class_docstr: LabeledVideoTensorDataset handles a direct tensor input data.
code: def __iter__(self):
      self._video_sampler_iter = None  # Reset video sampler
  
      # If we're in a PyTorch DataLoader multiprocessing context, we need to use the
      # same seed for each worker's RandomSampler generator. The workers at each
      # __iter__ call are created from the unique value: worker_info.seed - worker_info.id,
      # which we can use for this seed.
      worker_info = torch.utils.data.get_worker_info()
      if (
          self._video_random_generator is not None
          and worker_info is not None
      ):
          base_seed = worker_info.seed - worker_info.id
          self._video_random_generator.manual_seed(base_seed)
  
      return self
  
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 3 for calling the LabeledVideoTensorDataset obj's underlying member method code with example usage: obj.__iter__() and returns: self