Fix style

python/paddle/v2/layer.py

@@ -40,6 +40,7 @@ from paddle.proto.ModelConfig_pb2 import ModelConfig, SubModelConfig
 __all__ = ['data', 'parse_network']
 __layer_map__ = {}
 
+
 def __wrap__(f):
     def wrapped(*args, **xargs):
         out = f(*args, **xargs)
@@ -53,6 +54,7 @@ def __wrap__(f):
 
     return wrapped
 
+
 def __need_to_keep__(name):
     if name in ['StaticInput', 'LayerType', 'layer_support']:
         return False
@@ -99,6 +101,7 @@ def __data_layer__(name, type, **kwargs):
     l.data_type = type
     return l
 
+
 data = __wrap__(__data_layer__)
 
 LayerV2 = v1_layers.LayerOutput
@@ -107,6 +110,7 @@ LayerV2 = v1_layers.LayerOutput
 def __get_used_layers__(output_layers, extra_layers=None):
     layer_names = set()
     parents = {}
+
     def add_parent(child, parent):
         if child in parents:
             parents[child].append(parent)
@@ -181,28 +185,25 @@ def __get_used_evaluators__(layer_names):
     return evaluator_names
 
 
-def __trim_submodel__(old_submodel,
-                      layer_names,
-                      input_layer_names,
-                      output_layer_names,
-                      evaluator_names):
+def __trim_submodel__(old_submodel, layer_names, input_layer_names,
+                      output_layer_names, evaluator_names):
 
     submodel = SubModelConfig()
     submodel.name = old_submodel.name
-    submodel.layer_names.extend(filter(lambda x: x in layer_names,
-                                       old_submodel.layer_names))
-    submodel.input_layer_names.extend(filter(lambda x: x in input_layer_names,
-                                             submodel.layer_names))
-    submodel.output_layer_names.extend(filter(lambda x: x in output_layer_names,
-                                              submodel.layer_names))
-    submodel.evaluator_names.extend(filter(lambda x: x in evaluator_names,
-                                           old_submodel.evaluator_names))
+    submodel.layer_names.extend(
+        filter(lambda x: x in layer_names, old_submodel.layer_names))
+    submodel.input_layer_names.extend(
+        filter(lambda x: x in input_layer_names, submodel.layer_names))
+    submodel.output_layer_names.extend(
+        filter(lambda x: x in output_layer_names, submodel.layer_names))
+    submodel.evaluator_names.extend(
+        filter(lambda x: x in evaluator_names, old_submodel.evaluator_names))
 
     submodel.is_recurrent_layer_group = old_submodel.is_recurrent_layer_group
     submodel.reversed = old_submodel.reversed
 
-    submodel.memories.extend(filter(lambda x: x.link_name in layer_names,
-                                    old_submodel.memories))
+    submodel.memories.extend(
+        filter(lambda x: x.link_name in layer_names, old_submodel.memories))
     target_inlinkid = (old_submodel.target_inlinkid
                        if old_submodel.HasField('target_inlinkid') else -1)
     in_links = []
@@ -213,8 +214,8 @@ def __trim_submodel__(old_submodel,
                 target_inlinkid = len(in_links) - 1
     submodel.in_links.extend(in_links)
 
-    submodel.out_links.extend(filter(lambda x: x.link_name in layer_names,
-                                     old_submodel.out_links))
+    submodel.out_links.extend(
+        filter(lambda x: x.link_name in layer_names, old_submodel.out_links))
     if old_submodel.HasField('generator'):
         submodel.generator.CopyFrom(old_submodel.generator)
 
@@ -264,9 +265,8 @@ def parse_network(output_layers, extra_layers=None):
 
     for s in cp.g_config.model_config.sub_models:
         if s.name in submodel_names:
-            s = __trim_submodel__(
-                s, layer_names, input_layer_names, output_layer_names,
-                evaluator_names)
+            s = __trim_submodel__(s, layer_names, input_layer_names,
+                                  output_layer_names, evaluator_names)
             model_config.sub_models.extend([s])
 
     return model_config