code: def test_kmeans_dba():
      """Test implementation of Kmeans using dba."""
      X_train, y_train = load_basic_motions(split="train")
      X_test, y_test = load_basic_motions(split="test")
  
      num_test_values = 5
  
      kmeans = TimeSeriesKMeans(
          averaging_method="dba",
          random_state=1,
          n_init=2,
          n_clusters=4,
          init_algorithm="kmeans++",
          metric="dtw",
      )
      train_predict = kmeans.fit_predict(X_train.head(num_test_values))
      train_mean_score = metrics.rand_score(
          y_train[0:num_test_values], train_predict
      )
  
      test_mean_result = kmeans.predict(X_test.head(num_test_values))
      mean_score = metrics.rand_score(
          y_test[0:num_test_values], test_mean_result
      )
      proba = kmeans.predict_proba(X_test.head(num_test_values))
  
      assert np.array_equal(test_mean_result, expected_results["dba"])
      assert mean_score == expected_score["dba"]
      assert train_mean_score == expected_train_result["dba"]
      assert kmeans.n_iter_ == expected_iters["dba"]
      assert np.array_equal(kmeans.labels_, expected_labels["dba"])
      assert isinstance(kmeans.cluster_centers_, np.ndarray)
      assert proba.shape == (5, 4)
  
      for val in proba:
          assert np.count_nonzero(val == 1.0) == 1
  
imports: import numpy
  import pytest
  from sklearn import metrics
  from sktime.clustering.k_means import TimeSeriesKMeans
  from sktime.datasets import load_basic_motions
  from sktime.tests.test_switch import run_test_for_class
label_desc: Use image node_id 2 for calling a global function with example usage: test_kmeans_dba() without return types