code: def log_prob(self, value):
      shape = broadcast_shape(
          self.batch_shape, value.shape[: value.dim() - self.event_dim]
      )
      log_prob = self.base_dist.log_prob(value)
      return log_prob.expand(shape)
  
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 10 for calling the ExpandedDistribution obj's underlying member method code with example usage: obj.log_prob(value) and returns: log_prob