class_docstr: Base class for `neural (or range) augmentation <https://arxiv.org/abs/2212.10553>`_
class_docstr_tok: ["Base","class","for","`","neural","(","or","range",")","augmentation","<","https",":","\/\/arxiv.org\/abs\/2212.10553",">","`","_"]
docstr: Add model-specific arguments
docstr_tok: ["Add","model-specific","arguments"]
code: def add_arguments(cls, parser: argparse.ArgumentParser):
      """Add model-specific arguments"""
      group = parser.add_argument_group(title=cls.__name__)
  
      group.add_argument(
          "--model.learn-augmentation.mode",
          type=str,
          default=None,
          choices=["basic", "distribution"],
          help="Neural augmentation mode",
      )
  
      group.add_argument(
          "--model.learn-augmentation.brightness",
          action="store_true",
          help="Learn parameters for brightness",
      )
  
      group.add_argument(
          "--model.learn-augmentation.contrast",
          action="store_true",
          help="Learn parameters for contrast",
      )
  
      group.add_argument(
          "--model.learn-augmentation.noise",
          action="store_true",
          help="Learn parameters for noise",
      )
  
      # LR multiplier
      group.add_argument(
          "--model.learn-augmentation.lr-multiplier",
          type=float,
          default=1.0,
          help="LR multiplier for neural aug parameters",
      )
  
      return parser
  
imports: import argparse
  import random
  from typing import List, Optional
  import torch
  from torch import Tensor, nn
  from cvnets.misc.common import parameter_list
  from cvnets.neural_augmentor.utils.neural_aug_utils import Clip, FixedSampler, UniformSampler, random_brightness, random_contrast, random_noise
  from utils import logger
label_desc: Use image node_id 5 for calling the BaseNeuralAugmentor obj's underlying member method code with example usage: obj.add_arguments(cls, parser) and returns: parser