//  [python]
def genbaseline(sess, kvs, data, latents, save_exp_dir, frac=0.0):
    # tf.reset_default_graph()

    if FLAGS.joint_shape:
        model_baseline = DspritesNetGen(num_filters=FLAGS.num_filters, label_size=5)
        LABEL = tf.placeholder(shape=(None, 5), dtype=tf.float32)
    else:
        model_baseline = DspritesNetGen(num_filters=FLAGS.num_filters, label_size=3)
        LABEL = tf.placeholder(shape=(None, 3), dtype=tf.float32)

    weights_baseline = model_baseline.construct_weights('context_baseline_{}'.format(frac))

    X_feed = tf.placeholder(shape=(None, 2*FLAGS.num_filters), dtype=tf.float32)
    X_label = tf.placeholder(shape=(None, 64, 64), dtype=tf.float32)

    X_out = model_baseline.forward(X_feed, weights_baseline, label=LABEL)
    loss_sq = tf.reduce_mean(tf.square(X_out - X_label))

    optimizer = AdamOptimizer(1e-3)
    gvs = optimizer.compute_gradients(loss_sq)
    gvs = [(k, v) for (k, v) in gvs if k is not None]
    train_op = optimizer.apply_gradients(gvs)

    dataloader = DataLoader(DSpritesGen(data, latents, frac=frac), batch_size=FLAGS.batch_size, num_workers=6, drop_last=True, shuffle=True)

    datafull = data

    itr = 0
    saver = tf.train.Saver()

    vs = optimizer.variables()
    sess.run(tf.global_variables_initializer())

    if FLAGS.train:
        for _ in range(5):
            for data_corrupt, data, label_size, label_pos in tqdm(dataloader):

                data_corrupt = data_corrupt.numpy()
                label_size, label_pos = label_size.numpy(), label_pos.numpy()

                data_corrupt = np.random.randn(data_corrupt.shape[0], 2*FLAGS.num_filters)
                label_comb = np.concatenate([label_size, label_pos], axis=1)

                feed_dict = {X_feed: data_corrupt, X_label: data, LABEL: label_comb}

                output = [loss_sq, train_op]

                loss, _ = sess.run(output, feed_dict=feed_dict)

                itr += 1

        saver.save(sess, osp.join(save_exp_dir, 'model_genbaseline'))

    saver.restore(sess, osp.join(save_exp_dir, 'model_genbaseline'))

    l = latents

    if FLAGS.joint_shape:
        mask_gen = (l[:, 3] == 30 * np.pi / 39) * (l[:, 2] == 0.5)
    else:
        mask_gen = (l[:, 3] == 30 * np.pi / 39) * (l[:, 1] == 1) & (~((l[:, 2] == 0.5) | ((l[:, 4] == 16/31) & (l[:, 5] == 16/31))))

    data_gen = datafull[mask_gen]
    latents_gen = latents[mask_gen]
    losses = []

    for dat, latent in zip(np.array_split(data_gen, 10), np.array_split(latents_gen, 10)):
        data_init = np.random.randn(dat.shape[0], 2*FLAGS.num_filters)

        if FLAGS.joint_shape:
            latent_size = np.eye(3)[latent[:, 1].astype(np.int32) - 1]
            latent_pos = latent[:, 4:6]
            latent = np.concatenate([latent_size, latent_pos], axis=1)
            feed_dict = {X_feed: data_init, LABEL: latent, X_label: dat}
        else:
            feed_dict = {X_feed: data_init, LABEL: latent[:, [2,4,5]], X_label: dat}
        loss = sess.run([loss_sq], feed_dict=feed_dict)[0]
        # print(loss)
        losses.append(loss)

    print("Overall MSE for generalization of {} for fraction of {}".format(np.mean(losses), frac))


    data_try = data_gen[:10]
    data_init = np.random.randn(10, 2*FLAGS.num_filters)

    if FLAGS.joint_shape:
        latent_scale = np.eye(3)[latent[:10, 1].astype(np.int32) - 1]
        latent_pos = latents_gen[:10, 4:]
    else:
        latent_scale = latents_gen[:10, 2:3]
        latent_pos = latents_gen[:10, 4:]

    latent_tot = np.concatenate([latent_scale, latent_pos], axis=1)

    feed_dict = {X_feed: data_init, LABEL: latent_tot}
    x_output = sess.run([X_out], feed_dict=feed_dict)[0]
    x_output = np.clip(x_output, 0, 1)

    im_name = "size_scale_combine_genbaseline.png"

    x_output_wrap = np.ones((10, 66, 66))
    data_try_wrap = np.ones((10, 66, 66))

    x_output_wrap[:, 1:-1, 1:-1] = x_output
    data_try_wrap[:, 1:-1, 1:-1] = data_try

    im_output = np.concatenate([x_output_wrap, data_try_wrap], axis=2).reshape(-1, 66*2)
    impath = osp.join(save_exp_dir, im_name)
    imsave(impath, im_output)
    print("Successfully saved images at {}".format(impath))

    return np.mean(losses)