message: Proper markdown formatting in the tf.module docstring.
  Fixes the mess you see in as of 2019-03-20.
diff: @@ -36,6 +36,7 @@ class Module(tracking.AutoTrackable):
  functions which apply to user input. For example a dense layer in a neural
  network might be implemented as a `tf.Module`:
  + ```python
  >>> class Dense(tf.Module):
  ... def __init__(self, in_features, output_features, name=None):
  ... super(Dense, self).__init__(name=name)
  @@ -46,19 +47,24 @@ class Module(tracking.AutoTrackable):
  ... def __call__(self, x):
  ... y = tf.matmul(x, self.w) + self.b
  ... return tf.nn.relu(y)
  + ```
  You can use the Dense layer as you would expect:
  + ```python
  >>> d = Dense(input_features=64, output_features=10)
  >>> d(tf.ones([100, 64]))
  <tf.Tensor: ...>
  + ```
  By subclassing `tf.Module` instead of `object` any `tf.Variable` or
  `tf.Module` instances assigned to object properties can be collected using
  the `variables`, `trainable_variables` or `submodules` property:
  + ```python
  >>> d.variables
  (<tf.Variable 'b:0' ...>, <tf.Variable 'w:0' ...>)
  + ```
  Subclasses of `tf.Module` can also take advantage of the `_flatten` method
  which can be used to implement tracking of any other types.
  @@ -71,6 +77,7 @@ class Module(tracking.AutoTrackable):
  `with self.name_scope:` or you can annotate methods (apart from `__init__`)
  with `@tf.Module.with_name_scope`.
  + ```python
  >>> class MLP(tf.Module):
  ... def __init__(self, input_size, sizes, name=None):
  ... super(MLP, self).__init__(name=name)
  @@ -85,6 +92,7 @@ class Module(tracking.AutoTrackable):
  ... for layer in self.layers:
  ... x = layer(x)
  ... return x
  + ```
  """
  def __init__(self, name=None):
  