code: def load_data(
      self,
      tensor: Any,
      targets: Optional[List[List[Any]]] = None,
      bboxes: Optional[List[List[Dict[str, int]]]] = None,
      target_formatter: Optional[TargetFormatter] = None,
  ) -> List[Dict[str, Any]]:
      if targets is None:
          return to_samples(tensor)
      self.load_target_metadata(
          [t for target in targets for t in target],
          add_background=True,
          target_formatter=target_formatter,
      )
  
      return [
          {
              DataKeys.INPUT: image,
              DataKeys.TARGET: {"bboxes": bbox, "labels": label},
          }
          for image, label, bbox in zip(tensor, targets, bboxes)
      ]
  
imports: from typing import Any, Dict, Hashable, List, Optional, Sequence
  from flash.core.data.io.classification_input import ClassificationInputMixin
  from flash.core.data.io.input import DataKeys
  from flash.core.data.utilities.classification import TargetFormatter
  from flash.core.data.utilities.paths import PATH_TYPE, filter_valid_files
  from flash.core.data.utilities.samples import to_samples
  from flash.core.integrations.fiftyone.utils import FiftyOneLabelUtilities
  from flash.core.integrations.icevision.data import IceVisionInput
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _ICEVISION_AVAILABLE, lazy_import, requires
  from flash.image.data import IMG_EXTENSIONS, NP_EXTENSIONS, ImageFilesInput, ImageInput, ImageNumpyInput, ImageTensorInput
label_desc: Use image node_id 1 for calling the ObjectDetectionTensorInput obj's underlying member method code with example usage: obj.load_data(tensor, targets, bboxes, target_formatter) and returns: unknown, to_samples