code: def _update_damping_when_converged(
      mean_old: Tensor,
      mean_new: Tensor,
      cov_old: Tensor,
      cov_new: Tensor,
      damping_factor: Tensor,
      iteration: Tensor,
      threshold: float = 1e-3,
  ) -> Tensor:
      r"""Set the damping factor to 0 once converged. Convergence is determined by the
      relative change in the entries of the mean and covariance matrix.
  
      Args:
          mean_old: A `batch_shape x R`-dim Tensor containing the old natural mean
              matrix for the objective.
          mean_new: A `batch_shape x R`-dim Tensor containing the new natural mean
              matrix for the objective.
          cov_old: A `batch_shape x R x R`-dim Tensor containing the old natural
              covariance matrix for the objective.
          cov_new: A `batch_shape x R x R`-dim Tensor containing the new natural
              covariance matrix for the objective.
          iteration: The current iteration number
          damping_factor: A `batch_shape`-dim Tensor containing the damping factor.
  
      Returns:
          A `batch_shape x param_shape`-dim Tensor containing the updated damping
          factor.
      """
      df = damping_factor.clone()
      delta_mean = mean_new - mean_old
      delta_cov = cov_new - cov_old
      am = torch.amax(abs(mean_old), dim=-1)
      ac = torch.amax(abs(cov_old), dim=(-2, -1))
  
      if iteration > 2:
          mask_mean = (
              torch.amax(abs(delta_mean), dim=-1) < threshold * am
          )
          mask_cov = (
              torch.amax(abs(delta_cov), dim=(-2, -1)) < threshold * ac
          )
          mask = torch.logical_and(mask_mean, mask_cov)
          df[mask] = 0
  
      return df, delta_mean, delta_cov
  
imports: from __future__ import annotations
  from typing import Any, Optional, Tuple
  import torch
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.exceptions import InputDataError
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.model import Model
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.utils import check_no_nans
  from botorch.utils.transforms import concatenate_pending_points, t_batch_mode_transform
  from torch import Tensor
  from torch.distributions import Normal
label_desc: Use image node_id 10 for calling a global function with example usage: _update_damping_when_converged(mean_old, mean_new, cov_old, cov_new, damping_factor, iteration, threshold) and returns: df,  delta_mean,  delta_cov