Good. So how to use reducelronplateau scheduler in this plmodel? Change the previous code.

Sure, here's an example of how to use the ReduceLROnPlateau scheduler in a PyTorch Lightning model:

import pytorch_lightning as pl
import torch.optim as optim
from torch.optim.lr_scheduler import ReduceLROnPlateau


class MyModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Linear(10, 5)
        self.layer2 = nn.Linear(5, 1)

    def forward(self, x):
        x = F.relu(self.layer1(x))
        x = self.layer2(x)
        return x

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.mse_loss(y_hat, y)
        self.log('train_loss', loss)
        return loss

    def configure_optimizers(self):
        optimizer = optim.Adam(self.parameters(), lr=1e-3)
        scheduler = ReduceLROnPlateau(optimizer, 'min', patience=5)
        return {'optimizer': optimizer, 'lr_scheduler': scheduler, 'monitor': 'train_loss'}

trainer = pl.Trainer(max_epochs=100)
model = MyModel()
trainer.fit(model, train_dataloader)

Here, we define the ReduceLROnPlateau scheduler in the `configure_optimizers` method, and pass it as a dictionary to the `Trainer` object. The `patience` argument represents the number of epochs to wait before reducing the learning rate if the validation loss does not improve. The `'min'` mode indicates that we want to reduce the learning rate if the validation loss stops decreasing. Finally, we set the `monitor` argument to `'train_loss'` since we want to monitor the training loss for this example.