class_docstr: Common API for video dataset access
  
  Args:
      data_path (string): image directory of dataset
      train_list (string): training list file directory of dataset
      test_list (string): test list file directory of dataset
      image_modality (string): image type (RGB or Optical Flow)
      frames_per_segment (int): length of each action sample (the unit is number of frame)
      n_classes (int): number of class
      transform_kind (string): types of video transforms
      seed (int): seed value set manually
class_docstr_tok: ["Common","API","for","video","dataset","access","Args",":","data_path","(","string",")",":","image","directory","of","dataset","train_list","(","string",")",":","training","list","file","directory","of","dataset","test_list","(","string",")",":","test","list","file","directory","of","dataset","image_modality","(","string",")",":","image","type","(","RGB","or","Optical","Flow",")","frames_per_segment","(","int",")",":","length","of","each","action","sample","(","the","unit","is","number","of","frame",")","n_classes","(","int",")",":","number","of","class","transform_kind","(","string",")",":","types","of","video","transforms","seed","(","int",")",":","seed","value","set","manually"]
code: def __init__(
      self,
      data_path,
      train_list,
      test_list,
      image_modality,
      frames_per_segment,
      n_classes,
      transform_kind,
      seed,
  ):
      super().__init__(n_classes)
      self._data_path = data_path
      self._train_list = train_list
      self._test_list = test_list
      self._image_modality = image_modality
      self._frames_per_segment = frames_per_segment
      self._transform = video_transform.get_transform(
          transform_kind, self._image_modality
      )
      self._seed = seed
  
imports: import os
  from copy import deepcopy
  from enum import Enum
  import torch
  import kale.prepdata.video_transform
  from kale.loaddata.dataset_access import DatasetAccess
  from kale.loaddata.video_datasets import BasicVideoDataset, EPIC
label_desc: Use image node_id 1 to create a new VideoDatasetAccess object from inherited base classes: DatasetAccess with example: obj = VideoDatasetAccess(data_path, train_list, test_list, image_modality, frames_per_segment, n_classes, transform_kind, seed)