code: def get_Kxx_dx2(gp: Model, kernel_type: str = "rbf") -> Tensor:
      r"""Computes the analytic second derivative of the kernel w.r.t. the training data
      Args:
          gp: Botorch model.
          kernel_type: Takes "rbf" or "matern"
      Returns:
          Tensor (n x D x D) The second derivative of the kernel w.r.t. the training data.
      """
      X = gp.train_inputs[0]
      D = X.shape[1]
      lengthscale = gp.covar_module.base_kernel.lengthscale.detach()
      sigma_f = gp.covar_module.outputscale.detach()
      res = (
          torch.eye(D, device=lengthscale.device) / lengthscale**2
      ) * sigma_f
      if kernel_type == "rbf":
          return res
      return res * (5 / 3)
  
file_path: Ax/ax/utils/sensitivity/derivative_gp.py
imports: import math
  import torch
  from botorch.models.model import Model
  from gpytorch.distributions import MultivariateNormal
  from torch import Tensor
label_desc: Use image node_id 3 for calling a global function with example usage: get_Kxx_dx2(gp, kernel_type) and returns: unknown, res