</component>

	<component name="allChildUrlRuleMap" class="java.util.LinkedHashMap">
		<postConstruct name="put">
			<arg>"//A"</arg>
			<arg>"href"</arg>
		</postConstruct>
		<postConstruct name="put">
			<arg>"//AREA"</arg>
			<arg>"href"</arg>
		</postConstruct>
		<postConstruct name="put">
			<arg>"//FRAME"</arg>
			<arg>"src"</arg>
		</postConstruct>
		<postConstruct name="put">
			<arg>"//IFRAME"</arg>

        fileTransformer.setPropertyMap(propertyMap);
        Map<String, String> childUrlRuleMap = newHashMap();
        childUrlRuleMap.put("//A", "href");
        childUrlRuleMap.put("//AREA", "href");
        childUrlRuleMap.put("//FRAME", "src");
        childUrlRuleMap.put("//IFRAME", "src");
        childUrlRuleMap.put("//IMG", "src");
        childUrlRuleMap.put("//LINK", "href");

//
//   - Optional: Set server preferences for Backup & Replication parallel sessions, batch size of deletes, and block sizes (before
//     compression). This is an optional area; by default, there should be no <SystemRecommendations> section in the
//     system.xml. Vendors can work with Veeam Product Management and the Alliances team on getting approval to integrate

//
// An Arena must never be used concurrently by multiple goroutines.
type Arena struct {
	a unsafe.Pointer
}

// NewArena allocates a new arena.
func NewArena() *Arena {
	return &Arena{a: runtime_arena_newArena()}
}

// Free frees the arena (and all objects allocated from the arena) so that
// memory backing the arena can be reused fairly quickly without garbage

## Crear una función de tarea

Crea una función para que se ejecute como la tarea en segundo plano.

Es solo una función estándar que puede recibir parámetros.

Puede ser una función `async def` o una función normal `def`, **FastAPI** sabrá cómo manejarla correctamente.

En este caso, la función de tarea escribirá en un archivo (simulando el envío de un email).

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).

That's what we'll solve, let's load the model before the requests are handled, but only right before the application starts receiving requests, not while  the code is being loaded.

## Lifespan { #lifespan }

mc: File data successfully downloaded as inspect.6f96b336.zip
$ xl-meta inspect.6f96b336.zip
{

//go:build goexperiment.arenas

package arena_test

import (
	"arena"
	"testing"
)

type T1 struct {
	n int
}
type T2 [1 << 20]byte // 1MiB

func TestSmoke(t *testing.T) {
	a := arena.NewArena()
	defer a.Free()

	tt := arena.New[T1](a)
	tt.n = 1

	ts := arena.MakeSlice[T1](a, 99, 100)
	if len(ts) != 99 {
		t.Errorf("Slice() len = %d, want 99", len(ts))
	}

 *
 * <h3>Projects</h3>
 *
 * <p>{@link org.apache.maven.api.Project} instances are loaded by Maven from the local
 * file system (those projects are usually about to be built) or from the local repository
 * (they are usually downloaded during dependency collection). Those projects are loaded
 * from a Project Object Model (POM).</p>
 *