# Comma-separated list of files to include.
# Each item may be enclosed in quotes to gracefully include spaces. Items are trimmed before being loaded.
# If the first character of an item is a question mark, the load will silently fail if the file does not exist.
${includes} = ?"${maven.user.conf}/maven-system.properties", \
              ?"${maven.project.conf}/maven-system.properties"

#
# Settings
#

#

# Comma-separated list of files to include.
# Each item may be enclosed in quotes to gracefully include spaces. Items are trimmed before being loaded.
# If the first character of an item is a question mark, the load will silently fail if the file does not exist.
${includes} = ?"${maven.user.conf}/maven-user.properties", \
              ?"${maven.project.conf}/maven-user.properties"

#
# Maven Cache Configuration
#

```json
{
    "description": "Some data",
    "data": "aGVsbG8="
}
```

/// tip | Consejo

`aGVsbG8=` es la codificación base64 de `hello`.

///

Y luego Pydantic decodificará el string base64 y te dará los bytes originales en el campo `data` del modelo.

Recibirás una response como:

```json
{
  "description": "Some data",
  "content": "hello"
}
```

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

**FastAPI** **extraerá** los datos para **cada campo** de los **headers** en el request y te dará el modelo Pydantic que definiste.

## Revisa la Documentación { #check-the-docs }

Puedes ver los headers requeridos en la interfaz de documentación en `/docs`:

<div class="screenshot">

import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Nonnull;

/**
 * This annotation will mark your class as a Mojo, which is the implementation of a goal in a Maven plugin.
 * <p>
 * The mojo can be annotated with {@code org.apache.maven.api.di.*} annotations to

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	SSECustomerKeySize = 32

	// SSEIVSize is the size of the IV data
	SSEIVSize = 32 // 32 bytes

	// SSEDAREPackageBlockSize - SSE dare package block size.
	SSEDAREPackageBlockSize = 64 * 1024 // 64KiB bytes

	// SSEDAREPackageMetaSize - SSE dare package meta padding bytes.
	SSEDAREPackageMetaSize = 32 // 32 bytes

)

// KMSKeyID returns in AWS compatible KMS KeyID() format.

        return false;
    }

    // ===================================================================================
    //                                                                     Birthplace Mark
    //                                                                     ===============
    @Override
    public void markAsSelect() {
        __createdBySelect = true;
    }

    @Override

Aber FastAPI unterstützt auf die gleiche Weise auch die Verwendung von [`dataclasses`](https://docs.python.org/3/library/dataclasses.html):

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

Das ist dank **Pydantic** ebenfalls möglich, da es [„`dataclasses` intern unterstützt“](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel).

{* ../../docs_src/request_form_models/tutorial001_an_py310.py hl[9:11,15] *}

O **FastAPI** irá **extrair** as informações para **cada campo** dos **dados do formulário** na requisição e dar para você o modelo Pydantic que você definiu.

## Confira os Documentos { #check-the-docs }

Você pode verificar na UI de documentação em `/docs`:

<div class="screenshot">