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         id="text1225"
         style="font-style:italic;font-variant:normal;font-weight:bold;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana-BoldItalic;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(12.629692,179.09995)"><tspan
           id="tspan1223"
           y="0"
           x="0 11.62 20.916 26.823999 31.598 41.566002 50.862 55.509998 64.848 69.622002 79.870003 88.844002 98.377998 107.968 114.926 123.158 133.12601 137.774 151.032 160.62199 170.408 179.70399">Define a PyTorch Model</tspan></text>
<text
         id="text1241"
         style="font-variant:normal;font-weight:normal;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(12.629692,196.59994)"><tspan
           id="tspan1227"
           y="0"
           x="0 7.2800002 11.116 19.516001 26.712 33.992001 38.905998 50.694 59.051998 67.774002 76.103996 79.940002 86.295998 91.811996 100.212 106.218 113.498 122.346 127.302 136.14999 144.998 150.08 161.742 170.226 178.948 187.79601 191.632 199.836 206.23399">class Model(torch.nn.Module):</tspan><tspan
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           y="34.999985"
           x="0 19.614 28.336 36.666 41.580002 46.493999 55.383999 64.148003 68.026001 76.874001 80.709999 86.225998 95.115997 103.922 110.278 117.558 125.888 129.724 135.24001 141.59599"> def __init__(self):</tspan><tspan
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           x="0 39.27 46.549999 55.397999 64.120003 72.449997 78.288002 84.643997 96.431999 104.916 113.638 121.968 125.72 130.802 135.716 142.996 151.2 155.078 160.636 166.992 172.07401 180.964 189.854 193.606 202.45399 206.28999 211.806 220.696 229.586 235.942"> super(Model, self).__init__()</tspan><tspan
           id="tspan1233"
           y="70"
           x="0 39.27 46.549999 54.880001 58.716 62.748001 67.830002 71.666 79.898003 88.017998 96.348 102.312 107.226 118.678 123.466 128.98199 137.466 143.42999 150.71001 159.474 164.556 173.40401 182.12601 187.20799 194.992 198.828 207.67599 215.88 224.28 230.244 236.60001 240.436 249.2 258.09 263.004 271.33401 279.73401 285.25 294.01401 299.978 308.30801 315.46201 326.914 335.80399 344.694 353.58401 358.62399 363.53799 372.022 380.87 386.38599 395.19199 400.10599 408.436 416.836 422.35199 431.15799 437.12201 445.452 452.73199 464.10001 472.98999 481.88 490.76999"> self.layer = torch.nn.Linear(in_features=400, out_features=400)</tspan><tspan
           id="tspan1235"
           y="87.5"
           x="0 39.27 44.785999 53.27 59.234001 66.514 75.236 80.318001 89.166 97.902 102.984 106.82 115.668 119.434 124.95 130.032 138.306 146.552 154.826 158.662 166.992 172.83 181.72 190.56799 199.416 203.168 208.082 216.56599 222.53 236.138 244.944 251.3 258.57999 266.91 270.746 274.75 279.832 283.668 291.88599 300.01999 308.35001 312.31201 317.39401 328.84601 337.17599 341.01199 349.73401 358.45599 363.97198"> torch.nn.init.xavier_uniform_(self.layer.weight)</tspan><tspan
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           y="122.5"
           x="0 19.614 28.336 36.666 41.580002 46.493999 51.408001 59.765999 65.730003 77.028 85.428001 91.391998 100.114 106.47 113.666 121.996 125.832 129.85001 134.93201 139.84599 143.68201 152.40401 161.12601 169.974 175.49001 181.84599"> def forward(self, input):</tspan><tspan
           id="tspan1239"
           y="139.99997"
           x="0 39.27 45.234001 53.563999 59.080002 67.928001 73.765999 82.613998 87.528 93.043999 101.528 107.408 114.688 123.536 128.618 133.40601 142.254 151.10201 158.256 163.772 167.64999 176.134 184.98199 193.382 197.134 202.216 208.138 213.22 220.5 228.858 233.772 239.28799 252.896 261.29599 269.444 275.84201 283.12201 291.452 295.28799 299.32001 304.40201 308.23801 316.47 324.59 332.92001 338.884 345.23999 349.07599 357.84 366.56201 375.41 380.92599 387.28201"> return torch.functional.F.softmax(self.layer(input))</tspan></text>
<text
         id="text1245"
         style="font-style:italic;font-variant:normal;font-weight:bold;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana-BoldItalic;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(12.677529,564.96288)"><tspan
           id="tspan1243"
           y="0"
           x="0 7.6300001 22.441999 32.228001 41.818001 48.776001 55.146 59.793999 69.132004 73.905998 84.028 90.986 100.282 106.652 113.61 122.948 127.722 137.606 146.90199 156.688 161.46201 171.71001 180.68401 190.218 199.808 206.76601 214.998 224.966 229.614 242.87199 252.46201 262.24799 271.54401">Import a Pretrained PyTorch Model</tspan></text>
<text
         id="text1253"
         style="font-variant:normal;font-weight:normal;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(12.677529,582.46283)"><tspan
           id="tspan1247"
           y="0"
           x="0 13.608 22.091999 30.813999 39.144001 42.854 47.768002 59.220001 64.134003 75.921997 84.321999 93.043999 101.374 105.21 111.566">model = Model()</tspan><tspan
           id="tspan1249"
           y="17.5"
           x="0 13.608 22.091999 30.813999 39.144001 42.854 47.768002 59.220001 64.134003 69.650002 78.134003 84.014 91.293999 100.142 105.098 113.946 122.794 127.876 138.65601 146.92999 152.446 160.84599 168.966 177.366 183.078 191.478 195.314 199.14999 207.48 211.23199 217.588 231.196 239.67999 248.40199 256.73199 260.56799">model = torch.nn.DataParallel(model)</tspan><tspan
           id="tspan1251"
           y="35"
           x="0 13.608 22.091999 30.813999 39.144001 42.854 47.768002 59.220001 64.134003 77.741997 86.099998 94.821999 103.152 106.988 112.07 115.906 124.306 132.70599 141.42799 150.31799 157.59801 163.03 171.42999 176.946 185.276 194.166 202.888 206.724 213.92 219.436 225.834 231.35001 239.834 245.798 252.994 261.84201 266.92401 270.76001 279.11801 287.51801 296.23999 302.59601 306.34799 311.86401 317.56201 325.96201 329.798 338.646 346.97601 355.57199 364.504 378.112 386.59601 395.31799 403.522 407.358 412.44 421.16199 426.67801 430.42999 436.78601 441.86801 446.698 453.978 459.49399 465.45801 469.29401 476.48999 482.00601 493.45801 500.78 509.17999 513.01599 520.29602 528.5">model = model.load_state_dict(torch.load('trained_model.pt'), strict=False)</tspan></text>
<text
         id="text1257"
         style="font-style:italic;font-variant:normal;font-weight:bold;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana-BoldItalic;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(12.629681,373.05308)"><tspan
           id="tspan1255"
           y="0"
           x="0 9.5340004 16.492001 25.83 30.604 40.445999 45.220001 54.557999 59.332001 69.454002 78.554001 88.087997 97.678001 104.636 112.742 122.71 127.484 140.742 150.332 159.992 169.28799">Train a PyTorch Model</tspan></text>
<text
         id="text1275"
         style="font-variant:normal;font-weight:normal;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(12.629681,390.55307)"><tspan
           id="tspan1259"
           y="0"
           x="0 4.914 13.398 19.362 24.15 32.48 41.202 49.686001 56.966 65.688004 70.601997 74.438004 88.129997 93.856003 102.256 111.104 119.826 128.04401 134.39999 142.73 151.452 159.936 167.174 175.966 183.246 189.60201">for epoch inrange(epochs):</tspan><tspan
           id="tspan1261"
           y="17.5"
           x="0 29.441999 43.049999 51.534 60.256001 68.585999 72.421997 77.377998 82.893997 88.606003 97.005997 100.842 109.69 116.046"> model.train()</tspan><tspan
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           y="35"
           x="0 29.441999 34.355999 42.84 48.804001 53.717999 62.439999 70.713997 76.272003 83.552002 92.400002 101.164 105 113.722 121.996 131.908 138.306 147.028 155.42799 160.944 169.34399 174.42599 179.34 184.856 193.172 199.136 207.858 216.188 221.70399 228.06 232.974 236.726 245.57401 250.48801 258.81799 267.66599 276.388 289.996 298.32599 303.98199 312.38199 317.89801 326.228 332.58401 338.10001 343.742 352.142 355.978 364.82599 373.716 377.46799 385.952 394.35199 403.07401 411.40399 417.284 423.64001"> for batch_idx,(data, target) in enumerate(train_loader):</tspan><tspan
           id="tspan1265"
           y="52.5"
           x="0 49.098 57.82 66.220001 71.736 80.136002 85.218002 90.132004 95.648003 103.964 109.928 118.65 126.98 132.496 137.41 148.862 153.64999 162.37199 170.814 176.33 184.73 189.812 196.966 205.814 214.536 222.936 229.29201 235.64799 240.73 245.56 251.076 259.47601 265.44 274.16199 282.492 288.008 293.00601 300.28601 309.134 317.85599 326.25601 332.612"> data, target = data.cuda(), target.cuda()</tspan><tspan
           id="tspan1267"
           y="70"
           x="0 49.098 57.582001 66.304001 71.82 75.655998 89.264 93.099998 100.324 108.654 112.574 117.656 124.88 133.21001 139.174 147.658 156.464 165.186 170.87 179.27 187.992 194.34801"> optimizer.zero_grad()</tspan><tspan
           id="tspan1269"
           y="87.5"
           x="0 49.098 57.582001 66.43 71.945999 80.667999 89.515999 95.031998 99.82 111.272 116.186 129.79401 138.278 147 155.20399 159.082 165.438 174.16 182.56 188.076 196.476"> output = model(data)</tspan><tspan
           id="tspan1271"
           y="105"
           x="0 49.098 52.933998 61.417999 68.697998 75.851997 80.765999 92.218002 97.132004 104.412 110.292 114.128 119.644 127.974 133.938 137.774 146.174 155.022 161.37801 169.862 178.62601 184.142 192.864 201.71201 207.228 212.31 217.224 222.74001 231.01401 236.978 245.7 254.03 259.54599 265.90201 270.98401 279.70599 288.022 295.302 303.57599 314.84601 323.246 329.20999 337.93201 344.28799"> loss = criterion(output, target).backward()</tspan><tspan
           id="tspan1273"
           y="122.50003"
           x="0 49.098 57.582001 66.304001 71.82 75.655998 89.264 93.099998 100.324 108.654 112.574 117.656 124.936 130.452 138.782 147.504 153.73399"> optimizer.step()</tspan></text>
<text
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         style="font-style:italic;font-variant:normal;font-weight:bold;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana-BoldItalic;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(274.15024,535.69878)"><tspan
           id="tspan1277"
           y="0"
           x="0 6.7059999 18.606001 29.554001">-OR-</tspan><tspan
           id="tspan1279"
           y="-404.48544"
           x="308.67279 321.93079 331.52078 341.30679 351.27478 355.96478 365.26077 370.03479 380.28278 389.62079 396.49478 405.83279 420.64478 429.9408 436.31079 445.60678 452.56479 460.86679 465.64078 471.92679 481.51678 486.29077 496.53879 505.79279 512.16278 520.39478">Module Parameters to Patch</tspan></text>
<text
         id="text1285"
         style="font-variant:normal;font-weight:normal;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(582.82303,148.71334)"><tspan
           id="tspan1283"
           y="0"
           x="0 13.608 22.091999 30.813999 39.661999 43.414001 51.743999 60.633999 69.356003 77.755997 83.454002 91.853996 105.462 113.792 119.308 127.638 133.51801 140.798 149.688 155.20399 163.646 172.536 181.258 189.658 195.174 202.45399 211.25999 222.71201 229.06799 234.584 243.06799 249.032 256.26999 265.11801 270.20001 278.96399 287.81201 292.89401 300.67801 304.51401 313.27802 321.608 330.008 335.97198">module_parameters_to_patch=[torch.nn.Linear]</tspan></text>
<text
         id="text1291"
         style="font-style:italic;font-variant:normal;font-weight:bold;font-size:14px;font-family:Verdana;-inkscape-font-specification:Verdana-BoldItalic;writing-mode:lr-tb;fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"
         transform="translate(582.82303,183.71334)"><tspan
           id="tspan1287"
           y="0"
           x="0 13.258 22.554001 37.366001 44.324001 49.098 57.400002 63.686001 73.276001 80.234001 85.008003 98.265999 107.856 117.558 126.854">Memristor Model</tspan><tspan
           id="tspan1289"
           y="105.00004"
           x="0 10.122 17.08 26.67 34.972 43.273998 53.060001 62.355999 69.314003 74.087997 84.209999 93.800003 103.768 109.592 114.366 124.152 134.12 141.078 150.416 156.744 161.51801 171.108">Crossbar Configuration</tspan></text>
<text
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         transform="translate(582.82303,306.21338)"><tspan
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           y="0"
           x="0 7.2800002 14.56 23.323999 31.653999 45.262001 53.591999 65.043999 78.568001 86.898003 100.506 106.022 114.506 120.428 127.708 136.556 141.554 150.276 159.12399 164.20599 173.76801 180.964 189.812 198.142 211.75 219.996 225.078 235.858 244.342 253.106 261.828 265.664 273.99399 283.76599 292.25 296.086 304.892 318.5">scheme=memtorch.bh.Scheme.DoubleColumn</tspan><tspan
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           x="0 5.5159998 11.228 19.628 28.476 35.756001 39.507999 46.787998 52.304001 60.787998 66.751999 78.162003 85.526001 93.926003 97.762001 105.042">transistor=False</tspan><tspan
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           x="0 9.5620003 20.341999 30.114 39.004002 44.925999 53.256001 60.535999 69.019997 72.814003 81.662003 87.178001 91.014 99.498001 108.262 119.714">ADC_resolution=8</tspan><tspan
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           x="0 13.258 22.596001 32.382 42.167999 46.858002 56.826 66.612 71.386002 82.334 91.924004 101.808 108.178 112.952 122.92">Mapping Routine</tspan></text>
<text
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<text
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           x="0 8.7220001 14.686 23.17 31.892 37.562 45.962002 59.57 73.178001 77.014 85.778 94.5 103.432 109.396 117.88 126.644 132.16 136.03799 144.886 153.216 164.668 178.192 186.522 200.13 205.646 214.13 220.00999 227.28999 236.138 241.136 249.858 258.70599 263.78799 271.06799 276.98999 285.474 292.754 299.95001 308.672 317.07199 321.07599 326.15799 334.60001 340.564 348.96399 357.686 363.41199 371.81201 385.42001 390.50201 399.35001 407.66599 411.54401 419.67801 428.008 436.89801 445.62 451.58401 459.98401 468.70599 474.44601 482.84601">programming_routine=memtorch.bh.crossbar.Program.naive_program</tspan></text>
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           x="0 7.6300001 17.598 27.384001 37.352001 43.722 48.411999 58.352001 66.584 75.921997 80.612 85.386002 95.353996">Input Scaling</tspan></text>
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