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484 | 484 |     "# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n",  | 
485 | 485 |     "device = torch.device(\"cuda:0\")\n",  | 
486 | 486 |     "\n",  | 
487 |  | -    "UNet_meatdata = {\n",  | 
 | 487 | +    "UNet_metadata = {\n",  | 
488 | 488 |     "    \"spatial_dims\": 3,\n",  | 
489 | 489 |     "    \"in_channels\": 1,\n",  | 
490 | 490 |     "    \"out_channels\": 2,\n",  | 
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494 | 494 |     "    \"norm\": Norm.BATCH,\n",  | 
495 | 495 |     "}\n",  | 
496 | 496 |     "\n",  | 
497 |  | -    "model = UNet(**UNet_meatdata).to(device)\n",  | 
 | 497 | +    "model = UNet(**UNet_metadata).to(device)\n",  | 
498 | 498 |     "loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",  | 
499 | 499 |     "loss_type = \"DiceLoss\"\n",  | 
500 | 500 |     "optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",  | 
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539 | 539 |     "# initialize a new Aim Run\n",  | 
540 | 540 |     "aim_run = aim.Run()\n",  | 
541 | 541 |     "# log model metadata\n",  | 
542 |  | -    "aim_run[\"UNet_meatdata\"] = UNet_meatdata\n",  | 
 | 542 | +    "aim_run[\"UNet_metadata\"] = UNet_metadata\n",  | 
543 | 543 |     "# log optimizer metadata\n",  | 
544 | 544 |     "aim_run[\"Optimizer_metadata\"] = Optimizer_metadata\n",  | 
545 | 545 |     "\n",  | 
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