code: def test_latent_state_with_multiple_transform_fn(self):
      cov_reducer = covariance_reducer.CovarianceReducer(
          event_ndims=1,
          transform_fn=[
              lambda _, kr: kr.value,
              lambda _, kr: kr.inner_results.value,
          ],
      )
      latent_state = (
          {"one": tf.ones((3, 4)), "zero": tf.zeros((3, 4))},
          {"two": tf.ones((3, 4)) * 2},
      )
      fake_kr = FakeKernelResults(
          latent_state, FakeInnerResults(latent_state)
      )
      state = cov_reducer.initialize(latent_state, fake_kr)
      _, state = tf.while_loop(
          lambda i, _: i < 10,
          lambda i, state: (
              i + 1,
              cov_reducer.one_step(0.0, state, fake_kr),
          ),
          (0.0, state),
      )
      final_cov = self.evaluate(cov_reducer.finalize(state))
      cov_latent = (
          {"one": tf.zeros((3, 4, 4)), "zero": tf.zeros((3, 4, 4))},
          {"two": tf.zeros((3, 4, 4))},
      )
      self.assertAllEqualNested(final_cov[0], cov_latent)
      self.assertAllEqualNested(final_cov[1], cov_latent)
  
file_path: probability/tensorflow_probability/python/experimental/mcmc/covariance_reducer_test.py
imports: import collections
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.mcmc import covariance_reducer
  from tensorflow_probability.python.experimental.mcmc.internal import test_fixtures
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 5 for calling the CovarianceReducersTest obj's underlying member method code with example usage: obj.test_latent_state_with_multiple_transform_fn() without return types