class ResidualBlock(tf.keras.layers.Layer):
    def __init__(self, filters, kernel_size=3, strides=1, downsample=False):
        super(ResidualBlock, self).__init__()
        self.downsample = downsample
        self.conv1 = tf.keras.layers.Conv2D(filters, kernel_size, strides=strides, padding="same",kernel_regularizer=tf.keras.regularizers.L2(0.001))
        self.bn1 = tf.keras.layers.BatchNormalization()
        self.relu = tf.keras.layers.ReLU()
        self.conv2 = tf.keras.layers.Conv2D(filters, kernel_size, strides=1, padding="same",kernel_regularizer=tf.keras.regularizers.L2(0.001))
        self.bn2 = tf.keras.layers.BatchNormalization()

        if downsample:
            self.downsample_conv = tf.keras.layers.Conv2D(filters, 1, strides=strides,kernel_regularizer=tf.keras.regularizers.L2(0.001))
            self.downsample_bn = tf.keras.layers.BatchNormalization()

    def call(self, inputs, training=False):
        residual = inputs
        x = self.conv1(inputs)
        x = self.bn1(x, training=training)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x, training=training)

        if self.downsample:
            residual = self.downsample_conv(inputs)
            residual = self.downsample_bn(residual, training=training)
        x += residual
        return self.relu(x)

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