diff --git a/python/nano/tutorial/inference/tensorflow/cifar10.py b/python/nano/tutorial/inference/tensorflow/cifar10.py
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+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import tensorflow as tf
+from tensorflow import keras
+from keras import layers
+from keras import losses, metrics
+
+# keras.Model is injected with customized functions
+from bigdl.nano.tf.keras import Model
+
+# Model / data parameters
+num_classes = 10
+input_shape = (32, 32, 3)
+
+# Load the data and split it between train and test sets
+(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
+
+# Scale images to the [0, 1] range
+x_train = x_train.astype("float32") / 255
+x_test = x_test.astype("float32") / 255
+print("x_train shape:", x_train.shape)
+print(x_train.shape[0], "train samples")
+print(x_test.shape[0], "test samples")
+
+
+# convert class vectors to binary class matrices
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+
+def creat_model():
+    model = keras.Sequential()
+
+    model.add(layers.Conv2D(32, (3, 3), padding='same',
+              activation='relu', input_shape=(32, 32, 3)))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Conv2D(32, (3, 3), padding='same', activation='relu'))
+    model.add(layers.BatchNormalization())
+    model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(layers.Dropout(0.3))
+
+    model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))
+    model.add(layers.BatchNormalization())
+    model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(layers.Dropout(0.5))
+
+    model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
+    model.add(layers.BatchNormalization())
+    model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(layers.Dropout(0.5))
+
+    model.add(layers.Flatten())
+    model.add(layers.Dense(128, activation='relu'))
+    model.add(layers.BatchNormalization())
+    model.add(layers.Dropout(0.5))
+    model.add(layers.Dense(num_classes, activation='softmax'))
+    return model
+
+
+if __name__ == "__main__":
+
+    model = creat_model()
+
+    # this line is optional
+    # model = Model(inputs=model.inputs, outputs=model.outputs)
+
+    model.summary()
+
+    batch_size = 128
+    epochs = 20
+
+    model.compile(loss="categorical_crossentropy",
+                  optimizer="adam", metrics=["accuracy"])
+
+    model.fit(x_train, y_train, batch_size=batch_size,
+              epochs=epochs, validation_split=0.1)
+
+    score = model.evaluate(x_test, y_test, verbose=0)
+    print("Test loss:", score[0])
+    print("Test accuracy:", score[1])
+
+    # Quantization using Intel Neural Compressor
+    # pip install 'neural-compressor'
+
+    # calib_dataset only accept tf.data.Dataset object
+    tune_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
+
+    # Execute quantization
+    q_model = model.quantize(calib_dataset=tune_dataset)
+
+    # Inference using quantized model
+    y_test_hat = q_model(x_test)
+
+    # Evaluate the quantized model
+    loss = float(tf.reduce_mean(
+        losses.categorical_crossentropy(y_test, y_test_hat)))
+    categorical_accuracy = metrics.CategoricalAccuracy()
+    categorical_accuracy.update_state(y_test, y_test_hat)
+    accuracy = categorical_accuracy.result().numpy()
+
+    print("Quantization test loss:", loss)
+    print("Quantization test accuracy:", accuracy)
+    # Note: accuracy decrease varies from different tasks and situations,
+    # and sometimes the accuracy is even higher.
+    # You can set a quantization threshold when making a quantization model.