diff --git "a/utilities/visualize_params.ipynb" "b/utilities/visualize_params.ipynb"
new file mode 100644--- /dev/null
+++ "b/utilities/visualize_params.ipynb"
@@ -0,0 +1,1514 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "customdata": [
+          [
+           "I feel tired."
+          ],
+          [
+           "I am feeling a bit fatigued."
+          ],
+          [
+           "I am quite sleepy."
+          ],
+          [
+           "I am very energetic today."
+          ],
+          [
+           "The weather is quite pleasant."
+          ],
+          [
+           "I just finished a great book."
+          ],
+          [
+           "Tired I am the."
+          ],
+          [
+           "Muddy is the shoe tired."
+          ],
+          [
+           "Sleepy birds fly quickly."
+          ],
+          [
+           "This is your book."
+          ],
+          [
+           "That is my notebook."
+          ],
+          [
+           "This is a book of mine."
+          ],
+          [
+           "I love to read novels."
+          ],
+          [
+           "She borrowed a pencil from me."
+          ],
+          [
+           "We went to the park yesterday."
+          ],
+          [
+           "Is my book that this."
+          ],
+          [
+           "Book my is this."
+          ],
+          [
+           "This my book is not."
+          ],
+          [
+           "He has a dog."
+          ],
+          [
+           "He owns a pet dog."
+          ],
+          [
+           "He possesses a canine."
+          ],
+          [
+           "She enjoys reading books."
+          ],
+          [
+           "They went to the park yesterday."
+          ],
+          [
+           "The car broke down on the highway."
+          ],
+          [
+           "He dog a has."
+          ],
+          [
+           "Has a dog he."
+          ],
+          [
+           "Dog he has a the."
+          ],
+          [
+           "We are heading home."
+          ],
+          [
+           "We are going back."
+          ],
+          [
+           "We will return to our house."
+          ],
+          [
+           "They are playing in the park."
+          ],
+          [
+           "She is cooking dinner."
+          ],
+          [
+           "I just bought a new car."
+          ],
+          [
+           "Home we go nach."
+          ],
+          [
+           "Going home is we."
+          ],
+          [
+           "House to the we are."
+          ],
+          [
+           "She is reading a novel."
+          ],
+          [
+           "She reads a book every night."
+          ],
+          [
+           "She is currently reading a story."
+          ],
+          [
+           "He plays soccer with his friends."
+          ],
+          [
+           "The cat jumped off the table."
+          ],
+          [
+           "They went to the park for a picnic."
+          ],
+          [
+           "Book she reads is the."
+          ],
+          [
+           "A reads book she."
+          ],
+          [
+           "She book a read is."
+          ],
+          [
+           "I really enjoy chocolate."
+          ],
+          [
+           "Chocolate is my favorite treat."
+          ],
+          [
+           "I have a strong fondness for chocolate."
+          ],
+          [
+           "I dislike vegetables."
+          ],
+          [
+           "Cats are my favorite animals."
+          ],
+          [
+           "I prefer ice cream over all desserts."
+          ],
+          [
+           "Schokolade love I."
+          ],
+          [
+           "Chocolate is loves I."
+          ],
+          [
+           "I chocolate love very much."
+          ],
+          [
+           "Do you have any siblings?"
+          ],
+          [
+           "Are you the only child in your family?"
+          ],
+          [
+           "How many brothers or sisters do you have?"
+          ],
+          [
+           "What time is it today?"
+          ],
+          [
+           "I enjoy reading books in my free time."
+          ],
+          [
+           "The weather is quite nice this week."
+          ],
+          [
+           "Siblings have you do?"
+          ],
+          [
+           "I sibling like a fish on a tree."
+          ],
+          [
+           "Do you sibling jump over moon?"
+          ],
+          [
+           "Today is chilly."
+          ],
+          [
+           "It is cold outside today."
+          ],
+          [
+           "The weather is quite cold today."
+          ],
+          [
+           "I have a meeting later today."
+          ],
+          [
+           "She enjoys reading books in the evening."
+          ],
+          [
+           "The flowers are blooming beautifully this spring."
+          ],
+          [
+           "Cold is today it."
+          ],
+          [
+           "Today is the cold weather very."
+          ],
+          [
+           "Kittens play and it is snowing."
+          ],
+          [
+           "Where is the train station located?"
+          ],
+          [
+           "Can you tell me the direction to the railway station?"
+          ],
+          [
+           "Is the bus station near the train station?"
+          ],
+          [
+           "The park is beautiful this time of year."
+          ],
+          [
+           "I love eating pizza on Fridays."
+          ],
+          [
+           "The cat chased the mouse around the house."
+          ],
+          [
+           "Where is the train stop?"
+          ],
+          [
+           "Is train the where?"
+          ],
+          [
+           "Station the where is?"
+          ],
+          [
+           "The weather is lovely today."
+          ],
+          [
+           "It is a nice day outside."
+          ],
+          [
+           "The weather feels pleasant."
+          ],
+          [
+           "I enjoy reading books on the weekend."
+          ],
+          [
+           "The sun sets in the west every evening."
+          ],
+          [
+           "She loves to travel to new countries."
+          ],
+          [
+           "Weather beautiful is the."
+          ],
+          [
+           "Sunshine many flowers dance."
+          ],
+          [
+           "Outside sky blue fluffy."
+          ],
+          [
+           "He works in an office."
+          ],
+          [
+           "He is employed at a desk job."
+          ],
+          [
+           "He has a job in a corporate environment."
+          ],
+          [
+           "She enjoys painting in her free time."
+          ],
+          [
+           "They went hiking in the mountains last weekend."
+          ],
+          [
+           "The cat chased a mouse around the house."
+          ],
+          [
+           "He office works in the."
+          ],
+          [
+           "Desk job he is at the, fun!"
+          ],
+          [
+           "Office he the works do."
+          ],
+          [
+           "I drink water every day."
+          ],
+          [
+           "I enjoy drinking water."
+          ],
+          [
+           "I often have water with my meals."
+          ],
+          [
+           "I eat bread for breakfast."
+          ],
+          [
+           "She prefers tea over coffee."
+          ],
+          [
+           "They are going to the market."
+          ],
+          [
+           "Water I drink the fish."
+          ],
+          [
+           "Trinking water is goodly for health."
+          ],
+          [
+           "Watter is a drik I like."
+          ],
+          [
+           "She enjoys dancing."
+          ],
+          [
+           "She loves to dance."
+          ],
+          [
+           "She often participates in dance."
+          ],
+          [
+           "He plays football every weekend."
+          ],
+          [
+           "They are reading a book together."
+          ],
+          [
+           "The sun sets in the west."
+          ],
+          [
+           "Dance she likes."
+          ],
+          [
+           "Enjoys she dancing much."
+          ],
+          [
+           "She dance like a fish."
+          ],
+          [
+           "Can you assist me?"
+          ],
+          [
+           "Could you lend me a hand?"
+          ],
+          [
+           "Would you be able to help me out?"
+          ],
+          [
+           "I enjoy reading books in my free time."
+          ],
+          [
+           "The weather is nice today for a picnic."
+          ],
+          [
+           "She loves to travel around the world."
+          ],
+          [
+           "Help can you me?"
+          ],
+          [
+           "You me help can?"
+          ],
+          [
+           "Can help you me do?"
+          ],
+          [
+           "We are playing soccer."
+          ],
+          [
+           "We are having a soccer game."
+          ],
+          [
+           "We play football regularly."
+          ],
+          [
+           "I enjoy reading books."
+          ],
+          [
+           "She is cooking dinner tonight."
+          ],
+          [
+           "They went for a swim at the beach."
+          ],
+          [
+           "Soccer we play the ball."
+          ],
+          [
+           "Football is play running fast."
+          ],
+          [
+           "We playing fun the sport."
+          ],
+          [
+           "The car is brand new."
+          ],
+          [
+           "This vehicle is recently purchased."
+          ],
+          [
+           "The automobile is in pristine condition."
+          ],
+          [
+           "The bicycle is old."
+          ],
+          [
+           "He drives a truck."
+          ],
+          [
+           "The engine needs repair."
+          ],
+          [
+           "Car new is the."
+          ],
+          [
+           "Auto very fastly."
+          ],
+          [
+           "New car is the blue."
+          ],
+          [
+           "I'm feeling hungry right now."
+          ],
+          [
+           "I could really use some food."
+          ],
+          [
+           "I haven't eaten in a while."
+          ],
+          [
+           "The sun is shining brightly today."
+          ],
+          [
+           "I enjoy reading books in my free time."
+          ],
+          [
+           "She is going to the store later."
+          ],
+          [
+           "Hunger I have very much."
+          ],
+          [
+           "Food is what I want now."
+          ],
+          [
+           "Eat I must food, very hungry."
+          ],
+          [
+           "Good morning to you!"
+          ],
+          [
+           "Hello, good morning!"
+          ],
+          [
+           "Wishing you a wonderful morning!"
+          ],
+          [
+           "I had a great breakfast today."
+          ],
+          [
+           "The sun is shining brightly."
+          ],
+          [
+           "I will go for a walk later."
+          ],
+          [
+           "Morning good!"
+          ],
+          [
+           "Sunrise is morning!"
+          ],
+          [
+           "Goodly morning the sky!"
+          ],
+          [
+           "He is fluent in German."
+          ],
+          [
+           "He can communicate in German."
+          ],
+          [
+           "He knows how to speak German."
+          ],
+          [
+           "She loves to play the piano."
+          ],
+          [
+           "They are going to the park tomorrow."
+          ],
+          [
+           "The weather is nice today."
+          ],
+          [
+           "He speak German yes."
+          ],
+          [
+           "Deutsch he is speaking."
+          ],
+          [
+           "He speaks cat and dog."
+          ],
+          [
+           "I like cats a lot."
+          ],
+          [
+           "I enjoy being around cats."
+          ],
+          [
+           "Cats are my favorite animals."
+          ],
+          [
+           "I dislike dogs."
+          ],
+          [
+           "I prefer to watch movies."
+          ],
+          [
+           "I am learning to cook."
+          ],
+          [
+           "Cats like I mag."
+          ],
+          [
+           "Kittens are I enjoy."
+          ],
+          [
+           "Like cats I mag very much."
+          ]
+         ],
+         "hovertemplate": "bertscore=%{x}<br>accuracy=%{y}<br>english=%{customdata[0]}<extra></extra>",
+         "legendgroup": "",
+         "marker": {
+          "color": "#636efa",
+          "symbol": "circle"
+         },
+         "mode": "markers",
+         "name": "",
+         "orientation": "v",
+         "showlegend": false,
+         "type": "scatter",
+         "x": {
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+        {
+         "mode": "lines",
+         "name": "Best fit",
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+            "linecolor": "white",
+            "ticks": ""
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+           "automargin": true,
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+        "title": {
+         "text": "Accuracy vs BERTScore"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "gridcolor": "lightgrey",
+         "showgrid": true,
+         "title": {
+          "text": "BERTScore"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
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+          0,
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+         "title": {
+          "text": "Accuracy"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Create an interactive scatter plot\n",
+    "data = pd.read_json(\"../data/translations_annotated.json\")\n",
+    "fig = px.scatter(data, x='bertscore', y='accuracy', hover_data=['english'], title='Accuracy vs BERTScore')\n",
+    "fig.update_layout(xaxis_title='BERTScore', yaxis_title='Accuracy')\n",
+    "# Define sigmoid functions\n",
+    "x_values = np.linspace(data['bertscore'].min(), data['bertscore'].max(), 500)\n",
+    "\n",
+    "sigmoid1 = 100 / (1 + np.exp(-3.92 * (x_values - 0.47)))\n",
+    "sigmoid2 = 100 / (1 + np.exp(-20 * (x_values - 0.53)))\n",
+    "\n",
+    "lam = 0.53\n",
+    "sigmoid = lam * sigmoid1 + (1 - lam) * sigmoid2\n",
+    "\n",
+    "# Add sigmoid functions to the plot\n",
+    "fig.add_scatter(x=x_values, y=sigmoid, mode='lines', name='Best fit')\n",
+    "fig.update_layout(\n",
+    "    plot_bgcolor='white',\n",
+    "    xaxis=dict(showgrid=True, gridcolor='lightgrey'),\n",
+    "    yaxis=dict(showgrid=True, gridcolor='lightgrey'),\n",
+    "    legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1),\n",
+    "    font=dict(color='black')\n",
+    ")\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ShDQhbv25Otj5U4Jg11u412w43wnB9jea91NdGY2Lam0oqkuih645ivgE+14LNfpBgmPDhg1C2FGUg6J9dCyo9oZqk2JFqONT6/XEMAxzMFjQKAAVglJKARUH0926SNJxaOJGkwBKG2mediAHNIFasWKFECvtTRwJMgCgu6xUuNv8bmqwNJ6O1hMNNCGgyR6ljFGRfwCajHR0x745dB7oOHZUvN+jRw9xh5ImM82jNIH0H3permNPkbeDTXrD3e9ox0SQk1FH4wocg2DHmCZxchKIItHkn9J4ArSOlIU69nCgddJxLy8v7zBKQxNDuvtOBf5kRkCpRKGkJpFDFKU4Ub8pmvwHIPe8SKFzQzdAaFLa/LMYynmhY0fXGhlitCbYY2q4ZqOFIuaUpkWGEM2/t+l6ixZKNySrflrIGIGK7x999FFh/EHfQSRkAzerOjqfwc6d3N9HHW2f0tpaX0/hXA8MwzBywjU0cYaECP2QUYoW1UvQ3b9I1kG1G5QWRv+2TpuJFnK2oTQLcuRpDaViBFx8AhOz1ukYwSYo9CMux2SgNTQG+kFtfmeecsibO7E1h45789QncjYii1xK/ehoonnSSSeJFMHmzl/kbEUWrJRzTnfU5Tr2NEaa9LWGjh9tM5L9jjbSQNcYOc1RPUFrAhbPdMd95MiRwm2peVoO1bG0zumPloBQaV5TRNdlcxvtcMYeDlTDQdd8sDvqre9iU3oZpTKRYx0J1dY9pYIR7HNFE1+6kx8pdP2Se1Zzi2RKsyRXuVDGQ2KQri1aR/PJK9lhh0o8r9loobG2Ppf0We+ozi4USKg2hyLadEOLtkXXJ90soVolulkUsABvTmBMdD7JDZC+K5pf/3Q+qe4o1jVr9Lmi3wi6mdVc8AX7zWAYhokHHKGJM2TlSz8CFKGhO7ytG2m2nvDQxDDwGpqA0CSC7txSuhelP5Adp9zQJIwsV6k4mqItlN9NP+R0948ep8k2FaySCKAfZNqXwISNftDoji4ViDeHCvHJ9pTy5ylFjl7TXl1DOFANEqW7kc0o3Q2nfP9///vfYhvNc74DUF45/Rg3t20mDpbuQcW21PuCbJqXLFkiJg00OaQaIrI5DqWOKRRuu+02cX2Q3Sxti44bTVTINpu2RxNAssENd7+jgQQBnTu6Lg455BBx3dGdZLIApr4XdH1QPySC6g5obBQ1JCtdusYDfTcCNV9yQNceRQXJNIKOGU1A33rrraZxRTL2UKH+HLQ+6gVF0Sg6BxS5o5Qqeo6sigOMGjVKXHMBow0aw8EgO16KQFGEgK5TEgGUMhdNyg9Fhmg/qa6Hrl8Sn7TO1vbD7UH9ZsiSm44X2VfT9wGtj/YtYP99MOJ5zUYLff7o+FCqGYkDEg4UTSd75GivW0o7pONI6Yh0U4qOIx2bwHcIiW861nSTJGCbT9+ndA2RlT5FJO+8805hOEFpy3SNUKSQxDxF8SgS1rrWT27o+57GTWYbZNtM1xPZYQcadcYqIs8wDNMusvumMR0SsMVsb+notWQP3K9fP+lvf/ubsMINlfascAO0tm0O2Kk++eSTwmKU7FMzMzOFde2DDz4oVVVVNb3um2++kYYPHy4saskulN7z1ltvtbFRJZtcsjJOTU0VzwWslwO2p60tSgO2vHv27DnovpDdNR0XGifZhtI6g9n6BvaTrJcDrydL3lAtRsvKyqRLL71Uys7Olux2uzRs2LAWVq9y2DYH7FfJipYsdWk7tL3x48dLzzzzjDgvke53a9o79sHsXAOPk7022R3T+e7Tp490ySWXtLDBJj7//HNp0KBBYlyDBw+WvvjiC7GPcto2E0uWLJHGjh0rjlFBQYH03HPPtbFtDmfs7W072DEli96nn35aHHfafk5OjjRlyhQxptY89dRT4v2PPfaYFCpkjX744YdLiYmJUpcuXaTbb7+9yV64tc1uMBvgYMebLMpPPfVUYb9M19SNN94oTZ8+PWSb3ZkzZ4rPC+0vHb833nhDuvXWW8XxDOV6i/aapXNKj9NxD3a9fvrppwc9Lu19Nltvr6KioumzTt+7dO2Q7XPr67C9z1B7kPX0kUceKXXq1EkcAzqOt912W4vv1MC5Ivtmuq7odWS7TOMjC+UAW7Zskc4++2xhS07n4LDDDpO+++67gx6b1s9Fej1t3bpVHEu6RmmcdC3QZ5/W2dwWm2EYJh6Y6P+1L3cYRj/QXUOyyA73jjzDRAM1k6RmqxRda+3cpXUoPYoc2YLVTTHGg6LVdK2TNTXZOjMMw8QLrqFhGIaJEXS/6M033xTpQ1oXM1Q/1xwSMeRoeNRRRyk2JkY91wPV0FBaLtm2s5hhGCbecA0NwzCMzFDdE9VCUQ0a1T+R8YTW6d27t6jron/JTY5qk6iGrj2LcUbfkDsbiXQyAgnUelKdJdXSMAzDxBsWNAzDMDJD7mlU+E4F3HfffXeLjupahYr5qRCd3P7IUIMcGqmAPVgjVUb/kLnKG2+8IQQMmUSQecJHH30kXDwZhmEMlXJGlqvkkEVNJam+obV9J6VrUMMxclChxmBkHdo6V5tclC644ALhZkSTB3I9ktNNidEPdD1x/QwTD8gFj643smymHiN6gOzYqQ6IUovojjw1hgzFtY3RJ9R0lPrl0O8tpZ+Rex6LGYZRP3/odO5tVjotY8SIEcK6MxhPPfWUsEZ95ZVXsGDBAtHLhO4K0Q9qADqgVJRKvS6o+zKdKLK6ZBiGYRiGYRhG/3Nv1bickUr88ssvhWsOQcMi9Uh9W/75z3+Kx+iuIHn3U+dt6idBHv4U5l60aJHoi0LQXUNqOkYuK/R+hmEYhmEYhmH0O/dWbQ0NNQijXG0KdQWgJmdjx44VTc7ooNK/FOoKHFCCXk9NxUhVnnHGGUHX7XK5xBKAmuJR+IyapnFDMIZhGIbRBjQBq6mpEZOoWDcUDQbdtXa73XHfLhlyBBqZMupAqWsh8DkwtZq/Uq0jLWqZextW0NABJUgVNof+DjxH/1LH+eZYrVbRNTnwmmBQN/ODdYZnGIZhGEYbFBUVoVu3bnGfwCamdgK89Yg3eXl5YvLJokYd0LWQk5qOWq8ygiYlJaVNDcv999+PBx54QDVzb8MKmlhy11134ZZbbmn6m8JpZD95yWu/wp6UctD3Oz2+iLbr8voRKR5vZNsk3Pu3Swp+5Vv3YZy0F7dNHNNGzf+yaTteWL0DQ695Bgnp2VHvr9i2J/J9DuDZP/7KTUtQ88NLePaYQ9EjM63Fa5w+L26e/if29j8W3Y4+r+nx+tLtKP3kYdx76AAMSfDC4fMhwWJtOh53L1iHrRVeJFsc2JqcjH6XPQ2Tre0dDY878mPQcl86Xk/l6t/gWPIJXjxhHLKSWv5QVTvduG76n2gYeQaSBx0b9VicJZvhnvkCHh4/FIM7Z7ccp8+Pe35fgLXpw5Ax4eKg76fjt2/60+hcsgXDTAlwuGvRPS256bra4K7HGpMXt4wdisfnLceVh47CMb17NL2/1uXBrT/OxNSCLJw8ZAjMjuSm9T78259Yv6MEKT4TjrWlwiT5KZQKiwmolySs87uxzuTFqemd4JT8mFFfBVdGdySfdB9MZkubsbp2LEXSsg/EtZOf2vIz7vR4cdMv87Cj6xFIHHTigcc3zETulu/x0rh+yHUkovmnZXeDC9fOXomdlFKckIr+Xj/6JXZqcWyWNuzCkd3TcNnwPjCn5oFWILnqgIYKzCrai9dWF2FkQlesrt8Nh8eJw2BDPfywm2xwmBr3YbvfhUWox8XJaTjKngSTxda0DZ8k4YWafVjmk2CfdAssmY3H1rnma6Rv/RPjPBKyzGakmM343lONJACj4UA1/LDu35tUmLEVHqyBG+OQCLryXZDwKxrQAw6MNtO7ALffByc8SDbZsUpqwB54cQxSYTGb4ZckfCNVgY5QDfzIhwV9YMVSuDERCVgIF7rCgqGwg65+uodeCz8o13kLPNgJHybCgUxYmo5xCXxYBBf6wooN8KI3rCg0+XFm975IttrwdcUu5PQowJ51GzHKZUIKTE375IOEvfBjFhqQSHcOxchMcENCjtkCG0zYJXkxD07cmJqOPIsVj1ZVINlvQi9Y4YAJP6MeFQAKYEYlJAyBDX1hE2OmfaRt0euWwIVK+HEY7JBgwk54sQ1eVEHC4bmZePDQfrCYTUiACXaYxL6vLK/BvYs2YrjLLPa9BF7UiTUDh8OBLFjE6/bCJ9ZP28qBBYfADvP+dYjPDyQUwivOH42btpsOE+iSHAsH0luUyJpQCi/+hFM8PwGJ4tzPQT16wIJi+NAVVoyEvekdNCYvJPHcerjF2RkPhzh+tD/0L42HjvcsOJEOC+ohYQck+C029PN5cagpGdWSD7NRK8bfHTY44UcDJPH+JJjEe/fBhxPEmCziSDR+41tQAg8WiTH7MdiSiRJ/PbIlv7iOA9dKBXz4Bg1ITU1FvBF34731cAy9FLAcOHYxx+dG6eq3xfZZ0KgDOhckZm4fNhYOS9vfoFji8vnw1KoFQtRTkX6AcKMzWke1gobuPhBlZWXCaSEA/U2+94HX7N69u8X7vF6vSB8LvD8Y7YXhSMyEImj8EU7w7dGImihEhdRsmwXHnIu5Hz+Fj1auxznDByI9wQG3z4c523bi7TXbkHnIiUjN69ni/T5r5Nu2JQKuKMYuxr9fFGUNHo+KpdPx3IKVuGnsMAzNzRaT513VtXh10SrsMqegYOzJsCY0ToyJ1B6Dsaf3GLyyfAmuH9YTo1LMsJlMqHX78N8NO7BmXx2yLKliMpA9/kzYUrOCjsFvlkfQHOzayRh6NMo2/IHn5i3H9YcNQ99OmeLxbeWV+Pei1ahKykHnYcfCbzmwj5GS1GMYanMH499L1+OGMYMxKj8XZpMJu+vq8c7ytdjQYEbKkVNhdjROaoOResjpKPv5JaR4aFJlQnm9E1mJDuzwebDa68LInl3x3sZiVPqtmLuzDCPzc1GQniYm/HTenJIJH23ZjcysfBzTOw0OqwV1bg9yUlIwHxLqzcBivxOHWBKQKEmo8/uwSfKKSW4vRwJcfj/+aKiG02yFbfjpsCQEn9Qk9BqL6s1/4IWFq/CPMUMxIDtLXDs7q2vw+tI12IVkJPQ7Cmb7gX1N6DsJewrn4YVVhbh2cAEGpqWKSdSGqlo8v3Ir9rq8sOQNgbnraGxf9SUS3dXobk+H1WSGF34x9ZtRvBc5GZk4bWgeUux2eMxm/L6jEP/duBOpSESy2Y4+CVlY7ivGSr8HPWFFMkygn8Nd8GK9yQOTZMUMZz1yLFYcYrWJ9Zf7fPiqvgrr6W5g3ghYcgY0CUlHn6NRU7IKa/wVGOzzI8FkwwhLEv7w1WIl3OhjsolJslfyC0GxHrRdG1JgRhk8WAsvbGY7yvw+7JTcYqIrmSRIsGKd5ESRyY9BpiT4JD88ErAOLlhMVjgleoWEUviRDJ+YMC+DWwic7fAhEV4xefbAJCatu+HDFvjE/vphgoeOOX3Hw4eVJJ5gEp9Lmjzvgg/ZkgX1NbVCIGV2ycfll16Ehx56FKtcVUJw5JispCTF+0lM0SeNhAWtawQa97nG70OdGVgONwbZHbCaTHi7rhb1fgkD9wuWxXCjTkynaSwS6KyvF+Mwi/HTv3XwYzO8Yl9p2zTZLxITfy+6wizWtKq8Gm9t2IkL+nVBSqIDVglYtbcaL64pRKJbQjn82AGfEFuO/QJlFTwYRr9tsAiBlwEL9sAvREUKvOgPmxACtP1iIWZIxDQeXxKNdA6XwI018GAobEIc0dGma4mOCd07pn1cTfsPO7qZklAoNSALZjGWZHiFiKRv3ICY2QgPesAKikEshwcDYIUVZiHQ3PCL40stNpMgCSEqpXaHKbMvCot+R4rUgAFIQBfYxL4RmTAjSRxDCRvgFoKQBNc6eDEIJiHESCiR0FsBF9wmC7y2VBT63OhiSUSJtxYb4EG//eMMFAErmi5uscNkid/kURWFz0xQEi2Wppum8SJw6yItLa2FoFHb3BtGNwWgoiQqTiKqq6tFmKt1YdLixYsxevRo8ZqffvpJ9EsIpzCJ1ks5gle9vzCmEZpoozTuaCIlzbZbvPgX7Jz+NlK89eiWkoDyBjf2+ExIGX40Bp56FczWA3eBo91fIlpB42kW5XHXVKDom3/BVLIBXRIsSLRasL3GCVdqZ3Q5+XqkdBvQ5v0+Vz22fzsN0vYl6AwXUkw+7KiuR43bBz/9CCVnIPPw05A3/kyYOsjBdssQpfGEcCzclaXY+9O/kVBZhO5JdvHZ2FHngiutC7JOuA6OrK6yjIVw1dag6o83YC9bg3yHGal2G7bXNKDGno7E8ZcisdvQg66jduMc1M7/ELa6SiS5G8TdfI/FhKTEREiOFDgze8Ex7CS4lnyC5Po96JHigNcvoajeg/q0bpAyusFetAg5Vh9yEhNQUudEuWSHM3sApB3LgLp9cEiSmOBSNIYmQvRzkWy2iImnx54Ax+i/ImnIlA4nNb66CtTPfQPJ1TvQLckGu9mEwloXahydYD30YlizCtq8x1u1C+4505BaV4weyXb4JaCopgFVXsCfNxgpE/8uxKVrw0/wb/gZCR6niAc0SBKc9gT4O/VGYk0RssxudE1OwD6nG6UuP2o8gM3rRqq4r29Cpc8Nv98j7jun+LxwS344TYDPbIXkSIPfakNy3V7kmIBUsxm7fF7UkAjoPBgJ46+Fie4cNN/XfVvgWfoBrFXFSPN5RRSlGj4xASSBkGIyo16iO+WNkRKaZPpMZtRabPCYzLCbLLD43PD6PEJkJFjsqLPY4JK8kExmpJkssHpcqPV74DOZkGhNFHfnfT43JMkHi+QDfYvQ3pEUogmqf/9NnUSYxXZpck2P0R1+2m8SQB5I4u594//oeTo6kpiWUxSmjk6vw47+g4agwenCzqJCuGprxXpJeHgpKgtJRGMofmAx2+Dzu8T602CBC37xfKLJhDyLRYi2Oj9tH/sjTIH3WuBP6Qy/1w2zc5+YMNAY6Ng59gsKimSR6CFBEYg60JSd9psiD7UUxbACmXYbeqQliu8burZdbp+IOAb2n6D1ByITgcgFba8akhgzbYcW2jaJAdqWs+koNb6HtkvCgs4oradxnxuPKX1mPPtfbTcnwm0CbGYzEiUzPD4n3JJHrJ+ERfNzRNuhb5q0/ftNIozGmgyz+AzW7X+eTouHrqG0brD1OxUmayI8RbNhKVmMBMkn9qdy/yvpv0kUUqSwcd/MSDTbUed3CyFIY6ZjQ+fJY7ZAyugLS5fD4S9ZAGvNTli9Lvj8HvFaOhZ07CmKRpkW0U7mwiUwd3CMuDq+gsbngmvFq4rsM9PxtXD/yPFxFzSUofLg8rkRXQ9Kzr11JWgo32/z5s3iv0eNGoXnnnsORx99tMjDoxSwJ598Ek888QTeeecd9OrVC/feey9WrlyJtWvXNoVZp0yZIpQj2ct5PB5ceumlolDpww8/DHkc4QoapdLO5BI0hKe+FmWr/0RDxW5YE5LQefDhSM7pGvS9ahI0BF2ytTs3oHr7Kkg+L5JyeyKj35g2QqzlOnxo2L0dVVuWwVNbCW99lYjG2NOykTFgLOyUB30Q4iVoCEnyo75oDRp2bRTTkMS8fkguGNqUTiWXoAmsy7N3Oxp2robk88CW2QWJBaNgDpJ61x5+Vx3qty2Gq6IEvvpymGwJsCRmwJE3EPbcfuJLU/L74Nq5Cu592+lbFI7cAbDn9YeJIhq1e+EsXAq/qxaWpEwk9BgNS2IaXLVVcG5bCFfxSkheJ6wZ3WCy2uDdVyjOvS23H5IHnQBzO5GZ1njcbnj3bIFnzyZA8sOa2R22/CEwmdv/AaJxu3ethnv7AvidVTCndIaj9zjYchr3y7f/nPobquApXga/qwZmRypsXUfBnJgu9slTvBz+hgqYbEmwdR0Bc1IWvHs2wbdvqzjX1syesGT1gKdkNXxVO+Gv3QPYU2FOy4c1fzjMiZnwlm+DZ/Nv8DsrYU7IgK3vUbBm9W5/3D4PvGVr4Nu7Bf7aUpgc6TCl5gPuanj3bRfHD/YEmERkww9zeldYOg8FUnIh7V4Lf/0eQOSDS413oBPSYc4dCjgr4d+7Uay/cSrb+I85rStMmb3h370Wvt3rIFUWQjJbYLKnwJScDfh8kGp2iXMvSMiEyecCHOmQKB1v32agfm/j8wnpMGX1gzklFyarHf59WwBPLZCQASTlNN6RNFthSs6Fv7oY/vJNkGrLxEBoW+acIRQOFcJBsiVBqt4JNOyDZHHAktETkqcB/vLNkBoq6S4J4Pc2bteWDHQeCmvOIBH1gskMX8kq+IoXQKotFak+sNrFPknWRJi8TnEc6a3il9SeDHNSJ5gSMmAyWYThjFSxCVJDBeD3AdaEA9uiNEpKU6Jrjx73NkAcCPo/Wrc9GZZOA2FK7QZfySL49qwV505sSLyvUTKK9dGNGErNpONmT4VkTQZc5ZCqdtAdHYDWl9YN5sRsmB3pMGf1g+Sqhr9qG+BpEKuka0Sq2XmgHoTSGzsNgjmpM6SqrXT3A5LVAVNCNswWqxCuUsO+xtNvS4YlZwTMqY19LZq+F9w1YuySsxww22FK6974HleVGJM5tSeF4AEPSaPG90kNewCfB6aELFiy+sO8P5WTvvcl5z74a4oh+ZyQPPUAHX8aR9VWFjSMomhJ0NSqZO6tK0Hz+++/i4PYmosvvlgoQRoaFTW99tprqKysxMSJE/Gf//wH/fv3b3othbiuv/56fPvtt8Jh4ayzzhL+2VQgFSpGFDThoDZBE9k6ohcA8RQ08RiLnMc3gNdD98jlw+NuTFNR6/iIgKCRm1iMNVbHtTWS54CLZMzwHOiJENvtxGFfmuN2xnl7FNdRCBnOoZKTexY0TOtr4YFRygiaB5aFLmjUMvfWbcqZkrCgQWzT7GSY8EU76VaLoJFrLPKOxziChmBRE3tRw4JGQ4JGSVHDgiYiWNCoDy0JGr0Sf9N2g+OwGu+QO2zxdfxglMNKRQOM4QnmFCg7tji5O8VjX4xKvM4hw8QJs0ILw8dBU9ijEAZ2Awqp5thkEFV2uz6Fmc2m3mvDZm+/LophdIldgUm+vaWpBMMwjNbg26kMwzBRYrFZYpJ2RhGvWKadMYwqojTxSh9kmBhjJoOUODuIx3t7akW9t2ZVTgKnUTEKRov0HDGKNZwWF5/oF6edMQzDMPGCBQ2jCQGn5rQopiUsGBgmSjjtjGEYJix45qGQMUA0bmdaNQaQw+1MaSgqIqdlslogwSin25nckYRY2wwzTEhRmng7nhkFraedUR8haxyjeB00EGaUhc5MvM8OXw2N8G1vA8HGAJyixcS2jkZrES9OO2NawFEahmE0CkdoNOh0Fk0/GkY94kqOfjR6jRjFGi62ZzSRdqZETxql0HqUhmFEgb5JLPHeJsMRGk3XlRgNrqPRDlxHw+gSNgdgGIZRJTxDZOIm4LjBprpRs2DUSj8aTjtrC6edRYHRzAH0eh4Zhok56p3BMEwM4AabTACOIjEMwzCxMAWI98KwoFHU6SyaOpqI32twYwA1wf1oGEaDGCHtjM0BGIbRGDy7ZRhGExEQraSdxQpOOzNoupISaWdKotfzyBgC0/6JdTwXjtA0woImSoxmDKD0/qq5zkMP8PFVbx0NwzAMwzDB4dkLE1fUYAzAdTRMAK6jYTSRdsbmAAyjCcwKLQwfB83W0TCMFgQWCwZ54bSzg8ATYYZhGEPCM2qNwsYA+kAuYwCjpJ1ppY6G084MAJsDxBYWpwzDhAHfPmU0B024PR6/0sNgdBT18Hq8Sg+DkQu9dpyntDO3DveLYXSFCTDFu0yfbQEIFjQyFco7PT4YhWj3l+poXDo4XpTm5XZrfz8YpnkEzOP2xDTtTPK4YrZ+RmdoRZxSJMsax4idWb1RdIZRCv5UNMOh4lQbRv+pXtFilDoao6edcV2SimBzAIZhmsGmAMrBx8GgxgBcR6Me1Ciu1FxHwzAHhesvGIZhDAXPWgxqDMAwzAE46qEzt7N4orf9CQabAzAMo3JY0DARwQ021Z3mxagDTjtjdJF2xjBMSJhNyiwMCxrdTPC1hl4abKptLGoVWEato2EUhO/sywtHaRiGUTEsaFqRoJI7/wyjNGqJgsULjnocgNPOwsQI5gAMwxwUk0ILw4JGFbAxAMMw4cICjIk7HKVhGEal8C+iDowB3Dro6aLlBpvcj0aZBpax7pkiZx2Nz6CfUcV70miljwnDMLrAbDKJJd7bZDhCwyhYN8R1NOqvo+G0M+PCdUphwmlnsYejNAzDtIOxZisxho0BGIaJJyzAQoAnwfLCjTYZhlEhLGgMbgzAdTQMo137Zq3D5gBRwlEahlEVbAqgHHx7T0XGAC5vZPUgXEfDdTRqH4+W6mjkHqOW0UqdEqNAlMbdoPQo1IPdAVjjKLT4PiTDtIE/FkxUcB2Nfsdi1DoarcFpZwa/q89RGoZRDdxYUzl4piIzXEfDMEwwOO0sOJx2plG4loZhGBXBgoZhGE1HFNiNK7bo5vjyXX19weeTYZhmsKBRkTEAN9jUdkqUnHbJehyPltBaGpfWxqt7jGIOwFEahmmBWaGF4eOgG8gYQCm4jka/Y1GbaNQ6nHZmkLQzJj5wlIZhmP3wLIVhmLjDaWfaQjfHN54TYKNEaRiGaYJtm5WDBU0MYGMARi1w2plx0ri0Nt7WcJRGoyiddhZPu2SGYVQLCxpGwHU06hIQnHamX7SadsZRGg3AURqGURSuoVEOPg46MgbgOhr9iQjGgBNuA0dpdIdRok5KR2mYg1JTU4ObbroJPXr0QGJiIsaPH49FixY1Pf/FF1/ghBNOQKdOnWAymbB8+fKQ1ltZWYnrrrsO+fn5cDgc6N+/P3744YcY7gnDBId//RiGUWzy7fV4oWa0MEY9QWlnksel9DAYRndcccUVWL16Nd577z106dIF77//Po477jisXbsWXbt2RV1dHSZOnIhzzjkHV155ZUjrdLvdOP7449G5c2d89tlnYj2FhYXIyMiI+f4wTGtY0DBMDNLO3G4f9DgeSjvzePyyrMvoaWc+j3qukXCiYB63B7pIO/M447QtBxBPkUZpZ+447VuL7SYC7ob4b5c5KA0NDfj888/x9ddf48gjjxSPPfDAA/j2228xbdo0PPLII7jwwgvF49u3bw95vW+99RbKy8sxd+5c2GyNEfKePXvC6HCRvjJwylmM0KIxANfRqCvtTE1j0Qqcdqb9tDM2B2CY0Kiurm6xuFzBhbPX64XP50NCQssaK0o9mzNnTsTb/+abbzBu3DiRcpabm4uhQ4fiscceE9timHij7RlsHOA6mvihhjoaJr5oYfKthTEyEcDmADHYrkFraUiEx3sB0L17d6Snpzctjz/+eNDhpaamCuHx8MMPY9euXUJwUMrZvHnzUFJSEvFub926VaSa0fqobubee+/Fs88+KyI+RsVsUmZhOOWMkTkq5dRgGo0RUFsaHMNpZ4Yi3mlnjCEoKipCWlpa099UlN8eVDtz2WWXiToXi8WCQw45BOeddx6WLFkS8fb9fr+on3nttdfEOkePHo3i4mI8/fTTuP/++yNeL8NEAkdoGCYG6Ln/C9s3qx+tR5U47UwGOEqje0jMNF86EjR9+vTBrFmzUFtbK4TQwoUL4fF40Lt374i3T85m5GpGYibAoEGDUFpaKgwDGCae8MwkhnAdTfzhOhrtIffkOxZ1NFoXCHKjm1qleKadsUhjVEBycrIQIhUVFZgxYwZOO+20iNc1YcIEbN68WURqAmzcuFGs3263w4iYYVJkYVjQqBauo2EY/aPVJpvxgKM0MsBRGmY/JF6mT5+Obdu24eeff8bRRx+NgQMH4tJLLxXPk1sZ9Z4hG2diw4YN4m+KtgS46KKLcNdddzX9fc0114j33XjjjULIfP/998IUgEwCGCbesKBRqTGAVtFiVMooyJkGx2ln6oejSiHCURrGAFRVVQmhQSKGhAn1nCGRE7BbJseyUaNGYerUqeLvc889V/z9yiuvNK1jx44dLUwEyJSA1kENOocPH44bbrhBiJs777wTRsVkUmZh2BSA0SFq6ZUiVyE+pZ15NFg8rmQDy1gUrseqySabAzAxhfvSMIBomElLe1xyySVi6Yjff/+9zWPknjZ//nxZxsgw0cC3WWOMFiMWWq+jkQMj1K4wjNrRZdqZHveJYRiBWaGF4ePAqFDEcR1N7OC0M2PBaWchoueeNATX0jAMo3N4RhIi3GCTUVJAGCFiZGS3M62aA8TD7UyXURqGYRhGVljQMLpEb/bNDBMpHKUJEb2bA3CUhmFiDpsCKAcLmjjAdTSMmuC0M0ZOdNOThmEYhtEsPBthYgLX0TBqgNPO9EHc0s70HqVRCo7SMAaBG2sqB+cihFlH44yzHTDV0bi8/ojraNwatIPVG2zfrLw1stHh48ooauGsd6yOOBtLSHHcFsNoA47QMLqF62iYWMF1KTo2B+AoTWzgKA3DMDGEBQ3TLlxHo1+MVEejpRqPWKadsQhjFDUHYBgDYDYpszAsaOKGFo0BooXraA7A9s2hwxNvbaIl4agqOErDMAwTNSxowoT70WgLTjtjCDYH0Ae6TDtTAo7SMAyjM1jQMIxBMVLaGdMIR79UCkdpGIZhooJnIYyq62g47cyYESOeeGsTNgfQEBylYRhGR7CgiSNcR8MwysFpZ4yq4SgNw+hiUq3EwvBx0AxcRxM5XEfTPpx2ZjxiHf0KVThKnnpIDZWQ/OH3x9FrlEbyuSG5qsW/cYGjNAzD6ATO69BIg02GMRrcDFKf+Cu2Q9oxB4n1RbCYTfDAiobMIbD2mgSTLQlGRGqogLdoLixla2D2ueE3W+HLHgBzt8NhTsyCbqM07galR8EwsmKGSSzx3ibDgoYJsY7G7fUrWkfj8vigl4iI2+2TJWLk0ckxiScUPfC4PZoQXpR25ovROY61WGzvOPt2r0Xi1m9w+KihmDz5PGRlZWPdutX45rtvsX3lu5CGXQSTXWWihqI0HmfMVu+v3wdpxfvo1FCJAnMiUs3JqPN7UFiyEhUVW+Efcg7MyZ1jG6Vxx27/Ot42ixqGYeSBBY0CNSVOBSailHbmVmgCrNQ+t06H8kQZVdOriJBLZMl1nJvDURr9IHldsG/9AadPORbX33AbzObGFMVhI0bhiEnH4PbbbsbGbb/DNuCkkNPOJI8LWse/5WfkNFThUEcObKbGFNAsyY98azKWOPeidMvPMA07HyYT34VlGLVDH9N4f1T5q6ERTno3SB0NwzCxg80BDo5/91pkJADnXXBpk5gJkJ/fFaedehoSK9dC8skbQVNzLQ1FZyzlW9HHmtIkZgJYTWb0taXCWrMLUt1u6LaWRgcGASSu470wDNMSniFrqMGmkrB9s3ywfbOycEd7ZcwBpPq96NqlC3JygqdPDRkyHHaTVxTFh4rWJ3ZSQzksfg8yLcEFRaY5QTwvOcvjPjaGYRgtYaxZOaMYbN+sbtTsdsY9aXSCxY7qmmp4PMEjMBUV5fD7JZgsdhgF2lfJZIZbapXyaWr8DLkkHyT6mTbH4ZhwlIZhosakgGUzZ5w1woLGQJN7tm9m+2Ymdmgx7SyeURpzzkCU7a3Egnlz2rzO7/fjp59+QL0jHyZHqmEsnE1pXeFNSMdOT03Q54u9tfDak2FO7464wDbODMNoFBY0GoPraPQBp50pC6edxR9zSi5qU/rg5X+/iD9n/w6v19sUmXn9lRcxZ+EySAUTYSRMZitQMB5bTT5sdVfCKzUaavgkPwo9NdgEN/xdDzNG1IqjNIwOMCm0MOxyFhVG60fD9s36ht3O1IteLJzNA09D4bqv8cCjjyE/JwuZmRnYUbQT5fU+uHtMgaVT34i2ETfHsxhYOJvzR8PjdWFN4Wxsce1GksmMBskPp9UBX9cJsHQ9FHFFSRtnhmGYCGFBw8QNtm9m1ISWetLoBYo0WIb+BXU1pdi4Zx2kPS6Ys46AefAwWKzGTHciO2ZrwQRIucPRsHc96t01gC0ZluxBsCakATqwpg4Z7kvDMEyEsKDR4OSe0s5cEUZKlOxHwxiryabcURpGP5hT88SiSaKM0kheJ+D3AbZEmPYX/xNUOxT3aEx7cLNNhon4BkW8e0Zxj6pGWNAwmoLTzrSTdiY3Wol+xGqcekk7ixVqb7Tp27sB0s6FsFTvBCQJ/qQsoMsYmLsc0kLYtIFMD1S8XwzDMGqABU2UcB1NeHDaGaMm4jHRZnRImFEab9E8WLfMRBc/kG9Ngtlkwt7afdix8Qe4qnfCMvDUjkWNEnCUhmHCRokifY7PNKKyb1BjwfbNjN7dzuTuSWN09GLhHCvU2GjTX7cHlm2/YTBsGJWQgzxrMjpbkzDYkY0xllTYS1fCv2ddxytR4X4xDMOoCZ5taBS2b2a00GRTbrTSZFMr42Ri35fGX7YKKV4PetjS2zyXbU1CnmSCVLIshO0pIGq42SbDhAXbNisHz4plSjszEpR2pnQdjRpQa1SECQ/uSXMAjtLEgLo9yIZFpJkFI8vsgLl2d9yHxTAMoyf4NmIzEixmSEoPwgDopY5GLtjtLDzYHCB25gCM/LU0ktUOVwe/LG5qpmkJUegpYRDAtTQhCXWTLX43RiSTF9wpiGFaYqzQggrhOhpGjag57SwWcJQmfhgtSmPu1A97TH7U+duaT/gkP4olF/ydhygyNk3AqWeMhuCUM+VgQaNhjFxHw2lnjJHRsjmA0WppzNkD4UrNxzL3PlT5nJCkxmhNvd+D5a69qHGkwJI/KoztGayWhmEYJgT4l0sm2L45PDjtLDZw2pm60Mo49Yha+tKYzFZYhv4V5Ws/x5/VxUjzSKKephp+uJOyYBp0BkyJmVA9nHrGMAeHfZsVgwVNKxKsZjijmKgzjF4aWqpxTLGEe9Loq9GmmmppTAnpMI+6BL7KQuyr3CYaa5pS8mDOHiAET/jb42abDMMwzWFBowKiiVZQ2pkrQgFGdTRuFd7NDyftzKXh8TPGgM0BlEMtURqCGmeaMnvBnNkLmoWjNAzDqBTjFmEwits3K2WIIHfjR7XV0ahtPLFqshmLWg82B9CXhbMa+9LItz31GB/EDTYIYFQOmwIoBwsaGTFaPxpG/85iahwTEx9zAL2gJsczXcAGAQzDqBCegbdTRxP3bbJ9M2MAtBCliQVaGWe84ShNtNszoFjjKA2jYswKLQzX0OiCaOpolHY7U0MdjRwOXHK5i6mpyabf60Zd4Ur46qsh2VOQ2H0YzFY79I7WCtZjWUujNpc2sjyWqovhr9klalLMmb1hSsrSVC2NLlCyloZhGCYILOwYRVFDHQ3Tlqr1c7Dr3X/C+f3zsPz6BrzTX8CeD29DzfrZSg9Ns3CUJroojb9+L7DkNaQuewXdt32N/C1fIGHh8/Ct+gSSV0WTa6NEaZRMPeMoDcNERU1NDW666Sb06NEDiYmJGD9+PBYtWtTi5tF9992H/Px88fxxxx2HTZs2Qc3wL6zMaLEfjdbdzhh5qd44D/UzX8fxViuOzuuBTjYH9rpdmFW5G7/88RZgNiO1/wRd96TRWpQmlqghSiO5amBZ9jYOSWzAxROGYmjnbHj9fszfWYI3V63CthW1wCGXiKhNe3CURmew6xnDRMwVV1yB1atX47333kOXLl3w/vvvC9Gydu1adO3aFU899RRefPFFvPPOO+jVqxfuvfdeTJ48WTyfkKDOOjqO0OikjobSzowKpZ3pye1MrkL8SMYj+X2omf85JpotOCunuxAzRLbdgTNyumOS1Yr6RV9C8qknDUlLxCpKo3VzgINFaXw7F6I7qnHfxDEYnpsjGlPaLRYc2aMb7hw7DBk1W+Dfp6K7hxylMRRWq0V8tuO2WLX9eWeUpaGhAZ9//rkQLUceeST69u2LBx54QPw7bdo0EZ154YUXcM899+C0007D8OHD8e6772LXrl346quvoFaMOwtmZEMP9s1MIw2lm2GtKsWRGTkwmdqaQR6R3hn2mj1wlmyEmuB0Ln0fX3vZChxf0BnpCW0n7oNyOmFoZhL8pauM7Xim531rD049Y1SGSaH/EdXV1S0Wlyt4RNrr9cLn87WJtFBq2Zw5c7Bt2zaUlpaKiE2A9PR0jB07FvPmzYNaYUETA9i+mVEL4UZpfM5aWPx+ZLczOaKIjcXvg89VG+W41P8Z0ZoDl56jNGZvHTqnJLX7fNeUBJg99VAV8Y7SKIXSURoWNQwj6N69uxAegeXxxx9HMFJTUzFu3Dg8/PDDIupC4oZSzkislJSUCDFD5Obmtngf/R14To2of1ahIFpLOzOyfTOnncmDLSULXosVO11tJ4dmE1DsqhfPW1MO7izFqDPaocVxex2Z2FZRHfQ5vyRhQ0Ut/AkZIa2LozQ6FDUMoxKUbKxZVFSEqqqqpuWuu+5qd5xUO0OpZVQv43A4RL3MeeedB7NZu7JAuyNnVFVHw2ln+sCR0xO+zr3wc+VueKWWhfs+ScIvlbvh6VQAR+fehuhJw1Ga+NLe8fbmj8avO/diR1VbUfPnjmJsrvXA0mUUVIdRojQMwyhOWlpai4WESnv06dMHs2bNQm1trRBCCxcuhMfjQe/evZGXlydeU1ZW1uI99HfgOTXCgoZhZEauKI0S46G6mawjLsByawKm7dqK1bWVwuFsbV0VXinZiiVmG9LGn9+hmxRzcDhKEx6WLoegLLE77pu9FN9t3IpdNbXYVlGF91euw/NLN6Cq8xiY0rqFvL64RmnYIIBhGJWSnJwsrJkrKiowY8YMYQJArmYkXGbOnNn0OqrJWbBggUhVUyva/FXVAGzfbNwmm3IhV5PNcEnM749Op96OtfM/xdpd62H1+eCzWCDlD0Snw8+CNbsv1IoaLIb13GgzHgSzzDZZHZBGXoxNm3/Gv9YsR+Lq7ZAA1FnT4C6YDGvPI4KaWDAMw8ST5ilg8dxmuJB4oZSzAQMGYPPmzbjtttswcOBAXHrppeK7lHrUPPLII+jXr1+TbTPZO59++ulQKyxoQqijcXrjO8Gl9CtnhBMSSjtzxXm8zdPO3FFsO5r9ZuQlMb8vEs+4C+7KUnjrq2BNTIM9M188J6fIUpOAjHdPGq2KL6XGbbIlwjboVLj7Hg9X7W7RD8mUmg+rObKfsbj2paEojccZ3yiNEj13KErjVlGTU4ZhghKosdm5cyeysrJw1lln4dFHH4XN1pj2e/vtt6Ourg5XXXUVKisrMXHiREyfPl21PWgIFjQME6M0L4+KxFmk47Fn5IlFDZEjxthRmubCxpTZI+5j0hwsahgm7lCgON7B4ki2d84554il/XWa8NBDD4lFK3AifAzRon0zu52py1JYKbezeMLmAIxS6LqWhmH2Q7a8lDJEqUPUa4QKwsmyl1KOmhd8X3LJJSKtKCkpCSeeeCI2bTp4s1q6e3/dddeJOgwqQu/fvz9++OGHGO8Rw7SFIzQhwGlnocNpZwwTOpx2Fv9UP0PBURoGwJNPPik6wL/zzjsYMmQIFi9eLGolqFfJDTfcIIQN1UZQutHXX38tHLKee+450Vhx7dq1onA8GG63G8cffzw6d+6Mzz77TFgAFxYWIiMjNAt1PdK80WU8t8mwoGGYmKGXtLNgqD3tLBYTbq1NsLWedhYvdF1LwzAA5s6dK9yrpk6dKv7u2bMn/ve//wmrXoIiMfPnz8fq1auF4CFIAJHTFb3uiiuuCLret956C+Xl5WL9gdoLWjfDKIF6cmsYRiY47Ux7qOl4xxu2cG4fTvWTAbZx1i1kpdt8cbmCC/Px48cLC96NGzeKv1esWIE5c+ZgypQp4u/A+5oXfFODRUoho9e1xzfffCNsfCnljLrIDx06FI899phIcWOYeKPNX1INwfbN4cNpZ7HDSFEahqM0ocJRmhhhkNQzs80ilnjhlxq31b179xaP33///XjggQfavP7OO+8UgodseS0WixAc5Gh1wQUXiOfp8YKCAuF69eqrr4oUs+eff144YJWUlLQ7jq1bt+LXX38V66G6GbL/vfbaa0WDRhqLEdGKbbMeYUETIlxHE786Gj0hl4AwgniQ28JZS2lnXEujn1S/kDCKjTMTU6jDO9W7BGivM/wnn3yCDz74AB9++KFIKVu+fLnoM0IGABdffLFIF/viiy9w+eWXCwtfEj1UP0MRnObGAa3x+/2ifua1114T7xk9ejSKi4vx9NNPG1bQMMrBgobRJXprsskwkcJRGhVGaZSADQJ0B4mZ5oKmPahpIkVpzj33XPH3sGHDRPH+448/LgQNQWKEhA71J6Fi/5ycHIwdOxZjxoxpd73kbEZiiMRMgEGDBqG0tFSsw263w2hoxbZZjxg3cT2OsH1zZNEpJnZRI6PU92jJwplraQxWS2MkG2eup1GU+vp6URPTHBIhFGFpDTmfkZghowByQyMzgfaYMGGCSDNrvh6q0yGhY0QxwyiL9mbaCqedxX2bUUw+Ke1MKSjtjJFXQKhdPMiBkc0BYh2lYVTWl0YJ9L5/TFBOOeUUUTPz/fffY/v27fjyyy+FLfMZZ5zR9JpPP/0Uv//+u6iLIetmsmMmK+cTTjih6TUXXXSRqLMJcM011wiXsxtvvFEIGVo/mQKQSQDDxBtt3hLUOFWlRdg05wfsXT0PkteLlO790HviVHQZPFp0Z42U+sq92DpvBvYsn4WG8jK4PF6YHQlI6ZSPToPHoue4E5GY3glGoaF4A4oXTIe3eD1gMsPeawRyDzkeyfm9NJF2RrnLtYWrUb5iJrzFGwGLFdaeI5E5/FgkdOoGtdBRfY/PWYua9bPh3ToPcNbCa0+Gz5oAh6sKJr8P/uzeSB40CYldh0R17ctR5yH5vGjYvgieLX/CXLsbsCcDBYciqd9EWBIPpHX46ivh3DQH3q0LYHLXw5+WB1uf8XAUjIbJrC7h4KvZDefWPyHtXAnJ5wYyC2DrOR7W3EHw7dsC97a5wN4t4vOBvEFw9JoAS3pXQ9fS6N4ggFPPDMdLL70kGmtSwf7u3btF7czVV1+N++67r+k1VPx/yy23iAabFGEh8ULvac6OHTtaRHrIlGDGjBm4+eabMXz4cNGHhsTNHXfcAaPCpgDKYZI6qvhSGHLiIMeO999/X+Rk0oeQOtnec889TZMfGj4Vn73++uuiYy2FQMk/vV+/fiFvh9w/KMx622dL4UhO6fC10RgDkNtZ8ZrFWPrfx9DV5MJxPfKRaLNiwa49WFFRj/yjzsKo0y9vM7ELxRigsngrFr12P7Kc5ZiUnYwMmwmbahqwaF8d0rv2gAdmlLuBkRfejfQuofvER+N2JocxQKSmCEVzvkbF7x9gcKod47t2hk/y47cdZdjiBDpP+Tuyh00MeV1yCJpwjQHoui6d8yka5n2J/iZgaEIy3JIfC+trscueiE4nXY/U3ofEdUwdEUzQeGr2onL6c8huKMOx3XNQ7/Zg5pbtyEu04aiCfCSmZGBhyV6sqGiAb8hJyDz0jKZrX24RebDJtuTzoGrWq0jbsxoT8zIwoFMG9tY14Jei3SizZiLp6BtgTc+Dp3IX6n75FzrV7sVYRyLSrDZsdzVgudeD+m6jkDLhMpgs0aVHySUMPGXr4Zz7JlKdtehqssNqMmO3z4W9FjM8mQWwVhQhy+dFntkBOtq7/E5U2RNhHX0B7N1GKTbujoiXOUBca2mUcDxTqlYoBoJG8rrgmveEqP0IpZ5ETgJzh05/extme1Lctut312Pf+5cqss9Mx9fCL5OORbI1vrGCOq8Xx82aafjrwarl7rbEU089hRdffFG8plevXuKOwuTJk0V32+ae6mrAVVeNpe8+ieM6OXDbERNg319I99fhAzF94zY8/fvnyO41CAUjJ4Tldub3+7D03acw3NqA/ztmFBI89ejapSuSkpOwsKgUt/26FKdedAWWr1iOlR8/j4k3Pg9Tq3xaPbmdVRWuQ/nvH+Dvg7vjzCH9mybJfx0+CK8tXIFPfnwVKV37IiErT7VuZ9Vbl6Nh3he4ICUdEzI6Nz0+WZLwv9Lt+GP6NCRe/AysyelQK1Wz38YgVODhEyeKc3Dz97/gL33zcdsh/bC7vgFeRxrOGjIAMzZtw/PLf0BDbh8k9RihyFhrV09Hzr41eOjIQzAo50AU8y9DXXjoj0VYMvctpE6+A3Wz38Cg+nJcmd8bSaYD0ZitzlpM27kUNet7IHnIiVAamvA45/8X3Z31GJ6YBwtFYAD0kiSsb9iDTcXLMdiRiV6JeU2fjz6ShHXOvdiy5ENYMwtgTg4vmstRmgjhKA3D6AbT/v/Fe5uMymtomne3pe6zZ599tsjnDHS3pbvYL7zwgojY0Oso5Pnuu+9i165d+Oqrr1RXR7Nt4W9IdtXgH4ePbBIzAU7s3wuHZ6diyx/fhb3e0nVLIe0twg1jh8PuaUBGeroQM8Rh3fNwSq88zPrpR1xz7XUw1e7F7g3LoGdKF/+MwSk2nDW0P8zmAx90s8mEy8YMQ77Fg90rfld1XUfF8p8x2GRqIWYIi8mEM3O6I62hGpXr2294prQ5gGtvIaxlG3HZiAHISkrEb9t2wG6S8M9D+iPFbkNWgl2koEmSH5P79cKh2UmoX/cblChcp+gMtszGmX26thAzRJrDgctHDkJiVRHq1s6Eo3wHzszKR7LFClOza6t3QgqOSkiBtOmPxvXFaKyh4t6xBPb6SgxOyG4SMwSJF5ffixzJhB4+U4toMP33gIROSHLXw72j8TuWiRNsEMAwDKNfQXOw7rbbtm0TqWjklx6AojdkNThv3rx210tdcVt32I0H+3ZsxKjsVKQlBC/MHN89F9WF6zr0fQ9GxY6N6JJoQ0FaMuD3ITmlZdrcpF5dUbG7FKlp6cjP7YyKosbjqVe3M/fOdRjfrXPQmgwSkofnZcFFNSlxJhwB4dm5AcOTUoM+l2S1YojVjoaSLVArrj2FSDb5MCo/V/y9aW8FxnROR6q9cbKeZLPBBD8kr1v8fXiXXEh7NisiIr3Vu2F312B018axtmZgdhay7Wa4dq1GJuWNO4KnlgxOSoOtoQq+un1QGm/FDmTDDIe5rTiq8jnRxWSDiWpqWkHiJxcW+Pdtg9Edz9gggGGYaOpo4rUwGkg5O1h3WxIzRG5uy4kI/R14Lhjkvf7ggw8i3litFnh87YsVj8/fbmF0R2lnVIjs9R9Yb2tB5PbR+xrDoB6PN66Fy4qknZnM4li2h9fvh2RRtZYXKYGeDoStFxJMraJ8ajIHoOtYggSf3w+zxQKL2RT8OgjUzPj9Mb8u202JEmM1tfgMNYce9Up+wGwhCSb+DnxKKUoj7X+fT2p8ThTYx2qsoWIyo53dgblRSrb7VnGWVGZuYAg49YxhGCZiVD2ra97ddunSpaJO5plnnhH/RgPZDlLxVGChbrvxSDvL7z8cy/bVoKymrs1zJEJ+LdyFrIHhO5117jcMZS4fVu+pEAXJNa0iTj9t3oH8nr1QsqsYu/eVI7vPMOiZhD6jhAEATaaJ5oezzu3GnJJyJPcM7xjEO+3M3nskFtdXwx9E1FR53Vjj9SKp26CotyNn2llzEroMQC1smLtjl/h7WG4OFu+pwp76xslTjcsNyWSByWoX+/j7jhJI+UMUOebWtFy4kzrhz8KdQZ9fVlKGco8JSb0OQ4XJgk0NNcFfV1cBT1ouLCnZUBpbTl/sNUmoD5L+lmlNxA7JA8naNvXHI/lQCh8sOf0j3jZHaZiQ4NQzhmGMImiad7elzrYXXnihsAekCAuRl9dY1E02g82hvwPPBcPhcDR12A21064c9DrkCPgy8/H47MXYV9/QIoLy1uJVWFXtQb9Jp4a93k69BsPRYzBeWLAKZT4LqmtqhOObz+fHZ6s24efiCoyfdAxe+NcLsHfuiayeA8Nav9bSzrqMOQGFHjP+M385nM3ucte63Hh2zhJU2NKQM3wSlCBUAdFp1AnYZrHhmz07RfQiQI3Xg3fLCuHM6Iz0/uOgVmxpneHvPhqvr9yIjXvLcUSPbkiwO3DfgnUoqqlDhcsDJKTC6/PjnWWrsabGi9Qhx8R8XMEm2xQZsvQ/Bt8W7sbv23a0EJGFlVWYtmwdXDkDkdj7cHjyBuKTilKUug98fiUTsLBmH+a4XbAOOBomGSI07Y01VOxdR8CbnocVrr1w+g98Bmjf6GqqMAHrTB7h/tdczKxs2ANnUiZsBYdGPX5GI7U0LNpk+azGe2HUCd1AVWJhVJ5ydrDutuRqRsKF6mxGjhwpHqMUtQULFoiGT2rDlpCISdc8gFn/uQ8Xfv07xuVmINlmxcKyCpT6rBh09nXI7Tc87PVSRGf0Rbdj4esP4vpZKzEq1YYM6xasqahDsduPzG498M77HwDp+Tjk4lti1u9DLWlnybkF6Hr6Dfjy65cx5+vfcHhepojW/FlagUpbGrqfeQvsKRlhrzeePWmSu/RD1uSr8ONPb2Dhzo0YZnfAJUlY6XGjLi0HXc+4FRZHoiIObKGmnWVO/Bt2/VKFm39bjJGdUtA9Ix2/bC/C3JJ9OLxLZyQn7cOCskrs8lrgGHcREnL7KnbMkwZMQnX1bjyxZBY+37Adg7PShBPb4r01qM3ohZTxl4jPTeoRl2Pnry/jid1bMdhqRabFis0eF4okM7wDj0dyn9DtwGMJRb4SJ1yJPbNfxW81ZciTzLCRbbPkRa09AZZ+J2HrjiUobSgVNTMk4Sgy40zKgOPwy2B2dGxffzDY8UxjqWdKwalnDMMYoQ8N9Zz55Zdf8Oqrrwrb5mXLluGqq67CZZddJiydCfr3iSeeaGHbvHLlyrBsm8PpQyNHT5rqqmpsW/w7ytYuht/rRlq3vug77gSk5R68WWJH9s1etwvFK+eiZOVc1O8rRUNDHRKSU5GclYf84eOQM/hwWGz2iMYcTT8a8f4oBU0k/WiclXtQtvx31BU1NtZMLBiKnOFHwpYceUQu3j1pXJVlKF/1O1xkAGC1IbnncGQOngBrQkq7zSxjPaaD0Xxckt+H+h0rUL9lIUyuGvgdaTA7kiHV7oPJ74U5uydSBx4hIjrtjy0+PWnoq9CzdysatsyHqXYP/PYkOAoOQUL3ES16y5CLmXPHcrh2LBWNNaXUXCT1GQekdY3JzYJohAHZN7uLlsJbuhbweWDO7A5Hj7GwpOXCV18O55b5jQYAJjMsnfvD1n1M1GJGjnGrrS8Nwb1pYkgUokYNfWhyL30v7n1oyt6+0PB9R9RE4Fr47ajjkBLnPjS1Xi+O/v0Xw18PqhY0NTU1QqB8+eWXTd1tzzvvPNHd1m63t2is+dprr4k0q4kTJ+I///kP+vcPPQc83oKGGmzGu9Ek4YpSVGi1yWYAlwyTdrkm13IJCLlEjZyChpBXbMkfoYnFZDtWk+tYCgOfzOddz6ImroLGaKKGBU1YsKBRHyxolEfVKWepqamizwwt7UF3RB966CGxaIUEmzkqURMpDqs5alGjtSabzXHYLFGLmnimncUTOdPO5EYrxzxWKVCxTN+y2CwxFTWcesaEBKeeMQyjZ1MARp6+LIz6kMtdrHVDSyY0uKiWUT1GMwhg1zNGN3AnGiVgQRMFkdo3axWtuZ0xylk4yy204m2brTY74VgKMIrSxBK2cY4CFjUMwzAhoY1Zgg6htDMloLQzpaC0M6WhtDOjTK6ZjuEoDRMJ3JuGYZj2MJuUWRgWNJqEIxX6QO9pZ0aN0sQKjtKoI0oTdzhKwzAMc1CMPUOQAU47C/P9UR4vtYg5vU6u5Uw70wKxmGjrdmLNGCtKw6KGYVRfPcNVNAfQ56xMIxgx7UwNyJF2JhccpWHCgaM0BhWTSkRpGIZhNISxZ7YaRi2RCiUw8r4bPUoTi8gYR2mYSGCDgBjDURqGYcKABY0McNpZmO9XwfFicwBGq3CUpmNYTMoMixqGCRlOOVMOtvlphkOB30EjNtlkYtPUktK73G6f6hptyjkuwuiNNhkDN9ukKI2HG1Cq8Xozx1FI+8GinWFaw7eYNYyRU6+MvO+M/GjNwpmjNOqBU89iDEdpGIYJARY0MsFpZ2G+XwXHS01pZ2wOoM90P05/Ug4+9jLDooZhGBWjjVlBHEmwxT8bkd3OGDWiZnOAWKC1yAFHadSDIaI0SsOihmGYDuAZrcYxcuqVWvZdbdECjtKoC44UGMMgwBCixgj9dxgmCtgUQDm0MSPQCJx2Fub7VXC89NiTRu9jiiVaixxoOUoTL1hQygynnjEMo0KUn1GqEE47Y5jYwFEaRssC8mAYIkqjNCxqGBVjVmhh+DjoAiVTr5SO0six72wO0DEcpVE3HKU5OCwodZZ6xqKGYZhWsKCRGaOlnTGM0nCUJrawQUB4GCZKo7SoYRiGaYY2ZgIKwGln2oHNAYxtDhALtDbJ1tp4lYANAmKxXa6nYZjmsCmAcqhrBsYERZIk1FfuE4vk94c8qXfX16C+Yg98Xnk6ZtO2G6r2icXTUIf6it3wuhpkSzvzNNSioXIP/CGM1+dxidd6nHXQqzkAHQd39T5462tUMSafuwGemr3wd9AZna5Vb30VPLUVkPy+sLZBr/fWlsPXoPz+amVSTV3qfXXlMe9WH22URvJ54K+vgOSuV1SQdXT8JXcdpIZKSH5vXMbCyACLGoZh9sO39WKUdub0Bhce4UCTwy3zfsLWWV/DWbYDJkiwZ3dFzyNPRf+JJ8FkDq5H92xejU2/fobqTctgliSYElOQe+jxGHjc2bAnpYY/Dr8f2+dNR/HCGXDu24W6qnJYvW44HIkwJ6Uhc/gR6HnkmUjO6RLRfpZvX4etv32O+i2N40VCMjJGHi3WaU9Oa/FaZ9VebJ/1BapWzoLJ44JktiB5wGHIG38aUvJ7QQ/4XPUo+fNrNKydBbOzGn6YYO46CJ0OPQWpPYaGHQ1xu8MTFq1x7i3CvkXfwrd9CSx+L3xWB8y9D0fW6KmwpWY3Xas1mxeidvl0YPc2egRSag4Shx2LzOHHw2Sxtjsuv9eNqpUz4Nu+ABZPLegS8KV3Q9KgY5Hcc1TI0TGPJ/rPXOtJttejzsmtt7oUDet+gnXXclj9XnjMNni6jIK9/3GwpDSeEzXgd9XCtXEmpMIFMHnqIZksQN5gMU5rVg9VHG/fvk0wbZ+NhOptsJgAtyUJrvwxsPQ8EiarI6ooTayFZpsojccZv+01bdcBxHM/W2Pn1DdGRZhMjUu8t8mwoDlY2pnTI8V5m2Y4PX4xQVz6xesom/UFju+WhUnjB8NsMmFOYTFmfP4yKoo2Y+x5N7QQNRSl2bxkNta8/zQOSbXipNF9kZWUgDVl+/D13C8wd+NyjL/2kaCihtLOXEFEGI1j5RfTULdhPsaPGY2VC0owpFMyphb0RJbVhF1uCT+s/x3L1y/C8MsfQmpey0nKwShbtwib/vcURqZYMHV0X3RKTsS63fvw9ZJvsWrzcgy79EHYU9LFaykis/rNe9HFuRd/698dfbMzUVJTh+82LsLad5ei53l3I71gYETHXRwDmwUuT3ST/2gn1j5XA7Z98jgy927GeX26YGhuT1Q5XfhpyzbM/+opeI6/GlmDJyBe1Jduwb5vn8Zghxcnj+iBvNRkbKuoxLebZmPrzpXIPuUO2NM7o3zpd/DM+wRjLVaMTsuEzWTG2rpKzJn9PspKNyN38rUwmS1B79zvmzkNWZ5SnHL6FBwy5jDU19Xhl19m4I9576KqrhzpQ46FnqAogccdedTUW7ET7tn/xjCHC1OHdUe3tFQUVdXguy1LsW72WmDCtbCk5SEWURpfGJ8Pv6sGztn/RnJFEXqYE5BhTYbT78WOomXYW7Ye0uGXwZY7CEoef9+u5Uje+DnGZyfh2MP6Ic1ux5o9+/Dt1lnYWbEF0qhLWdRoQdQwDGN4WNColN1b1qDkj69w+5gBOLH/gcjDYd3zcei2nXhg/gwUjxiPbkMPa3rO42rA2k9extT8FNwycYwQQMSoLrk4pk8BbpwxD+t+/hQjTrss9HGsX4rqtXPxwL334NsvPsXABODlqScgxWFDVXUVSktLcdyAPrjvj6XY8M3rGHPVIyGv2+dxY/OX/8Hk3GT88wgab6M4G5HfGUf3LsAtP83F1t8+xcBTrhCPb5nxHnp6yvH0lInITGxMNRiRD7FvD/+2AHO/fQXDrnmu3ciVFihb9AMy9m7GM8eNRff0A9GpiT274bWFK/DJb28jrfcIWBNSYj4Wiszt+/UtjEsz4b5JE2G3WoTAHZ6Xg6N7FeDOmfOxcd7HyDzsTDgXfI5zklJwTOaBifTA5DQMr6/BS5sWoLrXIUgfOKFNlGbP0l+Q7izGE08/gwEDBzc9N27ikfjw3bfw+nsfwVswEtbUTgcdrxGiNHT8ncs+xdgUHx44cgIS96dqDcvNwaSe3XH/rAVYuOJzJB1xndJDhXPddKRUFOHwhBwkmg+keuXbUrGyYTd2LPsY1uPvaRG9i+fxpvQ3x6Zv8NdeObhk5GCY9n9f0rEc370L7pq1FIWFf8La5xhoCqVEjYGxWq0wx7GOze/nqZtaUaKmheMzjWh35qdzt7Ot839C/xQbJvfr2ea5I3p1w4jMJGyb91OLx3cs+xOOhkpccsiQJjEToEtaCk7r2xVli34WQiJUihb9guFDBqFbQQE2rVqOKw4ZJMQMkZaWhgSHA3ZPAy4YPgDuHWtRU1oY8rrL1i6EvW4fLj5kCCytREhuajLO6F+A6pWz4HU54aouR936BThncK8mMRPAbrHgopGDYK3Yicrta6BVcwCqIaldORMn985HQUZaiygync/zRgxCuq8elevnxaUIv75kI2yVRfjb8AFCzBCBSV9aggPnDOoFFK1AxYoZyPV6MCkjt806+ialYqTNhvq1vwfdhmfrPBx/7NEtxExgO2f99XzkZqaiZtNc6I1Ia2m8lcVIqNiG8wb3axIzAZLtNpwzuA8SK7bAV10KJWtpJE8DULQYvSxJLcRM4Fru58iEtXYvvGVrodTx95WuRCezC2cP7td0XQcoSE/DlJ65cJQsgiT5tWUQoBRG2U+GYVQJCxqVup01lO7AyM6ZbX5oA4zsnIWG0u0tHqvZU4zuKYnITk4K+p5hedmAsxbO6vKQ3c7c5SUYOWIEiouKYPJ6MKpLTtNzJpiQlJQkitfprr1d8qJu766Q97NuTzHyEh0ijUmsr9WuDs3NhtldD1f1XtSXl8Hmc2No7oHtN6dvpwwkm4GGMLavNnMAr7MOpoYqDO4cPBqRnuBAn7RkuCpiM1ltjbuiFInwY0B2VtDnB3fOhkPywr2nEP1tdljauVb7JSTBv29nm8eFaUD9PgwdNiLo+xISEjFsyCB4qsoUdZpTk4OYr6YMCSYfBuUEv0YG53QSz/tq90BJ/A2VMHucyLQEL9pOttiRCFPQccbreJtdFeLzlOqwB31+UHYWrN5awBPcyCAc2PWMYRgmtrCgUSlmRyLKG9pPG6hocIrXNMdqT0Clyw1vO05o9B7JZIal1fs6wmRPQEVFBRwJCYDJjPL6lmPyer37H28QxeuWMH7QLI4E1Lg9cPt87Y7XbzLBYk+Axe6A32QWjwWjTqzHL16rBiKZWJutdvhhRqUzeC66X5JQ7nLDHEFOfyRRGrMtAV4JqHG1jOgFRHal0wkfyVp7Iqo6cDSr9nmDT7AoxdBsRWV5cIFN7N23L6L91WuUhuo5fJJJ1FUFg64det4axmc8FlEak8UuvmvcUvDrwif54ZH8MFmCH4O4iBqLTXye6HMVjEqXC37JRB8EaBIWNQwTd9i2WTlY0Kg07Sx/xATMKanAnrq2dwdpMvPrzj3IG3lEi8e7DhuLfT4T/txe3OY99KP9w6YdSO49DAn7i+xDidJkDzoMv876Az169ERSVid8sWZL03Nerwe1tbWwJSZh+sZt8CVnIqvXkJD3MXfQoajwWzB7W5C795KEHzcVwtZtABLSs5Ga1xPI6oIZG1tGpQL8vHk76m1JyB80GtGiVJSGxJit9yhM31wE335R2jzosaS4FMVOH9L6Rr+PoZBcMFQc0582t3fMC+FJ6Yy0QZOw3udDqauhzWtcfh/m19fC3v/wNs+RMLJ0HYHpM36Ex902DXLDurVYt2krknqODGvceo7S2HP6od6Wgl+2Bj8nM7cUot6eDltOn5iO42CixpSUBSmrB4o8NeKz3JoSTy1cNgdseaF/X8iNNXcIdjZ4saK0bZSIPn8/byuGM2tAVKYAhkw9YxiGUQAWNCpNOxs07hh4O3XDPb8uwIY95U2Tgq3llbh35nzUpXZGn3EntHhPZpee6DTiCPxr8Tr8se3ApHhffQNenLsUiypd6Hfs2WGNo+DQY1Hrt+Hhhx7A+GNOwMebd+GdpetQXluL4uJi1PmAr7eU4ONNu9B10plIbCfdLRjJ2V2QNmISXl6yHr9v2SEiSzSBpyjMtAXL8ee+enQ/8izxWir07zrpbPxYXIF3l65G7f6oAUV3pm/YijdXbUXWoSc2OaJplc6HnozlNT68OHcZ9tY1NInRBUW78PzC1ZAKRiEpr3dE6w43SmNNTIFj6Al4d20hftiwFW5v4912OvYfrVyHb3fsQ9Kok5Hadwzcub3x+p6d2NJwYAK72+3EW6WFKEtKR/qQo4NuI2P4cdhYVIanHn8YpSWN6YJ+vx/Lly7GY48+DGdSPhK7KTfpVVuURrhm9TsWH23ahW/Wb0bD/gL6Bo8HX63bhE+3lAADyCbbpqgII7Fq6388dlmtWO/cC/f+3i50Le9yV2ONtxamHofDnNy+2UOsx2/O6I7azIH415K1WLyrtClSQ9+X0xavxNJKD0w9Wt40ihZOPWMYfUPffUosDGCSgt0+MxjV1dVIT0/HPV8vRUJy8D4tkdo3R9OPZndxEea/9Si8JVvRNdEOswkoqnPD0rkAh112txAwrSGns4Uf/gv7ls9GZxuQ4bBhR00DnIlpGHTWteg+6siDbre1fXN16Q6s+N+zVKQDq+RDTdkupFuA3ORElEs2VFkcyDvyLAycfL74YLnDsHb1uV1Y8+U01Kz6AzlWCVkJDjHeOnsyup90BbqMOqrF63fM/Q4lMz9Ams+JLikJ2Nfgxh6fGemjT0D/ky6Feb9jkjNK++Vo7ZsDROK6VbFxMXbPeA3Jrir0SE1CtcuNErcEU89D0PXEq2FxhC4aWxNuTxqqc9k952N41vyMbIsfOYkO7KpzohIO2EefjswRk8U5p0aae2b8G+aSDegsSbCZTCj1++FKz0OnydcgMa9vu9uoL16Hyj/+iyQ40bNHAerr61Fcugee9AJkHnkFLInh904i5HY8I2LlwBWOjTMVqdev+gHWzTORbfWhc6IDZfUu7PVZ4Ot/ApKGnNj0Axdrx7CD2Ti7dyyEZ8UXsDtrkGIywyX50WC1Az0OQ8KIv7TpT9SaWI+fzAu8Kz9G0r516JJgQZrdhsKaelSaUuAeeAYsneW3lY6rjXMApVzP4rCvktcJ16wHUVVVJYxqlJg79LzuM5ij+F4OF7+rHtv/fbYi+8x0fC0sPPYEpFjjm6Za6/XgsJk/Gf56YEETY0ETjagR/Wj8fpRsWIbdm1eLO985vQahy+AxMFs6vtteVrgFxavmw+t2IiU7H91HHQFbQuhfuK1FjZ8mtuuWoLxwPTzOeiFEqOllQlomuo48AonpB+60hiNoAtSU7UDZmgXwuhpgz8hF7tBxsCUGtyZ211ahbPVc0ZfGlpSK3CHjkNSpZd+NaAWNXKIm0km1z+1ExfoFcO4rhtnmQHLPkUjKbStgwyXSJpvuqj2o3rwQvoYaWFOykN5/LCRby/ND12dDyUbU71wrhFBCTg8xbnM7dRLNoeaalRsXw12+U/SrSew+DI7OvaO686RXQRPAV18B145l8DurYU5Mg6NgNCyJ6XEVBaH0pSF7ZHfxcvjry2GyJcCWPwyW1LaOeO0RDxtnX1Ux3LtWQ/J7YE7KgTl3iGypZqoQNUraOMd4X1nQGHcCqyZY0CgPC5oQBY1SURoSNZG/N/IJebAmm+EQiahp8f4ot68WQSPnxNoj03giFTWxHFMsxmYEURMKSkdp5CAeoiZWxx9GFzUsaGSHBY36CFwLi45TRtAc+gsLGq6hYXRJggyF/UpaODOM3gwN9NobSDNwPQ3DMDqGBY0GmmwqQbCeNOFgj1IM2DV4zOLpuhUNkTbaDIZNZtEn59i05nim1Ql1qM02tSDK4nkOFHE9Y1HDMDGF+vPF3RSAjZsF6pppqRylmmxG/l5jRxj0FqWRWzwwxiHWgiAeokaPGMrK2Uj7yjBM3OFcBKbDKE20tTSMOqFIiFz1KiS05K6lkTtKI3ctDQmEWNR1UIQgVrUcsRpzvIjX+GN5DlQBRWmUNAnQIVYbNZWO3/1hn5/vyKsV8rGJt4syuzY3whEaDaSdaTVKw2ln8qc+GSFKI3faGRMfOPUsMjj1jGEYJnr0M2PUcdoZEzl6SzszSi2N3HAtTSNsEKBOOPWMYRgmOljQxBE2B4jg/Ro8Zu3BUZrQ4SiNNuEojYZQKkojts2ihtF3ylm8F4YFjWbQatqZGuAoTftwlCY6OErTFhY1kWGo1DOGYRiZYUETAUZLO4s2SsMcgKM0+ovSaFHUMOrEUKKGozSMHom3ZTOHaJrgmWqc0WLaWbToJe2MozTaF1pq6wmkFBylCR0WljGERQ3DMDLBv+4Rwj1ptIXR9z8eeOur4a7eC79PPstbjtJoN/VMT3DqWSy3zaKGYZjo4V80Ji49aShK446iVwlFadw66YkjV18UOfu/RNOXpnbHalQunw7s2wazCfBZEmDpcSiyDpkKiyMJRuhLwwSP0vhi3J9I6711OhI1ksdlnP40JGrivb8MEwvoXne873dzxpmAIzQKYMSeNGqAzQHkp3L9n6iaOQ0TemfgoXv/D88/8zSuuuBsdNq3HLt/fAE+V33U2+AojXajNJx6FjmGi9QwMcPn8+Hee+9Fr169kJiYiD59+uDhhx+GJElNr2mvPuPpp5/ucN3FxcX429/+hk6dOol1Dxs2DIsXL47DXjFMSzhCE2XamdNz4AtB70QbpYkWjtLENkoTLj5nLarnf4JzT5uKv19/U2NxIoDhI0bhiElH4+abb0TF8unIHnumohGkYHCUholU1Hjc8qVUMs3gKE3MePLJJzFt2jS88847GDJkiBAcl156KdLT03HDDTeI15SUlLR4z48//ojLL78cZ511VrvrraiowIQJE3D00UeL1+fk5GDTpk3IzMyEUeEAjXKwoGHiRrRpZ3JFaZxRjoGiNC6F9yMWhCsaqjYuQEaCBeddeEmTmAnQvaAHTjnpJLz16beQDj0NJrP6oiyxEDWxSoGK5UQ61mlbnHoWOZx6xsjB3Llzcdppp2Hq1Kni7549e+J///sfFi5c2PSavLy8Fu/5+uuvhVDp3bt3h0Kpe/fuePvtt5seoygQwygBp5wpBKedMVq3cHZXlqFXrx7IyAh+N27IsOEwextEJEcOtJJ6Fis49axjOPVMRtgkQBNUV1e3WFyu4GJw/PjxmDlzJjZu3Cj+XrFiBebMmYMpU6YEfX1ZWRm+//57EaHpiG+++QZjxozBX/7yF3Tu3BmjRo3C66+/DiPDjTWVgyM0UcJpZ8Y0B+AoDWC2J6C8vAJ+vx9mc1txVlFRDr9fgtlqh1rRUpSGUQ+GSD3jSE1Y35uWON5w8UmN26LoSHPuv/9+PPDAA21ef+eddwrBM3DgQFgsFlFT8+ijj+KCCy4Iun5KTUtNTcWZZ3acLrx161aRynbLLbfg7rvvxqJFi0QKm91ux8UXXxzVPjJMuLCgUThK41Rgci5H2pWWUdP+a7mWJrXXKOxa/ysWzp+Lw8dPbPEc/WBO//EHWPIHwWxPlG2bctfSxApOPWsLp55pLPWMUT1FRUVIS0tr+tvhCB7d+uSTT/DBBx/gww8/FDU0y5cvx0033YQuXboEFR5vvfWWEDsJCR1H6uhmFkVoHnvsMfE3RWhWr16NV155hQUNE3dY0GgUSjtzKlTUzOYA+iZU0ZDQuSf8nQfgueefxU1+P8aOmyDu/u3ZsxvvvvUGlq7egKzJjQWnaoYNAuKHnkRNvKM0XE/DtIbETHNB0x633XabiNKce+654m9yIissLMTjjz/eRnjMnj0bGzZswMcff3zQ9ebn52Pw4MEtHhs0aBA+//xzGBWzySSWeG+TYUEjS12L0dLOooXNAfQRpSEjgLzjrkDZr2/h3gcfQl52lvhx3VG0E7U+CzKOvAxJXfqL18o5Lo7SaDdKozdY1MR62yxq5KC+vr5NWjDdfKIIS2vefPNNjB49GiNGjDjoesnhjMRPc6hOp0ePHjKMmmHCgwWNhtPOoonSqCntKhI4ShNbQhUNFnsiupx4HZx7d2Df1mXY43HDccg4dOszRtTYaAWO0hyAU8/Cg+tpYr1tFjXRcsopp4iamYKCApFytmzZMjz33HO47LLLWryO6mw+/fRTPPvss0HXc+yxx+KMM87A9ddfL/6++eabheEApZydc845wjXttddeE4thYd9mxWBBIxNGi9IobQ4gBxylkY+E7AKxxGtcHKUxwCQ6SvQabVKsnoZFjWZ56aWXRGPNa6+9Frt37xa1M1dffTXuu+++Fq/76KOPRLPN8847L+h6tmzZgr179zb9feihh+LLL7/EXXfdhYceekhYNr/wwgvtmg0wTCxhQaOCQn02B2DUiFZEg1GjNFpOPYtHlCaeGCL1TGlY1EQMOZaR0KClI6666iqxtMf27dvbPHbyySeLhWGUhvvQaJxoetLIEaWJBorSRPX+KLcvV18eitKoCaX60sR7XFrpSxOv/ihyw71pwoP708Rj+9yjhlE3VFuqxMKwoJE97YxhlG60aUTRoPZzEKuJdbwn0VpEq4JStSgtahiGYYKgvhmUht3OlNpmNFEaOSIUSiJHlEYOOEqjnyiNGoWlUughShNPDBGlUVrUcJSGUTEULFFiYVjQyI7RojRKp53JgZpEnRon00aL0sQKjtIEh1PPooNFDcMwDAsa3URp9DKhjwSO0gSHozT6EpZKTaL1krLFokZnGHGfGdXDNTTKoY9fKiaqnjTRwhbO+nfbMprjWazQqpWwXlzPtHr8Q8FwTTfF9h2AV8HtB4ZhtcQ1fdLsU+fNKoZREm3dhtQIRks7Yw7AUZrQ4CiN/HDqmbrQ+vnQjEkAR2oYhmFBo6+0MyXNAZSupVGLhbOeJ9Nqr6VR+/jikfrEqWcHh1PPdChqGEYlsCmAcqhv1sQwGkfPURo5RYNao0datnGONXpxPWNREwNY1DAMoyAsaGIUMYkm7YzNASKDozTag1PP5EfrqU56Sz1TAhY1DKMQHKJRDG39UjMxTTuLFqXTzhjtRWmMDEdplEXPURqGYRijwYImhmjRHEBNEQotR2nkSjvTWnQgEjhKo70JNKeehQ+nnjEMw8QObf1KK4SRzAGihaM06oWjNOpDqwYB8YBFTfSwqGGY+GJSaGFY0DA6hKM02sPIURqtpnDFY9xcTxM9LGoYhjEC+p8pKYxS5gBGtnBm2sdIURotiZpYofXUs3ih5ygNwaKGYeKD2aTMwrCgUb3zGBMZHKXRHlqwcY4VnHrWPpx6Jg8sahiG0TP6nyWpADYHCB+O0sQOjtJEB6ee6Tf1TO+iRjFY1DAGgV2blUObv6AKRmmcXr9mtklpZ05PfMfbPO3MFedjFSxK41Z4DIEojcvjk2Ui7VHofMZTbHnCOFZ+nwe1mxehfstCmJzVkFKykdJ/ApJ7DIPJxPdrmk+ePW5PTIWA1+ON2foZ+aI0kscV9XokSYJUWQh/yTKY6vZAsibAlDsE5s5DYbIqFAliGMbQ8C8+o9oojRrQ6zFQc5Qm1LH5GmpQ+vUT8M96FUe5t+K89HqMqVoD10/Po+ynaULsxGJ8Wo3SaD0iwFEadaSeSZIfvo0/wLzkTeTsXIQBFcXouWcjHKs/g3/Ra5AaKg0XpbHZLeJ7Jl4LbY9hIsXn8+Hee+9Fr169kJiYiD59+uDhhx8WNyoC0H/fd999yM/PF6857rjjsGnTJqgZjtDEMe3M6ZEie69BozSUduaWIbKhBjhKIz97Z72DgprteOD4seiZmd70+LJdZXhk7iKUL85H9tgzY7Z9Phfxj9KQqPHF4TshnhGnWEfP5I7U+IuXwL5jLkaYk5DvSIFpf75Lf78HS6pLsG/1J7CMubLp8TaixuOMdvgMo16UyAELc3tPPvkkpk2bhnfeeQdDhgzB4sWLcemllyI9PR033HCDeM1TTz2FF198UbyGhA8JoMmTJ2Pt2rVISFDnzQmO0ISJ0cwBtB6hUIs5gBrRcpTGXVkG7FiGK0b0byFmiFFdcnFO/+7wrPsN/v2TJ7XX+uglSsP1NPqO1FB0BkXz0UOyoIsttYVoSTTbMMSeAVtVkUhHaxcdR2oYRgvMnTsXp512GqZOnYqePXvi7LPPxgknnICFCxc2RWdeeOEF3HPPPeJ1w4cPx7vvvotdu3bhq6++glox1uzcoOYA3GhTHejd8SyeoqFh1wYkw4Ox3fODPj++RxfY3TVw7S2K6fg49YwxlKhxVsFSvwe51qSgT2eYHUjy++Gv6kDQECxqGJ2ipClAdXV1i8XlCh6FHT9+PGbOnImNGzeKv1esWIE5c+ZgypQp4u9t27ahtLRUpJkFoOjN2LFjMW/ePKgVdc6MVI4SURqjRYbkhKM02rRK7nhskrg7HDStRfQCMO3vnhxZmicTORyl0TOS+L/ApysYplA/dixqGEZWunfvLoRHYHn88ceDvu7OO+/Eueeei4EDB8Jms2HUqFG46aabcMEFF4jnScwQubm5Ld5HfweeUyP8ba2hWhqloMm8M4q8da6lMU4tDUVB3O7Yn6uE3D6okixYUlwWNEqzoKgELlsy0jt1j/n4YnUuYlnHoQfXM66nUaCeJiEDvqRM7K6tQJY1sc3T1X4X6kwmmNO7hbY+rqlhGNkoKipCWlpa098OR/AI7CeffIIPPvgAH374oaihWb58uRA0Xbp0wcUXXwytwrf9DRKlUTLtTA1wlEZfURpHp27wdxmKN5dvQFltXYvnNu4tx8cbdsDSbyIs9sQ4jZNTz/QM19Psf53JDKnbWBTCiz3e+hbPuf0+rHVXwpvWBabMXqFvnCM1jI4wKbQQJGaaL+0Jmttuu60pSjNs2DBceOGFuPnmm5siOnl5eeLfsrKyFu+jvwPPqRGO0DAhwVEafUdpwu3/0hFyR0HaG1v20Zdi83fP4u8/zsWkrlnIT0nGlopqzC2rREP+MOS243AWryiS0dFTlCbeqDlSY+l+OJw1JVi0axk6e2uRabLBJXlRDB8aUrJhGnpO+D2gOFLDMHGjvr4eZnPLz6jFYoHf3zifIFczEi5UZzNy5EjxGNXkLFiwANdccw3UCgsaBTCihbMakKPRZrTCTm7UmnoWD6zJmcg94/9QvWEuvts8H6isAlK6IWnSX5Hb91CYLe3f6ebUs0Y49Uy9zUPVKmpIrFgGnwFv5yEo3rUEJfsba0q5w2DJHwmTPTmyjbOoYXSAyWwSS7y3GQ6nnHIKHn30URQUFIiUs2XLluG5557DZZddJp6n2lRKQXvkkUfQr1+/JttmSkk7/fTToVZY0ERBNOJCiyg9mecoTWzRYpSGUsoyhx0L0KICWNS0hUWNTkVNzkCAFjlhUcMwMeell14SAuXaa6/F7t27hVC5+uqrRSPNALfffjvq6upw1VVXobKyEhMnTsT06dNV24OGYEGjEEaM0kSbdqa3KI0aU8+MBKeeMVoQNUoQTeNNhjEyoqYl3n01w3x9amqq6DNDS7vrNJnw0EMPiUUrGLtSXAaMZqesdGG8nvrSqBEtN9tUC2wQoG8rZyOYBCgKmwQwDBMBHKExoIUzR2k4ShMv1B4FUfv4WsOpZx3DJgE6idJoLPWMbrZY4yioTT5t3NxhmHhirPCCjlAyMsRRGn2j5khILMYmdyQpllEaxrhNN9Vs5xwTOFLDaJBAw+d4LwwLGll6tRgt7UyOKI3SqKkvDUVp9DyJ5tQzeeHUM/XAoibGsKhhGCZE1DkDMljaWcTvNXCjTbVEaZSOVsUKrYgGNUdpYgmLGmPW0xAsahhG3SjRVJNhQSMbRovSRDuR10uURi7UGKUxmkEAp57FDxY12oNFDcMwaoZ/bVuRqMAEhKM0kcFRGm2htSiIXHDqmXLEU9TEE6XOD4sahmHUirZntCqDozThwVEaY0Vp5EYrUZpYomVRE6+oBpsEyAuLGoZpHzYFUA71zOZUBEdptANHabSV6qSF1DMjnQ+jiJp4waImDrCoYRgmCNr8lWVUA0dp1BmlMaJoUHuURoupZ/GA62mig0UNw6gHCpYosTAsaGSP0kQXLVHmquQojTxwlEY/URpOPTsA19OEj1FEjaKwqGEYphnqnPEwhmq0yVGalnCURn/jU7vINErqmV5NAgxp50ywqGEYZj/a/HVVOVo0B+AojTzoOUqjZhvnWMCpZ/oUNfFCif0xpKixKrx9hlGyCQ03o2lC27PYGGMkc4Bo4SiN/qM0csOpZ/KjdVHA9TTaRHFRwzCM4THGt60CkLhwev3QWpTG6dHWmFtHadwen9LDEOLOKcM4SNS4ZFgPRQQ8Mp1XEg0eFRxjrSPnOQk2ifZ6vDGLAHjcHmgdEjW+OF3HsTwfajpHJGokjwtGxGY1wxrHG6Amr3puvjEtUaJIn00BGuFPxUHgKE0Y2+YoDRMmRo7ScD2NceppjGISwJEahmGUQpu/qBqBa2m0WUsjh6iRq5bGCM02jSxqYoXWU51Y1EQPixqGiT8mhf7HsKAJCY7SGCtKo1fULGqMSiyjNFq3cmZREz0sahiGMQo8e2R0B0dptIeRozSceqbfSJMaYFHDMIwR0OYvqQJordFmdNuN7rLgKM0B2MZZObQkamKF1q2c44WeozQEixqGiQ8mszILw4KG0SlqidLIBUdp9I1WU8/iAaeeyQOLGoZh9Ix6Zmw6hqM04cNRmrawQYC+ozRaFTV6Sj1jURMbWNQwRsFkMimyMCxoVG8OwESO3qI0cqJmUSM3LGpiD4uayGBRwzCMUdmyZQvuuecenHfeedi9e7d47Mcff8SaNWsiWp82fz01CEdpwoejNG3h1DPGyPU0LGrkgUWNsfD5fLj33nvRq1cvJCYmok+fPnj44YchSVLTay655JI2d/1PPPHEg667uLgYf/vb39CpUyex7mHDhmHx4sUx3iNG68yaNUtcKwsWLMAXX3yB2tpa8fiKFStw//33R7RObSdXKxSlaYhRh28mNlEad5QdwSlK4/bq75zL2a2eoiAeGTuvk6hxu+Vbn9zji8UYY3Fe4t21Xqku9Von1udFTeeKRI3kccV9u0bmySefxLRp0/DOO+9gyJAhQnBceumlSE9Pxw033ND0OhIwb7/9dtPfDkfHArSiogITJkzA0UcfLe6s5+TkYNOmTcjMzIRhofSveKeAaTDl7M4778QjjzyCW265BampqU2PH3PMMXj55ZcjWicLmjhC0RKnAhPjaLZLURpnFJMrikw4o5hIUpTGpRMxEe2xaB6lcck8OTcCLGqUmzxrcfwUpfHF8XOm9fMSDixq4svcuXNx2mmnYerUqeLvnj174n//+x8WLlzY4nUkYPLy8sISSt27d28hgigKxDAHY9WqVfjwww/bPN65c2fs3bsXkaCfnJ44orVGm0aHa2m0WUvDqWfahFPPIscojTcJTj+Lnurq6haLyxVcJI4fPx4zZ87Exo0bm9J65syZgylTprR43e+//y4mlAMGDMA111yDffv2dbj9b775BmPGjMFf/vIX8b5Ro0bh9ddfh6EJRGjivWiMjIwMlJSUtHl82bJl6Nq1a0Tr1OcsTcVEU9OiFFxLo+9mm2oWNXLDBgGNcD2NekVNvGFREz12m1l8J8droe0RFB2htLHA8vjjj7eb3nPuuedi4MCBsNlsQnjcdNNNuOCCC1qkm7377rtC+FDkhWocSPBQ/U17bN26VaSy9evXDzNmzBAiiFLYKLWNYTqCrsc77rgDpaWlol7L7/fjzz//xD//+U9cdNFFiAROOdNQLQ1FaZweSVPpbnqqpZELTj1TNqWLU8/0U0+jx/QzI9XTEJx+FjlFRUVIS0s7aM3LJ598gg8++ECk+FANzfLly4Wg6dKlCy6++OKmCWYAKtYePny4MA+gqM2xxx4bdL00CaUIzWOPPSb+JqG0evVqvPLKK03rNRwULIl3wER7ARrQNXPdddcJUU6iefDgweLf888/XzifRQILGgWQW1zUVZZjw9yfsKdwI0wmM7oOHIl+Y4+GPTFZtm3ooZaGDQLax0gGAbGCRY2+YVETO1jURAaJmeaCpj1uu+22pihNQLAUFhaKiE57wqN3797Izs7G5s2b2xU0+fn5YiLanEGDBuHzzz+PaH8YYyBJkojMvPjii7jvvvtEPQ25nJEgpmhfpKgjlycKS0A6MHRA6INFzx933HHCZcMotTRbl/2Jr+67DMXfvonhJSswoGgJ1n/4PL68/0rs3r6x5XtVkrrFqDP1TE6MmHrG6Dv1jGA759ihl/QzNVJfXw+zueXvv8ViERGW9ti5c6eooaG5VXuQw9mGDRtaPEZ1Oj169JBh1IxekSQJffv2FdcYRWhOOukknHPOOVGJGSLiGa7H4xHhTrqYy8vLEQsCloCU80mWgGvXrsWzzz7bwhLwqaeeEiqPQpzkZ52cnIzJkyfD6XRCzcghLvbt3IZ5bzyOk3Ic+OjMY3HfsePxyAkT8cEZR2OEqQ6//ft+uOoavb3lQA+1NGwQoM2mjrGoU+F6mka4nka9sKhh5OCUU07Bo48+iu+//x7bt2/Hl19+ieeeew5nnHGGeJ7ujlMUZ/78+eJ5qqMhVzSadNJ8KgBFappb6t58883iPZQ+RJEcSml77bXXRCqRYTGblFk0BIlrEi8HM50Ie73hvLimpkYUgE2aNEmEOcn6j8KL5D1OivzKK6/EokWLZBtcc0vAww47TNgBnnDCCSKvM6DyXnjhBZFvRx8+yvmkorZdu3bhq6++gt6jNGtmfYcCqw+3TByDpGY/Qp2SEnHv0YfBXr0HGxf82vK9Gp+Mq8UgQK9RGjUbBGjF9YxFjb5Fjd6dzwgWNfripZdewtlnn41rr71WzNmo8Prqq68WzTUD0ZqVK1fi1FNPRf/+/XH55Zdj9OjRmD17dou6HOrs3txS99BDDxXiiCyghw4dKtZHc7LmZgMME4wnnnhCiGiquZKLkL8pSc2TwicxQWr/7rvvFgVllOZFERoaFF38JDjGjh0rPkDRho/IEpDuDpAlIDlukJUbfSBJOBHbtm0TeXiUZhaAnD5o+/PmzWtR5NYcsjZsbm9IdodapGzVfFzQIw/WVqFkIiMxAYfnZmDFmsUYdsypuqmlkQM91tKo1SAgFgX4Rhqfnupp2CQgcrimhokGalxIQoOWYNA8jlzKDgZFb1pz8skni4UJwK4AoUBOZpQKOWLECNjtdnENNieSzK+QBQ1FXv744w/hkBEMiqBcdtllIvWLIiokbqIVNAFLQOokSgKKxkCWgLTzVMhGYobIzc1t8T76O/BcMKgQ7sEHH4TSRNfw0gS/14sEa1IH67dA8nl053jGzTa1O2mOFqO7njEHh0WNfLCoYRgmFrQnrqMhZEFDIcVQoPDk3//+d8hBrCwB77rrLiGSmkdoKLUt0nQsJSyciczegzGvcCn+OmJgm+dcXh8WlFUga9gA2bfLURp5ozRqtHFm17PoYdcz5SbILGrkg0UNwzByEwtb77Bm8ORCQIX68aI9S8AdO3aI/87LyxP/lpWVtXgN/R14rj3RFbA7DNX2MFZEU9My/JipWFHZgO/WbWnxuF+S8NbiVdgLOwZOnCz7dsX7FS4gV0stjV4NAoxGrFzPuJ5GXfUZsYBramIL19QwWsJkpsUU50XpvY4M6jtDFt+PPPKIWKgeq6NGrgcjrMNAFmuUckZOGfHgYJaAZBJAwoUcOZpHW8jtbNy4cXGdqCthDtBj6Bj0PuFcPLdsM279cTa+WL0RH69Yj2u+/R0fb9+HMeffgPSc9i0XlTQIkKsgXmnHM7lggwB9up7FEhY1xrRzJljUMAyjZTZv3iwCFFRL88UXX4iFWrSQxiDziUgI6xfxzz//FDZ9VKR/xRVXCKu/WHIwS0CTySS63ZKyIwMBas5DB4fMCk4//XRohUiFBe3/pL9ehnHXPYztXYbi5Y278dr2ClQNOgLH3fYsBk44AbGEbZzVGaUxSm8ao7uexRoWNeHBoib2ooaFDaN6TCZlFo1B9fBkMkbtX5YuXSoWyr6iQAU9FwnWcCfQZLNGLmeXXnqpaHL5j3/8A1Zry9VEOpjWBCwBqebloYceEjva2hLw9ttvR11dHa666ipUVlZi4sSJmD59OhISEuJeI6JELQ2dk57Dx4olErRuEKAn1FBbpCWDAC25nmmxniYe6Mn5jOCaGmPW1dB3dzwjvBavNm+SMEwAci6mgEVWVlbTY506dRJ2zpSdFQkR3eIZOHCg8Cmn4v/nn3++haChCbZcgiYUS0DaHokdWrRMtI5nTo8k+5i0YBAgh+MZGwR0jNEMAtjKWdkJcyxgUSMvLGoYJjhKBEw0GKAB1bJTb8vWUOYXORlHQti5MlRwTxEaitS8+eabIlxE/WACC1kt6wEt1dJEi9abbaoFTj3TT+pZLO62xjL1jOtp1JmiFS+Mln5GcPoZw2iXk08+WWRWUc27JElioYgNBUqowWskhPUr+NFHH4mCnYaGBqxYsSImtmtGJhphQVEapeBaGv0ZJsRzwmykOhWtjLM1LGrUXU9DsKhhGEYrvPjii6KGhgy8qESEFko169u3L/71r39FtM6wvgEpzYzy26huhlFfX5po0HotjVqabcqVeiYXRkk9iwVcT6PPHjXxIt6pZ0ZOP2MY9aBEkb72cs4yMjLw9ddfC8OvdevWicfI9YwETaSEJWiWL1+Ofv36wShEWx8S0Ta5lkYx5KilkQs1HI9Yw/U08sMmAcaupzGqqGEYRpuQgIlGxDQn5DwSym0LVczU19djzZo1MDJcSxN/1JJ6Jlctjd570xg5pYvradqH7Zyjx4jpZwyjCswmZRaNcdZZZ+HJJ59s8/hTTz0lWsNEQsi/fBdeeCEmT56MTz/9VNgkB2Pt2rW4++67RV7ckiVLoAeirQ+JaJtcS6N59GwQoOaGm7EgVmPUivjSa3E9ixr5YVHDMEwo/PHHHzjppJPaPD5lyhTxXCSE/I1HYmXatGm45557cP7556N///6igSUV8lRUVGD9+vXCbu2MM87ATz/9JHrUGB2upYk/arFxlgtOPdN36hnX0yibwsTpZ7ETNZyCxjBMe7Rnz2yz2VBdXY1ICPlWK22E+sts2LAB8+bNw5VXXomhQ4eia9euOOqoo/Dqq69i165d+N///qc7McNRmnC2zVGaAJx6phxasXKOJbE+R+x8Fj5GidQQHK1hjAj1RlRi0RqkEz7++OOgbsqDBw+OaJ0RfdONGTNGLMzB4SiNcZttqhF2PVMfsYrSEGwSEBocqYkNbBbAMEww7r33Xpx55pnYsmULjjnmGPHYzJkzRVCESlsiQd23WVUER2nC2bbylxUbBGgPozbcJNgkoH04UiMPHKlhmDhA0RIlFo1xyimn4KuvvhK2zddeey1uvfVW7Ny5E7/88gtOP/30iNapj+pOlaNUlCYaG+doozR6sHGWC+5NE876uJ5Ga8Tj7j9HauSBIzUMw6iFqVOnikUuWNDouC+N0dFb6pmcIk/NokZuWNRo3ySAYFEjDyxqYnPTyhZHZ0uTylw0mWZQsIT7aoaF0+kU9TTkoHz88cdH3O+SPxUa6EujVOpZtH1p2CDgAJx6pt/ie62gdZMAgtPP5IHTzxiGUYJbbrkF//jHP5r+drvdOPzww4XRGLV9GTVqlDAeiwSrHMqKrJuNghJRGiXReoRIb1EaOVFzlIZTz7RpEqDU3f9YwJGa2KDnSA0THtu2bcPs2bNRWFgoGrLn5OSICe24ceMMNa80Ej/99BMee+yxpr8/+OAD7NixA5s2bUJBQQEuu+wyPPLII/j+++/DXndEt+z8fj8efvhhYdmckpKCrVu3NrkWvPnmm5Gs0hBoMUoTLWqI0rBBQPuwlXN0sEmAfu2c4w1HahijQJPYww47TDRhv+OOO0RxOAmbN954AyeeeCJyc3NFoTgJHa1hMiuzaAUSL81tmUngnH322ejRo4ewn77xxhuxbNmyiNYd0WEg9fTf//4XTz31VIvGONSXhi5IvaMGF694Em3qWdTb11EKkhpFjVrRSuoZi5r4o8fUM4JFDaN3KALz4osv4pJLLhGCpaSkBEuWLMGcOXNEA3dqqvj111+LG+fUHiRSC19GnZjNZkjSAbOq+fPni5SzABkZGaioqIhs3ZG86d1338Vrr72GCy64ABbLgS/gESNGYP369RENxChwlEYZ1BKlUSNqjtJowcqZaQvX00QOixpGzzzxxBNYsGCBiMB07969zfMOh0M0a3/llVfEfLJ3797QpitAvBdtMGjQIHz77bfiv9esWSMiNkcffXTT8yRyKUIXCRF9ixUXF6Nv375tHidF7fEYIzeWa2nCfD/bOMtu48yuZ5HB9TT6qafRo/OZEWtqCK6rMQaTJ08O+bWdOnUSC6Mfbr/9dpx77rmiRoYEzUknnYRevXo1Pf/DDz+IdMS4CRrKf6N8R8p5a85nn30mwolM7PrSRCMsoulLowfUZBCgRlGjVmIhFljUNMKiJnRY1MQWNgswJj6fD19++SXWrVvXdAefGitardxVRI+cccYZQrR89913OOGEE1o4nhFJSUkiehcJEV0x9913Hy6++GIRqaGozBdffIENGzaIVDQapFHgKI32ojRqEjVqQ81RmliJBblhUdMWFjWRw6KG0TN0h/7UU09FaWkpBgwYIB578sknhdsZpSVRXbbmMJsal3hvU0Mce+yxYgnG/fffH/F6I0p4P+2008TF9ssvvyA5OVkIHFLX9Bg1xWEOjhFraRj1GwRwPU30sElAW7imJnKMVFNDcF2NcbjiiiswZMgQ7Ny5E0uXLhVLUVERhg8fjquuukrp4TEaI+xvLa/XKzykySv6559/htHhKE2Y7+coTUwwQupZLIhl5ENrcKQmdDhSE1s4UmMMli9fjsWLFyMzM7PpMfrvRx99FIceeig0icnUuMR7m0z4ERrKayS7ZhI2jDGjNGzjrL4ojZwYKUpDcH+a+MGRmsjhSA2jN/r374+ysrI2j+/evTuo8RTDdERE31aU+zZr1iz07NkTesJuNsc96hCNQUA0KGkQoIaolpqiNGo0COB6mujhehrl4EiNfHCk5uDYrJb42vp7tH1jI8Djjz+OG264AQ888EBTLxLqS/LQQw+JWhrqSRMgLS0NmoAjNNoSNFOmTMGdd96JVatWYfTo0aKOpjlU5GU0lJikR5v+peS2OfXMWKlnahc1sRIJLGqUmyCzqNGPqCG0IGyY8Dj55JPFv+ecc47oEk8Emi6ecsopTX/Tc+SGxuiH+++/X5SutHZLjrugCViqPffcc22e0/qF5zCb4fLHVyQYMUrDxCZKIydyRmm0gNbqaVjUHBwWNfKnn3G0hpGL3377TekhMArx9ddfi1qpSZMm4fLLL8dZZ50lmqrGXdCQVTPTFo7ShPl+jtK0XA+nnoW5PmP3pyFY1KgHI4gaNURrWNToB5rM6g3OOAvdEGLZsmV4++23ceONN+K6664TDTcpahOpIYT6KpJVEqWJN1o1CFAatRgExDV/WkPHpTVsEqAt2M45PIxgFECwWQAjJ/X19Vi/fj1WrlzZYmH0zahRo/Diiy9i165dePPNN4V994QJE4Rt97/+9S9UVVWFtb6If63IFIByHMmJghaqm5k9ezaMDkUdjES0jmdqOF4UpVELenc9iwVaEQtadT5jURMeLGpiD4safbBnzx5RR5Oamir60dAEt/miSQKNNeO9aBiqk/J4PHC73eK/ybr75ZdfRvfu3fHxxx+HvJ6Ifqnef/99HHfccUhKShIOFbQkJiYK97MPP/wQeoCjNPHZduP2o32/RRWiRq4oDTfcVB5uuhlfWNREh1FFDQsbbXPTTTehsrISCxYsEHPI6dOn45133kG/fv3wzTffKD08JsYsWbIE119/PfLz83HzzTcLEbtu3ToRMNm0aZOosSF9ESoRfRvRRqgXDQ0gAG2UTAIefvhhnH/++TAyarAlNppBgFrcvfTsesb1NPLAzmf6b7xJcE1NfKBz6lRs60w0/Prrr6I4fMyYMTCbzcLx6vjjjxcWzWTpPHXqVKWHyMSIYcOGiTTDE044QaSbUcaXxdLyxsx5550n6mtCJaJbpVu3bm2y1GsOpZ1t27YNesFIUZpoUTpKIwecehZfjFpPE0tiOd54RNY4UhMdRozUMNqlrq4OnTt3Fv9NaUaUghaY7C5duhSadgWI96IxyKp7+/bt+P7773H66ae3ETNEdnZ2WCZkEf1CUV7bzJkz2zz+yy+/iOcYdUzQw4UNAjj17GBwPY36x8mi5uDoXdQoIWxY1DDhMmDAAGzYsEH894gRI/Dqq6+iuLgYr7zyikhDYvTLvffei65du8q6zoi+gW699VaRYka2a+PHjxeP/fnnn/jvf/8rnAn0hNb60hjZxllNcOqZfjrTay31jOD0M2Onnxm1Vw2jLSidqKSkpKnR4oknnogPPvgAdrtdzCe1iGn//+K9Ta1BfWcOO+ww3HHHHS0ep3KWRYsW4dNPP42PoLnmmmuQl5eHZ599Fp988ol4bNCgQcKN4LTTToPeiFTURDNBV0rUKF1Lo5feNHpuuCk3XE+jHuEbKixqwscookYNdTWMNvjb3/7W9N+jR49GYWGhqKsoKCgQ6UaMfvnjjz/wwAMPtHl8ypQpQltEQsQx4jPOOEMsDKOWCJHaRA033AwdFjXaMgnQSnQtFFjUxAYWNUy4kHPuIYccAk2jhI2yBm2ba2trRSSuNTabDdXV1fETNBQOokKdsWPHtnicrPeosIccK/QGR2nis+3G7XPqWaxQs6iRGy2JmlihdVETr0kxi5rYYBRRQzfA4mnkYtKJaQz1HPnss8/w22+/Yffu3W0KwL/44gvFxsbEFjJ+oKyu++67r8XjH330EQYPHhw/QXPdddfh9ttvbyNoqJjrySefFMKG0fYEXenUM71EadSYeqZWUaOXO/5GqqchWNSED4sahmnsQ0NGAEcffTRyc3Nh0qBbFxO5KcCZZ56JLVu24JhjjhGPkdnY//73v4jqZyIWNGvXrg0aFqSmOPScXmGDgPhtWw4RqLfeNFxPo+8oDYua9mFRIw8sahg18d5774kozEknnQS9QJIs3rpMizLwlFNOwVdffYXHHntMROmoserw4cOFW/KkSZPiJ2gcDgfKysrQu3fvFo+TW4XVytaNweAojTaPmVxRGk49Cx0WNdoUNbFGr6KGMEoDToKFDRMgPT29zTySMQ5Tp06VtXlqRImY1NnzrrvuQlVVVdNjlZWVuPvuu0WXVz3DzTa1sW219aaRCznztLk/jTr7vXCPGuV7ncSzT42RGnAS3K+GCUAuVw8++CAaGhqgG7ixZli43W7s3LkTO3bsaLFEQkTfLM888wyOPPJI9OjRQ6SZEdSThnIgKYTIqCviwAYB6oBTz5RNYeJITSPco0Z9kRol0rKUitQQnILGBLrFU81E586d0bNnT+Fw1ZylS5cqNjYmtmzatAmXXXYZ5s6d28YogmqpfD5ffAQNdfdcuXKlaIC0YsUKkft26aWX4rzzzmtzQeoRrdXSaD31TE8GAWoTNUZKPYsVWhQ1sYRFjbZEDcF1NYwSXHzxxViyZInoR8OmAMbikksuESUq3333HfLz82U59xHHfpOTk3HVVVdFPQCjoUUb52hRQ5RGTaJGbRhJ1GhVJMiN1k0CCBY18sFmAYwSfP/995gxYwYmTpwI3aBECpgGheDy5cuFmB04cKBs6wwr6Xnjxo1YuHBhi8fIZo0s9w477DDhVmAUlKilURKK0kT1/ihrP0jU6AWK0siyHoPU08hdm8H1NLGvpyG4pkZbdSZK1dXQvnJdjTHp3r070tLSlB4GowDUa2bv3r2yrjOsX5w77rhDhIcCbNu2TVivUbfPcePG4fHHH8cLL7wg6wD1SDSTc60aBKgBNRkEqFHUGAkWNY2wqAkdFjWxhUVN7KB6BOr70atXL1Ei0KdPHzz88MOiXqF5gT7dLafsm8zMTBx33HEh9RSk/oOUMtapUyexbmqYuHjx4pDG9eyzz4qehtu3b4deMJlNiixag3pW0rn//fffsW/fPlRXV7dYIiGsbxC6SGkAAaiGpn///iJkSJCH9EsvvSSaJRmBaGpptFjsrgeDAE49ax8jpZ41rpNNAghOPwsdTj+LLZyCFrvJ47Rp0/DOO+9gyJAhYi5Hdc9km3zDDTeI19Bc7uWXXxY2yuQ69vzzzwtH282bNyMnJyfoeisqKjBhwgSRpfPjjz+K11GxNwmiUCAhVF9fLwRWUlJSmxrs8vJyGfaeUSMkmIljjz1WGVMACg9169at6e/ffvtNRGgCHHXUUbj11lthJLRmEBC9qODeNHKhNoMAI4qaWMCipi0saiKHRQ3THq3vZFOPQFpaQ05Sp512WlPPD3IUI3ex5iUE559/fov3PPfcc3jzzTeFAVTrSWdzoURpY2+//XbTYxQFChUSTWwEYEx+++032dcZlqDJysoSzTPpAvb7/ULl33LLLS38pJuHMJmOYYMAZVBTlIZFjT5NAljU6F/UEPFswKmEqCG4CWdo35lypRGHgnn/tmgu1pz7779fpI61Zvz48XjttddEHTRFYsidds6cOUK0BIPmcvR6iuCMGDGi3XF88803mDx5Mv7yl79g1qxZwgH32muvxZVXXhmy05X+IIEWb5GmPVE4adIk2dcZVoIzRWAo77KoqEjUypCooccCrF27Vih/PeJ2OVGxpxQNdbVtnmODAO0ZBKipniZSJL8fzupysdCNBLXW08Sy6abPVQ9X1V74PK4OX+epr4a7ei/8Pm9MalRove7qffDW1yDWyFlT4/c44anZC7+7sbEd19Sot65GqRoTrqtRLzQXowbngYUangfjzjvvxLnnnitqZCiti/oHUmnABRdc0OJ1VCOdkpKChIQEET35+eefkZ2d3e72t27dKlLZ+vXrJ0oPrrnmGpHCRqltoWCxWLB79+42j1NNBT3H6JvZs2eLtEMS3FSLRVAvSxLbkRDWt8Wjjz6K448/XjTUpIvtxRdfFAVkAWggxxxzDLTKJ/96GMeccykK+g1qeqxq3x78+vn7WDt7BvwuJySzBX3GTMRRZ16Arr37R71NI0Zp5MDIqWckZHYsmIHiP7+Fd08xJJhgzy1AtyNOReeRR8sSwpczShOLSXHVru0o/fMr1KxfAJPPA9gTkT70COSNPx2O9AM/wNXbVmHPwm/h27kWZkmCPzEVyUOPRt7hp8DiSIo68uFzO7F70XdoWP0bzM5q+GGCuctAZI2ZirReI2LYwDK6SI27ajcqlnwH/7YFsPg88JqtMPccg8xDToYjqwtHalSagqZU5IJT0NQJOYSF4hL2ySefiJrnDz/8UNTQkGUuCZouXbqIXjABqBaGnqPygtdff100viRjAGp8GQy6qT1mzJgmh1sSSqtXr8Yrr7zSYr3t0V5Gj8vlEmZTmoTu2cT73qI672V2yOeff44LL7xQiGpqoErnnCBhTtfTDz/8gJgKGoq+rFu3DmvWrBHFX/RhaM6DDz7YosZGazg2LMJ7DyzH2bc9ggEjDxMRmbfuvwmplSW4akABBuRkYldVLb5aNQf/XbEA5931JHoPHqFYLY2RDQKMmnpGYmb1l6+gZu73GGd3YGR6J/H40t1FmP/R86gpLUSfEy9RnaiRM/WspngTtr7/CFKqKjDSnopkSyqqXE5sm/8dNm1Zin4X3A9HZi72rZ6D8hnTMDYrEZMP7Yf0BAdWle7BN0u/wrYdq9HrnLthcSRGLBJIzBR9+TQy92zAOb3yMTy/J2pcLvy8pRDzvnkG3mOvRNbQI2MqDiLBXVGCvd8+ib6mWpwypAA9MtJQXF2D7zYtxrpvViFr6j+RkNODRU0YcF1NbGFREx233XZbU5SGICeywsJC4UzbXHjQDeq+ffuK5fDDDxeRF6qjaS/yQw0RyX63OYMGDRKT1Y6gm+EE/U698cYbIioUgIrB//jjD1n7kzBt5/J0/ltD6YL//ve/4XQ6RT38Rx99JIQGpRX+5z//Ec1P5eKRRx4Rwveiiy4S2wlAJhP0XCSEHc+lzp7t5VR2lGupBZ476Qg8O3spvpn2NG5++QP8+P5ryKouxcunHImspATxmtFdczF5QE/834x5+OaVZ3DDC+/AHGXKmVajNEqLGr25noUiavZsWoaqeT/gqqwcjErLanp8aGoG+lXswTt/fI3cIYcjteBAlFEtyCFqSNAVfvsqOlVX4fCMbjDvvzWV40hGd386/tyzCzt/fR89Troae2e+hXN6dMLVh41oEngj8jvjyF7dcesvC1C26Ad0mXhWi/WHI2r2LvsJ6bs34MljD0OvzPSmxyf26IY3F6/Eh7+/g7TeI2FPSlNVPU35nx9iuK0Bjxw7ASn774IOz8vBUb0K8MBvC7D4j3eRd+Y9XFMTJixqYovW6mrUBDmJtZ6nUJYNRVg6gp4P3DkPBk0+N2zY0OIxqtOhLJ6OoHS2QISGJrXN08soMkMTbnpcuyU08W6sGd7LFy1a1MJFjKJqlH1FtVDEzTffLJqefvrpp6KO6vrrr8eZZ56JP//8U7Yh03Vz5JFHtnmctldZWWmUQFXssJrNuPLQofDsLcGSWT9hy8JZuGBonyYxE8BuseCKQwejdtd2bF61VJZaGqUaR2q9N41aGm7Gq56meMHP6GcytRAzAQ7PyEaB5EPx4l9U2XBTjnqamqL1cJdsxaCkLFhM5ha/Gw6zFf0caajbuBi7l8xAuq8e548Y1CZaVZCRhqm98lC76ldIfl/Ewqpu1a+Y0jO3hZghaHvnDh+ETMmJinVzVdWjxl1ZCtOutfjrkL5NYiZAos2KC4b3g3XfNrj2NPaF4Jqa8DBKrxquq9EW5EZLJQM0SaWeL19++aUwBDjjjDPE83V1dbj77rsxf/58ceeeOrhfdtlloq4hMMklyO2MrJ0D0MSX3kMpQmTvTCltZCZw3XXXdTge6mFICxWGk0FB4G9aaKJL9Thjx46N4RExNjk5OcjLy2taqHaKrLPpfFDKF0Xl6PqgEpLRo0cLFztyyqNzLRe0XbpmWkP1M2QdHgnqmA2qiB6ZaeiUYEPhxrUwedwYnh/cf31AThaSzcDekiLFDQKiabapdYMANU7ao+FgTjnO0u3on3AgTar1RHqAIxHO0kJZDQLUdHyc5aUwez3ItAU/Bp3syTB5XKgr2YLeaUlIS2hrYUoMyc2Gqb4SXmddRCLBR0X0deUY3Lkx5a81KQ47+mekwFVR2vSYGkSNu7IMDnjbHffgztnieRI+8YJFTXSwWQBzMKg/4Nlnny1Siigl7J///CeuvvpqYfJEUIRk/fr1OOuss4QLGgkgKsynom2quQmwZcuWFt3dDz30UCGOyAJ66NChYn1kGNXabKAj695Qe9YwB6e6VXPKjqJrzR3t3n//fSFgaQ5BYtbj8TT1iSEo/a+goADz5s2TbazkhHfjjTeKGi3a7q5du0SdF12bZC4RCfyt0Aqn14tatwfdU1IhmcyoaHCie0Zqm9fVuNxw+fywOVpGb6KBU88i3b5xUs/MCcmorihr973VXi/MCUm6tXI22x2QTCa4JR8cpsavLwrABGpLXT6P+NxaHMkod7rhlySYg4T/Kxtc8JvMMFvtEaVz0fskkwVVzuA/GJRKsa/BBbPNrio7Z7PNIYwLKp0uUVPUmsoGp3jeajvwXKxTzwhOP9NmjYkaUtA88TvMmiU1NVUIDVqCQa5mX3zxxUHXQ9Gd1px88sliiZSdO3cK++cdO3aIyXVz2rOVVjX0exP3lLPG7YVq492cr776SqR4BSy0S0tLRdpfRkZGi9dR/Qw9JxdU00UpjRT1o5RISj+jHkokaP7xj39EtM6wb415vV489NBD4iLUIz9vLES9xY6JU05Han4Bvlm7Nejrfly/Dd6EJAwcdbgubJzVECmJBqOknmUPn4BFLidqvG1/xSs8bizzuJA9dHzTY2qN1ESaepbRewSk5HQU1rfMsQ38fhQ6K2Hp1AWdDzkexU4/lha3FX8+vx/TtxTB1msULPaEiCIfZqsNtl6H4MctRfAGyUNfUbIbhfVepPcd0+Y5JSM1CXl94ErMws+b2k5MiJ83F6LBnoqkri1rsGKdekZwpCY6jBipIaxW9USQmfCYOXMmBgwYIKyfn332WRGxofSmt956S7itMbGx8W4OpZdNmTKljclXrKGozP/93/+hvLxc1PBQOtuePXuaooaRYI7EFODpp58WwkZv/LKpEC8t3YBhR09Fdl5XHPOXi/FTSRVeXbBSRGQIt8+Hb9ZuwWsrt2LkCWcgNSNTNRNzTj3Tf+pZtzHHwpndBdOKt2Gns74pGlDYUItpu7bBm1uArqPaFtqpkUhEjTUxBTmHT8V6XwO21pXDJzWKCbffh/W1u1FoAXInnonkrv1g6jkSzy5YjQVFu0Skhthb14AX5y7D8hofOh96clQiIfvQk7C6TsILc5dgT13juaDtLNpZgmcWrILUfRiSuvRDPDmYqDFbbEgYORVfbCvDZ6s3oH5/FIEi09+u34IPNuyEfdhkmO1tU/pY1GhD1ChVV8Mw4UITbrojv2rVKhElInc0mpRTLUfz2h1NRmjiveCAjXdgoYhHR1C91C+//IIrrriiRW0LRcpaF+aXlZWJ5+SGokHklHfYYYe1cLuLhIi++ahQiLrC6q2J5tPLt2LY0afgzCtvFH+POeoE1NdW48MPXsUXm2aia0oi9tS7UAkLhk0+C1MuuKrddUVj46xUjxU1WClHgxFSzxwp6Rhx2f1Y9f6TeLRkO/JMJkiQUEZpQl37YuSFd8CW2PJLQa2pZ5Gmn3Wb9BfRSHPlgh+wvqoYiSYz6uCDLzEV+cdchE7DJom7PwUnX4+iH6bh3rlLkGdfhzSHHYU19ahzpKPzyTcgpVv/qNK5kvL6IHvqjfjhp9cw6/vZ6JGSiBq3ByVuCaaCEeg65Zp27bNjmcZ1sPSzjKHHoNxdj2nLvsXHG4qQl5yA3fVO7PNZYBk2FdmHTG33vZx+pv70MyUd0AilUtAY7UFtQKj+JnCzvKGhQUxqKQvotNNOi7iWggkNioZRj6GpUw9855MJADVfpegZ1VQRZNRAKYHjxo2DXFDPo47aS/z666/xETQUnqL8N1LVtPPNm2sSp556KrTIVU+8ip79W3qqH3ny2Rg6/mismPs7KveWIS8lDcPGTRIRHDUSTS1NtChdS6OmhpuxFDWpeQU4/JYXsWfDUlQWbhCWjQN6DkZ2v5EwtZPuqGZREy4mswU9T7gYeYdOQfm6+fA01KBzahayh4yHLbnRcYwmxNaEJPQ681bUlW5D1ealqPC6kdCpC/IHjO0w1SwckUC2zCmXP4/KjQuxbd8ukYqW02cUEjv3PGgvIKVEDY2r0+hT4B04ETWbF2NjXQUsSWno1GcMbKntdwXXo6gh4jHxD0RquK6GYQ5Ac8dA3Qz1tCHTgYAJQXPzAUZ+/H6/EDTUh4jEZHPb5Msvvxy33HILsrKyRKSHalpIzFBvIrkYOXJki7/JiIDSDCn9LJSmrLIJGnLKaK9gi34sm/tba4nOXVoWVAXIyuqECVMa7Q3DQakojZENAvQwYQ8Fs9mC3EGHiiVU9GYSkJDZGV3GH/zmSXJeL7HECiq0zxpyRETvVTJSY03OROaI4yNat15Ejd6jNSxqGDVDE2Sy6SXntZNOOkk0c6Qb5WRQIOfk2SimAOFAqWYUdSF3s2B9gqhvEUVomjfWlJNAL6LWkIlBbW1t/ATNwZoxMfKIGqVQg6iIBiOknukNOZpuxnoyHGkjSzWLmmhgURMZLGr0h8NmFku8MKnEBCda6KZ4YPL64IMPiv/++OOP0a9fP206nGmIE044QdTfBoPqmf7973+LJd787W9/E/U0zzzzTNjvZdvmELFbzHD74jvJVypKEy1qiNKwqNFWlIZgUcOiJhgsarQtagijCBsmPJo3UKT0s1deeQWah4IlcQ7QxH17MYR63ZCgioSIBQ11liVjgGDe4TfccEOkq9UlnHoW/+2rCRY1ocOihkVNMFjUaKtmyMjRGiY8yE3rs88+E/Uzt912m6jbWLp0qeh70rWrOmuVmeg588wzW/xN0aKSkhIsXrwY9957b/wEzbJly0S+IzXDIWFDFyAVcCUlJQnHBL0KGiWiNEqidVGhliiNGlGzqJEbFjWxh0VNZBjFAY1gUcO0ZuXKlaIjPRWiU9NO6h5P80mqoaGb5e+++y40h8ncuMR7mxqDznlzqGaHehKRwx2lw8VN0Nx888045ZRTRHiQBkUNccjmjXLfbryx0fJYr0QqarQYpYkWNURp1CJq1BalkRs1mAR0BIua2EZpCBY1kWE0UUOwsGEIctKiDvVPPfUUUlNTmx6nG+bnn3++omNjYgs5rMlNRLKOrNXIjYIUlcViES4I3bt3Fxfl3XffLfsg9QKJGq0RbcNLPTTclAsSNbFsuhn2elR8bCNpuqkEB2tkGSmxamIZq/Hqrfmm3htwEko04AzAjTgZYtGiRbj66qvbPE6pZqWlpYqMidEuEX2jUTSGxAxBKWYUGiTbPYrWUJdXvcMGAdpCLVEaNUZq1Jx6poV6mnhEPuSGIzXqjdQQXFfDGAXqZF9dXd3m8Y0bNyInJweaRCO2zUqQmZl50P5sAcrLy+MjaEaNGiWUNVnrTZo0Cffdd5+ooXnvvfcwdOjQSFZpGLSYeqYHgwA1iRq1waJGnWjVJIBgURM5RktBY1FjXKgJO9VMfPLJJ+JvmuzSDfI77rijqUs9ox9eeOGFmK4/IkHz2GOPoaamRvz3o48+iosuugjXXHONEDhvvfUWjAAbBGhP1KgFtUVpxLpY1BiqnoZgURM6LGpiB9fVGJdnn30WZ599tsj0aWhoEDfIKdWMutLT3FKTcISmXS6++GKoTtCMGTOm6b/pQpw+fTqMCBsEaAs1RWnUKGrkRO3RLBY1jbCoUW96lpFEDcHRGuNBZQo///wz5syZIxzPqLHmIYccIpzPGH1y33334c477xSuyERFRYVIRZMD9VYF6xylDAJI1BjZIIBETfTrUFdBK5sEqKe4XGsmAQQbBYQHmwXEDjYLMCYTJ07Etddei9tvv53FjM559NFHhXAN0KNHD2zdulWWdUf0zbVv3z6hsn777Tfs3r0b/lbRhkiKebSK1gwCot62DlLP1BKpkStKIydGSj0jOFLTCEdqwsMIts4Ep6AxseDFF18M6XWa7GlI923jnQGmjYyzpgaaHf0dd0Fz4YUXYvPmzbj88stFN9dQXQuYlnDqWWRwPU1buJ4mcljUNMKiJjz07oBGcAoaEwuef/75Fn+TO25+fj6s1gNTUppXalLQMIoRkaCZPXu2yHkcMWIE9ESCxRL3KE00oiYatOx6JgdqidIQLGqUFzWxgEWNcqKGYLMAeWBRc3ASLGbZ+oyFgsmi3vTgUNi2bVuLv6mp5qxZs9C7d29oHpO5cYn3NjUCCVUyFUtISBDRGfqbUtBa23enpaXFR9AMHDhQOFIwB+DUszDfz6lnMcEoJgFacD5rXC+LGiVEDcEOaPLBKWgMw8gBiZj+/fu3+JtawTT/m0SOz+eLj6D5z3/+I1wKqI6G+s5Qo81olZWaojTOCA5kNHDqmbZTz7ieJjRY1MgLi5qDw6JGXjhawzAHgW2bO4Rq72NFRIImIyNDhIeOOeaYFo9Ho6z0IGq4N40xU8/kQu+pZ1qARc0BWNSoX9QQnILGMIxWoF5DqhI0F1xwgYjKfPjhh2wKYPAojdKiRm+pZ3oXNWqP0hAsauIragi9mAUokZplxLoagoWNdmldLyFnHQVjXCISNKtXr8ayZcswYMAA6BUlojRaFTVKw6ImOCxqtOGWZXRRQ7ADWnQYra6G4GiNdqEsn+Y3wuWso1AeBVLOtOTbrDZBM2bMGGGzp2dBEw2ceqa91DO5YJOA0DCqqImlOGBRc3BY1OgvWuOXuBmn1ohlHQVjXCISNP/4xz9w44034rbbbsOwYcPamAIMHz4ceoANAsLYNqeeyQqbBIQPixoWNaHAokZfoobRHrGso1AcbqypLUHz17/+Vfx72WWXNT1G4UFthwn1kXoWDZx6xqlnB10XixqZ1smipj1Y1GjXLIBgYcN0RF1dHZKTk2P2esa4WOVoisQEh3vTxD/1TE9WzkYRNXLDooZFjRobcBIcrWGMTt++fUWGz8UXX4z8/Pygr6Gb47/88guee+45HHnkkbjrrrviPk4mtpBQfeKJJzBz5kzs3r0b/lY38bdu3RofQdOjRw8YCU4905aoUYso1HM9jZpNArQEixrj2DoTnILGGJ3ff/8dd999Nx544AGMGDFC1GR36dJFdI6vqKjA2rVrMW/ePFitViFkrr76amgKk7lxifc2NcYVV1yBWbNm4cILLxTCVg635IgEDbFr1y7MmTMnqLK64YYboh6YXtCiQYDSqWdcTxO7ehqjiBqtRGka18uipj1Y1Ghb1BAsbJjmkJnU559/jh07duDTTz/F7NmzMXfuXDQ0NCA7O1u4nb3++uuYMmUKLBY2fNArP/74I77//ntMmDBBtnVGJGj++9//CtVst9vRqVOnFsqK/luPgsZIUZpoUUPal1pEjdpSz9QMixoWNcFgUaPduhqjRGsctsYbcXGjpQ+TJikoKMCtt94qFl1hUsC2WYO9IDMzM5GVlSXrOiOKU91777247777UFVVhe3bt4uamsASSd6blkRNpFGaaERNNBPyaKI00QqKqN4fzx+HOEzU5YBEjVxRGrmgKI0aj1VzUROr2gv512uJeQNLrY07HuOPx7ntKIIRTwLCJt4osa8Mw6iXhx9+WOiI+vp62dYZ0bcMDeDcc8+FOYrJttEwYuqZ0vU0aonSqDFSo9bUM4IjNdqM1BCxjNboNVJDcAoaw+gEjtCExLPPPostW7YgNzcXPXv2bNP+ZenSpYiLoLn88stF7uOdd94Jo8GpZ9pCTaJGbbCoiQ4WNfFPQdOjqCE4BY1hGCNx+umny77OiATN448/jpNPPhnTp08P2liTrPYY9fSmMbrrmVpEjdqiNGoXNXLDokY/oobQk62zUhN9dkFjGEYJ7r//fvUImhkzZgi3CqK1KYDeiSZKw6ln2rRylgs1iho5UbPzGcGiRh+iRq/RGqOJGoKFDaMraAoc72mw/qfdsRM0lPv21ltv4ZJLLoFRMVrqGVs5cz2NErCoYVFjRFFDcAoao1eOPfZYXHfddTjzzDODPr93714cdthhujaZMjo+nw/PP/88PvnkE2Hh7Xa7WzxfXl4e9jojqup3OByyekcbDS26nkVLtK5nahmDkscwGOx8FhnsfhYf9zB2QIsco7mgsROacfjtt99wzjnntJt2RJPdwsJCaBGTyazIojUefPBBUZ7y17/+VTgm33LLLULgktkYNV2NhIiOwo033oiXXnoJRidSG+doRY1S6MHKWS2iJhaT9GhhURM9LGrawqImcowkaggWNcZh2rRpeOGFF3DGGWegrq5O6eEwceaDDz4QDVSpD5HVasV5552HN954Q1g5z58/P6J1RvTtsXDhQvz666/47rvvMGTIkDamAF988QWMAqeexReupzGuSQCnn3H6mVrSzwiuq4kNnIL2/+2dCbyV0/rHnzM1nVSaS3PSoFypVIrcSilDys1UV5RcJIQQushQ+ZuuK7mS4pZZUlxFoXtTUZG4aJ404mpSZ97/z7O2vdv7nL3P2Xu/633XWu/6fX1enbOHtd79nn3e8373Myw7GDBgAHXv3l3826VLF3r33XepWbNmqncLeMSePXtEUzGmcuXKIkrDcMMxXusyFVL6uKlatWoiNNSjRw+qWbMmVa1aNWoDiYHUM3P3QdYx1G3RTRsjNW6ASI2/IzUqFuH0OnrBUoMUNOAmrVu3ppUrV1LDhg2pU6dOtGjRIvJHU4A0jzfVLzp5GjRoQLt37xZfN2/enD788EPxNb8fuKwlFVI6Y8yYMYP8SFaKgqEiSqMSP3Q9Q5OA0rEpUuNGlIZBpEZdpIbxW7MABtEa4Df4Q/D333+fxo0bR/3796fJkyfTFVdcoXq3gMtwquHixYupc+fONHr0aBo6dChNnz5dNAgYM2ZMSmPiIxBJpCo1Jq5Nw0Bq9Fx004Z2zgykBlJjYwc0lVLDoL0zkEHx5T34+0mTJtGpp55K11xzjShpMBa0bU4I/nmH4MYAjRo1ouXLl1OLFi3oggsuIE+F5q233orbbu3LL78kU1GxToypUqMaXeppdFp0Uyay2zlDamSPC6kpC0iNXBCtif/hmKfp1BqkbjshEIj9d/uyyy6jVq1aubKKPNCbrl27is0JKQnN008/Tffcc49Yh4YLua6++mratGmTyH3j3uKmk6pgmJh65uRiXHWURgY67IMtqWcmAKnxVmoYNAswK3KhWmoYXcUGJN62uXr16jHv4yjN6tWrRRqakYTqWrye00B27dpFS5cupX379lFRsQ/mb7rpJm+E5tlnn6Xnn39etFmbOXMm3XHHHaI7BbdbS2UxHD9hWuqZU1RLjd9Sz/wuNbpHaRhIjbcy4KcOaLZEa1SmoOkerQFlww2lSqNGjRp05ZVXerY/wHvYHf7yl79QuXLlxM87Mg2Rv05FaFKKW3Ka2RlnnCG+rlixIh06dEh8/ec//5leffVV8gMq1okxtesZ1qcJ7Qc6n/ml85kba9Qw6H4WG3RAc4aqjmCq16xBJzQAzGT8+PEiCMLtmrdu3UpbtmwJb5s3b05pzJTOunXr1g1HYriQJ7QIDu9IvNxIm3Cy4KYqVLZyloEMqZEBpCbBsSA1xkmNX9o6+3kRTtsW4mQgNUAr0tLVbIZx5MgRUTOV7uDD+OKkNFLPnj1p3rx54muun+EWa+ecc47oVMCt2PyCTVEap6iO0shA1j7oJoeQmtSB1ETjB6lhvJQaG6I1KtesAQCYx4gRI+jNN9+UOmZKZz6unwkV8HATAM5/W7ZsGV144YUiJ85PqGgQYGrXM9TTyEXHzmeyQU2NWTU1DDqgJY8NdTWqGwYAoAVoCpAQEydOpPPPP58WLFhA7dq1o6ys6A9EnnjiCXJdaAoKCuiRRx6h4cOHi5U+GQ4b8Qbs7nqmA7pIDZoEJDEepMaFcSE1ieB3qWFsaxgAADBDaBYuXEgtW7YU3xdvCpAKScfCMzMz6dFHHxViYwtIPTMr9QxNAsxKPRPjIf3MhXHNTz9DswDn2NgwAERTWFgoirCbNm0qGjk1b96cHnzwwaia5zlz5lCfPn3CHafWrFmT0Nj79+8XmTr16tWj8uXL00knnUT/+te/XHw1wA88/vjj9OKLL9L3339Pn376qWjlHdpSXVg1pTNdr169aMmSJdSkSROyBaSemZN6phOI1CQxHiI1LoxrdqTGy7bOjB/Xq2GQgmY3kydPpqlTp9JLL71EJ598Mq1atUrUP1etWjXcHve3336j7t270yWXXEIjR45MaFxeVJ3rp2vXri0WWz/hhBNo27ZtVK1aNbIW/jzV6www8zLOiOW3W7duUsdMSWj69etHd911F33zzTfUoUMHys7Ojrqfa2nAMSA1dtfT6JbGB6lxBqTGn1LDIAVNPkhBUw/XOA8YMIDOO+888T1/GM1LbHzxxRfhx/CyGwy30E0U/oSdO97y+KEaCJs+6Aapc/PNN9Pf//53evrpp0mp0Nxwww1xi3Y4VMnhTT/iRC5MRLcLcZOlRreLc52lRjaQmmPpZ1iA026pYRCt8Q8HDx4s8ak3b8XhdQO5mdP69etFStjXX38tVmhPpfA6Eu5227VrV5Fy9u6771KtWrXoiiuuoDvvvJMyDFy+QgpoCpAQLNOcWvbee++JqGHxpgCcAumJ0IQ6nNmITalnTlEdpdFJanRLPdNZamRHaRhIjftSAKlJDdtS0Bi/iQ2f353WjybF73M1bNgw6ub77ruP7r///hIP54walp9WrVoJ0eAPnR9++GEaMmSIo93gBRD5opTH4bqZjRs3ig+88/Pzxb4AEA9OSxw0aBDJxKgVqSZNmkTjxo0ToaqnnnpK3JaTk0O33XYbvfbaa5Sbm0t9+/alZ599lurUqUO6YaLUmJ56JsaA1MQFUuMMSE00kJrUsSUFjUG0Rg47duygKlWqhL+PFZ1h3njjDZo9eza98sor4tNwLvi/5ZZbqH79+jRs2LCU5+cPt7l+hqM/LEpcgrBz5076v//7P3uFRsVClwYurDljxgzpYyYlNEePHqXFixeL3tEMywVLRAh+Q3PnjAoVKkjf0ZUrV9I//vEPOuWUU6Ju50U933//fbFADxe43XjjjcL6PvvsM3ILpJ6ZJzW6oKPUyARSIw+TpYZxu60z46dmATamoDEQm9RhmYkUmniMHTtWRGlCy2vwuh9cvM+tc50IDXc241ShyPSy1q1b0549e0TDgHLlyqU8NgCuCg13yGB5CAnNM888I2yf2wAyP/zwgzB+lgyZHD58WIQ0p02bRg899FD49gMHDtD06dPFpw49e/YMWx//Qq1YsYK6dOlCboHUM7PQJUqjo9TIjNLIBlJjptQwiNakhk1SwyBa4z5Hjhyh9GLLObCEOC0f4C5VfP3F44TG5zodFh3IDCgNbiFe2noznM7oqtBwyPKOO+6Iuo3fzM2aNRNfz5o1i6ZMmSJdaLjgjLtz9O7dO0poVq9eLXI1+fYQnCPaqFEjWr58eVyh4ahSZGSpeGGd25goNX6I0ugkNbLQUWp073zGQGpCY0NqEsWGuhoG0Rr/ccEFF4iaGb424g+hv/rqK9EQgBdID8HdyrZv3067du0S369bt078W7duXbExV155pWjNzJEd5vrrrxcfbHMZwOjRo2nDhg1i4fVQK2grQVOAhOCUx0j4Wp7flwsWLBARxVRISmi44ItDlSE4tSzS+k8//XQhHzLh2pgvv/xSpJwVh8Oa/ClA8Z7nXD/D98WDfxkfeOABx/vmRC6cSI0qIDUy90O/DnKQGudAakoCqUkdRGuADLg9Li+syQX7+/btE5k0f/nLX+ivf/1rVMcyXpsmRCg9LbLRAAtP5DUfNyXg1d75Q2wuB2DZYbnhLmcAlAa/T2LBQRFeJ8l1oeEVYSMjGz/99FPU/Rx2jLxfRsEbv+iPPvpIal0O1/7ceuutURGa4t1CdK6nUZl65gepkYFOUmNDkwAxHqQGUqOJ1DB+T0FjEK3xB8cdd5xopBRqphSLq666SmylwSu6F4fbNnOKP/gdLKzpCF7nkq/RU2kakFRrhAYNGtC3334b9/61a9eKx8iCU8r404TTTjuNMjMzxbZkyRKxEA9/zZEYLjxj0Ypk79694RBpLLgTSKiYLtGiOjfgKI0TqUkVlhqTYZlw9PwsfX77WWrkjCOv5z9LjSxYamTCUiMbmccuUmrcTEVyZ+wMT0TATfg1uP06vHw9kT93N3/2ZYmNCkJiAwCwh7feeouqV6+e0nOTOlv1799fhCi5nqV4xIQ7oHEaV2glWhn06tWLvvnmm6jbOCTKdTIc0uSoCnfY4M5rF198cTjvk8Oi/KmBV5JgYuqZyfU0MtAl9Sy4L4jUJDUeIjVGR2oYP0VrkILmHojWAOBP2rdvH9UUIBAIiFIRzvzipVdcF5q7775b9DNv2bKlaI/MK86GJIILwwoKCsRjZIZJ27ZtG3VbdnY21ahRI3z7iBEjRPoYGx1HWrgwjWXGzQ5nfkg9c4pqqfFTPU1wXyA1SY0HqTFWahikoKWObSloDGprgDHwmjBeZ8EYuA7NRRddFPU912bVqlWLzj77bBG0cF1oOMVr2bJlorMF9zRno2LYss455xwlC1o++eST4kBwhCZyYU2TJMHErmcMpEbefugqNTKB1JglNQzqahLH713QGERrAAAycGPh1cxUekdzWzVu8cddz5gTTzwx5Zy3ZClelMapb9wVgTdZpCoJqlLPbF6fxk9NAvzezpmB1Jj1aT2aBSSHLSloDKI1AMQCXQFKI9FlUlKpbU+54o8Fhts0A33SwFIF9TTOozS6dT6TCaRGf6lhTE1B80pqGL/W1TCI1gAAdIeXWSltQU3O/OL7C1P4gF9dCxPNsSn1zCmqpUaX1DNZ6Jh6BqnRW2pMr6vxSgL8Wldjc7SmiNR3Q+O/QbI6ViZCwIVujwB4wSeffBIlL9xs7IUXXhBrGDkFQlMKSD1LHEiNvP0I7gukJunxIDXGSw2DFDSzpEZ1tAYAreDoQykRCNfmNIQePXpEfZ+RkSGaeDVr1szx2NB8F1s5q1ifxgm2r08ja40aGfthwxo1DNapkY/Ja9V4uV6NV3i5Xo3qNWtUrlsDALAbs69gNceJ1Jg0p6wLcI7SON4HSI1xUiMbSA2kRsdFOFWIjQogNcBq0hRtAEKjqyQ4idI42V+nURodpEYXIDVqojRiTEiN61Ljtgx4JQB+j9aoANEaAECilNYkIBlwxkkQ1NMkDupp5O6LTHStqZFdTyPGRE0N2joniN/rahjU1gDgAfyhsNfp+waVCwwaNCjq+5ycHLruuusoOzs76vY5c+YkPTaExgNsa+UsAz9JjQxktnOG1OgpNQzWqrGnWQBjS8MABmIDAKhatWrU90OHDpU2NoQmCdDK2az1VHSRGt06nwXHgtToJjUMFuC0Z70aBtEaAIBNzJgxw7WxzYlTaYJpXc9sr6dBk4DSxkJNjW41NW7W1bhdT+GXZgF+r6thUFsDgFugK4AqIDQeAqlJHjQJiDGOpscEUmOG1KADmp5SY0t7ZwZSAwCQjZ5XRpqjsjWyCkyXGl2iNLL2RdfOZ7KB1KADmmoB8LK1M4NoDQA+WVjT6w1AaFLFptQzHRbdhNSYITW6L7wpxoTUCJCCljiQGveA1AAAZIAziQJMbOXsFDQJkL8vMo+rrk0CGDQKQFtnG1s7MzY1DGBMbRpQLj3d0w/9Cg1q0wuAV+C3wgFOoh6q0tZsr6fRKVIjC0RqUhwTkRoBIjWJgxQ0d0G0BhgPUs6UAaFxiAoxcZJ65hRIjX6dz2QCqXEOpEad1Pg1Bc22hgEQGwBAsuh3RWUgqKfxFl06n+kkNTa0c2YgNe5KjenNAhg/Sg2DaA0ABsC/MulpHm+qX7Qe4DBEUI7fGF7PaajUqL741qFJgCwgNckBqclAtKYMkIImD0RrAAAmoM9VnUbFfSk9D/U0xq2lokvqmax9EeNAaqyQGgZSo8/Fv99T0HSI1kBsgEEhGgUbwFGQCOppkgP1NPL3RYwDqUl9TEiN76QGKWj+iNYwkBoAQDwgNBKjNDamnkFqfh8DUpMQNkuNqXU1buLHuhpEa9wF0RoAQCwgND5JPTO1nkYGkJo440BqUh/TpRorSE28OfwjNQyiNe4DqQFakqZoAxAa3aRGFabX0/ip85kYB1KTFJAaszugBedACpoTrI3WZEJsAAAQGtcwrZWzU3SQGn0kQq+PSyA1DsaE1EThB6nxcwqardEaALSBr4VUbABCUxaopzFHanRJPdNtjRoxFqQm9TEhNVFAapLHBqlhIDUAAFXg7JMAqKdJHEiNXCA1yQOp8cdaNX6sq7GlYQDEBgDgNUg+dRkWhLzCIjIJ1fvMF945+UWOpOaog+eLfchMp5wCh2NkpVFOfsDRGLL2RdaxjR4rg3LyC6VJTa6k1xiSmjxJ+xYeMzOd8iTuoxvHsTgsNbkujc0XrfmS3kvx58igfJf2P0Tooj8vz915vHxNxV+fV68tkpDUuP0e0eVvppe1swUG1ulaQ1pacPN6ToAITaLYlHrmdG6nURoZ6BKp0a1JgM4gUuMOaBaQGH5PQUMaGgDAz+BMkwQ2pZ45RXXqGQOpMSv1zCSpwQKc0SAFLXlsaRjAIA0N2AP6NqsCZ5gksUlqTK+nYSA1ccaJcVwCgQDt3fgtrf/P+7RpxUd0ZP8vCY5ln9SIcSE1vl2vBtEad4DUAADcAmcXD7FtfRpdpEYXdJaa//24iT6aPJpW/X0s7Xz7adr8yv/RoglX08rXp1Bhfp4vpMaUFDQ+luiAZl8KGqI1APiohsbrLUl27txJQ4cOpRo1alDFihWpXbt2tGrVqqgPOP/6179SvXr1xP29e/emDRs2kM7grJICqKfxFj91PtNRag79tIuWPXsvtTqyk/72xw4079Jz6e0/nUO3tWtEh5fPp89nPyVObqZLDWOK1DDogKaH1CBa4w6QGgDU8Ouvv1K3bt0oKyuLPvjgA/ruu+/o8ccfp+OPPz78mEcffZSefvppeu655+jzzz+n7Oxs6tu3L+Xk5JCuoMuZA6nJKyrytIOYk+ey1OQUet/pJhSlyU3hWMlEl85nOnY/27BkLp1QcIgeObcHVSqXJW7jfy9ofSJVq1CB7lvxKf2v50Cq0aiF5x27ZHc/Y9ABzZsOaG53uApd/HvRMczLTmFed0Fj0AkNAHuYPHkyNWzYkGbMmBG+rWnTpuGv+QPMp556iu69914aMGCAuO3ll1+mOnXq0Ny5c+myyy4jHcFHJApAPY3d9TQ6RWoCRUW0a+XHdMGJDcIyE0m3JifQCRXSaduX/0l8nxCpkQrqasqaAylofojWIGIDfAFf66jYiOjgwYNRW25ubsxdnDdvHnXs2JEGDx5MtWvXpvbt29O0adPC92/ZsoX27Nkj0sxCVK1alTp37kzLly8nXcEZRFHqmao0LkiNPlIjCyf7U5CXS0V5uVSvSuWYabjpaWlUP7sC5R05lNw+QWqkAqkpaw6koMlCldQwkBoAUqdhw4ZCPELbxIkTYz5u8+bNNHXqVGrRogUtXLiQrr/+errpppvopZdeEvezzDAckYmEvw/dpyNIOVOUembqwpdO53aafiZjYUhd0s9kpZ45IbNcecrMrkIbfv4fndW0gZCayHKZvMJC2nTgCFU+vlbSYyP9TC6mpp95uQinX1PQGK8X42SQhgaAOQtr7tixg6pUqRK+uXz58jEfXlRUJCI0jzzyiPieIzTffvutqJcZNmwYmQo+DpGATa2cnc7NIFKjT+pZWno6Ne7Wj/61ZQ/tPfRb8LaIXZr//Sb6uTCdmnY8O7X9QqTGqA5opi/C6dcUNJXRGqShAWAGVapUidriCQ13LmvTpk3Uba1bt6bt27eLr+vWrSv+3bt3b9Rj+PvQfTqCs0UEmYZ1LzN10U0Z6NLO2Q9Sc3LPAfRbzUY05sNlNP+7jbTz4GH64adf6OnPVtPUtZup8TmX0nG16qe+X5Aa6SAFzd4UNKShAQCc0K1bN1q3bl3UbevXr6fGjRuHGwSwuCxevDh8P9fkcLezrl27kq4g5awYWRnplJ9CSpWT1DMTO5+pTj2TkX4mI/VMFio7n1WoXJV63TyZVs99kZ74cgllrNlIgbR0yqhel04cPJpadO/vfL8sTj9jkIIWDVLQzOyExiANDYDS4A8oPU45S3K+MWPG0BlnnCFSzi655BL64osv6PnnnxebGC0tjW655RZ66KGHRJ0NC8748eOpfv36dNFFF5GuQGg0kRpVmC41TtGlnka11FSqejydOew2OjpoBB3Y+yNlZGZRjYbNKT0j03HNku1SI8ZFXY2yi1SvLv69vuBXUVujssWzzmLDf0e9zHooMjzDAqilU6dO9M4779C4ceNowoQJQli4TfOQIUPCj7njjjvot99+o2uvvZb2799P3bt3pwULFlCFChVIVyA0lq9P4xTVUuOnJgE6rFFT8bhqYpN9jI+NBakxTWoYP0RrvLrwR7TGfbx4zwCQEulpwc3rOZPk/PPPF1s8OErDssObKSA5tZQojdeYWk+DJgH+XKMmaiyJeexu1NTIrqtxo6bG1LbOfqqr8XPDABtra1BfAwAIgbOBC1Kjan0aSI0zIDXmSg0DqXGvA5pfpMbPDQNs7ITGZEo6VwIgh3RuH+rthkt5AY6CSziRGlWg8xmkxmup0b0DGksNOqB5KzV+6oJmW7RGpdgAAOzGvKtug1LPTFufxqnUqI7SMJAas6QmOJ7eUsNAaryTGr+moNkQrWEgNQAAFUBoEsCmehqn+EVqZACpSWY8e6XGxLoatxfh9GMKGoNoDQCWdG32egMQmkRBPY03c8uSGh2iNAykJpnx7JQaMa6BUsMgBS15EK0BAAD5qL9ytABIjb1NAsS+QGqSGA9SIxvTmwX4MQWNQbQGeEWTJk1EG97i26hRo8T9mzZtooEDB1KtWrWoSpUqYrHFvXv3ljnuzp07aejQoVSjRg2qWLEitWvXjlatWkV2k65oAzgKmtfTqARSA6lJaCxIjbxxDa6rQQpa8tgWrYHYqGHlypW0e/fu8PbRRx+J2wcPHiwWT+zTp48QnI8//pg+++wzysvLowsuuICKSlkj7tdff6Vu3bpRVlYWffDBB/Tdd9/R448/Tscff7yHrwyAY2BhzRSkJt/jRSxVLrrJUpNTqGbxND8tvClz8U1ZyNwfmYtvBsfDApxu4OYinAxLjduLcDJeLMQZnMe/i3EyXi/IqeK1AhKRl0gmTZpEzZs3px49egi52bp1K3311VciOsO89NJLQkxYcHr37h1zzMmTJ1PDhg1pxowZ4dt4xXkAVGFe2EADbKqncYrKuXWM1OgUpRFjIVLjCERq/JuCFpzHvyloDKI1ZnPw4MGoLTc3t8zncPRl1qxZNHz4cBGV4efwv+XLlw8/pkKFCpSenk5Lly6NO868efOoY8eOIspTu3Ztat++PU2bNk3aazOW9DQ1G4DQeI2JUmN66plOUqNb6pkYC1Kj7Vo1aBZgn9TYUlvjJ7HhDznLebiFPlTlCEnVqlXD28SJE8vc17lz59L+/fvpqquuEt936dKFsrOz6c4776QjR46IFLTbb7+dCgsLRXpaPDZv3kxTp06lFi1a0MKFC+n666+nm266SUR3AFABhMaiehpIDaQm7liQGuuiNV40C/BTFzQbojXohmYWO3bsoAMHDoS3cePGlfmc6dOnU79+/ah+/frhdLQ333yT5s+fT5UrVxZixMJz2mmniShNPLi+hh/zyCOPiOjMtddeSyNHjqTnnnuOrEa0UU7zeFP9ovUANTQW1dM4xWk9jdN9d1pPo1NNjaz6FZaanPyA43Fk7pMpNTUM6mrMr6thWGrcrqsJzpPhaV0N43W9iZevUYfXazJc8xKqe0mEbdu20aJFi2jOnDlRt3NTAO509vPPP1NmZiZVq1aN6tatS82aNYs7Vr169ahNmzZRt7Vu3ZrefvvtFF4JAM5BhMYhttXT+CFSIwNEasyP1DCoqwmCFLRk5kG0xk38koamI1zAz/Uu5513Xsz7a9asKWSGmwHs27ePLrzwwrhjcYezdevWRd22fv16aty4MVmN59GZ3zcAoZEBpMZbdGjnzEBqEhgLUiN3XMOlxi8paDbU1jCqpIaB1MiFU8RYaIYNGyaiMJHw7StWrBBRGm4YwIX+Y8aMoZYtW4Yf06tXL3rmmWfC3/P9/BxOOdu4cSO98sor9Pzzz4fXtgHAayA0EWRmeG+5pkqNE2TMrYvUyABSk+x4kBpT62oYRGtSB9EakCqcarZ9+3bR3aw4HGm56KKLRMrYhAkT6J577qHHHnss6jGhlLQQnTp1onfeeYdeffVVatu2LT344IP01FNP0ZAhQzx5PQAUBzU0MaSmoDDgaT0NS02ew9oQr2taVNfTyECXehqxL6ipUV73YdJaNWJs1NVosWaNTbU1DOprzITrZAKB2H8beF0a3kqD16opzvnnny82EIGKFDCknAn0+ZjaB5EaJ53PTIy0+KGeRpfOZ2JfEKnxbaQGKWhqIjV+j9YgDQ0AAIJAaCSDehpv54fUxBkHUpMyaOvsr7oaL6UmOJe3F91IQwNAI9LS1WwAQhMP1NMkDqQmCKRGndRgrRp3pcYP0Rq/NgywOVoDsQEAhIDQaJZ6BqlJDUiNeVKje7SGpQbNAo7hB6nxcwqajdEaBlIDAGAgNGUAqTEHSI1ZUiPG01xqGJOkRoxtuNQgBc3caI1KsQFAC9IUbQBC4yYqmgQ4xYnUqI7SMJCaOONAahyBZgHHQApaKnPZEa1hIDUAABWgbbOLrZyd4KSVs8qWyDq0c2apyXXYBhstnb3ZJ9vbOrvZ2tnNts5etXZmvGjv7EVrZ6/bO9va4lkF/HfLywyHfAM/LLUH/tl4/fPB+wFHIQmQepY4iNQcA5GaJMazNFLDoK4mPojWmB2tQcQGAOAFEJokgNSYJTW6AKlJYjxIjTtjIwUtIVBb4w6QGmAN6WlqNgCh8QpIjffz6xKlYSA1SYwHqTFOavzWMMDv0Ro0DQAA+A0IjYfr09jWJICB1BwDUuM/qUGzAH+moPk9WsMgDQ0A4CfMu8LWAJsW3ZTxfKdAakoCqdEnNcq0aA3qahIH0Rr3gNQAX5KWpmYDEJpUsamexunznUZpnM7PQGpKGcciqQmOCanxS10NojVyQLQGAGA6EBoH2CY1TpAhNU6B1HgnNTLFBlJjptT4MVrjFTZGayA2wBcgQqMMCI0iTJQa0+tpGEiNN1IjxoPUSANSY1/DgOB8EBsAAEgECI2FTQIgNZAaU6XGlGYBbuB2swDTU9D8Gq2xLQ2NgdQAAJIFQhNBZooXuqY1CXA8N6RGAKnxR12NSR3QGERr7GsYEJwP0RoAtCctXc0GIDSysK2eBlITBFKT4HgWSg0DqSkJGgaYGa2B2AAAdCZT9Q7oGKUpKCrydE6WmvzCopSlJi/F/WUpyEtxXhnPZ6nJKSxUNn9IanId/rz54jonv0iK1ByVMA7LQ05BkTSpyckPOB5L5n7JPu7HxgteNOXkp/6ejCc1uRJfd6TU5Ene1+JSk+fCfkceb9nHujgsNbkuzxEpNfkS34+lzxd8r+Z78NoiYanJy/N2TtWvORHKpad5mjWRj5Xh9UVFkT6aAggQodEk9czWSI1TEKnxonYFbZ1trKvxUwoaojX+iNYwiNYAAGIBoYkDpMYbdFijhoHUlDEWpEZbqWFMlxrU1piXkoU0NACATkBofNIkwObOZzL2QRaQmgTHslhqTK2rQbRG/2iNjd3QsjIhNkAjsA6NMvS4CvQhpjUJEM+3XGpkRGkYSE2CY1kqNYyJUiPG94HU+DlaE5zTrmgNAAAwEBqXojROUCk1ToDUHANSo05q0AHN/LoaP6Wg2RatgdgAq0GERhkQmgRAPY1ZUuMUSI3ZUiPGdEFqUFcTMTZS0JLGlmgNA7EBAHgNhCZBIDXePd8va9QwkJoEx8pMtzYFzdS6GjE+ojVJY0u0hoHUAOvAwprKwFHwAEhN8kBq7JMaMZ6lUsMgBS0+iNbImBNpaAAA/wKh8aiexrbOZzKeD6kpCaQmhfEgNQKkoOkZrUEaGgAAOAdCkyS2NQlQ3Q4ZUlMSSE0K40FqBEhB0y9aw9gSrWEgNcD3pHm8AQGEJgVsqqdR3c6ZgdSUBFKjj9SgrsafKWi2RGuQhgYA8AMQmhSxTWpMjvLI2gdIjT+kBtEab+pqEK1JDa+lJjgn0tAAkAKaAigDR0EBJkqN6fU0MvaBgdSYLzViTEiNANGaxEG0xj1MFxv+gJP/Nnu1qUh9B0B38FthYJMASI26fWAgNQmMlZVm3Vo1wTEhNX6XGhuiNRAbAIBpQGgs7HwGqYHUeJfihbVqTKmrQQpa4vg9WhOcV51YQGqAsYhC/TSPN9UvWg8gNBIEw7TOZ06B1ASB1PgjBc2kZgEMojX2RmuQhgYAALGB0EjCtiYBkJogkJoEx7NQaoLjQmpKjI9ojSOQhgaAznjdsxm9m0NAaDRIA4PUpAakxn2psalZADqgeZeCJuZAtCZlbE1Dg9gAAOIBodEk9QxSkxqQGnelhrFJasSYhkmN6dEat/F7tMamNDQGYgO0Jj1NzQYgNPGA1JgDpCY2kBoHY6KuxlcpaH6O1tiYhsZAagAAkZh9JesypkmNrZ3PGEhNbCA1DsZEXY0vU9D8KjYq09BU1tdkQWwAABAa9zCx8xmkBlJjutSgWYD5KWh+itYwNkRrgvNCLACgtHQ1G4DQuBkxMa3zGQOpgdSYvACnGNPyZgGMyVIj5kC0xhE2RmsAAHYDoUkAmzqfOQVScwxITRLjaS41YkzU1fguBc2GaA3EBgAP8XxRzd83AKHxaz2NzZ3PZEmNDCA1SYxnqdQEx0UKmuoUNL9Ga1SnodkgNvy32usNABANhCYJIDV2SY2MfWAgNUmMB6lxBURrEgPRGrfm9r/UAADUAqHxCEiN98+XAaSmbGyVGtTVRINojTnRGqShAeAWaYo2oP6K0TBUNAlwis1So0s9DQOp8VcHNFPrakxOQfNztAZpaAAAkDoQGgs6nzkFUgOpUSENsqVGjGmI1JgcrfEqBc1v0RrGlmhNcG6IDfAfaWlpSjYAoUkZ2zqfQWr8LTW6RmsgNWZKDYNoTWrYlIYWnBtiAwBwDoTGATY1CZAhNU6B1Lh7Ia2z1JhSV+MGpqegIVqTGl5LjQ5iA4DxoG2zMiA0DoHUeCsDkBo7pUaMZ4jUIFpTEkRrzInWMIjWAABMA0KjEFOlxgmQmmggNUmOZ3GzgOC4kBpbozVIQwMAgPhAaCLITE8zqvOZre2cZYwBqSkdm6RGjAmp8U0KmtfRGqShuTk3xEYGTZo0iVlIPmrUKNq6dWvcQvM333yz1HG///57uvDCC6lq1aqUnZ1NnTp1ou3bt5PVpKWr2QCExnSpcYLpUuMUSE3pQGrsrKthEK1JDr9HaxiIjbmsXLmSdu/eHd4++ugjcfvgwYOpYcOGUffx9sADD1DlypWpX79+ccfctGkTde/enVq1akWffvoprV27lsaPH08VKlTw8JUBcIzMiK9BhNQUFAWSf15GGhUUJv88Jzidk6Umv7AoZanJK0rtueL5GemUV6ju+SGpySksdDSGjP0ISU2ug+MZeRGdk+98nJDUHJU0FgtDToGcscR4WWmUky/39032Pooxf78IlPUziR47g3Lynb1/S5OaXMnHIpbU5Lm0/+F5MtMpz8XXUVxq3Pp5FCckNbkezReCpSbfhfdyYnMHX3O+x6/ZdGrVqhX1/aRJk6h58+bUo0cPEYmpW7du1P3vvPMOXXLJJUJq4nHPPfdQ//796dFHHw3fxmNaj4oifTQFEKj/mLwUJk6cKEKYxx13HNWuXZsuuugiWrduXdRjcnJyRNi0Ro0a4pfv4osvpr179yrbZ9OaBDhFdaRGRoQEkRqz16oxoVmAGBcpaDHxUwqa19EaxrZoTXB+RGuYgwcPRm25ubllPicvL49mzZpFw4cPj7l+yerVq2nNmjU0YsSIuGMUFRXR+++/TyeddBL17dtXXJ917tyZ5s6d6/g1AeBLoVmyZImQlRUrVogQaX5+PvXp04d+++238GPGjBlD8+fPF7me/Phdu3bRoEGDlKWemSg1qts5Q2rskhoTUtBMbBaAFDS9UtCQhuZNGpoOcsN/u73eGE4X4/qV0MYfApcFS8f+/fvpqquuinn/9OnTqXXr1nTGGWfEHWPfvn10+PBhEek599xz6cMPP6SBAweKay++DgNABVqnnC1YsCDq+5kzZ4pPAvgThLPOOosOHDggfvleeeUV6tmzp3jMjBkzxC8jS1CXLl2UpJ45mzOdClJMO3KSfuYk9YxB+pm8/ZCdfiYr3Ulm+hmDFDSkoKlKQRPzeJSG5lUKmuo0NEZVKpqt7Nixg6pUqRL+vnz58mU+h6+ZuDamfv36Je47evSouJ7iWpjS4AgNM2DAAPGhMnPqqafSsmXL6LnnnhOpbNaClDNlaB2hKQ4LDFO9enXxL4sNR2169+4dfgwXqDVq1IiWL18edxwOyxYP1fqlSQAiNc7wY6RGZmTAtkiNGBMpaL5LQRPz+DRaw6iI1jAq09BshGUmcitLaLZt20aLFi2ia665Jub9b731Fh05coSuvPLKUsepWbMmZWZmUps2baJu5w+Tre9yBpRhzNmHPxG45ZZbqFu3btS2bVtx2549e6hcuXJUrVq1qMfWqVNH3BcPDstGhmk5bFsakJrEgdTI2w+dpQYd0CSNixQ05e2d/So2NqahgdLhDBbOcjnvvPPiRm+4DXPxJgLF4esurm8uXtO8fv16aty4MVlNKELj9QbMERqupfn222/ptddeczzWuHHjRLQntHHYtiwgNYkDqZG3H7pKDaO71NheVxMcG9GahObxedMAiA3gD4VZaIYNGyaiK8XZuHEj/fvf/44bveHsF+5+FmLs2LH0+uuv07Rp08Rzn3nmGVHPfMMNN7j6OgCIhxFnmxtvvJHee+89+uSTT6hBgwbh27nVIHfs4AK3SLjLWfE2hJFwWLZ4qNZNVHYhSxVIDaTGdKkxKVrDPxMTU9AYRGuSx7Y0NIiNejjVjNPBuLtZLF588UVxfcWNl2LB0ZhQ2j/DTQC4XobbNrdr145eeOEFevvtt8XaNFaDhTWVofVRCAQCQmb4U4GPP/6YmjZtGnV/hw4dKCsrixYvXhz1S8e/tF27dpW+PzZ1PpMhNU7xk9TI2BdbpAYpaG6Ma3YKGoNojbnRGgZioxYWFb6m4lbLsXjkkUfEtVN6nL8z/NzindFYjjZs2CCaCXCrZ24SAIAqMnVPM+OOG++++65YiyZUF8N1LxUrVhT/cq/0W2+9VTQK4EjL6NGjhcw47XAWD5s6n6leeNNP3c9k7UtIavy8AKfNHdBk/1y87oLGoBOa3gtyquyGpnphTgCAf9H645KpU6eKEOfZZ59N9erVC2+ctxniySefpPPPP18sqMmtnDnVbM6cOa7ul031NE4jNU5Tz2SgS6RG1r4wWKtGn0gNUtCiQbTGrDQ01NcAIBE0BVCG1hEaDnGWRYUKFWjKlCli85JUIzVOIiYqIzVOUL1GjR8jNbLXqrEpUsPYHq1xOyqAaI05a9cwLDWqojUMIjYAAKfgIxIFmBipMb1JgKwxEKkxP1IjxkRdze9jI1qT8DyI1rgGIjbAP6Qp2gDOIIY1CQjOC6lROQakxvy1asSYkBrPGgagE1pqQGwAACAxcOZwCKQmOSA18vdFttToGq0xqQOaaXU1wfERrdFZbLwGYgMAMAmta2i8JiPFi0snnc9U1bY4mddJ5zMGNTXy90VmTY2NdTWya2pMrKvxon6DpcbNuhova2vEXJnpntbWMLbU15jUEY3/nnq53pyJa9tZA19GOvigO+U5AQ5DcTIdSI3XOInSOAWRGn9HahhdIzWMG5EapKD5JwXN79EapKEBAEA0OEtIBO2ckwNSI39fGEiNgzENkxqkoOlRWyPm8lBqGKShAaAhaelqNgChkRmlEc+F1CQFpEb+vjCQGjvqasTYhksNojVmRWsYiA0AQDdwVnBBalKfE1KT0vMhNcZJjY3NAsS4hkoNojVJzmWR2KgCYgO0BR2blYCzgWb1NJCaFJ8PqTFKakyI1phYV4NoTdl4JTViLqShuU6Wx8cYAKAnOBOUAaQmcSA1QSA1/pEaMSZS0CLGRrQm6bksitaoFBsAQGLcf//9lJaWFrW1atUqfH9OTg6NGjWKatSoQZUrV6aLL76Y9u7dS7oDoUkASE3iQGr0lRodF+C0WWrEuIjWxATRGudAbABQ1rdZwZYcJ598Mu3evTu8LV26NHzfmDFjaP78+fTmm2/SkiVLaNeuXTRo0CDSHaxD4zJO1qhRhdO1cbBOjZ7r1MhcqyZ0sWzLWjUmrldj+po1fl23Rszl0do1qtevYVStYQMAiE9mZibVrVu3xO0HDhyg6dOn0yuvvEI9e/YUt82YMYNat25NK1asoC5dupCuIEKTIDZ1PnM6twwQqZG/L7qnoLkRqTGprgYpaOqjNX5NQ2NURGsYRGyAVaSlqdmI6ODBg1Fbbm5u3N3csGED1a9fn5o1a0ZDhgyh7du3i9tXr15N+fn51Lt37/BjOR2tUaNGtHz5ctIZCE0SQGq8Sz1jIDXy98UEqUEKmgvjGp6CxnghNQzS0NwBUgOAuzRs2JCqVq0a3iZOnBjzcZ07d6aZM2fSggULaOrUqbRlyxY688wz6dChQ7Rnzx4qV64cVatWLeo5derUEffpDFLOUpCaAknpP16kgLHUFDhIMXIyt9PUMxkg/cz99DM3UpuQgmZmCprbqU0hqUEamjOQhgaA/9ixYwdVqVIl/H358uVjPq5fv37hr0855RQhOI0bN6Y33niDKlasSKaCCE0K2NQkwOncqpsE6BapkRGt4X1BBzR9IjVIQSs+PqI1JqWhoXGAc/hvu9cb0BSFKWdVqlSJ2uIJTXE4GnPSSSfRxo0bRV1NXl4e7d+/P+ox3OUsVs2NTkBoUsQ2qXECpEbvFDTbpAYpaN6koKG2JsX5FKyrgvoaAOzl8OHDtGnTJqpXrx516NCBsrKyaPHixeH7161bJ2psunbtSjoDoXGATVJjejtnMQakJi42SQ0Dqfl9bERrEgbRGveA2ADfwH9LVWxJcPvtt4t2zFu3bqVly5bRwIEDKSMjgy6//HJRezNixAi69dZb6ZNPPhFNAq6++mohMzp3OGNQQ2NgO2dVNTWmt3PWqaZGx7oa2TU1sts6MzbX1TBu1NbI/lmVHB+1NSnPZ1F9DYMaGwDc58cffxTy8ssvv1CtWrWoe/fuoiUzf808+eSTlJ6eLhbU5E5pffv2pWeffZZ0B0KjsEkApCZ5IDXu7k8oUoNmAc4jNWgY4M91a1SJjZdSw0BsAEietN//83rOZHjttddKvb9ChQo0ZcoUsZkEUs6ktFb2vp2zU5B+pscYOqaf2ZaChroa/6WgIQ1NHqrS0BikogEAEgVCI+li3YnUqBILSI0eYzCQmuRAXY27XdBMbxjAeCk1fhcblfU1DKQGAFAWEBqLmwQ4BVIjbwwGUuNfqUG0Jtb4iNY4mk9RtAaNAwDQs22z7UBoZHcSM0xqVK5Rw0BqooHUJC81JqxXI8Y1VGoQrUkcG6I1DMQGAKAbEBqNIh+QmtSA1JgnNbpHa0ysq0G0Rq9oDcTGG7Ep5/J7E4CkQIRGGTgTaFZPA6lJDUiNWVLD6C41jElSI8Y2XGr8FK1RlYZmm9gAAAADoSkDSI03czOQGvekRscFOG2XGqSgxZsD0RrHcyqQGgZSAwBQBYQmASA13szNQGrckRpGptToXlcjGxPrahCtsVtsEK0BQAFp6Wo2gIU13V5Q0raFN53O7bfFNxmn48hafFPmApwMS43OC3AyshfhZNxYiNONRTi9WIiTMX0xTi8X5GRYarxakFPM9/v71qaFOb0mIyPd06UbeD4AQDT4rfAARGrsjdTIGkfHSI2NzQIYpKAVGx/RGmPS0BCxAcBl0BRAGRAazds5qwRSo5/UoFmAf6XG9BQ0P9TWqGgaYFN9DcQGAOAW5l1lK8amNWqCc0NqdJIaBlKjj9SgC1qx8RGtSQlbojUMxAYA4AYQmhSwaY2a4Nxq3yaQmpJAavRYhNPUFDREaxIDaWjuAbEBvgQpZ8qA0KSITZ3PgnOnKxVASI05UqNzBzTGFKkR4yJao8UFsQ1paGj1DAAwGQiNAyA13s3NQGrMWKuGQbMAieNCasqYA2lo0uZE4wAAnIEIjTIgNA6B1Hg3NwOpsbMDGmOK1CAFzfsUtOA8iNZImxdpaAAAw4DQKMRUqXECpMadcSA1/q+rEeMiWmNttMa2NDSIDTASETHxemFNRGgYCI0ESVDVztnWNWoYSE1JIDWpY5rUIFpjX9MABmIDAACxgdAUA1KT6LyQGpm1J5Aa/0oNojX+jtYwXkqNbfU1DMQGAFAWEBofSI0TIDX6RFlskBobmwUwpkoNojWJY0O0RswLsQEgPmmKNkCZqndAZ6kpKAok/7yMNCooTP55KvZVxj6z1BQUFSmZO1Jq8guLHElNnoPXEB4nI53yHOyHrDEipSansNDxWLL2KQRLTa6E4x2CL4pz8oukS81RiWMyLAc5BXLHjJSanHx3zjtu7bdbP7/Yc/z++5Dv/PehLEJSk+viMYskJDV5Hry2qHkz0ynPo9eo8ueZ6N9gL2tTnWRmAOBXEKFxAdM6nwXnRqTGb5EaW9o6M0hBMz8FzU/RGgZpaO6DiA3QD4RoVAGh0aw+BVKTOn6TGhtS0GyUGsZUqUFtTXIgDQ0AALwBZ6AysE1qnACpiRgHHdCUSQ1aO7vXBc1v0RqIjeR5ITYAAEXgzKOpJJjY+Sw4N6QmPA6kRkmzAJOiNSanoPkhWmNDGhrEBgAvQcqZKnDG8WnnM0iNc6nBWjXeSQ1jq9QwiNbEGR/RGimokBoxL8QGAOARONMkAaTGLqlhIDWlA6mRh7stkhGt0TFaY0MampgbYgNsIS1NzQYgNMkCqUlmbkiNLVKDuhq7U9DE+IjWpIQtaWhibkgNAMAlcHbxEEiN9/MzkJr4UqNrtEa21DCI1niTguanaI3f09BQXwOAbFBDowqcVQwSBEhN6kBq4qOz1CAFzR0QrUlmLv+moTEQG//TpEkTSktLK7GNGjVK3H/22WeXuO+6664rc9zvv/+eLrzwQqpatSplZ2dTp06daPv27R68IgBKkhnjNpCgIBQUBVK+QC8odGdVb133maWmwMHq8DKOGUtNvsMV71lq8hyucs8X/XkO90PmOCGpySmUs+q2zP1iWGpyHR5zt1emD0nNUcnjhsQgx4UV2UNSk5PvzrnIzX0Pz/H7cZf981S9Mj1LTa6Lxy0WLDV5Hr2+qHl/f5/kefx6bWLlypVUGHF+//bbb+mcc86hwYMHh28bOXIkTZgwIfx9pUqVSh1z06ZN1L17dxoxYgQ98MADVKVKFfrvf/9LFSpUcOlVAFA6EBrDBIGjNAUSLxaTmxtSw0Bq/CE1jBtiI1tqQnLglhiw2LglNW7vu5uSGnueDE+lhvFSbEKRGoiNv6hVq1bU95MmTaLmzZtTjx49ogSmbt26CY95zz33UP/+/enRRx8N38ZjWo+KDDBknAkQ73WITWvUBOdG+pmO6We6NguQCepqzE5B80samoqmAbakoYm5kYqWMAcPHozacnNzy3xOXl4ezZo1i4YPHy5Sy0LMnj2batasSW3btqVx48bRkSNH4o5RVFRE77//Pp100knUt29fql27NnXu3Jnmzp0r7bUBkCw4a0i4WLap81lwbkiNblJjUwc0U+pqTOyCZnptjV+bBjAQG33hv6leb0zDhg1F/UpomzhxYpn7ytKxf/9+uuqqq8K3XXHFFUJyPvnkEyEz//znP2no0KFxx9i3bx8dPnxYRHrOPfdc+vDDD2ngwIE0aNAgWrJkCVkNegIoAylnitPPnKRROUk/c5IuF5wb6Wcy088YnVLQZKafmZKC5kbKElLQ/F1b43Uamsr6GgapaHqxY8cOUbsSonz58mU+Z/r06dSvXz+qX79++LZrr702/HW7du2oXr161KtXL1EnEyuNjCM0zIABA2jMmDHi61NPPZWWLVtGzz33XFQqGwBeYcbHHx7iKGqCSE2S8yNSo3MHNJ3bOpuyXo3bKWiI1pQxB6I1UkHERi9YZiK3soRm27ZttGjRIrrmmmtKfRynjzEbN26MeT+npmVmZlKbNm2ibm/dujW6nAFl4Ozg0oWyl3NCapwBqSkdm6TGtBQ0BrU1erV4tkVsVAGxSZ0ZM2aIepfzzjuv1MetWbNG/MuRmliUK1dOtGhet25d1O3r16+nxo0bk90g50wVOCtoUk/jZM7gcyE1ToDUlA6kRg6mSo1fojV+XLuGsS1aI+aH2CQFp4mx0AwbNkxEV0JwWtmDDz5Iq1evpq1bt9K8efPoyiuvpLPOOotOOeWU8ONatWpF77zzTvj7sWPH0uuvv07Tpk0TkZxnnnmG5s+fTzfccIPnrw0ABmeDUoDUJDM3pIaB1PirWQBS0PwVrRHzIFrjO7EBZcOpZpwOxt3Nikdb+L4+ffoIabntttvo4osvFnISCUdjDhw4EP6emwBwvQy3bea6mxdeeIHefvttsTaN1XDnOBUbQFMA19aLUbSIJRoF+KNRgA1r1ZjQLMC0hTi9aBjA+GHdGi+bBgTn8u/6NaobB4CyYWEJBEr+3nKntEQ6k8V6LstRcUECQBX4aCMBEKlJZm5EahhEahIHKWjycb8uBdEaE9LQbIzYAKAW1NCoAkLjw4U3nQKp8a/UoAOaXVJjcgqaX2trIDYAACAfCE2C2NTOWQcRg9TovwAno7vUmFRXY2K0xquGARAb56iQGgZiAwDwAghNEkBqkpnb+UUOpMZOqUG0Rj6I1iQxD9LQXAFSA+wAKWeqgNAkiU1r1IjnQ2qkSY0MsZF5wa+r1DCQGndAtCbBeSyI1iANDQDgJ9DlLAVs6nymcr/91v2MQQc0/3VAY9AFLWL8rDRXu6B51QnNrQ53unRDY9ARTQ78N8bLDztVfLAKEkRFwARvBwEiNCliU+cz8XxEaqREanRMQbMtUoNojTcpaIjWpDofIjYAAJAsEBoH2CY1ToDU2CU1OjcLYCA1/mjv7HVtjZ/raxg0DgAAmAqERhEmSo3qds7BfYDUmNDW2eZmASZ2QUO0Jom5EK1xDYgNMB80BVAFhMbAzmeO54XUON4Hv0qN7LGQgiYXRGvsjdZAbAAAID4QmggyUrywgdQkOzekJhJITXIgBc1dqUG0Jom5LElDg9gAkChpRGkeb4jQCCA0ki6UITXJzg2piQRS41+pMS0FzU/RGqShyQNiAwDQGQiND6TGCZAaOVKDBTjVNAswoa6GQbRGTbRGzOPjaA3EBgDN8Do6E47SAAiNRlKTKk7nhNTI+ZnpJjU6dkAzIVpjYl0NojV2p6HZ1BGNgdgAAIoDoXEB09o5B58PqfGb1NjSAY2xPQWNMV1q/BStEXN5KDXB+eyK1jAsNVmZEBsAAITGtYtkSE2yc0NqikuNDSloNkoNY6rUIFqT5FwWRWtUig0A+oC2zarAGagMbJMaJ0BqjoFmAeqkBilo5qegQWyczgexAQDYBc48Hlwk29L5TDwfUhMGUqOmWYBJ0RqTU9D8EK2xIQ0NYgOAd6AngDoyFc5tFHyRXFBUlPzzMtKooDCQ4pxpVFCU4nOdzJuRTgWFyb9WGfvtdN+d/rwi94Fxuh8sNfkOjmWk1OQVSRgnI53yJOyP7LEYlpqcwkJp48nev5DU5Er4OcS60M3JL3JNao66MHZIBnIK3Bk7Umpy8p39HpY5z+8X326+lvBcv/9M3Ph5x58zKDU5+fJ+vxIhJDW5HhxX1WvYefnBZ6pr5gHgZ/Bb4eN2zojUOH97Y60a78ZibI3UuP3pPaI1ekZrbGgcwCBiAwBwG5xhkgRSk8TzITVaS41NHdBMqqsxMQWNQW1NivNZkobGQGyA/0FTAFXgzOIhkJpU5ofUmNDWWfZYsqWGQbTGm4YBEBtzojUqxQYAAGSCs4ohnc+C80JqUt8HSI2JUoMUNHcwPVoj5kAamqQ50TgAAHkgQqMKnE1SBFKT5PMhNWEgNclhitSYmIKGaE2C8/g8DS04J8QGAGAuOIsYKDVOgNRAauKOZZnUIFoTBNGaJObxeRpacF7U1wCQMujbrAycPSxbeNMpkBo9pUbXDmioqzFHahCtSWIupKG5BsQGAJAKOGtEkJWyYHh/GFWlngWfD6mRKTW6RWtkX+TbWldjWgoag2iN3mloEBsAAIgNzhaSsKmdc/D5kBpZ+8HoKDVIQbM3WuMmiNY4nM+i+hoGYgOMAilnysBZQlKUhoHUJPl8SI3WUiPGskhqGNOkxtSGAV5Ga/zaNABiAwAAx8DZQbLUpAqkJtX5ITWxgNTYkYLGIFqTwDxIQ5M8L8QGAKAXOCtoVE8DqUl1fkiN7VJjQl0NY6rUIFqjfxqaTR3RGIgNACCSzKjvQAmpyS8MpHRxXFBUlPJFcUEKcwbnTaOCooDn8wafn04FhUWpP9/BvsvYf6c/N5n7EZKafAfHM1Jq8hy+pvBYGemUVyhvLEbWeAxLTU5hobTx3NjHkNTkSvqZRBK6oM3Jlz82w1Jz1KWxIyUgp8C9OcQ8WWmUk+/8d1SX1xM1Z1a6az//sqQmJ1/e714y6CA1fK3gZWaHiiwSAHRH/ZlAcxCpSeb5iNTI2g8bIjVujIcUNERrdIzWIA0NAEtAUwBlQGgSAFKTzPMhNbL2Q+e1anRPQZONiSloqK1JYB6PpEbMBbEBAADXgNAkCKQmmedDamTth0yp0TlaY0pdjUld0BhEa/SK1thWXwOxAdaRpmgDEBovULHwZnBeSI0OUiNrAU4bpMbWaI2pKWh+i9b4NQ1NzAmxAQD4GAiNj9eocQqkRp6MypIa1NX4V2pMT0HzQ7TG72loYk4FUhOcF2IDbAHhGRVAaHwuNaqlAFIjd18YSI0+UoMUtGgQrUlxPkuiNcG5ITYAAPlAaFLApoU3nc4tAx32H1LjD6kxJVrjdgoaojUJzoM0NBfnhtgAAOQBoUkRm5oEOJ5bwgUfpMYcqdG5WQBjitQwiNboIQA2pKGpFBsA/AO6AqgCQuMASE0yz4XUyN4XXds6i/HQLEAaiNaUDtLQJM6JxgEAAEOB0DgEUpPMcyE1svfFlg5ottfVMIjWlA6iNRLnRRoaAKmBhTWVAaFRCKQmxTEgNSWA1Pi/roaB1NgbrbEtDQ1iAwBIhsykHu1zMlK8uOQoTX5hIOUL44KiotSem5FGBSnPm0YFRak91yksNQWFRc7GcLj/To7dsX0I/rFP9ecnc19CUpPv8LhGSk1ekaSxMtIpT9J+uTEew1KTU1godUw39jNSanIl/XwiCV3A5uTLHztSao66NH4IvvjPKXB3DjHP71KTk+/NuTQkNV68Nq/eE6XPHZSanHy5v5tuXDukev2Q6nwAgGgQoZEoNTZFalS3c9YlUiNzAU6/R2psratBtMbf0Roxl4fRGtvqa4JzI2IDACgdCE0MIDXuzxt8PqTGjX3RdQFOMR5S0IySGtMbBvi1xbPq+hqIDQBANyA0PpEaJ0Bq/Cc1tnRAc2M8xjSpMT1a45dOaGIuiI0Hc0NsgKagKYAyIDSaSY0ysYDUaNMsQNa+6J6CZqvUIFoTG6ShOZzPso5owbkhNgCAIBAalzCtnXNwbrulJrgfekiNrH2xTWpMqKthEK2JDdLQJMxpWbQGAAAYnIFc7CYCqUn2+ZAaN/ZFd6nRPVpjWl0Ng2hN2SANzYV5ITbAdpBypgyceSRITSAQoF9++YX27tlDBQUFUuZUKTVOsElqjh49Qvv27Bb/xt4PSE3C42kuNQxS0KJBtCaFuSxKQ4PYAAC8BOvQJCE1hTHWCvnk40U0e8Z0+mHNV0RFRVS9bj0acNnlNPTKq6l8+fJWrlHjdF0VHdapKe11/LhtC701+0VauXgBFeblUWb58nT6Of1p8NARVO+EhtJ+honsi1/WqrF5vRpT16xh+KLV7fVJWGq8WLeG8ePaNarWr1G9hg0AauDfb68/VEaEhvHNRyhTpkyhJk2aUIUKFahz5870xRdfuB6peWX2y3TvjddTxsqVdEOVanR7jdp06k8/0cuTJ9LtN91AeXl5VrZzdjq3zpGaLRvX0fhRV9Hmd9+iSzPL0R01atOf0rNo/ZzXafwNw2jH1s3WRWpsrKtBCpr3KWhIQ5Mwp4JojZgXERul8LVRWlpaiW3UqFHi/r/85S/UvHlzqlixItWqVYsGDBhAP/zwQ5njfv/993ThhRdS1apVKTs7mzp16kTbt2/34BUBmUyaNEm8H2655ZbwbTk5OeL9UaNGDapcuTJdfPHFtHfvXtIZX5xhXn/9dbr11lvpvvvuoy+//JL+8Ic/UN++fWnfvn2uSc3OH3+kqY9OogvKV6C7WrSiM2rUolOrHU/DmzS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+      "text/plain": [
+       "<Figure size 1000x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate z as a function of x and y\n",
+    "x_values = np.linspace(data['fluency_score'].min(), data['fluency_score'].max(), 500)\n",
+    "y_values = np.linspace(data['grammar_score'].min(), data['grammar_score'].max(), 500)\n",
+    "\n",
+    "fluency_scores = data['fluency_score']\n",
+    "grammar_scores = data['grammar_score']\n",
+    "fluency = data['fluency']\n",
+    "\n",
+    "X, Y = np.meshgrid(x_values, y_values)\n",
+    "lam = 0.8\n",
+    "Z = lam * (100 / (1 + np.exp(-0.03 * (X - 5.36)))) + (1 - lam) * (100 / (1 + np.exp(-0.03 * (Y - 5.07))))\n",
+    "\n",
+    "# Create the heatmap\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "heatmap = plt.contourf(X, Y, Z, levels=50, cmap='Blues')\n",
+    "sc = plt.scatter(fluency_scores, grammar_scores, c=fluency, cmap='Reds', edgecolor='black', alpha=0.8)\n",
+    "cbar = plt.colorbar(sc)\n",
+    "cbar.set_label('Human Fluency Score')\n",
+    "\n",
+    "plt.colorbar(heatmap, label='Trained scoring function')\n",
+    "plt.xlabel('Fluency Score')\n",
+    "plt.ylabel('Grammar Score')\n",
+    "plt.title('2D Heatmap of learned fluency and grammar scoring')\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "teach-bs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}