code: def predict_load_data(
      self, data: Union[torch.Tensor, List[Any], Any]
  ):
      if _is_list_like(data):
          return data
      if not isinstance(data, torch.Tensor):
          raise TypeError(
              f"Expected either a list/tuple of torch.Tensor or torch.Tensor, but got: {type(data)}."
          )
      if data.ndim == 5:
          return list(data)
      if data.ndim == 4:
          return [data]
      raise ValueError(
          f"Got dimension of the input tensor: {data.ndim},"
          " for stack of tensors - dimension should be 5 or for a single tensor, dimension should be 4."
      )
  
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 VideoClassificationTensorsPredictInput obj's underlying member method code with example usage: obj.predict_load_data(data) and returns: data, list, list