def test_add(self):
        prio_set_list = event._PrioritizedSetList()
        objs = [(i,) for i in range(5)]

        prio_set_list.add(0, objs[0])
        assert prio_set_list.list == [(0, {objs[0]})]

        prio_set_list.add(0, objs[1])
        assert prio_set_list.list == [(0, {objs[0], objs[1]})]

        prio_set_list.add(10, objs[2])
        assert prio_set_list.list == [(10, {objs[2]}),
                                      (0,  {objs[0], objs[1]})]

        prio_set_list.add(-10, objs[3])
        assert prio_set_list.list == [( 10, {objs[2]}),           # noqa: E201
                                      (  0, {objs[0], objs[1]}),  # noqa: E201
                                      (-10, {objs[3]})]
        prio_set_list.add(-1, objs[4])
        assert prio_set_list.list == [( 10, {objs[2]}),           # noqa: E201
                                      (  0, {objs[0], objs[1]}),  # noqa: E201
                                      ( -1, {objs[4]}),           # noqa: E201
                                      (-10, {objs[3]})]
def batch_norm(inputs, training, data_format, name=''):
  """Performs a batch normalization using a standard set of parameters."""
  # We set fused=True for a significant performance boost. See
  # https://www.tensorflow.org/performance/performance_guide#common_fused_ops
  return tf.compat.v1.layers.batch_normalization(
      inputs=inputs,
      axis=1 if data_format == 'channels_first' else 3,
      momentum=_BATCH_NORM_DECAY,
      epsilon=_BATCH_NORM_EPSILON,
      center=True,
      scale=True,
      training=training,
      fused=True,
      name=name)