def process_item(item: int | str):

                                            {
                                                "name": "Baz",
                                                "price": "thirty five point four",
                                            },
                                        ],
                                    }
                                )
                                | IsDict(

                {
                    "name": "Bar",
                    "price": "35.4",
                },
                {
                    "name": "Baz",
                    "price": "thirty five point four",
                },
            ],
        ),
    ],
):
    results = {"item_id": item_id, "item": item}

                {
                    "name": "Bar",
                    "price": "35.4",
                },
                {
                    "name": "Baz",
                    "price": "thirty five point four",
                },
            ],
        ),
    ],
):
    results = {"item_id": item_id, "item": item}

```Python
user_dict = user_in.dict()
```

то теперь у нас есть `dict` с данными модели в переменной `user_dict` (это `dict` вместо объекта Pydantic-модели).

И если мы вызовем:

```Python
print(user_dict)
```

мы можем получить `dict` с такими данными:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

                                        "value": {
                                            "name": "Baz",
                                            "price": "thirty five point four",
                                        },
                                    },
                                },
                            }
                        },
                        "required": True,

Un générateur de projet fera toujours une mise en place très subjective que vous devriez modifier et adapter suivant vos besoins, mais cela reste un bon point de départ pour vos projets.

## Full Stack FastAPI PostgreSQL

and then we call:

```Python
user_dict = user_in.dict()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python
print(user_dict)
```

we would get a Python `dict` with:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

## Code size

This example might seem verbose. Keep in mind that we are mixing security, data models, utility functions and *path operations* in the same file.

But here's the key point.

The security and dependency injection stuff is written once.

And you can make it as complex as you want. And still, have it written only once, in a single place. With all the flexibility.

I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.