The key factor is that a dependency should be a "callable".

A "**callable**" in Python is anything that Python can "call" like a function.

So, if you have an object `something` (that might _not_ be a function) and you can "call" it (execute it) like:

```Python
something()
```

or

```Python
something(some_argument, some_keyword_argument="foo")
```

// Copyright (c) 2015-2021 MinIO, Inc.
//
// This file is part of MinIO Object Storage stack
//
// 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

// Copyright (c) 2015-2021 MinIO, Inc.
//
// This file is part of MinIO Object Storage stack
//
// 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

Then you create an instance of that class with some values and it will validate the values, convert them to the appropriate type (if that's the case) and give you an object with all the data.

And you get all the editor support with that resulting object.

An example from the official Pydantic docs:

{* ../../docs_src/python_types/tutorial011_py310.py *}

/// info

import org.jline.console.impl.JlineCommandRegistry;
import org.jline.reader.Completer;
import org.jline.reader.impl.completer.ArgumentCompleter;
import org.jline.reader.impl.completer.StringsCompleter;

import static java.util.Objects.requireNonNull;

@Named("builtin")
@Singleton
public class BuiltinShellCommandRegistryFactory implements ShellCommandRegistryFactory {
    @Override

## Modelos aninhados { #nested-models }

Cada atributo de um modelo Pydantic tem um tipo.

Mas esse tipo pode ser outro modelo Pydantic.

Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos.

Tudo isso, aninhado arbitrariamente.

### Defina um sub-modelo { #define-a-submodel }

Mais si vous avez besoin d'accéder aux variables et objets de la même application **FastAPI**, ou si vous avez besoin d'effectuer de petites tâches d'arrière-plan (comme envoyer des notifications par email), vous pouvez simplement vous contenter d'utiliser `BackgroundTasks`.

## Résumé

    private final Map<ApiChange, String> acceptedApiChanges
    private final File mainApiChangesJsonFile
    private final File projectRootDir

    AbstractGradleViolationRule(Map<String, Object> params) {
        AcceptedViolationsProvider acceptedApiChanges = ((AcceptedViolationsProvider) params.get("acceptedApiChanges"))

   <span style="background-color:#007166"><font color="#D3D7CF"> module </font></span>  🐍 main.py

     <span style="background-color:#007166"><font color="#D3D7CF"> code </font></span>  Importing the FastAPI app object from the module with
             the following code:

// Copyright (c) 2015-2021 MinIO, Inc.
//
// This file is part of MinIO Object Storage stack
//
// 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