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    -

    +

    Decision Trees, Ensemble Methods and Boosting @@ -187,7 +193,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Dimensionality Reduction @@ -204,7 +210,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Deep Learning Methods @@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output" data-placement="left">.ipynb + onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf @@ -299,7 +305,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +
    Contents @@ -427,7 +433,138 @@ const thebe_selector_output = ".output, .cell_output"
    - + +
    +

    Linear Regression

    + +
    + +
    +
    +
    -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    -
    +

    + + By Morten Hjorth-Jensen
    + + © Copyright 2021.
    +

    +
    - + - \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index 10424c36c..2f4af6bb2 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -7,8 +7,8 @@ 14. Building a Feed Forward Neural Network — Applied Data Analysis and Machine Learning - - + + - + - + + + + - + - + - + + + + @@ -94,7 +100,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    About the course @@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Review of Statistics with Resampling Techniques and Linear Algebra @@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    From Regression to Support Vector Machines @@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Decision Trees, Ensemble Methods and Boosting @@ -187,7 +193,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Dimensionality Reduction @@ -204,7 +210,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Deep Learning Methods @@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output" data-placement="left">.ipynb + onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf

    @@ -299,7 +305,7 @@ const thebe_selector_output = ".output, .cell_output" -
    +
    Contents @@ -463,7 +469,174 @@ const thebe_selector_output = ".output, .cell_output"
    - + +
    +

    Building a Feed Forward Neural Network

    + +
    + +
    +
    +
    -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    -
    +

    + + By Morten Hjorth-Jensen
    + + © Copyright 2021.
    +

    +
    - + - \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index e4f10e2e7..9361d8bea 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -7,8 +7,8 @@ 15. Solving Differential Equations with Deep Learning — Applied Data Analysis and Machine Learning - - + + - + - + + + + - + - + - + + + + @@ -94,7 +100,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    About the course @@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Review of Statistics with Resampling Techniques and Linear Algebra @@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    From Regression to Support Vector Machines @@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Decision Trees, Ensemble Methods and Boosting @@ -187,7 +193,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Dimensionality Reduction @@ -204,7 +210,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Deep Learning Methods @@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output" data-placement="left">.ipynb + onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf

    @@ -299,7 +305,7 @@ const thebe_selector_output = ".output, .cell_output" -
    +
    Contents @@ -402,7 +408,113 @@ const thebe_selector_output = ".output, .cell_output"
    - + +
    +

    Solving Differential Equations with Deep Learning

    + +
    + +
    +
    +
    -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    -
    +

    + + By Morten Hjorth-Jensen
    + + © Copyright 2021.
    +

    +
    - + - \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index b5652d3e2..0bd3d3e60 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -7,8 +7,8 @@ 16. Convolutional Neural Networks — Applied Data Analysis and Machine Learning - - + + - + - + + + + - + - + - + + + + @@ -94,7 +100,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    About the course @@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Review of Statistics with Resampling Techniques and Linear Algebra @@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    From Regression to Support Vector Machines @@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Decision Trees, Ensemble Methods and Boosting @@ -187,7 +193,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Dimensionality Reduction @@ -204,7 +210,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Deep Learning Methods @@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output" data-placement="left">.ipynb + onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf

    @@ -299,7 +305,7 @@ const thebe_selector_output = ".output, .cell_output" -
    +
    Contents @@ -383,7 +389,94 @@ const thebe_selector_output = ".output, .cell_output"
    - + +
    +

    Convolutional Neural Networks

    + +
    + +
    +
    +
    -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    -
    +

    + + By Morten Hjorth-Jensen
    + + © Copyright 2021.
    +

    +
    - + - \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html index 66481abd9..42d4fab6a 100644 --- a/doc/LectureNotes/_build/html/chapter13.html +++ b/doc/LectureNotes/_build/html/chapter13.html @@ -7,8 +7,8 @@ 17. Recurrent neural networks: Overarching view — Applied Data Analysis and Machine Learning - - + + - + - + + + + - + - + - + + + + @@ -93,7 +99,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    About the course @@ -115,7 +121,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Review of Statistics with Resampling Techniques and Linear Algebra @@ -132,7 +138,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    From Regression to Support Vector Machines @@ -169,7 +175,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Decision Trees, Ensemble Methods and Boosting @@ -186,7 +192,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Dimensionality Reduction @@ -203,7 +209,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Deep Learning Methods @@ -280,7 +286,7 @@ const thebe_selector_output = ".output, .cell_output" data-placement="left">.ipynb + onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf

    @@ -298,7 +304,7 @@ const thebe_selector_output = ".output, .cell_output" -
    +
    Contents @@ -364,7 +370,76 @@ const thebe_selector_output = ".output, .cell_output"
    - + +
    +

    Recurrent neural networks: Overarching view

    + +
    + +
    +
    +
    -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    -
    +

    + + By Morten Hjorth-Jensen
    + + © Copyright 2021.
    +

    +
    - + - \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html index 8a7b8b427..31107dea7 100644 --- a/doc/LectureNotes/_build/html/chapter2.html +++ b/doc/LectureNotes/_build/html/chapter2.html @@ -7,8 +7,8 @@ 4. Ridge and Lasso Regression — Applied Data Analysis and Machine Learning - - + + - + - + + + + - + - + - + + + + @@ -94,7 +100,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    About the course @@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Review of Statistics with Resampling Techniques and Linear Algebra @@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    From Regression to Support Vector Machines @@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Decision Trees, Ensemble Methods and Boosting @@ -187,7 +193,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Dimensionality Reduction @@ -204,7 +210,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Deep Learning Methods @@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output" data-placement="left">.ipynb + onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf

    @@ -299,7 +305,7 @@ const thebe_selector_output = ".output, .cell_output" -
    +
    Contents @@ -384,7 +390,95 @@ const thebe_selector_output = ".output, .cell_output"
    - + +
    +

    Ridge and Lasso Regression

    + +
    + +
    +
    +
    -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    -
    +

    + + By Morten Hjorth-Jensen
    + + © Copyright 2021.
    +

    +
    - + - \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index 00caa630a..93fc20e67 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -7,8 +7,8 @@ 5. Resampling Methods — Applied Data Analysis and Machine Learning - - + + - + - + + + + - + - + - + + + + @@ -94,7 +100,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    About the course @@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Review of Statistics with Resampling Techniques and Linear Algebra @@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    From Regression to Support Vector Machines @@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Decision Trees, Ensemble Methods and Boosting @@ -187,7 +193,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Dimensionality Reduction @@ -204,7 +210,7 @@ const thebe_selector_output = ".output, .cell_output" -

    +

    Deep Learning Methods @@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output" data-placement="left">.ipynb + onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf

    @@ -299,7 +305,7 @@ const thebe_selector_output = ".output, .cell_output" -
    +
    Contents @@ -393,7 +399,104 @@ const thebe_selector_output = ".output, .cell_output"
    - + +
    +

    Resampling Methods

    + +
    + +
    +
    +
    @@ -657,10 +760,10 @@ number \(i\) is left out. Usin
    -
    Runtime: 0.0896981 sec
+
    Runtime: 0.0903549 sec
 Jackknife Statistics :
 original           bias      std. error
- 100.213        100.203        0.148564
+ 100.107        100.097        0.150184
    @@ -879,7 +982,7 @@ theorem.

    Bootstrap Statistics :
 original           bias      std. error
- 99.8879  15.0782        99.8894        0.149213
+ 99.9033  14.9678         99.904        0.151348
    @@ -1015,10 +1118,10 @@ We use a more compact notation in terms of the expectation value

    -
    Error: 0.013121574061370796
-Bias^2: 0.012073649472576395
-Var: 0.0010479245887943952
-0.013121574061370796 >= 0.012073649472576395 + 0.0010479245887943952 = 0.01312157406137079
+
    Error: 0.013121574062587286
+Bias^2: 0.012073649469946107
+Var: 0.0010479245926411787
+0.013121574062587286 >= 0.012073649469946107 + 0.0010479245926411787 = 0.013121574062587286
 
    
 
    
 _images/chapter3_61_1.png
@@ -1089,71 +1192,68 @@ Var: 0.004579219539673834
 Polynomial degree: 2
 Error: 0.10398646080125037
 Bias^2: 0.10077114273548984
-Var: 0.0032153180657605125
-0.10398646080125037 >= 0.10077114273548984 + 0.0032153180657605125 = 0.10398646080125036
+Var: 0.0032153180657605116
+0.10398646080125037 >= 0.10077114273548984 + 0.0032153180657605116 = 0.10398646080125036
 Polynomial degree: 3
 Error: 0.06547790180152352
-Bias^2: 0.06208238634231944
-Var: 0.003395515459204093
-0.06547790180152352 >= 0.06208238634231944 + 0.003395515459204093 = 0.06547790180152353
+Bias^2: 0.062082386342319454
+Var: 0.0033955154592040923
+0.06547790180152352 >= 0.062082386342319454 + 0.0033955154592040923 = 0.06547790180152355
 Polynomial degree: 4
-Error: 0.06844519414009442
+Error: 0.06844519414009445
 Bias^2: 0.06453579006728322
-Var: 0.003909404072811217
-0.06844519414009442 >= 0.06453579006728322 + 0.003909404072811217 = 0.06844519414009444
-Polynomial degree: 5
-Error: 0.052279218012057004
-Bias^2: 0.048187277304303056
-Var: 0.004091940707753948
-0.052279218012057004 >= 0.048187277304303056 + 0.004091940707753948 = 0.052279218012057004
+Var: 0.003909404072811221
+0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444
    -
    Polynomial degree: 6
-Error: 0.037813671417388985
-Bias^2: 0.033657685071527624
-Var: 0.004155986345861364
-0.037813671417388985 >= 0.033657685071527624 + 0.004155986345861364 = 0.03781367141738899
+
    Polynomial degree: 5
+Error: 0.05227921801205679
+Bias^2: 0.04818727730430286
+Var: 0.004091940707753925
+0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679
+Polynomial degree: 6
+Error: 0.03781367141738902
+Bias^2: 0.03365768507152769
+Var: 0.0041559863458613296
+0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902
 Polynomial degree: 7
-Error: 0.027609773491022407
-Bias^2: 0.0229994982603662
-Var: 0.004610275230656187
-0.027609773491022407 >= 0.0229994982603662 + 0.004610275230656187 = 0.027609773491022387
+Error: 0.027609773491022394
+Bias^2: 0.022999498260366198
+Var: 0.004610275230656182
+0.027609773491022394 >= 0.022999498260366198 + 0.004610275230656182 = 0.02760977349102238
 Polynomial degree: 8
-Error: 0.017355848195593354
-Bias^2: 0.010331721306655144
-Var: 0.0070241268889382116
-0.017355848195593354 >= 0.010331721306655144 + 0.0070241268889382116 = 0.017355848195593354
-Polynomial degree:
-
    
-
    
-
     9
-Error: 0.026605727637184613
-Bias^2: 0.010018312644139205
-Var: 0.016587414993045405
-0.026605727637184613 >= 0.010018312644139205 + 0.016587414993045405 = 0.02660572763718461
+Error: 0.017355848195593312
+Bias^2: 0.010331721306655165
+Var: 0.007024126888938144
+0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331
+Polynomial degree: 9
+Error: 0.026605727637184558
+Bias^2: 0.010018312644139219
+Var: 0.016587414993045335
+0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554
 Polynomial degree: 10
-Error: 0.021592704588021167
-Bias^2: 0.010516485576646513
-Var: 0.01107621901137465
-0.021592704588021167 >= 0.010516485576646513 + 0.01107621901137465 = 0.021592704588021164
+Error: 0.021592704588021178
+Bias^2: 0.010516485576646504
+Var: 0.01107621901137467
+0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174
 Polynomial degree: 11
-Error: 0.07160048164232467
-Bias^2: 0.014436800088896274
-Var: 0.057163681553428394
-0.07160048164232467 >= 0.014436800088896274 + 0.057163681553428394 = 0.07160048164232467
+Error: 0.07160048164232538
+Bias^2: 0.014436800088896381
+Var: 0.05716368155342902
+0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254
 Polynomial degree: 12
-Error: 0.11547777218875695
-Bias^2: 0.016285782696017055
-Var: 0.0991919894927399
-0.11547777218875695 >= 0.016285782696017055 + 0.0991919894927399 = 0.11547777218875696
+Error: 0.11547777218876518
+Bias^2: 0.016285782696017142
+Var: 0.09919198949274803
+0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518
 Polynomial degree: 13
-Error: 0.2284246870217459
-Bias^2: 0.019754165271682844
-Var: 0.20867052175006306
-0.2284246870217459 >= 0.019754165271682844 + 0.20867052175006306 = 0.2284246870217459
+Error: 0.2284246870217162
+Bias^2: 0.01975416527168255
+Var: 0.20867052175003364
+0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162
 
    
 
    
-_images/chapter3_62_3.png
+_images/chapter3_62_2.png

    The bias-variance tradeoff summarizes the fundamental tension in @@ -1366,7 +1466,9 @@ training data. 

    Degree of polynomial:   1
-Mean squared error on training data: 439230.69504801
+
    +
    +
    Mean squared error on training data: 439230.69504801
 Mean squared error on test data: 481979.17861098
 Degree of polynomial:   2
 Mean squared error on training data: 115822.95008046
@@ -1383,12 +1485,12 @@ Mean squared error on test data: 5.98822371
 Degree of polynomial:   6
 Mean squared error on training data: 3.66204648
 Mean squared error on test data: 8.14812206
-
    -
    -
    Degree of polynomial:   7
+Degree of polynomial:   7
 Mean squared error on training data: 0.47075725
 Mean squared error on test data: 2.00607783
-Degree of polynomial:   8
+
    +
    +
    Degree of polynomial:   8
 Mean squared error on training data: 0.04912436
 Mean squared error on test data: 0.21596432
 Degree of polynomial:   9
@@ -1403,72 +1505,72 @@ Mean squared error on test data: 1.35533773
 Degree of polynomial:  12
 Mean squared error on training data: 0.00813803
 Mean squared error on test data: 0.17446471
-
    -
    -
    Degree of polynomial:  13
+Degree of polynomial:  13
 Mean squared error on training data: 0.00759119
 Mean squared error on test data: 1.08131003
 Degree of polynomial:  14
 Mean squared error on training data: 0.00472199
 Mean squared error on test data: 0.81333804
-Degree of polynomial:  15
+
    +
    +
    Degree of polynomial:  15
 Mean squared error on training data: 0.00410478
-Mean squared error on test data: 92.09172408
+Mean squared error on test data: 92.09172409
 Degree of polynomial:  16
 Mean squared error on training data: 0.00315593
-Mean squared error on test data: 234.38533184
+Mean squared error on test data: 234.38533185
 Degree of polynomial:  17
 Mean squared error on training data: 0.00242999
-Mean squared error on test data: 1271.35771842
+Mean squared error on test data: 1271.35771826
 Degree of polynomial:  18
-Mean squared error on training data: 0.00228741
-Mean squared error on test data: 108.27092897
-
    -
    -
    Degree of polynomial:  19
-Mean squared error on training data: 0.00156382
-Mean squared error on test data: 1371.99049330
+Mean squared error on training data: 0.00228742
+Mean squared error on test data: 108.27092910
+Degree of polynomial:  19
+Mean squared error on training data: 0.00156376
+Mean squared error on test data: 1371.99051150
 Degree of polynomial:  20
-Mean squared error on training data: 0.00137823
-Mean squared error on test data: 1887.85953586
-Degree of polynomial:  21
-Mean squared error on training data: 0.00118504
-Mean squared error on test data: 14859.70127680
+Mean squared error on training data: 0.00137818
+Mean squared error on test data: 1887.86252988
+
    +
    +
    Degree of polynomial:  21
+Mean squared error on training data: 0.00118508
+Mean squared error on test data: 14859.69908626
 Degree of polynomial:  22
-Mean squared error on training data: 0.00092645
-Mean squared error on test data: 876.51214899
+Mean squared error on training data: 0.00092647
+Mean squared error on test data: 876.51191552
 Degree of polynomial:  23
-Mean squared error on training data: 0.00085883
-Mean squared error on test data: 5594.60685864
+Mean squared error on training data: 0.00085889
+Mean squared error on test data: 5594.60815105
 Degree of polynomial:  24
-Mean squared error on training data: 0.00084711
-Mean squared error on test data: 1277.60619654
-
    -
    -
    Degree of polynomial:  25
-Mean squared error on training data: 0.00079130
-Mean squared error on test data: 128664.09744272
+Mean squared error on training data: 0.00084705
+Mean squared error on test data: 1277.61702282
+Degree of polynomial:  25
+Mean squared error on training data: 0.00079129
+Mean squared error on test data: 128664.31650694
 Degree of polynomial:  26
-Mean squared error on training data: 0.00076919
-Mean squared error on test data: 19003.95079764
-Degree of polynomial:  27
-Mean squared error on training data: 0.00068941
-Mean squared error on test data: 2379.66226149
-Degree of polynomial:  28
-Mean squared error on training data: 0.00062582
-Mean squared error on test data: 4082.19994371
-Degree of polynomial:  29
-Mean squared error on training data: 0.00060708
-Mean squared error on test data: 3250.24770094
+Mean squared error on training data: 0.00076905
+Mean squared error on test data: 19003.94822514
    -
    <ipython-input-7-40a38ad763f1>:73: RuntimeWarning: divide by zero encountered in log10
+
    Degree of polynomial:  27
+Mean squared error on training data: 0.00068946
+Mean squared error on test data: 2379.66219404
+Degree of polynomial:  28
+Mean squared error on training data: 0.00062595
+Mean squared error on test data: 4082.19983530
+Degree of polynomial:  29
+Mean squared error on training data: 0.00060705
+Mean squared error on test data: 3250.17647619
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-<ipython-input-7-40a38ad763f1>:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
-_images/chapter3_65_6.png
+_images/chapter3_65_7.png
    @@ -1698,7 +1800,7 @@ cross-validation (LOOCV).

    -
    <ipython-input-9-6e75736fdab1>:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
@@ -1962,9 +2064,9 @@ Note also that we do not split the data into training and test.
    
 Fitted beta: [2.08376632 0.19569961 3.97898392]
 Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]
 MSE with intercept column
-0.00411363461744314
+0.004113634617443139
 MSE with intercept column from SKL
-0.004113634617443116
+0.004113634617443147
 Manual intercept: 2.083766322923899
 Fitted beta (wiothout intercept): [0.19569961 3.97898392]
 Sklearn intercept: 2.0837663229239043
@@ -2083,9 +2185,9 @@ intercept.
    
 
    Beta values for own Ridge implementation
 [ 1.03032441e+00  6.28336218e-02 -6.24175744e-01  5.21169159e-02
   2.80847477e-01  2.12552073e-01  8.13220608e-02 -1.69634577e-02
- -6.50846111e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02
- -9.80609616e-03  1.08299273e-02  2.41882036e-02  2.93492130e-02
-  2.64742912e-02  1.63249532e-02 -5.01831036e-05 -2.15098090e-02]
+ -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02
+ -9.80609616e-03  1.08299273e-02  2.41882037e-02  2.93492130e-02
+  2.64742912e-02  1.63249532e-02 -5.01831251e-05 -2.15098090e-02]
 Beta values for Scikit-Learn Ridge implementation
 [ 1.03032441e+00  6.28336218e-02 -6.24175744e-01  5.21169159e-02
   2.80847477e-01  2.12552073e-01  8.13220608e-02 -1.69634577e-02
@@ -2093,7 +2195,7 @@ Beta values for Scikit-Learn Ridge implementation
  -9.80609615e-03  1.08299273e-02  2.41882037e-02  2.93492130e-02
   2.64742912e-02  1.63249532e-02 -5.01831207e-05 -2.15098090e-02]
 MSE values for own Ridge implementation
-4.363295924430451e-07
+4.3632959215700067e-07
 MSE values for Scikit-Learn Ridge implementation
 4.363295916323784e-07
 Beta values for own Ridge implementation
@@ -2107,7 +2209,7 @@ Beta values for Scikit-Learn Ridge implementation
  -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724  0.01348565
   0.02976145  0.04543942]
 MSE values for own Ridge implementation
-5.19404282648955e-06
+5.194042827197027e-06
 MSE values for Scikit-Learn Ridge implementation
 5.1940428268204826e-06
 Beta values for own Ridge implementation
@@ -2121,7 +2223,7 @@ Beta values for Scikit-Learn Ridge implementation
   0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318
  -0.01708852 -0.01708781]
 MSE values for own Ridge implementation
-2.094082198966615e-05
+2.0940821989643363e-05
 MSE values for Scikit-Learn Ridge implementation
 2.094082198961999e-05
 Beta values for own Ridge implementation
@@ -2135,7 +2237,7 @@ Beta values for Scikit-Learn Ridge implementation
   0.01458337  0.01223198  0.00996754  0.00784393  0.00588657  0.00410387
   0.00249435  0.00105081]
 MSE values for own Ridge implementation
-0.0003153514830958235
+0.0003153514830957865
 MSE values for Scikit-Learn Ridge implementation
 0.00031535148309580783
 Beta values for own Ridge implementation
@@ -2151,7 +2253,7 @@ Beta values for Scikit-Learn Ridge implementation
  -1.84923989e-03 -8.13661243e-04  7.46984697e-06  6.56636616e-04
   1.16805821e-03  1.56912044e-03  1.88168312e-03  2.12318726e-03]
 MSE values for own Ridge implementation
-0.015072388895177239
+0.015072388895177157
 MSE values for Scikit-Learn Ridge implementation
 0.0150723888951771
 Beta values for own Ridge implementation
@@ -2165,7 +2267,7 @@ Beta values for Scikit-Learn Ridge implementation
  0.00719176 0.00630331 0.00556826 0.0049544  0.00443743 0.0039987
  0.0036237  0.003301  ]
 MSE values for own Ridge implementation
-0.26409315307910053
+0.26409315307910036
 MSE values for Scikit-Learn Ridge implementation
 0.26409315307910025
 
    
@@ -2266,7 +2368,7 @@ Let us see how we can change this code by zero centering.
    
   2.18613217e-01  1.02054837e-01 -4.25617658e-04 -5.90475506e-02
  -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02
   1.11482289e-02  2.88529063e-02  3.67047975e-02  3.38135733e-02
-  2.02198702e-02 -3.46383925e-03 -3.63025821e-02]
+  2.02198703e-02 -3.46383925e-03 -3.63025821e-02]
 Beta values for Scikit-Learn Ridge implementation
 [ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03  2.47116868e-01
   2.18613217e-01  1.02054837e-01 -4.25617654e-04 -5.90475506e-02
@@ -2274,11 +2376,11 @@ Beta values for Scikit-Learn Ridge implementation
   1.11482289e-02  2.88529063e-02  3.67047975e-02  3.38135733e-02
   2.02198702e-02 -3.46383925e-03 -3.63025821e-02]
 Intercept from own implementation:
-1.0330308045190182
+1.0330308045188872
 Intercept from Scikit-Learn Ridge implementation
 1.0330308045183219
 MSE values for own Ridge implementation
-3.13925595925919e-06
+3.1392559591206444e-06
 MSE values for Scikit-Learn Ridge implementation
 3.1392559585048734e-06
 Beta values for own Ridge implementation
@@ -2292,11 +2394,11 @@ Beta values for Scikit-Learn Ridge implementation
  -0.04355837 -0.03562355 -0.02348765 -0.00848904  0.00831018  0.0260906
   0.04423486]
 Intercept from own implementation:
-1.041148729430502
+1.041148729430595
 Intercept from Scikit-Learn Ridge implementation
 1.041148729430523
 MSE values for own Ridge implementation
-1.9601304850018328e-05
+1.96013048502692e-05
 MSE values for Scikit-Learn Ridge implementation
 1.960130485007504e-05
 Beta values for own Ridge implementation
@@ -2310,11 +2412,11 @@ Beta values for Scikit-Learn Ridge implementation
  -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081  -0.01416528
  -0.01290947]
 Intercept from own implementation:
-1.0495569966278238
+1.0495569966278295
 Intercept from Scikit-Learn Ridge implementation
 1.0495569966278269
 MSE values for own Ridge implementation
-5.4959161509356135e-05
+5.4959161509377256e-05
 MSE values for Scikit-Learn Ridge implementation
 5.495916150936645e-05
 Beta values for own Ridge implementation
@@ -2328,11 +2430,11 @@ Beta values for Scikit-Learn Ridge implementation
   0.00817631  0.00472512  0.00149311 -0.00149956 -0.00424967 -0.00676387
  -0.00905423]
 Intercept from own implementation:
-1.0399676689527968
+1.0399676689527966
 Intercept from Scikit-Learn Ridge implementation
 1.0399676689527975
 MSE values for own Ridge implementation
-7.571105947979336e-05
+7.571105947979352e-05
 MSE values for Scikit-Learn Ridge implementation
 7.571105947979394e-05
 Beta values for own Ridge implementation
@@ -2346,7 +2448,7 @@ Beta values for Scikit-Learn Ridge implementation
   0.00579953  0.006162    0.00642221  0.00660427  0.00672607  0.0068011
   0.00683964]
 Intercept from own implementation:
-0.9999555851685968
+0.999955585168597
 Intercept from Scikit-Learn Ridge implementation
 0.999955585168597
 MSE values for own Ridge implementation
@@ -2587,9 +2689,9 @@ linear system as an equation would reduce this down to
    -
    <ipython-input-20-6f7a6bd7d79f>:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-<ipython-input-20-6f7a6bd7d79f>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2733,9 +2835,9 @@ with the form utilized in linear regression, viz.
    -
    <ipython-input-25-5dd54edf2138>:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-<ipython-input-25-5dd54edf2138>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2783,9 +2885,9 @@ K
    -
    <ipython-input-26-fe5b9d300cc0>:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-<ipython-input-26-fe5b9d300cc0>:10: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2820,9 +2922,9 @@ K
    -
    <ipython-input-27-25845e8df859>:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-<ipython-input-27-25845e8df859>:9: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2875,43 +2977,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
    -
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    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
   model = cd_fast.enet_coordinate_descent(
 
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@@ -3058,9 +3160,9 @@ which polynomial fits the data best.
    
 
    
 
    
 
    
-
    <ipython-input-30-bc298b802fe2>:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection='3d')
-<ipython-input-30-bc298b802fe2>:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94529/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   fig.colorbar(surf, shrink=0.5, aspect=5)
 
    
 
    
@@ -3221,7 +3323,7 @@ Python program using
    
 
    
 
    ---------------------------------------------------------------------------
 NameError                                 Traceback (most recent call last)
-<ipython-input-31-d985fb40c43d> in <module>
+Input In [31], in <cell line: 1>()
 ----> 1 scipy.misc.imread
 
 NameError: name 'scipy' is not defined
@@ -3293,54 +3395,42 @@ of data presented here (either the terrain data we propose or other data sets).<
 
               
    
               
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-
    
-    
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-    
    
-      
    
-        
-          By Morten Hjorth-Jensen
    
-        
-            © Copyright 2021.
    
-      
    
-    
    
-  
    
+  
    
+    
+      By Morten Hjorth-Jensen
    
+    
+        © Copyright 2021.
    
+  
    
+
 
 
 
       
    
     
    
   
-  
+  
 
-  
   
 
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html
index 13316222e..c72d38972 100644
--- a/doc/LectureNotes/_build/html/chapter4.html
+++ b/doc/LectureNotes/_build/html/chapter4.html
@@ -7,8 +7,8 @@
     
     6. Logistic Regression — Applied Data Analysis and Machine Learning
     
-  
-  
+  
+  
 
     
   
     
     
-  
+  
 
     
     
     
     
-    
     
     
+    
+    
+    
+    
     
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+    
     
     
-    
     
+    
     
     
     
     
     
-    
+    
+    
+
+    
     
   
   
@@ -94,7 +100,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
-
    
+
    
  
   About the course
  
@@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
-
    
+
    
  
   Review of Statistics with Resampling Techniques and Linear Algebra
  
@@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
-
    
+
    
  
   From Regression to Support Vector Machines
  
@@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
-
    
+
    
  
   Decision Trees, Ensemble Methods and Boosting
  
@@ -187,7 +193,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
-
    
+
    
  
   Dimensionality Reduction
  
@@ -204,7 +210,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
-
    
+
    
  
   Deep Learning Methods
  
@@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output"
                 data-placement="left">.ipynb
         
         
+                onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf
     
    
 
    
 
@@ -299,7 +305,7 @@ const thebe_selector_output = ".output, .cell_output"
         
    
 
         
-        
    
+        
    
             
             
    
                  Contents
@@ -339,7 +345,50 @@ const thebe_selector_output = ".output, .cell_output"
 
    
     
    
         
    
-        
+            
+            
    
+                
    Logistic Regression
    
+                
+                
    
+                    
+                
    
+            
    
+            
               
    
                 
   
    
@@ -1052,9 +1101,9 @@ Please also refer to the documentation for alternative solver options:
 
    
 
    ---------------------------------------------------------------------------
 ModuleNotFoundError                       Traceback (most recent call last)
-<ipython-input-8-12adb44b1c20> in <module>
-     34 
-     35 
+Input In [8], in <cell line: 36>()
+     32 print(accuracy)
+     33 print("Test set accuracy with Logistic Regression  and scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
 ---> 36 import scikitplot as skplt
      37 y_pred = logreg.predict(X_test_scaled)
      38 skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
@@ -1089,54 +1138,42 @@ Please also refer to the documentation for alternative solver options:
 
               
    
               
-        
             
-
-
-
-
    
-    
-    
     
    
     
    
-    
    
-      
    
-        
-          By Morten Hjorth-Jensen
    
-        
-            © Copyright 2021.
    
-      
    
-    
    
-  
    
+  
    
+    
+      By Morten Hjorth-Jensen
    
+    
+        © Copyright 2021.
    
+  
    
+
 
 
 
       
    
     
    
   
-  
+  
 
-  
   
 
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html
index 80e5afcd2..76d73571c 100644
--- a/doc/LectureNotes/_build/html/chapter5.html
+++ b/doc/LectureNotes/_build/html/chapter5.html
@@ -7,8 +7,8 @@
     
     8. Support Vector Machines, overarching aims — Applied Data Analysis and Machine Learning
     
-  
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@@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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   Review of Statistics with Resampling Techniques and Linear Algebra
  
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@@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output"
                 data-placement="left">.ipynb
         
         
+                onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf
     
    
 
    
 
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@@ -361,7 +367,72 @@ const thebe_selector_output = ".output, .cell_output"
 
    
     
    
         
    
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    Support Vector Machines, overarching aims
    
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@@ -1277,7 +1348,7 @@ converge. So, welcome to the promised land of quadratic programming.
    
 
    
 
    ---------------------------------------------------------------------------
 ModuleNotFoundError                       Traceback (most recent call last)
-<ipython-input-4-703716d317a7> in <module>
+Input In [4], in <cell line: 2>()
       1 import numpy
 ----> 2 import cvxopt
 
@@ -1388,54 +1459,42 @@ With  the slack constants this leads to the additional constraint 
-        
-            
-            
    
-                
    previous
    
-                
    7. Optimization, the central part of any Machine Learning algortithm
    
-            
    
-            
+                
+
     
+    
+        
+        
    
+            
    previous
    
+            
    7. Optimization, the central part of any Machine Learning algortithm
    
+        
    
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+    
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+        
    next
    
+        
    9. Decision trees, overarching aims
    
     
    
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-          By Morten Hjorth-Jensen
    
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-            © Copyright 2021.
    
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+    
+      By Morten Hjorth-Jensen
    
+    
+        © Copyright 2021.
    
+  
    
+
 
 
 
       
    
     
    
   
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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index d8b307c4f..3f80fa6dd 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -7,8 +7,8 @@
     
     9. Decision trees, overarching aims — Applied Data Analysis and Machine Learning
     
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@@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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   Review of Statistics with Resampling Techniques and Linear Algebra
  
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@@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output"
                 data-placement="left">.ipynb
         
         
+                onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf
     
    
 
    
 
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@@ -414,7 +420,125 @@ const thebe_selector_output = ".output, .cell_output"
 
    
     
    
         
    
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    Decision trees, overarching aims
    
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@@ -564,9 +688,9 @@ predicting the target features of query instances is as follows:
    
 
    
 
    
 
    2nd degree coefficients:
-zero power:  3.975510299261579
-first power:  -0.12345260617257443
-second power:  -0.0003043256065368937
+zero power:  0.18790439176058887
+first power:  -0.014599964106338128
+second power:  0.00010403373827253124
 
    
 
    
 _images/chapter6_1_1.png
@@ -801,9 +925,9 @@ s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
 
    
 
    ---------------------------------------------------------------------------
 ModuleNotFoundError                       Traceback (most recent call last)
-<ipython-input-2-7c394b1e8b71> in <module>
-      7 
-      8 from IPython.display import Image
+Input In [2], in <cell line: 9>()
+      6 from sklearn.tree import export_graphviz
+      8 from IPython.display import Image 
 ----> 9 from pydot import graph_from_dot_data
      10 import pandas as pd
      11 import numpy as np
@@ -1435,54 +1559,42 @@ trees can be substantially improved.
    
 
               
    
               
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-        
-            © Copyright 2021.
    
-      
    
-    
    
-  
    
+  
    
+    
+      By Morten Hjorth-Jensen
    
+    
+        © Copyright 2021.
    
+  
    
+
 
 
 
       
    
     
    
   
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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html
index ab0d971ae..a4134fde8 100644
--- a/doc/LectureNotes/_build/html/chapter7.html
+++ b/doc/LectureNotes/_build/html/chapter7.html
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     10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods — Applied Data Analysis and Machine Learning
     
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   About the course
  
@@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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+
    
  
   Review of Statistics with Resampling Techniques and Linear Algebra
  
@@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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   From Regression to Support Vector Machines
  
@@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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   Decision Trees, Ensemble Methods and Boosting
  
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   Deep Learning Methods
  
@@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output"
                 data-placement="left">.ipynb
         
         
+                onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf
     
    
 
    
 
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                  Contents
@@ -395,7 +401,106 @@ const thebe_selector_output = ".output, .cell_output"
 
    
     
    
         
    
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+            
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+                
    Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
    
+                
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+                    
+                
    
+            
    
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@@ -473,12 +578,11 @@ predictor, averaged over all \(B\)
 
    ---------------------------------------------------------------------------
 NameError                                 Traceback (most recent call last)
-<ipython-input-1-eface79dac2c> in <module>
+Input In [1], in <cell line: 2>()
       1 heads_proba = 0.51
 ----> 2 coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
       3 cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)
       4 plt.figure(figsize=(8,3.5))
-      5 plt.plot(cumulative_heads_ratio)
 
 NameError: name 'np' is not defined
 
    
@@ -1445,54 +1549,42 @@ sketch for efficient proposal calculation. It introduces a novel sparsity-aware
 
               
    
               
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-        
-            © Copyright 2021.
    
-      
    
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+      By Morten Hjorth-Jensen
    
+    
+        © Copyright 2021.
    
+  
    
+
 
 
 
       
    
     
    
   
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+  
 
-  
   
 
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html
index 6f28965d2..3356d10f5 100644
--- a/doc/LectureNotes/_build/html/chapter8.html
+++ b/doc/LectureNotes/_build/html/chapter8.html
@@ -7,8 +7,8 @@
     
     11. Basic ideas of the Principal Component Analysis (PCA) — Applied Data Analysis and Machine Learning
     
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   About the course
  
@@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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+
    
  
   Review of Statistics with Resampling Techniques and Linear Algebra
  
@@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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+
    
  
   From Regression to Support Vector Machines
  
@@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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+
    
  
   Decision Trees, Ensemble Methods and Boosting
  
@@ -187,7 +193,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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@@ -204,7 +210,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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+
    
  
   Deep Learning Methods
  
@@ -281,7 +287,7 @@ const thebe_selector_output = ".output, .cell_output"
                 data-placement="left">.ipynb
         
         
+                onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf
     
    
 
    
 
@@ -299,7 +305,7 @@ const thebe_selector_output = ".output, .cell_output"
         
    
 
         
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                  Contents
@@ -388,7 +394,99 @@ const thebe_selector_output = ".output, .cell_output"
 
    
     
    
         
    
-        
+            
+            
    
+                
    Basic ideas of the Principal Component Analysis (PCA)
    
+                
+                
    
+                    
+                
    
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@@ -544,10 +642,10 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.03382304823545749
-4.021250026482402
-[[0.9252772  2.69061276]
- [2.69061276 8.90540529]]
+
    0.046785461905835435
+4.240670854503034
+[[0.94986593 2.88137798]
+ [2.88137798 9.93586895]]
 
    
 
    
 
    
@@ -587,10 +685,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 
    
 
    
 
    
-
    0.08328216846752691
-2.094472507965532
-[[1.         0.67697934]
- [0.67697934 1.        ]]
+
    0.08271198519070039
+1.7306310662842432
+[[1.         0.58084359]
+ [0.58084359 1.        ]]
 
    
 
    
 
    
@@ -619,30 +717,30 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
    
 
    
-
    [[ 2.38295101  5.51289697]
- [ 0.51803019  2.61399851]
- [-1.09849763 -1.02255619]
- [-0.54016188 -0.53794784]
- [ 0.28634473  1.32721178]
- [-1.66619972 -6.88017651]
- [ 1.52811546  4.29512284]
- [ 0.427017    2.43493232]
- [-0.80842254 -4.37964744]
- [-1.02917662 -3.36383443]]
+
    [[-0.50488131 -2.2493023 ]
+ [-0.26115367 -1.92631966]
+ [-1.43556723 -2.99992698]
+ [ 0.64528459  2.57643113]
+ [-0.55273102 -2.09964817]
+ [ 0.31681097  1.26466619]
+ [-0.04673082 -0.56607416]
+ [ 1.53394148  5.38629412]
+ [ 0.15092012 -0.22014758]
+ [ 0.15410688  0.83402742]]
           0         1
-0  2.382951  5.512897
-1  0.518030  2.613999
-2 -1.098498 -1.022556
-3 -0.540162 -0.537948
-4  0.286345  1.327212
-5 -1.666200 -6.880177
-6  1.528115  4.295123
-7  0.427017  2.434932
-8 -0.808423 -4.379647
-9 -1.029177 -3.363834
-          0         1
-0  1.000000  0.930583
-1  0.930583  1.000000
+0 -0.504881 -2.249302
+1 -0.261154 -1.926320
+2 -1.435567 -2.999927
+3  0.645285  2.576431
+4 -0.552731 -2.099648
+5  0.316811  1.264666
+6 -0.046731 -0.566074
+7  1.533941  5.386294
+8  0.150920 -0.220148
+9  0.154107  0.834027
+         0        1
+0  1.00000  0.95302
+1  0.95302  1.00000
 
    
 
    
 
    
@@ -699,37 +797,37 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.086074  0.080593  0.088526  0.083276  0.078269  0.081046  0.076604   
-2   0.0  0.080593  0.076130  0.082896  0.078254  0.073810  0.076216  0.072216   
-3   0.0  0.088526  0.082896  0.096472  0.091051  0.085879  0.091696  0.086956   
-4   0.0  0.083276  0.078254  0.091051  0.086112  0.081388  0.086890  0.082536   
-5   0.0  0.078269  0.073810  0.085879  0.081388  0.077083  0.082295  0.078299   
-6   0.0  0.081046  0.076216  0.091696  0.086890  0.082295  0.089501  0.085163   
-7   0.0  0.076604  0.072216  0.086956  0.082536  0.078299  0.085163  0.081144   
-8   0.0  0.072442  0.068461  0.082503  0.078436  0.074530  0.081072  0.077349   
-9   0.0  0.068538  0.064931  0.078314  0.074573  0.070972  0.077211  0.073761   
-10  0.0  0.073062  0.069085  0.084862  0.080738  0.076780  0.084484  0.080647   
-11  0.0  0.069275  0.065641  0.080706  0.076895  0.073230  0.080582  0.077013   
-12  0.0  0.065730  0.062411  0.076804  0.073280  0.069884  0.076905  0.073583   
-13  0.0  0.062409  0.059378  0.073136  0.069877  0.066729  0.073436  0.070343   
-14  0.0  0.059294  0.056528  0.069685  0.066670  0.063752  0.070163  0.067282   
+1   0.0  0.088104  0.081216  0.088760  0.086997  0.085010  0.080343  0.079306   
+2   0.0  0.081216  0.075612  0.080764  0.079618  0.078300  0.072530  0.071897   
+3   0.0  0.088760  0.080764  0.094925  0.092249  0.089310  0.089233  0.087615   
+4   0.0  0.086997  0.079618  0.092249  0.089982  0.087470  0.086257  0.084927   
+5   0.0  0.085010  0.078300  0.089310  0.087470  0.085410  0.083021  0.081989   
+6   0.0  0.080343  0.072530  0.089233  0.086257  0.083021  0.086109  0.084269   
+7   0.0  0.079306  0.071897  0.087615  0.084927  0.081989  0.084269  0.082642   
+8   0.0  0.078323  0.071329  0.086021  0.083629  0.080998  0.082431  0.081022   
+9   0.0  0.077372  0.070810  0.084426  0.082339  0.080026  0.080571  0.079383   
+10  0.0  0.071946  0.064637  0.081990  0.079001  0.075770  0.080642  0.078768   
+11  0.0  0.071044  0.064041  0.080699  0.077930  0.074922  0.079218  0.077511   
+12  0.0  0.070222  0.063524  0.079476  0.076927  0.074144  0.077846  0.076306   
+13  0.0  0.069475  0.063084  0.078314  0.075987  0.073433  0.076518  0.075145   
+14  0.0  0.068799  0.062719  0.077203  0.075102  0.072783  0.075222  0.074019   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.072442  0.068538  0.073062  0.069275  0.065730  0.062409  0.059294  
-2   0.068461  0.064931  0.069085  0.065641  0.062411  0.059378  0.056528  
-3   0.082503  0.078314  0.084862  0.080706  0.076804  0.073136  0.069685  
-4   0.078436  0.074573  0.080738  0.076895  0.073280  0.069877  0.066670  
-5   0.074530  0.070972  0.076780  0.073230  0.069884  0.066729  0.063752  
-6   0.081072  0.077211  0.084484  0.080582  0.076905  0.073436  0.070163  
-7   0.077349  0.073761  0.080647  0.077013  0.073583  0.070343  0.067282  
-8   0.073827  0.070492  0.077015  0.073629  0.070429  0.067402  0.064538  
-9   0.070492  0.067392  0.073575  0.070419  0.067432  0.064604  0.061923  
-10  0.077015  0.073575  0.080984  0.077452  0.074111  0.070950  0.067956  
-11  0.073629  0.070419  0.077452  0.074149  0.071022  0.068059  0.065249  
-12  0.070429  0.067432  0.074111  0.071022  0.068093  0.065314  0.062676  
-13  0.067402  0.064604  0.070950  0.068059  0.065314  0.062706  0.060228  
-14  0.064538  0.061923  0.067956  0.065249  0.062676  0.060228  0.057899  
+1   0.078323  0.077372  0.071946  0.071044  0.070222  0.069475  0.068799  
+2   0.071329  0.070810  0.064637  0.064041  0.063524  0.063084  0.062719  
+3   0.086021  0.084426  0.081990  0.080699  0.079476  0.078314  0.077203  
+4   0.083629  0.082339  0.079001  0.077930  0.076927  0.075987  0.075102  
+5   0.080998  0.080026  0.075770  0.074922  0.074144  0.073433  0.072783  
+6   0.082431  0.080571  0.080642  0.079218  0.077846  0.076518  0.075222  
+7   0.081022  0.079383  0.078768  0.077511  0.076306  0.075145  0.074019  
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+11  0.075797  0.074050  0.075201  0.073904  0.072647  0.071421  0.070216  
+12  0.074760  0.073183  0.073811  0.072647  0.071523  0.070428  0.069355  
+13  0.073768  0.072364  0.072452  0.071421  0.070428  0.069465  0.068525  
+14  0.072813  0.071585  0.071115  0.070216  0.069355  0.068525  0.067719  
 
    
 
    
 
    
@@ -918,10 +1016,10 @@ We can write our own code or simply use either the functionaly of numpy<
 
    
 
    
 
              0         1
-0  4.038506  2.027865
-1  2.027865  2.037559
-[[4.03850557 2.02786488]
- [2.02786488 2.03755944]]
+0  3.967536  1.983164
+1  1.983164  2.000755
+[[3.9675364  1.98316352]
+ [1.98316352 2.00075534]]
 
    
 
    
 
    
@@ -948,8 +1046,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
 
    
 
    
 
    Centered covariance using own code
-[[4.03850557 2.02786488]
- [2.02786488 2.03755944]]
+[[3.9675364  1.98316352]
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 _images/chapter8_65_1.png
@@ -1009,16 +1107,16 @@ questions.
    
 
    
 
    
 
    Eigenvalues of Covariance matrix
-5.299267190588216
-0.7767978193240488
+5.197738983259782
+0.7705527590466072
 First eigenvector
-[0.84924834 0.52799362]
+[0.84977962 0.52713812]
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-[-0.52799362  0.84924834]
+[-0.52713812  0.84977962]
 
    
 
    
 
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-[0.84924834 0.52799362]
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-[[-1.5378811  -0.94639099]
- [ 0.86145244  0.89288636]
- [-0.00445655  0.81633628]
- [ 0.07145103 -1.00433417]
- [ 2.03707133 -0.48476997]
- [ 0.72174172 -1.4557763 ]
- [-0.55854694  1.60673226]
- [ 1.6999536   0.43766686]
- [-1.10405456  0.31718909]
- [-2.18673098 -0.17953942]]
+[[-1.5378811   0.94639099]
+ [ 0.86145244 -0.89288636]
+ [-0.00445655 -0.81633628]
+ [ 0.07145103  1.00433417]
+ [ 2.03707133  0.48476997]
+ [ 0.72174172  1.4557763 ]
+ [-0.55854694 -1.60673226]
+ [ 1.6999536  -0.43766686]
+ [-1.10405456 -0.31718909]
+ [-2.18673098  0.17953942]]
 
    
 
    
 
    
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     13. Neural networks — Applied Data Analysis and Machine Learning
     
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   About the course
  
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   Review of Statistics with Resampling Techniques and Linear Algebra
  
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   From Regression to Support Vector Machines
  
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diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html
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     7. Optimization, the central part of any Machine Learning algortithm — Applied Data Analysis and Machine Learning
     
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   About the course
  
@@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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   Review of Statistics with Resampling Techniques and Linear Algebra
  
@@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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   From Regression to Support Vector Machines
  
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    Optimization, the central part of any Machine Learning algortithm
    
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+      By Morten Hjorth-Jensen
    
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+        © Copyright 2021.
    
+  
    
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diff --git a/doc/LectureNotes/_build/html/clustering.html b/doc/LectureNotes/_build/html/clustering.html
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     12. Clustering and Unsupervised Learning — Applied Data Analysis and Machine Learning
     
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@@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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   Review of Statistics with Resampling Techniques and Linear Algebra
  
@@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
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    Clustering and Unsupervised Learning
    
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     Index — Applied Data Analysis and Machine Learning
     
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     Applied Data Analysis and Machine Learning — Applied Data Analysis and Machine Learning
     
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     2. Linear Algebra, Handling of Arrays and more Python Features — Applied Data Analysis and Machine Learning
     
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   About the course
  
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   Review of Statistics with Resampling Techniques and Linear Algebra
  
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     Teaching schedule with links to material — Applied Data Analysis and Machine Learning
     
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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and 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     1. Elements of Probability Theory and Statistical Data Analysis — Applied Data Analysis and Machine Learning
     
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   About the course
  
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   Review of Statistics with Resampling Techniques and Linear Algebra
  
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   From Regression to Support Vector Machines
  
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                  Contents
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    Elements of Probability Theory and Statistical Data Analysis
    
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-          By Morten Hjorth-Jensen
    
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-            © Copyright 2021.
    
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+      By Morten Hjorth-Jensen
    
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+        © Copyright 2021.
    
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     Teachers and Grading — Applied Data Analysis and Machine Learning
     
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     Textbooks — Applied Data Analysis and Machine Learning
     
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\n",
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\n",
       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
      "metadata": {
       "filenames": {
        "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_9_0.png"
-      },
-      "needs_background": "light"
+      }
      },
      "output_type": "display_data"
     }
@@ -516,16 +515,15 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
      "metadata": {
       "filenames": {
        "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png"
-      },
-      "needs_background": "light"
+      }
      },
      "output_type": "display_data"
     }
@@ -585,27 +583,26 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [1.99274513]\n",
+      " [2.02408959]\n",
       "Coefficient beta : \n",
-      " [[5.03570747]]\n",
-      "Mean squared error: 0.27\n",
-      "Variance score: 0.88\n",
+      " [[4.92811987]]\n",
+      "Mean squared error: 0.25\n",
+      "Variance score: 0.89\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.42\n"
+      "Mean absolute error: 0.39\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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V7t27VVJS4tpwAhNstmzZogEDBqiyslJTpkxx/HMNDQ0qKSlRfX09wQYAgqa6Who/PvV1VVXSuHFej6ZwVFZKU6dKLW/xVsvGE09IZWW+vo5X92/fl3tbFi9erJ49e2ry5Ml+DwUA4JZt29y9DqnFYtKsWa3DhnTgsfJy8zo/Xue008zgc/LJ2b1+AoEINvv379fixYs1Y8YMtW0bqLYfAEA2evd29zqktnKltHVr4u8bhlRba16Xy9f5+GMz0Dz7rPn1669n9/oJBCLYvPjii3rvvff0gx/8wO+hAADcNHq0FI0emJpoKRKRSkvN6+COXFXJ0nmdb31L6tat+eMPPJDd6ycQiPLIxIkTFZBWHwCAm4qKpEWLzD6MSKT5tIUVdu66i8ZhN+WqSub056dPb/3Y/v3Snj3Sj36U3RhsBKJiAwAIsbIys4m0b9/mj0ej7jWx4oBcVclSvY6dBx4ww62H+84FomIDAAi5sjJpyhSz32LbNvP/2x89mkqNF3JVJUv2Onb27/c00Fio2AAAcqOoyFzSPW2a+ZlQ451cVckSvU68H//Y8ypNvMDsY5Mp9rEBACCBWCw3VbKaGmnAgNaPJ6nSeHX/ZioKAICwsqpkXrILLhddJN1/v7evmwDBBgAApG/jRmnQoNaPx2KSi4daposeGwAAkJ5IpHWoOfZYs5fGx1AjUbEBAABObdokDRzY+nGfqzTxCDYAACC1RKuaArYGKRjxCgAABFNNjX2o2bcvcKFGomIDAAASyZMqTTyCDQAA+c7t/Wq2bJH692/9+N69UttgR4dgjw4AACRXWSnNmiVt3XrgsWjUPO4gkx2G87BKE48eGwAA8lVlpXlWU3yokaS6OvPxykrnz/Xee/ahZu/evAk1EsEGAID8FIuZlRq70GE9Vl5uXpdKJCL162f/PAGfemqJYAMAQD5aubJ1pSaeYUi1teZ1iWzdal+l+fLLvKrSxMuvGAYAAEzbtmV3Xba9NLk6YDNNBBsAANyQ6xt9796ZXVdXZzYXt/Tll1K7ds6e0+2GZRcxFQUAQLYqK83l0ePHS9Onm5/790+veTddo0ebYSJR5SUSkUpLzeviH7MLNYaRXqhxq2HZAwQbAACy4deNvqjIrJBIrcON9fVdd5nXvf++fQBqbEyvl8bNhmWPEGwAAMiU3zf6sjLpiSekvn2bPx6Nmo+XlZmBpuX3rfG1b5/e67nRsOwxgg0AAJkKwo2+rEzavFmqqpIqKszPNTXSsGHuVGniZduwnAM0DwMAkKmg3OiLiqRx4w587dXuwZk2LOcQFRsAADIVtBv95s32oebTT93ZlyaThuUcI9gAAJCpIN3oIxHpiCNaP24YUqdO7rxGOg3LPiHYAACQqSDc6P/zH/tg9ckn3uwe7KRh2UcRw8jTPZP/q6GhQSUlJaqvr1eXLl38Hg4AoBDZbVhXWmqGGi9v9IkqRfv2eV81yXJDQq/u3wQbAADckMudhzdskI48MvH3A7ILcDIEmwQINgCAgpKoSmN3TQCmhhLx6v5Njw0AAPng3XedhRopMLsA+4F9bAAABwT0xOaC5zTQxIvfHDB+j5uQo2IDADD5cZAjkqupsQ81O3eauww74eMuwH4g2AAAAn9ic0GKRKQBA1o/bhhSt27B2xwwIAg2AFDo/D7IEc1t2WJfpdmxo/l7FKTNAQOEYAMAhS4IBznCFImY038tGYbUvXvzx4KwOWAAEWwAoNAF5SDHQlZba195+fDD5LsHB3wXYD+wKgoACh29Gv7K9iTusjJpyhRWs/0XwQYACp3Vq1FXZ38zjUTM7xdYr4bn3ntP6tev9ePbt0s9e6b3XEVFBbWkOxmCDQAUOqtXY+pUM8TEh5tC6tXI5R4+2VZpkBA9NgAAejVytYfP1q32oWbbNkKNSzgrCgBwQCHuPGzt4dPyduj2eUtUaZrhEMwECDYAgIzFYmZlJtFyd6u/qKYm84D3/vutK2GS+ZybNxdWiIzj1f2bHhsAQHilqkCls4dPJs25iao0S5eaY4l/7WjU7HUK+7Sfx+ixAQCEk5O+Ga/28PngA/tQs2WLGWo4vsIzBBsAQPg4PfvKiz18IhH76w3DnJLi+ApPEWwAAOGSztlXbp63tG2b/fNs2nTgdTm+wvy7V1dLjz/uydMTbAAA4ZJOeEh23pJ17cKFqZt6IxGpTx/7nz/iiANfF/rxFfHTgz/8oScvQbABAIRLuuEh0R4+liuvTNz3sn27fSDauNG+YlTIx1ckmh50GauiAADe8GtPnEzCQ1mZOd6zz259ndWX03I/m0z2pSnU4yuSTQ+6jIoNAMB9udrJN57Vu1FXJx16aOLr7PpmYjHpqqvsr2/Zl/PRR/ahZv361DfuZFNfYT6+ItX0oIsINgAAdzldkeT2a1pB6rzzpB077K9LFB6c9uW0bWt/QKVhSMcc42yshXh8RQ57hgg2AAD3pLMiyS3p9G4kCg+Z3njXrs1seqWszNx1uKpKqqgwP9fUhDPUSDntGaLHBgDgHq938m0pVe9GJGJOS/3qV2aFJFGfTyY33mz7RYqK3PkbBF0sZn506ybt2uX5y1GxAQC4J9fLmZ0EqY8+MkPNuHGJe1dGj5a6d3f2mv/8Z8EeXJk2a4pwwoSchBqJig0AwE25Xs7sVpD64x+lnTtTPw+BxrlEp6Z7jIoNAMA9bu7k64QbQcqazkqma1dp3z7Hwyp4TpZ3H3KIJy8diGBTV1en8847T927d1enTp00bNgwvf76634PCwCQrlwvZ3YjSDlZirx7d/4ec2Atg1+yxPyci3OonPxNP/7Yk5f2Pdh8/PHHGjVqlNq1a6fnnntO69ev15133qmuXbv6PTQAQCZyuZzZjSC1YYOz18rHYw782E9I8vVvFTEMfycMr7vuOr3yyitamWESbmhoUElJierr69WlSxeXRwcAyFgudx6urDSnPuKrBKWlZqhJFqQSVXrsVFXl1yqmRD0u1u/s5Z451dVmiEqiQVKJ5Pr92/dgM3jwYE2aNElbt27VihUr1LdvX11yySX60Y9+ZHt9Y2OjGhsbm75uaGhQaWkpwQYACl3LIDVypPS3v9kHq927nfd4WMcc1NTkz47AsZhZmUk0HeT172S9fpKjIxr69FFJXZ3r92/fp6I2bdqk++67T4MGDdLzzz+vmTNn6oorrtD//b//1/b6BQsWqKSkpOmjtLQ0xyMGAI/50RMRBta+MNOmmUuLBw60n4KJROxDTSQSnmMO0tlPyAtOpghvv92b1zZ81q5dO2PEiBHNHrv88suNk046yfb6L774wqivr2/6qK2tNSQZ9fX1uRguAHhr6VLDiEYNw7z1mB/RqPk4nFm61DAikeZ/Q8n+Mckwnn32wM+1/NuXlubn376iwv53bflRUeHtOJL8Tevr6z25f/u+j03v3r01ePDgZo8dc8wxWrp0qe31xcXFKi4uzsXQACC3EvVEJDpdGq05OdIh0WNlZdKUKf6cSO62XO8nlEiyv2lDgycv6XuwGTVqlP7zn/80e+ydd95Rv379fBoRAPgg1Q05EjHPWJoyJT9vtLni9BTpefOkm29u/XhYjjmwlsEn6XFRNOrefkLJ5Phv6nuPzZVXXqlVq1Zp/vz52rhxoyoqKvTb3/5Wl156qd9DA4Dc8bonolD6dpwuMx40yNtx+C3X+wkFiO/B5vjjj9eTTz6pJUuWaMiQIbr11lt111136dxzz/V7aACQO16eseTXXiZ+CMoUTBCUlUmPPdb6DCwv9hMKEN+Xe2eLfWwAhIKDfT8kpb+Xip97mfgh1b40+bh0O1N2e/v06CHde6/5b8JnXt2/CTYAEAQO9v1I+4bs914mLcfiZVPunj1SqnuAFXrmzjWnovK5OTiVPAi0Xt2/fZ+KAgDIm54Iv/cysXg9FRaJ2IeaaLT51926mR9z5oR7Ss7JyrDy8tD2WRFsACAo3D5jycu+HaesykHLgGUtYc8mVHz6qf3U08KF5g1882Zz6q6iwlwFtXOn+eH2OIImKIHWJ74v9wYAxHFzLxW/G2m9XMKeqJcm/rWsZcbWlFyi68O2lD4IgdZHVGwAIGjijwYYNy7zm621l0miEBCJmAdFerWXiZuVA2u5+v/5P/a/z4IF9gHK7XHkA78Drc+o2ABAvkrVkGv17UydaoaB+Bt/LvYycatyYLe6J16qNTCFVsEI0uZ8PqBiAwD5yGlDrtt9O+lwo3KQqEfHkuD4HdfHkU8KeHM+ieXeAJB/MlnK6/VyazvZLmGPxaS2SSYWnC5X92IpfT6wq3SVlpqhJgB7F7GPTQIEGwAFJUh70zhhhTDJfiosUdWosVHq0MHZazjZtDDTceQ7PwKtQ+xjAwDIv0bYTKbCIhHnoUZy1hvj55Scn9xqRM8jNA8DQD7xohHW6/+v3ukS9i+/lIqL039+p70xbi6lR2ARbAAgn7jdCGvXhxGNms2nblYxrMpBIomWpLu9uifVOJD3mIoCgHzi5t40Xu4K7NTevfa/y7nnmmGmgFf3IDMEGwDIJ24t5Q3CeUKRiNS+vf3r//735v8u1N4YZIxgAwD5xo2bvZ9NyPv22VdpzjzTPmiVlTU/96mqylz1RaiBDXpsACAfZdsI69duvE7OeAKyQLABgHyVTSNsrnfjTbTZ3uTJ0jPPJP/ZXDU4IxTYoA8AClEud+PNpkqTyS7LyAts0AcAcI+bTcjV1dKSJebn+Gbj/fvtQ82oUc5CTRAanJF3CDYAUKiybUJOdhBnJGIfigxDevllZ+PLt12WEQj02ABAIcu0CTnRFFFdnXTWWa2vP+YYaf369MbmV4Mz8hrBBgAKWSbHKTiZIkr1mJPxbN/u7GfcanBGKBBsAKBQZbraKNUUkaVPH7OCk814iooS99BkcqQCQo8eGwAoRNkcp+B06ueOO7IfT7JQI3GkAloh2ABAocl2tZHbe+AkG4+lZXjhSAUkwFQUAOS7dPtk0lltZLcB4PjxyceT7hSRk6mtWEz61a+kXr3S32UZBYVgAwBBkm5IyaRPJtPVRoYhtUlR6M9kisjpeHr1kqZNc3YtChZTUQAQFMn2hUl0fSZ9MplMJUUi9qEmGm39dbpTRE7Hs2GD8+dEweJIBQAIgnSPDrCOREg0hZPsSIR0j1NIdiRCJsvFW0o1nnhLl9JXExIcqQAAYZVJM6/TPpm7727dBOz0OIW2be1DjWEcGJd1EOe0aebnTPpe4seTTCTCEQpIiWADAH7L5OgAp30pV15pP52V6jgFu92DrbF4oaxMmjs3+TUcoQAHCDYA4LdMmnnT2W03Uc9NWZm0ebNUVSVVVJifa2vtQ018lcZOssMwnRo0yNl1HKGAJFgVBaBwuNEP4oVMmnlHjzarK076UgzjwDTOlCnNf2drKklK3kuTTKY7GLfk9v44KEhUbAAUhnRXHOWSFVISBYtIRCotbb4vTLI+GTvJpnEikdS9NIlks4NxS5n8HYAWCDYAws/Nm68XnDbztqwuJeqTSablNE6mVRop+x2MW8r07wDEIdgACDe3b75eSdXMm2hKx+qT+dWvnL2ONY2TTZXGkknTcyqZ/h2A/6LHBkC4ZXt8QC5YvT+NjdLDD5uPffih8z6goiLp8sulO+9MvTfN6NHZVWniZbqDcSplZWYvUBD7oRB4BBsA4ebVzdctyRpv0wla1jTO1KlmcIkPKVaQqa0196ZpKdMl3F42+8Y3NQNpYCoKQLgFeaWN270/yaZxEoWXbPalodkXAUSwARBuQb35etX703JvGsms1Ni9Rrab7dHsiwAi2AAIt6DefL1ovLVY0zjTpyd+brfQ7IuAIdgACL8g3ny97P1xY8VTOux2MK6pIdTAFzQPAygMQVtp41Xvj1srntJFsy8CgmADoHAE6eab6kiE+OXZTvgVaICAYSoKAPzgZu8PoQZoQrABAL9k2/uT614aIA8wFQUAfsq094cqDWCLYAMAfkun94dAAyRFsAGATFlnPOVqlRWhBkiJYAMAmUh2xpPb+7cQaADHCDYAginX1ZB0WGc8tQwW1hlPbm76F6RQE+T3BPgvVkUBCJ7KSql/f2n8ePNYgPHjza/TPRTSC16d8dRS0FY8Bfk9AeIQbAAEi9snXrvNyzOeLEGq0kjBf0+AOAQbAMERi0lXXOF9NSQbYTrjyYlcVagAlxBsAATHz35mVgESybQaEotJ1dXSkiXm52xuwmE74ymVXFSoABcRbAAEQ2WlNGeOs2vTqYa43RtinfGUKIhEIlJpaXpnPAWtShPPywoV4AHfg83cuXMViUSafRx22GF+DwtALlnTHU45rYZ40RtSaGc8eVWhAjzie7CRpK985Svatm1b08fatWv9HhKAXEo13RHPaTXEy96QQjrjye0KFeCxQOxj07ZtW6o0QCFLZxrDaTUknd4Qp8cZxCuUM56sCtXUqebY48eZboUKyIFAVGw2bNigPn366IgjjtB3v/tdbdq0ye8hAcglp9MY8+Y53/guF70h1hlP06aZn5Pd3POpStNSthUqIIcihuHvf1HPPfecPvvsMx155JHavn27brvtNr399ttat26dunfv3ur6xsZGNTY2Nn3d0NCg0tJS1dfXq0uXLrkcOgC3xGJmQ29dXeKbfDQqbd7svDJQXW02CqdSVZVZxSYd+ValSYSdh+GihoYGlZSUuH7/9j3YtPTpp59q4MCBuvbaa3XVVVe1+v7cuXM1b968Vo8TbIA8ZzX6SvbTHelWBlKFpUjEDEs1Nd7dnMMSaAAPeBVsAjEVFa9z58766le/qg0bNth+//rrr1d9fX3TR21tbY5HCMATbk93uLl6KROEGsAXgQs2jY2N+ve//63eCebci4uL1aVLl2YfAEKirMycbqqqkioqzM81NZn3cPjRG5LPvTRACPg+FXXNNdfo9NNP1+GHH64PP/xQt912m1asWKG1a9eqX79+KX/eq1IWgBDJVW8IVRrAMa/u374v9966daumTZumHTt2qEePHjrppJO0atUqR6EGQIHINphYq5e8QqABAsP3YPPoo4/6PQQAQdIyxHz0kXTVVc33pIlGzf6ZICwzJtQAgeJ7sAGAJpWV5m7BqXYhto5E8HMPFQINEEiBax4GUKASnetkJ9sjEbJFqHGPmyevAyLYAAiCZOc6JRJ/JEKu5POKpyAGCLdPXgdEsAEQBOkcgtlSNkcipCOfqzRBDBBenLwOiGADIAiyCSdOz5nKVD5XaaRgBggvT15HwSPYAPBfJuEkEpFKS82l317J5yqNFNwAkc7J60CaCDYA/Dd6tLmEO1GQaMnrIxHyvUpjCWqAyMXJ6yhYBBsA/kt2rpMdr49EsJNPgcYS1ADhtELn9TQjQol9bAAEg3WuU8t9bEpLpYULpUMP9fZIhDAFGktQA4RVoUt18rqX04wILd/PisoWZ0UBIZOrc53ihTHUSObfsn//1AGipsb7v3FLVlOz1Hxs1nvh5+aLyAmv7t9MRQEIFutcp2nTzM9e3nDD0kuTSLIpPq/7lFLx4+R1FAQqNgAKU1irNHbsjqooLTVDjd8Bwo8KHQLBq/s3wQZAYSmkQBOPAIGA8er+TfMwgMJRqKFGOjDFB4QcwQZA+BVyoAEKDM3DAMKNUAMUFCo2AMKJQAMUJCo2AMKHUAMULCo2AMKDQAMUPCo2AMKBUANAVGwA5DsCDYA4VGwA5C9CDYAWqNgAyD8EGgAJULEBkF8INQCSoGIDID8QaAA4QMUGQPARagA4RMUGQHARaACkiYoNgGAi1ADIABUbAMFCoAGQBSo2AIKDUAMgS1RsgEIWi0krV0rbtkm9e0ujR0tFRbkfB4EGgEsINkChqqyUZs2Stm498Fg0Ki1aJE2ZkrvAQ6gB4CKCDVCIKiulqVNbh4e6Oumss6Tu3aWdOw88bgWesjL3xkCgAeABemyAQhOLmZUauwBhPRYfaiQz8EydagYiNxBqAHiEYAMUmpUrm08/OWEFjvJyMxhlKhKxDzWGQagB4AqCDVBotm3L7OcMQ6qtNYNRJqjSAMgBemyAQtO7d3Y/n24w8jvQBGXlF4CcINgAUmHd/EaPNpuB6+oyCxfpBCO/Q02ylV9uNkIDCAymooDKSql/f2n8eGn6dPNz//7uNcoGTVGReWOXEgcPO5GIVFpqBiMn1/rdS2Ot/GrZT+R2I3QYxWJSdbW0ZIn5OZu+KiDHCDYobIV68ysrk554Qurbt/nj3bubn1uGEuvru+5KXcnyu0ojOVv5lW0jdFgVWtBH6KQVbGpra70aB5B7hX7zKyuTNm+Wqqqkigrz8/bt0tKlrQNPNGoGoWTTN0Go0lhSrfzKthE6rAo16CNU0go2Rx99tG666SZ9+umnXo0HyB1ufmb1Zdw4ado083NRkX3gqalJHWrs+LXiyWmDc6YrxMKo0IM+QiOtYLNs2TK98MILGjRokBYvXuzVmIDcKKSbX7o9E3aBx06mVRqvezicNjhnu0IsTAj6CIm0gs3IkSP197//Xbfffrtuvvlmfe1rX1N1dbVHQwM8Vig3P696JjKt0tiNp2dP6ZZb3As41sqvRGNMpxG6UBRS0EeoZdQ8/L3vfU/vvPOOTj/9dE2ePFlnnnmmNm7c6PbYAG8Vws3Pi56JbHppEo1n1y5pzhypVy93+jiSrfxKpxG6kBRK0EfoZbwqyjAMTZw4URdddJH+9Kc/aciQIbr66qu1Z88eN8cHeCfsNz8veiay6aVJNh7Lzp3mIZxuhJtEK7+cNEIXokII+igIaQWb3/zmN7rwwgs1dOhQlZSUaMKECXrllVd06aWX6t5779Wbb76pwYMHa/Xq1V6NF3BXmG9+bvZMuLHiKZ0zqtxqUs2kEbpQhT3oo2BEDMP5soXS0lKddNJJTR/Dhw9XcXFxs2vmz5+viooKvfXWW64P1k5DQ4NKSkpUX1+vLl265OQ1EUJh3Hl4yRKzhyWVigqzSTgRt1Y8OR2PparKbFxGbtnt1lxaaoYaAiFc5NX9O60jFZzsY3PhhRfqpptuynhAgC+sVUBhkm3PhNtLuNPtzaBJ1R9lZdKUKeEL+igYrp8V1bNnTy1fvtztpwWQrlRnQkUi5vfteia82JfGGo/T6SiaVP0TxqCPguH6kQqRSERjx451+2kBpCuTngkvdw+2xpPqfCqaVAFkgbOigDBLpzk6F7sHW+OxzqRqiSZVAFlKq3k4iGgeBhxI1hztx3EIsZj0s5+ZFZxduw48TpMqUDC8un8TbIBC5vcZT2FcjQbAkUCsigIQEn4HGgtNqgBcRo8NUGiCEmoAwAOBCjYLFixQJBJReXm530MBwsfLFU8AEBCBCTavvfaafvvb32ro0KF+DwUIH6o0AApEIILNJ598onPPPVcPPPCADjnkEL+HA+SvWEyqrjaPL6iupkoDoOAEIthceumlmjx5siZMmOD3UID8VVkp9e8vjR9vnsk0frz9dQQaACHm+6qoRx99VG+88YZee+01R9c3NjaqsbGx6euGhgavhoYwKJTlxJWV0tSpyUMLgQZAAfA12NTW1mrWrFl64YUX1KFDB0c/s2DBAs2bN8/jkSEU7E4pjkbNTeFysQFcrkJVLGb+nsmCS2mpeV0YQx0AxPF1g76nnnpKZ555pori/o9tLBZTJBJRmzZt1NjY2Ox7kn3FprS0lA360FyiCobVb9LyOAEvXj9Xoaq6OvG0U7yqKvaMARAYodx5eM+ePdqyZUuzxy644AIdffTR+ulPf6ohQ4akfA52HkYrsZjZa5LoFGnrVOuaGm8qGLkOVakOlbRUVEjTprn3ugCQhVDuPHzwwQe3Ci+dO3dW9+7dHYUawNbKlYlDjWQGjtpa8zq3KxjJpoUMwwwh5eXSlCnZh6r27aW9e51f37t3dq8HAHnA9+Zh5Bkv+kbcfs5t29y9Lh25ClVOqzTWtdGo+XcFgJALXLCprq72ewhIxIu+ES+e02llwosKhteh6uCDpU8+af340qXm9JfUvFpkBaC77qJxGEBBCMQ+NsgDVt9Iy2pEXZ35eGVlMJ5TMisT0WjiqkYkYq4S8qKC4WWoikTsQ41hmCHwiSekvn2bfy8a9b5RGgACxNfmYTfQPJwDXjTjet3ga4Umyb6C4dXN3vq96urs+2wy+b169pQ++qj143bPXyj79gDIe17dv6nYILV0+kb8fM54flUwiorMaTSpdcUok2mhSMR5qLFef9w4c/XTuHGEGgAFh2CD1LzoG8lFg29ZmbR5s7l/S0WF+bmmxvtpGTdC1eGHc8YTAGQgcM3DCCAv+kZy1eBrVTByrazMXNKdybQQJ3EDQMbosUFqXvSNePGc+crqi5k2Tfrgg9bf378/veXdAJAH6LGBf9zuG/HqOfNR/IncdqHG2tQPAOAIwQbOeNGMW+hLlCsrpbPOStxEPW+etGSJeRZULJbToQFAvmIqCunJh52H80EsJrVNo8Utl6eSA0AOhPIQTDcQbDxSiGEjV4YMkdatS+9ncnUqOQDkCD02yJ34vo/p083P/ftnvhMwDohE0g810oEG6/JypqUAIAmCDZrz6piDQjd8ePZNwNluWggABYBggwNiMfNASrvZSSoGmYtEpNdfb/14svOskvHiVHIACAmCDQ7w+piDQjN6tH1wicXMv2Wi5e6peHEqOQCEBMEGB+TimINCEYlIL7/c+nHDkNq0McNNt25mhax7d+fP6dWp5AAQEhypgANydcxBmE2YIL30UuvH9+07sKqsstIMNPHVsR49pHPPlQ45RJo713zM7lTyQti0EACywHJvHODV0QmFsmzcyRlPVnN2y79v/HJuqXXwKS01Qw1LvQGEBPvYJECwcZl145XsKwbp7KNiV5kI40Zzp50mPfts68f37m2+CZ8VHBP1McUHR6lwAiGAgkSwSaCgg41X1RC7QJJuxcBJZSIM4Sadk7irq809gVKpqvLnRHKvFFLVDoBjbNCH5rzcRK+sTNq82bzBVlSYn2tqnAeRQlg2ftZZ9qHmyy/tf2+pMJuz2ewRQI7RPJyPElVDrE303KiGFBVlXjVIZ9l4PlYm0qnSxCu05uxc/DsFgBao2OSbfKiGhLUyMX26fahpbEwdaiRzCibZpnxhWs6dD/9OAYQSwSbf5MMmel5XJmIxs19lyRLzcy5ujpGI+XotGYbUvr2z5ygqSrwpX9iWc+fDv1MAoUSwyTf5UA1xWpkYOTL9gJLrno0f/MD+9/j8c2dVmpbKyswpmL59mz8ejYZraiYf/p0CCCV6bPKNG9UQr1epWJWJqVPNUGC3bPy735UGDkxvKXiuezYy7aVJpaxMmjIl3CuFCq2fCEBgsNw732S7iV4u95ZJtGz8u9+V7rgjvaXg6ewBk21AuOIK6e67Wz/+2WdSx47ZPXeh8GKzRwChwnJvmLLp07AqHi3DgVXxcHs6x27Z+MaN5tRTuk2luerZiETsQ41hEGrSUUj9RAAChWCTjzLp0/BrlYq1bHzaNPPz3/6WWUDxumdj9mz7qadPPsl+6qlQ+dVP5EdzOYDAoMcmX6XbpxGUvWUyDShe9mx41UuD3PcTFcoxHgASItjks3Q20QvKKpVMA4q10ipVz0Y6e8Dccos0Z07rxxsapIMPdv48SC6bzR7TwYaAAMRUVOEIyiqVTDepc7tnIxKxDzWGQajJR2wICOC/CDaFIii73mYTUNzo2bj9dvu/QX09U0/5jA0BAfwXU1GFwsneMrlapWIFFLteiFQniGfTs0EvTXgFZaoVgO+o2BSSIO16m80J4i1XWqUKNQsX2oeajz8m1IRFUKZaAfiODfoKkdc7DwcJVZrEwvTvgA0BgbzDBn1wT7oVj3x09932oWbnTkKNlPszt7zGhoAA/otgg/CJRMxjEVoyDKlbt9yPJ2hyvQN1rgRpqhWAb5iKQnjcf780c2brxz/8UOrRI/fjCaJcnrnllzBNsQEh5tX9m1VRCAd6aZwJyg7UXsrVhoAAAompKOS3hx6yDzXbthFq7LAsGkDIUbFB/qJKkz6WRQMIOSo2yD//7//Zh5r33yfUpJJqB2rJ7EcaOTJ3YwIAFxFskF8iEel732v9uGFQZXAi2bJoy0cfSQMH5u/qKAAFjWCD/PDoo/Y34tpaqjTpSrQsOl6+L/0GULBY7o3go5fGG19+aU5LffSR/ffDsPQbQGCx8zAKz9Kl9qFm82ZCjRv+9rfEoUbiRGwAeYlVUfBONhul+VmlKZQN3lj6DSCECDZwT3wg2LBBeuCB5pvBRaNm42qyre1feEGaNKn14+++Kw0Y4P6YW6qslGbNSn/c+Yil3wBCiB4buMMuELRkVWESndvjdy+NdYZSy9dLNe58xYnYAHxEjw2CK9Ghii1ZN8/ycvOmalm+3D7U/OpXUlVV82u9EouZwczuBp9o3PmOE7EBhBDBBtlJFgjstGxIjUSkb37T/torr5TGjzerCl4vO07nDKUw4URsACFDjw2cSdRQmyoQJFJdbYaWliKR1iHJ2lPFyxttITfSlpVJU6YURsM0gNAj2CC1ZA21jY2ZPee8ea0fi0btQ5JhmIGnvNy8AXtxwy30RlpOxAYQEgQbJJeoodaqosydm/1r/Oc/5jlPdhUcizUVVF1t3oTdrixYZyilaqQdPTr71wIAeIYeGyTmpKH2gQdSH6qYjGFIRx7pfIrn7LPNADR9urv9NzTSAkAoEGyQmJOG2q1bpR/9yPw6nXCzfn3zwOR0imfXruZfu3mmEY20AJD3CDZIzGkVZdCg1IcqxjMM6Zhjmj9mTQWlW/lxeyl2WZl5ZENVlVRRYX6uqSHUAECe8D3Y3HfffRo6dKi6dOmiLl26aMSIEXruuef8Hhak9Bpq4wPB/Pn21731VuJl4cmmglJxeym21Ug7bZr5meknAMgbvgebaDSq22+/XatXr9bq1at18skna8qUKVq3bp3fQ0OqKkokIpWWHmioLSoy+15uuKH1tYYhfeUryV8v0VRQt27OxhvGpdgAgLQE8kiFbt266Ze//KUuvPDClNdypILHrFVRUvNqS8tjBt56S/rqV1v//Jo10rBh6b1myz1zYjFpwoTUP1dVxZJlAMgTXt2/A7XcOxaL6fHHH9enn36qESNG2F7T2Nioxri9UxoaGnI1vMJkVVHs9rG56y7z+26f8dRyT5VYjKXYAABHfJ+KkqS1a9fqoIMOUnFxsWbOnKknn3xSgwcPtr12wYIFKikpafooLS3N8WhDIBYz94NZssT8nKrpNlFD7THH2Iea11939+BKlmIDABwKxFTUl19+qffee0+7d+/W0qVL9eCDD2rFihW24cauYlNaWspUlFPJdhFOZ+WPHydx2429tPRA5SgTiY6KAAB4yqupqEAEm5YmTJiggQMH6v777095LT02aUi0i3DLfplk3nlHOuqo1o+vWiWdeKI740zGzSDiVsgDAKStIHpsLIZhNKvKwAWpdhF2chZTLqs0iQKMW2capToqgg35ACAv+d5jc8MNN2jlypXavHmz1q5dqxtvvFHV1dU699xz/R5afkrUP+NkF+FEe8HU1NiHmlde8SbUVFaaRyV4cXSC5OyoCLc2/AMA5JTvFZvt27fr/PPP17Zt21RSUqKhQ4fqL3/5i0455RS/h5Z/3DiFu+VeMLnupclFJSWdkMfycQDIK74Hm9/97nd+DyEc3DqF29ptuLZWOvzw1t9/5RVp5MishpqQG9NlTjjdyI8N/wAg7/g+FQUXOD2Fu29fZ7sIRyL2ocYwvAs1UnbTZelI56gIAEBeIdiEgdNTuC+6yPw60V4wN90ktbUp4v31r94u47bkqpKS7lERAIC8QbAJg2xP4Y5GzeBiBZ94hpG7G3yuKils+AcAoUWwCYNMT+GuqJCWLjWnd1rKVZUmXi4rKYkO3IxGWeoNAHkskBv0pYMN+mT22PTvn/ospZqa5lUIP3YPTsXpoZtuYedhAPCFV/dvKjZhkO7Uys6d9qFm+XJ/Q42U+0qKteHftGnmZ0INAOQ1KjZh4uQspSBWaexQSQGAUCuos6LSQbBpIVEg2L1bOuSQ1tcvX27u7AsAQA4V1FlRyILdWUr5UqUBACBL9NiEWUNDbs94AgDAZ1Rsci3T3pF0f44qDQCgAFGxyaVMT61O5+f27LEPNX7sSwMAQI7RPJwriQ6pTLU/Szo/lw9VGlY7AQDEPjb5zckhleXl5nWZ/FyiXpog7EsTL9OKFQAADhFsciHTU6ud/lxJif33grSM26o8tfx96urMxwk3AAAXEGxyIdNTqzM5xbq6OlhVGinzihUAAGki2ORCpqdWp3uKtWFIY8em9zO5kGnFCgCANBFsciHTU6tT/ZwliFWaeJlWrAAASBPBJhfSPaTS7ucSWbo0mFWaeJlWrAAASBPBJlcyPbX6tNPsqzE9e5qhxu3Trr2QacUKAIA0sfNwLpWVSVOmON/HJVEQqKrKr/1frMrT1Knm7xQf1JJVrAAASBPBJtfsDqlsae9eqX371o+//LI0apQnw/KcVbGaNat5I3E0aoaafKg8AQACj2ATNPmwe3Cm0q1YAQCQJoJNUOzbJ7Vr1/rxFSukMWNyPx6vOKlYAQCQIYJNEIS5SgMAQA6xKspP+/blxxlPAADkCSo2bnN6ejVVGgAAXEfFxk1OTq+OxexDzbJluQ01sZi5Y/GSJeZnzmkCAIQAFRu3WKdXtwwn1unVTzwhnXWW/c/mukpTWWm/7HrRIpZdAwDyGhUbO+lWM5ycXm0Xap5/3p9QM3Vq60MprQAWX10CACDPEGxacjKd1JKT06vtHps4MdvRpsdJACsvZ1oKAJC3CDbxMq1mpHMq9bPP+tcg7CSA1daa1wEAkIcINpZsqhlOT6WuqpJOPTXjIWbNaQBLJ6gBABAgBBtLNtWMVKdXS8E4vdppAHN6HQAAAUOwsWRTzSgqMkORXbUnEjE/gnB6daoAFokEI4ABAJAhgo0l02qGYSSv1ESj5lLvICyjLioyl3RLrcdsfR2EAAYAQIYINpZMqhmRiNTG5k9YVSVVVJifa2qCEWosZWVm0Orbt/njQQpgAABkiA36LFY1Y+pUM7DETyu1rGYYhn2gWb7cXB4edGVl0pQpzo5+AAAgjxBs4lnVDLtdee+6y/x+WM54KiqSxo3zexQAALiKYNNSompGmzb2oeaVV6SRI3M/TgAA0ArBxk7LakZYqjQAAIQczcPJJFrxtHIloQYAgAAi2CTy7W/bNwgbhvSNb+R8OAAAIDWmolpKtOJp3Tpp8ODcjwcAADhGxSbenDmJqzSEGgAAAo+KjcWul2bTJumII3I/FgAAkBEqNvPm2YcawyDUAACQZwq7YmMXaN59VxowIPdjAQAAWSucYBOLHdh0b9kyafHi1tfYLeGO/zmOHgAAINAKI9hUVrY+JiHexo3SwIHOfi4aNc+U4rBIAAACJ/w9NpWV5sGWiULN0qWJQ43dz9XVmY9XVro/VgAAkJWIYeT3FroNDQ0qKSlRfX29unTp0vybsZjUv3/iUBOJmBWYmprm00uZ/hwAAHAk6f07C+Gu2DzySOJwIpk9NbW1Zg9NvJUrM/s5AADgq/D22Jx7rlRR4ezabduSf51IXZ1UXU1jMQAAARG+YPP229Ixx6T3M717J/86kfJyaceOA1/TWAwAgK/CNRV13nmtQ02fPvb71Ujm46WlZqUl3ujRZkhJ9HOW+FAj0VgMAIDPfA82CxYs0PHHH6+DDz5YPXv21Le//W395z//Sf+JzjnH7Kmx/OY3Zi/M3XebX7cMKdbXd93VevqoqMisvCT7OTtWH3Z5udmADAAAcsr3YLNixQpdeumlWrVqlZYtW6Z9+/Zp4sSJ+vTTT9N7or/85cD/7tNH6tHD/N9lZdITT0h9+za/Pho1H080bZTo5w49NPk4aCwGAMA3gVvu/dFHH6lnz55asWKFxowZk/L6puVikpoWi1lVlfjgkukOwi1/rq7OnPJKpaJCmjYt9XX5gh2YAQAu8mq5d+Cah+vr6yVJ3bp1s/1+Y2OjGhsbm75uaGhofZFhmOGmvFyaMsW8ARcVSePGpT+glj9XXe3s55w2IOdaJgGFHZgBAHnC96moeIZh6KqrrtI3vvENDRkyxPaaBQsWqKSkpOmjtLQ00ZN5MyWUqrE4UUNyEFRWmhsPjh8vTZ9ufu7fP3mzMzswAwDySKCCzWWXXaZ//etfWrJkScJrrr/+etXX1zd91NbWJn9Sp3vSOOWksdiuIdlvmQSUWMys1NjNVtIoDQAIoMAEm8svv1x/+tOfVFVVpWg0mvC64uJidenSpdlHUk6mhGIxc4ppyRLzc6obdaYNyX7JNKCwAzMAIM/43mNjGIYuv/xyPfnkk6qurtYRRxzhzhNb5zmlmhLKtH+krMzs38mHhtp0Akp8P5HTapfbVTEAADLke7C59NJLVVFRoT/+8Y86+OCD9cEHH0iSSkpK1LFjx8ye1OmUkDU907KSYU3PpKq+ZNqQnGuZBhSnDdBBbZQGABQc36ei7rvvPtXX12vcuHHq3bt308djjz2W+ZM6mRIqpP6RTANKPjdKAwAKku8VG9e20XnmGamhwfmUUKbTM/nICih1dfZBLtG0ndUoPXWqeU38zwa5URoAULB8r9i4ZvRoc0O8ceOc3WgLqX8km5Vc+dYoDQAoaOEJNukqtP6RbAJKWZm0ebNUVWXuqFxVJdXUEGoAAIETuCMV0pXxlsyxmLk5XarpmZqacE21cDQCACAACuZIhZwp1P6RfFnJBQBABgp3KkqifwQAgJAp3IqNJZ822gMAAEkRbCSmZwAACAmCTSo02wIAkDcINslkeo4UAADwRWE3DydjnSPVcndi6xypykp/xgUAABIi2NgppHOkAAAIEYKNnXTOkQIAAIFBsLFTSOdIAQAQIgQbO4V2jhQAACFBsLEzerS5+qnlSdiWSEQqLTWvAwAAgUGwsWOdIyW1DjdhPkcKAIA8R7BJJNtzpGIxqbpaWrLE/MwKKgAAPMcGfclkeo4UG/sBAOCLiGHYbdaSPxoaGlRSUqL6+np16dLF7+Ec2Niv5Z/VmsLi1HAAADy7fzMV5SY29gMAwFcEGzexsR8AAL4i2LiJjf0AAPAVwcZNbOwHAICvCDZuYmM/AAB8RbBxExv7AQDgK4KN27Ld2A8AAGQs7zfos7bhaWho8HkkcSZMkP71L+lvf5M++EA67DBp5EizUhOkcQIA4BPrvu32dnp5H2x27twpSSotLfV5JAAAIF07d+5USUmJa8+X98GmW7dukqT33nvP1T8M0tfQ0KDS0lLV1tYGYxfoAsf7ERy8F8HBexEc9fX1Ovzww5vu427J+2DTpo3ZJlRSUsI/0oDo0qUL70WA8H4EB+9FcPBeBId1H3ft+Vx9NgAAAB8RbAAAQGjkfbApLi7WnDlzVFxc7PdQCh7vRbDwfgQH70Vw8F4Eh1fvRcRwe50VAACAT/K+YgMAAGAh2AAAgNAg2AAAgNAg2AAAgNDIi2Bz77336ogjjlCHDh103HHHaeXKlUmvX7FihY477jh16NBBAwYM0G9+85scjTT80nkvKisrdcopp6hHjx7q0qWLRowYoeeffz6How23dP+7sLzyyitq27athg0b5u0AC0y670djY6NuvPFG9evXT8XFxRo4cKAeeuihHI023NJ9Lx555BEde+yx6tSpk3r37q0LLrig6bgeZO6vf/2rTj/9dPXp00eRSERPPfVUyp9x5f5tBNyjjz5qtGvXznjggQeM9evXG7NmzTI6d+5sbNmyxfb6TZs2GZ06dTJmzZplrF+/3njggQeMdu3aGU888USORx4+6b4Xs2bNMn7+858b//jHP4x33nnHuP7664127doZb7zxRo5HHj7pvheW3bt3GwMGDDAmTpxoHHvssbkZbAHI5P0444wzjBNPPNFYtmyZUVNTY/z97383XnnllRyOOpzSfS9WrlxptGnTxli0aJGxadMmY+XKlcZXvvIV49vf/naORx4+f/7zn40bb7zRWLp0qSHJePLJJ5Ne79b9O/DB5oQTTjBmzpzZ7LGjjz7auO6662yvv/baa42jjz662WMXX3yxcdJJJ3k2xkKR7nthZ/Dgwca8efPcHlrByfS9OOecc4zZs2cbc+bMIdi4KN3347nnnjNKSkqMnTt35mJ4BSXd9+KXv/ylMWDAgGaP/frXvzai0ahnYyxEToKNW/fvQE9Fffnll3r99dc1ceLEZo9PnDhRf/vb32x/5tVXX211/aRJk7R69Wrt3bvXs7GGXSbvRUv79+/Xnj17XD/wrNBk+l4sXrxY7777rubMmeP1EAtKJu/Hn/70Jw0fPly/+MUv1LdvXx155JG65ppr9Pnnn+diyKGVyXsxcuRIbd26VX/+859lGIa2b9+uJ554QpMnT87FkBHHrft3oA/B3LFjh2KxmHr16tXs8V69eumDDz6w/ZkPPvjA9vp9+/Zpx44d6t27t2fjDbNM3ouW7rzzTn366ac6++yzvRhiwcjkvdiwYYOuu+46rVy5Um3bBvo/+7yTyfuxadMmvfzyy+rQoYOefPJJ7dixQ5dccol27dpFn00WMnkvRo4cqUceeUTnnHOOvvjiC+3bt09nnHGG7r777lwMGXHcun8HumJjiUQizb42DKPVY6mut3sc6Uv3vbAsWbJEc+fO1WOPPaaePXt6NbyC4vS9iMVimj59uubNm6cjjzwyV8MrOOn8t7F//35FIhE98sgjOuGEE3Tqqadq4cKFevjhh6nauCCd92L9+vW64oordPPNN+v111/XX/7yF9XU1GjmzJm5GCpacOP+Hej/1+3QQw9VUVFRq6T94Ycftkp1lsMOO8z2+rZt26p79+6ejTXsMnkvLI899pguvPBCPf7445owYYKXwywI6b4Xe/bs0erVq7VmzRpddtllkswbq2EYatu2rV544QWdfPLJORl7GGXy30bv3r3Vt29flZSUND12zDHHyDAMbd26VYMGDfJ0zGGVyXuxYMECjRo1Sj/5yU8kSUOHDlXnzp01evRo3XbbbVT5c8it+3egKzbt27fXcccdp2XLljV7fNmyZRo5cqTtz4wYMaLV9S+88IKGDx+udu3aeTbWsMvkvZDMSs33v/99VVRUMGftknTfiy5dumjt2rV68803mz5mzpypo446Sm+++aZOPPHEXA09lDL5b2PUqFF6//339cknnzQ99s4776hNmzaKRqOejjfMMnkvPvvsM7Vp0/xWWFRUJOlAtQC54dr9O61WYx9YS/d+97vfGevXrzfKy8uNzp07G5s3bzYMwzCuu+464/zzz2+63louduWVVxrr1683fve737Hc2yXpvhcVFRVG27ZtjXvuucfYtm1b08fu3bv9+hVCI933oiVWRbkr3fdjz549RjQaNaZOnWqsW7fOWLFihTFo0CDjhz/8oV+/Qmik+14sXrzYaNu2rXHvvfca7777rvHyyy8bw4cPN0444QS/foXQ2LNnj7FmzRpjzZo1hiRj4cKFxpo1a5qW3nt1/w58sDEMw7jnnnuMfv36Ge3btze+/vWvGytWrGj63owZM4yxY8c2u766utr42te+ZrRv397o37+/cd999+V4xOGVznsxduxYQ1KrjxkzZuR+4CGU7n8X8Qg27kv3/fj3v/9tTJgwwejYsaMRjUaNq666yvjss89yPOpwSve9+PWvf20MHjzY6Nixo9G7d2/j3HPPNbZu3ZrjUYdPVVVV0nuAV/fviGFQawMAAOEQ6B4bAACAdBBsAABAaBBsAABAaBBsAABAaBBsAABAaBBsAABAaBBsAABAaBBsAABAaBBsAABAaBBsAABAaBBsAATOkiVL1KFDB9XV1TU99sMf/lBDhw5VfX29jyMDEHScFQUgcAzD0LBhwzR69Gj97//+r+bNm6cHH3xQq1atUt++ff0eHoAAa+v3AACgpUgkop/97GeaOnWq+vTpo0WLFmnlypWEGgApUbEBEFhf//rXtW7dOr3wwgsaO3as38MBkAfosQEQSM8//7zefvttxWIx9erVy+/hAMgTVGwABM4bb7yhcePG6Z577tGjjz6qTp066fHHH/d7WADyAD02AAJl8+bNmjx5sq677jqdf/75Gjx4sI4//ni9/vrrOu644/weHoCAo2IDIDB27dqlUaNGacyYMbr//vubHp8yZYoaGxv1l7/8xcfRAcgHBBsAABAaNA8DAIDQINgAAIDQINgAAIDQINgAAIDQINgAAIDQINgAAIDQINgAAIDQINgAAIDQINgAAIDQINgAAIDQINgAAIDQINgAAIDQ+P9dR2WCXKGXQwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
      "metadata": {
       "filenames": {
        "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_19_1.png"
-      },
-      "needs_background": "light"
+      }
      },
      "output_type": "display_data"
     }
@@ -825,16 +822,15 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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GG4iJiSE8PJxzzz2XAwcONOuDiIiIiO9wOEDJz89nwIABPPPMM3Xuc+aZZ5KWllZ5++STT6o9f9NNN7FkyRIWLVrEV199RV5eHueccw5Wq9XxTyAiIiI+x+FS9xMnTmTixIn17hMcHEx8fLzd57Kzs3n55Zd5/fXXGTt2LABvvPEGycnJrFixggkTJjjaJBEREfExLslBWb16NR06dKBnz55MmzaNw4cPVz63YcMGSktLGT9+fOW2xMRE+vbty9dff233eMXFxeTk5FS7iYiIiO9yeoAyceJEFi5cyBdffMFjjz3GDz/8wOmnn05xcTEA6enpBAUF0a5du2qvi4uLIz093e4xZ8+eTVRUVOUtOTnZ2c32CFXzdwIDA4mLi2PcuHG88sor2Gy2Rh9nwYIFtG3b1nUNFRERcTGnByiTJ0/m7LPPpm/fvkyaNIlly5axa9cuPv7443pfZxgGFovF7nO333472dnZlbf9+/c7u9keoyJ/Z9++fSxbtowxY8Zw4403cs4551BWVubu5omIiLQIl08zTkhIoHPnzuzevRuA+Ph4SkpKOHbsWLX9Dh8+TFxcnN1jBAcHExkZWe3mqyrydzp27MigQYO44447+OCDD1i2bBkLFiwAYO7cufTr14/w8HCSk5O57rrryMvLA8zhtb/+9a9kZ2dX9sbMmjULMHN9UlNTiYiIID4+niuuuKLa8JuIiIincHmAcuTIEfbv309CQgIAgwcPJjAwkOXLl1fuk5aWxtatWxkxYoSrm+OYjHWwaiIsSTLvM9a5pRmnn346AwYM4L333gPAz8+Pp556iq1bt/Lqq6/yxRdfcMsttwAwYsQInnjiCSIjIytnUc2cOROAkpIS7r//fn788Ufef/999u7dy9SpU93ymUREROrj8CyevLw89uzZU/l47969bN68mejoaKKjo5k1axYXXXQRCQkJ7Nu3jzvuuIOYmBguuOACAKKiorj66quZMWMG7du3Jzo6mpkzZ9KvX7/KWT0eIWMdrBgNGGBYoSgd0lfA2NUQO7LFm9O7d29++uknwJymXSElJYX777+fa6+9lnnz5hEUFERUVBQWi6XWTKqrrrqq8ueuXbvy1FNPMWTIEPLy8mjTpk2LfA4REZHGcDhAWb9+PWPGjKl8PH36dACmTJnCc889x5YtW3jttdfIysoiISGBMWPGsHjxYiIiIipf8/jjjxMQEMCll15KYWEhZ5xxBgsWLMDf398JH8lJtj5AZXAC5r3F39w+ZlmLN6dqjs6qVat46KGH2L59Ozk5OZSVlVFUVER+fj7h4eF1HmPTpk3MmjWLzZs3c/To0crE299//50TTzyxRT6HiIhIYzgcoIwePRrDMOp8/rPPPmvwGCEhITz99NM8/fTTjr59y8nacjw4qWBYze1usGPHDlJSUvjtt98466yzuOaaa7j//vuJjo7mq6++4uqrr6a0tLTO1+fn5zN+/HjGjx/PG2+8QWxsLL///jsTJkygpKSkBT+JiIhIwxwOUFqNtv3MYZ2qQYrF39zewr744gu2bNnCv//9b9avX09ZWRmPPfYYfn5mCtFbb71Vbf+goKBaVXl//vlnMjMzefjhhyunaa9fv75lPoCIiIiDtFhgXfreBVjMoATK7y3Q926Xvm1xcTHp6en88ccfbNy4kYceeojzzjuPc845h7/85S9069aNsrIynn76aX799Vdef/11nn/++WrH6NKlC3l5eaxcuZLMzEwKCgro1KkTQUFBla9bunQp999/v0s/i4iISFMpQKlL7EgzITZ+HIR2NO/HroFY1840+vTTT0lISKBLly6ceeaZrFq1iqeeeooPPvgAf39/Bg4cyNy5c3nkkUfo27cvCxcuZPbs2dWOMWLECK655homT55MbGwsc+bMITY2lgULFvD2229z4okn8vDDD/Pf//7XpZ9FRESkqSxGfQklHionJ4eoqCiys7Nr1UQpKipi7969pKSkEBIS4qYWSkvQ71pExLvUd/2uST0oIiIirYWH1PdqDCXJioiItAYeVt+rIepBERERaQ3s1ffCKN/ueRSgiIiItAYeVt+rIQpQREREWoO2/Y6XzqjgpvpejeGzAYoXTk4SB+l3LCLiADfV92oqnwtQAgMDASgoKHBzS8TVKn7HFb9zERGph5vqezWVz83i8ff3p23bthw+fBiAsLCwykX2xDcYhkFBQQGHDx+mbdu2nrXIpIiIJ4sd6ZYFb5vC5wIUgPj4eIDKIEV8U9u2bSt/1yIi4lt8MkCxWCwkJCTQoUOHelf4Fe8VGBionhMRER/mkwFKBX9/f13EREREvJDPJcmKiIiI91OAIiIiIh5HAYqIiIh4HAUoIiIi4nEUoIiIiIjHUYAiIiIiHkcBioiIiHgcBSgiIiLicRSgiIiI+JKMdbBqIixJMu8z1rm7RU3i05VkRUREWpWMdbBiNGCAYYWidEhfYa5iHDvSzY1zjHpQREREfMXWB6gMTqD83ijf7l0UoIiIiPiKrC3Hg5MKhhUOrfK6IR8FKCIiIr6ibT+w2Fkk11YMhX9A+nJzCMgLghQFKCIiIr6i712AxX6QAl415KMARURExFfEjjQTYuPHQWhH8AuuvY9hNYeCPJwCFBEREV8SOxLGLIMLDkDcmNq9KRZ/cyjIwylAERER8VU1h3ws/ubjvne7s1WNogBFRETEV9Uc8okfB2PXQOwId7esQSrUJiIi4ssqhny8jHpQRERExOMoQBERERGPowBFREREPI4CFBEREfE4Dgcoa9euZdKkSSQmJmKxWHj//fcrnystLeXWW2+lX79+hIeHk5iYyF/+8hcOHjxY7RijR4/GYrFUu1122WXN/jAiIiLiGxwOUPLz8xkwYADPPPNMrecKCgrYuHEjd999Nxs3buS9995j165dnHvuubX2nTZtGmlpaZW3F154oWmfQERERMz1dVZN9LpFAevi8DTjiRMnMnHiRLvPRUVFsXz58mrbnn76aYYMGcLvv/9Op06dKreHhYURHx/v6NuLiIhITRnrzEUAMcxS9kXpkL7CrIESO7L+1219wCx937afWditvv1bkMtzULKzs7FYLLRt27ba9oULFxITE0OfPn2YOXMmubm5dR6juLiYnJycajcREREpt/UBKoMTaNyigBVBTfpyj1zp2KWF2oqKirjtttu44ooriIyMrNz+pz/9iZSUFOLj49m6dSu33347P/74Y63elwqzZ8/m3nvvdWVTRUREvFfWluPBSYWGFgW0F9RY/M3tHlDYzWUBSmlpKZdddhk2m4158+ZVe27atGmVP/ft25cePXqQmprKxo0bGTRoUK1j3X777UyfPr3ycU5ODsnJya5quoiIiHdp288c1qkapDS0KGBTgpoW5JIhntLSUi699FL27t3L8uXLq/We2DNo0CACAwPZvXu33eeDg4OJjIysdhMREZFyTVkUsG0/j17p2OkBSkVwsnv3blasWEH79u0bfM22bdsoLS0lISHB2c0RERHxfU1ZFNDDVzp2eIgnLy+PPXv2VD7eu3cvmzdvJjo6msTERC6++GI2btzIRx99hNVqJT09HYDo6GiCgoL45ZdfWLhwIWeddRYxMTFs376dGTNmcNJJJzFypGdkDouIiHgdRxcFrAhqqs3iudtjVjq2GIZhOPKC1atXM2bMmFrbp0yZwqxZs0hJSbH7ulWrVjF69Gj279/Pn//8Z7Zu3UpeXh7JycmcffbZ3HPPPURHRzeqDTk5OURFRZGdna3hHhERES/hyPXb4QDFEyhAERER8T6OXL+1Fo+IiIh4HAUoIiIi4nEUoIiIiIjHUYAiIiIiHkcBioiIiHgcBSgiIiLOkLEOVk2EJUnmvYcsuuetXLpYoIiISKtQsTJwxeJ7RemQvsIshBarIqRNoR4UERGR5rK3MjBG+XZpCgUoIiIizeXhKwN7IwUoIiIizeXhKwN7IwUoIiIizeXhKwN7IwUoIiIizVWxMnD8OAjtaN6PXeMxKwN7I83iERERcYbYkTBmmbtb4TPUgyIiIiIeRwGKiIiIeBwFKCIiIuJxFKCIiIhUpZL1HkFJsiIi0npkrDOru2ZtMWuU9L2reil6laz3GOpBERGR1qEi+EhfDoV/mPcrRlfvIVHJeo+hAEVERFqHxgQfKlnvMRSgiIhI69CY4EMl6z2GAhQREWkdGhN8qGS9x1CAIiIirUNjgg+VrPcYmsUjIiKtQ0XwUW0Wz921gw+VrPcIClBERKT1UPDhNTTEIyIiIh5HAYqIiIh4HAUoIiIi4nEUoIiIiIjHUYAiIiIilQzD4M4lW9j4+zG3tkMBioiIiFRa9MN+Fn73O1e+9B3ZBaVua4cCFBEREQHgj6xCHvx4BwD/HteTqLBAt7VFAYqIiIhgGAa3v7eFvOIyBnVqy19Hpri1PQpQRERE3CljHayaCEuSzPuMdW5pxlvr97N2VwZBAX48eskA/P0sbmlHBVWSFRERcZeMdbBiNGCYKysXpUP6CrMkf+zIFmvGgWMF3P+RObQzY1xPusW2abH3rot6UERERNxl6wNUBidQfm+Ub28ZNpvBLe/8RF5xGYM7t+Nvp3ZtsfeujwIUERERd8nacjw4qWBYze0t5I3vfuPrX44QGujPYx4wtFNBAYqIiIi7tO0HFv/q2yz+5vYWsDczn9mf/AzA7Wf1pktMeIu8b2M4HKCsXbuWSZMmkZiYiMVi4f3336/2vGEYzJo1i8TEREJDQxk9ejTbtm2rtk9xcTE33HADMTExhIeHc+6553LgwIFmfRARERGv0/cuwHI8SLH4m4/73t241zcjwdZqM5j59o8UlloZ0a09fx7a2eHmu5LDAUp+fj4DBgzgmWeesfv8nDlzmDt3Ls888ww//PAD8fHxjBs3jtzc3Mp9brrpJpYsWcKiRYv46quvyMvL45xzzsFqtdo9poiIiE+KHWkmxMaPg9CO5v3YNRA7ouHXViTYpi+Hwj/M+xWjGx2kvPTlr2z47RhtggOYc3F//DxkaKeCxTAMo8kvtlhYsmQJ559/PmD2niQmJnLTTTdx6623AmZvSVxcHI888gj/+Mc/yM7OJjY2ltdff53JkycDcPDgQZKTk/nkk0+YMGFCg++bk5NDVFQU2dnZREZGNrX5IiIi3mvVRDMoqZrDYvE3g5wxy+p96a5DuZzz1FeUWG3Muag/l56c7OLGmhy5fjs1B2Xv3r2kp6czfvz4ym3BwcGMGjWKr7/+GoANGzZQWlpabZ/ExET69u1buU9NxcXF5OTkVLuJiIi0ak1MsC212pjx1o+UWG2c3rsDl6QmubCRTefUACU9PR2AuLi4atvj4uIqn0tPTycoKIh27drVuU9Ns2fPJioqqvKWnNwykZ6IiIjHamKC7bxVv7Dlj2yiQgN5+MJ+WCyeNbRTwSWzeGp+WMMwGjwB9e1z++23k52dXXnbv3+/09oqIiLilZqQYLv1j2ye/mI3APef35cOkSGub2cTOTVAiY+PB6jVE3L48OHKXpX4+HhKSko4duxYnfvUFBwcTGRkZLWbiIhIq+Zggm1hiZUbF22izGZwVr94JvVPaNn2OsipAUpKSgrx8fEsX768cltJSQlr1qxhxAjzhA0ePJjAwMBq+6SlpbF169bKfURERJrMQ9a2aRGxI82E2AsOmPf1zP6ZvWwHv2TkExcZzIPne+7QTgWH1+LJy8tjz549lY/37t3L5s2biY6OplOnTtx000089NBD9OjRgx49evDQQw8RFhbGFVdcAUBUVBRXX301M2bMoH379kRHRzNz5kz69evH2LFjnffJRESk9fGQtW08zaqfD/PaN78B8N9LBtAuPMjNLWqYwwHK+vXrGTNmTOXj6dOnAzBlyhQWLFjALbfcQmFhIddddx3Hjh1j6NChfP7550RERFS+5vHHHycgIIBLL72UwsJCzjjjDBYsWIC/v3+t9xMREWk0e2vbWPzN7Q1MvfVVR/KKufmdnwC4amQKp/aIdXOLGqdZdVDcRXVQRETEriVJZtGymkI7msMgrYxhGEx7bQMrdhyiZ1wblv7zFEIC3dcZ4LY6KCIiIm7l5rVtPM2iH/azYschgvz9eGLySW4NThylAEVERHyHI1NvfTyZdm9mPvd9uB2Amyf04sRE7xpxUIAiIiK+o7FTb5u5jo2nK7XauGnxZgpLrQzv2p6rT0lx7AAekP2hAEVERHxLY6be2kumxSjf7v2e/mIPP+7PIjIkgMcuHeDYQoAlJXDmmfC//7mugY2gAEVERFqfJq5j4w1+2HeUZ8qrxT5wQT8S24Y2/sWGAf/4B3z+OVx7LRw54qJWNkwBioiItD4+mkybVVDCjf/bhM2AC07qyLkDEh07wJw5sGAB+PvD4sXQvr1L2tkYClBERKT1acI6Np7OMAxuffcnDmYX0aV9GPef39exA7z3Htx2m/nzU0/BhAnOb6QDFKCIiEjr4+A6Nt5g4Xe/89m2QwT6W3j68kG0CXagFuuGDfDnP5s/33ADXHedaxrpAIcryYqIiPiEimRaH7AzPZf7PzKnFN96Zm/6JUU1/sV//AHnnguFhTBxIsyd66JWOkY9KCIiIl6ssMTKP9/cSHGZjVE9Y7lqpANTivPyYNIkOHgQ+vSBRYsgwDP6LhSgiIiIeLH7P97O7sN5xEYEOzal2GYzh3U2bYIOHeCjj8CDlo9RgCIiIuKllm1J483vfsdigccvHUhMm+DGv/i22+CDDyA4GN5/H7p0cVUzm0QBioiIiBc6cKyAW981Vyn+x2ndOKVHTONf/Pzz8Oij5s/z58Pw4S5oYfMoQBEREfEypVYbNy7aTE5RGQOT2zJjfM/Gv/ijj+D6682f77sPLr/cNY1sJgUoIiIiXubRz3ay4bdjRAQH8NRlJxHo38jL+fr1MHmymX9y1VVw112ubWgzKEARERHxIsu3H+L/1v4KwKOdnqHTTxc1bpHDvXvh7LOhoADGjzeHeSwOrNHTwhSgiIiIeIn9RwuYsXgDAFfFLOXM0I8atxLz0aNw1llw+DAMGABvvw2BgS3T6CZSgCIiIuIFisvMeic5xQYDwnZxW/wr5hMNrcRcXAwXXAA//wxJSfDxxx41nbguClBERES8wOxPfubHA9lE+efzbKfZBPmVHX+yrpWYbTaYOhXWrjWDkk8+gY4dW6zNzeEZ5eJERESkTh//lMaCr/cBMLf/KpJsR8GoskNdKzHfccfx6rDvvQf9vGe1ZvWgiIiItLSMdbBqIixJMu/ryR/Zm5lfWe/kmlHdOOP0P9OolZifew4eecT8+eWX4YwznP85XEg9KCIiIi0pY52Z1IphDs0UpUP6CnN15diR1XYtKrVy/cKN5BWXcXKXdswc3xP8/cx9tz5gDuu07WcGJ1VXYv7wQ/jnP82f77sP/vKXFvpwzqMARUREpCVtfYDK4ATMe4u/ub3G6sqzlm5je1oO0eFBPH35IAIq6p3UtxLzt9/CZZd5Ra2T+miIR0RE3MuB4Q6fkLXleHBSwU6S66Lvf2fRD/uxWOCJyQOJjwpp+Ng7dhyvdTJhgsfXOqmPelBERMR9HBju8Blt+5mfs2qQUiPJ9cf9Wfzng20AzBjXk9N6xjZ83P37zQJsR4/CkCHwzjseX+ukPupBERER97E33FFfTQ9f0Pcu6ktyPZJXzLVvbKDEamPsCXFcN7p7w8c8csQMTg4cgN69zVonbdq47CO0BAUoIiLiPo0c7vApsSPNHqL4cRDa0bwfuwZiR1BmtfGvRZs4mF1ESkw4cycPwM+vgSGa/HxzWKeiENtnn0GMAysbeygN8YiIiOtlrKsx6+Qu80LdiOEOn1RHkuujn+9k3Z4jhAX588KVg4kMaWCIprQULr4YvvsO2rUzg5NOnVzU6JalHhQREXGtijyT9OVQ+Ef1tWMaGO5o8LjOSK71kCTdZVvSeGGNuQjgnIv70zMuov4X2Gzw17/Cp59CaKg5rHPiiS3Q0pahHhQREXGthqbVNlTTwx5nJdd6SJLunsO5zHz7RwCmnZrCOf0T63+BYcCMGbBwoVkl9t13YfjwFmhpy1GAIiIirtVQnkl9NT3q4kAtkRY5TjPkFZfxj9c3kF9iZVjXaG49s3fDL3rkEXjiCfPn+fNh4kSXttEdNMQjIiKu1bbf8SGcCs3NM3FWcq2bk3RtNoN/L97MLxn5xEeG8MwVVYqx1eXll+H2282f586FP//Z9Q11AwUoIiLiWs3JM6mLs4IeVwRPDnh8xS6Wbz9EUIAfz/15EDFtgu3vWJEnc0t7+PvfzG233gr//neLtNMdFKCIiIhr1TOttsmcFfS4InhqpI9/SuPpL/YAMPuCfpzUqZ39HSvyZJZ9DnOPgg0YbYHp57i8je6kHBQREXG9puSZNHS8piTXuuo4Dtp2MLsyKfZvp6Rw0eCkunfe+gBst8ETNrACw4C/WWDbg9ChZfJk3EEBioiIeCdnBT3ODp4akJlXzN9f20BhqZVTe8Rw28QGkmK/3wD/tUEpMAi4FrDYfLuYHRriERERaTElZTaue2Mjf2QVkhITzjOXN5AU++OP8MAxKAL6ADdgdi20gmJ2ClBERERayKwPt/H9vqO0CQ7gxb8MJiqsnkqxO3fCuHGQVwY9gRl+EESL5sm4k9MDlC5dumCxWGrdrr/+egCmTp1a67lhw4Y5uxkiIiIe5fVvf+PN737HYoGnLh9I9w71VIrdtw/GjoWMDDjpJPh4GXQZ77wkYy/g9ByUH374Aav1+JzyrVu3Mm7cOC655JLKbWeeeSbz58+vfBwUFOTsZoiIiHiMr3/J5N6l2wC4eUIvTu8dV/fOBw/CGWeYKxOfcIK5vk5sLHQ/s4Va6xmcHqDExsZWe/zwww/TrVs3Ro0aVbktODiY+Ph4Z7+1iIiIx/klI49rXt9Amc3g3AGJXDuqW907Z2SYPSe//gpdu8KKFWZw0gq5NAelpKSEN954g6uuugqL5fhy0atXr6ZDhw707NmTadOmcfjw4XqPU1xcTE5OTrWbiIiIU7lg0cCj+SVcteAHcorKGNSpLXMu7l/teljNsWMwYQLs2AFJSbByJSQ2sCaPD3NpgPL++++TlZXF1KlTK7dNnDiRhQsX8sUXX/DYY4/xww8/cPrpp1NcXFzncWbPnk1UVFTlLTk52ZXNFhGR1qa+FZdr7tfIIKa4zMo1r2/gtyMFJLUL5f/+kkpIoL/9nbOyYPx42LQJOnQwe066dHHSh/NOFsMwDFcdfMKECQQFBfHhhx/WuU9aWhqdO3dm0aJFXHjhhXb3KS4urhbA5OTkkJycTHZ2NpGRkU5vt4iIlMtYV6OI2V0tuspvi1k10QxKqq7LY/E3E1IraqTUXPm4YjaNnZWPDcNgxts/8t7GP4gIDuDd60bQM66OpNjsbHO2zg8/QPv2sGoV9PPNKcQ5OTlERUU16vrtskJtv/32GytWrOC9996rd7+EhAQ6d+7M7t2769wnODiY4OA61icQERHXqHlBLkqH9BV2L8j1HsMZAY6rA6XGLBrowMrH81b/wnsb/8Dfz8IzfxpUd3CSk2MO61QEJytX+mxw4iiXBSjz58+nQ4cOnH322fXud+TIEfbv309CQoKrmiIiIk3hwAXZLmcEOM48Tn3a9jOPW7MHpWoxtEaufPzRTwd59LOdAMw6tw+jetaR5JqTA2eeCd99B9HR5rDOgAHO+DQ+wSU5KDabjfnz5zNlyhQCAo7HQHl5ecycOZNvvvmGffv2sXr1aiZNmkRMTAwXXHCBK5oiIiJN1cgLcp3sBTgY5dsd4Kzj1KcxiwY2YuXjTb8fY8Zb5ho7V41M4cphne2/X24uTJwI33wD7dqZwcnAgU77OL7AJQHKihUr+P3337nqqquqbff392fLli2cd9559OzZkylTptCzZ0+++eYbIiLqKVgjIiItrxEX5Ho1N8Bx9nHq05gVlxsIYvYfLWDaaxsoLrNxRu8O3Hn2CfbfqyI4+fpraNvWDE5OOsl5n8VHuGSIZ/z48djLvQ0NDeWzzz5zxVuKiIiz9b3LHEqx+FfvvSg5Zg67NDS80phhk8Zw1nEa0tCigfWsfHwsv4Qp878nM6+YExIiefLyk/D3szOdOC8Pzj4b1q07HpwMGuTcz+EjtBaPiIjYV3FBjk6tvv3oevtTcGtqzLBJYzjrOM5QEcRccMC8jx1BUamVv722nl8z8kmMCmHBX0+mTbCd7//5+WZw8uWXEBUFn38Ogwe3/GfwEgpQRESkbrEjIahd9aGexuaANGbYpLFtcMZxXMBqM7hp0WY2/HaMyJAAFlw1hLjIkNo75ufDOefA2rUQGWkGJyef3PIN9iIum8UjIiI+ojk5IA0NmzSWs47jRIZhcP9H2/l0WzpB/n78319S7U8nzskxe06++goiIszgZMiQlm+wl1EPioiI1K+5ybI+6qUv97Lg630APHbpAIZ1bV97p2PHzCJsX311fFhn6NCWbaiXUoAiIiL186QcEA+x9MeDPPjJDgDuOvsEJg2ws2bOjo9gSGf4/nuIDIR350I3q9PX+/FVGuIREZH61TN7pTX69tcjzCyvdfLXkV24+pSU2jttXQpnngd/AFHAHVY49HdYUf68qwrO+RAFKCIi0jAPzAFxh+0Hc5j26npKrDYm9o3nrrNPrL068YEDMPFyMzhpB9wBJNrM56pW4HC0Mm8royEeERGRRvjtSD5/eeV7covLGNIlmscnD6xd62TfPjjtNDhQADHAfwA7oz+VnF1wzoeoB0VERKQBh3OK+PPL31UWYntxSiohgTUSh3fvhtNPN3tQOobBrUXQ3lb/gZVsXCf1oIiISNNkrPP9hM+MdWR/fj5/efxV9h8tpHNbP1696mSiQgOr77d9e3nPyQE44QRY9ibE+NVILPY375Vs3CgKUERExHEVKwynL4fCP8z7xlSX9SYZ6yj8fDxXfzuSnwuTiA04yuvx0+hQtKH6fps2wahRkJ4O/fvD6tXQ77zaxeXGrTULzHlgwTlPZDHsLZrj4XJycoiKiiI7O5vIyEh3N0dEpPVZNdEMSmqujxM/zmcSPktXns3fvx3CqtxUIv3zWNz1Nk4I21/9M65ZA5MmmQsApqbCZ59BdLR7G+7BHLl+qwdFRKS1ccbQTEusMOxGNpvBzRsHsyo3lRBLEa90uZcTQvdV/4xLl8KECWZwMno0rFyp4MSJlCQrItKaVAzNYDSvFkdLrTDsBoZhcN9H23n/yFACKOO5zg+TGm4WZav8jK++CldfDVYrnHceLFoEIXbW4JEmUw+KiEhrsvUBKoMTaPzCfzX5aHVZwzB45NOdlSXsH+30NGOiNplPVnzGL3vA1KlmcDJ1KrzzjoITF1CAIiLSmjhraMaDVxhujqe/2MPza34B4IHz+3LBJfce/4xxY+H7K+Cep82dZ8yAl1+GAA1GuILOqohIa+LMoRkfqy774tpfmbt8F2Cur/PnYZ2BzuZntFrh+uvhhRfMnWfPhltvhZpVZMVp1IMiItKa+OjQTHO9/u1vlYv/zRjXk7+d2vX4k8XFcPnlZnBisZj3t92m4MTFFKCIiLQmPjo00xzvbDjA3e9vBeC60d345+ndjz+Zl2dOI377bQgMhLfegr//3U0tbV00xCMi0tr42NBMc3z000FuecdcmXjqiC7cPKHX8cX/0tPh7LNh40YID4clS2DcODe2tnVRgCIiIq3S59vSuWnRZmwGXD4kmXsmVVmZeOdOOPNMc/G/mBj46CMYOtSt7W1tNMQjIiKtzufb0rn+zY2U2QzOH5jIA+f3Ox6crFsHI0aYwUn37vDNNwpO3EABioiItCoVwUmp1WDSgET+e8kA/P3Kg5N334UzzoCjR82g5OuvzSBFWpwCFBERaTVqBiePXzqAAP/yS+GTT8Ill5izds49F774AmJj3dvgVkwBioiItAp1Bic2m1l07aabwDDg2mvhvfcgLMzdTW7VFKCIiLRWzlg00EvUGZwUFZk1TubONXd8+GF49lnw93dvg0WzeEREWiVnLRroBeoMTo4ehfPPhy+/NGucLFgAV1zh7uZKOfWgiIi0Rs5aNNDDLduSZj842bULhg0zg5PISPjsMwUnHkYBiohIa+SsRQM92JJNByqDk3OrBierVpnBye7d0KkTfPUVjBnj7uZKDQpQRERao7b9jq/HU6GpiwZ6oDe/+53pb/2IzYBLBifx+OSBZnDy0kswfjwcO2YGKd9/D/184zP7GgUoIiKtkQ8vGvjyV3u5Y8kWDAOmDO/MIxf1x9+wwcyZMG0alJWZibGrVkFcnLubK3VQgCIi0hr56KKBz3yxm/s/2g7AP0Z1Zda5ffDLzzOTYR97zNzp3nth4UIICXFfQ6VBmsUjItJa+dCigYZh8OhnO5m3+hcApo/ryQ2nd8eyf7+5GvFPP5kByYIFMHmyexsrjaIARUREvJrNZnD/x9uZv24fAHeedQLTTusK330H550Hhw6ZQzkffKA1dbyIAhQREfFapVYbt77zE+9t+gOA+8/vy5XDOsP//gdXXWUWYuvfHz780JyxI15DOSgiIuKVCkus/OP1Dby36Q/8/SzMvXQAV6Z2NJNhr7jCDE4mTTKnEftKcNKKqv+qB0VERJwnY51Z7C1rizllue9dLqlMm11QylWv/sCG344REujHvD8N4vTYAJg4EVasMHe64w647z7fKVvfiqr/ggIUERFxlha6gB7KKeIvL3/PzkO5RIYE8MrUk0nNOQAnnw9790J4uJkMe/HFTntPj2Cv+q/F39zuI8nOVTl9iGfWrFlYLJZqt/j4+MrnDcNg1qxZJCYmEhoayujRo9m2bZuzmyEiIi2tBcrn783M56LnvmbnoVw6RATz1jXDSf1+BQwfbgYnXbvCN9+4Nzhx1TBMK6j+W5VLclD69OlDWlpa5W3LluMnb86cOcydO5dnnnmGH374gfj4eMaNG0dubq4rmiIiIi3FxRfQrX9kc/FzX3PgWCFd2ofx7t+H0vuJh8xpwwUFZoXYH35wb2XYil6k9OVQ+Id5v2K0c4IUH6/+W5NLApSAgADi4+Mrb7GxsYDZe/LEE09w5513cuGFF9K3b19effVVCgoKePPNN13RFBERaSkuvICu3nmYyS98w5H8EvokRvLuZSeQfOUl8Mgj5g633AKffALR0c1+r2ZxZS+SD1f/tcclAcru3btJTEwkJSWFyy67jF9//RWAvXv3kp6ezvjx4yv3DQ4OZtSoUXz99dd1Hq+4uJicnJxqNxER8TAuuoAu/uF3rn51PfklVkZ0a89bI9vQfswp5grEoaHmlOJHHvGMZFhX9iL5aPXfujg9SXbo0KG89tpr9OzZk0OHDvHAAw8wYsQItm3bRnp6OgBxNdY+iIuL47fffqvzmLNnz+bee+91dlNFRMSZKi6g1Wbx3N3kC6hhGDy+fBdPfbEHgAsHdWROwY8EnHotFBZCly7w/vswYIDTPkKzte1nJgdXDVKcOQzjQ9V/G2IxDMNw5Rvk5+fTrVs3brnlFoYNG8bIkSM5ePAgCQkJlftMmzaN/fv38+mnn9o9RnFxMcXFxZWPc3JySE5OJjs7m8jISFc2X0RE3KCkzMZt7x4vwPbvU5L51wdPY3npJXOH8ePN9XRiYtzYSjtqzmSq6EVqTE9HC03RdqecnByioqIadf12+TTj8PBw+vXrx+7duzn//PMBSE9PrxagHD58uFavSlXBwcEEBwe7uqkiIuIBcopKufaNDazbcwR/PwtPpkZwzn+mwqZNYLHArFlw552eMaRTU1N7kVpZjZPGcHkl2eLiYnbs2EFCQgIpKSnEx8ezfPnyyudLSkpYs2YNI0b45hiaiIg03sGsQi557hvW7TlCeJA/7ydlcM5Vk8zgJCYGFs+FU7+BpZ09t5JqxTDMBQfM+8YMcbXAFG1v4/QelJkzZzJp0iQ6derE4cOHeeCBB8jJyWHKlClYLBZuuukmHnroIXr06EGPHj146KGHCAsL44orrnB2U0RExIts3p/FtNfWk5FbTEKYP0vTlhF7/1PmkyNGwLyZsP1SSPfBXoZWVuOkMZweoBw4cIDLL7+czMxMYmNjGTZsGN9++y2dO3cG4JZbbqGwsJDrrruOY8eOMXToUD7//HMiIiKc3RQREfESS388yM1v/0hxmY3hIcUs+PBegtf/ZD55SRd44iHY+TA+W0nV1cm1XsjlSbKu4EiSjYiIeC7DMHh8xW6eWrkbgH/a9jF9/t34ZR6DUOAfwJDyRNOgtlCcWfsgoR3N4RRv1pzkWi/iUUmyIiLSCK1gBkdNRaVWZrz9Ix//lEaAtYzXfvuI4e+8jMUwoBNwIxBPlQs25r0v9jI4eYq2L1CAIiLibq1wBsehnCL+/tp6fjyQTdecdN5e+wztt202nxwXBlcUQFCVFxhWMKCyEFzVXgZfqaTaimqcNIbLZ/GIiEgDWtkMjp8OZHHeM+v48UA2l+1awecL/mEGJ1Ft4O234fbTINjOFGILkPpU0yqpumoBP3EZ9aCIiLhbY2dw+MAw0LsbDnD7ki34FxTw/KrHOHPzN+YTPYHrC2FUAnCX2YOEH2A7/uLio7D+X473LLXCHipfoB4UERF3a8wie65cJbcFlFptzFq6jRlv/0iPP3bzxRvXmcGJBbgAuAuIxQzAKvIxgmsu/GejST1LrayHylcoQBERcbfGLLLnxRfZzLxi/vTSdyxYt5e/rv+ADxbOJCHjMLQD7gQuBvyp3msUOxL87FQQb0ptkNZaY8TLh7U0xCMi4m6NmcHhpRfZnw5k8Y/XN1D2x0Fe++xpTtvzg/nEyA7wl0xoU2UIp2avkbNqg7TGGiM+MKylHhQREU/QUHn0xgwDeZh3Nhzg4ue/4aTvVrBi/j/N4CQ4GJ55Bt57FyL86u81akzPUmM46zjexIt73CooQBER8QZedJEtKrVy+3tbuO/1dTyyZA7zPniYqIIcGDQINm6E66+HDqeY3+brm5FT0bPUlFk7VTnrON6kOT1uHjI0pEqyIiLeotYsHs8r5PXbkXyuW7iRqK/X8t9PniAxNxPDzw/LHXfA3XdDUFDDB5HmWzXRTKSuOawVP67+Wit1VrRd7ZShIVWSFRHxRR5eyOuzbenc+eb3XP/ZS/x1w4fmxu7dsbz+Ogwb5t7GtTZ9y6dqO1rUzt7QkJvWO1KAIiLSWD5Qh8QVSq025nz6M9++9TmLPnqM7kfL18W59lp49FEID3dvA1ujppbO96BkbAUoIiKN4QOzIlwhLbuQf7/2PcMWv8B737xFoM2KkZCA5ZVX4Mwz3d281h1UNqXHzYNmPClAERFpDGd0ffvYxXLljkO88tQ73PPufzkhY5+58dJLscybB+3bu7VtgILKpmjq0JALKEAREWmM5nZ9+9DFsqjUyn8/+JH2/53Nq9+/R4Bhw9q+Pf7PPAOTJ4PF4u4mmjwon8JreNCqygpQREQao7ld3z5ysdxzOJd5D73O9a/Pplt5rol18mT8n34aYmPd3LoaPCifwqt4SDK2AhQRkcZobte3l18sDcPgnbU7KbjlNv77/VL8MCiOjSP4xRfwP+88dzfPPg/KpxDHqVCbiEhjNLfYlxdWgq2QXVjKU3e+wJDzxzDl+w/ww6Dwz38heOcO8NTgBLyquJ3UpkJtIiItoc4CWJ5d0fS7DXtIv/ZGzvvhEwByOyQQvuAV/CZ6wAydmuwlIYNH5FOISYXaREQ8jQclHzZGUUkZn9z+GKc+/zBDC7IAyLzyKmKeeRw88YthfUnIHpBP4WszuFqCelBERKSanV9uJO/qvzN49wYADid1JWL+S4SOHePmltWjqaXdW4KLy8d7E0eu38pBERERAMoKCvlm6k10GTOMwbs3UBQQxK833U6HX3Z4dnACnp2E7AMrC7uDAhQREXfxkFVjAf54fxnpXXsz/NUnCbaWsr3fMIo3bqbr4w95xwJ/LZ2E7MjvzpODJw+mHBQREXfwkMJtZYcOs2fKtfT+7D0AMsPbse+uBxj8135Ytk+HJV6SM9GSFVAd/d1punOTqAdFRMQd3N3tX1ZG+uzHKOzand6fvYcNCytHX0TZtm2kXj0AyxenmzkdhX+Y9ytGu7WHp0HNnQbuCEd/d5ru3CTqQRERcQc3dvuXfrGKrGnXEv/rTgB+juvKwUee4PS/nIPFYoFVV+GVVW9bqgKqo787L5vB5SkUoIiIuIM7uv0PHCDr+ptou/RdYoFjIRF8fOl1jH/ibnq3Cz++n3Im6teU352HlI/3JhriERFxh5bs9i8qouS+Byjp3pO2S9/FavHjrZPP4btPv+ZPC2bToWpwAl5d9bZFaMimRShAERFxB2fnTNibVWIY8OGHFPTsTdA9dxNUXMj3SSfy3wff4Iwv3uHMUX3NIZ2adAGuX0vmu7RiKtQmIuLt7BUC2w9FH6cSsvY7ANLbRPPCOddw2n/+xZgT4hp3zObkTKhyqtjhyPVbAYqIiLerWkX1GNjeAdaAnwElfgG8MvQCCmbcyrWTBhIa5N/g4ZpNlVOlDlqLR0SkNcnaAoVW+BisH1vwLza/d37cayQfX3YD/7pmIr3jW/DLnL1puN4wC0g8igIUEZGqvG1owmqFb9phffEg/lkG/hhsTOzF42OvYOIZOTzz50vx87OTZ+JKmgUkTqAARUSkgodUd220zz/HmDkTy5at+AO/R8XxyKiphA0vYm7iU8ROXAotHZyAKqeKUyhAERGp4MyhCVf2xGzYAHfcAZ9/jgXIDg7nqRGXsWn0ydyd8jonJUVA36Xum1XSkmXnxWcpQBERqeCsoQlX9cTs2AF33w3vvguYCbCvDTqbN8ZeyTUXnMydqcn4+f2j6cd3FlVOFSdQgCIiUsFZQxPOThLdtw9mzcJ4/XUsNhs2LCzpM5qnT/0ToycO44OxPYkKC3T8uK6kyqnSTApQREQqOGtowlk9Menp8MADGP/3f1hKS7EAn/YcztxT/kT8yJN54awT6BUf4dgxRbyE0yvJzp49m5NPPpmIiAg6dOjA+eefz86dO6vtM3XqVCwWS7XbsGHDnN0UERHHNKdCaNVKrrZiav15daQn5uhRuP12jK5d4dlnsZSWsrbLSZx35WM8fs3D3Hnrpbx21RB6+f9Uu3qsiI9weg/KmjVruP766zn55JMpKyvjzjvvZPz48Wzfvp3w8OPrPZx55pnMnz+/8nFQUJCzmyIivsxVSahNGZqomXOCH2A7ft/YnpijR+GJJ+DJJyEnBwuwMbEXj542hd19TmbG+J5cMjiJAH8/75txJOIgpwcon376abXH8+fPp0OHDmzYsIHTTjutcntwcDDx8fHOfnsRaQ2aenF2VVBTM+ekIjgJjga/4IaTRDMzYe5cePppyMsDYEdsF/572pV81WsYfx/VjRdHdaNNcJU/2SqGdpy31a6RRnF5Dkp2djYA0dHR1bavXr2aDh060LZtW0aNGsWDDz5Ihw4d7B6juLiY4uLiysc5OTmua7CIeL6mXJxd2eNgL+cEmxmcXHCg7tcdOgSPPQbz5kF+PgDbO6Tw5IjLWdl7OJcO6cya03sQHxXSuPdsjcXQ1JPks1waoBiGwfTp0znllFPo27dv5faJEydyySWX0LlzZ/bu3cvdd9/N6aefzoYNGwgODq51nNmzZ3Pvvfe6sqki4k2acnF2VY9DxrrynJMa6ss52foB3HsjLP0dSsyy9Fviu/PkiMtZ0X0IkwZ2ZPm4nqTEhNt/PXhWMTR39mCoJ8lnuXSxwOuvv56PP/6Yr776iqSkpDr3S0tLo3PnzixatIgLL7yw1vP2elCSk5O1WKCIL3HkIld1cbwKFn8zqbWui9KSJCj8o/b2oBhon9q0i2vFt3fDhjmsU8EPLH61E2z37YMHZsJr70KpuenHhB48MfIKVnVNZUzvDsyc0Is+iVENv+/GGXDku+PbKhfka2RSr7O4e2HAun6voR3r770St/CIxQJvuOEGli5dytq1a+sNTgASEhLo3Lkzu3fvtvt8cHCw3Z4VEfERjnbTN2U6sL0eB/yg5MjxYMfR4YGKb+/VghPM3JPTPjgeKPz4I8yZA4sXm2vnABs79uKJEVewNmUQQ9ps460+7zHkwlcafs+q56qq6FQYNLf5wYmjvSHu7sHwpJ4kcSqnByiGYXDDDTewZMkSVq9eTUpKSoOvOXLkCPv37ychIcHZzRERb+DoRa6xlUqrXmzDyr8oVQ1qKmbcNPXiajf3BDP3JGY4rFplBiZVJg+s6zKAeUMvYV3nAQxts5X/xd3B8DZbwNKx4fcDOwm55Z8pqJ1zghNH8zncnQujsvo+y+kByvXXX8+bb77JBx98QEREBOnp6QBERUURGhpKXl4es2bN4qKLLiIhIYF9+/Zxxx13EBMTwwUXXODs5oiIN2jKRa6h6cD2LrYG5nBOwQEzqDm6HoozHXvfqux9ezf84MdYmD0UfvgBAJvFj096n8LzQy5ka3x3hoVvOR6YgGPf+F0ZEDSlN8TdPRgqq++znB6gPPfccwCMHj262vb58+czdepU/P392bJlC6+99hpZWVkkJCQwZswYFi9eTESEKiKK+JT6hguqPldZ2KzKUElzL3J1XWyD2sGEb81tdeWyNPZ9q357L7LCOgt8bIP0zQCUBAazuO8ZvHjyBfzeLoFhXaNZlFrCsJ13l7cNx7/xNzYgaErialOCH0/owfDVsvqtfPq0S5NkXcWRJBsRcZP6kiehcYXNmpLwWfFHPe1zauWGQPXkyTrb6MD7/rgEHpkBH+6DPPPPaV5YJK8MmMirgydxJLwtY0/owLWjuzG4c3T1NjblG39j2tzUxNWmJB839/OIfe5OPnYRR67fClBExDXqu9hB7ecqCpsZgKV8U3Rq02bV1MzRqPn+VS+2Tbm4GgZ8+61Z9fXddysTXw+1T+CFAeewaMB4ikPCOHdAIv8Y1ZXe8U7+O9VQm5sTaDQ3YBPnaOrv0MN5xCweEWnlGhousFfYzABKs6i8QKYvb9qsmrqCE3tDD44MD5SUwNtvm6Xoy/NLADakDOCFgeewovsQAoMCmXxyMtNO7UpydFjjjuuohtpc17k/tMoMQuo6l8rn8BzuTj72AApQRMQ1GsqVsPecBZo8ZTVjnXkBthec4Gd+82zqxTYtDV56CZ57zvwZKAsM4oM+o3nppHPY0aEr7cODuGFYZ64c3pmYNk4si9CUPAS7U6oxc31WjK4/4PPVfA5v4+7kYw+gAEVEXKPe5EnD/nPQtG+NlQXTymo/19RucZsNvvgCXngB3n8fysxjH2sby8v9z+TNAWdyNCyKnnFtmHNKV84dmEhIoL9j79GQppZxrzj3dhmqsuoNPCH52M0UoIiIazQ0XGDvua33N21WTWXBNHsskHSeOabfmF6IzExYsMAMTPbsqdy8pUtfXux3Jst6jaTUP5DRvWK5+pQUTukeg8VisX+s5mpqEbSKc7/yjNpl+O0FfK18tohH0nCbkmRFxIM0NUmzrnLnfsEw+AlYf4OdY64+fhE2DPj6a3j+eTPHpHxpjcLQNrzTZwyv95/ArtguRAQHcNHgJP48rBPdO7RAWYTmlnFvTKKlj84WEc+kJFkR8U5N/dZY13h93Bg48AF19kL0eRUWLoRXXoGtWytfuju5Fy/2mcCHJ5xGYVAIfRIjmT2sM+cNTCQsqAX/bDY3D6ExwwTuLlVflXpypAr1oIhI3bzlglFfz8tXl1bvhSgDNgPrQmBjWWVuSUlwCB+eOIoF/c5kS0IPggL8OKd/AlcO68zA5LauG8apjzOm/TY0JdlTFttTT06roB4UEWm+piZoukN9PS8VvRC/W2ENsA7IASgCYEdyb9444XQ+POE0ckLa0Ck6jDuGdeKSLgdp98vtsGEL/OKm4MwZeQgNzcrxlNkintSTIx5BAYqI2OdtFwx7F+IjR+CHPvDCp/Dr8c3Z4eG81Wc8b/Udy+7YzoQG+jOxXzyXDE5maEo0fke+hhWn4xHBmaun/XrKbBHV/ZAaFKCIiH3eesHIz4cPPzRzSz79tHIIx+rnx5rug3mz75ms7jqYMv8ATu7SjjmDkzmrfwJtgqv8OfS24Kw5PGW2iKf05IjHUIAi0to0Jq8kY13t6anguReM0lJYscIMSt5/3wxSyu1K6MbiE8bw/omjORLeloSoEK4ZlMTFg5PoEhNu/3jeGpw1lScUZ/OUnhzxGApQRFqTxuSVVBY9q7nQnh8O1xSx9/7OSrq12cz1cBYuhLfeMuuXlDsYncg7vU5l6Ymj2BPTiYjgAMb3iee8gYmM7B6Dv18DCa/N+TbvLYnFnsZTenLEY2gWj0hr0pi6GPb2AQiOgf73111TBOq/MDtjlobNZtYreecdc5G+A8dnmRxr0473e53C0hNGsSmxF8GB/ow9IY5JAxIZ3SvWsSqvTZ09o5koIvXSLB4Rsa8xQxf29gGz6FldNUU2zoCjG6i3Z6auvI6NMyCoXd2BjdUKX35pBiXvvVe5Fg5AQXAoy7oP44MTR7Ouy0AsAQGc1jOWJwYkMvbEuOp5JY5o6rf51pS7IuJiClBEWpP6FpGrWOW2vuGNugKcY5tp8MJc12uPfHc876AisBm1AraVmL0k770HGRmVL8kNCefz7kP5pNdIvupyEtagYIZ3a8/9fROY2DeeduFBTjlVTcrLaG25KyIupABFxJfVzIdIOrd8ETk/oEqOSfHR46vc1pesuPV++8ELNHxhris4qtg3H/jRChstMG0s5B9f+C8rNILPug9jWa+RrOsygMDQEEb3iuWRE+MZ07sDUaGBzTpNTqOZKCJOowBFxFfVlRCb+hT89B8ozqyysw2wHO/xqGt4o67gpd1AOLq+/guzvdemW2EjsBGMn8Fiw2wvZRwJi+KzHsP5pNdIvu3Uj4iIUMaeEMdzfeI5pUeM81cOdgbNRPFdSn5ucUqSFfFV9SXEZm1pennzij/UR9ZDxWSYNt3MAAXqTyo9uAbevgXWbocNwG951Q69M6YTK7sPYUX3oWxO6Em3+ChG94rl9N5xnNylHQH+fo6ehZbXUGn5Fn9/XUibraHkZ53zRnPk+q0ARcRX1bfGStt+Dc/mqY+9P9gG0D4VCg5UvzD//rtZMO2zzzBWrMCSk1N5mDKLH9916svKbkNZ0X0IGe3bMbLNT4weMprRg4eS1C6suWfB+zTnYqdZRK5RX7Df9y6dcwdoFo+It3LmN7H68iGaOxRR12yVoHZw2ipz1s3sdzA+/RuWHTuOvz1wNDSSL7ucxMruQ1jddTAxCZGMidzIg6GLGZIcRHD/O1tv7Yvmrn+kWUSuUV/ys865yyhAEfEUzlycL2MdlByzk8xqOd6zUTPPJOk8Mwm2McFR1T/YVmAfsM2Kbcdq2BmNX7G5EJ8FsFr82JjYm7UpJ7Gm62AOdz+RYT06cEr3GG7p1r68l2QicKdjn9EXNfdip1lErtGUmW06582mAEXEUzjrm1jVQKeq6FQYNPd470TVabSOBEc2GxzpAmsPUrbVAjsgoMicEeRXvkJwWpv2rOk6mDUpg/ip12D69u3CiG4xzO3enm6xbbBYGqjk2lo192LnLbOIvC1noykz2zztnHshBSgiLaWhP8rO+iZWM9CB48MvdQ2d1BccnfYRbN2K8eWX5C//gsC1awjOOgpAQHkQlB0czred+vF15wFs7TWI6JNPIrVLNNd2a0+fxKiGS8uLqbkBhjfMInJmT2FLqa9wnzeccy+lJFmRltCY5MXGlKFvjLqSY/2CzXL19oKjqq8pAX6FvB0hFO2MoM2vRYTk51Y7VH5gCN8n9+HrTgPY2yOZqBPDOblfKqn9TqZbbHjtHhJv+8bsLk0tsV/zGJ68no2z/p17Ek8/5x5Es3hEPE1j/ig74+JU13tVVTM4OnyYrHnnk/3tXgJ3lRL7exaB1uqvzQsKZWNibzZ07svh1BGEjRzGoG5xpHZpR1xkSP3tcfXMEl8Lfnz9Ylff7LKGpriL11OAIuJuNS8yR9fXKIxWruYfZWdcnGoGBFXYiiF9TwzHdkXity+UmH1Hic1Mq32I8Lb8kHQie3v1o3TEaGJHnsyAlBh6xUcQ6GgtEld+Y9a0Wu/jiz0o0miaZiziTvbG2A0rtcrLQ/U1cKD2+i8Z68w/6I70DsSOxDhjFenfz+WPTWnk7bQS+Gsxsb8fIyXtIIm2TBI5HizZsLAnJpk9XXpztHs0fr1K6dInilOHX8VZSU64yLtyloMzp3g60hPja702LUk5G9JIClBEnKHqBctWDIaNymCkMjgxqHcNnJoXuEYkExqGweHcYnb/nsGRbzdi/WYVYds2k7j/D3oe/p2EspJaTT0SFsmepGSyeibhN+7PxIw5lV69OtIzyEV/Dpwxs8Re9dro1Nrl9aFpwY8jiZtNSfJUQHNcU1eKllZHQzwizVXPkEo1QTHmxbXmUE9d3dtVusKPlUWwtziRfSVJpBX1oXSfjZBfDhH/Rya90vbS/ch+Am213zs/KISDnVLITTAwulto1yub5KQMAv2MZiZeOqO6aSPfv+L1VYM+4HiwVyPoa8pwgSPDDo4OUfjiMJQCLmkiDfGINJcjf4DtTeutyeJvloG3983esGI7tpXMnCL2HyvkwLEC9mUWcPDb/lh/60VYWhHJhw/RK+M3RmRuIj5vpd23yAsLJzMpktLO/oSlFBHb4xjhCSX0SOxstr/y85zh2DfW5k4Lbc435ox1sPZ8MMrsPFkRnBjNHy5wZBjK0SErX6s06o3ThMUrKUARqanmH+DCNEj71JyiG53auPolVVn8MQwLGSl3cmDTSxzI+o0DJTEcKIljf0kcx/IiCTpURvL8u+iZ+Ts9M3/jgozf6JR9qM5D5rYLoygpCP8UKxFdCwnsHUGbNlm0seTX3jlrS+3cFkc44wLblPev7DmxF5xUsJk9UxXBX1OHCxwZhnJ0yMrXKo36WsAlHksBikhNtXpEyocPijPNrv205eYFMfcXsEBZcS4ZJe1JL2vPodL2pJXGkF4aQ7o1kfTSaA5Z48gsDCdu1VZSjnai61E/Uo7+weBjP5Ny9A865B+rsylFkYGUdfQnpFMpAclWSAI6QkRYAREUVNmzjmM4o6Kluy6wFb+H+lT0TDX3wuhI4qajSZ5NzcHx1GEUXwu4xGMpQBGpqfwPcLEtgCNlbcmscTtU2p70fe1JL72U9NIYMsraYsOfNsUFJGenk5x1iOTsQwzM/okuxw6ScvQgydmH8Ddsdb6lNS4OS89e+PXtA0FrIPJn6GgjJKIUKG3a53DW7Ah3lU9vqGcKP5w2+8ORYShHh6yaMmvFk4dRvKWcvng9JclKq2EYBgUlVo7ml5CRV0xmbjFH8kvIzC0mM6+YzLzy7Yd/IbMokBxrm2qvDy0pIjE3g6TswyRnHyIpK53kbDMYSc46RLui3Dreufz927TB0rMn9OoFPXtWv1X9d1xXISuH+EHCeOfMjnBWATlH1VVwzhIIQVHlw21eMvvD0fo2rqoV4oxeGXf9exCfoEJt4pvK/7gWH91BdvhgsrvcSHZIX7IKSskuNG9Zmb+Qc/B7svKLyLbEkR3YjayyEHLKny+12v/nHlZSSEJuJvG5R0jMySA+N5OE3EwSczPomHeYhJwjtCkqbLiNERZIjIT2JdApHlIvhtSzzSAkPh4as0ie3QtzRTKoX/XtgVFQmkuzZrE05qLljuqmrflC6Ipqq86cTeTr1W7FZRSgiMcrs9rIKSozg4qCErLTfyR7z3tkZx0m2xpGVlk42f6dyQ7tR7Y1kuy8LLKzD5NVFkGREdyo9wiwltG+IIvY/Cxi8o8Rm3+M2Pws4gqO0bEwg7j8TNoX5NAuJ4fQoqLGNTwEiAE6ALFVbh2AWAuEcjyIaOgCUFdgUNeFJPVpOPDB8f2TzoNfF8CR744fs6lTeD11CqyrLoSemt9RwRU9KKrg6j08/d9nM3hNgDJv3jweffRR0tLS6NOnD0888QSnnnpqg69TgOICVf9DhCWZ28qTQLGVgl+gua3KLBbDMMgtLiO7ag9G+hayf/2Y7NxjZFniySkNIavIINvSgWxLHFn5ReSUBpJrDXWoeRbDRmRRPu0Kc2hblEe7wmwSizOIK82hQ1EWMQUZ5nMFuUTl5dEmr5CQ/NpFyuoVBkQDPYZA136QnAxJSdAmGwqWQNAvENOpvDiYQbNqcjQUGDR0Ya6r9kr7oTBobuMv4p48lOAqTQnKWvrzuKL3SGvgeAdP/9LQTF5RB2Xx4sXcdNNNzJs3j5EjR/LCCy8wceJEtm/fTqdOndzVLM9QV9XM+rreq+7bppt5by/AqPGcYS2l0BZMdpGVbGsbsqztyLZayLa2Idt6KtnWcLKsEWSXtSnfFkH2qu1k2/4guywUG/bWZRlTa0uAtYw2JQVEFBeQXFxARHE+bUoKiC05RkxpFjElWbQryaFtSS6RJflEFBfQpqCAsIIiQgpKCCwow9KUUNofiATah0FEKUSWQaQBbYGoKrdozN4PAMsmCPq1xjmffvxcB7azH7jZW2+nrtkNDU3VbGharr3aKxZ/CGrn2AXMGTMyal68k86F9f863j5PSvAEx6fJuiNh1RXVVpXc6h00jbuS23pQhg4dyqBBg3juuecqt51wwgmcf/75zJ49u97XuqwHpebFvp4Le+VzjdnHkdeX5kLO9rrbGHkiBEaYxzFKoTQbML/QFxrBZJVFcMwaQbY1gmNlEWRZy2+VP7chqyyy/L4NhSUhWMogqKyUIGv5rfzn4PLHIaUlhJQVE1ZaRFhpMSHl96GlxYSWFhFRWkCkNZ+I0nzCywoJKysirKSI4NISgktLCS4qIaC07hksDgkB2ti5RVS5j+J4ABIOdmOoRvEzh2vGrjYfNvStpq7ckeBo8Auu/s27zkTYiuTWBr6hO+vbcHN7UOx927O37pAnDSU4eu58ZWikNef0eBMf7+ny+B6UkpISNmzYwG233VZt+/jx4/n666/d0STzP+/SUfCdzbzaV4RtRvmNzCo/U/tnACOz7ucNB15vA6vVQlFZEEVlwRSXBVFSFkhxWSCl1iJKy8qwWmOxWv2wWTths1qw2fzws9kIsNnwt1kJsxURYcunq+0PAq1lx4OPip/LSgmy1VcAy0WCMIdSQstvYTXuq26vGnyEl/8c2JKNtQGW8m800OC3mprTSSsu0sVHzfuq37ztfZuteM/05Q1/Q3fWt+HmLtxm79texeeoypPqZLTWQmtaA8c7qKerklsClMzMTKxWK3FxcdW2x8XFkZ6eXmv/4uJiiouLKx/n5OQ4v1FbH4AsA170jJxhfwzCKSac4oZ3dt6bmgFAQI37ICC4/Fb157q2BWH2dFT8XDXw8LbKO1UvRA1dpGpeAGzFx4OTiv0rgppawUyN4zbUpeusFWGbe9FqsFZJOU/6A9tShdY8UXMqCkvL0GrPldx6ubDUmHJpGEatbQCzZ8/m3nvvdW1jsrZAsA1O4nguh6XGz/a2WaAMP4qNIAqNYIpswRSW/1xohJTfB1NghFJiBGBYzBcZFjCwYFgs5ffHH1stfpT5+WPz8yPIv5TggBKCA8z7kIASQgOLCQkwb2EBxYQEFhMWUERYYBHBAaXmv2c/zICj4t5e4FH1PoBmDIU4WWCUeV+aw/HuJQdfX3PqbYPvlW3/+aoXosZcpKpeAJYk1W5DRVBTNTBI+7zu/erizG/Dzblo2e0JqjEl2tP+wLZEoTWRplJPVyW3BCgxMTH4+/vX6i05fPhwrV4VgNtvv53p06dXPs7JySE5Odm5jar4Qzuz+rfBMsOPQ6XtOVgay8GSWA6UduBgSaz5uDSWgyUx5Nra1HHQ2tr4FdAh4CgxgceICcimfUAW0f45xARkmT8HZJf/nE2Ufx5+TcoMbaaqeS4V+TJQ90W8vuNAPTk1NfIUas5AyVgHG2dUn0Zba7ZM+X3lOjl3A0b5WLulnm/35fkloz85vr/d1XItx4/p6EWqoW/eFYFBXTkODX1D94Rvw3VdvGtOifa0P7COnDtdMKSlecL/bQ/g1iTZwYMHM2/evMptJ554Iuedd557kmQz1pH2yUW8ljmRg6UxlUFIeml7rPg3+PJQSxFxgUfpEHiEuICjxAWatw4BR+gQeIy4wCN0CDhGG/9GFPuqyj8crHYWgLMnMMpMuG1Ksi40XJnToanId1cPNGr+ccdo/B/8WrNEzmv44ldfW+trY62ZUw18jvouUo1NSvT25EUV7RKRRvKKOiiLFy/myiuv5Pnnn2f48OH83//9Hy+++CLbtm2jc+fO9b7WVbN49vy8lrELapcrD7SUkRCcQ2JIDh39D9Ax+AiJgYdJDM4iMeAwcSG5tPErwhLhhFk8AAUHqv+hd/RiK56jsRdvXeRFpBXwigAFzEJtc+bMIS0tjb59+/L4449z2mmnNfg6VwUohSVWHvn0ZxLbhtCxbVj5fSgxbYLx82tEiXIRERGpk9cEKE2lSrIiIiLex5Hrt6fM2xARERGppABFREREPI4CFBEREfE4ClBERETE4yhAEREREY+jAEVEREQ8jgIUERER8TgKUERERMTjKEARERERj6MARURERDyOAhQRERHxOApQRERExOMoQBERERGPE+DuBjRFxQLMOTk5bm6JiIiINFbFdbviOl4frwxQcnNzAUhOTnZzS0RERMRRubm5REVF1buPxWhMGONhbDYbBw8eJCIiAovF0qjX5OTkkJyczP79+4mMjHRxC6WCzrt76Ly7h867e+i8u0dTzrthGOTm5pKYmIifX/1ZJl7Zg+Ln50dSUlKTXhsZGal/wG6g8+4eOu/uofPuHjrv7uHoeW+o56SCkmRFRETE4yhAEREREY/TagKU4OBg7rnnHoKDg93dlFZF5909dN7dQ+fdPXTe3cPV590rk2RFRETEt7WaHhQRERHxHgpQRERExOMoQBERERGPowBFREREPI5PBSjz5s0jJSWFkJAQBg8ezJdfflnv/mvWrGHw4MGEhITQtWtXnn/++RZqqW9x5Ly/9957jBs3jtjYWCIjIxk+fDifffZZC7bWdzj6773CunXrCAgIYODAga5toI9y9LwXFxdz55130rlzZ4KDg+nWrRuvvPJKC7XWdzh63hcuXMiAAQMICwsjISGBv/71rxw5cqSFWusb1q5dy6RJk0hMTMRisfD+++83+BqnXlcNH7Fo0SIjMDDQePHFF43t27cbN954oxEeHm789ttvdvf/9ddfjbCwMOPGG280tm/fbrz44otGYGCg8c4777Rwy72bo+f9xhtvNB555BHj+++/N3bt2mXcfvvtRmBgoLFx48YWbrl3c/S8V8jKyjK6du1qjB8/3hgwYEDLNNaHNOW8n3vuucbQoUON5cuXG3v37jW+++47Y926dS3Yau/n6Hn/8ssvDT8/P+PJJ580fv31V+PLL780+vTpY5x//vkt3HLv9sknnxh33nmn8e677xqAsWTJknr3d/Z11WcClCFDhhjXXHNNtW29e/c2brvtNrv733LLLUbv3r2rbfvHP/5hDBs2zGVt9EWOnnd7TjzxROPee+91dtN8WlPP++TJk4277rrLuOeeexSgNIGj533ZsmVGVFSUceTIkZZons9y9Lw/+uijRteuXatte+qpp4ykpCSXtdHXNSZAcfZ11SeGeEpKStiwYQPjx4+vtn38+PF8/fXXdl/zzTff1Np/woQJrF+/ntLSUpe11Zc05bzXZLPZyM3NJTo62hVN9ElNPe/z58/nl19+4Z577nF1E31SU8770qVLSU1NZc6cOXTs2JGePXsyc+ZMCgsLW6LJPqEp533EiBEcOHCATz75BMMwOHToEO+88w5nn312SzS51XL2ddUrFwusKTMzE6vVSlxcXLXtcXFxpKen231Nenq63f3LysrIzMwkISHBZe31FU057zU99thj5Ofnc+mll7qiiT6pKed99+7d3HbbbXz55ZcEBPjEf/sW15Tz/uuvv/LVV18REhLCkiVLyMzM5LrrruPo0aPKQ2mkppz3ESNGsHDhQiZPnkxRURFlZWWce+65PP300y3R5FbL2ddVn+hBqWCxWKo9Ngyj1raG9re3Xern6Hmv8L///Y9Zs2axePFiOnTo4Krm+azGnner1coVV1zBvffeS8+ePVuqeT7LkX/vNpsNi8XCwoULGTJkCGeddRZz585lwYIF6kVxkCPnffv27fzrX//iP//5Dxs2bODTTz9l7969XHPNNS3R1FbNmddVn/gqFRMTg7+/f61o+vDhw7WiuQrx8fF29w8ICKB9+/Yua6svacp5r7B48WKuvvpq3n77bcaOHevKZvocR897bm4u69evZ9OmTfzzn/8EzAunYRgEBATw+eefc/rpp7dI271ZU/69JyQk0LFjx2rLy59wwgkYhsGBAwfo0aOHS9vsC5py3mfPns3IkSO5+eabAejfvz/h4eGceuqpPPDAA+ohdxFnX1d9ogclKCiIwYMHs3z58mrbly9fzogRI+y+Zvjw4bX2//zzz0lNTSUwMNBlbfUlTTnvYPacTJ06lTfffFNjwk3g6HmPjIxky5YtbN68ufJ2zTXX0KtXLzZv3szQoUNbqulerSn/3keOHMnBgwfJy8ur3LZr1y78/PxISkpyaXt9RVPOe0FBAX5+1S9v/v7+wPFv9OJ8Tr+uNim11gNVTEN7+eWXje3btxs33XSTER4ebuzbt88wDMO47bbbjCuvvLJy/4rpUP/+97+N7du3Gy+//LKmGTeBo+f9zTffNAICAoxnn33WSEtLq7xlZWW56yN4JUfPe02axdM0jp733NxcIykpybj44ouNbdu2GWvWrDF69Ohh/O1vf3PXR/BKjp73+fPnGwEBAca8efOMX375xfjqq6+M1NRUY8iQIe76CF4pNzfX2LRpk7Fp0yYDMObOnWts2rSpcnq3q6+rPhOgGIZhPPvss0bnzp2NoKAgY9CgQcaaNWsqn5syZYoxatSoavuvXr3aOOmkk4ygoCCjS5cuxnPPPdfCLfYNjpz3UaNGGUCt25QpU1q+4V7O0X/vVSlAaTpHz/uOHTuMsWPHGqGhoUZSUpIxffp0o6CgoIVb7f0cPe9PPfWUceKJJxqhoaFGQkKC8ac//ck4cOBAC7fau61atarev9euvq5aDEP9XSIiIuJZfCIHRURERHyLAhQRERHxOApQRERExOMoQBERERGPowBFREREPI4CFBEREfE4ClBERETE4yhAEREREY+jAEVEREQ8jgIUERER8TgKUERERMTjKEARERERj/P/1zZ4QwQ3vXYAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
      "metadata": {
       "filenames": {
        "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_33_0.png"
-      },
-      "needs_background": "light"
+      }
      },
      "output_type": "display_data"
     },
@@ -842,7 +838,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.005000000000000011\n"
+      "0.005\n"
      ]
     }
    ],
@@ -1246,9 +1242,9 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Read the experimental data with Pandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n\u001b[0m\u001b[1;32m      3\u001b[0m               \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'N'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Z'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'A'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Element'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Ebinding'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m               \u001b[0mwidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m               \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m39\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_fwf\u001b[0;34m(filepath_or_buffer, colspecs, widths, infer_nrows, **kwds)\u001b[0m\n\u001b[1;32m    869\u001b[0m                         \u001b[0mlen_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    870\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlen_index\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolspecs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 871\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Length of colspecs must match length of names\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"colspecs\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolspecs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Read the experimental data with Pandas\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m Masses \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_fwf\u001b[49m\u001b[43m(\u001b[49m\u001b[43minfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m              \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mN\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mElement\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEbinding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m              \u001b[49m\u001b[43mwidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m13\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m              \u001b[49m\u001b[43mheader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m39\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m              \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Extrapolated values are indicated by '#' in place of the decimal place, so\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# the Ebinding column won't be numeric. Coerce to float and drop these entries.\u001b[39;00m\n\u001b[1;32m     10\u001b[0m Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_numeric(Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m], errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    306\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    307\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m    308\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    309\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m    310\u001b[0m     )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:871\u001b[0m, in \u001b[0;36mread_fwf\u001b[0;34m(filepath_or_buffer, colspecs, widths, infer_nrows, **kwds)\u001b[0m\n\u001b[1;32m    869\u001b[0m                     len_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index_col)\n\u001b[1;32m    870\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m+\u001b[39m len_index \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(colspecs):\n\u001b[0;32m--> 871\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of colspecs must match length of names\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    873\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolspecs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m colspecs\n\u001b[1;32m    874\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer_nrows\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m infer_nrows\n",
       "\u001b[0;31mValueError\u001b[0m: Length of colspecs must match length of names"
      ]
     }
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index 467a3cf09..966db3dc9 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
@@ -547,16 +547,15 @@
     },
     {
      "data": {
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       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
      "metadata": {
       "filenames": {
        "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_39_1.png"
-      },
-      "needs_background": "light"
+      }
      },
      "output_type": "display_data"
     }
@@ -1078,7 +1077,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1656,7 +1655,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1674,7 +1673,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1692,7 +1691,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1710,7 +1709,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1728,7 +1727,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1746,7 +1745,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1764,7 +1763,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1782,11 +1781,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1804,11 +1803,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1826,11 +1825,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1848,11 +1847,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1870,11 +1869,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1892,7 +1891,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1910,11 +1909,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1932,11 +1931,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1954,11 +1953,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1976,11 +1975,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1998,11 +1997,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2020,11 +2019,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2042,11 +2041,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2064,11 +2063,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      ":44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2129,23 +2128,23 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      ":4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
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+       "
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       ]
      },
      "metadata": {
@@ -2157,9 +2156,9 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
      "metadata": {
@@ -2716,10 +2715,6 @@
       "Learning rate  =  0.1\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.8805555555555555\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.8722222222222222\n",
       "\n"
      ]
     },
@@ -2727,6 +2722,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n",
       "Learning rate  =  0.1\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.8666666666666667\n",
@@ -2734,10 +2733,6 @@
       "Learning rate  =  1.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.08611111111111111\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
       "\n"
      ]
     },
@@ -2745,6 +2740,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.001\n",
       "Accuracy score on test set:  0.10555555555555556\n",
@@ -2752,14 +2751,6 @@
       "Learning rate  =  1.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.17777777777777778\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.08333333333333333\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.08888888888888889\n",
       "\n"
      ]
     },
@@ -2767,10 +2758,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.08333333333333333\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.08888888888888889\n",
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.09444444444444444\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.17222222222222222\n",
@@ -2782,20 +2787,10 @@
       "Learning rate  =  10.0\n",
       "Lambda =  0.001\n",
       "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.1388888888888889\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.11388888888888889\n",
       "\n"
      ]
     },
@@ -2803,10 +2798,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.10555555555555556\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.09444444444444444\n",
@@ -2854,9 +2859,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
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      },
      "metadata": {
@@ -2868,9 +2873,9 @@
     },
     {
      "data": {
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\n",
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       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
      "metadata": {
@@ -2973,10 +2978,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (, line 1)",
+     "evalue": "invalid syntax (2259440937.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    conda create -n tf tensorflow\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Input \u001b[0;32mIn [12]\u001b[0;36m\u001b[0m\n\u001b[0;31m    conda create -n tf tensorflow\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10_39_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter10_39_1.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
index e036b6549..44f6fad75 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
@@ -798,16 +798,15 @@
     },
     {
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\n",
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\n",
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        "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_50_2.png"
-      },
-      "needs_background": "light"
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@@ -1008,16 +1007,15 @@
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\n",
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@@ -1314,16 +1312,15 @@
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\n",
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VKmDFRMaDe+3atSYlJcWcPXvWzJ8/34SEhBh/f38TExNjoqKiDGBef/11p8sePHjQeHt7myeeeMKxrF27dgYwP//8c6brGjZsmPH19XVatmjRIgOYSZMmOS2fM2eOUwE0Jn1n5Orqanbu3Ok0NmPn2rt3b6flo0aNMoB5+OGHnZb37dvXBAcHZ3ufpKWlmZSUFDNjxgzj6upqTp06len2rVu3zukytWrVMl27dnX8PH78eAOYpUuXZns9s2bNMoD59ttvnZZv2LDBAI4/xLPLWKZMGVO3bl2TlpbmWH727FkTGhpqWrZs6ViWcf98/fXX2W4vQ8bj4YEHHnBaPmnSJAOYo0ePGmOMOXDggHFzc8tUZM+ePWvCwsLM7bff7lh2tQWscuXKTn8UZSU1NdWkpKSYO++80zRs2NBp3dUUsNKlS5v4+HjHspiYGOPi4mImTpzoWNa1a1dTtmxZExcX53T5hx56yHh5eTkeHxm/t4tfZDK0bt3a3HjjjY6fq1SpYv7zn/8YFxcXs2rVKmPMv4U644/U3HhuZNzOBx980MTGxprWrVubiIgIEx0dfcX757XXXjOA0x88l3rqqaeyfD7cf//9xmazOeW59IX9nXfeMYDTmFOnThlPT0/z2GOPOZZlV8By8jz8z3/+Y2w2W6Y/Drt27ZqjAnap1NRUk5ycbKpWrWoeffRRx/KM51iPHj2cxn/11VcGcPqj7VIxMTEGMAMGDLjsdffv398A5tixY8aYKxcwY4wpXbq0qVmzpuPnjD+msjpVrlz5ste/Z88e4+rqmumP1oudPn3a8Yf+xQ4cOGA8PT3NoEGDHMuGDRuWaf+XkpJiQkJCDOD4480YY2JjY42rq6sZPXq0Y1nG7b/55pudruvXX381gHnppZeyzGi3201KSorZv3+/Acz333+f6f557rnnnC6T09fA06dPGy8vr2wzXamAGWOMr69vlvuvAQMGGE9PT3PgwAGn5d27dzc+Pj6XfZ5ebrt5sc/PiYx9eMeOHZ3ur4zXgbp16zr9U2D9+vUGMLNmzbrsNhMSEoyvr695++23HcsvLWDGpP+uPTw8HM8nY/7dt2bsk40xpmfPnk6vUxe7dJ82cuRI4+7ubrZt25aTu8CR+Xpey/J6P53TAmaMMbVr187yMZ5VAWvevLkJDQ01Z8+edSxLTU01derUMWXLljV2u93peq70+CxKdAhiMdO8eXPc3d3x9/enV69ehIWFsXDhQkqXLs38+fOx2WzccccdpKamOk5hYWHUr18/02E8QUFB3HjjjTm63uXLlwNk+tDmbbfdhq+vLz///LPT8nr16mU7gUSvXr2cfq5ZsyZApsO8atasyalTp5wOQ9yyZQs33XQTJUuWxNXVFXd3d4YOHUpaWhp///230+XDwsJo2rRpplwXH/awcOFCqlWrluUH4DPMnz+fEiVK0Lt3b6f7tUGDBoSFhV328KidO3dy5MgRhgwZgovLv09XPz8/br31VtauXcu5c+eyvfyV3HTTTZluH/x7SOrixYtJTU1l6NChTtm9vLxo167dNc02dfF1u7u7Z1r+9ddf06pVK/z8/HBzc8Pd3Z1PP/0006FNV6NDhw74+/s7fi5dujShoaGO23nhwgV+/vlnbr75Znx8fJxua48ePbhw4UKmw2Ky0rFjR3799VfOnz/P/v372b17NwMGDKBBgwYsXboUgGXLllG+fHmqVq0K5O5zY+/evbRo0YL4+HjWrl1L/fr1r5g545DK22+/na+++irLWVGXL19OrVq1Mj0fhg8fjjHGcRuyMnjwYDw9PZ0OS5k1axZJSUmMGDHiivly8jxctWoVderUoVatWk7jBg4ceMXtA6SmpjJhwgRq1aqFh4cHbm5ueHh4sGvXriwfd1d63lwPYwxAlocDXekyl1q2bBkbNmxwOl1p5r2lS5eSlpbGgw8+mO2YqKgozp8/n+kxW65cOW688cZMj1mbzUaPHj0cP7u5uVGlShXCw8Np2LChY3lwcLDT8/Jil35GuWXLllSoUIEVK1Y4lh0/fpz77ruPcuXKOfYdFSpUAMjy93jxYcBAjl8Do6KiuHDhQraZrsfy5cvp2LEj5cqVc1o+fPhwzp07l+XhdFcjP/b5H374IY0aNcLLy8vxe/j555+z/B307NkTV1fXbPMAJCQk8OSTT1KlShXc3Nxwc3PDz8+PxMTEK74u3H///QBOh2G/99571K1bl7Zt217xtmRl4cKFdOjQwfG3R3Zy87Usr/fTeSExMZF169bRr18/p8nYXF1dGTJkCIcOHcp06HZe7lsLGhWwYmbGjBls2LCBLVu2cOTIEf744w/Hh72PHTuGMYbSpUvj7u7udFq7dm2m4+ivZiat2NhY3NzcMn3I3mazERYWRmxsbI63HRwc7PSzh4fHZZdfuHABSD82uk2bNhw+fJi3336bNWvWsGHDBsdnfM6fP+90+ZIlS2a6bk9PT6dxJ06cuOLsSMeOHePMmTN4eHhkul9jYmIy3a8Xy7hfsro/ypQpg91u5/Tp05e9/su59DZmTIKRcRszjme/4YYbMmWfM2fOZbNfSVa3ae7cudx+++1ERETwxRdfEBUVxYYNGxg5cqTj93gtrvS7jI2NJTU1lXfffTfT7cz4wzEnt7VTp04kJSXxyy+/sHTpUkqVKkXDhg3p1KkTy5YtA+Dnn392Kuy5+dxYv349f//9N/3798/xrF1t27Zl3rx5jj+6ypYtS506dZg1a5ZTxuwegxnrsxMcHMxNN93EjBkzSEtLA9KP92/atCm1a9e+Yr6cPA9jY2MzTUABZLksK6NHj+bZZ5+lb9++/Pjjj6xbt44NGzZQv379TPuFrDJd+rzJSqlSpfDx8WHv3r2XzbJv3z58fHwy7c+yk5iYSGxsrON3cbH69evTpEkTp1OdOnUuu72Mz6Ve7vFzpf3SpY8HHx8fvLy8nJZ5eHhkeRs9PDyyfK6HhYVluSzjuux2O126dGHu3Lk88cQT/Pzzz6xfv97xj5OsfjeX5s/pa2DGdWaX6Xpcz3MtJ/J6n//GG29w//3306xZM7799lvWrl3Lhg0b6Nat2zU/lwYNGsR7773HXXfdxeLFi1m/fj0bNmwgJCTkss85SN8H9O/fn48++oi0tDT++OMP1qxZc12ThOXkdT+3X8vyej+dF06fPo0x5qoyXcu+tbDSLIjFTM2aNZ1m0rpYqVKlsNlsrFmzJsvZ6C5ddjX/oS1ZsiSpqamcOHHC6Q9NYwwxMTGO/+5cy7Zzat68eSQmJjJ37lyn/1Jez/c5hYSEcOjQocuOyfgw6aJFi7Jcf/E7M5fK2BkdPXo007ojR47g4uJCUFDQVSS+OhkzqH3zzTdX/M+ul5cXcXFxmZZn94Kd1e/4iy++oGLFisyZM8dpfVJS0tXEvmpBQUGO/8pl95//ihUrXnE7zZo1w8/Pj2XLlrFv3z46duyIzWajY8eOvP7662zYsIEDBw44FbDcfG7079+fsLAwxo4di91u55lnnrliZoA+ffrQp08fkpKSWLt2LRMnTmTQoEFERkbSokULSpYsme1jELjiTHsjRozg66+/ZunSpZQvX54NGzbwwQcf5ChbTpQsWdLxh+PFYmJicnT5L774gqFDhzJhwgSn5SdPnqREiRK5ERFXV1c6dOjAokWLOHToUJZ/wB06dIhNmzbRvXt3p3cFLmfBggWkpaXl6LunciLjMXjo0KFM78JkuNJ+KS9mXszqdxkTE0OVKlWA9Ikrfv/9d6ZNm8awYcMcY3bv3p3tNi99LuX0NTDj9meX6eJJh67W9T7XrtfV7POz8sUXX9C+fftMz++zZ89eU564uDjmz5/P888/z1NPPeVYnpSUxKlTp3K0jUceeYTPP/+c77//nkWLFlGiRInrmvU5J6/7efFalpf76Yx/kCQlJTk99q/nn6xBQUG4uLhY+nguyPQOmDj06tULYwyHDx/O9F/TJk2aULdu3WvedseOHYH0ndLFvv32WxITEx3r81LGTvDinYsx5oozxF1O9+7d+fvvvy/71n6vXr2IjY0lLS0ty/u1evXq2V62evXqRERE8OWXXzodZpSYmMi3337rmBkxr3Tt2hU3Nzf++eefLLNfXOYjIyP5+++/nV5gYmNj+e2333J8fTabDQ8PD6cXrJiYmOuaOSonfHx86NChA1u2bKFevXpZ3s6MP7ou9x85d3d32rZty9KlS1m+fDmdO3cGoE2bNri5ufHMM884ClmG3H5uPPPMM7z11ls899xzjBkz5qou6+npSbt27XjllVcAHLOedezYkW3btrF582an8TNmzMBms9GhQ4fLbrdLly5EREQwdepUpk6dipeXV44PD8yJdu3asXXrVrZt2+a0fPbs2Tm6vM1my/QH94IFC3L9S+rHjBmDMYYHHnjA8W5ghrS0NO6//36MMTn+vR04cIDHH3+cwMBA7r333lzJ2KVLF1xdXS9bkFu0aIG3t3emx+yhQ4cch9DltpkzZzr9/Ntvv7F//35H8cxq/w5c1eyQOX0NbN68OV5eXtlmyolL38XN0LFjR5YvX55p5rcZM2bg4+NzxWm+s9tuTl3NPj8rWT2X/vjjj2s+dNJms2GMybTNKVOmZHoOZadx48a0bNmSV155hZkzZzJ8+PBM33t6Nfdb9+7dWbFixWVnPs3L17K82E9n/NPgjz/+cFr+448/Znn9ObmvfH19adasGXPnznUab7fb+eKLLyhbtmyx/q5SvQMmDq1ateKee+5hxIgRbNy4kbZt2+Lr68vRo0f55ZdfqFu3ruN46qvVuXNnunbtypNPPkl8fDytWrXijz/+4Pnnn6dhw4YMGTIkl29N1hk8PDwYOHAgTzzxBBcuXOCDDz64rkP4Ro0axZw5c+jTpw9PPfUUTZs25fz586xatYpevXrRoUMHBgwYwMyZM+nRowePPPIITZs2xd3dnUOHDrFixQr69OnDzTffnOX2XVxcmDRpEoMHD6ZXr17ce++9JCUl8eqrr3LmzJkcT1d9rSIjIxk/fjxjx45lz549dOvWjaCgII4dO8b69evx9fV1TIM+ZMgQPvroI+644w7uvvtuYmNjmTRpEgEBATm+vowp1h944AH69evHwYMHefHFFwkPD2fXrl15dTMBePvtt2ndujVt2rTh/vvvJzIykrNnz7J7925+/PFHR8muXLky3t7ezJw5k5o1a+Ln50eZMmUch1R07NiRxx57DMDxTpe3tzctW7ZkyZIl1KtXj9DQUMf15sVz45FHHsHPz4977rmHhIQE3nnnnWzfOXvuuec4dOgQHTt2pGzZspw5c4a3334bd3d3x/fJPfroo8yYMYOePXsyfvx4KlSowIIFC5g8eTL333//FV9EXV1dGTp0KG+88QYBAQHccsstWX5B8LUaNWoUn332Gd27d2f8+PGULl2aL7/80vFVEBd/fjIrvXr1Ytq0adSoUYN69eqxadMmXn311Vz/8tVWrVrx1ltvMWrUKFq3bs1DDz1E+fLlHV/EvG7dOt566y1atmyZ6bJbt251fB7n+PHjrFmzhqlTp+Lq6sp3332X5Xeobdq0Kcv7uVatWtk+LyMjI3n66ad58cUXOX/+PAMHDiQwMJBt27Zx8uRJXnjhBUqUKMGzzz7L008/zdChQxk4cCCxsbG88MILeHl58fzzz1//nXWJjRs3ctddd3Hbbbdx8OBBxo4dS0REBA888AAANWrUoHLlyjz11FMYYwgODubHH390fPYyJ3L6GhgUFMTjjz/OSy+95JRp3LhxOT4EsW7duqxcuZIff/yR8PBw/P39qV69Os8//zzz58+nQ4cOPPfccwQHBzNz5kwWLFjApEmTrvi8yW67OXU1+/ys9OrVixdffJHnn3+edu3asXPnTsaPH0/FihVJTU3NcY4MAQEBtG3blldffZVSpUoRGRnJqlWr+PTTT6/q3elHHnnE8RUtGY+Zi9WtW5e5c+fywQcf0LhxY1xcXLItm+PHj2fhwoW0bduWp59+mrp163LmzBkWLVrE6NGjqVGjRq6/luX1frpHjx4EBwdz5513Mn78eNzc3Jg2bRoHDx7M8r6aPXs2c+bMoVKlSnh5eWX7D/qJEyfSuXNnOnTowOOPP46HhweTJ09m69atzJo1K0+Odio08n3aD7FETmbSyvDZZ5+ZZs2aGV9fX+Pt7W0qV65shg4d6jQ1cLt27Uzt2rWzvHxWsyAaY8z58+fNk08+aSpUqGDc3d1NeHi4uf/++52mETYmfUagnj17Zrp8drP8ZXfbMmb1uXia2h9//NHUr1/feHl5mYiICPOf//zHLFy4MNPMSdndvktn9DMmfUasRx55xJQvX964u7ub0NBQ07NnT6fpq1NSUsxrr73muG4/Pz9To0YNc++995pdu3Zlup5LzZs3zzRr1sx4eXkZX19f07FjR/Prr7/m6P7JSnb3WVazSGVcf4cOHUxAQIDx9PQ0FSpUMP369TPLli1zGjd9+nRTs2ZN4+XlZWrVqmXmzJmT7SyIr776apbZ/vvf/5rIyEjj6elpatasaT755JMsZ2i6mlkQM6Znv9Ll9+7da0aOHGkiIiKMu7u7CQkJMS1btsw009qsWbNMjRo1jLu7e6bZpH7//XcDmKpVqzpd5uWXXzaA0wxvGa73uZHd7Zw1a5Zxc3MzI0aMcJpF82Lz58833bt3NxEREcbDw8OEhoaaHj16OL7iIMP+/fvNoEGDTMmSJY27u7upXr26efXVVzNt99L7I8Pff//tmIkvq1lDs5sFMafPw61bt5pOnToZLy8vExwcbO68804zffp0A5jff/89y9ue4fTp0+bOO+80oaGhxsfHx7Ru3dqsWbMm08ye2T3Hspr963KioqJMv379TOnSpY2bm5sJDQ01t9xyi/ntt98yjc24XzJOGb+jdu3amQkTJpjjx49nuszlZkHM7v6/1IwZM8wNN9zg2F81bNgw0+2bMmWKqVevnvHw8DCBgYGmT58+mWaizO71ILvf7aWP8Yzbv2TJEjNkyBBTokQJxwyMl+47t23bZjp37mz8/f1NUFCQue2228yBAweynfHt4teGi+XkNdBut5uJEyeacuXKGQ8PD1OvXj3z448/Zjsb7KWio6NNq1atjI+PT6aZE//880/Tu3dvExgYaDw8PEz9+vVz/NjKbrt5tc+/VFJSknn88cdNRESE8fLyMo0aNTLz5s27qteBS39fhw4dMrfeeqsJCgoy/v7+plu3bmbr1q2Z9uHZ3ZaMXJ6enqZbt25Z5j516pTp16+fKVGihLHZbE6vN1nt0w4ePGhGjhxpwsLCjLu7uylTpoy5/fbbnWZbzM3XsvzYT69fv960bNnS+Pr6moiICPP888+bKVOmZNov79u3z3Tp0sX4+/sbwPF7zW4/uGbNGnPjjTc6nk/Nmzc3P/74o9OYq318FgU2Y7KZPklERKQQu+eee5g1axaxsbGOSXmkcJk2bRojRoxgw4YNVzz8TSQ7P/74IzfddBMLFixwmpFTxCo6BFFERAq98ePHU6ZMGSpVqkRCQgLz589nypQpPPPMMypfIsXUtm3b2L9/P4899hgNGjSge/fuVkcSAVTARESkCHB3d+fVV1/l0KFDpKamUrVqVd544w0eeeQRq6OJiEUeeOABfv31Vxo1asT06dOL92eOpEDRIYgiIiIiIiL5RNPQi4iIiIiI5BMVMBERERERkXyiAiYiIiIiIpJPNAnHNbLb7Rw5cgR/f399qFNEREREpBgzxnD27FnKlCmDi8vl3+NSAbtGR44coVy5clbHEBERERGRAuLgwYOULVv2smNUwK6Rv78/kH4nBwQEWJxGRERERESsEh8fT7ly5Rwd4XJUwK5RxmGHAQEBKmAiIiIiIpKjjyZpEg4REREREZF8ogImIiIiIiKST1TARERERERE8ok+AyYiIiIikkfS0tJISUmxOoZcJ1dXV9zc3HLl66dUwERERERE8kBCQgKHDh3CGGN1FMkFPj4+hIeH4+HhcV3bUQETEREREcllaWlpHDp0CB8fH0JCQnLlnROxhjGG5ORkTpw4wd69e6lateoVv2z5clTARERERERyWUpKCsYYQkJC8Pb2tjqOXCdvb2/c3d3Zv38/ycnJeHl5XfO2NAmHiIiIiEge0TtfRcf1vOvltJ1c2YqIiIiIiIhckQqYiIiIiIhIPlEBExERERGRfNW+fXtGjRp1XdvYt28fNpuN6OjoXMkE6YeMzps3L9e2lxVNwiEiIiIiIgXa8OHDOXPmjFM5KleuHEePHqVUqVLWBbsGKmAiIiIiIlLouLq6EhYWZnWMq6ZDEEVERERE8pgxhnPJqZacrvaLoBctWkTr1q0pUaIEJUuWpFevXvzzzz/Av4f9zZ07lw4dOuDj40P9+vWJiopyXD42NpaBAwdStmxZfHx8qFu3LrNmzcr2+saPH0/dunUzLW/cuDHPPfcc48aNY/r06Xz//ffYbDZsNhsrV67M8hDEv/76i549exIQEIC/vz9t2rRxZN+wYQOdO3emVKlSBAYG0q5dOzZv3nxV901u0DtgIiIiIiJ57HxKGrWeW2zJdW8b3xUfj5z/2Z+YmMjo0aOpW7cuiYmJPPfcc9x8881ORWfs2LG89tprVK1albFjxzJw4EB2796Nm5sbFy5coHHjxjz55JMEBASwYMEChgwZQqVKlWjWrFmm6xs5ciQvvPACGzZs4IYbbgDgjz/+YMuWLXz99deEhoayfft24uPjmTp1KgDBwcEcOXLEaTuHDx+mbdu2tG/fnuXLlxMQEMCvv/5KamoqAGfPnmXYsGG88847ALz++uv06NGDXbt24e/vf1X36fVQARMREREREYdbb73V6edPP/2U0NBQtm3bhp+fHwCPP/44PXv2BOCFF16gdu3a7N69mxo1ahAREcHjjz/uuPz//d//sWjRIr7++ussC1jZsmXp2rUrU6dOdRSwqVOn0q5dOypVqgSkfxFyUlLSZQ85fP/99wkMDGT27Nm4u7sDUK1aNcf6G2+80Wn8Rx99RFBQEKtWraJXr145vn+ulwqYiIiIiEge83Z3Zdv4rpZd99X4559/ePbZZ1m7di0nT57EbrcDcODAAWrVqgVAvXr1HOPDw8MBOH78ODVq1CAtLY3//ve/zJkzh8OHD5OUlERSUhK+vr7ZXufdd9/NyJEjeeONN3B1dWXmzJm8/vrrV5U7OjqaNm3aOMrXpY4fP85zzz3H8uXLOXbsGGlpaZw7d44DBw5c1fVcLxUwEREREZE8ZrPZruowQCv17t2bcuXK8cknn1CmTBnsdjt16tQhOTnZMebikmOz2QAcRe3111/nzTff5K233qJu3br4+voyatQop8tndZ2enp589913eHp6kpSUlOmduCvx9va+7Prhw4dz4sQJ3nrrLSpUqICnpyctWrS4bK68UDgeBSIiIiIikudiY2PZvn07H330EW3atAHgl19+uaptrFmzhj59+nDHHXcA6cVs165d1KxZM9vLuLm5MWzYMKZOnYqnpycDBgzAx8fHsd7Dw4O0tLTLXm+9evWYPn06KSkpWb4LtmbNGiZPnkyPHj0AOHjwICdPnryq25YbNAuiiIiIiIgAEBQURMmSJfn444/ZvXs3y5cvZ/To0Ve1jSpVqrB06VJ+++03tm/fzr333ktMTMwVL3fXXXexfPlyFi5cyMiRI53WRUZG8scff7Bz505OnjxJSkpKpss/9NBDxMfHM2DAADZu3MiuXbv4/PPP2blzpyPX559/zvbt21m3bh2DBw++4rtmeUEFTEREREREAHBxcWH27Nls2rSJOnXq8Oijj/Lqq69e1TaeffZZGjVqRNeuXWnfvj1hYWH07dv3iperWrUqLVu2pHr16pkm67j77rupXr06TZo0ISQkhF9//TXT5UuWLMny5ctJSEigXbt2NG7cmE8++cTxbthnn33G6dOnadiwIUOGDOHhhx8mNDT0qm5bbrCZq/1iAAEgPj6ewMBA4uLiCAgIsDqOiIiIiBQgFy5cYO/evVSsWBEvLy+r4xQKxhhq1KjBvffee9XvuuWHy/1Or6Yb6DNgIiIiIiJiqePHj/P5559z+PBhRowYYXWcPKUCJiIiIiIilipdujSlSpXi448/JigoyOo4eUoFTERERERELFWcPhWlSThERERERETyiQqYiIiIiIhIPlEBExERERERySeWFrDVq1fTu3dvypQpg81mY968eZcdP3fuXDp37kxISAgBAQG0aNGCxYsXZxr37bffUqtWLTw9PalVqxbfffddpjGTJ092TCHZuHFj1qxZk1s3S0REREREJEuWFrDExETq16/Pe++9l6Pxq1evpnPnzvz0009s2rSJDh060Lt3b7Zs2eIYExUVRf/+/RkyZAi///47Q4YM4fbbb2fdunWOMXPmzGHUqFGMHTuWLVu20KZNG7p3786BAwdy/TaKiIiIiIhkKDBfxGyz2fjuu+9y9C3ZF6tduzb9+/fnueeeA6B///7Ex8ezcOFCx5hu3boRFBTErFmzAGjWrBmNGjXigw8+cIypWbMmffv2ZeLEiTm63oL0RcxnTsaw46f3qXPzY/j5l7A0i4iIiIjoi5iLotz6IuZC/Rkwu93O2bNnCQ4OdiyLioqiS5cuTuO6du3Kb7/9BkBycjKbNm3KNKZLly6OMVlJSkoiPj7e6VRQxH7QneZ73mHrgg+tjiIiIiIiIpdRqAvY66+/TmJiIrfffrtjWUxMDKVLl3YaV7p0aWJiYgA4efIkaWlplx2TlYkTJxIYGOg4lStXLhdvyfU5UbU/ABE7p2NPS7M4jYiIiIiIZKfQFrBZs2Yxbtw45syZQ2hoqNM6m83m9LMxJtOynIy52JgxY4iLi3OcDh48eJ23IPfU6Xkf8caHcuYIW1d9Y3UcERERERHJRqEsYHPmzOHOO+/kq6++olOnTk7rwsLCMr2Tdfz4ccc7XqVKlcLV1fWyY7Li6elJQECA06mg8PMvwdawvgC4rPvg8oNFREREJP8ZA8mJ1pyuYsqH9u3b8/DDD/PEE08QHBxMWFgY48aNc6y32WxMmTKFm2++GR8fH6pWrcoPP/yQB3dY0eVmdYCrNWvWLEaOHMmsWbPo2bNnpvUtWrRg6dKlPProo45lS5YsoWXLlgB4eHjQuHFjli5dys033+wYs3TpUvr06ZP3NyCPVOg2irRps6iTtIUD29dTvmZTqyOJiIiISIaUczChjDXX/fQR8PDN8fDp06czevRo1q1bR1RUFMOHD6dVq1Z07twZgBdeeIFJkybx6quv8u677zJ48GD279/vNC+DZM/Sd8ASEhKIjo4mOjoagL179xIdHe2YDn7MmDEMHTrUMX7WrFkMHTqU119/nebNmxMTE0NMTAxxcXGOMY888ghLlizhlVdeYceOHbzyyissW7aMUaNGOcaMHj2aKVOm8Nlnn7F9+3YeffRRDhw4wH333ZcvtzsvRFSszha/tgAcX/q2xWlEREREpLCqV68ezz//PFWrVmXo0KE0adKEn3/+2bF++PDhDBw4kCpVqjBhwgQSExNZv369hYkLF0vfAdu4cSMdOnRw/Dx69GgAhg0bxrRp0zh69KjTd3N99NFHpKam8uCDD/Lggw86lmeMB2jZsiWzZ8/mmWee4dlnn6Vy5crMmTOHZs2aOcb379+f2NhYxo8fz9GjR6lTpw4//fQTFSpUyONbnLc8Wz8Ii1dRN3Yx8SePElAq3OpIIiIiIgLg7pP+TpRV130V6tWr5/RzeHg4x48fz3K9r68v/v7+Tuvl8iwtYO3bt+dyX0OWUaoyrFy5Mkfb7devH/369bvsmAceeIAHHnggR9srLOo068zOZVWpnraL3xe8Q9NhOftOMxERERHJYzbbVR0GaCV3d3enn202G3a7Pcfr5fIK5SQckjWbiwun6t4NQKW9s0hNOm9xIhERERERuZgKWBHTsNswjhNMKU6zdel0q+OIiIiIiMhFVMCKGC8vL3aUHwBAQPTHVzXtqIiIiIiI5K1CNw29XFmNnv/H+clTqJT6D3s2LaVSky5WRxIRERGRQiCrORfmzZvnOJ/V/A1nzpzJu0BFkN4BK4JCS5dhU4muACSsesfiNCIiIiIikkEFrIgqeePDANSJ/4XYgzstTiMiIiIiIqACVmTVrN+Uze6NcbEZ9i98y+o4IiIiIiKCCliRltTkHgCqHfmOpMTTFqcREREREREVsCKsScfb2EtZ/DjPjoUfWh1HREREpNjJatIKKZxy63epAlaEubu5sr/qUABCt03DpKVanEhERESkeHB1dQUgOTnZ4iSSW86dOweAu7v7dW1H09AXcfV63suZN98m3B7Drl+/oWrbAVZHEhERESny3Nzc8PHx4cSJE7i7u+Piovc9CitjDOfOneP48eOUKFHCUa6vlQpYERdcogQrQvrS4eRMTNQHoAImIiIikudsNhvh4eHs3buX/fv3Wx1HckGJEiUICwu77u2ogBUD5bo9Qsrns6l2Pprjf28gtNoNVkcSERERKfI8PDyoWrWqDkMsAtzd3a/7na8MKmDFQJUq1fnVpw2tzq8kZskbhFabZXUkERERkWLBxcUFLy8vq2NIAaKDUYsJ15YPAlDj5BLOnTpscRoRERERkeJJBayYuKFVZ/50qY4Hqez+6W2r44iIiIiIFEsqYMWEq4uN47XvBKDcP7MxKectTiQiIiIiUvyogBUjN3QfylFTkiATx66fp1kdR0RERESk2FEBK0YCfLz5s2z6NPRemz4GfTO7iIiIiEi+UgErZqp3f5BzxpPyKXs4Er3Y6jgiIiIiIsWKClgxU6FsBFEB3QCIX/GOxWlERERERIoXFbBiKLD9QwBUi/uNhCM7LE4jIiIiIlJ8qIAVQ40b3cBatya42Az7f3rT6jgiIiIiIsWGClgxZLPZSGx4DwAVD80j7dxpixOJiIiIiBQPKmDFVMtOt7KLcvhwgd2LPrA6joiIiIhIsaACVkx5e7qxq+JQAIL/mgZpqdYGEhEREREpBlTAirEGPe8i1vgTknaMQ2u/sTqOiIiIiEiRpwJWjJUpFcz6kn0BSPn1PWvDiIiIiIgUAypgxVyZTg+RbFypeO5Pzuxaa3UcEREREZEiTQWsmKtXszq/eLYD4NjSt6wNIyIiIiJSxKmAFXM2mw2a3wdA5eNLSDlz2OJEIiIiIiJFlwqY0KptJzZTEzfS2PvTW1bHEREREREpslTABE83Vw7XGAFA2K7ZkHzO4kQiIiIiIkWTCpgA0Lz7EA6aEAJMPAdWTbM6joiIiIhIkaQCJgCEBPqwOaw/AO4bPgJjLE4kIiIiIlL0qICJQ9Vu95FgvAhP3sepPxdbHUdEREREpMhRAROHWhXLscq3GwBnlr9lbRgRERERkSJIBUyc+LV5ALuxUelMFElHt1sdR0RERESkSFEBEyetmt7AL643AHBw4ZsWpxERERERKVpUwMSJm6sLZ+rdDUDZA/Mw505ZnEhEREREpOhQAZNM2nXuyzYTiRdJHFj6gdVxRERERESKDBUwySTQ14PtFQYD4P/HVEhLsTiRiIiIiEjRoAImWarf/U5OmECC005wcv3XVscRERERESkSVMAkS1XCS7K6RB8ALvzynsVpRERERESKBhUwyVbpGx8gybhRNvEvzu2JsjqOiIiIiEihpwIm2WpZtwbL3dsBcGyxpqQXEREREbleKmCSLRcXG6k33AtA+WNLsZ8+YHEiEREREZHCTQVMLuvG9h1ZSx1csXNw8TtWxxERERERKdRUwOSyfD3d2F9lKAClds6C5ESLE4mIiIiIFF4qYHJFLbsPZp8pja9J4PiaaVbHEREREREptFTA5IrKlfRjbchtANjWfwB2u8WJREREREQKJxUwyZFKne8l3vgQknSQhL8WWh1HRERERKRQUgGTHLmhWjmWenUF4PRyTcYhIiIiInItVMAkR2w2G56t7iPN2Ch3ei2pR/+yOpKIiIiISKGjAiY51qnFDaywNQPgiL6YWURERETkqqmASY55ubtyos6dAITtmweJJ60NJCIiIiJSyKiAyVW5sXNv/rBXwoMUYpZ/aHUcEREREZFCRQVMrkrpQG+iIwYC4P37Z5CabHEiEREREZHCQwVMrlr9rsM5ZkoQmBpL3KavrI4jIiIiIlJoqIDJVasfGcoyvz4AXFj9LhhjcSIRERERkcJBBUyuSal293LBuFM6cQfJe3+zOo6IiIiISKGgAibX5MbGNVnk2h6A40s1Jb2IiIiISE6ogMk1cXd14XyjuwEIP/oz5tReixOJiIiIiBR8KmByzbp16MAaez1csROz7F2r44iIiIiIFHgqYHLNgnw92FVpCAAldsyGpLMWJxIRERERKdhUwOS6tOx6O//Yw/G2J3L6t2lWxxERERERKdBUwOS61AgvwargW9N/WPsh2NOsDSQiIiIiUoCpgMl1i7zxLs4YX4KSDpG07Ser44iIiIiIFFgqYHLd2teJZIFHVwBOL3/b4jQiIiIiIgWXCphcNxcXG27N7iHVuBB2agP2I79bHUlEREREpEBSAZNc0aN1E5bSHIDjy/QumIiIiIhIVlTAJFf4e7lzqMYIAEru+QESjlucSERERESk4FEBk1zTuXNPNtur4E4Kp1Z9aHUcEREREZECRwVMck1kKV/Wl+4PgMeWzyDlgsWJREREREQKFhUwyVV1Ow3hiAnGL/U057Z8ZXUcEREREZECRQVMclXLamEs8O4NwPnV74IxFicSERERESk4VMAkV9lsNoJb380540nJhL9J27vG6kgiIiIiIgWGCpjkuh5NazHf1g6Ak0vftDiNiIiIiEjBoQImuc7bw5W4+ncBEHJ0BcT+Y3EiEREREZGCQQVM8kTPDm1ZYW+AC4bYFe9aHUdEREREpEBQAZM8UaaEN1vLDQbAd9tsuBBncSIREREREeupgEmeadn5Vnbay+JlP0/i2qlWxxERERERsZwKmOSZRhWCWRJwCwBpUR9CWqrFiURERERErKUCJnnGZrNRof1wThk/ApKOkrJ9gdWRREREREQspQImeapbg4rMc+0KQNyKty1OIyIiIiJiLRUwyVMebi6YJneRbFwpFbsJc3iL1ZFERERERCyjAiZ5rk/bxiw0LQA4tVzvgomIiIhI8WVpAVu9ejW9e/emTJky2Gw25s2bd9nxR48eZdCgQVSvXh0XFxdGjRqVaUz79u2x2WyZTj179nSMGTduXKb1YWFhuXzrJEMpP0/2VBkGQOA/P0L8UYsTiYiIiIhYw9IClpiYSP369XnvvfdyND4pKYmQkBDGjh1L/fr1sxwzd+5cjh496jht3boVV1dXbrvtNqdxtWvXdhr3559/Xvftkex16dSV9fbquJHK2V8/sjqOiIiIiIgl3Ky88u7du9O9e/ccj4+MjOTtt9MPYfvss8+yHBMcHOz08+zZs/Hx8clUwNzc3PSuVz6qXSaQ14Nvo+mZl3DdNBU6PQnu3lbHEhERERHJV0X+M2CffvopAwYMwNfX12n5rl27KFOmDBUrVmTAgAHs2bPnsttJSkoiPj7e6SRXp3bHgRy0h+CTeoaULbOtjiMiIiIiku+KdAFbv349W7du5a677nJa3qxZM2bMmMHixYv55JNPiImJoWXLlsTGxma7rYkTJxIYGOg4lStXLq/jFzmda0cwzyP9s3iJq98DYyxOJCIiIiKSv4p0Afv000+pU6cOTZs2dVrevXt3br31VurWrUunTp1YsCD9C4KnT5+e7bbGjBlDXFyc43Tw4ME8zV4UubrY8G85kgTjRYmE3Zg9K62OJCIiIiKSr4psATt37hyzZ8/O9O5XVnx9falbty67du3KdoynpycBAQFOJ7l6N7eoxTzaA3Dm57cszSIiIiIikt+KbAH76quvSEpK4o477rji2KSkJLZv3054eHg+JCveAr3dOVFrBHZjI+jISjiZfekVERERESlqLC1gCQkJREdHEx0dDcDevXuJjo7mwIEDQPphf0OHDnW6TMb4hIQETpw4QXR0NNu2bcu07U8//ZS+fftSsmTJTOsef/xxVq1axd69e1m3bh39+vUjPj6eYcOG5f6NlExuurE1P9sbAXB21bsWpxERERERyT+WTkO/ceNGOnTo4Ph59OjRAAwbNoxp06Zx9OhRRxnL0LBhQ8f5TZs28eWXX1KhQgX27dvnWP7333/zyy+/sGTJkiyv99ChQwwcOJCTJ08SEhJC8+bNWbt2LRUqVMjFWyfZqRzix1fhA+h8fBOef82B7uPAJ/iKlxMRERERKexsxmgqumsRHx9PYGAgcXFx+jzYNVi54xilv+xETZcDJLV/Ds/2j1kdSURERETkmlxNNyiynwGTgq1ttVB+8OkLQGrUR5CWYm0gEREREZF8oAImlnBxsVG29R2cMAH4Jh3Dvu1HqyOJiIiIiOQ5FTCxTN8bKvONrQsAZ1e+bXEaEREREZG8pwImlvH1dCO54QiSjBuBsdFwcIPVkURERERE8pQKmFjq1raN+NHeEoB4TUkvIiIiIkWcCphYqmyQDzsqpH9Ztu/u+RB32OJEIiIiIiJ5RwVMLNf5xk5EpdXClTQu/PqB1XFERERERPKMCphYrmnFYJYG3JL+w+bpkJxobSARERERkTyiAiaWs9ls1Gx/G/vspfFKjSctepbVkURERERE8oQKmBQIvRuU42vXHgCcW/M+2O0WJxIRERERyX0qYFIgeLm74tV0GPHGG/+ze+Cfn62OJCIiIiKS61TApMDo37om39g7AHB25TsWpxERERERyX0qYFJghPp7cbjaUNKMDf/Dq+H4dqsjiYiIiIjkKhUwKVD6dGjBEnsTAM6ved/iNCIiIiIiuUsFTAqUemVL8Gup2wFw/+srSIy1OJGIiIiISO5RAZMCp3n7nvxhr4ibPYmUDZ9ZHUdEREREJNeogEmB07VOOHM9bgIgNepjSE22OJGIiIiISO5QAZMCx93VhbCWAzluSuCddByzbZ7VkUREREREcoUKmBRI/ZtV5kvTBYDEVe+AMRYnEhERERG5fipgUiAF+XqQUGcIF4w7frF/wsF1VkcSEREREbluKmBSYN3eriHfpbUG4Nzqdy1OIyIiIiJy/VTApMCqVtqf3yMGAOC1+yc4c8DiRCIiIiIi10cFTAq0zu07sCatDi7YSfntQ6vjiIiIiIhcFxUwKdA6VA9lvk9fAMzmGZB01tpAIiIiIiLXQQVMCjQXFxs12tzCP/ZwPFLPYt/ypdWRRERERESumQqYFHj9mpTnS1sPAC788j7Y7RYnEhERERG5NipgUuD5e7nj2nAgccYHn4T9sGux1ZFERERERK6JCpgUCoPb1GJWWkdAU9KLiIiISOGlAiaFQoWSvuypOJBU44LP4V8hZqvVkURERERErpoKmBQafds1Y5G9KQDJv75vcRoRERERkaunAiaFRovKJVkWcCsArlu/hoQTFicSEREREbk6KmBSaNhsNpq368YWexVcTQppGz61OpKIiIiIyFVRAZNCpW/DCOa49gIgde0nkJpkcSIRERERkZxTAZNCxcvdlVI33MZRE4xn0knY+q3VkUREREREckwFTAqdwa0q83laFwDOr3kPjLE4kYiIiIhIzqiASaETHuhNbI1BnDceeMf+Bft/tTqSiIiIiEiOqIBJodS/bT2+TWsDQNIv71mcRkREREQkZ1TApFBqVD6IqJDbAPDYvQhO7bE4kYiIiIjIlamASaHVpV1bVqbVx4Yhbe1HVscREREREbkiFTAptLrXCedbj5sAsG/+HC7EW5xIREREROTyVMCk0PJwc6Fai5vYZY/APTURs+VzqyOJiIiIiFyWCpgUaoOaV2CG6Q5A8q8fgD3N4kQiIiIiItlTAZNCraSfJ2l1bue08cMz4SDs/MnqSCIiIiIi2VIBk0LvjjY1mZnWEdCU9CIiIiJSsKmASaFXq0wAf0XcRopxxfPwWjgSbXUkEREREZEsqYBJkdC37Q0ssDcDIPW3yRanERERERHJmgqYFAmdapZmgU9fAGx/fQtnY6wNJCIiIiKSBRUwKRJcXWw0a92ZDfZquJpUzIYpVkcSEREREclEBUyKjNualGMmPQFIXfcppFywOJGIiIiIiDMVMCkyAr3dKdGoL4dMKdyTTsGfX1kdSURERETEiQqYFClDWlVhemoXAJJ/eR+MsTiRiIiIiMi/VMCkSKkc4sfhSreRaDzxOLUD9q6yOpKIiIiIiIMKmBQ5A9rU5eu0dgCk/qovZhYRERGRgkMFTIqcNlVLsSLwFuzGhts/S+HkbqsjiYiIiIgAKmBSBNlsNjq3acnP9oYA2Nd9aHEiEREREZF0KmBSJN3SKII5rr0AMJu/gPOnLU4kIiIiIqICJkWUj4cblW/oznZ7OVzTzsPmGVZHEhERERFRAZOia0jLSKaldQcgJepDSEu1OJGIiIiIFHcqYFJklQ3y4Vz1WzhpAnBPOAI7frQ6koiIiIgUcypgUqQNaVOdmWmdAEj59X2L04iIiIhIcacCJkXaDZFBbAi5mWTjivuRDXBok9WRRERERKQYUwGTIs1ms3Fz60b8aG8JgD1K74KJiIiIiHVUwKTI61U/nLkeN6X/sO17iDtsbSARERERKbZUwKTI83RzpUnz9qy118TFpMKGKVZHEhEREZFiSgVMioXBzcsz3Z4+JX3qhs8g+ZzFiURERESkOFIBk2Ih1N8Lnzq92G8PxS3pDPwx2+pIIiIiIlIMqYBJsTG8dRWmpXUFIPW3yWC3W5xIRERERIobFTApNuqWDWR3mT6cNd64ndoFe5ZbHUlEREREihkVMClWBratw1dp7QFI+01T0ouIiIhI/lIBk2KlS63SLPK5Cbux4bpnORzfYXUkERERESlGVMCkWHFzdaFjq2YssTcBwKz70OJEIiIiIlKcqIBJsTPghnJ8QQ8A7NGz4NwpixOJiIiISHGhAibFTgkfD8o37MRWeySuaRdg46dWRxIRERGRYkIFTIqlEa0qMiX1f++CrXkL4o9aG0hEREREigUVMCmWqpb251Slm9hir4JLSgIsftrqSCIiIiJSDKiASbF1V9sqjE0ZSZqxwV9zYfcyqyOJiIiISBGnAibFVpuqpQipegNT07qlL1jwOKSctzaUiIiIiBRpKmBSbNlsNp7tVYt37bdx1ATD6b2w5g2rY4mIiIhIEaYCJsValVA/+rWsyQspQwEwv74FJ3dZG0pEREREiiwVMCn2Hu5YlQ1erVie1gBbWjIsGA3GWB1LRERERIogFTAp9gK93flPtxo8lzqcC7jD3tXw59dWxxIRERGRIkgFTAS4rUk5SpSpwjspt6QvWPw0nD9tbSgRERERKXJUwEQAVxcbz/euzSdpPdllj4DEE/Dzi1bHEhEREZEiRgVM5H9uiAyme/3yPJMyEgCz8TM4tNHiVCIiIiJSlKiAiVxkTI8a/OFWh2/S2mLDwPxRkJZqdSwRERERKSJUwEQuEh7ozQPtKzMhZRDx+EHMn7DhE6tjiYiIiEgRoQImcom721bCJ6g0E1IGpC9Y/hLEH7E2lIiIiIgUCZYWsNWrV9O7d2/KlCmDzWZj3rx5lx1/9OhRBg0aRPXq1XFxcWHUqFGZxkybNg2bzZbpdOHCBadxkydPpmLFinh5edG4cWPWrFmTi7dMCjMvd1fG9qjJnLT2bDZVITkBFj1ldSwRERERKQIsLWCJiYnUr1+f9957L0fjk5KSCAkJYezYsdSvXz/bcQEBARw9etTp5OXl5Vg/Z84cRo0axdixY9myZQtt2rShe/fuHDhw4LpvkxQN3eqE0bxSCE8n30kaLrDte9i11OpYIiIiIlLIWVrAunfvzksvvcQtt9ySo/GRkZG8/fbbDB06lMDAwGzH2Ww2wsLCnE4Xe+ONN7jzzju56667qFmzJm+99RblypXjgw8+uK7bI0WHzWbjud61+JvyfJraPX3hgscg5by1wURERESkUCuSnwFLSEigQoUKlC1bll69erFlyxbHuuTkZDZt2kSXLl2cLtOlSxd+++23bLeZlJREfHy800mKtprhAQxuVoG3Um/lhK0UnNkPq1+zOpaIiIiIFGJFroDVqFGDadOm8cMPPzBr1iy8vLxo1aoVu3btAuDkyZOkpaVRunRpp8uVLl2amJiYbLc7ceJEAgMDHady5crl6e2QgmF052q4e/szNmlI+oJf34YTO60NJSIiIiKFVpErYM2bN+eOO+6gfv36tGnThq+++opq1arx7rvvOo2z2WxOPxtjMi272JgxY4iLi3OcDh48mCf5pWAJ8vXgsS7VWGJvwioagz0l/VBEY6yOJiIiIiKFUJErYJdycXHhhhtucLwDVqpUKVxdXTO923X8+PFM74pdzNPTk4CAAKeTFA+DmpaneukAxiYNJdnmCfvWwB9zrI4lIiIiIoVQkS9gxhiio6MJDw8HwMPDg8aNG7N0qfOMdkuXLqVly5ZWRJQCzs3Vhed71+KQCeHNlP9NGLN4LJw7ZW0wERERESl03Ky88oSEBHbv3u34ee/evURHRxMcHEz58uUZM2YMhw8fZsaMGY4x0dHRjsueOHGC6OhoPDw8qFWrFgAvvPACzZs3p2rVqsTHx/POO+8QHR3N+++/79jG6NGjGTJkCE2aNKFFixZ8/PHHHDhwgPvuuy9/brgUOi2rlKJb7TA++as7g7x+o9y5/fDzC9D7baujiYiIiEghYmkB27hxIx06dHD8PHr0aACGDRvGtGnTOHr0aKbv5mrYsKHj/KZNm/jyyy+pUKEC+/btA+DMmTPcc889xMTEEBgYSMOGDVm9ejVNmzZ1XK5///7ExsYyfvx4jh49Sp06dfjpp5+oUKFCHt5aKezG9qzJ8p3HGZ04jK89x8OmadBgMJRresXLioiIiIgA2IzRbALXIj4+nsDAQOLi4vR5sGLktcU7eW/Fbt73nULPtOVQug7cswpcLf1fhoiIiIhY6Gq6QZH/DJhIbnqgQ2XCArx4JvF2LrgFwrGtsO5Dq2OJiIiISCGhAiZyFXw83BjTowanCeCl5AHpC1dMgLhD1gYTERERkUJBBUzkKt1UvwyNKwQxM7kNe7zrQEoiLHrK6lgiIiIiUgiogIlcJZvNxrjetcHmwv1n7sDY3GD7j7BzkdXRRERERKSAUwETuQZ1ywZye+Ny7DTlmevVJ33hwv9A8jlrg4mIiIhIgaYCJnKNHu9aHX9PN5453ZNE73A4cwBWv2p1LBEREREpwFTARK5RiL8nD3esynm8eDZpaPrC396B49utDSYiIiIiBZYKmMh1GNYykkohvsw9V5+dJdqCPRXmjwZ9vZ6IiIiIZEEFTOQ6eLi58GyvWgDcfeI27G7ecOA3iP7S4mQiIiIiUhCpgIlcpw7VQ7mxRigH0krytd8d6QuXPgvnTlkbTEREREQKHBUwkVzwTM+auLvaGBvThoTAanAuFpY9b3UsERERESlgVMBEckGlED9GtKpIKm6MSR6ZvnDzDDiw1tpgIiIiIlKgqICJ5JL/u7EKpfw8+fF0eXaE901fOP9RSEuxNJeIiIiIFBwqYCK5xN/LnSe6VQfgriO9sXsFw/FtsPYDi5OJiIiISEGhAiaSi/o1Kku9soEcSvLm65L3pi9cORHOHLQ2mIiIiIgUCCpgIrnIxcXG871rA/DUnjokhDWFlHOw8EmLk4mIiIhIQaACJpLLGlcI4uaGERhj45mUkRgXN9i5AHb8ZHU0EREREbGYCphIHniyWw18PFyZdziAXZWHpy9c+AQkJ1qaS0RERESspQImkgfCAr14sEMVAO7adyP2wPIQdxBWvWJxMhERERGxkgqYSB65s3VFygf7cOAszAt/JH1h1PtwbJu1wURERETEMipgInnEy92VsT1rAvDU1gjOVeoO9lRYMBrsdovTiYiIiIgVVMBE8lCXWqVpVaUkyal2xqcNBXdfOBAF0TOtjiYiIiIiFlABE8lDNlv6tPSuLjZm7zTsrftw+oqlz0JirLXhRERERCTfqYCJ5LFqpf0Z0rwCAA/ubooJrQ3nT8Oy5yxOJiIiIiL5TQVMJB882qkaQT7ubDt+noWR//tS5i1fwP7frA0mIiIiIvlKBUwkHwT6uDO6S3UAxmzwJqne0PQV80dDarKFyUREREQkP6mAieSTQU3LUyPMn7jzKbxuHwg+peDEdlj7vtXRRERERCSfqICJ5BNXFxvjbqoNwJRNpznSbGz6ipWvwOn9FiYTERERkfyiAiaSj5pXKknPuuHYDYzeUQNToRWknoeFT4AxVscTERERkTymAiaSz8b0qIGnmwtr955mTbWnwcUd/l4EOxZYHU1ERERE8pgKmEg+Kxvkw73tKgMwZk0yqc3/L33FwicgKcHCZCIiIiKS11TARCxwf7vKhAd6cfjMeT7mFihRAeIPw6r/Wh1NRERERPKQCpiIBbw9XBnToyYA76w5RGy7l9NXRE2GmK0WJhMRERGRvKQCJmKR3vXCaRoZzIUUOy/sKAs1bwKTBvMfBbvd6ngiIiIikgdUwEQsYrPZeK53LWw2+OH3I0TXeQo8/ODQetjyudXxRERERCQPqICJWKhORCADbigPwNifT2Fv/3T6iqXPQeJJC5OJiIiISF5QAROx2ONdquHv5cZfR+L5yqU7hNWFC2dgybNWRxMRERGRXKYCJmKxkn6ejOpUDYBXl/5DQufXABv8/iXs+8XacCIiIiKSq1TARAqAoS0qUCXUj9jEZN7cHgBNRqSvmD8aUpOtDSciIiIiuUYFTKQAcHd14dletQCY/ts+9tQbDb4hcHInRL1rcToRERERyS0qYCIFRLtqIXSqGUqq3TBu2VFMl5fSV6yaBKf2WhtORERERHKFCphIAfJMz1p4uLqw+u8TLHdvD5FtIPUCLHwCjLE6noiIiIhcJxUwkQIkspQvI1tXBODFBdtJ7v4auHrAriWw/UeL04mIiIjI9VIBEylgHrqxCiH+nuyLPcdnO9yh1aj0FQufhKSzlmYTERERkeujAiZSwPh5uvFktxoAvPvzLo43eACCIuHsEVgx0dpwIiIiInJdVMBECqBbGkZQv1wJEpPTmPTzAejxevqKdR/C0T+sDSciIiIi10wFTKQAcnGxMa53+rT032w6RLRXE6h9M5g0mP8o2O0WJxQRERGRa6ECJlJANSwfxC2NIgAY98Nf2LtMAA9/OLwRNk+zNpyIiIiIXBMVMJEC7KluNfD1cCX64Bnm/WOHG59JX7FsHCQctzSbiIiIiFw9FTCRAiw0wIuHbqwKwH8X7iCh/nAIrw8X4mDJs9aGExEREZGrpgImUsCNbB1JhZI+HD+bxPur9kGvNwEb/DEb9q62Op6IiIiIXAUVMJECztPNlWd6pk/I8emavezzrAE33Jm+cv5oSE2yMJ2IiIiIXA0VMJFCoFPNUNpULUVymp2Xf9oONz4LvqEQuwt+e8fqeCIiIiKSQypgIoWAzWbj+d61cHOxsXTbMdYcSoFu//tS5tWvwak91gYUERERkRxRARMpJKqE+jO0RSQAL/y4jZSaN0PFdpB6AX76DxhjbUARERERuSIVMJFC5JFOVQn29WD38QS+WHcAer4Brh6wexlsm2d1PBERERG5AhUwkUIk0Nudx7tUB+DNpX8T61UOWo9OX7loDFyItzCdiIiIiFyJCphIIdP/hnLUCg8g/kIqry/9G1o/CsGV4OxRWDHB6ngiIiIichkqYCKFjKuLjXE31QZg1voD/HUiCXq+nr5y/UdwJNq6cCIiIiJyWSpgIoVQ04rB9KoXjjHpE3KYSh2gzq1g7DD/UbCnWR1RRERERLKgAiZSSD3doyZe7i6s33uKBX8eha4TwDMAjmyGTVOtjiciIiIiWVABEymkypTw5v52VQCYsGA75z1D0r+gGWDZeDh7zMJ0IiIiIpIVFTCRQuyetpWIKOHNkbgLfLjqH7jhTghvAElxsGSs1fFERERE5BIqYCKFmLeHK0/3qAnAh6v+4XB8MvR+C2wu8OfXsGelpflERERExJkKmEgh16NuGM0qBpOUamfCT9uhTEO44e70lfNHQ8oFawOKiIiIiIMKmEghZ7PZeL53bVxssOCPo6zdEws3jgW/0nDqH/j1basjioiIiMj/qICJFAG1ygQwsGl5IH1a+jSPAOg2MX3lmtch9h8L04mIiIhIBhUwkSLisS7VCfByY/vReGZvOAC1b4HKN0JaEvz0OBhjdUQRERGRYk8FTKSICPb1YHTnagC8tngncedTocdr4OoJ/yyHv+ZanFBEREREVMBEipDBzStQNdSP0+dSeHPZ31CyMrR5LH3lojFwIc7agCIiIiLFnAqYSBHi7urC871rA/D52v3sOnYWWo+CklUg4Rgsf8nagCIiIiLFnAqYSBHTumoputQqTZrdMH7+NoyrB/R8PX3lhilweLO1AUVERESKMRUwkSLomZ618HB1Yc2ukyzddgwqtYe6t4Gxw/xHwZ5mdUQRERGRYkkFTKQIKl/Sh7vaVATgpQXbuZCSBl1eBs9AOBoNGz61NqCIiIhIMaUCJlJEPdihCqUDPDlw6hyf/boX/EtDp+fSVy5/Ec7GWBtQREREpBhSARMponw93Xiqew0A3lu+m2PxF6DxCIhoDEnxsPhpixOKiIiIFD8qYCJFWN8GETQqX4JzyWm8snAHuLhCrzfB5gJbv4XdP1sdUURERKRYUQETKcJsNptjWvq5Ww6z+cBpCK8PTe9NH/DT45BywcKEIiIiIsWLCphIEVe/XAlua1wWgBd++Au73UCHp8E/HE7tgV/etDihiIiISPGhAiZSDPynW3X8PN34/VAc324+BF4B0G1i+spf3oCTu60NKCIiIlJMqICJFAOh/l483LEKAK8s2snZCylQqy9U6QRpybBgNBhjbUgRERGRYkAFTKSYGN6yIhVL+XIyIYn3VuwGmw16vApuXrB3VfqkHCIiIiKSp1TARIoJDzcXnu1VE4DPftnL3pOJEFwJ2j6ePmDRGDh/xrqAIiIiIsWACphIMXJjjdK0rx5CSprhpfnb0he2fBhKVoXE4+lf0CwiIiIieUYFTKSYeaZnLdxcbPy84zgrdx4HN0/o9Ub6yg2fwqFN1gYUERERKcJUwESKmSqhfgxvGQnA+PnbSEmzQ8W2UG8AYGD+KEhLtTKiiIiISJGlAiZSDD3cqSql/DzYcyKR6b/tS1/Y5SXwCoSYP2DDFEvziYiIiBRVlhaw1atX07t3b8qUKYPNZmPevHmXHX/06FEGDRpE9erVcXFxYdSoUZnGfPLJJ7Rp04agoCCCgoLo1KkT69evdxozbtw4bDab0yksLCwXb5lIwRbg5c5/ulYH4O1luziZkAR+IdBpXPqA5S9B/BHrAoqIiIgUUZYWsMTEROrXr897772Xo/FJSUmEhIQwduxY6tevn+WYlStXMnDgQFasWEFUVBTly5enS5cuHD582Glc7dq1OXr0qOP0559/XvftESlM+jUuR52IAM4mpfLa4p3pCxsNh7I3QPLZ9FkRRURERCRX2YwpGN++arPZ+O677+jbt2+Oxrdv354GDRrw1ltvXXZcWloaQUFBvPfeewwdOhRIfwds3rx5REdHX3Pe+Ph4AgMDiYuLIyAg4Jq3I2KljftO0e/DKGw2+PGh1tSJCISYP+GjdmDSYPC3ULWT1TFFRERECrSr6QZF/jNg586dIyUlheDgYKflu3btokyZMlSsWJEBAwawZ8+ey24nKSmJ+Ph4p5NIYdckMpg+DcpgDIz74S+MMRBWF5rdlz7gp8cg5by1IUVERESKkCJfwJ566ikiIiLo1Onf/+I3a9aMGTNmsHjxYj755BNiYmJo2bIlsbGx2W5n4sSJBAYGOk7lypXLj/giee6p7jXwdndl4/7T/PD7/z731WEM+JeB0/tgzeuW5hMREREpSop0AZs0aRKzZs1i7ty5eHl5OZZ3796dW2+9lbp169KpUycWLFgAwPTp07Pd1pgxY4iLi3OcDh48mOf5RfJDeKA3D7SvDMB/F+7gXHIqePpD91fSB/zyFpz427qAIiIiIkVIkS1gr732GhMmTGDJkiXUq1fvsmN9fX2pW7cuu3btynaMp6cnAQEBTieRouLutpUoG+TN0bgLfLjyn/SFNXtD1S5gT4EFo6FgfFxUREREpFArkgXs1Vdf5cUXX2TRokU0adLkiuOTkpLYvn074eHh+ZBOpODxcnflmZ41Afho9R4OnjoHNhv0eBXcvGDfGvjjK4tTioiIiBR+lhawhIQEoqOjHbMR7t27l+joaA4cOACkH/aXMXNhhozxCQkJnDhxgujoaLZt2+ZYP2nSJJ555hk+++wzIiMjiYmJISYmhoSEBMeYxx9/nFWrVrF3717WrVtHv379iI+PZ9iwYXl/o0UKqK61w2hZuSRJqXYm/LQ9fWFQJLR7Iv384qfh/GnL8omIiIgUBZZOQ79y5Uo6dOiQafmwYcOYNm0aw4cPZ9++faxcudKxzmazZRpfoUIF9u3bB0BkZCT79+/PNOb5559n3LhxAAwYMIDVq1dz8uRJQkJCaN68OS+++CK1atXKcXZNQy9F0Y6YeHq8vQa7gS/vbkbLyqUgNRk+bA0nd0KTkdDrTatjioiIiBQoV9MNCsz3gBU2KmBSVD33/VZmRO2nRpg/8/+vNW6uLrDvF5jWE7DBnUuh3A1WxxQREREpMPQ9YCJyzUZ3rkYJH3d2xJxl1vr0w4GJbA31BwEG5j8KaamWZhQREREprFTARMRJCR8PRneuBsDrS//mzLnk9BVdXgSvEnDsT1j/kXUBRURERAoxFTARyWRQ0/LUCPPnzLkU3lz6v+8A8y0FnV9IP79iAsQdti6giIiISCGlAiYimbi5uvBc7/RJab5Yd4CdMWfTVzQcCmWbQnICLHxC3w0mIiIicpVUwEQkSy0rl6J7nTDS7IYXfvwLYwy4uKTPgujiBjvmw/qPrY4pIiIiUqiogIlItp7uURMPNxd++yeWxX/FpC8MqwOdX0w/v/hp2P+bdQFFREREChkVMBHJVrlgH+5tWwmAlxZs50JKWvqK5vdD3dvAngpfDYP4IxamFBERESk8VMBE5LLub1+Z8EAvDp0+z5Q1e9IX2mzQ+20oXQcSj8NXQyE1ydqgIiIiIoWACpiIXJaPhxtPda8BwPsr/uFo3Pn0FR6+0P9z8AqEQxtg0VMWphQREREpHFTAROSKbqpfhiYVgjifksYrC3f8uyK4Etz6KWCDjZ/B5s8tyygiIiJSGKiAicgV2Ww2xt1UG5sN5kUfYdP+U/+urNoZOoxNP7/gMTi82ZqQIiIiIoWACpiI5EidiED6NykHwLgftmG3X/QdYG0eg+o9IC0J5gyBxJMWpRQREREp2FTARCTHHu9aHX9PN/48HMfXmw7+u8LFBW7+EEpWgfhD8M0ISEu1LqiIiIhIAaUCJiI5VsrPk0c6VQXglUU7iYm78O9Kr0DoPxM8/GDvavh5nDUhRURERAowFTARuSpDW0RSMzyAU4nJPPTlZlLS7P+uDK0BfSenn//tXdj6rTUhRURERAooFTARuSoebi5MHtwIf083Nu4/zaRFO5wH1OoDrUaln//+ITi2Ld8zioiIiBRUKmAictUqlvLl1dvqAfDJmr0s2nrUeUDH56BSe0g5B3MGw/kz+Z5RREREpCBSARORa9KtTjh3ta4IwH++/oN9JxP/XeniCrd+BoHl4dQemHsP2O3ZbElERESk+FABE5Fr9mT3GjSpEMTZpFTun7mZCylp/670LQn9Pwc3L9i1GFZPsi6oiIiISAGhAiYi18zd1YX3BjWipK8H24/G8/z3fzkPKNMAer2Zfn7lRNi5KN8zioiIiBQkKmAicl3CAr14Z2BDbDaYs/EgX2086DygwSC44e7083Pvgdh/8j+kiIiISAGhAiYi161VlVKM7lQNgGfnbWXbkXjnAV0nQLnmkBQHswdDUoIFKUVERESspwImIrniwQ5VaF89hKRUOw/M3ET8hZR/V7p5wO3TwS8MTmyHHx4CY6wLKyIiImIRFTARyRUuLjbevL0BZQK92Bd7jie/+QNzccnyD4PbZ4CLO/z1XfoXNYuIiIgUMypgIpJrgnw9eH9wI9xdbSzcGsNnv+5zHlC+GXSbmH5+2fOwZ1W+ZxQRERGxkgqYiOSqhuWDeKZnLQAm/rSdTftPOQ+44S6oPwiMHb4ZAWcOZrEVERERkaJJBUxEct3QFhXoVS+cVLvhwZlbiE1I+nelzQa93oDw+nAuFubcASkXrAsrIiIiko9UwEQk19lsNv57az0qhfgSE3+BUXOiSbNf9Hkwd2/o/wV4B8PRaFjwmCblEBERkWJBBUxE8oSfpxsf3tEYb3dX1uw6yTs/73IeUKI89PsMbC4Q/QVs/MyaoCIiIiL5SAVMRPJMtdL+vHxzHQDeWb6LVX+fcB5QuQN0fD79/MIn4eD6fE4oIiIikr9UwEQkT93SqCyDmpXHGBg1ewtHzpx3HtDqEajVB+wp8NVQOHvMmqAiIiIi+UAFTETy3HO9alEnIoDT51J48MvNJKfa/11ps0Gf9yGkBpw9Cl8Pg7SU7DcmIiIiUoipgIlInvNyd+WDwY0J8HJjy4EzTFy43XmApz/0nwmeAXAgChaPtSaoiIiISB5TARORfFEu2IfXb28AwNRf97Hgj6POA0pVgZs/Sj+//iP4fXb+BhQRERHJBypgIpJvOtcqzX3tKgPwxDe/88+JBOcBNXpA2yfSz//4CBz9PZ8TioiIiOQtFTARyVePd6lG04rBJCan8cAXmzmfnOY8oP0YqNoFUi+kf0nzuVPWBBURERHJAypgIpKv3FxdeG9gQ0r5ebLz2FnGzvsTc/GXMLu4wC0fQ1BFOHMAvr0T7GnZb1BERESkEFEBE5F8FxrgxbsDG+Jig7mbDzNnw0HnAd5B0P8LcPeBf5bD8pesCSoiIiKSy1TARMQSLSqX5PGu1QF47oe/2Ho4znlAWB246d3087+8Adt+yOeEIiIiIrnvugtYUlJSbuQQkWLovraV6VgjlORUOw/M3Ezc+Uu+/6tuP2j+YPr5effDiZ35H1JEREQkF111AVu8eDHDhw+ncuXKuLu74+Pjg7+/P+3atePll1/myJEjeZFTRIogFxcbr99en7JB3hw4dY7Hv/7d+fNgAJ3HQ2QbSE6A2YPhQrw1YUVERERyQY4L2Lx586hevTrDhg3DxcWF//znP8ydO5fFixfz6aef0q5dO5YtW0alSpW47777OHHiRF7mFpEiooSPB5MHN8LD1YWl247xyZo9zgNc3aDfVAiIgNhd6e+E2e3WhBURERG5TjaT6d/NWWvatCnPPvssPXv2xMUl+952+PBh3n77bUqXLs1jjz2Wa0ELmvj4eAIDA4mLiyMgIMDqOCKF3hdr9/PMvK24utiYdXdzmlYMdh5waBNM7QZpyXDjs9D2cWuCioiIiFziarpBjguYOFMBE8ldxhgenRPNvOgjhPp7suDhNoT4ezoP2jQdfnwYsMHgb6BqJ0uyioiIiFzsarrBNU3CMX78eM6dO5dp+fnz5xk/fvy1bFJEijmbzcbLN9elaqgfx88m8fCsLaTZL/n/UONh0Hg4YNK/H+zUXiuiioiIiFyza3oHzNXVlaNHjxIaGuq0PDY2ltDQUNLSiv6XpuodMJG8sfv4WW5671fOJafxUIcqjqnqHVKTYGoPOLwRSteFO5eAh481YUVERETIh3fAjDHYbLZMy3///XeCg4OzuISISM5UCfXnv7fWA+C9FbtZvuOY8wA3T7h9BviGwLE/4cdHQEdSi4iISCFxVQUsKCiI4OBgbDYb1apVIzg42HEKDAykc+fO3H777XmVVUSKiZvql2FoiwoAPDrndw6dvuSQ58AIuG0a2Fzhz69g3Uf5H1JERETkGlzVIYjTp0/HGMPIkSN56623CAwMdKzz8PAgMjKSFi1a5EnQgkaHIIrkraTUNG7/MIrfD8VRv2wgX93XAk83V+dBUZNh8Zj0IjbsR4hsZU1YERERKdbyfBbEVatW0apVK9zc3K45ZGGnAiaS9w6dPkfPd34h7nwKQ1tUYHyfOs4DjIG5d8OfX6cfknjvaggoY01YERERKbby5DNgiYmJjvPt2rW7Yvm6eLyIyLUoG+TDW/0bADAjaj/fRx92HmCzQe93oHQdSDwBc4akT9IhIiIiUkDluIBVqVKFCRMmcOTIkWzHGGNYunQp3bt355133smVgCJSvHWoEcpDHaoAMGbun+w+ftZ5gIcP9P8CvALTZ0Zc+KQFKUVERERyJseHIO7cuZNnnnmGH374gQYNGtCkSRPKlCmDl5cXp0+fZtu2bURFReHu7s6YMWO45557cHV1vfKGCykdgiiSf9LshiGfruO3f2KpEurH9w+2wtfzknfhdy2Dmf0AAze9C42GWpJVREREip88/QzYoUOH+Oqrr1izZg379u3j/PnzlCpVioYNG9K1a1d69OiBi8s1zW5fqKiAieSvE2eT6PnOGo6fTaJvgzK82b9B5q/DWP0qLH8JXD1gxCIo29iasCIiIlKs5PkkHKICJmKF9XtPMfCTtaTZDS/1rcMdzSs4D7DbYc4dsHMBBETAPavAL8SasCIiIlJs5HkBGz16dNYbs9nw8vKiSpUq9OnTp0h/KbMKmIg1Pl79DxN+2oGHqwvf3N+CemVLOA+4EAef3AixuyGyDQyZB67Fd8ZWERERyXt5XsA6dOjA5s2bSUtLo3r16hhj2LVrF66urtSoUYOdO3dis9n45ZdfqFWr1jXfkIJMBUzEGsYY7v18E0u2HSOihDcLHm5NCR8P50HHd8CUjpCcAC0egq4vWxNWREREioU8mYb+Yn369KFTp04cOXKETZs2sXnzZg4fPkznzp0ZOHAghw8fpm3btjz66KPXdANERLJjs9l49bb6lA/24fCZ8zz21e/Y7Zf8Hym0BvSdnH4+6j3485v8DyoiIiKShWt6BywiIoKlS5dmenfrr7/+okuXLhw+fJjNmzfTpUsXTp48mWthCxK9AyZira2H47jlg99ITrXzRLfqPNC+SuZBy8bBL2+Cuw/ctQxK1873nCIiIlL05fk7YHFxcRw/fjzT8hMnThAfHw9AiRIlSE5OvpbNi4hcUZ2IQMbflF6oXlu8k6h/YjMPuvFZqNQBUs7B7MFw/nQ+pxQRERFxds2HII4cOZLvvvuOQ4cOcfjwYb777jvuvPNO+vbtC8D69eupVq1abmYVEXHS/4Zy3NqoLHYD/zdrC8fjLzgPcHGFfp9BYHk4vRfm3pM+U6KIiIiIRa6pgH300Ud07NiRAQMGUKFCBcqXL8+AAQPo2LEjH374IQA1atRgypQpuRpWRORiNpuNl/rWoUaYPycTknho1hZS0y4pWD7B0P9zcPOCXUtg1X+tCSsiIiLCdX4PWEJCAnv27MEYQ+XKlfHz88vNbAWaPgMmUnDsOZHATe/9SkJSKve1q8xT3WtkHhQ9C+bdl35+4Gyo3j1/Q4qIiEiRleefAcvg5+dHvXr1qF+/frEqXyJSsFQK8eOVW+sB8OGqf1i67VjmQQ0GQtN70s/PvQdi/8nHhCIiIiLprquAiYgUFD3rhTOiVSQAj30VzYHYc5kHdXkZyjWHpHiYPQiSEvI3pIiIiBR7KmAiUmSM6V6ThuVLEH8hlQe+3MSFlDTnAW4ecPt08AuDEzvg+wfh2o/CFhEREblqKmAiUmR4uLnw/qBGBPm4s/VwPOPnb8s8yD8Mbp8BLu6wbR789k6+5xQREZHiSwVMRIqUMiW8eWtAQ2w2+HLdAeZuPpR5UPlm0P1/syEuGwd7VuZnRBERESnGVMBEpMhpVy2Eh2+sCsDY77ayM+Zs5kFN7oQGg8HY4esRcOZAPqcUERGR4kgFTESKpIc7VqVN1VKcT0nj/pmbSEhKdR5gs0HP1yG8Ppw/BXPugJTz1oQVERGRYkMFTESKJFcXG2/1b0BYgBd7TiTy5Ld/kOlrD929of8X4B0MR3+HBY9pUg4RERHJUypgIlJklfTz5P3BDXFzsbHgj6PMiNqfeVCJ8tDvM7C5QPRM2Php/gcVERGRYkMFTESKtMYVghnToyYALy3YxpYDpzMPqtwBOo1LP7/wKTiwLv8CioiISLGiAiYiRd7IVpF0rxNGSprhwZmbOZ2YnHlQy4ehVl+wp8BXQ+FsTL7nFBERkaJPBUxEijybzcakfvWoWMqXI3EXGDUnGrvdXDoI+rwPITUgIQa+GgapWRQ1ERERkeugAiYixYK/lzuTBzfC082FVX+f4P0VuzMP8vSD/jPBMwAOroUlY/M/qIiIiBRpKmAiUmzUDA/gpb51AHhj2d/8sutk5kGlqsDNH6WfX/8xRM/Kx4QiIiJS1KmAiUixcluTcvRvUg5j4JHZW4iJu5B5UI0e0O7J9PPzR8GR6PyMKCIiIkWYCpiIFDsv9KlNzfAAYhOTeejLzaSk2TMPavcUVO0CqRdgzhA4dyr/g4qIiEiRowImIsWOl7srHwxuhL+nGxv3n2bSoh2ZB7m4wC0fQ1BFiDsA34wEe1r+hxUREZEiRQVMRIqlyFK+vHpbfQA+WbOXRVuPZh7kHQQDZoK7D+xZActfzOeUIiIiUtSogIlIsdWtThh3t6kIwH++/oN9JxMzDypdG256N/38L2/Ctu/zMaGIiIgUNSpgIlKsPdGtBk0qBHE2KZX7Z27mQkoWhxnW7QctHko/P+8BOJ7FIYsiIiIiOaACJiLFmrurC+8NakRJXw+2H43n+e//ynpgpxcgsg0kJ8CcwXAhLn+DioiISJGgAiYixV5YoBfvDGyIiw3mbDzIVxsPZh7k6gb9pkJABMTuhu/uB3sWsyeKiIiIXIYKmIgI0KpKKUZ3rgbAs/O2su1IfOZBfiFw++fg6gE7F8Avr+dzShERESnsVMBERP7ngfZVaF89hKRUOw/M3ET8hZTMg8o2hp7/K17LX4ZdS/M3pIiIiBRqlhaw1atX07t3b8qUKYPNZmPevHmXHX/06FEGDRpE9erVcXFxYdSoUVmO+/bbb6lVqxaenp7UqlWL7777LtOYyZMnU7FiRby8vGjcuDFr1qzJhVskIoWZi4uNN29vQEQJb/bFnuPJb/7AGJN5YKOh0Hg4YODbO+HUnvyOKiIiIoWUpQUsMTGR+vXr89577+VofFJSEiEhIYwdO5b69etnOSYqKor+/fszZMgQfv/9d4YMGcLtt9/OunXrHGPmzJnDqFGjGDt2LFu2bKFNmzZ0796dAwcO5MrtEpHCK8jXg/cHN8Ld1cbCrTF89uu+rAd2nwQRTdIn45gzBJKzmMJeRERE5BI2k+W/d/OfzWbju+++o2/fvjka3759exo0aMBbb73ltLx///7Ex8ezcOFCx7Ju3boRFBTErFmzAGjWrBmNGjXigw8+cIypWbMmffv2ZeLEiTm6/vj4eAIDA4mLiyMgICBHlxGRwmNG1D6e+/4v3FxszLm3OY0rBGceFHcYPm4HiSeg7m1wyydgs+V/WBEREbHU1XSDIvcZsKioKLp06eK0rGvXrvz2228AJCcns2nTpkxjunTp4hiTlaSkJOLj451OIlJ0DWlegd71y5BqNzw4cwuxCUmZBwVGwG3TweYKf34N6z7M/6AiIiJSqBS5AhYTE0Pp0qWdlpUuXZqYmBgATp48SVpa2mXHZGXixIkEBgY6TuXKlcv98CJSYNhsNibeUpfKIb7ExF9g1Jxo0uxZHDAQ2Qq6vpx+fvFY2PdL/gYVERGRQqXIFTBI/8PpYsaYTMtyMuZiY8aMIS4uznE6eDCL7wkSkSLFz9OND+5ojLe7K2t2neTtn3dlPbDZfemHIJo0+Hp4+qGJIiIiIlkocgUsLCws0ztZx48fd7zjVapUKVxdXS87Jiuenp4EBAQ4nUSk6KtW2p+Jt9QF4N3lu1i583jmQTYb9H4HStdN/zzYV0MhNYtDFkVERKTYK3IFrEWLFixd6vy9PEuWLKFly5YAeHh40Lhx40xjli5d6hgjInKxvg0jGNysPMbAo3OiOXzmfOZBHj7Q/3PwKgGHN8LCJ/I9p4iIiBR8lhawhIQEoqOjiY6OBmDv3r1ER0c7poMfM2YMQ4cOdbpMxviEhAROnDhBdHQ027Ztc6x/5JFHWLJkCa+88go7duzglVdeYdmyZU7fGTZ69GimTJnCZ599xvbt23n00Uc5cOAA9913X57fZhEpnJ7tVYu6EYGcPpfCgzM3k5xqzzwouCLc+ilgg03TYNP0/I4pIiIiBZyl09CvXLmSDh06ZFo+bNgwpk2bxvDhw9m3bx8rV650rMvqc1oVKlRg3759jp+/+eYbnnnmGfbs2UPlypV5+eWXueWWW5wuM3nyZCZNmsTRo0epU6cOb775Jm3bts1xdk1DL1L8HDx1jp7vrCH+QirDW0Yy7qbaWQ9c/SosfwlcPWDEIijbOH+DioiISL66mm5QYL4HrLBRARMpnpZtO8ZdMzYC8N6ghvSqVybzILsdvhoCO+ZDQATcswr8QvI5qYiIiOSXYv09YCIiealTrdLc374yAE9+8wf/nEjIPMjFBfp+ACWrQPzh9JkR01LzN6iIiIgUSCpgIiJX6bHO1WhWMZjE5DTu/2IT55KzKFdeATDgS/Dwg/2/wLLn8z+oiIiIFDgqYCIiV8nN1YV3BzUkxN+Tv48l8Mx3W8nyaO6Q6unvhAFEvQd/fpO/QUVERKTAUQETEbkGof5evDuwIS42mLvlMLM3ZPPl7LVugtaPpp///iGI2Zp/IUVERKTAUQETEblGzSuV5D9dawDw/A9/sfVwXNYDb3wWKnWA1PMwZzAkxuZjShERESlIVMBERK7DvW0r0almKMmpdu6fuYm4cymZB7m4Qr/PoER5OL0PPu8D50/ne1YRERGxngqYiMh1cHGx8fptDSgb5M3BU+d57Ovfs/48mE8wDP4WfEMg5k/4oh8knc3/wCIiImIpFTARkesU6OPOB4Mb4+HqwrLtx/h49Z6sB4ZUgyHzwDsIDm+EL/tD8rl8zSoiIiLWUgETEckFdcsG8vxNtQCYtHgn6/Zk8zmvsDpwx1zwDID9v8LsQZByIR+TioiIiJVUwEREcsmgpuW5uWEEaXbDQ7O2cPxsNsUqohEM/gbcfWHPiv99UXMWnx0TERGRIkcFTEQkl9hsNl6+uQ7VSvtx4mwSj8yKJjXNnvXg8s1g4Cxw84K/F8K3d0FaFl/oLCIiIkWKCpiISC7y8XBj8uDG+Hi4ErUnljeX/Z394ErtoP8X4OIO2+bBDw+BPZvCJiIiIkWCCpiISC6rEurHf2+tB8D7K/5h+Y5j2Q+u2hlumwo2V/h9FiwYDVnNoigiIiJFggqYiEgeuKl+GYa1qADAo3N+5+Cpy8x2WLM33PIxYINNU2Hx0yphIiIiRZQKmIhIHnm6Z03qlytB3PkUHvxyM0mpadkPrtsPbno3/fzaybD8pfwJKSIiIvlKBUxEJI94urny/qCGlPBx549Dcbw0f/vlL9BoCPR4Lf38mtdg9at5H1JERETylQqYiEgeKhvkw5v9GwDw+dr9fB99+PIXaHo3dH4x/fzylyDq/bwNKCIiIvlKBUxEJI91qB7K/91YBYCnvv2T3w+eufwFWj0M7Z9OP7/4adjwad4GFBERkXyjAiYikg9GdapGm6qlOJ+Sxh2frrtyCWv3BLQalX5+wWiI/jKvI4qIiEg+UAETEckHri42PryjMTdEBnH2Qip3fLqOPw6dyf4CNht0GgfN7kv/+fsHYeu3+RFVRERE8pAKmIhIPvH1dGPaiKaOEjZ4Sg5KWLf/QqNhYOww9x7YsSDf8oqIiEjuUwETEclHvp5uTB3RlCYV/vdO2JR1/HkoLvsL2GzQ602o1x/sqfD1cNi9LN/yioiISO5SARMRyWd+nm5MG5lewuIvpDJ4ytrLlzAXV+gzGWreBGnJMHsw7F2Tf4FFREQk16iAiYhYIKOENf5fCbvj03VsPXyZEubqBrd+ClW7QuoF+LI/HFyff4FFREQkV6iAiYhYxM/Tjen/K2Fx51MYPOUKJczNA26fAZXaQ0oifNEPjkTnV1wRERHJBSpgIiIW8vN0Y9qIG2hUvkTOSpi7Fwz4Esq3gKQ4+PxmOLYt/wKLiIjIdVEBExGxmL+XO9NHNs15CfPwhUFfQZlGcP4UzOgDJ3fnX2ARERG5ZipgIiIFwFWXMK8AuONbKF0XEo/DjJvg9L58yysiIiLXRgVMRKSAyChhDf9Xwu74dB1/HblMCfMJhiHfQanqEH8Ypt8EcYfzL7CIiIhcNRUwEZEC5OISduZc+jthly1hfiEw9HsIqghn9qe/E5ZwPP8Ci4iIyFVRARMRKWAC/lfCGpT7t4RtOxJ/mQuEw7AfILAcxO5O/0zYuVP5F1hERERyTAVMRKQACvByZ8ad/5awQVPWXr6ElSif/k6YXxgc3waf94XzZ/IrroiIiOSQCpiISAGVUcLqO94Ju0IJK1k5/Z0wn1Jw9HeYeRskJeRfYBEREbkiFTARkQIswMudGSPTS9jp/5Ww7UcvU8JCqsPQeeBVAg6th1kDIOV8fsUVERGRK1ABExEp4AK9/1fCygZy+lwKgz65QgkLqwtD5oKHP+xbA3PugNSk/AssIiIi2VIBExEpBAK93ZlxZzNHCRs8ZR07Yi5TwiIaw+Cvwd0Hdi+Db0ZCWkr+BRYREZEsqYCJiBQSGSWsXtlATiUmM+iTK5SwCi1g4Cxw9YQd8+G7e8Geln+BRUREJBMVMBGRQiTQ253PLylhO2POZn+BSu2h/xfg4g5bv4Uf/g/s9nzLKyIiIs5UwERECplAb3c+H9mMuhHpJWzgJ2svX8KqdYF+n4LNFaJnwk+PgzH5F1hEREQcVMBERAqhQB93vrjz3xI26EolrFYfuPlDwAYbP4Ulz6iEiYiIWEAFTESkkMooYXUiAoj9Xwn7+9hlSli92+Gmd9LPR70HKybkT1ARERFxUAETESnEAn3cmXlnc0cJG/jxFUpYo6HQfVL6+dWTYM3r+RNUREREABUwEZFCL+OdsNpl/n0nbNflSlize6HTC+nnfx4Paz/In6AiIiKiAiYiUhSU8PFg5l3pJexkQvrEHJctYa1HQbun0s8vego2Ts2XnCIiIsWdCpiISBGRuYStu3wJa/8UtHw4/fz8R+H32fkTVEREpBhTARMRKUIySlit8ABOJiQx8JN17D6eTQmz2aDzeLjhbsDAvPvhr+/yNa+IiEhxowImIlLEXFrCBnx8hRLWfRI0vAOMHb696//bu+8oqer7/+PP2c4u7NKWhaX3IkqVJiggQlBRYscodsUa9WssMYmaGE1MYvKzN2yxV0Rjw0JTLCAoIkjvZVnKLnXr/P4YWFgpsgh3dpfn45w5cj9zZ+Y959yD8+Lzue8P/Ph+sAVLknQIMYBJUiVUIyUSwtqWCmEbd39yTAwMuQ8OPx2KC+GVc2HeJ8EWLEnSIcIAJkmVVI2UBF4oFcK+2EsIi4Whj0DbIVCUDy+eDQs/C7ZgSZIOAQYwSarEts+Etalbbds9YXsJYbFxcOqT0HIgFG6BF86ApZODLViSpErOACZJlVzNlAReuKQHbepWY/WGSAibt3oPISwuAc54FpoeDfkb4blTYMV3wRYsSVIlZgCTpEPALiHssb2EsPgqcNaL0LAHbM2B/w6FrFmB1itJUmVlAJOkQ8TOISzr50JYYlX4zSuQ2Qk2r4FnT4I184ItWJKkSsgAJkmHkJo73RO2PYTN31MIS0qDc96AjPawcRU8cxKsWxRswZIkVTIGMEk6xNSqmsjzF3endca2EPb4XkJYck04dxTUbgW5SyMzYbnLA61XkqTKxAAmSYegWlUTeeGSSAhblRsJYQuyN+3+5KrpMPwtqNEE1i2MzIRtzAqyXEmSKg0DmCQdompVTeT5S7rTKqMqq3LzOOuxSXsOYamZMHw0pDaANXPg2aGweW2g9UqSVBkYwCTpEFa7aiIvXNKjJIQNe+wLFu4phNVoDOeNhqoZkDUj0qJ+a06wBUuSVMEZwCTpELdzCFuZu5Wz9hbCajWPzIQl14LlU+H50yFvD/ePSZKkXRjAJEklIaxlnX0IYXXawLlvRrokLvkSXhoGBVuCLViSpArKACZJAnYNYcMe/4JFa/YQwup1iLSoT6gKC8bDy+dCYV6wBUuSVAEZwCRJJdKrRUJYizpVWZETmQnbYwhr0BXOfgXiqsDcMfDahVBUGGzBkiRVMAYwSVIp6dUSeXFfQ1iTo2DYCxCbCLPegVEjoLgo2IIlSapADGCSpF1EZsK60zw9hRU5Wxn22BcsXrN59yc37w9nPAsxcTD9VXj7t1BcHGzBkiRVEAYwSdJu1amWxIuX9qB5egrLc7Zy1mOT9hzCWv8KTh0JoRiY+l94/yYIh4MtWJKkCsAAJknao5+GsGGP72Um7LChMPQRIARfPQZj/mQIkyTpJwxgkqS9qlMtiRcv6UGz9BSWrd/CsMe/YMnaPYSwDmfCif+O/Pnz+2Dc34MrVJKkCsAAJkn6WXVSk3hppxB21mN7CWFdL4Bf/S3y57F3w8T/BFanJEnlnQFMkrRPSkJY7X0IYT0uh2Nvi/z5o9vgy8eCK1SSpHLMACZJ2md1UpN46dJ9DGF9roejb4z8+b3fwTfPBleoJEnllAFMklQmdVIjjTn2KYT1+z30vCry59HXwHevBFeoJEnlkAFMklRmGdtCWNPaOxpzLF23mxAWCsHAO6HrRUAY3hwBP7wVeL2SJJUXBjBJ0n7J2LYcsWntFJaui8yE7TGEHf9P6PgbCBfBaxfB7A+CL1iSpHLAACZJ2m8ZqZEW9T8bwmJi4KT74bBToLgAXj4X5o8NvF5JkqLNACZJ+kXqpkVCWJNaySxdF1mOuGz9ll1PjImFUx6D1idAUR68OAwWTQq+YEmSosgAJkn6xeqmRe4Ja1IrmSVrt3DWY5N2H8Ji4+H0p6D5sVCwGZ4/HZZNCb5gSZKixAAmSTog6qVV4cVLe9D450JYXCKc+Rw06QP5G+C/p8DK6cEXLElSFBjAJEkHTL20Kry0Uwgb9tgXLN9dCEtIhmEvQYNusHU9PDsUVv8YdLmSJAXOACZJOqDqpVXhxUsiIWzx2s2ctacQllgVznkN6nWEzdnwzEmwZl7g9UqSFCQDmCTpgMusHglhjWpGQtiwx/cQwpLS4Nw3oU472LgSnj0Z1i8JvmBJkgJiAJMkHRSZ1SPLERvVTGbRmkgIW5GzmxCWXBOGvwW1WkDOEnhmCOSuCL5gSZICYACTJB00mdUjjTka1qzCojWR5Yi7DWFV68Dw0VC9MaxbEJkJ25QdfMGSJB1kBjBJ0kFVv3oVXrq0Z0kIG7anEJZWH84bDan1IfvHSGOOLesCr1eSpIPJACZJOuh2DmELt4WwlTlbdz2xRpPITFhKHVg1HZ47FbbmBl6vJEkHiwFMkhSI+tsaczSoEQlhZz02afchrHaLyD1hVWpGNml+4UzI3xR8wZIkHQQGMElSYBrUSOalS3eEsGGP72EmLKNdpDtiYhos/hxeOhsKdnOeJEkVjAFMkhSonUPYguxNDHv8C1bl7iZcZXaEc16HhKowfyw8dwpsXht0uZIkHVBRDWDjx49nyJAhZGZmEgqFGDVq1M++Zty4cXTp0oWkpCSaNWvGI488Uur5vn37EgqFdnmccMIJJefcfvvtuzxft27dA/31JEl70KBGMi9e0oP61SMh7KzH9hDCGh4JZ78Ciamw6DMYORDWzg++YEmSDpCoBrBNmzbRoUMHHnjggX06f8GCBRx//PH06dOHqVOn8vvf/55rrrmG119/veScN954gxUrVpQ8vv/+e2JjYzn99NNLvddhhx1W6rzp06cf0O8mSdq7hjUjM2HbQ9iwPYWwJkfBhR9AWkNYMweeGABLvgq+YEmSDoC4aH744MGDGTx48D6f/8gjj9CoUSP+85//ANC2bVsmT57MP//5T0499VQAatasWeo1L730EsnJybsEsLi4OGe9JCnKtoewsx77gvnbQthLl/agTmpS6RMz2sHFH0UacqyYBk+fCKc8Cof9Oip1S5K0vyrUPWCTJk1i4MCBpcYGDRrE5MmTKSgo2O1rRo4cyVlnnUVKSkqp8Tlz5pCZmUnTpk0566yzmD9/70ta8vLyyM3NLfWQJP1yO8+Ezd+2HDFrdzNh1erCBe9Cq8FQlAevng8T/wPhcNAlS5K03ypUAFu5ciUZGRmlxjIyMigsLCQ7O3uX87/66iu+//57Lr744lLj3bt359lnn+WDDz7g8ccfZ+XKlfTq1Ys1a9bs8bPvvvtu0tLSSh4NGzY8MF9KkrRrCHt8DyEsIQXOeh66j4gcf3QbvHMdFBUGW7AkSfupQgUwgFAoVOo4vO1fPn86DpHZr/bt29OtW7dS44MHD+bUU0/l8MMPZ8CAAfzvf/8D4Jlnntnj595yyy3k5OSUPJYsWfJLv4okaScNa+5ozDF/daQ7YtaG3YSwmFgY/Hf41d+AEEx5Cl48E/I2BF6zJEllVaECWN26dVm5cmWpsaysLOLi4qhVq1ap8c2bN/PSSy/tMvu1OykpKRx++OHMmTNnj+ckJiaSmppa6iFJOrAa1YqEsMy0JOatjtwTttsQBtDj8shsWFwVmPsRPPkryFkWbMGSJJVRhQpgPXv2ZMyYMaXGPvzwQ7p27Up8fHyp8VdeeYW8vDzOOeecn33fvLw8Zs6cSb169Q5ovZKksmtUK5mXLu25byGszQmR+8JS6sCq7+GJY2HFd8EWLElSGUQ1gG3cuJFp06Yxbdo0INJmftq0aSxevBiILPsbPnx4yfkjRoxg0aJFXH/99cycOZMnn3ySkSNHcsMNN+zy3iNHjmTo0KG7zIwB3HDDDYwbN44FCxbw5Zdfctppp5Gbm8t55513cL6oJKlMGtVK5sVLe1BvWwg7+/Evd39PGED9znDJx5DeFjasgKcGw+wPgy1YkqR9FNUANnnyZDp16kSnTp0AuP766+nUqRN/+tOfAFixYkVJGANo2rQp7777LmPHjqVjx4785S9/4b777itpQb/d7NmzmThxIhdddNFuP3fp0qUMGzaM1q1bc8opp5CQkMAXX3xB48aND9I3lSSVVeNaKby0LYTNzdrICfdP5OuFa3d/cvVGcOH70PQYyN8YuSfs6yeCLViSpH0QCoft37s/cnNzSUtLIycnx/vBJOkgWrxmMxc/+zWzV20kNibELYPbcFHvprttvkRhfqQr4rTnIse9roYBf4aYCrXiXpJUwZQlG/h/JElSudaoVjJvXnEUJ3XIpKg4zJ3/m8lVL05lY95uWs/HJcDJD0D/P0SOP78fXj0PCrYEW7QkSXtgAJMklXspiXH8v7M6cvuQdsTFhPjfdysY+uBnzM3aTev5UAiO/h2c8gTEJsDM0fD0ibBxdfCFS5L0EwYwSVKFEAqFOP+oprx8WQ8yUhOZm7WRkx/4jHe+W777FxxxOgx/C6rUgGWTIx0SV88OtmhJkn7CACZJqlC6NK7JO1f3oUezmmzKL+KqF6by57d/oKCoeNeTG/eCiz6CGk1h/SIYOQAWTgy+aEmStjGASZIqnPRqiTx3UXdGHNMcgCc/W8DZj3+x+1b1tVvAxR9Bg26wNQeeHQrfvhxswZIkbWMAkyRVSHGxMdw8uA2PntuFaolxfL1wHcffN5Ev56/Z9eSU2nDeaGg3FIoL4M1LYezfwUbAkqSAGcAkSRXaoMPq8tZVR9E6oxrZG/M4+4kveXz8fHbZZSW+Cpz2FBx1beR47F0w6opI63pJkgJiAJMkVXjN0qvy5pW9GNox0qr+r+/O5MoXvtm1VX1MDBx3Bwz5fxCKhW9fgOdOgS3rolO4JOmQYwCTJFUKyQlx/PvMjvz55MOIjw3x7vSVnPTAROas2k2r+i7nw29egYRqsHACjBwI6xYGXbIk6RBkAJMkVRqhUIjhPZvw8mU9qZuaxPzVmzj5wc94+9vdtKpvMQAufB9S60P2bHhiACydEnzRkqRDigFMklTpdG5Ug3eu6U2v5rXYnF/E1S9O5Y63Z5Bf+JNW9XXbRzok1j0cNq2Gp0+AmW9Hp2hJ0iHBACZJqpRqV03k2Qu7cXnfSKv6pz5byLDHv2DVT1vVp2bCBe9By4FQuAVePhc+f8AOiZKkg8IAJkmqtOJiY7jpV214bFur+imL1nHCfROYNO8nreoTq8FZL8KRFwNh+PBWePcGKCrc7ftKkrS/DGCSpEpv4GF1efvq3rSpW43sjfmcM/JLHh03r3Sr+tg4OP6fMPCvQAi+fgJeOhvyNkatbklS5WMAkyQdEprUTuHNK47ilE71KSoOc/d7s7j8uW/YsLVgx0mhEPS6Cs54FuKSYM4H8NRgyF0RvcIlSZWKAUySdMiokhDLv87owF+Gtic+NsT7M1Zy8gOfMfunrerbnQTn/w+Sa8PK7+CJY2HVjOgULUmqVAxgkqRDSigU4twejXnlsp7US0tifvYmTn7gM96atqz0iQ26Rjok1m4Fuctg5CCY+1F0ipYkVRoGMEnSIalToxq8c3VvereozZaCIn770jRuH/2TVvU1m8JFH0KTPpC/AZ4/AyY/Fb2iJUkVngFMknTIqlU1kWcu7MZV/VoA8PTnCznrsUmsyNmy46QqNeCcN+CIsyBcBO9cC2Nug+Li3b+pJEl7YQCTJB3SYmNC3DCoNU8M70q1pDi+WbyeE++byOdzs3ecFJcAv34E+t4SOf7sP/D6hVCwdbfvKUnSnhjAJEkCBrTL4J2re9O2XiprNkVa1T88dqdW9aEQ9L0Zhj4CMfEw40149iTYtGbvbyxJ0k4MYJIkbdO4VgpvXN6LUzs3oDgMf39/Fpf9dwq5O7eq7zgMzn0TktJgyZeRDonZc6NXtCSpQjGASZK0kyoJsfzz9CP466/bkxAbw4c/rOKk+ycya2XujpOa9oGLxkD1RrBuAYwcAIs+j17RkqQKwwAmSdJPhEIhftO9Ma+O6En96lVYuGYzQx/8jDenLt1xUnpruPhjqN8FtqyDZ0+G716NXtGSpArBACZJ0h50aFidt6/uTZ+WtdlaUMx1L3/LH0d9v6NVfdU6cN470HYIFOXDGxfD+H/A9vvGJEn6CQOYJEl7UTMlgacv6MY1/SOt6v/7xSLOeHQSy9dva1WfkAynPws9r4ocf3InjL4Kigr28I6SpEOZAUySpJ8RGxPi+oGtefL8rqQmxTFtyXpOvH8in21vVR8TA4P+Csf/E0IxMPU5eO5U2LI+qnVLksofA5gkSfuof5sM3rm6D+3qpbJ2Uz7njvySBz+dS3HxtiWH3S6BYS9BfAosGAdP/grWL45u0ZKkcsUAJklSGTSqlcwbV/Ti9C6RVvX/+OBHLv3vFHK2bFty2GoQXPgeVKsHq2fCEwNg2TfRLVqSVG4YwCRJKqOk+FjuOe0I7j7lcBJiY/ho5ipOemAiM1dsa1Vfr0OkQ2JGe9i4Cp4+AWa9G92iJUnlggFMkqT9EAqFGNatEa9dHmlVv2jNZn790Ge8PmVbq/q0+nDBe9D8WCjYDC+dDV88Et2iJUlRZwCTJOkXOKJBdd65ujdHt0pna0Ex//fqt9z65nTyCosgKRXOfhm6nA+E4f2b4L2bobgo2mVLkqLEACZJ0i9UIyWBp84/kt8e25JQCJ7/cjFnPPoFy9Zvgdh4OPE/MOCOyMlfPgwvnwP5m6JasyQpOgxgkiQdALExIa47rhVPnnckaVXi+XbJek68bwIT5qyGUAh6XwunPw2xifDju/DU8bBhVbTLliQFzAAmSdIB1K9NHd65ujft66eybnMBw5/8igc+mRNpVX/Yr+G8tyG5FqyYBk8cC1kzo12yJClABjBJkg6whjWTeW1EL87s2pBwGP754Wwu/e9kcjYXQKPucPFHUKsF5CyBkQNh3qfRLlmSFBADmCRJB0FSfCx/P+0I/n7q4STExfDRzCyGPDCRGctzoGYzuGgMNOoFebnw/Gkw9blolyxJCoABTJKkg+jMIxvxxuW9aFCjCovXbuaUhz7ntSlLIbkmDB8Fh58OxYXw1pXw8V8gHI52yZKkg8gAJknSQda+fhrvXN2bfq3TySss5oZXv+WWN6azNRwHpzwOR/8ucuKEf8LrF0NhXnQLliQdNAYwSZICUD05gZHnHcl1A1oRCsGLXy3mjEcnsXT9Fuj/Bzj5QYiJg+9fg2eHwua10S5ZknQQGMAkSQpITEyI3w5oyVPnH0n15Hi+W5rDifdPZNzs1dDpHDjndUhMhcWfwxMDYM28aJcsSTrADGCSJAWsb+s6vH1Vbw6vn8b6zQWc/9RX3PfxHIqbHAMXfQhpDWHtPBh5HCz+MtrlSpIOIAOYJElR0LBmMq+O6Mmwbo0Ih+HeMbO56JmvWV+1OVz8MWR2gs1r4Jkh8P0b0S5XknSAGMAkSYqSpPhY7j7lcO457QgS42L49MfVnHj/RL7PTYLz/wetj4eiPHjtApj4bzskSlIlYACTJCnKzujakNcv70XDmlVYum4Lpzz8Oa98uxbOfA66Xx456aPb4e3fQlFBVGuVJP0yBjBJksqB9vXTeOeqPvRvU4f8wmJufP07bn5zBlsH/BUG3wOhGPjmGXjhTNiaG+1yJUn7yQAmSVI5kZYczxPDu/J/x0Va1b/09RJOf2QSS1qeC2c+D/HJMO9jePJXkLM02uVKkvaDAUySpHIkJibE1ce25JkLulEjOZ7py3IY8sBExoa6wgXvQtUMyJoRaVO/4ttolytJKiMDmCRJ5dDRrdJ5++redGgQaVV/wdNf858fUii+cAykt4UNK+DJwTD7g2iXKkkqAwOYJEnlVIMaybwyoie/6R5pVf+fj+ZwwahVrD/7HWjWFwo2wYtnwVePR7tUSdI+MoBJklSOJcbF8tdfH86/Tu9AYlwM42av5oRHv2N635HQ6VwIF8O7N8AHt0JxcbTLlST9DAOYJEkVwKldGvDmFUfRuFYyy9Zv4dTHvualur+D/n+MnDDpAXh1OORvjm6hkqS9MoBJklRBtMtMZfRVvRnQNtKq/uY3v+fGrOPIH/o4xCbAzLfhmRNhY1a0S5Uk7YEBTJKkCiStSjyPnduV3w1qTUwIXpm8lF+Pr8eqoS9DlRqwbAo8cSys/jHapUqSdsMAJklSBRMTE+LKfi149sLu1ExJYMbyXAa+UcgX/V6GGk1h/WIYeRwsGB/tUiVJP2EAkySpgurdsjbvXN2bDg2rk7OlgLPeyOahFo8SbtAdtubAf0+Bb56FcDjapUqStjGASZJUgWVWr8Irl/Xg3B6NAbhnQjYX8Ufy2wyF4gIYfTW8diFsWR/VOiVJEQYwSZIquMS4WP4ytD33ntGBpPgYPpmbS/8F57Ki640QioUZb8AjvWHRpGiXKkmHPAOYJEmVxCmdI63qm9RKZmlOHsdM6szbRz5NuEYTyFkCTx8Pn94FRYXRLlWSDlkGMEmSKpG29VJ566reHNcug/yiYq4eH8sZoXtY0+LUyKbN4/4OTw2GdQujXaokHZIMYJIkVTJpVeJ59Jwu/GVoe6olxfH18kK6zjiVFxvdTjihGiz9Ch7pA9+9Gu1SJemQYwCTJKkSiokJcW6Pxnzyf305pXN9wmG4ZXYrTij4G6trdIK8XHjjYnjjUtiaG+1yJemQYQCTJKkSS6+WyL1ndOSVy3rSOqMaP2ypQY8V1/NSym8Ih2Lgu5cjDTqWfB3tUiXpkGAAkyTpENCtaU3euaY3fzihLUkJCdy85gTOyPsj6xPqwvpF8OQgGPcPKC6KdqmSVKkZwCRJOkTEx8ZwcZ9mfPx/fTnxiHp8XdyaPrl38n6oN4SL4NM74ekTYf2SaJcqSZWWAUySpENM3bQkHji7M89d1J302umM2HI51+VfzpZQFVj8OTxyFHz/RrTLlKRKyQAmSdIhqnfL2rx3bR9+N6gN78Uew6CtdzGtuDlszYHXLoC3roS8jdEuU5IqFQOYJEmHsMS4WK7s14Ix1x1D67ZHcFr+bdxfOJRiQjD1OcKPHg3Lvol2mZJUaRjAJEkSDWsm8/jwrjx2fg9eST2PYXl/YHm4JqG18wiPPA4m/geKi6NdpiRVeAYwSZJUon+bDMZcdwzd+w3h5MJ7+F9RN0LFhfDRbRQ9ezLkLo92iZJUoRnAJElSKUnxsVw/sDWvXHc8Lze5kxsLLmFzOJHYheMpeKAnzHwn2iVKUoVlAJMkSbvVtHYKz1zYjX5n/R8XJPyT6cVNiM9fDy//ho2vXQX5m6NdoiRVOAYwSZK0R6FQiMGH1+PJG87m3e7/5dGiIQBU/f6/rP1PT/KXTotugZJUwRjAJEnSz0pJjOOmE46g/1UPc2fNu1gVrk7NzQvhiWOZP/oeG3RI0j4KhcPhcLSLqIhyc3NJS0sjJyeH1NTUaJcjSVJgwuEw7305g+QPrqVv+GsAfkjuSu1znqJOZqMoVydJwStLNnAGTJIklUkoFOL4Hu3pfOO7vNPwd2wJJ9Bu82RiHz2KD958hoIiZ8MkaU+cAdtPzoBJkhQxd8bXxL15CU0KFwDwVsKJZJ7+D45smRnlyiQpGM6ASZKkwLQ47Ega3TiJH5ueA8DJ+e9Q7b8DuefZN1i9IS/K1UlS+eIM2H5yBkySpF1t+P49GHU51QrXkReO51+hc2kw8Bp+06MJsTGhaJcnSQeFM2CSJCkqqrUfTLVrvyK3QV8SQwX8niep/94FDL//HaYuXhft8iQp6gxgkiTpwKpah9SLRlH8q79TFJPAsbFT+c/aK/nPIw9zyxvTWbcpP9oVSlLUGMAkSdKBFwoR02MEsZd+SmGt1qSHcngm4e80++YuBv1zDC9/vZjiYu+CkHToMYBJkqSDp2574kaMgyMvAeCSuHd5qugWHnvjfU575HNmLM+JcoGSFCwDmCRJOrjiq8AJ/4RhLxFOrsVhMYt4J+FW2ix7nSH3T+D20TPI3VoQ7SolKRAGMEmSFIzWgwld/jk060eVUD53xY/k4bh/M+rz6Rz7r3GMmroMmzNLquwMYJIkKTjV6sI5b8DAv0JMPINiJ/NRlVtosekbrn15GsMe/4I5qzZEu0pJOmgMYJIkKVgxMdDrKrjkY6jVktrhtbyQcBe/T3iJKfOzGPz/JnD3uzPZlFcY7Uol6YCLagAbP348Q4YMITMzk1AoxKhRo372NePGjaNLly4kJSXRrFkzHnnkkVLPP/3004RCoV0eW7duLXXeQw89RNOmTUlKSqJLly5MmDDhQH41SZL0c+p1gMvGQZfzCRHm0pjRjEm9k4bh5Tw6fj4D7h3He9NXuCxRUqUS1QC2adMmOnTowAMPPLBP5y9YsIDjjz+ePn36MHXqVH7/+99zzTXX8Prrr5c6LzU1lRUrVpR6JCUllTz/8ssvc+2113LrrbcydepU+vTpw+DBg1m8ePEB/X6SJOlnJKTAkP8HZ/wXkqrTJH82Y1L+yGWpn7EiZwuXP/8N5z31NQuyN0W7Ukk6IELhcvLPSqFQiDfffJOhQ4fu8ZybbrqJ0aNHM3PmzJKxESNG8O233zJp0iQgMgN27bXXsn79+j2+T/fu3encuTMPP/xwyVjbtm0ZOnQod9999z7Vm5ubS1paGjk5OaSmpu7TayRJ0l7kLIM3L4OFkVUpP9Y6lt+sHEZ2UTIJsTGMOKYZV/RrQVJ8bJQLlaTSypINKtQ9YJMmTWLgwIGlxgYNGsTkyZMpKNjRvnbjxo00btyYBg0acOKJJzJ16tSS5/Lz85kyZcou7zNw4EA+//zzPX52Xl4eubm5pR6SJOkASqsPw9+CY2+DmDhar/mYL2r8iUsarSC/qJj7PpnLcf8ex8czV0W7UknabxUqgK1cuZKMjIxSYxkZGRQWFpKdnQ1AmzZtePrppxk9ejQvvvgiSUlJHHXUUcyZMweA7OxsioqKdvs+K1eu3ONn33333aSlpZU8GjZseIC/nSRJIiYW+lwPF30INZsRt3E5v1/9Oz7qOIEGqXEsWbuFi56ZzCXPTmbpus3RrlaSyqxCBTCILFXc2fYVlNvHe/TowTnnnEOHDh3o06cPr7zyCq1ateL+++//2ff56djObrnlFnJyckoeS5YsORBfR5Ik7U79LnDZeOj4G0LhYlrMephxtf/OTd0TiYsJMeaHVQy4dxwPfjqXvMKiaFcrSfusQgWwunXr7jJLlZWVRVxcHLVq1drta2JiYjjyyCNLZsBq165NbGzsbt/np7NiO0tMTCQ1NbXUQ5IkHUSJ1WDoQ3Dak5CYRuzyKVw+83wmDs6ie9OabC0o5h8f/Mjg/0xg4pzsaFcrSfukQgWwnj17MmbMmFJjH374IV27diU+Pn63rwmHw0ybNo169eoBkJCQQJcuXXZ5nzFjxtCrV6+DU7gkSdp/7U+FyydCo56Qv4G6H/+Wl2o/yQOnNKd21UTmZ2/inJFfcuUL37AyZ+vPv58kRVFUA9jGjRuZNm0a06ZNAyJt5qdNm1bSDv6WW25h+PDhJeePGDGCRYsWcf311zNz5kyefPJJRo4cyQ033FByzh133MEHH3zA/PnzmTZtGhdddBHTpk1jxIgRJedcf/31PPHEEzz55JPMnDmT6667jsWLF5c6R5IklSPVG8F570C/WyEUS2j6K5z4+emMG5bM+b2aEBOC/323gmP/NZYnJsynoKg42hVL0m5FtQ392LFj6dev3y7j5513Hk8//TTnn38+CxcuZOzYsSXPjRs3juuuu44ZM2aQmZnJTTfdVCo4XXfddbzxxhusXLmStLQ0OnXqxO23307Pnj1LfcZDDz3EPffcw4oVK2jfvj3//ve/Ofroo/e5dtvQS5IUJUu+gtcvgvWLIRQLfW9mRvOL+ePomXyzeD0ArTOq8Zeh7enWtGZ0a5V0SChLNig3+4BVNAYwSZKiaGsO/O8GmP5K5LhRT4qHPsqr80L87b1ZrNsc2Z7mlM71uWVwW9KrJUaxWEmVXaXdB0ySJAmApDQ49XH49WOQUA0WTyLm0T6cWeVrPvm/vgzr1ohQCN74Zhn9/zWW/05aSFGx/+YsKfqcAdtPzoBJklROrF0Ar18MyyZHjjv+Bgb/nWlZRfxh1HS+X5YLwOH10/jL0PZ0bFg9erVKqpRcghgAA5gkSeVIUQGM+zuM/ycQhhpN4dSRFGV25oUvF3HPBz+yYWshoRCcdWQjbhzUmhopCdGuWlIlYQALgAFMkqRyaOFn8MalkLsUYuIiXROP+i2rNxVy93szeeObZQDUSI7n5sFtOL1LQ2JiQlEuWlJFZwALgAFMkqRyass6ePta+GFU5LhJH/j1o5BWn68WrOWPo77nx1UbAOjcqDp/GdqewzLTolaupIrPABYAA5gkSeVYOAzTnod3b4SCTZBUHU66H9qdREFRMc98vpB/j5nNpvwiYkIwvGcTrh/YitSk+GhXLqkCMoAFwAAmSVIFsGZeZM+w5VMjx53Pg1/dDQkprMzZyp3/+4F3vlsBQHq1RG49vi0nd8wkFHJZoqR9ZwALgAFMkqQKojAfPv0rfPb/gDDUagmnPgGZHQGYOCebP731PfOzNwHQo1lN/nxye1plVItezZIqFANYAAxgkiRVMPPHwZuXwYYVEBMPA26DHldCTAx5hUU8MWEB938yh60FxcSE4PjD63F53+beHybpZxnAAmAAkySpAtq8FkZfDbPeiRw36we/fgSq1QVgydrN3Pm/H/hgxqqSl/Rrnc6V/VrQtUnNaFQsqQIwgAXAACZJUgUVDsOUp+H9W6BwCyTXgpMfhNaDS075YXkuD4+bx/++W07xtl9K3ZrW5Mp+LTi6ZW3vEZNUigEsAAYwSZIquNU/Rhp0rJweOT7yEhj4F4ivUnLKwuxNPDp+Hq9NWUpBUeQn02GZqVzZrwWDDqtLrHuIScIAFggDmCRJlUBhHnz8Z5j0QOQ4vW2kQUfd9qVOW5mzlScmzOf5LxezpaAIgGa1UxjRtzlDO9YnIS4m6MollSMGsAAYwCRJqkTmfgRvXg6bsiA2EQbcDt0vg5jYUqet3ZTP058v5JnPF5KzpQCAzLQkLj26GWce2YgqCbG7eXNJlZ0BLAAGMEmSKpmNq+GtK2HOB5HjjPbwq79B0z67nppXyAtfLuLxCQtYvSEPgFopCVzYuynn9GhMWhU3dJYOJQawABjAJEmqhMJhmPxkZFni1vWRsXYnw8A7oXqjXU7fWlDEa1OW8uj4eSxZuwWAaolxnNOzMRce1ZT0aokBFi8pWgxgATCASZJUiW1eC5/cCVOegnAxxCVBr2ug97WQkLLL6YVFxbzz3QoeGjuX2as2ApAYF8NZRzbkkqOb0aBGcsBfQFKQDGABMIBJknQIWPk9vH8zLJwQOU6tD8f9GdqfCrtpRV9cHOajmat4cOw8vl2yHoC4mBAnd6zP5X2b0aJOtQCLlxQUA1gADGCSJB0iwmGYORo++APkLI6MNeoZuT8ss+MeXhJm0rw1PDh2Lp/NXQNE8tqvDqvLFX1bcHiDtICKlxQEA1gADGCSJB1iCrbA5/fDhHsjGzgTgs7nQv8/QdX0Pb5s2pL1PPTpXD78YVXJWJ+WtbmyXwu6N63pps5SJWAAC4ABTJKkQ1TOUhhzG3z/WuQ4MQ363hTZyDkuYY8vm71qA4+Mncdb3y6nqDjy86tzo+pc2a8F/dvUMYhJFZgBLAAGMEmSDnGLJsH7N8GKbyPHtVpGliW2HLDXly1Zu5lHx8/jlclLyS8sBqBN3Wpc3rc5Jxxej7hYN3WWKhoDWAAMYJIkieIimPpcpG395uzIWKtfwaC7oFbzvb40a8NWRk5cwHOTFrEpvwiAxrWSGXFMc07pXJ/EODd1lioKA1gADGCSJKnE1hwYdw98+QgUF0JMPPS4HI7+HSTt/XdCzuYCnpm0kKc+W8C6zQUAZKQmckmfZgzr1oiUxLggvoGkX8AAFgADmCRJ2sXq2fDBLTD3o8hxSh0YcBt0OBti9r60cHN+IS9+tYTHx89nZe5WAKonx3NBr6ac16sx1ZP3fH+ZpOgygAXAACZJknYrHIY5H8L7t8DaeZGxzM4w+B5oeOTPvjyvsIhRU5fx8Nh5LFyzGYCUhFh+06MxF/duSp3UpINZvaT9YAALgAFMkiTtVWF+ZEniuHsgf0Nk7IgzYcAdkFrvZ19eVBzm3ekreGjsPGauyAUgITaG07s24LKjm9OoVvLBrF5SGRjAAmAAkyRJ+2TDqkiTjmnPRY7jU6DP9dDzKoj/+dmscDjM2B9X8+Cnc5m8aB0AsTEhhhxRj8v7tqB13WoHs3pJ+8AAFgADmCRJKpNlU+C9m2HpV5HjGk1g4F+hzQmwj3uAfbVgLQ9+Opdxs1eXjB3XLoMr+janU6MaB6FoSfvCABYAA5gkSSqzcBi+ewU+ug02rIiMNesb2T+sTtt9fpvvl+Xw0Ni5vPf9Srb/kuvVvBZX9mtBr+a13NRZCpgBLAAGMEmStN/yNsLEe+Hz+6EoH0KxcOTF0O8WqLLvM1nzVm/kkbHzeHPqMgqLIz/pOjRI44p+LTiubQYxMQYxKQgGsAAYwCRJ0i+2dgF8+AeY9U7kuEpN6H8rdLkAYvZ9I+Zl67fw+Pj5vPT1YrYWFAPQsk5VrujXnCFHZBIXu/cW+JJ+GQNYAAxgkiTpgJn3aaRt/eqZkeOM9pFliU37lOltsjfm8dRnC3j280VsyCsEoEGNKlx2THNO79KApPh9D3WS9p0BLAAGMEmSdEAVFcLkJ+HTv8LW9ZGxdifDwDuheqMyvVXu1gKe+2IRIycsYM2mfABqV03k4j5N+U33RlRLij/AxUuHNgNYAAxgkiTpoNi0JhLCpjwF4WKIS4Je10DvayEhpUxvtSW/iFcmL+Gx8fNZtn4LAKlJcZzfqwnnH9WUmikJB+ELSIceA1gADGCSJOmgWvk9vH8zLJwQOU6tD8f9Gdqfus9t67crKCrmrWnLeXjsXOat3gRAlfhYhnVrxCVHN6VeWpUDXb10SDGABcAAJkmSDrpwGGaOhg/+ADmLI2ONesLgv0O9DmV+u+LiMB/+sJIHP53H9GU5AMTHhjilUwNG9G1O09plm2GTFGEAC4ABTJIkBaZgS6Rl/YR7oXALEILOw+HYP0FK7TK/XTgcZsKcbB4aO5cv5q8FICYExx9ejyv6tqBdpr9tpLIwgAXAACZJkgKXsxTG3AbfvxY5TkyDvjdBt0shdv8aa0xZtJaHPp3Hx7OySsb6tU7nyn4t6Nqk5oGoWqr0DGABMIBJkqSoWTQJ3rsRVn4XOa7dCgbdDS0H7PdbzlyRy8Nj5/HOd8vZtqcz3ZrW5Mp+LTi6ZW1CZbzvTDqUGMACYACTJElRVVwEU5+Dj/8Mm7MjY61+BYPuglrN9/ttF2Zv4tHx83h9yjLyiyKbOh+WmcqV/Vow6LC6xMYYxKSfMoAFwAAmSZLKhS3rYfw/4MtHoLgQYuKhx+Vw9O8gaf9/o6zM2coTE+bz/JeL2VJQBECz2imM6NucoR3rkxAXc4C+gFTxGcACYACTJEnlyurZ8MEtMPejyHFKHRhwG3Q4G2L2Pyyt25TP058v5OnPF5KzpQCAzLQkLj26GWce2YgqCbEHonqpQjOABcAAJkmSyp1wGOZ8CO/fAmvnRcYyO8Pge6Dhkb/orTfmFfLCl4t4fMICVm/IA6BWSgIX9m7KOT0ak1Zl/5qASJWBASwABjBJklRuFebDlw/DuH9A/obI2BFnwYDbIbXeL3rrrQVFvP7NUh4ZN48la7cAUC0xjnN7NubC3k2pXTXxFxYvVTwGsAAYwCRJUrm3YVWkSce05yLH8Slw9P9BjyshPukXvXVhUTH/m76CBz+dy+xVGwFIiIvhV4fV5bQuDTiqRW0bduiQYQALgAFMkiRVGMumwHs3wdKvI8c1msDAv0KbE+AXtpcvLg7z8awsHvx0LtOWrC8Zr5uaxCmd63NqlwY0T6/6iz5DKu8MYAEwgEmSpAqluBimvwof3QYbVkTGmvWFX/0N6rT9xW8fDof5flkur01ZwlvfLmf95oKS5zo1qs5pXRpw4hGZ3iumSskAFgADmCRJqpDyNsLEe+Hz+6EoH0KxcOTF0O8WqFLjwHxEYRGfzMzitSlLGTt7NUXbdnZOiIth0LYlir1doqhKxAAWAAOYJEmq0NYugA//ALPeiRxXqQn9b4UuF0DMgWstn7VhK6OnLefVyUv5cdWGkvGM1ERO6dyAUzs3oEUdlyiqYjOABcAAJkmSKoV5n0ba1q+eGTnOaB9Zlti0zwH9mHA4zIzlubw2ZSmjpi0rtUSxY8PIEsUhR2SSluwSRVU8BrAAGMAkSVKlUVQIk0fCp3+FrTmRsXYnw8A7oXqjA/5xeYVFfDorskTx0x9LL1Ec2C6D07o0oE/LdJcoqsIwgAXAACZJkiqdTWsiIWzKUxAuhrgkOOq3cNS1kJB8UD5y9YY83pq2jNemLGXWyh1LFOtUS+TXnetzepcGtKhT7aB8tnSgGMACYACTJEmV1srp8N7NsGhi5Di1Phz3Z2h/6i9uW78nOy9RfGvaMtbttESxw7Yliie5RFHllAEsAAYwSZJUqYXD8MNb8OEfIWdxZKxRTxj8d6jX4aB+dH5hMZ+ULFHM2rFEMTaG4w7L4LTODejTsjZxsTEHtQ5pXxnAAmAAkyRJh4SCLZGW9RPuhcItQAg6D4dj/wQptQ/6x+9piWJ6tURO6VSf07o0oGWGSxQVXQawABjAJEnSISVnKYy5Db5/LXKcmAZ9b4Jul0JsMMsCZyzP2bZEcTlrN+WXjHdokBbpotghk+rJCYHUIu3MABYAA5gkSTokLZoE790IK7+LHNduBYPuhpYDAishv7CYT3/ctkRxVhaFOy1RHNCuDqd1acDRLdNdoqjAGMACYACTJEmHrOIimPpf+PjPsHlNZKzVr2DQXVCreaClZG/M461py3l9ylJ+WJFbMp5eLZFfb1ui2MolijrIDGABMIBJkqRD3pb1MO4e+OpRKC6EmDhoexIceRE0PuqgdUzckxnLc3h9yjJGTVtWaoniEduXKB6RSY0UlyjqwDOABcAAJkmStM3q2fDBLTD3ox1jtVtD1wuhw1lQpXqg5eQXFjN22xLFT36yRPHYtpElise0comiDhwDWAAMYJIkST+x4lv4eiRMfxUKNkfG4pMj+4d1vRDqdw68pDUb8xj97XJem7KUGct3LFGsXTWRX3fK5LQuDWld1yWK+mUMYAEwgEmSJO3B1hz47pVIGFs9c8d4ZifoelEkkCUkB17WD8tzef2bpYyauow1Oy1RPLx+ZIniSR1coqj9YwALgAFMkiTpZ4TDsPgLmDwysqlz0bbQk5gGHYdFZsXSWwdeVkFRMWN/XM1rU5bw8cwdSxTjY0Mc2yYjskSxdTrxLlHUPjKABcAAJkmSVAabsmHqczD5SVi/aMd4kz7Q9QJoMwTigp992r5E8fVvlvL9sp2XKCYwtGN9TuvagDZ1/a2nvTOABcAAJkmStB+Ki2HeJ5FZsdnvQ7g4Mp5SBzqfC53PgxqNo1LazBW5vD5lKaOmLSN7444liu3rp3Jq5wac3LE+NV2iqN0wgAXAACZJkvQL5SyFKc/AN8/CxpXbBkPQcmCklX2LARATG3hZBUXFjPtxNa9NWcrHs1ZRULRjiWL/NnU4rUtD+rpEUTsxgAXAACZJknSAFBXAj+9GmnYsGLdjPK0RdDkPOg+HqnWiUtraTfmMnraM136yRLFWSgJDt2303LaevwUPdQawABjAJEmSDoLsuTDlqcj9YlvXR8Zi4qHtiZEOik16B77B83azVkaWKL45tfQSxcMyty9RzKRW1cSo1KboMoAFwAAmSZJ0EBVsgRmjIveKLf16x3gUN3guKa2omPGzI0sUP5q5Y4liXMz2JYoN6NemjksUDyEGsAAYwCRJkgKy4rtI98TvXoGCTZGxuCpw+KmRWbEobPC83bpN+SUbPU9fllMyXislgZM7RpYotsv0t2JlZwALgAFMkiQpYFtz4buXI2Es64cd4/U6Rpp2tD8VElKiVt6PKzfw+jdLeeObZWRvzCsZb1svldO6RJYo1naJYqVkAAuAAUySJClKwmFY8mWkaccPo0pv8NzhrMgSxTptolZeYVEx4+dsW6L4Qxb5RZFW+3ExIfptX6LYug4JcS5RrCwMYAEwgEmSJJUDm7Jh2vORWbF1C3eMN+4d2eC57UlR2eB5u3Wb8nn7u8gSxe+W7liiWDMlgZM7ZnJalwYclpkWtfp0YBjAAmAAkyRJKkeKi2H+JzD5qUhL+5INntOh07nQ5fyobfC83exVG3h9ylLemLqM1Rt2LFFsU7caJxxej/5t69CuXiqhKHV51P4zgAXAACZJklRO5SyLbO78zTOwYcW2wRC0PC7StKPlcVHZ4Hm7wqJiJszJ5rUpSxnzw6qSJYoA9dKS6NemDse2qUOv5rWpkhC9OrXvDGABMIBJkiSVc0UF8ON7keWJ8z/dMZ7WMLLBc6fhUC0jevUB6zfn8+70lXwyaxUT52aztWBHGEuMi6FX81r0b5tB/zZ1qF+9ShQr1d4YwAJgAJMkSapA1syLBLFpz8OWdZGxmDhoc2Kkg2KTPlHb4Hm7rQVFTJq/hk9mZvHJrCyWrd9S6vk2davRv00djm1bh44NaxAb41LF8sIAFgADmCRJUgVUsDXSOfHrkbD0qx3jtVpGuid2HAZVakStvO3C4TA/rtrAJ7Oy+GRmFt8sXkfxTr/aayTH0691Hfq3rUOflumkVYmPXrEygAXBACZJklTBrZy+Y4Pn/I2Rsbgqkf3Eul4Y2eC5nDTEWLspn3Gzs/hk1mrG/pjFhq2FJc/FxoQ4skkNjm2TQf+2dWhWO8VGHgEzgAXAACZJklRJ5G2IbPD89ZOQNWPHeL0OkaYdh58W1Q2ef6qgqJgpi9ZFZsdmZTE3a2Op5xvXSo4sVWyTQbemNd1vLAAGsAAYwCRJkiqZcBiWfAWTR8KMUVC0rVV8YupOGzy3jWqJu7NozaaSMPbF/DUUFO34eV81MY4+LWvTr00d+rWuQ3q1xChWWnkZwAJgAJMkSarENq3ZaYPnBTvGGx8VCWJth0Bc+QszG/MKmTgnm09mreKTWavJ3phX6vkODatzbJs69G9Th8My3XPsQDGABcAAJkmSdAgoLoYFYyNNO358D8JFkfHk2tB5+wbPTaJY4J4VF4eZviynZHZs+rKcUs9npCbSv00d+rfJ4KgWtUhOiItSpRWfASwABjBJkqRDTO7yyAbPU56BDcu3DYagxYBIK/uWA6O6wfPPWZW7lU9nZfHxrCwmzslmS0FRyXMJ2/cc2zY71qBGchQrrXgMYAEwgEmSJB2iigph9vuRe8XmfbJjPLVBZEasc/Q3eP45WwuK+HLBWj6ZuYqPZ2WxdF3pPcdaZ1Sjf9tIGOvUsDpxsTby2BsDWAAMYJIkSWLNPJjyNEx9DrasjYzFxEGbEyIdFJseXW5a2e9JOBxmTtbGkj3HJi9aW2rPserJ8fRtlU7/thkc0zKdtGT3HPupsmSDqEbZ8ePHM2TIEDIzMwmFQowaNepnXzNu3Di6dOlCUlISzZo145FHHin1/OOPP06fPn2oUaMGNWrUYMCAAXz11Velzrn99tsJhUKlHnXr1j2QX02SJEmHglrNYeBf4PqZ8OvHoGF3KC6EH96CZ0+CB46ESQ/BlnXRrnSPQqEQrTKqMeKY5rwyoiff/PE4/t9ZHTm5YyZpVeJZv7mAUdOWc82LU+l85xjOeHQSj46bx9ysDTiXU3ZRvdNu06ZNdOjQgQsuuIBTTz31Z89fsGABxx9/PJdccgnPPfccn332GVdccQXp6eklrx87dizDhg2jV69eJCUlcc899zBw4EBmzJhB/fr1S97rsMMO46OPPio5jo0tv+t1JUmSVM7FJ0GHMyOPld9v2+D5ZVgzBz64BT6+Y9sGzxeVqw2ed6d6cgInd6zPyR3rU1hUzDeL1/PxrFV8OiuL2as28tWCtXy1YC13vzeLRjWTS+4b696sJolx/qb+OeVmCWIoFOLNN99k6NChezznpptuYvTo0cycObNkbMSIEXz77bdMmjRpt68pKiqiRo0aPPDAAwwfPhyIzICNGjWKadOm7Xe9LkGUJEnSXuVtgOmvRjZ4XjV9x3jdIyJNO9qfBolVo1fffliydjOfbGvk8cW8NeQXFZc8l5wQS5+WtTm2TQZ926RTp1pSFCsNVlmyQYXqNTlp0iQGDhxYamzQoEGMHDmSgoIC4uN3XY+6efNmCgoKqFmzZqnxOXPmkJmZSWJiIt27d+euu+6iWbNme/zsvLw88vJ27KOQm5v7C7+NJEmSKrXEapE9w7pcAEu/jsyKff8GrPwO3v4tfPhHOOLMyDkZ7aJd7T5pWDOZ83o14bxeTdiUV8hnc7NL2txnbcjjgxmr+GDGKgCOaJBG/zZ1OLZNBodlphITU35n/YJUoQLYypUrycgo3VEmIyODwsJCsrOzqVev3i6vufnmm6lfvz4DBgwoGevevTvPPvssrVq1YtWqVdx555306tWLGTNmUKtWrd1+9t13380dd9xxYL+QJEmSKr9QCBp2izwG3QXTXoiEsbXz4OvHI4+qGZDeGmq3jvw3vTWkt4GU9HK7XDElMY6Bh9Vl4GF1KS4OM2N57rYwtopvl+bw3bbHfz6aQ51qifRrXYf+bevQu0VtUhIrVAw5oCrUEsRWrVpxwQUXcMstt5SMffbZZ/Tu3ZsVK1bs0kjjnnvu4W9/+xtjx47liCOO2OP7btq0iebNm3PjjTdy/fXX7/ac3c2ANWzY0CWIkiRJKrviYlgwLhLEZv1vxwbPP5VUPRLE0ncKZrVbQ1qDchvMALI2bGXsrNV8MiuLCXNWsyl/pz3HYmPo0bwWx267d6xhzYq/51ilXYJYt25dVq5cWWosKyuLuLi4XWau/vnPf3LXXXfx0Ucf7TV8AaSkpHD44YczZ86cPZ6TmJhIYmLi/hcvSZIkbRcTA837RR55GyB7Nqz+cafHLFi3ELauhyVfRB47S6gKtVtGwlntVjtCWo0m5WIz6DrVkjjjyIaccWRD8gqL+GrBWj6eGVmquHjtZsbPXs342au5bfQMWtapSv+2kaWKnRtV/j3HKlQA69mzJ2+//XapsQ8//JCuXbuWuv/rH//4B3feeScffPABXbt2/dn3zcvLY+bMmfTp0+eA1yxJkiTtVWI1qN8l8thZwRZYM3dHKMve9t818yB/IyyfGnnsLDYxEsxKQtm2/9ZsDnEJwX2nnSTGxdKnZTp9WqZz25B2zFu9iU9mreLjmVlMXrSOOVkbmZO1kUfHzSetSjzHtErn2LZ1OKZVOtWTo1PzwRTVJYgbN25k7ty5AHTq1Il7772Xfv36UbNmTRo1asQtt9zCsmXLePbZZ4FIG/r27dtz2WWXcckllzBp0iRGjBjBiy++WNKG/p577uGPf/wjL7zwAkcddVTJZ1WtWpWqVSNdZm644QaGDBlCo0aNyMrK4s4772TcuHFMnz6dxo0b71PtdkGUJElSVBQVwNoFkVmy7J1mzbLnQOGW3b8mFAs1m5VexpjeOhLUEqK3BDBncwHj5qzm01lZfPpjFus3F5Q8FxOCro1r0r9tZKliyzpVCZXTZZdlyQZRDWBjx46lX79+u4yfd955PP3005x//vksXLiQsWPHljw3btw4rrvuOmbMmEFmZiY33XQTI0aMKHm+SZMmLFq0aJf3vO2227j99tsBOOussxg/fjzZ2dmkp6fTo0cP/vKXv9Cu3b53nzGASZIkqVwpLoacxTuWMK6evS2kzYa8vXTwrt5o16WMtVtBleqBlQ5QVBxm6uJ1fDwri09nZTFr5YZSzzeoUSVy31jbDLo3rUlSfPSXWm5XYQJYRWYAkyRJUoUQDsOGFaXvL8veFs42r9nz66rW3bX5R3obSKkdSAOQpes28+m2Pcc+n7eG/MIde45ViY+ld8vaHNumDid1zCQ5Ibp3VhnAAmAAkyRJUoW3Kbv0/WXbHxuW7/k1VWruupQxvTWk1j9owWxzfiGfz13Dx9va3K/KjXQnT4iNYeqfjot6W3sDWAAMYJIkSaq0tuZE7ilbPat0E5B1i4A9xIeEajs6M6bvtJyxeuMD2pkxHA7zw4pcPpmZxbrNBfxpSPQ3sTaABcAAJkmSpENO/uadOjPu1ARk7XwoLtz9a2ITt91f1qr0vWY1m0WtM+OBVmn3AZMkSZIURQnJUO+IyGNnhfmREPbTpYzZs6EoD1ZNjzx2FhMXCWElzT+2zZzVahnVzowHmzNg+8kZMEmSJOlnFBfB+kU/CWXb/pu/cQ8vCu3ozFgya9Y68uektEDL31cuQQyAAUySJEnaT+Ew5C7fcY9Z9k4dGres2/PrqtXbtflHw+4QGx9c7bthAAuAAUySJEk6wMLhSGfG7J/sZbb6R9i4ctfzQzHw+xUQnxR8rTvxHjBJkiRJFU8oBFXTI48mvUs/t2X9tv3LdtrLrHBr1MNXWRnAJEmSJJV/VapDw26RRwUWE+0CJEmSJOlQYQCTJEmSpIAYwCRJkiQpIAYwSZIkSQqIAUySJEmSAmIAkyRJkqSAGMAkSZIkKSAGMEmSJEkKiAFMkiRJkgJiAJMkSZKkgBjAJEmSJCkgBjBJkiRJCogBTJIkSZICYgCTJEmSpIAYwCRJkiQpIAYwSZIkSQqIAUySJEmSAmIAkyRJkqSAGMAkSZIkKSAGMEmSJEkKiAFMkiRJkgJiAJMkSZKkgBjAJEmSJCkgBjBJkiRJCogBTJIkSZICYgCTJEmSpIAYwCRJkiQpIAYwSZIkSQqIAUySJEmSAmIAkyRJkqSAGMAkSZIkKSAGMEmSJEkKSFy0C6iowuEwALm5uVGuRJIkSVI0bc8E2zPC3hjA9tOGDRsAaNiwYZQrkSRJklQebNiwgbS0tL2eEwrvS0zTLoqLi1m+fDnVqlUjFApFtZbc3FwaNmzIkiVLSE1NjWotqhi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@@ -1640,31 +1637,29 @@
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\n",
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\n",
+      "image/png": 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@@ -1926,16 +1921,15 @@
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MI2+bj/S4mrse+C2VsGPkXRmm4/kIrADPXx4/pnnP7e/xfNNkBZ5vOn7lva418h3Es6F7Gc+b6CV4/qptqju8mRbh+VjhbjzHdjX5cVtrq/B8Y2Yanr1rD+M5Jmhdyx7VN+67Fk+hG4bnywl78bw5tvfO8gyeb25uA/6L5yO9I93fGjzHy8zHsxEYjOcYptbwAJ7n/r/GmIN4DqYf6/29ZXi/Rev9eGKc9zZz8WwsPz3MZTiG9cda+wGebxG+ZYwZ2cgso4EvjTEl3uw/sNZutdYW4nmj/hGej8HuBM601jb60Zy19lAR74rnTaHZrLXvAn8HPsFzsO9876TKRuY91vW+4f29hOc4uNvxrGNr8BwTOtH7XBwy3vtcFeMpzO2A0dbalQ3ussh88zxhPzzMr74cz7Fe6/Acs3abN89HeI6/eRXP3qVewEUtfXx49rDux7OH4Tk8B6ofen1+H/itd539FZ7n9LCsta/j2U68aDwfc63C89rHu36cD/wZz3rThyO/vg4VwUJjzFfeny/Gc3xQPp6PtX/tXY8b8y8g2/uaeuNIub35FuP5YtODeJ6PTXiOz2rM+3jW5Q14PgqroIkfrbZwO/Nv73wNP4r8E54/EIqMMXc0crtG1yE8x/bF4lmfFwDvNSW711RgtXddfwDPca8V1tr1eD4u/Yf3fs/Cc2qnqkbu42jb5qM9ruasB37LeP7IExEJLMaYAXje4KOt43PMBTLvnulnrbXN2Xsvbcy7B+tZPF++qDva/BIYtCdMRAKGMeYc70dmyXj2trytAibBzvvR3g+Ax1XAgotKmIgEkuvxfPy9Gc8xKDe6jSPiW949vkV4von7N6dhpNXp40gRERERB7QnTERERMQBvztx2dF07NjRZmZmuo4hIiIiclRLlizZa61NbWxawJWwzMxMFi9e7DqGiIiIyFEZYxqOLvI1fRwpIiIi4oBKmIiIiIgDKmEiIiIiDgTcMWEiIiLSOqqrq8nLy6OiosJ1lIAXExNDRkYGkZGRTb6NSpiIiEiIysvLIzExkczMTIwxruMELGsthYWF5OXlkZWV1eTb6eNIERGREFVRUUGHDh1UwI6RMYYOHTo0e4+iSpiIiEgIUwFrHS15HlXCRERERBxQCRMREZGA9tRTT3HzzTcfdZ78/PyvL1977bWsWbOm2b9rzpw5nHnmmc2+XWNUwkRERCToNSxhjz/+ONnZ2Q4TqYSJiIiIQzNmzGDkyJEMHDiQmTNnApCQkMD//d//MXToUMaNG8fu3bsBePvttxk7dizDhw/n5JNP/vr6Qw4ePEhWVhbV1dUAFBcXk5mZyX/+8x8WL17MpZdeyrBhwygvL2fy5MlfD4P43nvvMWLECIYOHcpJJ50EwMKFC5kwYQLDhw9nwoQJrF+/vtUfu05RISIiIvzm7dWsyS9u1fvM7tqOX5818IjzPPHEE6SkpFBeXs7o0aM577zzKC0tZdy4cfzhD3/gzjvv5LHHHuMXv/gFkyZNYsGCBRhjePzxx7nnnnu49957v76vxMREJk+ezKxZs5gxYwYvvvgi5513Hueffz4PPfQQf/3rXxk1atQ3fn9BQQHf+973+PTTT8nKymLfvn0A9O/fn08//ZSIiAg+/PBDfv7zn/Pqq6+26vOjEiYiIiLO/P3vf+f1118HIDc3l40bNxIVFfX1cVcjR47kgw8+ADznNbvwwgvZuXMnVVVVjZ6T69prr+Wee+5hxowZPPnkkzz22GNH/P0LFizg+OOP//q+UlJSADhw4ABXXnklGzduxBjz9d611qQSJiIiIkfdY+ULc+bM4cMPP2T+/PnExcUxefJkKioqiIyM/PqUD+Hh4dTU1ABwyy238MMf/pCzzz6bOXPmcNddd33rPidOnMi2bduYO3cutbW1DBo06IgZrLWNnl7il7/8JSeeeCKvv/4627ZtY/Lkycf8eBvSMWEiIiLixIEDB0hOTiYuLo5169axYMGCo86fnp4OwL///e/DznfFFVdw8cUXc/XVV399XWJiIgcPHvzWvOPHj2fu3Lls3boV4OuPI+v/rqeeeqpZj6upVMJERETEialTp1JTU8OQIUP45S9/ybhx4444/1133cX555/PcccdR8eOHQ8736WXXsr+/fu5+OKLv77uqquu4oYbbvj6wPxDUlNTmTlzJueeey5Dhw7lwgsvBODOO+/kZz/7GRMnTqS2tvYYH2njjLXWJ3fsK6NGjbKHvs0gIiIiLbd27VoGDBjgOkare+WVV3jzzTd55pln2vT3NvZ8GmOWWGtHNTa/jgkTERGRoHHLLbfw7rvvMnv2bNdRjkolTERERILGP/7xD9cRmkzHhImIiIg4oBImIiIi4oBKmIiIiIgDOiZMREQ86uqgphxbVUZVRSnlZSVUlB2koqyEqvISqspLqa4oo7ayhNrKUuqqyqirKofqMkx1OaamnPDacsJrKwirq6Zi1A2Mn3qJ60cl4rdUwkREAkFtDVSXQXW59//6P5dTXVFKVXkJleWl1FSWUF1RSm1lGXXesnRoPlNTTlhNBeG15UTUVhBRV0FUXSVRtoIYqgAwQLT331FjWUO5iaGSaKrCoqk2MVSHx5BQu4/282/lq3YdGTHhVF8+MyIBSyVMRKStHdhB7cLHqSnbT01FGbVVZdiqMmxVqbdYlRNWb69SZF0F4bbmiHcZ6f0XX++6ShtBBVGUE025jaKcGCqIoioshpqwJGrCY6iNjKEuIhbr/Wei4iAqjvDoeMKj4oiIiScqJp7ImHii4xKIjk0gNi6BuPh2xCUkEhUVTYIxJDTIU7JvJ/senEyP969hY8os+vQf0spPokjgUwkTEWlDtmA9pY+fRUxFASXEU040FTaKcm9Z8vzcjnI6Um6jqTDR1IbHUBce6ylLkbGYyDhMVCxhUfGERcURHpNAVEwckbEJRMXGExObQExsPPGxMcRHR5AQHUH76Ai6RocTHRHeJo8zIaULZVe8SsRTpxH14oXsuvEj0jp3bZPfLYFj27ZtTJs2jUmTJjFv3jzS09N58803mTZtGmPHjuWTTz6hqKiIf/3rXxx33HGu47Y6lTARkTZSl7uEin+fQ3k13JfxTzIGjCU+Opz46Iivy1LHKM//h66PjghrdHDhQNApcxDbzn6Krm9exPqZ3yH+9v+SmNBwn5n4jXd/CrtWtu59pg2GaX8+4iwbN27khRde4LHHHuOCCy7g1VdfBaCmpoaFCxcye/ZsfvOb3/Dhhx+2bjY/oBImItIGajZ9Qs3zF1NYk8Drgx/iF+edSlhYYJar5sgccQpr9/6FwfNuZ97DlzH6h68QGaG3HvmfrKwshg0bBsDIkSPZtm0bAOeee+63rgs2eiWIiPhY5Yo3CHvtWrbVpTF//ExumTo+YPdutcSAU7/Lin3bmLDuAT569DamfP8fIfX4A8ZR9lj5SnT0/74CEh4e/vXg2oeuDw8Pp6bmyMdEBiqdJ0xExIfK5j9BxGtXs6Iuk5WnPM/V0yaEZAEZcuFvWNlpOicVPMNHz//VdRwRv6ASJiLiIwc//Atx79/OF3WD2XvOS5x/XAh/Q9AYBl33OOviRzN5wx/5/L2XXCcScc5Ya11naJZRo0bZxYsXu44hInJ41lL01k9JWvoIs+xEUi55nPH99M1AgKrSInbdP5mU6l1sOvNVho2e6DpSSFu7di0DBgxwHSNoNPZ8GmOWWGtHNTa/9oSJiLSm2hr2v3AdSUsf4SUzlW7XPqsCVk9UfBJJ33uTirBY0mZdzuYtG11HEnFGJUxEpLVUV7DvyYtI3vAy/wq/kNE3Ps6QbimuU/mddp17UHvxSyRSSu0z51Owd6/rSCJOqISJiLSGimIKZ55FSt4HPBRzHWfc+nd6dkp0ncpvde47hoKpj9KzbjvbHr2Q0vIK15FCVqAdluSvWvI8qoSJiByrkgL2PXwq7fYs4W/tfsylt/6BtPYxrlP5vcxxM9g0+jeMrl7Mlw9dQ01NretIIScmJobCwkIVsWNkraWwsJCYmOa97nWeMBGRY2D3b6do5lnElu3kH51/ww3X3khclDatTdX/zFtZVbiVKVuf4N1//YKp1/0xJE/h4UpGRgZ5eXkUFBS4jhLwYmJiyMjIaNZttKUQEWmhut1rKXn8LMKqSnm0x73ccuVlRIbrA4bmGnT5vax9MIdpOx/mv6/04NTzb3AdKWRERkaSlZXlOkbI0tZCRKQFanIWUj7zVCqqqnl+wCPcetXlKmAtFRZGvxueZUvMIE5Y9Qs+/3iW60QibUJbDBGRZqpc/yE1T57F3uoY3hvzFDdceHZIjAPpS2FRsXS98XX2RaSSPfd6li//ynUkEZ9TCRMRaYbSr14h7IUL2VrbicUnvcAVZ5yoY5haSUz7TsRd/RrhBpJev4StuTmuI4n4lEqYiEgTFX8+k9i3rmVFXS9yp7/CeSc0ehJsOQbtMwZQft6zpLGX4icvYG/RAdeRRHxGJUxE5GisZd97f6Ldhz/mMzuMqkte5dSR/VynClppgyeTP/l+htatZc0/L6e8stp1JBGfUAkTETmSujr2vnoHKQv+zCxzPMnX/Ifx/bu5ThX0siZfzobBd3B85Vw++ect1NbpPFYSfFTCREQOp7aaPc9eQ8dVj/Ny+BkMuPE5hnRPdZ0qZPQ99xesS/8Opxe9wKyn/uQ6jkirUwkTEWlMdTl7Hj+fTlte46noSzj+ln/Rs1M716lCizH0/+6jbGo3jtO3/4X33nzOdSKRVqUSJiLSUMUB9jx8Oh3z5/BowveZ8YMHSEuKdZ0qNIVHkHXjf9gZncXEr37EF59/4jqRSKtRCRMRqcce3E3Bg6eQvG85j3T8GZff+juS4qJcxwpp4bHt6Hj9m1SGx9Hrg++ycu1a15FEWoVKmIiIV92+bex/cArxB7fxeLc/8r3v/1jjQPqJ2A7diLj8FRJNOVEvXcT2/N2uI4kcM5UwERGgeucqDj58EmEV+3m+39+5/rvXaxgiP5OUNYLisx6nFzns+tdF7DtY5jqSyDHRFkZEQl7llvlUPjaViuoaZo38F9dcfKGGIfJTXUaeSd6E3zO29isWP/xdKqpqXEcSaTGVMBEJaaWr38c+PZ2CmjjmT36eS8+epmGI/FzmqTexqe91nFr+Lu8++jPqdA4xCVAqYSISsooWvkDUfy5hq01j81mvMuPEia4jSRP1vuhuNnU6jXMKZ/Lmcw+6jiPSIiphIhKS9n7yMO1m38gy24cDF77JyaMHu44kzREWRq/v/Ztt8UM4fdNveG/2664TiTSbSpiIhBZr2f32b+k492d8xkhirn6DcdlZrlNJC5jIWDJueJ39kZ0Y8+XNfPHll64jiTSLSpiIhI66Ona+dBudl9zLu2GTybjhFQZnprlOJccgIrEj7a99g/CwMDJmX8HqTVtcRxJpMpUwEQkNtdXkP3UFXdY9xStR0xl+6wv0Skt2nUpaQWxaX2ovep40s4+aZy8id88+15FEmsSnJcwYM9UYs94Ys8kY89MjzDfaGFNrjPmOL/OISIiqKiP/0XPpmvM2z8Zfyck/eIy0pDjXqaQVpfQ7jn2n/oPBbGDzzMs5UFrpOpLIUfmshBljwoGHgGlANnCxMSb7MPPdDbzvqywiErps+X7yH5pG2u7PeDL5B5z7g/tIio92HUt8oMuEi8gd+TMm13zOJw/fTGVNretIIkfkyz1hY4BN1tot1toq4EVgeiPz3QK8CuzxYRYRCUF1xbvY8/eT6Vi0kqfSf82lN92lYYiCXI8z72RL5sXMKH2ZNx/7PdbqHGLiv3xZwtKB3HqX87zXfc0Ykw6cAzxypDsyxlxnjFlsjFlcUFDQ6kFFJPhU793Cvn+cSEJZLi/2uZerrr2NqAgdBhv0jKHn5Q+yLWUS5+76G6+99JTrRCKH5cstUmOnnG74J8nfgJ9Ya4+4z9haO9NaO8paOyo1NbW18olIkKrIW0HpP08ivKqYWcMe5fJLr9IwRKEkPIIe17/I7thenLb2p7z34X9dJxJplC9LWB7Qrd7lDCC/wTyjgBeNMduA7wAPG2Nm+DCTiAS5kg2fU/OvaZTXwOfHPcMF55yjYYhCkIlOpNMNb1IZkciwz65j/rIVriOJfIsvS9gioI8xJssYEwVcBLxVfwZrbZa1NtNamwm8AnzfWvuGDzOJSBDbv+wdIp4/hz11iayd9h/OOnmK60jiUGRSOrFXvUaiqSTl9UtZuy3PdSSRb/BZCbPW1gA34/nW41rgZWvtamPMDcaYG3z1e0UkNO354hkS37iCzTadgu+8wZRxo1xHEj8Q120Ilec+RS+zgwP/voT8wmLXkUS+ZgLtmyOjRo2yixcvdh1DRPxI/vt/o+v8X7OYbGKueJlBPbsd/UYSUvI/mUnXuT/m3ahTmXj7c7SLjXIdSUKEMWaJtbbRvwr1VSERCVzWkvvaL+k6/9fMDRtLyvVvq4BJo7qeeB05g25iWtV/ee+RO6murXMdSUQlTEQCVF0dOc/eRLcVf+e9yJPoe8ur9OzS0XUq8WPdz/sD27uewQUHnuTlJ+/XOcTEOZUwEQk8NVVsf/wSum9+jjfizmPsD16gS3Ki61Ti74yhx3efJK/dcL6T+0deee0/rhNJiFMJE5GAYitL2P7wdHrkv8tLSddy6m0zSU7QMETSRBHRpN/wGkXRXTl5xe28P/cz14kkhKmEiUjAqCvdR97fTyOjcD4vdL6Dc27+i4YhkmYzcSkkf+9NwsLDGfDxd1m4ar3rSBKiVMJEJCBUF+1g99+n0KlkHa/0/D0XXv8LDUMkLRaV2pPwS1+msyki+pVL2bhDwxdL29MWTET8XsWujRQ9OIXEip3MHvx3LrjiJg1DJMcsodc4Ss78J4PZRN6/Lmf3gTLXkSTEqISJiF87uG0p5TNPIby6hM8mPME537lUwxBJq+kw6jvsHvdLTqxbwOf/vInSyhrXkSSEqISJiN/at2YO5qkzqKg1rDj1ZaaddobrSBKEupz2Q/L6XM55Fa/x6qN3UaNziEkbUQkTEb+0a9EbxL98Pntse/LOeZPJEye6jiTByhgyLn6AvE4ncGnhgzz37GM6h5i0CZUwEfE7OZ8+Q8dZV7OJbpRd+g6jhw1xHUmCXVg4Gde+wJ6Efnxnyy955Z1ZrhNJCFAJExG/cnDXJlI//hGrTD9ivzebQX17uY4koSIqns7XvUFlZBLHL76JD+ZrnGLxLZUwEfEf1rLr2eupsWFEXvAEPdPTXCeSEBPWvgsJ17xGYlgV3d+7isXrtrqOJEFMJUxE/MbG9/5Jn5LFfJF1CwMHZLuOIyEqqssg6i54hl4mn+oXL2fTzv2uI0mQUgkTEb9QtjeHLl/+jqXhg5h8yZ2u40iISxhwMgdOvo/xrGTd49dQUFzhOpIEIZUwEXHPWnKevoFwW0PE9AeJiYp0nUiEDpOuYtfw2ziz9iPef+QOyqp0DjFpXSphIuLcpo+fon/xF3yacT2Dhwx3HUfka2ln30V+j+lcVvYMzz32V2rrdOoKaT0qYSLiVPn+3XT87FesDuvLcZf/0nUckW8yhq6XP87O5NFcsecvPP3CczqHmLQalTARcWrLMzcRZ0upOuMB4mKiXccR+baIKLpc9x+KYzM4Z8OdvPL+x64TSZBQCRMRZzZ9+hID933Ap12uZvjICa7jiBxebDIdrnuTsIgoxs6/no8WrXKdSIKASpiIOFFxcB9Jn/yEjSaTcVf83nUckaMKS8kk+or/0NkU0+GdK/lqc77rSBLgVMJExIkNT99KUt0Bik/7Gwlxsa7jiDRJdI/RVM6YyRCzmYLnr6dOB+rLMVAJE5E2t2XB2wwpeJtPUy9m5LgTXccRaZZ2w2awuc81nFLzGUvXrHEdRwKYSpiItKmqsmLi3v8h2+jKyCvvdh1HpEW6nXQDYcay67NnXEeRAKYSJiJtavUzd9CproC9U+6lfWKi6zgiLRKT1odtsQPpu+sdKnQSV2khlTARaTNbv/qQofkv83nKOYw6/nTXcUSOSe3gC+ljclm4YK7rKBKgVMJEpE1UV5QS+c6t7DSpDL7iXtdxRI5Z5vGXUU0E5Yufdx1FApRKmIi0iRXP/YyMuh3kH/dnkpNTXMcROWbhCR3YkjyR4Qc+pLC41HUcCUAqYSLic9tXfcHQnGf4ot3pjD7pPNdxRFpNwpjL6GSKWPLJG66jSABSCRMRn6qpqqDu9ZvYZ5Lof8UDruOItKr00dM5aBKIWvOy6ygSgFTCRMSnlr5wF1m1W9k67nd06NjJdRyR1hURTV7XqYypmM+WHbtcp5EAoxImIj6Tu24JQ7fMZGH8iYw57VLXcUR8ovNxVxFnKln/8XOuo0iAUQkTEZ+oramh4pUbKTFxZF3+IMYY15FEfCKl3yR2R3Sl49Y3NIyRNItKmIj4xJKX/kCfmvVsHPErUtMyXMcR8R1j2N9rBiNrV7J89WrXaSSAqISJSKvbsXkVQzb8g69ixzPmzGtdxxHxuR4nXk2Ysez8QsMYSdOphIlIq6qrreXASzdSRSTpl/0TE6bNjAS/2LS+bI0dSJ9dszSMkTSZto4i0qoWvXof2VUrWDvkJ3ROz3IdR6TN1A66iD7ksmjBp66jSIBQCRORVrMzZyMDV9/LyujhjDnnVtdxRNpU1uTLqCKC8sX6lqQ0jUqYiLQKW1fHnudvJIw6Olz0iD6GlJATHp/C1uSJDNMwRtJE2kqKSKtY+ObDDK1YxKoBt9E1q7/rOCJOxI+5lE6miK/mvOE6igQAlTAROWZ78nPov/yPrIvMZtT5P3EdR8SZjNEzPMMYrdYwRnJ0KmEickysteQ8+31ibBUJFzxCWHi460gi7kREk9t1KmMq5rFVwxjJUaiEicgxWTj7SUaVfcby3jeQ0Weo6zgizqUddxWxpor1n+gAfTkylTARabGCPTvpteguNkf0ZuTFv3YdR8QvfD2M0ZY3sFbDGMnhqYSJSItYa9n09K20tyVEnvsw4RGRriOJ+AdjKOx1DiNqV7J8zRrXacSPqYSJSIss+uAlxpf8l+WZV9M9e6zrOCJ+JfPQMEafaRgjOTyVMBFptn2Fe+k+7+fkhHdn2CW/dx1HxO/EpfVha+wg+ux6R8MYyWGphIlIs61++nZS7T7s2Q8SER3rOo6IX6oZdAG9yWXJl3NdRxE/pRImIs2y8JM3Oe7AWyzPuIQeQ09wHUfEb/WcfDlVRFCmYYzkMFTCRKTJig4U0WXuneSHdWHw5fe4jiPi18LjU9iSNJFhRR+y/2CZ6zjih1TCRKTJlv37Trqxi8ppfyMyJsF1HBG/Fz/6UlLNAb765HXXUcQPqYSJSJMs+eK/HFf4Mss7n0PW6Kmu44gEhG5jZ1BsEonQMEbSCJUwETmq4pISkj/8IfvCOtD/ivtdxxEJHBHR5HbxDGO0LX+36zTiZ1TCROSoFj39c3raXIpP+SvR8cmu44gElLTjriTWVLFOwxhJAyphInJESxd+xvG7n2Vlh2n0mnCO6zgiAadD/0nsiuhKx82vaxgj+QaVMBE5rNLyCmLf/QEHwxLoc8U/XMcRCUzGsK+nZxijFRrGSOpRCRORw/rimbvobzez/4Q/EtM+1XUckYCVOcU7jNHnT7uOIn5EJUxEGrVs6SJO2PE4a5Im02vyZa7jiAS0uLQ+bIkdRO+ds6is1jBG4qESJiLfUl5ZTdjbt1Bposm84mHXcUSCwqFhjBYv+NR1FPETKmEi8i1zn/0jQ+rWsnvCXcSlpLuOIxIUep5wGVVEUL74WddRxE+ohInIN6xctYLjch5iQ+JY+pxyres4IkEjIqEDm5MmMbToQ4pKNIyRqISJSD0VVTVUvX4zxhjSL58JxriOJBJU4rzDGC3RMEaCSpiI1PPxC/cxsnY5O0b/jPhOma7jiASd7mOnU2wSidIwRoJKmIh4rV63nklb7mdL3FD6TLvVdRyRoGQiosnpMpVR5fPI2alhjEKdSpiIUFldQ/ErtxBlaki97DEI06ZBxFe+HsboYx2gH+q0pRURPvzPI4yv+ZLcYbeT2LWf6zgiQa1j/0nsCu9KBw1jFPJUwkRC3LrNWxi//s9sjxlAn7N/4jqOSPAzhsJeMxheu4qVa9e6TiMOqYSJhLDq2jp2vXgbiaac5EtmQli460giISFzyjWeYYw++7frKOKQSphICHv/tSeYXD2Xbdk30q77ENdxREJGfFpvNscOotfOWVRV17qOI46ohImEqE05eYxe9Xt2RPWkz7m/ch1HJOR8PYzRl3NdRxFHVMJEQlBNbR1bn7udjuYA8Rc8AhFRriOJhJxeJ1zuGcZokb4lGapUwkRC0Htvvcgplf9lS5/vktR7rOs4IiEpIiGFzUmTGFL0IQdKyl3HEQdUwkRCzJYduxm67Ffsisyg9/m/cx1HJKQdGsboqzmvuo4iDqiEiYSQ2jrL2md/TLrZS/S5D2Oi4lxHEglph4Yxilj1H9dRxAGVMJEQ8u7s15lW9hZbMi8mecAJruOIhDzPMEanMap8Hrk797iOI21MJUwkRGzfXUj2ov9jX0Qnel18j+s4IuLV+biriTVVrNUwRiFHJUwkBNTVWZY+81N6mnzM9Acw0YmuI4mIV2r/iewMT6fjFg1jFGpUwkRCwLsfvMeZB19hS8Y5dBgyzXUcEanPO4zRsJqVrF67xnUaaUMqYSJBLm/vAXrN+wkHw5PIuvR+13FEpBGZU672DmP0tOso0oZUwkSCmLWWBU//kv5mOzXT7sXEJruOJCKNSEjrw+aYQfTc+Y6GMQohKmEiQez9T+Zw1oHn2JZ2Gqmjz3UdR0SOoHrQBfQij68WahijUKESJhKkdu0vpcunP6YyLI7ulz7kOo6IHEWvyZ5hjEo1jFHIUAkTCULWWuY8/VuGspHKk/9IWGKq60gichSRCSlsSprEkP0fcqBUwxiFApUwkSD0wefzmL7vCbZ3PIHUCZe5jiMiTXRoGKOln7zmOoq0AZUwkSCzp7iM5I9+TF1YBBmX/ROMcR1JRJqox9jpHDCJRKx62XUUaQMqYSJBxFrLf5++m9GspvSE3xCelO46kog0g2cYo6mMKv+CvJ27XccRH1MJEwkiH335FdMLHiUveQydTvie6zgi0gKdJ11JjKlmnYYxCnoqYSJBovBgBTHv/YhIU0fapTP1MaRIgOo0YBL54el00DBGQU8lTCRIzHruASaxlAMTfkZExyzXcUSkpYyhsNd0htasYs26ta7TiA+phIkEgU+WrOKsnX8nv90QOp98q+s4InKMMqd8lzBjyf/0366jiA+phIkEuKKyKmrfuYN4U0nqpY9BWLjrSCJyjBLT+rApdjA9d75DdY2GMQpWKmEiAe715x/hZDuf/aNvJ7Jzf9dxRKSVVA883zOM0ZcaxihYqYSJBLBPl6/nzNy/sju+H52n3uk6joi0ot5fD2P0nOso4iMqYSIBqriimoNv3kmyKSH54pkQHuk6koi0ov8NY/SBhjEKUiphIgHqlRef5Iy6ORQOvZGojGGu44iID8SOupSO5gDL57zqOor4gEqYSACat2YLp239M3tjMul81q9cxxERH8kc5xnGKHylhjEKRiphIgGmvKqWXa/+jC5mH4kXPQoR0a4jiYiPmIhotqdNZWT5PHbs0jBGwUYlTCTAzPrvu5xb+x67B1xFdOY413FExMe+HsboEx2gH2xUwkQCSHFFNWmL/0JJWCJdpv/GdRwRaQOdsz3DGKVsek3DGAUZlTCRAPLu268wiWUcHH0rxLR3HUdE2oIx7O05g6E1q1i3XsMYBROVMJEAsfdgBb1X3U9ReEe6nHyL6zgi0oYODWO0Q8MYBRWVMJEA8cGbzzDSrKdq0h0QGes6joi0oXZderMpZjA98zWMUTBRCRMJAPn7Sxm28R/sjUqn0/HXuo4jIg5UDTyfnuSxbOEc11GklaiEiQSAua8/ygCzHXPiz3VmfJEQdWgYo5KF+pZksFAJE/FzW3cXMX77o+yO6UWHsZe4jiMijkQlprAxaRKD939IcZmGMQoGKmEifm7Ba/8g0+wiZupdEKaXrEgoix15CR3NAZZ98prrKNIKfLpFN8ZMNcasN8ZsMsb8tJHp040xK4wxy4wxi40xk3yZRyTQrM3ZzeRdT7AjYTDth57lOo6IOJY1fgYHSCR81Uuuo0gr8FkJM8aEAw8B04Bs4GJjTHaD2T4ChlprhwHfBR73VR6RQLTi9fvoYvbR/szfgzGu44iIYyYimm1dpjKybB75u/e4jiPHyJd7wsYAm6y1W6y1VcCLwPT6M1hrS+z/Tv8bD+hUwCJeSzdu55R9z5KTPI6E/pNdxxERP5F23FWeYYw+esZ1FDlGvixh6UBuvct53uu+wRhzjjFmHTALz96wbzHGXOf9uHJxQUGBT8KK+BNrLZvevJsUU0LqjD+4jiMifqTzgImeYYw2v65hjAKcL0tYY5+dfGttsda+bq3tD8wAftfYHVlrZ1prR1lrR6WmprZuShE/tGDVBqYdfJVtnU4mtsco13FExJ94hzEaVruS9RrGKKD5soTlAd3qXc4A8g83s7X2U6CXMaajDzOJ+D1rLbtn/YlYU0XXcxv9u0REQlzmiZ4PjjSMUWDzZQlbBPQxxmQZY6KAi4C36s9gjOltjOdoY2PMCCAKKPRhJhG/N2fRcqaVv0NOxtlEpTX8LouICLTr6hnGKCv/HWo0jFHA8lkJs9bWADcD7wNrgZettauNMTcYY27wznYesMoYswzPNykvtPqAW0JYbZ2l7IM/YIyl2zm/cR1HRPxYpXcYo6WL5rqOIi0U4cs7t9bOBmY3uO6Rej/fDdztywwigeTDz+dxWtWH5Pa+lKwOma7jiIgf6zP5ciqX/I7Shc/C+Cmu40gL6PTbIn6iqqaO8Ll/pNpE0WP6L13HERE/F5WYwsb2kxi070NKNIxRQFIJE/ET//34A06u/ZzdA68mrF1n13FEJADEjPIMY7T0k1ddR5EWUAkT8QPlVbUkzb+bEpNAjzN/4jqOiASIXuOnc4BEIla97DqKtIBKmIgfeP/dN5hkl7B/+E2Y2GTXcUQkQJiIaLZ2mcrwsnns2rPbdRxpJpUwEceKy6votvQvFIWl0G3qba7jiEiA6TzpSmJMNWs/etZ1FGkmlTARxz58+3lGspbScT+EqDjXcUQkwHTJnkR+eDrJmzSMUaBRCRNxqPBgOf1X38/eiC6kT7nedRwRCUTGsKfnOQyrXcmGDRrGKJCohIk4NOf1x8k226g94ScQEeU6jogEqJ5TrgY0jFGgUQkTcWTn/oMM3/wwu6Kz6DzxCtdxRCSAtevSm40xg8ncoWGMAolKmIgjX7z6ID1NPuEn/wrCwl3HEZEAV5l9AT3JY/nCOa6jSBOphIk4sH1XIRNyHyM3LpvUUee4jiMiQaDPlMuoJJKSRc+5jiJNpBIm4sCS1+6jqykk4YzfgjGu44hIEIhOSGFD+4kM2veBhjEKECphIm1s/fZ8jt/9NNvajSZ54Cmu44hIEIkddQkdTDHL5rzmOoo0gUqYSBtb+/rddDTFdJj+e9dRRCTI9Bo/gyISCV/5kuso0gQqYSJtaPmGLUzZ/xKbO0wmsdc413FEJMh4hjGapmGMAoRKmEgbynnrjySYCrqc8wfXUUQkSH09jNHHGsbI36mEibSRRStWc8rBN9jc5QziMga5jiMiQapr9kR2hKfTYaOOC/N3KmEibcBay97ZvyfcWLqf+zvXcUQkmBlDQc9zGFK7io3r17hOI0egEibSBj5fuIiTy99nW4/vEJ3a03UcEQlyWSd6hjHK+/Qpt0HkiFTCRHysts5S9eEfqDXhZJ3za9dxRCQEtO/amw3eYYxqa+tcx5HDUAkT8bG5n83hxKq55Pa9koikrq7jiEiIqMw+nyx2aBgjP6YSJuJD1bV1RH36R8rC4ug1/f9cxxGRENJ3yuWeYYwW6luS/kolTMSHPv7gHSbVLmT3oOsIi092HUdEQkh0Qgrr209i4L4PKNUwRn5JJUzERyqqauj45Z8pCkui55l3uI4jIiEoxjuM0fK5Ol2FP1IJE/GRj2a/xEi7mqKRP8BEJ7iOIyIhqM/4GRTRjrAVGsbIH6mEifjAwfIqMpfdS0F4ZzJPu8l1HBEJUSYiiq1ppzG8bB57Cva4jiMNqISJ+MCcN59gIJupmHgnRES7jiMiIazTcVcRbapZ+9EzrqNIAyphIq1s38Fystf+nZ2RPeg2+WrXcUQkxKV7hzFK1jBGfkclTKSVff7ag/QyO7BT/g/Cwl3HEZFQZwx7smZ4hjHaoGGM/IlKmEgr2llYxIgtj5AT05+u4y5wHUdEBICsKd8FYMen/3acROpTCRNpRYtfvZ8Ms5eYqXeBMa7jiIgAkNS1N+tjhtAj720NY+RHVMJEWsn2nQWM3/EEWxOG02noVNdxRES+4dAwRisWzXEdRbxUwkRaycpX/0xHU0z7M3+vvWAi4nf6nniZZxijL/UtSX+hEibSCjZsz+H4gufZmHQcKf0nuY4jIvItMYmeYYyy931IWbmGMfIHKmEirWDT638kgXI6n/N711FERA4rZqR3GKM5Ol2FP1AJEzlGK9et58T9r7Kh01Ta9RjmOo6IyGH1Hj+D/bQjbKWGMfIHKmEix2jn278j0tTS7bzfuY4iInJEYZFRbEs7jWGl8yjQMEbOqYSJHIPFy5YyuWQ2G9PPJT6tj+s4IiJHlTrJO4zRxzpA3zWVMJEWstZS/O7vsCacrPPuch1HRKRJMgZOJC88gyQNY+ScSphIC81f8AWTKz5mS89LiUnJcB1HRKRpjGFP5nSG1Kxis4YxckolTKQF6uosdR/9njITS+9zfuE6johIs2RNuRqAvE+fchskxKmEibTAZ3PfY1LNfPIGXENkYkfXcUREmiU5vQ/ro4fQI+8dDWPkkEqYSDNV19YR/9kfKTLt6Xv2na7jiIi0SEX2d8hkB6s0jJEzKmEizfTpe68wqm4Fe4bdTFhsO9dxRERapN+Uy6m0kRQvfNZ1lJClEibSDBVVNaQtvoeCsFT6nH6L6zgiIi329TBGhR9QXl7hOk5IUgkTaYZP3/k3A+1GDoz9ESYy1nUcEZFjEjXKO4zR3FddRwlJKmEiTXSwrIKeK+4nPyKD3id/z3UcEZFj1tc7jJFZoWGMXFAJE2mieW88Qm9yqTr+5xAe4TqOiMgxC4uMYkvaVO8wRrtdxwk5KmEiTbC/uITs9Q+RE9WbzEkXu44jItJqOk28kmhTzbqPNIxRW1MJE2mCha/eTzezh7BTfg1hetmISPDoNsg7jNGm111HCTl6NxE5it2F+xi+7XE2xw0lY9RZruOIiLQuY9idOZ3BNavYslHDGLUllTCRo1j2yt10MkXET/stGOM6johIqzs0jFHu3H87ThJaVMJEjiA3fydj859hXbsJpA2e7DqOiIhPpHiHMcrc8TZ1GsaozaiEiRzBulf/QJIpJfXs37mOIiLiU+XZ59PD7mDV4jmuo4QMlTCRw9i0ZQsT9r7Mmg6n0KH3KNdxRER8qv+Uy6mwkRR/qWGM2opKmMhhbH/jt0RTTfo52gsmIsEvJjHZO4zRfzWMURtRCRNpxOo1q5h04G3WdZlO+4wBruOIiLSJqJGXkGIOskLDGLUJlTCRRux9x/NNyKzzfuM6iohIm+k34dAwRi+6jhISVMJEGli6ZAGTSv/Lhu4XEp/aw3UcEZE2ExYZxea0qQwtnc/egj2u4wQ9lTCReqy1lP/3t1SYaPqc92vXcURE2lyniVd4hjH6WMMY+ZpKmEg9C7/4iAmVX7Cl99XEtO/kOo6ISJvrPmgSuWEZJG18zXWUoKcSJuJVV2eJmPN7DpBI/3N/6jqOiIgbxrA7awaDalaxfct612mCmkqYiNe8j95gZM1ScgfdSGRckus4IiLOdB49A4Cdyz9yGyTIqYSJANU1tSTN/xMFpgPZZ93uOo6IiFPpvYdRZqOp2/GV6yhBTSVMBJj37vMMqltPwYjbCIuOcx1HRMSpsIhIcqJ7k1y0ynWUoKYSJiGvoqqarl/9hfzwrgyYdoPrOCIifqE4ZTCZ1ZuprKp0HSVoqYRJyJv/5kz62O0cHH8nJiLKdRwREb8Q1W0ksaaK7Wv1kaSvqIRJSCspK6f36r+zPbIn/aZc6TqOiIjf6Nx/PACFGxc4ThK8VMIkpC187QG6sYvayb+AML0cREQOScvK5iBxkL/UdZSgpXcdCVn7iw4wcOMjbIoeRM8J57qOIyLiV0xYODkx/ehwYLXrKEFLJUxC1lev/YXOZj9Rp90FxriOIyLid0o7DCazZivlZWWuowQllTAJSXsK9jBy+5OsjR9D9xGnuI4jIuKXYnqMJsrUsnXNl66jBCWVMAlJq1/5I0mmhKQzf+s6ioiI3+qaPQGAoo0qYb6gEiYhJy8vh9G7XmBl+xPpMmC86zgiIn6rY3ov9tGO8F06ON8XVMIk5Gx67bfEUknajN+7jiIi4t+MYUfsAFIPrnWdJCiphElI2bJ5PeML32Bl6pmkZg1yHUdExO+Vpw6hR20OxcVFrqMEHZUwCSn5b9yFwZJ5no4FExFpiris0YQby/ZVOmlra1MJk5CxdtVXjCt+j9Xp3yGpS0/XcUREAkKG9+D84s06OL+1qYRJyDgw6y6qTCR9zvu16ygiIgEjqXM39pgORO1e5jpK0FEJk5CwfNGnjCufy7rMK0jo0NV1HBGRgJIfn03nUh2c39pUwiToWWup/eA3HCCB7PP+z3UcEZGAU9VpKN3tTvbt3eM6SlBRCZOg99VnsxhRtZjN/b5HTGKy6zgiIgEnsedoAHJWf+E4SXBpcgkzxiQbYwYaY3oaY1TeJCDU1dYR/ekf2GuSGXzOHa7jiIgEpG6DJgFQumWx4yTBJeJIE40x7YGbgIuBKKAAiAE6G2MWAA9baz/xeUqRFlr04UuMrVnDsiG/omNMgus4IiIBKSGpIztMGjEFy1xHCSpHLGHAK8DTwHHW2qL6E4wxI4HLjTE9rbX/8lE+kRarqakh5cu7yQ9LY/BZt7iOIyIS0HYnZtO1eAXWWowxruMEhSOWMGvtKUeYtgRY0uqJRFrJl+88wcS6rawY8xe6Rka5jiMiEtBq0obTpfhjdu/KpXOX7q7jBIUmHdtljLmmweVwY4xOtiR+q6Kykm7L72NbeCaDp15z9BuIiMgRJfUeA0De6nmOkwSPph5gf5IxZrYxposxZhCwAEj0YS6RY7LojQfpbndSPulnmLBw13FERAJe94HjqbWGim06OL+1HO2YMACstZcYYy4EVgJlwMXWWn1PVfxSSWkJfdY+xIaoAQyYfKHrOCIiQSEmvj3bwrsRt3eF6yhBo6kfR/YBfgC8CmzDc0B+nA9zibTY0tfuJ41CzEm/Ah08KiLSagrbZ9O9Yh22rs51lKDQ1I8j3wZ+aa29HjgB2Ags8lkqkRaqqCinz+anWBc9mD5jT3cdR0QkqNR1GUEHDrAjZ5PrKEGhqSVsjLX2IwDrcS8ww2epRFpoyazHSGMvduIPXUcREQk6KX3GAbBzjQ7Obw1HLGHGmEkA1trihtOstRuNMe28B+qLOFdTU0P6qkfZGt6T/pNmuI4jIhJ0umePptqGU5WrM1S1hqMdmH+eMeYe4D085wQ7dMb83sCJQA/gRz5NKNJEX33wPGNsHstH3YcJ08haIiKtLTI6js2RWSQWrnQdJSgc7WSttxtjkoHvAOcDaUA5sBZ4RN+QFH9h6+pot/gf5Js0Bp9yhes4IiJBa3/7gfTZ+yG1tXWEh+sP3mNx1GfPWrsfaAesAD4APgf2Av2NMcN8mk6kiZZ/Pov+tRvIH3QdYRGRruOIiASv9BG0N6Xkbl7lOknAa2qFHQncAHQBugLXAZOBx4wxd/ommkgzfHE/hSQx5IwbXScREQlqqf3GA7B73XzHSQJfU0tYB2CEtfYOa+2PgFFAKnA8cJWPsok0ydqlnzGscgmbe11BVIxOXyci4ksZfYdTYSOp1cH5x6ypJaw7UFXvcjXQw1pbDlS2eiqRZij58K8cJJaBZ9/uOoqISNALj4xie1Rv2hfp48hj1aRhi4DngQXGmDe9l88CXjDGxANrfJJMpAm2b1zBiJK5LMm4gjHtU1zHEREJCQeSBzNw95tUV1cTGanjcFuqSXvCrLW/A74HFAEHgBustb+11pZaay/1YT6RI8qffQ81RND77DtcRxERCRkR3UYQbyrZtm6p6ygBral7wrDWLsFzrjARv7BrxzZG7nuX5alnMbpzd9dxRERCRuf+42EJFG5cQJ/BY1zHCVg6wYcErM1v/YVwask4Q1/QFRFpS117DaaEWOyOr1xHCWgqYRKQigoLGLrrVZa3n0KXrGzXcUREQooJCycnui8pRatdRwloKmESkFa/dT8JppyUU3/iOoqISEgq6TCYrJotVFSUu44SsHxawowxU40x640xm4wxP21k+qXGmBXef/OMMUN9mUeCQ1npQfpvf5YVsWPIHDTWdRwRkZAU1W0kUaaGrWsWuY4SsHxWwowx4cBDwDQgG7jYGNPwc6OtwAnW2iHA74CZvsojwWPZ2w/TgQNEnfBD11FEREJW14ETACja+KXjJIHLl3vCxgCbrLVbrLVVwIvA9PozWGvnecemBFgAZPgwjwSB6uoqeqz/F+sjB9B/7FTXcUREQlZqRl+KSMTs1GkqWsqXJSwdyK13Oc973eFcA7zb2ARjzHXGmMXGmMUFBQWtGFECzdJ3nyDd7qZy3K1gjOs4IiIhy4SFkRvTj9RiHZzfUr4sYY29Q9pGZzTmRDwlrNGjrK21M621o6y1o1JTU1sxogSSuto6Oi57mO1h3Rh84oWu44iIhLyy1KH0qM2hpKTYdZSA5MsSlgd0q3c5A8hvOJMxZgjwODDdWlvowzwS4JbP+Q8967ZTMPRGTFi46zgiIiEvrscoIkwd21YtcB0lIPmyhC0C+hhjsowxUcBFwFv1ZzDGdAdeAy631m7wYRYJcNZaohY8wC5SGTrtWtdxREQESB80EYDizQsdJwlMTR62qLmstTXGmJuB94Fw4Alr7WpjzA3e6Y8AvwI6AA8bz/E9NdbaUb7KJIFr7cIPGFi9mi/7/4S0qGjXcUREBEhJ60EBKUTsWuY6SkDyWQkDsNbOBmY3uO6Rej9fC2i3hhxV5Zz72E87hpx1i+soIiJST358fzqXrHEdIyDpjPni97asXsjw8vms73EJsfGJruOIiEg9FanD6GF3ULRfh3U3l0qY+L3C9++hzEaTffaPXEcREZEGEnqOBmD7qi8cJwk8KmHi1/K3rWf4gY9YkXYu7Tp0ch1HREQa6OY9c37pFg1f1FwqYeLXct65mzoMWWf92HUUERFpRLsOaeSbzkTtWe46SsBRCRO/Vbg7j2EFb7Es+TQ6Z/RyHUdERA5jd8IAupaudR0j4KiEid/a8Pa9RFFD52mNDqQgIiJ+orrzMLqyh4LdO1xHCSgqYeKXSor3MzDvJZYlHEePfsNcxxERkSNo12ssAHmr5jlOElhUwsQvrXrrAdpRSvxJd7iOIiIiR9F90DjqrKFsuw7Obw6VMPE7lRVl9Nz0b1ZGDaPfiBNcxxERkaOIS0whLzyd2IIVrqMEFJUw8TsrZj1KJ/ZhJ93uOoqIiDRRQeJAupWvw1rrOkrAUAkTv1JbU0PaqplsDO/N4Elnu44jIiJNVNdlGKnsZ2feFtdRAoZKmPiVFR8+QzebT/GomzFhWj1FRAJFUm/Pwfn5a+Y7ThI49C4nfsPW1ZG4+EFyTFeGnXK56zgiItIM3QeOocaGUbl9sesoAUMlTPzGmi/epnfNJvIHXkd4RITrOCIi0gzRsYnkRPQgoVAH5zeVSpj4Dfv5/ewhhWFnXO86ioiItEBh+0H0qFhPXW2d6ygBQSVM/MLmZZ8yqHIpm3pdSUxsnOs4IiLSAqbrcJJMCXlbNYRRU6iEiV8o/vAeim08g86+1XUUERFpoQ59xwGwa50Ozm8KlTBxLm/jMoYe/JxVGRfQrn2K6zgiItJC3fqPpNJGUpO7xHWUgKASJs7tnH0PlUTS52wNUSQiEsgiomLIiexJ+30rXUcJCCph4tTeHVsYuu89lnU8i9TOGa7jiIjIMdqfPIjMqo3U1NS4juL3VMLEqc1v30MYlowzfuI6ioiItIKIjBHEmwpyNi53HcXvqYSJM8X79zB452ssaXcS3Xr2cx1HRERaQcd+4wEoWLfAcRL/pxImzqx9817iTCUpp97pOoqIiLSSjN5DKbPR1O34ynUUv6cSJk5UlBbTb9tzfBUzjj6Dx7iOIyIirSQsIoLt0X1ILlrlOorfUwkTJ1a+/SBJHCTyhB+6jiIiIq2sOGUwmdWbqayscB3Fr6mESZurqaqk+/p/sTpyIIPGneo6joiItLKo7qOIMdVsX6fzhR2JSpi0uZXv/4vOdi/lY36AMcZ1HBERaWVpAzwH5xdu0MH5R6ISJm3K1tWSsvSfbA7LZMSU813HERERH0jrMYBi4jH5S11H8WsqYdKmVs95mR51OewZ+n3CwrX6iYgEIxMWRk5MPzoWr3Ydxa/pXVDajrVEzf8bO+jMyGlXu04jIiI+VNphCD1qtlNWVuI6it9SCZM2s3Hhe/StXse2ft8lKirKdRwREfGhmB6jiDS1bF/9pesofkslTNpM5Zx7KaQ9Q8+6yXUUERHxsfSBEwDYv2mh4yT+SyVM2kTOmgUMKl/Emu6XkpCQ6DqOiIj4WMeuPSkkiYidOjj/cFTCpE0UvncPJTaWgWff7jqKiIi0BWPIi+tPp5I1rpP4LZUw8bnd29Yy5MDHLOt8LikdO7mOIyIibaQidQjda/M4ULTPdRS/pBImPpc7625qiCDrrB+7jiIiIm0oLnMMYcaSu1onbW2MSpj41IE9uQze8w5LkqeR3i3LdRwREWlD3Qd5Ds4v3qKD8xujEiY+tfGtvxJBDZ2n3eE6ioiItLH2qensMqlE7V7mOopfUgkTnykr3ke/vJdYEn8CvfoNdR1HREQc2Bk/gLTSta5j+CWVMPGZtW/dTyLlxJ+kvWAiIqGqqtNQMuwu9hXsch3F76iEiU9UV5SSuelplkaNZODI41zHERERRxJ7jQEgZ/U8x0n8j0qY+MSq2Y/QgSLqJt7mOoqIiDjUfdBEAMq2LnKcxP+ohEmrq6uppvOqmawN78fwSWe6jiMiIg4ltO9AXlhXYgqWu47id1TCpNWt+ehputbt4sDImwgL1yomIhLqdidkk162Dmut6yh+Re+Q0rqsJX7Rg2w1GYw89VLXaURExA/Upg2jM4Xs2ZnjOopfUQmTVrXhi9fJqtlCXvZ1REZEuI4jIiJ+oH3vsQDsWP2F4yT+RSVMWpX97H520pFRZ17nOoqIiPiJHgPHUWsNFdsXu47iV1TCpNVsX/Yx/SpXsKHnlcTGxrqOIyIifiImvh254d2J37vCdRS/ohImrab4w79SZBMYevYtrqOIiIif2dt+IN0q1mPr6lxH8RsqYdIqdm5cyuCSL1iefhFJScmu44iIiJ+p6zKCFIrZsX2j6yh+QyVMWsXud/9MmY2m39k/dB1FRET8UIe+noPzd63VmfMPUQmTY7Z/xyYGFn7A4g5nk5aW7jqOiIj4oe4DRlNlw6nKWeI6it9QCZNjtvWde7BAtzN/7DqKiIj4qcjoWLZH9iRx30rXUfyGSpgck5L9uxiw83UWtTuFrJ79XMcRERE/VpQ0kMzK9dTW1rqO4hdUwuSYbHjzr0TbapJPucN1FBER8XMmfSSJppzcTatcR/ELKmHSYpWlRfTa9gKLYyeQPWS06zgiIuLnUvt5Ds7fs26+4yT+QSVMWmztOw/SnhIijr/ddRQREQkAGX1HUG6jqM3TwfmgEiYtVFdVQca6f7E8YgjDx5/sOo6IiASA8IhItkf1pv1+fRwJKmHSQqvff5yOdh/lY27FGOM6joiIBIjilEFkVW+iurrKdRTnVMKk2WxtDcnL/smGsJ6MmnKu6zgiIhJAwruNItZUsW3dUtdRnFMJk2ZbP/dFMmrz2D3k+0REhLuOIyIiASSt/3gA9m1Y4DiJeyph0jzWEjX/AXJIY/S0K12nERGRANO150AOEovdoYPzVcKkWbYumk3P6g1s7nsNMdFRruOIiEiAMWHh5ET3I+XAGtdRnFMJk2apnHMve2wyI8/+vusoIiISoEo6DCazZgsV5WWuozilEiZNlr96Hv3LlrCq+6W0S0hwHUdERAJUdI9RRJlatq1d5DqKUyph0mSF/72bYhvH4LNvcx1FREQCWNfsCQAUbfzScRK3VMKkSQq3r2Zg0VyWdD6P1NRU13FERCSApab3Zj/tMDu/ch3FKZUwaZLcd+6migh6nflj11FERCTAmbAwcmP7k1oc2gfnq4TJUR0syGHgnnf4MukMunfv4TqOiIgEgfKOQ+lRm0PJwQOuozijEiZHtfnNezBY0qbe4TqKiIgEidiskYQby/ZV811HcUYlTI6oongvffL+w5fxk+k3YLDrOCIiEiQysicCULwldL8hqRImR7T+7fuJp4L4KdoLJiIirSclrTt76EDErtAdQ1IlTA6rtrKUHhufZlHkaIaOnOA6joiIBJn8+P50LlnrOoYzKmFyWGtnP0wSxdROvA1jjOs4IiISZCo7DaO7zefAvgLXUZxQCZNG2ZoqUlfOZGX4AMYcf4brOCIiEoQSskYDsH3VPMdJ3FAJk0at/+gpOtftoWj4TYSFaS+YiIi0vm6DPAfnl2xd6DiJGyph8m11dcQvepBNpjtjTrvYdRoREQlS7VI6scOkEb1nuesoTqiEybdsmf8a3Wq2kzPgeqIjI1zHERGRILY7YQBdS0Pz4HyVMPkma7Gf3ssOUhlz1rWu04iISJCrThtOF/ZSsCvXdZQ2pxIm35C3/GN6Va5hXdZVJMTGuI4jIiJBrn0vz8H5eatD7+B8lTD5hoMf3kOhbcews29yHUVEREJA94HjqbOG8m2LXUdpcyph8rWCTUsYULKAZV0vokNysus4IiISAuISk8kNzyBu7wrXUdqcSph8bffsP1NiY+h31u2uo4iISAgpSMwmo3wdtq7OdZQ2pRImABTnb2BA4Qcs7DCdjK5dXccREZEQUtd1BB0pYlfeFtdR2pRKmACw7e27qSGc7mdooG4REWlbyX3GApC/JrQOzlcJE8r37aTfzjdZkHgqvXv1dR1HRERCTPfsMVTbcCpzQuvgfJUwYePbfyHS1pB8qvaCiYhI24uOiScnIpOEwpWuo7QplbAQV126n55bX2BBzCSGDBnpOo6IiISofe2z6VGxnrra0Dk4XyUsxK2f9XcSKCP8OH0jUkRE3DHpI2lvSsnbusZ1lDajEhbCbHU5XdY+yZKIYYyeMMV1HBERCWEd+o0DYPfa+Y6TtB2VsBC2/v2ZdLD7KRt9C2FhxnUcEREJYd36jqDCRlKTGzoH56uEharaGpKWPsxq04dxJ53jOo2IiIS4iKhotkf1ot3+Va6jtBmVsBC1+dPnSavdxe4hNxIZEe46joiICEVJg8is2khNdbXrKG1CJSwUWUvk/L+xla6MP/0K12lEREQAiMgYSbypJGfjMtdR2oRKWAjKXfwO3as2s6nPtcRGR7qOIyIiAkCn/uMBKFj/peMkbUMlLARVfvJXdtkURp11vesoIiIiX0vvNZhSG4PNW+I6SptQCQsxBRu+pHfZMlZkXEJyuwTXcURERL4WFhHB9ui+JB8IjYPzVcJCzO737+WgjSX7zFtcRxEREfmW4g6DyazeSmVluesoPqcSFkLKCrbTr/BDFiafQUaXNNdxREREviW620iiTTU5a4P/fGEqYSFk86z7CLN1pJ3yA9dRREREGpWWPQGAwo0LHSfxPZWwEFFbcZDMbS/zZcwkBg4c4jqOiIhIo9K696OIBMyOr1xH8TmVsBCx/t1/kkgZZsJNrqOIiIgclgkLIzemHx2LV7uO4nMqYaGgrpaUVf9iZVh/xhw31XUaERGRIyrtOJQetdspKz3oOopPqYSFgC2fv0xa7S72DrqWcA3ULSIifi62xygiTB3bVwf3SVtVwkKAnfcgeXRizDQNUSQiIv4v3XtwftEmlbAWM8ZMNcasN8ZsMsb8tJHp/Y0x840xlcaYO3yZJVTtXvM5vSpWsbbHZcTHRruOIyIiclQd07MoIJnwXctcR/GpCF/dsTEmHHgIOAXIAxYZY96y1q6pN9s+4FZghq9yhLq9H9xHrI1j8Jnfdx1FRESkyfLj+tP54JqjzxjAfLknbAywyVq7xVpbBbwITK8/g7V2j7V2EVDtwxwhq3jXZvrv+5jFHc4mLTXVdRwREZEmq+g0lG51OzhQtM91FJ/xZQlLB3LrXc7zXtdsxpjrjDGLjTGLCwoKWiVcKNg26z7qCCP9tNtcRxEREWmWuMzRhBlLzqp5rqP4jC9LWGNfw7MtuSNr7Uxr7Shr7ahU7dFpkuqyInrlvsqXccfTr98A13FERESapfugiQCUbAneM+f7soTlAd3qXc4A8n34+6Se9bMfIp5yoibd7DqKiIhIs7Xv2IWdphORe5a7juIzvixhi4A+xpgsY0wUcBHwlg9/n3jZ2mo6rXmS5eEDGTX+JNdxREREWmRn/AC6lK51HcNnfFbCrLU1wM3A+8Ba4GVr7WpjzA3GmBsAjDFpxpg84IfAL4wxecaYdr7KFCo2zX2BTnUF7B96HWE6OauIiASo6s7DSLe72bcnOD9I89kpKgCstbOB2Q2ue6Tez7vwfEwprcVawr98iBzSGDf1UtdpREREWiyx1xjYDLmr55PS6TzXcVqdzpgfZHasnEPPynVszLqCmKhI13FERERarPtAz5nzS7cG58H5Pt0TJm2v6KP7ibfxDDnrRtdRREREjklC+xRywtKJLVjhOopPaE9YEDmQt54BRZ+ypNO5pKakuI4jIiJyzPYkZpNevg5rW3SWK7+mEhZEts2+lxrCyJz2A9dRREREWkVd2nA6sY89+dtcR2l1KmFBovJgIX3y32RhwhR69ezjOo6IiEiraN97LAB5q4PvzPkqYUFi/awHiaOCuBNudR1FRESk1fQYOJYaG0bl9kWuo7Q6lbAgYGuq6LL+3yyNGMrw0ce5jiMiItJqYuISyYnoTkLhStdRWp1KWBBY//HTpNpCSoZfjzE6OauIiASXwnYD6VaxHltX5zpKq1IJC3TWErPoEbaSzphTL3CdRkREpPV1HUEyB8nftt51klalEhbgcpd9QGb1Rrb2uYroSJ2cVUREgk9yH8/B+fnr5jtO0rpUwgLcwY//xj6byPAzrncdRURExCd6ZI+mykZQk7PYdZRWpRIWwPZtX0P2wS9YlvYdkpPau44jIiLiE5FRMWyL7EnivuA6OF8lLIDlvPtXKm0kvU7XyVlFRCS4FSUNIrNyI7W1ta6jtBqVsABVcaCAfrveZmHiyfTokeU6joiIiE+FZYwgwZSTuzF4xpFUCQtQ6995gFiqaDdFe8FERCT4pfYdB8CeIDo4XyUsANVVVZCx6VmWRI5gyPBxruOIiIj4XEbf4ZTZaOp2fOU6SqtRCQtA6z56kg52P5Wjv6+Ts4qISEgIj4hge1Rvkvavch2l1aiEBRprSVjyKJtNd0ZPOdd1GhERkTZTnDKYzOpNVFdXuY7SKlTCAszWRbPoXrOV3H5XExkR7jqOiIhIm4noNooYU832tUtcR2kVKmEBpnzu39lr2zP8jOtcRxEREWlTaQM8x0EXbljgOEnrUAkLIAVblpNd+iUr0i+gfWKC6zgiIiJtqmvWQIqJh/ylrqO0CpWwALLj3XupsJH0O0OnpRARkdBjwsLIie5LyoHgODhfJSxAlO7byYCC2SxqP5X09G6u44iIiDhR0mEwmTXbqCgvdR3lmKmEBYgN7/yNaKpJOeU211FEREScie4xmkhTy7Y1C11HOWYqYQGgtrKMzC0vsChqDAMHj3IdR0RExJmu2eMBKNoY+Afnq4QFgLX/fZxkDlA39vuuo4iIiDjVKb0XhbQnbOcy11GOmUqYv7OW9stmssH0ZNTk6a7TiIiIOGXCwsiL7U/qwTWuoxwzlTA/t2n+G3SrzWXnwGsID9fiEhERKe84hO61uZQcLHId5ZjoXd3PVX/+D3aTzMjTv+s6ioiIiF+IyxpNuLFsXzXfdZRjohLmx3ZtWMyAsiWs6XYxCXFxruOIiIj4hYyBEwAo3hzY35BUCfNju96/jzIbzYAzb3UdRURExG+kdO7GLjoSuXuZ6yjHRCXMTx3cm8vAve+xOPl00jp3cR1HRETEr+yMH0BayVrXMY6JSpif2vT23winjrRTb3cdRURExO9UdhpKht1JUeEe11FaTCXMD1VXlNBz+4ssjh1P3+yhruOIiIj4ncSeYwDIWfWF4yQtpxLmh9a8N5P2lGDG3+w6ioiIiF/KGOQ5OL906yLHSVpOJczP2LpaOqx8nHVhfRg5aZrrOCIiIn6pfXIqeaYL0XuWu47SYiphfmbj56+SUbuDgsHXEqaTs4qIiBzW7sRsupatcx2jxfQu72fq5j3ITjoyatrVrqOIiIj4terOw0hjLwU7c1xHaRGVMD+yY818+lcsZ32PS4mNiXYdR0RExK8l9fYcnL9j9TzHSVpGJcyP7P3gPkpsDAPP0gH5IiIiR9N94DhqraF8W2AenK8S5icO7N5G9r6P+KrDWaR27OQ6joiIiN+LS0giN7wbcYUrXEdpEZUwP7HpnfsIo470qT90HUVERCRgFLQbSLfyddi6OtdRmk0lzA9Ulh2gT+4rLI47jl59s13HERERCRh1XYeTQjE7cze5jtJsKmF+YM3sR2hHKVHH3eI6ioiISEBJ6T0WgF1rA+/gfJUwx2xtDZ3XPMHq8AEMG3+K6zgiIiIBpXv2GKpsOBXbl7iO0mwqYY6tm/sSXet2UTTsOowxruOIiIgElOiYOLZHZtFuX+AdnK8S5lj4lw+RRydGnXaZ6ygiIiIBaX/7gXSv2EBdba3rKM2iEuZQzoq59K1czZaelxMdFeU6joiISEAyXUfQzpSRt2W16yjNohLm0P6P/kaxjWOQTs4qIiLSYh37jQNgz7r5jpM0j0qYI4U7NjGwaA7LOs0gJTnFdRwREZGA1a3fCMptFDW5gXVwvkqYI9tm3YvF0H3aba6jiIiIBLSIyCi2R/Wm/f5VrqM0i0qYAxUl++mX/zpLEk4gs2c/13FEREQC3oHkQfSo2kRNdZXrKE2mEubAmlkPkUA5cSfc6jqKiIhIUAjPGEGcqSRnwzLXUZpMJayN1dVU03Xdv1kZMYjBoye7jiMiIhIUOvcfD8De9QscJ2k6lbA2tubj50izeygdcb1OzioiItJK0nsN5qCNxe4InIPzVcLakrXELHqYXLow8tRLXKcREREJGmHh4eRE9yX5wBrXUZpMJawNbVn6Eb2r17Ot75VERkS4jiMiIhJUDnYYQmb1Fiory11HaRKVsDZU8skDHLDxDDnjRtdRREREgk5U95FEmRq2r17kOkqTqIS1kYLt6xhU/BnL086jffsk13FERESCTpfsCQDs2/Sl4yRNoxLWRra/ey81hNHr9NtdRxEREQlKad36sJ9EwvK/ch2lSVTC2kBp0V6yd73JV+1OIr1HT9dxREREgpIJCyM3pj8diwPj4HyVsDawdtY/iKOSdlNucx1FREQkqJV1HEL32hzKSotdRzkqlTAfq62uovumZ1geOYzs4RNdxxEREQlqMT1GEWHq2L7K/0/aqhLmY6s+eIpOtpCq0fpGpIiIiK9lDPTs8DiweaHjJEenEuZL1pL41aNsM+kMn/Id12lERESCXseuPdhDChG7lrqOclQqYT60cdH79KzZRF7/7xKhk7OKiIi0ify4/nQ66P8H56uE+VD53AfYRyLDzrjedRQREZGQUdFpGN1tPgeKCl1HOSKVMB/ZtWUlg0rms6brBSQkJLqOIyIiEjLiM0cBkLvqC8dJjkwlzEfy3r2PaiLofcZtrqOIiIiElO6DPQfnH9zi38MXqYT5wMH9uxm45x2+SjqFtPTuruOIiIiElPYd0thhOhO1e5nrKEekEuYD6975O7Gmig4na4giERERF3bFD6Br6VrXMY5IJayV1VSWk7X5OZZFj6Tv4DGu44iIiISk6rThdKGAwj07XEc5LJWwVrby/SfpyH5qx97sOoqIiEjISuzp2RGSt2qe4ySHpxLWimxdHUnLZ7IlrAfDT5jhOo6IiEjI6jFoPHXWULbNfw/OVwlrRRsWvENW7VZ2ZV9DWLieWhEREVcS2iWTG55BbMEK11EOS02hFVV99g/20p7hp3/PdRQREZGQtydxABnla7F1da6jNEolrJXs2LCUweULWdftImLj4lzHERERCXl1XYbTkSL27NzmOkqjVMJayc7376PCRtLvzB+4jiIiIiJA+95jAdjhpwfnq4S1ggMFOxi8912WpkwjtXO66zgiIiICZGaPpdqGU5njnwfnq4S1gvXvPEC0qabzaT90HUVERES8YuISyInoQXzhStdRGqUSdoyqKsrovf0FlsaMpWf/4a7jiIiISD2F7QfSvWK9Xx6crxJ2jFbOnkkKxYRN1MlZRURE/E7X4SRRQv42/xvCSCXsGNi6OlJXPc6msCyGTDzTdRwRERFpIKXPOAB2rp3vOMm3qYQdgzWfv073ulz2Dr4OE6anUkRExN/0GDCKShtJTc5i11G+Rc3hGNh5D7KHFIaffrXrKCIiItKIyKhotkX2pN3+Va6jfItKWAvlrPmSQRVfsTHzEqKjY13HERERkcMoShpEj8qN1NbUuI7yDSphLbTng/sps9Fkn6WTs4qIiPizsIwRxJsK8jYudx3lG1TCWqBwVw5D9v2X5R3PJLlDJ9dxRERE5Ag69RsPwO71Cxwn+SaVsBbYNOt+Iqij69TbXUcRERGRo8joM5RSG4PNW+I6yjeohDVTRdlB+uW+zLL4CfToM9h1HBERETmK8IgItkf3IanIvw7OVwlrppWzHiWJEqKOu9V1FBEREWmi4uRBZFZvoaqy0nWUr6mENUNdbS1pa59gfXgfBo491XUcERERaaLI7iOJNtXkrPOf84WphDXDqrkv061uB8XDrtfJWUVERAJI2oAJABRu8J+D89UkmiHiy4fZSUeGTb3SdRQRERFphq6ZAzhAPGbnUtdRvqYS1kRbVnxBduUKtva6nMjIKNdxREREpBlMWBg50f3ocGC16yhfUwlrov0f3U+pjWHgmbe4jiIiIiItUNJxCN1rtlNRVuI6CqAS1iQFO7YwpOhjVnSeTvvkDq7jiIiISAvE9BhNpKll25ovXUcBVMKaZPOs+wmjju7TfuQ6ioiIiLRQ12zPmfOLNqqEBYSykiKy819lWeLxpGf1cx1HREREWqhT1yz2kkT4rmWuowAqYUe16p2HaUcp8SdooG4REZFAZsLC2BHbn04H17iOAqiEHVFtTQ3p659ibeQA+o8+yXUcEREROUZlqUPpVptHSfF+11FUwo5kxccvkG53UzbiBtdRREREpBXEZY0mzFi2r5rvOopK2JHELnqYfNOZoSdf6jqKiIiItIJuAz1nzj+4xf3B+Sphh7FxySf0r15DTp8riIiMdB1HREREWkFKp3R2kkrE7uWuo6iEHU7xnAcotnEMOvMm11FERESkFe1MGECXEvcH5/u0hBljphpj1htjNhljftrIdGOM+bt3+gpjzAhf5mmqXTkbGFo8l9VdziGhXbLrOCIiItKKKjsNI93upmjvLqc5fFbCjDHhwEPANCAbuNgYk91gtmlAH++/64B/+ipPc2ybdR8WQ9YZP3QdRURERFpZYs/RAOSunuc0hy/3hI0BNllrt1hrq4AXgekN5pkOPG09FgBJxpguPsx0VAcP7GPQrjdY3u5E0rr1dhlFREREfKD7IM/B+SVbFzrN4csSlg7k1ruc572uufNgjLnOGLPYGLO4oKCg1YPWt3vbWorDEmk/RSdnFRERCUbtkjqyLjIb6mqd5ojw4X2bRq6zLZgHa+1MYCbAqFGjvjW9NfUeOpHagWsJj/DlUyMiIiIu9f+/4D5PWB7Qrd7lDCC/BfO0ORUwERER8TVflrBFQB9jTJYxJgq4CHirwTxvAVd4vyU5Djhgrd3pw0wiIiIifsFnu3ystTXGmJuB94Fw4Alr7WpjzA3e6Y8As4HTgU1AGXC1r/KIiIiI+BOffu5mrZ2Np2jVv+6Rej9bQGdDFRERkZCjM+aLiIiIOKASJiIiIuKASpiIiIiIAyphIiIiIg6ohImIiIg4oBImIiIi4oBKmIiIiIgDKmEiIiIiDqiEiYiIiDigEiYiIiLigEqYiIiIiAMqYSIiIiIOqISJiIiIOKASJiIiIuKASpiIiIiIAyphIiIiIg6ohImIiIg4oBImIiIi4oBKmIiIiIgDxlrrOkOzGGMKgO1t8Ks6Anvb4PdI02mZ+B8tE/+k5eJ/tEz8U1sslx7W2tTGJgRcCWsrxpjF1tpRrnPI/2iZ+B8tE/+k5eJ/tEz8k+vloo8jRURERBxQCRMRERFxQCXs8Ga6DiDfomXif7RM/JOWi//RMvFPTpeLjgkTERERcUB7wkREREQcUAkTERERcSCkS5gxZqoxZr0xZpMx5qeNTDfGmL97p68wxoxwkTPUNGG5XOpdHiuMMfOMMUNd5AwlR1sm9eYbbYypNcZ8py3zhaqmLBdjzGRjzDJjzGpjzNy2zhhqmrD9am+MedsYs9y7TK52kTOUGGOeMMbsMcasOsx0Z+/1IVvCjDHhwEPANCAbuNgYk91gtmlAH++/64B/tmnIENTE5bIVOMFaOwT4HTrg1aeauEwOzXc38H7bJgxNTVkuxpgk4GHgbGvtQOD8ts4ZSpr4WrkJWGOtHQpMBu41xkS1adDQ8xQw9QjTnb3Xh2wJA8YAm6y1W6y1VcCLwPQG80wHnrYeC4AkY0yXtg4aYo66XKy186y1+70XFwAZbZwx1DTltQJwC/AqsKctw4WwpiyXS4DXrLU5ANZaLRvfasoysUCiMcYACcA+oKZtY4YWa+2neJ7nw3H2Xh/KJSwdyK13Oc97XXPnkdbV3Of8GuBdnyaSoy4TY0w6cA7wSBvmCnVNea30BZKNMXOMMUuMMVe0WbrQ1JRl8iAwAMgHVgI/sNbWtU08OQxn7/URbfFL/JRp5LqG5+toyjzSupr8nBtjTsRTwib5NJE0ZZn8DfiJtbbW8we+tIGmLJcIYCRwEhALzDfGLLDWbvB1uBDVlGVyGrAMmAL0Aj4wxnxmrS32cTY5PGfv9aFcwvKAbvUuZ+D5y6S580jratJzbowZAjwOTLPWFrZRtlDVlGUyCnjRW8A6AqcbY2qstW+0ScLQ1NRt2F5rbSlQaoz5FBgKqIT5RlOWydXAn63nJJ2bjDFbgf7AwraJKI1w9l4fyh9HLgL6GGOyvAdFXgS81WCet4ArvN+cGAccsNbubOugIeaoy8UY0x14Dbhcf9G3iaMuE2ttlrU201qbCbwCfF8FzOeasg17EzjOGBNhjIkDxgJr2zhnKGnKMsnBs2cSY0xnoB+wpU1TSkPO3utDdk+YtbbGGHMznm9yhQNPWGtXG2Nu8E5/BJgNnA5sAsrw/AUjPtTE5fIroAPwsHfPS421dpSrzMGuictE2lhTlou1dq0x5j1gBVAHPG6tbfRr+nLsmvha+R3wlDFmJZ6PwX5ird3rLHQIMMa8gOebqB2NMXnAr4FIcP9er2GLRERERBwI5Y8jRURERJxRCRMRERFxQCVMRERExAGVMBEREREHVMJEREREHFAJExEREXFAJUxERETEAZUwEQlpxpjRxpgVxpgYY0y8MWa1MWaQ61wiEvx0slYRCXnGmN8DMXgGuc6z1v7JcSQRCQEqYSIS8rzj/C0CKoAJ1tpax5FEJATo40gREUgBEoBEPHvERER8TnvCRCTkGWPeAl4EsoAu1tqbHUcSkRAQ4TqAiIhLxpgrgBpr7fPGmHBgnjFmirX2Y9fZRCS4aU+YiIiIiAM6JkxERETEAZUwEREREQdUwkREREQcUAkTERERcUAlTERERMQBlTARERERB1TCRERERBz4fyU26442cDFiAAAAAElFTkSuQmCC\n",
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@@ -2309,31 +2303,29 @@
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\n",
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\n",
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\n",
       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
      "metadata": {
       "filenames": {
        "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_91_4.png"
-      },
-      "needs_background": "light"
+      }
      },
      "output_type": "display_data"
     }
@@ -3026,28 +3018,26 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0mlmb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m     \u001b[0mP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolve_pde_deep_neural_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_hidden_neurons\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlmb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;31m## Store the results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m\u001b[0m in \u001b[0;36msolve_pde_deep_neural_network\u001b[0;34m(x, t, num_neurons, num_iter, lmb)\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;31m# Let the update be done num_iter times\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mcost_grad\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mcost_function_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_hidden\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py\u001b[0m in \u001b[0;36mnary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0munary_operator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munary_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mnary_op_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnary_op_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnary_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnary_operator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py\u001b[0m in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0marguments\u001b[0m \u001b[0;32mas\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbut\u001b[0m \u001b[0mreturns\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgradient\u001b[0m \u001b[0minstead\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     should be scalar-valued. The gradient has the same type as the argument.\"\"\"\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0mvjp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_make_vjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mvspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mans\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         raise TypeError(\"Grad only applies to real scalar-output functions. \"\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py\u001b[0m in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_vjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mstart_node\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVJPNode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_root\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mend_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_node\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mtrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_node\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mend_node\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mvspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py\u001b[0m in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mstart_box\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_box\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mend_box\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_box\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_box\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mend_box\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trace\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mstart_box\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mend_box\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_box\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_node\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py\u001b[0m in \u001b[0;36munary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0msubargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubvals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msubargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0margnum\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m\u001b[0m in \u001b[0;36mcost_function\u001b[0;34m(P, x, t)\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0mg_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0mg_t_jacobian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_t_jacobian_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m             \u001b[0mg_t_hessian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_t_hessian_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0mg_t_dt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_t_jacobian\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py\u001b[0m in \u001b[0;36mnary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0munary_operator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munary_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mnary_op_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnary_op_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnary_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnary_operator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py\u001b[0m in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhessian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;34m\"Returns a function that computes the exact Hessian.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mjacobian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjacobian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0munary_to_nary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py\u001b[0m in \u001b[0;36mnary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0munary_operator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munary_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mnary_op_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnary_op_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnary_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnary_operator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py\u001b[0m in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0mjacobian_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mans_vspace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0mgrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvjp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mans_vspace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstandard_basis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjacobian_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0munary_to_nary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py\u001b[0m in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;31m# primitives defined in this file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'need at least one array to stack'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;31m# primitives defined in this file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'need at least one array to stack'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py\u001b[0m in \u001b[0;36mvjp\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mvspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0;32mdef\u001b[0m \u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mbackward_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mvjp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py\u001b[0m in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtoposort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_node\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0moutgrad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutgrads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mingrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutgrad\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mingrad\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mingrads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0moutgrads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_outgrads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutgrads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mingrad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py\u001b[0m in \u001b[0;36m\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     65\u001b[0m                     \"VJP of {} wrt argnum 0 not defined\".format(fun.__name__))\n\u001b[1;32m     66\u001b[0m             \u001b[0mvjp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvjpfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mL\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0margnum_0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margnum_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margnums\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py\u001b[0m in \u001b[0;36mmatmul_adjoint_1\u001b[0;34m(A, G, A_ndim, B_meta)\u001b[0m\n\u001b[1;32m    408\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# We need to swap the last two axes of A\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m         \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mA_ndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mA_ndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    411\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mB_is_vec\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py\u001b[0m in \u001b[0;36mf_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     42\u001b[0m             \u001b[0mparents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_node\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m     \u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mboxed_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0margnums\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margnum\u001b[0m    \u001b[0;32mfor\u001b[0m \u001b[0margnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m   \u001b[0;32min\u001b[0m \u001b[0mboxed_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m             \u001b[0mnode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf_wrapped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mnew_box\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py\u001b[0m in \u001b[0;36mf_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_raw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mf_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m         \u001b[0mboxed_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_constructor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_top_boxed_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mboxed_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m             \u001b[0margvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubvals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0margnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mboxed_args\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py\u001b[0m in \u001b[0;36mfind_top_boxed_args\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0mtop_node_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0margnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0misbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m             \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mtop_trace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m    140\u001b[0m num_iter \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m250\u001b[39m\n\u001b[1;32m    141\u001b[0m lmb \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.01\u001b[39m\n\u001b[0;32m--> 143\u001b[0m P \u001b[38;5;241m=\u001b[39m \u001b[43msolve_pde_deep_neural_network\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_hidden_neurons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_iter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlmb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;66;03m## Store the results\u001b[39;00m\n\u001b[1;32m    146\u001b[0m g_dnn_ag \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((Nx, Nt))\n",
+      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36msolve_pde_deep_neural_network\u001b[0;34m(x, t, num_neurons, num_iter, lmb)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;66;03m# Let the update be done num_iter times\u001b[39;00m\n\u001b[1;32m    119\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_iter):\n\u001b[0;32m--> 120\u001b[0m     cost_grad \u001b[38;5;241m=\u001b[39m  \u001b[43mcost_function_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mP\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(N_hidden\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m    123\u001b[0m         P[l] \u001b[38;5;241m=\u001b[39m P[l] \u001b[38;5;241m-\u001b[39m lmb \u001b[38;5;241m*\u001b[39m cost_grad[l]\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;124;03m    Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;124;03m    positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;124;03m    arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;124;03m    should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m     27\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     28\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36mcost_function\u001b[0;34m(P, x, t)\u001b[0m\n\u001b[1;32m     78\u001b[0m g_t \u001b[38;5;241m=\u001b[39m g_trial(point,P)\n\u001b[1;32m     79\u001b[0m g_t_jacobian \u001b[38;5;241m=\u001b[39m g_t_jacobian_func(point,P)\n\u001b[0;32m---> 80\u001b[0m g_t_hessian \u001b[38;5;241m=\u001b[39m \u001b[43mg_t_hessian_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43mP\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     82\u001b[0m g_t_dt \u001b[38;5;241m=\u001b[39m g_t_jacobian[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     83\u001b[0m g_t_d2x \u001b[38;5;241m=\u001b[39m g_t_hessian[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:78\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m     77\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturns a function that computes the exact Hessian.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 78\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:61\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     59\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m     60\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(vjp, ans_vspace\u001b[38;5;241m.\u001b[39mstandard_basis())\n\u001b[0;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m)\u001b[49m, jacobian_shape)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m     arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m     arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp..vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m toposort(end_node):\n\u001b[1;32m     20\u001b[0m     outgrad \u001b[38;5;241m=\u001b[39m outgrads\u001b[38;5;241m.\u001b[39mpop(node)\n\u001b[0;32m---> 21\u001b[0m     ingrads \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrad\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[1;32m     23\u001b[0m         outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(outgrads\u001b[38;5;241m.\u001b[39mget(parent), ingrad)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m     65\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m     66\u001b[0m     vjp \u001b[38;5;241m=\u001b[39m vjpfun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 67\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m,)\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m     69\u001b[0m     argnum_0, argnum_1 \u001b[38;5;241m=\u001b[39m argnums\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:82\u001b[0m, in \u001b[0;36m\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     80\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlog10,  \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m x \u001b[38;5;241m/\u001b[39m anp\u001b[38;5;241m.\u001b[39mlog(\u001b[38;5;241m10\u001b[39m))\n\u001b[1;32m     81\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlog1p,  \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m (x \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m---> 82\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39msin,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcos\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     83\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mcos,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39m g \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39msin(x))\n\u001b[1;32m     84\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mtan,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m anp\u001b[38;5;241m.\u001b[39mcos(x) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:37\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f_raw)\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf_wrapped\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 37\u001b[0m     boxed_args, trace, node_constructor \u001b[38;5;241m=\u001b[39m \u001b[43mfind_top_boxed_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m boxed_args:\n\u001b[1;32m     39\u001b[0m         argvals \u001b[38;5;241m=\u001b[39m subvals(args, [(argnum, box\u001b[38;5;241m.\u001b[39m_value) \u001b[38;5;28;01mfor\u001b[39;00m argnum, box \u001b[38;5;129;01min\u001b[39;00m boxed_args])\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:70\u001b[0m, in \u001b[0;36mfind_top_boxed_args\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m     68\u001b[0m top_node_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m argnum, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(args):\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43misbox\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m     71\u001b[0m         trace \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_trace\n\u001b[1;32m     72\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m trace \u001b[38;5;241m>\u001b[39m top_trace:\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11_50_2.png b/doc/LectureNotes/_build/jupyter_execute/chapter11_50_2.png
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