{
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["query", "query"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "42.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "a",
                },
                {
                    "type": "int_parsing",
                    "loc": ["query", "limit"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "b",
                },

@needs_pydanticv1
def test_union_scalar_list():
    # For coverage
    # TODO: there might not be a current valid code path that uses this, it would
    # potentially enable query parameters defined as both a scalar and a list
    # but that would require more refactors, also not sure it's really useful
    from fastapi._compat import is_pv1_scalar_field

    field_info = FieldInfo()

        {
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["body", "size"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "XL",
                }
            ],
            "body": {"title": "towel", "size": "XL"},
        }
    ) | IsDict(

        {
            "detail": [
                {
                    "type": "float_parsing",
                    "loc": ["body", "price"],
                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

        {
            "detail": [
                {
                    "type": "float_parsing",
                    "loc": ["body", "price"],
                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

else:
  _module_dir = _module_util.get_parent_dir_for_name("keras.api._v2.keras")
_current_module.__path__ = [_module_dir] + _current_module.__path__


# Enable TF2 behaviors
from tensorflow.python.compat import v2_compat as _compat
_compat.enable_v2_behavior()
_major_api_version = 2


# Load all plugin libraries from site-packages/tensorflow-plugins if we are
# running under pip.

        {
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["path", "item_id"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "foo",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [

                    {"data": "examples example_examples 1"},
                    {"data": "examples example_examples 2"},
                ],
            ),
        ):
            return item

    # TODO: enable these tests once/if Form(embed=False) is supported
    # TODO: In that case, define if File() should support example/examples too
    # @app.post("/form_example")
    # def form_example(firstname: str = Form(example="John")):

                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "a",
                },
                {
                    "type": "int_parsing",
                    "loc": ["query", "limit"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "b",
                },