code: def test_correct_meta():
      vect = dask_ml.feature_extraction.text.HashingVectorizer()
      X = dd.from_pandas(pd.Series(["some text", "to classifiy"]), 2)
      result = vect.fit_transform(X)
      assert scipy.sparse.issparse(result._meta)
      assert result._meta.dtype == "float64"
      assert result._meta.shape == (0, 0)
  
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 5 for calling a global function with example usage: test_correct_meta() without return types