code: def test_covar_is_writeable(self, implementation):
      h = hmm.GaussianHMM(
          n_components=1,
          covariance_type="diag",
          init_params="c",
          implementation=implementation,
      )
      X = self.prng.normal(size=(1000, 5))
      h._init(X, 1000)
  
      # np.diag returns a read-only view of the array in NumPy 1.9.X.
      # Make sure this doesn't prevent us from fitting an HMM with
      # diagonal covariance matrix. See PR#44 on GitHub for details
      # and discussion.
      assert h._covars_.flags["WRITEABLE"]
  
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 TestGaussianHMMWithDiagonalCovars obj's underlying member method code with example usage: obj.test_covar_is_writeable(implementation) without return types