code: def __init__(
      self,
      in_channels=512,
      out_channels=512,
      n_filters=256,
      last_padding=0,
      kernel_size=3,
      is_deconv=False,
  ):
      super().__init__()
  
      if kernel_size == 3:
          conv_stride = 1
      elif kernel_size == 1:
          conv_stride = 1
      elif kernel_size == 4:
          conv_stride = 2
  
      # B, C, H, W -> B, out_channels, H, W
      self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=2)
  
      self.norm1 = nn.BatchNorm2d(out_channels)
      self.relu1 = nonlinearity(inplace=True)
  
      # B, out_channels, H, W -> B, out_channels, H, W
      if is_deconv is True:
          self.deconv2 = nn.ConvTranspose2d(
              out_channels,
              out_channels,
              kernel_size,
              stride=conv_stride,
              padding=1,
          )
      else:
          self.deconv2 = nn.Upsample(scale_factor=2)
  
      self.norm2 = nn.BatchNorm2d(out_channels)
      self.relu2 = nonlinearity(inplace=True)
  
      # B, out_channels, H, W -> B, C, H, W
      self.conv3 = nn.Conv2d(
          out_channels, n_filters, 3, padding=1 + last_padding
      )
  
      self.norm3 = nn.BatchNorm2d(n_filters)
      self.relu3 = nonlinearity(inplace=True)
  
imports: from torch import nn
label_desc: Use image node_id 1 to create a new DecoderBlockLinkNetInceptionV2 object from inherited base classes: nn with example: obj = DecoderBlockLinkNetInceptionV2(in_channels, out_channels, n_filters, last_padding, kernel_size, is_deconv)