class_docstr: Implements the view augmentation for MAE [0].
  
  Input to this transform:
      PIL Image or Tensor.
  
  Output of this transform:
      List of Tensor of length 1.
  
  Applies the following augmentations by default:
      - Random resized crop
      - Random horizontal flip
  
  - [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.
docstr: Applies the transforms to the input image.
  
  Args:
      image:
          The input image to apply the transforms to.
  
  Returns:
      The transformed image.
docstr_tok: ["Applies","the","transforms","to","the","input","image",".","Args",":","image",":","The","input","image","to","apply","the","transforms","to",".","Returns",":","The","transformed","image","."]
code: def __call__(self, image: Union[Tensor, Image]) -> List[Tensor]:
      """
      Applies the transforms to the input image.
  
      Args:
          image:
              The input image to apply the transforms to.
  
      Returns:
          The transformed image.
  
      """
      return [self.transform(image)]
  
file_path: lightly/lightly/transforms/mae_transform.py
imports: from typing import Dict, List, Tuple, Union
  import torchvision.transforms
  from PIL.Image import Image
  from torch import Tensor
  from lightly.transforms.utils import IMAGENET_NORMALIZE
label_desc: Use image node_id 2 for calling the MAETransform obj's underlying member method code with example usage: obj.__call__(image) and returns: list