code: def test_ssd_loss_in_out(
      batch_size: int, neg_pos_ratio: int, label_smooth: int
  ) -> None:
      # These tests check the input and output formats are correct or not.
  
      # build configuration
      parser = argparse.ArgumentParser()
      parser = SSDLoss.add_arguments(parser)
  
      opts = parser.parse_args([])
  
      setattr(
          opts,
          "loss.detection.ssd_multibox_loss.neg_pos_ratio",
          neg_pos_ratio,
      )
      setattr(
          opts,
          "loss.detection.ssd_multibox_loss.label_smoothing",
          label_smooth,
      )
  
      criteria = SSDLoss(opts)
      num_anchors = 20
      num_classes = 2
  
      # prediction is a dictionary with scores and boxes as a key
      predictions = {
          "scores": torch.rand(
              size=(batch_size, num_anchors, num_classes)
          ),
          "boxes": torch.rand(size=(batch_size, num_anchors, 4)),
      }
  
      # target is a dictionary with box_labels and box_coordinates as a key
      targets = {
          "box_labels": torch.randint(
              low=0, high=num_classes, size=(batch_size, num_anchors)
          ),
          "box_coordinates": torch.rand(
              size=(batch_size, num_anchors, 4)
          ),
      }
      # this loss function does not care about it, so we can have any input
      input_sample = torch.randint(low=0, high=1, size=(1,))
  
      loss = criteria(input_sample, predictions, targets)
      assert {"total_loss", "reg_loss", "cls_loss"}.issubset(
          loss.keys()
      )
      for loss_name, loss_val in loss.items():
          assert isinstance(
              loss_val, torch.Tensor
          ), "Loss should be an instance of torch.Tensor"
          assert loss_val.dim() == 0, "Loss value should be a scalar"
  
imports: import argparse
  import random
  import pytest
  import torch
  from loss_fn.detection.mask_rcnn_loss import MaskRCNNLoss
  from loss_fn.detection.ssd_multibox_loss import SSDLoss
label_desc: Use image node_id 1 for calling a global function with example usage: test_ssd_loss_in_out(batch_size, neg_pos_ratio, label_smooth) without return types