'Scale each non zero row of X to unit norm
Parameters
X : {array-like, sparse matrix}, shape [n_samples, n_features]
The data to normalize, row by row. scipy.sparse matrices should be
in CSR format to avoid an un-necessary copy.
y : (ignored)
.. deprecated:: 0.19
This parameter will be removed in 0.21.
copy : bool, optional (default: None)
Copy the input X or not.'

def transform(self, X, y='deprecated', copy=None):

    if ((not isinstance(y, string_types)) or (y != 'deprecated')):
        warnings.warn('The    parameter    y    on    transform()    is    deprecated    since    0.19    and    will    be    removed    in    0.21', DeprecationWarning)
    copy = (copy if (copy is not None) else self.copy)
    X = check_array(X, accept_sparse='csr')
    return normalize(X, norm=self.norm, axis=1, copy=copy)
