code: def test_seuclidean(spatial_data):
      v = np.abs(np.random.randn(spatial_data.shape[1]))
      dist_matrix = pairwise_distances(
          spatial_data, metric="seuclidean", V=v
      )
      test_matrix = np.array(
          [
              [
                  dist.standardised_euclidean(
                      spatial_data[i], spatial_data[j], v
                  )
                  for j in range(spatial_data.shape[0])
              ]
              for i in range(spatial_data.shape[0])
          ]
      )
      assert_array_almost_equal(
          test_matrix,
          dist_matrix,
          err_msg="Distances don't match " "for metric seuclidean",
      )
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal
  import umap.distances
  import umap.sparse
  from sklearn.metrics import pairwise_distances
  from sklearn.neighbors import BallTree
  from scipy.version import full_version
  import pytest
label_desc: Use image node_id 42 for calling a global function with example usage: test_seuclidean(spatial_data) without return types