code: def price_prior():
      values = [50, 51, 500, 501, 1000, 1001, 5000, 5001, 10000, 10001]
      probs = torch.tensor(
          [
              0.4205,
              0.3865,
              0.0533,
              0.0538,
              0.0223,
              0.0211,
              0.0112,
              0.0111,
              0.0083,
              0.0120,
          ]
      )
      ix = pyro.sample("price", dist.Categorical(probs=probs))
      return values[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 3 for calling a global function with example usage: price_prior() and returns: values