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: Get image-text pair for zero-shot dataset along with classification label.
  
  Args:
      img_index: Image index
  
  Returns:
      A tuple of PIL image, captions, and class label
docstr_tok: ["Get","image-text","pair","for","zero-shot","dataset","along","with","classification","label",".","Args",":","img_index",":","Image","index","Returns",":","A","tuple","of","PIL","image",",","captions",",","and","class","label"]
code: def get_zero_shot_pair(
      self, img_index: int
  ) -> Tuple[Image.Image, Union[str, List[str], List[List[str]]], int]:
      """Get image-text pair for zero-shot dataset along with classification label.
  
      Args:
          img_index: Image index
  
      Returns:
          A tuple of PIL image, captions, and class label
      """
      img_path, captions_str, class_label = self.zero_shot_dataset[
          img_index
      ]
      input_img = self.read_image_pil(img_path)
      return input_img, captions_str, class_label
  
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 10 for calling the BaseMultiModalImgText obj's underlying member method code with example usage: obj.get_zero_shot_pair(img_index) and returns: input_img,  captions_str,  class_label