class_docstr: Image & mask augmentation that zero out mask and image regions corresponding
  to randomly chosen object instance from mask.
  
  Mask must be single-channel image, zero values treated as background.
  Image can be any number of channels.
  
  Inspired by https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114254
  
  Args:
      max_objects: Maximum number of labels that can be zeroed out. Can be tuple, in this case it's [min, max]
      image_fill_value: Fill value to use when filling image.
          Can be 'inpaint' to apply inpaining (works only  for 3-chahnel images)
      mask_fill_value: Fill value to use when filling mask.
  
  Targets:
      image, mask
  
  Image types:
      uint8, float32
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code: def apply_to_mask(
      self,
      img: np.ndarray,
      dropout_mask: Optional[np.ndarray] = None,
      **params,
  ) -> np.ndarray:
      if dropout_mask is None:
          return img
  
      img = img.copy()
      img[dropout_mask] = self.mask_fill_value
      return img
  
file_path: albumentations/albumentations/augmentations/dropout/mask_dropout.py
imports: import random
  from typing import Any, Dict, Optional, Tuple, Union
  import cv2
  import numpy
  from skimage.measure import label
  from ...core.transforms_interface import DualTransform, to_tuple
label_desc: Use image node_id 5 for calling the MaskDropout obj's underlying member method code with example usage: obj.apply_to_mask(img, dropout_mask) and returns: img, img