'Scale back the data to the original representation
Parameters
X : {array-like, sparse matrix}
The data that should be transformed back.'

def inverse_transform(self, X):

    check_is_fitted(self, 'scale_')
    X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy, estimator=self, dtype=FLOAT_DTYPES)
    if sparse.issparse(X):
        inplace_column_scale(X, self.scale_)
    else:
        X *= self.scale_
    return X
