code: def test_compute():
      a = np.random.randn(4)
      b = np.random.randn(4)
      c = np.random.randn(4)
      d = np.random.randn(4)
      ground_truth = np.random.randn(4)
      np_prod = 1.0
  
      m = GeometricMeanAbsoluteError()
      m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
  
      errors = np.abs(ground_truth - a)
      np_prod = np.multiply.reduce(errors) * np_prod
      np_len = len(a)
      np_ans = np.power(np_prod, 1.0 / np_len)
      assert m.compute() == pytest.approx(np_ans)
  
      m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
      errors = np.abs(ground_truth - b)
      np_prod = np.multiply.reduce(errors) * np_prod
      np_len += len(b)
      np_ans = np.power(np_prod, 1.0 / np_len)
      assert m.compute() == pytest.approx(np_ans)
  
      m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
      errors = np.abs(ground_truth - c)
      np_prod = np.multiply.reduce(errors) * np_prod
      np_len += len(c)
      np_ans = np.power(np_prod, 1.0 / np_len)
      assert m.compute() == pytest.approx(np_ans)
  
      m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
      errors = np.abs(ground_truth - d)
      np_prod = np.multiply.reduce(errors) * np_prod
      np_len += len(d)
      np_ans = np.power(np_prod, 1.0 / np_len)
      assert m.compute() == pytest.approx(np_ans)
  
imports: import os
  import numpy
  import pytest
  import torch
  import ignite.distributed
  from ignite.contrib.metrics.regression import GeometricMeanAbsoluteError
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
label_desc: Use image node_id 3 for calling a global function with example usage: test_compute() without return types