class_docstr: A generic multi-modal dataset.
      Args:
          `meta_processor`: a meta processor,
              handling loading meta data and return video_id and text_id.
          `video_processor`: a video processor,
              handling e.g., decoding, loading .np files.
          `text_processor`: a text processor,
              handling e.g., tokenization.
          `aligner`: combine the video and text feature
              as one training example.
code: def __len__(self):
      return len(self.meta_processor)
  
file_path: fairseq/examples/MMPT/mmpt/datasets/mmdataset.py
imports: import torch
  from collections import OrderedDict
  from torch.utils.data import Dataset
  from torch.utils.data.dataloader import default_collate
  from ..utils import set_seed
label_desc: Use image node_id 2 for calling the MMDataset obj's underlying member method code with example usage: obj.__len__() and returns: len