docstr: Convert this Module in to a batch Module by loading parameters from the given `samples_dict`. `samples_dict`
  is typically produced by a Pyro sampling mechanism.
  
  Note that the keys of the samples_dict should correspond to prior names (covar_module.outputscale_prior) rather
  than parameter names (covar_module.raw_outputscale), because we will use the setting_closure associated with
  the prior to properly set the unconstrained parameter.
  
  Args:
      samples_dict (dict): Dictionary mapping *prior names* to sample values.
code: def pyro_load_from_samples(self, samples_dict):
      """
      Convert this Module in to a batch Module by loading parameters from the given `samples_dict`. `samples_dict`
      is typically produced by a Pyro sampling mechanism.
  
      Note that the keys of the samples_dict should correspond to prior names (covar_module.outputscale_prior) rather
      than parameter names (covar_module.raw_outputscale), because we will use the setting_closure associated with
      the prior to properly set the unconstrained parameter.
  
      Args:
          samples_dict (dict): Dictionary mapping *prior names* to sample values.
      """
      return _pyro_load_from_samples(
          module=self, samples_dict=samples_dict, memo=None, prefix=""
      )
  
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 31 for calling the Module obj's underlying member method code with example usage: obj.pyro_load_from_samples(samples_dict) and returns: _pyro_load_from_samples