code: def __init__(
      self,
      out_channels,
      in_channels=32,
      num_layers=1,
      kernel_size=3,
      padding=1,
      norm_layer=nn.BatchNorm2d,
  ):
      super(ConvHead, self).__init__()
      convhead = []
  
      for i in range(num_layers):
          convhead.extend(
              [
                  nn.Conv2d(
                      in_channels,
                      in_channels,
                      kernel_size,
                      padding=padding,
                  ),
                  nn.ReLU(),
                  norm_layer(in_channels)
                  if norm_layer is not None
                  else nn.Identity(),
              ]
          )
      convhead.append(
          nn.Conv2d(in_channels, out_channels, 1, padding=0)
      )
  
      self.convhead = nn.Sequential(*convhead)
  
imports: import math
  import torch
  import numbers
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ConvHead object from inherited base classes: nn with example: obj = ConvHead(out_channels, in_channels, num_layers, kernel_size, padding, norm_layer)