/** 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.defaultPerformanceBaselines: Provider<String>
    get() = gradleProperty(DEFAULT_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?

  // device. If is_async=false (constructor argument) this means the ops have
  // run and have results. If is_async=true it means that all of the
  // device-specific executors have scheduled the op.
  //
  // Accepts inferred shapes for outputs (`expected_output_shapes`), which if
  // fully defined will avoid querying the shapes of the underlying

          fail();
        }
        throw new MyError();
      }
    };
  }

  // TODO(cpovirk): top-level class?
  static class ExecutorSpy implements Executor {

    Executor delegate;
    boolean wasExecuted;

    public ExecutorSpy(Executor delegate) {
      this.delegate = delegate;
    }

    @Override
    public void execute(Runnable command) {
      delegate.execute(command);

          fail();
        }
        throw new MyError();
      }
    };
  }

  // TODO(cpovirk): top-level class?
  static class ExecutorSpy implements Executor {

    Executor delegate;
    boolean wasExecuted;

    public ExecutorSpy(Executor delegate) {
      this.delegate = delegate;
    }

    @Override
    public void execute(Runnable command) {
      delegate.execute(command);

=== ":material-language-kotlin: Kotlin"
    ```kotlin
      private val executor = Executors.newScheduledThreadPool(1)
      private val client = OkHttpClient()

      fun run() {
        val request = Request.Builder()

```
./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.

```

//
// Each call to Session::Run() that identifies a new subgraph (based on feeds
// and fetches) creates some datastructures that live as long as the session
// (the partitioned graph, associated executors etc.).
//
// A pathological case of this would be if say the initialization op
// (main_op/legacy_init_op) involves the use of a large constant. Then we

import java.security.cert.Certificate
import java.security.cert.X509Certificate
import java.time.Duration
import java.time.Instant
import java.util.Date
import java.util.concurrent.ExecutorService
import java.util.concurrent.Executors
import java.util.concurrent.TimeUnit
import javax.net.ServerSocketFactory
import javax.net.SocketFactory
import javax.net.ssl.HostnameVerifier
import javax.net.ssl.SSLContext
import javax.net.ssl.SSLSocket

DeviceCompiler<ExecutableType, ClientType>::~DeviceCompiler() {
  // Since programs are owned by the cache, ensure any use of our programs have
  // completed by waiting for all stream executors to complete.
  compiler_client_->WaitForProgramsToFinish();
  // Wait for all outstanding compilations to finish.
  // Resetting the pointer explicitly in the top level destructor.