class_docstr: Double Convolution and BN and ReLU (3x3 conv -> BN -> ReLU) ** 2.
  
  >>> DoubleConv(4, 4)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  DoubleConv(
    (net): Sequential(...)
  )
class_docstr_tok: ["Double","Convolution","and","BN","and","ReLU","(","3x3","conv","-",">","BN","-",">","ReLU",")","*","*","2",".",">",">",">","DoubleConv","(","4",",","4",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE","DoubleConv","(","(","net",")",":","Sequential","(","...",")",")"]
code: def forward(self, x):
      return self.net(x)
  
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 DoubleConv obj's underlying member method code with example usage: obj.forward(x) and returns: self