def training_forward(self, batch_data):
        """
        Forward pass for training the model.
        Abstract method to be implemented.

        Parameters
        ----------
        batch_data : tuple
            The batch data for training the model.

        Returns
        -------
        loss : float or tuple of float
            The loss.item of the model, or a tuple of loss.item 
            if there are multiple losses.
        """
        # An example implementation:
        # x = batch_data
        # x = x.to(self.device)
        # # x, y = batch_data
        # # x = x.to(self.device)
        # # y = y.to(self.device)
        # self.optimizer.zero_grad()
        # output = self.model(x)
        # loss = self.criterion(output, x)
        # loss.backward()
        # self.optimizer.step()
        # return loss.item()
        pass

        Forward pass for training the model.
        Abstract method to be implemented.

        Parameters
        ----------
        batch_data : tuple
            The batch data for training the model.

        Returns
        -------
        loss : float or tuple of float
            The loss.item of the model, or a tuple of loss.item 
            if there are multiple losses.
        
pyod/models/base_dl.py