message: Fixing example indentation and text
  Summary:
  Fixes T10839
  Remove reference to AdamWOptimizer, fix indentation and improve text in code example.
  Test Plan: Unit tests run.
  Reviewers: #tensorflow, georgep
  Subscribers: georgep
  Maniphest Tasks: T10839
diff: @@ -35,29 +35,31 @@ class MapGradientOptimizer(optimizer.Optimizer):
  Example
  .. code-block:: python
  - #Define function which should modify computed gradients. Decay gradient function.
  + # Define function which will modify computed gradients.
  + # This is a gradient decay function.
  def map_fn_decay(grad, var):
  return grad + (WEIGHT_DECAY * var)
  - # To run the code we need session.
  + # To run the code we need a session:
  with self.cached_session():
  - # We can employ AdamWOptimizer
  - optimizer = weight_decay_optimizers.AdamWOptimizer(WEIGHT_DECAY)
  + optimizer = gradient_descent.GradientDescentOptimizer(0.000001)
  # We define MapGradientOptimizer
  map_optimizer = map_gradient_optimizer.MapGradientOptimizer(
  optimizer, map_fn_decay)
  # Gradients are computed by compute_gradients(), where our map function
  - # modifies computed gradients. compute_gradients(loss, var_list) argument
  - # are loss and var_list. Define some and employ map_optimizer, compute_gradients().
  + # modifies computed gradients. compute_gradients(loss, var_list) arguments
  + # are loss and var_list so define arguments and call
  + # map_optimizer.compute_gradients().
  values = [1.0, 2.0, 3.0]
  vars_ = [variables.Variable([v], dtype=dtypes.float32) for v in values]
  grads_and_vars = map_optimizer.compute_gradients(
  vars_[0] * vars_[1] + vars_[0] * vars_[2] + vars_[1] * vars_[2],
  vars_)
  - # The output grads_and_vars contains modified computed gradients by decay map function.
  - # grads are 5.01, 4.02 and 3.03. If we did not use MapGradientOptimizer they would
  - # be 5, 4 and 3.
  + # The output grads_and_vars contains computed gradients modified by
  + # the decay map function.
  + # grads are 5.01, 4.02 and 3.03. If we did not use MapGradientOptimizer
  + # they would be 5, 4 and 3.
  Args:
  wrapped_optimizer: tensorflow (derived) optimizer.
  @@ -67,6 +69,7 @@ class MapGradientOptimizer(optimizer.Optimizer):
  Returns:
  compute_gradients() returns a list of (gradient, variable) pairs.
  """
  +
  def __init__(self,
  wrapped_optimizer,
  gradient_mapping_function,
  