code: def test_parametrization(self):
      err_msg = "`m` must be a positive integer as the `Periodic` kernel approximation is only implemented for 1-dimensional case."
  
      with pytest.raises(ValueError, match=err_msg):
          # `m` must be a positive integer, not a list
          cov_func = pm.gp.cov.Periodic(1, period=1, ls=0.1)
          pm.gp.HSGPPeriodic(m=[500], cov_func=cov_func)
  
      with pytest.raises(ValueError, match=err_msg):
          # `m`` must be a positive integer
          cov_func = pm.gp.cov.Periodic(1, period=1, ls=0.1)
          pm.gp.HSGPPeriodic(m=-1, cov_func=cov_func)
  
      with pytest.raises(
          ValueError,
          match="`cov_func` must be an instance of a `Periodic` kernel only. Use the `scale` parameter to control the variance.",
      ):
          # `cov_func` must be `Periodic` only
          cov_func = 5.0 * pm.gp.cov.Periodic(1, period=1, ls=0.1)
          pm.gp.HSGPPeriodic(m=500, cov_func=cov_func)
  
      with pytest.raises(
          ValueError,
          match="HSGP approximation for `Periodic` kernel only implemented for 1-dimensional case.",
      ):
          cov_func = pm.gp.cov.Periodic(2, period=1, ls=[1, 2])
          pm.gp.HSGPPeriodic(m=500, scale=0.5, cov_func=cov_func)
  
imports: import arviz
  import numpy
  import pytensor
  import pytensor.tensor
  import pytest
  from scipy.spatial import distance
  import pymc
label_desc: Use image node_id 1 for calling the TestHSGPPeriodic obj's underlying member method code with example usage: obj.test_parametrization() without return types