code: def test_tfidf_vectorizer_get_feature_names():
      corpus = [
          "This is the first document.",
          "This document is the second document.",
          "And this is the third one.",
          "Is this the first document?",
      ]
      vectorizer = TfidfVectorizer()
      vectorizer.fit_transform(Series(corpus))
      output = [
          "and",
          "document",
          "first",
          "is",
          "one",
          "second",
          "the",
          "third",
          "this",
      ]
      assert (
          vectorizer.get_feature_names().to_arrow().to_pylist()
          == output
      )
  
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 24 for calling a global function with example usage: test_tfidf_vectorizer_get_feature_names() without return types