...But here I want to show you the community.

---

**FastAPI** receives a lot of support from the community. And I want to highlight their contributions.

These are the people that:

* [Help others with questions in GitHub](help-fastapi.md#help-others-with-questions-in-github){.internal-link target=_blank}.

# This workflow will build a Java project with Maven
# For more information see: https://help.github.com/actions/language-and-framework-guides/building-and-testing-java-with-maven

name: Java CI with Maven

on:
  push:
    branches:
    - master
    - "*.x"
  pull_request:
    branches:
    - master
    - "*.x"

jobs:
  build:

    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v2

You can connect the debugger in your editor, for example with Visual Studio Code or PyCharm.

## Call `uvicorn`

In your FastAPI application, import and run `uvicorn` directly:

```Python hl_lines="1  15"
{!../../docs_src/debugging/tutorial001.py!}
```

### About `__name__ == "__main__"`

The main purpose of the `__name__ == "__main__"` is to have some code that is executed when your file is called with:

<div class="termy">

```console

   *   absent.
   */

  /**
   * If {@code key} is not already associated with a value or if {@code key} is associated with
   * zero, associate it with {@code newValue}. Returns the previous value associated with {@code
   * key}, or zero if there was no mapping for {@code key}.
   */
  long putIfAbsent(K key, long newValue) {
    while (true) {

After having a Container (Docker) Image there are several ways to deploy it.

For example:

* With **Docker Compose** in a single server
* With a **Kubernetes** cluster
* With a Docker Swarm Mode cluster
* With another tool like Nomad
* With a cloud service that takes your container image and deploys it

## Docker Image with `uv`

The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

///

### About `**user_in.dict()`

#### Pydantic's `.dict()`

`user_in` is a Pydantic model of class `UserIn`.

Pydantic models have a `.dict()` method that returns a `dict` with the model's data.

So, if we create a Pydantic object `user_in` like:

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

        # By default, queries listed here will override any specified in a config file.
        # Prefix the list here with "+" to use these queries and those in the config file.
        # queries: ./path/to/local/query, your-org/your-repo/queries@main

    - name: Compile with Gradle with Build Scan
      if: ${{ matrix.language == 'java' && github.repository_owner == 'gradle' }}

# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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
#

				{Name: "temporary/0/"},
			},
		},
		// ListObjectsResult-34:
		//    * Listing with marker > last object should return empty
		//    * Listing an object with a trailing slash and '/' delimiter
		34: {
			IsTruncated: false,
			Objects:     []ObjectInfo{},
		},
		// ListObjectsResult-35 list with custom uncommon delimiter
		35: {
			IsTruncated: false,
			Objects: []ObjectInfo{