)
            else:
                return create_model("RunnableLambdaInput", __root__=(List[Any], None))
        if self.InputType != Any:
            return super().get_input_schema(config)
        if dict_keys := get_function_first_arg_dict_keys(func):
            return create_model(
                "RunnableLambdaInput",
                **{key: (Any, None) for key in dict_keys},  # type: ignore
            )
        return super().get_input_schema(config)
    @property
    def OutputType(self) -> Any:
        """The type of the output of this runnable as a type annotation."""
        func = getattr(self, "func", None) or getattr(self, "afunc")
        try:
            sig = inspect.signature(func)
            return (
                sig.return_annotation
                if sig.return_annotation != inspect.Signature.empty
                else Any
            )
        except ValueError:
            return Any
    def __eq__(self, other: Any) -> bool:
        if isinstance(other, RunnableLambda):
            if hasattr(self, "func") and hasattr(other, "func"):
                return self.func == other.func
            elif hasattr(self, "afunc") and hasattr(other, "afunc"):
                return self.afunc == other.afunc
            else:
                return False
        else:
            return False
    def __repr__(self) -> str:
        """A string representation of this runnable."""
        if hasattr(self, "func"):
            return f"RunnableLambda({get_lambda_source(self.func) or '...'})"
        elif hasattr(self, "afunc"):
            return f"RunnableLambda(afunc={get_lambda_source(self.afunc) or '...'})"
        else:
            return "RunnableLambda(...)"
    def _invoke(