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: Data transforms during validation or evaluation
   The order is Resize, CenterCrop, ToTensor
  
   Args:
      size: Size for resizing the input image. Expected to be an integer (width=height) or a tuple (height, width)
  
  Returns:
      An instance of `data.transforms.image_pil.BaseTransformation.`
docstr_tok: ["Data","transforms","during","validation","or","evaluation","The","order","is","Resize",",","CenterCrop",",","ToTensor","Args",":","size",":","Size","for","resizing","the","input","image",".","Expected","to","be","an","integer","(","width=height",")","or","a","tuple","(","height",",","width",")","Returns",":","An","instance","of","`","data.transforms.image_pil.BaseTransformation.","`"]
code: def _validation_transforms(
      self, size: Union[Tuple, int], *args, **kwargs
  ) -> T.BaseTransformation:
      """Data transforms during validation or evaluation
       The order is Resize, CenterCrop, ToTensor
  
       Args:
          size: Size for resizing the input image. Expected to be an integer (width=height) or a tuple (height, width)
  
      Returns:
          An instance of `data.transforms.image_pil.BaseTransformation.`
      """
      aug_list = [
          T.Resize(opts=self.opts),
          T.CenterCrop(opts=self.opts),
          T.ToTensor(opts=self.opts),
      ]
  
      return Compose(opts=self.opts, img_transforms=aug_list)
  
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 8 for calling the BaseMultiModalImgText obj's underlying member method code with example usage: obj._validation_transforms(size) and returns: Compose