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*.

import org.apache.maven.api.xml.XmlNode;
import org.apache.maven.execution.MavenSession;
import org.apache.maven.execution.MojoExecutionEvent;
import org.apache.maven.execution.MojoExecutionListener;
import org.apache.maven.execution.ProjectDependencyGraph;
import org.apache.maven.execution.ProjectExecutionEvent;
import org.apache.maven.execution.ProjectExecutionListener;

     *
     * <p>If the Thread in use is interrupted before or during execution of the tasks in {@link
     * #queue}, the Executor will complete its tasks, and then restore the interruption. This means
     * that once the Thread returns to the Executor that this Executor composes, the interruption
     * will still be present. If the composed Executor is an ExecutorService, it can respond to

        MojoDescriptor mojoDescriptor = execution.getMojoDescriptor();

        if (mojoDescriptor == null) {
            mojoDescriptor = pluginManager.getMojoDescriptor(
                    execution.getPlugin(),
                    execution.getGoal(),
                    project.getRemotePluginRepositories(),
                    session.getRepositorySession());

            execution.setMojoDescriptor(mojoDescriptor);
        }

              <model>src/main/mdo/archetype.mdo</model>
            </configuration>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-dependency-plugin</artifactId>
        <executions>
          <execution>
            <id>copy</id>
            <phase>process-test-resources</phase>
            <goals>

            </configuration>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-compiler-plugin</artifactId>
        <executions>
          <execution>
            <id>test-compile</id>
            <goals>
              <goal>testCompile</goal>

      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr immediate_execution_ctx_;
  GradientRegistry registry_;
  Status status_;

            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-pmd-plugin</artifactId>
            <executions>
              <execution>
                <goals>
                  <goal>cpd-check</goal>
                </goals>
              </execution>
            </executions>
          </plugin>
        </plugins>
      </build>
    </profile>
    <profile>
      <id>reporting</id>

  TFE_Context* context_ TF_GUARDED_BY(execution_mutex_);
  const char* operation_name_ TF_GUARDED_BY(execution_mutex_);
  absl::optional<int64_t> step_id_ TF_GUARDED_BY(execution_mutex_) =
      absl::nullopt;
  std::vector<TFE_TensorHandle*> op_inputs_ TF_GUARDED_BY(execution_mutex_);
  const TFE_OpAttrs* attributes_ TF_GUARDED_BY(execution_mutex_);
  int expected_max_outputs_ TF_GUARDED_BY(execution_mutex_);

import java.util.Set;
import java.util.stream.Collectors;

import org.apache.maven.artifact.Artifact;
import org.apache.maven.execution.BuildFailure;
import org.apache.maven.execution.ExecutionEvent;
import org.apache.maven.execution.MavenExecutionRequest;
import org.apache.maven.execution.MavenSession;
import org.apache.maven.internal.MultilineMessageHelper;
import org.apache.maven.lifecycle.LifecycleExecutionException;