code: def __init__(
      self,
      in_: int,
      out_: int,
      size: int,
      skip_norm_and_gate: bool = False,
  ) -> None:
      norm: Module
      nonl: Module
  
      if skip_norm_and_gate:
          norm = nn.Sequential()
          nonl = nn.Sequential()
      else:
          norm = nn.InstanceNorm2d(in_)
          nonl = nn.PReLU(in_)
  
      dropout = nn.Sequential()
      conv = nn.Conv2d(in_, out_, size, padding="same", bias=True)
  
      super().__init__(norm, nonl, dropout, conv)
  
imports: from __future__ import annotations
  from typing import Any
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor
label_desc: Use image node_id 1 to create a new Conv object from inherited base classes: nn with example: obj = Conv(in_, out_, size, skip_norm_and_gate)