code: def test_similarity_weighted_column_averaging():
      X = np.array(
          [
              [0.1, 0.9, 0.2],
              [0.8, 0.1, 0.01],
              [0.95, 0.2, 0.3],
              [0.14, 0.85, 0.3],
          ]
      )
      X_incomplete = X.copy()
      X_incomplete[1, 1] = np.nan
      X_incomplete[3, 0] = np.nan
      missing_mask = np.isnan(X_incomplete)
  
      solver = SimilarityWeightedAveraging()
      X_filled = solver.fit_transform(X_incomplete)
      eq_(X_incomplete.shape, X_filled.shape)
      diff = (X - X_filled)[missing_mask]
      abs_diff = np.abs(diff)
      mae = np.mean(abs_diff)
      print("MAE", mae)
      assert mae < 0.1, (
          "Difference between imputed values! MAE=%0.4f" % mae
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_similarity_weighted_column_averaging() without return types