code: def test_multinomial_basic_fit_predict_dense_numpy(
      x_dtype, y_dtype, nlp_20news
  ):
      """
      Cupy Test
      """
      X, y = nlp_20news
      n_rows = 500
      n_cols = 10000
  
      X = sparse_scipy_to_cp(X, cp.float32).tocsr()[:n_rows, :n_cols]
      y = y[:n_rows].astype(y_dtype)
  
      model = MultinomialNB()
      model.fit(
          np.ascontiguousarray(cp.asnumpy(X.todense()).astype(x_dtype)),
          y,
      )
  
      y_hat = model.predict(X).get()
  
      modelsk = skNB()
      modelsk.fit(X.get(), y.get())
      y_sk = model.predict(X.get())
  
      assert_allclose(y_hat, y_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 2 for calling a global function with example usage: test_multinomial_basic_fit_predict_dense_numpy(x_dtype, y_dtype, nlp_20news) without return types