class_docstr: Patch Description Network small
  
  Args:
      out_channels (int): number of convolution output channels
code: def __init__(self, out_channels: int, padding: bool = False) -> None:
      super().__init__()
      pad_mult = 1 if padding else 0
      self.conv1 = nn.Conv2d(
          3, 128, kernel_size=4, stride=1, padding=3 * pad_mult
      )
      self.conv2 = nn.Conv2d(
          128, 256, kernel_size=4, stride=1, padding=3 * pad_mult
      )
      self.conv3 = nn.Conv2d(
          256, 256, kernel_size=3, stride=1, padding=1 * pad_mult
      )
      self.conv4 = nn.Conv2d(
          256,
          out_channels,
          kernel_size=4,
          stride=1,
          padding=0 * pad_mult,
      )
      self.avgpool1 = nn.AvgPool2d(
          kernel_size=2, stride=2, padding=1 * pad_mult
      )
      self.avgpool2 = nn.AvgPool2d(
          kernel_size=2, stride=2, padding=1 * pad_mult
      )
  
imports: from __future__ import annotations
  import logging
  import random
  from enum import Enum
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from torchvision import transforms
label_desc: Use image node_id 1 to create a new PDN_S object from inherited base classes: nn with example: obj = PDN_S(out_channels, padding)