message: enrich docstring for jit_scope
diff: @@ -70,6 +70,34 @@ def experimental_jit_scope(compile_ops=True, separate_compiled_gradients=False):
  h = tf.gradients([f], [a, b], name='mygrads2')
  ```
  + Ops that are not in the scope may be clustered and compiled with ops in
  + the scope with `compile_ops=True`, while the ops in the scope with
  + `compile_ops=False` will never be compiled.
  +
  + For example:
  +
  + ```python
  + # In the example below, x and loss may be clustered and compiled together,
  + # while y will not be compiled.
  + with tf.xla.experimental.jit_scope():
  + x = tf.matmul(a, b)
  + with tf.xla.experimental.jit_scope(compile_ops=False):
  + y = tf.matmul(c, d)
  + loss = x + y
  + ```
  +
  + If you want to only compile the ops in the scope with `compile_ops=True`,
  + consider adding an outer `jit_scope(compile_ops=False)`:
  +
  + ```python
  + # In the example below, only x will be compiled.
  + with tf.xla.experimental.jit_scope(compile_ops=False):
  + with tf.xla.experimental.jit_scope():
  + x = tf.matmul(a, b)
  + y = tf.matmul(c, d)
  + loss = x + y
  + ```
  +
  Args:
  compile_ops: Whether to enable or disable compilation in the scope.
  Either a Python bool, or a callable that accepts the parameter
  