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
```