code: def generate_test_results_clusterers():
      """Generate test results."""
      X_train, y_train = load_unit_test(
          split="train", return_type="numpy2d"
      )
      print(" Shape = ", X_train[0].shape)
      s = StandardScaler()
      X_train = s.fit_transform(X_train.T)
      X_train = X_train.T
      d1 = X_train[0]
      d2 = X_train[1]
      print(" Shape = ", d1.shape)
      d3 = np.zeros(d1.shape)
      print(d1)
      print(d2)
      print(d3)
      # d1, c1 and c2= distance =  0.9166666666666666
  
      c1 = np.array(
          [
              -0.70310574,
              -0.87214184,
              -1.00698691,
              -1.06674154,
              -1.12436733,
              -1.15538656,
              -1.15125921,
              -1.03439438,
              -0.85569435,
              -0.76934041,
              -0.47880807,
              0.13013539,
              1.24150173,
              1.47580796,
              1.21524191,
              1.03776906,
              0.9338891,
              1.11147877,
              1.35705589,
              1.29823615,
              0.81583304,
              0.30507612,
              -0.14019819,
              -0.5636006,
          ]
      )
      c2 = np.array(
          [
              -0.56887401,
              -0.78355128,
              -0.97270354,
              -1.05610923,
              -1.16663863,
              -1.21763814,
              -1.21428503,
              -1.14248176,
              -0.9842895,
              -0.91458795,
              -0.55085488,
              0.03584731,
              1.08807594,
              1.32503268,
              1.08481383,
              0.90703813,
              0.87781706,
              1.03376858,
              1.33278512,
              1.38938799,
              0.99061415,
              0.59018919,
              0.1635356,
              -0.24689165,
          ]
      )
      x = create_test_distance_numpy(10)
      y = create_test_distance_numpy(10, random_state=2)
      curr_X = X_train[19]
  
      curr_X = curr_X.reshape((1, c1.shape[0]))
  
      c1 = c1.reshape((1, c1.shape[0]))
      print(f"instance 19  = {curr_X} shape X = {curr_X.shape}")
      print("Centroid 0 =", c1)
      d = lcss_distance(curr_X, c1, epsilon=0.01)
      print(" DISTANCE = ", d)
      print("Shape centroid = ", c1.shape)
      d = lcss_distance(curr_X, c2, epsilon=0.01)
      print(" DISTANCE = ", d)
      print("Shape centroid = ", c2.shape)
  
      italy1 = np.array(
          [
              -0.71051757,
              -1.1833204,
              -1.3724416,
              -1.5930829,
              -1.4670021,
              -1.3724416,
              -1.0887599,
              0.045966947,
              0.92853223,
              1.0861332,
              1.2752543,
              0.96005242,
              0.61333034,
              0.014446758,
              -0.6474772,
              -0.26923494,
              -0.20619456,
              0.61333034,
              1.3698149,
              1.4643754,
              1.054613,
              0.58181015,
              0.1720477,
              -0.26923494,
          ]
      )
      italy2 = np.array(
          [
              -0.41137184,
              -0.67348487,
              -1.1103399,
              -1.2413964,
              -1.4161385,
              -1.3287674,
              -0.84822689,
              -0.58611386,
              -0.80454138,
              0.069168714,
              0.68076578,
              0.76813679,
              0.63708028,
              0.069168714,
              -0.36768633,
              -0.49874285,
              -0.36768633,
              -0.018202296,
              1.7729034,
              2.0350164,
              1.5544759,
              1.4234194,
              0.63708028,
              0.025483209,
          ]
      )
      d = dtw_distance(italy1, italy2)
      print(" Italy DTW DISTANCE = ", d)
  
file_path: sktime/sktime/_contrib/distance_refactor.py
imports: import numpy
  from sklearn import metrics
  from sktime.clustering.k_means import TimeSeriesKMeans
  from sktime.datasets import load_arrow_head, load_basic_motions, load_unit_test
  from sktime.distances import ddtw_distance, dtw_distance, edr_distance, erp_distance, euclidean_distance, lcss_distance, msm_distance, wddtw_distance, wdtw_distance
  from sktime.distances.tests._utils import create_test_distance_numpy
  from sktime.clustering.utils.plotting._plot_partitions import plot_cluster_algorithm
  from sklearn.preprocessing import StandardScaler, normalize
label_desc: Use image node_id 3 for calling a global function with example usage: generate_test_results_clusterers() without return types