class_docstr: Base class for Image-Text multi-modal learning
  
  Args:
      opts: command-line arguments
class_docstr_tok: ["Base","class","for","Image-Text","multi-modal","learning","Args",":","opts",":","command-line","arguments"]
docstr: Returns the number of classes in detection dataset along with super-class arguments.
docstr_tok: ["Returns","the","number","of","classes","in","detection","dataset","along","with","super-class","arguments","."]
code: def share_dataset_arguments(self) -> Dict[str, Any]:
      """Returns the number of classes in detection dataset along with super-class arguments."""
      share_dataset_specific_opts: Dict[
          str, Any
      ] = super().share_dataset_arguments()
      share_dataset_specific_opts[
          "dataset.text_vocab_size"
      ] = self.vocab_size
      share_dataset_specific_opts[
          "dataset.text_context_length"
      ] = self.context_length
      return share_dataset_specific_opts
  
imports: import argparse
  import os
  from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
  import torch
  from PIL import Image
  from torch import Tensor
  from data.collate_fns import COLLATE_FN_REGISTRY
  from data.datasets.dataset_base import BaseImageDataset
  from data.datasets.multi_modal_img_text.zero_shot import BaseZeroShotDataset, build_zero_shot_dataset
  from data.datasets.utils.text import caption_preprocessing
  from data.text_tokenizer import build_tokenizer
  from data.transforms import image_pil
  from data.transforms.common import Compose
  from utils import logger
  from utils.ddp_utils import is_master, is_start_rank_node
label_desc: Use image node_id 4 for calling the BaseMultiModalImgText obj's underlying member method code with example usage: obj.share_dataset_arguments() and returns: share_dataset_specific_opts