code: def __init__(
      self, kernel_size, in_ch, out_ch, skip, exp_ratio, stride
  ):
      super().__init__()
      self.kernel_size = kernel_size
      self.in_ch = in_ch
      self.out_ch = out_ch
      self.skip = skip
      self.exp_ratio = exp_ratio
      self.stride = stride
  
      self.conv = nn.Conv2d(
          in_ch,
          out_ch,
          kernel_size,
          padding=kernel_size // 2,
          stride=stride,
          bias=False,
      )
      self.relu = nn.ReLU(inplace=True)
      self.bn = nn.BatchNorm2d(out_ch, momentum=BN_MOMENTUM)
  
imports: from nni.retiarii import basic_unit
  import nni.retiarii.nn.pytorch
  import warnings
  import torch
  import torch.nn
  import torch.nn.functional
  from nni.retiarii import model_wrapper
  import sys
  from pathlib import Path
label_desc: Use image node_id 1 to create a new RegularConv object from inherited base classes: nn with example: obj = RegularConv(kernel_size, in_ch, out_ch, skip, exp_ratio, stride)