from pytorch_lightning.callbacks import EarlyStopping early_stop_callback = EarlyStopping( monitor='val_loss', patience=10, mode='min' ) trainer = pl.Trainer( callbacks=[early_stop_callback], max_epochs=100 )In this example, the EarlyStopping callback is instantiated with the parameters `monitor='val_loss'`, which means that it will monitor the validation loss; `patience=10`, which means that training will stop if the validation loss does not improve for 10 epochs; and `mode='min'`, which means that the validation loss should be minimized. The EarlyStopping callback is then added to the callbacks list in the Trainer class, and the maximum number of epochs for training is set to 100. This is just one example of how the EarlyStopping callback can be used in pytorch_lightning. Other parameters and settings can be customized to suit specific use cases.