code: def __init__(
      self,
      dim_in,
      dim_out,
      ksize=3,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      bias=True,
      transpose=False,
  ):
      super(ConcatConv2d, self).__init__()
      module = nn.ConvTranspose2d if transpose else nn.Conv2d
      self._layer = module(
          dim_in + 1,
          dim_out,
          kernel_size=ksize,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=groups,
          bias=bias,
      )
  
imports: import os
  import argparse
  import logging
  import time
  import numpy
  import torch
  import torch.nn
  from torch.utils.data import DataLoader
  import torchvision.datasets
  import torchvision.transforms
label_desc: Use image node_id 1 to create a new ConcatConv2d object from inherited base classes: nn with example: obj = ConcatConv2d(dim_in, dim_out, ksize, stride, padding, dilation, groups, bias, transpose)