code: def test_chunked(sellf, covtype, init_params="mcw"):
      np.random.seed(0)
      gmm = create_random_gmm(3, 2, covariance_type=covtype, prng=0)
      gmm.covariances_ = gmm.covars_
      data = gmm.sample(n_samples=1000)[0]
  
      model1 = GMMHMM(
          n_components=3,
          n_mix=2,
          covariance_type=covtype,
          random_state=1,
          init_params=init_params,
      )
      model2 = GMMHMM(
          n_components=3,
          n_mix=2,
          covariance_type=covtype,
          random_state=1,
          init_params=init_params,
      )
      # don't use random parameters for testing
      init = 1.0 / model1.n_components
      for model in (model1, model2):
          model.startprob_ = np.full(model.n_components, init)
          model.transmat_ = np.full(
              (model.n_components, model.n_components), init
          )
  
      model1.fit(data)
      model2.fit(data, lengths=[200] * 5)
  
      assert_allclose(model1.means_, model2.means_, rtol=0, atol=1e-2)
      assert_allclose(model1.covars_, model2.covars_, rtol=0, atol=1e-3)
      assert_allclose(
          model1.weights_, model2.weights_, rtol=0, atol=1e-3
      )
      assert_allclose(
          model1.transmat_, model2.transmat_, rtol=0, atol=1e-2
      )
  
imports: import numpy
  from numpy.testing import assert_allclose, assert_array_less
  import pytest
  from sklearn.utils import check_random_state
  from ..hmm import GMMHMM
  from .test_gmm_hmm import create_random_gmm
  from .None import assert_log_likelihood_increasing, normalized
label_desc: Use image node_id 1 for calling the TestGMMHMM_MultiSequence obj's underlying member method code with example usage: obj.test_chunked(sellf, covtype, init_params) without return types