class_docstr: MultiModalDatasetBuilder is a utility class designed to construct datasets
  suitable for multi-modal tasks. This class simplifies the creation of 
  datasets that incorporate data of multiple modalities, such as text, 
  images, video, or audio.
class_docstr_tok: ["MultiModalDatasetBuilder","is","a","utility","class","designed","to","construct","datasets","suitable","for","multi-modal","tasks",".","This","class","simplifies","the","creation","of","datasets","that","incorporate","data","of","multiple","modalities",",","such","as","text",",","images",",","video",",","or","audio","."]
code: def _get_dataset_args(self, info, is_train):
      dataset_args = dict(self.config.build_info.get("kwargs", {}))
  
      for modality in self.data_type:
          proc_name = (
              f"{'vis' if 'image' in modality else modality}_processor"
          )
          dataset_args[proc_name] = self.processors[
              "train" if is_train else "eval"
          ][modality]
          mm_path = self._get_absolute_path(
              self.config.build_info.get(modality).storage
          )
          dataset_args[
              f"{'vis' if 'image' in modality  else modality}_root"
          ] = mm_path
  
      dataset_args["text_processor"] = self.text_processors[
          "train" if is_train else "eval"
      ]
      dataset_args["ann_paths"] = [
          self._get_absolute_path(path) for path in info.storage
      ]
      dataset_args["modalities"] = self.data_type
  
      # Conform to base
      for key in ["vis_processor", "vis_root", "test_processor"]:
          dataset_args.setdefault(key, None)
  
      return dataset_args
  
imports: import logging
  import os
  import shutil
  import warnings
  import lavis.common.utils
  import torch.distributed
  from lavis.common.dist_utils import is_dist_avail_and_initialized, is_main_process
  from lavis.common.registry import registry
  from lavis.datasets.data_utils import extract_archive
  from lavis.processors.base_processor import BaseProcessor
  from omegaconf import OmegaConf
  from torchvision.datasets.utils import download_url
label_desc: Use image node_id 8 for calling the MultiModalDatasetBuilder obj's underlying member method code with example usage: obj._get_dataset_args(info, is_train) and returns: dataset_args