diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index d41852c9d7f4f5..82ae908e2b10cc 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -822,7 +822,6 @@ class Model(object):
             nn.Tanh(),
             nn.Linear(200, 10))
 
-        # inputs and labels are not required for dynamic graph.
         input = InputSpec([None, 784], 'float32', 'x')
         label = InputSpec([None, 1], 'int64', 'label')
         
@@ -970,6 +969,7 @@ def test_batch(self, inputs):
               import numpy as np
               import paddle
               import paddle.nn as nn
+              from paddle.static import InputSpec
 
               device = paddle.set_device('cpu') # or 'gpu'
               paddle.disable_static(device)
@@ -980,7 +980,9 @@ def test_batch(self, inputs):
                   nn.Linear(200, 10),
                   nn.Softmax())
 
-              model = paddle.Model(net)
+              input = InputSpec([None, 784], 'float32', 'x')
+              label = InputSpec([None, 1], 'int64', 'label')
+              model = paddle.Model(net, input, label)
               model.prepare()
               data = np.random.random(size=(4,784)).astype(np.float32)
               out = model.test_batch([data])
@@ -1092,15 +1094,20 @@ def load(self, path, skip_mismatch=False, reset_optimizer=False):
             
               import paddle
               import paddle.nn as nn
+              from paddle.static import InputSpec
               
               device = paddle.set_device('cpu')
               paddle.disable_static(device)
 
+              input = InputSpec([None, 784], 'float32', 'x')
+              label = InputSpec([None, 1], 'int64', 'label')
               model = paddle.Model(nn.Sequential(
                   nn.Linear(784, 200),
                   nn.Tanh(),
                   nn.Linear(200, 10),
-                  nn.Softmax()))
+                  nn.Softmax()),
+                  input,
+                  label)
               model.save('checkpoint/test')
               model.load('checkpoint/test')
         """
@@ -1165,13 +1172,18 @@ def parameters(self, *args, **kwargs):
 
               import paddle
               import paddle.nn as nn
+              from paddle.static import InputSpec
 
               paddle.disable_static()
 
+              input = InputSpec([None, 784], 'float32', 'x')
+              label = InputSpec([None, 1], 'int64', 'label')
               model = paddle.Model(nn.Sequential(
                   nn.Linear(784, 200),
                   nn.Tanh(),
-                  nn.Linear(200, 10)))
+                  nn.Linear(200, 10)),
+                  input,
+                  label)
               params = model.parameters()
         """
         return self._adapter.parameters()
@@ -1483,7 +1495,7 @@ def evaluate(
 
             # imperative mode
             paddle.disable_static()
-            model = paddle.Model(paddle.vision.models.LeNet())
+            model = paddle.Model(paddle.vision.models.LeNet(), input, label)
             model.prepare(metrics=paddle.metric.Accuracy())
             result = model.evaluate(val_dataset, batch_size=64)
             print(result)
@@ -1591,7 +1603,7 @@ def __len__(self):
             # imperative mode
             device = paddle.set_device('cpu')
             paddle.disable_static(device)
-            model = paddle.Model(paddle.vision.models.LeNet())
+            model = paddle.Model(paddle.vision.models.LeNet(), input)
             model.prepare()
             result = model.predict(test_dataset, batch_size=64)
             print(len(result[0]), result[0][0].shape)