code: def module(
      name: str,
      nn_module: torch.nn.Module,
      update_module_params: bool = False,
  ) -> torch.nn.Module:
      """
      Registers all parameters of a :class:`torch.nn.Module` with Pyro's
      :mod:`~pyro.params.param_store`.  In conjunction with the
      :class:`~pyro.params.param_store.ParamStoreDict`
      :meth:`~pyro.params.param_store.ParamStoreDict.save` and
      :meth:`~pyro.params.param_store.ParamStoreDict.load` functionality, this
      allows the user to save and load modules.
  
      .. note:: Consider instead using :class:`~pyro.nn.module.PyroModule`, a
          newer alternative to ``pyro.module()`` that has better support for:
          jitting, serving in C++, and converting parameters to random variables.
          For details see the `Modules Tutorial
          <https://pyro.ai/examples/modules.html>`_ .
  
      :param name: name of module
      :type name: str
      :param nn_module: the module to be registered with Pyro
      :type nn_module: torch.nn.Module
      :param update_module_params: determines whether Parameters
                                   in the PyTorch module get overridden with the values found in the
                                   ParamStore (if any). Defaults to `False`
      :type load_from_param_store: bool
      :returns: torch.nn.Module
      """
      assert hasattr(
          nn_module, "parameters"
      ), "module has no parameters"
      assert _MODULE_NAMESPACE_DIVIDER not in name, (
          "improper module name, since contains %s"
          % _MODULE_NAMESPACE_DIVIDER
      )
  
      if isclass(nn_module):
          raise NotImplementedError(
              "pyro.module does not support class constructors for "
              + "the argument nn_module"
          )
  
      target_state_dict = OrderedDict()
  
      for param_name, param_value in nn_module.named_parameters():
          if param_value.requires_grad:
              # register the parameter in the module with pyro
              # this only does something substantive if the parameter hasn't been seen before
              full_param_name = param_with_module_name(name, param_name)
              returned_param = param(full_param_name, param_value)
  
              if param_value._cdata != returned_param._cdata:
                  target_state_dict[param_name] = returned_param
          elif nn_module.training:
              warnings.warn(
                  f"{param_name} was not registered in the param store "
                  "because requires_grad=False. You can silence this "
                  "warning by calling my_module.train(False)"
              )
  
      if target_state_dict and update_module_params:
          # WARNING: this is very dangerous. better method?
          for _name, _param in nn_module.named_parameters():
              is_param = False
              name_arr = _name.rsplit(".", 1)
              if len(name_arr) > 1:
                  mod_name, param_name = name_arr[0], name_arr[1]
              else:
                  is_param = True
                  mod_name = _name
              if _name in target_state_dict.keys():
                  if not is_param:
                      deep_getattr(nn_module, mod_name)._parameters[
                          param_name
                      ] = target_state_dict[_name]
                  else:
                      nn_module._parameters[mod_name] = target_state_dict[_name]  # type: ignore[assignment]
  
      return nn_module
  
imports: import copy
  import warnings
  from collections import OrderedDict
  from contextlib import ExitStack, contextmanager
  from inspect import isclass
  from typing import Callable, Iterator, Optional, Sequence, Union
  import torch
  from torch.distributions import constraints
  import pyro.distributions
  import pyro.infer
  import pyro.poutine
  from pyro.distributions.torch_distribution import TorchDistributionMixin
  from pyro.params import param_with_module_name
  from pyro.params.param_store import ParamStoreDict
  from pyro.poutine.plate_messenger import PlateMessenger
  from pyro.poutine.runtime import _MODULE_NAMESPACE_DIVIDER, _PYRO_PARAM_STORE, InferDict, Message, am_i_wrapped, apply_stack, effectful
  from pyro.poutine.subsample_messenger import SubsampleMessenger
  from pyro.util import deep_getattr, set_rng_seed
label_desc: Use image node_id 10 for calling a global function with example usage: module(name, nn_module, update_module_params) and returns: nn_module