code: def __init__(
      self, C_in, C_out, kernel_size, stride, padding, dilation
  ):
      super(SepConv, self).__init__()
      self.op = nn.Sequential(
          nn.ReLU(inplace=False),
          nn.Conv2d(
              C_in,
              C_in,
              kernel_size=kernel_size,
              stride=stride,
              padding=padding,
              dilation=dilation,
              groups=C_in,
              bias=False,
          ),
          nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
          nn.BatchNorm2d(C_out),
      )
  
imports: from typing import Callable, Dict
  import torch
  import torch.nn
  from nni.nas.nn.pytorch import ModelSpace
  from .modules.nasbench201 import NasBench201Cell
label_desc: Use image node_id 1 to create a new SepConv object from inherited base classes: nn with example: obj = SepConv(C_in, C_out, kernel_size, stride, padding, dilation)