not_: Optional["SchemaOrBool"] = Field(default=None, alias="not")
    if_: Optional["SchemaOrBool"] = Field(default=None, alias="if")
    then: Optional["SchemaOrBool"] = None
    else_: Optional["SchemaOrBool"] = Field(default=None, alias="else")
    dependentSchemas: Optional[Dict[str, "SchemaOrBool"]] = None
    prefixItems: Optional[List["SchemaOrBool"]] = None
    # TODO: uncomment and remove below when deprecating Pydantic v1

        return [{"item_id": "i1", "user_id": "u1"}, {"item_id": "i2", "user_id": "u2"}]
    else:
        return [{"item_id": "i2", "user_id": user_id}]


@item_router.get("/{item_id}")
def get_item(item_id: str, user_id: Optional[str] = None):
    if user_id is None:
        return {"item_id": item_id}
    else:
        return {"item_id": item_id, "user_id": user_id}

<img src="/img/tutorial/custom-response/image01.png">

## Available responses

Here are some of the available responses.

Keep in mind that you can use `Response` to return anything else, or even create a custom sub-class.

!!! note "Technical Details"
    You could also use `from starlette.responses import HTMLResponse`.

By validating the data with Pydantic we can make sure that we have, for example, exactly a `list` of `str` with the scopes and a `str` with the `username`.

Instead of, for example, a `dict`, or something else, as it could break the application at some point later, making it a security risk.

We also verify that we have a user with that username, and if not, we raise that same exception we created before.

=== "Python 3.10+"

        dependant.request_param_name = param_name
        return True
    elif lenient_issubclass(type_annotation, WebSocket):
        dependant.websocket_param_name = param_name
        return True
    elif lenient_issubclass(type_annotation, HTTPConnection):
        dependant.http_connection_param_name = param_name
        return True
    elif lenient_issubclass(type_annotation, Response):

        if field_info.description:
            parameter["description"] = field_info.description
        if field_info.openapi_examples:
            parameter["examples"] = jsonable_encoder(field_info.openapi_examples)
        elif field_info.example != Undefined:
            parameter["example"] = jsonable_encoder(field_info.example)
        if field_info.deprecated:
            parameter["deprecated"] = True
        parameters.append(parameter)

        # All this is only relevant locally, on GitHub Actions all this is done through
        # artifacts and multiple workflows, so it doesn't matter if directories are
        # removed or not.
    else:
        dist_path = site_path / lang
        shutil.rmtree(dist_path, ignore_errors=True)
    current_dir = os.getcwd()
    os.chdir(lang_path)
    shutil.rmtree(build_site_dist_path, ignore_errors=True)

So, a Python class is also a **callable**.

Then, in **FastAPI**, you could use a Python class as a dependency.

What FastAPI actually checks is that it is a "callable" (function, class or anything else) and the parameters defined.

                    errors=[error], model=RequestErrorModel
                ).errors()
                use_errors.extend(new_errors)
            elif isinstance(error, list):
                use_errors.extend(_normalize_errors(error))
            else:
                use_errors.append(error)
        return use_errors

    def _model_rebuild(model: Type[BaseModel]) -> None:
        model.update_forward_refs()

And as the code related to SQLAlchemy and the SQLAlchemy models lives in separate independent files, you would even be able to perform the migrations with Alembic without having to install FastAPI, Pydantic, or anything else.

The same way, you would be able to use the same SQLAlchemy models and utilities in other parts of your code that are not related to **FastAPI**.