code: def test_parallel_custom_transformer():
      """Regression test for running parallel training with custom transformer"""
  
      def _sigmoid(x1):
          with np.errstate(over="ignore", under="ignore"):
              return 1 / (1 + np.exp(-x1))
  
      sigmoid = make_function(function=_sigmoid, name="sig", arity=1)
      est = SymbolicClassifier(
          generations=2, transformer=sigmoid, random_state=0, n_jobs=2
      )
      est.fit(cancer.data, cancer.target)
      _ = pickle.dumps(est)
  
      # Unwrapped functions should fail
      sigmoid = make_function(
          function=_sigmoid, name="sig", arity=1, wrap=False
      )
      est = SymbolicClassifier(
          generations=2, transformer=sigmoid, random_state=0, n_jobs=2
      )
      est.fit(cancer.data, cancer.target)
      assert_raises(AttributeError, pickle.dumps, est)
  
      # Single threaded will also fail in non-interactive sessions
      est = SymbolicClassifier(
          generations=2, transformer=sigmoid, random_state=0
      )
      est.fit(cancer.data, cancer.target)
      assert_raises(AttributeError, pickle.dumps, est)
  
file_path: gplearn/gplearn/tests/test_functions.py
imports: import pickle
  import numpy
  from numpy import maximum
  from sklearn.datasets import load_diabetes, load_breast_cancer
  from sklearn.utils._testing import assert_raises
  from sklearn.utils.validation import check_random_state
  from gplearn.functions import _protected_sqrt, make_function
  from gplearn.genetic import SymbolicRegressor, SymbolicTransformer
  from gplearn.genetic import SymbolicClassifier
label_desc: Use image node_id 4 for calling a global function with example usage: test_parallel_custom_transformer() and returns: unknown