code: def cholesky_from_fn(linop, cholesky_fn):
      """Compute Cholesky factor with respect to `linop`.
  
      Computing a Cholesky decomposition via `tf.linalg.cholesky(linop.to_dense())`
      can be both numerically unstable and slow. This method allows using alternate
      Cholesky decomposition algorithms via `cholesky_fn` to enable numerical
      stability, while trying to be as efficient as possible for some structured
      operators.
  
      Args:
        linop: Positive-definite `LinearOperator`.
        cholesky_fn: Callable which takes a single (batch) matrix argument and
            returns a Cholesky-like lower triangular factor.
  
      Returns:
        cholesky_factor: `LinearOperator` representing the Cholesky factor for
          `linop`.
      """
      if isinstance(linop, tf.linalg.LinearOperatorIdentity):
          return linop
      elif isinstance(linop, tf.linalg.LinearOperatorDiag):
          return tf.linalg.LinearOperatorDiag(
              tf.math.sqrt(linop.diag),
              is_non_singular=True,
              is_positive_definite=True,
          )
      elif isinstance(linop, tf.linalg.LinearOperatorBlockDiag):
          return tf.linalg.LinearOperatorBlockDiag(
              [
                  cholesky_from_fn(o, cholesky_fn)
                  for o in linop.operators
              ],
              is_non_singular=True,
          )
      elif isinstance(linop, tf.linalg.LinearOperatorKronecker):
          return tf.linalg.LinearOperatorKronecker(
              [
                  cholesky_from_fn(o, cholesky_fn)
                  for o in linop.operators
              ],
              is_non_singular=True,
          )
      else:
          # This handles the `LinearOperatorFullMatrix` case among others.
          return tf.linalg.LinearOperatorLowerTriangular(
              cholesky_fn(linop.to_dense()), is_non_singular=True
          )
  
label_desc: Use image node_id 2 for calling a global function with example usage: cholesky_from_fn(linop, cholesky_fn) and returns: linop, tf, tf, tf, tf