code: def _potential_fn_jit(self, skip_jit_warnings, jit_options, params):
      if not params:
          return self._potential_fn(params)
      names, vals = zip(*sorted(params.items()))
  
      if self._compiled_fn:
          return self._compiled_fn(*vals)
  
      with pyro.validation_enabled(False):
          tmp = []
          for _, v in pyro.get_param_store().named_parameters():
              if v.requires_grad:
                  v.requires_grad_(False)
                  tmp.append(v)
  
          def _pe_jit(*zi):
              params = dict(zip(names, zi))
              return self._potential_fn(params)
  
          if skip_jit_warnings:
              _pe_jit = ignore_jit_warnings()(_pe_jit)
          self._compiled_fn = torch.jit.trace(
              _pe_jit, vals, **jit_options
          )
  
          result = self._compiled_fn(*vals)
          for v in tmp:
              v.requires_grad_(True)
          return result
  
imports: import functools
  import traceback
  import warnings
  from collections import OrderedDict, defaultdict
  from functools import partial, reduce
  from itertools import product
  import torch
  from opt_einsum import shared_intermediates
  from torch.distributions import biject_to
  import pyro
  import pyro.poutine
  from pyro.distributions.util import broadcast_shape, logsumexp
  from pyro.infer import config_enumerate
  from pyro.infer.autoguide.initialization import InitMessenger, init_to_uniform
  from pyro.infer.util import is_validation_enabled
  from pyro.ops import stats
  from pyro.ops.contract import contract_to_tensor
  from pyro.ops.integrator import potential_grad
  from pyro.poutine.subsample_messenger import _Subsample
  from pyro.poutine.util import prune_subsample_sites
  from pyro.util import check_site_shape, ignore_jit_warnings
label_desc: Use image node_id 3 for calling the _PEMaker obj's underlying member method code with example usage: obj._potential_fn_jit(skip_jit_warnings, jit_options, params) and returns: self, self, result, self