I created a dataset and to creat a dataloader with Pytorch. I have the following problem, I would like to create a dataloader with only the features data and y_vector. As I apply torch.utils.data.random_split if I want the nth element of the test_dataset if I don’t include z_vector I can’t understand which element of z_vector I have to take. I do not have to give in input z_vector to the network and it seems useless to include it in the dataloader. How to do?
class Dataset():

    def __init__(self, X_np_array, y_np_vector,z_np_vector, transform=None): 

        self.transform = transform
        self.data = torch.Tensor(X_np_array[:,None, :, :])
        self.y_vector = torch.Tensor(y_np_vector)
        self.z_np_vector= torch.Tensor(z_np_vector)


    def __getitem__(self, index): 
        return self.data[index], self.y_vector[index],self.z_np_vector[index]

    def __len__(self): 
        return len(list(self.data))
    
    def train_test_split(self,dataset,splitting_percentage):

        torch.manual_seed(int(1996))
        train_dim = int(len(dataset)*splitting_percentage)
        test_dim = len(dataset)-train_dim
        train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_dim, test_dim])
        return train_dataset, test_dataset