code: def Ucf101(
      data_path: str,
      clip_sampler: ClipSampler,
      video_sampler: Type[
          torch.utils.data.Sampler
      ] = torch.utils.data.RandomSampler,
      transform: Optional[
          Callable[[Dict[str, Any]], Dict[str, Any]]
      ] = None,
      video_path_prefix: str = "",
      decode_audio: bool = True,
      decoder: str = "pyav",
  ) -> LabeledVideoDataset:
      """
      A helper function to create ``LabeledVideoDataset`` object for the Ucf101 dataset.
  
      Args:
          data_path (str): Path to the data. The path type defines how the data
              should be read:
  
              * For a file path, the file is read and each line is parsed into a
                video path and label.
              * For a directory, the directory structure defines the classes
                (i.e. each subdirectory is a class).
  
          clip_sampler (ClipSampler): Defines how clips should be sampled from each
                  video. See the clip sampling documentation for more information.
  
          video_sampler (Type[torch.utils.data.Sampler]): Sampler for the internal
                  video container. This defines the order videos are decoded and,
                  if necessary, the distributed split.
  
          transform (Callable): This callable is evaluated on the clip output before
                  the clip is returned. It can be used for user defined preprocessing and
                  augmentations to the clips. See the ``LabeledVideoDataset`` class for clip
                  output format.
  
          video_path_prefix (str): Path to root directory with the videos that are
                  loaded in ``LabeledVideoDataset``. All the video paths before loading
                  are prefixed with this path.
  
          decode_audio (bool): If True, also decode audio from video.
  
          decoder (str): Defines what type of decoder used to decode a video.
  
      """
  
      torch._C._log_api_usage_once("PYTORCHVIDEO.dataset.Ucf101")
  
      return labeled_video_dataset(
          data_path,
          clip_sampler,
          video_sampler,
          transform,
          video_path_prefix,
          decode_audio,
          decoder,
      )
  
imports: from typing import Any, Callable, Dict, Optional, Type
  import torch
  from pytorchvideo.data.clip_sampling import ClipSampler
  from .labeled_video_dataset import labeled_video_dataset, LabeledVideoDataset
label_desc: Use image node_id 1 for calling a global function with example usage: Ucf101(data_path, clip_sampler, video_sampler, transform, video_path_prefix, decode_audio, decoder) and returns: labeled_video_dataset