code: def test_gaussian_mixture_fit():
      # recover the ground truth
      rng = np.random.RandomState(0)
      rand_data = RandomData(rng)
      n_features = rand_data.n_features
      n_components = rand_data.n_components
  
      for covar_type in COVARIANCE_TYPE:
          X = rand_data.X[covar_type]
          g = GaussianMixture(
              n_components=n_components,
              n_init=20,
              reg_covar=0,
              random_state=rng,
              covariance_type=covar_type,
          )
          g.fit(X)
  
          # needs more data to pass the test with rtol=1e-7
          assert_allclose(
              np.sort(g.weights_),
              np.sort(rand_data.weights),
              rtol=0.1,
              atol=1e-2,
          )
  
          arg_idx1 = g.means_[:, 0].argsort()
          arg_idx2 = rand_data.means[:, 0].argsort()
          assert_allclose(
              g.means_[arg_idx1],
              rand_data.means[arg_idx2],
              rtol=0.1,
              atol=1e-2,
          )
  
          if covar_type == "full":
              prec_pred = g.precisions_
              prec_test = rand_data.precisions["full"]
          elif covar_type == "tied":
              prec_pred = np.array([g.precisions_] * n_components)
              prec_test = np.array(
                  [rand_data.precisions["tied"]] * n_components
              )
          elif covar_type == "spherical":
              prec_pred = np.array(
                  [np.eye(n_features) * c for c in g.precisions_]
              )
              prec_test = np.array(
                  [
                      np.eye(n_features) * c
                      for c in rand_data.precisions["spherical"]
                  ]
              )
          elif covar_type == "diag":
              prec_pred = np.array([np.diag(d) for d in g.precisions_])
              prec_test = np.array(
                  [np.diag(d) for d in rand_data.precisions["diag"]]
              )
  
          arg_idx1 = np.trace(prec_pred, axis1=1, axis2=2).argsort()
          arg_idx2 = np.trace(prec_test, axis1=1, axis2=2).argsort()
          for k, h in zip(arg_idx1, arg_idx2):
              ecov = EmpiricalCovariance()
              ecov.covariance_ = prec_test[h]
              # the accuracy depends on the number of data and randomness, rng
              assert_allclose(
                  ecov.error_norm(prec_pred[k]), 0, atol=0.15
              )
  
imports: import copy
  import itertools
  import re
  import sys
  import warnings
  from io import StringIO
  from unittest.mock import Mock
  import numpy
  import pytest
  from scipy import linalg, stats
  import sklearn
  from sklearn.cluster import KMeans
  from sklearn.covariance import EmpiricalCovariance
  from sklearn.datasets import make_spd_matrix
  from sklearn.exceptions import ConvergenceWarning, NotFittedError
  from sklearn.metrics.cluster import adjusted_rand_score
  from sklearn.mixture import GaussianMixture
  from sklearn.mixture._gaussian_mixture import _compute_log_det_cholesky, _compute_precision_cholesky, _estimate_gaussian_covariances_diag, _estimate_gaussian_covariances_full, _estimate_gaussian_covariances_spherical, _estimate_gaussian_covariances_tied, _estimate_gaussian_parameters
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.extmath import fast_logdet
label_desc: Use image node_id 17 for calling a global function with example usage: test_gaussian_mixture_fit() without return types