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 = FractionalAbsoluteError()
  
      m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
      np_sum = (
          2
          * np.abs((a - ground_truth))
          / (np.abs(a) + np.abs(ground_truth))
      ).sum()
      np_len = len(a)
      np_ans = np_sum / np_len
      assert m.compute() == pytest.approx(np_ans)
  
      m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
      np_sum += (
          2
          * np.abs((b - ground_truth))
          / (np.abs(b) + np.abs(ground_truth))
      ).sum()
      np_len += len(b)
      np_ans = np_sum / np_len
      assert m.compute() == pytest.approx(np_ans)
  
      m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
      np_sum += (
          2
          * np.abs((c - ground_truth))
          / (np.abs(c) + np.abs(ground_truth))
      ).sum()
      np_len += len(c)
      np_ans = np_sum / np_len
      assert m.compute() == pytest.approx(np_ans)
  
      m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
      np_sum += (
          2
          * np.abs((d - ground_truth))
          / (np.abs(d) + np.abs(ground_truth))
      ).sum()
      np_len += len(d)
      np_ans = np_sum / 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 FractionalAbsoluteError
  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