class_docstr: Bayesian probit regression with a Gaussian prior.
  
  ```none
  # The `+ 1` is for the bias term.
  for i in range(num_features + 1):
    weights[i] ~ Normal(loc=0, scale=1)
  
  for j in range(num_datapoints):
    label[j] ~ Bernoulli(probit=concat([features[j], [1]) @ weights)
  ```
class_docstr_tok: ["Bayesian","probit","regression","with","a","Gaussian","prior",".","``","`","none","#","The","`","+","1","`","is","for","the","bias","term",".","for","i","in","range","(","num_features","+","1",")",":","weights","[","i","]","~","Normal","(","loc=0",",","scale=1",")","for","j","in","range","(","num_datapoints",")",":","label","[","j","]","~","Bernoulli","(","probit=concat","(","[","features","[","j","]",",","[","1","]",")","@","weights",")","``","`"]
code: def _prior_distribution(self):
      return self._prior_dist
  
file_path: probability/spinoffs/inference_gym/inference_gym/targets/probit_regression.py
imports: import functools
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import prefer_static
  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 german_credit_numeric_probit_regression
label_desc: Use image node_id 2 for calling the ProbitRegression obj's underlying member method code with example usage: obj._prior_distribution() and returns: self