class_docstr: Bayesian logistic regression with a Gaussian prior.
  
  This model uses the German Credit (numeric) data set [1].
  
  #### References
  
  1. https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)
class_docstr_tok: ["Bayesian","logistic","regression","with","a","Gaussian","prior",".","This","model","uses","the","German","Credit","(","numeric",")","data","set","[","1","]",".","#","#","#","#","References","1.","https",":","\/\/archive.ics.uci.edu\/ml\/datasets\/statlog+","(","german+credit+data",")"]
code: def __init__(self):
      dataset = data.german_credit_numeric()
      del dataset["test_features"]
      del dataset["test_labels"]
      super(GermanCreditNumericLogisticRegression, self).__init__(
          name="german_credit_numeric_logistic_regression",
          pretty_name="German Credit Numeric Logistic Regression",
          **dataset,
      )
  
file_path: probability/spinoffs/inference_gym/inference_gym/targets/logistic_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_logistic_regression
label_desc: Use image node_id 1 to create a new GermanCreditNumericLogisticRegression object from inherited base classes: LogisticRegression with example: obj = GermanCreditNumericLogisticRegression()