class_docstr: Base class for other collate implementations.
  
  Takes a batch of images as input and transforms each image into two
  different augmentations with the help of random transforms. The images are
  then concatenated such that the output batch is exactly twice the length
  of the input batch.
  
  Attributes:
      transform:
          A set of torchvision transforms which are randomly applied to
          each image.
class_docstr_tok: ["Base","class","for","other","collate","implementations",".","Takes","a","batch","of","images","as","input","and","transforms","each","image","into","two","different","augmentations","with","the","help","of","random","transforms",".","The","images","are","then","concatenated","such","that","the","output","batch","is","exactly","twice","the","length","of","the","input","batch",".","Attributes",":","transform",":","A","set","of","torchvision","transforms","which","are","randomly","applied","to","each","image","."]
code: def __init__(self, transform: torchvision.transforms.Compose):
      _deprecation_warning_collate_functions()
      super(BaseCollateFunction, self).__init__()
      self.transform = transform
  
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 BaseCollateFunction object from inherited base classes: nn with example: obj = BaseCollateFunction(transform)