class_docstr: A fake dataset that returns randomly generated images and returns them as PIL images
  
  Args:
      size (int, optional): Size of the dataset. Default: 1000 images
      image_size(tuple, optional): Size if the returned images. Default: (3, 224, 224)
      num_classes(int, optional): Number of classes in the dataset. Default: 10
      transform (callable, optional): A function/transform that takes in a PIL image
          and returns a transformed version. E.g, ``transforms.RandomCrop``
      target_transform (callable, optional): A function/transform that takes in the
          target and transforms it.
      random_offset (int): Offsets the index-based random seed used to
          generate each image. Default: 0
code: def __init__(
      self,
      size: int = 1000,
      image_size: Tuple[int, int, int] = (3, 224, 224),
      num_classes: int = 10,
      transform: Optional[Callable] = None,
      target_transform: Optional[Callable] = None,
      random_offset: int = 0,
  ) -> None:
      super().__init__(
          transform=transform, target_transform=target_transform
      )
      self.size = size
      self.num_classes = num_classes
      self.image_size = image_size
      self.random_offset = random_offset
  
file_path: vision/torchvision/datasets/fakedata.py
imports: from typing import Any, Callable, Optional, Tuple
  import torch
  from ..None import transforms
  from .vision import VisionDataset
label_desc: Use image node_id 1 to create a new FakeData object from inherited base classes: VisionDataset with example: obj = FakeData(size, image_size, num_classes, transform, target_transform, random_offset)