class_docstr: LabeledVideoTensorDataset handles a direct tensor input data.
docstr: Retrieves the next clip based on the clip sampling strategy and video sampler.
  
  Returns:
      A dictionary with the following format.
  
      .. code-block:: text
  
          {
              'video': <video_tensor>,
              'label': <index_label>,
              'video_label': <index_label>
              'video_index': <video_index>,
          }
code: def __next__(self) -> dict:
      """Retrieves the next clip based on the clip sampling strategy and video sampler.
  
      Returns:
          A dictionary with the following format.
  
          .. code-block:: text
  
              {
                  'video': <video_tensor>,
                  'label': <index_label>,
                  'video_label': <index_label>
                  'video_index': <video_index>,
              }
  
      """
      if not self._video_sampler_iter:
          # Setup MultiProcessSampler here - after PyTorch DataLoader workers are spawned.
          self._video_sampler_iter = iter(
              MultiProcessSampler(self._video_sampler)
          )
  
      # Reuse previously stored video if there are still clips to be sampled from
      # the last loaded video.
      video_index = next(self._video_sampler_iter)
      video_tensor, info_dict = self._labeled_videos[video_index]
      self._loaded_video_label = (video_tensor, info_dict, video_index)
  
      return {
          "video": self._loaded_video_label[0],
          "video_name": f"video{video_index}",
          "video_index": video_index,
          "label": info_dict,
          "video_label": info_dict,
      }
  
file_path: lightning-flash/src/flash/video/classification/utils.py
imports: from typing import List, Optional, Tuple, Type
  import torch
  from flash.core.utilities.imports import _TOPIC_VIDEO_AVAILABLE
label_desc: Use image node_id 2 for calling the LabeledVideoTensorDataset obj's underlying member method code with example usage: obj.__next__() and returns: dict