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 add_arguments(
      cls, parser: argparse.ArgumentParser
  ) -> argparse.ArgumentParser:
      if cls != RegNet:
          # Don't re-register arguments in subclasses that don't override `add_arguments()`.
          return parser
      group = parser.add_argument_group(title=cls.__name__)
  
      group.add_argument(
          "--model.classification.regnet.mode",
          type=str,
          default="y_4.0gf",
          help=f"The RegNet<mode> to use. Must be one of {', '.join(supported_modes)}. Defaults to y_4.0gf.",
      )
      group.add_argument(
          "--model.classification.regnet.stochastic-depth-prob",
          type=float,
          default=0.0,
          help="Stochastic depth drop probability in RegNet blocks. Defaults to 0.",
      )
      group.add_argument(
          "--model.classification.regnet.stem-width",
          type=int,
          default=32,
          help="The number of output channels of the first conv layer. Defaults to 32",
      )
      return parser
  
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 3 for calling the RegNet obj's underlying member method code with example usage: obj.add_arguments(cls, parser) and returns: parser, parser