class_docstr: Action recognition video data set for EpicKitchen-55 Dataset.
  <https://epic-kitchens.github.io/2019/>
  
  This dataset handles the loading, decoding, and clip sampling for the videos.
docstr: Args:
      frames_per_clip (int): The number of frames per clip to sample.
  
  Returns:
      A function that takes in a list of frame indicies and outputs a subsampled list.
docstr_tok: ["Args",":","frames_per_clip","(","int",")",":","The","number","of","frames","per","clip","to","sample",".","Returns",":","A","function","that","takes","in","a","list","of","frame","indicies","and","outputs","a","subsampled","list","."]
code: def _frame_filter_generator(
      frames_per_clip: int,
  ) -> Callable[[List[int]], List[int]]:
      """
      Args:
          frames_per_clip (int): The number of frames per clip to sample.
  
      Returns:
          A function that takes in a list of frame indicies and outputs a subsampled list.
      """
  
      def frame_filer(frame_indices: List[int]) -> List[int]:
          num_frames = len(frame_indices)
          frame_step = int(num_frames // frames_per_clip)
          selected_frames = set(range(0, num_frames, frame_step))
          return [
              x
              for i, x in enumerate(frame_indices)
              if i in selected_frames
          ]
  
      return frame_filer
  
file_path: pytorchvideo/pytorchvideo/data/epic_kitchen_recognition.py
imports: import random
  from dataclasses import fields
  from enum import Enum
  from typing import Any, Callable, Dict, List, Optional
  import torch
  from pytorchvideo.data.dataset_manifest_utils import EncodedVideoInfo, VideoClipInfo, VideoDatasetType, VideoFrameInfo, VideoInfo
  from pytorchvideo.data.epic_kitchen import ActionData, EpicKitchenDataset
  from pytorchvideo.data.video import Video
label_desc: Use image node_id 3 for calling the EpicKitchenRecognition obj's underlying member method code with example usage: obj._frame_filter_generator(frames_per_clip) and returns: frame_filer, unknown