code: def _compute_correlation_index(
      x1: list, x2: list, tol: float = 5e-16
  ) -> float:
      """Computes the CorrIndex from the L2-normalized A matrices.
  
      Parameters
      ----------
      x1 : list
          A list containing normalized A matrix(ces) from the first tensor decomposition.
  
      x2 : list
          A list containing normalized A matrix(ces) from the first tensor decomposition.
  
      tol : float, optional
          Precision threshold below which to call the CorrIndex score 0, by default 5e-16
  
      Returns
      -------
      score : float
           CorrIndex metric [0,1]; lower score indicates higher similarity between matrices
      """
      # generate the correlation index input
      c_prod_mtx = tl.abs(tl.matmul(tl.conj(tl.transpose(x1)), x2))
  
      # correlation index scoring
      n_elements = tl.shape(c_prod_mtx)[1] + tl.shape(c_prod_mtx)[0]
      score = (1 / (n_elements)) * (
          tl.sum(tl.abs(tl.max(c_prod_mtx, 1) - 1))
          + tl.sum(tl.abs(tl.max(c_prod_mtx, 0) - 1))
      )
      if score < tol:
          score = 0
      return score
  
label_desc: Use image node_id 2 for calling a global function with example usage: _compute_correlation_index(x1, x2, tol) and returns: score