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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Customizing the convolution operation of a Conv2D layer
Author
Step1:  A Simple StandardizedConv2D implementation
There are two ways to use the Conv.convolution_op() API. The first way
is to override the convolution_op() method on a convolution layer subclass.
Using this approach, we can quickly implement a
StandardizedConv2D as shown below.
Step2:  The other way to use the Conv.convolution_op() API is to directly call the
convolution_op() method from the call() method of a convolution layer subclass.
A comparable class implemented using this approach is shown below.
Step3:  Example Usage
Both of these layers work as drop-in replacements for Conv2D. The following
demonstration performs classification on the MNIST dataset.
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Python Code:
import tensorflow.keras as keras
print(keras.__version__)
Explanation: Customizing the convolution operation of a Conv2D layer
Author: lukewood<br>
Date created: 11/03/2021<br>
Last modified: 11/03/2021<br>
Description: This example shows how to implement custom convolution layers using the Conv.convolution_op() API.
Introduction
You may sometimes need to implement custom versions of convolution layers like Conv1D and Conv2D.
Keras enables you do this without implementing the entire layer from scratch: you can reuse
most of the base convolution layer and just customize the convolution op itself via the
convolution_op() method.
This method was introduced in Keras 2.7. So before using the
convolution_op() API, ensure that you are running Keras version 2.7.0 or greater.
End of explanation
import tensorflow as tf
import tensorflow.keras as keras
import keras.layers as layers
import numpy as np
class StandardizedConv2DWithOverride(layers.Conv2D):
    def convolution_op(self, inputs, kernel):
        mean, var = tf.nn.moments(kernel, axes=[0, 1, 2], keepdims=True)
        return tf.nn.conv2d(
            inputs,
            (kernel - mean) / tf.sqrt(var + 1e-10),
            padding="VALID",
            strides=list(self.strides),
            name=self.__class__.__name__,
        )
Explanation: A Simple StandardizedConv2D implementation
There are two ways to use the Conv.convolution_op() API. The first way
is to override the convolution_op() method on a convolution layer subclass.
Using this approach, we can quickly implement a
StandardizedConv2D as shown below.
End of explanation
class StandardizedConv2DWithCall(layers.Conv2D):
    def call(self, inputs):
        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        result = self.convolution_op(
            inputs, (self.kernel - mean) / tf.sqrt(var + 1e-10)
        )
        if self.use_bias:
            result = result + self.bias
        return result
Explanation: The other way to use the Conv.convolution_op() API is to directly call the
convolution_op() method from the call() method of a convolution layer subclass.
A comparable class implemented using this approach is shown below.
End of explanation
# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
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)
model = keras.Sequential(
    [
        keras.layers.InputLayer(input_shape=input_shape),
        StandardizedConv2DWithCall(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        StandardizedConv2DWithOverride(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation="softmax"),
    ]
)
model.summary()
batch_size = 128
epochs = 5
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=batch_size, epochs=5, validation_split=0.1)
Explanation: Example Usage
Both of these layers work as drop-in replacements for Conv2D. The following
demonstration performs classification on the MNIST dataset.
End of explanation
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