code: def debug_clusterers():
      """Debug clusterers."""
      X_train, y_train = load_basic_motions(
          split="train", return_type="numpy3d"
      )
      #    X_train, y_train = load_unit_test(split="train", return_type="numpy3d")
      #   X_train2, y_train2 = load_unit_test(split="train", return_type="numpy2d")
      parameters = {"window": 1.0, "epsilon": 50.0, "g": 0.05, "c": 1.0}
      for dist in distances:
          kmeans = TimeSeriesKMeans(
              averaging_method="mean",
              random_state=1,
              n_init=2,
              n_clusters=2,
              init_algorithm="kmeans++",
              metric=dist,
              distance_params=parameters,
          )
          kmeans.fit(X_train)
          y_pred = kmeans.predict(X_train)
          train_rand = metrics.rand_score(y_train, y_pred)
          print('"' + dist + '": ' + str(train_rand) + ",")
  
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 2 for calling a global function with example usage: debug_clusterers() without return types