code: def transform(self, X, y):
          # pylint: disable=anomalous-backslash-in-string
          
          # pytorch DataLoader cannot deal with None so we use 0 as a
          # placeholder value. We only return a Tensor with one value
          # (as opposed to ``batchsz`` values) since the pytorch
          # DataLoader calls __getitem__ for each row in the batch
          # anyway, which results in a dummy ``y`` value for each row in
          # the batch.
          y = torch.Tensor([0]) if y is None else y
  
          # pytorch cannot convert sparse matrices, for now just make it
          # dense; squeeze because X[i].shape is (1, n) for csr matrices
          if sparse.issparse(X):
              X = X.toarray().squeeze(0)
          return X, y
docs: Additional transformations on ``X`` and ``y``.
  
          By default, they are cast to PyTorch :class:`~torch.Tensor`\s.
          Override this if you want a different behavior.
  
          Note: If you use this in conjuction with PyTorch
          :class:`~torch.utils.data.DataLoader`, the latter will call
          the dataset for each row separately, which means that the
          incoming ``X`` and ``y`` each are single rows.