code: def test_metrics_mod(second_operand, expected_result):
      """Test that `mod` operator works and returns a compositional metric."""
      first_metric = DummyMetric(5)
  
      final_mod = first_metric % second_operand
  
      assert isinstance(final_mod, CompositionalMetric)
  
      final_mod.update()
      # prevent Runtime error for PT 1.8 - Long did not match Float
      assert torch.allclose(
          expected_result.to(float), final_mod.compute().to(float)
      )
  
imports: from operator import neg, pos
  from typing import Any
  import pytest
  import torch
  from torch import tensor
  from torchmetrics.metric import CompositionalMetric, Metric
label_desc: Use image node_id 10 for calling a global function with example usage: test_metrics_mod(second_operand, expected_result) without return types