this.analyzer = analyzer;
        return this;
    }

    /**
     * Sets the thread pool.
     * @param threadPool The thread pool.
     * @return This builder instance.
     */
    public SuggesterBuilder threadPool(final ExecutorService threadPool) {
        this.threadPool = threadPool;
        return this;
    }

    /**
     * Sets the thread pool size.

     * @param threadPool The ExecutorService for thread pooling.
     */
    public Suggester(final Client client, final SuggestSettings settings, final ReadingConverter readingConverter,
            final ReadingConverter contentsReadingConverter, final Normalizer normalizer, final SuggestAnalyzer analyzer,
            final ExecutorService threadPool) {
        this.client = client;

     * @param settings The suggest settings.
     * @param threadPool The thread pool.
     */
    public SuggestIndexer(final Client client, final String index, final ReadingConverter readingConverter,
            final ReadingConverter contentsReadingConverter, final Normalizer normalizer, final SuggestAnalyzer analyzer,
            final SuggestSettings settings, final ExecutorService threadPool) {
        this.client = client;

        connected = false;
    }

    /**
     * Gets the thread pool from the underlying client.
     *
     * @return The thread pool.
     */
    @Override
    public ThreadPool threadPool() {
        return client.threadPool();
    }

    /**
     * Gets the admin client for cluster and index operations.
     *
     * @return The admin client.
     */
    @Override

  // TODO(b/113217601): This is needed for EagerContext::runner_ to use a
  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback

# config to build OneDNN backend with a user specified threadpool.
build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl_threadpool --define=build_with_mkl_opensource=true
build:mkl_threadpool -c opt

# Config setting to build oneDNN with Compute Library for the Arm Architecture (ACL).
# with Eigen threadpool support

 * Usage: groovy CheckBadMerge.groovy <commit1> <commit2> ...
 * If any "bad" merge commit is found, it will print the details and exit with non-zero code.
 */
class CheckBadMerge {
    private static final THREAD_POOL = Executors.newCachedThreadPool()

    private static final List<String> MONITORED_PATHS = [
        "subprojects/docs/src/docs/release/notes.md",
        "platforms/documentation/docs/src/docs/release/notes.md",

    * Add optional `use_unbounded_threadpool` argument to `map`, to specify that the `map` should use an unbounded threadpool instead of the default pool that is based on the number of cores on the machine. This can improve throughput for map functions which perform IO or otherwise release the CPU.

    "@zlib//:__subpackages__",
    "@dlpack//:__subpackages__",
    "@arm_neon_2_x86_sse//:__subpackages__",
    "@cpuinfo//:__subpackages__",
    "@ruy//:__subpackages__",
    "@XNNPACK//:__subpackages__",
    "@pthreadpool//:__subpackages__",
    "@FXdiv//:__subpackages__",
    "@FP16//:__subpackages__",
    "@clog//:__subpackages__",
    "@flatbuffers//:__subpackages__",
    "@nccl_archive//:__subpackages__",