docstr: Defines local behavior of this Module when loading parameters from a samples_dict generated by a Pyro
  sampling mechanism.
  
  The default behavior here should almost always be called from any overriding class. However, a class may
  want to add additional functionality, such as reshaping things to account for the fact that parameters will
  acquire an extra batch dimension corresponding to the number of samples drawn.
code: def local_load_samples(self, samples_dict, memo, prefix):
      """
      Defines local behavior of this Module when loading parameters from a samples_dict generated by a Pyro
      sampling mechanism.
  
      The default behavior here should almost always be called from any overriding class. However, a class may
      want to add additional functionality, such as reshaping things to account for the fact that parameters will
      acquire an extra batch dimension corresponding to the number of samples drawn.
      """
      self._strict(False)
      for name, (
          prior,
          closure,
          setting_closure,
      ) in self._priors.items():
          if prior is not None and prior not in memo:
              memo.add(prior)
              setting_closure(
                  self,
                  samples_dict[prefix + ("." if prefix else "") + name],
              )
      self._strict(True)
  
imports: import copy
  import inspect
  import itertools
  import operator
  from collections import OrderedDict
  from typing import Union
  import torch
  from linear_operator.operators import LinearOperator
  from torch import nn, Tensor
  from torch.distributions import Distribution
  from .constraints import Interval
label_desc: Use image node_id 30 for calling the Module obj's underlying member method code with example usage: obj.local_load_samples(samples_dict, memo, prefix) without return types