}
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }
  absl::Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                       int* num_retvals) override {
    auto* tf_opdesc = op_.release();
    if (tf_opdesc == nullptr) {

	return cs.processors["delete"]
}

func (cs *callbacks) Row() *processor {
	return cs.processors["row"]
}

func (cs *callbacks) Raw() *processor {
	return cs.processors["raw"]
}

func (p *processor) Execute(db *DB) *DB {
	// call scopes
	for len(db.Statement.scopes) > 0 {
		db = db.executeScopes()
	}

	var (
		curTime           = time.Now()
		stmt              = db.Statement
		resetBuildClauses bool
	)

		return true
	case reflect.Float32, reflect.Float64:
		return true
	default:
		return false
	}
}

// ExplainSQL generate SQL string with given parameters, the generated SQL is expected to be used in logger, execute it might introduce a SQL injection vulnerability
func ExplainSQL(sql string, numericPlaceholder *regexp.Regexp, escaper string, avars ...interface{}) string {
	var (
		convertParams func(interface{}, int)

    * Es enthält den gesendeten `username` und das gesendete `password`.

{* ../../docs_src/security/tutorial006_an_py39.py hl[4,8,12] *}
Wenn Sie versuchen, die URL zum ersten Mal zu öffnen (oder in der Dokumentation auf den Button „Execute“ zu klicken), wird der Browser Sie nach Ihrem Benutzernamen und Passwort fragen:

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

## Den Benutzernamen überprüfen

Hier ist ein vollständigeres Beispiel.

  GradientRegistry registry;
  s = RegisterGradients(&registry);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto tape = std::make_unique<Tape>(/*persistent=*/false);
  s = Execute(check_numerics_op.get(), ctx.get(), absl::MakeSpan(outputs),
              &num_retvals, &forward_op, tape.get(), registry);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  string read_message;

Eles tendem a exigir configurações mais complexas, um gerenciador de fila de mensagens/tarefas, como RabbitMQ ou Redis, mas permitem que você execute tarefas em segundo plano em vários processos e, especialmente, em vários servidores.

Para ver um exemplo, verifique os [Geradores de projeto](../project-generation.md){.internal-link target=\_blank}, todos incluem celery já configurado.

  virtual absl::Status AddInput(AbstractTensorHandle* input) = 0;
  virtual absl::Status AddInputList(
      absl::Span<AbstractTensorHandle* const> inputs) = 0;
  virtual absl::Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                               int* num_retvals) = 0;

  virtual absl::Status SetAttrString(const char* attr_name, const char* data,

    return status.status;
  }

  absl::Status Execute(const ImmediateExecutionOperation* op,
                       ImmediateExecutionTensorHandle** retvals,
                       int* num_retvals) override {
    std::vector<TFE_TensorHandle*> outputs(*num_retvals);
    TF_Status status;
    device_.execute(tensorflow::wrap(op), num_retvals, outputs.data(), &status,
                    info_);

/// tip | "Dica"

Normalmente, você faria isso **uma vez**, logo após criar o ambiente virtual.

///

Certifique-se de que o ambiente virtual esteja ativo (com o comando acima) e execute:

<div class="termy">

```console
$ python -m pip install --upgrade pip

---> 100%
```

</div>

## Adicionar `.gitignore`

        it.proceed(it.request())
      }
      .build()
  }

  private fun makeRequest(client: OkHttpClient): Handshake {
    val call = client.newCall(Request(server.url("/")))
    return call.execute().use { it.handshake!! }
  }