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.
class_docstr_tok: ["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","."]
code: def __init__(
      self, d, w_sizes, tau_label=None, init_value=0.1, **kwargs
  ):
      super().__init__()
      # start in train mode
      self.train()
      if not isinstance(d, (tuple, list, torch.Tensor)):
          d = (d,)
      self.means = nn.ParameterDict()
      if tau_label is not None:
          w_sizes[tau_label] = 1
      for l, mu_l in tensor_to_dict(
          w_sizes,
          init_value * torch.ones(*(d + (sum(w_sizes.values()),))),
      ).items():
          self.means[l] = nn.Parameter(mu_l)
      self.scale_trils = {}
      self.w_sizes = w_sizes
  
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 1 to create a new LinearModelLaplaceGuide object from inherited base classes: nn with example: obj = LinearModelLaplaceGuide(d, w_sizes, tau_label, init_value)