import tensorflow as tf

    # First convolutional layer - maps one image to 32 feature maps.
    with tf.variable_scope('Conv_1'):
        conv1 = tf.layers.conv2d(
            inputs=x_image,
            filters=32,
            kernel_size=[5,5],
            padding='same',
            use_bias=False,
            name='conv1'
        )
        conv1_bn = tf.nn.relu(tf.layers.batch_normalization(conv1, training=train))
        pool1 = tf.layers.max_pooling2d(
            inputs=conv1_bn,
            pool_size=[2, 2],
            strides=2,
            name='pool1'
        )

        conv2 = tf.layers.conv2d(
            inputs=pool1,
