import tensorflow as tf

    current_image_id_ph = tf.placeholder(tf.int32, [])
    progress = networks.compute_progress(
        current_image_id_ph,
        stable_stage_num_images,
        transition_stage_num_images,
        num_blocks=3)
    z = tf.random_normal([2, 10], dtype=tf.float32)
    x, _ = networks.generator(
        z, progress, _num_filters_stub,
        networks.ResolutionSchedule(
            start_resolutions=(4, 4), scale_base=2, num_resolutions=3))
    fake_loss = tf.reduce_sum(tf.square(x))
