ExecutorService threadPool = Executors.newFixedThreadPool(nThreads);
    final CountDownLatch tasksFinished = new CountDownLatch(nTasks);
    for (int i = 0; i < nTasks; i++) {
      final String s = "a" + i;
      @SuppressWarnings("unused") // https://errorprone.info/bugpattern/FutureReturnValueIgnored
      Future<?> possiblyIgnoredError =
          threadPool.submit(
              new Runnable() {

    protected SuggestWriter suggestWriter;

    protected ExecutorService threadPool;

    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;
        this.index = index;

#include "tensorflow/compiler/mlir/tfrt/transforms/passes.h"
#include "tensorflow/compiler/mlir/tfrt/utils/host_context.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/platform/threadpool.h"
#include "tensorflow/core/platform/threadpool_interface.h"
#include "tensorflow/core/runtime_fallback/kernel/kernel_fallback_execute_compat_eager.h"
#include "tensorflow/core/runtime_fallback/runtime/kernel_utils.h"

            }
            logger.info("Disconnected to {}", address);
        }
        connected = false;
    }

    @Override
    public ThreadPool threadPool() {
        return client.threadPool();
    }

    @Override
    public AdminClient admin() {
        return client.admin();
    }

    @Override

// clang-format off
#define CPP_CLASS {{TFCOMPILE_CPP_CLASS}}  // NOLINT(whitespace/braces)
// clang-format on

namespace tensorflow {
namespace tfcompile {

int Main(int argc, char** argv) {
  Eigen::ThreadPool pool(1 /* num_threads */);
  Eigen::ThreadPoolDevice device(&pool, pool.NumThreads());

  CPP_CLASS computation;
  computation.set_thread_pool(&device);

  benchmark::Options options;
  benchmark::Stats stats;

#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "xla/client/local_client.h"
#include "tensorflow/core/framework/metrics.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/thread_annotations.h"

namespace tensorflow {

// Compiles/lowers a given Tensorflow graph/function/cluster into a compiled XLA

On top of that, an async framework could run some sync code in a threadpool (using `asyncio.run_in_executor`), but belonging to the same request.

             const std::vector<Operation>& run_outputs,
             std::vector<Tensor>* outputs, RunMetadata* run_metadata) const;

  /// Same as above. Additionally allows user to provide custom threadpool
  /// implementation via ThreadPoolOptions.
  Status Run(const RunOptions& run_options, const FeedType& inputs,
             const std::vector<Output>& fetch_outputs,
             const std::vector<Operation>& run_outputs,

### Fonctions de chemin

Quand vous déclarez une *fonction de chemin* avec un `def` normal et non `async def`, elle est exécutée dans un groupe de threads (threadpool) externe qui est ensuite attendu, plutôt que d'être appelée directement (car cela bloquerait le serveur).

  // 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