code: def get_lanczos_eig(self, compute_m=True, feed_dict=None):
      
      if compute_m:
        min_eig, min_vec = self.sess.run([self.m_min_eig, self.m_min_vec], feed_dict=feed_dict)
  
      else:
        min_eig, min_vec = self.sess.run([self.h_min_eig, self.h_min_vec], feed_dict=feed_dict)
  
      return min_vec, min_eig
docs: Computes the min eigen value and corresponding vector of matrix M or H
      using the Lanczos algorithm.
      Args:
        compute_m: boolean to determine whether we should compute eig val/vec
          for M or for H. True for M; False for H.
        feed_dict: dictionary mapping from TF placeholders to values (optional)
      Returns:
        min_eig_vec: Corresponding eigen vector to min eig val
        eig_val: Minimum eigen value