class_docstr: Add random uniform noise to integer values.
class_docstr_tok: ["Add","random","uniform","noise","to","integer","values","."]
code: def add_arguments(
      cls, parser: argparse.ArgumentParser
  ) -> argparse.ArgumentParser:
      group = parser.add_argument_group(title=cls.__name__)
  
      group.add_argument(
          "--image-augmentation.random-uniform.enable",
          action="store_true",
          help="Use {}. This flag is useful when you want to study the effect of different "
          "transforms.".format(cls.__name__),
      )
      group.add_argument(
          "--image-augmentation.random-uniform.width-range",
          type=int,
          nargs=2,
          default=[-5, 5],
          help="The range of values from which to add noise. It is specified"
          " as [low, high] (inclusive). Defaults to [-5, 5].",
      )
      return parser
  
imports: import argparse
  import io
  from typing import Dict, Union
  import numpy
  import torch
  from PIL import Image
  from data.transforms import TRANSFORMATIONS_REGISTRY, BaseTransformation
label_desc: Use image node_id 3 for calling the RandomUniformNoise obj's underlying member method code with example usage: obj.add_arguments(cls, parser) and returns: parser