code: def test_bayesian_mixture_fit_predict_n_init():
      # Check that fit_predict is equivalent to fit.predict, when n_init > 1
      X = np.random.RandomState(0).randn(50, 5)
      gm = BayesianGaussianMixture(
          n_components=5, n_init=10, random_state=0
      )
      y_pred1 = gm.fit_predict(X)
      y_pred2 = gm.predict(X)
      assert_array_equal(y_pred1, y_pred2)
  
imports: import copy
  import numpy
  import pytest
  from scipy.special import gammaln
  from sklearn.exceptions import ConvergenceWarning, NotFittedError
  from sklearn.metrics.cluster import adjusted_rand_score
  from sklearn.mixture import BayesianGaussianMixture
  from sklearn.mixture._bayesian_mixture import _log_dirichlet_norm, _log_wishart_norm
  from sklearn.mixture.tests.test_gaussian_mixture import RandomData
  from sklearn.utils._testing import assert_almost_equal, assert_array_equal, ignore_warnings
label_desc: Use image node_id 13 for calling a global function with example usage: test_bayesian_mixture_fit_predict_n_init() without return types