class_docstr: `MovingMNIST <http://www.cs.toronto.edu/~nitish/unsupervised_video/>`_ Dataset.
  
  Args:
      root (string): Root directory of dataset where ``MovingMNIST/mnist_test_seq.npy`` exists.
      split (string, optional): The dataset split, supports ``None`` (default), ``"train"`` and ``"test"``.
          If ``split=None``, the full data is returned.
      split_ratio (int, optional): The split ratio of number of frames. If ``split="train"``, the first split
          frames ``data[:, :split_ratio]`` is returned. If ``split="test"``, the last split frames ``data[:, split_ratio:]``
          is returned. If ``split=None``, this parameter is ignored and the all frames data is returned.
      transform (callable, optional): A function/transform that takes in a torch Tensor
          and returns a transformed version. E.g, ``transforms.RandomCrop``
      download (bool, optional): If true, downloads the dataset from the internet and
          puts it in root directory. If dataset is already downloaded, it is not
          downloaded again.
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code: def __init__(
      self,
      root: str,
      split: Optional[str] = None,
      split_ratio: int = 10,
      download: bool = False,
      transform: Optional[Callable] = None,
  ) -> None:
      super().__init__(root, transform=transform)
  
      self._base_folder = os.path.join(
          self.root, self.__class__.__name__
      )
      self._filename = self._URL.split("/")[-1]
  
      if split is not None:
          verify_str_arg(split, "split", ("train", "test"))
      self.split = split
  
      if not isinstance(split_ratio, int):
          raise TypeError(
              f"`split_ratio` should be an integer, but got {type(split_ratio)}"
          )
      elif not (1 <= split_ratio <= 19):
          raise ValueError(
              f"`split_ratio` should be `1 <= split_ratio <= 19`, but got {split_ratio} instead."
          )
      self.split_ratio = split_ratio
  
      if download:
          self.download()
  
      if not self._check_exists():
          raise RuntimeError(
              "Dataset not found. You can use download=True to download it."
          )
  
      data = torch.from_numpy(
          np.load(os.path.join(self._base_folder, self._filename))
      )
      if self.split == "train":
          data = data[: self.split_ratio]
      elif self.split == "test":
          data = data[self.split_ratio :]
      self.data = data.transpose(0, 1).unsqueeze(2).contiguous()
  
file_path: vision/torchvision/datasets/moving_mnist.py
imports: import os.path
  from typing import Callable, Optional
  import numpy
  import torch
  from torchvision.datasets.utils import download_url, verify_str_arg
  from torchvision.datasets.vision import VisionDataset
label_desc: Use image node_id 1 to create a new MovingMNIST object from inherited base classes: VisionDataset with example: obj = MovingMNIST(root, split, split_ratio, download, transform)