But you don't have to worry about them either, incoming dicts are converted automatically and your output is converted automatically to JSON too.

## Bodies of arbitrary `dict`s { #bodies-of-arbitrary-dicts }

You can also declare a body as a `dict` with keys of some type and values of some other type.

Apart from all the fancy words used here, the **Dependency Injection** system is quite simple.

Just functions that look the same as the *path operation functions*.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).

/// tip

All this might not seem as useful with these simple examples.

But you will see how useful it is in the chapters about **security**.

#    -X prop       set non-standard JAVA system property
#   --prop=val
#   --prop val     set fess property (i.e.  -Des.<prop>=<val>)

CDPATH=""
SCRIPT="$0"

# SCRIPT may be an arbitrarily deep series of symlinks. Loop until we have the concrete path.
while [ -h "$SCRIPT" ] ; do
  ls=`ls -ld "$SCRIPT"`
  # Drop everything prior to ->
  link=`expr "$ls" : '.*-> \(.*\)$'`

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

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

Sem ter que saber de antemão quais são os nomes de campos/atributos válidos (como seria o caso dos modelos Pydantic).

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

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

`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

{* ../../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.

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


## Response con `dict` arbitrario

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

      return method.invoke(delegate, args);
    }

    @Override
    public String toString() {
      return "some arbitrary string";
    }
  }

  private static class DelegatingInvocationHandlerWithEquals extends DelegatingInvocationHandler {

    DelegatingInvocationHandlerWithEquals(Object delegate) {
      super(delegate);

     *
     * @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