fix code in save/load

doc/fluid/user_guides/howto/training/save_load_variables.rst

@@ -270,12 +270,12 @@ save_vars、save_params、save_persistables 以及 save_inference_model的区别
     pserver_endpoints = "127.0.0.1:1001,127.0.0.1:1002"
     trainers = 4
     training_role == "PSERVER"
+    current_endpoint = "127.0.0.1:1002"
     config = fluid.DistributeTranspilerConfig()
     t = fluid.DistributeTranspiler(config=config)
     t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers, sync_mode=True, current_endpoint=current_endpoint)
 
     if training_role == "PSERVER":
-        current_endpoint = "127.0.0.1:1001"
         pserver_prog = t.get_pserver_program(current_endpoint)
         pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
 
@@ -284,7 +284,7 @@ save_vars、save_params、save_persistables 以及 save_inference_model的区别
         exe.run(pserver_prog)
     if training_role == "TRAINER":
         main_program = t.get_trainer_program()
-                exe.run(main_program)
+        exe.run(main_program)
 
 上面的例子中，每个PServer通过调用HDFS的命令获取到0号trainer保存的参数，通过配置获取到PServer的 :code:`fluid.Program` ，PaddlePaddle Fluid会从此
 :code:`fluid.Program` 也就是 :code:`pserver_startup` 的所有模型变量中找出长期变量，并通过指定的 :code:`path` 目录下一一加载。

doc/fluid/user_guides/howto/training/save_load_variables_en.rst

@@ -179,23 +179,23 @@ For the PServer to be loaded with parameters during training, for example:
 
     exe = fluid.Executor(fluid.CPUPlace())
     path = "./models"
-	pserver_endpoints = "127.0.0.1:1001,127.0.0.1:1002"
-	trainers = 4
-	Training_role == "PSERVER"
-	config = fluid.DistributeTranspilerConfig()
-	t = fluid.DistributeTranspiler(config=config)
-	t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers, sync_mode=True)
-
-	if training_role == "PSERVER":
-		current_endpoint = "127.0.0.1:1001"
-		pserver_prog = t.get_pserver_program(current_endpoint)
-		pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
-
-		exe.run(pserver_startup)
-		fluid.io.load_persistables(exe, path, pserver_startup)
-		exe.run(pserver_prog)
-	if training_role == "TRAINER":
-		main_program = t.get_trainer_program()
-				exe.run(main_program)
+    pserver_endpoints = "127.0.0.1:1001,127.0.0.1:1002"
+    trainers = 4
+    Training_role == "PSERVER"
+    current_endpoint = "127.0.0.1:1002"
+    config = fluid.DistributeTranspilerConfig()
+    t = fluid.DistributeTranspiler(config=config)
+    t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers, sync_mode=True, current_endpoint=current_endpoint)
+    
+    if training_role == "PSERVER":
+        pserver_prog = t.get_pserver_program(current_endpoint)
+        pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
+    
+        exe.run(pserver_startup)
+        fluid.io.load_persistables(exe, path, pserver_startup)
+        exe.run(pserver_prog)
+    if training_role == "TRAINER":
+        main_program = t.get_trainer_program()
+        exe.run(main_program)
 
 In the above example, each PServer obtains the parameters saved by trainer 0 by calling the HDFS command, and obtains the PServer's :code:`fluid.Program` by configuration. PaddlePaddle Fluid will find all persistable variables in all model variables from this :code:`fluid.Program` , e.t. :code:`pserver_startup` , and load them from the specified :code:`path` directory.