fix code style

fluid/image_classification/caffe2fluid/kaffe/layers.py

@@ -91,6 +91,8 @@ LayerType = type('LayerType', (), {t: t for t in LAYER_TYPES})
 
 #map the layer name in V1 to standard name
 V1_LAYER_MAP = {'_not_init_': True}
+
+
 def get_v1_layer_map():
     global V1_LAYER_MAP
     if '_not_init_' not in V1_LAYER_MAP:

fluid/image_classification/caffe2fluid/kaffe/paddle/network.py

@@ -285,8 +285,5 @@ class Network(object):
     def dropout(self, input, drop_prob, name, is_test=True):
         fluid = import_fluid()
         output = fluid.layers.dropout(
-                input,
-                dropout_prob=drop_prob,
-                is_test=is_test,
-                name=name)
+            input, dropout_prob=drop_prob, is_test=is_test, name=name)
         return output

fluid/image_classification/caffe2fluid/kaffe/paddle/transformer.py

@@ -132,8 +132,7 @@ class TensorFlowMapper(NodeMapper):
         # just scales by alpha (as does Krizhevsky's paper).
         # We'll account for that here.
         alpha = params.alpha / float(params.local_size)
-        return TensorFlowNode('lrn',
-                              params.local_size, alpha, params.beta)
+        return TensorFlowNode('lrn', params.local_size, alpha, params.beta)
 
     def map_concat(self, node):
         return TensorFlowNode('concat', node.parameters.axis)