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)
  
      m = WaveHedgesDistance()
  
      m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
      np_sum = (
          np.abs(ground_truth - a)
          / np.maximum.reduce([a, ground_truth])
      ).sum()
      assert m.compute() == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
      np_sum += (
          np.abs(ground_truth - b)
          / np.maximum.reduce([b, ground_truth])
      ).sum()
      assert m.compute() == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
      np_sum += (
          np.abs(ground_truth - c)
          / np.maximum.reduce([c, ground_truth])
      ).sum()
      assert m.compute() == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
      np_sum += (
          np.abs(ground_truth - d)
          / np.maximum.reduce([d, ground_truth])
      ).sum()
      assert m.compute() == pytest.approx(np_sum)
  
file_path: ignite/tests/ignite/contrib/metrics/regression/test_wave_hedges_distance.py
imports: import os
  import numpy
  import pytest
  import torch
  import ignite.distributed
  from ignite.contrib.metrics.regression import WaveHedgesDistance
  from ignite.engine import Engine
label_desc: Use image node_id 2 for calling a global function with example usage: test_compute() without return types