self.dependencies = list(dependencies or [])
        self.description = description or inspect.cleandoc(self.endpoint.__doc__ or "")
        # if a "form feed" character (page break) is found in the description text,
        # truncate description text to the content preceding the first "form feed"
        self.description = self.description.split("\f")[0].strip()
        response_fields = {}

    * With <a href="https://github.com/ciscorn/starlette-graphene3" class="external-link" target="_blank">starlette-graphene3</a>

## GraphQL with Strawberry { #graphql-with-strawberry }

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

# See https://docs.microsoft.com/en-us/cpp/preprocessor/preprocessor-experimental-overview
common:windows --copt=/Zc:preprocessor
common:windows --host_copt=/Zc:preprocessor

# Misc build options we need for windows.
common:windows --linkopt=/DEBUG
common:windows --host_linkopt=/DEBUG
common:windows --linkopt=/OPT:REF
common:windows --host_linkopt=/OPT:REF
common:windows --linkopt=/OPT:ICF

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

    * [Path Parameters and Numeric Validations - Order the parameters as you need, tricks](https://fastapi.tiangolo.com/tutorial/path-params-numeric-validations/#order-the-parameters-as-you-need-tricks)
        * [Better with `Annotated`](https://fastapi.tiangolo.com/tutorial/path-params-numeric-validations/#better-with-annotated)

        ),
    ] = None,
    include_in_schema: Annotated[
        bool,
        Doc(
            """
            To include (or not) this parameter field in the generated OpenAPI.
            You probably don't need it, but it's available.

            This affects the generated OpenAPI (e.g. visible at `/docs`).
            """
        ),
    ] = True,
    json_schema_extra: Annotated[
        Union[dict[str, Any], None],

        if (method.changeStatus == JApiChangeStatus.REMOVED) {
            return false
        }
        // @Override has source retention - so we need to peek into the sources
        return repository.isOverride(method)
    }

    Violation acceptOrReject(JApiCompatibility member, Violation rejection) {

            # (e.g. `TypeAdapter(Annotated[int, Field(alias='b')])`). In some cases, we
            # end up building the type adapter from a model field annotation so we
            # need to ignore the warning:
            if shared.PYDANTIC_VERSION_MINOR_TUPLE >= (2, 12):
                from pydantic.warnings import UnsupportedFieldAttributeWarning

                warnings.simplefilter(