code: def __call__(self):
      mask = np.zeros(shape=self.get_shape(), dtype=int)
      mask_count = 0
      while mask_count < self.num_masking_patches:
          max_mask_patches = self.num_masking_patches - mask_count
  
          delta = self._mask(mask, max_mask_patches)
          if delta == 0:
              break
          else:
              mask_count += delta
  
      return mask
  
imports: import logging
  import math
  import numpy
  import random
  import torch
  import torchvision
  import torchvision.transforms.functional
  from PIL import Image, ImageFilter
  from scipy.ndimage import gaussian_filter
  from torchvision import transforms
  from .rand_augment import rand_augment_transform
  from .random_erasing import RandomErasing
label_desc: Use image node_id 5 for calling the MaskingGenerator3D obj's underlying member method code with example usage: obj.__call__() and returns: mask