code: def test_space_ngrams(ngram_range):
      data = ["abc      def. 123 456    789"]
      data_gpu = Series(data)
      vec = CountVectorizer(ngram_range=ngram_range).fit(data_gpu)
      ref = SkCountVect(ngram_range=ngram_range).fit(data)
      assert (
          ref.get_feature_names()
      ) == vec.get_feature_names().to_arrow().to_pylist()
  
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 13 for calling a global function with example usage: test_space_ngrams(ngram_range) without return types