code: def test_count_binary_occurrences():
      # by default multiple occurrences are counted as longs
      test_data = Series(["aaabc", "abbde"])
      vect = CountVectorizer(analyzer="char", max_df=1.0)
      X = cp.asnumpy(vect.fit_transform(test_data).todense())
      assert_array_equal(
          ["a", "b", "c", "d", "e"],
          vect.get_feature_names().to_arrow().to_pylist(),
      )
      assert_array_equal([[3, 1, 1, 0, 0], [1, 2, 0, 1, 1]], X)
  
      # using boolean features, we can fetch the binary occurrence info
      # instead.
      vect = CountVectorizer(analyzer="char", max_df=1.0, binary=True)
      X = cp.asnumpy(vect.fit_transform(test_data).todense())
      assert_array_equal([[1, 1, 1, 0, 0], [1, 1, 0, 1, 1]], X)
  
      # check the ability to change the dtype
      vect = CountVectorizer(
          analyzer="char", max_df=1.0, binary=True, dtype=cp.float32
      )
      X = vect.fit_transform(test_data)
      assert X.dtype == cp.float32
  
imports: from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import cpu_only_import_from
  from cuml.internals.safe_imports import gpu_only_import_from
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_extraction.text import HashingVectorizer
  from sklearn.feature_extraction.text import CountVectorizer
  import pytest
  from cuml.feature_extraction.text import CountVectorizer
  from cuml.feature_extraction.text import TfidfVectorizer
  from cuml.feature_extraction.text import HashingVectorizer
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 11 for calling a global function with example usage: test_count_binary_occurrences() without return types