* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient

from ...utils import needs_py39, needs_pydanticv1


@pytest.fixture(scope="module")
def client(tmp_path_factory: pytest.TempPathFactory):
    tmp_path = tmp_path_factory.mktemp("data")
    cwd = os.getcwd()
    os.chdir(tmp_path)
    test_db = Path("./sql_app.db")
    if test_db.is_file():  # pragma: nocover
        test_db.unlink()

from fastapi.testclient import TestClient

from ...utils import needs_py310, needs_pydanticv1


@pytest.fixture(scope="module", name="client")
def get_client(tmp_path_factory: pytest.TempPathFactory):
    tmp_path = tmp_path_factory.mktemp("data")
    cwd = os.getcwd()
    os.chdir(tmp_path)
    test_db = Path("./sql_app.db")
    if test_db.is_file():  # pragma: nocover
        test_db.unlink()

from fastapi.testclient import TestClient

from ...utils import needs_py39, needs_pydanticv1


@pytest.fixture(scope="module", name="client")
def get_client(tmp_path_factory: pytest.TempPathFactory):
    tmp_path = tmp_path_factory.mktemp("data")
    cwd = os.getcwd()
    os.chdir(tmp_path)
    test_db = Path("./sql_app.db")
    if test_db.is_file():  # pragma: nocover
        test_db.unlink()

            )

        def get_default(self) -> Any:
            if self.field_info.is_required():
                return Undefined
            return self.field_info.get_default(call_default_factory=True)

        def validate(
            self,
            value: Any,
            values: Dict[str, Any] = {},  # noqa: B006
            *,
            loc: Tuple[Union[int, str], ...] = (),