code: def aggregation(
      self, transform_fun: Callable[[torch.Tensor], torch.Tensor]
  ) -> torch.Tensor:
      gradients_measure = torch.tensor(
          [
              torch.mean(
                  transform_fun(not_none(self.mean_gradients)[:, i])
              )
              for i in range(self.dim)
          ]
      )
      if not (self.bootstrap):
          return gradients_measure
      else:
          gradients_measures_btsp = [gradients_measure.unsqueeze(0)]
          for b in range(self.num_bootstrap_samples):
              gradients_measures_btsp.append(
                  torch.tensor(
                      [
                          torch.mean(
                              transform_fun(
                                  not_none(self.mean_gradients_btsp)[b][
                                      :, i
                                  ]
                              )
                          )
                          for i in range(self.dim)
                      ]
                  ).unsqueeze(0)
              )
          gradients_measures_btsp = torch.cat(
              gradients_measures_btsp, dim=0
          )
          return (
              torch.cat(
                  [
                      gradients_measures_btsp.mean(dim=0).unsqueeze(0),
                      gradients_measures_btsp.var(dim=0).unsqueeze(0),
                      torch.sqrt(
                          gradients_measures_btsp.var(dim=0)
                          / (self.num_bootstrap_samples + 1)
                      ).unsqueeze(0),
                  ],
                  dim=0,
              )
              .t()
              .detach()
          )
  
imports: from copy import deepcopy
  from typing import Callable, List, Optional, Union
  import torch
  from ax.utils.common.typeutils import checked_cast, not_none
  from ax.utils.sensitivity.derivative_gp import posterior_derivative
  from botorch.models.model import Model
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.posteriors.posterior import Posterior
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.sampling import draw_sobol_samples
  from botorch.utils.transforms import unnormalize
  from gpytorch.distributions import MultivariateNormal
label_desc: Use image node_id 3 for calling the GpDGSMGpMean obj's underlying member method code with example usage: obj.aggregation(transform_fun) and returns: gradients_measure, torch