code: def utterance_prior():
      utterances = [
          50,
          51,
          500,
          501,
          1000,
          1001,
          5000,
          5001,
          10000,
          10001,
      ]
      utteranceLogits = -torch.tensor(
          list(map(utterance_cost, utterances)), dtype=torch.float64
      )
      ix = pyro.sample(
          "utterance", dist.Categorical(logits=utteranceLogits)
      )
      return utterances[ix]
  
file_path: pyro/examples/rsa/hyperbole.py
imports: import argparse
  import collections
  import torch
  from search_inference import HashingMarginal, Search, memoize
  import pyro
  import pyro.distributions
  import pyro.poutine
label_desc: Use image node_id 8 for calling a global function with example usage: utterance_prior() and returns: utterances