code: def test_maskrcnn_loss_in_out(
      batch_size: int,
      classifier_weight: float,
      box_reg_weight: float,
      mask_weight: float,
      objectness_weight: float,
      rpn_box_reg: float,
  ) -> None:
      # These tests check the input and output formats are correct or not.
  
      # build configuration
      parser = argparse.ArgumentParser()
      parser = MaskRCNNLoss.add_arguments(parser)
  
      opts = parser.parse_args([])
  
      setattr(
          opts,
          "loss.detection.mask_rcnn_loss.classifier_weight",
          classifier_weight,
      )
      setattr(
          opts,
          "loss.detection.mask_rcnn_loss.box_reg_weight",
          box_reg_weight,
      )
      setattr(
          opts, "loss.detection.mask_rcnn_loss.mask_weight", mask_weight
      )
      setattr(
          opts,
          "loss.detection.mask_rcnn_loss.objectness_weight",
          objectness_weight,
      )
      setattr(
          opts, "loss.detection.mask_rcnn_loss.rpn_box_reg", rpn_box_reg
      )
  
      criteria = MaskRCNNLoss(opts)
  
      prediction_losses = {}
      for loss_key in [
          "loss_classifier",
          "loss_box_reg",
          "loss_mask",
          "loss_objectness",
          "loss_rpn_box_reg",
      ]:
          prediction_losses.update(
              {loss_key: torch.tensor(random.random())}
          )
  
      # 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, prediction_losses)
      assert {
          "loss_classifier",
          "loss_box_reg",
          "loss_mask",
          "loss_objectness",
          "loss_rpn_box_reg",
          "total_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 2 for calling a global function with example usage: test_maskrcnn_loss_in_out(batch_size, classifier_weight, box_reg_weight, mask_weight, objectness_weight, rpn_box_reg) without return types