class_docstr: LSMDC dataset.
code: def __getitem__(self, idx):
      face_ind = 1
      if idx < self.n_MSR:
          vid = self.train_list[idx]
          text = self.text_features[vid]
          r = random.randint(0, len(text) - 1)
          text = text[r]
          flow = self.flow_features[vid]
          audio = self.audio_features[vid]
          video = self.visual_features[vid]
          face = self.face_features[vid]
  
          if np.sum(face) == 0:
              face_ind = 0
      elif self.coco:
          video = self.coco_visual[idx - self.n_MSR]
          text = self.coco_text[idx - self.n_MSR]
          audio = th.zeros(1, 128)
          flow = th.zeros(1024)
          face = th.zeros(128)
          face_ind = 0
  
      return {
          "video": video,
          "flow": flow,
          "face": face,
          "text": text,
          "coco_ind": self.coco_ind[idx],
          "face_ind": face_ind,
          "audio": audio,
      }
  
file_path: moe-embedding-expert/MSRVTT.py
imports: import torch
  from torch.utils.data import Dataset
  import numpy
  import os
  import math
  import random
  import pickle
label_desc: Use image node_id 4 for calling the MSRVTT obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: dict