class_docstr: Augmentation used to generate a mask for Vision Transformers
  Typically used in approaches such as iBOT (https://arxiv.org/pdf/2111.07832.pdf)
code: def __init__(
      self,
      pred_ratio_mean: List[float],
      pred_ratio_var: List[float],
      patch_size: int,
      log_aspect_ratio: Tuple[float, float],
  ):
      self.pred_ratio_mean = pred_ratio_mean
      self.pred_ratio_var = pred_ratio_var
      self.patch_size = patch_size
      self.log_aspect_ratio = log_aspect_ratio
  
file_path: vissl/vissl/data/ssl_transforms/masked_image_modeling.py
imports: import math
  import random
  from typing import Any, Dict, List, Tuple
  import numpy
  import torch
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
label_desc: Use image node_id 1 to create a new MaskedImageModeling object from inherited base classes: ClassyTransform with example: obj = MaskedImageModeling(pred_ratio_mean, pred_ratio_var, patch_size, log_aspect_ratio)