Pero tampoco tienes que preocuparte por ellos, los `dicts` entrantes se convierten automáticamente y tu salida se convierte automáticamente a JSON también.

## Cuerpos de `dict`s arbitrarios { #bodies-of-arbitrary-dicts }

También puedes declarar un cuerpo como un `dict` con claves de algún tipo y valores de algún otro tipo.

Mas você também não precisa se preocupar com eles, os dicts de entrada são convertidos automaticamente e sua saída é convertida automaticamente para JSON também.

## Corpos de `dict`s arbitrários { #bodies-of-arbitrary-dicts }

Você também pode declarar um corpo como um `dict` com chaves de algum tipo e valores de outro tipo.

{* ../../docs_src/extra_models/tutorial004_py39.py hl[18] *}


## Response con `dict` arbitrario { #response-with-arbitrary-dict }

También puedes declarar un response usando un `dict` arbitrario plano, declarando solo el tipo de las claves y valores, sin usar un modelo Pydantic.

{* ../../docs_src/extra_models/tutorial004_py39.py hl[18] *}

## Resposta com `dict` arbitrário { #response-with-arbitrary-dict }

Você também pode declarar uma resposta usando um simples `dict` arbitrário, declarando apenas o tipo das chaves e valores, sem usar um modelo Pydantic.

	// By default, MinIO always verify the client certificate.
	//
	// The client certificate verification should only be skipped
	// when debugging or testing a setup since it allows arbitrary
	// clients to obtain temp. credentials with arbitrary policy
	// permissions - including admin permissions.
	EnvIdentityTLSSkipVerify = "MINIO_IDENTITY_TLS_SKIP_VERIFY"
)

// Config contains the STS TLS configuration for generating temp.

For that, use the standard Python `typing.List` (or just `list` in Python 3.9 and above):

{* ../../docs_src/extra_models/tutorial004_py39.py hl[18] *}

## Response with arbitrary `dict` { #response-with-arbitrary-dict }

You can also declare a response using a plain arbitrary `dict`, declaring just the type of the keys and values, without using a Pydantic model.

`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you
are working with. When loading untrusted checkpoints, the values of the traced

 */

package com.google.common.base;

import com.google.common.annotations.GwtCompatible;

/**
 * A time source; returns a time value representing the number of nanoseconds elapsed since some
 * fixed but arbitrary point in time. Note that most users should use {@link Stopwatch} instead of
 * interacting with this class directly.
 *
 * <p><b>Warning:</b> this interface can only be used to measure elapsed time, not wall time.
 *

    # has its own internal pool (e.g. a database connection pool)
    # to avoid this we let __exit__ run without a capacity limit
    # since we're creating a new limiter for each call, any non-zero limit
    # works (1 is arbitrary)
    exit_limiter = CapacityLimiter(1)
    try:
        yield await run_in_threadpool(cm.__enter__)
    except Exception as e:
        ok = bool(
            await anyio.to_thread.run_sync(

     *
     * @throws CIFSException if an error occurs during authentication
     *
     */
    boolean close() throws CIFSException;

    /**
     * Authenticate arbitrary credentials represented by the
     * <code>NtlmPasswordAuthentication</code> object against the domain controller
     * specified by the <code>UniAddress</code> parameter. If the credentials are