code: def test_mean_error():
      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 = MeanError()
  
      m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
      np_sum = (ground_truth - a).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 += (ground_truth - b).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 += (ground_truth - c).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 += (ground_truth - d).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 MeanError
  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_mean_error() without return types