def _construct_loaders(self, X_train, y_train, eval_set):
        """Generate dataloaders for train and eval set.

        Parameters
        ----------
        X_train : np.array
            Train set.
        y_train : np.array
            Train targets.
        eval_set : list of tuple
            List of eval tuple set (X, y).

        Returns
        -------
        train_dataloader : `torch.utils.data.Dataloader`
            Training dataloader.
        valid_dataloaders : list of `torch.utils.data.Dataloader`
            List of validation dataloaders.

        """
        # all weights are not allowed for this type of model
        y_train_mapped = self.prepare_target(y_train)
        for i, (X, y) in enumerate(eval_set):
            y_mapped = self.prepare_target(y)
            eval_set[i] = (X, y_mapped)

        train_dataloader, valid_dataloaders = create_dataloaders(
            X_train,
            y_train_mapped,
            eval_set,
            self.updated_weights,
            self.batch_size,
            self.num_workers,
            self.drop_last,
            self.pin_memory,
        )
        return train_dataloader, valid_dataloaders
Generate dataloaders for train and eval set.

        Parameters
        ----------
        X_train : np.array
            Train set.
        y_train : np.array
            Train targets.
        eval_set : list of tuple
            List of eval tuple set (X, y).

        Returns
        -------
        train_dataloader : `torch.utils.data.Dataloader`
            Training dataloader.
        valid_dataloaders : list of `torch.utils.data.Dataloader`
            List of validation dataloaders.

        
pytorch_tabnet/abstract_model.py