-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathindex.html
583 lines (537 loc) · 821 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge,IE=9,chrome=1"><meta name="generator" content="MATLAB 2022b"><title>Quantization Aware Training for Transfer Learned MobileNet-v2</title><style type="text/css">.rtcContent { padding: 30px; } .S0 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left; }
.S1 { margin: 2px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left; }
.S2 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 20px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 20px; font-weight: 700; text-align: left; }
.CodeBlock { background-color: #F5F5F5; margin: 10px 0 10px 0; }
.S3 { border-left: 0.994318px solid rgb(191, 191, 191); border-right: 0.994318px solid rgb(191, 191, 191); border-top: 0.994318px solid rgb(191, 191, 191); border-bottom: 0.994318px solid rgb(191, 191, 191); border-radius: 4px 4px 0px 0px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }
.S4 { color: rgb(33, 33, 33); padding: 10px 0px 6px 17px; background: rgb(255, 255, 255) none repeat scroll 0% 0% / auto padding-box border-box; font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; overflow-x: hidden; line-height: 17.234px; }
/* Styling that is common to warnings and errors is in diagnosticOutput.css */.embeddedOutputsErrorElement { min-height: 18px; max-height: 550px;}
.embeddedOutputsErrorElement .diagnosticMessage-errorType { overflow: auto;}
.embeddedOutputsErrorElement.inlineElement {}
.embeddedOutputsErrorElement.rightPaneElement {}
/* Styling that is common to warnings and errors is in diagnosticOutput.css */.embeddedOutputsWarningElement { min-height: 18px; max-height: 550px;}
.embeddedOutputsWarningElement .diagnosticMessage-warningType { overflow: auto;}
.embeddedOutputsWarningElement.inlineElement {}
.embeddedOutputsWarningElement.rightPaneElement {}
/* Copyright 2015-2019 The MathWorks, Inc. *//* In this file, styles are not scoped to rtcContainer since they could be in the Dojo Tooltip */.diagnosticMessage-wrapper { font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 12px;}
.diagnosticMessage-wrapper.diagnosticMessage-warningType { color: rgb(255,100,0);}
.diagnosticMessage-wrapper.diagnosticMessage-warningType a { color: rgb(255,100,0); text-decoration: underline;}
.diagnosticMessage-wrapper.diagnosticMessage-errorType { color: rgb(230,0,0);}
.diagnosticMessage-wrapper.diagnosticMessage-errorType a { color: rgb(230,0,0); text-decoration: underline;}
.diagnosticMessage-wrapper .diagnosticMessage-messagePart,.diagnosticMessage-wrapper .diagnosticMessage-causePart { white-space: pre-wrap;}
.diagnosticMessage-wrapper .diagnosticMessage-stackPart { white-space: pre;}
.embeddedOutputsTextElement,.embeddedOutputsVariableStringElement { white-space: pre; word-wrap: initial; min-height: 18px; max-height: 550px;}
.embeddedOutputsTextElement .textElement,.embeddedOutputsVariableStringElement .textElement { overflow: auto;}
.textElement,.rtcDataTipElement .textElement { padding-top: 2px;}
.embeddedOutputsTextElement.inlineElement,.embeddedOutputsVariableStringElement.inlineElement {}
.inlineElement .textElement {}
.embeddedOutputsTextElement.rightPaneElement,.embeddedOutputsVariableStringElement.rightPaneElement { min-height: 16px;}
.rightPaneElement .textElement { padding-top: 2px; padding-left: 9px;}
.S5 { border-left: 0.994318px solid rgb(191, 191, 191); border-right: 0.994318px solid rgb(191, 191, 191); border-top: 0.994318px solid rgb(191, 191, 191); border-bottom: 0px none rgb(33, 33, 33); border-radius: 0px; padding: 6px 45px 0px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }
.S6 { border-left: 0.994318px solid rgb(191, 191, 191); border-right: 0.994318px solid rgb(191, 191, 191); border-top: 0px none rgb(33, 33, 33); border-bottom: 0px none rgb(33, 33, 33); border-radius: 0px; padding: 0px 45px 0px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }
.S7 { border-left: 0.994318px solid rgb(191, 191, 191); border-right: 0.994318px solid rgb(191, 191, 191); border-top: 0px none rgb(33, 33, 33); border-bottom: 0.994318px solid rgb(191, 191, 191); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }
.S8 { margin: 10px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left; }
.embeddedOutputsMatrixElement,.eoOutputWrapper .matrixElement { min-height: 18px; box-sizing: border-box;}
.embeddedOutputsMatrixElement .matrixElement,.eoOutputWrapper .matrixElement,.rtcDataTipElement .matrixElement { position: relative;}
.matrixElement .variableValue,.rtcDataTipElement .matrixElement .variableValue { white-space: pre; display: inline-block; vertical-align: top; overflow: hidden;}
.embeddedOutputsMatrixElement.inlineElement {}
.embeddedOutputsMatrixElement.inlineElement .topHeaderWrapper { display: none;}
.embeddedOutputsMatrixElement.inlineElement .veTable .body { padding-top: 0 !important; max-height: 100px;}
.inlineElement .matrixElement { max-height: 300px;}
.embeddedOutputsMatrixElement.rightPaneElement {}
.rightPaneElement .matrixElement,.rtcDataTipElement .matrixElement { overflow: hidden; padding-left: 9px;}
.rightPaneElement .matrixElement { margin-bottom: -1px;}
.embeddedOutputsMatrixElement .matrixElement .valueContainer,.eoOutputWrapper .matrixElement .valueContainer,.rtcDataTipElement .matrixElement .valueContainer { white-space: nowrap; margin-bottom: 3px;}
.embeddedOutputsMatrixElement .matrixElement .valueContainer .horizontalEllipsis.hide,.embeddedOutputsMatrixElement .matrixElement .verticalEllipsis.hide,.eoOutputWrapper .matrixElement .valueContainer .horizontalEllipsis.hide,.eoOutputWrapper .matrixElement .verticalEllipsis.hide,.rtcDataTipElement .matrixElement .valueContainer .horizontalEllipsis.hide,.rtcDataTipElement .matrixElement .verticalEllipsis.hide { display: none;}
.embeddedOutputsVariableMatrixElement .matrixElement .valueContainer.hideEllipses .verticalEllipsis, .embeddedOutputsVariableMatrixElement .matrixElement .valueContainer.hideEllipses .horizontalEllipsis { display:none;}
.embeddedOutputsMatrixElement .matrixElement .valueContainer .horizontalEllipsis,.eoOutputWrapper .matrixElement .valueContainer .horizontalEllipsis { margin-bottom: -3px;}
.eoOutputWrapper .embeddedOutputsVariableMatrixElement .matrixElement .valueContainer { cursor: default !important;}
.embeddedOutputsVariableElement { white-space: pre-wrap; word-wrap: break-word; min-height: 18px; max-height: 250px; overflow: auto;}
.variableElement {}
.embeddedOutputsVariableElement.inlineElement {}
.inlineElement .variableElement {}
.embeddedOutputsVariableElement.rightPaneElement { min-height: 16px;}
.rightPaneElement .variableElement { padding-top: 2px; padding-left: 9px;}
.outputsOnRight .embeddedOutputsVariableElement.rightPaneElement .eoOutputContent { /* Remove extra space allocated for navigation border */ margin-top: 0; margin-bottom: 0;}
.variableNameElement { margin-bottom: 3px; display: inline-block;}
/* * Ellipses as base64 for HTML export. */.matrixElement .horizontalEllipsis,.rtcDataTipElement .matrixElement .horizontalEllipsis { display: inline-block; margin-top: 3px; /* base64 encoded version of images-liveeditor/HEllipsis.png */ width: 30px; height: 12px; background-repeat: no-repeat; background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB0AAAAJCAYAAADO1CeCAAAAJUlEQVR42mP4//8/A70xw0i29BUDFPxnAEtTW37wWDqakIa4pQDvOOG89lHX2gAAAABJRU5ErkJggg==");}
.matrixElement .verticalEllipsis,.textElement .verticalEllipsis,.rtcDataTipElement .matrixElement .verticalEllipsis,.rtcDataTipElement .textElement .verticalEllipsis { margin-left: 35px; /* base64 encoded version of images-liveeditor/VEllipsis.png */ width: 12px; height: 30px; background-repeat: no-repeat; background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAZCAYAAAAIcL+IAAAALklEQVR42mP4//8/AzGYgWyFMECMwv8QddRS+P//KyimlmcGUOFoOI6GI/UVAgDnd8Dd4+NCwgAAAABJRU5ErkJggg==");}
.S9 { border-left: 0.994318px solid rgb(191, 191, 191); border-right: 0.994318px solid rgb(191, 191, 191); border-top: 0.994318px solid rgb(191, 191, 191); border-bottom: 0.994318px solid rgb(191, 191, 191); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }
.S10 { border-left: 0.994318px solid rgb(191, 191, 191); border-right: 0.994318px solid rgb(191, 191, 191); border-top: 0.994318px solid rgb(191, 191, 191); border-bottom: 0px none rgb(33, 33, 33); border-radius: 4px 4px 0px 0px; padding: 6px 45px 0px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }
.S11 { border-left: 0.994318px solid rgb(191, 191, 191); border-right: 0.994318px solid rgb(191, 191, 191); border-top: 0px none rgb(33, 33, 33); border-bottom: 0.994318px solid rgb(191, 191, 191); border-radius: 0px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }
.S12 { margin: 10px 0px 20px; padding-left: 0px; font-family: Helvetica, Arial, sans-serif; font-size: 14px; }
.S13 { margin-left: 56px; line-height: 21px; min-height: 0px; text-align: left; white-space: pre-wrap; }
.S14 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 18px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 17px; font-weight: 700; text-align: left; }
.S15 { margin: 10px 10px 5px 4px; padding: 0px; line-height: 18px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 15px; font-weight: 700; text-align: left; }</style></head><body><div class = rtcContent><h1 class = 'S0' id = 'T_5E1BFFD0' ><span>Quantization Aware Training for Transfer Learned MobileNet-v2</span></h1><div class = 'S1'><span>This example shows how to perform quantization aware training for transfer learned MobileNet-v2 network.</span></div><div class = 'S1'><span>Low precision types like </span><span style=' font-family: monospace;'>int8 </span><span>propagate quantization error that may degrade the accuracy of the network. Quantization aware training is a method that introduces quantization error at training, thus giving the network the ability to adapt and ultimately produce a network more robust to quantization. In most cases, a quantized network or integer-arithmetic only network constructed after quantization aware training can produce accuracy on par with the original floating point network.</span></div><div class = 'S1'><span>This example takes you through the quantization workflow of a transfer learned MobileNet-v2 network. MobileNet-v2 was chosen for this example because it contains depthwise-separable convolution layers that are especially sensitive to post-training quantization.</span></div><div class = 'S1'><span>The flowchart below highlights the steps necessary to convert a trained network into a quantized one via quantization aware training.</span></div><div class = 'S1'><img class = "imageNode" src = "data:image/png;base64,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" width = "292" height = "522" alt = "qat_workflow.png" style = "vertical-align: baseline; width: 292px; height: 522px;"></img></div><div class = 'S1'><span></span></div><h2 class = 'S2' id = 'H_FCCD6D67' ><span>Load Flower Dataset</span></h2><div class = 'S1'><span>Download the flower dataset [</span><a href = "#M_2AA33B76"><span>1</span></a><span>] using the supporting function </span><a href = "#H_D08A0BC5"><span style=' font-family: monospace;'>downloadFlowerDataset</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S3'><span style="white-space: pre"><span >imageFolder = downloadFlowerDataset;</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsTextElement" uid="26772109" prevent-scroll="true" data-testid="output_0" style="width: 938.026px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" data-width="908" data-height="16" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">Downloading Flower Dataset (218 MB)...</div></div></div></div><div class="inlineWrapper"><div class = 'S5'><span style="white-space: pre"><span >imds = imageDatastore(imageFolder, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > IncludeSubfolders=true, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span > LabelSource=</span><span style="color: rgb(167, 9, 245);">"foldernames"</span><span >);</span></span></div></div></div><div class = 'S8'><span>Inspect the classes of the data.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S3'><span style="white-space: pre"><span >classes = string(categories(imds.Labels))</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsTextMatrixElement embeddedOutputsVariableMatrixElement" uid="669818DB" prevent-scroll="true" data-testid="output_1" data-width="908" style="width: 938.026px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement" style="width: 908px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">classes = </span><span class="veVariableValueSummary headerElement" style="white-space: normal; font-style: normal; color: rgb(179, 179, 179); font-size: 12px;">5×1 string</span></div></div><div class="valueContainer" data-layout="{"totalRows":"5","totalColumns":"1"}" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 82px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">"daisy" <br>"dandelion" <br>"roses" <br>"sunflowers" <br>"tulips" <br></div><div class="horizontalEllipsis hide" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="verticalEllipsis hide" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><h2 class = 'S2' id = 'H_A091A87E' ><span>Perform Transfer Learning on MobileNet-v2</span></h2><div class = 'S1'><span>MobileNet-v2 is a convolutional netural network 53 layers deep. The pretrained version of the network is trained on more than a million images from the ImageNet database.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S9'><span style="white-space: pre"><span >net = mobilenetv2;</span></span></div></div></div><div class = 'S8'><span>Split the data into training and validation sets and create augmented image datastores that automatically resize the images to the input size of the network.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span >[imdsTrain,imdsValidation] = splitEachLabel(imds,0.9);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >inputSize = net.Layers(1).InputSize;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >augimdsTrain = augmentedImageDatastore(inputSize,imdsTrain);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >augimdsValidation = augmentedImageDatastore(inputSize,imdsValidation);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span >validationActualLabels = imdsValidation.Labels;</span></span></div></div></div><div class = 'S8'><span>Set aside a portion of the training dataset to use during the calibration step of quantization. This datastore should be representative of the data used for training but ideally separate from the one used to validate.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S9'><span style="white-space: pre"><span >augimdsCalibration = subset(shuffle(augimdsTrain),1:320);</span></span></div></div></div><div class = 'S8'><span>Perform </span><a href = "#H_C9FB9125"><span>transfer learning</span></a><span> on the network on the flowers image dataset. The learnable parameters of the trained network </span><span style=' font-family: monospace;'>transferNet</span><span> are in </span><span style=' font-family: monospace;'>single</span><span> precision.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S3'><span style="white-space: pre"><span >transferNet = createFlowerNetwork(net,augimdsTrain,augimdsValidation,classes);</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="41BDF570" prevent-scroll="true" data-testid="output_2" style="width: 938.026px;"><div class="figureElement eoOutputContent"><img class="figureImage figureContainingNode" src="data:image/png;base64,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" style="width: 987px; padding-bottom: 0px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport"></div><div class="outputLayer activeOutputDecorationLayer doNotExport"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport"></div></div></div></div></div><h2 class = 'S2' id = 'H_1B4CE57B' ><span>Evaluate Baseline Network Performance</span></h2><div class = 'S1'><span>Evaluate the performance of the </span><span style=' font-family: monospace;'>single</span><span> precision network. Performance in this case is defined as the correct classification rate.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S3'><span style="white-space: pre"><span >netCCR = evaluateModelAccuracy(transferNet,augimdsValidation,validationActualLabels) </span></span></div><div class = 'S4'><div class='variableElement' style='font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 12px; '>netCCR = 0.9101</div></div></div></div><div class = 'S8'><span>Quantize the network using the </span><a href = "#H_138C14B1"><span style=' font-family: monospace;'>createQuantizedNetwork</span></a><span> function provided at the end of this example and evaluate the performance of the quantized network. Post-training quantization of the original network yields poor performance due to the range of learnable values in the depthwise separable convolution layers. An accuracy of roughly 20% is the equivalent to guessing one of the 5 possible labels for each image.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span >originalQuantizedNet = createQuantizedNetwork(transferNet,augimdsCalibration);</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S11'><span style="white-space: pre"><span >originalQuantizedCCR = evaluateModelAccuracy(originalQuantizedNet,augimdsValidation,validationActualLabels)</span></span></div><div class = 'S4'><div class='variableElement' style='font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 12px; '>originalQuantizedCCR = 0.2452</div></div></div><div class="inlineWrapper"><div class = 'S5'><span style="white-space: pre"><span >bar( </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > categorical([</span><span style="color: rgb(167, 9, 245);">"Original network"</span><span >,</span><span style="color: rgb(167, 9, 245);">"Post-training quantized network"</span><span >]), </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > [netCCR,originalQuantizedCCR] </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > )</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S11'><span style="white-space: pre"><span >ylabel(</span><span style="color: rgb(167, 9, 245);">"Network Accuracy (%)"</span><span >)</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="50F59A12" prevent-scroll="true" data-testid="output_5" style="width: 938.026px;"><div class="figureElement eoOutputContent"><img class="figureImage figureContainingNode" src="data:image/png;base64,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" style="width: 616px; padding-bottom: 0px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport"></div><div class="outputLayer activeOutputDecorationLayer doNotExport"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport"></div></div></div></div></div><h2 class = 'S2' id = 'H_07814550' ><span>Replace Network Layers with Quantization Aware Training Layers</span></h2><div class = 'S1'><span>Replace the </span><span style=' font-family: monospace;'>Convolution2D</span><span> and </span><span style=' font-family: monospace;'>GroupedConvolution2D</span><span> layers along with their adjacent </span><span style=' font-family: monospace;'>BatchNormalization</span><span> layers with custom layers that are quantization aware using the </span><a href = "#H_157F752C"><span style=' font-family: monospace;'>makeQuantizationAwareLayers</span></a><span> function provided with this example. The quantization aware layers are </span><a href = "./QuantizedConvolutionBatchNormTrainingLayer.m"><span>custom layers</span></a><span> that have modified </span><span style=' font-family: monospace;'>forward</span><span> and </span><span style=' font-family: monospace;'>predict</span><span> behavior that inject quantization error similar to that of post-training quantization. The quantization error comes from the </span><a href = "./quantizeToFloat.m"><span>quantizeToFloat</span></a><span> function that quantizes, then unquantizes a given value using best-precision scaling to </span><span style=' font-family: monospace;'>int8</span><span> precision. </span></div><div class = 'S1'><span></span></div><div class = 'S1'><span>Quantization to float can be expressed as follows.</span></div><div class = 'S1'><span> </span><span mathmlencoding="<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mtable columnalign="left"><mtr><mtd><mrow><mi>𝑥</mi><mi>̂</mi><mo>=</mo><mi mathvariant="normal">quantizeToFloat</mi><mo>(</mo><mi mathvariant="normal">𝑥</mi><mo>)</mo></mrow></mtd></mtr><mtr><mtd><mrow><mtext> </mtext><mtext> </mtext><mtext> </mtext><mo>=</mo><mi mathvariant="normal">unquantize</mi><mo>(</mo><mi mathvariant="normal">quantize</mi><mo>(</mo><mi>𝑥</mi><mo>)</mo><mo>)</mo></mrow></mtd></mtr><mtr><mtd><mrow><mtext> </mtext><mtext> </mtext><mtext> </mtext><mo>=</mo><mi mathvariant="normal">rescale</mi><mo>⋅</mo><mi mathvariant="normal">saturate</mi><mo>(</mo><mi mathvariant="normal">round</mi><mo>(</mo><mfrac><mrow><mi>𝑥</mi></mrow><mrow><mi mathvariant="normal">scale</mi></mrow></mfrac><mo>)</mo><mo>)</mo></mrow></mtd></mtr></mtable></mrow></math>" style="vertical-align:-34px"><img src="data:image/png;base64,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" width="230.5" height="80" /></span></div><div class = 'S1'><span>The quantization step uses a non-differentiable operation </span><span style=' font-family: monospace;'>round</span><span> that would normally break the training workflow by zeroing out the gradients. During quantization aware training, bypass the gradient calculations for non-differentiable operations using an identity function. The diagram below [</span><a href = "#M_2AA33B76"><span>2</span></a><span>] shows how the custom layer calculates the gradients for non-differentiable operations with the identity function via straight-through estimation.</span></div><div class = 'S1'><img class = "imageNode" src = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABI0AAAIYCAYAAADgsxz+AACAAElEQVR42uydB3gUVReGpfdepBcBidJFEBAsFEFRmgUEFQSs+Kv03hREwALSQXpvCkoLkEKH0HuRHnoNKL2cf7+7mcn27GY32fa9z3OeJDNzZ+/MZmbOfPecc58QQgghhBBCCCGEEEIseIKngBBCCCGEEEIIIYRYQtGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhBBCCCGEWEHRiBBCCCGEEEIIIYRYQdGIEEIIIYQQQgghhFhB0YgQQgghhPgUd+7ckcOHD8vNmzcD4nhOnDghe/bskUePHiXJ5z1+/Fidu/v37/OfiRBCiFtQNHIRPOzxAH748KHN9ViO9XhY2wLrXH2AY5///fef3Lt3z+5+bQHn5M233pRRo0YlmZNCCCGEkLgX9wcPHsjJkyfl77//lqlTp8rChQvln3/+8auXefgho0ePlrFjxiRZv6dNmybJkyeXLl262N1m+/btsn79erl7967VOggma9askUOHDtlse+7cOYmMjJTLly+75ANOmjRJfv31V+WTuUL58uXV8Vy6dClR/9/g+3388ceSJ08eSZ8+vWTOnFmqVq0q69atc8mHJIQQQjQoGrkIHIyXX35ZunXrZtOZ+Pzzz9X61atXW62PiYmRBg0aKLMnOtliypQp6sH/xRdfuPTADw0NlWTJkslHH31E0YgQQghJQvC8PnjwoDRq1EhSpUolTzzxhIklk9q1a8vOnTt96kX+woULsm3bNvn333/Nlh87dkxSp04tmTNlUoJXUgDfB+eqY8eOdrcpXry4pDSc22XLllmtG/TjINW+SJEicuvWLav1TZs2VT7S5EmTnO5TdHS0ZM+RXYk/+/btc+l4ypYto/oTn2iE/4czZ84oQcxWvx21mzFjhmTNkkXy5skrw4cNUwLSvHnz5Mknn5S0adOqfRJCCCGuQtHIRTZu3KgevDlz5rR6mCP0OFu2bMopaNu2rVXblStXKscRo02ugEihTAZHDSNHFI0IIYQQ3wcCQ9GiRZVPULJkSenbt69MnDhR+vXrJyVKlNAFDVfFh8SkXbt2ShBBNJQpiOSBL4H1iHxOCpwRjdq0aaO26WpjIO+1unXVunTp0smGDRvM1iFKKG++vGpAbu/evU73Ce2+/PJL5Y+5mjbnrGgEf+2DDz6QFClS2ByAtAeiqnA8uXLlUgOcpgwcOFB9dtNmzXhhEkIIcRmKRi6CHPvChQtLqpSp1APaFDhZKVOmVA/mYsWKWYUu9+7dW63r1rWbS59548YNiYqKUiHUFI0IIYQQ3+ebb75Rz/xq1arJ0aNHzdZhkOm5555T699//309+hjPePxu61mP57it5fA1rl69qqKZba3HMs0HwO/Xr1+3G8ECQQSi0YIFC2x+fkJ9CXwefBlHPgzWQZDS+oZUvvhEI/QT21SuXNksghvHmDt3bj2ya/DgwWbttmzZoiKnnn32WZU+aHmcaG8v/cz0fNrqP9raiiYvW7asLhphW0Rz3b592+Z5btGihRKNVq1a5dT5xb6wf/h8P/74o01/EN9rSMkQpqgRQghxGYpGCaBVq1bqwf/dd9+ZLW/fvr1aniZNGjXaYxoGDCegevXqar3pCBAcC4QPz5kzR8aMGaNGleD8mXLx4kX566+/VF6+6cMey5csWaIikZD7D6cAFhERoZwuU9EI+1yxYoXaFsvhXDoCn4PRz+HDh6vRPtQwwDFgRG7s2LEybNgw5fQSQgIT3ANYQJWQhIF6OZkyZ1ZRLpZRH9r1hehj+AT58+fXn6eoe/Tiiy/K7NmzzbbfunWrvPTSS/LTTz/p4hHaIHqpxks1pFSpUko0eOedd6xSkNp3aC9NmjSR3bt3y6effiplypSRSpUqybfffivnz59X22Bfb775puTNm1f1CdsgfQ4RNRBBwFtvvSWNGzfW/YHu3btL7Vq11HamtmjRIv2zkX6HfVR6/nkpV76cvP322yqVzFRUwbEg5a1169byvGE79A11jIYOHRqvaATfBuJPjhw55MiRI/rysLAw5Ys9/fTTah+vvfaamdCD84jlOAcaGBREJNjrr7+ujh/fQ4f2HVSqmCnNm7dQx6mJSug/zvmHH34oz1V8TrXFd6WdjwkTJhhFozLGSCNErH/22WdKNHzhhReka9eucuXKFbUNCn/XrlVbF7ywDfaB0geOUtVQZwmiENLQrl27ZrUevmUKw3rs11b9J0IIIYSikYeBwIOHOZwCTcSBE/X885UkQ4aM0qxZM7UegosGimDmzpVbsmfPrhwTgBGpnj17qmUQd9AG6WtVqlQxC5fGSBrWf/311/rnIfKodOnSyknQRtLwOwz727Vrly4awSmpWLGiXlMBP9944w27IdJwrOCwFixYUDlj2OfqsNVKcELYM/aB/W7evJn/DIQEKLhn/fLLLzwRhCQARIjgOVmuXDm74isGb5566ilJnz6D/jwd8dtvNgelli5dqvbXsmVL9YyGYPHuu++q5zMEI03owDaFChVStYk04KtAQMEzPa3hJ4SZrFmzqm0R5QQRAYLL889X1FPsCxQooPYLoUgTIZAmj9R8RMegD5988olKu9NMi7QeMmSI2j48PFwJYvAjatasKfXq1ZOMGTOqwszwo0wFNtQm0j4X4hf8FK0vjkQj9AN1JHEeTIW2AQMGqLYYjEO/0Q+kC2rUr19fkhnWYzAN4HzCx0JfcZ4aNmwoFSpUMGyTTPlkpucT0eYpU6TQRRzUrUJ0OdpCnKpbt67xXCR7QooVL6bfR7VII/hRGTJkUH4Z+oa+f/rZp+r/BIN1pUqXkixZsqht8VmIhoIYaC8tEOcAwhK2x6CmLVDXCN83jo2RRoQQQigaJQFIE4NDA+dJG4HC6BAcgVdffVU9nPHwxqidxp9//qmcCDz4tVHCsePGqvBjjCTBIYTQg3oBaAsHQBsN0vb31Vdf6eHPtWrXklSpU0n//v2VwzJixAhJkTKF6tMff/yhttFEI7TFLBoQfTCKVrhIYbXcVNQyBQ5clReqqAgj1F5A+1defUU5tzgOiGL58uWTs2fP8p+BkAAFqTV4gdMiEQghzvP777+rZycifOyBASQIOtgOEUZKNDI8y50RjRCpM27cOFm8eLGeSgVxB+JM8mTmAor2GXhuQ8hBLR5EJEOUyZwls/IhsE+IWKgRhM9Bahj2i201kcFUNALwMxDpA0MkFIQpRPwgehrrVF+SJ5fJkyerNugj/BNsV6lyJT2CqVfPXrrfA2EH/UCbTJkzxSsaoW8//PCD2g7RO9oy+Ej47NOnT8trrxlFHERmA/QXEVWZMmbUhR9EgyFCHMIbBDSkrKF/EOZwPoYN+9VMNEphIhr9MND4+ShBgO8UvluXLl3Vsv6G71GLSNJEI9SxgkiIc4s+4bxisA/+JL4H9E8TBOFz4XtF+pk9sQdRYjlz5VT7ht9nC/w/YX2N6jV4cRJCCKFolBTgwY2wZTghWhg2ooHwgO/Tp3dsuHQa9cKFFDKgpa4hTFmrKYBIIYwmIWpIcwbgcMCxwEgcnDBT0QiCEraDWANnD6NU2gwncDQQUQTnQyuqqYlGEIwQlo62MIQxY39wSuzVP0DtAeyzR48ealuMimnTtcKBOXXqlEszwBFC/Adc37iX4P6BEXtCiGtoL+m2JsXQgJhQ7/XX1XYQaVwRjbRntSWoZ4P2SO2yFI3gE5hGRyPyB8vxbNdwVNPIUjTSgC+A6GX0DwWX8Rk7duxQ2yNKxzRCBkIJlsHHwWAbgC+EgTjTos84Rk0MciQaAaTAaSl1+CwMfCGyCH9D/EHkk+l+8DkQrkwH9jQfDUKc6XmFuKb5S/ZEI5QAwDbr16/Xt4EIiGWff/6FvkwTjVBPSQMCU9WqVdVyzXdztaYR0v1w/iB62avDhAgoy3Q8QgghhKJRIqPNRIHReDgYiALS6hXBgXrllVdUqDJCn+EUIBwcD3WkfGgvZXBaChUqrEScWbNmKZs5c6YSpOC0Lf5rsdp2/vz5yhnTIo0gGkFsQui0NnsHPrNcufLKEdOmw42radTSLJcfzhz2X6tWLYe57fismrVqquPq2asnv3RCggBc9//73//0tFekM5imdRBC4ge1/+KLNIL4giLZ2A4ROOA3J9PTAAQR1MdBmjtEBohAWg2fQYMGWYlGmkijoYkd2LfGF198ESsazbchGmW0Eo1wv0AEMwQO1CLS0u8hZGDf8EnKlimrBBMYhBwMQmHdtm3blHiVLWs2yZwpkx55pOHM7GkAg2c4bnwWagvB94H/hfsYzhVEJZw7+GHouxZBPWb0aP08NmjQQC3DbHdaX2HaLHfwy7TzXrhQIXW8mhiGulJapBH2j3Pwxeefq2W//vqrlWh0KXYwUUP7bHyXCRGNRo0cqdojesuWkHj23DnJkT27+l61dDxCCCGEolESsG37NvWQhnBz7fo15Yw8mTu3iizCQ1vLp0ehyP3790uOnDmkapUqurOFkSash1MAAcjSEOINp0uJRvPmq7x6TTTC6BZEqRQpUsqXX7aT5cuXq1EyOEWYQUSrn2Bv9jSMZqE4J1LpNAfPFiqEO09e1UcWvSYkOEBqBlIlNNFIe4FlHQxCnAeRJnj+li9Xzmp2Lg0UP0bhYjyPN23aFCsaGSONkHruSDTCPhFNhIhnRPSgdlKdOnWkZEhJp0UjFJ62LxpZRxplzJRRclmIRsePH1c1lCDSmEbQaLUfITqjblLz5s3N7EvD56BOEIQirc6RZQSTs6IRBs0++aStUQgaO0YXcTCjLcBnoOYSIrQRdQ3/CceozWgHP0irCYSUOsu+QsAxjUDSIo000QjCTooUySW5wRA1BPEM+yr5dEk1QGglGlnUk2zSpLFavmHDhgSJRlpUG4qU27pPQ4hMHlvrytZsbYQQQghFo0QCwg1GpPByhfQxiDyvv/6GHrkD5wkOHkYRJ0+ZrH6HgKQ90FFDAMtQ4BDbogaAqWG9Jv5YRhohPQwOIkbr4GxBUEJYMoQrRBFp2BONIGKlS5c+XtEIIevYLz6LEBL44P6iTRNuarjXmb78EEIcA4FGSxkyTf8yvdZQtwfXV0hIiKqVCCBOYBkGgkxBJBKWa6IRhAcILailiDpFWrq4lt7mjmgEvwF+jSWWkUZIhUJ/NGHHVLDAMUPQQkSzIz8DxwLhDOcJM8SaokVrxScaaX6SFtkFUQj702pO4txgBjesR5QR6hmVL19e7xf6jZnPsN5y1jpbFC4UJxph3zgHEO4g9GDgDqUCUE7A8nzbF42a2BSNIGzZq1FkCupT2hON8Flly5W1inoihBBCKBolAXiow+GCc4VcfjyQTWt/oJAjih3CoatRo4baznTaXRSXzZkzl6o3ZOkoWTIPzpCJaIQQZs15xOjkkr//Vrn0+ExTHItG8Ucaac4npnolhAQ+EKvxAmcpGsG+//57RhsR4oKPUL16dWPx4RrV9bRxTaRYu3atqrsDYQCRMRoofAxBAnV/tEEoRPpCCDEVjRAxjL8xqKNth8EszJ7ljmgEgQbL8PyPTzRCNBJ8iWefecZKCEEEEuo6YnALRZ9N7x2IksLgl7YMEVLwVXCP0XyVAwcOKGHHWdEIojYG8SCkYRZbRPyYTlGP1H+tGHjKlCnUPk39Isxwpk1ggpqOpt8V0t9MI3RMaxpBOHqx+osqQhx1qRDJBJ/u5MlTVrOduSIaoSA5lk2fPj3eY0cxbQiUzxi+B9OoNvxfoIaRcQDzRb2cASGEEELRKAnRwq+1qe7hBJo6RRgpwsMa6zBSf9Ekjx0jdB/G1hNAgUU4SHBK4EhhP5gmVitoqEcaxRbCRn4+6iFhylwIVhhBg6CEopEYcdTauSsaIaQc/cNoKCEk8MG9BlNs48UJ020j5QRTeWPZ1//7H186CHEBDOogChnPUUwpj+c0UonwTNaWZ8maxayAMqJjcufOrfyGpk2bqjQ11AFKYfjbVDRCahsGpfAsRxQzxIUmjZsoMQPbwR9IiGikRfeElCyphKeePXrqIoppIWwIJi/VqKG2RWRN586dpUuXLtLZYNOmTVN9RL+SxaZF4ThQt3HYsGGqhs97771nJj5hu0yZMqtzhGigEobzhfuPs6IRfBmIT5rI3b59BzOhCqJdNoPPpK23TPvCeQ95JkSdP/hVo0ePVkJTt27dpOLzzxv8vbk2RSMcp7EcgdHXg1+G769gwUJS6flKBl9urB41Hp9opNU0Aj8azr0mCuJ70Ool2QLfDyLNsf3IkSNV1BoGAFDTCb4ixDf8TdGfEEIIRSMvgJEtjGrhQV24cBGrKei1WcpgmKbesq4BHuKaEwHnD4UcIS4hrBrT22uOmiYaabOnYdSr2ovG4pkIs8bIGZw5/I22KIqJ7dwRjTBChtQ57BP9JIQEPtoMizCMvOMFkUIRIQm/npCmVad2HUmbNq1Z5B6Eh+eee07/HQNF2jMf4krOnDn0bZG+BhEI/sbHH7dWz3MYav6YRgYicqlDhw7quT948GC9H3jWY5lptBP45JNPVLsVy5fryyA4QATS9oki1hCogCaIwG8wFSosTZuVDNHPvXr1tKqRBn8HM7NqPhEGuiAqwS/BelW0uuLzSgDBss6duzh1rlHjSRvIMxXCAAQe1CtSU94bzrelcIP2mLEW2yBqR+srxCH4aVoUEIB/lipVSiXioO9I6UWfGzVqrKKlIJZBGINfhu99eez5hXiD7bRURA0MHOKzEJFl6l+iZqbWD3wn2my5tti3b79KBUTJAvw/4Rzju4JwdPr0aQpGhBBCKBp5CzgLP//8swoHh3NjKQohBQ1CD0bz7NU0QCFIvJwhnx6FGDHihBEl1CbSHvK7du1S+4B4hGUIgYYjg20hVMEBwc+5c+cqBwWFJ/HZCJFu07aNGoE0dRiwDtPqIr/dXoFOFMGG+ITRQ0fRSISQwISikWf5fdd0abXkKzlw5TBPRpAJRxAXkK61Zs0aNZiD1DIIMXjOIv0bIgXq/yAFDUKL5hsgAgm+A7aDv4Ei9RA7tOc5hCM8z7ENopqwDtthoMc0XR3+AaKMLJ/38B3gJ1jOhgZBCD4IIpcxc6I26HTs2DF1HJqwjPZYb2norwZq/iDKGrOaQTzBjGk4Ni36RgN9g6i1cuVKJZ6g/ziWw4b+IQLbGSAM4TixH8up57X+Iqob58N0IM0UDJhhllsce1hYmNofzoepDwVBB5+BZTgnSMGDwKOJa5p/2LVrV7PSBTg3+A61+lMaOB/4Hkxns8W+UcAb5ysiIkLOnTsXr/CD40d/tIgrpKbZ8/EIIYQQikZJ7BDC+bD3MI9vvQa2gRMFZ8JyW9N9aLWUtJlBTLeFg4G8eow2wimy99nO9okQQtGIopFnGL51rDRY0Fz2XNrPk0HMxJKw8DCpVbu2Sgm1jIAhvg0itzGIh2gwRPTAt3r46KH6Hl9++WUVWYRSBknpk2pR7ugTREUsW716tfIRCSGEkIRA0cgPQX47HBE4BIhwQhFEOAkvvviichQ+aNGCghAhxC0QiUjRyHNQNCLxgYEjPrv9C9wfUfcNKXEZM2ZQBcxfqPKCSk2DmNSwQQOHaWWJAQQr1NCCP4gIdpQoQIqgad1NQgghxBUoGvkhGC1q27atmnkN+f4oFgkHBX+jRoFlvjwhhLgKRSPPQtGIkMAE6XgonI2SAdWqVVN1kRARPm36tCQXjACER6TWId0Rs/jWr19fzd5LQZIQQkhCoWjkp+Dhj7x61EhADYCoqCirQtyEEJJQdNHoBkUjT6CJRrspGhFCkgCkyqHOEcUiQggh7kLRiBBCiBUUjTwLRSNCCCGEEOKPUDQihBBihSYaadOAE/cYvnWcNFjQgulphBBCCCHEr6BoRAghxAqKRp6FohEhhBBCCPFHKBoRQgixYtiwYRSNPAgLYRNCCCGEEH+EopEPgGl279y5k2jFChN7/4SQwIOikWehaESClXv37sndu3e99vkPHz5UPhB+EkIIIcR1KBp5GTgydevVlVy5csmlS5fUMog7cLCwztIePHigt8XMGKbb4Xcss6RN69aSJk0aOXDgAE84IcQpKBp5FmN6GkUjElxgyvlSz5aSokWf0pfBj8GsXqb+DPweTVyyNcCFZfBzMAhmy/+xZ/iMP/74Q1KmTCm//PILB88IIYSQBEDRyMtERkZK+vTp5e2331YOE4C489prr0nNmjWtrG/fvnrb5cuXS506dQzLa6l1+P2DDz6QmTNnys2bcTMeLZi/QFKkSCGff/EFTzghxCkoGnkWRhqRYAT+CASbb775Vl+2cOFCqVKlisyYMUNfduzYMalXr57yfebNm2cl7ly8eFH5OZ06dVJ/b9myxa6fZGo//fSTnD17Vp588kkpU6aMXLlyhV8KIYQQ4iIUjbwIRsratm0ryZIlk8WLF+vL4TA98cQTkilTJilevLiZffb55/p2vXr3Vtvlzp1bQkJCpGjRopIqVSpJnjy5vPPOO3Lt2jW13X///SeFCxdWL4B0mAghTokcw4dTNPLk+aRoRIIMpIPVrl1b+SR79uzRl3///ffKd/nuu+/0ZYsWLVLLYIUKF5Kj//xjtq8TJ06odTVq1FB/r169WkqWLCnFihVTlidPHrU+Q8YM+jJYly5dVLQRfC0Mnv3555/8YgghhBAXoWjkRS5cuCAlSpSQggULypkzZ/TlGJmD89O0WVM1umZqphFEvXr1Utv169dPCUQYTZszZ47kyJ7d4KSlkHHjxunbtmnTRjlMc+fM5YknhMTLMIpGnj2fFI1IkHHo0CF1D3n22Wf1SGqgi0b9++vL5s+fr5ZBYMLPb7/91ix97eTJk2aiEdLU4BPBj4JNnjxZrW/QsIG+DIb0OKCJUh9/3MpmGj8hhBBC7EPRyItEREQoB+mtt97S8/SBJhq1atXKYXtNNBo8eLC+DM5Q+/bt1fLmzZvry+fOnWvcZ8tWPPGEkHjRIo1iYmJ4MjwARSMSbEyaNElFUrdr185MqLEVaaSJRi+//LJkzpRZsmXLJjt37tTXa6JR9erVbX4WIoiw/r333rO5Hu1zP5lbRSdBbCKEEEKI81A08iJDhgxVTk7v3r3NlrsjGmkve1iOFDWN48ePq2WlSpXiDCKEkHihaOTh86lEoxaym6IRCRK++OIL5XdAPDJFE43624g0+vrrr6X1x63V7++//74eoeS8aNTU5npEab9QpYqaFGTv3r38cgghhBAXoGjkJTDqhqLVcHJmzZplts4d0QgOFiKMsLxnz576cghFGdKnV7O0nT59ml8AIcQhFI08fD7V7GktGGlEggL4HC+++KLyRTZt2mS2bsCAAXZFow4dOihRB75KqpQpZc2aNWq9ZXqaJfFFGsHnatq0qdoGdSMJIYQQ4jwUjbwEpoqtW7eucmAi10SardNEo0yZM6maR6Y2bdo0fTtNNII4hHpGmE3kq6++UilvcLh27Nihb4uZSFBIO3PmLLJr1y5+AYQQh1A08vD5ZHoaCSIQ2VO6dBnlo1gOVMUnGsFf6datmy4SYTDMXdEIaKn7v/32G78gQgghxAUoGnkJzGhWrVo1NRUtxB5TNNEoa9asKp3M1EyjkjTRyNQgGBUpUkRmz55ttk84YRUqVJD06dPLhg0b+AUQQhxC0cjD55OiEQkiUDeoeInikjp1alWQ2hRdNPrOWjSCsAMwOchTTz2lRwZZzp5miTOiUZ8+fdQ2SI8jhBBCiPNQNPISmmiUIkVK2bx5s9k6V9PTEAL+zTffKEcIYpHpTGwaEI3Kly8vGTJkkI0bN/ILIIQ4RBONrl+/zpPhifMZKxqxphEJBjTRKFXqVFaikaNC2Ig00q+ZYcPUTLCYfe3gwYPGmkZuiEaoH4ltBgwcwC+IEEIIcQGKRl7izp07Uue1OsqBCQ8PN1vnbiFsW0A0eqroU5I5c2azGUkIIcQWFI08fD4pGpEgAhGKpUuXVj4KooRMiS89TePGjRtStmxZNQNbjx493I40wuAatsG9jRBCCCHOQ9HIS6BIZLNmzZQDY1qnCLgqGv3ohGh0//59SZc+vXoJPHXqFL8AQohDUPcjR44cFI08BAthk2ACPkfVqlWVj7Ju3Tqzdc5GGoEFCxZI8uTJJFu2bE7Onma/EPa7776rtpkzZw6/IEIIIcQFKBp5EW20DQUfTUmMSKOjR4+qbUNCQuTBgwc8+YQQh1A08iwUjUiw0aZNG+V3jB833mz597G+jzOiEcSnV15+Wa/bmND0NBTmrly5sqqxxMlACCGEENegaORFVqxYoZycevXqqdlBNBJDNEIBbWz7/vvv88STJAVRdUeOHJGwsDAJDQ1V6ZGm/+/+AKLzJk6cKIcPHw6a7w2iEdPTPAcLYZNgY8yYMcrv+OSTT1Skj8YAB6KRVgjbFNRhTJU6tVr/0ksv2fys+EQjpMjhflasWDE5d+4cvxxCCCHEBSgaeREUrMZMZ3nz5jWbknbv3r3Srl07FZbtiFWrVkm7r9pZhX7botXHH0uy5MmsUuEISSxQR2vZsmVSsWJFNWtfqlSplKVNm1b930+YMEFt40tcvnzZpkjSsWNHVVfjY8N1FCww0sizGEUjRhqR4GHPnj0qrQxCjelAQWRkpPJxIiIi9GUHDhyQr776SpYtXWq1HwhOC/9YqASlP/74w+ZnHTp0SLU3nWHWFLTTBs4wkEEIIYQQ56Fo5EXgCH344Yf6lLKJ9QKNmdry5cunnDfMaEJIUjB16lRJnjy5MtS2QCFTRMe98sorkiJFCiXC4G/TEWhvgqKrELWKFy9utQ7XZ6VKldQxBQsjRoygaORBjOlpjDQiwQPEGRSuho+zdetWr/YDkdsYOENEEyGEEEJcg6KRl8HMaenSpZMGDRrI3bt3E+Uz5syZrV7SP/vsM55wkiRcvXpVCZX4v+vcubPZ/zYc+IEDByoxKWPGTLJlyxZ9HYRTW+KpttxyHf7GviGMolaXrfWm7fDZt2/ftilUxcRcV6LRU089ZfOYEipu4bPxmfZGt037h/od9vqX1Gii0bVr1/gP7QGYnkaCEUQ3pzQ8B7788kuv9SE6OlqlppUpU0ZFkxJCCCHENSgaeRm8IL7zzjvy7LPPJpozg2lmCxUqJPv3H+AJJ0kC6mwhkuiFF15QQoglEEm0EeguXbqoZRBVOnXuJB988IFVzaMhQ4aotIKzZ8+qvyGqREVFSadOnaRu3bry4osvSsOGDWXKlCnqmtI4duyYtGjeXObOnatSIpobfn/55ZelZcuWEhEergs506dPV7Uw0OeMGTOq9bClsakSa9asUev//vtv9ffJkydVkVdtu49afqR+fvvtt0rAAjiGxYsWSevWrVV0Fa7zYcOGWUXuIE0DxfCjorZKkyZNVP9QP8nbUDTyLBSNSDCCAtS4PyNS01vgvl2gQAF1TyOEEEKI61A08gGQFoOX4cTKs8dLHws/kqQEkXMQhEaPHm13m3Hjxqlt6tevr/6GyFKiRAm1zFT4AbVq1VLLUSMDoH5FpkyZVK2kUqVKSfny5SVDhgwqemnw4CF6u82bNyshqGTJkpIlSxbJlSuXFC5cWO0LI8/r169X23Xr3l29VGA5oqMgssK0l4yxY8eqdX379lV/796927CfQpI/f35l2bNn1/d5/vx5JZT17NlT1W/CZ0I8Qx0n9A/nxlQ4evLJJ5U4U7BgQbV91qxZ5dNPP/X6d0jRyLMMo2hEghRv+yB4niDayN8mYCCEEEJ8BYpGhBCPU7p0aSWQIELHHoj8gdBSrlw5JbK4Ihoh0qd1m9aydu1a9UKC0WzMOpgmTRpVdFVDE43QtsFbb8m+fftU22bNmqlliBYCEG6xLmXKlEq8wWxpMC1qyFI0Qn+x/vjx48qQ+on1jRo1UuIv+gVRKyQkRLZt26aODbP31KxZU32GaW0kiEZomydPHjUDEArhY7Y5b0PRyLNQNCKEEEIIIf4IRSNCiEe5c+eOEkAQsQMBxB47d+5UYkmJEsUlJibGoWhUs6a5aGSrvhFqG0GAQY0w9AFootFzzz1nVgQey7Xpm1ELCaAP9moaWYpGpqBPiDTKlj2bHDx4UC37+uuv1fZjxo4x6+eiRYvUchRl1UCfca5mzJjhU7PJjRgxkqKRB9FmT9tN0YgQQgghhPgRFI0IIR4F9YaKFi2qIo02bNhgdztEIUFAeSYkRIlEEH3sRhrVrGkmGgEIQ8uWLZMBAwaoukAff/yxEowQbfTvv/+qbTTRqHHjxmbpn4g2wnaVnn9ejyZKiGiEPtR7vZ5a9/PPPyvRByLUG2+8oZZVrlxJ/a4ZZpHD8tq1a+vFriEaIc1O67OvMHIkRSNPYpw9rQUjjQghhBBCiF9B0YgQ4nFefuklJdbMmjXL7jbjx49XAsqrr76qxBbTSKNbt26ZbVuzlrloBIEHKWtI9YL4A5EKKXHa3/GJRqdPn1Y1kJ53QzRCn1GXCZ9ZuXJlfYY49L169epq+woVKqjC1pY2dOhQPaoIohH6YnnM3oaikWdhIWxCCCGEEOKPUDQihHictm3bGusINWhgc/p4CDjVqlVT2/Tp00ctQ4QOClZjGYpJa6A9hCVT0QizqeHvN15/3azAKopNK9Ho5k31tyYaNWrcKF7RCMWpIRpBgLJME7MlGh0+fFgVwcZ+tm7dqi9HW4hU2F6bfc0RFI2CA4pGhBBCCCHEH6FoRAjxOMuWLlUROGnTpVXT2Wt1gwAiijTRB7ONmdY90iJ0Fi9erP6GYAThRZudTBONUBMIf0+ePFlvi0LTmKksoaIRRJtUqVIrEUdbpmEpGiEtrXnz5mpZjx49rGY+7N27t1rXsmVLvb4SgKCEv1FIW8PXRaOrV6/69f/i7fu3Zd3pTTJi2wQTGy8jtht/H2m6fLvRRm6fYLG9+20/X9FBiUasaUQIIYQQQvwJikaEEI8Dkei9995Twgnq9UDkgfACIaJJkyaq8DPWvfvuu3paF+jVq5dajhnQkPqFKCQIRqiPZCoa/fDDD+pviExhYWEyY/p0KVOmtFqW0PQ0UKhQISV2de3aVcaMGaP2DSxFI8z8hs+Boc/Dhw9XhuM7c+aMikLCLGypUqdSUVd///23REREqG3q1asny5cv1z+TolHiEnFqnRJrXLMWCWjjuO2wqLESejxcrtxm5BYhhBBCCPEfKBoRQhIFpHuhQLU2pbxmEGVKly6lF63GdPUQWRBVBMEF09JrIhHSxerXr68Mf2tRSUhJg2CkbZcyRQqpUqWKVKpUWe1TE4K2bNkiKVIkl7fffttKNMqUKZNUqlzJTDQaNmyY6hf2mcxgQwYPVsvHjTPWX+rfv7/6e8GCBUqMMj0urb8QhBBRtGTJEilXrpwukMHQXwhiKAKukTdvXsmYMaNV8W9vM2rUqIASjX7aMlJ2XNjjtO10YVtn2l65fZU3BUIIIYQQ4ndQNCKEJBoQav755x9ZumSJTJo0SebMmSObNm2Sq1evyPz581U0DsSUfPnyydy5c1Ub1NCBqILtw8PD5ebNm3Ls2DH1uxaNA1EG261evVqmTJmitr9y5YocOnRI/a7VUULEEdodPHjQrE4RopvWrl0rO3bsMKu5hAipqKitMnPmTFm0aJFcvnxZLb948aKsWrVKoqOj1d/oU1RUlIpkMjUsu3Hjhr4/tNu4caNMmzZNpekhQgnCmOln4nysW7fOZu0nb6KJRjiv/owmGk3fN48XJCGEEEIIIS5C0YgQ4hUg4kAMQjQSaht99913PCk+RKBFGlE0Iv4MhG4I4Bs2bFBid7AZJhtAFCfE+WA8fnzvq1attqqfRwghhFA0sgPSOBo2bCh16tRR9VGCzXDslStVkjfrvxmUx//aa6+pacvpPAUG+B4vXbqkprwnvicaMdKIEO9z/PhxFZmJ9FnUQAs2Q208pP/iZzAeP773nDlzmU2sQAghhFA0cgBSRgoWKCAVK1WVt9/7wMRamJit5c5s4/ttX6/fSFKkSCmv1qobFMdruc1zz78gWbNmpWhESCIyevRoikaE+AgXLlxQhfp79PlBVkZuj7M1283/dsX8qO3EaQslfYYMMnrCzKA4Xsu2X37dWUoUL242cQQhhBBC0cgBKFxbtGhRGfTTKPnn9A35J/qG+U97Fu1gmR+1jdy0V9KlzyAL/goLiuO1bDtg8G9SoEABikaEUDSKl3CKRiQAwHUIv+fXkRMD77l/Ov62oeFbJVOmzLJo+VqX27rzub7Stnf/wfLMM8/IvXv3eDEQQgihaJQg0chVB+a0G86PD7SN3BgrGi0OD4rjtTSKRoRQNKJoRIJeNAqU574TtiIiVjRatjZB7RP6ub7Stnf/IRISQtGIEEIIRSPPikYBbEbRKL0sWBwWlMdP0YgQikbOwvQ0ErCiURBZqLuikZ8bRSNCCCEUjTwlGvn5SJJropET6WkBOupG0YgQikauiUYtKBoRikZ+/NzXRaPla4MmuojpaYQQQigaeVo0CsAcdqcijYLgeC3bUjQiJClEozGMNCLEF0WjaDeECD9t65JoFADHS9GIEEIIRaPEEI0cPYij3Rjx8cG2umhkK9LIUdtoNz7Xh9pSNCIk8RkzhqIRIT4pGgXhc9+splEQHK/leopGgcHjx4/l9OnTsnbtWpk3b55MnDhRxo8fT6PRfNQmT54ss2bNkpUrV8o///wjt27domgUsKJRINY02uSB9DTWNCKEOCEaXb58maIRIb4sGgWBrQj6mkYUjfyV+/fvqxfOb9u3l2LFihn+jzNJmjRpJEWKFPLEE0/QaDQft+TJk0vq1KklY8aMUqRIEWncuLHykc+ePUvRKHBEoxg3HtK+29Z+IezAPF6KRoRQNKJoRIIZXIdwVikaUTQivg8iim7fvi0zZ86U0qVLS7JkyZSlTp1GipYIkaov15F3PvxMWrXrLG2+7k6j0XzUWn7RUZp93E5ee+tdKVmqnGTImEmSJzcKvhCAu3TposQjXPMUjXxZNBoa5LOnMdKIngkhiSgaZc+RnaIRIf4mGgXgpCAORaMgmASFopH/gMiiZcuWSdWqVdWLZZo0aaXc81Wl28DfZE7oNllz4DKNRvNTWxZ1XAaMmCp1GzaVrNlyqGs8f/78MnToULl58yZFI58VjRKSnhYAzoVLolEAOlMUjQhJItEoO0UjQnyBuPS0SfE/K6PdeM76aNsV4W6KRn5+rnr3H0LRyE/eTzp37qxeJFOlTi3lK1WToRPmSfje83zhptECzGYuj5K33v1IMmbOoq752rVry6FDhygaBYxoFABCisuRRgEmHFE0IiTxGTt2LEUjQnxZNAqi574uGtmbPS3Ao40oGvk+Fy9elEaNGqmXx7z5C0mvIWNl1c5ovlzTaAFsYXvOy+hZy+X5qi+raz8kJEQiIiLk0aNHFI18XjSKduMh7SdtzUSjIDhey3UUjQhJOtHo0qVLFI0I8RHRaJizolGA1jRaHLQ1jSga+TLnzp2TypUrG18aS1eQyYvW8IWaRgsi+2vDYXnr3Q8lZcpUkjtXLlm6dClFI78XjQJgZM44e5qTolEAjkQOpGhESJKJRow0IsR3RCOnC2EHmG8QGp9oxEgj4iWuX78ub775pip0XaFydVm6+Shfomm0ILTQ7afkk297SerUadV76p49eyga+bRoZO9hHO3CA9rH29pMTwvg47U0ikaEJJ1oxEgjQvxENIp2Q8Tw8bZ6IWzT9LQAPl6KRv4Bvo9OnTqpKblDylSQvzYe4cszjRbEtmrnGTXbWjLDPQHRh0hbpWjkC6KRy7OnBcaU9M7XNAqM46VoREjSM27cuIASjWZQNCKBLhoF8HPfpmgUwMdrUzQKCaFo5GMsXrxYUqdOLU/mLSBT/1of9/K434kXzP0uLmdbtmVbv2i7fOsJqfpSHZWu2rJlS3n8+DFFI6+LRlqkUbQbtXL8sK0xPS1D/KJRgByvZVvWNCKEopGzhDPSiASTaOSPz/3T8bcNja8QdiJ9rq+07d1/MCONfAykpVWsWFFFGf04ZhajLGg0mm4LI/dKvgKFlagcFRVF0chnRCNXHZjTbjg/PtDWGGmUQRYsDg+K4+XsaYRQNKJoRCgaWYhGgfLcd6EQ9qKEFsL285pHiDQKCaFo5CsgcmDUqFEqkqDaK3VVSorDiIT9CXz5ZFu2ZVu/bftNj0FKVH7rrbfkwYMHFI18VjQKYNPT0xaHBeXxUzQihKKRs7CmEQlY0SiILNRd0SgAZk+jaOQ7xMTESLFixSR9hkwyasZSRlbQaDQrWx51XIqWeEZSpUoly1esoGjkU6JRkMwkpkca/RUWFMdr2dYXRaOdh6/K/NUn5dGjx/SmSECJRv5exM8oGrWgaETMuHD1toycd0j+vX0/eEQjP37uh0a4kJ4WgD6hv6en3fjvvoyce1D+vXU/IO4fqGWEKKOqL9eRsN3nLF4Wr7jxosm2bMu2gdS2+8AR6l7RqFEjikY+IxoFYA67U5FGQXC8lm19UTRaveWcvN01Uo6eucm3MULRyOdEI0YaEXNOnvtXmvVYK39EnPI/0SjaDSHCT9u6JBoFwPEGmmh0JeauNOwYLosiT/n9vePRo0fSokULlXbSc/BoRlTQaDS7tmTTP5I6TVrJmzevnDt3jqKRT4hGQTQFrcPZ0xy1jXbjc32ora+KRm91CJd+43fxbYxQNKJoRPxENGrea61cvn7Hv0SjIHzum9U0CoLjtVwfCKIRfKRPBmyUi1fv+PW9A99B0aeKSo6cuWV++G4PRinQaLRAtOqv1pO0adNKZGQkRSOfFo0CsabRJg+kp7GmkWdFo6hz8lb7MGnUMVzW7rzANzJC0YiiEfED0QgvsmMXHvZv0SgIbEXQ1zQKANGofbg0MFxvYxYekgcPH/ntvePo0aOSLFkyebpUWYncf4kvxTQazaF93qmvSlH76aefKBr5rmgU4/KD+YihzeFT1+Xwyevq94Q94GP0/Rw5FePifmJcS09z83it+nwy9tgNfXd+fzE2z586/nj3ERNQohGcos8HbZLbd/y/Sj4JbsaPH0/RiAQsu/+5Jk26RCjR6O0ukXLg+HWf7u/ly5elSJEiXhONDp28JktXbZZlq7eoZ3swiEbwXw4cuyx/h26QqbMWyR9LImT73lPKr6NolBDRyOgjvdst0vA/dMNv7x1Lly5VL4ANmrZK+Ivk/stsy7ZsGyRtf/59vrpntGnThqKR10SjoZ6NNDp04qrMmLtE6r7RUGrWriebdx5N0H72H70sv4yYKK/UrCvvt/hYdh08mzizp3ko0ghO0Yx5S+X9D9pIxUpVpELFylLPcA6+/3GY7Dp01inRC07k6nU75dtOPaX6yzWlXIWKUuOV2tL2s69l7ZYDbgpafiIaGV4+lFNk+DlrxXE1JSshfi8aXaBoRAIP00gjWI+ROwzPFN+NfnBJNPJgIWYMIC0P2yI1XqqlnN6MmTJLxMY9SV482qFolAifu+/IRWnfqZfkzp1HHbdmqVKllpatP5edB88kabHsQIk00nyk3mN3yt17/jkD7siRI9X/wmcdezOKgkajxWszl29R94xatWtRNPKaaJSQ9LRo26NJ67ceMjgCX0iaNGnUF4sCdysjt7n0kMfo08rI7VK3XgPdwShUuKhs3vGPR50Ll0QjJz63xUdtVagt+psyZUrDsafQ+/9qzbqy+9C5ePcxbtJc5dBp5w770faRJ08+10YHo/1YNOoQppyiln3Xy6nz//LNjFA0omhEfFY0WqOL/Y07R8iqLb5bpDIuPW1S/M/KaDeesybLd+yPlm49B0iWrNn057nbolEC+7wi3E3RyMXPHfLLWHW8adKklSJFi0nZ8hUle46c+nlo1ebLRD1ey2169x8SEDWNNB+pYccIWbP9gl8Org0cOFD9D3Ts9xNfiGk0Wry2IHy3umdUq1aNopFfiUZWD+sYmTp7sRQvUVJ9oXCOIHrYFI3iedD/MGSE5Mr1pNpPtuw54kQjLWLJQ8KRy5FG8Xwu+pgvXwH58utOMmHKfBk+arI8V/EF3Tn60Ynz/Hm7DpIhQ0Z5s+E7Mnz0FPl92gJ5/4PWkjyFUYB66ZXaroV0R/uraBRnQ6bt8+u8fULRCKLRhQv+XaOLohGxLxqt1V9iEQXRbvAWifnXN1/KbYpGiRhthMG05h8aB5SyZcsh9d9qovwiiEaR9kSjRIw20kUje7OnefhzR4ydJi9UqyGTpi+U/UcvqfOBGdyeLvmM8mnSpk0n+/65mGTRRoEjGsVZ2+83ys3/7vvdsfTr10/9D3Qb+BtfiGk0Wry2dMsxdc8oW7YsRSOfEo2iXQ29jpEGjd+T9OkzSNP3W8rY32cZnKJMLkcaIT2rxNPPKLHoq2+6yo8/jzYXjaLdcBwcRRpFu+GURGtRQnNUcW3LUHDMDIFjaPzO+/Hud8nKjTJ7YagcOnFNX4/fX3q1jtrHk3nyytY9JxJ0vJbrfFc0Mr54aKNp73aPlJ2Hr/LtjFA0omhEfFI0ios00or0IrX40SPfi37QRKNhzopG7tbzMfg07zT9QF6v30hF9/yxJFJFEDuMNEqCmkaLk6im0YHjV5RYZLm8R59BekQ1RKSkq2k0JGBqGukRRwabHXqCohGNRqNoRNHIB0UjGzZl5iKZOW+pchKQuw/HxCgabXdpP6hjhIKJEKLGT55rHWnkoZE54+xpTopGCfxcFLIuGVLKmIP52hsJ7vM3HXuqfeTIkUvWbNrvkRHBgb4sGhnsy8Gb9d+7jdwuN2/dF0L8jQkTJlA0IkEgGoVJy37rY4tiG38/cc73Uotdnj3NA77Btr2ndOEEwpFTolEi+SSh8YlGSRTxM2joSF00itiwh5FGrohGsX4RrrGmsdfe+z3XyqkL/pXKT9GIRqNRNAoE0cjewzjauQe0uWi0zaW2pp/rkmjkYp9tpqcl8Hjttd2296Sh70XUMSDNLKF9RlvsA+lvOw+c8ch35JOi0Za4mkYzlx+Tr4duUb836BguS9dH8w2NBKVo9N+9/2TViUiv2shtEygaEYeiUd9xO9U04Fq06K+zDsj9B76VWuyUaBTthngST9s/l60xEY32JtnnWhXCNk1PS4LPtbR3m36ofJqs2XKoSU+S6nMDqaZRH8P1NnLeQV1EGjh5D0UjGo0WuKJRFEUj74tGLs+eFpMw0SgBU8Obi0b/uBGWHOPG7GkJn7Fs/JS5kipVKnUMo8bPSFCfUUA7T978ah/1G7ztZH9i/Fs0MrxwLIo8LVv2XVbTOMMh+rj/Brl8/Q7f0kjQiUZhJ9cqwabBghaxP5ub/G25zHJ9c4+0HbDhJzlz85zcuHuTXyqxKRr1n7BLzl66JR/2Xaf+xr175yHfSi12PtIoxqP+hmaLzESjPUn2uQ5FoyT4XFNbtXaH5MptrFvZtHmrJPtcXTQKCfF/0cjgI/Ufv0suXr0tbb7fqK43RPlF7b/sn6LRfjem5GZbtmXboGjLSCNfEI0sI408UN/HVDQKNRWNXKxJ5HSkUQL6bBSNMsQvGiWwjhJmTHnu+Sqq/yVKPmM9e1q0c0U0kZqGIpqYfQTpf576jny9EPbiNafl3oNHMmjKHn3kesKiIz5ZJ4OQxBSNfo0ao8SbYVvHyqz9C0xsoYUtsGH21rvWduOZKH6ZxLFoZHiJffTwkfwZcUqPhug1Zofcues7zxinRSMP1k90STSKTtxUudD4CmEncsra/mNXVP1LNdFJthyyZvP+JE3R691/sDwTEhiFsCHSYta0v9ZGqzpiqHXU8detcvvug8ARjQ648dLJtmzLtgHVdhkjjXxQNIrvwZvQ9LTTrqd6jfNEelp8otHicLeP13K7Qyeuytftu8sTyZ6Q1GnSyNRZi13u8xGDzfkj1HAes6hz8NHHn3n0O/JZ0QhFHiEaRZ5WDtGJszelRe+1almjTuFy+GQM39SI3/D777+7LxptNYpGey8d4AklPioahUs/vMQalqH+XPtfovSIiOUbz/jMlOA2RSMPPfedE43WJlw08oCgokcaJbQQthtCzuGT16R3v8GSIkUKZahrdMTZaCEPCUiINAoJCYBC2BCNxu9W19X9Bw/lm5+jYlP5w2RB2Emfud6cFo2cfbnc78aLKduyLdv6ddtljDTycdEogWY/Pe2GG+lpRz3aRz09bXGYR/eL4tdDfh2rppKFY/TVt13l4PGrrjt34VulWPGn1fGXK19Rdu6P9mg/B/hselqYHmkEx+fhw0cydclRfSQNI9cPHj4SQvyBCZ4QjaIoGhEfF43aG9PToBrhvr1u5wVjUWzD8lb91vvMRAYuF8L2sDldCDuRLNRd0Sihs8gZ/KJfR05SM+zCL2zV5ks5cOxKkh9/wIhGSE+bYBSNYNsPXpFGnYzXW5vvN6g00YATjWg0GgthUzTyMdHIAyM6CRKNEjM97bQb6Wku9Bkzvo35fbZ+7Jhqd49lWpoTfQ7fsFvKP/e8Ovanij0tq9fu9Ph3NMDHC2FrohFeQq7fvCdf/rhZOUSINlq38yLf1ohfoEUanT9/nqIRCVDRaK0uGmkvsffuP5SBk+JSiyctPhI4olG0l0QjT6SnRSR9epo2E27mLFkkmcEvavT2+7Lr4NkkTYszS08LkELY2vUGHwlp+0On7zNGGxlszIJDasDNv0WjK268YLIt27JtoLWlaORrolG0Gw9rk7bLw6JcE43sfK5TolEC+2wWaeTm8WrLps35S7JkzaZqEL3xVhPZvu+Uy31et/WgVKlaQz/uxQbnziyE20Pfka+LRotMRCNY6KYzxpE0w7rPB232mZFrQigakaAXjdRL7O7YezaEI5FDJ2OMU4K3D5P3uq+R6Av/+ZZoFO2GEJHAtgkSjTzwudrvLolGHvrcGfOWqFnS4Be9Xr+RmlU2qY43sEWj3SY+0mM5eT4uVbSp4Xrbd/San4tGNBqNRtHId0UjD01vahZptGabzbYRG/bIsFGTZMFf4YmTnhZPnx3OnuaorZ3l8xevlpw5c6v+1qzzukTtPmG3LUbe8LnDRk82cxzR5qVX66h95MtfQOYtWh0nGEW7cbzR/pSeZqyDYYw0En3k+vadB9J77E61HqlqM1cc5xsboWhEiE+IRuFmkUZ4j3306JFMXPxPbGpxuPwweY/heePdWisOI40S8Nx3te2ftkSjJPhcmzWNkuBzF69YL7lyGWdKq1LtJeX37Tp01mgHjbb/6OVEO17L9QEjGrXXrrc4HwmRRbNDj8euD5O+43b6dFFsx6LRFb4k02g080LYFI38RDRywpas2iQ//jRafvx5tLTv3FvN9oWRpY5d+6plv4yYaBZ582bDd9SXX7BQEdl7+Ly+HDOEYXvYRx9/rrbJniOn9Or3o1o2ctz0BNUIshKNNnkgPS3Wtu45YTiXxVRf06RJI20/+1o6d+9vZl0Mhs/E9pt2HJXiJULU9k3ebS6HTl5TtZBafPSJWgarXbe+dO7WT2+r9tOjv0ya/kfg1jTSCmHHikaivYDEGkauG8fWyfiwzzo57QMj14TEJxply5bNA6JRC4pGxH9Eo1i7dO2OtOq/Xt2z3+4SKZv2XvJd0SiR7H/tu6k086eKlZB8+Quq5zt8I/g+WIaahRjESoq+rEjimkaNmjTTfZrMmbNI3nz5DVbAxPJLnbpvJmFNowAQjdrHzZ5m6SNdv3lXvhwcl8ofuf2Czx4LI41oNBojjQJONHJudosWLdvqzoEtg4g0989V+vYvx0bU5MyV2yy/vULFyg73kylzZtmx/7TbfY6bPS3MtbY2RrP+Cl2vopYc9Rv20/DxxhS0qIO681ivfiM5dOKaHDx2RcpXeD7efdSs/brrxxvtv6KRmVP06LEMm7U/tlh2mAydsY9FsYlPM3HiRPdFI86eRnxaNFpjVzSCrdh4Ro9+6Dhsq9y6473ohzjRaFKS1RN6p+mHDp/pEFNWrtmeJHV9nBaNPPS5773fMl6fpvxzlRK1jlHgiUZhZqKRpXAEP6phR2MK26c/bJKYf33zWD0iGnF6crZl26BpS9HIF0SjoZ6JNML08N926qVbe5PfYV17DZDNO+JSzGYtWCEftvpUBv882qxmz4hx06zamu6v7/dDZf+xy56bPc0DkUZ7Dp+XXn1/tNtnzcI3GMPRD564qqKyPmr9ucw1nDeIPkhZGztxtt222rIJU+cFdqRR7MvF4sjTNl9Art64K+/3NNbQeLtrpGzZd5lvbsTnRaNz584lXDRiehrxadForUPRCEWxv/5pixoMaGB4mf0z4pTXpgS/fPmyFClSJEkjjTDV/CHDMx/PfURJ4+ehWDsYa84OzvlbpBEiqG0dr+nvOD+MNHJFNAqPLTy/2+b1dufuA+k7fpde+2jKkqNeu94SXTSi0WiMNKJolISiUULS0xJ5NCgp2rokGgXA8fqVaKTPniY2naKl66PVDCFwnLr8tl3+u82i2CSARaOtTE8jviwaGSON+o23Fo20KIidh69Kw9iJDBD9cOHqbe+8dNuKNIp24/nrZ21XhDsQjQLweC236d1/SIDNnmbbRzpsOFY1uGbwkd7tFilnL9+iaESj0SgaUTTygmgUAEKKy5FGASYc+bZoZFoIW6wcI4hEHX/dqrZDGPbfa0+rKWcJCUjRiDWNiE+LRrGzp5mJRmImGqFI75Bpe/X7+2gvTQnucnpagD33ddHI3uxpAThAZh5pFCiiUXhspJG5f2R6vY3/47CxCL3h2hw0ZY/PpfJTNKLRaBSNAkE0inbjIe0nbc1EoyA4Xst1visahZnMnmYyeibmuftIS2vS2Thy3eb7DXLhym0hJHBFI6anEV8VjWzVNBKr30+cvRmbWhwuTQ1tMLGBt0SjYa7WNAoQ09LTFidRepqvWUCIRrF1Hy3T09SUhSZ/nzP4RG2/32hM5e8SIZu9XISeohGNRqNoFIyiUQCMzBlnT3NSNArAkciBviwaaelpYi4aWdbJGDptn779+D+PeH06Z0IsmTRpEkUjEgSiUZiZaGRdoFfk4aNHMn35Mf2e/d3vu+XO3aR9/rg8e1qA+Qah8YlGjDTyfdEo9vpR15s8tiPUGq+/Jeui9aLY7X+J8moReopGNBqNolEgp6dF2/g7OoEPch9razM9LYCP19I8JRohwufgyRjdDsVj2jYHT+DnDbO205cds6hp9NiBiRwzHOeHfdapNu91XyNHTt3gWxyhaESIA5Cm4vD+fML8/nzIwb199ZZz8k7XSBs1ViRO7Je4l9gr1+/IZz9sMqYWdwqXjXsu+p5oFO2GiOHjbfVC2KbpaQF8vL4gGt28dT/e68jZaxAR1pqP1M+q8LxY/bx15750Gr4tNpU/wjgrLUUjGo3mj6JRFEUj74tGLs+e5s4sH77T1vmaRoFxvIklGk3487AqtGhlXWOtm/PWWKWbhZulp1mGXGvLVN7+o8cybekxPaWtx6jtXqmTQQhFI+IvXL95z/H9Ob77tknbJl0izCMf1P1a7Ir9qD2HiFLtXv/V4M3y362km8jA+UijwHzu2xSNAvh4bYpGISFJKhpt3nfJdR/JznKjQGv0kbQaYtbXm/k1uOvwVWNtI0ObVv3Xy9lLvlEU20w02u/GlNxsy7ZsGxRtGWnkC6KRZaRRtBujO37UNnLjPkmXPoNj0SiAjteyradqGqGgqTbyFWfhJsuMzspbsbOd6Wa1nfmyONHI6AHZewm5GnNXvvhxk952/a6LfCskASgasRA28Zxo5Pz9Ocy5e7tVIezHVpFGphMZdBuxXW87b9UJ3xKN/PW570REcmi4nUijIKnp2Lv/4CQXjTbuuWTnGrK4jto7cw2GmxTC3mVW/Nq+PZLBU+OK0I+cd9B/RKMDbrx0si3bsm1AtV3GSCMfFI3ie/BGu5Fn7kNtjZFGGWTB4vCgON7Emj1NF40MTk/vsTtl6PR9btkPU/aon9sOXLHtANkQkCK2nzeOXBscrU8HblTh4IT4ApMnT1ai0dmzZxMuGm1lpBFJBNHIcM/+qO96t+/Z6r49eY/8GXkqNjDU/j1bq3W0/9h1ea+7Ma2tafc1cvn6He+JRoHy3HehEPaihBbC9vOaR4g0Cgl5xguikfF6+2rIFsP1st+Na22/DJ62T36culcWhp+04yNZX4MoI6CK0Bt8JKTy7z5y1bdEI2dfLve78WLKtmzLtn7ddhkjjXxcNApg09PTFocF5fF7TDSar4lGYXLgeIzd0S6x87szFl/bu/ceSq8xO/WRuGlLj/LNkPhGpJEnRCOmpxFPi0bt44R+63usOH0/Tmhb1FX6ZeZ+PbX415kH5NHjxJ/IwOVC2AFmoe6KRgEwe5r3RKMwmbXiuFN+kKvXYHxtHz16JLNDj+szr/UcvUNu3/VuUWzWNKLRaCyE7e+iUZDMJKZHGv0VFhTHm2jpafPj0tOMopFYOS0S+y5gWwAyLd6Y8LYYuW7aY63qR/Nea+X42Zt8OySBEWlE0Yh4PNLI+PLYZ+xOs3vs43jvz4/t3p9dbRt94T9p2W+96sd73SJl//Hr/iEa+fFzP9RuTaPgmEFOpac94yXRqINRNLJfpzH+68idtjGG6/5/Q7eofqAo9srNZ43b+axodMWNF0y2ZVu2DbS2FI18TTSKduNh7WdtzSKNguB4LdsmRk2jAwan35ZjI3acncdWzo5zbcVG24cPHsnYhYf1aKNfZuxXo9mEUDQixIZo1MFENHLiHqsJPwm5P9tri3pGDWKjMLqO2C537j1M1GM3E42i3RAi/LStS6JRAByvr4lGs1Wkkb3ryDqV0/lrMP62KEK/bucFNWsh+tNu8Ba5duMuI41oNBojjSgaJTDSKEimoHU4e5qjttFufK4PtfWsaGR8AdFEI91xkcc2R8DMcu/FNAffvbZXYu7KB73Xqf683SVSNu29xDdEQtGIEEvRKLYItZaelhT3Z1ttb/x3T75UExmES+NOEWpmNa9FGgXBc9+splEQHK/leu+JRkYfSRONvOUj3bn7UPpP2K1HGk7++x8lJvmeaHSFL8k0Gs28EDZFIz8RjQKxptEmD6SnsaaRiWhkWdNIElS/yN22KMaqOUSdh2+Tm/+xKDYJBNGIs6cRT0YaGUWjPo5EI7v35oTen2233bD7ov5SjULBiVkUO9hrGq0I+ppG3hSNtEijxLmOnG277+h1aYZUfoOP1LTHGjl57l9GGtFoNEYaUTTyhGgU48ZD2nfbxs2eFuZa22g3PteH2iaGaHQwtqaR6aw54lIRbPfbYiStw69RKt2hUacI+Wvtab4lEq8xZcoUzp5GfFY06q3S01y9P4sb93brtkgj7j5qh96nmaj7kuii0aSgmjXMZdEoQGtb+oZolDjXkbNtEVk0cfERY1FsQ58GTdnjv6IRpydnW7YNmrYUjXxBNBoa5LOnMdLIfdGovSYaXU9AVJHnLcpwI2oQG23U+rsNcvHaHb4pEq+KRmfOnHEz0oiiEfGgaNTeGNnT2+VIo8Sxo4YX/ne7r1H9QorxibOJE/1w+fJlKVKkCCONGGmUtKKRmrUsTM1g5gvX24Urt6VVbBH6hh3DZfvBK4w0ovm1zVy+RUZMXyLLt57k+WCkEUUj70UaBeYolEuiUQCOuiWGaGSenmYjesjl6WMT1vbeg4fy84z9sXU7wmXEvIPy4AGLYhM/FY22Mj2NeDrSyPgS64xoZD+SQTzW9uHDR2YzcQ6Ztlfu3/d8UWybkUbRbjx//aztinAHolEAHq/lNr37D/GeaKRFGokb15EH2y7dcEYadTJeb+2GbJZ/byVtKj9FI+dt1c4zMn3ZZpn29wYbtlGWbz0R9OcopHQF9f/U9fvhDreL2HdJFkTsMTuHs1ZEyV8bj8jqXWf5/0bRiKKRx0WjABBSXI40CjDhKPFmT9MKL4rJVLDms3iIo9pF8bR97GTbU+f/lQ/7rFN9e697pBw6GcO3ReLHkUYUjYgnRaNwk0gj1+6xYuf+LG62RS0jRIaiX026RMj2Q56PfnA5PS3Anvu6aGRv9rQATUvzumikzZ4WGpueZvNaeOzEdeS5tpipsMtv21TfEG20KDJpU/kpGjlvn3zbU50re9au63cUjWJFoy7fD3O43dzVO1VpEstzmCZtOqlU7RX5acJ8idh3kf93FI0oGjktGkW78ZD2k7ZmolEQHK/lOo+JRhgdjp2J58CJGItpXy1eRKxy8M2nknWm7WMn2z569EjmrDyuvxz1HL1D7ibydM6E2BONoqOj3RSNmJ5GPCgatQ+3Tk+L7x5rNsW3i/dnR21jnwm4Z/8RcUq9wKJ/nYZv9Xj0gyYaDXO1plGApactDtr0NC+IRrsv6T7SLJP0NFvXgjbyFV8kkafa7vnnmjTpHKHuBS37rpczl25RNPJBe/uDT9W5ypg5ixJHQkqXl5KGnyXV7xWk/7BJFI30SCPHotG0JRvVdilSpJCQMhWk3PNVpXhIacmQMZNanj5DJvmO55OiEUUjD4pGATAyZ5w9zUnRKABHIgd6ND1NE42uW496ifmLheWLgj0nxxNtL127I+0Gb1Z9w4tIxLbzfGMkfhppRNGIeDLSKEwvhO3SPVbcuD/ba2tiMf/ek86IfoittbJs45nY6cU9HWk0MSgjjULjE40YaZQEopGja0HcuI5cb4u00MHT9irRqIGWyv8waVL5KRq5IBp9aBSNXqpTn+fDTdFoeqxolDlLNiUgYVnY7nMycuYyyZOvoFpXslRZWbr5GM8rRSOKRk6JRvYextEuPKB9vK3N9LQAPl5LG5gYNY1OxLg4DazEvXDI40Rpu2b7eWmMkTSD0/bJgI1y684DvjUSRhoRikYd7NU0Ejfuz+633bznkkpPQ/8+6rtOrt24m7SiUbQbIoaPt9ULYZumpwXw8fqOaBSmi0biVB1H29dRYrS9ePW2vN9zrfKR3uu+RvYevUbRyOcijT6JFY3edGr75VHH5ZeJC6Vz/1/lm54/Sv9fJ8rs0G1W2y3ZfFQGj50tUxavk9Dtp+WHUdMN2w+SoePnScTeC/LzxAUqXWvZFnMBJXT7KcM2c2X41MUSaZHK9deGwzJk3BwZMydUX7Zi20kZOWOpdP9hpPyv+wDpPXSc+kzL2bOwDP1Bv+at2qFSzTr2+1lmLNusbxO5/5KMn7daug4YLu17D5Fhk/+U1bvOqOgrZ9LTppmJRhvM9tuh709qXabMWWTyorX6ur83HZGff1+gzufXPX6Q74dPlnmrd1rtG/uYvnSjOt84j31+Gq/qJVluh/3hHHfoO1Q6f/eLjJq1TMJ2s55SvKJRFEUj74tGLs+eFuNGeLDvtHW+plFgHG+iikYdws1EI9vh0NbLnBGH3G175+5D6Td+lz5b0Mzlx/jWSJKMqVOnUjQivicatTemgEE0spXW4vgeKw5TYtxtizTigZP36MLWpL+OeCHSKDCf+zZFowA+XpuiUUiI12oazVpx3O618NiN68idto8ePTa8oJ/Q7wl9DPeE23cTf3DNTDTa78aU3EHQ1kw0iqfthAVhKuUqVerUer2eZMmSSe4n80n7PkPMij1D+EmeIoW8UKOW1G3wnqRMmUptny1HLlVcG8JKylSppMcPI80+F+lbKVOmlMxZs6lC3Kb9+LxjH5X6VbHqS+pviFclni0radOlN6shlCVbdvm4XRez+kGv1muk+tP2mx5SoPBT+rZNDMeP9eF7zqs26WNTyWCp06SV1wx9z1ugkFE0GjDM4fm0JxrBRs5Yotahr+PmrVLLOvf/RfIVKiqpUpmcz+TJ5cl8BaXP0HFKKFIFtvdekC8695es2XOq862dd/yt7Qv9GTt3pUqH08619nkt2n4dNP/PCW3LSCNfjDSKdmN0x4/aRm7cp4qhORSNAuh4LdsmSiHsE9ftzt4h4kDgEfvpCp5ou+/odXm32xrlELXovVZOnL3JN0fiZ6IRC2ETT0YahcdGGu3y+v3Z1jo8o5r1WKv62LTHGjlqeG75TKSRrz73nYhIDg23E2kUJDUde/cf7CXRKMy8ELbTM8hapn8mTltE82EGNQyuITJ7xaazviEaHXDjpTOA2uqiUe03HbbFDGtP5s2vts1t+Nm4RVtp1vorefrZcsY6PiljBaDY7X8cMyu2vk9KJXA8mbeAVHv1NQkp85ys2HZKXqxZT61/tW5Ds89t8N5HuiiCSKC4PlySshWrqnUtv+iklkHQgRBUrlI1af2/rvJV1++kYtWX1TZ4Bxsx/W+9fY3a9dVyCDQQZspUeEHKPl9VPordFwSc5MlTqM8t89wL8u6Hn0mxkqXMxCizSCMXRaMeg0aqdTlz55E5K42RWYWLlVDCGQS7zzv2lbbf9pJiT5fSt/tzzX613fgFqyVV6jSSJk06efejz6Xnj6Pl/Tb/k2zZc6oILi3iqnylF1Xb56rUUP/7X3UbKM+WqxgXRcZrwa4tY6SRd0WjIkWKSM++g2TTjn8MdjT2p/nvm/VllnY03t99ue2fSyMlbbp08vu0BS62PerG5/pO2269BkrevPk8Jxq1tx9p5JTJ40RtO3L+Qb0o9uCpez1aJ4MQh6JR1qzuiUZbGWkUyMyaPVumT58ut27dSpL7ki4aGe7ZfWympyX9/dnaHsn4Pw/r/ew7bqdHzg1qi2GwpO4bDeR/7bsFnX3Q8hNJbXixadbi46A8/pdeqSP58xeQu3fvJtn1rYtG7TXR6LFP2rpdF3Qx+bMfNsn1fxNXWHM5PW2/Gy+nft5WK4T91NPPylfdBshXXQ3W7XtlSJeCGIFIl4ZNW6ntCj31tBI9EAUDW7bluFR7pa5aV7TEM/L3xiNmopGq41O6vCwI323Yz0UJ231etcN3g3V58heSP9ce0KN9TKOAatR6w7CtsZ9zVm2XzFmzq+Xj5682Ru/MXCqjZy1T7bSonJU7TqtjwXaIztGOVxONIBh99HlHCdtzTsINx4VjW7XzjBQu9rRRxKrXUP2Nz10WdUJeb/y+Ht3jbHpapixZZerfG1SfcMwzl2+WfAWKqHUVXqguq2IjsgaOmKrS4/SIKMNnTl+6SbLnzK22HT5lsVqOVDn8XapCJbU/LV1txfZThuM4bzw/hu8EkUcQvqb8tV7tC8eA9dguWP6fE9p2GSONvMfly5elYMGCKkQuTZq0QWdwnJSinTp1UB4/QkvTpEnjmdnTNNHoeIyDVDEXRsYSoe3fa0/rKWoYUfuPtY08xoMHD2T16tXSvXt3mTFjhty5c8drfcEMTFFbt0qvXr1k7Nixcv36da+LRlndFY08kJ6G7yg0NFS6desms2fPTtKRdnsRH6NGjZI+ffrIzp07g1rE/e6779SzqEyZMko8unHjRqKeD8v0NOt7rDh9P07MtqGbzsb2Ey+xG9UU4e5y/PhxyZsnjxQsVETKlK1gsOdirYKJWf5tz/yvbYmSzxheWJJL8RIhQXG8lm3z5isguXLnTtJnlJ6e1t48PU3iSa135TryRNuT5/6VRp2MA2vvdI2Uf6ITNyKbNY1cnz3NliGK5/eFYbJo/UHJX8goeqBWjuU+Rkz7W1IY/H5c/5NRT8hENMqQKbOMm7vSqs3CyD3qXQntfpowTy2b9CcG3DNIxkxZjBFNefLJn+uMglLvIWPV/hGxpAlEtur+QAh6o0lz1f6djz61ijRCFA4EI9N2E/+IkDRp06l31nHzzPu6cke0FHqqhEuiEaKCar/5thLaqrxUW0U9YXnWHLlk3JyVdtujhtOqndHydClj9FavwaPV8n6//G5snz2nSsmzVaPojzX7JF+Bwmo7iGJIt4rk/zcLYfsDMTExUrhwYfnsy/Yye8FyZbNgC5eb/61+X6H/jvWz7G3rR21/GzNViSffDxoWFMdr2bbt599IboPz5DHRqIMmGontqWBF7AhApkUbE6ctXpLaDtignLaGBqcocjtnUfMk8+bNk3Tp0ulODIQJb7F582Yl0mh9ad68uVujyv/e+0/O3Dwba+dctpHTR0vekvll25EdNtbHt0/j5w7c8IvbotG0adOUSKydFzjs3uK/W/9JkyZN9L5kz55ddu/eHfSikWbly5dX4iue0YkXaRSm1y8xvcc+jvce+9ju/dmTbW/8d0++GrJF9RGzOq3acs5jYqVLs6cF2AxjoXZrGgXHDHIqPS2pC2Fb1DSyX6cx/usosdref/BIRWCrgTWDjTL4dQ8fJe4savGLRlfceMEMrLaaaFSkeEn55NsesdZT2Wcd+siyrSdkTug2JeagntDEheFW+0AUUe7Y1LXBY+eYiUaIHAqPjYYxNdQ/qvBCDbVNs4/bqWUd+gyVJ5IlkwZNW0reAoVV6pba3/5LUq9RM2MNouZtzUQiiCV9f54gLT75RkUFIVIof6Gixm1btLESjVDTyLIvKJAN8Qr1jML3XLBaXzJ29rQuTs6eZmo4ZxDBKr34qoyZtcJK5JqzcrtKw2v68Zcq3Q79R3ob2naPTfdDJNZTJZ4x1llKnUZerPm6/DBqhooC075f9LtZ63ZK6NMix77s3F8WRuyJJwqH1wJFIy9js6ZRtBsPaz9rqxfCXhwWFMdr2TZRahodv27TORE7DstjK2fHubbiYtsZy4+pFw/0EyPr9+4/TPTrC2kQLVu2lMaNGwe8QXw2fQBnzJjRa30pVco8vx1CyRtvvJGgfTX9qJk0+6mVEmzMrUWsxbe8hUfa1h7TUF4a+rq82bJBgs8LrnWzIpRZsnjtO6pbr67BqUpt1p9y5coFxbViy/ASa2sEuVLlSjJz5kyPp9LEzZ5mIho5fY997Mb92XZbWy+688NOSoOOxuiMbiO3q+LYHheNot0QIvy0rUuiUQAcr6+JRrNXHHdwLZincrp2HbnXdveRq9IEs8wa+tn6uw1y5uKtRD8vjDRyPdJI1TSysw1m+1J1gtJl0KeSt4pyKWj01VDI2kw0KvKUnkJlmRb0Rae+aptnVJ2jk/JK3Qbq74Ejp8tb7xprGzVt9aWa8axg4WLqb8w0pgkuo2cv16OAULQadYBQtDp9hoyxolFbp0SjXkPGKrEKRbptHb8mGnV1UjRCZNHXPQfJgN+mqtneJv4RKat3n7MSjDADWpZsOYxtDH3O9WQ+1X+t0LgmGmlpa7XeaCKZs2TVaz5BiPpz7f64meS2npRPO/SW/IWf0lPqkP6H88n/dUYa+ZdoFERT0DqcPc1R22g3PteH2npWNDK+gGiike6QiO3ii3G1LkSbDNZ6Vg8PtT1+9qY077VW9e9thFyfvpEk19fp06fl3XfekTp16gS8Wb705s2b12t9qVixollfUIS6Vq1aCdpXvXZvKvHmk+XfSp+1g+Js3SDzv+0tM1jbuf+TGv1fky6r+rrcVrMWXVvKa2/UkzqvvZbg81KyZEmz85I/f36vfUc1a9ZUopVpfypXrhwU14otK1asmE3RqGy5sjJhwgSPp9Lo6WmqEPZOr96fbRleWPHiint2w47haoIFT6ZF2o00CoLnvj572rK1QXG8luu9JxoZfSRNNPIlHwmDaN/8HKVfb39GnEqS8+JYNLrCl2R7s6fZ2QbRMBBCkB5mK9UM65E6hf1o0TSmkUY2RSOVjrZGRRMhwmf41L+UYJI6TRr5a+MRGfDbFL0eEmZiQ/ZG9pxPyrKo47GFi09I6QqV1TYofj1s8iKZHbpNFkbulTcav6+WN7YZadTdqh/oKyKNMNMYaiJZrg9xUjQyL4S90eG2s1duUyIXtq//9gcyft4qmbtqp+r/08+WNReNYg3RRBCgEJWkCWON3m9tU8TrOuA3KVi0uNEnK1RULeP/u4NC2BSN/EQ0CkCL3LQ3/tnTAtg8LxpZ1jSSBNUg8mRbOENDpu3TR/kQck08z5EjR+SVV15R0SPFixeXsLBwr/Xl0qVLKnojXdp06v8bNWIeJTDMPvzkWiUazdw/3620MKTLQUT0JgcOHJDq1atLKoPzBwFp3br1XusL7jkQQ/Lly6fSGps2ber12lO+lJ727LPPqnpcqDuYeOlpxnui04WwbUSHSoLuzY7bPnj4SIbPPqA/U36dtV9NCZ4kolEQmJloFITH713RSIs0SvzryJW2S9dH6/eDDr9ulZv/3fcB0YhmWzSqb3ebxaqmkTHlq/X/uunFqeMidcaoyBYUgIbwYRRiZjpMT4MtjzohJUuVV9vVer2xpEmbVspWrCLhe88rkSNT5iySIWMmeeW1t9Q2dd58RxWuRtu5q3aoOkSIyhkze4VZBE+D91q6lJ42YUG4+uzkKVPIzxMXWEQPbVJileui0QaH2w4aNUOlkkH8Cd0eJ1ShJpMmUlmKRpqhcHardp2NolqpcnY/A0W2URgbqYWm54jGSCM/FY1i3HhI+25bY6RRBmN6mitto934XB9qmxii0cHYmkams+aIS4WsPdt2Kx62XYwh1y37rZfL1+8ISRxQvBdFZvGi6+2ixpiF6uSJk3Lh/PkEC0YgTIlGzQNCNAKokYPvCC/O3gb3nfPnzsvJkyfl9u3bQX3taKJRSEiILhYleiHsDnGRRq7fY8WN+7PjtgdPXJemPdaoviFC9Nxlz6bJxIlGk4KmjlGCRKMAqmPke6JR4l9HzraNuXlXPjL4RioS2+ArbTL0NanwiGgUJNOTOyMaQchA1I6KsM6RS0X+aLOnIfIof+yMZ4iY0aKK4qtppAk8zVp/Zaz9kzKl+tnyy05q+erdZ6Vy9Zpm6/r8NEFvOz9sl0rpgijS/9dJ+rH/MHK6ZMqc1UGkUQ+rc4XPKhY741qZ516QResO6MdWvGRcWQK9ptF+x+lpDiON9pvXUUKk1ewVW/U6T5jxDal2RtFohOpH90Ej5TvDMWqzrkE0eq/lF8ZI6uqvqmVT/14vn3XobRZR9PuCcCXmoT9TMaMarwWKRj4tGg0d5Vpo8Gk3wop9qK3D9LQAPF7LdQMTRTS6bi3gOHBmJD7xx422N/+7J9/GhlzDMJoWzDM0kYSKRog0WhAQohHxTfA/MmbMGLl27VqS3KOM6WlxNY2s76fixv054W1Rt6jn6B26oDVv1QmPnw8IckWKFEl4pJGfiymhrkYaBZh45FXRyHDNzQ49bqPWkCvR1p5tO3LeQd1HGjR5r0ej+hhplAg1jRykp8FmLt8iefIV1IsxFyxSTAoULqqifYy1i4rJjOWbzVK+4ktPgw2fulilvWnCzMjpf8e+9F+SLzr105dDCFoQvkdvh+gc1PTRZmhD/ytWqSGpUsXVNHS2phGsx6BReh2gLFmzS7GSpQzvcRnVsuw5c7s0e5ozkUYQd5COh+1z5M4jteu/LSGlyqvC2Vr/IRpBIELfITDhnNeo/YY8W+55XUjr+aNxhrXvf5ui+ooaSZWqvSLVXq0rGQ3nBdtUeamOrN55lv/vTE/z90ijAE1Pc0Y0Ynqac6JR+zCTQtiup5QlLA3Ncdsl66Jj63aES4dfo+TW7Qd8OyVBHWlEfJO4OiNJQ9zsafHUNErE+7O1PVKzWmqCUbvBm9UMap7GpUijAIy2WRHuQDQK0OginxCNYmclU5FG4sZ15MG2OB/vdTdG9TU1/Dx57t8kve9RNHLePvysvRJ+MDtZfJEbiO555bUGKpMC4gwM9YjqNmyqZvgyje74ZdJCtV/U59FSymzZXxsOS0jp8mrbgkWKy9LNR+MKcC9eJ1myZVfrar7RWMJMi0kbPgs1jDClvUpTM/QFETqYWazLd8PUsuZtvta3x8xkWNau6/d2o566fj9c1WZCyhsMKXm9h4xTs7ulSZfO8PtYh+do1v/ZOw/4psruj8tGAdkIuBAcgChO0NdXXwd/x/sqS0GlDobIVCxluNhDhiA4WArKHrKKQFu6S8vooqUTaCltKaOFTrqbnn+eJ8ltkiZp0qx7b35fP4+0SU5y701zxzfnnMc7nGc/3dX1HtrhFV7n9vxt+xF6qPdjfLnZ8rdoeScN+2Q8z75iy8pmhWOlgKz594O9+giPY32f2Gu4z1rGl1szw9prym3EnoO/N8rlv71FC579dSA4AX/ryDSSsTSS+AmGxdJIZidU9ps9TdN4kbSmgtX9hotMfSNWR2y1GbHZuaU0Ypaq+fWQ6YF05txNXJkCK6RR/TONWE8lSCMgJmrK0wLU0siyfSwZ2T+TFbGsj8q4xSdUzXinBVJQ5FW7rLvF5WkyO+4L0sjY7GkyF0ez5i1z7uxpmkwjg58FczL3bBNbWamgb3+LFvYDW4+mOHw/BGlk/mCZLKy5dIAJsaM//jl5nv7yDKG/Dh3nGRqGJUwOeUWk8ZKrup6Pla+xi3Z/vRnG2PA9c5kvn1HxlJBNnmHJvKm2pjSLiRTWL0l7nVifJP48yvU1tSw+Uem05Z9Q2nb0pLDs7HmORlwUBI3xkc0zoAw10za+/bNpb+BZvvxsu2pejy2/9usFKR+3PyieP26P/xmjJX+sQTgrRWNZT2xd8DcOaSRtaeQCZWo60siFyvI099lMGv2dLMzEk5iWrzftq96FSK0afN3pYM2Jra4jtkpRTat3aTdSTVSuowJXisCp0ig9PR0bFIhHGrlrSyMz97E603RbuH82Fasc271ShWVasDGWT2JgT2m0ytKeRjJrhO3pso2wnSCNYrOFc6QdPhdNfhY033yZKum0RaxveBYNnqZaps8WnqDS8iqH74cgjTAwMCCNXEEayeCbOdXsaWZKIxl+E2nTnkaCNMqr/a0X6V5Y6KdNGzvJqW9s8qV8GsqbX/vTx7ND6WJWEa4SgRXSyA2ZRkCGmUb+QiNsi/axZMX+2UhsVnaxUCbD/k1Itd9MehbPniazcwOfuqQRMo0cII1MfI5qiSALPoNmxuYWlNHk5aeEfUBgxFWn7IcgjTAwMCCN5CCNjB2MMy04QIs81mB5mozXV38sskdPI5ZpZFEfC6q54KBqq2NZyvWMnyP5sgycGsBTwQGwPtOo/j2NkGkEJCONDM2yZPH+2fLY5VvihSyjdfvOUaUdM0PNkkaZVkgMkccKs6dpl6fJeH3FI438BWlEJj9HZPJzZG0sa3S99UgqPz9inzfWeJ6dN0EaYWBgiF4ahUMaOV8aLbe0p1G+FenB4ok1v6eRPNbXrtJoaoCONDKcSl37NvOaOJof6x9+VXUypLwAmbzsFL84Avbn0qV0+vHHH2nYsGE0dOhQfjJ4+fJlUSxbZGQkjR8/nn755RenSKNt27ZBGgHxSSN31X6SSSNDZS2m989ksiTGktiYczf5VN9sWT6dG0rXb5bYdd3NzzSS53HfoDSS8foalEY9ezqtp9EO74tGPwvVVnyOzI1lza7dWL9H5edt2NfBlKQ8Z3MWOtIowYopuRGLWMS6RCwyjcSYaZRpxbc7EooNOhHPZxcwKY1ktL76sXZphJ2WZ3T2DjI16wcZnxXE3Nj8onKauPQUXxZWqx8YeRVXhw7g2rVr9OSTT1GTJk3ooYceoo4dO/Kd+hN9n6Ds7GynLVdhYSEtmL+A2rdvz5fnf//7nxXSqP7laWKRRhEREfTJJx/T7DlzqLLSuTMJMqG4ePFieuutt+iVV16hCRMmUFxcnENnEEOmUYA60yjGIftnQ/eVlVfR1J8ieBbGII8AOhBk/8+ITTKNxHrcNyMj2SfASKaRi/R05LOnOUUa+es2wrZ0lkEy3qfI3NiKSgWt2pkofPZ/25Ps1H6PZkmjRCsuOhGLWMTKKvYoMo1EKI1cpMZflWnUou5MI5nW+C+0a6YR1dmM0byTIvNjtx2taaQ6d0MMb4gN7M/169dpypQpdPr0ab5PYaKof//+1KBBA9q8ebPTlmvMmDF0++23k5ubG7Vo2cJlpRF7T+bOmUutW7fmB9uXXnrJoRdM+hQVFVG/fv2oUaPG9PDDD9O9997Ll6tdu3aUlpaGD5QjpZG7P802WJ5m+/2zoVhVM95AvhyTlp3iEkkU0kiqx31zGmFrpBEaYTtZGtXjcyQ8tn6xZy/k0kAP1TnS6PlhfJZZZ2JxeVqCFReniEUsYiUfC2kkRWkkkyGUp3n6u+T623T2NPUFyJHQyxSXmkvxbKTkKf/N478Lt+kM1f3xKTW3acfeyCsz+E2aoW/VLl4upA+/C+EnZazU4Xx6Pq4MnciPK37kO3aWTeIsdu7cSVFRUVxidejQwUppJN3ytC+++EJ5kdiKRo8eTZ06dXK6NCopKaHZs2fz94Znm5SV0Ycffsj/XubNm4cPj6Okkbo8jWX66OyfU7T2zYb2zynG9+2Z127VWXas2X/n5JXSqPmhqv5zygvZ6OQbDll3ixthy2xoGmG7sjTq2bOXc8rTlOdIbGbXeJOfozyjn8E47c+oMvbS1SKjJf36t5WWV9KUFeF8GVgJ//5A55dLo6cRBgYGGmFLXRq5yExiZmcayTQLyebSiH2Tpr4Q0fl9aoDuv6whpLt/zc9GYj2DM2p6ZhAZkUfEmzgu2xInxP5+8DyuCp0MExXOzjTSYL00crOJNLp06ZJT1t/b25v3dcrNzaUePXo4XRoZYseOHfzvZdKkSfjwOCzTyLr9c+1Yf5qz4Uyt/bN2doRm/82a8W46dEGInfd7rMPW3SbSSMLHfR+jPY1cI9ual6f16uW0nkYmP0fCZ868z+C832O0JJGxsjXV7UdCM4XYaasjqaSs0un7obql0Q0rLjARi1jEyi0W0khs0ijTioO1xGJ1Mo1cYH31Y+1RnlZzgqP+Xfg5QJhyVvhX+z4DsQe1pZFes1XSuhBhJ2RDpgfyuJHzwnhvI+A8WI+jzp07U5cuXZwmSmpJo47OlEbbnSqNNIhZGi1csICfCCxfvhwfIEdKI4P7Z/19s/n7ds1MbGSkjEZzIzsGuX0fwmOHfR1EF7MKnSONMq0QERKNtUgayWB9xSeN/HWlkaWfQR1pFKvbjJ4MnyPlFZTRiO+Ceex7ys9bZOINUeyHkGmEgYGBTCOpZxq5yBS0JmdPMxWbacXriijWVtIoOPoa/bI7iaddr92XrBznDI+954zfx0cyn/5Vc/KkyjQyUeqgfO3C4gryWBUhnEwdDbuMZrp2hJUUscwVzYiPj9e5n5UajRw5kho2bMBnU7M3+fn55OfnJyzPsWPHeANsW2UaBQjSqP49jbZvhzQyBeuJxS7i7lDuiy9cuIAPmQMoLq3k+9wV2xMs2j8bun3RpljexFqVMRSjs39WT/St03uloqKKfvjzrHCBvPlwCs88EkWmkQsc9721y9NcYH3173eGNGLl8zWft2TjnzUzPoM/7UgQzpFUnzfT50js/yxOc460dEscb4gtfml0AxfJGBgYuo2wIY0kIo3k2NPopA3K09DTyKb4hV8RTm6YNKprVpCDQek0UN1gctqqSCouqSRgP15//XVq2rSpMMaOHSvcx/6OFi1aRI0bN6ahQ4fy3jX25uTJk9TqzlbC8rDePdHR0XaQRvXPNII0Mg6TjF9++SU1bNiQvv/uOwhfCcKm8P7g25Da0shIE9+TmsxQ5T5+1Pww3tvIkbh6TyNvl+9p5HhpZNO/3/wyrbLOmDrPkZIv5fNsPvb4Ed+HODSrzzpphIGBgYFMIwlKo3wrDtLijRV6GtVqhJ1vRZ29dGKlIo2MNcG+klNMn8xRNVIdqrwIiTl/E1dwdoZNlZ6cnCwMVorGUCgUtGbNGp4t8uJLL/ILM0dQWlpK58+fF5bn3LlzXETYShrZohG2mKRR9+7dRSON2H5n9erV1KxZM3rzzTd51hiQqjQKNkMaqTKc3H8KF0ptfE5lkcLBorBGGm1yyVljzZZGMu1tKQ9p5K8jjYydI5VXVKmzt1WPZzPMigmbSCNMT45YxLpMLKSRGKTR8t8sSw3OsCKtWESxJsvTZLi++vctEqs0UjdV9QzKMDpjWpVCQev2nxOmsV2+NZ6fIAHHw96PLVu28Cyf/v37c7EkpowRlTTqaGVPI5Sn2RImGVmT9JYtW4rybwZYKo20M43I6CyXh4IzhAvY6asjuURyNDk5OdStW7f6ZxpJXKZYPHuazOSRbDKN3Gt6GhkStOzfkOhr/As19tixi06Irt8jMo0wMDBQniY1aeSq5WnmSCOUpzlHGmnNnmZonLuUr+qjoTwZcpt1nNKv3sLVm5NISEjgUoTtyIcPH05fffUVubu787Fw4UIqLi52ynIdPXqUFixYQF9//TXPgHr44Yf576tWreL7PvOlkY0yjdKQaaSRjAcOHKCWLVtR3759KS0tTXfGHyBBaaTKNJprINNII49yC8rog+9C+L79vZlBFJXknGa8FmUayTDbxjvAhDRygZlzZSGN9GZPMzTyCsto/A8nhdnYgqKuim5dII0wMDCQaeQq0kjiJxgWSyOZnVCJWxppN8LWnYWnskpBM3+JEh6361gartycCOshxGZLa9++fa3xxBNPOKxUTZ8vvvjC4DL17t2brl417wS6RhrVP9OITSfPpBGTI86RZ17k4eFBkydPptatW/PPPBN63333HeXl5Tl8eVhJY1u1ZBwwYACNHj1aGBMmTHTKMgFrpZFKBs3bEFMr20EjjdbzzFDVPvvHrfHK445zBKHF5Wl2Ov6ez8inhJRsOnvuKsVfuE7nLuU65HUFaWRs9jSHrW+eU86RZs1bJhNpFKDONNI9P2K/sMby271Shcd9uybaoc3mIY0wMDAgjVxJGtmwfOp8ej6FhifTuk27aNc+H0rWPzkyEstOMo74nqK/D/rVDE8/3d+Vw8vvtPI18ixeZh1pZKP1jU7IUD5fAC37aR0tXbmW/tiyj0JOJ9C59DyLn5edXHkHhtO6jTtp3qIV/CTXy/80Jabm2OQ9Eq808teaPU17JhDVCVFAxFXhZGjSstN8+mgA7IGtpBGTNc6SRosXL+YZRvqDHXQzMzMdvjxM2LGLNkPL1KdPH7OFHhCTNDLU06jmQpYdcwZNU2WGfjz7OGVcK3LeRbdaGq2ytKeRjQY7H/ILiabJU2bSc/96iXr26kNPP/scDXlvBO054FtbptipEbangxphs/U9FhRFE7+cTv2ff5F69lat79BhbryfpL3XV5bSSN33Ub88TT1VIaUpP5Nus1Qlo0NnBPFm2GIE0ggDAwPSyBWkkZmygn2ztOLn3+m++7rxN/qRnr0pMu6SWbHJaTeofYeOPM7UuP/+7hQVn2HxMqtmTzNTGpmxvr//tYce7dOXGjRooLN8nTp1pvmLfzIsyzINn2Sxk8cX//MaNWrUWOe5mjVvzl/HFu/RIjFLI015GulKI5ZyPXpBGL+fzcATGnMdV23AztLITdKZRgA4RhrplstoUiAqKxU0e90ZfgE7UDm2HE1x7kW3pbOn2TgDZs9BP2rXvj0/njdo0JA3gtcc3+9o0YJ+Xb9VNbGFnTJvfOqSRjZ+XfZFYZu27VTr21B3fVu0aEnrN+5CppGl0kh9jsQ/b6Tby4j1dlyyOU54zIYD50SZZQRphIGBAWkkF2lk7GCcaf4B+lhwFP33naE64uPhuqSR1uswyfLO4OH01DPPGRzt2nXgz9m9x0N0JvGyxctssDzNivV96OFefLrxJ57qR5+MGkfDP/yU7urchS8jex2TjSe1XmPfPwHU6S5V3P3dutOosZNo+jfz6MOPx1Cfx56g9Zt22eQ9EqU0Om26p9Ff/1wQ7p+9/gxVKC9IALCvNLKupxHPNGrTGtIIuIg0olrNeIdMC+SZEeMWn3R6ZqhZ0ijTColRR+zi5b9S89tvp3cGDVNlX+8/RtO+nst7fLFj/t333Kc898mz+evWmj1NuzzNjuu74IdVdPvtd9CgIcOV5y67+fpOnTGbCyPVOU4POpeeb7f1lW95mr+BHmLEe4UNZp835f0j54XRtRslol0XSCMMDAyLpFE4pJHzpdFyS3sa5ZuVkvzoY335m9u7z+P03gcfa0mjdLNfi9X5J6beUI8c4WdWunX/Az34c3488nNeymbpMpvf08i8E5pPRo+nX9ZtptjkK8I22LzTk1oqT9DYck77ek6dz3H23BXq99y/+eOfefZ58g2J5s+jKdeLScqqKU+zcplFLY20ytM0KdcXMgtVKdfK+96dEUQpyt8BELs02rlzp1PL0wBwtDQitTAqKq6gSUtP8fvYxAXB0decf9FtdqZRvhVlUMZjQ8OTaPeBY5R08WbNeU56Hv9ySPPlWvCpBJu/rklpZMf1PX46kfYc9KXkNN31/XjUOL6uDRs2pOPKbWKv9TUojXr2lL40YuVp6h5ims9baVklfbUiXDW7mvIx/xzPFPXkAjrSKMGKKbkRi1jEukQsMo3EII30M40yrfh2J7NGGr3y2hs0deZsijx7iZeo1VmeZsHrrv1juzq9uRUdOBJUr2UOOhFPt9/RwrQ0yjS/n1GtvkXK+1jDxx4PPsyX9dMxE+pc3+17jlLDRo2o1Z138oyjevdYMmOZpdEIW1XiUFFZRYs2nVXX8vvTRs8LuFoDDpRG9S9PgzQCLiONNuhmPuz0uUgDPVRZER6rIvkkBpKQRpmW9W20RezKXzYK0uio32mre0kau98nwEimkY16WJp7P+v7qJFGrDm3o16Xz54mB2mk1UNM02z+oPKcSXOONOOXKCourRD1upgljRKtuOhELGIRK6vYo8g0EqE0slH9eNz5a6oG1cqfV/z8h3k9jcx4XZZx01+djfPyq68bbzJdxzKrMo1a1J1pZEWtfUBoLHXseBdf1m9nLa7z8eMmuQvrlXTxhl1r/EUrjdTfknkGZQglDifPZtO7MwL5yZDb9yFofg0gjQAQlTQK0CmXycoupk/nhqp7qwSIJjPU7EwjB894ykrQVX2ObqPIs2n2a4StkUYOaoRtbLhP+56vLyvpj7Ig+xyNsLWk0YaactDcgjJym3WcnyMNVZ4rnYwTf79Hi8vTEqy4OEUsYhEr+VhIIylKo3oMTabRw9ZII01jxf0+1LRpM2rUuBFt+Gt3vZ9HKE/z9LfZerKZQFgadlLqDf78rwx4k69369ZteJq2qVgW9/Kr/8cfP36yB/kERdKSFWvIY+YcmrfoJz4rW5JWire1Y6FYy9Pc/bWkUTUVFJXTVyvVKdfK+46dysKVGnCwNEJ5GgAmpZG7qjyNlcqwjKJVOxLVZTT+tHpnomia8VrcCNsBg5Xb933yGX7sf+qZ/maU21s/e5ozpVGscn0ffewJvr79nv+3XdfXkDTq2VN+s6f9uidJyDJatiVe1GVp9ZZGyMJALGJdOhblaWKURnb4hm2lOdLIjNdlWUXvDB7Gn+uhR3rV9AXIrK80MiPTyIL1nTDZg/r1f4Ee6fkoNWvWnC8na4a9eeeh2idGerGsVxHr/8Ri2rVrT40aNdKZOY3NyvZ/b7xNJyLP2+Q9WiiFRtjKC429fpeEGn6PVRF8dhAAHCeN3GwijS5evIgNCmQqjUIEacQuVmPO36Sh0wP5fvuTOcfp2k3xNOO1iTSy4TkSK+VnzaLZ8Z6Vav26fotdX9fHaE8jx63v7AXLlevaiI91G3c6NNuLl6fJpBG25vOWdDGfzybLM7FnhVD61SJJrEvd0uiGFU1zEYtYxMotFtJIbNIo07yDNZM34bEX6VR0Ss04k8IbNRuKrbOnkZmv6+V/mmftsOeat3ilRbEmM40yrTgp0Yp9462B1LZtO2p1Z2tq3KQJX86One6iMZ9P1m0AbiA2ISWbunV/kMc0b347Pfevl/isKnMW/shnkWvStKnQG4mX5Fm5zGKXRqw+XyhxcA/gJ0VnL+TiKg1ITBrtgjQC8pdG/CI2lkrKKmnWujNCCc0+/0ukEFHWg440yrRCRBiJPZOYSQuXrKY5C5bTnPk1Y9cBH4Oxh3xCqXOXrvzY/vbAd/lEH/V5XXOX2SJpZMPX1fx88GiwMDvsoCHv6zTItufrylMaxfIv0b5bE83PkQYqb9vunSqZdcHsaRgYGBbNngZpJDJpZGZzR9ar57HHn6R77+vGx33qfz/6dKzBWLMaYdfxuixTZ+z4Kfx52rZtT9EJGVZNyWpy9jRTsSae89SZVF5W5ul9nNb/uZv++/YQaqKWR4Pe/UDo8WRoxKfkUPceD/HHTv5qJiVq9TRiZW9un3ymnqK2Oxd21i7zQpHPnrY/MJ1+2Z0k9MT4WfmzGBqpAtchQJBG9e9ptGsXpBFwBWkUQHM3xJCvVokxKysuLBZXM16TmUb1PO5rx/7t6UdNmzXTyRJm4733P659DnIynh5/4ml+f89efZS/x9X7dc1dZp3yNBusryWxAWFnqY/yvJHPqvvo4xRiqGTfDq+rfb9spJG7qhG2f8RVGjxNJWgnLDlFRSUVklkX09LoBi6SMTAwdBthQxpJRBrpjUDlwf+Zfs/Tw4/00hq9aez4r0z2NFJJo/o1PTx+Oonuufd+/jxjPv/C6jp4doJmdXlaHSP+QjYNHeammiWkUSPDJ0lCedoNerRPX1XT7NmLat2/Zechfl/7Dp3INzhanj2Nwq8I9fqL/zxL784I4idDH88+ThnXbuEKDThJGtU/0wjSCLiKNGLlw2MXnuD7cJYZGhx9TXwX3XbuacQyjVi5Geudoz32HPTTedzJ6BT617//w4/p7Au3/YcDHdLTx1k9jcIiz1H/51/k63tft+7k6aSeSrKQRurJQr5aEU4Tl5zkPw9UjhARft7qL40wMDAwkGkkQWlUW84wYROVkEERZ9O0xiU+Y5qh2PplGum+7vfKEy/W16d58+Y8m8fSZTba06hWI+x8K+r7a8eu+X07NWqo6k+068CxOhphv8EfZyhja/ueI+pyt84UEBpj9TKLXRoNVtfos0yjv1mJg6KaAHAktmiEDWkE5C+NVLOnDVb3MWL77Pm/x1Jpufj6z9VIo01O62N0JiGTz5Cq6XnIju1GvwSzcV8fs6WRDV83Mj6dXvzPAL6+XbrcQ7v2eTtsfeUpjVSZfIM8VCVp7PM2e90ZqqiUVia2TaQRGgkjFrEuEwtpJAZptPw3y1KD63GfOT2NWAmWsfItNiWrJgvnrf8NrmmAbcUymyxPq8f6nr+UZ/D+uQtXUAP1VLpC+nmmpuwsV6dBpKb8js0swvtDaUm6KR7f8vt6PPQIxSRetnqZF4lVGqln3NFcfExZEU4Ft8oJAOdII9uUp6WmpmKDAplKoxChhxEbTCKdTy8Q5fLm5ORQt27d6p9pZKXUiLtwnd5UnsPwMvt27emv7QfNy5q2kUzxsTTTyNr1PX+d/u+N/6mzpDvS1l3/OHR9ZZtppHWO9N7MIErJLJTcuiDTCAMDA+VpUpNG9ShPs3SsqGP2tK27D/NmkA/0eIj8j8fUuv/X9Vv5zCINlGPzTk+bLJNZ0sjMsXOfNz36WF9atOwXik7IFJqFs5KyLl3v4eve7YEHuRhi98UkZ9H/vfk2nyXNY+Zs4SRqz0Ff5To24DOpTP7qa6Gv0eZdh4QG4BO/mG6TKWoXilkaTVVNHZO/zyQAAIAASURBVMtm4JFayjWwHT+e+pXcDn2uHuO0fjb0u+7tI2rdNs7i2OEHR9usPA3SCMhXGgXX7LeV/7JedGKd8luTabTKnEwjO4iLr6Z9J/Q5eurp/jRu0lTl8OBjPBuTPWjx8l/slm2jyTQyWB5mh/Vl/Rk168taGhha3yUr1jhsJjs5NcJWZWX707q95ySZiQ1phIGBgUwjV5FGdRysn+3/AjVr3pxPPd+4UWNh2vhmzZrxErP773uAktWZNt/MWiScWGz/+6jO87Bvql548RV+39PP9KfY5Cs2OcGwWBqZWF/27RlbT7aMTZs25b2XOna8i4sudluLFi1p9wFf4fEhpxKoS5e7+X2vv/kOJaflCjOoDR76gbAt2rXvQO3bdxSe54knn+Wz1NniPRK9NFKOBX/EUmUlml+7GjdKbtLgfR/zwcTN8IOjaPgB7TFaffto1e/sfs3P+vdbGZtRkEmllWWQRgCYlEaqC1g222VeYZl49y2WlqfZWKT8b+C7tZpk6w+WTWyvTBvvgDpmT7Px67IZZetaX9YE3N5laTXSaJmspNHoBWGUk1cqyXX54Ycf+Ps/dfYyXBBjYGDUOQ6EJPJ9xrPPPgtpJCppZIMytVcGvEl33dVFd3SuGY89/pQgjTZt3c/vv79bD/IN0W3yfDw8ibp170GdOnWmlb9srP+JQ6aJTCMr1zfp4g3ldlzDv0ljoqfVna2pdZu21LnL3fT2oPfoiO8pnb5DZxIu039eeZ0LoSlTvxUykNhgmUrjJrpT16730J2t29Cdyufq3LkrjRn3BZ3RLkuzcpnFK41UPY3eV16IXLxcSMA1pRHL8JkZOE/y6wJpBFxDGqn6qxwKyRD3vsWSTCM7jL3/BNDs+ctMDtYH0d6NsB3ViJrNJlfX+q7buMuBjbCXyaOnkfIcaaBHAHmFXZbsvmPNmrX8AvCzr77FBTEGBkadY/OhML7PeOONNyCNJCWNbP7NXD4XIrUbaWtmIbtOceeu6cgVa19XNXuamdLIzPU9rxysKbhvcBSfTjcxNcfEbGk5FBWfwcvYDN3PMqz8Q2PJLySaYpOu2Pw9WiRmaaS8APnt72Rckbm0NBpBMwPnSn5ddu/eDWkEXEAa+dOo+WFUJsLm14akkdk9jRx+LmTfWJ+6pJHM1leemUaqTOypP4VT4a0Kye47fHyOqS4ABw3HBTEGBkadY9Gv2/g+4/PPP4c0EpU0MnYwzrTgAC3yWIPlaTJeX/0hSml0WlWe9vHs45RfhObXri2N3CCNAJCQNApPyBH/vsUcaZRphcQQeawwe5p2eZqM11eu5WlDZwRK4vNmCjajKLsA7N33GfI/ewUXxRgYGCbHeI/ZfJ+xatUqSCOnSaPlv1mcFVT/9GDxxJrf00ge6yslaeRzKgtXYy4vjZBpBIBUpNGSv+KksW8xO9NInsd9g9JIxutrUBr17Cl5abRyW4Lk+z0WFxfzmQxbt2lHW4+cqv+U3Ak5iEUsYl0g9qnnXuQ9kQMDAyGNnCaN9DONMq34dkdCsUEn4un2O1qYlkYyWl/9WDH2NIpLyaV1+86JvsQBOEoaSb+nEaQRkDPZuaX0864kyrh2Sz7SSKrHfTMykn0CjGQaZdj3dcUSy2dPk7A0KiyuoFU7Ein9apHk9x0KhYImT55MjRo3pjkrfq9/BkJCDmIRi1iZx3qGJlPTZs3p/vvvp6Iiae//XGv2NJnUvKsyjVrUnWkk0xp/MUojAOQqjVJSUvDGAiAFaSTV4745jbA10shBjbDFNqReniY3goKCeLnJk/3+TX4xWXVfXCZYcWGKWMQiVrKxX3yzWBb9jOQtjWTcFFEoT/P0d5kmkJBGANLIwdJozx5IIwDELI1c6LgvlKfVVxpJfFsxadSzJ6SRWCgoKKA+fR6jps2a0Yo/9iILA7GIRWyt8c/JC9Tl7vuomXI/ERkZCWkkKmnkIidQZmcayfQECtIIQBrZnz2QRgCIWxq50HHfx2hPI9fItublacg0EhX79u2jhg0b0qN9n6Gj4Re1LhZvWNE0F7GIRaxcYkd/8Q3PMvr0009lsc+TjzTKtOJgLbFYnUwjF1hf/VhIIyBuaeQGaQQAsJ80yrRCREg01iJpJIP1hTSSxrXIa6+9Rg0bNqLp83+iwPjrmCkKAwODj62HT1L7jnfxLCO59AaVf08jGU7JanL2NFOxmVa8rohiIY2A+KWR9GdPgzQCQKTSyAWP+zrlaS6wvvr3QxqJE19fX2rZsiW1a9+Rft12mIIScLGMgeHqgzW/frL/v3mWkbu7O1VXV0MaSUIayXAEnbRBeZqEB6QRELc0GgFpBABwnDRygWF1TyPJN8KGNBIjFRUVtHDBAmrSpAnd3+Nh+jsgBhfNGBguPHyiMuidYR9zYfS///6XcnNzZbO/k7k0yrfiIC3eWKGnkae/ZbGZVryuiGIhjQCkkf35+++/IY0AEJ002uSSs8aaLY1k2tsS0ki8lJSU0NixY3l/oz5PPEu7faPQDBixiHXB2GPRmTT4w9G8ZLVPnz6yKUuThzRa/ptlqcEZVqQViyjWZHmaDNdX/75FkEYA0shh0ujChQt4YwFwMjk5OdStW7f6ZxpJXKb4WJppJDN5BGkk/s/noEGDqEGDBnTP/T3o582eyLrAwHChcfB4EvX796tcGHV74AE6ceKE7PZzKE+TYnmaOdII5WkAOE0azYA0AgDYct+izjRaZU6mkQyzbTSZRp6GpJELzJwLaSR+8vLy6Msvv+RlKa1at6EJ0+eRd+QlXFBjYMh8LF27kx7s2Yd/9vv27UuJiYmy6WMEaSTxEwyLpZHMTqggjYC4pZGbbDKN7mx9J6QRACKSRmaXp8nsuO8dUMfsaTIXR7PmLYM0kgDsvHTVqlXUvHlznnV07wMP0uzl68n3zGVcXGNgyGSwhvdB8ddpwx5feu6lAdSwUWNenjp06FC6efOmbPdv8pNGLlCmpiONXKgsT3MfpBEQvzSaJw9pdOeddP78ebyxAIhEGq2ytKeRzBphe7psI2xII6mgUCgoLi6OX0AyecSyD7rcfR+N95hDq/86SEfDL9KxM5kUEHeNghOycRGOgSF2SRR/nfxissgr4iLt9ImkKd8voSf7vUCN1LKI7ZsPHDgg+32b60kjGXwzp5o9zUxpJMNvItHTCIhbGsmjp9HevXtRngaAyKSR2T2NZHZu4FOXNEKmERAZ7BzVz8+PXn31VWrXrh2XR2x06NSZej3+FP3n9XforSEf0tvvfYSBgSHi8ep/h9DTz79Ene++V/gcN2vWjHr37k0rVqyg4uJil9inybc8LdPA75n1PJCLLNZgeZqM11d/QBoBcUsjN9lII2QaASAhaZRphcQQeawwe5p2eZqM1xfSSD5UVlbyHiesbO2ll16iBx98kNq3b0/NlRedrIRNcxGKgYEhztGkSRP+JSqbjOLZZ5+ladOmkZeXF5WWlrrUvkw+s6fZaDp7KcSa39NIHusLaQSkJY1GQBoBABwvjWR83DcojWS8vgalUU9II6lTUVHBP8vx8fEUGhpKvr6+5OPjg4GBIeIRHBxMZ86coStXrlBZWZnL7r/klWmUacW3OxKKDToRT7ff0cK0NJLR+urHoqcREL80kn5PI0gjACQmjaR63DcjI9knwEimkYv0dOSzp/XsCWkEAAAA0shqaeQiNf6qTKMWdWcaybTGH9IIQBrZn3379kEaASAlaSTV4745jbA10giNsPFhAAAAAGlkM2kk46aIQnmap79LNQCHNAKQRo5jL6QRAOKWRi503BfK0+orjSS+rZg06onyNAAAAJBGNpBGLnICZX5PI3meQEEaAUgjSCMAXF4audBx38doTyPXyLbm5WnINAIAAABpZKU0yrTiYC2xWJ1MIxdYX/1YSCMgbmnkhvI0AIBNycnJ4TO3/LD8V4pOzKToBAtGopGfJRS7/3AgtWzVinbsPeoS66t///Rv5tEjjzzi0k1YAQAAQBpZRGFhIT95enXAWzR5ygzlmEmTv5pBk5RjMh8zVbdN0fyuHprHTpmhNaQX++mo8dS4cRN674OPLYv9yorXFVHsK6+9SW3atIE0AiKWRtKfPQ3SCADxcOnSJerapSt16NCJ7u/Wne5j43716KY9HtD5/X6tcZ/Wv3aJvb++sd3rjO3S9R4+PXnnLndbHGufZXZsbNt27amt8rzH1aZ4BgAAAGlUb/Ly8ujtt9+mfv36YbjoeOGFFyCNgEil0QhZSaNz587hjQXAybBMo/Xr19PGjRtp06ZNLjfWrl1Lv/zyi0uuOxts3bds2YLzHgAAAJBGAAAgZeQkjfbv3w9pBAAAAAAAgIsDaQQAADYC0ggAAAAAAAAgJyCNAADARmik0QxIIwAAAAAAAIAMgDQCAAAbIadG2JBGAAAAAAAAAEgjAACwETXSaJ7k10UjjZKTk/HGAgAAAAAA4KJAGgEAgI2QU0+jAwcOINMIAAAAAAAAFwfSCAAAbIScytM00giZRgAAAAAAALgukEYAANmwMnwtLQj7sR5jeT3jdGNnhfwgu0wjSCMAAAAAAABcF0gjAIAsiLwaQ0P2f8KljXnDzYLHmhfrduhzOn0lihJzpC9aII0AAAAAAAAAkEYAAFnglxbMxc1v0RvpYt4lvZGuGvmaoXVbnv7P9Y/NKLgsm+0JaQQAAAAAAACANAIAyAKNNNqRsA8bwwZAGgEAAAAAAAAgjQAAssAvLQjSyIZAGgEAAAAAAAAgjQAAsqBGGu3FxrABBw4c5NIoKSkJGwMAAAAAAAAXBdIIACALII1sy8GDkEYAAAAAAAC4OpBGAABZAGlkWyCNAAAAAAAAAJBGAABZIEijRPQ0sgWQRgAAAAAAAABIIwCALIA0si0qadQK0ggAAAAAAAAXBtIIACAL/NKCUZ5mQ5g0atWqFSUlQhoBAAAAAADgqkAaAQBkgd8ljTRCppEt0EijxKREbAwAAAAAAABcFEgjiVGae53O/v4t5SScoOrqamwQANTUNMKGNLIFnp6eKmmUCGkEAAAAAHly+Xox7fFNo13HxDH+9r9EeYXleGOAqIA0kgjVCgXdPBdFwdP+j7w+7Ul+E/pTmvdfpKjETgUAht8lSCNbAmkEAAAAALlz8mw2DfQIoHem+quH8md3f63flcNd7/6p/kaG9bFDZwRS2pUivDFAVEAaSYCqijK65Ledjo19kgsjzfAe2ZviNn5HZfk52EjA5anpaQRpZAsgjQAAAAAgd8JirxuQRgE1gsddWwQZuG+qbWOHzgiitCxIIyAuII1ETnlhLsX/OYe8Rz0qyKJA95fp2GdPkLf695MLPqT8SwkoVwMuDcrTbMuhQ4cgjQAAAAAga07FZdOgaSp5s2jTWTp+5nrNiGH/XlP/fk33vlrjWr1jQ6Kv0vTVkWppFAhpBEQHpJGIKUhPolOLPhJkkc+YxylhywIquZFF6QG7yH/Sc8J9Qe4v09XTXthowGWpkUaYPc0WQBoBAAAAQO6cisuhwdMCubDZciSFfwlvcJDmZ6p1HxmLMTNWoVDQ0s1xNeVpkEZAZEAaiRDWv+jK6aMU5PGaIIV8J/SjjMDdpKhQ9TCqrlbQjcTTFPr9IOExxz5/ilIO/kaVpcXYiMDlqClPgzSyBZBGAAAAAJA7KmmkKg3bcjjFtPwRxA/p/V5t5jAcW6W89lumLY3Q0wiIDEgjkcGE0bm/fyLfsU8JMihk5lt0IzmciyJ9SnKy6MwvU3h/I97naHQfilb+XnLzKjYmcCnQ08i2QBoBAAAAQO6w8rTB6vI0lTQioeWHKQFERqWQ5bGqTKN4Lo3ehTQCIgTSSERUlt6iiB/HcgHk/WkvLoGiVk2k4pzLJvsVVZQUUYrn2hpxpIw9/t1Ayj0fhY0KXAb0NLItkEYAAAAAkDuCNHL31ylPIwMyqLomVciABKJ6xyoU1SppNBXSCIgTSCMRwHYYeamxFDz9dbX0Yf2L+tKFfaupsqzYzOdQ0JVwLwr48kXlc/Tiz+E3oR9lBv9NiqpKbGQge9DTyLYcOvQPl0YJCQnYGAAAAACQJTWNsFl5WqqRjCDdkrLa4sdUn6O6Y1XSKK4m0wg9jYDIgDRyMkzoZIbsJ99xT6vL0XpRwOR/0bVIX4ufi+14WPPsE3Pf0ylXi/9rDlUUF2JjA1kDaWRb/vkH0ggAAAAA8ob1NBrkoV2ept/EmrQaWetlDlEdPY3MjK3SNMKeGkDvzghCphEQHZBGTqS8KJ/i/5pL3qMfE/oXhX0/mG5dvWjd8xbepLO/f0M+Ws97csEHVHTlIjY6kC2QRrYF0ggAAAAAcoc3wvZQZxodSTWjkbVuQ2uyqAm24dgaaeQPaQRECaSRE2A7ByZwwpeOEqSOz+g+dPb3r6m8MNcmr8FmWbvotYn8J/9LeI3Aqa9S9plAqlZU4U0AskOQRonoaWQLII0AAAAAIHe0M402q6URmTX7mSqLSL9PUX1iq7QaYWP2NCBGII2cwPWYIAqa+qrQv8h3wrOUevgPUlRW2PR1mBzKORtCITP/y1+HvR4rg7vguZaqykrwRgBZAWlkWzTSKD4+HhsDAAAAALKkRhoF0GadnkbGMonIdElaPWKFnkbKZYA0AmIE0siBVJWXUuqRP8h33FNC9k/wjDcoJ+EEVSsUdnvd4uxMilw5TnhN1ucoZq0HleZex5sCZINfWjDK02wIpBEAAAAA5I62NNJvhG2wsTWZW45mfmyNNEJ5GhAnkEYOggma2PUzyHvUo0KGUfiSkXTr2iWHvH5FyS1K3r2cl8F5f9qLN9w+MW845aXG4c0BssDvkkYaIdPIFkAaAQAAAEDu8NnT9KQR1ZFFxKh5nG72UH1iq3RmT4M0AuID0sgB5KclUNisIWpZ05PPbJa04weeeeRIFJWVlHXiH/Kf9JyQdRQw+QW6HHpQucNS4I0CkqYm0wjSyBYcPnwY0ggAAAAAsoZLo2nq2dMM9jQiw/JHPYxmFVkQC2kExA6kkR1hPYWyTh7WaUbtP+l5Lm6qNarZ0cukfN3Cy+cp9LuBgsTy+exxSty2yOY9lQBwJH6XgiCNbAikEQAAAADkTk2mEZNGKWb0K9ITRKRdela/WGH2NDTCBiIF0shOVJbcoqSdS4VyNDbCZg/lWUdioCw/h2J+cyefUX3Uy9eLz+ZWfC0dbx6QJMg0si2QRgAAAACQOzqZRodTdGc40/5X/R/VOUOa5bEKQRqxRthBlJYFaQTEBaSRHbh1PZ0iln9W03h6ZG+KWTuNixoxUVVRRin/rCe/Cc8Ks6sFT3+dcuLCSFFViTcSSAph9jRII5tw5MgRSCMAAAAAyBrd8rSUWmVk1XUKIsOzpFkSqyuNAiGNgOiANLIxNxJPUZDHAEEY+Y1/llL+WUeVpcWiXF4mh7JjginI/dWaZZ7Yny4e3URV5WV4Q4FkqJFGmD3NFkAaAQAAAEDusNnTBk8L5MJGVxrpDTLep4jqKkerI1ahX54GaQREBqSRjWD9gNJ8NpPfhP6CfAnyeI2uxwRJImun6HIKL08TsqNG9aGzG2ZSaV423lwgCWrK0yCNbIFGGsXFYYZFAAAAAMgTlTQKUEmjwylm9CMiPfljfh8jY7Gsp9Ey9DQCIgbSyAZU3Cqgs79/Qz5jHhPKvMKXjKSirBRJrUd5UR4lbf9BuR6Pq8vqetHJBR9QYcY5vMlA9KCnkW2BNAIAAACA3GHlaYMN9DRi1NnI2kjZmaWxqkyjePXsaZBGQHxAGllJ4eULdGLOu+Q1srdKtChH/F9zqKqsRJLrU1VeSpnBe8l3/LO8OTZbn4Av/k1XI33xZgNRg55GtgXSCAAAAAByR5BG7ro9jchEDyIyKIGo3rEKRbVKGk2FNALiBNKonlRXK+hqlB/5T3peyC46NvZJygj6W/JNpNm65V2IoePfvM3XjQ2fz/rS+X0/8zI8AMQIehrZFiaNWrZsCWkEAAAAANlS0wiblaelGskIIp0G17XFj6k+R3XHqqRRXE2mEXoaAZEBaVQPmDi5cOA3QRZ5jexFITPforzUWFmtZ1nBTYr+ZQp5j3pUJY+U6xm54nMqzb0mpF4CIBYu3LxIm8/upJhrkBy24OjRo5BGAAAAAJA1rKfRIA/t8jT9Jtak1chaL3OI6uhpZGZslaYR9tQAendGEDKNgOiANLKQkptXKXLlOKEUzXv0oxT10wQqk2nDaCbIzu1dRcc+6ytIMibIbiaepmrlDg4AIE8gjQAAAAAgd3gjbA91ptGRVDMaWes2tCaLmmAbjq2RRv6QRkCUQBqZSXVVFd1IPE2h3w8WZhjz/fwpOr//Z6qqKJf3uit3ZldOH6Ug91cEceQ/6TlK999JCpmvOwCuCqQRAAAAAOSOdqbRZrU0IrNmP1NlEen3KapPbJVWI2zMngbECKSRGTAxku6/g/wmPS8Io4Cv/kNXTh52mW3AdnD5F+Pp1KKPBHHEytZY0++Konz8kQAgMzTS6OzZs9gYAAAAAJAlNdIogDbr9DQylklEpkvS6hEr9DRSLgOkERAjkEZ1wKahT9gyn4599jifTYzJkpMLPqTCzPMuuT3KCm5Q/J+zyWfMY4JAYyKpMOMc/lgAkBGQRgAAAACQO9rSSL8RtsHG1mRuOZr5sTXSCOVpQJxAGpmg8PIFCl86irxH9hYya+I2fs/FiStTVVZCl/y2k+/4Z4Wso6BpA+hKuDf+aACQCZBGAAAAAJA7fPY0PWlEdWQRMWoep5s9VJ/YKp3Z0yCNgPiANDLC9ZggCnR/RWh4fWzsk3TJdxt6+KhhTbBvJp6i4Blv1PR4GvcMpfyznkslAIC08fLygjQCAAAAgKzh0miaevY0gz2NyLD8UQ+jWUUWxEIaAbEDaaQHkyEph9aRz5jHtWTIU5QRvJduXUun4usZdEs5VP+qfi/OVv1uaAiPEX5XPb6qosw566fcMZXl51i+zAbXN51yU2IoeMZbwrbyGd2HolaO57PJVWt0OgBAcmikUWxsLDYGAAAAAGRJTaYRk0YpZvQr0hNEpF16Vr9YYfY0NMIGIgXSSIuygpsUtXoyeat7F2l6GGlKsDQ/a/9ufBiP9fnscSrMSnHKOlaW3qITc4dbvMyWrm/w9NcpLzUW4ggAiQJpBAAAAAC5o5NpdDhFd4Yz7X/V/1GdM6RZHqsQpBFrhB1EaVmQRkBcQBoR++wqKC8lhsJmDdaRJYbkiHet33sZuM10rM+YvlSQkeyUdWXS6OS84RYvc33W13f8M3Q57BApKlHSB4DUgDQCAAAAgNzRLU9LqVVGVl2nIDI8S5olsbrSKBDSCIgOSCMlZYW5FLfxO4paPYmP00tHUcTysTzrSHXb5JqfV00SHldzv95YNdlgrP+k57hMOfZZXyq6kuqUddWWRmwGtIjlY0wuc836TrZ4fVnsmV+nUH5aAv7IAJAYkEYAAAAAkDts9rTB0wK5sNGVRnqDjPcporrK0eqIVeiXp0EaAZEBaeRAolZNFIk0Gqaa8cz9Fd57CAAA9IE0AgAAAIDcUUmjAJU0OpxiRj8i0pM/5vcxMhbLehotQ08jIGIgjRwIk0beIpFG3lwavQppBAAwiJe3N6QRAAAAAGQNK08bbKCnEaPORtZGys4sjVVlGsWrZ0+DNALiA9LIgaikUS/yEUl5GqQRAMAYkEYAAAAAkDuCNHLX7WlEJnoQkUEJRPWOVSiqVdJoKqQRECeQRg5Ek2nEpVGWGKQRytMAAIbxhjQCAAAAgMypaYTNytNSjWQEkU6D69rix1Sfo7pjVdIoribTCD2NgMiANHIgqp5GvZwujU6oexoFQhoBAIwAaQQAAAAAucOlkYd2eZqBkjOqrn8PIzNiq7RmT3t3RhAyjYDogDRyIGJphH1i3nBVI+ypkEYAAMNopFFMTAw2BgAAAABkCW+E7aHONDqSakYja92G1mRRE2zDsYI04uVpkEZAfEAaORCNNBJDTyPMngYAMIWPjw+kEQAAAABkDZNGmkyjzWppRGbNfqbKItLvU1Sf2CqtRtiYPQ2IEUgjB1IjjZ5wbqbRXPQ0AgCYJjQ0lJ599llKSkrCxgAAAACALKmRRgG0WaenkbFMIjKjRM2yWKGnkXIZII2AGIE0ciCiK0+DNAIAGNtXVFZSYWEhVVVVYWMAAAAAQJZoSyP9RtgGG1tTtQU9jcyLrZFGKE8D4gTSyIFEi0QaoTwNAAAAAAAA4OrUNMKukUZURxYRo+ZxutlD9Ymt0pk9DdIIiA9IIwdii0yjTZs20fjx42np0qVUVGT5DgWZRgAAAAAAAACglkbT1LOnGexpRIblj3oYzSqyIBbSCIgdSCMHopNplFU/afTwww/TbbfdRh07daTk5GSL4zF7GgAAAAAAAABoZxoxaZRiRr8i7awhrZIzqq53rHYj7HfR0wiIEEgjBxKtPXtaPaXRgw89pJJGHTvWq0GtphE2Mo0AAAAAAAAAroyONDpsQhqReeVn9YlVcGnEMo3UjbCzII2AuIA0ciDRNpg97SEbSCPW04jPnjb1VUgjAAAAAAAAgEuiW56WUquMzBxBREZK0MyNhTQCYgfSyIHYoqeRTaWRO6QRAAAAAAAAwDVhs6cNnhbIhY2uNDKcLVRdZx8jy2NrpJE/pBEQJZBGDkQ80mgYytMAAAAAAAAAoqWqSkF5heV2HX6nr6jK05g0OpxiVj8iXfljbh8j47Gsp9EytTQaMj2Qzl7Itfl63swv0/m9tKwKf2DAbCCNHAiTRt4ikUbINAIAAAAAAACIlWs3S+irlRE07oeTNWOxkZ/VY7yJ+wzFfjInlMsa/Z5GDFMCiEyUnVkaq9BqhP3O1AAaPT/M5us7duEJ4efpP0dSQmoe/sCA2UAaORCVNOqlaoRtQhrduHGDVqxYQUuXLq01WrVqxaVRixYtyMPDo9b9y5cvp/j4eKPPjfI0AIA5lJeXU1xcHDYEAAAAAJwCm4o+7Gw2fb7opCBUmNxhWUF8TA3Q+1lzn7/6cVrDjFjt8jQy0YOIDEogqnesQrmeXBrVY5ktXd8vfwynhIt5gtwCwBwgjRyIJtOortnTdu3axcVQfce0adOMPreuNEJ5GgDAMLt376YBAwZQVRXSlwEAAADgHJjcSMsqJI9VkboiRPnzp3NDdbNt6hqLa2fksNgxC0/Q6AVhtD8g3UhGEOk0uK4tfkz1Oao7lkmj9fvPWbTMda2r26yQGqk0VTUW/3mWcvJK8UcFLAbSyIFEa2camZBG7GLNGmnEMpCMod3TKBDSCABgaD9RWUmP932cmjVrRj4+PtggAAAAAHAqt0oq6be/k7UyagLoi+Wn6UJmAZWVV1JpeZVy6P+rGmXCbZU6t6lGJe/vU6L8t6JSodW0Wkv6kP7MaEbK06yILa+ssmyZTaxv7PmbXKhpthVr9L3TJ41KyirxhwTqBaSRAzG3EXZRURFt2bKFNm3aJIyNGzcq/91InTp14mKIlaktWrRI5zGblI/566+/KD093fjFYOktOjFvuKoR9lRIIwBAbXbs2EENGzbg+5rXXnuNKioqsFEAAAAA4HSOncyi978JFjKORs4LoxNnr/O+QOY3o9ZtSk0WNbIWb2xllYK8T1ym974OUm0f5XCbdZwiEnPwhwOsAtLIgWikkY/TG2EPx+xpAACDlJSU0FNPPSVkLjZv3pyOHj2KDQMAAAAAp8PkSOz5XJq49JTQu+fdGUG03SuVSkor62hUrT+DmSoTSD/7R4qxBbfK6eddSTR4WoAgjDxWRfBm4gBYC6SRA6mRRk84VRqdmIueRgAAw2zfvp0aN26sU/L6xhtvcJkEAAAAACAGmAyZ/0csDfZQ9esZNC1A+XsMn1q+1mxmJiWM6ewesceyfkhpVwrpm9+ihObYrByNCSSUowFbAWnkQMwtTzPFQw/aQBoh0wgAYABWGtu/f/9afdJuv/12OnrUCxsIAAAAAKKB9TliM54NY+VY6qwj1ucoLiXX9FT3ZG5ZmLhjFcpxKi6bPpmj7l+kXP/3vw2mg0EZVFWlwB8IsBmQRg4k2gbS6EGUpwEA7MSOnTuoSZMmBhvsv/Xft6i4uBgbCQAAAACigcmR4KhrNGp+mDC72kezQsgr7DKVl1cZzeZh1EgZ3SweqiMTSAyxrCH2Hr80enemWpgpB5s1LT41j2cfAWBLII0cSJRNpNGDyDQCANiFq9euUnx8PB8zZsygtm3bUnh4OP/9/PnzaIgNAAAAANHBJMnFrCKa+lO4II7enRlI6/YlUzHvc0SGJYx6GM3u0WlCLZ7YG/lltGxLHA1UZxcN8gigBRtj6WoOvtwD9gHSyIHoZBplObGnEWZPAwDUwcqVK6lDhw5UWFiIjQEAAAAA0ZNfVE4/7UgQevsM9Aig79dEU9b1YhPZO1qlX1RtRs8h58Ymp+XTlz+eFppds8bXf/6TwjOPALAXkEYOJFp79rR6SqNjx47R8uXLadeuXVRWVmZxvKYRNjKNAACmgDQCAAAAgNSorFTQ4ZBMGjazZtr50fPDKDr5htllYAZvJ+fGsuEfcYU++C5Y3b/Jnz78PoRCzlxDORqwO5BGDiTaBrOnWb0jVfc04rOnTX0V0ggAYBBIIwAAAABIESZRzl7Ipc8WnhDK1YZMD6R9AZeoolJhUNSQkVIwcySPvWNZid3vB8+rM6gCaKDy36k/RdDFyzhHA44B0siB2KKnkbXoSCN3SCMAgGF++uknSCMAAAAASJZrN0pozoYYXqamyTpavjWecgvK6ugnZCrjhxwam37tFn27JlotvwJo8LRAWrk9gZfiAeAoII0ciHik0TCUpwEATAJpBAAAAACpU3irgv7wPK+aZUzdONp9ZTidS8/nGUkmS8OMZglR3WVlNohlJXWjNbPCuQfQB9+F0P7AdJ4tBYAjgTRyIEwaeYtEGiHTCABgCkgjAAAAAMiBKoWC/E5foY9mHxemp/94znHyC79CFZVVBkvHGKaEDpkoO7M2lkmhPb5pNOybYC6L2PJ+vugkRSTmoH8RcAqQRg5EJY16qRphozwNACBiII0AAAAAICeSL+XTVyvCVSJmagC9NzOINnqep+KSSrXIMdZrSPd2MiiBbBObX1hOP26N57OiafoxzVl/hjKv3cIbCJwGpJED0WQaWTN7mrXoSiOUpwEADKORRgUFBdgYAAAAAJA8TMpcv1nKpYymRxBrKj1rXbTy9hITGUGk06S6tvghm8SmZBby0rmBUwPUM6QF0IYD56mkrBJvHnAqkEYOJFo70yjL+T2NAiGNAABGWLVqFaQRAAAAAGRHWXkV/e1/iWca8T5H7gG8d1BCap7OFPeC9CH92c2MlJjVM7ZKUU0hZ66pyuem1mRBhURfo6oq9C8CzgfSyIGIpRH2CY00mgppBAAwDKQRAAAAAOSKorqaopJu0Mh5YUIZ2AffBtOhkAwuaqpN9B2q3zAcy/oXbTp0QWuGtwDevygls0DocQSAs4E0ciAaaSSGnkaYPQ0AYApIIwAAAADInazsYvrut2hB2AzyCKDVuxIpt6CsjtnPVFlE+plDZNbMaarYnLxS+n7tGSG7iL324k1nqai4Am8MEBWQRg6kRho94dxMo7noaQQAMA2kEQAAAABcgdLyKvp1TxINmR6o6nPkEUDTV0fSxcuFpFAoTMgf01lFZEQcVVVVU+yFXJqw5JQgjIZ/E0w7fS7yzCMAxAakkQMRT3kaMo0AAKaBNAIAAACAq1BZqaAjoZdpxPchQtbRyHmhNX2FjPYtqntox5ZVVClfJ5Pe/zZY1ezaPYBGzQ+jU3G4JgPiBdLIgUSLRBqhPA0AUBcaaZSfn4+NAQAAAADZw/ocxV3IpS+WnxYygN6dGUTbvFLpVklFTTNr0s4k0s08IhPyiJW8/bw7iQazjCb17Ggso+lydjE2PhA1kEYOBJlGAACpsHr1akgjAAAAALgUTO5cu1FCizadVTenVpWrLdwUy3sQ1cyCphpGs4r0ml+nXSmir3+N4plFbAydHkhr9iZTXmE5NjoQPZBGDkQn0yhLBNIIs6cBAIwAaQQAAAAAV6W4tJJ2eF/kcoeLnqn+vAdRXEquVsaRVrkaGZ8hLSz2Oo2Zr56lzd2fl6YdDbuM/kVAMkAaOZBo7dnTspzbCBuZRgAAU0AaAQAAAMCVqVJU815DH80+LvQ5+uC7YPI6cZn3OTI9Q5rql+1eqSrxpI5n4inhYh4vhQNAKkAaORAdaSSK2dNehTQCABgE0ggAAAAArg6TP5evF9OUFeHqTKEAGjwtgNbuS6ai4gqjs6flFZTR3A0x6t5I/jRQ+e+CP2IpO7cUGxVIDkgjBxIlokbYkEYAAFNAGgEAAAAAqCgpq6RVOxNp8LRAoUn2zF+juFDSFkYKRTXFp+bRhCUnBWH07oxA2no0lcrKq7AhgSSBNHIg4pFGw1CeBgAwyc+QRgAAAAAANddRVQr6JySDRsw6rm5o7U+j5oVRREIOVSkUVKG83/dUFn34fYi6HM2fPlI+NizmOjYekDSQRg6ESSNvkUgjZBoBAEzx888/c2mUl5eHjQEAAAAAoIT1IopMukETl5wSxNG7MwNpu/dF+u3vZBqilYnksSqC0rKKVP2NAJAwkEYORCWNepH3yN50Ys67dHrRR3ycYv8u1vpZ6/ZT+rctrvlZP/a0GbGnFnxIvuOehjQCAJgE0qj+VCqQfg4AAADIGVaWNu/3GBrkoRJHGoHEsotYCdvqnYmUX1SODQVkAaSRA9FkGnmpBxdIWr/X3M7+7aX+vZfwOFvHBnz5IpXevIY3BgBQi59X/0zt27eHNLKQ8CtRtOz0z3S1CKnoAAAAgJwpuFVBmw+nqMWRShoN/zqY9gVcovIKBTYQkA2QRg4kWp1ppJE7hqSPl8HbNeLHmtietWID0dMIAGAElmkEaWQZWYVXaLz3VBq4140OX/DBBgEAAABkTpWimoKjr9GH34XQ6PlhdPZCLm+GDYCcgDRy5E6lrJgqiguVo8DAKNQbBUZ+t11sZQmrsYUFB/YhPT2dpnl40IABA6h///700UcfkechT6qsrKQTJ07QqNGjaPTo0SbHoUOHqLS0lBYvXizcNmrUKCF2zJgxFBERgY1tByCNLOParWya5DOdBu4dQWujN1FZZRk2CgAAAOACsJ5Fl7OLKTu3FBsDyBJIIwCAzYmJiaHWrVtTgwYNqFWrVtSmTRtq2rQp3XbbbRQcHEwbNmygO+64QxhNmjTh9zVu3Fjn9rlz51JGRgbdf//9/LnYbS1atNAZ27Ztwwa3A7/88gukkZlk38qhcd5TadBeN1oX/SeVV1VgowAAAAAAAFkAaQQAsDmDBw/mEujzzz+n3NxcKisro6CgIHr//fcpOjqaSkpK6MKFC8KYPWsWfzzLHNK+vaCggNLS0uiee+6hu7t2pTMxZ7hE0ozMzEz+3MD2QBqZR2llKc0PXc5L0uYeXwphBAAAAAAAZAWkEQDApigUCurevTs1atSQoqKizIphpVBMGnl4eNS6TyON7rvvPsrORg8uR6GRRkz6AcMUVxTT4hMreUnaN0ELKLcUgg0AAAAAAMgLSCMAgE1hdd1PP/0Ml0CzZs3iPYzqAtJIfEAamaa8qpyWnlrNM4y+DpxHeaX52CgAAAAAAEB2QBoBAGzO0qXLeA+iZs2a8YbVsbGxPAPJGOZIoy5dulBYWBglJSXxkZiYSNevY1pzewFpZJxKRSVtT9hLQ/Z9TCMPT6KswqvYKAAAAAAAQJZAGgEAbE5xcTFNnDiRGjZqyGUQkw+fffYZpaamGny8OdKoYcOG1K5dO+rQoYMwWM8kYB8gjQzDhNHOxP00dP8nNOrIZDp/M5Vn1wEAAAAAACBHII0AAHahvLycfH19acCAAVz4MCnEeh2xrCN9zJFGrVq2orFjx9KXX34pjJ07d2JD24lff/0V0sgAB84d4RlGKmGUAmEEAAAAAABkDaQRAMBusAtqJo+8vLzogQce4GLo7bff5rdpU5c0uhs9jRwOpFHtv+XTWZH0/sEx9N7+kXQ84ySEEQAAAAAAkD2QRgAAh8DEUdOmTenuu7tSQUGBzn1ohC0+II1qUAmjKBp2YBQNPzia/C+F4A8EAAAAAAC4BJBGAACbwmZLKywsrJWFceLECWrZsiUvUSsqKtK5D5lG4gPSqIb47CQujN47MJJ804LwxwEAAAAAAFwGSCMAgE0JDw+nF//9b/rzzz+58MnJyeEznQ0ZMoSLITc3N6qoqNCJMSfTqGvXrnTmzBlKT0/XGbdu3cJGtwO//vYbl0Y3b9506e2QUXCZxnlNpcH7PqI/z+7AHwYAAAAAAHApII0AADaFiR02yxmTQEz09O7dm890xn5nPyclJdWKMSmNLqmkUaNGjfjzsZ81495776U//vgDG90OQBoRXcrPpIk+02jgXjfaGLuNKqoq8IcBAAAAAABcCkgjAIBNqaqqotDQUJo4cSK9+OKL9PTTT9Prr79Os+fM5llDhpoHe3t705tvvklbt26tdR/LJJo5cybPVDI0WCywPb/+6trSKKf4Bn1x7GsauHcErT/zF4QRAAAAAABwSSCNAAB2gcmh4uJi3vSazZZmaqYpdh/rhYTZqMTDby6caVRYXkQLwpbToL1u9F3wQiqpKMEfBAAAAAAAcEkgjQAAANTCVaXRrYpimh+2nGcYfRu0kArKCvHHAAAAAAAAXBZIIwAAALVYs0YljW7cuOEy61xeVUHLT/3ChdH0gDkQRgAAAAAAwOWBNAIAAFCLNWvWuJQ0qqiqpK1xu7kw+tzLnS7mpeOPAAAAAAAAuDyQRgAAAGrhStKoUlFF2+L3qISRtzul5qbhDwAAAAAAAACSqDS6fPkyH9nZ2S43rl+/ThcvXqSrV6+65PpfuXKF0tPTSaFQ4NMLgB1xJWnkfdGfBu51o08PT6QUCCMgMthEAjt37qTNmzfTnj17XG7s3r2b91jbsWOHS64/m1V07do1fCZRAAAAANLIDIqKiujll1+mBx54gHr27Oly45FHHqGuXbvSQw895JLr36NHD+revTvl5eXh0wsApJHVhGScoPc9x9D7B0dTaOYpvPFAdGRlZfHjXvt2bemB++81f9x3r2WPF2nsPV07U4MGDejuLne5xPrqj47t21GbNq35l4YAAAAApJEZsOm7e/fuTcMG/Zd+/2kxbdAbv2sN7dsMPU6KsUtmz6CmTZvQN19NsCh2gxWvK6bYcZ+OoLZt21Jubi4+vQBAGllFcEYYfeg5lt47MJJCMk7iTQeihB3vHn30UVrw7VS6GB2iHMFaI0Rr6P8eYuQx0or127+N7rj9djqyc6NLrK9+7Ir539ID3brhvAcAAACkkbmUlJRQ3759aemcr6k0K6meI1mysedPB1CLO26n4H92ucT66o/t63+izp07U35+Pj69ANiRtWvXcmmUcyNHluuXkJNMbofG8T5GfmlBVF1djTcdiBKWWdunTx/6+Yc5Lnncjw05Si1b3EGnjx1wifXVHxtXL6EeyLAGAAAAaeRoaSTdcT68PtJIPmP7+lWQRgBAGlnFpfwMPkMaE0abz+6iSkUl3nAgWtjx7rHHHoM0cmFp1L1HD0gjAAAAkEaQRmZKo9OQRpBGADhQGuXIRxqxbKJL+Zn02dEvadDej2hLHIQRkKs0ks8wTxrJd/BMI0gjAAAAkEaQRuZLI39II0gjACCN6nPxXVZAU/y+4RlGG85spsoqCCMAaQRpJO7xB880QnkaAAAASCNII0gjM6URehoB4BBptG4dtW/XTjbSqLC8iL4PXkiD9rrR/NBlVFhWhDcZSAImCx6zuKcRpJGcpBEyjQAAAEAaOVQa2b/+vMSOsZZLI3HX6lu6rSCNAHCcNGonE2nEBNHskMU0cK8bzQtdRgVlhXiDgWQQMo0WzzHzGJpkxfFXfLHa0sgV1lc/FtJIHhQWFlJERARt3bqVZs2aRSNHjqTBg4fQO++8g4GBIdIxdOi75ObmRh4eHrRhwwYKDg6mzMxMqqx0vUx1F5NGyVaIEHHEnrNIGkl/fVGeBoCTpNFaeUijiqoK+i3qD16S9pXfNzzjCABJSiMjmUYlVnzZI4VY/Uwjua8vehrJh4qKCjp9+jRNmTKFnnjyCWrevDnddtttGBgYEh3NmjWjbt260cCBA2nz5s0udT2KTCOJxZ6zePY0ec0oAmkEgGNYJ4NMIyaMNp7dRoP3fURjj06hjILLeGOBzKWRZcdfKcSalkbyW19II+mjUCgoNjaWPv74Y2rdujW/2GzV4nZ6/oneNHXUe7Tq2wl0aM18Ct62ksJ2rKJQzdip+jfMwAjVG7VuRyxiEWvz2MAtP5LX74vprx+m0zfjPqR3XnmeOndoxz/TjRs3pueee47+/vtvLoghjWQnjSTe0ygcs6dBGgEAaWSOMNoUu50Lo8+8IIyAfKURehph9jQgHnJybtDEiROpTZs2/MKy+71daO7kTyju0Aa6Grqb8iMPGhwF/F9Prds8+W0Fwn0HEYtYxDo59sapfXTO60/asnQmvdyvLzVs2IBnH7024DU6c+YMpBGkEaQRpBEArsX69SpplJ2dLcnl90sLoqH7PqFhB0ZRal4aVVdX400F8pFGl604lkos1mppJPFthZ5G0iEjI4P69evHZVH7NnfSrAludNF3C+VHaF+wKi9EIwxdrNa+qK19sYtYxEojtsAFYrNP7qVty76mPg9145/5rl270r59+yCNII3EMc6dhjSCNALAEdJovSSlEZNDAZeO03v7P6UPDn5Gp7Ii8GYC+UkjZBph9jQgquNOWFgYderUiV88vvnvZyn56Cbh4rIg0vAFuc7P2hexTDI5KzZSDrGeiEWsw2Kvhe6hb8d9SI0bN6KmTZvSwgULqKysDNII0sjJmUYWz54GaQQAcA1pxE7cgzNO0HsHRtKIQ2MpLPM03kgAaQRphPI0YFfCwk7Qna3vpEYNG9LEEQPpyvHaZWgFEboZCwUG7jdaNoNY82MjnRSLbeXS2yr39AH6feFU6tSuDTVq1Ijmz58vuxnWII0kV56mkUa7IY0AAHaXRtevX5fMMp/PTaURnp/zsrSjKb54EwGkEaQRpBGwK0lJSfz9aay8UJw+Zniti0rVhaYnH9qZQAUGLkYRa32sfsYVYhHrqNi88P10aO0C6tKxHe9ztGPHTt4UH9II0sg55WnhyDSCNAIA0kif1Nw0GnVkMg3Z9wntSNhLFVWVeBMBpBGkEcrTgN24ceMGDRgwgJekjRryOt08vU+3dCVCI0FMlVwZbtTrtNhI82PznRRbIMFYV9tW+S4amxt+gP5YNJXuaN6M7rqrM50+LZ+MdwlLo5lohA1pBACANKL0gss05ugUGrTvI9qRsI8qFRBGQD4wWdCnTx9II1fONOreHdJIhCxZsoQaNGhAb7zwNKX5b6s9A1OE4RmbCoyUvYgiNtL82HwnxRZIMNbVtlW+C8eyGda+HT+CZx+yxvjl5eWQRs6VRtZkGiVLNvZ8vRphJ8tmW21f/xOkEQCQRjUX1KX5NDNwHg3c60Yrw9dAGAFII5kd982TRsmyPSeENBInaWlp1PXuu6l1yzvo+Pafal086pS01HEBiljEIlZesZdDdtG/nnyUGjZsSDt27IA0kq40QqYRMo0AAFKXRvmlBfRt8AIauHcEzQtdRiUVpXjjgOyoX3kapJGcpFF3lKeJiqqqKvryyy95WdrEEe/wkhT9WZhUF52eBjMSjF2AIhaxiJVP7O5V31PTxo3osT59qKioCNII0sjRs6dBGkEaAWB/NmzYIGppVFBWSN8GqYTR/NDlVFIJYQQgjVCehkbYYqNaoeBDTrDj4p133skb3p45uE7vwlH3YlOn90mEelrvCFMzNTk71lPisQcRi1hRxOaFH6DXX3ial7CuWbMG0gjSyNHSCI2wIY0AcG1pVFZZRuvP/MWF0SSf6ZRXVoA3DEAaQRrJthF29x7SLE9jsihq9SS6dGyrbD6P1dXVtGrVKp5lNPq9N01cONb0F8qP0JrSW+t3xNopNhKx9o4tQKxZsUfWL+T7iueee07yM6lBGkEaSUwaoacRAK4sjZgw2nBmMxdGk4/NpMzCLLxZQNYwWfAYGmFj9jQJSqPrMUHkNbKXrKQRK017/fXXqVmTJrT/1zm1e5tEHDSYnaDfVNhg42HEIhaxsorNObmX7mrfhtq2bUvnzp2DNJKWNLJ//XmJHWMtl0birtW3dFtJSRqV5l6nW9cu4YoHQBrZ8BvejbHb6J29I+gL35l81jQAbEVFcQEVZp4jRWWFqJZLyDRaPMfMY2iSFcdf8cVqSyNXWF/9WKlKo4riQgqbNZS8Pu1Jl3zlI40KCwupQ4cOdG+XjpQRtEOnpEp75rJaPVAiTE/bLZbYfMQi1urYg4jVGp8OeZ2aNGlCBw8ehDSSjjRKtkKEiCP2nEXSSPrrK9XytMqSWxS+ZCRF/jgWV2JA0tLo2rVrolmmYxcD6N39n9JH/4yj+JwkvEnAZiiqqihp1zIKmvoaldy8Kk5pZCTTqMSKL3ukEKufaST39ZVLT6OUQ2vJe2Qv8vpUXplGsbGxvNzk5X599S4ePYVhUN6YnI2ppg+Ks2PzLYr1dFLsQQnGutK2OmjFMssvdtW3E/k+Y+7cuZBG0pFGxg/WhenxlHTKj6ICDvETlJspZ+qfPXM5kS5EBFLamePKn22btXPO4tnT6ve6WfEnKTrwMB+pUUFWLfP1cxFcduUa3KaWZhpJQxpdDj1IPqP7QBoByfL777+LShr5XAygYQdG0YhDYyn8ShTeIGBTCjOSyXdCPxlII/OO+wVpZ+nI7k303dRJNHH0x/TV+NG06eeldCXhdL1ECDtvmjvzK1o+7xu6eSG6Htk3yfWURpad5xiKZbcln/ajn5fMpS/GfkJffPYpzVOuywmvvTqPN/d1z4Z48fhlc78WtoU1yyxFaVScnUmB7i/zLCOeaSQjabRnzx5+ATj5o8FaF456jXMjPHWm7S4wMV13rebViEUsYmUV67NxCd9nfPjhh5BG0pJGuqM4M5G89/xJLz3fjzp1aE9t27Sm9u3aUq+He9Cvy+bRrfR4i56PiaLRbsOoc6eO9PijygNlzHHb9jQKt+/saVcSTpH7hNH0wH33UjvltmCjy10dadig/1LyKT+Lt+3ODaupp3Jb3tWxA62Y/51LNMIuy80m/8kv8BMlSCMAaWQ9oZmnaPjB0TT8wGiKvBqDNwfYlKryMgpfMorvs4M8XqNSiUkjS8eN81Hk9t4gur15c34iqxkNGzagp/r2oYQTPmY/V15qDD+236U852HP0UF5/sTOU6TS04h9yXdgyzq6/967+Qw32tuj9Z2taNH30/hjzN0WKxd8z8//WHy7tm1ssi2kKI3ObprFM4y8ZSiNNE2wF7mPMuMi0nD2T75ZF6CIRSxi5RB79tAGvs/4z3/+A2kkVWnEvvk5uvtPurNVK/5mtm19Jz3d9zF+oGe/N23SRHkCYJ7ouJWRQNvW/UQP3HePcMLRuVMHSokMkow0YtlVb7/xKj9xatiwIfV6+EE+NCdSTz3+KGWcDTPruVIiAmn44P9R06ZNhO0xe/qXspdGrOfKub9X8hMlb0gjAGlkNck3LtDIw5No4F432n/uMN4YYHNyzoYKGRGSkUaX6y9JZnw5Tnlcv42aN2tGE8d8RFvXrqSvp4znYoYdq1976V/KcxojX5hpvW6430F6+YX+1KhRI+E4b1IaXXaSNDLxuoknfKljh/Z82Z98rDet/XEBbVi5mPo//QS/7Q7l+ZaX8jyxrteI8POkV/79nM62UEkjf6uXWWo9jXISTtKxz58UhNH/s3cd0E1cTfent9AJKfBBAikU00LvkITeIfTee2+BUAIESOgJkNAJHYMNFqHYGGMbMMaFZrCxacbYFAMuAvc2v+ZJWqusZMmSrF1p5px3ZO/uXb23q92dd3fmjquNkUYrVqxg53fb0mm8qSpSnsmkVCPtR5c+CmEJaytYKWG5ZY/c/mX3jCZNmhBpJFbSKO7pXahb61t2Ihs3qMsIng8R9yDqni/82LYlW17ps0/h6S3vHJywByy8G52FAgXyQ5Pv6lmMNArzswxphATa7i1rWb+LFC4MOzetZuHr758FwbFdf7Fx4bpVi+fkuC9/dxf47NOKkE+2fZVKn8uO4ScMu9wOSKMPLx7DxXH1OWeJSCMyIo1yT8AqCaNeTkPB6cEZyMjMoBNDZt7fWWYmXFnY2W5Io8h71+Gj4sXZy6D508ZD4vNgjkz6Z4O8NDBGIF06dVgvmYHbf9+6OeSX7ad5owawZc1S0ZFGOIYZE0axfn9RpRKLsFJKCkSHBkD1L6uwdb27ddQrZo37+bFtK3YsmjWqD5tXL+NIozA7I43SU5IgYP047nqyxfQ01CXB8/v38unaEQcB6n9LA3RMvPlKdgsFG2gLWAlhCSsYLArm4z2jbt26RBqJlTRydz7ETmLJj0qAp+So2rp711xZqDa2g/9syjENq12r5tCwngOcP74PLp0+rCCNPjZ/pJHR1dMMa6jp1Lp5Y9bvIf16wgeVtLykFyHwU88ubF2j+nVA+uye3n25HNrJnKW5U8fDqwd+3H5tPdIoSzah9f9jlJqjFECkERmRRrmyF+9fwUhGGA2Dkw8kRBiRWcSent/LRUMoSaMkG05PO7h9I3seY8q4JrkTHx7EUtJx/W+L5+b4smz5gpmwA18wyXyCq2cdRZeelhgZDNW+kBNDS+ZO01q/4udZbB2mrqHOo759oYbRjo2/sZdtV8+dZKScSZFGIkxPQ6I/0tuJ839cR9eyyfQ0JWm0ffl0wwgOlXXSQCPJEcIajw20EpaOFR0rHdhIIo3ETxr9unAmO4m1vv0KYp+oizQnyJwJXI7r50wZl+O+Htxw58SzPU4fsRxp5K8kjRzNnJp2C4oWLcIcnQPbN2itR4FMHNPnn1aEUD/9TtDbh4HMacS3b0mRIXZDGkXfugyuo2pxqWmuI2sSaUQmetLo1au8n0C/S4yB+ZeXM8Joo992SE5PphNCZnZLjo0Gj+ktuXu2PWgaYRoaPo+bN27Ans+a679v04KtHzagj1H7vSJC0ghFqpVSBGeO7NZa73rygFy6oExpCPQ4Y/B+7ZU0Sop9DVcWyKP23EY7yPyfsdy1ZauRRlKeCaWUJ71HyvRNJGqRQIS1DFYz4oqwhLU2lkgjGyCNJowczE7iTz26aL9FkzlTGJLM1vfsYtR+s0kjTE/zNG96mr9lIo2wn9jnUiU/Al9XZ631PhecFOtLgK+bs1Fi2PZAGqUnJcCVhV24N9bKRqQRmVhtz549ViGNYpJiOcJo3Y2/ICEtkU4GmUXs3r5lamK99kAajR7anz2PB/Xtwbt+jGJ9h3atbJ40woq5SsHrAHcXrfWhivX4Qg39OsNJoxNmJY3EkJ6GaZ5hJzZx11Hgponw7OJBm440Uk1PkypEbzUjEOSTSwmv1okmVioUbKDh2HgrYaUixNrbsYonLLeO0tOsThotNPlB3LtbB3YSx48YzBt6PW74wFw5T6qRRk9EIoR93dVZ7vCVLwtBVy/wfi/TOypSGC67HCHSSMMeuWxnodjoHHkv6JjtOBFpREakkcGWmpEKi71XMdHr1dc3EWFEZjGLfXgT3Cc2ZPdpFO29PLOlYEkjJAscHBzMQhp17/g9ex5PGDGId/3MiaPZeqwom3vSyFMUpNGVs8dZn8uXLcMkCTTXY0oai0QqXIhFHVmLNGKRRtWqCZo0kj4LAfcJ8uvp0uTGEPc0CCK9T9oRaaQZZSDR0jbRPenUjFSwMjbQcGy8lbBSEWLt7VjFE5ZbTqSR1UkjUyKNQtlnGwWZMWvSGF7SaPr4kfJKIm1bamH1k0aHdQhhh5rc54e5EsLO+XvPHtvDEV1haulncmxkkI/CeSoMHgaTRqFqpJFxQtihOkijzYIjjRJehcsmHG1ZOprr6Nrw8sY50jQiI9IoF4TRtpt7GGE0030RvEl8RyeBzCKWkZoMgRsmcBPakKNrwGd5HzlpNMdWSCP+Z+j3bZqz5/HUMcN518+dOo6tx6poeR9pFGoCacSPjX96F27IcDfcTqk1JIRcT/7L9fmBr7sWFsWwlcVBLjodNPh7DSeNQm2CNMIoo5t/TuX8npDDqyArI8MOSKMZevVO1FJacpiAEpawhLVdLJFGoiaN5K1ty2bsJCI5xEcaTR07nK3v0flHE9LThBNpdMvzLEu169cDW2f5Z/fOcPrgDibgjX2u+HF5Xufp2Z1rbD1+NzpEBldls/FIo8zMDLizYz6XlnZ31wJIjntDkUZkRBoZSxgF7mFV0qa6LYDXCW/oBJBZzF76XQC3sXXZPdtzdjtI/RAnaNIod+lp/IRE1w7t2PN49JD+vOunjhvB1vfp3tEmSKOzx/aytHvNtmrRHPA8c4zTLLrrfV4LG3FX7veULFmCpejnhjQK03ssDCeNqgk4PS36lge4jnZg14/nnO8h6d1LThTb7oSw1aonSbSiDqQ5TEAJS1jC2h6WNI1sgDTqpgjTHjagNy9pNOSnXmz9mKEDhJOe5pd70shx71bWL822avEcJqwtd55K8Qo+3vE6Jw/jljmEt73OUnqawt7c9Qa38fXlIdlTmkDi2xccaeRKpBGZiG3v3r1QtmzZPCGNjgU7M8Jo3PmZ8CbxLR18MotZSvw7uLqoO6c7F+VzmkUe+SzrYxeaRsP692bP464/ttORtt+JrZ84crCRpNEJjjR6JCBNI/RXuvzYFjp930atOe7bxlLxlS/DvM4c08L6up5i6z8uX443bT+n9LTyOZJG4hfCzkxLhauLuyuqpdWG516OkJWlqKTmlV1JzVYjjdQrJklUPiV6tVGkWtWWJGp/WxcrETnWhbCEFRSWSCMbII2UQtgtmzSExKgQreppTRvWZ+sXzZoinEgjv9wLYWMpWH93F+Z4+bmfln9edIE3YYGcEHahQoXg3PF9WtiT+7ez9f+r9JlRY2KkUTPbJI3SEt+D76ohCqeoJoS7/QtZmVmQQpFGZEQaGTbhyMqEs4/doN+pkTBEMh6CooPZZIOMzBKGv60nZ3dx92e/P0ZChmzSm5FiP6TR0vnT2fP4qy+rQtyTO1ovy774X2W2fs0v82xCCBuruCZGBTNfJCkSP4PZ/7g8Vjb+QoUKQr58/8fEpjWx29etYGP6skpleBl8I1eaRuYgjfawSCNhpqc9ObsbXEfVZNfO9RX9IS05gV1n9kIa6SqxHa8aaRCgaMqJpsr/hLUQNpCwlsZKCWswNtLrCJFGYieNtv4hv/F/VvFjrTLy4bevMAKlQIECcHzPVi0nxNBIIyGRRvpa3NM78NmnH7N+L5k7DZJehKiNd9wwuSh4iyYN4UPEffVjEaXfYcudptEDQWsaoUMU7naAc4gwtSFd4SylUKQRmY2QRuXKloWXL19ajDA690hOGA07MxFuvw6ig05mUUP9OY/pLeQlwcfWhbhHt9g9Wx5p1JsjjZIEqGlUx0xC2K4n/uUqgmmWmT+nSFMvXqwYeEqOcs/wyHvX4VHAZZmfcNcw0ihAHNXTkDxq1qgB63e3ju3h/bMgbt37Z/egeePv2DpM5+d8pSe3ZePzZMfEMNLIdqunJb6JhMszWzNf5+K4evAu1J/zj5TpaRdsXAibVzw4QGOZ5qRTJQKBsIQlrO1jiTQSLWmUnUMefP0iFC1ShD3c50+fwJwBXP5O5khivj+e4C+qVGZ57UrsBZnDNeSnnixqBt9YGU4aGZa7npRDn40njUINjAgKhhGD+rJ+V/uiCtM/SoySv3lEZxC/M7/sOK37dVG283nyAEvhWzpvOnuLx/e9qqQRX6RRkpF9FgpphDn7XvM6KCYfdSD61iX0lJijROlpZLZCGpW1EGmE14nfi5uMMBrgMob9TUZmScvMSIe7uxZyk9jgg7/KlmVkk0bL+wiWNOIijdYsN/AZqv8F0TdffcmeyXVq1WB6hqhjiNo/1b+sKi/+0aYFfIi4x7Z/etMbvq72BXv+r106X+17n925Cre9zrF28J9NDFumVCnmJ+EyrEim+pIpt31WJY2STCBf+LDb161kPiCKXS+ZMw3uesv6fdWV07QsLvN9kExTYpcvmAn58+eHzz+tqBaphbqPmAqH4z60Q34sSpUqCecd97PleCzeK46psX0WImmE187dXT9zaZ53dsyVR4kq/CAt0sjdHkgj9cplWhooAfrLdgsFG09YwpqMdSGsSov0pPQ0EZJGoWqERMLz+1yKGlYFa9uiKfsf06nQiShUsKDKmz05dmCf7tybuNcP/Ll9bVy5GHp36Qi9unaAlk0bceXpMXcel48c3A9ehfiZ3Ocwo0ijUKMqtvm6nWKCkEqyDAkh1D8oX64MW9aofh2IDMp+uza4X0/5G8siRdSOxf1rbjCwb3fo3VV2PLp0YG/bcLsa31SXLevA2rHdf+aqz0JIT8vKzIDgAytZSho6Q7f/ns3y+kGRx59NGtUk0oiMSCMewujemxAY8d9k6Hd6FLiEnYN02TVFRmZJiwkNZNFFTKx3Zmv48OIxd89WkkauCiHsJJGlpyUZ8aIIXwSdObILypaWP+uRDMLnPkZVK9PW8KWRcnvUOCxbuhRbN2n0ULV9DejVleGwoc+k1ElEUqVAgfyyZ39pCL1xyeQ+a0YaJeXixZiu730TdhO6dfqe6z+SRPhdzC8sVAh+njVZ5isGc1ilJhSO+aWKTzdI5hvimHmPhazhMQ7hioyEil7TKEbm87lPasz8HPdJjSDxbRRHFoEWaVTTptPTpFppKRJ+8kZvNSaJmh6KNbHxRmElVsK6iBBrT8fKxYQ+2x6WNI1ESRppP6yx7OqIgX05J0HZPvukouw7FrIQZVUsOk2syliF8hCriEzC1r3T97wi08pWRuZ05T5VTYU0Mrp6WqgR+kPBcHzPX/BN9S/VnB50nDq2b60lBKl8E1ehfDmIeZx9LC46H2Jv7fQdjwUzJuWqz0IgjeIe34GL4+Ti1x7TmoH0+QMm/KhQf2SkkStFGpGJ3Pbt22cR0ijkbRgMOzOJRRkRYUSWF4ZRRr4rB8nvy6NqwtPze1iZ8CwN0uiCTZBGOT/3UcPxsstRGNy3BzSoUwtqfl0dmnxXj1WSfeB7SavsPBIlqP146sAOtXXoI3WS+Qad2rdRaa251r9XV4gM8jG5z/pJI+P8HD4s+oGrf5nLXhjW+vYrqFurBhMKPyrzN1SjgxB7ZNcWaNm0IQySHTslmYRt/a+L1Maudly+b8OORXbUunF9FhpplJGSBH5rR3B+zpPzuyELsiOM5A3sID1NlTSSqE+0AyRqpbelesp1x2tORglLWMLaFJZII9GSRtotPvwuBF6SMEJi58bVrAQ9RvTwaRdhpM25Y3vh5mX1CmMoJI0i0/J2WuVv+f8YnvyeJ0zbaE0j/8sW0TRS1SB6dvsqnDu+F3ZtXgMH/t4I186fZCl7WsfiHh6LfRDoIVFbHvv4NgTKjo/28cg+Ls91OJJiEML2WzOMKyP76PQ2LsVB2VIUpBEJYZMRaaRubxPfwfgLs1iltGMhzpBBhBFZHljk1VPZ+nPLekPqh1i1e7Y9VU/TLPiBpFCU7FmOz3hdeo0fnt9jaW1JUQ8s4ndYS9NIrcnGhsLYUfd9WVS4rlSyJMV2GKmeV+MXGmn03OskuI2uzfycKz93grREqQZhlB1p5GpnQthSHZWUpPpKdBOWsIS1aSyRRjZEGompWZo0EnqzNmn04voZbvJx9ZfukBL/Vi2HP4sijciINOInjJJiYI7HEujpNAQ2+m2H1IxUOshkFre0D/GMCFKKX7+4LuGiQpUREaIkjaJMI0jEhDWZNBL5sRKSphH6PFd+7sL5OK/8z8uuoeyovSweTSNXGxbC5ktVkfJMJqUaaT+69FEIS1hbwUoJS6QRkUbWbdnpaY5EGuWxpX6IA695P8onH2McIOraad63a8kUaURGpJGaxSTFwpxLcsLod98/IT0znQ4wWZ7Yo1N/cfpzgRvGqUWGgmqk0XIijYg0ItJIn2FKZ+jxdUzHiF1PG8dDRloyrx9kj6QRryhuQLbOiT5RXUFhA20BKyEsYQWDfU6kEZFGVok0Mrp6GpFGZnGWZA5Q2MlNXJRRwIaxMmcpVeWNNaiRRtnbEWlEJlLSaP9+Rhq9ePHCtHt2WhL85rOREUZLvFdDWkYaHVyyPDFpxANwn9iQ3YvxE0vG801uRVM9zczpaWJpeZKeJuAmlPS095EP4eKEBvLraXJjiA+/p5MwYqSR10mbTk/brlE9Td9kUrlOqi+Cg7DmwQZaCUvHio6VDixFGhFpZKX0NA+KNLICaRQfHgyXWKWQGsxpin8qd5ZAVfxRQS6lcOlpNYk0IhOt7TcDaZSYlgRbAnZAT6ehMOfSUniT+I4OLFmeGIpf3/l7Lkfghxz+TSW6CFRaFmSk2KemEZFGRBoZfj1lQOCmyRwJFHJkNWSxqD3QitzLjjQ6yaWx2WqkkZRnQinlSe+RsogDlRLmAS6EtRA2Xi3iirCEtT6WSCMijayUnkaRRnlNGmFE0W3F5AOdn/sHfuXN31dOQlJUNI2INCKzV9IoOT0Z/gzYySKM5nksg9cJ0XRQyfLMom97wsVx9eRVLqe3gJT3MSBn9kHjnm2/QthEGomjCSE97fXNSxwB672gEyTHvtLygYCnehqXqr9pEjw+swOeyNpjRXvCtZ2Klr2Or2GqKbbomx6CIY1Uy3HzRSBIA9TX8ZXqluoQ27UaNtBwbLyVsFIRYu3tWMUTlltH6WlWJ40WkhA2kUZ5Yq/83ZiAKjo+nrPaaFXe0WwpKulppGlEZo+kUWZWJpx8IGGEEVZLi5BG0gElyzNLS3wPvisHchPWiMtHuYksyIuDq0SICl/TCMkCBwcHIo3sOdKoWjWrkUYZKUlw9Zee8hdnYxwg/OIB7euIVwg7Oz1NtZm67O7O+QIkjTSjDFQiZfRpn/BGKlgZG2g4Nt5KWKkIsfZ2rOIJyy0n0sjqpJEpkUahosU+9MsNaRRqM8fqyM7NeUoapb6PBZ+lfTiHRTn54MKKINthUr7BTo7NJo2u//oTvL1/XdZ84J2sZf+t+ilvymXqTb7szV1viHngR7NBMlGQRqfDzkHfUyNg1NmpcDf6Ph1Msjy1iEtHwVVREvzakl6Qnpygl+hXJY08prWEN7e9QPosxKQW+/AW01SyHmlkO899w0ijUJv1Ca1NGj35bxcTk8fryXfVYEhPSdIZaa0adaRKGilT9g0lhlxV1mlihUMazdCrd6KW0pLDBJSwhCWs7WKJNBI1aUSRRhRpZJg9PbcbXEfVYo7KjdVDID0pQUdqWrbjpCqE7TpK5miNdWBv59wUTfVvzf+z/64j/1+BxT5cnt6SZoNkgieNXBhhNBJG/DcJgt4E04Eky1sfIeYVeM35XhEVURve3r/GO6mVE/3apBET+Z3wHVya3Jjp2LHPyU3Y3+6a/yu3USx3V/n74vgGcHlGa7OMKXfpaUQa2RJpVM1K6WmJbyLBQ+Z74HVxcVx9eBvso+M6AgVZlH2dJUZHwAtfiaKd0d+u8y3Pxj45t0uFNFogCNKIVwibp3oSX0SCrgkoYQlLWNvDkqYRkUZWqp5GpFFekUYJryPg8vQW7C2X27i68O6Br0YYNj95pCSNNN+UXdDTXHW8aVN9y+YxpSnNCMnyzP7991+jSSOMKhroMhZ6Ow0D36gAOohkeWoo1htyZA13H721fSZkpKfyRERkqVS+zIL0lETwXtjRhPszP+7i+PpWJI0oPY2EsE23u7t+ZlFG+Hu+8/dsyMpI17qO+KOOsrTIJb5r0FDs++dh2aTRDmGQRhhppF4xSaLyKdGrjSLVqrYkUfvbuliJyLEuhCWsoLBEGhFpZCXSiISw84I0ykxPg6Ddi7KdlF0LeCYf2ZoYqk4O6mkEH1zJ20KUfx9SLlslb4dWZjcNzEVFyehLRBqRCZQ0wt99UPR9GHpmIquUduHJJUV1QTKyvDOscumuuF96TG0qLwkOWXpT01ikUVoKBB9eCTc3T2QtUFfbonudKhajjOSkUQMijYg0MosQdrXqeZ+ehunxWC2WRd9NagTJsa9zvJb0RWGbghUqaaSrxHa8aqRBgKIpJ5oq/xPWQthAwloaKyWswVgijYg0ItLIKqSR5TWN0EmJCQuUp4fJHJTLs9rAh5dPFZNg45wd1TdpucV6zm7P+kGkEZk1SKOoqCgDIoyCYciZCdDn1HA4/9idCCOyPDck9W9umcJNLMNObsqhupPm/RkMvpfnhL2ysLNZSSMkC+qQEDZVT8tD0ig9OZHpFymj6J6c3aHzWlCLHjLyOjIU+z4yjIvqu7tDOELYvOLBARrLNCedKhEIhCUsYW0fG+l1hEgjcZJGls8/T7Ig1njSSNi5+sYeq7wgjeT6Fv3kqQeja7Fcel0lZfn0MVS3VSWKcov1nCMnjSg9jUyIpNGLD69gitt86OU8DPYHHWWV08jI8tpe+btyE1zvhZ0hSaMkeO7vz8ZjryzobJlIozXLDXyGPjDh+Ss8rCppZA/j1cRagzSK8DjGtBUxNf7Koq6MRIIcyFN915GpWCVpdEFg1dM0S2+rVi7T0kAJ0F+2WyjYeMIS1mSsC2FVWqQnRRqJkDQKNYEIEQY2zCjSSPzjtUZ62nNPR+5ttc/yvpD6IS6HcGtQq56W7ejwLTMei5FGF4g0IhMgafTywyuYdWkR0zDaGrgbUtJT6MCR5X2UUWoyXF0sLwnuNtoBIr1P8Nxjs0y4PxuHlUca1cyz9LQkE172iAGrGWlk6+O1tqZR0tsX4D2/g+LFmQNE377M+Sl814LuaCJQqy5rClaNNNohrOppUq20FAk/eaO3GpNETQ/Fmth4o7ASK2FdRIi1p2PlYkKfbQ9L6WkUaWQVbJjR1dNsq6KIpUmjFOk7uDy9FSdi+vqWuw4xR9DQygDeN2n8efzGYZWkEaWnkQmJNHqXFAsz3RdBb2c5YZScnkwHjcwq9vTsHo7o91szDDKwJHge3Z/5sBaLNDKINDLu+SsGrH7SyPbGa03SCMXkHxxfx11PgZsnMFJW/7WQxaMdZuh1ZBhWmJFGqqSRRH2iHSBRK70t1VOuO15zMkpYwhLWprBEGomWNBK5ppE/VU+zJGl0/+Aq9oYYnZObmycxQWy9+flgaM5+7rFeRBqRCYw0Sk5PgdU+G6Gn0xD4xXs1fEhNoANGZh2f4O0L8JjWQh5lNL4+vA25nqf3Z76W16QRaRpR9TRzmTQiFC5NbqIQv24M0mfBVruOVLFIGimrFQqNNNI/ieSP/ok3aAJKWGOwT9wPwLWjWyDc45BNjPftDSe47bIDQs7v1Vmhi34b4sASaUSkEZFGNkYaxT8JYk4Sc5YmNmROChhQ+QN0LDMX1ovS08isYAcOyEmjyMhILcJo3Y2/GGE0//JyeJv4jg4WmdXs3v5lHNF/55/ZkMmVBNetk5K7+7PhWCSNXBXRqhYjjaJMeJaKDGsyaSTyY5VXmkaZ6akQuGkSF2X04NjvLPJIp3C1gdeRObDK6mmuAtM04ktVkfJMJqUaaT+69FHEin3tcwKeXT7MWoSsRXofgxi/U3na56lDe0LpkiVg4biBNnGc3fashc8rloevv6gEsX6n9WLjAk4z3ZwIDzwHRyDC8whEXz+pI40qb8YrJSyRRkQaWbdlp6c5EmlkVmcpHQLWj1M4JTUh9OQGo9+K6RJwNBVLpBGZdUijA7yk0Ua/7Ywwmn3pF4hNiqMDRWY1i314C9wnfCePxJzcGJLeRuVQuQlyfX82BouaRkQaEWkkNtLoTdBVcB1VSy4mP78jJMe81EH4gBEkkXmw6ppGCwRJGvGK4gZk65zoE9UVFDbQOCxG9lT/32dQrnRJtYaER58fW4LXoQ0W+V5N7IheHdh5mTqkZ3aFq1x9rzCwZ/5eCUUKFWJEWKzfKb3YtXPGah3/j8uWhiZ1voXNiyfDW18nwY/XlrHPiTQi0sgqpJHR1dOINDLEonwkrFIIOiSes9tBakK8gROJ3BBDxmFJ04jMWqRRmTJlONIIq6KdeOACfZyHw9AzE+B1QjQdJDu3iIgI+PDhA1cuOy8NdYv8fh/FRR88OrPdKvdn/ZFGlJ5G6Wmmt7wgjTCiyGvuD1zV2OdMTD5TnSCF3F1H5sAqI42EJIS9XaN6mtZkUlFuO15HZSXBYgONwwad2cWOR758+aBiuTLw2cfloESxolAgf362vGSJYnBu529m/15NrCpplOvxBlr2WBmDlWiQRvqwo/t2YmMvLNu+QtnSUEp2vyxUsAB3Xnr/2AKifU6Yd7wCOlZ5gjVhvEQaEWlkpfQ0D4o0MjNplBwbDVcXdeOijF76XVA4K6DSdJSIVXWEAFS20cZm5RKLkUauRBpZzF6+fAkbN2xgjuDDhw+t2pf09HRwdXWFX375BTw9PSFD5sgLgTTKyMzgCKMRZyfDw5gnedqXtLQ0OHPmDCxcuBBOnTrF/reW4Tnx8vaGxYsXM92nhAT71XNauXIlfPvtt7Bnzx42oc1L8ijqqgtH9Hsv7ARpCVK991jQc4+FHO7PxmJJ04hII7FpGj2W/MMRsH5rh0E6J36tei2Ajmshy4DryDSsMDWNpqtNIKU6JpVKUVy1EuYBLjaDVZJGZUuXhBuOW9kyTE1TplfhuvZN6sn2cdqifVaPNMrdeNXIBStjdUUaaWFVSKOfOrfh1uN5GdS1HVteoEB++OfXmYIery1jKT2NSCMrpadRpJE5SaOszEwIc9qcXXnnjxFM/Bp4K6bllIMPXHnYHCvsGIH1mkORRpay2NhYaN26NbuZY6tcubJWOlZe2tmzZ6FYsWKsLyVKlIBr165ZnTSKeB4BLmHnoO+pEYwwCnkXlud92b59OxQsVFDh/BSAjRs3WvUc4blR/mamTZtmlUgboZBGyuNQu3Zt2L17N8TExFj8e1Pi38K1X3rKJ5GjasIr/wsG3WNBxz0WzIzNJo0smJ5GpBFFGpnJEt9EcmLy7hO/g3fB1428FrJMuI4Mw2KkkauSNBJgeppUJYVFqhF9IOVLudIo5c2niWI1bKDh2HhN0ujEVrV1y6YMY+uq/e8zNnFWxb6+5gh3JDvAbe/vcOafleB/YhtEeR/T+b2vrp2Ae2d2w0XZ9p4H1kOY636I9c8morRII0V74n4Qbp/ewT5x/7dP/8OwmuN96nGICU8/9zyiNd6Hbv8y3Kurx+XLA+TRI4FO2+H8rtVwdudvcEu2XqkjpIp9eukQ+37UfcJjcGn/Oriwew1brjwWiAtw3g5nd6wCz4MbmDaURFd6muY50iCNVM/RG9l+q35eka3r37ltdt8C5P3ydfxLduxXyc7BWrh/dje8u+Gs9dvAbcM9DjNC8D/ZtlePbmb904zgeSw7vteP/8m2wc9IryMm/a5sCUuRRlYnjRaSEDaRRiab9HkoeExvwTn4cY9vZ7/hAu2SsGBoxQ/QV07WOGxek0Z4jd28eRNu3Lhh8w0nuYVkD2XlxBfb0qVLrdafvn37qvVl1KhRVuvL8uXLoWTJknDE25Glo3U9PADWOW+2Sl/wnq96XDC6xVrHpXv37mp9QQLJ19fXLq4XzTZhwgS1Y4ENzxVGHr17ZxmBdDnRv4Uj+v3Xj4aMtBSDtVFyf3/mwerRNDInaYRkgYODA5FG9hxpVK2aRUijrMwMuLtzAUfIBO1emMtrAUy4jsQvhK0dZSBRS4eJ1zvp1IxUsDI20HCsnDTaqUEaZW/328xRbF31Kp+rkEYu4PTXMmjdqA58VLwo9+zAdKrm9WvBxX2/Q5xaqs9p8Dm6Bbq2aQKlPirObV/pkwqweNIQedqVDtLI59ifUOebL1na1rifusDWpdNkfxcEh6+rynAn1UiPrm2bsnVjf+qsNt7X107Ad7W+YuuOb/qFESt//jIZ6tf8CooUzvYfixYpDL1+aM5INNVjMKLXj1BYtt3mRZOhbaO6kC+ffPvxA7qy7ZDMGti1HRQtXJgtz58vHzSs/TUsnjiYHRN10kjCq5+jJI36q5FG8ta9XVO27vum9dnyKO+jsHD8QCawnT9/Pq7/ZUuVhEkDuzMhc05g2/80HN2wCGp/VZVFK7H+yTA1q1dhhBPuD/uGUUxfV83eH6YmNq7zLQRJdub6d2XKb1JoWCKNRB1pFCpa7EO/3JBGoTZzrI7s3Gw20igzLRVub5vNiSuGHFmtVXlHn0g1aIZe63V8co/Na9IoNDQUPv74Y63JIDX7a+UbfA6d9/djhFHlH6rD/6k4GNSo6WoFCxaE8ePHW6TKZcLLp3B5Rms5KTOhAcQ9umXQPdaYAgSmYoVBGtnOc98w0ijUZn1CS5JGb4Ovg9v4BswPwhdoCa/DdV4LOfs4YDGsmqaRYEijGTqrJ2mltOQwARUzVpM0Uo3OqVejGluHpIiSCEIMkkgFCxaA72p/BUN7fA8DOreFzz8ux7ZFQkc14gj3+UXlT9m6TyuUg06tGjHCCXWTUC/J6+BGXk2ju7J+1a9RXR4J+1VV2X7+giuHNzFCo1iRwiziRzneR7K+oh4Tizj/tIJa5TGM/EFyC3FBkl1sXIjHZa2+qw0jeneAnt83l/0vjxLv17EVFwGF7aeO8mh2FKdGwuhL2ViQYJk0uDu883WGzrLxKEmnZvVrMuIMySilJpSqphHvOdIZaSRf10y2P1w3rOcPDHtq63JGAJUvUwo6tPyOYds3rcdIsXyy7VbOGMGqsbFj6LKT9RvPVceWDWH2qH7QvV0zRt7tWzOPbXP5wHqmoYR9xuOA23SUjanURyVAsn2FXV0LurBEGlnJUD8Cw7SH/tQbDu/cAod3bJa3nRqfmk1zuUixf65ZJrswC8OKhbOMw+4w4XsFhJ0xYRSUK1fOLM7Tm7ve4DragTlLnrPaQOKb5zyTBOCJLjKMHDIX1nNO3moaJScnw907dyAwMNAuGjqAFSpUYBEjGDlhzb54eHhAq1atoUiRItChQwe4evWq1fqy6p810Pnf/vDT6VHgEnwObt66abW+nD9/Hho1aiRzXApB/fr14b///rNaX1BrCs8NnqNqsonc4cOH7eZa0WyTJk3SIowaNGjAtJ4skaaGpD6K4Conj8EHVkCWCtGvvzIT6L0/mxNr7vS06Oho+Oabb+Cral9AmxZNoE1zRWuhbE1V/s5e11bRtNaJDNuoQR3IL5tAfVfXwS7Gq4n99qtqLFX4zZs3Zr2eMlKTwe/3kVwET8jRtSZcC2BRrDppJIz0NF4hbJ7qSXwRCbomoGLE3lWkpyFp0K9TaxjXvwv07dgKqlf5TEECfQG3Tv2jhl0+dTic/HMpI4eUwt2YolayhDyS6MqRTZw20qg+nbgUN1yOhMzbG84sLaxfx9Zwx2UH6zOSN0rSCKN3Wn5Xm/1fq3pVli6F34H7+1JBQGEflOPFyKfCCuHoIoUKsmgn5XhXzx4jf7bV/Irhsc8Lxg0E931/cNFKsf6nYMuiyXJiq3xZRkJJNUgjRijJjk/ohf3w+OIB1keX7SugqOy4IZn05y9T4LVsf5jGtmvlbChb+iMt0ojvHOnSNMLlh9YtZIQPRi9tWTyFYW86/w2LJw0G/xNb2XhYepzsezESC/fR8jsHlgqI+8B+sHP4VVXZMkf2vTF+zizK6JmHPCJppSKarF2TeiwdDnEYjeVzbAt3fu3lWtCFJU0jKxmSBTVr1oSSH5WATytWyKF9rPb/JzqWiwlbobyciS9XtoxdjFezlS5VEooWLWryhASdJZ+lfTgn5JnHEZb2wKdXATr1jCCH0rLmwcqFsGuCB2kaWcRQfDoqKopVgkpNTbVqX/B8Y3RGeHg4vH//3mpaOa8SomHi+dnQ02ko/B24FzKyMgVx73/69CnTobK24bnBc4STOHvVM0JbuXIF5wzjy5z9+/dbVKz3XcgNcB3jIK9yOastJEZH6CHmDb/HmhtrCdLo66+/hvatmsPMiaOy24TRsk9lG2VcExxW975GDOwDhQoWhCH9ehmNNeV7hYLt2L41fPLJJ/D27Vszk0YpcHPzZC417f6BZTlcCzlpelkOq149TSiaRjM0Sm9LVD4lerVRpFpluyVqf1sXKzEKq9Q00tU+qVAWti+bDnF+p/V+L5IS335ZmWGObFjElqHuTjUFyfPbrNGKaKXsFLy3vs4KgW0JF2k0tl9nFo2Ef9eo9j/wPf6XSjUrCUwa1F1BjtSGOP9TbNmcUf044gs/l0wewr4/VtbnH5o3YMt+Hj+QE/PmO1bYV0byyOanvsf/5I5VPwVphH2RawFlY5HgYmndNaox4ileJS1s08+ToEC+fBrV07S/V5U0wjS6TQsnwvJpw6Fvh1Zc+l/TejVVorckvBEyzluXy9P/ZedA2c99a+bKibCPy4Kf41Ze7Jo5Y9k233xRif0WDPtduZjwmxQflkgjK1liYiI76Ct+ng2vQwPg9QNs/ooWoPIZANHK5aHyz+gHKtuLFHvz8n9QvFhROHd8n12MVxO7e8ta5jyZOjF5cm4354D4LOsDaSkJ2o4KGKh5wZfGYEas52x5ehqRRmR5Ya8T3sAUt3nQ4+QQaL2gA4RHPKODQqaDNFoJNWrUYGSRVCq1KIGGUUa+Kwdy9+3HZ3dCVqYx99gsE+7PxmGpehppGplb06hadcukp0kjHsClyY25dM9Yha5jztHQxuk1mooVYqSRanqaepNoR4OoluUO4C/nrSqsa12sxCisMtLooxLF4K8l08Dl7xVwdONi+H3uWEaU4LriRYswPSBVHEajnNu1GrYtnQbLpg5j0TvKFLH9a+ez773/3x4oWCA/08q5tP8PvX1WkkaY5iVPl/sIbjj+pdVvrEqG67E0PYpbv7txChrX+YYtmzy4B/tsVq8GI6juycamrADndWij2vdGeh8Dpz+XMa2iXyYNhnlj+rPtMFoK0+CU22J0ES6fO+onrT4r9YamD++tdZw1q6fpO0dK0oivIWl177/dWthHFw/AoXU/w/r542HRhMEwrOePioiuTyH80iG2zYPze6Fiefk5wXS2KUN6gv/JbezYKIkTjCgqU+ojBblUDuaO+Un2fbv0/q6kJvwmxYgl0shKZu/V0x762Xv1NPNoGj05t4dzQK4t7Q1pSR8Mc16McnTMg1WSRlQ9jczSJk15DzPcf4aeTkNg4JYRUKZsGXj+/DkdGDJew0qD+CInL6KtMKrookJ7BaON3ty7wnOP1a8/pLeQgRmx5iaNkCyoQ0LYdl09zVKkEf5ew05sYtHMF2TN/4+RTBxbCNeRWnpapGr1NOEJYWump/BONPWltIgYizo/Ss0eJGlUsRGeRzjiqEOL75hWDq5z3bOWaQfly5ePafdgJCHT1FGIRO9fK9fLCXDaLiekiheVp5jp6fOI3nLSA7WRcD+YlrV50SSV6KDsKmwflyvNvnvPb3Mg0Hk7VChbilUZwypniMWxIKF0ZP0ith2O4cWVY1wqGkZOIVGC65DUwr4XVlR41SKNFJFG88cO0OozCl7juhXTR2gdZ83qaTrPkWx8StKo9tdVYemUofDrtBGwdck0uHH8Lxa1pIp96+sEs0b2VaTF5WPHCfteSJGeh6TRUwVphDiPf9dB/ZrVOSHsggUKwPCeP7DjoYx0Or3tV3aMlDpMhWT7mzG8D0RfP8HbZ6kJv0kxYrGSHJFGoiSNLC9amGRBrPGkkbAFHo09VuYijdISpHB1cQ/mKLmOrgURivQ04HFwAAwpCwsWwyo1jSjSiMyS9j7lA6y5vokRRgu9VsDfe/9hOhpEGpEJwfBeGHpiI1e4IGDjOEhPSTTwHptlwv3ZeKzFIo3WLDfwGfrAhOev8LCqpJE9jFcTi6RR9erVLZb6mZYQD97zO7LfrNvYOhB17ZReofecrg1LYJWk0QWBVU+TakUYqE8wpZoRO3rKdgsFG28kVkkaMSFsx61a2CmKFKy6334JL64chwfn90ElRfQO6uCg7o73oU1M8PqbqpXkpNGaeQwbfG4PFClSCAoUKAAe/67X22esUobYKYN7MDFqeUWwj8Btz1oVAkzCNJFQmJsJdHdpxwSdkSga1K0d0/hBggQ1gHaumAVj+nVm26FotXIfuD8kXJAsGtytPYs2unZ0M1w/9qcaaSTVQRqp9rl9E3llWBSP1jzOfKQRL3GhUT0tp/O7YcEERu5gRNbc0f3g3M7VjJDbu3quGmkk1UgDRN2nLq0bc+QYRkep9gOr2B1e/zO0bVyX7R+P4aoZo3Lxu3Ix4TcpTGykJ0UaiZA0CjWBCBEGNswo0kj849UmjbaYhTRCR+R1oDu4janDnBDv+T8o9DE0Jwugp+oHqIk7GoKFXGApPY0sLyKMfvfdwgijxV6rIDY5Hg4ePESkEZmgLPV9LHgv6CQX7h3jAC/9zhlxj80y4f5sHFZePa1mnqWnJZnwskcMWM1II1sfL2/1NAuSRmhR11y46Ouri7tCSly01rUAOq4F3dFE5sOqkUY7hFU9Taqma5KtZyTVRdDwTjwlanoo1sTGG4WVcJpGmtXTlATN0O7fs/UYVYPEwrGNixkJhBW4IhU6O1JFBA+KZisjjXAfTy4dhCqfV2TL1i+YoN7nALmAszJ1S0kaoU4QCk0r91Xnmy8h+OweNey/a+ezdVjFrUf75uzvf36Va1TNHytPM0NdpNpffcGicVCwWolFEWlcj1XOVKusoQi0ZqSRVEUIm5FGGsd5qCIlrGWDWmpRKhgdtPHniYx8kZNGp3WeI03SSN/5xW27tm7CtkXB8jiVKm9ue9dqpKdp/zZQBHvasF6cwDnfbwPTDgd0acO2QT0o439XLlrXkdixlJ5GkUZWwYb5X7apSKNkoyONzEMaoaEY9q2tMzhHKeTIb5CVkZFDxTOVyQRkaUULGVYtzTgspaeRWdLSM9Nha+AuRhjNu7wM3iXJhaYPHSLSiEx40UbPPR3BdUxtNoG8tqQnJMe9Mfkea25sXmsaJZnw/BUDVj9pZHvjtQZplJGeBn5rFZXURtWEMOfN8uIgBl0LfOn3YFasMCONVEkjiQahIVErvS3VU65bS4NIZFgu0qjUR+Bz7E8WrYPEAYopo5CyUox5ypAeDIuEDUahlP6oOIR7HGLfGyPbfu9vc6FkiWJqkUZvrztBnw7yqKEaX1YGvxNbmZ4OEkWY4tajXTOmqSNV0TRC0gjJEdRAKl+6pLyqWKfW8PKqI9fn0Av7WHoZplph5EzRIoXg1ml5hbeLe3+Xp1gVLMjSrL6o9AkjoZRYpWg26h4p94nk1bIpwwwgjdSP886VsxTpeQXAcfMv8spwvk5s/KghlF097bTOc6SbNNI+v7ht+6b12baj+3Zk38fIOfeDXPSVqqbRfztWsfQ01H1SftfKGSPZdk3qfMvwKKB99ehmrhIbnp+pQ+XRZT3aN7Ora0EXlkgj0ZJGItc0Mpo0sjVNI/ORRswZf3oPPKa1YI6I++TGEB9+L+e8e9W3YmBkzn4usF5EGpFZaqKQmQFH7p+EPs7DYfyFWfAw9gm3jkgjMiFaWqIU/NeNkeubyCa3T87t1Kh8mbf3ZxLCJk2jvBDCtjRphL/buEd3wG1sXXl1wtltGFFjzetIFYt9uaDUNBIYaaR/Eskf/RNv0ARUHFglaYTpWpiC1rReDWhc51uoUa0Kp5ODUSl3JDsZJtD5b44QQeJl6eShjNQpUawoJ96sJI3iFSQOEjG4vPInFRgRgcRHKdkyFNh2k62PVyONeim0h07DlsWTGSmExAySHUpiA4mZzooKa8qqY5g6p5zgV/qkPLduUNe2apXNTmxZysgm1ALq1q4p0xD6sXkDTs9HU9NIM9JI9TgjsVarehW2Hkmszm0aQ8eWDVnBoxLFi7Jjmi2EzX+O+Egjfed3iex4I2lXrEgRGNWnIyyaOAga1PyKG6+qplGfDi3Zce7cuhHMG/MTjOzdQdbPEmw7FC/HSKt2TesxDaie3zeDBbIxYsoenhc8Hn8tmWpX14IuLJFGRBoRaWQDpBFGFoU6bsjWyVg/BjLT03ly8MEoYUdzYr0oPc2ihuWsr169Cl5eXoIiSNJlv8M7d+7AkydPLPYdSsJo3PmZEPruodq6w4flpFFERAT9SMgEZe+CfTlR7EtTm0LC62cG3mPBhPuz4VgkjVwZaVTfcqRRlAnPUpFhTSaNRH6s9uQBaYSWKfOH7u9fxkVf3/l7NmSkpuhMzTf0OjIHVlk9zVVgmkZ8qSpSnsmkVCNlSJc+ihixGIVTrkxJrapdSBx8XbUSTBrUA+6d2a2WToXH7tMKZbltkYTp9X0L6P1jS/b/wT8WcN8bc+MUS2nDku6YKqbEYKU1rLj2ShHtoyROUIBZicUIoDH9OjFcmZIlWLqbsu9Y9SyfYl/Thvbk9GiQoBnaQ55Slz9/fjjw+wK18WIa2ryxAzgii1VsK1wIJg/uzsgnTLtDjSPleAd2acu2WTRhEO9xRh0o1FFS7gsJHdzPntVz4OOypRkhE6sgu/jOEfZ7fP+uDIuETU7nN9zjMAzs2pYj9FgluTKlWPW3Sp9UgK+qfM4iwBCDQuGqBBojxWRzUExtwygrJObWzxvPVVhTNjzWuL83151y9buSmvCbFCKWSCMijazSstPTHIk0MpMlx7yCqz93UTgkNeGl7xktEUbjKp6ZF0ukkeXuJcuXL4fKlStD8eLFoVixYlCxYkX47bffIBNF0a1oSNQMHToUSpcuDf3797fId7iHe0Fv52EwWDIOwmIea60/fPiwYEgjJND8/PwgJCTE6v0ICgqCM2fOgLe3N8TGxtKFZAXLTEuFu7sWcpPIe/sWy+6VmTlHLnBzVePvz8ZgUdOISCMijcRGGqF9eBkOnjPbyiPlxtWDN3c8eQgfMIIkMg9WXdNogSBJI15R3IBsnRN9orqCwgYah0VdnDDX/YwYwtLuyobl2jGSBokFTSxWUUPM+V2rmXjy1SOb4fW1E/DE/RBLcXt5zVH9e2XtmcdhJth8eP0iljb1UIaPVVQGwz4/dj8I145tkae8KVODZOuQ3MCqbgEnt7FII+V4X/ucYOluvrJ9RnodUyMFIi4fgeuyffnJMBiVpHmscD8YYeWybQUc27SYVWDDlDzUTsKS9O9UMA/d/oVrR7dAhOdhnccZq8xhuh2Kgrvv/4ORDJiShvu9eeofViFN5zmS/Y+RQZimp5pGp+/8IpkT4LQNHDcvYQLX2G/s/y3Zd905vUN2Tk9x5xb3idFc2LezO35j51Z5HLEPSGgpzyVuc0H2GXJuL4vO0v+7kpjwmxQX9jmRRkQaWYU08qNII3OTRuiURHo7getoB+aQeM37AdKS3hsUTg0GTDxMxZKmkWUsPDwcqlatCgMGDIADBw7A2rVrGXlUtFgx8PT0tFq/Tp48CZUqVWIkFr7l6tatm9m/w//lLRjgMoZFGXlF+PBuIxTSCCOtevbsCUWKFIEePXpYrR+PHj2CFi1aQNGiRaEglgcuXJj9fjw8PPKk5DyZumF00eWZbRST27oQ8zDAKvdn/aQRpadReprpLS9JI/z9PnM/zEUb+f42ENKTE3VoDxl2HZkDi5FGQhPC3q5RPU1rMhnA87++6B6hYANFjlXoyeQ5NkCEWDpWeTJeIo2INLJSepoHRRqZmTRib67T0yBgw3jOUXp4aguozBt0l4hVdYQAVLbRxmblEouRRq5EGpnd0tLSIDIyUm3CP3++vKLG+vXrrdavIYMHw/Dhw+HWrVtQoUIFs5JGONYbLwKhv8to6Os8HK4+99W5rZI0ehbxzGrH4uDBg1CuXDkoX748I2q6dOlitb78+++/bOKGv41z587BuHHjWAWYKlWqQAYK6JPluT0+vV12b6zF7tnXV/4EmRnpOu+xoOceCzncn43FkqYRkUZi0zRSezYmvofrKwbIdcNkLeLSIUUkH+i4FrIMuI5MwwpT02i62gRSM0VFfZ1Evfx5gAthLYTVJM8IS1hrYyk9jUgjK6WneVCkkQVII7SYB/7gPrEhlwqmFIHkd2x0VM7RKsUMelPQDMF6zaFIo7wyjDbCG/uWLVus1ofk5GSWHvfmzRuzk0ZPYsNhxH+TobfTMLj49LLeCBmONHpmPdJo/ITxMHXqVLh79y5LHbQmaSSVStUmbfgs+uabb9jv5eXLl3TxWMFSP8SBz9I+8snk6NoQedVJ7z0WdNxjwYD7szHYbNLIgulpRBpRpJEFLfqmB1yc0EDuD01vDknvovRcC1kmXEeGYZWRRq4CTU/TFYEg1Yhs4CvVLdUhtms1bKDh2HgrYaUixNrbsYonLLeOIo2sThotJCFsIo3MG22UkQ73D6xgukbYbm2bDhnpqWqh1WBoxQ/QV07WOCyRRnljqFfTtm1bKFqsKNOrsbaZmzQKj4uAyW7zmI7R7juHID0zXe/2QiCNUlJSGIH29u1bq5NGfL8XBwcHJrD57t07uoCsZC98/gPXMQ5sMum9sBOkxL/Rq42S+/szD1ZPepo5SSMkC/C3RqSRHUcaVauWp6QRRl/f+WceF90TcnhVDtcCmHAdiV8IWzvKQKIzxYV/cqm6DytjAw3HxlsJKxUh1t6OVTxhueVEGok60ihUtNiHudI0CrWZY3Vk52aLkUYsyiPmNVye0Zo5J27j6sGrQDedItWgGXqt1/HJPZZII9MMjyGmoqk2vggb1BLCdKMunTszssLafTInaRQeHwFT3OZDb+fhsOfuIUjNSMsRc+TIEauTRkoTImnk7+8PpUqVYk4ApadZz5DsD9w4QZ5aPKomPHTezHuPNaYAgalYYZBGtvPcN4w0CrVZn9AapBEaEjUe01syogb9obgnt3O4DnRfRzn7R/qxStLogqBIoxk6qydppbTkMAElLGEJa7tYIo1ETRpRpBFFGum2CI9j2SKQKwdAakK8AellfOuAJzLJeKznHNI0MsVQG6hly5bQuHFj1lq1agWhoaFq22Bpe6yihsTE/fv3Ld4nTLdq2Sq7TyiwrFkZzFykUXyKFOZfXg49nYbAJv+/ITUj1SAckUa6LTExEfr27cuEyjEii4SwrWvxT4Jkk9r67D7pMb0FxD8NynFiqnl/1l/VyTisMNLTiDSyJdKoWh6np7GXG5kZEOq4AVxH1WS/54B1oyEjNUXHtQAmXEc5Y9VJI2Gkp/EKYfNUT+KLSNA1ASUsYQlre1jSNCLSyDqkEVVPszhplJ6SBNdUdDKeXtirQ88Icigtm40xBSsXwq7JdJbIjLfHjx/DtGnTYeLEiaxNnz4dXrx4wa1/+vQpNGjQgFVOw1LqeWH4ndgPZZ9QtycqKsrspFFCWiL8evUP6Ok0FJZf/R1ikw2fdBBppOP+kJ4Oq1atYsLcgwcPZhpUZNY1nNwGH1ylqK5UE+7snA8Z6WkG32OzTLg/82FJ04jS08QshK1qqBt2ZWEXefT1mDrwwveMHk0v9Wsh7vEdeHB8LQQfWin7/J39LW+/sxaq9v9aCD32O7cue1v5+qDdC7NJI8FoGs3QKL0tUfmU6NVGkWqV7Zao/W1drETkWBfCElZQWCKNiDQi0shGSSN0dt4EXWE6GeigeM5uCwmvwnVHCPGlMYCBehkGYD1nt+fEucnMa0geKcuoHzp0iOnnCMVMJY2S0pNhOSOMhsCSK2sgPtm4a4ZII23D38fevXsZwdi6dWt4/fo1XUQCseSYV+AxrYUitbguRN++rPsebPT92TgsVU8j0sjcpFG16tWsp6gocAAASmZJREFUQhqx5+T1M3BhVC1GyF5b3A1SpO8Mun4ivZw4ogex2X8b29SxQok0Uk1PU28SrUgDtbLcyqYjUsH6WAlhCZsjVkpYg7FEGhFpZCXSyN6rp222OGnEJofpqXB3R7YIZNDeX5gwpCEiqOmJ7+HBsT8MbL8rmu5tlBXdKD3NvBYbGws//vgjEzJevXo1vHr1CqKjoxkREBMTY7WUI9THwWgWrMiFpeaRKMH/sRnaJ9xu1+0DLMJoqtt8eJ/6weh+CJE06tyls/WiWWTH9MSJE1CiRAlo2LAhPH/+nNLSBBVtlAlPz+/jJpZ+a4ay0uG6KzRlt8zMDHgfFQoxDwMhLjyIpbdhw7+5/8M1msYy1W2VOnTmIo2QLKhDQth2XT3NmqRRRmoy+K0dIfeHRtWCMKeNeojW7Bdgkd6apJG8yAimu6l9jqzF/a+23cjs/1WxwiGNputMT+GdaOpLaSEsYUWGlRLWYGyk1xEijcRJGlk+/zzJgljjSSNh5+obe6zyijRCS3gdAZdntpaXeR1TG2LC/PWUhQVuXXLcG45sclV5O6b+d001Z4pvW83/KdLIvObj48Nu4nztf/+rYrUoktGjR/P26fPPP1dLq9Nl6bIJ8P6go9DLeRiMOTcdot7nrhz8kaNHrU4affiQwAgj1KDCqKsffviB/Y+kXl5GheF17eHhIZu4lpDdfz4BV1dXCAsL49rbt1Q9TQiWnpQAvisHcanF4e4HZecuk5cwUhXaxUmxz/I+Ou7PNQy6P/NhzR5ptGa5gc/QByY8f4WHVSWN7GG8mtg9VkxPU5o0/D64KaoU4gssaUSIzuuKjzS6/++v8Oaut6xdUXx6w1vFp+pyzWVvVbZXbiONeCAY0kiqFWGgPsGUakZ06CnbLRRsPGEJazLWhbAqLdKTIo1ESBqFmkCECAMbZhRpJP7xWiM9TfXN9ZMzO9ibNfbmeu0wSE9O0KF/AVx1kGzSSPFmbLQDuMkaTmLwkzle+L/i041rteXLVZap/n15WkuaFZrRkHjYt28f7Nq1S6sdP37c4hXUdNm1a9d4+4SRP3j/y4kwOnjfkVVJG3d+JkTER+a6H0eOyEmj8PBwq52jsWPH8RJoeA/Q1ICyNGk0bdo0nSTjuHHj6YISiL0OdAe3sXUYGX95VhtIinnNW5QAVO7ZStIomwyqqUXoaxJDrhopNHzYvEpPSzLhZY8YsJqRRrY+XiFpGikNo/FCDq/mfud3ds6FjLQU3qpooEUa1YRnFw/ZzD1GMz1NqqZrkq1nJNU1CeedeErU9FCsiY03CiuxEtZFhFh7OlYuJvTZ9rCUnkaRRlbBhhldPc22KorkJWmElhwbDT7L+ireXNeCCM9j2UQRZGlFGoGCNFI6VteX94M3d7y0WjTPspza23vXaEZIptupz8qEy8+uwE+nRsFAl7EQ/DbUpP0dPWp90ggr32FKmGZDwfK8FKDGazw4OJi3L9hu375NP0CBGBJAt7fP4cgcFNLNysjUK16tGmmEYr83t0yDoF0LZe1njbZQY/lCuCv7vKu1rfz/e3uXWoE0Mu75KwasftLI9sYrRNIILTH6OXjOast8G7fxdeH1bQ89+l+gFmlk+6SRRH2iHSBRK70t1VOuW0uDiLCEJaxNYYk0Ei1pJHJNI38Sws5L0gjtpe85JqyKjs/lma0gRRqT/UaNR+MoRUEa4faBGygCgSxvCKMrz6/DIMk4GOQyTva3L1smdtKIjCw3Fvf4Llya2ozdh7GCmTKVRpeuESONlslJI6+5PzBRbSEZCWGTELYQSCO8VpD8wehrvLaQaNXUDcvSSE9ztQPSSP8kkj/6J96gCShhCUtYW8ASaUSkEZFGdkIapackwe2tM7k3ZqGO6/SKq6pGGhFpRJYXjvz1SH8WXYSEkffz6yYTRmjHjh2F0qVLE2lEJr5rIjNTdp/ewKWXBW6eqFUmHFQiIkRJGkWZ8CwVGdZk0kjkx2qPQEgjtNQPcXDjt8FyH2dUTXjmfpDpy2XxpKkpI41cbZY0ms6bqiLlmUxKNdJ+dOmjEJawtoKVEpZIIyKNrNuy09MciTTKQ5M+C4FLkxszB8h9UiOQRgTrfLuWTJFGZHlo4fERMOrcVOjtPAxOhZ01C2EkJ42OEWlEJlpLfBsF3gs6KQoZOMAr//MK/RXQIvxV09PsmTSKeXwLAi5JwOeCE9z2OgfS8CDjiBDZutehAeB30QVuXDwNT296Q2JksGBJo3ePcLwubLx3vGXjfRZkNPHzIeI+BPu4sX3cvXJBdgxv2zRphBZ90wPcxtVn1xZGXye9e6Ez0sieSCNeUdyAbJ0TfaK6gsIG2gJWQljCCgb7nEgj2yWNIu76wNqlC6Bfjy7Qo/MPMOSnXrB7y1qIkTkYhu7j7cNA9qAfNfgn6NHpexg2oDesXjIPou5dN4008jN/pFGSzDk5fXAnjB06ALp3/B76dOsEC2dMZE6joU5PtMxR3L15LYwc1E823h9k4+0jO4bz4UXwDZsgjdABQhFIpchp4KbxkJmexquToRTCxhZApBGZBS3y/QsYfW4a9HYaBv8GHYX0zHSz7ZtIIzIxG96Ln192ZAUG8F6MkUTqhQyyiSNN0ijJztLT3kfch42rlsAXVSozcqZ4sWJQ8qMS4FDzWzi5bxskRobkuI/QG+4wXPbc/+TjCjIfpThrZUqXgrYtmoCvq7NJFcTMnZ72PuIerPt1EVStXAlKqIy3bq0a4HzgH4PGi2TRX2t/hTq1voVSJT/i9lHps09g7bIFbL25zo/QSCMUwL69fTbn59zb9wtv2mek18ns9DR32yONtmtUT9OaTAbw/K8vgkMo2ECRYxV6MnmODRAhlo5VnoyXSCMbJI3QqfE5fxKq/q8SO7n58+WDAgXys7/zyf5u37o5vLifMwmCb9j+9/lnDKPEKqvsfPZpRQj0kJiQnuZhVtLo/bMgGDmoLxTILx9ngQIF2Ljx79KlSsIJmcOYFKXfgcJjVvnzT3nHW+nzT+CW11nRk0ZoKdJ34DWnvVwsdWxdeOF7htdRItKILE8II+kLGHNuOvRyHsoII3NFGGmSRk+fPqWDTSZKw8nt9V8HcBXPHp/5W6PypSLSKEU9Pc2eSKOkyBBYMnca83Xw2f11tS+gdbPGjPDBZziSP/8d2c1eLunbj0ONb9j2hQoWZMQR4pW+AP4ffvuKIEgjHO/CGZMgf375eL+p/iW0atqI+TvYV/yeC47/5jjenZtWMzz6S0gWfS7z7XDsSh9ozdL5NqdppGrSiFC4PKMVu2Yujq8L0mfBOkkjW4004iWNdBIdErUoBP4JKWHNgY0nLGEFhqX0NBskjZAQqlu7Bjux1b+swggK91OHYPakMVC0aBG2fM7kMTm+hfpj2UIoWLAANP6uHotY2rNlLfTv1Q0KFijA9tGyScNcv3ULMzNp9Mfyhcx5KihzdmZPGQsep4/Av9vXQ61vv2Z9rVihPETe9dG7j98Wz2X4pg3rw++ysWNUFkZpFVCMt13LZia9ZRQKaYT24vp/XLTRlZ+7QOr7WI1KPMAJYbsSaURmqUlkshSWeK+Gnk5DYPX1TRb5DiKNyGzBsOokVkTDe7bn7HaQ+CaCv3qanQph+19ygXJlyrBn9cSRgyHhuTxCJvj6RXCoKSeCWjVrBLFPbuvdT73aNWFY/z5wW/GSCLefO3U88y9wH8vmzxAEaeTr5gxlSskJsaljh8vGK0+fu3fNFWp+85XCZ2kKcU/v6t3P3+tWQm2Zn3Rw+0aIe3KH+TjeZx3h808/YfuoUK4si2iyxUgjpT06vZ2JYuN1c2PNUBZ9rVk9zbaFsKfnGIEg1Yhs4CvVLdUhtms1bKDh2HgrYaUixNrbsYonLLeOIo2sThotNLvzhA9mjLgpXrwYXDnrqBKGfA8mjR4qjxT6pCK8CvHTH2nkdhoObN+glh+Pocq9unZg+8Aw5qj7vlYXwn71wJ+9ZcM+YVqZakQRjl/5pjEnRxVz+Q/9s0lrvF07tGd4DN02JEJLDKRRZnoq+K0ZIX9zPaomPJJsh8zMDK3qaRdI04jMQhabHAfLrqxlhNGvV/+AxLQkIo3IyPRY0K6FXLTRvf1LIDMjXa1UuNA1jZAscHBwsAhptHz+DPac/urLqvBSw7fBF0gYNYMRNPevuen3J0L8tZa9DL4BVSp/zvbfv1dXQZBGi2dNYf3BiKrosAC1dfjCi423UEEI8/PQux8klWKf3NFajlFbuP8iRYpAiM9F80UaVasmONIoJf4tXPm5q/zaGlMbIq+c1NA0OmlHpJFmlIFEZ4oL/+RSdR9WxgYajo23ElYqQqy9Hat4wnLLiTSyOmlkSqRRKO/ygb27sZPa5cd2WvnoGIFTrFhRtl6VUDKm/blmGcMXlTkTQVcu5KrPD3OlacQ/3kunDkGxovIx3fL8T20dvm1s36o5W/dTj865Gu+GFYsV4e3F2Fs8c5yjIzs3W5U0YpP2h7fg4rh6nAikvJwzKERWsyA5ltLTyCxjCamJsPLaekYY4ef7lA8W+67jx48TaURmG9fNq3C4NElRyGBiQ4gJ9VNPT1MljebYCmkUaoCeYQh0+r419+IoW7Q6VBF9c4FFG+P643u2Gv296Ed8rXgxNahvj1z32TDSKOfxJsrG+31ruV8zbthA9r8q9o7XORYhhOtR2yg3x/lP2TmS+3mFIfTGJZPPkZBJI7QXvmfBbbQDu3au/tINkuOiOa0w2yeNZuisnqSMMtA32SQsYQlrH1gijURNGvFp+9yDxg3qspO68ufZWvnsjwO94H+VPmPr9/31R66+Y5nijR5qBAgh0mj/tvWK6KmPIZ4nFHvO5LFsfT2Hmrna/6LZUzhtpBe5HK/QIo3Q8C31/X1Luaogd3bMYdoZypDsZIo0IrOApWakwp67h6C383CY4jYP3iS+s+j3EWlEZiuWlZUJj05vy74vbxrPRLFBJKRR7tLTciYkYh/f5vyeTat+0cJiVHXNr6uz9etXLDL6e/FlUYXyZRXpadOtThq9k423fp1arD9bf/9VC4uRURiBhOu3rFmWq+M8cnA/eWr/x+VZ2pq5SKNqAkxPU/pDAevGcNHXYU6bubRPW09P06VppFk9iS8iQdcElLCEJaztYUnTyMZIIwytVur47Nu6TrsU7aPbsvXyfPffly0wev/x4XehicI5a9b4uxzFpXWSRmasnrbu159ZfxrWc+Ct9LF59RIuJc/o8T69Cw3q1Gb41s2b5Hq8QiSN2Jvr1+EsjUEuAlkPou94aAlhuxJpRGYmw6pou+8chD4Kwig8LsLi30mkEZktGabSeC/sokilcYAXvhJ10sgONY3w5VXNb6rr9Hsw5fy7ug5s/dJ5040TnI56AL8unMnSvVDfMNDjjNXT054HXYevq8tJIUyp1xpveBDUqSXXtVy5aLbR+3/g685ewiF+9JD+Ni2ErWpxj++A2/j68ujr6S0g7mmQQgjbiSNqbTXSSL30tkTlU6JXG0WqVbZbwi8EbRWsRORYF8ISVlBYIo1sjDR6fNOLq5p2ct/fWusTIu4zoUdlJJKx+8fopEKFCjH8fh7nzBqk0bxp41l/2rZoyolfquf3r2Hry5crY/S+d21aw1US4XPOxE4aoUOETpDSIfJdOYAr50yaRmTm/q0dvOcIvZ2HwSTXuRAe/zxPvpdIIzJbu46wkIHr6NqKVJrukJoQl109bbn9kUaqfo8jT/oZpm81+a4eW49aPcbsG0miTxUEyoiBfXl9jLwmjTBSu3KlT1mfTh3YoT3eyBBoUFf+smvFwllG7RtfvI1SRBmVKlkSgn3czEoaVateTbCkEeo6Bh/6TeH31IS7O+dBZlqqXZBGvJEGbNIo0ZhIqpTlVjYdkQrWx0oIS9gcsVLCGowl0kiEpBEKWl/5zxHOO+5Xa7c8zzLRw0qfKZ2Jf3idJ2VYM1YLM+rt0w13qPbF/xj2x7YtWT9yTxqZr3ra9PEjWZ/at24OCZyWgTrRhes/rlDOqP2is6R0RDv/0IZ332InjdBSE6Tgu3JgtlPkfhCyMrO46mlEGpGZw648v85S0ga4jAH/lzfz7HuVpNGTJ0/oJJDZhKUlfQB/LpWmBjw8tUV2z85URBr1FrQQdp1cCmG/CPZlpA36Hqpt7pRx8OSmN3xZpTJ7VqNeoNbLsuf3oVF9eYQ0VoI1pshGu1bNGA6FsDG935TnvrlIo0cBnpww94m923jHW99B7uehJqMx+965aTUUKVKYYTeu+sVs0dXKIi1CJo3QkmOjwWt2O3YNuY2tA9G3PVh62gU7EcLWPYFUmWjyRCwQlrBixUoJazA20usIkUZiI42w+senFT9m4dKqDdOnnsqcpy8UzhNfZAymWynLz+7YuFp/WLZq+Pc9X2jXoinDVf78Mwi5ftFgrKVJo/nTJ7J+NW1Ynzc9bdsfK9j6Wt98ZUT4tw+0atqIcxYf+nuYNF4hCmGrvrl+LXOM3MbVY06Rx9RmkBT7mtLTyMz2+/KO8GEpaQNOjwH/F7fkAqNEGpGR5fqaigm7ycSwXdk9uykkvHmu0DTKJo2SREQa5fQMDb3hAV9U/R8UKVxYrSGpExl0Hb79uppC40d732/CAjm/B6NdDPned49uwaA+3RmmbOlS4OZ0ULZtiEnPfVXSKMkEnyfijg98Va2qwo/7TXu8oYFQQ6HhdPDvDQb1GckhjFrCKrH5ZLhRg/qxNDdTxquJ3SPw9DR2bWVmwtML+1mFQnn0dX94en6vzZNGUq0IA/XKZfo0UISKjScsYU3GuhBWpVGkkQhJo+jQAFg0azKMGz5QrSE58vqBP9SqIdc0+mP5Qi1s+O0r8IUieuaUgVU1UESyd7eOXLSOu/OhXFZ6yxZLDDOKNArVK7SIwpbYN4wKQiFwTeyCGRO59DVD+ooikj06/8CJQHq6HDV5vEKONELLzMiAuzsWMEcJJyEYlp1NGtUk0ogs1xb46jb0OzUKfpK1gJe38vz7iTQis0niSHbPvrd3CXfPvrVtOieE7aoQwk4SWXpakh5xZSQ1MC0LiRfVFnHXh5WNb9awPntmz5o0RqsAyIPr7kzTENf/d3Rvjt+LJejHjxikSNH6CA7v2Ky1T0P6nFOkUVIuxKSxoTB1w/p1WP/mT5+g1bf7Pm7wyccV2HrXkwdy7DMeWzfZduXLlWUvIHt2+ZERbaaOV2yaRkrDSL4bvw3hIq2vLu7OpazZcnqaVE3XJFvPSKprEs478ZSo6aFYExtvFFZiJayLCLH2dKxcTOiz7WGJNBIhaaTvYY2lZtsrSrEO6ddTpfSsvHlKjkLxYkUhf/78LJ0tp+95+/AmDBvQm+2vXNky4LR/u07nyZg+hxldPU23c3JS1iccDzYMU1cP0w6GH9q0YP0fM3SAQYTc4L492PZYLeX0oZ1mGa/QSSO0989D4fKM1sxRQlHs14EX2d8UaUSWWwuNeQRjz89gUUZOoWes0gdHR0cijchs0j68eAyeilQa1zG14e39a2rV08RNGhlBSsie0SMH9mXPbdQuwmpqqlgkffLnz8cIG9UUMyRLWNOoQDt70hi2L9z+n42/QVJkiBFRxqG5JI2MGy/6d9jH5o0bsAIlqlisKJs/Xz4oWaIEPLtzTYt8S9IQ+r5y1pEJXyNh1Kl9G4i8d90s4xUraYT2JugqXJzQgIswsgdNI6mWCDZGHEjUSm9LA/VFL2hMRglLWMLaFJZII9GSRrrbkrnTFaRHOXgU6KniDN2HCSMGs3W1a3zNwq+VKWvoNPicd1I4H9mpbBNHyrcvU7oUHPx7I9NEMkcfH/qbTwg7+PpFFgHFVynE68wx1ndVgUx0mkKuu4PH6cPw9Ja3StneOzB22AB5OHqZ0nB01xazjVcMpBHa4zM7wHVULfZG7eribqRpRJZ7wujdQxh/YRb0cR4BR+6fhMysTCKNyMjMbE8v7FPcs2swPaMrP3exOyFsbMd2/QkFCxaEAvnzw+7Na+Wp6lGoTXgRGtaXV07r1bWjzMcJ4nyQ/r26wqC+PZheo1JAevkCeaW0woUKwdql82V+0k2IfXKba3GylludH3NpGmFDf6xgwQJQoEB+2L9tHXx4Lh/vvWuunG7lTz06g1ShPYnHY53M3+zWoT2TJlCO4Y73OahS6XOOgELpAdXxYjNFv1KspBFG7d3dMZ8ji1ztTAhbqqOSkiE6KIQlLGFtE0ukkQ2SRoEeEvi4vJxEaduyGUtDwwgjrDJWTBFl9OfaZZzTcPrgTihUqCAULVIELjof5PaD6W3oPOF+uvzQluWjYzlb1Xbp1CGrk0bo6CnfupUuVZKlq+F4j+7cwukYoCjk24eBnL5B88bfseUDeneXO1uy5SgMrhxv947f8473sssRmyaNUt7HwBVlOWeVRqQRmTH28sNrViGtp9MQOHz/BKRnplutL0QakdmypX6Ig2u/9GJEP7tfKwgk0ZBGUSY8S6PUo4S/V0RZo2/RtmVT2fO9G1SrWkWeal6hPFw9d4Lb/sDfG9hybMd2/8WWIeFS8qMSbBlWTa3vUBMa1nNQa43q1wGP00esQxqpjBelCNq0aCIfr2yfqO00oFdX+LKKvFgJpqf5ujqriXrX+Equc4Q4JXmmjKxW6jdqjhfbxFFDzHKO9oiINEKTYvT19JYcYeRqJ0LYUj3aJ1KNtB9pgAthCWvTWClhiTSyZdIISZQtq5cxgghPLr6JKs79XQDGDO0P7x7ezC5Jv3kt5zQ4q+gcdWrfmluuqzVrVD9XfcxOT3M0y5hDb1yCurVrcP3CsReUjRX/xqoqV8+e4NLMXtz35UQksfqK9Jncefq+VfMcx9u6WWObJo3YhN/vPDfxoEgjMmMtJikOFnutYoTR775bIDEtyar9IdKIzNbtlf8FcB3jwN2v7ZE0woYRxKhHiFFCymc2viRD4uP88X3MN1Jue+7YXuYb4bYXHP/lom6Q1NHnA+CLJce9W61OGsm1iy5Ct47t2Us/Zf8w0qpx/brgeuJftfFiZHmLJg3ZNn26deJelvXv1SVHv6dVs0Z2SRplZWXCI5ftMn+otk2SRuvXr2fnd9388XoiDhSiuAHZOidaZc6Fig20BayEsIQVDPbB+b3sntGiRQsijWyFNFKGIqNg9egh/dkbt1bNGsPgvj3h+O6/WLixOoHjAcP694axQweoaQLhm7jxwwfK2iCVNlBt2Z9rluaONPIzX6QRV1Hk7jVYtWg2i4rCSCL8XDpvOiOUVMPJMdR6w8rF0K97Z1ZhTkkmYSRRTuPd9vuvNk8aocBqwPpxann8AUQakRnyZjblPfzsuZIRRmt8N8P71A9W7xNHGj0m0ojMNi0zPY3do1WJfiFWT7NkehqXZv7kDtz2OgenDv4DJ/ZtZ9E2GIWUrFH5DLWLAi5J4Obl/7gCGqiB+DjAU+afXGLFOrKb+v+Yum/t9DT18Z5lL/xO4njdTsnGG6g1XvRzwm9dgavnTqppFr0M9stxvJFBPmbpq9hII7SUuDcs+ppLT3O3HdJo9+7dcjH1sf31ExwBPP/ri+AQCjZQ5FiFnkyeYwNEiKVjlSfjvbR/Hbtn9O7dm0gjWyKNVIWxpeH3WCgyOkS6hA3xjVRiZIhF+6KenuZhdtJI6RghYYZO3fuIezq1B3A7PB5JeTReMZFGaO+fPwT3id8RaURmlP169XdGGP3mswESUhME0SclafT48WM6QWQ2a+8jH4LbmLp2TxoJuVmCNBJT2ytC0gjtpe9Z2bXlYHORRr6+vvIJ4I8tdKaj8FVWUk4q2WeAS55ipSLE5nq8gVbCBogQS8cqT8Z7aN1CeZXSWbOINLJF0kioLcxCpJFYmtBJIwzLDj68htM0ItKITJ+lZ2bAtpu7GWE0xW0evE6IFkzfTpw4QaQRmV1YyOHVHNHvOasNJL6JJNKISCPBtD0iJY0y01MhYP1YmxPCjo6OZumWX39RCV5cOa43AkGqEdnAV6pbqkNs12rYQMOx8VbCSkWItbdjFU9Ybt2M4fJq6vv/3U+kkXVIo4V26TyYUwibSCPLWEr8W/Ce9yNpGpHptdSMNPj71j7o5TRUcIQRkUZk9mSYSuM5ux27Z1+e0RoSo58Lqn9IFjg4OBBpZM+RRtWqiY40Qnsf9QjcxtWzKdIoPT0dmjZtCiVlv8krhzfpiDKQ6Exx4Z9cqu7DythAw7HxVsJKRYi1t2MVT1huee2vq0LpUqUgKCiISCPrkEamRBqFihb7MFeaRqE2c6yO7NwseNIIo42ee54A19G1KdKIjNcyMjPgxIPT0Nt5GIw+Nx1efXgtuD4SaURmL5aVlcUmtVjIwGuO8ISwc0ca2c5z3zDSKNRmfUIxk0Zob4OuQMLLpzZ1v1izZg2LNlo5YyRv9SRllIG+yaauykuEJSxhbQfre/xPrnJaWloakUbiI40o0ogijSz8Jio5Efz/GE2RRmS8hJHbk8vQ//RoGHJmAgS8vM2cUCKNyMisZ6kf4sB35SBBkka5S08j0siWSKNqIkxPs2W7f/8+FCpUCGpU+x88vXRIb/UkvogEXRNQwhKWsLaFnTiwGyOY165dK/r7HpFGYiON/Ig0EgNphBYT4gePTm0l74osmzDKygS3p0gYjYGhZyaA/8tbgiSM0E6ePEmkEZldWUxoANzb+wukvo+1AdKI0tNICJvMUpaSkgJ9eveB/Pnzw/oFE9TKccfzlI6X8mifqJftlvCK7loHKxE51oWwhBUE1uf4n1D6o+Js3hoZGSn6+x6RRkQaEWlERpZHFhR9n0UY/XRqFPhE+kFmVqZg+0qkERkZkUZEGgmDNKpWvRqRRgIzf39/KFWqFFStVBEenN+rMWmUaEwk1SsvxQfoiFIIdBEAVkJYwuaIlRI2R+yQ7u1ZapotRBkRaSRK0oiqpxFpRCZGC4t5DCPPTYU+zsPh5AMJS1MTsilJo0ePHtHJIyOzoiFZUIeEsO26ehqRRsIz1CeZMGECmxSOH9AV3vo68eudqEwy+SpS6Z58EpawwsVKCau3OW5eAsWKFoHPbGjOSqSRBVqSBbG2RholGU0abSbSiEx09jguHIaemcgII8cHLoKOMCLSiIxMPKRRkkB9FXNiVUkjexivJnYPpacJ1oKDg6Fy5cpQpHBB2LJ4MsT5n1arXKZPA0WqFZ3gIghsPGEJazLWxa6xvo5/QcVyZZiW0f79+wUrQ0GkUY6ig6EmCBZaHxtmFGkk/vFSpBGZ2C02OQ7GnJsOPZ2GwIF7x0TTbySNMPSeSCMyMutaTulpSSYIM4sBqxlpZOvjJU0jcZmrqyuULVsOihcrAmd3rOI0TaS6JuG8E0+Jmh6KNbHxRmElVsK6iBBrT8fKxYQ+ixt7/7/dUK9GdUYYLV++HDIyMmzmXmeHkUbirrARZnT1NNuqKEKkEZmY7F1iDMy7vAx6Og2FdTe2QnJ6imj67uTkRKQRGZnoSCPjnr9iwOonjWxvvEQaic/++ecfKFasGJQpVQIOr/8ZYm6cUiu9LQ3UF72gMRkNkBCWsIQVGTYu4DQEOm+HBrW+YimrgwYNgoSEBJu6z1F6mtg0jfxJCJtIIzKxEEYLPVcwwuiPG39BiogIIyKNyMjEQxqRphFVTyOzrqWnp8Mff/wBJUuWhKJFCsOyqcPh5dXjOqN/4g2avBKWsIQVAzYuwAXO/LMKKpYvwyoq9u3bF16+fGlz9zkijYg0ItKIjMzMlpyeDBv8trGUtCXeqyEhLVF0Y1CSRg8fPqQTSkYmNNIoyoRnqciwJpNGIj9WpGkkHuLI2dkZKlasyCaOHVs1gnO7VkOMn7PGpFOiUa6bX4uIX6SYsIQVB1ZqJ9ggyS6YMKArlPqoOBQpUgRmzJgBUqnUJu9xRBqJrGWnpzkSaURGJkjCKAW2Bu6G3s7DYNalxfDyw2tRjoNIIzIyIo2INCLSiMw4u379OrRo0YKlqHwk89cHdm3HyKNYv1PqZc71ie2ioG5Atr6KVol0S2IDbQErISxhLYbFa/m2yw5YPHEwVP60ArvWK1WqBDt27GBVFW3ViDQSG2nkR5FGRBqRCdVSM1Lhr8Bd0Mt5GMx0XwQvP7wS7ViINCIjEzBpROlpdtOINBKXYaUkPFd79+6FGjVqsAll8aJFoNV3tWHq0J7gvHUZq64U4XkEYnQRSQE8/+uL/jAnNlDkWIUWTZ5jA0SIpWNlEBZTTe/9t4uloK2dMxZ+bPEdVChbmoldFytWHGbOnAlRUVE2f28j0kh06WkeRBoRaUQmUEfR5eF56OU0DIb/N/n/27sTcJvq/Y/jhnPMZSaiohtliK4ooVuG5igaFHKFKA0ISaRBt8JN40UapUHXPGRMA/8MKZkjUzJ0zBHRoe//fH7O2nft4UzOYHPer+dZj87ae6+99tqxvufzm2zjvp9P6c+jbvaERgChEaERcxrhxGqCuLg4Gzx4sNWtW9cNWVOAFBsTY4UK5LNihc+wksUKJ2xF2NjYongrXuTM48PPYmPd32FtpUuXtm7dutmyZctOqxXSCI1Oq+FphEaERojG4nDa+tkuMGo16T5bGrfC7SM0AkBoRGhET6PsXR9ovqN169bZ888/b23btrU6depY2bJlrWDBgm4eFDY2tujdSpQo4e7BN998s/Xt29e++uorO3jw4Clf52ej0OgxJsImNAKiwvQNn7tV0lpN6mTL4laeFp+J0AiIDgoLqlWrRmiUnXsaVaxIaAQAIDRKe2iUnp5GP56yr117QnMa/XjaXKsPhw8hNEJUWbZjpbWc2MFajGtrczcvOG0+lxcarVm7hi8ZOOVCo9Pnvp+60OjH07YmJDQCABAanZTQiJ5G9DQC0m9p3HJrO+UBaz72Hhu/Zupp9dkIjYDocGLD0wiNTqfQqCLD0wAAhEaERqkOjVg9jdAIUWHVzjXWbmoXaz5OgdGU025s87hx446HRmsIjYBTLzRieBoTYQMAQGhEaERoBJwU23+Ps66z+1jTMXfbsCXvWvyx+NPuMxIaAYRGhEbRERpVPJ/haQAAQiNCo1SHRqyeRmiEk2nHwZ326Of9XGD0yrfD7OCfh07Lz0loBESH3bt3W5UqVazngx3tm2ljkt+mp3F/NLw2hWN+POJVy58vn73/xuA0vzY97xstr+3X4yErX768+/8AAABCo1Q4sH+/mxCySuUL7KZrGiVsDSNsjUIe8/8c+uep9dpGV15huXPnsnqXXZrG1zZMx/tGz2tr1ajmliileMLJsP/wAes663gPoyGLhp62gRGhERA9tm7dahUqVLDY2BgXnmS3LW/ePJYjRw73Z3b8/LGxsXbGGYUsLi6OvwwAAEKj1FD33E6dO1nr1q2tY8eOyW4dOnRI0/5T4bX33HOP3dLsFmvXrl22+LyhW5s2baxVq1Z24MAB/vYiS2kI2ovzX3GBUa85T7kA6XRGaAREh8OHD9vixYttxfLltm7dOlu/fn3kLbnHErZT9bWrVq2y+fPn248//pgtPm/ovh9++MGWLFli8fHx/GUAABAaAUA0OnL0iA1a+Jo1HdPKun/e13YeOv17uo0fP57QCAAAAMjmCI0AIBlHjx21Id8OdYGRJr/eeXBXtvjchEYAAAAACI0AIAnxx47ahDVTrfm4ttZmUmfbuO/nbPPZvdBIQ0IAAAAAZE+ERgAQgQKj8WumWgsFRpPvtxU7Vmerz09oBAAAAIDQCAAimL3xS2sx7p92z+T7bdmOVfbXX39lq89PaAQAAACA0AgAQizZvtTaTO7sehnN2PB5tguMhNAIAAAAAKERAPgsi1tpd0+6z5qPbWuT182wY38dy5bXwQuNVv+4mv8pAAAAgGyK0AgAEm3Y+7PdMeFeN/G1AqPsLBAarSY0AgAAALIrQiMASBD3+w57YEZPazamlb3x3dt25OiRbH09JkyYQGgEIOCPP/5w26nm2LFjtmHDBvvuu+9s7ty5tmXLlgy/LgsWLLAlS5bY0aNHIz7nyJEjbqjvokXf2tdff2179+5N8bgaFh0fH58th0cDAKILoRECxcmff/5pBw8etN9//90VWRlJx96/f7/7M7nCToWVzkEbhRKyyrYDv9pDM3tb0zGt7PXFb2X7wEgIjYDoovvi1q1bbeOmjfbr9l/t8OHDWfr+N910kzVr1izwvrpH//zzz7Zq1SoXbkQrhTr33Xef5cuXz3LkyGGvvPJKoOYYPHiwPfhgl8TtQfvwww/TfHyFQDp2ixa3uRomkl9//dWuu+46y50rl+VK2L766qskj7dh4wZ7+eWXrXXr1ta0aVN3XlOnTk3y2AAAZDZCoww2duw4e//994Nam1RYRXsAoqLvmmuusXr16tmNN94YaIlTgLR48WL79ttv3bZ48bcu/EkLffYRI0ZYncvq2MSJk5J83uTJk61hw0ZWt25da9OmDQUSssTvfx60nnP6W9Mxd9uQRUMJjBIRGgHRYdu2bTZw4EBr0KCBVahQwc466yy74G8XWOPGje3111+33bt3Z8l5FCpUyEqUKOHqAlFvmVq1almBAgXs+++/z7T3VQ2h90xPSKYGqxYtWgSFRjruxIkTrWTJkm6/Pot6I6WFgqcuXbq4IGjs2LHJPleh3wUXXJBkaKS6cdSoUVa2bFkrVaqU3X///fbMM8/Y3//+d8ufP7/1798/wxv0AABIjdMmNNLNX92P3333Xeverbu1a9fOHn30URfgrFu/PstCm3POOcdiYmLswIED7ucVK1bY9ddfb08//XRUhyAqZkaOHGm5c+d2Bcv6hGsma9assUsvre0KFm2XX36525fWY9e+9FIrWapksr+A7tq1y5544gnLmTOnXXLJJVneiorsR4HRwAWvWrMxra3nnCdt/5EDXJREhEbAyacw5tKE+6fui2eccYZdccUVdu2117p9BQsWdAFE7969s+RcQkMj1TkNGza08847zzU8ZRaFYjVq1HQ9b9Kja9euQaGRKEy6+eab3X7VaWmlQO/cc861Cy+80OLi4lJ8/pVXXplkaKR6VXXWueeea/Pnzw/Uraq5zj77bBfOZeZ1BgAgKadFaKQwZtDAgVauXDlXWOnm7236uezZZe3NN9/MknPxQiOvN84nn3zizuOyyy5Lcw+dtFDXcBVC77333gkfY+fOna4F0x8ayaJFi9xnUME4b968NB/3//7v/1yRpFa+pMb7e7755htXGBEaIbMd/PNQYmDUyvU02nVoNxfF53hodAahEXCSaEhTjRo13P23Tp067v6rwEb3+99++83NodO5U2fr169flpxPaGgkO3bscMFJZvaA0fF1DdTjJj3UkBgaGqkmuf2O293+QYMGpfmYCnr02l69eqWqcfLqq6+OGBqtXLnSyiTUX3ps2rRpQY/puBoaqPfJqloWAAC/Uz400g1fvVMU1MTGxlqH9u3dRIc//fSTG2fep08fK1umjBsXnhVCQyMVVP/5z39cgZCZRdW+ffssb968ruv6iVJrns4/NDTSZ1CxUqJ4CVu3bl2ajqnPfN99HS02JjbFrtuiIXBqTSU0Qqb+u3HsqI34YaQ1G9vKHpzZy3YdJDAKNWHiRPd3kdAIyHoKCgYMGODuvRdddJGbOyhSKKEAyT859fLly10osnHjRvvss8/skUcescGDBgdqEt3DNaStW7dubtPQ8T179oS9t3q0vPrqq9ajRw8XjOj1kUIj3ddfe+01O3ToUNA5qeZ58cUXXVCj99D5+/3yyy/uPH/44Qc3T5MCG52PQhGFZR4d5/nnn3fXoXz58jZs2DC3aVLp5KgxUUPPdP6az+ipp55yPbQihUZ33HGH26/5jdJCNYqG9evfyYULF0asy9SQp+9AQ810PSpXqhwWGul6t0+oXXUOCociNa61atXKPf7444/zlwMAkOVO+dBIvVg0pEq9U9566+2wyRh1M94RF+cKE38xoU2PqXeNiiOv4NE+3bDVzVhDy1TM6OfQYk0/KxDR0CsVYSratC80NNI+PRa6Aob3PioqFHDpOTqe/zn6Wfu999dz9QuczjX0eRralSdPHtdNXO+lLaWQSsdQa+WyZctcK9fmzZsjhka6Ri40SigWNQQwLXT8woUL29/+9jf335FCJR1fLaZr1651LalnnBkcGuk8dT2XLl3qroE3R5QKV31HOqdonoQT0RYYHbOxP06yW8a0trZTutiaXT9xUSKFRhOOh0YMhwCynu791apVs1y5c7kgJbVD7IcMGeJCCc1NqJpAva0Ln1nY1Skavq9aSY+rbtKfelzvo9483v123LhxVrx4cfdYrsSJm2vXru0a5kJDo9q1L3X1gUIg0WMKafTe/vcpXbq0zZo1K/C6GTNmuOMryClTpkzgXLTVqFkjcLzbbrvNPeb1HvfORxNFJ0UhWLOmTV0tdsMNN7i5gP7xj38EjpFRoZH+bdQ1Ue+h0OkHVENdfPHFbgjh3XffbX379rUqVaoEPoM/NNJzy5Y92z02evToiN+1N4ROxwEAgNAojW6//Xi34jtb3pnqnjzVq1/seuSoFc1bTaNly7vc61VUqYDyD3HTjf+LL74IupGrKNFkzRp/rucUKVLEnnvuOTe8yx8aaVJFFRX+oVk6jpZn1fxAeq5er0KuZcuWQWPiNQG1Hn/ooYdca533XvpTP3uFmx5XYeadr95PBZvOJykKmR54oIv7pbBYsWJu0kWdg14fGhrpuScaGr311lvuteoNFmrTpk2uVS1f3nxuIsqiRYsGvg8vNNI10xA/XVftr1+vnpt8Uy2XKmq1r2LCd5lVE4HiVA+MjtqYHye6Sa/bTX3Q1uwmMErKxImERsDJoh6+ClB0f1bjiEeNRmqA8W/+wOKll14K1AKa/+i///2vvTniTXff7PdkPzfHonogqdZQr6T69eu752qiba+20VB/1R7qIaMaRrWS7s9eHeAPjTS3khcaqYZSryP9rJDGa+jRPvU21rw/Xm3khUZ6rsIUndPnn3/u6i3te/bZZ93z1GNKQ2W1r1KlSq4nubak7vmqr7xhaAqvvLpQn1/D2zIqNNL7PPbYYy4Aevvtt4Me03tefXVDV5fps/vDIV3H0NBoZOIQt5IlS0X8XGoUq1mzpnvOG2+8wV8OAAChUVroRqrgQMOyZs6clerXqXBRqKKiSCtZNG7cxB5++GFXRFWsWNFtWuL0X//6lyuwdINXIaMizisWvLBKPXs6duzoCi8vAPKHRhpupderlcgLjVQAaoJZbSpqVKhoMkk9r0mTJoHnaS4h7dPnU5iiFrO2bdu6X+S0X8GJfPTRR3bnnXe6fYUKFnRhlrbQcfH+orPlXS3d81VcqZhSq2bPnj0zNDRS4aTV2NTSpt5MfscntqzhPtvw4cPd++v6Nm3WNCg0UlinLunqnq7vTcGWQrLzzz/ftR6qu/w1Ta6J+tXpEB3m/bLAmo5pZXdPus9W7GDYFaEREJ309073Qk2K7NUe0qlTJzcpsrcp4BmQGLD4Q6NKlS4I9Nbx35ND75Wffvqpe36HDh3cYx9//HEg9PEPEZ8+fXpCbRObbGikntcaSqdGNDV6+TVu0tjVXbNnzw4KjVSDqZezRyGXjnfLLbcEAh9vTiPVBSnZvn27q+Ficse4HtR++owR5zRKrOfSEhqpjlJtouuvHul+qvFy5srpHgsd+qfgyh8a6ZrrO9X7q0dSpKFp6o2uhj09R/M+AgBAaJQGKqQU0KhA8c+1o192NCmhf5syZUpQaOQtr6pixBtqplU01H1aw6U8aiWrVevvlidvHjeEyivm1JtHvWPUUicqlvQ+MTG5kwyNvCFU6lGkFih1I/eKIj1fxYT2qxeSPzRSYKQixxtypm7Z3th3rwD05jRSiJUSFSuxeWKtatWqLhDyqHArkL9AhoVGul46JwVioYWqWi5VMOq6+E2dOtVdPy80UqClVUQUKtWtW9e9pnz5cq71U8XVmDFjIq5CAoRatO17u2tiR7t9fDubsWEOFyQFkyYRGgEni+bs8Rpx/PMBae4fDenS5vXKUWNSaGj0zDORVwLTime6N6smcqvNdu8eCCxUX2juH/2sRrNQkeY08odG3jB59Y5S72L1FvI2TeSt52mVVn9odM899wT1EtdUAqoBVDd4czVt3bY11aGR6iY1Lql3cmhg433W8J5GaQ+NVGd6vZlCeZNjX1HvirDHFMb5QyP1ErvyygbJvr+CNNWGxYuXiDjMHwAAQqNkaHiTigsVKP6WHvVEUe8Wbd5wp2bNmoWFRl44E4lu5CqAVAjdeNON7vlffvmle0xdkfVz6PKvKnDOTijwkguNFJ6o6CpapIjNmTPHVq9e5eYp0nbvvfe643pdnb3QSL2P/C1+Wn5VxZZWZPP2pyU0erLfk+592rVrFxTm6POqQE1uTqP161MfGqkXU86cOeyDxCLRT/Mt6JiakNNv/oL57nsLnQhbRXO5cuVd4aSJJelZhLRYsHWxtZrUyVqMa2szN37BBUlVaDSJ0Ag4SdQbVwGM7oeauzGSjz76OMnQSJMuh9J9XfPvqC7SsUuUKO4a3fyhUefOnd3PGlqe1tBIdYw3RF49pFSP+DfNbahhaMmFRuqloyH4V111VYTQKOXV09Q7WcdVb2TVRX49Hu2RptBIdYZqwUiLcmhxFdVcXl3opwZBHU/BXqjQ1dN0jt7QMw3DC6W6UfM6uZqzTesUV6AFAIDQKIRutipO1KrkD4C08oaGQ2kbOnSou9ne7Fs9zQuN1IsllHrSKOxQIaTx8yp8vLl+5sw5/svmU0897X7WkKlQoRNhhw5PUyGYK2cut2lonXoreZv3PkMSij5/aKTAy18oqDjTc9UzSa2GaQ2N/vnPf7r30cpyfmqVOyfh8ybX0yi1oZGOpXmj9Bm9a+HntZCOGTsmaL+ul4bthYZGWglPgZFeF9p6CCRn496frf3Uh9ywtIlrp3FBCI2AqKf73+WXXebukw888EDExR7SEhopANFwez12b7t2rmZSA9QHH3zg6gwvNFKPaf8cR2kJjTTEX/fpihUruH83tM+/qXHP+xxJhUYaUhYaGqVleJo+l16v8wydH6hr165pmtNI56prHzono+pEXQuFPeqhHuqdd95xx6tTp3bYYw0aNHCTm/tDIw3V1/O9oXt+6hWmHkYK+ebOncdfDAAAoVFaqQiqXKmSKzwGDYrcrVfz+uhm3DQVoZHGwl+WWKRdeeWVbmnTF154IeGGXjMxNDo+pEWTSernSBNNpxQaKfBwq5kULux62Yx4a4Rr0fM2FRveKiZJhUZbtmxxBYuG13nFW1pCI/Uw0vkrHPNTgRVp9bTkhqcptNJwudDldD8ZPdpdBy0zG4lXJH344UepCo3UJV/PV9FHLyOk1i+/bbUHZz7mJr5+c8n7djj+CBeF0Ag4JeobrZqm+57uiVoQIrSXiTf/UGpCI9UeWsFMDW1eY5Ood0vOnP8LjYYOG+Zer4DJH1Qdn9MoJtnQSMfVPD/6d0PDypOTUmikHjleaKQwSu+h+ZJSWilVQZjm/9Gx/fP/qJ7QkH4XGvlWXktuTiMNBStdqnRYHaMGQx3/3//+d8RzmD1rljuezkP1mkd1oWo01XVeDyW9f6NGjdzzFeD56Trfcuutge84dIU2AAAIjVJJvWV0Q1XYEWm+HS80urnpzSmGRuPHj3c3c3Up9hdVrVu3CgqNvN5LmpvIT8GNirLkQiMVgsWKH+8SriXmk3MioZEmXkwpVFGrmc6/efPmQQWYCjOt7JGW0EjXJG+evEHd51UAesWZPkMkXovn008Hz7ugQkqfwx8a+btna2w/kBpb92+3LjN7ucBo+JL37HD8YS7KCYRGK1et5GIAJ4HqCDV46d6XL38+N4RdE1erx4nm89PQ9dSGRgocVCeppvj00/+6iZz1d1w9gv3D0zS3oYbE6T48YMAAF8IMGzbM1S2pWT3Nq8lq167t7ucKXlRbjBs3zs0ppPkfkwuNVkQIjfSa/AUKuPBMdZrqCn+NEvo5r73uWncOmnxbdZ5qCV0P9dYJbfDT873h8qGhkVYq034tEuLROWmFXQ3x257YwBdK51umTBn3+TRdgq61zqNjxw7u+udK2O8N05OXX37FPVeLnXifWdfYWxlX19gfPgEAQGiURuodpELIa4XSkq0qENRlWMXAsMRWs9T0NNKkkF4YpNerkNGEyxUqnBcUGmnMvW78Cli8CZk1749CmORWT/MCGi8A6fJgF1dc6H206Xy1RG3o6mk695RCIx27QMECVqRoEVf06eekxr6r6FKBolYwteopZNJz1Wqm89IEklqtw6PhfpFCI71OLXCaVFvn7dHk4JpnShNfRpoLQJ588vi8SlWqVnXXSsdSYeUNndMwNK94Umil79b9AruSX2CRskPxf1i/r//lhqQNXPCqHTlKC+0Jh0b8nQNOCt0XVadodS1v+Lru3QoSVBvov888s3DQku/JzWn04gsvuPpEr8+bL19gBVmFRK0SQyPVAi+9NMQFLDq+eibp+ep9XalS5WRDI+9+7a06q9fpONr032rU8uYZ8kKjtmGh0Yqw0EjnpPl89D46js7bP8Qs1Ny5c630WaXd83XdNExeIZYCKu3T3EqTJ09276saUZ9f+3UtFMTpsx5fmCQmcVLxZwLH1nkrUFPvpOQa6N57730XLLnvqPCZrqFQNaImwtYxtbLs999/756rXmC33dbCfa66l9d1vcG9VX5btGjhaj56WAMACI3SWVSpG7SKAN2cdTNWC48CFS276hVZ/pa4pEIjBR8qiFSUaIz7XXfd5YIhFQj+0EhuTewyrHBEBZLmPlLhUTihMEiup5FouEfxxFY7jYlXgaBJtVWwqEhQSOMPjVLT00hUhOiYWhVNRc+QIS9HvGYqULQS2fFleStZ78cfd0GZrmHJUqXc5x09erQLzrR9+OGH7rn6BXLof4ba2LFjbeQHI63vE32PT6iZN09g9Tp9H+o9pOe/9tprSRY6aiU8K+F7ciuMXHGF6/2kLtrVq1d3RZ5aFPV9qKjTL60qXPUY8xkhxcDoz0P29LyBLjDq89Wztu8PVps5EfqlitAIiI46R6up6Z76yCOPWPv27d3cQ6NGjQrUCx7NgaN7sBq0Ih1Hw8x69OhhXbp0ccOhNM+QAhitpubdr1VvzJw5072XAisNk1MDl4bDaTi6v4eyjqGV1vy1iBrA9O+HVmLTuWpY+YgRI4J6y2hIu3r86Hz8dYKGyWs4euhwPPX+Vs8fzTGk3kxJ9TTyqIeUjvPwww+7xTN0fjqn3r17uxBIobiOr8cUtCW3LV68OHD92rRp4+qWadOnpao21TXQ9dZqa6qnFFJp6gP14lq4cGHg+XpMq8d68y6pnvTOEQAAQqMMolau/v37uyBFrVnqLaOx9QokNP58m68bsboAK6DxD0HzaPiTWpg0REuhk7qDqweOVuLwD7VS4KReMXoPhVRqbVPRpYJGoZTXBVu9kipXruz2ezd/FRMKRNSCpKBG56pz1pAsFXteQaYeO2rZU3HnLxxUJCpsUquV1xLnBVTq3XP22WfbueeeFzY+3k9D42655ZbACicKrnSuCp70sz6DPq8Kz4trXOzCpaQ2hTk7duwIBFI1Lq7hQrHQeY5Cab4BLUmra6jj9O3b1+bNm2dVq1VzXeb1vrrOKl71+P0P3B/UIgmE+iP+sD07b1BiYDTA9v6xj4tCaAQA6bZx40ZX76l+9Gq8jKZaT73n1XipoE1UE6rGorcRAOBkyXG6fSDv5vrz5p9dN+lIN1m16GjYVFI3YLWSqUXM6y2koELhTOjztV/vpcDKP/+Of0iWXqPXJjV5owoPDSfTmP/QFqWkXqv93hC8SJ9fQ/bUWpdSgaHHFfJozgH9tz6PQhr9rGtwIgWKWsrUK0g9pyKdX6Rz0DXU0DTv/PcknIMCvdS8Hgj8v38s3t5eOsoFRp2ndycwSidCIwD4Hy1eop7rGl6fmQHOk4mLrajBUT2xtGJvw4YNI65ECwBAVsjBJUCG/dIeH+/CotwxMW4CciDL/t87Fm8frRxrt45tY/dOfcjW7dnARUknQiMAOE4NfA0aNHDD5zP730T1JlevbzXAaQoA9ULXXEr0tAYAnCyERsgwmiRbq8fVqFHD9XQCssKxv47ZJ6vGWfOx91i7KQ/auj0buSgZwAuNNGwVALIzzaGkVWEffuSRJBf4yCheL3DNeaRt185dDE0DAJxUhEbIMJqk01tphAIHWUW9jN5d9qG1nNjelu9Yxf97GWTylCmERgBgx6cj0DB6Dd0HACC7ITRChvll82a3covmRAKy0uH4w7Zh7yYCoww0eTKhEQAAAJDdERoBAMJMoacRAAAAkO0RGgEAwhAaAQAAACA0AgCEmTqV0AgAAADI7giNAABhpk6dSmgEIENpQunhw4fbnXfeab/88kumvMc777xjt912m23atIkLDgBABiA0iiI7d+606dOn25YtWzLl+EePHrVvvvnG5s+f7wo3AEhKIDRaTmgEIGNs3brVKlSoYFWqVLEdO3a4fQcOHLChQ4fayy+/HLaNHz/e1SuqX1Qf+R9T+DR79mz3er93333PcufObX369KHWAQAgAxAaRZHHH3/cFToTJkwI7Pvzzz9dkaUWudDt4MGDgecpcPL2K3Tat29f2EpSWiq2YaOGdtZZZ9mqVau44ACS5IVGy5cv52IAyBAKenLkyGF9+/UL1CgLFixw+yJtdevWtfj4eLcqa/Xq1cMez5Mnj11+eV1bunRp4D1+/fVXO/fcc+2s0qXDAiUAAJB2hEZRQi1u5cqVszJlygSFQSqwSpYsGXF74YUX3HPWrl1r559/ftBjOk7jxo3ts88+C7S0qUAbNnzY8YKtb18uOoAkKTQqdEYhQiMAGeLw4cN27bXXWoECBVxQ5Jk1a5arS2rXru16E02bNs39qe27775ztYsawi666CIrXLiwffDBBzZnzhx76623AkFS7dp1AgGRnt+uXTu3f9KkSVx4AADSidAoSowcOdJy5cpl3bt3D9rfq2cvV/ioMGrWrFnQ9umnn7rnLFy40BVhRYsWtbvuustuvfVWq1ixontd/vz5bcyYMYHjqSeSQiUdb/fu3Vx4ABG50KgQoRGAjKH6o2DBglalalXbu3dvYL8XGt1www1JvtYLjUqUKBHUU1rHzJcvn9u+/PLLwP6PPvrI1VQKjwAAQPoQGkUBtYop6FHRpNYzvx49erj9Gr+fFC80uuSSSwL7/vjjD+vcubN7bc1LatqhQ4fcfg1Ru+mmmyw2NjaowAIAP/VSPKMQw9MAZIyJEye6mkSNW37pCY3Uk7pmzZqWM2dOGzt2bGD/mjVrEv79KuR6YYcO1QcAAGlDaBQF4uLi7JxzzrEzzzwzbPx9zxMMjWTx4sVuThL1NlLB5Rk4cKA7Zv/+/bn4ACJSaERPIwAZQcGNag7VHs8++2zQY/8LjW5M8vVJhUZqENPQftVAc+fODezXUDjN35g3b17bvn07XwAAAOlAaBQFNLZfv5zVrlPb9QTy69mz5wmHRpoYUkPWtGkSSc+MGTPcMZs2beommASAUIRGADKKegS1bdvW1R6jRo0KeswLjdQrSCue9enzeOKffQK9r73QqHjx4q62UVi0bdu2QI2k1dh27doVdNxatWpZTGysLVq0iC8AAIB0IDSKAp988klC0ZPT7r77bresrJ9XEPXq1cuWLVsW2FasWBGYMDtSaKRV155++mk3pr9+/fpuuJpn5cqVbl4Bdenes2cPXwCAMIRGADKKahs1VGkYmYap+XmhkeoVb34ib3viiSfcc7zQSEPr69Sp4+oazd2o45UuXTrihNeNGja03LljbObMmXwBAACkA6FRFHj99dddwfTQQw+Fjb33QiMNMStSpIgVTtiKFC5sxYoWC7TAeaGRlphVC56OpwBK+1R0TRg/Iei4GzZscEWWCi4tTQsAobzQSCE1AKSHejVfc801ljt3bvdvi58XGtWrV88Nq/dvXo3ihUZ6/QUXXGAXX3yxNWzY0M3dqH+jIs1bdN1110V8PwAAkDaERlHgueeecwVT796Phz3mhUYNGjRwoZK3devWzdatW+ee44VGep63qcWuatWqbgUR9Try02ojmkOpbNmytmXLFr4AAGE+mzaN0AhAhlBPo+bNm7v6xD9htaRlIuxixYrZN99843pJa96i5Ca5blC/vuWOibEvvviCLwAAgHQgNIoCgwYNcgVT9+7dk+xplJo5jSpUqGATJkywWTNn2k8//eSKrEgF1aZNm1xgVL58edu6dStfAIAwn31GaAQgY2hOo3vb3+vqmeHDhwc9lp7V05Jz4UUXuuFsS5Ys4QsAACAdCI2iwIgRI1zB1KFDB1dY+aVnIuykrF271g1vq1y5su3YsYMvAEAYQiMAGUUNWN7KrV27dg16LDNCo927dyfUOUVdbbR//36+AAAA0oHQKApMmTLFjbtv1LhR2FCyzAiN5s+f74av1atX337//Xe+AABhpjE8DUAGmj17tqtnGjVqFLToR1pCI62elprQSHWO5nTUgh8AACB9CI2igAqg4sWLWbly5YJWOZPMCI3effddd8x7722f7HwAALIvLzTS8tYAkF5xcXFWqlQpV+ts3LgxqIbRQh6dOt2X5GsPHDjgJrauVauWW8wjJYMHD3Z1jmooAACQPoRGUeDgwYNWvXp1V+CsXr066LHHHnvM7X/11VeTfP2iRYtcaKRiKiUa/nZfx47umBoWBwCREBoByEhaQa1ly5aup/P4ceMD+9V4dejQoaDeR5GoJ7Ymv06JjtOkSRNX52jSbAAAkD6ERlHi0UcfdQXOKy+/ErR/zZq1bqWRzZs3J/lajdefOGmizZs3L8X32bNnr1WrVt118V69ehUXHkBEhEYAMtqkSZPccHyFR6HD8TOKhtQWLVrUatSoYUeOHOGiAwCQToRGUeLrr7+2/PnzW/369TN1yJjeJyYmxq6/4fpUtdgByJ4IjQBktN9++83q1q3rhqmtX78+U95jwIABrhHu7bffZgg+AAAZgNAoSqjb9lVXXWV5YmMzbeJZddnu2LGjW4J2zNgxXHQASZo+fTqhEYAMpRBnxYoVrge1AqTMsGTJEhs3bhwLfQAAkEEIjaKIVhZpck0TmzZ9WqYcXz2LOnXu7IIjTSoJAEkhNAIAAABAaBRF1AKn3kCZ1Z3aO35Kk00CgBca/bD0By4GAAAAkE0RGgEAwhAaAQAAACA0AgCEmTFjxvHQ6AdCIwAAACC7IjQCAIQhNAIAAABAaAQACLNt2zYbPXq07dmzh4sBAAAAZFOERgAAAAAAAAhDaAQAAAAAAIAwhEYAAAAAAAAIQ2gEAAAAAACAMIRGAAAAAAAACENoBAAAAAAAgDCERgAAAAAAAAhDaAQAAAAAAIAwhEYAAAAAAAAIQ2gEAAAAAACAMIRGAAAAAAAACENoBAAAAAAAgDD/D+5uk0sm6v+nAAAAAElFTkSuQmCC" width = "869" height = "400" alt = "ste.png" style = "vertical-align: baseline; width: 869px; height: 400px;"></img></div><div class = 'S1'><span></span></div><div class = 'S1'><span></span></div><div class = 'S1'><span>For 2-D convolution layers, the weights and biases of the replacement layers include the batch normalization layer statistics. Convolution operations during training use the adjusted and quantized weights [</span><a href = "#M_2AA33B76"><span>3</span></a><span>].</span></div><div class = 'S1'><span>As the batch normalization layer statistics are incorporated into the convolution layers, the </span><span style=' font-family: monospace;'>makeQuantizationAwareLayers</span><span> replaces each batch normalization layer with an identity layer that returns its input as its output. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S9'><span style="white-space: pre"><span >quantizationAwareLayerGraph = makeQuantizationAwareLayers(transferNet);</span></span></div></div></div><div class = 'S8'><span>Inspect the layers of the network.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S3'><span style="white-space: pre"><span >quantizationAwareLayerGraph.Layers</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement scrollableOutput" uid="569A54B1" prevent-scroll="true" data-testid="output_6" style="width: 938.026px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" data-width="908" data-height="2325" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> 154×1 Layer array with layers:
1 'input_1' Image Input 224×224×3 images with 'zscore' normalization
2 'Conv1' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
3 'bn_Conv1' Identity Training Layer No operation to forward behavior
4 'Conv1_relu' Clipped ReLU Clipped ReLU with ceiling 6
5 'expanded_conv_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
6 'expanded_conv_depthwise_BN' Identity Training Layer No operation to forward behavior
7 'expanded_conv_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
8 'expanded_conv_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
9 'expanded_conv_project_BN' Identity Training Layer No operation to forward behavior
10 'block_1_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
11 'block_1_expand_BN' Identity Training Layer No operation to forward behavior
12 'block_1_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
13 'block_1_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
14 'block_1_depthwise_BN' Identity Training Layer No operation to forward behavior
15 'block_1_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
16 'block_1_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
17 'block_1_project_BN' Identity Training Layer No operation to forward behavior
18 'block_2_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
19 'block_2_expand_BN' Identity Training Layer No operation to forward behavior
20 'block_2_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
21 'block_2_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
22 'block_2_depthwise_BN' Identity Training Layer No operation to forward behavior
23 'block_2_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
24 'block_2_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
25 'block_2_project_BN' Identity Training Layer No operation to forward behavior
26 'block_2_add' Addition Element-wise addition of 2 inputs
27 'block_3_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
28 'block_3_expand_BN' Identity Training Layer No operation to forward behavior
29 'block_3_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
30 'block_3_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
31 'block_3_depthwise_BN' Identity Training Layer No operation to forward behavior
32 'block_3_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
33 'block_3_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
34 'block_3_project_BN' Identity Training Layer No operation to forward behavior
35 'block_4_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
36 'block_4_expand_BN' Identity Training Layer No operation to forward behavior
37 'block_4_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
38 'block_4_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
39 'block_4_depthwise_BN' Identity Training Layer No operation to forward behavior
40 'block_4_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
41 'block_4_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
42 'block_4_project_BN' Identity Training Layer No operation to forward behavior
43 'block_4_add' Addition Element-wise addition of 2 inputs
44 'block_5_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
45 'block_5_expand_BN' Identity Training Layer No operation to forward behavior
46 'block_5_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
47 'block_5_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
48 'block_5_depthwise_BN' Identity Training Layer No operation to forward behavior
49 'block_5_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
50 'block_5_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
51 'block_5_project_BN' Identity Training Layer No operation to forward behavior
52 'block_5_add' Addition Element-wise addition of 2 inputs
53 'block_6_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
54 'block_6_expand_BN' Identity Training Layer No operation to forward behavior
55 'block_6_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
56 'block_6_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
57 'block_6_depthwise_BN' Identity Training Layer No operation to forward behavior
58 'block_6_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
59 'block_6_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
60 'block_6_project_BN' Identity Training Layer No operation to forward behavior
61 'block_7_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
62 'block_7_expand_BN' Identity Training Layer No operation to forward behavior
63 'block_7_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
64 'block_7_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
65 'block_7_depthwise_BN' Identity Training Layer No operation to forward behavior
66 'block_7_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
67 'block_7_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
68 'block_7_project_BN' Identity Training Layer No operation to forward behavior
69 'block_7_add' Addition Element-wise addition of 2 inputs
70 'block_8_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
71 'block_8_expand_BN' Identity Training Layer No operation to forward behavior
72 'block_8_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
73 'block_8_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
74 'block_8_depthwise_BN' Identity Training Layer No operation to forward behavior
75 'block_8_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
76 'block_8_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
77 'block_8_project_BN' Identity Training Layer No operation to forward behavior
78 'block_8_add' Addition Element-wise addition of 2 inputs
79 'block_9_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
80 'block_9_expand_BN' Identity Training Layer No operation to forward behavior
81 'block_9_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
82 'block_9_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
83 'block_9_depthwise_BN' Identity Training Layer No operation to forward behavior
84 'block_9_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
85 'block_9_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
86 'block_9_project_BN' Identity Training Layer No operation to forward behavior
87 'block_9_add' Addition Element-wise addition of 2 inputs
88 'block_10_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
89 'block_10_expand_BN' Identity Training Layer No operation to forward behavior
90 'block_10_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
91 'block_10_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
92 'block_10_depthwise_BN' Identity Training Layer No operation to forward behavior
93 'block_10_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
94 'block_10_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
95 'block_10_project_BN' Identity Training Layer No operation to forward behavior
96 'block_11_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
97 'block_11_expand_BN' Identity Training Layer No operation to forward behavior
98 'block_11_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
99 'block_11_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
100 'block_11_depthwise_BN' Identity Training Layer No operation to forward behavior
101 'block_11_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
102 'block_11_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
103 'block_11_project_BN' Identity Training Layer No operation to forward behavior
104 'block_11_add' Addition Element-wise addition of 2 inputs
105 'block_12_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
106 'block_12_expand_BN' Identity Training Layer No operation to forward behavior
107 'block_12_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
108 'block_12_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
109 'block_12_depthwise_BN' Identity Training Layer No operation to forward behavior
110 'block_12_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
111 'block_12_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
112 'block_12_project_BN' Identity Training Layer No operation to forward behavior
113 'block_12_add' Addition Element-wise addition of 2 inputs
114 'block_13_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
115 'block_13_expand_BN' Identity Training Layer No operation to forward behavior
116 'block_13_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
117 'block_13_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
118 'block_13_depthwise_BN' Identity Training Layer No operation to forward behavior
119 'block_13_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
120 'block_13_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
121 'block_13_project_BN' Identity Training Layer No operation to forward behavior
122 'block_14_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
123 'block_14_expand_BN' Identity Training Layer No operation to forward behavior
124 'block_14_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
125 'block_14_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
126 'block_14_depthwise_BN' Identity Training Layer No operation to forward behavior
127 'block_14_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
128 'block_14_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
129 'block_14_project_BN' Identity Training Layer No operation to forward behavior
130 'block_14_add' Addition Element-wise addition of 2 inputs
131 'block_15_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
132 'block_15_expand_BN' Identity Training Layer No operation to forward behavior
133 'block_15_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
134 'block_15_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
135 'block_15_depthwise_BN' Identity Training Layer No operation to forward behavior
136 'block_15_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
137 'block_15_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
138 'block_15_project_BN' Identity Training Layer No operation to forward behavior
139 'block_15_add' Addition Element-wise addition of 2 inputs
140 'block_16_expand' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
141 'block_16_expand_BN' Identity Training Layer No operation to forward behavior
142 'block_16_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6
143 'block_16_depthwise' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
144 'block_16_depthwise_BN' Identity Training Layer No operation to forward behavior
145 'block_16_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6
146 'block_16_project' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
147 'block_16_project_BN' Identity Training Layer No operation to forward behavior
148 'Conv_1' Quantized Fused Convolution Layer Quantization Aware Conv-BN Layer Group for Training
149 'Conv_1_bn' Identity Training Layer No operation to forward behavior
150 'out_relu' Clipped ReLU Clipped ReLU with ceiling 6
151 'global_average_pooling2d_1' 2-D Global Average Pooling 2-D global average pooling
152 'new_fc' Fully Connected 5 fully connected layer
153 'Logits_softmax' Softmax softmax
154 'new_classoutput' Classification Output crossentropyex with 'daisy' and 4 other classes</div></div></div></div></div></div><div class = 'S8'><span>To apply quantization aware training to a network that contains convolution layers without an adjacent bach normalization layer, use the </span><a href = "./QuantizedConvolutionTrainingLayer.m"><span style=' font-family: monospace;'>QuantizedConvolutionTrainingLayer</span></a><span> provided with this example instead of </span><a href = "./QuantizedConvolutionBatchNormTrainingLayer.m"><span style=' font-family: monospace;'>QuantizedConvolutionBatchNormTrainingLayer</span></a><span>.</span></div><h2 class = 'S2' id = 'H_9D6B450E' ><span>Do Quantization Aware Training</span></h2><div class = 'S1'><span>Using the layer graph with quantization aware training layers, train the network. Compared to the training of the original network, the training options have been modified to increase the number of </span><span style=' font-family: monospace;'>MaxEpochs</span><span> to 10 and the </span><span style=' font-family: monospace;'>ValidationFrequency</span><span> to every 2 epochs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span >miniBatchSize = 32;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >validationFrequencyEpochs = 2;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >numObservations = augimdsTrain.NumObservations;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >numIterationsPerEpoch = floor(numObservations/miniBatchSize);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >validationFrequency = validationFrequencyEpochs*numIterationsPerEpoch;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span >options = trainingOptions(</span><span style="color: rgb(167, 9, 245);">"sgdm"</span><span >, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > MaxEpochs=10, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > MiniBatchSize=miniBatchSize, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > InitialLearnRate=3e-4, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > Shuffle=</span><span style="color: rgb(167, 9, 245);">"every-epoch"</span><span >, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > ValidationData=augimdsValidation, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > ValidationFrequency=validationFrequency, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > Plots=</span><span style="color: rgb(167, 9, 245);">"training-progress"</span><span >, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > Verbose=false);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper outputs"><div class = 'S11'><span style="white-space: pre"><span >quantizationAwareTrainedNet = trainNetwork(augimdsTrain,quantizationAwareLayerGraph,options);</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="4E2C58EC" prevent-scroll="true" data-testid="output_7" style="width: 938.026px;"><div class="figureElement eoOutputContent"><img class="figureImage figureContainingNode" src="data:image/png;base64,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" style="width: 987px; padding-bottom: 0px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport"></div><div class="outputLayer activeOutputDecorationLayer doNotExport"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport"></div></div></div></div></div><h2 class = 'S2' id = 'H_5AA96E0C' ><span>Quantize the Network</span></h2><div class = 'S1'><span>The network returned by the </span><span style=' font-family: monospace;'>trainNetwork</span><span> function still has quantization aware training layers. The quantization aware training operators need to be replaced with operators that are specific to inference. Whereas training was performed using 32-bit floating-point values, the quantized network must perform inference using 8-bit integer inputs and weights. </span></div><div class = 'S1'><span></span></div><div class = 'S1'><span> </span><img class = "imageNode" src = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAa8AAAKfCAYAAADZ+UcxAABEh0lEQVR42u293XNU193ny7kA7Krkyv/D5NSk6nD1VM0kZ2qciwMnz6maJzVU5eFmXDVzYfwQ43floQJVdkQZEAgbkGwsgwQ24iVSsLAlGkkgCakbrLdGQhIgJEEEloXB+E0JGAfw7/R3OVuz2e4WAiR1792fT+oXqff7Wj95f1hrr716ngEAQNa5dftbuzoxZsNX+y15qc2On//I6voqrap7u+0+UWzvHC+0kqbVtrnxFSs68pyti62wtXXPWGHt06lY7n7qs5ZrvbbT9u+0Ftruk8VW1bU9dbw91nq+1h1f59H5dN4wMo8/GQCAueObm1+kxNFnieF6q+mpsJ3xdbap/kXbEFuZks0aK09ssL0dpVadLLfavn1Wf7bGWoZilrjQbO2jceu+3G69Y0k7Pd5rA1f67cxnA5Ohz1qu9dpO22s/7a/j6Hg67r7U8XUenU/n1fl1HbqexEi9uz5dJ/ICAMjT1tTw1QFrGfzQ9nz8phU3vOxaROXxolSL6l07PFBlbcNH7dRY1z0SmuvQ+XUdup6qrjJ3fZsbXnHXW/nxFms+d8iVI5daacgLAGCGuPv9XRu5NmANZ6qsrHWtrU+1asrjG+xgzy5rGqy15CedWZXUg4auV9et69+ZKofKo3I1psqncqq8yAsAIIT87bsJO3Upbvs7Suz12ArbEV9vNb3vW3zkaKhENd1QuQ6lyqdyqrz7O0pd+VUPyAsAIIf57vYtd8N+/+RmK6p/wfamxHX03CHrG++NpLAyhcqrclemyq96eC9VH6oX1Q/yAgDIEf7y+aAdTO60dbHf2Z72bXZssDavZHW/UH2oXlQ/qifVF/ICAMgS3aOt7lmPhqsf7j+Qdy2sh2mRHe474Ibpq96So23ICwBgLrhz9461DcXsjcaCVGtii7UONyCmhwjV2572ra4e24Zjrl6RFwDALHBipME2NbxkB7q2u3elkNCjh+pxf+d2V696xw15AQDMEANjnW5WCr0k3D6aQDqzIrGE7e0sdfXcP9aBvAAAHpYvb1xLtQpKbUd8nbWO0D04J92JqXpWfaveVf/ICwDgAei42OymSKrrP4BUshCqd9W/8oC8AADuw+27f7fq7jIrTxRZB12EWQ3Vf0UqD8qH8oK8AADScOWby/Z2y6tW07sbeeRQKB/Ki/KDvAAAfAx91ue6qTTLOsLIvVBelB99ZQvyAgBIMfBplxUded6O885WTofyozwpX8gLAPKawSu9trH+RUtcaEIQIQjlSflS3pAXAOQlY19dtPWxZ61tuBExhCiUL+VN+UNeAJBXaIbzkuY11sgzrlCG8qb8BWeqR14AEGkOJnfYwVO7EEGIQ1+GqTwiLwDIC86Md7mZ4BFA+EMz1J/xDeBAXgAQWUpb1ljL0BFu/hEI5VH5RF4AEGm6Ro/b7pObufFHKJRPfbca8gKAyKLuQlpd0Wt9Ka/ICwAiyZWvL9m2pj/k3iS0J+rsn3/zaztx7n/Ppbjy98/aL3/1yx8tU2Q6TnI0ab996rdW+dGeKc+nYxS9teGB1+VybGta7fKLvAAgcrSer7M/db+bczdeCUryksQ8Cf2vFf/TfvHkLyZFNF0xTSeiKC/ltXWoDnkBQPSobN9qR899mJM3X780JDHJ67VNr062tCQ4yctriWn5vHnzXHj7BQWn5Vr/s5//zB3PO5Z+/vsff+9adlrvLfe2946p69C+3jE8ueZiKK/KL/ICgMix9dgq67x0IidvvhKOJxHvd09ikpKWSUz6XWLxfvcEo/V+eWm5Wm76KeFJVH55eV2S/v39Eg2K0H99uRjKq/KLvAAgcrx+eIWdHu/NzS9f9D33kiQ8GUleWieh+KXi79pLJxx99svG/9n/7CwoqUzyyvVQXpVf5AUAkWNt3fKcvfl6ojp47M9OIP7uQa/V429ded17Xmi9XzjBwR1BeWXqavSv81psOn5w8EguhvKLvACAltcch6Sh51xeV6HXXec9+5I8pmoRPUjLazryCnZrel2VtLwAAOaQXH7m5Qni1//y/94jD++51x+LX7tHRJ5IvNZRsKvvfs+87iev4AhInnkBAGSJXB5t6O+m87eq0j3jCo42zPT8yhs9qGNqdOF05OXto59a5p0j17sNGW0IAJFF7wHl4ntec/E8LZ0AoxS85wUAkeWHGTZW54Ww/K0mf+ssqsEMGwAQaZjbkLkNAQBCB7PKR3NWeeUVeQFApOH7vPg+LwCA0KFv3nXfwMvNn29SBgAIEweTO+xgzy4EEOJQ/pRHP8gLACLNd7dvWUnTGms8ewgRhDAaz9ZYSfMal0fkBQB5xdhXF2197FlrG25ECCEK5Ut5U/6CIC8AyAsGr/TaxvoXLXGhCTGEIJQn5Ut5SwfyAoC84cyn3VZ05Hk7PtyAIHI4lB/lSfnKBPICgLxi6LM+2xBbaY3neAaWi9GQyovyozxNBfICgLzjyjeX7e2WV+2D3t0II4eiJpUP5UX5uR/ICwDyktt3/27V3WVWkSiyjtEE8shiqP4rEhutuqvM5WU6IC8AyGs6Lja7bqq6/gOIJAuhelf9Kw8PAvICgLznyxvXbH9nqe2Ir7PWEQZzzEWonlXfqnfV/4OCvAAA/sHAWKeVNK+2vR2l1k5X4qyE6nVvR4mrZ9X3w4K8AAACJEbqbVPDS3aga3vqZhtHOjMirbirT9VrYrj+kXOEvAAA0nDn7h1rG4rZG40Ftqd9i7XybtjDdQ+m6m1P+1ZXj6pP1etMgLwAAO5D92irlbWudTObH+7/k/WN9yKmKUL1o3pSfaneVH8zDfICAJgmf/l80A4md9q62O9SrYltdmywFln5QvWhelH9fJCqJ9XXbIG8AAAeEM1wfupS3N47udmK6l9wAxCOnjuUdy0ylVflVvlVD6oP1UtwBnjkBQCQY/ztuwl3w97fUWqvx1bYjrb1VtP7vsVHjkZSWCpXTc97rpwq7/6UuFR+1cNcgrwAAGaIu9/ftZFrA9Zwpso961kfW2nliQ3uyxSbBmst+UlnqESl69V16/p3xje48qhcKp/KqfJmC+QFADBL3Lr9rQ1fHbCWwQ+t8uM3rbjhZdvc8IqVx4usqqvMDg9UWdvwUTs11pVVSen8ug5dj65L17e58RV3vZUfb3HXr3KoPLkC8gIAmEO+uflFSgR97l2nmp6KVItmnW2qf9FNkaRvfC5PFNm+jlL786lyq+3bZ/Vna6xlKGaJC83uXanuy+3WO5a00+O9NnCl/x4J6bOWa7220/baT/vrODpedbLcHV8tQp1P59X5dR26Hl2Xrk/XmcsgLwCAHGmlXZ0YS4mj35KX2qz1fK3V9VVaVfd2232i2A07L2la7VpERUees3WxFba27hkrrH06FcvdT33Wcq3Xdtpe++0+WWzV3e+44+m4Or7Oo/PlUmsKeQEA5BHz/o/8u5UjLwCAsN/I5yEvAABAXsgLAACQF/ICAADkRdoBAJAX8gIAAOSFvAAAAHkhLwAA5IW8AAAAeSEvAAAA5AUAAMgLAADmiJs3b9ry5ctd12BlZWXG7QoLC902/u38+yq0DfICAIBZZ3Bw0JYtW2bXr1/PuE0ikXCSkqy0/eLFi91PScwTlvZfsmTJlAJEXgAA8Mh4wlGrSXKqqKiwgoKCydZUupbUVLLT9sgLAADmtOUl8SxatMgt88SmVpdfTpm6F7W9jqN9kRcAAMypvPytrXQtKe85l3+5t8wvOuQFAAA5Iy9vuf9ZV5RaXMgLACCE8lJXoX73dxsqggMztEy/q8U11WAP5AUAALMuL40k1HOv4ICNdEPl/cuC65AXAADMCRKPNyQ+30FeAADIC3kBAAAgLwAAmPpGzqzyAACAvJAXAAAgL+QFAADIC3kBACAv5AUAAMgLeQEAAPJCXgAAyAt5AQDAnFJcXGyPPfaYbdmy5R556bOWaz3yAgCAnGJiYsLmz59vjz/+uD3xxBNOXvqpz1qu9cgLAAByjlWrVtmCBQvumTFen7U8H0BeAAAhbX0tXLjwHnnpcz60upAXAEBEWl/51OpCXgAAEWl95VOrC3kBAESg9aVBGvnU6kJeAAARaH0tXbo0r1pdoZXXrdvf2tWJMRu+2m/JS212/PxHVtdXaVXd2233iWJ753ihlTStts2Nr1jRkedsXWyFra17xgprn07FcvdTn7Vc67Wdtn+ntdB2nyy2qq7tqePtsdbzte74Oo/Op/MCQDi59dWgXeteY6M1/2Tnd/3Uzu2YR+RoKD/Kk/KlvIVOXt/c/CIljj5LDNdbTU+F7Yyvs031L9qG2MqUbNZYeWKD7e0otepkudX27bP6szXWMhSzxIVmax+NW/fldusdS9rp8V4buNJvZz4bmAx91nKt13baXvtpfx1Hx9Nx96WOr/PofDqvzq/r0PUkRurd9ek6ASB3uXpyZeqG+BO72vSU3eh/1+5cbjEb7yZyNJQf5Un5cnlL5S9n5aVWzfDVAWsZ/ND2fPymFTe87FpE5fGiVIvqXTs8UGVtw0ft1FjXPRKa69D5dR26nqquMnd9mxtecddb+fEWaz53yJWDVhpA9rl941P3L/jxhqUIK8QiU/6UR+Uz6/K6+/1dG7k2YA1nqqysda2tT7VqyuMb7GDPLmsarLXkJ51ZldSDhq5X163r35kqh8qjcjWmyqdyqrwAMLfohvd5678hgQiE8qh8ZkVef/tuwk5ditv+jhJ7PbbCdsTXW03v+xYfORoqUU03VK5DqfKpnCrv/o5SV37VAwDMfleh/sXOjT86oXx6XYizLq/vbt9yN+z3T262ovoXbG9KXEfPHbK+8d5ICitTqLwqd2Wq/KqH91L1oXpR/QDADD+G+GrQPSuhqzB6XYjKq/I7a/L6y+eDdjC509bFfmd72rfZscHavJLV/UL1oXpR/aieVF8AMDNolJoe9nPDj14or8rvjMure7TVPevRcPXD/QfyroX1MC2yw30H3DB91VtytI07D8AMPOvSaDVu9tEL5VX5nRF53bl7x9qGYvZGY0GqNbHFWocbENNDhOptT/tWV49twzFXrwDw4Og9IboMo9x1+NNHl9eJkQbb1PCSHeja7t6VQkKPHqrH/Z3bXb3qHTcAeDD0ois3+uiG8vvQ8hoY63SzUugl4fbRBNKZFYklbG9nqavn/rEO7kgAyIt4WHl9eeNaqlVQajvi66x1hO7BOelOTNWz6lv1rvoHAOSFvB6AjovNboqkuv4DSCULoXpX/SsPAIC8kNd9uH3371bdXWbliSLroIswq6H6r0jlQflQXgAAeSGvNFz55rK93fKq1fTuRh45FMqH8qL8AADyQl4+hj7rc91UmmUdYeReKC/Kj76yBQCQF/JKMfBplxUded6O885WTofyozwpXwCAvPJaXoNXem1j/YuWuNCEIEIQypPypbwBAPLKS3mNfXXR1seetbbhRsQQolC+lDflDwB5zb28rp9psiVP/mebN2/eZCz6j//BBuMHEc5sy0sznJc0r7FGnnGFMpQ35Y+Z6gF5ZU9eiQ/LJ5dVlq615f/jv9vNiyeQzmzK62Byhx08tQsRhDj0ZZjKIwDyyr681Opa9i+L3Trvs1pjapVpW2+5Qvt5LTZPeAr9XrbxD5P7SYiFBc/86Bjett4xvOvQORf/1//kwtvefy7/tvqp61UEW47+lqXOo22y1aq8R15nxrvcTPAIIPyhGerPMIADkFdOtLwkGv96LdNnLfck5UlN+3oS0nrvd287Tzo6hrfOfzzvXNrOE4//2H6ZeeLxtw694wevI3h87ZPNLtF75FXassZaho5w849AKI/KJwDyyu4zL3/LKF0rzJOIZOHvXtRn7TuWjN0jqOAxJBOtC4rTE48+B2UVDP+5vfN6x/fkq8/+llbwc9bk1TV63Haf3MyNP0KhfOq71QCQV3a7Df2C8ovBL4DgszFPUp68/K2mqeTlF6fXQgvu4+3n384vL/91ePIKCjBn5KXuQlpd0Wt9Ka8AyCt78gq2gB625TUdeWWSSXCfoESDLa908srJlteVry/ZtqY/hHOi2hN19s+/+bWdODfz8y3qmDq2zhFWgW1rWm3KLwDyyn7L61Geed1PXsFnUv7jTSWvdM/U0skrJ595tZ6vsz91v4u8Iigv5bV1qI47GSCvLD3z8o/ke5TRhtORV3C0YabnZP7tdC2Hdm2eXD+VvPzle+O1l7Lf8qps32pHz30Yenl5v//2qd/+7+R9tMdtp59a/r9W/E+3/Je/+qXbJzmadMu97TxhfdD8weRxfvbzn4VWYMqr8guAvIiZjGDXY1bktfXYKuu8dCIS8pJoit7a4Nat/P2zTkASlOQkEfnXKTLJS8eKQstLeVV+AZAX8SgRbNVle+YQJ6/XD6+w0+O9kZDXL578xaRsvNaWJy+vteXfr7mnKdLyUl6VXwDkRURuho21dcvD+83CaboNPUEF5eX9nk/yUii/wT74KMeTTz6ZVzdplTef8jvdQF55IK8otbymkpe/5eV9DsrL33qj5RVOdOOivIC88kBeUXrmNZW8/AM4gs+89LuW65mYN0CDZ17czCkv8iJyWF5RG22YSV5ap9B/7MEuRAlLy//9j7+fFJYnNkYbcjOnvPktLw0XD85aMZvD7b0h6MFh8nM1MKNgxVP3vJeWbqqr4AwdwW0yrZ+puvvhPa+h8L7nNd0IPvPKl8jH97yQF8ykvHQTDk7ppM/eu0+zKa9stGh0XslLM3v4X6j2JB4cHp/uxezgC82zPMPGauQVwcjHGTaQF8yUvDJNaOtfnm72iuBLvsHh5f4ZNLx1/tk0vG17j+2fbHkFW3/Bl5AzvfgcnKIq3Tta/hedtS7dXIaZRJVVeSnRzG3I3IbczCkv8so823o6GaSb9HaqGdq9G3q6KZk8sU2n29C/TVAe/imngl2fnqC8SYC9c3jdhN7+/imqgjOETGdKrDmVF7PKR3NWeeUVeVFe5PXwz7oyfQuy11q5X8sreDy/vDyZBGU0lbyCsggKMt0M8sHrenfTajcllHf9kpeWax9tk2nKqnQim0pewZbiTL7YzPd58X1e3MwpL/KapZZXcFaKYPegd8OfrryC0ksnl0yS8MR0PvGBu4aeo/vc/IReN6H/eVc68WbqRs16y0vom3fdN/By8+eblLmZU17k9aObtX7qpj/VMy9/ay3Ycgu2vB5UXv5nU/cTbLqQrNTi0k9vRKBk5R3f37Xol1qmZ2c5Iy9xMLnDDvbsQgAhDuVPecxXkBfMlLyCow0/ScacWNINlvA/J0onL/8oxYeRVyYZBOWRbkSgv9vyX//b4slrl8S8bsLgs690UvI/m8s5eX13+5aVNK2xxrOHEEEIo/FsjZU0r3F5RF6UF3nN/HteunmXbfzDZBegf723LigsbxSgWj1a98XZ5ozy8oTlH23YVL39R92P/vP7RxtO9Vwp2JIMft1J8HlX8D2vTN2R033m5b/mGZeXGPvqoq2PPWttw40IIUShfClvyl8+g7xgpuVF5OgMG+kSP3il1zbWv2iJC02IIQShPClfylu+g7wAeeWxvH4YwNFtRUeet+PDDQgih0P5UZ6UL0BegLzyXl5i6LM+2xBbaY3neAaWi9GQyovyozwB8gLkhbx8XPnmsr3d8qp90LsbYeRQ1KTyobwoP4C8AHkhrzTcvvt3q+4us4pEkXWMJpBHFkP1X5HYaNVdZS4vgLwAeSGv+9Bxsdl1U9X1H0Ak2fgKmFS9q/6VB+BmjryQF/J6AL68cc32d5bajvg6ax1hMMdchOpZ9a16V/0DN3PkhbyQ10MyMNZpJc2rbW9HqbXTlTgroXrd21Hi6ln1DdzMkRfyIh5RXh6JkXrb1PCSHejanrrZxpHOjEgr7upT9ZoYrudOxM2c8iIvYqblJe7cvWNtQzF7o7HA9rRvsVbeDXu47sFUve1p3+rqUfWpegVu5pT3wTm/66d253ILN/oIhvKq/M74X373aKuVta51M5sf7v+T9Y33IqYpQvWjelJ9qd5Uf8DNnPI+GqM1/2Q3+t/lZh/BUF6V31n7y//L54N2MLnT1sV+l2pNbLNjg7XIyheqD9WL6ueDVD2pvoCbOeWdGa51r7GrTU9xs49gKK/K76z/5WuG81OX4vbeyc1WVP+CG4Bw9NyhvGuRqbwqt8qvelB9qF7yeQZ4buaUd7a49dWgnd/1E7oOI9ll+BOX3zn9y//bdxPuhr2/o9Rej62wHW3rrab3fYuPHI2ksFSump73XDlV3v0pcan8qgfgZk55Z5erJ1faeMNSbvoRCuVTeXV/+9n6w7r7/V0buTZgDWeq3LOe9bGVVp7Y4L5MsWmw1pKfdIZKVLpeXbeuf2d8gyuPyqXyqZwqL3Azp7xz/+zr89Z/48YfgVAelc/Jv/2caebf/taGrw5Yy+CHVvnxm1bc8LJtbnjFyuNFVtVVZocHqqxt+KidGuvKqqR0fl2HrkfXpevb3PiKu97Kj7e461c5VB7gZk55s8vtG5+6G57+xU4XYni7CpU/5VH5zDl5peObm1+kRNDn3nWq6alItWjW2ab6F90USfrG5/JEke3rKLU/nyq32r59Vn+2xlqGYpa40Ozeleq+3G69Y0k7Pd5rA1f675GQPmu51ms7ba/9tL+Oo+NVJ8vd8dUi1Pl0Xp1f16Hr0XXp+nSdwM2c8uZ2F6Kelehhv0arIbLcF5bypHy5vP2jq/Cev/0w/iGqVXN1Yiwljn5LXmqz1vO1VtdXaVXd2233iWI37LykabVrERUdec7WxVbY2rpnrLD26VQsdz/1Wcu1Xttpe+23+2SxVXe/446n4+r4Oo/OR2uKmznlDS96yK9RavoXvN4T0ouuRG6G8qM8KV/KW9q//Xz7A/4+9T/gZk55AUL+t08VADdzyguAvAC4mVNeAOQFwM2c8gIgL+BmTnkBkBcAN3PKC4C8ALiZU14A5AXczCkvAPIC4GZOeQGQFwA3c8oLgLwAeQEA8gJAXgCAvACQ15TcvHnTli9f7spTWVmZcbvCwkK3jX87/74KbQOAvACQ16wzODhoy5Yts+vXr2fcJpFIOElJVtp+8eLF7qck5glL+y9ZsmRKAQIgLwDk9ch4wlFZJKeKigorKCiYbE2la0lNJTttj7wAeQHAnLa8JJ5Fixa5ZZ7Y1OryyylT96K213G0LwDyAoA5lZe/tZWuJeU95/Iv95b5RQeAvADmkHwbwPCg8vKW+5910eIC5AWQQzfzTERpAENQXrpm/e7vNlQEy6Vl+l31MFVdASAvgFkmHwcwBOUlEeu5V7C86Vqa/mXBdQDICyCLN/N8GsCgcngtSgBAXhBieeXTAAbkBYC8IE/k5S1nAAMA8gLIGXkxgOEf/wEzlyMgL4DwyIsBDMgLkBdAqOAZEPIC4C8fkBfyAkBeAIC8AJAXADdzyguAvICbOeUFQF4A3MwpLwDyAuBmTnkBkBdwM6e8AMgLgJs55QVAXgDczCkvAPIC5EV5AZAXADdzyguAvAC4mVNeAOQFgLwAkBcAN3PKC4C8ALiZU14A5AXwA8XFxfbYY4/Zli1b7rmZ67OWaz3lBUBeADnFxMSEzZ8/3x5//HF74okn3M1cP/VZy7We8gIgL4CcY9WqVbZgwQJ3I/dCn7Wc8gIgL4CcbY0sXLjwnpu5Pke1FZJv5QVAXpAXra98aIXkW3kBkBdEvjWSD62QfCsvAPKCSLdGNGghX1oh+VZeAOQFkW2NLF26NG9aIflWXgDkBVnhsy9uWO2Ji7ZxX9JeLmmzZ99oIXI0lB/lSflS3gCQF+Ql1c3D7oa468iwtfR9Yf2Xb9nQZ3eIHA3lR3lSvpQ35Q8AeUHe8PVfb7l/wZd9dA5hhVhkyp/yqHwCIC+IPLrhVR67iAQiEMqj8gmAvCDyXYX6Fzs3/uiE8kkXIiAviCx6yK9nJXQVRq8LUXllEAcgL4gkGqWmh/3c8KMXyqvyC4C8IHLo2YhGq3Gzj14orzz7AuQFkYQuw+h3HQIgL4gcetGVG310Q/kFQF6AvAjkBYC8AHkRyAuQFwDyIpAXIC9AXgTyAkBegLwI5AXICwB5EcgLkBcgLwJ5ASAvQF4E8gLkBYC8COQFyAuQFzd55AWAvAB5EcgLAHkB8iKQFyAvAOSFvACQFyAvAnkBIC9AXgTyAuQFEF15HfiozebNmzcZxW+9j7SQFyAvQF65La7/8+f/lzWcOOM+943+1ZY99TQCQ16AvAB55WZkEpVE9v/95l+t89zVH7XMtL328/Z9/vevTa7T79peP/3H1O/eOuQFgLwAeT1SSFL/95P/z2SrK9M2aplJYH5heb97MvO34PR7UHJahrwAkBcgrxmRl7+Flalb0ROR9/m//GqxxXsu3dNq0zF0LB3TL8XpnAN5ASAvQF4z2vKSnPzy8mTkyctrUfnl5W9thb3LEHkB8gLkFZJnXhKRluvn/Vpe6eTlf86lCHOXIfIC5AXIK0SjDb3W0v2eeWWSl7efRBfmLkPkBcgLkFdI3vMKdvNNNdowk7yCEkReAMgLkBeBvAB5ASAvAnkB8gLkRSAvAOQFyItAXoC8AJAXgbwAeQHy4iaPvACQFyAvAnkBIC9AXgTyAuQFgLyQFwDyAuRFIC8A5AXIi0BegLwAkBfyAkBegLwI5AWAvAB5EcgLkBcA8iKQFyAvQF4E8gJAXoC8COQFyAsAeRHIC5AX5A0vl7RZ/+Vb3OgjGMqr8guAvCBybNyXtJa+L7jZRzCUV+UXAHlB5Kg9cdF2HRnmZh/BUF6VXwDkBZHjsy9u0HUY4S5D5RcAeUEkqW4etrKPznHTj1Aon8orAPKCSKNnI5XHLnLjj0AojzzrAuQFecHXf73lbnj6FztdiOHtKlT+lEflEwB5QV51IepZiR72a7QaIst9YSlPypfyRlchIC/IW/SQX6PU9C943RD1oiuRm6H8KE/KF4MzAHkBRPU/pHn8pwSAvACQFwAgLwDkBYC8AAB5ASAvAOQFAMgLAHkBIC8AQF4AyAsAeQEA8gJAXgDICwCQFwDyAkBeAIC8AJAXAPICAOQFgLwAkBcAIC8A5AUAyAsAeQEgLwDkBQDICwB5AQDyAkBeAMgLAJAXAPICQF4AgLwAkBcA8gIA5AWAvACQFwAgLwDkBYC8AAB5ASAvAOQFAMgLAHkBIC8AQF4AyAsAeQEA8gJAXgDAf3EAyAsAeQEgLwBAXgDICwCQFwDyAkBeAIC8AJAXAPICAOQFgLwAkBcAAADyAgAAQF4AM8XNmzdt+fLlrluwsrIy43aFhYVuG/92/n0V2gYAkBfArDM4OGjLli2z69evZ9wmkUg4SUlW2n7x4sXupyTmCUv7L1myZEoBAgDyAnhkPOGo1SQ5VVRUWEFBwWRrKl1LairZaXvkBYC8AOa05SXxLFq0yC3zxKZWl19OmboXtb2Oo30BAHkBzKm8/K2tdC0p7zmXf7m3zC86AEBeADkjL2+5/1kXLS4A5AWQVXmpq1C/+7sNFcGBGVqm39XimmqwBwAgL4BZl5dGEuq5V3DARrqh8v5lwXUAgLwA5gSJxxsSDwDICwB5AQDyAgAA5AUA9/sPiVnlAZAXAPICAOQFgLwAkBcAIC8A5AWAvAAAeQEgLwDkBQDICwB5ASAvAEBeAMgLAHkBAPICQF4AyAsAkBcA8gJAXgCAvACQFwDyAgDkBYC8AAB5ASAvAOQFgLwAAHkBIC8AQF4AyAsAeQEA8gJAXgDICwCQFwDyAkBeAIC8AJAXAPICAOQFgLwAkBcAIC8A5AWAvAAAeQEgLwDkBQDICwB5ASAvAEBeADlBcXGxPfbYY7Zly5Z75KXPWq71AIC8AHKKiYkJmz9/vj3++OP2xBNPOHnppz5rudYDAPICyDlWrVplCxYscOLyQp+1HACQF0DOtr4WLlx4j7z0mVYXAPICCE3ri1YXAPICCF3ri1YXAPICCFXrS4M0aHUBIC+AULW+li5dSqsLAHlNza3b39rViTEbvtpvyUttdvz8R1bXV2lV3dtt94lie+d4oZU0rbbNja9Y0ZHnbF1sha2te8YKa59OxXL3U5+1XOu1nbZ/p7XQdp8stqqu7anj7bHW87Xu+DqPzqfzQv5y+eqExU9/avuPnretVb32WkW7FbwVt+e2HLdn32hxP/VZy7Ve22l77QcAeSSvb25+kRJHnyWG662mp8J2xtfZpvoXbUNsZUo2a6w8scH2dpRadbLcavv2Wf3ZGmsZilniQrO1j8at+3K79Y4l7fR4rw1c6bcznw1Mhj5rudZrO22v/bS/jqPj6bj7UsfXeXQ+nVfn13XoehIj9e76dJ0QTXqGrtl7sbP2728n7I8VnfbOR+esuu2yHev53DqHJ6zvk1t2fvy2DX12x/3UZy3Xem2n7bWf9tdxdDwAiJC81KoZvjpgLYMf2p6P37Tihpddi6g8XpRqUb1rhweqrG34qJ0a67pHQnMdOr+uQ9dT1VXmrm9zwyvueis/3mLN5w65ctBKCy/Xv/7WDrVdcMIpPtBrf05JKHnxhhPUw4b213F0PB1Xx9d5ACBk8rr7/V0buTZgDWeqrKx1ra1PtWrK4xvsYM8uaxqsteQnnVmV1IOGrlfXrevfmSqHyqNyNabKp3KqvJDbfDlxy3X1vbCt1XYdGXYtqEcRVqbQcXV8nUfn03kBIIfl9bfvJuzUpbjt7yix12MrbEd8vdX0vm/xkaOhEtV0Q+U6lCqfyqny7u8odeVXPUBu0dh5yT2z2t0w4rr+ZkNawdB5dD6dV+cHgByS13e3b7kb9vsnN1tR/Qu2NyWuo+cOWd94bySFlSlUXpW7MlV+1cN7qfpQvah+IHt8cu2vVrz/lG39c9+stbSm0xLT+XUduh4AyKK8/vL5oB1M7rR1sd/ZnvZtdmywNq9kdb9QfaheVD+qJ9UXzC0n+8fdKEE9i8qGtIKh69D16LoAYI7l1T3a6p71aLj64f4DedfCepgW2eG+A26YvuotOdrGX+Uc8FHighsFmDj7dU6Iywtdj65L1wcAsyyvO3fvWNtQzN5oLEi1JrZY63ADYnqIUL3tad/q6rFtOObqFWaeA8eGbNP+njl7tvUwz8J0fbpOAJgleZ0YabBNDS/Zga7t7l0pJPTooXrc37nd1avecYOZFdeb1adzUlrB0HUiMIAZltfAWKeblUIvCbePJpDOrEgsYXs7S10994918Nc6A12FatGEQVxe6HrpQgSYAXl9eeNaqlVQajvi66x1hO7BOelOTNWz6lv1rvqHB0eDIPQsKVe7CqfqQtR1M4gD4BHk1XGx2U2RVNd/AKlkIVTvqn/lAaaPhp9rFF+uDc54kEEcun6G0QM8oLxu3/27VXeXWXmiyDroIsxqqP4rUnlQPpQXuD96fypXhsM/yjB6lQMApimvK99ctrdbXrWa3t3II4dC+VBelB/IjGau0AvAYRaXFyoHM3EATENeQ5/1uW4qzbKOMHIvlBflR1/ZAj9GcwZq6qVszZwxGzNxqDzMhQgwhbwGPu2yoiPP23He2crpUH6UJ+UL7kWT3mruwCiIywuVR+UCQF5pGLzSaxvrX7TEhSYEEYJQnpQv5Q1+QF83olnbwza6cDqjD1Uuvk4FkFeAsa8u2vrYs9Y23IgYQhTKl/Km/IG578vS145ESVxeqFwqHwDy+gea4bykeY018owrlKG8KX/MVG/uCx+j8qwr3bMvlQ8Aef2Dg8kddvDULkQQ4tCXYSqP+UzP0DX3jcVRFJcXKp/KCZD38joz3uVmgkcA4Q/NUH8mjwdwvBc7G/r3uqbz3pfKCZD38iptWWMtQ0e4+UcglEflM5+7DJMXb0RaXiofXYeQ9/LqGj1uu09u5sYfoVA+9d1q+cblqxNuLsAoi8sLlVPlBchbeam7kFZX9Fpfymu+ET/9qb3z0bm8kJfKqfIC5KW8rnx9ybY1/SHUN+qVv3/W5s2bd08UvbXBrUuOJu23T/3WKj/ak3cC29a02pTffEIv8FZH/HmXFyonLyxD3sqr9Xyd/an73dDLSzE5+/qJOvvFk79wP/O59aW8tg7VzegfzM2bN2358uXuHwiVlZUZtyssLJz8h4S3nX9fhbZ5UIqLi21iInNX2daqXjvW83leyEvlVHkB8lJele1b7ei5DyMlL39rK9jy0k9/C81b7m0XbLmFOZRX5XcmGRwctGXLltn169czbpNIJJykJCttv3jxYvdTEvOEpf2XLFkypQDT8dhjj9n8+fNt1apVaSX2WkX7nL/fVfzW+y6y8b6XyguQl/LaemyVdV46EbmW1z//5td24lziHnkFW2QSlNZpG/3uHUP7abl+hrlelFfld6bwhCO5S04VFRVWUFAw2ZpK15KaSnba/kHltWXLFiewBQsW2MKFC38ksYK34nM+JVS25KVyqrwAeSmv1w+vsNPjvZF75hVsUaV75qVl6eQVlVBeld/ZanlJPIsWLXLLPLGp1RXsOkwnKG2v42jfB+WJJ56YzHNQYpp1/fz47dkXx+hfbdlTT//o7+6//GqxdZ67OifyUjlVXoC8lNfauuWhv0ln6jaUkILyCorOk1dwXVQGeCi/wRvso0RQXv7WVrqWlPecy7/cW+aJbiauS12JS5cudd84POezXWSp5aVQeQFoeUVEXl6XoJb55aX45a9+Odkd6G95+ffVenU7hn3Ax1y0vO4nL2+5/1nXw7a4cqnllQvyouUFPPOK2DOvTC0vv7y85Z68tL83SINnXtOXl7oK9bu/21ARHJihZfpdLa6pBntM55nX448/nlPPvLL59Sg884K8lVdURhsGu5E8maUbeaj1P/v5z6xk97bJgR0KiS1K3YazPdpQ8tJIQj33Cg7YSDdU3r8suG665OJow2zOLs9oQ8hbeek9oLC/50XM3XtefiQeb0j8XMF7XrznBeDk9cMMG6u52TPDRijkdT+YYQMgT+Sl/2NuQ+Y2jArMbQiQR/JiVvloziqvvOYbzCoPkEfyEnyfF9/nFRX4Pi+APJKXvnnXfQMvN3++STnk8E3KAHkkL3EwucMO9uxCACEO5U95zGd6hq5Z8YHeSMtL5VM5AZBXiu9u37KSpjXWePYQIghhNJ6tsZLmNS6P+Y661KL6vpfKRZchIK8AY19dtPWxZ61tuBEhhCiUL+VN+QOzQ20XbNeR4UjKS+VS+QCQV4DBK722sf5FS1xoQgwhCOVJ+VLe4Aeuf/2tvbCtNXJTRak8KpfKB4C80nDm024rOvK8HR9uQBA5HMqP8qR8wb3oBd7dDSORkpfKw4vJAFPISwx91mcbYiut8RzPwHIxGlJ5UX6UJ/gxX07ccrOuR+XZl8qh8qhcAMjrPlz55rK93fKqfdC7G2HkUNSk8qG8KD+QmcbOS7b1z32RkJfKofIAwDTkJW7f/btVd5dZRaLIOkYTyCOLofqvSGy06q4ylxe4P8X7T4X+vS9dv8oBAA8gL4+Oi82um6qu/wAiyUKo3lX/ygNMn0+u/dV943Di7NehFJeuW9evcgDAQ8hLfHnjmu3vLLUd8XXWOsJgjrkI1bPqW/Wu+ocH52T/uJsLMGyjD3W9um5dPwA8grw8BsY6raR5te3tKLV2uhJnJVSveztKXD2rvuHR+ChxwTbt7wmVvHS9um4AmCF5eSRG6m1Tw0t2oGt76mYbRzozIq24q0/Va2K4nr/SGeTAsSF7s/p0KMSl69T1AsAsyEvcuXvH2oZi9kZjge1p32KtvBv2cN2DqXrb077V1aPqU/UKsyMwtWhytQtR16XrQ1wAsywvP92jrVbWutbNbH64/0/WN96LmKYI1Y/qSfWlelP9wdx0IepZUq4N4tD16LroKgSYY3l5/OXzQTuY3GnrYr9LtSa22bHBWmTlC9WH6kX180GqnlRfMLdoEIRG8eXKMHpdh66HwRkAWZSXh2Y4P3Upbu+d3GxF9S+4AQhHzx3KuxaZyqtyq/yqB9WH6oUZ4LOLhp/r/Sm9AJytmTh0Xp1f18FweIAckZefv3034W7Y+ztK7fXYCtvRtt5qet+3+MjRSApL5arpec+VU+XdnxKXyq96gNxCM1do6iXNHThXz8J0Hp1P52XmDIAclpefu9/ftZFrA9Zwpso961kfW2nliQ3uyxSbBmst+UlnqESl69V16/p3xje48qhcKp/KqfJCbqM5AzXprWZt19eOzFZLTMfV8XUenY+5CgFCJK8gt25/a8NXB6xl8EOr/PhNK2542TY3vGLl8SKr6iqzwwNV1jZ81E6NdWVVUjq/rkPXo+vS9W1ufMVdb+XHW9z1qxwqD4QTfd2Ivi9LX/iobyzWs6jkxRuPJCzt76Z4Sh1Px9Xx+VoTgAjIKx3f3PwiJYI+965TTU9FqkWzzjbVv+imSNI3PpcnimxfR6n9+VS51fbts/qzNdYyFLPEhWb3rlT35XbrHUva6fFeG7jSf4+E9FnLtV7baXvtp/11HB2vOlnujq8Woc6n8+r8ug5dj65L16frhGjSM3TN3ouddcLRKMB3Pjpn1SkJHev53LWg1PV3fvy2E5R+6rOWa7220/baT/vrODoeAERcXlO10q5OjKXE0W/JS23Wer7W6voqrap7u+0+UeyGnZc0rXYtoqIjz9m62ApbW/eMFdY+nYrl7qc+a7nWazttr/12nyy26u533PF0XB1f59H5aE3lN5evTlj89Keuq29rVa+9VtFuBW/F3TMrjRLUT33Wcq3Xdtpe+wEA8nokvk/9D2DG/0OaN49KAEBeAMgLAJAXAPICQF4AgLwAkBcA8gIA5AWAvACQFwAgLwDkBYC8AAB5ASAvAOQFAMgLAHkBIC8AQF4AyAsAeQEA8gJAXgDICwCQFwDyAgDkBYC8AJAXAPICAOQFgLwAAHkBIC8A5AUAyAsAeQEgLwBAXgDICwB5AQDyAkBeAMgLAJAXAPICQF4AgLwAkBcA8gIA5AWAvACQFwAAAPICAABAXgBzyM2bN2358uWuW7CysjLjdoWFhW4b/3b+fRXaBgCQF8CsMzg4aMuWLbPr169n3CaRSDhJSVbafvHixe6nJOYJS/svWbJkSgECAPICeGQ84ajVJDlVVFRYQUHBZGsqXUtqKtlpe+QFgLwA5rTlJfEsWrTILfPEplaXX06Zuhe1vY6jfQEAeQHMqbz8ra10LSnvOZd/ubfMLzoAQF4AOSMvb7n/WRctLgDkBZBVeamrUL/7uw0VwYEZWqbf1eKaarAHACAvgFmXl0YS6rlXcMBGuqHy/mXBdQCAvADmBInHGxIPAMgLAHkBAPICAADkBQD3+w+JWeUBkBcA8gIA5AWAvACQFwAgLwDkBYC8AAB5ASAvAOQFAMgLAHkBIC8AQF4AyAsAeQEA8gJAXgDICwCQFwDyAkBeAIC8AJAXAPICAOQFgLwAAHkBIC8A5AWAvAAAeQEgLwBAXgDICwB5AQDyAkBeAKGhuLjYHnvsMduyZcs98tJnLdd6AEBeADnFxMSEzZ8/3x5//HF74oknnLz0U5+1XOsBAHkB5ByrVq2yBQsWOHF5oc9aDgDICyBnW18LFy68R176TKsLAHkBhKb1RasLAHkBhK71RasLAHkBhKr1pUEatLoAkBdAqFpfS5cupdUFgLwgitz6atCuda+x0Zp/svO7fmrndswjcjSUH+VJ+VLeAJAX5CVXT65M3RB/YlebnrIb/e/ancstZuPdRI6G8qM8KV8ub6n8ASAvyBtu3/jU/Qt+vGEpwgqxyJQ/5VH5BEBeEHl0w/u89d+QQARCeVQ+AZAXRL6rUP9i58YfnVA+6UIE5AWRRQ/59ayErsLodSEqrwziAOQFkUSj1PSwnxt+9EJ5VX4BkBdEDj0b0Wg1bvbRC+WVZ1+AvCCS6D0hugyj3HX4U/7IAXlB9NCLrtzooxsuvwDIC5AXgbwAkBcgLwJ5AfICQF4E8gLkBciLQF4AyAuQF4G8AHkBIC8CeQHyAuRFIC8A5AXIi0BegLwAwi6v62eabMmT/9nmzZs3GYv+43+wwfhBhIO8AHkB5La8Eh+WTy6rLF1ry//Hf7ebF08gHeQFyAsgHPJSq2vZvyx267zPao2pVaZtveUK7ee12DzhKfR72cY/TO4nIRYWPPOjY3jbesfwrkPnXPxf/5MLb3v/ufzb6qeuVxFsOfpbljqPtslWqxJ5AfIC5DXLLS+Jxr9ey/RZyz1JeVLTvp6EtN773dvOk46O4a3zH887l7bzxOM/tl9mnnj8rUPv+MHrCB5f+2SzSxR5AfIC5DWLz7z8LaN0rTBPIpKFv3tRn7XvWDJ2j6CCx5BMtC4oTk88+hyUVTD85/bO6x3fk68++1tawc/IC5AXQMS6Df2C8ovBL4DgszFPUp68/K2mqeTlF6fXQgvu4+3n384vL/91ePIKChB5AfICiLC8gi2gh215TUdemWQS3Cco0WDLK528aHkBIC/I05bXozzzup+8gs+k/MebSl7pnqmlkxfPvACQF0RcXsGuu6DMHma04XTkFRxtmOk5mX87XcuhXZsn108lL3/53njtJVpegLwAoiCvfIpg1yPyAuQFgLxyLoKtumzPHIK8AHkB8iKYYQMAeQHyIpAXIC8A5EUgL0BegLwI5AWAvCDi8tJw8eCsFbM53N4bgh4cJj9XAzMKVjx1z3tp6aa6Cs7QEdwm0/qZqjvkBcgLkNcUoZtwcEonffbefZpNeWWjRaPzSl6a2cP/QrUn8eDw+HQvZgdfaKblBcgLYA7llWlCW//ydLNXBF/yDQ4v98+g4a3zz6bhbdt7bP9kyyvY+gu+hJzpxefgFFXp3tHyv+isdenmMswkKuQFyAsgx+QVFFFQBukmvZ1qhnbvhp5uSiZPbNPpNvRvE5SHf8qpYNenJyhvEmDvHF43obe/f4qq4Awh05kSC3kB8gLIorym+hZkr7Vyv5ZX8Hh+eXkyCcpoKnkFZREUZLoZ5IPX9e6m1W5KKO/6JS8t1z7aJtOUVelENpW8gi3FmXyxGXkB8gLkNUstr+CsFMHuQe+GP115BaWXTi6ZJOGJ6XziA3cNPUf3ufkJvW5C//OudOLN1I1KywuQF0COySt4s9ZP3fSneublb60FW27BlteDysv/bOp+gk0XkpVaXPrpjQiUrLzj+7sW/VLL9OwMeQHyAshBeQVHG36SjDmxpBss4X9OlE5e/lGKDyOvTDIIyiPdiEB/t+W//rfFk9cuiXndhMFnX+mk5H82h7wAeQHksLzSjRgs2/iHyS5A/3pvXVBY3ihAtXq07ouzzRnl5QnLP9qwqXr7j7of/ef3jzac6rlSsCUZ/LqT4POu4Htembojp/vMy3/NyAuQF8Asy4tghg0A5AXIi0BegLwAkBfyAkBegLwI5AWAvAB5EcgLkBcA8kJeyAuQFyAvAnkBIC9AXgTyAuQFgLwI5AXIC5AXgbwAkBfkHud3/dTuXG7hRh/BUF6VXwDkBZFjtOaf7Eb/u9zsIxjKq/ILgLwgclzrXmNXm57iZh/BUF6VXwDkBZHj1leDdn7XT+g6jGSX4U9cfgGQF0SSqydX2njDUm76EQrlU3kFQF4QafRs5PPWf+PGH4FQHnnWBcgL8oLbNz51Nzz9i50uxPB2FSp/yqPyCYC8IK+6EPWsRA/7NVoNkeW+sJQn5cvlja5CQF6Qr+ghv0ap6V/wek9IL7oSuRnKj/KkfDE4A5AXQFT/Q5rHf0oAyAsAeQEA8gJAXgDICwCQFwDyAkBeAIC8AJAXAPICAOQFgLwAkBcAIC8A5AWAvAAAeQEgLwDkBQDICwB5ASAvAEBeAMgLAHkBAPICQF4AgLwAkBcA8gJAXgCAvACQFwAgLwDkBYC8AAB5ASAvAOQFAMgLAHkBIC8AQF4AyAsAeQEA8gJAXgDICwCQFwDyAkBeAIC8AJAXAPICAOQFgLwAkBcAIC8A5AUA/BcHgLwAkBcA8gIA5AWAvAAAeQEgLwDkBQDICwB5ASAvAEBeAMgLAHkBAPICQF4AyAsAkBcA8gJAXgCAvACQFwAAAPICAADkBQA/cPPmTVu+fLnrFqysrMy4XWFhodvGv51/X4W2AQDkBTDrDA4O2rJly+z69esZt0kkEk5SkpW2X7x4sfspiXnC0v5LliyZUoAAgLwAHhlPOGo1SU4VFRVWUFAw2ZpK15KaSnbaHnkBIC+AOW15STyLFi1yyzyxqdXll1Om7kVtr+NoXwBAXgBzKi9/aytdS8p7zuVf7i3ziw4AkBdAzsjLW+5/1kWLCwB5AWRVXuoq1O/+bkNFcGCGlul3tbimGuwBAMgLYNblpZGEeu4VHLCRbqi8f1lwHQAgL4A5QeLxhsQDAPICQF4AgLwAAAB5AUAGVq1aZQsWLHDPs/RTnwEAeQHkNBMTE7Zw4UInL/3UZwBAXgChaH3Nnz+fVhcA8gIIV+tr6dKltLoAkBfA3HPr9rd2dWLMhq/2W/JSmx0//5HV9VVaVfd2232i2N45XmglTattc+MrVnTkOVsXW2Fr656xwtqnU7Hc/dRnLdd6baft32kttN0ni62qa3vqeHus9XytO77Oo/PpvACAvACm5JubX6TE0WeJ4Xqr6amwnfF1tqn+RdsQW5mSzRorT2ywvR2lVp0st9q+fVZ/tsZahmKWuNBs7aNx677cbr1jSTs93msDV/rtzGcDk6HPWq712k7baz/tr+PoeDruvtTxdR6dT+fV+XUdup7ESL27Pl0nACAvyNPW1PDVAWsZ/ND2fPymFTe87FpE5fGiVIvqXTs8UGVtw0ft1FjXPRKa69D5dR26nqquMnd9mxtecddb+fEWaz53yJWDVhoA8oIIcvf7uzZybcAazlRZWetaW59q1ZTHN9jBnl3WNFhryU86syqpBw1dr65b178zVQ6VR+VqTJVP5VR5AZAXQAj523cTdupS3PZ3lNjrsRW2I77eanrft/jI0VCJarqhch1KlU/lVHn3d5S68qseAJAXQA7z3e1b7ob9/snNVlT/gu1NievouUPWN94bSWFlCpVX5a5MlV/18F6qPlQvqh8A5AWQI/zl80E7mNxp62K/sz3t2+zYYG1eyep+ofpQvah+VE+qLwDkBZAlukdb3bMeDVc/3H8g71pYD9MiO9x3wA3TV70lR9v4IwLkBTAX3Ll7x9qGYvZGY0GqNbHFWocbENNDhOptT/tWV49twzFXrwDIC2AWODHSYJsaXrIDXdvdu1JI6NFD9bi/c7urV73jBoC8AGaIgbFONyuFXhJuH00gnVmRWML2dpa6eu4f6+CPDpAXwMPy5Y1rqVZBqe2Ir7PWEboH56Q7MVXPqm/Vu+ofAHkBPAAdF5vdFEl1/QeQShZC9a76Vx4AkBfAfbh99+9W3V1m5Yki66CLMKuh+q9I5UH5UF4AkBdAGq58c9nebnnVanp3I48cCuVDeVF+AJAXgI+hz/pcN5VmWUcYuRfKi/Kjr2wBQF4AKQY+7bKiI8/bcd7ZyulQfpQn5QsAeUFeM3il1zbWv2iJC00IIgShPClfyhsA8oK8ZOyri7Y+9qy1DTcihhCF8qW8KX8AyAvyCs1wXtK8xhp5xhXKUN6UP2aqB+QFecXB5A47eGoXIghx6MswlUcA5AV5wZnxLjcTPAIIf2iG+jMM4ADkBflAacsaaxk6ws0/AqE8Kp8AyAsiTdfocdt9cjM3/giF8qnvVgNAXhBZ1F1Iqyt6rS/lFQB5QSS58vUl29b0h/BMTnuizn7285/ZvHnzXKz8/bPT2u/EuYT9829+7fZ/lHPrGDpWGOpqW9Nql18A5AWRo/V8nf2p+91Q3IwrP9rjxOUXkOT126d+a8nRJPIKhPLaOlTHHzkgL4gele1b7ei5D3P+Riw5SVISWKblwW08YX3Q/IFbrpaa5Od9fm3Tq5OtuKK3NqQVlI6lbZt7mu2Xv/ql21Y/wyAw5VX5BUBeEDm2HltlnZdOhKK7MFOrR60vRSZ5aV//7952noS07BdP/sL9zCQv7RO2lpfyqvwCIC+IHK8fXmGnx3tDLS+1mh5GXl5ryxOgPkdJXsqr8guAvCByrK1bHpqBGjMtL38XZBTlpVB+AZAX0PIK0TMvf3fgVC0v/+egoLSMlhcA8oIcIyzPvKYz2tCTkDd8XuLxtk8nL7+U/M+8tI9fhjzzAkBekGOEZbThdN/z8q//9z/+/kfC8o821L7ecfwtOknPG5moEYmevCQtDfJgtCEA8oIso/eAwvKe12x3QUYteM8LkBdElh9m2FiNvCIYzLAByAsiDXMbMrchAPKC0MGs8tGcVV55BUBeEGn4Pi++zwsAeUHo0Dfvum/g5ebPNykDIC8IEweTO+xgzy4EEOJQ/pRHAOQFecN3t29ZSdMaazx7CBGEMBrP1lhJ8xqXRwDkBXnF2FcXbX3sWWsbbkQIIQrlS3lT/gCQF+Qlg1d6bWP9i5a40IQYQhDKk/KlvAEgL8hrznzabUVHnrfjww0IIodD+VGelC8A5AWQYuizPtsQW2mN53gGlovRkMqL8qM8ASAvAB9Xvrlsb7e8ah/07kYYORQ1qXwoL8oPAPICSMPtu3+36u4yq0gUWcdoAnlkMVT/FYmNVt1V5vICgLwA7kPHxWbXTVXXfwCRZOPrYFL1rvpXHgCQF8AD8OWNa7a/s9R2xNdZ6wiDOeYiVM+qb9W76h8AeQE8JANjnVbSvNr2dpRaO12JsxKq170dJa6eVd8AyAtghkiM1NumhpfsQNf21M02jnRmRFpxV5+q18RwPX9kgLwAZoM7d+9Y21DM3mgssD3tW6yVd8MernswVW972re6elR9ql4BkBfAHNA92mplrWvdzOaH+/9kfeO9iGmKUP2onlRfqjfVHwDyAsgSf/l80A4md9q62O9SrYltdmywFln5QvWhelH9fJCqJ9UXAPICyBE0w/mpS3F77+RmK6p/wQ1AOHruUN61yFRelVvlVz2oPlQvzAAPyAsgx/nbdxPuhr2/o9Rej62wHW3rrab3fYuPHI2ksFSump73XDlV3v0pcan8qgcA5AUQQu5+f9dGrg1Yw5kq96xnfWyllSc2uC9TbBqsteQnnaESla5X163r3xnf4Mqjcql8KqfKC4C8ACLGrdvf2vDVAWsZ/NAqP37Tihtets0Nr1h5vMiqusrs8ECVtQ0ftVNjXVmVlM6v69D16Lp0fZsbX3HXW/nxFnf9KofKAwDIC/KQb25+kRJBn3vXqaanItWiWWeb6l90UyTpG5/LE0W2r6PU/nyq3Gr79ln92RprGYpZ4kKze1eq+3K79Y4l7fR4rw1c6b9HQvqs5Vqv7bS99tP+Oo6OV50sd8dXi1Dn03l1fl2HrkfXpevTdQIA8gK4byvt6sRYShz9lrzUZq3na62ur9Kqurfb7hPFbth5SdNq1yIqOvKcrYutsLV1z1hh7dOpWO5+6rOWa7220/bab/fJYqvufscdT8fV8XUenY/WFMCD8/8DRyWHO91+R50AAAAASUVORK5CYII=" width = "258" height = "401" alt = "quantized_training.png" style = "vertical-align: baseline; width: 258px; height: 401px;"></img><img class = "imageNode" src = "data:image/png;base64,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" width = "359" height = "400" alt = "quantized_inference.png" style = "vertical-align: baseline; width: 359px; height: 400px;"></img><span> </span></div><div class = 'S1'><span></span></div><div class = 'S1'><span>Remove the quantization aware layers and replace with underlying learned convolution layers using the </span><a href = "#H_D546E6DD"><span style=' font-family: monospace;'>removeQuantizationAwareLayers</span></a><span> function, provided at the end of this example. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S9'><span style="white-space: pre"><span >preQuantizedNetwork = removeQuantizationAwareLayers(quantizationAwareTrainedNet);</span></span></div></div></div><div class = 'S8'><span>Perform post-training quantization on the network as normal using the function </span><span style=' font-family: monospace;'>createQuantizedNetwork</span><span>, provided at the end of this example. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span >quantizationAwareQuantizedNet = createQuantizedNetwork(preQuantizedNetwork,augimdsCalibration);</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S11'><span style="white-space: pre"><span >quantizedNetworkDetails = quantizationDetails(quantizationAwareQuantizedNet)</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsVariableStringElement" uid="C2062F56" prevent-scroll="true" data-testid="output_8" style="width: 938.026px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" data-width="908" data-height="79" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">quantizedNetworkDetails = <span class="headerElement" style="white-space: pre; font-style: italic; color: rgb(179, 179, 179); font-size: 12px;">struct with fields:</span></span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> IsQuantized: 1
TargetLibrary: "cudnn"
QuantizedLayerNames: [105×1 string]
QuantizedLearnables: [60×3 table]
</div></div></div></div></div></div><h2 class = 'S2' id = 'H_62BFFDFA' ><span>Evaluate the Quantized Network</span></h2><div class = 'S1'><span>Evaluate the performance of the quantized network.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div class = 'S3'><span style="white-space: pre"><span >quantizedNetworkCCR = evaluateModelAccuracy(quantizationAwareQuantizedNet,augimdsValidation,validationActualLabels)</span></span></div><div class = 'S4'><div class='variableElement' style='font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 12px; '>quantizedNetworkCCR = 0.8937</div></div></div><div class="inlineWrapper"><div class = 'S5'><span style="white-space: pre"><span >bar( </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > categorical([</span><span style="color: rgb(167, 9, 245);">"Original network"</span><span >,</span><span style="color: rgb(167, 9, 245);">"Post-training quantized network"</span><span >,</span><span style="color: rgb(167, 9, 245);">"Quantization aware training network"</span><span >]), </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > [netCCR,originalQuantizedCCR,quantizedNetworkCCR] </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > )</span></span></div></div><div class="inlineWrapper outputs"><div class = 'S11'><span style="white-space: pre"><span >ylabel(</span><span style="color: rgb(167, 9, 245);">"Network Accuracy (%)"</span><span >)</span></span></div><div class = 'S4'><div class="inlineElement eoOutputWrapper embeddedOutputsFigure" uid="3B0056AE" prevent-scroll="true" data-testid="output_10" style="width: 938.026px;"><div class="figureElement eoOutputContent"><img class="figureImage figureContainingNode" src="data:image/png;base64,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" style="width: 616px; padding-bottom: 0px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport"></div><div class="outputLayer activeOutputDecorationLayer doNotExport"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport"></div></div></div></div></div><div class = 'S8'><span>The accuracy for the quantized network after quantization aware training is on par with the accuracy of that from the original floating point network.</span></div><h2 class = 'S2' id = 'H_5CF3214F' ><span>References</span></h2><ol class = 'S12' id = 'M_2AA33B76' ><li class = 'S13'><span>The TensorFlow Team. </span><span style=' font-style: italic;'>Flowers</span><span> </span><a href = "http://download.tensorflow.org/example_images/flower_photos.tgz"><span>http://download.tensorflow.org/example_images/flower_photos.tgz</span></a></li><li class = 'S13'><span>Gholami, A., Kim, S., Dong, Z., Mahoney, M., & Keutzer, K. (2021). A Survey of Quantization Methods for Efficient Neural Network Inference. Retrieved from </span><a href = "https://arxiv.org/abs/2103.13630"><span>https://arxiv.org/abs/2103.13630</span></a></li><li class = 'S13'><span>Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., & Kalenichenko, D. (2017). Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. Retrieved from </span><a href = "https://arxiv.org/abs/1712.05877"><span>https://arxiv.org/abs/1712.05877</span></a></li></ol><h2 class = 'S2' id = 'H_592CCEAB' ><span>Supporting Functions</span></h2><h3 class = 'S14' id = 'H_D08A0BC5' ><span>Download Flower Dataset</span></h3><div class = 'S1'><span>The </span><span style=' font-family: monospace;'>downloadFlowerDataset</span><span> function downloads and extracts the flowers dataset, if it is not yet in the current folder. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10' id = 'M_67532D8E' ><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">function </span><span >imageFolder = downloadFlowerDataset</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > url = </span><span style="color: rgb(167, 9, 245);">"http://download.tensorflow.org/example_images/flower_photos.tgz"</span><span >;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > downloadFolder = pwd;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > filename = fullfile(downloadFolder,</span><span style="color: rgb(167, 9, 245);">"flower_dataset.tgz"</span><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > imageFolder = fullfile(downloadFolder,</span><span style="color: rgb(167, 9, 245);">"flower_photos"</span><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">if </span><span >~exist(imageFolder,</span><span style="color: rgb(167, 9, 245);">"dir"</span><span >)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > disp(</span><span style="color: rgb(167, 9, 245);">"Downloading Flower Dataset (218 MB)..."</span><span >)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > websave(filename,url);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > untar(filename,downloadFolder)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">end</span></span></div></div></div><h3 class = 'S14' id = 'H_C9FB9125' ><span>Perform Transfer Learning</span></h3><div class = 'S1'><span>The </span><span style=' font-family: monospace;'>createFlowerNetwork</span><span> function replaces the final fully connected and classification layer of the MobileNet-v2 network and retrains the nework to classify flowers.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">function </span><span >transfer_net = createFlowerNetwork(net,augimdsTrain,augimdsValidation,classes)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Define network architecture.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Find and replace layers to perform transfer learning.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lgraph = layerGraph(net);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Replace the learnable layer with a new one.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > learnableLayer = lgraph.Layers(end-2);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > numClasses = numel(classes);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > newLearnableLayer = fullyConnectedLayer(numClasses, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > Name=</span><span style="color: rgb(167, 9, 245);">"new_fc"</span><span >, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > WeightLearnRateFactor=10, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > BiasLearnRateFactor=10);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lgraph = replaceLayer(lgraph,learnableLayer.Name,newLearnableLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Replace the classification layer with a new one specific to the type</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% classes seen in the flowers dataset.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > classLayer = lgraph.Layers(end);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > newClassLayer = classificationLayer(Name=</span><span style="color: rgb(167, 9, 245);">"new_classoutput"</span><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lgraph = replaceLayer(lgraph,classLayer.Name,newClassLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Specify training options.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > miniBatchSize = 64;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > validationFrequencyEpochs = 1;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > numObservations = augimdsTrain.NumObservations;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > numIterationsPerEpoch = floor(numObservations/miniBatchSize);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > validationFrequency = validationFrequencyEpochs * numIterationsPerEpoch;</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > options = trainingOptions(</span><span style="color: rgb(167, 9, 245);">"sgdm"</span><span >, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > MaxEpochs=5, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > MiniBatchSize=miniBatchSize, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > InitialLearnRate=3e-4, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > Shuffle=</span><span style="color: rgb(167, 9, 245);">"every-epoch"</span><span >, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > ValidationData=augimdsValidation, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > ValidationFrequency=validationFrequency, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > Plots=</span><span style="color: rgb(167, 9, 245);">"training-progress"</span><span >, </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > Verbose=false);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Train the network.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > transfer_net = trainNetwork(augimdsTrain,lgraph,options);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">end</span></span></div></div></div><h3 class = 'S14' id = 'H_EC33AD21' ><span>Evaluate Mode Accuracy</span></h3><div class = 'S1'><span>The </span><span style=' font-family: monospace;'>evaluateModelAccuracy</span><span> function compares the classify output of the network with the actual labels.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">function </span><span >ccr = evaluateModelAccuracy(net,valDS,labels)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > ypred = classify(net,valDS);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > ccr = mean(ypred == labels);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">end</span></span></div></div></div><h3 class = 'S14' id = 'H_138C14B1' ><span>Create Quantized Network</span></h3><div class = 'S1'><span>The </span><span style=' font-family: monospace;'>createQuantizedNetwork</span><span> constructs a </span><span style=' font-family: monospace;'>dlquantizer</span><span> object for </span><span style=' font-family: monospace;'>GPU</span><span> target, simulates and collects ranges of the network with a representative datastore using the </span><span style=' font-family: monospace;'>calibrate</span><span> function, then quantizes the network using the </span><span style=' font-family: monospace;'>quantize</span><span> function.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">function </span><span >qNet = createQuantizedNetwork(net,calDS)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > dq = dlquantizer(net,ExecutionEnvironment=</span><span style="color: rgb(167, 9, 245);">"GPU"</span><span >);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > calResults = calibrate(dq,calDS);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > qNet = quantize(dq);</span></span></div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">end</span></span></div></div></div><h3 class = 'S14' id = 'H_157F752C' ><span>Make Quantization Aware LayerGraph</span></h3><div class = 'S1'><span>The </span><span style=' font-family: monospace;'>makeQuantizationAwareLayers</span><span> function takes a </span><span style=' font-family: monospace;'>DAGNetwork</span><span> object as input and replaces 2-D convolution, grouped 2-D convolution and batch normalization layers with quantization aware versions. The layer replacement works for this particular network where the layers are in topologically sorted order but may not work for other networks. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">function </span><span >lg = makeQuantizationAwareLayers(net)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lg = layerGraph(net);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">for </span><span >idx = 1:numel(lg.Layers) - 1</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > currentLayer = lg.Layers(idx);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > nextLayer = lg.Layers(idx + 1);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Find 2-D convolution layers or 2-D grouped convolution layers.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">if </span><span >(isa(currentLayer,</span><span style="color: rgb(167, 9, 245);">"nnet.cnn.layer.Convolution2DLayer"</span><span >) </span><span style="color: rgb(14, 0, 255);">...</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > || isa(currentLayer,</span><span style="color: rgb(167, 9, 245);">"nnet.cnn.layer.GroupedConvolution2DLayer"</span><span >))</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">if </span><span >isa(nextLayer,</span><span style="color: rgb(167, 9, 245);">"nnet.cnn.layer.BatchNormalizationLayer"</span><span >)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Replace convolution layer with quantization aware layer.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > qLayer = QuantizedConvolutionBatchNormTrainingLayer(currentLayer,nextLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lg = replaceLayer(lg,currentLayer.Name,qLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Replace batchNormalizationLayer with identity training</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% layer.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > qLayer = IdentityTrainingLayer(nextLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lg = replaceLayer(lg,nextLayer.Name,qLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">else</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Replace convolution layer with quantization aware layer.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > qLayer = QuantizedConvolutionTrainingLayer(currentLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lg = replaceLayer(lg,currentLayer.Name,qLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">end</span></span></div></div></div><h3 class = 'S14' id = 'H_D546E6DD' ><span>Remove Quantization Aware Layers from Network</span></h3><div class = 'S1'><span>The </span><span style=' font-family: monospace;'>removeQuantizationAwareLayers</span><span> function extracts the original layers from the quantization aware network and replaces the quantization aware layers with the original underlying layers.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div class = 'S10'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">function </span><span >net = removeQuantizationAwareLayers(qatNet)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lg = layerGraph(qatNet);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% Find quantization aware training layers and replace with the</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(0, 128, 19);">% underlying layers.</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">for </span><span >idx = 1:numel(lg.Layers)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > currentLayer = lg.Layers(idx);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">if </span><span >isa(currentLayer,</span><span style="color: rgb(167, 9, 245);">"QuantizedConvolutionBatchNormTrainingLayer"</span><span >)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > cLayer = currentLayer.Network.Layers(1);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lg = replaceLayer(lg,cLayer.Name,cLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > bLayer = currentLayer.Network.Layers(2);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lg = replaceLayer(lg,bLayer.Name,bLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">elseif </span><span >isa(currentLayer,</span><span style="color: rgb(167, 9, 245);">"QuantizedConvolutionTrainingLayer"</span><span >)</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > cLayer = currentLayer.Network.Layers(1);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > lg = replaceLayer(lg,cLayer.Name,cLayer);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > </span><span style="color: rgb(14, 0, 255);">end</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S6'><span style="white-space: pre"><span > net = assembleNetwork(lg);</span></span></div></div><div class="inlineWrapper"><div class = 'S6'> </div></div><div class="inlineWrapper"><div class = 'S7'><span style="white-space: pre"><span style="color: rgb(14, 0, 255);">end</span></span></div></div></div><div class = 'S8'><span style=' font-style: italic;'>Copyright 2023 The MathWorks, Inc.</span></div><h4 class = 'S15' id = 'H_5FC10B62' ></h4>
<br>
<!--
##### SOURCE BEGIN #####
%% Quantization Aware Training for Transfer Learned MobileNet-v2
% This example shows how to perform quantization aware training for transfer
% learned MobileNet-v2 network.
%
% Low precision types like |int8| propagate quantization error that may degrade
% the accuracy of the network. Quantization aware training is a method that introduces
% quantization error at training, thus giving the network the ability to adapt
% and ultimately produce a network more robust to quantization. In most cases,
% a quantized network or integer-arithmetic only network constructed after quantization
% aware training can produce accuracy on par with the original floating point
% network.
%
% This example takes you through the quantization workflow of a transfer learned
% MobileNet-v2 network. MobileNet-v2 was chosen for this example because it contains
% depthwise-separable convolution layers that are especially sensitive to post-training
% quantization.
%
% The flowchart below highlights the steps necessary to convert a trained network
% into a quantized one via quantization aware training.
%
%
%
%
%% Load Flower Dataset
% Download the flower dataset [1] using the supporting function |downloadFlowerDataset|.
imageFolder = downloadFlowerDataset;
imds = imageDatastore(imageFolder, ...
IncludeSubfolders=true, ...
LabelSource="foldernames");
%%
% Inspect the classes of the data.
classes = string(categories(imds.Labels))
%% Perform Transfer Learning on MobileNet-v2
% MobileNet-v2 is a convolutional netural network 53 layers deep. The pretrained
% version of the network is trained on more than a million images from the ImageNet
% database.
net = mobilenetv2;
%%
% Split the data into training and validation sets and create augmented image
% datastores that automatically resize the images to the input size of the network.
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.9);
inputSize = net.Layers(1).InputSize;
augimdsTrain = augmentedImageDatastore(inputSize,imdsTrain);
augimdsValidation = augmentedImageDatastore(inputSize,imdsValidation);
validationActualLabels = imdsValidation.Labels;
%%
% Set aside a portion of the training dataset to use during the calibration
% step of quantization. This datastore should be representative of the data used
% for training but ideally separate from the one used to validate.
augimdsCalibration = subset(shuffle(augimdsTrain),1:320);
%%
% Perform transfer learning on the network on the flowers image dataset. The
% learnable parameters of the trained network |transferNet| are in |single| precision.
transferNet = createFlowerNetwork(net,augimdsTrain,augimdsValidation,classes);
%% Evaluate Baseline Network Performance
% Evaluate the performance of the |single| precision network. Performance in
% this case is defined as the correct classification rate.
netCCR = evaluateModelAccuracy(transferNet,augimdsValidation,validationActualLabels)
%%
% Quantize the network using the |createQuantizedNetwork| function provided
% at the end of this example and evaluate the performance of the quantized network.
% Post-training quantization of the original network yields poor performance due
% to the range of learnable values in the depthwise separable convolution layers.
% An accuracy of roughly 20% is the equivalent to guessing one of the 5 possible
% labels for each image.
originalQuantizedNet = createQuantizedNetwork(transferNet,augimdsCalibration);
originalQuantizedCCR = evaluateModelAccuracy(originalQuantizedNet,augimdsValidation,validationActualLabels)
bar( ...
categorical(["Original network","Post-training quantized network"]), ...
[netCCR,originalQuantizedCCR] ...
)
ylabel("Network Accuracy (%)")
%% Replace Network Layers with Quantization Aware Training Layers
% Replace the |Convolution2D| and |GroupedConvolution2D| layers along with their
% adjacent |BatchNormalization| layers with custom layers that are quantization
% aware using the |makeQuantizationAwareLayers| function provided with this example.
% The quantization aware layers are <./QuantizedConvolutionBatchNormTrainingLayer.m
% custom layers> that have modified |forward| and |predict| behavior that inject
% quantization error similar to that of post-training quantization. The quantization
% error comes from the <./quantizeToFloat.m quantizeToFloat> function that quantizes,
% then unquantizes a given value using best-precision scaling to |int8| precision.
%
%
%
% Quantization to float can be expressed as follows.
%
% $$\begin{array}{l}\textrm{𝑥}̂=\textrm{quantizeToFloat}\left(\textrm{𝑥}\right)\\\;\;\;=\textrm{unquantize}\left(\textrm{quantize}\left(\textrm{𝑥}\right)\right)\\\;\;\;=\textrm{rescale}\cdot
% \textrm{saturate}\left(\textrm{round}\left(\frac{\textrm{𝑥}}{\textrm{scale}}\right)\right)\end{array}$$
%
% The quantization step uses a non-differentiable operation |round| that would
% normally break the training workflow by zeroing out the gradients. During quantization
% aware training, bypass the gradient calculations for non-differentiable operations
% using an identity function. The diagram below [2] shows how the custom layer
% calculates the gradients for non-differentiable operations with the identity
% function via straight-through estimation.
%
%
%
%
%
%
%
% For 2-D convolution layers, the weights and biases of the replacement layers
% include the batch normalization layer statistics. Convolution operations during
% training use the adjusted and quantized weights [3].
%
% As the batch normalization layer statistics are incorporated into the convolution
% layers, the |makeQuantizationAwareLayers| replaces each batch normalization
% layer with an identity layer that returns its input as its output.
quantizationAwareLayerGraph = makeQuantizationAwareLayers(transferNet);
%%
% Inspect the layers of the network.
quantizationAwareLayerGraph.Layers
%%
% To apply quantization aware training to a network that contains convolution
% layers without an adjacent bach normalization layer, use the <./QuantizedConvolutionTrainingLayer.m
% |QuantizedConvolutionTrainingLayer|> provided with this example instead of <./QuantizedConvolutionBatchNormTrainingLayer.m
% |QuantizedConvolutionBatchNormTrainingLayer|>.
%% Do Quantization Aware Training
% Using the layer graph with quantization aware training layers, train the network.
% Compared to the training of the original network, the training options have
% been modified to increase the number of |MaxEpochs| to 10 and the |ValidationFrequency|
% to every 2 epochs.
miniBatchSize = 32;
validationFrequencyEpochs = 2;
numObservations = augimdsTrain.NumObservations;
numIterationsPerEpoch = floor(numObservations/miniBatchSize);
validationFrequency = validationFrequencyEpochs*numIterationsPerEpoch;
options = trainingOptions("sgdm", ...
MaxEpochs=10, ...
MiniBatchSize=miniBatchSize, ...
InitialLearnRate=3e-4, ...
Shuffle="every-epoch", ...
ValidationData=augimdsValidation, ...
ValidationFrequency=validationFrequency, ...
Plots="training-progress", ...
Verbose=false);
quantizationAwareTrainedNet = trainNetwork(augimdsTrain,quantizationAwareLayerGraph,options);
%% Quantize the Network
% The network returned by the |trainNetwork| function still has quantization
% aware training layers. The quantization aware training operators need to be
% replaced with operators that are specific to inference. Whereas training was
% performed using 32-bit floating-point values, the quantized network must perform
% inference using 8-bit integer inputs and weights.
%
%
%
%
%
%
%
% Remove the quantization aware layers and replace with underlying learned convolution
% layers using the |removeQuantizationAwareLayers| function, provided at the end
% of this example.
preQuantizedNetwork = removeQuantizationAwareLayers(quantizationAwareTrainedNet);
%%
% Perform post-training quantization on the network as normal using the function
% |createQuantizedNetwork|, provided at the end of this example.
quantizationAwareQuantizedNet = createQuantizedNetwork(preQuantizedNetwork,augimdsCalibration);
quantizedNetworkDetails = quantizationDetails(quantizationAwareQuantizedNet)
%% Evaluate the Quantized Network
% Evaluate the performance of the quantized network.
quantizedNetworkCCR = evaluateModelAccuracy(quantizationAwareQuantizedNet,augimdsValidation,validationActualLabels)
bar( ...
categorical(["Original network","Post-training quantized network","Quantization aware training network"]), ...
[netCCR,originalQuantizedCCR,quantizedNetworkCCR] ...
)
ylabel("Network Accuracy (%)")
%%
% The accuracy for the quantized network after quantization aware training is
% on par with the accuracy of that from the original floating point network.
%% References
%%
% # The TensorFlow Team. _Flowers_ <http://download.tensorflow.org/example_images/flower_photos.tgz
% http://download.tensorflow.org/example_images/flower_photos.tgz>
% # Gholami, A., Kim, S., Dong, Z., Mahoney, M., & Keutzer, K. (2021). A Survey
% of Quantization Methods for Efficient Neural Network Inference. Retrieved from
% <https://arxiv.org/abs/2103.13630 https://arxiv.org/abs/2103.13630>
% # Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H.,
% & Kalenichenko, D. (2017). Quantization and Training of Neural Networks for
% Efficient Integer-Arithmetic-Only Inference. Retrieved from <https://arxiv.org/abs/1712.05877
% https://arxiv.org/abs/1712.05877>
%% Supporting Functions
% Download Flower Dataset
% The |downloadFlowerDataset| function downloads and extracts the flowers dataset,
% if it is not yet in the current folder.
function imageFolder = downloadFlowerDataset
url = "http://download.tensorflow.org/example_images/flower_photos.tgz";
downloadFolder = pwd;
filename = fullfile(downloadFolder,"flower_dataset.tgz");
imageFolder = fullfile(downloadFolder,"flower_photos");
if ~exist(imageFolder,"dir")
disp("Downloading Flower Dataset (218 MB)...")
websave(filename,url);
untar(filename,downloadFolder)
end
end
% Perform Transfer Learning
% The |createFlowerNetwork| function replaces the final fully connected and
% classification layer of the MobileNet-v2 network and retrains the nework to
% classify flowers.
function transfer_net = createFlowerNetwork(net,augimdsTrain,augimdsValidation,classes)
% Define network architecture.
% Find and replace layers to perform transfer learning.
lgraph = layerGraph(net);
% Replace the learnable layer with a new one.
learnableLayer = lgraph.Layers(end-2);
numClasses = numel(classes);
newLearnableLayer = fullyConnectedLayer(numClasses, ...
Name="new_fc", ...
WeightLearnRateFactor=10, ...
BiasLearnRateFactor=10);
lgraph = replaceLayer(lgraph,learnableLayer.Name,newLearnableLayer);
% Replace the classification layer with a new one specific to the type
% classes seen in the flowers dataset.
classLayer = lgraph.Layers(end);
newClassLayer = classificationLayer(Name="new_classoutput");
lgraph = replaceLayer(lgraph,classLayer.Name,newClassLayer);
% Specify training options.
miniBatchSize = 64;
validationFrequencyEpochs = 1;
numObservations = augimdsTrain.NumObservations;
numIterationsPerEpoch = floor(numObservations/miniBatchSize);
validationFrequency = validationFrequencyEpochs * numIterationsPerEpoch;
options = trainingOptions("sgdm", ...
MaxEpochs=5, ...
MiniBatchSize=miniBatchSize, ...
InitialLearnRate=3e-4, ...
Shuffle="every-epoch", ...
ValidationData=augimdsValidation, ...
ValidationFrequency=validationFrequency, ...
Plots="training-progress", ...
Verbose=false);
% Train the network.
transfer_net = trainNetwork(augimdsTrain,lgraph,options);
end
% Evaluate Mode Accuracy
% The |evaluateModelAccuracy| function compares the classify output of the network
% with the actual labels.
function ccr = evaluateModelAccuracy(net,valDS,labels)
ypred = classify(net,valDS);
ccr = mean(ypred == labels);
end
% Create Quantized Network
% The |createQuantizedNetwork| constructs a |dlquantizer| object for |GPU| target,
% simulates and collects ranges of the network with a representative datastore
% using the |calibrate| function, then quantizes the network using the |quantize|
% function.
function qNet = createQuantizedNetwork(net,calDS)
dq = dlquantizer(net,ExecutionEnvironment="GPU");
calResults = calibrate(dq,calDS);
qNet = quantize(dq);
end
% Make Quantization Aware LayerGraph
% The |makeQuantizationAwareLayers| function takes a |DAGNetwork| object as
% input and replaces 2-D convolution, grouped 2-D convolution and batch normalization
% layers with quantization aware versions. The layer replacement works for this
% particular network where the layers are in topologically sorted order but may
% not work for other networks.
function lg = makeQuantizationAwareLayers(net)
lg = layerGraph(net);
for idx = 1:numel(lg.Layers) - 1
currentLayer = lg.Layers(idx);
nextLayer = lg.Layers(idx + 1);
% Find 2-D convolution layers or 2-D grouped convolution layers.
if (isa(currentLayer,"nnet.cnn.layer.Convolution2DLayer") ...
|| isa(currentLayer,"nnet.cnn.layer.GroupedConvolution2DLayer"))
if isa(nextLayer,"nnet.cnn.layer.BatchNormalizationLayer")
% Replace convolution layer with quantization aware layer.
qLayer = QuantizedConvolutionBatchNormTrainingLayer(currentLayer,nextLayer);
lg = replaceLayer(lg,currentLayer.Name,qLayer);
% Replace batchNormalizationLayer with identity training
% layer.
qLayer = IdentityTrainingLayer(nextLayer);
lg = replaceLayer(lg,nextLayer.Name,qLayer);
else
% Replace convolution layer with quantization aware layer.
qLayer = QuantizedConvolutionTrainingLayer(currentLayer);
lg = replaceLayer(lg,currentLayer.Name,qLayer);
end
end
end
end
% Remove Quantization Aware Layers from Network
% The |removeQuantizationAwareLayers| function extracts the original layers
% from the quantization aware network and replaces the quantization aware layers
% with the original underlying layers.
function net = removeQuantizationAwareLayers(qatNet)
lg = layerGraph(qatNet);
% Find quantization aware training layers and replace with the
% underlying layers.
for idx = 1:numel(lg.Layers)
currentLayer = lg.Layers(idx);
if isa(currentLayer,"QuantizedConvolutionBatchNormTrainingLayer")
cLayer = currentLayer.Network.Layers(1);
lg = replaceLayer(lg,cLayer.Name,cLayer);
bLayer = currentLayer.Network.Layers(2);
lg = replaceLayer(lg,bLayer.Name,bLayer);
elseif isa(currentLayer,"QuantizedConvolutionTrainingLayer")
cLayer = currentLayer.Network.Layers(1);
lg = replaceLayer(lg,cLayer.Name,cLayer);
end
end
net = assembleNetwork(lg);
end
%%
% _Copyright 2023 The MathWorks, Inc._
%
##### SOURCE END #####
-->
</div></body></html>