code: def load_data(
      self,
      inputs: Optional[Union[Collection[torch.Tensor], torch.Tensor]],
      targets: Union[List[Any], Any],
      video_sampler: Type[Sampler] = torch.utils.data.RandomSampler,
      target_formatter: Optional[TargetFormatter] = None,
  ) -> "LabeledVideoTensorDataset":
      if isinstance(inputs, torch.Tensor):
          # In case of (number of videos x CTHW) format
          if inputs.ndim == 5:
              inputs = list(inputs)
          elif inputs.ndim == 4:
              inputs = [inputs]
          else:
              raise ValueError(
                  f"Got dimension of the input tensor: {inputs.ndim}"
                  " for stack of tensors - dimension should be 5 or for a single tensor, dimension should be 4.",
              )
      elif not _is_list_like(inputs):
          raise TypeError(
              f"Expected either a list/tuple of torch.Tensor or torch.Tensor, but got: {type(inputs)}."
          )
  
      # Note: We take whatever is the shortest out of inputs and targets
      dataset = LabeledVideoTensorDataset(
          list(zip(inputs, targets)), video_sampler=video_sampler
      )
      if not self.predicting:
          self.load_target_metadata(
              [sample[1] for sample in dataset._labeled_videos],
              target_formatter=target_formatter,
          )
      return dataset
  
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 VideoClassificationTensorsBaseInput obj's underlying member method code with example usage: obj.load_data(inputs, targets, video_sampler, target_formatter) and returns: dataset