class_docstr: Architecture based on U-Net: Convolutional Networks for Biomedical Image Segmentation.
  
  Link - https://arxiv.org/abs/1505.04597
  
  >>> UNet(num_classes=2, num_layers=3)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  UNet(
    (layers): ModuleList(
      (0): DoubleConv(...)
      (1): Down(...)
      (2): Down(...)
      (3): Up(...)
      (4): Up(...)
      (5): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))
    )
  )
class_docstr_tok: ["Architecture","based","on","U-Net",":","Convolutional","Networks","for","Biomedical","Image","Segmentation",".","Link","-","https",":","\/\/arxiv.org\/abs\/1505.04597",">",">",">","UNet","(","num_classes=2",",","num_layers=3",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE","UNet","(","(","layers",")",":","ModuleList","(","(","0",")",":","DoubleConv","(","...",")","(","1",")",":","Down","(","...",")","(","2",")",":","Down","(","...",")","(","3",")",":","Up","(","...",")","(","4",")",":","Up","(","...",")","(","5",")",":","Conv2d","(","64",",","2",",","kernel_size=","(","1",",","1",")",",","stride=","(","1",",","1",")",")",")",")"]
code: def forward(self, x):
      xi = [self.layers[0](x)]
      # Down path
      for layer in self.layers[1 : self.num_layers]:
          xi.append(layer(xi[-1]))
      # Up path
      for i, layer in enumerate(self.layers[self.num_layers : -1]):
          xi[-1] = layer(xi[-1], xi[-2 - i])
      return self.layers[-1](xi[-1])
  
imports: import os
  import random
  from argparse import ArgumentParser, Namespace
  import numpy
  import torch
  import torch.nn.functional
  import torchvision.transforms
  from lightning.pytorch import LightningModule, Trainer, cli_lightning_logo
  from PIL import Image
  from torch import nn
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 2 for calling the UNet obj's underlying member method code with example usage: obj.forward(x) and returns: self