Do not change API in doc PR

python/paddle/fluid/clip.py

@@ -215,7 +215,7 @@ def set_gradient_clip(clip, param_list=None, program=None):
 def append_gradient_clip_ops(param_grad):
     context = dict()
     for p, g in param_grad:
-        with p.block.program.optimization_guard(p):
+        with p.block.program.optimized_guard(p):
             clip_attr = getattr(p, 'gradient_clip_attr', NullGradientClipAttr())
             if clip_attr is None:
                 clip_attr = NullGradientClipAttr()
@@ -228,7 +228,7 @@ def append_gradient_clip_ops(param_grad):
 
     res = []
     for p, g in param_grad:
-        with p.block.program.optimization_guard(p):
+        with p.block.program.optimized_guard(p):
             res.append(clip_attr.create_operators(param=p, grad=g))
 
     return res

python/paddle/fluid/framework.py

@@ -1103,7 +1103,7 @@ class Program(object):
         self._op_role_var = [var_name]
 
     @contextlib.contextmanager
-    def optimization_guard(self, var):
+    def optimized_guard(self, var):
         """
         A with guard to set :code:`Optimization` :code:`OpRole` and
         :code:`OpRoleVar` automatically.
@@ -1116,7 +1116,7 @@ class Program(object):
         Examples:
 
             >>> p, g = backward(...)
-            >>> with program.optimization_guard(p):
+            >>> with program.optimized_guard(p):
             >>>     p = p - 0.001 * g
         """
         OpRole = core.op_proto_and_checker_maker.OpRole

python/paddle/fluid/optimizer.py

@@ -226,7 +226,7 @@ class Optimizer(object):
 
             optimize_ops = []
             for param_and_grad in parameters_and_grads:
-                with param_and_grad[0].block.program.optimization_guard(
+                with param_and_grad[0].block.program.optimized_guard(
                         param_and_grad[0]):
                     if param_and_grad[0].trainable is True and param_and_grad[
                             1] is not None:

python/paddle/fluid/regularizer.py

@@ -43,7 +43,7 @@ def append_regularization_ops(parameters_and_grads, regularization=None):
     """
     params_and_grads = []
     for param, grad in parameters_and_grads:
-        with param.block.program.optimization_guard(param):
+        with param.block.program.optimized_guard(param):
             # If no gradient then we don't need to do anything
             if grad is None:
                 params_and_grads.append((param, grad))