code: def test_hash_collisions():
      X = [list("Thequickbrownfoxjumped")]
  
      Xt = FeatureHasher(
          alternate_sign=True, n_features=1, input_type="string"
      ).fit_transform(X)
      # check that some of the hashed tokens are added
      # with an opposite sign and cancel out
      assert abs(Xt.data[0]) < len(X[0])
  
      Xt = FeatureHasher(
          alternate_sign=False, n_features=1, input_type="string"
      ).fit_transform(X)
      assert Xt.data[0] == len(X[0])
  
file_path: scikit-learn/sklearn/feature_extraction/tests/test_feature_hasher.py
imports: import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.feature_extraction import FeatureHasher
  from sklearn.feature_extraction._hashing_fast import transform
label_desc: Use image node_id 10 for calling a global function with example usage: test_hash_collisions() without return types