diff --git a/docs/guides/custom_op/new_cpp_op_cn.md b/docs/guides/custom_op/new_cpp_op_cn.md
index a75666c2251..4ae535d4dc5 100644
--- a/docs/guides/custom_op/new_cpp_op_cn.md
+++ b/docs/guides/custom_op/new_cpp_op_cn.md
@@ -1680,28 +1680,32 @@ paddle.jit.save(net, path,
 import numpy as np
 
 import paddle
-import paddle.nn as nn
-import paddle.static as static
-from paddle.vision.transforms import Compose, Normalize
+from paddle import nn
+from paddle import static
 from paddle.utils.cpp_extension import load
+from paddle.vision.transforms import Compose, Normalize
 
 EPOCH_NUM = 4
 BATCH_SIZE = 64
 
 # jit compile custom op
 custom_ops = load(
-    name="custom_jit_ops",
-    sources=["relu_cuda.cc", "relu_cuda.cu"])
+    name="custom_jit_ops", sources=["relu_cuda.cc", "relu_cuda.cu"]
+)
 
 
 class LeNet(nn.Layer):
     def __init__(self):
         super().__init__()
-        self.conv1 = nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
-        self.max_pool1 = nn.MaxPool2D(kernel_size=2,  stride=2)
-        self.conv2 = nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1)
+        self.conv1 = nn.Conv2D(
+            in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2
+        )
+        self.max_pool1 = nn.MaxPool2D(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2D(
+            in_channels=6, out_channels=16, kernel_size=5, stride=1
+        )
         self.max_pool2 = nn.MaxPool2D(kernel_size=2, stride=2)
-        self.linear1 = nn.Linear(in_features=16*5*5, out_features=120)
+        self.linear1 = nn.Linear(in_features=16 * 5 * 5, out_features=120)
         self.linear2 = nn.Linear(in_features=120, out_features=84)
         self.linear3 = nn.Linear(in_features=84, out_features=10)
 
@@ -1726,7 +1730,7 @@ paddle.enable_static()
 paddle.set_device("gpu")
 
 # model
-image  = static.data(shape=[None, 1, 28, 28], name='image', dtype='float32')
+image = static.data(shape=[None, 1, 28, 28], name='image', dtype='float32')
 label = static.data(shape=[None, 1], name='label', dtype='int64')
 
 net = LeNet()
@@ -1737,36 +1741,37 @@ opt = paddle.optimizer.Adam(learning_rate=0.001)
 opt.minimize(loss)
 
 # data loader
-transform = Compose([Normalize(mean=[127.5],
-                               std=[127.5],
-                               data_format='CHW')])
+transform = Compose([Normalize(mean=[127.5], std=[127.5], data_format='CHW')])
 train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
-train_loader = paddle.io.DataLoader(train_dataset,
+train_loader = paddle.io.DataLoader(
+    train_dataset,
     feed_list=[image, label],
     batch_size=BATCH_SIZE,
     shuffle=True,
     drop_last=True,
-    num_workers=2)
+    num_workers=2,
+)
 
 # prepare
 exe = static.Executor()
 exe.run(static.default_startup_program())
 
-places = paddle.static.cuda_places()
-compiled_program = static.CompiledProgram(
-    static.default_main_program()).with_data_parallel(
-        loss_name=loss.name, places=places)
+compiled_program = static.CompiledProgram(static.default_main_program())
 
 # train
 for epoch_id in range(EPOCH_NUM):
     for batch_id, (image_data, label_data) in enumerate(train_loader()):
-        loss_data = exe.run(compiled_program,
-            feed={'image': image_data,
-                  'label': label_data},
-            fetch_list=[loss])
+        loss_data = exe.run(
+            compiled_program,
+            feed={'image': image_data, 'label': label_data},
+            fetch_list=[loss],
+        )
         if batch_id % 300 == 0:
-            print("Epoch {} batch {}: loss = {}".format(
-                    epoch_id, batch_id, np.mean(loss_data)))
+            print(
+                "Epoch {} batch {}: loss = {}".format(
+                    epoch_id, batch_id, np.mean(loss_data)
+                )
+            )
 
 # save inference model
 path = "custom_relu_test_static/net"