code: def __init__(
      self, dim_in, dim_out, kernel_size, padding, stride=1, bias=True
  ):
      super().__init__()
      self.net = nn.Sequential(
          nn.Conv2d(
              dim_in,
              dim_in,
              kernel_size=kernel_size,
              padding=padding,
              groups=dim_in,
              stride=stride,
              bias=bias,
          ),
          nn.Conv2d(dim_in, dim_out, kernel_size=1, bias=bias),
      )
  
imports: from math import sqrt, pi, log
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new DepthWiseConv2d object from inherited base classes: nn with example: obj = DepthWiseConv2d(dim_in, dim_out, kernel_size, padding, stride, bias)