code: def score_parts(self, value):
      shape = broadcast_shape(
          self.batch_shape, value.shape[: value.dim() - self.event_dim]
      )
      (
          log_prob,
          score_function,
          entropy_term,
      ) = self.base_dist.score_parts(value)
      if self.batch_shape != self.base_dist.batch_shape:
          log_prob = log_prob.expand(shape)
          if isinstance(score_function, torch.Tensor):
              score_function = score_function.expand(shape)
          if isinstance(entropy_term, torch.Tensor):
              entropy_term = entropy_term.expand(shape)
      return ScoreParts(log_prob, score_function, entropy_term)
  
imports: import warnings
  from collections import OrderedDict
  from typing import Callable
  import torch
  from torch.distributions.kl import kl_divergence, register_kl
  import pyro.distributions.torch
  from .None import constraints
  from .distribution import Distribution
  from .score_parts import ScoreParts
  from .util import broadcast_shape, scale_and_mask
label_desc: Use image node_id 11 for calling the ExpandedDistribution obj's underlying member method code with example usage: obj.score_parts(value) and returns: ScoreParts