code: def predict_load_sample(self, sample: str) -> Dict[str, Any]:
      video = EncodedVideo.from_path(
          sample, decode_audio=self._decode_audio, decoder=self._decoder
      )
      (
          clip_start,
          clip_end,
          clip_index,
          aug_index,
          is_last_clip,
      ) = self._clip_sampler(0.0, video.duration, None)
  
      loaded_clip = video.get_clip(clip_start, clip_end)
  
      clip_is_null = (
          loaded_clip is None
          or loaded_clip["video"] is None
          or (loaded_clip["audio"] is None and self._decode_audio)
      )
  
      if clip_is_null:
          raise ValueError(
              f"The provided video is too short {video.duration} to be clipped at {self._clip_sampler._clip_duration}"
          )
  
      frames = loaded_clip["video"]
      audio_samples = loaded_clip["audio"]
      return {
          DataKeys.INPUT: frames,
          "video_name": video.name,
          "video_index": 0,
          "clip_index": clip_index,
          "aug_index": aug_index,
          **(
              {"audio": audio_samples}
              if audio_samples is not None
              else {}
          ),
          DataKeys.METADATA: {"filepath": sample},
      }
  
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 2 for calling the VideoClassificationPathsPredictInput obj's underlying member method code with example usage: obj.predict_load_sample(sample) and returns: dict