class_docstr: Implements the view augmentation for MAE [0].
  
  - [0]: Masked Autoencoder, 2021, https://arxiv.org/abs/2111.06377
  
  Attributes:
      input_size:
          Size of the input image in pixels.
      min_scale:
          Minimum size of the randomized crop relative to the input_size.
      normalize:
          Dictionary with 'mean' and 'std' for torchvision.transforms.Normalize.
class_docstr_tok: ["Implements","the","view","augmentation","for","MAE","[","0","]",".","-","[","0","]",":","Masked","Autoencoder",",","2021",",","https",":","\/\/arxiv.org\/abs\/2111.06377","Attributes",":","input_size",":","Size","of","the","input","image","in","pixels",".","min_scale",":","Minimum","size","of","the","randomized","crop","relative","to","the","input_size",".","normalize",":","Dictionary","with","'mean","'","and","'std","'","for","torchvision.transforms.Normalize","."]
code: def __init__(
      self,
      input_size: Union[int, Tuple[int, int]] = 224,
      min_scale: float = 0.2,
      normalize: dict = imagenet_normalize,
  ):
      transforms = [
          T.RandomResizedCrop(
              input_size, scale=(min_scale, 1.0), interpolation=3
          ),  # 3 is bicubic
          T.RandomHorizontalFlip(),
          T.ToTensor(),
      ]
  
      if normalize:
          transforms.append(
              T.Normalize(mean=normalize["mean"], std=normalize["std"])
          )
      super().__init__([T.Compose(transforms)])
  
imports: import math
  from multiprocessing import Value
  from typing import List, Optional, Tuple, Union
  from warnings import warn
  import torch
  import torch.nn
  import torchvision
  import torchvision.transforms
  from PIL import Image
  from lightly.transforms import GaussianBlur, Jigsaw, RandomSolarization
  from lightly.transforms.random_crop_and_flip_with_grid import RandomResizedCropAndFlip
  from lightly.transforms.rotation import random_rotation_transform
  from lightly.transforms.utils import IMAGENET_NORMALIZE
label_desc: Use image node_id 1 to create a new MAECollateFunction object from inherited base classes: MultiViewCollateFunction with example: obj = MAECollateFunction(input_size, min_scale, normalize)