def log_metrics(self,
                    train_metrics: dict,
                    val_metrics: dict = None,
                    epoch: int = None,
                    log_to_console: bool = False) -> None:
        """
        Sends all of the train metrics (and validation metrics, if provided) to tensorboard.
        """
        metric_names = set(train_metrics.keys())
        if val_metrics is not None:
            metric_names.update(val_metrics.keys())
        val_metrics = val_metrics or {}

        # For logging to the console
        if log_to_console:
            dual_message_template = "%s |  %8.3f  |  %8.3f"
            no_val_message_template = "%s |  %8.3f  |  %8s"
            no_train_message_template = "%s |  %8s  |  %8.3f"
            header_template = "%s |  %-10s"
            name_length = max([len(x) for x in metric_names])
            logger.info(header_template, "Training".rjust(name_length + 13), "Validation")

        for name in metric_names:
            # Log to tensorboard
            train_metric = train_metrics.get(name)
            if train_metric is not None:
                self.add_train_scalar(name, train_metric, timestep=epoch)
            val_metric = val_metrics.get(name)
            if val_metric is not None:
                self.add_validation_scalar(name, val_metric, timestep=epoch)

            # And maybe log to console
            if log_to_console and val_metric is not None and train_metric is not None:
                logger.info(dual_message_template, name.ljust(name_length), train_metric, val_metric)
            elif log_to_console and val_metric is not None:
                logger.info(no_train_message_template, name.ljust(name_length), "N/A", val_metric)
            elif log_to_console and train_metric is not None:
                logger.info(no_val_message_template, name.ljust(name_length), train_metric, "N/A")