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:

                **function***.
            * `"request"`: start the dependency before the *path operation function*
                that handles the request (similar to when using `"function"`), but end
                **after** the response is sent back to the client. So, the dependency
                function will be executed **around** the **request** and response cycle.

common:windows --host_linkopt=/OPT:REF
common:windows --linkopt=/OPT:ICF
common:windows --host_linkopt=/OPT:ICF

# Verbose failure logs when something goes wrong
common:windows --verbose_failures

# Work around potential issues with large command lines on windows.
# See: https://github.com/bazelbuild/bazel/issues/5163
common:windows --features=compiler_param_file

# Use old toolchains for Windows builds.

* 🌐 Sync Portuguese docs (pages found with script). PR [#14554](https://github.com/fastapi/fastapi/pull/14554) by [@YuriiMotov](https://github.com/YuriiMotov).
* 🌐 Sync Spanish docs (outdated pages found with script). PR [#14553](https://github.com/fastapi/fastapi/pull/14553) by [@YuriiMotov](https://github.com/YuriiMotov).

    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": {

    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