code: def _replace_pareto_diagonal(A: Tensor) -> Tensor:
      """Replace the pareto diagonal with identity matricx.
  
      The Pareto diagonal of the omega factor shouldn't be updated because does not
      contribute anything: `omega(x_p, x_p) = 1` for any pareto optimal input `x_p`.
  
      Args:
          A: a `batch_shape x M x (N + P) x P x 2 x 2`-dim Tensor.
  
      Returns:
          A `batch_shape x M x (N + P) x P x 2 x 2`-dim Tensor, where the Pareto
          diagonal is padded with identity matrices.
      """
      tkwargs = {"dtype": A.dtype, "device": A.device}
      batch_shape = A.shape[:-5]
      P = A.shape[-3]
      N = A.shape[-4] - P
      M = A.shape[-5]
      identity = torch.diag_embed(
          torch.ones(batch_shape + torch.Size([M, 2]), **tkwargs)
      )
      for p in range(P):
          A[..., N + p, p, :, :] = identity
  
      return A
  
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 4 for calling a global function with example usage: _replace_pareto_diagonal(A) and returns: A