code: def time_clusterers():
      """Time tests for clusterers."""
      k_means = TimeSeriesKMeans(
          n_clusters=5,  # Number of desired centers
          init_algorithm="forgy",  # Center initialisation technique
          max_iter=10,  # Maximum number of iterations for refinement on training set
          metric="dtw",  # Distance metric to use
          averaging_method="mean",  # Averaging technique to use
          random_state=1,
      )
      X_train, y_train = load_arrow_head(split="train")
      X_test, y_test = load_arrow_head(split="test")
      k_means.fit(X_train)
      plot_cluster_algorithm(k_means, X_test, k_means.n_clusters)
  
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 1 for calling a global function with example usage: time_clusterers() without return types