File "c:\Users\sean\miniconda3\envs\keras+torch+pl\Lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py", line 459, in _update_learning_rates
    raise MisconfigurationException(
lightning.fabric.utilities.exceptions.MisconfigurationException: ReduceLROnPlateau conditioned on metric val/loss which is not available. Available metrics are: ['lr-AdamW/pg1', 'lr-AdamW/pg2', 'train/a_pcc', 'train/loss']. Condition can be set using `monitor` key in lr scheduler dict

    @final
    def configure_optimizers(self):

        decay, no_decay = [], []
        for name, param in self.named_parameters():
            if not param.requires_grad:
                continue
            if "bias" in name or "Norm" in name:
                no_decay.append(param)
            else:
                decay.append(param)

        grouped_params = [
            {"params": decay, "weight_decay": self.weight_decay, "lr": self.lr * 0.3},
            {
                "params": no_decay,
                "weight_decay": self.weight_decay,
                "lr": self.lr * 1.7,
            },
        ]

        optimizer = self.optmizer_class(
            grouped_params, lr=self.lr, weight_decay=self.weight_decay
        )

        scheduler = self.lr_scheduler_class(
            optimizer, **self.lr_scheduler_args if self.lr_scheduler_args else {}
        )
        scheduler = {
            "scheduler": self.lr_scheduler_class(
                optimizer, **self.lr_scheduler_args if self.lr_scheduler_args else {}
            ),
            "monitor": "val/loss",
            "interval": "epoch",
            "frequency": 1,
            # "strict": False,
        }
        return {"optimizer": optimizer, "lr_scheduler": scheduler}

  lr_scheduler_class: torch.optim.lr_scheduler.ReduceLROnPlateau
  lr_scheduler_args:
    mode: min
    factor: 0.5
    patience: 10
    threshold: 0.0001
    threshold_mode: rel
    cooldown: 5
    min_lr: 1.e-9
    eps: 1.e-08

# Error messages and logs here please