<img src="/img/deployment/https/https05.drawio.svg">

### Response HTTP { #http-response }

La aplicación procesaría el request y enviaría un **response HTTP simple (sin encriptar)** al TLS Termination Proxy.

Imaginemos que cargar el modelo puede **tomar bastante tiempo**, porque tiene que leer muchos **datos del disco**. Entonces no quieres hacerlo para cada request.

}

// Simple tests of bucket lifecycle: PUT, GET, DELETE.
// Tests are related and the order is important.
func testBucketLifecycleHandlers(obj ObjectLayer, instanceType, bucketName string, apiRouter http.Handler,
	creds auth.Credentials, t *testing.T,
) {
	// test cases with sample input and expected output.
	testCases := []struct {
		method     string

	}
	if err := scanner.Err(); err != nil {
		fmt.Fprintln(os.Stderr, "shouldn't see an error scanning a string")
	}
	// Output:
	// true
}

// Use a Scanner to implement a simple word-count utility by scanning the
// input as a sequence of space-delimited tokens.
func ExampleScanner_words() {
	// An artificial input source.

            .create().generate()
        projectLayoutSetupRegistry.templateOperationFactory.newTemplateOperation()
            .withTemplate(templateFolder.template("build.sample.conf"))
            .withTarget(settings.target.file("../tests/build.sample.conf").asFile)
            .create().generate()
    }

    private
    fun tasksExecuted(descriptor: CompositeProjectInitDescriptor): Int {

    assert response.headers["WWW-Authenticate"] == 'Basic realm="simple"'


def test_security_http_basic_invalid_credentials():
    response = client.get(
        "/users/me", headers={"Authorization": "Basic notabase64token"}
    )
    assert response.status_code == 401, response.text
    assert response.headers["WWW-Authenticate"] == 'Basic realm="simple"'
    assert response.json() == {"detail": "Not authenticated"}

And the `dict` you receive as `weights` will actually have `int` keys and `float` values.

///

## Recap { #recap }

With **FastAPI** you have the maximum flexibility provided by Pydantic models, while keeping your code simple, short and elegant.

But with all the benefits:

* Editor support (completion everywhere!)
* Data conversion (a.k.a. parsing / serialization)
* Data validation
* Schema documentation

- Improve CPU sample sanitization in HPA by taking metric's freshness into account. ([#68068](https://github.com/kubernetes/kubernetes/pull/68068), [@krzysztof-jastrzebski](https://github.com/krzysztof-jastrzebski))

  <modelVersion>4.0.0</modelVersion>

  <groupId>ut.simple</groupId>
  <artifactId>parent</artifactId>
  <version>1.0</version>
  <packaging>pom</packaging>

  <name>Simple Unit Test Parent</name>

  <dependencyManagement>
    <dependencies>
      <dependency>
        <groupId>ut.simple</groupId>
        <artifactId>dependency</artifactId>
        <version>1.0</version>

        super();
    }

    @Test
    public void testResolveParentThrowsUnresolvableModelExceptionWhenNotFound() throws Exception {
        final Parent parent = new Parent();
        parent.setGroupId("ut.simple");
        parent.setArtifactId("artifact");
        parent.setVersion("0");

        UnresolvableModelException e = assertThrows(
                UnresolvableModelException.class,