// Creates a new operation - see `TF_NewOperation` for more details.
//
// The lock for `graph` must be held when calling this function.
//
// Unless implementing advanced behavior, like custom gradient functions, you
// most likely need to call `TF_NewOperation` instead.
TF_CAPI_EXPORT extern TF_OperationDescription* TF_NewOperationLocked(
    TF_Graph* graph, const char* op_type, const char* oper_name);

Если вы хотите перехватывать исключения и формировать на их основе пользовательский ответ, создайте [Пользовательский обработчик исключений](../handling-errors.md#install-custom-exception-handlers){.internal-link target=_blank}.

## Зависимости с `yield` и `except` { #dependencies-with-yield-and-except }

{* ../../docs_src/query_params_str_validations/tutorial014_an_py310.py hl[10] *}

## Кастомная валидация { #custom-validation }

Бывают случаи, когда нужна **кастомная валидация**, которую нельзя выразить параметрами выше.

type BaseOptions struct{}

// RenameOptions represents rename API options, currently its same as BaseOptions
type RenameOptions struct {
	BaseOptions
}

// DiskInfoOptions options for requesting custom results.
type DiskInfoOptions struct {
	DiskID  string `msg:"id"`
	Metrics bool   `msg:"m"`
	NoOp    bool   `msg:"np"`
}

// DiskInfo is an extended type which returns current
// disk usage per path.

Nós criamos uma função assíncrona `lifespan()` com `yield` assim:

{* ../../docs_src/events/tutorial003_py39.py hl[16,19] *}

Aqui estamos simulando a operação de *inicialização* custosa de carregar o modelo, colocando a (falsa) função do modelo no dicionário com modelos de machine learning antes do `yield`. Esse código será executado **antes** de a aplicação **começar a receber requisições**, durante a *inicialização*.

 different computation approaches and orders.
    *   To verify if oneDNN optimizations are on, look for a message with *"oneDNN custom operations are on"* in the log. If the exact phrase is not there, it means they are off.

* Making the `tf.function` type system fully available:

    * `tf.types.experimental.TraceType` now allows custom tf.function inputs to declare Tensor decomposition and type casting support.

# TF_LoadLibrary/tf.load_op_library) register their ops and kernels with this
# shared object directly.
#
# For example, from Python tf.load_op_library loads a custom op library (via
# dlopen() on Linux), the library finds libtensorflow_framework.so (no
# filesystem search takes place, since libtensorflow_framework.so has already

		TraceType: madmin.TraceFTP,
		Time:      startTime,
		NodeName:  globalLocalNodeName,
		FuncName:  s.Cmd,
		Duration:  time.Since(startTime),
		Path:      objPath,
		Error:     errStr,
		Bytes:     sz,
		Custom: map[string]string{
			"user":   s.Sess.LoginUser(),
			"cmd":    s.Cmd,
			"param":  s.Param,
			"login":  fmt.Sprintf("%t", s.Sess.IsLogin()),
			"source": source,
		},
	}
}

	globalIsErasureSD = false

	// Indicates if server code should go through testing path.
	globalIsTesting = false

	// This flag is set to 'true' by default
	globalBrowserEnabled = true

	// Custom browser redirect URL, not set by default
	// and it is automatically deduced.
	globalBrowserRedirectURL *xnet.URL

	// Disable redirect, default is enabled.
	globalBrowserRedirect bool

Wenn Sie Exceptions abfangen und darauf basierend eine benutzerdefinierte Response erstellen möchten, erstellen Sie einen [benutzerdefinierten Exceptionhandler](../handling-errors.md#install-custom-exception-handlers){.internal-link target=_blank}.

## Abhängigkeiten mit `yield` und `except` { #dependencies-with-yield-and-except }