code: def __init__(
      self,
      input_channel,
      output_channel,
      num_iteration,
      kernel_size,
      pad,
  ):
      super(GRCL, self).__init__()
      self.wgf_u = nn.Conv2d(
          input_channel, output_channel, 1, 1, 0, bias=False
      )
      self.wgr_x = nn.Conv2d(
          output_channel, output_channel, 1, 1, 0, bias=False
      )
      self.wf_u = nn.Conv2d(
          input_channel, output_channel, kernel_size, 1, pad, bias=False
      )
      self.wr_x = nn.Conv2d(
          output_channel,
          output_channel,
          kernel_size,
          1,
          pad,
          bias=False,
      )
  
      self.BN_x_init = nn.BatchNorm2d(output_channel)
  
      self.num_iteration = num_iteration
      self.GRCL = [
          GRCL_unit(output_channel) for _ in range(num_iteration)
      ]
      self.GRCL = nn.Sequential(*self.GRCL)
  
imports: import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new GRCL object from inherited base classes: nn with example: obj = GRCL(input_channel, output_channel, num_iteration, kernel_size, pad)