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 __init__(
      self,
      meta_processor,
      video_processor,
      text_processor,
      align_processor,
  ):
      self.split = meta_processor.split
      self.meta_processor = meta_processor
      self.video_processor = video_processor
      self.text_processor = text_processor
      self.align_processor = align_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 1 to create a new MMDataset object from inherited base classes: Dataset with example: obj = MMDataset(meta_processor, video_processor, text_processor, align_processor)