From 048d2ddd0d60ce682a57f1dfb81df7a565f8d43f Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Thu, 3 Nov 2022 08:04:30 +0100
Subject: [PATCH] cleaning up

---
 doc/pub/week44/html/._week44-bs000.html      |    2 +-
 doc/pub/week44/html/._week44-bs033.html      |   10 -
 doc/pub/week44/html/._week44-bs053.html      |    4 +-
 doc/pub/week44/html/week44-bs.html           |    2 +-
 doc/pub/week44/html/week44-reveal.html       |   16 +-
 doc/pub/week44/html/week44-solarized.html    |   16 +-
 doc/pub/week44/html/week44.html              |   16 +-
 doc/pub/week44/ipynb/ipynb-week44-src.tar.gz |  Bin 294283 -> 294283 bytes
 doc/pub/week44/ipynb/week44.ipynb            | 1420 +++++++-----------
 doc/src/week44/week44.do.txt                 |   13 +-
 10 files changed, 533 insertions(+), 966 deletions(-)

diff --git a/doc/pub/week44/html/._week44-bs000.html b/doc/pub/week44/html/._week44-bs000.html
index 8fc5cd4c3..3e2d78be7 100644
--- a/doc/pub/week44/html/._week44-bs000.html
+++ b/doc/pub/week44/html/._week44-bs000.html
@@ -318,7 +318,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Nov 2, 2022</h4>
+<h4>Nov 3, 2022</h4>
 </center> <!-- date -->
 <br>
 
diff --git a/doc/pub/week44/html/._week44-bs033.html b/doc/pub/week44/html/._week44-bs033.html
index 499a798fb..79e85f2e5 100644
--- a/doc/pub/week44/html/._week44-bs033.html
+++ b/doc/pub/week44/html/._week44-bs033.html
@@ -356,12 +356,6 @@ display(grades)
 X <span style="color: #666666">=</span> grades<span style="color: #666666">.</span>loc[:, grades<span style="color: #666666">.</span>columns <span style="color: #666666">!=</span> <span style="color: #BA2121">&#39;Grade&#39;</span>]<span style="color: #666666">.</span>values
 y <span style="color: #666666">=</span> grades<span style="color: #666666">.</span>loc[:, grades<span style="color: #666666">.</span>columns <span style="color: #666666">==</span> <span style="color: #BA2121">&#39;Grade&#39;</span>]<span style="color: #666666">.</span>values
 <span style="color: #008000">print</span>(X)
-<span style="color: #408080; font-style: italic"># Create the encoder.</span>
-encoder <span style="color: #666666">=</span> OneHotEncoder(handle_unknown<span style="color: #666666">=</span><span style="color: #BA2121">&quot;ignore&quot;</span>)
-<span style="color: #408080; font-style: italic"># Assume for simplicity all features are categorical.</span>
-encoder<span style="color: #666666">.</span>fit(X)    
-<span style="color: #408080; font-style: italic"># Apply the encoder.</span>
-X <span style="color: #666666">=</span> encoder<span style="color: #666666">.</span>transform(X)
 <span style="color: #408080; font-style: italic"># Then do a Classification tree</span>
 tree_clf <span style="color: #666666">=</span> DecisionTreeClassifier(max_depth<span style="color: #666666">=2</span>)
 tree_clf<span style="color: #666666">.</span>fit(X, y)
@@ -375,10 +369,6 @@ export_graphviz(
 )
 cmd <span style="color: #666666">=</span> <span style="color: #BA2121">&#39;dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png&#39;</span>
 os<span style="color: #666666">.</span>system(cmd)
-
-
-<span style="color: #408080; font-style: italic">#data_pandas = pd.DataFrame(data,index=[&#39;Frodo&#39;,&#39;Bilbo&#39;,&#39;Aragorn&#39;,&#39;Sam&#39;])</span>
-<span style="color: #408080; font-style: italic">#df.columns = [&#39;First&#39;, &#39;Second&#39;, &#39;Third&#39;, &#39;Fourth&#39;, &#39;Fifth&#39;]</span>
 </pre>
 </div>
       </div>
diff --git a/doc/pub/week44/html/._week44-bs053.html b/doc/pub/week44/html/._week44-bs053.html
index 587196cb9..ad7480cb2 100644
--- a/doc/pub/week44/html/._week44-bs053.html
+++ b/doc/pub/week44/html/._week44-bs053.html
@@ -360,8 +360,8 @@ simpleprediction <span style="color: #666666">=</span> simpletree<span style="co
     <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Var:&#39;</span>, variance[degree])
     <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;</span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> &gt;= </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> + </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> = </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121">&#39;</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
  
-mse_simpletree<span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> simpleprediction)<span style="color: #666666">**2</span>)
-<span style="color: #008000">print</span>(mse_simpletree)
+mse_simpletree<span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> simpleprediction)<span style="color: #666666">**2</span>))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Simple tree:&quot;</span>,mse_simpletree)
 plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">1</span>,maxdepth)
 plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;MSE&#39;</span>)
 plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bias&#39;</span>)
diff --git a/doc/pub/week44/html/week44-bs.html b/doc/pub/week44/html/week44-bs.html
index 8fc5cd4c3..3e2d78be7 100644
--- a/doc/pub/week44/html/week44-bs.html
+++ b/doc/pub/week44/html/week44-bs.html
@@ -318,7 +318,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Nov 2, 2022</h4>
+<h4>Nov 3, 2022</h4>
 </center> <!-- date -->
 <br>
 
diff --git a/doc/pub/week44/html/week44-reveal.html b/doc/pub/week44/html/week44-reveal.html
index be9a7a6e2..7f88535f6 100644
--- a/doc/pub/week44/html/week44-reveal.html
+++ b/doc/pub/week44/html/week44-reveal.html
@@ -184,7 +184,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Nov 2, 2022</h4>
+<h4>Nov 3, 2022</h4>
 </center> <!-- date -->
 <br>
 
@@ -1139,12 +1139,6 @@ display(grades)
 X = grades.loc[:, grades.columns != <span style="color: #CD5555">&#39;Grade&#39;</span>].values
 y = grades.loc[:, grades.columns == <span style="color: #CD5555">&#39;Grade&#39;</span>].values
 <span style="color: #658b00">print</span>(X)
-<span style="color: #228B22"># Create the encoder.</span>
-encoder = OneHotEncoder(handle_unknown=<span style="color: #CD5555">&quot;ignore&quot;</span>)
-<span style="color: #228B22"># Assume for simplicity all features are categorical.</span>
-encoder.fit(X)    
-<span style="color: #228B22"># Apply the encoder.</span>
-X = encoder.transform(X)
 <span style="color: #228B22"># Then do a Classification tree</span>
 tree_clf = DecisionTreeClassifier(max_depth=<span style="color: #B452CD">2</span>)
 tree_clf.fit(X, y)
@@ -1158,10 +1152,6 @@ export_graphviz(
 )
 cmd = <span style="color: #CD5555">&#39;dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png&#39;</span>
 os.system(cmd)
-
-
-<span style="color: #228B22">#data_pandas = pd.DataFrame(data,index=[&#39;Frodo&#39;,&#39;Bilbo&#39;,&#39;Aragorn&#39;,&#39;Sam&#39;])</span>
-<span style="color: #228B22">#df.columns = [&#39;First&#39;, &#39;Second&#39;, &#39;Third&#39;, &#39;Fourth&#39;, &#39;Fifth&#39;]</span>
 </pre>
 </div>
       </div>
@@ -2228,8 +2218,8 @@ simpleprediction = simpletree.predict(X_test_scaled)
     <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
     <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
  
-mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**<span style="color: #B452CD">2</span>)
-<span style="color: #658b00">print</span>(mse_simpletree)
+mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**<span style="color: #B452CD">2</span>))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Simple tree:&quot;</span>,mse_simpletree)
 plt.xlim(<span style="color: #B452CD">1</span>,maxdepth)
 plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;MSE&#39;</span>)
 plt.plot(polydegree, bias, label=<span style="color: #CD5555">&#39;bias&#39;</span>)
diff --git a/doc/pub/week44/html/week44-solarized.html b/doc/pub/week44/html/week44-solarized.html
index 9a05309ee..ff8c75e04 100644
--- a/doc/pub/week44/html/week44-solarized.html
+++ b/doc/pub/week44/html/week44-solarized.html
@@ -262,7 +262,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Nov 2, 2022</h4>
+<h4>Nov 3, 2022</h4>
 </center> <!-- date -->
 <br>
 
@@ -1147,12 +1147,6 @@ display(grades)
 X = grades.loc[:, grades.columns != <span style="color: #CD5555">&#39;Grade&#39;</span>].values
 y = grades.loc[:, grades.columns == <span style="color: #CD5555">&#39;Grade&#39;</span>].values
 <span style="color: #658b00">print</span>(X)
-<span style="color: #228B22"># Create the encoder.</span>
-encoder = OneHotEncoder(handle_unknown=<span style="color: #CD5555">&quot;ignore&quot;</span>)
-<span style="color: #228B22"># Assume for simplicity all features are categorical.</span>
-encoder.fit(X)    
-<span style="color: #228B22"># Apply the encoder.</span>
-X = encoder.transform(X)
 <span style="color: #228B22"># Then do a Classification tree</span>
 tree_clf = DecisionTreeClassifier(max_depth=<span style="color: #B452CD">2</span>)
 tree_clf.fit(X, y)
@@ -1166,10 +1160,6 @@ export_graphviz(
 )
 cmd = <span style="color: #CD5555">&#39;dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png&#39;</span>
 os.system(cmd)
-
-
-<span style="color: #228B22">#data_pandas = pd.DataFrame(data,index=[&#39;Frodo&#39;,&#39;Bilbo&#39;,&#39;Aragorn&#39;,&#39;Sam&#39;])</span>
-<span style="color: #228B22">#df.columns = [&#39;First&#39;, &#39;Second&#39;, &#39;Third&#39;, &#39;Fourth&#39;, &#39;Fifth&#39;]</span>
 </pre>
 </div>
       </div>
@@ -2224,8 +2214,8 @@ simpleprediction = simpletree.predict(X_test_scaled)
     <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
     <span style="color: #658b00">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
  
-mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**<span style="color: #B452CD">2</span>)
-<span style="color: #658b00">print</span>(mse_simpletree)
+mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**<span style="color: #B452CD">2</span>))
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Simple tree:&quot;</span>,mse_simpletree)
 plt.xlim(<span style="color: #B452CD">1</span>,maxdepth)
 plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;MSE&#39;</span>)
 plt.plot(polydegree, bias, label=<span style="color: #CD5555">&#39;bias&#39;</span>)
diff --git a/doc/pub/week44/html/week44.html b/doc/pub/week44/html/week44.html
index 80cdc072d..a14dfbb93 100644
--- a/doc/pub/week44/html/week44.html
+++ b/doc/pub/week44/html/week44.html
@@ -339,7 +339,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Nov 2, 2022</h4>
+<h4>Nov 3, 2022</h4>
 </center> <!-- date -->
 <br>
 
@@ -1224,12 +1224,6 @@ display(grades)
 X <span style="color: #666666">=</span> grades<span style="color: #666666">.</span>loc[:, grades<span style="color: #666666">.</span>columns <span style="color: #666666">!=</span> <span style="color: #BA2121">&#39;Grade&#39;</span>]<span style="color: #666666">.</span>values
 y <span style="color: #666666">=</span> grades<span style="color: #666666">.</span>loc[:, grades<span style="color: #666666">.</span>columns <span style="color: #666666">==</span> <span style="color: #BA2121">&#39;Grade&#39;</span>]<span style="color: #666666">.</span>values
 <span style="color: #008000">print</span>(X)
-<span style="color: #408080; font-style: italic"># Create the encoder.</span>
-encoder <span style="color: #666666">=</span> OneHotEncoder(handle_unknown<span style="color: #666666">=</span><span style="color: #BA2121">&quot;ignore&quot;</span>)
-<span style="color: #408080; font-style: italic"># Assume for simplicity all features are categorical.</span>
-encoder<span style="color: #666666">.</span>fit(X)    
-<span style="color: #408080; font-style: italic"># Apply the encoder.</span>
-X <span style="color: #666666">=</span> encoder<span style="color: #666666">.</span>transform(X)
 <span style="color: #408080; font-style: italic"># Then do a Classification tree</span>
 tree_clf <span style="color: #666666">=</span> DecisionTreeClassifier(max_depth<span style="color: #666666">=2</span>)
 tree_clf<span style="color: #666666">.</span>fit(X, y)
@@ -1243,10 +1237,6 @@ export_graphviz(
 )
 cmd <span style="color: #666666">=</span> <span style="color: #BA2121">&#39;dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png&#39;</span>
 os<span style="color: #666666">.</span>system(cmd)
-
-
-<span style="color: #408080; font-style: italic">#data_pandas = pd.DataFrame(data,index=[&#39;Frodo&#39;,&#39;Bilbo&#39;,&#39;Aragorn&#39;,&#39;Sam&#39;])</span>
-<span style="color: #408080; font-style: italic">#df.columns = [&#39;First&#39;, &#39;Second&#39;, &#39;Third&#39;, &#39;Fourth&#39;, &#39;Fifth&#39;]</span>
 </pre>
 </div>
       </div>
@@ -2301,8 +2291,8 @@ simpleprediction <span style="color: #666666">=</span> simpletree<span style="co
     <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;Var:&#39;</span>, variance[degree])
     <span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;</span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> &gt;= </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> + </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121"> = </span><span style="color: #BB6688; font-weight: bold">{}</span><span style="color: #BA2121">&#39;</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
  
-mse_simpletree<span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> simpleprediction)<span style="color: #666666">**2</span>)
-<span style="color: #008000">print</span>(mse_simpletree)
+mse_simpletree<span style="color: #666666">=</span> np<span style="color: #666666">.</span>mean( np<span style="color: #666666">.</span>mean((y_test <span style="color: #666666">-</span> simpleprediction)<span style="color: #666666">**2</span>))
+<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Simple tree:&quot;</span>,mse_simpletree)
 plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">1</span>,maxdepth)
 plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;MSE&#39;</span>)
 plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bias&#39;</span>)
diff --git a/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz b/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz
index e304f1a88004c4af7c5893984aedfd8ba60647d7..5d3b425a198c84502ca0b57dc985e3ec5df93717 100644
GIT binary patch
delta 30
lcmeDFE!h2AkX^o;gW*$pawB^yJ7X(5Q!6|3R(6(_S^%gk36}r>

delta 30
lcmeDFE!h2AkX^o;gJA_zQX_jSJ7X(5Q!6|3R(6(_S^%4Z2<HF*

diff --git a/doc/pub/week44/ipynb/week44.ipynb b/doc/pub/week44/ipynb/week44.ipynb
index 61b8298bd..12e8ded62 100644
--- a/doc/pub/week44/ipynb/week44.ipynb
+++ b/doc/pub/week44/ipynb/week44.ipynb
@@ -2,8 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d71885d1",
-   "metadata": {},
+   "id": "59fb2b9e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
     "doconce format html week44.do.txt --no_mako -->\n",
@@ -12,21 +14,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51c93be7",
-   "metadata": {},
+   "id": "d408fbc4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "# Week 44: Decision Trees, Ensemble methods  and Random Forests\n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Nov 2, 2022**\n",
+    "Date: **Nov 3, 2022**\n",
     "\n",
     "Copyright 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dc2cab51",
-   "metadata": {},
+   "id": "d0ae087e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Overview of week 44\n",
     "\n",
@@ -47,8 +53,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bf703ef",
-   "metadata": {},
+   "id": "8374c026",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Digression First\n",
     "\n",
@@ -63,8 +71,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98778a6c",
-   "metadata": {},
+   "id": "5103d90f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Decision trees, overarching aims\n",
     "\n",
@@ -92,8 +102,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03ad6396",
-   "metadata": {},
+   "id": "17974e99",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Basics of a tree\n",
     "\n",
@@ -110,8 +122,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d56e4131",
-   "metadata": {},
+   "id": "e1141b9b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A Sketch of a Tree, Regression problem\n",
     "\n",
@@ -122,8 +136,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15e9ccca",
-   "metadata": {},
+   "id": "16a3a7d7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A Sketch of a Tree, Classification  problem\n",
     "\n",
@@ -133,8 +149,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d3d956e",
-   "metadata": {},
+   "id": "0cf0e0ad",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n",
     "\n",
@@ -149,8 +167,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26b7124c",
-   "metadata": {},
+   "id": "7f882015",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## General Features\n",
     "\n",
@@ -170,8 +190,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63bd32f3",
-   "metadata": {},
+   "id": "de640bd9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## How do we set it up?\n",
     "\n",
@@ -191,8 +213,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e07dde87",
-   "metadata": {},
+   "id": "73a83c4f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Decision trees and Regression"
    ]
@@ -200,40 +224,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "3b672587",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2nd degree coefficients:\n",
-      "zero power:  5.5512545666537125\n",
-      "first power:  0.05202946060040184\n",
-      "second power:  -0.000187081702984667\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "id": "1af5ee76",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -329,8 +325,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71707f31",
-   "metadata": {},
+   "id": "8e7a1f48",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Building a tree, regression\n",
     "\n",
@@ -349,8 +347,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9d1aa76",
-   "metadata": {},
+   "id": "b0cdf698",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n",
@@ -359,8 +359,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bd688f8",
-   "metadata": {},
+   "id": "73f15329",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $\\overline{y}_{R_j}$  is the mean response for the training observations \n",
     "within box $j$."
@@ -368,8 +370,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c75f76be",
-   "metadata": {},
+   "id": "f7e6d1d8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A top-down approach, recursive binary splitting\n",
     "\n",
@@ -388,8 +392,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69684b47",
-   "metadata": {},
+   "id": "1e1df1bc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Making a tree\n",
     "\n",
@@ -399,8 +405,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55e24148",
-   "metadata": {},
+   "id": "ecc178b3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\left\\{X\\vert x_j < s\\right\\},\n",
@@ -409,16 +417,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "337c3963",
-   "metadata": {},
+   "id": "51e92bd7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1ce2e95c",
-   "metadata": {},
+   "id": "dcae0bf1",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\left\\{X\\vert x_j \\geq s\\right\\},\n",
@@ -427,16 +439,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b43c87d",
-   "metadata": {},
+   "id": "f5493ac0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "so that we obtain the lowest MSE, that is"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5a752b5a",
-   "metadata": {},
+   "id": "c285a4a5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sum_{i:x_i\\in R_j}(y_i-\\overline{y}_{R_1})^2+\\sum_{i:x_i\\in R_2}(y_i-\\overline{y}_{R_2})^2,\n",
@@ -445,8 +461,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02d8d908",
-   "metadata": {},
+   "id": "c40f850e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "which we want to minimize by considering all predictors\n",
     "$x_1,x_2,\\dots,x_p$.  We consider also all possible values of $s$ for\n",
@@ -476,8 +494,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf3546d3",
-   "metadata": {},
+   "id": "2de1df16",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Pruning the tree\n",
     "\n",
@@ -498,8 +518,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56d9f28b",
-   "metadata": {},
+   "id": "62c0ce6a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Cost complexity pruning\n",
     "\n",
@@ -508,8 +530,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb5a288c",
-   "metadata": {},
+   "id": "fae9d5d9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n",
@@ -518,8 +542,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "074d2165",
-   "metadata": {},
+   "id": "89fb5670",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "is as small as possible. Here $\\overline{T}$ is \n",
     "the number of terminal nodes of the tree $T$ , $R_m$ is the\n",
@@ -544,8 +570,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16c88686",
-   "metadata": {},
+   "id": "35d5d8d1",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Schematic Regression Procedure\n",
     "\n",
@@ -568,8 +596,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8025e3a5",
-   "metadata": {},
+   "id": "fe67bc38",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A Classification Tree\n",
     "\n",
@@ -589,8 +619,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "edc69600",
-   "metadata": {},
+   "id": "a295171f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Growing a classification tree\n",
     "\n",
@@ -614,8 +646,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b34390f9",
-   "metadata": {},
+   "id": "2ca5b9ba",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Classification tree, how to split nodes\n",
     "\n",
@@ -631,8 +665,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72bb5da6",
-   "metadata": {},
+   "id": "f1001e0e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n",
@@ -641,8 +677,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bea46b9",
-   "metadata": {},
+   "id": "fdeba790",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We let $p_{mk}$ represent the majority class of observations in region\n",
     "$m$. The three most common ways of splitting a node are given by\n",
@@ -652,8 +690,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83b2f14e",
-   "metadata": {},
+   "id": "2d487375",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n",
@@ -662,16 +702,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93c3becf",
-   "metadata": {},
+   "id": "b71a7fae",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "* Gini index $g$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e34f4ce3",
-   "metadata": {},
+   "id": "b4a99914",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n",
@@ -680,16 +724,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d09bc7d",
-   "metadata": {},
+   "id": "9a6ce2de",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "* Information entropy or just entropy $s$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f4d1aaeb",
-   "metadata": {},
+   "id": "8622aaae",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n",
@@ -698,8 +746,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47632237",
-   "metadata": {},
+   "id": "61315602",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Visualizing the Tree, Classification"
    ]
@@ -707,119 +757,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "85348704",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
-      "0          17.99         10.38          122.80     1001.0          0.11840   \n",
-      "1          20.57         17.77          132.90     1326.0          0.08474   \n",
-      "2          19.69         21.25          130.00     1203.0          0.10960   \n",
-      "3          11.42         20.38           77.58      386.1          0.14250   \n",
-      "4          20.29         14.34          135.10     1297.0          0.10030   \n",
-      "..           ...           ...             ...        ...              ...   \n",
-      "564        21.56         22.39          142.00     1479.0          0.11100   \n",
-      "565        20.13         28.25          131.20     1261.0          0.09780   \n",
-      "566        16.60         28.08          108.30      858.1          0.08455   \n",
-      "567        20.60         29.33          140.10     1265.0          0.11780   \n",
-      "568         7.76         24.54           47.92      181.0          0.05263   \n",
-      "\n",
-      "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
-      "0             0.27760         0.30010              0.14710         0.2419   \n",
-      "1             0.07864         0.08690              0.07017         0.1812   \n",
-      "2             0.15990         0.19740              0.12790         0.2069   \n",
-      "3             0.28390         0.24140              0.10520         0.2597   \n",
-      "4             0.13280         0.19800              0.10430         0.1809   \n",
-      "..                ...             ...                  ...            ...   \n",
-      "564           0.11590         0.24390              0.13890         0.1726   \n",
-      "565           0.10340         0.14400              0.09791         0.1752   \n",
-      "566           0.10230         0.09251              0.05302         0.1590   \n",
-      "567           0.27700         0.35140              0.15200         0.2397   \n",
-      "568           0.04362         0.00000              0.00000         0.1587   \n",
-      "\n",
-      "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
-      "0                   0.07871  ...        25.380          17.33   \n",
-      "1                   0.05667  ...        24.990          23.41   \n",
-      "2                   0.05999  ...        23.570          25.53   \n",
-      "3                   0.09744  ...        14.910          26.50   \n",
-      "4                   0.05883  ...        22.540          16.67   \n",
-      "..                      ...  ...           ...            ...   \n",
-      "564                 0.05623  ...        25.450          26.40   \n",
-      "565                 0.05533  ...        23.690          38.25   \n",
-      "566                 0.05648  ...        18.980          34.12   \n",
-      "567                 0.07016  ...        25.740          39.42   \n",
-      "568                 0.05884  ...         9.456          30.37   \n",
-      "\n",
-      "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
-      "0             184.60      2019.0           0.16220            0.66560   \n",
-      "1             158.80      1956.0           0.12380            0.18660   \n",
-      "2             152.50      1709.0           0.14440            0.42450   \n",
-      "3              98.87       567.7           0.20980            0.86630   \n",
-      "4             152.20      1575.0           0.13740            0.20500   \n",
-      "..               ...         ...               ...                ...   \n",
-      "564           166.10      2027.0           0.14100            0.21130   \n",
-      "565           155.00      1731.0           0.11660            0.19220   \n",
-      "566           126.70      1124.0           0.11390            0.30940   \n",
-      "567           184.60      1821.0           0.16500            0.86810   \n",
-      "568            59.16       268.6           0.08996            0.06444   \n",
-      "\n",
-      "     worst concavity  worst concave points  worst symmetry  \\\n",
-      "0             0.7119                0.2654          0.4601   \n",
-      "1             0.2416                0.1860          0.2750   \n",
-      "2             0.4504                0.2430          0.3613   \n",
-      "3             0.6869                0.2575          0.6638   \n",
-      "4             0.4000                0.1625          0.2364   \n",
-      "..               ...                   ...             ...   \n",
-      "564           0.4107                0.2216          0.2060   \n",
-      "565           0.3215                0.1628          0.2572   \n",
-      "566           0.3403                0.1418          0.2218   \n",
-      "567           0.9387                0.2650          0.4087   \n",
-      "568           0.0000                0.0000          0.2871   \n",
-      "\n",
-      "     worst fractal dimension  \n",
-      "0                    0.11890  \n",
-      "1                    0.08902  \n",
-      "2                    0.08758  \n",
-      "3                    0.17300  \n",
-      "4                    0.07678  \n",
-      "..                       ...  \n",
-      "564                  0.07115  \n",
-      "565                  0.06637  \n",
-      "566                  0.07820  \n",
-      "567                  0.12400  \n",
-      "568                  0.07039  \n",
-      "\n",
-      "[569 rows x 30 columns]\n",
-      "     malignant  benign\n",
-      "0            1       0\n",
-      "1            1       0\n",
-      "2            1       0\n",
-      "3            1       0\n",
-      "4            1       0\n",
-      "..         ...     ...\n",
-      "564          1       0\n",
-      "565          1       0\n",
-      "566          1       0\n",
-      "567          1       0\n",
-      "568          0       1\n",
-      "\n",
-      "[569 rows x 2 columns]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "id": "a7aa6248",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import os\n",
     "from sklearn.datasets import load_breast_cancer\n",
@@ -858,8 +801,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16feb17e",
-   "metadata": {},
+   "id": "cd6e1605",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Visualizing the Tree, The Moons"
    ]
@@ -867,20 +812,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "bf5f4030",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "id": "92f3f3d5",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -910,8 +847,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db30e9db",
-   "metadata": {},
+   "id": "368a97c9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other ways of visualizing the trees\n",
     "\n",
@@ -921,46 +860,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "d62f4144",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Text(0.5, 0.9166666666666666, 'X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
-       " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
-       " Text(0.5769230769230769, 0.75, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
-       " Text(0.3076923076923077, 0.5833333333333334, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
-       " Text(0.15384615384615385, 0.4166666666666667, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
-       " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
-       " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
-       " Text(0.46153846153846156, 0.4166666666666667, 'X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
-       " Text(0.38461538461538464, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
-       " Text(0.5384615384615384, 0.25, 'X[2] <= 5.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
-       " Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
-       " Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
-       " Text(0.8461538461538461, 0.5833333333333334, 'X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
-       " Text(0.7692307692307693, 0.4166666666666667, 'X[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
-       " Text(0.6923076923076923, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
-       " Text(0.8461538461538461, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
-       " Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "id": "5cc14381",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn import tree\n",
@@ -973,8 +878,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66ef59a0",
-   "metadata": {},
+   "id": "749201ad",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Printing out as text\n",
     "\n",
@@ -985,24 +892,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "eb003507",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|--- petal width (cm) <= 0.80\n",
-      "|   |--- class: 0\n",
-      "|--- petal width (cm) >  0.80\n",
-      "|   |--- petal width (cm) <= 1.75\n",
-      "|   |   |--- class: 1\n",
-      "|   |--- petal width (cm) >  1.75\n",
-      "|   |   |--- class: 2\n",
-      "\n"
-     ]
-    }
-   ],
+   "id": "af6b920f",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
@@ -1016,8 +911,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e23af78",
-   "metadata": {},
+   "id": "4b221e5c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Algorithms for Setting up Decision Trees\n",
     "\n",
@@ -1034,8 +931,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d45f60f4",
-   "metadata": {},
+   "id": "ef4ff80a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The CART algorithm for Classification\n",
     "\n",
@@ -1049,8 +948,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b82a6ee",
-   "metadata": {},
+   "id": "577342d4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}G_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}G_{\\mathrm{right}},\n",
@@ -1059,8 +960,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ded4f78f",
-   "metadata": {},
+   "id": "5e8aa9db",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $G_{\\mathrm{left/right}}$ measures the impurity of the left/right subset  and $m_{\\mathrm{left/right}}$\n",
     " is the number of instances in the left/right subset\n",
@@ -1074,8 +977,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e45062ff",
-   "metadata": {},
+   "id": "277f39a4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The CART algorithm for Regression\n",
     "\n",
@@ -1085,8 +990,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4eb8f40",
-   "metadata": {},
+   "id": "a7ed9f9f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n",
@@ -1095,16 +1002,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d04a8b2b",
-   "metadata": {},
+   "id": "c1cdfa33",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Here the MSE for a specific node is defined as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c2c78ab6",
-   "metadata": {},
+   "id": "b6647d29",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n",
@@ -1113,16 +1024,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5330ffab",
-   "metadata": {},
+   "id": "13867677",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "with"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "44796ffa",
-   "metadata": {},
+   "id": "a92987fc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n",
@@ -1131,8 +1046,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3336c40e",
-   "metadata": {},
+   "id": "baac08a1",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "the mean value of all observations in a specific node.\n",
     "\n",
@@ -1142,8 +1059,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce17050d",
-   "metadata": {},
+   "id": "62011b0f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Why binary splits?\n",
     "\n",
@@ -1155,8 +1074,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f368dbe",
-   "metadata": {},
+   "id": "68483628",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Computing a Tree using the Gini Index\n",
     "\n",
@@ -1179,8 +1100,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66ce0b1b",
-   "metadata": {},
+   "id": "58d114ab",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The Table\n",
     "\n",
@@ -1205,8 +1128,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f363c6b9",
-   "metadata": {},
+   "id": "48bfbac4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Computing the various Gini Indices\n",
     "\n",
@@ -1220,8 +1145,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5017e81e",
-   "metadata": {},
+   "id": "407c354e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Computing the various Gini Indices, Hours slept\n",
     "\n",
@@ -1232,8 +1159,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23c27590",
-   "metadata": {},
+   "id": "68ffb69c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Computing the various Gini Indices, Hours studied\n",
     "\n",
@@ -1246,165 +1175,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0238bc31",
-   "metadata": {},
+   "id": "249b41f6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A possible code using Scikit-Learn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "6d3ad401",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Trend</th>\n",
-       "      <th>Sleep</th>\n",
-       "      <th>Studied</th>\n",
-       "      <th>Grade</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Trend  Sleep  Studied  Grade\n",
-       "0      1      0        1      1\n",
-       "1      0      1        0      0\n",
-       "2      1      0        1      1\n",
-       "3      1      1        1      1\n",
-       "4      0      0        1      0\n",
-       "5      1      0        0      0\n",
-       "6      0      1        1      0\n",
-       "7      0      0        1      0\n",
-       "8      1      0        0      0\n",
-       "9      1      1        1      1"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 0 1]\n",
-      " [0 1 0]\n",
-      " [1 0 1]\n",
-      " [1 1 1]\n",
-      " [0 0 1]\n",
-      " [1 0 0]\n",
-      " [0 1 1]\n",
-      " [0 0 1]\n",
-      " [1 0 0]\n",
-      " [1 1 1]]\n",
-      "Train set accuracy with Decision Tree: 1.00\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 6,
+   "id": "2be74ca8",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -1453,6 +1240,7 @@
     "X = grades.loc[:, grades.columns != 'Grade'].values\n",
     "y = grades.loc[:, grades.columns == 'Grade'].values\n",
     "print(X)\n",
+    "# Then do a Classification tree\n",
     "tree_clf = DecisionTreeClassifier(max_depth=2)\n",
     "tree_clf.fit(X, y)\n",
     "print(\"Train set accuracy with Decision Tree: {:.2f}\".format(tree_clf.score(X,y)))\n",
@@ -1464,13 +1252,15 @@
     "    filled=True\n",
     ")\n",
     "cmd = 'dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png'\n",
-    "os.system(cmd)\n"
+    "os.system(cmd)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5bf4e701",
-   "metadata": {},
+   "id": "516b46c3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Further example: Computing the Gini index\n",
     "\n",
@@ -1511,36 +1301,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b161aa79",
-   "metadata": {},
+   "id": "454f2366",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Simple Python Code to read in Data and perform Classification"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "078a139d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train set accuracy with Decision Tree: 0.93\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 7,
+   "id": "387d844f",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -1595,9 +1372,9 @@
     "encoder.fit(X)    \n",
     "# Apply the encoder.\n",
     "X = encoder.transform(X)\n",
-    "#print(X)\n",
+    "print(X)\n",
     "# Then do a Classification tree\n",
-    "tree_clf = DecisionTreeClassifier(max_depth=5)\n",
+    "tree_clf = DecisionTreeClassifier(max_depth=2)\n",
     "tree_clf.fit(X, y)\n",
     "print(\"Train set accuracy with Decision Tree: {:.2f}\".format(tree_clf.score(X,y)))\n",
     "#transfer to a decision tree graph\n",
@@ -1607,14 +1384,16 @@
     "    rounded=True,\n",
     "    filled=True\n",
     ")\n",
-    "cmd = 'dot -Tpng DataFiles/ride.dot -o DataFiles/ride.png'\n",
+    "cmd = 'dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png'\n",
     "os.system(cmd)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e47d975e",
-   "metadata": {},
+   "id": "ca2917fd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Computing the Gini Factor\n",
     "\n",
@@ -1628,74 +1407,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "47e6cb0e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 1.000 Gini=0.394\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X1 < 1.000 Gini=0.394\n",
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X1 < 0.000 Gini=0.408\n",
-      "X1 < 1.000 Gini=0.394\n",
-      "X1 < 1.000 Gini=0.394\n",
-      "X1 < 2.000 Gini=0.394\n",
-      "X2 < 0.000 Gini=0.408\n",
-      "X2 < 0.000 Gini=0.408\n",
-      "X2 < 0.000 Gini=0.408\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 2.000 Gini=0.407\n",
-      "X2 < 2.000 Gini=0.407\n",
-      "X2 < 2.000 Gini=0.407\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 2.000 Gini=0.407\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X2 < 0.000 Gini=0.408\n",
-      "X2 < 1.000 Gini=0.407\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X3 < 1.000 Gini=0.367\n",
-      "X3 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "X4 < 0.000 Gini=0.408\n",
-      "X4 < 1.000 Gini=0.405\n",
-      "Split: [X3 < 1.000]\n"
-     ]
-    }
-   ],
+   "execution_count": 8,
+   "id": "b7d31788",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Split a dataset based on an attribute and an attribute value\n",
     "def test_split(index, value, dataset):\n",
@@ -1761,29 +1479,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "277cae80",
-   "metadata": {},
+   "id": "91ed45c3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Another example, the moons"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "34a3f693",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1100x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 9,
+   "id": "68d548c2",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from __future__ import division, print_function, unicode_literals\n",
     "\n",
@@ -1853,29 +1565,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad6c5f89",
-   "metadata": {},
+   "id": "50cf5c83",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Playing around with regions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "89770dc2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1100x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 10,
+   "id": "3ac8c09c",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "np.random.seed(6)\n",
     "Xs = np.random.rand(100, 2) - 0.5\n",
@@ -1901,17 +1607,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d2ec3db",
-   "metadata": {},
+   "id": "efce1a5d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Regression trees"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "72026bb5",
-   "metadata": {},
+   "execution_count": 11,
+   "id": "3d01a515",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Quadratic training set + noise\n",
@@ -1924,21 +1635,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "38fbe6f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DecisionTreeRegressor(max_depth=2, random_state=42)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 12,
+   "id": "93eb18bd",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.tree import DecisionTreeRegressor\n",
     "\n",
@@ -1948,29 +1651,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc4785b1",
-   "metadata": {},
+   "id": "1037deae",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Final regressor code"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "54b15ed3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1100x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 13,
+   "id": "28d2785f",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.tree import DecisionTreeRegressor\n",
     "\n",
@@ -2014,21 +1711,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "92e9f134",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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2NDTY5yxlzZo1WLp0KX7++WdUVlbKcrS13gc15syZQ/2SCa9CwpYgdHL//ffj3//+N55//nnk5uaq7iMUuhDyYJUIVQGrq6t1v67auU6cOAGAb0X02WefaR576tQpAEC3bt2wZcsWzJ07F1988QU++OADAHxxrHnz5mHs2LG656M1Jy0Egdi5c2e3XkMvzfmfC3lAUkJC+Mui1FC/+uqr6N69O1asWIFnnnkGzzzzDCIiInDLLbfgpZdeouqhBEEEDMrcUEC8rjnbJogYZ6hdM4VzKIvu6SE2NhZbtmzB7NmzsXbtWnz++ecAeKE8a9YsTJs2zb7vfffdh3//+9/o0qUL/vGPfyA1NRXh4eEA+MJEdXV1qq+h9Tc7u/6r/S8SExNhMDjWXBVsjyu7Ltjt//73v073E+z24MGD8e2332LBggVYtWqVveDSgAED8MILL2DEiBFOz6OHyspKMMZw+PBhzJ071+WcAOCll17Cww8/jKSkJFx55ZUwmUyIjIwEALzyyiua70MgILznWu9VTU2N5mecCA5I2BKETiIjIzFnzhxMmTIF8+fPx5gxYxz2EQzosWPHVM8hjKsZWi3Umo0Lx7/22mu45557dJ2nf//++Oijj9DQ0ICffvoJX3zxBV599VWMGzcOnTp1wtChQ5s1Jy0EI6G2Au4NfPE/VxIaGopHHnkEjzzyCI4cOYLvv/8eb731Ft5++20cPXoU69ev9/jcBEEQbZmuXbuioKAAVqsVO3fuxFdffYVXX30V06dPR3x8PMaPH4+ysjK8/vrr6N+/P7Zs2SKLzjl69KhTUeYtKioqYLPZHMStYGNcCSLBBq1duxajR4/W9ZrDhg3DsGHDcObMGWzbtg1r167Ff/7zH1x77bXYuXNns1vrCXMaMGCAajVoJY2NjZg3bx46deqEHTt2ICkpyb6NMYbnn3/erdf/7rvv7AWd9HDBBRfg+uuvd+s1pAhRY8XFxRgwYIBsW2VlJY4fP44hQ4Z4fH7C/5CwJQg3uOuuu7Bo0SK8/vrrOP/88x22C2FeP/zwAxhjMgHIGENhYaFsP08Rqh1v2bJFt7AVEMKpLrnkEvTo0QN33nkn1q1bZxe2QtVnq9XqsgK0HqKionDeeedhz549KC4udhmO7O7r9+rVCxERESgqKsLp06cdwpGFCofeqsLYqVMnjB8/HuPGjUOvXr2wYcMGnDlzxr5iTRAEQbiP0WjEBRdcgAsuuACDBw/GZZddhk8//RTjx4/H33//DcYYrrjiCodrvGBXfU1DQwO2bt3qIHz02nWp3dYrbAUiIyMxfPhwDB8+HHFxcXj66aexYcOGZgvb6Oho9O7dG7t370ZVVRXi4uKc7n/8+HFUV1fj8ssvl4laAPjxxx/tLaX08t1337m1KJGdnd0sYTts2DAsWLAAX331FW699VbZtq+++sq+DxG8UB9bgnADo9GI+fPno66uDv/6178ctqelpWHEiBH29j5S3nzzTfz+++8YOXKk7vxaLQYOHIhBgwZh1apVeP/99x2222w2Wcn6oqIih/5ygLjSLBVlCQkJAACLxdKsOUqZPn06rFarrJeiwNmzZ+0hWp68flhYGMaPH4/jx49jwYIFsm0bNmzAF198gR49erjlkZZSV1eHb7/9Fowx2fipU6dQW1uL0NBQrywAEARBtDV27dql2r9VaZvS09MBAJs3b5aFPFssFq+1vdHDU089JQtT/vPPP/Hmm28iNjYW1113ndNjr7vuOqSlpWHRokWq/c8bGhrwf//3f/bnhYWFsj6+Amp2uzncd999OH36NCZPniwLORbYv3+/vU99cnIyIiMj8fPPP+P06dP2fSorK3Hvvfe6/dpz5swB4wvZ6vppbv/byy+/HN27d8fKlSuxY8cO+3htbS3mzZuHkJAQTJgwoVmvQfgX8tgShJvccMMNGDx4sGahjCVLluDSSy/F5MmTsXbtWpx33nn4448/8OmnnyIpKQlLlizxyjxWrVqFESNG4NZbb8Urr7yCAQMGICIiAgcPHsSWLVtQXl6Os2fPAuBzev7zn/9g+PDh6NGjB2JiYvDHH3/g888/R4cOHXDXXXfZzzty5Eh8+OGHGDt2LK655hpERESgX79+uPbaaz2e6913343vv/8eq1evhtlsxj/+8Q/ExMTg4MGDWL9+PfLz8+2rsJ68vtDH8JlnnsHmzZsxaNAgex/bdu3a4a233lLNjdLDmTNn7MZw0KBBSEtLw8mTJ7Fu3TocPXoUM2fO1F1IiyAIghDZsGEDHnroIQwdOhS9evVCYmIi/v77b3z66aeIjIy0RySlpqbipptuwkcffYSLL74Yl19+OY4dO4Z169Zh5MiR+Pvvv30+19TUVFRVVeGCCy7Atddei+rqaqxatQpnz57FsmXLEB0d7fT48PBwfPjhh7j66qsxbNgwXH755ejbty8A4ODBgygsLERiYqK9UNNLL72Er7/+GiNGjED37t0RERGBn3/+Gd988w169OiBG264wSt/V25uLrZu3YqCggJs2rQJV1xxBTp16oRjx47hzz//xLZt27By5Up07doVBoMB06ZNw0svvYTzzz8fY8aMQU1NDb744gukp6ejU6dOXpmTO0iFaGlpqcPYiy++aK+DERISguXLlyMrKwuZmZkYP348YmJisGbNGuzfvx/PPPOMx0U1iQChpcswE0QwoGz3o+SHH36wl59X9rFljLGSkhI2ceJElpqaykJCQlhqaiqbOHEiKykp0T0Hoe2M0IJBjRMnTrAnn3yS9e3bl0VGRrKoqChmNpvZbbfdxtasWWPfb+vWrSw3N5f17duXxcXFscjISGY2m9l9993n0HqooaGBPfrooywtLY2FhITISuwL/xdnJffV2gkwxrcCWL58ObvkkktY+/btWbt27ZjZbGZTp06VzcHZ6zOm3seWMb4H7X333cfS09NZaGgo69ChA7v55pvtLY+kOPvfCu0HNm7cyBjjew8+99xz7Morr2Qmk4mFhYWxlJQUNmzYMPbee+9p/h8IgiBaEmdtVdTazwgor3nO2v2oXdsZc92mTYs//viDzZgxg1144YUsMTGRhYeHs+7du7MJEyawP/74Q7ZvbW0te+ihh1jXrl1ZeHg4M5vNbN68eay+vl7VLjibk7O/Re1cwv4VFRVs0qRJLDk5mYWHh7OLL75Ytc+ss/+3xWJhM2bMYGazmYWHh7OYmBjWu3dvNmnSJPbNN9/Y9/vyyy/ZnXfeyc4991wWHR3NoqKi2HnnnceefPJJr/axFXj//ffZFVdcweLj41loaCjr3LkzGz58OHvppZdk/e7r6+vZs88+a59/Wloae/DBB1ltba3q+Z19Lr0BVNoCSX/U7Py2bdvYVVddxWJjY1lkZCS7+OKLHfogE8EJx5givo4gCIIgCIIgCACwt0ESQnIJgghMKMeWIAiCIAiCIAiCCGpI2BIEQRAEQRAEQRBBDRWPIgiCIAiCILyC3t6kze1JSgArVqzQFR59/fXXe63lHUEEMiRsCYIgCIIgCK+gtzdpc3uStiSBmlu7YsUKWWs/Lbp27UrClmgTUPEogiAIgiAIgiAIIqihHFuCIAiCIAiCIAgiqKFQZJ3YbDYcOXIE0dHR4DjO39MhCIIg/ABjDLW1tejUqRMMBlob1gvZUIIgCMLXNpSErU6OHDmCLl26+HsaBEEQRABw6NAhmEwmf08jaCAbShAEQQj4yoaSsNVJdHQ0AP6NiPniC2DKFH5Dz57AX3/xj599FuyJJ6Bci26EAce/3YWOAzq33IQJgiAIr1NTU4MuXbrYbQKhD5kNjYnR3O/wYaBPH0Ba/cNgAHbtAjqTCSUIgghqfG1DSdjqRAidiomJQUxoqLghLk58XF6ucbQNB389hpgRvX02P4IgCKLloHBa95DZUCfC9qef5KIWAGw24NgxoDeZUIIgiFaBr2woJQh5QkOD+Fi64hAXB6byRjXCiJShPVpgYgRBEAQRvJjNvIdWitEI9CATShAEQbiAhK0naAnbiAhwzz4r29UGDluylyI1g3KxCIIgAh6LBVi9GliyBJXzl2DX06tRWmTx96zaDCYTkJfHi1mA/710KT9OEARBBA8WC7BxI/+7paBQZE+orxcfS4Xt2bPA+PHA44/bh05Ovh+ZeTktODmCIAjCI/LzgUmT7E/jm35s8zgUZi9D5gq6lrcEOTlAVhawdy/vqSVRSxAEEVzk5/PliGw2PgonL4+/tvsa8th6gpbHtq4OsFplu8aktG+hSREEQRAeY7EAkyerbjKAYXBBLnluWxCTCRg+nEQtQRBEsGGxiKIW4H/n5raM55aErSdoCduzZx2ELRobW2ZOBEEQhOcUFztWLZIQAiuObdrbghMiCIIgiOCjuFgUtQJWKx+F42tI2HqCM2GrfCdJ2BIEQQQ+ZjPgpEojFQEkCIIgCNf4swggCVtPcCMUmYQtQRBEEGAyAa+8orqJigASBEEQhD78WQSQhK0nOCseRcKWIAgiOBk/XnW4/H+bqHAUQRAEQegkJwcoKeGrIpeUtEzhKICqInsGhSITbsIYQ0NDA2zKzwfhFwwGA0JDQ33WIJwIUjSu1yn9Ulp4IgRBEAQR3JhMLV8AkIStJ2gI25rf9qN+Zyk6aO1LtDmsViuOHz+O2tpaNNBnIaAIDQ1FdHQ0OnToAKMQL0O0bZQRN67GCa9hsfAFR8xmqoRMEARBeAYJW0+QCpTvvrM/jNn7C2yTbpDvSx7bNovVasWhQ4dQV1eH2NhYREVFwWg0kpfQzzDGYLVacfLkSVRVVeHMmTPo0qULiVtC+3pNwtan+KvfIUEQBNG6IGHrCRJhy557DlKZYoCiXQQJ2zbL8ePHUVdXh7S0NERGRvp7OoSCqKgoxMbG4uDBgzh+/DhSUijctM2jdb2mFAKfodXvMCuLPLcEQRCEe1DxKE+QFI/inPQ9BEDCto3CGENtbS1iY2NJ1AYwkZGRiImJQW1tLZir7zLR+qFQ5BbHn/0OCYIgiNYFCVtPkHpsXYWVkrBtkzQ0NKChoQFRUVH+ngrhgujoaPv7RbRxKBS5xfFnv0OCIAiidUHC1hMkN8DcCy/AJglGdvD5kLBtkwjVjylvM/AR3iOqWE2QsG15/NnvkCAIgmhdBLywra2txaOPPoorr7wSSUlJ4DgOc+bM0X18WVkZJkyYgA4dOqBdu3YYPHgwvvnmm+ZNSurZufNOGL5ab39qTUmV70vCtk1DhaICH3qPCDtaAjaIFz0C0oYq8Fe/Q4IgCKJ1EfDCtqKiAnl5eairq8P111/v1rF1dXW4/PLL8c0332Dx4sX45JNPkJKSgquuugrff/+955OS5NgiNBTo1Mn+NATUx5YgCCIoaYUe24C0oSqYTMDw4eSpJQiCIDwn4Ksip6eno7KyEhzH4fjx41i+fLnuY/Pz87Fr1y5s3rwZgwcPBgCMGDEC559/Ph599FFs27bNs0lJPbZhYUCI5N8oFb0ACVuCIIhgoRUK24C0oQRBEAThAwLeY8txnMehgh9//DHOPfdcu0EGgJCQENxxxx3Yvn07Dh8+7NmkpMI2NFRMDgJI2BIEQQQrrTAUOSBtKEEQBEH4gIAXts1h165d6N+/v8O4MPb7779rHltXV4eamhrZjx2psA0JIWFLEATRGmiFHtvm4DMbShAEQRA+oFUL24qKCiQkJDiMC2MVFRWaxy5YsACxsbH2ny5duogbBWEbEgJwnFzYKluGkLAlCJ+xe/duzJkzB0uWLPH3VIggpbTIgl8WbURpkYWErQKf2VCCIAiiVWCx8IX/LBZ/z4SnVQtbwHnFU2fbZs2aherqavvPoUOHAACHDwOnyk8JJ+DfSWctXUjYEoRPaGxsxB133IG5c+di2rRp+Pjjj/09JSLI2D72BaQMTMOFD41E8sB07Hz+c/Ud26iwBXxjQwPpJoggCILwjPx8ID0dGDmS/52f7+8ZtXJhm5iYqLqifOLECQBQXYkWCA8PR0xMjOwHABae9zbaHdrD79TQAKSlAatXa09C6cElCMIrLFy4ED///DPmz5+P7t274+6777Z/twnCFaVFFmR8+CgMTd3HjbDhvA2vijtIiwIGcY5tc/CFDe3TJ7BuggiCIAj3sViAKVNE82izAbm5/l+0bNXCtl+/fti5c6fDuDDWt29ft8+5GPdCtkbNGPDww9oHkMeWILzOrl27MG/ePNx8882YNWsWVq9ejcrKStx7773+nhoRJBwtLIbS32iUtmsLCxMft1GPrS9sKOPXEQLmJoggCIJwn+JixzVfqxXYu9c/8xFo1cL2hhtuwJ9//ilrSdDY2Ih3330XgwYNQidJ/1m9qP7DnK3mk7AlCK9itVoxceJEmEwm5De5fAYMGICXXnoJK1euxCeffOLnGRLBQMdMs8OYVXqFDw+XbGibwtYXNlRKINwEEQRBEO5jNgMGhSgyGoEePfwzH4GgELZffPEFPvzwQ6xduxYA8Mcff+DDDz/Ehx9+iNOnTwMAcnJyEBISggMHDtiPu+uuu9CnTx+MHTsWK1euxIYNG3DLLbdgz549eO655zyaC1MbVL6zUkjYEoRXMRqNKCoqwr59++zhjQBwzz33gDGG6667zo+zI4KF1AyT7LkVBvx12SRxQCpsgzwUOZBsqJRAuAkiCIIg3MdkAvLyxDJDRiOwdCk/7k9CXO/if+6++26Zsf3ggw/wwQcfAAD279+Prl27wmq1wmq1gjFReoaHh+Obb77Bo48+invvvRenT5/GBRdcgC+++ALDhg3zaC4H0QX9cEgcMBiAf/8bmDZN/QAStgRBEAFPxfsb0LvhCPBDHj/QikKRA8mGGgz8OkGg3AQRBEEQnpGTA2Rl8ZE3PXoExvWcY1IrRmhSU1OD2NhYVHTphoRD+2GNbA9jwVvA4MFAUhIQEaF+YKdOfBlIok1x9uxZ7N+/H926dUOE1meDCAjovWrDSKv6/vYb8MsvQHY2/7xHDzFO9v33gVtuASDagurqalnEAOEc4f/2xx/VOHYsJmBuggiCIIiWw9c2NCg8toFEyMkqAIAxNQUYO5YfdOaVbYbH1mLhk7PNZroBIAiC8CkNDfLrdSsKRQ4kOncGevdumdciG0oQBNG2CIoc24Ciupr/HRcnjvmgj20g9oYiCH9w7NgxcBwHjuOwfv16p/vec8894DgOQ4YMAQWjEE5Rfj4aG+Uhx60oFLktQjaUIAii7UEeW3cRVu6lwpbj+B+1G2kPhK1Wb6isLFp1JtoeKSkp6N69O/7++29s27YNWVlZqvv9+uuveOONN2AwGPDaa6+B45TNXAhCQn29/Hljo/x6TcLWN7z4IpCaClRWAmVlQHIyPy48jo93vi0xERgyxKkxJBtKEATRNiFh6ylSYQsAISF8KJsSD4Sts95QZJSJtsjQoUPtwlaLe++9F1arFVOmTMGAAQNacHZEUHLqlPy5s1BkErbeY9685p+D44Bly/jKJSqQDSUIgmibUCiypyiFrVY4sgfCNlB7QxFBisUCbNzI/w5ShgwZAgCawvbdd99FYWEh4uPj8eyzz7bk1IhgRSlsnYUiU45tYMEY74LVuKaRDSUIgmibkLD1FB8K20DtDUUEIa0k0Wzo0KEAgIqKCuwVKtU2UVtbi0cffRQAMG/ePHTo0KHF50cEIeSxDW4EF6wKZEMJgiDaJhSK7CE1JznIilRrCVubjf9RLh+7IBB7QxE+4OKLgaNHfXNuq1V+bpsNmDQJePJJ5wXPmkPHjsCPP3r9tH369LGXh9+2bRt6SFwvc+fORWlpKfr374+pU6d6/bWJVoqax5ZybIMHFy5YsqEEQRBtDxK2HhKd9xIK63ojc0VTjk+Ik39lY6P8JkknJhMZ41bP0aMt3+fYV0LahxgMBgwaNAhfffUVtm7dittvvx0A8Oeff+LVV18FALz22msw+kqwE60PNY8thSIHBxynywVLNpQgCKJtQcLWQzgAgwtyUTo9C6kZJtctfzwQtkQboGNH351b6bGVvqYvPbY+YujQofjqq69kebb33nsvGhoacNttt+Gyyy7z2WsTrRDy2PqFp/E0ziAVcahECsoxcWYS4mMBlJcDSUliVWThOZq2ffcd8Msv/PMNG/j0CoIgCIKQQMK2GYTAimOb9uoTtgShhg/CdmXk5/NFVqxWMdFMo5JooCMUkPr1119RV1eHdevWYcOGDYiKisLzzz/v59kRQcfJk/LnyhxbErY+YTEeAiSJPBddBQwfruPAKVNEYUt59ARBEIQKJGybQSOMSBnalONDwpYIRFpRotkll1wCo9GI+vp6bNq0CQ899BAA4Mknn0Tnzp39PDsi6HBVFZmKR/kctyoVh4aKj9Va6xEEQRAtisXCt1czmwPn9pKqInuIFQZsyV7Ke2sB1zm2BOEvTCbeJRIoVx0PiYqKQr9+/QAAOTk5OHDgAMxmMx544AE/z4wIRva+8bXs+V9LN2p7bCnH1msIdRTdrlQstbFkUwmCIPxKoDbdII+tm1Te/zQOxfZFwrWDkZkhscjksSUInzN06FDs2LEDJSUlAIDFixcjjPLXCTcpLbKg+7ZVsrFzNr+Nk+l3IUoYoFBkn7BrF3DsmAcBJOSxJQiCCAgsFj47RFjztdn4rLesLP/7UMhj6ybxcx9CnzljRU+tAAlbgvA5Qp4tAIwZMwZXX321H2dDBCtHC4thAJONGWHDGcsJcYBCkX1C584eBpCQx5YgCCIgKC52DGRy0lq8RSFh6y1I2BKEz4mMjAQAhIeH4+WXX/bzbIhgpWOmGTZwsjEbOLRLjhIHpMKWQpH9D3lsCYIgAgKzWUwrEXCrZoIPIWHrLUjYEoRPsVqtmDNnDgDgkUcewTnnnOPfCRFBS2qGCYf7XCkbK7n4ZrRPjBAHKBQ5sCCPLUEQREBgMgF5eaL0cbtmgg8hYestnBSPOvD0chxbV9SCkyGI1serr76K3377DV27dsWsWbP8PR0iyOkywix73v3GC+UCloRtQGCxABs3AtWnyWNLEAQRKOTkACUl/PW5pCRwOkmSsPUWTjy26e+/gOQxA7H7/LH4ZdFGlBZZWnBiBBH8rFq1CjNnzgTHccjLy0O7du38PSUi2Kmrkz9vbJR7AinH1u+8+KJYdfPZF0Rhe/wYeWwJgiD8TSA23SBh6y2chSID4AD0+u1DXPjQSCQPTEfhhACpi00QAcpnn32Grl27IjY2FrfddhsaGhrw5JNPYtSoUf6eGhFElBZZ1BcU1YStlseWcmxbnBdeAB55RPzXNzAxKure3IaAaS1BEATRmhGiZixB4pMjYestXAhbAPZSJUbYMLgglzy3BOGETZs24cCBA2hsbMSFF16I5cuX41//+pe/p0UEEYUT8pE8MF19QVEpbBsatPvYkse2RbFYgJkz5WMNED22BtaI3NzgudEiCIIIRgK1V60zSNh6Cyc5tqq7w4pjmwKgLjZBBCjz588HYwynTp3Czz//jJxASeAggoLSIguGFEyBEbzLz2FBkUKRA5biYoDJuzGhEaKNDUVDwLSWIAiCaI1o9aoN9AVFErbeQofHVkojjEgZGgB1sQmCIFohRwuL7aJWQLagqOaxpVDkgECtlYTUYxuCxoBpLUEQBNEaCeRetc4gYestVIStVePfa4UBW7KXIjUjgLKtCYIgWhEdM81QOP3kC4rksQ1YlK0kDAbgwotFj2041xAwrSUIgiBaI4Hcq9YZJGy9hYqwPf7JJpQPvd5h/MS7XyBzBYVVEgRB+IrUDBNOxYvKhwHyBUXKsQ1opK0kDhwA7nlA9Ng+O6chYFpLEARBtEYCuVetM0jYegsVYZtykQlJVw1wGE/qGd8SMyIIgmjTRPXpan/ccG4f+YIiVUUOeIRWEgCwa4/osY2LonY/BEEQviZQe9U6g4Stt1ArHmUwoPjzYsfxNWt8Px+CIIi2TnS0/WGYrV6+zZXHlkKRAwKhKudT/xI9tmho8N+ECIIg2hCB2KvWGSRsvYWKx/bob8dwzpa3HcbZ888HflkxgiCIYCcqSnxcWyvfdvas/Lkyx5ZCkf2OtCqntCpydQV5bAmCIAhHSNh6CxVhW150QPUfzNlsgV9WjCAIItiRilOlsFV4bCt/+Rv1Bw6JAydOiI9J2PoFaVVOaVXkyjLy2BIEQQQKFgsfrhwIPjsStt5CRdh2uOQcWME5jDPOEPhlxQivwJTNGImAg96jVoxUvJ46hcqZ84HVq3nrqxC28bu3IuzAfnHg8svFx5Rj6xekVTmlHtuEGPLYEgRBtBTOhKuQLjJyJP87P7/l5yeFhK23UMmxTR2Yhs3ZyxzELXf31OAJVic8wtB0N2YlT0/AI7xHBmVdeyL4UYrX558Axo0D0tKAmhrnx0oXPOh77BekVTmlHtuYSPLYEgRBtATOhKs0XQTgf+fm+tdzS3dy3kLFYwujEZkrclC2/SBKr7xTHL/sspabF+EXQkNDERoaipMnT/p7KoQLamtr7e8X0bo4e+iY+gbGgDNn9J+IhK3fEKpyvvyqZPG4kTy2BEEQvsaVcJWmiwhYrf7NtiRh6y3UhG2TByg1w4TUfwwUx1UqOh5bV4TiaYtwbF2Rr2ZItCAcxyE6OhrV1dU4484NNNGinDlzBjU1NYiOjgbHOaYNEMENO1runRNRKLJfMZmAiwY5r4ocSDleBEEQrQFXwlWaLiJgNPo321KlRw3hERoeWzvSIib18rYT+wbeiu5F7yMFAFsC/DUkGz03rfDJNImWo0OHDjhz5gwOHjyImJgYREdHw2g0koDyM4wxWK1W1NbWoqamBuHh4ejQoYO/p0V4wLF1Raj5vBAx12QiZXSGw3ZjVITnJ+c4MRyZPLb+J0TbY5ufL3oVDAY+fDkY+i0SBEH4E4uFF69ms3qGpCBcpeJWKlyFdJHcXN5MGo3A0qX+zbYkYestnHhsAQDSMEeJsD22rgjdi963Z+FyAMybC3Bs3XTVGzUieDAajejSpQuOHz+O2tpaVFVV+XtKhITQ0FDExcWhQ4cOMKp9f4mA5q+hE2DeXOB0QTAskjdxDFAp4+fIyZuzEXX5JUBiIm/RL7yQ30DC1v+EqntstULlsrKolAVBEIQWehYE9QjXnBz+ert3Ly94/X3dDQphe/LkSTz55JNYvXo1Tpw4gV69euGxxx7Drbfe6vLYjRs3Yv78+fj1119x+vRpdO/eHZMmTcL06dO9ezOrUjxKj8e25vNCpCgO4wDUfrmJhG0rwGg0IiUlBcnJyWhoaICNQhoDAoPBgNDQUPKeBynH1hXBvLnA9YJgU/Eo1j4aB6+dgvTVL/HjF10E/Pyzw3mjJt4CXHMN/6SiQtwQ5MI2KGyoKzQ8ts5C5fx9g0UQBOF3ioqAtWuBiAggPh6orETNvjLsfTMZjwBIRhnKbMn4aVI8bvqrEnF1ZUByMn9sWRlykpNx00NAzd4yRPdIRnx9PDC/EigT9zOVlcGUnAzs5s8v3WZ/3PTaOHRIfZ5eIiiE7Y033oiioiIsXLgQPXv2xMqVKzF+/HjYbDbcdtttmsdt2LABWVlZuOyyy7Bs2TK0b98en376KWbMmIF9+/Zh8eLF3pukK4+tVNhKVptjrskEWyL3JjAA0VcN9d7cCL/DcRzCpJ8BgiA8RveC4NmzAABDdHukr3wO+GARH15crpF7m5QkPpZe04N8QSoobKgrNDy2rkLlCIIg2iwTJgAFBQ7DMQAWqO3/vPpp4pp+ggIW4Hz22WcMAFu5cqVsfNSoUaxTp06ssbFR89jbb7+dhYeHs5MnT8rGr7zyShYTE+PWPKqrqxkAVl1drb7DjBmM8bdM/I/BIN/+v/+J2+bPl206FZMiO3bPkGy35kYQBNGWOLp2O7NJr7cAswHs6Nrt8h2Tk/ntXbvyzxMT5ddp5c/+/eKx1dXi+KhRkmEXtiDACBob6or9+8X3Y9w42ablyxkzGvlNRiP/nCAIok2zfbtze+enn2ref+czGxrwVZE//vhjREVFYezYsbLxiRMn4siRI9i2bZvmsaGhoQgLC0NkZKRsPC4uDhERzSgqoobSY6t87qR4VLt+8qXlnoVvenNmBEEQrYqU0RloDBWv4QxA8ZBsx/SNJo8thOu9EBqlhZbHNohDkYPGhrpCw2MLiC2BNm7kf1PhKIIg2jyFhf6egV8IeGG7a9cu9O7dGyGKHNb+/fvbt2sxdepU1NfX47777sORI0dQVVWFd955Bx9//DEeffRR705UmWPrTNgqWxUon+/bh8r5S7D/zqep/Q9BEIQKoSFiAoe1Q7J6JXmlsJUKVxUKp78nPmklochBY0Nd4aQqMsDn0w4fLubVUvsfgiDaNJmZ/p6BXwj4HNuKigp0797dYTwhIcG+XYtBgwbh22+/xdixY/H6668D4Iv5LFiwAA899JDT162rq0NdU+ERAKipqXE+UaWQVTZ20qiKDMBB2LKePREPIB4Ae2des9r/lBZZcLSwGB0zzUjNoEoaBEEEF6otfRoaAEl/6BCOOR7ImHitDQ/nf5865fS1BhfkonR6Fn+tbCUe26Cxoa5w4rFV4s32P67aYRAEQQQkGRlAQgJw4oS/Z9KiBLywBeC0cqmzbT/99BNuuOEGDBo0CEuXLkX79u3x7bff4sknn8TZs2fx1FNPaR67YMECzJ07V/8kmxGKrDTSnOKxp+1/CifkY0jBFKTCBisMKMzOQ+YKitEiCCI42DvoNpyzfZVjS5/qavmOgmdWikRUISKCVyg//eT09UJgxbFNe3lhK12cDGJhCwSJDXWFC4+tgDfb/1B/XIIggpnjUenocOIEGsDhKTyL0bfH49I+lXwBRSGCSXgsVC1W2yZ5XIl4PPd4JZJQjjLw25KbHlchHo9OqsQ50U7Ob7EA//mPz/7mgBe2iYmJqivKJ5pWIIRVZzWmT5+OlJQUfPzxx/a2BCNGjIDBYMCcOXNw++23q65kA8CsWbPw4IMP2p/X1NSgS5cu2hP1orBV4kn7n9IiC4YUTIERvHU3wib3RhAEQQQwx9YV4Zztq9Rb+vRWXPfVhK10LCKCd7u5oBFGpAxtqnnQSoRt0NhQV+j02Hqr/Q/1xyUIIpixWIATBxvRAUA9IvEcZuHF9/g6BM25hu3YCDynsc1oBJ6aDcDZ+WtqfCpsAz7Htl+/fti9ezcaFSu0O3fuBAD07dtX89gdO3ZgwIABDr32MjIyYLPZsHv3bs1jw8PDERMTI/txijLHVhmK7CzHVil0FXjS/udoYbFd1Nqn2OSNIAiCCHRqPi+E0pcoLPKhqkq+oaHBUXwqha3Z7PT1GmHEluyl4sIfx4nX8SDOsQ0aG+oKnR5bof2PFE/a/zgTyARBEIFOcTEQAv5aaQV/DffGNUztGgvwY0uX+n/hL+CF7Q033ICTJ0/io48+ko0XFBSgU6dOGDRokOaxnTp1wo8//gir4oZny5YtAACTN//7Sg9tQ4O8aoUbObZSNKt9uqBjphk2xW2hzBtBEAQRwMRckwll5qx9kU8ZigzIQ4+VzyMieGu7fLnDYTYAGzMeRvn2EsdUDcF6B7HHNmhsKFwUfJIKWyc202TiQ4YFk2w0enaz5S2BTBAE4Q/MZsAI/trd2BSg641rmNo19uGHgQMHAiRVwydNhLzMqFGjWHx8PMvLy2Pffvstmzx5MgPA3n33Xfs+d911FzMajaykpMQ+9uqrrzIA7Oqrr2b/+9//2FdffcVmzpzJQkJC2BVXXOHWHFz14Nt30U2O/Zo4Tmyo9/ff4vitt8oPTknR7PfUkNTRrXlK2Z05WXauH7KpuR9BEMHDmfYJsj619h7fa9Y4Xi8rKmTHli9ZLW676SZxw6FDjC1ZwqonPcD+fGAJO7L9kPYEwsP5488/3z4UbH1sGQsOG7p8Od/+XWgDr9qLVmhWO2CAy9c7dIixjRv5355C/XEJgghmqpO6MwawY0hy+xp26BBj336rfQ319BrraxsaFMK2traW3Xfffaxjx44sLCyM9e/fn61atUq2T3Z2NgPA9u/fLxv/6KOP2KWXXso6dOjA2rdvz/r06cPmzZvn0HDeFc7eiCPbDzGrVjNio5F/1y0W9ZssxhhLSNAUtiwpya15ynj3Xfm5CIIggol+/ezXL2tkO35s+3bGxoxxvFYePmw/bM8l/2Q2yTYb4JkqadeOP0e/fvahYBS2gW5DDx0SRa3SdMqIiOA39u/v1ms3B28IZIIgCL+QlsYYwM4mdHS4hjkTrroWGj3E1zaUY4yp9EkglNTU1CA2NhbV1dUOuUK/LNqICx8aqX3wxo3AeecBKSn88zFjgE8/FbfHxAC1terHtm8PnDzp2aTffRf45z/F5/RWEwQRoKi2JjvvPEDI4wwJAW6/HSgoUD/Bvn1ARQWOzXsDyWvfdMjPZQYDuAMH3IpJtbWPguH0KTQmdUTI3KeBykrUHDqE2DfeULUFhDbObOjGjcBIFRO6cSPfm9ZO+/bA6dNAx47A0/z7gbIyIDmZ3y48Fqpvam0DgMREYMgQ/yeEEQRBeAHV1mQmE3D4MP/70CH7vs4qvhcVAYMGySWD0ehZ0Sm1OTmzBd4g4KsiBwMdM82wgoPRISMMYkC7p1WRT5/mP11OWjJo4g8ha7EAmzejcm8FDp9NROKYIVSFmSAIp2i2JpNeGxsbtUUtgLO3TkB4USFSNLZzNptbpXELJ+Tj0tN839uQ8qPAtGl6/xzCTYR8VmmxJodcsPx83h4CwFEvvR8cByxbFiCJYXKofy5BEHrRFKpCoT1JHSBnFd/Xr+e3KeWDJ5Xl/dUuLeCLRwUDqRkmbM5eBqvSRyAtEeasKrIzYcuYY1EUvbS0sM3PB9LSgHHjEP/ENPSdNw4pA9NQOCG/ZedBEETQoNmarMjitPqtkvAixyrKUpjBoLtqBj+nyU7PR3gPlwWfLBZg8mTvvzBj/B2darUq/5GfD6Sn817s9HT+OUEQhBpaQtVigWhDJcX3tCq+b9kiP48Ud4tOOZ2TjyFh6yUyV+SgbPtB/D57NSrnLwFWr5aXCNPy2NpsrituCqvU7tKSwla48VC8pgFMvEklCIJQ4LQ1mRvC1qmoBQcuL0/3cjM/J0rdaElycvhQt40b+d+ylf3iYt/ZswDr4ePPG0KCIIIPp63JBH0hEbZaFd8ZUxe1nrTx8We7NApF9iKpGSakZoxV3yhtByQVts68tQKnTwMJCe5PqCWFrZMbD+EmlUKSCYJQIrQmM0iEpL012XP6ha0aDMChu+cj7fF/umWVnaaXED7DZNJ4m8xmPmzYFzYtwHr4OLshpJBkgiCUOE3lUAlFFiJkcnP5a4sQITNkiON5DAZg61Ygw72Oo/rSS3wEeWxbCo4TvbaeCFsJpUUW/LJoo2svaEsKW+HGQwXqn0sQhBapGSbsHXKnbGxL9lJ+IUzr+titGypnzsexYRoLiU3UtYtH2n9mua0INNNLCP9gMvG5sJ7UmnAC4zxwRfgY6p9LEIQ7OE3laPLY1rMQWY9wtQgZtfPk5bkval3OyceQx7YlCQkB6utxxnIcVUUW5zduUiTCtvDOPAx5527HIitqqMUU+AqTCXj0UeC55+RTAIct2UuRSd5agiA06HlXJrBZLAxlv6ZphSJffDHiF84Cnn8e+P4DzfNGdIz3eE6ZK3JQOj0LJz7bgk7hFXwx3cpK/s7gP//x+LyEh+TkAFlZqFi3BeV7KpCSDMSjEigvB5KSAAA1f5ejnCUh4Zx4h23C48rFbyH+WDEAoCf7E4/BjEAqHaXlTQkg7U0QRIDRdHnE3r38Ipj9etFkQ3fuDsHIkfIiTmoRMprn8eacfAwJ25YiPx/s9GlwACKPWxA+MA2F2cuQ+fwY18c2CdvSIguGvjPVHrJnL7IyPUs9zFePaPYm11zjIGzLP/oBmTde2rLzIAiiVWBraFQNKzoZEosoAIiIcH4CaW0DD1BNL6mpIWHrJ/LXmzBl+lh5lc1ZTdvygSlPQnWbgMUC/P7498gCL2zLkGyvBhpIwtFfN4QEQQQvakKVNTaCA2AF7zqVVkDWuq5opoR4aU6+hkKRWwKLBWyyvMKmUFTp6PYDro8/fRqwWFD38uuyPDRAUmRFjZYWtioe4pR+Ws03CIIgnMMa1D22J2xx/ANXwjY83LsTIvyGs6JKegsuFRcD9Qi1Pw9DfaDVjrJjMvE9fEnUEgThETYbuKaUxEaJHzNQr3negjy2LUFxsf3DJSUEVlS//i46ujr+k0+AUaPQVWWT0/xVZb9cX6NW3dkfvXQJgmgVGKzqi3OxXWL5Bz722BKBg7MWFR066Cu49OOPQHeIn4kw1FP+KkEQrRPJPbngsQVaf84+eWxbArMZTKPoxblfvur6+H//W3WYAfi/axdqVxsOAI9ti+b5EgTRerDZwGlcP2LTSNi2NdSKKgHArbcCP/3kuuCSxQI89hhQLxG2oWjAwoXkFSUIohUiqVFhbfJjtoWcfRK2LYHJBG7ZMriSeNLtTEc1Tg5A3BUXa+/Q0sKWPLYEQXgLZ/294+L43yRs2wxCUSWlgLXZeMG6cKHzCpyCx7dBEYp8sRMTShAEEbRIbOglmSHqPcKbsFggq5oczFAockuRkwNDdDQwbpzmLnWXXo6/L89Fx45AYn0pMGMGAN4zqyZzrTA4b6NDHluCIIIVZ9cvYdt33zk/BwnbVkVODqBmRq1WviVFSYm84JLFwgtas1n0+NbbxM9EpKG+VYfkEQTRhpF2FTAYNf1M+flijQJp1eRghYRtSzJkCBjHqebbAkDkOSb0mcNX4PxjxDSc1zSu5bv9c+Q09BHCkC0WVKzdjPI9FUg6NxGJY4YEhrAljy1BEK5QS9XQavUDAJMmARUVYC+/4jy2hYpHtTqGDOFvvqTmRgg7llbgVLtZy8sD6ieHQajBOPfJBplXVyqEW3OoHkEQbQCJx/ab70MweqSjcNUqvBdoleLdgUKRWxIhJFmryXxCAgC+rU+v75ao7iKViX0mDOQfLF8O1qULEqeNQ6/F05A4bRxYWhqfeNSSqIUOkseWIAhPcCZsGQMeewwcXCyckce21SGEJDsLO3Z2s3bHRDEU+fprxAKL+flAejowciT/Oz+/Jf4agiAIHyGxoQ1NfkxlxXitonzBXDWZhG1Lk5MDw8GDOHPJMMdtobzBPVpYrPnGyCRxdXVTK6EpDl4LjjGwDRu8MGE3oFBkgiC8hTNhCwA2m+taBCRsWyU5OXzYsVbOmLObtegEyWeiqXOA3nZBBEEQQYOseJRYFVkqXNWK8gV71WQStv7AZELkwPMdx5uEbcdMM6w6ikehqopvJaThtdAKefYZVDyKIAhv4SqVwmgE98Lz2hEwAAnbVoyzPq9Ob9bCHIVta/RaEATRxpHck0v72EqFq54ImGCDhK2/iIlxHGsStqkZJmzOXqYubq+6SnxcVcW3EtJ4Ca0WQz6DPLYEQXiC2nVCstrssNVg4K3vww/DcPAgKpasxp4HlqBiyWr5fiRs2yROb9akn4mmxZPW6LUgCKKNI7WhHH8xVBOuriJggg0qHuUvnAhbAMhckYPS6Vk48dkWdD67F3ERdcC11/LtLb78EgBw4pufUTcOSL3tNmDlStmpGMeBu+QSvnt9S0EeW4IgPEEt7FgydvaqG7B/0Hh0Cq9AfI9EYPBg0TKbTEicOhaJws53S85BxaPaLDk5fE6ttEoyAJmd/e3HeiT0FYVwbi5vxlqD14IgiDaOxIb+48YQbLxHcS2UIC28F+yQsPUXLoQtwHtuUzPGyvd54QX7w4QdG2EbmIZ9GbfgHMWpuPXrgbfekgtbxtSrj3oL8tgSBOEJaotiEqPcLiXGXjHeLchj26ZRvVmTfCb+9VQ9Pp4tVglVFcIEQRDBiMSuto8JwfDh/ptKS0LC1l/oELYOWCxgM2fKApQNYOhWtNpx3/Bwe/6QHasVCPHhW07tfgiC8AQ1j600x9bT6xYJW0JB1ekwxDU9DkO9Q3sLErQEQbQKpHbVaNTer5VBObb+whNhW1ysWhDKoJZlW1XlWHzFVZXR5kLtfgiC8AQXHlsStoS3OHZCtLOh4G0kFYoiCKLVIbWrvnRqBRgkbP2FmrB1dRNmNqsWhLKpFZnyh7Aljy1BEJ7gIsfW5aKfFiRsCQVJJvEzEQY+qokKRREE0erwxuJwEELC1l+oCdvaWufHmEzgli2TCVkG4ND5ox33VRO2rtpnNBfy2BIE4Qm+8thS8ahWj8XCV/PU23M2IUUubKlQFEEQrRIKRSZalC++cBhiDz0M5Oc7Py4nB4bP1tmfnr7pTqSP7u+4XzM9tqVFFux6ejXfPkPvHQN5bAmC8ARf5di2oVXqtkh+PpCeDowcyf92ZT4ByLz/D0xv8Fl7C3cFN0EQhFehUGSixbBYwB5/3GGYAwObkuvaEkqWltsnRqjeFJ7885DHwrZwQj5SBqah77xxSJw2DiwtTd8dA1VFJgjCE1x5bD0NRSZaLRYLMGWKaGKEIlAuhaQkPL1HWr2Dp9YbgtQjwU0QBOFNKBSZaDE0ikABAGfTUcVC+gFtaFAVrO1W5aN2z2H5oA5hW1pkwZCCybKCVBzTKbipjy1BEJ7gKse2DRllQh/FxY7rprqKQEnzrhWdA7whSD0W3ARBEN6EQpGJFkOjCBQAMIOOKhbSm7zGRtWbQgMYoioOyAd1CNujhcUwqlRZ1iW4yWNLEIQnqC2KeSMUmWi1mM2AQXEHo6sIlNT7LxG23hKkHgtugiAIb0KhyESLIRSBUohbxhnA5emoYiE1zBrCFoBjrWQdxaM6ZpphVamyrEtwk8eWIAhP8JXHlq4/rRaTCcjLEx0RuotAST22EpvoLUHqseAmCILwJm3UY9t2JHygkZMDQ1YWKtZtwfG/KtChZyISRw/WV5pRRygywFdMlklUHR7b1AwTfst6CP3Xvyg5D6dPcJPHliAIT6AcW8IDcnKArCxefPboobOysUYosiBIpSbLE0EqCO7cXP5jTVWXCYLwC200naft/KWBiMmExKljkejucTo8tgxAXVQiIk5WyPfVQf8HRwESYcvNf1Zf2UiqikwQhCf4yGNbfaASsR5OiQgOTCY3RaOGsPWmIPVIcBMEQXiTZoQiWyx8FIvZHHzXLwpFDkZ0eGwbzL0R0V7xQdbb7kdRUAOxOm8NqY8tQRDuYLGgYslqVGzZIxv+ZdFGVP55VBzwUNhGvzIPhROoJC0hQbowrEjPyckBSkr4qshCGyBPqySbTMDw4cF3U0gQRHChvEYJzyuOeRaKHOxV3UnYBiM6ikeF1VaCHTsmG/v0I9c5tgAcha2aYFWDPLYEQeglPx+sSxoSp41D4o9fyTZd+NBIxM6+XxzQKWxLi+TqwwCGwQW5DuNEG8ZJVWRALkiD/QaPIIjWjfIaNWGC+Pzeae57bFtDVXcStsGInlDko0cdSkAVLDii78PpqbAljy1BEHqwWMAmTwanUoFdQNpyTG+O7dHCYoexEFhxbBOVpCWacCFsBVrDDR5BEK0XtWtUQYH43MDcT+dpDVXdSdgGIzpCkdWaCZnYAX0fTvLYEgThS5z08lZFp1Hmq7rLzVojjEgZSiVpiSY02v0oaQ03eARBtF7UrlFSQuB+KHJrqOpOxaOCEWce2/btgVOnVA8r5zrq+3Aqjb1erytVRSYIQg9ms2PVdmfoFLapGSYUZudhcEEuQmBFI4zYkr0UmRmU6Eg0IfXY7tkDzJ8PlJUByclAfDxQWQmUleGC8GQ8BiAJZShDMqoQjwRU4qJ3yoDNyfzxZWXAuecCY8ZQMi1BEC2KWiV3KUa4H4rcGqq6B4WwPXnyJJ588kmsXr0aJ06cQK9evfDYY4/h1ltv1XX8J598gkWLFuGXX36B1WpF165dMWPGDEyZMsXHM/cRBgPAcbw3VOmxdSJs75wRp+/D6c1QZPLYEgShxGQCd/vtwH//q7mLTPi6UTwqc0UOSqdn4dimvUgZ2oNELciGyli9Wnz822/8jwrxABaobXhTZWz6dGDZMn3dAwiCILyAmgi94w7gnXd4sRvGNcKe0eNG8ahgr+oeFKHIN954IwoKCjB79mx88cUXyMjIwPjx47Fy5UqXxy5cuBA33ngj+vbti9WrV+PTTz/FtGnTUO8kBCkoEG70GhvlgrJ9e81DBnY5qrlNhjdDkcljSxCEGuef73RzMSThJW72sU3NMOGC+4cjlUQtALKhdiwW4IEHvH9exvhkN0rAJQiiBVFWcs/MFLcZ4X6OrUAwV3UPeI/t559/jq+//horV67E+PHjAQAjRozAgQMH8Mgjj2DcuHEwaqxE/PTTT3jiiSewYMECPProo/bxyy+/vEXm7lNCQ0VvrdRj266d5iHVv1uQoOfc5LElCMJLlBZZULF2M1JTgcQxQ0RLqRFZItAOZ8Qnbai5vLchGyqhuNh3Nslm410cwXgnSBBEwOKqp6zQy1tZTMrAPO9jG8wEvMf2448/RlRUFMaOHSsbnzhxIo4cOYJt27ZpHvvvf/8b4eHhuPfee309zZZH+JCqhSJrEGdO0nduyrElCMILFE7IR8rALug7bxwSp40DS0sTe6a4ELZJKBeftCGj7G3Ihkowm/k0Hl9gMARXhRWCIAIed1qOKYtJeVI8qjUQ8MJ2165d6N27N0IUNzb9+/e3b9fihx9+QO/evfHRRx/h3HPPhdFohMlkwmOPPeYyjKqurg41NTWyn4BCCM1zw2Mbtulb4OmngaIi5+d202MrNIOuraaqyARB8JQWWTCkYLLMyHCMgU1p6pniQtiGQ3IdqqryyRzbAmRDJZhMfC6sL8RtXp7H3lrBhlIkM0EQAu62HFNWNPakeFRrIOD/0oqKCnTv3t1hPCEhwb5di8OHD6O8vBz33Xcf5s2bh/POOw/ffPMNFi5ciEOHDuG/TgqXLFiwAHPnzm3+H+ArtDy2ToRt+3WrgXUA5s0DsrOBFSvUd3RD2Obni1+8V2DFDOUO5LEliDbJ0cJipKr0qeVsTT1TXAhbKeyOf4I7e5aK83gA2VAFQmWULVuAigq+CnJ5OZCUJFZFbnpeVQ38+nU5Pv85CVWIRzwqcdsV5eg/Mon//D77LH/O4cM9/mxKbajBwOtj+pgTBOGs5ZhWSLK0mJTUY/v1xhCMutHHEw4QAl7YAgDnZHXV2TabzYba2lqsWrXKXv1xxIgROHXqFF555RXMnTsXPTRCh2bNmoUHH3zQ/rympgZdunTx8C/wAdLiUYKwNRqB8HB9xxcU8JUcMzIct+kUtsrVJA4UikwQBE/HTDNscAwLYgYjuB493BK2HHhPL5eVRTmMHkA2VIHJBChCs9U4aQFGvgCZZXtxI1DyFmBKOC0KWw89wFoeGfqYEwSh1s7HVU/ZnBygf39g0CAghInC9rX/GNF7Ztu4rgS8sE1MTFRdUT5x4gQAcdVZ69ijR48iKytLNn711VfjlVdewc8//6xplMPDwxGuVyT6AyEUWeqxDQnRL2wBYNMmfcJWQ5wqV5NkYQ8CFIpMEG2S1AwT9g66DT22iZV3GThweU1N8dwQtoDE09sWLLMXIRvqOU49JpmSv+3sWe+fnz7mBNGmMZmAdxda8MnMzYhjFUhAJcYPL4PpbXnPbSRL+monJ4PbF49HWSWuwWf2c0XbKtvMdSXghW2/fv2watUqNDY2ynKEdu7cCQDo27ev5rH9+/fH0aOOLW5Yk9gyGAI+xVgbNY+tirCV9YJUMnSo+rhOj61yNclAHluCICT0uL4fIKlNxL2xRIyzdFPY2j29hFuQDfUcpx4To1HsTuChsPXEI0MQRBshPx/jZ07GeKmD6JumHydc3PQj5R3ciaof64DhrT/PIeCt0g033ICTJ0/io48+ko0XFBSgU6dOGDRokOaxN910EwDgiy++kI1//vnnMBgMyFDzVgYLasWjQkKAsDDZbjVJ3WFVk7bZ2ereWkC3sBXi+YViayEceWwJgpBw8qT8eUyM9jYnME7i6SXcgmyo5yhtnNEILJV+DCMi+N8eCluX5ycIom1isQCTJ3vtHtoAhoTHnFSeakUEvMf26quvxqhRo3D33XejpqYGPXr0wKpVq/Dll1/i3Xfftfffy8nJQUFBAfbt24f09HQAfDuDpUuXYtq0aTh+/DjOO+88bNiwAa+//jqmTZtm3y8oUSsepeKxje3fDaULvseJz7bgvLm38BK3Vy/twlGAW8WjhDoce/cCGUttwHuKHchjSxBtF6V4lRa6cyfH9v33deVEEo6QDW0eUhvXo4dCdEZEALW1Hgtbl+cnCKJt4oue220kzyHghS0ArFmzBk888QSefvppnDhxAr169ZIVswAAq9UKq9VqD5ECgNDQUHz99dd4/PHHMX/+fJw4cQLdunXDwoULZUUtghItj60yp6ldO6RmmJCaMRZYGA7U1Tl4dR1ws4+t0Bwab1O7H4IgJHhB2DKjEdzgwV6cVNuDbGjzsNs4Jc302Lo8P0EQbROh57Y376HbSJ5DUAjbqKgoLF68GIsXL9bcZ8WKFVih4oVMSEjAG2+8gTfeeMOHM/QDUo9tQ4M4phS27duLj8PCeGEr7K9FXZ38uYs+tk73I48tQbRdlMJWeu1xImyF2gA2gxEGis1sNmRDfYSXhC1BEIQMkwlYuBCYOdM75zMY2kyeQ1AIW0IFabNlwcOqkmMr62srraTchMXCRzyYzZLPuxuhyDLURCx5bAmi7aLmsbVYULF2M+KrqjSLPNR16Iza/7yLpMEUm0kEME3C1nr6LEot9FElCMKL3HijKGwzMoAbbrD32K5EPCr2VSKZK0dM9yR+H8m2E39XIglN23r0AAYPbjMXKBK2wYogUgFxtVgjFNnhmCbhqtkY3lNhSx5bgiCkKIVtYSHY9OlIdHFYRFwkIsYO99WsCMIrlNdGIAkAV3cW6WkMecs4e9FvgiCIZiGNcOrTB5g1C4CTe/cm4pt+2ioBXxWZ0EDqsT1zRhxzJmwFb25Dg2ZjeIsFbufYOt2PPLYE0XZRCFv23nva7cekSBfuCCIAsViA3SW8x9YABiNrEG0oQRBEc5HeizfZRKf37gQAErbBi/TGz0m7H1mOrSQU2VljeK+GIpPHliDaLgphq0vUAiRsiYCnuBg4iwj78wicFW0oQRBEc5F6bJvu7Z3euxMAKBQ5eAlReevcCEV22hjem6HI5LEliFZDaRGfH5uaCiSOGeI6Z0fpsYVOcat2fSOIAMJsBn5RCNvTxpi2UHSUIAgPUa1ro4VU2Dbdvzu9dycAkMc2ePFE2EpCkZ02hiePLUEQCv7vn28gZWAX9J03DonTxoGlpfHJPk6wVdfInnMq1y3VpS/y2BIBjskE9LlYFLbtDWfbStFRgiA8ID8fSE8HRo7kf7swnzJhW3uWt4lO790JACRsgxe1Gz8VYVt5VNKGQFEVOScHKCkBNm7kf9uTzz3NsSWPLUG0SkqLLBjy7t0yg8ExBjZFO7mnMHs5uJO18kFJH9vSK+9E9QOzwSUlORxbd5YWxAj/YrHwttFZ7lr33qKw3bLxLBWOIghCFU9yY7/4RLwXf+2NULsQ1rx3h77rVmvHbWF77NgxcBwHjuOwfv16p/vec8894DgOQ4YMkTV9J7yAlsf2hx9kQ7HzHkbhhKZvgyBsGxvtgtNkAoYPV6z2kMeWIAgJRwuLVY0FZ1NP7iktsmDI27lOw45TH/knYhfNAWJjHbaF/VokXrcIooXR7VmJEIVtxzjqZUsQhDru5sZaLMC/XxY9tnUIkwlhtXt3tz3CrRS3hW1KSgq6d+8OANi2bZvmfr/++iveeOMNGAwGvPbaa+A43WVDCD2oeWytVrDXX5cNGcAwuCAXpUUWeWEpaey+EmWLDupjSxBtmo6ZZqgtUTGDenLP0cJiGFWPkNB0PWowRjhs4gDxukUQLYhbnhWJsLW33ZOcp617TgiC4BFyY6U4y40tLgaMTLxPb0CophC2WIDVq4HJk6laMuBhKPLQoUMBOBe29957L6xWKyZNmoQBAwZ4NjtCGzWPbV2dqockBFYc27RXLoa1hO0ddwBVVbKhQyXUx5Yg2jKpGSaUnjdKNsY4DlyeenJPx0wzrK7MSygfWlW954jqZvt1iyBaELc8KxrCljwnBEFIcTc31mwGwjm5sFUTwsK1Ztw4Rz9SW62W7JGwHTJkCABtYfvuu++isLAQ8fHxePbZZz2fHaGNmrCNiQFT8Yw3woiUoT3kwlYZbgwARUVg//2vw/ChnSf0rfqQx5YgWi2dh5tlz7kNG6CVVJiaYcLOax5zer5jlWFYOqkIiTihut1+3SKIFsQtz4qKsKU+kwRBqOEsN1aJyQRMmyTep1u5UAchrLzWKGmr1ZI96qkgeGwrKiqwd+9e9JD852pra/Hoo48CAObNm4cOHTp4YZqEA2qhyFFR4JYtg23yZBiaBKUVBmzJXorMDJPrUOTCQlWPbwJOYO9eHVXXyGNLEMGPhW/pU1rKt/RJzWj64isXw+LjnZ7mgnszgc+1tx8oDcNQfK16zbGBE69bBNGCCJ6V3FzepDn1rEiF7TvvAJ9/jrraZDxiA5JRhjIkowrxiLNWwjajDBiQzO9bVgYkJ/PfocpK8blyW2IiMERHWy2CIAIKrbY+JpP+r/OISxuAZfzjp+aFIV4hhNWiSwTacrVkj4Rtnz59EBsbi+rqamzbtk0mbOfOnYvS0lL0798fU6dO9dpECQVaxaNycmDIykLFui04ehRIuHaweHPoJBT52LoiNH65A51VXqoW0bJVH81eluSxJYjgJj8ftkmTkQiGRAC2eRwKs5chc0WOo7A966JYTmWl081pPcKwCZkOvW0ZgIq3PkXmhNEe/AEE0XxycoCsLD6Mr0cPJzeHv/4qPn73XQDAOQAWqu27punHXTgOWLbMuXuHIIiAIT9f9KQaDPxCmUdfX8l9enyyozNLq6ftqlXA4MFtU9QCHoYiGwwGDBo0CACwdetW+/iff/6JV199FQDw2muvwSgEkxPeR6vdDwCYTEicOhZ95owVvS3KYyQ3qX8NnYDkMQPR+et3VF/K1DPK/gUpnJCPlIFp6r0sqSoyQQQvFgvY5MkwSDrLyorPKYVtXZ3z87kQth27hCJ3eQbeRrb9FRkALjsbSSRqCT+j2jFAilCxxdcwRrHMBBEkeDUVQeqAUrnn18rbHTu27YpaoBl9bNUKSN17771oaGjAbbfdhssuu6z5syO0UfPYquXNSlEJRT62rgjmzQXO23Ik8yHGpUUWDCmQ3/jKellSH1uCCE4sFlS/mAdO5ftqL+LkrrBVFKFzICwMOTnA5YdW4Ocl21E1+2Vw27cDK1a4NXWC8JRmVS4uLvb6fDRpq1VgCCKIENa63Gnr4xSpzZXev0twJ2+3reBRKDIgFpD69ddfUVdXh3Xr1mHDhg2IiorC888/77UJEuoc+Hwn0pWDa9fy3lOtT7ZKKHLN54VIcfViTd/So4XFSIXjja+9lyV5bAki+GgKP45V+W4DkiJO33jXYysYapMJME3NAJChc8IE0XyaHS5oNvNhwi2xeNtWq8AQRJAgvZ4o8fjr68JjK+BO3m5bwGOP7SWXXAKj0Yj6+nps2rQJDz30EADgySefROfOapmahLcoLbKgy2+fqW+cMkV7+VklFDnm6ks1bmclNHlitXpZ2gCUl5wkjy1BBBsWC9ikSbIoDClCEafUDJPXQ5G1VqAJwtd4JVzQZAKWLVPtROBVOK7tVoEhiCDAWXXiZhVx0ilsCTkee2yjoqLQr18/7NixAzk5OThw4ADMZjMeeOABb86PUIH3nGpgs0GzhLFKKHLKZeeqn8dg4EUpY7xgtViQun8zquLSEVd1QL4rgA4Tx+Bs+wREKM9DHluCCFyKi52mIVS88wUy78jin7gSthYLHzWyZw+QnIy6zT8i3Nlrk6Em/ISzXrVu3YDm5IDLysI3z2zBhqV70QHlOI4k3HADMLBrOZCUJFY+Lueff/8D8NOX5TiGJFQjHrGoRDLKUY4k3Cgc9913wC+/8K+xYQPfEJcgiIBEqzrxyy8DN9/s+ZpU9fF6xApPyF7qxmNhC/B5tjt27EBJSQkAYPHixQijVXif0zHTDCsA1dJcBoN2zINaVeSjR+1Dtd36ovGW2xA/oAdfUq17d36/sjIgLQ1gDHEac+IAhJ9S6UdJHluCCFzMZoeqxFKSzokRnzgTtvn5wKRJss1ORS1AHlvCb2hVE/UoXNBkwuVvjMW5T7quopyfD0z5io9yUotifvFTPk/OVDtZFLZJSR5MiiCIlkLretIcUZufD5S+1IAnm55/8U0Yrr622VNtE3gcigyIebYAMGbMGFx99dXNnhDhmtQMEzZnL3cMC+Y4PlFI65ukVhX52DH7UPRNVyF+4SyxpJrQpf7gQV0CVfXmmDy2BBG4mEzgLrlEe/uZM+JjpYdWeG6xOIhaXagVwCOIFkCrmmhzon1dVVFWhiuqmVR7kRlpRwm1FB+CIAIGb19PhGtFCMRQ5BcXh1JhdJ00684iMjISABAeHo6XX37ZKxMi9JG5Igel07NQs2odUmv/QsyAnsDo0c6/SSqhyFKPLVIUZaTcbNek6vkhYUsQ/sXC950u31OBjuGViKsrA849Fxgzhr9edO0KSNq2yZAKWy2PrSfVYcPC+IU4gvATunvVegmtcEUpdq8xCVuCCCgsFv47bDarXyu8eT0RrhWhEmF71hbqfqpEG8VjYWu1WjFnzhwAwCOPPIJzzjnHW3MidJKaYUJqxlT9B6iEIldv+1OM4VeGBroQtlIhywDUR8Yi/Ey1YicKRSYIv5GfDzZpMhLBkKjcNm0asHy5vECFkrNnxcdawtZsdn9elC9EBADeqCbq6oZXQC1cUQiKstkUXh4StgQRMOitoO6t6sTCtSLMJtpcmyGUCqPrxONQ5FdffRW//fYbunbtilmzZnlzToSvUIQiF96Zh9hFs+1DbMb9/DdYwImwZQD2D7gZ9V35BQ0OQHiHGMcdyWNLEP6hqAhs0iRwzuqeT5kC1NRob9fjsTWZ+Jx8JzjMgPJriVZAfj6Qns7XdkpPl5tPJWrhinl5wIEDKj0opba3sdFX0ycIwgVFRcDkyc2soO4mwrUijBMXnR99Moy8tTrxyGO7atUqzJw5ExzHIS8vD+3atfP2vAhfILmZrNx9FEPfmSbbzIGBTckFl5Ulz7GVcOqqG1CW0h/tbr4W3UdnAOPGASX7+I1Hjji+JnlsCaLlyc9vErUusNmA6mrt7XqELQB07Cg+njQJ6N4dlYhHWTnQoWciEu+7Xe4ZJmFLBDlaLYME86mGVriiw/7ksSUIvyN4apW3sR5VUHeTnBzg1NcNwPv88xtuoSgnvegWtp999hmmT5+OyspK1DSt8D/11FMYNWqUzyZHeBmJx7b6dwviVXbhbJJvrIrHtv0dN6Hb7beLA6mSxkNqBpg8tgTRsjT1ptWVwWowOBeZeoVtba34+KWXgJgYxAPiNeahCSRsiVaFpy2D1MIVHcKZpYXVSNgSRIvjqjdtS4QFtw+jPraeoDsUedOmTThw4AAaGxtx4YUXYvny5fjXv/7ly7kR3kbyxYjtnuBYVRkAM0i+sWqhyIob0v2FLuIxyGNLEC2Li960MqSxkWroFbbScOaoKMfzKCsgk5EmghwhD06KJze8quHM5LElCL+iVezNYGh+BXXdSG0u2Uzd6Ba28+fPB2MMp06dws8//4wctcxpIrCRiNL4LtGo6tRXtplxBnB5km+s2g1vuNidsrTIgvSf1zh/TfLYEkTL0tSbVgkDUHaVJNpizBg+3slJ8ajqMh3FowDRYxsVpZrC4GCUyWNLBDneaPGhFc5cc4qELUH4E7WFK4OBbyCgR/5YLHzufLNycSnKySOa1ceWCDIUVZETknjjaeMMqHj9PXAHD8i/sS6E7dHCYhicFaYByGNLEC2NyQQuM1M2xAAUD8lG8lsvioNCux0nwvaNRWfEgjh6hG10tPqJlB5bMtJEKyAnhy/65FD8SSda4cwV1SRsCcKfaBV7y8hwfaw7ReWc0kChyJ5AwrYtIf1irFsH659/AgBsKR2ROG2c41KzmudFckPaMdMMq6uPEHlsCaLlkfSkPnjT/Shbux09N60AmnqPAxBb+TgRthE4w1eAPGB1vMEmYUu0cYTcWE/7VmqFMyd0IGFLEP7Gk4UrrSgMjzy3JGw9goRtW+KHH8THX34JY9ONqfHoERROUFlScuGxTc0w4bdrXbR6Io8tQbQ8VVX2h2lv/Qspo5uWmaXCVsifdSJsI3EGVivw95/1jhsFYcuYKGxjVFp+AY5GmYw0EeR4wyujFc4cm0DCliACAZMJGD5c/8KVs6JybiONkqLFYN2QsG0rWCzAkiWqmzgAQwqmoLRIsaTkQtgCwIUv3O64jxTy2BJEyyMIW4NBXswpNFQMQdYpbI1G4JwuToTt6dPi95w8tkQbwJteGVWvEBWPIoigxFtF5QCQx9ZDSNi2FYqLnW42woZjmxRLSjqqIqN9e+evSx5bgmh5BGEbFycKWYB/LHhtdYQiR+Isli4FOic5EbbSVj8kbIk2gFe9MlDxCpGwJYigxBtF5eyQsPUI3X1siSDHbOZvajWEphUGpAxVLCmp5dgqPLaqrT2kkMeWIFoeqbBVEhnJe1l1eGyvvfwMInMAHHYUtvX7ShC2ZIl80UyrdRBVRSZaEYJXRmrevNrbkoQtQfgPiwXYvBmoqAAqK4GyMiA5GYiPlz8HVLflJCfjpoeAmr1liO6RjPj6eGC+eFxVtWRbVyfnFEJAOA44fLiFegwFPyRs2womE7BsGTB5soO4tYHD5uw8ZGYovjQ6QpHJY0sQAQZjzoVtRAT/W08oMmvaR1kRGUDYwf3AtGnywY8/5pMNlVU2qI8t0YoQvDK5ubzubJZXRg0StgThH/LzVe+T3SWu6cfdbaowBqSl8ffw1GrVJUERinzy5Encf//96NSpEyIiInDBBRfgvffec/s8Tz75JDiOQ9++fV3v3BrJyQEOHgRWrwbmz0fNpAew54ElOLb9IDJXqHxZ9AjbsDDHm1Yp5LEliJbl9GmgsZF/rOWxBXSFIp/+429ULFnNXzf0MmWKY7Kh8hohzI9oEciGep/mtvpxCglbgmh5LBaviFqfwBjYlFxsXm1pXm/cNkBQeGxvvPFGFBUVYeHChejZsydWrlyJ8ePHw2az4bbbbtN1jh07duDFF19EiqQNRpvEZALGjgUAxDT9aKInx5bjeK9tdbX6OQLxAkEQrRlJReQztjBEKre74bFtd7QE7aaNAwMHTnMvBTYbn2wocV+d3l2CdpJd2Lp14NQ8u4RPIBvqG0wm73lphdZBZjNgki4EkbAliJahuDig71k5mxWPj9uLQoMJeXlkPrUIeI/t559/jq+//hr/+c9/kJubixEjRmDZsmUYNWoUHnnkEVh1XPQbGxsxceJE5ObmolevXi0w61aCnhxbAKc4eTiy7LJAHluCaFF+mib2HQn/br1jKy/BY3vmDG/EnQhbAQ5uGHuDQZZseGxdESJPlivOBzA1zy7hdciGBj7K1kGFmyWLyhTdQBAtg1CLJkBphBF70aN5vXHbAAEvbD/++GNERUVhbJOXUWDixIk4cuQItm3b5vIcCxcuxIkTJ/Dss8/6apqtEx2hyBYLUF4l3092WQjg1S+CaG2UFllwwadz7c8NYBhckCtv5SXtZVtfr0vYSnH6jeY4PvlQ4saq+bxQ1dvLCZ5dwqeQDQ1s1FoHvfU2hSITRItjMqFyzmJ/z0IVKwzIxVIcBm9bm1OFvbUT8KHIu3btQu/evRGiyNHq37+/ffuQIUM0j//jjz/wzDPPYM2aNYhyVcGXkKMUthznMFZcDPSAE8NLHluCaDGOFhYjFfLvXAisOLZpL1KF4nBCKDLA5+O6eePMAbACOHz3fKT1b6roWF4O9OwJjB7tEJsZc00m2BI4iFtmMIDzWhlZQguyoYGNWuugBkbCliD8we/9x+NS3AcA+A198F/cjmSU4/pJSThngMTeJSXxBwiP48VtlWFJeO45IAnlKEMSqhCPOFQiuek5AKSgHDffnSS3oWrnBFCBRFw4bTAOMdG2erUKeysj4IVtRUUFunfv7jCekJBg366FzWbDXXfdhRtvvBHXXHONW69bV1eHOqFPI4Camhq3jm8VKIVteLhDmIbZDJQjCV1wRP0c5LEliBajY6YZNnAwSPyqjTDKW3lJPbbSHrQSGByFqBQjgBM9ByNt6nCXc0oZnYG/hmTDvLnAfk4GDpzCs0v4BrKhgY1a6yDGGcXQCBK2BNFidE8TQ///Rg88j1kwGoFxswHoNFfx4L/XQtV05fdb4KJbgLThrs+XCGB2qA+rsLcyAj4UGQA4JzHvzrYtWrQIxcXFeOWVV9x+zQULFiA2Ntb+06VLF7fPEfQoc2xV8mtNJsDUVV5QinJsCcI/pGaYUDLgJvtzKwzYkr1U9NYCcmGrITa4MWNQOX+JZtixat9rJ/TctAJla7djf/ZsnJi/BNyhg1T5ogUhGxq4CK2DhHVkoxG4azJ5bAnCH3RKFoVtI0I8FpHSqulbtzreTrvrcfVpFfZWRsAL28TERNUV5RMnTgAQV52VHDx4EE8//TRmz56NsLAwVFVVoaqqCo2NjbDZbKiqqsIZoSqoCrNmzUJ1dbX959ChQ975g4IJpcdWWRG5iSTuuP3xGdM54D74QNxIHluCaFG6Xy5656qWvOfYyksaiqzhsUVcHOJnTQW3fLnDJqHvdaqy77ULUkZnoNuKOUiYNZWWmlsQsqGBT04OsGULsGgR/3vkKBK2BOEXJDUnLhsR0iwRaTIBw4cDGRmOi1eeiGXhfGQ+nRPwocj9+vXDqlWr0NjYKMsR2rlzJwBo9tP7+++/cebMGcyYMQMzZsxw2B4fH48ZM2ZorkSHh4cjXMVD2aZQC0VWofHQEfsHKdzyN4pW/oUMYSN5bAmiZZGImMShKhVsdYQi27/rOTlAVhawbh1qfvoLpdE9ETN+NDLdFLWE/yAbGvjk54sFpAwGYP1UI64QNpKwJYiWQ1KFPLlzqO7wY1cIpnTvXt5TS+LUdwS8sL3hhhuwbNkyfPTRRxg3bpx9vKCgAJ06dcKgQYNUj7vggguwceNGh/H7778f1dXVeOutt2CiT5ZzdAjb0iILUhvFPCoDGC76+ClxB8FjW1QErF2LyroIVBwHkqLPIvb2MfxSFkEQ3uO4GEGBDh0ct0s8tmcWLnbscwvw1ZIFTCZg6lTXfa+JgIRsaGCjVhX5329oC1tZv1v69xOEd5G21wpxLZGk30fA+XfTm32vCW0CXtheffXVGDVqFO6++27U1NSgR48eWLVqFb788ku8++67MDaJr5ycHBQUFGDfvn1IT09HXFwchg8f7nC+uLg4NDY2qm4jFCiTAlRCkfkqrHKM0qqsNhuQnQ28/TYAPqk+vmkTWzwPXHY2sGKFt2ZMEIQ07DQx0XH7nj32h5EbP1c9BXv7bXCXXUaJPK0AsqGBjWpVZJt6KLLSs5uXR19RgvAqbghb6fdRKFXAGH03/U3A59gCwJo1a/DPf/4TTz/9NK666ips27YNq1atwu23327fx2q1wmq1glFOp/fQ4bHtmGmGVfExaoTkuOPH7aJWCQeAFRTw3lyCILxCg6UUAGCLbOe4GGWxAJ+ri1kpHAA2hTrAtxbIhgYuQlVkGQZHYavm2c2lryhBeBdJju3Js9rCVvl9ZEwMUKTvpn8JCmEbFRWFxYsXo7S0FHV1dfj1119x6623yvZZsWIFGGPo2rWr03N999132LVrlw9n24rQIWxTM0zYnJ1nF7ONMOLnm+aLO5SVOX0JDgA2bWrmRAmCAIDCCfkIKdkHAODOnEbhhHz5DsXFus/F2agDfGuBbGjgolYV+Z4ZjsJWzbNrpa8oQXiVTz4SPbZvvRuK/Hz1/dS+j1Lou+k/gkLYEn5CKWwlPQmlZK7IQfn2Eux4eSPKt5dg4MvjxY1qoZASGAAMHdq8eRIEgdIiC4YUTLH3iuUADC7IRWmRZNlYSATSATNQB3iCaC4WC9+iw5n3RtnK4+oxEk9Rk7BV8+y62zKEIAhtLBbghQWisG1AiKbnVTXSQgJ9N/0HCVtCk/KiEtlztmMHMGGC6r6pGSZccP9wvgWI9NseGysmHyhgAJ9jSwWkCKLZHC0slue3AwiBFcc2SZaNTSZApYUPIO8/zTgDuDzqAE8QzSE/H0hPB0aO5H9reX8ARSsPo6PHVs2z60nLEIIg1CkuBgxM3sdWy/Oq/D4aDOKtLn03/QsJW0KV0iILOpTIc19158RKheyJE2LiQffuqJw5H2dj+UqtXGQkFY4iCC+hle+eMlSxbJyTAxw6BCxZIhs+NW4ifp+9GhVLVoM7eIAqXxBEM2hWTqyKsAUcPbv0FSUI72E2A+GcmGPbiBCnnlfp9/HAAeDgQfpuBgIkbAlVjhYWQ83PqisnVuqxPXpUfHzppYhfOAsR6Z28MEOCIKSkZpiw/dZF9uc2cNiSvZSPolDS1MJHSpQpAX3mjEXi1LG01EwQzaRZObFSYSut0gqFZ5cgCK9hMgEPzRC/b1Yu1KXnVfp9pO9mYBDw7X4I/8B7fwBFli0fPuwqJ1bqsZUK2+Rk/rdQQl1SfY4gCBc09YJGRASqqoGavWWIGnAuEu4cY7ekgxfdArx3PwCgbngWMle4sWxM1XAJwmsIOXhScas7707DY0sQhHfQ6gd91RWNwCv84/seDEEseV6DDvLYEqrw1Y6XyzL2dOfESj221dXiY6H1SGgo/7uxkW6mCUIP2dnAwIHAvHnAE08g7vknkLZmMRKemAbWJU1M3pMsFkUmx7g+r7TS+cmTXp40QbRdmpUTKxG2hw9aqW0IQXgRp7nvkgiJ2ETy/QUj9K4RmmSuyEHp9CzUrFqHjjiK2PHX6iv0pFEsCgsWAN27y5teW62qTbAtFuDgZgvMKEbSEH5JrbTIgoq1m5GaCiSOGdJy8R4W/nXL91Qg6dzEln1tgigq0uwFDQAcGNiUKeCysoD6enGDsoetGtHRYrVzErYE4VVycoCsLD78uEcPN8yGRNiu+9SKaet4kexO3p6aR0rLS9US+PO1CUJAK/c9K6vpcymNJFS5NyUCH3rXCKekZpiQmjHV9Y4SSn8pRaraBsb4K4hUHDc2Olw88vOB3ZNewgt4GBwAG2fA3sH/RI/NbyO1qXYrm8aBW77M9xn6+fmwTZqMRDAIjYvYdA7cshZ4bYIAgMJCl7twNht/9yyE+wP6hG1UFHD8OADgzKFyRHo6R4IgVBFy79zhaLkRHZseG2F1vPl2QX6+ePNuMPCiGHAcaykTpjYfMp+EP3CW+24yQZ7TLkQXuoAWbQILCkUmvE75tv3aG61WuVdJkWdrsQBzJ1vsohYADMwG8+YCGCQNSXgvld4Skx5isYBNnix7XQDgWAu8NkEIZGa63IUZDLxLyE2P7ckqcf+Iwq9ROMFJPxKCIFqEAxbRY2sEn2Ort/CUlkdq8mQPKzQ3k2ZVhyYIL+OyH7RU2Orw2LrT0otoGUjYEl4naYiT6hhGIxAjyf1ruogITew3bwbOYY4VmVUrNNv0lpj0kOJicBo5wD5/baJNUlpkwS+LNqK0SHLXl5GhHd6Pptz3vDx+qdgNYVtaZEH7qiP25xyAwQW58tcmCKLFSe/uKGxdFZ6S2lA1j5TSlOmu0NxMmlUdmiDcRPgeaC2cuMx9d0PY0qJNYELClvA6qRdrxGIYDPwVJDpaHGtokK143XorsBdmh0PV5CUz6C0x6SFms+rrtshrE22OLeNfRcrANFz40EgkD0wXvafSu9JOnYD581F9133247hx48S4PjeErVpLrxBYcWwT3XEShD/p2FkubF0VnlLaUOU6mNGoPtYSJsylh4wgvIRe76nTftBu5NjSok1gQsKW8D4KK3Ym41Jg9Wq+g3VOjixv4cjBRtmKF2PAYU5uvRlnQPGQbPkYAC5Pb4lJDzGZwF1/vcMw4wy+f22iTVFaZMEl782wh70bYRO9p0JxJwA491xg1izEzpwujkkFrBvClm/pJf+uNsKIlKF0x0kQfkVyQz1qhNXx5luC0mskrIEpPVLLlnlYobmZNKs6NEHoxF3vqWbPWTdybGnRJjAhYUt4H8XScOSQAcDYseIVRGK0S4obHFa8lCFT3KuL0fP/3pKPjRjRMtUn+vSRPbWFhYM7eIAqXxBexan39MwZcTCyqbyTdCVZaojdELZ8S688NDZ1q26EEVuylyI1g+44CcKvSKoiJydanYpANa8RY8CqVXKPlFMvlY/x52sTbQOveU/dCEWmRZvAhKoiE95HuYSlvMGWrIJ169Ko2sQe0p70ycnyiw3QcmXYpaICgCHESFctwut0zHQMv7d7T9WErXQlWRo6JRW2Oio6Ci29jm3ai5ShPZBJopYg/I9E2MJq1d4PotdIaUMHD3Y0VZ5UaPYW/nxtovWj9T1w23vqZvEoj1t6ET6DPLaE91Em8yiFreRikdqhQXXFS0Z9vfyGHXAUur5CIWwdlgQJwgsovaQy76mPPLbS177g/uHkqSWIQMENYUteI4Lw4vfAgz62mmHNhF8gjy3hfZQe2/Bw+XPFTbnqitckcZcTR+uRoGgLpGwT5DNI2BJ+oHx7ieg9dcdjK32sU9gSBBFguCFsAW2vEfXXJNoSXvGeetDHlggsSNgS3seVx1blptxZmNJTj9ZhiLEet0sHW0rYnj0rf07ClmgBZN5TH3tsCYIIMNwUtoCjDc3PF4vpGAy8N4tyW4nWTrND3t0MRSYCDwpFJryPqxxbrZvyJpRV7EJZHWY9rBCyFIpMtGakN7PSz2BEBP9bT44tCVuCCE6kwtYDW0f9NQnCQ0jYBj0kbAnvo/TYKkORtW7Kmyj+S14WOQz1MNoUObYUiky0ZqSfb/LYEkTbwgOPrRTqr0kQHkLCNuihd43wPm4Uj1JbjTan1cmeh6MOEYYGQGqopTf+Fguwdi1qftyDciQj7uIeSBwzBDCZUFpkQcXazUhNhX3MLZTCFuB7KSj/RoJoDsq70IYG0TsrDYd3pyoyCVuCCE6aKWw9rRCrlpNLebpEm0JqTynHNighYUt4Hw9ybKWYEk7LnkdydXh2dj0wWzIoCOL8fGASX2kqpukHbwJsOofiwXeix+a3kQreA8ymc+CWLXMv0UhN2Nps8huPNk5pkQVHC4vRMdNMlXU9Rfk9kC74qHlsDQb+e8YYeWwJorXRTGErVIjNzeUP11MhVi0nF6A83ZaAFg8CCPLYBj30rhG+x02PLU6dkj29d0odokcrhG1DA28NJk2CGhxjMG8uAKcYY1NywWVl6bceJGydUjghH0MLJiMVDFYYUJidh8wVdOfjFAsfRVC+pwIdwysRF34WuOIK+T6uQpEBfoGovp48tgTR2mimsAXcqxCrlpM7ZYr4WPidm8ufk8SX96AiX57hs8UAErZBD71rhO9xM8dWKWyjw+sd92to4K9qTlALFuZsTYlGzRW2BEqLLBhSMAWGJo+4ETYMLshF6fQs8txqkZ8PNmkSEgEkSsfnzZPvp0fYhoTwQlbLY0thVAQRnHCcGJHhobAF9FeIVcvJVTNzVjfNJ+EcrSJftHjgHJ8uBpCwDXqoeBThe9z12J6WhyKjrk5+wy4cZzY7fVmmNmbQkWgkhYStJkcLi2GE/H8RAiuObaIKJapYLGCTJ6suuDig12Or3Jc8tgTROhDsZDOErV6EnFwpBoPjmJ48XUI/VOTLfXxe8VtqT0nYBiUkbAnf406ObVMhKBl1deoeW5MJuOoq1Zdk4FDZqY98jOPA5blINFJCwlaTjplm2BQyrRFGpAylOx9ViovBMbXlFhX0emwBNFafxP47n8aJ+UuAEyfE7W4KW4sF2LiRWoIQREAghCN7KGzd+T4LObnCSxqN/HPlmKs8XcI91BYUaPHAOb5YDJB9V6TOFjejnsiGBgYkbAnfowxF1vLY5ucDaWnAnDmy3Y99v9vRYyvc+PfqZR86e/7F9sfcIw8jYaw8b5H76Sf341VI2GqSmmHCX5feJRvbkr2UwpC1MJtVowhUccNjazx6GN3emYeEJ6aBLV8ubndD2ObnA+npwMiR/O/8fN2HEgThC5ohbD35PufkACUl/I15SQn/XG2M8B5qCwq0eOCcqCjvLgYovyt7//QsFJlsaOBAwpbwPXo8thYLMHkyn1OkIGn/Npz4vVQ+KAjiOrE1UMStN4nbIyIcvbwdO7o3b5tNdn7ZOAEA6DV1hOw5FY7SJn+9CSVI17Vv8eJ1qJo5H7j/fuC778QNtbX2h40N/A2vrECa9CQ6ha3PQ7sIgnAfD4Vtc77PJhMwfLhcWKmNEd6DFg/0k58PXHKJYxsr5WKAXs+p2nflp+3uC1uyoYEFCVvC9+jJsS0uVhW1AP8hrd55UD4oiFapJzc6WnysrBYLuC9Ipf1Dm3Oe1oxy6ZRQxWIB5k62oCsOyMa1PLjmvJmIe/4JYPFiYOtWccP119uXgm31KvnpEn58+lNdc6M8L4IIQDwUtvR9Dj5o8cA1SvEI8LcfW7bIFwPc8ZyqfVdCmHjfuOoDfcKWvnOBBWVGE75HT1Vks1msAqmAAYg7J1E+2NjI7yv1qEZFiY+9IWzVwpA9OU9rRtmzmFCluBg4hxU7FI5y+7/HmL1sJhfVHqit1Nz1wv89jdKibJeh4UKel3IVnPK8CMKPCMK2rAyYP198nJwsfxwfD1RW2p9fWA0sQhmOIRlViEccKpGCMpz/RTKwWfs4Z+dEfDyQmAgMGULqi/ALWpW7pU003K0yrWb7QiAuGD/0WCgyx7v+yJMNDSxI2BK+R4/H1mQCXnsNuOceh8OrU3oiPj3G8bxWq7bHVq3gFAlb7xPEwra0yIKjhcXomGn2eV6w2Qzs48xgTC5mGTwQt01LwaHxMUCp9m5G2HBs016Xf5uQ55Wby5+a8rwIIgA4eZL/XVUFPPGE7sPiADygtuH55k8JHAcsW+ZWvKzP+o0Sfqcl31s94tGZ51RtfkrbB8iFbZ0tRFd7K7KhgQXFERK+R29V5Ouusz+s632+/XFctwTH4lHCsVKPrTIUWXmMm4K06L631TeQsBUJUmFbmL0cKQPTcOFDI5E8MB2FE3xb6cFkAmbndZaFHtsMRnCeWD7BmrvI/7HCoLtCNeV5EUQAUVSknQrjT4SIEZ3Jg1RQp/XS0u+tnkJbnlSZFmzf6tX87YxU2DJDiG6vK9nQwIGELeF79FZFrqoSDxlysbifmvcV4Mek4tWLocilRRZc9PFT6htJ2IoEobAtLbJg6NtTYGiSmUbYMLggF6VFvq30kHP7WfsFt773+TAcKHG/oJnBIFpz6QKR0ejQemn7+Ffc8kRTnhdBBAiFhf6egTY6kwepoE7rxV/vrSvx6GmVaZMJGDuWD0YIg3jf+PJrIW7ZQ7KhgQGFIhO+R6/HViJsER/PC+LGRnXvKyBuE/Bi8aijhcVIhcb+JGxFglDY8u+tPJc7BFZdYbvNQpIMFNbdxFs/J5WLT107Fu17moCkJDHHbfBg0WpKF4jat4fhu++Aiy6yDw2eq97jmSCIACcz098z0EZn8qC7YaFE8ODP99ZkAkylRUDeWr77BSDLBc+prMTY7DKUc8lITATidpYB813klDc9zklOxpnOB4HD/FD2VccAdPftH0R4HRK2hO/Rk2ML8Bcdgbg4XtieOuXcY6sVitzMHNuOmWZYYYBRTdySsBUJQmHbMdPskNvaCKPusF2PEXLmAKB9e/63E2HbfuI44KabNLfLFohCQpD/0wWQLmDbzu0Fw7I8iokiiGAjIwPIzgYKCvw9Ezkcpzt5kArqtF78+t5efjnw7bdOd4lp+vEESad4sB49wLmZU074HxK2hO/xxGMbFyceV1ennWMrjHMc0K6duK2ZObapGSbsvOI+9NvwiuNGErYiQdjuJzXDhArT+Ui0/AqAL+C0JXspMn1cQEpWvlGHsLWvRmshWSCyGkIwd8phmbA1MBtYbi44rZKQBEEELitWANOnA599JqbzlJfzERzSx4Inylfbvv4a2LWL3755M99IVAdUUKf14rf3tqjIpaj1JhxjYFPIhgYbQSFsT548iSeffBKrV6/GiRMn0KtXLzz22GO49dZbnR63Zs0afPDBBygqKsLhw4eRkpKCoUOHYs6cOTCbzS00e0ImZAFdObaIi0NjSDhCAFhPVMK4f7/jeRsbRY9tWJg8l9cL7X76TbsMIGHrnCD02AJA4sBzgCZha03uiMwV3luRLS2yoPq/a9Gpdg9iLj4XGDOGN4pSYSvkgyvzz6VERmpvA2Tfq0ZmxDms2GEXjmL/CJANDVoyMvgfD/FK1do77hCFbYcObh2ak8O3Wtm7l/fm0WWo9eDL91bzc+uH3HPORjY02AgKYXvjjTeiqKgICxcuRM+ePbFy5UqMHz8eNpsNt912m+Zxzz33HDp27IgnnngC3bt3x6FDhzB//nxcdNFF2Lp1K/r06dOCf0UbRil+dHhsv9oeh0GWSsQCMJ46CbZ8uWNbFKnHNjxc7v2iPrYtQ5AKW+niSgjn2DvZUwqzl2Ho21OQKgy8Cd7rsmwZ0F2Sq+Nlj60xPAT7ODOsTB4+z4xGcBT71+YhG9r2yM8XC/wYDLyHzaOISq2FaJ2YTKQJWiu+eG+dfm79kHvODGRDg42AF7aff/45vv76a7shBoARI0bgwIEDeOSRRzBu3DgYhRJoCtauXYtkITG8iZEjR6Jr1654+eWXsXz5cp/Pn+A9WLKiPCqGsrTIgpDPt6IpIAobXv0do1Bj301VPklzbMPC5CJBJce2/LdSHPtgN1JTgcQxOhrNk7B1TbD+L6RhvHUNMAKAxYLKt9ei9qc9iO6RjPgBPYAhOj4nTYjVlhUwxltq6fVGj7B15bGVXPdCwkMwe5kJUyfnYQnLRQissBmMMFDsX5uHbGjw4o7HVbovoF611qOISslC9NFDDdhdSj1pCR61z2dzogS0qi3bP7cXX+zV+buCcQZweWRDg42AF7Yff/wxoqKiMHbsWNn4xIkTcdttt2Hbtm0YMmSI6rFKgwwAnTp1gslkwqFDh3wyX8KR5IHpKMzOE8M9pR7bxkYUZi/H0LcnywTBQsxUF7NSpFWRw8P55b2QEM1Kyol3ZNmFM5vOuS4KIBW2RqPYwTtYxZwvCNL/xd59HIQ12FNVDfg1exkufXsK4gHES3fkON7bqsPVwVdb1sBmA6Th9N7w2AqfRwAICWkKDctB0ZYs9MBeJA2m2D+CbGiw4o7HVbnvgw96sWqtZBFwdFYDfmLN9AATrQK1zyfQvCgBl9WWpfdk6em86gWan1MueVyJeJSVAx16JiJx9GCyoUFIwAvbXbt2oXfv3ggJkU+1f//+9u1aRlmNv//+GwcOHMD111/vdL+6ujrUSSru1tTUONmbkFJaZJHd4Nv7hE7P4j23kvfy1OFKDPnY0ctlABwq1zqg9NgKvwVhq/DYSl9DV1EA6UW0fXtA+AwEqZjzCVJxFSRYLMBv287YhW0oGjD07SnqnzXGdLs61Kot2zEY5FW7hRxbbwnbJo+byQSYxpoAkDEmeMiGBh8uPVcu9l20yItVayUL0QbW6HI+ROtH7TM3ZYr4WPjt7mfEZbVlaZ2Kvn2BWbOa9Xeo4bC4TQQdAV/StKKiAgkJCQ7jwlhFRYXuczU2NiInJwdRUVF44IEHnO67YMECxMbG2n+6dOni3sTbMEcLHYvYCH1CAcgMZf32X2CEeo4jB2hsaUKaYysVtoB6jq3y/DYXjeaVwlbAH8LWYkHFktX48/4lqJy/BFi9OjA63QehyC8uBiJx2v48BA3OL4RWF5+TJlIzTDhh6q++MS9PLmKFz1NzikdJ891CAn6NkvATZEODD2eeKyWbNzvua7PxXlshwrxZVWsl9joUok3VeVkMKCwWYOPGwDCdwYza59Nm0/+Z1UKotqz5uVXrLEAQCgJe2AIA56RAjbNtUhhjyMnJQWFhId5++22XRnbWrFmorq62/1DYlX6EHrBSZH1C//c/+3j84T80z8MMRlReMFz7haRVkQWBIPzW6n2rOL+zJezav4+JT6SthFpazOXnw9YlDYnTxqHX4mmIf2IaMG4ckJbGxwP5kyAUtmYz0E4ibENhVetWLOKGqyOxd4rj4B138PFY3m73Q8KW0AnZ0OBC8FxJUbsM5ecDTWnTDvvOmAGUlPBCrqSkGWHD0kJ7EK85ei6LgSQk8/P56NWRI/nf/jadwYza59Ng0PeZdUVOjpPPLQlbQgcBL2wTExNVV5RPnDgBAKor0UoYY5g0aRLeffddrFixAtddd53LY8LDwxETEyP7IfSRmmHC5uw8NPIledAII7ZkL+XDkC0W4KmnXJ/EwCftJ4y+VHsfVx5btd63TTBwTosCbLvlRUQX/Mf+/OQJMaSuRcWcxQI2eTIMar5rIUzWn3cNiv9FaZEFvyzaiNKiALiT0cBkAs7relo2VpV4jvrOHOeeq0MSemlHuDEsLbUPVRxqev3mFI8iYUvogGxo8OHScwXHcFABg0Hc12QChg/nf3ssMiUe2whDg+Z8lASSkNQK7Q4Ewa0kkBYDtFB+Pg0G4LnnXH9m3Tm/8LmVIRG2JxkJW0KdgBe2/fr1w+7du9GoKDO/c+dOAEDfvn2dHi8Y5LfeegvLly/HHXfc4bO5EiKZK3JQvr0EO17eiPLtJWLhqOJiXpBpcCS2Fx9me+AAv1TnbJX/7FnRUgmeWp2hyNzbBZpL2KVFFgz84BHZWPtKiZVpSWFbXAzOyf/L7/Fgihzb5IHpuPChkXzBsAmBuySe1E4ubOMr9gHgQ9/LBv9D3PDgg/LPicUCrF6NyvlL8Of9S1CxRBESfvas44udPQvk54P9+9/i6917O///cSZsnW0DHIpHEYQaZEODE6eeK6iHgwK8gFPu2yyRKbm2vLuiUZcHONCEpDuh3f4kkBYDXJGTAyxYwK/92mzAzJn8uN4oAU8E/OcfiML29RXtA/r/Q/gRFuB8/vnnDAB77733ZONXXXUV69SpE2tsbNQ81mazsZycHMZxHMvLy2vWPKqrqxkAVl1d3azztHkOHWKM4xjj5a3Djw1gR9dut+9rg/a+7I03xMcjRvDH9O7NP4+OZiwqSvvY//1Pc4o/v/St9nEAYz/+2AL/qCYOHWI2J/8vZjTy/1N/8eabmnNrgJEd2e7HuTmhoWNn1Tk3xiUw9vXX4tiTT4oHLV+u+tm1cRy/jTHGzj/f8bxXXaX6HjbAyKqnPqL93rpiwABx30sv9cn/iXAk2GwB2dDWiZYpVZqEQ4cYMxiaYTaeeUY88NNPdR3yrYYJ3bjR7T/TKzT7f9ACBMMcpTRnvsuXi8caDKL5dPV6Y7i19hd7Ev8K6P8PoY2vbUHAe2yvvvpqjBo1CnfffTeWLVuGjRs3YsqUKfjyyy/x/PPP2/vv5eTkICQkBAcOHLAfe9999yE/Px8TJ05Ev379sHXrVvvPL7/84q8/qW1jMvHtUzTyujgAtV9u4p8UF4NzVj5K2j7F3RxbNc9aEx0Hd3MYs0lr3bakx9ZkArdokfo2d8NkfYGT/4WsYFiAwWpPqo7Xh8c49kMG+GXlyZNVow2ECtuwWNRDkU+cUPW6h8CK2sPNqBQr9cBp9CElCLKhrROTCXjoIcdxpSey2d5KaXs+F3UrBPTmCLcUekK7/U2weJUFPJ2vp9784mIgkoke21NoH9D/H8J/BEX82po1a/DEE0/g6aefxokTJ9CrVy+sWrUKt956q30fq9UKq9UKJrmBXLt2LQDgzTffxJtvvik7Z3p6OkpKSlpk/oQCvuEmqv79DmKfe1zWHoUBiL5qKP/EbAbjOO1QXGn7FLUcW2dFUdQESBOpSY0OY+XdBiJl/zb+SUsXTLrpJkCtAun33wOZmS07FyVO/heygmEBhtGqnn/NJcXLKxULedouQujtFbaFz1VUFHCySTwbjaptgBphRJS5k2d/AEA5toRuyIa2TmbM4Fv7OGvr47KFiiuk15ZGR9uohskELFwIPPYY/7qBICSbbjuwdy//tweSqAW88D61MJ7O12WvWievF82dsrfKOIX2Af3/IfxHwHtsASAqKgqLFy9GaWkp6urq8Ouvv8oMMgCsWLECjDF07drVPlZSUgLGmOoPGWQ/YzIhbuEsFA/JtvtkGYDiIdlIGZ1h34dbtgw2LYEq9awpc2wBp0LEmcd2592vO4ylXCFp46Il5ppa8ux6erV3iydpifDkZO+9hqc46WNrLxgWaDAGQ536+x++6xfg88/FAeF/bzY7XSixV9gW9lcUyuGuvVb23AoDtmQvRay5Ge8h5dgSOiEb2jrR44lstrfSA49tfr4oag0GXuTqrcrsy+JJmkWJAoBg8CpLEeYr9czbbMD69c6P89SbbzIBObeKHtvTXFRA/38I/xEUwpZovfTctAJla7dj7/SXUbZ2O3puWiHfIScHhoMHUbFkNfY8sAQnx91l33Rwm1hl1i5onfUFlaIhFkuLLOizYbHD+MlaiZhVE7b5+WBNLXn6zhuHlIFpKJyQjx3rLNhw0xIcnvQ0UFSkb25KtES4ztVzn+LEY2svGBZo7NunuejBAWDPPCMOCB5bkwmYNk31GMYZxArbwucqMlL8TJ49y1vzJv6+ax7Kth/g/z+uCkQ5Q/L+nzmp74aTIIjWhasiU1r76BaQbnps1UJNH3tMn1BVK55UVMR7pT01n8GEnvcykOjfX34LoKdRQ3ME/OB+YgrRy3ntvfL/CYYq1IR70DI/4XdSRmeIXlo1TCYkTh2LRAC4c7M4/NEr4j5qHltnKMSixQIc3GxB5LrVSFXJ6z117CSihCdKMdfUkkeaD2wAw9CCyUABs68esfx54LKzgRUr9M1RY652AlzYBiT5+XyurBM46d8kXQC5+GL1/fcWA92780+E9yo8nO9DW1/Pj0naFHSf8Q+gf5MVb4awPXu0EkKn24hN36JwQn7gLiYQBOEzhNY+evfJzxfFp8HACw1NkaDTY2ux8GGm5eWehZqqCWJlWQNPzGewoee99DsWC759ZjM+WFqBXABxqEQyylCGZMAK2GaUAQOSgfh4oLISKCsTI8zKypCTnIwb/xWPE39XIomVIeZYMjAf4n4ax+HXX+1T6KDobOAJbn0PiKCBhC0RPFgsYO++a89VlPZ2PVVZj/aAR8I2Px/YOjkfb7ApMMKmmg/Zrouk16PSamu05FH2nuUAsIICcNOnAxm8kC8tsqBi7WZ0jqhAPJou5OeeC4wZI1o3rVBkErbu4aQAlBRmMIjiVtoLuapK/QBpSLjwXkVE8D81NQ7CVtZY3kNhW1pkQceTYm9SDsDgglyUTs8KzPBvgiACAq3iPVlZGoJKh7CVCgSO43+kl1lPcy+Vl+qCAkBiPu1/T3ExHxQT8IKwNZCfDzZ5MkYyhpFa+6xp+nFCfNOPx9xxB3DmjIMS1ft5cPt7QAQNFIpMBA9Oerq2+/hd3rrqFQpNAsRiAeZOtthFLeAoagGARceJT5TW12x2VrtZBgcAm/iqz4UT8pEyMA19541D/BPTgCeeABYv5kNe09LEJnZaHlsn+a0tRiDMQS8uCkABAAMH7sUXxQHpooKWsBX2sdnExQbBYws4F7ZOQued9QE+Wljs8DkN5CrUBEEEBnqr2QohmhU1zkORlQJBuMRKQ03Vah8qUcu9VKPJfAIIrr6vrYKmxWHNgp4tiUrcszufh2CrQk3oh4QtETw0VUlWgwN466r3qtQkFouLgXNYsV3UavHD67+JT5RXQ5MJnCK81Qaoil0GAEOHorTIgiEFkx28uuKOjP97LBYKRfYWGgWgGsFhER7A/plLwB06CEycKG7U8timpDjuIxXBSmF7UswN0uuxHVyQq1mErGOmGVbF5TuQq1ATBBEYREWpj0svS1KBMH2Gc4+tlqd11Srg4Yf5bS++6FpoKHMvtUTu0KamCZ62jSGagY7F4RZFokTd/TwEWksqwnuQsCWCB5MJh8aqNO4TsNnA9PZWbBIhZjOwjzO72Bm4BpJKuWpi7tJLZU+rFy5FaUwv2RgD+BzbjAwcLSyG0ZWf12aTt5BRQsLWPYQeyhKsMOBubhlily9Ct4VT+X3U+tgCcmGrFn6sFLaCN1bpsW3XTnzsRNg688CmZpiwOTsPjeDvAhthDNwq1ARBBAzSNTYpwiVKKRDqmXOPrZZA6NqVL/ok6CA9wlNaPOnAAT6nVkqT+QRAHje/4KI7QIsjUaLufh6CrQo1oR8StkRQEfrwDGgGv3KcahixKmfPAhYLwjdvxJQp8k1qclPmWVUTcwqDH58eg0595Bkk3IUX2itfdMw0u/ARg79b6NFD7rGVLquTsNWktMiCXxZtdPR45uTYDXNDl+7YvvoAZh/MkafpSMWmtzy2wl1jZKRoSQHgq680/wZXHtjMFTko316CHS9vRPn2EiocRRCES1x5qpQCoQGuc2wffNBRIJw86ZnwlLbkWbEC2L4dePll/re0cBR53HyLarVglcVhv2EwyJToTz857uLq8xBsVagJfVDxKCKoSM0woTB7OYYWTJKvynAc8NxzYDNn6sv/+PVXsC5pSALDLMX6jpo4toETxa0OYWuvhislMlL2d/w1JBs9NxdozzEvj79oS88TFSWKpEDIbw2EOSjYPvYFXPzhTKSCwQoDCrPzRNFntdpdCKFdOmLwWJXl2ZAQsfN8czy2grBljK/wCMgXJiwW4IUXVP8GBqDwmoUY4cIDm5phIi8tQRC6ETxVubn85VDpqRIEo2DmnAlbZdGohx8GZszgz2WxyM8DeCY8MzLkxaL0/h2E5zitFjxhAjBpEv+4Wzfg0Ud5+1ZeDiQl8ePCY6G6sbe3JSYCgwfb32yLBZg50/HvWLDAvWrhROuAhC0RdGSuyEHp9CzUrFqH1Nq/EDOgJzB6NJ/rmpAANmmSa89tUZF9H1f5tY0wYh3G4Hr8jx/QI2wbGviKfcoxCT2zugNN3Ytq75iK6K4dAKGH6mWXiZZEKWyPHVN/TX8QYB7b0iILMj58VPbeyqoFS98DabVPJWFh/P+9yRtbWmRB3O97EQnwwjc2VtxX8NhK3yehKrJARVMFY6mwdZKvxAGIH6XeXoggCKI55OTw1V/37uWFpvTGXikYmSEEdhMpsTlqRaNefpkXtmrn8YXwdPZ3EJ7hslqwdAG3a1dg6lTd5/VV9Wq1MGRAfUGEaP2QsCWCEt5TpXJBzckBV14OzJrlldcpL/gMeyP7Y8SPbwPP/48f9NRjKwggiwXYvBln1q6H4MONHj8GuPJKUdhKzyc1JBSK7JSjhcVIVYwJuaoOwtZZBe3wcLuw3Xz767hk5T1iP+LGRnDffSfu68pjC4iLHNL3T8hXUhG3VAiKIAhf4sxTlZMD9O8P/N//AVe3CwUEUyu5fjrLaRTO62vhSa1+vI/L91Vp53Tg636xyigDgMLS2zKUY0u0OnZ+W+a1cyWNuxyDx5oQGy/5quj12CqFbUMDf4VPSwPGjUPkj5vtm9i1o/kmfYIYklb4oBxb3XTMdCwEJhOJ0pxZVx5bAI01p2SiFmjqR7x7tzjgKsdWirQkqZCvpCjGYYWBCkERBOE38vOBSy7hc2cn361ePEpvjqs0Z9bbc6RWP97H5fvqprBtierVVAiKkEIeW6JVUVpkwXlfv+LWMbL8WSWCV88gF7bH1hXh9Oq1iO2VioQ7x6h7bJWhyKdPA5Mnq3roODCwKbngYmMc28MoQ5EFAiG/NRDmIEEpBgWRmCmM6w1FbjLYtpNnVC+SMimqx2MrIF2YAESXxpYtqNxbgSN1iUi4drA4X4IgiBZEKUTqmHqOrctQY4sFWLsW2LOHr0kg5E2WlQHJyaiqBmr2liG6RzLiu8q3ARAfxztuq9lXhr1vJmMy4hGHSiTbyrBvUjKq/gLi6nScQ8jTHDKE1I8Cl++rm8JWj2ffG1BYOiFAwpZoVfChqO71WTt068NIf0+liE9oqOhNkwjbivtmI9my0y5u2BPTwF1/vfxYtVDkM2ec9oDjbFZRSNfWihsoFNljql5ficxp48QBvaHITduMBitscAxtYZCIWzc8tmcaDIhUDppMwNixiAdwygL8WQxYLWSYCYJoedypiqwpJvLzxQJDGsQ1/XhCDIAFahued/NEHMdHzVA5XBlORaKbwtZbYcJ6ws6F8eJi+XOibUGhyESromOmGVb9TX8AAOnpGl8D6UVbImwTJaIWaApN/eQT+bH19er9Z530gGMGIxAXxz/xJBTZYkHl/CU4eNP9qJw5H1iyBFi92rcd6/0obDVb+khI7B4rH9Abitz03httVpxIu0C2iQHgBgwQB7Q8tvv2OZw24v++QeEE9Zg5Cq0jCMLfKENRG6EeiizgEGpssbgUtQEDYw5xsaptblopzv5WzRByN4WtN8KE9dpGsqEEQMKWaGWkZpiwOXuZe+K2pkZ9XEPYquHQYkjqcRWw2cSSkQoYZwCXtxRISOAHzpwRw3z1hCK//jpYly6If2Ia0tYsRvzzTwDTpgHjxvE5vb66wreEsLVYeIG+ZAkwfz5w//34+8IbkTIwDRc+NBLJA9M1xaLD4oLE43CqIUz7Bkbw5tbVoUM3URxX3/M4uO3bgTvvFPdVq4pcVwd8+63DaTkAQwqmOIjxlshDIgiCcIVSiNgM4gJg6aEG19ckwV0WLEia67YlYSSU+xg50s1bBIlNPdkQrmsRoDn9YvXaRrKhhAAJW6LVkbkiB2XbD+L32atx8rYprg9QE6GAPFTVhbBlSk+smliur+eXQJs4Peof2PPAElQsWQ3u4AH+ai8VroLX1lUossUCds892lJeZVXaa/g6x1ZSbAvTpgFPPAEsXozuOz6250UbYcOQgkn4fUWR4/GnT8ufSzy2Kz8I1b6BERY16uvFHrTh4Yh97Vm+h4D0s1FfDxQV4VTeO+LY4cOaf5IRNhzbtFc25iwPiSAIoiWRCpEN34ke22/WN9qvl5rePrNjAb+Apikuti0JI4tFXu6DMf55kYoJdUByP5JXEK57EUBPETG1z5Re20g2lBAgYUu0SlIzTOgzZywaurlO5Kj4fqf6Bp0eWwaAu/JK+aCasG1okBmFdlmX4dxFU5E4dax4tVcTtq5CkYuLXfunfXWF96XHVml9nWAE0HviIEfPrZNewvVNuWOqNzCCcGUMOH6cfyyEiQPyz8aiRWADB6L9lx/bh9jbb2vO1QqDQysfvRVGCYIgWgJBiEhTNkLRAJuNvyxrejZNJuDiIOnBzXH2uNi2JIw2b3Y0q4wBgwbp8NxK7mHOoqnIohcWAbS85XptI9lQQoCKRxGtmpq95Yh3sU/CoV/VN+gUttw11wA9ewLr10te2LWwVc1PcSVspdsFYWs2y4sZqeGrK7wvhW1xsS5RK2AAw+CCXPmg0mMrE7ai19WhSqPUI3v0KP87XvJJkm7fudPhf8+B9+IrQ9Rt4LA5O8+h6rHLSpQEQRB+4O9Dofbe4KHgr5+MiZdmQdRkZUmuV9Jr5d13A126iJWJy8uBpCS+KvK+ckSfkyRWRW7aBkB8HO/hNq39tmwBtm7lt3/yCTBmDADqhQqIwV2y91KJ5B6mDuI9THMqHWt5y4V56LGNZEMJARK2RKsm5txUl/toCkK9ocihoY5FNbwhbOfMAfr1Q93ve0TzIS1+JIQBm0zgrroK+PJL9flJVqW9ji+FrdnMz90NcRsCRWi0k1BkabVPhxsY6Xsj/I1aHlsNOMb43OCKCtT89BdKo3siZvxozVY+1K6AIIhAo5tZvE0MgXolfgdRU1XF/+Y44N//VrWfcfC8KrK7yCrqvvqoKGxjYuz7tCVhNGSItml1KVA1hG1zFgFctQTSaxvJhhIACVuilRPfo4PsuZpnU9Pbqbd4VEODo7DVyts9dUr9/AL794uP33sPeO89SPdiTz0tzlX6muedZxe2x4aNRTRXi3bfNQndJUt8187Alzm2JhPw9NPA3Lm6D2mEUS5unYQiW7lQgGncwKi1AtLy2GrADEZwgwcDJhNiwLeocIXJRMaYIIjAoVO6PBTZYJB7bAEVUSMI29hYl/UpfE1+vugNNBiAomsjcZGwUdGSr60II5OJ73Ik9ZIKuBSoEmHbwIVr21A30OMt12sbyYYSlGNLtG4URrUx/RyHLreNBg3vW3OErValZangVQpbi0Xb69oEJ5299DUlgi3luYfQ7ubR4rZ27Zyes1k489h6w5t77bXi42HD+KrIDzwAzJ+PUzfeIXsvbeCwJXup/HgnocjTHgjTrtKosuhwxhrmdLsUe5VrsrAEQQQzIaL/Y9CARhw4wIsiafuWBx5QHCMIW2mUix9QC3H9cJ2kk7hy4RP6ihy1BnJygAMHgIcfFm9vdAlUibB9cl64rkrHrlooeaMlEEEIkLAlWjcKQRp6wxhw27ej+oHZqDPyBi7UptJvFtAfilxfr1/YSvvTKsWRu20SNIQtQkOBSOfG21NKiyz48/4lqL7/ab4S8DGNvxPwjjdXuqKekQHMmgUsWgTMmoX2H70DbtUq++ZT2dOQuUJhXZ2EIscmhmrfwBw86DAU8dUnYnEqFY9tY0oq9k2aL69yTRAEEcxIikclRDXYQ0NLSnhRZLMBL74oKfjDWMAIW7UQ11PMN7ZRL0qR58++uSYT8MILvMDV3YpHImzjO4a7XATQ20KpOS2BCEIKhSITrRu1MnkZGTh7DIh52UWIqy88ts6ErZttEqpPWPH7agvMKEaScCMBOApbpbjzkMIJ+RhaMMleSIQtnof2zg6wWmU3RR7hKidZYlGjE1W2OwlF1gwntljEHCwJ9h6007OQqjKXkAvPxznLZqmfkyAIIhiRXsOl10/wa4zKIlJXXXYGnYX9/Cxs1UJc67kICKE+Jw6fwa8bm3JvW8A7qAyL/uc/gXfeEZ/n5flH0LkVvuvKJktwVRSqWfMgCA3IY0u0bpSCtCmsqubzQtctcpojbLW8lc5CkU0mYPlyp1Nikllvf+kHXDIuDUnjRoK99764U2ioPPzYC6vSpUUWDCmYJLtg6Gox1FykHtuICMft0v9hnYrn3YnHVlN0O/Gc23vQqonihATN4wiCIIISqe2T2Dmtgj8Hfq0SB/wsbNVCXG+dKC76Pv3oGd19WJuLmsgrKAjCvrluCNu21EKJCBzIY0u0btQ8tgBirskEW+JCnDUnFFkLZx5bQKxesW4d8NdfQFISKhGPsnKgQ89EJMZZgfHjAQCjsMF+mCz31gehyEcLi+2eWt3o/Z84w5URdVfYKkO21XDiObf3oA2vdNxIwpYgiNYGx/ELwo2NwJEjfDHCykoM2FeGx5AMBiAZZShDMmoQjz5f/Cke28yIHVk1Yw89eQ4FobZEAm/y28IZv3DqypPoDdREnpLmtMxpMdwQttRCifAHJGyJ1o2GxzZldAb+GpIN8+YCbXHbHI+tFlJhq+aBBHirNnWq/Wl80w8A4PvvXb+GD4Rtx0wdvXKVtLTHVuqNFfAkFFnwnE+aJBuW9aDdc8rxOBK2BEG0NvLzRftmsQDTpgHgq7wvUNv/TcnjDz7gj/cgvlYZttucMF1ZiKvENkZCtA++FpVqIk9JUIg+N4RtW2qhRAQOFIpMtG40PLYA0HPTClhTOmkf6wth6ywUWQ8hOtaifJBjm5phQl27eNc7StEQtsoCVE5xNxRZ0Zjv1M69qJy/hO8na7HoC0UG+DuoQ4eAJUtQM+kB7HlgCY5tPygWp1J77/zc1oIgCMKrWCzA5MnNO8eUKW7H12rlZnolTFdD2PpaVKqFRWdne1YJuKUKTqm+jhvCFnBdFMqfxbOI1gl5bInWjYbH1v40PgY4dkT9WL2hyJ56bFtK2Hqj8mNDAyLOVrt3jMr/ZNMdSzD4v9NkBai47GxgxQr1c7gbiqwQ0+1L/0b7J3gPAzgOGDdO3OiqF22T51y1B+1HHznszubMBSeUDCUIggh2iosdFgvdxmZz2xXqLDez2d4+yQJpe+6MV/qw6kWtT+4zz7jXN9ebnmyPXsdNYQtoF4Vqqb+FaFuQi4Fo3Tjx2AJwemE+WSnx7rVUjq0rlPNXIyTE68Wjjn3+o/t9aRUis7TIgsH/neZQgIoVFGh7bt312JaUaM+HMeB9RZEtT7BYwB55xGGYAwObEgzVPwiCIHRgNvMLgs3BYHDbFSqE7UrxmkdVsug76fYzLdZeRvBMAvI+ue70zfWpJ1vv63ggbN1+DYJoBiRsidaNC4+tM69d+/fyxb6lzfDYyta7/RGK3ExhWzghH0nXD3H/QIWwPVpYrHrB4QBg0yb1c7gyotL3r64O2LPH+ZyYosiWJxQXg9PwYnA2KvlIEEQrwWQCli3zWNzawKHwn3luu0LVwna95lGV2MYo4xndorI56O3l6iost6WqDDt9HS8JW6qYTPgKCkUmWjfN8NhyAAYX5PJ9S10JW0V/P4ED4x5BQvhJRL+9hB/wRyhyM3Js+TY/kz1bAVOI/Y6ZZtjguJrGAHBDh6qfw12PbXq68zlxnChuXYUia2E2g3GcqrhlBiO4gK/+QRAEoRMhfnbLFqCigh+rrATKy4GkJP550+NKxOO5xyuRhHL8hZ74DKNx9F0TSp5xXzyqhe16BaltlNoXH6G3l6uesFynVYYtFmDzZv49qqwEysqA5GQgPl7+HFB/LNnvgvBkPAYgqanadRXikYBKXPROGfDTT+KLnzjh8f+FKiYTvoKELdG6aYbHFgBCYMWxTXuR2t2zUOT0e/4BrF8vDkgNqS+FrZRmeGz5Nj+e5ViV/XIYyeecY3+emmFCRZcLkHhoh32MAXyObUaG+kmkq8NqwjYkRBSrdXWisVaD44BrrgE++4x/7qnH1mQCt2wZbJMnwyARt4wzgMujko8EQbQyTCZg7FiXu+3YCDynHGxGbqxWbmaz8Hb9CRfoyRfWK341qwyvz3eo4t8c4qGj4jUADBvGe/Q9iOOmismEr6BQZKJ10wyPLQA0wsj3LfU0FDkxUVtA+SLH1mjkBVxoqDjnZhjvjplmWN1r8mOn4sf9DmOJPeSVlbnoaO3CUYDrhQCOE8fr6mTvQ935Gfj/9u48Oooy3xv4tzqBgMSEgAQIzTphmwDiEjHKpg5ERkYuzs0ZtleWRsGFCHoNIo7AVTFeObgMiuxEo/HAhbggOoDAwIssGdF3wIUJAglhCySEECBA0s/7R9OdXqq7q6uru6u7v59zcrSrqypPPwF++dXvWU5ljm84d948IC2t4bXaii0AmEwwlJaiYvEaHJqxGBWL10AqLeHKF0QUtQI6N1Yr9g9Ig5DYKukTX4bluqwynKnBytVqCeHXxFhvKyYTqcGKLUU2bxVbD8llPQzYPX6JZd/Sjf9y/z08DEVGixbyCVRsrLrtYbxVbK1JtCRZFpCqqfEteFuHMx0+jOrfynEZyfjt939Ct5+/sJ2idD/bln1ktlKqrnZ8Lbf3rD1vFVvA8jOsrXVJbOO6d0bbR7OAv+dZDgjh+HNSW7G1MhrRcmoWWvp3FyKiiBAWVbggVmzLyixJa24uMGuW+z7xdViuQyV7mwYrV/vDz+WqA1KVp6gWFoltTU0NXnrpJaxZswaVlZXo0aMHXnjhBYwaNcrrteXl5cjJycGGDRtw+fJl3HrrrXj11VfxwAMPBKHlFHLeKrZuqnZnBmXB/OZCS1Irdx9n9gmYvaQk+QRK7aILShNbwBLAa2qAEydwfuZ8VJwDklGOhN8lWyLmPfe4TvJ57DFbkJTb5uZy5gjcdPY4sH+/16Ym/76V60H7xbMAS79ZJxXJUTJ023r82jXHxDU21vJgwaqy0rGy7m9iSxQmGEMpWAI2N1YrjRo1TF8pKQEWL3Y/B9Vu3un5uGTHGOpp7mpSEvZtOo9dn5XjDJIhAdh9bzla9EhGiy5JSDpzHpjecJ2xvBz/GNUdYz/9E0rNRt8eCFhXrg5Vcqu7kjxFu7BIbB955BEUFRUhNzcX3bp1wyeffILRo0fDbDZjzJgxbq+7evUqHnjgAVRVVeGdd95BcnIy3nvvPTz44IPYsmULBg0aFMRPQSGhsmLb+s8DgHS7qOItsZVZoMncpAkMjRuHLrG1tunCBST9z2wkOZ8rSQ3zY8rKHJJad27q0Qm4Vu3xHJv6eqCsDBVffoezhyrQqntLtDx/3vW8K1eAZs3k76G0Yms91zlxdU5s7Z/W+zMUmSiMMIZSMPlbhbNWOrt2DUBivHJlQ5w7fhx48klFlyXd+FLqrhtfNrtufLnRH8AxPIlD/7Uc8c+YHD63x/4wGoFp04B33/WhdRoxGHRYkqdop/vEduPGjdi8ebMtEAPAfffdh5KSEjz//PP4y1/+ghg38w5XrFiBgwcP4rvvvkNGRobt2ltvvRU5OTnYu3dv0D4HhYjKii2SnEKYt8RWZkiTVFuLf987Ad3+z92u56tNbL3NsbUmtkVFwKVLns+1zo/JzLRETSVPfKurlbd9/XqI+fPRErAN15UdxuwpsVVSsbX+DK9e9V6xtV9cihVbigKMoRROlKwOrFpZCOejKiAB6LHwceCZTACWZFFRf9x5Z8P/Z2UBt93WsGq1tbIss4q1w/97Os/5vaQky/ohGRlMakl3dJ/YFhYWIj4+HllOK/JNnDgRY8aMwd69e3HPPfJ7bBYWFqJ79+62gAwAsbGxGDduHF588UWcOHEC7dq1C2j7KcTUzrG1T4jk7uNMLrEF0PW7PFTd0Q7Nnd8MdMV2505l97POj+naVdn5bdpYApwCYv58lyRWdm6up+2I/KnYxsY6PKC4/v2PuHLTLQ3Dq61bVxBFMMZQChdKVwdWTekD3FAym21zVhX3h31MHjECGDs2IE0LaCWdSCO6XxX54MGD6NmzJ2KdfqHv06eP7X1P11rPk7v2p59+cnvt1atXUV1d7fBFYUjtqsgaVGwBSyJX+5Pr6sAuSyAqpTSxHTBA8S0vrPkah+d8iLpEBQOtbrlFcVKueC1lTwt4eNvHFvA8FDk21tZnjU6fQMKR/9fw/sCBlsfhRBGMMZTChbfVgcvKLCvoqlyEt2E+qp4ZDLY5q4pXS7bfT9b5dxeNrFhh2Sb+/vst/2XoJL3SfWJbUVGBFs7VM8B2rMJD1cWfa19//XUkJibavtq3b+9r00kP1O5j62tieyP6OD8LFgASGrkmbqK0FJgwwfM95ShNbNPTgfHjPZ97Q+Li/0HqytmIvWD31HfkSFzp/wf5+7tLMJ0ofi6uNLH1tnhUXZ1jhTc2FigqgnC3FZOfWxUQhQPGUAoXnrbG0SSxMhot60roObldutRWDlW8fZJ9xVbm76u/3FWOGTpJj3Q/FBkAJA//CHl6z59rZ82ahWeffdb2urq6moE5HKmt2NonVHL3ceN6XDwaXa2BBEtid/SO/0Tnv/+vy3kSAJGXB+mppyxJqFLe5tjaJ76rVwNPPQV89RXO18ahsgJIvnQENxcs9/59Pv8cTRctAv7vFsfjPiS20v33A1u3ej9RyVDk2Fj3n93+Z2g/r7hRI2DnTs+VYz+3KiAKB4yhFAz+DlV1t10QoOEQZeuyzbt3N0xHUTC39HzjVqisAFrhLBK6WN6rOnYeK984izOwXJeMsziHVsiZn4QkKJyvCgALFwLnzll+z7B74C3XH7m5lj62vg8g4BVbT5Vjhk7SG90nti1btpR9Klx54y+y3NNkLa6Ni4tDnNp5kKQfKiu25tvvgGGZ3SoNChPbxp3a4cyCj3Dxm124+cF70aVZDXC/a2IL3Biqu2uXb4mtL6siA5Z7p6c3rOi4bRugJLE1m4GqKvn7K/17ofSX2CtXHFZPbhN3Hs2v3tgK4cQJyzkGg+W3Jrko6i6xjY0FBgzwvO8utyqgCMcYSsGg1aJPctsFbdumcWJlNFoWWfKB3KrIP2wDnnvD9dzMDGDwYB9uvns38OWXlg85e7blwfqNhQ5N5eV45LlklF9LwqlfzuOH58vxdyRjE4AxD5Sj9/3JwA8/2G61b0ctUpppm3D6us8uUSjpPrHt3bs3CgoKUFdX5zBH6MCBAwCAXr16ebzWep49JddShFBZsTUIM8SUKZCsj4QVJraIjUXr4eloPfxGslpWBiFJkGQWrBAApHvvVXZfW8O8tMPbSr9KF4mym+fjoHFjxRVbKJ1T99lnEIsWOaye7OLaNaBDh4btiex5SmzT0yGNH2+pjjvfk1sVUBRgDKVA03rRJ+ftgvSaWGnWLvtY+YZrpmxNqrsDGGz/xrc3vuzc+fhtmCItw93LTJqtJu2uks7QSXqk+zm2I0eORE1NDdatW+dwPC8vDykpKejXr5/Ha3/99VeHLQnq6uqQn5+Pfv36ISUlJWDtJp1QO8cWgGS/SoNcQhkf73rMObE0GiEtWwaz05A9AUAaP963ai1gmRvkqWrrLbE1GoHlXiq2kmSJYh07yt9faRWmpkbRaWLRImULTbmbE+tpKDIArF4Nad8+XJgxB79Nno/z8xcDa9YAJSUa7iNBpE+MoRRoihc5UsmaWFmfS+slsdKkXWVlwD/+oVmbDBBYLKbgvx8v03QOrMkEHDtmqZ4fO8bQSfql+4rtsGHDMGTIEDzxxBOorq5GamoqCgoK8M033yA/P9+2/57JZEJeXh5+++03dLzxC/mkSZPw3nvvISsrC7m5uUhOTsb777+PQ4cOYcuWLZ6+LUUKtXNsAYiYGEjWR69yiW2zZq7Jm1zSaTLBkJmJig27UfX9Ydxy81Ukjn7I96TWKibGcfVfe0r2ZrWO9dqwAfj3vx3nDt3RDRg+3BKZ5VY89WGOLS5eVHSaT8t4yI0/s3s4ceHnMiRaX9j/LNLTkZie3vAeUZRgDKVAC0ZFVW6Ish743S7rhFkNxaIenc2HcfiwUXF7lMyPdq6kE+mR7hNbAFi/fj1mz56Nl19+GZWVlejRowcKCgowatQo2zn19fWor6+HsBvyGRcXh2+//RY5OTmYNm0aLl++jL59++Lrr7/GoEGDQvFRKNh83MfWDAkGCJgNMTDYP3qVS2xvusn1mLtqqtGIllOz3A+19UVsrOPqv/aUJLY32oOpUwHIzx0CYEnc5e6vtGLrYSiytZ8BeJ4D60zmt6XT+0rQ5sb/3/zua45tJSLGUAqoYA1V1Wti5Ve7rFsQabi/bh1icNSQqvjBglbzo4n0QBJC77tV60N1dTUSExNx4cIFJCQkhLo5pNShQ0CPHg2vd+8G7r674fUnnzhsZl6a9SyaZv0JrTKcHr3++CNw220Ot77epTsaHTnk+P369wd27tTwA8hISHBfDX3wQeDrr7X5PmfP2hawsNm4EThyBHj6ae/Xt28PHD/ucrjy9gcg/fnPSJr9pOVAfLyyYcsyEfdUURla39XBliQ7eOstYPp07/cl8gFjgTrst8hXVqascunv6sla0Us7sGIF8NhjmiS39TBgqrRU8RzbsjLLrCPnavuxY/p8iEDhL9CxICwqtkSq+Vix7dC3JZA12Pt9AMQ6J7Vy99faihWeh/hqWaWUq9g2bqy8YuumnS3u6Qn0sVsx2S6pLRvxBOJ7tEfza3ZbIwBAy5ZARoZLpD29sxht3e2Yy4otEVHQKKlc6qU6qJd2AHDcgujwYeXbBN34//NIwplyIDa5Jc6mZmBOhvIhyNzKhyINE1uKbN7m2DovHuUuMZVJbGWHz9ovXqS1sjLLU11PtEzm5ObSajHHNi4O2L5d9q12n38A6U/LANMsRd+izYCuDsOaHQT6IQMRESnmbvXkPn0szzeDVTnVehVnf9rRUDH2fQsiQD5Bz/DhM+h1xWkitXS/KjKRX3ys2PqS2MoRRUUOG6xrqrjY+1AlLRNbg8F1HnGjRpa9d5Wor5c/fvWqZUN6GRIExOMyKx+70TbdiIOZz9leO/QOE1siIt1wVx28+27g/vstQ2JXrAhdO7RaxVmJFSssn9efz+0uQfdlNWS9rjhNpBYTW4psPlZsL5xwM9dTYWIrARB5eUBRkcIG+sC6yIQnWg+/dR6OXFkJ8cEH/t2zpsZjgi6ZffsNo8/SaQ3X2r/BochEREFRVmbZCsZTUmWtDjrzJzFTQ64dwaxSapGQAtol6NatfNassSw7kpnp2/VEesLEliKbcyLoXMXbtMnh5c0L52DnBJlHpwoTW+BGcqW0qukLoxFYtsxzchvoxPbUKd+255HTtq3HzyAMPv6G0a6dyz7BAHD+2AU1rSMiIh8orT46VwflwmowKqehrlJqlZBqmaD//e/AqFHAX/4CdOgAPP984B8wEAUCE1uKbJ4qtmVlEK/nOp4OICNvCk4VOf2LLhOBhZsUTwDAvff63lYlTCagtBRYswbn5y/GtXinjXoCndj+7ncQ3qrG3iQnu03QhWSAtNS33zDOfL0fkkwFuPmcbPmHFEREpAlfq4/W6uC2bcCePaGrnNq349ixwC4c5VzN1ioh1SpBd/4ZCgEsWBC8oeFEWmJiS5HN0xzb4mJIMosOxaIeZ3Y5PTp1vk/TppCWL3OpFAoA0vjxQHq6H4324sYiE0mzpqJxq+aO79XWavqtqs9cdnhd9Lc9kJa5fm6fxMW5JOiHZixGxeI1kEpLfP4No3rjTtlHDBLcPKQgIiJNqKk+Go3A4MGWMBnKyqm1HYD3YdRqyVWztawYa5Ggy/0MgeANDSfSEldXocjmqWLbtSuEJLlU++oQg9b3Oj06db5PUhJgMsGQmYmKDbtR9f1h3HLzVSSOfiiwSa2Tq6cqYL/8lfjwQ0gDB2ry+PlUURnanDvqcOy2wr/i1KxjaFtaivMrC5E0J9v3G1sX7LIm6ACSPF7gWcIfB0Asll+l2vqQom06V8IgItKav6vqWne6UbL/bSAEctsfT6sve/rcvu6vq2SbJU/kfoZW3PqHwg0rthTZPFVsjUaX6mM9DNg9folrIuR8nxYtbPdoOTULv1s2C4kL5wY1qT2zoQiNa6sdjkkAxOOPa/KI9fTOYpdk0VbNNhqRNPE/1N1Y6T64CrUeno6jd/yn7G62sg8piIhIE1pUH62V02AnT1ot4uSOt2q23Oe2r/AGa66r9WcoN+eZW/9QuGFiS5HN26rIJhMMpaWoWLwGP81Zg/J9JRiwWuZxrVzFNsTcDsE1mzVZfcOyR6wjh0RR7XxejRNbAOjyz7Wonjbbob1uH1IQEZFmgjlfVUuB3vbH17m0oZzrajIBJSXAf/0Xt/6h8MahyBTZvO1jC9iqri19uY8OElt3Q3CFwQBJg0esbdONONl9MFIObbcd2zf6LdxjTRR1lNgCQOK7rwI5U1GxYTdOnwZaPJSBAUxqiYgCzt/hsKHg7zBqb6yV0ClTLAmzt0TR21zXzMzA9rHRCLz5JvDMM6EbGk7kL1ZsKbJ5q9iqvM/lusZuTgye1sPTUXzPeIchuAISpKVLNYtGKSPvdnh9z3K7R/E6S2wB2B5SpM3NYqWWiEhnlOx3GyzB2PbHl2q2u31+geBsg2QVqqHhRFpgYkuRTUnFVomCAoeXTTf+ry62kum2azXKv9yHo+PnoHL+YkjHS7UdB5aQ4PjaPpnVY2JLRES6pHS/22AKxjBqpYki57oS+Y+JLUU2LSq2ZWUQzz3ncEhPW8m0Hp6OzqvnosWsqdo/YnVObO0fDHhIbA8bB9kqyS6LOjGxJSKKKoFeqMkfoaxQOlewOdeVyD9MbCmyaVGxLS522RIIcLPfbaRJTHR8bbeC9E7TareXpR7fjvIv9+HwU2/h7KovHd9kYktEFFUCvVBTOHJXwbbOdQ3HBbmIQo2LR1Fkc05s3U1g8cSX/W4jjXPF9oZTRWW458MpHi9tPTwdrYenA2fPOr7BxJaIKKoEeqGmcONpj1trZTYcF+QiCjVWbCmyOSeyktwGOV74st9tpHGT2J7eWYwYl82A7NiPL3MesszElogoqgRjoaZw4q6CvXatPoZnE4UrJrYU2dRUaOUo3e820rhJbNsM6Ip6D/98mDvYjatq7LSCtPNrIiKKeOG6320guFsB+dln9bOwFlE4YmJLkU2rxBaIzq1k3CS2bdON+G78UtTB8vi9zumfEoMwQ1hXBnFOZFmxJSKKStxKxsK5gm1PTwtrEYUbJrYU2dQMPaYGTomtfaAdsNqEs/uOYe2T2zAGBXAmWVcGcY7cTGyJiCiKyO3fa61gL1zoen60L6xFpBYTW4psTGz9s369w8t5HVY4DJFqm25ExqzB2CPd4zI0WVhXBnH+GTCxJSKiKOFp/16jEcjKkt+ZMFoX1iLyBxNbIpJXVgbx1FMOhxaLKfjvx8scnjobjcCcZUZMlRqGJpsNMZDcrQzCObZERBQFlOzfy4W1iLTD7X6ISF5xMSSnZRtjUY/O5sM4fNjoEHRNJiAz04Si3ZlIxWG0ykh1G5VPfX8yeuYoExFR1PK0f69rDLUcT3UTPsvKLPfr2pVJL5E7rNgSkbyuXSGcxkfVIQZHDamyQ6SMRiAjy4hWWYM9Rt3kuzpi5wQu+UhERJFNbvVjd8OMPS2s5Wk4MxE1YGJLUYWrDPrAaIS0dCnMBuvKxzF4QlqCl5cafXpafKrIsdNjYEZG3hSX40REpE9yix+Rd1oMM1YynJmILJjYUmRzeqzpvPgReWEywVByDGfXbEPRmmOYU2ryee/B0zuLXY7Foh5ndnHJRyIivWO10D/+7t/raTgzETmShBAi1I0IB9XV1UhMTMSFCxeQ4GZvT9KZsjKIjh0d5onWIQa/MxzDrhLfqo6k3qmiMiTf1RExcPw5nN13jHNtKewwFqjDfgtPZWWWZNY+sYqJsSRojKHBwZ8BRZJAxwJWbClyeVz8KERtikJt0434bnzDisl1iMHu8UuY1BIR6RyrhaHHVZOJlOOqyBS5bix+5Fyxdbf4EQXOgNUmnHoqE2d2HUbre1MxgEktEZHuWRc/cq4WMoYGl5JVk4mIFVuKZBotfkTaaJtuRN/pg1mpJSIKE6wW6oenVZOJyIIVW4psJhMMmZk4u/swDiMVczKY1BIRESnFaiERhQsmthT5jEa0yjKiVajbQUREFIaMRia0RKR/HIpMREREREREYY2JLREREREREYU13Se2NTU1mD59OlJSUtCkSRP07dsXn376qaJr169fj9GjRyM1NRVNmzZFp06dMHbsWBQXFwe41URERKHHGEpERNFC93NsH3nkERQVFSE3NxfdunXDJ598gtGjR8NsNmPMmDEer33jjTfQpk0bzJ49G126dMHx48cxf/583H777dizZw/S0tKC9CmIiIiCjzGUiIiihSSEEKFuhDsbN27EQw89ZAvEVkOHDsVPP/2E0tJSxFjXoJdRXl6O5ORkh2MnT55Ep06d8Oijj2L58uWK21JdXY3ExERcuHABCQkJvn8YIiIKe+EUCxhDiYhITwIdC3Q9FLmwsBDx8fHIyspyOD5x4kScPHkSe/fu9Xi9c0AGgJSUFBiNRhw/flzTthIREekJYygREUUTXSe2Bw8eRM+ePREb6zhiuk+fPrb3fXXkyBGUlJRwCBUREUU0xlAiIoomup5jW1FRgS5durgcb9Gihe19X9TV1cFkMiE+Ph4zZszweO7Vq1dx9epV2+sLFy4AsJTQiYgoOlljgI5n8dgwhhIRkZ4EOoYGLbHdvn077rvvPkXn/vDDD+jbty8AQJIkt+d5es+ZEAImkwk7d+7EunXr0L59e4/nv/7665g3b57LcW/XERFR5KuoqEBiYmLQvh9jKBERRYpAxdCgJbbdu3fHsmXLFJ3boUMHAEDLli1lnyhXVlYCaHjq7I0QApMnT0Z+fj7y8vIwYsQIr9fMmjULzz77rO11VVUVOnbsiNLS0qD+MhPOqqur0b59exw/fpyLhfiA/eY79pk67DffXbhwAR06dFAcf7TCGBp9+PdTHfab79hn6rDffBfoGBq0xLZt27aYPHmyT9f07t0bBQUFqKurc5gjdODAAQBAr169vN7DGpBXrVqFFStWYNy4cYq+d1xcHOLi4lyOJyYm8g+vjxISEthnKrDffMc+U4f95juDIbhLVDCGRi/+/VSH/eY79pk67DffBSqG6nrxqJEjR6Kmpgbr1q1zOJ6Xl4eUlBT069fP4/VCCDz22GNYtWoVlixZgokTJwayuURERLrBGEpERNFE14tHDRs2DEOGDMETTzyB6upqpKamoqCgAN988w3y8/Md9t8zmUzIy8vDb7/9ho4dOwIAsrOzsWLFCkyaNAm9e/fGnj17bOfHxcXhtttuC/pnIiIiCgbGUCIiiia6TmwBYP369Zg9ezZefvllVFZWokePHigoKMCoUaMczquvr0d9fb3DKltffvklAGDlypVYuXKlw/kdO3bEsWPHFLcjLi4Oc+bMkR1aRfLYZ+qw33zHPlOH/ea7cOszxtDwxT5Th/3mO/aZOuw33wW6zyQRDnsWEBEREREREbmh6zm2RERERERERN4wsSUiIiIiIqKwxsSWiIiIiIiIwlrUJ7Y1NTWYPn06UlJS0KRJE/Tt2xeffvqpomvLy8sxYcIE3HLLLbjpppuQkZGBb7/9NsAtDj21fbZ+/XqMHj0aqampaNq0KTp16oSxY8eiuLg4CK0OPX/+rNl76aWXIEmSoj0ow52/ffb5559j0KBBSEhIQLNmzZCWloalS5cGsMX64E+/bdu2DUOGDEFycjLi4+PRp08fvPvuu6ivrw9wq0Pr4sWLyMnJwdChQ9GqVStIkoS5c+cqvp7xgDFUKcZQdRhDfccYqg5jqO90E0NFlBsyZIho3ry5+OCDD8TWrVvF5MmTBQDx8ccfe7yutrZW9OrVSxiNRpGfny82bdokRowYIWJjY8X27duD1PrQUNtnd911l3j44YfFypUrxfbt28VHH30kevbsKeLj48XBgweD1PrQUdtv9n744QcRFxcnWrduLdLS0gLYWn3wp89ef/11YTAYxJNPPim+/vprsWXLFrFo0SLxt7/9LQgtDy21/bZ582ZhMBjE4MGDxWeffSY2b94spk2bJgCI7OzsILU+NI4ePSoSExPFwIEDbf01Z84cRdcyHjCG+oIxVB3GUN8xhqrDGOo7vcTQqE5sv/rqKwFAfPLJJw7HhwwZIlJSUkRdXZ3ba9977z0BQHz33Xe2Y9evXxe///3vxV133RWwNoeaP3125swZl2MnTpwQjRo1EiaTSfO26ok//WZ1/fp10bdvX5GdnS0GDRoU8UHZnz775z//KQwGg3jjjTcC3Uzd8affxo4dK+Li4kRNTY3D8aFDh4qEhISAtFcvzGazMJvNQgghzp4961NQZjxgDFWKMVQdxlDfMYaqwxiqjl5iaFQPRS4sLER8fDyysrIcjk+cOBEnT57E3r17PV7bvXt3ZGRk2I7FxsZi3Lhx2LdvH06cOBGwdoeSP32WnJzsciwlJQVGoxHHjx/XvK164k+/WeXm5qKyshKvvfZaoJqpK/702aJFixAXF4dp06YFupm640+/NWrUCI0bN0bTpk0djjdv3hxNmjQJSHv1QpIkSJKk6lrGA8ZQpRhD1WEM9R1jqDqMoeroJYZGdWJ78OBB9OzZE7GxsQ7H+/TpY3vf07XW8+Su/emnnzRsqX7402dyjhw5gpKSEqSlpWnWRj3yt99+/vlnvPrqq1i8eDHi4+MD1k498afPduzYgZ49e2LdunXo3r07YmJiYDQa8cILL+DatWsBbXeo+dNvU6dOxbVr15CdnY2TJ0+iqqoKH330EQoLC5GTkxPQdoczxgPGUKUYQ9VhDPUdY6g6jKHBp2U8iOrEtqKiAi1atHA5bj1WUVERkGvDmZafu66uDiaTCfHx8ZgxY4ZmbdQjf/rNbDZj0qRJeOSRR/DHP/4xYG3UG3/67MSJEyguLkZ2djays7OxZcsWTJgwAQsWLMDEiRMD1mY98Kff+vXrh61bt6KwsBDt2rVDUlISJk6ciNdeew3PPfdcwNoc7hgPHDGGuscYqg5jqO8YQ9VhDA0+Lf9djPV+SmTzVDb3VlL359pwpsXnFkLAZDJh586dWLduHdq3b69V83RLbb8tXLgQxcXF+OKLLwLRLF1T22dmsxkXL15EQUEBRo0aBQC47777cOnSJbz99tuYN28eUlNTNW+vXqjtt++//x4jR45Ev379sGTJEjRr1gxbt27FSy+9hNraWvz1r38NRHMjAuOBb+/5e204YwxVhzHUd4yh6jCGBp9W8SCqE9uWLVvKPgWorKwEANmnB1pcG860+NxCCEyePBn5+fnIy8vDiBEjNG+n3qjtt9LSUrz88svIzc1F48aNUVVVBcDypN5sNqOqqgpxcXEu8zkigb9/P0+fPo3MzEyH48OGDcPbb7+N/fv3R2xQ9qffnnrqKbRu3RqFhYWIiYkBYPllxmAwYO7cuRg7diy6dOkSmIaHMcYDR4yh7jGGqsMY6jvGUHUYQ4NPy3gQ1UORe/fujV9++QV1dXUOxw8cOAAAHvc46927t+08X68NZ/70GdAQkFetWoXly5dj3LhxAWurnqjttyNHjuDKlSt45plnkJSUZPvatWsXfvnlFyQlJWHWrFkBb38o+PNnTW6uBmD58wcABkPk/tPnT7/9+OOPuOOOO2wB2So9PR1msxm//PKL9g2OAIwHjKFKMYaqwxjqO8ZQdRhDg0/TeODTGsoRZuPGjQKA+PTTTx2OP/jgg16X9H7//fcFALFnzx7bsevXr4u0tDTRr1+/gLU51PzpM7PZLEwmk5AkSSxdujTQTdUVtf12/vx5sW3bNpevW2+9VXTq1Els27ZNFBcXB+MjBJ0/f9aWLFkiu+dcdna2MBgM4tixYwFpsx7402+dO3cWvXr1cjnnxRdfFADEjz/+GJA2642vWxUwHjCGKsUYqg5jqO8YQ9VhDPVfKGNoVCe2Qlj2pUpKShJLly4VW7duFY899pgAIPLz823nTJo0ScTExDj8Ra6trRVpaWmiffv24uOPPxabN28WI0eOjJrN5dX02dNPPy0AiEmTJondu3c7fO3fvz8UHyWo1PabnGjYg08I9X127do1cfvtt4vExETxzjvviM2bN4uZM2eKmJgY8fTTT4fiowSV2n579913BQAxbNgw8dlnn4lNmzaJmTNnitjYWPGHP/whFB8lqDZu3CjWrl0rVq5cKQCIrKwssXbtWrF27Vpx6dIlIQTjgTPGUN8xhqrDGOo7xlB1GEPV0UMMjfrE9uLFiyI7O1u0adNGNG7cWPTp00cUFBQ4nDN+/HgBQBw9etTh+OnTp8Wjjz4qWrRoIZo0aSLuvvtusXnz5iC2PjTU9lnHjh0FANmvjh07BvdDhIA/f9acRUtQ9qfPKioqxJQpU0Tr1q1Fo0aNRLdu3cSbb74p6uvrg/gJQsOfflu3bp3o37+/uOWWW0SzZs1EWlqaeOWVV1w2nI9Env6NsvYT44EjxlDfMYaqwxjqO8ZQdRhD1dFDDJWEuDFgnoiIiIiIiCgMRe7sbyIiIiIiIooKTGyJiIiIiIgorDGxJSIiIiIiorDGxJaIiIiIiIjCGhNbIiIiIiIiCmtMbImIiIiIiCisMbElIiIiIiKisMbEloiIiIiIiMIaE1siIiIiIiIKa0xsiYiIiIiIKKwxsSUiIiIiIqKwxsSWiLBr1y5IkgRJkrB27VrZc/bu3Yv4+HhIkoScnJwgt5CIiEifGEOJ9EESQohQN4KIQm/EiBH44osv0KNHDxw8eBAxMTG29w4dOoT+/fvj3LlzGD9+PFatWgVJkkLYWiIiIv1gDCUKPVZsiQgAkJubi5iYGPz666/Iz8+3HT958iQyMzNx7tw5DB8+HMuXL2dAJiIissMYShR6rNgSkc3kyZOxYsUKdO7cGYcOHcKlS5cwcOBAHDhwAP3798emTZvQtGnTUDeTiIhIdxhDiUKLiS0R2Zw4cQJdu3bFlStX8NZbb6GwsBA7duxA7969sWPHDjRv3jzUTSQiItIlxlCi0OJQZCKyadeuHbKzswEAM2bMwI4dO9CpUyd88803sgG5pqYGc+fOxfDhw9GmTRtIkoQJEyYEt9FEREQ6wBhKFFpMbInIwTPPPAODwfJPQ4sWLbBp0yakpKTInnvu3DnMmzcP+/fvx5133hnMZhIREekOYyhR6MSGugFEpB91dXV4/PHHYTabAQCXL1/2OB+obdu2KCsrQ7t27VBbW8u5Q0REFLUYQ4lCixVbIgIACCEwefJkbNiwAa1atULnzp1RW1uLOXPmuL0mLi4O7dq1C2IriYiI9IcxlCj0mNgSEQAgJycHeXl5iI+Px1dffYXXXnsNAJCXl4eff/45xK0jIiLSL8ZQotBjYktEWLBgARYsWIBGjRph3bp1SE9Px6hRo9CnTx/U19dj1qxZoW4iERGRLjGGEukDE1uiKPfhhx8iJycHkiRh9erVGDp0KABAkiS88sorAIAvvvgCu3btCmUziYiIdIcxlEg/mNgSRbGNGzfCZDJBCIGFCxdizJgxDu8//PDD6NevHwBg5syZoWgiERGRLjGGEukLE1uiKLV7925kZWWhrq4OM2fOxPTp02XPs84T2rVrFz7//PMgtpCIiEifGEOJ9Ifb/RBFqYyMDFy6dMnreQ888ACEEEFoERERUXhgDCXSH1ZsiYiIiIiIKKyxYktEflm0aBGqqqpQV1cHAPjXv/6FV199FQAwcOBADBw4MJTNIyIi0i3GUCLtSILjI4jID506dUJJSYnse3PmzMHcuXOD2yAiIqIwwRhKpB0mtkRERERERBTWOMeWiIiIiIiIwhoTWyIiIiIiIgprTGyJiIiIiIgorDGxJSIiIiIiorDGxJaIiIiIiIjCGhNbIiIiIiIiCmtMbImIiIiIiCisMbElIiIiIiKisMbEloiIiIiIiMIaE1siIiIiIiIKa0xsiYiIiIiIKKz9fwN+SUYm67SfAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1100x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 14,
+   "id": "b1884a94",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
     "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
@@ -2062,8 +1751,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6dfb6d9",
-   "metadata": {},
+   "id": "96cde198",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Pros and cons of trees, pros\n",
     "\n",
@@ -2084,8 +1775,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6683775c",
-   "metadata": {},
+   "id": "43ba8c79",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Disadvantages\n",
     "\n",
@@ -2110,8 +1803,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf52cfd7",
-   "metadata": {},
+   "id": "f0edf840",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n",
     "\n",
@@ -2139,8 +1834,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c39236d",
-   "metadata": {},
+   "id": "1f77bcab",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## An Overview of Ensemble Methods\n",
     "\n",
@@ -2153,8 +1850,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "437f82d0",
-   "metadata": {},
+   "id": "9a9f1f37",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Why Voting?\n",
     "\n",
@@ -2175,8 +1874,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12c69836",
-   "metadata": {},
+   "id": "a1ec804f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Tossing coins\n",
     "\n",
@@ -2204,17 +1905,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8614384",
-   "metadata": {},
+   "id": "5a687873",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Standard imports first"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "60dd5ef6",
-   "metadata": {},
+   "execution_count": 15,
+   "id": "c2eefbdb",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -2258,29 +1964,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0323fdfd",
-   "metadata": {},
+   "id": "bb895740",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Simple Voting Example, head or tail"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "74dfd159",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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AIGghF5wlPDQ01KW1u7i4GMCllbwx4uPjGThwIFu2bHE4B8CAAQNsAhzAx8eHIUOGsGvXLrfj6fV6AgICHH6caegXbj+21O6w/m822GvruWGHxLQiIt7upy5JEuFx8baMmlc/8zIAEfHt3M6xIa07J7utSxkxmkeW/EBghOvNmgKBQCAQCASCc88FZwlPTk5m0aJFmM1mB7/wffv2AdbIJ81FURRU9Vw36vzLPWnbEtT6CiwGV+IczLWbUOu7krn3N1tZXSp7T/AJCOT+hV/Zku5IKhWKLCOpZHyja6jK80axOM6/oduKK/Q+fsBpj+chEAgEAoFAIGg5F5wlfOLEiVRWVvLNN984lC9cuJCYmBj69u3brPFOnjzJxo0b6devn60sOjqa/v37s3HjRoeNldXV1axbt86h7bnBUWSbax3TxNelsvcUnZe3bXNl38utUWRiB54mcXwmrS5yFNKTn5nl2Zg+3g7HdZZ3gUAgEAgEAsG554KzhI8dO5aLL76Yu+++m/LychITE1m0aBG//PILn3/+uS1G+K233srChQs5fvw4bdq0AWDkyJEMHjyYlJQUAgIC2LdvH7Nnz0aSJJ5//nmH88yZM4dhw4YxevRoHn/8cSRJ4rXXXqOwsNCp7bnGYnAU4UozLOENqSgqBCC0UykAYZ1Kyd0SgcWoZuRt9xDrwh/cFTovRxFuMrjP/CkQCAQCgUAgODsuOEs4wLfffsv111/PjBkzGDNmDFu3bmXRokVce609mofFYsFisTjExE5OTmbJkiXccMMNjB49mtmzZzN8+HB27Njh5MYyYMAAVq1ahV6v59prr2Xq1KlotVrWrl1L//79z2r+7b0kanyyqfXK96i9bGm5CD+wbpVTWedrj6FSq0kZOdbjccxGg1OZ3CDUoUAgEAgEAoHg3HDBWcIB/Pz8ePPNN3nzzTfdtlmwYAELFixwKHv99debdZ6BAweydu3aFsywcQKiDlBZZgTAKy8CVwl86tMcn/CGxHVNIXP/XocytU6mdX9zk8l+6tNwDABjdQ1efn4tnptAIBAIBAKBwDUXpCX8747Bq7hBSeMi+2zcUSY+8Sxab61TeXCXQ1gstVgstZSW7nBI6uMpxprqphsJBAKBQCAQCJrNBWkJ/7ujaGoAqwXZojIgNSGAz8YdRa1R0fXGQyguhjCby9iwcQAAQYG96dlzsUdj+gQGUV1WiqG6qsXzEggEAoFAIBC4R1jCzwPGdr/YXisqMwruRbhK1xFDtdltfVNYrdxGl3V1AhygtGy7g/98Q25/+xMAohKT0HlbN2kahCVcIBAIBAKB4LwgRPh5xqAvojF3FLU2CYDyopoWjb9r91SP2xqN7jeKBoRH8MiSH7j2xbnofXyt7auFCBcIBAKBQCA4HwgRfp6RVQaa8gkHOHW4pNljl5fva1Z7o7HQo3Y6bx9AWMIFAoFAIBAIzhdChJ93JGjEHcVaX/9/z5BlM9t3XN6sPrt239BgDOewhACqM7HYKwoLmjW+QCAQCAQCgcAzhAj/K2jEF9vepHmbM0+m/6/Z0zCbSwGoqcni8JGZrFnbmYKClU7tMvbuBmD9lwuafQ6BQCAQCAQCQdMIEX7eUfDEEt7cMIWZmR+5LA8K7N1ov4OH/sumzUPJzv4MgL377mrWeQUCgUAgEAgEZ48Q4ecbCRr1CT/jhbL2i8PNGlaWXUdE6d79c1q1cr9ZMydnSbPOYza6Po9AIBAIBAKBoOUIEX6+USQaE+GSyt/joQoKV7F2XQo7dlzpsj4+/l5UKg1RkZc1d5YO3DT3Pdvrd2655qzGEggEAoFAIBA4I0T4eUZqYmOmSh3u8Vh7996BxVJFWflup7rOnWbTNuFhAIKCetGr59fNnmsdoa1a216bTUZMRiPb/niAHev/2+IxBQKBQCAQCAR2hAg/z6gsXlhMGS7rWndJOWfniYqahCTZI6wEBna3ve7T+7uzGnvP7ruoMP9ImWkJq1a34+TJt89qPIFAIBAIBIJ/O0KEnzesLigasw+y8U+XLXpPmHTOzlZfgNcxYvhxhg09jL9/F1KS32/WePHdethel1Wtc6g7cfL1ZkdzEQgEAoFAIBDYESL8vONerPoGh5z3s6tUGgDCwy8mOvoql22qqk44lUXEtyWobTmJl6W77LN//yNYzOZzNk+BQCAQCASCfxNChJ83lHr/ukZSnf2vv1u3j+nf73eP2nbuNIsRw48zdMgBQkOH2cq3bL3YoZ3ZXEVQ+9PEX3wKv6gal2PlF6zg/btucFknEAgEAoFAIGgcIcLPN5JrGR4UFU1YbBwXXZkIgHeArkXDh4UOxccnoVl91GovUrvNcygrKlpve33s+CzyKz5sdAxDmZbainJqKiuadW6BQCAQCAQCgRDh5w0XLtoO3PrmR0gqFVFtAwHQ6pr/VsTGXt+Sqbkkbc9NmM1VAOTmfuOyTe72MNvrqtPeAPz45uxzNgeBQCAQCASCfwua/+8J/HPxbOOiSm1V67KleRsdu6d+RkjIgGbPqjGOHn2ByMgJyLLBZf3pXeGYqjXEDclDrbeGXaxLcS8QCAQCgUAg8BxhCT/vNC6uVWrrW2BpKMIVBcpz3fY7FwJ86JD9Dsc5uUvZnebeuj7l+TlYDGoA1DrLWZ9fIBAIBAKB4N+KEOH/z6hUVku40lCE//EqzO0IW96v11YPQLcUR3/ulqJWe3vctl2b54hJ6mgX4Xq7CK8qLTkn8xEIBAKBQCD4t3BBivDKykoeeughYmJi8PLyIjU1lcWLFzfZb8GCBUiS5PInLy/Pbb+amhqSkpKQJIk5c+ack2uQzmzI7HxwAZI6wm07uztKg6yaa160/v/L47YiRbG28fPrcE7mCDB0iOsY5vUJDx9Dm7ZTAWwiXKO3z7coO+uczUcgEAgEAoHg38AF6RM+adIktm/fzqxZs0hKSuLLL79kypQpyLLM1KlTm+w/f/58Onbs6FAWGhrqtv306dOpqqo663m7QqWYQNI6lLX1S6HmQBHeXUI99gmXZQOKYrKOqfICQFEUPv3vJmorTdzxvyEuE/Y0hVqtb7JNSvI7ttcWg/W5rb4lvAWnFQgEAoFAIPhXc8GJ8J9++omVK1fahDfAsGHDyMjI4NFHH2Xy5Mmo1epGx+jatSu9evXy6Hzbtm3jrbfe4osvvuCqq1wns2kZdXHCJat/9xn8NMH0Dh9L0Wd/EjtrkM0nXJYbF+HZWT/ZXlcUyWg01exdk01liXUT5d7V2XQb0foczh8CA3sR1/pmh7JJj7/CoewrUWkUYjq2J+fQUWorK8/peQUCgUAgEAj+6Vxw7ijLli3Dz8/PSRDffPPN5OTksHXr1nN2LqPRyC233MK9997rsWj3lDrrsCJJgEKnwP6MjL4eP22QQ7v6lnB3qeCLTlWy/qtdtuPFz6bx+fQt7F2dbSs7vju/xXPt2WMJADpdBEOH/Elqt/n07/c7vXouISJijEPbmPbdbK8D2lndUP5cv4asA3tRZBlZtoiU9gKBQCAQCARNcMFZwvfv30+nTp3QaBynlpKSYqsfMKDxyCDjx4+noKCAwMBAhg4dynPPPUfXrl2d2j333HNUVVXx/PPPU1BQcO4uArBFRZFApRhJCRkMwJCoq20tir44iM/4tgSpJSosCoqsIKmdfTsWP78Nv1aB9Uqcn51yj5VRUVyLf4hXs2caFNSL4cOO2dxZQkMHu20rSfZz60KzgHCObd/Mse2bGX7znez6+TssZjMVhQV06D+I8Q897nYsgUAgEAgEgn8rF5wlvKioiJCQEKfyurKioiK3faOionjqqaeYN28ea9as4fnnn2f79u3069ePPXv2OLRNS0tj9uzZvP/++/j6+no8P4PBQHl5ucNPYyhI6GpdW6lr9hVS9PI2hvhrGB+ktbukHF3pPI7FmlHTVBPoVFfHp//d5OFVONMSf3KVuY3D8er5H1Cal0tFofWB5vDm9a66CQQCgUAgEPzrueBEODQuCBurGzNmDC+88ALjx49n8ODB3Hvvvaxfvx5JkpgxY4atndls5pZbbmHy5MmMHj26WXN7+eWXCQwMtP20bt24H7YiSUiK2aOxZYsCx1bBF1c61UlqIwCmqjCnur+aqMjLrS/8d5J650Fi+p/GL+bcbmw1mcrIzv4Co7H4nI4rEAgEAoFAcCFwwYnw0NBQl9bu4mKrGHNlJW+M+Ph4Bg4cyJYtW2xlb7zxBidOnOCZZ56htLSU0tJSm0W7traW0tJSLBbXyWiefPJJysrKbD9ZWa7D89WFKEQCxUMjs2xR4KubXNap1NbIKIpF67LePoZMbZXJsxO2kLzTyx2OI1KKSZyQiUpzJmyhpOAVUgsoKLLs1L8pZNnIwUNPcPjIDNZv6N2svpWVh1m1uh3Hjp+bUJMCgUAgEAgE54MLzic8OTmZRYsWYTabHfzC9+3bB+DSt7spFEVBpbI/b+zfv5+ysjLat2/v1Hb69OlMnz6d3bt3k5qa6lSv1+vR65sO61eHLEnWCCmetLUoYHB2b9F4lRLTz5qgpykR/t69awGI6xLKmDu7otU1HkmmJWg0/pjNFU7l7cZncHR5AjF984noZn1o2r72GSr4EoDevZYREJDS6NgHDkwj7/SKFs9t67ZLAMjIeI/Edv9p8TgCgUAgEAgE55MLzhI+ceJEKisr+eabbxzKFy5cSExMDH379m3WeCdPnmTjxo3069fPVvbEE0+wZs0ah59FixYBcNddd7FmzRoSExPP/mIAvNz7cDfEXazwVgPfrtdG59FYmQeK2P7DSY/P3Rw6dHjeZblvZC1qvcUmwAGbAAfYvmNik2O7EuDCJUUgEAgEAsE/jQvOEj527Fguvvhi7r77bsrLy0lMTGTRokX88ssvfP7557YY4bfeeisLFy7k+PHjtGlj3SA4cuRIBg8eTEpKCgEBAezbt4/Zs2cjSRLPP28Xjh07dnRK5pOeng5Au3btGDp06Dm4EqugNvsEoVR7JiI3f3uUVtUjCVTn0kp/wFbuHZJhH7UJS3h9Dm/NY8Ckc/QwUY+w0OFu6xJGZbutawq3IRqL/yA66vIm+xcWrXU4NpnK0Go9fwgSCAQCgUAg+Ku44CzhAN9++y3XX389M2bMYMyYMWzdupVFixZx7bXX2tpYLBYsFseY1MnJySxZsoQbbriB0aNHM3v2bIYPH86OHTta5MZyNtTtHy318Txk4LHt+awpv5flJS/Y8vvU6hzfIkVxdC/pNCDa7XimWtd+7WeLRuNLUFAfl3V+MdWN9lUU93Pas/d2l+V//vkIitK0b3lh4SqH4+PHX22yj0AgEAgEAsH/B5IiMqucFeXl5QQGBrLiu3h8fe2CecvmKzGZvIkoqsJyai8Tk55ucqyjtRZyTQolFoXefovo47eUch8123sFO7Q79tU7mBWrW0ry0Fj6XprAvGnO4QC1XmrueGPIWV5h4xQVrScvb5nHftw9un9JcLCzS5HRWMj6De5djbqlzCMsbJhTeUnJNjIy38fbuw3Z2Z861Y8YftyjeQkEAoFAIPjnUqfXysrKCAgI+P+eDnCBWsL/aSgexuBu76VmsL8GUDgU3JZKHzUyzhsrxwS9Ynut0anQ+7h2UQmO9GnRfJtDaOgg2rZ9yGVddYHzKsChwzNctISCBlbshuzZext5p7+n1pDnUL5r9xSKita5FOACgUAgEAgEFypChJ9nWrLM4Bu9l1b957G1VzBfFzu7VBhku7iWVO4Fflhr/xac3QU1pbDxTSg75bLa2zvOqaxD+9kc+yGO8ixfMtfaXWb8/Dpw+vSP7Nh5NSUlWzCZrNFgDh36r61NQsKDXDRgA+3aOkY3OXDgIbZuHWM7NhgLXc6nc6fZttelpTuavj6BQCAQCASCvxghws8Xkl1+e2oJVyQLBe2XEtb5R/swKsdEPx2PVFBuibIdH/jDWRi36x4OgMnQuE94YU0hBouh8Ul9ewe80gZWzoDPJzV1CTaCgpN5+LOfOfFTHMWHgyg6aN0gmZ//I/sPPEBZ2U527b6WzVtG2IR4Ha1jb8LLK5q4OGcfcbO5gqLiDQBs3XqJy3NHRIy3vd65a7LHcxYIBAKBQCD4qxAi/DxRX3Z7ag0vavsdxQk/4R1qDy0oqe2Jd/x9OxKTZyBad9BWZqi2ivRWHex+46GxfgCYje5FeE5lDsOWDmP8svFu2wCwd4n9dcEhj64DwNfXGpUlsXd/AGpLXcdWN5mKnUIXarVWXy2VSkP37p879UlLu/FMX+ekTp07vYpa7XiuTZuHkZn5icdzFwgEAoFAIDjfCBF+vpFs/zRJcZtfncpUKqsIry2NpXfnj5GACO1RW31CN2sa+/H32pPg+AZZRWjpafeRSv6zzurqkVeVh+xB5JGmSGz3uO31gP5rkSTrRytlpNV9xDvUvcW9pibd9nrI4DSHuqDAXi77mM1VLss1GqsLTnSU3WpfU5PJ0WMvehRhRSAQCAQCgeCvQIjw80RAQAGANVum5Omv2dlmLqmtlm7FrEMyW4WsBrt1vPNFMdYynZqbXrmIm2cPpLrM2q4krxqLybXw3Fe4z/Z6TdYaD+fnntjYG9HpwgkI6I63d2tbeXy3HgBU5no3OUZY6HCbiK5DpXK96XTdH/aHjh7dFxEVeTm+vu0JCRkEgMnsnHm0piYDk6mk6YsRCAQCgUAgOM8IEX6eCAjIt72WPBThPiUdnMoktVVQy7IWzLVnxrOL9fru5r6BenwCdBTl2K3EK+f/6fJcQ2OH2l4/tOYhp/qjJUdZnbnauaObiJZqtZ6LBqyjV88lDuXSmQmWpze9SbSwyMX5gN69lhMXdxvDhh52WX/4sIm2bV+gb5+fUau9qKiooLbmJqd2m7eM5I/1vThy9EVWrW7n8HPs2GwURaG2Noeysl1NzlUgEAgEAoHgbDgrEf7ll18yatQoIiIi0Ov1hIeHM2rUKL788sumO//TOSOOFUBy447i1SnEdaf6bYKsGy8VixZMNU71IdVbnMq6jbBboo/vyuerl7dzbGe+Q5u12Wud+imKQnGtNbvnpO8m8eCaB9mns8Yj/9nXh0X+fmCocHktKAoqlR5Jcg6pOOrOBzAbHMtbx95M717LHcq02lCXQwcEJNM+8UlUKtcJXn/5ZSWvvPIKzz77LACvvfYaP//8K5s2TsbHp61T+6wsZ//wjMwPWL0mkY2bBrFj51WsWt2O0rKdLs8nEAgEAoFAcLa0SITLssxVV13F9ddfz++//05VVRUxMTFUV1fz+++/c/3113PFFVcgy8IHV2+2uBSmAKFTOxJ8VZLtuCp8j1MbSW0EIFjKtVnCAa4Lu4srQx/Ff8tTYKp16OMf4hifOz+jgl8/2u82xCDA7xm/k/JpCkOWDOH+Vffbyqe2isIEPBYRxkthIWz9bCzmnx+HmYH2nxNr4cMhsGiKy7GTh4/i8kef4eRvrSjYF8yejzrgx5UEBCQT1/pWW7vUbh+7nV8dQ4e4tuzXUVVlXwWwWHQUFjxAQECPJsd1xc6dV7eon0AgEAgEAkFTtEiEv/XWW3zzzTcMHjyYzZs3U1VVxcmTJ6mqqmLLli0MGTKE5cuX89Zbb53r+f6NOOO2IUl0DrnIqTbm2f5IWjW+PSOptLiPnxIQtw0AbcBpB0t4oOY0kdpjUJIOL0Y69PENdB2JxPhaD6jIo9rkvGHz4bUP2143tJL/5Odre32brpzHTn7t2PnTyyB3Dxz+CXJ2g8UEWdsdmrTr2YeR17zOqU1RKLKK/WtXAtC+/X8ZeNEWevX6loCAZNe/hHo0jHyyc8cEh+NXX3WMq75z506ys3xpKUePvtTivgKBQCAQCATucL2+3wQLFiygQ4cOrFy5Eo3GcYg+ffrw22+/kZKSwvz583nwwQfPyUT/btQ5lvj4xtBB1dupXqW3/9781O6jp2i8rO4f1T4aMDcR07sJDtSMpvsP0xgqH2tWvzeDAx2OV/o2kolz3kiQ68U2f3APBMcDEJ/a01YcmdDO9lqvD0evD/d4Plu3TiIk5BSn89qhKK5XGepz4EA0/Qc4l/fssYTAwB5kZLzP8ROvueybmfUxJSVb6NFjESqVBpXK9QOOQCAQCAQCQXNokSX88OHDTJgwwUmA16HRaBg/fjxHjhw5q8n9rTmzeTJQG+ZxF5WpiQgiJ9edzYzYVnkNEyt3UmN29i1vjAIX7/P0sBA2ejunpXcQ4ABvdnM47DpsFAA1FW58y5vAZDJhNPiSl5vkIMDj4pyzdtZhNjcUzhJ9+vxIUFAvJElFfPw9BAZ0ByAiYhwJCY4PjhWVB1j3Rwp/rO+FojSeAEkgEAgEAoHAE1okwnU6nYPvrSuqqqrQndnU929GaUbiet/Cbo032PKux2ONvLmzU5lZ8eLYOXpPlvv7cVdURLP7+QRarerV5aXN7mswGHjxxRedym+44QZuueUWrrrqKlvZpEmO2T3Tdo+hqiqQXTvH0af3Rvz9OjrUJye/R4cOz9O50yzaJjxAeNjFTuexWKrZvGUkBkMBlkYyjZrNZtLT0zGbzW7bCAQCgUAg+HfTIhHevXt3li5dSk5Ojsv63Nxcli5dSo8eLdsQ90+gzsFE8SxPz5m2ZyHajI4PRR36RpE8NLZBo3O/UbZQ7cFHqLLA9tInIAiA3T9/j+Im3GF9CgoKSE9PB+Dll1922aZNmzYAdOnShZkzZzJz5kxSUlK49957bW0qKsLZtfNSqqpCeO2196isrHQYQ68PJ7bVVNRqq6tNcvJ7xMXd5nSumppMNmzsx9p1zg85dcyaNYsFCxbwzjvvsHbtWrKzs5u8ToFAIBAIBP8uWiTCH3nkEYqKiujVqxevvfYaO3bsICsrix07djBnzhx69uxJcXEx06ZNO9fz/RuhNPjfTav6QlTl3tXBq7YJN4iXYpyKtDTcgKmiX/plxJQlOrWNK+nMXZvfpEveQFtZ//TLGXPodqRGniTKVR58hObYz+ftb48XvnGJY0r6MpOZqDVpvHoyF7BalN955x0WLFjAc88953Z4tdq1X3h4eDhPPvkkiYmJ3HDDDY5TmjPHZqmuqanBZDJhsVj47rvvyM7ORpIk2ic+yfBhR/H1TXI1PGvXpbgsrxu3pKSEtWvXMm/ePDIyMpg5cyZr1px9YiSBQCAQCAR/f1okwsePH8/rr79OYWEhjz32GH379iU+Pp6+ffvy2GOPUVhYyJw5cxg/fvy5nu/fh3pxwt1RWVnJa6+9xlaNNQ29onJvCY867cGmzNy9jsc5zklnUnOHc+mf93Pr1tksPWQV7r6GQC45dCcAg05eZZt4t9xhxJd0JbbUOYlQHe8GBbqtc0C2PkQYa+z+6FuXLYHqYttxhw37AXgt/TQmWaGwsNDe3U24yyeffLLR0+r1eq677jratm1Lly5dHOpeeOEFCgoKeOWVV3jxxRf55ptv2LVrF/PmzbO1kSQV/fr+TGyosyuQxVLFuhWfOpQdO+Z60+v8+fMBWLfu7Pz6BQKBQCAQ/DNoUXQUgAcffJBLL72Uzz//nLS0NMrLywkICKB79+5MnTqVtm2dk6T8G2nMJ3zLli1UVlayT1NJX3P7Rt1RWuc02Eyp1oHF6FhWUy8l+57FcGonEO9yPK2sZ3XJ4/yWcTXf5rzpVH/XFntZz1OjyQo+xPVl5eRqNGRqNBzRW33Lf/Xz5eXRH6GO7YVqlvvNkdSUoviE0Lqro/W49uUO6GfmITWwZset28MVJ/fhalvr9OnT3Vq/G+Oqq64iMzOTinqbQt955x3b6z//tMcgr60yUl1uIiTaF0VR+P09LWqvV5FN3nS44j5bO7P/szz33BFkWUNwcDAGQ9MPS3UuMwKBQCAQCP69tFiEAyQkJDB9+vRzNZd/FNIZNxNXEjzs1q7WugY+0coZd5SYtPsoaP8VJt/TtjqdqcFID+6BuZ0cyz69FFr1hGu+hGV3Atc3Oc9T5Zc5lcXVOrqf5PmfZMvULfi+aLWcV0oS/ePtWTl7bHwIrUqLg929/Wjofh0stc5BObGOd98JcjrXO0f60+W5zxj8yFSHcgX4OiGZOzKPOy3XtESA15EaNpb1FUubbPf200vxrolhxE2dWLXgIACW2iAA/vzqHTpfZfc39/KqoLo6mJIS+0NQt27dGDduHC+95DrO+Jw5c/jPf/7T4usQCAQCgUDw9+as0tYL3GM0uk4QE/10X7zaB7usq3NHkWQNUX/eZCuPrHbh8hEQA5e941x+aif8bk3f7klclq2V1zqVXbL7DYfjkKBAfLX26/FTFC5JuMShjUk2YXj6NCSOBL8omPw5dL7UVl+x7jPba51f/UyUMsfyYnlj5iaX89se7/igER7ueTxxV6i1asLq+b27ozLwGAqKTYDXYVEZKIrcQnq6PZKNt7dzuEVvb290Oh1XXHGF6/ErK5k5cyYmk4kFCxbw0UcfsW/fPrebnQUXBiZTKYoiMgELBAKB4OzxyBL+xx9/ANZEPF5eXrZjTxg8eHDLZvY3p7LC6kjR0B1F7WcPEVg/hF2Qdi7ZqkwAJEWDtjrKVhfKs+QZyonQ/QeVVG+zZffrYIXdImvjmDUbpaKcm2esPiH9rS9iulszYgLXdbqOn07+5NCu1FBG5HXfuBzDkJtue63SOkZtMZR9yC+DXFuFd7fpwID9W7D4BZKcnMzll1/u3Ki2HLwCPLqWo9vykFDhU9GGav+MRtsWRq0nsKgbOpP9IcjglQ9AVmYKPj5lRESk4+1dTkTkccwmHcXF1hWCnj2tiYm6dOnCN99Yfyc33ngjCxcudDhH/ZCLde2efvpptzH4BeeWVavbORx37fI/IiPHNdo2JvpqOnWyR+rJzJpPYGAP/Hzb26Lr/F3JNRjpvsnqlvVrryS6+f+9r0cgEAguZDz6ph86dCiSJHHw4EGSkpJsx55gsfw7k5v4+JRTVBLTqDW6vvuCt3otqAIADZKsRpLtb031rmr0ShxVlrH4a76Bca6zO9qosoYEbOe1ibTqy/HXlVBhdG1994RAU6j1xc0/w9u94aIHaRfUzqldZkUmkb6RjoVaXzBVUWhs3aB4PKaqH6wHSjVHWtkfToZu+om1A+yWdu+sY4DCFWf8qGXZwuavFxPXJZnW6ixYXM+VJbY3DJ8ObYe4vBbjmSgzvlVt8K1qg1FXgtrsTXHENpfty0L3EJ5nfZBMmapj1eqTtrqaGqvwT2i721amVj1Maup1BAUFAaBSqRz8vz3xB1+wYAG33eYcHlFwbikq3uBUtv/AAyCpiIwYS3V1BlptEIpiYv2GvrY2OblLycldSudOs/nz4GMO/Tt1nEVMzFUoigVZNqJWN5GAqx517mme3lvPlmqLjEVR8Neo2VJayaOHszhabd/TMHrHEXKHdvvL5iMQCAT/NjwS4TNmzECSJMLCwhyOzxeVlZU8/fTTLF26lOLiYjp27MgTTzzBNddc02i/BQsWcPPNN7usy83NJSrKal0uLy/nrbfeYuXKlRw6dIjKykoSEhK47rrrePDBB/HycpEJspnEt9tO1qmONOYUUt8nXEZCVll/p5KscRDh0hmvIVk5k/mxfhSUblNhz5cux4/SHWVK2P34qQr5KH9Ro/PtPy6KzT/muaw7vDWPw1vzuOe9YUgPWyOYuLKP3fLrLSwYs4B2ge0I8gqyFrbuAyfWsLriIYe2Kk0rt3PpdGyfgwjPiYyl1eksjDVmVi/YT3XZak7uXs2WbxZxT9JmvOu7iGdvt/rGX/MldKxn0Tz6O79+Ww34ATA59CF0Ug06VQ0f53+KV3U0tT65XDHqBr75zTHiiYLMFf/txgefOLr/KC5CN1rk1/H2noyiyEiSClk2kJ//C0HBfVFJWnS6UGbOnMn//vc/iouLnfoDZGdns3z5ctdWf8FZU1DwG3v33e22fv/++9jvwTgNBTjAwUNPcPDQEw5l3VLmERo6GElyv5ehOCeb+Q/f5bb+6mdeJioxCa2uYfZXz/n2dAlPHsmmzOxoGPkmtR1XpB132Sd67R4AZifF8tiRbL5MacvwUM9WnerYW1HNqB3W7MlPt43mvjaRTfQQCASCfwceifCGlrvzHdlh0qRJbN++nVmzZpGUlMSXX37JlClTkGWZqVOnNtl//vz5dOzomBExNDTU9jozM5M33niD66+/nmnTpuHn58f69euZOXMmK1euZOXKlefsIcPTfJkW1LbEPgHDEjB85+x3KnPGmq2tZ12b+B5kbIRS164VIRrPEsV0G9vRrQivo+hUJWGx1jjf6XsLuWvzm5wKOMK+6HWMOXw7f0Zs5KZfbgJg1eVr2PrpKfq0n8LXmx5wGCe45BAlwfb3p/7v6Kal7+BX7ehjXRIYRqvTWbx100S0fhMxVa621W3Mb8PIaBcCYvFUmFl25gQKfHEFx/KW2apDNZlIkvXMlwVPZ0XJ81x2+aV0GhDN8fxU0tLSbG0LozbwwSeOVtOLL76YTZuKiY/f43TqDRv74efXkWDVa2SVO7o2hIePoX3i4zzwwANkZ2fzyy+/cPnll2MwGFiyZAnl5eUApKWlkZ2dza233oq3t+fWVIGV6uqT6PXRqNVeFBSsZO++u4iPv5f0dOd9FO3bP01c65udXFNcERoymKJiz93xAPbsta5qjBh+nFpDHlpNMEcPv8qpvPmUHPcnuJ318x6Y0IqKbF9kk7NYX/qsPRTnXR98hm9Q81a23s3M57njrvcbuBPg9XnsiPU+MnXvCd7uFMeVUSGNtpcVBbOiELfOMWzqCydyeeFELnnDUj2buEAgEPyDaZHjaWZmJkFBQQQEuLeIVFRUUFJSQlxcI2HrXPDTTz+xcuVKm/AGGDZsGBkZGTz66KNMnjy5yegYXbt2pVevXm7rExISSE9Px9fXvtlw+PDh+Pr68uijj7Jx40YGDmx6854nNBaisE2bNhw9ao0RLiMhnxH+ai9vJNkIshpUFjS11i88RTnjstHrFseB7t8Jz7sK5neGLpOgnr6O8N5Ofk1vhyZqTdP+43knym0i/Md3rV+urcqTaFVuTWbTOf8iNiZ8i6So+Opxa33mAeeNlN33vMXqoe+gD3qAisr3ePO2Z2x1geVFyBotd61bzvtDLgdAVqmo1XlxLKETMXnrqP/1v6c0xrUIB5BlUKmgupg/q0c6VNUJcIBY/X7ujZoIA6yi/fLLL2fChAk8//zzbn8XvXr1on///qxZ+6PL+srKQ1Ti7FtcUPALBQW/MGzoQWJjYx3cTm655RbeeOMN23FhYSGvvPIKqampTJgw4ayiwvyTkWUDe/beSatWU9i37x6HuuHDjrF3n9XC7EqAA8S2us7WdteuKZSWbXfZLinpGVrH3oAsG1iz1poxNSX5fcLDLwZg7767KSj4ze08XYn8OgEOkDDqFAAHvkhEsUgoFgmL0fk9//OP1fS+1LrhV1EUMrM+RqcNJjx8NCqVDkWRUavtq3lGWXYrwOvjr1bxQ88kOvh6UWW20G79Ppft7juYSUdfL7rW8xfPqDEw7VAWr3SIpZ23npi1zg+n9YlakwbA6x1bMzEimElpx2jv48UbHVsL9xeBQPCvoUUiPCEhgZkzZzYanvDdd9/lv//9b7N9wpctW4afnx9XXXWVQ/nNN9/M1KlT2bp1KwMGDGjJtG3UF9/16dOnDwBZWVlnNb6n1N98J6NGPqODJY0OCRPt17yLgoxKtorvankEIbwOfhGOA6m1Vl/obNfigYkfwCq7JfeqwJcgEN6pZxkG6D+pHZu/tQva4Td0ZPWnh2zH6748TEK3MDYvc285S84dQr/MS93Wp+75H60/+pBLv/uBQ7vKeORuxyV4ndlIVbzVSt4xN51D0fEcb9OBX4dOtLUZt+orOh+1f8kfUOvp2OsW1LIZtn9kHyxrC7QZgDKnA2vK7WEJLw5041OfuweirVFP1Go1od4SRTXOD1ETJkxAr7e6BXRIeo7DR2a4vV53ZGbNJ77NnQ5lQUFBTJ06lS+/dHQvSktLIy0tjWeeeeZfIVBqa3PYtv1SevRYhJ9v+0bbKopiE8TFxeud6levcc4OCxAaOpTYVtcRFjbMViZJEj17Lqai4gA7dk5mQP/V6PURTn1VKj0jhjv/DaQkv0dNzSl27ppMxw7PExCQwuYtIzGbyxu9hoZ0udYx4ZOv5Vo2LdiBYrbeINYv/phyP2dXGBq4xwwdsp89lTKX7DpqK3slKZZLwgNJ3ngAgM+SE7h+30k6+HqxtncH2+fLV6Mmb1gqRlnmtv3p/FZUTpyXjsxaa26CkTuO8HxiK6YfO+VwzoFbD+GKeG8dt7YKd2r/8KEsHj5kvd/uKq9mSZ7dRaslri8CgUDwd6JF4TMURXGKce2qTUvYv38/nTp1cooOkZKSYqtvivHjx6NWqwkJCWHSpEke9QFYvdrq5tAws2J9DAYD5eXlDj+N0ZglvH4WSBnJ5o6i1lhFt8qiR21p4Iow9L/g7WIp2p0AjxsAGh1x/a2+0EditlrdNKYdYvA1Vgv2yJusYQBTR7Tm0gdSuerJXky4vxudBsQ4Dbfg8Y0c3uLebaUxAe5XkUVsO3/8Bg2iZtMGYk+tIz/U0T9U1miRvf0cyo4ldHY4/nGE4wPa0hPd+SA6jrWlt/FO3jLWlp0R9kXHwVhNhSnIoX2S95kHkoQGkXuW17Oimmq5v2YuoTj6bc8cG2OLfAIQG3stPXssIcbnNw5/7ZxVsw4fb8fkVaUlW1y2S0pKolu3bi7rnn32WX780bXl/UKmtjaH7TuupKhoPWZzBRZLtct25eX7WLW6HRs3DcJkKmHr1jHU1Di6UymKQlXVcWTZjNlcweo1jYt0VwwauI3Ubh87CPD6+Pt3YdjQ/eh04c2+j3l7t2LgRRsICxuGThfK4EG76NjqO8w1ja1iNL7CUaX+gm63Hib1zoOk3nmQbrcddqjPoRUv8QyruJi3eRj5TLrej9ZNdBDg4VoNVwQWcergXRzv40fesFQuDgskb1gq6/p0dPmAp1Op+DSlLXnDUtnWvzMz29nvCQ0FtSu8VBKHB3ZlS7/O3N46nPTBKU32qWPq3hPsrXD9WWkOBllu8feRQCAQnE/OWxy07Oxs/P39m92vqKjIZbbNkJAQW707oqKieOqpp+jXrx8BAQHs27ePWbNm0a9fPzZu3OhW3ADs3buX2bNnM3HiRJvgd8XLL7/Ms88+6/H1lNULKbg97CCxDLId1xfh1Xgjq6xfFCqV1u14RdkT8MssRx/noYXolp8BiB2l43/lb6KJMgFPQkA0yUMheag9XKBKraJ1Z0dfz+tf6M9nT2/27FyNoJJN9No1m9jt1igkX/UwMGklpBw9yN729ljgVe3t79HJMOeHAFcEVGtJf/9bZKUtkqTjQM1oUn2/I3DF/cgrHuKzwq9sbcfflwJdyxwHmHkmBGH+n+zP2sin313Pf4pLiQDuZyEzeRiA2/kSfj4Nfe8AYOGj91GYmQ7AI0t+ILFHPNu+/4mj+36l9WB7xtGMVY8T32EQI27oxNFjL5OZOY+i4j94567VJHQL45K7HT9vEydOpF1cB7793jmp0Pbt2xk7diwq1d8nxP/GTdbPfNqem9y2iWt9K5lZHzuVb9o8xGZ1Lixay549tzZ5vuCgfiQlPcPWbWNtZV06z6W8fC/x8feg04U6tFcUhbxjRwiPb4tGa/3be23yeFv9LW9+SGFmOu37OK7A/fi/Vzm0cR1Xz3iJ1l1c3zO+e+1Fjm3fAiQ51d03fyl6H7tLh6Ioto2QCcoxnuYZvKht9Fp30ou5ktVf/ADWOWxmIA8ps3lDslvGtYqBN4xXsPVMEKCiorUMHLiVDWeivnRP/YyQkKZXGO+Ki2CmB64tAHsHdCFC73gv81KryBuWyqGqGhblFPNBtjWSUwdfLw5XOV/rqB1H+L1Xks31RVYUSs0WDLJMtF7n1L6OKouFdn+4dqep22QKsK5PRzr4evHKiVxezzjN16ntGBjc/O+tv4pik5n1JRWMCwtCo/rnr4oJBP90PBbhzz33nMPx2rVrXbazWCxkZ2ezePFi+vbt67JNUzS25N5Y3ZgxYxgzZoztePDgwYwbN47k5GRmzJjBihUrXPZLT09n/PjxtG7dmnnz5jU6tyeffJJp06bZjsvLy2ndurXb9jnqEjBZXx/3c7QcmTPt1uvNdCdAZQ11J0nuRXjN/iJq9hcRO2uQY0VEF8g/YDvcp9Pxk58PFRueJjksmdYBrckLOEGS3lkMNEZA2NltCjRq4JUrrMJ+ic8Y1H6+WGQLG9vUMglQ1XsQmbrV0Z928sZMPh3q2p1AHzwNQ8lc23FkiRcG3kYf9ACSpOGLwndQFAWLYScqzWlUGqvFvU1XF77zQ56AdbMolGDK6rvAz5ef/XzZnp6Fl6Jw36g4Jvz5EbkmmYE1/nTf/Aad+9xvE+AA5YUFBISFc9EVHeh/eXs2rEgm+8TvlBwbDkgc3JSLJEFge/vnV6Wp5eSeQkrzqwmK8OHE7gIsFpn2vSJZ/1EeYQzCoqmCVrmUGHJt/UpLS20PpS3BaCxEpdKj0ZxbsSHLZk7lfMmRI9aH1JDgi+jcZW4Tvay4EuB1VFQc4Nix2RSXOIcUrKNr17cICb4IgyEPP78OAAwfdoTPZk6k8IiFNPkjpjz/qpMAB9i24ms2LLLGb7/9nfl8/IBjeMhPHrzD9rr/lVM4mbaTvGNHbGVLn/uv05jBMbH4h4SSud/ZN7rToGFcct8jgKPwrs9JKZFb+YKvwldgzP/UqR7gk7xbWRV9icu6+gK8q5LGkzjvb9hQL+zi7jR7dt2IiHHERF9BcPBFqFTOXxE5Q7sxescR9lXWcFOrMGRF4a7WEbT18TxyS0dfb55t34pegb5E6jT0CfJjR1kVDx/K5MMu8czLLuCLXOsq1MgdR7gmKoTDVbXsrmcZT/TRU2KyUGQyo5UkTB5au+sEOMCQbY7uM1emHad/kC9fdUtsVOQeqKwhq8aIXiUxZe8J3u/chssigs7dhn5F4e3MfBbmFPJzzyTMisKa4gqmHapzlbRvxN93URfCde6/MwQCwYWLpHi4Tlff8iZJUpPLezExMSxbtozevXs32q4h/fv3x2KxsG2bY9zmAwcO0LVrVz744APuuOMON71dM3bsWHbt2sXp06ed6jIyMmxxz//44w9iY2NdjOCe8vJyAgMDWfFdPL6+jtbJ9X9Yv9huqx0BwBcJv/L4nS/Y6utHmZEkmYGDvgCgb+waSj/JQEFBHeyFXGKP3VtHq5cHOt7wc3bDZxOhpoRlfr7MCHcUGzd0voFP/7R+me+70bWFyB1rPjvInxtzm27YgMHrp3Hx2wscysIz7V/2S182c8Mzc8iKasWEPRtoVVro0DYsbyAvTLaL5mF7q1mTYrWI/WdZCdqyzZhrHTNtav0uR61ti6JYMJTardH6oIeJ7RDM5dN6OE90+zz48RGeCQvhW39HVxi1omBx8cU6cnsEsQWODyiSpLJlU5y2+HskSeL9+9ZiMdsfNCS1gQ5X3OfQz1gZiM6vjLwd4yg5NoKpM0ey6LmtDm3MmkpKwnYBMHr0aPr37+98HR5QU5PFps1DbccJ8ffTtu1DtuOKigPknf6e8vK9dOr4Mj4+bdyOZTAUsGFjP1KS36emNpujR19w27Y5xERfTVLSdNauS/aofXLXd4iIGIPFbGLFqy/QqmMX9D6+rPrkvUb7jXvwMZL6XcTrUy47F9NulEvu/w8dBlgfnCsqKvn6668ZOXIkN2xM43C0+98xWP2pt/TrTEHBb5RWHGVQxkWoAU9221ys/MxNNG5Y8ISYmMl06vhSs/qYTFbrg1bbMoH4UVaBRy4v54sxYQG83zmeUrOFEK3aKdKLK+Z2aM2H2QWMCg3gnrgIJCBQq2FedgFPH3W8lu39O9Pay9GaLysKY3YcYW9lTYvmvLVfJ9p4tzyMpUDwT6ZOr5WVlTUaWOSvxGNL+Jo1awDrE/rw4cO56aabuPHGG53a1flid+zYsUVL5snJySxatAiz2ezgF75vn1U4du3atdljKorici51AlxRFNauXdtsAd7secjuvzYlyVLvtRYZheW6behqtYyjOxINhKBFAU29spju8NhJkC3M+Ly70/h1AtwTZEVBVU94Dru+k4MIzwrV8NVAPy7bWkW7POsXbfujX3G0vd1Xe8gfD6GWTU5jK5IWSbGXFwVa/dt9DI5L0boKBS8/M6N2V/Fbd19GplUzeeUPbO80nkqtD+XeKiKN/ZAt+ZyIVPCtqiC8JB9T5XJUgXdgKFvgMJ7FsJ1LH3rcaT5FNUUEd7+ea3a/ykEXy9v1BXi3o4F0PxpEWmKpkwAHHNKZ//Lu64y9dxqTn+7NlzPtglqxOH9B6vys7jFRvX4kf88xPvvvEdS69kgq+01CY7Y/HPz6668tFuH1BTjAyfS3OJn+lvX8kZeTd3q5rW7zluEkJDzIyZP2h5n6GxI3bOwHYIs+4inDhx0hLe1moqOvJCLiEtastVquExOfRKu5hNDQUNRq90KiS+e5HPjTuhrVvcvPzL9/Gir1+8gWawbak2k7PZrHj2/O5sc33ddfPeMlByv37i590BtqMer07Ei5iMnff4x/VYX7AerRaeBQwJold+7rr7M2qTtz96ZT5EKAJ+VlciTKHlUqvcZI1Jo08oaNInm/daNo/TtJr4M76ZVvtZBqj+9j8aW3URAWzfCNP/Jwnw5U+YRTcbwD6ZuLueKx1/niqQfodrujX3lT5OQsISdnCeDsOpTa7RNCQx2TY+Xl5fH+++8D1o38N954I9XV1Xh7e3tsLb69dTj5RhNvZeY3a651JHjr2NzPup+k1iJTarbgo1YxcOtB5naMY352IauK7Xt65nZsXc/SDL8UlhP/R9PCuz7TDlv7H6qq5X9NzLv35j+bNXYdMXotOQbneytA3y0HXZYv755IvyA/l3UCgeD/D48t4fV59tlnGTZs2HlJSf/zzz9zySWXsHjxYiZPnmwrHzt2LHv37iUzM7NZodpOnjxJSkoKI0eOZNkyezSQzMxMhgwZgsViYe3atS790D2hOZbwI9rDDH/evtRd3xKu1dbQr//XAAyI38rxj3bwtd6+ea9ujDpiZvZH5eX6GSp5YeMWRHeW8NVF5dzzZ4bty+pEvU1U79xlj839/GS7K8Sau6egABJQHJREWuqDdP5zPlH5O7BIEiPfdYz04VP6Fb7l3wHQNkfL1n6fAHDzhh/QnxFR47//Ht+qamRJxZohb1HmoyKwWmbYuvsZ8e4XtrGmLynmRKSGL4ZaxeqI9d/T44CjBbk+USldYVIys7bNsl7TiHe4d9W9jf6u6lBbJK7/tXnhNh9ZYs0Iun7pIfauzkFRTJhrt5N0+Qr0ga4T9QCkfWD1kfcNewCLpV7mVN9MqvzTbccqlYrrJt5F66RQck8vpNaQS1L7pxqdkyexsJuiY4e5mC35HDs2y+M+hQXTOXgw3XZ8ww03EBAQQHZ2NqmpqRRlZ/H76tUcPGHPSDp16lVkZds3+loKBrLv2yK6DhvF/jXuQwE2ZNDUm1j/5QKP2wNc/tgM2vXsQ21VJRaTiXa7XcfhB7gmKoTFecXcWFvI3a1C0Xl58ccX8zHW1BCR0I4xdz8EQIXJRPsNB1yOMfzgDkp9/EnJPoaX2US+fxDf9hja5Dy7ZR2l/wn7mJqyYrQl+ahrKpGA6rgkLL4BeKcfQrKYURutD7umgBBqWznf89RqIwMuWtLked2Rkf4UmZmZTbbrlziWzO21pFztzdr1qwkKCqKoqIiHHnrIIXqVoih8mVXII8etVuQfe7SnZ6AvG0oq8FGriPfWU1VUi+KvQadRE6bVoFFJ5GeUo9aoCG3VuPBUFAUZUJ95MGjMl7whg4L9OFhZy2+9khi98wgFRrNH/TwlXKfhxpgw5qTn0d5Hz/jwIK6LCaWVl85mKLkq7RjrSyo9Gm9lrySS64WVFAj+bVyIlvAWifDzzahRo9ixYwevvPIKiYmJLFq0iI8++ojPP/+ca6+9FoBbb72VhQsXcvz4cdq0sVqTRo4cyeDBg0lJSbFtzJw9ezYVFRVs2rTJZkXPz8+nf//+nDp1io8//ph27RyFSWxsrMdW8eaI8GKpiJSXL7fV1xfhCQk7iW1ttYwMG3yUOc/PoloyOoxXX4hHP9UXtb/rjUktFeF1sXvrOD44Gd8zDzxbvzvBjp/SAWcR7gqLSsWnl0zi03FXONXVuaS0qxzIls53orGYuXXDD0hAwokT9Nlm95VfPdQa2zkybxtdDi1k2HuNZ/68e+Es/GrcfyktuMS9mKpj3qh53Pabo1/wTT+5dhn4tc9pRm9znQHw7vmLOLpxA2sWfkjqmLvZ+f3/APAKrqXj1Sdd9gE49l0clbm+gEJU72QqMvtjMfpjVldTEr7D1q590iaiohxD5fn7J9On93LbcVnZbmoNeURGjMVkKueP9dZVki6dX+fAnw+7nUNL2f1tb6p9Y8FXz0UDF6MosGH99U328848Sk2cc6STGM1WBl/Rn1aRd/Ph3a6z4bqj14RJtO97CVtWnCLnaCmtOwXSY7Q3S2c+jkqtsVnOwfrAVFFUyA9vvMK4Bx8jIMwe3/63wjJu2Of+/WrI3I6tmRptdQc7UnKEVSXwbIb7KB+JpzbiVbGMUn0pFsnCZRl295hSb18W97nYZb9L9m0irrj5VmJ1ZRkWv0DP2qqNPProY2zfMRqjsbDpDoDB4M3uXeMwmRrfT6KvCcfgXVCvRMbbu4KIyvFUF0iYtOWUhqYBEFLQ2yFS1MCr2rPhq6MO4015pi+/z/+Tgkz76sSw6ztiMcn8sfgIN71yEb6BnrlqFBhNtjCOdQwN9ueLbm1tgr0hy06X8FlOEbOSYtlSWungew5W9xNftQpftYqDlbWM2XnE5TiXhAUyr2u8w2pkU9RaZDJqjU4+7q6YFh/JYwnRHo37cXYBb2ac5oce7QnTafFRe766LZ+RFs25DoHgfPKPFOFZWVnk5ORgMDj7LQMtspZXVlby1FNPOaStf/LJJx3S1t90000sXLiQkydPEh8fD8DDDz/Mb7/9RlZWFjU1NURERDB8+HCmT59OUpJ9Q+LatWsZNsx1eDKAZ555xuOsoJ6KcAWZQq8TdJ9pFxL1zzFo8Ge21yOGH3d5/qsNAwhQrF9EUY/3RhPs5dQGzp0ITxvQhagz0Q0qTGaW/3ycdkvmM+k2+/vgSoSfiGnNrdNnuz1/aNadTG43jlNbg1jRfTABNVVM3baSyYudLXAVvq3YkBxCrbSfiZsVRr+5EKPOfVQEjdnEw/PcR6/Zk1jKvrblmDXuP/b7btxHtakajUrDG7veYOnuL5iyyvXm2wWXZDB+YxRhZXq+uDiTiBI9F+9oOi23d3gN5moNpirr77frDUfQeNudDNI+6ETC6CwC460PFD277uPzGZspjPqDQYM/b3TsiIh36NpllFP4vvrZHocPO0ZGxnscP+EcM33Y0EOcOvUlR47aN2MbjV7odM7RKwry23D6dDssFi21tX4Yjefe0qYpLURdW42hnouGd+YRFEmFdMYFSFNVjgIYEkYSFaRlyr3XM//RHS7Hu/f94Y2er8JsQa+S0KlUZNQYGLvzCMWm5uU78ARt7SG8K35FX+M4z4HBA4ncZf8MmVRqPh40wXZ8/eZfOOW1lxpNDd2KrdGEgnoGUbqztNlzCD6dQa3RiCJJaKorqExydmWrQ5JkIqOO0b69dbVpz55ReHtVkNTBdfSkU7uvpPjkAErDrBvOu3b9neCQXCorQti9+xKo52IXGXWUpCTXYTsB0tJGI2f3x6e6VbOvsSF6Hw1Dr+1I2+7hvHfPGqf65GGxDJ7sfgO7bJHJPlRCdGIQJ/cUkHGgCL2Xht4TErCYZLz9dJxOL2fb9ycYeXNnfIP07Pw5ndoqM+26h/PHkiO0TQ2n19h4MgxG/A0K2TsL6DqklUdJ05qiLoJMgFqNRiWxpbSSy3cfc2rX1lvPf9tGc9uBdABWdE+ks583/ho1FWYL7d0kazo1tBu5BhNaSaLMbGHwtkNE6bQsTW3H4txiJlTns7RSZr7Bfi3XRYfyea5jZDOxoVTwV/OPEuHff/89jz76qC3jozuam6zn74anIvzIiNtQ1Gbatp1GQrzVBeLjmbeTRStUKhMXDVxs6+dOhI81dqeVbLVCRz7SE224s+BRFIWUT92HWFxz9RrCvF1n12wowr9IacuIM8ky6uoe3LqWN/sOtbU5KhdR+N776Nq0ofbgQYwnTri0VmsMxzHr7SsOMZJMl/07WNmlD1FlRTw37w3aH7N+UWj69MC8bZetbXoEvDNezaufWMiKiOKGZ193e30Aj77/tO31ioE5XLbBOdThsVaVbOh25ktBgWG7wvGv1jD5pTmoD1qtc7+82/h5or3LGZS4n0tat8Lmk4N7q3ljTFv0HavX2kVz2ocdSb2jaauWO9TqSCwW543IABqNP0MGpwFQVXUck6kEo7GQffvvpXvqZwQH92f7t+9TjpZDuRsoLIwHFGJaHaJdO0fBWPcZd4dP+kGq23SEZljDmmOpbYywvIFIDVIhKMALZ1ZyFnRNIEir5t4/M+gf5Md9UWEM3eP+fvZYtRfdCmWCIn3YsvwE+YFqPhhjnafaXIxF41nEmrDMW5Bw9OntGNKRrybYQ2l+suQTMg86u3Uc9z9OWlia7Vhr0aJSVBg0BuIq4uhd6PlG+DJtGbffcRv5P2+mxyWXUpBxkuWzn0fWaAGJmrj2yHrPoiNJkoWBg750WZe94V5iB7rOVtoc0tO7kZWZgr4mgoCyjh73U2mrUGkMmA0BIHselTd5WCxhrX2JSQwiKMLuIlPfNe9cM/TaDuRnVvDnemsYyItv6UxSn6gm+51Mf4e8vGWkJH+ATheOVusoMA5V1TB0W/P2AfwVhFLKdOUJwikgNGQwIaGDOXr0BbqnfkqAX2/yTh4kK30prdr1JaJ1H1AUvLysVvys7M84ffo7IiIuISb6SlQqHSrVP39TqqIoLvdVVFtkdJKEWoLjNQZWF5WTVWvk0YRoAjQXTrZlY62ZLcuOExrrR+tOIeh9NKTvKyI8zp+QaNeJFM8V/xgRvnbtWkaOHElUVBRXXHEFb731FkOGDKFjx45s2LCBAwcOMH78eHr27MkzzzzT9IB/Y9yJ8KzMLqSnWyNx3FY7gsOjbrLVjRh+HA7/woJFS0knDo3GQP8BSx3qXYnwS4w9iJGtGxkjHuiOLsbZ33F/4X6m/OjaRQTcW8Ev3n6YfS525OcNSwWcBXod36YmMiDYOg9Tbi5lP/9C1wRna1rg6Rcoi3zaoSygppLyM4l5DlRmUfbxJ7R6fS6qNrEc62x/kPh8mIo1KRIfv2l9oMuIiuGmZ1xnvdQoMg9/YM1guXxQDqX+Jtqe8mXwHucHj0UjszDoZMJKdYzfZL2xd7xoCIc2rnM5NsCVT7/A1y9Yr+PhjutxiGL2eAZ4B/HasumweLfbMepz8R33kTLCGlazvHwv23dMbKKHazIzuxIX51lSqhqpB6MGf4FOreNw8WEMZgN+lX6c3LWL0JUrKfuzgv1dbqMgcgNIslN/v7L2mCN3YTD4oCjWm7vvsX3URrfB4nvmxiZb8DuShtTE7UXW6qlKtK7c+GnVsHcrEmDReVHdrvmbsBsSljcICYluE9vy7eYsfAwy80a1TOA/8XUxWhc2hY/6PoJFZUZBQ3XgpVQHOr+HPdMP0TvjEAeCDjBmxBi8Nd50Du1MqFcorQNcr7LM/GEmp6pO8eyQZ5EVmYjwCOuXrwSyItN/4UVYJDOyyoKkSHiZ/Li97830jurNjT/eSOuq1uT65GJU293aEsra0aM4FYDlbZZjUdkv6PNLPufZDx/kor3Wv0lZraEqKdVhTvrcdCw+/pgDQ4kuL6Xy1DFMgTF4qyegUawuVNF95xHYxjG61fmgY+ttqLwPEhTSls9mrKPKqKXP+CpqNc+57ZO1/n6qclMABe+wo2i8y6jI7gFK4yJlfeJSBh2r23j+17pXXPFYT7IPl9CmSyganQrfQD0anQpJJaEoZtasdf9A4uvbnsS418jYdZTywgKqUXFrXE+37V0xt2NrNpVU8vXpkrO9lEbpouzlFj4giFLUmCkknAhOo8b5HtQUvXp+Q2BgaqNtvsorptBo5q7W4Y1uFjabLGi0//8i9kS1gSeOZPGHh3sAGnJDTCjPJbbCS63CIMusK66ge4DPX7IaUVFcy6f/3dRku16XxNNteGsURcHbjbvt2fCPEeFjxoxhy5YtHD58mMjISFQqFTNnzmTGjBkoisKsWbN44YUX2LhxI6mpqedh2hcO7kR4ndUG3IjwD4bwSW57MolFo6ml/4CvHOpdifDLDL0JV858cDQqYl+4yKnNxlMbuet319EqOgR34OtLv3ZZ505kT28Xw10RAbTabLXK+lZXUeVjf1q9NjqEJ8N8SEtLY/jw4Tx8ONsh9TRYreABhW9T3Mq9ZblO7Nex7q4riVhr9cn0XvEpbZJ6caiTPXPmm5NvYvnQ0QAs+e+9ZEVE85+HrOI4d2g3p9WALicC6H3IOdPoFxdncu1KzzdcPrLkB6slAuDZIMfKwY/C8KeZv38+f77zBVHFzu5CPS65jJCYWCSVipQRox3qMsozOLajcVeJ+pzaFEHBvlCrG0ZUHNpIFT37WZMz/cIlfCZZE9uMLpjCDWF2IZb2QUdAoqJTL7dje1VFU+trj4gTkptKTUAB+towtKZACqL+sNWpDDX4ntkcOHrvcdQKbOz3PAYvq2W4Tcav1Bg3kRnmWvze+M58VCo15adklr18L7KlylZXf45+pYnojKEUR7jffOuKGTNmMHbXUfZUtCzsG8CYnVX0Puba5Q5gYc+naVfZhg5lHWxlClCp98bfYD2vOlbN9NumN3muTd8cY/dKuxW8z4QEeo9LAKC20sQXz2yhtspuSf+492Pcut3q/hWdGMik/1hF1p6CPdz/9SN0Ke9P78JR1FSYzszLesuXkKjWVpAZ9CcWlYm0mFVcu9tqNDHXbMFcuwlTYCgWLx/0p7OalJ5XPL2AiDYB+AToqKg4zLbtzjHMBw/abduX0JBX87w4ZVLxw8QfaBNgXU0ymKt5aEUPrgx2HQ3kXJHYdiYhYRO5/5d76P37TaAy4xWUgW/UAUKSfkets3928veGkru1Axrvfqi0HVnc/SViLQkM2jfVYczRt3fl14/2I6lMeAVnENVax7Fti4jqWUBopwYJw+qRu+N6VGoj1QXtSRj1AhajN8e+ew1FbiCWVGY6Xnm3R9d3cElbOk0+AVg/lxY0ZO2L4mTXZ4n69V2KKjWsvXIchyR7tuibst4j+VgAU5+djSRJZGV/xmtH0lgsXY9OMTCclegxMIqf2EN30ujBaH7iJy7lNFE8z+PoMCIjcZQkosnFn3IkYAUTWSpd59HcAa5QFjOQdUTQvD0Q/futRta2YsnpEn4rLGddietoRnM7tOaa6BCeOXqKj07Z9z7c/Hs5sUXWvSMx7YO49KFU1M3wi28ue1Zl2fY6mON9mddDz7iwQBaUuf+8nC2PxEcyrXUkiqKg0apRFIUj206zd3UWIdG+lBXU0HFANIc25ZLYK4LOF8VgMcvUVJpQqSUsJpngKNcW7ENbclm1wHXEHjv1lpHrcdWTvTi6I5/jO/NRUAgM96GmwkhxTpXzEB5SY6zi0fmX/v1FeGhoKBMmTGDBggWANUrDjBkzHITjwIEDCQkJ4bvvvjtXc70gcSfCM9K7kdmYCH/vIuad7kI2Meh01fTt941DvSsR3sOUQA+LPaKBU8IeIC0/jet/trsI+Gh8qDbbN4V56g9en4UzH+HGma4tz8NC/OmwzOrPHhUVxcwO/ZzahGbdTnCtnmPt33Y5xvR2MdwbF+FQpigKf469GEt4MMmfLkWSJCrXryfrdnuM+Pp/ukdax3Pnf1+21QWbj6PKexWVbP+DvXdPX6pO5bm9zqaoi/1tY83LsK5BhJCZZRRUFzD8q+GkHA2kY6YfPgb7Evi0Rd8huQiXWWWqot+X/bgzrJZO3nbLT9lJP2qK9UT1tPtTHlzcFkOZ62VXRYLv75jKYamzU91zGx6jOCsalbGW6nhrBBYZKPILJKyyzK3I0mYe4c+EzrSq8KbGN5lDMQrHWvli1uq5ZeOP+B/ayaBDmQTU2oW+LGlYO8Qe/y86dxNxJ5ewrpOjq47Wy5srp3+EIissn2tfPYhpH8TER3rwzl2rkVVGUFSoFA1R17fjXmMJ9/5wmidfGYmskviloJQZu/4kz8uPx/avZ0TqOJatsofkNGi0zL9onJurc2b6kmKMaqjVSejMoDdZH7rMmios6hq0pgBq1NX4moIAqAg4Sq2P6zj6d911F1FRTbsT1PHD23vI2O+cFXj07V3ZvPw45QUtf5BoLj90eo/xB+9GUUwYSt/yqM/h1hVsTrY+hEdqZJ6Mtu4j+KxIx85q+99BFy8z/mrYXa3GoDh/8rZO3cqmnE08vNa6eThaK/N4VOPZQ88WU7Wa7A1RJIzyLDb5n4vaYSy3W+u+HnqKSm8zKkkh1cfCSH8TMbpzG/dAsaipLYtF538atdbx93Hk23jajc9ErWu+5bgOGQkJxeFeYCjXog84tw9BtSU6vIKNrPu1Px+NmobSjHDG7ZQjPMNTYFbYP78DklomqG0FcUMd/war8OV7Lud7adJZzXXKugoS8xq//vA4f654rKfNp/9YZQ0fnCrER62iu78PFwX7cdv+dLaWOQrIaL2Wa6JC2J9RSpvf8gmtsHA8Ssuyfn5Y1J6tuGjMCn61MgmnTVR5qTCpJXodq0UtQ3Sxmd+6+3CgjWduOlN2ZBFVYmFH6xCCylXUalWs7O5DSIWFcm8VUaVmIksthJdZWNfVm+F7a+hxwtE4ofEuJnGCczhggPKsnuhMV5A0OB/F7EdWnj23RNWJ28ja0YfzudL0jxHh3t7eTJs2jRdffNF2fPfddzN3rj073n/+8x/mz5/faJr5fwLuRXgKmZnWTVMuRfjbffigsCe5RKLXV9Kn7zKH+tdfnUtZlT2GbR31I6S4EuG9P+9NrcV6c/5p0k9Umaq46nvrMuq3l35L+2DnCBSyohBTL2vfpMhgvm3G0uOd65YD1j+d94dcbit/sE0kbUhn4a6Z9N/fnxqtjpWde5MTFO7Q/5WkWG5s5dpPvSGmnByODR/hVK4Aw135ohvTCcqbjgSkXZ9GTWkpH9ztHN/eHfcvWIpGr0elcrMcWZIOn02C4jNRSh47CfoAkuvFap+0NoaAaqsVqy5sYUMmfTeJoyVH0UsKr8TahdaTGd7UqCSuWdWKYD8zNUV66m5SPS6byDvHrIlY+h8IZW2/0WxPdf5M1OeuM+8VOG/4u2rHakIbfOayg8L4odtAt+ONXruMR5d+hbrBbSTsgfsp6j6RVQvtVhC1uRazWo+p+lcUSwmSpEPrNxFJcv4CHn9fN6K8S6kqqGDJp8WMvb4N8f3bOTws5g1L5dljp3gvyx5hY1iIP//rFMcNe44Tu+F3FAmX87/9j+9Qn9nUGXq6H/mB3pR7q9gc8zg12ko0shbzGTeOObFz2LzecfPh6pjVDM9pfNUiIS6RG266FqkZ6cXPp7+xp8R2COayh7uTV5XHi1teJGNPCVO0d1B8wGoRVOQajFXfgVxLTdhQvPK/cTmORaVQEGggqsS6IrQutYBanUyrAi8UCZJPBPL5qEzMGgW9UYVKlqjxcvb30ZokTBrF9t2sk9XMbuM+PvvxY73IyenkVB4UfILk5I0OZXm7Qonq8ff/jqoLawqg1lmQzRKKrMI3uor2lzYdMrKl5O8JobZUR6DXSEbfcx9bdjoaYYYM3sv2HZOorrZvDB3Qfx3e3rGYTSaObNlA687JqHV6Pj1dyi/7D9C3bQLzSg1cGhHEolz3oVwb0io3nY7H9rGq3j2tMcYryzCh41ep6Qf0tnkmEk6bKPVVsTPR+nkev62S5AwjmgbPPHlBaj4affZ7WhrSTjnC47yAL3YhL5t1nE6bTNmJwWh982k3zjFEbcaqxzFWRiCb9VRqvFiT7M3udl60P2WkS6aR5f3PT+z4LspermIRiRxptqT2N3zG9hXn/oFbkkBWG7n/nbF/fxGekJDAxRdfzIcffghAYmIi7dq149dff7W1ufnmm1m2bBmlpaXnbLIXIg1FeHlZOAGBBWRkpJCZYRXhtxgHcnTknbY+I4Yfh+8e4L1dZk4TgZdXOb37rADA2zuOAf3XuI3O0jBeeMDFbQgYYXWnsMgWUj9LtdXtu3EfBouBQYsHEaAL4Perfncaz3TqFK+s3MDbbazL6MuT4zktK9x5oOlQfnUEVldi1GiYunWlg6irczGRZZnnnrP7adYX6uAYhcUTCt59l8L/2S1z7Tdv4mj/AW7DF/qWLMKn4ifbKkBFUSEf3nNTo+fwDwtnyHW30qG/ewFq49jv8LljKEYLkJpgd3NpLUXw/dRfUWs0lNaW4qP1Qae2W9HqR7R5o7V15eKEQcX/8u0uLWqLxLW/tSYnrJbfezsuy7bNac/WfjOanOr1m3/B90ys6MzgCH5KGeC2bXBVOSW+Td+oXvnfywzx1iCpVFTv2EHUzJkEXzMZRVF4927n6BOeMEFZQtU6u8vLnGtv58eBjqL3wy7x3HEmskNzGHQkjS65zv02RWwi94wLjpfZC1mSia+IJ7nEs8ydAN5VsUiyGp+qOFuCrXvfH86B9aeITAggLNbfbd/6AnzqzL6oNSpWf3qQU0dKHdr5Bum58aUBSCrJoc/lD3dn+euu9yK06RrKyJs74+Vr/zurKjWw4AlHUeoToOOmVy5y6yNrMcnsWZOFb6Ce3bsPowzJ5X9rX+HKtWcftaSOujCikgI3/mxfOblp7nu8cOg1fs9eBUDX4q42959v479Fkep9lSlwRbpzeFSVykTvPsvR6WrZtOFqvA4dJCi2hLZjsp3ams0aMtJTqagIo6LCajjQaAy0bbeDyMgTLbo2WYFjBhXLC/TEnQikrSYB3f5SW73W10TcsBwkCfxiqjm0tC2yRaLzlOMuxzNWavjziwaGFUkPihmwoPbqR0gHiZCkVRTuv4yqvGRAQa2rpP3l0xy6FR8dQk1BPOBFeVYqIUmriexud5M0Vmk4sKQD2sBY9IHdKKuqQmOAqjADssZAt5TujBg5zKW4kWUTIKFSeb4xto4yk5mnjp46K5/0B5Q5dOQAgTgaGYqPDiWk/VqO0p79dKM7O4nHGo60JPA67iv3bI9OcGUVJX7ndlNhgnKMp5mBDiMqzn5FJcC/BzHRk8HYgW3LDJzOOIWkq2HtmFOsUTsbts4VKcouWpGNAS9WS6Ns5QOVtUzka+K9dNTWOusNf82ddE5+AL9gL8zmKjQaX4zGInS6UE4bTJw2mig0mjlQWcOLJ5xXItv76DlabWBAkB8LkxPwUqmoqaz4Z/iEX3755eTn57Npk9XR/vbbb2fhwoV8+umnTJgwgQ0bNjBx4kR69erFH3/80cRof28aivDS0kiCgk6TmZFMRkYqADeb+3Ns+D22PiOGH4f93/DO12spIBQfn1J69voegODgAfTo/pnHIhzsFvE52+ew8M+FtvI60VljrkEjadCqHYVuZo2BPg0yrK3LO0zSNVcTXc8y7opgjZoSs6PVavT+rfzatS8Av/dKouuZxBAffPABubn2P5K02ES21Nt019Af3BNqDx/BXFiA30VWv/iijz8hb84cRrzrHKHh6shg/tfZ0Q1CURTmXmN/YJi26DvmTrEnhnFnsXaJqQZedHY5eCE0mCUBdtE1Z8gcIn0ibe5CO6/biU6tY8H+Bby20+ru83DPh/nfzrmEaxTyzJ4t0VrUwRS3+p9T+Z3rlrMuKZXMkEiq60W58DXUUOVh1AtP6Rfoy5ayKu6MDeeW2DCH1Nnfvr6btLxyKr0kdrXz4spNlS6tI7nBai77+WGuf+51BuzdyU8Xee4f7wl3rFvO+fLm9K5qhW9FW+fMth5S5z9cR10YRdki8969awEIjvJhwBWJxCc3vmqkyAqfz9hMQJg3Y+9KRucmqVfDPoZqM15+LdukVVFs9aP98O6bWtT/bAjs3Yk3w38hwieC/GrHh9OumdF0LUjEFBLhpncdMkkdNlNT40/OqY5YLI1vCvPyqqB3n+Vu60+nhVKV6015ph91JvxfLypnQLH1fc3wy2BHvZj/OqMKo052u4ncjoKkVlBpZCwGNfWX7jXeQ9F49XDb89IHUolJCkKtUXFkex6rP9+CSluDqTKSm2cPRFEUhzjqFpPM27M/plp1BIPBB0/dBPp2GsGoKwdQUVGB2Wzmvffew2KxcPXVV9OpYyeqygz4BukdHvQyMzPZtGkTJ06coF27dvRK7c/+bcfI3GSmKNJxQ192UDjrE/tQ5tv0Z/X6b94lsiAH/1ZVGEp1mGo0+EVXU1usx1xj/7tQ680k3+QcGekUrXhMcr63NoWvUsEspmFERzGhbKcfnThAHxzDcJpRU4M3flSyilHkEkMPdhDPCXxxn1vgfCBJPhz0mUmmuYShhjnIqMklmnhOoiChQsGMGjUWMmlDJKdZxShWMAkJqJTcGxg84engXaQXp/Ejl5HKLgazhnBOE0g5u+jJ69ITZ32NclUlBRMG/f1F+CeffMJ9993HwYMHadOmDSdPnqRnz56U1ds8oNFoWLly5XnJqnkh0RIR3rfPz/gd381byzZSRAjhESfo2NFqkWqJCG/1/AAkrdopPrg7/+86YtemYW7w7q+6ZypdDlqTBjXmJ35xaAArixytCiGVZRSfCS1XX1g3vJaGbhAtEeGuqNmzhzG7jnE43jH50qURQXzYJd6p/c4fl3No4zqmvvAakkrF+i8XsG3F19z02ruExjYvOybzLoZs54gQyQmNj9M2sC0nyuxWtX037iOn1si+yhqePZTGCVMgAQVvsnrcbMZ+bf0CN2tiqAy5CZOX85J7HTevOYBeZf9Sabj6UJ926Qc5Hu9+LIAHP34OncnqnvH9gFy+ue0PumxrPDzpo/FRvJru2gd/8dPPcjq8E/smXc681me/C37N3VO457HnOJjg7G4Fdjccv/IKar292BVxkFyfXEafGu2yfUNyvXMJ6R/Cgz0eZMXiFeRm5xIUFERpaSn33nsv4eHh1FQYObgpl7L8ao7uzMdU27LwrLfOGdRiMXyhIFssvD7VmnTogYVfU5B5kkXTHwVAo9NjNhrwCwmlsvjcuYI8suQHLLKF7bnb2fzYLGSzPRmTolJT2aE7ocGB7K89RHRN48lqrrvuOnx9fdFqtVRUVLBz504OHz6MyeToGyxJMlptDUajL3p9JcEhOZzOS0RVUYF31jFuePFdvl26nDyj6+v8Ie4HehX0wqA2EGwIJtcnF2+zN3FV1vuG36Fdtlj4AL4jUuhz2ZV0j+hOeXEx7779PyxIXDpuHEmdUpEtClWni1iz8EcSLVHsis/FL9CfwsJCIiMjSU9P54YbbiAy0jGXwbp161izxrpqlZycTN++fZk3b57nv3wPUZm9CCnsDZJMafBezDr3bkXNpf/4Sfx4+AhBRw+glmDswP6U5uUSn9SFgppaJI2K3BPHObJ+FaaQSLwzDqOql7CrjsCEcqd9AVX4YERHAOVYUKPDxH6SeVma6dS/nXKEO3iHWJxXV5pLWNgIqqqOUVOTQWK7x2jT5k6HekWxcOzYK2RmfYy3dxyp3T7BxyfhTJ1MYdEaCgt+Jyd3qavhbZQejyD99xDqHrR8AoO45X9vo9F6U1l5EJOpmJqaTI4cfd6pr1YbTEBAKhERY4iJvhKAPRXVfJdfyjuZzU8mdj75x4hwVxw/fpy5c+dy4sQJ2rRpw1133fWPj4wCziK8pCSK4OA8MjO7kpFu9Qu+2dyPY8Pt6dG7dnmTyJwy3lixg1ICHRL1BAX1pWePL92K8AdTrqVqm6Ow0bUJIOLubh6J8PoxRsftPMLOcsen7TV3T6HDnjRUen2jIrz/8X1sbud+mT5vWCqyLJOZmWnbwFuf+qLwXIlwgPen3MjMOx5GbTFjUdstHVlDuqFthm9us0n7EpY7Ryo4rVYzMq7ppXoFFYVxCxtt46WC2ib2XPmUfo1PxS/cvWm2Q8bB0/7BLOsxxGWfLzsnsOqNND52EbrvvbgQJrazCoIvDn7BrG3Wjai3J9/O/d3vb3LF5Fyz8t7rkBSZkfVWPH554Ab0JueNUxtTepIbGs6Va35BliQUSUIt23+BVz9p/3zcUHQDVeWud90HBAVwz3334KVxnRzLHV+/soPTJ533dTRG3YbUfwuFmeksfPQ+bpj9Flq9Fx8/eLtDfddhoxh91wOkp+3km5fPLtxt/dWtmpoaFi9eTEaG8zL49OnTUasbD0l35MgRVq5cyciRI1m0qPEsvueCKqmMnQF/UBBiJMgQRJAhiJ5FzqEGIwPCOV1e4GIE10yePJlt27Zx8qTnGWHr6N+/PwAlJSW08Utly2/7KQvxLEzqhYa2KM8aTlWR0fr5U+XtD0hERB6nQ4fGQ+vJsopT1W2I9TvpsFZQWhpJ/um2JHXYTGlpJH5+RYBEXmY8sW2tBozCgjiKimMpyI8/E+5VQa+vQq0JZdzY8ZgtMn/++ScjR44kPDzc1emdyMzM5Pjx4yR37Qq11eh9fSkqr2TRondQFInIyOPEJ6RRXBxDSXEMOTkd4Mwaoaq2Gl1hLiqTAXWtXRvIag29xk5gwOWTUXJNqAN1aCMbd79RLDLVaQXoUsI4XHqQa366FgkZjaJGb/ZiYP7lZIZG8kf71GblkQCIU9K5hfcJpwAJhUCco8cY0KHDSAER/ElXvmEyhdVe/1wR/m+lToSHhKhQqSRkWY1KZcFi0WI2W61Zvugw60tp317P8y9EWUV4diFzv9/HB4t+oLjY0d9Pr4+ioqKC+m9N//796d+/P/d3vIaatAIqDdUMm2ePgqIO1HG62p6cJdLHbulYsWIFPXv2pPDDjyiYO5dtXTrz1NatlJstVFnsosTLaMC/ugokFZqIcKrVGnQLljvMreL916ld/UuTLg2XDx5IcrKjSP/kw48prbT+sRi8vDErEKzV4NVAHM+ePZupU+3hvg4fPsyIEZ75rP3+wguc+PQLWuWf5t0rr2NRRQVVn35IiFaDvhERnpSUxOrVjpvirr32Wtatcx8zvI7bb7+dZx5/GN7qAdHd4LpviI1tBeVWFxyDJFHaIKyVovJHkTTE3RqAvltnSqOsPvPGtB2UvfSU0zlcEb70V4fjyoUfoPrpC2rMNYRqwjBUO1p5anQSJrWErltPAp96CYCBQX583T2R4cOHc+SINY12rcFACSp8UAjUO1qplYsVQoZaQw/+NOkniopkevbu49F8g+d8gCYu3j6fVT9T+cEbjfYJ0qrRhIQhvfUpvXc8QK2mmBodhD5bwE8WGR9DLd4Gx0081XrwObNh/xL/AB6NcHRFGHfyBNWyjFErUeGlEKALwFvjTVVVFWq1Gj1gKStj+pNPcvMTT+DjY3Wr2rlzJ5dddhmecPDgQfz97cuzc+fOZe7cuRhrzCgKaHQqZLOCRq+mtsqEbJZRa1QMGNzXKaLUpZdeyq5duxqewolp06YxbZrdz7eiooJOnRpf4aij7h5Rxw8//MBdd7kOdVofPz8/Dh1yTCr16KOPeiRMx40bxwcffOBQ1qtXL/Ly8pBlCxKSUyQhs8kIisLcN95k6tSpVBQX8uHdN5FfXskH61yHrpQkCd9geyKl7du3Ex1ttYQbjUZeeeUV3n33XSfhLVcYqTNCq7zUxPtEs2TKm2givPHpHkn5r+nc//1zbMncgyzJmDBjdhPTukePHgwdOpSJhj4EKD4s9FrrEMigMSZNmmTLCl2uLaf4aDHffvutR33rfx7AmuPDk89SfHw8kyZZo4rcc+dtRHzQheELqzhSJIPW2+qCB6BSW3+8Q5BlidoqIwMHDqJHnxRQJDQmP+TcUF7/6lEsKvchPgFUsp4Hxs8hNCwYk7YcJaiMLXtXs379erQarcMGZ7PZjKIoGA1GLLIFPz8/7rjjDofxvv/++yaTCQJ07dqVUaNGOZS9/fbbGI1Gl+0lSUE5E9Fn/PjxDtm4c3JyWLx4sct+Dbn33nvR6+3uP5s3b2bzZtfZZ+vw8vKib9++fP/999TW1qLRaMjJyWHcuHEuHygbUqcj6jAYDLzzjmeJtK655hoeCbkKX6zGiN+PbeLJX+1R0xRJQUbGjIyCcibOjoJap+G+++5zGOu3335j/37HBzaTWoMKBbXFApKELEmEp/Sg7R330aq0gPjCXFTAhx9+SGVl47HSVSqZ8RdfzK2DO1LeZiVms47Dh3yY8+oGSkoNF5QIb/4uCUCtVnPNNdfwxRdfnOv5/G0pLq67+Vrq/W8VBxVn/gkPr7c8LZuRUWGxFFBY2HDZ2jlElsFgvYEZIqxfSgoKeZX1LB4NPpOnSuxjGGpqKP7ySwrO3PSLt2zhVE6O0zmqz/xYBziFr0rFgIN72dHJGmpxSoaR9yvLkQvzz1yT+6XEQ4cOOYlwY0UtFZVn+pzp62qRtrra0TpvNps5dcqzsGE+gweTMOsVAB5cPJ8vh1+CXJhPYRP9AgOdrcCFhYUenbesrAy8AuA/R21P9KdO1f/9KuD05WzdZFQaMhN9lH1zpGIyIhc2fwnvuuhQ5BBvXi+w/l5P48IN5Iyht9f+XXwzoAsR9TbDnj592ulaK3H6WHF58OUcwxrp4JJvL+H+hPs9nq+/pPBZajuuTLM+dCq1NU32LQZaqVQEZ15PegTULZf61tRgrqygHHCyNZvP/M0B5bLj31a731eS36YNVbIMZqAGinEdgcH48cdkLP2KgPHjaTXnVYxGo8efw4a2jfLyco/6FhQ4WzELCgo86lte7vibUBTF4/k2FBw1NTUe9a3/oFFHSUmJR30LcwuQq01Ieg3SmXBspw5nOt7X3FB3j/APCeOejxcx48rxlNW4j6hQWm2fT/0szjqdDl9fX/LymghdWgl+oVbxYc6vofzXdACKq8s8mm9qlxQHV8LbakfwbMWzTfYD6/2vjgBTAPnmfCoauffWZ6ixC4GKDxrUnFaVcrx6D+s86KtVLDwkLydQSsfy/meUmW/gVMUPnKpIBwdfZbP1p8S+56e/KYsnWWX9U9VBVpDCM1WezLeCBN06rg35EZ2qFi7/kHmtxnr0wOGFjvaWaPqbklAhofKSWVnzsUe/p9pa589NRUWFWxFen4buSb28svjIw/emDrUsYcGCwWBocr4VFRWkpaU5rZSXlpZ6dK0GgwFt8WkkWcYYFm0b0xMsFguLvOwbuauVAo8++zqds7thbW2tR+dtU3SaQcf2OpRVVlZ61LfcYGFdkR6KxgOQn59PSemqJvv91bRIhAcEBNC6tessb/9WGrOEe2tkZHUtQUH1LC0Zm7EQRkSEgbIyRwtMfUu4j6JH8lHbnpjzfSoJw5poI8rPvjylCtTaNiUF6gIdls5zrrueYC/7sZekQh8eganBGkhoWSkmnRbLGetTmMHA8CO70Gol2hbmcEdZTwpKvPjuzBevO0u4WpHx8nJeuo/wC6VWMqIAvnofsCiovDVO+33qLI91aDQaWrXyLPqC1tubmFdnk/PoY0iA5O2DKsx5U1ZDC3xDH0mAsLAwl+dVgDyD9ebro1bZBPzBqlreyczntY6tz/RToDwXs6SmRu2FXu+DRTGjVnlRZLJ+sUpaxxuUpNWhCotAr5IwyAqhOg1FRme/RZ1KcnLjmennR5SXF8qZL20FCZPOH0VSobKY0JqrkHR64kaMcBDgdddf5kFCiDEdxvA29njvc3fPRROsQavSEuLVeNr2X/p2pkOwPx18vThcVct97eN411tri8Nd7I8tBkDdSo6iKBRpiwjGMdHSga4aInfpQFFA0qAJDQKVCotiQS3Z/54SpkyhzXWPUbzIaq3VRkYTHRZGRXHToc+C+z+Af+IAypffQfkPP3CqtpZITb3NXKGhSGeOLeVGUEDlrUbSqZ2iiwQEBHj0GXa15BweHu5R34aWHUmSPP67afhF6e3t7VFfPz/nEGfBwcFu+ypmBflMoiHvTDM5zzluVAv3c/4MqbzUSFoV9dPT1r9HqPItTOz/CIv2u44g0pCGFm8/Pz/bfBWzjFJrQbE4LxCH+Ton+wrxCbTdh1U+Gus8XRAcEUrsM4MwF9ZQvacAv4ExRH8YgWKyhl5U+WhAAdlh9UrBKJkZLidToFiokayiUKPREOIfhFbRICGh8tGgmCwYzSZkFAxqA5JGhUW28FjiK3x+7CV8ZG+CLb5E6ILPzFdBkaAaq3HHV7GHPgWIk2OoNM5weAgP891IucHuslWlrsbXYn0fZMmASrF+R6nUQ8munUuo/ma8pQI0KoVW/hKgxqySKFaBpEgokmJLHKVWJAIsXnT134RWMqIoGqRld+C1V0UrfwlZ8kGRHb8X6hPuG8IQU73cCLVqEnTdOeFnQC2VYDWKqbEoQTT8wumj7cCU2ouQ1WCWLfgqej73m0+V0R4qViVVY42ibsB6l1KhaP24IeooE/gNhj4JQx5n565dtPp6N1S6Ni5Y8AbFFwWFG2qH4K/YXTpkVTGH/az7sYxyDeYzBj1JUZA19vt1w+/HurK6B2JJtiDJMoqkQqn3WVeZTQSU5kOxN91aj6NNTRwY1SyopyPq3g+LZMGEBbleYq+GfzfZ+hKXD+ENcSXCE3TR5PplUf+9kBUzKsl+fzVLFpc6ol2gHwVnPpkGdKiQsaDGiON3mlZrP1bVVqOSJPz8/Jq0ov/VtMgdZfTo0ahUKn7++efzMae/FQ19wouLWhESeoqsrM6kn+yJSpEY3yafkvhfbH26dnmTyPeu5WXuoc9gxw0TWm0wAy/aZgvp98iD01DU2JYuJ116OSFLnZ8CS273Y+qGGwB4dfCrjEkYY6s72NF5SbphOL+V916HRraw5JrJtrLLli1nxcTLbcf+sheTjRcxz8v6NHkyNIpfuzon50k8ncXIQzudym+rHXHmz1pBg/UP2qdHBCFXd3Bqe7ZU/P472ffdzx1PvsTRuASXbdb16UgH3+b5+ULjG1bryBuWyor8EodQj1eF6HirW2cWnirk8SPuN+08Gh/FIwn2aCuFRjMjth/ivrhIrooKJkjr/Oys5Ozl0PDJTuX1if/6a7y7dnEstJg44x/R5DXV0TAhVB2P9HyEG7tYY7DXZSzddu02vDWuH9Ze2fYKnx/83GVdz8ieLBizwGmfw/Zrt9seME15VZx+w7q0rk8MIvw25z0K5qIa8l7d4VAWMqUjWTePQTZJSBodSk2D0GcqLf6XOi7RypX5qPwiqPx9Okql3e2r06GDGE6UUfCh3VrT6uWBjabB/rtQ/nsGmggffFKsX9KWSiPVaQX4XRTjcH2KWabmQCGWchN+A2JsVm04Y41/csM5nZcm0gdLmRGl1vnhtD4h13ZCG+VDydIjhF7fGXWADrnaBBoVKp31/mMuNSBpJHJfcO3K0uqyw5jirqHwl5N8GQaZezbzWHUnVP5aIu5JRRPc/PtHU1TtyKPka6sbRcjUjqgD9RS8t4c8qRRZkomQA9GgRhPuTfgdKagbSe9tkS3c8MsN7M3fy8+H3j3nc/WEMnUFgRbXYm12zHzS9Tm8e9IzFzxXrAmex7vhu3gx+yqSqoe1eJyWclyfRc0AL/pfNByNTsvXR79mdOuRmLL2Eps0AlPBQVbnHKb1b3oCyxs3VDRGXk06q8p+pCYuCVQqNOXFoCjoSgrxNYKX2pvk4MFUm8s4XLaDPSlGFB89JZpyDvhYH1DjDFF8cKLxULZb/fbRt9J+L32x1Uds8N9NYk0cXfK7o1Fc2271p7PQVJTQNfowfX1zMJh1VBoCsJgVkgIKrM/Qoe2h6CgVJp3V9USVgKwY8VanO7iFy4oPFiWMItOTaKRsQrRzkbAgSY2sTsR0hwlvwo+PUJuxm23FsdQEt6VPaxXBlQdQukwiL/leYmJjLyh3lBaJ8C1btjBkyBA+/PBDbrzR88Qn/0QaivCiolaEhp4i51hbjudchEZlov9ARx+xLl3eIOq963iR++g32LFOq3mf1avtSz5PPPEEFouFV199FYCJEycSush5s1fJbX5M3WgV4XOGzGF0vDXqg2IycSg5xal9fRF+6R8reXjRJ3j36smCxMRGr3egqSMbtHYfUINaSz9zW+4c2gmt2YSXyciEvRsJqLepo4u5Nf3NSa6GQxcfQMRd3Ro9Z0spWrCAE2/8jwmvf9Jou+ODkvHVNL4Rq45dZVVcsqtpP0NXRBoK2TN6BE8ePcX8U84OMi3eoPr5FVSsW0/2+lC3TaKefZbgyVfbCxQFXk2E6nrzuO4bMoL78/mWDB4Z1YHbP93B+qOFHHlhLDqNs4WvoUB2R3xAPN9P/L7J/n4WH746MgeA7b77qW4n8VvJal7Mup8/vU+wPHUT715m9yHOfmK905iB49riP+iMVVNWOPXf5glAuaYUlXdQk+1q9y3FdPx3Wr3zIeUrXbeJeW4AlX9kU/67PVlKq5cGYik1kDd7OyGTO+DTvanQeeePsmoT3Z77DYC+CSFsPWldHdg9/WKqnnctSusIHN8Wc1ENVZtdZwoF8BscS+UfzY8QofLXIlec3zT1nhClvxmNZF1uH2h4kw36B2118bVfIKGQGO7PykeGuh3DYLZwOK+CAC8t8WEtiyNtzMpCqa1F36YV5p9ep3x7LQHqr9CocuHqz6DzpdaH6ecbhDbsNAE6jIPld1MjQZ/41szIupP+ldb77ZVJj1ClrkGtqBhVOoDxJYNpa4gF4IhXBiWacvpWJiMjsyZgO59ELEejqFl4/IWGU7RxfeJTzMy6i3aG879Svs5/J29Gf06N2tHXXKsodKxO4sWs+9AqjUcZMmFG68Ih4Meg9ZzSneaO/CvP6Zzr+DVwE29Efw4SeMl6Li0eys0Fnu03OZ/cmPg0BGqcQn1eVF3D8OoaetfWEm62oFZUvBMYwc8BEvEmM3PzCwiUz8H2wuuXQ7thUFMKfy6H9XOhtAlf9963w5DHwCcMGsm8qigK+9Lz6NY25u8vwp977jk2btzI77//Tvfu3enTpw+RkZFO1h9Jkpg+ffo5m+yFiJMIL4wlNCyb0/vDOVI8hsTEzUTHHHPo06Xz60S9fz0zedghMgrA+j8cLYxPPfUUFouFWbOsUSm6d+9Oz83OT9MlN/kydav1gai+CC/78UdyHvmPU/v6Ivz7h2+h+8pfMRw/zqu//OLUtiluqx2Bpl0AH+V9h6+vr4Nvaldza/q5EeAA6lAvoh/t7bbeIivsyS6lS0wAR/Iq2Z5ezE0D4lF5EOlENho5nNLNbRKfOvzVKo4MSvbIeumJFdwdWtlE1vqRTLpsJ5tKK2nvreNojfXJvleADz/0dP97csvB72HJdZz4NQxDiXuLWKdD9eLByzI857y0XscVhmfYqTiuTpx8+RKueG8TveJDqKg1MyElmr5tgx2SQzXF/wa+SZWpkh+yf2LjKfuDZqA+kMuyBjOlaKxH4/gNiKFyk/Oehjoi7kvFmFVB6QrP3BP+P9FG+RD5kHOUi+ZyvKCSEa9ZNxEffmEM+noPlfkVtQx7dS1VRgvjUqJ5Z2oPNh4r5Np5jkJbDUQhMQYdN+NZmuuWEHJNB3xSHR8+FFnBdLoabZgXklaNcuYLvfz3DCrXn0IxtTwVe3PQSsfwU/+Aj3odkuTZg8BU8zPMm/EAPjqrmFMUhYQnf2qy36OjO3DP0HaO9x1DJUUVVeQ8OxvNyqbHaIh/6xpi+pai0rj+WpeBWknCJEGxWk2oxUKArEBwPFzxCUqrHqzOWs3mnM34aHywWDRYLFrSCnZwoNQagrXi0PNoFC3tz6SQKcDMOH0xvwVuplj2wVIdT6LfIT7On+Jw7lxtAdGmcEq8IcgAUoO31NDLi59O/EgbQww9qqyrtyf02bYHg7sTXkQCTnp5ts8BINIYyoLj9rB6VyRNo1pt9QFXKRIKMkP3KvjWSqSclFnfVWJ9V0chp5U19C7vyJgDfvQOucFWbinLRh0Y6/Fc6ri0wwOYVO5XcQLMviw5ajW6HfZKx0vW0cYY0+zzNMZMqrn25u4MTbKuckmShMkic+R0BR/+cYLBHf1ZlPE0hlo/3hz5Mq2DQtGeWA1fOCfAqs9+OZ6uqnS+s/SnQAniVo1rb4nMiGEUXvw23RNbsSuzhBVpOcwY3xmN2r2IBqDwKAS0Ap2jS47BbOHiuX+QWdx4bHXZUE3WG1f//UW4qpGnDYfBJclhE8w/kYYivLCwNWFhWZzeH8aR4rH07PkdPr6OvradO81h/wfz2EhvBxFeXBzDgf2OUUBmzJiBSqVy2IjhKlZ4/lQNN+627g5/ddCrjE4YjSRJVKxaRfa99zm1v+vxFzgc347h2zcx/ZO3CF+zmhNZWWz6/ntKmwjPVZ++pvYkW+JQ+WnxHxJL5cYcPqi1f3kMNnUiyWK9gbR68SJOPbXRaYzYWYOwVJnIfX6Lkyh5f91xZv18CD+9hkqD9cb1xuRULu/uma9r9c6drP3PE9wy41XmvPkiwzu1p8eoq53aJft5s7J3424xBlmmzbq9jbZpiiV7H+HuLs9RrPblp113ckkPq2U3Z2g3VJ66MHz/IOxcAFgN2ke+jUI2Wf8m9UEm2o4pQFGg+IgvFoOK8Es6I932m73/zKZTKsfXOic9akidhVxWZLp96n41I762FQ/mTqVjrdUtKM3nEKnVHXkt+lN+D9rCG9IXdPizaX90dwSMiaf8l/RG27R6eSCnX9uJubCm0XYN+5iyK8l/J82j9oZDP6DvON7j8RsSdHkifv0aj19dh6IomGWFV389TFyID08vt0YaiENFIBJfv3Ax+06WUPTxPrrUs/Stw8RTSjVXHFtLln8k2yI7ES6pWMbZJdpQB+uxlLiPfhH9dF/UfmcfD75ycw5FP51EbZL5AxP/xfH97IWaUhT8kXgLq+VZRkHVRKIZL9UmwnQvtXhef8ptGG98kf9r777jm6reB45/kqZJ96AtHUDZpYwCsvcSZCgiKCIoskRF/SqiIAhCVZaKAxcqylCGoqCiIAoCyhZkyCj8GC0bCt27TXN+f4SmTZN0sQo879crL5pzz7333NyUPjk55zkmtIDiZd0yhjr9zv3ZUzmh8gMov4wkspycaXzpKLsCw8nUGRiXfYjOq698W6dRoK7NUCbXgCxCO8Zhd5HKyi0gPQ4G/wi+tguZJaTnsOnoJV74dm+JzlUv2ItD5wt/Q6v4++m66HP0bFg1jTr9hhJhqI7O1xeNXo/KVVx4dxe5CZmEvNYKZcrGqdCqk+k56bRc0tLyvN/lmnQK7M9T+9zJdnJGo0tEmfT4p2WREXAIbZX8b9yUyZnUoxPBlD9kKDj1Mh9vfB83o+P3qpOvL4mD7uF1zw2c0sTTIMbE5KV5aXKcMQ/eLxBEa7RovUPRhdyFc6XmaN3zv5EwXv4/4iq44BIWTIVOgfhUq8HKYyuZusPxtwn2jG4ymp7bq2E6bvs7lJt4kuwjq1C52eRe/j8w5aILbY1KjycrK4lj9R+geZA5XXJaZhLqrxmojPw5MWPbjaJCZgrbg+ujAJ+sVJL17mSVYIiiDiMGckijpIu/KUq66JMjr/YKZ9muMxyLLf3Y7tsmCC9J2rY8HTvaz018u7AJwi+F4h9wCtNWN7YYH7QbhNet+xZffG7+o1kwCC/cCw75C90UF4RnVlQ87PsCCnjk8iNUr1qdhx9+mNTNWzj9xBM29fN6h4f8+gNDVy1n2cBHbDI6FKd/Vmu8lKvNKoF5Y8YBumY3JKJzU9zuqoizv6vdIQQVBoUTvyR/iEulN9uicdZizDVRa6L9T9JTetdjWFv7Y70LKzwm/mjlajw5cYZNvT+ahdHQ03bSS0auiZknzvP5mfyZ4JNqBDO1wFK5CyOq093fm98vJzFkv3XO3XfqVGbsEeuv5TXKxPHNPXBt9wJ0frXoXvhDK2HZYGg6FMJ6wtL8sd8X93gRfyR/clxop8u4B9mOmztkqso04yCad+7L6C35KQVHZL9EIp4s10da1V9q7MwE40iKc2xaT3ROWvacSqD/4o9wDTEvc60734dmie2Z1qU62esvFnMUa9kRfuj3l2wRl4CnGmKobv5QcWbSZmxWnwICX26Gs3/+H4mci2lcfN88lrzis43JOpGIR9tKpGw6Q/Lv5q8+81ahBTAmZJKbko2+sicZ+y9bJnkWlLp2Eio7Fc97P7DZlnnge4wX/sOjq+1CFzY04Brhj1fXqjhXdMOUnkPm/yWgq+hG7Id78L6vBmnbzrHQQzHnpPn92Aods3A8aa2w9K0fogtpTE70Rtw7Ox4fmlzbk8vnNzIyMRSNzoAz5qwzddDyFeb3XHzvapzxcGLprtO80acBVTRaTFm5GC9lkLLhNAFPN0RbghU7HVm26zQ7TsTzSs86tJhmP7NB21p+nE/K5MQl+3neATSYOKh/FheNETDxtbEfnprzPKizTktKr1nQbARJmbn0+WQzHWr788beduZtHV+Bzq+i1k9D8/fbDs91QfkSpMmfZ2Ayakg45kbs3uI//DqidTZZPmhfjRc6Ps9PMwaS/MnHeHa9m78SNFSb/Dya1Px5Rn9VasxHjR4kTe9KQHoCXU7v5pcabUl3dgGlcNE7sX3C3fi4mYM0lZPDufETyE1MJHj2bJSLK3qdlgtvvEnCEscf5rXe3lRbupTcuMucHJzfw+zRsSNBr0eSsXcvrk2akOui5/i4F9Fu3O7wWBYuLlAg20ncC68Ss2s/NS6d5KhXMC12/V7EztefLjiY0K++4kCmjsFf7cDFmE2vmO1sqHwXl119yNLpcc7NYdajzenTuBKmrCwurl5D4gR7q0VqyJ/Kfv2kOLty0a0CtZLM30C82OE5Dleo5rC+m96J9OxcAtIT8MtM4v98Q9EoRa5Ga8ke5puZTILLjQ+CI7tXZ1iX+rd+EC7yFQ7CL10KJSDgFGqbK5tzHqJps59xc7PuIYiI+JRPPzHnAy1pEP7666+jlKJRo0Y80OcBEn74P9L3xFr9Di72X8XfLvtpfrm5Zd+zY8eR/IvteNy8ILzbjk28uuBTqwmZJWXvwwBYB+GvvfSq1cQhe0F4YR5tQvC5vyZtZ67nbKLjnstfnmtHROXi/7CdHDaM9G3W/4HbG6LS0deTvxJSGBLix7TaldFpNbxx7Byfnrad6X6hc2NOZmTRcnsUd3m68Vsz66EkR9IyGbY/mpeqBfJgUAW7w1gu/NURAurC05vA6crYRVMu7PwKat0NFWqAMsEb9ifzKAWHv8vvZUswePD7G/N5/Z5K8FY1u/v0yprOasOrAPTMmkGUyusFU7yi+5ZRuvz3SvXMRcx5rDlPL7KeZDtJ9w1P6H5jv6kaE3NGsHLG89w3fg2f4c4xcplKBguwzZpRnF/I5q0raT01Tqn0u/sAke3G4ak399LmpmRzfvoOy3ve3gTIgu8v5xB3Ap5uZJmEdy1ln04hJy6RnDMX0LpkcfY5Rx9Y8v9QVhg2jIrjxlraHLckioz/ikue6dgY0niPso0zLoopyAWiT5P2x2RQ5m8yf5i1nK82F7+gy4npvayGih29mML01VFEnU/hQrL53va9qxLvD2hsd//Z645yMj6NFbtLPuQgZlJzmFVopVT3AEi7RG7r5/lnzx5aZxb//w6P/gC1u5XonMpoROXkoJlVFYxZmIwa/m95yb7FKKmwfueZGDiLF1JnE5xziu5ZMzmWW4lcbf772Rkj1TQXiFZBGLJzGHngF7qfsl2593oI276NtB3/cPaFF4qvXI59E34Pf1ZpyiVXH6bsmE+Li7YfsgHebTKA/Q3acTHpSpCv0aDPzaFKSixGrZbP1r9rd7/r4WSDVnzm1RCtUhz1rUyuxoneJ7agN+Uw6Mi6G9YORzTOzig7C6jZk+bmhaZadfbHZfN+k4dJMhT/rZzOZOTh/1vP4MPmb3j3+teiUZ1KhA4fjHuLFmRFR6NycnCtb52IIC9ekyD8NmIThMdWJaDiyQJB+E+4uVlnM2kYMYdPPjGvwlW33kb8/U8TE9OI06dsJ1DmBeFr1qxh+/bttG3blm7dzH8oEpYfJW1nfn7b3PjjfNJ2L27RPpZ9C/cCK+BcQCCPvfEBAAtef4nQC+dYVsogvHdWMwKV/QC4YBBeOJ9pxsHLxC0+jE+fmiT+eAxHKk1tyyuT/mQdOcQ7+LTv72Fg16SuVmXKaOLsJPOQl7zeTGNCAkdbt7Gql+riyuq2Xfj9kcGcsNN7CvBfm/o03HrQ7rbSTqI8mpZJ+3+s/3O/8FeBb4najYHWz8LC+yHW/jkLm3RkCI/uMc8KPO/mx/B7JgAQM/Neqo1fhQtZHHYZ5nB/e0NOPjB8zgOaAt90BTfmZ9/HeWePljMqgBPjG6P9wPo/tjOZv1JS5zERjP0evXa2Gb+t7JtyD96uzuRcSEPn5+owHVzarguABvdmtmknr5e837Oa69ahr1yJy59/waX337dbN2jKZNzbt8e5UiVSN5/DlJ5Dzvk0Mg8XnzaxtBJObsE9N5ucmE04+Yfh0vARh3VTfnrS4TYA3aDBfL3rPOtDm3K2QFoze8ICPfi/i46/Lv7ssabUCHAn16ToObsEAfIVrbSH+FZv/ir/cr0h+Cfsg/N7AchJc0LnllvaxfcAUK/FcfLRwWTsNR9L4+ZG4CuvcGGKeZXOyh9/hGdX8/81F6ZNJ+Gbbxwdqkxq3nsRZ49cLrReTmCndmgNBqtVBJVSLN5xioqeBu6pb86cdD4pg4Nnk9l/NonZf+ZPFh/brTbR5xJQP/3AEwdL/rt5PQVPfZPzk15DX7Uq2SVYVMYR10aN8O7Xz3JfyqLqkiW43tXY8mH4n+h4grxcMCnF8t1nmLPuMAHpicxbN9Oyz4iur3CumPe8IxXT4nlh7/c0uVS2Cf159vvV4ELku/yvax00Gg2ZObl89tdxHmxSGS9XZzwNOpu5UkopVEYGmYeP4OTjg87fD62Hh2URrJy0dC7PnYtb7VqcG/cKXr164dmlM2dfHGOvCTeUR5cuaF0MJK/+DbdWrfAbMYKzo0djSnP8jVdxXF8aQ/Unn5Qg/HbiKAh32qJnY+4AmjX/CVdX6yA8osEnfPqpuWc2ryc8JqY9p09Vszl+XhC7bt06Nm/eTMuWLenZ0zyBzZRh5Nzr1itsfZ/zA0me5kl3kydP5ki9/ICp6jdf80iWnn8KJAhfPXooLk+MYFmc46//2+fUZZNz/sS+yMhIEn46hinDSG58Jtmnra+vqCAcQOWY0DhrS9QrDkUHZ3+N7UQVX1e0Wi05l9K5+G5+r23F/92FvpK5R9ZemkaAXI2Gt777lbVxtuf4tlENHtl3wqb8xaqBvFKj9L1eJqUIubLM+4gzy5l2/MNSHwNse8ABevZ5x/JHu3EVH/aeTgSgr3YT7+vn2D1O6oQ4GkzJ/3r2n1fvpqKXS9FjxoMawoX/UEpHhqkN8TnjStTmORX3Mi15Ell6X7SPbUXzVQd0mkuk3LePDT9EM5kMEkv41eqOV+/GoNPi46YnPduIi86pRBN1bzRTejop6zfgGtGA49172Gw/2fJuqg3qT517OpCTayJxTQzZmx1POP2WLB5xMGHSs1MVvLpVJXbWO8TPnw9OenPGjEKvqcf9c9BonXDyMZCbmEX69o8xpZxHpZV8qfOC+t43jUyd40mc+twc2p77jx1B5vGmK1ZNAuCnGu1ZEt6VFL2jnnzF/dqtvO68EF9N0WM/c3M0HP0xCGXKfw/41kojsEkS5//xAQ0EN09Eo4XkcRcYPXUWc10+xAkTPPkX5z5aStLykq1AWVZ+D92DZ9JiXP0K9A7qPcwfvJs8DmWY4FdQZk4uyZk5VPS0Tpm4+1QCAz76G28nE2ueaMyl+3oB8HdIQ9qcP8BlV28mtnmSOk3qMn94S4wJCSStWEHsO7NKdX7fxwfj0a4dp598ylKmCwqi+vfL0DlYbj3n/HmiH3wInZ8fVZcsxsnTk+yTJ8k5f4FTQ4fi3qE9Kiub3KQkgqdOtU2vCqjsbNDp0Gi1KKXITUxE52v++6dyckj9+2+cfH3RVahAxn//4dW7d4km4MelZlHBXU+W0cR9H20ucvxx82q+TL6vPg0qeaHRaFBK8eiXO9h63PZvas3EM3SIO0InbQI164Ti7OpCxZdfJnn1ahK+/Y6MPXtwa9GC9H/M32ZsDo7gm7rd+WjCgzSq4lNsu68HpRQqMxOtqyumjAyO97oX43nHWZHyePe5H9emTUnbtIn0Xf+Sm5A/RMu1aVMy/rVNY3y9pebm0uLYUQnCbyeFg/DY2GpUrBiDboszG3Ifscl+AtCg/of8vmADJzI8LNvPng3nxHHbLCF5QezGjRvZuHEjTZs2pXfv3pbtBQNZZczmK4/85+PHjye6sXlChnPVUGr9/rvNsIh5M8ejrxrK5hDroK5vVgt+NPwDCp7IutsqsB47YRLuBvM4T2NiFhdmWn/9uccpmn+dT9Dznh60bGObR9xe24uyc0B17msYwg//nmFXTALLd+ePr26NjndwwynAldxLtkNXKj7XGH1lT7KioznRs5fd4z+xYAXHM2wn6njptCQb86fwR7VrwL6UdDr4epZ8EmUhH528yO+Xk/ilUTU0U0ves5LbbARZa77m5DrbfS64+TL+oTe4mGx/slEIl9nq8rx14WPLoVZXEtOzSc4wEurnhjExE0yg2zUVtn7ksC1puV1IyCm+p8SjymG053fipfvOcSXf6iSO/IcjF1KIqOzNA59sKbIH1ZHoGb1uam7uXJMiIyeX6Etp6Jw0BHu7WMbMApwdN47klbbDwgqa1eQR2j/7OJG/HLJfQSne//tDwhNOk/nYNAJSze+F4nqwAQInTaLCY4/abkiPJ2PjT8SMecdmU62//kLjpOVou/a2+xWgr1mT0KVLCZu2EaUx97C55WTw7t8fUy2l5PMBnuj6CgbPHEI0cSzSW8/ZyEzQEf27OaNK5fZxeFbKQpngyPJgVG7J7nudPbvRulpPIEvfvYeTgwaVuI3FCY86ZP6UrBSaUkxwv5FyTYrvdp7m94MXeOehhuYP3sVQRqNlYSqA3NQ0To8cScaePdT49RcMxaS2vROlZxt567fDLNx2kg8H3sX9jUqf4SQpIweNBrxcik61WJ4oo9H8/ncuWZtzU6/0bCsTR9u1R2U5njSbx/uhB/Ho2BGvK6MCTOnpaFxcyNi7l5ODHqXi2JfJOHCAlN+ss71JEH4bsgnCL1anYmB0kUG4RnM3sbvcOZzmbdl+4UJNjv6f9ZAJJycnS4rHzZs3s27dOho1akTfvn0tdc6M30Q6WWxz/j/q5lZmvdpB5pXVEF985hnOdTAPedB6eFBn106bIPzpv34iPMfI4UILwDyReTcJmlRclR4X9Li/0oC53/3CtyddqV8rlG9G5M9YLxxMh0xrS3KK+XUpKjDKPpNC7Mf57XmRNN63M8b11PBw2oTlB59R55MtX2FvpvhfpLxhKUopso4cIWbQo6j0/FRGIb/8Qp0zRQd+Zc7hXZQjv8FSx8MD8qzIbcdf9afydKT9YMF30VKCmjWm2vhVVuUVPQ3EpmTx6//a0SDQBaZWhIfmQQPbNFMFh/EA+DlH4qLdjaZQHrGknMdIybVtc0X9/3DWRBOX8xqZppb46D7DQ1fCr8KnJFp68VXiKTK1nmTp3Gn8hnmozZKRLRk0t+i81QVFz+hFapYRV2en4lNeXQMpmTlERP5hU+7voWfBsBZMWxXFthNxBKXFMX+t7YRgR/b61yQ0JZZsJx0ZOhcavPQsaVMmlaptuoAAqv/wPbqjSyCwgfn9lpfZof3LsMm6x9Nk1JDrWhXnZvfBPVMt9yX79Glzb77p+qYKrHhXEl6hGRz7Oaj4ymXk2qQJGr0ev2FDOf3U05ZyfbVqVP/R3COetm0byWvW4Dd0KCo7m5hHrNPt2QvohRDXjjEhAa2bG1qDAVN6Osb4BDTOzjgHlm1tBWU0cvngQSo2blyugvCyT1u/jlJTU5k0aRLLli0jPj6e8PBwxo8fzyOPFB2wLFiwgGHD7I+BPX/+PEFB1v+xr1u3jtdee419+/bh5ubGfffdx9tvv03FimVfQENdyRSiKeKrdaX+5HDaYAp+VaxMtsFCwfSOuiu9EPv27SM8PJy6dc3DK1wb+bH20AZOOl0i2ikWCizdeuS+3pbEY1p3d2Kz7E+UKByA5/FV5qEczlU88fX15dMY82Iwm446nkzm1aMaWictPj4+DuvkSfHR045kS3IjR5lTQ/6LhwJBeN1gL9z1TmRlly79pUajwSU8nKrz5xEzIP+9dK53b0K+XMa5nFzeqBXC5GPWQwKGVfIvfKhro05PiEyCxFPwQf7CNYdNVTChpZ7WPHZyTM4zOP97iqcL7b7rsdEMevVJnK4MxTg6rSe1C2ST2T7hbuthGpGO0wCm77GefBqXE4lX7dN4nR4FgGngKoyLn7cbgFea3ADN2+ZJe/76EmQAKezkFsjJhMUPogFcrzxiAkMh6RS4/kWMyyBONXiWDrvaUDjFlYFsjrgMBeD+rDetcjSP61GHIa2rWb65uSqZSTAz1KqodubXVxax1l5JT5fvcmo2932Uv1jQBXc/ej5gDnoHHf7DMqnIkcaXrfOclzQAr7F6NYYaBTIH/fc9rIu0rbjJdsiBVqfQ5sTAto/Nj3HRYDKi/7oldR+2Hot5ZosvKadLF4i6NmuK74ABnBtrfxhT7B5vYveULYtItWXf4dqwIca4OI62beewXsZuc2ac9O35k7Urf/opnl3yV1v07NIFzy5dLM/D/tlB2vbteHbtahlPK4S4fvKGFQFo3dzQu5U8A5Q9Gp0OQ/WSZVS7kcplEN6vXz927tzJzJkzCQsLY8mSJQwcOBCTycSgEnx1OH/+fMLDw63K/PysVxP866+/6NmzJ/feey8///wzsbGxvPLKK9x9993s2rULg6GMi1XkxdUl+oa0QBCuiv6PXVfgq8DvvvvOMkzFvZkv5w8n2N3HcPcUDMf+4esrQ+naRkXZrVdYuxzr1y5gpO3KiJuOXuLIhRRGtKtOpentOPvqZpyD3PHsUPKxjU2nmmdxFxxEchETgYWCGd2uWHiojlU2id+LyAqxhCwGFRg7q4wmNAVWfHRt1Iha6//kWJf87C7fTx9PzdXmnuTCQfj02iXLSV5mPqEQmURizD6+nPsRH+c+gDO5DHDawKrclnQ8s4fxuxZb7aKvWZPBk56yKnN20hIz894yNSFhue2koeSjVUjW/op7QzfS5qcDs622F0zjR8934Lextgeu3hEaDYSfnoZhv0HMFog7al5eeNqVD8ULHLQ56cpKk1+Yv80JPfAJMfdVhOYjGP9GJD/mtmOW36/0TvvBsstKw2u0y/qAM8r8QfrtNUd4e80RPnusKfVDvKhSoYT/kadcBHd/KJCJonAADnDUJT+1Woes9zmlSjYZdEn4PQz9fComk4mdHw+j695dJB4vWaYTnxppJJ4w180bZnZ+8hQSly0jbNcu61zLJhOssE1RWmJvO/6jVbltAsmnMji71X72HreALPzqp+Lqm0161afxqOmKZst78O8veF/5LJeVpCMrSUfsPi9y0or+c+QSEUHg+Fc4+ehjVuXh/+1Do88f+qPz87NenArHc0IA/J95xioAt8fJywuve+4pso4QQpRWuQvCV69ezdq1ay2BN0Dnzp05efIkY8eOZcCAATgVM9auQYMGNGvWrMg6Y8eOJSwsjB9++MES4FavXp22bdsyb948Ro0aVab2K0v0XfwoH602/6tdUymCcDC/Tv/88w/Bzs5ka+z3IWdpjGTVvgvYA8DKteugWRe7dQFc9AYeSzb3IM2u6swLJ3NwaxlsN8Xb4K/M48DDg7xoV9ufSjPM+13tuNzHSeV3O0NMclOzS5TObQrp/ImRfzDywZVAPTcpC52fdY+dc6Ex8E7e5t63pLUnOeIfTJ3E/IknyetO4d3NekGL0jDGx2NKSeHi2+8Q+Mo49KG2wRxA48/OAOahRjnouFB7EEtmDrGpVzjAKC1lUsR+tIec82kET2xJ+t4iJuWZIG2v7SpkVgE4QIuR5rSK/86HhBh4aAE4FXjPNr7ydX7VAkOuIh6G/ctK1/h1U2DdFGY6w0znL8HORPnNhtHclfkZCQXeR4XTLALc3yiEDwfeZXuAghNT8749KMEwjL8NL5p/iOjPkVPn6X5xFKAhhMvMfTCUe5enE0wcdbxzWDBhBGx+H9ZFEu4MNIegpkkcXhaCVmfCr24ql/Z7odEqPCtnkHwq/8NDcIsk/BukkJulxcX3HER6E6yF4EeAdyub01oWpWo7cHaFY2vhgTnQ+ErHRtxx+KhJsddZkFdoJl6h51Am81AWJ739//c8L8yBC7blht4vYvj7HbxCM0lP8iX2Qksy9pgXxKrz3z60BYLrPHUPR5kni6Wno3Uv2QeXvN+ZnNhYUv5YS8LixWRHR9v0gAshxI1UpiD84MGD/PvvvzzwwAOWcTUZGRmMGTOGlStX4ubmxrhx4xg5svjFPgr78ccf8fDwoH///lblw4YNY9CgQezYsYM2bdo42Ltkzp49y86dO5kxY4ZVcNumTRvCwsL48ccfSx2EZ2aa0GohMyOXjAwTudkaso3ZZGTY/4OYk5OJVptr2e7pWYHsbOtFVrp3707alXQ82ZmZVts3bzZ/zX2y0D4FbTUdItxYiWzMddINuagM68mLBY+pRUt6dgZOgW58d/QsK4HljWtjuNIGU3YmhR04eRE/g4lnF+/m2KU0pj7QgAebFt8bfiY+3ep4EZW82H82mRSgzZVc0U+gt2SDOD55Y5HHG0gqXmg4jvn1/Ac4jCIUJ5LOxePi4mOzT8ia3zjRfySuzUcSv/k9PPefJe63IwB866rht2Bnhp7I5iKx6NqUbUjKxb+3kvBC/oqlsWvXEr5nt+V5wQlPhV/fSM1hLhYK/gJeesnyniir7DMpJJ00f6A5PmWj1baQN9qQvP40qRtPO9w/ZEpr+21wCYK25jSJZGYBxUywuXsm/Fto0ma3NyDlAmz/tJirKNpm7VO823wjX25ynNt6/65NPLDrN/rU9ebhXt3AuxIcWQPZBQLJ1wJg3AmYYf42JEvrSof0t2mkPcEXevspCPl3GZWBg9oCcyV+hIN5n7NTIO3VF+3uGtovPz92aO38rEM+zRLI8boLp/C2pP3zMehyQZdLms2vfxFDtCY6yGiQdy9dguDFGNBozY8Zhb4BGvEHVKhps2S0FaVgegknoDm7QssXofYD5ucVqlNw2nFGTg4Ul2u4tL8L7u4Y+j5AUN8HChzi6n6fhBC3hnL5u67KYODAgSo4OFiZTCZL2ejRo5VGo1Genp5Kr9crrVar1q1bV+pjt2rVSjVv3tym/MCBAwpQn3/+ucN958+frwAVGBiotFqt8vX1VX379lX79++3qrdmzRoFqFWrVtkc46GHHlLBwcEOz5GZmamSkpIsj9OnTyvM3d7ykIc85CEPechDHvIox4+kpKRSRKXXV5lmmPzzzz907tzZMvQgJyeHefPm0aJFC2JjY4mOjiYgIID3HSxWUZS4uDgqVLAdY5hXFldEPuugoCAmTpzIl19+yYYNG3jzzTfZuXMnrVq1Yt++fVbnKHjMwucp6hwzZszA29vb8qhSpUqJr00IIYQQQggo43CUixcvElpgXOuOHTtISUnh6aefxsXFhZCQEPr06cPq1auLOIpjRY0rLmpbjx496NEjf1GMDh06cO+99xIREcHkyZP5+eefS3Ssos4xYcIExozJz5GcnJxMlSpV+G5ZKG5uWi6cr0VQ8DH8K/Tkl199ad3GdlXCPKdONiS06n9oNDqys95m586dVtsfeugh6tSpw+G7mrCvYUOOhdV2cKSSWVu3Gaf8gnHPSueRnX9abXNRzjyc1QbtlTHtXcn/KjzqzR40eWMtGTkly0byZIfqvNitjk153dfW2KltPn5B6krWTI1Gw9nXtlhtCxzT1LzQSFKWZWGekDfaWN0zpRS//neeJj9EW8omk8HWAvlXPDQaflJFL62ec3o7zlVs85yn/Po/XJs2pUfwg8zcPIc6iafRuAWgMuIodjxuGVR67108O5d93GpWdBKX5x3AuYonOYUWVsoT9HIznLzLOBn5ZomPBmM2VCz0Xtv+Gfz5uvnnB+dBzU7wdo1SHfquzM+4S3uMefr8DCJDs8exw2Se4Pfzc20JCyx+eWULpeDCf+BfB3YvNA/FqN0dPG/cqp5CCHEnS05OJiSk9Pnar6cyBeFOTk5kFUiovmnTJjQaDZ0LBAp+fn5cvlz8RLrC/Pz87PZEx8ebl3S213tdlGrVqtGuXTu2F0hHlZcpxdF5ijqHwWCwmznFxUWLq6sWg4sOV1ctrq7OGAwGXF0df9ng6ZlzZbuJ2IuJ6AtNQqpUqRLu7u64abU0iInhlJ0Vw0rjdOUaaIB0V1ebcw3N7IJWnx/Iaskfi+nm5kaWxhmtvmTJ91fsj2PSA+YJU5uOXqKKrxvV/N3R6u0vCuFexOQqN33+hMpKU9vmZznx8MDr7a6g0aBxsv3Q9HCrWtRZ+X9suDI5rwc6thfIw/JHEfnFM/d8Q87JTWgBJ2MGhjqFFvkJaoA6Hktv027uSrmAxwOfo7mSRaMkC6eUhpOvL0H33VeqfZRJYbyUjtZDz/k3ze97N70rXDTiXOD1DHmjDbkJmegC3NCUwxUni+XewH753S/BpjfMP/8ywvyv3sH1TU5g9+lE5v60jt667bx8ui3puIAedtAU9yv7vZg9iu9nlS5Ht41aV+aydLn5S0ILIcSdpmDa5/KiTMNRqlWrxoYNGyzPf/jhB6pXr07VqlUtZWfPnrVJC1gSERERREVFYTRaZ/zYv38/YM58UlpKKbQFcrvmHSPvmIXPU5Zz5J8sPztKcZlCQiodsfx88uRJy889e/bkwQcfxCctzZJayyXTdlJkaVz09C1yu7ZATsUNWE+GSsooZnJUIfFp2eSaFHtPJzL4q3/oNGsjJy7ZXwznvoZFL/8ePKEFOn9Xgia0sEozCKDRae0G4ABarYa5w/JXIO1aIH96JEXnNs45mZ/bOTvqJ5vtbm1G495lCi+mJKKvfY8lAAf4pWYnq7ovtX8W9dMfPD74I4bc82qR57Wn8se2K1dmxSSRvPG05RuDgjKPJXL21c1cfH+3JQC3e9yZ7dHqnXAOdL81A/DitLM/8dEiIBwmJ4BWS5OqFZjzwsP0evY9Ds18kAOvd6dFdfMH8WqZS6iWuYQej46+/m0WQghxRylTT/jgwYMZO3YsrVq1Qq/Xs3fvXl591TrA2L17N7Vrl374RN++fZk7dy7Lly9nwIABlvKFCxcSEhJCy5Yti9jbVnR0NFu2bKFr166WskqVKtGiRQsWLVrEyy+/bEl5uH37do4cOcLo0aNL3e48qsBPZU3XFxYWhq+vr1VuW+1VrlS3JiI/o4xLdv63GPWMlWlsrGZV9z2sA/7WM9ZbfvZy0fFqr7o0qORttRBJYYnp2Wz6v/zUd13e/cumzpKRLWkSWvSHAydvA0EvF51u0pFOdSpyRnPEclP6o6cVOloWeNt/RiZLyeYvq55x6+A2bcNUnPxr4xIxwKrcpaHtwjWDIgZBhDnl2+SEwxzyDSE8rDLLn/ajwzsb6NnnyvLgBd4by1a9hmeOuZe+1sYNHO91L/5PP43voEHWOZ+BtD2xJHxn/vCWvCbGKlXgxdm7yTlf/OzvStMcL2Ry2+gaaU4BWNDA76BaWzAUPYzEw6Bj2VOtr1/bhBBCCMrYE/7cc8/Rv39/du7cyebNm+nevbtVEL5z504OHjxIlwIrjpVUz5496datG6NGjWLu3Lls2LCBJ598kjVr1vD2229bAuYRI0ag0+msepC7du3KG2+8wU8//cT69euZPXs27dq1Q6PR8Oab1iv5vfXWWxw+fJj+/fuzbt06lixZwsMPP0yDBg0crrpZIqXoCXfEXh704oLwDF3RwVeGc35P8H37t1p+9lZuuBVY2GYIqSQUCkILjgUf0qYaj7QIpbp/fnA4tE01m/PFp2VTwcM2xy9Ax7AAfnymDW1q+uPiXHTO96sVMjk/mHoBF6sAHGAR2eQCz6afIev/1pCyarTV9hc6Ps82F3dyjluPoS+JN3zNix5ptRpC/dyYN7SZOfjWaPh7bP7Qrcfun4Z27jfU+XcXzkFBhO/+F/8nR9oE4MbETEsAnidlizmlXeb/JRQZgAc83RCP9pXwe6yuw28PbjtDfs3/eehqqNOj2ABcCCGEuFHK1BNuMBj47rvvSE5ORqPR4Olp/YetevXq7Nmzh2rVqpWpUStWrGDixIlMnjzZsmz90qVLrZatz83NJTc31+or+YiICL777jtmzZpFRkYGFStWpEuXLrz22muEhYVZnaNTp06sXr2ayZMn07t3b8uy9e+8807ZV8ssQF1FEK7ValGF8n9rgNCTJzlVYMgPwEPLvsfJZOKpMf50OXc3JeGelT82ul5ufk7vN8iw5NkGqO7vTvTlNNrU9GPrcfP4+THdzK+ju0HH8LbVycjJZUrvekTeX5+//+8Sj88zL+LT7f2/HZ5/4fAWJWrntaB1dfwWTwz3gcPJAEx+qTtVnNqTsacVxosXiZ31LgAfvf4olZK6crrP/aSuGYdHj7dLdf6vC1xrl/BA1o3pSE6uiVA/N6Jn9CIuLZvMqf/AqgzOrTLnD6/4/F2ozFz01b0s7yFjYhYXZu60OX7SLydIWnUCCn1G8+5VHWNCJi61fXGtZx4WZqjmXaq23/Kqt89fbEcIIYQoZ65qxcy8hXoK8/f3x9+/bIubAHh4eDB79mxmz57tsM6CBQtYsGCBVVlpUyJ269aNbt26laWJDuWtmJmTfYkaNY+X+Tg5587ZlDXbucsmCHe60kOeYEjk7uwG/Kk/gLNyIkfjeAKC3mikolsFKiV7o7nS3sDRTTi6aCdcNo//jpl5L82nmZeVzwvAn+pYw+qDxeTe9ayO2yEsgOJ8MbhpsXWuNY82IaRutX49ffrUpFKrYGI0ERhzTeictEAF9JUro0wmcpOScImIwCvIC4K88HtiBLuWrWaySqKCxol5WGdWCXm9DeembKWwFlrrX7FaFc37KaU4O8H+cJ7YD/dYfs4bbhL74W67dQGbANxmNUshhBBClDtlGo4iinBlOEpy6r8EBBwt0S5uPn1JKBDUGQwG4hYutKnnbLS/PH2eaqaK9My+i34XbCc7+maZI7W7Th3BSZm4P/4umhrz07Y5B7nTqLK5p/TVXuZhFJdSrFc89HYtWXaUonSrd+NTsvncX9OmzKN1iOUDhTkAz6fRaqn40kt43XOPpaziyy/T4LdfadkwhPnjOxEyOT91oUsdX7QGJyrNsB1rffnLA1bPc2LTufDuLocBeGGJq6NRSqGrmL9Kof/ICCpNtz+uO/g125SKQgghhCh/ShSEa7VanJycSv0ouCS8cGz7iSRWZYaRo7S0b98e4uJIXPptsfuFFhgPDxDVLJZQ3xA83WOsytvkhKG7MmqnxiXbHnbf/mGs3HeOn/aat3m62A+2SxKEf15MT3dZh+hcLf+REVd9jFA/Nz55tAmVfFzRuuW/FhUGmj+0aDQaKs9sb9MTnZucP7To0uf7MF7KoKRS/z7D2QmbyY4xD5sx1PDGpaYPGq2GSjPaofXMH3fvP7wBTu5X/0FJCCGEENdfiaLkDh062ARPCQkJ/Pfffzg5OVGlShUCAwO5ePEip0+fJjc3l4YNG+LrW3Tmi9uRovRBZkWXExhxYnFWU6bdfTepmzaVaL/m/5jHCF/0gUYBjejcvQ/OTs5Ac4iMtNQLz63MJRfz5y3nXCP3Z+VnG3GN8MepoT/PF1hIx+tKEH53eEX+PBxrKXfWFv+Z7R47Pd3rX+rIyfh07qriU6Lruh5cavrgfW91klZFE/Dk1QfkgLnn24TdiY5B45tbxnBnHkvArXFF0nfHYkqz/22G9301MF7OwKNNCBmH4jBeyiD934s29fICfjAH/SETS5ctSAghhBDlQ4mC8I0bN1o9P3PmDG3btmXQoEFMnz7davXMU6dOMWHCBLZs2cKvv/7KHcc2dbOFfo+O7LtsgzBfw3mr59kxJ23q2ON0JfH83hoaxjUfdyUAN3v66af57LPPCDB5EeOen4FkSHprAgpMZnVvHkSvD62Dfk8X89vi40FNqDs5PzjfdOwyDzevUmSbCn9Y+/V/7agR4EGNgKJXp7wRPNtXxrN95eIrlpBGowEHyV10PvkLEyUs+z8wKRJ+sB2e5NuvNq4N/Kx61p0rupGblGU3CNd6SE+3EEIIcTso05jwl19+meDgYBYtWmQVgAOEhoayePFigoKCGDt27DVp5O3Cfan9FSONJutI7lIJJ5jmhbudD2ppGNDQaltQUBCRkZH0yW5OZoHDuxltPyWcuGSd2i4vCHfVW7drap/SLWJUM8CdBpXusIwcBRX47SocgIdMbkXlme1xbxFkFYDncfI2YKjtA4D/Ew3w7lmdStPb3bThPEIIIYS4tso0aHvdunU89dRTRdbp0qULc+fOLVOjbmWOhqN4LXdC5Tpx4UJNgoKss6aYlPU+pvT0Up3TUNlx73RquA+PV83PlFKxaRBp/1ywPD+XlW2zj1eBsd9eLjqSM8299952gkV7omf04lJKFhW97H/ouFNUer0NZ1+zzZjiUs/PbuBdWMCI/GEzLrXuvKFdQgghxO2sTEF4ZmYm58+fL7LOuXPnyMgo+QS0251TnAaTRsPR/2tDSoo/tWvvsGwrGIJnZNsOV3Fdv5yMLg8C0GnDBjZ27my1XVu7hs0+eZ62zmiIxsW6d7vL4l02++T1hAP8F9mdXJPCqRRLm2s0mjs+AAfQ2FmIyKdPTTxah9yE1gghhBCiPCnTcJSmTZvy7bffsm3bNrvbt27dynfffUfz5s2vqnG3JOUgWFWgrgwlMBUaflJgiDbx/+613dc9Pz1dxYuxHPQ5yLaK29hdw3y8gEcHO2zOYazzhZdkOINXoewopQnAhbXCaQvdmwXdpJYIIYQQojwpU0/4tGnTuPvuu2nfvj29e/emXbt2VKxYkdjYWDZt2sSvv/6KTqdj6tSp17q95Z6j4Shx1XPwPm5/mxMmqqRcxD0nE5XmbrNdFZjtqQEO+x4G4K3+WlZ2/AbfGnfZb4uyHf+duj3/G4z+pFhtWzKyJVqN5rovJX8n0Wg0VJralsxjieireKJxltT8QgghhChjEN6uXTtWr17Nk08+yc8//8zPP/+MRqOxBH3Vq1fniy++oG3btte0sbcEB9lRjC4KdaVHWZeTY7XNIz2DL/58x1yvi/UHl/+qabjLZF0/zw8PrKCab5jDpvxfepZtYYHA/HyBxlZw19OmZtlXORWOaXRaXMMr3OxmCCGEEKIcKfNqOnfffTfHjh1j8+bN7Nu3j6SkJLy9vWnUqBHt2t3JWRzsX7dSCpNGa7eGU3x+SW5iktW2FFdIzU4l7IvPOf3kU2yMyK+rKSYn+Y8XE6yez6pTBR9vozllXiFvljLziRBCCCGEKLurWtJSo9HQvn178yqPAnCcJtykyx8Tjp1hInmys6x7r7/tqGX2miH89/h/1D0cxYCFDS1nKS4IP5yWPzF2bbMwIjzdUEGKfzedYvl56wDdt4SZT4QQQgghxNWTAarXmoOJmYnuRksQrikUqmsLJJFZsDE/n/S/NTVc9DXvYzSZs6YUHB9e3bt6kU25xy8/R3eEp3lyp0arYZGvhp+xHuIiky+FEEIIIW6cq+oJ37ZtG+vWrePcuXNkZdmOP9ZoNHz11VdXc4pbjqM+7lxTfk+4plAl72/zb4POlJ+i8Jxffp1sU7bVipjNApvhpC16AuWFbHOgfXcFL6vyPw7ZrsTorJPPY0IIIYQQN0qZgnCj0cjAgQNZsWIFSimrSZmA5fmdGIQ7HBOucTwcRVdgTPhjR9Zafj4enF+elZuFu3N+5pRWwa2Kbcnb0eZFef6MT7aUpWZZ5yG/v1EIGTm5NK7sU+zxhBBCCCHEtVGm7s93332X5cuXM2zYMHbt2oVSitGjR7Nt2zbeeustfHx86N+/P8ePHy/+YLcbR13hGizZUVJjA0p0qK1184PwS+mXrLbdW+PeMjXvzV8OWT3/cOBdzH28GVoZjiKEEEIIccOUqSd88eLFNGjQgC+//NJS5uPjQ8uWLWnZsiW9evWiRYsWdOnSpdjl7W83jvKEGwpkR8nNNJTsYAUyzCw8uNBqDLiLLn9Fyr/jU4jLMdI30P7S5rXc8s/33a7TJTu3EEIIIYS4bsoUhB87downnnjC8lyj0ZBTIPd1/fr16d27N3PmzLnjgnBH+qSmscMyJtxxdhRHMowZfLjnQ8tzZ60zObkm3vrtMB+7ZwPg7qRl4tGznM7Mttr3jZohpGcbcdNb3+6KniX8MCCEEEIIIa6pMg1H0ev1uLnlL6Xu4eFBbGysVZ2qVaty9OjRwrve/hxkR3FTqsDEzNIH4XX96lo9d9G5UHvib8zdHmMpe3x/tE0ADjB07j/Um/w76dnW48E3ju1U6nYIIYQQQoirV6ae8CpVqnD6dP6whvDwcP7++2/LZEyA7du3U6HCnbdKYFHhtSUINxUfhL/zoPXno9Mp1sNILqeYMLnryG4XWHybDOZjfbrBeox+4Z5xIYQQQghxY5SpJ7xjx46WoBtgwIABHDlyhPvuu49PPvmEgQMHsnnzZnr06FGmRqWmpjJ69GhCQkJwcXGhcePGfPvtt6U+zqRJk9BoNDRoYLsaZFZWFu+88w4NGjTA3d2dwMBAevbsydatW8vUZgsHPeGQPzGzJD3hxkJ35qdjP1k9j0vNKlEADqBNNPeOf7zhmKXs0BvdS7SvEEIIIYS49srUFTp8+HByc3M5c+YMVapU4X//+x8bN27k119/5bfffgOgRYsWzJw5s0yN6tevHzt37mTmzJmEhYWxZMkSBg4ciMlkYtCgQSU6xt69e5k1axaBgfYD1ZEjR7J48WImTJhAly5diI+PZ+bMmXTs2JEtW7bQokWLMrW9KAWHo8SleuDnkYr+sP2g3egEyuiGRpdud/uCrTFQsiQraLJNNmWuzkXnGBdCCCGEENdPmYLwJk2aMGfOHMtzZ2dnVq5cya5duzh+/DhVq1alRYsWaLWl72hfvXo1a9eutQTeAJ07d+bkyZOMHTuWAQMG4ORUdABpNBoZNmwYTz31FPv27ePy5ctW27OysliyZAmDBg1i6tSplvK2bdsSEhLC4sWLryIId9wTfjbAvE2rTMxOyqWZ0ZmR8xxcg5OG9NPDcK/+id3t8WnZEKAvYxuxDBsSQgghhBA33jVdJrFZs2YMGDCAVq1alSkAB/jxxx/x8PCgf//+VuXDhg3j3Llz7Nixo9hjzJw5k/j4eKZNm2Z3u1arRavV4u3tbVXu5eWFVqvFxcXF7n4lkZ3teN9cnXnFyyy9gWSThvUpzjil2g+Gc7WAcvxho10t/2Lb4hSdgsvvZ4utJ4QQQgghbqyrCsKzs7NZvXo17733Hm+++aalPDMzk9jYWEwm22EQxTlw4AB169ZFp7PupG/YsKFle1EOHTrE1KlTmTNnDh4eHnbrODs788wzz7Bw4UJ++uknkpOTiYmJYeTIkXh7ezNy5MhStxsg9mJ1TEUEzqdrmnvXk328HdbJY3QC0JKbUcnu9o83HEN7IcPuNv2OS+g3X8T5/5LtbhdCCCGEEDdXmdNjrFy5kieffJJLly5ZsqK89tprAPz333+0bt2ab775psRjuPPExcVRo0YNm/K8TCtxcXEO9zWZTAwfPpx+/frRq1evIs/z/vvv4+3tzYMPPmj5sBAaGsr69eupVauWw/2ysrLIysqyPE9Ozg90jbnORadHKQXjlVg+O64zrpUXWW1LPzmS3PQcux+hnE6lWiZiOiLjwYUQQgghbq4y9YRv2bKFhx56CIPBwOzZs20C7RYtWlCrVi2WL19epkYVNV65qG3vvfceR48e5YMPPij2HNOmTWPWrFlERkayYcMGfv75Z+rUqUO3bt3Ys2ePw/1mzJiBt7e35VGlSpXCLbS7X66D8m3hjidmAnazreSm1zT/4FS2cd2LnmhZpv2EEEIIIcS1Uaae8KlTp+Lj48OuXbsICAiw2zvdtGlT/vnnn1If28/Pz+7x4uPjARzmHj916hSTJ09m5syZ6PV6EhMTAfMkTZPJRGJiIgaDAVdXV6Kiopg8eTJvv/02L7/8suUYPXv2pF69eowZM4YNGzbYPc+ECRMYM2aM5XlycrJVIO6oI/wvWtkt/7mVltaHc23Kc7VgyvFB6+y45z8v5WFB2vgsm7IQbxfOJWVanjetan95eyGEEEIIcWOUqSd8+/bt9OnTh4AAxznyqlSpwoULF0p97IiICKKiojAarVd33L9/P4DdnN8AJ06cICMjgxdeeAFfX1/LY8uWLURFReHr68uECRMA2LdvH0opmjdvbnUMZ2dnGjVqVOS4c4PBgJeXl9XDSqGe6/Pna8G2Gmx3bmf3eEnu9s9jdAJMxUwQvdITrknJwXnnZZx3x6G9mGlVpVFlb94f0NjyvE1Nv6KPKYQQQgghrrsy9YRnZWXZZBYpLCkpqUwZUvr27cvcuXNZvnw5AwYMsJQvXLiQkJAQWra0P5SicePGdnuvR48eTVJSEvPnz6dy5coAhISEAOYPEx07drS6rt27d1vqlZqdoSMJ8ZUIyrkAzvnbdDk5lp8LL8pjKb8yHEUVGsaSElUg9/qVnnBdVCJOCfbHge87k4SPW34qw14RwUVeghBCCCGEuP7KFITXqFGDXbt2FVln27ZthIeHl/rYPXv2pFu3bowaNYrk5GRq1arF0qVLWbNmDYsWLbLkCB8xYgQLFy605CX38fGhU6dONsfz8fHBaDRabWvXrh3NmzcnMjKS9PR0OnToQFJSEh999BHR0dF88803pW53nsJBs72yWkePQZj5Z6ODOZJ5wbkp0/oDQb+7KrFiz1kUoDzMKQ81JutBMO893Igxy/ZZnrsb8k+yMyaex1pVLcmlCCGEEEKI66RMw1EefPBBNm3axNdff213+6xZszhw4IBVT3ZprFixgsGDBzN58mR69OjBjh07WLp0KY8++qilTm5uLrm5uagSLAFfmFarZe3atbz00kt8//333H///YwaNQowLxb02GOPland9ig0KDRkF+j9Lrhsvam4nnCjFxlnzIsWZSe0pHIFN3O5t3N+5Vzr18BNnx90P9OpplU2lA2HY8t0HUIIIYQQ4topU0/42LFjWb58OcOGDWPRokVkZprHIY8bN45t27axdetWGjduzHPPPVemRnl4eDB79mxmz57tsM6CBQtYsGBBscfauHGj3XJvb2+mTp1qtWLmNVF4SIqy7QnXqvz86cUNRwEwpjQi5XA4KD2a+lcO61rg1hXqCXdxduL5LrVYue8cI9vXwNs1P2D//cUOJb8WIYQQQghxXZQpCPfw8GDTpk0899xzLFu2jNxcc3aPWbNmodFoePjhh/n0008xGAzXtLG3IoWGbTS1Kst2yg+ac5w1rGmiocdu60A6t3BwrsyvZZJGkdm90AI+hb4MMOicGHNPHcbcU8dSdnRaTwCcna7pIqlCCCGEEKIMyrxYj6+vL4sXL+bDDz9k586dxMfH4+XlRfPmzQkMDLyWbbyl2AyOsTNZc01T67J53Z3wzMilbVSBvR3kQ//MyXaVTI3R+qwnLqfSulAWFAm+hRBCCCHKjzIH4Xn8/Pzo0aPHtWjLLU+B3aC7sHSD7Th2nW2qcBtNq/qyxU55y0o+/BMTb3lew9+j+IMJIYQQQoibRrpHrznrINxuthSNbRBeOEvKvO7zrJ4PblWVjwfdZfeMe08nWn729zDY9IILIYQQQojypcQ94cOHDy/1wTUaDV999VWp97t1aTC4pFoX2UneYrJTuKy9lrZR+d3hzYOa4+q8howcc9mbD9hfpMhLp+XDgXfx9KJ/Adg58e4ytl0IIYQQQtwoJQ7CHWUi0Wg0DtME3nlBOOj1hcdsW/eEn/XxZ2e9V3BLmIdL+jZL+Xk/2x7zfVPu4f8uplA/xMtmW563wqrQI9CXpSNbERbogcbBWHIhhBBCCFF+lDgI37Ztm03Zl19+ybx58+xuE2aq0BjxVRGtMWmdSPF/BpdTRb9uep2WBpXyVyaNSrWdlJlkNPeUyxAUIYQQQohbR4mDcHvLxa9Zs8bhtjuRVmssQa2y91Sn55psyrJMtmVCCCGEEKJ8k4mZ11Bw8DGbMeC2EzNLv8JnHnvhu14rt1AIIYQQ4lYjEdz1VoKUhYVd8Cl5XV+dU/GVhBBCCCFEuSJB+HVWuN+7JCF5zpVBQqsuJdJ3z1HOZWYD0Gv3UZu69wX4XFX7hBBCCCHEjSdB+PVWip7wFW3MdRd0Nd+WEQdi2JaYxsSjZx3uo9NKNhQhhBBCiFvNVa+YKUonV+t4+Mi3HZ34sbUiS28dWF/OLsmETyGEEEIIcasocRDeq1cvm7Jjx4453AbmPOGrVq0qY9NuDwVTFMa7eVpta+jfkGxTNofjD1vKCgfgAApFjqnsEzqFEEIIIUT5UuIgPC8dYWm23ZkLxxS+5vznByrVsNoS6B4IYBWEA1TxrGL1fFdyuiUfeJ7abgYZDy6EEEIIcYsqcRAeHR19Pdtx28jOcbF6btV/7WBl0cL0Wr1NmanAvrPDQxkQXKEszRNCCCGEEOVAiYPwqlWrXs923Days1ytC4qZmNmuUjvWnlyLq86VDKN5RcyIgAibelkFgnAJwIUQQgghbm0yMfMaS0wMtnqe4uzGeW9/KiVdtqmrlOKBWg/grfemgX8DUnNSWXViFcMaDLOpm7cyprfkBRdCCCGEuOVJEH7NWfd8f9HoEQB679uMt7eXTW2tRsvdVe8GIJBAnm/yPGAO0AvKujIxUy8pCYUQQgghbnkShN8gZ30CiHaxDsJbh7R2WD+rUDaUlCsTM/V35GRXIYQQQojbiyzWcwMl6KwnXD5Y+0GHdXMK9YT333scABet3DIhhBBCiFtduYzoUlNTGT16NCEhIbi4uNC4cWO+/fbbUh9n0qRJaDQaGjRoYHd7WloakydPJiwsDIPBgJ+fH507d+boUdvl4a8HpyIW7sku1BOeF5TLcBQhhBBCiFtfuRyO0q9fP3bu3MnMmTMJCwtjyZIlDBw4EJPJxKBBg0p0jL179zJr1iwCAwPtbk9NTaVz586cO3eO8ePH07BhQ5KSkti6dSvp6enX8nIASNO7FF+pgGxlsltukJ5wIYQQQohbXrkLwlevXs3atWstgTdA586dOXnyJGPHjmXAgAE4ORWdIcRoNDJs2DCeeuop9u3bx+XLtplJJk2aRFRUFP/99x81auQvonP//fdf2wu64khw6VI8Fu4Jz2OQnnAhhBBCiFteuetW/fHHH/Hw8KB///5W5cOGDePcuXPs2LGj2GPMnDmT+Ph4pk2bZnd7eno6X375Jf3797cKwG+0whlQCio8JjxPhsl+D7kQQgghhLh1lLsg/MCBA9StWxedzrqTvmHDhpbtRTl06BBTp05lzpw5eHh42K3z77//kpaWRu3atRk1ahS+vr7o9XqaNWvGqlWrijx+VlYWycnJVo+ySi60FL3VeRz0hP+XklHm8wkhhBBCiPKh3AXhcXFxVKhguyJkXllcXJzDfU0mE8OHD6dfv3706tXLYb2zZ88C8NZbb7F//36+/vprfvzxR7y8vOjduze///67w31nzJiBt7e35VGlSpWSXlqR3jpxntCN+1h7OQmAcUdOX5PjCiGEEEKI8qfcBeEAmiJyYRe17b333uPo0aN88MEHRR7fdGVIh16v57fffqN3797ce++9/PrrrwQHB/Pmm2863HfChAkkJSVZHqdPlz1YzrjS2707OY33T14kWykG748G4N/kaz85VAghhBBClA/lLgj38/Oz29sdHx8PYLeXHODUqVNMnjyZKVOmoNfrSUxMJDExEaPRiMlkIjExkYyMDMs5ANq0aYOnp6flGG5ubnTs2JHdu3c7bJ/BYMDLy8vq4UgGRWdE+fa8+TovZuVYlb929EyR+wkhhBBCiFtbuQvCIyIiiIqKwmg0WpXv378fwGHO7xMnTpCRkcELL7yAr6+v5bFlyxaioqLw9fVlwoQJQP74cnuUUmivURrAH3m4yO0pueYeeU2hpe7nnrHN5iKEEEIIIW4f5S5FYd++fZk7dy7Lly9nwIABlvKFCxcSEhJCy5Yt7e7XuHFjNmzYYFM+evRokpKSmD9/PpUrVwYgODiY1q1bs2XLFpKTky292enp6fz111+0atXqmlzLKk2fIrefSM8CoKiV6J+sHMAXZy5dk/YIIYQQQojyodwF4T179qRbt26MGjWK5ORkatWqxdKlS1mzZg2LFi2y5AgfMWIECxcu5Pjx41StWhUfHx86depkczwfHx+MRqPNtlmzZtG5c2e6d+/OK6+8gkaj4d133+Xy5ctFjgm/ln67MgmzqMzfhVfIXN209nVskRBCCCGEuBHK3XAUgBUrVjB48GAmT55Mjx492LFjB0uXLuXRRx+11MnNzSU3N7fIXNtFadOmDX/++ScGg4FHH32UQYMG4ezszMaNG2nduvW1upSrVjgIb+LlfpNaIoQQQgghrhWNKmsUKwBITk7G29ubn1dWw91dy6a/B9O+wzcAPKpZXuz+Fzo3Zu3lJEtWlMK6+nmxLi7Zqr4QQgghhCi5vHgtKSmpyKQaN1K57Am/kyTkGIvcfjBVFucRQgghhLjdlLsx4Xea7YmpRW4/Xyh9oRBCCCGEuPVJT/hNlmVS5BQzIOjjuqEAzKlX9Qa0SAghhBBCXG/SE34dNVa72KtpVmSdbKWKvAkRHq48FFSBh4LsL1IkhBBCCCFuPdITfpM9H3UKYxFzY3+VlIRCCCGEELcdCcKvo+J6wfMYTY6DcMM1Wr1TCCGEEEKUHzIcpRz45VLizW6CEEIIIcoJk8lEdnb2zW7GLUev16O9hTovJQgvBzbEp9zsJgghhBCiHMjOziY6OhqTyXSzm3LL0Wq1VK9eHb1ef7ObUiIShAshhBBClANKKc6fP4+TkxNVqlS5pXp1bzaTycS5c+c4f/48oaGhaDSa4ne6ySQIL8caerre7CYIIYQQ4gYxGo2kp6cTEhKCm5vbzW7OLScgIIBz585hNBpxdna+2c0plnzEugn8nUv22ef50MDr3BIhhBBClBe5ubkAt8xwivIm73XLex3LOwnCb4LOfp4lqpcu48GEEEKIO86tMJSiPLrVXjcZjnKd5Dr4fLOqSW1qu7vw/YWEYo+hv8XeTEIIIYQQomSkJ/w6+YdWdsubervjpXOyuy3c3cXqeVc/r2veLiGEEEIIcfNJEH6dZFH6SZUzwipbPXdzktsjhBBCiPItMjISjUZj9QgKCrJsX7FiBd27d8ff3x+NRsPevXttjjFmzBgqVKhAaGgo3377rdW2ZcuW0bt37+t9GTecRHnXjeNVMB3x0TmxonEty3OtDEcRQgghxC2gfv36nD9/3vLYv3+/ZVtaWhpt27Zl5syZdvf95ZdfWLJkCX/88QdvvfUWw4YNIy4uDoDExEQmTpzIJ598ckOu40aSMeHXQfCFTDRBpQ/CnTQaarsbrkOLhBBCCCGuH51OZ9X7XdDgwYMBiImJsbs9KiqKTp060axZM5o1a8bo0aM5ceIEfn5+jBs3jmeeeYbQ0NDr1fSbRoLw6yD8aCo+Jh1Usi5v7uVe5H7OGg0Bemf+ahGOhwxFEUIIIQTw3nvv8d577xVbr0mTJqxcudKq7P7772f37t3F7jtmzBjGjBlT5jYePXqUkJAQDAYDLVu2ZPr06dSoUaNE+zZq1IgvvviChIQETpw4QUZGBrVq1WLz5s3s3r2bOXPmlLld5ZkE4deBVsHblZ62KR9R2d/y8+omtem1+6jVdp3WPPykTqEJmkIIIYS4cyUnJ3P27Nli61WpUsWm7NKlSyXaNzk5uUxtA2jZsiVff/01YWFhXLx4kalTp9KmTRsOHjyIn59fsft3796dxx57jObNm+Pq6srChQtxd3dn1KhRLFiwgDlz5vDRRx/h7+/PF198Qf369cvc1vJEgvBrKDq6cZHbL2UbLT838bbtFbefM0UIIYQQdzIvLy8qVapUbL2AgAC7ZSXZ18ur7BnZevbsafk5IiKC1q1bU7NmTRYuXFji3vXIyEgiIyOtnnft2hVnZ2emTp3K/v37+fXXX3n88cf5999/y9zW8kSC8GsoJ6foHuz18cmMrGL7CyKEEEII4cjVDBUpPDzlRnB3dyciIoKjR48WX9mOw4cPs3jxYvbs2cO8efPo0KEDAQEBPPzwwwwfPpzk5OSr+tBQXpTLgcepqamMHj2akJAQXFxcaNy4sU26mpKYNGkSGo2GBg0aFFkvIyODsLAwNBoNs2bNKmuzi6WKmatZ+qmcQgghhBDlS1ZWFlFRUQQHB5d6X6UUTz75JO+++y4eHh7k5uaSk5MDYPnXdJusKF4ue8L79evHzp07mTlzJmFhYSxZsoSBAwdiMpkYNGhQiY6xd+9eZs2aRWBgYLF1X3vtNdLS0q622cUqLsgONjhf9zYIIYQQQlxLL7/8Mr179yY0NJTY2FimTp1KcnIyQ4YMASA+Pp5Tp05x7tw5AI4cOQJAUFCQTUaVuXPnUrFiRe6//34A2rZtS2RkJNu3b+e3336jXr16+Pj43LiLu47KXRC+evVq1q5dawm8ATp37szJkycZO3YsAwYMwMmp6NHTRqORYcOG8dRTT7Fv3z4uX77ssO4///zDRx99xOLFi+nfv/81vZbCTMWE4ZIXXAghhBC3mjNnzjBw4EAuX75MQEAArVq1Yvv27VStWhUwD4kZNmyYpf4jjzwCwJQpU6zGgV+8eJHp06ezdetWS1mLFi146aWXuPfee6lYsSILFy68MRd1A5S7IPzHH3/Ew8PDJiAeNmwYgwYNYseOHbRp06bIY8ycOZP4+HimTZvGfffd57BednY2w4cP59lnn6VZs2bXpP1FKWo4ysgCmVOEEEIIIW4VxQ0ZHjp0KEOHDi32OIGBgXZziU+ePJnJkyeXsXXlV7kbE37gwAHq1q2LTmf9+aBhw4aW7UU5dOgQU6dOZc6cOXh4eBRZ94033iAtLY0333yzxO3LysoiOTnZ6lFSA4IrWD0vGHjX9yj9MvdCCCGEEOLWVO6C8Li4OCpUqGBTnleWt4ypPSaTieHDh9OvXz969epV5Hn27t3L22+/zWeffYa7e9GL6BQ0Y8YMvL29LQ+rnJyq6OEkTbzcrJ6HuuSvjimTMoUQQggh7hzlLggH0BQxNrqobe+99x5Hjx7lgw8+KPL4RqOR4cOHM2DAALp3716qtk2YMIGkpCTL4/Tp08Xu09DDlU/qhlLTzTqFYcFLaeTphhBCCCGEuDOUuzHhfn5+dnu74+PjAez2kgOcOnWKyZMnM3PmTPR6PYmJiYA54DaZTCQmJmIwGHB1deWDDz7gxIkTLFu2zFIvb1hJZmYmiYmJeHp62p0AajAYMBgMNuVFaefryYNB9tudp54MRxFCCCGEuGOUu57wiIgIoqKiMBqNVuX79+8HcJjz+8SJE2RkZPDCCy/g6+treWzZsoWoqCh8fX2ZMGECYB5XnpSURO3atS31GjVqBJjTFfr6+lrOdy0oB4NNJBeKEEIIIcSdqdz1hPft25e5c+eyfPlyBgwYYClfuHAhISEhtGzZ0u5+jRs3ZsOGDTblo0ePJikpifnz51O5cmUAxo8fbzNL98KFCwwcOJCnn36aAQMGUKtWrWt2TY7Ge0tKQiGEEEKIO1O5C8J79uxJt27dGDVqFMnJydSqVYulS5eyZs0aFi1aZBkiMmLECBYuXMjx48epWrUqPj4+dOrUyeZ4Pj4+GI1Gq23h4eGEh4db1ctLiVOzZk27x7kajoJwCcGFEEIIIe5M5S4IB1ixYgUTJ05k8uTJxMfHEx4eztKlSy3J3QFyc3PJzc1FFbcWfDlwdwUvu+U+uqIXHRJCCCGEELenchmEe3h4MHv2bGbPnu2wzoIFC1iwYEGxx9q4cWOJzlmtWrWrDuiVg77tEBf7y9HfF+DDAxWTaOFd8hSJQgghhBDi1lcug/DbTWWD3m65Tqvhs/rVbmxjhBBCCCHETVfusqPcjlyc5GUWQgghxO0pMjISjUZj9QgKCrJsV0oRGRlJSEgIrq6udOrUiYMHD1odY8yYMVSoUIHQ0FC+/fZbq23Lli2jd+/eN+RabiSJDoUQQgghxFWpX78+58+ftzwKpnp+++23ee+99/j444/ZuXMnQUFBdOvWjZSUFAB++eUXlixZwh9//MFbb73FsGHDLGvGJCYmMnHiRD755JObcl3XkwxHEUIIIYQoh5RSpKen35Rzu7m5FblKeWE6nc6q9zuPUooPPviAiRMn0q9fP8CcdjowMJAlS5bw1FNPERUVRadOnWjWrBnNmjVj9OjRnDhxAj8/P8aNG8czzzxDaGjoNbu28kKCcCGEEEKIcig9PR0PD4+bcu7U1FTc3UueOOLo0aOEhIRgMBho2bIl06dPp0aNGkRHR3PhwgXuueceS12DwUDHjh3ZunUrTz31FI0aNeKLL74gISHBsvhirVq12Lx5M7t372bOnDnX4xJvOhmOIoQQQgghyqxly5Z8/fXX/P7778ydO5cLFy7Qpk0b4uLiuHDhAgCBgYFW+wQGBlq2de/enccee4zmzZszdOhQFi5ciLu7O6NGjeLzzz9nzpw51KlTh7Zt29qMJb+VSU+4EEIIIUQ55ObmRmpq6k07d0n17NnT8nNERAStW7emZs2aLFy4kFatWgHYDG1RSlmVRUZGEhkZafW8a9euODs7M3XqVPbv38+vv/7K448/zr///lvGqypfJAi/lgqkGa+efoZot8q8fvxT6PzFzWuTEEIIIW5JGo2mVENCygt3d3ciIiI4evQoDzzwAAAXLlwgODjYUic2NtamdzzP4cOHWbx4MXv27GHevHl06NCBgIAAHn74YYYPH05ycjJeXvYXQryVyHCU68TFlAVAPb3xJrdECCGEEOLGycrKIioqiuDgYKpXr05QUBBr1661bM/Ozuavv/6iTZs2NvsqpXjyySd599138fDwIDc3l5ycHADLvyaT6cZcyHUmQfh1YrryFYvm6hbhFEIIIYQo115++WX++usvoqOj2bFjBw899BDJyckMGTIEjUbD6NGjmT59Oj/++CMHDhxg6NChuLm5MWjQIJtjzZ07l4oVK3L//fcD0LZtW9avX8/27dt5//33qVevHj4+Pjf4Cq8PGY5yneQtYa9RuTe5JUIIIYQQ18+ZM2cYOHAgly9fJiAggFatWrF9+3aqVq0KwLhx48jIyOCZZ54hISGBli1b8scff+Dp6Wl1nIsXLzJ9+nS2bt1qKWvRogUvvfQS9957LxUrVmThwoU39NquJwnCrykNNY4fh5qgrnzJoEG6woUQQghx+yq8wmVhGo3GZuKlPYGBgcTExNiUT548mcmTJ19FC8snGY5yjTXdZZ6xq65M+NWq22PckhBCCCGEuHYkCL/GtMrc823K6wk33Pqzd4UQQgghxLUlQfh1kjcmXNt0yE1uiRBCCCGEKG8kCL+GTrqGMK93f3I0TvnZUVw8i9lLCCGEEELcaWRi5jW0sNojUA1qHTuX3xMu8zKFEEIIUQpKSfBQFrfa6yY94dfBcbcqlp5wIYQQQoiScHJyAsyL2YjSy3vd8l7H8k56wq8TS0+4rNYjhBBCiBLQ6XS4ublx6dIlnJ2d0Wqlr7SkTCYTly5dws3NDZ3u1ghvb41W3oIsecK9Kt3klgghhBDiVqDRaAgODiY6OpqTJ0/e7ObccrRaLaGhoWhukdEIEoRfJ5Y84Tr9zW2IEEIIIW4Zer2e2rVry5CUMtDr9bfUtwflMghPTU1l0qRJLFu2jPj4eMLDwxk/fjyPPPJIqY4zadIkpk2bRv369Tlw4IClPDk5mY8++oi1a9dy+PBhUlNTqV69Oo899hgvvPACLi4uV3cBdXphSjEPQ7k1PosJIYQQorzQarVXH4uIcq9cBuH9+vVj586dzJw5k7CwMJYsWcLAgQMxmUwMGjSoRMfYu3cvs2bNIjAw0GbbqVOn+OCDDxg8eDBjxozBw8ODTZs2ERkZydq1a1m7du1VfZWhPINQWUmQbUR7i3wlIoQQQgghbpxyF4SvXr2atWvXWgJvgM6dO3Py5EnGjh3LgAEDip31ajQaGTZsGE899RT79u3j8uXLVturV69OTEwM7u7ulrIuXbrg7u7O2LFj2bJlC+3atbuq6zBdmY8pIbgQQgghhCis3A2c+fHHH/Hw8KB///5W5cOGDePcuXPs2LGj2GPMnDmT+Ph4pk2bZne7u7u7VQCep0WLFgCcPn26DC23lpcTRTrChRBCCCFEYeWuJ/zAgQPUrVvXJr1Mw4YNLdvbtGnjcP9Dhw4xdepUVqxYgYeHR6nOvX79egDq16/vsE5WVhZZWVmW50lJSQCkp5swaVIByEzRY0xLwZSTS1pyCsm5OaVqhxBCCCGEuHaSk5OB8rWgT7kLwuPi4qhRo4ZNeYUKFSzbHTGZTAwfPpx+/frRq1evUp33v//+4+2336Zv376WgN+eGTNm8Prrr9uUD3zkFNAegI8LlDcrVSuEEEIIIcT1EhcXh7e3981uBlAOg3CgyEmRRW177733OHr0KCtXrizV+WJiYrjvvvuoUqUKX375ZZF1J0yYwJgxYyzPExMTqVq1KqdOnSo3N1VcP8nJyVSpUoXTp0/j5eV1s5sjrjO533cWud93Frnfd5akpCRCQ0MtnbrlQbkLwv38/Oz2dsfHxwM4fPFOnTrF5MmTmTlzJnq9nsTERMA8SdNkMpGYmIjBYMDV1dVqv5MnT9K5c2d0Oh1//vlnsTfHYDBgMBhsyr29veWX+A7i5eUl9/sOIvf7ziL3+84i9/vOUp7yiJefllwRERFBVFQURqPRqnz//v0ANGjQwO5+J06cICMjgxdeeAFfX1/LY8uWLURFReHr68uECROs9jl58iSdOnVCKcWGDRuoXLny9bkoIYQQQgghCih3PeF9+/Zl7ty5LF++nAEDBljKFy5cSEhICC1btrS7X+PGjdmwYYNN+ejRo0lKSmL+/PlWQfapU6fo1KkTubm5bNy4kapVq177ixFCCCGEEMKOcheE9+zZk27dujFq1CiSk5OpVasWS5cuZc2aNSxatMiSI3zEiBEsXLiQ48ePU7VqVXx8fOjUqZPN8Xx8fDAajVbbYmNj6dy5M+fPn+err74iNjaW2NhYy/bKlSuXuFfcYDAwZcoUu0NUxO1H7vedRe73nUXu951F7vedpTzeb40qT7larkhNTWXixIlWy9ZPmDDBatn6oUOHsnDhQqKjo6lWrZrDY3Xq1InLly9bLVu/ceNGOnfu7HCfKVOmEBkZeS0uRQghhBBCCBvlMggXQgghhBDidlbuJmYKIYQQQghxu5MgXAghhBBCiBtMgvAySk1NZfTo0YSEhODi4kLjxo359ttvb3azhB3r169n+PDhhIeH4+7uTqVKlejTpw///vuvTd3du3fTtWtXPDw88PHxoV+/fpw4ccLucT/66CPCw8MxGAxUr16d119/nZycHJt6sbGxDB06FH9/f9zc3GjdujV//vnnNb9O4diXX36JRqPBw8PDZpvc89vD5s2b6dWrF76+vri6ulK7dm3efPNNqzpyr28Pe/bs4YEHHiAkJAQ3NzfCw8N54403SE9Pt6on9/vWk5KSwrhx47jnnnsICAhAo9E4nKN3s+/vunXraN26NW5ubvj7+zN06FCrJB8lokSZdOvWTfn4+KjPPvtMrV+/Xj3xxBMKUIsXL77ZTROFPPTQQ6pz587q008/VRs3blTff/+9atWqldLpdOrPP/+01IuKilKenp6qffv2atWqVWr58uWqfv36KiQkRMXGxlodc+rUqUqj0agJEyaoDRs2qLffflvp9Xo1cuRIq3qZmZmqQYMGqnLlymrRokXqjz/+UH369FE6nU5t3Ljxhlz/ne7MmTPK29tbhYSEKHd3d6ttcs9vD4sXL1ZarVY98sgjauXKlWr9+vVq7ty56vXXX7fUkXt9ezh48KBycXFRjRo1Ut999536888/1ZQpU5STk5O6//77LfXkft+aoqOjlbe3t+rQoYMlrpoyZYpNvZt9fzdu3Kh0Op3q06eP+uOPP9SiRYtUpUqVVIMGDVRmZmaJr1eC8DJYtWqVAtSSJUusyrt166ZCQkKU0Wi8SS0T9ly8eNGmLCUlRQUGBqq7777bUta/f3/l7++vkpKSLGUxMTHK2dlZjRs3zlJ2+fJl5eLiop588kmrY06bNk1pNBp18OBBS9knn3yiALV161ZLWU5OjqpXr55q0aLFNbk+UbT77rtP9e7dWw0ZMsQmCJd7fus7c+aMcnd3V6NGjSqyntzr28PEiRMVoI4dO2ZV/uSTTypAxcfHK6Xkft+qTCaTMplMSimlLl265DAIv9n3t3nz5qpevXoqJyfHUrZlyxYFqE8//bTE1ytBeBk88cQTysPDw+rFV0qpJUuWKEBt2bLlJrVMlEbnzp1VWFiYUsr8i+bq6qqeeuopm3r33HOPql27tuX5okWLFKC2bdtmVe/cuXMKUNOmTbOUde3aVdWpU8fmmNOnT1eAOnPmzLW6HGHHN998ozw9PdXp06dtgnC557eHyMhIBaiYmBiHdeRe3z7y7velS5esyseNG6e0Wq1KTU2V+32bcBSE3+z7e+bMGQWoGTNm2NQNCwtT3bp1K/E1ypjwMjhw4AB169ZFp7Ne66hhw4aW7aJ8S0pKYvfu3dSvXx+A48ePk5GRYbmHBTVs2JBjx46RmZkJ5N/fiIgIq3rBwcH4+/tb3f8DBw44PCbAwYMHr80FCRuxsbGMHj2amTNn2l18S+757eHvv/+mQoUKHD58mMaNG6PT6ahYsSJPP/00ycnJgNzr28mQIUPw8fFh1KhRnDhxgpSUFH799Vc+//xznn32Wdzd3eV+3+Zu9v3N28dR3dLEgBKEl0FcXBwVKlSwKc8ri4uLu9FNEqX07LPPkpaWxsSJE4H8e+boviqlSEhIsNQ1GAy4u7vbrVvw/st75eZ55plnqFOnDqNGjbK7Xe757eHs2bOkp6fTv39/BgwYwLp16xg7dixff/01vXr1Qikl9/o2Uq1aNbZt28aBAweoWbMmXl5e9O7dmyFDhjB79mxAfrdvdzf7/hZ3/tK8D8rdsvW3Co1GU6Zt4uZ77bXXWLx4MR999BFNmza12lbS+1qa+y/vlRtv+fLl/PLLL+zZs6fY11ju+a3NZDKRmZnJlClTGD9+PGBeKVmv1zN69Gj+/PNP3NzcALnXt4OYmBh69+5NYGAgP/zwAwEBAezYsYOpU6eSmprKV199Zakr9/v2drPvr6O6pXkfSE94Gfj5+dn9pBMfHw/Y/3QkyofXX3+dqVOnMm3aNJ577jlLuZ+fH2C/JyM+Ph6NRoOPj4+lbmZmpk06rLy6Be+/vFduvNTUVJ599ln+97//ERISQmJiIomJiWRnZwOQmJhIWlqa3PPbRN597N69u1V5z549AXMaM7nXt4/x48eTnJzM77//zoMPPkiHDh0YO3YsH3zwAfPmzeOvv/6S+32bu9n3t7jzl+Z9IEF4GURERBAVFYXRaLQq379/PwANGjS4Gc0SxXj99deJjIwkMjKSV1991WpbzZo1cXV1tdzDgvbv30+tWrVwcXEB8seWFa574cIFLl++bHX/IyIiHB4T5L1yPVy+fJmLFy/y7rvv4uvra3ksXbqUtLQ0fH19efTRR+We3ybsjcsEUEoBoNVq5V7fRvbu3Uu9evVshhc0b94cwDJMRe737etm39+8fx3VLdX7oMRTOIXF6tWrFaC+/fZbq/IePXpIisJy6o033lCAmjRpksM6Dz/8sKpYsaJKTk62lJ08eVLp9Xr1yiuvWMri4uKUi4uLevrpp632nzFjhk3Ko08//VQBavv27ZaynJwcVb9+fdWyZctrcWmikIyMDLVhwwabR/fu3ZWLi4vasGGD2r9/v1JK7vnt4Pfff7fJcqCUUu+9954C1KZNm5RScq9vF507d1YBAQEqJSXFqvyLL75QgPrpp5+UUnK/bwdFpSi82fe3RYsWqkGDBlbx3rZt2xSg5syZU+JrlCC8jLp166Z8fX3VF198odavX69GjhypALVo0aKb3TRRyKxZsxSgevToobZt22bzyBMVFaU8PDxUhw4d1OrVq9WKFStUgwYNikz+/+qrr6qNGzeqd955RxkMBrvJ/+vXr6+qVKmiFi9erNauXav69u0rizvcBPbyhMs9vz307t1bGQwG9eabb6q1a9eqGTNmKBcXF3XfffdZ6si9vj38/PPPSqPRqFatWlkW65k2bZry8PBQ9erVU1lZWUopud+3stWrV6vvv/9ezZs3TwGqf//+6vvvv1fff/+9SktLU0rd/Pu7YcMGpdPpVN++fdXatWvV4sWLVZUqVWSxnhslJSVFPf/88yooKEjp9XrVsGFDtXTp0pvdLGFHx44dFeDwUdCuXbvU3Xffrdzc3JSXl5d64IEHbBaFyDN79mwVFham9Hq9Cg0NVVOmTFHZ2dk29S5cuKAef/xxVaFCBeXi4qJatWql1q5de12uVThmLwhXSu757SA9PV298sorqkqVKkqn06nQ0FA1YcIEmz+Gcq9vD+vXr1f33HOPCgoKUq6uriosLEy99NJL6vLly1b15H7fmqpWrerw73V0dLSl3s2+v3/88Ydq1aqVcnFxURUqVFCPP/643cUBi6JR6srAOSGEEEIIIcQNIRMzhRBCCCGEuMEkCBdCCCGEEOIGkyBcCCGEEEKIG0yCcCGEEEIIIW4wCcKFEEIIIYS4wSQIF0IIIYQQ4gaTIFwIIYQQQogbTIJwIYQQQgghbjAJwoUQoghDhw5Fo9EQExNzs5tyTSxatIjGjRvj4eGBRqMhMjLyprQjJiYGjUbD0KFDb8r5hRDiZpMgXAhxQ+QFXRqNhvvuu89unY0bN6LRaHj66advcOvuDFu3bmXw4MGkp6fz7LPPMmXKFDp16nSzm3VdLFiwAI1Gw4IFC252U4QQwi7dzW6AEOLOs2rVKv7++286dOhws5tyR1m9ejUAX3/9Na1atbqpbalUqRJRUVF4e3vf1HYIIcTNIj3hQogbqlq1ami1Wl555ZWb3ZQ7zrlz5wAICgq6yS0BZ2dnwsPDCQ4OvtlNEUKIm0KCcCHEDVWnTh0GDx7M9u3bWbFiRYn2qVatGtWqVbO7rVOnTmg0GquyyMhINBoNGzduZP78+URERODq6kr16tX58MMPAVBKMXv2bMLDw3FxcSEsLIxvvvnGYRtyc3OZMWMGtWrVwsXFhdq1a/POO+9gMpns1v/777/p3bs3/v7+GAwGateuzaRJk0hPT7eqlzcEJzIykm3bttG9e3d8fHxsrsmRrVu3cu+991KhQgVcXFwIDw8nMjLS6jx555g/fz4A1atXtwwNKono6GiefvppqlevjsFgoGLFinTq1MnuUI+FCxfSqlUrPDw88PDwoFWrVixcuNCmnqMx4Xn302g08uabb1rOGRYWxqefflqi9g4dOpRhw4YBMGzYMMu1Fr7eU6dOMWLECCpVqoRer6dy5cqMGDGC06dP2xzz/PnzvPDCC9SuXRtXV1cqVKhAREQEzzzzDMnJyZZ6SUlJTJ48mXr16uHh4YG3tzfh4eEMGzbM5rhKKebNm0fbtm3x8vLCzc2NZs2aMW/ePJvzZ2Zm8u6779KoUSO8vb3x8PCgZs2aDBw4kP3795fodRFClC8yHEUIccO98cYbfPvtt7z66qv06dMHJyen63KeDz74gI0bN9KnTx+6dOnC8uXLeeGFF3Bzc2Pfvn18//333HfffXTp0oVvv/2Wxx9/nOrVq9OuXTubY40ePZrt27fz8MMP4+LiwooVKxg3bhzHjh3j888/t6r72Wef8cwzz+Dr60vv3r0JCAhg586dTJs2jQ0bNrBhwwb0er3VPlu3bmX69Ol07tyZJ598klOnThV7fcuXL+eRRx5Br9czYMAAKlasyLp163j99df5448/2LBhAwaDgWrVqjFlyhR++ukn9u3bxwsvvICPj0+JXsNt27bRs2dPkpOT6d69O4888ggJCQns2bOH2bNnWwXRL774Ih988AGVKlVixIgRaDQali9fztChQ9m3bx/vvfdeic4JMHDgQHbs2EHPnj1xcnJi2bJlPPvsszg7OzNy5Mgi933ggQdITEzk559/pk+fPjRu3NimztGjR2nXrh2xsbH07t2b+vXrc/DgQebNm8evv/7Kli1bqFWrFgDp6em0bduWmJgY7rnnHvr27Ut2djYnTpxgwYIFjBs3Di8vL5RSdO/enR07dtC2bVt69OiBVqslJiaGH3/8kSFDhlClShXAHIA/9thjLFmyhLCwMAYNGoRer2ft2rWMGDGCQ4cOMWvWLEt7hwwZwrJly2jYsCHDhg3DYDBw6tQpNmzYQPfu3YmIiCjxayuEKCeUEELcANHR0QpQ3bt3V0opNWbMGAWozz//3FJnw4YNClBPPfWU1b5Vq1ZVVatWtXvcjh07qsL/lU2ZMkUBqkKFCur48eOW8lOnTim9Xq+8vb1VWFiYio2NtWzbsWOHAtT9999vdawhQ4YoQAUGBqqzZ89aylNSUlRERIQC1N9//20pP3jwoNLpdOquu+5ScXFxVseaMWOGAtSsWbNsrhlQX331ld1rtCc5OVn5+Pgog8Gg9u3bZyk3mUxq0KBBClBvvvmm3WuJjo4u0TkyMzNVlSpVlFarVb/99pvN9tOnT1t+/vvvvxWg6tatqxITEy3liYmJKjw8XAFq06ZNlvK898OQIUOsjpl3P1u2bKmSkpIs5YcPH1Y6nU7VqVOnRG2fP3++AtT8+fPtbu/SpYvN+08ppT7//HMFqLvvvttStnLlSgWoF1980eY4ycnJKisrSyml1H///acA1bdvX5t6mZmZKiUlxfL8iy++UIAaMWKEysnJsZRnZWWp3r17K0Dt2rVLKWV+DTUajWrWrJkyGo1WxzUajSohIaHoF0MIUS7JcBQhxE0xceJEvL29ef31122GaFwrzz//PDVq1LA8r1KlCu3atSMpKYmJEycSEBBg2daiRQtq1KjBvn37HB4rJCTE8tzDw4PJkycDWA23+PzzzzEajXz44YdUqFDB6hjjxo0jICCApUuX2hz/rrvuYvjw4SW+tp9++onExESGDx9Ow4YNLeUajYaZM2ei0+muOjPIypUrOX36NI899hg9evSw2V65cmXLz3nnioyMtJps6e3tzZQpU6zqlMSMGTPw8vKyPK9Tpw5t27blyJEjpKSklPJKrJ0+fZr169dTr149m171kSNHUrduXf7880+b4SOurq42x/L09LT5VsNePYPBgIeHh+X5xx9/jLu7Ox9//DE6Xf6X0nq9nmnTpgFY3icajQalFAaDweZbIycnpxJ/qyGEKF9kOIoQ4qaoUKECr7zyCq+++ioffPABr7766jU/x1133WVTljcR0N4QheDgYHbs2GH3WO3bt3dYtnfvXkvZ9u3bAVizZg3r1q2z2cfZ2ZnDhw/blLdo0cLueR3Zs2cPgN0Ug1WqVKFmzZqWgNXT07NUx87zzz//AHDPPfdcVXvyygq+TsVp0qSJTVle0J+YmFjma4L8tnbs2NFmnLhGo6FDhw5ERUWxb98+qlSpQocOHQgKCmLGjBns3buXe++9l3bt2hEREWG1f926dYmIiGDJkiWcPn2aBx54gPbt29OkSROr4Dk9PZ39+/cTEhLCzJkzbdqXk5MDYHmfeHl50aNHD9asWUOTJk146KGHaN++PS1btrT5ACCEuHVIEC6EuGlGjx7Nxx9/zNtvv81TTz11zY9fsCc1T16vo6NtRqPR7rEqVqxot0yr1ZKUlGQpi4+PB7D0ZpZUYGBgqernTQZ0tF9QUBBHjhwhOTm5zAFrYmIiYE4nWJL2aLVaq28X8gQGBtq8TsWxl7ow797l5uaW+Dj2lOS1Ayzt9fb2Ztu2bUyZMoVffvnFkuqxcuXKTJgwgWeeecbSvvXr1xMZGcmKFSt46aWXAPD39+d///sfEydOxMnJiYSEBJRSnD17ltdff91hO9PS0iw///DDD0yfPp2lS5cyceJEwNwLP3z4cKZPn46bm9vVvCRCiJtAhqMIIW4aV1dXIiMjSUpKYvr06Q7rabVah8FxaQK7qxEbG2u3zGQyWQWMecF9cnIySimHj8JKmqmk8HkuXrxod3teub0PGyWVN8zh7NmzJWqPyWTi0qVLNtvyXqeracu1VJbXrlq1aixcuJBLly6xZ88e3nrrLZRSPPvss1bDi/z9/fn44485e/Yshw4d4uOPP8bPz48pU6bw9ttvWx23adOmRb5HNmzYYDmuu7s706ZN48SJE5w4cYKvvvqK8PBwZs+ezYsvvnhtXyAhxA0hQbgQ4qYaPnw44eHhfPLJJw4zgvj6+hIbG2sTiKelpXH06NEb0Uw2bdrksKzg0JaWLVsC+cNSrpe8oTYbN2602Xb27FmOHz9OjRo1rmrYRt4QmT/++OOq2vPXX38B9ocAXS95wz/s9ZrntePvv/+2+UCklLJ7Xwset3HjxowbN84SfK9cudKmnkajoW7dujz77LOsXbvWqp6npyd169YlKirK8m1DaVSvXp3hw4fz119/4eHhYff8QojyT4JwIcRN5eTkxPTp08nKyuKNN96wW6dZs2bk5OSwePFiS5lSigkTJlh9ZX89ffjhh5bFbgBSU1Mt7X388cct5c888ww6nY7//e9/dvNNJyYmWsYkX40+ffrg7e3N/PnzOXjwoKU873XJycmxycFdWvfffz+VK1dm0aJF/P777zbbC/aQDxkyBIDXX3/dKm92cnKyZchFXp0bIW9S7JkzZ2y2hYaG0rlzZ0tKwoLmzZvHwYMH6dKliyWd4IEDBzh58qTNcfJ6zPMmYkZHR3Po0KFi64F5om96ejojR460+x6Ojo4mJiYGgEuXLlnG5xeUkJBAVlaW3YmgQojyT8aECyFuur59+9K6dWu2bdtmd/tzzz3H/PnzeeKJJ1i7di0BAQFs2rSJxMREGjVq5DCjybXUvHlzGjVqxIABAzAYDKxYsYKYmBhGjhxJhw4dLPUaNGjAp59+yqhRo6hTpw69evWiZs2aJCcnc+LECf766y+GDh3KZ599dlXt8fLyYu7cuQwcOJCWLVsyYMAAAgIC+PPPP9m1axctWrRg7NixV3UOg8HAsmXL6NGjBz179qRHjx40atSI5ORk9u7dS3p6uuUDRYcOHfjf//7HRx99RIMGDXjwwQdRSrFixQpOnz7N888/b/U6XW+tW7fG1dWVDz74gOTkZMtY9fHjxwMwZ84c2rVrx8iRI/nll1+oV68ehw4dYuXKlQQEBDBnzhzLsdatW8dLL71E27ZtCQ8Px8/PjxMnTrBy5UpcXV157rnnANi3bx99+/alefPmNGjQgKCgIM6ePctPP/2Ek5OTZYw4wFNPPcX27dtZuHAhW7ZsoWvXroSEhHDx4kUOHz7Mjh07WLJkCdWqVePs2bO0bNmS+vXr06RJEypVqkRcXBw///wzOTk5jBs37oa9rkKIa+hG5kMUQty5CucJLywvzzR28oQrpdSff/6pWrZsqQwGg/Lz81ODBw9WFy5cKDJP+IYNG2yOU1SubHvHyqt/7NgxNX36dFWjRg2l1+tVzZo11VtvvWWTtznPP//8ox555BEVEhKinJ2dlb+/v2rSpIkaP368ioqKstTLyxM+ZcoUu8cpzt9//6169uypfHx8lF6vV2FhYeq1115Tqamppbr2ohw7dkyNGDFCVa5cWTk7O6uKFSuqTp06qa+//tqm7rx581Tz5s2Vm5ubcnNzU82bN1fz5s2zqVdcnnB7Stv+VatWqebNmytXV1fLe6ugmJgYNWzYMBUcHKx0Op0KDg5Ww4YNUzExMVb1Dh06pF544QV11113KT8/P2UwGFSNGjXU0KFD1aFDhyz1Tp8+rcaPH69atWqlKlasqPR6vQoNDVUPPfSQ2rFjh902fvfdd6pr167K19dXOTs7q0qVKqlOnTqpd999V126dEkppVRCQoKKjIxUHTp0UMHBwUqv16uQkBDVo0cP9fvvv5fotRBClD8apezMEBJCCCGEEEJcNzImXAghhBBCiBtMgnAhhBBCCCFuMAnChRBCCCGEuMEkCBdCCCGEEOIGkyBcCCGEEEKIG0yCcCGEEEIIIW4wCcKFEEIIIYS4wSQIF0IIIYQQ4gaTIFwIIYQQQogbTIJwIYQQQgghbjAJwoUQQgghhLjBJAgXQgghhBDiBvt/CkWTCUCgivkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 800x350 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 16,
+   "id": "99276411",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "\n",
     "# Common imports\n",
@@ -2309,8 +2009,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f8db29e",
-   "metadata": {},
+   "id": "02b45e4c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Using the Voting Classifier\n",
     "\n",
@@ -2319,25 +2021,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "1f4c6479",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LogisticRegression 0.864\n",
-      "RandomForestClassifier 0.872\n",
-      "SVC 0.888\n",
-      "VotingClassifier 0.896\n",
-      "LogisticRegression 0.864\n",
-      "RandomForestClassifier 0.872\n",
-      "SVC 0.888\n",
-      "VotingClassifier 0.912\n"
-     ]
-    }
-   ],
+   "execution_count": 17,
+   "id": "7abc7cb3",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
@@ -2385,31 +2075,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b578c34",
-   "metadata": {},
+   "id": "c849ad5c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Voting and Bagging"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "891c848b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
-       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
-       "                             ('svc', SVC(random_state=42))])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 18,
+   "id": "9699c6a6",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
@@ -2433,21 +2115,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "47b632ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LogisticRegression 0.864\n",
-      "RandomForestClassifier 0.896\n",
-      "SVC 0.896\n",
-      "VotingClassifier 0.912\n"
-     ]
-    }
-   ],
+   "execution_count": 19,
+   "id": "73d1c087",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -2459,24 +2133,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "e2f5031e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
-       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
-       "                             ('svc', SVC(probability=True, random_state=42))],\n",
-       "                 voting='soft')"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 20,
+   "id": "44404489",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "log_clf = LogisticRegression(random_state=42)\n",
     "rnd_clf = RandomForestClassifier(random_state=42)\n",
@@ -2490,21 +2153,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "b0719293",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LogisticRegression 0.864\n",
-      "RandomForestClassifier 0.896\n",
-      "SVC 0.896\n",
-      "VotingClassifier 0.92\n"
-     ]
-    }
-   ],
+   "execution_count": 21,
+   "id": "f50a0d71",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -2516,8 +2171,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9966fed",
-   "metadata": {},
+   "id": "686f4568",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Bagging\n",
     "\n",
@@ -2536,8 +2193,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8613549",
-   "metadata": {},
+   "id": "95cff28c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## More bagging\n",
     "\n",
@@ -2566,8 +2225,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72be9bd5",
-   "metadata": {},
+   "id": "d14d63ca",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Making your own Bootstrap: Changing the Level of the Decision Tree\n",
     "\n",
@@ -2577,63 +2238,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "b2f5c535",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial degree: 1\n",
-      "Error: 0.04846118259421969\n",
-      "Bias^2: 0.04674244183114465\n",
-      "Var: 0.0017187407630750422\n",
-      "0.04846118259421969 >= 0.04674244183114465 + 0.0017187407630750422 = 0.04846118259421969\n",
-      "Polynomial degree: 2\n",
-      "Error: 0.053295125716988155\n",
-      "Bias^2: 0.04404166206019625\n",
-      "Var: 0.00925346365679191\n",
-      "0.053295125716988155 >= 0.04404166206019625 + 0.00925346365679191 = 0.05329512571698816\n",
-      "Polynomial degree: 3\n",
-      "Error: 0.025869524957393485\n",
-      "Bias^2: 0.01949474430645311\n",
-      "Var: 0.00637478065094038\n",
-      "0.025869524957393485 >= 0.01949474430645311 + 0.00637478065094038 = 0.02586952495739349\n",
-      "Polynomial degree: 4\n",
-      "Error: 0.02383973446087422\n",
-      "Bias^2: 0.01664706573250269\n",
-      "Var: 0.007192668728371525\n",
-      "0.02383973446087422 >= 0.01664706573250269 + 0.007192668728371525 = 0.023839734460874215\n",
-      "Polynomial degree: 5\n",
-      "Error: 0.022249830075416054\n",
-      "Bias^2: 0.014974343924433729\n",
-      "Var: 0.007275486150982323\n",
-      "0.022249830075416054 >= 0.014974343924433729 + 0.007275486150982323 = 0.022249830075416054\n",
-      "Polynomial degree: 6\n",
-      "Error: 0.021603137741425225\n",
-      "Bias^2: 0.01463608324610759\n",
-      "Var: 0.006967054495317632\n",
-      "0.021603137741425225 >= 0.01463608324610759 + 0.006967054495317632 = 0.02160313774142522\n",
-      "Polynomial degree: 7\n",
-      "Error: 0.021752908985436126\n",
-      "Bias^2: 0.014615048785845125\n",
-      "Var: 0.007137860199591002\n",
-      "0.021752908985436126 >= 0.014615048785845125 + 0.007137860199591002 = 0.021752908985436126\n",
-      "MSE simple tree: 0.4127109797033181\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 22,
+   "id": "e74280fa",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "\n",
     "import matplotlib.pyplot as plt\n",
@@ -2685,7 +2296,7 @@
     "    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
     " \n",
     "mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2))\n",
-    "print(\"MSE simple tree:\",mse_simpletree)\n",
+    "print(\"Simple tree:\",mse_simpletree)\n",
     "plt.xlim(1,maxdepth)\n",
     "plt.plot(polydegree, error, label='MSE')\n",
     "plt.plot(polydegree, bias, label='bias')\n",
@@ -2697,8 +2308,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "decc7681",
-   "metadata": {},
+   "id": "50b0a599",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Random forests\n",
     "\n",
@@ -2718,8 +2331,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b047a99c",
-   "metadata": {},
+   "id": "2f2734fa",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "m\\approx \\sqrt{p}.\n",
@@ -2728,8 +2343,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfb22949",
-   "metadata": {},
+   "id": "61611116",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "In building a random forest, at\n",
     "each split in the tree, the algorithm is not even allowed to consider\n",
@@ -2751,8 +2368,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8bec594f",
-   "metadata": {},
+   "id": "7b5744be",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Random Forest Algorithm\n",
     "The algorithm described here can be applied to both classification and regression problems.\n",
@@ -2775,8 +2394,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23480abb",
-   "metadata": {},
+   "id": "5932ad3e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Random Forests Compared with other Methods on the Cancer Data"
    ]
@@ -2784,8 +2405,11 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "c7542bff",
-   "metadata": {},
+   "id": "5203653a",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2853,8 +2477,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4bead63",
-   "metadata": {},
+   "id": "37c9161d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Recall that the cumulative gains curve shows the percentage of the\n",
     "overall number of cases in a given category *gained* by targeting a\n",
@@ -2867,8 +2493,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78db991e",
-   "metadata": {},
+   "id": "b386dd37",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Compare  Bagging on Trees with Random Forests"
    ]
@@ -2876,8 +2504,11 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "c12e0ecd",
-   "metadata": {},
+   "id": "ad092b18",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "bag_clf = BaggingClassifier(\n",
@@ -2888,8 +2519,11 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "9b3dadd3",
-   "metadata": {},
+   "id": "8b0d6548",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "bag_clf.fit(X_train, y_train)\n",
@@ -2902,25 +2536,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.10"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/doc/src/week44/week44.do.txt b/doc/src/week44/week44.do.txt
index f9aa59072..cfabf19da 100644
--- a/doc/src/week44/week44.do.txt
+++ b/doc/src/week44/week44.do.txt
@@ -742,12 +742,6 @@ display(grades)
 X = grades.loc[:, grades.columns != 'Grade'].values
 y = grades.loc[:, grades.columns == 'Grade'].values
 print(X)
-# Create the encoder.
-encoder = OneHotEncoder(handle_unknown="ignore")
-# Assume for simplicity all features are categorical.
-encoder.fit(X)    
-# Apply the encoder.
-X = encoder.transform(X)
 # Then do a Classification tree
 tree_clf = DecisionTreeClassifier(max_depth=2)
 tree_clf.fit(X, y)
@@ -763,9 +757,6 @@ cmd = 'dot -Tpng DataFiles/grade.dot -o DataFiles/grades.png'
 os.system(cmd)
 
 
-#data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])
-#df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']
-
 !ec
 
 
@@ -1525,8 +1516,8 @@ for degree in range(1,maxdepth):
     print('Var:', variance[degree])
     print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
  
-mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2)
-print(mse_simpletree)
+mse_simpletree= np.mean( np.mean((y_test - simpleprediction)**2))
+print("Simple tree:",mse_simpletree)
 plt.xlim(1,maxdepth)
 plt.plot(polydegree, error, label='MSE')
 plt.plot(polydegree, bias, label='bias')