class_docstr: Compact Dilation Convolution based Module
class_docstr_tok: ["Compact","Dilation","Convolution","based","Module"]
code: def __init__(self, in_channels, out_channels):
      super(CDCM, self).__init__()
  
      self.relu1 = nn.ReLU()
      self.conv1 = nn.Conv2d(
          in_channels, out_channels, kernel_size=1, padding=0
      )
      self.conv2_1 = nn.Conv2d(
          out_channels,
          out_channels,
          kernel_size=3,
          dilation=5,
          padding=5,
          bias=False,
      )
      self.conv2_2 = nn.Conv2d(
          out_channels,
          out_channels,
          kernel_size=3,
          dilation=7,
          padding=7,
          bias=False,
      )
      self.conv2_3 = nn.Conv2d(
          out_channels,
          out_channels,
          kernel_size=3,
          dilation=9,
          padding=9,
          bias=False,
      )
      self.conv2_4 = nn.Conv2d(
          out_channels,
          out_channels,
          kernel_size=3,
          dilation=11,
          padding=11,
          bias=False,
      )
      nn.init.constant_(self.conv1.bias, 0)
  
imports: import os
  import math
  import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from typing import Optional
  from cftool.cv import to_uint8
  from ....parameters import OPT
label_desc: Use image node_id 1 to create a new CDCM object from inherited base classes: nn with example: obj = CDCM(in_channels, out_channels)