code: def _patched_parameters(
      self, recurse: bool = True, time: _typing.Optional[int] = None
  ) -> _typing.Iterable[_torch.Tensor]:
      r"""Returns an iterator over monkey patched module fast parameters.
  
      Args:
          recurse (bool): if True, then yields fast parameters of this module
              and all submodules. Otherwise, this *still* yields parameters of
              this module and all submodules, and raises a warning. This keyword
              exists only to satisfy API compatibility with
              ``torch.nn.Module.parameters``.
          time (int or None): if None, the most recent fast parameters are
              provided. The int provided stands for the number of steps since the
              module was created. *Note* that the step counter is incremented
              every time parameters are updated, so this may not align with number
              of training or evaluations steps.
  
      Yields:
          Parameter: module fast weights.
      """
      if getattr(self, "_fast_params", None) is None:
          raise Exception(
              "Tried to get fast weights of a monkey patched module which does "
              "not encapsulate fast weights."
          )
  
      if not recurse:
          _warnings.warn(
              "Calling parameters with recurse=False on a monkey patched module "
              "still returns all the fast weights of of nested patched modules."
          )
  
      time = -1 if time is None else time
  
      if not self.track_higher_grads and time not in (-1, 0):
          raise ValueError(
              "The patched model is not tracking higher gradients. Only the "
              "latest parameters are available."
          )
  
      return iter(self._fast_params[time])
  
imports: import abc
  from collections import OrderedDict
  from contextlib import contextmanager
  import typing
  import weakref
  import warnings
  import torch
  from .None import utils
label_desc: Use image node_id 2 for calling a global function with example usage: _patched_parameters(recurse, time) and returns: iter