"""
    Run the language model on the example.
    Args:
        llm: The language model to run.
        inputs: The input dictionary.
        callbacks: The callbacks to use during the run.
        tags: Optional tags to add to the run.
        input_mapper: function to map to the inputs dictionary from an Example
    Returns:
        The LLMResult or ChatResult.
    Raises:
        ValueError: If the LLM type is unsupported.
        InputFormatError: If the input format is invalid.
    """
    if input_mapper is not None:
        prompt_or_messages = input_mapper(inputs)
        if isinstance(prompt_or_messages, str):
            llm_output: Union[str, BaseMessage] = llm.predict(
                prompt_or_messages, callbacks=callbacks, tags=tags
            )
        elif isinstance(prompt_or_messages, list) and all(
            isinstance(msg, BaseMessage) for msg in prompt_or_messages
        ):
            llm_output = llm.predict_messages(
                prompt_or_messages, callbacks=callbacks, tags=tags
            )
        else:
            raise InputFormatError(
                "Input mapper returned invalid format: "
                f" {prompt_or_messages}"
                "\nExpected a single string or list of chat messages."
            )
    else:
        try:
            llm_prompts = _get_prompt(inputs)
            llm_output = llm.predict(llm_prompts, callbacks=callbacks, tags=tags)
        except InputFormatError:
            llm_messages = _get_messages(inputs)
            llm_output = llm.predict_messages(llm_messages, callbacks=callbacks)
    return llm_output
def _run_chain(
    chain: Union[Chain, Runnable],
    inputs: Dict[str, Any],