get_up_and_running_with_xgboost_in_r.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Get Up And Running With XGBoost In R\n",
    "By James Marquez, April 30, 2017\n",
    "\n",
    "The goal of this article is to quickly get you running XGBoost on any classification problem and measuring its performance. It won't explain feature engineering, model tuning, or the theory or math behind the algorithm. There's already a plethoral of free resources to learn those elements. In my opinion, I learn better when I run my data through an algorithm and then use various resources to learn how to improve my prediction performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading The  Dataset\n",
    "We'll be using the popular [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic/data) dataset from [Kaggle](https://www.kaggle.com/) where we'll be predicting which passengers survived the sinking of the Titanic. You can download and learn all about the dataset from the provided link. We're going to load <span class=\"ic\">train.csv</span> and split it into a <span class=\"ic\">train</span> and <span class=\"ic\">test</span> set. We'll train the model on our <span class=\"ic\">train</span> set with the known outcomes and then test the model's performance on the <span class=\"ic\">test</span> set with unknown outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t891 obs. of  9 variables:\n",
      " $ Survived: int  0 1 1 1 0 0 0 0 1 1 ...\n",
      " $ Pclass  : Ord.factor w/ 3 levels \"1\"<\"2\"<\"3\": 3 1 3 1 3 3 1 3 3 2 ...\n",
      " $ Sex     : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ...\n",
      " $ Age     : num  22 38 26 35 35 NA 54 2 27 14 ...\n",
      " $ SibSp   : int  1 1 0 1 0 0 0 3 0 1 ...\n",
      " $ Parch   : int  0 0 0 0 0 0 0 1 2 0 ...\n",
      " $ Fare    : num  7.25 71.28 7.92 53.1 8.05 ...\n",
      " $ Embarked: Factor w/ 4 levels \"\",\"C\",\"Q\",\"S\": 4 2 4 4 4 3 4 4 4 2 ...\n",
      " $ Cabin   : Factor w/ 148 levels \"\",\"A10\",\"A14\",..: 1 83 1 57 1 1 131 1 1 1 ...\n"
     ]
    },
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     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Fare</th><th scope=col>Embarked</th><th scope=col>Cabin</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0      </td><td>3      </td><td>male   </td><td>22     </td><td>1      </td><td>0      </td><td> 7.2500</td><td>S      </td><td>       </td></tr>\n",
       "\t<tr><td>1      </td><td>1      </td><td>female </td><td>38     </td><td>1      </td><td>0      </td><td>71.2833</td><td>C      </td><td>C85    </td></tr>\n",
       "\t<tr><td>1      </td><td>3      </td><td>female </td><td>26     </td><td>0      </td><td>0      </td><td> 7.9250</td><td>S      </td><td>       </td></tr>\n",
       "\t<tr><td>1      </td><td>1      </td><td>female </td><td>35     </td><td>1      </td><td>0      </td><td>53.1000</td><td>S      </td><td>C123   </td></tr>\n",
       "\t<tr><td>0      </td><td>3      </td><td>male   </td><td>35     </td><td>0      </td><td>0      </td><td> 8.0500</td><td>S      </td><td>       </td></tr>\n",
       "\t<tr><td>0      </td><td>3      </td><td>male   </td><td>NA     </td><td>0      </td><td>0      </td><td> 8.4583</td><td>Q      </td><td>       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Survived & Pclass & Sex & Age & SibSp & Parch & Fare & Embarked & Cabin\\\\\n",
       "\\hline\n",
       "\t 0       & 3       & male    & 22      & 1       & 0       &  7.2500 & S       &        \\\\\n",
       "\t 1       & 1       & female  & 38      & 1       & 0       & 71.2833 & C       & C85    \\\\\n",
       "\t 1       & 3       & female  & 26      & 0       & 0       &  7.9250 & S       &        \\\\\n",
       "\t 1       & 1       & female  & 35      & 1       & 0       & 53.1000 & S       & C123   \\\\\n",
       "\t 0       & 3       & male    & 35      & 0       & 0       &  8.0500 & S       &        \\\\\n",
       "\t 0       & 3       & male    & NA      & 0       & 0       &  8.4583 & Q       &        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Survived | Pclass | Sex | Age | SibSp | Parch | Fare | Embarked | Cabin | \n",
       "|---|---|---|---|---|---|\n",
       "| 0       | 3       | male    | 22      | 1       | 0       |  7.2500 | S       |         | \n",
       "| 1       | 1       | female  | 38      | 1       | 0       | 71.2833 | C       | C85     | \n",
       "| 1       | 3       | female  | 26      | 0       | 0       |  7.9250 | S       |         | \n",
       "| 1       | 1       | female  | 35      | 1       | 0       | 53.1000 | S       | C123    | \n",
       "| 0       | 3       | male    | 35      | 0       | 0       |  8.0500 | S       |         | \n",
       "| 0       | 3       | male    | NA      | 0       | 0       |  8.4583 | Q       |         | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Survived Pclass Sex    Age SibSp Parch Fare    Embarked Cabin\n",
       "1 0        3      male   22  1     0      7.2500 S             \n",
       "2 1        1      female 38  1     0     71.2833 C        C85  \n",
       "3 1        3      female 26  0     0      7.9250 S             \n",
       "4 1        1      female 35  1     0     53.1000 S        C123 \n",
       "5 0        3      male   35  0     0      8.0500 S             \n",
       "6 0        3      male   NA  0     0      8.4583 Q             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the dataset into our environment\n",
    "mydata <- read.csv('/Users/JamesMarquezDev/Downloads/train.csv')\n",
    "\n",
    "# Select and rearrange the order of the features we'll be using\n",
    "mydata <- mydata[, c('Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Cabin')]\n",
    "\n",
    "# Convert the Pclass feature to an ordered factor 1 < 2 < 3\n",
    "mydata$Pclass <- factor(mydata$Pclass, ordered=TRUE)\n",
    "\n",
    "str(mydata) # View the structure of our dataset\n",
    "head(mydata) # View the first six rows of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding Missing Data\n",
    "Before we split our dataset into a <span class=\"ic\">train</span> and <span class=\"ic\">test</span> set, we must deal with missing data. We'll find every feature with missing data and simply remove those rows for simplicity sake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Survived</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Pclass</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Sex</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Age</dt>\n",
       "\t\t<dd>177</dd>\n",
       "\t<dt>SibSp</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Parch</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Fare</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Embarked</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>Cabin</dt>\n",
       "\t\t<dd>0</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Survived] 0\n",
       "\\item[Pclass] 0\n",
       "\\item[Sex] 0\n",
       "\\item[Age] 177\n",
       "\\item[SibSp] 0\n",
       "\\item[Parch] 0\n",
       "\\item[Fare] 0\n",
       "\\item[Embarked] 0\n",
       "\\item[Cabin] 0\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Survived\n",
       ":   0Pclass\n",
       ":   0Sex\n",
       ":   0Age\n",
       ":   177SibSp\n",
       ":   0Parch\n",
       ":   0Fare\n",
       ":   0Embarked\n",
       ":   0Cabin\n",
       ":   0\n",
       "\n"
      ],
      "text/plain": [
       "Survived   Pclass      Sex      Age    SibSp    Parch     Fare Embarked \n",
       "       0        0        0      177        0        0        0        0 \n",
       "   Cabin \n",
       "       0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sapply(mydata, function(x) sum(is.na(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <span class=\"ic\">Age</span> feature is missing 177 observations so we'll simply remove those rows altogether."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get all rows that are not NA in the Age feature\n",
    "mydata <- subset(mydata, !is.na(Age))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting The Dataset Into Train And Test Sets\n",
    "Now, we're ready to split our dataset into a <span class=\"ic\">train</span> set (75%) and <span class=\"ic\">test</span> set (25%). We'll be using the really great [caret](http://topepo.github.io/caret/index.html) package to perform the split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the caret package\n",
    "library(caret)\n",
    "\n",
    "# Set the seed to create reproducible train and test sets\n",
    "set.seed(300)\n",
    "\n",
    "# Create a stratified random sample to create train and test sets\n",
    "# Reference the outcome variable\n",
    "trainIndex   <- createDataPartition(mydata$Survived, p=0.75, list=FALSE, times=1)\n",
    "train        <- mydata[ trainIndex, ]\n",
    "test         <- mydata[-trainIndex, ]\n",
    "\n",
    "# Create separate vectors of our outcome variable for both our train and test sets\n",
    "# We'll use these to train and test our model later\n",
    "train.label  <- train$Survived\n",
    "test.label   <- test$Survived"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-hot Encoding\n",
    "XGBoost requires your dataset to be a sparse matrix, which is a memory-efficient way to represent a large dataset that holds many zeros. We're going to use the [Matrix](https://cran.r-project.org/web/packages/Matrix/Matrix.pdf) package to convert our data frame to a sparse matrix and all our factored (categorical) features into dummy variables in one step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "\\begin{enumerate*}\n",
       "\\item 536\n",
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       "\\end{enumerate*}\n"
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       "1. 536\n",
       "2. 158\n",
       "\n",
       "\n"
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       "[1] 536 158"
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       "<ol class=list-inline>\n",
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       "\\begin{enumerate*}\n",
       "\\item 178\n",
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       "1. 178\n",
       "2. 158\n",
       "\n",
       "\n"
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       "[1] 178 158"
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   "source": [
    "# Load the Matrix package\n",
    "library(Matrix)\n",
    "\n",
    "# Create sparse matrixes and perform One-Hot Encoding to create dummy variables\n",
    "dtrain  <- sparse.model.matrix(Survived ~ .-1, data=train)\n",
    "dtest   <- sparse.model.matrix(Survived ~ .-1, data=test)\n",
    "\n",
    "# View the number of rows and features of each set\n",
    "dim(dtrain)\n",
    "dim(dtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training The Model\n",
    "For simplicity sake, we'll use the below hyperparameters, but you can improve the performance of your model by tuning them using the [caret](http://topepo.github.io/caret/index.html) package. We're using <span class=\"ic\">objective = \"binary:logistic\"</span> because this is a logistic regression for binary classification problem. We're also using <span class=\"ic\">eval_metrix = \"error\"</span> which is used for classification problems. You can learn about all available options here [XGBoost](https://cran.r-project.org/web/packages/xgboost/xgboost.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttrain-error:0.149254 \n",
      "[101]\ttrain-error:0.052239 \n",
      "[201]\ttrain-error:0.029851 \n",
      "[301]\ttrain-error:0.016791 \n",
      "[401]\ttrain-error:0.013060 \n",
      "[500]\ttrain-error:0.009328 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   user  system elapsed \n",
       "  0.419   0.009   0.432 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the XGBoost package\n",
    "library(xgboost)\n",
    "\n",
    "# Set our hyperparameters\n",
    "param <- list(objective   = \"binary:logistic\",\n",
    "              eval_metric = \"error\",\n",
    "              max_depth   = 7,\n",
    "              eta         = 0.1,\n",
    "              gammma      = 1,\n",
    "              colsample_bytree = 0.5,\n",
    "              min_child_weight = 1)\n",
    "\n",
    "set.seed(1234)\n",
    "\n",
    "# Pass in our hyperparameteres and train the model \n",
    "system.time(xgb <- xgboost(params  = param,\n",
    "                           data    = dtrain,\n",
    "                           label   = train.label, \n",
    "                           nrounds = 500,\n",
    "                           print_every_n = 100,\n",
    "                           verbose = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate The Model's Performance With A Confusion Matrix\n",
    "The Confusion Matrix provides us with a large amount of information regarding the performance of our model. But first, we must choose a threshold. The threshold value is the cutoff at which we determine a positive prediction and a negative prediction. Down below, we have chosen 0.34 as our cutoff threshold. The model produces a probability for each prediction and we convert it to either a 1 (Survived) if the probability is above or equal to 0.86 and a 0 (Did Not Survive) if the probability is below 0.86."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction  0  1\n",
       "         0 97 18\n",
       "         1  6 57\n",
       "                                          \n",
       "               Accuracy : 0.8652          \n",
       "                 95% CI : (0.8061, 0.9117)\n",
       "    No Information Rate : 0.5787          \n",
       "    P-Value [Acc > NIR] : < 2e-16         \n",
       "                                          \n",
       "                  Kappa : 0.7173          \n",
       " Mcnemar's Test P-Value : 0.02474         \n",
       "                                          \n",
       "            Sensitivity : 0.7600          \n",
       "            Specificity : 0.9417          \n",
       "         Pos Pred Value : 0.9048          \n",
       "         Neg Pred Value : 0.8435          \n",
       "             Prevalence : 0.4213          \n",
       "         Detection Rate : 0.3202          \n",
       "   Detection Prevalence : 0.3539          \n",
       "      Balanced Accuracy : 0.8509          \n",
       "                                          \n",
       "       'Positive' Class : 1               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create our prediction probabilities\n",
    "pred <- predict(xgb, dtest)\n",
    "\n",
    "# Set our cutoff threshold\n",
    "pred.resp <- ifelse(pred >= 0.86, 1, 0)\n",
    "\n",
    "# Create the confusion matrix\n",
    "confusionMatrix(pred.resp, test.label, positive=\"1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without creating new features or tuning our hyperparameters, we were able to get a 76% true positive rate and a 94% true negative rate with a Kappa score of 0.7173."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine Which Features Are Most Important\n",
    "Now, we can view which features had the greatest impact on predictive performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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6lU6gFnlVERAggggAACgQlocJTUm40MkALbr/l2h3eb8xVj+RAFetUp+87R93M6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pnOx3+5RX6LloP7flSOh1+lSfvK9qnPEp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\nCCCAAAI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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the trained model\n",
    "model <- xgb.dump(xgb, with_stats=TRUE)\n",
    "\n",
    "# Get the feature real names\n",
    "names <- dimnames(dtrain)[[2]]\n",
    "\n",
    "# Compute feature importance matrix\n",
    "importance_matrix <- xgb.importance(names, model=xgb)[0:20] # View top 20 most important features\n",
    "\n",
    "# Plot\n",
    "xgb.plot.importance(importance_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting The ROC To View Various Thresholds\n",
    "An ROC curve allows us to visualize our model's performance when selecting different thresholds. The threshold value is indicated by the dots on the curved line. Each dot lets us view the average true positive rate and average false positive rate for each threshold. As the threshold value gets lower, the average true positive rate gets higher. However, the average false positive rate gets higher as well. It's important to select a threshold that provides an acceptable true positive rate while also limiting the false positive rate. You can read more at https://en.wikipedia.org/wiki/Receiver_operating_characteristic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tXvWIhOUzdoNOR16E7GsrGmHCS/E07H\n19iemFkBmWNyOshdeYQnPJP8u5trIgIiIAIiIAIiIAK5I+B1kLw3Nsnm+ZSc3cf85u029Jh/\nZYrl+Qg3x+BNKO03kAnjdAcGBmhB7T8oB4lP3auS5M/cMJNBDdTZE+dDzBtGf/KE/84TzlYC\nPrH3Grtvme3+4l2RYn4DT/xFmKcDQPM6AGZ/5zWsSpSXCaMzY7vhnHIfXPdXNyybCfdp9p+J\ng3So72CjPPvh/kzeGM3rIKUqn5me7f/OjTy2D+ZNGtla1cZdd7wnnM4KHc3NIcOIDm4y8ztI\n6eZlQ+zMpINTrwNkjsNz1cSZYwIxHekJ5/qoZ92LnnVsefLbTQgw++TUOEjZ5t+/fy2LgAiE\ngID3ohuC7CgLIiACISewHPmbAvEGzmtsWToI6gU97V3hzvNG0lhfM9PEtBLr+BTbmNme3Y68\nlk5Lhzd+c/OtvX9zfHPTa5ZTTf+DFXSA/MYn7NPcQLKiQ0b7BWRamegE3c9A13hDbGw6Zhhv\nb4++xryx9c1ME9NvsO5zd30XTIdCbaFhbtjbmNa68zu7UzoHxlhPeINM2wTiPmhvNUxy+v9T\n39Ho0HuNjJNZsvKhw9PbE5mOn5dzGZbr3PV0fvu4829iyu5xtHbQFdAn0AKIjgrZeVuOsJjU\n0s3LCM/W/rpiVv3DzGDKc817TnpWNZod6Fl6xTNvZl82M75pUPn37VaLIiACxUhADlIxlprS\nLAKlS+BdZH0jiDdqvOmdC9F4A3kXtCMXkthXnrABnvlUs+thhe1ZOcWdN1Ozig5ZU8Yb70yu\ns62x/2THL/ck2ptPT3Bi9nt/gLscx9R783q0G36sO+XkBWiOZ5ktPsZ4E8v1Xh1hVmLqvcH3\nBK8163WG6QTsAFW4sdgVbJw7z3A6Boxj7N9mBlOup/FGnXUs1+bnbBw7k45UZeTfjvHpXHrj\nX4plL2d2JfSWv2E9FeEnQGTgta5YGAU9CjHO8VBT5k9TqrwYx4z7mg354zH8O/7zGM+n5qy/\nJ4K5PniCGtVJb3hQ+ffuU/MiIAJFSiDZD2eRZkXJFgERKDECbyC/+0N1br67Y8obwc3cZe/E\n63jwhnAn78ok87wh9JrZ/kcELvSs2MUz759dBwGTIDoJd0NDoOasNfbvvRk2x/e2SJhWFLPO\nO13pXfDN349lw56tNv2hwyFj95gZd+q9AV6AsFeb0Le+bVMteh0kdvXa1RNxLOZfc5fZIvIb\nyNQNluE77jpOdnDnP8aUrZS5Nj/npsrEmzb/dlzn5czl8VBTrL3HehBx2eXsGsj7UAGLCaOz\ndC+0c8Ni0v/+NHn3791giWehvWfeO9vRu4B5toY1ZzyHjCVr8WprViaZBpH/JLtVkAiIgAiI\ngAiIgAgES4A3urzJol5OsuuLPesZh118TCuCic7uXDHI7Gci5tmikMzWReBSyMT9HvPVnoh0\nDMy6OObNTbcnSmL2Mk88xucNfDoWxP4PwoG8afTn9XzP+hpfoui8mG3ZotCUPYGVJu7jnvkZ\nmI/6Nrzas54tBkE9oGP5MA0s3+/ceTpANjTUXeZ6Ol0mrX7nbaa77gZMW2LMtznG7U3siHXB\nxOPUbzshwLu+jSdCc+XDbnOsl2b7kz3bpjtryqYXNjgSegqqh8w+b8O8sWzz4q2j3C8dM78d\njwBzzNWYN87+SE8413vr2jjPur9j3m+/Q4DZZ6rjZpJ///61LAIiIAIiIAIiIAKtTqA5B4k3\nR3xK7r3puSJJqu7wxWELQj9fvO2xbG64zf78gz5waGQ+/TbreVM8DPLasVjw3lB+g2XesKdj\nQez/FziQSR+nW3sOzBtotpSY9bzx9Jr3BvxY74ok87shzOzHO708Sdx9fHF39sRhGb4LTYDu\ng/aG0rWbEdF7bM7/n7sxb3R/SLJ+T3c9J/0hs/3+DGiBsS6YfeXCQTo2RVrf96TjTV+cQ7E8\nFXoOGg2xdY12JUTngq00z0J+48MJk7e7PCuzdZDaYh/LPft8BvPGAeLuu0HG4eVx2TpsrCkH\nic6bSedizPc3G2HKuv8ZZNZzOgiiZZv/hq31XwREQAREQAREQARySKA5B4lJ2QjiiFzmxoc3\n/RtDXuuEhXmQicMpn7RPgXjzNwvyruP8f6FkdiYC/XHZQsERtLw3dYwTg/aDMrGW7r8CB/M6\naF9g+WjoWOgtyJt2svKa10E6xrsiybyNMDp/3v0xv31TxJ3sifs95o+CdoUe8oRzXztC6dpO\niOg9PudP8mz8pG89nVvyMcY0cBvWhXVMYJbTXDtIx6RIJ7sTepncjeUR0InQbMisex3zxs7H\njAnnlK1pLIdtoPMg1hOzfhTmjWXrIHH7P0Jmn5x+CP0JouPmPR/pSPWHjDXlIPVGpJWQ2S/r\nGVuZT4G4fxNupsZByjb/2KVMBERABERABERABHJLYCwOZ25mXm7i0Od44jE+HQHewHttABa8\n+zP79U95s3wrxKfcqexUrFgB+bf1L/PGLBtr6f75lN+fFrPsfcrOG1+vZeIgcTv/jeXz3p35\n5rfEsteJMOnxTm/ybdPcYhQRFkLefZibXm77e9+6RxjoMXa347afesKynfXm7fYmdtISpyKd\n8mG9vwXyMvHPz8X6wZ40snXlpWa24T7Y0uM9r1qSF+zKOh3i+eZPn1mmc3QY5LWmHCTG8zte\nZl+c0mGmk2zCTF3JNv/YlUwEREAEREAEREAEckvA69A05SBFkCx2ETI3Ppz+LklSeXPHm+YP\nIO+TZsZnl5z/QTtB6RhvMOkQLIK4vVEM8+ym9EuoJdaS/ZPH3dAyyKSLrVzHQuy+ZMJqMO+1\ndG7AvfG5LzpZZn/NdVNj/Kch700qt50O0SnMxu7DRub403w74A2wWcfpQb71X7nr7/CFZ7NY\nKA6SSfsxmJkCsT4aBmxZ/D9ofchvlQi4EPK23pjtWC/OhuhIeK2lDhL3dSj0X8hbJ1gvX4IG\nQn5rzkFi/AOguZBJ/0+Y/xvE88LrUHv3n03+sTuZCIhA2Ah4nwKFLW/KjwiIgAg0R4A3SwOg\nPhC7is2BsjXugzedvCmbBtVCQVq2+y9DIraA2L2Q3asKyQx73pDPgHgjn2vb2z3gF5hOz/XB\nc3Q8toRuBtGR/Q6is9CU8bzoCfV1I03HdD5EZ6M1jcdlOunE0bHjtKXGOtYJmgylW7/ylf+W\n5lXbi4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI\niIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEDABOyA96fd\nZU9ga2xanv3m2lIEREAEREAEREAEREAE8kagFkf+MG9HD/DAcpAChNmCXdE5er8F22tTERAB\nERABERABERABEcg3Ad7TFr2TVJZvijp+goBpOWqPJXrfMhEQAREQAREQAREQAREoFgIVSOgy\niNOiNzlIhVWEdI7kIBVWmSg1IiACIiACIiACIiACJUQgUkJ5VVZFQAREQAREQAREQAREQARE\noEkCcpCaxBOulRMmTLieCleu0s+N8q/yV/3X+Z/+FSNcMXX90/VP1z9d/8J1VWvd3KiLXevy\nLai927bdtaASlOPEKP8q/xxXuYI6nOq/6n9BVcgcJ0b1X/U/x1WuoA5X6vU/m8KQg5QNtSLd\nJh6Pf1mkSQ8k2cq/yj+QilSkO1H9V/0v0qobSLJV/1X/A6lIRbqTUq//2RSbhvnOhlrw2wzH\nLt+FKiEN0hA8X+1RBERABERABERABESg9Qhw9LrV0HbQ+NY7zFp7PhEhHdYKbT6A9925TGfz\nKVKMtQjQQXKgUAyNuFbuFCACIiACIiACIiACIhBmAryH5b0s72lzaR/jYDxupvpzU4lUF7um\n6GidCIiACIiACIiACIiACIhAoRLYCwn7N0TH7Fnofigdm5JOpFKK0x+Z3R7aE9oK6gbl23LS\ngjRu3LhBVL4zm6/jK/8qf9V/nf/5uv7k+7i6/un6p+ufrn+tfB3KVwsSs8VXVCZAq6EhUIut\nVFqQDgepy6ENoGRDm9ch/FvoIOgrKJQWjUYvcTN2fCgz2EymlH+Vv+p/goDO/2auFWFcreuf\nrn+6/un6BwJhvf7TMfod9BF0G/QrqEWWzFlo0Q4LcOOxSNMYaDAUh5ZB86EZ0EJoOUQOG0Of\nQ7dDYTUOylHKA3Mo/yp/1f+wXt2az5fOf53/Ov+bP0/CGkPnf/jPf97DXwxxwIbNWlqRw96C\ndAMA7QLREToLegxKZjxxToOucaeTMb0HCpVhHPyHHVioMpVBZpR/lb/qv87/DC4ZoYqq65+u\nf7r+6foXqota8szwvp+SNUNgGtZz2Oz2zcQzq/tjhq1M9EJzaTl5BymXGdKxREAEREAEREAE\nREAESoZAPt9BChxy2LvYrQtisyB2q0vHpiMS43ZJJ7LiiIAIiIAIiIAIiIAIiIAIhItA2LvY\nrUBx9YKqoVVpFF1/xGFr05Q04iqKCIiACIiACIiACIiACIhAYRHojOR0hDi6HccaWALRJ5C5\nBO7AlO/czIWOd8OSTcw7SGw9Yhe7k5NFasWwnHSxGz9+/GFUK+ajoHet/Kv8Vf91/hf0RaoV\nE6frn65/uv7p+teKlxjuOt9d7Di8973QAoj3/n5NRdjdUDeoWQt7C9LpIMAWpP2h+yAOvMCW\npJXulC1LVBsoChHm09BdUOgsEons5Wbq8dBlLo0MKf8qf9X/BAGd/2lcL8IWRdc/Xf90/SvJ\n6x+/ffl7fAPrt275b4UpnQQ6C2Gyy5CZK9wMzcB0PPQDxNYjtiStA/WDToJ+A50JpRq4Dass\nK+wOEh2eA6GdoAehPlA7V5gkjHH4HaSPIY5k9x4USsMINhywomRN+Vf5l2zlR8ZV/1X/Vf9L\nl4DO/5I8/49FjaczNKlt27bsSUXj/fAfoN9DD0BhsEOQCTpHL0H83ie/hZTM2FtsB4ij3D0K\nTYfehZJa2B0kk+k3MdPfXeiNKcVx0qdAM6GSsHg8fmFJZDRFJpV/lX+KqlESwar/qv8lUdFT\nZFL1X/U/RdUIazAdIXY3OwO6c4sttmALCo2tKqdAXHcc1BWaDh0KFes7Ogcg7d9BnK6GUhkb\nRN6CRkLfQ0dDKR0krJMVAIHhSAMLrqIA0qIkiIAIiIAIiIAIiIAIFC8BOgJ8tSSVjceK6e7K\nqzA9x51vyYT3sLyX5T1tLu1THOyRDA/4DuL/p6ltSqUFqSkGrbGO7zOdC1WmufN+Xbp0sZ54\n4ok9KysrP9l+++3p2Sbsrbfe6lZRUTEUT7/iq1evfmPnnXeuMesmTJiwOebZbfCHYcOGTTDh\nl19+eWTPPffcFcvlsVjsc+1P/Fg3VF8azhCdH7q+6Hqq60HD1cCydD3Q9SBs14OhQ4fufdVV\nV23fsWPHW1988UVUdYf3+psdc8y82lNO+X495neXXXapq6mp6YtwvoP/KFqY7oZ9ifmW3E/G\nsT1tO4ijyKVjfMVlLGS2TWcbf5y5CNgKKoe4v+aMaeP9891NRZSD1JjOY1hkEx0Hajiy8aqM\nln6F2FdDdrpbLV682IJzNAYv0rLAzjbblZeXj8IX0EcjPA5HaR+E86lAwhB+K2aGom8xR+wY\n0BBqWbvuumt/rPs/LFdof+Jn6oXqSwMJnR+6vuh6ih8n/X4kLgi6Huh6EKbrwQ47/HpAdXX9\nY6NHj4688075FS+++F/eT3J0t6o+fVY9j/N+F+YXDtGCiRMnRhDO100Wn3XWWVtg3RMtvJ98\nH/uisUUqlphr/h/fjf8lNK35qCljPIg1j0BPQTz2RCiZ8Z6c9+fXQ3QMn4FkaRL4H+KxefD1\nNOMHFW20e9xOQe0w2X7QgnA9lWxdKYQp/yp/1X+d/6VwrUuWR13/dP3T9S+M1z+nF25b94f+\nCr0MLbGsetzHtkWLzN0YfMy5Etrvtdc+vMNX/uyWxhHe2ONpIMQBDtK2OXAw5uEgXuFlJw6f\nzXvoE9PeUTAR6fj8EeI7VDz+LIi9ql6AxrjT8Zgi2Yn1bGXiQBVNmlqQGuPhsOAbQV81Dg7H\nEp4O8GW8kjXlX+VfspUfGVf9V/1X/S9dAjr/w3D+O11Qg9nasg00FBoG9YCWQZMgtuDcD58H\n0xWXYKC6HaD7EDa9ffuJB+FXoC3maRUQ7wcZny097BLH7dOy1VbFkRHLZotNI7sGvsdtVi2H\nyev5ZqM1rb5Ap+gm6FmILUg7QttCXuPnfeggcQS7W6CZUJMmB6kxns+xSIXS0O2U/UtL1pR/\nlX/JVn5kXPVf9V/1v3QJ6PwvtvPf4SdptoToCBmtj/nVEFt/6Nxc5E6/gPODFqNGRmeA8fm6\nxaGe8u+P5bsgGlqbEq0sPNa+DEjHbMvpjHhIh12ZLP46Dd8XTbaqtcO+wwFGuQdh18GOUBXE\n11B+gmRFSCAnXeyKkIuSLAIiIAIiIAIiIAIhJuCgRcdBy5DzewjDbzuTIbTsOPUQHto7D0Cn\nQWg5csrTBPEO4p0MvQvReWLPKIrzDGO3OhrfxcnI6qzym+usijgU82oFxgXDjhz0r7stox0W\naGS1IBVowShZIiACIiACIiACIiACYSLgcGCEwZBpFeJ0CAQnyZoOvQc9BLGF6AO00qzANBvj\nYAQ0thbxGFtxAfYhxH0bY9ezDM1Z2NBiZfP9pbUMLzatWiuwsAL4HSg6j3dCpjVtrRTKQVoL\niQJEQAREQAREQAREQAREoKUEnPWwB68zxHl2aZsP0VH5L3QFhJHX7B8wbQ3jcbxOUUuPQWeO\nAyMkNXiAdAIL2XogcRzmm9OUFnYH6UHknH0lM7X/YIN/ZLpRoccfN27cIKYR30WaWuhpbY30\nKf8qf9V/nf+6/un63xq/L4W+T/3+5eL3z+ENN1tq0BUu4RRti2kX6EfoY4itQ7dDdIZmYxqU\n9cGOnoM4AMGjyXYaZPnHrUgbeEBwkBzfe08cK8GKwHtKtxtgsqTmIuxOHOTfEJ3UlBZ2B+lg\n5Dzj/pXYht596BykaDSKUU0Sdrw7LamJ8q/ydyu86n9JnfkNmdX5r/Nf53+CgK5/gVz/HA4A\ngPeGGrUOsbWIXdY+gdhiM6Zhak/BtDXtVex8AfR8qoMEef2LWPF58IPwfpSd1BFaWvhd7OgY\nNekckWPYHaRhyOM4qD00F7ofSsfeSSdSEcZJ2SRahHnJJsnKfzbUwrONyj88ZZlNTlT+2VAL\nzzYq//CUZTY5aUH5O9U4ILtkma5ybCHie0RwEqwvIDpDV7tTDLBgc7CCXNrvcDC+W4SR5VJa\nC/LfeJ+OFd0AI9nBf3ASTUY/r00s2t0bPj77c3CRzoXdQfoU5dIbmgOtC30G/QsqScN3EB7G\nV5J9Fbp0UCj/Kn/Vf53/pXPFa5xTXf90/dP1L53rH2/8rU0g4wxxSueIAxJ8C7GbHF/sp1P0\nEZyhGkzzbe82l4Bgz/847qWZb5uO41qG5iUNqb0WlcIN2A9JY1/JQi200UgbHZdOkEwEREAE\nREAEREAERKBVCThoVXHWh/DtHOdG6G0IXeToSDkzoX9DF0G7QR1aNSmZ7fxQRN8is02Ci11r\nld9bZ1U6fq2wKngfy/HI/x7c0fK3p7C3IBmyfHmNfTN3hw6CUOllIiACIiACIiACIiACpUHA\n6YV8skVoG3fKeT6YXgSxi9ob0N+giWgd4Ts9hWiXIVGXQr+G+K5TPgy9s5xaMKpIdnB02cp1\nY8SJSEc2Dixb3sYnywPDSsVBYl734z+ZCIiACIiACIiACIhAmAk4HD1uK8h0leOIcnzVYhnE\nEeXYRe6+hqn9HabFYoOQ0H2hl/OVYDS7DcCxMUBD8lHsujUMdJbL5J2Kg22ZxQEvxzZykLIA\nF7pNxo8ffxgzNXz48MdDl7k0MqT8q/xV/3X+6/qn638aPxehixLu3z+nLQpsCGScIbYQ0ZHg\noAVo7bDeP/fc758eNGjV1FNO2egmtHzwlYtitWOySXiw5d/0h2LRfLQqmzS2YJu9sC17hg2H\nnoXuh9KxKU1FKqUWpKY4lMS6SCTCSkQrSQdJ+Vf5N1R/1X+XQ0lNdP7r/Nf5nyBQ5L//Drt1\nbQYZZ4jTTSG+/8IbXg6icCPEFqJP4AyhK5hlHXLIxAcwwXZF5xyhpcbiqHgtcuoCvv6xDFKO\niocER7E+l4ZxIaydoTch3udeAbGVsEUmB6lF+IprY4xgk7hQFFeqg0ut8q/yD642Fd+eVP9V\n/4uv1gaXYtX/Yqz/Dr5HmhhO2+sMsaWIN+jTIDpBD7nTD3DPvgLzSa1Iy38AMvMSdA90Q9KM\npRkYbP4j1TaO66ToYgcHKR++BVsLfwd9BN0G/QpqkeUjEy1KsDbOnkA8Hr8w+62Lf0vlX+Vf\n/LU4+xyo/qv+Z197in9L1f9iqP/OeqhpdIbMIApbY74dNBf6EPovdDmEViL7B0zTtiIt/6eQ\nwenQ3WlnNEXEYPMfn+9YEbRqFdyHYj9H9i+G2A0RrYWJ7pWYyIqZwGgkns3DGua7mEtRaRcB\nERABERABEUiDgNMDtz37QJdDL0ALIdwHOT9Cr0G4L3Iw6rDTN42dhTVKf2Ss4Boyaq3Ku+qs\nirhfK6xydgN0TtEw32Gtj8qXCIiACIiACIiACIhAMAScjtjPLyG2DpkWon6Y58v8kyB2lRvj\nTr9GywQfGMsaWo8KkENpfCi24DzTAqwJSpIIiIAIiIAIiIAIiECzBJwqROGQy8YZ4nQwxIEG\nPoc4iMJfITpFn8EZqsdU1kDgJEy+h14ucCAoX7s6VRr7WBE4xC0aUyLVrnMaLgcpp7jze7AJ\nEyZczxQMGzbs3PymJD9HV/5V/qr/Ov91/dP1Pz+/QPk9auv8/jm8h9wE8jpDm2OZo5h9A9EJ\nusud4uV5uwbzebHWyX+gWaHTyHuz/QPdq7uzgPM/GT3pMOhX8g/FzrLiGOm7+E0OUvGXYdo5\nsG27a9qRQxhR+Vf5h7Bap50l1X/V/7QrSwgjqv63tP47HLiM3xaiM2QGUWC3ObYkzILoDD0J\nnQ9xRLmCukkugvKHw2HtAb0JBW6B5r8M9SCa5EOxfIWs1op0y/2HYgPnxR3KQWoVrIW5U4xi\n8mVhpiw3qVL+Vf65qWmFeRTVf9X/wqyZuUmV6n+m9d/pjZLxtgxxngNJLYLgAFmvQ9dCHFFu\nPqYFbUVQ/n9pTYBB5t+OOPMtG9+Tsu3G3zty3xz7KYJ3y4q/h11rFof2nQEBjNaiUewy4KWo\nIiACIiACIiACgRBw1sEtyEjoEugZaA7EEeWWQWjRcK6HDoMGBnI47YQE2kMdihFFXVXZFXXt\nKmJ17Sodr1a0raCL5Pw20rJvNhUKE7UgFUpJKB0iIAIiIAIiIAIi0KoEnLbYvXdEObYMsesc\nu3jh3ZLEIAoXY8ouc+h1gpYCWdAEOGgFB2J4BPpT0Dtv7f1FyiNViU/4+j4Ua9OntiysRge8\nENQaOUitXZO0fxEQAREQAREQARHIOQGnHIfkoAnernIcVIHvE7HLPZ2gG93pJwimkyRrfQL8\n8Osk6KrWP1TwR4hb8YW2jQ/F2r4Pxbpd7JZaVt4G4wgyt3KQgqRZ4PsaN24cnxJZ22+//dQC\nT2qrJE/5V/mr/uv81/VP1/9W+YHJ+04Tz/TZMkFnyAyisAXmK6FpG2648ov27ete+PDDjqdj\nmSPKLce0ZKzAfv9/DfArINelaP1iCDL/dnl0fSvCEQwbmozWpL5h0e4eRdfBEAzeLgdpTcmG\nfyYajaJ/ccKOD39u186h8q/yd2uF6v/ap0foQ3T+6/wP1/nv9Ed+vC1DW2GZ77XMg9gy9Dz0\n54Z5e/FDD024H/M9MMz9W5iWnBXY+Z9z5zTI/Mft+GcRm0O2J/8W0jzHKqgRDLOt7HKQsiVX\nnNuxWb2UTfkv5dJv6FZSygRU/0u59FX/i7j+O91Rdb3OEOe7QbwR5YhyE6DbIDhG9kxMk1kR\n5z9ZdjIOy2f+z0Nqf4TuzTjVwW0QXP7LrCFWNIlz5LaH9YnqQ7HBFZv2lBMCGAf/YQeWk4MV\n4EGUf5W/6r/O/wK8NOUkSbr+FeL1LzFgAp2fLhC/U2jmzXQnhLFVqCe0CvoYYuvQY+4UH2O1\n0/pNV/nnrfyvQVmdAbXKB2Cx37QsyPKPRq1PnKY+FBsLx4dig/Mo0yoiRUpBgMN8Xwh1hpak\niKNgERABERABERCBgiTAdzIsDJedcHTYukOHh1Pj7HCejpAJ43q+H2RsNWYWQos8U3536E2I\nI559BmcoBG92ICelZUcju2ZAjFDkvLZX+a2RqM132Ro557V4/tZmVl3kpGrr9ntWJZzCos6v\nutgVdfEp8SIgAiIgAiIgAsETcNphn15Hhw6NcXqSTfmA0zx05iDHfNjpd3i+TxLGOJC9AlNZ\n+Ag8FLYsRW0Hrxkl+VCsm9Gf4onWzqLPthykoi9CZUAEREAEREAERCA1gUTrjunG5nd6zLLf\n6fG27rB7G1t2vK0707H8AWScIO86zOv7QWBTisYWwzbQ9LBmPh6xquzomocBP2fTbU9KfAfp\n59CinZODVLRFl3nCx48ffxi3Gj58+OOZb138Wyj/Kn/Vf53/uv6F4frv8L0c49iYqd/B8S53\n2m23xYnWndde68LWHb4wbxwbM52eJIzrIHslpkVt+v3Lye8fvzn1IsR7rLMLqcIEWf74Emxl\ngy/k8Fz62RpecW34UOzPoUU7JwepaIsu84RHIpG93K1K0kFS/lX+qv8JAjr/M798Fv0WhXv9\nS3zMtLnWHb8TVOEpEDovbL3xtuB8h+X3IOP8LDz33O/PrqqK18BBOrIUW3cKt/xRSjmwHOX/\nT8jKGxDfKS8oCzL/cQcfio3iQ7FW8g/FLovrQ7EFVfhKTPMEMIJXSX8lW/lX+Td/loQ3huq/\n6n9uarfTAccxDo1/6m3VMes4EIExPpH+AVrj2GCejg8/bu53gkzrDru/NWudOk04qiFSaXZ9\n0/mfk/OfvXQaDVzQbMXMUYQgy98uw4di7dQfiu1Wjg/FpnVW5ijzWR5GLUhZgivGzeLxeME9\n1cglR+Vf5Z/L+lZox1L9V/3Prk46fbCdGZ0tmYNjHB2zrtxznBWY97bscP5biN/t8TtBXAfn\nqHUcGNV/1X/Ur9a2gnSOmOkg63/iQ7EWPhRrJ/kWEo41LxaOD8WaEVdau9Jo/00T0DDfTfPR\nWhEQAREQgZwScDbA4V6AOKWhS03S1p1kjo4bZofgOXIi7/onAn4CvH/+C0Sn6DL/yjAv125c\nfh+67B3vz2NtHMN8f1lrndE5cudtP8ZP9a8vtmW1IBVbiSm9IiACIiACItBqBJwIdv1H6Ero\nHehE6FMIAxuk91FSxJWJQNgJ8ME2nYD9wp7RtfJnW59YfGXDtr3vAq6JNqs2HB+KlYO0pkg1\nIwIiIAIiIAKlTMDZGLm/H+L0DDhE95UyDeVdBJog8ArWPQx93kScUK6yI9YgK2KhK61vFLuG\n5yeRbhVWO4uda4vc5CAVeQFmkvwJEyZcz/jDhg07N5PtwhJX+Vf5q/7r/Nf1L9n1P/GdoPNw\nfvwZeg3aFM7RbJ4vYTFd/3X9Z10O8Px/vZjOjUDrf8SZj9YjDKiCryF5zX1p50d9KNZLRfPF\nQMC2bb5EW7Km/Kv8S7byI+Oq/6r/yeu/sxnC/wkNgNCdzuZT8dCZ6r/qfwsr9XrYvgfE4eOL\nzoKs/3Hbqo5Gknwo1qVSZVuhaHwJRSaKrqbmKcEYxeTLPB26IA6r/Kv8C6Ii5ikRqv+q/42r\nXuL7QxchjN9ueR76NZyjeY3jhGdJ9V/1vwW1eSts+yL0BFSUDlKQ9T8SjVQ0uEe+LnYNI5xH\nyiLogCcrOgJtkeL1oaHQttBGED9Ql2/jy34cCcX7PYh8p0nHFwEREAERCCUBZwh+ciZBGG3O\nOSyUWVSmRCA4AtdhV3dCalQAhLqhZefWb11RWz+00vFq5VYVvI91Du5oXYNp0VupFPYOKCkW\n2DZQsjx/h/CnoUuhVZBMBERABERABEJGwOFDuH9AB0D/hnZHqxGcJJkIiEATBPh+nswlYNtR\nDNKQ5EOxDS1IdrdKfCg2BFYKzWDoU22NhTgqz0RoPkRbDd0BPcUF2DnQh9AWXJCJgAiIgAiI\nQDgIOOg54dyKvMyC9oHwDRMbLUdyjsJRvsqFCOSOQNyKf44udvxQrL2WkIx5deH4UGzYHaTt\nUFb3QHdBvaFfQb2gEdDH7jy7F+DHw9oZqoImQH2g0Nm4ceMGUaHLWJoZUv5V/qr/Ov/TvFyE\nJJqD3hMOW4qmDBiwcsQOOyzh+0Z4uhvOgRiaKjRd/3X9T/P6z/vim6GHmqpPxbYuyPofKbOG\nWGV2daI/FvtkeYXF3uURtlQXvTFbYbZ9kbnJ0B8g9o2kYWhC603ot9C3EB2j16A3oF2gqdDJ\nEH9IQmXRaPQSN0PHhypjaWZG+Vf5q/4nCOj8T/OaUZzREoMvHIK0nw3hXSPrWWjEmDGfHofp\n5hjmuB7TkjNd/3X9dyt9c9e/vyDe0RDvH0NjQdZ/3ER/ErEc9MKyK5MBmh2LL0kWXmxhkWJL\ncIbpRf9qfPH3Z+fIuzkdoRXQbp7A6Zhny9KmnrAwzdrIDFWqpvyr/FX/S/Xsb6j7IS5/pzN+\n6i5A8U6D7obGQRsg2wdBb2Ne178GBkBRkqbyT6/8H0Pt2Bri+RMmC6z87TJ8KDZqVeA9pHgj\nRRtGtetWhg/FhsDC3oK0CGVkBmbwPzUbiHVtfWXIj171hb72hYdiEd1FH3ZgochMFplQ/lX+\nqv86/7O4dBT4Jg6coEQvieMw5W8euwdhIAb7J0zXmK5/uv7p+pfW9e+LNSdNiGYCPf8dZ54V\nwYdiMVpDI0TsnwX7MabBzhpIFPZ/DrxAh+A+yOsMcWjvNyCu2xuidYQ40h3DTodyaRrmO5e0\ndSwREAERKHoCzgj8XD0H4bbEwbuzHK6bI0vJREAEMiAwGHEPzCB+yUddPaLsyvpdKmOQ49XK\nEQ3DfB/Vw7oxDJDCfjG9BYV0KMQ+p/tBH0C0YRBfInsC+i9E+wzqA7GLHQd2kImACIiACIhA\nARFwKpAYDizE94s2g56GfoXWoncxlYmACGRGYFtEfxH6P4jnkiwNApFIpNz9FKzbZtRoo0h5\nJByvcoT9HSR2q9sZug5iU+Ceruow5RfEj4SM8evIV0L4sbFqTaCmIiACIiACIpBfAg56PTgX\nIw3Toduh/0HrwzHCYAxyjsBCJgLZEBiKjdAd1To1m41LdhvHXoy8x9DFDq6QT1ixtD7xGZ2i\nxxP2FiQW0ErofFfrYhqDFkJ++40/IGzL48eP55NHa/jw4Y+HLW/p5Ef5V/mr/uv8L67rn7MR\n6uwfoGMgfsOPD/vQZdxeimlGpuufrn+sMMVV/zOq4k1GTlH/+bChJCxF/rPKO9qPBjg2B/f2\nv9PFN1Qsu1tVBJ8SSNa4lNXh8rZR2FuQ/GDnISCZc+SPF8plNIvuRYUyc2lkSvlX+av+6/xP\n41JRAFGcXXHz8QIS8gW0OXQ0xBajm7JxjrCtpeufrn+6/iWuf40HFuDJUQIW5Pkfs3FdaupD\nsbXxRgPEFCveUmhBKtayCTzdGMGmpLsOKv8q/8BPqiLaoep/odd/h98UGQX9EdoEegoaDodo\nIqYtNpV/oZd/i4u4yR2UevnXwc455xy+QvE6tFOTsEK4MtDyjzhDrGikei1MbqNR7wp+KLb4\nW5DkIDUu4VOweDJ0J3RX41UZLXEI8Q8hvlCbjpWfddZZ1qhRo2bH4/G70QTOF3AThmbRM+H5\nj0bljsdisX223377t8y6iRMnvoH5oVi3AB//G2DC33777YEVFRWJ43v3h/kLsa+TsN2KIPbH\n4wWZvtbeH4a5PBwMxxtOmfJr7fS1tHybSx/WbwIGQyZMmLB7OvWluf0VGz/Wf+Yp1fkRtvwy\nPzRvflH+DyQC3X/FdP629Pwo3OvfsNEoDv7unDZq1LwOp58+sywadVbHYvV/x/V+jXPU0vNN\n1z9d/3jae68H3vsDrgvz9eDee+9tM3nyZL5iwdbYhIU5v8hghbd8A73+2fYnaOFevWDowZWz\ndz0Js3Fr/TEXWJXTEQybXRuOD8XKQUoU55p/PTDH7gyctsSmY+NDofI0d3LMM888c+iOO+54\nbI8ePThYxBrDQ48xcHa+ReWO19bWNloHJ+dMROwD/bBmA8yMHTt2+p577pk4Pjb73Kzbbrvt\nfnjrrbfuw/4mB7E/7jfI9LX2/lavXv3GzjvvXGN4ZMqvtdPX0vJtLn1YfyrynHZ9aW5/xcaP\n9Z95uvzyy5ckOz/Cll/mh+a9HtTX16+5HnBdMZ2/LT0/Cu369/LLXTd/6KGe26EYZkBzoKsH\nD17yXDQa36Q1rs+6/un6hzpWste/LbfcsiseEH9z9913m9GMdf1jhXAtk9/zSBTdfSNWRecp\nr8Yrl86ybDhIbed/adWXJd5BinSvsNqb/WoaHgJBOUiZEuETRNYsDj0uEwEREAERCDUBB119\nnFXQN9CBUKm9Dxzq0lXmRCDMBOoOqLio/oDKuvoDqxyvVu5fyftY55Be1t/CkH9dlBuX4nws\nToY4lYmACIiACIhAwAScPbHDl6H7oQ3xjhG+v4Kv0stEQASCJLAldsYRIGVBE4hYbfDhnEji\nW0j0IrzCYlWUI9wVv5Wig9QZxdYfGgz1htpCJWF49+R6qiQymySTyr/KX/Vf53+SS0MOg5zD\ncLDnoBvhFJ0GJfqk5CIBuv7p+ldC1z92XX0L4n1ewlT/A6z/Nl4fSThF6FsX8Qm0y+1wfCg2\nFF6eW/+bmgzBSvwYWftB3ZJE/A5hr0F/gkI7DDhe0u2aJO8lE6T8q/xLprInyajqf77rv/N7\nFMsd0HlwjDBcd25N5Z/v8s9tefuPVmLl3x35vwbi6wsJK7H8m2yvmQaa/wg+FGtzmDo7qQ+h\nD8WuwV7wM5chhVe4qZyB6XiIL2svhzpC60D9IAzFYfFjsWdCj0GhM7z4+2XoMpVBhpR/lX8G\n1SV0UVX/81n/HY6geCV0Am4qHshH5VL557P881HijY9ZYuX/DHJPrbESy/+afJuZIPMfiSQ+\nFIvvSfk+FNuwGJoPxRp2YZ0egoyxC8OL0C+byKSNdTtC70OMz+bZXBqfcvC4GqQhl9R1LBEQ\nARFodQIOXlh2MHomB2OQiYAItAIB3sPxYbcsBwRqD6s8vX5U5ar6URikwaOVhzYM0nBgr59b\n7nKQnFY7RNjfQToA5L6DOP2oCYp0TthfdSS0DDoakomACIiACIhAlgQ4Mp3zD2zM7+vtg5Yj\nDMYgEwERCJhABfbHXj+8h5PlgECEH4ots6sSQzGwk51XWOzdnh+KLX5jtsJsmyNz7FK3Os1M\n/oh4kyEO3iATAREQAREQgSwIOLxpexTaBdoVzlGjb9hlsUNtIgIikJzAqQhmD6C9kq9WaNAE\n4hF7UgQfisV1rTLZvmevCMeHYvGEK9Q2F7nbCkr3g60c4Y5O1VdQ6GzcuHGDqNBlLM0MKf8q\nf9V/nf9pXi5aEM1pi43/A7GrNm7cCsM50vVP17+QXv/uxHm2EcSH2ylN9T/A+h/F5wnKrAqr\nDCPY+YUS6N4mHB+KDXsL0oMoq0egp6CroIlQMmP/VXy4z+IQ2G2gZ6DQWTQavcTN1PGhy1wa\nGVL+Vf6q/wkCOv/TuF6kHyXRWtQX8deDNoAwSl3C8JtiT3Pn8z7R9U/XP7cShu38Zw+hZnsJ\nqf4HV//tqDPHsu0Yrm+NfQjeScN+rLVWNMwV9//GmSvuvCRLPfulcrjHv0L7QrOhWdBiaCnU\nAeKLffxh6wnVQ+dA46Awmlt9w5i1tPKk/KeFKbSRVP6hLdq0MpZl+TvV2Hs/qD/E3wpOKc6b\n3w72xuDvB3st0LbBzcO8htmC+Z9l/gsm/S1NiPLfUoKFsf0wJOMI6MwMk6PyzxBYyugRfD/U\nTnwJqXEUp2GxsiztXluNty+wpbA7SCwufm/iWYgtSOynui3ktZVYmAPdAN0CzYRCaRgH/2EH\nFsrMpZEp5V/lr/qv83/tS4XTHmF0dKj+SaZ8yEarhWZA30PToZfcqVnGAzg+VS1M0/VP178Q\nXP/Y0+dl6N5MzzLV/0Drf1mDe4Qudl6LO3RC7bB8KJaZKTVjqxG/f1QFLYB+gvJto5GACyG+\nA7Uk34nR8UVABEQg3AScK5G/YyF2qWYvAhofltEBmg4Zp8c7ReuQXbIPmMBEJgL5JoCWWWtr\n6O/5TkgpH7/upMrzbNu6GtfDRo0stTHHanPvauvg/ta1T05P3NMWNaZGmSvqnKSfeHato2Qi\nIAIiIAIlR8CJIsvHQJ9DfBLtOkH2QszLREAECpfAe0gaJcsjARsfikUXO1xHfT0SzIdi20bQ\nENG4cSmPyc360KXoIGUNSxuKgAiIgAgUPYFDkQO2Gh2JJ6B8H1UmAiJQmAT4MGMw9EVhJq80\nUwXX5wu8dLkaAzWwJ9bP5vZJm7ciXgg9s35OV5ZzcpCyBFeMm40fP/4wpnv48OGPF2P6W5pm\n5V/lr/qv83/4cOsC1AN8wLW0nCNd/3T9K7LrHwdHGQMNgfh+YItM9T+4+h8px4diI5HGzhFL\nh0PVwHp34Idi1YLUQEP/i4JAJBLZy01oSTpIyr/KX/U/QaBkz/+ZM6v4EfBNoH3dulAyE13/\ndP1zK3uxnP8HI72bQyODOElV/4Or/3G7mQ/FLg/Hh2LVghTEmVck+8AINhyFqWRN+Vf5l2zl\nR8ZV/53ar7+u3hQoHkXr0cxSqwsqf13/iqzOP4z08lMtgYwMqfofYP238aHYKD4Ua/lGsbMS\nY9hEurfVh2KL7FxTcuPxOEfKK1lT/lX+JVv5kfFSr/+nnfaLJ7/5pvIEoPhbKdaDUi9/5b8o\nr/+BOEc831X+wZW/HXFmN/mh2JpwfCjWfaWqFH8uCirPo5EaDfNdUEWixIiACISLgPMc8sOv\nvx8YrnwpNyIQCgIjkIvzoF9DGk4fEArV6s6u+KtdZl+Ea6n71lFDShPDfN+w2jpqY+umh7+0\nzi7U9KebLnWxS5eU4omACIhA/ghsjEP/FtoQWg69AfEF5rB3m90IeWS+OZLVCugNiPleDWVg\nDrvW8cZreAYbKaoIiEBuCGyHw/DDy7dBco5ywzz7o0TRwS7hGvm62DUM8x2JlmEQ8BBYI+8v\nBPlRFkRABEQgbAT+jAx9Cu0CzYfQ99u6EWIYHYew2qXI2GfQ7hDzzQd6N0DMNx3GTIwj172J\nJ54TM9lIcUVABHJCYC6OchzEFiRZoROw7R/gAsXRzS6ylpD25aszfYBVmBmWg1SY5dIqqZow\nYcL1VKvsvAh2qvyr/Iuw/p+GU4s39wdBbP04HcL3e6yB0BToZQgf5WvektT/Qu5B8Hvk6GLo\nEGhbiPlmS9IAiN9EYb47QmmYsx4ijXr44c8WF2H5p5G/9KIkKf/0NgxJLOW/oK//01DN2DLc\naqbyD678I+VWf7hGUbyL5PjFAuzaLtK+1QoyhzuWg5RD2Pk+lG3bXal8pyNfx1f+Vf5FVv/5\nnYm/QnyqyvdnvMYP8R0GsTvKGZ4VgzA/DvoKusYTjgf1yVZuAABAAElEQVR9jcp/V6zz79Mb\nPZ/zbCG7GuJ7mU/7ErIUy4dD7Fr4B986s+hncA5WfLrhhiuXu+VfjuWnoM+hF6C2EO1KaDY0\nyxW+5REe85V/eDKWZk6U/0bnf5rUWi0az8HdW23vSXas8g+0/L9ECxI/FGuvJbCfvzzO63TR\nWyE/QSx6uIWWAYzi8mWhpSmX6VH+Vf65rG8BHIv98nnz/s81+9rd2brc/rmPft07m7xq1c46\n1Nph6SSrzppnTeh+vdVpr4esvpdOtj4deovV7dgjrUE30Vmypsz5rOFHq/M+11lLXjvKirad\nZm21eKvEvmNWFVyOSuw59RfQ4+h17lhd0bFiwZr0+Gfi6Jtea3VGnEX+VY2WY9a6iDNvTVgd\nlpl+2opzR1j1N6BV7IMPrfKtGtK3JqI7U7fZq5Y15wjLWkwHx2dgYIGBBQbWdndY1vvojjf0\nGJz/bH2inQqx695voKugkyF23fsVNAyaCYXOdP3T9a9AKjWvaf+GfgH1gwIbqQ77Smmq/wHW\n/7IUH4q1G/CH5UOxbnZS1imtyA2B0TgMn5Z2hpbk5pA6igiIQIETYAvRLdC6TGf5Hs42mEyA\n1ly347PutWLfXWtFh37DliQ79v5gzLPnHYa1nfN33HostyJ9z08sm3/x769Dp7ydLGfGpVZk\nU/ZUQzNUXYNMnKRTfhi9uY+j81ZnddKtfw5kSusTTl5DPri8wrO84km0D51iWVULf97GP1cH\nnzH2F2w5bQ2Ln6MMxmwDA8sCA+snpOiiNsBjUs+bM77fxBakTSA4VNbe0FToJKgb9AxUA8lE\nQASCJcCWo79BB0HTgt219pYLArErKtlrAT8kdqX3eLX1jlV95WrrwI2sa57+yrrIu64Y59WC\nVIylpjSLgAiUAgG2ZLBL7DrWrujqXWY9gUGCHqxd6elS9+WZf7XidUNiMYzQNumSNtaqb96M\nLba2TsCZdtlBVmzl1rE25/Ndnp+t6jzb+mro+lbdpOviP1kH/Lwijbn5cM56JJyZ5JHZaXsp\n4nhauZJGXIgY3Tz7+RHLa0av+tNQy1r0mlVzJN6zenRx0u2t0y63rDq29uzZeP0leDr9zZsI\na2BggYH10xC8zmScI0bvBy3iDIz77wHRKVoB9Ya6Qx9Bm0P1kEwERCA4Amj9tXBOyoqWgPlQ\nrJN8FLt12+tDsUVbtkq4CIiACIAAb8r5vktzbR75gsUR1+ZA51RWWlvDOfqh9kd0Dxtvr3IT\nxJt6DlxwmTXOXoYpb/DLrc/t5ZjS+ABsjmc5Eej+W4lpLMU6b7y1541rsfaazEJSNxDRwYFz\n+BiakR5jy7rfuiDgaAjvDK3Jq4nDfOH9hjXhZDDfrHSn7GqIFqWEwaGymBKKDpGxLTGzB/SC\nCdBUBERABEQABPihWHRYwPtHjRtZ+IsKWxySD8VqkIaG8iyJ/+PGjRtElURmk2RS+Vf5u/V/\nB1SPlyF2oaKmQ1dAvFkuJGOHNXRlsC+s//bqHWqXzj7M4xyxHxnzwC4q90I0tpIsgNhCQuM7\nTJMSc/hXRPWf+TgdOheigwSHZ41tgDnmm61r96wJ/XkmJQNP/j9AdLKhGUZ9MP9EIqThH4/z\nuWe56Gc9+S/6vGSTAeU/b9d/tvJ+BjXqjpVNGbZkG5V/gOVvR9pxDDu8cWqtJQRVRxMPHltS\nXAWxbWPvryCSpES0FoFoNHqJu+/jW+sYhbxf5V/lP2XKlA1RR4dBj0HsXrYE4kAA50H7QTtD\nDGuJ8boaSNesyr2c2vq5j1mxT46NWV9f8g72i4EHrE4Qu6i8Ah0F1ULGzsLMXRBbxvhOzUsQ\n7eNly5ZN7dSpE1tPiuH8fx7pPBK6EzoH+gQy+R6Lea5L1fKXjEH3sWPHTho5ciSdoIugm6Ej\noHbQvhC5TIGehXpBZDsdCo3p+qfrn1uZc3n+D8Ux/wONhlKdr26yWnei+h9c/Y/jQ7GRhuYV\nvAfqsYYwfB2JM3xWVdwmB6m4yy/T1LsNoJluFpr4yn9oijLzjPzwww+dp06dOhxbngjd79nD\neMzTYXoT4lv9vHH2G1sx/gVtBE2HDoVWQH5bFwHsGreeu4I39V9A5tfin5i/1F3X9GRPp79j\nW49Eex1xbWzSkXypmTfydPDYhe40iMfxG8P2htiFjN3NjA2Bc8Rjm3OAzsAeZmWBTh9Huujg\nMd+DIfJmy9IEqClLxmABnKMnsRHzPx8aBfkZ/QlhdCz5614Dhc1M2YctX+nmR/lPl1Rw8b7F\nrvaB+MAh36byD6gEIlb8R1wm+aHYxj6ES3h5XX6d4YCymeijHtS+tJ8CJ4Ah6x92YAWezFZL\nnvJf2uV/2223VaxatYov33udI1PffsDMqdCbELt2zYG8xnXsJvIb6CroZOgGyGvDsPAPqLMn\ncAvMvwj9zhPW/OxeTmVFxHrSdqxJq/+bcKhi2Ojh5jdcE8PrHCUCi7T+/4TEP7ImV5nNNGKQ\nJP+N1ru7rs3sEMUTO0n+iyfxAaRU+c/L9R830gXhHPFzPbr/Cer+r8xeD4+R0MHOdz/ZsGh3\nbRuOQRpK3aMO4LIbyC5GYy8XQryxWhLIHrUTERCBBIFONzhPY6ydOUuv7H1oxaYHflZ94O0f\nO7XWVk7M+hQRatG2szEckbnx+viSZde2ObVy56tfrtj67Bi2WY3XUOdyJyseGbpvxY6jx5f1\n3m1A/fdjv6t9++wdqg//5G0Mj93FrreW4XehsnbStf0i3XadV/v6HjtU/2bxfdyu9oPTh1ir\nF7Sx2w1aEumx29xo913XwX5/wvEWcn0qi9VaIzAodc/VddYW1iv2glTxFC4CIiACBUSgEmlh\n99cHINNqjllZmAjEbqxES759HVTlzVdimO/za6wDN4tc8/Sn8Yu864pxvnHzWDHmQGkWAREQ\ngRQEOtzsbIux2vbHk6CxVqSs0mrbZR1EHQT1RWcrtiCwxaAfBpyuRr/pn6xouW2XV/fE98Gj\n+Hmvwzp2w7Kc2p+6Rjuuvy720yfacdBKp35lZzg5A7GqE5pk2fWrvHzTC2y8edQVx+F1lcew\nnHhdTytWUxWp7mvXTb5gy3jfUbVlG5wzA8fuwPWpDMevRPe6s+QcpSKkcBEQgQIjwPf5XoAG\nQP+CkrXQIlhW7ASciP1LO9rYOUrkyW1y6d0p8b5osWdT6S8QAmxBYtc3vq8gEwERCIhAp5uc\nRzrf7LCLG20M9ERiLvk/dofjeZhwbnxRXscyf/hpdIz4bkwqm55ixfoIfz/FOgWLgAiIQDET\n2BKJfxXiiJCyEBOovaXyjNhtVTWx26odr1bdVMXfTwctSLynLXrjy6iyEiEwfvz4w6gSye5a\n2VT+i6r8f4ECPAr6LTR4rcJMI6DtVU4PRDsEH1G9jdGvvfbar3bfffeDMDuSyz7jy/m3QPyB\nn+pbx8UPoO3ccE4nufPNTfi+0i5upA0x5XtMeTHV/6Kq/4HXEZW/yr+Vf/95TdwdmhV45Q1g\nh6r/wdX/SBS/yeUY0KYMncv9Qlmt2zHePoAiy/su1MUu70WQuwREIpG93KNxdKiSM+W/KMq/\nPyrmg9COEH9o+RCnF8RWoOOheVBaVt7GOgkRZyw9J7GtNWLEiAGdO3f+7NVXX30O4XzCxW4g\nS6CtoMuhnpBxgjDbyDggw83QERC7knBkNdqtEN9l+gcXktgjCLsROgPqDx0O5cVU/4ui/rda\n3VD5q/zdyhXk7z87VbHVoOBN9T+4+u9EnVm208SHYldG0O28+F9Bk4NU8Kd1cAnEACa1we2t\n+Pak/Bd8+dNBGQd9CW0ETYFom0N3QW9D20AcGalpO8nBy0TWyRgQ4Xq8SJr4AWf5b7755hOx\nIV4utf4MXQ7RVkPserc/NBdKZvMROAriO0nevvVnJonc3xPGvOwF8Z2jpZ7wnM+q/hd8/W/V\nOqHyV/kHXMEOxP7uhdjCvyjgfQe+O9X/4Op/xIq0x1u6a/dAc0Oq2boUApODFIJCTDcL8Xic\nI+WVrCn/BV/+16ByzoH2hrzO/GQss+sGu7ldBv0RopVDbAWiMzUdOhTigAlWx02sg/Bcs+OS\nS7rXY/EdyDnssMOeGzNmDEeX+wF6BGJfebYGzYC8Tg8WU1q68fw7yKtzxMSo/hd8/ffXmUCX\nVf4q/wAr1GbYFx8qXQoVvHPEfKv+B1f/42VWJMK2Q/8w35HEs0g7ievEyDIRyIoAu/uwZnXK\namttJALFT6ASWaDzsV8TWTkW6xZ61v8B81e4y1dheo5Z1/km552OVy7g0012f+PTLD4MoqO1\nDiQTAREQARHIngCvqUOy31xbFjOB2L0V58fuq6qL3YdBGjxadXfDIA0HbxXhw86iN7UgFX0R\nKgMiUFwE+i52xmA47H0xiHZFvB7d22otu276dxULRg4q7/7c9DGRbuvF2X0ZHQJsp96Ko6dz\nFK+C1td/9VH5D3/cqrrLAz8ut6s6xZf+bf/q6gP+urqsz2Yn18/4rM3KJ86tbn/GSxeBRtSp\nsTqunD/tLMyzO51pjWJrUm+ILUgyERABERCB7Ajwmvpxdptqq6InEE3xodiG19Hsrh3C8aFY\nOUhFX1OVAREoLgJ2mbUevvHzKpyfSbieLnAiVpltxem4XLD647fvqN5jve/xnSC8OoSGetuq\njTl478e2ltV8+jqeWNonO5HKP2H1qrrv3ru4Ml53B5yn6U59Te/6WZPPxv7OwrtHTixidam7\nfVt2yTP2V8zwfSC2KMlEQAREQATSJ8B3L9m1+RZoefqbKWYoCUQSv6V4dzfJt5CQ4flL8/u+\nbSiZl3CmctLFbsKECddTpcpZ+c9r+XdGvTvHsu2nqvfeL1ax3Y5PYnmAry7yiSRHi0tl/8SK\nsZ6Vr2Pe7GMg5pN9m4gPgdjV7k4M83qD6r/Of9SFkjRd//J6/ct7nWtB+bdD4jm4DT9/0DHv\nGckyAS3If5ZHLKzNgsx/7KGq+2KPoXudT6seauhid/rukTsLK/fZpWbtUSiy208xb9UWiT8E\n6lbMmUgn7bZtd6XSiRvGOMp/3sp/V9Snb6HTrDZtlpQNGBip++TDX2D5K+gkyBgHYOB7RRxs\nwW+MdxR0uWfFB5jfzl3mdJJnHWfZDkVH7HPoFAzz2kX1X+c/6kJJmq5/ebv+FUR9a0H5871N\nXr95jf2pIDKTRSJakP8sjlZ4mwSZ/1jEwW+tw9Ffk9rsJYnPZyRdl8dAvje3BcR7/rSsVLrY\n3Qwau0CzoOshPnnuAb0H9YV4I8VBEqZBI6CZUOgMo7iwi1HJmvKfl/LfEBXuWehu6II+c5Z3\nKKuyju947U2HzGpnb4swDt89G3oB+g90PjQGOgHieXokxFaiauh0iEN9G2Nr01vQbW7A0e6U\nP+btIF7f+OO+OXTs/fff323bbbd9BvMlaar/ean/BVPXVP4q/ywr4wxsx+twUZvqf3D1P8oP\nxZZh8COMnd64UiQW7R4dE5+1aLwqN0uH4TA7QLyHfwoy9wL3YZ6f22gPxaFHoTOgonX4kfYW\nWxR7mAux1IximB8G0Vlg2GLoC2iZu7wEUzpMubScdLHLZYZ0LBFwCTyC6WuGRp+fnHX6r3ac\nPsudzdywGzH1vxfEpzx0nL6HeKGjA3QndA7ktZ2wQGeK/ePpRE2AaH+GjkrM6Z8IiIAIiECm\nBPiASSYCSQnEHq+4IPYERrF7At3sPFr1mDuK3XY5H8UugoTyQay5z+f0R6gf9A83fCymfFDL\nhhGu58PWJu/1udMw24PI3LoQnaArIX4zhQ7Sm9Bg6AaoC7QJ1B7ijVxH6BZIJgIi0HICfGpz\nr9mNXV03EJckx6qoY4sQjRevX0B9ueDaJ5ieDH0I7QmdAdFJ2hXy2tdYoNPE4cGXQbwY0raE\neJ7/DuoOyURABERABNIjcASiLYbWTy+6YpUcgUikA4ZQ4p+1lhDUtsKqzDGTE3E8fiKE9/Cc\nngbRQaJTxPsAvkbD+4ffQ9tAl0K/gg6HUlrYHaSRyDmb0wZBl0GjoL9B7Iu4EDoX8hpvxuqh\nEd5AzYuACGRFgNeXzu3PPa1XP2fZSf2cmt2j5ZFrrbLYG7MqKvgUhzanYZJ4UOHOrpnQ4Vnk\nLvEHu8eaNQ0zczHh9uxTzJYqthzRuLwexHP5f9CmkEwEREAERKBpAmyJfwg6D/q26ahaW7IE\nOL4sf90j6GLnF6FwXW5tfxzuB2hf6D/Q36GzIDr5L0JPQl67GgszITpJKa0s5ZpwrOiAbPAG\nytvPkK1DF0PfQ37jU2cOYRnKp87jxo2jo2htv/32UzktNVP+c1v+/Zz5R87uM8SqWH/9v2Ek\nb354GyN2WxF82IgPKoxthBkEJ94PNGFmuhQzbdwFOj0LzQrPlOfq0xBboh6AaCMbJon/5fh/\nLHSeyj+35Z+gX0D/VP4qf1ZH/f41+fs/DYj6Q7OgUJnO/wDP/0h8iRWJxC0HH+3wGn7kaStq\n1nx7sCGg9f+vh0O8DtV4DsXWIzaQfOEJM7MMZ13nQ9iU1jhzKaMV7QreYPWC2G3OOEkXYJ7F\nSKB+Y7Mg437qXxGG5Wg0eombj+PDkJ9M86D85678uzozeuEh0s0VW2763uKT/9gB2grltSpJ\nmV2EMF7YTEuRN8oHWNgO4oWM00mQ1+gc/RdiS/AbEI3nNp8gHQ3xidKG0GeQpfLPXfmTd6GZ\nyl/l79ZJ/f41fXKGzjlidnX+B3f+O/hQrB2xoni26TSqSg2LdpcO6Y8U12j77BdmYNNdoSrI\nOEns3o/bkMQrNJg0Mvo+v4QeaBTqW+DGYbZXkTnmkTdY10AvQWx2Wwl1g26HjKGwE44Rb7Be\nNoEhmzJvVKma8p99+bdDpWEXt7SuGe2s6B54eNNxVX3NgdiGrUB0WvpCxvgggq0+u0F/NIG+\n6Q1Y/jXEEe7Yx/hqiPYxNBj6EzQAYqvwJ64qMH0AGgPxPGb3uichmso/+/JvIFjc/1X+Kn/W\ngVK1ZPWfvWxuhXqXAJRk+S+BbK/JYmD5t23rS2g1LifYp1+WtWBZ4p3gNQfOwcyzOEZniA9M\nD4T44PVmiA9V6SgdARnjPQzvPXhP8waU0sLegsSnyDtDPSG2HNHi0DbQE9Bp0DHQT9C6EJ0k\nvutwPhQ6wzj4D2NUxsYef+hymTpDyn9W5b8PiP4ZGuqSXYLpI9Bl0I9u2FoTx6qbh7EY6qyX\n356LlSMgOix8UPEJhAtrYiAFrtsdStViOx/rRkF0sPhQw9gQd+ZMTCm/0SGi+OPPVuSEqfyz\nKn+Dr+inKn+Vv37/Gv3+87r6FsSHSpdDoTad/wGe/+X2EPhFVfS4vGaWe3exOnnDczB/D47B\nMQT2h3jPT1sIMexK6FHoD9AsaBjUC2IDylNQyRtbj96DHoPY1YfWD/oG4ntHdBroONHb7Azl\n2kbjgExDritVrvOp4xUXgXOQXA50cAs0HNoIOhL6AuK5wxalpLaeM32v/s40f5e6HRCZL/9e\nBNHx4g+zTAREQAREIPcEOuKQN0Lr5P7QOmIxE4j9t/KM+EtVNfGXqx2vap5vGOb7wO0jvKfN\nhx2Ig14HnQL1dhPA++oHIDpMvM/mw1a2mlZDsmYIsNWIN27lzcRrzdVykFqTrvadDYGtsREf\nHhycZOO2CJsIPedbdymW+ZDh0y4P3njqAGe630Hik5s3oA+hyyGZCIiACIiACIhAERGIvVJ5\ne/z1qnh8bGPVvFTJhgbn5H0jdxRgdti1bgDEe/60jBuUuvEm8G2ortRBKP8i4CFwOuZfhNhV\nzW8rEHAatC/UH7Kq9hmxW6RPj9/0W/nJm93+fXvFyv/7z224TvovRNci6skQnS8+6RkIyURA\nBERABHJDIKMbxNwkSUcpOgJRZybSHEM3O3stYcWi3L+DlA5COm/s5s97/rSsLK1YpROJXfAO\ngJ6G2JUoW+ON3/NQuq1SiSbusWPH/r1NmzbPDh8+/HFz4IkTJ+6CftNHYdmJxWJXeYcoHT9+\n/AWRSGRjrF80bNiwc80277777joIvwZ1tyIej79o9of4hyFsU8TrG8T+eLwg09fa+wMntm7M\n9/DIiF9rp6+l5ZssfUdtN+Vox4piBLho+0um9OnVpj7aeW5VfM7tg5bCybEnc5t1aqMVJ05r\nt1XUsSOTO9bOfr7nyukz+h27X/vjRi4cedbvP9h9fnnHOtspH9MvtmRWddkiyypDO3V5+51O\nuqN+00P3/6xm/b71Nz/3ZMc2u2+/KlLdZt7AvXZ9ao9PF/1h5PsLno6izhneONTeqC/brlix\n4nIMuTqgpqam+1VXXfUd00DLtD5zm0zqH+s/t0E9eDXZ+ZHp/hiflup847pM0sf4tFbc34Y8\nB3CtOKvhSAWXvkbXK6YxSH66/pXe9c/3e/kozvveOAc+SOf3Muj6l+/97b777ne99tprJ2yy\nySa733fffaP89wf5Th+PT9P1rwju/6L4UCy+hLQ0MsRaFBmJWcfqGXsE3/CYnSjDhUscPjzl\ne/3pWC0iXQj9kE7kgOKcgv3wYe2d0F2p9ikHqTGZnlishjgNneHHYS9kal38QPDl+JIz/CDs\nBC1Dxtc4oGGG8Gbv+d0dy+F7RJ8xn11rou3LrUj53MTrdi3Juc1+vAnDhTHeqyb20U83Pzgz\nNmPmiz+cddW/HoOD/qpln2LVolE22qgRacWLL764YWVl5W5t27Zd3LNnz+VmP7mYuvXfgiP6\nai6OV4DH6I36X4DJyk2SdP0rreufv1ah7g9GGN+7+cC/rgSW18ED2BOOO+64iSeccMInyO+o\nEsizP4u6/gV1/8e+Z6b/mflJ4dQf5i+BwlnugaRsDnEqS5MAW1d+A3GaS8vJO0gTJky4h8pl\nxgrpWCWW/3br3HLo/d2fPKnW6tZ2S5ZDhvl/AJuwFTSVbY0VdJT6uxEuw/Q4d56TryBz6fQE\nr5m9DnNnr1nKwUyG+c9BinJ7COVf1z/WgdzWusI5WqnX/9dff/0+lb/qfxBnZOztigvi71TV\nxd/BIA0e1fyvYZCGg3eOXBvEcVpxH2k5SGpBalwCn2ORCqWhux2bMUvWSiT/bLK5FDrvhz8+\nURFpXxmxfqr5GMuTzjzzzHNuvvlmdjNMx25HpInQwZD/PaS2CLsD+g80HaLxqSzfK/ontB60\nGFrT0oR5OksvQnxy+SPEfsCMkzMrkfJPyVP51/UvZeUogRWlXv+rqqrOA4MSKOnkWSz18g80\n/1G7L1qLcK/RaNh4s2h37Zz4xlDygiiM0PlIBiXzEeiP5e2hPaGtoG5Qvi0nLUj5zqSOnxMC\nD+EoeE/IOqLf0muvGejcthrz/aGHIQ5vuQ2UrrF7XrrDfNMBuh5iq9NHEFuYaHSwmCYaHSg6\nSU9BdKTSfUcPUWUiIAIiIAJpEuB7zWOgzdKMr2gikDaB2DgM8z2halV8AlqQPKp52x3me0Te\nhvn25qEzFvpDg6HeEB/sypIQOBxh7PLDp9Z8qu0XXxLjt134nZd8mBykfFAP3zH3QZZYl4cw\nawOd644Y4Nwwh/OuPYDpZxCdmWTGlie2MH0K8aJC4z75DTFzzuDFosT3BHhO0TpBPMYsV6zL\nTRmPzQ8UykRABERABIInUIld8n6G1/LEAFDBH0J7LGUCsfer7ot/2MbxqwbOErg4px8euTNP\nfHjvcy+0gOlIoqkIuxtKq2HEvFKF+KG1scjZGIg3fGxfXgaxaW0GtBBaDpHDxhC7190OyUSg\nGAkcg0Q/DrFLHSv7BpjQgTF2MWY2gbY2AZ7plpj/FTQU4rtBbA2ivQCx1akPxIcMfAqzKXQV\nRNsCYqsQ11MXQU0ZL1orm4qgdSIgAiIgAlkTqMeW90M7QrkcGSzrBGvD4iIQsWzcYzjsnZLU\nZs+zliRd0bqBl2H37L3yO2gVNB7i/QvviV6C+KCXD2dPgr6EjoCatLA7SDcg97tAdISOhNil\npwO0LrQe1B1qDzH8DIhQT4MIMHSGFzSvp0KXsTQzVAL5ZwsoLwKuxfEQoJ4PABKG/J997rnn\n8gFBspZS/pg+B7GF6FXI3zVjNsLoWLGFik7WUohGx4oPHI6HGF6wVgLl3yR75V/XP9aBJitJ\niFeWUP1nTxmWs7lGJ0q1hPKftBYr/8Fd/+JlaFAotyqsMryD5Bfor9vV5n11Lu0QHOwKiI7Q\nVhDv77eDfg0dDu0FbQv1gnaCpkGPQoyT0sLuIB2EnPOGbxD0WEoKDU1xbDn6BeRAf2gibtGu\nwjCnXamizUALE14C+acz5GkxivVAOxKfmCSM+W/fvn0VFhjPb/0QsMgTyIcF/ovcCoTREfon\n9C+I1gXigwb+GN8MFezDhRIof+BPbcq/rn+sA6lrSLjXhLz+b43Sa9dUCYY8/01lPbFO+Q/w\n+he1vsedcsoPxS5c5iS7x2i2jFoQ4QBs+x3EKVuRUhnv79+CRkJ8WHw0lNLCPoodW4r4bgRB\npGPTEYlxedMXOsMoJl+GLlMZZKgE8v8mcPBJylVQ3LFql+JbbnxAkLCPPvooNnXqVIw8Y41z\ng7wTOjhrnCnM812hZBe5+xFOB2kixJamyyBjkzDD7qz3mIBCmpZA+TeJW/nX9a/JChLylSGu\n/2ei6G6EdobeTlWMIc5/qiw3Clf+A7z+OXYnq9yJ8KvrjSEnFu22lVZlo/DWX9gch2CXupTd\n/nxJ+BHLk6HevvCSWuQT8RqoOs1c90e8OPRemvGDijYaO2LN6hTUDrWfkiTQE7leAt0E2QOc\nS68c6PzZXDDYijoDuh1KZnsj8B/uivUw9TtRfL/o3+56Trh+IERnbBeIxn38MzGnfyIgAiIg\nAq1NoAIHmA0d0doH0v5FwBCIfV51VfzL6lj8SwzU4FHNJw2DNBxzQIT3ILm0V3AwNgCUp3lQ\njnDHh8LXNRU/0tTKEKx7HHmgJ/sddHwT+eHTcr579Kkbh0/JZSJQDAQ2QSLvgCZAz0BvQCdA\n7y848t9bLP3HB+yPfivE1p3PoHOgZPYiAn+Cnoeehv4A0bjtiRBbYvnEhesY51mI59Uj0HkQ\nw+ksXQPJREAEREAEWp9ALQ7Bp+CPtf6hdAQRcAlE7aXoYxJP9DPh3bNXWFyxOvGuci5xPYiD\nbQQ9BW3bxIGZ0h0gvqvUBuI9U8kaYfDGja1CbKHh6C7sQjcfmu5O6UUynOsZj4BzbWpByjXx\ncBzvZGSDdfd/0IXQBRBHbWTY2Ei3NjPL+nXg/MvQb6F0Hog019rKBw7spue3Dv4ALYuACIiA\nCIiACISLQOyb6tvj31bH/ar5oipxr33yqAgf2ubSeK//R2gFxHt5PtDlQ+MXoDHudDymcyCu\nr4PMQ2DMlrbthOxPh3izSDhesUDZDel9iMMZ58NG46BMU6fWPPi4ceMGUa15jELed8jyvytY\nsz4fk4Q5naH6dW7d+bGBztmmi135Bhts8FL//v2/xjpeNNom2c4EcT33ESoLWflnXDbKv65/\nuv4X/e9fD5z4r0KmW3Pa1wGd/zr/gzr/Y99WnwHnqCb+LbrYeVTzRUMXuwP3zNuHYgfihKBD\nxG6n3vt8ztN5+ga6HuoLNWtlzcYIR4Q3kY3+blbYHE3xifcUaCZUEhaNRi9xM9pUd8PQsghZ\n/q9AQd0LPZikwNjtbfhP10zcu9MZm69y15+KIb57bLHFFu8MGzZsLsLY+nSDu847YVfTrSFe\nZEJlISv/jMtG+df1z600uv5nfPYUxAYRpOJt6Efo40xTpPNf579bZ1p+/kedLa1IpHKtOkg3\nBNa7R+s+7G84StL/3yF0lLuG9/gdoSpoAcRXCDKyUnGQvFDoWVKlaHYpZtqT56LJfx/nl+uX\nWeX72JY9F02cHWyrvD3mZztWtH/9/NjiWeu+tV3nmzYa3+GsgSfYVrS9Y0VqI1YZ+oiWdUKc\nmSsf+D6y6Lhx68WWreSFgbZT3759p2NKBo9CfIrid5A2Rti+0P1QGK1oyr+V4Cv/rQS2SHar\n8i+SgkqRTPZ2uRT6D5TNx7ZV/inAlkhwYOUfse2PHcvBAGg2nY+1bPb8vHwo1p8Ovj5DZW2l\n6CBlDavYN8R3AB7GqIyuj1/suck8/cWS//5O/6qIVfGSY9m94BQti1rRSnyNrRxXt+V4jahD\nxIpxZEa7ze7dT8G6ejhH5XCO8OOJJStageWV1dv15AuIdmzBT1+4pPotWLBg9DrrrPMDlhdD\n7KrhNY6GdDt0LPR7KHRWLOXfWuCVf13/dP0v+t8/DjyVlen81/kf2PmPD8XaEQyA5ruftPFx\nEZjds7utd5KzOku1UTICoxHImtWq7yAlO7DCCo/AAGfo/wY628zs4WzOD7AmM3a1YDcL05Sc\nLM4hCOTTE/MQ5HXMD3AjDsSUo7h4jU8mn4B+B3GUOnbf6w/JREAEREAE8kNgJA7LVwJkIlAw\nBGKzq8935lTXOXPaOF6tnt7wDtLB+0auKZjEtiAhvNGSiUC+CayLBLB7V3uoK7QJ1JyzyP6v\ng6H+EBpX1hj3xe25r34Q4yRtBkZ4KivHig0gOhLmHGF6uN9uUDq2DiIxPqfJrCcCmeckXz93\ndnKs+r/MtycvSLYhwtjVYgx0MVQN+Y355ftm/4Lq3ZUfYLqdO8/pJHfeTDj63XNQLRSDuB2P\nIxMBERABEcg9gfNxyP9C/J2QiUDBEIjYVmd0V4lYUbTI+oVUtqtOfF6nYNKrhBQ3gdFIfim2\nIP0a+Z7s5p355w05pxRv0l+DtoC8RkflTgjdzdbEneWGfeIJ8+6Lo5ewRaQL1JS1wcobIL7M\nZ9KxCPNfQ0yPCZuA+R2hZLYNAt+EzPE5fQtiOO0A6DPI7IsOyVPQAChhA5wt4us5W+xsllNM\n6UhOg7jvTT1x+GP6BvQ91B0yxi51dKpegJg+0wR+K+ZPhLz2Vyz81hugeREQAREQgZwSGIej\nHZjTI+pgIpAGAWdB1VXOwuqYsxAtSB6tnt3QgnTsYTn/UGwaqVaUYiWQEwdp/Pjxh1EFAul0\npIOtFHRIzobqID4tew9i97EjIToOK6EREK0z9AX0JXQwxNaiQRDj0RGh4/EYtBy6B+IPDPd1\nJjR5n332mfvUU0+dgPlkxmGv34e+g3jsXhC7qa129QamdDh+Cf0DYnoPhby2FxYY/1FoG4it\nTZxymeFME7f7G0SnhvvbFXoDoiOWeFKYpoOE6ImuF69gSmeLTiLFebYG9YUaGcv+lVdeOapR\nYAktFFj9zzl55b+grn8q/xwTUP1X/WcdyHG1K5jDBVn/nQXVFzqL0cVuERwkj1bPaXCQDjsg\ncm3BZLwFCSlrwbbatMgIRCIR3sDTHm+Y5O3/RjjyTdBxEN+FmQpdCl0DsUsb03c+NAS6GaKD\nsT7EbWLQF9CDUBS6DtoPuhy6BBoM3QmxBYT7+n/2vgNQjqpse3b3lvSEEBJIgSSEEkpAEEkI\nRQTpUsxPVeGjiEhVPlQEURAwFopgAZQeBAEpigqfVAmXJKBUISAtgRRCIAlpN7fszv88e3du\n5u7dvbuzd2b2zOzzwpPp55z3ed85d949jQsDnwJ8Y+edd256+eWXGaSw1WQFwJal8wEGWK8A\nYwEGWjMABjJMn/lxfw/gHeBtgIHd/wEs12XAGICBGAOeWcBXAJaJ6bKl533gUYDlOAm4FaAt\n2JIzEGA5mfaM5PBk6v1Rr1mZ5el7ccyAb0uAZeJ9zcBS4P8BbwILgf2AicDOQAJgHrcAHwBs\nbSNXfN569dVXP8Y03y9hl/rVnBjk/1XhXvobU//J/lVgQP4v/8+5XbW/f6rg/fjI8PP7r84a\nja+WVP4kDehuR90SGw4tNHSgKmr3KlN+mElqhAFMONJKGKDu11EGthTxQ/1rAD/62apC4cc8\nW3y2BaYCDGD6AccAxwG/BxhcbA6cADCQ+hfwY2ARwLQYHJ0IbAoMBbYBrm9sbPyora0Nu9k8\nmO9eAAOc/sBI4EGgHngZYJoMoNjycxhwOVAHfAmYAjDIY/D1GMBgZSbAvPcB+gBXAEuALQC+\nZ9sD5J7pU3YDmlGNjB7+wOhjkhulNsbxTok+qR02XbjNf0eu3YrXfwQ8DBwCzAYYiJ0LMG23\nzMXBHcAggOVyZAfs8PnRxKRJk54zxP5O+ULdGuT/oertZCb9jan/HJOEupX9jbY/6+gXgOOC\ncgrZ32j7B2X2znR9tX8i+83TamFqxG5AjkuW9m567c5CV3mHH3ySGmEgk8kw2DBBdkQhnmBB\nhjRaux28ZXL07afUr7GG4l1jpzSMFNr22JbEyV9NPfS/Z9T/c9f91rUOG2Zd+vdH7fqvHpM8\na/bzmcxbc/vOsesSVl3d2vrDj0pud/1dje9tPqR5TGurlaxrQOTz9dS94w5M2tcf1Fbff7Rl\nr1lkbfvAwivm//cPaasReUz4WvLaXa5MrXz0oPYRow5OPvncmek+e92XGp5psZ599afp0S3L\nELpgru02hEDb/LHuwcV/SNctvd1unPBs6uW25fbKpVdnhqC9Jt3+oXVi/VaJZWsfs7dtmGit\nHvNs/ezm5zODlhybHjHwG6mh9eOTf1/56/To9qVIrd1K9t298ephNw7/4aI9lgzLrLQzg07f\n8L0+uw3o07hDn4+bm9bY6QVtH76XeOVisMA/mAyqGGR9B2BAyejuUYBBYr5MxAkGbze7LpBn\nPn8SMNsg+7uKGN6u9Dfm/Q/P6K6cZH/Z3+UOpu2yXv8I+FtQBZP/y/998616LBSb6GGh2NEl\nJ9nyrShBJqQAKUh2DUt7t912W2ZIkdpRjmxLCgKkvpmM1WClE0sxesZG2w6QSCDQGdrWlliB\n43dWrbZ2HDAg25oz6rXXM4nGxsSneH4Bh9vg94vRSxbbbOXBsTUax/beX0m2/P3qdJ/ENWlr\n6JZWugUd02y0Sy2cu6J55adYJajRqlv4SKb/Z69MvZzqa41Y9V5mBZ617JQ9/8ULM/tse0Hi\nqX+fZx+cXosp4sZZbS3v2qtb3u2Y1GDp9elMoiGRbn7GGtBnF2uh3Ww1Zpbaa9E2ZLf+x+qz\n7MftidRmyRUo0dDVt2QaG3e329resW0EQ4nkkGS7naYeicXpj9McT9Xa/kHrnPdHvTEOwdNL\nOGZ3OZQ024VvGrZ7AnOAAcChwAfA7wBolO1yxxYsCkLCgmsYIdTMdvvjL5O/hP3/hC2fr0kx\nyP+rwr/0N6b+k/2rwIDh/n8yKPk3wL9lgYjh+geisztR6e9j/WcnXrSSWCjWLrJQ7AIjFop1\nm1/7EWYglEkaDOLnUpSFY34SB45N/nJEv2xQ0OgqH7ulMVDYCWAXOQQVFgMGbp8AngEcYbDw\nNsDxN/MA/gq3DuDYH55bDDAtBhdsYWErzIu5Y2ys2wF2meMfpusApj8e4P2tANOhPADw+GAe\nQJjPJ8DpAO9ntzwGbvwjtyfA8UOXAZQZwEqAeX8FoPCe27J7HeOG3sI+O/BunzvHsr8LDAF+\nCHwPeB6gvAHwuiMXYede4GTgr8CNwFjALRNw4DzvPq99MSAGxIAYEANiQAyUxYC9ru9v7La+\nGbu1K1pW98kgAfv0r6d+U1ZCht+UNLx8Kl48GbgeajGouOCGfZMfMSqAXAXwo78fwA/8fwIM\notjiMR/4M8AAhgEEu5OlgHFAGtgYuA0YDTCgagcYTPUFBgEMdnj/74HbAT43F+A5pseAg2N8\nTgSeAHYDKGyVYsvO54EDAb78vJfCtNkKxrROAf4BNAPMjxMjUI8RAPOYBNQBfN82BRLAhcCO\nAK9vC3B80avAwQCFgdFAYCfgX8DeAIOuzYBPgBxt2OuYte4v2DKAIx/Un2W9HPgCQNkS4PMS\nMSAGxIAYqC4DRyF7/l2QiIHoMZDKfpOl0YWn4Bikj5fbTu+W6OmmEhvHQCgtSLNnz76CMET7\nI1COlgmDrX9fvGsKndmyU3MzaGFQQpwFsMVjCcAAgsJWJgYw6wC+gHzuDuDvAAMCBkW3AAwS\neE8bwKCB/C4/55xz2s8991zesxKYCTD9jwHKKQDvJ94DeP4kgGkx7RXAP3NggMIWp8eApwAe\nPwQweOLz/wZ+C1APtioxcGJ5ZwBM70/AT4G3AZ5nQPMhwOCHgc5q4HXgTOBhgK1X5OFJ4AXg\nswDlWuDr2b31/1yG3a/mDidi6zz/4kMPPXSTQfZfX+KQ9gzz/5C0Xp+N9Deq/ltvmJD2ZH9j\n7P9DmJx/J/YPyfTZbGR/Y+wfptk78/LT/mg9mm5nsA5SGi1ILrQ0d7QgnfQ/8VgHqa6TPe3E\nngEE+8MMUpIf/bumbet3P/1Xug/22arCAIGtLAyEfgTcDxwGLAIoDEr4R+Uc4FSAXfGOBtj6\nczawD3AUwDQoTJM+fiTwi6OPPnrrlpaWhquuuuqY3Hle5x8qyo3AzQDTOQVgWX4HvAwsA9ja\nsyfAgIZBxw+AtwC2FK0FBgEMvPjcxcCxAFuBGFjdA/CP4nzglwB1Yz5shVoMMJ9LAd57KDAA\nYJoZ4NeAkzbvbwYcYRr5wnI5Mhc7BwLZ54cPH34r9k3yARQnPDHM/8NTPJeT9Deq/pP9Q2bA\nIP/fHKp/Cfi/MCkwSP8w1e7MS/r7WP/VYTQ1P69AaifB3Ml9eWXsLmcje6AAKbKm815wzGLD\nD2aT5KU3vtpwZcpKXlP323UMLBgAURgwsQWokDCguSKHBmzbAQYSFAYTFAZYTIuvK32crUh8\nl7/Xpw+TzgqfyxemwwCGYPc5HrPFx5FC5WIgQ2FwRGEQdGIOhe5n6xKDIIpTzo6j9f+uXr+b\n3XPSdgdHebf0eJh93kD791hovy9Kf+Pef79N3GN6sr/s36ODhHfxhPCyWp+T/F/+v94beruX\nWI0YiN9Hzo/R2QSduKh5Xcc3V29z0fNigAxMB+hbQ3hQS9JyesPR7af3+bCWdJauYkAMiAEx\nUDMM8Me2Lh+SNaO5FI0lA7bd91cZu18GSLuxrqUvgyb7tNM1SUMsDS+lxIAYEANiQAyIATHg\nAwPjkMZ/gG/7kJaSEANGMIAo6L/4Rb8VSOYh2+VuyRLb6fViRHkrLYS62FXKnJ7zhYG6tLWV\nnbQ38CUxJSIGxIAYEANiwBwG7kNR5gE3mFMklUQM9I4BTF+3IwIjDhHoIs64hZGjrcFdLkT0\nQAFSRA1XSbGbmpo4ONSaOnXqO5U8H8Qz7W3WO6m6RCi/NpiofxCcFktT+pvn/8VsFcR52V/2\np1+ZVP8H4efF0qyS/09DeT4AnG/HYsUL/HyV9A9cr3IzkP5+1n/2i+Ad48QLLxS7YEF29t5y\nTWPsfQqQjDWN/wVLpVIX5lI9yf/UK0wxiT8cdpeJECpMqPRjRupfuti+3SH9DfR/36xbOiHZ\nX/bPeYk59X9pt/Xtjir5/3u+KdDLhKqkfy9L7d/j0t+/+g/R/kS2INmWjc16aetYnjExfESC\nM+dGXjRwMPIm9KQA+4dm+4h6eio+N0t/2V/+H5/32asmev/1/gf9/nP5if29OmZI98v/5f++\n+D/GIM1DZMSpvpFePrCI5MfxWChWLUgh1UwmZINprmfYEBPKUo0ySH/ZX/6v978adY8Jear+\nC7z+uwx2Pg/g2n3GiewfuP2Ns7m7QH7aHwHSUHxIooHF5qx1nZLuaEFKDhjQfXxS503aEQMe\nGajdab5PwTTfX9c03x79RbeLATEgBsSAWQz8CMXZy6wiqTRiwH8GVth9Lltu900vt/vZbixp\n6csf4O3jTkhe5X+u4aeoFqTwOVeOYkAMiAExIAbEQLwYuCRe6kgbMVCYgYyV4GL2mY5WpPX3\nOM1J69ZZbevPRndPAVJ0baeSiwExIAbEgBgQA9VhYGAu21XVyV65ioHqMIBJGkZh8FEqkdfF\nDpM2sEDJQUOt/tUpmb+5apIGf/k0OrVZs2YdTRhdyAALJ/1lf/m/3v8Aqxijk1b952v9txWM\n/SrwXaON7iqc7O+r/V3MRmPXZ/tnF4pFS1IyH2Tj44+sWPxooAApGr7tSymTyeSBhC+JRTAR\n6S/7y//1/kew6vKlyKr/fK3/uPDrS8BPfDFOCInI/r7aPwSL+ZuFn/bPWPaO7VaikYt75YOl\n3lgLxfprPKUWPAOYwas1+FzMzUH6y/7memfwJZP/y/+D9zJzc/DZ/w+BpmuAbJ8ic7VeXzKf\n9V+fcET2pL9/9V/aSryI7nVFF4pdpIViI/JWqJidDGQymfM7D2pwR/rL/jXo9p0qy//l/53O\nUIM7Pvs/B6lHSnzWP1K6s7DS37/6DwsgbQNKiy4UOywmC8VGzsljWmBN8x1Tw0otMSAGxIAY\niDwD34EGJ0deCykgBnxgYJ7d9zvz7b5t8zHNtxtv5ab5Pvio5E99yKbqScR9DNJ4MLxD1VlW\nAcSAGBADYkAMiIEoMsCPvYuB+VEsvMosBvxmAP1KN0QrUhILw2bywbz69bf6+J1nNdKLe4D0\nI5DKgZQcUNlQDYKVZ88MJDOJUYmEHYspIXvWVFfFgBgQA2Igggy8jjJ/HngsgmVXkcWA7wwg\nKGrnmkf5M9jxmJllMrgUA4l7gOSY6FTszAJ2dk7U4nb27NlXECbpbrdl1mLoYCgvk4n6h2kL\n6W+e/8v+4TEg/5f/V/j373Z46fPheWowOcn/5f8V+n83h0QL0hp8tCEOYpDUFby5WQvFkobI\nCAMkTsc5B2Br0lXAO0BNSSKRGGaawnYmsdyyE81hlMtE/cPQ28lD+pvn/45twtjK/rJ/GH5m\nah4e/H84dOgHzDNVl0rK5UH/SpI3/hnp71/9hym+R9bCQrF1xnu1PwW8B8n8BbgeOB34JvAI\n8BvgYSCUFgzkU1XBLC5zq1qAApmjCROzodhDC1zy/ZSJ+vuuZA8JSn/z/L8Hc/l+SfaX/X13\nqgglWKb/T4JK/Ca4Gzg3QuqVLGqZ+pdMJ6o3SH9f67/sQrE21kJy+4PzIb0sJgvFunWL4/5t\nUIrrFAx2KXcc9tmnmOcJtiRdAOwLbAJUQ2p1FrvPHLRJcsb3tklxytSvAUOqQb7yFANiQAyI\nATEABvhj6h8AjVmWO4iBIgy8Zve56VXMYJePF3Kz2H3l7OR1RR6N1OlaGYPkNsqdOECrhbUH\nwL7FDIouBx4FFgGfAKcCkuAYYMB6P/DvOZ9k9np2aYZ/jK4E5gHHAxIxIAbEgBgQA2EzcDQy\n/ApQ04uqh0268osWA1woFu0LWCi2sCxZaK0ofCVaZ2uli10hqzyDk8TZwJcBTgc+EWDwVK2W\nJGQde0lBw4eADYEdPjy04aBEMvGjuntaRuKYtrgZ4IvHX/IkYkAMiAExIAbCYoC9SiRiQAz0\nwEC6xEKxG8ZkodhaDpAc83+KnVucg9w2li1rTU1Nm1O/qVOnslthteSryJjBKAPRhZk2a5tU\nIrES++3AVUA/4Frgz0ALQLkImAYwuPp/wJuAWz6Hg8sAdtF7EPgJ0E0M0b9bucI6If2N8P+w\nzN0tH9lf9qdTVLn+7+aXYZ0o4P8YZ25dAjAo+lFY5ahWPgX0r1ZRqpKv9Pev/sNYo/dgxHTC\nShSMIT75yF5VFSP7nGlB5XzOI4rJOWPNolj2omVOpVIX5i6eVPSm4C8wwLkDWMisUhjlZyXx\n33r5JXYZEO0OPA7smNvfBdvPA5ym/EuAWxgQ/QB4DngA2AOYCXQRQ/TvUqYwD6S/Ef4fpsm7\n5CX7y/45h6hm/d/FJ8M8KOD/HPt7OnBomOWoVl4F9K9WUaqSr/T3r/6zLRs9gBJJbLt8K2ey\nvzVYyQEDtVBsVZzcY6bfwP2DALYSSeDRIIGopmyKzDtbgPDT3daWbbPlxxFO2MDgifdR9gT+\nArQBHCe2PeCWehxsCcwG+LI+CUwFCokJ+hcqV1jnpH/1/T8sWxfKR/aX/atd/xfyy7DO5fv/\nP5DxFOCpsApQ5Xzy9a9ycULPXvr7VP/hQ6uNwRC2yXzQqq1pKx26dQPIMO4tSBzLUnQgWQB8\nGp0k1gGYYUOqXMilyH+0UwZ0sZubSiWWOcfYMuDhOhS8j7Ip4F6krxnHAwGnCZeB00vAicCz\nAFuoXga6iSH6dytXWCekvxH+H5a5u+Uj+8v+BtT/3fwyrBMF/P+JsPI2IZ8C+ptQrNDKIP39\nq/8yVmYtYyN8TLp7/3RGRe1tnbuh2TeIjOIeIAXBWTlp0mn2AfixX46MHzt2rHXVVVd9ccSI\nEc+hj/h856Gnn356o4aGhl0wh3+mpaXlqb333rsz4MOqyJNwH4ONZZMnT2YLSlYuvvji5AEH\nHJDNP51Ov+akt+uuuz7B9PDcQX6kx8wqKN/fUVGd+5e//GXmRhttlF5153eHDXmXPeM6ZIst\ntjgNHKTOPffchg022KAP9OX4JI5Lsqjv97///QHTp0/f1q1v//79v37IIYdMb29v//Lbb7/9\n5Msvv4yXt0Pc5Vu7dm2v+GOK7vR6a4+w08PH0cfIczR4BH2l/SXs8nn1Z6/lo//zmWLvh9f0\neD8lKunB/vWsDzpK3fFvlP3Zq78YUv9VzV9qvf67884765ubmyfCb5pV/3X/PmCNEOf6QPWf\nf99/mMVuE7hLKtE2KJNYsw3apdKWPfAltCm10I2S/Yd0fLPxIMqiAKmr9biA7GkA53C/vusl\nT0djcfc9QEOZT9Uffvjh1qhRo25F4HIDnjnXea6+vv5YBBTTk8lkBoHSwTj/tHMN5zmZwS54\n8T/Cdpxzfp999hmLa9n88Zxp6V0/ZsyYswYOHHgfyti2+PMnPukKkL742c9+9udnnXUWip28\nDR9z1PdfwBHALQhIbvjxj3/MF/MuoFPfCRMmfA8BFe9pePzxx8ciQDoH+1mJIX+W4fZV+eL9\n/sq+sm8U/x7tfNNNN/1lv/32S+APA7twd/79UH0a6+8N1VcB1FfoVvc2XqTWxCf7NiYWnIrX\nCW1KW55vZfp0dN5ZroVis9+fcfvnYijELmg/ClkxDhZlvu6xOCEXIdTsNkdurwPLtupnzTx1\n09Qa7DPwY7/VnwNu4R80TszwV+AF4LMAhV3pbs/uWdaR2D4CcPXzy3LntBEDYkAMiAExQAZ+\nAfCHT/0oTDYkYqAXDPwTC8X+0+5r5+Oxlj78jrWPiMlCsb2pLPqCiAlAP2AO0B/gh26UhRXo\n/cCSKCtRrOyzZs06mtemTJnCQKKa8g4y51TfxyST1skL1tnsksgugmcB+eOH+MKdB9DfmgFH\n/oQdgnIvQLvRn7NtvNh2E4P071a2ME5If2P8Pwxzd8tD9pf96RQG1P/dfDOEE9/J+f+0GtXf\n0vuv99+v9x8TNLyIjzYM90j0KfTuflLDC8Vy0Dx/0ecv+Px1/xmA0yrfAbB/+6VA0Y9UXDNZ\nGBjFMjgi6ei2dmCO/Ltz22puOLnCjJf2aGhNpZIT6/667rslCuMOjgrdytYnoqgYpn/RcgZ1\nQfob5f9BmblourK/7J9zDhPq/6J+GtQF+b/8X/6fZaDX7z9+td6m3bIa0VjEH7A7pT3bEcpK\nDN3YGtx5MsI7/MXdi3D8B7s5YQ50ay7A1iNHGCxdCBwOsBtU52QC2DdJNkBhaDwY11oNrACi\n3vIFFUoLxiq1lr4r3DvqMta2dsKmTQIXE/UPXGlXBtLfPP93mSfwXdlf9g/cyczIgD0SrgL4\nd+UEp0jyf/m/4wu1uPXT//FL9Hv44Mem60KxTrS0bInFybUiL14DJE4K0BfYA2DLEbs1bQRQ\npgGXAAySWCndAJgin0FBzgAOBZzyusv2Lg4eA34ALHVfiNM+JoA43zR92tut11LtXab5DqyI\nJuofmLIFEpb+5vl/ATMFdkr2l/0Dcy6zEv4xinM80GVBcfm//N8sNw23NH76v20lNsREDfgh\noutCsZjLjkol+8dkoVivFlqGB9yD6BkgzXQlwmmt2SJzi+tctXd/iALQasR8gGvl/BX4I/Aw\nMAdYDPD6x8BxQNhSa5M0dPLbsk/D0e379Pmw84R2xIAYEANiQAxUzsA2eHR85Y/rSTEgBnpi\n4GG7z6VA+hFM1ODGc4ZD4gAAQABJREFUQ7lJGr54fPLKnp6PyjUvLUiDoBSbrN/sQTmOK+E4\npFC6TPVQDufSkdhhqxZnOGPL1gtAIUFrYbZVjEb9AzAPYCAlCZgBdLEbbyftWPRXDZgqJS8G\nxIAYEAOlGeAMqRIxIAYCYiC3UCwbFbqIMwi8NSYLxbKvbrnCPoX8pX+XHh5gELUt8EYP94R5\nieOh2H2O22LBEctDQz8N7AesAtg8LwmBgXS7vdjK2J0Lu4aQpbIQA2JADIiB+DCwFVTh33iJ\nGBADITCQsRIbs4sdZrPL5IPZ9xmcHYoTQkmCzcJLgMSSsEvaKcCZwADALUNwcDvA1oBH3Req\nuD8Jec8Cyp1VbznufQUYBcROsIL4FYRJitmJRLNlJ9jyGLiYqH/gSrsykP7m+b/LPIHvyv6y\nv2n1vw9OvyvS4N/4/UulJf+X/8fQ/0u5fed1P/0fLUXvoFWhFWORkvlghquXZidA68w7qjte\nuthRx3OBfYFfAZcDzQBb1R4EOHHDUOBW4HHABFmMQuwMcGxUOR/h7BrIoMqkCSZQHH8kkUgM\n8ycl/1JJZOwNMVk8J/4IXEzUP3ClXRlIf/P832WewHdlf9k/cCcLP4PPIcvfA98vlbX8X/5f\nykfifN1P/0dQtEPaSjTm88VggDJ4lMUGk8iL1wBpBTTeCbgMOBFglzrKYcAy4Gzgt4ApchsK\ncgdwH8CAjhMyFBKOQdodYOsKpy5nwBc7wSwmc41TKp0NXr22ZFakhpH6V6RJZQ9JfwP9vzJT\nVvSU7C/7V+Q4Zj/EH2vLEvm//L8sR4npTf76v/0iWpDWJYosFLs8JgvFenWF8Xhgw9xDKWx5\nvBswMneOH7p7ATvmjqu9YeDzbWANwHFGC4DZwN+Au3LbWdguAnidrUznAGHLdGTI/GMRdXsh\nr2UyZrHbtVGz2HkhTfeKATEgBmqXAX57SMSAGKgSA/fajdfda/fJ5OOulkYMTbLs/c9ImtRQ\nUjFLXn+551pBHH9EYWsaJ0DgbG8MMChscnsKOBUwQRh0XA1sD/wRYMDEPssHAcfktuxSxwCK\nM9gx4LsGkIgBMSAGxIAYEAPmMMAeLzcCpnThN4cZlUQMhMgAoiCOQWpHV7tEPliMT2tkodgt\noOueLt4HYp9d7E52nXN2GWw5LUfsbmeSMJA7NlegQdgOBvoAHwGfAhIxIAbEgBgQA2LAXAY4\nzuhQ4GBzi6iSiYH4M4DgaCMESWjJLbxQbL/B2e/ryBPBX2R6kiW4eCmwiesmVlBEMWFrzAPF\nLhpwfiXKQNScNDU1bU6lp06d+k7NKQ+Fpb/sL//X+6/6L7L1/w14fzkhQ0VdslX/q/5X/e9P\n/Y8JGlrJJcYgdemFxi5blLaYrINUKkBiIHEIwJWpKVcBM4FCARACSovr2bwAvA9IDGMglUpd\nmCvSSYYVLZTiSH/ZX/6fZUDvfyg1jlmZxKD+Y4+PiiUG+lesOx+U/vr7l3MgH+r/TLNtJW0n\nIHIck0EApb1GAiTqyoCHoHwWeBq4nweSyDHAMVi1LNK/lq3fMQaxlhmQ/9ey9aPn/+yyvxfg\n17hg+b/8v5YZ8M3/0YI0AkQm87vYYdFY8pvsG5OFYku1IOU707fyTxQ4Rr9Ei+vtsHuexCAG\nMA/+DBtiUJFCLYr0l/3l/3r/Q610DMosYvUfZ8d9BLjDLwojpr9fanemI/3198+vv39oKXoH\n0Ra62XVdC8n5uPw0JgvFVhJRHo43bhrAiQ64ACuF6TDY4oKfE4DrgIsBSXkMcJrv84ENgBXl\nPRKPuzjNd8pOXFM3p2XjeGgkLcSAGBADYqCXDPA7g137nSUwepmcHhcDYsAvBm62+9yE2eu6\nddVrb7WtbzSus/Y9O3n9Y9dmvulXftVKh0GNFzkRN99c4oG3cP2lEvfoshjoYCCNwDppe/VD\nsScGxIAYEAPxZeBBqEZIxIAYMIwBdKV7Ea0i69A2wtmgu0lcFopFH0JP8l3czYkbjgdGAasB\nTr25FXAcsBzgWkmq2ECCpDQDiIzGo9vqgNJ36g4xIAbEgBiIKQPshTI0prpJLTEQKwbQxW47\nLITamLZsOx9UdNAIa1AcFPYSIHFsEaeJZr/gGQAXh50DsK/wf4G7gH2AbwC7ABLDGJg1a9bR\nhEnFak/DdzKJFWGUyUT9w9DbyUP6m+f/jm3C2Mr+sr9p9X/O7xuwvRN4Osj3QP4v/zfU/4N0\n+860/fR/BEhvY7xR0YViV3xkrerMOMI7XgIk/srPMUfuSuwNHO/g0v9F7DNYOsx1TruGMJBM\nJg8kDClO6MWQ/rK//F/vf+gVjyEZGlz/nQGK9gTYCyUwMVj/wHR2Jyz99ffPr79/CI42QgtS\nCq1HmXzQ5zCLXcGud25/jMK+lwDpUyj0MbC1SzEGSJsCnPLPkfex46yb5JzT1gAGMINJK2FA\nUdxFQNAdzhgkQ/V3cxHovvQ30v8Dtbk7cdlf9jew/qeL/hbgd8UrPAhK5P/yf0P9PyiX75Ku\nn/6PMUgtNsZGIFBK5oOZtrchboqBeB0c/xJ0Phy4A2D3ulcBCs9xleuBwB7APYDEMAYymcz5\nhhXJSrZbR2GaBq9+WJEaJupfkSIVPiT9zfP/Ck1Z0WOyv+xfkeME/1ALsiACFfm//D9QBzM8\ncT/9H0HROgD/dxV0vctKXBaK7apd6aMdcQtbIMjDVIAtUO8A64AHAa50TdKOByTlM+BMZTqk\n/Eeif2fbxLp927dpSLdtU8egWiIGxIAYEAPxZ2AyVPRr8df4syUNxYBhDFxjN14LZIC0G1e2\nNLDlyJ56mvUrw4pcUXG8dLFjBi8BBwD/AJYCDJSOBJYBHHe0EfAHgC1MEjFQlIEPR1j9E4nU\n7zFN5G/rX2+fWfRGXRADYkAMiIG4MLA7FHkiLspIDzFQiwygFYSTNGDVowS62HUF+VgZk4Vi\nK+naxMrNXcG9gOMxACdrWAG8C0jEQI8MDNuw8ae84eNPWozr9tdjwXVRDIgBMSAGKmWAPVC+\nA/ym0gT0nBgQA9VlABMz4Hs/2ZhfCjYfUQaNSqI3VKbjIML/eg2QWKk1A/yobXfpTV4YKEkM\nZmD27NlXsHiTJ08+r5rF7OhSZ59u25n9N15irQmrLKboH5a++flIfzP8P98uYR3L/rI/fa3K\n9f9zKAIRusj/5f90uir7f+h+72Top/9nrAQWirXXJYosFLtyYYaNJZEXLwESo8UTgAVAVT+w\nI896lRRIJBLDqpR1Z7YfjLb64peHm6xE4mZ0reOiwqGJCfqHpmyBjKR/9f2/gFlCOyX7y/6h\nOdv6jFLY3RKYu/5Udfbk//L/6nieGbn66f/oXrcd2ocQE9jYXS/tHfM2JAZubA1efza6e14C\nJDaNc/GnfgBXve5CDI4lhjOAWUyq/kdq40ENP8aL2m9lc0voQbYJ+lfTRaR/9f1f9q8eA/L/\n0P0fP4ZlF5DfEdux1bN8R86yf+j2r7bJu+Qv+/tnf3QfexsTGGCT4NqoneIEBauWWCs7T9bQ\nDmef4TpHDwH7A5sDgwqgW99E3CMpzsB0XKJvod9mfKV12/rPtW/b0N4+sfHg+GopzcSAGBAD\nYgAMHA9wTPJWYkMMiIH4MPATu/Fnl2MGu3xckpvFbueTrV/GQVuvs9hxDAtbkA4BHgHeBriA\nbD6+j3MSMdDJwGvbWg0JO3EzwsC76ua2/K3zgnbEgBgQA2IgjgzcDqW2AN6Mo3LSSQzUKgNY\nKJbrIOFzrusMdjwmJ3FZB8lLFzvq/QawnDslRBViCYJq7fJWmYbbEglrs9WJ1j1rTXfpKwbE\ngBioUQacia1qVH2pLQbixwDGH3FhZ6dHXaeCOJ+VdJsVi/fea4B0SicT2okcA01NTewSaU2d\nOvWdsAtvZ6wNkOeDg9/MrpkVdvbZ/Kqpf1UUzstU+lfP//NMUZVD2V/2p+MFWP9/HslzbOmX\ngG4fTzhXVZH/y//pgAH6f1X9u1Tmfvo/ZrEbgfzQWoQvO5fYHa99ss9ATsYVffEaIEVf4xrW\nIJVKXZhT/6SwacB0kFtl7Mx9Yefrzq+a+rvLUa196V89/6+Wzd35yv6yf84fgqj/d0Pa7Hb/\nK8C44Ih6y//l//QDSBD+35Gywf/67P/8kZ0LxXaZb8B58Vd/Yq02mIqyi6YAqWyqYnEjZx8M\nXT6ZkJ3IYyz+bL4SeuZdM6yK/l2LUNUj6V9V+queuexfdRNUtQBB2n8xNDsRuKuqGvaceZD6\n95yzGVelvxl2qFYpfLM/pq+bhBnsugRHVMrpVzegRheKrZZhla8PDGB67Rk2xIekPCUxKFW3\nPabLzyxb2fYfTw/6fHO19PdZjYqTk/7V8f+KDebzg7K/7B9g/f8e3JUwVuT/8v8A/d9Yv3cK\n5qf/4yPyJSwU24wgqWBXurgsFOtwp211GZiO7Bm4xHKa79Yt609v37LxrepSrNzFgBgQA2LA\nJwa4/skXfUpLyYgBMRAhBr5vN14PZPLx3ZYGjkmydz7D+m2E1ClaVHWxK0qNLvjFQDKRnIRf\nbqrdvc4vdZSOGBADYqCWGegP5e8HtgM2BZyeNdiViAExEHcG8Gs+f/Bux9jyLgvFOnrHZaFY\nBUiORbX1iwH2Sx17SX9r0/MGWvUNdXVr7Iw91UpYVZ2gwS/llI4YEANioMYZmAr9hwO7AwqO\natwZpH7tMYBmIs5il0JjUZdZ7NLOLHZD4jGLHabpq1jY93B7YNdcCvxVSWIwA7NmzTqaCKiI\nnMb7euAT4I0frbH+seVS62+/XWM9hfFH29pt9tKA8i072YD1L7sc1bpR+gfq/9Uya9n5yv6y\nv0/1/z/gdJ8BjB5zlP9iyP/l/z75f75rReLYT//HdN7NHBNSdKHYVgvzOERfKgmQ2KR+D7AG\nYLepKwDKHcBlQLeZLXhRUn0GksnkgUQAJRmKNJ8F9gBO6GdZo97cqH7Vdqnk7ecsb19V/3bb\nzQ3vtf0mgHw9JRmg/p7KUa2bpX9g/l8tk3rKV/aX/QOq/z35YbVulv/L/+X//nz/odm4Fcg2\nH7EJyQ2+33FZKNZrgLQJdH8BOBJ4A5gPOMIpBLnOzr+BPs5Jw7fsP/kFgEFf7AXjgFqJABT9\nJdJkumxNvG/FJvWbbF6fHPinDTLfwTH5/QpAnykkXLy2CaA//bTADRvj3AzgeeBuwOnzein2\nFwILcig5wUWA+qMI5ov0D8z/zTc+Sij7y/4V1v8HwH1eBSL946f8X/5fof9Hon4vVUg//R8t\nRxshKEpmECPlg+VoMG+hWHQHtCYAJb8TWX5H6pydMrfX4j52rWNLwTMAB2puBFCmAZcADJJO\nAG4ATJBtUIh7AX5Yb5kr0BhsHwZ4jYEdWwtbgEeBQ4FYSiaTOT8AxehwxwIHA7nFwZJ74mV8\nY+AS6yOcI24EvgnQDvnCFsgfALOAvwE7AwyyHTkFO68BXwO4Sjt1YHDE/u+TgQ+AsiQg/cvK\n24SbpH8g/m+Cacsqg+wv+5flKF1v2gWHDwHTAf6NjKzI/+X/kXVeHwrup/+j9eg9tK60IVBq\ncBeNH9KU5uosFMtxkT8G+EPOiQBlMMAf3nnM82zs4vfkbcCVgK+yDKn93JUiA6SZrmMGISuA\nW1znqrnLgIh9IWm35bmCMMBbkztHsqgTP7Lbcue4Vk8KCFP4x4dl9BTdhlnAHvKaimvksfNF\naduk4cH2TRrdAfKXcZ08F5I3XSdPx/53XcfcfQLYOnduR2wfye2/g+0+wDFAn9w5bcSAGBAD\nYsA/Bji2dD//klNKYkAMRJ2Bs+3GG8+y+9j5OL2lkd+x9qSzkmFP8z0M+S5g3sA/AQrjEf7Y\nznOI6awnAf5IPx/guesAxHnFpceLeY8NwjErS/cHbd4t2SCD0RnvM0HuRCEY7PCjetNcgXiu\nHzAX2AHg+JkxAIOTl4BtAbZUSMpjgAFoAnBaI7EembVHOmE7TspU6Ki8L18G4ASDK0c+wc4I\n5yC3/Su2ZwPjgFMB2mojgEHuKGA08ALg5I9diRgQA2JADPjAAH9Y5KQMEjEgBsRAlgFEF/hW\n5kKxhWX1osynha8EdvYCpMzvwe8Dzg86Z2J/J+D3AK/tDRwJbAGwN9xpAH9kLypeAqSVSOVD\nYJeiqVkWgygGGG/0cE+Yl7ZEZm3AIcCqXMafw5Yf5ezK9WruHDf84P4swA/5IwBJeQyQQ74o\nB/P2lhEN2yFeGppe1/o0j3PCa885B67tWuw3uI77Y3+p65i7/CXieeAi4FGAvxLwnknA7cAV\nAAOk/QGJGBADYkAMVM6A0z3Fy7dB5bnpSTEgBiLHAJpjtsOHcp92jGzNB5XpNzLJrm1hyhRk\n9h7AHm4tuYz3wHYFcAbA2MURjpf/NvABsK9zstDWayX4MBI5BWBkNiAvQbbA8IOVxPBD1gSh\nfh8BsGenMDgigYWiX963DhgDxE5mz559BeGzYgxyrgeY7qhEwt4TjZfv9luWDWSY1ZeA44BC\n/T1pC9pnU4CyG8BWPLcchoOXgZMA+twcgK1G9wKO8BeB15yDYtuA9C+WnXHnpX8g/m+cnYsV\nSPaX/UvU/6xf/wFcAsSu27L8X/5fwv+LVZ2xOO+n/+PD7S2QghgJ/YW6AYPRF2fYoBKmsAfR\niwC/KR3h9/z7ABtJ8oX3LQL47VhUmKgXORc3M+L6FXA5wCCDhXgQ2AMYCtwKPA6YIPNRiInA\nBODtXIFmYzsN2BrIb+naHuf6A88DsRP0fWM/zSDkAiT6GeDFH65KN09psOmUXwS+DJwC/Ah4\nEigk38JJBlhsSXoHeAQYDrDFaSzAgOk3wBKgHjgeWAe8CfwZGAnwj/o8oEcJUP8e8zXlovQP\nzP9NMXGP5ZD9Zf8eHaTj72Qr7tkN4A9fsRL5v/w/Vg7tURk//R9d7DYGMHyl60KxmNGOpUo2\nhr9Q7L+R73HAhsAnAOVp4BBgI2Ap4JaNccAeY5e6T+bvew2QViCBnYDLgBMBdqmj8Ff+ZcDZ\nALtEmSI/RUFuA+YC/BDnh/ZpwH7AswD7HzLqpBwFzMjuWdavc9tYbTCLCXkIQhiwkNNv3d2c\nueynazJjsP93YBZwKPAwUEzYInQQ0A9w/iizVWksQGEgtC/AwHUN4MgPsMOgKgkw/5ISoP4l\n8zbhBukfmP+bYN6SZZD9Zf8STsIfo/jDVixF/i//j6Vjl6mUn/6PQGhtIjv0PMHvr07Jhkc4\nSoe/UOyNyJYxCeswBkozgZuAo4F7gK8AiwDKjgDPoQUsOxM3Nv5LCkmOB/hr00j/k/ctRZLE\n5jSCH+D/BRgoOOf4cZ0GaFueY0AVtkxHhsx/SNgZ+51f25CGpuUD6i5Cul1eHL/zUXpiQAyI\nATEgBsSAGBAD4TJwit1w4Sl2Yztgu3FiSwO/Y+1xx1i/CLdE2dwYIDUD/I7nsIybgd/njlux\nfR1gTyTnW//r2O9RvH7EXo7U9gISAIOKdwG2xDiRGXaNk5NRInadY9c6dtGaALB7HXUgOCiV\n2/kAu4SdAEh6wcCAVJK+QSeViAExIAbEgJkMHIFisTtKUF2vzdRapRIDYqBXDKSxUGzasosu\nFFs/qCpjGG+BUmOBnwEbAMcDpwD8vue3/0SAPZH+CEwCGDz1KHU9Xu1+8VicugBgYHQrcBvw\nPmC6vIYC7pYrJIkiOQyaVgAvAFHQAcWUiAExIAbEgBjoNQP8+3cvwNb+j3udmhIQA2KgZhiw\nrcx76GJXdKHYddVZKJb8s4Xo+zmwlxvHGo0C2HtsAcBv/rLFa4DE8SSMyo4DfgxcDDwBMHJ7\nAGDzlunShgL+OwfTy+pr+ZqamjZnglOnTn3H14Qjkpj0l/3l/3r/Vf9l63+O7fwcwB8Ia0JU\n/6v+p6Pr/e/9959tJbe3uy7Rkq1DMrmapP+oJIaLOEe5k+Fv2JNpYQ4V5e41QPoPcvkucD7w\nBeBrALul7Qt8CrDpisHSHEBiGAOpVOrCXJFOMqxooRRH+sv+8v8sA3r/Q6lxzMokr/5jn/ya\nCY5oiTz9zTJOCKWR/vr7l3OzXtf/aEF6CT3XmtGK1LeQ665ZmPHUUlMoDRPOeQ2QnDIzNHws\nh9OxPRw4Cvgf4BsAA6ifAVGTO1Fg6sLWsK/0ovB74Vm2rLHvY9nyzDPPLEQldsOUKVPOdR6a\nNWvW2clkcrpt25l0On0wfv142rk2Z86cp7C/C659NHny5HHO+ZkzZ45vaGhgK1kDZi5xp8fy\nTMRza3xKz8ovn3Xw3k4xrArK1y29XurbJT0UbCbAJtismFY+H+zbRd/89DDN54FQfBjWQ9i7\nTH/pMb0I8pd9H3t4P+Kmb9bPXfpynRu2HHRK/vvr5/uW738G+Evg9Z+p/DU3N2euvvrqhSef\nfPLzjvENsEeo7xvrP/zdU/1X/PsgVHuE7X+w/QD4gOo/H77/EACwBanPjkum2bstOA2zHmSs\nP2/5HeuDRsRN+O5tWWb9L7ZnO3VNiS1/rNkZeLfEfX5e/iYSOw24Dri+WMKVBkju9DimhxMd\nsL+fI+zGFkXZBIXuC3DbG3kGD18AkJdyZB/ctOeCBQv+Z8yYMc+5H2hra7sLwc7bCHQyra2t\nXa7hhacDjgaWuZ95/PHH5x1wwAEMWOvxGMdfZQWVwwzsDMJzrX6kx0T9LF/Q6SH4hOo2X8as\neOWPD0VJ33x/gc0vgg9wQPZT1MWRYv7C61HWN9++9H86QK3om29f/NCyM3xgnnOe2zjZ19Gr\nmH1rtf5bsmTJgpNOOuknsPWggw466G6Hp/z3wzlfjD9ej7K/qP5T/af6z5/vP0zQ8GbSSrS/\nOfSx+uWN7MWWsZb0fwMTZ2d/g2Q08CBO3gqUI4wX5pVzo4/3jEBakwBufZcGpMgZcO4D1gEI\nJrMLMV2NLTONqmyLgnMRWW7DlOnIjBwOCTPTIPJqH9w4r31gPeekl4gBMSAGxED1GdgURWDv\niEA+BqqvnkogBsRAmAwcYzf84li7IQ20u3FUSz3H/dibn2JdE2Z5KsirrADJawvSVBTkeOBI\ngNPokYyHgVuAh4Cothyh6Flha0tni0vunDYeGLDb7bX4MWGlh0d0qxgQA2JADATHwPtI+rjg\nklfKYkAM1BYDmTWYqAEqJ9w9x7K/8pOH9vAXivVK/xI8QPQoXgMkdtEaB6AtzfopwOPFQJRk\nLAo7ChgILAX4x4NbiQ8M2GlrOQbuadpYH7hUEmJADIiBChngeLPVFT6rx8SAGBADRRnAGKR2\nXGSvpy6C81nJtFZ/CjsUhI04gwEOdWFduAJYA5QtlQRIjyD1WWXnYMaNx6AYFwNbAAx784Ut\nX28DnJGPwV8sBQOyj6ZimASisx96LBUtopT0l/3l/3r/a6D+Y2sRe3Wwqzj/rmVF9Z/qPzpC\nDfh/h8Pn/Sv/98//sVAsxzInE5btxERZtu2OmClZV52FYlmGzwBnAIcCGwH58i5OPAb8ACjZ\nMFIqQGIExkkYOAlAO3AtwCa14UBPwijNU6TWU2K9vPY4nv9CLg3qwHKtBVqAvi5MxP5rwHXA\nmUDsBIO0D8wpVZMBkvSX/eX/WQb0/seudu9UiD08bgc4E+rbnWexo/pP9V/OH/T+u1+MGtn3\n+f1/D7SZtlDsD1GmS3LmZM8wNuQwdmHrEVuShgKbAqcCnGvgbIBjM4tKqQDpSTy5A7AL8C/g\neYAVcCm5GDc4BS11b5DXr0TiDI4YKX4LKEZGAtcYdbLbILevAL8DYiWYtahzBrdYKVamMtJf\n9i/TVWJ5m/y/JvyfHy5jgQX5Tiz714T9883eeSz7y/6dztDLnTQWisVHc0N+Mk5zUt+RSQQk\nzlH+XYEcH4lUGXOwh9uFwAtAIeG3/h4AY4M/APOAZ4GCUipAegxPvQUszz3NCRlKtR7x1tdz\n91d782UUgN3nNgdW9VAYG9d+DfwVeBc4B4hdgIQpLs+HXoFK7qVhq6NxEob+xintKpD0D97/\nXXQbtyv714z9uwVHdEbZv2bsX7Dukf1l/4KOUdHJjoViMUlD30KPNy/KfFrofIDnDkfa/Hbn\nlr3Digm/9Z8G9gPmA8cDRQMkXIu1NEM7kuZFaNgPvTzgw73TkQYNN8SHtKqaRFtj49K2uoZ7\nq1oIZS4GxIAYqA0GBkHNa4GRtaGutBQDYqDaDBxq199wqN2QycchLfUZlM0ee5b125DL+Cry\nu8Njns/gfs6+XVSSRa8UvjAepzcsfCl7luntBezYwz1hXuJ4I/7hKBjlFijIWJzj7HbvF7im\nU2UwYLfY/8VotRfLuFW3iAExIAbEQOUM9MOjTwP7AusqT0ZPigExIAbKZyCDhWJxN8b0J9Bp\nKB+ojBaHvtTLYpRnZ6Dc3kucX2ES0OOkbAxovAi73PU0gUEjrj8FnAqYIByMyDKxFemkHgrE\nfokce8QolHJzx0b/emUAU3xvgWdMCZC9Fl/3iwExIAaiwgA/Bp4AdgeWRaXQKqcYEAPRZiBh\nJTdBU1EKgVImH9SsYUjZjRJ+EXEbEtoauA/YtYdE+a3PMUgcq8QfmB4Eikpd0SsdF/ixu6fr\nHrau7ASc7Drn7DLYcj6MTamsGcyxBekw4CaA44rY7Y6z2HHLliWCRHF2PnZzewC4HoidzJ49\n+woqNXny5POCUg7TPL4PDzRlDFoXNcPQv0uGhh1I/+D93zCTdymO7B87+7M7+LldjNzDgewf\nO/v3YO3ul2R/2Z9e4cf3X9rKrEGQhNQSXRpZ+AFNaQt/odg7ke1w4DLgS8BCYAHwCbASYFfk\nocBmwCYAWr+s/wWagKJSKkBagicvBZigI4dihygm7NbGIMMEob2OAPYCGGGOBgbkgE1WeA8n\ncmC3MLYiPQfEUhKJ7Nz1geqWtBIbZqwMeTZOwtDfOKVdBZL+wfu/i27jdmX/WNh/LBzrAyDt\n1cFk/1jY36vZO++X/WX/Tmfo5Q5aj9rxQzi/nbsIzmcF8wU6u12uB3jAslwN/Bm4HGDDTn5L\nEhtGFgFXAtcArEd7lFIBEiOvQ4Btcqlche1MoFAAREJYgBeA9wGT5J8ozNhcgUZhSwwC2I+y\nJEm4JxaCWWzmBq0Imlub8eLQb4yTMPQ3TmlXgaR/8P7votu4Xdk/8vY/BU51HcA//Pw760lk\n/8jb35O982+W/WX/fJ+o9NjGD+GISExcKPZd6HRsTi9+4w8G+gAfAWxxD1R+idS/HGgOtZl4\nbGaxa7Uamtqshgtq04zSWgyIATEQCAPsHtICMEiSiAExIAaqxsD+dv239rcbWva3G203vtjS\nwJYce8Q0i9+0kZdSLUgbQMN6YBnAPnuXAimAff16EnazIyRiQAyIATEgBsRA7xjg32D+GqrZ\n6nrHo54WA2KglwxkrOR2SKIhPxmnX11D+AvF5hfFl+MuA6wKpPgkznEckjP5wvO5Y57rCYFN\nAoB8JQYzgFnsJqB4jr8YXFIVTQyIATEQKQYUHEXKXCqsGIgnA7aVeQkTcjUX025d+AvFFitK\nr86XakF6DKm/BSzP5fIwtqVaj3jr67n7tTGIgaamps1ZnKlTp74TVLHw0nyAMUhzg0q/N+mG\noX9vyhf0s9I/eP8P2oa9SV/2j5T92aXuN8BPAGf5id6Y35L9I2X/Xtm60MOyv+xPv/Dj+w/9\n6LYHMLbHZpe6TsEYdO4nGkZmW7s7z0d1p1SAlN8SxFneJBFlIJVKXZgr+kkBqsB+8oRxEpL+\nxuntFEj6h+L/Dt3GbWX/yNifg4qfAVoBTlfri8j+kbG/L/bOT0T2l/1zPtHr7z8EQm8iDsJM\ndgkOwekm6xYFPyFCt0wDOFGqi125WTLQ2gpA44HEYAZon1q2kfSX/eX/BldQARctKu9/G3i4\nBeBUtRx75JdERX+/9M1PR/qr/lf9n/9WVHCcthIjMd4olcZCsflgcnUbZNcWrSBlsx4p1YJU\nqLTTcHI/4Bu5i1yU6Q6AU+otBk4G2BVPYhgDWAdhhg0xrFihFUf6y/7yf73/oVU4lWfENY5+\nUfnjhZ9U/af6T/Wf6r/CtYPXs/ZqxNr4lkxw0rZuYrdk1xbtdj7uJw6HgvzA5uAsRuKcVWcF\nwMkr/g/gPOM8zo51wVZSHgOcEpG8DinvdnPv0jTf5tpGJRMDYsBYBnZGybiIuUQMiAExYDQD\nu9t1P9jDbmjfA9N8uzE1N833sGOtK4xWoMzCee1i9yOk+x6wK8AP+sMABkkkY3/gM7ljBlIS\nMSAGxIAYEANioGcGzsblOQD/fkrEgBgQA0YzgLFHG6JVJImxSJl8sOCpgVaj0QqUWTgvARLv\n3Rq4C3gll/5Bue19ue272HIGs51yx9qIATEgBsSAGBADhRngWiLnA8cDMwvforNiQAyIAXMY\nwKKo81EajJVMIC7IBxZN/cRCF7zoi5cxSAOhLmfX+TCnNvseciwSB5FyfSRHeA8rfYlhDMya\nNetoFmnKlCl3G1a0UIoj/WV/+b/ef8PqP85UNzKMClD1n+o/1X+q//yp/5LboQtZt+98diuj\npEYlMVyEI2+iLV5akDi+iMHQHjmVv4jtBsAjgMMEuwiMA9iSJDGMgWQyeSBhWLFCK470l/3l\n/3r/Q6twDMtI9Z/qP9V/qv/8qZZ6Xii2bWGGcxFEXrwESFSWs9UdCTyV22fA+DuAchHwDMBg\n6VZAYhgDmMGmlTCsWKEVR/rL/vJ/vf+hVTiFMxqB048CXyh8Obizqv9U/6n+U/3nRw2Dj/zt\nM1aiL6b4tvPB9OtrZKHYfC6/hxNsNWKQtAo4C/gnQNkL4Mx2JwAchyQxjIFMJsO+7jUr0l/2\nr1nnh+Ly/6r7P3+Q5Dij5cCLQKgi+1fd/qHaOz8z2V/2z/eJSo/t7EKxdluxhWLbYrJQbKX8\nsO8hgyG3TMIBxylJvDOgab69c6YnxIAYEANRY+AYFLhf1Aqt8ooBMSAGHAZ2seuv2MVuSH8O\nU327sXNLPddwszf8unWtc2+Ut14maXDryW5amwGc1W4osBR4AWCrkkQMiAExIAbEgBjozsAf\nu5/SGTEgBsRAdBhAC9IqtB5h1d0iC8Wus2IxlKOSAGkbmPE6YM88c2LKv+z5b2HLsUkSMSAG\nxIAYEAO1zAAnM3oNWFTLJEh3MSAG4sMAPvA510C3D/3sSZzPYB7wOIjXAGkMlJ4FDAI4ex37\nUXO2Cp7nmkhnA1wN/OuAwxV2JSYwMHv27OzqxpMnTz7PhPKEXQbpL/vT5+T/ev9Dqnu+i3x+\nAhwAVD1AUv2n+o9+r/pP9R/9oDeClqOh+MDHmEq7y3c+Fo1lssm4LBTrNUC6BspznaN9gccB\nt5yLg6uBM4BbgGcAiUEMJBKJYQYVJ/SiSH/ZP3SnMyhD+X/o/n84zM8JjR4zwQ1k/9Dtb4LZ\nO8sg+8v+nc7Qyx1ERfMxCQEXiuV8BN2kFheKJQl7ATcA+cERr7GLHbvXHQV8HlCABBJMEsxi\nU9OzC0p/2d+k9zHsssj/Q/f/3cK2cU/5yf6h278nc4R+TfaX/f1zuuS2aCvqFhxl24+QSd0m\n8Vgo1gtfg3Ez9T+1xEOcxvT+EvfoclcGNItdVz50JAbEgBgQA2JADIgBMWAYAzvY9WfuaDes\n3dFutN2Y1NLAGMEeOM3iN23khesylCuf4kZixx4eYEQ5EXivh3t0SQyIATEgBsRAnBgYDWX+\nDRwXJ6WkixgQA2IgnwF0scNCsVZfjDnCIKSu4L11MVko1kuARL05MQMnYDiEB3nCsUmc3W5D\n4Mm8azoUA2JADIgBMRBXBjjOaDXwt7gqKL3EgBgQAzkG3kBDEYfVcD3UfFjtMVko1uskDd8D\nGfsDDwEcY8RZ7JYDnMWO05nyV7Q/AX8FJIYx0NTUtDmLNHXq1HcMK1ooxZH+sr/8X+9/QPXf\nSfAttiC1hFKZVZCJ6j/Vf3SbgPy/Ao8M9xH5v3/+j3WQRnENpIRlp91WxHkepuqGWv3d56O6\n7zVAmg9FtwNuBDh16e6AI2ux80PgF84Jbc1iIJVKXZgrEf+Y15xIf9k/5/Ty/5p7+/FXO9j6\n71nTKQ1Yf9PVD9r+0t9wBuT//v39R/e6Veh+1m2h2Gx4BD/I1PBCsQuh/4HAAGBrYATAMUds\nlTD21zOUTdLRFFrLPLApuJZF+tey9fX+++n/nK0V3UysVyLkUn7qHyG1O4sq/TupqMkd2d8n\ns4NIxEgds7a5k+wMkNrdZ6O777UFya0pg6FWoBn4EFBwBBJMFqyDMMOGmFzGIMsm/WV/+b/e\nfx/qGPaUuAjgWNzIBEiq/1T/qf5T/edD/YfoKLEBgiQ2ImUDpfVpZj8vY7NQ7Hq9yt/bBLf+\nH8CAiGw4eA/7pwES7wxomm/vnOkJMSAGxEA1GLgdmbKLuUQMiAExUHMMTLTrv7WNXd+yjd1g\nuzGxpT4bD/SPyTTfXluQdoIncJYedqt7FHgd4Mw9nJxhX+A6gN3uvg2QKIkYEANiQAyIgTgx\ncHyclJEuYkAMiAEvDODjfhv02C66UGzDJtaQNV4SNPRerwHS/0KPPsAuAGfscQvJugo4B7gX\naAIkYkAMiAExIAaizAD/TrIrSV53kiirpLKLATEgBipm4BXMWNecsBJ9C6XQuthaUeh81M6h\nD2HZksKd7FZwOZAfHDERjkdicLQYOBiIgmyGQu4HcPHbgoaOghLllnHWrFlHE+XeH7f7pL/s\nL//X+++xXhuH+18DvuXxOeNuV/2n+k/1n+o/nyqmSWhB6osgCYO6uoLp16EFyad8qpqMlwCJ\nv6Jx5jrOYldMOCf6PIB/VEyQb6AQdwL5wc/2OPc8MA/geCqu58TA7nsAA8FYSjKZPJCIpXJl\nKCX9ZX/5v97/MqoK9y3342Ae8Dv3ySjuq/5T/af6T/WfH3UXmtLnYhQNFopNYK6GfFhWe0xa\nkLx0seOkDM8B/wPcDRTqbsAWGbbGzABMkF1RiGOBbwLNuQKNwXYmMBj4F8DWsEHAnsBPgRHA\nuUDsBHE+W/lqVqS/7F+zzg/F5f8V+f+XQd0HQOQnrpX9K7J/bKoM2V/298+Z7dGYyS6Fvyps\nFOkUZ6HYRI0uFHsymGBw8RBwMfAywI/ufsC+ABeJ5cQNDwAbAo5wEVknQHHOVWvLIIjB0VnA\nr12FoA6/BzjBxN+Bx4BYSSaTOT9WCnlURvrL/h5dJla3y/8r8v/34uIEsn9F9o+L+S3ZX/b3\ny5nROrISTUc2xiAhSOou9tpsXND9QszPsLWFwQ4msciC0eOnrmPnfP72+7inGnIzMmVZGBA5\n8h525jgHedu+OP4Y+Ene+aAPpyMDljPy/TZbrYamNqvhgqAJU/piQAyIgQAYOBVp7h9AukpS\nDIgBMRALBraw6y7awq5v3wLTfLsxwZnm+yvWFXFQ1EsXO+rLLnbzK1D8zQqeCeoRdqd7vEji\nbOXi6ujbFbmu02JADIgBMRBPBi6DWucBh8VTPWklBsSAGOg9AzYWikUq3RaKzXWxSyb6W429\nz6X6KXgNkDiWJ+rCVrDtiyjBboGcwvzWItd1WgyIATEgBuLJAAYdZ1uP/hlP9aSVGBADYqD3\nDKCL3QeIjtrQxa6hUGr2J9n1UQtditQ5rwFSpJRzFZYtX86EDM9i/yLgUOAvgCObYudnAA0e\nyz+Qs2fPzjZ7Tp48mb+S1pxIf9mfTi//1/tfpPK7pMj5WJxW/af6j46s+k/1X28rNARH2xQL\njph2KibTfMc9QOJkCxzXw5n1jssBm6xwggYnQOK6TQ8C5IMB1F1A7CSRSAyLnVIeFJL+sr8H\nd4ndrfL/bv4/MGfkVbEzdgGFZP9u9i/AUnxPyf6yv1/ejQHzmKCt+EKx6Rqc5tsvbsNM50/I\njKBwogYGSg4SPJkTzsTB8UcMjDiLHSdMiJ1gFpu5sVPKg0LSX/b34C6xu1X+38X/t4KB/w+Y\nAbBHQexF9u9i/9jbO19B2V/2z/eJSo/x8TypY6HYrik4H85xaUHqql3tHnH2uvoqqj8deWsW\nuyoaQFmLATFQUwywGzV7DbDul4gBMSAGxECZDIy36789wa5vmYBZ7NzY3JnFbprFb9rIS9y7\n2JVrIFPWaCq3vLpPDIgBMSAGKmfgEDy6GnB+9Kw8JT0pBsSAGKgpBuwxmMmuDrPWcamfTulc\nKHZDa0DnyQjvKEDqajzO0ncacB1wfddLno6G4m4uSFtwho8CKe10wAEHWBdeeOFv6urq/jJl\nypS7nXvmzJnzBayA/TUc2+l0+vKpU6e+41ybNWvW95LJ5ERc/9g98PLZZ58divM/RZ/jBjSr\nPxxmetbUvZ3iWSaWz3T+VD6z/Fn2kD2qWZ/K/+R/8r/ofg/F+P1dgV+W7G1X7pza4+P98SuT\nbT048nZrSXJh9vvPXmO1dH4IRninNwESuyZMAPoBc4D+wBogyjIChUffSovb2ElTU9PmCJo2\nQUAVO93KUQh6bwoOLHelVc5zcbkHQfNQcMCB6R/HRScvetD/c/cv9/JcXO7N2T6WdVsZNjpv\n/Pjxdaj/PlD9t/5HtjJ4i80tqv9U/8GZa7X+s/z8/sM031gEicP4CUoCIVIHOo5r919Oh30P\nQI74pT0ToDwAXAY08iCiUq0AKZQxSJjm9WYiSNu0Wg1NbVbDBUHmUWnaYehfadnCeE76B+//\nYdix0jxq2P5szV9z0UUXPRx0/VepbcJ4robtn6VX+qv+0/vvz/cfxiBdNc5uzABpN8a2NLDL\nnd3vVOtXYdRpQeeB6cw9ySa4+wXgSOANYD7gCEPJCwEuxNrHORmx7RKU9xWA2ziKO+SPo36l\ndJL+63/yKcVVHK/L/rVp/7lw5s8ffPDBH2JLH6hVkf/L/vL/Wn37O3zfF/tnrOQHSIgLxSbz\nQXprdaHYa6E7u9btATwD3A9sBFCmAZcADJJOAG4ATJQNUChO+c2WLg7SXQFEvWsgVCgt6F4y\nA91LarN/HeiR/rK//L8m3//bWDvq/df7r/e/Jt//7MeR3n//3n8ERROxHGyBMfYdn5f1I1OD\nmy02JtWWLIO6P3epzADJ6WLH05wqmwHHLTwwSD6DstwIfATQgvngxAcM6JxgD7uhSihd7MLQ\nyOQudmHorzzEgBioOgPDUYLNql4KFUAMiAExEEMGxtl9zxxn91s73u5vuzGupV/227rvtCS/\naSMvXrrYDYK2bH15swet23Dttdx9PdwW6qUfIrcXgJOBZmAW8DfgbuAR4DmgH3AqwK4YxwES\nMSAGxIAYiB4Dk1DkF4Fzold0lVgMiAExEAUGUjskrBR6kzGEyAfalkYm2Esr8uJlFruV0JZ9\nuHcBbiqiOYOobYHeTJFdJOmKTh+Jp9jtj4EQu/4xUCok6E6Z7TZ4JbZ/AOYBzwISMSAGxIAY\niA4DF6GoTwHnR6fIKqkYEANiIDoMYFrv1/HR3IqOywW62VlWepH9aXS0KV5SLwESU3kYOAX4\nD3Ar4JYhOLgVYOT4KGCCHI5CvAtw29JDgdgs+DSwHzAfOB6IXYCEdYmOhl6We10kHteKSH/Z\nn74u/1+/rkgM3/2joBPr826i91/vP51C73+s3/9u771zQu+/n+9/kgvFckhNxuGX21zFm0xs\nmIjFQrFeuthR/3OBRQCn8FsI7AaMBx4EOI7nMOBW4HHABGF3C3ap6yk4cpdzOQ44i90o98m4\n7GMdiAOJuOjjVQ/pL/vL/2P//hcMjlhX6P3X+6/3P/bvf9HPAr3//r3/WPGI38qZ/BnseEwD\nJNYkyv3mLmovEy54DZBWoNA7ATcAnMp7BDASYGBEORtgC5MpshgF2RlgpFuOcIwVg6o3yrk5\navdgBp9WImrl9qu80l/2l//H6v1n1+gfA5eUU0fo/df7r/c/Vu9/Oa995z16//17/2383IQw\nCNwWQiflkd/x2sWOCn8MnAacAWwGbAzMA9iyZJrchgLdAdwHXA7MAQoJrbw7cAXACRvYIhY7\nyWQyNd0vX/rL/rF7qT0oFEP/nw71TwcOLYeGGOpfjtqd90h/1X+dzlCDO/J///wfH8yDEBwl\n0VxfsIud3a/w2KQadDujVWbg821gDcCuFwuA2QBnsbsrt52FLYM7XucsfOcAYQv/0DP/IWFn\n7Hd+mubbb0aVnhgQA0UY2AfntytyTafFgBgQA2IgAAbG2kO+Pd4e0jLe3gDTfK/HuJYh/I61\n+06r4zdt5MVrCxK71rFbXSn5I24gqi001tXAnwG2IO0J7Aq4ZS0OGCBxBrtrgA8AiRgQA2JA\nDJjNgCljXc1mSaUTA2JADPjIQMdCsckGzGbHb+xOyR0k6jHNd3Pn2ejueA2QvghVx5VQl600\n/yxxT9iX30WGx+YyRdNgdqa9Pth+BHyaO6+NGBADYkAMmMvAZigaF4F93twiqmRiQAyIgbgz\nkHwFwVAzAiWshdRd2hYlYvFd7TVA+gyoyJ/YgcejAXZ1YGsNW464NVW4nhNRczJ79myOsbIm\nT558XlDKo08juzUaKWHob6TiuUJJ/+D9X/YPjAFOtvMwcC9QUYAk/5f/0zuD/PvH9E0V+b/8\nn77pj/8nJiWsZIHgqKMNKTXSHmzqe+ClXF4DpGJR4SfI9GXgNeBFYCbwF0BiEAOJRGJY0MVB\ndLQVXhETJ+ywwtA/aH57k770D97/e2OfoJ+NuP3ZA4CT7ZxTKU8R179StTufk/56/zudoQZ3\n5P/++T++8XILxVoNhVwpXaMtSIW4cJ97CQfzAXbFU4DkZsaAfcziMjfoYuDFsTGtCT9kjJMw\n9DdOaVeBpH/w/u+i27jdiNu/163eEde/1/4k/fX+99qJIpyA/N9P/09uWgsLxfrt7o1IcBVw\nt98Jxzy9WMxitxyz8LVZjVhooX6nmNtL6okBMSAGxIAYEANioOYYGGcPv2icPaJ9vL0xZrFb\nj3EtI9jHzu5/XMMv4kCK1y52nNig0BgTprMRcBkwAPgXIKkxBvpb9VtQ5VVW29s1prrUFQNi\nwD8GOK71KmAD4AT/klVKYkAMiAEx0FsGuFAsJmgonkwq0cPF4o+ZdsVrgPQ6FBhXQgnOGPe7\nEvfocgwZQJPrFngrlmxYo5NgxNCkUkkMVIOBS5Hp8cCXqpG58hQDYkAMiIHiDGCChoG4Wnyh\n2D7JgmOTiqdo5hWvAdLTUOO/BVTBsJPszHCvYHsjUGwyhwKP6lRYDDQ1NW3OvKZOnfpOEHni\nZ98t0L5qbOtR0PoHwamfaUr/YP3fT1sFkVaE7H8n9L8J4I9tvkmE9PdNZ3dC0l/vP/0hqL//\nbl8zcV/+75//Y/2jhUkr1YbedAUDIXuZvcZEH/BaJq8B0v94zUD3m8NAKpW6MFeak4IoFYIj\ndLGz3woibT/SDFp/P8oYZBrSP1j/D9J2fqQdIftzNlTfJUL6+647E5T+ev9zjhXI3/9AnNbH\nROX//vk/WpC2RgNSgYViOQTJwkKxyVgsFIsf/T3Jb3A319LxGlh5ykQ3B8YA+4UG2TeUXeyM\nDZByugepf2CG8ynhoO3vUzEDS0b6B/v+V2q4rfDg4ZU+7OE52d9M+3swYa9ulf1lf/pArYqP\n/p94Fa1IXCgWg426guS2LcrEoheZl0CHM9RxwOwCoNdTriINScgMwI1n2JCgssXbNwF9LY0N\nkILWPyhe/UpX+gfr/37ZKah0DLX/rtCXC8By5tMHg9Kd6Rqqf5Aqd0lb+uv9D/LvfxdnM/BA\n/u+n/6d26Hmh2LpYLBTrxY0ZfS4G3gdqOQr3wlm590Z+mm/8XDA0N8X3juUqrfvEgBioeQbO\nAgM/A7z2Zqh54kSAGBADYqAaDIy1N/3WeHtcC4BpvtdjXMtY/gBv953Wl9+0kRcvLUhU/Ajg\nHoCLwP4a4ID8pUC+tOAEIakRBvrmpvheoSm+a8TiUlMM+MLAr3xJRYmIATEgBsRASAwkN8NU\n3/WIhThBW6fkuiclExsmuNxP5MXrr3Ycf9QPOAR4BGCAxL6G+fg+zklqiAFO8Q11Fw+3rNU1\npLZUFQNiwDsDKe+P6AkxIAbEgBgwgQGMO1qGcmTQzQ7rIXUFy5dYnVhnQjl7WwYvLUjM6w1g\neRmZvlnGPbolZAZmzZp1NLOcMmUK+/v7LZzi29jxR1Q2YP395tP39KR/oP7vu738TtAA+/Pv\nzfXABODzQKhigP6h6pufmfTX+0+fCOjvf767GXcs//fP/20rVWKh2HgMw/EaIP0EXs/Wok+K\neD9bpPbI3VPkFp2uFgPJZPLAXN6+B0gYlMYpvo1dA4l6B6l/tWzqJV/pH5z/e7FDte41wP7s\nWXAocHA1ODBA/2qo3Zmn9Nf7n3MG3//+dzqZwTvyf//8H61GJRaKTRVcH8lg9yhYNK9d7B5D\nKmcWTKnjJGe6ewo4teNQ/5rEAGawaSUCKpPpU3xbAesfEK3+JSv9A/V//wwVUEoG2P93UG0S\n8HxAKvaYrAH691i+oC9Kf73/9IGg/czU9OX//vk/Bh4tQje7NoAtSV1A+9vL0jWxUCzHlezp\ncnhGjTsBJ7vOObsMtpwZzNg/UWIYA5lM5vygimT6FN/UO0j9g+LVz3Slf3D+76edgkrLAPsv\nCUq3ctI1QP9yihnYPdJf739gzhWBhOX//vl/suRCsXWxWCi2lFsPwg2LgOzUfWVuOUh/Z0BS\nPgORnuZ7pWUN4xTfLVYDfx2WiAExIAbIAH8wO1tUiAExIAbEQHwYGGdvfeY4e+La8fY2mOZ7\nPca1TMzGCn2n9a+Jab7x7ZudsW6bnGmvwnYm8EDu2L3hdH9rgReA990XtB9vBvpadRx/ZH9i\ntRo9BineVpB2YsAoBnZDaR4B7jCqVCqMGBADYkAM9JKB1I6FF4rtmPU7NaphiGVFv5ddOZM0\nMOAhKJ8Fngbu54FEDJABTvGNfqiLRnYEyCJFDIgBMTACFPwM+ImoEANiQAyIgfgwgGai/yQs\njmdLNBTSKr2wfUWh81E7V06A5NbpW+4D7UeLgdmzZ3MdK2vy5Mnn+VnyjgDJ7Cm+qW9Q+vvJ\nZZBpSf9g/D9Im/mZdsj2Zy+DQj0N/FTJU1oh6++pbGHcLP31/tPP/P77H4bv+pGH/N9P/09h\nodhEPX4e72gyyhkI57iHhWLrBvhhs2qn4TVAqnZ5lX8vGEgkEsN68XjRRznFt+lrILHwQelf\nlBjDLkj/YPzfMDMXLU7A9udfxg2AZUULUOULAetfZe1KZy/99f6X9pL43iH/98//0WMIS/0k\nsFBsouCi34nVmXVx8CQFSHGwYpk6YBaXuWXe6vU2dLGzjV9bIUD9vfJVlfulf2D+XxV7es00\nQPuzm8VtwPbAdl7LFdb9Aeoflgq9ykf66/3vlQNF/GH5v3/+j5aiOoxBgkdwTga3ZFuQLCsV\nj4Vi3Zppv3oMRHoWuzar4VPMYHdE9ehTzmJADFSRgW8j74XApCqWQVmLATEgBsRACAyMsz9z\nxTh7pwyQdmNsy45pZG/3P2XoNSEUI/AsGAJKxEDFDGBOdwzGTgxCAm9VnIgeFANiIMoM/BaF\n3xp4JcpKqOxiQAyIATFQmgEMPMotFJvEIrFdwaczy9Oc0Tryoi52kTdhdRVotOom4AcD+yOr\n9Z3qlkS5iwExUCUGWpAvIREDYkAMiIGYM5C06rbCD+P16GDXpY9d7iBRP7I+FgvF9qYFqS98\ngH3Od835Qv+Y+0Tk1Wtqatqc8FMRzmAHLBhjWc1+phtEWkHoH0Q5g0pT+vvv/0HZKoh0fbT/\nZJQvcl0ofNQ/CPMEnqb01/tPHwjc0QzNQP7vp/8nXsV33zpM0pDIB83ftij9qaFu4KlYlbQg\nbYocOF30/wM4IusZYA/gDuA14FJAvyaCBNMklUpdmCvTSX6VjQESouxIdK8LQn+/eAwjHenv\nv/+HYTe/8vDJ/rujPP8Afu9XucJKxyf9wyqu7/lIf73/Oafy7e+/704aYILyfz/9P1lkodiO\nSRpSaEEK0JShJe01QNoEJXsB2BDgjGj9AEfIDD/ADwc+C8Rimj/oESfJTTHinxexqjoAAEAA\nSURBVEoIjiIxxXdOY9/194/JUFKS/qHQbGwmfti/Ddp9B/iNsVoWL5gf+hdP3fwr0t98GwVZ\nQtk/SHbNT9s3++OH8Z4Xil3UVpMtSNfCB9i1ji1GbDm6H9gIoEwDLgEYJJ0A3ABIDGIAjaEz\nbIifRUJimOI723roZ7KBpBWE/oEUNKBEpb///h+QqQJJ1if7z0HhiMiJT/pHTm+nwNJf77/f\nf/8d34rCVv7vp/8ncwvFWnkLxWY9AQvFNsRioVivfr0MD/zc9RADpJmuY6ysa60AbnGdM32X\nsy8dCKSqWNDITvONKb5XYYrvw6rInbIWA2IgOAZYL04MLnmlLAbEgBgQA1FiYJy9+w/G2Xu2\nj7f3tN0Y17I7f4C3+x87zB0nmKIaG3P4vY+OT+VJ2TciuUHABsCbPSTN7hev5e7r4TajLt2K\n0vwd2MyoUkWgMGssC10uE/yl4K0IFFdFFANiwBsDfXH7fcDD3h7T3WJADIgBMRBXBmwryeE5\n6DzEXnv5wKmG8oOQEDk6D3lxaNCQcvP0MgZpJRL9ENgFuKlIBgyitgWuL3I97NPs9je0RKbD\nc9ePwXZpbv9FbP+V29emCAMNVt0W+LEg876m+C7CkE6LgUgzcCRKPwnYP9JaqPBiQAyIATHg\nGwNY+4izVicwFimvix0bkBAc1afYmyxM4d+pUjNpj8oViDEM4xnKB8CC7F6Bf7wESHycvySe\nAvwHuBVwC6OyWwHOXvEoYILMQCH4K2g5crnrpiex/wXXcSx2Z82adTQVmTJlyt1+KISXYwLe\nkQ+2iMishX7r7weHYaYh/f31/zBt50deFdj/duT7ByDtR/7VTqMC/atdZF/zl/56/+lQfv39\n99U5Q0hM/u+f/2cse3HKSrHHWENX03V0SsusaG/uej7wI/6t2qHMXB5x3Xcx9i9xHXfZ9Rog\nnYun9wV+BTCgIAn84/kgwIkb2FpzK/A4YII8gEIcC7ANkK1f84B82Q4n2E3sJcCZeY+BYOwk\nmUwemFPKrwApMlN8U2+/9Y+ag0h/f/2/Ruwfi+BI77/qP9V/qv9ydbYv3z81Uv8XVDNRYqHY\n1CZ92JssTGGvtauBPsBfgLlAvuyNE58DrgWcAK4p/yb3sdcAaQUe3gm4DDgRcEg4DPvLgLOB\n3wKmyFdQEL4MfwRGAM8ARwHZdkBsKbOBXQF2x3sXiK1gBptWP5VD1AnOMov8TDPItPzWP8iy\nBpG29PfX/4OwUZBplmH/zyN/9tP+EuCuI3EYfSlD/+gr2YMG0l/vfw/uEftL8n///B+Lw2Kh\nWIsLxfbt6jhsi0CryaLWsKf5ZoA0E7gT+CLwGPBrwP137Gc4ZoB0CcB4paR4DZCY4MfAacAZ\nwGbAxsA8wNQPZUaTDI6eABgEcZwR+9T/G6gpyWQy5/usMPqfJkzpTllStQD0L5mnSTdIf9/9\n3yTzlixLCfvvhgQeAdg7wP1HpWS6UbmhhP5RUaPickp/vf8VO08MHpT/++n/CSwUm8oLjugk\nHQFSamSfwVVwmdeQJwMg9m67BuAPfScCCwFJGQyci3vYb5LdRkgghS1I/CAYz4MqyXTkyzJw\nHFdkpM1q/BBTfB8TmQKroGJADBRjYBwusDuyRAyIATEgBsRAUQbG2vufM94+oAXANN/rMa5l\nP37H2n2njeA3bTXlC8icEzCwpcj5RmULEss3FChLvLYgcUzPyBIpt+A6m9fYXe1egN3aTJGr\nUJD7gX8CZwOHAk5fROxKvDFgD8lYmY+8PaO7xYAYMJCB91AmQiIGxIAYEANioAcGkuMwSVc9\nWozQi2i94BwPsFBsPcf1V1PYY2wScD1wF8Bv/dWAJ/EaIHEavYnAwFwuK7FlphsDHdNX5C7k\nNuyG9zWABTRF5qEgmwEk7lQga1FsJR4YgOGHgbrGpJVUgOSBN90qBgxhAH/crL0A9tWWiAEx\nIAbEgBgoiwGMPcJQm2QGH8+prg90fE4nVoU+i13XYnQcLcfmaOCvwK+BQYAnKRTU9JTABbjI\noOoOYCzAfoajgD4AAyGOT7oC6AfsAcwFeO/WgGnCcVQs4wKARMa+JWn27NlXENC119JoNYxg\nIq1Wa2QCJD/17zWBVUhA+vvn/1UwX6+zdNmfP3Txj8ZtQN4fuF5nY2wCLv2NLWOQBZP+ev/p\nA0H6mMlpy//98/+OhWKTiIYYQuQDpxqSXmOLIF1nBhLfAfgT8BTAYTZlCYMdL8IZ6p4Bjgds\n14PMkIEQW5T+DLBAvO9A4AOAgcgbgGnShAKNMa1QQZUnkUig1ccfQde64Ul8W71vWZzZMBLi\np/6RUDivkNLfP//PozYShy77T0WB+QPH7kA6EoX3oZAu/X1ILXpJSH+9/9HzWv9KLP/3z/8x\nQQN/ZCuwUGzWXlgoto49FEySeSgMFz73JF4CJM5Y8RngHMAdHLkzfAQH7JPIP7yvAAuAt4Ap\nwO+BWhH+Knse0FimwpxByvrjH//4xTFjxjw3derU+c5zTz/99EYNDQ27YAaWTEtLy1N77733\nOucafhFhH8vRwLLJkydzsomsXHzxxckDDjhgHxzUp9Pp15z0kMRcTHXZH88d1Nv00LVuONL/\n6JOnnx4826fysfB+6pufHo7fgt7tPE/xyh+fCbJ8vbVvqfLB9mwpHQy94S6l/aVUelHjj/5P\nnYq9H3HTl/pQHH1hf9bNS4B/5BBpf/bqf7Q/PpJ8qf/AX1aiVB9Af/4trtn6D7ZfDg5U/xX5\nPqBDR8mfvf69dNV/2Xc37vpCv8C+/3ILxbYPbOtTv/makQgIbOv1QfPRo4h/YvCr27y1G2Nz\nUPag9D9tuOVxoOPh0veHdoeXAIkf5vzA4gd5MRmFC2xaW+O6geOVvOTjejT0Xc6hfjjwAPCV\nXuS+O579CYAmyPJl9OjRt2IxuxvwxLnOU/X19ceiYp+O8xkESgfj/NPONZy/Fvu74MVnN7dx\nzvl99tlnLK7dg2M0dK5PDyto/wyrSZ+Na/f2Nr2EZQ9HlPyRn+Vj+YNMr729/WAEixXzF3T5\nemvfMsrHHzjoL3tjW9JfykjPk/9VOz36P8swc+bM8YXej2qXj/lTir2/vNbb9wMfiKxfOqW3\n6bnrFyZqcnpO/Ycy97r+cwg0Wd98e+DHspqu/1DvfQbvveo/1X/O62t0fZX//vb2+8Cp//z5\n/ktuhc/but2WbW8ft2CvbIA0fct7rLmN6FPElqW0dQS2h/CgDGnFPTsB75Vxr1+3fBMJcZjN\ndcD1fiXKrnOrgV0KJMgmt78BbF1yxhzxQ4zHHLsUBXkShWR5nwi5sNNz+UZmmm9M730ppvlm\n1C8RA2LAbAYOQPFeBcpt0TZbG5VODIgBMSAGqsbAOHvamcDa8fY0TPO9HuNajuD3M6b53oTf\ntCbLxSgcy/qjngrptWXnSiTGrlvszvV34D8AW5Y4jofT6G0EXA68ARwL3AE0AzcDUZAzUUgG\ndyy/pAcGMIsJWpCyLVc93KVLYkAMVJkBLpz3EMA/WC1VLouyFwNiQAyIgcgzkMRCsUkOu8mT\njiGtqVF9Tf+xny1H9wPscl5UvAZILyGlnYHbAf4q6W5CW4xjBhicyIGyA/ACcA7wIRAFeQ2F\nJGIpTU1Nm1MxdDN7p7cKoovdCARI83ubTpjP+6l/mOX2Ky/p75//+2WTENLhuBN2zf2H7F+T\n9u90Mdlf9qcz+PH3v9OpIrQj//fP/9H08iq+AdE1LtFQyAXSC9etKHTeoHMMjHoMjlhWrwES\nn2Eg9EWgEeAkAZsA7wL/BdiX0JHznR3DtmNRHo6V4tiopQA7TXIbe0mlUhfmlDypt8riBRmO\nVqTneptOmM/7qX+Y5fYrL+nvn//7ZZMQ0uG40X8wH9m/Ju3f6WKyv+yfc4Ze//3vdKoI7cj/\n/fN/TNKFhWKTmKkuf6HYrENgodi+AwxwjQ1QhsEAYxUODWLQtgYoWyoJkJzE2V3jeefAtU1h\nfxhQMjpzPRP07jHI4GJgCyAJ5EsbTrwNfBmIc/e6RL7ilR+zi12Gk0NESXzUP0pqd5ZV+ndS\nEdsd/jE4DrgNyJ8VSPaPrdnLUkz2L4um2N4k+8fWtGUp5pv9ERyhUSGBhWIT/N53SUcWVVwo\nlhNRnQE4Q35cZcvusjHnMeAHQMmGkUoCpMOR8DSAkRkiyKyQFabVF5gAsH/fxYAJwokEvpAr\nSDu2jCDXAgzwWF4HE7HP7nUsO7sKxk4we8kMzOTDgWm9Fs5il7aSkQqQ/NS/1wRWIQHp75//\nV8F85WTJX+3+BowD7gZYz3WK7B97+3fautCO7C/7+/X3v5B/mX5O/u+f/yNAasAYpAIBV+7z\nsqFgQ0TQLvJDZHBJLpP3sZ0FLAPYesR4ZSiwKXAqwBjmbIAzV/smJyIlMtAT2NWOQZQJwkkl\nWFZ+yPNX1WJCQzMoIpG8nwSGKdORGfMdEmamleZ1D3rrYAY7dECtn1xpGnpODIgB3xngr2f8\ndWy07ykrQTEgBsSAGBADYGCcffwvgMw4+4S0G2NbvspZGuz+p2x+TchEHcl8gYcBThleTPit\nvyfA3m+8fzegqBTqblb0Zlz4LrASOB7gOB4GFN8HtgIYgLC/O/9APwiYIOwyx+5znJygp0iR\nRP0a2A7g/jmApAgDX8p9gGWstki1IBVRR6fFQFwYeBGK7AssiItC0kMMiAExIAZMYyDxIVqQ\n2tHFDs1IXcGSZla0NYdcYjbKvAtw+0IPefP7/mlgP2AVwFimqHgJkNjXkIHGI8AMYBEwB2AE\n9l/gLmAf4BvALoAJsjEKwY8FElGOzMNNvHfDcm6u1XtSVv1w6r7CrHFmtWoO6S0GxIAYEANi\nQAyIgVAYQBe7LYA6AGM2uoIFSI3oPyiUgqzPZBJ2ZwEcOlOOsDHnFYANPUXFS4DE/u0cc8To\nyxFOaLCDc4Atf8FksHSY61w1d9cg85EAxxmVI2NxE2e3e7+cm6N2z6xZs44melvujJVEgGS3\nI/okv5ERv/SPjMJ5BZX+/vh/Hq3VPDwCmX8ClPWDjuwfO/t78j3ZX/anD3hymhjdLP/3z//R\nT+0/cI11aD1K5IMuk/6w+dOQXWcx8tsZcOZFKJX9BriBQRVjmKLiJUCiwh8DXEjVESa+KTDC\nOYEtg4ttXMfV3OVA5UaATW8n9VAQ9kvkzBev5u65uYd7I3spmUweSPRWgaSVwQx2ich14/FL\n/97yV63npb8//l8t++Xluz2O7wWuABgklRTZP1b2L2nv/Btkf9mfPpDvF7VyLP/30/9TWCi2\nHg0PdXCffKAFaWTfwSH71W3Ij7HJfcCuPeTNb/09gEeAfkCPw4GomRd5CTezj98dwBzACSh4\n7gZgIMDMMY7fCDkTpWALElu0bgJ+BzQDnN2JWxg4CxLFLoTsn/gAcD0QO8EMNq1+KIVfDBAg\n2R/5kVaYafilf5hl9jMv6e+P//tpk16k9Sae/RzQU3/rLsnL/rGyfxfblnMg+8v+5fhJXO+R\n//vn//iBvOeFYheF3oLEOQY49OMy4EvAQmABwB8POW8Cu/wNBTYDNgE4o/X/Ak1AUfEaIH0H\nKT0HsK8fA6GZAFtnrgH4y8RuAIOOJwAThAHPEcBeACNMzu7EroKEI7yHEzm8CLAVifrFUjKZ\nzPl+KIbgaASCpMgFSH7p7weH1UhD+vvj/9WwXYE8+WNH2cERn5f9Y2X/Ai7R8ynZX/bv2UPi\nfVX+75//J60UFopNoDtbsYVi+7m/scNwLH7HXw38Gbgc2BPIb0liwwjnTrgSYMzyAeC7fAEp\nsnlqy1zKO2HLTFlAgq1LXrru4fZQhYOy+MvrvsCYUHMuntl0XCJ3Q4rfYs6VVqvhjlar8UZz\nSqSSiIHYM9APGl4AhP2HJ/bESkExIAbEgBgon4Fx9ukXAu3j7TNsN8a1nJaNA/ofu8XPy08t\nsDvZasRv/C2AwZXk4rUFiXmwdcjdQsRfMVmIHYAVwLuAybIQhSMkFTLALnYZy/5XhY/rMTEg\nBrwxwKDocWAY8Btvj+puMSAGxIAYEAP+MYDWo54Xiq23OWSl2sKudUTF4jVA4h9njt1hVy32\n4XMkjR1P3T2cB7WNHgPoYjc8il3sose0SiwGsgyw7/Q7wKHAp9kz+kcMiAExIAbEQBUYwBpI\nGEqTZLenjDt7fBvyMGk1NHiNLdzJGLPvpSscZ4M7ATgEcAdHxiijgvTMwOzZs68ger6r9NWo\nTtLgl/6lGTLzDunvj/9XwbrvI8/jgCW9yVv2j6z9e2P2zmdlf9mfPtDpEDW2I//30/+TS/Ad\nmAaKLBS7jg0pkRcvUR4HBa8C/j97ZwIgR1G2/5qZ7G5IQkISsgkJRwIeIMofPkQToyAeCCKi\noiB4oHjhB4IXyAceQUE8ODxBPEFERLwVOZSbZYMoIHIqRyLhCEeAQI7dzUz/n2dmejPTOzPb\nPdMz01X9FDyZPqq76/297/R2TVVXsS88h8orVhXxqWQJAYxYzy46MSRvumcK1g3SEJ/9MSDs\nwilkf1zx3xHnsVvdc3FeSf63yv9xur54Lvlf/o89qCw6oeI/vvhHFzu815Nl/aGqHlBuQcrk\nZk/h+z/WpygVJILgiHC/hP4AfQe6F3ocCibOZkspJYgARnG5q9XioEPnTNSPJ6KJ1boKUhz2\nt8qvm8fL/tbjv0P+exeu82PoRRC71sWS5H9r/B+Lv4Mnkf/l/2BMpGld8R9f/KPl6HY8B2Ki\n2OKo1RVhxLYTThS7NpVdwa+H7U9ArCw10hLsVwpPwJpR7IZM7w4jps/DT9uzw5unnCIgAiEJ\nbIt87MJ8VMj8yiYCIiACIiACHSOwwPv0D7f1PoMR7Kq1YOjTxXrBlKN3ObNjhWnjhaK0ILEY\nd0NPhSgPJzFUcpBA1hQwGVfOPFIasdBBC2WSCHSVAEcBnQ+t6GopdHEREAEREAERqEHAM9nb\n0FbE1256a+w2+RVrnGhBilpB+mAtGNqWHgIFk+3HyB5PoAOqulCmx+2ytLMEVDnqLG9dTQRE\nQAREICQBvGKxHSpJPWMnii12sctmZm7Cd2itT1FGsQsai2H+zEugl5d3TA5m0HqyCAwMDGxH\ntVKqDIb4xot41r1/RJvjsL8Vdt0+Vva3Hv9t8CFfZv0mNLcN5646pfyfSP9X+aidK/K//M8Y\naGeMJfnciv844z+HEVUzhXqj2GWe3ZC6Uez82N8aCxwq8u0Qq4vXQ6+CfgbdAX0JUusCICQt\n5XK5E8plOqzZsmH0ktlZk7GyghSH/c1yS8Jxsr/1+I/Zj/xR6VqI3RSWQG1N8n/i/N9WfwdP\nLv/L/+WYaPrvfzCmbFpX/McX/2g96kPlqNhcVB0DfAUJqQfvYTiQorYgbQGbb4beAfF9pOWQ\nnwiLD+D/gCb6G/WZKAL0UY2gDl9GfCnYgtTSfCzhrxZ7zpbtj71EnT2h7G8x/mN21wSc70ro\nlVCYdztbvbz8nyz/t+rPqMfL//I/YyCtSfEfW/xn8YzPiWKzhaCKwdXT40QFiX+go6RvITO7\n1rHF6HroN9AsiOkA6ESIlaRDobMhpQQRwDwA53lIrRSp1MUus6KVc3Tr2Djs71bZ47iu7G89\n/uPwQ8U5+CLrJyvW27oo/yfO/231d/Dk8r/83+rf/2BM2bSu+I8v/vFD+eNQHv7He0hjU+GZ\ndevHbnV/yyqY+LUKM1lBuq5inbCehn5SsU2L4xOwZpjvYdN3/Yjp/ez4JimHCIhADQLzsc2J\nX9dq2KZNIiACIiACjhOY7534vQXeSYWg5g8tKcB0b8oRL3NimO8oXeymwvDp0D0NfD+CfXwP\nifmUnCRg7yANTrpDRtlE4IMo7H+g/2dToVVWERABERABEfAJoPUIE8UaThTLF5GqxDz5R59j\n7wjrU5QK0mpY+yi0WwOrWYnaEeL7SUoOEsCXYTYGarBykAYH3SGT7CEwA0X9LvRR6GZ7iq2S\nioAIiIAIiMBGAp7J7ZIxE/C6Dd/SCQpdJLacOm1jbnuXaFmUdAky81dQ1h7PgSrTZlg5ByKY\nv0BKCSMwODh4EIu0aNGiC5sp2hK+lWfMppgsdmUzx3f7mFbt73b5W72+7G8t/lvkvwrH897Y\ntb7Z8n9X/d9i+LR+uPwv/zOKmv3733oEdvcMiv844z/zT/Skw0SxmToTxT6buhYkRjdfKH4Y\n+jb0EPQKaFvod9B90P7QOdAVkFLCCGSz2X2oZot1rDHz2JqaNxusbEFq1f5muSXlONnfWvzH\n4MeuVY5Ydvm/6/6PIYSaP4X8L/8zBpqPILuPVPzHF/+YKPZ5nunp8cwEjGJXLUZJbtbkVE4U\nywEY/gfiCHUcyns2xAkOWTFiOgpiC5NSAglgBJthqtmiTTA9/Tz2WWOsrCC1an+z3JJynOxv\nLf4j+pFd6i6AXhzxuLZll/876v+2+bHZE8v/8j9joNn4sf04xX+c8T8Br9vUnyjWe2Y4tRPF\nPoEvyuHQEdA20BxoGcSWJaUEEygUCse1UryCyfbnjJfHuO6oI9mXWrXfPourSyz7W4v/apoN\n1/jj0fUQH0YSc1+U/zvm/4bB0a2d8r/8363YS8J1Ff9xxn8Gf+OKYzMEXFse1qAnl8qRWk8G\njT0gvKuvFCMBK4b5HjY9h2KY7+Ux2q1TiYCLBPhu5zEQB61REgEREAEREAFnCMz3zvjqAu+b\n+aDmD53GuZG8yR94+TdcMDbKKHa092Doauhe6HPQ1pBSSghgaEd0qfSs7F6XEhfJzGQQ2IBi\nfB1anYziqBQiIAIiIAIiEA8BtJAUJ4rFM2E2KF6h8Mz6rr5vG4+VpVHJopzrzcjMP/x90Beh\nByCOWHcItAmk5DABz3j9+GJYOYKdw26RackgsCuK4cSLqcnAqVKIgAiIgAgkkQCG+cYgDbkJ\nkBcUy5ubtakTvSeitiBxeG8MZlZsOXo9Pn8GLYTOhx6Bvge9HFJKIIGlS5eeSjVftEy/Z+kA\nDbS5dfubJ5eEI2V/q/Ff14scnOZGaOe6ORKwQ/5vm/8T4N3xiyD/y/+MgfEjxc0civ/44j9j\nco0nin3MjWG+2Ve+mVTAQX8t63/x+RboQOh90EcgDgbwVUgpQQQymczmrRQHTan9BePd2so5\nunlsq/Z3s+xxXFv2txb/dXzQi+28370X4sAMiU3yf1v8n1h/Bwsm/8v/wZhI07riP77490x2\nl6zJ1eg1VhqeILflDM75Z31qtoJUaXgPVtjlrnLUipHKDFpOBgGM4nJXKyVhF7usyVj7DlKr\n9rfCLgnHyv7W4r+ODzlS3dw6+xK1Wf5vi/8T5eNGhZH/5f9G8eH6PsV/nPGfxUSxxVFa+QPh\nmJRf8bQTE8WOMSzkBkJ5K/RraD2EnlfmcegMaCdIKRqBU5CdDDeLdlhnc4+YvoeGTO+7O3tV\nXU0EREAEREAEREAERCAJBBZ4P/jmAu9HBQgj2W3U/KGzi6PYTTl8928noZytliFqC9JiXJBd\nSd4BTYcI4xLoJ9AfIbUcAYK7yZvumYIGaXDXwbJsfAIYybH47uWX8XnV+NmVQwREQAREQARc\nIpDFcyAniq3qOQYDS13s8s+sX+eCtVErSOfB6AXQ3dBXIK5zcAYlxwk8XawQZzaxuYud4y6S\nee0nwG7E10FPQda+i9d+TLqCCIiACIiAuwQyeP8II3yPSaVx37ITclHrFmPOlIQNUY1ghehS\naLBB4TmLPPvk398gj3Z1gcDAwMB2vOzixYvvi3r5iaavn8eMmBFr30Fqxf6ovJKYX/Y3H/9l\nf7LF/PPQH6C15W3WfMj/LfvfGl/XKqj8L/8zLpr5+18rnmzbpviPL/4xSEMvRrLjeyGFyjjA\ne+pczZrenqgjZFeeJjHLUStIX2hQ8udj3+HQ+yD2P1wC2ZA4wMQ2ECsNfAByNuVyuRPKxh0W\n1cicyaOClDOoHfHXcytTK/ZbaXCg0LK/+fivQPmLimWrFuX/WPxvlc8rCyv/y//leIj8978y\njmxdVvzHF/+oHBUnikUscJC2ilRqVCo8s45jE1ifWq3lsYL1Nugv0D3QJ6EZUNJGsGC3QFbe\nOLCEP5njFlj+FfQExLKvhk6GAg7HFncSo7dGs+j4BhZMFu9eeKsA0ubAb9r+8QlZkUP2R4//\n18OzVoxSFyIC5f/o/g+B1Zos8r/8zxhIa1L8xxT/aEHaruFEsbOnOzFRbNQWJP+LtSUWPgR9\nEPIfHljR+Bn0I4iTSCUlfQIFOb2iMMuw/DLoG9ABEF6vKQ40sTM+j4eeBx0EOZcwD8B5HlIz\nhmUwxLdnMlYP0NCK/c0wS9oxsj9y/H8GPuSPJm+AHk6aP6OWR/6P7P+oiBOdX/6X/5v9+5/o\nwA5ZOMV/fPGPmuYdwL4en3gXaWzKP/pU0hpJxhYy5i2sfe8F/RbaAPFB29dHscyhv5OW9kaB\n2EeSFTbOds8Wrichthix7MdCvoMnYvmn5e18IOpkOgUXY3k26+RFo1wLw3svGTZ9V0c5RnlF\nwHICN6D8bHVWEgEREAEREAERAIH53kU/2tb7lRfUgqELinWCKUftc5YLoMK0IG0OQ98PfQQq\nvuSPT45idw6Ug/gL6zXQMJS0xIebNdDLy58sHwePYCVvBXQa5L93xK5jtHGfsi7Dp1KZQMZk\n+tGKZO0ADXKkCDRB4BVNHKNDREAEREAERMBhApl/wrghqK+WkfmHVrFnlvVpvAoSKxBHQITA\nLnTfg86FlkJM7yv+m9x/WDG6FGIlyU+XY4GVoT9BfuXI37cOC3dDz/c36LNEoNzF7lHxEAER\nEAEREAEREAERSCeBTPEdpAx7jQVGsSuuZnOzpk5xgcx4gzS8tmzkF/A5D2JXOr9yVN5V/GCz\nWhITR1xjJanSTg7P+1noLiiY2MVtN+jh4A4X1gcHBw+imrEF7x9Z34LUiv3NMEvaMbK/Yfzz\nvcp/QAcnzW9xlUf+b+j/uDAn9jzyv/zPGEhsgLa5YIr/+OIfPYr4Yzkmis1gMqRq0Y35Z9au\na7M7O3L6yopDrQtehY2s/JwIsdLAd3ReD413HLIkIrGb3FYQB2mYU1Eitox9q2Kdiz3QlyG2\nll0JOZey2ew+VHOGebMxxr3VXexas785akk6SvY3jP+/wlfPQX9Oks/iLIv839D/caJO5Lnk\nf/mfMZDI4OxAoRT/8cU/KgWT8IZNplQVYHWgUlib0Dte77QOeLz1S4xX0fkELjEXOhJaDr0H\nYhe1ZRDfPfLfSQKoRKZvoFQ3Q0dDy6DpUK3E0ez4ThJbyFgp/AXkXMIINsNUM4bBwdaPYteK\n/c0wS9oxsr9h/H8A/toLeiZpfourPPJ/Q//HhTmx55H/5X/GQGIDtM0FU/zHF/+e6UGDAieK\nzRWCKrqxh5Un+1OYWh67qX23rJ3xeRj0Luh4yE8HYuFMKGktDHzX6JUQJ0jlww9tqZUmY2Mv\nxFalz0BsNXMuFQqF45oxaknp54FpWVNImn8jmdOs/ZEukuDMsr9h/A8k2HWxFE3+b+j/WBgn\n+STyv/yf5Phsd9kU//HFP95JfxKTxeIdfo89rypSqV5UWL2WAzikNrEbGvuysgsbBzpghWIE\nuhh6J7QJZFNieXNdLPApuDYZ8h2oxCW8tDVvxPR5603f8xNXOBVIBJojwB91XtLcoTpKBERA\nBERABNJJYL536ZkLvMsLQc0fupijNHhTjtiPDSbWp/G62NUzkLXDC6E3QAugL0APQm+ELoCO\nhWxKfKGMFT2lGgQmmJ5+bl5rhqxuQaphmjalk8DnYfb50Nx0mi+rRUAEREAERKA5AmgnuhNH\ncqLYTFA8Y/7RVU50VW+2glRJ9b9Y+SLE95FeC/0cYtc2JUcIFIw3Gz8K5GcYs9oRk2RGugk8\nD+bvB7EFXEkEREAEREAERCAkAbx3tEvGTEDPK76lExS6Y83bPJG9oUKaN5otzDtIo5nHWWAX\nMY7+ZvMIcB9F+Q+HzoK+BzWbtsWBHDKY7zWFST0f//jHzcEHH/wQ+smevWjRok/6B2FoyqMw\n+sopeMGwkM/n9128ePG1/r4bb7zxaizvhn2PLVy4kC15xXTddddt29vbW7x+5fmWLl16aiaT\n2QWZFkY537OveMWOy7/0hcyNU6Y8V3k+XizO8rX7fOD4J5T/IbD6NK8VlV+7y9eqf0OUjz9m\nzIHvyWDceAlxvquRJ3T8dft8jH+WYWRkZEmt70e3y8frM9X7/nJfi9+3yfD9bfD9S3kuphbP\n12vT/aDZ+18b/dFRfrr/3aj7H77zuP+dqftf8fan+1/Tz5OliWL3fnRC3ztX9BbfD/nqC9ab\nuyaWuOYfevLp0pLd/8ZZQbKbRKn0aCkxO0H8bCUtw8EHQoEX2Oqe8tDf/e53B+6+++7vmz17\n9t8qc+FmdgFuZvfiQaQwPDxctQ8PO0chL+dvWVV5zBVXXLFs7733Ll4fh93h70PlaHPoMWx7\nR5Tzbbr0puw2Xzzl3vu+etInKs/H88ZZvnafD7YfQgY+j6j82l2+Vv0bony3IM8k6HPM66d6\n8cL9Nvm3Hr/Vq1ezuT/r+951e2FrvfvBsWBQ9YfLBf82sLen8n5F/0OR73+uxAts1/0vhfc/\n//vB+MeycSWe693vfXtpK1OFvbr/xXT/w0Sxz/PMhN7rZ2YKj07MGw/97O6dglHtNhSrFNlc\n/4xNS/T1r0sE4qogRWXSkUEa8GvxZ6iohRsyvV/DIA1/jnpc0vI3a3/S7Gi2PCm1ny1l90Cf\nTKn9o+Ei+5u7/40CtHxB/pf/GQOWh3HTxVf8xxf/23rX/t+23nUjkFepBUNXsieZt8nBr/1a\n047SgSIQINCRClLgmqFXh03vOVToA5RRBJJDgK1mfNdoSnKKpJKIgAiIgAiIgJ0EFnjXfWmB\nd0N+W28AFaSNWjB0dbGCNPk9e59up2XVpU5jF7vpQDAN6oOeg9jlZA2kVIdAxmRmY6CGf9XZ\nrc0ikGQCb0PhOMLmhiQXUmUTAREQAREQARsIoHtdD7rZ8d0jDus9mrzi20jY0YPdDqSsAzaE\nMYEDE/wQegxi//wHoLuhFRArSfdBZ0OzIKUAAQR9f9ZkVgY2a1UEbCDA77oqRzZ4SmUUAREQ\nARFIPIHSRLHFyhHrEEGZwupnhxJvRIgCpqEFiXOenFhmwVFsBiFWklgxYksSRq82W0Mfhg6A\njoI4VLlzaWBggEOxG4yExwph6IQWpH7PFFi5tDo1a7/VRlcUPiX283u8DLq8wvTiYkrsD5o9\nui77m7v/jQK0fEH+l/8ZwlH//lse9qPFV/zHF/9oQVrgmWwOcNmlbjThOZHLmdysmVNHN1q8\n4HoF6R3wDStHl0InQDdDtRKbA18FnQZxAsll0A2QUymXy5EB02Glj7D/ejPQxc76ClLz9ofl\nlOx8KbD/JHjg09Cba3kiBfbXMnt0m+xv9v43itDqBflf/i8HcMS//1aH/WjhFf/xxb9nMnei\nFWkIP56XB/YexVxcyD/2JEeOtT65XkF6Czx0P8TPRk1+rAVfC+0FLYfeCzlXQYJNrAhGSquK\nrWyZSehiZ30FCYZHtj8SrORndt1+dqV7A3RNHVe4bn8ds0c3y/5RFKlckP9T6fZRo+X/URSp\nXIjR/z2YKLZ25Yhkc/PmbOYCYdcrSDvBSexS16hyVOnHp7ByGzSvcqMry5gH4TzM/VPVJDqe\nbZNMXz/zbDAj1leQmrF/PD427U+B/Usa+SMF9jcy38j+6Pe/hkAt2yn/y/9R//5bFuINi6v4\njy/+8RB5K2DzubqvFvT8Q489XWu7tiWLAN9DuAsKO2ErR7hbDX0d6mQ6BRdjxSVxte4RM+GV\nmAPJwzBgm3QSiK4lAiEIcDI6TUgXApSyiIAIiIAIiEAcBLbxbvv2fO+Ownzvznylthn6Zx7n\n96Z89O3fieM63T5HttsFaPP1z8X5t4d+Db28wbXY9Mh3kPiu0iTod5ASCBRMFi1I3lNbGbNO\nQEQgQQReiLJw6PljE1QmFUUEREAEREAEnCaQMbmH0cUujwfnbLW8Yp0iv+pZJ6bOcb2L3c8R\npewixpe394MeglZAT0JsKZoKcRS7baAtIL7D8CloAFICAbyIhxHsnHj/SP50i8D3YQ6b+b/s\nllmyRgREQAREQASSSwDPhJPxphEqR8E3NkptLtmJE8P22kqukSiZ6xUkeu8M6PfQydDuULAl\naS22PQydBn0T4qSSTqbBwcGDaNiiRYsuDGtgaYhvNypIzdgflpMN+Ryz/01gzqH6g3fouq5w\nzP66dtbbIfuj3//qsbRxu/wv/zNuo/z9tzHO65VZ8R9f/GfMBNQditWj4rjePnPP5NgbCw1M\nbkwU63oFyfcbR7I7uLzCViPOf8ThCTnwgBPDEcKOcVM2m92nnCl0BQm/FPTja2D9AA20uxn7\nx4VqUQbH7H82KnrH7I9qvuK/iftfZMgJPkDxH/3vX4LdGblo8r/8Xw6a0M9/9YIMv0o+ibYi\nVI4ygToE60fY8Zwmiq3HLunb2bWOSl3CCDbDUY1G5Wi2K13smrE/Kq8k57fcfs5vhFHnzY+b\nZWy5/c2aPXqc7I9+/xuF58CC/C//OxDGTZug+I8z/nswUWym/kSxM2ezIcL6FKj9WW+PDGhA\noFAoHNdgd81d5Rak22vutGxjM/ZbZmLD4lps/1dg2Meg/RsaOM5Oi+0fx7Jwu2V/9PtfOLJ2\n5JL/5X87IrU9pVT8xxn/3p1oPcJEscWeWGMcln/ssdT0zBpjvDbETiCxw3wPm967hk3PR2O3\nWCcUgfAEDkXW3cJnV04REAEREAEREIF2ENjG+++PFngPeUHNH3qA7wR7U4467Kx2XLfT51QL\nUqeJW3Y9/ELQX9AodpZ5zbninuucRTJIBERABERABCwkgMEYOIJsg4liH33aQrPGFFkVpDFI\ntMEnsATjGmB5s6wpODFIg2+XPhNPgEPzbwItT3xJVUAREAEREAERSBGBjMm+wDPZXnSzC4xi\nVxxUNpvrn7WpCzhKg5a7YIlsGJfA0qVLT6XGzVjOcIwxc/AFyOZNzokKUlT7w3KyJZ8l9u8E\nnrdAR8fN1RL74zZ79HyyP9r9bxScIwvyv/zPGHAknCObofiPL/4xjvdDqCTVmCi2+KO6ya96\nRhPFRo5QHdBVAplMZvMoBegxPfwl36wzQyujHJfUvFHtT6odzZbLEvs/B/uuhiIPKDIeF0vs\nH8+MpvfL/mj3v6ZBJ/RA+V/+T2hodqRYiv/44h+tR1P44zlewQgkDmyHrkcTezVRbICMVhNO\nAKO43BWliAXj9eeMV5juyFxRUe2PwsqGvJbYfyBYFtvp42Zqif1xmz16Ptkf7f43Cs6RBflf\n/ncklJsyQ/EfX/yj9Qg1IU4UG+xiV6wgZV2ZKLapQNNBsRNI5Ch2Q6b33SOmb0Xs1uqEIiAC\nIiACIiACIiAC1hHYxnv6mAXe0yMQRrLbqPlDjxVHsdvkwP05NYf1SYM0WO/C9hmQNZl+z3hO\nvH/UPko6cwsE2EJ/IsQXPZdASiIgAiIgAiIgAokmkJmPbnY1JoottiBlcptvrkEaEu0/Fa5l\nAuxil9EQ3y1z1AnqEmDL6cehq+vm0A4REAEREAEREIEkEcDrGsWJYjP4lbNKLGR+5WOrk1TY\nZsuiFqRmyVl43MDAwHYs9uLFi+8LU3wE/myXWpCi2h+GkU15Emj/X8DvZ9DtneCYQPs7Yfbo\nNWR/tPvfKDhHFuR/+Z+hHPbvvyNhP2qG4j/O+J+wC0aymzgKd3Sh9Ppwbt6Wm41usnhBFSSL\nnRe16Llc7oTyMYeFORYVJEwSa+4Mk9eGPFHtt8GmKGVMoP1XRCl/q3kTaH+rJkU6XvZHu/9F\ngmtBZvlf/i+Haai//xaEdKQiKv7ji//xJ4p92ImJYjUPUqSvmPWZUefB0CMhE34L6Md7SC69\ngxTJ/pCYbMrWbfu3Aazdugis2/Z30fTipWV/hPtft53VhuvL//I/YyCtSfEfU/yXJoqd0OuZ\nnkJQDK7cnDlOvIOkFqQU3SowD8B5HlJ4kzlIQ8GZClJ0+8OTsiFnl+3fFYwugX4J3dQNXl22\nvxsmV11T9ke9/1Xhs35F/pf/o/39tz7kqwxQ/McX/6WJYk0egGvWIfJPPOXERLFVAaSVrhFI\n5DDfI6b3ufWmb5+uUdGFXSLAGdzPgmreUF0yVLaIgAiIgAiIgKsEtvHyJ80vFPKQV6lt1q/n\nD/De5PcdeoYLtuthxQUvtsGGJ42ZitbYyVmHWpDagEmnDE/g0+GzKqcIiIAIiIAIiEASCWB6\n2JzHsesCk7pj4lhuQw88N3pyqoJEdyqNITAZ7x9x4wYzsnLMTm0QAREQAREQAREQARFIHYGC\nyT6FSlIB1aDqOgRnNEQqrF47VFqy+99q4+y2RaUfh8Dg4OBBzLJo0aILx8lqcmZCsYKEoUhW\njZfXlv1R7LfFpijl7KD9HPzldIhDfb4PSkTqoP2JsDdYCNkf/v4XZOfCuvwv/zOOw/z9dyHe\ngzYo/uOL/0zebGMwlB3601W90+7xrSS0IOVmaqLYYPxpPeEEstnsPlSYYuIXAlSQvGfmGrM2\nTH4b8kSx3wZ7opaxg/Z/CWV7L/SDqGVsZ/4O2t9OM5o+t+wPf/9rGnKCD5T/5X/GQIJDtK1F\nU/zHGP8Fg4lizRCqR5kxwo78oys1UWxbo1knj50ARrAZDntSRD1GsHNqiG8Txf6wnGzK10H7\nfw4uP4LuTxKfDtqfJLNHyyL7w9//RqE5tCD/y/8OhXNkUxT/Mcb/BvM/qBiNnSh2pOSWzNx5\nmig2coTqgK4SKBQKx4UtQMZkZrtWQYpif1hONuXroP13JJFLB+1PovlG9oe//yXSgS0WSv6X\n/1sMIasPV/zHF//oSndLJmsOQUD01QqKwoqHnJgotpZt2tZ5Aokb5nvY9H0bw3z/uvModEUL\nCbwQZd7fwnKryCIgAiIgAiIgAhEIbPWU952tn/IKW62q1paPDnGYBm/SB4/4ToTTJTYrX6ZW\nEoEaBNzrYlfDSG1qncDLcYpBaO/WT6UziIAIiIAIiIAIJJmA55kVUB6j2HGw7yqx3PlVTzgx\nUawqSEmOwi6WDV3s8A6S91gXi6BL20GAFSQOxnCEHcVVKUVABERABERABJolkNlgNjV5k8U8\nMJwLplpYzfZt0tvsuZN0nIb5TpI32lyWpUuXnspLLFy4cNxJO1E56sdADU5VkKLY32ZXdOX0\nbbL/W10xpomLtsn+JkrSnUNkf/j7X3c81N6ryv/yPyMszN//9kZid86u+I8v/j0Mc8xmozHD\nfJcG/c5ky/MhdcfT8V1VFaT4WCb+TJlMZvOwhWQLEmLcqQpSFPvDcrIpX4z252B3acYDiwDE\naL9FVm8squwPf//bSM2dJflf/ncnmqNboviPMf498xRqR6wGVdchyhWj/FpNFBs9QnVEVwlg\nFBeOXR8m8beB6VlTcKqCFMH+MIysyxOD/bwZfg/aDtrTNgAx2G+byVXllf2h739V3FxZkf/l\nf1diuRk7FP/xxT9akIoTxcIPNSeKzU53Y6LYZuJMx8RPIFGj2D1nzJwR0+cNmd7t4zdVZ7SY\nwOdQdlaad7PYBhVdBERABERABESgSQJbPugdudWD3jp8epWad98QK0zexL0P4DOt9UmDNFjv\nwvgN6DG9/TzrejPsVAtS/KRSd8bvw+KdoJtSZ7kMFgEREAEREAERYAf7XbwNmCg2OEAD15Gy\nc9yYKDZtFSS+O9Eo9WDnfGhao0yu70PXUlSQvMIZxmiyL9edHc2+lcj+aLRDlFsEREAEREAE\nRMAVAoWCuRW2DNWzp/Bo1yeKHa9uw7rAdGhiPRu4fbyTNDrWln07oqDLIb5UPgKtgg6HaqW3\nYuMD0G9r7bR928DAwHbUeHZkTBYVpMzKJab4Et542a3ZH9Z+awyKWNAm7N8Zlzgq4mUSm70J\n+xNrSzMFk/3h7n/NsLXhGPlf/mcM2BCr7Sij4j/G+PfMC9GC1OuNGC8o+i47c/bUdvhwnHPO\nxv4LIT7jr4aughZDtdJLsJH5PlNrp7+tegQKf6s7nwTGmi7tZN9IVpA2g86E3g29EkpNyuVy\nJ5SNPayR0RjBbraLcyCFtb8RG5v3RbT/FbD1UuhnNttcWfaI9lce6sSy7A93/3PC2TWMkP/l\n/3JYNPz7XyN0nNik+I8v/jGMN94+Kk4UW7MOUVj1xLMdDpopuB67/m8FsXK0AtoDuhb6CuQ/\n+2IxfHK9BYm1STrwamge1Ae9B1oPLYb+DqUpYXQ6QzVMpTmQMuxO5VoKZb9rRlfYE8V+/rjw\nVcilCWCj2F+BzZlF2R/i/ueMt8caIv/L/4yBtCbFf0zxn99gpnKiWLQimaAYXJm+SXzW7mQ6\nBhdj5ehEaEtoe4iDSd0OHQ+dDkVONWt/kc+S3ANejKJxZPa9Ib+/5PlYZq3yX9Cu0J+gN0HO\nJ8wDcJ6HNJ6huIv0u9iCFNb+8fjYuj+i/exm6lRX04j22+rmuuWW/eHuf3UBWr5D/pf/w/z9\ntzzM6xZf8R9f/KMFKVN+kKx6niyvZMrTIdX1RRt2sMcLBxU7CUK1rZj+gX93h/4IfQJ6BPo6\npFQmwJaiB+vQYIvSWoi+9GuXB2KZPr4S6mTikIi8Lrv/dT2NmN4/Dpue07peEBWg0wRmdPqC\nup4IiIAIiIAIiIA9BObe7h079zZvBPIqtcU/SsN8973hQPY+6WS6Exf7VZ0LTsX2f0J81ucz\nPhPfr+Yz9xe4Ui+53sWOfRFZEZpWA8BD2PYqKA99HKo3cAN2pSshavrxHpKG+E6P23th6gXQ\nNekxWZaKgAiIgAiIgAhEJVAYMdt4eZODvKB4ruy0WZOjnrPF/Mtx/OugiTXOw3rAG6EV0LkQ\nX68JlVyvIN0ICugxZq6AdqlBhE1w+0GsSZ4JfRBScnSQBjm2LgG+Z8Sm6HfVzaEdIiACIiAC\nIiACqSeALnZ3oY/dEJ6cM2MEOvknHu30IA18xmdDyJehuVAwsUHk9RDL9WdoX2jc5HoF6d0g\nwHeP+K4RK0PfhIKJI3UdDLGSRIBMrFQ5lwYHBw+ixjMMET+zYLLODdIQ1v7x+Ni6v4H9/HFg\nB+g2W20LU+4G9oc53Po8sj/c/c96R9cxQP6X/xkDdcLD+c2K//jiH/MgNZ4otr/jE8V+BwF8\nJ8R3jfhazTuhYLoHG/aC2NWO7yoxNXzWd72C9AwAbAPdCvHFLb6TVCv9EhvZPPd4rZ2ubMtm\ns/tQjexBv7opiJkpWVNwrotdGPsbsbF9XwP7+SMCm6GdTg3sd9pu3zjZP/79z2fl4qf8L/8z\nBlyM7TA2Kf7ji38veRPF8tn+5dC3oP9Cw1CtxLrASyE2jIybJoybw/4MbAnxu9flGphzFfb1\nQ++FnmuQz9pdGMGmXtCM2oS32cgAL2aNOFdBCmP/KAgHFyrsXwjz3gnx3bvUpAr7U2NzpaGy\nf/z7XyUv15blf/nftZiOYo/iP774z+QxUawxveh3xZ5XowmTxjJlcjO6MlEsn9uPLqtR4899\nyMMfCjgMOCtWdVMaKkiVxnNAhvHST8fLYOv+QqFw3Hhlz5qeYgUJTW9PjpfXtv1h7LfNpijl\npf3vec97+CsL++v+MMqxLuSV/8f//rvg53o2yP/yf73YSMN2xb/iP7Y494rd2PLon1azDpF/\nuuMTxQZNYze68dJN42Vo2P9uvIMd3P9z2PQWiPO/tPLC+rY4/k9QDxQmzUCmGVdcccUFkyZN\n+v2iRYsu9A+68cYbX4NfPt6DdS+fz5+8ePFi1n6LCX1qP4Nm4x2w/4mFCxd+2t9+ww03zMD2\nr2Dc/17cFC+Jcr5NHnxwr6mDf3vltmecOjrRVyvni7t8Op8xrfjj+uuvf9Xf/va3iRdddBG7\nlBZTK+eTP1rzh/iJX5T7s+JF8aJ46dzzkL5vtb9vs6/3TsZkR8e9JLM6u2f2iWIz0kX5ueaR\n4axZ+bo+07PLnneO3HLVuBWQ8iMIezbxx/tV5fVOfHwUF+HI1WdB36t3wUbNUPWOcXn7FjBu\nE4ifqUy5oaGJ3oSetak0PgVG77777k986lOfYj9cJREQAREQAREQARGIRgBv9HvsXFfZwS64\nHu2Mnc49GxfcCeKnUkgCOyLfARA/O5kSM1EsJok9Ydj0DnTSeF2rrQT43t0Obb2CTi4CIiAC\nIiACIpAKAnOu8j4z+0pvBPIq1X9ZaaLY3j06PlFsVO6hKkg1+w9GvZJD+e+ALZSTaenSpafS\nsMrueEFD8c6ds5PEhrE/yMPydbaGcgLYnaH5KbS/yn2yf/zvfxUwx1bkf/mfId3o759jIV9l\njuJf8R9X/BfyZiu0HvHH18o2JLwHUgy5TG7q5hgNOdFpJUpHNUxprCDNB5F50KbQ49B/y5/4\ncDvhnaTNx7cw04+5v5wbwY52h7N/fEIW5XgHyspm5DewzCm0n2aPJtkf5vs/isu5Bflf/ncu\nqCMYpPhX/EcIl4ZZURG6J5stThQ7sSpjubqUX7UyCdOGTEfZOHks36fnCHdPQ2ug0CktFaR3\ngsgS6PlQrfeuODjhvdDboLshJxMGbLhrPMMyJtNfMB5ZOJfC2O+Y0T+FPRx4BD2GMTtaCP8z\nn6tJ9o///XfV97RL/pf/XY7v8WxT/Cv+x4uRCPt3xlNFdeWIB5dakEx2ZscnivWLzil9joDe\nDM3yN1Z83o/lv0KfhdhAkvp0BQj4r4+xIsSa7aPQcogtJc9CfIBkHrr3O1CnU2LeQcL7R7cP\nm56PdRqAricCIiACIiACIiACIpBsArP+7B05+8/e+tmX4B2kCvX/ofwO0uID+Ezb6fR5XNB/\n1ufz/Q3Qn6BfQJdAN0KPQMzzBHQI1DDVak1peIBlO09DeV8DsabIYbs57PZUaA60DcQ5f9jV\njttZKVgHsfb5YSiViS1I6GI3bt/MVMJJvtGvRhH/mPxiqoQiIAIiIAIiIAJWEiiYHdDNrhcT\nw3pB0Z7MtDl8ru5k4usEJ0KXQrtCfL5/BfQmiD3I9oFeDs2F9oAegM6HmKducr2CxC5zbDXa\nDmJXo3rJww62HL0Y4jJn401jwrxY3oysKTj5DpLjDl0M+3hzGLcbpeMcZJ4IiIAIiIAIiECb\nCGCQhuUY5ps9rvDMOEbGW/043/npZOL8pew+x8+bG1yYz/fXQntB7D32Xqhucv0dJLYUrYAI\nIkxahkzMOzNMZtvyDAwMsKJoKiebrbQBhqPPZiZXMFknK0jj2V/JwsJlNh2/H7qgXtkdt7+e\n2aPbZX/j7/8oKEcX5H/5n6Fd7++fo2E/apbiX/EfV/xnC2YzNCVkvUyxQWE0xvxR7LzeSX2j\nGzuzwMGoBqGhkJd7Cvlug+Y1yu96BYkjVrBJbROI3efGS/ORgU2D94yX0cb9uVzuhHK5D6tV\n/l7Tyy6HiLBhJytI49lfi4lF2/jrCVU3OW5/Xbv9HbK/8fff5+Tqp/wv/5dju+bfP1fj3rdL\n8a/4jyv+CxjJq9h0hPcx/PgqfhbKa/5n1c62rvAH4l0hvi7DXmPjJY5wx0rV2Y0yut7F7kIY\nz5osHxwb3RTpZL579C+I6celD+f+LcZ0PasKpjAbXewK6KfF2rWLqaH9lhnMG8HrIpbZJfsj\nml7MLvtL3SGaYefCMfK//M8YSGtS/Cv+44n/AgY7wwMj/sPwoAFhNTO8drjDX7Jzcb3toV9D\nfNeoXqL9r4L4OsIk6HdQ3eR6C9KRsJwtSPtDP4K+D7ElaW35ky1LFEH5k179Fsvfg5xLmAfh\nPA+pnmFZk0ULUuaxA0cHa6yX087t49lvkVWTUdbfQDtC20DsCzxucsj+cW2tlUH2N/7+12Lm\n0jb5X/5v9PfPpVivZYviX/EfW/wXShPF4mGy6nnSY2UJD5GZKR2fKJZjDLAH1EnQftBD0Aro\nSYgjV0+FZkB8XtoC4sjVn4IGoNSnPUBgGeSnb7v4AABAAElEQVQP502n+qJL2W/xJuhlUDfS\nKbgoy7NZNy7uX3PY9B2NYb7/6a/rM7EE+ILhrdCCxJZQBRMBERABERABEXCOwMyfex+bdYG3\nfvMLPK9SM88rD/O9W1eG+SbnbaELIFaQ/Gd8/5Ov3PwHOhXaCho3ud6C5AO4Bgvzyyt8KYti\njZLvGj0IKYGAZ7z+LFqQBCPxBC5HCSklERABERABERABEegcgYLZGX2R+PpKdWITBFJms65N\nFHs/Ln9wsRClZ/xpWOaEtnyufaa8PfRHWipIlUBYs6SUAgRKcyB5qiAFuGhVBERABERABERA\nBEQABDxzC/5dD7HyMSZ5qx56eszGzm9g1zqq6eT6IA1Ng3HxwMHBwYOoerZhxEb04XS3gjSe\n/fW4JGD73igDBxAZ+4tNhMJZbH8EK+tnlf2Nv//1ybmxR/6X/xkDbkRzdCsU/4r/2OKfE8Vu\nMH2QFxQj05s6mz20rE+qIFnvwvAGZLPZfah6R6Cj5my0IjnbgjSe/fW4dHk734v7I8TBQ/iu\nXNPJUvubtjd4oOxv/P0P8nJtXf6X/xkDrsV1WHsU/4r/uOK/OFFsAYNDcZjvoBCQmdVPPBs2\nLpOcL41d7JLsj7aWDSOYDDe+QKbfM4WVjfPYu3d8+xNpG18q3Bdq+Z0jS+2PzSmyf7zvf2yo\nE3ki+V/+T2RgdqhQin/Ff1yhls2b6XgHqf5EsRM7PlFsXKZVnUcVpCocbq8UCoXjGlnILnZ5\nk3W2BWk8+xux6eK+p3DtlitHLL+l9seGXvY3/v7HBjqhJ5L/5f+EhmZHiqX4V/zHFWiYKLbA\nCYUy9SaKLQ/WENf1dJ50E+j6MN8P4GW7EXQpHTY93RrqPN0RsNF6vmf0fkjdXzcy0ZIIiIAI\niIAIiEACCEw/yztuxlneCORVavq3y8N8/89BX01AMVsuglqQWkboxgnmGIP/OePoiLMtSBZ4\nagrKeDG0ALoQWgspiYAIiIAIiIAIiEAyCOTNll7G5FAYzjE0mvyJYr1JM/ksY31SBcl6F8Zj\nQM70zOaZ8GbdE/GcUWdpgsDzccwI9ApIlaMmAOoQERABERABERCB9hHA+0f3oIsd32mvHlm3\nXF3KPLuypeG121fyaGdWBSkaL6tzL126lDMIm4ULF346aEjBZPtzxnuu35jngvtcWW9kf0Js\n5NwCr2tXWSywv12mF88r++t//9sKPiEnl//lf4Zirb9/CQnRthZD8a/4jy3+PbMzuhtVV454\n8vK7R97Urk0UG+t3SBWkWHEm+2SZTGbzeiXMmgJGsHN3gAba3cj+elxc2i7768e/S36uZ4v8\nL//Xi400bFf8K/7TEOf1bIwz/jMbMFFsDhPFenUmin06ERPF1kMRersqSKFR2Z8Ro9jcVc8K\nzH+ECpK7k8TS7kb21+PS5u1vxfl/CL0AerLN10qi/e02uer8CfR/VfnavSL769//2s0+CeeX\n/+X/JMRht8qg+I8v/jGK3Q6ZQrEFqfodpFILUiazqRsTxXYrVnXdagJdH8UOo9edMWJ6f19d\nLK21kcBOODdvJ//Xxmvo1CIgAiIgAiIgAiIQG4FpX/OO3ezr3sj0r3tepTY7pTSK3YSXHPiV\n2C7WxROpBamL8JN16cxsDGmvEew655S7cSkOqX5z5y6pK4mACIiACIiACIhACwQKZjqOzqL5\nqLoFKV88Z8b0TRn7flILl+vWoZprpVvkE3bdche7lQkrlsvF4Qgwqhy57GHZJgIiIAIiIAKO\nEcAodnmI1aPMGGFzBn3wXEiqILngxZA2DAwMbEfVyo73j/pRSXK6BamR/bWYxLxtEs53PDQ5\n5vOGPl2X7Q9dznZllP31v//tYp6k88r/8j9jIEkx2cmyKP4V/3HFPypHz5mCKXDeo6AY097I\nOv4AbH1SFzvrXRjegFwud0I592HBo9iCVHB8kIZG9gd5xLzOSdOugDiK4HdjPnfo03XR/tBl\nbGdG2V//+99O7kk5t/wv/5djcczfv6TEaDvLofhX/McV/xigYR4qRpwotqqpyNuAFiUOGDxR\nE8W287usc7eHAIO3VsJ2byZ+EHC6BQmG17O/FpM4t83Eye6D3gw9E+eJI56rW/ZHLGbbssv+\ntqG14sTyvxVualsh5f+2obXixPJ/XG7Km3/jVMMAWvNdI2+NJoqNC7XO0yECGAf/PA8peDlM\neYwH+MyErPvzINW0P8ijDevLcc5D2nDeSKes5/9IJ7E4s+yv/f232KWRii7/y/+1/v5FCiKL\nMyv+Ff9xxT9ainbG+zljK0elQRpMZvKW0yz+qowWPe016lEQXV7gMN/HQRwZ5OlOl2XI9O6Y\nNZnb15uh2Zsa43orUqfwslvdc526mK4jAiIgAiIgAiIgAu0mMPUE70iTNV9HBWJi5bW8/LBZ\n/eU+M+EFB3xlw79/bf0UJhqkodK7KV1G17p+dLHzLunAZKUpQczWIk78mtoXglPiZ5kpAiIg\nAiIgAuki4JkXYRbHPrQkeUERRGaKGxPFapCGdIV1TWvRtQ4VpMzjBxpTbiCtmU0bwxHYFtl+\nCn0S4ntHSiIgAiIgAiIgAiLgBAEM0PAA3ujOowWpZh3Ce+6JZ10wtKZxLhgmG8YSGBwcPIhb\nFy1adGHlXgxk348Xk5zvWlfP/koWMSzfj3MsgB6M4VyxnqJD9sda5jhPJvtrf//jZJzkc8n/\n8j/jM/j3L8kxG2fZFP+K/7jiv+CZGdl8caLY6lHsSj+xZzVRbJzfXJ2rIwSy2ew+VPBimOvL\n+TmQaHM9+4M8YlhPXOWow/bHgDD+U3TQ//EXPoYzyv7a978Y0FpxCvlf/mcMWBGsbSik4j++\n+M/kRyeKzWKi2GrBd4VCfsxgYG1wadtPqXeQ2o44ORfACCbDVLBEmANpNiaKdb4FqZ79QR4R\n16ci/zehuRGP63j2NtnfcTuavaDsr/39b5anbcfJ//I/Y8C2uI2rvIp/xX9c8e9PFFt8KYOt\nRpViwA6vH4krbrt5HnWx6yb9Dl+7UChwpLwxqdTFzls2ZodjG+rZ34KZk3HstVAvtARKdGqD\n/Ym2N1g42V/7+x/k5Oq6/C//uxrbYexS/Cv+w8RJmDyZDWYeWo5yXiYwUWypw102M3EGn42U\nRCAWAhzmm02Sm8VytognGTa9N4yY3uMjHqbsxnCs/zOgGYIhAiIgAiIgAiIgAq4TmHKU9zFo\nPeRVavIRQ3yO9bLbHsBnWuuTWpCsd2EcBmQwSEPB+S52cZAKnOMZrH8isE2rIiACIiACIiAC\nIuAkAS9vdsYIdvUnip3ixkSxegfJyfCNZhS72BVMVhWkcNjmI5u+N+FYKZcIiIAIiIAIiIBD\nBDKeuQXvIa2vZ5K3egV/PLY+6UHPeheGN2Dp0qWnUpVH/Kf4K0Bm02wKWpBq2V/JIsTyB5EH\nyMzOIfImLksM9ifOpigFkv1jv/9R+NmeV/6X/xkDtsdxs+VX/Cv+44r/QqE8UewIJooNiPGZ\nmayJYpv9nuq4LhHIZDKbBy+9pTFzuC1vRlYG97m2Xsv+CDbORN7vQh+Fbo5wXGKytmh/Yuxo\ntiCyf+z3v1mWNh4n/8v/NsZtXGVW/Cv+44ql4kSxeGysO1HsGk0UGxdrnadDBDCKzV3BS+VM\nTz+3oT3U+S52tewP8miw/iT2cVCGus3KDY5NxK4W7U+EDa0UQvaP/f63wtO2Y+V/+d+2mI2z\nvIp/xX9s8ZQ3/ME4ixEZak8Um5s8MbZr6USpJ9C1UezWm759MYLdutR7QABEQAREQAREQARE\nQAQaEpj0fu+kSYd5+cmHeV6lJh1aHsXueYee3vAEluzUO0iWOKpdxcS7RxjBLvNou85v8Xk5\ndPcF0IsttkFFFwEREAEREAEREIHYCOAdpDV4L6OA0exMUMWLbNBEsbHBbuOJPoRzT23i/Dfg\nmMEmjrPukIzhEN+e893rIjqGzcPXQ5x1/eGIxyq7CIiACIiACIiACDhJAKPYzcV7SDlTZ6JY\n0+vGRLGuz4P0v4jOZkYcW4LjnKsgDQwMbMdv6+LFi+/jJxMqR7NRSUpFBamW/SUKY/7dgC3n\nQN+DVkNOpAj2O2Fv0AjZP/b7H2Tk8rr8L/8zviv//rkc70HbFP+K/9jiP2/+jcrRMB4g+6ri\njNPEMq197NnSgt3/ul5B2gfu+Q20CPo99GMoTLonTCbb8uRyuRPKZT5sY9nZgpRxfgQ72lvb\n/o0kKpZYQfpaxboTixHsd8LeoBGyv9b3P0jJ3XX5X/4vR3fF3z934z1omeJf8R9X/KP1qOFE\nsWbyvM2C8WfjuusVJL5bsyd0DcTK0onQLVBaE0ZlrE7sYlcw3t+rtzq7Nsb+Ckt3xfLd0JqK\nba4tNrLfNVtr2SP7a1FJzzb5Pz2+rmWp/F+LSnq2yf8x+RoVpFtM1qwH0Nqj1T370NMxXaqr\np3G9gkS4Q9AHIM5d823olVAqE+ZBOM9DqjQeXez609LFrpb9ZRZH4fN06NXQ9ZCTqYH9Ttob\nNEr2j/3+Bxm5vC7/y//Bv38ux3vQNsW/4j/G+H+RGTF9eJisfp4sDfqdMRPdmCg2+B1yef1T\nMO426CUJNPIUlImB1vFmScT4w0Om95AEMulUkdiHlgMxpJlBp1jrOiIgAiIgAiIgAhYT6DvA\nO2biAd7IJm/3vEpNfFt5mO+5b/+KxeaNFj0NLUi+sadhgVKqIuBt7plCKgZpqDJ74wpbGOdu\nXNWSCIiACIiACIiACIhALQLoYjcT3evGThRbakHKmgmb1u56V+tkCd6WpgpSgt3QnaJheLaZ\nxmR6MiabikEaukNZVxUBERABERABERABRwh4ZoRdnlhJqrKo3OGukM9jhiT7U7Vx9tsjCxoQ\nGBwcPIjys/SZ3n4uj5jhVLQg0fazzz77wzD5L9CetD1NKej/NNlOW2V/9fdf/k8XAcW/4p8x\nkK6o32it4j/G+N9g1jacKDY/4kQFSS1IG78/cS6x4vlaqCfkSbedP3++Of30018/e/bsv2Ge\nhuX+cddee+2s3t7e3QpIQ0NDV++5557r/X1Lly7dCctbQqsWLly41N++ZMmS7N577128Piry\nd/jny2az++BFzT4c9yzPN/KWt+f7HnvCu8mYJ3ls1PPxmDjL1+7zweQ3HX/88W/GdTha3a2u\n2xuMF/ie71lNgt3Lw8RLu/0RLF+7/cH4p034flxU6/vhmr20h6nifvBBxABHF7qwuAP/2PT9\nbTVegve/Vs9Hhjbxg/37osjsBFP0f7u/b+TDVBF/PZV/j7ivk/x0/9P9T/e/jc9/rdz/MFHs\nFpwodrPJI4Xt564xnpcxtyzb1Az5Xex6NpvE77ftSRWkag9+FKuHQ2dBnCS02TQfB/4S6g15\ngp63vOUtZt68eefgIf5sHPNJ/7ienp6D8aU+BX/cCqgo8Q/ctf4+bP8WlnfDyCRsAVrgb3/t\na187H/uK18dxo+dDvmHkeT72XcTzPbnvvl+f+5OfPommFM77Y6Kej8fEWb52nw/2XXXggQf+\n/ayzztoP11qL9d/iMzS/dpevVf+GKB9tnYV8F0DjxkuI80WKv26frxz/pt73o9vl4/WZ2lU+\n2D8Fuqt0ldK/Nn1/W/1+lP0/ev9r9XwkaBM/FPcqMHi05Hn37/c1/Kv7H5zfrvsLTt1b+bzB\nOEvS9wOxr/tfxfNfje9H6L/nhby5lxPFvm7HJ/s+8roVpoCudcee/wJz67Lyq0fDbkwUyxhW\n2khgCRbZi/ILGzd1ZKkjo9jdcMMNMyjfomHTc+Sw6b3DX3f9M2i/6/YG7ZP91fEf5OP6uvwv\n/1fe/12P96B9in/Fv+J/4/Nf8PsRZb3nDd6PIC+oCa8vjWJntjqKjQxKjhGYDXvYbY2fnUwd\nqSAFDcLw3l/EMN9XBrc7tv562KNR6hxzqswRAREQAREQARHoPAFUjI6csJe3rm4FadYBfKa1\nPmmQhmoXrsTqbRA/nU+YILYfE8Wye56r6TMw7BJoB1cNlF0iIAIiIAIiIAIi0CkC6GL3IgzS\n0OdtwOtHARXLMHHOtE6VpZ3XSeM7SNMBlM7rg56D+NLyGih1KWM8VJAyKxw2/C2w7R3QFQ7b\nKNNEQAREQAREQAREoCMEvLx5AO8g5TMZU7sOsXbl6o4UpM0XqW1cmy/ahdPvgmseAXEEM76k\nHkz3Y8Nfoc9Cjwd3urKOUYtOpS0YwezT/ETlqB+VpJu57GhaVGlX0P7KfWlYlv3V8Z8Gn1fa\nKP/L/4wH//5fGRtpWFb8K/4V/zF9/z3MoVnARLFecVTM0dsHRrZj0kSxo0SSv/B5FPHEcjH/\ni89BaBXE1iO2JHHQgq0hzo9zAHQU9HPIuYRR2zavNootSE53sasyd6z9VbudX5H9wfh33uVV\nBsr/8n9VQKRsRfGv+E9ZyFeZG2v8FzBRLIYzQwtS7dd0PDcminW9BYndq1g5uhQ6AarXWpLB\nvldBp0HnQ8ugGyCnEoYQv6vSIBg9u2AyrryDxPmgfg+xleyCSjv95aD9/va0fMr+6vhPi999\nO+V/+d+PhTR+Kv4V/2mMe9/mWOPfw0Sx+HWdlaTKVG5BwsQxbkwUW2mbi8us7NwH8X2jMInv\nJ7HvZCtzIIW5TjDPKdjAUNssuKNd63cY04sR7LwRM6GqG1q7rteB83Ly12sgJ14O7AAvXUIE\nREAEREAEREAEohF4hffN7CKvAOUrlXn5UB4n8szsw78d7YTJzO16C9JOwD4IDYXE/xTycRS7\neSHzW5ttQXko87zZ4EoL0gfgjL9DYX1tre9UcBEQAREQAREQARHoCoGCuc/DRLG4dlXjw2iD\n0vDjfIXF+lS7/6D1Zo0a8AiWdoV6Rrc0XmALEitVbI1wOk0wPf008FljXKkgDcAcVY6cjloZ\nJwIiIAIiIAIi0FUCBfP/zAZUjjagFEGxYD3zOtYbqp0cXK8gnQt420O/hl7eACRexym+g8R3\nlSZBv2uQ19pdAwMD21E0AAOQoILkDWFIP9SRrEwHotQviVLySvujHOdKXtm/Mf5d8WkUO+R/\n+d+//0eJG1fyKv4V/4r/0vNfy9/pgrkFrUXr655n6KGn6+6zaIfrXew4Gh1bSk6C9oMeglZA\nT0J812gqxFHstoG2gFgX/hTE1gjnUi6X40AVTIdlTWG2Z7K2TojLkQk/B70J+hcUKlXaH+oA\nxzLJ/o3x75hrQ5kj/8v/5UA5LFTAOJZJ8a/4V/wXCbT+/c+bHfGmUR8qSaO96opnLg3znTE9\nmijWhtsnnXcGxNHNToZ2h4ItSWux7WHoNOib0IOQq4ktZcWUwRxIGITE1grS82EEK7yXlawJ\n/e+o/aGPcCuj7HfLn1Gtkf+jEnMrv/zvlj+jWiP/RyXmVv74/O8VBz9jg0JPTURDmii2JpeE\nbrwf5Tq4XDa2GnGks4kQ3795BkpFwjj452FcxmKNH5UjTBJr7RDf72nGYZX2N3O87cfI/o3x\nb7svmym//C//+/f/ZuLH9mMU/4p/xX9wYO4mv9V5MwttRzkcXWoz8k9TWsua3DQ+XyuJQCwE\nOj7M97Dp/Sn041hK3/6TsCuo6+/LtZ+iriACIiACIiACIiACrRDY0fuS2dHLmxejwlWpFw3x\nB3jPTH0ve2RZn/TQab0LmzOg1MXOihHsFsBCTNtkjm7OUh0lAiIgAiIgAiIgAiIQCwHPrEPb\nkWc461Gl/PakvBsTxbo+SEMsseDiSdDFbnbWji52vwH/ZdAPXPSDbBIBERABERABERABawjk\nzWxUj9jA4leJSkUvDdmQNT3TNrHGlgYFVQWpARzXdg0ODh5EmxYtWnQhW5DwA4ANgzQcgCL/\nF+ILgS2lSvtbOpGlB8v+jfFvqQtbKrb8L/8zgHj/bymQLD1Y8a/4V/zH9P3HRLFgOWwy1RPF\njt4aRjRR7CgLLdhBIJvN7kOVSutt7pmCDZPE3o/ytlw5os3V9pcopOlf2V8Z/2nyfMlW+V/+\n33j/V/ynjYC+//r+x/b9z2Oi2HyDiWJzbkwUqxakFN0lMYLLMM3FDF7Tjcn0JrSL3YdRxGXQ\n5VCsybc/1pNadDLZX4p/i1wWa1Hlf/k/1oCy7GSKf8W/ZSEba3FjjX/P3ILCrUcLUu3R6kY0\nUWysztPJ2k+gUCgcx6tMNH2cPNeMmJGktSCdhGJ9Gnozyxd38u2P+7y2nE/2l+LfFn/FXU75\nX/6PO6ZsOp/iX/FvU7zGXdZY4x/j1+EdpD6UsfTWkV/Y0lrG5GZzOh0lEYiFQEeH+R4xE141\nYnoLf683yVcsJjV1kiU4ao+mjtRBIiACIiACIiACIiAC7SUw1/u02QotklthmO9KbVke5nvi\n27/S3gJ05uzqYtcZzom6SsFkMYKdeeqlbERKVlqSrOKoNCIgAiIgAiIgAiIgAhUEZuHpcexE\nsf4odhk3JopVBanC42lZzBiv3zOZJIxgt2mZ+bNpYS87RUAEREAEREAERMBaAgUzVBzgO1Mc\n6nujGX6Huw2aB2kjFC1ZQWDp0qWnsqDewt2fwzDf3X7/6IUoymXQedDnoLYn3/6FCxfyPafU\nJdlfin/5X/Gfui8/DNb3X99/xr3uf7r/tXz/G8EADXz/KBM4k19ByqiCFCCj1aQTyGQym7OM\nqBxNQitStytInPj1VujLLFMnkm9/J66VxGvI/lL8J9E3nSiT/C//dyLOknoNxb/iP6mx2Yly\nxRr/BTMHZa4/UayniWI74VNdI0YCGMXkLp4OlaPdUPV/NMZTN3OqfXHQc5D/m0Mz54h0jG9/\npIMcyiz7S/HvkEsjmSL/y/+RAsaxzIp/xb9jIR3JnFjj3zP3oovd2Ili/ac5z42JYiMBVua2\nEejoKHbDpu86jGL3+bZZoxOLgAiIgAiIgAiIgAi4R2CS9yMzESPYBdVXHsUud9RZLhjNJjKl\n1BHgIA1epwdp4Hs/h6UOtQwWAREQAREQAREQAVcI5DFRrFd8D6m2RXlNFFsbjLYmngDeq+sv\ndHaQBo6J/zFo/8TDUQFFQAREQAREQAREQARqE9iAiWLzNSaKLeXGI+acabUPtGurhvm2y18t\nlXZgYGC73ieemGD2P3CzrMl3cpCGu1HwV0M3tWRAiwfTfp5i8eLF97V4KisPl/3yv+Jf33/d\n/3T/t/IPWIuF1t+/GP/+5fEOkjEboJ7ablm5uvZ2u7aqgmSXv1oqbS6XO8FMnjyJJ8mbHCpI\njO+OpHM6cpVxLlK0v5QnlV39ZD/iX/4nAcV/ORDS9KHvv77/5XjX9z9NX/yyrTF//2fhtGMn\nii1dK2uMJopNYYhZb3Imt379JrRirRlqZwtSPy7B6yzntRKUgqP2J6hoHSmK7O8I5sReRP5P\nrGs6UjD5vyOYE3sR+T+xrulIweL0P+dBYkJlqFbSPEi1qGhbgglgHPzzpl9++UsxsvYbZhjz\nTJuKuhPOewn0C+hTbbpGU6el/R5SUwc7cJDsl/8V//r+O3Ara8oE3f90/9P9L7b73xC+hA2e\npdyoIDV1o9FBsRPo2DDfw6bnvRjm+7+xW7DxhL/C4vlQ78ZNWhIBERABERABERABEbCfgPdN\n1I8KUL5aQ1hnxenD37bfRmP0DpILXoxgQ8ZkOcR3O7vXvaP0BYlQKGUVAREQAREQAREQARGw\ngQAHuhqG+moX9snnam+3a2ud/oN2GaHShidQMF5/pr1DfDdodg1fTuUUAREQAREQAREQARFI\nHAG+SlGncsSyztsscSVuokCqIDUBzdZDBgcHD3r8kHe+LMYWJL7090VoiQ1MaD9lQ1nbUUbZ\nL/8r/vX9b8e9xYZz6v6n+5/uf7Hd/27Fd35d/e+9GxPFqoJU38PO7clms/s8t+vOz0MTT1xd\n7DgB7Mehq22ARfspG8rajjLKfvlf8a/vfzvuLTacU/c/3f90/4vt/vdifOcnQuwxFBQ2aaJY\nQFCyiQBGcBnOrl3XlzWZlTGV+3Kc5zzo9pjO19bT0P62XiDhJ5f98n/CQ7StxVP8K/7bGmAJ\nP7niX/EfY4j+B+fSRLExAtWpukygUCgct9Vp33ijZwpxtSBd0WWTIl2e9kc6wLHMsl/+dyyk\nI5mj+Ff8RwoYxzIr/hX/MYb0bJyrwUSxmxXn24zxejpVigl0bJjvEdM7tN5MeEOTrLfBcbs1\neawOEwEREAEREAEREAERsJqAd2J5eG90r+PcSr6Gyt3t3nua1eaVC693kFzwYkgbVhkzzZhM\nL7rYNdOCtCsucxN0aMjLKZsIiIAIiIAIiIAIiIBbBPi6AitDdZIbE8WmcR6k+fDoPGhT6HHo\nv+VPfLidJpk+Noui4+hIMxWkQ3DoryEOyqAkAiIgAiIgAiIgAiKQPgKzYDIbWDBZ7JiE7Zuq\ni90YLMnd8E4U7W6oPMvvmFE3WBu+E9oe6kZqdxc7tv785Phjjnnqi0cd5SF6z8E6t6UqLV26\n9FQqVUZXGCv75X/Fv77/FbeEVC3q/qf7n+5/cd3/vKPRgDS0sWtdsIvdAXymtT6loQWJAwm8\npuwpjrqxBloLwbmGtVxfO2D5Dugs6EjIlfQpGPI16NIXb73141OeXDUFVX7W/v8GHQv5fUX5\nwh0rkM6mTCazubPGhTBM9sv/IcLE2SyKf8W/s8EdwjDFv+I/RJiEzfISZOytn3kuXuewP7le\nQeLDPytH7ErHrmE/h2olTnh6BMR5ffh5G/R9yPa0Lwz4KsQWtIt2XbPuN30PPboZlrn9HdAF\nEFvWdoNYYfRr/WhkMqdDzPs+yImEUXzucsKQJo2Q/fJ/k6HjxGGKf8W/E4HcpBGKf8V/k6FT\n67BbsXEdxAaGGunhZ2ps1KaEEXgA5WH3uU1Dlms+8rFPJVuSOpna1cWOrUTf8g3BCHafHzZ9\n1/rr+PwO9Cj0BPR/kJ9OxgLGdDCL/Q36FAEREAEREAEREAERSDsB73voXodn5aCG+PyMwRs+\ndqYLhNhS4HKaA+NWQM+GNHJZOe/MkPmTnG0KCseWIbYS+Wk3NJWxtcxPl2GBAzec5G8of7Kl\n7aXQQGC7VkVABERABERABERABNJLwJ8ols+TQWHTI6tdQON6F7s1cNJciM2AbA4cL81HBrY2\n3TNeRgv2s4LE9FTpg/96kzxTNZs0g5wpGAedbkErlUL/ioAIiIAIiIAIiIAIJJkAGx+cnyjW\n9RakC+HEPuh+6DCoXmINmO8e/auc4cf1Mlq0nUN5Pw39j1/mNdvv8PRayF/H564Q1ydDO0BO\np4GBge0op41sYJzsl/8V//r+N7hFOL1L9z/d/3T/i+3+x4HOmFiHCAqbhjkgmvUp2HJgvUEB\nAzgaHVuQ9od+BHHgBbYk0bn+C2ZsXZoEsTbMia9+C6F/pfWJfUHPh06Afg+teeSjH+LII8Yc\n/TH+y0rR8dBfoGOg2yGnUy6XIwumRpXlUg4H/5X98n85rBX/Dn6/xzNJ3399//X9LxLQ/W+8\nm8X4+4eRpcFEscN8/rQ+sebncqID3wrtCS0vG8quZ/3QNuVPrnN4679DC6EDIFfS52EIh2Lk\nUOe7ehmTobDM94u4jftuhq6H/gC5nmg7ldYk++V/xX9av/0b3xVIKwHd/3T/0/0vnm//LJyG\n9QdWhILCpqkT8U+303QUYD70QmgexEaBSMn1FiQfxjVYmF9eIShqKsR3jR6EXE0cie6V0A+g\nv3/kvJ8Oe5zPy5iboD9CbFlbCXF4c+cT5oE4D/Y3+NXDbQSyX/5X/Ov77/Zdrr51uv/p/qf7\nX2z3vwfwTRuB+CN7jfTkczU2dmLTLrjIEdCbIVbigul+bPgr9Fno8eBOrSeTQLuG+a60dsFp\nJnclhY2pfQ+nEoiWRUAEREAEREAEREAEohDwfogedvixOagh/gANHXVWlLPFlPfzpWsXu/4t\nx/IN0J+gX0CXQDdCj0As4xPQIZCSBQQ6UUEyz5mec/cymX+Dx1UWMFERRUAEREAEREAEREAE\nEkXAOxL1DLzLX6+CdACfaTuZ3oGLseLDitDowGQ1CpDBtt0h9qJi/ldAdRMzK20k8HMsvgXi\nQA3v2rg58tIeOIItNVH4ZjDCytpsNnv2okWLPulfcXBw8ChsOwVNw4V8Pr/v4sWLr/X33Xjj\njVdjeTfse2zhwoUL/O3XXXfdtr29vf/Aei9mzx4932Em9/df9U3Y6Ywzz8zvuOOOG1o9H68X\nZ/l0vtb8K37ihxgIfT9QvCheFC/G1Pt7qe+Hvh/6ftT7fnhng82HDjxwpTnyyBWmUDDm4x9/\ngbn11j5s7sNz757ofncVu+CFSRzwgSMq3x8mc508HJCMYwi8CBqqk6dyM99PWg7xmf/wyh2V\ny2l5B6nS5kbLW2AnR7XjZyuJgx4cDzFawqStZ86c+YGhoaGD+/r6/ll5wMjIyAWo7NyLik5h\neHj4b5X7UDE6CutbQnzXaDRdccUVy/bee+8DsaEHh93h79jr5C9euFdPz8Xzd9zxxjjOx/PG\nWb52nw+jOG2NCt1BqIBeyGtF5dfu8rXq3/HKhwrxxahsDyLf75nXT/Xihftt8u94/Oh72nTZ\nZZddVOv74Zq9tIepwr+vwXsYK0pbS/+65F/frgp7q+5/9D/ify2+9+/Q/S999z/Ex8Xwve5/\nuv/5twqn/r75RjW6/yFPXPe/e3CuDZdfPqNnxYq+YgXpzjsnc1O5GJuejQW25oRJrEgtC5Ox\nQZ6dsI/f7aEGeSp3PYWV26B5lRu13JjAjtjNUez42cm0CBdjc1+dF97iKQpanM6h4jmbfWeR\n/fK/4l/ff/vuXPGUWPc/3f90/9P9L567SbGLHSo23vpqDbGCgmfZoz8Uz3VCn+Vy5LwL6gl5\nBFuQVkNfb5RfLUjVdNjaMtriUr3L/jX8esamzNQm2S//pzb4YbjiX/Gv+E8vAX3/9f2PMfov\nxbm2ggKvkYxw6O9PofGole5yzRTzXBz0M+jX0MkQB2SolVjeV0KnQpz/9HdQ3ZTGCtJ80GCz\n2qbQ49B/y5/4cDuhW8lxblvY2DrZL/83jhC39yr+Ff9uR3hj6xT/iv/GEeL23njjP3MvaH2m\nBjH2gkIF6d8YwKGjie8ScX7Tk6D9oIegFdCTEFuKpkIzoG2gLSD2BUQ5zQCU+vROELgb4oSw\n7MoWFFtW7oS2h7qROtLFrhuG6ZoiIAIiIAIiIAIiIALOE2AFic/XfKbtRtoWF70AYgUp+Jy/\nBtv+A7H1iK1fSiBwBeSDQp/JYm3yUXxyBIvHoGch1iaZhxWo70CdTqogdZq4ricCIiACIiAC\nIiACIhAXgW5XkCrtYKsRK0LPh6ZV7tByicBp+GDFhxWhQ0qbav7LfolHQpz9l/k/DHU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      "text/plain": [
       "Plot with title “ROC Curve w/ Thresholds”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ROCR)\n",
    "\n",
    "# Use ROCR package to plot ROC Curve\n",
    "xgb.pred <- prediction(pred, test.label)\n",
    "xgb.perf <- performance(xgb.pred, \"tpr\", \"fpr\")\n",
    "\n",
    "plot(xgb.perf,\n",
    "     avg=\"threshold\",\n",
    "     colorize=TRUE,\n",
    "     lwd=1,\n",
    "     main=\"ROC Curve w/ Thresholds\",\n",
    "     print.cutoffs.at=seq(0, 1, by=0.05),\n",
    "     text.adj=c(-0.5, 0.5),\n",
    "     text.cex=0.5)\n",
    "grid(col=\"lightgray\")\n",
    "axis(1, at=seq(0, 1, by=0.1))\n",
    "axis(2, at=seq(0, 1, by=0.1))\n",
    "abline(v=c(0.1, 0.3, 0.5, 0.7, 0.9), col=\"lightgray\", lty=\"dotted\")\n",
    "abline(h=c(0.1, 0.3, 0.5, 0.7, 0.9), col=\"lightgray\", lty=\"dotted\")\n",
    "lines(x=c(0, 1), y=c(0, 1), col=\"black\", lty=\"dotted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it. Please leave a comment if you have any questions, spot any errors, or if you know of any other packages or graphs to display correlation matrices. You can grab the notebook from my GitHub here correlation_matrices_in_r.ipynb. Thanks for reading!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}