new_contents: from sklearn.ensemble import RandomTreesEmbedding
  
  def rt_embedding(X, n_estimators=100, max_depth=10, n_jobs=-1, **kwargs):
      """Embed data matrix X in a random forest.
  
      Parameters
      ----------
      X : array, shape (n_samples, n_features)
          The data matrix.
      n_estimators : int, optional
          The number of trees in the embedding.
      max_depth : int, optional
          The maximum depth of each tree.
      n_jobs : int, optional
          Number of compute jobs when fitting the trees. -1 means number
          of processors on the current computer.
      **kwargs : dict
          Keyword arguments to be passed to
          `sklearn.ensemble.RandomTreesEmbedding`
  
      Returns
      -------
      rt : RandomTreesEmbedding object
          The embedding object.
      X_transformed : sparse matrix
          The transformed data.
      """
      rt = RandomTreesEmbedding(**kwargs)
      X_transformed = rt.fit_transform(X)
      return rt, X_transformed
  
subject: Add RandomTreesEmbedding for robust clustering