code: def __init__(
      self,
      in_c: int,
      out_c: int,
      depth: int,
      group_width: int,
      se_ratio: float,
  ):
      super(RegStage, self).__init__()
      for i in range(depth):
          block_stride = 2 if i == 0 else 1
          block_in_c = in_c if i == 0 else out_c
  
          name = "b{}".format(i + 1)
          self.add_module(
              name,
              Bottleneck(
                  in_c=block_in_c,
                  out_c=out_c,
                  stride=block_stride,
                  group_width=group_width,
                  se_ratio=se_ratio,
              ),
          )
  
imports: from typing import Optional
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
label_desc: Use image node_id 1 to create a new RegStage object from inherited base classes: nn with example: obj = RegStage(in_c, out_c, depth, group_width, se_ratio)