logger.removeHandler(logHandler);
    }

    assertEquals(1, logHandler.getStoredLogRecords().size());
    assertThat(logHandler.getStoredLogRecords().get(0).getMessage())
        .isEqualTo("Exception while executing callback: MyListener custom-label");
  }

  public void testEnqueueAndDispatch_multithreaded() throws InterruptedException {
    Object listener = new Object();
    ExecutorService service = newFixedThreadPool(4);

      logger.removeHandler(logHandler);
    }

    assertEquals(1, logHandler.getStoredLogRecords().size());
    assertThat(logHandler.getStoredLogRecords().get(0).getMessage())
        .isEqualTo("Exception while executing callback: MyListener custom-label");
  }

  public void testEnqueueAndDispatch_multithreaded() throws InterruptedException {
    Object listener = new Object();
    ExecutorService service = newFixedThreadPool(4);

     *       thread cleared it, we still know that it's not associated with our thread
     *   <li>If this field's value == null because it was associated with our thread and was
     *       cleared, we know that we're not executing inline any more
     * </ul>
     *
     * All the states where thread != currentThread are identical for our purposes, and so even

        for (final String sid : sessionIdList) {
            // remove config
            ComponentUtil.getCrawlingConfigHelper().remove(sid);
        }

    }

    /**
     * Inner thread class for executing data store crawling operations.
     * Each thread handles crawling for a single data configuration,
     * processing documents and updating the search index.
     *
     * <p>The thread manages:</p>
     * <ul>

mkdir -p "$TFCI_OUTPUT_DIR"

# In addition to dumping all script output to the terminal, place it into
# $TFCI_OUTPUT_DIR/script.log
exec > >(tee "$TFCI_OUTPUT_DIR/script.log") 2>&1

# Setup tfrun, a helper function for executing steps that can either be run
# locally or run under Docker. setup_docker.sh, below, redefines it as "docker
# exec".
# Important: "tfrun foo | bar" is "( tfrun foo ) | bar", not "tfrun (foo | bar)".

   * the {@code shutdownNow} method. First, "best-effort" with regards to canceling running tasks is
   * implemented as "no-effort". No interrupts or other attempts are made to stop threads executing
   * tasks. Second, the returned list will always be empty, as any submitted task is considered to
   * have started execution. This applies also to tasks given to {@code invokeAll} or {@code

#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Abstract interface to an operation.
// This interface allows building and executing an operation in either
// tracing or immediate execution mode.
class AbstractOperation {
 protected:
  enum AbstractOperationKind {
    kGraph,
    kMlir,
    kEager,
    kTfrt,
    kTape,
    kOpHandler

      }
    }.apply {
      timeout(client.callTimeoutMillis.toLong(), MILLISECONDS)
    }

  private val executed = AtomicBoolean()

  // These properties are only accessed by the thread executing the call.

  /** Initialized in [callStart]. */
  private var callStackTrace: Any? = null

  /** Finds an exchange to send the next request and receive the next response. */

 private:
  absl::flat_hash_map<string, GradientFunctionFactory> registry_;
};

// TODO(srbs): Figure out if we can avoid declaring this in the public header.
// Wrapper for a tensor output of an operation executing under a tape.
//
// `GetID` returns a unique id for the wrapped tensor which is used to maintain
// a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of

   * <i>happen-before</i></a> its execution begins, perhaps in another thread.
   *
   * <p>Guava implementations of {@code ListenableFuture} promptly release references to listeners
   * after executing them.
   *
   * @param listener the listener to run when the computation is complete
   * @param executor the executor to run the listener in