class_docstr: Bayesian hierarchical model to predict radon measurements in houses.
  
  This model uses the Radon data set that accompanies the example in [1],
  filtered to include only houses in Indiana. It uses the form of the model with
  a `HalfNormal` prior on the scale parameters. Unlike the classical Minnesotta
  dataset, this dataset is somewhat larger and produces a posterior that does
  not require 64 bit precision to sample from.
  
  #### References
  
  [1] Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and
      Multilevel/Hierarchical Models (1st ed.). Cambridge University Press.
      http://www.stat.columbia.edu/~gelman/arm/examples/radon/
class_docstr_tok: ["Bayesian","hierarchical","model","to","predict","radon","measurements","in","houses",".","This","model","uses","the","Radon","data","set","that","accompanies","the","example","in","[","1","]",",","filtered","to","include","only","houses","in","Indiana",".","It","uses","the","form","of","the","model","with","a","`","HalfNormal","`","prior","on","the","scale","parameters",".","Unlike","the","classical","Minnesotta","dataset",",","this","dataset","is","somewhat","larger","and","produces","a","posterior","that","does","not","require","64","bit","precision","to","sample","from",".","#","#","#","#","References","[","1","]","Gelman",",","A.",",","&","Hill",",","J",".","(","2007",")",".","Data","Analysis","Using","Regression","and","Multilevel\/Hierarchical","Models","(","1st","ed.",")",".","Cambridge","University","Press",".","http",":","\/\/www.stat.columbia.edu\/~gelman\/arm\/examples\/radon\/"]
code: def __init__(self, dtype=tf.float32):
      dataset = data.radon_indiana()
      for key in list(dataset.keys()):
          if key.startswith("test_"):
              del dataset[key]
          elif dtype_util.is_floating(dataset[key].dtype):
              dataset[key] = tf.cast(dataset[key], dtype)
      super(RadonContextualEffectsHalfNormalIndiana, self).__init__(
          name="radon_contextual_effects_halfnormal_indiana",
          pretty_name="Radon Contextual Effects HalfNormal Indiana",
          prior_scale="halfnormal",
          **dataset,
      )
  
imports: import functools
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import dtype_util
  from inference_gym.internal import data
  from inference_gym.targets import bayesian_model
  from inference_gym.targets import model
  from inference_gym.targets.ground_truth import radon_contextual_effects_indiana
  from inference_gym.targets.ground_truth import radon_contextual_effects_indiana_halfnormal
  from inference_gym.targets.ground_truth import radon_contextual_effects_minnesota
  from inference_gym.targets.ground_truth import radon_contextual_effects_minnesota_halfnormal
label_desc: Use image node_id 1 to create a new RadonContextualEffectsHalfNormalIndiana object from inherited base classes: RadonContextualEffects with example: obj = RadonContextualEffectsHalfNormalIndiana(dtype)