code: def predict_load_data(
      self,
      paths: List[str],
      clip_sampler: Union[str, "ClipSampler"] = "random",
      clip_duration: float = 2,
      clip_sampler_kwargs: Dict[str, Any] = None,
      decode_audio: bool = False,
      decoder: str = "pyav",
  ) -> List[str]:
      paths = list_valid_files(paths, valid_extensions=("mp4", "avi"))
      self._clip_sampler = _make_clip_sampler(
          clip_sampler, clip_duration, clip_sampler_kwargs
      )
      self._decode_audio = decode_audio
      self._decoder = decoder
      return paths
  
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 VideoClassificationPathsPredictInput obj's underlying member method code with example usage: obj.predict_load_data(paths, clip_sampler, clip_duration, clip_sampler_kwargs, decode_audio, decoder) and returns: paths