* specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.plugin.lifecycle;

/**
 * A set of goals to execute.
 *
 * @version $Revision$ $Date$
 */
@SuppressWarnings("all")
public class Execution implements java.io.Serializable {

    // --------------------------/
    // - Class/Member Variables -/
    // --------------------------/

    /**

which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.

        <plugin>
          <groupId>build</groupId>
          <artifactId>managed-plugin</artifactId>
          <executions>
            <execution>
              <phase>test</phase>
            </execution>
            <execution>
              <phase>test</phase>
            </execution>
          </executions>
        </plugin>
      </plugins>
    </pluginManagement>
    <plugins>
      <plugin>

  /**
   * Returns an {@link Executor} that runs each task executed sequentially, such that no two tasks
   * are running concurrently.
   *
   * <p>{@linkplain Executor#execute executed} tasks have a happens-before order as defined in the
   * Java Language Specification. Tasks execute with the same happens-before order that the function
   * calls to {@link Executor#execute execute()} that submitted those tasks had.
   *

      if (executeImmediate) {
        new RunnableExecutorPair(runnable, executor).execute();
      }
    }

    public void execute() {
      synchronized (runnables) {
        if (executed) {
          return;
        }
        executed = true;
      }

      while (!runnables.isEmpty()) {
        runnables.poll().execute();
      }
    }

    private static class RunnableExecutorPair {

```

ne sera pas exécutée.

/// info

Pour plus d'informations, consultez <a href="https://docs.python.org/3/library/__main__.html" class="external-link" target="_blank">la documentation officielle de Python</a>.

///

## Exécutez votre code avec votre <abbr title="En anglais: debugger">débogueur</abbr>

  }

  // Takes a description of a single operation being executed on the
  // ParallelDevice, and in turn runs one operation per component device with
  // its corresponding inputs from the input ParallelTensors. Wraps the
  // resulting per-device and per-output TFE_TensorHandles into one
  // ParallelTensor per output of the original operation.
  //
  // Attributes are forwarded to executed operations unmodified.
  //

     * ScheduledExecutorService#scheduleWithFixedDelay} method.
     *
     * @param initialDelay the time to delay first execution
     * @param delay the delay between the termination of one execution and the commencement of the
     *     next
     * @since 28.0 (but only since 33.4.0 in the Android flavor)
     */

 *      </td>
 *   </tr>
 *   <tr>
 *      <td>execute</td>
 *      <td>@Execute
 *       ( phase=LifecyclePhase.&lt;phase&gt;, goal= "&lt;goal-name&gt;", lifecycle="&lt;lifecycle-id&gt;" )</td>
 *      <td>No</td>
 *      <td>When this goal is invoked, it will first invoke a parallel lifecycle, ending at the given phase.
 *          If a goal is provided instead of a phase, that goal will be executed in isolation.

  // Block until the previous `StartExecute` operation has executed. Forwards
  // the status from `TFE_Execute` and returns outputs if the status is OK.
  std::vector<TensorHandlePtr> Join(TF_Status* status);

  // Block until all Ops finished running on the thread.
  void AsyncWait(TF_Status* status);

 private:
  void Run();

  void Execute(TFE_Context* context, const char* operation_name,