code: def test_basic(vect, container):
      b = db.from_sequence(JUNK_FOOD_DOCS, npartitions=2)
      if type(vect) == dask_ml.feature_extraction.text.FeatureHasher:
          b = b.str.split()
      elif container == "series":
          b = b.to_dataframe(columns=["text"])["text"]
      elif container == "array":
          b = b.to_dataframe(columns=["text"])["text"].values
  
      vect_ref = vect._hasher(**vect.get_params())
      X_ref = vect_ref.fit_transform(b.compute())
      X_da = vect.fit_transform(b)
  
      assert_estimator_equal(vect_ref, vect)
  
      assert isinstance(X_da, da.Array)
      assert isinstance(
          X_da.blocks[0].compute(), scipy.sparse.csr_matrix
      )
      result = X_da.map_blocks(lambda x: x.toarray(), dtype=X_da.dtype)
      expected = X_ref.toarray()
      np.testing.assert_array_equal(result, expected)
  
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 1 for calling a global function with example usage: test_basic(vect, container) without return types