class_docstr: Given a list of video files, computes all consecutive subvideos of size
  `clip_length_in_frames`, where the distance between each subvideo in the
  same video is defined by `frames_between_clips`.
  If `frame_rate` is specified, it will also resample all the videos to have
  the same frame rate, and the clips will refer to this frame rate.
  
  Creating this instance the first time is time-consuming, as it needs to
  decode all the videos in `video_paths`. It is recommended that you
  cache the results after instantiation of the class.
  
  Recreating the clips for different clip lengths is fast, and can be done
  with the `compute_clips` method.
  
  Args:
      video_paths (List[str]): paths to the video files
      clip_length_in_frames (int): size of a clip in number of frames
      frames_between_clips (int): step (in frames) between each clip
      frame_rate (int, optional): if specified, it will resample the video
          so that it has `frame_rate`, and then the clips will be defined
          on the resampled video
      num_workers (int): how many subprocesses to use for data loading.
          0 means that the data will be loaded in the main process. (default: 0)
      output_format (str): The format of the output video tensors. Can be either "THWC" (default) or "TCHW".
docstr: Gets a subclip from a list of videos.
  
  Args:
      idx (int): index of the subclip. Must be between 0 and num_clips().
  
  Returns:
      video (Tensor)
      audio (Tensor)
      info (Dict)
      video_idx (int): index of the video in `video_paths`
docstr_tok: ["Gets","a","subclip","from","a","list","of","videos",".","Args",":","idx","(","int",")",":","index","of","the","subclip",".","Must","be","between","0","and","num_clips","(",")",".","Returns",":","video","(","Tensor",")","audio","(","Tensor",")","info","(","Dict",")","video_idx","(","int",")",":","index","of","the","video","in","`","video_paths","`"]
code: def get_clip(
      self, idx: int
  ) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any], int]:
      """
      Gets a subclip from a list of videos.
  
      Args:
          idx (int): index of the subclip. Must be between 0 and num_clips().
  
      Returns:
          video (Tensor)
          audio (Tensor)
          info (Dict)
          video_idx (int): index of the video in `video_paths`
      """
      if idx >= self.num_clips():
          raise IndexError(
              f"Index {idx} out of range ({self.num_clips()} number of clips)"
          )
      video_idx, clip_idx = self.get_clip_location(idx)
      video_path = self.video_paths[video_idx]
      clip_pts = self.clips[video_idx][clip_idx]
  
      from torchvision import get_video_backend
  
      backend = get_video_backend()
  
      if backend == "pyav":
          # check for invalid options
          if self._video_width != 0:
              raise ValueError(
                  "pyav backend doesn't support _video_width != 0"
              )
          if self._video_height != 0:
              raise ValueError(
                  "pyav backend doesn't support _video_height != 0"
              )
          if self._video_min_dimension != 0:
              raise ValueError(
                  "pyav backend doesn't support _video_min_dimension != 0"
              )
          if self._video_max_dimension != 0:
              raise ValueError(
                  "pyav backend doesn't support _video_max_dimension != 0"
              )
          if self._audio_samples != 0:
              raise ValueError(
                  "pyav backend doesn't support _audio_samples != 0"
              )
  
      if backend == "pyav":
          start_pts = clip_pts[0].item()
          end_pts = clip_pts[-1].item()
          video, audio, info = read_video(
              video_path, start_pts, end_pts
          )
      else:
          _info = _probe_video_from_file(video_path)
          video_fps = _info.video_fps
          audio_fps = None
  
          video_start_pts = cast(int, clip_pts[0].item())
          video_end_pts = cast(int, clip_pts[-1].item())
  
          audio_start_pts, audio_end_pts = 0, -1
          audio_timebase = Fraction(0, 1)
          video_timebase = Fraction(
              _info.video_timebase.numerator,
              _info.video_timebase.denominator,
          )
          if _info.has_audio:
              audio_timebase = Fraction(
                  _info.audio_timebase.numerator,
                  _info.audio_timebase.denominator,
              )
              audio_start_pts = pts_convert(
                  video_start_pts,
                  video_timebase,
                  audio_timebase,
                  math.floor,
              )
              audio_end_pts = pts_convert(
                  video_end_pts,
                  video_timebase,
                  audio_timebase,
                  math.ceil,
              )
              audio_fps = _info.audio_sample_rate
          video, audio, _ = _read_video_from_file(
              video_path,
              video_width=self._video_width,
              video_height=self._video_height,
              video_min_dimension=self._video_min_dimension,
              video_max_dimension=self._video_max_dimension,
              video_pts_range=(video_start_pts, video_end_pts),
              video_timebase=video_timebase,
              audio_samples=self._audio_samples,
              audio_channels=self._audio_channels,
              audio_pts_range=(audio_start_pts, audio_end_pts),
              audio_timebase=audio_timebase,
          )
  
          info = {"video_fps": video_fps}
          if audio_fps is not None:
              info["audio_fps"] = audio_fps
  
      if self.frame_rate is not None:
          resampling_idx = self.resampling_idxs[video_idx][clip_idx]
          if isinstance(resampling_idx, torch.Tensor):
              resampling_idx = resampling_idx - resampling_idx[0]
          video = video[resampling_idx]
          info["video_fps"] = self.frame_rate
      assert (
          len(video) == self.num_frames
      ), f"{video.shape} x {self.num_frames}"
  
      if self.output_format == "TCHW":
          # [T,H,W,C] --> [T,C,H,W]
          video = video.permute(0, 3, 1, 2)
  
      return video, audio, info, video_idx
  
imports: import bisect
  import math
  import warnings
  from fractions import Fraction
  from typing import Any, Callable, cast, Dict, List, Optional, Tuple, TypeVar, Union
  import torch
  from torchvision.io import _probe_video_from_file, _read_video_from_file, read_video, read_video_timestamps
  from .utils import tqdm
label_desc: Use image node_id 13 for calling the VideoClips obj's underlying member method code with example usage: obj.get_clip(idx) and returns: video,  audio,  info,  video_idx