class_docstr: Dummy Dataset for CI/CD testing
  
  Args:
      opts: command-line arguments
docstr: :param batch_indexes_tup: Tuple of the form (Crop_size_W, Crop_size_H, Image_ID)
  :return: dictionary containing input image, label, and sample_id.
docstr_tok: [":","param","batch_indexes_tup",":","Tuple","of","the","form","(","Crop_size_W",",","Crop_size_H",",","Image_ID",")",":","return",":","dictionary","containing","input","image",",","label",",","and","sample_id","."]
code: def __getitem__(self, batch_indexes_tup: Tuple) -> Dict:
      """
      :param batch_indexes_tup: Tuple of the form (Crop_size_W, Crop_size_H, Image_ID)
      :return: dictionary containing input image, label, and sample_id.
      """
      crop_size_h, crop_size_w, img_index = batch_indexes_tup
  
      input_img = torch.randn(
          size=(3, crop_size_h, crop_size_w), dtype=torch.float
      )
      text = torch.randint(
          low=0,
          high=self.vocab_size,
          size=(self.context_length,),
          dtype=torch.int,
      )
  
      return {
          "samples": {
              "image": input_img,
              "text": text,
              "padding_mask": None,
          },
          "targets": torch.randint(low=0, high=1, size=(1,)).long(),
      }
  
imports: from typing import Dict, Tuple
  import torch
  import torch.utils.data
label_desc: Use image node_id 2 for calling the DummyMultiModalImageTextDataset obj's underlying member method code with example usage: obj.__getitem__(batch_indexes_tup) and returns: dict