code: def test_more_verbose_output():
      """Check verbose=2 does not cause error"""
  
      old_stdout = sys.stdout
      old_stderr = sys.stderr
      sys.stdout = StringIO()
      sys.stderr = StringIO()
      est = SymbolicRegressor(
          population_size=100, generations=10, random_state=0, verbose=2
      )
      est.fit(diabetes.data, diabetes.target)
      verbose_output = sys.stdout
      joblib_output = sys.stderr
      sys.stdout = old_stdout
      sys.stderr = old_stderr
  
      # check output
      verbose_output.seek(0)
      # Ignore header rows
      _ = verbose_output.readline().rstrip()
      _ = verbose_output.readline().rstrip()
      _ = verbose_output.readline().rstrip()
  
      n_lines = sum(1 for l in verbose_output.readlines())
      assert 10 == n_lines
  
      joblib_output.seek(0)
      n_lines = sum(1 for l in joblib_output.readlines())
      # New version of joblib appears to output sys.stderr
      assert 0 == n_lines % 10
  
imports: import pickle
  import pytest
  import sys
  from io import StringIO
  import numpy
  from scipy.stats import pearsonr, spearmanr
  from sklearn.datasets import load_diabetes, load_breast_cancer
  from sklearn.metrics import mean_absolute_error
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.utils._testing import assert_almost_equal
  from sklearn.utils._testing import assert_array_equal
  from sklearn.utils._testing import assert_array_almost_equal
  from sklearn.utils._testing import assert_raises
  from sklearn.utils.validation import check_random_state
  from gplearn.genetic import SymbolicClassifier, SymbolicRegressor
  from gplearn.genetic import SymbolicTransformer
  from gplearn.fitness import weighted_pearson, weighted_spearman
  from gplearn._program import _Program
  from gplearn.fitness import _fitness_map
  from gplearn.functions import add2, sub2, mul2, div2, sqrt1, log1, abs1, max2, min2
  from gplearn.functions import _Function
label_desc: Use image node_id 22 for calling a global function with example usage: test_more_verbose_output() without return types