)
    result = None
    try:
        if isinstance(llm_or_chain_factory, BaseLanguageModel):
            output: Any = _run_llm(
                llm_or_chain_factory,
                example.inputs,
                config["callbacks"],
                tags=config["tags"],
                input_mapper=input_mapper,
            )
        else:
            chain = llm_or_chain_factory()
            output = _run_chain(
                chain,
                example.inputs,
                config["callbacks"],
                tags=config["tags"],
                input_mapper=input_mapper,
            )
        result = output
    except Exception as e:
        error_type = type(e).__name__
        logger.warning(
            f"{chain_or_llm} failed for example {example.id} "
            f"with inputs {example.inputs}"
            f"\nError Type: {error_type}, Message: {e}"
        )
        result = {"Error": repr(e)}
    return result
## Public API
def _prepare_eval_run(
    client: Client,
    dataset_name: str,
    llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
    project_name: str,
    project_metadata: Optional[Dict[str, Any]] = None,
) -> Tuple[MCF, str, Dataset, List[Example]]:
    wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name)
    dataset = client.read_dataset(dataset_name=dataset_name)
    try:
        project = client.create_project(
            project_name,
            reference_dataset_id=dataset.id,
            project_extra={"metadata": project_metadata} if project_metadata else {},
        )
    except ValueError as e:
        if "already exists " not in str(e):
            raise e
        raise ValueError(