fix style problem

demo/seqToseq/seqToseq_net.py

@@ -131,8 +131,9 @@ def gru_encoder_decoder(data_conf,
 
     decoder_group_name = "decoder_group"
     group_inputs = [
-        StaticInput(input=encoded_vector, is_seq=True),
-        StaticInput(input=encoded_proj, is_seq=True)
+        StaticInput(
+            input=encoded_vector, is_seq=True), StaticInput(
+                input=encoded_proj, is_seq=True)
     ]
 
     if not is_generating:

python/paddle/v2/config_base.py

@@ -114,7 +114,8 @@ class Layer(object):
 
         # 4. parse myself and add myself into context.
         ret_val = self.to_proto_impl(context=context, **kwargs)
-        if self.context_name() is not None and self.context_name() not in context:
+        if self.context_name() is not None and self.context_name(
+        ) not in context:
             context[self.context_name()] = ret_val
 
         # 5. parse children that should be pased after this layer.

python/paddle/v2/layer.py

@@ -292,7 +292,8 @@ class RecurrentLayerInput(Layer):
         else:
             self.__parents__ = parent_layers.values()[0]
         self.__recurrent_name__ = recurrent_name
-        name = self.__parents__[index].name if index >= 0 else self.context_name()
+        name = self.__parents__[
+            index].name if index >= 0 else self.context_name()
         super(RecurrentLayerInput, self).__init__(
             name=name, parent_layers=parent_layers)
 
@@ -402,9 +403,7 @@ def recurrent_group(step, input, name=None):
     extra_input = None
     if len(non_static_inputs) == 0:
         extra_input = RecurrentLayerInput(
-            recurrent_name=name,
-            index=-1,
-            parent_layers={})
+            recurrent_name=name, index=-1, parent_layers={})
 
     def __real_step__(*args):
         rnn_input = list(args)

python/paddle/v2/layers/__init__.py

@@ -1 +1 @@
-import beam_search
+import beam_search

python/paddle/v2/layers/beam_search.py

@@ -63,6 +63,7 @@ class RecurrentLayerGroupSetGeneratorV2(Layer):
     def use_context_name(self):
         return True
 
+
 @wrap_name_default()
 def beam_search(step,
                 input,
@@ -75,9 +76,10 @@ def beam_search(step,
     if num_results_per_sample is None:
         num_results_per_sample = beam_size
     assert num_results_per_sample <= beam_size
-        # logger.warning("num_results_per_sample should be less than beam_size")
+    # logger.warning("num_results_per_sample should be less than beam_size")
 
-    if isinstance(input, paddle.layer.StaticInputV2) or isinstance(input, BaseGeneratedInputV2):
+    if isinstance(input, paddle.layer.StaticInputV2) or isinstance(
+            input, BaseGeneratedInputV2):
         input = [input]
 
     generated_input_index = -1
@@ -107,8 +109,8 @@ def beam_search(step,
 
         args = list(args)
         before_step_layer = gipt.before_real_step()
-        before_step_layer.append_child(layer=generator,
-                                       parent_names=[before_step_layer.name])
+        before_step_layer.append_child(
+            layer=generator, parent_names=[before_step_layer.name])
         args.insert(generated_input_index, before_step_layer)
 
         predict = gipt.after_real_step(step(*args))
@@ -125,8 +127,6 @@ def beam_search(step,
     #     name=name,
     #     is_generating=True)
     tmp = paddle.layer.recurrent_group(
-        step=__real_step__,
-        input=real_input,
-        name=name)
+        step=__real_step__, input=real_input, name=name)
 
     return tmp