code: def test_check_unknown_missing_values(missing_value, pickle_uniques):
      # check for check_unknown with missing values with object dtypes
      values = np.array(
          ["d", "c", "a", "b", missing_value], dtype=object
      )
      uniques = np.array(["c", "a", "b", missing_value], dtype=object)
      if pickle_uniques:
          uniques = pickle.loads(pickle.dumps(uniques))
  
      expected_diff = ["d"]
      expected_mask = [False, True, True, True, True]
      _assert_check_unknown(
          values, uniques, expected_diff, expected_mask
      )
  
      values = np.array(
          ["d", "c", "a", "b", missing_value], dtype=object
      )
      uniques = np.array(["c", "a", "b"], dtype=object)
      if pickle_uniques:
          uniques = pickle.loads(pickle.dumps(uniques))
  
      expected_diff = ["d", missing_value]
  
      expected_mask = [False, True, True, True, False]
      _assert_check_unknown(
          values, uniques, expected_diff, expected_mask
      )
  
      values = np.array(["a", missing_value], dtype=object)
      uniques = np.array(["a", "b", "z"], dtype=object)
      if pickle_uniques:
          uniques = pickle.loads(pickle.dumps(uniques))
  
      expected_diff = [missing_value]
      expected_mask = [True, False]
      _assert_check_unknown(
          values, uniques, expected_diff, expected_mask
      )
  
file_path: scikit-learn/sklearn/utils/tests/test_encode.py
imports: import pickle
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.utils._encode import _check_unknown, _encode, _get_counts, _unique
label_desc: Use image node_id 5 for calling a global function with example usage: test_check_unknown_missing_values(missing_value, pickle_uniques) without return types