeZQbODVxQeZRRehKhhFFHEOWEsmBk/xmuEUCPqFCKOEXHeKAgVExXUcmq7xnJutxtOpxOzs7PweDyy71arVQo/qrAk6nA6naivr8fWrVtN50o1CoV3iHrMeI3oo/rXWLbY8WLU1tZiw4YNqK2tnXONmQhXVlYGh8Nhet/MDE31+EL6o4phdrsdpby4zLBarchms8hkMgAgPaSuhIUIBsXW4dVqz0zMUz9fiVdaqfquRjtmdZj1o9h6eLOeKGbihrFts7VpVk6UNX6fb07UZ8YoEBn7aFan+h5T+yoEYlUUNbZlHIsqQBvHo15vbNf4nlvss0gQBEEQBEEQxFvDov9lbiYwmIk4ZmXV86rBrh4zljN6zQgjw0wkms+4KvaLtrjGZrPB6/XC4/EUeBUZrzUzxqanp5HP57Fnzx5p9BQzII3GmXH+FsJ8hiBQaMAxxuBwOKRwsxBxpaWlBatXr0ZDQ0PRe1zqs5lnjxmMMaxfvx719fVwOBwlx61e43Q6kcvlkE6nkc1mYbfbF3TtQuq+llRXV8Pv98u5uJL+lBIz1DLz1b3Qto1r3WKxoKmpCatWrYLP5ytaT7H3SbF6za5dyDNf7J20kPG/mfWw2Oe7mIeN2lfVY8is3lLtGUX0Ul4+1/o5IAiCIAiCIAjiTYo2xl+DjSKB2bXzGRTGX5PNyqjl5uuj8bjxGjWkx+VyobKyEj6fDx6PR4Y4qe2q16vXTk1NIRKJYM2aNXC73XNEG/V6EYqgzsl8fRd1LBSzelOpFABg9+7dc+6VmZdAS0sLtm/fjrVr15p6By3UAC4lEIl6WlpaEAgECoSy+bDZbHC5XNIDyhgCV6wfCynzVhqsou6qqirU1tbC6/UW3INVq1ahubkZbre75HyXOl6s3bdiXMY5EwLhunXrsGPHDgQCgZLPqtmcz7fWzCgl2ho9X1wuF8rKygq8s+YTjMwEjvnaNgok6vdSdavnxDp3uVylhj+n30J49vl8sFgssNlsUlBW/1Of+1LvVYIgCIIgCIIg3n6uSLRR//Fvs9nmCDeqEaIeV8uJY0YjwfhdCC3qr8umA9HrVo0wYz3Ca0ccF+EA4pjT6YTL5YLT6YTFYkEkEikI1TL+Cq0eSyQSiEajSCQSqK+vNxVB1GvNxmz0QFKN1vnCIsyOGecyHo9jdnZWetqYGZHqMc45GhoasHTpUun1of5Sb1wX6rwXu1/Fftk/evQoRkdH595YE0QduVwOPp8PlZWVsNvtyGazBf0S/RQhHzabrahwaBQgS3kjGPsxnxipllefi1tvvRUHDhzA+vXrC9btmjVrpIBlXEfFRLZSqGKF8ZlYCGo5o+BgVi8AlJWVYdmyZVi2bNmc0DczTw71mSglgBjLq2vROC9mubTE95aWFixZsgQVFRUFbZo9Y2bvJHUcZs+EGg5qrN+Y26rYfRDnKysr0dTUhLq6ujnvWbG+zep1Op1oaGjAzTffDKvVCr/fD7/fD6fTKcuJNQ+8kYRdfVcSBEEQBEEQBHFtWXQSENUAFuEoIjzFzPAxJuw1GjyqaAK8YYwZE2iKMkZDxygMiD4Z8zWYXSe8XtQcDqlUCj09PfjFL34hBRR1DGp9qpHEGEM8Hkd/fz927tyJrq4uU1FKbds4ZjPjzUz8MdalGoxGI1FNdprJZJBKpWCz2VBZWYloNGpqyIvrstksPB6PNPRSqVRBwlNj/0U7xntq/GuEc45IJGJ6rhSpVApVVVUAAI/Hg3Q6PWe9lZWVwefzobGxER6PB6dPn0Y8Hjc1mOczVI1i20IxE9+EIMYYQ2NjI6LRKLq6ugAAjzzyCJxOJ5LJ5Jx1YVzPpTBeayY2LKQOFWNCcGO9Yi36/X7U1NTAZrNhcnISAOTzYCZazCcmqWvMWMasj8UQ6zeTyUixbGpqquD5MYoxajvFRCDje0/ti/G9oSb9FaKJeG8lk0kwxmSZbDYLxhjq6+uxfPlyPProo8hkMrKMqCOfz0vBRdSdz+fh8Xhw8OBBPP/889ixYwfy+Ty6u7sxMjIi+ygSvYu2xDxRXhuCIAiCIAiCuPYsWrTZtm0bDh48iCVLliCVSiEYDCKXy8Fms2FwcBAvv/wyBgYGMDMzM+eXZTWBr/prOGNMehqo3jBGUcS484kwAB0OB6qrq7F//37k83k4nU688sorGBgYQDqdBgAZWpBOp6VBpAo3jDGk02nZN5Hc1u/3IxKJyDJWqxUulwubNm1CMBjEyMiINHaSySTGxsawYsUKWK1W01/oRf/VcCO1jJm3QTHPEPV6UVZ4GzHGpMiiGrHpdBqhUAjl5eWYnp4GoAldom9C1AE0w9btdoNzLsU6ETJWyovGzJBVUftqJkIVq08tI4xWu92OTCaDbDZbMLe7du3C0qVLwRjD9PQ0ZmZmsGLFClRXV+Opp57C7OxsSW+HYn02Oybmx8zDw3hvVaN4amoK0WgUANDU1ISenh7k83mk02kZymZGKS+UUsLY1cD4HIq61eccAMrLy2G1WpFIJOQ6KyYWqfNSTAxS21c/G8+ZXW8UPS0WC6anp7FlyxYAQGdnp7xnxh2dir1/VBGmmKiqeumZzaMov3TpUqxcuRLpdBpHjhyZ8w4MhUI4deoU2tra5HtJvIuMIo3an1wuh2w2C5/Ph0AggGAwiDvvvBOJRAIjIyMFQo0q0og+X601QxAEQRAEQRDElbNo0cbhcOCJJ57A2rVr0djYiGeeeQZTU1Ow2Wxobm7Gnj17sHPnTly8eBGnT5+WIoDYulsYAuLXYovFAo/Hgw0bNsBiseD06dOynNmOT3a7HUuXLsXo6CiSySQaGxuxfv161NXV4eTJk4hGo7jrrrvg8XjgdDrhdruxbds2VFZWYnBwEH19fYjFYjKURvQLgDSW7XY7nE4n1q1bh/Xr1+OnP/0pYrGY7E8ikUBXVxfuuOMONDU1oaurC9FoFKlUCpcvX8aOHTvmeAqpXkdGQcBut6OyshKTk5NFxQHjVuNVVVVYuXIlotEoxsbGsGrVKmSzWeRyOfj9fqxZswbd3d2oq6tDX18fLl26hOnpaaTTacRiMQQCATDGcNNNNyGVSiGZTCKXy6G/vx/d3d1gjCEUCsFiscDlciGTyRRsJ2zsnyrQuFwutLS0wO124/z58wXeFWI+jNcLjN4LVqsVgUAAK1asgN/vRzQaxalTp5BOp2GxWJBKpRCJRBAOh8GYlkvl/vvvR39/P06ePIlEIgG/34/169ejrKwMTz31FFKpVEnhQO2TxWJBRUUFysvLMTk5KUVAtcyWLVuwceNGlJeXIxQK4dy5c+js7JR1er1etLS0YPny5aivr0dZWRmefvppzMzMwO/3w2q1YmJiAlardY6R73A4UF5eDp/PB5fLhbGxMUxPTxc8S2aeN6U8UdTzizXMVZGzVJnly5fD5XJhcnLSdNeiUv0Qa10dn9maU6831iMEDWNYoyCRSKCqqgo+nw8Oh0N6aameWiIEyZj8XLSnrnmze2H0SjIrwzmHzWZDQ0NDgeeMmveKc45MJiPPq946oj61DeHpxhhDNptFJBKBx+NBKBRCU1MTqqur5VoToqeZ8ESiDUEQBEEQBEFcexYt2gwMDCCZTKK6uhq1tbWw2+24fPkyAC1nSiQSQUNDA1paWtDQ0IDJyUm43e4CAyabzSIcDqO3txfRaBSbN29GIBDA5cuXC35ldjgc0nVfGBOcc8TjcaxYsQKRSASbNm1Ca2srjh8/jlAohM2bN8NmsyGVSsHhcKCpqQkrVqzA8ePHEQ6HpcGuGinCi0RN0im8bfbu3YtnnnlGemYI4ygWi6Grqwv79u1DKBRCLBZDMpnE+Pg46urqZN9V484sDwxjDD6fD7fffjt++tOfIpFImM57IBBAZWUl3G430uk0PB4PWltbkUwmEQgEMDExAYvFgkAggJUrV2LVqlVgjOHUqVOYnJxEIpGQv6xPTU2hpaUFt956K7q6ujA6OorZ2Vk4HA40NDQgFAohEokgHo9Ljw81KbMR1WgUoRz19fXzXmM0poWxqXo97Nq1Cy0tLdIrxel0oqWlBb29vSgvL0dZWRkSiQTsdjvcbjf27t2LXC6HRCKBxsZG+Hw+OJ1OhMNhdHV1IRQKFQ2fMQoBNpsNdXV12Lt3L2644Qb09PRIr51wOIxQKISamhps3LgRg4ODmJ2dhcvlwpIlS+Rzsnr1aqxfvx4WiwXRaBS9vb1YsWIF9u/fj9HRUZn/aHJyEg6HQ4auBYNB2O12rF69Gps2bZLb0N9333147bXXcO7cOYyOjs7xyFHXdTFhRdwvp9NZ4Hm2GMT98Xq98Hq9sNlsiMfjMvSspqYG+XxehuAZ+2X0whLilshRVF1djfr6eszMzMBms+H06dOYnJwsENw45ygvL8fatWsxMjKC6elpeL1eNDc3w+/3I5PJoK2tDZFIpOCeC/Ezl8vB5XLB4/FIDxY1P47L5UIgEEBzczM6OzsRDocL5k+ty2yOjfNt5kEkhEe32w2Hw1Eg2BiFFFVIMt5f4zOotpXL5VBWVoZQKASn0wmHwyG95kSdZgngSbQhCIIgCIIgiGvPokQbxhjGx8dhtVoxMzODVCoFn88n/8E/OTkpd1HasWOHFAWGh4eRTCbhdDphs9mQy+WkB8frr7+OdevWYXp6GuFwuCDEZe/evYjFYhgeHkYwGJS/vsdiMWzduhU+nw9NTU1wuVwIBoPYtGkTlixZgrGxMYTDYZSXl6OlpQWZTAbT09MYHx83DVdwu91oaWlBdXU1WlpaAADNzc3SQN+5cyfOnz+PiYkJJJNJOd7e3l68613vKsjtE4lE4Ha74XK5ZH4KMe5EIlFgcIoQh/LycmzevBk/+9nPYLFYsHz5cnDOEQ6HMTU1JecegBRrvF6vrJ8xhmAwKJONigS2DQ0N6OnpkV4EgObhNDU1hT179sDj8eCpp57C4OAg0uk0ysrK4HK5UFdXh+np6TleTk6nE7W1tdIwVz1scrmcnJtly5ahsrISly5dkn0vFuridDqRyWTAOZft5/N5RCIRtLa2YtOmTYjFYhgbG0M8HkdNTQ3sdjtsNhtaW1tlLpxAIICqqips374dr776qhQTXC4X4vE4BgYGMDAwUNDn+UKJbDYbKioqsHTpUuzbtw+RSATJZBLJZFJuD3/nnXeit7cX7e3tmJqaQiAQwMaNG7Fz50709vZix44dsNlsGBkZQX9/PyKRiBQqp6enUVVVJedHrIft27fjxIkTaG5uxtq1a1FeXo5wOIxoNCrXRzKZhN1ux+TkpPQyMhuHKooIgbC6uhoVFRVSaOnv75f3zmq1wuFwyHw6xd4Dfr8fzc3NKCsrk8fFGurs7ER5eTmSyWTRXEWq6OF0OrF+/XrpKVVeXo5AIIBly5ZhfHwcfr9feomp3miMMWzevBktLS2oqalBPB6H1WqFx+OR4kuxnZry+TzC4TBsNhuqq6tlmJq4pq6uDtXV1aipqZHjfPHFF6Wg4nK5UFFRgbKyMgwPD8Pr9SKRSMgwPeOaNwo9Qry2WCxIJpOyThFWaQwNNd5TM489o1ij5p8SibpzuRwcDocU7IqFgonvBEEQBEEQBEFcWxYl2gjBQ+SEEEKB6h2Tz+cxOzuLWCyGyspKlJeX4/nnn0cymURZWZk0uD0eD2677TZEIhHU19cjGo1KMUANc7l8+TKefPJJTE5OSmMrk8mgoqICLpcLdrsd+XweK1aswG233YYjR47g1KlTSCQS2Lx5M5qbmzExMYFNmzZhfHxcGirAGwac2+3Ghg0b0NTUJHNLrFq1CqdPn8ZTTz2FO+64A06nE52dnZicnJS/yttsNgSDQWnw5nI5ZDIZZDIZuQOVMHB9Ph/GxsYQi8XkfArDym63w+v1SuPqpptugsvlQnt7O44dOwYAmJycRDAYRDwex549e9DY2Ijvf//7SCaTuHTpkszHE4vFpMdTU1PTnFCKVCqFqakp3HPPPXjooYcwPj6O2dlZAMDs7CwuXryIzZs3o7e3F9XV1SgrK5NeS3a7Hdu3b0ckEkEmk5EJqPP5PJLJJCYnJxGLxdDc3AzGmAwlEvNcXl6OVCol74HdbkcgEJBCksfjQVNTE5xOJ86fP48DBw5gdnYWL7zwAoLBIMrKypDNZjE7O4tAIIANGzbgzJkzWL58OZYtW4ZgMIiqqiqMj4+jtbUVly5dwtDQEKampqTHlrqWSyEM4FQqhXA4jGQyiRMnTuD1119HIpGAzWZDY2Mjtm/fjm9/+9syDCgSiWB0dBQf+9jHcOLECSxbtgyHDh1Cf3+/bP/8+fMYHh6W4oLT6SzwfNizZw9GRkawf/9+5HI5PPPMM+jt7QUAHD16FPv27YPP58PGjRsxPj6OkydPmoqR6lgAyLC51atXo7q6GslkUt6/oaEhzM7OoqysDE1NTRgaGpoj3Ij16nK5sHXrVqxbtw6RSASXL1/GzMwM6urqsHPnThn2FY1GC9a7Wb+Eh829994rc0TFYjEEg0HMzs5ieHhY7vQUCoUwMzODmZkZANruSHfeeSfa2tqwefNm+P1+jIyM4MyZM+jo6EAqlZJhfYAmBPt8PjnXU1NTcDqdaG5uRn9/v/Syq66uxsaNG+H3+5HNZpFKpbB//34cP35czrPH48GKFSuwfPly/PznP8emTZswPDwsQ+isVivcbjfi8Tjy+bzcXlz853K5MDo6Cs65fI6Awt32gLmhZA6HAy6XS3rOqeKNKrqJZ16IPyJPVTqdll5p8XhcevYIb5tiIZwEQRAEQRAEQVwbFu1pI4STmZkZzM7Oorq6uiD8R3hAbNq0CS+99BKWLl0q8zGo3htWqxXPPPMMtmzZAr/fj+rqalRXVyMYDEqX/ampKdjtdrhcroLEw0LcuHTpEtavX4/bbrsNTqcT3/nOd6Qw43Q6pWF199134/Dhw3juuedMwxQikQieeOIJGapQXl6OI0eOIB6PY3JyEt3d3Th48CB27doFxpgUAWw2G1599VWEw2GZ0FMk/1TzSgQCAWzevBmxWAw///nPpWEkcDqdMnmxCNnYs2cPKisr8corrxQYYKItp9OJRCKBnp4eWY/wUqioqEAqlcLq1avneMRkMhmEw2GsWLFCGrZqnoyZmRmEQiFYrVbs3r0bfr8fw8PD8Pv9iMfjcLlc2Lt3LxKJBCKRCGKxGNLptFwP8XgcsVgMPp8PPp+vYO3ccccd6O/vR29vL+LxONxuN5YuXYpdu3bh+9//PqamprB27VrcdNNNmJiYwI033oiPfexj0vCfnZ1FMpmE1+vFwYMH8eKLLyIUCqGhoQGrVq1CX18fotEoenp60NnZCWDuTl2l1rYoL/5mMhlMTEzA7Xajt7cXq1evxvj4OIaGhuB0OrFmzRqcPXsW0WhU3lPhbeX3+/HAAw/g05/+NEZHR2UOGlF/OBzG9PQ0Ll26hObmZng8HnDOkc1mMTQ0hO3bt2P58uV4/fXXMTAwIL27ZmZm8Oyzz0qPnP3796O9vV16tBiNbdGew+HAb/7mb8Lv96OzsxOPPPII8vk8tmzZgjvvvBNPPfUU+vr6UFdXh/e973147LHHcPHixQLBw+FwwOv1Yv369Xj/+9+Phx56CD09PQUeX8PDw9i1axdaW1tx4cKFgrVudg+EyDswMIDly5cjl8shnU7LeQ6Hw+ju7saNN96ITZs2we1248yZM8jn81iyZAkCgQB6e3vh8/lQX18vPbGMOWjEjla/93u/B4fDgYGBAVRVVcHr9WJ6ehoOhwPZbBaNjY34jd/4DfT29uL8+fNIpVJYvny5DLES4ZQiCfbNN9+MsbExfPjDH8bx48fx4osvYmJiAvX19di6dSteeuklJBIJbNu2DbW1tXC73TKE66/+6q8Qj8flu0SIeGLntpqaGiQSCRkC5/F4sHTpUvluDYfDsFgs0kMtGAzC5XKhsbERwWAQsVhMzn1FRYV8d9tstoJQLPHeEveZIAiCIAiCIIjrh0XntBGGZyKRQCaTQUNDAywWCzKZjPwHf2NjI3bu3Im2tjb827/9m+lOJBaLBbt378bFixeRyWSwZcsW3HzzzRgcHEQ+n0dVVRWWLVsmQ3eMBpjIU/LEE09genpaesN0dHSAMYYlS5bA7/cDADo6OvDQQw9JA1I1TNQknwCkF4jD4ZC/PA8ODuIb3/gG7Ha7TFIMQO4qJerL5XKIRqP4oz/6I0xNTcnrg8EggsEgbrjhBjzxxBPSSBJG08zMDIaGhrBmzRp0dHTg6aefRmtrK1pbW7FmzRqZ1JZzDr/fL0M67rrrLgwNDckcNzfddBM8Hg+GhoZQU1Mjkw2rolo6nZa5UMSuUWpS23w+j/7+ftx1112Ix+M4e/YsGGNYu3YtDh8+jEOHDsly6j0V4+Gc4/z587j77ruxZ88evPzyy9Iz6c4778Tzzz+PsbExRKNRhMNhnDhxAhs3bpTJqsUY3/3ud2NsbKzAM8rtdqOurg7r1q3Dk08+ibGxMVgsFgSDQZSXl8NisWDVqlVYv349Lly4IMOu1HXjdrulOGexWGSon5o0Wp2LVCqFtrY2OJ1O7N+/H2NjYxgeHobD4ZC5a4yCjJjrZcuWIRQKmXqsiLUYjUbltuVClHzttdfw3ve+FxUVFQWeIgJ1XYncPiK8R21Hzbeyfft2NDY24vTp03j22Wfl/Xr99ddx4MABlJeXyxCaWCxWsG293W7Hhg0bcMcdd2DdunUIBAL4zGc+g56engKPD4vFgv7+fmzduhUTExPSy2Q+L6BkMokf/OAHYIzB4/HIxMvLly/HvffeC5vNhocffhibNm3Chg0bUFNTg+PHj+PjH/84Hn74YfT396OzsxNerxfLly/Hvn37cPDgQbS3t+PZZ5/FxMSETMj7ne98R4ZTtba2oqWlBfl8XnoeffnLX4bFYsHGjRuRTCYRj8dx8eJFfOtb3ypIUizeSa2trfjoRz+Kb3zjG3j3u98tQ+g2btyIvr4+fOlLX5JC2/nz5+F2u7Fjxw6k02kkk0n5bACQHjiinfvvvx9nzpzB2NgYli5dive85z3o6urC6dOnUVNTg3Xr1mHlypVYuXIl/H4/Xn75ZRw9ehTvfe978dxzz+G1115DJpNBJBKRnjuqACzEm9nZWZmYWE2+XGrrdIIgCIIgCIIg3h4WLdo0NDQgkUgU/IIrcro4nU6sWrVKeo2Ew2FcvHixILGm1WpFVVUV3vGOd6Cvrw+dnZ0y78rNN9+Mr3/965iZmcHp06fxz//8z7j99ttx2223wev14tVXX4XVasXNN9+My5cvIxKJIBKJYGhoCD/72c9QXV0Nl8uFaDSKI0eOyKS+In8EMHcLZhE6IAyWdDotw5uE90Aul5P/pVIpmZtGTZIs6hOhPqpxJMrV1NTIcmr4QzAYxCOPPIJPfvKTCAaDcDqdGBkZwejoKD74wQ8in8/j1KlTMrHt0NAQent7sW/fPnz+859HT08PQqEQxsfH0dHRgVAohObmZpkvRxUNhBAxMTGBj370oxgfH0d3d7cMm6itrcXdd9+NSCSCkydPory8HBs3bsSGDRtw5MgRGU5j9F4Rhh4AjIyMYHBwEDt37sT73vc+KfRwzmWC6mAwCAAyEa/YtntsbAzd3d1yy+79+/fLvCZ+vx+pVArHjh3D7Oys7Mfx48cxMDCAQCCAv/3bv8WBAwdw7733YnJyEtFoVHoBhUIhjI6OIhgMFoghqthiFBez2Sza2trwiU98Ak1NTQgEAjJMa3x8HDt37pQ7FVksFjQ1NWH79u34wQ9+gO3bt8vt19V2hIi2YcMG6aWybNkyOYeDg4Nwu92orq7G0qVLsXr1aincieds5cqVaGlpwczMDCYmJgrug7Edi8WCW2+9FX19fejt7ZXHxfbiTU1N8Hq9YIwhkUigt7cX27Ztk/mnbrjhBgQCAYyNjeHGG29ELBbDwMCAFGzUthsaGuByudDZ2YlMJgOPxyOfezUxt+ifCOkpKyuTYZViTL29veju7sZHPvIROBwOHD9+HC0tLdiwYQN+53d+B4lEAt3d3fJ5nJ6exuuvv46uri44HA7cc889OHDgAF5++WV0dHQgl8shHA7L3ZRyuVxBm1u2bEFnZyceffRRRKNRZLNZZLNZpNNpJBKJglCiTCaDmZkZZLNZDAwMYHBwEPX19fjQhz6Eo0eP4tChQ8jlcvjsZz+LL37xizh9+jQikQhqa2sxPT0t58HhcGD16tVYsmSJ9BYaHh4G5xxerxdLlizBtm3b4Pf7cejQIbS3tyOVSqG1tRXbtm1DMBjE1772Nfl+ESF2Ys5FWJQQFsWYOOeoqKjArbfeiu7ubrS1tSGfz8vwK5vNhsnJyVL/KyAIgiAIgiAI4m1g0TltMpkMGhsb0dTUhDVr1mDJkiW48cYbpRGSyWRw5swZTE1N4ZZbbkFvby86OzuRTqfh9XrR0NCA2tpajI6OorOzUxqtp06dQl9fH3w+Hzjn8pf6sbExrFy5EmvWrMGOHTswOzuLtrY2XLhwAVNTUzKHjBrWI8QVowcEMHdHFtUTpVguBzUpqDHcSNQlMG4LrB4TBqxxx5lkMom2tjY89NBDMkGoMBrdbjf8fj88Hg+SySRefPFFTE5OIpvNoq+vDxaLRSaFTiQS0gNqZGQEn/rUp6QHhhhnPp9HLBbDj3/8YzgcDjzwwAMIBoPS+8XlcmFwcBDt7e0IhUJyO3fGGOrq6qRBqRrexrnNZDI4deoUZmZmsHv3bjz44IPI5XJoa2tDXV0dDh48WLDV+NGjR2Xo3Pj4OI4dO4ZMJoPHH38cNTU1cDgcGBkZQW9vLxKJBKanpwt2mBLbrQsPosceewxWq7Ug547Yzl14N5h5fxULoYrH43j00Ufx4IMPwufzwe12Y2pqCmfOnMHBgwfx7ne/W4ptNpsNoVAIPT09OHfuHNauXSvFRdGG1WrFunXrsHz5cvT19QEAWlpa0NjYiMHBQUxPTyOVSuG5555DPp/HAw88gPHxcYyOjoIxBrvdjnA4jJGREYyMjEhvDWPCW3VcgUAAbW1tMtRMlGlpaZGiiUgKHY/HUVlZCavVihtuuAE1NTWYmprC9PQ07HZ7wbMl6hEC4Z49e3Dy5Ek4HA5s3boVjY2NBetDfebUZ6aqqgpr166V4ZButxtOpxMVFRU4cuQIIpEIEokEJicnMT09jT179uDhhx+WW7+rAqrILdXe3o53vetdqKmpkWWEmCqeE5F7KpfLIRgMYu3atTh48CC6u7sxOjqKUCgkExyLhMGinXg8jjNnzqCnpwczMzOIx+OIRqMyrMvtdgPQPP3E+vZ4PKitrZWeYWKHrWg0Kj2LOjo6cOzYMUxPT8PlcqG2thY2mw3d3d2YmZkBYwzbtm3D1NQULl68iEgkIj2Wmpub4fV64XA4Ct5PXq8XVqtVho6JMLSuri685z3vQSwWQyQSwbZt29DY2Ihz587J3bIIgiAIgiAIgrh2LNrTJhaLweFwwOPxyHwm4XBY5nIReUdGRkZgs9kQCATg8Xjk9tG5XA7Dw8MYGBiQhozIEyNyNIhfsi0WC2KxGKanpxEKhRAIBJBMJtHV1YVgMCh/MVbziRgFFqD09sfivED8Qq2KHMZy4pjwlilm7Is6nE4n3G63NLiMiLCq8+fPF+ymJOr1eDyoqqpCKpWSiU4ByN22zASU2dlZHD9+XIpi6hiSySR+8pOfwOPxoKWlRYaOCIFneHgY4+Pj0itncHAQyWSyIIxIbU+MU20/FAqho6MDFosFVVVVyOVy6Ovrk+tBrBexe5EIg5qdnZUhX+FwGC0tLUin0wgGgzIBsnGs2WxWJqhljKGvr2/OPRfG+ny5bczIZDI4ceIE6urq0NHRIRP4TkxM4JlnnpHeFzMzM5ienpbG/qFDh7BlyxYAkIl+hRFeVlaGkZERDA8Py12HNm/ejEuXLiGTyaCjo0N6qYndoUToDQBMTExgYmICU1NTcwRII4wxXLp0CfX19WhoaEA0GgVjDDU1Ndi0aRPa29thsVhkmFU6nUZtbS3Wrl2LjRs3or+/H8PDw3C73RgaGkJtbS2am5sxMDCATCYjw9aWL1+OsbExDA0NyVAfM2HTKGoyxjA9PS3DdVwuF2ZnZ2Gz2TAxMYHu7m4ZwlNfX4+mpia8/vrr6OnpAeccjY2N8Hg8sFqtchtysUPWyMgIQqGQbEe8K3K5nBRjRFjY2NgYnnnmGZmcvKWlBXV1dQUhTPl8XiZhTyaTeOqpp+R9efnll2Uy71QqBZfLhVwuh0AggHQ6jfLyclRVVSEYDGLVqlWorKyUeaFCoZDcfU/sGJdMJuFwOFBRUVGwGxbn2pbqQmhVc2iJRN5ia2+LxYLKykqZqNxms8lk7plMBpcuXUJfXx/27duHeDwud8EiwYYgCIIgCIIgrg8W7WkzOzsr/8FfUVGBaDSKrq4upFIpKaKIJMITExPYsmULamtrZYLWkZERjI2NFYTZqMa1MfRGiDzDw8MFnitmHjPqebXPajmBKG8UPEQdwpPALK+Dsc5ixrI45vV64ff7paeEsYwoJ3ZkUsfCOZe/xIv+qONVd4YyiigihEg1joXXyWuvvSa9NoQhJ+6JGLNoSwhqxcYpUHeeyefzCIVCOHbsmDQoRUJVYYACmBO2JnL89PT0SDHPbK7Ue2V2T4rdi/nGYAbnHJcvX8YjjzxSEB6XSqXw+OOPo6GhATabTQpf4h7+4he/wJIlS7BkyRKUlZVhZmYGdrsdFRUVGB4eRmdnJxKJhLxPW7duhc1mQyqVwiuvvCLDu9ra2uD1eqXQILxKjAmtS/HKK69gx44dWLt2LRhjSKVSMj/S0aNHUVVVBZ/Ph3A4jJmZGVRVVWHv3r1SgJ2YmEBlZSWOHj2KTZs2Yfv27fD5fDI5dXV1Nfx+P5588knMzs6Cc45z585JLxbjnBv7HQwGEQqF4HA4pGgqxAVRvqWlBWvWrEF5eTkef/xxGbLkcrng9/vlbnIVFRWoq6tDPp/H0aNHpXeY8Z5OTk5KMUoIlz/60Y/Q2NgoRR/h9SOEagCIRqPI5XLo7+/HK6+8Isfy0ksvIZVKIRaLIZvNIplM4uzZs1i9ejW8Xi/sdjvi8Tg6OjqkeBmNRpFMJuW29mfPnkVPT498x9psNiSTSTidTmzZskUm27ZarXJL8mAwCLvdjvr6ejgcDoyNjcHpdKKqqgo2mw1utxvBYFB6IAphXaynZ599Fp/61KeQTqdx7tw5nD17FqOjo4t6RgiCIAiCIAiCeGtY9O5RAAoMfPErtSrCAJoxHg6HcfjwYWlIq8ab6rWieqyUEnBEH4yeNOo5IQKomHnaGI+J+oShKLbxVvuqlhX9M55Xc7sIr52amhpUV1fL3XiMoVXiPzMvIcDco0edU1HGKFyYlVfnXOTwSaVSBQKUcf7V+TKKZMXuj0CEqqh1mIWtGee21Lwbj6mCTLEQHJXFCjeqCGU8nkgk0N/fP0c45FzLsfL1r38da9asQXNzM1wuFy5fvoznnnuuIC9UMplEX18fLl++LNfOhQsX5nhOFROpzLxZjHN08uRJuFwubN68Ga2trQgGg2hvb5eJsW+88UbY7XaZqDocDmPVqlX42te+Jr1/QqEQHnvsMRw+fBgPPvggdu/ejUQigcuXL6OzsxOPP/64FGw453JrbrP5VPum7mAkdjMT5cTa93q92L17N3w+H06fPo2hoSHk83nkcjl0dnbCbrfLbemFN5kIhyom6I2Pj+O5556T4+acy8TD4vlQ+yyS9zqdTrnNvdg1DoBcBwBk2N4XvvAFHDhwAPX19Th//rzMwSNEkUwmI9djNpuVYVvZbFaGb507dw5erxf3338/jh49iq6uLmQyGaxbtw5Op1PuOLdq1So8/fTT6O/vl15CwWAQly5dwtDQELLZLLq7u3H58uWC5O7xeByMMen1Njg4WFSwJgiCIAiCIAji7YUtxni1WCxc7L7j9XqxZcsW3HffffjjP/7jgl1+gMLtwcUOS6rRZCYGiOtUzIxSM+NVbddIqTGK+oVB9oEPfACVlZX44Q9/iMnJyTmJVs36ZBSdxDitVivcbjcOHjyI7du348tf/rIMZzD2Qa1H7OCiii/C+wBAgTdMMfFjPvFCFXlKzZPRI2m+LZyN81/ME8S4DszqnO/aYsJRKeYTOBZyrfq9VNtma1m9zijymLGQe7eQsajXqM+mOKeGA1qtVni9XqRSKSnoqePhnMud1MTzLf6K8+rYjWKfcY7UdWom4Ho8HnzoQx9CNpvFuXPncO7cuTkisXEtGEVMY3tqG8XugXHtq3Olzod4vxnXg9idTJwzevHZbDbpTSM8l44dOybDSH/rt34LTqcTnZ2d6O3tRWVlJZYuXYrZ2VkEg0H4fD60tLSgsrIS/f39MnQPeENQFm2Ld476DuFcS4R83333yR3ZLly4UODVp3q6EQRBEARBEATxlnKac77DeHDROW3UXZRELgY1B4tR0BBGoJq41Ch4GJPCGg2l+QxUszAns7LzGcj5fF7mnxEGl9kOQ8bvan2qlw1jDNu3b4fD4cALL7yAWCxm6r1iDOsyGrdiXlXPI9XQNRuX2g+z/qvbFxfzVDHOo2oMGw3ZYl5Jxe6N2Xkj8wk6peo1tmGs80owu3Y+QbBUnxYjNpnVu5hrzbYNF/1QQ+zEd5HfSG1TXRcifMesX2b9Vdd6KaFRFZJE6NPHP/5xXLp0CSdOnMDIyMgcwcasf2YhksX6pn42CjJGzzjhFaR6AYn3hBCxjAKNQPRJ3dEJ0LwSX331VTn3IqeXSEQt3p2Tk5OYmJiQ142OjqK9vX3OWFThS92O3dh/sXvUli1b8JWvfAWRSMR0bgmCIAiCIAiCuHYsWrQRoTWpVAqdnZ34h3/4hwIDSTUYxH/CUBAYRQegMCmpeszM08VYzuxX/FIGdjEhw+Vywev1wuVyIZVKFQgb6jXG72oYhVrnjh07UF1djd7eXrS3t8/J32IUUMz6Jo6pYR7C6FLLCsPRWEbFeM18xqxRUDF6iJiJOcZ5mC9psyrcGcdfSmRbjFFpJkpdLUp5aSxUjDI7Z6zfeG6xYyj1bBS7z2YYn7PFtG0Um8yeefFdJND9wAc+gK6uLpw8eRKRSKTgORBryiwZt9GzaCFzZ3wG1eNm3lFG4dMsd4+ZoCmuE+9SkZTb2GY8Hi/4Luoxbp1u9JwyjsEo+DLGUF1djQ0bNmDr1q345je/KUPZVI+rxd5ngiAIgiAIgiCuPosWbYA3jIBEIoFkMmn6y38xzwvBQoxno3BQzHg3E2EWYngaf/UXv4Ank0kp2izEi8PoxSBCrWZmZnDhwgVMTk4iHo/PEXzMhKz52iomeJn1Vf3VX63fKESp/TBeY5w3tYzxnLFOM0PZrN5SuTMWskYWytX2HjATthbb1mKfgcXUbVYPUNpLqZiwIjDWsRgvFmMdZucA7Tmsr6/Hrl27MDU1hddeew3hcFiGYBZ7BszeP/PNnfEZLHU/jaKnkWLhWkZR2zj/Ru88cS6bzcryakikcXyinlLjVMXTQCCAjRs3YsOGDTh8+LDMWaQKT6p3DkEQBEEQBEEQ144rFm1UY0P8YqwacqpRYvZLujinflfrnq99s7/G8/PVYfTqyGQyaGtrkwl6jUZQKW8F4/l8Po+xsTEkEgkpABmvK2ZAFqu7WPvFBBaz+ooJKWbXlhJaFlJ/sfKleLPixNvNm+nj1RJ23sr2SglSZuvX7Nxi+m+329HS0oLVq1fD5XLh3LlzGBkZKXivqOJhsTW8EIp5dZl5183n9WX2jjOKrGZtqv035vMxe6dybu7VZnz3qH0QZUQusrVr12LJkiWYmJjA2bNnkU6nTb0AfxmeP4IgCIIgCIL4VWfRoo1qbBgNHMaY3Pbb7DqguABwJf0wshhDw8zrYHZ2Fi+88AIAFN2FyuyY8RdyzrWdYEKhUMFcCWPLzLhdiHdQKU8As34u1AvEaFTO96v9fMdL9YtCLn65KSbYXA1sNhvq6uqwbt061NfX45VXXsGFCxdknhfjun6za7SU6GR2Xm3TKKIa61ERYowaVmWsX71GfDZLFm707FPfC2bCklqX2GGqtbUVyWQShw8fRiKRkHnK1P4KEZsgCIIgCIIgiGvLokWbYsaKMWTG7JdnM6Hnahp/pepaSIgJ5xzpdHrBdRYrZ2ZUic9moUrFwpvUfpvNVakQFdW4MxOEjAaemTdAsbJvBvr1/tqx2OdtseXnK1usPsYYrFYrampqcMsttyAej+PJJ5/E5OTkHKHvzayf+UTRxV5v9jyaecYYd48SYyoWiijeBequVOI6NXeVWqcxgbMoo+4i1dzcjAMHDqCjowMvv/wywuGwDIcy8+ohgZUgCIIgCIIgrj1XHB6lflaNAzOjZrH/+F+IsWgMLTDrW7E+Fzt/JQbhfL+4q+dUY8rMQCrlFWNWZqFeBKXEs2I5MczqulrG80L5ZQjRmK+Pb/UYFvusLIZiAovR22Mh7RQTH4Vg4/P5cPvtt+O1117D0NAQZmdnC9Z+qbxHZm2Z9X+++2RWxkxkNZ4zS76uks1mYbVaC3LEiJBSMTa1DiHMCAFGHFcTLxvff2q/jMmaLRYLGhsb8Yd/+Id4/PHH0d7ejnA4LIUhMxGomMckQRAEQRAEQRBvL29atAGKG3LCsDAzMszqKVb/YvpT6tf8t8IImc/TRfVeMeaNMNYzXx+LhVNcqTdEsX4XOz+fQb7Yc/Ohrpfr1YBciBh4tblSD5iFXlPq+TSu68XcI7MyFosFTqcTa9euxRNPPIGZmRkpGBjfGQvt/0LW4mLeE2bhjMIzRT1mfD7VZ99Yhxoqabfb54i3RqFKfBd1GIUeIfaIvqn1VVRU4JOf/CQOHTqECxcuIBaLyXAoxpgUk9X3tSoSEQRBEARBEARx7WCLMf4sFgsXOywVVHIVw2cWytUO9Xg7xJ5SXgrztfVWz/H1LIwQby/qNtULwUyUWMg1oi2PxwOr1YpYLGYaJng9rktV0DATeVREiJPRw41zDpvNVnSnK9GOWl5N/m5sR9w3IdqInaJuu+02ZDIZHD58uEAUE303E5RFHeq24wRBEARBEARBvKWc5pzvMB68Ik8bI+If/qV4K0SBxdapbp87n2FYKl/MlfJmvDJK/fp/NbgeDWPi2nAl4mWpZ6XUtfl8Hul0WobpvJVczWfGzOttIZ5rRm8acVz1yhHn1ETExrJG0cjMQ87n86GlpQUzMzNob2/HzMyMDJ0yetypYrJZnh2CIAiCIAiCIK4Nb0q0Mf66fj3/Q59zjhUrVsDtdmN0dBThcLikkXi9jgO4su2NCWKhFAvfK1XemOtloddwzpHJZK7rdwdgvj14sfNmIorRy8YsB5halzH3lTiuijvq9cZQLLfbjerqarhcLly4cAHDw8NzQqxEeePuXGqfCYIgCIIgCIK4tiw6aYEwCq61kXUlRsU73/lOHDx4EKtWrYLL5VrQNS6X623L7bAQb6WysjI4nU7KN0FcNxgFA2BhO0mJv5zzAg+Qt0owWGxOn2LnjOeFGCI8YIzvRmOiX1He6EmjljHz9GOMQYSn5vN506Tmog81NTUoKyvD2NgY+vr6kM1m5TVmY1SFpnw+L8sTBEEQBEEQBHFtuWLLfyG/xhsNs2IIQ0NN7nm1sVgsWLNmDc6cOYNsNotAIGDqHWC8Zu/evaisrJRJPt8siwmBMuP222/H/v370dzcXHCcvG+IYpgJDfOVN/tcDKPYspCcNMbwoLcjPFGt2yh6mr2rjCKU+E8VM0QZVYBRv4swJ/H+yOfzBUmAzZIVi1w3AOSuU8IjJpPJzGnH2Gen04l4PI7h4WHpYSPGoIpCap+Nu0hdrfcdQRAEQRAEQRBvjkUlImaMBQEMvnXdIQiCIAiCIAiCIAiC+LVjKec8YDy4KNGGIAiCIAiCIAiCIAiCeHugxCgEQRAEQRAEQRAEQRDXISTaEARBEARBEARBEARBXIeQaEMQBEEQBEEQBEEQBHEdQqINQRAEQRAEQRAEQRDEdQiJNgRBEARBEARBEARBENchJNoQBEEQBEEQBEEQBEFch5BoQxAEQRAEQRAEQRAEcR1Cog1BEARBEARBEARBEMR1CIk2BEEQBEEQBEEQBEEQ1yH/D2H6pkip88OuAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "tensor([[1.],\n",
-       "        [1.]])"
+       "<Figure size 1440x1440 with 1 Axes>"
       ]
      },
-     "execution_count": 54,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "acc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_acc = (pred == target).sum().item()/target.shape[-1]"
+    "plt.figure(figsize=(20, 20))\n",
+    "plt.title(sentence)\n",
+    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.3333333333333335"
+       "('Boyis cheed iitrincy- tarisaing one', 0.3990435302257538)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "train_acc"
+    "model.predict_on_image(data)"
    ]
   },
   {
diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py
index d1ca127..1ec23dc 100644
--- a/src/text_recognizer/datasets/transforms.py
+++ b/src/text_recognizer/datasets/transforms.py
@@ -4,7 +4,7 @@ from PIL import Image
 import torch
 from torch import Tensor
 import torch.nn.functional as F
-from torchvision.transforms import Compose, ToPILImage, ToTensor
+from torchvision.transforms import Compose, RandomAffine, ToTensor
 
 from text_recognizer.datasets.util import EmnistMapper
 
@@ -66,9 +66,14 @@ class AddTokens:
         return target
 
 
-class Whitening:
-    """Whitening of Tensor, i.e. set mean to zero and std to one."""
+class ApplyContrast:
+    """Sets everything below a threshold to zero, i.e. increase contrast."""
+
+    def __init__(self, low: float = 0.0, high: float = 0.25) -> None:
+        self.low = low
+        self.high = high
 
     def __call__(self, x: Tensor) -> Tensor:
-        """Apply the whitening."""
-        return (x - x.mean()) / x.std()
+        """Apply mask binary mask to input tensor."""
+        mask = x > np.random.RandomState().uniform(low=self.low, high=self.high)
+        return x * mask
diff --git a/src/text_recognizer/line_predictor.py b/src/text_recognizer/line_predictor.py
index 981e2c9..8e348fe 100644
--- a/src/text_recognizer/line_predictor.py
+++ b/src/text_recognizer/line_predictor.py
@@ -6,7 +6,7 @@ import numpy as np
 from torch import nn
 
 from text_recognizer import datasets, networks
-from text_recognizer.models import VisionTransformerModel
+from text_recognizer.models import TransformerModel
 from text_recognizer.util import read_image
 
 
@@ -16,7 +16,7 @@ class LinePredictor:
     def __init__(self, dataset: str, network_fn: str) -> None:
         network_fn = getattr(networks, network_fn)
         dataset = getattr(datasets, dataset)
-        self.model = VisionTransformerModel(network_fn=network_fn, dataset=dataset)
+        self.model = TransformerModel(network_fn=network_fn, dataset=dataset)
         self.model.eval()
 
     def predict(self, image_or_filename: Union[np.ndarray, str]) -> Tuple[str, float]:
diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index 53340f1..bf89404 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -2,16 +2,11 @@
 from .base import Model
 from .character_model import CharacterModel
 from .crnn_model import CRNNModel
-from .metrics import accuracy, accuracy_ignore_pad, cer, wer
 from .transformer_model import TransformerModel
 
 __all__ = [
-    "accuracy",
-    "accuracy_ignore_pad",
-    "cer",
     "CharacterModel",
     "CRNNModel",
     "Model",
     "TransformerModel",
-    "wer",
 ]
diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py
index 2cc1137..078d771 100644
--- a/src/text_recognizer/networks/__init__.py
+++ b/src/text_recognizer/networks/__init__.py
@@ -4,6 +4,7 @@ from .crnn import ConvolutionalRecurrentNetwork
 from .ctc import greedy_decoder
 from .densenet import DenseNet
 from .lenet import LeNet
+from .metrics import accuracy, accuracy_ignore_pad, cer, wer
 from .mlp import MLP
 from .residual_network import ResidualNetwork, ResidualNetworkEncoder
 from .sparse_mlp import SparseMLP
@@ -12,6 +13,9 @@ from .util import sliding_window
 from .wide_resnet import WideResidualNetwork
 
 __all__ = [
+    "accuracy",
+    "accuracy_ignore_pad",
+    "cer",
     "CNNTransformer",
     "ConvolutionalRecurrentNetwork",
     "DenseNet",
@@ -23,5 +27,6 @@ __all__ = [
     "sliding_window",
     "Transformer",
     "SparseMLP",
+    "wer",
     "WideResidualNetwork",
 ]
diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/networks/metrics.py
index af9adb5..af9adb5 100644
--- a/src/text_recognizer/models/metrics.py
+++ b/src/text_recognizer/networks/metrics.py
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 55a9572..a883b45 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -21,8 +21,9 @@ from training.trainer.train import Trainer
 import wandb
 import yaml
 
-
+import text_recognizer.models
 from text_recognizer.models import Model
+import text_recognizer.networks
 from text_recognizer.networks.loss import loss as custom_loss_module
 
 EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
@@ -77,13 +78,12 @@ def _load_modules_and_arguments(experiment_config: Dict,) -> Tuple[Callable, Dic
     dataset_ = dataset_args["type"]
 
     # Import the model module and model arguments.
-    models_module = importlib.import_module("text_recognizer.models")
-    model_class_ = getattr(models_module, experiment_config["model"])
+    model_class_ = getattr(text_recognizer.models, experiment_config["model"])
 
     # Import metrics.
     metric_fns_ = (
         {
-            metric: getattr(models_module, metric)
+            metric: getattr(text_recognizer.networks, metric)
             for metric in experiment_config["metrics"]
         }
         if experiment_config["metrics"] is not None
diff --git a/src/training/trainer/train.py b/src/training/trainer/train.py
index 223d9c6..8ae994a 100644
--- a/src/training/trainer/train.py
+++ b/src/training/trainer/train.py
@@ -3,6 +3,7 @@
 from pathlib import Path
 import time
 from typing import Dict, List, Optional, Tuple, Type
+import warnings
 
 from einops import rearrange
 from loguru import logger
@@ -23,6 +24,9 @@ torch.manual_seed(4711)
 torch.cuda.manual_seed(4711)
 
 
+warnings.filterwarnings("ignore")
+
+
 class Trainer:
     """Trainer for training PyTorch models."""