Repository: Lightning-AI/pytorch-lightning
File: pytorch_lightning/loggers/base.py (Python)

Code before:
        logger_iterable: An iterable collection of loggers
    """

    def __init__(self, logger_iterable: Iterable[LightningLoggerBase]):
        super().__init__()
        self._logger_iterable = logger_iterable

    def __getitem__(self, index: int) -> LightningLoggerBase:
        return list(self._logger_iterable)[index]

    def after_save_checkpoint(self, checkpoint_callback: "ReferenceType[ModelCheckpoint]") -> None:
        for logger in self._logger_iterable:
            logger.after_save_checkpoint(checkpoint_callback)

    def update_agg_funcs(
        self,
        agg_key_funcs: Optional[Mapping[str, Callable[[Sequence[float]], float]]] = None,
        agg_default_func: Callable[[Sequence[float]], float] = np.mean,
    ):
        for logger in self._logger_iterable:
            logger.update_agg_funcs(agg_key_funcs, agg_default_func)

    @property
    def experiment(self) -> List[Any]:
        """
        Returns a list of experiment objects for all the loggers in the logger collection
        """
        return [logger.experiment for logger in self._logger_iterable]

    def agg_and_log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
        for logger in self._logger_iterable:
            logger.agg_and_log_metrics(metrics, step)

    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
        for logger in self._logger_iterable:
            logger.log_metrics(metrics, step)

    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
        for logger in self._logger_iterable:
            logger.log_hyperparams(params)

    def log_graph(self, model: "pl.LightningModule", input_array=None) -> None:
        for logger in self._logger_iterable:
            logger.log_graph(model, input_array)

    def save(self) -> None:
        for logger in self._logger_iterable:
            logger.save()

    def finalize(self, status: str) -> None:
        for logger in self._logger_iterable:

Review comment (suggestion):
```suggestion
        Returns a list of experiment objects for all the loggers in the logger collection.
```

Code after:
        logger_iterable: An iterable collection of loggers
    """

    def __init__(self, logger_iterable: Iterable[LightningLoggerBase]):
        super().__init__()
        self._logger_iterable = logger_iterable

    def __getitem__(self, index: int) -> LightningLoggerBase:
        return list(self._logger_iterable)[index]

    def after_save_checkpoint(self, checkpoint_callback: "ReferenceType[ModelCheckpoint]") -> None:
        for logger in self._logger_iterable:
            logger.after_save_checkpoint(checkpoint_callback)

    def update_agg_funcs(
        self,
        agg_key_funcs: Optional[Mapping[str, Callable[[Sequence[float]], float]]] = None,
        agg_default_func: Callable[[Sequence[float]], float] = np.mean,
    ):
        for logger in self._logger_iterable:
            logger.update_agg_funcs(agg_key_funcs, agg_default_func)

    @property
    def experiment(self) -> List[Any]:
        """
        Returns a list of experiment objects for all the loggers in the logger collection.
        """
        return [logger.experiment for logger in self._logger_iterable]

    def agg_and_log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
        for logger in self._logger_iterable:
            logger.agg_and_log_metrics(metrics, step)

    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
        for logger in self._logger_iterable:
            logger.log_metrics(metrics, step)

    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
        for logger in self._logger_iterable:
            logger.log_hyperparams(params)

    def log_graph(self, model: "pl.LightningModule", input_array=None) -> None:
        for logger in self._logger_iterable:
            logger.log_graph(model, input_array)

    def save(self) -> None:
        for logger in self._logger_iterable:
            logger.save()

    def finalize(self, status: str) -> None:
        for logger in self._logger_iterable: