Fix merge error before

python/paddle/v2/optimizer.py

-import py_paddle.swig_paddle as swig_api
 import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
 import paddle.trainer_config_helpers.optimizers as v1_optimizers
 """
@@ -17,6 +16,7 @@ __all__ = [
 
 class Optimizer(object):
     def __init__(self, **kwargs):
+        import py_paddle.swig_paddle as swig_api
         if 'batch_size' in kwargs:
             del kwargs['batch_size']  # not important for python library.
 
@@ -25,8 +25,6 @@ class Optimizer(object):
 
         self.__opt_conf_proto__ = config_parser_utils.parse_optimizer_config(
             __impl__)
-        if swig_api is None:
-            raise RuntimeError("paddle.v2 currently need swig_paddle")
         self.__opt_conf__ = swig_api.OptimizationConfig.createFromProto(
             self.__opt_conf_proto__)
 
@@ -37,18 +35,22 @@ class Optimizer(object):
         For each optimizer(SGD, Adam), GradientMachine should enable different
         buffers.
         """
+        import py_paddle.swig_paddle as swig_api
         tmp = swig_api.ParameterOptimizer.create(self.__opt_conf__)
         assert isinstance(tmp, swig_api.ParameterOptimizer)
         return tmp.getParameterTypes()
 
     def __create_local_updater__(self):
+        import py_paddle.swig_paddle as swig_api
         return swig_api.ParameterUpdater.createLocalUpdater(self.__opt_conf__)
 
     def __create_remote_updater__(self, pass_num, use_sparse_updater):
+        import py_paddle.swig_paddle as swig_api
         return swig_api.ParameterUpdater.createRemoteUpdater(
             self.__opt_conf__, pass_num, use_sparse_updater)
 
     def __create_new_remote_updater__(self, pserver_spec, use_etcd):
+        import py_paddle.swig_paddle as swig_api
         return swig_api.ParameterUpdater.createNewRemoteUpdater(
             self.__opt_conf__, pserver_spec, use_etcd)