class_docstr: Collator for IJEPA model [0].
  
  Experimental: Support for I-JEPA is experimental, there might be breaking changes
  in the future.
  
  Code inspired by [1].
  
  - [0]: Joint-Embedding Predictive Architecture, 2023, https://arxiv.org/abs/2301.08243
  - [1]: https://github.com/facebookresearch/ijepa
class_docstr_tok: ["Collator","for","IJEPA","model","[","0","]",".","Experimental",":","Support","for","I-JEPA","is","experimental",",","there","might","be","breaking","changes","in","the","future",".","Code","inspired","by","[","1","]",".","-","[","0","]",":","Joint-Embedding","Predictive","Architecture",",","2023",",","https",":","\/\/arxiv.org\/abs\/2301.08243","-","[","1","]",":","https",":","\/\/github.com\/facebookresearch\/ijepa"]
code: def step(self):
      i = self._itr_counter
      with i.get_lock():
          i.value += 1
          v = i.value
      return v
  
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 2 for calling the IJEPAMaskCollator obj's underlying member method code with example usage: obj.step() and returns: v