class_docstr: This class implements the `RegNet architecture <https://arxiv.org/pdf/2003.13678.pdf>`_
class_docstr_tok: ["This","class","implements","the","`","RegNet","architecture","<","https",":","\/\/arxiv.org\/pdf\/2003.13678.pdf",">","`","_"]
code: def _make_stage(
      self,
      opts: argparse.Namespace,
      width_in: int,
      stage_config: Dict,
      stage_index: int,
      stochastic_depth_probs: List[float],
      *args,
      **kwargs,
  ) -> Tuple[nn.Sequential, int]:
      stage_depth = stage_config["depth"]
      stage_width = stage_config["width"]
      groups = stage_config["groups"]
      stride = stage_config["stride"]
      bottleneck_multiplier = stage_config["bottleneck_multiplier"]
      se_ratio = stage_config["se_ratio"]
  
      stage = AnyRegNetStage(
          opts=opts,
          depth=stage_depth,
          width_in=width_in,
          width_out=stage_width,
          stride=stride,
          groups=groups,
          bottleneck_multiplier=bottleneck_multiplier,
          se_ratio=se_ratio,
          stage_index=stage_index,
          stochastic_depth_probs=stochastic_depth_probs,
      )
  
      return stage, stage_width
  
file_path: ml-cvnets/cvnets/models/classification/regnet.py
imports: import argparse
  from typing import Dict, List, Tuple
  from torch import nn
  from cvnets.layers import AdaptiveAvgPool2d, ConvLayer2d, Dropout, Flatten, Identity, LinearLayer
  from cvnets.models import MODEL_REGISTRY
  from cvnets.models.classification.base_image_encoder import BaseImageEncoder
  from cvnets.models.classification.config.regnet import get_configuration, supported_modes
  from cvnets.modules import AnyRegNetStage
label_desc: Use image node_id 2 for calling the RegNet obj's underlying member method code with example usage: obj._make_stage(opts, width_in, stage_config, stage_index, stochastic_depth_probs) and returns: stage,  stage_width