format fluid example.

fluid/image_classification/se_resnext.py

 import os
+
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import reader
@@ -21,10 +22,12 @@ def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
 def squeeze_excitation(input, num_channels, reduction_ratio):
     pool = fluid.layers.pool2d(
         input=input, pool_size=0, pool_type='avg', global_pooling=True)
-    squeeze = fluid.layers.fc(
-        input=pool, size=num_channels / reduction_ratio, act='relu')
-    excitation = fluid.layers.fc(
-        input=squeeze, size=num_channels, act='sigmoid')
+    squeeze = fluid.layers.fc(input=pool,
+                              size=num_channels / reduction_ratio,
+                              act='relu')
+    excitation = fluid.layers.fc(input=squeeze,
+                                 size=num_channels,
+                                 act='sigmoid')
     scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
     return scale
 
@@ -129,8 +132,7 @@ def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
     for pass_id in range(num_passes):
         accuracy.reset(exe)
         for batch_id, data in enumerate(train_reader()):
-            loss, acc = exe.run(
-                fluid.default_main_program(),
+            loss, acc = exe.run(fluid.default_main_program(),
                                 feed=feeder.feed(data),
                                 fetch_list=[avg_cost] + accuracy.metrics)
             print("Pass {0}, batch {1}, loss {2}, acc {3}".format(
@@ -139,8 +141,7 @@ def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
 
         test_accuracy.reset(exe)
         for data in test_reader():
-            out, acc = exe.run(
-                inference_program,
+            out, acc = exe.run(inference_program,
                                feed=feeder.feed(data),
                                fetch_list=[avg_cost] + test_accuracy.metrics)
         test_pass_acc = test_accuracy.eval(exe)