class RegressiveModule(pl.LightningModule):    def __init__(self, model_name, train_config, model_config):        super().__init__()        self.save_hyperparameters(ignore=["model_name"])        # self.model = model        self.load_model(model_name, model_config)        self.train_config = train_config        self.criterion = torch.nn.MSELoss() # compute_sequence_loss(water_pred_seq, target_seq)                self.training_step_outputs = []        self.validation_step_outputs = []        self.test_step_outputs = []        self.testTime = []        self.predictions= []        self.targets =[]    def load_model(self, model_name, model_config):        name = model_name        # Change the `snake_case.py` file name to `CamelCase` class name.        # Please always name your model file name as `snake_case.py` and        # class name corresponding `CamelCase`.        camel_name = ''.join([i.capitalize() for i in name.split('_')])        try:            Model = getattr(importlib.import_module(                '.' + name, package=__package__), camel_name)        except:            raise ValueError(                f'Invalid Module File Name or Invalid Class Name {name}.{camel_name}!')        self.model = self.instancialize(Model)    def instancialize(self, Model, **other_args):        """ Instancialize a model using the corresponding parameters            from self.hparams dictionary. You can also input any args            to overwrite the corresponding value in self.hparams.        """        # model_config = Model[1]        # Model = Model[0]        class_args = inspect.getargspec(Model.__init__).args[1:]        inkeys = self.hparams.keys()        args1 = {}        for arg in class_args:            if arg in inkeys:                args1[arg] = getattr(self.hparams, arg)        args1.update(other_args)        return Model(**args1)    def forward(self, img):        return self.model(img)    def training_step(self, batch, batch_idx):        print('in train loop')        signal, time_feat, target_seq, pattern_labels = batch        water_pred_seq = self.model(signal,time_feat)        loss = self.criterion(water_pred_seq, target_seq)        self.training_step_outputs.append(loss)        self.log("train_loss",loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)        return {"loss": loss}    def on_train_epoch_end(self, *arg, **kwargs):        epoch_average = torch.stack(self.training_step_outputs).mean()        self.log("training_epoch_average", epoch_average)        self.training_step_outputs.clear()  # free memory        # print("\n\ntrain_loss_manual", epoch_average)    def validation_step(self, batch, batch_idx):        # print('\n\nvalid step')        signal, time_feat, target_seq, pattern_labels = batch        pred_seq = self.model(signal,time_feat)        val_loss = self.criterion(pred_seq, target_seq)        self.validation_step_outputs.append(val_loss)        self.log_dict({'val_loss': val_loss},                 on_step=True, on_epoch=True, prog_bar=True,logger=True) # Critical: on_epoch aggregates batch losses        return {"val_loss": val_loss}    def on_validation_epoch_end(self, *arg, **kwargs):        epoch_average = torch.stack(self.validation_step_outputs).mean()        self.log("validation_epoch_average", epoch_average)        print("\n\nvalid_loss_manual", epoch_average)        self.validation_step_outputs.clear()    def configure_optimizers(self):        optimizer = torch.optim.Adam(self.parameters(), lr=self.train_config.get('learningRate', 1e-3),                                     weight_decay=self.train_config.get('weight_decay', 1e-3), betas=(0.9, 0.999),                         eps=1e-08, )        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=self.train_config.get('patience', 1e-3),                                                               factor=self.train_config.get('factor', 1e-3))        return {            "optimizer": optimizer,            "lr_scheduler": scheduler,            "monitor": "val_loss",            'interval': 'epoch',            'frequency': 1        }

valid_loss_manual tensor(0.4408, device='cuda:0')Epoch 13/199 ━━━━━━━━━━━━━━━━━ 9/9 0:00:00 • 0:00:00 95.71it/s v_num: 273.000                                                                  train_loss_step:                                                                0.539                                                                           val_loss_step:                                                                  0.501                                                                           val_loss_epoch:                                                                 0.421                                                                           train_loss_epoch:                                                               0.313            in train loopin train loopin train loopin train loopin train loopin train loopin train loopin train loopin train loopvalid_loss_manual tensor(0.3893, device='cuda:0')Epoch 14/199 ━━━━━━━━━━━━━━━━━ 9/9 0:00:00 • 0:00:00 95.85it/s v_num: 273.000                                                                  train_loss_step:                                                                0.489                                                                           val_loss_step:                                                                  0.388                                                                           val_loss_epoch:                                                                 0.390                                                                           train_loss_epoch:                                                               0.309            in train loopin train loopin train loopin train loopin train loopin train loopin train loopin train loopin train loopvalid_loss_manual tensor(0.3756, device='cuda:0')Epoch 15/199 ━━━━━━━━━━━━━━━━━ 9/9 0:00:00 • 0:00:00 95.63it/s v_num: 273.000                                                                  train_loss_step:                                                                0.465                                                                           val_loss_step:                                                                  0.420                                                                           val_loss_epoch:                                                                 0.361                                                                           train_loss_epoch:                                                               0.306