- Sélectionnez "Python".
- Lancez le <abbr title="En anglais: debugger">débogueur</abbr> avec l'option "`Python: Current File (Integrated Terminal)`".

Il démarrera alors le serveur avec votre code **FastAPI**, s'arrêtera à vos points d'arrêt, etc.

Voici à quoi cela pourrait ressembler :

<img src="/img/tutorial/debugging/image01.png">

---

Si vous utilisez Pycharm, vous pouvez :

- Ouvrir le menu "Run".

def get_full_name(first_name: str, last_name: str):
    full_name = first_name.title() + " " + last_name.title()
    return full_name

  - type: dropdown
    id: source
    attributes:
      label: Source
      description: TensorFlow installed from

from unittest.mock import patch

from docs_src.python_types.tutorial006_py39 import process_items


def test_process_items():
    with patch("builtins.print") as mock_print:
        process_items(["item_a", "item_b", "item_c"])

    assert mock_print.call_count == 3
    call_args = [arg.args for arg in mock_print.call_args_list]
    assert call_args == [
        ("item_a",),
        ("item_b",),
        ("item_c",),

from typing import Union


def process_item(item: Union[int, str]):

def process_items(items: list[str]):
    for item in items:

            'code': authorization_code, 'redirect_uri': callback_uri}
    id_token_response = requests.post(
        token_url, data=data, verify=False,
        allow_redirects=False, auth=(client_id, client_secret))

    print('body: ' + id_token_response.text)

    # we can now use the id_token as much as we want to access protected resources.
    tokens = json.loads(id_token_response.text)
    id_token = tokens['id_token']

-   type: dropdown id: source attributes: label: Source description: Tensorflow

            }
        }

        int pad = lastLength - buffer.length();
        lastLength = buffer.length();
        pad(buffer, pad);
        buffer.append('\r');
        out.print(buffer);
        out.flush();
        buffer.setLength(0);
    }

    private void pad(StringBuilder buffer, int spaces) {
        String block = "                                        ";

from typing import Annotated

from fastapi import Depends, FastAPI

app = FastAPI()


def get_username():
    try:
        yield "Rick"
    finally:
        print("Cleanup up before response is sent")


@app.get("/users/me")
def get_user_me(username: Annotated[str, Depends(get_username, scope="function")]):