docs: def compute_update_ratio(weight_tensors, before_weights, after_weights):
      """Compute the ratio of gradient norm to weight norm."""
      deltas = [after - before for after,
                before in zip(after_weights, before_weights)]
      delta_norms = [np.linalg.norm(d.ravel()) for d in deltas]
      weight_norms = [np.linalg.norm(w.ravel()) for w in before_weights]
      ratios = [d / w for d, w in zip(delta_norms, weight_norms)]
      all_summaries = [
          tf.Summary.Value(tag='update_ratios/' +
                           tensor.name, simple_value=ratio)
          for tensor, ratio in zip(weight_tensors, ratios)]
      return tf.Summary(value=all_summaries)