fix codestyle

doc/PIPELINE_SERVING.md

@@ -2,6 +2,7 @@
 
 ([简体中文](PIPELINE_SERVING_CN.md)|English)
 
+
 Paddle Serving is usually used for the deployment of single model, but the end-to-end deep learning model can not solve all the problems at present. Usually, it is necessary to use multiple deep learning models to solve practical problems.
 
 Paddle Serving provides a user-friendly programming framework for multi-model composite services, Pipeline Serving, which aims to reduce the threshold of programming, improve resource utilization (especially GPU), and improve the prediction efficiency.
@@ -404,4 +405,3 @@ Specific operation: open Chrome browser, input in the address bar `chrome://trac
 The profile function can be enabled by setting `profile=True` in the `predict` interface on the client side.
 
 After the function is enabled, the client will print the log information corresponding to the prediction to the standard output during the prediction process, and the subsequent analysis and processing are the same as that of the server.
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doc/PIPELINE_SERVING_CN.md

@@ -130,6 +130,7 @@ def postprocess(self, input_dicts, fetch_dict):
   return fetch_dict
 ```
 
+
 **preprocess** 的参数是前继 Channel 中的数据 `input_dicts`，该变量是一个以前继 OP 的 name 为 Key，对应 OP 的输出为 Value 的字典。
 
 **process** 的参数是 Paddle Serving Client 预测接口的输入变量 `fetch_dict`（preprocess 函数的返回值），该变量是一个以 feed_name 为 Key，对应 ndarray 格式的数据为 Value 的字典。
@@ -405,4 +406,3 @@ python timeline_trace.py profile trace
 Client 端在 `predict` 接口设置 `profile=True`，即可开启 Profile 功能。
 
 开启该功能后，Client 端在预测的过程中会将该次预测对应的日志信息打印到标准输出，后续分析处理同 Server。
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