fix pep8

edward/models/dirichlet_process.py

@@ -155,29 +155,29 @@ def _sample_n_body(self, k, bools, theta, draws):
     n, batch_shape, event_shape, rank = self._temp_scope
     k += 1
 
+    # If necessary, add a new persistent state to theta.
     def fn():
       theta_k = self._base_cls(
           *self._base_args, **self._base_kwargs).sample(batch_shape)
       return tf.concat([theta, tf.expand_dims(theta_k, 0)], 0)
 
-    # If necessary, add a new persistent state to theta.
     theta = tf.cond(tf.shape(theta)[0] - 1 >= k, lambda: theta, fn)
     theta_k = tf.gather(theta, k)
 
+    # Assign True samples to the new theta_k.
     if len(bools.get_shape()) <= 1:
-      bools_broadcast = bools
+      bools_tile = bools
     else:
       # ``tf.where`` only index subsets when ``bools`` is at most a
       # vector. In general, ``bools`` has shape (n, batch_shape).
       # Therefore we tile ``bools`` to be of shape
       # (n, batch_shape, event_shape) in order to index per-element.
-      bools_broadcast = tf.tile(tf.reshape(
+      bools_tile = tf.tile(tf.reshape(
           bools, [n] + batch_shape + [1] * len(event_shape)),
           [1] + [1] * len(batch_shape) + event_shape)
 
-    # Assign True samples to the new theta_k.
-    theta_k_broadcast = tf.tile(tf.expand_dims(theta_k, 0), [n] + [1] * (rank - 1))
-    draws = tf.where(bools_broadcast, theta_k_broadcast, draws)
+    theta_k_tile = tf.tile(tf.expand_dims(theta_k, 0), [n] + [1] * (rank - 1))
+    draws = tf.where(bools_tile, theta_k_tile, draws)
 
     # Draw new stick probability, then flip coin.
     beta_k = Beta(a=tf.ones_like(self.alpha), b=self.alpha).sample(n)