new_alternate: List[Dict[str, str]] = []
    # Language names sourced from https://quickref.me/iso-639-1
    # Contributors may wish to update or change these, e.g. to fix capitalization.
    language_names_path = Path(__file__).parent / "../docs/language_names.yml"
    local_language_names: Dict[str, str] = mkdocs.utils.yaml_load(
        language_names_path.read_text(encoding="utf-8")
    )
    for lang_path in get_lang_paths():

    *,
    path: str,
    call: Callable[..., Any],
    name: Optional[str] = None,
    security_scopes: Optional[List[str]] = None,
    use_cache: bool = True,
) -> Dependant:
    path_param_names = get_path_param_names(path)
    endpoint_signature = get_typed_signature(call)
    signature_params = endpoint_signature.parameters
    dependant = Dependant(
        call=call,
        name=name,
        path=path,

But don't worry, you can show it as you wish to your final users in the frontend.

And your database models can use any other names you want.

But for the login *path operation*, we need to use these names to be compatible with the spec (and be able to, for example, use the integrated API documentation system).

The most secure is the code flow, but is more complex to implement as it requires more steps. As it is more complex, many providers end up suggesting the implicit flow.

!!! note
    It's common that each authentication provider names their flows in a different way, to make it part of their brand.

    But in the end, they are implementing the same OAuth2 standard.

```

!!! tip
    Peewee creates several magic attributes.

    It will automatically add an `id` attribute as an integer to be the primary key.

    It will chose the name of the tables based on the class names.

    For the `Item`, it will create an attribute `owner_id` with the integer ID of the `User`. But we don't declare it anywhere.

## Create the Pydantic models

Now let's check the file `sql_app/schemas.py`.

        bool,
        Doc(
            """
            Pydantic's `by_alias` parameter, passed to Pydantic models to define if
            the output should use the alias names (when provided) or the Python
            attribute names. In an API, if you set an alias, it's probably because you
            want to use it in the result, so you probably want to leave this set to
            `True`.
            """
        ),

It would depend mainly on the tool you use to **install** those requirements.

The most common way to do it is to have a file `requirements.txt` with the package names and their versions, one per line.

You would of course use the same ideas you read in [About FastAPI versions](versions.md){.internal-link target=_blank} to set the ranges of versions.

<img src="/img/tutorial/generate-clients/image06.png">

In this case you have:

* `ItemsService`
* `UsersService`

### Client Method Names

Right now the generated method names like `createItemItemsPost` don't look very clean:

```TypeScript
ItemsService.createItemItemsPost({name: "Plumbus", price: 5})
```

                )
            }
        )
    else:

        class Config:
            schema_extra = {
                "description": (
                    "response_model_by_alias=False is basically a quick hack, to support "
                    "proper OpenAPI use another model with the correct field names"
                )
            }

    contributors_results = get_contributors(pr_nodes=pr_nodes)
    authors = {**experts_results.authors, **contributors_results.authors}
    maintainers_logins = {"tiangolo"}
    bot_names = {"codecov", "github-actions", "pre-commit-ci", "dependabot"}
    maintainers = []
    for login in maintainers_logins:
        user = authors[login]
        maintainers.append(
            {