code: def __init__(
      self,
      mask_window_size,
      num_masking_patches,
      min_num_patches=16,
      max_num_patches=None,
      min_aspect=0.3,
      max_aspect=None,
  ):
      if not isinstance(
          mask_window_size,
          (
              list,
              tuple,
          ),
      ):
          mask_window_size = (mask_window_size,) * 2
      self.height, self.width = mask_window_size
  
      self.num_patches = self.height * self.width
      self.num_masking_patches = num_masking_patches
  
      self.min_num_patches = min_num_patches
      self.max_num_patches = (
          num_masking_patches
          if max_num_patches is None
          else max_num_patches
      )
  
      max_aspect = max_aspect or 1 / min_aspect
      self.log_aspect_ratio = (
          math.log(min_aspect),
          math.log(max_aspect),
      )
  
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 1 to create a new MaskingGenerator object  with example: obj = MaskingGenerator(mask_window_size, num_masking_patches, min_num_patches, max_num_patches, min_aspect, max_aspect)