code: def __init__(
      self,
      input_size: Union[int, Tuple[int, int]] = 14,
      total_mask_patches: int = 75,
      mask_group_max_patches: Optional[int] = None,
      mask_group_min_patches: int = 16,
      mask_group_min_aspect_ratio: Optional[float] = 0.3,
      mask_group_max_aspect_ratio: float = None,
  ):
      if not isinstance(input_size, tuple):
          input_size = (input_size,) * 2
      self.height, self.width = input_size
  
      self.num_patches = self.height * self.width
      self.total_mask_patches = total_mask_patches
  
      self.mask_group_min_patches = mask_group_min_patches
      self.mask_group_max_patches = (
          total_mask_patches
          if mask_group_max_patches is None
          else mask_group_max_patches
      )
  
      mask_group_max_aspect_ratio = (
          mask_group_max_aspect_ratio or 1 / mask_group_min_aspect_ratio
      )
      self.log_aspect_ratio = (
          math.log(mask_group_min_aspect_ratio),
          math.log(mask_group_max_aspect_ratio),
      )
  
imports: import math
  import random
  from functools import lru_cache
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  from PIL import Image
  from transformers.image_utils import PILImageResampling
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
  from ...utils import TensorType, logging
label_desc: Use image node_id 1 to create a new FlavaMaskingGenerator object  with example: obj = FlavaMaskingGenerator(input_size, total_mask_patches, mask_group_max_patches, mask_group_min_patches, mask_group_min_aspect_ratio, mask_group_max_aspect_ratio)