class_docstr: Combination of MaxPool2d and DoubleConv in series.
  
  >>> Down(4, 8)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  Down(
    (net): Sequential(
      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (1): DoubleConv(
        (net): Sequential(...)
      )
    )
  )
class_docstr_tok: ["Combination","of","MaxPool2d","and","DoubleConv","in","series",".",">",">",">","Down","(","4",",","8",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE","Down","(","(","net",")",":","Sequential","(","(","0",")",":","MaxPool2d","(","kernel_size=2",",","stride=2",",","padding=0",",","dilation=1",",","ceil_mode=False",")","(","1",")",":","DoubleConv","(","(","net",")",":","Sequential","(","...",")",")",")",")"]
code: def __init__(self, in_ch: int, out_ch: int):
      super().__init__()
      self.net = nn.Sequential(
          nn.MaxPool2d(kernel_size=2, stride=2),
          DoubleConv(in_ch, out_ch),
      )
  
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 1 to create a new Down object from inherited base classes: nn with example: obj = Down(in_ch, out_ch)