}
            },
            "/level1/level2/level3/level4/override5": {
                "get": {
                    "tags": [
                        "level1a",
                        "level1b",
                        "level2a",
                        "level2b",
                        "level3a",
                        "level3b",
                        "level4a",
                        "level4b",

    return [pet1, pet2]


client = TestClient(app)


def test_filter_top_level_model():
    response = client.post(
        "/users", json={"email": "******@****.***", "password": "secret"}
    )
    assert response.json() == {"email": "******@****.***"}


def test_filter_second_level_model():
    response = client.get("/pets/1")
    assert response.json() == {
        "name": "Nibbler",

import mkdocs.commands.build
import mkdocs.commands.serve
import mkdocs.config
import mkdocs.utils
import typer
import yaml
from jinja2 import Template

logging.basicConfig(level=logging.INFO)

app = typer.Typer()

mkdocs_name = "mkdocs.yml"

missing_translation_snippet = """
{!../../../docs/missing-translation.md!}
"""

docs_path = Path("docs")
en_docs_path = Path("docs/en")

    name: str

    if PYDANTIC_V2:
        model_config = {
            "json_schema_extra": {
                "x-something-internal": {"level": 4},
            }
        }
    else:

        class Config:
            schema_extra = {
                "x-something-internal": {"level": 4},
            }


@app.get("/foo", response_model=Item)
def foo():
    return {"name": "Foo item"}

            ) && !oauth2.auth.code) {
                if (!isValid) {
                    oauth2.errCb({
                        authId: oauth2.auth.name,
                        source: "auth",
                        level: "warning",
                        message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
                    });
                }

  Or install the latest stable TensorFlow 1.X release:
    * `pip install -U "tensorflow==1.*"`

  Otherwise your code may be broken by the change.

  """)

# Make sure directory containing top level submodules is in
# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.
_API_MODULE = _sys.modules[__name__].bitwise  # pylint: disable=undefined-variable

  platforms = os.path.join(android_sdk_home_path, 'platforms')
  api_levels = sorted(os.listdir(platforms))
  api_levels = [x.replace('android-', '') for x in api_levels]

  def valid_api_level(api_level):
    return os.path.exists(
        os.path.join(android_sdk_home_path, 'platforms',
                     'android-' + api_level))

  android_api_level = prompt_loop_or_load_from_env(
      environ_cp,

    head_commit: PartialGithubEventHeadCommit


class PartialGithubEvent(BaseModel):
    workflow_run: PartialGithubEventWorkflowRun


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    settings = Settings()
    logging.info(f"Using config: {settings.json()}")
    g = Github(settings.input_token.get_secret_value())
    repo = g.get_repo(settings.github_repository)
    try:

TensorFlow API v2 init files and is stored in the wheel file after the build.

See README.md file for "how to run" instruction.
"""

import logging
import unittest
import pkg_resources

logging.basicConfig(level=logging.INFO)


class ImportApiPackagesTest(unittest.TestCase):
  """ImportApiPackagesTest class. See description at the top of the file."""

  def setUp(self):
    def _get_api_packages_v2():

    return f"baz_{route.name}"


class Item(BaseModel):
    name: str
    price: float


class Message(BaseModel):
    title: str
    description: str


def test_top_level_generate_unique_id():
    app = FastAPI(generate_unique_id_function=custom_generate_unique_id)
    router = APIRouter()

    @app.post("/", response_model=List[Item], responses={404: {"model": List[Message]}})