text: import torch
  import torch.utils.data as data
  from .. import transforms
  
  
  class FakeData(data.Dataset):
      """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 datset. Default: 10
          transform (callable, optional): A function/transform that  takes in an 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
  
      """
  
      def __init__(self, size=1000, image_size=(3, 224, 224), num_classes=10,
                   transform=None, target_transform=None, random_offset=0):
          self.size = size
          self.num_classes = num_classes
          self.image_size = image_size
          self.transform = transform
          self.target_transform = target_transform
          self.random_offset = random_offset
  
      def __getitem__(self, index):
          """
          Args:
              index (int): Index
  
          Returns:
              tuple: (image, target) where target is class_index of the target class.
          """
          # create random image that is consistent with the index id
          if index >= len(self):
              raise IndexError("{} index out of range".format(self.__class__.__name__))
          rng_state = torch.get_rng_state()
          torch.manual_seed(index + self.random_offset)
          img = torch.randn(*self.image_size)
          target = torch.randint(0, self.num_classes, size=(1,), dtype=torch.long)[0]
          torch.set_rng_state(rng_state)
  
          # convert to PIL Image
          img = transforms.ToPILImage()(img)
          if self.transform is not None:
              img = self.transform(img)
          if self.target_transform is not None:
              target = self.target_transform(target)
  
          return img, target
  
      def __len__(self):
          return self.size
  
      def __repr__(self):
          fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
          fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
          tmp = '    Transforms (if any): '
          fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
          tmp = '    Target Transforms (if any): '
          fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
          return fmt_str
  