code: def combine_meaning(meanings, c):
      possible_combos = can_apply(meanings)
      N = len(possible_combos)
      ix = pyro.sample(
          "ix_{}".format(c), dist.Categorical(torch.ones(N) / N)
      )
      i = possible_combos[ix]
      s = meanings[i].syn()
      if s["dir"] == "L":
          f = meanings[i].sem
          a = meanings[i - 1].sem
          new_meaning = CompoundMeaning(
              sem=apply_world_passing(f, a), syn=s["out"]
          )
          return meanings[0 : i - 1] + [new_meaning] + meanings[i + 1 :]
      if s["dir"] == "R":
          f = meanings[i].sem
          a = meanings[i + 1].sem
          new_meaning = CompoundMeaning(
              sem=apply_world_passing(f, a), syn=s["out"]
          )
          return meanings[0:i] + [new_meaning] + meanings[i + 2 :]
  
imports: import argparse
  import collections
  import torch
  from search_inference import BestFirstSearch, HashingMarginal, memoize
  import pyro
  import pyro.distributions
label_desc: Use image node_id 10 for calling a global function with example usage: combine_meaning(meanings, c) and returns: unknown, unknown