def METHOD_NAME(self):
    tf_dataset = load(self.test_data_path)
    # Extract records into a list.
    dataset = list(tf_dataset)
    self.assertEqual(len(dataset), 1)
    lidar_tensors = next(iter(dataset))
    num_boxes = lidar_tensors["label_box"].shape[0]
    self.assertEqual(num_boxes, 16)
    self.assertNotEqual(lidar_tensors["frame_id"], 0)
    self.assertNotEqual(lidar_tensors["timestamp_micros"], 0)
    self.assertEqual(lidar_tensors["timestamp_offset"], 0)
    self.assertGreater(lidar_tensors["timestamp_micros"], 0)
    self.assertAllEqual(
        lidar_tensors["label_box_detection_difficulty"],
        np.zeros(num_boxes, dtype="int32"),
    )
    # Laser points.
    point_xyz_mean = tf.reduce_mean(lidar_tensors["point_xyz"], axis=0)
    self.assertAllClose(
        point_xyz_mean, lidar_tensors["pose"][:3, 3], atol=100
    )
    point_feature_mean = tf.reduce_mean(
        lidar_tensors["point_feature"], axis=0
    )
    self.assertAllGreater(point_feature_mean[0], 0)
    self.assertAllGreater(tf.abs(point_feature_mean[1]), 1e-6)
    self.assertAllGreater(point_feature_mean[2:4], 0)
    self.assertTrue(tf.math.reduce_all(lidar_tensors["point_mask"]))
    # Laser labels.
    self.assertEqual(lidar_tensors["label_box_id"].shape[0], num_boxes)
    self.assertEqual(lidar_tensors["label_box_meta"].shape[0], num_boxes)
    self.assertEqual(lidar_tensors["label_box_class"].shape[0], num_boxes)
    self.assertEqual(lidar_tensors["label_box_density"].shape[0], num_boxes)
    self.assertTrue(tf.math.reduce_all(lidar_tensors["label_box_mask"]))
    self.assertAllGreater(
        tf.math.reduce_max(lidar_tensors["label_point_class"]), 0
    )
    # Multi-frame tensors for augmentation.
    augmented_example = next(
        iter(tf_dataset.map(transformer.build_tensors_for_augmentation))
    )
    self.assertEqual(augmented_example["point_clouds"].shape, [183142, 8])
    self.assertEqual(augmented_example["bounding_boxes"].shape, [16, 11])