*       servers {@code alpha}, {@code bravo}, and {@code charlie} and you occasionally need to
   *       take each of the servers offline, {@code consistentHash} will be a poor fit: It provides
   *       no way for you to specify which of the three buckets is disappearing. Thus, if your
   *       buckets change from {@code [alpha, bravo, charlie]} to {@code [bravo, charlie]}, it will

 * This communicates to your callers all of the semantic guarantees listed above, which is almost
 * always very useful information.
 *
 * <p>On the other hand, a <i>parameter</i> type of {@link ImmutableList} is generally a nuisance to
 * callers. Instead, accept {@link Iterable} and have your method or constructor body pass it to the
 * appropriate {@code copyOf} method itself.
 *

			objectName:     "foodir/fooobject",

   * close or flush the reader.
   *
   * <p>This is identical to {@link #copy(Readable, Appendable)} but optimized for these specific
   * types. CharBuffer has poor performance when being written into or read out of so round tripping
   * all the bytes through the buffer takes a long time. With these specialized types we can just
   * use a char array.
   *

//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Affero General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

## Importe o `BaseModel` do Pydantic { #import-pydantics-basemodel }

Primeiro, você precisa importar `BaseModel` do `pydantic`:

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Crie seu modelo de dados { #create-your-data-model }

Então você declara seu modelo de dados como uma classe que herda `BaseModel`.

Utilize os tipos Python padrão para todos os atributos:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}

あなたのAPIは独自の <abbr title="念のためですが、これはジョークです。クッキー同意とは関係ありませんが、APIでさえ不適切なクッキーを拒否できるとは愉快ですね。クッキーでも食べてください。🍪 （原文: This is a joke, just in case. It has nothing to do with cookie consents, but it's funny that even the API can now reject the poor cookies. Have a cookie. 🍪）">クッキー同意</abbr> を管理する能力を持っています。 🤪🍪

Pydanticのモデルの Configuration を利用して、 `extra` フィールドを `forbid` とすることができます。

{* ../../docs_src/cookie_param_models/tutorial002_an_py39.py hl[10] *}

## Enabling Parquet Format

Parquet is DISABLED by default since hostile crafted input can easily crash the server.

If you are in a controlled environment where it is safe to assume no hostile content can be uploaded to your cluster you can safely enable Parquet.
To enable Parquet set the environment variable `MINIO_API_SELECT_PARQUET=on`.

## Example using Python API

### 1. Prerequisites

### Step 1: Add AMQP endpoint to MinIO

The AMQP configuration is located under the sub-system `notify_amqp` top-level key. Create a configuration key-value pair here for your AMQP instance. The key is a name for your AMQP endpoint, and the value is a collection of key-value parameters described in the table below.

```
KEY:
notify_amqp[:name]  publish bucket notifications to AMQP endpoints

ARGS:

✅ Deployment successful!

🐔 Ready the chicken! Your app is ready at https://myapp.fastapicloud.dev
```

</div>

É isso! Agora você pode acessar sua aplicação nesse URL. ✨

#### Sobre a FastAPI Cloud { #about-fastapi-cloud }

**<a href="https://fastapicloud.com" class="external-link" target="_blank">FastAPI Cloud</a>** é construída pelo mesmo autor e equipe por trás do **FastAPI**.