diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index c951d522f..5db4f2e57 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/week39.doctree b/doc/LectureNotes/_build/.doctrees/week39.doctree
index f4ba859ea..420e34c60 100644
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diff --git a/doc/LectureNotes/_build/html/_images/week39_153_1.png b/doc/LectureNotes/_build/html/_images/week39_153_1.png
index d71eae3c9..2553e4ed5 100644
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diff --git a/doc/LectureNotes/_build/html/_images/week39_166_1.png b/doc/LectureNotes/_build/html/_images/week39_166_1.png
index 1cc777a81..91b413b23 100644
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diff --git a/doc/LectureNotes/_build/html/_sources/week39.ipynb b/doc/LectureNotes/_build/html/_sources/week39.ipynb
index 4a48fabca..bde575030 100644
--- a/doc/LectureNotes/_build/html/_sources/week39.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/week39.ipynb
@@ -1012,7 +1012,7 @@
     {
      "data": {
       "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x150835f40>"
+       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11f758f40>"
       ]
      },
      "execution_count": 1,
@@ -1154,7 +1154,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x161847e80>]"
+       "[<matplotlib.lines.Line2D at 0x12a472ee0>]"
       ]
      },
      "execution_count": 5,
@@ -1814,16 +1814,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.30768763 4.39313488]\n",
-      "[[3.95814165]\n",
-      " [3.0184648 ]]\n",
-      "[[3.95814165]\n",
-      " [3.0184648 ]]\n"
+      "Eigenvalues of Hessian Matrix:[0.39227722 4.38168146]\n",
+      "[[3.8976899 ]\n",
+      " [3.03844474]]\n",
+      "[[3.8976899 ]\n",
+      " [3.03844474]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1899,9 +1899,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.94758046]\n",
-      " [2.92347862]]\n",
-      "[3.94453204] [2.94467121]\n"
+      "[[3.81448757]\n",
+      " [3.3421718 ]]\n",
+      "[3.83247778] [3.40246301]\n"
      ]
     }
    ],
@@ -2034,16 +2034,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.32374405 4.06221646]\n",
-      "[[4.0585026]\n",
-      " [2.9999519]]\n",
-      "[[4.05843037]\n",
-      " [3.00001709]]\n"
+      "Eigenvalues of Hessian Matrix:[0.31252985 4.57630515]\n",
+      "[[4.06031839]\n",
+      " [3.01099015]]\n",
+      "[[4.05965043]\n",
+      " [3.01153127]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3993,34 +3993,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "cd18340b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[3.94499279]\n",
-      " [3.03306538]]\n",
-      "Eigenvalues of Hessian Matrix:[0.31248425 4.44418124]\n",
-      "theta from own gd\n",
-      "[[3.94499279]\n",
-      " [3.03306538]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients for OLS\n",
     "from random import random, seed\n",
@@ -4083,87 +4059,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "ac458170",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.]\n",
-      " [3.]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30306759 4.65944342]\n",
-      "0 [-20.62275788] [-25.73690387]\n",
-      "1 [-0.03662823] [0.02925855]\n",
-      "2 [-0.0342458] [0.02735547]\n",
-      "3 [-0.03201832] [0.02557617]\n",
-      "4 [-0.02993573] [0.0239126]\n",
-      "5 [-0.0279886] [0.02235723]\n",
-      "6 [-0.02616812] [0.02090304]\n",
-      "7 [-0.02446604] [0.01954342]\n",
-      "8 [-0.02287468] [0.01827225]\n",
-      "9 [-0.02138683] [0.01708375]\n",
-      "10 [-0.01999575] [0.01597256]\n",
-      "11 [-0.01869515] [0.01493365]\n",
-      "12 [-0.01747915] [0.01396231]\n",
-      "13 [-0.01634224] [0.01305415]\n",
-      "14 [-0.01527928] [0.01220505]\n",
-      "15 [-0.01428546] [0.01141119]\n",
-      "16 [-0.01335628] [0.01066897]\n",
-      "17 [-0.01248754] [0.00997502]\n",
-      "18 [-0.0116753] [0.0093262]\n",
-      "19 [-0.01091589] [0.00871959]\n",
-      "20 [-0.01020588] [0.00815244]\n",
-      "21 [-0.00954206] [0.00762217]\n",
-      "22 [-0.0089214] [0.0071264]\n",
-      "23 [-0.00834112] [0.00666287]\n",
-      "24 [-0.00779859] [0.00622949]\n",
-      "25 [-0.00729134] [0.0058243]\n",
-      "26 [-0.00681708] [0.00544547]\n",
-      "27 [-0.00637367] [0.00509128]\n",
-      "28 [-0.0059591] [0.00476012]\n",
-      "29 [-0.0055715] [0.0044505]\n",
-      "theta from own gd\n",
-      "[[3.98281205]\n",
-      " [3.0137297 ]]\n",
-      "0 [-0.00520911] [0.00416103]\n",
-      "1 [-0.00487029] [0.00389038]\n",
-      "2 [-0.00445186] [0.00355614]\n",
-      "3 [-0.00403677] [0.00322456]\n",
-      "4 [-0.00364967] [0.00291535]\n",
-      "5 [-0.00329616] [0.00263296]\n",
-      "6 [-0.00297571] [0.00237699]\n",
-      "7 [-0.00268602] [0.00214559]\n",
-      "8 [-0.00242441] [0.00193661]\n",
-      "9 [-0.00218823] [0.00174795]\n",
-      "10 [-0.00197505] [0.00157766]\n",
-      "11 [-0.00178263] [0.00142396]\n",
-      "12 [-0.00160895] [0.00128523]\n",
-      "13 [-0.0014522] [0.00116001]\n",
-      "14 [-0.00131072] [0.001047]\n",
-      "15 [-0.00118302] [0.00094499]\n",
-      "16 [-0.00106776] [0.00085292]\n",
-      "17 [-0.00096373] [0.00076983]\n",
-      "18 [-0.00086984] [0.00069482]\n",
-      "19 [-0.00078509] [0.00062713]\n",
-      "20 [-0.0007086] [0.00056603]\n",
-      "21 [-0.00063957] [0.00051088]\n",
-      "22 [-0.00057726] [0.00046111]\n",
-      "23 [-0.00052102] [0.00041619]\n",
-      "24 [-0.00047025] [0.00037564]\n",
-      "25 [-0.00042444] [0.00033904]\n",
-      "26 [-0.00038309] [0.00030601]\n",
-      "27 [-0.00034576] [0.0002762]\n",
-      "28 [-0.00031208] [0.00024929]\n",
-      "29 [-0.00028167] [0.000225]\n",
-      "theta from own gd wth momentum\n",
-      "[[3.99916114]\n",
-      " [3.00067008]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients for OLS\n",
     "from random import random, seed\n",
@@ -4230,29 +4129,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "e6b06cdb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.26281735]\n",
-      " [2.84478178]]\n",
-      "Eigenvalues of Hessian Matrix:[0.33978889 4.51659846]\n",
-      "0 [-14.67490857] [-18.08539155]\n",
-      "1 [-4.46170878e-15] [-1.45781905e-14]\n",
-      "2 [-1.21430643e-15] [-1.24768421e-15]\n",
-      "3 [4.92661467e-16] [4.50610071e-16]\n",
-      "4 [4.92661467e-16] [4.50610071e-16]\n",
-      "beta from own Newton code\n",
-      "[[4.26281735]\n",
-      " [2.84478178]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Newton's method\n",
     "from random import random, seed\n",
@@ -4305,43 +4185,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "a010a209",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.0586484]\n",
-      " [3.0718316]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]\n",
-      "theta from own gd\n",
-      "[[4.0586484]\n",
-      " [3.0718316]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "theta from own sdg\n",
-      "[[4.02496085]\n",
-      " [3.12081773]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using SGD\n",
     "# OLS example\n",
@@ -4428,27 +4275,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "3320d6c6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[3.96075051]\n",
-      " [3.02148021]]\n",
-      "Eigenvalues of Hessian Matrix:[0.27470622 4.24106503]\n",
-      "theta from own gd\n",
-      "[[3.95906059]\n",
-      " [3.02296298]]\n",
-      "theta from own sdg with momentum\n",
-      "[[3.95611042]\n",
-      " [2.99475306]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using SGD\n",
     "# OLS example\n",
@@ -4529,25 +4359,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "19414323",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own AdaGrad\n",
-      "[[2.00025662]\n",
-      " [2.99802696]\n",
-      " [4.00167329]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4617,25 +4432,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "6724b3d2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own RMSprop\n",
-      "[[1.99456598]\n",
-      " [2.99848815]\n",
-      " [3.99835783]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4703,25 +4503,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "28aa6aad",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own ADAM\n",
-      "[[1.99989103]\n",
-      " [3.00042093]\n",
-      " [3.99954925]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4794,19 +4579,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "f3dde7c5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial loss: 2.772588722239781\n",
-      "Trained loss: 1.067270675787016\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
     "from autograd import grad\n",
@@ -4861,18 +4637,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "291c9b6d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.25       0.19661197 0.10499357]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import jax.numpy as jnp\n",
     "from jax import grad, jit, vmap\n",
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index e17d39d5a..cbac24241 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
+++ b/doc/LectureNotes/_build/html/searchindex.js
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient 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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods"],titleterms:{"1":[0,15,16,17,18,23,28,29],"16":32,"2":[0,15,16,17,18,28,29,30],"2023":26,"2024":[23,33],"21":[],"23":33,"26":29,"27":33,"3":[0,15,16,28,29],"34":[15,28],"35":[16,29],"36":[17,30],"37":[18,31],"38":[19,32],"39":[20,33],"4":[0,29],"5":0,"7":23,"9":31,"case":[8,10,25,29,30,32,33],"class":32,"do":[1,30,31,33],"final":[12,29,30,33],"function":[0,1,6,7,8,10,11,12,13,23,25,28,29,30,31,32,33],"import":[5,22,28,29,30],"new":[4,30,31],A:[0,1,4,8,9,28,30,31,32],And:[28,29,30,32,33],But:33,For:29,In:26,Ising:6,The:[0,1,2,3,5,6,7,8,9,11,12,17,21,28,29,30,31,32,33],To:[28,29],With:[4,30],about:[28,29],abov:30,activ:[1,12,30],ad:[0,6,17,23,28,29],adaboost:10,adagrad:[13,33],adam:[13,33],adapt:[10,33],adjust:1,adversari:4,again:[3,9,32],ai:[23,28],aim:[8,9,17,18,19,20,28],aka:[28,29],algebra:[22,28],algorithm:[9,10,11,12,29,33],algortithm:[13,32,33],all:8,an:[0,4,10,28],analys:[5,29],analysi:[0,5,6,11,21,23,25,28,29,30,31],analyt:[0,16,17],ani:[13,32,33],anoth:[9,30,31],appli:21,approach:[0,8,14,28,31,33],approxim:12,architectur:1,argument:33,arrai:[22,28],assist:26,assumpt:[30,31],august:29,autocorrel:25,autograd:[2,13,33],automat:[13,33],avoid:33,b:[17,23,33],back:[1,11,12],background:[21,23,31],bag:10,base:[13,31,33],basic:[0,5,7,9,10,11,22,29,30,31,32],batch:[1,33],bay:[5,30,31],befor:11,beta:[30,31],better:8,bia:[6,23,31],binari:1,bind:28,bird:10,boldsymbol:[29,30,31],boost:10,bootstrap:[6,10,31],boston:0,breast:1,brief:[28,31,32,33],bring:12,build:[1,3,9],c:[23,28],calcul:29,can:[28,31,33],cancer:[1,7,9,11,32],cart:9,center:29,central:[13,21,25,31,32,33],chain:12,challeng:32,chang:10,channel:28,chi:[0,28],choos:1,cifar01:3,classic:11,classif:[1,9,10,32],classifi:[8,32],clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,28,29,30,31,32,33],collect:[1,3],come:32,commun:28,compact:32,compar:[2,10],comparison:30,compet:33,complet:29,complex:[0,6,23,29],complic:[6,33],compon:11,comput:[9,33],computation:31,computerlab:28,con:9,concept:25,condit:[30,31,32,33],confid:31,conjug:[13,33],contn:28,convex:[8,13,32,33],convolut:[3,12],correctli:[30,31],correl:[11,29,32],correspond:[32,33],cost:[1,10,29,30,31,32,33],cours:[21,27,28],covari:[5,11,25,29],cover:28,cross:[6,23,31,32],cython:28,d:23,data:[0,1,3,6,7,9,11,15,16,21,23,25,28,29,30,32],dataset:[1,3],deadlin:[23,28],decai:[2,33],decis:[9,10],decomposit:[5,11,17,22,29],deep:[1,2,28,32],defin:[1,28],degre:[0,29],deliveri:23,delta:31,dens:[0,28],deriv:[5,12,29,30,31,32,33],descent:[2,10,13,32,33],descript:23,design:29,detail:[3,28],develop:1,diagon:11,differ:[8,33],differenti:[2,13,33],diffus:2,dimension:[2,3,8,23,29],directli:33,disadvantag:9,discret:25,discuss:32,distribut:[5,25,30,31],distrubut:31,doe:[29,30],domain:25,dot:33,down:1,dropout:1,e:23,each:32,economi:29,electron:23,element:[0,25,28,33],elimin:22,energi:28,ensembl:10,entropi:[9,32],environ:[0,15,28],equat:[0,2,12,28,29,30,32,33],error:[0,10,28,29,31],essenti:28,estim:[30,31],etc:28,euler:2,evalu:1,exampl:[0,1,2,3,4,6,7,8,9,10,28,29,30,31,32,33],exercis:[0,6,15,16,17,18,19,20,28,29],expect:[18,25,30,31],expens:31,experi:25,explor:[0,15,16,28],exponenti:2,express:[17,18,29,32,33],extend:[32,33],extrapol:4,extrem:[10,28],ey:10,f:23,fall:26,famili:[1,28,29],famou:22,fantast:29,featur:[9,22,29],feed:[1,12],find:[31,33],fine:1,first:[4,12,28,29,30,32,33],fit:[0,10,28,30],fix:29,fold:[31,32],forc:3,forest:10,format:[23,28],forward:[1,2,12],fourier:3,frank:[6,23,29],freedom:[0,29],frequent:29,frequentist:[0,28],fridai:[],from:[5,10,12,29,30,31,32,33],full:2,further:[3,5,29],g:23,gan:4,gaussian:22,gd:[13,33],gener:[4,9,28],geometr:[11,32,33],gini:9,good:[0,28],grade:[26,28],gradient:[1,2,10,13,32,33],grid:32,group:32,growth:2,ha:21,handl:[22,28,29],happen:[30,31],hessian:[29,32,33],hidden:2,histogram:31,homework:[32,33],hous:0,how:32,hyperparamet:1,hyperplan:8,i:1,id3:9,idea:11,ideal:[32,33],ident:[30,31],identifi:31,ii:28,iid:[30,31],illustr:30,implement:1,implic:[5,29],improv:[1,33],includ:[13,32,33],increment:11,independ:[30,31],index:9,inform:26,input:2,instal:[21,23,28],instructor:26,intercept:29,interpret:[5,11,28,29,30,31,32,33],interv:31,introduc:[11,13,29,33],introduct:[0,6,21,22,23,28],invers:[5,22,30],invert:29,iter:[10,33],its:29,jacobian:29,jax:[13,33],julia:28,jungl:10,k:[31,32],kera:[1,3],kernel:[8,11],lab:[31,32,33],lagrangian:8,lambda:32,lasso:[5,6,23,29,30,31,32],last:[29,32],later:[5,29],layer:[1,2,3,12],learn:[0,1,2,11,13,14,15,16,21,23,28,29,30,31,32,33],least:[5,6,18,23,28,29,30],lectur:[28,29,30,31,32,33],level:10,librari:[21,28],likelihood:[7,30,31,32],limit:[1,13,25,31,32,33],linear:[0,8,13,22,28,29,30,32],link:[5,11,24,27,29,30,31],literatur:23,logist:[7,28,32,33],loop:33,loss:[29,32,33],lu:22,machin:[0,8,13,21,23,28,32,33],made:[30,31],main:[25,28],make:[0,9,10,15,16,28,29],mani:[10,12],manipul:29,margin:[30,31],mass:28,materi:[23,24,28,29,30,31,32],math:[5,29],mathemat:[3,5,8,29,33],matric:[5,22,28,30],matrix:[1,5,11,12,22,28,29,30,32,33],matter:[0,28],max:29,maximum:[30,31,32],mean:[0,29,30,32],measur:32,meet:[5,10,25,28,29],mercer:8,method:[6,9,10,13,23,28,31,32,33],midnight:23,min:29,mini:33,minibatch:33,minim:[28,32],ml:28,mle:[30,31],mlp:12,mnist:[3,4],model:[0,1,4,6,12,28],moment:33,momentum:[13,33],mondai:[29,30,31,32,33],moon:[8,9],more:[3,6,22,23,28,29,30,31,32,33],multilay:12,multipl:[1,3],multipli:8,need:[23,28],network:[1,2,3,4,7,12,28,32],neural:[1,2,3,4,7,12,28],newton:[32,33],noen:[],non:8,none:33,normal:[0,1,31],notat:12,note:[23,29,30],now:[1,9,13,30,31,33],nuclear:[0,28],nueral:32,numba:28,number:[0,2,25,29,33],numer:[2,23,25],numpi:[22,28,33],object:3,obtain:11,octob:23,od:2,off:[6,23],ol:[5,6,23,30,31,33],one:[2,12,32,33],oper:22,optim:[1,8,13,21,28,29,32,33],order:[13,33],ordinari:[5,6,18,23,28,29,30],organ:[0,28],oslo:27,other:[4,9,11,12,22,23,28,29,32],our:[0,4,5,11,13,28,29,32,33],outcom:[21,28],output:2,overarch:[0,4,8,9,17,18,19,20,28,29],overview:[10,28,33],own:[0,10,11,15,16,28,29],packag:[22,28],panda:[28,29],paper:23,paramet:[28,29,32,33],part:[13,21,23,29,32,33],partial:2,pass:1,pca:11,pdf:25,pencil:23,perceptron:12,perform:[1,9],period:3,perspect:1,plan:[29,30,31,32,33],plot:[31,32],point:4,poisson:2,polynomi:[3,30],popul:2,popular:28,practic:[13,26,28,33],pre:[1,3],preambl:23,predict:4,predictor:32,preprocess:29,prerequisit:[3,21,28],princip:11,principl:3,pro:9,probabl:[5,25,30,31],problem:[1,2,13,28,29,30,32,33],procedur:[9,28],process:[1,3],product:33,program:[2,13,23,32,33],project:[6,23,28,32],prop:[13,33],propag:[1,12],properti:[5,25,29,32],python:[0,9,15,21,22,28],quick:8,r:28,random:[10,11,25,32],raphson:[32,33],rate:33,read:[9,28,29,31,32],real:[6,23,28],recommend:[28,29,33],rectangular:30,recurr:[4,12],recurs:33,reduc:[0,29],reduct:3,refer:23,reformul:2,regress:[0,5,6,7,9,10,13,17,18,23,28,29,30,31,32,33],regular:[1,32],relat:29,relev:[27,29,32],relu:1,remark:3,remind:[6,8,28,32,33],repeat:29,replac:[13,33],report:23,repositori:31,requir:[2,21],resampl:[6,23,31],rescal:[6,30],residu:29,resourc:2,result:[29,30],revisit:[13,32,33],rewrit:[28,29,31],rewritten:32,ridg:[0,5,6,17,18,23,29,30,31,32,33],rm:[13,33],rmsprop:33,rule:12,s:[8,10,31,32,33],same:[13,31,33],sampl:11,scale:29,schedul:[24,28],schemat:9,scheme:2,scienc:28,scikit:[0,1,11,15,16,28,29,30,31,32,33],search:32,second:[13,33],select:32,semest:26,sensit:[32,33],septemb:[30,31,32,33],session:[29,30,31,32,33],set:[0,2,3,9,12,15,28,29,32],sgd:[13,33],should:1,similar:[13,33],simpl:[0,4,9,13,28,29,30,32,33],singl:10,singular:[5,11,17,29],size:29,slightli:33,soft:8,softmax:1,softwar:[23,28],solv:[2,30,32,33],solver:13,some:[13,22,29,32,33],specifi:2,split:[0,15,16,28,29],squar:[0,5,6,10,18,23,28,29,30],standard:[13,29,31,33],state:[0,28],statist:[5,6,21,25,28,30,31],steepest:[10,13,32,33],step:[31,32,33],still:29,stochast:[13,25,33],stop:33,strongli:28,studi:32,subtract:29,suggest:[28,32],sum:31,summari:[26,28],superposit:3,supervis:1,support:8,svd:[5,29,30],syntax:33,systemat:3,t:[29,30],teach:[24,26],teacher:[26,28],technic:30,techniqu:[6,11,23],technolog:21,tensorflow:[1,3],tent:28,term:31,test:[0,1,15,16,28,29,30],text:28,textbook:[27,28],than:[32,33],theorem:[5,8,11,12,25,30,31],theori:25,thi:[17,18,19,20,28],think:29,thursdai:[],time:33,tip:[13,33],togeth:12,tool:[23,28],top:1,topic:28,toward:11,trade:[6,23],tradeoff:[6,31],train:[0,1,4,15,16,28,29],transform:3,tree:[9,10],tuesdai:30,tune:1,two:[3,8,21,23,29,32],type:[2,4,12,28],uio:28,understand:31,univers:[12,27],unsupervis:14,unsupport:33,up:[0,2,9,12,15,28,29,31,32],us:[0,1,2,3,7,13,21,23,28,29,32,33],usag:[30,31],valid:[6,23,31,32],valu:[5,11,17,18,25,29,30,31,32],vari:33,variabl:[25,32,33],varianc:[6,23,30,31],variou:[0,15,28,31,32],vector:[8,12,22,28,29],video:[32,33],view:[0,4,10,29],visual:[1,9],vs:3,wai:[9,31],wave:2,we:[28,33],wednesdai:30,week:[15,16,17,18,19,20,28,29,30,31,32,33],weekend:32,weekli:24,what:[0,28,29,30,31],when:33,which:[1,33],why:[28,29,31],wisconsin:[7,32],wrap:[29,31],write:[4,11,23,30],x:[29,30],xgboost:10,yet:30,your:[0,10,15,16,28,29],yourself:32}})
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/week39.html b/doc/LectureNotes/_build/html/week39.html
index bd8990824..4f2487bc5 100644
--- a/doc/LectureNotes/_build/html/week39.html
+++ b/doc/LectureNotes/_build/html/week39.html
@@ -1797,7 +1797,7 @@ which equals</p>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1273ffac0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x120cc1b20&gt;
 </pre></div>
 </div>
 <img alt="_images/week39_82_1.png" src="_images/week39_82_1.png" />
@@ -1855,7 +1855,7 @@ which equals</p>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x1279bd460&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x121cf68e0&gt;]
 </pre></div>
 </div>
 <img alt="_images/week39_90_1.png" src="_images/week39_90_1.png" />
@@ -2149,11 +2149,11 @@ when <span class="math notranslate nohighlight">\(||\nabla_\beta C(\beta_k) || \
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvalues of Hessian Matrix:[0.39517073 4.17377547]
-[[3.7074705 ]
- [3.25970297]]
-[[3.7074705 ]
- [3.25970297]]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvalues of Hessian Matrix:[0.28457291 3.97480604]
+[[3.34620097]
+ [3.61325173]]
+[[3.34620097]
+ [3.61325173]]
 </pre></div>
 </div>
 <img alt="_images/week39_153_1.png" src="_images/week39_153_1.png" />
@@ -2184,9 +2184,9 @@ when <span class="math notranslate nohighlight">\(||\nabla_\beta C(\beta_k) || \
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[3.95872127]
- [3.20357736]]
-[3.9712983] [3.26048873]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[4.08179906]
+ [3.05087018]]
+[4.03853268] [3.07469645]
 </pre></div>
 </div>
 </div>
@@ -2286,11 +2286,11 @@ minimum of this function.</p>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvalues of Hessian Matrix:[0.25986026 4.46387965]
-[[4.18118338]
- [2.86240935]]
-[[4.18234308]
- [2.86143379]]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvalues of Hessian Matrix:[0.32932635 4.53640693]
+[[4.15709522]
+ [2.9838929 ]]
+[[4.15605625]
+ [2.98473694]]
 </pre></div>
 </div>
 <img alt="_images/week39_166_1.png" src="_images/week39_166_1.png" />
@@ -3590,18 +3590,6 @@ first example shows results with ordinary leats squares.</p>
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
-[[3.94499279]
- [3.03306538]]
-Eigenvalues of Hessian Matrix:[0.31248425 4.44418124]
-theta from own gd
-[[3.94499279]
- [3.03306538]]
-</pre></div>
-</div>
-<img alt="_images/week39_265_1.png" src="_images/week39_265_1.png" />
-</div>
 </div>
 </div>
 <div class="section" id="id9">
@@ -3664,80 +3652,6 @@ theta from own gd
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
-[[4.]
- [3.]]
-Eigenvalues of Hessian Matrix:[0.30306759 4.65944342]
-0 [-20.62275788] [-25.73690387]
-1 [-0.03662823] [0.02925855]
-2 [-0.0342458] [0.02735547]
-3 [-0.03201832] [0.02557617]
-4 [-0.02993573] [0.0239126]
-5 [-0.0279886] [0.02235723]
-6 [-0.02616812] [0.02090304]
-7 [-0.02446604] [0.01954342]
-8 [-0.02287468] [0.01827225]
-9 [-0.02138683] [0.01708375]
-10 [-0.01999575] [0.01597256]
-11 [-0.01869515] [0.01493365]
-12 [-0.01747915] [0.01396231]
-13 [-0.01634224] [0.01305415]
-14 [-0.01527928] [0.01220505]
-15 [-0.01428546] [0.01141119]
-16 [-0.01335628] [0.01066897]
-17 [-0.01248754] [0.00997502]
-18 [-0.0116753] [0.0093262]
-19 [-0.01091589] [0.00871959]
-20 [-0.01020588] [0.00815244]
-21 [-0.00954206] [0.00762217]
-22 [-0.0089214] [0.0071264]
-23 [-0.00834112] [0.00666287]
-24 [-0.00779859] [0.00622949]
-25 [-0.00729134] [0.0058243]
-26 [-0.00681708] [0.00544547]
-27 [-0.00637367] [0.00509128]
-28 [-0.0059591] [0.00476012]
-29 [-0.0055715] [0.0044505]
-theta from own gd
-[[3.98281205]
- [3.0137297 ]]
-0 [-0.00520911] [0.00416103]
-1 [-0.00487029] [0.00389038]
-2 [-0.00445186] [0.00355614]
-3 [-0.00403677] [0.00322456]
-4 [-0.00364967] [0.00291535]
-5 [-0.00329616] [0.00263296]
-6 [-0.00297571] [0.00237699]
-7 [-0.00268602] [0.00214559]
-8 [-0.00242441] [0.00193661]
-9 [-0.00218823] [0.00174795]
-10 [-0.00197505] [0.00157766]
-11 [-0.00178263] [0.00142396]
-12 [-0.00160895] [0.00128523]
-13 [-0.0014522] [0.00116001]
-14 [-0.00131072] [0.001047]
-15 [-0.00118302] [0.00094499]
-16 [-0.00106776] [0.00085292]
-17 [-0.00096373] [0.00076983]
-18 [-0.00086984] [0.00069482]
-19 [-0.00078509] [0.00062713]
-20 [-0.0007086] [0.00056603]
-21 [-0.00063957] [0.00051088]
-22 [-0.00057726] [0.00046111]
-23 [-0.00052102] [0.00041619]
-24 [-0.00047025] [0.00037564]
-25 [-0.00042444] [0.00033904]
-26 [-0.00038309] [0.00030601]
-27 [-0.00034576] [0.0002762]
-28 [-0.00031208] [0.00024929]
-29 [-0.00028167] [0.000225]
-theta from own gd wth momentum
-[[3.99916114]
- [3.00067008]]
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 <div class="section" id="but-none-of-these-can-compete-with-newton-s-method">
@@ -3785,22 +3699,6 @@ theta from own gd wth momentum
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
-[[4.26281735]
- [2.84478178]]
-Eigenvalues of Hessian Matrix:[0.33978889 4.51659846]
-0 [-14.67490857] [-18.08539155]
-1 [-4.46170878e-15] [-1.45781905e-14]
-2 [-1.21430643e-15] [-1.24768421e-15]
-3 [4.92661467e-16] [4.50610071e-16]
-4 [4.92661467e-16] [4.50610071e-16]
-beta from own Newton code
-[[4.26281735]
- [2.84478178]]
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 <div class="section" id="including-stochastic-gradient-descent-with-autograd">
@@ -3884,23 +3782,6 @@ beta from own Newton code
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
-[[4.0586484]
- [3.0718316]]
-Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]
-theta from own gd
-[[4.0586484]
- [3.0718316]]
-</pre></div>
-</div>
-<img alt="_images/week39_271_1.png" src="_images/week39_271_1.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own sdg
-[[4.02496085]
- [3.12081773]]
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 <div class="section" id="id10">
@@ -3977,20 +3858,6 @@ theta from own gd
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
-[[3.96075051]
- [3.02148021]]
-Eigenvalues of Hessian Matrix:[0.27470622 4.24106503]
-theta from own gd
-[[3.95906059]
- [3.02296298]]
-theta from own sdg with momentum
-[[3.95611042]
- [2.99475306]]
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 <div class="section" id="similar-second-order-function-now-problem-but-now-with-adagrad">
@@ -4048,18 +3915,6 @@ theta from own sdg with momentum
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
-[[2.]
- [3.]
- [4.]]
-theta from own AdaGrad
-[[2.00025662]
- [2.99802696]
- [4.00167329]]
-</pre></div>
-</div>
-</div>
 </div>
 <p>Running this code we note an almost perfect agreement with the results from matrix inversion.</p>
 </div>
@@ -4124,18 +3979,6 @@ theta from own AdaGrad
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
-[[2.]
- [3.]
- [4.]]
-theta from own RMSprop
-[[1.99456598]
- [2.99848815]
- [3.99835783]]
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 <div class="section" id="and-finally-adam">
@@ -4204,18 +4047,6 @@ theta from own RMSprop
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
-[[2.]
- [3.]
- [4.]]
-theta from own ADAM
-[[1.99989103]
- [3.00042093]
- [3.99954925]]
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 <div class="section" id="and-logistic-regression">
@@ -4258,12 +4089,6 @@ theta from own ADAM
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Initial loss: 2.772588722239781
-Trained loss: 1.067270675787016
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 <div class="section" id="introducing-jax">
@@ -4287,11 +4112,6 @@ It provides composable transformations of Python+NumPy programs: differentiate,
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.25       0.19661197 0.10499357]
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 </div>
diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
index 133dd2d9c..2933c6089 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
@@ -1012,7 +1012,7 @@
     {
      "data": {
       "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1273ffac0>"
+       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x120cc1b20>"
       ]
      },
      "execution_count": 1,
@@ -1162,7 +1162,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1279bd460>]"
+       "[<matplotlib.lines.Line2D at 0x121cf68e0>]"
       ]
      },
      "execution_count": 5,
@@ -1826,16 +1826,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.39517073 4.17377547]\n",
-      "[[3.7074705 ]\n",
-      " [3.25970297]]\n",
-      "[[3.7074705 ]\n",
-      " [3.25970297]]\n"
+      "Eigenvalues of Hessian Matrix:[0.28457291 3.97480604]\n",
+      "[[3.34620097]\n",
+      " [3.61325173]]\n",
+      "[[3.34620097]\n",
+      " [3.61325173]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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VEBUVZRD0lffGG28gKysLnTp1wrPPPou4uDjcvHkTx44dw9q1a7FgwQJER0cjKysLaWlpmDFjhj41Q1paGl544QUkJibiscceA6ANrhYtWoT4+Hi0bNkSv/32G959912zgUll1a5dG927d8eMGTNQtWpVzJ8/HwcPHrS6vP7TTz9F79690atXL4wePRr16tXDpUuXcODAAezevRvLly93SXuJHOLhCdxEkrRnzx4xZswYERsbKwIDA0VQUJBo3LixePLJJ8Uvv/xisO+oUaNE1apVTR5n//794uGHHxbVq1cXtWrVEkOGDBEnTpwQAMTMmTMN9l2zZo1o2bKlCAgIEPXr1xfvvPOOmDlzptXVZEIIce3aNfHaa6+JuLg4ERAQIEJCQsR9990npkyZYrASCICYOHGiUTtNHXP69OkiKipK+Pj4GK0UMuXzzz8XTZo0EQEBAeLee+8VX375pRg1apTBajIhtEvi//3vf4tWrVqJoKAgUa1aNREfHy/GjRsn/vrrLyGEdhXYY489Jho0aCACAwNFWFiY6Natm1izZo3BsW7cuCFef/11/XnDwsJE9+7dxdatWw32+/LLL0WHDh1E1apVRXBwsGjUqJF48sknxa5du/T7dOvWTTRv3tzoukxdQ0ZGhoiPjxf+/v4m38uKzp8/L5599lkRGxsr/P39RWhoqGjXrp149dVXxbVr18Tp06dF3bp1Rffu3Q1WWWk0GtGvXz9Rs2ZN/cq+y5cvi7Fjx4q6deuKKlWqiC5duojc3FzRrVs30a1bN/1zdSu8yi+BF+JuGoSKqw91n7Xz58/rt+k+L/PnzxeNGjUS/v7+Ij4+XixevNjguaZWkwkhxN69e8Xjjz8u6tatK/z9/UVERITo3r27WLBggcXXi8jdVEII4alAjIiIpEulUmHixIn46KOPPN0UIpfiajIiIiJSNAZDREREpGicQE1ERCZxFgUpBXuGiIiISNEYDBEREZGiMRgiIiIiRVPEnCGNRoPTp0+jevXqJssmEBERkfQIIXD16lWrdQgrSxHB0OnTpxETE+PpZhAREZEDCgoKXJZlHVBIMKQrIVBQUGBTGQUiIiLyvOLiYsTExJgsBeRMigiGdENjNWrUYDBEREQkM66e4sIJ1ERERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoHg+GNm3ahH79+iEqKgoqlQrffvut2X3HjRsHlUqFuXPnuq19RERE5N08HgyVlJSgVatW+Oijjyzu9+2332LHjh2IiopyU8uIiIhICfw83YDevXujd+/eFvc5deoUJk2ahJ9++gl9+vRxU8uIiIhICTzeM2SNRqPByJEj8eKLL6J58+aebg4RERF5GY/3DFkzZ84c+Pn54dlnn7X5OaWlpSgtLdX/Xlxc7IqmERERkReQdM/Qb7/9hnnz5mHRokVQqVQ2Py8tLQ0hISH6n5iYGBe2koiIiORM0sFQbm4uzp07h/r168PPzw9+fn44fvw4nn/+eTRs2NDs86ZPn46ioiL9T0FBgfsaTURERLIi6WGykSNHokePHgbbevXqhZEjR2LMmDFmnxcYGIjAwEBXN4+IiIi8gMeDoWvXruHIkSP63/Pz87Fnzx6Ehoaifv36CAsLM9jf398fERERiIuLc3dTiYiIyAt5PBjatWsXkpKS9L9PnToVADBq1CgsWrTIQ60iIiIipfB4MJSYmAghhM37Hzt2zHWNISIiIsWR9ARqIiIiIldjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESK5ufpBhAREREZUKuB3Fzg6FG3nI7BEBEREUlHZibw3HPAyZNuOyWDISIiIjKk65kpLAQiI4GEBMDX1/XnzcwEBg8GhHD9ucphMERERER3meqZiY4G5s0DBg503XnVau153RwIAZxATURERDq6npmKQ1SnTmm3Z2a67ty5uW4dGivP48HQpk2b0K9fP0RFRUGlUuHbb7/VP3b79m28/PLLuO+++1C1alVERUXhySefxOnTpz3XYCIiIm9kqWdGty0lRbufKxQWuua4NvB4MFRSUoJWrVrho48+Mnrs+vXr2L17N2bMmIHdu3cjMzMThw8fxj/+8Q8PtJSIiMiLWeuZEQIoKNDu5wqRka45rg08Pmeod+/e6N27t8nHQkJCkJWVZbDtww8/xAMPPIATJ06gfv367mgiERGR97O1Z8ZVPTgJCdq5SadOuX3ekMd7huxVVFQElUqFmjVrmt2ntLQUxcXFBj9ERERkga09M67qwfH11U7SBgCVyjXnMENWwdDNmzcxbdo0DB8+HDVq1DC7X1paGkJCQvQ/MTExbmwlERGRDOl6ZswFIioVEBOj3c9VBg4EVqwA6tVz3TlMkE0wdPv2bQwdOhQajQbz58+3uO/06dNRVFSk/ykoKHBTK4mIiGTKUs+M7ve5c12fb2jgQODYMSA7G/j8c9ee6w5ZBEO3b9/G448/jvz8fGRlZVnsFQKAwMBA1KhRw+CHiIiIrDDXMxMdrd3uyjxD5fn6AomJwJAhbjmdxydQW6MLhP766y9kZ2cjLCzM000iIiLyXgMHAv37eyYDtYd4PBi6du0ajhw5ov89Pz8fe/bsQWhoKKKiojB48GDs3r0b33//PdRqNc6cOQMACA0NRUBAgKeaTURE5L10PTMKoRLCA3mvy8nJyUFSUpLR9lGjRiE1NRWxsbEmn5ednY1EG9+o4uJihISEoKioiENmREREMuGu+7fHe4YSExNhKR7zcKxGREREXk4WE6iJiIiIXIXBEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESman6cbQERERAqnVgO5uUBhIRAZCSQkAL6+bjs9gyEiIiLynMxM4LnngJMn726LjgbmzQN69HBLExgMERERkWdkZgKDBwNCGG4/dUq7/euv3dIMzhkiIiIi91OrtT1CFQMh4O62adPc0hQGQ0REROR+ubmGQ2MVCaHtIXIDBkNERETkfoWFnm6BHoMhIiIicr/ISE+3QI/BEBEREblfQoJ21ZhKZfpxlQqoV88tTWEwRERERO7n6wt88IHpCdS6AOmdd9zSFC6tJyIiIvfLzASmTDH5kCayHjbf9wzeTHFPzxCDISIiOfFwpl4ipzCTX0j328jTs7Hk9EgAxW5pDofJiIjkIjMTaNgQSEoChg/X/tuwoXY7kVxYyC+kAiCgQhpeRWPfv/FSx1y3NInBEBGRHOj+kq6Yl0WXqZcBEcmFlfxCPhCojwIcXn8cr65LcEuTGAwREUmdLZl6U1K0+xFJ1KWjl/HpiE1IfXS7Tfurzp5xcYvu4pwhIiKpsyVTb0GBdr/ERLc1i8iam1du4oe385C+WIUfCtviNrqiGzS2PdmNeYgYDBERSZ2tmXollNGXlEtTpkHux/uQ/kkxlh9qhSJ01D/WKugQ+nVXo2xXJPzOnzG/rD46Wrs4oKTELW1mMEREJHW2/oUsoYy+pDx/rPoL6XNOYcmuJihQt9Zvj/Y9jRH3H8aIF6Nw36A4AHFA5kfauW4qlWFApMsvNHeuW1dJqoQwFZZ5l+LiYoSEhKCoqAg1atTwdHOIiOyjVmtXjZ06Zfkv6fx8LrMntzq1qxAZqYeQ/ksk9t6M02+vgSIMuXcfkp+pjq6TWsLHz8QU5cxM7Vy48kPAMTHaQGjgQADuu38zGCIikgPdajLA9F/SK1bobyAkUV6SI6r4ZDEyU/chPbMKNlxuDXFnLZY/bqFP5G6MGCbQd0YbBNUMsn4wK68JgyEnYjBERF7Bhr+kSaJMvXfR0cC8ebJ4725du4Wf5uxB+ldlWFPQBjcRrH+sS429SO5bhCFv3IfQRrWcel533b89vrR+06ZN6NevH6KioqBSqfDtt98aPC6EQGpqKqKiohAcHIzExET8+eefnmksEZEnDRwIHDsGZGcDS5Zo/83Pl8XNVNFkmiNKaAS2ffY7JrXYgCHVf8SSt47ibMEt3EIA4gOO4q0eOfh7YwFyi1ph3OKuTg+E3MnjE6hLSkrQqlUrjBkzBoMGDTJ6/F//+hfef/99LFq0CPfeey/eeustPPzwwzh06BCqV6/ugRYTEXmQry+Xz8uJtRxRKpU2R1T//pIZMjv8Uz4Wzz6O9K2xaFX2F+bhOcTgbiB3KzQc/p9+DNVg43u2XElqmEylUmHVqlUYMGAAAG2vUFRUFFJSUvDyyy8DAEpLSxEeHo45c+Zg3LhxNh2Xw2REROQROTnasinWZGd7NMg99+d5LJu5H+nrwvBrSQsAwGPIxAoMhgoCqvI7u3GemmKGySzJz8/HmTNn0LNnT/22wMBAdOvWDVu3bjX7vNLSUhQXFxv8EBGRC6nV2ht/Rob2X2bD1pJwjqiScyVYMnELHq27E1EtauHZld3wa0kL+KIMfWpvx/+qjjcOhACvzHru8WEyS86c0abiDg8PN9geHh6O48ePm31eWloaZs2a5dK2ERHRHTKfHGwXe1eESSxHVNnNMmz4YC/SP7+BzL9bowSd9Y+1r/onkntdwBOzmiH8wk0g6bz5A3lZ1nNJB0M6KpVhXCqEMNpW3vTp0zF16lT978XFxYiJiXFZ+4iIFEs3ObjijAvd5GBXD6W4c7m6I0FfQoJ2H2s5ohIqWZDUwusgNAJ5GQeR/v5ZZOxpijOadvqn3eN3HCM65mPE9PqI69387vEypNuj5QqSDoYiIiIAaHuIIstFzefOnTPqLSovMDAQgYGBLm8fEZGieXpysDt7pBwN+nx9te1xZbZlM6/DufGv4/ONTZC+MQYHbjUF0BQAEKa6iCea/4HkyaF48J8toPJpYHxMifVouZqk5wzFxsYiIiICWVlZ+m23bt3Cxo0b0alTJw+2jIiI7Cog62zuXK5uLegDLM+fGThQGyzVq2e4PTq68j1nZl4HzclTqP3aOOzKuoQDtxohCDfweMxWrHntV5wuro6Pf++Gjk/fB5WPmVEWXY+WuVEYlUqb46qyPVoS4fGeoWvXruHIkSP63/Pz87Fnzx6Ehoaifv36SElJwezZs9GkSRM0adIEs2fPRpUqVTB8+HAPtpqIiDw2OdjdPVL2BH3m5s8MHKhtjzOH9O68DkIYT3L2gYAGKixQTcA/ngzFY2+0QUh9OzoR3NGjJSEeD4Z27dqFpHLLDnVzfUaNGoVFixbhpZdewo0bNzBhwgRcvnwZHTp0wPr165ljiIjI0zw1lOKM4MQezgr6nJgjSlOmwb6UhWh98qTxaq87fCBQV5zF6NEA6ofYfxJdj5apoUgvy3ru8WAoMTERllIdqVQqpKamIjU11X2NIiIi69w1Obgid/dISWj+TPnK8J3VVZFhy5Mq8zq4okdLgjweDBERkUx5aijF3cGJp4K+O4wrwzcBABTDxhGSyr4OCsh6LukJ1EREJHGunBxsjrsn9+qCPt2xK54LcHrQV3yyGIv+uRk9Qncjpn04XvwhEXtvxsEftzAgcjuWT92GlReSFDXJ2ZUkVY7DVViOg4jIxdyZ7we4u4oKMN0j5YpAzNQS9pgYp82fuX39Nn56Jw/pi8qw2tbK8J54HdzIXfdvBkNERCRPLg5OTHJy0Cc0Ats//wOLP7qEpX+0wEURpn8sLuBvjOx6AsNnNEJsVwuJgz3xOrgJgyEnYjBEROSl3N0j5SS6yvCLt8biaNndpIfhPucwrNUBJE+ti7bD483nAapIpq+DNQyGnIjBEBEReZqpyvAAUBXX8FjsHiSPDcJDz7eGXxDXNum46/7NV5yIiMhFSs6VYPWsPVi8PAA/nW8DNboBAHxRhp518pA85Bb6z2yNqnW7eLilysZgiIiI5EfCw0I2V4Zv0d6DraTyGAwREZG8uLNAq40cqgwvRxIOQiuDwRAREVknhZugWg28/TYwc6bxY9aqx7vIsc0nseSNI45VhpcbCQahzsIJ1EREZJkUboKm2lCRLhN0fr5LA7VLRy9j+eu/Y/H3IcgtbqXfHoQb+EdMHpJH+aHXy60RUC3AZW1wO10+o4ohg4vzGXE1mRMxGCIicpCHboI2tcGc7Gynl4+4eeUmfng7D+mLVfihsC1uQxvoqKBBUq09SB5QgoGpLRHiSEFUqVOrgYYNzQeiLgxCuZqMiIg8S63W9saYCkKE0N4EU1K0hTxd1RNjqQ3mOKlAq6ZMg9yP9yH9k2IsP9QKReiof6xl0CEkdy/EsNQ4RLdv65TzSVZuruUeOSGAggLtfjKtYcZgiIiITJPCTdBaG0ypZGHSP1cfQfo7J7F4ZxMUqFvrt0f7nsaI+w9jxItRuG9QHIC4Sp1HNmwNLp0UhHoCgyEiIjJNCjdBe45dierxp3efQcbMg0j/JQJ7bsQDaAwAqIEiDLl3H5KfqY6uk1rCxy/K7mPLnq3BZSWDUE9iMERERKZJ4SZo77HtqB5ffLIYman7kJ5ZBRsut4ZABADAH7fwaEQekodr0HdGGwTVVHjV94QEbZB56pTp4cpKBKFSwWCIiIhMk8JN0FobdGxc3WZcGf5u5mfDyvAdnHUF8ufrq31tBw/Wvufl3wfdRHo7glAp8vF0A4iISKJ0N0Hg7k1Px103QUtt0Jk1Czh2TBsIqdVATg6QkaH9V62G0Ahs++x3TGq5EZHVitHvzQewrKATbiIYcQF/460eOfh7YwFyi1ph3OKuCG1Uy3XXI1cDB2pXDtarZ7g9OtrtuZ1cgUvriYgskUKyQVu5qq2mcvzExGgDIU/mGarYBhP7FAXUwfSyN/GJZpx+m8OV4cnt3wfmGXIiBkNE5BApJBu0lavbKoWg0FIb7uQiEkKgfGijufNbMv4H39gGrAwvMwyGnIjBEBHZTQrJBm0lp7a6wPWzVyEaN0KVa+dhqo9HABBR0fA5cUy6vXpkkrvu35wzRERUkbVkg4A22aBa7dZmmSSntjqR+pYaWe/8hlGNNmNQRC6qmgmEAEAFwOf0SW2vEpEJDIaIiCqyJ9mgp8mprZUkNAK7Fx/A1HY5iA6+gJ7T2+Hrv7ugJopsO4CMkwKSa3HQlIioIikkG7SVnNpqjpX5SNYqwz/z8A3gAxvOI+OkgORaDIaIiCqSQrJBW8mpraaYmfh97ZXZWLKlAdK/01WGjwZgqjJ8N0DdBVg+06uTApJrMRgiIqpICskGLSnfk1K3rjb3y+nT0myrJWYmfmtOnkSVCaOwDiuQi64mKsN3MjyOApICkmsxGCIiqkjKN1dTPSlhYXeryEuprZbcmfhdcSk8oJ3MqgHwMSaiU+8QDJ3VzHpleF1SQFPpBdyZD4lkiUvriYjMkUKywYrtMbeEXghtUHTx4t3tnmyrFflvLUbsjGTrO2ZnA4mJth9YCvmQvI0HX1N33b/ZM0REZM7AgUD//tK4uVpbQq9SAcHBwM8/A+fOSTIQOL37DJa+/if2Z51Cq1s7MdmWJ9k78dvX177giSyTU+LRSmAwRERkiVRurrYsoT95UtveYcPc1y4ryleGD7n8N+ZiCmJg4ToqkurEbyUw1xN56pR2uxcl82SeISIiOZDREvrb12/j+9d/xdD6WxEe448xX3RBjcvHsByPo56tgZBKpR3mk+LEbyVQWDJP9gwRkTwpbW6IxJfQC43Aji//RPp/LmL57/Foiuuoh1MYh1/h76NGqu+bUN02nixtkpQnfiuFPck8pdBzWkkMhohIfhQyj8GARJf7/5V1DEveOorDm89DoxFojL+wC8mGQ2GaOz+24gowz5NRT6QzMBgiInmxNI9h0CBg1iygSRPv6y2ytNxfx009Kef+PI9lM/cjfV0Y6pUcxjw8ZxD8OLxEedIk7XvoTe+bXEm8J9LZOGeIiOTDlnkMM2cCw4cDSUlAw4ba4Mlb6HLphIYaP2ZqmxNdv3AdGZO3ok/dnYhqUQvPruyGeiWHsQKDEV1hHpBNQ2GmDBqkHXJhIOR5up5IlZl308vmdDEYIiL5sDaPoSLdqhdvCogAw1xCOpcuOf1ay1eGD6+jxvCPOmHt+fZQww8dquzDV8HjoYKN84AssXRjVauBnBwgI0P7r5dM2JU8XU8kYBwQeeGcLgZDRCQf9s5P8LZVL7qeMVOcdK1CI5CXcRDP35+DmODz+srw11AdsX4nMCMhBwfX/o3tP1xC9RvnnRMIAaZvrJmZ2t69pCTv7e2TMl1PZL16htujo71qWT3AOUNEJCeOzE/wplUvLlzhY1gZPh5APAAgVHUJTzT7A8mTa6HjUy2g8qmvfcKUDx27horMTZZWUI4bSZNS4lEXknwwVFZWhtTUVCxevBhnzpxBZGQkRo8ejddeew0+PuzYIlIUayuqLPGGVS9OXuFzOf8Kls/YZ1QZPhA30T9md7nK8F0Nn5iZqQ1gHFWnDvDBB9oeB1M3VluybaekaG/SXnZTliSpJB51IckHQ3PmzMGCBQvw1VdfoXnz5ti1axfGjBmDkJAQPGeuu5iIvJMtK6rM8YZVL05Y4XPzyk2snb0H6enAD4VtcAvaQMdqZXgdS0N1tlCpgAULLPfqKCzHDXme5IOhbdu2oX///ujTpw8AoGHDhsjIyMCuXbs83DIi8ghz1cnN8VD+HZdwMNeQpkyD3I/3If2TYqw43BJXxIP6x1oGHUJy90IMS42zXhkesH8Se3m2Fo5VWI4b8jzJB0NdunTBggULcPjwYdx7773Yu3cvNm/ejLmV6aIlInmrOI/hr7+A1FTtY+WDBG9b9aLrGRs0yPTjQhhc65+rjyD9nZNYvLMJCtSt9btF+57G8HaHMeLFKLQcHAcgzvY22BqADB4MDBgAnD+vHRYzNyRmisJy3JDnST4Yevnll1FUVIT4+Hj4+vpCrVbj7bffxjALhQhLS0tRWlqq/724uNgdTSUid6o4j6FFC9NZqRWWyfjS35exsF8O0n+JwJ4b8QAaAwBqoAhD7t2H5Geqo+uklvDxi3LsBLYGIBMnOj6EJdFs2+S9VELYOwvRvZYuXYoXX3wR7777Lpo3b449e/YgJSUF77//PkaNGmXyOampqZg1a5bR9qKiItSoUcPVTSai8txZQ8zb65Wp1dql5WaGqTQATiIGsciHBr7wxy08GpGH5OEa9J3RBkE1g5zXBmuBSn5+5V573WoywHRvH1eTKUJxcTFCQkJcfv+WfDAUExODadOmYeLEifptb731FtLT03Hw4EGTzzHVMxQTE8NgiMjd5FRDTA6BVE6ONteOFZODP0fzAU0wZFYLhDVxQWZqdwUqpj4/ts47Iq/grmBI8sNk169fN1pC7+vrC43GfNW/wMBABAYGurppRGSJnPLEyCRoE6dO25Tk8MMvqgDDulrf0VHmJrE7e1hSITluyPMkHwz169cPb7/9NurXr4/mzZsjLy8P77//Pv7v//7P000jInPklCdGBkHbX1nHsPjtY/gr1weLbXmCOyYWuytQUUCOG/I8yQ+TXb16FTNmzMCqVatw7tw5REVFYdiwYXj99dcREBBg0zHc1c1GRHfYOJyD7GzP3uiszMFx2vwXB+gqwy9eF4YdJS0AAD5Q4zgaIAqnTNdS8mB7iVyBw2R3VK9eHXPnzuVSejmSwxwMcg255ImRWHK/6xeuY/WsPUhf5o+fzreBGt0AaIOgnrV3I3nILYS1mwOfp0bebZ+Ot6URIHIjyQdDJFMymYNBLuKOPDHOCLYlELSpb6mx4f09SP/vDWT+3QrXcDfzc/uqfyK51wU8MasZwlu0v7O1M1ArmGkEiJyIwRA5nwzmYJCLuTpPjLOCbQ8l9xMagT3LDiH9vTPIyItHoaad/rFYvxNI7vg3Rkyvj7jezU0fgBOLiZzKrjlDBQUFiImJcWV7XIJzhtxIwnMwyM1ctfzaXLDtyHEdyZlTiR4pXWX4xZuisb+0sX67cWV4W9aMEXk/d92/7Sr7Hh8fjxkzZqCkpMRV7SG5s2cOBnk33fLrevUMt0dHOx4IWVulBmhXqanVth1PV94CuBtM6Ziag5OZqQ2ekpKA4cO1/zZsqN1uxuX8K/gseRO6huxFbEI0Xs1KxP7SxgjETTwesxVrXvsVhcXVMP+Prug07j4GQkQeYFcwlJWVhfXr16NJkyZYuHChq9pEciaBORgkIQMHAseOaVeNLVmi/Tc/3/FhUlcE27YGbboeqYrn1w3/lguISotLkfnSdgyM2o6Ie4IxbnFX5Ba3ggoadK+1G1+OycXZ46VYdqIT+r35AAKq2bYylohcw645Q506dcKOHTvw9ddf49VXX8V//vMffPDBB0hkDgjSYYFFqsiZeWJcFWxbm4NjQ94k8VwKcgsaIn3BNSw/VMnK8ETkVg7nGbpx4wbS0tLw3nvvoWfPnnj33XfRuHFj60/0AM4ZciN31S0iZfJU/iIbz5uIbGyE9rz1fAox4v5DdyrD3+u8thApiCTnDJUnhEDPnj3x9NNPY82aNWjRogWef/55XL161ZntI7mxdw4GkT10q9QqfrbKq1MH6NTJ/OOOsLGn6R4cxdh7c7HhvTycKA3HnB2JDISIZMCuYGjBggUYO3YsWrZsiZCQEPTo0QNbtmzBxIkTMX/+fOzZswfNmjXDrl27XNVekgNXTJwlAu4G25Y6tM+fBxo1sjip2W42Dut+sjoanx9KQNLUNvDxc/hvTddTq7W9XRkZ2n9tnXBO5KXsGiaLiYnBgw8+qP+5//77jQqizp49G0uWLMEff/zh9MY6isNkHsIM1OQKmZnAoEGW93FS9fTb12/jp3fysGRRKf5VMAxROA0fyHz4lwlRlUfG/xe76/7t9NpkZ8+eRVRUFNQS+kuDwRCRl7CWx6o8BwMUoRHY8eWfSP/PRSz7ozkuiNoAgMeQiRUYDBWEYeV4JwVebuHMHE0kDzIPfiU/Z8icunXrYsOGDc4+LBF5M1uHbawtrS/PzmX2f2UdQ2piDpoEnkDHp1rg49+74YKojXCfc0hpsxGv/q8pVMuXQxUdbfhEuQz/OjtHE0mfHekglM7p5ThUKhW6devm7MMSkbey5y9XR/JTWXjO+QMXsOz1P5H+o64yfEMAQBWUYGBsHpLHBuGh51vDL0j3f1pT4LEB8hxykFhRWgCyHr6RPBvSQSAlRZtSgq85a5MRkQfZU8dOrQbOnrX/HBUmP+sqwy/+xg/rzrU1WRm+/+utUC2ii+njOTNvkjtJLSGqzIdvJE+Kwa+EMRgiIs+wZdjmqaeAkBDg8mVgyhTbh8gAg2Kw9leG90JSSojKYs6uJ7XgV+IYDBGRZ9gy/+fSJaBHD/uPrVJBAMgf9AI+7pDrWGV4b6PL0WQtIWpCgmvbweEb95BS8CsDDIaIyDNc+BdpUUBtvK6eif/MnajfZlwZvv7dJyhh7oouR9PgwdqAo3ww4s6EqBy+cQ+pBL8ywWCIiDzDiX+RXn/lTWT/WhW/bbmJDTc6Irc0ARr4IhA38Y/oPCQ/6YNHprdBQLWuxk9W0twVXUJUU9c7d657rpfDN+4hleBXJpyeZ0iKmGeISIKs1bGzw0h8hXQ8CQBQQYOkWnuQPKAEA1NbIqR+iPknKjXvjid7wjxVX06pTAX7MTHuC34rSbZJF6WIwRCRROmCEaBSAVEisnE5KLJcZXgbep2sJXBUqbQlZRYtAs6dMwwabA0mlDD8Zi8Wc3Y/GX8OGQw5EYMhIgkz9ZerjTRQoTigNk58vREtn2hq35Nt7aEoLzoaGDZMmxzS2rCakobf7GUuCPb2Hjmym2wzUBMR2WXgQODYMeDnn4HQULO7VfyrTUAFlQqombHA/kAIcGxOysmTwLvvWs/oy8y/lrGYM0kMe4bIe8m4a1ipbny+GEFPJUPA8C81DQDVnR+9ys57cKRnyBLd8M6RI0CjRpaH3zgMpMXvKFnhrvs3V5ORd+IQhWzcvn4b6/+1B+kLb2P1icfwCFZiHp5DDO6+dyKyHnzmfQDUqeO8G6e1pcf20i0Jnz+fS8dtJdds3uR1GAyR92F2W8kTGoFfF/6J9HkXsfSP5rgg7mZ+3h/QGl93WYQx/S4gKlwDREbC1xU9BpaWHlfG0aO27acbpmPvCJHHMRgi78LstpL2V9YxLH77GNK3xOJoWQv99nCfcxja8gCSp9RBu+SmUPnc454Gmcu7UxmNGtm2X2QkezCJJIJzhsi7MIeJ5BhXhtcyrgzvwb/NyvfO1K0LjBoFnD5tX29RxTlD1paOv/ce8MQTystxRGQHzhkicoStK4ROndIGThyacInrF65jzRt7kL7Mwcrw7lZx7sp//mPf8Fn5jL4BAdYz/77/vrbwLHswiSSBPUPeTmnzEWztGapTBzh//u7vHJowz5bPkFoN9YYc7P9iO77fVAPvFI5EMWrqHzasDF/H8+21hbnMvUOHGucZMrWyzVLm39BQ9mAS2cBt92+hAEVFRQKAKCoq8nRT3GvlSiGio4XQ/q2p/YmO1m73VmVl2mtUqQyv29qPSqX98ebXxhFWPkMatUYcTZknLvvXMdjnBKLFeJ8FYkZCtji49qhk2mu3sjIhsrOFWLJE+29ZmeXttj5/yRLbPpdLljjWbiIv4a77N3uGvJVSay4Bjpd4cFX+F7mWbrDwGRICyGyVik1/huGDsskAhEFeIAEVoAJU7vycyekzz7ltRDZhOQ4nUlwwZEvNJW9P+mZqiKLi0Jg5lm5A9gYstq4WktqqIiufIQ1UOIl6UAGoh5OmU9k763Nm4zCdrD7zrM9FZBMOkzmR4obJsrNt64LPzvZ0S12r4hBFenrlhibsHYJZudL0cF3FITlb93MnWz9Drv6cmXvNly83fG9//ll+n3nd+17xveeQLZGeu+7fXE3mjWxdUeVIbSY5qbhCKCfHtudFmqh4bm8iR1vzHfXtK7m8SJoyDQ4t2g4Hqn2Z5ujnzNxrfvIkMGSI4baqVV3bFlcwl+MoOrpyZUaIyG4s1OqNTN3MK7Oft9CVX9DNIalIpdKu9klIMNxuLbABtAGLWn13e26ubSUZ7Cnd4GL71xzBK51yEBt0Gs989aDzDuzI58zSa25KSYnr2uJKuiK12dnAkiXaf/PzGQgRuRl7hryRtZpLuvkIFW/63s5S+YXyeWIq9sDYGtiUrzVlaw+EvaUbnOz07jNYOusg0rPCkXejKYDGAIBiVMElvzqoVXYBKqN68dC+XrqK49bm6TjyObP2mttLyp951uci8jj2DHkj3U0fMO4FsXTTVwLd0ITuRq4THW1+tZEjw4629kDYU7rBSa6evoqvntqMh8N+Q0y7Onh+TSLybjSFP26hf8QOfDNlG05fDEbosgXaj4u5z9C8ecCwYZZP5ujnzJnBn9I/80RknUtnJEmE4iZQ65iafBoTw4mZQtieJ0YIxyakW8t3pFJp34vSUtv2s9Q+G9wquSW+n/mrGFp/iwhGicEpOlffKz4ZtlFcOHzR+ImWPkPmJn7rfl580fEGO3MCNz/zRLLFPENOpLil9eVJLXeNHDm6DNpcvqOKeW9s3c9OxpXha+sfiwv4G8kJJzD8tXtwT2J9ywcy9RkCXLuU3dprbotJk4BBg/iZJ5IxLq0v5+TJk2LEiBEiNDRUBAcHi1atWoldu3bZ/HzF9gyR8zi6DNrW3jkn9uIdXp8vZnbLFo398w0OF+5zVjzXOkfs/OpPoVFrHHgRynFH+gZzr7k7lvQTkSRwaf0dly9fRufOnZGUlIQff/wRdevWxdGjR1GzZk1PN42UxNFl0AMHapfFW+uds3U/M4wrwzcEYKoyfDe7L90ke+ZROZqBu39/06+5NVKeLE1EkiT5YbJp06Zhy5YtyK3E0mJFD5ORc0lo2NG4Mrz2bxvjyvDVnH9yW8tJPPEEsGVL5TJwVwwSz58HnnkGuHjR+HxSLL1BRA5jOY47mjVrhl69euHkyZPYuHEj6tWrhwkTJuCpp54y+5zS0lKUlpbqfy8uLkZMTIxygiFP3rAlFCx4I/UtNbLn7kX6Z9ex8mgrXEN1/WP3V9mP5F7n8URqU0S0rOvihlgpf2GJuTlTFf8rshTYqNXA229rg6VLl+5uN1U9nohki3OG7ggMDBSBgYFi+vTpYvfu3WLBggUiKChIfPXVV2afM3PmTAHA6EcRc4Y8Wanek+f2Yhq1RuxeckBMbZctIn0KDV7ehn4nxGtdssWBH9xYGV5n1izHV3hVXE1nbT97q8ITkVfgarI7AgICcP/992Pr1q36bc8++yx27tyJbdu2mXyOYnuGPFm1W04Vw2Xi+JaTWPLGEaRvjMb+0sb67aGqS3ii2R9InlwLHZ9qAZWPmYzarpaRAQwfXrljfPABMGWK9f1YvZ1IkdzVMyT5CdSRkZFo1qyZwbamTZti5cqVZp8TGBiIwMBAVzdNWmytheWKGleePLeXuZx/BStm7kP6mhrYVNQaQDQAIBA38Y/oPCQ/6YNHprdBQLWuHm0nAOckgvRwBm4iIkAGwVDnzp1x6NAhg22HDx9GgwYNPNQiiXKkZIQ3nNsLlBaXYu3beUhPB74/3Qa3oA10VNAgqdYejOhfgkGzWiKkfkcPt7QCa2VfbOGBDNxERBVJPhiaMmUKOnXqhNmzZ+Pxxx/Hr7/+is8++wyfffaZp5smLZ6sVO/Jc8uUpkyDzZ/8jsXzi/DNoZa4Iu4WRm0ZdAjJ3QsxLDUO0e3berCVVliq9WaNbvn7hAnAe++xjh4ReZTka5O1b98eq1atQkZGBlq0aIE333wTc+fOxYgRIzzdNGnxZKV6W4+5f792SXb56u7eQK3WXldGhtXrK18ZvtuzrfDZwa64Imqink8hXnogB3uXH8beG3F48YdERLeXQW+IuVpvMTHAiy9qgxlL9fECAlhHj4g8TvITqJ1BEXmGHC0Z4Y5zV2Qqz4xcWcqPc+f6jCvDa9VAEQY32Yfk8dXQdVJL+Aa48Ybv7BQI5o5n6vUxtfzd1v2ISFGYZ8iJFBEMAS6rcVWpc5viLSvMLKygEwByur+J2XmPYMOl1tBAG2j44xZ6R+QheZgGfV9rjeDQYM+020oA51SOZqBmjioixWMw5ESKCYYAz/6Fberc5riyp8odrCQd1ECFk4hGLPKhgS86V9+H5L5XMGRWC4Q1CXVvW8tjCgQikhEGQ06kqGAIkEYG6l9+Ad56y/r+cs0fY2M5iv+1/QCd3xtovTK8O1jLGi33AJWIvA7zDJHjfH09F2Dozu3lK8zOZO1DhA37jXwhHJBCIAQwBQIRkRkMhsg1PLm6zUV0leEXrwtF4LWWyLHlSVK6Pi8PUImIHMVgiFzDWkI+S/ljJDSRtnxl+J/OtUEZugEA/HAL533qorbmHEwWw5BifhwvDFCJiJxB8nmGZM2O/DNeR5eQD7Avf0xmpnZeS1KStu5VUpL298xMFzf4LvUtNX7+126MbrwZ4XXUGPZhJ/xw7gGUwR/3V9mPuY9tRMHeK6iz/BOorOXRkdLcG12AWrG9OiqVdrK9lAI4IiI3YDDkKhK4qXucuYR80dGmVy3pVjpVnNdy6pR2uwtfO6ER2LPsEF64Pwcxwefx8Mtt8dXRLriG6mjoV4DXuuTgwA9/Y2dJMzyX2Q0RLeuav77atbWr6kJDpRUAOxqgEhF5Oa4mcwUuXzZky7BXZVc6OTi0dmLbKSyZ9RfSc+rhz9Im+u12VYbXnXv1amDxYuD8+buPSTHBJBMcEpFMcGm9E7k1GOLyZcfYuFTd5FJ8O5MIGleG1zKuDB9ge/vlFgBLaF4WEZE5XFovV1y+7BhHVzqZC0J0Q2t3ghBLleETa+5F8oBrjleGV6u1wZipvyuE0AZEKSlA//7SCTg8mX6BiEhiGAw5G5cvO8aRlU5WghChUqF07ASkvFILyw63MagMf1/QYSQnncawmfcipkMb0+eytffEkwEwe3iIiCqNwZCjzN2EvHX5sqtvuo4sxbcShKiEQNCVszh4RYUr0FaGH3H/IYx4MQotB98L4F7z7bFn6M1TAbC7a4wREXkpriZzhKWVYt64fNkdK+McWelkY3AxLCIbG97Lw/EbdTFnR+KdQMgCe1e1eSIA9uDKOyIib8NgyF7WbkKrV3vX8mV33nTNLVWvVQtITdXOuSnnun+ITYcdl5GEpKlt4Btgw2tubf4PoJ3/U37J/Pnzlt9PZwfAjrSRiIjMYjBkD1tvQv3725dfR6o8cdMdOBA4dgyYNUubpwcALl0CZs4EGjZEWcY3+CF1J4Y33IK6QxJQgGhoTOeAdiwIsWf+D6ANBp94wvpr4MwA2N42EhGRRZwzZA97bkIDB2qDIjlPbvXUxODVq7U9QRWCMHHyJHyGD8UXWIFV0AaUc3xfwYfqCRBQQYVy+zvaC2fP/B9LwaKOry+wdKlzA2BO0icicioGQ/aw9yYk9+XLnrjpWggwVAAEgA8xGQ1a1cSIqRFolzweqm/DTU8kdiSJoD3zf6wFi4D2emrXtq8NuueZC6S9dZI+EZGHMBiyh5RuQu5YUu2B672S/j1qWggwfCBQD6fxwVwfILGZdqMze+HsWdX2zTe2HdPeYNHaKrHKFMElIiIjnDNkD6msFHNX3TM3Xe/1C9ex9Nmt6Bv+KyaNLrbtSRUDDF0v3LBh2n8dDQztWdXmimDRlgnrrDFGRORUDIbsIYWbkDtXd7nwes1Vhj+JGNsO4MreN1sLzDo7WLRnwrq9RXCJiMgs1iZzhKcKXd66pb35Xbhg+nFX1T1z0vUKjcDe5YeR/m4hluTFo1AToX+soV8Bkh88ihEv1UP8hO7Wh4DcUdvNlqFIXXAKGLbXkZpkjtRnYwZqIvJiLNTqRC55Md19E8rMBMaPN6yIbo6pYqaVVYnrtbsyvDMDDHdwVnCckaEd9rRmyRLtcCARkZdjoVapc+dKMXPFSM1xxZJqO6/XuDK8djjHuDJ8V+Mn64aAnLVCzNWcNYFbShP0iYgUhD1DUqdWaydHW1vCXZ4reoZsYFwZPhCAqcrwtmWOVtwQkO69lsIQIRGRBLBniLRsyWWj44El1ZoyDbYs+B3pHxfhm0Mt7a8Mb4nc8zTZSzdhffBg7XtpaoiQq8SIiJyOwZDU2Tvk9c9/avPfuLgnZf+aI1g85yQW72iM4+pW+u12VYaXCin1QMltiJCIyAtwmEzqbF1hVKMG4O8PXLx4d1v5RH1OULjnLDJmHkB6VjjybjS9e2oUYXCTfUgeXw1dJ7W0rSCqVFhLcOgpUgrQiIg8hKvJnEjWwZC1eSSANhAqNpGs0Amrr66evopVqXuRvjIYv1xqDQ20N2Q/3MajEbuRPEyDvq+1RnBosEPHdxtTwcXq1aYnpkt11RoRkcIwGHIiWQdDgPWl5qGhhj1C5Tkw6fb29dvIencP0hfewrfH2+AGqugf61R9H5L7XMGQ1OaoHRfmyNW4n7nenxs3nPq6ERGRc7nr/s0M1HJgKdtwaqr5GzpgWFneAqER2PHFH5jcciOiqhWhT2p7ZBzvjBuognv98/HmQzk4mn0CW4pb4pmMrvIKhExl7D550imvGxERyR8nUMuFuVw2lSwWeuSX41j8Vj7StzTEkdst9Nvrqs5jWKv9SJ5SB+2Sm0LlE+uMq3AvS+UtbOWKnE1ERCQpDIbkxNRScwcS9Z0/cAHfzPwT6T+GYvu1+wA0AABUQQkea7gHyWMD0eOF1vAL6uacdnuKPWkJzGGCQyIir8dgSO50xUKtJOq73rQdvntuK9KX+mHduTYogzbQ8YEaPWvvRvKQW+j/eitUi+js3PZ5clVUZXp1PJCziYiIPIPBkNxZSNQnVCpAAB/dGodXIoBr6KR/7P4q+5Hc6zyeSG2KiJbtXdM2Ty9bd7RXhwkOiYgUhavJvIWJwOMUojAZH2IVtIGHvjL89PqIf/Qe17fH08vWbSlvERoKBAVp99FxpMgqERE5HZfWO5ESgqET204hI/UgDmYX4uZtXxQiErlIQIiqGE80/R3Jk2qi07j77laGdyVr9dTcuWzdWlqCFSucU2SViIicjsGQE3lrMHTleBFWvL4P6aurY2NRa/12XWX4ESN90PuVNgioFuDehtmaNdtdBWVNDdex94eISPJYqNWMtLQ0vPLKK3juuecwd+5cTzfH7Ywrw2sn+BpXhu9o/WCumtxs68Rldy1bN5eWgL0/REQEmQVDO3fuxGeffYaWLVt6uilu5ZLK8K6c3OzAcn+XM5WWgIiICDIKhq5du4YRI0bgv//9L9566y1PN8ctXFYZ3tzk5lOntNsrO7nZxuX+XLZeAYuzEhF5hGzKcUycOBF9+vRBjx49rO5bWlqK4uJigx+5KNxzFu/3z0HbKgfQvH9jzN6aiOPqaNRAEf6vSS42vJeH4zfqYs6OxDuBkJ0sZWXWbUtJ0e7nKN1yf+DuRGUdLls3LTNTO+k8KQkYPlz7b8OG2u1ERORSsgiGli5dit27dyMtLc2m/dPS0hASEqL/iYmJcXELK+fq6av4+unN6Bn2G6Lb1MbzaxKRd6Mp/HAb/4jYgW+mbMOZiwH44nACkqa2gW9AJYIIa1mZnVWTy1I9NVaDN2Sufpqup44BERGRS0l+mKygoADPPfcc1q9fj6CgIJueM336dEydOlX/e3FxseQCIuPK8F30jxlWhu/g3BO7c3IzJy5bZ62nTqXS9tT178/XjYjIRSQfDP322284d+4c2rVrp9+mVquxadMmfPTRRygtLYVvhZtEYGAgAgMD3dtQG+Z7CI3Azq/2I33uBSz9vRnOi7uZn+/1z8fIrscx/LV7cE+iCyeIu3tyMycuW2ZPTx1fRyIil5B8MPTQQw/h999/N9g2ZswYxMfH4+WXXzYKhDzCysosXWX4xVsa4K/bzfW7eKQyPCc3S4vU0hAQESmQ5IOh6tWro0WLFgbbqlatirCwMKPtHmFmZZY4dQoYNAjTg97HnJtTIJnK8BZqmXFyswdIMQ0BEZHCyGICtWRZmO+hEgICKky4+QH8cAu9wnbhf+O34GyhQHp+Zzzy2v3wC/JQLMrJzdKh66mruOpOR6XSZstmTx0RkcuwHEdl2Fh24tLnmQgd+5jzzusszGuj5enXwZb6aQxQiUiBWI5DasrdMEV4BPaeDcefr/6CETY8NbTKTZc3zyGc3OzaTNy20vXUmWoH66cREbkcgyFbVLhhqgCEIRoqdLbt+ZzvIU2uzsRtD6YhICLyGA6TWVHy6f9QZfyTALRBkI6m3O9mZntoRUcDx47xpiY1arU2w7O5Ze26VXX5+XzviIg8xF3DZJxAbUJpcSlWvbwdQ6K24PL46RAwDnh87myzGAgBwFNP8WYqRe7KxE1ERJLHYOgOTZkGuR/txbimmxBR8wYG/utBnC+8jWicqtyL1KSJs5pIzsT8PkREdIfi5wwd+P4o0tMKTFaGf7rhL8DflTwB5wtJE/P7EBHRHYoMhgr3nMXS1ANIXx+O3TeaAmgEAKiBIgxqsg/J46qh2+SW8N36EJD0lmMnYSZnaWMmbiIiukNRwVDGs9uQ+X0Yfr7YBhokAgD8cBuPRuxG8jAN+r7WGsGh5W5+1m6YOszkLD/MxE1ERHcoas7Q+K86Yv3F+6GBLzpV34f5Qzeh8GAxVhd2wJD3OyI4NNjwCbobJmCcIVil0v68+CIzOcsVM3ETEREUtrS+kd9ejO526U5l+Pq2H8BUYr6YmLsJ8TydwZgqh+8fEZEkuWtpvaKCoSuXryCkZohjB7F2w+QNlYiIyKlYjsMFVD5WswKZZ6l0hRRKOhAREZFDFDVnyCV0JR0qJvDTlXTIzNT2GuXkABkZ2n/Vak+0lIiIiExQ1DCZ07vZbCnpEBoKBAVpgyMd9hoRERFZxWEyObClpMPFi8bbnVUIVI7zlOTYZiIi8mocJqsMR0s16DrjUlIcHzLLzNT2SiUlAcOHa/9t2FC7Xark2GYiIvJ6DIYqozKlGipTCNSWeUpSI8c2ExGRIjAYqgxdhuqKCRntYW/vklqtXblmaqqXM3qcXEGObSYiIsVgMFQZljJU28re3iVb5ik52uPkKnJsMxERKQaDocqyVNIhLMx8kKRSabNY21sI1NaeJEfnM7mCHNtMRESKwWDIGQYOBI4dA7KzgSVLtP8eOwZ89pn2cVN1zQDHCoHa2pNUmflMzibHNhMRkWIwz5CrWatrZi9dbqNTp0zPwVGptL1S+fnSWbIuxzYTEZHHuev+zZ4hVzPVa5Sf73h+IUvzlCrT4+RKcmszM4YTESkKgyF30NU1GzZM+29lb/qW5ilVNpGjq8ilzcyFRESkOBwmkzM5ZnOWcpt1uZAqfiV0vVdSCtqIiBTAXfdvBkNEgG115jiviYjIrThniMidmAuJiEixGAwRAcyFRESkYAyGiADmQiIiUjA/TzfA7aQ8gZc8R1dnzlouJHszhhMRkeQpq2dozRoumybT5JYLiYiInEZZwdDIkcaTZE+d0i6nZkBEcsmFRERETqWspfUATC7Ms3fZNIfavBvfXyIiSXDX0nrlzRkypfyy6cREy/uaqjUWHa0dYmHPgXfQZQwnIiJFUNYwmTXWlk3rMhRzqI2IiMhrMBgqz9KyabVa2yNkalRRty0lhUU9iYiIZIbBEKCdMxQTY3nZNDMUExEReSXlBUOOLptmhmIiIiKvJPlgKC0tDe3bt0f16tVRt25dDBgwAIcOHXLsYP/7n+PLppmhmIiIyCtJfmn9I488gqFDh6J9+/YoKyvDq6++it9//x379+9H1apVbTqGwdK8qlUdWzatq2puLUMxq5oTERE5hbuW1ks+GKro/PnzqFu3LjZu3IiuXbva9BynvZi61WSAYUCkG2pjYj4iIiKncVcwJPlhsoqKiooAAKGhoWb3KS0tRXFxscGPUzBDMRERkdeRVc+QEAL9+/fH5cuXkWth1VZqaipmzZpltN1pkSUzFBMREbkch8lMmDhxIn744Qds3rwZ0dHRZvcrLS1FaWmp/vfi4mLExMS4/MUkIiIi52E5jgomT56MNWvWYNOmTRYDIQAIDAxEYGCgm1pGREREcib5YEgIgcmTJ2PVqlXIyclBbGysp5tEREREXkTywdDEiROxZMkSrF69GtWrV8eZM2cAACEhIQgODvZw64iIiEjuJD9nSFUxY/QdCxcuxOjRo206hrvGHImIiMh5OGfoDonHakRERCRzssszRERERORMDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBRNNsHQ/PnzERsbi6CgILRr1w65ubmebhIRERF5AVkEQ8uWLUNKSgpeffVV5OXlISEhAb1798aJEyc83TQiIiKSOZUQQni6EdZ06NABbdu2xSeffKLf1rRpUwwYMABpaWlWn19cXIyQkBAUFRWhRo0armwqEREROYm77t+S7xm6desWfvvtN/Ts2dNge8+ePbF161YPtYqIiIi8hZ+nG2DNhQsXoFarER4ebrA9PDwcZ86cMfmc0tJSlJaW6n8vKioCoI0wiYiISB50921XD2JJPhjSUalUBr8LIYy26aSlpWHWrFlG22NiYlzSNiIiInKdixcvIiQkxGXHl3wwVLt2bfj6+hr1Ap07d86ot0hn+vTpmDp1qv73K1euoEGDBjhx4oRLX0ypKS4uRkxMDAoKChQ1V4rXzetWAl43r1sJioqKUL9+fYSGhrr0PJIPhgICAtCuXTtkZWXhscce02/PyspC//79TT4nMDAQgYGBRttDQkIU9SHSqVGjBq9bQXjdysLrVhalXrePj2unOEs+GAKAqVOnYuTIkbj//vvRsWNHfPbZZzhx4gTGjx/v6aYRERGRzMkiGHriiSdw8eJFvPHGGygsLESLFi2wdu1aNGjQwNNNIyIiIpmTRTAEABMmTMCECRMcem5gYCBmzpxpcujMm/G6ed1KwOvmdSsBr9u11y2LpItEREREriL5pItERERErsRgiIiIiBSNwRAREREpGoMhIiIiUjRZBkPz589HbGwsgoKC0K5dO+Tm5lrcf+PGjWjXrh2CgoJwzz33YMGCBUb7rFy5Es2aNUNgYCCaNWuGVatWuar5DrPnujMzM/Hwww+jTp06qFGjBjp27IiffvrJYJ9FixZBpVIZ/dy8edPVl2IXe647JyfH5DUdPHjQYD9ve79Hjx5t8rqbN2+u30cO7/emTZvQr18/REVFQaVS4dtvv7X6HG/4ftt73d7y/bb3ur3l+23vdXvL9zstLQ3t27dH9erVUbduXQwYMACHDh2y+jx3fMdlFwwtW7YMKSkpePXVV5GXl4eEhAT07t0bJ06cMLl/fn4+Hn30USQkJCAvLw+vvPIKnn32WaxcuVK/z7Zt2/DEE09g5MiR2Lt3L0aOHInHH38cO3bscNdlWWXvdW/atAkPP/ww1q5di99++w1JSUno168f8vLyDParUaMGCgsLDX6CgoLccUk2sfe6dQ4dOmRwTU2aNNE/5o3v97x58wyut6CgAKGhoRgyZIjBflJ/v0tKStCqVSt89NFHNu3vLd9ve6/bW77f9l63jty/3/Zet7d8vzdu3IiJEydi+/btyMrKQllZGXr27ImSkhKzz3Hbd1zIzAMPPCDGjx9vsC0+Pl5MmzbN5P4vvfSSiI+PN9g2btw48eCDD+p/f/zxx8UjjzxisE+vXr3E0KFDndTqyrP3uk1p1qyZmDVrlv73hQsXipCQEGc10SXsve7s7GwBQFy+fNnsMZXwfq9atUqoVCpx7Ngx/TY5vN/lARCrVq2yuI+3fL/Ls+W6TZHj97s8W67bW77f5TnyfnvD91sIIc6dOycAiI0bN5rdx13fcVn1DN26dQu//fYbevbsabC9Z8+e2Lp1q8nnbNu2zWj/Xr16YdeuXbh9+7bFfcwd090cue6KNBoNrl69alTs7tq1a2jQoAGio6PRt29fo78sPaky192mTRtERkbioYceQnZ2tsFjSni/v/jiC/To0cMoS7uU329HeMP32xnk+P2uDDl/v53BW77fRUVFAGCxCKu7vuOyCoYuXLgAtVptVK0+PDzcqKq9zpkzZ0zuX1ZWhgsXLljcx9wx3c2R667ovffeQ0lJCR5//HH9tvj4eCxatAhr1qxBRkYGgoKC0LlzZ/z1119Obb+jHLnuyMhIfPbZZ1i5ciUyMzMRFxeHhx56CJs2bdLv4+3vd2FhIX788Uf885//NNgu9ffbEd7w/XYGOX6/HeEN3+/K8pbvtxACU6dORZcuXdCiRQuz+7nrOy6bchzlqVQqg9+FEEbbrO1fcbu9x/QER9uYkZGB1NRUrF69GnXr1tVvf/DBB/Hggw/qf+/cuTPatm2LDz/8EP/5z3+c1/BKsue64+LiEBcXp/+9Y8eOKCgowL///W907drVoWN6iqNtXLRoEWrWrIkBAwYYbJfL+20vb/l+O0ru3297eNP321He8v2eNGkS9u3bh82bN1vd1x3fcVn1DNWuXRu+vr5G0d65c+eMokKdiIgIk/v7+fkhLCzM4j7mjulujly3zrJlyzB27Fh888036NGjh8V9fXx80L59e8n8JVGZ6y7vwQcfNLgmb36/hRD48ssvMXLkSAQEBFjcV2rvtyO84ftdGXL+fjuL3L7fleEt3+/JkydjzZo1yM7ORnR0tMV93fUdl1UwFBAQgHbt2iErK8tge1ZWFjp16mTyOR07djTaf/369bj//vvh7+9vcR9zx3Q3R64b0P7FOHr0aCxZsgR9+vSxeh4hBPbs2YPIyMhKt9kZHL3uivLy8gyuyVvfb0C7WuPIkSMYO3as1fNI7f12hDd8vx0l9++3s8jt+10Zcv9+CyEwadIkZGZmYsOGDYiNjbX6HLd9x22eai0RS5cuFf7+/uKLL74Q+/fvFykpKaJq1ar6WfXTpk0TI0eO1O//999/iypVqogpU6aI/fv3iy+++EL4+/uLFStW6PfZsmWL8PX1Fe+88444cOCAeOedd4Sfn5/Yvn2726/PHHuve8mSJcLPz098/PHHorCwUP9z5coV/T6pqali3bp14ujRoyIvL0+MGTNG+Pn5iR07drj9+syx97o/+OADsWrVKnH48GHxxx9/iGnTpgkAYuXKlfp9vPH91klOThYdOnQweUw5vN9Xr14VeXl5Ii8vTwAQ77//vsjLyxPHjx8XQnjv99ve6/aW77e91+0t3297r1tH7t/vZ555RoSEhIicnByDz+3169f1+3jqOy67YEgIIT7++GPRoEEDERAQINq2bWuwLG/UqFGiW7duBvvn5OSINm3aiICAANGwYUPxySefGB1z+fLlIi4uTvj7+4v4+HiDL5dU2HPd3bp1EwCMfkaNGqXfJyUlRdSvX18EBASIOnXqiJ49e4qtW7e68YpsY891z5kzRzRq1EgEBQWJWrVqiS5duogffvjB6Jje9n4LIcSVK1dEcHCw+Oyzz0weTw7vt27ptLnPrbd+v+29bm/5ftt73d7y/Xbkc+4N329T1wxALFy4UL+Pp77jqjsNJCIiIlIkWc0ZIiIiInI2BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiEiWMjIyEBQUhFOnTum3/fOf/0TLli1RVFTkwZYRkdywNhkRyZIQAq1bt0ZCQgI++ugjzJo1C59//jm2b9+OevXqebp5RCQjfp5uABGRI1QqFd5++20MHjwYUVFRmDdvHnJzcxkIEZHd2DNERLLWtm1b/Pnnn1i/fj26devm6eYQkQxxzhARydZPP/2EgwcPQq1WIzw83NPNISKZYs8QEcnS7t27kZiYiI8//hhLly5FlSpVsHz5ck83i4hkiHOGiEh2jh07hj59+mDatGkYOXIkmjVrhvbt2+O3335Du3btPN08IpIZ9gwRkaxcunQJnTt3RteuXfHpp5/qt/fv3x+lpaVYt26dB1tHRHLEYIiIiIgUjROoiYiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIr2/21HS/0LLjbZAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1915,9 +1915,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.95872127]\n",
-      " [3.20357736]]\n",
-      "[3.9712983] [3.26048873]\n"
+      "[[4.08179906]\n",
+      " [3.05087018]]\n",
+      "[4.03853268] [3.07469645]\n"
      ]
     }
    ],
@@ -2050,16 +2050,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.25986026 4.46387965]\n",
-      "[[4.18118338]\n",
-      " [2.86240935]]\n",
-      "[[4.18234308]\n",
-      " [2.86143379]]\n"
+      "Eigenvalues of Hessian Matrix:[0.32932635 4.53640693]\n",
+      "[[4.15709522]\n",
+      " [2.9838929 ]]\n",
+      "[[4.15605625]\n",
+      " [2.98473694]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -4029,38 +4029,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "cd18340b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[3.94499279]\n",
-      " [3.03306538]]\n",
-      "Eigenvalues of Hessian Matrix:[0.31248425 4.44418124]\n",
-      "theta from own gd\n",
-      "[[3.94499279]\n",
-      " [3.03306538]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_265_1.png"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients for OLS\n",
     "from random import random, seed\n",
@@ -4123,87 +4095,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "ac458170",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.]\n",
-      " [3.]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30306759 4.65944342]\n",
-      "0 [-20.62275788] [-25.73690387]\n",
-      "1 [-0.03662823] [0.02925855]\n",
-      "2 [-0.0342458] [0.02735547]\n",
-      "3 [-0.03201832] [0.02557617]\n",
-      "4 [-0.02993573] [0.0239126]\n",
-      "5 [-0.0279886] [0.02235723]\n",
-      "6 [-0.02616812] [0.02090304]\n",
-      "7 [-0.02446604] [0.01954342]\n",
-      "8 [-0.02287468] [0.01827225]\n",
-      "9 [-0.02138683] [0.01708375]\n",
-      "10 [-0.01999575] [0.01597256]\n",
-      "11 [-0.01869515] [0.01493365]\n",
-      "12 [-0.01747915] [0.01396231]\n",
-      "13 [-0.01634224] [0.01305415]\n",
-      "14 [-0.01527928] [0.01220505]\n",
-      "15 [-0.01428546] [0.01141119]\n",
-      "16 [-0.01335628] [0.01066897]\n",
-      "17 [-0.01248754] [0.00997502]\n",
-      "18 [-0.0116753] [0.0093262]\n",
-      "19 [-0.01091589] [0.00871959]\n",
-      "20 [-0.01020588] [0.00815244]\n",
-      "21 [-0.00954206] [0.00762217]\n",
-      "22 [-0.0089214] [0.0071264]\n",
-      "23 [-0.00834112] [0.00666287]\n",
-      "24 [-0.00779859] [0.00622949]\n",
-      "25 [-0.00729134] [0.0058243]\n",
-      "26 [-0.00681708] [0.00544547]\n",
-      "27 [-0.00637367] [0.00509128]\n",
-      "28 [-0.0059591] [0.00476012]\n",
-      "29 [-0.0055715] [0.0044505]\n",
-      "theta from own gd\n",
-      "[[3.98281205]\n",
-      " [3.0137297 ]]\n",
-      "0 [-0.00520911] [0.00416103]\n",
-      "1 [-0.00487029] [0.00389038]\n",
-      "2 [-0.00445186] [0.00355614]\n",
-      "3 [-0.00403677] [0.00322456]\n",
-      "4 [-0.00364967] [0.00291535]\n",
-      "5 [-0.00329616] [0.00263296]\n",
-      "6 [-0.00297571] [0.00237699]\n",
-      "7 [-0.00268602] [0.00214559]\n",
-      "8 [-0.00242441] [0.00193661]\n",
-      "9 [-0.00218823] [0.00174795]\n",
-      "10 [-0.00197505] [0.00157766]\n",
-      "11 [-0.00178263] [0.00142396]\n",
-      "12 [-0.00160895] [0.00128523]\n",
-      "13 [-0.0014522] [0.00116001]\n",
-      "14 [-0.00131072] [0.001047]\n",
-      "15 [-0.00118302] [0.00094499]\n",
-      "16 [-0.00106776] [0.00085292]\n",
-      "17 [-0.00096373] [0.00076983]\n",
-      "18 [-0.00086984] [0.00069482]\n",
-      "19 [-0.00078509] [0.00062713]\n",
-      "20 [-0.0007086] [0.00056603]\n",
-      "21 [-0.00063957] [0.00051088]\n",
-      "22 [-0.00057726] [0.00046111]\n",
-      "23 [-0.00052102] [0.00041619]\n",
-      "24 [-0.00047025] [0.00037564]\n",
-      "25 [-0.00042444] [0.00033904]\n",
-      "26 [-0.00038309] [0.00030601]\n",
-      "27 [-0.00034576] [0.0002762]\n",
-      "28 [-0.00031208] [0.00024929]\n",
-      "29 [-0.00028167] [0.000225]\n",
-      "theta from own gd wth momentum\n",
-      "[[3.99916114]\n",
-      " [3.00067008]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients for OLS\n",
     "from random import random, seed\n",
@@ -4270,29 +4165,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "e6b06cdb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.26281735]\n",
-      " [2.84478178]]\n",
-      "Eigenvalues of Hessian Matrix:[0.33978889 4.51659846]\n",
-      "0 [-14.67490857] [-18.08539155]\n",
-      "1 [-4.46170878e-15] [-1.45781905e-14]\n",
-      "2 [-1.21430643e-15] [-1.24768421e-15]\n",
-      "3 [4.92661467e-16] [4.50610071e-16]\n",
-      "4 [4.92661467e-16] [4.50610071e-16]\n",
-      "beta from own Newton code\n",
-      "[[4.26281735]\n",
-      " [2.84478178]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Newton's method\n",
     "from random import random, seed\n",
@@ -4345,47 +4221,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "a010a209",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.0586484]\n",
-      " [3.0718316]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]\n",
-      "theta from own gd\n",
-      "[[4.0586484]\n",
-      " [3.0718316]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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YIiIiAtSzxmx5HLkNBkNERESAevp8VBQgSfr3SxIQHa0+jjwKgyEiIiJAXUdoyRL1/9cOiDS3Fy9mvSEPxGCIiIhIIzUV+PprIDJSd3tUlHo76wx5JBZdJCIiqik1FUhJYQVqGWEwREREVJu3NzBokLNbQQ7CYTIiIiKSNQZDREREJGsMhoiIiEjWnB4Mbdu2DaNGjUJERAQkScLq1au1+yorKzF9+nR07NgRjRo1QkREBB5//HFcuHDBeQ0mIiIij+L0YKi0tBSdO3fG0qVL6+y7desW9u7di1dffRV79+5FdnY2jh8/DoVC4YSWEhERkSeShNC3PK9zSJKEVatWYfTo0QaP2b17N3r16oUzZ86gVatWJp23uLgYQUFBKCoqQmBgoI1aS0RERPbkqO9vt5taX1RUBEmS0LRpU4PHlJeXo7y8XHu7uLjYAS0jIiIid+T0YTJzlJWVYcaMGXjkkUfqjRDnzZuHoKAg7V90dLQDW0lERETuxG2CocrKSjz88MOorq7Ge++9V++xM2fORFFRkfYvPz/fQa0kIiIid+MWw2SVlZUYM2YM8vLysGXLFqPjhv7+/vD393dQ64iIiMiduXwwpAmETpw4gZycHDRv3tzZTSIiIiIP4vRg6ObNmzh58qT2dl5eHn799VcEBwcjIiICaWlp2Lt3L9atWweVSoXCwkIAQHBwMPz8/JzVbCIiIvIQTp9an5ubi+Tk5Drbx48fj9mzZyM2Nlbv/XJycjDIxEX0OLWeiIjI/chmav2gQYNQXzzmQmWQiIiIyAO5zWwyIiIiIntgMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZM3pa5MRERGRhVQqYPt2oKAACA8HkpIAb29nt8rtMBgiIiJyR9nZwHPPAefO3dkWFQUsWQKkpjqvXW6Iw2RERETuJjsbSEvTDYQA4Px59fbsbOe0y00xGCIiInInKpW6R0iIuvs029LT1ceRSRgMERERuZPt2+v2CNUkBJCfrz6OTMJgiIiIyJ0UFNj2OGIwRERE5FbCw217HDEYIiIicitJSepZY5Kkf78kAdHR6uPIJAyGiIiI3Im3t3r6PFA3INLcXryY9YbMwGCIiIjI3aSmAl9/DURG6m6PilJvZ50hs7DoIhGRO2HFYdJITQVSUvh+sAEGQ0RE7oIVh6k2b29g0CBnt8LtcZiMiMgdsOIwkd0wGCIicnWsOExkVwyGiIhcHSsOE9kVgyEiIlfHisNEdsVgiIjI1bHiMJFdMRgiInJ1rDhMZFcMhoiIXB0rDhPZFYMhIiJ3wIrDrk+lAnJzgZUr1f/l7D63waKLRETughWHXRcLYro1SQh9hSs8S3FxMYKCglBUVITAwEBnN4eIiNxZ7SVRrlwBxoypWwdKM4TJnjuLOer7mz1DREREptLXA+TtXX9BzL/+FQgKUi+bwV48l8ScISIiIlMYWhLFWG7Q1avA4MFATAyXTXFRDIaIiIiMqW9JFFNxHTmXxWCIiIjIGGNLopiC68iZrOxGGb59fTee7/WjQx6POUNERETG2Gqpk5rryA0aZJtzeogrx65i/cIjUH7jgw0XElGKngCKHfLYTu8Z2rZtG0aNGoWIiAhIkoTVq1fr7BdCYPbs2YiIiECDBg0waNAgHDp0yDmNJSIiebL1UidcRw4AcOzb3/HPEblICvoNoXFNMeGj/si+0AelaIwo7wt4Is4xPUNOD4ZKS0vRuXNnLF26VO/+BQsW4O2338bSpUuxe/duhIWFYciQISgpKXFwS4mISLaMLYkCmDdTTKbryKkqVPjhvf14uVcu2vvlIW54G7z8zSD8UNwZ1fBG1wZHMGtgLn759AjOVoTj7Z/6OaRdLlVnSJIkrFq1CqNHjwag7hWKiIhAeno6pk+fDgAoLy9HaGgo5s+fj6eeesqk87LOEBERWU0zmwzQn0g9axbQrx/w8MPAtWv6zyFJ6qAqL0820+xvFt7E928fhDK7Cut/j8MV0UK7zxcV+FPz/VAMLsXI5+5Cq766FdZZZwhAXl4eCgsLMXToUO02f39/DBw4EDt27DAYDJWXl6O8vFx7u7jYMWOORETkwTRLotSuM6QxZ4460HniCWDhQvW2mkGTjNaRO7+nAOsWnYByYwNsvtwR5eij3ddMuo4RMYegGO2NYRkdEBjVw4ktVXPpYKiwsBAAEBoaqrM9NDQUZ86cMXi/efPmYc6cOXZtGxERyZBmSZS5c9U9QbWdP68OhF58Ub1GWe3lORYvdlw16tqVsu24dIuoFtj/9XEoPyiAclcI9txKAHBnKLCtzxmkdM6DYlxT9HsqET4B/e3SDku5dDCkIdUaoxVC1NlW08yZM5GRkaG9XVxcjOjoaLu1j4iIZObf/9a/XQh1D9DnnwOnTgE7djhnHTkHrJVWcbMCW5cegDLzJpSH2+Ksqj2A9gAACdXo0/gQFP2uImVqNOKGt4Hk1domj2sPLh0MhYWFAVD3EIXXSDa7dOlSnd6imvz9/eHv72/39hERkQwZqzmkmT6/Y4dzps9rcptq5zVpij5asVba9bwb+HbhISjXSvg2vwOK0V27rwFuYWjYASjuq8CIF+IQmtjRmqtwKJcOhmJjYxEWFoaNGzeia9euAICKigps3boV8+fPd3LriIhIlkydFu+M6fP1VcrW9Fqlp6uH+kzspfo99yyUS/KgzA3EthsdocKdGV6hXpcwqt1RpIwJwL3pHdEguLf57XXQUF59nB4M3bx5EydPntTezsvLw6+//org4GC0atUK6enpePPNN9GuXTu0a9cOb775Jho2bIhHHnnEia0mIiLZMnVavDOmz5vaa1VP0cfqqmrs/u9hKP9zBcpfInGwvB2AVtr9if4noOh+Hoq/tEDP8Qnw8hlgWVsdMJRnKqcHQ3v27EFycrL2tibXZ/z48VixYgVefvll3L59G5MnT8b169fRu3dvfP/992jSpImzmkxERHKmqTl0/rz+HhjN9PmkJMe3zcJeq1tXbmHzkoNQflWGtSficLE6UbvPG1UY2Gw/FINKMOrZWLQZ1A5AO+vaacehPEu4VJ0he2GdISIisilDNYc0k3sc/GWulZsL1OhgMCgnBxdbdMC6hUeh3OCHjYUdcRsNtbsDUYT7ow9BMUrg/hc7oFlsU9u1UaUCYmIM92DVqMVUXFrqkO9vBkNERESW0DfMEx1t2+nz5ubUaAINA71WAhJKAlpguPf32FHaCaLGQhStvM8hpcMpKB5tggGTE+HX2M8211CbGQFbcbduLLpIRETksjQ1h+yVAGxJTo23t3p/Wpq6h6VGQFQNda/VhLJl+BFdAAA9Gh6Gos8lKJ4KR6e0uyF5Rdmm7fVxwQR0BkNERESW8va2z/R5K3JqinsNxsERc9H+20Vorrqs3X4OUXgJC1DeMhrLhmzDyOfbIbJHAoAE27e/Pi6YgM5hMiIiIldiRk6Nphfq7M7zWLvoJJRbGiHnaidUwg9eUCEJ23E3jqNNRDnaP9INQ17ojMZhjR13LfoYGcpzRs4Qe4aIiIhciYnT44+/9ikyf2yNNT+H4dfbcQDuLHLa3u93KDqfhWJCMPpOegLefi60Flo9Q3nOWr+NwRAREZErMTFXZtabfvgcgwAAXlDhnsCDSEm6jlFTWqH9/W0AtLFfG61laNFbR6/f9gcGQ0REnsZFqvqShUzMlbmBpkiN2AXF8CoMz4hDy/jOdm6Yjdk7Ad0MDIaIiDyJC1X1JcucKItGWEBLNCq7Ai/omx4PlAWGYtXvAxDQvJHjG2hL9kpAN5OX8UOIiMgtaGYg1c430cxAys52TruoXqoKFX58fz+m985FvP8p3H1/W4wvWwbgznR4DSFJkCQJDZa/5/6BkAthzxARkSewwwKdZD+ll0qxcdFBKLMqsO5kHC6LTtp9PqjEzeDW2NfiIXQ7+SVQfec1lby8gIwM9vLZGIMhIiJPYIMFOsm+Cn69iLULj0G5MQCbLnVCOe6s8N5UuoHhrQ5BkSLhvhc6IGjPGSDti7rBrUoFLFwI9OnDgMiGGAwREXkCF6zqK3eiWuDgqhNQLrsA5c4W+Lk0EUCodn+sz1mkdMyD4rFA9H86Eb4N+6l3qFRAPwO9fBrs5bMpBkNERJ7ABav6ylHlrUpse/cAlJ+VQHmoDU5X3Q3gbu3+3o0OIqXfFSgmRyFhVFtIXq3qnoS9fA7HYIiIyBMkJalnjRmr6puU5Pi2ebgbZ4rw3VuHsGYN8O3ZDihCN+2+ANzGkND9UAyrwMgX2iOsU6LhE2lKImRlmfbA7OWzGQZDRESewAWr+noMPXWb8n68gLVLfocypwm2Xu+IKtyjPTxEuoxR7Y5CkeaHwc93RMMWves5+R/0lUQwhr18NsNgiIjIU7hYVV+PoCdIKUQYXsC7WIU7z2eC/0koup6DYmJz9JqQAG8/M3rgDC3Kagh7+WyOC7USEXkaVqC2ifL/roTfhEcAQKfaj6b2z+yGC9BsSA+MmhaLu+5tbdmDGFuUtTZNL189q9Z7Ekd9f7NniIjI07hIVV93dOnQZax/6yjWfeuFJYUvIQJ1qxN7QUBIEl5v/g6QlWddoGksWbo29vLZBYMhIiJHYG+NSxLVAse+y4Ny6VkofwjGjpJECCRhIHIRhfMG7yfZakaXqUnQU6cCDz7I942dMBgiIrI3rhfmUqrKqrDj34eg/O91KPe3xolK3RXeuzU4gmmxm4HDJpzM0hldmuD4sCkPAnUgxN4+u2EwRERkjDW9OoaSYzXrhckk98PZSi6U4PtFB7EmW4X1eQm4Ju6s8O6HcvypxX4oBt/GqIx2iOoZD+TeCyS/YfzElszoMmfmGJOlHYIJ1ERE9bGmV8dYcqzmiy7PyrwT0uvc7gKsffsElJsaYMuVTqiAv3ZfsHQNI2IPIyXVG0OfT0STiCa6d9a8dsbqNpn72pkzc0xmydL6MIGaiMjZrO3VYSVhhxLVAr99dRxrlhVA+VMo9t6OB3Cn5+Yu39NI6XQaivHNcM+THeAT0N/wyexRt6m+xXT1YbK0wzAYIiLSxxarwMthvTAnJ4aXF5dj67sHoVx5E8rDdyFf1R5AewCAhGrc0+QgFP2vQTG1FdrfFwvJK8b0k9u6bpOpM8f+/nfg3nuZLO1ADIaIiPSxRa+Op68X5qTE8GunruObhYehXOeF7851QAm6a/c1RCmGhh+A4r5KjHghDiEdOln3YKmp6oDXFgGfqUFvQgJ7Ch2MwRARkT626NXx5PXC6htCfPDBO71m5gQO9fQyndpyBsp38qDcGoTtNzpChX7au4V5XYTi7mNQPNQAf3o2EQ2C+9joIv9gq7pNnh4cuzEGQ0RE+tjii8tT1wszNoQIqK9r8eK6PUWGAh49vUzlzULxVcvJmHfmERwuvwvAnSrPHQOOQ9H9AhRPtESPcfHw8gm12+XajCcHx26Os8mIiPSx5WwifcNJ0dHumxybmwskJ5t2bM0ZUYD+YbWxY4GFCyGE0LvsRRq+xlqMwsBmB6BILsGo59ogdkC01ZfhFJoeNUB/cCzjmWP6OOr7m8EQEZEhtvzi8qQK1CtXAo88YvrxkgQEBwNXr9bZVfMLSKqzF6gGcLtxCCp/O4KmbYLNbalr8rTg2I4YDNkQgyEishi/uOoyp2fIVnJyPCup2JTg2JMCaAuxzhARkSuw5WwiT2Es98Ue3Ln8gD7GkrK5hItDMRgiIjKGq8Drqi8x3F7kNMOKS7g4nJezG0BEZDKVSj1Es3Kl+r8qlbNbJF+agoSRkUYPrbbmcSRJPSwplxlWpszUS0/ne9/GGAwRkXvIzlbP7kpOVifvJierb2dnO7tlslWtGI09//gG/+m4CJ94j0c16gY+AoAECSK4+Z3Ec0Nq73fn8gOWMqfYJ9kMgyEicn2aYYPaXxKaYQMGRA5TdqMM376+G08nbEO0/0X0nNgRTxxIx+OqFRiDr3BF0q33I7VsCenLLyD9+8M/NugJeCQJeOmlur1MUVHyGxKSwxIuLoizyYjItXHld6e7cuwq1i88AuU3PthwIRGlaKzd1wg3cV/kASiGqzDipQQ0/20LMHkycPnynRNoEn+B+mfm1Z49dc89wI4djklcd5WZW6bO1PO02XUGcDYZERHAld+d5PiGPKx55wyUPzTDjuJEVOPOCu+RXgVQxJ+A4uGGGDQ1EQFN+6p3ZGcDY8bUn/h7+rThoKNmonp2NtC2rfWzqUwJclxp5harVDuHkIGioiIBQBQVFTm7KURkrsxMIdRfC/X/ZWY6u6Vuraq8Smx/9zfxUs8c0d7vVJ2nt2uDw2LWwBzxy6eHRbWqWs8JqoSIijL8+kiSENHR6uOMycpSH6/vHJKk3m+KrKy6bYqK0r2/rR7LljRtqt0uZ7bJSRz1/e3ywVBlZaV45ZVXRExMjAgICBCxsbFizpw5QqVSmXwOBkNEbiwnx7RgKCfH2S11OyUFJSLrpZ1ifNvtooV0Wefp9EW5GNp8t1j651xxZsc54yez1etkq6DKlCDHlgGcrekL5KKjZRUICeG472+XHyabP38+li1bhv/+97/o0KED9uzZg4kTJyIoKAjPPfecs5tHRPbGYQOburC3EGvfOg7lxgbYfLkjynFnhfdm0nWMiDkExWhvDMvogMCoHqaf2FaJv7YYFjU2PV2S1NPTg4JcdwiWxT4dyuWDoZ07dyIlJQUjRowAAMTExGDlypXYs2ePk1tGRA7z5JPArFl1t8tx6rWZRLXA/q+PQ/lBAZS7QrDnVgKAMO3+Nj5nkNIpD4pxTdHvrx3g27C/4ZPVx9SiiMaOs0VQZWpAlZtr/WPZE4t9OozLB0P9+/fHsmXLcPz4cdx999347bff8MMPP2Dx4sXObhoR2UPNhNcTJ4B//9vwF1tUlLzXCDOg4mYFtr13EMrPSqA81BZnVO0BtAcASKhGn8aHoOh3FYrJUYgf2RaSV+v6T2hKErKtevBsEVTZOniRU/VrmXL5YGj69OkoKipCXFwcvL29oVKpMHfuXIwdO9bgfcrLy1FeXq69XVxc7IimEpG19M3qMWTOHOCVV9gj9IfreTfw7cJDUK6V8G1+BxSjm3ZfA9zCkLADSLmvAiNeiENoYkfTT2zqTKv6lugwpwfPFkGVqcHLoEHAihUcgiXXn022cuVKERUVJVauXCn2798v/ve//4ng4GCxYsUKg/eZNWuWgLrwqc4fE6iJXJihhFdXS2x1IadyzohFo3NFctO9whuVOk9RqNdFMan9VqH8+0+i9HKpZQ9gyUwrWyT+WjubSpMYbej9VPP9w5lbLs1RCdQuX3QxOjoaM2bMwJQpU7Tb3njjDXz66ac4evSo3vvo6xmKjo5m0UUiV2WssKIhMik8p1FdVY3d/z0M5X+uYN2eMDSrKEQ4ClCAcGxHEuL9f4ei23mkPNECPccnwMvHikUGrCl2aYsChvp6pGoWaDTl/mlp6v/X10tVs7K1tY9FdsOii3+4desWvLx0P9De3t6orja89J+/vz/8/f3t3TQishVjCa+G2Co3xFnVh0143NvXbmPTogNQflWGtSficLE6EQ8gG+swDtG485xVtQyHz7KltvvytmZWly0Sf62dTaVZSFbfEF/tIIczt2TP5YOhUaNGYe7cuWjVqhU6dOiAffv24e2338Zf/vIXZzeNiGzF0qDGFomtzqo+XM/jXrw7CevfOgrld374vrAjbqOX9pBH8Qk+weN1TudzpfBOlWdbtNsV1siyNqgyJ8jhzC1Zc/lhspKSErz66qtYtWoVLl26hIiICIwdOxavvfYa/Pz8TDoH1yYjcnGmrsekYav1yDRDKbX/GdQ3lGJLBh5XfUtCGr5ENtK021t5n4Mi4RQUDzfA4PdSIZ0/r/+8tlyn7fXX9ZczqE1mQ5XkWI76/nb5YMgWGAwRuThNfoqhWT012SpQcdYCsH88rjh3DpKe3dWQcA5R+HOD9RjZ9yoUT4WjU9rdkLwkyxfxNHcY0NQcrqgo9VpjHE4iO3HU97cV2XVERDaimZYN3Al2DImKsk2PjTk5MTZSfK4YuQ8vAwwEQgDgBYFWyMdP31zFq5sHofOY9upACLBs6Co7Wx3YJCcDjzyi/m9MjHq7IabmcD35pPMDIZVKHSSuXKn+r0rl3PaQW2IwRESuQZPwGhmpuz0qSl1TKDNT3eORl+dWOTH5P13Aew9vxbAWe9AiOgAffB1s+eOaW5BQMxxXO7DRrCJvKCAy9ZrbtTPtOHuxJNAj0sOsBOr8/HxER0fbqy1EJHeOnNVjq+UjahHVAvtWHoXy3xeh/DkU+27HA4jQ7vfxBmBK54W+xzWnIKGp63OlpNR9fu303NiUoXwvTaBnr3wv8khm5Qw1atQIGRkZmDFjBho1amTPdtkUc4aIqA5jeUpm5AyVF5cj550DUH5+C2uP3oVzqjvBjxdUuCfwIBT9r0MxtRXaD21t3eOaWj/H0vwiwKbPjV04K9+LHM4lc4Y2btyI77//Hu3atcPy5cvt1SYiIvurL0/JhOUjrp64hk+e/gF/jtqJFkEVuP/VHnj/0ACcU0WgEW4iNWIXVkz6AYWHr2N7UWe8tH4Q2t/fxurHrXc4sWZviDXDgNa20d6ckO9FHs6SstX//e9/RVRUlOjSpYvIycmxUTFs+3FUOW8ickNmLB9x/Ps8sXBkjhgQtE94oUrnLuFeBeKp+K1i/eyfxe3rt236uHpVVQmRkyNEZqb6v7WXJsnJMW1pk/r+DbfF0hr2kJlp2rVlZjq3nWQ1l1+O4/bt25g3bx7eeustDB06FP/85z9x11132TZSsxEOkxFRvQxMPVdVqPDT8sNQLr8K5b5oHKloq3O3zgHHoOhZAMWkEHR7JM785S/sWfnaVkNdzqrOXR9rhgDJrbh8naFbt25h7969yMrKwjvvvANfX19MmTIFs2fPRpMmTWzdTqswGPJQrviPNLm90kul2LjoIJRZFVh3Mg6XRUvtPh9UYlDwfiiSb0LxfFu07hflxJaawJz1udyJq+c0kc24ZDC0bNky7N69G7t378aRI0fg7e2NTp06oU+fPujSpQs+++wzHD9+HKtWrUKPHj3s1mhzMRjyQM5aQoE8UsGvF7F24TEoNwZg06VOKEeAdl9T6QaGtzoERYqE+17ogKBWQU5sqQU8dRFSTw30SIdLBkPR0dHo06eP9q9Hjx51FkR98803kZmZiYMHD9q8sZZiMORhnLWEAtmPg3v5RLXAwVUnoFx2AcqdLfBzaaLO/lifs1Ak5iFlXCD6P50I34a+dmuLQ3hqL6qnBnqk5ZLBkCkuXryIiIgIqFyoCiiDIQ/CKbWex5JePgu+3CtvVWLbuweg/KwEykNtcLpKt2Za70YHobjnChTPRKJDyl13qj7bg6cGJ87A59KjOer72+ar1oeEhGDLli22Pi2RmjlTapk46fosKZxnRvB040wRvnvrEJRKgW/OJKII3bT7AnAbQ0L3QzGsAiOevxvhXXR7h+yGQ7y2xdXmyQa4UCu5l5Ur1WX3jcnMBMaOtX97yHKW9PKZMER6OqQXlItOQZnTBFuvd0QV7gxxtZQuY9RdR6FI88Pg9EQ0CnFw8VgO8RKZxW17hsjFuXuXsjssEyB3pr7HzO3lM7K8hABQ+OAUtMU5VOPOLK8E/5NQdD0HxcTm6DUhAd5+SVZfokWsWR6DiOyKwZCceEL3vDlrM5HjmfMeM7dCspHgSQIQjkIMRC6qg5pDMfAGRk2NQbshdwFwgRponjzE6+4/skj2GAzJhacsaqhZJiAtTR346JtS68xlAuTM3PeYmb18xXuOw5RO8jVv/44mz99r2rkdyZrlMVyFvqBnzRr3/5FFsmdmuVRyS8a65wF197wLzQCsl6lrM5HjWPIe0/Ty1V77SnM3SUJli3D8c74K/QP3I+Ul03p3mnRtZ167c3PVuWi5ufb9DLj7EG92tjrHKzlZnbeXnAyEhgIPPli3x0sTAGdnO6WpROZiArUceGrpenbNuw5L32MGCucJAAIS0vA1VkEd3HpBhQuIRAguQm/4ZG5ZBUcPG7tz1WRDvX71ceXrIbfhkqvWk5vyhO55fTRTaseOVf+X/+A6j6XvsdRU3P7wE9xq1FJncz6ikYavsQ4jMaz5Hrz38Dac3nURoVnvQZIk61dS13y5O7JHw9VXgjekvl6/+nDleHIjzBmSA3fvnnc3cuyxMvW9c/gwkJuLcwF3Ye2S36Hc1ABbrqShCg8jCdsRjgKUohGaxQbhkQf8sOL5MgRG1Vza548hUn09OqZWHXbmrK5UG7Tf0Ywlfhvjbj+ySJY4TCYH7tw97yyWBjSeMGPPEkbeYwLQGdrKRxSewxLtENhdvqeR0uk0FOOb4Z4nO8AnwMjvNGsCTlcYNnangNnU2l6GuNvwO7kU1hki2+EMLPNYGtAYyqs4d06dZKrpbXDlLz5L1fMe0/drKxLnkYU0rO4yG3H/eBRxw9tA8oox7/Es/YJ1hWFjd6qabGmPMctckBthzpBccAaWaSzNJTElr2LxYnWPREyMZTkpjpz5ZInUVJQsXYHShi3q7Kqd8OwFAUkCHrj6EeLvj7mzDpgjrtGTho0d8XwZmfWnF39kkbsRMlBUVCQAiKKiImc3xfmqqoTIyREiM1P936oqZ7fIdVRVCREVJYQ6pKn7J0lCREfrf85ycgzfT995JEmIrCzT25aVVbdtUVHmncNOTm4+Ld5OyRGDmu4V3qgUXqgSA5Ej5uDvpj0fOTnqEznqGjWvsySZ/zq7Eke+J7Ky7rxvaz9XgBDNm+tuj452ifcmuT9HfX8zGCLSMDWg0Xx515SZaXowZO4XruaLyBZBlQ2oKlVixwf7xYw+OSLB/0SdZnUMOCZe6ZcjTvx1gWnPRWam46+xvi93JzynZnPGe0Jf8KUJevgji+zEUd/fTKAm0rBmEVhTk3JrM5ZcaslipnZw68otbFp0AMqvK7D2RBwuiTtT4b1RhYHN9iMluQSjnmuD2AHR6h2mPiebNgETJjj+GvXlhkVHu+6sLg1nvifcKfGbPAITqMm9eMI/ktbkkhhbM80QY0m6TlzPqnD/Jax76xiUG/yw8WInlKG3dl8gijC81SEoFMB9GQloFtvtzh0174Xz54GWLYErV+qfxQg47hprv09PnQJ27HCv960z1zhzp8RvIjMwGCLrecp0cmsWgdXMpnrwQfMe01gAZuqMpvPnzXtcPUS1wOG1p6B87xyUO5rjp5sdIBCi3d/a+xxSEk9B8WgTJD2TCL/G99Q9ib73gj41E2wvXTKtgdbO7qrvfVq7p8+VucJsOCIPw2CIrOMpC8AC1pcgSEkBmjcHrl41/limTjs2tbfq+eeBBg3Mfq4rb1Xih2UHofy0GMoDMfi9SneF956NDkHR5zIUT0egY2o7SF5Rhk9mzpINNYsM5uaa1lhrZnd50vvUk2bDEbkI5gyR5VQq9UKNhr783bWYo6W5JObkDUmSaV/Axgpm1jwfYNI5i84WYcOiQ1izSuCbsx1wQzTV7vNHGQaH7IdiSBlGZtyNiG5hpl2PsTwWQD1ktmiRurxDzeEoexcFdZG8K5thEVWSEYd9f9s1PdtFcDaZncyZY/nsK1dnyewYU2eUBQebP63e0DRwE2eonf4hX/wrLVcMCd4jfFGuc7cW0mUx4a5tIvvlnaKkoMT0dtVkzUy8mtdoj9ld1rbNFbn7bDgiEznq+5vDZGQZlerOopPGuGPugiWJoqYOS3z5JXDvvaafV1Mw86mn1MnIhtRInBUDBuKXT49A+dElrPs5BIHllxCOAlQgHCp4I87vFBRd8qGYEIw+T3SAt5+VVYKtzWOx55pdjsyxcdREAndc44zIhTEYIuP0/QO/fTtw7Zpp95dL7oKpCdiWzMZJTQVu3wYee8zooR8/tgWvFbTHheoEPICjWINxiMadL8zKFmHw/eBd235h2iKPJTVVnXdl62DCUTk2jp5IYK/ni0iGmDNE9TP0D3xamvoXqDHNmwMXL8rnH2hNoi6gPwHbmkRdE3OSBiEHWzEIY/EZPoM6eNJZSMHatugLjgHXzWNxRI6NoQRtW7zuRDLmqO9vrk1GhtW3TpcpgRAAPPusfAIhwD5rwGnWn/qjbo+hXy/VkHAeEUiIF/h2zk/4LGI6JNRdF0z7hZ2ebv5aVtnZ6sAiOVldoFKz1tqaNXeGTWuvYeXsdao0swRrtkXDFm2rb106a55rInIY9gyRfqbMwPHyqv8feLn1CtVkq9yR7GyIZ5+DdP7O6yBQN8BRf4gl4KuvIKU9aPrMNmMVsGu1xWjvB+C8qs7GnnN7VZy2x3NNRABYgZqczZQqt5pAqHZNHo0PP5RnIARYXan3ZuFNHHpmKXqtnqk3+KlNAoCXXgTS/ij6aOukYWO9H5Kk7v3Iy7NdHos5AaUp+Tr2yrFhEUQit8dhMtLP1H+409PrDglFRwNZWcyRMNOFvYX44NFtGBGyGyHhEiJWvwuBuh9SvYGRJAGff34nQLV10rA5S0BoAsGxY9X/tbBHTO9wXHa2/mMNDeempenexxZtq41FEIncHnuGSD9T/+FOSQEWLrTs17YnrGdmTD3XKKoF9n99HMoPCqDcFYI9txIAqIscDkSuzgwwo2qvR2XN0iL6OLL3w5xq0ab2WKWk2O+9Zevnmogcz65VjGzk3Llz4tFHHxXBwcGiQYMGonPnzmLPnj0m359FFy1QVSVEVJThYn/1FPgzSVaW+vw1zxkV5XrF4iwpvqih5xqrIyPFr+P+KaZ1yhWtvfN1n1KoRJ/G+8WbQ3PE2Yy3TSsUWPsvM1P38W1VmM9RBTY17ztTC0u6SkFFFkEksgtHfX+7fDB07do10bp1azFhwgTx008/iby8PLFp0yZx8uRJk8/BYMhC9voH3lBFZVf74rAmYDNwjSpIQgVJPIAsAQjRAKVCEbZLfDR+myj47eKd+5v6JW/sS1/fNURH274CtrXBsbnXrblOU6t+1wwS7cUWzzUR6WAw9Ifp06eL/v37W3UOjw+GrOm9MMbW/8Cb+8vfWawJ2KqqRGXLcFFt4BpVkMRVnxCh/NsOUXq51OA56u2ZM+d5s+b9Yez1qvlniy99c4MbV+kZ0rDnZ5FIhhgM/SE+Pl6kp6eLtLQ00bJlS9GlSxfx4Ycf1nufsrIyUVRUpP3Lz8/33GDIEcNNtvwH3tW+vPSxIGBTVarEro8OiL/dkyMm+H5im2s01DNnSYBmKVNfrzlzHPt4mufO3sO5RORUjgqGXH422e+//473338f7dq1w4YNG/D000/j2Wefxf/+9z+D95k3bx6CgoK0f9HR0Q5ssQOZM4vGGracgeMO05BNnDlVvn4T1r76M56M24YI/yvoMykRb+4YhLJKE58fY9doqIBj7effmoKOxpj6OrRrZ5vH0yQj1y6OqCFJ6tmKmmRkexdUJCJ5sGuoZQO+vr6ib9++OtumTZsm+vTpY/A+bt0zZGovjLsMN9XmDj1DJg7VPI7lOpuaoEiMif5RfK94x7bXWPs9UV7uuKEYZ7xeluSqMV+HyCNx1fo/hIeHIyEhQWdbfHw8srKyDN7H398f/v7+9m6a7Zmz0KM5dV9cqeqtO0xDNrGsQDPcwFRpKe6KqULCpHsw8Nku8Gt8D6DqDcQssN016ivg6KjX1BmvlyUrsnPRUiKygssHQ/369cOxY8d0th0/fhytW7d2UovsxJzaKoB7DDfpoxnWSEurW7na0cMaBmoAVfXsi6qmofC/cVFvgUMBQMALi/G8+kYegPcigbh31K+RK12jtZx1LZYEN1ZW/SYiGbNrv5MN/Pzzz8LHx0fMnTtXnDhxQnz22WeiYcOG4tNPPzX5HC4/m8ySIS93GG6qj7OHNfQ8fmnjELE45A3RTLomHkCWdhp8zWMMzRDT/tVsv7Ov0ZY86VqIyG046vvbLRZqXbduHWbOnIkTJ04gNjYWGRkZePLJJ02+v8sv1GrJQo+ahVSNDV/k5Vn3q92eVaKdVYH6j144IYROz0/1H7fS8DW2SQPxWsgyTCp9Bw1vXrpzkJcXUF1t+Ny1F6f1pCrbnnQtROQWHPX97RbBkLVcPhhauVK9/pIxmZnqGV0amqE1QP/whbUzjMzJYXIDolpg32eH0HbSn9Ck4rLehfkEgPKmofA5dwY+jfx1A4ALF4AXXzT+QJs2Affea+vmExHJjqO+v11+ar0sWLrQo6Gp17aYau2oaft2Vl5cjg1z92BKx61o5VeAjMevIMhAIASoF0ENuHERPrt3qjfULCtw44ZpD5qba3W7iYjIcVw+gVoWrJmxY0qiqbnDG66w+KUVrp64hm/eOgzlOm98dz4RN9FDu+9ebDDtJK6WeE714xAeEVmBwZArsHbGTn2zaCwZ6nLDafsnNp6G8p3TUG5vih+KOqIa/bX7wr0KoWh/HIqHGmBw90hglAkn1NdbN2gQ8MYbxu/rIs+JbHjYcC4ROR6DIVdhSW0VY8ydrq/hBtP2VRUq/LT8MJTLr0K5LxpHKtoCiNHu7xRwDIoeBVBMCkH3R+Pg5RP2xx1VlvfCDRqkTpC+etVww5o3ZzDkSJa+x4mIamACtauxVXe/ZraZoR6e+mabWTK7zQFKL5Vi46KDUGZVYN3JOFwWLbX7fFCJQcH7oUi+iVHpbRHTP8rwiaxJPM/OBh580PC5s7L45eso1rzHicgtcDaZDblVMGQr1gQ0jpq2b4KCXy9i3dvHoNwQgE2XOqIMDbT7glCE4a0PIiVFwn0vdEBQqyDTT6xvaCU62rReOA7LuAYXDdqJyHYc9f3NYTJPZc1QlxMrKItqgYOrTkC57AKUO1vg59JEAKHa/TE++UhJ/B2KxwKR9EwifBv2s+yBrFm+gUs/uAY3GM4lIvfAYMhTWTpdX8MeOUwGVN6qxPb3D2LNJ8VQHmqD01V3A7hbu79Xo4NQ9L2ClMmR6JByFySvaNs8sDXLN3DpB+ez9j1eH85OI5IVDpN5KlsNddnpS+HGmSJ899YhKJUC35xJRBHuDHEF4DYGhxyAYlgZRma0R3iX0HrORLJlr+FcDoMSuQzmDNmQywZD9v71ae8K1WY6/cM5KBedgjKnCbZe74gq+Gr3tZQuY9RdR6FI88Pg9EQ0CmnksHaRG7P1e9zQ7DQnfWaI5I7BkA25ZDDkqF+f1iQKW6m6qhq/fHYUyo8uQbknHPvL2uvsj/c7BUXXfCgmNkfviQnw9uMwBFnAVu9xzk4jcjkMhmzI5YIhR//6dGD+w+1rt7HlnYNQfnEba4/fjYLqMO0+L6iQFHQAioE3MGpqDNoNiXGptjNPxI3Z4rXj7DQil8PZZJ7KGUtdWJvsa+SL5vKRK1j/1lEov/HBhoKOuIWe2n2NUYL7Ig9CMVKF4S8koHm7LqY/riNzN5gn4t5skdDO2WlEssVgyNHcbakLPUGCiIrC+cf/hsx98VD+EIwdJYkQNZa/iPK+AEX8CSgeboRB0zrCP7CvZY/rqMrCrGJMgH1npxGRS+MwmaOtXAk88ojx4zIz1SulO5OBIKEa6uG8NHyNVVAHCV0bHEFK74tQ/DUMXR5qD8lLsvxxHZm7wTwR0nChYqNEpOao728vu52Z9DP1V+Xhw+ocBpXKrs0xSKVC9dRp0Bcre0G9bZnXZLz35y04u+sC9t6Kx6ycQeg6Ns66QAgwr/fMWo58LHJtmmKjwJ38PQ07FxslIudiMORoSUnqX5e1/7Gt7Y031MmcMTHqHhoHObe7AO+P3YaXg/8Nr4ILMNRKLwiEVF/EM5O9EN07wraNcGTuhrmPpVKpg9SVK50brJJ9aIqNRkbqbo+K4nApkQdjzpCj1bfUhT52zlsR1QK/fXUcyg8KoPwpBL/cSgAQjodx3rQT2COZ1JG5G+Y8FpOs5YHLrRDJDnOGnEXfF6shNs5VqLhZgdx/HYBy5U0oD9+FfNWdX8ESqtG3yUFMiduCR3Y/b/xk9phm7MjcDVMf6623gIceYjE+IiIHYp0hG3LJYAi4M2V982b1sJgxVgQe105dx7dvHcaatV747lwHlODO89AAtzA07AAU91Vg5ItxCOnQ0vnJpI6snm3ssb78Enj+eSZZExE5GBOo5UBTGyUhwbTjzRySOrXlDBaNzkVys30IuasJHnu/H7461xclCESY10U8GbcNa1/9GVevSlhd0Bt/WZ6kDoQ0bXNmMqkjczeMPVaLFkyyJiLyYMwZcgU2ypGprqrGzysOY83HV6DcF4XD5XcBaK3dn+h/Aoru55EyqSV6jIuHl4+RBVAduHK9wcd3VO5GfY+1cqVp52AxPiIit8RhMldgxZDUrSu3sGnRASi/rsDaE3G4JFpq93mjCgOb7YdiUAlGPRuLNoNaWd4+OSeTcpkGIiKn4HIcclLfDDM9Q1IXD17G2n8ehXKDHzZe7IQy9NYeHogiDG91CAoFcF9GAprFdrNN+9z1S94WgZymHIKxYDUpyTZtJiIih2Iw5CrqGZISby/CYe9OUA7LhXJHc/x0swME7nzxtvY+B0WHU1A82gQDJifCr/E9TrgAF2SrqfCmBquAuhdJrj1oRERuisNkruaPnoyq0/k48HMZPtnZFmsOtsXvVa11DuvZ6BAUfS5D8XQEOqa2s77qs6cxtN6YNbPR9AVX0dF3AiHWICIisilOrbchvU+mC+bBFJ0twoZFh6BcLfDNmQRcF820+/xRhntbHoBiyG2MfL4dIntwsUiD7LnemL73zZo1tg+8iIiIwZAt1XkyXaiS8Jkfz2Ht4lNQbmmM3GsdUQk/7b4W0hWMbHsEilRfDHk+EY3DGju0bW7LkQnPXOiViMhumEBtL4aGT+y87IWGqBbYm3kUaz68COXucPxW1h5AlHZ/e7/foeh8FikTg9HniQ7w9mNSrtkcubaZOQu9umsSOhGRh5NXMKRSqXuE9HWGCaH+FZ+erq43Y8Nf8WU3ypDzzgEov7iNtUfb4Xx1PIB4AIAXVOgXeBCKpOsYNaUV2t/fBkAbmz22LDlybTNHBl5ERGQX8gqGduxw2K/4K8euYv3CI1B+44MNFxJRip7afY1wE8MiDiJlRBWGvxCPFu07W/VYVIsjp8I7MvAiIiK7kFcwVFho2nEW/oo/viEPyn+dgXJ7M/xYnIhq9Nfui/AqgCLuBBQPNUDysx0R0LSPRY+hwwWTwF2CmXWbrMIaREREbk9ewVBYmGnHmfgrXlWhws6PDkG54hqUv7XCsYo2AGK1+7s0OIqUHucxNukc7k7whRQZASR1s82XsAslgbskRy0l4sjAi4iI7EJes8muXUNgp05WrcR+s/AmNi46CGV2JdadiscV0UK7zxcVGBR8AIo/3cSo9LZoffFn+wQs9qih46kc1XtWXw0ivhZERBbh1Hob0nkyN21SBxKA/l/xegKJC3sLsfat41BubIDNlzuiHAHafU2lGxjR+hAUoyUMe74DgloFqXfYK2DhVG7XxWFLIiKbYjBkQybVGarxK15UCxzIPgHlsgtYszMEe24l6Jwv1ucsUjrmQfFYIPo/nQjfhr66D2jPgIWLhhIRkUywzpA9paaqp8/X+BVf0bU3tr1/EIfbLsbJPB/sF4nYjiRUwxsSqtG78SEo7rkKxeQoJAyPgfTj70DBUeDnoro9APasPcOp3ERERDYlz2AIALy9cb11F3z7lS+UayX45H+NefgbBuNOEHPFKwS/DpiGxCV/RVinjuqN2dlAm+T684DsGbBwKjcREZFNeTm7AeaaN28eJElCenq6RffP25aPJalbcW/wXoS0aYRH3+uHivxC/A/jEQnd3pwW4jIGb30NYSd/UG/Q5AHV7vXRVK/OzlbftmfAopnKrck9qk2S1EN+nMpNRERkErfKGdq9ezfGjBmDwMBAJCcnY7FmtXAjNGOO8X57caSiq86+Tr6H8aO4B42qiqA3vNDk95w8CbRta1oeEKDOGbJi1lq9NEEZYHISOBERkbtxVM6Q2/QM3bx5E48++ij+/e9/o1mzZsbvoMeRirbwRhUGNd2Ht1NycWHWB/gtaCAaGwqEgDv5Pe+9Z3oekKb2DFC3B8cWtWc0NXQiI3W3R0UxECIiIjKT2wRDU6ZMwYgRIzB48GCjx5aXl6O4uFjnDwA+fGIXLp0sQc71rnj+8WsIf/0Z4MoV0xqwYYNpx2nygOwdsKSmAqdPq2eNZWaq/5uXx0CIiIjITG6RQP35559j79692L17t0nHz5s3D3PmzKmz/aG3+6i72epbsNWQ774z7biaeUB6Zq3ZtPaMt7f10+etrY3D2jpEROTmXD4Yys/Px3PPPYfvv/8eAQEBxu8AYObMmcjIyNDeLi4uRnR09J0DjE19r0mSAC8v9Ze+seP0rUFli4DFXqxd0oNLghARkQdw+QTq1atX44EHHoB3jd4GlUoFSZLg5eWF8vJynX361EnAWrkSeOQR2zc2K8t9ggBrK2RzSRAiIrIzVqD+Q0lJCc6cOaOzbeLEiYiLi8P06dORmJho9Bx1nkxTqzibIz0dWLTItue0F2srZHNJECIicgBWoP5DkyZN6gQ8jRo1QvPmzU0KhPTS1OoxNPXdEikptjmPI1hbIdueFbaJiIgczG1mk9lUfVPfzeWORQ6trZDNJUGIiMiDuHzPkD65ubnWn0Qz9b12ArA5bFEzyBmsrZDNJUGIiMiDyLNnSKNmrZ6pU82/vy2LHKpU6lymlSvV/zU2e80a1i7pwSVBiIjIg8g7GALuTH1/8EHz7teypXqJDlsEQtnZ6oTk5GT1LLfkZPVtzVpntmZthWx7V9gmIiJyIAZDGsZ6O2q7fBnYscP6xzV18Vdbs7ZCNpcEISIiD+HyU+ttweSpeYZq5xiSmQmMHWt5w1xhijorUBMRkYvi1Hpn0PR2PP20uufHGGsThF1hirq1FbJducI2ERGRCThMVltqqjpAadHC8DG2ShDmFHUiIiKnYzCkj58f8MEH6qDHngnCnKJORETkdAyGDHFEgjCnqBMRETkdc4bqk5qqXmbDXgnCminqaWnqwKdm4janqBMRETkEgyFj7J0gbKgSdlSUOhDiFHUiIiK7YjDkCuzdA0VEREQGMRhyFZyiTkRE5BRMoCYiIiJZYzBEREREsiavYOirr+y/IjwRERG5FXkFQ5Mm2X9FeCIiInIr8gqGNOy9IjwRERG5DXkGQ5rihunpHDIjIiKSOXkGQ4DuivBEREQkW/INhjS4IjwREZGsMRjiivBERESyJt8K1JKkXv+LK8ITERHJmjx7hrgiPBEREf1BnsFQVJR6pXiuCE9ERCR78hom++gjoG1brghPREREWvIKhv78ZyAw0NmtICIiIhciz2EyIiIioj8wGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGTN5YOhefPmoWfPnmjSpAlCQkIwevRoHDt2zNnNIiIiIg/h8sHQ1q1bMWXKFOzatQsbN25EVVUVhg4ditLSUmc3jYiIiDyAJIQQzm6EOS5fvoyQkBBs3boVAwYMMOk+xcXFCAoKQlFREQIDA+3cQiIiIrIFR31/+9jtzHZSVFQEAAgODjZ4THl5OcrLy7W3i4uL7d4uIiIick8uP0xWkxACGRkZ6N+/PxITEw0eN2/ePAQFBWn/oqOjHdhKIiIiciduNUw2ZcoUrF+/Hj/88AOioqIMHqevZyg6OprDZERERG6Ew2S1TJs2DUqlEtu2bas3EAIAf39/+Pv7O6hlRERE5M5cPhgSQmDatGlYtWoVcnNzERsb6+wmERERkQdx+WBoypQpyMzMxJo1a9CkSRMUFhYCAIKCgtCgQQMnt46IiIjcncvnDEmSpHf78uXLMWHCBJPOwan1RERE7oc5Q39w8ViNiIiI3JxbTa0nIiIisjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREsuY2wdB7772H2NhYBAQEoHv37ti+fbuzm0REREQewC2CoS+++ALp6el45ZVXsG/fPiQlJeH+++/H2bNnnd00IiIicnOSEEI4uxHG9O7dG926dcP777+v3RYfH4/Ro0dj3rx5Ru9fXFyMoKAgFBUVITAw0J5NJSIiIhtx1Pe3y/cMVVRU4JdffsHQoUN1tg8dOhQ7duxwUquIiIjIU/g4uwHGXLlyBSqVCqGhoTrbQ0NDUVhYqPc+5eXlKC8v194uKioCoI4wiYiIyD1ovrftPYjl8sGQhiRJOreFEHW2acybNw9z5sypsz06OtoubSMiIiL7uXr1KoKCgux2fpcPhlq0aAFvb+86vUCXLl2q01ukMXPmTGRkZGhv37hxA61bt8bZs2ft+mS6muLiYkRHRyM/P19WuVK8bl63HPC6ed1yUFRUhFatWiE4ONiuj+PywZCfnx+6d++OjRs34oEHHtBu37hxI1JSUvTex9/fH/7+/nW2BwUFyepNpBEYGMjrlhFet7zwuuVFrtft5WXfFGeXD4YAICMjA+PGjUOPHj3Qt29ffPjhhzh79iyefvppZzeNiIiI3JxbBEMPPfQQrl69itdffx0FBQVITEzEN998g9atWzu7aUREROTm3CIYAoDJkydj8uTJFt3X398fs2bN0jt05sl43bxuOeB187rlgNdt3+t2i6KLRERERPbi8kUXiYiIiOyJwRARERHJGoMhIiIikjUGQ0RERCRrbhkMvffee4iNjUVAQAC6d++O7du313v81q1b0b17dwQEBKBNmzZYtmxZnWOysrKQkJAAf39/JCQkYNWqVfZqvsXMue7s7GwMGTIELVu2RGBgIPr27YsNGzboHLNixQpIklTnr6yszN6XYhZzrjs3N1fvNR09elTnOE97vSdMmKD3ujt06KA9xh1e723btmHUqFGIiIiAJElYvXq10ft4wufb3Ov2lM+3udftKZ9vc6/bUz7f8+bNQ8+ePdGkSROEhIRg9OjROHbsmNH7OeIz7nbB0BdffIH09HS88sor2LdvH5KSknD//ffj7Nmzeo/Py8vD8OHDkZSUhH379uFvf/sbnn32WWRlZWmP2blzJx566CGMGzcOv/32G8aNG4cxY8bgp59+ctRlGWXudW/btg1DhgzBN998g19++QXJyckYNWoU9u3bp3NcYGAgCgoKdP4CAgIccUkmMfe6NY4dO6ZzTe3atdPu88TXe8mSJTrXm5+fj+DgYPz5z3/WOc7VX+/S0lJ07twZS5cuNel4T/l8m3vdnvL5Nve6Ndz9823udXvK53vr1q2YMmUKdu3ahY0bN6KqqgpDhw5FaWmpwfs47DMu3EyvXr3E008/rbMtLi5OzJgxQ+/xL7/8soiLi9PZ9tRTT4k+ffpob48ZM0bcd999OscMGzZMPPzwwzZqtfXMvW59EhISxJw5c7S3ly9fLoKCgmzVRLsw97pzcnIEAHH9+nWD55TD671q1SohSZI4ffq0dps7vN41ARCrVq2q9xhP+XzXZMp16+OOn++aTLluT/l812TJ6+0Jn28hhLh06ZIAILZu3WrwGEd9xt2qZ6iiogK//PILhg4dqrN96NCh2LFjh9777Ny5s87xw4YNw549e1BZWVnvMYbO6WiWXHdt1dXVKCkpqbPY3c2bN9G6dWtERUVh5MiRdX5ZOpM11921a1eEh4fj3nvvRU5Ojs4+ObzeH3/8MQYPHlynSrsrv96W8ITPty244+fbGu78+bYFT/l8FxUVAUC9i7A66jPuVsHQlStXoFKp6qxWHxoaWmdVe43CwkK9x1dVVeHKlSv1HmPonI5myXXX9tZbb6G0tBRjxozRbouLi8OKFSugVCqxcuVKBAQEoF+/fjhx4oRN228pS647PDwcH374IbKyspCdnY327dvj3nvvxbZt27THePrrXVBQgG+//RaTJk3S2e7qr7clPOHzbQvu+Pm2hCd8vq3lKZ9vIQQyMjLQv39/JCYmGjzOUZ9xt1mOoyZJknRuCyHqbDN2fO3t5p7TGSxt48qVKzF79mysWbMGISEh2u19+vRBnz59tLf79euHbt264V//+hfeeecd2zXcSuZcd/v27dG+fXvt7b59+yI/Px8LFy7EgAEDLDqns1jaxhUrVqBp06YYPXq0znZ3eb3N5Smfb0u5++fbHJ70+baUp3y+p06div379+OHH34weqwjPuNu1TPUokULeHt714n2Ll26VCcq1AgLC9N7vI+PD5o3b17vMYbO6WiWXLfGF198gSeeeAJffvklBg8eXO+xXl5e6Nmzp8v8krDmumvq06ePzjV58usthMB//vMfjBs3Dn5+fvUe62qvtyU84fNtDXf+fNuKu32+reEpn+9p06ZBqVQiJycHUVFR9R7rqM+4WwVDfn5+6N69OzZu3KizfePGjbjnnnv03qdv3751jv/+++/Ro0cP+Pr61nuMoXM6miXXDah/MU6YMAGZmZkYMWKE0ccRQuDXX39FeHi41W22BUuvu7Z9+/bpXJOnvt6AerbGyZMn8cQTTxh9HFd7vS3hCZ9vS7n759tW3O3zbQ13/3wLITB16lRkZ2djy5YtiI2NNXofh33GTU61dhGff/658PX1FR9//LE4fPiwSE9PF40aNdJm1c+YMUOMGzdOe/zvv/8uGjZsKJ5//nlx+PBh8fHHHwtfX1/x9ddfa4/58ccfhbe3t/i///s/ceTIEfF///d/wsfHR+zatcvh12eIudedmZkpfHx8xLvvvisKCgq0fzdu3NAeM3v2bPHdd9+JU6dOiX379omJEycKHx8f8dNPPzn8+gwx97oXLVokVq1aJY4fPy4OHjwoZsyYIQCIrKws7TGe+HprPPbYY6J37956z+kOr3dJSYnYt2+f2LdvnwAg3n77bbFv3z5x5swZIYTnfr7NvW5P+Xybe92e8vk297o13P3z/cwzz4igoCCRm5ur8769deuW9hhnfcbdLhgSQoh3331XtG7dWvj5+Ylu3brpTMsbP368GDhwoM7xubm5omvXrsLPz0/ExMSI999/v845v/rqK9G+fXvh6+sr4uLidD5crsKc6x44cKAAUOdv/Pjx2mPS09NFq1athJ+fn2jZsqUYOnSo2LFjhwOvyDTmXPf8+fNF27ZtRUBAgGjWrJno37+/WL9+fZ1zetrrLYQQN27cEA0aNBAffvih3vO5w+utmTpt6H3rqZ9vc6/bUz7f5l63p3y+LXmfe8LnW981AxDLly/XHuOsz7j0RwOJiIiIZMmtcoaIiIiIbI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEicksrV65EQEAAzp8/r902adIkdOrUCUVFRU5sGRG5G65NRkRuSQiBLl26ICkpCUuXLsWcOXPw0UcfYdeuXYiMjHR284jIjfg4uwFERJaQJAlz585FWloaIiIisGTJEmzfvp2BEBGZjT1DROTWunXrhkOHDuH777/HwIEDnd0cInJDzBkiIre1YcMGHD16FCqVCqGhoc5uDhG5KfYMEZFb2rt3LwYNGoR3330Xn3/+ORo2bIivvvrK2c0iIjfEnCEicjunT5/GiBEjMGPGDIwbNw4JCQno2bMnfvnlF3Tv3t3ZzSMiN8OeISJyK9euXUO/fv0wYMAAfPDBB9rtKSkpKC8vx3fffefE1hGRO2IwRERERLLGBGoiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrP0/sCxKf+B5Q7YAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_271_1.png"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "theta from own sdg\n",
-      "[[4.02496085]\n",
-      " [3.12081773]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using SGD\n",
     "# OLS example\n",
@@ -4472,27 +4311,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "3320d6c6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[3.96075051]\n",
-      " [3.02148021]]\n",
-      "Eigenvalues of Hessian Matrix:[0.27470622 4.24106503]\n",
-      "theta from own gd\n",
-      "[[3.95906059]\n",
-      " [3.02296298]]\n",
-      "theta from own sdg with momentum\n",
-      "[[3.95611042]\n",
-      " [2.99475306]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using SGD\n",
     "# OLS example\n",
@@ -4573,25 +4395,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "19414323",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own AdaGrad\n",
-      "[[2.00025662]\n",
-      " [2.99802696]\n",
-      " [4.00167329]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4661,25 +4468,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "6724b3d2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own RMSprop\n",
-      "[[1.99456598]\n",
-      " [2.99848815]\n",
-      " [3.99835783]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4747,25 +4539,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "28aa6aad",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own ADAM\n",
-      "[[1.99989103]\n",
-      " [3.00042093]\n",
-      " [3.99954925]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4838,19 +4615,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "f3dde7c5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial loss: 2.772588722239781\n",
-      "Trained loss: 1.067270675787016\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
     "from autograd import grad\n",
@@ -4905,18 +4673,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "291c9b6d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.25       0.19661197 0.10499357]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import jax.numpy as jnp\n",
     "from jax import grad, jit, vmap\n",
diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.py b/doc/LectureNotes/_build/jupyter_execute/week39.py
index c5e936e73..e64fafae7 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week39.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week39.py
@@ -2098,7 +2098,7 @@ a /=b
 # for linear regression and logistic regression using **autograd**. The
 # first example shows results with ordinary leats squares.
 
-# In[28]:
+# In[ ]:
 
 
 # Using Autograd to calculate gradients for OLS
@@ -2154,7 +2154,7 @@ plt.show()
 
 # ## Same code but now with momentum gradient descent
 
-# In[29]:
+# In[ ]:
 
 
 # Using Autograd to calculate gradients for OLS
@@ -2214,7 +2214,7 @@ print(theta)
 
 # ## But none of these can compete with Newton's method
 
-# In[30]:
+# In[ ]:
 
 
 # Using Newton's method
@@ -2260,7 +2260,7 @@ print(beta)
 # ## Including Stochastic Gradient Descent with Autograd
 # In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using **autograd**.
 
-# In[31]:
+# In[ ]:
 
 
 # Using Autograd to calculate gradients using SGD
@@ -2340,7 +2340,7 @@ print(theta)
 
 # ## Same code but now with momentum gradient descent
 
-# In[32]:
+# In[ ]:
 
 
 # Using Autograd to calculate gradients using SGD
@@ -2414,7 +2414,7 @@ print(theta)
 
 # ## Similar (second order function now) problem but now with AdaGrad
 
-# In[33]:
+# In[ ]:
 
 
 # Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent
@@ -2471,7 +2471,7 @@ print(theta)
 
 # ## RMSprop for adaptive learning rate with Stochastic Gradient Descent
 
-# In[34]:
+# In[ ]:
 
 
 # Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent
@@ -2532,7 +2532,7 @@ print(theta)
 
 # ## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf)
 
-# In[35]:
+# In[ ]:
 
 
 # Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent
@@ -2598,7 +2598,7 @@ print(theta)
 
 # ## And Logistic Regression
 
-# In[36]:
+# In[ ]:
 
 
 import autograd.numpy as np
@@ -2646,7 +2646,7 @@ print("Trained loss:", training_loss(weights))
 # 
 # Here's a simple example on how you can use **JAX** to compute the derivate of the logistic function.
 
-# In[37]:
+# In[ ]:
 
 
 import jax.numpy as jnp
diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png b/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png
index d71eae3c9..2553e4ed5 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png and b/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_166_1.png b/doc/LectureNotes/_build/jupyter_execute/week39_166_1.png
index 1cc777a81..91b413b23 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/week39_166_1.png and b/doc/LectureNotes/_build/jupyter_execute/week39_166_1.png differ
diff --git a/doc/LectureNotes/week39.ipynb b/doc/LectureNotes/week39.ipynb
index 4a48fabca..bde575030 100644
--- a/doc/LectureNotes/week39.ipynb
+++ b/doc/LectureNotes/week39.ipynb
@@ -1012,7 +1012,7 @@
     {
      "data": {
       "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x150835f40>"
+       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11f758f40>"
       ]
      },
      "execution_count": 1,
@@ -1154,7 +1154,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x161847e80>]"
+       "[<matplotlib.lines.Line2D at 0x12a472ee0>]"
       ]
      },
      "execution_count": 5,
@@ -1814,16 +1814,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.30768763 4.39313488]\n",
-      "[[3.95814165]\n",
-      " [3.0184648 ]]\n",
-      "[[3.95814165]\n",
-      " [3.0184648 ]]\n"
+      "Eigenvalues of Hessian Matrix:[0.39227722 4.38168146]\n",
+      "[[3.8976899 ]\n",
+      " [3.03844474]]\n",
+      "[[3.8976899 ]\n",
+      " [3.03844474]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1899,9 +1899,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.94758046]\n",
-      " [2.92347862]]\n",
-      "[3.94453204] [2.94467121]\n"
+      "[[3.81448757]\n",
+      " [3.3421718 ]]\n",
+      "[3.83247778] [3.40246301]\n"
      ]
     }
    ],
@@ -2034,16 +2034,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.32374405 4.06221646]\n",
-      "[[4.0585026]\n",
-      " [2.9999519]]\n",
-      "[[4.05843037]\n",
-      " [3.00001709]]\n"
+      "Eigenvalues of Hessian Matrix:[0.31252985 4.57630515]\n",
+      "[[4.06031839]\n",
+      " [3.01099015]]\n",
+      "[[4.05965043]\n",
+      " [3.01153127]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3993,34 +3993,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "cd18340b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[3.94499279]\n",
-      " [3.03306538]]\n",
-      "Eigenvalues of Hessian Matrix:[0.31248425 4.44418124]\n",
-      "theta from own gd\n",
-      "[[3.94499279]\n",
-      " [3.03306538]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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FbGwsHnnkEZw+fdp7BSYiIiKf4vUwVFJSgvbt22PevHlV9pWWlmLXrl3429/+hl27diEzMxNHjhzBkCFDvFBSIiIi8kWSEEJ4uxBmkiRh2bJlGDZsmN1jtm/fjq5du+LEiRNo0qSJQ+ctLi5GeHg4ioqKEBYWJlNpiYiIyJ089f0d4LYzu0lRUREkSUL9+vXtHlNeXo7y8nLL7eLiYg+UjIiIiNTI681kzigrK8PUqVMxatSoahPizJkzER4ebvlJSEjwYCmJiIhITVQThioqKvCnP/0JRqMRH3zwQbXHTps2DUVFRZaf3NxcD5WSiIiI1EYVzWQVFRUYMWIEcnJysGHDhhrbDYODgxEcHOyh0hEREZGaKT4MmYPQ0aNHkZWVhcjISG8XiYiIiHyI18PQlStXcOzYMcvtnJwc7NmzBxEREYiNjcXw4cOxa9curFq1CgaDAQUFBQCAiIgIBAUFeavYRERE5CO8PrQ+Ozsbffv2rbJ9zJgxmD59OpKSkmzeLysrC3369HHoMTi0noiISH00M7S+T58+qC6PKWgaJCIiIvJBXg9DREREpGIGA7B5M5CfD8TEAKmpgL+/t0vlFIYhIiIick1mJjBpEnDq1PVt8fHA3LlAWpr3yuUk1cwzRERERAqSmQkMH24dhAAgL8+0PTPTO+VyAcMQERGRUhgMQHY2sGiR6bfB4O0S2WYwmGqEbPXrNW+bPFm55b8JwxAREZESZGYCiYlA377AqFGm34mJyqxh2by5ao3QjYQAcnNNx6kAwxAREZG3qa3JKT9f3uO8jGGIiIjIm9TY5BQTI+9xXsYwRERE5E1qbHJKTTWNGpMk2/slCUhIMB2nAgxDRERE3qTGJid/f9PweaBqIDLfnjNHNfMNMQwRERF5k1qbnNLSgCVLgLg46+3x8abtKppnyOtrk3kC1yYjIiLFMhhMo8by8mz3G5IkU8DIyVFmTYsbZ6DWzNpkREREmmZucho+3BR8bgxEamhy8vcHHFw4XanYTEZERORtPtTkpEasGSIiIlKCtDRg6FDVL3qqRgxDRERESuEDTU5qxGYyIiIi0jSGISIiItI0hiEiIiLSNIYhIiIi0jSGISIiItI0hiEiIiLSNA6tJyIi8kVuXCbD1zAMERER+ZrMTGDSJODUqevb4uNNy35wNusq2ExGRETkSzIzTeuc3RiEANNCsMOHm/aTFYYhIiIiM4MByM4GFi0y/TYYvF0i5xgMphqhGxd7NTNvmzxZfdflZgxDREREgKnGJDER6NsXGDXK9DsxUV01KZs3V60RupEQQG6u6Th3UGmYZBgiIiLylaal/Hx5j3OGisMkwxAREWmbLzUtxcTIe5yjVB4mGYaIiEjbvN20JKfUVNOoMUmyvV+SgIQE03Fy8YEwyTBERETa5s2mJbn5+5uGzwNVA5H59pw58s435ANhkmGIiIi0zVtNS+6SlgYsWQLExVlvj483bZd7niEfCJOcdJGIiLTN3LSUl2e7qUeSTPvlbFpyt6FDgfBw04guAOjTx/Qj9wzUBgNw5oxjxyo4TDIMERGRtpmbloYPNwWfGwORu5qW3MVgAGbMMF3PhQvXty9cKP/s07ZmubblxjCp0CVC2ExGRETk6aYld8jMBKKjgddesw5CgPyjuuyNHrvZjWFyxQrFDr2XhLBVJ+hbiouLER4ejqKiIoSFhXm7OEREpFQKrbmokTmcVPeVbq6hycmp3TUZDKYQU1MQAkwj1+bMMf3bVvnMYclO4PTU9zfDEBERkZo5E04AICvL1H/IVdnZplqdmsyeDUyYYPp3deWrJqR56vubzWRERKReKl3+QVY1DW2/WW1HdTl6/+hoU7hRwdB7dqAmIiJ1stWBNz5e/o7CSudsuKntqC5npyJQwdB7r9cMbdq0CTqdDrGxsZAkCcuXL7faL4TA9OnTERsbizp16qBPnz7Yv3+/dwpLRETKoPLlH2TlTLiRY/ZpZ2e5VsE8Tl4PQyUlJWjfvj3mzZtnc/+sWbPwzjvvYN68edi+fTsaN26Mfv364fLlyx4uKRERKYIPLP8gq5rCyY3kmCLA2VmuHSmfl+dx8noYGjhwIF5//XWk2ajSFEJgzpw5eOWVV5CWloaUlBR89tlnKC0tRUZGhhdKS0REXqeCPigeVV04MYuMBJYula/50JmpCBwp39WrpqH3NxFGz4zx8noYqk5OTg4KCgrQv39/y7bg4GD07t0bP/74o937lZeXo7i42OqHiIh8hAr6oHicvXASGQmkp5tmiXY1CNnrpJ6WBhw/bhqdlpFh+p2TY/txzOWLiLD9GBcuWJo3yy6V4dvXd2B8241IjvTMa6joDtQFBQUAgOjoaKvt0dHROHHihN37zZw5E+np6W4tGxEReYkK+qB4RVqaaRkOOedJqqmTur+/48P0hw41ncsWISAAnHvwGdxqPILL6PzHDs9UZii6ZshMuqlaTQhRZduNpk2bhqKiIstPbm6uu4tIRESe4mwHXi0xh5ORI2u/FpncndRraN6UADQyFqIjdiPWLx9/abUJX764w/lyu0DRYahx48YArtcQmRUWFlapLbpRcHAwwsLCrH6IiMhHONuBl5znhk7qlTmOVUx8Om4HTlU0xr8P9sK9r3Su+Q4yUHQYSkpKQuPGjbFu3TrLtmvXrmHjxo244447vFgyIiLyKl9YS0zJZOqkfv7oBXz+9BY8GL8VQx6z01/oJs0e7AzJz4GRcTLyep+hK1eu4NixY5bbOTk52LNnDyIiItCkSRNMnjwZb7zxBpo3b47mzZvjjTfeQGhoKEaNGuXFUhMRkde5o48MmbjYSV0YBQ6t+R36D3Kh/6EBfixOgRF3AgD8YMBpxKAx8m3XxNy4ur2HeT0M7dixA31vWONkypQpAIAxY8Zg4cKFePHFF3H16lWMGzcOFy9eRLdu3fDdd9+hXr163ioyEREphTMdeMlxTnRSryitwJaP9mHlF8XQ703Eb5W3ArjVcki7kMMY0jkfuscaofEt78LvoRGmHTc2wXm5eZMLtRIREWmVwWC7Zs28+Gtens1+Q0KSUFq3EZ5ssARrctuhCOGWfUEoR9/IvdDdVYLBk25F057x1ne2NULNvLr9Tc2bXLVeRgxDREREN6lp2Lx5NBlgFYiMMNXiDMcSLEMa/GDAYKzCwOhdaNMnGrf/3yjUS6hf/WPbC2E3YRiSEcMQERHRDcxB5+YIYG6uWrIElfcNweFn30Xcf/+F+hVnLYecRAImYw6OBLfF1KaL8GDhBwi+dOb6OWRcLJdhSEYMQ0REpAmO1LiYm8DsjBYTAC74R6GVYR/OoRH8YEAqNiMeuYiuV4rEe5pj0MTb0OzCjhoDVW0Dkae+v73egZqIiIhkUFOzl5kDkx9GGgqRjP3YK7XDfYkHoBsahAHPDUF4kz/6BhkMQGJP+/MQSZJpHqKhQ63DmIPNY57GMERERKR29pq9zLNF31BLY8zNc2iSwY/GbMNtH92JgJA7q+50Zh4i82g/R8OaFzAMERFpiUL/MlcUtT1HNc0WLUkwTpiIZT9EQb8CuPjbLai6PnxVrcZ2B0LsxARn5yFyIqx5A8MQEZFWKPgvc8VQ43PkQC2N3+k8vPdOJTaiD/xgQB5iEYPTrk9+6MxiuQ6ENZtNah6k6OU4iIhIJnIvuumL1PocOVhLc7v/r3iuYza+f/tXRGfMhp8kub62mzOL5cq0tIc7MQwREfk6Nyy66XNU+hyVFJZgi/6iQ8e+811bvLOzD/pOuR0BI0fUbm03ZxbLdXFpD09iGCIi8nUq+Mvc61T0HJ3ano+PRm3CoKjtiIz2R+9FTyEX8ZbJEKv4o5ZG6t3LentaGnD8OJCVBWRkmH7n5DjeHOjoYrnONKl5CfsMERH5OhX8Ze51Cn6OjJVG7Mo4BP0nhdBvj8buq60BXA8OSQEn8V3cX/DYib9DSBIkZ9b8qu3abo4slmtuUrOztIc3F2g1YxgiIvJ1KvjL3OsU9hxdvXAV6+fshf7rMqw60hynjW0AtAEASDCi+y37obvjPHRPxyF56G2Q/P4GZCbb7vxtY80vWdUUqMxNasOHm4KPghZotRSDM1ATEfm4GhbdtPxlnpOj7CHk7uTp58jG8P38veew6p3D0H8XjO/PtMVVhFoOr4srGBC7D7qBlbjvuZaISm7k8HkV85o6sUCrGZfjkBHDEBFpnp1FN+VcOkH1PPUc2QgFZ6RoPCM+wDJcP3+Cfx50rY9hyJ/qos+EtggOC679Y3ubk2GNYUhGDENERHDpL3PNcfNzdO3zxQh8ZCQAWHV3Nnd+nhb8Durd2QG6v8Sg3fAWkPzsdIrWCIYhGTEMERH9QcnNKEoh83NUuP8sVr99CKu/8cOcgocQC9vLYQhJgqT15sqbcKFWIiKSX21HD2lBLZ8jYRTYt+wo9P8+Df3WSPx0JRkCqeiNbMQjz+79JFvreZFHMAwRERHV0rUr17Bx3l7oF12B/kAzHK9sAaCFZX/HOgcxIXE9cNCBk9kavs8aPbdiGCIiInLBucPnsebtg9Cv8cfavGRcRifLvhBcxd1Re6G7pwyDn2uOuM6tgey7gb6v13zim4fvq3G9NJVhnyEiIlIuBdWICKPAoTW/Q/9BLvQ/NMCPxSkw4npZov0KoWt+CLoHgnH3pBTUjaprfQJXhu/bW+1dI6MA2YFaRgxDREQqpIAakYrSCmz+cB/0/yuGfm8ifqtsarW/fchh6DrnQ/dYI3Qe3Rp+ATWscuXM8H1zeLK3TIgG5odiGJIRwxARkcp4sUbkYs4lfPPWfuhXSfjmZDKKEG7ZF4Ry9I3ciyH3lGDwpNvQpEdcNWeyw9Hh+9nZQN++NZ8vK8tnO1xzNBkREWlTTSvIS5JpBfmhQ2WrETmyNgf6eSeg31wfW4pSYEBPy75G0lkMuvUQdPcHot/kZNSL7Vy7B3NkPS9A0eul+RqGISIiUhZnVpB3sUaksqwSP/5nP/T/vYiVvzTFkYokAEmW/cnBRzGkYx50YyPRdWwb+AfJvIioI8P3FbZemi9jGCIiImVxU41I0ckifPv2fuhXCqw50QYXRXvLvkBcQ++IvdD1uQLdpGZI6tUcQHOnzi87Faz27isYhoiISFlkrBH5Pfsk9O/mYGV2PWy62BaVuMOyryHO4rmYxbirwwW0fbQz6qbdq6yOyCpY7d1XsAM1EREpSy1WkDdcM2DbJ/uh/+wC9HvicaD8Nqv9rYJ+g659Lsa224XW386GlOfASDVvD+/X8JpyHE0mI4YhIvJZ3v6idhcnhqBfPn0Z383eh5WZBqzJaYVzoqHlcH9Uolf9vdD1LoJuQhJuu7upcyPVFDC8H4Dvvs41YBiSEcMQEfkkpXxRu0s1NSInortCP+c36LPqIvt8W1xDsOWQ+tIlDGyyHzqdhHuntEGDpPrX7+/M3D0rVmh6wkMlYBiSEcMQEfkcrcxM/EeNiPFUHg79cg0ZPzTByt0J2FvWwuqw2wKPY0i749A9XB89/5KMwNBA2+dzdO6e778Hxo7V9ISHSsB5hoiIyDYvzMPjDSWFJVg3ex/0S/2x+rd+OGOMsuzzgwE9w/ZBd+dF6MY3Qct7kyD5JdZ8UkdHoGVnu314PykHwxARkdp4YB4ebzm1PR/6d45Cv74ONpxti3J0s+yrh2IMTNgH3SCBgVNaI7J5+2rOZIfcc/JwwkOfwDBERKQ2PjQzsbHSiF0Zh7Dq43zk/XQKV64FIR8x2IyOMMIfSQEnoUvOgW5UPfQal4KgW+6o+aTVcXTunj59gNddWGGeVIlhiIhIbVQ+M3HpuVKsn7sP+iVlWHWkBbobD2EuJiEB12u7ioMb4dLk15DwxjhIfk3ke3BH5+7p04cTHmpIDcvrEhGR4phrN8xf3jeTJNOoKwV9UefvOYP/PLIJQxr/hIaNgCGvd8V/DvVCd+OPWILhiId1s1/YtXNoMmsCpOXL5C9MWpqpg3ncTYusxsdf73huDk1A1eeZEx76HI4mIyJSIyfm4fEGYRTY8+Vh6P9TAP1PUdhR2sZqfxP/UxjS+gj+7+RIBBcXwmasc/eILUfm7tHwhIdKwKH1MmIYIiKfpLAv6rJLZch6dy/0X13FqkO3ItdgXfPSte4+6Lqfg+4vMWg3vAWkTRsdG+aeleXdjuAanfBQCTi0noiIqpeWZho+78Uv6jP7zmL124egXxuIdfkpKEEXy75QlKBf433QDbiGQVNaonG7FOs7q6UjuCMrzJOqKT4MVVZWYvr06fjf//6HgoICxMTEYOzYsXj11Vfh58cuT0SkcR7+ohZGgX3LjkL/79PQb43ET1eSIZAKPxiQis1Ilg7g1oQKtBzTDXdNbo86Ed3sn0zlHcHJdyg+DL355pv46KOP8NlnnyE5ORk7duzAo48+ivDwcEyaNMnbxSMiT2OThceVF5dj4/v7oF90BasONsPxyhYArs8A/deguXhVmoHw8rOAAHASwIJ4oEMNy4KYO4JXN2cSAJw9K8dlENml+D5DgwcPRnR0ND755BPLtgceeAChoaH4/PPPHToH+wwR+Qg1r8WlshB37vB5rHn7IPRr/LE2LxmXcf3/zhBcxd1Re6G7pwzD2x9F5NQnXV8WZMkS4MEHqy9MQoJvLnuhsveEN3js+1so3MyZM0XTpk3F4cOHhRBC7NmzR0RFRYmMjAyHz1FUVCQAiKKiIncVk4jcbelSISRJCNPX7vUfSTL9LF3q7RLat3SpEPHx1uWOj1dUmY0Go9i/4qj4171Zome9X4QfKq2K29ivQDzRcqNY8fI2UXK2xHSnysqq13Xza5OQYDrOnqws+/e/8ScryxNPg+eo4D2hBJ76/lZ8M9lLL72EoqIitGrVCv7+/jAYDJgxYwZGjhxp9z7l5eUoLy+33C4uLvZEUYnIXdS8Fpe9BVXz8kzbvTgEvqK0Aps/3Af9/4qh35uI3ypvA3CbZX/7kMPQdc6H7rFG6Dy6NfwCoq1PIMeyIGrpRC0nBb8ntErxYejLL7/EF198gYyMDCQnJ2PPnj2YPHkyYmNjMWbMGJv3mTlzJtLT0z1cUiJyG7WuxaW0EGcwoHjxGvy6cCc2/NIQc86OwkXcbtkdhHLc1fBX6O4uxeBJt6FJj5YAWto/nxxBxlc7URsMpsVes7NNt/v0uf7eVNJ7gkzcWu8kg/j4eDFv3jyrbf/85z9Fy5Yt7d6nrKxMFBUVWX5yc3PZTEakZhkZjjWlONF87naVlULMnq2IJqDD3/4ulndKF2ekaKvHPYl4MRafirG3bRJLX9gqivOKnTuxHE1c5qY2W02gjja1Kc3SpUJERla9lshIIdLTFfGeUAs2k/2htLS0yhB6f39/GI1Gu/cJDg5GcHCwu4tG5PuU0sFTbbUHtjp6V0fmJqDKskr88O990H9+CfpfmyK5YjeWYDpMQ72ui0cePpUeh/Smi80yZ8+a3g8Gg+39jqzf5ehaYc6877z5vs3MBB54wPa+8+eB115z7Dy+1CyoBm6NWjIYM2aMiIuLE6tWrRI5OTkiMzNTNGzYULz44osOn4MdqIlcIEcHz8pK01+4GRmm367+da+m2gN7Hb3dXAtw6cQlsXjiD+LPiVtEA+mC5dR+qBQnESeMtenk7Op1OtOx3db7LSHB+Q7F3uyYXFkpRFycc689a4aq5anvb8WHoeLiYjFp0iTRpEkTERISIpo1ayZeeeUVUV5e7vA5GIaInCTHyC25v5TMZbq5XEoaTVbT6CqZQ9yx9cfF7GHZ4q4GO0UArlm3yEjnxOhmm0XW8Hnyf/k6cp3+/kJ8/bVzF1Tb8OztEYeONhu68T3haxiGZMQwROQEOYZLu+tLSa7aA3dx5svQheeisrxSbPngF/FStyzRJvholVO2CjomXuyaJTa//4uoLP/j9ZG7v5WC+kJVKVdt37e15ehzfWOZlBrsFYJ9hojIO2o7csudI6gUsBZXtZzp5xEf79CCqpdPX8bat/dBv9yANTmtcE60s+zzRyV61d8LXe8i6CYk4ba7bwVwq/UJ5Oxv5eW+UNVy9X0rZ/8iZ/qspacD//lP1QlEvbTIrtYxDBGpndydRWs7XNrdw+CVvGimo1+Gs2cDEybYfZ1O/HAK+jm/QZ9VF1nn26ECPSz76kuXcF+T/dANkXDv88mo3/R2m+ewMC95kZdnO6A60skZsD83TnXc1aHd1nvelfet3DOap6YCcXGm57o68fHAK6+YfpQa7LXGmWqkkydPuquGyq3YTEY+yx2dRWs7XFqNw+Dl4mJHb0OFQWz9z17x8h1Zom3I4Sp3ax74u5jSKUtkzd4trpVcc75cte1v5eG+UDVei633vLND1t3ZlFtTGdgM5jBF9hkKDQ0Vr776qrhy5Yq7yuMWDEPkk9z1n3ltR25pdXkFMweDx5UzV8SyqdvEY803iSip0OpQP1SKXuG7xf8NyhKH1vwmz6i82vS3cnNfKKeuwd57HjDN4+PI+9bd/Yuqm2eIQcgpigxDP/zwg+jatauIiYkRn376qbvKJDuGIfI5nvjP3NWaBG8Ng5drGL8cli6tOsQ6Pl6cffNj8cGfNoqBjX4WwbhqtTsMl8SIhB/E509vEeeOnLc+lyO1f45cv6vPkTMdg93Vod2R97w5DNX0vvVEYK+sFOL774V49VXTz/ffc4SYCxQZhsw+++wzER8fLzp06CCyVPDXHcMQ+RxP/Gdem5oETw+DV9qilzbKU4BocT+WWhUxKeCEmNQhW3w/a6cov2xjuhBHa//ccf03BidHR4/Nnu2+kPvqq46VIT295vetGptylRT2PUjRYUgIIUpLS8Xf/vY3ERoaKoYNGyaOHj0qZ7lkxTBEPsdT/5nX5j9gTw2D9/bcMjcpW5AhjECVSQ4NkIQBkpgW8raYOSBL7Ft+VBgNRvsncrT276uv5L9+W6+dv3/NZZH7C9pWORx5z9f0vlVbU67Swr4HKT4MlZSUiM2bN4vJkycLPz8/ERwcLKZMmSKKi51c28YDGIZIlar7D10t/5m7+69ZJcwtI4TI25kv/v3njWJIox9FLuKEwU55jM6Ux9HXuGHD2l3/za+RvXBV3WO4q7bP2Zm8HX3P+8KM5hqZk0iRYejDDz8Ujz32mGjbtq0ICAgQwcHBokuXLmLChAnik08+EXfddZeIj48X27dvd1d5XcIwRKpT01+CNf1nDgjRqJEQTszUrkpeCoVGg1Hsyjgo0vtmic6h+y0P0xsylsfZCfxceTxna4Bs7XdHbZ+zo9dcCTC+MKO5kkKbmygyDMXHx4vhw4eLt956S2zZskWUlZVVOWbGjBkiOTlZtgLKgWGIPK62zUuO9hOx9Z+5vQDlizzY9+Pqxati9fSfxdNtNop4/7wqD9Gt7l6RmeJgvxZHyiPH0g7VPZ6rNS+AqW+QO/uuOHvtrgYYX5nR3Ns1wG6kyDDkiIKCAuHn5yf3aWuFYYg8qjbt+87+JVhTn4ra/JWrhg6bznxZuHA9BXsLxSdjN4lhMVtFXVy2OmUoroihjbeJj8dsEvm/nHG+PDVxpCmnUSPXHs+Vmhdnw1xtOFsrVpsAo+T3uRo7estMtWHIaDSK7OxsuU9bKwxD5DG1bd935cu0vLz2/UZsXYcaOmw62vfj668duh6jwSh++fqweP2eLNGt7l4hwWB1lzi/0+LpNhvF6uk/i9Lzpa6XR66mHPN1Oft4ta11cndNhKPle/VV5QUYObFmSL1hSIkYhsgj5Gjfd+UvQbn/w1RSh01H/mqvKTC88EK113Pt88Vi7Rs7xLNts0VT/9wqh3UK3S+m98kSO784UP3oL0fLI3dTjiuP52p/JE/1UVFTB2d34vPAMCQnhiHyCDlCiSvnkLMqXUkdNp2pnbIXGL76qtrrMQDiJOKEHyotm0NQKgZH/ST+/eeNIm9nvnxld2dTjrOP50rNkKfDsBo6OHuCxp8HhiEZMQyRR8gRSlz5S1DOmiGlVMu7UjtlKzA4eD33S0vFEy03ihUvbxMlZ0vkuQZP90Vx5vEcGY3oiVFjNVF6B2dP0fDz4Knvb65aTyQXR1foru44f3/TitnDh5tWExfi+j5JMv2eM8d6ZWu5ViUHar9ivRwMBtNK4rauRQjT9UyeDAwdav083LSafUVpBQ7P/xEpDjzkkv9ehd/DvWpbcms3lcdltlZot7WyuTOP58j7bPFioGFD766onpZmep21vrI7nwf3c2vUUgjWDJFHyNm+7+xfgnJVpSuhZqgWZTh/7IL44pkt4qGEH0Q4Lsk77483uLsju4ZrHEgdPPX9LQlh688v31JcXIzw8HAUFRUhLCzM28UhX5aZafprG7D91/aSJaa/8hzhaI3AjY89aRJw6tT1bQkJppokZx4zMbHmWqacHPf9VbpoETBqVM3HZWQAI0fi8De/Q//+Sei31McPRSkw4HqFdzQKcCCgHRpUnoVk6xyeuB5Xmd9LN78OrryXquPs+4zIgzz1/c0wRCQ3OUKJq+T4YpMz0LkiOxvo27fGwz5sMRtzcobiSEWS1faU4KPQdcyDbmwkuo5tA/9VK7x7Pa4wh9Ib30M3UnKII5IRw5CMGIbI49T+17a3A101tVNGSDiFeCQhB0b4IxDX0CdiL3R9r0A3+VYk3hlf9ZzevB5XOBgIkZUlT78kIoViGJIRwxCRC7wZ6DIzIR4YDkBYNW8Z/7j1KBZANLsVuqH+GDAlGWHxDnyuDQZTyMjONt3u08f0o8SQ6mRTIZGv8tT3N0eTEZFtco2GcpDhmgFbP94P/WcXoN/THq2wBHMxCQm4XptTHNQQ+WOm4tN5D8M/yMkQs2KFde3Q66+bmprmzlVe7ZAcIxOJyGGsGSIiryk+VYzvZu+HfrkBq3Na47yItOwLQAV6h/+Cp1K2ILW3Hxr3a+d67ZSnOiPLRQkd2YkUgM1kMmIYIlKO41tOQT/nN+izbkH2hbaoQJBlX33pEu5rsh+6IRLufT4Z9ZuG1/4B1doZ2dsd2YkUgM1kROQTjJVG/LzwAPQLzkG/KxZ7y1oAuN7JuXlgDnTtTmDI6Pro+VQKAkJ6yluAzZvtByHAFDRyc03HKakzclqaKfDc3PE7Pl65Hb+JVIphiKg21D5qzE2uFFzButn7oF9WgdXHWqFQXJ8H2g8G3Bm+F7o7L0E3vglaDmwGIMn+yWpLCbNqu4ozDxN5BMMQuZ+vBgZbw7WV2iHXA3J/Oo1Vc45Bv74ONpxti3J0t+wLQxHuTdgP3SCBgVNaI7J5B88VTO2dkT3ckZ1Ii7TdZ8hXv6SVxFcDg9o65LqBsdKInf87BP0nhdDvaIw9V1tZ7W8WcAK6lOPQjaqH1GdSEHRLkJ0zuRk7IxOpFjtQy8jmk+mrX9JK4quBQa0dcmVQeq4U6+fug35JGVYdaYF8Y2PLPglG9Ki3D7o7LmDIuHi0HnwrJD+bi2B4HjsjE6kSw5CMqjyZvvolrSS+HBg0Njvw6V0FWPXOEejXheD7wrYoQx3LvltwGQPi9kE30ID7prRCo9YNvVjSGqhtFmoi4mgytzEYTP8h2sqAQpi+pCdPNnVaVNuXtJKodQSPI9TcIdcBwiiw58vD0P+nACt/isLO0jYArtcANfE/hSFtfoNu5C3oPT4FwWE9vFdYZ7AzMhHZob0w5Mtf0kriy4FB7R1ybSi7VIYNc/dC/9VVrDp8G04ZWgEw9QGSYETXugeg63EOuqdi0TatOSQ/G+t/qQE7IxORDdoLQ778Ja0kagsMznSmT001NfHV1CE3NdW9Za6lM/vOYvXbh6BfG4jv8tuiFF0s+0JRgv4xe6EbUIFBz7dCdEpKNWciIlI37YUhtX1Jq5WaAoOznen9/U37hg83XYetDrlz5iiu+UUYBfZmHoX+36eh39oQP5e0gcD15z/OLx+6VkehG1EHd01qi5D63as5GxGR79BeB+q6dTnM1lPUMIKnNp3pvdUh14larPLicmx8fx/0i65Af+BWnDBYN291Dj0AXbdC6J5sjA4PtVTO6C8iInA0mazsjiYDlPsl7Ss8HRicae6SY8Sbp+eqcqAW6+zBc1jzziHov/HH2rwUXEE9y6EhuIp7ovZC168Mg6e0QGzHxjc/AhGRYjAMycjheYY4zFZe5qCQlwecPQs0agTExbkvMDjb3KW2IfJ2arGEJAECWN5hOt76bRi2Xk6BgJ9lf4xfAQa3OALd8BDcPSkFoQ1DPV1yIiKXcGi9u3GYrXtVF0zcFYRsNXfl5Zm226rtU1Nn+mqmhJCEgBESOu35GNvwCgT80KHOIeg6F0D3eBQ6/bkV/ALcUAPEGdyJyEdoNwwBHGbrLq4Ek9pwde4oNXWmr2FKCD8INEEuMvvOQ8eZDyKh2/Wh8W7BGdyJyIf41XyI9+Xl5eHhhx9GZGQkQkND0aFDB+zcudPbxSJbagomgCmYGAzyPaYzc0fdyDziTbLTaViSTE2nXhzxdvib3/HW4Gz8476tDh0/9MkoJHSLdW+hzGH35ufcHHYzM937+EREMlN8GLp48SJ69uyJwMBAfPPNNzhw4ADefvtt1K9f39tFI1tcDSa14Wpzl3mIPFA1ELlriLzBYOqrtGiR6fdNobCyrBLZc/bg+c7ZaBGUg1b3NcMLq/tgw1UHZ3l2dy2WN8IuEZGbKb6Z7M0330RCQgIWLFhg2ZaYmOi9AlH1vNEPpzbNXWlppmY7W00+cnemt9O0VPLqG9DvvxX6lQLfnGyDi6IDAMAPBtyNdbir7s9o3bEOKg82RsD5M96dt4kzuBORD1J8GFq5ciUGDBiABx98EBs3bkRcXBzGjRuHJ5980u59ysvLUV5ebrldXFzsiaIS4J1+OLWd4NETnent9KMynspDnafH4CsswTKYglekdB5/j/oIT5S8i9ArhUAJgM0AIiOv94Hy1kSPaup0TkTkIMU3k/3+++/48MMP0bx5c6xduxZPP/00Jk6ciP/+97927zNz5kyEh4dbfhISEjxYYo3zRj8cOZq7zJ3pR440/Za5aUxMnARbs1j4wbTtfTyLl7qsx+b3f8WZxVmYWPg3UxC60YULpt8REdbbGzQA/v53IDzcbvObbNTU6ZyIyEGKn2coKCgInTt3xo8//mjZNnHiRGzfvh1bt9ruVGqrZighIcHt8xTQH7w1qaXC5o4qPlWMte/sx9HFO/By/sSa75CVZQqJNU0EGRcHPP448N571wPSzdw1sss8USVncCciD/DUPEOKrxmKiYlBmzZtrLa1bt0aJ0+etHuf4OBghIWFWf2QB5n74cTFWW+Pj3fv7N5pacDx46ZQkZFh+p2T49EgdHzLKbw3fCP6R+5Ew4QQjJjdA3vzGzp25/x8x/rknDoFpKfbD0KA+0Z2eaPTORGRmym+z1DPnj1x+PBhq21HjhxB06ZNvVQicoi3JrV099xRN000aOh2B37+/DD0C89DvysO+8qbA7i+/leLwBz0SjoDHHHg3DEx8vW1qW5+pdryZKdzIiIPUHwz2fbt23HHHXcgPT0dI0aMwM8//4wnn3wS8+fPx5///GeHzuGpajafwFmF7bPRDJeHWEzAe5bOz/6oRM/wfdDdeQm68U3QcmAz55qWNm92bIkQZ8yeDURHy/968r1CRG7mse9voQJ6vV6kpKSI4OBg0apVKzF//nyn7l9UVCQAiKKiIjeV0EcsXSpEfLwQpq9s0098vGm7xp2d+R9hBITxxucGEAZIwgBJvBU5Q3zxzBZx/tgF2ydYulQISTL93HgO8+3Jk4XIyhKivFyIyEjrY+T84etJRCriqe9vxdcMyYE1Qw6wt4SGuzs9K5Sx0oid/zsE/SeFWL29EZaX3Ys4nLLZyU5IEiRHOg3b6uDt72898is+Hrh0CbhyRa5LsabR15OI1Imr1suIYagG5mac6kYwaWCEUOm5Unw/ey/0S8ux6mhLFBijAQC9kY1syLS6vblpacUKU/8ab9DI60lE6sdV67XIW30wNDyr8OldBVj1zhHo14Xg+8K2KEM3y75bcBkD4vZhYotsIMuBkznS+dnf3/S6jh7tcplrzYdfTyIiVzAMKYU3VwHX0KzCwiiwe9Eh6D8+A/3PUdhZ2gZAY8v+pv6noGvzG3Qjb0Hv8SkIDusBZJcDWek1n9zRiQZrCp+e4gOvJxGRHBiGlMBefx3zXDHu7t/h47MKl10qw4a5e6H/6ir0h5ojz9gaQGsAgAQjutY9AF2PcxjydCxS7m8OyS/e+gS1Xe7jZnKGEPNIsago0+3CQuDMGeC552q+r0pfTyIiubHPkLcpob+OD84qXPBrIVa/cxj6tUFYV5CCUtS17AtFCfrH7IVuQAUGPd8K0SmNaj6hnLNqZ2fXfvh8da+JD76eRKRNnIFaK5zpr+MuPjCrsDAK/LrkCGb0y0a3W/Yhpn0UnvgsFSsKuqEUdRHvfxpPt9mENenbcf6iP5ad7o7HFqQ6FoQAeWfVdmT9tsjIqmuQ3bgfsP+a+MDrSUTkSQxD3qaU/jreWkKjFsqLy7F2xg48224jEoPy0P7BFnj1+z74uSQFANA59ADS+2ZjV8YhnLwWgw/398LAv3dBSP0Q1x5QruU+HAkr8+ebmrzS06uGIkdeExW+nkRE3sJmMneraYSYo00mjgzbloPCZxU+e/Ac1rxzCPpv/LE2LwVXUM+yLwRXcU/UXgwZUIZBk1sgtmPjas6kAI4uLFub10ThrycRUXU4z5CMvBaGHBkh5q3+HSr5khRGgQP636D/8BT0P0Zg6+UUiBsqNGP8CjC4xRHohofg7kkpCG0Y6sXSukAlrwMRkTcwDMnIK2HImRmd5eyc62jZvDWM3wHXrlzD5g/3QZ9xGSv3JSGnsonV/g51DmFIlwLoHo9Cx1Gt4BfA1l4iIl/EMCQjj4chV0aIOdpkUlsKXXbj/NEL+Oadg9CvlvBtbjKKEW7ZF4wy3NVoL3R3X8XgybchoVusx8tHRESexzAkI4+HIVf7Abm7yUQJw/hvcPib36F//yRWbm6AH4pTYMT1x4ySzmLwbYegeyAI90xKxi2Nb3F7eYiISFm4HIeauTpCzN/fvZ2kZ8xwbtkNmcNZZVkltny0D/ovLkH/a1McrWgGoJllf9uQI9B1PA3dow3RdWwb+AU4OIkhERFRLTAMuYMSZ3TOzARee82xY/PzZetXdOlEEb59ez9WrhT45mQyLokOln2BuIa+kb9C17cEgyfdisQ7WwBo4fC5iYiI5MBmMndQ2gzANTWP3WzsWGDhwqrbHexXdGz9Cejfy4F+Yzg2X0pBJQIt+yKl8xiUdBC6Yf7o/1wywuIVNiM4EREpBvsMyciro8kAz4wQq44cyz+Y2QhyhmsGbP14P/SfXcDKPQk4dO1Wq7u0CT4GXYdT0I2JQPfHk+EfxKHjRERUM/YZUjvzDMC2mprkHiFWEzlnr/6jX1FJ5rdYszUC+hUGrMlpjfOineWQAFSgV4O90PW+DN2ERNx6120AbpOvDErDuYKIiFSNYcid0tKAoUO9/0Xphr5JT4+4gC8wyHK7gXQR9zU9AN0QCQOmJKN+046yP6YiKXzOJiIiqhmbybSgpj5MLuiDLOQHNoWu3QnoRtdHz6dSEBCisWxtb84ms6++Ah580LNlIiLyIWwmI/mYFwYdPtzU56cWgUgAuBoYhvnL49HiviQASbIVU1UMBlONUHXP5ciRpufb3HeMiIgUiesYKJXBYOr4vGiR6bfBULvz2VvF3EkSgNCwQLQYoNEQZLZ5c82j8wwGU81QZqZnykRERC5hGFKizExTs1bfvsCoUabfiYm1/1JNSwOOH4fx+/U49pdZ+DT5bRQgGkZIzp3n/HlTGNAyZzqlT55c+zBLRERuw2YypbHXDyUvz7TdxSH5pedK8f3svdAvLceqo8koMN4FALgfiViC4TBCgh+caD6rzQg1Xxh95Uyn9Btn9SYiIsVhGFKS6vqhCGHqfzJ5smmEmgPhIW9HPlbNPgr99yFYX9gWZehm2VcPxRgQtx+6+6Jwpe0ChM161fFJGQHXR6j5yuir1FRTuR19zuSc3oCIiGTF0WRK4uoCr38QRoHdiw5B//EZ6H+Ows7SNlb7m/qfgq7Nb9CNvAW9x6cgOCz4+k5zbU1enilwnTtn+7FrM3u2vVovb0xEKYfMTOCBBxw79sbXzBdqxoiIPICjybTIhQVer164ig3v7oP+q6tYdbg58oytAbQGAEgwotst+6HrcR66p2KRcn9zSH7xts954yKxdepUP3v2nDnOf3nLXOulCGlppuHzI0fa7xNkDo+pfyw66ys1Y0REPoRhSEkcbHq6UBqCZWM3Q782COsKUlCKLpZ9oShB/5i90A2owKDnWyE6pa3z5XDH7Nk1jb76Y2Zr1fWtefBBU+CxNZ/QzeHRTf3BiIiodthM5ihPNG3UMDmiAHBGaow4cQpGXH/seP/T0LU6Bt2IOug7sS1C6ofIVx65rnnRItPIuJpkZJhqWtTGVo1PQsL18FjTYrmeXryXiEgF2EymJJ5q2rhhckQhSZBuCETm4e/jxPswwh+dQw9A160QQ56KQfsHW0Dyi5WvHDeWR65aGkc7XLth6RCPBNmall7x1ZoxIiIfwDBUEw82bZw9eA6r10ShsP4bGHXxPcTj9PWHQxy+CB+PgUMaYt7kAsR2bAOgjf2TKY159JW9JUFu7lsjF0/20akuPLrQH4yIiDyDzWTVcXPThjAKHND/Bv2Hp7Dyh0hsu5IM8cc8mH4wYJi0AvfG7EHK3TFo/38PIzS6ntOPoSjmYAnY7pgtd58ZJY1eq+VIQSIiLfJUMxnDUHXc8AV27co1bPpgH/QZl6Hfn4ScyiZW+2+vcxC6LmegezwKHUe1gl+Aj00SXlPfGrkorY9OTYvlss8QEVEV7DOkBDI1bZw/egHfvHMQ+tUSvs1NRjE6WvYFowx3NdqLIfdcxeDnmiO+y/Wh8T6ppr41clFaH53qFsutzZQFRERUawxD1XGx068wChz+Ngf6909Cv6UBfihOgRE9LfujpLMYfNsh6B4IQr/nUlA3qsvNZ/RtcnbMtkeJfXTcMWUBERHVmrbDUE2jjJzo9FtRWoEf5u+H/otLWPlrIo5VNAPQzHJo25AjGNLpNHSPNkSXMW3gFyBzR2Gy5s3Ra9XxVM0YERE5TFt9hlatQti9916fAM+RUUbVdPoVALbc+zo+PNAb35xMxiVR37I7ENfQN/JX6PqWQDf5VjTtaWfmZ3IP9tEhIlI9dqCWkeXJBBAWH2+a1O+ttxwfZWQjOJ2RojFevIeluD7zcEPpHAY1OwTd/f7o/1wK6sWqfPSX2nl69BoREcmKYUhGVmGopoNvqjGoLKvE1o/3Y9Vn55C/pwAVlX7IRww2IxVG+KNN8DHoOpyCbkwEuj+eDP8g1jIoiqdGrxERkewYhuyYOXMmXn75ZUyaNAlz5sxx6D5OhaE/ZA+fh092tsea421wQURYtgegAr0b7IWuz2UMfjYRt97V1OlrIA/jKvFERKrEofU2bN++HfPnz0e7du3c/lj/XhKBxbgTANBAuoj7mh6AboiEe59PRniTjjXcmxTFE6PXiIhItVQzo9+VK1fw5z//Gf/5z3/QoEEDtz9eYIDAXztnY+O7v6CwtB6+yOmJh+begfAm4W5/bCIiIvIc1dQMjR8/HoMGDcI999yD119/vdpjy8vLUV5ebrldXFzs8OMIAPDzw39LhgNBQS6WVoXYlERERBqlijC0ePFi7Nq1C9u3b3fo+JkzZyI9Pd2lx5IAwGgEfvxRO00rzi5myuBEREQ+RPHNZLm5uZg0aRK++OILhISEOHSfadOmoaioyPKTm5tr2Wd09IHlmpnYYDCtcbZokem3wSDPeWvLXK7nngMeeKDq0hV5eaZh6ZmZ1tszM03z9/TtC4waZfqdmFj1OCIiIpVQ/Giy5cuX4/7774f/DTUPBoMBkiTBz88P5eXlVvtsuXE0Wb1GjSCdPVvzA8uxerizNS6eYqtcttw8MaGSVoEnIiKfx6H1f7h8+TJOnDhhte3RRx9Fq1at8NJLLyElJaXGc1jNQN2vH3Drre6fmVipwcFeuaqTlWVqClPSKvBEROTzPBWGFN9MVq9ePaSkpFj91K1bF5GRkQ4FISupqaZO0XPnmm6bg4mZXKuHGwymmhdbgcO8bfJkzzeZVVeu6uTnO7cKPBERkYooPgy5hXn18Lg46+3x8fLU2Cg1ONRULntiYty3CrxS+1TZo7byEhFRjVQxmuxm2dnZtT+JO1cPd1dwqC1nH8/c9JWa6nhwc2YVeKX2qbJHbeUlIiKHqDIMycZdMxM7GgicCQ5ycObxbm4yTE01ffHX1NcqNdWx89vru2QexeZqDZ27hv27q7xEROR12mwmczdzcLi5T5KZJJkWC3U0OHiqXDe6ucnQ31++vlbu6lPlrmH/Su0DRkREsmAYcgc5g4OcHCnX5Mmm0WM5OVVrOuTqa+WOPlXmmhtH50tyhlL7gBERkSwYhtzF3Z203VGupUuB2bNNTYf2glpaGnD8uCkwZWTYD07VkbtPlbtrbpTaB4yIiGSh7T5D7ubOTtreLFdt+1rJ3afKmZobV8qt1D5gREQkC4Yhd3NXJ+3a8ma55O6M7e6aG7nLS0REisJmMvI8uftUubvmRql9wIiISBYMQ+Qdcvap8sToPaX2ASMiolpT/NpkcvDU2ibkArnmBTKPJgOsm7LkXgvOXfMYERFRFVyoVUYMQxpha4bohARTExZrboiIVMdT39/sQE2+Q6mj94iISNEYhsi3KHX0HhERKRY7UBMREZGmMQwRERGRpjEMERERkaYxDBEREZGmMQwRERGRpjEMERERkaYxDBEREZGmMQwRERGRpjEMERERkaYxDBEREZGmMQwRERGRpjEMERERkaYxDBEREZGmMQwRERGRpjEMERERkaYxDBEREZGmMQwRERGRpjEMERERkaYxDBEREZGmMQwRERGRpjEMERERkaYxDBEREZGmMQwRERGRpjEMERERkaYxDBEREZGmMQwRERGRpik+DM2cORNdunRBvXr1EBUVhWHDhuHw4cPeLhYRERH5CMWHoY0bN2L8+PHYtm0b1q1bh8rKSvTv3x8lJSXeLhoRERH5AEkIIbxdCGecPXsWUVFR2LhxI3r16uXQfYqLixEeHo6ioiKEhYW5uYREREQkB099fwe47cxuUlRUBACIiIiwe0x5eTnKy8stt4uLi91eLiIiIlInxTeT3UgIgSlTpuDOO+9ESkqK3eNmzpyJ8PBwy09CQoIHS0lERERqoqpmsvHjx2P16tXYsmUL4uPj7R5nq2YoISGBzWREREQqwmaym0yYMAErV67Epk2bqg1CABAcHIzg4GAPlYyIiIjUTPFhSAiBCRMmYNmyZcjOzkZSUpK3i0REREQ+RPFhaPz48cjIyMCKFStQr149FBQUAADCw8NRp04dL5eOiIiI1E7xfYYkSbK5fcGCBRg7dqxD5+DQeiIiIvVhn6E/KDyrERERkcqpamg9ERERkdwYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTGIaIiIhI0xiGiIiISNMYhoiIiEjTVBOGPvjgAyQlJSEkJASdOnXC5s2bvV0kIiIi8gGqCENffvklJk+ejFdeeQW7d+9GamoqBg4ciJMnT3q7aERERKRykhBCeLsQNenWrRs6duyIDz/80LKtdevWGDZsGGbOnFnj/YuLixEeHo6ioiKEhYW5s6hEREQkE099fyu+ZujatWvYuXMn+vfvb7W9f//++PHHH71UKiIiIvIVAd4uQE3OnTsHg8GA6Ohoq+3R0dEoKCiweZ/y8nKUl5dbbhcVFQEwJUwiIiJSB/P3trsbsRQfhswkSbK6LYSoss1s5syZSE9Pr7I9ISHBLWUjIiIi9zl//jzCw8Pddn7Fh6GGDRvC39+/Si1QYWFhldois2nTpmHKlCmW25cuXULTpk1x8uRJtz6ZSlNcXIyEhATk5uZqqq8Ur5vXrQW8bl63FhQVFaFJkyaIiIhw6+MoPgwFBQWhU6dOWLduHe6//37L9nXr1mHo0KE27xMcHIzg4OAq28PDwzX1JjILCwvjdWsIr1tbeN3aotXr9vNzbxdnxYchAJgyZQpGjx6Nzp07o0ePHpg/fz5OnjyJp59+2ttFIyIiIpVTRRh66KGHcP78efzjH/9Afn4+UlJSsGbNGjRt2tTbRSMiIiKVU0UYAoBx48Zh3LhxLt03ODgYr732ms2mM1/G6+Z1awGvm9etBbxu9163KiZdJCIiInIXxU+6SERERORODENERESkaQxDREREpGkMQ0RERKRpqgxDH3zwAZKSkhASEoJOnTph8+bN1R6/ceNGdOrUCSEhIWjWrBk++uijKscsXboUbdq0QXBwMNq0aYNly5a5q/guc+a6MzMz0a9fPzRq1AhhYWHo0aMH1q5da3XMwoULIUlSlZ+ysjJ3X4pTnLnu7Oxsm9d06NAhq+N87fUeO3aszetOTk62HKOG13vTpk3Q6XSIjY2FJElYvnx5jffxhc+3s9ftK59vZ6/bVz7fzl63r3y+Z86ciS5duqBevXqIiorCsGHDcPjw4Rrv54nPuOrC0JdffonJkyfjlVdewe7du5GamoqBAwfi5MmTNo/PycnBfffdh9TUVOzevRsvv/wyJk6ciKVLl1qO2bp1Kx566CGMHj0av/zyC0aPHo0RI0bgp59+8tRl1cjZ6960aRP69euHNWvWYOfOnejbty90Oh12795tdVxYWBjy8/OtfkJCQjxxSQ5x9rrNDh8+bHVNzZs3t+zzxdd77ty5Vtebm5uLiIgIPPjgg1bHKf31LikpQfv27TFv3jyHjveVz7ez1+0rn29nr9tM7Z9vZ6/bVz7fGzduxPjx47Ft2zasW7cOlZWV6N+/P0pKSuzex2OfcaEyXbt2FU8//bTVtlatWompU6faPP7FF18UrVq1str21FNPie7du1tujxgxQtx7771WxwwYMED86U9/kqnUtefsddvSpk0bkZ6ebrm9YMECER4eLlcR3cLZ687KyhIAxMWLF+2eUwuv97Jly4QkSeL48eOWbWp4vW8EQCxbtqzaY3zl830jR67bFjV+vm/kyHX7yuf7Rq683r7w+RZCiMLCQgFAbNy40e4xnvqMq6pm6Nq1a9i5cyf69+9vtb1///748ccfbd5n69atVY4fMGAAduzYgYqKimqPsXdOT3Plum9mNBpx+fLlKovdXblyBU2bNkV8fDwGDx5c5S9Lb6rNdd9+++2IiYnB3XffjaysLKt9Wni9P/nkE9xzzz1VZmlX8uvtCl/4fMtBjZ/v2lDz51sOvvL5LioqAoBqF2H11GdcVWHo3LlzMBgMVVarj46OrrKqvVlBQYHN4ysrK3Hu3Llqj7F3Tk9z5bpv9vbbb6OkpAQjRoywbGvVqhUWLlyIlStXYtGiRQgJCUHPnj1x9OhRWcvvKleuOyYmBvPnz8fSpUuRmZmJli1b4u6778amTZssx/j6652fn49vvvkGTzzxhNV2pb/ervCFz7cc1Pj5doUvfL5ry1c+30IITJkyBXfeeSdSUlLsHuepz7hqluO4kSRJVreFEFW21XT8zdudPac3uFrGRYsWYfr06VixYgWioqIs27t3747u3btbbvfs2RMdO3bEe++9h3fffVe+gteSM9fdsmVLtGzZ0nK7R48eyM3NxVtvvYVevXq5dE5vcbWMCxcuRP369TFs2DCr7Wp5vZ3lK59vV6n98+0MX/p8u8pXPt/PPvssfv31V2zZsqXGYz3xGVdVzVDDhg3h7+9fJe0VFhZWSYVmjRs3tnl8QEAAIiMjqz3G3jk9zZXrNvvyyy/x+OOP46uvvsI999xT7bF+fn7o0qWLYv6SqM1136h79+5W1+TLr7cQAp9++ilGjx6NoKCgao9V2uvtCl/4fNeGmj/fclHb57s2fOXzPWHCBKxcuRJZWVmIj4+v9lhPfcZVFYaCgoLQqVMnrFu3zmr7unXrcMcdd9i8T48ePaoc/91336Fz584IDAys9hh75/Q0V64bMP3FOHbsWGRkZGDQoEE1Po4QAnv27EFMTEytyywHV6/7Zrt377a6Jl99vQHTaI1jx47h8ccfr/FxlPZ6u8IXPt+uUvvnWy5q+3zXhto/30IIPPvss8jMzMSGDRuQlJRU43089hl3uKu1QixevFgEBgaKTz75RBw4cEBMnjxZ1K1b19KrfurUqWL06NGW43///XcRGhoqnnvuOXHgwAHxySefiMDAQLFkyRLLMT/88IPw9/cX//rXv8TBgwfFv/71LxEQECC2bdvm8euzx9nrzsjIEAEBAeL9998X+fn5lp9Lly5Zjpk+fbr49ttvxW+//SZ2794tHn30UREQECB++uknj1+fPc5e9+zZs8WyZcvEkSNHxL59+8TUqVMFALF06VLLMb74eps9/PDDolu3bjbPqYbX+/Lly2L37t1i9+7dAoB45513xO7du8WJEyeEEL77+Xb2un3l8+3sdfvK59vZ6zZT++f7mWeeEeHh4SI7O9vqfVtaWmo5xlufcdWFISGEeP/990XTpk1FUFCQ6Nixo9WwvDFjxojevXtbHZ+dnS1uv/12ERQUJBITE8WHH35Y5Zxff/21aNmypQgMDBStWrWy+nAphTPX3bt3bwGgys+YMWMsx0yePFk0adJEBAUFiUaNGon+/fuLH3/80YNX5BhnrvvNN98Ut956qwgJCRENGjQQd955p1i9enWVc/ra6y2EEJcuXRJ16tQR8+fPt3k+Nbze5qHT9t63vvr5dva6feXz7ex1+8rn25X3uS98vm1dMwCxYMECyzHe+oxLfxSQiIiISJNU1WeIiIiISG4MQ0RERKRpDENERESkaQxDREREpGkMQ0RERKRpDENERESkaQxDREREpGkMQ0RERKRpDENERESkaQxDREREpGkMQ0SkSosWLUJISAjy8vIs25544gm0a9cORUVFXiwZEakN1yYjIlUSQqBDhw5ITU3FvHnzkJ6ejo8//hjbtm1DXFyct4tHRCoS4O0CEBG5QpIkzJgxA8OHD0dsbCzmzp2LzZs3MwgRkdNYM0REqtaxY0fs378f3333HXr37u3t4hCRCrHPEBGp1tq1a3Ho0CEYDAZER0d7uzhEpFKsGSIiVdq1axf69OmD999/H4sXL0ZoaCi+/vprbxeLiFSIfYaISHWOHz+OQYMGYerUqRg9ejTatGmDLl26YOfOnejUqZO3i0dEKsOaISJSlQsXLqBnz57o1asX/v3vf1u2Dx06FOXl5fj222+9WDoiUiOGISIiItI0dqAmIiIiTWMYIiIiIk1jGCIiIiJNYxgiIiIiTWMYIiIiIk1jGCIiIiJNYxgiIiIiTWMYIiIiIk1jGCIiIiJNYxgiIiIiTWMYIiIiIk1jGCIiIiJN+3+0i07aOseSSwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients for OLS\n",
     "from random import random, seed\n",
@@ -4083,87 +4059,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "ac458170",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.]\n",
-      " [3.]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30306759 4.65944342]\n",
-      "0 [-20.62275788] [-25.73690387]\n",
-      "1 [-0.03662823] [0.02925855]\n",
-      "2 [-0.0342458] [0.02735547]\n",
-      "3 [-0.03201832] [0.02557617]\n",
-      "4 [-0.02993573] [0.0239126]\n",
-      "5 [-0.0279886] [0.02235723]\n",
-      "6 [-0.02616812] [0.02090304]\n",
-      "7 [-0.02446604] [0.01954342]\n",
-      "8 [-0.02287468] [0.01827225]\n",
-      "9 [-0.02138683] [0.01708375]\n",
-      "10 [-0.01999575] [0.01597256]\n",
-      "11 [-0.01869515] [0.01493365]\n",
-      "12 [-0.01747915] [0.01396231]\n",
-      "13 [-0.01634224] [0.01305415]\n",
-      "14 [-0.01527928] [0.01220505]\n",
-      "15 [-0.01428546] [0.01141119]\n",
-      "16 [-0.01335628] [0.01066897]\n",
-      "17 [-0.01248754] [0.00997502]\n",
-      "18 [-0.0116753] [0.0093262]\n",
-      "19 [-0.01091589] [0.00871959]\n",
-      "20 [-0.01020588] [0.00815244]\n",
-      "21 [-0.00954206] [0.00762217]\n",
-      "22 [-0.0089214] [0.0071264]\n",
-      "23 [-0.00834112] [0.00666287]\n",
-      "24 [-0.00779859] [0.00622949]\n",
-      "25 [-0.00729134] [0.0058243]\n",
-      "26 [-0.00681708] [0.00544547]\n",
-      "27 [-0.00637367] [0.00509128]\n",
-      "28 [-0.0059591] [0.00476012]\n",
-      "29 [-0.0055715] [0.0044505]\n",
-      "theta from own gd\n",
-      "[[3.98281205]\n",
-      " [3.0137297 ]]\n",
-      "0 [-0.00520911] [0.00416103]\n",
-      "1 [-0.00487029] [0.00389038]\n",
-      "2 [-0.00445186] [0.00355614]\n",
-      "3 [-0.00403677] [0.00322456]\n",
-      "4 [-0.00364967] [0.00291535]\n",
-      "5 [-0.00329616] [0.00263296]\n",
-      "6 [-0.00297571] [0.00237699]\n",
-      "7 [-0.00268602] [0.00214559]\n",
-      "8 [-0.00242441] [0.00193661]\n",
-      "9 [-0.00218823] [0.00174795]\n",
-      "10 [-0.00197505] [0.00157766]\n",
-      "11 [-0.00178263] [0.00142396]\n",
-      "12 [-0.00160895] [0.00128523]\n",
-      "13 [-0.0014522] [0.00116001]\n",
-      "14 [-0.00131072] [0.001047]\n",
-      "15 [-0.00118302] [0.00094499]\n",
-      "16 [-0.00106776] [0.00085292]\n",
-      "17 [-0.00096373] [0.00076983]\n",
-      "18 [-0.00086984] [0.00069482]\n",
-      "19 [-0.00078509] [0.00062713]\n",
-      "20 [-0.0007086] [0.00056603]\n",
-      "21 [-0.00063957] [0.00051088]\n",
-      "22 [-0.00057726] [0.00046111]\n",
-      "23 [-0.00052102] [0.00041619]\n",
-      "24 [-0.00047025] [0.00037564]\n",
-      "25 [-0.00042444] [0.00033904]\n",
-      "26 [-0.00038309] [0.00030601]\n",
-      "27 [-0.00034576] [0.0002762]\n",
-      "28 [-0.00031208] [0.00024929]\n",
-      "29 [-0.00028167] [0.000225]\n",
-      "theta from own gd wth momentum\n",
-      "[[3.99916114]\n",
-      " [3.00067008]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients for OLS\n",
     "from random import random, seed\n",
@@ -4230,29 +4129,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "e6b06cdb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.26281735]\n",
-      " [2.84478178]]\n",
-      "Eigenvalues of Hessian Matrix:[0.33978889 4.51659846]\n",
-      "0 [-14.67490857] [-18.08539155]\n",
-      "1 [-4.46170878e-15] [-1.45781905e-14]\n",
-      "2 [-1.21430643e-15] [-1.24768421e-15]\n",
-      "3 [4.92661467e-16] [4.50610071e-16]\n",
-      "4 [4.92661467e-16] [4.50610071e-16]\n",
-      "beta from own Newton code\n",
-      "[[4.26281735]\n",
-      " [2.84478178]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Newton's method\n",
     "from random import random, seed\n",
@@ -4305,43 +4185,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "a010a209",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.0586484]\n",
-      " [3.0718316]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]\n",
-      "theta from own gd\n",
-      "[[4.0586484]\n",
-      " [3.0718316]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "theta from own sdg\n",
-      "[[4.02496085]\n",
-      " [3.12081773]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using SGD\n",
     "# OLS example\n",
@@ -4428,27 +4275,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "3320d6c6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[3.96075051]\n",
-      " [3.02148021]]\n",
-      "Eigenvalues of Hessian Matrix:[0.27470622 4.24106503]\n",
-      "theta from own gd\n",
-      "[[3.95906059]\n",
-      " [3.02296298]]\n",
-      "theta from own sdg with momentum\n",
-      "[[3.95611042]\n",
-      " [2.99475306]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using SGD\n",
     "# OLS example\n",
@@ -4529,25 +4359,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "19414323",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own AdaGrad\n",
-      "[[2.00025662]\n",
-      " [2.99802696]\n",
-      " [4.00167329]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4617,25 +4432,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "6724b3d2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own RMSprop\n",
-      "[[1.99456598]\n",
-      " [2.99848815]\n",
-      " [3.99835783]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4703,25 +4503,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "28aa6aad",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own ADAM\n",
-      "[[1.99989103]\n",
-      " [3.00042093]\n",
-      " [3.99954925]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4794,19 +4579,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "f3dde7c5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial loss: 2.772588722239781\n",
-      "Trained loss: 1.067270675787016\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
     "from autograd import grad\n",
@@ -4861,18 +4637,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "291c9b6d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.25       0.19661197 0.10499357]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import jax.numpy as jnp\n",
     "from jax import grad, jit, vmap\n",
diff --git a/doc/pub/week39/ipynb/week39.ipynb b/doc/pub/week39/ipynb/week39.ipynb
index 4a48fabca..bde575030 100644
--- a/doc/pub/week39/ipynb/week39.ipynb
+++ b/doc/pub/week39/ipynb/week39.ipynb
@@ -1012,7 +1012,7 @@
     {
      "data": {
       "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x150835f40>"
+       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11f758f40>"
       ]
      },
      "execution_count": 1,
@@ -1154,7 +1154,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x161847e80>]"
+       "[<matplotlib.lines.Line2D at 0x12a472ee0>]"
       ]
      },
      "execution_count": 5,
@@ -1814,16 +1814,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.30768763 4.39313488]\n",
-      "[[3.95814165]\n",
-      " [3.0184648 ]]\n",
-      "[[3.95814165]\n",
-      " [3.0184648 ]]\n"
+      "Eigenvalues of Hessian Matrix:[0.39227722 4.38168146]\n",
+      "[[3.8976899 ]\n",
+      " [3.03844474]]\n",
+      "[[3.8976899 ]\n",
+      " [3.03844474]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1899,9 +1899,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.94758046]\n",
-      " [2.92347862]]\n",
-      "[3.94453204] [2.94467121]\n"
+      "[[3.81448757]\n",
+      " [3.3421718 ]]\n",
+      "[3.83247778] [3.40246301]\n"
      ]
     }
    ],
@@ -2034,16 +2034,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.32374405 4.06221646]\n",
-      "[[4.0585026]\n",
-      " [2.9999519]]\n",
-      "[[4.05843037]\n",
-      " [3.00001709]]\n"
+      "Eigenvalues of Hessian Matrix:[0.31252985 4.57630515]\n",
+      "[[4.06031839]\n",
+      " [3.01099015]]\n",
+      "[[4.05965043]\n",
+      " [3.01153127]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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Kq+3Hjx+3eF8jW/elI9HR0ejWrRsAoGfPnujZsyc6duyICRMmYO/evXY/Z+7cA7Gxsfjqq6+clvPXOSR5YssQBdTMmTOh1+tx//3349KlS17ty/iLuP4Dd9GiRRbfN2zYED169EBxcbHFX+Vnz57Fxx9/7PR9brnlFvz888+IjY1Ft27drL48SfhmrLMrLSLp6elITExEYWGhRXK6w4cPW8zqMtb1zJkz0Ov1Nutq65d7aGgoevbsifnz5wMAdu/eDQC46aabcPHiRavZN/XfTwiBY8eO2Xy/Dh06OD2++tw5N8Y6fPvtt2jVqpXNOpgHQ0888QQ2bdqEpUuXYtmyZdi3b59V65BGo7H6TH366ac4duyY28fiii+++MJiNpVer8eyZcvQqlUrJCcn23xNeno6WrdujX379tk85m7dulkEvq4aMGAAvv/+e9NnwOjdd9+FRqNBdna22/t0pHXr1nj44YfxzTffYNmyZXbLuXMP9OvXD2fPnsWaNWssttefoeevc0jyxJYhCqg+ffpg/vz5mDx5Mrp06YL77rsP7dq1Q0hICCorK1FUVAQALiU9zMjIQKtWrTBjxgwIIRATE4OPP/4YJSUlVmWfeuop3HjjjRg4cCAeeugh6PV6PPfcc2jYsCF+/fVXh++Tl5eHoqIiXHfddZg6dSquueYa1NXV4ciRI/jss8/w0EMPoWfPnm6dB2OQMHfuXOTm5iI8PBzp6ek2f/mGhITgqaeewj/+8Q8MHz4c9957L37//XfMmjXLqovgjjvuwNKlS3HzzTdjypQp6NGjB8LDw3H06FGUlpZi6NChGD58OBYuXIj169dj8ODBSElJwcWLF00tX9dffz0AwxiLt99+G/fffz/279+P7Oxs1NXVYfv27WjTpg3uuOMO9OnTB/fddx/GjRuHnTt34rrrrkPDhg1RWVmJzZs3o0OHDnjggQfcOjfGrORLly5FmzZt0KhRIyQlJVkENeaefPJJlJSUoHfv3njwwQeRnp6Oixcv4tChQ1i9ejUWLlyI5ORklJSUID8/H48//rgpNUN+fj6mT5+OrKwsDB8+HIAhuFqyZAkyMjJwzTXXYNeuXXj++eftBibeiouLQ//+/fH444+jYcOGWLBgAX788Uen0+sXLVqEm266CTfccAPGjh2LFi1a4Ndff8UPP/yA3bt3Y/ny5W7XZerUqXj33XcxePBgPPnkk2jZsiU+/fRTLFiwAA888ACuvvpqTw/TrunTp2PhwoWYPXs2br/9doSGhlqVceceyM3Nxcsvv4zRo0fj6aefxlVXXYU1a9Zg3bp1pn0Z+eMckkwFcfA2qdjevXvFuHHjRFpamtBqtSIyMlJcddVV4q677hJffPGFRdnc3FzRsGFDm/v5/vvvxcCBA0Xjxo3FFVdcIW677TZx5MgRAUA88cQTFmVXrVolrrnmGhERESFSUlLEs88+K5544gmns8mEMEzXfeyxx0R6erqIiIgQ0dHRokOHDmLq1KkWM4EAiIkTJ1rV09Y+Z86cKZKSkkRISIjVTCFb3nzzTdG6dWsREREhrr76arF48WKRm5trMZNGCMN05RdeeEF07NhRREZGikaNGomMjAwxfvx4ceDAASGEYRbY8OHDRcuWLYVWqxWxsbGiX79+YtWqVRb7+uOPP8S///1v0/vGxsaK/v37iy1btliUW7x4sejZs6do2LChiIqKEq1atRJ33XWX2Llzp6lMv379RLt27ayOy9YxFBYWioyMDBEeHm7zWtZ36tQp8eCDD4q0tDQRHh4uYmJiRNeuXcWjjz4qzp07J44fPy7i4+NF//79LWYI1dXViSFDhoimTZuaZvb99ttv4p577hHx8fGiQYMGom/fvmLTpk2iX79+ol+/fqbXGmc8mU+BF+LyFPD6sw+Nn7VTp06Zthk/LwsWLBCtWrUS4eHhIiMjQyxdutTitbZmkwkhxL59+8Ttt98u4uPjRXh4uGjevLno37+/WLhwocPzJYTt2WRCCHH48GExatQoERsbK8LDw0V6erp4/vnnLc6bcTbZ888/7/R9nL2fEJdngBlTLNg7XlfvgSNHjogRI0aIRo0aicaNG4tbb71VrF692mqWqRDenUNSDo0Q9RaFISKigNBoNJg4cSLmzZsX7Koo3pw5c/DYY4/hyJEjfmvlI/liNxkRESmKMbjMyMjApUuXsH79evz3v//F6NGjGQiRTQyGiIhIURo0aICXX34Zhw4dQk1NDVJSUvDII4/gscceC3bVSKLYTUZERESqxqn1REREpGoMhoiIiEjVGAwRERGRqqliAHVdXR2OHz+Oxo0bu50+noiIiIJDCIGzZ886XSfRW6oIho4fPw6dThfsahAREZEHKioq/JoWQRXBkHGJg4qKCpeWeSAiIqLgq66uhk6n8/s6caoIhoxdY02aNGEwREREJDP+HuLCAdRERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkakEPhjZu3IghQ4YgKSkJGo0GH374od2y48ePh0ajwSuvvBKw+hEREZGyBT0YOn/+PDp27Ih58+Y5LPfhhx9i+/btSEpKClDNiIiISA3Cgl2Bm266CTfddJPDMseOHcOkSZOwbt06DB48OEA1IyIiIjUIesuQM3V1dRgzZgz++c9/ol27dsGuDhERESlM0FuGnHnuuecQFhaGBx980OXX1NTUoKamxvR9dXW1P6pGRERECiDplqFdu3Zh7ty5WLJkCTQajcuvy8/PR3R0tOlLp9P5sZZEREQkZ5IOhjZt2oSTJ08iJSUFYWFhCAsLw+HDh/HQQw8hNTXV7utmzpyJqqoq01dFRUXgKk1ERESyIulusjFjxuD666+32HbDDTdgzJgxGDdunN3XabVaaLVaf1ePiIiIFCDowdC5c+dw8OBB0/fl5eXYu3cvYmJikJKSgtjYWIvy4eHhaN68OdLT0wNdVSIiIlKgoAdDO3fuRHZ2tun7adOmAQByc3OxZMmSINWKiIiI1CLowVBWVhaEEC6XP3TokP8qQ0RERKoj6QHURERERP7GYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqBT0Y2rhxI4YMGYKkpCRoNBp8+OGHpp9dunQJjzzyCDp06ICGDRsiKSkJd911F44fPx68ChMREZGiBD0YOn/+PDp27Ih58+ZZ/ezChQvYvXs3Hn/8cezevRvFxcX46aef8Le//S0INSUiIiIl0gghRLArYaTRaLBy5UoMGzbMbpkdO3agR48eOHz4MFJSUlzab3V1NaKjo1FVVYUmTZr4qLZERETkT4F6fof5bc9+UlVVBY1Gg6ZNm9otU1NTg5qaGtP31dXVAagZERERyVHQu8nccfHiRcyYMQOjRo1yGCHm5+cjOjra9KXT6QJYSyIiIpIT2QRDly5dwh133IG6ujosWLDAYdmZM2eiqqrK9FVRURGgWhIREZHcyKKb7NKlS7j99ttRXl6O9evXO+031Gq10Gq1AaodERERyZnkgyFjIHTgwAGUlpYiNjY22FUiIiIiBQl6MHTu3DkcPHjQ9H15eTn27t2LmJgYJCUlIScnB7t378Ynn3wCvV6PEydOAABiYmIQERERrGoTERGRQgR9an1ZWRmys7Ottufm5mLWrFlIS0uz+brS0lJkZWW59B6cWk9ERCQ/qplan5WVBUfxmITSIBEREZECyWY2GREREZE/MBgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqVrQV60nIiKSFb0e2LQJqKwEEhOBzEwgNDTYtSIvMBgiIiJyVXExMGUKcPTo5W3JycDcucCIEc5fz0BKkthNRkRE5IriYiAnxzIQAoBjxwzbi4udvz41FcjOBkaNMvybmur8dcGg1wNlZUBhoeFfvT7YNfIrBkNERETO6PWGFiEhrH9m3JaXZz9o8DaQCiQ5BW0+wmCIiIjImU2brAMZc0IAFRWGcvV5G0gFkpyCNh9iMERERFRf/W6iY8dce11lpfU2bwKpQJJT0OZjHEBNRERkztYg6bg4116bmGi9zVaAZIur5fzFnaAtKytg1QoEBkNERERGxm6i+q0jp087fp1GY5hVlplp/TNbAZItrpbzF7kEbX7AbjIiIiLAcTeROY3G9vevvGJ7mnxmpiFQqv8689frdLYDqUCSS9DmBwyGiIiIAOfdREb1u8ySk4EVK+znGQoNNeQhAtwPpAJJLkGbH7CbjIiUicntyF2udv+8/DLQooV7n60RIwwBk62Eja+84lrCRn8zBm05OYbAx7yFTEpBmx8wGCIi5fE2SzCpk6vdPy1aeDaAeMQIYOhQaQfpcgja/EAjhLPOUfmrrq5GdHQ0qqqq0KRJk2BXh4j8yd4AWONfto66M0jd9HpDcsFjx2yPGzIOki4vl1YA4w8SaVkN1PObwRARKYfxYWZv3IeaHmbkGWMwDdjuJmIwHVCBen5zADURKYdcktuRdBm7iVq0sNzubJA0yRrHDBGRcqg4T4riBLObRg5je8inGAwRkXKoOE+KokhhAHxoqOKyLJN97CYjIuVQcZ4UxVDpQqEUXAyGiEg55JLcjmxT8UKhFFwMhohIWTgAVr44AJ6ChGOGiEh5OABWnjgAnoKEwRARKRMHwMoPB8BTkLCbjIiIpIED4ClIGAwREfmLXg+UlQGFhYZ/OfDXMQ6ApyBhMERE5A/FxYalQbKzgVGjDP+mpnJquDMcAE9BEPRgaOPGjRgyZAiSkpKg0Wjw4YcfWvxcCIFZs2YhKSkJUVFRyMrKwnfffRecyhIRuYK5crwzYgRw6BBQWgoUFBj+LS9nIOQKtkZ6JOjB0Pnz59GxY0fMmzfP5s//85//4KWXXsK8efOwY8cONG/eHAMHDsTZs2cDXFMiIhcwV45vGAfAjxxp+JddY86xNdJjklq1XqPRYOXKlRg2bBgAQ6tQUlIS8vLy8MgjjwAAampqkJCQgOeeew7jx493ab9ctZ6IAqaszPAQcqa0lLPdyHeMrZH1H+nGsVYy6WIUdQKrHvsKv528hDv/2xN/1P7BVevLy8tx4sQJDBo0yLRNq9WiX79+2LJli93X1dTUoLq62uKLiCggmCuHAk3mrZHnT57Hv3qXQaMBQkI1GJbfE+Pe6ov3Jm0LWB0kHQydOHECAJCQkGCxPSEhwfQzW/Lz8xEdHW360ul0fq0nEZEJc+VQoMkwc/cPn/yM62N2Q6MBGiU0RP7WLKsyAx+4KmD1kXQwZKSpN8VSCGG1zdzMmTNRVVVl+qqoqPB3FYmIDJgrhwJNBq2Rok6gcPIWNNFUQ6MB2g5phS9+62JVbsyVm3F8zy8QAkjuHrg/GCQdDDVv3hwArFqBTp48adVaZE6r1aJJkyYWX0REAcFcORRoEm2NrDpShbzOG0zdX6Pm9cZZWD+PX83ZgEt/1EII4N2f+yKxk/3nu79IOhhKS0tD8+bNUVJSYtr2559/YsOGDejdu3cQa0ZE5ABz5VAgSag1cu+y/ejd+BtoNEDTltGYu7efVZnOUT9g6xvfQghDD96k5f0QFhnc1cGCvjbZuXPncPDgQdP35eXl2Lt3L2JiYpCSkoK8vDzMmTMHrVu3RuvWrTFnzhw0aNAAo0aNCmKtiYic4GKx6qLXB+9aG1sjc3IMgY/5QGo/t0bWXqzFfVeX4e2K6//akm6z3Pg2G/H0ynaIS48F0Mb+Duufx44dfV5nm0SQlZaWCgBWX7m5uUIIIerq6sQTTzwhmjdvLrRarbjuuuvEN99849Z7VFVVCQCiqqrKD0dARESqVlQkRHKysaHD8JWcbNge7HrodD6vx4+rf7Z4C1tf4agRb43dKPSX9F7VvyopKSDPb0nlGfIX5hkiooAKZisB2eeP6yK1/D5++uz9s3sZXtiZ5bBMn8ZfY94bWnT6u+3WIYfsnMdqANGA35/fDIaIiHypuNiQ88V8qnNysqEbg2OFgscf10WvN2R4tjetXaMxvEd5ueyC4VPfn0J8u2YulT289ThSeiV5/mYOzmOggiFJD6AmIpIVrkkmTf66LjLM7+PIpA6GxIcaDRwGQkOafwV9rTD1ZXkVCAHOz2MABH0ANRGRIjjLAqzRGLIADx0qu1YCl0mxe9Cf18XVvD1ffOHaOQnC+bOcgJZls0wI9Hin26sYPS3BrF72c/25TQLZ2NkyRETkCwprJXCbVBcJ9ed1cTVvz9NPOz8nATp/Bz8/ZGr9cZC7GABQ+vJeiKJi6JNTMXrnVP/VSwLZ2BkMERH5ggyyAPuNlLsH/XldnOX3scXWOfHz+bsxbocp+Gk9MNVh2Tr95e6vrJT/Bea6enIefYzBEBGRL0g0C7DfSX2RUH9eF0fZxu2pf078dP7MW3/Wnelut1yPht9ZTIrXhPx1HIG8rp6cRx9jMERE5AsSygIcUFLvHvT3dbGXbdwR83Pio/O34ZU9Lnd/fbPyoCn42X6une1Cgb6u9s6jO+fVCxxATUTkC0HMAhxUUu8e9Oa6uDqguX628e+/N4wTcsadc2KjrGXQ09nhyy0beFxYDT4Y19VW1vaOHYGYGN+9hx1sGSIi8hU1rkkmh+5BT66LuwOaQ0OBrCxg5EhgwADX6pWY6Nb5q6utc7n152/Nv7Lo/nKLXg/88otrZePjgbIyoLDQ8K+33Wbm5zErK2B/PDDpIhGRr0lxirm/GBPmHTtm+6krpcSDrl4Xb7NKu3NOAIdlBYAK6JCGctTB8fkr33QUqX2THZZxylZySls0GkOLTVSUXxOMBur5zWCIiIi8YwweANvdUHJqFfNVVml3zomdsnUwlM3BCqyE7fPn0ye4vSCwvvrdjfV/Bvjsmgfq+c1uMiIi8o6Sugd9NXDYjXNysf/NGCFWoEJYlj2KZKtA6P52mzzv/nLE0eyx+lq0AGJjbf9MCjMIPcAB1EREciaVLjlbg1/l2D3oy4HDDs7JszeWYua67L8KRgIYgY8wFJnYhERUohKJ2IRM1CEUv/7vd1yR1vSvsn6ajejqkhgvvwx06ABcf739MuYBY1aWz6roTwyGiIjkSmqLwhoHv8qZrweEm50Ty0HP2VZF6xCKDX8tiWHZQNPUtffyhqtBYEICcPKkb/cpAewmIyKSIylnfZYzH+YlOvH1SZdnfz1/ywb/dH+5yp0gUA4zCN3EAdRERHLjq0G+ZJsXA8KvbfwNtp3r4NLbXKyqgbaJ1pua+o4PZ8D58vPHAdRERGSbq4N8X31VVoNYJcPNAeHmrT/OAiHz1h/JBEKA4yUx6iendKesTDAYIiKSG1fHYkydKo2V4+VoxAjg0CGgtBQoKDD8W14OjBiBfct+dLn7a+6tQe7+coc7QaCSZhCC3WRERPJTVmbIiOwKOeb6kSB31g/VX6pDSJiM2xrcmaHo59mMTLroQwyGiEhRnI3vqI9jiDziTgCk/CdpcHDMEBER2eZozIYtwV45XiYKJ33pcvfXB1O3yqf7i5xiniEiIjkyjtlwZR0pIxnlfQkUy6Cnj8OylkHPtX6oDQULW4aIiOTKOMj35ZddKy+jvC/+IuqEy60/ANj6oxIMhoiI5Cw0FJg82WeJApVoRNI2U/ATEuo4Alr7zC4GQCrEbjIiIrkzjiHKybFeUVymeV/scnH2kmVc2MvhLi2Dnq6+qCXJDFuGiIiUQGF5X2wqLjbMosvOBkaNMvz7Vx6lC6cvsPuLPMap9UREgRKIFealsoq9rxmXyKj3yKqDIfLJwQqshP2Ab8e7P6DbmDZ+rSL5HvMM+RCDISIPKO2hGuzjkdoK83LiZC22OmhwFMlIQznqcPmaKv/ppnzMM0REweOgO0KWgn08XGHeYxXbjyMrzPFabCEQSEEFMrHJ8+4vvd6Q2buw0PAv13RTFQZDRGRJaQ/uYB+PXm9oEbL1dDZuy8vjw9eM+diflF5JSIRr+ZHKCjzMoxTsYJmCjsEQEV2mtAe3FI7H1RXmfZUdWqYtHI4GP1fCxfxInuRRCnawTJLAYIiILgv0g9vfpHA8rmZ99kV2aBm1cGz8716XZ38dCrnSP3mUpBAskyQwGCKiywL54A4EKRyPq60V3maHlkELh3nw029KJ4dlL1bVmMb+HNKn2F+LzZs8SlIIlkkSGAwR0WWBenAHihSOJzPT/9mhJdzC4WrrTwj0EKVlEAWFEKVl0DaslxPYH3mUpBAskyQwGCKiywLx4A6k3r2BuDj7Pw/E8ThaYd5X2aEl1MIxd8QGlwOg4UnbDK0/RcXQJ6c6794zrsVWWgoUFBj+LS/3PDWBFIJlqZLp2DNPMRgiossC8eAOlOJioFUr4PRp2z8P5PH4Ozt0kFs4zIOfvJX9HJat0wtT91fxsV7ud++FhgJZWcDIkYZ/vbl2Sgv+fUVGY898hcEQEVlSwrIO9h6w5gJ9PL5u1TAXhBYOT5e+0ISYvSDY3XtKCv59RQZjz/yBGaiJyLZgZ2z2lJNsxQCAZs0MP4+ICFi1/EqvBxISgDNnbP9cozEEf+XlHl/DO1K2YFlFb5fKPjWgDI99nuW8YFmZodXBmdJSQyuQrxk/4x99BCxdCpw6dflnOp0hEJJD8O8rzu4dH3yO3BWo57fkV62vra3FrFmzsHTpUpw4cQKJiYkYO3YsHnvsMYSEsGGLyG+M3RFy42z8DGB46G3ZIs/js+Wjj+wHQoChlcWDFg7LxhLLQCgEemRiExJRiUokoqzWPFjOcu0Ngtm9Z2t5lLg4YPRoYOhQ+QT/vuTO2DOl3Dt/kXww9Nxzz2HhwoV455130K5dO+zcuRPjxo1DdHQ0pkyZEuzqEZHUqG2GkLGryZHYWMMD3olLFy4homG4wzIh0ONfeAZ5mItY/Hr5B6kerLMWrAHMdhZ9xZkzhmNQYyAEqO/eMSP5ppWtW7di6NChGDx4MFJTU5GTk4NBgwZh586dwa4aEUmR2mYIudISduaM3ZlkWk2NaeyPs0Box23/gT42AU/hCctACPBsTEkwBjAHe5ySlKnt3jEj+WCob9+++OKLL/DTTz8BAPbt24fNmzfj5ptvDnLNiEiS1DZDyIO/5s0HP/8JrcOXmQY/FxWj2/JH7HfHeRJIeDOA2dOp3xJKQyA5art3zEg+GHrkkUcwcuRIZGRkIDw8HJ07d0ZeXh5Gjhxp9zU1NTWorq62+CIilVDbDCEX/0rPGpXo0ewvAK51xRlf6G4g4cnsRW+mfqu4K8gptd07ZiQfDC1btgzvvfceCgoKsHv3brzzzjt44YUX8M4779h9TX5+PqKjo01fOp0ugDUmoqBTQnoAVzn5a74OGhyBDptg/6/5bz/62ToAMudKV5w5dwMJd9IOeDv1W8VdQS5R071jRvJT63U6HWbMmIGJEyeatj399NN477338OOPP9p8TU1NDWpqakzfV1dXQ6fTcWo9kdrINT2Au/4KEOoEEILLv9LrYAiQcrACK2H5EHPrN39hoaEFxlX+nArv7dRv4z6OHbN9EoIwfVySJHLvcGr9Xy5cuGA1hT40NBR1dXV2X6PVaqHVOu4HJyIVkGt6ABdtfHUf+j3YEcAIDMcKzMUU6HA5UDiKZOThFVMg5PGfvu60kvhzTIkvpn4bu4JycgyBj/lJUXhXkFsUfu/UJ/lgaMiQIXjmmWeQkpKCdu3aYc+ePXjppZdw9913B7tqREQBZ9kb1tH0v5UYgY8w1CL3T/FPHVDcOtb7NzV2xdlrTTGvnD8DCU/H+9Rv5Rg61NDlUz/PUHKy+hItEgAZBEOvvvoqHn/8cUyYMAEnT55EUlISxo8fj3//+9/BrhoRUUC4MugZAOoQijKR5fsKOGpNMYqNBV5/3b+BhCfjfWwlV0z+KyfSoUOS6Aqi4JP8mCFf4HIcRBQ0Hoy9ePFvGzD9Y9sLntbP/LwJmdALNx/gno4HsRVYxMQYtj36qP8DCXfH+9hLrmiMLhU8IFgpAvX8ZjBEROQvjlol6j2EXWn9GY5iq3FB9vbnizrZFOyBtcYAB7A93scY4EhwnS1yH4MhH2IwRBRAwX5YSoULrRKaW11vlRBFPmjlUEpLia2Arv7CqsFeBJZ8IlDPb7fyDFVUVPirHkSkBN4kw1MSJ0s+1AngyK15CIH9rMn9ovdezv1T64MlJJS0DIUreYmYXJHc4FYwlJGRgccffxznz5/3V32IpM3TJQDUwNtkeEriZAp4CARSUIFMWGZqNk98WPZ7J5f351LmZ6UtQ2Gc+j1ypOHf+q2PTK5IbnArGCopKcFnn32G1q1b4+233/ZXnYikydVWDzUGTEpqdfBSXW0dRma71tqQiErHmZ+NfNHKobaWEhWvs0XucysY6t27N7Zv345nn30W//73v9G5c2eUlZX5qWpEEuJqq4dau4mU1urgJvOFT0PDQ1AJ11obCkt93HrhqJyr+4iPd62c1Kl4nS1yn0drk91111346aefMGTIEAwePBjDhw/HwYMHfV03khMlt4a42uqxYoV6u4nU1uoAywCovk3IRAWSTcth2HyxO60SvmjlcLYPo9xc5XxWVbrOFrnP44VahRAYNGgQ7rvvPqxatQrt27fHQw89hLNnz/qyfiQHSm8NcbXVY8IE9XYTqWB8xpkDvzoMgMzVIRS6orkI0cA3rRK+aOVwtA9zx48rK3h3ZxFYUi/hhtdee03cfffdokOHDiIsLExotVrRvXt3MXnyZPHWW2+J/v37i+TkZLFjxw53dut3VVVVAoCoqqoKdlWUp6hICI3GfNiD4UujMXwVFQW7ht4rKLA+Pk+/SkuDfTT+UVsrRHKy7c+C8fOg0xnKyYg7l3bF9K3WOygqMpwX84I6nef3ha39JSe7tz9b+1DI9SLlCdTz263lOJ555hn06tULubm56NWrF7p162axIOrdd9+NOXPmYOzYsfj22299HLaR5DjrPtJoDK0hQ4fKu1/el60ZCuomsqCgxS/rN5rYyvhcB8NxWH70e1nvbMQIw+ffl3mX6t9v7qaKGzECiI4Grr/e8Xs4W/AUkFdOKTnVlQLP19HViRMnREhIiK936xW2DPlJaak6WkNcafVo1kwd58IZX7eEBMCOd76ze7mGo0gcgZctMb7iy1ZYV1s7Cwoc18fbVqpAkVNdyUKgnt8ejxmyJz4+HuvXr/f1bkmK1DJo1pXxGvPncxovIJvxGeZjf7rntrVZZjiKsQI5SIYEBsT7OnWBt2O85JRTSk51paDhchzkObWlu3e2BICrayZRULi68jsAQ8ZnKa1r5et7zZt1u+S05pec6ko2SXI5DiILaktq5qzVg9N4JWXBHRtcnv0FWPYPSS5vkq9bYUNDDZmbHbE3xktq58YROdWVgsqtAdREFhQ0aNZlxiUA7PHHgFlymWXQ089h2fOnLqBBXAPbP5RaF7CvUxcUFwMvvGD/59On2w/epXZufFEHKdSVgootQ+QdtoZYc7ZmEvmUp60/dgMhQHp5k3zZCuto/JFxX++/b3/8kdTOjS/qIIW6UlBxzBD5BqetUoAMaf4VPvmlh8vlPfoNZxxrcuyY7R0EY6yJr8akeTv+SIrnxh451ZVs4pghkhe2hpAfmbf+OAuE6vTCcvyPJ6S4rpWvWmG97Tpy9dwAwV+iR4rXkSSJwZDUKHmNLyI3eNr9pQlxY9qYI1LsAvZF6gJfdB05OzeAdJbokeJ1JMlhN5mU2Jq6nZxs+MuGNywpnDtT32+I3Ym1p7v5rzLmlNYF7MuuI1vn5qOPDN159fcd7BQTSruOKhGo5zeDIakwjgeQ2i8QIj9yK/eP4n9TBYheDzzzDPDEE9Y/8/b3DfP6kI9xzJCa+Dq7LJFE/fHrH57n/iHvFRcbghVbgRDgfdcR8/qQTDEY8jdXxgDxFwgp0V+f/ZGaQmRpyhCq0aNBbJTDl7yas4EBkL/YW5bCaPZs75dOYV4fkikGQ/5k/CvM2SBC/gIhhRmhKUZFWCqQnY1CjEIZsnEIqRgO6wG05sHPpOWOEyWSh1zJLfTmm96/D/P6kEwxGPIXdxYH5C8QkrnyjRWmrq8RGsMCpy3qLXDaAsewAjkYjmK2/gRaoFqf1bZEDykGgyF/cHcMEH+BkAyZj/25sp8OABACPeZiCgBh9cslBAIhGqBYl+f/8W9MUWEpUK3PzOtDMsVgyB/c/SuMv0BIJpwNfs7EJuhw1P4vlkCMf3O1e1pNAtn6zLw+JEMMhvzBk7/C+AuEJOjTWTvcmv1VVhDk8W/udE+rSaBbn32RHJIogLhqvT94+lcYVzwnCbB8XnZ3WPbUD6cRlxF3eUNZEMe/Oeue1mgM3dNDh6rvnjK2PufkGM6DrbXNfN36bFyih0gGmHTRH7g4IMmMz5IfBvOz7+0CpGpgK8u9TmcIhNhqQxLEpItypsQxQByQqiiPZ5b5J/lhMD/7TFHhHLuviGxiN5m/GMcA2VprTG5/hXHNNEWwjE2yHJatrdEjNMLDgMVXn31315JiigrXsPuKyAq7yfxN7osDcs00WQvq2l/efPY9CcDZPU2kOFyo1YdksVCrFClp0UW5B6Uu6trwB+y+0Malsl2ivseuC239XCMPeBOAG18L2B4kzOCdSFY4ZoiCTylrpik874z52B9ngZD52B9JBkLeLlrMFBVE5AGOGZIqKbRkKGFAqr1WBmPeGRk+IPV/6hGmdf2zIKu2X3cCcHvjXpiigojcxJYhKZJKS4bcB6R628ogIeatP84Cocf7lsp37S9fBeDGQcIjRxr+ZSBERA4wGJIaKWXQlfuaaTLv5vN06vuTm1zItSNVcg/AiUiWGAxJidRaMuSeL0lm3XxnDv7mu9w/cs0LJbUAXK7nkYjcIotg6NixYxg9ejRiY2PRoEEDdOrUCbt27Qp2tXxPii0Zch6QKoNWBvPgJ671FQ7Lrp71lWvdX1LpZvWElAJwOZ9HInKL5AdQ//bbb+jTpw+ys7OxZs0axMfH4+eff0bTpk2DXTXfk2pLhlwHpBpbGZzlnQlwN5/nuX96OH+BEgaM20vaGBcH3HknEBNjaKHx5+dPCeeRiFwm+TxDM2bMwJdffolNXrSGyCbPENdW8j0J5J3Zs+wndLnjapfLe3xHKikvFHB5RuVHHwFLlwKnTl3+mT8zoCvtPBLJGPMM/WXVqlXo1q0bbrvtNsTHx6Nz58544403HL6mpqYG1dXVFl+yILXxEkoQpG4+8+4vZ4FQ+cYK38z+kmI3qzdCQ4FffzUEPeaBEODfCQVKO49E5JTkg6H//e9/eO2119C6dWusW7cO999/Px588EG8++67dl+Tn5+P6Oho05dOpwtgjb0Q7PESSh0sGqDFKT0d/Jya6aPPp1S7WT0VrAkFSjuPROSU5IOhuro6dOnSBXPmzEHnzp0xfvx43HvvvXjttdfsvmbmzJmoqqoyfVVUVASwxl4K1oBlpQ8W9UPemaWTtrocAIVAD1FaBlFQCFFa5p9AUwYDxt0SrBYapZ1HInJK8gOoExMT0bat5bIBbdq0QVFRkd3XaLVaaLVaf1fNfwI9YJmDRV1mGfRc67DsH79dRGTTyMuLjmY7WXTU26zjUhww7s0xBauFRornkYj8SvItQ3369MH+/fsttv30009o2bJlkGoUIIHKoCu13EYS5Gn3lykQciWJpi9a5oLdzVqft8cUrBYaqZ1HIvI/IXFfffWVCAsLE88884w4cOCAWLp0qWjQoIF47733XN5HVVWVACCqqqr8WFOZKi01f37b/yotDXZNA+bhXmUunRJAiKvDf7a/o9paIZKT7b9YoxFCpxPigw8M/7f1c41GiKIi9w6gqMj6fXU69/fjjaIi74/JeP5s7cf8/NXW+u8Ygn0eiVQuUM9vyU+tB4BPPvkEM2fOxIEDB5CWloZp06bh3nvvdfn1splaHwxTpxr+ynWmoMDQSqVQnuf+ccDVVAlxccDp07Z/5uk07mAu9OvLqenBTo0ghQWTPSHXehPVE6jntyyCIW8xGLKjuBi49VbXyiost5GoEwgJdT0C8uguKSw0dA/5gpzOv6/zZRnHXJkHVzqdIYjnWDZrts6XP/MyEfkR8wyRfxnHCrlCIbmNujX6wTT2x1kg9GhvH6z87suxLHKaxu3rgc8BSo2gCFJa6JlIRiQ/m4z8xNm0ZXMyHixq2f3VxmFZy6DHg5Xf63dN9O7tfFZSXJx1QkFb5DSN2x8Dn40TCsg+Z5MhNBrDZIihQ2V7PxP5C1uG/E2qiQxd/av81luBmhpp1d2Bi1U17s3+qr2c/8erY7Q1c6pVq8vjrOzNSnr1VecPptBQQ2AVSN58bplJPTiYOZvIYwyG/EnKiQxd/au8qEh6da/HPPiJauo4v9SHM7Zd7v4q8tH1cdQ18cILwPTp9pNoJiQ4DzT0emDLFvfq5A1vP7ecmh4czJxN5Dm/zlWTiKBMrffF1GJ/cjZt2d5UZinUXbheZbufcF9dH1enz9fUGNITFBQY/jVOBy8ocO0gCgp8cNZc4MvPLaemBxbTZJACcWq9DwV8NplcVr22N23ZkSDV/fjek2jROd7l8g4Px5fXx9uZU76eeeUNf3xuOcU7cIzXz1nm7GD/3iFyA2eTyZmU+u4djf2wtw6aIwGsu3n3l7NA6EDJIddnf/ny+njbNSGl8TX++NwGKpM6sXuSyAsMhvxBKn33roz9qD9t+bHHXNu3n+ru6dIXV12f6vqb+PL6eDtzytMHmD8G5kvlc0ueC9ZCz0Qyx2DIH6Sw6rU7+UbM/3ofMMC1/fuo7tve+s7jAMhjvrw+vmjZcfcB5q+B+VL43JL3mJeJyG3qGjP0669osm+f/8cvBLvv3tnYDwCIjQWWLbPuughA3d1Z+qL6aDUat/BxP7Gvj9FXS0a4Mr7G+F716+2L5SmC/bklIqonYGN+/To8WyJMo9GTkixnVSQn+29mi3FWTv2ZOYGYkeXqrBJ758APdfd69pev+foYAzFzytWZa94sXBrMzy0RUT2Bmk2mrm6y48ctv/dnivpg9t27M6bD1jnwQd0X373Z5e6vjpE/+qb7yx2+vj6B6JoIxMB8jjkhIhVSVzcZAKtGtkB0WQV6arGr07WN7J0DN+vuTveX/lIdQsIkEIvLaeq3qwu/FhRcznztKTmdFyJSrEB1k3FtMvO/pv2RxyUYayoZB/XaG/tRn71z4ELd3QmALKsigUAIkNeaV64OXP7lF0Mw403wIqfzQkTkJYk8kSRASdOFHU3XdsSFczCl80aXu7+mdPTByu90mbOZa0ZTp0p26RQiIiliMGSktOnCniRUtHMOzIOf/+69zuEuzIOfV/Z6sPI72edOkOvP8XBERArDYEjJK2gbB/V+/jkQE2O/XL1zUFdbF/jcP+QaV4Nc48XIy/NNQkYiIgVTdzCkhhT1oaGGRIpvvGE7uvnr+6d+nwxNWCg0GiA03PHHYtmDX0o/APJHhmapMAa5L7/suFwgl30hIpIxdQVDSUmW36tpurCdFoUjIhkjxAr8++w/Hb7cPPi5fW4ff9bUe/7K0CwloaFAQoJrZZU0Ho6IyA/UNZvs228BZxmo5Tql2IV6n712EJoePYRMbEIiKlGJRGxCJupg+/gk2+rjiL0MzcYxNEoKfrl8BhGRT6grz5CzPAXFxcCUKZaJ7ZKTDYNWpfwAdVDvln/viSO1rg2i/u7DA2g7tLWfKhkAzpYhUdpyElw+g4gULlB5htTVTeaIOwubSomdetcdPYa6W3PQtXa7w5ebd3/JOhACApOhWUo8XfGeiIgsMBgCDH9hT5li+69rKc/K+avethr3QmDY9gryEAK92Xa948HPch547OrYGCWNoeHyGUREXlPXmCF73GlRqJ+VN0hjjJpozqILdqEMR2Fv9nsIBFJQgarCNWh0xy1/bXVQN1vdbc2aAQsWXF6VXcrUOoZmxAhg6FB5jnUjIpIABkOA5y0KAR5jZNkT0hiJcK3ejcRZ54XsDTw+dQq47Tbgn/8E/vMfl+saFM6WITGOoVFiTikun0FE5DF2kwGetSgEYIzRj2vKHSY/rISPWkIcdRMaPf88sHy5a+8XLBxDQ0REHuBsMsD9WTl+nLWkCzuOo/ok5wUBXDpXg7CMq7yfTeTqKvfNmhlax6QeTNhqsdPpDIEQx9AQEckGZ5P5g73Bwe62KPh41pJ564+zQMh88HNYQ61vWkJc7SY8dUoeM7GMGZpLS4GCAsO/5eUMhIiIyCZ1BUPt29vPSuzOrBwvZy1tef0bl9f+Kpy42fHsL1/MJnJnQLFcZmIZx9CMHGn41x+tWXKeeUdERCbqGkB9/Ljl9/WzEg8dCkRHGx5sgOEhautB6sEYI8ugp4PDl1kEPfprDfVxNEvI29lEmZmGLrBTp5yXVdpMLE/JNUEnERFZUdeYIQBWPY7GcTUvvghMm+baw82FMUYiORlhFeV2l7qoz+ZVCOQDd8UKw6wxR3Q6ZjMG7M+8M0a8zO9DROQTHDMUKMbxPbff7vrMMAdjjOoA1AmBf1f8w+HbfrX4W8fdX4HOiJ2TY5g+b49Gw5lYgHwTdBIRkV1sGXLG0Yys4mJU3joRiThh86UVSMYUzMVKGFoJXD7TwVxja/lyYMIE4PTpy9s4E+syV2felZYy7w8RkZfYMiQV9WaG6f/UXx78fOsIJOMoHsdsCAD1Y50WOIpiTQ5EUbF7K8AHc42t224DTpzgTCx71LjkBxGRwqlrALUXRmZX4n0AtpazuA9vQMA6sjR9n5dnGODsaitOsB+4zGZsn1qX/CAiUjB1tww5m9duxl6250xsgg5H7Z9IT1px+MCVLuOSH/Y+OxqNoVtRiUt+EBEplLqCoaR6CQ2Tkw1jZJKTIew83OqgwRHosAmXH25zh5eZBj6XFfihFYcPXOnikh9ERIqjrm6yb78F9u0z5eI5UJOCq2+8EsMRghXIgYAGIWYjf+r+Wg8+D6/g3Jk/ERUT9ddPsi7v0x+tOMYHbk6O4QFrPuCID9zgMya6tJX2gAPNiYhkR3YtQ/n5+dBoNMjLy3P/xaGh+PL7K6AZNRKa7CxcfeOVAICVGIEcrMAxWGZxDtElI6RoBYrFCLNAqB5/teL4IrM0+Y+jJT+YmZqISFZk1TK0Y8cOvP7667jmmms8ev3P721B34k32vzZSozAs590ABoes87irNfbz+7sz1YcbzNLq4Wj6+NPtgaaMzM1EZH8CJk4e/asaN26tSgpKRH9+vUTU6ZMcfm1VVVVAoDYX7RZmC91OiR+m9Bf0jt+cVGREMnJwuKFycmG7c7K6XTW5ci3XL0+gaqLRmNZF8CwTaPhZ4GIyE3G53dVVZVf30c2SRdzc3MRExODl19+GVlZWejUqRNeeeUVm2VrampQU1Nj+r66uho6nc79pE3uLrsQrBYKtZLSshjBTJRJRKRQTLpo5v3338fu3buRn5/vUvn8/HxER0ebvnQ6nftv6smyC4FYKd0RNY1VkdqyGMFMlElERF6RfDBUUVGBKVOm4L333kNkZKRLr5k5cyaqqqpMXxUVFe6/sdwebsXFhpaJ7Gxg1CjDv6mpvl/DTCqkdn2CnSiTiIg8JvkB1Lt27cLJkyfRtWtX0za9Xo+NGzdi3rx5qKmpQWi9FhitVgutVuvdG8vp4Wavu+joUeDWWy9nwFZSt53Urs+BA66VY6JMIiLJkXzL0IABA/DNN99g7969pq9u3brhzjvvxN69e60CIZ+RSxZoR91FRq+8oryWIildH70eeOMN5+WSk5kok4hIgiTfMtS4cWO0b9/eYlvDhg0RGxtrtd2njPmDjh2zHWgYB8QG++HmrLvI3LFjhhYkJeQpktL1cfUa3HuvclrmiIgURPItQ0Ejl2UX3OkGCsbAYn+R0vVx9Rq0bu3fehARkUdkGQyVlZXZnVbvU3LIAu1uN5DUBn57QyrXR0pddkRE5DbZ5Bnyhtd5CqScP8iY38Zed5E9BQWGFABKEOzr4+waMMcQEZFHApVnSPJjhiTB1rILUuFoORBHlNRKEezrw4V1iYhkTZbdZFSPve4iWzxdOJYck0qXHRERuY3dZEpi7C766CNDS4S9Vgo+nP0n2F12REQKEqjnN4MhpbK1erpOZwiSHAVCxiU9ysoM32dlBWdpESIiUj0GQz6kymAIcL+VorgYuO8+4MwZy+2xscDrr7M1iYiIAooDqMl77gwsLi42LN1hy5kzhp8VFTEgIiIixeEAajK0ID34oPNyU6bIP1kjERFRPQyGyNCVduyY83JHjyojWSMREZEZBkPk3pIegVoFnoiIKEAYDJF7CRiVlKyRiIgIDIYIMMwycyVhY6BWgSciIgogBkNkmHX23/86Lzd3LvMNERGR4jAYIoMRIwxT52NjrX8WG8tp9UREpFjMM0SXjRgBDB3KDNRERKQqDIbIUmgoMGCA4YuIiEgF2E1GREREqsZgiIiIiFSNwRARERGpGscMubuyOxE/M0REiqLuYKi42LD46NGjl7clJxvy6XAaOdnCzwwRkeKot5usuBjIybF8qAGGBUtzcgw/JzLHzwwRkSJphBAi2JXwt+rqakRHR6OqqgpNmjQxdHOkplo/1Iw0GsNf++Xl7P4gA35miIgCzur57SfqbBnatMn+Qw0AhAAqKgzliAB+ZoiIFEydwVBlpW/LkfLxM0NEpFjqDIYSE31bjpSPnxkiIsVSZzCUmWkY36HR2P65RgPodIZyRAA/M0RECqbOYCg01DAVGrB+uBm/f+UVDoSly/iZISJSLHUGQ4AhJ8yKFUCLFpbbk5MN25kzhurjZ4aISJHUObXeHLMJk7v4mSEiCohATa1XdwZqwPAQy8oKdi1ITviZISJSFPV2kxERERGBwRARERGpHIMhIiIiUjUGQ0RERKRqDIaIiIhI1RgMERERkaoxGCIiIiJVk3wwlJ+fj+7du6Nx48aIj4/HsGHDsH///mBXi4iIiBRC8sHQhg0bMHHiRGzbtg0lJSWora3FoEGDcP78+WBXjYiIiBRAdstxnDp1CvHx8diwYQOuu+46l14TqHTeRERE5DtcjsOOqqoqAEBMTIzdMjU1NaipqTF9X11d7fd6ERERkTxJvpvMnBAC06ZNQ9++fdG+fXu75fLz8xEdHW360ul0AawlERERyYmsuskmTpyITz/9FJs3b0ZycrLdcrZahnQ6HbvJiIiIZITdZPVMnjwZq1atwsaNGx0GQgCg1Wqh1WoDVDMiIiKSM8kHQ0IITJ48GStXrkRZWRnS0tKCXSUiIiJSEMkHQxMnTkRBQQE++ugjNG7cGCdOnAAAREdHIyoqKsi1IyIiIrmT/JghjUZjc/vbb7+NsWPHurQPTq0nIiKSH44Z+ovEYzUiIiKSOVlNrSciIiLyNQZDREREpGoMhoiIiEjVGAwRERGRqjEYIiIiIlVjMERERESqxmCIiIiIVI3BEBEREakagyEiIiJSNQZDREREpGoMhoiIiEjVGAwRERGRqjEYIiIiIlVjMERERESqxmCIiIiIVI3BEBEREakagyEiIiJSNQZDREREpGoMhoiIiEjVGAwRERGRqjEYIiIiIlVjMERERESqxmCIiIiIVI3BEBEREakagyEiIiJSNQZDREREpGoMhoiIiEjVGAwRERGRqjEYIiIiIlVjMERERESqxmCIiIiIVI3BEBEREakagyEiIiJSNQZDREREpGoMhoiIiEjVGAwRERGRqjEYIiIiIlVjMERERESqJptgaMGCBUhLS0NkZCS6du2KTZs2BbtKREREpACyCIaWLVuGvLw8PProo9izZw8yMzNx00034ciRI8GuGhEREcmcRgghgl0JZ3r27IkuXbrgtddeM21r06YNhg0bhvz8fKevr66uRnR0NKqqqtCkSRN/VpWIiIh8JFDPb8m3DP3555/YtWsXBg0aZLF90KBB2LJlS5BqRUREREoRFuwKOHP69Gno9XokJCRYbE9ISMCJEydsvqampgY1NTWm76uqqgAYIkwiIiKSB+Nz29+dWJIPhow0Go3F90IIq21G+fn5mD17ttV2nU7nl7oRERGR/5w5cwbR0dF+27/kg6G4uDiEhoZatQKdPHnSqrXIaObMmZg2bZrp+99//x0tW7bEkSNH/Hoypaa6uho6nQ4VFRWqGivF4+ZxqwGPm8etBlVVVUhJSUFMTIxf30fywVBERAS6du2KkpISDB8+3LS9pKQEQ4cOtfkarVYLrVZrtT06OlpVHyKjJk2a8LhVhMetLjxudVHrcYeE+HeIs+SDIQCYNm0axowZg27duuHaa6/F66+/jiNHjuD+++8PdtWIiIhI5mQRDP3973/HmTNn8OSTT6KyshLt27fH6tWr0bJly2BXjYiIiGROFsEQAEyYMAETJkzw6LVarRZPPPGEza4zJeNx87jVgMfN41YDHrd/j1sWSReJiIiI/EXySReJiIiI/InBEBEREakagyEiIiJSNQZDREREpGqyDIYWLFiAtLQ0REZGomvXrti0aZPD8hs2bEDXrl0RGRmJK6+8EgsXLrQqU1RUhLZt20Kr1aJt27ZYuXKlv6rvMXeOu7i4GAMHDkSzZs3QpEkTXHvttVi3bp1FmSVLlkCj0Vh9Xbx40d+H4hZ3jrusrMzmMf34448W5ZR2vceOHWvzuNu1a2cqI4frvXHjRgwZMgRJSUnQaDT48MMPnb5GCfe3u8etlPvb3eNWyv3t7nEr5f7Oz89H9+7d0bhxY8THx2PYsGHYv3+/09cF4h6XXTC0bNky5OXl4dFHH8WePXuQmZmJm266CUeOHLFZvry8HDfffDMyMzOxZ88e/Otf/8KDDz6IoqIiU5mtW7fi73//O8aMGYN9+/ZhzJgxuP3227F9+/ZAHZZT7h73xo0bMXDgQKxevRq7du1CdnY2hgwZgj179liUa9KkCSorKy2+IiMjA3FILnH3uI32799vcUytW7c2/UyJ13vu3LkWx1tRUYGYmBjcdtttFuWkfr3Pnz+Pjh07Yt68eS6VV8r97e5xK+X+dve4jeR+f7t73Eq5vzds2ICJEydi27ZtKCkpQW1tLQYNGoTz58/bfU3A7nEhMz169BD333+/xbaMjAwxY8YMm+UffvhhkZGRYbFt/PjxolevXqbvb7/9dnHjjTdalLnhhhvEHXfc4aNae8/d47albdu2Yvbs2abv3377bREdHe2rKvqFu8ddWloqAIjffvvN7j7VcL1XrlwpNBqNOHTokGmbHK63OQBi5cqVDsso5f4258px2yLH+9ucK8etlPvbnCfXWwn3txBCnDx5UgAQGzZssFsmUPe4rFqG/vzzT+zatQuDBg2y2D5o0CBs2bLF5mu2bt1qVf6GG27Azp07cenSJYdl7O0z0Dw57vrq6upw9uxZq8Xuzp07h5YtWyI5ORm33HKL1V+WweTNcXfu3BmJiYkYMGAASktLLX6mhuv91ltv4frrr7fK0i7l6+0JJdzfviDH+9sbcr6/fUEp93dVVRUAOFyENVD3uKyCodOnT0Ov11utVp+QkGC1qr3RiRMnbJavra3F6dOnHZaxt89A8+S463vxxRdx/vx53H777aZtGRkZWLJkCVatWoXCwkJERkaiT58+OHDggE/r7ylPjjsxMRGvv/46ioqKUFxcjPT0dAwYMAAbN240lVH69a6srMSaNWvwj3/8w2K71K+3J5Rwf/uCHO9vTyjh/vaWUu5vIQSmTZuGvn37on379nbLBeoel81yHOY0Go3F90IIq23Oytff7u4+g8HTOhYWFmLWrFn46KOPEB8fb9req1cv9OrVy/R9nz590KVLF7z66qv473//67uKe8md405PT0d6errp+2uvvRYVFRV44YUXcN1113m0z2DxtI5LlixB06ZNMWzYMIvtcrne7lLK/e0pud/f7lDS/e0ppdzfkyZNwtdff43Nmzc7LRuIe1xWLUNxcXEIDQ21ivZOnjxpFRUaNW/e3Gb5sLAwxMbGOixjb5+B5slxGy1btgz33HMPPvjgA1x//fUOy4aEhKB79+6S+UvCm+M216tXL4tjUvL1FkJg8eLFGDNmDCIiIhyWldr19oQS7m9vyPn+9hW53d/eUMr9PXnyZKxatQqlpaVITk52WDZQ97isgqGIiAh07doVJSUlFttLSkrQu3dvm6+59tprrcp/9tln6NatG8LDwx2WsbfPQPPkuAHDX4xjx45FQUEBBg8e7PR9hBDYu3cvEhMTva6zL3h63PXt2bPH4piUer0Bw2yNgwcP4p577nH6PlK73p5Qwv3tKbnf374it/vbG3K/v4UQmDRpEoqLi7F+/XqkpaU5fU3A7nGXh1pLxPvvvy/Cw8PFW2+9Jb7//nuRl5cnGjZsaBpVP2PGDDFmzBhT+f/973+iQYMGYurUqeL7778Xb731lggPDxcrVqwwlfnyyy9FaGioePbZZ8UPP/wgnn32WREWFia2bdsW8OOzx93jLigoEGFhYWL+/PmisrLS9PX777+bysyaNUusXbtW/Pzzz2LPnj1i3LhxIiwsTGzfvj3gx2ePu8f98ssvi5UrV4qffvpJfPvtt2LGjBkCgCgqKjKVUeL1Nho9erTo2bOnzX3K4XqfPXtW7NmzR+zZs0cAEC+99JLYs2ePOHz4sBBCufe3u8etlPvb3eNWyv3t7nEbyf3+fuCBB0R0dLQoKyuz+NxeuHDBVCZY97jsgiEhhJg/f75o2bKliIiIEF26dLGYlpebmyv69etnUb6srEx07txZREREiNTUVPHaa69Z7XP58uUiPT1dhIeHi4yMDIubSyrcOe5+/foJAFZfubm5pjJ5eXkiJSVFREREiGbNmolBgwaJLVu2BPCIXOPOcT/33HOiVatWIjIyUlxxxRWib9++4tNPP7Xap9KutxBC/P777yIqKkq8/vrrNvcnh+ttnDpt73Or1Pvb3eNWyv3t7nEr5f725HOuhPvb1jEDEG+//bapTLDucc1fFSQiIiJSJVmNGSIiIiLyNQZDREREpGoMhoiIiEjVGAwRERGRqjEYIiIiIlVjMERERESqxmCIiIiIVI3BEBEREakagyEiIiJSNQZDREREpGoMhohIlgoLCxEZGYljx46Ztv3jH//ANddcg6qqqiDWjIjkhmuTEZEsCSHQqVMnZGZmYt68eZg9ezbefPNNbNu2DS1atAh29YhIRsKCXQEiIk9oNBo888wzyMnJQVJSEubOnYtNmzYxECIit7FliIhkrUuXLvjuu+/w2WefoV+/fsGuDhHJEMcMEZFsrVu3Dj/++CP0ej0SEhKCXR0ikim2DBGRLO3evRtZWVmYP38+3n//fTRo0ADLly8PdrWISIY4ZoiIZOfQoUMYPHgwZsyYgTFjxqBt27bo3r07du3aha5duwa7ekQkM2wZIiJZ+fXXX9GnTx9cd911WLRokWn70KFDUVNTg7Vr1waxdkQkRwyGiIiISNU4gJqIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkav8P16b0NppvCKcAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3993,34 +3993,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "cd18340b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[3.94499279]\n",
-      " [3.03306538]]\n",
-      "Eigenvalues of Hessian Matrix:[0.31248425 4.44418124]\n",
-      "theta from own gd\n",
-      "[[3.94499279]\n",
-      " [3.03306538]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients for OLS\n",
     "from random import random, seed\n",
@@ -4083,87 +4059,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "ac458170",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.]\n",
-      " [3.]]\n",
-      "Eigenvalues of Hessian Matrix:[0.30306759 4.65944342]\n",
-      "0 [-20.62275788] [-25.73690387]\n",
-      "1 [-0.03662823] [0.02925855]\n",
-      "2 [-0.0342458] [0.02735547]\n",
-      "3 [-0.03201832] [0.02557617]\n",
-      "4 [-0.02993573] [0.0239126]\n",
-      "5 [-0.0279886] [0.02235723]\n",
-      "6 [-0.02616812] [0.02090304]\n",
-      "7 [-0.02446604] [0.01954342]\n",
-      "8 [-0.02287468] [0.01827225]\n",
-      "9 [-0.02138683] [0.01708375]\n",
-      "10 [-0.01999575] [0.01597256]\n",
-      "11 [-0.01869515] [0.01493365]\n",
-      "12 [-0.01747915] [0.01396231]\n",
-      "13 [-0.01634224] [0.01305415]\n",
-      "14 [-0.01527928] [0.01220505]\n",
-      "15 [-0.01428546] [0.01141119]\n",
-      "16 [-0.01335628] [0.01066897]\n",
-      "17 [-0.01248754] [0.00997502]\n",
-      "18 [-0.0116753] [0.0093262]\n",
-      "19 [-0.01091589] [0.00871959]\n",
-      "20 [-0.01020588] [0.00815244]\n",
-      "21 [-0.00954206] [0.00762217]\n",
-      "22 [-0.0089214] [0.0071264]\n",
-      "23 [-0.00834112] [0.00666287]\n",
-      "24 [-0.00779859] [0.00622949]\n",
-      "25 [-0.00729134] [0.0058243]\n",
-      "26 [-0.00681708] [0.00544547]\n",
-      "27 [-0.00637367] [0.00509128]\n",
-      "28 [-0.0059591] [0.00476012]\n",
-      "29 [-0.0055715] [0.0044505]\n",
-      "theta from own gd\n",
-      "[[3.98281205]\n",
-      " [3.0137297 ]]\n",
-      "0 [-0.00520911] [0.00416103]\n",
-      "1 [-0.00487029] [0.00389038]\n",
-      "2 [-0.00445186] [0.00355614]\n",
-      "3 [-0.00403677] [0.00322456]\n",
-      "4 [-0.00364967] [0.00291535]\n",
-      "5 [-0.00329616] [0.00263296]\n",
-      "6 [-0.00297571] [0.00237699]\n",
-      "7 [-0.00268602] [0.00214559]\n",
-      "8 [-0.00242441] [0.00193661]\n",
-      "9 [-0.00218823] [0.00174795]\n",
-      "10 [-0.00197505] [0.00157766]\n",
-      "11 [-0.00178263] [0.00142396]\n",
-      "12 [-0.00160895] [0.00128523]\n",
-      "13 [-0.0014522] [0.00116001]\n",
-      "14 [-0.00131072] [0.001047]\n",
-      "15 [-0.00118302] [0.00094499]\n",
-      "16 [-0.00106776] [0.00085292]\n",
-      "17 [-0.00096373] [0.00076983]\n",
-      "18 [-0.00086984] [0.00069482]\n",
-      "19 [-0.00078509] [0.00062713]\n",
-      "20 [-0.0007086] [0.00056603]\n",
-      "21 [-0.00063957] [0.00051088]\n",
-      "22 [-0.00057726] [0.00046111]\n",
-      "23 [-0.00052102] [0.00041619]\n",
-      "24 [-0.00047025] [0.00037564]\n",
-      "25 [-0.00042444] [0.00033904]\n",
-      "26 [-0.00038309] [0.00030601]\n",
-      "27 [-0.00034576] [0.0002762]\n",
-      "28 [-0.00031208] [0.00024929]\n",
-      "29 [-0.00028167] [0.000225]\n",
-      "theta from own gd wth momentum\n",
-      "[[3.99916114]\n",
-      " [3.00067008]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients for OLS\n",
     "from random import random, seed\n",
@@ -4230,29 +4129,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "e6b06cdb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.26281735]\n",
-      " [2.84478178]]\n",
-      "Eigenvalues of Hessian Matrix:[0.33978889 4.51659846]\n",
-      "0 [-14.67490857] [-18.08539155]\n",
-      "1 [-4.46170878e-15] [-1.45781905e-14]\n",
-      "2 [-1.21430643e-15] [-1.24768421e-15]\n",
-      "3 [4.92661467e-16] [4.50610071e-16]\n",
-      "4 [4.92661467e-16] [4.50610071e-16]\n",
-      "beta from own Newton code\n",
-      "[[4.26281735]\n",
-      " [2.84478178]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Newton's method\n",
     "from random import random, seed\n",
@@ -4305,43 +4185,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "a010a209",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[4.0586484]\n",
-      " [3.0718316]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]\n",
-      "theta from own gd\n",
-      "[[4.0586484]\n",
-      " [3.0718316]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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YIiIiAtSzxmx5HLkNBkNERESAevp8VBQgSfr3SxIQHa0+jjwKgyEiIiJAXUdoyRL1/9cOiDS3Fy9mvSEPxGCIiIhIIzUV+PprIDJSd3tUlHo76wx5JBZdJCIiqik1FUhJYQVqGWEwREREVJu3NzBokLNbQQ7CYTIiIiKSNQZDREREJGsMhoiIiEjWnB4Mbdu2DaNGjUJERAQkScLq1au1+yorKzF9+nR07NgRjRo1QkREBB5//HFcuHDBeQ0mIiIij+L0YKi0tBSdO3fG0qVL6+y7desW9u7di1dffRV79+5FdnY2jh8/DoVC4YSWEhERkSeShNC3PK9zSJKEVatWYfTo0QaP2b17N3r16oUzZ86gVatWJp23uLgYQUFBKCoqQmBgoI1aS0RERPbkqO9vt5taX1RUBEmS0LRpU4PHlJeXo7y8XHu7uLjYAS0jIiIid+T0YTJzlJWVYcaMGXjkkUfqjRDnzZuHoKAg7V90dLQDW0lERETuxG2CocrKSjz88MOorq7Ge++9V++xM2fORFFRkfYvPz/fQa0kIiIid+MWw2SVlZUYM2YM8vLysGXLFqPjhv7+/vD393dQ64iIiMiduXwwpAmETpw4gZycHDRv3tzZTSIiIiIP4vRg6ObNmzh58qT2dl5eHn799VcEBwcjIiICaWlp2Lt3L9atWweVSoXCwkIAQHBwMPz8/JzVbCIiIvIQTp9an5ubi+Tk5Drbx48fj9mzZyM2Nlbv/XJycjDIxEX0OLWeiIjI/chmav2gQYNQXzzmQmWQiIiIyAO5zWwyIiIiIntgMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZM3pa5MRERGRhVQqYPt2oKAACA8HkpIAb29nt8rtMBgiIiJyR9nZwHPPAefO3dkWFQUsWQKkpjqvXW6Iw2RERETuJjsbSEvTDYQA4Px59fbsbOe0y00xGCIiInInKpW6R0iIuvs029LT1ceRSRgMERERuZPt2+v2CNUkBJCfrz6OTMJgiIiIyJ0UFNj2OGIwRERE5FbCw217HDEYIiIicitJSepZY5Kkf78kAdHR6uPIJAyGiIiI3Im3t3r6PFA3INLcXryY9YbMwGCIiIjI3aSmAl9/DURG6m6PilJvZ50hs7DoIhGRO2HFYdJITQVSUvh+sAEGQ0RE7oIVh6k2b29g0CBnt8LtcZiMiMgdsOIwkd0wGCIicnWsOExkVwyGiIhcHSsOE9kVgyEiIlfHisNEdsVgiIjI1bHiMJFdMRgiInJ1rDhMZFcMhoiIXB0rDhPZFYMhIiJ3wIrDrk+lAnJzgZUr1f/l7D63waKLRETughWHXRcLYro1SQh9hSs8S3FxMYKCglBUVITAwEBnN4eIiNxZ7SVRrlwBxoypWwdKM4TJnjuLOer7mz1DREREptLXA+TtXX9BzL/+FQgKUi+bwV48l8ScISIiIlMYWhLFWG7Q1avA4MFATAyXTXFRDIaIiIiMqW9JFFNxHTmXxWCIiIjIGGNLopiC68iZrOxGGb59fTee7/WjQx6POUNERETG2Gqpk5rryA0aZJtzeogrx65i/cIjUH7jgw0XElGKngCKHfLYTu8Z2rZtG0aNGoWIiAhIkoTVq1fr7BdCYPbs2YiIiECDBg0waNAgHDp0yDmNJSIiebL1UidcRw4AcOzb3/HPEblICvoNoXFNMeGj/si+0AelaIwo7wt4Is4xPUNOD4ZKS0vRuXNnLF26VO/+BQsW4O2338bSpUuxe/duhIWFYciQISgpKXFwS4mISLaMLYkCmDdTTKbryKkqVPjhvf14uVcu2vvlIW54G7z8zSD8UNwZ1fBG1wZHMGtgLn759AjOVoTj7Z/6OaRdLlVnSJIkrFq1CqNHjwag7hWKiIhAeno6pk+fDgAoLy9HaGgo5s+fj6eeesqk87LOEBERWU0zmwzQn0g9axbQrx/w8MPAtWv6zyFJ6qAqL0820+xvFt7E928fhDK7Cut/j8MV0UK7zxcV+FPz/VAMLsXI5+5Cq766FdZZZwhAXl4eCgsLMXToUO02f39/DBw4EDt27DAYDJWXl6O8vFx7u7jYMWOORETkwTRLotSuM6QxZ4460HniCWDhQvW2mkGTjNaRO7+nAOsWnYByYwNsvtwR5eij3ddMuo4RMYegGO2NYRkdEBjVw4ktVXPpYKiwsBAAEBoaqrM9NDQUZ86cMXi/efPmYc6cOXZtGxERyZBmSZS5c9U9QbWdP68OhF58Ub1GWe3lORYvdlw16tqVsu24dIuoFtj/9XEoPyiAclcI9txKAHBnKLCtzxmkdM6DYlxT9HsqET4B/e3SDku5dDCkIdUaoxVC1NlW08yZM5GRkaG9XVxcjOjoaLu1j4iIZObf/9a/XQh1D9DnnwOnTgE7djhnHTkHrJVWcbMCW5cegDLzJpSH2+Ksqj2A9gAACdXo0/gQFP2uImVqNOKGt4Hk1domj2sPLh0MhYWFAVD3EIXXSDa7dOlSnd6imvz9/eHv72/39hERkQwZqzmkmT6/Y4dzps9rcptq5zVpij5asVba9bwb+HbhISjXSvg2vwOK0V27rwFuYWjYASjuq8CIF+IQmtjRmqtwKJcOhmJjYxEWFoaNGzeia9euAICKigps3boV8+fPd3LriIhIlkydFu+M6fP1VcrW9Fqlp6uH+kzspfo99yyUS/KgzA3EthsdocKdGV6hXpcwqt1RpIwJwL3pHdEguLf57XXQUF59nB4M3bx5EydPntTezsvLw6+//org4GC0atUK6enpePPNN9GuXTu0a9cOb775Jho2bIhHHnnEia0mIiLZMnVavDOmz5vaa1VP0cfqqmrs/u9hKP9zBcpfInGwvB2AVtr9if4noOh+Hoq/tEDP8Qnw8hlgWVsdMJRnKqcHQ3v27EFycrL2tibXZ/z48VixYgVefvll3L59G5MnT8b169fRu3dvfP/992jSpImzmkxERHKmqTl0/rz+HhjN9PmkJMe3zcJeq1tXbmHzkoNQflWGtSficLE6UbvPG1UY2Gw/FINKMOrZWLQZ1A5AO+vaacehPEu4VJ0he2GdISIisilDNYc0k3sc/GWulZsL1OhgMCgnBxdbdMC6hUeh3OCHjYUdcRsNtbsDUYT7ow9BMUrg/hc7oFlsU9u1UaUCYmIM92DVqMVUXFrqkO9vBkNERESW0DfMEx1t2+nz5ubUaAINA71WAhJKAlpguPf32FHaCaLGQhStvM8hpcMpKB5tggGTE+HX2M8211CbGQFbcbduLLpIRETksjQ1h+yVAGxJTo23t3p/Wpq6h6VGQFQNda/VhLJl+BFdAAA9Gh6Gos8lKJ4KR6e0uyF5Rdmm7fVxwQR0BkNERESW8va2z/R5K3JqinsNxsERc9H+20Vorrqs3X4OUXgJC1DeMhrLhmzDyOfbIbJHAoAE27e/Pi6YgM5hMiIiIldiRk6Nphfq7M7zWLvoJJRbGiHnaidUwg9eUCEJ23E3jqNNRDnaP9INQ17ojMZhjR13LfoYGcpzRs4Qe4aIiIhciYnT44+/9ikyf2yNNT+H4dfbcQDuLHLa3u93KDqfhWJCMPpOegLefi60Flo9Q3nOWr+NwRAREZErMTFXZtabfvgcgwAAXlDhnsCDSEm6jlFTWqH9/W0AtLFfG61laNFbR6/f9gcGQ0REnsZFqvqShUzMlbmBpkiN2AXF8CoMz4hDy/jOdm6Yjdk7Ad0MDIaIiDyJC1X1JcucKItGWEBLNCq7Ai/omx4PlAWGYtXvAxDQvJHjG2hL9kpAN5OX8UOIiMgtaGYg1c430cxAys52TruoXqoKFX58fz+m985FvP8p3H1/W4wvWwbgznR4DSFJkCQJDZa/5/6BkAthzxARkSewwwKdZD+ll0qxcdFBKLMqsO5kHC6LTtp9PqjEzeDW2NfiIXQ7+SVQfec1lby8gIwM9vLZGIMhIiJPYIMFOsm+Cn69iLULj0G5MQCbLnVCOe6s8N5UuoHhrQ5BkSLhvhc6IGjPGSDti7rBrUoFLFwI9OnDgMiGGAwREXkCF6zqK3eiWuDgqhNQLrsA5c4W+Lk0EUCodn+sz1mkdMyD4rFA9H86Eb4N+6l3qFRAPwO9fBrs5bMpBkNERJ7ABav6ylHlrUpse/cAlJ+VQHmoDU5X3Q3gbu3+3o0OIqXfFSgmRyFhVFtIXq3qnoS9fA7HYIiIyBMkJalnjRmr6puU5Pi2ebgbZ4rw3VuHsGYN8O3ZDihCN+2+ANzGkND9UAyrwMgX2iOsU6LhE2lKImRlmfbA7OWzGQZDRESewAWr+noMPXWb8n68gLVLfocypwm2Xu+IKtyjPTxEuoxR7Y5CkeaHwc93RMMWves5+R/0lUQwhr18NsNgiIjIU7hYVV+PoCdIKUQYXsC7WIU7z2eC/0koup6DYmJz9JqQAG8/M3rgDC3Kagh7+WyOC7USEXkaVqC2ifL/roTfhEcAQKfaj6b2z+yGC9BsSA+MmhaLu+5tbdmDGFuUtTZNL189q9Z7Ekd9f7NniIjI07hIVV93dOnQZax/6yjWfeuFJYUvIQJ1qxN7QUBIEl5v/g6QlWddoGksWbo29vLZBYMhIiJHYG+NSxLVAse+y4Ny6VkofwjGjpJECCRhIHIRhfMG7yfZakaXqUnQU6cCDz7I942dMBgiIrI3rhfmUqrKqrDj34eg/O91KPe3xolK3RXeuzU4gmmxm4HDJpzM0hldmuD4sCkPAnUgxN4+u2EwRERkjDW9OoaSYzXrhckk98PZSi6U4PtFB7EmW4X1eQm4Ju6s8O6HcvypxX4oBt/GqIx2iOoZD+TeCyS/YfzElszoMmfmGJOlHYIJ1ERE9bGmV8dYcqzmiy7PyrwT0uvc7gKsffsElJsaYMuVTqiAv3ZfsHQNI2IPIyXVG0OfT0STiCa6d9a8dsbqNpn72pkzc0xmydL6MIGaiMjZrO3VYSVhhxLVAr99dRxrlhVA+VMo9t6OB3Cn5+Yu39NI6XQaivHNcM+THeAT0N/wyexRt6m+xXT1YbK0wzAYIiLSxxarwMthvTAnJ4aXF5dj67sHoVx5E8rDdyFf1R5AewCAhGrc0+QgFP2vQTG1FdrfFwvJK8b0k9u6bpOpM8f+/nfg3nuZLO1ADIaIiPSxRa+Op68X5qTE8GunruObhYehXOeF7851QAm6a/c1RCmGhh+A4r5KjHghDiEdOln3YKmp6oDXFgGfqUFvQgJ7Ch2MwRARkT626NXx5PXC6htCfPDBO71m5gQO9fQyndpyBsp38qDcGoTtNzpChX7au4V5XYTi7mNQPNQAf3o2EQ2C+9joIv9gq7pNnh4cuzEGQ0RE+tjii8tT1wszNoQIqK9r8eK6PUWGAh49vUzlzULxVcvJmHfmERwuvwvAnSrPHQOOQ9H9AhRPtESPcfHw8gm12+XajCcHx26Os8mIiPSx5WwifcNJ0dHumxybmwskJ5t2bM0ZUYD+YbWxY4GFCyGE0LvsRRq+xlqMwsBmB6BILsGo59ogdkC01ZfhFJoeNUB/cCzjmWP6OOr7m8EQEZEhtvzi8qQK1CtXAo88YvrxkgQEBwNXr9bZVfMLSKqzF6gGcLtxCCp/O4KmbYLNbalr8rTg2I4YDNkQgyEishi/uOoyp2fIVnJyPCup2JTg2JMCaAuxzhARkSuw5WwiT2Es98Ue3Ln8gD7GkrK5hItDMRgiIjKGq8Drqi8x3F7kNMOKS7g4nJezG0BEZDKVSj1Es3Kl+r8qlbNbJF+agoSRkUYPrbbmcSRJPSwplxlWpszUS0/ne9/GGAwRkXvIzlbP7kpOVifvJierb2dnO7tlslWtGI09//gG/+m4CJ94j0c16gY+AoAECSK4+Z3Ec0Nq73fn8gOWMqfYJ9kMgyEicn2aYYPaXxKaYQMGRA5TdqMM376+G08nbEO0/0X0nNgRTxxIx+OqFRiDr3BF0q33I7VsCenLLyD9+8M/NugJeCQJeOmlur1MUVHyGxKSwxIuLoizyYjItXHld6e7cuwq1i88AuU3PthwIRGlaKzd1wg3cV/kASiGqzDipQQ0/20LMHkycPnynRNoEn+B+mfm1Z49dc89wI4djklcd5WZW6bO1PO02XUGcDYZERHAld+d5PiGPKx55wyUPzTDjuJEVOPOCu+RXgVQxJ+A4uGGGDQ1EQFN+6p3ZGcDY8bUn/h7+rThoKNmonp2NtC2rfWzqUwJclxp5harVDuHkIGioiIBQBQVFTm7KURkrsxMIdRfC/X/ZWY6u6Vuraq8Smx/9zfxUs8c0d7vVJ2nt2uDw2LWwBzxy6eHRbWqWs8JqoSIijL8+kiSENHR6uOMycpSH6/vHJKk3m+KrKy6bYqK0r2/rR7LljRtqt0uZ7bJSRz1/e3ywVBlZaV45ZVXRExMjAgICBCxsbFizpw5QqVSmXwOBkNEbiwnx7RgKCfH2S11OyUFJSLrpZ1ifNvtooV0Wefp9EW5GNp8t1j651xxZsc54yez1etkq6DKlCDHlgGcrekL5KKjZRUICeG472+XHyabP38+li1bhv/+97/o0KED9uzZg4kTJyIoKAjPPfecs5tHRPbGYQOburC3EGvfOg7lxgbYfLkjynFnhfdm0nWMiDkExWhvDMvogMCoHqaf2FaJv7YYFjU2PV2S1NPTg4JcdwiWxT4dyuWDoZ07dyIlJQUjRowAAMTExGDlypXYs2ePk1tGRA7z5JPArFl1t8tx6rWZRLXA/q+PQ/lBAZS7QrDnVgKAMO3+Nj5nkNIpD4pxTdHvrx3g27C/4ZPVx9SiiMaOs0VQZWpAlZtr/WPZE4t9OozLB0P9+/fHsmXLcPz4cdx999347bff8MMPP2Dx4sXObhoR2UPNhNcTJ4B//9vwF1tUlLzXCDOg4mYFtr13EMrPSqA81BZnVO0BtAcASKhGn8aHoOh3FYrJUYgf2RaSV+v6T2hKErKtevBsEVTZOniRU/VrmXL5YGj69OkoKipCXFwcvL29oVKpMHfuXIwdO9bgfcrLy1FeXq69XVxc7IimEpG19M3qMWTOHOCVV9gj9IfreTfw7cJDUK6V8G1+BxSjm3ZfA9zCkLADSLmvAiNeiENoYkfTT2zqTKv6lugwpwfPFkGVqcHLoEHAihUcgiXXn022cuVKERUVJVauXCn2798v/ve//4ng4GCxYsUKg/eZNWuWgLrwqc4fE6iJXJihhFdXS2x1IadyzohFo3NFctO9whuVOk9RqNdFMan9VqH8+0+i9HKpZQ9gyUwrWyT+WjubSpMYbej9VPP9w5lbLs1RCdQuX3QxOjoaM2bMwJQpU7Tb3njjDXz66ac4evSo3vvo6xmKjo5m0UUiV2WssKIhMik8p1FdVY3d/z0M5X+uYN2eMDSrKEQ4ClCAcGxHEuL9f4ei23mkPNECPccnwMvHikUGrCl2aYsChvp6pGoWaDTl/mlp6v/X10tVs7K1tY9FdsOii3+4desWvLx0P9De3t6orja89J+/vz/8/f3t3TQishVjCa+G2Co3xFnVh0143NvXbmPTogNQflWGtSficLE6EQ8gG+swDtG485xVtQyHz7KltvvytmZWly0Sf62dTaVZSFbfEF/tIIczt2TP5YOhUaNGYe7cuWjVqhU6dOiAffv24e2338Zf/vIXZzeNiGzF0qDGFomtzqo+XM/jXrw7CevfOgrld374vrAjbqOX9pBH8Qk+weN1TudzpfBOlWdbtNsV1siyNqgyJ8jhzC1Zc/lhspKSErz66qtYtWoVLl26hIiICIwdOxavvfYa/Pz8TDoH1yYjcnGmrsekYav1yDRDKbX/GdQ3lGJLBh5XfUtCGr5ENtK021t5n4Mi4RQUDzfA4PdSIZ0/r/+8tlyn7fXX9ZczqE1mQ5XkWI76/nb5YMgWGAwRuThNfoqhWT012SpQcdYCsH88rjh3DpKe3dWQcA5R+HOD9RjZ9yoUT4WjU9rdkLwkyxfxNHcY0NQcrqgo9VpjHE4iO3HU97cV2XVERDaimZYN3Al2DImKsk2PjTk5MTZSfK4YuQ8vAwwEQgDgBYFWyMdP31zFq5sHofOY9upACLBs6Co7Wx3YJCcDjzyi/m9MjHq7IabmcD35pPMDIZVKHSSuXKn+r0rl3PaQW2IwRESuQZPwGhmpuz0qSl1TKDNT3eORl+dWOTH5P13Aew9vxbAWe9AiOgAffB1s+eOaW5BQMxxXO7DRrCJvKCAy9ZrbtTPtOHuxJNAj0sOsBOr8/HxER0fbqy1EJHeOnNVjq+UjahHVAvtWHoXy3xeh/DkU+27HA4jQ7vfxBmBK54W+xzWnIKGp63OlpNR9fu303NiUoXwvTaBnr3wv8khm5Qw1atQIGRkZmDFjBho1amTPdtkUc4aIqA5jeUpm5AyVF5cj550DUH5+C2uP3oVzqjvBjxdUuCfwIBT9r0MxtRXaD21t3eOaWj/H0vwiwKbPjV04K9+LHM4lc4Y2btyI77//Hu3atcPy5cvt1SYiIvurL0/JhOUjrp64hk+e/gF/jtqJFkEVuP/VHnj/0ACcU0WgEW4iNWIXVkz6AYWHr2N7UWe8tH4Q2t/fxurHrXc4sWZviDXDgNa20d6ckO9FHs6SstX//e9/RVRUlOjSpYvIycmxUTFs+3FUOW8ickNmLB9x/Ps8sXBkjhgQtE94oUrnLuFeBeKp+K1i/eyfxe3rt236uHpVVQmRkyNEZqb6v7WXJsnJMW1pk/r+DbfF0hr2kJlp2rVlZjq3nWQ1l1+O4/bt25g3bx7eeustDB06FP/85z9x11132TZSsxEOkxFRvQxMPVdVqPDT8sNQLr8K5b5oHKloq3O3zgHHoOhZAMWkEHR7JM785S/sWfnaVkNdzqrOXR9rhgDJrbh8naFbt25h7969yMrKwjvvvANfX19MmTIFs2fPRpMmTWzdTqswGPJQrviPNLm90kul2LjoIJRZFVh3Mg6XRUvtPh9UYlDwfiiSb0LxfFu07hflxJaawJz1udyJq+c0kc24ZDC0bNky7N69G7t378aRI0fg7e2NTp06oU+fPujSpQs+++wzHD9+HKtWrUKPHj3s1mhzMRjyQM5aQoE8UsGvF7F24TEoNwZg06VOKEeAdl9T6QaGtzoERYqE+17ogKBWQU5sqQU8dRFSTw30SIdLBkPR0dHo06eP9q9Hjx51FkR98803kZmZiYMHD9q8sZZiMORhnLWEAtmPg3v5RLXAwVUnoFx2AcqdLfBzaaLO/lifs1Ak5iFlXCD6P50I34a+dmuLQ3hqL6qnBnqk5ZLBkCkuXryIiIgIqFyoCiiDIQ/CKbWex5JePgu+3CtvVWLbuweg/KwEykNtcLpKt2Za70YHobjnChTPRKJDyl13qj7bg6cGJ87A59KjOer72+ar1oeEhGDLli22Pi2RmjlTapk46fosKZxnRvB040wRvnvrEJRKgW/OJKII3bT7AnAbQ0L3QzGsAiOevxvhXXR7h+yGQ7y2xdXmyQa4UCu5l5Ur1WX3jcnMBMaOtX97yHKW9PKZMER6OqQXlItOQZnTBFuvd0QV7gxxtZQuY9RdR6FI88Pg9EQ0CnFw8VgO8RKZxW17hsjFuXuXsjssEyB3pr7HzO3lM7K8hABQ+OAUtMU5VOPOLK8E/5NQdD0HxcTm6DUhAd5+SVZfokWsWR6DiOyKwZCceEL3vDlrM5HjmfMeM7dCspHgSQIQjkIMRC6qg5pDMfAGRk2NQbshdwFwgRponjzE6+4/skj2GAzJhacsaqhZJiAtTR346JtS68xlAuTM3PeYmb18xXuOw5RO8jVv/44mz99r2rkdyZrlMVyFvqBnzRr3/5FFsmdmuVRyS8a65wF197wLzQCsl6lrM5HjWPIe0/Ty1V77SnM3SUJli3D8c74K/QP3I+Ul03p3mnRtZ167c3PVuWi5ufb9DLj7EG92tjrHKzlZnbeXnAyEhgIPPli3x0sTAGdnO6WpROZiArUceGrpenbNuw5L32MGCucJAAIS0vA1VkEd3HpBhQuIRAguQm/4ZG5ZBUcPG7tz1WRDvX71ceXrIbfhkqvWk5vyhO55fTRTaseOVf+X/+A6j6XvsdRU3P7wE9xq1FJncz6ikYavsQ4jMaz5Hrz38Dac3nURoVnvQZIk61dS13y5O7JHw9VXgjekvl6/+nDleHIjzBmSA3fvnnc3cuyxMvW9c/gwkJuLcwF3Ye2S36Hc1ABbrqShCg8jCdsRjgKUohGaxQbhkQf8sOL5MgRG1Vza548hUn09OqZWHXbmrK5UG7Tf0Ywlfhvjbj+ySJY4TCYH7tw97yyWBjSeMGPPEkbeYwLQGdrKRxSewxLtENhdvqeR0uk0FOOb4Z4nO8AnwMjvNGsCTlcYNnangNnU2l6GuNvwO7kU1hki2+EMLPNYGtAYyqs4d06dZKrpbXDlLz5L1fMe0/drKxLnkYU0rO4yG3H/eBRxw9tA8oox7/Es/YJ1hWFjd6qabGmPMctckBthzpBccAaWaSzNJTElr2LxYnWPREyMZTkpjpz5ZInUVJQsXYHShi3q7Kqd8OwFAUkCHrj6EeLvj7mzDpgjrtGTho0d8XwZmfWnF39kkbsRMlBUVCQAiKKiImc3xfmqqoTIyREiM1P936oqZ7fIdVRVCREVJYQ6pKn7J0lCREfrf85ycgzfT995JEmIrCzT25aVVbdtUVHmncNOTm4+Ld5OyRGDmu4V3qgUXqgSA5Ej5uDvpj0fOTnqEznqGjWvsySZ/zq7Eke+J7Ky7rxvaz9XgBDNm+tuj452ifcmuT9HfX8zGCLSMDWg0Xx515SZaXowZO4XruaLyBZBlQ2oKlVixwf7xYw+OSLB/0SdZnUMOCZe6ZcjTvx1gWnPRWam46+xvi93JzynZnPGe0Jf8KUJevgji+zEUd/fTKAm0rBmEVhTk3JrM5ZcaslipnZw68otbFp0AMqvK7D2RBwuiTtT4b1RhYHN9iMluQSjnmuD2AHR6h2mPiebNgETJjj+GvXlhkVHu+6sLg1nvifcKfGbPAITqMm9eMI/ktbkkhhbM80QY0m6TlzPqnD/Jax76xiUG/yw8WInlKG3dl8gijC81SEoFMB9GQloFtvtzh0174Xz54GWLYErV+qfxQg47hprv09PnQJ27HCv960z1zhzp8RvIjMwGCLrecp0cmsWgdXMpnrwQfMe01gAZuqMpvPnzXtcPUS1wOG1p6B87xyUO5rjp5sdIBCi3d/a+xxSEk9B8WgTJD2TCL/G99Q9ib73gj41E2wvXTKtgdbO7qrvfVq7p8+VucJsOCIPw2CIrOMpC8AC1pcgSEkBmjcHrl41/limTjs2tbfq+eeBBg3Mfq4rb1Xih2UHofy0GMoDMfi9SneF956NDkHR5zIUT0egY2o7SF5Rhk9mzpINNYsM5uaa1lhrZnd50vvUk2bDEbkI5gyR5VQq9UKNhr783bWYo6W5JObkDUmSaV/Axgpm1jwfYNI5i84WYcOiQ1izSuCbsx1wQzTV7vNHGQaH7IdiSBlGZtyNiG5hpl2PsTwWQD1ktmiRurxDzeEoexcFdZG8K5thEVWSEYd9f9s1PdtFcDaZncyZY/nsK1dnyewYU2eUBQebP63e0DRwE2eonf4hX/wrLVcMCd4jfFGuc7cW0mUx4a5tIvvlnaKkoMT0dtVkzUy8mtdoj9ld1rbNFbn7bDgiEznq+5vDZGQZlerOopPGuGPugiWJoqYOS3z5JXDvvaafV1Mw86mn1MnIhtRInBUDBuKXT49A+dElrPs5BIHllxCOAlQgHCp4I87vFBRd8qGYEIw+T3SAt5+VVYKtzWOx55pdjsyxcdREAndc44zIhTEYIuP0/QO/fTtw7Zpp95dL7oKpCdiWzMZJTQVu3wYee8zooR8/tgWvFbTHheoEPICjWINxiMadL8zKFmHw/eBd235h2iKPJTVVnXdl62DCUTk2jp5IYK/ni0iGmDNE9TP0D3xamvoXqDHNmwMXL8rnH2hNoi6gPwHbmkRdE3OSBiEHWzEIY/EZPoM6eNJZSMHatugLjgHXzWNxRI6NoQRtW7zuRDLmqO9vrk1GhtW3TpcpgRAAPPusfAIhwD5rwGnWn/qjbo+hXy/VkHAeEUiIF/h2zk/4LGI6JNRdF0z7hZ2ebv5aVtnZ6sAiOVldoFKz1tqaNXeGTWuvYeXsdao0swRrtkXDFm2rb106a55rInIY9gyRfqbMwPHyqv8feLn1CtVkq9yR7GyIZ5+DdP7O6yBQN8BRf4gl4KuvIKU9aPrMNmMVsGu1xWjvB+C8qs7GnnN7VZy2x3NNRABYgZqczZQqt5pAqHZNHo0PP5RnIARYXan3ZuFNHHpmKXqtnqk3+KlNAoCXXgTS/ij6aOukYWO9H5Kk7v3Iy7NdHos5AaUp+Tr2yrFhEUQit8dhMtLP1H+409PrDglFRwNZWcyRMNOFvYX44NFtGBGyGyHhEiJWvwuBuh9SvYGRJAGff34nQLV10rA5S0BoAsGxY9X/tbBHTO9wXHa2/mMNDeempenexxZtq41FEIncHnuGSD9T/+FOSQEWLrTs17YnrGdmTD3XKKoF9n99HMoPCqDcFYI9txIAqIscDkSuzgwwo2qvR2XN0iL6OLL3w5xq0ab2WKWk2O+9Zevnmogcz65VjGzk3Llz4tFHHxXBwcGiQYMGonPnzmLPnj0m359FFy1QVSVEVJThYn/1FPgzSVaW+vw1zxkV5XrF4iwpvqih5xqrIyPFr+P+KaZ1yhWtvfN1n1KoRJ/G+8WbQ3PE2Yy3TSsUWPsvM1P38W1VmM9RBTY17ztTC0u6SkFFFkEksgtHfX+7fDB07do10bp1azFhwgTx008/iby8PLFp0yZx8uRJk8/BYMhC9voH3lBFZVf74rAmYDNwjSpIQgVJPIAsAQjRAKVCEbZLfDR+myj47eKd+5v6JW/sS1/fNURH274CtrXBsbnXrblOU6t+1wwS7cUWzzUR6WAw9Ifp06eL/v37W3UOjw+GrOm9MMbW/8Cb+8vfWawJ2KqqRGXLcFFt4BpVkMRVnxCh/NsOUXq51OA56u2ZM+d5s+b9Yez1qvlniy99c4MbV+kZ0rDnZ5FIhhgM/SE+Pl6kp6eLtLQ00bJlS9GlSxfx4Ycf1nufsrIyUVRUpP3Lz8/33GDIEcNNtvwH3tW+vPSxIGBTVarEro8OiL/dkyMm+H5im2s01DNnSYBmKVNfrzlzHPt4mufO3sO5RORUjgqGXH422e+//473338f7dq1w4YNG/D000/j2Wefxf/+9z+D95k3bx6CgoK0f9HR0Q5ssQOZM4vGGracgeMO05BNnDlVvn4T1r76M56M24YI/yvoMykRb+4YhLJKE58fY9doqIBj7effmoKOxpj6OrRrZ5vH0yQj1y6OqCFJ6tmKmmRkexdUJCJ5sGuoZQO+vr6ib9++OtumTZsm+vTpY/A+bt0zZGovjLsMN9XmDj1DJg7VPI7lOpuaoEiMif5RfK94x7bXWPs9UV7uuKEYZ7xeluSqMV+HyCNx1fo/hIeHIyEhQWdbfHw8srKyDN7H398f/v7+9m6a7Zmz0KM5dV9cqeqtO0xDNrGsQDPcwFRpKe6KqULCpHsw8Nku8Gt8D6DqDcQssN016ivg6KjX1BmvlyUrsnPRUiKygssHQ/369cOxY8d0th0/fhytW7d2UovsxJzaKoB7DDfpoxnWSEurW7na0cMaBmoAVfXsi6qmofC/cVFvgUMBQMALi/G8+kYegPcigbh31K+RK12jtZx1LZYEN1ZW/SYiGbNrv5MN/Pzzz8LHx0fMnTtXnDhxQnz22WeiYcOG4tNPPzX5HC4/m8ySIS93GG6qj7OHNfQ8fmnjELE45A3RTLomHkCWdhp8zWMMzRDT/tVsv7Ov0ZY86VqIyG046vvbLRZqXbduHWbOnIkTJ04gNjYWGRkZePLJJ02+v8sv1GrJQo+ahVSNDV/k5Vn3q92eVaKdVYH6j144IYROz0/1H7fS8DW2SQPxWsgyTCp9Bw1vXrpzkJcXUF1t+Ny1F6f1pCrbnnQtROQWHPX97RbBkLVcPhhauVK9/pIxmZnqGV0amqE1QP/whbUzjMzJYXIDolpg32eH0HbSn9Ck4rLehfkEgPKmofA5dwY+jfx1A4ALF4AXXzT+QJs2Affea+vmExHJjqO+v11+ar0sWLrQo6Gp17aYau2oaft2Vl5cjg1z92BKx61o5VeAjMevIMhAIASoF0ENuHERPrt3qjfULCtw44ZpD5qba3W7iYjIcVw+gVoWrJmxY0qiqbnDG66w+KUVrp64hm/eOgzlOm98dz4RN9FDu+9ebDDtJK6WeE714xAeEVmBwZArsHbGTn2zaCwZ6nLDafsnNp6G8p3TUG5vih+KOqIa/bX7wr0KoWh/HIqHGmBw90hglAkn1NdbN2gQ8MYbxu/rIs+JbHjYcC4ROR6DIVdhSW0VY8ydrq/hBtP2VRUq/LT8MJTLr0K5LxpHKtoCiNHu7xRwDIoeBVBMCkH3R+Pg5RP2xx1VlvfCDRqkTpC+etVww5o3ZzDkSJa+x4mIamACtauxVXe/ZraZoR6e+mabWTK7zQFKL5Vi46KDUGZVYN3JOFwWLbX7fFCJQcH7oUi+iVHpbRHTP8rwiaxJPM/OBh580PC5s7L45eso1rzHicgtcDaZDblVMGQr1gQ0jpq2b4KCXy9i3dvHoNwQgE2XOqIMDbT7glCE4a0PIiVFwn0vdEBQqyDTT6xvaCU62rReOA7LuAYXDdqJyHYc9f3NYTJPZc1QlxMrKItqgYOrTkC57AKUO1vg59JEAKHa/TE++UhJ/B2KxwKR9EwifBv2s+yBrFm+gUs/uAY3GM4lIvfAYMhTWTpdX8MeOUwGVN6qxPb3D2LNJ8VQHmqD01V3A7hbu79Xo4NQ9L2ClMmR6JByFySvaNs8sDXLN3DpB+ez9j1eH85OI5IVDpN5KlsNddnpS+HGmSJ899YhKJUC35xJRBHuDHEF4DYGhxyAYlgZRma0R3iX0HrORLJlr+FcDoMSuQzmDNmQywZD9v71ae8K1WY6/cM5KBedgjKnCbZe74gq+Gr3tZQuY9RdR6FI88Pg9EQ0CmnksHaRG7P1e9zQ7DQnfWaI5I7BkA25ZDDkqF+f1iQKW6m6qhq/fHYUyo8uQbknHPvL2uvsj/c7BUXXfCgmNkfviQnw9uMwBFnAVu9xzk4jcjkMhmzI5YIhR//6dGD+w+1rt7HlnYNQfnEba4/fjYLqMO0+L6iQFHQAioE3MGpqDNoNiXGptjNPxI3Z4rXj7DQil8PZZJ7KGUtdWJvsa+SL5vKRK1j/1lEov/HBhoKOuIWe2n2NUYL7Ig9CMVKF4S8koHm7LqY/riNzN5gn4t5skdDO2WlEssVgyNHcbakLPUGCiIrC+cf/hsx98VD+EIwdJYkQNZa/iPK+AEX8CSgeboRB0zrCP7CvZY/rqMrCrGJMgH1npxGRS+MwmaOtXAk88ojx4zIz1SulO5OBIKEa6uG8NHyNVVAHCV0bHEFK74tQ/DUMXR5qD8lLsvxxHZm7wTwR0nChYqNEpOao728vu52Z9DP1V+Xhw+ocBpXKrs0xSKVC9dRp0Bcre0G9bZnXZLz35y04u+sC9t6Kx6ycQeg6Ns66QAgwr/fMWo58LHJtmmKjwJ38PQ07FxslIudiMORoSUnqX5e1/7Gt7Y031MmcMTHqHhoHObe7AO+P3YaXg/8Nr4ILMNRKLwiEVF/EM5O9EN07wraNcGTuhrmPpVKpg9SVK50brJJ9aIqNRkbqbo+K4nApkQdjzpCj1bfUhT52zlsR1QK/fXUcyg8KoPwpBL/cSgAQjodx3rQT2COZ1JG5G+Y8FpOs5YHLrRDJDnOGnEXfF6shNs5VqLhZgdx/HYBy5U0oD9+FfNWdX8ESqtG3yUFMiduCR3Y/b/xk9phm7MjcDVMf6623gIceYjE+IiIHYp0hG3LJYAi4M2V982b1sJgxVgQe105dx7dvHcaatV747lwHlODO89AAtzA07AAU91Vg5ItxCOnQ0vnJpI6snm3ssb78Enj+eSZZExE5GBOo5UBTGyUhwbTjzRySOrXlDBaNzkVys30IuasJHnu/H7461xclCESY10U8GbcNa1/9GVevSlhd0Bt/WZ6kDoQ0bXNmMqkjczeMPVaLFkyyJiLyYMwZcgU2ypGprqrGzysOY83HV6DcF4XD5XcBaK3dn+h/Aoru55EyqSV6jIuHl4+RBVAduHK9wcd3VO5GfY+1cqVp52AxPiIit8RhMldgxZDUrSu3sGnRASi/rsDaE3G4JFpq93mjCgOb7YdiUAlGPRuLNoNaWd4+OSeTcpkGIiKn4HIcclLfDDM9Q1IXD17G2n8ehXKDHzZe7IQy9NYeHogiDG91CAoFcF9GAprFdrNN+9z1S94WgZymHIKxYDUpyTZtJiIih2Iw5CrqGZISby/CYe9OUA7LhXJHc/x0swME7nzxtvY+B0WHU1A82gQDJifCr/E9TrgAF2SrqfCmBquAuhdJrj1oRERuisNkruaPnoyq0/k48HMZPtnZFmsOtsXvVa11DuvZ6BAUfS5D8XQEOqa2s77qs6cxtN6YNbPR9AVX0dF3AiHWICIisilOrbchvU+mC+bBFJ0twoZFh6BcLfDNmQRcF820+/xRhntbHoBiyG2MfL4dIntwsUiD7LnemL73zZo1tg+8iIiIwZAt1XkyXaiS8Jkfz2Ht4lNQbmmM3GsdUQk/7b4W0hWMbHsEilRfDHk+EY3DGju0bW7LkQnPXOiViMhumEBtL4aGT+y87IWGqBbYm3kUaz68COXucPxW1h5AlHZ/e7/foeh8FikTg9HniQ7w9mNSrtkcubaZOQu9umsSOhGRh5NXMKRSqXuE9HWGCaH+FZ+erq43Y8Nf8WU3ypDzzgEov7iNtUfb4Xx1PIB4AIAXVOgXeBCKpOsYNaUV2t/fBkAbmz22LDlybTNHBl5ERGQX8gqGduxw2K/4K8euYv3CI1B+44MNFxJRip7afY1wE8MiDiJlRBWGvxCPFu07W/VYVIsjp8I7MvAiIiK7kFcwVFho2nEW/oo/viEPyn+dgXJ7M/xYnIhq9Nfui/AqgCLuBBQPNUDysx0R0LSPRY+hwwWTwF2CmXWbrMIaREREbk9ewVBYmGnHmfgrXlWhws6PDkG54hqUv7XCsYo2AGK1+7s0OIqUHucxNukc7k7whRQZASR1s82XsAslgbskRy0l4sjAi4iI7EJes8muXUNgp05WrcR+s/AmNi46CGV2JdadiscV0UK7zxcVGBR8AIo/3cSo9LZoffFn+wQs9qih46kc1XtWXw0ivhZERBbh1Hob0nkyN21SBxKA/l/xegKJC3sLsfat41BubIDNlzuiHAHafU2lGxjR+hAUoyUMe74DgloFqXfYK2DhVG7XxWFLIiKbYjBkQybVGarxK15UCxzIPgHlsgtYszMEe24l6Jwv1ucsUjrmQfFYIPo/nQjfhr66D2jPgIWLhhIRkUywzpA9paaqp8/X+BVf0bU3tr1/EIfbLsbJPB/sF4nYjiRUwxsSqtG78SEo7rkKxeQoJAyPgfTj70DBUeDnoro9APasPcOp3ERERDYlz2AIALy9cb11F3z7lS+UayX45H+NefgbBuNOEHPFKwS/DpiGxCV/RVinjuqN2dlAm+T684DsGbBwKjcREZFNeTm7AeaaN28eJElCenq6RffP25aPJalbcW/wXoS0aYRH3+uHivxC/A/jEQnd3pwW4jIGb30NYSd/UG/Q5AHV7vXRVK/OzlbftmfAopnKrck9qk2S1EN+nMpNRERkErfKGdq9ezfGjBmDwMBAJCcnY7FmtXAjNGOO8X57caSiq86+Tr6H8aO4B42qiqA3vNDk95w8CbRta1oeEKDOGbJi1lq9NEEZYHISOBERkbtxVM6Q2/QM3bx5E48++ij+/e9/o1mzZsbvoMeRirbwRhUGNd2Ht1NycWHWB/gtaCAaGwqEgDv5Pe+9Z3oekKb2DFC3B8cWtWc0NXQiI3W3R0UxECIiIjKT2wRDU6ZMwYgRIzB48GCjx5aXl6O4uFjnDwA+fGIXLp0sQc71rnj+8WsIf/0Z4MoV0xqwYYNpx2nygOwdsKSmAqdPq2eNZWaq/5uXx0CIiIjITG6RQP35559j79692L17t0nHz5s3D3PmzKmz/aG3+6i72epbsNWQ774z7biaeUB6Zq3ZtPaMt7f10+etrY3D2jpEROTmXD4Yys/Px3PPPYfvv/8eAQEBxu8AYObMmcjIyNDeLi4uRnR09J0DjE19r0mSAC8v9Ze+seP0rUFli4DFXqxd0oNLghARkQdw+QTq1atX44EHHoB3jd4GlUoFSZLg5eWF8vJynX361EnAWrkSeOQR2zc2K8t9ggBrK2RzSRAiIrIzVqD+Q0lJCc6cOaOzbeLEiYiLi8P06dORmJho9Bx1nkxTqzibIz0dWLTItue0F2srZHNJECIicgBWoP5DkyZN6gQ8jRo1QvPmzU0KhPTS1OoxNPXdEikptjmPI1hbIdueFbaJiIgczG1mk9lUfVPfzeWORQ6trZDNJUGIiMiDuHzPkD65ubnWn0Qz9b12ArA5bFEzyBmsrZDNJUGIiMiDyLNnSKNmrZ6pU82/vy2LHKpU6lymlSvV/zU2e80a1i7pwSVBiIjIg8g7GALuTH1/8EHz7teypXqJDlsEQtnZ6oTk5GT1LLfkZPVtzVpntmZthWx7V9gmIiJyIAZDGsZ6O2q7fBnYscP6xzV18Vdbs7ZCNpcEISIiD+HyU+ttweSpeYZq5xiSmQmMHWt5w1xhijorUBMRkYvi1Hpn0PR2PP20uufHGGsThF1hirq1FbJducI2ERGRCThMVltqqjpAadHC8DG2ShDmFHUiIiKnYzCkj58f8MEH6qDHngnCnKJORETkdAyGDHFEgjCnqBMRETkdc4bqk5qqXmbDXgnCminqaWnqwKdm4janqBMRETkEgyFj7J0gbKgSdlSUOhDiFHUiIiK7YjDkCuzdA0VEREQGMRhyFZyiTkRE5BRMoCYiIiJZYzBEREREsiavYOirr+y/IjwRERG5FXkFQ5Mm2X9FeCIiInIr8gqGNOy9IjwRERG5DXkGQ5rihunpHDIjIiKSOXkGQ4DuivBEREQkW/INhjS4IjwREZGsMRjiivBERESyJt8K1JKkXv+LK8ITERHJmjx7hrgiPBEREf1BnsFQVJR6pXiuCE9ERCR78hom++gjoG1brghPREREWvIKhv78ZyAw0NmtICIiIhciz2EyIiIioj8wGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGTN5YOhefPmoWfPnmjSpAlCQkIwevRoHDt2zNnNIiIiIg/h8sHQ1q1bMWXKFOzatQsbN25EVVUVhg4ditLSUmc3jYiIiDyAJIQQzm6EOS5fvoyQkBBs3boVAwYMMOk+xcXFCAoKQlFREQIDA+3cQiIiIrIFR31/+9jtzHZSVFQEAAgODjZ4THl5OcrLy7W3i4uL7d4uIiIick8uP0xWkxACGRkZ6N+/PxITEw0eN2/ePAQFBWn/oqOjHdhKIiIiciduNUw2ZcoUrF+/Hj/88AOioqIMHqevZyg6OprDZERERG6Ew2S1TJs2DUqlEtu2bas3EAIAf39/+Pv7O6hlRERE5M5cPhgSQmDatGlYtWoVcnNzERsb6+wmERERkQdx+WBoypQpyMzMxJo1a9CkSRMUFhYCAIKCgtCgQQMnt46IiIjcncvnDEmSpHf78uXLMWHCBJPOwan1RERE7oc5Q39w8ViNiIiI3JxbTa0nIiIisjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREsuY2wdB7772H2NhYBAQEoHv37ti+fbuzm0REREQewC2CoS+++ALp6el45ZVXsG/fPiQlJeH+++/H2bNnnd00IiIicnOSEEI4uxHG9O7dG926dcP777+v3RYfH4/Ro0dj3rx5Ru9fXFyMoKAgFBUVITAw0J5NJSIiIhtx1Pe3y/cMVVRU4JdffsHQoUN1tg8dOhQ7duxwUquIiIjIU/g4uwHGXLlyBSqVCqGhoTrbQ0NDUVhYqPc+5eXlKC8v194uKioCoI4wiYiIyD1ovrftPYjl8sGQhiRJOreFEHW2acybNw9z5sypsz06OtoubSMiIiL7uXr1KoKCgux2fpcPhlq0aAFvb+86vUCXLl2q01ukMXPmTGRkZGhv37hxA61bt8bZs2ft+mS6muLiYkRHRyM/P19WuVK8bl63HPC6ed1yUFRUhFatWiE4ONiuj+PywZCfnx+6d++OjRs34oEHHtBu37hxI1JSUvTex9/fH/7+/nW2BwUFyepNpBEYGMjrlhFet7zwuuVFrtft5WXfFGeXD4YAICMjA+PGjUOPHj3Qt29ffPjhhzh79iyefvppZzeNiIiI3JxbBEMPPfQQrl69itdffx0FBQVITEzEN998g9atWzu7aUREROTm3CIYAoDJkydj8uTJFt3X398fs2bN0jt05sl43bxuOeB187rlgNdt3+t2i6KLRERERPbi8kUXiYiIiOyJwRARERHJGoMhIiIikjUGQ0RERCRrbhkMvffee4iNjUVAQAC6d++O7du313v81q1b0b17dwQEBKBNmzZYtmxZnWOysrKQkJAAf39/JCQkYNWqVfZqvsXMue7s7GwMGTIELVu2RGBgIPr27YsNGzboHLNixQpIklTnr6yszN6XYhZzrjs3N1fvNR09elTnOE97vSdMmKD3ujt06KA9xh1e723btmHUqFGIiIiAJElYvXq10ft4wufb3Ov2lM+3udftKZ9vc6/bUz7f8+bNQ8+ePdGkSROEhIRg9OjROHbsmNH7OeIz7nbB0BdffIH09HS88sor2LdvH5KSknD//ffj7Nmzeo/Py8vD8OHDkZSUhH379uFvf/sbnn32WWRlZWmP2blzJx566CGMGzcOv/32G8aNG4cxY8bgp59+ctRlGWXudW/btg1DhgzBN998g19++QXJyckYNWoU9u3bp3NcYGAgCgoKdP4CAgIccUkmMfe6NY4dO6ZzTe3atdPu88TXe8mSJTrXm5+fj+DgYPz5z3/WOc7VX+/S0lJ07twZS5cuNel4T/l8m3vdnvL5Nve6Ndz9823udXvK53vr1q2YMmUKdu3ahY0bN6KqqgpDhw5FaWmpwfs47DMu3EyvXr3E008/rbMtLi5OzJgxQ+/xL7/8soiLi9PZ9tRTT4k+ffpob48ZM0bcd999OscMGzZMPPzwwzZqtfXMvW59EhISxJw5c7S3ly9fLoKCgmzVRLsw97pzcnIEAHH9+nWD55TD671q1SohSZI4ffq0dps7vN41ARCrVq2q9xhP+XzXZMp16+OOn++aTLluT/l812TJ6+0Jn28hhLh06ZIAILZu3WrwGEd9xt2qZ6iiogK//PILhg4dqrN96NCh2LFjh9777Ny5s87xw4YNw549e1BZWVnvMYbO6WiWXHdt1dXVKCkpqbPY3c2bN9G6dWtERUVh5MiRdX5ZOpM11921a1eEh4fj3nvvRU5Ojs4+ObzeH3/8MQYPHlynSrsrv96W8ITPty244+fbGu78+bYFT/l8FxUVAUC9i7A66jPuVsHQlStXoFKp6qxWHxoaWmdVe43CwkK9x1dVVeHKlSv1HmPonI5myXXX9tZbb6G0tBRjxozRbouLi8OKFSugVCqxcuVKBAQEoF+/fjhx4oRN228pS647PDwcH374IbKyspCdnY327dvj3nvvxbZt27THePrrXVBQgG+//RaTJk3S2e7qr7clPOHzbQvu+Pm2hCd8vq3lKZ9vIQQyMjLQv39/JCYmGjzOUZ9xt1mOoyZJknRuCyHqbDN2fO3t5p7TGSxt48qVKzF79mysWbMGISEh2u19+vRBnz59tLf79euHbt264V//+hfeeecd2zXcSuZcd/v27dG+fXvt7b59+yI/Px8LFy7EgAEDLDqns1jaxhUrVqBp06YYPXq0znZ3eb3N5Smfb0u5++fbHJ70+baUp3y+p06div379+OHH34weqwjPuNu1TPUokULeHt714n2Ll26VCcq1AgLC9N7vI+PD5o3b17vMYbO6WiWXLfGF198gSeeeAJffvklBg8eXO+xXl5e6Nmzp8v8krDmumvq06ePzjV58usthMB//vMfjBs3Dn5+fvUe62qvtyU84fNtDXf+fNuKu32+reEpn+9p06ZBqVQiJycHUVFR9R7rqM+4WwVDfn5+6N69OzZu3KizfePGjbjnnnv03qdv3751jv/+++/Ro0cP+Pr61nuMoXM6miXXDah/MU6YMAGZmZkYMWKE0ccRQuDXX39FeHi41W22BUuvu7Z9+/bpXJOnvt6AerbGyZMn8cQTTxh9HFd7vS3hCZ9vS7n759tW3O3zbQ13/3wLITB16lRkZ2djy5YtiI2NNXofh33GTU61dhGff/658PX1FR9//LE4fPiwSE9PF40aNdJm1c+YMUOMGzdOe/zvv/8uGjZsKJ5//nlx+PBh8fHHHwtfX1/x9ddfa4/58ccfhbe3t/i///s/ceTIEfF///d/wsfHR+zatcvh12eIudedmZkpfHx8xLvvvisKCgq0fzdu3NAeM3v2bPHdd9+JU6dOiX379omJEycKHx8f8dNPPzn8+gwx97oXLVokVq1aJY4fPy4OHjwoZsyYIQCIrKws7TGe+HprPPbYY6J37956z+kOr3dJSYnYt2+f2LdvnwAg3n77bbFv3z5x5swZIYTnfr7NvW5P+Xybe92e8vk297o13P3z/cwzz4igoCCRm5ur8769deuW9hhnfcbdLhgSQoh3331XtG7dWvj5+Ylu3brpTMsbP368GDhwoM7xubm5omvXrsLPz0/ExMSI999/v845v/rqK9G+fXvh6+sr4uLidD5crsKc6x44cKAAUOdv/Pjx2mPS09NFq1athJ+fn2jZsqUYOnSo2LFjhwOvyDTmXPf8+fNF27ZtRUBAgGjWrJno37+/WL9+fZ1zetrrLYQQN27cEA0aNBAffvih3vO5w+utmTpt6H3rqZ9vc6/bUz7f5l63p3y+LXmfe8LnW981AxDLly/XHuOsz7j0RwOJiIiIZMmtcoaIiIiIbI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEicksrV65EQEAAzp8/r902adIkdOrUCUVFRU5sGRG5G65NRkRuSQiBLl26ICkpCUuXLsWcOXPw0UcfYdeuXYiMjHR284jIjfg4uwFERJaQJAlz585FWloaIiIisGTJEmzfvp2BEBGZjT1DROTWunXrhkOHDuH777/HwIEDnd0cInJDzBkiIre1YcMGHD16FCqVCqGhoc5uDhG5KfYMEZFb2rt3LwYNGoR3330Xn3/+ORo2bIivvvrK2c0iIjfEnCEicjunT5/GiBEjMGPGDIwbNw4JCQno2bMnfvnlF3Tv3t3ZzSMiN8OeISJyK9euXUO/fv0wYMAAfPDBB9rtKSkpKC8vx3fffefE1hGRO2IwRERERLLGBGoiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrP0/sCxKf+B5Q7YAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "theta from own sdg\n",
-      "[[4.02496085]\n",
-      " [3.12081773]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using SGD\n",
     "# OLS example\n",
@@ -4428,27 +4275,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "3320d6c6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[3.96075051]\n",
-      " [3.02148021]]\n",
-      "Eigenvalues of Hessian Matrix:[0.27470622 4.24106503]\n",
-      "theta from own gd\n",
-      "[[3.95906059]\n",
-      " [3.02296298]]\n",
-      "theta from own sdg with momentum\n",
-      "[[3.95611042]\n",
-      " [2.99475306]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using SGD\n",
     "# OLS example\n",
@@ -4529,25 +4359,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "19414323",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own AdaGrad\n",
-      "[[2.00025662]\n",
-      " [2.99802696]\n",
-      " [4.00167329]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4617,25 +4432,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "6724b3d2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own RMSprop\n",
-      "[[1.99456598]\n",
-      " [2.99848815]\n",
-      " [3.99835783]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4703,25 +4503,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "28aa6aad",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Own inversion\n",
-      "[[2.]\n",
-      " [3.]\n",
-      " [4.]]\n",
-      "theta from own ADAM\n",
-      "[[1.99989103]\n",
-      " [3.00042093]\n",
-      " [3.99954925]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent\n",
     "# OLS example\n",
@@ -4794,19 +4579,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "f3dde7c5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial loss: 2.772588722239781\n",
-      "Trained loss: 1.067270675787016\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
     "from autograd import grad\n",
@@ -4861,18 +4637,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "291c9b6d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.25       0.19661197 0.10499357]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import jax.numpy as jnp\n",
     "from jax import grad, jit, vmap\n",