diff --git a/tensorflow_probability/python/experimental/mcmc/windowed_sampling.py b/tensorflow_probability/python/experimental/mcmc/windowed_sampling.py
index 643b409864..c8297d24ef 100644
--- a/tensorflow_probability/python/experimental/mcmc/windowed_sampling.py
+++ b/tensorflow_probability/python/experimental/mcmc/windowed_sampling.py
@@ -641,12 +641,14 @@ def windowed_adaptive_nuts(n_draws,
       Where to initialize the step size for the leapfrog integrator. The
       structure should broadcast with `current_state`. For example, if the
       initial state is
+
       ```
       {
-       'a': tf.zeros(n_chains),
-       'b': tf.zeros([n_chains, n_features]),
+          'a': tf.zeros(n_chains),
+          'b': tf.zeros([n_chains, n_features]),
       }
       ```
+
       then any of `1.`, `{'a': 1., 'b': 1.}`, or
       `{'a': tf.ones(n_chains), 'b': tf.ones([n_chains, n_features])}` will
       work. Defaults to the dimension of the log density to the 0.25 power.
@@ -690,6 +692,7 @@ class defaults are used otherwise.
     **pins:
       These are used to condition the provided joint distribution, and are
       passed directly to `joint_dist.experimental_pin(**pins)`.
+
   Returns:
     A single structure of draws is returned in case the trace_fn is `None`, and
     `return_final_kernel_results` is `False`. If there is a trace function,
@@ -766,10 +769,14 @@ def windowed_adaptive_hmc(n_draws,
       Where to initialize the step size for the leapfrog integrator. The
       structure should broadcast with `current_state`. For example, if the
       initial state is
+
       ```
-      {'a': tf.zeros(n_chains),
-       'b': tf.zeros([n_chains, n_features])}
-       ```
+      {
+          'a': tf.zeros(n_chains),
+          'b': tf.zeros([n_chains, n_features]),
+      }
+      ```
+
       then any of `1.`, `{'a': 1., 'b': 1.}`, or
       `{'a': tf.ones(n_chains), 'b': tf.ones([n_chains, n_features])}` will
       work. Defaults to the dimension of the log density to the 0.25 power.
@@ -801,6 +808,7 @@ class defaults are used otherwise.
     **pins:
       These are used to condition the provided joint distribution, and are
       passed directly to `joint_dist.experimental_pin(**pins)`.
+
   Returns:
     A single structure of draws is returned in case the trace_fn is `None`, and
     `return_final_kernel_results` is `False`. If there is a trace function,