class_docstr: This class implements a 'stage' as defined in the `RegNet paper <https://arxiv.org/pdf/2003.13678.pdf>`_.
  It consists of a sequence of bottleneck blocks.
  
  Args:
      opts: command-line arguments
      depth: The number of XRegNetBlocks in the stage
      width_in: The number of input channels of the first block
      width_out: The number of output channels of each block
      stride: Stride for convolution of first block
      groups: Number of groups for the intermediate convolution (bottleneck) layer in each block
      bottleneck_multiplier: The number of in/out channels of the intermediate
          conv layer of each block will be scaled by this value
      se_ratio: The numer squeeze-excitation ratio. The number of channels in the SE
          module of each block will be scaled by this value
      stage_depths: A list of the number of blocks in each stage
      stage_index: The index of the current stage being constructed
      stochastic_depth_prob: The stochastic depth probability
code: def __repr__(self) -> str:
      return "{}(depth={}, width_in={}, width_out={}, stride={}, groups={}, bottleneck_multiplier={}, se_ratio={}, stage_index={}, stochastic_depth_probs={})".format(
          self.__class__.__name__,
          self.depth,
          self.width_in,
          self.width_out,
          self.stride,
          self.groups,
          self.bottleneck_multiplier,
          self.se_ratio,
          self.stage_index,
          self.stochastic_depth_probs,
      )
  
file_path: ml-cvnets/cvnets/modules/regnet_modules.py
imports: import argparse
  from typing import List, Optional, Tuple
  from torch import Tensor, nn
  from cvnets.layers import ConvLayer2d, Identity, StochasticDepth
  from cvnets.layers.activation import build_activation_layer
  from cvnets.modules import BaseModule, SqueezeExcitation
label_desc: Use image node_id 3 for calling the AnyRegNetStage obj's underlying member method code with example usage: obj.__repr__() and returns: str