val extension = this.extensions.create<ErrorProneSourceSetExtension>("errorprone", project.objects.property<Boolean>())
        // Enable it only for the main source set by default, as incremental Groovy
        // joint-compilation doesn't work with the Error Prone annotation processor
        extension.enabled.convention(this.name == "main")

        project.dependencies.addProvider(
            annotationProcessorConfigurationName,

[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

Compiling models via the recommended entry points described in
[XLA](https://www.tensorflow.org/xla) and
[JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)

    "groups" : [ {
      "testProject" : "largeJavaMultiProject",
      "comment" : "We only test the multi-project here since for the monolithic project we would have no cache hits. This would mean we actually would test incremental compilation.",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {

# Simples OAuth2 com senha e Bearer

Agora vamos construir a partir do capítulo anterior e adicionar as partes que faltam para ter um fluxo de segurança completo.

## Pegue o `username` (nome de usuário) e `password` (senha)

É utilizado o utils de segurança da **FastAPI** para obter o `username` e a `password`.

#ifdef __cplusplus
extern "C" {
#endif

// When `enable` is true, set
// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by
// clients that cannot read/write the tensorflow.ConfigProto proto.

* Juju - K8s master charm now properly keeps distributed master files in sync for an HA control plane. ([#41351](https://github.com/kubernetes/kubernetes/pull/41351), [@chuckbutler](https://github.com/chuckbutler))
* Fix zsh completion: unknown file attribute error ([#38104](https://github.com/kubernetes/kubernetes/pull/38104), [@elipapa](https://github.com/elipapa))

// here.
void TF_SetTracingImplementation(const char* name, TF_Status*);

// Creates a new TensorFlow function. A Function is an execution context, and as
// such it can trace operations through TF_ExecuteOperation. After completing
// tracing, a function can be obtained by TF_FinalizeFunction.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* status);

// Creates a context for eager execution of operations.

- plugin dependency metadata missing
- plugin dependency metadata retrieval problem
- plugin configuration problem
- plugin execution failure due to something that is know to possibly go wrong (like compilation failure)
- plugin execution error due to something that is not expected to go wrong (the compiler executable missing)
- asking to use a plugin for which you do not have a version defined - tools to easily select versions

* Support completion for kubectl config delete-cluster ([#48381](https://github.com/kubernetes/kubernetes/pull/48381), [@superbrothers](https://github.com/superbrothers))

<img src="/img/tutorial/security/image12.png">

## Verifique o usuário

Aqui está um exemplo mais completo.

Utilize uma dependência para verificar se o usuário e a senha estão corretos.