code: def test_count_vectorizer_remote_vocabulary():
      m1 = sklearn.feature_extraction.text.CountVectorizer().fit(
          JUNK_FOOD_DOCS
      )
      vocabulary = m1.vocabulary_
      r1 = m1.transform(JUNK_FOOD_DOCS)
      b = db.from_sequence(JUNK_FOOD_DOCS, npartitions=2)
  
      with Client() as client:
          (remote_vocabulary,) = client.scatter(
              (vocabulary,), broadcast=True
          )
          m = dask_ml.feature_extraction.text.CountVectorizer(
              vocabulary=remote_vocabulary
          )
          r2 = m.transform(b)
  
          assert isinstance(r2, da.Array)
          assert isinstance(r2._meta, scipy.sparse.csr_matrix)
          np.testing.assert_array_equal(
              r1.toarray(), r2.compute().toarray()
          )
  
          m = dask_ml.feature_extraction.text.CountVectorizer(
              vocabulary=remote_vocabulary
          )
          m.fit_transform(b)
          assert m.vocabulary_ is remote_vocabulary
  
file_path: dask-ml/tests/feature_extraction/test_text.py
imports: import dask.array
  import dask.bag
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import scipy.sparse
  import sklearn.feature_extraction.text
  from distributed import Client
  import dask_ml.feature_extraction.text
  from dask_ml._compat import dummy_context
  from dask_ml.utils import assert_estimator_equal
label_desc: Use image node_id 7 for calling a global function with example usage: test_count_vectorizer_remote_vocabulary() without return types