code: def __getitem__(self, index):
      vid_id = self.ids[index]
      vid_dict = self.vids_dict[vid_id]
      clip_num = len(vid_dict["segments"])
      sent_num = len(vid_dict["segments"])
  
      # load video frames
      vid_frames_len = vid_dict["num_frames"]
      if vid_frames_len > self.max_frames:
          vid_frames_len = self.max_frames
      vid_frames = torch.tensor(
          self.get_frames_from_video(vid_id, num_frames=vid_frames_len)
      )
      vid_frames_len = int(vid_frames.shape[0])
      if self.frames_noise != 0:
          vid_frames_noise = coot_utils.truncated_normal_fill(
              vid_frames.shape, std=self.frames_noise
          )
          vid_frames += vid_frames_noise
  
      # load segment frames
      clip_frames_list = []
      clip_frames_len_list = []
      for i, seg in enumerate(vid_dict["segments"]):
          c_num_frames = seg["num_frames"]
          if c_num_frames > self.max_frames:
              c_num_frames = self.max_frames
          c_frames = self.get_frames_from_segment(
              vid_id, i, num_frames=c_num_frames
          )
          c_frames = torch.tensor(c_frames)
          if self.frames_noise != 0:
              clip_frames_noise = coot_utils.truncated_normal_fill(
                  c_frames.shape, std=self.frames_noise
              )
              c_frames += clip_frames_noise
          clip_frames_list.append(c_frames)
          clip_frames_len_list.append(c_frames.shape[0])
  
      # load text
      seg_narrations = []
      for seg in vid_dict["segments"]:
          seg_narr = seg["narration"]
          if seg_narr is None:
              seg_narr = "undefined"
              print(
                  "WARNING: Undefined text tokens "
                  "(no narration data, is this a test set?)"
              )
          seg_narrations.append(seg_narr)
      list_of_list_of_words = self.preproc_par_fn(seg_narrations)
  
      # load precomputed text features
      par_cap_vectors, sent_cap_len_list = self.text_data[vid_id]
      par_cap_len = int(par_cap_vectors.shape[0])
      par_cap_vectors = torch.tensor(par_cap_vectors).float()
  
      # split paragraph features into sentences
      sent_cap_vectors_list = []
      pointer = 0
      for i, sent_cap_len in enumerate(sent_cap_len_list):
          sent_cap_vectors = par_cap_vectors[
              pointer : pointer + sent_cap_len, :
          ]
          sent_cap_vectors_list.append(sent_cap_vectors)
          pointer += sent_cap_len
  
      return {
          "vid_id": vid_id,
          "data_words": list_of_list_of_words,
          "vid_frames": vid_frames,
          "vid_frames_len": vid_frames_len,
          "par_cap_vectors": par_cap_vectors,
          "par_cap_len": par_cap_len,
          "clip_num": clip_num,
          "sent_num": sent_num,
          "clip_frames_list": clip_frames_list,
          "clip_frames_len_list": clip_frames_len_list,
          "sent_cap_len_list": sent_cap_len_list,
          "sent_cap_vectors_list": sent_cap_vectors_list,
      }
  
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 7 for calling the VideoDatasetFeatures obj's underlying member method code with example usage: obj.__getitem__(index) and returns: dict