This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
    where an integer (but not int typed) is used. Encoding this as a float
    results in failed round-tripping between encode and parse.
    Our Id type is a prime example of this.

    >>> decimal_encoder(Decimal("1.0"))
    1.0

    >>> decimal_encoder(Decimal("1"))
    1

    return client


@pytest.mark.parametrize(
    "path,expected_status,expected_response",
    [
        ("/", 200, {"message": "Hello World"}),
        ("/nonexistent", 404, {"detail": "Not Found"}),
    ],
)
def test_get_path(client: TestClient, path, expected_status, expected_response):
    response = client.get(path)
    assert response.status_code == expected_status

        # The hero is no longer found
        response = client.get(f"/heroes/{hero_id}")
        assert response.status_code == 404, response.text

        # Delete a hero that does not exist
        response = client.delete(f"/heroes/{hero_id}")
        assert response.status_code == 404, response.text
        assert response.json() == snapshot({"detail": "Hero not found"})

        # Update a hero that does not exist

    response = client.get(
        "/items/bar?token=jessica", headers={"X-Token": "fake-super-secret-token"}
    )
    assert response.status_code == 404
    assert response.json() == {"detail": "Item not found"}


def test_items_plumbus_with_no_token(client: TestClient):
    response = client.get(
        "/items/plumbus", headers={"X-Token": "fake-super-secret-token"}
    )
    assert response.status_code == 422

                            "description": "The GraphiQL integrated development environment.",
                        },
                        "404": {
                            "description": "Not found if GraphiQL or query via GET are not enabled.",
                        },
                    },
                    "summary": "Handle Http Get",
                },
                "post": {

        "paths": {
            "/items/{item_id}": {
                "get": {
                    "responses": {
                        "404": {"description": "Item not found"},
                        "302": {"description": "The item was moved"},
                        "403": {"description": "Not enough privileges"},
                        "200": {

register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64_cuda")

# 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.

/// 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. 🤓

///

## Dataclasses in `response_model` { #dataclasses-in-response-model }

You can also use `dataclasses` in the `response_model` parameter:

    items = {"foo": "The Foo Wrestlers"}


    @app.get("/items/{item_id}")
    async def read_item(item_id: str):
        if item_id not in items:
            raise HTTPException(status_code=404, detail="Item not found")
        return {"item": items[item_id]}
    ```
    """

    def __init__(
        self,
        status_code: Annotated[
            int,
            Doc(
                """

        response = client.delete(f"/heroes/{hero_id}")
        assert response.status_code == 404, response.text
        assert response.json() == snapshot({"detail": "Hero not found"})


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text
    assert response.json() == snapshot(
        {