code: def test_mahattan_distance():
      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 = ManhattanDistance()
  
      manhattan = DistanceMetric.get_metric("manhattan")
  
      m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
      np_sum = np.abs(ground_truth - a).sum()
      assert m.compute() == pytest.approx(np_sum)
      assert manhattan.pairwise([a, ground_truth])[0][
          1
      ] == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
      np_sum += np.abs(ground_truth - b).sum()
      assert m.compute() == pytest.approx(np_sum)
      v1 = np.hstack([a, b])
      v2 = np.hstack([ground_truth, ground_truth])
      assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
      np_sum += np.abs(ground_truth - c).sum()
      assert m.compute() == pytest.approx(np_sum)
      v1 = np.hstack([v1, c])
      v2 = np.hstack([v2, ground_truth])
      assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
  
      m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
      np_sum += np.abs(ground_truth - d).sum()
      assert m.compute() == pytest.approx(np_sum)
      v1 = np.hstack([v1, d])
      v2 = np.hstack([v2, ground_truth])
      assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
  
imports: import os
  import numpy
  import pytest
  import torch
  from sklearn.metrics import DistanceMetric
  import ignite.distributed
  from ignite.contrib.metrics.regression import ManhattanDistance
  from ignite.engine import Engine
label_desc: Use image node_id 2 for calling a global function with example usage: test_mahattan_distance() without return types