class_docstr: Dummy Segmentation 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
      )
      target = torch.randint(
          low=0, high=self.n_classes, size=(crop_size_h, crop_size_w)
      ).long()
  
      return {"samples": input_img, "targets": target}
  
imports: from typing import Dict, Tuple
  import torch
  import torch.utils.data
label_desc: Use image node_id 2 for calling the DummySegmentationDataset obj's underlying member method code with example usage: obj.__getitem__(batch_indexes_tup) and returns: dict