import tensorflow as tf

    self.path = path
    self.tol = tol

  def update(self, labeled_sdfs, labeled_classes, labeled_poses,
             predicted_sdfs, predicted_classes, predicted_poses):
    """Update."""
    if labeled_sdfs or labeled_classes:
      print(labeled_sdfs)
    mean_x = tf.reduce_mean(labeled_poses[1][:, 0])
    mean_z = tf.reduce_mean(labeled_poses[1][:, 2])
    samples_world = grid.generate(
        (mean_x - 0.5, 0.0, mean_z - 0.5), (mean_x + 0.5, 1.0, mean_z + 0.5),
        [self.resolution, self.resolution, self.resolution])
    samples_world = tf.reshape(samples_world, [-1, 3])

    status = False
    if status:
      _, axs = plt.subplots(3, 3)
      fig_obj_count = 0

    # Do the same for the ground truth and predictions
    num_collisions = 0
    prev_intersection = 0
    sdf_values = tf.zeros_like(samples_world)[:, 0:1]
    for classes, sdfs, poses in [(predicted_classes,
                                  predicted_sdfs,
