code: def test_verbose_output():
      """Check verbose=1 does not cause error"""
  
      old_stdout = sys.stdout
      sys.stdout = StringIO()
      est = SymbolicRegressor(
          population_size=100, generations=10, random_state=0, verbose=1
      )
      est.fit(diabetes.data, diabetes.target)
      verbose_output = sys.stdout
      sys.stdout = old_stdout
  
      # check output
      verbose_output.seek(0)
      header1 = verbose_output.readline().rstrip()
      true_header = "    |{:^25}|{:^42}|".format(
          "Population Average", "Best Individual"
      )
      assert true_header == header1
  
      header2 = verbose_output.readline().rstrip()
      true_header = (
          "-" * 4 + " " + "-" * 25 + " " + "-" * 42 + " " + "-" * 10
      )
      assert true_header == header2
  
      header3 = verbose_output.readline().rstrip()
  
      line_format = "{:>4} {:>8} {:>16} {:>8} {:>16} {:>16} {:>10}"
      true_header = line_format.format(
          "Gen",
          "Length",
          "Fitness",
          "Length",
          "Fitness",
          "OOB Fitness",
          "Time Left",
      )
      assert true_header == header3
  
      n_lines = sum(1 for l in verbose_output.readlines())
      assert 10 == n_lines
  
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 20 for calling a global function with example usage: test_verbose_output() without return types