code: def load_data(
      self,
      path: str,
      clip_sampler: Union[str, "ClipSampler"] = "random",
      clip_duration: float = 2,
      clip_sampler_kwargs: Dict[str, Any] = None,
      video_sampler: Type[Sampler] = torch.utils.data.RandomSampler,
      decode_audio: bool = False,
      decoder: str = "pyav",
      target_formatter: Optional[TargetFormatter] = None,
  ) -> "LabeledVideoDataset":
      return super().load_data(
          *make_dataset(path, extensions=("mp4", "avi")),
          clip_sampler=clip_sampler,
          clip_duration=clip_duration,
          clip_sampler_kwargs=clip_sampler_kwargs,
          video_sampler=video_sampler,
          decode_audio=decode_audio,
          decoder=decoder,
          target_formatter=target_formatter,
      )
  
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 VideoClassificationFoldersInput obj's underlying member method code with example usage: obj.load_data(path, clip_sampler, clip_duration, clip_sampler_kwargs, video_sampler, decode_audio, decoder, target_formatter) and returns: super