import tensorflow as tf

    # 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 = []

    status = False
    if status:
      _, axs = plt.subplots(labeled_translations.shape[0], 5)
      fig_obj_count = 0
    for class_id in range(self.max_num_classes):
      # Do the same for the ground truth and predictions
      sdf_values = tf.zeros_like(samples_world)[:, 0:1]
      for mtype, (classes, sdfs, poses) in enumerate([
          (labeled_classes, labeled_sdfs, labeled_poses),
          (predicted_classes, predicted_sdfs, predicted_poses)]):
        for i in range(classes.shape[0]):
          if class_id == classes[i]:
            sdf = tf.expand_dims(sdfs[i], -1)
            sdf = sdf * -1.0  # inside positive, outside zero
            samples_object = centernet_utils.transform_pointcloud(
                tf.reshape(samples_world, [1, 1, -1, 3]),
                tf.reshape(poses[2][i], [1, 1, 3]),
                tf.reshape(poses[0][i], [1, 1, 3, 3]),
                tf.reshape(poses[1][i], [1, 1, 3]), inverse=True) * 2.0
