def fit_transform(self, X, y=None, input_type='<STR_LIT:data>'):
self.fit(X, y=y, input_type=input_type)<EOL>return self.embedding_<EOL>
Fit the model from data in X and transform X.

        Parameters
        ----------
        input_type : string, one of: 'data', 'distance' or 'affinity'.
            The values of input data X. (default = 'data')
        X: array-like, shape (n_samples, n_features)
            Training vector, where n_samples in the number of samples
            and n_features is the number of features.

        If self.input_type is 'distance':

        X : array-like, shape (n_samples, n_samples),
            Interpret X as precomputed distance or adjacency graph
            computed from samples.

        Returns
        -------
        X_new: array-like, shape (n_samples, n_components)