Custom LR scheduler not supported

[ErikHartman]

Bug description Trying to set a custom learning rate scheduler with the set_lr_scheduler_class throws an NotImplemented error.

To Reproduce

from torch.optim.lr_scheduler import ReduceLROnPlateau
from your_package import LR_SchedulerInterface  # Replace with the actual import path

class CustomReduceLROnPlateau(LR_SchedulerInterface):
    def __init__(self, optimizer, num_warmup_steps, num_training_steps, patience=10, factor=0.1, mode='min', threshold=1e-4, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-8, **kwargs):
        super().__init__()
        self.scheduler = ReduceLROnPlateau(
            optimizer,
            mode=mode,
            factor=factor,
            patience=patience,
            threshold=threshold,
            threshold_mode=threshold_mode,
            cooldown=cooldown,
            min_lr=min_lr,
            eps=eps,
            **kwargs
        )

    def step(self, metrics, epoch=None):
        self.scheduler.step(metrics, epoch)

    def get_last_lr(self):
        return self.scheduler.optimizer.param_groups[0]['lr']

from peptdeep.model.generic_property_prediction import (
    ModelInterface_for_Generic_AASeq_Regression,
)
from peptdeep.model.generic_property_prediction import (
    Model_for_Generic_AASeq_Regression_Transformer,
)

transformer = ModelInterface_for_Generic_AASeq_Regression(
    model_class=Model_for_Generic_AASeq_Regression_Transformer
)
transformer.target_column_to_train = 'normlogintensity'
transformer.target_column_to_predict = 'transformer_predictions'
transformer.train(data_train, warmup_epoch=10, epoch=50, verbose=True)

Expected behavior No error.

Version (please complete the following information):

• Installation Type: pip
• peptdeep version 1.2.1

Additional context I see in the source code that this isn't implemented. Would be nice if it was.