diff --git a/doc/pub/week41/html/._week41-bs000.html b/doc/pub/week41/html/._week41-bs000.html
index cafd1f5fd..8b355d07a 100644
--- a/doc/pub/week41/html/._week41-bs000.html
+++ b/doc/pub/week41/html/._week41-bs000.html
@@ -292,7 +292,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Oct 20, 2022</h4>
+<h4>Week 41</h4>
 </center> <!-- date -->
 <br>
 
@@ -331,7 +331,7 @@ MathJax.Hub.Config({
 </footer>
 -->
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 </body>
 </html>
diff --git a/doc/pub/week41/html/._week41-bs001.html b/doc/pub/week41/html/._week41-bs001.html
index 23aa84847..18cb326c8 100644
--- a/doc/pub/week41/html/._week41-bs001.html
+++ b/doc/pub/week41/html/._week41-bs001.html
@@ -277,17 +277,11 @@ MathJax.Hub.Config({
 <h2 id="plan-for-week-41" class="anchor">Plan for week 41 </h2>
 
 <ul>
-<li> Thursday: Building our own Feed-forward Neural Network and discussion of project 2.</li>
-<ul>
-  <li> <a href="https://youtu.be/yzbxJI6LgL0" target="_self">Video of lecture</a></li>
-</ul>
-<li> Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks.</li> 
-<ul>
-  <li> "Video of lecture":"https://youtu.be/CPj4mh7M9no</li>
-</ul>
+<li> Building our own Feed-forward Neural Network and discussion of project 2.</li>
+<li> Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks.</li> 
 </ul>
 <p>"
-Reading suggestions for both days: These notes, <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_self">Aurelien Geron's chapters 10-11</a>.
+Reading suggestions: These notes, <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_self">Aurelien Geron's chapters 10-11</a>.
 For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7. Bishop's chapter 5 on Neural Networks is an additional good read.
 </p>
 
diff --git a/doc/pub/week41/html/week41-bs.html b/doc/pub/week41/html/week41-bs.html
index cafd1f5fd..8b355d07a 100644
--- a/doc/pub/week41/html/week41-bs.html
+++ b/doc/pub/week41/html/week41-bs.html
@@ -292,7 +292,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Oct 20, 2022</h4>
+<h4>Week 41</h4>
 </center> <!-- date -->
 <br>
 
@@ -331,7 +331,7 @@ MathJax.Hub.Config({
 </footer>
 -->
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 </body>
 </html>
diff --git a/doc/pub/week41/html/week41-reveal.html b/doc/pub/week41/html/week41-reveal.html
index 685a05cdc..745b4fbee 100644
--- a/doc/pub/week41/html/week41-reveal.html
+++ b/doc/pub/week41/html/week41-reveal.html
@@ -184,13 +184,13 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Oct 20, 2022</h4>
+<h4>Week 41</h4>
 </center> <!-- date -->
 <br>
 
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 </section>
 
@@ -198,22 +198,12 @@ MathJax.Hub.Config({
 <h2 id="plan-for-week-41">Plan for week 41 </h2>
 
 <ul>
-<p><li> Thursday: Building our own Feed-forward Neural Network and discussion of project 2.</li>
-<ul>
-
-<p><li> <a href="https://youtu.be/yzbxJI6LgL0" target="_blank">Video of lecture</a></li>
-</ul>
-<p>
-<p><li> Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks.</li> 
-<ul>
-
-<p><li> "Video of lecture":"https://youtu.be/CPj4mh7M9no</li>
-</ul>
-<p>
+<p><li> Building our own Feed-forward Neural Network and discussion of project 2.</li>
+<p><li> Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks.</li> 
 </ul>
 <p>
 <p>"
-Reading suggestions for both days: These notes, <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapters 10-11</a>.
+Reading suggestions: These notes, <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapters 10-11</a>.
 For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7. Bishop's chapter 5 on Neural Networks is an additional good read.
 </p>
 </section>
diff --git a/doc/pub/week41/html/week41-solarized.html b/doc/pub/week41/html/week41-solarized.html
index f5c7a7510..9d79fd083 100644
--- a/doc/pub/week41/html/week41-solarized.html
+++ b/doc/pub/week41/html/week41-solarized.html
@@ -242,7 +242,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Oct 20, 2022</h4>
+<h4>Week 41</h4>
 </center> <!-- date -->
 <br>
 
@@ -250,17 +250,11 @@ MathJax.Hub.Config({
 <h2 id="plan-for-week-41">Plan for week 41 </h2>
 
 <ul>
-<li> Thursday: Building our own Feed-forward Neural Network and discussion of project 2.</li>
-<ul>
-  <li> <a href="https://youtu.be/yzbxJI6LgL0" target="_blank">Video of lecture</a></li>
-</ul>
-<li> Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks.</li> 
-<ul>
-  <li> "Video of lecture":"https://youtu.be/CPj4mh7M9no</li>
-</ul>
+<li> Building our own Feed-forward Neural Network and discussion of project 2.</li>
+<li> Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks.</li> 
 </ul>
 <p>"
-Reading suggestions for both days: These notes, <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapters 10-11</a>.
+Reading suggestions: These notes, <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapters 10-11</a>.
 For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7. Bishop's chapter 5 on Neural Networks is an additional good read.
 </p>
 
@@ -2695,7 +2689,7 @@ features).
 
 <!-- ------------------- end of main content --------------- -->
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 </body>
 </html>
diff --git a/doc/pub/week41/html/week41.html b/doc/pub/week41/html/week41.html
index 582a8a14c..f6f34ab4a 100644
--- a/doc/pub/week41/html/week41.html
+++ b/doc/pub/week41/html/week41.html
@@ -319,7 +319,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Oct 20, 2022</h4>
+<h4>Week 41</h4>
 </center> <!-- date -->
 <br>
 
@@ -327,17 +327,11 @@ MathJax.Hub.Config({
 <h2 id="plan-for-week-41">Plan for week 41 </h2>
 
 <ul>
-<li> Thursday: Building our own Feed-forward Neural Network and discussion of project 2.</li>
-<ul>
-  <li> <a href="https://youtu.be/yzbxJI6LgL0" target="_blank">Video of lecture</a></li>
-</ul>
-<li> Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks.</li> 
-<ul>
-  <li> "Video of lecture":"https://youtu.be/CPj4mh7M9no</li>
-</ul>
+<li> Building our own Feed-forward Neural Network and discussion of project 2.</li>
+<li> Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks.</li> 
 </ul>
 <p>"
-Reading suggestions for both days: These notes, <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapters 10-11</a>.
+Reading suggestions: These notes, <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapters 10-11</a>.
 For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7. Bishop's chapter 5 on Neural Networks is an additional good read.
 </p>
 
@@ -2772,7 +2766,7 @@ features).
 
 <!-- ------------------- end of main content --------------- -->
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 </body>
 </html>
diff --git a/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz b/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz
index 9e9941c1f..9e963fd57 100644
Binary files a/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz and b/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz differ
diff --git a/doc/pub/week41/ipynb/week41.ipynb b/doc/pub/week41/ipynb/week41.ipynb
index e2255d1f9..880d4e64c 100644
--- a/doc/pub/week41/ipynb/week41.ipynb
+++ b/doc/pub/week41/ipynb/week41.ipynb
@@ -2,8 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ad99bc77",
-   "metadata": {},
+   "id": "fe50dc63",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
     "doconce format html week41.do.txt --no_mako -->\n",
@@ -12,41 +14,41 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b600d246",
-   "metadata": {},
+   "id": "fda369ac",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "# Week 41 Constructing a Neural Network code, Tensor flow and start Convolutional Neural Networks\n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n",
     "\n",
-    "Date: **Oct 20, 2022**\n",
-    "\n",
-    "Copyright 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license"
+    "Date: **Week 41**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8637f719",
-   "metadata": {},
+   "id": "c14090dd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Plan for week 41\n",
     "\n",
-    "* Thursday: Building our own Feed-forward Neural Network and discussion of project 2.\n",
+    "* Building our own Feed-forward Neural Network and discussion of project 2.\n",
     "\n",
-    "  * [Video of lecture](https://youtu.be/yzbxJI6LgL0)\n",
-    "\n",
-    "* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks. \n",
-    "\n",
-    "  * \"Video of lecture\":\"https://youtu.be/CPj4mh7M9no\n",
+    "* Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks. \n",
     "\n",
     "\"\n",
-    "Reading suggestions for both days: These notes, [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf).\n",
+    "Reading suggestions: These notes, [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf).\n",
     "For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7. Bishop's chapter 5 on Neural Networks is an additional good read."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1eb37715",
-   "metadata": {},
+   "id": "24b17eef",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Videos on Neural Networks\n",
     "\n",
@@ -59,8 +61,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57d9f9f4",
-   "metadata": {},
+   "id": "a4b68d95",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Review of the back propagation algorithm\n",
     "\n",
@@ -72,8 +76,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "928c65c1",
-   "metadata": {},
+   "id": "27603656",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Setting up the Back propagation algorithm\n",
     "\n",
@@ -93,8 +99,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c820227e",
-   "metadata": {},
+   "id": "cfc83d3e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\delta_j^L = f'(z_j^L)\\frac{\\partial {\\cal C}}{\\partial (a_j^L)}.\n",
@@ -103,16 +111,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94267523",
-   "metadata": {},
+   "id": "c52a5c90",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Then we compute the back propagate error for each $l=L-1,L-2,\\dots,2$ as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0d835709",
-   "metadata": {},
+   "id": "f87049f3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\delta_j^l = \\sum_k \\delta_k^{l+1}w_{kj}^{l+1}f'(z_j^l).\n",
@@ -121,16 +133,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28995178",
-   "metadata": {},
+   "id": "3b402468",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Finally, we update the weights and the biases using gradient descent for each $l=L-1,L-2,\\dots,2$ and update the weights and biases according to the rules"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f8e000ce",
-   "metadata": {},
+   "id": "c3e371c5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "w_{jk}^l\\leftarrow  = w_{jk}^l- \\eta \\delta_j^la_k^{l-1},\n",
@@ -139,8 +155,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4bbf4d4",
-   "metadata": {},
+   "id": "05459156",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "b_j^l \\leftarrow b_j^l-\\eta \\frac{\\partial {\\cal C}}{\\partial b_j^l}=b_j^l-\\eta \\delta_j^l,\n",
@@ -149,8 +167,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4b9ddfa",
-   "metadata": {},
+   "id": "8ee0290c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The parameter $\\eta$ is the learning parameter discussed in connection with the gradient descent methods.\n",
     "Here it is convenient to use stochastic gradient descent (see the examples below) with mini-batches with an outer loop that steps through multiple epochs of training."
@@ -158,8 +178,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d9e1324",
-   "metadata": {},
+   "id": "c48f1301",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Setting up a Multi-layer perceptron model for classification\n",
     "\n",
@@ -184,8 +206,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "986a1be8",
-   "metadata": {},
+   "id": "4ef49cf0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\frac{1}{1 + \\exp{(- \\boldsymbol{x}})} ,\n",
@@ -194,16 +218,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9eb4dacf",
-   "metadata": {},
+   "id": "19f1dc57",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8277b759",
-   "metadata": {},
+   "id": "16155951",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "P(y = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = 1 - P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) ,\n",
@@ -212,8 +240,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce81bbac",
-   "metadata": {},
+   "id": "0566b8db",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $y \\in \\{0, 1\\}$  and $\\boldsymbol{\\theta}$ represents the weights and biases\n",
     "of our network."
@@ -221,8 +251,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dae87872",
-   "metadata": {},
+   "id": "93b66473",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Defining the cost function\n",
     "\n",
@@ -231,8 +263,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "305aa69e",
-   "metadata": {},
+   "id": "59609222",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\ln P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = - \\sum_{i=1}^n\n",
@@ -242,8 +276,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b9f595b",
-   "metadata": {},
+   "id": "541c0474",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "This last equality means that we can interpret our *cost* function as a sum over the *loss* function\n",
     "for each point in the dataset $\\mathcal{L}_i(\\boldsymbol{\\theta})$.  \n",
@@ -265,8 +301,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df7a99c3",
-   "metadata": {},
+   "id": "f1d7b488",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "P(y_{ic} = 1 \\mid \\boldsymbol{x}_i, \\boldsymbol{\\theta}) = \\frac{\\exp{((\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_c)}}\n",
@@ -276,8 +314,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "990ad682",
-   "metadata": {},
+   "id": "82c5f484",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "which reduces to the logistic function in the binary case.  \n",
     "The likelihood of this $C$-class classifier\n",
@@ -286,8 +326,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f46e3f7",
-   "metadata": {},
+   "id": "c7dfc681",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = \\prod_{i=1}^n \\prod_{c=0}^{C-1} [P(y_{ic} = 1)]^{y_{ic}} .\n",
@@ -296,16 +338,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "130feb29",
-   "metadata": {},
+   "id": "27afae96",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Again we take the negative log-likelihood to define our cost function:"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c5b8dd70",
-   "metadata": {},
+   "id": "4c7b4937",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\log{P(\\mathcal{D} \\mid \\boldsymbol{\\theta})}.\n",
@@ -314,8 +360,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9692ca73",
-   "metadata": {},
+   "id": "10004f54",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "See the logistic regression lectures for a full definition of the cost function.\n",
     "\n",
@@ -324,8 +372,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32190b69",
-   "metadata": {},
+   "id": "d188ab09",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Example: binary classification problem\n",
     "\n",
@@ -334,8 +384,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0355eaed",
-   "metadata": {},
+   "id": "17ee12d5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\mathcal{C}(\\boldsymbol{\\beta}) = - \\sum_{i=1}^n \\left(y_i\\log{p(y_i \\vert x_i,\\boldsymbol{\\beta})}+(1-y_i)\\log{1-p(y_i \\vert x_i,\\boldsymbol{\\beta})}\\right),\n",
@@ -344,16 +396,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a10d8c23",
-   "metadata": {},
+   "id": "17ffedce",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where we had defined the logistic (sigmoid) function"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "48469a18",
-   "metadata": {},
+   "id": "b0f0cc69",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "p(y_i =1\\vert x_i,\\boldsymbol{\\beta})=\\frac{\\exp{(\\beta_0+\\beta_1 x_i)}}{1+\\exp{(\\beta_0+\\beta_1 x_i)}},\n",
@@ -362,16 +418,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd40a298",
-   "metadata": {},
+   "id": "b384259f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e80ef21d",
-   "metadata": {},
+   "id": "f357a71b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "p(y_i =0\\vert x_i,\\boldsymbol{\\beta})=1-p(y_i =1\\vert x_i,\\boldsymbol{\\beta}).\n",
@@ -380,8 +440,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90e9d3d9",
-   "metadata": {},
+   "id": "4a172fb6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The parameters $\\boldsymbol{\\beta}$ were defined using a minimization method like gradient descent or Newton-Raphson's method. \n",
     "\n",
@@ -391,8 +453,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56ecaeeb",
-   "metadata": {},
+   "id": "52c754bc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "a_i^l = y_i = \\frac{\\exp{(z_i^l)}}{1+\\exp{(z_i^l)}},\n",
@@ -401,16 +465,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a8bd9d7",
-   "metadata": {},
+   "id": "211abd96",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "with"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4a7b30e6",
-   "metadata": {},
+   "id": "13f05813",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "z_i^l = \\sum_{j}w_{ij}^l a_j^{l-1}+b_i^l,\n",
@@ -419,8 +487,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fbb407d",
-   "metadata": {},
+   "id": "9c92564e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where the superscript $l-1$ indicates that these are the outputs from layer $l-1$.\n",
     "Our cost function at the final layer $l=L$ is now"
@@ -428,8 +498,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85e01cbc",
-   "metadata": {},
+   "id": "8b15007b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\mathcal{C}(\\boldsymbol{W}) = - \\sum_{i=1}^n \\left(t_i\\log{a_i^L}+(1-t_i)\\log{(1-a_i^L)}\\right),\n",
@@ -438,16 +510,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ced7306",
-   "metadata": {},
+   "id": "e317235b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where we have defined the targets $t_i$. The derivatives of the cost function with respect to the output $a_i^L$ are then easily calculated and we get"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a59f5fa2",
-   "metadata": {},
+   "id": "f0bf3e1d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial \\mathcal{C}(\\boldsymbol{W})}{\\partial a_i^L} = \\frac{a_i^L-t_i}{a_i^L(1-a_i^L)}.\n",
@@ -456,16 +532,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01925119",
-   "metadata": {},
+   "id": "2e7dbb17",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "In case we use another activation function than the logistic one, we need to evaluate other derivatives."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2e5de297",
-   "metadata": {},
+   "id": "ffea970b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The Softmax function\n",
     "In case we employ the more general case given by the Softmax equation, we need to evaluate the derivative of the activation function with respect to the activation $z_i^l$, that is we need"
@@ -473,8 +553,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aebd954a",
-   "metadata": {},
+   "id": "bcefe435",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial f(z_i^l)}{\\partial w_{jk}^l} =\n",
@@ -484,16 +566,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "169213b3",
-   "metadata": {},
+   "id": "36ca4b8e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "For the Softmax function we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "214da92a",
-   "metadata": {},
+   "id": "79c1417a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "f(z_i^l) = \\frac{\\exp{(z_i^l)}}{\\sum_{m=1}^K\\exp{(z_m^l)}}.\n",
@@ -502,16 +588,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c7b95b12",
-   "metadata": {},
+   "id": "2592db6e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Its derivative with respect to $z_j^l$ gives"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "343e174c",
-   "metadata": {},
+   "id": "da359e00",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial f(z_i^l)}{\\partial z_j^l}= f(z_i^l)\\left(\\delta_{ij}-f(z_j^l)\\right),\n",
@@ -520,16 +610,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca1a02a0",
-   "metadata": {},
+   "id": "12521ab8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "which in case of the simply binary model reduces to  having $i=j$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "975ee3db",
-   "metadata": {},
+   "id": "e835a921",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Developing a code for doing neural networks with back propagation\n",
     "\n",
@@ -550,8 +644,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d04c9ad0",
-   "metadata": {},
+   "id": "199c93d2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Collect and pre-process data\n",
     "\n",
@@ -597,30 +693,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "9b1e2335",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n",
-      "labels = (n_inputs) = (1797,)\n",
-      "X = (n_inputs, n_features) = (1797, 64)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1200x1200 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 1,
+   "id": "5ac526a9",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -670,8 +749,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bb279fa",
-   "metadata": {},
+   "id": "9a2b47ab",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Train and test datasets\n",
     "\n",
@@ -688,19 +769,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "63b5f925",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of training images: 1437\n",
-      "Number of test images: 360\n"
-     ]
-    }
-   ],
+   "execution_count": 2,
+   "id": "5414fbee",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
@@ -733,8 +808,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f10e308",
-   "metadata": {},
+   "id": "6106cbfc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Define model and architecture\n",
     "\n",
@@ -775,8 +852,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc52eae6",
-   "metadata": {},
+   "id": "67e9f13a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Layers\n",
     "\n",
@@ -813,8 +892,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a8b89e7",
-   "metadata": {},
+   "id": "2f8ebd5d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Weights and biases\n",
     "\n",
@@ -831,9 +912,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "21742490",
-   "metadata": {},
+   "execution_count": 3,
+   "id": "439f735b",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# building our neural network\n",
@@ -855,8 +939,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c5cca55",
-   "metadata": {},
+   "id": "a6bd9d44",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Feed-forward pass\n",
     "\n",
@@ -881,8 +967,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d6e5e7c",
-   "metadata": {},
+   "id": "6cfe367f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Matrix multiplications\n",
     "\n",
@@ -915,27 +1003,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "655edfa8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "probabilities = (n_inputs, n_categories) = (1437, 10)\n",
-      "probability that image 0 is in category 0,1,2,...,9 = \n",
-      "[5.41511965e-04 2.17174962e-03 8.84355903e-03 1.44970586e-03\n",
-      " 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03\n",
-      " 9.84443254e-01 3.11507992e-04]\n",
-      "probabilities sum up to: 1.0\n",
-      "\n",
-      "predictions = (n_inputs) = (1437,)\n",
-      "prediction for image 0: 8\n",
-      "correct label for image 0: 6\n"
-     ]
-    }
-   ],
+   "execution_count": 4,
+   "id": "0a74c4ad",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# setup the feed-forward pass, subscript h = hidden layer\n",
     "\n",
@@ -976,8 +1050,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6587577e",
-   "metadata": {},
+   "id": "1a87eda5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Choose cost function and optimizer\n",
     "\n",
@@ -1005,8 +1081,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4179cedb",
-   "metadata": {},
+   "id": "004473d8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Optimizing the cost function\n",
     "\n",
@@ -1041,8 +1119,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6eea00ee",
-   "metadata": {},
+   "id": "6e78b93a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Regularization\n",
     "\n",
@@ -1073,8 +1153,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b5658f7",
-   "metadata": {},
+   "id": "e94ad101",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Matrix  multiplication\n",
     "\n",
@@ -1111,33 +1193,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "70a11914",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Old accuracy on training data: 0.1440501043841336\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "New accuracy on training data: 0.09951287404314545\n"
-     ]
-    }
-   ],
+   "execution_count": 5,
+   "id": "0603a76a",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# to categorical turns our integer vector into a onehot representation\n",
     "from sklearn.metrics import accuracy_score\n",
@@ -1211,8 +1273,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8fe45e7",
-   "metadata": {},
+   "id": "17d62cce",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Improving performance\n",
     "\n",
@@ -1230,8 +1294,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97fbfe63",
-   "metadata": {},
+   "id": "c27a9b11",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Full object-oriented implementation\n",
     "\n",
@@ -1241,9 +1307,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "9f565f04",
-   "metadata": {},
+   "execution_count": 6,
+   "id": "a651821c",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "class NeuralNetwork:\n",
@@ -1349,8 +1418,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7c1c598",
-   "metadata": {},
+   "id": "17d3f194",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Evaluate model performance on test data\n",
     "\n",
@@ -1365,18 +1436,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "e864f3aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy score on test set:  0.9444444444444444\n"
-     ]
-    }
-   ],
+   "execution_count": 7,
+   "id": "e91aa14d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "epochs = 100\n",
     "batch_size = 100\n",
@@ -1398,8 +1464,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f175e5a",
-   "metadata": {},
+   "id": "15fda52a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Adjust hyperparameters\n",
     "\n",
@@ -1409,559 +1477,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "a7eeea76",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.11666666666666667\n",
-      "\n",
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.20833333333333334\n",
-      "\n",
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.12222222222222222\n",
-      "\n",
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.14722222222222223\n",
-      "\n",
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.17777777777777778\n",
-      "\n",
-      "Learning rate  =  1e-05\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.16111111111111112\n",
-      "\n",
-      "Learning rate  =  1e-05\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.20277777777777778\n",
-      "\n",
-      "Learning rate  =  0.0001\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.5305555555555556\n",
-      "\n",
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.5944444444444444\n",
-      "\n",
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.5888888888888889\n",
-      "\n",
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.6111111111111112\n",
-      "\n",
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.5222222222222223\n",
-      "\n",
-      "Learning rate  =  0.0001\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.5555555555555556\n",
-      "\n",
-      "Learning rate  =  0.0001\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.8055555555555556\n",
-      "\n",
-      "Learning rate  =  0.001\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.85\n",
-      "\n",
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.85\n",
-      "\n",
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.875\n",
-      "\n",
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.8666666666666667\n",
-      "\n",
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.8638888888888889\n",
-      "\n",
-      "Learning rate  =  0.001\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.9555555555555556\n",
-      "\n",
-      "Learning rate  =  0.001\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.925\n",
-      "\n",
-      "Learning rate  =  0.01\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.9472222222222222\n",
-      "\n",
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.9277777777777778\n",
-      "\n",
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.9472222222222222\n",
-      "\n",
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.9305555555555556\n",
-      "\n",
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.9555555555555556\n",
-      "\n",
-      "Learning rate  =  0.01\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.7694444444444445\n",
-      "\n",
-      "Learning rate  =  0.01\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.19166666666666668\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.08611111111111111\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.08888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.08611111111111111\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.08888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.09166666666666666\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": 8,
+   "id": "03099d3d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "eta_vals = np.logspace(-5, 1, 7)\n",
     "lmbd_vals = np.logspace(-5, 1, 7)\n",
@@ -1987,55 +1509,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ec1196b",
-   "metadata": {},
+   "id": "a7cf1e6c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Visualization"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "b53e874c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 9,
+   "id": "192fd489",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# visual representation of grid search\n",
     "# uses seaborn heatmap, you can also do this with matplotlib imshow\n",
@@ -2074,8 +1564,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b11c6a31",
-   "metadata": {},
+   "id": "f555ba04",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## scikit-learn implementation\n",
     "\n",
@@ -2095,8 +1587,11 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "48478b6c",
-   "metadata": {},
+   "id": "93868564",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
@@ -2119,8 +1614,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3748070b",
-   "metadata": {},
+   "id": "b938525d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Visualization"
    ]
@@ -2128,8 +1625,11 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "0a425870",
-   "metadata": {},
+   "id": "e65fc56d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# optional\n",
@@ -2170,8 +1670,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6304a99e",
-   "metadata": {},
+   "id": "1705a4be",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Testing our code for the XOR, OR and AND gates\n",
     "\n",
@@ -2195,8 +1697,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75c6f9a7",
-   "metadata": {},
+   "id": "6ef76cb6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The AND and XOR Gates\n",
     "\n",
@@ -2231,8 +1735,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8681d7c3",
-   "metadata": {},
+   "id": "ce3434c9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Representing the Data Sets\n",
     "\n",
@@ -2241,8 +1747,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "becf04ff",
-   "metadata": {},
+   "id": "af28a402",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{X}=\\begin{bmatrix} 0 & 0 \\\\\n",
@@ -2254,16 +1762,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d8c9533",
-   "metadata": {},
+   "id": "c33c7c5a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "while the vector of outputs is $\\boldsymbol{y}^T=[0,1,1,0]$ for the XOR gate, $\\boldsymbol{y}^T=[0,0,0,1]$ for the AND gate and $\\boldsymbol{y}^T=[0,1,1,1]$ for the OR gate."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c9e5cbe2",
-   "metadata": {},
+   "id": "63299029",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Setting up the Neural Network\n",
     "\n",
@@ -2273,8 +1785,11 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "e21b6b05",
-   "metadata": {},
+   "id": "c5677a08",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "\"\"\"\n",
@@ -2346,16 +1861,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e4e97ce",
-   "metadata": {},
+   "id": "e4278da5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Not an impressive result, but this was our first forward pass with randomly assigned weights. Let us now add the full network with the back-propagation algorithm discussed above."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c7694996",
-   "metadata": {},
+   "id": "d159f207",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The Code using Scikit-Learn"
    ]
@@ -2363,8 +1882,11 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "019432d0",
-   "metadata": {},
+   "id": "7264c3ce",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# import necessary packages\n",
@@ -2428,8 +1950,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afb2ee6d",
-   "metadata": {},
+   "id": "a799df9d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Building neural networks in Tensorflow and Keras\n",
     "\n",
@@ -2444,8 +1968,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68f0ed4c",
-   "metadata": {},
+   "id": "989e19b4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Tensorflow\n",
     "\n",
@@ -2477,8 +2003,11 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "8c4f086d",
-   "metadata": {},
+   "id": "3310ee6d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "pip3 install tensorflow"
@@ -2486,8 +2015,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13c0215c",
-   "metadata": {},
+   "id": "cbfcd955",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and/or if you use **anaconda**, just write (or install from the graphical user interface)\n",
     "(current release of CPU-only TensorFlow)"
@@ -2496,8 +2027,11 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "ad3eabbd",
-   "metadata": {},
+   "id": "0e7abca7",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "conda create -n tf tensorflow\n",
@@ -2506,8 +2040,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2996f448",
-   "metadata": {},
+   "id": "f002da9e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "To install the current release of GPU TensorFlow"
    ]
@@ -2515,8 +2051,11 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "be33424d",
-   "metadata": {},
+   "id": "90156829",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "conda create -n tf-gpu tensorflow-gpu\n",
@@ -2525,8 +2064,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8e9fe7e",
-   "metadata": {},
+   "id": "a77fb74b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Using Keras\n",
     "\n",
@@ -2538,8 +2079,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "16f70fd0",
-   "metadata": {},
+   "id": "fe9bcc62",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "conda install keras"
@@ -2547,8 +2091,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f92ed6ad",
-   "metadata": {},
+   "id": "13a135b9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "You can look up the [instructions here](https://keras.io/) for more information.\n",
     "\n",
@@ -2557,8 +2103,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9072373",
-   "metadata": {},
+   "id": "19dc5832",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Collect and pre-process data\n",
     "\n",
@@ -2567,30 +2115,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "62854fe2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n",
-      "labels = (n_inputs) = (1797,)\n",
-      "X = (n_inputs, n_features) = (1797, 64)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x1200 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 18,
+   "id": "50939b73",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# import necessary packages\n",
     "import numpy as np\n",
@@ -2639,9 +2170,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "bbb70e18",
-   "metadata": {},
+   "execution_count": 19,
+   "id": "dc47cb3a",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "from tensorflow.keras.layers import Input\n",
@@ -2665,9 +2199,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "ee5585fa",
-   "metadata": {},
+   "execution_count": 20,
+   "id": "bc5c2fe1",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "\n",
@@ -2692,278 +2229,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "f2f3fa89",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metal device set to: Apple M1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
-      "  super(SGD, self).__init__(name, **kwargs)\n",
-      "2022-10-20 08:47:09.846624: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12/12 [==============================] - 0s 26ms/step - loss: 2.3578 - accuracy: 0.1278\n",
-      "Learning rate =  1e-05\n",
-      "Lambda =  1e-05\n",
-      "Test accuracy: 0.128\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 2.3709 - accuracy: 0.1278\n",
-      "Learning rate =  1e-05\n",
-      "Lambda =  0.0001\n",
-      "Test accuracy: 0.128\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 2.5019 - accuracy: 0.1278\n",
-      "Learning rate =  1e-05\n",
-      "Lambda =  0.001\n",
-      "Test accuracy: 0.128\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 3.8106 - accuracy: 0.1278\n",
-      "Learning rate =  1e-05\n",
-      "Lambda =  0.01\n",
-      "Test accuracy: 0.128\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 16.8209 - accuracy: 0.1278\n",
-      "Learning rate =  1e-05\n",
-      "Lambda =  0.1\n",
-      "Test accuracy: 0.128\n",
-      "\n",
-      "12/12 [==============================] - 0s 10ms/step - loss: 139.3979 - accuracy: 0.1278\n",
-      "Learning rate =  1e-05\n",
-      "Lambda =  1.0\n",
-      "Test accuracy: 0.128\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 800.9124 - accuracy: 0.1139\n",
-      "Learning rate =  1e-05\n",
-      "Lambda =  10.0\n",
-      "Test accuracy: 0.114\n",
-      "\n",
-      "12/12 [==============================] - 0s 10ms/step - loss: 2.3165 - accuracy: 0.1333\n",
-      "Learning rate =  0.0001\n",
-      "Lambda =  1e-05\n",
-      "Test accuracy: 0.133\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 2.3297 - accuracy: 0.1333\n",
-      "Learning rate =  0.0001\n",
-      "Lambda =  0.0001\n",
-      "Test accuracy: 0.133\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 2.4605 - accuracy: 0.1333\n",
-      "Learning rate =  0.0001\n",
-      "Lambda =  0.001\n",
-      "Test accuracy: 0.133\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 3.7615 - accuracy: 0.1333\n",
-      "Learning rate =  0.0001\n",
-      "Lambda =  0.01\n",
-      "Test accuracy: 0.133\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 16.0185 - accuracy: 0.1333\n",
-      "Learning rate =  0.0001\n",
-      "Lambda =  0.1\n",
-      "Test accuracy: 0.133\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 82.1668 - accuracy: 0.1222\n",
-      "Learning rate =  0.0001\n",
-      "Lambda =  1.0\n",
-      "Test accuracy: 0.122\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 5.9034 - accuracy: 0.1056\n",
-      "Learning rate =  0.0001\n",
-      "Lambda =  10.0\n",
-      "Test accuracy: 0.106\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 2.1629 - accuracy: 0.5222\n",
-      "Learning rate =  0.001\n",
-      "Lambda =  1e-05\n",
-      "Test accuracy: 0.522\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 2.1757 - accuracy: 0.5222\n",
-      "Learning rate =  0.001\n",
-      "Lambda =  0.0001\n",
-      "Test accuracy: 0.522\n",
-      "\n",
-      "12/12 [==============================] - 0s 10ms/step - loss: 2.3064 - accuracy: 0.5278\n",
-      "Learning rate =  0.001\n",
-      "Lambda =  0.001\n",
-      "Test accuracy: 0.528\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 3.5376 - accuracy: 0.5167\n",
-      "Learning rate =  0.001\n",
-      "Lambda =  0.01\n",
-      "Test accuracy: 0.517\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 10.1971 - accuracy: 0.5194\n",
-      "Learning rate =  0.001\n",
-      "Lambda =  0.1\n",
-      "Test accuracy: 0.519\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 2.6673 - accuracy: 0.2278\n",
-      "Learning rate =  0.001\n",
-      "Lambda =  1.0\n",
-      "Test accuracy: 0.228\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 2.3055 - accuracy: 0.0889\n",
-      "Learning rate =  0.001\n",
-      "Lambda =  10.0\n",
-      "Test accuracy: 0.089\n",
-      "\n",
-      "12/12 [==============================] - 0s 7ms/step - loss: 0.9916 - accuracy: 0.9139\n",
-      "Learning rate =  0.01\n",
-      "Lambda =  1e-05\n",
-      "Test accuracy: 0.914\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 1.0080 - accuracy: 0.9139\n",
-      "Learning rate =  0.01\n",
-      "Lambda =  0.0001\n",
-      "Test accuracy: 0.914\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 1.1547 - accuracy: 0.9139\n",
-      "Learning rate =  0.01\n",
-      "Lambda =  0.001\n",
-      "Test accuracy: 0.914\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.0397 - accuracy: 0.9028\n",
-      "Learning rate =  0.01\n",
-      "Lambda =  0.01\n",
-      "Test accuracy: 0.903\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.2562 - accuracy: 0.4806\n",
-      "Learning rate =  0.01\n",
-      "Lambda =  0.1\n",
-      "Test accuracy: 0.481\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.3076 - accuracy: 0.0778\n",
-      "Learning rate =  0.01\n",
-      "Lambda =  1.0\n",
-      "Test accuracy: 0.078\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.3084 - accuracy: 0.0778\n",
-      "Learning rate =  0.01\n",
-      "Lambda =  10.0\n",
-      "Test accuracy: 0.078\n",
-      "\n",
-      "12/12 [==============================] - 0s 10ms/step - loss: 0.0887 - accuracy: 0.9806\n",
-      "Learning rate =  0.1\n",
-      "Lambda =  1e-05\n",
-      "Test accuracy: 0.981\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 0.1090 - accuracy: 0.9806\n",
-      "Learning rate =  0.1\n",
-      "Lambda =  0.0001\n",
-      "Test accuracy: 0.981\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 0.2472 - accuracy: 0.9806\n",
-      "Learning rate =  0.1\n",
-      "Lambda =  0.001\n",
-      "Test accuracy: 0.981\n",
-      "\n",
-      "12/12 [==============================] - 0s 10ms/step - loss: 0.4871 - accuracy: 0.9583\n",
-      "Learning rate =  0.1\n",
-      "Lambda =  0.01\n",
-      "Test accuracy: 0.958\n",
-      "\n",
-      "12/12 [==============================] - 0s 10ms/step - loss: 1.4946 - accuracy: 0.7000\n",
-      "Learning rate =  0.1\n",
-      "Lambda =  0.1\n",
-      "Test accuracy: 0.700\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.3065 - accuracy: 0.1250\n",
-      "Learning rate =  0.1\n",
-      "Lambda =  1.0\n",
-      "Test accuracy: 0.125\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 1594.4144 - accuracy: 0.0722\n",
-      "Learning rate =  0.1\n",
-      "Lambda =  10.0\n",
-      "Test accuracy: 0.072\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 0.0669 - accuracy: 0.9778\n",
-      "Learning rate =  1.0\n",
-      "Lambda =  1e-05\n",
-      "Test accuracy: 0.978\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 0.0910 - accuracy: 0.9778\n",
-      "Learning rate =  1.0\n",
-      "Lambda =  0.0001\n",
-      "Test accuracy: 0.978\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 0.4291 - accuracy: 0.9556\n",
-      "Learning rate =  1.0\n",
-      "Lambda =  0.001\n",
-      "Test accuracy: 0.956\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 1.7840 - accuracy: 0.5944\n",
-      "Learning rate =  1.0\n",
-      "Lambda =  0.01\n",
-      "Test accuracy: 0.594\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.4207 - accuracy: 0.0778\n",
-      "Learning rate =  1.0\n",
-      "Lambda =  0.1\n",
-      "Test accuracy: 0.078\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 363.3102 - accuracy: 0.1139\n",
-      "Learning rate =  1.0\n",
-      "Lambda =  1.0\n",
-      "Test accuracy: 0.114\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: nan - accuracy: 0.0778\n",
-      "Learning rate =  1.0\n",
-      "Lambda =  10.0\n",
-      "Test accuracy: 0.078\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.4347 - accuracy: 0.0889\n",
-      "Learning rate =  10.0\n",
-      "Lambda =  1e-05\n",
-      "Test accuracy: 0.089\n",
-      "\n",
-      "12/12 [==============================] - 0s 8ms/step - loss: 2.5265 - accuracy: 0.0889\n",
-      "Learning rate =  10.0\n",
-      "Lambda =  0.0001\n",
-      "Test accuracy: 0.089\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.4089 - accuracy: 0.1056\n",
-      "Learning rate =  10.0\n",
-      "Lambda =  0.001\n",
-      "Test accuracy: 0.106\n",
-      "\n",
-      "12/12 [==============================] - 0s 9ms/step - loss: 2.5464 - accuracy: 0.0889\n",
-      "Learning rate =  10.0\n",
-      "Lambda =  0.01\n",
-      "Test accuracy: 0.089\n",
-      "\n",
-      "12/12 [==============================] - 0s 11ms/step - loss: 3079.4927 - accuracy: 0.1056\n",
-      "Learning rate =  10.0\n",
-      "Lambda =  0.1\n",
-      "Test accuracy: 0.106\n",
-      "\n",
-      "12/12 [==============================] - 0s 11ms/step - loss: nan - accuracy: 0.0778\n",
-      "Learning rate =  10.0\n",
-      "Lambda =  1.0\n",
-      "Test accuracy: 0.078\n",
-      "\n",
-      "12/12 [==============================] - 0s 12ms/step - loss: nan - accuracy: 0.0778\n",
-      "Learning rate =  10.0\n",
-      "Lambda =  10.0\n",
-      "Test accuracy: 0.078\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": 21,
+   "id": "00800a93",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
     "        \n",
@@ -2984,141 +2256,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "02c13b5b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-     ]
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 22,
+   "id": "8ace914b",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# optional\n",
     "# visual representation of grid search\n",
@@ -3155,2646 +2299,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b9be2b5",
-   "metadata": {},
+   "id": "51a8d259",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The Breast Cancer Data, now with Keras"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "e4543f99",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The content of the breast cancer dataset is:\n",
-      "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
-      " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
-      " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
-      " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
-      " 'smoothness error' 'compactness error' 'concavity error'\n",
-      " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
-      " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
-      " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
-      " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n",
-      "-------------------------\n",
-      "inputs =  (569, 30)\n",
-      "outputs =  (569,)\n",
-      "labels =  (30,)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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tboeMlONtfLy74/Op6yGjWqkioHGEeAAAAACdVvKJB8vdLa76iXw9DMtUzNA+Spw4vJUrA+pHiAcAAADQaVnhYUp/8TZZke46XeYNy1RYQheNe/5mGUZTfe6B1kGIBwAAANCpJRw0RFO+/LdSL5omV5coSZK7a6wGXvkbHfblY4oZ2KuNKwR+ZjiO09BiCp2Wz2crL6+0rctAJ+dymUpIiFZ+fqm8XrvpA4BOhnsEaBj3B9Awf+4Px7ZlNNC9HmgJiYnRsvycPJF/mQAAAADwCwR4tGf86wQAAAAAIEQQ4gEAAAAACBGEeAAAAAAAQgQhHgAAAACAEEGIBwAAAAAgRBDiAQAAAAAIEYR4AAAAAABCBCEeAAAAAIAQQYgHAAAAACBEEOIBAAAAAAgRhHgAAAAAAEIEIR4AAAAAgBBBiAcAAAAAIEQQ4gEAAAAACBGEeAAAAAAAQgQhHgAAAACAEEGIBwAAAAAgRBDiAQAAAAAIEa62LgAAAABA5+HYtvZ8t0rlWblyxUUracoYuaIj2rosIGQQ4gEAAAC0iuwPvtdPtz+riu27a7ZZ0REaeMWpGnzdGTJMOgoDTSHEAwAAAGhxO99foCWXPFBnu6+0QusfelWVuws16v4/tEFlQGjhqy4AAAAALcr2eLXyxiclR9V/6pH5/IcqWpPZqnUBoYgQDwAAAKBF7friR1XtLmy0jWGZynrp0ybPVZqRrewPFyr38yXyFJcFq0QgZNCdHgAAAECLKs3IlkxDsht4DC/J8dkq3bKzwf0lm7brp5ue0u5vVtRsMyPcSj3/WKXddp6sCHdQawbaK0I8AAAAgBblio5sNMBLkkxTrpioeneVbtmpb4+/Ud6S8lrb7YoqbXn2PRWvy9L4l++Q6bKCVTLQbtGdHgAAAECL6n7MOBlWE9HDtpV84qR6d635+wvylpTL8dn1HOdo97xl2jn32yBUCrR/hHgAAAAALSqie4L6nHVkdZf6ehiWqah+yUo+bnydfZW5Bcr+aGH9AX4f01TmrI+CVS7QrhHiAQAAALS4Ef+4TD2OGidJPz+V3xvqI3p108TX7pYZVne0b2nGzqa74tu2itdvDWq9QHvFmHgAAAAALc6KcGvci7dqz/yV2vrSpyrLzFFYfLR6/eYwpZx8SIMT01mR4f6d3892QKgjxAMAAABoFYZhqNvkA9Rt8gF+HxM7PFXhyYmqzM5r+LyWqZQTDw5GiUC7R3d6AAAAAO2WYVkaeOVvGmlgSKapfhdPa72igDZEiAcAAADQrvW/7ESlXnS8JNWe5d40ZYa5NG7mTYrun9JG1QGti+70AAAAANo1wzA08r7L1es3k5Xx/IcqXLFJZrhbPY5JV+r5xyiyV1Jblwi0GkI8AAAAgHbPMAwlThiuxAnD27oUoE3RnR4AAAAAgBBBiAcAAAAAIEQQ4gEAAAAACBGEeAAAAAAAQgQhHgAAAACAEEGIBwAAAAAgRBDiAQAAAAAIEYR4AAAAAABCBCEeAAAAAIAQQYgHAAAAACBEEOIBAAAAAAgRhHgAAAAAAEIEIR4AAAAAgBBBiAcAAAAAIEQQ4gEAAAAACBGEeAAAAAAAQgQhHgAAAACAEEGIBwAAAAAgRBDiAQAAAAAIEYR4AAAAAABCBCEeAAAAAIAQ0a5C/IwZM3TeeefV2pabm6vrrrtO48aN04QJE3T99dcrLy+vZr9t25o+fbomT56s0aNH6+KLL1ZmZmZrlw4AAAAAQItrNyF+1qxZmj59eq1tVVVVuvjii5WVlaXnn39eTz31lFavXq2bbrqpps2MGTP06quv6t5779WcOXNkGIYuu+wyVVVVtfZbAAAAAACgRbV5iM/JydGll16qf//73+rfv3+tfe+99562b9+u//73vxo1apTGjBmjW2+9VVu2bFFJSYmqqqo0c+ZM/fnPf9aUKVOUlpamRx99VDk5Ofr000/b6B0BAAAAANAy2jzEr1q1SnFxcZo7d65Gjx5da98333yjiRMnqlu3bjXbJk+erM8++0wxMTFau3atSktLNXHixJr9sbGxGj58uBYtWtRq7wEAAAAAgNbgausCpk6dqqlTp9a7LyMjQ+PGjdMTTzyht99+W16vV4ceeqhuuOEGxcbGKjs7W5KUkpJS67ju3btr586d+1WXy9Xm32+gk7Mss9bfAGrjHgEaxv0BNIz7A6GuzUN8Y0pKSvT2229r0qRJ+te//qXCwkLdd999uuKKKzR79myVl5dLktxud63jwsPDVVhY2OzrmqahhITo/aodCJbY2Mi2LgFo17hHgIZxf8BxHMlxZJgE1l/j/kCoatchPiwsTFFRUfrXv/6lsLAwSVJcXJxOP/10rVy5UhEREZKqJ8Db97MkVVZWKjKy+TelbTsqKirbv+KB/WRZpmJjI1VUVC6fz27rcoB2h3sEaBj3B/IWrdXGGW8r+6MfZHu8iu7fUwMunaZ+5x8rKzK8rctrU9wfaI9iYyP97h3SrkN8cnKybNuuCfCSNHjwYEnStm3b1Lt3b0nVy9D17du3pk1ubq7S0tL269peLzc02gefz+bfI9AI7hGgYdwfndPWlz7ViuufkGGacvaG1NItO7TytueU9X/zNPH//i5XNE+huT8Qqtp1v5px48Zp7dq1qqioqNm2fv16SVJqaqrS0tIUExOjhQsX1uwvKirS6tWrNW7cuFavFwAAAGhLxeuytOL6GZKjmgAvSXIkOY4Klm/Smr+/0Gb1Adh/7TrE//73v5dlWbr++uu1fv16LVmyRLfffrsmTJigESNGyO1269xzz9XDDz+szz//XGvXrtW1116r5ORkHX300W1dPgAAANCqMmZ9KMM0Gm7gs5X1yufyFJW2XlEAgqpdd6dPTEzUSy+9pPvuu09nnHGG3G63jjrqKN1yyy01ba6++mp5vV7dfvvtqqioUHp6up577rk6k90BAAAAHd3uL5fWfgJfD7vSo4KlG5Q0ZUzrFAUgqAzHcZy2LqK98fls5eXx7STalstlKiEhWvn5pYzXAurBPQI0jPuj8/pi/B9UlpnTZLvxr/xN3ace2AoVtT/cH2iPEhOj/Z7Yrl13pwcAAADgv4T0NBlNBQHLVOzI/q1TEICgI8QDAAAAHUS/i6Y12p3esEz1PHGSIrontGJVAIKJEA8AAAB0EAnjhmrwtadXv/jVBHeGZSoipauG33tpG1QGIFgI8QAAAEAHMvTmczR2xrXqMrRvzTYrKlypFxynQz9+mKfwQIhr17PTAwAAAAhcr99OUc/TDlNFdp7syipF9EiUFRne1mUBCAJCPAAAANABGYahyJSubV0GgCCjOz0AAAAAACGCEA8AAAAAQIggxAMAAAAAECII8QAAAAAAhAhCPAAAAAAAIYIQDwAAAABAiCDEAwAAAAAQIgjxAAAAAACECEI8AAAAAAAhghAPAAAAAECIcLV1AQAAAABa3655y5Xx7Hvas2CV5DhKSE9T/8tOVNLUA2UYRluXB6ABhHgAAACgk1l730va+NjrMixTjs+WJO3+erl2fblU/S8/ScP/fjFBHmin6E4PAAAAdCLZH3yvjY+9Lkk1Af6XP295+l1tf2Nem9QGoGmEeAAAAKAT2fTkO5LZSAwwDW2e8U7rFQQgIIR4AAAAoJPwVXqUv3CNZNsNN7IdFa3aoqqCktYrDIDfCPEAAABAJ+F4ff639XhbsBIAzUWIBwAAADoJKypckX27S03MWRfePV7urrGtUxSAgBDiAQAAgE7CMAz1v+RENZriTUP9Lpomo7Fx8wDaDHcmAAAA0ImkXnS8EicOl8x6grxpKn70IA344ymtXxgAv7BOPAAAANABeEvKtf2NecpbuEaO4ygxPU29Tj9cYV2iarWzwsM04dU7teHR15U560N59k5g5+oSpb7nH6Mh1/9eVlR4W7wFAH4wHMdx2rqI9sbns5WXV9rWZaCTc7lMJSREKz+/VF5vIzPIAp0U9wjQMO6PzmfXvGVacvED8paUS9bezra2LSsyXAc+9Vf1OCa93uN8lR6VbtwmSYoe2EtWhLu1Sm4z3B9ojxITo2VZ/nWUpzs9AAAAEMKKVmfoh3PulbesonqDz67+40i+8iotvuh+FSzbWO+xVniYYkf0V+yI/p0iwAMdASEeAAAACGGbHn+ret13u54Oto4jOY42Tn+j9QsD0CIYEw8AAAAEqGhVhra/9Y08hSWK7NlNvU8/XJG9k1q9Dtvr046538rxNdwt3PHZyv7we/nKKhnrDnQAhHgAAADAT97Scv34x38p95PFMlxW9UbH0boHXtaAP56sYX+7oFWXZvOVV8rxeJtuaDvylpQT4oEOgO70AAAAgB8cx9GSSx5U7uc/Vr/2+qr/+GzJcbT5v+9o3f0vt2pNrugIWdERTbYzw8MUFh/dChUBaGmEeAAAAMAPBYvXadeXS6snjWvAphlvqyq/uNVqMkxTfc46SkYjs1oblqneZxwh0x3WanUBaDmEeAAAAMAP296Y93MX+gY4Xq92vregVeopXLFJ6/81R47PJzPCLZlG3UaGISPMpb7nHtMqNQFoeYyJBwAAAPxQtbuw0QnkJMmwLFXtKWzROipzC7TksgeV9/3q6ifwpiHH4/s5xBuS9k1U7ziyK6r07Yk3afA1p2vw9WfKMOoJ+wBCBiEeAAAA8EN49wQZlinH62uwjeP1Kbx7QovV4Cuv1ILf3qHSTdurr+ezpX3lOHuTe30rzXl8Wv/QqzJMU4OvO6PF6gPQ8uhODwAAAPih9xlHNBrgpeoJ5FJOnNRiNWx/6xuVrM+qv0dAPeH91zY89ro8hSXBLwxAqyHEAwAAAH6IHzNIPY6fUP/Y870GX3O6wmJbbhb4rJc/a/T6TbGrPNr5buuM2QfQMgjxAAAAgJ8O/O916nnKoZKqZ303wlySachwWRp8/ZkadO3pLXr9ip17JNuPR+4NMCxLFTl5QawIQGtjTDwAAADgJysyXAc+eb2G3nS2drw9X56CYkX0SlKv0w5TeLe4Fr++u1ucyrfv8qvrfH0cny1319jgFgWgVRHiAQAAgABF90/R4BZ+6l6fPmdOVeHyjc0+3rBMpZx4cBArAtDa6E4PAAAAhIjeZxyhyF5J1UvLNaShJeQMQ/0vO7FVegwAaDmEeAAAACBEuGIiNemtexU9oKckyXBZMlyWZBgywlwa/veL1f3IA2vtMyxTMqTUC4/TsDvOb8vyAQQB3ekBAACAEBLVt4emfD1du+YtV+4ni+SrqFKXoX3U+4wj5E6M1YA/nKzClZu1/c2v5ckvVkTPbup9xhGK7pfc1qUDCAJCPAAAABBiDNNU9yPGqvsRY+vdHzdqgOJGDWjlqgC0BrrTAwAAAAAQIgjxAAAAAACECEI8AAAAAAAhghAPAAAAAECIIMQDAAAAABAiCPEAAAAAAIQIQjwAAAAAACGCEA8AAAAAQIggxAMAAAAAECII8QAAAAAAhAhCPAAAAAAAIYIQDwAAAABAiCDEAwAAAAAQIgjxAAAAAACECEI8AAAAAAAhghAPAAAAAECIcLV1AQAAAEBLKlqdoaxXv1DFjt0Ki49Rz1Mnq+shI2UYRp22juPIU1AiSQqLj6m3DQC0JUI8AAAAOiTb49WK62do25wvZFiWHNuWYZnaOvsTJaSnKf3FW+VOjJUkOT6fMl/4WJuffldlW3ZKkqL6p6j/ZSeq34XHybCstnwrAFCD7vQAAADokFbf+by2vfalpOqQLseR4/VJkgp+XK9F5/9TjuPI8fm05LKH9NOtT6ssI7vm+LKMbK267RktufTB6uMBoB0gxAMAAKDDqcwtUOasDyXHqXe/47OVv2it9nz7k7b+71Nlf/C95Kh2e8eRHCn7w4XKnP1J6xQOAE0gxAMAAKDDyf7wezl2/QF+H8Nlafub87T5qXclNTb23dCWp9+T08AXAgDQmgjxAAAA6HCq8otlWI1/1HV8tqr2FKl00/YGn9hXN3RUumm7vMVlQa4SAAJHiAcAAECHE9mrW83494YYpqGI5ET/T8pM9QDaAUI8AAAAOpzkaRNlRYU32sbx2ep7/rGKGdKn8YBuGIoZ0kdhXaKCXCUABI4QDwAAgA7HFR2ptNvOa7iBYaj3GYcrbkR/9b/8pCa70/e//KTgFwkAzUCIBwAAQIfU/9ITNeIfl8qKjpBUPZGdDEOGZarfxcfrgEeukiT1Peco9TztsOqDzF88kd/7c8/TDlPfc45q1doBoCGGwzSbdfh8tvLyStu6DHRyLpephIRo5eeXyuu127ocoN3hHgEaxv1Rm7e0Qtkffq/ybbvkTuii5GkTFZ4UX6uNY9vKevULbXlqrorXbpUkdUnrq/6Xn6Q+Zx0pw+TZV0fB/YH2KDExWlYTk3HuQ4ivByEe7QG/YIDGcY8ADeP+2D++sko5jiPX3if46Fi4P9AeBRLiXS1cCwAAABBSmpoQDwDaEv2CAAAAAAAIEYR4AAAAAABCBCEeAAAAAIAQQYgHAAAAACBEMLEdAAAAOoSi1RnKnPWR9ny7UjIMdT1kpFIvPF6xw1LbujQACBpCPAAAAELe5qff1eo7npNhmXJ81cuGlW7eocwXPtKIey9V/0tPbOMKASA46E4PAACAkJb7xY9afcdzklQT4Gt+dqRVtz2rXV8tbavyACCoCPEAAAAIaZsef0uyGv5Ya1imNj3xdusVBAAtiO70AAAAnUDBso3KfPEjFa/OlBUVrh7HTVCfM49QWFxMW5e2X7ylFdVj4Bvh+Gzt/nq5fOWVsiLDW6kyAGgZhHgAAIAOzHEc/XTrM8qc+YEMy5Lj80mGtOe7Vdrw8Ksa/8qdSjhoSFuX2Wx2ZZX/bas8hHgAIY/u9AAAAB3Y5hlvK3PmB5JUHeAlyZHkOPIUl2vhmXepcldBm9W3v8LiYxSW0KXJdu7EWLm6RLVCRQDQsgjxAAAAHZRd5dHG/7zZSANb3tJyZb38WesVFWSGaSr1guMaHRMv01TqRcfLMPnoCyD08V8yAACADipv0Tp58osbb2Q72v72/Ba5vuPzKfujH/Tjnx7RwrP/rpU3PqmCpRuCfp2BV5yi6P4pMuoJ8oZlKmZATw3448lBvy4AtAXGxAMAAHRQ3tJy/9qV+NcuEBW5+Vp4xl0qXpNZs3a7YVnKfOEjpZx6qMb+5y8y3WFBuVZYXIwOmXuffrr9We18Z37NMnOGZarnqYdqxL2XKiw2OijXAoC2RogHAADooKL7pTTZxrBMxQzqFdTrOj6ffvj93SpZn7X3tV2zXZJ2vvOtHK9PZphLOZ8sluPxKmZoH/W7eJp6n364zLDAP6K6u8bqwP9ep8q7L1bBsg2SYSh+zCCFJ8UH7X0BQHtAiAcAAOigugzprfhxQ1Xw43rJdupt4/hspZ53TFCvm/vlUhWtymi4geMo+70FkmVIvuq6ilZt0YprH9eOt75R+v9ulxXevKf04d3j1eOY9GYdCwChgDHxAAAAHdjIey+V6XJJplF3p2ko6fCx6nFscEPvzrnf1js+vQ7fL75Y2Pslw+75K7T+wZeDWg8AdCSEeAAAgA4sfuxgTXrrXnUZ2rfWdiPMpdTzj9W4F26RYVlBvaansLSmC33AbEcZsz6Sr6wyqDUBQEdBd3oAAIAOLmHcUB325WMqXLZRJRu2yYxwq9vkA+T2Y3315ojq071mMrvm8JWUq3DlJiVOGO5X+z3fr1LGcx9oz4JVkqRuB49Qv0tO8Pt4AAglhHgAAIBOwDAMxY8drPixg1v8Wn3OOkpbnnlvv85he31+tVv30Cva8PAcGS5Tjrf6S4Od7y3Qjne+1ZAbz9KQ68/crzoAoL2hOz0AAACCKnZEP/U5+0ipnmH4/jBclmLTUptst/OD77Xh4TmSVBPgpZ9nw1//4CvK/nBh84oAgHaKEA8AAICgG/XQFRrwp1Nl7Fsubu/Eeq6YyEaPMyxTKScfInfX2CavsemJtySzkY+zlqlN/33b35IBICTQnR4AAABBZ7osDb/zQg26+rfK+WSRPAUliuzTXd2PPFCr73xemc9/WP2k/hcT1BuWqYiUrhp+90W1zuUpKtW2OV9q2xvz5MkrVlTf7ur1u8NVsHhd40X4bOUvXCNfWaWsqPDgv0kAaAOEeAAAALQYd0IX9Tlzaq1tI++7XLHDUrXx8TdVvjVXkmRGuNX7jCM09IazFN49vqZtycbtWnDa7arMLZDkSI5UlpWj3d+s8LsG2+ORJUI8gI6BEA8AAIBWZRiGUi84Tn3PO0alGdmyKz2K6ttdrujaXe3tKo8WnnGXqnYXSk7dNeX9EZbQRZuenCvDNJQ4fpi6TT5ARmNd8AGgnSPEAwAAoE0YpqmYAT0b3J/94UKVb9+1X9fw5Bdr0/Q3JEmO16eofskaN/MmxY7ov1/nBYC2wteQAAAAaJdyPlkkw/Lj46rR+DT4jtcnZ++SdWVZufrulNtUmpEdjBIBoNUR4gEAANAu+cor5TTVdd40lDBuqKzIn8e8G5bZ8PJ2Plu+sgptnvF20OoEgNZEiAcAAEC7FDO4jwyzicXmbUeDrv6tjv5plg5+735NeucfksuqNev9rzk+W1lzvpC99+k8AIQSxsQDAACgSb7ySm1/82tt/d+nKt++S+7EWPU+4wj1OetIuRO6tMg1+55zlDb++/WGGxiGwpPilDT1QJkuS4npaarKK5JT6Wny3HZFlbzFZS1WOwC0lHb1JH7GjBk677zzGtx/++23a+rU2kuU2Lat6dOna/LkyRo9erQuvvhiZWZmtnSpAAAAnUbVniLNP/4GrbjuCRUs26DKnHwVr8nUmr+/oK8m/1klG7a1yHWj+vbQkBvPqn+nYUiGdMAjV8l0WTWbrehIGb943RDDZckVE9lkOwBob9pNiJ81a5amT5/e4P7PPvtMr79e95vYGTNm6NVXX9W9996rOXPmyDAMXXbZZaqqqmrJcgEAADqNH//0L5Ws3xvUfzlG3XHkySvSwt/fLdvjbZFrD772DI166E8K75FQa3uXtL6a8Opd6nH0uFrbrfAwpZx0cKMT4hmWqZ6nHiozjE6pAEJPm/+XKycnR7fddpuWLFmi/v3rX+ojNzdXd9xxh8aPH6/t27fXbK+qqtLMmTN1ww03aMqUKZKkRx99VJMnT9ann36qE044oVXeAwAAQEdVvHards9b3uB+x2erfNsu5Xz8g1JOPDjo1zcMQ6nnH6s+Zx+lgiXr5CkoUUSvbood0V9GA7PSD/rzb5X9/vdyHKfumvKmIcOyNPDK3wS9VgBoDW3+JH7VqlWKi4vT3LlzNXr06Dr7HcfRzTffrFNOOUXjx4+vtW/t2rUqLS3VxIkTa7bFxsZq+PDhWrRoUYvXDgAA0NHlfr5EMhv/yGhYpnI/XdKidZguS4kThqvHseMVN3JAgwFekmJH9NP4l26XKyaquj6XVdPF3hUTpfEv36HY4f1atF4AaClt/iR+6tSpdca5/9KsWbO0a9cuPfnkk3rqqadq7cvOrl7fMyUlpdb27t27a+fOnftVl8vV5t9voJOz9nYDtPxZHxfohLhHgIYF9f7weGWYhhy74SaOIzkeT7v6/JQ8dayOWzVL29/6RnsWrpZhGEqcMFy9f3NoreXo0Pnw+wOhrs1DfGPWrl2rxx9/XC+99JLcbned/eXl5ZJUZ194eLgKCwubfV3TNJSQEN3s44Fgio1l0h2gMdwjQMOCcX+kjBuiNU0sxWZI6j5mUPv7/JQQraSrTpGuOqWtK0E7xO8PhKp2G+IrKyv117/+VX/605+UlpZWb5uIiAhJ1WPj9/2879jIyObflLbtqKiorNnHA8FgWaZiYyNVVFQun6+Rxx9AJ8U9AjQsmPdHzCGjFJ4Up8rdRdWP3OtjSN1PO0z5+aV1dhWt3aotz76vnR8ulF3lUezwfup/yQnqeeJEGU100wdaAr8/0B7Fxkb63Tuk3Yb45cuXa8OGDXr88cf1xBNPSJI8Ho+8Xq/Gjh2ru+++W/369ZNUPfFd3759a47Nzc1tMPj7y+vlhkb74PPZ/HsEGsE9AjQsKPeHYeqAx/6sRef/U3JUd6I4ScPuukiubvF1rrXjnfla+qdHJFVPgCdJuxes0u75K5U8baIOfPqvzBCPNsPvD4SqdvtfzQMOOECffPJJrW2zZ8/WJ598otmzZ6tr165yu92KiYnRwoULa0J8UVGRVq9erXPPPbctygYAAOhwehw1ThNfu1tr/j5Lhcs31WyPSu2hITeerd6/m1LnmJKN27X0T4/UhPcae19nf7hQGx59XUMbWgceAFCvdhviIyIilJqaWmtbXFycXC5Xre3nnnuuHn74YSUmJqpXr1566KGHlJycrKOPPrq1SwYAAOiwuh06SpM/+ZeK12epYucehcXHKO6AgQ3OEp8x84PGT+g42vLsexr0l9/JCg9rgYoBoGNqtyHeX1dffbW8Xq9uv/12VVRUKD09Xc8991y9E+EBAABg/3QZ0kddhvRpsl3OJ4vqPoX/FW9hqbI/+F7uxFi5E2IUO6rxpeMAAJLhOA3NUNJ5+Xy28vLqTswCtCaXy1RCQrTy80sZrwXUg3sEaFh7uD8+HXWRKnPzAzomql+yht54lnr9tm73fCBY2sP9AfxaYmJ06E9sBwAAgPahKq9IO96Zr4KlG1WRm6fo/r2UOD5NydMmNtgVPnZUf+36qrBmDLw/yjKytfSKR1W5u1AD/nBysMoHgA6FEA8AAIB6OY6jDY++pg2PvCbH8/Na8bu/XKbMme/LFR+tMf/+i5KPG1/n2H4XTdOuz39s1nXX3D1LPU85VBHJic2uHQA6KhbnBAAAQL02PvZ/Wv/AK7UC/C95C0q1+KL7tPubFXX2dT/qIPX63RSpGUPcHcdR1sufBX4gAHQChHgAAADU4Sks0YZHX2u6oeNozb0v1tlsGIbGTL9aabedL3e3uF/u8CPYGypeuzWgegGgs6A7PQAAAOrY+e4C2VWephs6UuGyjSrZuF0xg3rV2mVYlgb9+TQN+OPJKlqVIbvKo11fL9eGf82RGptb2bZVvnOPHMdhtnoA+BWexAMAAKCOipw8GZblf/vsvAb3mWEuxY8ZpMTxw9Tz5EMku+nFkfJ/WKPlf/mPWEgJAGojxAMAAKAOd2KXJtd5r9W+a6xf7boM6aOkw8fI8GMppW1zvtC2OV/4XQMAdAaEeAAAANSRcuLBMkz/urLHDOmjLml9/T732BnXKWZIn6YbmoY2P/Wu3+cFgM6AEA8AAIA6wpPi1e/SE/2aXX7oLecENHbd3TVWE+bc2XRD21Hx6gx5Syv8PjcAdHSEeAAAANRr2N8uUN/zj2u4QZilA/51pVKmTQz43KY7LIDWjIsHgH2YnR4AAAD1Ml2WDnjwjxr4p1OU9ernKliyXlV5RYrsnaSuh4xS7zOOkDuhS7POHRYfo6h+ySrLzG44oxuGogf2lCs6svlvAgA6GEI8AABAJ1e0OkO5X/wou8qj2LRUdT8mXabr55npo/unKO2Wc4N6TcMw1P/SE7XqjufUcIp3NODyk4J6XQAIdYR4AACATqpyV4F+/MPD2vPtT5JpyjANOV6f3EnxGjP9anWfeqBf5yleu1XF67NkRbiVOGmEwrpE+XVc6kXHa9e8Zcr9bEntdeON6v9JPn68+p57dOBvDAA6MMNh8c06fD5beXmlbV0GOjmXy1RCQrTy80vl9fq/xA/QWXCPAA3z5/7wllZo/rF/VenmHXWXkjMMGaahiW/co66TRjR4ncKfNmvljU+qYMn6mm1mhFv9Ljpeabee69e4d9vrU8Zz72vLM++pPCtXkhSV2kP9LztJqRcdX6tHABAM/P5Ae5SYGC3Lj6U3JZ7EAwAAdErb/+8rlWzYVv9Ox5HjSGvufVGHvv9AvU2KVmXouxNvka/KU2u7XVGlzU/OVenmHRo36xYZZuMfSk2XpQF/OFn9Lz9JVbsKJUNyd4sLaLZ7AOhMmJ0eAACgE8r83ydSY0HZdlSweJ1Kt+ysd/eq25+VXeWRfv0UX5IcRzkfL1LOJ4v9rscwDIV3j1d4UjwBHgAaQYgHAADohMqzcmuPQ2+o3Y7ddbaVZuzUnu9+qtsN/xcMy1Tmix/tV40AgLoI8QAAAJ1Mzsc/yJNf4lfb+paQK920o8njHJ+tkvUNdNcHADQbY+IBAAA6IMdxlL9kvXZ+ulh2pUddhqUq+YRJqtixW4svedCvc4R1jdX2N+Yp74c1Sj5uvHZ/s0I7312gvB/W+HW8FRW+P28BAFAPQjwAAEAHU75jj76ddpN2LVwjwzIl05Dj8SksPkYJE4ZLtn8zcnvyirX5qblyPD79dNNTgRVhmko56eBmVA8AaAwhHgAAoAPxlpRr/sm3qCxrl6Tqbu3yVe/zFJYq9+Mf/D+Z48jx+AIvwjRkRbiVet6xgR8LAGgUIR4AAKADyXr1c5Vm5NQ/aZ0fE9ntN0OyIsM1/qU7FJGc2PLXA4BOhhAPAADQgWx96TNJrRDWG9D3nKM19OZzFJ4U32Y1AEBHxuz0AAAAHUhldl5bZnj1PuMIAjwAtCBCPAAAQDvgKSpVyeYdqsor2q/zuLvFBqmiwFmR4Yod0b/Nrg8AnQHd6QEAANpQ0ZpMbfjXHGV/8H31JHSSuh02WoOvP0NdJ44I+Hy9z5yqtf+YLdmBP453xUbJW1QW8HGSJNNUn3OOlismsnnHAwD8wpN4AACANpK/eJ3mH39DrQAvSbu/XakFp92hne8vCPicfc85WuFJ8dVLy/kpPDlRI+67vPkB3jAUP3aQ0m49t3nHAwD8RogHAABoA47PpyWXPyS70lsrwEuSfLZkO1p65aPyFAcWrN0JXTR57j8V0y/Z72Mqs/OU9b9PpQCC/z4RPbtq+F0XatIb98gVHRHw8QCAwNCdHgAAoA3s+mqZKrbvbriB48iuqNL2179Sv4unBXTumEG9dNraWfr+1pla99Crfh1TsnFb9ZcHfnB1idQBj1yp7kenyxUZHlBtAID9w5N4AACANlCwdIMMl9VoG8M0VbB0Q7POb1qWuh0y0u/2dqXH/7Yen/YsWC3Pfk7CBwAIHCEeAACgDRimKTlNTz5nmM3/uJaYniYzwt3s4xtiV1Rp6wsf6ZujrlfJpu1BPz8AoGGEeAAAgCAp37ZLRasy/Fomruuho+qOhf8Vx2er66H+P03/NSvCrf6Xn9Ts4xvj+Gx5Ckv04x/+1SLnBwDUjzHxAAAA+yn7ox+04ZE5Kly+qXqDaSj5+IkaevPZ6jKkT73HJKSnqcvwfipZt7X+MG+acifEKOWkQ/artrSbz9bur5ercNnG/TpPfRyfraKVm1WwdIPixw4O+vkBAHXxJB4AAGA/ZMz6UIsv+KcKV27+eaPtKOejhZp/3A21t/+CYRgaN/MmubvG1pkV3rBMWZFupb9wq6z97A5vWJYO/egh9b/85CbH4DeLaSjvhzXBPy8AoF6EeAAAgGYq37FbP93ydPULu/b4dsdny1dRpaVXPiqngbHv0f1TdNjnj2nQn38rd7c4SZKrS5RSLzhOh33xqBLS04JSp2EYGnHPxRp07elBOR8AoO3QnR4AAKCZtv7vU0mGpAYmqPPZKlmXpYLF6xoM5OHd45V2yzlKu+UcOba9XxPZNcaxbW198ePgn9h2lDhxePDPCwCoF0/iAQAAmqlw+UbJbmJtdcNQwXL/xqO3VICXpKrdRarMyQ/qOQ3LVNzogYofPSio5wUANIwQDwAA0ExGmEsyjCZaOS0zFj1ARlhwazAsU2HxXTT2yeuDel4AQOMI8QAAAM2UdNhoNdiVfh9H6jb5gFappyFVeUUq3bxDUf1Tqnv/N8Jwu5psY0a41e+SEzT580cUM6Bn8AoFADSJMfEAAADN1Ov0w7X2H7PlLauoM7GdVP20utvkAxQzsFcbVCeVZeXqp7tf0M653za5Jv0+Q284S+sfelW211v3PZmGEicM18TX7pTp3r9Z8wEAzcOTeAAAgGYK6xKl9Nm3yXSHyfjVMnEyDEX1S9aY/1zTJrUVZ2Trq6Ou1453mg7w+2ofeNVvNPDPp2nSm/coZlDv2m3CLPU952hNeOVvBHgAaEOG09CaJ52Yz2crL6+0rctAJ+dymUpIiFZ+fqm8Xv+engCdCfcI2pPSjJ3a8sz72v7GPHlLyhXZq5tSLzhOfc87RmFdovw+j7ekXHmL1squ9KhLWl9F90uWJJVs2q6S9dtkRYUrMX2YrKjwRs/jcplafME/te2DH5qceM9wh6nbwSPV//IT1f3Ig2q2O46jgh83qGR9lszwMCVNGVO9pj0Q4vj9gfYoMTFa1q+/DG4AIb4ehHi0B/yCARrHPYKOxFfp0bp/zlbGrI9kV1TVbI8/aIgcr0+FyzfVbLNiItX/0hM15Ibfy7BMVWzfLV+VR5E9u8mKqH5CXpWzR5+MvlTy42Ne799P1Zh/Xx38NwW0U/z+QHsUSIhnTDwAAEAbsr0+Lb7gn9o1b1mdMegFS9bXae8rKdfGx17X9je+kuFyqWzLTkmSFR2pvuccpcHXnaGi1Zl+BXhJ2v7m1xr9yJUyrLafQR8A0DTGxAMAALShne9+p11fLq13YrzGlGftqgnwkuQrLVfGc+/r22k31nqa3xSnyitfuf/tAQBtixAPAADQhjJnfSiZwflI5vhslWXmaMcH3zc5bn4fKzrC77YAgLZHiAcAAGhDxeuzmpx8LhCOz9b2t+Zr0AXHNt3YNNTnrKNkBOlLBABAy+O/2AAAAG3Iigj+U3DH41W/0yYrcXxaw41MQ2Gx0Rr4p1OCfn0AQMshxAMAALShlBMnSaYR9PO6osJ16Nx/asiNZ8lw/2IuY6P6WtEDeurgd/6pyN5JQb82AKDlMDs9AABAGylYtlG7v1kR8KR2TXF1iVTi6IEqrvRpyPVnavC1p2v318uVt2itDMNQ4oTh6nroKBlG8L88AAC0LEI8AADAfnAcR47HKyPM5Vco3te+aHWmvjvlVtlVnuAWZBrqf+HxckVFSJWlkiTDNJV0+FglHT42uNcCALQ6QjwAAEAzVGTnafNTc7X1pU/lLSyVFRmuXqcfrgF/OkUxA3o22b66W7sjBfMhvCF1nTRSaTefHcSTAgDaE0I8AABAgEo2btd3J90iT2GJHF/1zPK+8kplvfSptr/+lSa8dpcSxw+r3f7kW+Qp+Lm9nOB2oTcsUyPv/4P6/H6qrAh3UM8NAGg/mNgOAAAgAI7jaPFF99cK8DX7fLZ8lVVadP4/5auo+rn9xffXDvAtoPvR45R6/rEy3WEtdg0AQNsjxAMAAAQg7/vVKlmf1XAgtx158ou1891vq9svXK2SdY20D5JBV/+uRc8PAGgfCPEAAAAB2LPgJxmW1Wgbw2Vpz3erJEl5362SYbXgRy7TULcpYxR/4OCWuwYAoN0gxAMAAATA8dlynKaeqjty7Oo2juPUrM0eMNNQ3JhBGnbXhUo+aZKk6rHvskwZruovErpNHq2DnruR5eIAoJNgYjsAAIAAFCzb2OS67o7XVsJBQ2V7vOqS1leO19e8ixmGYgb31sA/nSpJKl6fpW1zvlTFzj1yJ3ZRz9MOU/zYwQR4AOhECPEAAAB+Klq1Rbs+W9JkOzPCrdwvftRPNz9VPRbeMJo3G73jqMuQPjUvuwzpo2F3nB/4eQAAHQbd6QEAAPy09aXParqxN8aurFLuJ4uCspxc7zOOaPaxAICOhxAPAADgp9ItO/zrGu8oOLPR245W3/W8HF8zu+MDADocQjwAAICfXDFRktm6H592vP2NNj3+VqteEwDQfhHiAQAA/JRywiTJbtn13utwpM1PzpXt8bbudQEA7RIhHgAAwE/J0yYoKrVHy677Xo+qvCIV/bSlVa8JAGifCPEAAAB+Mt1hmvDa3Yro2a16Qyuu7GZXeVrvYgCAdosQDwAAEIDofsk6fP7jGvP4XxQ7akCrXNOwTEUP7NUq1wIAtG+EeAAAgABZEW71Pv0IjXns6ha/lmGZSjlxksK7xbX4tQAA7R8hHgAAoJliR/Srfhpvtky/esMy5e4ap2F3Xtgi5wcAhB5CPAAAwH5Iu/08yXEa3B+WGOv/yYxffBlgmUo+YZIO/ehBRfZK2o8KAQAdiautCwAAAAhVvrJKrbl7ltRwhpcnr0hh8THyFJVKdiMNJclxNPSWc5Q4YbhiBvVSeFJ8MMsFAHQAPIkHAABopk0z3lLxmswm2xnuMBlm0x+7DJelsswcdZ00ggAPAKhXwCF+9uzZWrFiRUvUAgAAEDIcn08ZMz9o9Cn8PlW5+epx/AQ/TurI8fr2vzgAQIcVcIifPn26zjnnHOXn57dEPQAAACGhIrdAVXuK/G4f58dydI7tKHZEv/2oCgDQ0QUc4pOTkxUWFqbo6OiWqAcAACAkmC4roPY9Tz1U7qT4RmeyN8Is9T7jiP2sDADQkQU8sd0FF1ygu+++W6effrqmTZumpKQkRURE1Gozbdq0oBUIAADQHrm7xSlmSB+VrM/yq/2e+Ss19olr9cPZf5cjR7Ltn3eahuQ4OuChP8kdyGz2AIBOx3CcRtZEqUdaWpoMo5FvkA1Dq1ev3u/C2pLPZysvr7Sty0An53KZSkiIVn5+qbxeu+kDgE6GewT7w7FteUvKZYa7ZYWHNfs8W1/+TCuufbzphobUJS1VU776t/IXrdXa+17Snm9X1uyOGz1QQ244Sz2OHtfsWn6J+wNoGPcH2qPExGhZln8d5Zu1xFxjuT/A7wQAAABajaeoVJufnKvMWR9Wj2c3pG6HjdGgq36jboeNDvh8fc46UnsW/KTtr33VeENHKl6TKbvKo4T0NE168x6Vb9+liux8ubt2UXS/lOa9IQBApxNwiF+7dm1L1AEAANCiqvKK9N1Jt6pk846fu7I70p75K7R73jKNvP9y9bsosCGBhmFo2O0XNB3i9/nFMnORvZIU2SspoOsBANCsJ/H75OTkKCcnRwcccIAcx2m0mz0AAMCvlW/bpczZnyh/8VoZlqluh4xSn7OPapE10n+69RmVbtlReyy6JMdX/fqnW55W14NHqcvQPgGdN7x7vKL6p6gsI1tqqEeiaSp+7KCAJ8MDAODXAp6dXpK+/PJLnXDCCTr88MP1+9//XpJ07rnn6qWXXgpqcQAAoOPKnP2JPk+/XBunv6E981dq97zlWnv/S/r8oMuU/dEPQb1WRW6+ds79tiaw18cwTWXO+jDgcxuGoQF/OKnhAC9Jtq0Bfzg54HMDAPBrAYf4b775RldeeaU2bdokx3HkOI68Xq+WLVume++9V++8805L1AkAADqQ3C+XauVfZ0j2r2Zptx3ZVR4tueQBFf60OWjXK1y6odEAL1U/kd/9i8nmApF6wXHqedph1S9+uYTc3u7z/S45QSknH9KscwMA8EsBh/gnnnhCjuPonnvuUbdu3Wq2n3/++XIcR7NmzQpmfQAAoAPa+O/Xa40Pr2XvA+3NT84N2vX8nne3mfPzGqapsU9cozH/+YtiR/bfu1FKGDdEBz57o0b841KGHQIAgiLgJebGjBmj8PBwLVy4UFOmTFFubq7WrFkjSRo/fryqqqq0bNmylqi11bDEHNoDlj8BGsc9Erqq8or0ybDzm2xnusN0/NbXghJ+K3Ly9NnYS6XGnsabhvqef6ySj0nXzrnfyVNUqsg+3dX37KPUJa2vqgpK5FR55e7aRYbV+Nh2x+eTDENGQ19UtDDuD6Bh3B9oj1p0iTm3263y8nKVl5fX2r5161YVFxcrPj4+0FMCAIBOxFtc3nQjSXaVR47XJyNsv+bhlSRF9EhUygmTtPO976q78Nd7QUe75y3X1lkfyXBZcny2DMvQlqfmKiyxizx5xZIkd9dYpV50vAZecapc0ZH1nqqpkA8AQHMF/PXw5MmT5fF4dPnll6ukpESS9NBDD+mcc86RJB188MHBrRAAAHQo4UnxMsPDmm7XI0FmEAL8PiPvu1xWVESjbcq27JQkOV6f5Dhy9j6l2xfgJalqT5E2PDxHHw89V6vveUEVuflBqxEAgKYEHOJvvPFGpaSkaNGiRSotre5yPnPmTO3atUtdu3bVtddeG/QiAQBAx2FFhavX76bIaKzboGkq9YLjgnpdX1mFfKX+9QLwh+PxafMTb2neYX9W0eqMoJ0XAIDGBBzie/Toobfeekt//OMfNWbMGPXt21fjxo3TFVdcoffee0+9e/duiToBAEAHMvjaM+SKi643yBuWqag+Sep3yQlBvebO9xdIRpDHqDuSp6hMP5xzj2yPN7jnBgCgHgH3UVu0aJHcbreuueaaWtt9Pp+++uoruVwuTZkyJVj1AQCADiiqT3cd8v4DWvbnf6tg8bpa+6IH9lTPkw9R0crN6nroqKDN6u7JL5FhGnKCPY+Vz1bFjj3K+fgHpZzIsEIAQMsKOMSfd955SklJ0Zdffllru2VZuummmxQVFaWvv/46aAUCAICOKWZATx36/gMqWpWhXd8sV9Yrn6tk7VaVbNiu9Y+8LtlzFNUvWWMev0aJ6Wn7fb3I3klNrhXfXIbL0q4vlxLiAQAtrskQ7ziOrr/+eu3evbtm2549e3T++bWXhikrK1NJSYl8Pl/wqwQAAB1WVL8eyvrjpyrdtKN6g+PULOxetjVH3//2Dh3y3v2KO2Dgfl2n5ymHaNVtz8qu8uxvyXU5jmwvn4EAAC2vyRBvGIYOPvhg3X777TWvPR6Pfvjhh3rbp6enB7dCAADQoWW98rlKNmyT6lv5zXbkeH1a+4//acKcO/frOmFxMRpy41lae++L+3We+ji2o9gR/YN+XgAAfs2v7vS/+93vtGXLFpWUlGjOnDmKiorSSSedVLPfMAy5XC717t1bp556akvVCgAAOqCtsz9pdL/js7Vr3lKV79yjyJSu+3WtgVf9RlV5Rdry7HtyqoI3EZ0Z5lLvM44I2vkAAGiI32Pib7jhBklSZWWl4uPjdfPNN7dYUQAAoPMo3767/qfwv+RIFdt371eIL83I1vJrpitvweqAjw1PSVTlzrwG9496+I9yx8c0uzYAAPwV8MR2999/vyQpNzdXCxcuVE5Oji699FJlZWWpT58+QS8QAAB0bK7YKHmLy5psF7YfIbl8x259e8JN8uQXN+v48S/epuL1WVp+zeNy6llKbvv/fa2eJx0qKyq82TUCAOCPZi2W+uCDD2rq1Km68cYb9cgjj0iSLrjgAl1zzTXyeFpgshgAANBh9f7tFKme9eJrGIa6pPVV9MCezb7Gxsf+T5784oBnpzcsU3FjBilmSB+t/cdsya7/+N3zV2jlzU81uz4AAPwVcIifNWuWZs6cKcuyZJqmHMdRWVmZduzYoY8//lgzZsxoiToBAEAHlXrR8XJFhUtmAx9LHEeD//r7Zq8X76uoUtacL5oV4F1x0Rr7xLXa+d53qtixp+Fz2I62/d9XqshpuMs9AADBEHCInzNnjkzT1Ouvv65u3bpJkqKiovTkk09KkubOnRvcCgEAQIcW2bObJrx2t8Lio6s3mIZkqDrUm4ZG/ONS9Twp8PXXPYUl2vz0u1p45l2yK6oCOtaMcKvPOUfrsE8fUcygXsr56Ifquhrjs5X72ZKA6wQAIBABj4nftm2b4uLiNGTIkFrbDz/8cMXGxmrXrl1BKw4AAHQOCQcO0VFLntX2t7/Rri+XyvZ4FTeyv/qcc3SzJrPbNW+ZFp33T9mVgYX3mMG9Ne75mxXZp7usCHfNdm9puWQ3MfueYchXVhlwrQAABCLgEN+1a1ft2rVL27Ztq7X9yy+/VGFhoXr2bP54NQAA0HlZUeHqe/ZR6nv2Uft1nu1vzNPSKx5t1rElm7YrY+b7ihnaV4ZhKHHiCHUZ2kcxA3tp97zljXfJdxxFD+rVzKoBAPBPwCH+xBNP1LPPPqszzjhDJSUlkqTTTz9dq1atkmEYOv7444NeJAAAQH0Klm9UxvMfKn/hGsk0FDuiv3bOnd/8E9qOMmZ+WN2df++D964Hj9Tga0/Xlmfea/g4w1BEcqKSDjug+dcGAMAPAYf4q6++Whs3btRXX31Vs23lypWSpEMOOURXXXVV0IoDAABoyIZHXtO6B16WYZk1T8hLN21ves15f/ziHHkLV2vZNf9R6gXHKvOFj+u2NarH8I96+E8yLCsIFwcAoGEBh3i3260nn3xSCxYs0Pfff6/8/HwlJSUpPT1dEydObIkaAQAAatnx7nda98DLklS7i3swAvyvOD5bFdl5CkuM1bC/XaCN09+Qp6CkZn/MoF4a8Y9LlTRlTPAvDgDArxiO47TAr7vQ5vPZyssrbesy0Mm5XKYSEqKVn18qrzewZZGAzoB7pGNzfD7t+fYnVeTkyd01Tt0OHSXTHaayrTnKeO59bXn2fTleX6vW5E6M1TFrXpSv0qO8BT/JU1SmqD7dFTdmULOXv2sp3B9Aw7g/0B4lJkbLsvxbPC7gJ/E+n0+zZ8/WokWLVFpaKtuu/Q/fMAy98MILgZ4WAABAkrTt/+ZpzT0vqDL75zXXwxK6qPfphyvzxY9lV3manim+BVTlFclX6ZEVHqakw8e2+vUBAJCaEeIffvhhzZo1S5JU30P8/fkmesaMGVqwYIFmz55ds+2LL77QE088oc2bNyshIUHHHnus/vKXvygiIkKSZNu2Hn/8cb3++usqKirSQQcdpDvvvFOpqanNrgMAALSNrS99qhXXPVFnuye/WFuefrfWhHPNZhqS7dQaS+8Pw+2S6Q74oxMAAEEV8G+it99+W5I0aNAgjRw5Um63u/ED/DRr1ixNnz5d6enpNdsWL16sq666Stdcc42OPfZYZWZm6m9/+5sKCgp03333SaoO/q+++qruu+8+9ejRQw899JAuu+wyvffee0GrDQAAtDxvablW3f5s442C8AB+9L//rLiRA5T5wkfK+361ZJryFJaoYueeBp/wG5apnicf0u66zQMAOp+AQ3xVVZUiIyP15ptvKiwsbL8LyMnJ0W233aYlS5aof//+tfa9+uqrmjhxoi6//HJJUmpqqq699lrdeuutuvvuuyVJM2fO1A033KApU6ZIkh599FFNnjxZn376qU444YT9rg8AALSOnXO/k6+ssmUvYkgVO/aozxlTNeqBP9Zs3j1/pb7/3R0NHGNIhqEBfzqlZWsDAMAP/o2c/4VjjjlGVVVVys3NDUoBq1atUlxcnObOnavRo0fX2nfxxRfrxhtvrHOM1+tVSUmJ1q5dq9LS0lqz4sfGxmr48OFatGhRUOoDAACto3TzDhlhLbtEm2GZKt2ys872boeO0pjpf5FhmdXd7WsOMGS6XRo38ybFjRzQorUBAOCPgJ/E33rrrVq2bJnOOOMMHXnkkerWrZtMs/Z3AYGsFT916lRNnTq13n3Dhw+v9bqqqkrPP/+8RowYocTERC1evFiSlJKSUqtd9+7dtXNn3V/QgXC5Av5+AwiqfbNT+jtLJdDZcI90PGFdIlthwjpDYTGR9f6e73f2kUqeOlYZ//tE+YvWSYahboeMVOo5R8mdGNvCdQUX9wfQMO4PhLqAQ/wHH3ygjIwMSdLrr79eb5tAQry/vF6vbrzxRm3cuFEvvfSSJKm8vFyS6ox9Dw8PV2FhYbOvZZqGEhKim18sEESxsZFtXQLQrnGPtC++Ko92fLJYZTvzFJEUp17HpssVGe7XsWlnHaE1//hfi9bneH0aetYRDf+eT4hWyj8uadEaWhP3B9Aw7g+EqoBD/IwZMyRJlmUpOTk5KOPim1JSUqJrrrlGCxcu1PTp02u63e+bob6qqqrmZ0mqrKxUZGTzb0rbdlRUVLZ/RQP7ybJMxcZGqqioXL4AZk8GOgvukfYnY/YnWn3Pi6raU1SzzdUlSkNvOFODrji16UnhUpLU4+iDlPvF0vpnjfd3ZvoG2hmWqdgR/RQ+erDy80v9OFHo4v4AGsb9gfYoNjay5daJLyoqksvl0ieffFKnG3tLyM3N1WWXXaZt27bpmWeeqTX+fd/1c3Nz1bdv31rHpKWl7dd1vV5uaLQPPp/Nv0egEdwj7cOW597XqlufqbPdW1ymVX97XlVFZRp6w1lNnmfME9fqh7PvUf7idT8vAbd3STh3Qqyq8ooaP4Flyp3QRVW7C+scHz2gp9L/d7t8PkdBmeY+BHB/AA3j/kCoCjjET5kyRfPmzVN8fHwLlFNbYWGhLrjgApWUlOjll1/W0KFDa+1PS0tTTEyMFi5cWBPii4qKtHr1ap177rktXh8AAJA8RaVa8/cXGm2z4ZHXlHresYpITmy0XVhcjCa980/lfrpYWa98rvIduxWeFK/eZxyhyj2FWn1bE0vQ+Wx1PXikep52mLa9+rnKt+89/vTDlXzCJFnhLd+DEACAlhRwiD/33HO1aNEiXXDBBTrllFMUFxdXZ2K7adOmBaW4++67T1lZWXr22WeVmJioXbt21exLTEyU2+3Wueeeq4cffliJiYnq1auXHnroISUnJ+voo48OSg0AAKBxO96eL7uyqolWhrJe/VyDrzm9wRaO42j318uV8ez7ylu8VoZpqtvkA9T/0hMVP3aQvj7qOr/q2Tn3W7miIzRu1i2s6w4A6HACDvHnnXeeJCkvL08rV66ss98wjKCEeNu29cEHH8jj8eiCCy6os//zzz9X7969dfXVV8vr9er2229XRUWF0tPT9dxzz9WZ7A4AALSMsswcGS5LjsfXYBvDNFSWmdPgfsdxtPpvM7Xl6Xd/7gYvaee732nHW9+o95lTVbw60++asl75XN0mH6Bev53i/xsBACAEBBziHafxMWRN7W/M/fffX/OzaZpasWJFk8dYlqUbbrhBN9xwQ7OvCwAAms8V49/ScK6Yhied3fbal9ry9LuSVGtSO8db/cXAtjlf1Ixt94tpaMsz7xHiAQAdTsAhfu3atS1RBwAACFHJJ0zUuvtfarSN4/Up5aSD69/nONr0+FuSYUiNPQwIZA1521HB0g1yfD4ZluX/cQAAtHP+zWEPAADQgC5D+qjHseOlBpbGMSxTCelpSkivf+WYytx8lazPajzAAwAASX4+iZ82bZqSkpL0wgsvNDne3TAMvf/++0EpDgAAhIYxT1yjRef9Q3kLVtWMad/3d8zg3ho9/eoGJ5nzFpYFvyDTUPzoQTyFBwB0OH6F+M2bN6usrKzm58YwCywAAJ1PWJcoTXrzHu3+ermyXv1CZZnZqsovUcXOPSpeu1VfTbpCkX27K+6AQUpMH6qUkw9RZM9ukqTceUv9u8i+zxj+PLG3HfW/7MRmvhsAANovv0L8VVddpZiYmJqfAQAAfs0wTSUdPlZd0lL17Qk3qWLnnlqT1JVvzVX51lxlv79Aq++epdTzj9WIey9V1suf+XeBX4Z3c+/4+V/n+b2T3/U560j1PO2w/X9TAAC0M36H+H0OO+wwDRs2TGFhYS1WFAAACF0rb3qyToCvZW/4znzhY9lVXpVn7Qr4GgOv/I3McLfKMncqb+FalW+tXr6uy9C+GvDHk9X7zKn0DgQAdEgBz05/xRVXyOPx6MMPP1RiYmJL1AQAAEJU+fZdyvl4kX9d3h1HWS9/Jnf3eKnY/3HxhmXKW1KuUbefv/c0jrzFZTJMs9Fl7AAA6AgCnp3eMAw5jkOABwAAdRQs3xTQLPOGZSqqb4/qbvB+cny28has+vkchqGw2GgCPACgUwg4xF999dUqKSnR3/72Ny1dulTbtm3T7t27tWfPnpo/AACgcwq4A7thKLJnN5nuAIfp0VUeANBJBdyd/l//+pdM09Trr7+u119/vc5+wzC0evXqoBQHAABCS/y4odXrxTc0Hr4eUX27a8Lrd2vBKbdKdtNP8Q3LVLfDRu9PmQAAhKyAn8QXFBTI6/XKcZwG/wAAgI6vaHWG1j34ilbd/qy2PPOeqvKKFNE9QT1PPkSG5d9HDMfrU6/Tpqjr+GE66Lmb/HqU79iOUi84dj+rBwAgNAX8JP7FF19siToAAEAb2bNglTY/NVe7vlwqx+tT7Ih+6nfxNPX63eEyXVad9p6iUv34x39p1+c/Vod105DjtbX67uc19OZzNOKfl6loVYZKNm5r/Mm6aSj5uPGKHdFPkpQybaLSX7hVy697QlW7C+ttL8fRAf+6QjEDewXp3QMAEFoMh0fndfh8tvLyStu6DHRyLpephIRo5eeXyuv1v1sq0FlwjwTH5iff0eo7n5dhmT8vCbd3rfXux6Rr3MybZIb9/J2/Y9ta8JvblbdobYNd5kf841L1PnOqMp55T1ue/0BVuQXVOwxJplk9Sa7Xp+RpEzT2ietkRYXXOt72+pT9wffKevkz5S9ZJ29RmWQa6nH0OA244lR1nTiiBf6f6Fi4P4CGcX+gPUpMjJblZy+2ZoX47OxsPfXUU/ruu++Uk5OjZcuW6f7779eJJ56okSNHBlxwe0OIR3vALxigcdwj+y9v0Vp9d+LNDTcwDA258fcact2ZNZt2fbVUC8+8u9HzuuKidfSK52VFuOU4juyKKpVtzdH2N79RZXae3N1i1eu0KTVP4JviK6+U6XbJsOr2CkD9uD+AhnF/oD0KJMQH3J1+y5YtOuuss1RYWCjHcWTsnR12zpw5mjNnjmbNmqXRo5lsBgCA9m7L0+/WfgL/a46jjGfe16A//7bmafy2175q/BhJ3sJS5X7xo1KmTZRhGLIiw9VlaF+l3XJOs+q0IsObbgQAQCcR8MR2jzzyiAoLC3XqqacqLi5OklRZWalhw4apvLxcjz32WLBrBAAALWDXvGWNhnFJqsorUsn6bTWvK7L3NHmMDKkyNz+gWopWZWjH3G+V8+lieUvLAzoWAIDOJOAn8QsXLlRkZKTuuecefffddyoqKlJ4eLief/55TZw4UT/99FNL1AkAAILNz2XgHJ+v5ufw7glNPomXUz35nT/yf1yvn256SoUrNtVss6LC1e+SEzX05rPrnVgPAIDOLOAn8ZWVlTJNUy5X7fzv9Xrl9Xpl24wrAQAgFMSNGdzkUnBWZLiiB/ased379MObfhIvad0//qevplytkk3bG2yT/+N6LTj1NhX+tKXWdl9ZpTY9/qaWXvEIS9cCAPArAYf4Aw44QKWlpXrooYdUVVUlSZo3b56uvPJKeb1ejRjBjLEAAISC/pdMazSQG5ap3mcdKVd0ZM22pCPGKiE9za914EvWbtXXU69R0eqMevf/dMvTsj0+qb4HAI6jne98q91fr2jyOgAAdCYBh/hrr71WLpdLM2fOVEFBgSTpj3/8o77//ntZlqUrr7wy2DUCAIAW0OP4Cep95tTqF0btfYZlKnpATw296eza201T4164VbGjBvh1DbvCo6V//netbZ4qn+a9tkRfRg/WgqmnacnkE7S9X5q8Vu1efoZlauvsjwN7UwAAdHABj4k/8MAD9eKLL+qhhx7S8uXL5fP5ZFmWxo4dq7/85S9KT09viToBAECQGYah0Y9dpbjRA7X5v++oPCtXkmTFRKrvOUdrwJ9OVvZ7C7Rr3lLZVV7Fjhwg0+1SxswPVJmd5/d1in/aooJlGxU/ZpCKiyr15MPzlbOzWEpMkgxTlZHRKo7vpq2DRmrMtx8psrxEkuT4bBWv29oi7x0AgFAVcIiXpLFjx+rll19WZWWlCgsL1bVrV1ms3QoAQMgxTFP9LzlB/S46XmVbc1WZm6+8RWtVvDpDX066QnZ5lWQaku0o5+NFUjPHqBeu2KT4MYP04pM/aFdOyb6L7/27uhtAZUSUVk48Sulfvl3TMcAVE1n3ZAAAdGLNCvGVlZWaO3euFi9erKKiIsXHx2vChAk64YQTFBYWFuwaAQBAK9g6+2Nt/u87cmxb+mVWt/e+2I9J5gzL1LbMAm3ZsKfhRqapsi7xyk/qqcRdOyRJycdPbPY1AQDoiAIO8bt27dKFF16ozZs3S5Icx5FhGHr77bf1/PPP6/nnn1diYmLQCwUAAC1n7T9ma9Pjb7XMyQ2p6yGj9O2KbJmmIdtu+MsAw/Zpd3KfmhBftCajZWoCACBEBTyx3T333KNNm6rXcj3wwAN1wgknaNy4cZKk9evX69577w1uhQAAoEVV5OZr83/faZmTm4a6Hz1O0f2S5fX49vWcb4Qh2/x5iN6ON79pdJk6AAA6m4CfxH/zzTcyDEP//e9/dfjhh9dsX7BggS666CJ99dVXQSwPAAC0tB1vfdNi67HHDOylMY9dLUnqkdJFPl/j13EMQ9HFBTWvDcvUtte+Utot57RIfQAAhJqAQ3xUVJQsy6oV4CVp0qRJiomJUURERLBqAwAALcBbUq6sOV8o6+XPVJGdVz0GPkjMCLdsr09Rfbqr34XHqe+5x9RMTnfAQb301isrVFHubfB4w7HVY9umX2wwApoJHwCAji7gEP+73/1OTz/9tBYsWKBJkybVbP/iiy9UUlKiyy+/PKgFAgCA4CnfuUcLTr1NZZnZ1RuC/AB+2N8uUP9LTqh3X5jb0u/OG6v/Pb2oel36X17bcSTD0KCffpC7qrLWce5uscEtEgCAEBZwiI+Li1PXrl112WWXadKkSUpKSlJWVpaWLFmi6Ohobd++XXfddZek6vVn77zzzmDXDAAAmsFxHC256P7q9eBbpve8Vt36jGKHparrwSPr3T8mvZfc4Zbe+79Vyt1ZXLM9oqxE/df+qB7bt9Su2etTr9OmtEyxAACEIMMJcBBcWlqaDMOoGTv3y5/3vZZ+nrV+zZo1QSy3dfh8tvLyStu6DHRyLpephIRo5eeXyusNXldXoKPgHglc/uJ1+vaEm1r0GoZlqvtRByn9xdsabec4jnZkFWrFo28p/8Pv1CV/l+rMeWcaSj5+gsbNvLnF6u2ouD+AhnF/oD1KTIyWZfk373zAT+LT09MDLggAALS9nE8WSYbR9Hrv+9J0M57WOz5buZ//WPNlfoOXMAz16huvlAfP00qVKuvlz2RYpvZNX+94fUo+foLGPn5t4EUAANCBBRziZ8+e3RJ1AACAFuQ4jrI//L7JAG9YpqIH91bJ2q3Nv5bXJ9m2ZFlNtjXDXBr96FUa+OfTtP21L1WRnSd31zj1+u1hih3er9k1AADQUQUc4gEAQOgpWLJeJeu3NdnOsR1F909RybqtzRs3b0jRA3rJ8CPA/1LMgJ4aejPLyAEA0BT/Ot0DAICQtvWlTyWz4e7t+xguS77yyv2Y+M5Qv4uPb+7BAACgCYR4AAA6gdItOyW76WTe47jx2v318uZdxDSVMD5Nfc87tnnHAwCAJtGdHgCATiAsNrr6SXxTQd6PoF/DZUlenyTJigpX3/OOVdrN58gKD9uPSgEAQGMI8QAAdAIpJx+inI9/aLJdycbtfgX5uLGDNeGVv6l4TaYMy1TsyAFyRUcEo1QAANCIZoX4wsJCLVq0SKWlpapvmflTTz11f+sCAABBlHLSwVr3wMsq35bbaEgvWeffrPR9fn+k3Ald1PXgkcEqEQAA+CHgEP/111/rmmuuUXl5eb37DcMgxAMA0M5Y4WGa+PrdmjflatkVVft9vm6HEN4BAGgLAYf4hx56SGVlZTWvw8LCZBhNz3YLAADaVlh8jOzK/QvwhmUqceIIxQzuHaSqAABAIAIO8ZmZmTIMQ4888oiOOeYYWQGuAwsAANqGJ794P5aOq+b4bBWv36q1//yf+l0yTRE9EoNTHAAA8EvAS8z1799fMTExOv744wnwAACEEHdiF7/Wim9K1a5CbXr8Tc2bcrWKVmfsf2EAAMBvAYf46667TmVlZXrllVdaoh4AANBCwuJi1O2w0UE5l+Oz5Skq0w/n3it77zJzAACg5QXcnf7NN99Ujx499Pe//12PPPKIEhMTaz2RNwxD77//flCLBAAAwZE0ZbR2f7UsOCfz2arYvlu5nyxS8rSJwTknAABoVMAh/uOPP675ubi4WMXFxbX2M8kdAKCjKN+xW0UrN0uWqYRxaXLHx7R1SfstKjU5qOczXJZ2fbOCEA8AQCsJOMRfddVVLVEHAADtRvn2Xfrp5qeV8+liyameCc4Ic6n3mUdoxN0XyxUT2cYVVqvKL1bVniIZlqmcTxdr11dL5VR5FTd6oPqed6yi+9UN7AnpaZJlSj676QsYRs37b4zj8TanfAAA0AyEeAAAfqEiO0/zp92kql0FtQKs4/Eq6+XPVbwqQ5PeuldWZHhQruc4TsC92ApXbNL6h16t9SXDL+357idteuItDbvzQg3806m19kV0T1DKSQcr+93v5DQU5A2p6yGjFBYfo+z3FjRev9enuAMGBlQ/AABovoBDvCRVVVVp8+bNKikpkbP3w4Nt2yoqKtJ3332nO++8M6hFAgDQWtY//KqqdhXUH3BtWwXLNmjrS5+q/6UnNvsaFTl52vLM+8p65TNV7SlUWHwX9fn9VPW/7ERF9kpq9Njd81dq4e/vlmy7wafk+2pfc9csRfbspp6nHFprf+r5xypvwSpV5hbUOodhmXJsR2P+c7V6n36E7CqPPltwiaryiyW7nmsZhqwIt3qddlhg/wcAAIBmMxzHj35yv7BixQpddtllKioqarDNmjVr9ruwtuTz2crLK23rMtDJuVymEhKilZ9fKq/Xj26vQCfTEveIt7RCnww7T3alp+FGhhQ9sJeO+PaJZl2jeH2WFpxymzyFJbW+KDAsU1Z0pCa9dY/iRg6o91jb49VnYy5RVV5R/aG6Tq2GYgb31pSvp8swDJVs2KalVz2mwmUb6za1TPU4foIG/vGU6i73e+2at0w/nH1P9Zf2v/xiY+9SdQc+fYN6nnSwn+8erYXfIUDDuD/QHiUmRsuy/Fs8LuAl5h599FEVFhbKcZx6/0ydOjXgggEAaA8qduxuPMBLkiOVbdnZrPM7tq3FF/yzToCXqp+e+0rLtei8f9Ys2eY4jqr2FKmqoLrnW/ZHP6hqd6F/Ab76BCpZn6XSLTtVlpmjb0+8uXqivl8zDUX0TtLoR66sFeAlKWnKGB089z51O/SAWtsTJwzTxNf/ToAHAKCVBdydftWqVXK5XHr77bf13HPPaffu3XriiSc0e/ZsPfTQQxo0aFBL1AkAQIvzd5y76Q5r1vl3f7NCpZsb/gLA8dmq2LFb2R98r/KsXG159n1V7NgtSYoZ2leRvbr6PyndL3iLy5Qx8wN5i8saGCbgqDwrVxkzP9Tga0+vszvhoCGa+NpdqsjNV2Vuvtxd4xSZ0jWgGgAAQHAE/CS+vLxccXFxGjRokMaNG6dly5bJ7XbrkksuUUxMjD788MOWqBMAgBYX0aubYob2qZ6VvQGGZTZ7ObXd36yU4bIab2SZ+ummp7Tm7y/UBHhJKlmfpV1fLK0eCx+g3M8Wa/sbXzc8kZ0k2Y4yX/io0fNEdE9Q3MgBBHgAANpQwCE+KSlJBQUF+umnnzRmzBgVFxfr66+/1vr161VeXq7c3NyWqBMAgBZnGIYGXf3bRpdVcxxH/f9wUrPO79g+qamJ6H129Zj3uhfe+3fg113/4Kt+LQNXsXOPnGZ8SQAAAFpPwCH+yCOPlM/n04033qiBAwcqOTlZf/jDH3TKKafItm316dOnJeoEAKBV9P7d4Rp83RmSqp+672NYpgzL1NjHr1H86OYNHYsfM1iOxxeUOgNi+reEnRUZLsMM+KMBAABoRQGPib/++uuVl5dX8/q6667TzTffLNu2FRYWpmuvvTaoBQIA0NqG3nS2ehw7XhmzPlT+orUyXZaSjhir1AuPU3S/lGaft8cx42S6w2RXNTF5nj9Mw/8J7mxHVlS4fBWeBrvjG5allFMPrXcfAABoPwJeYm4fr9crl6v6O4BNmzZpw4YNGjlypHr37h3UAtsCS8yhPWD5E6BxoXiP7Hhnvn68/OH9P5FpKPmESdr9zQp5C0r8OsTVK0ne7bvq32kYMlymJn/6iGKHpe5/fWhzoXh/AK2F+wPtUSBLzAX8JH4f27b1448/Kjs7W9OmTVOPHj0UExPT3NMBANCh+SqqtGnG24E9QW+I7Sj73e8kSZF9uqt8W26jY+Udw9DmiO5KOX2Cwt54X5Lx8xN5w5AZHqaDnruRAA8AQAhoVoh/6aWXNH36dBUVFckwDE2bNk1nnnmmDj/8cN1www3BrhEAgJBVkZOnDY/9n7a98rl85ZVBP39NgDeMeifkc/b+z45+Q7W5MlanTL9d3TetVf6S9TJcprodeoD6nHWk3ImxQa8NAAAEX8Ahfu7cubrnnntqbausrNTmzZu1efNmJSUl6cILLwxWfQAAhKzybbs0f9qNqtpd2PjybvtjX253nOos/4tdtmHIcBytGz1JFdHVIf3rJbt1yz/PlunnZHcAAKB9CXgK2pkzZ8owDD3//PPq0aOHJMntduv222+X4ziaM2dO0IsEACAUrbh+RssG+F8yDBUm91KVO1xSdbbP695Lyw4+TtmpQ2qa5e8pV252ccvXAwAAWkTAT+I3b96s+Ph4TZo0qWabYRg655xzNH36dG3fvj2oBQIAEIpKM7K166ulzTt477j5mKF9lf7ircp47n1lPP9Bo8vTGZapiqguWpZ+lCyfV7ZpyjGtett6PUzkBABAqAo4xMfExKioqEj5+fm1tq9cuVKFhYVKSkoKWnEAAISqwmUbAz/IkKJSkxXdL0W9zzxCKSdOkukOkysmst7x7r8WERshwzTkM8IabGNZhromRQVeGwAAaBcC7k5/9NFHy+v16pxzzlFRUZEk6ZprrtH5558vwzB0xBFHBL1IAADammPbylu0VjvfX6C8havlNLDeeo3mjDk3TFXuKtDIB/6gXqcdJtNdHca7Hz1OThPLIDlen/oePqrRrG+ahsaM763IKHfgtQEAgHYh4Cfxf/3rX7Vy5UqtXr26ZttHH30kSRo0aJCuueaaoBUHAEB7sOOd+Vpzz4sqz8qt2RbRq5vSbj1PvX83pd5jEtLTAl9OzrZlV1RpyzPvaeQ/Lq3ZHD92sOLHDVXh0g2Njq8vemy2Jvbvp+UDD1J5VJda+0zTUEyXcE37zXD/6wEAAO2O4Th+9M/7FY/Ho7ffflvff/+9CgoKlJSUpPT0dJ188skKC2u4C1+o8Pls5eWVtnUZ6ORcLlMJCdHKzy+Vt4kncEBn1Fr3yNaXP9OKax9vcP/IB/6gfhceX+++xZc8oOz3FgR8TVdctI5b/1KtbRU5eVpw2h0q3bi90S8HDMuUHRmhHw87UcWu6m7zhiGNHJuiU84cpfhEutJ3BvwOARrG/YH2KDExWpblX0f5Zq0THxYWptNPP12nn356cw4HACAkeIrL9NOtTzfaZtUdz6nnqZPljo+psy+6X0qzrustKpXjODIMQx6PT2tX5qiosELxD/xVqRvWKuO/b6ssM6feYx2fLbO8Qie4dyj+ugtk+xwl94xVbHxEs2oBAADti18h/pZbbvH7hIZh6J///GezCwIAoL3Y8dY3siuqGm3jeHza/n9fqf+lJ9ba7iurVMasD5t1XcOqnlV+/heb9dHbq1VR7q1eAN6RwsNdOkSuRp/GOz5bOe8t0AEP/LHeLxcAAEDo8ivEv/XWWzKM6gl6Gup9bxhGzVMDQjwAoCMo2bhdhstqfGk3l6mSjXWXV909f4V8JeU1rx0Z2tOjl3anpMrrClNkWbFSMjcoqrSozrGO16ePZy/SZ9/s+MXG6r8qK73ybMuR1cRYe8frU/nWHEI8AAAdTEDd6cPCwnTAAQeoT58+LVUPAADthhUZXhOeG+TsbfcrnqKymp8rIqK0YuLRKotNkGHbcgxDchxlDRqlXptWa9CqH/TLuew9YW59PX9HnXPu47MsWT6vf/UDAIAOxe8Q7ziOqqqqtGTJEm3fvl3p6ekaP368xo8fr759+7ZkjQAAtInk48Zr42OvN9rG8fqUfNz4OtujU3tIkmzD1PJJx6o8unq2eMfcO2nN3h5u2wcMU5inQv3Wr6g5NrfXgEYntd+dkqrkrRtkNjQ3rWEoKrWHogf1arR2AAAQevwK8V999ZUWLFig77//Xt9//7127typuXPn6t1335Ukde/eXenp6ZowYYLS09PVr1+/lqwZAIBWET92sBLS01Tw4/p6l3YzLFOxI/srYfywmm2O4yh/8TplvvixzPAw5XbtpfIucQ1fxDCUNWiU+mxaXfN0vTw6RqZpyG4gyW/rP0zJWzfIkVTvavSOo4FXnVYzFA4AAHQczVpibsuWLTWh/ocfflBBQUHNBwXDMGqtIR+KWGIO7QHLnwCNa617pDK3QAt+9zeVrNtaZ58R5tLwOy9Q/8tOkiQ5Pp+WX/MfbXvtKxkuU47X1sr0qdqT3FsyGl82ZsSiL5W0M1OGZWrXscdptbtHgyFekpKyt2rEknky5NR8wWBYlhyfTwOuPFXD7riAEN+J8TsEaBj3B9qjFl9irn///vJ4PPJ4PKqoqND8+fPlOE6Dk94BABCqwrvHa+x/r9N3J9woX3ntmeodj1erbn9OnqIyDbn+TK178FVte/2r6n17Pxh63OFNBnipehy8TEPRA3rqgBtO0k+PLmy0/a7kvhr2xv2q+vx75X6+RI7Hq/ixg5V64fFKOGhI894sAABo9/wO8Tt27NB3332nBQsWaOHChdqzZ4+kn2erT0pK0vjx45Went4ylQIA0EZW3fq07KqGJ5Jb/+ArSjp8jLY8PbfORHgRZSUqSkiSzMaDfGyXMA27/Xz1Pf9YhXWJ0ojRyVq9MkdOPU/jTdNQ/8FdNXDSYGnSYA27/bxmvS8AABB6/ArxxxxzjLKysiT9HNp79OhRa3I7xsEDADqi4vVZyvu+8WFihmVq/cNz5CurrLMvJWuDcvsMbPhgx1GE49Hpn95bqxvdWZcepFmPL9TGdbtrxsfv+7tPv3hd8Ke6k+kBAICOz68Qv3Vr9ThAt9utUaNGafz48UpNTa3Zv2zZMi1btqzm9amnnhrUIgEAaCtFKzc32cbx2SrZuK3effG7s5WYnaW8Hr3qdqt3HMkwdNT47nXGwUVEhOkP1x+ijWt3a/F3W1VYUK4usRE6aFIfDRneXabJeHcAADojv7vTG4Yhj8ejH3/8UT/++GOjbQnxAICOwgjz71elKzqy/uMljVj8ldYfMFE5fQZVb3QcyTTl8lRpYrcqdZ8wQQu/yVBMl3ANHdFdrjCr+ljD0OBhSRo8LCkYbwUAAHQAfn0y6dmzZ0vXAQBAu9R10ggZllnvEnM1TEPJJ076+Yn8r8axW7ZPw5Z9q/5rl2pPch/5XGGKKC2We8RA/egbpPmP/zyJXWRUmI49OU2HTB3A7PIAAKAOv0L8F1980dJ1AADQLoUnxavXbw/Ttje+luoL8oYhM8yl1POOUddJI7Tw9DvlGKp+2v4rYZ5K9di2WZbXo+39h2l1VF+ptPaM9+VlHr396kpVVvp05DRmmQcAALU1a4k5AAA6kxH/vFzF67epcNlGyTBqArphmZJp6qDnblJEj0RF9EjUhNfu1vJr/6PyrbmSqier35WSqqyBI1WcWN0t3l1WoqqIqEav+fE7azT+0L7qEhvRou8NAACEFv9WkwcAoBML6xKlg9/+h0Y9+Ed1SesrM8KtsMQu6nv+sZry5WPqcfS4mrbdDh2lyZ8/Ku2deG7zsIO0Ov0IFcd3rWlTFRld/WVAIxzH0ZIFWS3zhgAAQMjiSTwAoNPyVXpUnpUrwzIV1be7DMtqsK0VGa7UC45T6gXHNXne4tUZkmFoT1JPZQ0eVb3xl+vE+zHW3TAN7dlV2mQ7AADQuRDiAQCdjrekXBsefU2ZL3wsb3GZJMkVG6UuQ/sqIT1NKSdMUvxBQxqcWM6xbZVtzdX2N+epMrdA4d3i1Ou3UxTdP0VVBSVadO69km1r+4Bhkm3XDvD+cqTwCH5NAwCA2vh0AADoVLwl5frulFtVvCaz1ozz3qIy5S9aq/zFa7V5xttKGD9M456/WeHd4qr3l5Yr4/kPlfH8h6rYtqv6IGPvuHhHWv/Qq+p9xhHqMixV3pIKyZEKu/ZoXoCXZNuORh3I6jAAAKA2QjwAoEOp3F2orJc/04535stbXK7ogT2Vev6x6nHMOBmWpQ2PvqaiNZn1zzQvVc9EJ6lgyTotPOMuHfrxQ3uD/20q2ZBVe/k4R3K8P59n2+tfKbx7fM3Ed3Xnp/ePaRpKHZigvv0TmnkGAADQURHiAQAdRsHSDfr+jLvkLSmrCdvl23K164sfFZ6cqLiRA7Tr62UNB/hfcHy2ilZtUfaHC7Xzve9UWs/673UPclSZk1/zMm5PjvKTejb5NN4wJceuHgfv2I569onVhVdMZJ14AABQByEeANAheIpKtfD3d8tbUl4rbO/rMl+Znafc7LzATmqa2vrix9r97cqmA/w+xt7/cRz13rJG+T16N9r8gIOqu8wXFlQoNi5cB03qq2GjesiyWEAGAADURYgHAHQI217/Sp7C0pqu7EFh2yrNzJZjOyqO76by6FhZPo/id+2Uy+et/xjTrHnSn5i7Xb03/aRtA0fWnuDOcWSYhlIHJuqsiw9SmLvhWfEBAAB+iRAPAOgQcj76Qc0fhd4Ay1Rhj15aNmCSymJ/Hp9uer3qvWW1+q1dKrOeLw2iBqSobPNOGZIGrlqsLgV7lDVwhEriu0mSusSEafKxQzT5qIEKCyPAAwAA/xHiAQAdgre0POgZviA+SSt6jpZt1x5Db7tc2jpolCoiozXsx29Ua+S6z1bZ5p01Lw1JPbZvUY/tWxR3+IEacuPZ6n7gQMa7AwCAZmHAHQCgQ+gyLFWGFbyn2o6kjQdNrh4Kb9Tz69IwlNt7oAoTe/h9zsShvdXjoEEEeAAA0GyEeABAu+Y4jgqWblDOJ4tUsHSDnAbGvKeef6wcny9o1y1O6KaSiJhG2xi2rR39hvh9zsxZH8pTWLK/pQEAgE6M7vQAgHZr53vfac29s1W25efu6VH9UzTs9vPU59RDa7WNHz1IqRdPU+bMD4Jy7fKY2CbbOKapspg4JZ8wUVX5xcpbsLrRifXsSo92fblMPX9VOwAAgL94Eg8AaJeyXv1cSy55UGUZO2ttL8vYqSWXPKjMlz6rc8zIf1yqtDvOlxUTud/Xtzyephs5jlw+r8J7JMrx+PyaGd9bWr7ftQEAgM6LEA8AaHc8xWVaefNT1S9+nYv3vl5+45OqKiqttcswTQ266jT1Pfuon5dza6b4XTtl+NE9Pyk7U6bLUszg3n6NyY/un7JfdQEAgM6NEA8AaHd2vPWN7IqqRtvYlVXa/PIX9e4zTEOGtX+Tx7l8XkWVFDb8dN22FVZVqR6ZG5V0+Bj1PfeYxsfkG4ai+iUrcdKI/aoLAAB0boR4AEC7U7wuS4ar8afahstSwerMevfFHzS0unv7fvCEuVUaHavEnKzq6+1bZs5xJMdRmKdKo77/RLG9uyrpiLGKP3Cwev9+qlTfdweGIcM0NOqBPzAzPQAA2C9MbAcAaHesCHfTa747khXprndX8vET5O4Wp6q8IlWvERe4HalDFVlRqh5bN6rPxlXa2W+IymLiZHk96r4jU923bZZjmhr//t9l7O26P/qRKxWR3FVbnp4rX1llzbmi+6do5P2XK2nKmGbVAgAAsA8hHgDQ7vQ4Nl2bHn+z0TaO16e+J02qd58Z5tJBz92ohWfcJdvrk3x2QNd3TFP5yb01bt67MmyfTMdRQl5O9b69bXb2GaS8gw/RaYN71xxnWJbSbjlHg64+TbvnLZe3tFxR/VKUMG4oT+ABAEBQ0J0eANDuJKSnKW7MIBlW/b+mDMtU/JhB6n7IyAbP0XXiCB360UPqedLBDZ6nIYYcHbBivkxfdYCvva9at9xtGndUWr3Hu6IjlTxtonqffoQS09MI8AAAIGgI8QCAdscwDKW/cKui+iVXbzCNWn9HpfbQhNm3NRmOY4f304FP/VXHbnhZRy59VsesmS1Xl6jGL24aik5NlllULKOBPv2GJHdlhQZV5ATytgAAAPYbIR4A0C6F90jQqAf/pJTfTFZk7+4KT0lU/IFDNOrhK3TY548psmdXv8/lio5QZM9ucid2Ud/zj2l8+TnbUfTAnn5NrFeyfKPfNQAAAAQDY+IBAO1O0aotWnrlYypekykZRs0yb7FpqUo+drysqHDlLVqrFbM/Vs53qyXTVPepY5V60fGKGdirwfM6Pp+SjjhQ2e99r7KsnLqT3hmGUk6cpMg+3bXrq2Ut+A4BAACahxAPAGhXSjZu17cn3ypf+d7Z3X8xJn3318v13am3qvuRB2nL0+/KcFlyvNVLyWVk7FTGzA80evrV6v27w2ud03EcZcz8QBunv6HK7Lya7YZlytk76V1ESqL6X36yBvzhJGV/+IM2e99utE7H61Pi+PrHxAMAALSUdtWdfsaMGTrvvPNqbVuzZo3OPfdcjRkzRocffriee+65Wvtt29b06dM1efJkjR49WhdffLEyM+tfNxgA0P6te+Bl2eWV9c4o7/hslW7eoS1Pv1v92uurtc/x2Vr253+r4Ffd3Nfc84JW3fpMrQAvVYd7wx2mg567UUcueUb9Lp6mjf95Uz/d8nTjRZqG3N3ilHz8hGa+SwAAgOZpNyF+1qxZmj59eq1t+fn5uuiii9SvXz+98cYb+vOf/6x///vfeuONN2razJgxQ6+++qruvfdezZkzR4Zh6LLLLlNVVVVrvwUAwH6qKihR9vsLap6O16uJZd8Nw6gJ+VJ11/zNT7xdf2PbkeP1af2/XpOv0qPvT/+b1j3wsipz8xs+v2XKdIdp3PM3y3SHNV4MAABAkLV5d/qcnBzddtttWrJkifr3719r32uvvSa326277rpLLpdLAwcOVGZmpp555hn99re/VVVVlWbOnKkbbrhBU6ZMkSQ9+uijmjx5sj799FOdcMIJbfGWAADNVLFzT+MB/lccScXx3ZTdZ6A84ZEKqyxXctYmGR8u1Ni9bTJf+LhWt/k6bFvFqzO06vbnlL94Xd1x8r/kMtXrt4dr4FW/UZchffyuEwAAIFja/En8qlWrFBcXp7lz52r06NG19i1evFjp6elyuX7+rmHixInasuX/27vz+Kjqe//j73Nmsu97IBASlrCGPQiCCLgvqJRau2CVatVatdZatdJb7b311t5rXbu41qWtdbn406ptte6IILLJDgIhQMhCQvZlkplzfn8EAiELgcxMZpLX8/HwIXPmnO/3E+UwvOf7Pd9vvsrLy7Vt2zbV1dVp+vTpre/HxsZqzJgx+uKLL/z2MwAAvCMkLqrb53ocDm2aNk9rZ1+soiEjdXDAEBUNGam1sy/W+txZcrncklpG4rvzxcCB15d1HeBNQwmTR2rio7cQ4AEAQK/p9ZH4efPmad68eR2+V1xcrJycnDbHUlNTJUkHDhxQcXGxJGnAgAHtzikqKupRXU5nr3+/gX7O4TDb/BvoD2IyUxU/abgqv9zVdaCWtG3iGSpPGyRJsg9vGWcf3jf+UEqGXv7TWn3v5ulyRIR1q29PXWPXJ1i2Ktfu4PMBQYHPEKBz3B8Idr0e4rvS2Nio0NDQNsfCwlr+MuZyudTQ0CBJHZ5TVVV1yv2apqGEhO6PBgG+FBsb0dslAH415d6r9P6l/9Hxm6YhwzBUHxGtgxlZnTdiGNqw9oDqa5s1dMEslX26sc0q9+04zA4X0mvXLJ8PCDJ8hgCd4/5AsAroEB8eHt5ugTqXq2XLocjISIWHh0uSmpqaWn995JyIiFO/KS3LVnV1/SlfD3iDw2EqNjZC1dUN8pzEM8JAsIuZNUET/vcGfXnnEzIMo2UqvCFJhhwRYZr86C167Q/LJMuSzGNGUWxbhm23jsqbhvTBA39XwqYNLdd3luFNU5lXzFXJe2tPuKBd0oyxqqio89aPCvgMnyFA57g/EIhiYyO6PTskoEN8enq6SktL2xw78jotLU1ut7v1WGZmZptzRo3q2d69bjc3NAKDx2Px+xH9zuDvnq/ks6ao4M/v6tCKzZKklDmTlHXNhQqJjVLCfkNa3fLYlKO5SfFlRRq2ebUi62vkCovQgSE5iqs8KPcbB1TmMDuemm8akmUrafoYjb3vOkVmvaltv/5rpyP2tsdS1rUXcz8iqPAZAnSO+wPBKqBDfF5enl566SV5PB45HA5J0ooVK5Sdna2kpCTFxMQoOjpan3/+eWuIr66u1pYtW7Ro0aLeLB0A0ENln25U0Rufqm53S1g/tGqrKr/cqdBFl2rd+lLJMCXblickVOVpg1Wenqnhm1ZpUP5WZe34srWdDhe1MwwlTB2l7GsuUvpF02WGODX0xstU/vkWHfxgrSTjaJg3TcmyNPQHlyr1nKl++MkBAAA6F9CrOSxcuFC1tbVasmSJdu7cqddee03PP/+8rr/+ekktz8IvWrRIDzzwgN5//31t27ZNP/7xj5Wenq5zzjmnl6sHAJyqHQ+8pC9veVR1+ccsUmrZ2rN8m/764lZ5PIcD9uGF7GSakmFoZ+5pOpieKUOHZ+B3YcD80zXwslkyQ1q+zzZDnMp7/m6Nu/96RQ0b2HpewtQcTfnTnRp9z9UyjBO1CgAA4FsBPRKflJSkp59+Wvfdd58WLFiglJQU3XHHHVqwYEHrObfccovcbrd+/vOfq7GxUXl5eXrmmWfaLXYHAAg81VWNWrWsQNs3l8jjsTVkaIJyB4dpx/++1HLCcTPb9w0Z1boCfYcsSwU5E5RSvFeS1BgRpbL0THmcIQqvq1Fy8V45LI9k2yr55+caet38NpebIU5lXX2Bsq6+QB5XswzTaA35AAAAgcCw7a6W6+2fPB5Lhw6xcBF6l9NpKiEhShUVdTyvhT5py5fFeuHxVfJ4rGNmrhuyLFtDt65R5lcb213z6fnfkjv0xFvG5X3wmgpyJqg0Y6gktS5452h2acTGz5W+f7fiJg7XGe884NWfCQgUfIYAneP+QCBKTIzqGwvbAQACl8djadvGEu0vqJTDYWrE6BRlDk3o1pTz4sJqPf/Hz49Oiz/MOrwA3e7RUxReW63UooLW9xoiouUOCelWbTtyp6sqOb11uv2R0XuPM1TbJs+WKUODRmV21QQAAEBAIsQDAE7arh1l+utTq1Vd2SjTbAnI/3pjqzIy43TVD6YpMbnrvdSXvb+ry23bZdst0+KLCmQbhr7KPU1FQ0a2vqeuviiwLUXU1agqeUD794yWBet2jp6sSxexSB0AAAg+Ab2wHQAg8OwvqNRTD32mmqpGSS2j50dG0Iv2V+v3/7NMdbVNnV5vezz68ovC1ms6ZBiqi0uUKyLqaIA3jK7Du3Q44JsqHjKiy7abIqJUHpXQdVsAAAABiBAPADgp/3p9qzyW3eFIumXZqq5s1IqP8tu95yqr0pb/fE7vjP6uXPWubvVVFxV3NMAf63DnYQ11iqksU1h9bdv93Q3zhIG/8lBDt2oAAAAIJEynBwB0W021S9s2lXR5jm1LK5ft0dkXj2w91lBUrs8uukuNxeWyPZbC62vVEBXbZdA2LI+qk1LbT583DMWVFyt76zrFHzpaS2ViqvaMmqTKjqbRdyAyml1MAABA8GEkHgDQbTXVjd07r6rteRtu+31rgJekjPxtXV5vWJZSC/PlDgmVcdw+c0nFezXxs3cUd6i0zfG4Qwc14bN3lHzMYnidCQt3auSY1O78KAAAAAGFEA8A6LaoqO6NXkccc17dnmId/HBta4CXpAF7v1JUdYVkdbC1j2XJ4W5W1vb1CnU1ytbRUXjT49botcsk224X7o+8HrXuU5lud5f1nXVhjkJCHd36WQAAAAIJIR4A0G1xCRHKGp7Y5ePmhmlo6oyj27dVfLFVx+VtOTxuTfzsX0opKpDstkE+rqJUk5e9rYj6WqUW7m4zlT6lcI+c7mZ11r0hyeFu1oTP3lF0ZXmH58w5b4Tmnt/FwncAAAABjGfiAQAn5bxLRuvJh5Z3+J5hGgoLc2jWvGxJkm3b2vuXf7c5x5bUGBkt2zA0eu0yDd+0SpXJ6bINQzGV5YqqrWo9N7y+Vg53szzOEMkwFFNdLsswZdodjOAfqUFSbOVBTVn2lrZMOVMHB2ZJkmLjw3XjT89QcmrX298BAAAEMkI8AOCkjBidovNzo/TO+mpZhnl4artkm6YiQqTrfjJL8YmRkqSiNz/ToZVbJLWE98Ls0do3bKxckdGSJGeTSwMLtitzxwY5Pe2nwNfFxMkTcnRqvmWYajes3wFDLV8gjF7ziaoSUtQUEaXLvzuJAA8AAIIeIR4AcFL2PPdPNfzqCc1whqpk8FDVxCXLsC0lHCxSSlGBPKdFyF50jlyNbu18+p+Sacq2LG2dfIZKM4a2acsdGqa9w8fpUEqGJq58R86mo/vLW5K2TJ7T5vyKlAxl7trcrToNSbJtZez7Snn3Ldbo3LSe/eAAAAABgBAPAOi2pspabfmPP0mSQtxNGnTcKvOWYeqfTy5T+SZLFYcapbSpijl9iGIrDqp00LCOGzVM1cUnan/uVGWvXynbY6k2Jl6b8uapMTq2zakVKQNUHxWr8PoamR1tVH9807KVG9mgabOGnNoPDAAAEGAI8QCAbitc+rGs5o5XfrdMUxtOO0eVyenSoaNbzNUkJKsmMaX9fu/HsGWoaOho5eQk6fN9blUldjJqbhjaeNpZmrj8Xwp1NR6+smtmN6bfAwAABAtWpwcAdFvdzkIZzo4/OgpGjG8J8McH9SOvu1rSXpLL5dG7dUmdB/jDGqLjtHrOpdozaqIaI6JlddGu4TCVkDeqy/YAAACCCSEeANBtjsiwDteVswxThVmjThjUT6gbU+QlqTksXAU5E/T5OV9XedqgzpuzLA256rye1QQAABBACPEAgG5LO2+abLen3fGGqBi5w8J73sHJfglg24qpLG8/Gm+2vB7zn9coelhGz+sCAAAIEIR4AEC3JeSNUvzkETIcAfLxYRhad8ZFKs4cIR1TU/zkHOW9cLeGXje/F4sDAADwPha2AwB0m2EYmvrcz7Ry4S9U+9X+lhFvy1ZEQ62cTS65Q8P8XpMrIko7xs9QSlWJzn7jvxSaFKuwlHi/1wEAAOAPhHgAwEkJT0vUGe89qKI3l2vf3z5QY8khhacnafKoWH2R7+ruY+3eZRg6FJUoSQR4AADQpxHiAQAnzREeqkGXz9Wgy+e2Hpva5NHBh5Yrf+ehXqnJNgwZoXysAQCAvi1AHmoEAAQT18FK7XvpfeU/87ZKP1gr2+NRSKhD1902U3OmJMq02i9+52sp4baistL93i8AAIA/MWQBAOg2T2OTNv/8ae178T3ZHqtlNXnbVviAROX+5gY1VdZK9zyqWYahr3Knq2TwMFmOox81hscj2zAk04vfIVuW4ipKNf7qs2R4s10AAIAARIgHAHSLbdta+/3/Vcl7qyXLPnJQktRYXKEvrvq1JFsH0zO1d8R41SQkt5xjWS2h3bZlOxxt94K37Z7tLW9ZcrqbdGaGpaxrLjz1dgAAAIIEIR4A0C1ln2xQybtfdPzm4WC+b+gY7Ro3rSW4H3FkdPxIWD82tPcgwBuWR1lWlc67bJSGXXaFjJ58GQAAABAkCPEAgG7Z99d3ZTjMlmn0HfCYpuqjYmV63G2m0HubYRqalJehb1w1SQMGxquiok5ud8c1AQAA9DWEeADo4zz1Lu198d8qeP5fqt9bKkdkmAZedoayr71I0cMyut1OXX5xpwFekkzLUs7GlRqwb6e+nHGuPCGh3ii/HafD1LmXjFJ4RIhP2gcAAAhkrAAEAH1YU2Wtll98lzb//BnVfrVfVmOTmg/VaO/z/9Inc27VwY/WdbutkPjoLqe/G4f/iaks14iNK3tefAecIaau/dEMJadG+6R9AACAQEeIB4A+bONP/6CabQUtz6wfu56cx5LV7NYXV/9arrKqbrU18LJZbRel64QhW6mF+QppbDjVsjuUMyZF9z54gYaNTPZquwAAAMGEEA8AfVTDgTIVvbWi8ynwti3L1ax9L73frfYyFsxWSEL3RsBN21Z8eXF3Sz22KDkchsIjjj7tlT08UVf9YJqu+/FMhYczhR4AAPRvPBMPAH1U+Webjm4F1xnL1sEP12n4TV87YXuOyDAlz56gojeWd6t/wz7JxeYObzd37Y9O14jRKXI3e2QYhhxOvm8GAAA4ghAPAH2U3ezp1nlWs7tb55V98qWK3+7+s+61sYndO/HwFH3TtvTNa/M0YnSKJMkZ4uh2XwAAAP0FIR4A+qi48UNPeI7hMBU/ccQJz6vdWahVi34l22OpKSRMoc2uTs+1Zag6IVn1sQldN2pZMj1uJZXsV0z1Ic2+a4HGTc88YS0AAAD9GXMUAaCPih2brfhJI2Q4Ov+j3vZYMsNC1FRe3WVb+U+9KdvtUWl6plac+w3l50xouf648yzDkNvp1PaJM9s3YujwAnst0+wj6qo15ZM3FX+oREXZo/Tye8WqPOTdxfAAAAD6GkI8APRhEx6+WY7I8M6DvGlo12Ov6d8TFmv343/vtJ3C/7dM5Qlp2jJ1jmzTVMGoSdo6aZYaomJbz7ENQ2XpmVo7e77qY+Jbj2cPT9TiG09TXHx4yxZ1hinZthojovTFvK/pq/Ez1BARrcZGt5Z/uNtbPzoAAECfxHR6AOjDYkZlata7D2j7r/+qorc+a7/Q3eHXdrNHW+75k5zREcpcdE67dprrGrX5nEtbXhzeK75k8HCVDBqmiLpqOdzNckVEqzksvN21+TsPae+eL+RxH7PQnWHIdrZdad62bH3x2V5dtHBsD35iAACAvo2ReADo46KHDtSUp36q8Q/ceMJzt/36Lx0udFc9eqw8IaGtAb6VYaghOk618ckdBvgjrM62uTtOfV1Tt84DAADorwjxANBPFP9jpWR2/cd+U1mVypdvbHfcnTepdRX5U9HdS2Niwk65DwAAgP6AEA8A/URjSYVknXhEvPgfn+vA35fLVVopj9tSfV2TokYO9nl9hiGddkaWz/sBAAAIZjwTDwD9RFhKfLfOK3j+X9qydIX2jchV6eBhsmTINI32U+lPkmkasg8PyR8/Mm+ahqJjQnX63Owe9QEAANDXMRIPAP2EGdq9722rElK0ZvbFKs4YKkstwd06fkG8U2BZts69ZLSiDk+ZNx2HvxyQlDogRjfeMVvRTKcHAADoEiPxANAPeBqbVPZp+2fdj2cZhjblzZXlcLRsBeclhmkoLj5cZ12Yo3nnj9CWDcXaX1Ap0zQ0fHSKho5IktHDkX4AAID+gBAPAP1AXX6RPLUNJzyvLD1TzeGRXu3bNA2Fhjm0+IentYy8m4ZyJw9U7uSBXu0HAACgPyDEA0A/0N1R7qrENBmWR7bp6GF/Lc+9h4U7lTczU7PPHqbE5KgetQkAAABCPAD0C/bhEXCd6Nl2L81oN01D9z12sRxOk2nyAAAAXkSIB4A+zl3XqM8vv0fqIr/XRcfpQNYolQ4c0uNReEkyHaacIT1vBwAAAG0R4gGgD2sqr9YXi38tV/GhTs8pGjxc2yee3hLyzcOL2dl2h1vKHTl0/BZxx4tPjDjFigEAANAVtpgDgD6qqbxan154hyo+39rpOdXxydo+caYk42iAlzreE962Zdtq3RauKweLa7Vu1f5TqBoAAABdIcQDQB+1/X9eVMO+0k7ft0xTO8dOa3nR2XPrR4bcjxl693hOvGe8YUjv/2O77BMN2QMAAOCkMJ0eAPogd12D9r30gWyP1eH79VGx+nLGuXJFRnfd0LHh/iQWqLNtqbiwRhWHGpSY5N0t6wAAAPozQjwA9EF1+cUqjU/T/qzRqo1PkmHbSizdr4zdWxVVW6X1p5+nprCTeG79FFeYb3K5T+k6AAAAdIwQDwB9jGXZ+vt7+7Vp2lmSZbU+616aMVQlg4Ypdf9uNYVHnnIw7y6Hw2SBOwAAAC8jxANAH/PpB7v05ebylhfHLFZnHwnzg4b6vAbTNDT5tEEKDw/xeV8AAAD9CQvbAUAfYlm2Pn5354lP9OEovGkaiogK0XmXjvJZHwAAAP0VIR4AgpDrYKVqtu9TU3l1m+NlJbWqqmjs+mLDOPFG7900cmxquy3nho1M1i0/O1PxiSxoBwAA4G1MpweAIHLoi23a8T9/U9knX7YcMKSUuZOV89NvqiwiQe//Y3v3GvLCSPzCRRM048xs1VS7tGdnuTweW4OGxCk59QQr3gMAAOCUEeIBIEiUvPuFVl/9a7UZQ7elgx+uVemH67ThtHNUkTrQ53U4HNI1t5yunDGpkqSY2DDlTvZ9vwAAAGA6PQAEBU+9S+tufFC2ZUvH7/1uS7JtjV77sQzL49M6YuLC9NP/PLs1wAMAAMC/GIkHgCBw4I1P5a5p6PR9Q1Jok0vJRXt1MCPb6/07nabmnj9C514ySoaPt6YDAABA5wjxABAEKr/cKSPEIbu585F2yzAUU1nm1RCfmh6tCxaM0ejx6XI6mbwFAADQ2wjxABAEDIdD6saC8rbh3aA9bdYQnncHAAAIIIR4APCzhv0HVfj6MjWVVSksLUEZC2YrPD2xy2uSZ+Vqz9NvtTvudjjVEBUrw7YUWVulyuQBXq11yoxMr7YHAACAniHEA4CfWM1ubbr7Ke3987uSYchwGLI9lrb+5/PK/v7FGnPP1S0j7sfw1LtU9NZnqt19QM64qJbn4i1LzSFhyh81ScWZw2U5Wv4od7oa5Q4N81q9qenRion1XnsAAADoOUI8APjJxjuf0L4X35NsW7Jt2ccsMp//xJva+9f3FJYSr9Szpihr8QU6tHKLNv/iGXnqGmU4HbI9HsmWmkPDtHbWhWqIjJHMo9Pn3WHhXq33oq+P9Wp7AAAA6DlCPAD4QV1+kfb99d8dvxcdp6IhOaqOT5ZpWUr8uEA7/vwThboaW8+x3UcXtMsfOaldgPe2IcMSNDo33WftAwAA4NQQ4gHAD/b/30cyHKbs4/Z4Lxieq/wxUyTLagnltq3K5HTlj5ig+PJi1ccmyDYMxR4qVcaebYqpKFNR5gifBvhR49L0ne9PkWmylRwAAECgIcQDgB+4Siqk4/ZXL8nIbgnw0tFQfvgc2+FQRWpG6+vy9EyVDcxS6r6dso97br6nDEMaO3GABg6KU+6UgRqQEevV9gEAAOA9hHgA8IOw5LiWZ+EPsyUV5ExoOWZ0MOJ93DH7cMgvHTzcq3WlDYzR1xdNVPaIJK+2CwAAAN8gxAOAH2QsPFNfPfRq6+uGqBjVx8SffENHvgjoKPifBMOQvnHVJE09PVNGD9sCAACA//juoUoAQKvoEYM08GuzpcPPmXscIafWkGH0OMBL0o+WnKm8mUMI8AAAAEGGEA8AfjLhoZs08LIzJEkRTS37vfeGKTMGa9CQhF7pGwAAAD1DiAcAP3GEh2ryH2/TnE9/p9E/uFiDHfUyvBnkj3nmvjORUSG6+OvjvNcnAAAA/IoQDwB+Fj1ikEbe9R19+z8vkzPM0a3w3S0nmBpvGNKPfj5HMbFh3ukPAAAAfkeIB4BeYFm2lr2/S83NXgrw3WDbkuX2X38AAADwPlanBwAfaK6qVeH/W6b6ghI5YyI04KLTFTNycOv7/35zmz77KL/lhR8Xl3M4WcgOAAAgmBHiAcDLdj/5prb91wuymt0ynKZk2drxm78p9ZypmvTH2+QJCdFH7+70e11JqVFKSIr0e78AAADwHkI8AHhRwQvvaMt/PNP62mU4VZSdo8rkAbLrDK2/4XmNuvpsNTd5jl5k234ZjZ973gi2lAMAAAhyhHgA8BKPq1nb/vvPra/L0gZpy9Q5skxHywHDUKVlaccrm9te6IdgPWNOlk47Y4jP+wEAAIBvEeIBwEvKPlqn5opaSVJtTLw2582VbZhtQ7rp3/VEMzLjdMGCMRo5NpVReAAAgD6AEA8AXtJYUtH66/3DxsqW4ddF645lGFJ0bJhuuftMORxsRAIAANBXEOIBwEvCkuNaf10yMMvvo+5HmKYhh8PUVTdMI8ADAAD0MYR4AOiCbdvy1DfKcDjkCA/t8tyUeZPljIlUc029bGeInypsy3QYmjAlQ2ddmKP0jNheqQEAAAC+Q4gHgA54Gpu059l/aM/Tb6th/0FJUkLeKA298TINuHB6h9c4wkOVc/sVWn/f3/y24rwkJSRF6rxLR2nEqBRFRIYoNIw/2gEAAPoq/qYHAMfxNLi08op7VbFqW0sYP6xizQ6tWXy/ht96uUb97DsdXpt9/SXaeqBJKvJ9gB+dm6azLsxR5tBEmSaL1gEAAPQHPCwJAMfZ8dtXVPFF2wAvSbIsSdLOh19V2SdfdnitYRhKOjvP1yVKkk47I0tZw5MI8AAAAP0IIR4AjuFpbFLB8/+ULLvTcwyHqfyn3+r0/bQBMb4orQ1niKmRY1N93g8AAAACCyEeAI5Ru3O/3NX1XZ5jeyyVr9jc6ftDhiV4u6x2zr5opEJCHT7vBwAAAIGFEA8Ap6KTgXrLsrVzW5lPu545N1vzLsjxaR8AAAAITCxsBwDHiB6WIWd0hNy1DWqIiFZpRraaw8IV2livtMJ8hTXWy3CYSjxtdLtr6+ua9KffrdSenYd8Vt+ss4bqsm+O91n7AAAACGyEeAA4hiMiTBlXnqd3Pzuooszhkm3LkC3bMLR7zBRl7N6q4ZtXK+vai1W4t1Kr3tuh0s37FFpfqxJFqMzd9V7yPREZHaqLF471WfsAAAAIfIR4ADjOpoyxKsrc37LPu2G0mTlfOHSMYnOH6e0tLm1+8SMZliVbhqQYyfTdE0rJaVH60d1z5AzhOXgAAID+jBAPAMcoLa7RmlWFLQG+I4ahrfXhMtYXSTJk+yC4R8WEKiEpUrZlK31gjM6+aKRS0n2/4j0AAAACHyEeAI6xZsVeGbYl2+g6nLeMvvtGXU2Trrv1dGVkxvusDwAAAAQnVqcHgGMc3FrY6crzrewTndBz7/9jh8/7AAAAQPAhxAPAsYoPnviczqbae9Gh8q73qgcAAED/RIgHgGNkOep88pz7yYqM9N0q9wAAAAhevf83VQAIIJkTBiupeK9fpsx3ZcaZWb3aPwAAAAITC9sBwGEet6XPPckqT2s8evBImPfDFPojIqNCNW7SAL/1BwAAgOBBiAcASc019Xrh/n9ra6nVNrD7MbwfcdNdZ8johX4BAAAQ+JhOD6DfK1u2QX+f8SNtLbUlH24d1x2JyZFKZU94AAAAdIIQD6Bfq/1qv1Z95790IGmQZFm9WothSDPnDu3VGgAAABDYmE4PoF/b/fgbst0eNYVFyNCJt4j3FdM0lJgcqdPOGNJLFQAAACAYEOIB9GuFr30i22MpxNUg298z6Y2Wyfu2LWWPSNJ3vj9V4REhfi4CAAAAwYQQD6Dfsj0eeepdkqSYijLJ8M8TRnPPH6GsYYkqLamVw2FoxOhUDciI9UvfAAAACG6EeAD9luFwKCw1Xq7SSpUPyGwZEvfxqvBzzxuuixaOlSSN9WlPAAAA6IuCYmG75uZmPfTQQ5ozZ44mTZqkb3/721q7dm3r+1u3btWiRYs0ceJEzZkzR88880wvVgsgmGReeZ5kmiodmO3zAB8RGaJZZw/zaR8AAADo24IixP/xj3/U0qVL9atf/Uqvv/66hg4dqu9///sqKSlRRUWFFi9erKysLC1dulQ333yzHnnkES1durS3ywYQBLKvuUhh6YmynL6dmBQTF6YbfjJTcfERPu0HAAAAfVtQTKd///33dfHFF2vWrFmSpLvuukuvvvqq1q9frz179ig0NFT33nuvnE6nhg0bpoKCAj311FNauHBhL1cOINA5E2JUcsUVCtlSrubQMK+Pxo8Ylaxps7KUO2WgnM6g+N4UAAAAASwoQnx8fLw+/PBDLVq0SAMGDNDLL7+s0NBQjR49Wv/3f/+nvLw8OY8ZRZs+fbqeeOIJlZeXKykpqRcrBxCISotr9OkHu7Vh9QE1NjbL3WxJYeEtz8R70aXfzNUZZzF9HgAAAN4TFCF+yZIl+vGPf6yzzjpLDodDpmnqkUceUWZmpoqLi5WTk9Pm/NTUVEnSgQMHTjnEM2KG3uZwmG3+De/YvL5If/rdStm2ZFnHhXYvjMKbpqExE9K14Ju5SkqN7nF76Bz3CNA57g+gc9wfCHZBEeJ37dql2NhY/f73v1daWppeffVV3XnnnfrLX/6ixsZGhYaGtjk/LCxMkuRyuU6pP9M0lJAQ1eO6AW+IjeUZ6o40llfJVV6t8JR4hSXEdOuaQ+X1evb3n8vj8e6I+xEDMmL1699dIsPHC+ShLe4RoHPcH0DnuD8QrAI+xBcWFuqnP/2pnnvuOU2dOlWSlJubq507d+qxxx5TeHi4mpqa2lxzJLxHRkaeUp+WZau6ur5nhQM95HCYio2NUHV1gzweq7fLCRjlq7Zq+/++pNIP10m2JNPQgAtO08ifflPxuUO7vPavz6yW2+27/5bRsWGqrOTPDn/hHgE6x/0BdI77A4EoNjai27NDAj7Eb9iwQc3NzcrNzW1zfMKECfrkk080cOBAlZaWtnnvyOu0tLRT7teXf9EHTobHY/H78bDif63Smu/dL1tqCfCSZNkq/tcqlby3Rqe9cq+Sprfsvu7xWNryZbHWrNynmmqXwsOd2r65tLOme86QhgxN4P9VL+AeATrH/QF0jvsDwSrgQ/yAAQMkSdu3b9f48eNbj+/YsUNDhgzRxIkT9dJLL8nj8cjhcEiSVqxYoezsbBa1A/oQd12D1t34oGzLbrcAne2xZNvS2use0Flrn1Z9g1tPPvSZDuyrkmEaLdf4wfTZWX7pBwAAAP1XwK/mMH78eE2dOlV33nmnVq5cqT179ujhhx/WihUrdN1112nhwoWqra3VkiVLtHPnTr322mt6/vnndf311/d26QC8qPC1ZfLUNXa+grxlyVVSoZJ3VunZ33+u4sJqSfJbgL/silzFJ57aIzwAAABAdwX8SLxpmvrDH/6ghx9+WD/72c9UVVWlnJwcPffcc5o4caIk6emnn9Z9992nBQsWKCUlRXfccYcWLFjQu4UD8Kqq9V/JcDpkuz2dnmM4Hdr+eb4KivwbpofmJGkWW8kBAADADwI+xEtSXFyc7rnnHt1zzz0dvj9+/Hi9/PLLfq4KgD8Zhx+X6YrHY2nZlloZiZHe3vK9UyGhDn3t2xP80xkAAAD6vYCfTg8AkpQ0c1yXo/Ae06GSQcNUHZPotwAfFR2qH9w+S+kZsf7pEAAAAP1eUIzEA0D6BacpLC1BrrIq6bjtYCoTU7Vp2llyh4Z1/sy8lyUmR+iu+86VabInPAAAAPyHEA/A76o379Hup95U8dsrZDU2K2roAA1ZfIEGf/MsOcJDO7zGDA1R3gtL9Nmld8vyNLUer4+K0Ybp58g6Mt3e8E+ovvL6aQR4AAAA+B3T6QH41YE3P9Oyc25T4SsfyV1dL6upWTXb92nTnU9oxdd+LnddQ6fXxk8crtixWZIheQ6H9oIR42U5nJLhvz/Ozr1klAZnJfitPwAAAOAIQjwAv6nfW6J1N/xWtmXJ9hzzfPvhKfCV63dqyz3Pdnq9bVmqXPeVZEs7x07TRxddqZLMEX4bfZek8VMG6tz5o/zWHwAAAHAsQjwAvyl44V3Zti119ti6x9K+lz5QU0VN540cDvyR1RWS6b8/wkJCHTr/stG68vo8v/UJAAAAHI9n4gH4zcEP1rZblO54drNbFau2Ku28ae3eM0xTsWOzVb1lj5oi/LMXfExcmK64apJyxqbxDDwAAAB6HSPxAPzGanZ377wugn72tRdLlq2iwX6YRm9It/1irkblphPgAQAAEBAI8QD8Jn5KjgzHCf7YMaS4sdmtL+3jtowb9I05SrpwutzhEb4osY3Jpw1WTGy4z/sBAAAAuovp9AD8JuvqC7T/b+93+r7hMJU8e4LK7VC99tRqbVp3QO5mSyGhZuv279HRYQrPzJMOdPHcvJecfykL2AEAACCwMBIPwG/iJw7X8Fsvb3lx3FR4w2EqJD5G9pUL9eh/f6wNqwvlbm6ZVt/cZMnd3PJPZUWDin0c4E3T0JgJ6UpMjvJpPwAAAMDJIsQD8KuRd31bEx69RVHDBrYeM0Icylh4psa+/Cst/fsu2bZkWZ0tYe9bpmkoNj5cCxdN6JX+AQAAgK4wnR6AXxmGocFXzNOgb8xVfUGxPHUuRQxOUUhslN76v00y1PkOdL4WFu7UtFlDNO+CHMXEhvVSFQAAAEDnCPEAeoVhGIrKGtDm2I4tpX4fgXc6TU2dmamzL8xRdGy4nE4mKAEAACBwEeIBBAzL478AP37qQM04M1vDcpLZPg4AAABBgxAPIGBkDU9SSVGNbB9neYfT1OVXTlREZKhvOwIAAAC8jHmjAAJG6oBonwd4SRo6IokADwAAgKBEiAcQEGzb1hfL9/qlr0nTBvmlHwAAAMDbmE4PICCUldSpaH+1T/swTCkqOkwT8zJ82g8AAADgK4zEAwgIm7484NP2DUOKjAzVdT8+XaFhfH8JAACA4MTfZAH0qtoal556+DMV7q3yWR/xCRGafc4wTT09U5FRPAsPAACA4EWIB9Bramtc+u29H6im2uWzPsZOHKDv3pAnh4OJRwAAAAh+hHgAveYfr232WYBPz4jRrHnDNG3WEPaBBwAAQJ9BiAfgU263pY1rD2jtyn2qqXYpPjFCeadnKmtEktas3O+9jmxbMgz94PaZGjIsSU4nI+8AAADoewjxAHymvKxOv79/maqrGluPFe6t1KZ1RUpKjZTHbfW8k8PhPbSxXjfdf7EGDorreZsAAABAgCLEA/CJjWsP6IXHV8m22x4/8rq8tL7nndi2YioOKrG0UAO+fykBHgAAAH0e800B9JinwaX6faVqqqyVJO3cdrDDAO91ti3bMFUwcqImThvk484AAACA3sdIPIBTVr+vVF899IoKX/1YVlOzJClp1nhtShsh247xfQGmqdqEZBmGlJGZ4Pv+AAAAgF5GiAdwSmp3Fmr5xXfKXV0v23P02fbyzzYpVZu187wr5Anxw57sRss2chGRIb7vCwAAAOhlTKcHcErW/fChdgFekmRZKsnI8k+Al+RwmDrn4pF+6QsAAADobYzEAzhpVRt2qWr9zk7fL8we45c6oqJDdeX1ecrIjPdLfwAAAEBvI8QDOGkVa3dIhqROFq5rjPL98/BjJqTruzdMYz94AAAA9CuEeAAnzTCMTgO8JNmG4cO+pdAwp7727fEEeAAAAPQ7/A0YwElLnDG2y/dDXY3e7/Tw9wLhESG67tbTFZ8Y6f0+AAAAgADHSDyAkxY9YpCSZubq0MrN7Re2k5S+7yvtGTmpZdi8h7JHJCoiMlQOh6mRY1M16bRBCgvjjy4AAAD0T/xNGEC31O8tUf6Tb2r/qx+pqapWIQmxckSGy13XKFlHg7zhcGjgvl3akzOxxyE+Z0yKrv3R6TJN303PBwAAAIIJIR7ACVWu+0orvv4LVYTFaN+wKSpLz5TtcCi8rkaDCncqs3SPrENVMkKd8kwYqy8jh0hmz57WmTx9sL71vcktz98DAAAAkESIB3ACVlOzVl15nwoTMrRt4kzJtlsDemNktHaOmKiCoWM1Om+wGl2Wdmw52KP+DEM679LROuvCHAI8AAAAcBxCPIAuFb29UpX1Hm077fSWA8eOsB8O2c2OEG1YV9LjviZMHaj5l+cqPjGix20BAAAAfRGr0wPoUvmnG1SUPbrlRWcj414aMZ85dygBHgAAAOgCIR5Al2yPpUPJA3r8jPuJxCdEKGt4kk/7AAAAAIIdIR5Al+InjZDth0fTL/jaGFahBwAAAE6AEA+gSxkLz1R81aE228h528S8DE2ZPthn7QMAAAB9BSEeQJccUeHKmJvrs+n0zhBTVyye7JO2AQAAgL6G1ekBdGr75lK9/rcNOlhS75P2TdPQTXfOVkiIwyftAwAAAH0NIR7oR6qrGvX5sj3auLZITS630gfGasacbOWMSWm3J/vWjcX602MrZdu+qSV1QLRu/OkZio4J800HAAAAQB9EiAf6iV07yvTMoyvU3ORpDeaHyuq1aX2RJkzN0LevnSKHo2XKvGXZWvrnL+Wj/K6QUId+cPssAjwAAABwkngmHugHqqsa2wV4qSWsS9KXqwv17ze3tR7/autBVVY0yFcp/mvfHq+Y2HDfNA4AAAD0YYR4oB9YtaygXYA/3rL3d6u6qlHNTR4dLKmV4aPd3iIiQzRlRqZvGgcAAAD6OKbTA/3AhrUHTvhsu6vRrf+8/V+SpKTUKJ89Cz//62PZDx4AAAA4RYzEA/1Ak8t9UueXl9b5pI68mZmadkaWT9oGAAAA+gNG4oF+YMCgWB0qq299Bt7fomPDdOk3cjXptEG90j8AAADQVxDigX5gxpnZ2ri2yO/95oxJ0fmXjdHgrPh2W9gBAAAAOHmEeKAfGDE6RcNGJmvX9jK/9GeY0nevz1Pu5Ay/9AcAAAD0F4R4oI9rcrm17P3dKth9yOttm6ahpNQoNdQ1qb6uWWHhTk2ZMViz5g1Vcmq01/sDAAAA+jtCPNCHuRrdevy3n2p/QaVPVpu3LFv1dU365YMXer9xAAAAAO2wOj3Qh/3r9S3aX1Dls+3iJMny9M5ieQAAAEB/RIgH+iiXy63PlxXI9mWClxQXH+7T9gEAAAAcRYgH+qjiwmo1NXl83k/agBif9wEAAACgBSEeQI8MzIzr7RIAAACAfoMQD/RR6RmxCg11+Lyf3MkDfd4HAAAAgBaEeKCPCgtzatjIZJ/2MWZ8mlLTmU4PAAAA+AshHuijvlxTqK0bS3zWfsbgOH3rmqk+ax8AAABAe+wTD/RBHrelV59f75O2DUOad0GOzrt0tEzT8EkfAAAAADpGiAf6oO2bS9XY0Oy19mLjwxUVHaox49M1fXaWEpIivdY2AAAAgO4jxAN9UFlprVfaGTAoVhcvHKuR49K80h4AAACAniHEA0HK7ba0aV2R1n6+T7XVLsUnRihv5hCNHJsq2wvtx8SG6bZfzJVhMGUeAAAACBSEeCAIVVc26omHlqvkQI0MQ7JtaX9BpTasOaDsEUmqqmjoUfuGaWjKjEwCPAAAABBgCPFAkLFtW888tkKlRTWHX7cct6yWX+R/Vd6j9g1DCnGamjk3u0ftAAAAAPA+QjwQZHZuK1Ph3iqftR8a5tT3bp7O4nUAAABAACLEA0Fmw9oDrVPovW3WvKE695JRiowK9X7jAAAAAHqMEA8EGVd9s08CfGx8uC65Ipe93wEAAIAAZvZ2AQBOTmiYb757O++SUQR4AAAAIMAR4oEgM2JMitfaMoyWlegv+vpYnXZGltfaBQAAAOAbTKcHgkxNdWOPrjcdhgYNiVdKarRS06OVN3OIYuPDvVQdAAAAAF8ixANBwrJsvfnqJi17b9dJXRcTG6ao6FClDoxR3oxMjcpNY/93AAAAIEgR4oEg8e6b2046wEuS223p1v+YK6eTp2cAAACAYEeIBwJAWWmdVq/Yq8pDDYqMCtGkaYOUPTyp9f2G+mZ9+M8dp9R2Q32z9uws1/BR3nuWHgAAAEDvIMQDvciybL3x0gYt/zC/zcrwn/x7l0aNS9NVN0zT55/u0cZ1B+TxnPq+co2Nbm+UCwAAAKCXEeKBXvT20s1a/mG+pJZAf6xtm0r0s5velGGox/vCJyVH9qwBAAAAAAGBEA/4UJPLrXWr9uuLz/aqttqluIQITZuZqQlTM+RqdOvT90/8jHtPArxhSAMHx2nAoLhTbwQAAABAwCDEAz5Seahef/ztcpWX1rWOppcfrNOu7WX68J2vlHf6kB5NkT8Rw5AMw9ClV+T6rA8AAAAA/kWIB3zAtm0989hKVZTVH36tNv8uLqzRv9/c5tMaElOidPl3J2poTrJP+wEAAADgP4R4wAd2bitT0f7qLs/xxWJzp8/J1sDBcUpJj9bQEUnsBw8AAAD0MYR4wAe2fFkk0zTaLVbnS6FhpuZ/Y5xCQhx+6xMAAACAf5m9XQDQFzU1eSQ/D4LPOTeHAA8AAAD0cYR4wAdS02Nk+3EUfsr0wTr74pF+6w8AAABA7yDEAz4wdcZgmabvh+Jj4sJ0w+0z9c3vTfZLfwAAAAB6FyEe8IGomDDN/4Zvt3YzTUPjJw/U8JEpLGAHAAAA9BOEeMBHZs0b6tMRcsuyddoZWT5pGwAAAEBgIsQDPjR1RqZuuH2mT4L8tFlDNHBwnNfbBQAAABC4CPGAjw0dkawf3nGG4hMivNZm2sAYff3KiV5rDwAAAEBwIMQDfpCcGqXqqkavtXfhgjEsZAcAAAD0Q87eLgDoD9as3CfLC1vOmaahlPRojR6f7oWqAAAAAAQbQjzgAx63pW2bSlReVqewMKdWfJx/Su2YDkOWx5ZhSrYlpaRF67pbT2cUHgAAAOinCPFAF2zb1s5tZdq+uVQej6WBg+M0cWqGQkIdnV6z/otCvf63DaqtcfWo74sWjlVDXbOKCqsUHhGiCXkZGp2bJoeDp2AAAACA/ooQD3SirLROz/5upUqKamSahgxD8nhsvfHSBn1z8WSNmzSw3TVfri7UX578okf9pg2M1kULx2n85IFKSIhSRUWd3G6rR20CAAAA6BsY0gM6UFfbpD/87zIdLKmV1LInu8fT8kx7Y4Nbz/9xlXZuO9jmGo/H0usvbehRvw6HodvvPUtjeOYdAAAAQAcI8UAHVn6yRzVVjV0uRvfP17e2ef3V1oOqqerZFPoRo1NlGDzvDgAAAKBjhHigA6s+LZDdxWLyti0V7Dqk8oN1rccOldX3uN/Z5wzrcRsAAAAA+i5CPNCB7i5KV1t99LyIyJAe9Zk2MEY5Y1J71AYAAACAvo0QD3QgOiase+fFHj1v1Lg0OUNO/Za6aOGYU74WAAAAQP9AiAc6MG1mprp6NN0wpCFDE5SUEqUml1srP9mjJx5c3uU1JzJqHIvZAQAAAOgaW8yhX6uva1L+znJZHlsZQ+KVmBQpSZp+ZraWf7hbtTVNnS5ud/5lY1Rd1ahH//tjVR5q6FEdyWlRMk0WtAMAAADQtaAJ8a+//rqefPJJ7du3T5mZmbrpppt0wQUXSJK2bt2q++67T5s2bVJ8fLyuvPJKXXPNNb1cMQKZq9Gtv7+yUas/29u6dZzUMiX+a4smKDEpUj/46Rn602MrdbCkVoYh2ZJkt2wDd8kVuYqMCtF/3/WuV/Zwz+1gz3kAAAAAOF5QTKd/4403dPfdd+uKK67QW2+9pQsvvFC33Xab1q1bp4qKCi1evFhZWVlaunSpbr75Zj3yyCNaunRpb5eNANXc7NETDy7Xqk/bBnhJ2rGltHVkPSUtWt/9wTRFRYe2rFR/+FTLlv7fixv08K8+8kqAl6RhI5O90g4AAACAvi3gR+Jt29Yjjzyiq666SldddZUk6Yc//KHWrl2rVatWadWqVQoNDdW9994rp9OpYcOGqaCgQE899ZQWLlzYy9UjEK36tEB78ys6fM+ybNXXNumdN7bqkivG6YkHl6u+vrnNOfbh6fVdbUF3MhxOU0OGJninMQAAAAB9WsCPxO/evVuFhYWaP39+m+PPPPOMrr/+eq1evVp5eXlyOo9+HzF9+nTl5+ervLzc3+UiCHz2UX6X71uWrbWf79dnH+WrrsbVGtp9wTAN5c3MVERkqM/6AAAAANB3BPxI/J49eyRJ9fX1uuaaa7RlyxYNGjRIP/jBDzRv3jwVFxcrJyenzTWpqS17bR84cEBJSUmn1K/TGfDfb+AUlZXUnvAcj8fSyo/3eG20vTOZ2fFa8M3xHf5+czjMNv8G0Bb3CNA57g+gc9wfCHYBH+Jra1sC15133qmbbrpJt99+u9555x3deOONevbZZ9XY2KjQ0LajmGFhLXt3u1yuU+rTNA0lJET1rHAErJBQpzwNzSc8r6KHK8636/fwHvJut630gTE664KROvPcEQoNdXR5XWxshFfrAPoa7hGgc9wfQOe4PxCsAj7Eh4SESJKuueYaLViwQJI0evRobdmyRc8++6zCw8PV1NTU5poj4T0yMvKU+rQsW9XV9T2oGoFs/OQBWr1iX6dbx/mEIZ17ySidc/Eo2bYt4/CG8nV1jaqr6/gSh8NUbGyEqqsb5PF4ZwE9oC/hHgE6x/0BdI77A4EoNjai27NDAj7Ep6enS1K7KfPDhw/XRx99pIyMDJWWlrZ578jrtLS0U+7XW6uOo/ft3HZQyz/crT07D8mybIVHOv0W4A2jZQG80blpmn328GN+X3W/f4/H4vcj0AXuEaBz3B9A57g/EKwCPsSPGTNGUVFR+vLLLzV16tTW4zt27FBmZqYmT56sl156SR6PRw5Hy7TkFStWKDs7+5Sfh0ffYNu23vq/zfr43Z2tYVqS6mqbur7Qi9IHxmrWWUM19fRMnrsCAAAA0GMBH+LDw8N17bXX6ve//73S0tI0fvx4vf3221q+fLmee+45DR8+XE8//bSWLFmia6+9Vhs2bNDzzz+vX/7yl71dOnrZmhX79PG7OyV5bzu47rr0inGaOW+YTNPwb8cAAAAA+rSAD/GSdOONNyoiIkIPPfSQSkpKNGzYMD322GM67bTTJElPP/207rvvPi1YsEApKSm64447Wp+fR/9k27Y+/NdXkqGTmbnuNfGJkQR4AAAAAF4XFCFekhYvXqzFixd3+N748eP18ssv+7kiBLLqqkaVFNX0St8RkSEanXvq6zEAAAAAQGd4SBd9kru59xYpuWDBGDlDut42DgAAAABOBSEefVJcfLjCwns+0eTIVnDdERLi0KXfzNXpc7J73C8AAAAAdCRoptMDJ8MZ4tD02Vn65L1dsk9yOznDkJJSotTU5FFyapSmTB+sFZ/s0f49le3Ok6SxEwZo9Pg0TZiaofCIEC/9BAAAAADQHiEefdZZF+Zoy5fFKj9Yd1L7wl/2rfGaOXdom2OTpw/W58sKtOKjfB0srVVIiEO5kwdo9tnDNXBwnLdLBwAAAIAOGbbt7823Ap/HY+nQobreLgNeUFfbpLeXbtLqz/a1CfLH7ht/REpatM67dLQm5mX4ucqOOZ2mEhKiVFFRJ7e7957xBwIV9wjQOe4PoHPcHwhEiYlRcji697Q7I/Ho06KiQ/WNqybr4q+PU1FhtWqrXSopqpGr0a34xAhlZieoucmjyOhQDciIPaln4AEAAADA3wjx6Bcio0I1dEQSIR0AAABAUCPEo0+rrmzUpx/s0qpPC1Rb06SIyBBNPT1TZ5w9TIlJkb1dHgAAAACcFEI8+qzS4hr9/n+Wqb6uuXWF+ob6Zi3/YLe+WF6gG34yS4OGxPdukQAAAABwEgjx6JNs29Zzf/hcDccE+CMsy5ar0a3H7v9EqenRSkyO1LRZQzQ6N12myXR7AAAAAIGLEI8+adf2MpUW1Xb6vm1LHrelov3VKjlQo83rizU0J0nfu3m6wsPZ6x0AAABAYOreGvZAkNm57WC3R9WPbD2Xv/OQXnl2nS/LAgAAAIAeYSQefYLHbWnT+iKtWblPNdUuNdY3yz7xZW3Ylq0Naw+o/GCdklKifFInAAAAAPQEIR5Br6a6UU88uFzFhTUyjJap8jKkk07xkgxD2rSuSGeeO9zbZQIAAABAjzGdHkHNtm396bGVrc+/20eC+ykEeEkyDENNTW7vFAcAAAAAXsZIPILa7h3l2ren0mvtWZat5NRor7UHAAAAAN7ESDyC2sZ1B7y6LVxEZIjGTRrgtfYAAAAAwJsYiUdQa2p0y7ZPPHd+5rxszZw7VCuX7dEn7+7q9LwF3x6vkBCHN0sEAAAAAK8hxCOoJSZHqhsZXu5mS6npMZr/9XGKT4jQv9/crob65tb34xMjNP8b4zRhSoYPqwUAAACAniHEI+gcKqvTB//8SpvXF6mhoalb15QWtyx8ZxiGZp89XKefma2vtpWpvq5J8QkRyh6R5NVp+QAAAADgC4R4BJV/vb5V7/1j+0mvPl95qKHNa2eIQ6Nz07xYGQAAAAD4HiEeQePtpZv14b++OqVrI6NCvFwNAAAAAPgfq9MjKOwvqDzlAG8Y0pTpg71cEQAAAAD4HyPxCAqffZR/SteZpqHI6FDlzcz0ckUAAAAA4H+MxCMoFOw+dErXRUaF6ge3z1JEZKiXKwIAAAAA/yPEIyic6srxl3xznNIGxHi5GgAAAADoHYR4BIWRY09tJfm4uAgvVwIAAAAAvYdn4tErDpXV6bOP9mjLhiK5my1lZMbp9DlDNXxUsgyj/aj7jDOz9NE7J7ewXWx8uLJHJHmrZAAAAADodYR4+N3m9UV64fFVsmzJtlo2fK881KCNa4s0bdYQff3Kie2mzyelRGnQkDjtL6jqdj/nXzr6lKfhAwAAAEAgYjo9/Opgca1eeHyVPB67NcBLknX416s+LdAn7+3s8NqzLx7VrT4MQ7r48rGaNmtIzwsGAAAAgABCiIdfLf9ot2y763M+fmenPB6r3fEx49M1ZGhCl6PruZMH6hcPnK85547oaakAAAAAEHAI8fCrTWuLWkfdO1NT7VLR/up2x03T0LU/mqGRY1MlSYZpyHS0BPqQUIcWLpqgq34wTTGx4d4vHAAAAAACAM/Ew6+amz3dO6+p4/MiIkN1zS0zVFxYrU3riuRyuZWcGqUJeRkKDw/xZqkAAAAAEHAI8fCr9IGx2v1VWZdT6g1DSk6L6rqdjFilZ8R6uToAAAAACGxMp4dfzZiT3WWAN01DYycOYEo8AAAAAHSAEA+/Gj9loEaPT1MHW8HLNA2FRzg1//Jx/i8MAAAAAIIAIR5+ZZqGLv/uJA3LSW4X5EeMSdEtd89RUkrXU+kBAAAAoL/imXj41d78Cj350GdqbGyWDk+rNwzJtqWYmDAlJkf2boEAAAAAEMAYiYff1NW49ORDy+U6JsBLan1GfvWKfXrvre29UxwAAAAABAFCPPxm1fK9cjW6u1zY7pP3dna6vRwAAAAA9HeEePjNl6sLuwzwktTY4NbuHWX+KQgAAAAAggwhHn7jcrm7dV4TI/EAAAAA0CFCPPwmLT1GptnB3nLHSU6L9kM1AAAAABB8CPHwmxlnZsmyOp9PbxjS4Kx4DciI9WNVAAAAABA8CPHwm5yxqcqdPKDd/vBSS4B3OEwt+PYE/xcGAAAAAEGCEA+/MQxDi76fp9nnDFdIiKPNewMGxekHP52lzOyEXqoOAAAAAAKfs7cLQP/icJqaf/k4nTN/pHZtK1OTy6PUAdHKyIzv7dIAAAAAIOAR4tErwsNDNHbigN4uAwAAAACCCtPpAQAAAAAIEoR4AAAAAACCBCEeAAAAAIAgQYgHAAAAACBIEOIBAAAAAAgShHgAAAAAAIIEIR4AAAAAgCDBPvHoliaXW0WF1ZItpWXEKDw8pLdLAgAAAIB+hxCPLjU3efSvN7Zq5cd75HK5JUnOEFN5M4fowgVjFBFJmAcAAAAAfyHEo1PNzR49+dBy7dl1SLZ99Li72dLKj/OV/1WZbrpztsIjCPIAAAAA4A88E49Orfg4X/nHBfgjbFsqLqzRH//3U9XWuPxfHAAAAAD0Q4R4dGr5B/lSBwH+WIX7qvSrO97Rru1l/ikKAAAAAPoxQjw65HZbKj9Y1+1zn350haoqGnxcFQAAAAD0b4R4dMg0DRlG9893N3u04pM9PqsHAAAAAECIRydM09DwUSndDvK2LX35xX7fFgUAAAAA/RwhHp2ac+7wDhe164zL5fFdMQAAAAAAQjw6N3JcmmacmdWtcw1DSkuP8W1BAAAAANDPEeLRpa99Z4KSUiJPeJ5tS9PnZPm+IAAAAADoxwjx6JJhGPr+racrIiqki3OknLGpyp000I+VAQAAAED/Q4jHCSWnRuuO/zxL02ZlyjTbrnTncBiaOXeoFv/wtHbvAQAAAAC8y9nbBSA4xMSG6xtXTdY3rpqsA/uqVFJUo5AQU8NGJisiMrS3ywMAAACAfoEQj5M2cHCcBg6O6+0yAAAAAKDfYTo9AAAAAABBghAPAAAAAECQIMQDAAAAABAkCPEAAAAAAAQJQjwAAAAAAEGCEA8AAAAAQJAgxAMAAAAAECQI8QAAAAAABAlCPAAAAAAAQYIQDwAAAABAkCDEAwAAAAAQJAjxAAAAAAAECWdvF4CTY1m2Nq8v0ufLClR+sE6RUSGaNG2Qpp6eqfCIkN4uDwAAAADgQ4T4IOJyufWnx1Zq1/YyGaYh27IlSQW7K/TBP3fohp/MUuqAmF6uEgAAAADgK0ynDyJL/7xeu3eUSVJrgD+itqZJTzy0XO5mT2+UBgAAAADwA0J8kKg81KB1q/bLtjt+37JsVVU0asPaA/4tDAAAAADgN4T4ILFtU0mnAf4Iw5A2ry/2T0EAAAAAAL8jxAeJpia3DKPrc2xbanK5/VMQAAAAAMDvCPFBIjU95oQj8aZpsLAdAAAAAPRhhPggkTMmVbHx4VIXo/GWZeu0M4b4rygAAAAAgF8R4oOEaRq6/LuTWjJ8J0F+7vkjlJrOSDwAAAAA9FWE+CAyOjdN37/1dKUdF9SjokN1yRXjdOHXxvRSZQAAAAAAf3D2dgE4OTljUnX7L+epcG+VKsrrFR4RoqEjkuRw8n0MAAAAAPR1hPggZBiGBg2J16Ah8b1dCgAAAADAjxi+BQAAAAAgSBDiAQAAAAAIEoR4AAAAAACCBCEeAAAAAIAgQYgHAAAAACBIEOIBAAAAAAgShHgAAAAAAIIEIR4AAAAAgCBBiAcAAAAAIEgQ4gEAAAAACBJBFeLz8/M1adIkvfbaa63Htm7dqkWLFmnixImaM2eOnnnmmV6sEAAAAAAA3wmaEN/c3Kzbb79d9fX1rccqKiq0ePFiZWVlaenSpbr55pv1yCOPaOnSpb1YKQAAAAAAvuHs7QK667HHHlNUVFSbY6+88opCQ0N17733yul0atiwYSooKNBTTz2lhQsX9lKlAAAAAAD4RlCMxH/xxRd6+eWX9Zvf/KbN8dWrVysvL09O59HvIqZPn678/HyVl5f7u0wAAAAAAHwq4Efiq6urdccdd+jnP/+5BgwY0Oa94uJi5eTktDmWmpoqSTpw4ICSkpJOuV+nMyi+30Af5nCYbf4NoC3uEaBz3B9A57g/EOwCPsTfe++9mjhxoubPn9/uvcbGRoWGhrY5FhYWJklyuVyn3KdpGkpIiDrxiYAfxMZG9HYJQEDjHgE6x/0BdI77A8EqoEP866+/rtWrV+vNN9/s8P3w8HA1NTW1OXYkvEdGRp5yv5Zlq7q6/sQnAj7kcJiKjY1QdXWDPB6rt8sBAg73CNA57g+gc9wfCESxsRHdnh0S0CF+6dKlKi8v15w5c9ocv+eee/TMM89o4MCBKi0tbfPekddpaWk96tvt5oZGYPB4LH4/Al3gHgE6x/0BdI77A8EqoEP8Aw88oMbGxjbHzj33XN1yyy268MIL9fbbb+ull16Sx+ORw+GQJK1YsULZ2dk9eh4eAAAAAIBAFNCrOaSlpWnIkCFt/pGkpKQkZWRkaOHChaqtrdWSJUu0c+dOvfbaa3r++ed1/fXX93LlAAAAAAB4X0CH+BNJSkrS008/rfz8fC1YsEC/+93vdMcdd2jBggW9XRoAAAAAAF4X0NPpO7J9+/Y2r8ePH6+XX365l6oBAAAAAMB/gnokHgAAAACA/oQQDwAAAABAkCDEAwAAAAAQJAjxAAAAAAAECUI8AAAAAABBghAPAAAAAECQIMQDAAAAABAkCPEAAAAAAAQJQjwAAAAAAEGCEA8AAAAAQJAgxAMAAAAAECQI8QAAAAAABAnDtm27t4sINLZty7L4z4Le53CY8nis3i4DCFjcI0DnuD+AznF/INCYpiHDMLp1LiEeAAAAAIAgwXR6AAAAAACCBCEeAAAAAIAgQYgHAAAAACBIEOIBAAAAAAgShHgAAAAAAIIEIR4AAAAAgCBBiAcAAAAAIEgQ4gEAAAAACBKEeAAAAAAAggQhHgAAAACAIEGIBwAAAAAgSBDiAQAAAAAIEoR4AAAAAACCBCEeCDB/+MMfdOWVV7Y5tnXrVi1atEgTJ07UnDlz9Mwzz/RSdUDv6uj++NnPfqaRI0e2+Wf27Nm9VCHgP5WVlfrFL36h2bNna/LkyfrWt76l1atXt77PZwf6uxPdI3x+IFg5e7sAAEc999xzevTRR5WXl9d6rKKiQosXL9bZZ5+tX/7yl1q/fr1++ctfKj4+XgsXLuzFagH/6uj+kKTt27frhhtu0KJFi1qPORwOf5cH+N1tt92m8vJyPfjgg0pMTNSLL76oa665Rq+99poSExP57EC/19U9MmzYMD4/ELQI8UAAKCkp0ZIlS7RmzRplZ2e3ee+VV15RaGio7r33XjmdTg0bNkwFBQV66qmn+IsY+oWu7g+Px6OdO3fqxhtvVEpKSi9VCPhfQUGBli9frr/97W+aPHmyJGnJkiX65JNP9NZbbyk8PJzPDvRrJ7pHbrrpJj4/ELSYTg8EgM2bNysuLk5///vfNWHChDbvrV69Wnl5eXI6j37nNn36dOXn56u8vNzfpQJ+19X9sWfPHrlcLg0bNqyXqgN6R0JCgp588kmNGzeu9ZhhGLJtW1VVVXx2oN870T3C5weCGSPxQACYN2+e5s2b1+F7xcXFysnJaXMsNTVVknTgwAElJSX5vD6gN3V1f+zYsUOGYej555/XJ598ItM0deaZZ+rWW29VTEyMnysF/Cc2NlZnnnlmm2P//Oc/tXfvXs2aNUsPPfQQnx3o1050j/D5gWDGSDwQ4BobGxUaGtrmWFhYmCTJ5XL1RklAwPjqq69kmqYyMjL0+OOP684779THH3+sG2+8UZZl9XZ5gN+sWbNGd999t8466yzNmzePzw7gOMffI3x+IJgxEg8EuPDwcDU1NbU5duQvYJGRkb1REhAwbr75Zl199dWKjY2VJOXk5CglJUVXXHGFNm7c2G76PdAXvffee7r99ts1YcIEPfjgg5L47ACO1dE9wucHghkj8UCAS09PV2lpaZtjR16npaX1RklAwDAMo/UvYEccmUJcXFzcGyUBfvWXv/xFN998s2bPnq2nnnpK4eHhkvjsAI7o7B7h8wPBjBAPBLi8vDytWbNGHo+n9diKFSuUnZ3NM43o937yk5/ommuuaXNs48aNkqThw4f3RkmA37z44ov6r//6L33nO9/Rww8/3Gb6PJ8dQNf3CJ8fCGaEeCDALVy4ULW1tVqyZIl27typ1157Tc8//7yuv/763i4N6HUXX3yxli9frj/+8Y/au3evPv74Y9199926+OKLWXEYfVp+fr7++7//W+ecc46uv/56lZeX6+DBgzp48KBqamr47EC/d6J7hM8PBDPDtm27t4sAcNRdd92lwsJC/fnPf249tmHDBt13333asmWLUlJS9L3vfU+LFi3qxSqB3tHR/fHOO+/o8ccf1+7duxUTE6P58+fr1ltvbV3EC+iLHn/8cT300EMdvrdgwQLdf//9fHagX+vOPcLnB4IVIR4AAAAAgCDBdHoAAAAAAIIEIR4AAAAAgCBBiAcAAAAAIEgQ4gEAAAAACBKEeAAAAAAAggQhHgAAAACAIEGIBwAAAAAgSBDiAQAAAAAIEoR4AAACzLx58zRy5EiNHDlSZ599dpv3ioqKWt8bOXKk7rrrrl6q0n/279/f+vOuX79ekvTYY49p5MiROv/883u3OAAA/IwQDwBAANu3b5/279/f+nrFihW9WE3giI6OVlpamlJSUnq7FAAA/IoQDwBAgAoJCZHUNrivXLmyzXv91eLFi/XJJ5/oz3/+c2+XAgCAXxHiAQAIULm5uZI6DvHjx49vd351dbXuuecezZgxQ7m5ubr00kv1xhtvtDmnoaFB9913n+bOnatx48YpLy9PV199tb788svWc45MVb/lllv00Ucf6dJLL1Vubq7mz5+vjz/+uMuar7zySo0cOVKPPvqovve972nSpEm67bbbJEkFBQW65ZZbNHPmTI0bN04zZ87U7bffrtLS0jZtPP/885o3b55yc3N15ZVXavfu3e366Wg6/ZHHEJ588snWY2+//XbrVPwjSktLtWTJEs2dO1e5ubmaNWuWfvKTn6iwsLDLnw0AgEDg7O0CAABAx6ZNm6a1a9dq5cqVsm1b+fn5KikpUXp6ujIzM7VmzZrWc5uamnT11Vdr8+bNcjqdio6O1rZt23THHXeopqZGixYtkiT9/Oc/11tvvSXTNBUfH6+qqiqtWLFCmzdv1qeffqqwsLDWNjdu3Kj33ntPERERampq0o4dO3Trrbfqgw8+UEJCQpe1HwnSISEhGjVqlJqamrR48WIVFhYqNDRU0dHRKisr05tvvqnKyko9/fTTkqSnnnpKDzzwgCQpNDRU69at0y233OLV/6433nijNm7cKKfTqdjYWB06dEhvvfWWNm7cqLfffrvfz3IAAAQ2RuIBAAhQQ4YMUVpamsrLy7V9+/bWEfm8vLx2577xxhvavHmzsrOztWzZMn3++ed69tlnJUmPPPKIXC6XmpubZdu2srKy9PLLL2vFihWtI/XV1dXatWtXmzYPHDigJUuWaM2aNXrooYckSfX19Vq9evUJazdNU2+//bY+//xzfetb39K+ffs0dOhQjRs3TsuWLdPKlSv1i1/8QpK0bt06SVJzc3Nr+J89e7ZWrVqlVatWadKkSafyn69Dhw4d0saNGyW1/DdbsWKF3n33XeXl5Wnq1KkqKyvzWl8AAPgCI/EAAASwvLw8vfXWW1q5cqXWrl0rSTrttNPajMJLR6fZFxcX67LLLmvzXnV1tTZu3KipU6fqwQcflG3b2rVrl5YuXaovvvii9by6uro210VHR+vb3/62JOncc8/t9LyOTJkyRUOGDJHUMqIeExOjp59+Ws3Nzdq8ebM2bNigf//7323a2717t6qrqyVJN910kyIiIiRJ1113nT777LMT9tkdcXFxysjIUGFhoa699lrNnj1beXl5evjhh5WcnOyVPgAA8CVCPAAAAWzatGl66623tHz5cm3YsEFSS7A/PsRXVFRIannmvaGhoV07JSUlkqS///3vevDBB1VUVKTo6GhNnDix9RzLstpck5iYKMMwJElOp1MhISFqbm5ud15Hjl813rIs/fa3v9Xf/vY31dXVKS0tTRkZGZIk27YlSTU1NR1en5aWdsL+jnWkPUlyu91t3nM4HHrqqaf0m9/8RsuXL9fLL7+sl19+WaZp6vzzz9f999/f5pECAAACDSEeAIAANm3aNEnSp59+KsuylJqaqqysrHbnHQm95513nh599FFJksfjUXNzs8LDwyVJu3bt0p133inLsvTEE09o9uzZcrvdrQvoHc/hcLR5fSTQd8eRPo945ZVX9PTTTys1NVWvvvqqhg0bpmXLlunaa69tPSc+Pr711yUlJRo4cGDrr7vjSH1NTU2tx2pra9udN2zYsNYZCatXr9aaNWv04osv6h//+IfGjBmj73//+93+OQEA8DeeiQcAIIBlZ2crJSWldfT7SKg/3pHn5D/++OPWEfuXXnpJkyZN0gUXXKCamhrt2LGjtZ309HQZhtFmi7bujLCfqu3bt0tqWeguLS1NDQ0NevXVV9v0PXTo0NYp7X/84x/V0NCguro6Pf74493qIy4uTpJaZym4XC699dZbbc5Zt26dpk+frry8PG3btk1z587VD3/4w9ap/4cOHerZDwoAgI8xEg8AQICbNm2a3n777dZfd+SSSy7Rs88+q507d+ryyy9XXFycqqqqJElz5sxRTEyMxo0b1zol/mtf+5oiIiLajFQfeR7dFyZPnqwXX3xRhYWFmjlzpmzblsvlan2/qqpKCQkJuuWWW/SLX/xCH3/8sfLy8mQYhqKjo1vr7sqMGTO0efNmrVixQnPmzJHL5Wo3NX78+PHKzs7W2rVrtWjRIiUkJKiurk5NTU0KCwvT/PnzffLzAwDgLYzEAwAQ4I5djb6jlemllsXjXnjhBX3jG99QSkqK6uvrlZWVpTvuuEN33HGHJGnw4MF6+OGHNWLECDmdTsXHx+v666/XvHnzJMlri8d1ZP78+frRj37UOgMgKytL999/v5KSkiSpdeX9K664Qr/61a80aNAgmaapSZMm6YUXXujWtm833XSTLr/8csXExKi+vl5nn31263Z1Rxx5Jv66667TkCFDVF9fr7i4OM2ePVsvvPCCxowZ4/0fHgAALzLsY1d/AQAAAAAAAYuReAAAAAAAggQhHgAAAACAIEGIBwAAAAAgSBDiAQAAAAAIEoR4AAAAAACCBCEeAAAAAIAgQYgHAAAAACBIEOIBAAAAAAgShHgAAAAAAIIEIR4AAAAAgCBBiAcAAAAAIEj8f0PQhfst47AXAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1200x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/100\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
-      "  super(SGD, self).__init__(name, **kwargs)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6/6 [==============================] - 0s 24ms/step - loss: 0.7171 - accuracy: 0.5820\n",
-      "Epoch 2/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 0.7169 - accuracy: 0.6289\n",
-      "Epoch 3/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 0.7167 - accuracy: 0.6289\n",
-      "Epoch 4/100\n",
-      "6/6 [==============================] - 0s 14ms/step - loss: 0.7165 - accuracy: 0.6289\n",
-      "Epoch 5/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 0.7162 - accuracy: 0.6289\n",
-      "Epoch 6/100\n",
-      "6/6 [==============================] - 0s 9ms/step - loss: 0.7161 - accuracy: 0.6289\n",
-      "Epoch 7/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 0.7159 - accuracy: 0.6289\n",
-      "Epoch 8/100\n",
-      "6/6 [==============================] - 0s 10ms/step - loss: 0.7157 - accuracy: 0.6289\n",
-      "Epoch 9/100\n",
-      "6/6 [==============================] - 0s 9ms/step - loss: 0.7155 - accuracy: 0.6289\n",
-      "Epoch 10/100\n",
-      "6/6 [==============================] - 0s 10ms/step - loss: 0.7153 - accuracy: 0.6289\n",
-      "Epoch 11/100\n",
-      "6/6 [==============================] - 0s 10ms/step - loss: 0.7151 - accuracy: 0.6289\n",
-      "Epoch 12/100\n",
-      "6/6 [==============================] - 0s 10ms/step - loss: 0.7150 - accuracy: 0.6289\n",
-      "Epoch 13/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 0.7147 - accuracy: 0.6289\n",
-      "Epoch 14/100\n",
-      "6/6 [==============================] - 0s 10ms/step - loss: 0.7145 - accuracy: 0.6289\n",
-      "Epoch 15/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 0.7143 - accuracy: 0.6289\n",
-      "Epoch 16/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 0.7141 - accuracy: 0.6289\n",
-      "Epoch 17/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 0.7140 - accuracy: 0.6289\n",
-      "Epoch 18/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 0.7138 - accuracy: 0.6289\n",
-      "Epoch 19/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 0.7136 - accuracy: 0.6289\n",
-      "Epoch 20/100\n",
-      "6/6 [==============================] - 0s 10ms/step - loss: 0.7134 - accuracy: 0.6289\n",
-      "Epoch 21/100\n",
-      "6/6 [==============================] - 0s 10ms/step - loss: 0.7133 - accuracy: 0.6289\n",
-      "Epoch 22/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 0.7131 - accuracy: 0.6289\n",
-      "Epoch 23/100\n",
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-     ]
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-     ]
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-     ]
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-     ]
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-      "Epoch 30/100\n",
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-      "6/6 [==============================] - 0s 12ms/step - loss: 334339212574720.0000 - accuracy: 0.6289\n",
-      "Epoch 70/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 326402750545920.0000 - accuracy: 0.6289\n",
-      "Epoch 71/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 318654663098368.0000 - accuracy: 0.6289\n",
-      "Epoch 72/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 311090521047040.0000 - accuracy: 0.6289\n",
-      "Epoch 73/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 303705962315776.0000 - accuracy: 0.6289\n",
-      "Epoch 74/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 296496658382848.0000 - accuracy: 0.6289\n",
-      "Epoch 75/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 289458482053120.0000 - accuracy: 0.6289\n",
-      "Epoch 76/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 282587373240320.0000 - accuracy: 0.6289\n",
-      "Epoch 77/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 275879372521472.0000 - accuracy: 0.6289\n",
-      "Epoch 78/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 269330654691328.0000 - accuracy: 0.6289\n",
-      "Epoch 79/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 262937344212992.0000 - accuracy: 0.6289\n",
-      "Epoch 80/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 256695817207808.0000 - accuracy: 0.6289\n",
-      "Epoch 81/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 250602399465472.0000 - accuracy: 0.6289\n",
-      "Epoch 82/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 244653668433920.0000 - accuracy: 0.6289\n",
-      "Epoch 83/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 238846134452224.0000 - accuracy: 0.6289\n",
-      "Epoch 84/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 233176475631616.0000 - accuracy: 0.6289\n",
-      "Epoch 85/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 227641386860544.0000 - accuracy: 0.6289\n",
-      "Epoch 86/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 222237714022400.0000 - accuracy: 0.6289\n",
-      "Epoch 87/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 216962286223360.0000 - accuracy: 0.6289\n",
-      "Epoch 88/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 211812083564544.0000 - accuracy: 0.6289\n",
-      "Epoch 89/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 206784136478720.0000 - accuracy: 0.6289\n",
-      "Epoch 90/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 201875559284736.0000 - accuracy: 0.6289\n",
-      "Epoch 91/100\n",
-      "6/6 [==============================] - 0s 11ms/step - loss: 197083516633088.0000 - accuracy: 0.6289\n",
-      "Epoch 92/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 192405189951488.0000 - accuracy: 0.6289\n",
-      "Epoch 93/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 187837911662592.0000 - accuracy: 0.6289\n",
-      "Epoch 94/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 183379064520704.0000 - accuracy: 0.6289\n",
-      "Epoch 95/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 179026064834560.0000 - accuracy: 0.6289\n",
-      "Epoch 96/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 174776379244544.0000 - accuracy: 0.6289\n",
-      "Epoch 97/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 170627558277120.0000 - accuracy: 0.6289\n",
-      "Epoch 98/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 166577253122048.0000 - accuracy: 0.6289\n",
-      "Epoch 99/100\n",
-      "6/6 [==============================] - 0s 13ms/step - loss: 162623081414656.0000 - accuracy: 0.6289\n",
-      "Epoch 100/100\n",
-      "6/6 [==============================] - 0s 12ms/step - loss: 158762778230784.0000 - accuracy: 0.6289\n",
-      "16/16 [==============================] - 0s 10ms/step - loss: 156281545424896.0000 - accuracy: 0.6289\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/2 [==============>...............] - ETA: 0s - loss: 156281578979328.0000 - accuracy: 0.6562\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "2/2 [==============================] - 0s 38ms/step - loss: 156281578979328.0000 - accuracy: 0.6140\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1608181771.py:136: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
-      "  cbar=fig.colorbar(cax)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1608181771.py:152: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
-      "  ax.set_xticklabels(['']+x)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1608181771.py:153: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
-      "  ax.set_yticklabels(['']+y)\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1200x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 23,
+   "id": "13a062b9",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "\n",
     "import tensorflow as tf\n",
@@ -5966,8 +2487,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4cd2098",
-   "metadata": {},
+   "id": "9b1579eb",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Fine-tuning neural network hyperparameters\n",
     "\n",
@@ -5992,8 +2515,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d214193a",
-   "metadata": {},
+   "id": "0d7e41b0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Hidden layers\n",
     "\n",
@@ -6013,8 +2538,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9191060",
-   "metadata": {},
+   "id": "b000195c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Which activation function should I use?\n",
     "\n",
@@ -6042,8 +2569,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c99bf5d",
-   "metadata": {},
+   "id": "2b02569e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Is the Logistic activation function (Sigmoid)  our choice?\n",
     "\n",
@@ -6072,8 +2601,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bb9e82b",
-   "metadata": {},
+   "id": "9789688f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The derivative of the Logistic funtion\n",
     "\n",
@@ -6108,8 +2639,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac8e2413",
-   "metadata": {},
+   "id": "b259486d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The RELU function family\n",
     "\n",
@@ -6131,8 +2664,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fb72862",
-   "metadata": {},
+   "id": "05bcd0f9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "ELU(z) = \\left\\{\\begin{array}{cc} \\alpha\\left( \\exp{(z)}-1\\right) & z < 0,\\\\  z & z \\ge 0.\\end{array}\\right.\n",
@@ -6141,8 +2676,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dc439ba",
-   "metadata": {},
+   "id": "e1a8592e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Which activation function should we use?\n",
     "\n",
@@ -6162,8 +2699,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ebcb4c9",
-   "metadata": {},
+   "id": "c27319dc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## More on activation functions, output layers\n",
     "\n",
@@ -6180,8 +2719,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb1c86c5",
-   "metadata": {},
+   "id": "13edfd00",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Batch Normalization\n",
     "\n",
@@ -6200,8 +2741,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "116ff585",
-   "metadata": {},
+   "id": "2058a4fe",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Dropout\n",
     "\n",
@@ -6216,8 +2759,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b2393c6",
-   "metadata": {},
+   "id": "98d5373c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Gradient Clipping\n",
     "\n",
@@ -6233,8 +2778,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b05d371",
-   "metadata": {},
+   "id": "e762e70c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A very nice website on Neural Networks\n",
     "\n",
@@ -6243,8 +2790,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe8cb1f9",
-   "metadata": {},
+   "id": "92e6fbe6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A top-down perspective on Neural networks\n",
     "\n",
@@ -6286,8 +2835,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03e2eeb7",
-   "metadata": {},
+   "id": "58d322ba",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Limitations of supervised learning with deep networks\n",
     "\n",
@@ -6313,25 +2864,7 @@
    ]
   }
  ],
- "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/week41/week41.do.txt b/doc/src/week41/week41.do.txt
index 9c57d0f4f..e4fe80e29 100644
--- a/doc/src/week41/week41.do.txt
+++ b/doc/src/week41/week41.do.txt
@@ -1,17 +1,15 @@
 TITLE: Week 41 Constructing a Neural Network code, Tensor flow and start Convolutional Neural Networks
 AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University
-DATE: today
+DATE: Week 41
 
 
 !split
 ===== Plan for week 41 =====
 
-* Thursday: Building our own Feed-forward Neural Network and discussion of project 2.
-  * "Video of lecture":"https://youtu.be/yzbxJI6LgL0"
-* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks. 
-  * "Video of lecture":"https://youtu.be/CPj4mh7M9no
+* Building our own Feed-forward Neural Network and discussion of project 2.
+* Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Solving differential equations with neural networks. 
 "
-Reading suggestions for both days: These notes, "Aurelien Geron's chapters 10-11":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf".
+Reading suggestions: These notes, "Aurelien Geron's chapters 10-11":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf".
 For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7. Bishop's chapter 5 on Neural Networks is an additional good read.
 
 !split