code: def test_gaussian_parameters(priors, var_smoothing, nlp_20news):
      x_dtype = cp.float32
      y_dtype = cp.int32
      nrows = 150
      ncols = 20000
  
      X, y = nlp_20news
  
      X = sparse_scipy_to_cp(X[:nrows], x_dtype).todense()[:, :ncols]
      y = y.astype(y_dtype)[:nrows]
  
      if priors == "balanced":
          priors = cp.array([1 / 20] * 20)
      elif priors == "unbalanced":
          priors = cp.linspace(0.01, 0.09, 20)
  
      model = GaussianNB(priors=priors, var_smoothing=var_smoothing)
      model_sk = skGNB(
          priors=priors.get() if priors is not None else None,
          var_smoothing=var_smoothing,
      )
      model.fit(X, y)
      model_sk.fit(X.get(), y.get())
  
      y_hat = model.predict(X)
      y_hat_sk = model_sk.predict(X.get())
      y_hat = cp.asnumpy(y_hat)
      y = cp.asnumpy(y)
  
      assert_allclose(
          model.epsilon_.get(), model_sk.epsilon_, rtol=1e-4
      )
      assert_array_equal(y_hat, y_hat_sk)
  
imports: from cuml.internals.safe_imports import cpu_only_import
  import math
  from sklearn.naive_bayes import GaussianNB
  from sklearn.naive_bayes import ComplementNB
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.naive_bayes import BernoulliNB
  from sklearn.naive_bayes import MultinomialNB
  from numpy.testing import assert_array_almost_equal, assert_raises
  from numpy.testing import assert_allclose, assert_array_equal
  from cuml.datasets import make_classification
  from cuml.internals.input_utils import sparse_scipy_to_cp
  from cuml.naive_bayes import GaussianNB
  from cuml.naive_bayes import ComplementNB
  from cuml.naive_bayes import CategoricalNB
  from cuml.naive_bayes import BernoulliNB
  from cuml.naive_bayes import MultinomialNB
  from sklearn.metrics import accuracy_score
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 12 for calling a global function with example usage: test_gaussian_parameters(priors, var_smoothing, nlp_20news) without return types