/*
 * Copyright (C) 2007 The Guava Authors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,

  - login: DMantis
    avatarUrl: https://avatars.githubusercontent.com/u/9536869?v=4
    url: https://github.com/DMantis
  - login: hard-coders
    avatarUrl: https://avatars.githubusercontent.com/u/9651103?u=95db33927bbff1ed1c07efddeb97ac2ff33068ed&v=4
    url: https://github.com/hard-coders
  - login: supdann
    avatarUrl: https://avatars.githubusercontent.com/u/9986994?u=9671810f4ae9504c063227fee34fd47567ff6954&v=4

Sie können für Typ Deklarationen auch `str`, `float`, `bool` und viele andere komplexe Datentypen verwenden.

Mehrere davon werden wir in den nächsten Kapiteln erkunden.

## Die Reihenfolge ist wichtig

Wenn Sie *Pfadoperationen* erstellen, haben Sie manchmal einen fixen Pfad.

Tatsächlich gibt es in einigen Fällen sogar **zwei JSON-Schemas** in OpenAPI für dasselbe Pydantic-Modell für Eingabe und Ausgabe, je nachdem, ob sie **Defaultwerte** haben.

Sehen wir uns an, wie das funktioniert und wie Sie es bei Bedarf ändern können.

## Pydantic-Modelle für Eingabe und Ausgabe

Nehmen wir an, Sie haben ein Pydantic-Modell mit Defaultwerten wie dieses:

//// tab | Python 3.10+

```Python hl_lines="7"

```Python hl_lines="27"
{!> ../../docs_src/body_multiple_params/tutorial004.py!}
```

////

/// info

`Body` hat die gleichen zusätzlichen Validierungs- und Metadaten-Parameter wie `Query` und `Path` und andere, die Sie später kennenlernen.

///

## Einen einzelnen Body-Parameter einbetten

Nehmen wir an, Sie haben nur einen einzelnen `item`-Body-Parameter, ein Pydantic-Modell `Item`.

/*
 * Copyright 2020 the original author or authors.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,

/*
 * Copyright (C) 2008 The Guava Authors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,

/*
 * Copyright (C) 2009 The Guava Authors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,

# Release 1.13.0

## Major Features and Improvements

*   TensorFlow Lite has moved from contrib to core. This means that Python
    modules are under `tf.lite` and source code is now under `tensorflow/lite`
    rather than `tensorflow/contrib/lite`.
*   TensorFlow GPU binaries are now built against CUDA 10 and TensorRT 5.0.
*   Support for Python3.7 on all operating systems.

   to use a memory-hard function (Argon2) that (on purpose) consumes a lot of memory and CPU.
   The new KMS-based approach can use a key derivation function that is orders of magnitudes
   cheaper w.r.t. memory and CPU.
- Root credentials can now be changed easily. Before, a two-step process was required to
   change the cluster root credentials since they were used to en/decrypt the IAM data.