class_docstr: Laplace approximation for a (G)LM.
  
  :param tuple d: the shape by which to expand the guide parameters, e.g. `(num_batches, num_designs)`.
  :param dict w_sizes: map from variable string names to int, indicating the dimension of each
                       weight vector in the linear model.
  :param str tau_label: the label used for inverse variance parameter sample site, or `None` to indicate a
                        fixed variance.
  :param float init_value: initial value for the posterior mean parameters.
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docstr: Compute the Hessian of the parameters wrt ``loss``
  
  :param torch.Tensor loss: the output of evaluating a loss function such as
                            `pyro.infer.Trace_ELBO().differentiable_loss` on the model, guide and design.
  :param list target_labels: list indicating the sample sites that are targets, i.e. for which information gain
                             should be measured.
docstr_tok: ["Compute","the","Hessian","of","the","parameters","wrt","``","loss","``",":","param","torch.Tensor","loss",":","the","output","of","evaluating","a","loss","function","such","as","`","pyro.infer.Trace_ELBO","(",")",".differentiable_loss","`","on","the","model",",","guide","and","design",".",":","param","list","target_labels",":","list","indicating","the","sample","sites","that","are","targets",",","i.e",".","for","which","information","gain","should","be","measured","."]
code: def finalize(self, loss, target_labels):
      """
      Compute the Hessian of the parameters wrt ``loss``
  
      :param torch.Tensor loss: the output of evaluating a loss function such as
                                `pyro.infer.Trace_ELBO().differentiable_loss` on the model, guide and design.
      :param list target_labels: list indicating the sample sites that are targets, i.e. for which information gain
                                 should be measured.
      """
      # set self.training = False
      self.eval()
      for l, mu_l in self.means.items():
          if l not in target_labels:
              continue
          hess_l = self._hessian_diag(
              loss, mu_l, event_shape=(self.w_sizes[l],)
          )
          cov_l = rinverse(hess_l)
          self.scale_trils[l] = torch.linalg.cholesky(
              cov_l.transpose(-2, -1)
          ).transpose(-2, -1)
  
imports: from contextlib import ExitStack
  import torch
  from torch import nn
  import pyro
  import pyro.distributions
  from pyro import poutine
  from pyro.contrib.util import iter_plates_to_shape, lexpand, rmv, rtril, rvv, tensor_to_dict
  from pyro.ops.linalg import rinverse
label_desc: Use image node_id 3 for calling the LinearModelLaplaceGuide obj's underlying member method code with example usage: obj.finalize(loss, target_labels) without return types