code: def test_compute(compute_with_cache):
      """Test that `compute` method works as expected."""
      metric = DummyMetricSum(compute_with_cache=compute_with_cache)
      assert metric.compute() == 0
      assert metric.metric_state == {"x": tensor(0)}
      metric.update(1)
      assert metric._computed is None
      assert metric.compute() == 1
      assert (
          metric._computed == 1
          if compute_with_cache
          else metric._computed is None
      )
      assert metric.metric_state == {"x": tensor(1)}
      metric.update(2)
      assert metric._computed is None
      assert metric.compute() == 3
      assert (
          metric._computed == 3
          if compute_with_cache
          else metric._computed is None
      )
      assert metric.metric_state == {"x": tensor(3)}
  
      # called without update, should return cached value
      metric._computed = 5
      assert metric.compute() == 5
      assert metric.metric_state == {"x": tensor(3)}
  
imports: import os
  import pickle
  from collections import OrderedDict
  from typing import Any
  from unittest.mock import Mock
  import cloudpickle
  import numpy
  import psutil
  import pytest
  import torch
  from torch import Tensor, tensor
  from torch.nn import Module, Parameter
  from torchmetrics.classification import BinaryAccuracy
  from torchmetrics.regression import PearsonCorrCoef
  from unittests.helpers import seed_all
  from unittests.helpers.testers import DummyListMetric, DummyMetric, DummyMetricMultiOutput, DummyMetricSum
  from unittests.helpers.utilities import no_warning_call
label_desc: Use image node_id 8 for calling a global function with example usage: test_compute(compute_with_cache) without return types