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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def transform(self, X, y):
 # pylint: disable=anomalous-backslash-in-string """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. """

        # 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