class_docstr: Dummy SSD Detection 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.
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.randint(
          low=0, high=255, size=(3, crop_size_h, crop_size_w)
      )
      input_img = input_img.float().div(255.0)
  
      n_boxes = 4
      min_dim = min(crop_size_h, crop_size_h)
  
      boxes_x = torch.randint(
          low=0,
          high=crop_size_w // 4,
          size=(n_boxes,),
          dtype=torch.float,
      )
      boxes_y = torch.randint(
          low=1,
          high=crop_size_h // 4,
          size=(n_boxes,),
          dtype=torch.float,
      )
      boxes_w = torch.randint(
          low=crop_size_w // 2,
          high=crop_size_w,
          size=(n_boxes,),
          dtype=torch.float,
      )
      boxes_h = torch.randint(
          low=crop_size_h // 2,
          high=crop_size_h,
          size=(n_boxes,),
          dtype=torch.float,
      )
  
      boxes = torch.stack([boxes_x, boxes_y, boxes_w, boxes_h])
  
      boxes[:, 0::2] /= crop_size_w
      boxes[:, 1::2] /= crop_size_h
  
      labels = torch.randint(
          low=1, high=self.n_classes, size=(n_boxes,)
      ).long()
  
      # convert to priors
      anchors = self.generate_anchors(
          height=crop_size_h, width=crop_size_w
      )
  
      gt_coordinates, gt_labels = self.match_prior(
          gt_boxes=boxes,
          gt_labels=labels,
          anchors=anchors,
      )
  
      # Make sure there are no NaNs in the coordinates
      gt_coordinates[gt_coordinates.isnan()] = 0
  
      return {
          "samples": {"image": input_img},
          "targets": {
              "box_labels": gt_labels,
              "box_coordinates": gt_coordinates,
              "image_id": torch.randint(
                  low=0, high=1000, size=(1,)
              ).long(),
              "image_width": torch.tensor(crop_size_w),
              "image_height": torch.tensor(crop_size_h),
          },
      }
  
file_path: ml-cvnets/tests/dummy_datasets/ssd_detection.py
imports: import math
  from typing import Dict, Tuple
  import torch
  import torch.utils.data
  from cvnets.anchor_generator import build_anchor_generator
  from cvnets.matcher_det import build_matcher
label_desc: Use image node_id 3 for calling the DummySSDDetectionDataset obj's underlying member method code with example usage: obj.__getitem__(batch_indexes_tup) and returns: dict