/// info

Notice how `Offer` has a list of `Item`s, which in turn have an optional list of `Image`s

///

## Bodies of pure lists { #bodies-of-pure-lists }

If the top level value of the JSON body you expect is a JSON `array` (a Python `list`), you can declare the type in the parameter of the function, the same as in Pydantic models:

```Python

/// info | Informação

Observe como `Offer` tem uma lista de `Item`s, que por sua vez têm uma lista opcional de `Image`s

///

## Corpos de listas puras { #bodies-of-pure-lists }

Se o valor de primeiro nível do corpo JSON que você espera for um `array` do JSON (uma` lista` do Python), você pode declarar o tipo no parâmetro da função, da mesma forma que nos modelos do Pydantic:

```Python

/// info | Información

Observa cómo `Offer` tiene una lista de `Item`s, que a su vez tienen una lista opcional de `Image`s

///

## Cuerpos de listas puras { #bodies-of-pure-lists }

Si el valor superior del cuerpo JSON que esperas es un `array` JSON (una `list` en Python), puedes declarar el tipo en el parámetro de la función, al igual que en los modelos Pydantic:

```Python

/// info | Info

Beachten Sie, wie `Offer` eine Liste von `Item`s hat, die ihrerseits eine optionale Liste von `Image`s haben.

///

## Bodys aus reinen Listen { #bodies-of-pure-lists }

Wenn das äußerste Element des JSON-Bodys, das Sie erwarten, ein JSON-`array` (eine Python-`list`) ist, können Sie den Typ im Funktionsparameter deklarieren, mit der gleichen Syntax wie in Pydantic-Modellen:

```Python

/// info | Информация

Заметьте, что у объекта `Offer` есть список объектов `Item`, которые, в свою очередь, могут содержать необязательный список объектов `Image`

///

## Тела с чистыми списками элементов { #bodies-of-pure-lists }

Если верхний уровень значения тела JSON-объекта представляет собой JSON `array` (в Python — `list`), вы можете объявить тип в параметре функции, так же как в моделях Pydantic:

```Python
images: list[Image]

	Recursive bool

	// Separator to use.
	Separator string

	// Create indicates that the lister should not attempt to load an existing cache.
	Create bool

	// Include pure directories.
	IncludeDirectories bool

	// Transient is set if the cache is transient due to an error or being a reserved bucket.
	// This means the cache metadata will not be persisted on disk.

            encryption(cliRequest);
            return execute(cliRequest);
        } catch (ExitException e) {
            return e.exitCode;
        } catch (UnrecognizedOptionException e) {
            // pure user error, suppress stack trace
            return 1;
        } catch (BuildAbort e) {
            CLIReportingUtils.showError(slf4jLogger, "ABORTED", e, cliRequest.showErrors);

            return 2;

	for id, rl := range rMap {
		if !rl.Expiration.IsNull() || !rl.NoncurrentVersionExpiration.IsNull() {
			if _, ok := newRMap[id]; !ok {
				// if rule getting removed was pure expiry rule (may be got to this site
				// as part of replication of expiry rules), remove it. Otherwise remove
				// only the expiry part of it
				if rl.Transition.IsNull() && rl.NoncurrentVersionTransition.IsNull() {

* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀

In some cases, for pure data validation and processing, you can get performance improvements of **20x** or more. This means 2,000% or more. 🤯