KNN-regression.ipynb

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    "Copyright 2020 Dale Bowman, Natasha A. Sahr, Andrew M. Olney and made available under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0) for text and [Apache-2.0](http://www.apache.org/licenses/LICENSE-2.0) for code.\n"
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    "# $K$-Nearest Neighbor Regression"
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    "Unlike clustering, where the goal is to uncover latent patterns, and classification, where the goal is to predict a class label, $K$-nearest neighbor (KNN) regression is a technique that is used to predict a numeric value.\n",
    "KNN regression is a _non-parametric_ regression method and is simple to understand and implement.\n",
    "Non-parametric methods are statistical models that do not assume the data come from a particular probability distribution.\n",
    "Like the KNN classifier, KNN regression is based on the idea that observation vectors that are close to each other in space tend to be similar.\n",
    "\n",
    "## What you will learn\n",
    "\n",
    "In the sections that follow you will learn about KNN regression and how it can be used to model your data for prediction. \n",
    "We will study the following:\n",
    "\n",
    "- KNN regression\n",
    "- Choosing K\n",
    "\n",
    "## When to use KNN regression\n",
    "\n",
    "KNN regression is a non-parametric alternative to simple or multiple linear regression.\n",
    "It is useful in situations that involve response variables that are not normally distributed and when the relationship between the predictors and response is not linear.\n",
    "\n",
    "## How it works"
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    "KNN regression predicts the numeric value of a new datapoint as the *average distance* between the datapoint and its $K$ nearest neighbors.\n",
    "We illustrate this using the data in the table below on home loans, age, and House Price Index (HPI).\n",
    "The two numerical variables, `Age` and `Loan`, are used to predict the `HPI` and so they are called **predictors**. \n",
    "The `HPI` being predicted is called the **response** or **dependent variable**. \n",
    "We observe the data in the table, and using these predictors, we would like to predict the `HPI` for an individual 47 years old with home loan of $150,000."
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    "In the table below, the `Distance` column is calculated as the Euclidean distance between each predictor datapoint and the values of age (47) and loan (150,000) that we want to predict the HPI for.\n",
    "The formula looks like: \n",
    "\n",
    "$$Distance = \\sqrt{(\\text{Age}-47)^2 + (\\text{Loan}-150000)^2}$$"
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    "<!-- <table>\n",
    "<thead>\n",
    "<tr class=\"header\">\n",
    "<th align=\"left\">Age</th>\n",
    "<th align=\"right\">Loan</th>\n",
    "<th align=\"right\">HPI</th>\n",
    "<th align=\"right\">Distance</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr class=\"odd\">\n",
    "<td align=\"left\">47</td>\n",
    "<td align=\"right\">150,745</td>\n",
    "<td align=\"right\"><font color=\"red\">244</font></td>\n",
    "<td align=\"right\"> 745</td>\n",
    "</tr>\n",
    "<tr class=\"even\">\n",
    "<td align=\"left\">34</td>\n",
    "<td align=\"right\">198,333</td>\n",
    "<td align=\"right\">269</td>\n",
    "<td align=\"right\">48,333</td>\n",
    "</tr>\n",
    "<tr class=\"odd\">\n",
    "<td align=\"left\">42</td>\n",
    "<td align=\"right\">187,338</td>\n",
    "<td align=\"right\">154</td>\n",
    "<td align=\"right\">37,338</td>\n",
    "</tr>\n",
    "<tr class=\"even\">\n",
    "<td align=\"left\">44</td>\n",
    "<td align=\"right\">139,505</td>\n",
    "<td align=\"right\">173</td>\n",
    "<td align=\"right\">10,495</td>\n",
    "</tr>\n",
    "<tr class=\"odd\">\n",
    "<td align=\"left\">41</td>\n",
    "<td align=\"right\">142,113</td>\n",
    "<td align=\"right\">256</td>\n",
    "<td align=\"right\">7,887</td>\n",
    "</tr>\n",
    "<tr class=\"even\">\n",
    "<td align=\"left\">44</td>\n",
    "<td align=\"right\">102,839</td>\n",
    "<td align=\"right\">274</td>\n",
    "<td align=\"right\">47,161</td>\n",
    "</tr>\n",
    "<tr class=\"odd\">\n",
    "<td align=\"left\">32</td>\n",
    "<td align=\"right\">120,020</td>\n",
    "<td align=\"right\">191</td>\n",
    "<td align=\"right\">29,980</td>\n",
    "</tr>\n",
    "<tr class=\"even\">\n",
    "<td align=\"left\">49</td>\n",
    "<td align=\"right\">191,772</td>\n",
    "<td align=\"right\">110</td>\n",
    "<td align=\"right\">41,772</td>\n",
    "</tr>\n",
    "<tr class=\"odd\">\n",
    "<td align=\"left\">39</td>\n",
    "<td align=\"right\">142,664</td>\n",
    "<td align=\"right\"><font color=\"red\"> 138</font></td>\n",
    "<td align=\"right\">7,336</td>\n",
    "</tr>\n",
    "<tr class=\"even\">\n",
    "<td align=\"left\">45</td>\n",
    "<td align=\"right\">165,817</td>\n",
    "<td align=\"right\">238</td>\n",
    "<td align=\"right\">15,817</td>\n",
    "</tr>\n",
    "<tr class=\"odd\">\n",
    "<td align=\"left\">34</td>\n",
    "<td align=\"right\">163,728</td>\n",
    "<td align=\"right\">140</td>\n",
    "<td align=\"right\">13,728</td>\n",
    "</tr>\n",
    "<tr class=\"even\">\n",
    "<td align=\"left\">34</td>\n",
    "<td align=\"right\">143,132</td>\n",
    "<td align=\"right\"><font color=\"red\"> 198</font></td>\n",
    "<td align=\"right\">6,868</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    " -->\n",
    "<!--  Note: concerned about single table presentation implying distance was part of data, so split -->\n",
    "<table>\n",
    "    <tr><th>Training Data</th><th>Age=47 and Loan=150,000</th></tr>\n",
    "   <tr><td>\n",
    "       \n",
    "| Age | Loan    | HPI |\n",
    "|-----|---------|-----|\n",
    "| **47**  | **150,745** | <font color=\"red\"> **244**</font> |\n",
    "| 34  | 198,333 | 269 |\n",
    "| 42  | 187,338 | 154 |\n",
    "| 44  | 139,505 | 173 |\n",
    "| 41  | 142,113 | 256 |\n",
    "| 44  | 102,839 | 274 |\n",
    "| 32  | 120,020 | 191 |\n",
    "| 49  | 191,772 | 110 |\n",
    "| **39**  | **142,664** | <font color=\"red\"> **138**</font> |\n",
    "| 45  | 165,817 | 238 |\n",
    "| 34  | 163,728 | 140 |\n",
    "| **34**  | **143,132** | <font color=\"red\"> **198**</font> |\n",
    "\n",
    "</td><td>\n",
    "    \n",
    "| Distance |\n",
    "|----------|\n",
    "| **745**      |\n",
    "| 48,333   |\n",
    "| 37,338   |\n",
    "| 10,495   |\n",
    "| 7,887    |\n",
    "| 47,161   |\n",
    "| 29,980   |\n",
    "| 41,772   |\n",
    "| **7,336**    |\n",
    "| 15,817   |\n",
    "| 13,728   |\n",
    "| **6,868**    |\n",
    "    \n",
    "</td></tr></table>"
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    "If we use $K=3$ then to predict the HPI for age = 47 and loan = 150,000 we average the HPI values for the 3 nearest neighbors.\n",
    "These are the neighbors with the smallest distance as shown in red in the table.\n",
    "The predicted HPI is found as \n",
    "\n",
    "$$HPI = \\frac{244+198+138}{3} = 193.33$$"
   ]
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   "source": [
    "## Residuals"
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    "Typically the KNN regression is trained on a training set and the performance of the model is tested by comparing predicted values with the actual responses in the the testing set.\n",
    "The table below shows the performance of the KNN regression model on 3 members of the test set."
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    "<!-- <table>\n",
    "<thead>\n",
    "<tr class=\"header\">\n",
    "<th align=\"left\"><span><strong>Age</strong></span></th>\n",
    "<th align=\"left\"><span><strong>Loan</strong></span></th>\n",
    "<th align=\"left\"><span><strong>HPI Observed</strong></span></th>\n",
    "<th align=\"left\"><span><strong>HPI Predicted</strong></span></th>\n",
    "<th align=\"left\"><span><strong>Residual</strong></span></th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr class=\"odd\">\n",
    "<td align=\"left\">44</td>\n",
    "<td align=\"left\">107,889</td>\n",
    "<td align=\"left\">133</td>\n",
    "<td align=\"left\">222</td>\n",
    "<td align=\"left\">-89</td>\n",
    "</tr>\n",
    "<tr class=\"even\">\n",
    "<td align=\"left\">32</td>\n",
    "<td align=\"left\">127,052</td>\n",
    "<td align=\"left\">207</td>\n",
    "<td align=\"left\">188</td>\n",
    "<td align=\"left\">19</td>\n",
    "</tr>\n",
    "<tr class=\"odd\">\n",
    "<td align=\"left\">35</td>\n",
    "<td align=\"left\">163,463</td>\n",
    "<td align=\"left\">242</td>\n",
    "<td align=\"left\">207</td>\n",
    "<td align=\"left\">35</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table> -->\n",
    "\n",
    "<table>\n",
    "<tr><th>Test Data</th><th>KNN Regression Results</th></tr>\n",
    "<tr><td>\n",
    "       \n",
    "| Age | Loan   | HPI |\n",
    "|-----|--------|-----|\n",
    "| 44  | 107889 | 133 |\n",
    "| 32  | 127052 | 207 |\n",
    "| 35  | 163463 | 242 |\n",
    "\n",
    "</td><td>\n",
    "\n",
    "| HPI Predicted | Residual |\n",
    "|---------------|----------|\n",
    "| 222           | -89      |\n",
    "| 188           | 19       |\n",
    "| 207           | 35       |\n",
    "       \n",
    "</td></tr></table>"
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    "The last column in the table titled `Residual` is computed as the Observed HPI minus the Predicted HPI and can be used to diagnose the model fit.\n",
    "Checking the residuals is an important part of model diagnostics.\n",
    "The residuals can be summarized in a single measure called the _root mean square error_ (RMSE).\n",
    "It is the square root of the sum of the squared residuals divided by the number of sample points.\n",
    "The smaller the RMSE the better the fit. The formula for the RMSE is \n",
    "\n",
    "$$RMSE = \\sqrt{\\sum_{i=1}^n \\frac{res_i^2}{n}}$$\n",
    "\n",
    "where $res_i$ is the residual for the $i$th observation.\n",
    "For the testing set in this example, the RMSE is $56.29$.\n",
    "Alone this does not tell us anything about the fit of the model, but we can use it to compare various models.\n",
    "For example, if you wanted to try different values of $K$ you could use the RMSE to compare the fits."
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    "## Standardization"
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    "This example highlights one issue that you may run into when you are running a KNN regression model (or KNN classifier). The magnitude (size) of the Loan variable is much bigger than the magnitude of the Age variable. This difference in magnitude can lead the distance measure to favor differences in the Loan variable and the Age variable can have much less of an impact. One solution to this problem is to standardize each variable by subtracting the sample mean, $\\bar{X}$, and dividing by the sample standard deviation, $S=\\sqrt{S^2}$, as \n",
    "\n",
    "$$Z_i = \\frac{(X_i - \\bar{X})}{S}$$\n",
    "\n",
    "and performing the KNN regression on the standardized variables."
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   "source": [
    "## Choosing $K$"
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    "Similar to the KNN classifier, the choice of $K$ has a big impact on the regression.\n",
    "When $K$ is low, the regression fit will be very choppy and gets smoother as $K$ increases.\n",
    "The figures below show a progression of values of $K$ from $K=1$ to $K=n=506$.\n",
    "For this data, there is only one predictor on the x axis.\n",
    "The points in blue are the predictor (x-axis) and the associated response (y-axis).\n",
    "The orange line is the KNN regression prediction.\n",
    "As you can see, in Figure 1 where $K=1$, the regression prediction is very choppy and smooths out as $K$ increases.\n",
    "When $K=n=506$ as in Figure 6, the prediction line is completely smooth and horizontal.\n",
    "The choice of $K$ is a compromise between accurate but choppy predictions (low $K$) and less accurate but less variable predictions (high $K$)."
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;  **k = 1**\n",
    "<!-- TODO: put k=1 in image -->\n",
    "<div>\n",
    "<img src=\"attachment:image.png\" width=\"500\"/>\n",
    "</div>\n",
    "\n",
    "**Figure 1**\n",
    "\n",
    "<!-- ![image.png](attachment:image.png) -->"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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M1iG2+ceXYNFBx92Pi8ArISJoTH+hb/PDnH3GbSY3qQw6IneNWYWp280SZ7u9PbTbLqmPtbGPudnHvIRzTwAiqjZiRofBzbhTN3Gt/F0fG6EezIZk2ZppulSqFzIJWeonPLEEC2sgo25aVZ6208GK15YzsnFmSgH78UxgVeuqfw9myPZf9e25quv8lwRIgARIwOkJaEXQ5OXlHT58OD/faOVhpFpeXq7T6YYOHdqrV68nnnjit99+Ky1tEvPO66677sEHH6z3l4w9LxYq5Xr5e9nVw7x3k10kiuS0cXXp/nliIOwwfbgOLwdLS5N1y93+8I01m9F0cjObtrwUDF2MWeXEGPebLF1N2SJCwXb5p7+fmAMDslG13KiHALGY8Y2VyjfDMSLIuphh6Sc0AffOtZQaP6M3CJnqhskPzBe73ctK3nvFgEaVIpLENMeUlhwS2yBVikmRrUDTbpfqEoskQAIkQAKuQUArgmbevHmKoqxevdqEtaSkxBQGoU2bNv/v//2/P/7xj4qi9OrVKz09vdG5N1DQlOlx2yzzSsPoUHywTgaoixFJYQA2n5a/5aodEMsrhCWir9GAxrTj82UUng2SLacu7rL0UlIhqxpdPWSfqJe36J5pMZZbcaEUN8+UlQnbhYeP12NUiDz3uy1iR1JRCdPixLFsJOfKXsyiQ9LhcZu4WHoDnl0up6uaNI0Ow/tGMpanbDkjI0k3msJYKi+ZOZwpcI7aLPZUGvDCCnweZb415aI02GVjUgPgndWy0MVEAiRAAiTgwgQ0KmgmT56sKMo777yTl5cHoLKycvHixR06dHj22Wcb/WM0UNAAclq7uyc8dot86eUltrfdPOT89oIDYszraW87Yjt+vfGPsens0vBATNspOqaz0VymswdeDMYtM6U3k6XwgNmyTjNxLfakIvAw+s+S4lr7A0qp+aKuxqzC1jO4Y46sVfTxxaeRYq181xx8aJQUn0SKJXJYItIKZD3ptTBZ3sg22gjbjq1x80k5ctro4/U4kinLUSYyHrvr85CvNuJ2PwQnmMf/VrgUbYXRbzvEgGnJIZwvwKEMIXaLjxhoM5EACZAACbgwAY0KmltvvfW2226rrLQ7lDxp0iRFURpoJlz9WzZc0ADiEOWhBbKI0slNjHBvNGbu9pcDR7WnkgpZTenmab63k5vIFzmtbezB9HtggBzSKSzDG+HWS31niZOV6unsRTk9ZDIr7uFlzvT1xYxd5rUcvUE0lul4uelguWnDq3pXjVuTmC0Sre5kanp6pQE+sWLgLKzcxZz5nHWjUm4yQI4+3TlHGtzoJlZKJ2rz11jTc1hPAiRAAiTgTAQ0KmiuuuqqDz/8UAVy3bp1iqJs27ZNVd/AYqMIGtMYckvM5r1F5eZz0XUcm94g21LJuThndJdXqpfihVIpXrA3HKqolD/P2ZeKZ2XqAUCZsavq5iMGyCPKJABos6bLJVPT4Ooy/swi2bZjIgESIAESaA0ENCpoevToUT2wdnBwsKIosbFGA9fG+ziNKGgab1DsiQRIgARIgARI4DIIaEvQvPPOO6tWrTp27NiPP/74t7/97eTJk5ZXKS8vf/DBB6+66qqLF2v2+W9pfTkZCprLocW2JEACJEACJKBFAloRNIGBgVdccYXFO3CbNm0URRk6dKiJ2ZYtW/r166coyqRJkxqdIgVNoyNlhyRAAiRAAiTQzAS0ImjEDX9FRVJSUkREhJub2/jx44cMGfLSSy+ZcEyePPnPf/7zd999ZzBUtwZpKDEKmoYS5P0kQAIkQAIk0NIENCRoakFx+vTpkpKSWho05BIFTUPo8V4SIAESIAES0AIB5xA0TUqKgqYmvAaI3+GvNuKj9eI5prCspoasJwESIAESIIEWJkBBAwoah3OwVI/RYeIY8JNIfB0tsRHum0v3dA5RsZIESIAESKDlCVDQUNA4noU/bJVI4BYPvGV6jIvA40sYEckxLtaSAAmQAAm0LAEKGgoaBzNQbxB/xxuMcS4tl/NKJAxn9RiTlgbMkAAJkAAJkEBLEaCgcTJBU1KB32NwT4CEjx66WII6NUXKKJS4AWkF6r4fWlBj5HB1U5ZJgARIgARIoBkJUNA4k6Ap1eOppRLHe8VR7D4H31gJuzhlc+PPl3K9hJfabR+zulQvJjVbzzT+49gjCZAACZAACTSQAAWNMwka770YNM982qiiEutOyBGkzu5YYx9zu4FzwnT7xLUYGWQX7Om3Hbjb3xzhslEewU5IgARIgARIoLEIUNA4k6AZtgyz98mnP5WHhxeiry/eDJdFmhvd4LG7saaEuZ/sItw/D48shP9+BB6RCN69vbHLfs2mkR/J7kiABEiABEigvgQoaJxJ0DwwH2GJskby8EK8uxpF5fLZv4zC62G4yRsrj9V3FtRwX0EZpu/EsGV4dBG+iJJg4EwkQAIkQAIkoE0CFDTOJGjeWS3yZUMy+viY1YzeIIer58bDc4+cqWYiARIgARIggdZJgILGmQTNnlSx1R2/Bi+HyHStqBSXdwNm42KpnKbu6kEnMa3zv2K+NQmQAAmQAChonEnQAHJOu6sHenpizCrcNQd3zkF8uszjdSdw2yxOaBIgARIgARJopQQoaJxM0AA4lCEnm8avQUiCeeOpohLDAzFpYyudxHxtEiABEiABEqCgcT5BA8AvTpz2TtuJvalYc1zUzL1zkdtU8cj5nwkJkAAJkAAJaJ0ABY1TChrTHtPwQJE1A2bL2kxOsdanGsdHAiRAAiRAAk1HgILGWQVN080J9kwCJEACJEACTkeAgoaCxukmLQdMAiRAAiRAAmoCFDQUNOo5wTIJkAAJkAAJOB0BChoKGqebtBwwCZAACZAACagJUNBQ0KjnBMskQAIkQAIk4HQEKGgoaJxu0nLAJEACJEACJKAmQEFDQaOeEyyTAAmQAAmQgNMRoKChoHG6ScsBkwAJkAAJkICaAAUNBY16TrBMAiRAAiRAAk5HgIKGgsbpJi0HTAIkQAIkQAJqAhQ0FDTqOcEyCZAACZAACTgdAQoapxQ0pXr4xOLpZRg8H+9FYF+a0008DpgESIAESIAEGpMABY3zCZqicjy5VMJr+8UhJAGfRKKbJ8ISG3NasC8SIAESIAEScC4CFDTOJ2h+3oZHFqKwzDrTAo+glxeyGXDbioQ5EiABEiCB1kWAgsb5BM3d/ghNUE/TB+ZjySF1JcskQAIkQAIk0EoIUNA4n6Dp5onY8+r5OToU7rvVlSyTAAmQAAmQQCshQEHjfILm4YUI2G83P8v1uN0Pq5LsKlkgARIgARIggdZDgILG+QTNggPo64tj2eZZWmnA5E242x+l+tYzb/mmJEACJEACJGBHgILG+QSNAfgyCt098e5qfB2NBxfI8szBDLvvygIJkAAJkAAJtCoCFDQaEjTZRdh1DkezUGm49CTcfQ7fb8HH6+G/H3kll27PFiRAAiRAAiTgwgQoaDQhaIor8NVGdPXATd7o7I775mJnigvPOr4aCZAACZAACTQyAQoaTQiaMatk58jk8LegDL/HoIcnd5Eaea6zOxIgARIgARcmQEHT8oJmX5q4+k25aDfNPo3Ea2F2NSyQAAmQAAmQAAnURICCpuUFzaw4jAxSf6AdZ9F7prqSZRIgARIgARIgAYcEKGhaXtD4xeH5aoJm2xn8h4LG4ZxlJQmQAAmQAAlUI0BB0/KCJj5dzIFP5dl9nA/W4a1wuxoWSIAESIAESIAEaiJAQdPyggbA+DW4f56cbDIAuSX4cSt6eiEhq6avxnoSIAESIAESIAE7AhQ0mhA0pXp8u1l85XXzRCc3OfFUPVqT6buFJYrBzT0BeG45lh8RAcREAiRAAiRAAiRAQaMJQWOaiBdLRcck59YoUyZtFMMaXQwikuC2C7f6imM9JhIgARIgARIgAQoaDQma2qfj5tPo5YXEqhBOgEif/8zE2uO138erJEACJEACJOD6BChonEbQfBKJTzeoZ+TX0Rgboa5kmQRIgARIgARaGwEKGqcRNK+vxIxd6vnp8Mi3uhHLJEACJEACJODqBChonEbQfL/Fge/gcRESeZuJBEiABEiABFo5AQoapxE0J3IlwNOyw9YZG5Yop6KOZFprmCMBEiABEiCB1kmAgsZpBA2AlcfEP83TyyQ093PL5Zj38iMyb/elYeZe+Yk73zqnMd+aBEiABEigtROgoHEmQQMgNR+ee/B5FDx248wFFFfg3dWyTvNisPx09ZBiSUVrn9Z8fxIgARIggdZGgILGyQSNaoJ+tVG88J25YK4+cwEPzMf/NqlasUgCJEACJEACLk6AgsaJBU1uiazNqHwK70mVyrwSF5+4fD0SIAESIAESsCVAQePEgibuvGgXVfQDA8S2RqVybD858yRAAiRAAiTgegQoaJxY0CTl4EY3XCi1m5Z5JVKZlGNXyQIJkAAJkAAJuDYBChrtCZpT67FtEgrOXXLmGYDB8/HLdruGP20TqxrVso1dCxZIgARIgARIwOUIUNBoT9CEPgWdgqML6zLZdp8T5zQfrceOs/Lz0Xo51703tS63sg0JkAAJkAAJuA4BChrtCZrgx0XQHFlQx1l2JBNvrETvmRKo8o2V9LNXR2xsRgIkQAIk4FIEKGi0J2hWDBVBc3ieS000vgwJkAAJkAAJNCUBChptCZqDGTgxewh0SnhwAA17m3Lms28SIAESIAGXIkBBoyFB47VHXP0e9XkYOmXOfP9ungg0RjZwqRnHlyEBEiABEiCBJiBAQaMVQROTIv5jYlKAwMGy5XRw9spj4mbGsk6TnCsxm1SHtJtgSrBLEiABEiABEnA+AhQ0WhE0E9fiiyjjBFp2vwiaA34ARofhh62IT8eji9DJTdZvunli0kYJ4cREAiRAAiRAAiRgIUBBoxVB8/QyzI83fpdl9xkFjS+A32PwcjBu8sY30cguEu8ye1LFzcxb4ZYvyAwJkAAJkAAJkAAoaLQiaN5ZjSmbjTNy6X9F0MT7ABI6+765GBdhN1PP5cvm1K5LO96zu4sFEiABEiABEnBhAhQ0WhE065PR2xuJ2ShbOBA6ZUOwty4G3bxwqy8ik9UzcHQo3HapK1kmARIgARIggVZLgIJGK4IGwP82oYcX9uvuhE6Z7uN1o7uomVt9EXVSPT9fC8MMCho1FZZJgARIgARaLwEKGg0JmvXJ6OqOEx4DoFMCF3pEn8LH68WARrXldL5A4hvIeSgmEiABEiABEiABIwEKGg0JmueDxAoYC/uLDU2cG4ByPfrNQncP/LQNF41RtQ9mYMgivBbG8JP8L5gESIAESIAErAQoaDQkaPr6YvNpYEE/ETSxM0xf6d3V+GAdHpiPzu5iZNPFXZZtCsutn5A5EiABEiABEiABChoNCZpB8xB0FJh/q1HQTDPNzmcC4RMr6zFHs7DzHLKKnGzSGoDoU3Ls/IsoLD1EDzpO9vk4XBIgARJwFgIUNBoSND9vEwd6lQE3i6DZqwOw/awsyZzKc5bppB5nmV5c5vT2xsS1+DIKd/vLUtOZC+pmLJMACZAACZBAAwlQ0GhI0BSW4eGFOOvWGzrlzIbfZuxCLy947WngJ27J23/ZjkHzkJpvHkOpHmNW4amlLTkkPpsESIAESMAlCVDQaEjQACgoQ7Z3L+gUnfvU4YFYneTEs05vwM0+WG/vRCenWLwC7ktz4vfi0EmABEiABDRIgIJGW4JGpoh/D9ly2v2LZbrEpOCVEPT3w71zJbSTs8SnzCiU+FNpBZb3MGceWoAVR9WVLJMACZAACZBAQwhQ0GhM0BSlw6+TCJpdP5m+69JDEpByymZsOiU64IklsomTW9KQj95M95bpJZrm3lS7x5XqZdlmyxm7ShZIgARIgARIoIEEKGgaUdAYEPUewp+v7WfHN7V9sPIiTL9S1IxbB+z6EUB2MXp4YuUx603legwPFANbp0jj1+ClYPGmY0nTd+LOOXY1lkvMkAAJkAAJkEC9CSzhe5AAACAASURBVFDQNKKgAXb9jK1f1Piz6gV4/a22T1WcJWom6yCCH8PO7wEsP4L756nviExGH4lc6QQps0i2yYYuxoIDCE7AO6vFzHnHWScYOYdIAiRAAiTgXAQoaBpV0NT+8VO2wP2q2poUpIqgKUhF8OOI+Q6AXxxGBqnvOJRe2cXdUFGprtdm+UIppu4QTfPgAnEJmJSjzWFyVCRAAiRAAs5NgIKmGQVN2m5Ma2ueLwf8sPtX88/e31F4XuovnhZBU5yJkCex41tA7GZu8VH7BT6ydPyCmV8797zj6EmABEiABEigUQlQ0DSjoMk6KHpFX4qSXMks/S+CHpYfz78i3riHlHccOuXD8Lzd3sPWzP8mJgV6g3imeS8CpVVmKPHpWDvjheOBYxt1GrAzEiABEiABEnBuAhQ0zShocpNExwQOxrL7JXOx6qjPsvsQOx2A+9qj0Ck/biw8u+DpmPkTdG4/z96Rm5wr3nUHzMYEo4FtF3ckBjyDdW8697zj6EmABEiABEigUQlQ0DSjoKmsQJy7bDPN6S6CpqDqQHPQI9j18/pkPOMVL/WV5VjzqmR0ykj37fHpsjwTmiAeaNx3S0QnhDyBiFGNOg3YGQmQAAmQAAk4NwEKmmYUNJapsmKI6JWiDHNF6FPY8c07q+G3Zi90bQCDaJqSXLj98fflW7+OttxWlQl6RE6GM5EACZAACZAACVQRoKBpCUET8oQImpKqAz/hz2PF0Jn+sw6t+EL80FiS+5+Wrds8ZpWlXJVZdj/Cnq4q8F8SIAESIAESIAFQ0LSEoAkbLoKm7KJ5Asa5Y+HtZ71vP+d9O8KGW2elx1+mLt34w1ZrhTm3+C6sGFKtlhUkQAIkQAIk0HoJUNC0hKAJf14ETXmR7byLSZGojTtTrHUV7te86rbBgeMWtw5y++ZPkbTCusxjvY85EiABEiABEmh1BChomlXQ6A3YfBqnFr8EnVJWVqaabp57JGzTuAjoYvDGSuTp/rZl83pVGylOvwLT2ommmfEH8U3MRAIkQAIkQAKtngAFTfMJmiOZcgD7PzOx0Xs0dModsw226zGmqbg/DZM34a1wfL8Feq+/49RaB1NU1wZufxRBs+hO1B4cysHNrCIBEiABEiABFyRAQdNMgianGLf7SVDJonKIF5lp7X6PQe+ZOH2h5lk18x84GVHtskGkjOdf5XfgYGz9sloDVpAACZAACZBAqyNAQdNMgmbmXjy6CJUG4wzbMBYz2gMYFYJvqp/KtkxCn3/iRLilZM4YKkXKeF8nv0OeRPRH6gYskwAJkAAJkEDrI0BB00yC5r0I/LStan7t+Q3z+wKYG4/hgVWV1f/1/ReOh6mrKytEyvj+S36vegFR76kbsEwCJEACJEACrY8ABU0zCZpPIvHVRvX8ct+Nl0PUldbyrH8jqdplfalImdld5Pe6NxE5xtrePlemx8pjmLYT8+KRVmB/jSUSIAESIAEScC0CFDTNJGhWJ+FWX2QUWqdPQRnuCcCcfdYadc6vsxzMVqWKEpEyAb3k96YPsPwh1XVT8WgW/hsgEaDeDJetrm6eWHDAYUNWkgAJkAAJkIArEKCgaSZBU2nAq6G4fx7WHMeZC9h0Co8vwRNLrGG0Hcym2V2RWG1HqrxIpMy8PnJy++AczL+1+o2F5bjbH59HoawqRvfa46JptlVFw6x+C2tIgARIgARIwKkJUNA0k6ABRF5M3YE+Pujkht7emLQRBWpPNPZzaU53JCy1rwLKC0TQLOwPtw44FCDKploKPIyB/qiotLswZTNGh9rVsEACJEACJEACLkOAgqb5BI1l0uSVwHTayVLjOOPfE0cXqy+VXRRBs2Qg3P+Ew/Mw92Z1A4gPm4nV/NdEJuOO2dXbsoYESIAESIAEXIEABU0LCJq6TpyAm3B0obpxaZ4ImsDB8PqbXA24Sd0AcNuFl4LV1YsO4uFqnakbsUwCJEACJEACzkmAgkbDgmbuzTgyXz2vSnJE0JyNRuoOWb/x76luABzNQlcP7EuzXikqx4ML5MQTEwmQAAmQAAm4JAEKGg0Lmnm34PBc9bQrzhJBc/GU1CcsgX8PdQNj+dft4oZ45l7sTUVYohx0enSR0Umxw9asJAESIAESIAEnJ0BBo2FBM78v4n3UE6wowyhojAeWEgMxu6u6QVU5OAGPLZYI3nf744etKKzdALnqLv5LAiRAAiRAAs5IgIJGw4JmwW2Y1hYbJ8Jgc2CpME0ETX6KzLZjQfDr7IzTjmMmARIgARIggcYloDlBEx8fP2XKlDFjxgwbNmzQoEEjRoyYOHGit7d3Tk5O4765pbfrrrvuwQcftBQ1lNk+GZ7XiHwpOGcdVUGqsSZVapKC4dfJeok5EiABEiABEmitBDQkaGJjY/v3769UpTZt2rRv376qpHTo0GHs2LElJSWN/qW0K2gABD5gXI85a33r/BSpKTRa/CaFYNa/rZeYIwESIAESIIHWSkArgiY1NfWaa65p3779J598snXr1qysLL1e3NwWFxenpKQEBgYOGzZMUZRRo0Y1+pfStKAJekTky8XT1re+eEZqijKk5niYRKlkIgESIAESIIFWT0ArgsbX11dRlMjIyFq+yIQJE6688sri4uJa2tTjkqYFTcgTRkFjPNNkereLp6SmOEtKJ8Lh8896vDJvIQESIAESIAEXI6AVQfP2229ff/31tcPds2ePoig7duyovdnlXtW0oFn5jMiXvBPWlzobbRQ02VJzMgIzLwHNeiNzJEACJEACJOC6BLQiaL7//vt27drl5ubWgnrJkiWKoqSkGA/41NLuMi9pWtCsGmkUNMet7zS/r2wzVRhtiU6thfd11kvMkQAJkAAJkEBrJaAVQbN9+3ZFUQYOHJiYmFj9W+j1+vDw8I4dO/bs6cAxbvX2l1WjaUET8YoImtxj1jcK6I0jC8zFU+vg3dF6iTkSIAESIAESaK0EtCJoAHz11VemM009e/YcPnz4m2++OX78+FGjRg0dOvTaa69VFKVjx467du1q9C+laUGz/i0RNDkJ1rcO6IWji8zF0xvgda31EnMkQAIkQAIk0FoJaEjQAIiLixs3btw///nPtm3b2h7Y7tevn5ubW3a20XCksT+VpgVN9hERNNlHrC/t30MiHpjSmSh4/tV6yXly5XrklzrPcDlSEiABEiABzRPQlqCx4NLr9WlpaUlJSRcuXLBU1iNz4sSJIUOGPFxruvLKK/v161ePzpvjlopio6A5bH3WnG5IXGYunt0Ejz9bLzlD7uxFvB2OLu7o5CYxGRYcgMEZhs0xkgAJkAAJaJyARgWNLbWysrLy8nLbmrrnL168qNPpfq01XX311QMHDqx7n83aUl8qgibroPWhs7vg2HJzMWUz3K+2XtJ8LuUi+vpizCrEncfJPCw8iD4+mNrIp9Y0T4EDJAESIAESaAICTiBoBgwYMHTo0CZ4d3OXmt5yqiwXQZMZb319vxuRtMJcTNkKtw7QO03YyQlr8MZKuyWZPano7I5Tedb3Y44ESIAESIAE6kGAgkbDwSkBGPQiaDL2WT/trH8jKcRczDok0Sv3/Ga9qu3cLT6ItvERaBrs40swz0awafsNODoSIAESIAGNEtCKoBkxYoTFCrj2TKOD1PQKDQwiaNJjrW/t+y+JeGBJocOw/WtLScsZA9DdE7Hn1WN8MRg+Nu+nvswyCZAACZAACdSBgFYETVBQ0J/+9CdFUfr06TPcPl1zzTX/+Mc/LHV1eKnLa6JtQQOjoNlrfSWfG5C8ylpc+Ry2fmktajs3PBC/2VvMZBejlxd22ATf1PYbcHQkQAIkQAIaJaAVQQMgKSlpwIABV1999ezZs21ptWobGkA2ldJ2W4HM/IdEPLCk1S8h+mNLSeOZTadkkSY0wWxGk1GIEUF4PsjOqkbjr8DhkQAJkAAJaJOAhgQNgPLy8s8++6xNmzbPPvtsTk6OCVlrFzTTr8D5ndbZ4/13nFprLa4ZjY0TrUXN55Ydxk3ecmB76GJ088QLK5BdpPlBc4AkQAIkQAKaJ6AtQWPCFRkZecMNN/zrX//atGkTgFYvaK5Eaox1Inn9DafWW4vr30bku9aiM+QulCL8GJYewv40Zxgux0gCJEACJOAMBLQoaABkZmY+8cQTbdu2/eKLL2699dbWe2wbwIw/4Nx261zyvAanN1iLG8Zh3ZvWInMkQAIkQAIk0CoJaFTQmL6Fh4fHH/7wB0VRWrWgcfsjUrZaJ6fHX3Bmo7W46QOsedVaZI4ESIAESIAEWiUBTQsaAAcPHtTpdCtWVLmSa4KPpPVTTu5XI2Wz9b3d/4Sz0dbi1i/Ft56tmbD1GnMkQAIkQAIk0FoIaF3QNMN30Lqg8fgzzootkTm5X4WULVUFoCQHgYPlaLetcxrrZeZIgARIgARIoFUQoKDRtqdgQOJpR47B7l9xIlympFsHnNtmNzfLC+HfMyVm3oxd+GErwhJRpre7zgIJkAAJkAAJuDwBChrNC5rIdxH0MObeLD9iI9weqXbO6SoNSPfu8/mMgFEhmLAG/f3w4AKcaVCQcpef9nxBEiABEiABVyNAQaN5QWOaconL4HUttn4hfvZs3dIAc/bh2PS+52PmmBqWVEg468cWo9LgapOV70MCJEACJEACNRGgoHESQXM8TAxlgh/H6pdQnGn7OQf6I9fvNhy0ule+WCrO6xhPwJYS8yRAAiRAAq5NgILGSQTNqbUiaArVruhKKtDJDaXz+uPALNuZ+vQyhrC25cE8CZAACZCAixOgoHESQXM2WgRNWb5qPhogizEF/gMQ72O5VGmQ2AIrj1kqmCEBEiABEiABFydAQeMkgiZtt1jPGBycX/poPY573Fm5z9syVZccQu+ZyC+1VDBDAiRAAiRAAi5OgILGSQRNZbldCCebaZldhCNud/nO8QpJwMaTmLxJgj6GJdq0YJYESIAESIAEXJ0ABY2TCJpaJ2LFooFrl3vcOxe3++G1MOw+V2trXiQBEiABEiABlyNAQeMKggZL7kGcm8tNTr4QCZAACZAACdSVAAWNSwiapfcidkYt3/xQBr6Mwssh+HAdtp2ppSEvkQAJkAAJkIBTEqCgcQlBs2wQYqfVNAHnx4tVzXsR8N6LTzeghyd+so+dUNONrCcBEiABEiABZyFAQeMSgibwAez93eGcS8gSNROZbL14IB29vBB10lrDHAmQAAmQAAk4OwEKGi0KmpKKywxcEDgYe35zOBd/3IrXV6qvfBMt4RGYSIAESIAESMBlCFDQaEvQhCXioQXi/Lebp2iOusaYXP6QhON2lMavcbDBtOSQBHtiIgESIAESIAGXIUBBoyFB4xMrbn99YnEsG7vOiaC51bdumiboEez62eGk5AqNQyysJAESIAEScDECFDRaETQZhejqgU2n7CbYmFV12xta8Sh2/Wh3Z1UhMduxDc1G2tBUIeK/JEACJEACLkCAgkYrgmbFUdw/Tz2jdp5Dd0+xp8ktwboTEp7p9AV1GymvGIqdPzi6IHWWU04+sfhsA3p6OdiEqule1pMACZAACZCAUxCgoNGKoJl/AMOWqedMQhZudJNNqJ5eGDAbg+ahszs+jURJhX3L4McR8x3S99rXWksH0vFFFF5YIX5otp5BYRlS81FRaW3AHAmQAAmQAAk4NQEKGq0ImrjzshiTUWg3nWbuxR2zJdLkuhMwGK8czZKFnE832DVDyJNY86qE4y51uIBjbXz2ohx66uwudse3+EAXQ1ljhcMcCZAACZCA8xKgoNGKoAFkBeW55cgsMk+njSfFYUzfWZh/wG6CHc4URXL2ok1l6FMIHSaCpjjLpladTSvAbX5ilHM4E9lFWHscd/vj/XXqZiyTAAmQAAmQgNMRoKDRkKDJLhZN090TzwTigfliIzxtpyylpBWo59V/A7A6yaYy7GkEDhZBU5RhU6vOfhmFF4PFImdnikRCGLMKkzaiqydiz6tbskwCJEACJEACzkWAgkZDgsY0dWLPI2A/lh+R7acLpSJoknPtJpUBstCywcb5L1Y+g4CbRNAU1qZN/huAsER8HS3RDz5Yh6k7MDIIXdwxYY1d/yyQAAmQAAmQgNMRoKDRnKBRzaGnluK7LXZ1q5LEXc3FUpvKTe+LmtEpKDhnU6vO3jkH32/BzT44arMxNWSRnOu27HOp72GZBEiABEiABJyBAAWN1gVNrNFYeMpmWadJK8DceFEzAfurTa6idBE0+WerXbBWjFmF22djxi5rzcVS9PHBPf6Yvc9ayRwJkAAJkAAJOB0BChqtCxoAceflRHcnN/kZ6I+go46mWXGmCJqLpx1dM9cdSMeN7vh4Pcr1UpNWICY7w5bho/WYHF3LfbxEAiRAAiRAAlonQEHjBILGNInyStSHuu0mV3G2CJoLl3AAPHg+envLrpPJ6PjFYNlsenoZvGt0YWP3EBZIgARIgARIQJsEKGicRtBcYgKV5IqgyTtRe7P5B3DbLAQfRWiC2ZJm5TE5V3Uuv/b7mulqVpH4NZ66A0sPId/WSKiZns/HkAAJkAAJOCsBCpqWFzSVBuxLQ2Sy+jTT5c2p0gsiaHJtD3M76KDSgHdWo68vft2OBQfkrFM3TwQecdCy+avCEsXvzsML8e5q3DtXBrmjNoug5h8gn0gCJEACJKBdAhQ0LSxo4s6L59/unuIRuLM7RofW98BReYEImpyES841AxCcgNfCzNYz+9MueUdzNDiUYZRWh83PqjSID55bfBz44GmO0fAZJEACJEACzkaAgqYlBU1yrli0/LAVBWUycc5eFCvdx5eYjXYvby6VF4mgydbGYsvlDV1afxLpwB3O08vgZnMm6/J75R0kQAIkQAKthQAFTUsKmk8j8Va43VQrKBMbl7BEu8o6FSpKRNBkHapT46pGZXrMisPwQAyej/ciZOerpdKwZeoIDwB+3oZxEZc3IgPEJ+ELK8Tq+fWV2HgJI+nL65ytSYAESIAENEuAgqYlBc2DC2T3R5U+Wi/OfC876ctE0GTah32qtZfiCjnfdE+AaJqQBFkj6eYpmQamMxdkt+iTSIl8eTKvrp29GirtVemDdfhqo6qutqIBIoD6+oqvndAE8SLYw9PO705tN/MaCZAACZCAMxOgoGlJQTNkEZZVWY1YZtGENWrXwJZLtWUqK0TQZFT3uFfjTVN34KEFdoeJgo6ip5fErax3Wn5ENMTIIHFsM9IYlyqw2gvadp5TjFVJWHFUZMftfnaPTsoRG+FtZ2ybXyIfdBT9ZiHFJmznnlQJiRWffokbeZkESIAESMDZCVDQtKSgmbIZzy6XaJGWlF6I3jPrtVFiqBRBkx5n6eqSmXsCREmo0oMLsOigqq6uxaNZssZju1+28pjUHMl03INfnKifu/1FV3V2R38/yS8+hJ3n4Bcn0uTzKMc31lT7aih+26G++Ha4LNUwkQAJkAAJuDYBCpqWFDSZRbIsMTYCJ3JRppc/5A8vlINOthLnMuafrg3S9tS9fU8v7K4W+umNlZi+s+592LWcHC0RvFXp3dUS07t6CjoqBtHrqvzmJOdiyCIMWSi2L7298egiLDwIG6VXvQMHNUMXiwMbVfphKyauVdWxSAIkQAIk4GoEKGhaUtAAOH0BL4fgRmNYgx6e8re/uKK+k2xaO5y/jENBjy7CHPsQThWVcnrcdonlsobyaig8dqvv8InFi8HqSkA8zagiUiXnyjrN8RwHjetYNTbCgXh6bnn9JVodn8tmJEACJEACLU6AgqaFBY1pBmQXISELheUNmw/Tr8Sp9XXvYtFB3OqLxGzzHZUGfLsZd81BSX0V1UfrHWwSfbXRwQLJhVKJS3X6gnqwDy5wsAumblRzOSZFdri22JjdzDPG8tSIH+SaB84rJEACJEACDSVAQaMJQdPQz2i6f14fRL5r7SoxEFHvobSaaqhqYYCsZ3T3FM+8326W3a7+fmiIn73o02LGm5BV9QCIWrrJG5tOWWtMuZIKWZSqbltz1xxEXMLXsborVXlWnFgBjw6TN3omUJ5u2dVStWSRBEiABEjAlQhQ0LiQoNn2PwT0wrnt5gkaNrwuZsK7z8mhqg/XYfY+5JY0dG5PjsZ/Zopl7spj+D1GomD+b5PjPkeuUC/nrDshZ6waPoaELPyyXd7IfbdWYlQ5RsBaEiABEiCBxiNAQeNCgub8Tszphsgx5ukROgw6pTLdGkdbf7lGtvWaZ6uSxCro/nnyuxZznMOZIl++iJIYmefy4b9fVlNm29v01Ov5vIkESIAESKA1EqCgcSFBA2DDWKx/C5DzQefmPQmdMmzGnltm4rHFcgq6kxv+GyAnout5iqqx/wM5kikeazoZDaLvnYugamfIG/uB7I8ESIAESMBlCVDQuJag2TgBa18H8E00ts54HDol8fDuQfNkLeTOOdhzTry83O4nQba1k4rKkVOsneFwJCRAAiRAAk5JgILGtQTNpg8QMepghhjGFiwZCp0SsmnXfXNFMTy6SCxLAAlH0MvL7ihQQ2ZudrHsFk3ZDN9YpOY3pCfeSwIkQAIkQAL1J0BB41qCJvpjrH5p+k6MCgFWPAqdMmlhzEyjFU3APtzlj8hkpBfKOerJ9QgXVW2abUgWE+Ahi2TJ56ml4va3emiqajexggRIgARIgAQanwAFjWsJmi2fYdVIs2/coEegUz6eu2PpITG27eElbusGzJbFm0cX4YMGO889e1HMeP1sYi0EJ4gbmEMZjT9N2SMJkAAJkAAJ1E6Agsa1BM3WL7HyuaCj4h+vMvAh6BSv0G1PLpV1lFdD8cZKmQy7jfEaRyyvfWJc+urUHXhhhbrZ+DX4dIO6kmUSIAESIAESaGoCFDSNL2gKy8XX/tDFEpZobETzhnre9j+EDS+ukNNM+z0GQ6eM89vayQ2PLpaFmd3nUK4XxzB9faWYUSizq1QP7714fAkGzcNb4Q6iO9U0Bd9Z7SAS5Nx4PL2spjtYTwIkQAIkQAJNRYCCppEFzYVSiR390ALMPyBeWD5eL7swqxvm/fYyPv6ObxD6VFaRhK3eNf0B6JSJfltM56JvnomXgnHbLLl0MEPOOm1IlrhRTy3FfXPFsDcsEV9tlNEuO1ynB34RhY+qBVr4aRveDK/T7WxEAiRAAiRAAo1IgIKmkQXNpI0YtkyWPSxp4UHZ8blYaqlozEx6oQQ/WnJI3NNJivkOwY9PXCvxICuXDoJOWbY2upObKJX/+EjkyLXHJax3pQF9fOSg09Qdor0Ky6xDWnlMgiGkGxdvrLWOclvPyGmpY1WhoAA55dTXF6EJjlqzjgRIgARIgASakgAFTSMLmj4+cpLINlUaZFGk0f/MGyAbW109xCfv0MXo4i4hmcpjfjSseLSbJ3aeA5beK6EPzm56aIHsDfX1RdRJ87hWHkPvmSgok5DX1d3ZDVmEefG2b1Bj/ssoEUZee7D8CH7cittn453V4tOPiQRIgARIgASamQAFTWMKmopKibloXiyx+ZIjguxOA9lcqX92fryIiW1VkaVP5cnx6fCFv1QEPtzJDSkXgSX3iKA5E7X1jGwk3TZLXMXkFMteWG9vs2Tp7Y2dKeoxvBUOXYy6sqayLga9vM3efrt74tcdqKisqS3rSYAESIAESKCpCFDQNKagAcQad8khu69VVI5bfBxEnLZrdJmFSgPumI2l9g9KysHP038rXjy4ry9WJQFLBoqgOS2HjtYelzPbJmOau+Yg8Ij5eY8vgU+s3bMrKsWncB3Xk45miQ/iKZtli6pcj40nZS3qM55ysiPKAgmQAAmQQHMQoKBpZEEzex/6++F4jvnjVVSK07nB8xt53SKjUNRJWoF6inj6TMueO2j6Tgz0R9mCu4yCJrJUjzGr8ORSnC+QlRvbEJXLj0hAbIvnmEqDONy72x8lFeqeHZZfCxOrZ9uUkCWbXwkmgx7bC8yTAAmQAAmQQFMSoKBpZEFTaRBHLN09RUN8HiVGKgP9kVSlbxrrUxaUiaBRdVtpwC9eXhVuf6kozhsXgQPT7oROmR287m5/OUB+5oLjh3+/RQxx3gyXwNeD58vppwPpjltWr73J0Y7V08sQsL96W9aQAAmQAAmQQBMSoKBpZEFj+la7z+GHrXK2aNFBuzNEjfgln1su/dumkATc550mqzK5cky8wH8AdMq80DWBh+V4di1pfxp+2oYvozA3HhfqfBrLAIl1sCdV3fFzy8UxMRMJkAAJkAAJNCcBCpomETTN8AkPZYj9yqcbxKnM8Rx47hF5EXigTARN5gEZwML+kj+5pukG88IKfG0fE+rMBVnvia/zGk/TjY09kwAJkAAJtCoCFDTOKmgAOU71UrDZ2vfhhVhz3Dh1p1+B87skt6CfCJrk1U03ofemyvmpmXvNNjeHMsSrzbtN+MCmexX2TAIkQAIk4NwEKGicWNCYpl6ZHkXlNrPQ4884OFvK8/uKoDnRtI57N56UU1HdPdFvlpgDfxJpPxibcTFLAiRAAiRAAk1HgILG6QWNenKEPIE1o6VyXh+joDFGpFQ3asxyRSXiziP6dJ38Czfmg9kXCZAACZAACVQRoKBxOUET/SEiXpHvO/dmETTHw6q+dav7NzkXK4+Jf2Tb2A6tjgJfmARIgARaBwEKGpcTNJs/xeoXZfYG9DYKmtDWMZPt3rKgDBPWyBbYoHmyF9Z7JgLrFnHTrhcWSIAESIAEnIcABY3LCZqtXyB8hMzAgF4iaJKCnWc2NtpIx6zCo4vM7g0rDRKvqrsn1tvH2Gq0h7EjEiABEiABDRCgoHE5QbPtfwgbLlPLv6cImmNByEnE6pc0MNmaaQiHM+Xo+NmLdo+bsUtCeDKRAAmQAAm4KgEKGpcTNDu+FXPgTR9gWlujoFkuizRe17rqDK7+XosP4fEl6uqELIkbWseQDuqbWSYBEiABEtA8AQoaTQiaMr3Ej5wVJ0ashbZnsOsxgXZ+LzrG91/YBS+oKgAAIABJREFU9SP8OiFhCeJnwvOv9eip9lv0BkSfkijiQUeRW1J722a9GnRUYjio0r40WbZhJHAVFhZJgARIwGUIUNC0vKDZlybxIG/zw8gV4tOlr68IhbqnUr142EvIknjXknb9JIJm40TJz+mOxXfJUo3Hn+veYV1aJufikYUS2HJkkMSr6uUlskYjKTVftMvOFLvhfLgOo1ujebQdBBZIgARIwIUJUNC0sKDJKsJtszBlM8qMckRvEMe7vbxwIrdOs27hQbm9s7vsp9zuhxVHgT1TRdBsmyT3+/cwmwa7X12n7urWqFQv0S7fizAfhzZAntvNEzvP1e3+pm/1ewxu9ZWTTZlFQvLLKNzkjcTspn8wn0ACJEACJNBCBChoWljQeO4Rgw+D/ed/YyUm2QeetL9uLgXsx39mIvAISvUSfnL+AYnudGC1TgTNAV9pFNALs7uKAY1bB4c91KWyolI01n8DRDN19xTx1NMTPbxkTcg2TY7GaM24vDFAAm3e7icxyTu54dnlOJxpO1jmSYAESIAEXI0ABU0LC5r3IiTStSrNj8fTy1R16mJROW7xQViiiKGIJImg9HwQRizH754zRNCk7pAbAnph1r/hcwNmtA9OwNvhskP07WYk1235B5DO3wqXHbH/bUJXT4wKxR1z0N8Pd80R5y62mibqpAgITaVKA84XXEb8cE0NnoMhARIgARK4LAIUNC0saD7bgM82qD/ZjF14JURdqSrHnZf1kvJK8SB3s49sWvnF4Y2V+Hq6pwiabKMjuYDe8LnBMOtG/bQr+s3Cj1ulzehQ2dLaeFLVn+Pi6iT08cHpC+Kebl68tMkuEinz0EJ8GonnllvvWn5E9qEakgrKsOQQft0O//2MotAQkLyXBEiABFojAQqaFhY065NFjqTmWyffhVJZETGpB2tttdzeVPTwxIKDYk1s8blSUoFn3GI2TX9y47GCqJOoCLgFntfkefcw/N4ms8jaxaw4Wd25WGqtqSn3wTpZm9l+VkSM5YjQx+txo7scy+rkZj7fVK6XjbMft9bUzaXrd6aIMdB9c2Wp6dFF8mpiD8REAiRAAiRAAnUjQEHTwoLGAIxZJQpmxVGxWo1IwsML8Uxg1ZGlmr9iYbkY0Nw/TwxcLGnpIdzii24ecsynvx/26e42TGt7coYxqJONoY4BcjQp8Ijlvhozb4Vj2k6sOyEjtCS/ODmN1cdXBM2mU7LY8+xyGUldFJKlE9tMRqGsA/0eg8oqY6KQBLEy3p9m24p5EiABEiABEqiRAAVNCwsaQFY+PHaLROjkJjs7P2wVC9+6JJ9YMdH9eZvogIpKhCSIuW4XDzy2GJM2SQeFS4dApxzW3SabUAbTqW5zx6+vxIxdl37I7zGyr3QqTx5ksbwZHYpvt8hOWRd3GfN/Zkq+Ia5oPPfgyaU2gss4rg/XYeLaS4+QLUiABEiABEgAAAVNywsay0Q0ndy2FC+ZMQAD/cWSpofx5xYfPLwAn0eJj//Z+4x3r30NOiVOd7cImkqrw75Kg9xou6eTViDGwkMXy87R91uQUWh+eGq+7Ij9HoN3VuOJJWJjO3OvHIHekCwmwJ57zKfNLznU2ht8sE5sgFRp2WHZe2IiARIgARIggboQoKDRkKCpywdTtQk8LJs1YYmIPS9+/fv44OtN6O0t/lckFaRCp8S7D4JOOW+zhOK2S5aCCsvMnSVkSfH5ILHJXXRQNrz6+1nXY8S6xQ93zEY/Xzm53cVd/PB2dsfkTdYdItWoLrf4TTTGr1Hf5LYLL6xQV7JMAiRAAiRAAg4JUNA4t6AxQE4b9fbGF1Gyb9XNUwxo1p2o+tb6UuiUg94PQ6c87X14cjTcd4s/4t4zxc7Xkp5Ygk83WHd8cktE09w/z+q7pagcoQnS/9QdcNuFOftwrFGd1O08J4tMtofAs4tFQi04YBkjMyRAAiRAAiRQGwEKGucQNEXlOJ6DvBpCJm08iQ+Mrv0fmKc+710Y9Iz7jG+hU46sc5+4Vnzf/bIdKTaRqJNzZd0lq+oM1MKDsqM0wOiSrquntLdZ2altJjXw2v82yfKSTyx2nJVVov8G4KVg67mqBnbO20mABEiABFyeAAWN1gVNYbkcnO7uKea3nd3x+kq7M96qCZqcK6svX0bhXL6ck4o+jXsC8OPKY2JDEztD1dhU3HlOFIwprTshKyUrj6ESck5qfbL497ukRxyH3V5upQESqeDxJWJiPHi+rAaZQ1NdbkdsTwIkQAIk0CoJUNBoWtDoDbJDNGSRhFosKpcglK+vlBPUtayaHMnEsGWifjq5iVL5aRsqco4bBc00hzP8fIGs0JhCRz22WPakANls6uKO7GKkFYiWijvv8FZWkgAJkAAJkIBWCFDQaFrQRCRJkMXsYut0qaiUZYxft1trHObOF4hJivn494WTImj2/u6wJSAhEZ4Pkv2sLu44lCGxAp5eZj0y/UwgAvbXdGtD6ysNsiD0/joZg8ducUPMRAIkQAIkQAL1IEBBo2lB879N+CRS/VnnxeOpperK2soXz4ig2TO1pjbZRXIk+3Y/2WZ6Y6WcaXp6mTUE0sMLsfRQTbc2qL5UL/tZfX0xOVpOhj+2WE5XHcpoUJ+8mQRIgARIoHUSoKDRiqCpNIin4N3n7LaTPo/CV9XCbi89dJkOWgyVWNAPu3+pZYqX68UtzUB/scYNTbDar2w/KyrHNjKDw04cDt5hS9vKn7dJ+CeLPXKlQayF7p1LW2BbSMyTAAmQAAnUiQAFjSYEzc5z8qf9Rjf09JKj1//bJE5lACw7LHGtVY6DXwkRHzCXl1YMwa6fLnnLyTxxozdhDQ6ky0mohQeleEmHwjUN/pKPu9UXa47btSqukCdGn7arZIEESIAESIAELkmAgqblBc3BDDlbNHUH8o3RIvekirgZFyHfrqQCDy3Aa2FinCt+8srEo+4tPnbnri/5jaVB8GPY+b25ZfBjmNNdFQnB0klyroTjvtFoU3y3v5ygrj3VMvjabywoE7NlSzgFS+OnltL9jAUGMyRAAiRAAnUlQEHT8oJmdJj4tbNNp/Jkoyc+XepS8zEySIp3G6McDJpXr5CNIU8gZor5EbO7iklNaZ7tE1X5Uj1ybCyRVVdti7UP3ralKm+AnNCOOmlXXaYXkxpVpV0LFkiABEiABEjAEQEKmpYXNL1nijc5VXo+CLPirHWJ2Vh7HPvS6httIPQp7PjW3J3fjSJo8lP0BvEp/NM2Mcjdfc76rMvK1WXwNXX4TbREjyqoisAAyDLVnXMaJz5UTQ9lPQmQAAmQgEsSoKDRqKAZEQQ/G0HjcPIl54oNyubTZoMbh23MlWFPY/vX5vys/wedkpt6dNgycc77XoT4tunuKdGUKipr68P22qk8EVjRp8XVTXU1VpfBSzDwMjmuNdBfzHT89+PlEFmzqbe0sh0e8yRAAiRAAq2NAAVNywua11eqz2afNG45HTBuOVWfkRWVOJ6LMavEbcxAf7Gi7euLiKTqDa01FaHPlkRPMpd9/w86ZfLSvS+swEWj1Q6AU3niU/i3HdZbasoVlImLmi7usgXWx0f2wkYE2bWtffB2TSGLMXPjMSoEzy7Hd1tw5oLqOoskQAIkQAIkUCcCFDQtL2gOZ4pR8K/bzfJi9zkJDDmhWvRpWdIox9fR6OVlDoPwXoTYEVdUiibo5inehKunmBRxxLdm2nPe078cPF+iGWDmP6BTnnPboTqMvT5ZFkguGXDg7XA5NG4KTqk3QBcjgxkXcenBVx8ba0iABEiABEigsQhQ0LS8oAFkn+WB+aIMTMe2J29CqV79iSsqJQj2o4vgGys7RKuO4cml4hDP1HLKZgmSoErRp0Xo/LwNBcEj8zd87rFbbizz+Dt0yni/zarGuSUygLM2cStVDQDxetfVQ93mo/XoYdRYtQy+elesIQESIAESIIFGJEBBowlBA8AAWfbYk2p10av6zIGHMWC2XPWNxQtG7VJQJltOc+Ol4e5zIoYMNvcYICs9bruMVatfxOZPATkRfXHatdApb3pGltlrpiOZclrbdHTcphu77IIDDpwUH86UqJlHMmobvF0vLJAACZAACZBAYxOgoNGKoLnklx2/RqxMALGffXqZufnvMWLSC2DLGTGmsU2nL8iKy5x94p2vIPRlRH8MyI5Sge4v0Ckf+qz22mNtXmkQo5zRYdYah7llh/HIQvWV2POyDqS3FVPqJiyTAAmQAAmQQNMSoKBxGkHzdrgYrABIypEVkcOZkveJxYvBkhmzyuyLzzRfKirl+FInN9Efjy5C6PRR2xd8aDrEVDztauiULVEhPTzx0XpEJiP8mCz59PdT7yWZurL9nXJRzIH3ptrW4cN1l1ZCdjewQAIkQAIkQAKNTYCCxmkEjcduCd9YaVwI+d8m2X4KT5INoC+i8PF6WZ45aeMq76dtuHsObp4pYgVAXsjo5Z4Tf9qGOftRovsjdMpXM3zjpt/7+MLiAbPlfNOXUWZnxJecYD9tQ79ZCEkQz3sn8yTUVC8viULFRAIkQAIkQAItSICCxmkETXaxBMT+Ikrct+gNmL5LzkZ1MsYoeD7ITlLkl4o9zdYzsn5z2yyjn5h1b6SGju/jUfDLjKkVuiuhU/QRo6FTXvCJ99icdVnzzwBxkHPbLPOjn17WiuJjn8qT02f9Zkn0idfCzK6cL4seG5MACZAACTQRAQoapxE0AI5mYfB89PbGkEXoPRP3+MuGUW6Jem7sS5OzSJUG+fk9RvKrvN4K9x777Izt4iNYp2DeLXDrAJ1S4fG3bN11542BotS91FquqJR4UnUMj1BrT05z8WiWHGt/OxwbT2LbGYlW0d1TvBoykQAJkAAJaIEABY0zCRpANMrOFAQewfazDhz7HkjHzL34drOcV7KEFEgvxOmgd0rcr4uYNsIsaDa9b87oFMPvbcITyrUwFzU+huGB+DzKboweuyUWet3dK9vdzAIJkAAJkECjEqCgcTJBU9PXL9fjg3WyGPPCCrwSLILm/nlWTYPsI0lznzVtNomUObMRB/wQ0Nskaw6v+rmmbllvIpBZJFtsp+0dGRdXyK5f7HmtQzIYHQhtPn35Qdq1/mYcHwmQAAlYCVDQuIig+Wkb7p2LE7nmTxucgBvdZX/qaJb8/LAVb7ivtazKIOugtAt9CjoleVovw/T21hnBnCMCJ3JF0BRVW8nq7yc7UFpO+9Lw4AJRurf6iswds6p1bRRq+dNwbCRAAo1LgILGFQRNcYUEiYy2t+cIPCJ/g280/jy9DAcP7rUIGn2+HLzOCXpRzm8v+BTTr2zcWeV6vZVUiOnSplN2b2Y6P59Sq29luxuavZCcK7ZW320xB6ZIzhVn008v4zZZs38JPpAESKDpCWhO0MTHx0+ZMmXMmDHDhg0bNGjQiBEjJk6c6O3tnZOT00Q0rrvuugcffLCJOm+ebo9li3YpqVA/rd8sif30QrAYEf8YHG8SNCXTrrrZq/y2WZgyw1OOO237GtOvUN/JcjUCP2/DfwOQXLUGllkkQbLGOwq5Ve3WFqv4eD3eCrd7+oVSWapZc9yukgUSIAEScAECGhI0sbGx/fv3V6pSmzZt2rdvX1VSOnToMHbs2JKSakd6GvwRXEDQnC8QQXMu345FQbn43+vjg+k7xYh4WsQR6JSLM3uUFWbvOifLOTk52fD9Fw7MwrR2dney4IhAuR7vG62UXg6RM9u9vPBS8CUiRTjqplnr7p+HsET1EyeuNbucVl9gmQRIgAScmYBWBE1qauo111zTvn37Tz75ZOvWrVlZWXq9hBoqLi5OSUkJDAwcNmyYoiijRo1qdNouIGgA8bA3aaMdmzdWildf64ZIbhJ0StT0Ybb+9+SGtD3QtbG7U5OF3BKsO4GVx3DG3jK3mQe7Pw2eeyRC1vazdpGzmnkYdXzc4PlYcVTddlyE2FQxkQAJkICLEdCKoPH19VUUJTIysha+EyZMuPLKK4uLi2tpU49LriFoDmWIkce4CAnqtDNF/O/d6G5/zPjiKeiU9bPec99tDyndaFtjX6e10qw4cRU4YDYGzZNlp08jHeyvaW3MWhjPpI0SGcM2ylZ6oVjVqIyBtDBUjoEESIAEGkhAK4Lm7bffvv7662t/mT179iiKsmPHjtqbXe5V1xA0gBxxene1OLHtPROjQ+XPf0SSDYyCc9Ap0fM/Uy3kID1WbGs0nJYeEo92606Y/zAfyZQT6Z9t0PCINTO01Hzxa/zBOonSpTdIEK5HFuLVUHMADc0MkwMhARIggUYgoBVB8/3337dr1y43t8rk0tGrLVmyRFGUlJQURxfrX+cygsaCILtIbIF7euE/3hgbURWaoCgDOmWhz7d+cZaGxkzGPhE0Btnga3g6lScBL+8JkJ9PItWOW0z9J2aLLe3d/rhvroSCSqvVT3GlQVouOGA3tEMZ9rtpdhdZsCNwIhfPLRcTq05uEhT9iygHh8/tbmCBBEiABJyTgFYEzfbt2xVFGThwYGJiNSNGQK/Xh4eHd+zYsWfPno3O2cUEzbl83DEbzy4Xf8E9vGSppruncYvBoD+8ZNzL3juziuwRZuwXQVNZ7YiUfau6lOLTZSnljZWyMrQqSSxnb/bBEWNUcMvt28+K0hq/RlZcwhIxcgVu88Mpm7CalpamTHqh/CVOL1RVi2CyW39SX2fZjkBqvoRnzy+1q2SBBEiABFyJgFYEDYCvvvrKdKapZ8+ew4cPf/PNN8ePHz9q1KihQ4dee+21iqJ07Nhx165djU7fxQTNW+F4M1z2FwD8sl3+p/yRhaIhnl4mW1HR9p5UpFGm8Th3YwiaIYvwTTQulEos7tn75CDVF1FyttmSKipluWWGzTc0QNaQXg6xNFFn8kpE0KgMmQ2Q6JhR2vZop34TlkmABEiABJqSgIYEDYC4uLhx48b985//bNu2re2B7X79+rm5uWVnZzcFClcSNCUVshezP0046Q0SgnvZYXwVha6e+CgSqfbnus0wMw/ICo2+rIFsj+eIE78FB8Q2+Z4APB8kyzOD59vJkT2pIq2K7ReDErPlxuohNi3jeXIpvt9iKUkm/Jg4EryomfWGcr0sRB3MUL+a3aBZIAESIAESaEoC2hI0ljfV6/VpaWlJSUkXLjTokO7hw4e7d+/etdbUrl27W265xfJop86YNmgyCsUf/z0BIia6eojCuNUXwdWO75rfNOuQUdA0VB3sSZW9rW6e4vPGdKymsAzj1shhq51VVk/rk3HnHDXggjIZp8VhnfoyxJS1u6e4TjmZJwY3AftFzcyNr96wZWpCEsT+urO7oO7jI0tTtqeKWmZMfCoJkAAJtD4C2hI0+fn527dvX7NmjUnH6PV6T0/P4cOHjxw5curUqfn5DlcYavtoZWVlISEhy2tNf/nLX+67777aenGea3qD/E2dtlOEhdceFJbJOs2Ko6IYPlhXw2tkHxZBU9FQj4VZRbLQ8p6959w9qfLo+VUmvcm58off6hrHOKLoUyK5Sms1So49L452TJat9wQ48K1Sw7s1eXVYosSnnB8vx8grKsUq6BYf+MQ2+XP5ABIgARIgARUBDQmawMDAv/71r6adpmuvvTY2NnbcuHGWjSdFUTp37nzixAnVCzS8qP0tp/h08bzy3HIxN1mffIk31sWgm4fVyV5hOV4JwWOLZCvK8ZZTtngQRkUjePfp5S0Hl7KrjI4zi/DoItwbYLdhNDoUI4Osu0Xn8uWWH+vm5y2vBBnVrIMvgaMpL+uNJ7AC9ts9Y+1x0Wf1Nr+NPoX3IuRbf7TeCeJ42705CyRAAiTQogS0ImhOnTr1hz/84brrrvv666+nTZvWp0+fnj17tmvXbuLEiZmZmXl5eXPmzPnjH//42GOPNToujQua+QdkueX9dZizTw5j9/bGl1G1MbhQKisZ3TwliM/EtWI8O3SxnBIa5MgLvnSUc1QETXkjKIXB88Vo5j8z5U/yuAixQX45BC8Hy4qRJWUVYdgy2QKbuFYiP/f0kt9ltS7PWO7VWuZUnqDOs1/bqjQIgR1n6zPYb6IlosLkTfKtP1ovH3HOvvr0w3tIgARIoBUS0Iqg+fHHH9u2bXv48GHTNzh9+nSbNm26dOlSWVlp+SqTJ09WFKXRTYO1LGiO58hfNdtVmaQcMbZdecxCRZ0pNJqkhCRg6g5ZGlmVhHKjXLh/HkIT1I3Fdjg7ETplU1LB2QZHjf5pm5xpWn9CVlx+2iZnxQ9liGXJwQy751ZUSnDEH7bi1+3OEUDAbvQ2BZOgUdkmGyAfaPvlC5p1J0StHs2yPiD6lNBTnXu3XmaOBEiABEjAhoBWBM3rr7/etWtXm4Hh3//+9zPPPGNbs379ekVRtm/fblvZ8LyWBc1vO2TDSJV+3S7OXmtJQxfj9xi76/vSZMtJFb0SEM0xwu8YdMoDfvmd3WVlpd57JYBsJA2aJy5wok/hWDYWHRQ3tV9H243ElQp6A+6aIwY0tmndiXqewHo73EHMyDfD7TbsbB/EPAmQAAmQgC0BrQia999//7rrrrMd2ZgxY3755RfbmuDgYEVR9u7da1vZ8LyWBc3H68WziyqtOIoHF6jq7IqbT8u6jl+cWKoaINGd7pwjfvZUKe68NFu0RYJWovRCUo6sr4wKadAhndwSCSDVe6bsxdw7V04kufaRnxVHZZNo6SGxCK40yMrTrb5ijl2P9ORSkYCq9Mt2MZxiIgESIAESuCQBrQgaf39/RVF8fX1rGfHw4cPbtWvXwIPc1fvXsqCZthMvrFAP+Yet4oS39rTuhIiYrh5ipNLDUzzsVVj37sy3vhpqtB3OO2EUNOKsN71QGltOWdf+iNqvltg7m6m9sVNfDTwsC1HdPAVd75nw3ltPDffuakyuJl5Hh8rmHRMJkAAJkMAlCWhF0JSXl998882KovTr1++7776zHXdRUZGXl9f999+vKMqECRNsLzVKXsuC5lSeuGAJsbF9OZAuOxq2VjW2EAwQ/7nfbhbD0qCjiDuPPak17iKZTT0uJIugKckx9fNKCDzrtcBgOwxLvlwvg5+8SYa0IbkqJuLsrkhThfy23OGUmVI99qXJoaSi8vqPP/q0SKK489YeVieJTjpu/jLWeuZIgARIgASqE9CKoAGQnZ09duzY//u//3vggQdsB3r69GlFUdq2bfv+++8XFVWdCbZt0bC8lgUNID5XuhuPLHnsxsfr5W9eTf/LXlwhkZt6z5QDRJ9Eor+fnJpOL5RYBKuTsPyI2ry0r68xEsLFUyJoosabKD4fBN9GcqOSXigHrPr7yWAmrpWzP6+GGn3puv0RgYOrf7RKgywOLTsslj1Oeu6p+ktdVs3vMfKtP1wHj91y+Kubp2xmMZEACZAACdSFgIYEjWW4paV2XmsLCgqioqLy8moOYGi5s14ZjQsaQAxs/z973wEexZVl3RN2dmZnZ2d2ZnZm/5lZEUTGNmAMzja2sQHniCM4giMOGHsc8Djj1JI6K+ecc0ABIQmQhJAQKCGBIsooZ6nD/b9TVV1d3RIiWLaF9N7HJ1W9evXq1ekSdfrec+/9eD/Iyjtpk4XP7M6k2/wsdRwHxhA1fYsfLDprPVDRab6Knom1hBm/nEjbE4h660Bo3Oz4C81XoYrhlLTHI6FoHjDXVGgdwPLgVXH8Ja54zE16lZOddEcAlroxELkBr3KnQ+b8wtJhM347vxEipK3REFOz+KYZ/3GzG2QIMASmEIHpSGim8PbOZ6rpT2jO5y6G9ZDL2EQLB5eSnRO5FAqqjtO9yAHzlDlsqroL5pw9SfWgF4pf76/qvcYT1pQpaae4pMA2qfwONNBijQmX0/6J8r4QL9Q3Qtd6IpcxH2M1akDqmqXayeohiOeyDYYAQ4AhwBBgCBARIzQ0MwhNDZfkrd9sDuEf7k2ByMSfIsmu3NwPR0YRV72SCFlPng9sAMOQy+7VlHxzcALt8MX9naRV0xp361M97IfPnJqvGMPl3OdTzgfiYa+jtM7X9tLPxJ4jhaB4OttgCDAEGAIMAYYAIzQzhND0jKCUUoUkLZveiNwz9iornSkR3R+Kyo5GE3Lu7UimnZGNPKEZaxFy+FecQaz4M7H0z3Roii+uFbVADiL6m4hgmBkIvuNNJ39czm8VZb4pzvzWRNHpgSUII/+e7VgrfbgP9/LhPjrW+j0nY6czBBgCDAGGwPRFgBGaGUJoiCC82BaPapR8MxEIzRp3Sw/ff5sfBR6nrTGQquzOJNesJp7QnChBdFNQCUw42+LJKQ/1FuzVJLfO0dc2gHQpLkdQ0Fu81vgH3GhCkSaLftlk4K+S7fYsNkLXUdIW8az3MyZwdbkWIkff92muhVj/y4m4l5cTse1W+H3mY+cyBBgCDAGGwPRFgBGa6UtoDCboc/MbLTLeyZ+j072gL/eHIjQmqgK1nOYr6bFIq5MONEAa/EkWst618uWbBpp5qvG8y6GSNrz1E6sspxQ0Ia5qf53Q43UUA9b7IzvOcmdEUdX3WAbbbB1phkbnuTgsJqxEz19lMOZJbKS9TP5XiuNTTmG2ln6xA8FQ63y/VwB52YlyL6e3suotc2Zy+QZFd5vlANtiCDAEGAIMgUsfAUZopimhyayFTnaeElLfBWrkv+dLMk3+yHUMYuTt/mADu1JBRJZxUdzH21D3wP84eINTHniPpa7TQItAaHRZ2+JhubFpu/fRiwnoS60GuRFT4PSPwpBzu7+gfZnQWlPTjWWs86VN/px0Ri4j3ytwuYMfw+tkbkYTvEI3+UDu09yPyo4PhII2nTOty4QX5WcNC/PChUxWdS93JCOGiDWGAEOAIcAQmHkIMEIzHQlNbiMMIerDwhv9YANd50W70i7m8TvVhbCmOQrUIrjGE94ivRHbliKIg208ofnUP2NjIO0Zl5dW1LI8GGZbImpgDJn+P80C+ZirBHnalUpnpKmCDKOkH8K6DSP8VfDTawkdcyG/ldLcPVC0AAAgAElEQVT7GTWgVuVyZ6xtgRoZd2yqWEsH643kfASEz06BPDd7clDkwaa5+7rjWkarVHe6Anrc2mRlcxbbZQgwBBgCDIFLFAFGaKYjoXkgFMWopa38DBjDRSeNHTFQB8cr+DmvdIMiWGhD7TzVeFWX8kwMHFU27ZP9sMQQwX80PjfMTT7QHbscgeQ2o4Y2h4M2dYicJvJOcv5fnKwfthCa0FuoxBOmmnHNaAIfGl+lQTrQRLAYrfVACr7SdhQev9UPFh0bC1Z4iCuuqB+WnvvW3gnEOtIBbJshwBBgCDAELlEEGKGZdoSGj04aH5KzMZACpyhv7J4ckADBBDJ0hqcar2sTClugsBEVM0TI7bZEC2cTEThEgkiDuOe9tpvmKFHZQGx6Iz0URu9nmDuCriOnX2FnbBBX0fw3yWVt3rfFRXp16C6v7DAPu5DfqdUwBUkrh/eMgEV5W1e9rsnUkVx29DQvFMIFDjfB9pPXeCEXY2MZAgwBhgBD4BJBgBGa6Uho5qsmiDHeEIAQpClpw3p6OBwE5btDFFDQyROaitxoIsQB2athxvAuRrLaJVpLme4P98EQIjWf/CsTzqw6a11w0klaLSYBDryG5D/DmscGSC4b9L+R5LJ85e3+wd4NqsvmKW1DqM7n7t5Nh2PLpjnlIS0yEY0YKO9E4xn/TSO+V5Nctkrd91467uW9dAiAHHNtzmO7DAGGAEOAITBDEGCEZtoRGiKwjX9ZF14ubYfLqbpryh47vREa4adj6KFwU1SIzqD+o1iLoKAJ5YQeCEWocxpnm8FVxwYGjnpv8mh/OBz1L4tb4WZaoKKbfWyXlNdIizXmzoA1YEtGPY31w16iXEdymTF8A5X5kveyw01IBxxzwjz4/H6/miSJBjef4nsMSZATqlD4eqsmi6doJJdlVna/loR72ZFMOZKIJ/N57DdDgCHAEGAIzBAEGKGZjoSmoAlmEqc8lAIwmiirHi4Vix9nip69YT1CopZqIa1Nc7ovw+edTonOxvYiR5yQGe+w5p00ZB+ep0TppU+zkMxmwEp3C6uPJX+M/2pwC/0QjfaSXJanWI/dyDupzI+8lhKhCuPdwbaXmnxffRgJ98zZdoSxL8TRs7EAzbeYDHWZIqF5JeKi3FqTr4AdZQgwBBgCDIHphwAjNNOR0BDBnHCjtxDys0iDQB6pr+f8H6S4SuSMud4LVp+wMgsPMJgQ73OrH0pbtw9Se8z2dJcXbvWT5vblLlIVIRTiTuTyxxQq+UsbOUKhNyIVzfNxJJZcSDkFnUp6jXmBfivBLUZ7aaSH5LIS3QbsRt1N5f6IdSIoWizmHPNJk//uGKRVrqBiPCBGE9xkizR0ZyB9wSupG/aJhGatur2wefL5fqij7YP0yX7Ef93ChdDX/FDFVX+o9bN5GQIMAYbApYUAIzTTlNAQwTZT2YHCBSJduNBna3cmQpO+OwRfjFMeQqzf2ivMEV+FXUs40sF/kcPPt3hVqpEu2NzqM2jvC6i7RERxj4AlFCrMx4Tfp3tRH3uZjjZHoJi2vdpanOtzOc4a6qDhLpLLDqvvxG70vVQeSF6LieBvusbTZspz7xa10NUeCNh+PBKnX+YMCrVAbS7yUJ8hEpoXQlrci8494ZSPqO2GkOjBMAotpYhyZNlZqrOU0Jryy7EJGQIMAYYAQ4ARmulLaL7n05ldD+OHtLpTTTcSvfCJgN9Js04xZxgh7Z8yE7weDjdfdpgXC/+MnP8fumLuA0s44mg+bPltNFFmLbkWQrPcZgkq4gZ4L8dZAy3EzZbhdA92Y+6niiDyXDish4HHJkDdMu+kWyMGsDRdAdIQ84RvidYcVV6fTk7/znOarQGNvtbRT5POOmUHH4ukVxJBScX2WRYSDEp7xENsgyHAEGAIMAS+PwKM0MxYQvNOGtLT2bSP9wtpf99IgUPEqsU+eNr3nntERUv3KZ4TkPr3GBbJGVcK5FannHPHawkm6WuAkUYuq/N7gOSyE14PlmUH9+sW3BGARMOSApbnnG6yAS/E0WtJ3IC6NKyZKyF+naJ+CpXUk11ecqxjEMFfp6wV3H0jCIkvZgUyJUCxTYYAQ4AhMIUIMEIzYwnNc3HkMC5K2aMIeWKIyOsoDAZWupz8r0q1t30kRFeZKOdDgdA4/YpOhBAfr1Tw3cQPX9TdVM3VR7A57LkIkzTsIz59X9zDkAY7P/xPbWiTk/1XB6aMzRBRVScEzv9Mp5bSVJPmjybHX5Jc5pRaa7OiH2G3qhPip/HJi1e7SdRFP8I62CUYAgwBhsBsQoARmhlLaD7PnqAw06tJgqepfxTlFHYkU88InvdhPaWFKwodr2/o5R5/nn8gi4zM6t/hbyb46xhoJsWvKfdzm0Op1dSisCe57B2X2O/SuQIL8Y9itvjNVBlG7vNsxn//3fIzMPlscUrpcvjTkMN/kFzW1HCKn9ZEyCy8MRCZdW70hqJo1KrK0/e/uGWGgTHkvMm1zuBX34PA+1omDbbg9GNsDY6hpMZ1Xvjc7wm+4BwBP8YS2TUYAgyBKUKAEZoZS2iqu/BaDZbk4ourhGi3pE14dqq7IGFZpEEkzjIdfa1zHfA0V8AebAXzqE7Az9gHyP8qgdbkf2X74A22ksPPcXT/Lukh10JcvUczD5n0MqO2BKAE5lj8ExiZ8BhVhpPbXOn4KdlOrcYNukYkj6r+rFf+geSye3SVvMvp/QxEmLsWgmeElKIQ5sPh1gaqKVmBeZIPMkCt2s0lIPpHURTi6XGFP83D2e8fBIHBMVDY9f4UWYHPXZUPG953h36Qa7FJGQIMgZ8cAUZoZiyhQWRSJfjKfSH0QQY9Eo6XfWip1SNnIipoovByRIkbS/3I5W80dAYj+BLcdangH2kvIS6JN9Xk7bE6n4i6KoVDkXeKhxr7oBfJrCNys8PRqgh9XzMS63lvxW7ik1QVQW5zxPFTsjFqoFVuCOGmmiTS/g/+yWXfRBU+Fong8IVqOiFJSdM9jPAoz6NTcuUJJhkcoyejQBN3JCPt8io35M6xKts5wUmsa4oRcMhF5J20ZvuRZqRQkirlp/iSbDqGAEPgp0OAEZqZTGiIqKkPX0zfSSNF3rn8HW1HSfmfVOKFp7G/Cczj9H78zHgNQUnxjyJ+O+8L22e1vRhj3OdLi036FNOmQG6g6z9w9EQoP2Gc6lnsJm2hqihy/T/bqb7f/sEGhEajRGVNIun+Qi5/J7msOd93jgJ87qVE29nVh2Gk+eGa0YQaWB/vR62rmBO2tTN/uOuymUUE7ghAOmyb9ngkHI6sMQQYAjMPAUZoZjihubBH1m+VUAChvxHMo+kQfu57Q5gkchPlfmY7YXMuOf4SeWU8F4mHVPm0FYWhCCHfchn4UN9pkstCnV7AbvLTdDKaXP8hjp+SjeSTdC2f0qY6ASW+Y+4nxa+H8hzmK8a2x6MulU0LK4NXiLWZgcCEuZqu9qAUQUNlucsJ4vssB9kWQ4AhcAkjwAgNIzSSx9f/Kip2xj7HP6j1CPhH5k5hROSddOhTyWhusy6NVL+zsbiknEJNpaExI+/3oXL/7vYGkssCHV8kuSxO9Wzq3hhYUKa0VXfBm1DfQ1QdL+TOidhocvhFsvIxXQHUQpKkMLjwW3vhD2LtkkZAb4QB8ko3hJUt0aIEaS8ncudvaks0MkpLm94IgXCIteNVOoBtMwQYApcuAozQMEKDp3dIT9oCqlJf7eyheS+d6hvqQWXOHMfPrHeF5zvqbjr4se2zHnwD4pV42Yr5mN6IfP9hgRqc7vjLwaM+d7vUkVxWFPAqyWU1Ic+/oY3rVf7NPHzKfj8bizqUvWWxEAMRdez/FpfzvLNjCARrTw4ZzKQmsgKKIpYVZsqg/ykmMhGSKl3jSdEVqNu6rxZxTLf7W0Qz2fX4lFPNBVb1RjCeazxtq4/9FGtn12QIMASmHgFGaBihQeT27f5IS9Pidl1BvPKZWLpNA/5BnSfwM+d94bmLvpcO/sv2GfRchNLZjQfgeBq0pI1rqKs6obgqWvXCafUVn2k8r1fUYqp9r+Nn6ram4vh2+f8WtdhO9j33O4egfX5VFdOl+PvjkfS0Yi/JZabITUR0uAm1CK7zou0JKKu5UI3IF9YuaQQyaxG1dJpPNMDdyZAej7Eq33JbPsXQp98VRNviaa0H2ExZu+Uo22IIMARmEgKM0DBCQ7vS6P5QGGko5Ea+uIHH/hpEWXfiJx34SHjiY+6jA7uxPdxJqS+i5CQRPEcno8lkJKdfUV2q5W/j8Dcm7V8P5Oe2Oa/6QuPml81NlfkWR2hepOqEXqe//hABtEYTlRyM7lf/Q3OYTpVyGqCIjfyqBkZRnvPbg6jIbVuiwbJutnXJIPBR5gROQ7dChPVJW1MfeRQhWju6gnvIpcfYNkOAITCDEGCEZrYTGqMJod3Z9dxDHbqOuFzAY53VJJdll7eRhz2VegsPfOwDlPMBtptzwUvyvyKTgVT/JfCYgDUkDerO/Yw40wj5X/WNzjmrmCuksH8XTkx7mWqS+hV/+dRa3zBlf1ZiCBXnMjNF3NHUJynDOWWXYRP9xAjsSkMEmU0LLYMFjjWGAENgFiLACM1sJzTdwxBUQktLRGG3Uf7X2OAKOYUXWee1jX1IcD815oCXyGXUdhQ/e7nyAhEbrZIF53yIOCMiCrw60E/zbdJJjKxJRNXuU3Fj1cndDn+O/oGcPlWRyH9DRD0wC+UpbrdT4B7vCqIjzehmbWYg4FNMN/vYZkd8MQGJf1hjCDAEZiECjNDMCkJT2YHCjdd64gWwe5+Vw8VEdIULJZ/kHv6IOyjvSyIabKsiuSzoSM8LcVAe3OYHRa0+5mHK/ifGNWTyhKY+4UO9/N/WuCOGqMbrbn2ORDK8/22kriGioOtOZyrXq5B/zzgCL1XfCClCU3od/zTyAxUfQNY+pCF2O9BOclmL9/ozg1TXg2KcC9Q05cKdWfi/xjS55b4RaGJ27hVUwAYTuRyBubFSkkFxmiyVLYMhwBD4ERBghGbmE5oDDfhf/pVESjpJURXIJrfajYSaTdwj9s1BlAJo6Sc4iXI/0xvpy1jwjxXqvn+m095q1E/YGEiZ6s3DGe/gjLo0ntC0Ovx9QPHX1GqoUnJU94W57bZUZMx4DflmSNDl7CuCvvhW9777Q7nwWo+9I4o/vpsy9EbimHsRTZhE5OKffq5Q1JlBWq4ewjrDbhOn2p2JMCjWZgwCJzoQT7dMBxHYVe60wgWxTqwxBBgCsxMBRmhmOKHRG/EtVpoa1WhCpM8WPvEd99SPGuiZWFruTOVud+8P+vgWX9rsVkFyWeSxfvGvYtRAOc6P7fflCjbVJvOERq/6I2/RISJDzIMBuvcdxfreKc9R6naczutyOjFhUvmAdzFeOZ/5pfXJ/9CmWV7s+9zNPnS9F9VYe7fE60o3mvqQQcSnmI5OHh51IpTc5ydU4cYp6i4Ku1WcpOIMfE8DY2LHJbyhN1JaNao3JFRZApUv4fu52KUbTLS/jryLwdelVQ4udj52HkOAIXCpIsAIzQwnNPmNtFiDYtrSVn6G5iiEOtt8v4mQU7XM/b4U393uRZSYX46A57EB6VmtoY/7KLgke9XxJr4gpeI3yBHMt/jN5ZHv3uJrPsNvlSCpCbsN5bVb8sGBxlCt8cN99J5Xukn9eyQLjnt4zADr0V1B5hPP8tshF/G3t/khhmW+CoXEpSnUrE46EUIe9tEViOClw99S6C3i0boeEJruYbHjUt041goWuMKFHgqDZWKlK2Xxsu5L9YbYuhkCDAGGwPdFgBGaGU5oUk5xhgrr56R3BO/12vFGkdgHedlv1IEy8A/9kPS83qgnPRzfhPDlZLTe6T84I83PUKeJbwmPn47ducLFfIbnIoHrHPiIFL+hnA/5CUcN8H+VFGQgPMr5fyn2QSLqHDpHmju/Y6iVfbBBmLypj+4OpufjzNey+V0RTB4LarpprpLas76DicjcnI9wLMe8e4n+7hhCbtzdmcSLkPRGUh+GI4+vK36J3hRbNkOAIcAQ+J4IMEIzwwlNVScKAjT1WT0n+7iMZKPjNblJW1CfUj9ccKyE5LKBQStTRk3gU41OC8hzIeV8OKL8M0doZD7hEZc5c7W1tU9leL5hKfeI/DQxwlWDrkWGG/nPyDDa2Acu1XMyEwUTtH8WIqEIIp7YSqtFSneu8aQA6yqD9T24L2kBbcv4iiAskituoHOWD/jfTERGE9KQLNLAQXOpNy1XycFoznrM384zsajByRpDgCHAEJi1CDBCM8MJDRE9GUWPRVqEt419dKM3fZkz0TPfCU8TDZ0xchlc3kwcEUlPxRmKU2zlSQxF3mly/T9+W+4fs6+WCpupwPNpH8fX3k3jph3pATES8+xlvkkOv+AtJUN6sJ/K4v2k/C2p/0DR9xLRwCj8YvmNEy2JqIsLLJeqmPlxt/lRRPlEp5QHktdivp5DXJBDnsNN13rCO7NUa8uKJjr5Euh7NYm+yLZdp28x3WudUM52BNtnCDAEGAIzGgFGaGY+oWkfhEJlpSu9ngI58GIN0sCLTMXq8eaLbA80U3sxgpJ8xq72QBHHZ2LBQgq9ngGJcfk7qhx4La5TryS57D23+LdT6fFICnN6PtvzpQVqOjNIVB4Aj1Kf2UVERPUZ1FbEX+u1JPoo4AAyCyt/S9H3mIg+3o94crHQktWSCAKgOQoqabPqNhG0I0K0udURonJ/8loi9B1xHAm8MbEKSuQpjqWyueiPuPtOGr3DE0fJRZX5+BRYYwgwBBgCsxYBRmhmPqEhQvKxhCr6NAvpZHLqbetOW57+ISRuod46PmPe8Kg+vJz+lYlyAUjfciqWUrdRVxVFbKDEp27WNJJclrEveXcmoqi647ZT6rabfVBegJK28OIYy8ySrY5BesT7NG/gOe1z16MR0MdMXifyiSh6M8U8RXUC5X+9L69oqfYsuuAyP/JaKowuVFDwDeYzZ8jvlFMISZO6EftGkGTI8+gMuUF2GwwBhgBD4CIQYIRmVhCa830yRnrAM7qqYE2Ry1Ch6SxtzECrlW0YI/qV0l6mlOceDONeq9H3WCpATTTDwCgVhaAMQqFm0yf7qdFa4jP+jMoOWqpDbsDC06N6zV/71f8guWzvgcPjR6KnzJe8lwmHCpUUfP3Ewy7ZXhNBEH2tJ8WcoJOdSBS0IQD+pomtbpfsbbKFMwQYAgyBC0KAERpGaCQPjJ7LRNdRSq1HQFbObsohovt8ujCmwVxNJ2PHaNyTizWU28iXUPhKMu9Em5VhOD1iw0THJug71QXP1wZlKcllW/wb9MrfI7/fhK3Uh7yXC0eKVBR03YSjLulOvRFWsdVuUFhf7gyf3cxIrnNJfyhs8QwBhsBPiwAjNIzQSJ5AkwEko7WA6tMRlDRpizreT3JZaZFQYXI0fafpu59tD6ozGUbJ/0oqVE56NtGpOI7Q3HGOYdaHjXz1KKMeEVK1ydYHzXul3uRzmbBTpKaga80HZuBvlkpuBn6o7JYYAgyBi0KAERpGaKwfHIdfwEej/gM5/Nz6wLg9wwjJZQ8pDt4ZhECqm3TNRvkvhpK2IR5bLqPKsHEnWHfw9RPC11v3nmuvtUBgWrq/UnXCxKNLvMj3CuHQUQ0FXj3xMNbLEGAIMAQYAjMIAUZoGKGxfpxj7kepaoefI9B68mYyklzWejLPp5g0h5F+HlYT3V+RmVca33S2SfiS3ZK6BGcbaNXfdIgc/w09Ln+HSHnCdtyDfFcIR45qKWDthKNYJ0OAIcAQYAjMJAQYoWGEZtzzXOwMEwvPG8YdtOrIfo+Guyw9qdtwYsqzlp5JtoY7QTUkaXwnGWs5dDobeYdRStuOqs4SpnzcnfxWCqcU6yhgjeV0tsUQYAgwBBgCMxQBRmgYoRn3aB/3AC9x+tW4A+fqqAzHiemvnGuc+XiBnEKQxvcCWj1XM4GI3Oed1at1zI38VglzFjuT/1UXMD8byhBgCDAEGAKXJgKM0DBCM+7JLfMluUzv+Ovb/BBBc1cQalxb59kfdwrfUR0PQpPJFbA8yxCr7iNOKIlwQa12L2n+iDM8F1KxbuJTj7lClcy3Yy7kv3riYayXIcAQYAgwBGYQAozQMEJj+zgbyoJILhuR/8b3GGXXk64AtOb9s9QJaupD2r1nY2nnXirMTQOhOfCR7Yxn20fWuwtMElOTSLq/YD6vJcLG+MmlJEZKbsaPZD0MAYYAQ4AhMFMQYISGERrbZzk7DZ4jk+I/xAMnOlDWUSx2LfZn1dMyHT0cTo659M90ekqdjRNrEsUB59hAkpizx1Tv30WdFbYznIyBHJgIgdnK39oe5feLnSlgTVELuRZSToLbsJfZ/TTxaNbLEGAIMAQYAjMBAUZoZjuhqe2mvEZq6bc8zY7+MTC0KP/T0kW0I5neTqXc08hLm9+EgpG9I3SFC6nyLaOaqlEBKuogKkYOjNKRZiptR9WFs7ajGlPA1aXtVNA0rtASnxGn1Nv23KoIcpuDTt8VkC3XJFLy0zQ2IB02ekRbrVk7V0m3+JLKw73cceXOvdTQi9us65EOZNsMAYYAQ4AhMHMQYIRm9hKaU110fyhSzS7WoPrjtnjqGMKT/YlPIgiN6nfiYz44Rnf4Y+QchfBzvpoei6AbvG21NWG5tev9YbBZoqUFapqnpDXulHRSnMlqozxdV65YM1eJkYs1cF1Z2M8YsvbRcQ+rE0xGUv9eyALsvxoDUl/EzzPHxWHDevLQfJzrsM6OW+q7jh61qhWLNGTnhEvYKeihMKrtFoezDYYAQ4AhwBCYIQgwQjNLCU3bAF3phvrbrZx142QnPEd3BqEekDoKniNpUYJnYzl2ogLnaOylz7NpoZoucwFZsWm5jajLfZU7pdeA6wzrya2Q7NWUUWMzkDLraLfSpd3lqiE9RmbWYrZP9puH8WUyj7ma97nfhlEs7HQ2diI3YTv+UfxsOiQOeyScDjvcEOf5fu8I9Y1QYLBnucMVS7VwmQ2MoaDjy4m01kOgbuJZbIMhwBBgCDAELnUEGKG5NAjN0RZ6ay/dF4KqhHGVtnaRi3gKP8+mB0LJKAle6h9FbaDwcjrWSjepamNKOXMNoTbTPBXNVdJKN3o8EsKUIT3qRN7qT3OUGExEbQP01QEYP27whhVkf63Vipzy6DY/qx4i1FPcGyUJRyIqbIZFRygi3VsPpmITx6QfRueZEszVeQLbERvw01wgs7gVVy91vLI8w3V7AuBSeHqfcLh8jhK2pbJ2nGcwIW7rOwsFsl0Y22cIMAQYAgyBSxEBRmguAULjfxxGjjdTyLcY1pHLnGFZkVCRi3nw7g0hn2LbE9/PoF1cwceQUlxxlSvd7g/nkZ0TuMtHmVDM3OgNLhJfBQHNXBVtjwdRWOUK75VTHgbPUdI6XzozaJm8vgc8o3MI7McxF7cQWoqe9jxJBjxu+E0+qCCNxqe0OaqxzEJEYu1MIuqpAZXxW4mfvKqGYA2ap6RTDkveVfn9KxNw7XH1OeFw2VwVLtfQK0zmWki3B9DXB/Avs9aK1Vldju0wBBgCDAGGwKWDACM0053QVHeBW6ScsjxTdT20woVCyyw9F7rVNQzB7AcZIBnSxhOayg4YWla7013BdI0HqMByHS3V0qHTGMvraTYGgFfd5gcCsUwHY8/XB2iVG5xWV3vSY5H0oqTOEk9o3t4Lb9SWaOiLr3TFtFVZnqiW0G2+t9Fenct3hzPDyWSk/W+DqdhUuBwbRGcHd+f9TdiWyyjjNfzkCB5PaBrlc0KiI/j7CgjxbVAut1MAMb71j4JvzVPR9gT8W6qlJ6IgYWaNIcAQYAgwBC5pBBihme6EximPNgtvZ8uThs5wy+75bxlNpMiDAmaxhuxV2HDIFUwUfSNwOYWW0k0+cG+NGTDrwQZawGlpb/Klt/fCH7Q5AiTGTgEn1BwFfZiBjbuD6bk4cKymfrCWwBKM6RsR1uWQi5lXuVHFGaFnWA+S9LHGG1yk3B+9YwP1mSqBo3SU0t7nsX3EUTiB/zU2gE4+lttkpKNaKlKjNrhcBg5EsADZKajL4X+eUyZ9sI+ST1F0pF+VfJmdAs4yvr21F3qaT4Ua4dQ+SBsDcb/Tv3UOWSCd/qtlK2QIMAQYAj8yAozQTHdC8146vZdu+1TEVcKIchFNW0ArXWlfLV7kq93o0XCYLlT5VNUJBczdwZRYhQEjHJshoqASutwFlIUPGlqopofD6I1k7K71oFv9QA6udLNayJ4c9MAA0wm1DS8KXuWKdMPSlnSKdjr6kFw2eszHRFSVphTYjFxGLn9D0LhcRgXfSU+h0T6O0PBOKfORtqMcoRFWfJmOhuW/edUtax7nZnrDyf+Uw5IFKnqCIzSl7fCIrXZD2LnYjrWChHVIfGTioWmykVgFqxL/EdwTTIebpsm62DIYAgwBhsA0QoARmulOaDSHIW61aV8foCejbPrOvTs4BveQGERd3QVXkZ2C/o8zt2yLhwdKc9gy86lO+JXsnGiFKxewbaY1c7iND/aB9zwYCrojVcwYTPROGqZdoMahtR4Ue0LgN9Ildg3T2+owksvecfSYq6RvFd8KhEb5WxRpqgxH+YLD30hPodFejOmqtOpsR/IbMur5zlcTxkgu2+h0lH/9v6EIqHVcDA2NE7gXIs+VVDsuG81C9cQsYdSARMmbAulaT3o6hnLqra784+wElcCQpiugxj461UWfZcEFmdt47ot3D9OXObTeH9z3lURBE33u09gIhgBDgCFwaSLACM10JzRNfXAPBVgyrdDxNig/RF5y/g/e8TaYIiy5Xrgzq7vRmW6OSwosESKSjrXSQg1epWvcIbixV4OgPBQGg80VrsgLHFEOBfHDYejfHi+4qIiQcO+uIHozGVFLZVxiPRPhFrIlbKB1gK7xpEVqY4vmsv1xrk9GklzxlUBotH8W7ij8dsrbY3V3Iz0Y022d1qb9GPpahYwAACAASURBVMll32WPvp6CqHLvxBySyxILq6MqKKqCBo4F9jsvmqOkN/ZSfiMdaACnsTHGdAyCrp2SVA3nLzpiAJW8wZu8jsJw9fF+gOA9Tkk9OEb+x+G0ej8DUidp4JjV4i9qZ3CMljsDamnbk0N3BEg7JthuG6DrvPBBBJdAwb0jGR/lPvOnPMEJrIshwBBgCFziCDBCM90JDRElVOFt9GQU9C5vpGD7cy4Vy4U+e+VnYDIZEmwZwtkjBhCa423CbvsgCEp8lfDNfk8OlbShZ4UL/i3UwM5xhQt0J6vdICt+KAwC24VqEJSt0fRUNLQy94XYqj3e2gvxjcilXkrE/Mt0ZPBfw8dm50V8LhAa3V+FpURspNzPre5xuIsjNGYRMXcs/tBxksuejRr5NAtCmSKHa44pr1+kNrySCLhcA4NqHRa+nkz3BAszbQig3fusZt29D3Fb45tTHhw9ohKIiNKqgZUYLUWEHH03+dD1XggBezsV5GNLtMVhN37OC+050MChZB3S1tQHWiZN7jx+2leTIHYWASci5yMIRhu2/vTHn8h6GAIMAYbAJYoAIzSXAKEhoppuuA+ei4Oeho82uogHTm+EucXGxuBbDGoyZsDLr7oL//yP47U9RwnWsj0eL/t1vuSUC8+LnQJilCtdIUnuH8UpdwXhXc77pC5zpqU6mqugz7NsDRUdgzAY3BOC16pvMXjVfDUS6lDgNRD2EhkOfpKluGvQdQkENHyLuosOfWJ1m8OdIDQ91WJncSttUpWgUy+IYgyOv3rGKTGqgj7cB7giooLH3BaElSH+nG/H20ARno+DiSv5JL0Qh90SM58TZyaCQiiQS3kj7bw3BJl4+GYi3P5LichGyLfWAWB1cXRTmML6V0aNrUSJt4HZKSbLd6w34hPMt3ZL6Y1gouMzHFpfkO0xBBgCDIFLFQFGaC4NQjNVz1dsJcwtLkfgdukYhGJ3oRqpXyLKIXbhdSfXeNLrybC7LNbAJPNltlBoSW+Ea2YBJ7YtaafyM/RMLAwzO5JhopjLiW35Geap6M1xcUPBpaA7/AA7BX3EJwUOvp4KFbi7A7sPqu/rc11GbnbCzUbfQwf/ZXXjQ2fAXXotjpN302lPTKmF0BghoLlPXfyhaIM5EUIe9i/ECfl1+Nlqu5HU5xpP/HsjBRxuwrbSFemMbdoriWCWfCtqAZhSfTERUiQv1VqZRmxmuKDd1gFQk6IWq5NCy8BypNYXq8MELZSdApobm3ZHwPeK9reZje0yBBgCDIFphQAjNLOI0PSPwniw0hXWF55YXO8F71JQCbiLbzGIS+8IeR4Fa5mjgE52lStMHYNjeGgPNoC1IIrbCafPVYLQFHHpfb86iJ6vDqC8QM8I8v7NUVK8RLwbXQHm5FGE+XtHOKqkxnUp5GYqkBPRUOb7KY4PDnlcQe7zhL+Q2Aco50Orvxa+HkKvhWU8GkGhWWUgNGNckNJIN8lld+uqVrtTN2ey0VdGG+W/2KSuOBtrsZrfeudWP7rGCy62Ne4AoXMIWYav80JkO99iTsAeY9PODAIKId+xzbGL2v1wH5IZ8mmOiWh/HZTa45MiSuc2EfyD8VXSPqx/oRrCJtYYAgwBhsCMRIARmtlCaPpH4UPZFAh7TO5pMJvFWjiSRgx4+Ylp+o63wat1qy/4ynp/jLzZB5aV2/zAUZbroHu147hOD5dmJp3ziSzjTC+nzal4+0YwZo0H5CY7UmD7maugdT5CnQQiVB5Y6UqX6cgYeisd/mZIT0me7xzQbSa/VeSxQPhLi32Ist/jtyvO0O5MeiW8jeSy/HKLunhHMjkllnOEhitJdXo/yWXvRjetcAERuc6LFqkNw/LfOIZn1VxgQUrPozCN2KvI7xg0TPeHYrb30kFuxCx8h04DGRtVSkETThTj3r///xojBno3HR/HOl8EW9mrkUnIWlQzwUUcc2F/EquLD+vhZbsv5NwnTjAX62IIMAQYApcCAozQzBZC82UONDHSFy2KNCkRvzNfJcQoeRThfflKIgoUoP6RErRgRwrEtgs1tCkIxhUiCGx37hVejYdOY8w8JdTBYjvdC0Jjp8BscxQw89gpcJW5SkHBozcilbCdgo5rb0/033OVO4Xrdg5GP07+V5HXYmGe+M2UsYMIFhF7NYqBex5sJbnsJvVpMS1eRg1tUnNFncb6+0fpWMI3beqlC5XGlGp6MhLGpGdjaUTxh2bV4idVWXst2htxpRNv8I6etGoQr/kqiJ3f2Is1zFPRNwdhguKb3gjS8M1ByySjBmifdyQLPfU9MHc55tLe6sk8RJbzz75V30PRFaBW0gj5sw/H5V5LwkfzYgI+rDXuoLMi45zkRHaIIcAQYAhcoggwQjNbCM1NPig8KW1dw7Q+gF6MhxDEaIIw1t4c2TtqAEcJKqH5SuiCP82i1GqLzreqk1a6QCz81QG8sOdxKYOrJSaQj/fTBk5BPEcB28xdwaA1izSYbZ5SyBdsNOG6x7QbY30+9ywiQ8brlLQFGmHv5cIis96hyE3VXRgG+TARDTSTXHairnGpFnpevjklVZJctjOhd6mWnJRfHXBcf5kzLdIi4EiwT+j+QnJZboJmqc42YFuYYtyvIHPsOhF8Pa+n0CI1lDH2Ktirljsj3IlveY0w0jwTC9bldRTR1Df7CFdR5OFm7wkGqbrcGYek4VHjrvmDdBxsoC+yodoOL7e1JP0g12OTMgSmBwIt/QieKGmz/K81PdbFVvHDIsAIzWwhNFKJq94ILrJQzVlNVGSnRLq2f2XStgThtRdVgYgY3tkh2hv4J3HEAO8P747hs+dtDICtZUcynehA+Pd76Zj5+Th0LtFCO8x7tXyLkdNlrlIIAlLnY3uf410a1SdzlZTj/Yo+6VkKuo58rxAe+bw9FH67/BAqQwmNr9800PxFNm2NMXd2VZFcdrmq90o3ivD+4ozfBhPByWWvMqt9Xf8Bn1SRar3/OaQn5hkhmn7UXG6isQ+URZUPZe58FWQ07kUgZ6Iop6Ybt7whAA4d+SEhzDu6AgRIjEcbGEM41YYAnM4aQ4Ah8MMh0D8KyT9fY26eElkVbML9frhLs5l/cgQYoZkthGZzBOpH8u1fmXS1B9KsXeWO+CaBfGhgR7FTQKixWAt7AxF+3h9q9ZS+ngIjRAGXfX9Ijwy29mr4pPhSA/OU8LloDsOsMkeJlMF88zoKKeueA5j/iSg4nuYoQRoMMQ+Qy98qa08nal/MdH2Bwm6jgLXCOQXfUei6nXvBtITW3whqMtAcUQ4HysAYfbKfNulOkly2St2NolSHPqPITURwct0TbD7RfT7OOuL0WtL5BlTvq4UZhtfKfHNQKJulKwAj4dszsTB7TNI2BAAZaesfxZyZlggt6UG2zRBgCEwBAiZCvq5NgYKIvn8U/+kt0uBrFWuzAQFGaGYyoekYRIzxvSF4u2+LR+zSvloE4MxTIoLpzRSEz4wYqLITPXYKkJIlWgybbxa7vJcOKYbYTnRgpE1S3c+y6MEwKEUWaeBbWaaDSYOXyLxrJjQmQvqWhVzxAV5MsyGQU+GcKSHHX1LTwZHwe0KcXjx8osESlV2ooODrvzsEAiS0vtOgJoOte3LoqSjkgNkQQAeOnyK57K3Yruu8KNbnE4q6iwhFv5+PM9fv9FqCswrkdwTY5uAxz2v722DCzNviEd71XBwS9OU2wuISXSGMdCukR85eHNREsOXYxFoTgfbZsBzbC7N9hgBD4HsgkFOPCMp267psO5Jpe8L3mJSdeukgwAjNjCU01V0wwNwXgnhs32Mo27REBzpygzf+5le7YaOqE4/qlmh6IV4gNM5H4DRJqAI7yaqH80hasiDgOLiCTTvWimn1RkRKZ9cjuntglFoHuAmVlhzEZwZplQs6t8ZgAdvjUYgAHhjV76hhHzn9ysfP1TFXMvdRDQVeHVxCc1X0bBxEM/ruepLLTp5uW6qD0PV6L86I0lNNctmXaZ3N/eSs/Kgt4F4iuJYWa5C2GC3oOpLLCuO+Wao9X0UtEdX3IBffCheYsq50g3fMKc+yti9z4EKapK1wgerIpq33typhYXOU7TIEGALfEwGnPNRcs2kZNUhfztpsQIARmhlLaB4Kg3FFFG0YTTDJbAykXamIvo6rFPLbjhnwtk46CaqhyIP/6M0UWFOWaGFm+FYSwsMX3xbdLuKfx1FOXMLneRsxwKsSUQ453sZAzDxXCVL1aARijuYo8G+5CzS294eBM+Gbk/ZPdCqO5LLdocUKCWkYLXKtUa9eqkUN8DlK+LAecK8juew6bfvufTDbKPO5JfTUkFx2va6jsY8y/D4scbufiJr7YQ1aooVPyrNgpFx363eKr0QdsbjyyTf0RsSov5yIS0uLQbYNwKVlU1/JZqp30+nhcKvIptRqYNvKRZfbDJ5810Qwp0WUg/9Nkkxv8knYUYbAbEBAmY+vZzYttRrf31ibDQgwQjMzCU0jV+7HJky3dQB8gk+UcrBBeLz5nDEfZIB/ENGRZtQkeiQckpcvsmz/BKq7QFBK2636P8gQdLup1fgmdIULktbMV8HPtUQL88YyHXb5QG5eprNYC/PJ6V5YifpVf6NSb5LLblWdksr3IsI8axRX8AygpA2ph2/X1pJclnWigwjKHiG5XC9Yzs6YM2vcKdbj/XzdgyGlkAE9E0txVfB8bQ6nk24bOvZ9IV30mAGJdPtHpX0Tb5uIPoyu2aN1jc0/eaSZ+MmfjjlH9ETHINbwYBgoUUETnFYL1YIsaeLLnKX3ZCecX4s1gHSpDrlwzpkZr30QtGmmio9NhLuz8SmcBTzWPesQyG/E1w+bHNnb4i2ZFGYdIrPshhmhmZmEhg/JGf9WW6pFrrxvDsLGkFiFb/y9I6AgiDOy5KsD1ZivmlhJ90GGICjWG5H1/5uD+B/kaAv+2atRS4G3IrQOQOOyiBPN8FRmGefwahug5GqEINkpUZ46tIwaHed1+W8kueztmFbxr69/lHYpfMe0f6Njlu9W9fXwLjW1w0+2ex8oC1ov/FCGgTbXQnJzejfR8ZGbfBCUZGXMiLyTDn3KTz5mQHYZPhMgn+zYhvaJaxA3TDnvk1wWoX3dXo2gCdvJxXHWGx1DuMEr3YDP/aFgNhfaekeE4gx8+p8hPUTNlznb/n8tTptZh1yIPNo3esNvOMNaYhXw52/wNr8JClPMsPtlt3MRCGyLRwrK3EYYp1sHkON7mQ618FibDQgwQjMzCU3HIHQtNtr+U12w0DT1wbqgOYygm7mcFnilC2wAWWZC09wP48pzZ9GI6I301l4hpslOgRPX+cKie5kzvEj+x3HuajfBSINyB4WoQ7nOF+/1la7C31R8FbxI13rh5XRSvpTksuOejw7rLQSs/AztcAqCmFf7Z8vfYTf0vweqUHupugvmH3CLngaSy0b7Wz/IID/VrrHYRy3jxS2UhfqY33s5EXaOvdXUMQS/2OZwsK41HtBHf5AxkUuoIoh0f8VK/FZRsTP9KLYPE6Eu5jWe+BDvDYZZSITm0Qj40cY33qXlkAu6AznREdiEPsuCs2+1G8w8qnxLEc3xp/9oPX0jtCeHbvGFwOvZWHjTzrOFleGOXI7g7hr7YPSyV6NyFmsMASkCw3rklVigFv5z2xRoa1GWDmbbMwwBRmhmJqEhgvjjwTAhLQqS0o3i5S2Vsg7p8ad+shP8RpUPk8wtvtC72KuRG1fMhys+8Zm1yMH/QAhUujuSyL0Q8VNzlSi1HX0CX4PsVXjlOB/Ba+ZKNxCF67zgKpqjpMSTkOPYKUEjUFvbhM7XkmG32KzI7i3woCE4ksTWOkAvO4WBRqj+S+zsb0OEdvnpHr4nux4erk0uCH26RddynRd1JO2kxCfE8ZaNmPso9iHqqck9jRWKCe5yONXzKlcICb87hHS6C9X08X5Kq0FU9qtJ6OxNfZfkMqPn4kav9SaHnztH7UuosiiTLJeYui0TQfy0ypU2cpFW2gJok99OFS4wPpCeCJ/g9V6ktQ4Ufz4OIH+Rg4/DtxiM7YHQn5jTdA7BxLIxEJa5tGowSHs1RZljxyaBkE9lFGxd/Fx9GC5Lo8j1JjmfHZplCPSOoNBKvfBfxSy7+Vl8u4zQzFhC0zUMOe1qNxhdd+/D23pj4GSpclv68ZrxLYbzSNq6hymsDOcu0oCC2KtgOVjlSl5cJFFZG2jTrjS60h3Wmsud8X5q40KcDp6GgeHpaJhhgkvxKp2rhDM75gSpD6Pz7VS4jZ63NgUNjkGw7FZIb3rlGOS/JMVvxMWo9yIpsGnUXDKKaEhPGceRnCa7vBk+pn1vUOJT4njLRuyD4EYpz313yGJ50hvhO1PmU0QFjFUL1aARN/lgw05Bj0Qg4n1zOHk57SS5LEy9Y50vNTmviQ9yvNwZuS6kRSQsF5p0q6kPyZc9j57DLJFYBXNXQy/w+SADM1Z3IWicT0+sPmxJ+iderboLa+brcfKdVZ1gqPYqiKL41jsCTqM+LJ70E2y8m46nZcxguXRoGSxt0pVbjkm2CpvxndvKjUg4y05hyXAoGc42GQIMgdmIACM0M5bQDIzS0WbSHaG30/BqDC079zu4bYCKW0FHxBZRjvfNFS54zS/VgQFc5UFGgtdjiRY1AYiQ22apjm7wobXuSLT/XBw8AnYKvErnKGHs4dWsBhNO4etDreDit+3VMLFIC1Nn1sEttdYDTOIKF3pAkWeU/yK6AoWjX4ijOzVc2abRPnF52ODTB/dzrouMHZS01eoovxP3CAhN7ANfZMPuwreUatxUfQ/Y3lwlYoiKW0nFMa039+LqvI2qPOx1ksuSvd7D2zT8dsrb0z4ID9rn2RNcZ5IuRR4Yxu3+eKMvUENG3ckZq8af8kqikBIw5gTQ4G1a76XTGykgcDf7wO1i0050wJko5ViqfNocCeTFbMV8NPv4IDWbqX7Q3eXOtk4iE8GMd04jTS5XB9RmbcN63HUFy5lmgwvbZQjMVgQYoZmBhEZvRA7+pVr8d2+nwNtXjGk623PeOgBBAx9WPUcBUtI2AGGdvRplgG7woeASOK02BSEcesSA78oL1KgDRYSKB3YKui8UXqc7AqBLbR2AqeZyZ6Ts3BpDISXgMavdMOzVJIT/2ClokY5u86er3CxOseou2Hg0hwWHzqiBnOIKUY3S23iDN4LJG2orwEvGJITLXOCJ+huxlPRXKeXZCe4x4TGcGHRtYhUoQm03kubN4fRDc8zpBIV75zqLWsAbAjkHR1vsq4ccbnHezxk6oMX5FxGMJUt1F+B4Ci6BEUiUXbcNwPvzpJgw0HrFj0XCbcc75jZHAM/9dUh4em8IzrrdH+n+bNqIAQws5oSl+5P99FgEjGpS12FqNQx1P1XTG/GA2ei6iBDf7l50jkV1D4N9SlMiEeF+V7j8xE60c6ybHWYIMAR+RAQYoZmBhGb3Phg50mtAO7qHIQSxV1tcD+OfroFRkJ6nooQ8e5UdyPJyiy8iqz/KhEZhnhLWCyLKqMU7KYR701/nBQUDEZLFXe8F58iWGKhreaKAjMNqaFF5SwZfvpuPT5mnguNJb8S/m33M0dcE19izfOCSeYnG9hKSy7S55vLWHWUcobHOAzqIEtzUdxonpb1Ee583ny35nfgExih/qzfCd7ZEi9rgqdW4rzdSwK7sVRAIEwk59O4IQLz3lznoqQ59MVL5giBhid9MWe8QUUs/znosEgxsgRpMbr4S1qYPMhD5Nb7d6G25Tf5ocz8MNscscV2Wk3bvA9/i25Aemp4lWlxunhKsbsL5iUvEvMLFEvjzeTbm/8Zc7IKf7cscSdplywV/vK2bfMjvmNXlBkaB3v46q84Jd746gKdaFBFn1oHDnZMJTTgV62QIMARmJAKM0Mw0QlPbDQfKce71LD6yH2XiFX625loIQjMqUTYM66EmWaYT0t2ucRe+/RtMsLvc7APfxxp35Mr7Ihs2D+9iWP7nKRH9VNiCOBSDibZx9SlXuMKnMFeJigrXecEDdcCcAoc4ErPLXB7hgdBxmVo64WN6I87sY+ooJbksv35oRzIcN68mcZFZg20gK71cjFbqdkrdJt5m+RmomB8Mo4Nuz2CM/GdkMirzYbeYz9XNns/l/bNX0WpXFCU43YtD94XAs3a1J32WDVddsu55f8eXXkzkZk1+GhFP9enuhWAYO1ORsHiRBoYoexVp8nHuTT62ipCBMQwWi1nyppfQMthsbvJBRIb0EG/xWqBG+W6xeRfDMHZiUt+KiZDLeIEan8s1nrjBxVqUSRc1s7wCOuMnDQvyKcbTIhppRg30SiJsTmL6R/GWx2/ojSDH8zkJF549Ne6XCYLHA8V6GAKzFgFGaGYaoQktg5PCppW0gW2MGUB0VPkIeU2rtrxFno2Fi8qmfXcIhoGwMnR/dQDFIPlsKA+GIaxpORfTtDEQr+oVLni18+FR9mqEbe/ci3f8Ei1FViBAens8Xj9XuqHAwjtp2BaS/HKhWGIQ8pZoFISyalyOmUIPs873DAw2y9XD76RBWvt+BtwQ6qx2jtBw3/H3vgAjDdciK2CXejkRI50ymj9Q+yFYaaRvewJup/wMKFp8JS3TgovYq/CiXaShO4Ox8UgEGNh8NURCR7ye9nF8bb4K3Ig6SslryVjutws541NNN1DN45xdX2QDolEDbt9STZNbid6I17CYEG/MADPJKi7f4Ft76YlIHH0pARHvovUlthK2n7s5JO8MApLnmea4YxCBY8knYUMqbAbmN/vg49gcgav85JWkTCSUat8aA2vTWg8sT8wRcrgJuaodc+GbOxtTaelHVuuUU4K0yOppYTsMAYbA7EaAEZqZRmhCSuHosWnlZ/CG3pmKt9rjkZDILHcGBTnDeW+eibUqVMSf65BL13sjOd6YAfaY+0Pxvf/DDMzzKPcOfi4ONCizFmb/jzIh++AzoLgcwa5boSAu5g0DR1tApNb5Yqo8TppT0ATLxCKNRbXqU4xL8LSJX0NTH7kr3+vxuprfLS07zlloLPl9i1poreoMR2i4MtYpz1L6K0QQGi/k4oGH9Zg/vYZKTiG6u9H37ldjB6X1HDZHILD8cmdQk7dSKKAE7jPeNbZ7H0hMtONTuX5vLNHC+LQrleq87/LUfWKnoNM9uMf7QgSkO4dw1qku8KRrPIVO8dezsfQSb+Ph7Cg3ekOTtEiDAKulOtCO+SpYa5Y7Q2HNt5Z+iH95JC+iYAI/Sd8IXDwfZQL8k1zdLnFJF7RR0gZiKlpWLujc8YOLW+EG/Xg/+O6wHseH9SiCsYDLF/A0V+rryajzyuM8fnLWwxBgCMxaBBihmWmE5hRXncAm9OOLbLhR1rhDwMu3nhF8a388EnuawzDqSGNixwywNzjlI9D3wTC8Zcva6OUEWDJWuSJI22b+Sf5+3kiBVYa4EOsNAahtmVpNz8Rg2tXuyNEntjEDaNN6f3wFr+xA5Mu1nuTv70ouf6f8r6mrShlfDO5itNLEfprSgc4ezpWS/DRlvEYEKnBvCGZY4QLGsNqN7JVGX7evSS4LPlAt9a99d4juCqb1AbAePR4Jw9KOFKEO1MZAeLXOhD1G+9+u7wG3W6ajXN3D+/3eXajG69Yh1xIEbjSBlBS1QH+9TCfek7BR0w3OtC0eaZrXumNjoVqoh+BTLGSRCS3DspdoqW46Jc840QHVkb0a1pT5KhC4H2J5H2WC7Ir5gdoGuA8i2RZGts8QYAgwBCZBgBGamUZoiGBIuMGb+LpIw3pUmpyvQmqTuEqrJ4HXpZa1wyhynRe+Irf0Y0BzP2Kkb/BG/FHnEKwUS3UwP9zECXjP5guwmlqy82gEFsC3/lFYCxZzElfeCvJwmFXamyE9RDl8UPdaDzggjMe9wFfkMtL8sVPxD47QcF/qzZfwy+/kCA1X29r/Ksr5kLiocpTDVCNSiSdqR1tonXsvyWWDbZU3eiP5DR8uXtEBS8liDVVzydFP90Lls1xH76WbLxD3MGX/kwj06BZfGozdesDt6eWKHt7XtlQHmxMR7EAL1UDym4MI2xnf6nqAMJ/k5mYfkLaPMi18aGuM4IZ7PFIQI4+f4cfvOTMI69HbqYIvrH0QWunrvSyBaVOypP5RwJLLqbrFCXmbIv9Aip1sgyHAEGAITIIAIzQzkNCMGmDPt1cjcnu+CoaZ8HIwkvGvh1t8hRQg9T14DdspwHvsFLAx3OSDWCQ+9ofIKsfJ+OepexhxxZsCYV95N91K5fp2qpUZJqwMPOM2fxg/StsRFrRgoggsPqXKsJ5SE/xRv0lhX6m7pUF7pcnh52QyShewO6kLhKabK5Wk+DWdRkVN9yJYWUSlDj/+0/RBkssaqssa+2CdslPAamKnwLI3BiB6i999IgoVAzaLpCT2AZ4kfZFNd/iTu9M7JJeVOK5d6Qq+NU9J63zgi7nJB7l50qpxO5Pk4zcR3ewrBIRvDkdU1/2htM4PpErO6YeU+Qh0nybtu0MwdImyYiL4H2/xha9tCltZO8CXGgj5yS93to3TnsKLsqkYAgyBmYcAIzQzkNDwj2nHEL71Hm3Bq2JID+2LTZCw3ojA10yzaIOIjrdCN3O9F4S0URUgNAu4epN7ckBT3AonzgXX3A/VyJ1BkLWGleElvVRnCa893ARqxUc2DelBmD7hyJYYh/VpFijF+NY/CjHQJ26hJJcdSfd7I4VuVVY95pQpjdM50EAr1T0coTmJGeQ/oxakwq3rBlP57qBl1soOWuU8SnLZ3lwhbri2G+9LMcLoZCd2+VzpdT2wGURWEJkM5D6PDn58rBXutrlKut7TmJ8ZNeb42xXq3jlKMDPe1HSVO3LwzFcBusmbyxG4b2q7oT5eqoWb6YV4WqQFSsElYHh8usLJJ/lxjj4WOYGO+OsDMDVNYWvi0jBK8ysiXeIowBT59BRejk3FEGAIzFQEGKGZsYTG5pF9JhbvIanDyPMo3qm8KpMfvDsTiavKygAAIABJREFUkTW8D4XveYorXLAlGi6Y2/0xnvdk8UdPdMD2c3cw1CfSb9hfHYAPS+zRFeBt/WoSnBfIx6+2Cs9u6AUn4NMTjxmQScWtEPrl2/yg++krjQJfqUTQud8x8Ax7NQS2ynxUKrBXk0ceHEnUVQnyIZdRGwgFJLpOOHpvMEbuSoM25f00I8llmXnnZV6IrACZey0K5h+nmHyezVzhwhXNHoaTKzb3xGItPR85cK0nPRsHa43vMXjrztkMJs73pIHrbZEGUVHLnUE94yqx4CVa7hLnnOUsA3pGUGQ7rGxqBLxbom2tXESInX7FLHA+yyouuPvuYMSsSds3B2H0khqHpEfZNkOAIcAQGI8AIzSzhdDU90DP+0QU1BvZ9chiZ6/Gu3NwDFYKPrZIeGGbH5P4KpgQVpuT0JgIfqVVrhjcNwI5xTwlaIedAmnl+OS2/KmDY1Cl8PHMfE9RC95YdwWh38ZQ1DOCGWq7kZj4ag+4inibB/9zuzIeNKUameZMBJmwMh/FCrbGwJhR1EI02ocBnSdIP4yNMyVRXG0mezVd7oI6mle40KvJMEQdOk1G+c87avLN93eO31Wd5JCKQlGfJTVvi0cE+DIdNL/xZbjQZpeSjS6n9fJfalSffXmBZRBMBCPNvSHAdpkOd5R7GiRvnhKGrvNveiNMSqIn0e8Y4L3aA9RzvgqmMmma4POfVhzpfARaXWlFhb4RrDzIukikOP6iN0rbYdXbFg+fXWYtsh0u0lhR54uemZ3IEGAIzB4EGKGZLYSGV/vuSqOr3CEW2RpNOXUgGQu4WoxzldDJ2mSmfzgckTgPh1s0E0YTEof4H8e7Z1MgKMtjkRb+8WSkxbpzgzcKMNm0pj68s238CJEVIEnV3XilbY2GW+f+ULyn7ZxopQs975xFclldmZDydnMEfDRWbawfPKa1gGc2haUn7NUUUkoFnKvrw32Y7Z5g1ANf6UoGx19Rk8QRZTXRRDtdVZh8pOeVRGh1H42AcWKRGqYgY2tR/CEk+gt1en6dryWpz0SzTNDHC0QMJmB7mx/u/Y4AuNi+4tITT3DCuK6gEqijeNq33p8c80AC4ioFI1xDL+56a/S40y6kg08h/Xgk1E4jBrgv7wuBQU6ag/FC5ptsbE03V2PcDR/TtvipsTBNdj12jCHAEJhxCDBCM4sIjfTp1RuRO3hjIGwDA6NIuPdkFAwt3hIJyGo3mHMudyapbGVHMlKizeMUOVd7ELKGaGnvKfikFqhRP8Fogv9ovmrid9KuNHzpF3Oi5J5GTJNHEfKtbYlGyr7LnAWTwA1eKIT51QG6WtHIl0QY1mNwKhfPZLkX4xhp/0Txm4nzBO0IqRbLRuY3giXwr/xlOo4JKf+D8r6go1rL6YR1bk9AaPdaD+iXxeBhjKkMB6Exjn17EGwmpx58CzWYHH5BzXlfxxyDvkezyabmYk035Edr3MEdX0kUCkpIr0gE84zGuvC1wQQ/XSiXydBm8PhdlyMw7QQcR1bipj769iAqTuxKtRrY1IdPwaZ2utWI89g5Mwhw5nIlruZzNSu+p9XnPK7JhjAEGAIMgYtBgBGaWUpoYk7ALiKmpiXC1+7VXPpaUSd7hz/dHwL+IaphiGDweJlLV/9FNr6vG0wQ+d7qRwXNoA5LtZRYBZYzYegyn43mjRS8IDcEYOZ5SryMTdy0ugLM8LJZn+FeBJPDMzGgBVe540LvpCEvjnQxwiN/6FOKvJO4ok63ahtsKnE29dHDYfTZflxoyPG/GjUrBlwWiVqigw3gYbf40d1BUALdGwICV8ln6xnpgcrYYwERLEaLNDBNLeGEL6MOv37VPWeTUxFMNZo/PRsLUxbfjrfB3PJCHNx5e6txO0u0ljTB4t9oRDmkM6KxqnMY/HKBCvU+Qznz0vsZgPHDfbZVLIjgSFqssZLajBlgXbvZR5xe2LgvhHyLbTsn3x/So+zU9gSYSTyKaIBL+tM9jAxGfeaaWpPPYHO0sgMxd0/H4OPL5bIq2wy4FHf7R5GMYFs85Fz+x628cpfi7bA1MwRmBgKM0MxSQrMrTZJqxfwsuxaC5SzSwK6wOxPG//kqKylDajV6vIuhZdkUiP/K+TSvW6NpiQ5Gght9wGlu9ROyvJgntv1d1QmvUGSFoKLlw54hQHZCKQC+GU2IiJ6nxBWXacF+1npMbPXpyvq62evWwEPIBXy7a8teGxMOIZh8oQaMYVjxx27VnGZHu+0JYEgGExQ2c5XIB6PIg3lmgZrW+yHpH9pAM8wzfB1vAlFbrIHw6MlIGnT43eOKfe5JhzHA4RebI0xiVYGNgYBO2j7PRqizSKHEQ59mYbZt8TDn2Ktxm7tSETG+lEPypQQs6aVEHPKSmM2IkOdwpavVhEYThtkphNTP4iVu8T1fkw9/StsA/F/Xe8Ewxsty1/me46MUrzXhRmgpFvY8B++bKYD3a+t6mROeNc0763oA0S2+yHf8ZQ5C/DYGWn03mObrZ8tjCMxUBBihmaWE5s0UfG+2ab7FMFEcaIDR5e1UvEcf4ioeuBVSbCUSwdmr8Y2/YxBvpms9hUpPRHAzPRoJOeo6X6R4kcZJ2VzCZnfMgEpD85R0rReI1BwlLA1VnIGkbQDGkpfi0XObH1iUtCoCP4+JaE8OfeqkLFdd5+flQHLZrS4dj0VYvezzGvGm/4gnGbq/kPoPBvX/rPWAByrxJA5Jy0Aea8UyhKir3jrwlaF2cc2NfRDtrnajIeWfGo+lUHMuBshlCseP+bIAdT3mc8VzCF6hucqJcysXtYDBLNchGquNK0OR1whsb/SG5JlvGTXgOmKUOxEMP2s9LBdo7off8CEujZA0zCq7HidKeyznnGXruTjIxsWPb8QAy8oTUWcZfa7u6i7cizSd49EWkMLxeXraBmC8qfoexRnOtZapPH5/KIjmmLmY68AoGPBrSVN5CTYXQ4AhcBEIMEIzSwmN7zHkApaqO00ELvIp8tJZmt4I1eq9XBHp5+IshbJdjiAvy3p/VKgubYdpYYmWilvhG4oot5x+zq3Ps/Flt6oTL9E7AuA0sVOA3+xMxRffazxBBVZycVUTTuVRBFVNXbYr+V9JcQ+PRD18ZyAcN9viEQDV1Af7xDIdbB5CALD3clAQ5X8GHMe1Xk/GXdi0j7OwBtSIMCuCpQNa+pE8t8fpf9viXhvzXGZw+Pd45dYjPi9gjMnQEfN8tMNTxpFe6SlEsFrZZMIVBxQ0gUKJ3py39sJQlF2PTvGV+WICArvE1tIPplLUggKN2zjCt5RPvuwED93xNhTV8jsGl5Y0teAErjpxRm6jfZBjk9asAupshbWuyPqsSfa+PcjpjaxHfLIfBhux9Y7AQDVXCYjsFHgGpNRNHDZ9Nio78HzyWQbEVR1twScynnCLA9gGQ4Ah8CMgwAjNLCU0A2N4o2+LF5wU/aNwlFzhcgH+BecjeA/ZKfAWfDQC7+At0XghSUnS5E/wwChe29n1wqieEcgs7FWYk/83R0EPhNq6UfjRURWgU3YK+JKCQn31nsso6m46+DECqVTI88uv7Sp3rMqSezf3M95JdKIkn69xPUeJipLStucAzh0c42pry2WkH5IeNZoglwlzeuG4w+pu+R+H5P9xNPgtSnwCYzgRD8llZWVC7j7+RL7UeTtngJFOxW/HnIBZi29RFeCFMFNxwC7XoXB3/yh4ieUWuKG7M2Ehu8UPWqXKDtjPFmtRlGqBGh+HnQJcMNAcXB1aCifgHAUozrvpE2dHJIJ8eJ7SyrjFr2qpzlJAVFjo+f16PQX2J5sWWoaHhG8GEz0SjrAp3r7VOQTettwZMfzTtu2rRaV0mzZqAObnX+DM5nS2yxBgCEwJAozQzFJCQ4Sv3Q+E4pvldV54Ea7zveAvxwVNeK0u0uDnfI5GiDlRzufpLGkDdbCxHOiNkONk1IJn2BwS53TIhdFFkY+3SFgZOfqGNzvOHQtZT/lfESELS1gZeFUHxyHCymCLEiQsR7W8zrfZ964bvKGBWOUGaXOHmbQ09NJlOgCC1gbNL/L1SdqbKbDQBByn4haqDdoy6PC7YOcPDDEPYEjTId4D9Yp3gejoaRuA/HmnpAanZDJs5jWCxAyMkWMurBQrXGiBhj6IrvF3fPktTRQfy/1SgnXeuai79D0NT0bh9le7g4Yu1ULJazSB33yUabkdIog8ljtD3lvSBl/Pg2FAo3vYZhXYbekH6REl4fwIPo3vxTGMrw7Y8jAi1Op6Jla4emo11tZpBp/vfSYW7s5p28rPgPaJDwy/ztJ2PMkTojptb4QtjCEw8xBghGb2Ehr+aS5rR2x2UYvZKXOBz7jBhCoHySctdbzPf4JarkABTzvEswbGwI3E8B+xX9yo68EbJa8RlGWuEn4uY01yn9Ofa51voCNORhM0LimnEFjkkAuFTWQ5QsGF8kP9jVTiOXDI8ZjyBlU+osftORUwn9XtqWhceq4SmuiabqJTcQjPlrSDDWBvlld+ynMm1R/cnb8YVv6ZwteT8j8588/Pvwg5tFANXnWjN5jiI+HwUASWIMeursDWfWMw0Y1esFLMUcBgNl+FU+SuHiSXFWvv2JlKV3vC5oQUgnzjEyLXJL2XjsLgqdW0vw5WHL4FHLdKzcf7R6SR23oj8tOMl0/xpz8VhXR8oqtLb0T6xEfEslbCRc7314kOwCtVzFScARNN4spU8GRLDGoTJ42tBG7Tud0ZhMwFBrPMe8SA+LipLQcxnW+frY0hMG0RYIRmthOan/bRXO9vKS7d3I/X8xvJsPeIb4vxy/M6itc/37bGgHxQY47R8d/rnBZTsXNYGV6Zr3JVEbbGIGr6cmck0UcEeCxkzl8doE+1nl2Kvxn6mohQr8BeDUXzPcHYmMMVFX84zPCZk6LNdS0F3yBdwKdZlqhy9Ke+SNo/hR6o8nX/mvK/pqQtpP49KX9bkL9/iRbSn9v96QYfEJTlznhJ70iGNWi+yiqUOuUU1jZXCZa2iFvAYg197uSIOpq6tYu0YDNSCTCNocQmxT74fgY8dDbN35rQ6AomiJ+PrjCboGxOJjgcb/CGL09zGNxrQwBcV3x9q3Fjz6vDuxiovpkC5D/KhCVJGgK2JwfqWpsWc2K6E5qqTmjF7gqCrlyVj3Cndb4TO0Ztbo3tMgQYAj8oAozQMELzgz5g55i8oAnlmd7aiy+481ScgkSBF7z32bOnqPLp8QhO40IwllzuTO9G1g0p/9Ij/+/wxBQECcfjNSzaUfpGkM74vhC8UB8MgyI1Oa8MzKNUsAMcbUGufXs1eI9gPOitJ7ksz3Fd4WErDcg7aZbgI9xYxmuk+2tEOYKwuGai0V6j+r+fU6e6HIFVRm+EP2WpFnyl1BwslVqNa/HJcirO4PY9ihADDzW0ilPPONFe3/eN8l8MOfznQqVefsgissFVRrpBaPyvQiYhNyshqtEEH6KYV5AIPrVtKBph1bLq4aU6W+sdwUv68Uj8c8qbgmjkohZ43B4MAxe0SYqYUQPJ9hmJushE0DyNJ2pnW+1P1d8xBGwfjYDqWVdgsZD9VOth12UIMASIiBEaRmh+4j+E0na6xgOmEXsVXqIFTQj0XaJF2cvxLawMmXJ4yfAj4WAJ9T14U/LGlftCKL0WFEd0avAz8GFBZWZKgU6/VXTMou0MKoFlwmIW6j5JcpnD/u7HzUlx+Hm8i8EtLMMy3yKXv+1ItqqPPaD8y5uuiYu52O9FGrzIH/DpSPJ+/710y93s3odY6Oe4WhN2CpTaXqbDLbsXgVRtDKAGh/ltvneQXPZoyOBbe2FwsjReeuz4b4bq5HtDkOeQV9Q298MCtNqNpOrjhCqQHoRhG8cobw8NQ//8eTZwng7NaALX3BQIvyERVv7PdHx8p22jxKbDYtkaGAIMgemOACM0jND8xM8oX+DpaKuViMe7GFYWm2LLugK89Z+MglVjkVbQMpe1QzFjr0LGlFv94K2wU0Cxa9PW+VLMCUlfwBoq1on7n2eDDVhaZznJZfsqB69yt/QRwRyy1gMJCYV6jVnvDmj+b4HaYn0ZM1Cr4z/e1kbvr4PGNqseIt/ndAdI/rOHws2aC4KAd54StpMHwuj9dNhvVrji39ZoWusO51eHw/9z0shRFzOtz15tXc6Tsx6R6z+oUNk9DI/bXCW8WnYKkBvRLsWve8wATF5MoKGWMth16tL4mt455sgyq9v7KXb6R2G/4ZVDdgoIgHh+9lOshV2TIcAQuLQRYISGEZqf+AlOOYWMfDatbQBvaDFWiAghS/YqFEi6I4BUh+l6b7wF5yjwLp+vQrq2xyJh1Akqgddmmc7qvThmgIolS/IWNwVdW5ysei4WepF7QxDmfZ9Zl4OVtBeTXBZQPHa7v826qKwdtpyVrricj3Z3o+M8qTUosIQa5PN9QkPF0z7LoqeUmSSXOQeGU6GSDCgfcHcwYqlMBNPOk1EUpX65MS9wngrkY7EWXKfL4U+7nTQkl63VdNvW+GzJ51xOq6HaIaSc+Swb9p5306x5j3kFtd2kc1VUOywhuexdj+QF6qmvlW2+1MX/7hiCZe7iYqku/qrsTIYAQ2BmIcAIDSM0U/NEjxhgKfE7hqCbs4VbT3iljBp4kSzmC24Qn89NGgCVUAXzzENhQp4bowkXejQCvOd2f9qVZpnh2TjIijcFWq7mfASx1mKCnK5hKlPf+JXSca6SbvCCxmWHMvxeRb5arBbZcpjkP7vZxyoxnTjdGHenLkcoqbB+4JQ5oS93eHsCNauW/lPlK1pKStpoi9Neksv0yt+DiLQWdA2DsvCRyXurIRhqcFxAmTsfDxsJ2l8RVobMOkOOv9vl5ElyWW+PJc/dSa5eRK/rZUbV7yliA2X/M+mkabEGUUifZ0MbNF818YJNcY90+m8cVfw+OzNeKlgR74htMAQYAgyBGYAAIzSM0Fg9xs39MEJcaM7TAw2I+1jlCnfJMh1S9p2/42B8qUXiChpslDCStgFknZmrBImRtmou8NtOgRTGghuIoMCAzsYJkpS0amQxXqBG/SOxvZxIRcpbvnH6tgBxTig23uP011bH/9vvsEGVVOJ/nMLTDuodfnVviGVO8VzpRscgwsulaVSejqE2l6sigp1XuJDzEazWo4ieVyaAynD/krIO3eCNquaZ5vW8lEgt8r/n+Lzu7KExffez9a5tryWR3uHf5Z6BJJdVnm5v6sMy3k4FDdoQQJ2O//u8U1y51yMkl6kVnxTF7KHU7bTvDRobzG2EMy7TGiIsOPoeOvgveKlORkvXz7YZAgwBhsBMQoARGkZohOf5VBdCfHm9rb0aShFkyz2PVt0F24lTnpC/ZHAM6WjXuF9AnjHXQsRaBxxHTE1jH8oi2quFyswNvXDKiLmDH4+0VAkggp+FT6prp0DkjluhYKdpG4C54ko3iGRfiBM0p/ytdAyCGRzV3LEv5Evx5ozK3/GEY6eTr52CXnCM+f/sfQd8VGXW/rC7+q3rfuv6ueXb/e8XQi82QBQsCLqgiIqoiCJlFUVs2BVFFBVRlEkyLb33RgohgVBCIAkJqYQkkEIghCSk954p5/977p25c+fOJKSRBLjvz5/c+r7vPXMz7zPnPOc57dI/cpiDu4zbqGozUnqt5cg9ZpXWbFIoV7FQmyYNzEVc6V4nOIq+dIrgAM3H7glsDtG3hgKWOqJWuzt8bd7ZabebpBLP+OKfEkknHecQGE5SyX12lVDPc4HPCUK0xz4l25sKso/JlL9WyqegW8WfKHYDNmohDLzdtLCAfsKhS8AIdrGmwsFKynBPLm6IFhAtIFpgrFpgzAGa7Ozs7777buPGjcuXL3/00UdXrly5efNme3v7+nqj7314jfnXv/718ccfH94+r7neKlqw+n4Yi6QhtRYVtpf4IoFWQMu1+FxfHwUhl980OnBTnDL4x66wHZALDMQClyW+eq39ug6QcDfuA9dVo4OjZbISEEGtBXCRJuP6iQq97lxkAR7hu+Og997thFOvhEHOX9BYgf9E2TJH5ffT7QE7IvIJAnqMB8XV07lbQ2rv+2oc75mkBLHDvLV0IRdpXQS0BLU6MIJfCoXvpFONPJ1Eu2VlDg+09yCG1t4DLf8tqmCd4k9woshuoUuoyXSyHIjNN0efMKWT/2Gf/cZfZT+TVJJp86DcxYmkkq/cDyBQ1VxxuoqsZeDcVLQQOf8fi132n0MP+gKWOi0OVmUSoZCWIYecN/HARyjDltwmUUEQ7+hY2UwpQ9bVTAc4+T4/TFVtY2Vi4jxEC4gWuLYsMIYATUZGxpw5cySGNm7cuJtvvtmwJ/n973//9ttvd3Za0mwfmslFQEMEZf3VYUYaChHWlRn2JpGa3sz8XBDSdgTtxwTTZGPBaQIa2HaU1oaDGMv6QtRaqOhWMfItgbnAMQu86H43qjSscHYnEVKxkgH6zGMyve9zAZLYfAC4pL4DPh7gmD1Irn45FI6iyUpjcIedwvkGXBNntzza85uMooqQw8mH7F5g0QxJJXFhcupuhuZvyYHtxyDrYt7s08He5Rg5CFr1ICfLh6ngVJq+h6SSex261niXLbE//6xHTdlJP/KYhn5Ut9NFvRZeVCH0i2e70DqfizrpuHjn11sTfmSnoZH9gaSS+qJj2G25lFiKK18NR0ozOfyNTkNDpospHvRiCJDfxwdJY/M7h6iU01UAecKU7CwlRHeyHTGHs4YqSoan0uqQ/LUpGglWOxPhHhvhFpYPZPbdMQjz7D8HUtRc1wEUFBvh2YrDiRYQLTCWLTBWAE1FRcWf//znm2+++dNPP01ISKitrdVoUEOno6OjrKwsODh4+fLlEolk7dq1w25NEdAQAQGE5QtN+3YMKu9csb0SBik2QfviCIi6vbXQs1jGNu6DIu2WI8hR+tpQTbqtB+nHc12xvs5zQ272bGd9kSmNDpqz1gpkOL/IRMcedENqj+cpesgdYa9ZLnDYTLeH+ksT0onIPh3ohyMpV7RAL3iSgo4pXoiWr+uIeo2kEq3dLTrpuJ9k0jM2szt8HyHPmSS/lXraz9Yii6pTLXyIdREWuLc/JhhEb+vySCrJystD0SiphGx/R+5TyOsuZjZ/oRJjdnhbDx08T4eTM3DZ/nWU/D023CY3K626bP9IbZXYbS45XdpYYTO+R3F7oWwOKf/MQqKadsCy8XI876eHSG33h90hx63leLQvDpvqvEWuQD9F4eR5J+V58R+mRwMcc68TSqwrUpHqNZNXh/JSM+QNZSfBQzJK7/DvH/J2SxdCjcFnjB1pdfD2bYo2HhG3RAuIFhAt0E8LjBVA4+TkJJFIDh3qqyrd+++/f9NNN3V0mNay6+eD9n6ZCGiIEEMxkWlhzLX5gIVqyeaGdMlEJnMbj3BT2QqnQmyx+bU4crEJjhP+cAV1WEr3FuLstng4XVg48kwgShNsi4e6DAdK3tyHcNinh7Cie2cjH/tRLxRBvJcJM01RAoRxF3dpgJZOlqNn50ws/1MZReCv7RzV0puabP4Hi71U8qWds5WMLnkuw27AQ3QBSnaFdQA05kSiV8OBwwRtaxxQFGgu9fnoxGMawEdnA8V/gl3/+bje8X/pvJlwL1vSMno1JX1DTv+kLGW507x6uTXulUqosVhzNlgt/V14kL1u9zgErcogXrwrCVBvogK4cNtR6rT782uKw9ZMme77mXKVxhz18GcocStG976Xct3507Y7iQ+u2uAAI0L1q9nOeGRFKphGTwfAsHc54hO5GmJ3+88BuQrCmqnleC7uE+RPWNwWLSBaQLRAHxYYK4DmzTff/Pvf/97HRIkoLS1NIpGcOHGi78sGelYENEQQN3sjysRyjZ3QbO0NlPAv7VSD1/J0AOJTF5sougj44/W9JgEs/vWykxbqHf56AlEVrc4kzvXdMbgN2nrwO57VgmvrAasm+Aw6f4gp8e1xSj9QMRNLspJRDiM7y434gBscIQfOIUE6vgSxoQPnqKxR85OLT5v0j002tx+VLrvfrsJKRsVBbwFDMPouRPTrCZNCj1yHtik4zjktKlv1tOVpTImDjT5F6MTxf6mEKbHdXAonzf71RKRz/ld3gVme0SWo1FDUSz3Ht9JeVO2+GPlRlGxdU1MTSSXdtUUU+Eh54OolMkYZTyopOZvybTwQ4UtMYe2kSxi9Tvq3L1xiQs7S61H0/THAkRn2hiqYe5bSyR+RAuY7h047c09BBFMI5Jg1Oph321HcnnRJf21bN96NJ/2Mj8zvZCjbQXnoVtAuNgGqsohWcErcFS0gWkC0QB8WGCuA5ocffvjtb3/b0ABp9t5aQECARCIpKyvr7YLBHRcBDTFFkWY4wB/TwkRqzjeAPvJ8cH9/KLd2I2Y03R5L0b1OgAJcxWbzD+WLIwY2K+/c3kI4Wuo70APnDLjcit62HAHFOCgPx18NxxLIZmivDYcXISBXP8njF+FQudsRbgaunW9AsvfFJoSxbJnj6yPB2NARuqqQ/l9a+E9qLbARS8oJYcIf3RpEWyYphVnibLf1DFV58wEUIepQI0d9jgv8JW3dUP/bua8YZF7Pe7k5dDZV7jrWMc2eyqTWb9uFTmaCRA0GMpiuBCo1x5XPO9huOWi38pODcJm8EELLvFpJKnnMLr/YZsaPTj4LFReBe6SSZbKsF0Io4zL0AFnWzo4Eqpf9iwpDqHivw9FLbNnnV8L04cI67397OO8aL6Nsmwd8fVScQI5WB+uxNQe4qRJjlrmuqE/Eb63dcKH1kfbFv7j/21mVIEXx1YaIKDDXgtBi//sUrxQtIFrghrXAWAE0SUlJEonkwQcfLCjgC9TrPxeNRhMVFXXHHXdMnTp12D8qEdCwJs2qBJNmogJBh/EyZDtzi24/ba5jigNc8WKLHprdDJuV9dDwF86ztQgwWTFpPuNlIBFzIZIXQ2hzLBDPVBVwzGQlKlDe5QiPBdvq2rHqswv8DAM7JLEUSEWeirs+I0z9AAAgAElEQVTK5TPjw2y/isMK2tCBssnWcqR/T1GCrhtd1OujHL2AC1BOUo7/rwg20mk1TSUklZyRzVvqD7ZyWjlw4V2OCI01qiYf2R841wUpS4u89djRPyIKAMjuD2onq5qQV14KpYc9gMlmyDtIKnnOPq9C+n8fKMMecqhhAU13dR47rU3RKLdJhNpP52V3d9rcSlJJpPMnPzD1NJVpsJVtCp20WShTSZcF0Bm35w96fj7Tgc4Z8gXnuyMrit9YD421nLLMssNWhwlRDv/GQW+/GILPi4XRRND1meWM0tw3YDtdBYL8Un9k5/nm9Pe3xA1oKPGRRQv0ZoGxAmiQaPPVV2xO09SpU1esWLFhw4b33ntv7dq1S5cuvf322yUSyR133HHy5MnenmTQx0VAw5lOqwP9Nr7E6CPhTg3jhjmHprAO6z2fQ8Mp+7V0I7Yy057ejhaWnHxtL1xBXRokmSeWwruj1iLaZS2H9+W1vfAYrdqjD17McyOuhoD3aVxzpwOdtHv8K6XPfHd9MaZjF4E8jpfC/9GHhynuAsDQxn1kexLDzXBAuI2lEKm19HEoKnVnyhYG5iKQN0GByU9SUlE9kcd0SthS3QbM9KA7/ZwEDcPP5N4a1d8o5HHglZg1ai0tCwSsjC3sIqmkIvwdrd3vdSUHN8doY9y/YAX02M/iaAnoQW/uwxAfqyBaQ1KJt817XzBVML+NR6EoKzs6q3w4I1rmkE6RqncOOrz+7n4AHbaxHBp+MUvbFAC1exxBBBa0xb5IIuvRIMv923hoEx+72GtUUXBvH7uVrUwhCEdEtVaF4nPZzvjP+rjlujzF0uQ/jIXH0e4kqEVrwkVMc11+1OJDXUULjCFAQ0SZmZnvvPPOP/7xj9/85jf8hO1Zs2bJZLK6urpBWKK1tbWhz/aXv/xF1KEZhGGHckvIGazxm6KhVbM1DoyNrwwlBNp64Gh5wA28121HcdkEBW3YC4AyUWFCUg7IheLc5TYgsIh8aBOXNgFeOGeARGKbQgmlRsLpN/HolqOa7kqCV2aKvNstiylGzTxM3AW4B/pubT16BT/2sk8PQYHwST+EsYig7PeMSzmynFRL2QuWBcDd9VQAZli97wMKWwbi81G4jhb7knNSg9rmv8j3PjryHhAJQ7X59hieVKdVsxiFwp6iznpWPoefK04Es1jZof+X7Y6SVNIivS3A9q3xcgrIgctqmgpxIvKfR6dURNQT93G8w6ubogEa2NStHg3UdGY7A504pCPfe4YD0qc/PwwQyZKEKlrgqfolCVM6XYUnneOCRPF3YoAX34gySV/v23S9ndXogJ9sUjAHsKpvvFbZCqcg9JAMra4dkFcQ+DOcFP8VLSBawLIFxhag4eao0WgqKyuLiopAjRxCy8rKGjduHIeNetuYMWPGEAYRbx2MBc7WAsq8vAdxGYE/oEcDZ8Brkahq9HSAMZqTUw1HTrA+5AJ08oQfgj7T7cFvRdFpxnFicTaNnfCmPBcERnDmZaAQKxn9mGhy7VvRKF7dd9tXhDxwjhEcehY/piPygXKIQBb2S7lMUkmx53NsP8v8MTFrOeJZUrufMpVLzjdALebZQFro3tFje6va9vfU3ULHPwd8id1AhCTqiQpwiFlA01O8v7QJcM1KJixJ8ShToXOaPa2yT9dJx+V4rT/i+PpEBYZbHgzP0OPeBLTESNdQ4lc1gS9MU6EfLpio0SHqtHEf0MyOBD2PuK4d9nwxBCDSmqEljZcDVj7sAQDEpbNVtIA/tKMfif19m/RaOduhBmLuMEvjH/r83U8h0iRo3qcBecUmWkC0QP8tMLYATUBAwIoVK+699941a9ZkZWUJHmP79u2LFi0SHOx7V6fT5eTkZPTZbr/99oF22/eg4tlhscDeQmAFzqfC9umUYfzqz7wM/826CH0C8yOegCzLA41oQzCNhk4wl+e44K6nA+i5YNCQT1zCEJWtODXDAZLEfTf3U2Dscq1Hg4jJk/5ACZ0aOHjW+1WTVNIT/iIxVaWgbqyCC6pbQx3Ju4scFz3oTk/7A/p8GllFUslax1SskSe2A74c2kgEQUIrGcTltJ53t8j+8bii2EoGjDLDQV9ggR1drcXByQqKKSJ1T/f+mPBg1UeRtmugTyMDzljsC2DU42HI1k7+vj1omZWsX5TbunZQlyBso4Qz6cA5EJCtZIiG8NvREv2j8Q9ef9tNXYgeTmJEHScpsc2FRIflYXclGUSMeN0llMLNJjbRAqIF+m+BMQRoPvroI4lEMm7cuD/96U8SieSmm27as2cP/0lWrlwpkUj4R4ZlW+TQDIsZh70T2UlhRQWk7lfAOc+2/0TqA1U6Qg7Uc0GIQPWfhNGlgS+ElR62kiHzPLf6yg9x5AKcMVwhTCKUrFoRjMWe/e9pjzpAk+hXiBCHWh4M+TtrOWg3vl6yRs+HwV9WAWrkFYM+/JRb7Sxn2uywn6SSvIC3g5hg3EIvUHwmyqGa8+URBH2m2tMiL5Q1KKxHuc1HPOFEYUdESQS2Hf+c9q2abg+Q8WMC4k1ePg5a6W8bs5jc6NRdpa6PIyCFok9XaHXtWL9TGf0e9tL958C/nuVswi5q6sIcLg7Ji3qFmYz66U41Am3PBcEaTV3QNFoeBDzKfweGOMmAXPi6BAXn7dPhxhObaAHRAv23wFgBNGlpaePGjVu8eHFVVZVWq01MTPzXv/71hz/8objYqM4mApr+f67XwZU+p+EkELTwfHAL2DbbRZ9I/CGT66RKo0PnUZRgqgqye/yWXoHw1lvR4OUIVt/WbjCCLzXzL+9rW61F5GVrnNEPVNYMn8fuZFBMvLJpvj0jiBcDSevlQUAV1jJMaYKCvpGpsmzmsblRUYVE9WdJKrlH2TbbGcG1A6r/fCgLsJaB54s0K2dghfmM1s54OVwjnWr4oqaoIHYXVYjEchbQLPY1VKhI3Fru+/z/yRCDO1KCRKrDXlvO2j82Q9m1IphkTqoe6c1fBggdnxaf1tw9cLIcpByo9TBOrA41fDbrIwHXPE8NA5PG4jT4B4+WQHv67RiIF3OZbvwLrtK2VzaTk8/TjWztxufC5swPy6DNXQCLrKwA22F+LfjprIjAsAwhdiJa4EawwFgBNN99991NN91UU1PDGf306dO33nrrsmUgUbJNBDQGS1xX/2ZVIk03KE9Ywae6DZnYWPgNra0bYZRdSfr9+e6ItsQWAzSUNOoPrg6DstwMe4Abtu1MhLPh/f248eU9wBb7z+lPZVcBFgTmGmk6+hN9/pNbjVjYEl8wlD89hEjQ2zFY5v1zoVz8RUwzSSUn3V7bkYD5j5eDIdSppn2FFBbsfEE5Z7yc3orSUltl897XSSpJKdNqdcjScs1CD3Nd4c5Jq0Ch0LdjYBmPUyho5YLSk3SfKz3gio2UcjiK7nHCxWyFzsW+FOa2vdDmrlmqtjku8B+crSU/5Wfxypff34/42j2qjka7f2gLTbye/Adt7KTwfHLLwmTiSzB5l0yKu6CP+qm1+uqhu5NBu3ncmx70gKPoYU8cf8ofWWZXqWl1IFrNsIdr6qdE+C3ucTLxHl2lcdlu34xC+XdB+zkJxKNhbCcuAcEsD0KscPMBIFquGMgwjiJ2JVrg+rbAWAE069evnzx5ssDWP/zwg0QiOXaMSSAhEgGNwD7X+m5DJ1KIJyuh1PJvH0RhZKYUjcBcYJGvjwK4eGXD5fB0gLEWweeHkZ79/gEkDbGtsA6dnK5CKGozUzTpwDkshHz5ONsUrBZZl8F4nSAHLnncBxu7k4U+/z7M29CJ/KzNByD6d7AYoaUJcpB7IIQjhyBemueb7+8HeJrtwqsGleuh8bh7goLCAu3ZSk97Xb8QjLIuAgiJCGlEPzGc5dImpEkvC0A+uZUMlJ22HnS7O5kqWgGArGTwnVjL6Q3VEY30t6vkSfEX9b12Hf6k2Hf1B7Go0wnRZ6+7BdUPuNFDzsCv87AHrQxl/ElMmGyRN2gci7xRSfTAOfB1IJzojIedogAj+wE3wNC2bjCI37kSn5oba6Abrll4TM61pmMqP8xxMb4JA+1wQNevj0TSnKBZjIcKrhnobnUb5JHe34/3Obkf6qFtPXjVi+qNqXwDHVG8XrTAdWaBsQJotmzZcuutt3Z1MTq1Bht3dHRYW1tPnTq1pQU0ARHQGAxznfy7PgIIoLJV/ziJpfC1BJoyPE6W04YohJmW+uPrnp9jcrkVcZnZzvTtMajNRhdhff2cqYipStPL662LgNwL205VggzB1nRkyzqyvo2JClrHVCwyrxneH0MrUrHcckp0JbUQxEv2fIcIEOEhDyz2aRXg8+Yn+l6STV/g3Nim/CebwfT5AaFbY10k0AmbAf6IJzwrLCOY4+hYy2ltBM1z18e86juA4e50AMgYL6cum1sv5up/AKCX+I8oZo3xKYIWkM8s465h68QlAMewfEC63GpAzCf94XeZokR1z3diQJ2ZwCRPBebRG/swpZkOQIqlBsfYmRpMoE74NIYBhvbvY94o6cVvGh3CfPw8Z/7Z4d22TQGM5hec0uj0b+PwDtT/3jQ6/C3McMBHbyUDDI270P+7xStFC1y3FhgrgCYqKkoikbz22mutrYb1jbH5kSNHJBLJSy+91NXVJQKa6+k1zKvB17GR0Mo8m2c2vp3730qbEPgYz4Rd7nGCg4dNqH5tL/TZ2CribBXMgjoAhe8Y6LMhCsvADAeQeVeHYf2eIMfyPNt5AE4adpLdGkQKYviawhoI4nnZbr7MSMb9egLBI3bh+UQe2Cb7a67tA902tzTY/KVFets0e0qvMHncV8IwEx1TGtNaBjwUcw63z3WFs8RKRqp0BFzmMoEnVlp3ogJFGNIr8CCkvI1KGWU9ttejH7DaNvoxiqPI5jdICDdta8LBj2bb5gPQq23vgTV+SEAiGIulpqn01Z1OXAKaUWsRb/LN0d/VWyEF03EGuTdZKbQSEQjjAn/eIHu/0m117SCbf3ZYn9nUzGQ83e96FUNsV5oRwlL3ucDlptZCZFmZhjeEq711xdvFC0QLXK8WGCuAhoieeeYZiURy2223LVy4kHXJsEZnA09///vfraysxCyn6+ZFDD6DmIWglTRi+RxQTmxhHRwJzhlGlq5/Lo6w3NVVe/SZxhv36YNQREAGExX40W8lg/vhZDlEiicyqCjVFF4Ipme+e56piNnazTvDCOJ5yz9+IRjaMxMVFHYWjJbyFuoqTW61++sp6QPyoJjUgrLEvNKpKrJWUFyJ/vakSwjrzLRHOOlrRglwWSBysK1kACsLvNDbI570axIwXFYlPCJP+dNHsbg9OA+nyP4OKmH22S6PvEcHXuMmV1iICpdrQtrfiTFyjIgQwOLKTfzbR18PYRPDoSYCA/cuB2M90eIGTKmmHelXWwzY6TxzsIpXuJsbtP8bGh3Kjr4RhbDX9mNGXtTDHkJ6rI6pD88pEvV/iMFdmc/U35jM1GmfqACEfToA2fWc0QbX7eDuqmjBayDAwbuSEJQUm2iBG9wCYwjQaDQapVL54IMP3nbbbQJRYB8fnylTprCyeMP+gYlp28Nu0v50GF2EVBGBr8CiHu4Ve9tbCNrHs4EgjT4TCE8MV4Yp+Aw4H7uScMEPx8E+OXgeaGCGI8Ioi7xJlabvfm0EcMOLoVcczeSCqjbcVc4lTsP7odNKf6uy/WpTNHi+j3njgiW+oA8/4glc8k60sYf0CprugGjOJwdBWJ6oQIgnpRxzm6xErGpVKG5/JQwLmFYH+PWAG3xLVnJs3OmI2g6t3UA2j3gyHguHv9MFHpnl8DusWB8RCMsLmAqXfiert8XDD7TVoM78iKcxfLM8CLxgIvSsNBjnbkdMj2vLg+B2emufXlVPrQUK4SoqcJc1dcGF81MieM38AgvcBfyNbg28ZbNdcL1LJuSFptsTSwayTYGjiBMDJAKVe6bDiFbk1hH0GD+MRWzuo4P4ZLfG4U3jPFv8Z7mq21GFDHI1HYMtNW8CrE0vEPdEC9wIFhhDgKZvc+t0uqKiothY3q/Pvm/o91kR0PTbVMN5YWMnQAaHPNiu342xoD3Tn1Evt5JjBtiUThl0mRe0dD8FQDBBAafCDAesQKi+yWjrTVWBU8yt2Q97wI1vJRvwMrksQF/3gJ1ncB49r8x6xKmBK1OgSgVj9+ODcCNZyaA2y2/V7TRJgZRy2Umm3hNzrrGTZjmBqPFtPM10RNoR2z6KpW+OIm1qljP6fD4YS7ttCvgur4QxCjFO/6Tivcb+D22ig28SITwxQUE+6U1k81s68i4ReL4zHfT1rb7l1YWQpwKEpVYgDMcWIjjfgFX8IXcju/lcPdKMreWojumSCS/RfHcTVAf2zkUwex5ww8SW+MLybK0u49xMt1jUwq+8bc8E11q7kRO+ag+6sk2BX+3tGHjgwHEe2XamBnbgCNdESPifzsunG5npsHXpBWOxnsIBuTYFPYi7ogWuAwtcM4Dm6tlaBDRXz7Z99+ydjZVJkYqFM60CC9VdjlfW6u27T/5Z1yzgGDZGoI/aeGAl/igWLOPxDNAJPYtSlJui4fNQpQFwlPVbk4YdK6sST/H5YcSAztSArGqtMGaGs9c8HwzkxP6M5koHcFOd42JC6iyqR0TDWoZuJyrobid9AlFjJ9gzMUWY4SQlhZ8FsWN5EOjPPqf1ydXkYkVFYVzPdGhjy75NzwcDfExmetvruLk14lX2gp8SaX0ENhs6QdZZFYp6kznVgEcTFUhLLqpHrGeuK1LJFvvCB3b4Aj6pwDyAvyd8gahWhqKSA999QkRnahkQKce9k5Ww9o/HsXHWUKqpucsIj9jJPGhW+lurA04KYyocqbV4xlfD8byfHx6dkk8/HIcjStC2Hxvm/G1B/+a75S3AmhwJnb1AmgzCu9hEC9zgFhABDYmAZhT/Bg6cwxfxeBlW6DeihhPNFNVjFX82EEnFRPBqWMvwK/9hD/hCWI24GfYM4lECSC0LoMgCwB1BsYX+GCevBmvtRAVWmilKMGAE7YsjyCTv0sBLdMQ0ISW/FvPkyNF17QABH8UCWNzlCL/Oi8GY5KeHUdXh+WDKrESKe68iKK4TqNBYmqEnZkOY8t23YwCJth9DZCre69NY+Wo2HWnPWXTFtrp2hL2m2wPSscnbdzIc5PlMiUSNDjEj5IgxTKPxcloR1CvVScMAkTsNdSTae4B4pqpoTQSyx6MKEeljicarQvXqzDqC9QSLNBHCWJwLTWDSkd9915BIzx/aP3cUyCvfxAM3JzB+uy4NQoQC1xF/huK2aIEbxwIioBEBzei/7V0ak7TYwU0o8zKW7ce8QT1xywIFeLq9Sf5RXAmgw3g5QjwTmP+zuU7WcjiH0iqAJIayfKq1CPqs2mMi+co+y7OB+srJv56A16GoXv+Ila2IK7GSOUw5SiCVmQ54iv9Ekke2Hu2x6nnjZTSdCZl9ftgkfd3EXO5TKN/IDi3wey3C/n21FpDiFcZxozv++XHVy1IGcu1IgBiPoPET47lttRYcmsd9wEAqbkDYZVkA3EgW5f/jLtBEJZLt10fgQV4NB1LcfICWBkCFb6oKYcGSRsRrthzBbg5TceIRT2PZUXZKai0AKJukJpjkqOzuTLQQD912FO69EW5qLcQIpij1IdS5ribv+QhPRhxOtMDYsYAIaERAM2pvo44Q49h+DD/c/XOHpJPGSvB9fhhat/bpAA1TlPSQJygm/PZuDOP/OIg858ZOxC8mKxBqWRkKrPNVHNTb7E6CXCxNpgsGkRV+D1fcjsgHV7egznihfy4WHpbZo9aCHTxBjkHXhmOS6yPA6mXRzOYDAFuvhOEpdiRgsZcmo6sPY3FxXg34wrXtxp4tbHnMoDMGrwtRiuu6DP8PiPAsk5UMOEjYUuz90uowaLL1n/8Rno8sbr7MTGs3AkkepyxMYXcyeD8TFbQzEQ/y6wkYZE04hpsgF7KmvorTF/tUpeFT4/Ofdiejkmg7r+aAhcFG8FBhHRwhnMw0MTThKUq8w6PS2MJS2VWD8SmOyoTFQUULXG0LiIBGBDRX+x2z3H+PBt6I6faQRv0qDqvjQi9jpq7le3o5erkVq/U+nhhMQycW1CW+yI7hp36sDAFw4ZgcOkK1bUUqfAZ+uYAaE+W00Bu/uVftweolUPnrZXwc7tFgYQvOQxL4V0eAYD47jJ7/EwnUIuA+59VgRFUaAArXIguQT/5SqLGidQZTTjyrEpUm3+alLnG3WNiAFrBRySfVdW2q30fsZSFM2csYj6/SHF9gRfN2MkrEFjoRHMqUK8KPbYsXHIUjah1DwRGc+DgWMbKpKmMA8Vw9jDCekeMT5LUV1iH81NKFVfmNKFjgm3jEBFeGAgallNO5eqSRxxSNpu4L94B+OXgr3orGJ/tRLN468/Aid7G4IVpAtMAIW0AENCKgGeFXTj/c7mRkn3IJz90awIin/IWJ3P2ZnPsp3ChoK0Owpi4PAmMjKA+JQj8mkJWdMb7DXZ9eATjFlt0ez5BnJynhNvBlVi82IMJdbHEjpQychrscESGapoK+TmAeKCnrI5DWe84QYLJ4b3MXIlCt3Yj+/JQIoLPI2xjK2RQNeDTf3ShhZ7ET40Gf2XTcWE6hxH91gPITjoZcUEfxfl8ftlvxhB/ovf1tLtbRnt+Y1zNyzQLs0OpQ2rO0yagD9HoUPDGsvrBzJiCjV7Y+xrfAUzjm5VYAGjapW0dApZsPAAX+eoKK66H1MkEOos88N3xArv0qrCkcYnj3z9bStngguS+OmODR4R1F7E20gGiBQVhABDQioBnEazPUW3SMuh3fe0+EGNBUlVAxrD8j/XLCAo8hsgAugXf34xf/o55wBU1Rgfkh8BCUNcMT8EEsUpMW+2AOnx5Cms/qMLBb3o3ButV3u9AITZfdyfqK0y1dWJIfdIfXoe9W147+JxiqS97rTC5ZUMpZ5I3Rz9bC6/NxLDK0n/I3Qpy++4SMnlRC7QwthUgT9fIex89eCEGAqUcDMVkX5TeHbJazNR8+OXjlSWI4539lBn+51F/Ic1q1hzbsBYOYZfjOd9fzXTZEgW2z0BvZ7Ev9Yfm7HBFKW+ID9xjHH2IfxPs04JrgQ2FPvRMDgHieKe7NZp5Pt9cnPV3BCOJp0QKiBW5IC4iARgQ0o/Di13VgFeTcM9wMnvQTMkO5U31ssCK5/Go7RAjoPOmHyBG73E5R4od1p1rYzffHgR42RGFZtWNKY2p0iFXJU5Hx9MlBo0Ku8E7D/tY4IVdUrUX4zCK/xHATgMtj3mDa5lSD4ZtVieHudQYqqm1HMI4jAi/2ESZFc50QQRDvuSCAniW+IA/1tDcB0DQa6olHrew8umXzAQTgWDs4K7fX+T/boUay+jOBuLdHw+8P29lViP7c5wJY9lUcae3/3nnk0/tcIA3MMlq6NODHTFMh5uKciWBQQyeI2FOUFHoWAobrI/F/NpXMSgZ312uRiMFtPoB5chjlaAnSvoLyhBMgAu6ZIBfSmFyzyNzHY+Fm8ZBoAdECN6QFREAjAppRePHVWnAR+AwSIvgkJisRJNpyBEyU/rfWbmjl7Uoy/tDPq4HfhZUwqWlHz1zYheuWDXDMcYHCykx7WhkC3wzbfk4CZ+XtGHh03jUlrxTVw/Hw+l5MMpWZ5LOBkEgRtO+OgcnbRxOElqBEV4IMrO2GmkoNnWAET1EKhfj4fX5/HClRilTQd7yzoSWzJkyDOt5VmfrL9r5AiVuJoBb4zVF6zIfUSd9R+NPs2aYuoJaQM/wukcc0SQnwceQCpPBeCqUm2zvaDn5wpgauoxkOsMldjvSAK6T/BOU8/XLAHT5Tg88xLB/oJ78W4aTYYvSZXYVP4b39wGpLfOHamaQE7rTYwvLh5hG00ibAssZOwWFxV7SAaAHRArCACGhEQDM6fwkfxMJ9wrkHQs5Aio1lWX7A0C1/PTGAiaUy5QKe9APaeCsaK2XfmvQ9GrhV7naEY+blPRiX9TewfOFtR0H5fDEEgZJD543TCD6DKzdEgQ/LTnJ3MngkThnGa9itzw4b6xwJzzH76yOM5F/ugpf2ANOsj8RTrAxFAM6k7CV3HbPBUobzaoxH6zqQ59xj998U+zppuqijhvzn0Ylv2SteCWPQQ8oOCnuKu+ebeDwI1zrVcPb4p1aT7BaS30rdzVodddjddsx1ExFYMomlFJCLtO3cKkT0BClI3Rq4guIvAg9NkNNiP9oej/ysSUoTDlBhHVBUZEFfGVsxTGUMbmLsxpkaDMolkwvOiruiBUQL3OAWEAGNCGhG50+grh0pSE/6wb3hmIFf7ZMUeq0wIoRgpqpM9HOvOMu6DgCLD2MR7BCU7jO/1yYFlOSqNij3L/AE+HjADd6OGQ70/TFsvBoOZwBfGfZ8A9AMX7+fneTHBxE84gezqtrgw+hbm391mF6Zhj+3X08gZ/vnJDyF3UmQbftoOxIsyOspUinY/QfynQNMY3czyX5P5yLZTlaGMqTakztpz5Nctz8mwAVV0QJvWUkjvER3O5KmMgtxK6mEmqHdppXdesj2ua4qE09OXg0gC4dH2Q61OgCaSQrQeF9kSNkzHRC06psWzU2Gv1HfAe8UH00SQSOYX1KKf724LVpAtIBoARHQiIBm1P4KWrrApX3KH16Bua4m2i1E0Kfh44nhneVcVz2DtVMNUPV0ACgsExkNmGkqLMmTlfQSrxwjEf1yAmIqgvZtPNghj/uAJpJSRpWtWIMXMbJ4Ak6P4MbdyVAx5l+j1gIYORuCRYLrzXc/PQS+s6CFnqWfXX3J/i96RCKVUNkx9pqfEiE0rEv9mUKXsEe6NVC6W+ILtwerEbzRLcNXtYViXkXJJ6mEGgohkGN7M0klapdJ/LE61QBtfHhHRDbJ9H8yfbFuIrhSNh8AbOWjPX4nfW87Z4Jt7XEKeCu/FqBzmsqYct/3veJZ0QI3pgUEvzFuNCOIgBvwDxAAACAASURBVEYENKP/zn/AuFUE8wg+g/znq9F6NPC+cGo0zV346T/ZQJu1kgHcKFKNecjsHDYf0BeX5k8pOA94qKET9OHJSnQ70wH54VxlSv7F/O3advBXPj2kZ4TUtSPP+SGP/qUdMR05ZljIcv/8MLkEhZPNbwBHWFBSrc91rmoDweWA/y5N8L+JQG3ZEMXgtlA9S7eylaLcvrxs86+W0Jco4Uv0UJsDQCNlenP4O3/+SH7KRG2pw+f11KX4EgDBF411F3B5twZQNZypxyS4vT+7oWeNWVQvhUJaUGyiBUQLWLRAVCG+MK3lwP3v7zdWU7F48fV6UAQ0IqAZ/Xf7J0ui8j8mCLOHhnGis12EYnedaqz3EflU3WYkF/NH3JlooVAAv3pAjwb38p0u/NvNtwvqAIYmKcHjmaiAw6ZkINrEte3AE7uTjbONKUJvhVlH9O6ZE9/Skfeox1h8/GwtuTr9mmL7GDviI55IUOfzYHTxn8TIVj/kQa3dOpCLK9OK67UklXTK7yDFnwSPoCNUipimAv670wHfpBMUtCJYGGt7NwYIj2tqLSpmrw2Hu2jbUWPGE3cBu5FdhTpWT/kjMrjAk14OQ3BwcJ4eQc/i7ti3wPkG+voo3qV1EWBfDaK82th/xuGdoUM6/hLt00HJTyyFAtYclxsR04iARgQ0w/uXNZje8muxoh8tMd5bUAc6i0Coxnh6yFs7E8HzaOJJxdidxFdAH4TTs71M8oAhRXoQk9IR0rZji/E1NIiWUoZo3UIvRHaWBwHNBOQS6TTk9E+SSrSanrgLYOT8coKOl+pxjy5d2uqzMLYYDqqPD5oFreI25/usn24PqKS2/f2PgYnTld3oKtuZbH9ncYZNXQwFWAHezxxnROVm2GM14tqqPXB3sa1DjVVqnhtY1Z7ZoD9Pt8fcBI2VNFwbDh7Vgx74/xN+iI4t9Wc8WDotiEFF4aQdM2URBA8g7g7BAkcu4BPfEIU3RJoMzL0qtN86TEMY99q9tbIV35/xvBIcOgKmeX//tftMg5y5CGhEQDPIV2d4b3M/hfX4k4PIBN5+DIvi1rjhHcGktw41WC8PuOEb0+MUfgjOsIembd+NLWv86SHjJPkrd9/3smfVWiR7HzgnJAz1516L1zR34SfsD8dBiDbq+njfSza/XbUHoHBTNLjD01RAD3DGZNhS0AK2q08OQpvHpB3aVBDw5oogQMl22V/1nh6phIr2YPusmR4zoVz2JCV+FBJBnOYpfxyZ4YBnZMndE+TGh92RgCT5Zh6OVKQCk/G9RBca0eH+c3Bc/ZyETi42wQnkexpOrC1HiOrOMBMbRxVmZc1NHkbcufYs0NIFbpYLj0nW0AlSmlhioo/PMijPgsZBQil+LViUrOyjq2v9lAhoREAzVt7hjMtIll4RDAGYvlOEhmXGai0SrNZHANlsP6YP91S3URtTKrK3IYYyyZRysGSmqbBUT5BD0K+qrbdxhnZ8/7oK5d3PBlJdO8I0la1wPi/2hTIeZcoo8GGNDgf9cwDp+M+rPbAhxuEdfcJ80wWqysB/tTnU005BC2nPE+bT+uE4vRmlP9zShbVneRAUhFeGYlm60xH6QFwzj/SptSDZ8OtwyU7iF/nxUtzLVfOWnYQizvFS/HbXVJ0CoJHfSqVXE/NykxY3RtAC4fmQcxTEbcPz4dUTW28WcD+FLzFBy6tBFPhGi9aJgEYENII/hBt01z8XX5qsnO6qUCNleLjMUVQPKLM7WR/VqmhBZvhSf2Hm87AMV9mK77LjpYAarOLwvU6o4TBJSW0p8nLnh+50xJNOVODX8PNB+nIEFS2U6ro2UPVRfYelWaTuohCwiQXtvf1wzHCtqQvMmBkOyJxa4mui+6zWmnCxuVteYqAPt/tVHNwwAkr4viKwgyta0EPTxTTwe+z/ShdMRQ+5LsSNa9YCDukA+oJ2ugqvsQDlCK65kXdTyvDdIvizZaXSbzSziIBGBDQ32jtv4XntTsIf4JsDv0V+LUgh0+2xMYzto1hhwak2RuA4skA/iFZHfjmgyi7wBGdWIMHSz5l0qFG0YaEXBPqmqOAmya4CVXlvIbgI42Xk7qE6I5sXW4yDGZfhC5mmwmoxRUkTZJos1VPNBz+zPFaGDQU9an7q5yRjze1uDSSFngmku53ofje9kjL/lgfchMWYujUIOfEdcvbpKMiQUoYgIOc9+uUE8N+h80gi05Ynk+1N5GJFRWZLH38wcfsatMC+InjsBH6FwFxww8XWmwV0hO+N1WFGTBNfgr9rvuOzt3uvs+MioBEBzXX2Sg/4cVhKXZIpgeajWKCKYWyLvPXKN/w+P4qlbxkWi1YHb8ocF+QNRRUiLWiqimxS+NdeebulC+TZx7wR5RkvR9LWJCV6Y1vSJbg3tssdenzu5/pSa6HBs/UopVVQXWYgQjnpUu6syUaWggIeNDnC7BQzeoORBYhtPReEhefbePh+XtuL0QNzTe6QnURA4XIrgZQT9ZKuMGT7MQTg+FnuFS34LvbJwYN8dhjJ8yDl2FNQLo7sSCAqS4CQsfsUyg8w6V3cGZQFjl2EU+2jWFTj4tPkB9XZUG/qVAPQ/JRoJH+UtyAw6mgmxj3Uka6v+6vbgGmmqRCZ/bcP/gDN5cuvrye2/DQioBEBjeU348Y5GpYPl4agZVXiS0HwS1FwzYB2F5vGXxo6kSK+zB+1jYiw8M9xgX+Ia+kVmMDpKu7AlTe+jQc66VBjMWAhRUAuoABb/Oizw1CdsXNxIt/7+H355xr0fnLcyPte/imT7WwH8jMiIf4ptiLEAi8U13ybqTshY8p87ivCiPyHUmuRvXKXI2V6/Iekkovyu96z31+Uc4J0Wn6HMUXAcyuCEbq62xF8o6cCgM9eCWMyt0vjkEPudTdFvURpv7Byxvzbr952TjV4V6FnTR7q6g13tXtWa6ETPd0eGWrfHQNefMCtX8HW0iYEE/1zjXTvYZxqWgWS7JYHIT775RG4TjdFC0WhhnG466YrHVFyGdTAA3Ovk/dzEB+NCGhEQDOI1+a6uiUgF7k5glZYN8xlg747Rs+HoHx0QR2SpKapsHiwBR9eCIEena2ZP+aNKAtSfoJ58nehm2fIIb/XCVGkV8ORH7TtKPwldznSPHeSubqQz2z+XZEFBjx3Woh1+JdRjqvgRv7Zonogj6UBSNjOuGw886Qfkm/5TUcoUJXltvq8/YP1Dndq3KaAEFNjehGBo70zEcGsZwNhnA9j4d/SsDkbJQdJ9T+U9A2FLibV7ZRhw++f3e7RUGEdOhku4kVzF6AYKxe0yBsbLGgzH/oaOqJMA2+stEk/ZbUWCpOPevWF47U6pNRNYMqLLgvAC/xB7PDrA1W3ofTHG1FQnowtNnprriHbilMdFQuIgEYENKPy4o2hQc/UYH0SFE5SpBr8FsM009jzWAZY0rGVDJ7hRd5YI2s7ADum2VOQSa0kjHrFkt38qWl0WF1yqvXHlvoD0LDDjZeBK3O2Br/Fv1O5ab1M3DDvxAAuoJ1SoZhlby3Pk7zu6u0kEdgt5nnvG/f1knC790WIEecHkvLPiHMZ6jP00b/x1IUY4mSLQxdT6s/GU4TFz+MUeDnssy/wHFhFMH5X/O03o+D9KjNU10pgkrD8TQNq/Ouvie15bvA28VunGsA07gL/mMm2IhUK15kGzHquHq8xcunFJlpgDFhABDQioBkDr+FoT+HNKCQcXWCEenUECu0UpdHbMfTZpVeAULItHkETDmQsC9CnJTd2AlG9vlc4znNBRkk64TlL+wu99EWtvbIRspmgJEUavPefHUJRgiW+WJJ3OXuVye9cFwGHzT1OkK+YrDQEDjLlFPiwpY6ZY2d9yWN6r2cJXODQo+kQiTE0tRb8mDCLdQ8ilsPFUrwX7hmphIoNmd+Ge/v691wkOf0//QVhT1HKD/yLFalYkvecReitoRMGnKQ00RzjX9zP7cI6gFHOk8He5X0aT3fttm6mAIg58/3FEJBpLLZuDV4bgdzlKSY4e7UECCzOQzwoWqAXC4iARgQ0vbwaN9Lh1m6I31jLscDPdUU8yBdVjIatsVI3bHdTlGDPnCjDGslJ4T0dQNamWp9OGWCQ8AkoV5yNxykQTXKqsepE5COyg9wlJjpgrcD25VbKiAsskU6eIIcizgx7ZEJNUSETCq2XPCb9uAVBECC+FC/gu3CzCj1LJ+0ebwxbwx7R6qDu84Cbmfhy2XE4ZvYspZQddPEQ0IxU0he9V6elMz502pnORdDFw9RQBEKxy3j9uCwwMkyioROPKXAw2KSAJjmUFp4PfomglTM55HUWU9wFl47V3bsd6eB5k8lpdMiG6y07prgBcLzNTJ95tjPqtItNtMCoW0AENCKgGfWXcKxMoLgBv+xji02kbIc+uW4NoBJX3GCWMwo6EsFXv8fg8H8mEFlOExUIP319FK6UGQ4mtSD6M43mLkSyxssx3McH6VFvRF7+EwnecXg+lqKietrmHNaissqqpJAz8Ft0a5CU9CRbBDTtFwp5vNeBKk4wRbzHIcmol3bJ+eFY2xc2RNFXcYCGc1zoVKXZpd73AMH4zAaft+KEHtCc6R1xNJ3HNS5W+L/df5H8j+Q6ESlObGNDV4ZB4i7QbBfDjuFfFnnUtBv2B/5vbDFgrkB09UzNMLOsBj6vod7xTTwtCzCRaXZhCo6aQxZ2JFYHCElqvNajwQ+A1HLeIXFTtMAoWUAENCKgGaVX74YZtlONla+gTv/AnxxEwSMdMbpzTHwmtRx+lItNABy7k5Gr7JA+YBHh46Xg/z7iiW7Hy4GNZjmD1/L6XqQLQV7Pjg4U0+t20T3KvyeXGfuvbgPWudhEdHKnRS1gkw/K4W90PtrkCH/H7/7mgGU7EyHi53FKn13FP49t96mAJu5TKMOONF2U64H4Ua6H8DJuv6EQ1xeFkVRSlx1SdNyj8oQzlRpYG9GrKf4T7tpD5+FgELQq5gEH5OsS9NDchTWbS4Bnz3580IIEnODGMb7b2o1I60IvaDqHnkW9d3P/luARnvQzKTVKDGNptotJ4r3gFnFXtMCIWUAENCKgGbGX7cYdaFmAkRtb2QrXxYuh+hiTdzYSU4dYqqasGR4dRSqSelq6QP6d5Yza1zmGrO9fkgCq3o6mtXaHmqS3z3AAhPrkILW31msLQl6XxWRX6Sj5ewpbdoUPyXUCFZqprHP3+Mzqy8fDXsb6Whz/Qafs9ff5zEIKVW+NqdxU29iyK+j4JJn6TkeAsyf8DLKH+9dT3Gbu1uo2IDmBt8AlEzhP4F/hbunnhs9pLPbKNCRPZVUitWeGA7av9dapRonmFcHAwZ8cvHLOdnoFSFdb4xDZzK+lX0+AohRTdK2bQZz/dWIBEdCIgOY6eZXH8mPEl+B73ytbX+gg9hz4K+OZpKcFnhAIHmLblUSr9hj7+OwwOl/qj2LaGh1cPuxYVnb0kt1xre3NFP+xRnl7q+3t3bZ/UMtvI6mkre4SWLoRy429WNzyugt0lt6a58y+aMVN5+noh6TEcKT4b8oxlOfxnUvZjr11STWnSSp52rf75T1U3EDOmYj+WMnwRO8foO79b9DBN+G/KQyhetCPdySAuMPmW6m1KKEw1cy50utYfZ7Yfw6rPlsyYn3E9YBm+nzcXk9mV+Fls5YDIj8TOFTCda/DiCdECwzcAiKgEQHNwN+a0bjjfAOyQ58NRAayS6axbOFozGUwY+4rAr1jqgq83YkKaMN0qE3oC4Pp1HDP63tNZGy8T2NRn6SgiXIAKSs5ognLg7AYP+l8+ZjNU63+i+nE9rr8w+tkh593uQCEUZkG3by9Lxi67OXf4MfI9nfU2WD5tNtksr8Dbp4SNhHc9KqzvhiI/c/mN3TWV3/afx6iXV1MjpnpHdirziKpZL6rpq0HarZzXcH+OdcAbDHXlSLt3wWxRvZ78Ht851J3s1oLndnJSuRwTbdHxlNQnnmngz/Soe5LpmXw/V5rd/ZorqW/wS4NvjRWheIL5MsjdL6X9/da+xDE+QotIAIaEdAI34kxuB9/ESSGN6Kg06pMQ53CZwKNhX7G4IQtTqlbgwoD8RdpKARViz1/GIsoANciC1BhYFs8BAOt5ZRegTMzHQBxGjpRaGmCHJp1a8JxdrlvC0BGtiPZ3Yw86r5bcwkubilLugQuxfZjSKcyFi5gw0lSCTRmzFuelxHQSCVUEKy/JPBhHHebbH4HjlSm6XaP+/IIpZQj6MMtRX45cEGt87kYtS+EqjLBL5bdwoGk+g7U5kwpN8uxsjyGePR6tkBLF16VRzxJlUbep8Eqm25PiaXX8yPfsM8mAhoR0Iz1l7+9B4wQh3TjPNu6kZiz/ZjxyA2+deAciDhcEnhjJ1xBMx0gbL8qFLbR6oBmXmbCUpEFNMcZhBtlGj0dSM6pncATJ3eS4z+ubMaOGpJKtkZcnKqCIP1HseADPeGHOtikVUMlL1MGD83RDy10leuOC6Jf0cOac5H6a4IexRGb3wCUmLfLKVqb331+GH6XjfuMp71PA9R6n8ZapW/e9xrDWIZj5v+qtZR0CfllaRVDJdaYdy4eGYMWYNMG+alb8lS4SzvVY3Cy4pSGZAER0IiAZkgv0AjcHFMEcVK97L1hvNhiRBNGvbV2Yy0fxpJPg3siHUFH535XyOekV6CYyz1O8L6sCKaVIdAaeXkP3BsbGP/LsYvIhyKmvMBEBZ26rAaeCH+aPKZdefTOBpJKVrkVc8LK7T20PgLVCajlEvppOk+H3qJDG7mumroMJjptqLpg+ztcWXJAf03I49i1vYmc/0V5ZlW1yhM1dr+f7UxfHIZCINs0OhTC3JkICcRHuTt851LiVmos5oY238i8jF/qMx2gTDNVBSh2rt78KvHI9WMBHeObPGKqfazWIvLLr/F+/Tzwjf0kIqARAc1Y/wvwzMbCLGhF9WCEdIzeb6xLzSiQhHRomT5NaXRhjUYHlsAib9BHFnrB+xJ3Acv2eDnNcgEv+GQ5PO3fxiOnek046tg96sWWxtRBrtfmN5S0TWBk8111J+JT9V6LAD46atkLatrBCjpXmANcou6EeyZmLRFos6v2wD5WMvjYjkc5guZChEpMUglVJOv7D12C3bRfKPAROrRJOGjZMZLfuiIYQGSuK8JbpU2gBN3vSnXtcBGx1T1xl/989GPzG+oyVCcy7ausGZ/UjgT9a9PchWSl+e7DLDtkOqa4N8oWaOvB61dsRpp5Loi8hQXERnmq4vBDt4AIaERAM/S36Or2EHcB/gaBfzg8H8vbaLWqNoRaNu6jvBqq68BPvQfd6X2mbjYRUpns0xEUm+OCiA9XMJKbbdIlUFjuc4H63K8nLEivclcOcUOthRNiQxQ1daGnU1VYv61kwGFTlIjZ6ekvtr+jsKeoo+aKw1U1dpBUolXdAejAZBWxt4TYf9LqNB0sFiIAI7uba0+o7nTQ+gf5OGXqlgcBSXxrpypSzgPsa6+mZp6ybNhT6O1SPB1+hw6+IZxD6RFS3tbURe/uh5QOm661KhR1Kryygd5yDeWrKGgB+pFKenPSfH8cpSf4Ta3FR+CSyT8mbl9vFpjtAk8ev3Wo8UIeu8g/Jm5fDxYQAY0IaMb6e6zW4tf51jhj5eSKFizM8tRRm/nWOPge+JWcSxqxuKaWA828GALasnc2fCS7k0FntjtpnKr3aWQefX8cZwNzabEvMEdrt/GCIW6dqkS29jsxtCsJ5abLmqE7PN2eXghBuYNpKnLMoNNVkKsxNtnvKbdfdYk6uroBaOxuAW6oyUZhSN/7dH7319n8rTpkrT6K1NVI4c8kur65LbKUpJLH7S/JTuJhY0PkGTYPrQwVRg8p/Bn0VpEMRZn9642zIqLLKQhgqf6HPZh5GYZln2WeG9Ykk6hByOOIW0nHUZbSpBPDzgsh5H7KsGP4d2cimEZiu44tIE3Ga8MJHGt0cFIu8h79SPF1bPPRejQR0IiAZrTevQGMm1sNf8wTfogXfHYYHIiN+0bz+2iBJ0UWCOf/2l7AF8cMfHuyHhH2itRyRGTOMvGZylagGf4y3KEGrXVHr+UEhKP0vS9NRv9vx4BC+0oY3DD7ikB9TS7DWh5ZYDIxY1eK/6aCIONuH1s6LUklut1MRcnKVPB/I5/rsf0jSSWaHE/jfSe+JalEbQvcU1x4Wn88wzZXsWCmAwmLVGcp4R9qr4bmb/RqYydEFPUSsI7j/3IHNTr8sHY/Bd1eIQrc8ySpbqe9z1PQQu56/sbqMETiBO3roygTIbbr2AI9Gjgp73Skzw/jD22JL+KV7N/jdfzUN+ajiYBGBDTXxpvf0Ams8P5+fCvFFo9yfsp8dxNQwlrwnRjQVJ8NJFezYsVrwvVawN6nAcsELbKA5hlE5gSnBrQbdwFE14zLxpsCcnGEI/AaTwi2VLfTeV4GkeCsYNfmNwAZUklcUmK954JwP5sKG6bQUn4Ad6Gup3WjfB97GXRrqpgUtfTdZ1SL1ob3XjEgYQtFreQ6wUbEcnSSsMXkYG874c8gUStTTgEPWbzEMQPsIj7vqq4d5B5BPMLiveLBa9oCOsIf7GeH8QXilAHxArFdlxYQAY0IaK7LF/vqPtSmaOHP+pYuEH1iixEdizZTgv/8sF4nRp5K6w3ZytwU0yrgShl62xCFSJagrQw10dwTnNXvZin6Q6DRX2x3M4tUvncNKbObEuDv0uV+N44U8bSKid4KLNcDGuWficnQbknclWz771+SDLUwzaeStI0iV5gc3rMUnfRRbIF/9d4XIWaTbaAe808x2+09CPA9Hwx+dG07PD2LfeHHEiTQmd0nHhAtIFrg2rCACGhEQHNtvKljapa51WDM2KfrSbWVrVgXnw1EFOw/kUJUodWBeerJpFTEFIEpbFSiY57KPh1Fj4felviixKCgfXeMPrQk2yu4bAC7MoZAI5UgzVt2C5Udo9jXATtMfTwni6r1gMZlPBUElTSSj8vOXIcnPjtkZE8LB03+Dtnj/BbyOPnO6S/YaiyGwl6OG3nfiz4up8C1w/5Xpef9NnYCiUI9WQYuzjfxJg4b/sji9rBYILIA+HWiAmnSW+NE18iwGFXspFcLiIBGBDS9vhziiT4sEH8RaUp3OiKKMVEBQMPq/x4vBdaJNyRQ6AhxqNku+tLT3Rq4cD47ZCQAZVWizGE4yhANtXGBLX5H6yPo5yT9Aa0Owr4b94HR/G28UXWXf33f2+091GP33+AFS39LUknn4U/auikreAtJJbv8D8lTeRW2uxrBtpGOO2dz53Z7z/Eyslf+cMr+qYlKFHe03MzLfQc+gqLcA2pnvMlzJu44/A65TgQLx/lfAuXibo1BGmdAPYsXGywQX4Js+VWhiP9mGwqgGk4a/7VJQc0y+3R84rHFkA5a4Ml7Q4wXiluiBYbHAiKgEQHN8LxJN2Av3RpKKAXTVkAwdMkExFkZCmfAIm/8NmWLD7Amyq9Fjvd8d5x9NRxVCHYZAMcQbRiWD7HBEl5NpPgSzKSAKQqt1kL0/V4n8CKdMxH5mqoigeBY3xOobYerqd72HySVRMYe9HDetcop50F3kjo562x+6x93+gk/PFcpqwLT0wbcY3NTjs3cr+0cF3n2dEhvjbN9ZpKCDp/vZZy0X8nrLioMpbP+yGzKcUV5qWyHXq7u5XB+ALlPwbkDr9GR97CxbxUd/7yXq8XDA7bAtnjkym2NQ7r7+/vh7vIx0L75fZ1vwLvNr3yu1qKg2Lfx/KvEbdECw2kBEdCIgGY43yexL9YCFxqRUPNtPL7r+RlP7Nn2HlSB3n6MZCeFYGgoBtQRokt3OyLFKSAXtTwnKY0MZYd0oI2qNuMILpnwMJlPz3iF6damaDiiulprWRUZjY7mutGdDsYImlpLb0UzqsFEpEGCt9ruD4myZ/L8N+480kxSScee51yzMGijRVZm8V74VGS3kIs1wlX7VpH7VIvVDPJqIH98xqJuTtEechmPie97mY59ho3o1XTsU9NHGYa98hYgs5NMov4wdHeNdBFTBIdivl5SEZOOLcZrVmQmuOySCZeMoO0txEsoNtECV8kCIqARAc1VerXEbmGBtu4RLUqsI3iM1keiLOV7+ymlzPgpLPImvxzjLhEyxR71ghZOf1pzF35wG1XsiKrbaLyMJhgy0tlOLrfi4AV4iSBA3GbzpzN7PkfchykCRbkeOkKQLqCPQSOWo3o2Wx7B5jd0waBXyAxwrh6yOhMVKCc+UQHeEleuUv8UxXvJ4e+owBC6mJK+wcGYNRT/kf7soP9J3MqUdNhEDYWt3QCOE+TI/p2mAinqYG8+p0EPN1ZvfG0vipIK2jpeWJM7ZZuCZGlBO3EJmgtiEy1wlSwgAhoR0FylV+tG7zamiB7zBvnUSgbOCh8KjIppptuj+rSgvRFFUkMFAsEpwa55rYnTVVAcnusqDCHd6Yjqj2i2NzXa3FEW+x3kalqZpKc25JS/GaVPYhcMod+NWQP3jIAdzJyr6wCG+CgW6sxEKH3w3n784jdxMpUn6snIUoleYW//Ojr6geWx+nlU24M+o1aSxwyuc7WTFRG8Uy6ZcFGcNLNtP/u+ti570o+C84RT3pFAmw2Fubhz+88h2MpPkieiX04gFCs20QJXyQIioBEBzVV6tW7obn1Og6Finw7/QW41lEmnqqDPO4rtcR/UpuY3rQ4MZfP1iX8Nt93SBY8InwFa2w5AYy0zCUCUt8BDc5Gl0UQ8e1T5SvpeKfnPo+osQIGOOh2BGRqYRyFnUDJzkTf8SYfPg1L9w3FkxEQ4fojgVLipIA1TUGJdBFKTHvNBoW+WlqTWIvXanleJHRPuaqLOBvzHyhVxZBruYQa6oWYKktfmUmdDfl7Gc4qMxix/PI5Oy/a0/Rgw643QNkThYxK0V8OBVAStRwPG1Tsx1N6jP3P4PPjycaZ1IgV3ibuiBYZiARHQiIBmKO+PeK8FC7R0noEylgAAIABJREFUIblDINe2NQ4KKKPYXDLh3ihvMU5BmQbtHJMaCMaTFrbe3w9+TJthfVJr6X6GQ8OV2erWgHfM1kvqVAMqrQimz+V+WPuVt+H/Pa0qZtBN0aAn26YgQLYjgaaoaJo97BOQS2GnW528Pd9SHszjUWS6NDg7VYUaWBH5AIiTlHooJk22ENowmX3sBmQ8DaUxBGeqR068axZDEqo/yz4O22s6oyTEL4UxlNHG8r2HzgNT5nD1swiSzZOVZoE/5hkuNKI86t2OqMfOJm97mJWeGMsPK87tmrOACGhEQHPNvbTDMOH0CgrKA5+R+/k4DJ0aujhyAc52wfJ2jikP3syvoGS4vo9/O9V06Dymmlo+VHFkjQ6OjbscaVs8KVIh1zvDno6X9jG48FR9B5alB92RlmWTAgn5eW5wtyzwRInN3ckIsS3wBGa61IzteW7QZp3nRh/YMf4MqWTtnu4Z9shjv8fJRL94mT+cPZzGsY7oqzhIKusMU7BNgS9nXYQxQBZZgHW0shVJ6Rv7Vjk+tBH0lz5bdhVo2jFFvcC7bjCaqQEVDj2zkapDzRdxpF2/sJ+4hGWem22fQ13zJ39MgA7kp4dAe38r2ogsLT6YWosXWJEK72Blq8VLxIOiBYbNAiKgEQHNsL1M10RHZc1Ivpiqwto821kv7zu8M48uQihH0KraQKYZ0Hd63AUI2MxyRrGnaSqI7+lDOYKu+72rI6zZ7+9HlGdnoiG/ut+3E6F8km0Kbn99Lzmkg7zS3oPKSm9G0RtRyAZn/T3LgwAyWM8NBk1MJ6mkS3rLT0m60iZoD3KZ6s1ddLICllkbjrQvrjV0AuJw+fCLvIHqXLIApwrq9Co+T/pBxn6+uzCUxnWi3zi0iQ6+KTxo2K9pB7abpMT7MNcVIM9cnBDRK0MR78I6TKyovBZHvO8h3/vId26In8IpIASKxoUhDIv5Osc2Jy4B0KwNBzi2nGtmMK/4r2iBkbSACGhEQDOS79soj9WtAeViQxTVM6xStRaL8SSlCTVk6FO82IQ1r5BRf+F688uBr0LgtuHOmm+cqcHvYKcM/eLd2Ilfw4u8hSxL8xuv0hEdATfc56KnOS/xNUmh4g96qhK+E9bC3PFjOaWPOlSzRQaeDkC+VXUbPEbWcn2Hi33pLVNHyz1ORgfSnY5AS4/76C++1wmeklWhMOlS/yvlkR15l2I3cDPhb2h1CIqt2qPPZtfqwOyZpDSOq7+4g4EvzSXs7pdHkGOVfTxYfcq5IdX5lMfaS9KJPU4TkXPOJpw33TBZT3xrituiBUbbAiKgEQHNaL+DIzh+ZAGSbAWZF58dulLMYuAz3HwAERkuo/h4KcRXBFnTffe6+YAwc6RLg/U7+Ezf912ts4pUhKuC86i5CzK7u5Kw8FtM7YnIR7yJawmlYNXc6wQs8mUcbn9/P0QFH/WCrmBONV1uQ+73VCVQglrPskXi93gZlTXru1nkTdYKRLXO1NKb+4ww6Lmgfqjpx70PkT1LLaEULhlBqcIfE8wycdqr4I9pYXO3MEnbFKQfsylszwTyUth4bBtLA4rHRAuIFriKFhABjQhoruLrNda63pFgoZBQbDGAwvC2th5kFFvLEchY4In8IHnqwEZY5A3CrKB9doi+Pio4NhK7dR1wuhzVeyj0I+5IgCSMeUtg0Btbr8rnNHDPjgTkNk9QwJuy0AvyxNYKesjDqMj3XCBsdY8T7WFqUbV200uhJlTf+e4gDnPRjcYuZEiNl1NFf2gZRz+g/evM50mE9KhXw4VnUssRkTRpbZcBaFor+Ae7NXDC8YUKcZaRE6Qa03Qy/m3itmgB0QJXzQIioBEBzVV7ucZex7uT6bW9wmmFnkUo52q0gjq4NCLyEV4ZaFvqT76mOnhEiNHsMJM1G2jPg7g+thgZUoJ2vgEuChMNGOaKHg0ulqfiqScr6cA5uDRWhwHh9WhAYPr6KC3wAoJZFwEx5RdC4J553IcmKeDIWbUHXrRlAZCZYVt9Bwb6lCkquXEfblniC/7T3U5CCRzBDPW78R9DW89Scz9lQc027gKglUlrKQOgae9P2r0OV1ZlmNwu7oysBRo6wbiyT6f95/DKie3GsYAIaERAc+O87cijmaSk4gbjI7P1ZcylNYxXjOBWeQsll7Eyu4jpPOVv8nVc0gjPwQl93GMEp0UUXUQPewhHrGgBzuBgB/90Yimm+nQgkI1fDhw5D3voi3fGFgOLPBdMHxwAsfTDWECf6HMg5E5Rgvs83R7/HeKxUKoNfOrMy8BzHx9E7nR9B+jAB87xh+1l+9inFP1KpxolEjMum+S1nW8AruIXy9QRYNZHgvrkbE5TB0/wv5ehcNj2dyj0LbZRskBEPt6fhUxAc5YzXjyOWj5KMxKHHTkLiIBGBDQj97aNhZE+OYhVNiIfRJC0CloTjsQZAYti5OdZ1Ybcn/EyFMqxkkFz5XQ1UqVW7YG8b0UL1D7mu1uIl43MVMuaofTPVx8hAkv3Ua9exy+qp2X++myynxKNxZsyK4EhJir0/7/PBVxj1J9Kokc8QPXV6sgxA8iGYyDpCPEpgYRJViU6udyfkNPxL0r9V93rBCw7WQnui1OGMcV6RwLcQkF54OtkVYJNNdsZBjdpTefhd4FSXz+a7BYqGw03Wj+mdt1fksn8YuF4Zl0aSBbNc0N23iAax+gaxL2jeMs1Ou1hsZgIaERAMywv0jXTiUYH/YzZzsANExX0bowZDWLEH6VDDf2xNeH6QtmVrYjOPOBG5+rpg1gsw1YyvQzdKH5VfXkEAIt1Zmh1FJYPsBIFZZZe2+HziN0IlH5eCgWZZs9ZPNGWI+SYDmSzwAtI4lEvlL5i238ikRLMtbB8QJzIAn2aWMZlwNBveBdwV5pvnA378qDdyuAziHxpdbSvCPjJ0RAU0jHSMve7wsjWchB39NXC+R01FAHQdBsoyvxT5tvK26j0iPlh8cgIWGBTNH1+2GQctRYkNov1wE2u4+1caobA8TQVvh+eDjBxFvKuGnObXRqw5tk8xHluKHw7il8Xo2UdEdCIgGa03r1RHreufaz8wfuchgeC09sFtVSHb9LdTJUltdZyWGeEzdet0dOcJyrwRT9VdeVFQq0FM+b9/ca06iMXgBt2MTL5eTU4O1mJ3qxk4LJc4gGGX08gCLUiGOQb91OgD3ufhvvqbkccn6gAmukPPQKZ+YqtZb7P8821/xy64sATe6quw0hS5l+M7fp8AJqe/riDiOz/QiVmlY2EPYr7V8UCFqn0XxyhL/uNMC804h17Iwr1yE5XQUByspL8+6ilelWeY8CdqrXw5i70Aug/U4MfDA95gC/Yf52IAQ85Jm8QAY0IaMbki3kjTWrzAfrerD6OS6ZZ8vCI2EStBWPmxwR8lWeilKS+7WUU7l/fCw3fzQeg+Lcu4sqI8HwDCNf3ueAn76o9KPzEL7as1cHl83wwAE2tgQJMRImlWEVmOSMCJU+FM+bZQJRcaOumYxdBBO4/yfpcPcltv9GELEa5b0PT6kCzsJhzbrjE9N+6Mwyg4U3R9LzJntM/yWcWJX5tclDcGRELPBMoDE2yxVB3JvZ3+Nf24l3lt8gCeGvMye/8a0Z9OzAXQJ8fOq9shX+0bx/qqE972CcgAhoR0Az7SyV2ODALfBBL3/FEctmbnTORujzCraIFmdWznOGMWR8BR8gnB+Euqm0HAgjk/U6tbQfOEFaFtDTdbg1+Nf6UiEifR7aFbCm/0wA0bEp2czfJUmmqPfKeuOFau5H9ztcRtjSO5WPHS+lH292AI7Y3cS4WjQ5LVGr/62PX5qAHTf/qVhQEUfRqcp9qeULi0atpAXkqADQ/ynm2FkFbPjTvY3y1FlwxPkmcmAKn89wQqRzL7Y0o1AARtG1HhVpWgguuv10R0IiA5vp7q6+xJwrIBeGXK/pIBM/HUn+ot41wez4YZQ24WExxA/AHW4hnsa9wLr45JgJ6wtOW9mvbsbokmhaQskuFut08N0jpW8uhLsMK1j3gRuWGINTB85D1G1Br60YK1QQ5TVJolynzSCqJya5ke9hbCJ1DvsEt99x0gS4epgwb8p8PQKNVW77M/GhBMFSDR7A1dA6S9zqCcxyJoTrUwL5L/VHi43QVeWcDnfdfuqmlC+/ehUbhVJ/0A218LLeX94DqLmi2KShRckM1EdCIgOaGeuHH4sN2afAtvCpUXy3hUjMYsvPdjZlBIzPp7CoEegRp2JEFWBJ2J2NKgpZcBibKQNvuZDCyWXGath4I7k1SIor02l49jrnbkea5Q3xvmgpDs62Y0by5MgThzeataJS3LKgDn2C+spqkkkcUlw4VU3g+wl7u/Sn7HPgIyf9AqttROSHPrDoXbyzhZlEYuVgJD16d/f3n4JNgIeDyICTu3eCtrRsB3NkuAMeLvMkr25jR1h/LPOQhJIdVtMBVKUjx609XI3nN98fxe4DfdAQKmpTh4fGPX9/bIqARAc31/YZfG09X1w7EYC3X5zS9vMeEITsyz7DnLJKtBI0Vm3HLQqqIgGC45Qg8Kw97gODimnVlPg3bs5bJMpvpgEVivAy4LbYYZ+5yoGcCsfYcLQErs1NNOVVYp70Z0d3IAixR/W9natA/l68UmY3qko/aFY9n9Ig9s/vXk9/9lD1wyEZExXvJ6Z/9G0N4VacaLrGl/jD4m1GU3idACcyFeI99OnLOz9VjIZ+kRJ6/2NhQ0SDs4JcDXyBnw9p2cLzWRQyipxG95WIT/hgVqQgQsy7en5PwdyRUsh7RSY3CYCKgEQHNKLx24pAWLVDdBuU3oQiKxUuHdrC1G79cPz5I244CQLC1oeMuIJVakOqZcRnIoK4dp+xOGkfdkYDY0OowcAucMxEwWh0mvNd4tdlWew+dqqT8Wv33r5b02OVMDaTzptsDJJU1Y2nZEIUS5Q97gKTc/+aXA61hY2NKLMVn5lvLB8Lu9J1Dpw0+ImNf/di6EEMOf+vHdcJL2roBZVi/wt5C5OZMUlqq/s3c194D+whqg/+UCG/fNdE61RSQS58dAs18/zn9mzAWZi5NRqRyqT9IbFOUILMLKq2OhUmazyGhFB7NB5i/xPtcwMRPq8CXid1JxF53JlKekRZvfveoHdHq4K/9Kg5vQkCusNDeQKclAhoR0Az0nRGvv7YtUFgHPu9CL9QQ+DAWP+zeigYW6VQDtfBjMRodqMGbovG8SZfAO3k+mH49AWeSlQw/W1kkRER1jG6vS+bgLTNeRmsZGvKGKDAZOeW9hz3wQ/P1vQNAS0Qo4WnibdL2kFRSkhK4XJ6habzQ31n6zKIcs4oP/bn54iEEqgbediQAjvA5razejyAOyHZ84hJiZ+wvcm4o1qNW2b8Ec+6ukd8obwEBa747IPVnh/ARrw4zefCRnxJ/xEvNCDy5ZiEVjnvJ+ReMze22HhDwHTOQqNipBlCYbo/SIt8dw1/QRAWp0sbWxNt78Lnf5UifHcabMN8db0W5QNZyIFMWAY0IaAbyvojXXuMW0Oig/vLxQSM+KG8BvmEJyLHFcAl8fhiklsgCejEEP/g4Nd7LrXCTbNwH58dSMzeASybcKoNu9zgjAhVZAKH69h4gpKcCAJteDMG3syDadcVRylvwI9uEUCIdB2KvVELyP17xdv0FXndTLio+1LajnjY/J/YKPVw6Sor/vsI1lk4/4AYLCNpj3pZ1UOIuoOiVoDUztNYSM1qr4LJR330hBL43Tnuptp0W+2JJE9twWaC8BSw0b1509cQlkOSOm1Lyh2u4wfWzjanLxkk2dKrxVrwQMrjOcJcIaERAM/i3R7zzmrNA0iVGU67HZOJgqBhCK6cqoSp2nwuiPNuO6gswmVxN0PD99JDgGH4OzncXHuz//hJfwBf2v2kqWh4E0ZqpKgieDq79lAgPfHg+gEhJI/XY3Zpm++j5IiYBW93Zrz49ZzZmer+2F0jLSgaE9N7+/sGa8kSS3dKvIUwvmqREzFHQ1keAG2Heqtrwm1uQYxxyBihHEDc0v3d0j5ytxcxrTGV92PpfHMQZ3RleB6PLTlpABtviLbD7R+thuzT4AxfkPNa0491gRRwGMTER0IiAZhCvjXjLtWoB/1xoEAtaSSMW7Ob+yawQQbtssa/QFf9jAvIsztaCynqPE8DE2zFgqvan5VQDNDzlTzMdjbDmflfIAQvrRPanO+YaHSGFiq1xYSWjTX7FZy5WUcslOGkin6OolyjtF7aY152OAHCfHhKylzTu07539PtPJLLPtDrkubwQAu9U1xULOF9OQYnKgbcn/Mgty+S2bg3Y0NG9iKBsjQN3mCu+eLwU3vv+Up5NxhnRndhiC9jXmDLdXo1I32ln/X85btTFeJzORyMZnv0v5tURnfE1ONjHBy1IN4WexV/uGGmlTfhjbzT7cfGwR68v/BVnLgIaEdBc8SURL7h+LHDwPNZ4wS/4+IuItfc/rFPfAbaNNNmIaVLKQJ90yMD/P4yFnu/REgCaGfaQA2FbajlAw+owhLQEQmdrwhHbYmm/5xoopQyOB2s5+Jg/9Vvj1eKHpNYi16muw3BSp6Hk7yhhC0WuaHeYMEkJb1PSJWRarQmHZbjEKCJqVE3e6RrIt1VrN+bJKf4ZOjX7tyqdpOMYSTazU30e8M8Fm+TwBVhpjgv+e8iD5nv0WpOhSwNjsvnJD3kgXGh30vih9DnUMJ/U6JAe/1Y0mFU/JQqhoWCwjMt4TwRJ+DnV8IG1dBFlyhEW9L1P/5/sFjrDZN9l2JDnTCoMoaMfkvsUQZ/irsACOxMtiNDsTqZXTbO7BXeN5G5bNz5x7vuBHbqtB24bk2DxQOYkAhoR0AzkfRGvvcYt0KnGss1X+O1UI76zpd/FblgDJJfBDfOEH25cx5B5FamoLvkrU6eJM9I38fqkG9sULLcfHySHdHB+JymNOmBaHUL7u5Lg1OHzARd6Yanm3A9cn8Oy0ZUfWW3z/5iahTqU0dZ06Qje+Nd4QmTVsomx+03i+Rca6YVgeBc+OEB+Ob2Hn1hlYcd/YPVtOt//CesIRdetZMggu88Fq/7/b+874Ju6zrcvkPAn//Zrs9o0/VJmIKEhCSF7fAnNhCYhkJLRkIRmkBBGdrMamgDZYXgbzF7GZtkYDLYx2BhPsI3B4ImxjTHee9uS3u/3SLJ0dSXLlizJV9J7fv615957xnuee8J99J53DPcAOGcqTI+hIkq4QEuOAtJVadJDHNN97HC3XQEGdsdqeKh5pcA56BZ/c5klFCoolpaKUpJ3KbGLtLGOTiyjoEf0Ym66nU6vxeXxn2nHo6jk7qSAkfoGXDOFwBl15ILEEv2zCw34HSJxi9M/HojanH147+LfDEuOYm9ITN37LhoTGiY0fd8t3NIVEIgpwm+gd/fjn7a16bARfmKLJc7M3RhUt+JM54tohN3LqsLfSM0v7O4GREi6NNyDQnLwbRb/6oorBqfR2H8oVDg1TytDDp2Jq2GevCcb2vKRnrDmsVM5mXigdvkf8C/p4QU4hFJ/IDPKManOjKPKY0RU5C6dAL8k4Knmb5QXjfWBWutAvu65uKKiklja9RQsaXY9KX5gvo6fp95gBt/G0leHEYMnvwY2T5MCTOhdatvopV0w/HwtBKbTIz3h+2r1l8C8YOaf/pIAV3OxK9bP6tyiOiSNu6eVQfJntsMlZ1MGDvIeWN8dNCXlJ9rxmL7LlrsoQx0lOvl7LZj5obTq/+obcK0HBLyPgw1/E4v/oFYk4T+u+QdMbKQeejvidnkz3vvUbdgDwWcQpvyvflKzMIvkYELDhMaiDcONXQGBvBr4yj6+BTmuPVOkyn8zK2ztOfp/0kUcMElKuwJa5dmhcMuUlHnh+ABrysydCAqnUrtbv7wbHOuf6kyWmRUGv94kI/TnMj4+umn57zFC2EzkklQ7JZU1gX7pMl82et3w/YYQzUlcYCb+qR3vCyOV9SdxMvJaCOymb/Q2q0M68SsF/63vcu7Kgm4m0TAy3qZTuKkhf50KPSBv7EUoWJ2HSFYV0hOaNB/uuwDWtbwzQOqc1aUECeuB7cFl7Mmt2Bg3qlVQE/zp26MidReIy1N6SbbdR+neuExcTLv/jkrBfvK7Tt+Aaz0jcLQYvwrwH9RuMAYZuqDXt0Ot+8x2qHK/PAz7/f4UJjRMaPqzf7ivWyBQ24ajpfG++N7fvw7xOYwNbjTuCboI8SfLYAEzypOGrwQVMM6n45WCIDeaknRRewilsbfNKMexzh2r0X2UJ44wjH1/+ol7YWZc27IrEK9l91ToAzx/Q4QfsncE6Jem9PvTh377PoyA7mHyJnwSJvjT5M3acCm1bdAxvLDLBFfTy4bTk4f1l73VfkoAwpIwbqcrgOFPCWrPL09os2btoYgCICO2+AE3y8U5oFiB39uENnjepYTMxieDL+yEAs+4XFDHS/zPEa016Pk6JP14OpA6dabWid/Snqf1HYP+H6WuwGX817DmJqLCSPK5Wt+Aa4xANwJMaJjQdO8F/n9GwBQCDe2Iwjc9mGIKKa8GJrF3BJi2ufk4Ej+zLjVRYgkIygcRYD8LDtCjm6BjiDYMaDf/gF5DQ4TIN/esxU/221fRKC+0//oIfsqfKofd7hhvmO6aKQoVBHt5NwRYcKAPBKgsRbV88IKggq4t91LodPIYllUFpYK/OMOf3x8vnQp/Zrualqk9t+9da2DuOj2Y5h3Aj8seS5oHbX+wx6dGD3rS0AxX58T4KR68Ib0M2rUxXtDHSEp1K7iF2A5J0sBOl3etsUBD89VhadahxnbsqJDsbuniF1HItO4Lgt3M8Z9xGfcF7f0HKtaG+dGPyTUXRYAJDRMaF93avCwbIfBjPA659T+gCW7MY7ylnkpESNM9OxTmMjf50kMbwE7e2Q8n56QScBSxd9VRtQ3NSW32a62gXUqMeaQQzEYS0nRZIiyOjdVCmp5dSpy+TwqAoUDQGfhSjfFGGHVzpS6flg+G9cwy4cia9xQrho7yhNmKwRS+11BhBBGdrcJaXtoFo2ZxeWgDFjjLjNtIuhcF3i/uYr7eorahuXedPuF5eTPd7Af7mM2nDbq+tY9GekmVMTnV8H7vu/u9wYj9uPg1EZTX2IbGpH/7k1tx9iEpn0YBfG059hXtndF9QTh+Sv4Ol7Gf0r6XUEGYn2H6BlxjBLoRYELDhKZ7L/D/MwKmEHhii4lItf/aK3Vo0nUNyYae4Kd4AyvgXxJx81+hcG56PwKEY7Wp8wgirXGxxKe3vh2f6rweotqsSUcyKXGgtv15mKKkQSeUqUpHo7K1Ni63NjAqWbV8iDTTTfL3YDzF2s/swoP0922wAj7ffcYflgvqdmeANDmzwUwnfWnrPQZ3jC7q2ymygPbmUlE9nkWeA33RpBD6xw4a7QllzE0+Uu6SXwM8vQ0j2S88iHMxx5cOjZdTAByXfI7jCOkWf0oxtATSSTVlmwmuibRiMd1Njn6GKEG6sucZSvgGVzEfUvgsVC4l0/IhpAtUo6lc7J9/v246rjgzAkxomNA48/5l2e2PwIPrKdwwsJtCBSepT42CBWtkOVuJT7KxJcc4XxjSvrQLoVPM2MScKMXnXGK9qPGEkmh0NDmkShrgI7PGMB4dEeyd/U70DR2EjRH0TS8cwcdy0210cDYptTGVy5pwxDNpNcxolsbRx5E4h5oUgOUYr1Q/VIY/bblLf9lda2iH+Uu7Al5mN/mAFT2s9lH/JAo+VgW1Wi/oSQGIUrg7Cw0kRROGbrQXEnIlX6SYIth+TvCnc7WShg66VKjgNDdnH/RVPxwzd+y1+CjiE4o1YdWtiL5zUOcvFvMx7f+nXu69MyjmY7jWR83BGyGiplLadp8+UM2WOylgBG2ZpO/CNXdFgAkNExp33fu87r4hMGcffSaKUrMzC+oQTY6CJ7ZII5cTwWZ2vC9OjsTleClUJvoAd+JnhvW6NpzvJBn+vo8twoe/qUPfNKsKvsoaMUZ60kcRBt9IIoQY/iZW395crTwNhEalpNpcRI7x/xOtGwciUmLQv6IZh1k3+0JXNMIT5GZ5Uo8h77TTnQqgzXeIpy5tRLizkZ6Q/EZvaF/25mjZW1YVTI+Nc0qUqvNSSaxu9+bC2Ci+GNRnnA8IwfwDWh2PeDoZ1iuaQQQ10ipVMAmaug3243qH8yMfUPireskPvKY5GcT/Rs/X3xfXMtfTxlvFN7junggwoWFC4547n1fdVwTSy8BFtmXiu7v+JN3sA5+UiavheTte/XX/3kjZ/0sC3JR0hzj5NQiW9V/dmUJvM38bCxOc3Gptu6wqjPZjvL5bbjWIxWfRUGZUt9LfNsNqZ/FRfQMVIdv2RlFmPv0z41plBj6Wyk7a8wzcZ9aNowYbZfDLXAdNT8VJKk+l6rNVLXT3GqTfO1sJL+VJAbAreitMr47SxPLRnD2JxfwoEj72Oo/W9DJY0fZV/yQeSB51jWeThoyO9oK6S0xVwVoO/ksvaWcTghNq/joN8z/pGp3dTOvH66644rYIMKFhQuO2m58X3lcEwnIR3u3etTBEHecLN+zbV8Ma5lABzlxGeYksOtVDdilxZ6QnDoOe3Y7KhxF9SIHULU6HAoqKkZ5wIHo6EJXPog1Odt7Yi9i4uhKaA/3NSE/ak4NAf+/uxy/+W/xEoU10TU1WqjJBaLraELct+XuTTay8eWYDciBosnwvE7wi82cEa1URGo+k9DIws9gi/fAPb4Q9jaS0dCKPxGgvnKM9tRUrXXJUqpGSdJFexn1OPlfROVEgZCIqO44sEHpCJe1k1+tLTUiPVW+cQezQXIp827KpswM5GYJliLloayY0TGhcdGvzsmyKQGM77HzH+cBC4rZVeu/lS+pgdGN96KiRUqOgFvkBNp+inG5di0US5Vaj75bT0gyXKgJ9SRLFdCeCegYnQR6IIXb3Gnzyx/pIHcV7nL36LDhHZwsC0qT81GMzKx6c3YyR/f6r6AC1AAAgAElEQVSIrp7/+9naQ0HdDj5NHTh1yq8BU9ElAVARLHUizhnOlPIjrb2RCiPOViKgamCmVUdLIc9CkjRPg6HjF+Fmu9oa2eDBwF10tdL6m+jQXMskyN1Ba0ZZ1oVbuyICTGiY0LjivuY12QGBw+dxSvJ2GH0nysJT24YP8zv7YOrrmKIxENZEztXNGJIN7dHUbdDceCQjFk5AGrRKukC6upYmKjU5+K53NFLwZDrxq4kGVt/K2oqRNXH6t9y53m9xaI5+rOeCYOXzUaT+MO5gPigj8g+31VL0PIp6B3/rb8Ig+BukTdOoH6PHWkM7zFMKarvVL8GTMYImoIuuU9znuNnSnT5Ud38AKw1FEMlSl6W83RQwfACl5qllggATGiY0MtmKLIbcEdDEAn5yK607qRd162loRH5NhC2Iw8qMYGkW7mlBIDQSlvPUVhPeTyaErMvHR7S9nnRBaU00supW9naMrFEehE6P2vSfd/frx0kvg13wOB8IWd4M7ct4326BL8TQyqFgM+vGYoToebDCCby/LydibV1gSDd602yPyP3LX4j1fqF2xwuwDVomIHuAuMR8hJsNohMv8dMBqdedg0htFjprndtLq/48IPLypLJCgAkNExpZbUgWRtYIfBMLm49X1KHkVARX2/F+2hC9P4mMdu29Bk1uyy2ntYY152phX/JskHTaT6JE0U2kD0XX9QXqj2gNBT4gPZQRtbKmmrsDI68bh75hM2siP7/Jh5YlavNfljcjm8FN6oQSwz1g+KyPOIf8i+ov9MHZGEETiCVkGsUv6lWMueHwlsKRXPT8rg23JWz7fI3H59WRn8PP+diXBt2j52PwGpHWyODxQFzotGUWTX4+XHuuZ1EvbuxyCDChYULjcpuaF2Q3BDoVSCww3INu84eHzk0+ONlZmw6Wc8F8FDtbi7RXnbdogj+yK4z0hFmPR7J0junB5Gs2FM2BfFgQvxeEY47ckkradi+d9JGO0p/rvF1gDBsnYIz9L1PsJ0klIC43+yKx5WgvxKArb6bG0jPVhanQwZSn0oUYtNx2L234K3pFzcEImlC5+15CtFyzJb0Mw2pzPOHEag4RjuHeDiMEd1l/E2LW6f42TsDglX10BjM7sa0eVp+BSF2tlo3H2Z0sw8tlWzOhYULjspubF2YnBNalI4Lt3zYh5u/UbTgoiSqw01Tmhm3rgn/QgXz4M286hehzpY369mG5OHbpKViwSv2Zv8Wfvo2lwMQSWibc41Veu+YuyvDXD9H/Wn4IPs+aUDThr9KRD4ioS6E8dfrkseOp53PUDEZjZ9PtCYX2m26D81FOMObXKFE0ti8HZ1PUu1SeBt5Tp4tDZyDlqlR4eGlLxBuawC1HixFz71hCbErQ5xk7P2+M/hzjb7gFcy0TyOt3tPov1Nnc3a0f/3/+AAL5mPw7H94ndyqdC71FUhRHk7c6d7pFvbixyyHAhIYJzcBv6qJ6fBHTLomCaw28UCyBOQRKGxEH5esYaGgqbPEpNDdZH54pVQhTO3E1/ZwA36gP1dkV9Cc4RiMgxebqbnehplJaJoSfLM1acUdtilFQXqO+Ftw4txeMYdu96HLwX9q4cAX7tExCR2K2P2Q8Zm0b6Fpp2IdorEk3HfUuDGs0vZYPIYWxxzOSa76kIzThs5AugCjwNJRYd60BRM9uhwoHebAPzcVQYTNJo0bqNfRORwNMW3R/HSLyqJG+6SIGDBhBa0ZL/wJG4lFfjHXKU9Gy25TZGBbTd0piacXlWhvqqHfo8AJqrTTdku+6NAJMaJjQDOQGr23DP7IjPWFYOtYbxwcJZpMqD6SsPLe8EVAR7cqi10Pwzf4wQmogLJH9+R2iREit1XAgWnFZx/IrjuxbJ2nZr8vCSHyeg/+GQSLfRiqi+gJK/oE23W5mWIUKdjZj1YnH13l8RsuEhL1eaF90CGmoT62m+vMYtrW6vl0a3Sf1kiiJ1b4X6ei/27pwwnXfWn1Q45giDF4QvhiDJC2lziZUanTZrk2JVpMtDqiD9suHUEuFQVOtKZIpe96OBnSpNQqwY9BffXEpmVZcZny7lzut1RQ9Txn5TvsBtV/YmtF09N+9dOHHrogAExomNAO2rxUqRK+fHgznUk3I/OVJcPqQRHkfMPl4YtdF4P51OKvSl5osWn0DLRN27dyov9n/mkpJFenUfAkjaTQiGv1K6HNmxl6WCPf4GLXvkerY17RMWOzlv+mUqEdLOS0TXlhXrAm2+9Iug/9k3g5DlGTkvdo7oyP2q7fCkKjhlGFi858T6OUdXSBGyi7kfFg2CMdYZsrFY7Tyf/QReyvSQVDqDAPmgPQIoEfGpasVj6q7g/AYN9DdsSqTdmkjbKFGqRNK3BlAuVtnQ0nDxf0QYELDhGbAdv3h84g5W9tmIMDCg/i3iYtdEcitRrz5xzbDy8YjmSSprW01dXoZzQtHpLvnd+CMo1Nhq4FtMM4LO42MiDfeSsuEw+FbbDC6ySEOL8BHPXcHTm0UoqxUho3r28HpY3SZsIqjaffUqMS0O1brAdx4opGWCTuOZRfWUUnyVtWv+mDEdGJZSycCK4/2opgVz3is+ObWVTRTdwjVPdfRYphR64vHFUifFPc5/LpNnWQRrG6v0rfvgABSjUvVadw02V3ZhUcVInd//ViGtQsx5Plbw1u9XJU3w3zqX3uR8fRCA+04SyFebx3fZGFovl4m4cfOgQATGiY0A7ZTf01UO18Yzh9VgMN+LvZD4EghThze3Y8DmnUnQTge26wO5mbTKXecxdnHp1G0JxvWNvevA61plw2n2Z0NO9m8Gv2aq9feS8uEupOB+lu2rcWorWFKE8yPmlQCI2tJsvGWTgRB1iS3qm2jm70V0Kls+Cs8sVf9mXZNWbk39YttqTjSWjNa48TUEfJCp8+fKo98558K5CVlx1nk99aX2E/UvWaCdlSd1t/X1eBG/n91V4iqDI3LWf0dIlgra3J8Gtztvlg+GJkWei1Fh8j7yl5biRt8Fk0v7zZIBFETNjdoxdus6BWj5CZ1JjRMaAZsq69Iotmh0tnD8+DXysVOCLR2wXJ2leh4oa0LGYJsG+e3ohmcKURklVHfTo9sRHpq+ZQvomFc8u9D5JWCfZi0/G/4Hmt8iwgfyKQSCjoDy1zb6JaSv4fham+GscdLoaHRp55W41XfDld5zcnsgXy6Zy2p8nbBmEbzV5V5thKMp/3SSWhZxH9VmQW1OItJFGWKaOtCYlHTcYNWDqWL8QiXLB4k7nMKfc4gWZKiA1hVio/BiMybv6z8H1p/MxiY+G/rPdooOO11UBGFvYDYhr7XWrRJ7l1L4XmGPQ4viPWfbd5j37ADX7kIAkxomNAM2FZOvojPnjh+iYrotRD8rOdiJwQiC5CouUtpMHxsESLKKCVqAYMmll1sOkVPGB3dBJ9Bzm1ZlZgiZB54PQS5HiuT1yIAjNodOqca2a3H+yIT5AR/ZOVMueggwVu7MONuERckAgF9YL2W5ezJhsO8pFxoAOORnN7q2nil4P2uTKaki2CZU7bh7Zg+Z/S9llJ+BFnZ84w+XI0mbo04bQLMbgSoZMSlJI48rhDfMKh7/hYBBnUkTFPxu45y1PEQS+Jgo6NhUadWG3Ts7eL2VUZ5xGI+TAqYtSyxt5783OUQYELDhGYgN/W7+6H6PloMF4zCOjin3LaKLho5hA6kiK4197ZM5K+WlMI6fA4bTHgBSxr29XJ5kolMCEkXoRGRf6lvB4P5OJKa1IYu7QooM/7q1x2tzv4L2HwKQG0+BQFq28BmxnjrM1bm10DjotHW6GRZd5IeMksWw/Ogh7vZF2d/S+O0S9N111e23g2m4nuNGSsfbWMcIaXoO55eCw7k9Tv9HUnN5yo0kJTtD5Ln/8I6B3RnrORhHy9f3aNPhqXp0nH404MrXzo0ELGR+igzN7MTAkxomNDYaWv1adgOBf55vckHH9ThHnB6yheZNfRpCG5kCQIJF5CsoNnQJjU0B2obG5bd2Tg3lJybrE5DFD75l4A0GBVJhJ9l9NW060KCzyKQgeY/igfW0QcHoTGauBqkJCSb3guH6qiwTivCoQIok3Zl2VUio8FXXE6lIh3Ihlvgna6JaGzUFjcCRsJxXVIaiuCLrvmrlZwbSZr2eJl0EYQvMFOrYqxuob1rv0jw+YfkDer7V5w0HfpPEg9QVknI9dJzzRwCTGiY0JjbH4551tJJ2VV9S4zsGIFcdxalCl/rDyL0p04lDSAfnqIf2/1ffXMnLLuXxumNW7Oq6FZ/Ua6i/s9htxEWHoTkkrIxA2TCkaVLCTVMQR0i/z6wHqGQY4vwmsb70X9jaW449DRPbMGjG70NjKIcJKTHFVQigmnNaJ35kWkB2mqpy9Ch0XQ7a+6G5tAt/thyT20FGjsDvm7f3bNj/PYHYaZjHP1PfGflUDprdLBnjWjcx6EIMKFhQuPQDceTDTgC+TX4Cj68kRbFIHfBeF98HSVWNf0XMvUSQvE+uRW5BeaG4zPzdUz/R3XECJ9G0RfR0om8j4si8Eof2vF63UnQTbFxzKlyKCTSyyinGi7Ke3OpusWOAvQ4tNf/oQtH9E/9/0QF+/SXDq81dyCwUPAZdUDFxG9pz9M9irBxAp0xez5HhGwVp2yqtOxRGts96Gql4mi9xkun+nKKSvHh3k85+wAVExomNH3YJtzEtRBo7qBNGTAT+ToGIU9sZw1sAFNNK8LPfBABk1uHGdUaSGDVxb48GHJViVhCSyf432pDE1giau3C5/NkmR3d0WfuJB8jZ+e3w0zokKxaaz86+VxFx3/Wfz49f4uvqUxK8nfwlqovMP236s+Ut7sXSbfeTRl+vbSR2+PTaxFk2ecqp/zzvZbKzSaS7RvaTGiY0PRtp3ArZ0PANs7Gzrbq/surVNGsPTR5Ex0qoEtNyMXxXBDOMsRG0ypCjvEJ/lCWjPai21fRVlPRW/ovzONbaKeRccyiGJDRAS7bHzT4cPpdR9Yawdh+IadWw7S5x79BJDZnNjl94AOU5mnyiXxvpnlQ4APyFc8hkjGhYULjkI3GkzgQgT3ZsCEd4QGvlg8iqFwGySMduHobTNWutlUf7wez3LHeYDMTA1C/ew3iC3cpoa2Z4E+hObAd7lJS8FlAvT3TBlNLhpgbLj3/UhHkEUcSknThSyDQXq/Po6lLqKmpmEzOIEEt6GFKXS65J/fLlB9p5+NyF9LO8jGhYUJj5y3GwzsWgZXJMItZnUZnK2FG+soefIblkBDbsTDYYDaFChqa54MR92VfHvIl7c6G2/NrIfDi3m/olLMtEwmYbK4VO14KJZAu7VRaGQyBR3vRa6GwnrHTWaENsHP2IXY8Sik/OdkiEv5LIc86mcy2FpcJDRMaW+8pHm/gELjQAOeXJFFYWKUK1qyfcKxCq17KltMIy1svitBT1gQ2M9pL6tfd2gWVWHaVVdOY7bT1NDjNc0H0zHakmbzRmxYfhQ3NBH/YdDOnMQuetQ93TUEechkWRTttvdu0i5b3lYgM6d6FCQ0TGvf+L8C1Vr8pw0Ssl0MFMPLgYgUC/9pLv4qCrWhGmLMPrFES5kSTbskehIYIoSZ/OEYjveirI3pTnqJ6vNZgw3xKVqyRu5hAYM8zlPCNifsDfquhCLZBqSukMZc1kZdrDCNMD7i0DhdAdoQmIyPj22+/nTNnzrRp0x5++OGZM2cuXLjQ19e3psZeAdf+8Ic/PProow5HnidkBGyPgN8J+qeRA0faJfzE55/yVsD9wk44aknKD/E02hsnPuKy5TTioEi83+vaDDyuxe0tra9IMuE3vjKZXtxp6UjO0b6hfYDc0TXw7J0BA1tJQqteL817g2dvN81CJOkgzF/GL0JeUmWXc7xFh0spI0KTmpo6adIkobsMGjRo6NCh3VfCsGHD5s6d29Zm+9BMTGgcvut4QnshEFOEAxGxPw4RUkI6OCicvZbn8HG/iUWmJ3FRET27HWY0t/gjkXiXElk7tmci2vUOkbLkaDFMdzWhfh/bTNHnxWNYU/88mr48LO24N1d26bGkIlp+faIUCGuge3ijlDhaPp5VPc5skKay0iS0MvO/2x8i/z/1OFlrFTQrG281yM0pztPZ9/reGT3O4vYP5EJoSktLr7zyyqFDh37yySdxcXFVVVUKhQKRHlpbS0pKgoODp02bJgjCq6++avNXxoTG5pDygAOFgFKFVE2vh+jNPiIL4KcTOUB5bSLOwSr5oQ00I5i2dQenHyhwrJi3sA7uS14p2gOmLiWMVyaupsoW2pCBcDWjvfB3x2rk5daVyAKoxH5NpKJ6ZIBamYxLiRGxrnEfK97HaXqwtO2P8fTqHulNR143ttPPCaBukzfRggOwm+5n0eQxWHwUUZJLG2HbPs4HO8cJyvlw8vtjj3I2lYLQNF/qsQE/sAUCciE0q1atEgQhKsqc7eKCBQsuv/zy1tZWWyxcPwYTGj0WXHN+BEob8QN3vC+9sBPuOaO9aP3JgVnVD8egt/gxHi5CXilIFzU33PlOvo4Wg8HcsxYnPpMC4DKW1v1VauuijHLKrICSRlcUKsT2lbhVr003cSCl69KXSmkjwBRHuzlVDrIVca4vve3SproF+qGntkKqkGwQmrHedLh/uqintmLDiMvubCgdJdnHxA3kUi+MQFLPnorG9qW134yvp/H5vhoBuRCat99++7rrrjP/Uo4fPy4IQkJCgvlmlj5lQmMpYtxe5ggoVRR/AcYfQWcsdthWESVfxCdqf16/viInSuGPk1Guh+piI4xYjcPE6VvItdbSSWG54CgH8nsMCtzWBW6x5TQwH+EhTWfd1gVmebqiXyvcnwfGMGsPLUuEf9ON3vT9sX4N2M/O70cgz5TYU31VKshfm7UGHpUtOGkqaTCQS6GCP9fRYoObdrrIr0HyhF1ZUA5ZXIqiEGmwp1KXDw0NJ7zsCR8b3ZcLoVmyZMmQIUNqa2vNrCswMFAQhJISkU+qmdZ9fsSEps9QcUMXR6CkgaYFQcn/90BoFG7xh3LFuvKNOoWTpO/PCVKTFEkDJ708UohTpzsCcN53ky8ITa6hD4OKgOrx0v6ur7AO7k5vhcGeJulif0frT3+FCowq0fAf4y4lCE2UteebJQ0gNDVGKvi719j9zLStiz6KhPPak+rjs1GewFlpkSF98WHy+l2PkNZkgdB0ihJq9NiUH1iPgFwITXx8vCAI999/f05OjvFqFApFWFjYNddcM27cOOOn/bzDhKafAHJ310CgU4H4wm+GaR1zlCroG8Z4U2r3CYtFy1xwwES+ocBME17lFg0rw8bZVVCc+KdqXZwqW+ADNSnAQL8VU4gDo0ZRPBsZLsQikeraQD6K66WdpmyjQGtNXhQqujNAmpL9ZBmUW5eapBPZ9vrzaJgB5VZrR025CIbqn2rJJBdiyPO3PXaoPAVCo+zssQE/sAUCciE0RPTll19qfJrGjRs3ffr0N998c/78+a+++uqUKVOuuuoqQRCuueaa5ORkW6zaYAwmNAZw8IW7IhCWCx1Dq+F5wZeH6Y291iCyPAlGPJLy6SFaeFByz+kvP4qkeeEGq1iRRCM9aUmc9mbqJVjeGMezMejjbBcqwidf4rte3w7e1p9EpJsycMB0pFALx9lKmOn8R5TV2x44lTeDM4mPR4mQ1+K2VQYHar1MXRJHHldo26iUtPd5JCLY+bg252J5GgiN85mQ9bJouT2WEaEhorS0tPfee+/6668fPHiw2GF74sSJHh4e1dXd/NmmKDKhsSmcPJizIvBjPIx2JSX6PH40W1FKGmCyuk5kjxyl9v050e9jFyuEsWuXJ7YYOGwTwSVqylZwmoc24Hs8yhOxfSUhauwqkmMG90iG+XNRt5KmQ0Hv7MdhpUUHNcaieqXgMOu+dVCZjPSkfx8ysLk2bt//OzFFIGeS0twBFdQ5c0YQhj0uxtPK/9He6mgEfYn9hAJGULo3bl5KRipsLnZGQF6ERrdYhUJRVlaWl5dXX9/9n4vumSUVlUp1+vTpVLPlqquumjx5siWjcltGwAUR8Eg24QO8J9v6SCeRBXC2emY7fXUYLkKjvWhThgviNi3IgLdpVrjgAH0aBVPisFyrLEydAacuJc0/ANugueGgHfeto0c26vlNf1ZQ3QKb9NAcE0da/Rm2p77JF0G+JYyztBGExgLr4EtJesrSXgdCU3eOdj1J0fOoPJVOryWPYT0JwPdthYBMCY2tlpeenj5o0CCdsqenym233WarGXkcRsBJEcgoB+fIEalBu5SIyPdNrPULKm8m3xP42i1PQmQRlywrk2F71C7y3C6uxwfSMY45Noe0ugXmxull0g98TxPFX6AlR8FZg85Qi3OaiLQr4H+35bTBEpfGwSPdglKWQssHa9trwug1FFLUHDAbzV/AcAtG46ZWIeDihIaImpqaas2WJ554YsmSJVahx50YAZdC4ItoWLPuOEvn6/BVe2UPkksbe53Yb81tXX39jtpPBktHbu5AvJ8ZwRRXDP3E3lzoKiRWNZaOOSDt2xUgr2O8oVcb7QW7n4GKx+j45YfmYOErkiivBmnqv47BsZdlx6PlqSAumgO35jLUG0tw2Var/VN0OH5d7jaj6xOaXt/o1KlTmdD0ihI3cAcElCpEr7lrDZTtN3ojVFpFs4PWHVmgzRUw2gsnX3bK8minxdS10aeH4Os03APWGCuSnI+WEdHHkThe1Jj0tnUhSu8YbwQ0cpNy+Lx2B47woH/sgI7KslKRDhKjUqJXYwnqzZYOYdmE3NoYASY0xITGeFvwHTdHoKHdoZ/kwEywAY9kHHill9GHEXCW6X8cfQe/RIVKn3HCMVMfK4Yd7pRtNDu0v6kV8mtggavzW9bI//0xt8sC1txpcHpowXuszACJ0ThmNxSiznGBLYDPNk3lQmh+/vnn6X0rtlm3aBQmNCIwuMoIOBqBpg6ccYRkG8z770M008jr26CF21+sTIYW7T9HkBrzuzjkB/g82npQgs6YMBk5WwmWIzYPsn4Cl+9ZlQkSo1DHGtLGBa5z+UXLbYFyITSbN2++8cYbezLaFd+3OYJMaGwOKQ/ICPQdgejzOKaRRGXNqcbxTRNbHfSAY3oZzoPEoYfzasBpDuT30KG328FnYdosKZkV8DkXJzeQNOBLPQLVZ0FoNLGAa7LVdTtHA9TPzTUtAnIhNETU0dHx9NNPC4IQHd2PHxqWv1kmNJZjxj0YAZshEJZrwjO8vBmEptxRFjw2W4yjBvpvjImgQYuP0tthVkpQVA9ljCS43KIYE9ERrZzA5buJSYxGW9PV5vKLltsCZURoiCguLo4Jjdy2CMvDCNgVgYJaqAEkTt2BmUhw3c8QbXYVe2AHnxtOPxlmpSaizacQ9cfqsigGmEefh/lUXRtCG4/x1qcWt3pYd+lYmwetTIc6tabYnsZd1i+LdcqL0DQ1NQ0aNIg1NLLYGiwEI+AoBN4LhwHHhe40y4kliH+/6ZSjpnfCeX44Rq+FSOX+LBqOaVaXLiXyeI/3hapmuAcC9Sa4jYuT1aDpO9adA6FpU0dbErtw61twze4IyIvQEFF7e7tSqfZ8s/vatRPwkZOjkOZ5GAHTCDR10Jx9iH3y7HZ6dDMUNssSTbfkuxoEztXCIniPyJI6rhgKleR+p+Bu7qTMCijMWD1m2WZrKAKhCXoE+Zu23kPLh1jWnVvbAgHZERpbLMqyMZjQWIYXt2YE7INAZgW0MsFnLYk3bx9JnGLUXVngNLP20A/HYDoz2otWWZQd2ikW6URCqhSU5kkpP2n/Mtc7kewuIyoTGo5D4zKbmRfCCPQXgdo2OlmmP/zq73B27l9QSz/G03vhSD6QWWHnydxgeIUKkXgyK6Q5591g6S6yRCY0TGhcZCvzMhiB/iDQ1EGfREHPMUptQfJcEKLgc3EfBCLOIdHHcA9sgFv8ye8EH7o538tnQsOExvl2LUvMCNgWAYWKXtxJU7chTrGKcOb1YQRNXM2HX6ZhTrmI9OljvWm8H0IVn3P+tKPR52GB5J9KzR1w8tqXh7e/PMn08vmubBFgQsOERrabkwVjBByEQMQ5+FVVt+qnUxG+2f85or/DNQ0CB/Px7f9vDLI+xRUjHM54P+fLUyF+myqCKbpnivgexRbBRKm6xeAmX8gcASY0TGhkvkVZPEbABgi0K+AT9EsCfoWfNwpJv+SoCYfnnVn4znERI9ChQFjn9SfF95CY8/kdBnckF50KJCH/NZF8T1C+/A7yqltw0nSxUSI1srS6T75x6eKd85oJDRMa59y5LDUj0GcEsqvooQ109xo4h08PhpGE93GDzouP0vsRBneIaHc2YrHYr5Q2Iopd2iWH5gHt53KSShCoRpIMoaAWhECs3xLPUlBLf9tEkwIA/j92APxfZeaTX6kmNKVGhOaetRRxTrwUedUrWyimEI76nGxL92KY0DCh0W0GrjACLohAu4IeWE8fRFCHQru6+As0zscg7dGBfLptlTRX9msh/Ur3aAbKxnZaeBDx6yYFwBLl3rU44LBVaerQr9RWY+rGiSxANGFJaWgHoSk00nsRgas9uhnar7YubafjpTii2pUlGWMgL1VEj2yEFbC4JJbgZK1SlkdO7Qr6+ggM2O9Yjbz0E1dTaI5YdvetM6FhQuO+u59X7vII5FbTk1vxuR3uQQ9v1EeiW55EM4L1q+9SQnMzPVh7IFLbBuuZCf528d9WEb26BwbIOdUQoLWLvFJgrnGyTC+PdbXQHHyYh3vQCA96ZY92fOuG6qmXJuWTxAo4PI9u9ZeqbTQjRBWAKbZ2sxnNTf9UE5m9e5rRMfcP5INZbj6FVagImo87A+ASL8/ycSQ2c9olSNepgNhjvOnweXkK61CpmNAwoXHohuPJGAGHIZBdRTf7gtC8uBOhbwPScBmQhvkTLiA3tbjUttG7+6E1+asf/veJLfaK7JJ0EfohSd7NzyyqInsAABi/SURBVKLp9VCxOBbXdasrqKVT5fRRJM6GsqssHqfXDu+F09OBVNadSTqrCrYmXoYWtbpB/E6AWklKehm0C3KLRLwnm+4IADO4yQcv6NdEUshNRDWOBbXYnxo2rAN2eZLsOKJONkdWmNAwoXHkfuO5GAHHIfDKHoSWWZNO04K0k0acww/x6lbYxzxgKpRraSO4juRrYVuJfU194+ONCJZFk1a3QscTVWDQ6aNIE2TCoIVVFw3tSCM11pv+uRtarpGe9OXhHr/92zLp8S3SaQ7m46zE3qVDgYOtLw/DISuygJR9YCcdCvjtp1ykhnZ7S2f9+Luy6DEjW/X8GmjmmjutH9Y1ejKhYULjGjuZV8EIGCDQocC39lQ5FddDH3C0WPv0vnW0Ows/Z78/ZtDeYRerUqExkpSYQhzNWF325dF966S9T1cAATtZjCZfhNHJmvRelEAVzdB5iE1rOxX0XBBMQOxayprwiu8MoH8fgrn3Lf7IESE5+bKrAPYbPCQbB4uSklWFc0adoZLkqftcMqFhQuM+u51X6kYINKotVTUe2qtSoVH4NZGOFePDf+9anCg1dwwMGqfKQbAKDIPRvbMfGQysLjuzsCJJKazDr/b6gVY2bMqA9ujHeAStCc2hZ7bje2xvqV7eDUMlncaiohmKInuzKAn+drosbcT+STHMQroophfPeTsJI7dhmdAwoZHbnmR5GAHbIPDwRlqbrh0qsgD/4o/3o+Er6eNIahogNqOR5oMIHHjFFMKi82IjfRENwSQUxyIIcqrhDl1Ub9BpTTpMR+VQYgqhlJrgj9wC38banc3k10A1JXHDTrgAUtviEocyPxyDuc++PKhkKlvop3iowTQ2wnJ43QMoAxMaJjQDuP14akbAjgjsyoJh7KFuy5ILDfAtmrPPjjP2cehOBTQWN/tCgzLcg57dTmcr+9i1x2Zz9mF1xd2c5lABxt+d3WN7F35wqAAxhySlpRNQyzCsn0TOvlwqVQhReKu/dv88tpmSDBU2fRnEJdswoWFC45IbmxfFCACB1Wk473hwPSwqRnnS22HUONBHMLoX09oFA5RL3e5CuvvWVRrbwdVGeWKlD67HT/ZVqdaN5PS90svw0iVHitlqK5PaNqdfnW4BHQrkBi9p0N3gCjGhYULD/xkwAq6MQHULTDeCzjh3vqE+vqGsKqw0NIeqZBkRro+r6GczpQpnbUvj9MMoVPRWGM3un2O8fjiuyRUBJjRMaOS6N1kuRoARGCAEqltgdioJoDdAslgzbdolmCX9ay88twMz4Vd1z1oT2ZqsGZr7yBgBJjRMaGS8PVk0RoARcCwCTR0I3jPaC8HlhnvgAOt0hWMlsNFshXXw2X50M5awNM7RebO7lDZaBg9jCQJMaJjQWLJfuC0jwAi4LgJKFWLxTdmG+D1EiED4dQxCJxvnJ3ddDPq1sqoW0MHxfogK89hm2nG2X6NxZ0sRYELDhMbSPcPtGQFGwDURiCmCX5jE/uatMPrQKBW5a66/f6uqakGUoxd3wSH/TCWSbIz3o2UySy3evyXKvTcTGiY0ct+jLB8jYCcEqlto3UlachTfHklyJTvNKPNhf0mAI5ikhJsKQyxpMyCXpytoZTKOk0JzbJxg/GQZrUjCyGG5ppNumlzvF9H0wk6DLBDJF+F3xvotk3DZ4yYTGiY09thXPCYjIHcEIgtwmPLkVlp4EBFcbvKhvblyl9ne8v2SYCJOjwwJjVKFsL+jvRAOeMEBmhQAWxldDJ7+oKRU0WfRGPm1EJp/ADmnntwqjdHX0/h3raED+dKH04L00R2lz/ja1ggwoWFCY+s9xeMxArJHoLAODGbdSb2gQWcQvMQe6an1c9i0plRBq2TbVE2x6iOnSkOX7zfld+S0Nh2hcrO6c4m3dYGHTd3WpwyU5l+C3wkkD8+t1rZq6YSr1HNBfcoNPt6Xkkqkw88OJY9k6U3Xvm7toormgVkiExomNAOz83hWRmAAEfj+GLIVSsqcffTVYck9OV4qVOR9nCauhhfSSHW0QEmYf6uFVqoAy5RtWs+mmm6j4MI6q4e0S8f719H2TIORG9oRGTnhgsFNKy7uWiMNr1zTipwJJ0p7H+z5HfRLgkGz5g4kfNDFqjZ45ooXBbXQbI1Qx7++MwCqqT7kOLclEExomNDYcj/xWIyAUyDwZhiMJCQlII1mGuXBlrSRw+XHkQjtH5oDDU16Gb2xF0oFW/0mbu5ArqvRXtBgDffAgYvc3LbbuiCYTomieyPPBdHGDN2VNZUGdUJTY/Y2dRuC2fRaos9DyXew+9SpsR2vZso2A6uaXgdx3gaFdWBvCw5QZgV2ZvAZun0VfScKb+iApTGhYULjgG3GUzAC8kLg40iYSkjKkqMmLEgkbQb8Mr0MbEMc8k6pgi2qbXVL1S2ULNfAeiqCMiau2OBVKFXwMOqnFZRChZQRyYZ5kRQq2OhEnDOYrqcLTWrxv21CMs7xfjgFu9jYU1tXu/9euPQ/n7RL0CA60iaaCQ0TGlf774rXwwj0isDh8/BPFie4LmlAtr8w2dsFe6aYOCzbm4v03e5TPooEYxAHrwvMBIHof6Ku+QcAr0J0UrIxAxujuc9puitbcBwWkEZHi21g0+NE7/SvfliypDwd2F+1mWRA85dMaJjQmN8h/JQRcE0EPomCStw/lWKK8Pm5M4DeC3f0kb8VyP6SQO/sl/Y7fB5aBPcp1S0gcNOCaE82HT4Pj6cx3jiD638pb4amZ3owRos+T19EY2Rj36X+T+RiI6gIx22pl6TLenGXQ5OkMqFhQiPdgnzNCLgDAirCL+lntsO69u+BtCHDOX5PR5yj21ZRU4fBK/osmt40ih9j0MLlLmrbaPFRemgDmOjsUGSeslWpbqX/xmhHfjPMxEfaVhO52DjP76Af4w3WVNkCe2rJEZ5BC1tfMKFhQmPrPcXjMQKMgN0Q6FKCfs3aQxrn6i4lnM/HeNPJMrtNyQMzAn1AILYI+zD4rPaHQWkj3N3/ubsPPW3XhAkNExrb7SYeiRFgBOyPQFkTrIDHeMMF6fZVUNj00WTV/qLxDG6NwK4sBKucFEBPbIHp+ushVNvmUECY0DChceiG48kYAUbAJgicKkfuw8PnqbXLJuO5+CAqgmOUVwrspfJqXHyxA7i85g7Q651Z+rCHjhSGCQ0TGkfuN56LEWAEGAFHI1DVQv/YAb+210NoRjB8ib+JdQIDcEfD5PzzMaFhQuP8u5hXwAgwAoxAzwj8czfcvGtatS1OlSNJU0Bazx34iXMiwISGCY1z7lyWmhFgBBiBPiBwphL2HGVNBk13nIV7lCjcjMFTvnBSBJjQMKFx0q3LYjMCjAAj0DsCu7ORi1tSShuRP6HaMA2npA1fOh0CTGiY0DjdpmWBGQFGgBHoKwJRBUjNLQ7+S0QZ5bCksW2u8r4KxO3shgATGiY0dttcPDAjwAgwAgONQHMn3eJvkJ1bRYi2/MbegZaM57c1AkxomNDYek/xeIwAI8AIyAmB0ByE7Vkah4DCRwrp9VCEhy6ul5OILIstEGBCw4TGFvuIx2AEGAFGQMYIJFygF3chEv9tq+j9CDdKgi3jd2J70ZjQMKGx/a7iERkBRsB9EOhS0uo0mrKN7loD72hO5eg+r15uK2VCw4RGbnuS5WEEGAGnQaBLSS/vRobq9SfpUAH9mkg3+dCyRKeRnwV1JQSY0DChcaX9zGthBBgBhyKwNh1sRhezjohOlCLuS2aFQ8XgyRgB7L0TJwRBsCEUthzLhmKZGWrq1KlLliwx04AfMQKMACPACJhEYEYw+Z2QPpkdSj/FS2/yNSNgbwSY0LCGxt57jMdnBBgBl0Vg8iYKzZGu7qvD9O9D0pt8zQjYGwEmNExo7L3HeHxGgBFwWQTeCqNFMQarUxE9uZUzJRlgwheOQYAJDRMax+w0noURYARcEIH4CwjxcqRQuzQVwS749lVU3Z0J0gXXzEuSKwJMaJjQyHVvslyMgHsgoFBRZAE8g3yOU1aV86153UlYAb+wkz6Josc2g80kXXS+VVS20IYM+jmBgs5QU4fzyc8Ss1Ew9gAbBfN/CYwAIzBQCFxqoqcDaYI/vRWGyG8jPem/Mc6XBfp8HXml0NcxtDGDatsGCkvr592dTeN8cFL2zn56cD3dsZqSnZCTWb9+V+nJGhomNK6yl3kdjIATIjAjmF4LocZ2reiZFYjKvybdCVfitCKfLMOp2a4s7QKUKuhpbltFlZyL29neKRMaJjTOtmdZXkbAVRBIu4Rg/BJzk6AzdM9aV1mhM6zj/Qj6MMJAUBXR3wNxAsjFuRBgQsOExrl2LEvLCLgOAkFn6Kmt0uUU1dNwD6rv1tlIH/O1rRGYuo0CM6WDLjlKCw9Kb/K1zBFgQsOERuZblMVjBFwWgYP5yH+kVBksMOUibGy7lAY3+cJ+CLy8mzxTpMPPPyB1R5e24Gv5IcCEhgmN/HYlS8QIuAcCTR30Vz+41eiKUkWvh9B74bobXLE7ApsycMYnzt6QXYWjQGf01bI7WPKegAkNExp571CWjhFwaQRCsmGR+k0sxV+g8Dw4Ot29hi41ufSaZba4LiW9tIse2oCDp6SLyBx+2yr6zxGZScni9AEBJjRMaPqwTbgJI8AI2A2BpBJ6ZQ/d4g89wWfRVNFst5l44B4Q6FTA7fyRjTTelzQmNYbHgD1049syQ4AJDRMamW1JFocRYAQYAUaAEbAcASY0TGgs3zXcgxFgBBgBRoARkBkCTGiY0MhsS7I4jAAjwAgwAoyA5QgwoWFCY/mu4R6MACPACDACjIDMEGBCw4RGZluSxWEEGAFGgBFgBCxHgAkNExrLdw33YAQYAUaAEWAEZIYAExomNDLbkiwOI8AIMAKMACNgOQJMaJjQWL5ruAcjwAgwAowAIyAzBJjQMKGR2ZZkcRgBRoARYAQYAcsRYELDhMbyXcM9GAFGgBFgBBgBmSHAhIYJjcy2JIvDCDACjAAjwAhYjgATGiY0lu8a7sEIMAKMACPACMgMASY0TGhktiVZHEaAEWAEGAFGwHIEmNAwobF813APRoARYAQYAUZAZggwoWFCI7MtyeIwAowAI8AIMAKWI8CEhgmN5buGezACjAAjwAgwAjJDgAkNCM1dd931Dhf3QOCVV1659tprx3NxVwT++Mc/3njjje66endf91/+8hf+196F/6WfMWOGIAg2ZFm2HMuGYpkZateuXS78gnlpEgSmTZsmCMLNN9/s7v+0u+v6L7vsshtuuMFdV+/u677yyitHjBgh+TeBL10JgcWLF5v53Fv6yPkIjaUr5PZOjUBaWpogCEql0qlXwcJbjcD1118fFhZmdXfu6NQIvP3223PnznXqJbDwjkSACY0j0ea5LEaACY3FkLlWByY0rvU+LVsNExrL8HL71kxo3H4LyBsAJjTyfj92l44Jjd0hlvEETGhk/HLkKBoTGjm+FZZJhwATGh0U7llhQuOe712zaiY07vz2rVg7ExorQOMujkOACY3jsJblTExoZPlaHCQUExoHAe0q0zChcZU36aLrYELjoi+2r8tiQtNXpFyxHRMaV3yrdlwTExo7gstD9x8BJjT9x9CpR2BC49Svr5/CM6HpJ4Du1p0Jjbu9cSdbLxMaJ3ththaXCY2tEXWm8ZjQONPbkoGsTGhk8BJYhJ4RYELTMzZu8YQJjVu85h4WyYSmB2D4tmkEmNCYxoXvygQBJjQyeREDJQYTmoFCXg7zMqGRw1twIhmY0DjRy3JHUbOysoYNG6ZSqdxx8bxmotGjRx86dIiRcE8EFi5c+NFHH7nn2nnVViDAhMYK0LiLQxGora116Hw8mZwQqK2tZTorpxfiUFlaW1vb2tocOiVP5swIMKFx5rfHsjMCjAAjwAgwAoyAGgEmNLwRGAFGgBFgBBgBRsDpEWBC4/SvkBfACDACjAAjwAgwAkxoeA8wAowAI8AIMAKMgNMjwITG6V8hL4ARYAQYAUaAEWAEmNDwHmAEGAFGgBFgBBgBp0eACY3Tv0JeACPACDACjAAjwAgwoeE9wAgwAowAI8AIMAJOjwATGqd/hbwARoARYAQYAUaAEWBCw3uAEWAEGAFGgBFgBJweASY0Tv8KeQGMACPACDACjAAjwISG9wAjwAgwAowAI8AIOD0CTGic/hXyAhgBRoARYAQYAUaACQ3vAUaAEWAEGAFGgBFwegSY0Dj9K+QFMAKMACPACDACjAATGt4DskNg8+bNP//8s7FYiYmJS5cunTNnzpo1ay5dumTcgO84NQL79+//8ssvX3/99SVLlhw/ftx4LbwBjDFxjTudnZ3r16//4IMP3njjje+//76oqMh4Xfz2jTHhOxIEmNBIAOHLAUago6Nj7Nix06dPl8jx1VdfCYIwZMiQ6667ThCEq6++Oi0tTdKGL50UAaVS+eyzzwqCcMUVV4wZM+ayyy4bNGjQRx99JF4ObwAxGq5Ur66unjBhgiAI11xzzahRowYPHjxs2LAdO3aI18hvX4wG13tCgAlNT8jwfUcj0NTUdOzYsWeeeUYQBAmhSU9PFwThvvvuq6mpIaLIyMihQ4feeeedjhaR57MPAj4+PoIgzJ49u62tjYgqKioef/xxQRC2b9+umZA3gH2Al8Wo7733niAI/v7+GmkyMzP/8Ic/XHnllY2Njfz2ZfGGnEcIJjTO865cWtL29vZBgwYJ3UVCaObNmycIQk5Ojg6DTz75RBCEjIwM3R2uOC8C999//+9///umpibdEsrLywcNGqTbBrwBdMi4XuXPf/7zxIkTxetatGiRIAjx8fGam/z2xeBw3QwCTGjMgMOPHIeAUqmM7i7GGprrr79+3LhxYmni4uIEQfj222/FN7nupAj8/ve/f+SRR8TCK5XK3/3ud7fffrvmJm8AMTiuVG9vb588efLixYvFi1q+fLkgCOHh4fz2xbBwvVcEmND0ChE3cDQCEkKjUCgGDx6s+7GukaaqqkoQhDlz5jhaOJ7PDghUV1frzhc0wx87dkwQhFmzZhERbwA7QC7fIYuLi2+99dYrr7yyvr6e375835MsJWNCI8vX4t5CSQhNWVmZIAhvvvmmGBWFQjFo0KBp06aJb3LdNRA4derUDTfccPnll6enpxMRbwDXeK29rmLOnDnDhw8fNGjQ2LFjdW5u/PZ7xY0b6BBgQqODgityQUBCaPLy8gRBmD9/vkS+oUOHPvzww5KbfOnUCLS2ti5duvQ3v/nNFVdcofNz4Q3g1O+078L/+OOP06dPHzly5JAhQ+bNm6dQKIiI337fAeSWTGh4D8gOAQmhqa+vFwTh1VdfFQva2toqCMLMmTPFN7nu1AgcPnx45MiRgiBMnTo1Ly9PtxbeADoo3KGiUCgWLFggCIK3tzcR8dt3h5duqzUyobEVkjyOzRCQEBoiGjZs2JQpU8QTlJSUCIKwcOFC8U2uOy8C33333aBBg8aNGxcbG2u8Ct4Axpi4xp2mpqaioqKWlhbxciorK8X/CPDbF4PDdTMIMKExAw4/GhgExP+WaSSYOHHi1VdfrVFBa+4EBwcLguDr6zswIvKsNkVg8+bNGn2bxDRYNwlvAB0ULlaJjo4WBMHHx0e8rrq6OkEQXnzxRc1NfvticLhuBgEmNGbA4UcDg4AxoVm5cqUgCPv27dMJNG3atGHDhmni7OlucsUZEVCpVOPGjbvuuus0UfVMLoE3gElYXOBmXV3d4MGDJ0+eLF7LunXrBEHQ5T/hty8Gh+tmEGBCYwYcfjQwCBgTmurq6quvvvraa6/dvn37iRMn3n//fUEQ3n333YGRj2e1KQL5+fmCIAwfPnymUfnmm280U/EGsCnk8hpMYzHz/PPPh4aGRkVFLVq06IorrhgxYkRDQwO/fXm9KtlLw4RG9q/I/QQ0JjRElJiYOHbsWE0k4csuu2zWrFlmftC7H2ZOvOLIyMjuANHS/xf/cOcN4MTv2Kzozc3N8+bNGzJkiO71P/XUU/n5+eJO/PbFaHC9JwSY0PSEDN+XIwIXLlyIj4+XmBDKUVCWyT4I8AawD64DP2pdXd3x48ePHTtWVVXVkzT89ntChu9rEGBCwzuBEWAEGAFGgBFgBJweASY0Tv8KeQGMACPACDACjAAjwISG9wAjwAgwAowAI8AIOD0CTGic/hXyAhgBRoARYAQYAUaACQ3vAUaAEWAEGAFGgBFwegSY0Dj9K+QFMAKMACPACDACjAATGt4DjAAjwAgwAowAI+D0CDChcfpXyAtgBBgBRoARYAQYASY0vAcYAUaAEWAEGAFGwOkRYELj9K+QF8AIMAKMACPACDACTGh4DzACjAAjwAgwAoyA0yPAhMbpXyEvgBFgBBgBRoARYASY0PAeYAQYAUaAEWAEGAGnR4AJjdO/Ql4AI8AIMAKMACPACDCh4T3ACDACjAAjwAgwAk6PABMap3+FvABGgBFgBBgBRoAR+P/CfHPyB4fNzwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"attachment:image.png\" width=\"500\"/>\n",
    "</div>\n",
    "\n",
    "**Figure 2**\n",
    "<!-- ![image.png](attachment:image.png) -->"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"attachment:image.png\" width=\"500\"/>\n",
    "</div>\n",
    "\n",
    "**Figure 3**\n",
    "<!-- ![image.png](attachment:image.png) -->"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"attachment:image.png\" width=\"500\"/>\n",
    "</div>\n",
    "\n",
    "**Figure 4**\n",
    "<!-- ![image.png](attachment:image.png) -->"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"attachment:image.png\" width=\"500\"/>\n",
    "</div>\n",
    "\n",
    "**Figure 5**\n",
    "\n",
    "<!-- ![image.png](attachment:image.png) -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does the KNN algorithm work?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now seen that the KNN algorithm can be used for both _classification_ and _regression_ problems.\n",
    "The KNN algorithm uses __feature similarity__ to predict values of new data.\n",
    "This means the new data is assigned a value based on how closely it resembles the closest $K$ data in the training set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The step-by-step instructions for KNN regression is as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0. Pre-process the data. \n",
    "\n",
    "The data needs to be *standardized* and split into testing and training sets.\n",
    "\n",
    "When we say standardized, we mean put onto the same scale.\n",
    "Otherwise variables with large magnitude (like `Loan` above) will contribute more to distance than variables with small magnitude (like `Age` above)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Calculate the distance between the new point and each training point.\n",
    "\n",
    "There are various methods for calculating the distance between the new data and each training data.\n",
    "The most common methods are _Euclidean_, _Manhattan_, and _Hamming_.\n",
    "We will use the Euclidean Distance calculated as the square root of the sum of the squared differences between a new point and an existing point. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. The closest $K$ data points are selected based on the calculated distance. \n",
    "\n",
    "In general, the KNN model overfits on the training data for a very low value of $K$ and performs poorly on both training and validation sets for a very high value of $K$.\n",
    "The _elbow curve_ and _grid search techniques_ are methods to help you find the __best__ values for $K$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Average the response of the closest $K$ data points to get the final prediction for the new data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: House prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start to perform KNN regression, we need data. \n",
    "The `boston` dataset was collected in the 1970s and has been used to examine the relationship between clean air and house prices.\n",
    "It contains 13 variables:\n",
    "\n",
    "\n",
    "| Variable | Type | Description |\n",
    "|:----|:-----|:----------|\n",
    "|crim | Ratio | per capita crime rate by town | \n",
    "|zn | Ratio | proportion of residential land zoned for lots over 25,000 sq. ft. | \n",
    "|indus | Ratio | proportion of non-retain business acres per town | \n",
    "|chas | Nominal (binary) | Charles River dummy variable (=1 if tract bounds river, =0 otherwise) | \n",
    "|nox | Ratio | nitrogen oxides concentration (parts per 10 million) | \n",
    "|rm | Ratio | average number of rooms per dwelling | \n",
    "|age | Ratio | proportion of owner-occupied units built prior to 1940 | \n",
    "|dis | Ratio | weighted mean of distances to five Boston employment centers | \n",
    "|rad | Ordinal | index of accessibility to radial highways | \n",
    "|tax | Ratio | full-value property tax rate per \\$10,000 | \n",
    "|ptratio | Ratio | pupil-teacher ratio by town | \n",
    "|lstat | Ratio | percent lower status of population (defined as non-high school graduate, manual labor) | \n",
    "|medv | Ratio | median value of owner-occupied homes in $1000s | \n",
    "\n",
    "<div style=\"text-align:center;font-size: smaller\">\n",
    "    <b>Source:</b> This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
    "</div>\n",
    "    \n",
    "### Load the data\n",
    "\n",
    "First import `pandas` so we can work with dataframes:\n",
    "\n",
    "- `import pandas as pd`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"A]5Vf/x,=8dk=KKk3OrJ\">pd</variable></variables><block type=\"importAs_Python\" id=\"/k@:}OLWtAh)hpx[.2^]\" x=\"150\" y=\"308\"><field name=\"libraryName\">pandas</field><field name=\"libraryAlias\" id=\"A]5Vf/x,=8dk=KKk3OrJ\">pd</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataframe:\n",
    "\n",
    "- Create variable `dataframe`\n",
    "- Set it to `with pd do read_csv using \"datasets/boston.csv\"`\n",
    "- `dataframe` (to display)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        crim    zn  indus  chas    nox  ...  rad  tax  ptratio  lstat  medv\n",
       "0    0.00632  18.0   2.31     0  0.538  ...    1  296     15.3   4.98  24.0\n",
       "1    0.02731   0.0   7.07     0  0.469  ...    2  242     17.8   9.14  21.6\n",
       "2    0.02729   0.0   7.07     0  0.469  ...    2  242     17.8   4.03  34.7\n",
       "3    0.03237   0.0   2.18     0  0.458  ...    3  222     18.7   2.94  33.4\n",
       "4    0.06905   0.0   2.18     0  0.458  ...    3  222     18.7   5.33  36.2\n",
       "..       ...   ...    ...   ...    ...  ...  ...  ...      ...    ...   ...\n",
       "501  0.06263   0.0  11.93     0  0.573  ...    1  273     21.0   9.67  22.4\n",
       "502  0.04527   0.0  11.93     0  0.573  ...    1  273     21.0   9.08  20.6\n",
       "503  0.06076   0.0  11.93     0  0.573  ...    1  273     21.0   5.64  23.9\n",
       "504  0.10959   0.0  11.93     0  0.573  ...    1  273     21.0   6.48  22.0\n",
       "505  0.04741   0.0  11.93     0  0.573  ...    1  273     21.0   7.88  11.9\n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.read_csv('datasets/boston.csv')\n",
    "\n",
    "dataframe\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</variable><variable id=\"A]5Vf/x,=8dk=KKk3OrJ\">pd</variable></variables><block type=\"variables_set\" id=\"FJGldw(qFp?V=E]uaN)4\" x=\"31\" y=\"222\"><field name=\"VAR\" id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"4s18v%0iqE%_l@{fLYCo\"><field name=\"VAR\" id=\"A]5Vf/x,=8dk=KKk3OrJ\">pd</field><field name=\"MEMBER\">read_csv</field><data>pd:read_csv</data><value name=\"INPUT\"><block type=\"text\" id=\"tN4Jmaffy/={ZFo;uu;W\"><field name=\"TEXT\">datasets/boston.csv</field></block></value></block></value></block><block type=\"variables_get\" id=\"yN6MT1B[)Pk)cEECN_YZ\" x=\"31\" y=\"286\"><field name=\"VAR\" id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the data\n",
    "\n",
    "Let's look at the relationship between percent of lower status (`lstat`) and the value of a home (`medv`). \n",
    "\n",
    "First, import `plotly` for plotting:\n",
    "\n",
    "- `import plotly.express as px`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"x2E1re-+t1/@.:kt|~XV\">px</variable></variables><block type=\"importAs_Python\" id=\"+cd]v=]w[Y{ypm^J_~c}\" x=\"198\" y=\"290\"><field name=\"libraryName\">plotly.express</field><field name=\"libraryAlias\" id=\"x2E1re-+t1/@.:kt|~XV\">px</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the scatterplot:\n",
    "\n",
    "- `with px do scatter using` a list containing\n",
    "    - `dataframe`\n",
    "    - freestyle `x=\"lstat\"`\n",
    "    - freestyle `y=\"medv\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "lstat=%{x}<br>medv=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "type": "scatter",
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         "xaxis": "x",
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          48.3,
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          35.2,
          32.4,
          32,
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          35.1,
          45.4,
          35.4,
          46,
          50,
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          22,
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('2359386f-a58a-4cc5-be62-879a8be995ad');\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "    x.observe(outputEl, {childList: true});\n",
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     },
     "metadata": {},
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   ],
   "source": [
    "px.scatter(dataframe, x=\"lstat\", y=\"medv\")\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"x2E1re-+t1/@.:kt|~XV\">px</variable><variable id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</variable></variables><block type=\"varDoMethod_Python\" id=\"#{`uqQJ7o((KWM@lD9qM\" x=\"189\" y=\"257\"><field name=\"VAR\" id=\"x2E1re-+t1/@.:kt|~XV\">px</field><field name=\"MEMBER\">scatter</field><data>px:scatter</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"06@|Mq6tTAw|c;Zqa,w0\"><mutation items=\"3\"></mutation><value name=\"ADD0\"><block type=\"variables_get\" id=\"%+W1]bM6TMW-dcT(D)!q\"><field name=\"VAR\" id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</field></block></value><value name=\"ADD1\"><block type=\"dummyOutputCodeBlock_Python\" id=\"_db~Ig?DLuc}U+3QVLY+\"><field name=\"CODE\">x=\"lstat\"</field></block></value><value name=\"ADD2\"><block type=\"dummyOutputCodeBlock_Python\" id=\"}PsvFr/L@QtObEW4I4*|\"><field name=\"CODE\">y=\"medv\"</field></block></value></block></value></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appears to be a non-linear relationship between `lstat` and `medv`. \n",
    "\n",
    "### Prepare train/test sets\n",
    "\n",
    "Now, we need to separate the dataset into predictor features and corresponding response (`medv`). \n",
    "In other words, split the dataframe into training data and testing data.\n",
    "\n",
    "Let's start with everything but the response.\n",
    "We'll use a new function called `drop` to remove `medv`.\n",
    "This is faster than listing all the columns we want to keep:\n",
    "\n",
    "- Create variable `X`\n",
    "- Set it to `with dataframe do drop using` a list containing\n",
    "    - freestyle `columns=[\"medv\"]`\n",
    "- `X` (to display)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>lstat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        crim    zn  indus  chas    nox  ...     dis  rad  tax  ptratio  lstat\n",
       "0    0.00632  18.0   2.31     0  0.538  ...  4.0900    1  296     15.3   4.98\n",
       "1    0.02731   0.0   7.07     0  0.469  ...  4.9671    2  242     17.8   9.14\n",
       "2    0.02729   0.0   7.07     0  0.469  ...  4.9671    2  242     17.8   4.03\n",
       "3    0.03237   0.0   2.18     0  0.458  ...  6.0622    3  222     18.7   2.94\n",
       "4    0.06905   0.0   2.18     0  0.458  ...  6.0622    3  222     18.7   5.33\n",
       "..       ...   ...    ...   ...    ...  ...     ...  ...  ...      ...    ...\n",
       "501  0.06263   0.0  11.93     0  0.573  ...  2.4786    1  273     21.0   9.67\n",
       "502  0.04527   0.0  11.93     0  0.573  ...  2.2875    1  273     21.0   9.08\n",
       "503  0.06076   0.0  11.93     0  0.573  ...  2.1675    1  273     21.0   5.64\n",
       "504  0.10959   0.0  11.93     0  0.573  ...  2.3889    1  273     21.0   6.48\n",
       "505  0.04741   0.0  11.93     0  0.573  ...  2.5050    1  273     21.0   7.88\n",
       "\n",
       "[506 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataframe.drop(columns=[\"medv\"])\n",
    "\n",
    "X\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\".X4`4]:_GBz1DeII8[o2\">X</variable><variable id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</variable></variables><block type=\"variables_set\" id=\"j12HEn?u}2$P-S)Ax6d(\" x=\"-16\" y=\"265\"><field name=\"VAR\" id=\".X4`4]:_GBz1DeII8[o2\">X</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"PMH^/O2y?rD(Y}r-N6eY\"><field name=\"VAR\" id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</field><field name=\"MEMBER\">drop</field><data>dataframe:drop</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"bGn#S3b1DV-0-egwl87D\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"dummyOutputCodeBlock_Python\" id=\"8N-YF(7ms%)oox.~Z;b/\"><field name=\"CODE\">columns=[\"medv\"]</field></block></value></block></value></block></value></block><block type=\"variables_get\" id=\"*Q(nsPMh9xg`HceqC3eN\" x=\"-11\" y=\"350\"><field name=\"VAR\" id=\".X4`4]:_GBz1DeII8[o2\">X</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save a dataframe with just `medv` in `Y`:\n",
    "\n",
    "- Create variable `Y`\n",
    "- Set it to `dataframe [ ] ` containing the following in a list\n",
    "    - `\"medv\"`\n",
    "- `Y` (to display)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     medv\n",
       "0    24.0\n",
       "1    21.6\n",
       "2    34.7\n",
       "3    33.4\n",
       "4    36.2\n",
       "..    ...\n",
       "501  22.4\n",
       "502  20.6\n",
       "503  23.9\n",
       "504  22.0\n",
       "505  11.9\n",
       "\n",
       "[506 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = dataframe[['medv']]\n",
    "\n",
    "Y\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"fnSh@+0BWBw|hcm3M|WE\">Y</variable><variable id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</variable></variables><block type=\"variables_set\" id=\"_:J1([xryT4i+F7k{#[%\" x=\"17\" y=\"175\"><field name=\"VAR\" id=\"fnSh@+0BWBw|hcm3M|WE\">Y</field><value name=\"VALUE\"><block type=\"indexer_Python\" id=\"22hOF=T:mHvJrMybHkPs\"><field name=\"VAR\" id=\"$%drMkQ:qhLh-L8dv:ow\">dataframe</field><value name=\"INDEX\"><block type=\"lists_create_with\" id=\"qT2G_{7q~*L;[5j:vtKq\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"text\" id=\"=UVLwuCW$1d9ZJ]6HF_,\"><field name=\"TEXT\">medv</field></block></value></block></value></block></value></block><block type=\"variables_get\" id=\"pog__n6Jf{VK_sNEB)B$\" x=\"17\" y=\"239\"><field name=\"VAR\" id=\"fnSh@+0BWBw|hcm3M|WE\">Y</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we are going to split our `X` and `Y` into train and test sets. \n",
    "That means we'll end up with four data frames: `Xtrain`, `Xtest`, `Ytrain`, `Ytest`.\n",
    "\n",
    "First, the `sklearn` import for splitting:\n",
    "\n",
    "- `import sklearn.model_selection as model_selection`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.model_selection as model_selection\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"uASGz64Zb$AOvQyV4pRj\">model_selection</variable></variables><block type=\"importAs_Python\" id=\"sN1YO5FEzpHyxb31@j,Z\" x=\"16\" y=\"10\"><field name=\"libraryName\">sklearn.model_selection</field><field name=\"libraryAlias\" id=\"uASGz64Zb$AOvQyV4pRj\">model_selection</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do the splits:\n",
    "\n",
    "- Create variable `splits`\n",
    "- Set it to `with model_selection do train_test_split using` a list containing\n",
    "    - `X` (the features in an array)\n",
    "    - `Y` (the labels in an array)\n",
    "    - freestyle `test_size=0.2` (the proportion of the dataset to include in the test split)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "splits = model_selection.train_test_split(X, Y, test_size=0.2)\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"_ut$e0PL4OMi4o1MXTpw\">splits</variable><variable id=\"uASGz64Zb$AOvQyV4pRj\">model_selection</variable><variable id=\".X4`4]:_GBz1DeII8[o2\">X</variable><variable id=\"fnSh@+0BWBw|hcm3M|WE\">Y</variable></variables><block type=\"variables_set\" id=\"oTGRJ#{R!U^we@Bl@pkT\" x=\"31\" y=\"224\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"f?j@ker(a#hJv;Nh)IGX\"><field name=\"VAR\" id=\"uASGz64Zb$AOvQyV4pRj\">model_selection</field><field name=\"MEMBER\">train_test_split</field><data>model_selection:train_test_split</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"er6r2]}|nA;1;}VsM5I7\"><mutation items=\"3\"></mutation><value name=\"ADD0\"><block type=\"variables_get\" id=\".mm}`*H4)i%Eq5z={e-$\"><field name=\"VAR\" id=\".X4`4]:_GBz1DeII8[o2\">X</field></block></value><value name=\"ADD1\"><block type=\"variables_get\" id=\"I3dOV;CPBf^~E%BvgthZ\"><field name=\"VAR\" id=\"fnSh@+0BWBw|hcm3M|WE\">Y</field></block></value><value name=\"ADD2\"><block type=\"dummyOutputCodeBlock_Python\" id=\"@Hg?ib/!8fH$;f3pWJy2\"><field name=\"CODE\">test_size=0.2</field></block></value></block></value></block></value></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may have noticed we haven't scaled (standardized) the predictor features yet.\n",
    "We'll do that next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN regression model\n",
    "\n",
    "We need to import libraries for:\n",
    "\n",
    "- KNN regression\n",
    "- Mean squared error\n",
    "- Ravel\n",
    "\n",
    "As well as libraries we need to standardize:\n",
    "\n",
    "- Scale\n",
    "- Pipeline\n",
    "\n",
    "So do the following imports:\n",
    "\n",
    "- `import sklearn.neighbors as neighbors`\n",
    "- `import sklearn.metrics as metrics`\n",
    "- `import numpy as np`\n",
    "- `import sklearn.preprocessing as pp`\n",
    "- `import sklearn.pipeline as pipe`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.neighbors as neighbors\n",
    "import sklearn.metrics as metrics\n",
    "import numpy as np\n",
    "import sklearn.preprocessing as pp\n",
    "import sklearn.pipeline as pipe\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"MvUG^P$o3=I*6/67?Rk:\">neighbors</variable><variable id=\")}+w@N9iB/j=:+PLkMv8\">metrics</variable><variable id=\"Zhzp)s*VL?V@ES3(j:*b\">np</variable><variable id=\".T`xqZHAu7r0$MQ4s=Yg\">pp</variable><variable id=\"=z9KfSBWpoDS,oj-?yx,\">pipe</variable></variables><block type=\"importAs_Python\" id=\"Jkis0rqxTm[rs0.yA(O7\" x=\"183\" y=\"249\"><field name=\"libraryName\">sklearn.neighbors</field><field name=\"libraryAlias\" id=\"MvUG^P$o3=I*6/67?Rk:\">neighbors</field><next><block type=\"importAs_Python\" id=\"*G_SVgZ;}hIr,Hi1~$Z6\"><field name=\"libraryName\">sklearn.metrics</field><field name=\"libraryAlias\" id=\")}+w@N9iB/j=:+PLkMv8\">metrics</field><next><block type=\"importAs_Python\" id=\"1ODp#_M`@7Fj/@US}H%#\"><field name=\"libraryName\">numpy</field><field name=\"libraryAlias\" id=\"Zhzp)s*VL?V@ES3(j:*b\">np</field><next><block type=\"importAs_Python\" id=\"/YAkt4vqDQp/.5;pPcY.\"><field name=\"libraryName\">sklearn.preprocessing</field><field name=\"libraryAlias\" id=\".T`xqZHAu7r0$MQ4s=Yg\">pp</field><next><block type=\"importAs_Python\" id=\"O15G]dI#1oM;`7^r@Pr,\"><field name=\"libraryName\">sklearn.pipeline</field><field name=\"libraryAlias\" id=\"=z9KfSBWpoDS,oj-?yx,\">pipe</field></block></next></block></next></block></next></block></next></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can train the KNN regression model, we need to scale the data.\n",
    "To do this easily, we introduce a new concept, **pipelines**.\n",
    "A pipeline is a series of steps, or stages, that process your data.\n",
    "You can think of it like an assembly line, with each stage adding something (or taking something away).\n",
    "\n",
    "The reason we need a pipeline is because when we scale our data, we need to scale not only the training set but also the testing set, and we need to scale them all the same way (so they match).\n",
    "Pipelines make this easy because we can wrap the scaler and our KNN model in the same pipeline and then forget about it.\n",
    "\n",
    "Let's make a pipeline with both a scaler and KNN regression:\n",
    "\n",
    "- Create variable `std_clf`\n",
    "- Set it to `with pipe do make_pipeline using` a list containing\n",
    "    - `with pp create StandardScaler using`\n",
    "    - `with neighbors create KNeighborsRegressor using` a list containing\n",
    "        - freestyle `n_neighbors=10`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_clf = pipe.make_pipeline(pp.StandardScaler(), neighbors.KNeighborsRegressor(n_neighbors = 10))\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</variable><variable id=\"=z9KfSBWpoDS,oj-?yx,\">pipe</variable><variable id=\"MvUG^P$o3=I*6/67?Rk:\">neighbors</variable><variable id=\".T`xqZHAu7r0$MQ4s=Yg\">pp</variable></variables><block type=\"variables_set\" id=\"}81D/tZY#o}$E:M}:u4x\" x=\"77\" y=\"398\"><field name=\"VAR\" id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"(Noh/e~gvTM3-4|;g]s[\"><field name=\"VAR\" id=\"=z9KfSBWpoDS,oj-?yx,\">pipe</field><field name=\"MEMBER\">make_pipeline</field><data>pipe:make_pipeline</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"(Q-}ch^M{g#1!2LN+l,@\"><mutation items=\"2\"></mutation><value name=\"ADD0\"><block type=\"varCreateObject_Python\" id=\"e9G_EPu`q{*0`$!~:jh~\"><field name=\"VAR\" id=\".T`xqZHAu7r0$MQ4s=Yg\">pp</field><field name=\"MEMBER\">StandardScaler</field><data>pp:StandardScaler</data></block></value><value name=\"ADD1\"><block type=\"varCreateObject_Python\" id=\"ar7keIh-Yv)+b+#Edsp_\"><field name=\"VAR\" id=\"MvUG^P$o3=I*6/67?Rk:\">neighbors</field><field name=\"MEMBER\">KNeighborsRegressor</field><data>neighbors:KNeighborsRegressor</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"Fdc{)AkUpH{!v_dA{/89\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"dummyOutputCodeBlock_Python\" id=\"Q!`Pnb+=Gq!aL4Xa}S{z\"><field name=\"CODE\">n_neighbors = 10</field></block></value></block></value></block></value></block></value></block></value></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because our KNN regression model is inside the pipeline, we can go ahead and train it.\n",
    "In fact, we can treat the whole pipeline like it's a classifier:\n",
    "\n",
    "-  `with std_clf do fit using` a list containing\n",
    "    - `in list splits get # 1` (in menu LISTS)\n",
    "    - `with np do ravel using` a list containing\n",
    "        - `in list splits get # 3` (in menu LISTS)\n",
    "        \n",
    "Remember the first and third elements in `splits` are `Xtrain` and `Ytrain`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('standardscaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('kneighborsregressor',\n",
       "                 KNeighborsRegressor(algorithm='auto', leaf_size=30,\n",
       "                                     metric='minkowski', metric_params=None,\n",
       "                                     n_jobs=None, n_neighbors=10, p=2,\n",
       "                                     weights='uniform'))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_clf.fit(splits[0], np.ravel(splits[2]))\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</variable><variable id=\"Zhzp)s*VL?V@ES3(j:*b\">np</variable><variable id=\"_ut$e0PL4OMi4o1MXTpw\">splits</variable></variables><block type=\"varDoMethod_Python\" id=\"Z)$q-zn.KTC{+^l-wH6u\" x=\"259\" y=\"169\"><field name=\"VAR\" id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</field><field name=\"MEMBER\">fit</field><data>std_clf:fit</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"e_B;36VOJ^lH70V=aWY}\"><mutation items=\"2\"></mutation><value name=\"ADD0\"><block type=\"lists_getIndex\" id=\"C#,#1*rEm+]qEx?L1x[L\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"b_Sz{9#d7d=ystO|k?l_\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\":s{r1~S,,@.CSh#9`$R;\"><field name=\"NUM\">1</field></block></value></block></value><value name=\"ADD1\"><block type=\"varDoMethod_Python\" id=\"zYBlZ,!^P^%Ib5R@Ch.H\"><field name=\"VAR\" id=\"Zhzp)s*VL?V@ES3(j:*b\">np</field><field name=\"MEMBER\">ravel</field><data>np:ravel</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"9s({WSn={~Ink.5O+6Cc\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"lists_getIndex\" id=\"^)UBt0jM;BnGmWeG7pw*\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"4Vo!*g]qQ=D}XtD2i39/\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\"RysCD3.C27sBxztz(T}2\"><field name=\"NUM\">3</field></block></value></block></value></block></value></block></value></block></value></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step is to predict house prices (median value of owner-occupied homes, in $1000s) using the test data:\n",
    "\n",
    "- Create variable `predictions`\n",
    "- Set it to `with std_clf do predict using` a list containing\n",
    "    - `in list splits get # 2` \n",
    "- `predictions` (to display)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.88, 23.74, 25.95, 27.31, 14.48, 16.82, 21.01, 20.35, 18.42,\n",
       "       20.64, 23.5 , 17.85, 19.89, 17.85, 29.13, 30.13, 15.06, 20.75,\n",
       "       22.15, 29.57, 37.13, 26.87, 19.51, 23.78, 12.85, 22.71, 30.  ,\n",
       "       28.11, 18.88, 14.7 , 26.76, 24.91, 26.1 , 18.98, 25.41, 22.57,\n",
       "       20.98, 24.85, 19.8 , 30.14, 19.43, 25.42, 19.64, 21.11, 11.91,\n",
       "       23.19, 10.02, 20.57, 15.26, 16.99, 22.29, 10.87, 17.41, 27.54,\n",
       "       20.97, 19.78, 26.86, 22.55, 23.5 , 29.28, 22.88, 23.57, 17.25,\n",
       "       14.12, 30.41, 15.54, 27.45, 21.85, 22.87, 21.12, 15.4 , 19.18,\n",
       "       24.98, 16.99, 15.4 , 28.76, 21.11, 29.78, 17.25, 22.44, 22.1 ,\n",
       "       16.76, 13.03, 24.79, 22.78, 30.44, 30.59, 17.45, 17.25, 38.96,\n",
       "       17.25, 19.06, 15.33, 21.9 , 19.88, 24.86, 42.09, 27.85, 16.62,\n",
       "       39.44,  9.41, 20.78])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = std_clf.predict(splits[1])\n",
    "\n",
    "predictions\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"5lkT$zi8W,`TXDny?Cv7\">predictions</variable><variable id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</variable><variable id=\"_ut$e0PL4OMi4o1MXTpw\">splits</variable></variables><block type=\"variables_set\" id=\"NHw$/HH988vNLbZgn)CM\" x=\"88\" y=\"212\"><field name=\"VAR\" id=\"5lkT$zi8W,`TXDny?Cv7\">predictions</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"N}3ds6:i%0MtTA:(2im4\"><field name=\"VAR\" id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</field><field name=\"MEMBER\">predict</field><data>std_clf:predict</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"3Ru6U*^.a`oD7$bu/I%y\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"lists_getIndex\" id=\"@lpyN+:CEcPQ#Q:Svm|9\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"ng))4fZyb@U1|eswo1}:\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\"Lo)w=2LL|Tf-L/gkeTdT\"><field name=\"NUM\">2</field></block></value></block></value></block></value></block></value></block><block type=\"variables_get\" id=\"I#4y[,+*#I5s;b;h3o/M\" x=\"75\" y=\"307\"><field name=\"VAR\" id=\"5lkT$zi8W,`TXDny?Cv7\">predictions</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `sqrt` of `mean_squared_error` to calculate the root mean squared error (RMSE) between `Ytest` and our predictions:\n",
    "\n",
    "- `square root` of `with metrics do mean_squared_error using` a list containing:\n",
    "    - `in list splits get # 4` \n",
    "    - `predictions`\n",
    "    \n",
    "Remember `square root` lives in the MATH menu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.843666590051684"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "math.sqrt(metrics.mean_squared_error(splits[3], predictions))\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\")}+w@N9iB/j=:+PLkMv8\">metrics</variable><variable id=\"5lkT$zi8W,`TXDny?Cv7\">predictions</variable><variable id=\"_ut$e0PL4OMi4o1MXTpw\">splits</variable></variables><block type=\"math_single\" id=\"K+~AfTut!=kX_{{*?zjq\" x=\"76\" y=\"293\"><field name=\"OP\">ROOT</field><value name=\"NUM\"><shadow type=\"math_number\" id=\"TwxVz{fz8[Th0JEpyxPq\"><field name=\"NUM\">9</field></shadow><block type=\"varDoMethod_Python\" id=\"dtb+F,1WedaH`AXMNxY[\"><field name=\"VAR\" id=\")}+w@N9iB/j=:+PLkMv8\">metrics</field><field name=\"MEMBER\">mean_squared_error</field><data>metrics:mean_squared_error</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"-=NmFJemtj{-P/.D|`Zg\"><mutation items=\"2\"></mutation><value name=\"ADD0\"><block type=\"lists_getIndex\" id=\"%Q5nelLnQN_U[o);kAqv\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"eCZagv7%IlQ;!+X*/wn7\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\"+UoB2X],lB*~xVCJOMRM\"><field name=\"NUM\">4</field></block></value></block></value><value name=\"ADD1\"><block type=\"variables_get\" id=\"Y`5[z@^QpYeXO!b/zuki\"><field name=\"VAR\" id=\"5lkT$zi8W,`TXDny?Cv7\">predictions</field></block></value></block></value></block></value></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have the time, try taking the scaler stage out of the pipeline, train the model, and assess it.\n",
    "You should find that your RMSE about doubled without the scaling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the optimal K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In figures 1-5 above, we saw how different values of K affect the shape of the line in KNN regression.\n",
    "Both small values of K and large values of K don't seem very good.\n",
    "Instead we want a K that is \"just right,\" somewhere in the middle.\n",
    "\n",
    "One way to find the optimal K is to train models for a range of K values, say 1 to 100.\n",
    "It would take you a long time to do this, even copy/pasting the code above!\n",
    "\n",
    "To make this kind of task doable, we're going to introduce a basic programming concept called a loop (see the LOOPS menu).\n",
    "But don't be fooled, even though loops are basic, they are one of the hardest things to understand in programming.\n",
    "\n",
    "The basic idea of a loop is that you create a template of the thing you want to do many times, and you put that template inside the loop.\n",
    "Then you have a few values that change depending on the *iteration* of the loop.\n",
    "\n",
    "Let's look a simple example, a loop that prints the numbers 1 to 3:\n",
    "\n",
    "- Get the **green** `for each item i in list` block\n",
    "- To its right, attach a list containing 1, 2, and 3\n",
    "- Inside the loop block, put `print i`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for i in [1, 2, 3]:\n",
    "  print(i)\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"eV4EZ~~D[TKTsoJ=Y9i/\">i</variable></variables><block type=\"controls_forEach\" id=\"FQ8X,Yn$Q`D}l2yQmxgn\" x=\"99\" y=\"276\"><field name=\"VAR\" id=\"eV4EZ~~D[TKTsoJ=Y9i/\">i</field><value name=\"LIST\"><block type=\"lists_create_with\" id=\"gJi:5JuudMItfG.a90l7\"><mutation items=\"3\"></mutation><value name=\"ADD0\"><block type=\"math_number\" id=\"x%u{x?:sZy@{o?3py;AZ\"><field name=\"NUM\">1</field></block></value><value name=\"ADD1\"><block type=\"math_number\" id=\"8uQpfZhd]$_:!+h{X+gX\"><field name=\"NUM\">2</field></block></value><value name=\"ADD2\"><block type=\"math_number\" id=\"Y@I~L`X)g,BYB$-Yk;i_\"><field name=\"NUM\">3</field></block></value></block></value><statement name=\"DO\"><block type=\"text_print\" id=\"GOAMMS}Lq-d0X-vGEKQn\"><value name=\"TEXT\"><shadow type=\"text\" id=\"$wPTCb2SBOUHe@SW]e-A\"><field name=\"TEXT\">abc</field></shadow><block type=\"variables_get\" id=\"$;paAJN}M27hOi7BpPVE\"><field name=\"VAR\" id=\"eV4EZ~~D[TKTsoJ=Y9i/\">i</field></block></value></block></statement></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example:\n",
    "\n",
    "- `print i` is our template\n",
    "- `i` changes at each iteration of the loop to the next thing in the list\n",
    "\n",
    "Next, we're going to follow the same logic, but instead of just printing inside the loop, we will build a KNN regression and print out its RMSE.\n",
    "\n",
    "<!-- First, the imports:\n",
    "\n",
    "- `import plotly.graph_objects as go` -->"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the steps:\n",
    "\n",
    "- Get the **green** `for each item j in list` block and attach a list containing 1, 10, 25, 50, 100, and 250\n",
    "- Copy the entire `std_clf` block (that makes the pipeline) and put it inside the loop\n",
    "- Change the freestyle `n_neighbors = 10` to `n_neighbors = j`\n",
    "- Copy the entire `std_clf.fit` block (that trains the pipeline) attach a freestyle to the front of it (see below) and put it in the loop (this adapter block lets you but `std_clf.fit` in the loop block)\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "- Copy the entire `predictions` block (that does the prediction) and put it inside the loop\n",
    "- Create a variable `rmse` and set it to the entire `square root` block and put it inside the loop\n",
    "- Inside the loop do `print create text with`\n",
    "    - `j`\n",
    "    - `\":\"`\n",
    "    - `rmse`\n",
    "    \n",
    "**In each of the above steps, when you put something in the loop, it goes below the block from the previous step**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:5.433104770845156\n",
      "10:5.843666590051684\n",
      "25:6.231880124196304\n",
      "50:6.552589911555552\n",
      "100:7.345896891943068\n",
      "250:8.396098233131397\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "for j in [1, 10, 25, 50, 100, 250]:\n",
    "  std_clf = pipe.make_pipeline(pp.StandardScaler(), neighbors.KNeighborsRegressor(n_neighbors = j))\n",
    "  (std_clf.fit(splits[0], np.ravel(splits[2])))\n",
    "  predictions = std_clf.predict(splits[1])\n",
    "  rmse = math.sqrt(metrics.mean_squared_error(splits[3], predictions))\n",
    "  print(''.join([str(x) for x in [j, ':', rmse]]))\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"nr%W_%EA(*n$_f,PuH#c\">j</variable><variable id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</variable><variable id=\"=z9KfSBWpoDS,oj-?yx,\">pipe</variable><variable id=\"5lkT$zi8W,`TXDny?Cv7\">predictions</variable><variable id=\".T`xqZHAu7r0$MQ4s=Yg\">pp</variable><variable id=\"MvUG^P$o3=I*6/67?Rk:\">neighbors</variable><variable id=\"RN+myW;vx){}Q0LuFA9y\">rmse</variable><variable id=\"Zhzp)s*VL?V@ES3(j:*b\">np</variable><variable id=\"_ut$e0PL4OMi4o1MXTpw\">splits</variable><variable id=\")}+w@N9iB/j=:+PLkMv8\">metrics</variable></variables><block type=\"controls_forEach\" id=\"MbWYZ/u3Q#+3*u`TM}ce\" x=\"-3\" y=\"182\"><field name=\"VAR\" id=\"nr%W_%EA(*n$_f,PuH#c\">j</field><value name=\"LIST\"><block type=\"lists_create_with\" id=\"Q6_EXa41T:y(.uy[pm.V\"><mutation items=\"6\"></mutation><value name=\"ADD0\"><block type=\"math_number\" id=\"+BJ@yT0IgJORgO^/))c~\"><field name=\"NUM\">1</field></block></value><value name=\"ADD1\"><block type=\"math_number\" id=\".~r[qy7$p-Jy_g:ACHYr\"><field name=\"NUM\">10</field></block></value><value name=\"ADD2\"><block type=\"math_number\" id=\"w!m`o}C;PB7OhYP5ZTq~\"><field name=\"NUM\">25</field></block></value><value name=\"ADD3\"><block type=\"math_number\" id=\"?!EK)M.j{u~@|;ytL|nd\"><field name=\"NUM\">50</field></block></value><value name=\"ADD4\"><block type=\"math_number\" id=\"cLtaU]#w-;S(sUq8pw/l\"><field name=\"NUM\">100</field></block></value><value name=\"ADD5\"><block type=\"math_number\" id=\"910qY[[4~4nziBPT;K+G\"><field name=\"NUM\">250</field></block></value></block></value><statement name=\"DO\"><block type=\"variables_set\" id=\"8APEX{xGs3Qz/$NzTFrY\"><field name=\"VAR\" id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"W=ep`rJjDXvsAV^R+NIG\"><field name=\"VAR\" id=\"=z9KfSBWpoDS,oj-?yx,\">pipe</field><field name=\"MEMBER\">make_pipeline</field><data>pipe:make_pipeline</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"ed7Q+#-kPzf4R,8)[q_Z\"><mutation items=\"2\"></mutation><value name=\"ADD0\"><block type=\"varCreateObject_Python\" id=\"ME+Z@t~BnN}LL22r%Hxt\"><field name=\"VAR\" id=\".T`xqZHAu7r0$MQ4s=Yg\">pp</field><field name=\"MEMBER\">StandardScaler</field><data>pp:StandardScaler</data></block></value><value name=\"ADD1\"><block type=\"varCreateObject_Python\" id=\"p=kmgsx6gSLD(RSPi]We\"><field name=\"VAR\" id=\"MvUG^P$o3=I*6/67?Rk:\">neighbors</field><field name=\"MEMBER\">KNeighborsRegressor</field><data>neighbors:KNeighborsRegressor</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"%{hTJRYu`A$B-B}N|W^|\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"dummyOutputCodeBlock_Python\" id=\"4*MO9`pDvGbjv^%|]#Jm\"><field name=\"CODE\">n_neighbors = j</field></block></value></block></value></block></value></block></value></block></value><next><block type=\"valueNoOutputCodeBlock_Python\" id=\"nCe{WO~.SVUJ*REfHN;r\"><field name=\"CODE\"></field><value name=\"INPUT\"><block type=\"varDoMethod_Python\" id=\"Fm%sV]3?)?kc0h:9S9IQ\"><field name=\"VAR\" id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</field><field name=\"MEMBER\">fit</field><data>std_clf:fit</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"bkqo.bU~bUHGJR3.PCNj\"><mutation items=\"2\"></mutation><value name=\"ADD0\"><block type=\"lists_getIndex\" id=\"c-OmE5.Qf*x_-O+oEcL(\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"xDX74mH}[mNJlpBTYt{)\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\"qrLzq]pv]HBj!dNAu-9i\"><field name=\"NUM\">1</field></block></value></block></value><value name=\"ADD1\"><block type=\"varDoMethod_Python\" id=\"?U(hh`0ZFjUnfsrL}xQK\"><field name=\"VAR\" id=\"Zhzp)s*VL?V@ES3(j:*b\">np</field><field name=\"MEMBER\">ravel</field><data>np:ravel</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"(UU{x68[{=A[p}uG##Eq\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"lists_getIndex\" id=\"kcy}-[{;=}P{onxAUnZc\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"di`_Ko4qTbeTQ^v!pa2A\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\",i2p$Oad~vpnzpz8YDss\"><field name=\"NUM\">3</field></block></value></block></value></block></value></block></value></block></value></block></value><next><block type=\"variables_set\" id=\"t4FH|^O1fvcXbQ/%eX{o\"><field name=\"VAR\" id=\"5lkT$zi8W,`TXDny?Cv7\">predictions</field><value name=\"VALUE\"><block type=\"varDoMethod_Python\" id=\"@~[XlP)bAA[7?k^=6%J2\"><field name=\"VAR\" id=\"`P)`+jl|;h{.[r}{78q6\">std_clf</field><field name=\"MEMBER\">predict</field><data>std_clf:predict</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"~`MOj}QguYf}BCZOc7C!\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"lists_getIndex\" id=\"h$p4(I@O~CtLDZv)+/nX\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"Kh1qgk~(:R(}S`sfh}?{\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\"jyi_*c[[~,{K#t[0Gghj\"><field name=\"NUM\">2</field></block></value></block></value></block></value></block></value><next><block type=\"variables_set\" id=\"6eYPMqa.T*II`(7!/U;_\"><field name=\"VAR\" id=\"RN+myW;vx){}Q0LuFA9y\">rmse</field><value name=\"VALUE\"><block type=\"math_single\" id=\"SI.B#46RorSW7}ZGS{D-\"><field name=\"OP\">ROOT</field><value name=\"NUM\"><shadow type=\"math_number\"><field name=\"NUM\">9</field></shadow><block type=\"varDoMethod_Python\" id=\"7PlC*+UVpHU@Lyq2@_?h\"><field name=\"VAR\" id=\")}+w@N9iB/j=:+PLkMv8\">metrics</field><field name=\"MEMBER\">mean_squared_error</field><data>metrics:mean_squared_error</data><value name=\"INPUT\"><block type=\"lists_create_with\" id=\"ExgoS4A$UNPym77J;1L2\"><mutation items=\"2\"></mutation><value name=\"ADD0\"><block type=\"lists_getIndex\" id=\"JoSeD*7Q$:S1sXw#)5=+\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"OR=]poUbkR)eqh!eV:XW\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\"gJ*8#!h/B4O65oy_[TKL\"><field name=\"NUM\">4</field></block></value></block></value><value name=\"ADD1\"><block type=\"variables_get\" id=\"::s[{k@xAHJRWvDND!GY\"><field name=\"VAR\" id=\"5lkT$zi8W,`TXDny?Cv7\">predictions</field></block></value></block></value></block></value></block></value><next><block type=\"text_print\" id=\"qD^5qCGb|~Z/e)-2,/Xe\"><value name=\"TEXT\"><shadow type=\"text\" id=\"jjAd~$4y5!nZc!%qRsJ}\"><field name=\"TEXT\">abc</field></shadow><block type=\"text_join\" id=\"G#,=(,gYP!hQgbLLojMo\"><mutation items=\"3\"></mutation><value name=\"ADD0\"><block type=\"variables_get\" id=\"N0QQN{Ql.g=*FApk}*2b\"><field name=\"VAR\" id=\"nr%W_%EA(*n$_f,PuH#c\">j</field></block></value><value name=\"ADD1\"><block type=\"text\" id=\"`f+]K88}p[|+.2@^Mpb9\"><field name=\"TEXT\">:</field></block></value><value name=\"ADD2\"><block type=\"variables_get\" id=\"5es9)`8wQ`Wh@fitgT3Y\"><field name=\"VAR\" id=\"RN+myW;vx){}Q0LuFA9y\">rmse</field></block></value></block></value></block></next></block></next></block></next></block></next></block></statement></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimal K (in terms of rmse) is 10 for me, but may be different for you depending on your data split.\n",
    "This is an example of what is sometimes called an *elbow*: errors start and end high, but have a distinct minimum somewhere in between."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the data and the model predictions\n",
    "\n",
    "Previously we used a scatterplot to examine the relationship between percent of lower status (`lstat`) and the value of a home (`medv`). \n",
    "Although our model uses many other predictors, we can only easily plot one of them (e.g. `lstat`) against `medv` at a time, because scatterplots are 2D.\n",
    "\n",
    "However, we can still plot our predictions, which are based on all predictor features, on the same 2D plot.\n",
    "This allows us to look at the model's fit, at least with respect to the one dimension of `lstat`.\n",
    "\n",
    "To do this, we need to import a more sophisticated part of `plotly` called `graph_objects`:\n",
    "\n",
    "- `import plotly.graph_objects as go`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"`iml3^SQ8#}M{aSRdsjz\">go</variable></variables><block type=\"importAs_Python\" id=\"I=D{X(f@]J?iDq8EKrNP\" x=\"65\" y=\"163\"><field name=\"libraryName\">plotly.graph_objects</field><field name=\"libraryAlias\" id=\"`iml3^SQ8#}M{aSRdsjz\">go</field></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The way `go` works is that we first create an *empty* figure, then we add data to it in layers.\n",
    "This is in contrast to `px`, where we created the figure and added data all at once.\n",
    "\n",
    "To create the empty figure:\n",
    "\n",
    "- Create variable `fig`\n",
    "- Set it to `with go create Figure using`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"@U5l^JIp,}.H=ECCW?@g\">fig</variable><variable id=\"`iml3^SQ8#}M{aSRdsjz\">go</variable></variables><block type=\"variables_set\" id=\"jhv+dBJpS#?]Xz;mB/y}\" x=\"63\" y=\"341\"><field name=\"VAR\" id=\"@U5l^JIp,}.H=ECCW?@g\">fig</field><value name=\"VALUE\"><block type=\"varCreateObject_Python\" id=\"U[~m;7}PfW266d{R4B_w\"><field name=\"VAR\" id=\"`iml3^SQ8#}M{aSRdsjz\">go</field><field name=\"MEMBER\">Figure</field><data>go:Figure</data></block></value></block></xml>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to add two kinds of data to `fig`:\n",
    "\n",
    "- The test data (both `Xtest` and `Ytest` from `split`\n",
    "- The predictions (both `Xtest` and `predictions`\n",
    "\n",
    "Each of these can be done with the function `add_trace`.\n",
    "However, notice the both times we need just a column from `Xtest`, not the whole thing.\n",
    "So we need to get the `lstat` column on its own first.\n",
    "\n",
    "Here are the steps:\n",
    "\n",
    "**Getting `lstat` out of `splits`**\n",
    "- Create variable `Xtest`\n",
    "- Set it to `in list splits get # 2`\n",
    "- Create variable `lstat`\n",
    "- Set it to `Xtest [` a list containing `\"lstat\"` `]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtest = splits[1]\n",
    "lstat = Xtest[['lstat']]\n",
    "\n",
    "#<xml xmlns=\"https://developers.google.com/blockly/xml\"><variables><variable id=\"fe.98]:,bU@x*=}zaJ1/\">Xtest</variable><variable id=\"rVUZld`HfW,n/PC{X!9-\">lstat</variable><variable id=\"_ut$e0PL4OMi4o1MXTpw\">splits</variable></variables><block type=\"variables_set\" id=\"Q=9pQHE7F@.Gw^K.L-dt\" x=\"11\" y=\"385\"><field name=\"VAR\" id=\"fe.98]:,bU@x*=}zaJ1/\">Xtest</field><value name=\"VALUE\"><block type=\"lists_getIndex\" id=\"57k*lJ0;vmKpSg*!H`]8\"><mutation statement=\"false\" at=\"true\"></mutation><field name=\"MODE\">GET</field><field name=\"WHERE\">FROM_START</field><value name=\"VALUE\"><block type=\"variables_get\" id=\"F4hyTfPiqx9)S?z+xUIq\"><field name=\"VAR\" id=\"_ut$e0PL4OMi4o1MXTpw\">splits</field></block></value><value name=\"AT\"><block type=\"math_number\" id=\"|irh~@jIqN~E)ZBhA5j/\"><field name=\"NUM\">2</field></block></value></block></value><next><block type=\"variables_set\" id=\"%B!g^jAyg[JC}a?Gd;]2\"><field name=\"VAR\" id=\"rVUZld`HfW,n/PC{X!9-\">lstat</field><value name=\"VALUE\"><block type=\"indexer_Python\" id=\"L]ClG49hl?`v1EmAZY)h\"><field name=\"VAR\" id=\"fe.98]:,bU@x*=}zaJ1/\">Xtest</field><value name=\"INDEX\"><block type=\"lists_create_with\" id=\",YhZdkj[DdlL=cwFv4FA\"><mutation items=\"1\"></mutation><value name=\"ADD0\"><block type=\"text\" id=\"opa@$sxEyy6xpto~juJ:\"><field name=\"TEXT\">lstat</field></block></value></block></value></block></value></block></next></block></xml>"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adding scatterplot of observed data**\n",
    "- `with fig do add_scatter using` a list containing\n",
    "    - freestyle `x=` then `with np do ravel using lstat`  (see image below for this freestyle)\n",
    "    - freestyle `y=` then `with np do ravel using` a list containing\n",
    "        - `in list splits get # 4` \n",
    "    - freestyle `mode=\"markers\"`\n",
    "    - freestyle `name=\"observed\"`\n",
    "    - freestyle `line=dict(color='royalblue')`\n",
    "        \n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "**Adding scatterplot of predictions**\n",
    "- `with fig do add_scatter using` a list containing\n",
    "    - freestyle `x=` then `with np do ravel using lstat`\n",
    "    - freestyle `y=` then `with np do ravel using predictions`\n",
    "    - freestyle `mode=markers`\n",
    "    - freestyle `name=predicted`\n",
    "    - line=dict(color='orange')        "
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",
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       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"e13b24c5-5f66-4e16-a066-33e645f78ff1\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    \n",
       "                if (document.getElementById(\"e13b24c5-5f66-4e16-a066-33e645f78ff1\")) {\n",
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    "It's always a good idea to look closely at your model predictions!\n",
    "\n",
    "In this plot, we see that although our RMSE is lowest at K=10, there are some ranges of our predictor features where the model is doing better than others.\n",
    "\n",
    "Looking at this plot for `lstat`, it seems that when `lstat` is below 10% and above 20%, the model isn't doing as well as we'd like.\n",
    "\n",
    "It just so happens that most of the data is between 10 and 20%, so our overall model fit scores are pretty good even though we're having problems with some ranges of the data."
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   "source": [
    "## Strengths and Weaknesses"
   ]
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    "Unlike other types of models which only work for linear relationships, KNN regression can be used to model non-linear relationships.\n",
    "KNN models are also easy to implement and fitting the model tends to be fast.\n",
    "However, the model is slower to predict since to predict an outcome for a new value, the algorithm needs to search through all the points in the training set to find the nearest ones.\n",
    "This can take awhile for large datasets."
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    "Due to its non-parametric nature, KNN regression models are not directly interpretable like some other models are.\n",
    "This makes it difficult to determine which features have the greatest effect on the response.\n",
    "For this reason KNN regression is not useful in _feature selection_, a technique used to weed out features that are less important thereby reducing the dimensionality of the data."
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