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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code: def _hessian_diag(y, x, event_shape):
      batch_shape = x.shape[: -len(event_shape)]
      assert tuple(x.shape) == tuple(batch_shape) + tuple(event_shape)
  
      dy = torch.autograd.grad(
          y,
          [
              x,
          ],
          create_graph=True,
      )[0]
      H = []
  
      # collapse independent dimensions into a single one,
      # and dependent dimensions into another single one
      batch_size = 1
      for batch_shape_dim in batch_shape:
          batch_size *= batch_shape_dim
  
      event_size = 1
      for event_shape_dim in event_shape:
          event_size *= event_shape_dim
  
      flat_dy = dy.reshape(batch_size, event_size)
  
      # loop over dependent part
      for i in range(flat_dy.shape[-1]):
          dyi = flat_dy.index_select(-1, torch.tensor([i]))
          Hi = torch.autograd.grad(
              [dyi],
              [
                  x,
              ],
              grad_outputs=[torch.ones_like(dyi)],
              retain_graph=True,
          )[0]
          H.append(Hi)
      H = torch.stack(H, -1).reshape(*(x.shape + event_shape))
      return H
  
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 2 for calling the LinearModelLaplaceGuide obj's underlying member method code with example usage: obj._hessian_diag(y, x, event_shape) and returns: H