def handler = blockingHttpServer.expectConcurrentAndBlock(maxWorkers, calls)

        when:
        args("--max-workers=${maxWorkers}")
        executer.withTasks("parallelWorkTask")
        def gradle = executer.start()

        then:
        handler.waitForAllPendingCalls()

        when:
        handler.release(1)

        then:
        handler.waitForAllPendingCalls()

    tf_device::ParallelExecuteOp parallel_execute_op, OpBuilder* builder,
    llvm::SmallVectorImpl<tf_executor::IslandOp>& executes,
    bool legacy_graph_export, int parallel_group_idx) {
  const int num_regions = parallel_execute_op.getOperation()->getNumRegions();
  executes.reserve(num_regions);

  for (int i : llvm::seq<int>(0, num_regions)) {
    Block& execute_block = parallel_execute_op.GetRegionBlockWithIndex(i);

    def setup() {
        requireOwnGradleUserHomeDir()
    }

    @Override
    TestFile getGradleUserHomeDir() {
        return executer.gradleUserHomeDir
    }

    def "can configure caches via init script and execute multiple builds in a session"() {
        def initDir = new File(executer.gradleUserHomeDir, "init.d")
        initDir.mkdirs()
        new File(initDir, "cache-settings.gradle") << """

        when:
        run("build")

        then:
        result.assertTaskSkipped(":test")
    }

    @Requires(UnitTestPreconditions.Jdk9OrLater)
    def "compiles and executes a Java 9 test suite"() {
        given:
        buildFile << java9Build()

        file('src/test/java/MyTest.java') << standaloneTestClass

        when:
        succeeds 'test'

        then:

        });
    }

    private void maybeStartWorkers(MergedQueues queue, Executor executor) {
        int executorCount = workerLimits.getMaxWorkerCount();
        state.maybeStartWorkers(() -> {
            LOGGER.debug("Using {} parallel executor threads", executorCount);
            for (int i = 1; i < executorCount; i++) {

This means that it a given test class is executed by multiple test tasks, then the test report will include executions of that class, but it can be hard to distinguish individual executions of that class and their output....

  return %value : tensor<*xf32>
}
```

## TensorFlow Executor Dialect

The `tf_executor` dialect is intended to model the current TensorFlow executor
semantics and (when combined with the `tf` dialect) can represent arbitrary
TensorFlow 1.x and 2.x graphs. As such it follows the executor model, including
deadness propagation, concurrent semantics, and control dependencies. The

              </models>
            </configuration>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.codehaus.mojo</groupId>
        <artifactId>buildnumber-maven-plugin</artifactId>
        <executions>
          <execution>
            <id>create-buildnumber</id>
            <goals>
              <goal>create</goal>

            executer.withTasks("d").withArguments("-x", ":sub:c").run().assertTasksExecuted(":a", ":b", ":c", ":d", ":sub:d")
            executer.withTasks("d").withArguments("-x", "d").run().assertTasksExecuted()
            // Project defaults

        when:
        def executor = factory.create('<display-name>')
        def action = {
            executor.stop()
        }
        executor.execute(action)
        executor.stop()

        then:
        IllegalStateException e = thrown()
        e.message == 'Cannot stop this executor from an executor thread.'
    }

    def stopThrowsExceptionOnTimeout() {