code: def test_gaussian_random_number_generator():
      # Tests normality of the gaussian RNG with shapiro and D'Agostino/Pearson's tests.
      # Caution: can fail with extremely low probability. TODO: Harsha calculate this failure prob.
  
      stddevs = [1, 2]
      n_iter = 1000
  
      native = Native.get_native_singleton()
  
      for std in stddevs:
          norm_results, shapiro_results = [], []
          for i in range(n_iter):
              rands = native.generate_gaussian_random(
                  None, std, count=1000
              )
              norm_results.append(normaltest(rands).pvalue > 0.05)
              shapiro_results.append(shapiro(rands).pvalue > 0.05)
  
          assert 0.9 < np.mean(norm_results) < 0.99
          assert 0.9 < np.mean(shapiro_results) < 0.99
  
file_path: interpret/python/interpret-core/tests/utils/test_native.py
imports: from interpret.utils._native import Native
  import numpy
  from scipy.stats import normaltest, shapiro
label_desc: Use image node_id 6 for calling a global function with example usage: test_gaussian_random_number_generator() without return types