code: def load_data(
      self,
      tensors: Any,
      targets: Optional[List[Any]] = None,
      video_sampler: Type[Sampler] = torch.utils.data.RandomSampler,
      target_formatter: Optional[TargetFormatter] = None,
  ) -> "LabeledVideoTensorDataset":
      result = super().load_data(
          tensors,
          targets,
          video_sampler=video_sampler,
          target_formatter=target_formatter,
      )
  
      # If we had binary multi-class targets then we also know the labels (column names)
      if (
          self.training
          and hasattr(self, "target_formatter")
          and isinstance(
              self.target_formatter, MultiBinaryTargetFormatter
          )
          and isinstance(targets, List)
      ):
          self.labels = targets
  
      return result
  
imports: import os
  from typing import Any, Callable, Collection, Dict, List, Optional, Type, Union
  import pandas
  import torch
  from torch.utils.data import Sampler
  from flash.core.data.io.classification_input import ClassificationInputMixin
  from flash.core.data.io.input import DataKeys, Input, IterableInput
  from flash.core.data.utilities.classification import MultiBinaryTargetFormatter, TargetFormatter, _is_list_like
  from flash.core.data.utilities.data_frame import resolve_files, resolve_targets
  from flash.core.data.utilities.loading import load_data_frame
  from flash.core.data.utilities.paths import PATH_TYPE, list_valid_files, make_dataset
  from flash.core.integrations.fiftyone.utils import FiftyOneLabelUtilities
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _PYTORCHVIDEO_AVAILABLE, lazy_import, requires
label_desc: Use image node_id 1 for calling the VideoClassificationTensorsInput obj's underlying member method code with example usage: obj.load_data(tensors, targets, video_sampler, target_formatter) and returns: result