code: def collate_fn(self, data_batch):
      def get_data(key):
          return [d[key] for d in data_batch]
  
      batch_size = len(data_batch)
  
      # collate video frames
      list_vid_frames = get_data("vid_frames")
      list_vid_frames_len = get_data("vid_frames_len")
      vid_feature_dim = list_vid_frames[0].shape[-1]
      vid_frames_len = torch.tensor(list_vid_frames_len).long()
      vid_frames_max_seq_len = int(vid_frames_len.max().numpy())
      vid_frames = torch.zeros(
          batch_size, vid_frames_max_seq_len, vid_feature_dim
      ).float()
      vid_frames_mask = torch.zeros(batch_size, vid_frames_max_seq_len)
      for batch, (seq_len, item) in enumerate(
          zip(list_vid_frames_len, list_vid_frames)
      ):
          vid_frames[batch, :seq_len] = item
          vid_frames_mask[batch, :seq_len] = 1
  
      # collate paragraph features
      list_par_cap_len = get_data("par_cap_len")
      list_par_cap_vectors = get_data("par_cap_vectors")
      par_feature_dim = list_par_cap_vectors[0].shape[-1]
      par_cap_len = torch.tensor(list_par_cap_len).long()
      par_cap_max_len = int(par_cap_len.max().numpy())
      par_cap_vectors = torch.zeros(
          batch_size, par_cap_max_len, par_feature_dim
      ).float()
      par_cap_mask = torch.zeros(batch_size, par_cap_max_len)
      for batch, (seq_len, item) in enumerate(
          zip(list_par_cap_len, list_par_cap_vectors)
      ):
          par_cap_vectors[batch, :seq_len, :] = item
          par_cap_mask[batch, :seq_len] = 1
  
      # collate clip frames
      list_clip_num = get_data("clip_num")
      clip_num = torch.tensor(list_clip_num).long()
      total_clip_num = int(np.sum(list_clip_num))
      list_clip_frames_len_list = get_data("clip_frames_len_list")
      clip_frames_max_len = int(
          np.max(
              [
                  np.max(len_single)
                  for len_single in list_clip_frames_len_list
              ]
          )
      )
      clip_frames = torch.zeros(
          (total_clip_num, clip_frames_max_len, vid_feature_dim)
      ).float()
      clip_frames_mask = torch.zeros(
          (total_clip_num, clip_frames_max_len)
      )
      list_clip_frames_list = get_data("clip_frames_list")
      clip_frames_len = []
      c_num = 0
      for batch, clip_frames_list in enumerate(list_clip_frames_list):
          for i, clip_frames_item in enumerate(clip_frames_list):
              clip_frames_len_item = int(clip_frames_item.shape[0])
              clip_frames[
                  c_num, :clip_frames_len_item, :
              ] = clip_frames_item
              clip_frames_mask[c_num, :clip_frames_len_item] = 1
              clip_frames_len.append(clip_frames_len_item)
              c_num += 1
      clip_frames_len = torch.tensor(clip_frames_len).long()
  
      # collate sentence features
      list_sent_num = get_data("sent_num")
      sent_num = torch.tensor(list_sent_num).long()
      total_sent_num = int(np.sum(list_sent_num))
      list_sent_cap_len_list = get_data("sent_cap_len_list")
      sent_cap_max_len = int(
          np.max(
              [
                  np.max(len_single)
                  for len_single in list_sent_cap_len_list
              ]
          )
      )
      sent_cap_len = []
      sent_cap_mask = torch.zeros(
          (total_sent_num, sent_cap_max_len)
      ).long()
      cap_feature_dim = list_par_cap_vectors[0].shape[-1]
      sent_cap_vectors = torch.zeros(
          (total_sent_num, sent_cap_max_len, cap_feature_dim)
      )
      c_num = 0
      for batch, sent_cap_len_list in enumerate(list_sent_cap_len_list):
          pointer = 0
          for sent_cap_len_item in sent_cap_len_list:
              sent_cap_vectors[
                  c_num, :sent_cap_len_item
              ] = par_cap_vectors[
                  batch, pointer : pointer + sent_cap_len_item
              ]
              sent_cap_mask[c_num, :sent_cap_len_item] = 1
              sent_cap_len.append(sent_cap_len_item)
              c_num += 1
              pointer += sent_cap_len_item
      sent_cap_len = torch.tensor(sent_cap_len).long()
  
      return {
          "vid_frames": vid_frames,
          "vid_frames_mask": vid_frames_mask,
          "vid_frames_len": vid_frames_len,
          "par_cap_vectors": par_cap_vectors,
          "par_cap_mask": par_cap_mask,
          "par_cap_len": par_cap_len,
          "clip_num": clip_num,
          "clip_frames": clip_frames,
          "clip_frames_len": clip_frames_len,
          "clip_frames_mask": clip_frames_mask,
          "sent_num": sent_num,
          "sent_cap_vectors": sent_cap_vectors,
          "sent_cap_mask": sent_cap_mask,
          "sent_cap_len": sent_cap_len,
          "vid_id": get_data("vid_id"),
          "data_words": get_data("data_words"),
      }
  
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 8 for calling the VideoDatasetFeatures obj's underlying member method code with example usage: obj.collate_fn(data_batch) and returns: dict, unknown