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 __len__(self) -> int:
      return self.size
  
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 3 for calling the FakeData obj's underlying member method code with example usage: obj.__len__() and returns: self