) -> T:
        if isinstance(input, BaseMessage):
            return self._call_with_config(
                lambda inner_input: self.parse_result(
                    [ChatGeneration(message=inner_input)]
                ),
                input,
                config,
                run_type="parser",
            )
        else:
            return self._call_with_config(
                lambda inner_input: self.parse_result([Generation(text=inner_input)]),
                input,
                config,
                run_type="parser",
            )
[docs]    async def ainvoke(
        self, input: str | BaseMessage, config: RunnableConfig | None = None
    ) -> T:
        if isinstance(input, BaseMessage):
            return await self._acall_with_config(
                lambda inner_input: self.aparse_result(
                    [ChatGeneration(message=inner_input)]
                ),
                input,
                config,
                run_type="parser",
            )
        else:
            return await self._acall_with_config(
                lambda inner_input: self.aparse_result([Generation(text=inner_input)]),
                input,
                config,
                run_type="parser",
            )
[docs]class BaseOutputParser(BaseLLMOutputParser, Runnable[Union[str, BaseMessage], T]):
    """Base class to parse the output of an LLM call.
    Output parsers help structure language model responses.
    Example:
        .. code-block:: python
            class BooleanOutputParser(BaseOutputParser[bool]):
                true_val: str = "YES"
                false_val: str = "NO"
                def parse(self, text: str) -> bool:
                    cleaned_text = text.strip().upper()
                    if cleaned_text not in (self.true_val.upper(), self.false_val.upper()):