#
# 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")

tf_workspace3()

common:elinux_armhf --copt -mfp16-format=ieee

# Config-specific options should come above this line.

# Load rc file written by ./configure.
try-import %workspace%/.tf_configure.bazelrc

# Load rc file with user-specific options.
try-import %workspace%/.bazelrc.user

# Here are bazelrc configs for release builds
# Build TensorFlow v2.

    tmp_file.write_text(json.dumps(openapi_json))

    with patch("pathlib.Path", return_value=tmp_file):
        importlib.import_module("docs_src.generate_clients.tutorial004_py39")

    modified_openapi = json.loads(tmp_file.read_text())
    assert modified_openapi == {
        "components": {
            "schemas": {
                "HTTPValidationError": {
                    "properties": {
                        "detail": {

                "input": '{"name": "Foo", "price": 50.5}',
            }
        ]
    }


def test_other_exceptions(client: TestClient):
    with patch("json.loads", side_effect=Exception):
        response = client.post("/items/", json={"test": "test2"})
        assert response.status_code == 400, response.text


def test_openapi_schema(client: TestClient):

Used by FastAPI:

* <a href="https://www.uvicorn.dev" target="_blank"><code>uvicorn</code></a> - for the server that loads and serves your application. This includes `uvicorn[standard]`, which includes some dependencies (e.g. `uvloop`) needed for high performance serving.
* `fastapi-cli[standard]` - to provide the `fastapi` command.

Used by FastAPI:

* <a href="https://www.uvicorn.dev" target="_blank"><code>uvicorn</code></a> - for the server that loads and serves your application. This includes `uvicorn[standard]`, which includes some dependencies (e.g. `uvloop`) needed for high performance serving.
* `fastapi-cli[standard]` - to provide the `fastapi` command.

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()