stream!!.receiveHeaders(headerBlock.toHeaders(), inFinished)
    }

    override fun rstStream(
      streamId: Int,
      errorCode: ErrorCode,
    ) {
      if (pushedStream(streamId)) {
        pushResetLater(streamId, errorCode)
        return
      }
      val rstStream = removeStream(streamId)
      rstStream?.receiveRstStream(errorCode)
    }

    override fun settings(

  }

  /**
   * 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) {

  }

  /**
   * 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) {

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 {

    }

    /**
     * Sets whether streaming is enabled.
     *
     * @param stream true to enable streaming
     * @return this request for method chaining
     */
    public LlmChatRequest setStream(final boolean stream) {
        this.stream = stream;
        return this;
    }

    /**
     * Gets the thinking budget for models that support thinking tokens.

   * overkill when nearly all Streams are produced using well-tested JDK calls. So, we cheat and
   * just test that the toArray() contents are as expected.
   */
  public void testStream() {
    assertThat(FluentIterable.of().stream()).isEmpty();
    assertThat(FluentIterable.of("a").stream()).containsExactly("a");
    assertThat(FluentIterable.of(1, 2, 3).stream().filter(n -> n > 1)).containsExactly(2, 3);

 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are
 *       noted in the method descriptions below.
 *   <li>Streams include primitive-specialized variants such as {@code IntStream}, the use of which
 *       is strongly recommended.
 *   <li>Streams are standard Java, not requiring a third-party dependency (but requiring <a

    // 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]

 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are
 *       noted in the method descriptions below.
 *   <li>Streams include primitive-specialized variants such as {@code IntStream}, the use of which
 *       is strongly recommended.
 *   <li>Streams are standard Java, not requiring a third-party dependency.
 * </ul>
 *
 * <h3>Example</h3>
 *

* 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).