code: def test_gaussian_fit_predict(
      x_dtype, y_dtype, is_sparse, nlp_20news
  ):
      """
      Cupy Test
      """
  
      X, y = nlp_20news
      model = GaussianNB()
      n_rows = 500
      n_cols = 50000
      X = sparse_scipy_to_cp(X, x_dtype)
      X = X.tocsr()[:n_rows, :n_cols]
  
      if is_sparse:
          y = y.astype(y_dtype)[:n_rows]
          model.fit(X, y)
      else:
          X = X.todense()
          y = y[:n_rows].astype(y_dtype)
          model.fit(
              np.ascontiguousarray(cp.asnumpy(X).astype(x_dtype)), y
          )
  
      y_hat = model.predict(X)
      y_hat = cp.asnumpy(y_hat)
      y = cp.asnumpy(y)
  
      assert accuracy_score(y, y_hat) >= 0.99
  
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 10 for calling a global function with example usage: test_gaussian_fit_predict(x_dtype, y_dtype, is_sparse, nlp_20news) without return types