code: def test_noiseless_is_consistent_with_cumsum_bijector(self):
      num_timesteps = 10
      ssm = AutoregressiveMovingAverageStateSpaceModel(
          num_timesteps=num_timesteps,
          ar_coefficients=[0.7, -0.2, 0.1],
          ma_coefficients=[0.6],
          level_scale=0.6,
          level_drift=-0.3,
          observation_noise_scale=0.0,
          initial_state_prior=mvn_diag.MultivariateNormalDiag(
              loc=tf.zeros([3]), scale_diag=tf.ones([3])
          ),
      )
      cumsum_ssm = IntegratedStateSpaceModel(ssm)
      x, lp = cumsum_ssm.experimental_sample_and_log_prob(
          [2], seed=test_util.test_seed()
      )
  
      flatten_event = reshape.Reshape(
          [num_timesteps], event_shape_in=[num_timesteps, 1]
      )
      cumsum_dist = chain.Chain(
          [invert.Invert(flatten_event), cumsum.Cumsum(), flatten_event]
      )(ssm)
      self.assertAllClose(lp, cumsum_dist.log_prob(x), atol=1e-5)
  
file_path: probability/tensorflow_probability/python/sts/components/autoregressive_integrated_moving_average_test.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import cumsum
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.distributions import linear_gaussian_ssm
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.sts.components.autoregressive_integrated_moving_average import AutoregressiveMovingAverageStateSpaceModel
  from tensorflow_probability.python.sts.components.autoregressive_integrated_moving_average import IntegratedStateSpaceModel
label_desc: Use image node_id 2 for calling the IntegratedStateSpaceModelTest obj's underlying member method code with example usage: obj.test_noiseless_is_consistent_with_cumsum_bijector() without return types