'Fit X into an embedded space.
Parameters
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is \'precomputed\' X must be a square distance
matrix. Otherwise it contains a sample per row. If the method
is \'exact\', X may be a sparse matrix of type \'csr\', \'csc\'
or \'coo\'.'

def fit(self, X, y=None):

    self.fit_transform(X)
    return self
