code: def test_issue_385(self, implementation):
      model = hmm.GaussianHMM(
          n_components=2, covariance_type="spherical"
      )
      model.startprob_ = np.array([0.6, 0.4])
      model.transmat_ = np.array([[0.4, 0.6], [0.9, 0.1]])
      model.means_ = np.array([[3.0], [5.0]])
      model.covars_ = np.array([[[[4.0]]], [[[3.0]]]])
  
      # If setting up an HMM to immediately sample from, the easiest thing is
      # to just set n_features.  We could infer it from self.means_ perhaps.
      model.n_features = 1
      covars = model.covars_
      # Make sure covariance is of correct format - the spherical case would
      # throw an exception here.
      model.sample(1000)
  
imports: import numpy
  from numpy.testing import assert_allclose
  import pytest
  from sklearn.utils import check_random_state
  from ..None import hmm
  from .None import assert_log_likelihood_increasing, make_covar_matrix, normalized
label_desc: Use image node_id 1 for calling the TestGaussianHMMWithSphericalCovars obj's underlying member method code with example usage: obj.test_issue_385(implementation) without return types