class_docstr: Randomly Drop Channels in the input Image.
  
  Args:
      channel_drop_range (int, int): range from which we choose the number of channels to drop.
      fill_value (int, float): pixel value for the dropped channel.
      p (float): probability of applying the transform. Default: 0.5.
  
  Targets:
      image
  
  Image types:
      uint8, uint16, unit32, float32
class_docstr_tok: ["Randomly","Drop","Channels","in","the","input","Image",".","Args",":","channel_drop_range","(","int",",","int",")",":","range","from","which","we","choose","the","number","of","channels","to","drop",".","fill_value","(","int",",","float",")",":","pixel","value","for","the","dropped","channel",".","p","(","float",")",":","probability","of","applying","the","transform",".","Default",":","0.5",".","Targets",":","image","Image","types",":","uint8",",","uint16",",","unit32",",","float32"]
code: def get_transform_init_args_names(self) -> Tuple[str, ...]:
      return "channel_drop_range", "fill_value"
  
file_path: albumentations/albumentations/augmentations/dropout/channel_dropout.py
imports: import random
  from typing import Any, Mapping, Tuple, Union
  import numpy
  from albumentations.core.transforms_interface import ImageOnlyTransform
  from .functional import channel_dropout
label_desc: Use image node_id 4 for calling the ChannelDropout obj's underlying member method code with example usage: obj.get_transform_init_args_names() and returns: str,  str