def __init__(
        self,
        metrics: Optional[
            Tuple[Callable[[jnp.ndarray, Array], Union[float, Array]], ...]
        ] = None,
        early_stopping_patience: int = 0,
        early_stopping_monitor: str = "val_loss",
        early_stopping_min_delta: float = 0.0,
        eval_every_n_epochs: int = 1,
        disable_training_metrics_computation: bool = False,
        verbose: bool = True,
    ):
        """
        An object to configure the monitoring of the posterior fitting.

        Parameters
        ----------
        metrics: Optional[Callable[[jnp.ndarray, Array], Union[float, Array]]]
            Metrics to monitor during training.
        early_stopping_patience: int
            Number of consecutive epochs without an improvement in the performance on the validation set before stopping
             the training.
        early_stopping_monitor: str
            Validation metric to be monitored for early stopping.
        early_stopping_min_delta: float
            Minimum change between updates to be considered an improvement, i.e., if the absolute change is less than
            `early_stopping_min_delta` then this is not considered an improvement leading to a potential early stop.
        eval_every_n_epochs: int
            Number of training epochs between validation. To disable, set
            `eval_every_n_epochs` to None or 0 (i.e., no validation metrics will be computed during training).
        disable_training_metrics_computation: bool
            if True, during training the only metric computed is the objective function.
            Otherwise, all the metrics provided by the user at runtime will be computed for the training step.
        verbose: bool
            Whether to log the training progress.
        """
        if metrics is not None:
            if type(metrics) != tuple:
                raise ValueError("`metrics` must be a tuple of callable metrics.")
            for metric in metrics:
                if not callable(metric):
                    raise ValueError(
                        f"All metrics in `metrics` must be callable objects, but {metric} is not."
                    )

        self.metrics = metrics
        self.early_stopping_patience = early_stopping_patience
        self.early_stopping_monitor = early_stopping_monitor
        self.early_stopping_min_delta = early_stopping_min_delta
        self.eval_every_n_epochs = eval_every_n_epochs
        self.disable_training_metrics_computation = disable_training_metrics_computation
        self.verbose = verbose

        An object to configure the monitoring of the posterior fitting.

        Parameters
        ----------
        metrics: Optional[Callable[[jnp.ndarray, Array], Union[float, Array]]]
            Metrics to monitor during training.
        early_stopping_patience: int
            Number of consecutive epochs without an improvement in the performance on the validation set before stopping
             the training.
        early_stopping_monitor: str
            Validation metric to be monitored for early stopping.
        early_stopping_min_delta: float
            Minimum change between updates to be considered an improvement, i.e., if the absolute change is less than
            `early_stopping_min_delta` then this is not considered an improvement leading to a potential early stop.
        eval_every_n_epochs: int
            Number of training epochs between validation. To disable, set
            `eval_every_n_epochs` to None or 0 (i.e., no validation metrics will be computed during training).
        disable_training_metrics_computation: bool
            if True, during training the only metric computed is the objective function.
            Otherwise, all the metrics provided by the user at runtime will be computed for the training step.
        verbose: bool
            Whether to log the training progress.
        
fortuna/prob_model/fit_config/monitor.py