for param in parameters
                    if param.get("required")
                }
                # Make sure required definitions of the same parameter take precedence
                # over non-required definitions
                all_parameters.update(required_parameters)
                operation["parameters"] = list(all_parameters.values())
            if method in METHODS_WITH_BODY:

        else:
          ver = int(sm_compute_match.group(2))
          if ver < 30:
            print(
                'ERROR: TensorFlow only supports small CUDA compute'
                ' capabilities of sm_30 and higher. Please re-specify the list'
                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:

try:
    import email_validator

    assert email_validator  # make autoflake ignore the unused import
    from pydantic import EmailStr
except ImportError:  # pragma: no cover

    class EmailStr(str):  # type: ignore
        @classmethod
        def __get_validators__(cls) -> Iterable[Callable[..., Any]]:
            yield cls.validate

        @classmethod

    try:
        from pydantic_core.core_schema import (
            with_info_plain_validator_function as with_info_plain_validator_function,
        )
    except ImportError:  # pragma: no cover
        from pydantic_core.core_schema import (
            general_plain_validator_function as with_info_plain_validator_function,  # noqa: F401
        )

    Required = PydanticUndefined