* or more contributor license agreements. Licensed under the Elastic License
 * 2.0 and the Server Side Public License, v 1; you may not use this file except
 * in compliance with, at your election, the Elastic License 2.0 or the Server
 * Side Public License, v 1.
 */

package org.elasticsearch.gradle.internal;

import com.github.jengelman.gradle.plugins.shadow.tasks.ShadowJar;

## Quick Start

//
// 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

Doing this, our `GzipRequest` will take care of decompressing the data (if necessary) before passing it to our *path operations*.

After that, all of the processing logic is the same.

But because of our changes in `GzipRequest.body`, the request body will be automatically decompressed when it is loaded by **FastAPI** when needed.

/// warning

This is a more or less advanced section. If you are just starting, you can skip it.

You don't necessarily need OAuth2 scopes, and you can handle authentication and authorization however you want.

But OAuth2 with scopes can be nicely integrated into your API (with OpenAPI) and your API docs.

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

#### MinIO Custom Access and Secret Key files

To use other secret names follow the instructions above and replace `access_key` and `secret_key` with your custom names (e.g. `my_secret_key`,`my_custom_key`). Run your service with

```
docker service create --name="minio-service" \
  --secret="my_access_key" \
  --secret="my_secret_key" \
  --env="MINIO_ROOT_USER_FILE=my_access_key" \

If you have improvements to TensorFlow, send us your pull requests! For those
just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are

## Regression
<!-- Is this issue a regression? (Yes / No) -->
<!-- If Yes, optionally please include minio version or commit id or PR# that caused this regression, if you have these details. -->

## Your Environment
<!--- Include as many relevant details about the environment you experienced the bug in -->
* Version used (`minio --version`):
* Server setup and configuration:

pydantic==2.8.0
```

///

## Execute seu programa { #run-your-program }

Depois de ativar o ambiente virtual, você pode executar seu programa, e ele usará o Python dentro do seu ambiente virtual com os pacotes que você instalou lá.

<div class="termy">

```console
$ python main.py

Hello World
```

</div>

## Configure seu editor { #configure-your-editor }