class_docstr: Implements the augmentations for I-JEPA [0, 1].
  
  Experimental: Support for I-JEPA is experimental, there might be breaking changes
  in the future.
  
  - [0]: Joint-Embedding Predictive Architecture, 2023, https://arxiv.org/abs/2301.08243
  - [1]: https://github.com/facebookresearch/ijepa
  
  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[str, List[float]] = 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"])
          )
  
      self.transform = T.Compose(transforms)
  
file_path: lightly/lightly/transforms/ijepa_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 1 to create a new IJEPATransform object  with example: obj = IJEPATransform(input_size, min_scale, normalize)