code: def _potential_fn(self, params):
      params_constrained = {
          k: self.transforms[k].inv(v) for k, v in params.items()
      }
      cond_model = poutine.condition(self.model, params_constrained)
      model_trace = poutine.trace(cond_model).get_trace(
          *self.model_args, **self.model_kwargs
      )
      log_joint = self.trace_prob_evaluator.log_prob(model_trace)
      for name, t in self.transforms.items():
          log_joint = log_joint - torch.sum(
              t.log_abs_det_jacobian(
                  params_constrained[name], params[name]
              )
          )
      return -log_joint
  
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 2 for calling the _PEMaker obj's underlying member method code with example usage: obj._potential_fn(params) and returns: unknown