import tensorflow as tf


        conv2 = tf.layers.conv2d(pool1, filters=64*amp_factor, kernel_size=[5, 1],
                                 data_format='channels_last', padding= "same",
                                 strides=(2, 1),
                                 activation=tf.nn.relu)
        pool2 = conv2

        conv3 = tf.layers.conv2d(pool2, filters=128*amp_factor, kernel_size=[5, 1],
                                 data_format='channels_last', padding= "same",
                                 strides=(2, 1),
                                 activation=tf.nn.relu)
        pool3 = conv3

        conv4 = tf.layers.conv2d(pool3, filters=256*amp_factor, kernel_size=[5, 1],
                                 data_format='channels_last', padding= "same",
                                strides=(2, 1), 
                                activation=tf.nn.relu)
        pool4 = conv4

        conv5 = tf.layers.conv2d(pool4, filters=256*amp_factor, kernel_size=[5, 1],
                                 data_format='channels_last', padding= "same",
                                strides=(2, 1), 
                                activation=tf.nn.relu)
        pool5 = conv5        
        pool5 = tf.transpose(pool5, [0, 3, 1, 2])
        size = pool5.shape[-1] * pool5.shape[-2] * pool5.shape[-3]
