class_docstr: LabeledVideoTensorDataset handles a direct tensor input data.
code: def __init__(
      self,
      labeled_video_tensors: List[Tuple[str, Optional[dict]]],
      video_sampler: Type[
          torch.utils.data.Sampler
      ] = torch.utils.data.RandomSampler,
  ) -> None:
      self._labeled_videos = labeled_video_tensors
  
      # If a RandomSampler is used we need to pass in a custom random generator that
      # ensures all PyTorch multiprocess workers have the same random seed.
      self._video_random_generator = None
      if video_sampler == torch.utils.data.RandomSampler:
          self._video_random_generator = torch.Generator()
          self._video_sampler = video_sampler(
              self._labeled_videos,
              generator=self._video_random_generator,
          )
      else:
          self._video_sampler = video_sampler(self._labeled_videos)
  
      self._video_sampler_iter = (
          None  # Initialized on first call to self.__next__()
      )
  
      # Depending on the clip sampler type, we may want to sample multiple clips
      # from one video. In that case, we keep the store video, label and previous sampled
      # clip time in these variables.
      self._loaded_video_label = None
  
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 1 to create a new LabeledVideoTensorDataset object from inherited base classes: torch.utils.data with example: obj = LabeledVideoTensorDataset(labeled_video_tensors, video_sampler)