### Security and authentication { #security-and-authentication }

Security and authentication integrated. Without any compromise with databases or data models.

All the security schemes defined in OpenAPI, including:

* HTTP Basic.
* **OAuth2** (also with **JWT tokens**). Check the tutorial on [OAuth2 with JWT](tutorial/security/oauth2-jwt.md){.internal-link target=_blank}.

If you want to secure your API, there are several better things you can do, for example:

* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.

## Resources

*   [TensorFlow.org](https://www.tensorflow.org)
*   [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
*   [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official)
*   [TensorFlow Examples](https://github.com/tensorflow/examples)
*   [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)

Arreglaremos estas cosas añadiendo unos **modelos extra**. Aquí es donde SQLModel brillará. ✨

### Crear Múltiples Modelos { #create-multiple-models }

En **SQLModel**, cualquier clase de modelo que tenga `table=True` es un **modelo de tabla**.

Y cualquier clase de modelo que no tenga `table=True` es un **modelo de datos**, estos son en realidad solo modelos de Pydantic (con un par de características extra pequeñas). 🤓

import okio.buffer
import okio.source
import org.junit.jupiter.params.ParameterizedTest
import org.junit.jupiter.params.provider.ArgumentsSource

/** Runs the web platform URL tests against Java URL models.  */
class WebPlatformUrlTest {
  class TestDataParamProvider : SimpleProvider() {
    override fun arguments() = ArrayList<Any>(loadTests())
  }

  /** Test how [HttpUrl] does against the web platform test suite.  */

platforms/core-configuration/kotlin-dsl-provider-plugins/   @gradle/bt-tide
platforms/core-configuration/kotlin-dsl-tooling-builders/   @gradle/bt-tide
platforms/core-configuration/kotlin-dsl-tooling-models/     @gradle/bt-tide

# Core automation platform (Declarative DSL)
platforms/core-configuration/declarative-dsl-api/                   @gradle/bt-declarative-gradle-project

import org.apache.maven.model.Activation;
import org.apache.maven.model.Build;
import org.apache.maven.model.Dependency;
import org.apache.maven.model.DependencyManagement;
import org.apache.maven.model.InputLocation;
import org.apache.maven.model.InputLocationTracker;
import org.apache.maven.model.InputSource;
import org.apache.maven.model.Model;
import org.apache.maven.model.Parent;
import org.apache.maven.model.Plugin;

    version: Literal["1", "auto"] = "auto",
) -> ModelField:
    if annotation_is_pydantic_v1(type_):
        raise PydanticV1NotSupportedError(
            "pydantic.v1 models are no longer supported by FastAPI."
            f" Please update the response model {type_!r}."
        )
    class_validators = class_validators or {}

    field_info = field_info or FieldInfo(annotation=type_, default=default, alias=alias)

<img src="/img/python-types/image06.png">

Notice that this means "`one_person` is an **instance** of the class `Person`".

It doesn't mean "`one_person` is the **class** called `Person`".

## Pydantic models { #pydantic-models }

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

Imaginemos que tienes algunos **modelos de machine learning** que quieres usar para manejar requests. 🤖

Los mismos modelos son compartidos entre requests, por lo que no es un modelo por request, o uno por usuario o algo similar.