class_docstr: Dataset for GTEA, ADL and KITCHEN.
  
  Args:
      root_path (string): The root path in which video folders lie.
      annotationfile_path (string): The annotation file containing one row per video sample.
      dataset_split (string): Split type (train or test)
      image_modality (string): Image modality (RGB or Optical Flow)
      num_segments (int): The number of segments the video should be divided into to sample frames from.
      frames_per_segment (int): The number of frames that should be loaded per segment.
      imagefile_template (string): The image filename template.
      transform (Compose): Video transform.
      random_shift (bool): Whether the frames from each segment should be taken consecutively starting from
                      the center(False) of the segment, or consecutively starting from
                      a random(True) location inside the segment range.
      test_mode (bool): Whether this is a test dataset. If so, chooses frames from segments with random_shift=False.
      n_classes (int): The number of classes.
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docstr: Load data from the EPIC-Kitchen list file and make them into the united format.
  Different datasets correspond to a different number of classes.
  
  Returns:
      data (list): list of (video_name, start_frame, end_frame, label)
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code: def make_dataset(self):
      """
      Load data from the EPIC-Kitchen list file and make them into the united format.
      Different datasets correspond to a different number of classes.
  
      Returns:
          data (list): list of (video_name, start_frame, end_frame, label)
      """
  
      data = []
      i = 0
      input_file = pd.read_pickle(self.annotationfile_path)
      for line in input_file.values:
          if 0 <= eval(line[5]) < self.n_classes:
              data.append(
                  (line[0], eval(line[1]), eval(line[2]), eval(line[5]))
              )
              i = i + 1
      logging.info(
          "Number of {:5} action segments: {}".format(self.dataset, i)
      )
      return data
  
file_path: pykale/kale/loaddata/video_datasets.py
imports: import logging
  import math
  import os
  from pathlib import Path
  import pandas
  from PIL import Image
  from kale.loaddata.videos import VideoFrameDataset, VideoRecord
label_desc: Use image node_id 3 for calling the BasicVideoDataset obj's underlying member method code with example usage: obj.make_dataset() and returns: data