code: def __init__(
      self,
      dataset_path_dict,
      split: str,
      max_frames: int,
      is_train: bool,
      preload_vid_feat: bool,
      preload_text_feat: bool,
      frames_noise: float,
  ):
      self.frames_noise = frames_noise
      self.split = split
      self.max_frames = max_frames
      self.is_train = is_train
      meta_file = dataset_path_dict["meta_data"]
  
      self.vids_dict = json.load(
          meta_file.open("rt", encoding="utf8"),
          object_pairs_hook=OrderedDict,
      )
      self.ids = [
          key
          for key, val in self.vids_dict.items()
          if val["split"] == self.split
      ]
      print(
          "init dataset {} split {} length {} ".format(
              dataset_path_dict["dataset_name"], split, len(self)
          )
      )
  
      if dataset_path_dict["dataset_name"] == "activitynet":
          self.text_data = BertTextFeatureLoader(
              dataset_path,
              self.ids,
              dataset_features,
              preload_text_feat,
          )
          self.preproc_par_fn = self.preprocess_bert_paragraph
          self.vid_data = ActivityNetVideoFeatureLoader(
              dataset_path, self.ids, preload_vid_feat
          )
      elif dataset_path_dict["dataset_name"] == "youcook2":
          self.preproc_par_fn = self.preprocess_bert_paragraph
          self.text_data = BertTextFeatureLoader(
              dataset_path_dict, self.ids, preload_text_feat
          )
          self.vid_data = Youcook2VideoFeatureLoader(
              dataset_path_dict, self.ids, preload_vid_feat
          )
      else:
          raise NotImplementedError
  
imports: import json
  from collections import OrderedDict
  from pathlib import Path
  from typing import List
  import re
  import numpy
  import torch
  import torch.utils.data
  from tqdm import tqdm
  from gluoncv.torch.utils import coot_utils
label_desc: Use image node_id 1 to create a new VideoDatasetFeatures object from inherited base classes: data with example: obj = VideoDatasetFeatures(dataset_path_dict, split, max_frames, is_train, preload_vid_feat, preload_text_feat, frames_noise)