* similar to the way Maven configures plugins from the POM, i.e. some configuration like {@code <param>value</param>}
 * is mapped to an equally named property of the bean and converted. The properties of the bean are supposed to either
 * have a public setter or be backed by an equally named field (of any visibility).
 *
 * @since 3.0
 */
public interface BeanConfigurator {

    /**

}

// fixLongPath returns the extended-length (\\?\-prefixed) form of
// path when needed, in order to avoid the default 260 character file
// path limit imposed by Windows. If path is not easily converted to
// the extended-length form (for example, if path is a relative path
// or contains .. elements), or is short enough, fixLongPath returns
// path unmodified.
//

* `skip`: with a value of `0`
* `limit`: with a value of `10`

As they are part of the URL, they are "naturally" strings.

But when you declare them with Python types (in the example above, as `int`), they are converted to that type and validated against it.

All the same process that applied for path parameters also applies for query parameters:

* Editor support (obviously)

# Experimental filesystem C APIs for TensorFlow.
# Will be moved in proper place once all filesystems are converted to the
# modular framework.
load("//tensorflow:tensorflow.bzl", "tf_cc_test")
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    licenses = ["notice"],
)

# This is only for plugins
cc_library(
    name = "filesystem_interface",

  // `apiGroups:["apps"], apiVersions:["v1"], resources: ["deployments"]` and `matchPolicy: Equivalent`,
  // an API request to apps/v1beta1 deployments would be converted and sent to the webhook
  // with `kind: {group:"apps", version:"v1", kind:"Deployment"}` (matching the rule the webhook registered for),

You couldn't get this kind of editor support if you were working directly with `dict` instead of Pydantic models.

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

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

So, a `datetime` object would have to be converted to a `str` containing the data in <a href="https://en.wikipedia.org/wiki/ISO_8601" class="external-link" target="_blank">ISO format</a>.

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

Para esos casos, puedes usar el `jsonable_encoder` para convertir tus datos antes de pasarlos a la respuesta:

```Python hl_lines="4 6 20 21"
{!../../../docs_src/response_directly/tutorial001.py!}
```

!!! note "Detalles Técnicos"

#include "tensorflow/core/platform/file_system.h"

/// This file builds classes needed to hold a filesystem implementation in the
/// modular world. Once all TensorFlow filesystems are converted to use the
/// plugin based approach, this file will replace the one in core/platform and
/// the names will lose the `Modular` part. Until that point, the `Modular*`

  // `apiGroups:["apps"], apiVersions:["v1"], resources: ["deployments"]` and `matchPolicy: Equivalent`,
  // an API request to apps/v1beta1 deployments would be converted and sent to the webhook
  // with `kind: {group:"apps", version:"v1", kind:"Deployment"}` (matching the rule the webhook registered for),