code: def norm2d_cx(cx, in_c, trainable=True):
      """Accumulates complexity of norm2d into cx = (h, w, flops, params, acts)."""
      c, params = cx["c"], cx["params"]
      assert c == in_c
      cx["params"] += 4 * c
      cx["freeze"] += 2 * c  # moving_mean, variance
      if trainable is False:
          cx["freeze"] += 2 * c  # beta, gamma
      return cx
  
label_desc: Use image node_id 3 for calling a global function with example usage: norm2d_cx(cx, in_c, trainable) and returns: cx