cmdline string
	layout  [][]string
}

type disksLayout struct {
	legacy bool
	pools  []poolDisksLayout
}

type serverCtxt struct {
	JSON, Quiet               bool
	Anonymous                 bool
	StrictS3Compat            bool
	Addr, ConsoleAddr         string
	ConfigDir, CertsDir       string
	configDirSet, certsDirSet bool
	Interface                 string

	RootUser, RootPwd string

type Session struct {
	DryRun                   bool
	PrepareStmt              bool
	NewDB                    bool
	Initialized              bool
	SkipHooks                bool
	SkipDefaultTransaction   bool
	DisableNestedTransaction bool
	AllowGlobalUpdate        bool
	FullSaveAssociations     bool
	PropagateUnscoped        bool
	QueryFields              bool
	Context                  context.Context

This can be very useful for setting up **resources** that you need to use for the whole app, and that are **shared** among requests, and/or that you need to **clean up** afterwards. For example, a database connection pool, or loading a shared machine learning model.

## Use Case { #use-case }

Let's start with an example **use case** and then see how to solve it with this.

Isso pode ser muito útil para configurar **recursos** que você precisa usar por toda a aplicação, e que são **compartilhados** entre as requisições e/ou que você precisa **limpar** depois. Por exemplo, um pool de conexões com o banco de dados ou o carregamento de um modelo de machine learning compartilhado.

## Caso de uso { #use-case }

Vamos começar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso.

        ).build()
    assertThat(client.retryOnConnectionFailure).isTrue()
    executeSynchronously("/").assertBody("seed connection pool")
    executeSynchronously("/").assertBody("retry success")

    // The call that seeds the connection pool.
    eventRecorder.removeUpToEvent(CallEnd::class.java)

    // The ResponseFailed event is not necessarily fatal!

    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

   * instance. This method is guaranteed to only be called once.
   *
   * <p>By default this returns a new {@link ScheduledExecutorService} with a single thread pool
   * that sets the name of the thread to the {@linkplain #serviceName() service name}. Also, the
   * pool will be {@linkplain ScheduledExecutorService#shutdown() shut down} when the service
   * {@linkplain Service.State#TERMINATED terminates} or {@linkplain Service.State#TERMINATED

      MockResponse
        .Builder()
        .body("This connection won't pool properly")
        .onResponseEnd(ShutdownConnection)
        .build(),
    )
  }

  @Test
  fun serverShutdownInput() {
    testServerClosesOutput(
      MockResponse
        .Builder()
        .body("This connection won't pool properly")
        .onResponseEnd(
          CloseSocket(

Esto puede ser muy útil para configurar **recursos** que necesitas usar para toda la app, y que son **compartidos** entre requests, y/o que necesitas **limpiar** después. Por ejemplo, un pool de conexiones a una base de datos, o cargando un modelo de machine learning compartido.

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

   * instance. This method is guaranteed to only be called once.
   *
   * <p>By default this returns a new {@link ScheduledExecutorService} with a single thread pool
   * that sets the name of the thread to the {@linkplain #serviceName() service name}. Also, the
   * pool will be {@linkplain ScheduledExecutorService#shutdown() shut down} when the service
   * {@linkplain Service.State#TERMINATED terminates} or {@linkplain Service.State#TERMINATED