import tensorflow as tf

        for pt in [pt_0, pt_1, pt_2, pt_3]:
          if pt[0] < box_limits_x[0]:
            box_limits_x[0] = pt[0]

          if pt[0] > box_limits_x[1]:
            box_limits_x[1] = pt[0]

          if pt[1] < box_limits_z[0]:
            box_limits_z[0] = pt[1]

          if pt[1] > box_limits_z[1]:
            box_limits_z[1] = pt[1]
      mean_x = tf.reduce_mean(box_limits_x)
      mean_z = tf.reduce_mean(box_limits_z)
    else:
      mean_x = tf.reduce_mean(labeled_translations[:, 0])
      mean_z = tf.reduce_mean(labeled_translations[:, 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 = grid.generate(
    #     (box_limits_x[0][0], box_limits_y[0], box_limits_z[0][0]),
    #     (box_limits_x[1][0], box_limits_y[1], box_limits_z[1][0]),
    #     [self.resolution, self.resolution, self.resolution])
    # samples_world = grid.generate(
    #     (-5.0, -5.0, -5.0),
    #     (5.0, 5.0, 5.0),
    #     [self.resolution, self.resolution, self.resolution])
    samples_world = tf.reshape(samples_world, [-1, 3])
    ious = []
