code: def fit_transform(self, X, y=None):
          
          embedding = self._fit(X)
          self.embedding_ = embedding
          return self.embedding_
docs: Fit X into an embedded space and return that transformed
          output.
  
          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.
  
          Returns
          -------
          X_new : array, shape (n_samples, n_components)
              Embedding of the training data in low-dimensional space.