code: def test_hyperprior_on_common_effect():
      rng = np.random.default_rng(121195)
      data = pd.DataFrame(
          {
              "y": rng.normal(size=100),
              "x1": rng.normal(size=100),
              "g1": ["a"] * 50 + ["b"] * 50,
          }
      )
      slope = bmb.Prior(
          "Normal", mu=0, sd=bmb.Prior("HalfCauchy", beta=2)
      )
  
      priors = {"x1": slope}
      with pytest.raises(ValueError):
          bmb.Model("y ~ x1 + (x1|g1)", data, priors=priors)
  
      priors = {"common": slope}
      with pytest.raises(ValueError):
          bmb.Model("y ~ x1 + (x1|g1)", data, priors=priors)
  
imports: import logging
  from functools import reduce
  from operator import add
  from os.path import dirname, join
  import arviz
  import numpy
  import pandas
  import pymc
  import pytest
  from formulae import design_matrices
  import bambi
  from bambi.terms import CommonTerm, GroupSpecificTerm
label_desc: Use image node_id 20 for calling a global function with example usage: test_hyperprior_on_common_effect() without return types