code: def test_mean_euclidean(mock_det, mock_obj):
      euc = get_distance_by_name("mean_euclidean")
  
      # perfect match
      det = mock_det([[1, 2], [3, 4]])
      obj = mock_obj([[1, 2], [3, 4]])
      np.testing.assert_almost_equal(euc.distance_function(det, obj), 0)
  
      # foat type
      det = mock_det([[1.1, 2.2], [3.3, 4.4]])
      obj = mock_obj([[1.1, 2.2], [3.3, 4.4]])
      np.testing.assert_almost_equal(euc.distance_function(det, obj), 0)
  
      # distance of 1 in 1 dimension of 1 point
      det = mock_det([[1, 2], [3, 4]])
      obj = mock_obj([[2, 2], [3, 4]])
      np.testing.assert_almost_equal(
          euc.distance_function(det, obj), 1 / 2
      )
  
      # distance of 2 in 1 dimension of 1 point
      det = mock_det([[1, 2], [3, 4]])
      obj = mock_obj([[3, 2], [3, 4]])
      np.testing.assert_almost_equal(euc.distance_function(det, obj), 1)
  
      # distance of 2 in 1 dimension of all points
      det = mock_det([[1, 2], [3, 4]])
      obj = mock_obj([[3, 2], [5, 4]])
      np.testing.assert_almost_equal(euc.distance_function(det, obj), 2)
  
      # distance of 2 in all dimensions of all points
      det = mock_det([[1, 2], [3, 4]])
      obj = mock_obj([[3, 4], [5, 6]])
      np.testing.assert_almost_equal(
          euc.distance_function(det, obj), np.sqrt(8)
      )
  
      # distance of 1 in all dimensions of all points
      det = mock_det([[1, 2], [3, 4]])
      obj = mock_obj([[2, 3], [4, 5]])
      np.testing.assert_almost_equal(
          euc.distance_function(det, obj), np.sqrt(2)
      )
  
      # negative difference
      det = mock_det([[1, 2], [3, 4]])
      obj = mock_obj([[-1, 2], [3, 4]])
      np.testing.assert_almost_equal(euc.distance_function(det, obj), 1)
  
      # negative equals
      det = mock_det([[-1, 2], [3, 4]])
      obj = mock_obj([[-1, 2], [3, 4]])
      np.testing.assert_almost_equal(euc.distance_function(det, obj), 0)
  
file_path: norfair/tests/test_distances.py
imports: import numpy
  import pytest
  from norfair.distances import ScalarDistance, ScipyDistance, VectorizedDistance, create_keypoints_voting_distance, create_normalized_mean_euclidean_distance, frobenius, get_distance_by_name
label_desc: Use image node_id 3 for calling a global function with example usage: test_mean_euclidean(mock_det, mock_obj) without return types