Our codebase uses `null` extensively to represent an absence of value or optionality of the argument.
We utilize nullness annotations in Java code and somewhat rely on IDE warnings to guide us.
However, the Java code we have is not fully annotated, which causes several consequences:
* IDE warnings can be misleading
* Redundant null checks may increase binary size, hurt performance, and increase cognitive load for readers

import warnings

import pytest
from starlette.testclient import TestClient

warnings.filterwarnings(
    "ignore",
    message=r"The 'lia' package has been renamed to 'cross_web'\..*",
    category=DeprecationWarning,
)

from docs_src.graphql_.tutorial001_py39 import app  # noqa: E402


@pytest.fixture(name="client")
def get_client() -> TestClient:
    return TestClient(app)


def test_query(client: TestClient):

        yield c
    # Clean up connection explicitly to avoid resource warning
    mod.engine.dispose()


def test_crud_app(client: TestClient):
    # TODO: this warns that SQLModel.from_orm is deprecated in Pydantic v1, refactor
    # this if using obj.model_validate becomes independent of Pydantic v2
    with warnings.catch_warnings(record=True):
        warnings.simplefilter("always")
        # No heroes before creating

        yield c
    # Clean up connection explicitly to avoid resource warning
    mod.engine.dispose()


def test_crud_app(client: TestClient):
    # TODO: this warns that SQLModel.from_orm is deprecated in Pydantic v1, refactor
    # this if using obj.model_validate becomes independent of Pydantic v2
    with warnings.catch_warnings(record=True):
        warnings.simplefilter("always")
        # No heroes before creating

    )


def is_pydantic_v1_model_instance(obj: Any) -> bool:
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", UserWarning)
        from pydantic import v1
    return isinstance(obj, v1.BaseModel)


def is_pydantic_v1_model_class(cls: Any) -> bool:
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", UserWarning)
        from pydantic import v1

jieba==0.42.1
# For image processing by Material for MkDocs
pillow==11.3.0
# For image processing by Material for MkDocs
cairosvg==2.8.2
mkdocstrings[python]==0.30.1
griffe-typingdoc==0.3.0
griffe-warnings-deprecated==1.1.0
# For griffe, it formats with black
black==25.1.0
mkdocs-macros-plugin==1.4.1
markdown-include-variants==0.0.8

import sys
import warnings
from typing import Union

import pytest

from tests.utils import skip_module_if_py_gte_314

if sys.version_info >= (3, 14):
    skip_module_if_py_gte_314()

from fastapi import FastAPI
from fastapi.exceptions import PydanticV1NotSupportedError

with warnings.catch_warnings():
    warnings.simplefilter("ignore", UserWarning)
    from pydantic.v1 import BaseModel

            assertThat("Has errors", richReport.errors.map { it.message }, inAnyOrder(errors))
        }

        fun assertHasWarnings(vararg warnings: String) {
            assertThat("Has warnings", richReport.warnings.map { it.message }, inAnyOrder(warnings))
        }

        fun assertHasInformation(vararg information: String) {

import re
import warnings
from collections.abc import MutableMapping
from typing import (
    TYPE_CHECKING,
    Any,
    Optional,
    Union,
)
from weakref import WeakKeyDictionary

import fastapi
from fastapi._compat import (
    BaseConfig,
    ModelField,
    PydanticSchemaGenerationError,
    Undefined,
    UndefinedType,
    Validator,
    annotation_is_pydantic_v1,
)

    assert jsonable_encoder(subclass_model) == {"dt_field": "2019-01-01T08:00:00+00:00"}


def test_json_encoder_error_with_pydanticv1():
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", UserWarning)
        from pydantic import v1

    class ModelV1(v1.BaseModel):
        name: str

    data = ModelV1(name="test")