Test if `last_loss` is NaN and interrupts training.

def on_batch_end(self, last_loss, epoch, num_batch, **kwargs:Any)->None:
        "Test if `last_loss` is NaN and interrupts training."
        if self.stop: return True #to skip validation after stopping during training
        if torch.isnan(last_loss):
            print (f'Epoch/Batch ({epoch}/{num_batch}): Invalid loss, terminating training.')
            return {'stop_epoch': True, 'stop_training': True, 'skip_validate': True}