diff --git a/tensorflow_probability/python/experimental/mcmc/particle_filter.py b/tensorflow_probability/python/experimental/mcmc/particle_filter.py
index 4f18f4782d..4bfaf2dd1a 100644
--- a/tensorflow_probability/python/experimental/mcmc/particle_filter.py
+++ b/tensorflow_probability/python/experimental/mcmc/particle_filter.py
@@ -237,7 +237,6 @@ def observation_fn(_, state):
   with tf.name_scope(name or 'infer_trajectories') as name:
     pf_seed, resample_seed = samplers.split_seed(
         seed, salt='infer_trajectories')
-
     (particles,
      log_weights,
      parent_indices,
@@ -377,7 +376,7 @@ def seeded_one_step(seed_state_results, _):
     # Return results from just the final step.
     traced_results = trace_fn(*final_seed_state_result[1:])
 
-    return traced_results
+  return traced_results
 
 
 @docstring_util.expand_docstring(
@@ -393,6 +392,7 @@ def particle_filter(observations,
                     resample_fn=weighted_resampling.resample_systematic,
                     resample_criterion_fn=smc_kernel.ess_below_threshold,
                     unbiased_gradients=True,
+                    rejuvenation_kernel_fn=None,  # TODO(davmre): not yet supported. pylint: disable=unused-argument
                     num_transitions_per_observation=1,
                     trace_fn=_default_trace_fn,
                     trace_criterion_fn=_always_trace,
@@ -448,6 +448,7 @@ def particle_filter(observations,
       Filtering without Modifying the Forward Pass. _arXiv preprint
       arXiv:2106.10314_, 2021. https://arxiv.org/abs/2106.10314
   """
+
   init_seed, loop_seed = samplers.split_seed(seed, salt='particle_filter')
   with tf.name_scope(name or 'particle_filter'):
     num_observation_steps = ps.size0(tf.nest.flatten(observations)[0])
diff --git a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py
index b586e44b86..2a9302a420 100644
--- a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py
+++ b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py
@@ -96,8 +96,7 @@ def testMarginalLikelihoodGradientIsDefined(self):
         WeightedParticles(
             particles=samplers.normal([num_particles], seed=seeds[0]),
             log_weights=tf.fill([num_particles],
-                                -tf.math.log(float(num_particles)))
-        ))
+                                -tf.math.log(float(num_particles)))))
 
     def propose_and_update_log_weights_fn(_,
                                           weighted_particles,
@@ -111,8 +110,7 @@ def propose_and_update_log_weights_fn(_,
           particles=proposed_particles,
           log_weights=(weighted_particles.log_weights +
                        transition_dist.log_prob(proposed_particles) -
-                       proposal_dist.log_prob(proposed_particles))
-      )
+                       proposal_dist.log_prob(proposed_particles)))
 
     def marginal_logprob(transition_scale):
       kernel = SequentialMonteCarlo(