class_docstr: `UCF101 <https://www.crcv.ucf.edu/data/UCF101.php>`_ dataset.
  
  UCF101 is an action recognition video dataset.
  This dataset consider every video as a collection of video clips of fixed size, specified
  by ``frames_per_clip``, where the step in frames between each clip is given by
  ``step_between_clips``. The dataset itself can be downloaded from the dataset website;
  annotations that ``annotation_path`` should be pointing to can be downloaded from `here
  <https://www.crcv.ucf.edu/data/UCF101/UCF101TrainTestSplits-RecognitionTask.zip>`_.
  
  To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5``
  and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two
  elements will come from video 1, and the next three elements from video 2.
  Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all
  frames in a video might be present.
  
  Internally, it uses a VideoClips object to handle clip creation.
  
  Args:
      root (string): Root directory of the UCF101 Dataset.
      annotation_path (str): path to the folder containing the split files;
          see docstring above for download instructions of these files
      frames_per_clip (int): number of frames in a clip.
      step_between_clips (int, optional): number of frames between each clip.
      fold (int, optional): which fold to use. Should be between 1 and 3.
      train (bool, optional): if ``True``, creates a dataset from the train split,
          otherwise from the ``test`` split.
      transform (callable, optional): A function/transform that takes in a TxHxWxC video
          and returns a transformed version.
      output_format (str, optional): The format of the output video tensors (before transforms).
          Can be either "THWC" (default) or "TCHW".
  
  Returns:
      tuple: A 3-tuple with the following entries:
  
          - video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): The `T` video frames
          -  audio(Tensor[K, L]): the audio frames, where `K` is the number of channels
             and `L` is the number of points
          - label (int): class of the video clip
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code: def __len__(self) -> int:
      return self.video_clips.num_clips()
  
file_path: vision/torchvision/datasets/ucf101.py
imports: import os
  from typing import Any, Callable, Dict, List, Optional, Tuple
  from torch import Tensor
  from .folder import find_classes, make_dataset
  from .video_utils import VideoClips
  from .vision import VisionDataset
label_desc: Use image node_id 4 for calling the UCF101 obj's underlying member method code with example usage: obj.__len__() and returns: self