}

  /**
   * Returns an {@link IntStream} containing the elements of the first stream, followed by the
   * elements of the second stream, and so on.
   *
   * <p>This is equivalent to {@code Stream.of(streams).flatMapToInt(stream -> stream)}, but the
   * returned stream may perform better.
   *
   * @see IntStream#concat(IntStream, IntStream)
   */
  public static IntStream concat(IntStream... streams) {

 * <Integer>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code IntStream} features (like {@link IntStream#sum}) using {@code stream()}
 *       instead of the awkward {@code stream().mapToInt(v -> v)}.
 * </ul>
 *
 * <p>Disadvantages compared to {@code ImmutableList<Integer>}:
 *
 * <ul>

	var buf bytes.Buffer
	e := json.NewEncoder(&buf)
	d := jstream.NewDecoder(r, 0).ObjectAsKVS().MaxDepth(10)
	sset := set.NewStringSet()
	for mv := range d.Stream() {
		if mv.ValueType == jstream.Object {
			// This is a JSON object type (that preserves key order)
			kvs, ok := mv.Value.(jstream.KVS)
			if ok {
				for _, kv := range kvs {
					if sset.Contains(kv.Key) {

import java.util.List;
import java.util.Queue;
import java.util.concurrent.ConcurrentLinkedQueue;
import java.util.concurrent.ExecutionException;
import java.util.function.IntConsumer;
import java.util.stream.IntStream;
import junit.framework.TestCase;
import org.jspecify.annotations.NullUnmarked;

/** Test Java8 map.compute in concurrent cache context. */
@NullUnmarked
public class LocalCacheMapComputeTest extends TestCase {

    })
```

Sample call:
```go
    // Get a connection to the remote host
    conn := manager.Connection(host).Subroute("asubroute")

    payload := []byte("request")
    stream, err := conn.NewStream(ctx, grid.HandlerDiskInfo, payload)
	if err != nil {
        return err
    }
    // Read results from the stream
    err = stream.Results(func(result []byte) error {

        }

        try {
            final PrintStream originalOutStream = System.out;
            final ByteArrayOutputStream outStream = new ByteArrayOutputStream();
            System.setOut(new PrintStream(outStream, true));
            final PrintStream originalErrStream = System.err;
            final ByteArrayOutputStream errStream = new ByteArrayOutputStream();

    AtomicLong count = new AtomicLong(0);
    ImmutableLongArray.of(0, 1, 2, 3)
        .forEach(i -> assertThat(i).isEqualTo(count.getAndIncrement()));
    assertThat(count.get()).isEqualTo(4);
  }

  public void testStream() {
    ImmutableLongArray.of().stream().forEach(i -> fail());
    ImmutableLongArray.of(0, 1, 3).subArray(1, 1).stream().forEach(i -> fail());

    // There are several well-known algorithms for rotating part of an array (or, equivalently,
    // exchanging two blocks of memory). This classic text by Gries and Mills mentions several:
    // https://ecommons.cornell.edu/bitstream/handle/1813/6292/81-452.pdf.
    // (1) "Reversal", the one we have here.
    // (2) "Dolphin". If we're rotating an array a of size n by a distance of d, then element a[0]

* Add external link [Apache Kafka producer and consumer with FastAPI and aiokafka](https://iwpnd.pw/articles/2020-03/apache-kafka-fastapi-geostream) to docs. PR [#1112](https://github.com/tiangolo/fastapi/pull/1112) by [@iwpnd](https://github.com/iwpnd).