*   <li>All threads generated must be joined inside each test case method (or {@code fail} to do
 *       so) before returning from the method. The {@code joinPool} method can be used to do this
 *       when using Executors.
 * </ol>
 *
 * <p><b>Other notes</b>
 *
 * <ul>
 *   <li>Usually, there is one testcase method per JSR166 method covering "normal" operation, and

 *   <li>All threads generated must be joined inside each test case method (or {@code fail} to do
 *       so) before returning from the method. The {@code joinPool} method can be used to do this
 *       when using Executors.
 * </ol>
 *
 * <p><b>Other notes</b>
 *
 * <ul>
 *   <li>Usually, there is one testcase method per JSR166 method covering "normal" operation, and

    barrier.await(1, SECONDS);
    assertFalse(executor.isShutdown());
    assertFalse(executor.isTerminated());

    executor.shutdown();
    assertTrue(executor.isShutdown());
    assertThrows(RejectedExecutionException.class, () -> executor.submit(() -> {}));
    assertFalse(executor.isTerminated());

    // WAIT #2
    barrier.await(1, SECONDS);
    assertFalse(executor.awaitTermination(20, MILLISECONDS));

    barrier.await(1, SECONDS);
    assertFalse(executor.isShutdown());
    assertFalse(executor.isTerminated());

    executor.shutdown();
    assertTrue(executor.isShutdown());
    assertThrows(RejectedExecutionException.class, () -> executor.submit(() -> {}));
    assertFalse(executor.isTerminated());

    // WAIT #2
    barrier.await(1, SECONDS);
    assertFalse(executor.awaitTermination(20, MILLISECONDS));

## Multi-Tenant environments

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are

  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager
  // Executor is only used in current TF but not in TFRT.
  virtual EagerExecutor& Executor() = 0;
  // Update the Eager Executor for current thread.
  virtual void SetExecutorForThread(EagerExecutor* executor) = 0;

  // Return a list of local tensorflow::Device*.

    /** Synchronization control for AbstractFutures. */
    private final Sync<V> sync = new Sync<V>();

    // The execution list to hold our executors.
    private final ExecutionList executionList = new ExecutionList();

    /** Constructor for use by subclasses. */
    protected OldAbstractFuture() {}

    /*

    /** Synchronization control for AbstractFutures. */
    private final Sync<V> sync = new Sync<V>();

    // The execution list to hold our executors.
    private final ExecutionList executionList = new ExecutionList();

    /** Constructor for use by subclasses. */
    protected OldAbstractFuture() {}

    /*

val Project.defaultRfrPerformanceBaselines: Provider<String>
    get() = gradleProperty(DEFAULT_RFR_PERFORMANCE_BASELINES)


// null means no limit: use all available executors
val Project.maxTestDistributionRemoteExecutors: Int?
    get() = gradleProperty(MAX_TEST_DISTRIBUTION_REMOTE_EXECUTORS).orNull?.toInt()

val Project.maxTestDistributionLocalExecutors: Int?

```
./bin/spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn-client \
    --num-executors 1 \
    --driver-memory 512m \
    --executor-memory 512m \
    --executor-cores 1 \
    examples/jars/spark-examples*.jar 10
```

The job should produce an output as shown below. Note the value of pi in the output.

```