code: def __init__(
      self,
      in_c,
      out_c,
      residual=False,
      kernel=(3, 3),
      stride=(2, 2),
      padding=(1, 1),
      groups=1,
  ):
      super(DepthWise, self).__init__()
      self.residual = residual
      self.layers = nn.Sequential(
          ConvBlock(
              in_c,
              out_c=groups,
              kernel=(1, 1),
              padding=(0, 0),
              stride=(1, 1),
          ),
          ConvBlock(
              groups,
              groups,
              groups=groups,
              kernel=kernel,
              padding=padding,
              stride=stride,
          ),
          LinearBlock(
              groups,
              out_c,
              kernel=(1, 1),
              padding=(0, 0),
              stride=(1, 1),
          ),
      )
  
imports: import torch.nn
  from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
  import torch
label_desc: Use image node_id 1 to create a new DepthWise object from inherited base classes: Module with example: obj = DepthWise(in_c, out_c, residual, kernel, stride, padding, groups)