sub := &Sub[T]{ch: subCh, types: Mask(mask.Mask()), filter: filter}
	ps.subs = append(ps.subs, sub)

	// We hold a lock, so we are safe to update
	combined := Mask(atomic.LoadUint64(&ps.types))
	combined.Merge(Mask(mask.Mask()))
	atomic.StoreUint64(&ps.types, uint64(combined))

	go func() {
		<-doneCh

		ps.Lock()
		defer ps.Unlock()
		var remainTypes Mask
		for i, s := range ps.subs {
			if s == sub {

		},
		cli.BoolFlag{
			Usage: "combine inline data",
			Name:  "combine",
		},
		cli.BoolFlag{
			Usage: "combine inline data across versions when size matches",
			Name:  "xver",
		},
	}

	app.Action = func(c *cli.Context) error {
		ndjson := c.Bool("ndjson")
		if c.Bool("data") && c.Bool("combine") {
			return errors.New("cannot combine --data and --combine")
		}
		// file / version / file

// qProp contains analysis info about an SQL term.
type qProp struct {
	isAggregation, isRowFunc bool

	err error
}

// `combine` combines a pair of `qProp`s, so that errors are
// propagated correctly, and checks that an aggregation is not being
// combined with a row-function term.
func (p *qProp) combine(q qProp) {
	switch {
	case p.err != nil:
		// Do nothing
	case q.err != nil:
		p.err = q.err
	default:

It will all be combined and included in your OpenAPI, and shown in the API docs:

<img src="/img/tutorial/additional-responses/image01.png">

## Combine predefined responses and custom ones

You might want to have some predefined responses that apply to many *path operations*, but you want to combine them with custom responses needed by each *path operation*.

    byte[] murmur3Hash = Hashing.murmur3_32().hashLong(42L).asBytes();
    byte[] combined = new byte[md5Hash.length + murmur3Hash.length];
    ByteBuffer buffer = ByteBuffer.wrap(combined);
    buffer.put(md5Hash);
    buffer.put(murmur3Hash);
    HashCode expected = HashCode.fromBytes(combined);

    assertEquals(
        expected, Hashing.concatenating(Hashing.md5(), Hashing.murmur3_32()).hashLong(42L));

   */
  // TODO(user): Evaluate whether it makes sense to always piggyback the computation of a
  // HashCode on an existing OutputStream, compared to creating a separate OutputStream that could
  // be (optionally) be combined with another if needed (with something like
  // MultiplexingOutputStream).
  public HashingOutputStream(HashFunction hashFunction, OutputStream out) {
    super(checkNotNull(out));

"Contributor" means any person or entity that Distributes the Program.

"Licensed Patents" mean patent claims licensable by a Contributor which
are necessarily infringed by the use or sale of its Contribution alone
or when combined with the Program.

"Program" means the Contributions Distributed in accordance with this
Agreement.

"Recipient" means anyone who receives the Program under this Agreement

// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,
// inputs and outputs and the gradient function These data structures combined
// allow us to trace the data dependencies between operations and hence compute
// gradients.
//
// `ZerosLike` is not expected to be called and returns a nullptr. The creation

            return propertyValue;
        }

        /**
         * Checks if the passed in line should be combined with the following.
         * This is true, if the line ends with an odd number of backslashes.
         *
         * @param line the line
         * @return a flag if the lines should be combined
         */
        private static boolean checkCombineLines(String line) {
            int bsCount = 0;

  /**
   * All node connections in this graph, in edge insertion order.
   *
   * <p>Note: This field and {@link #adjacentNodeValues} cannot be combined into a single
   * LinkedHashMap because one target node may be mapped to both a predecessor and a successor. A
   * LinkedHashMap combines two such edges into a single node-value pair, even though the edges may
   * not have been inserted consecutively.
   */