code: def test_none_const_range():
      """Check that const_range=None produces no constants"""
  
      # Check with None as const_range
      est = SymbolicRegressor(
          population_size=100, generations=2, const_range=None
      )
      est.fit(diabetes.data, diabetes.target)
      float_count = 0
      for generation in est._programs:
          for program in generation:
              if program is None:
                  continue
              for element in program.program:
                  if isinstance(element, float):
                      float_count += 1
      assert float_count == 0
  
      # Check with default const_range
      est = SymbolicRegressor(population_size=100, generations=2)
      est.fit(diabetes.data, diabetes.target)
      float_count = 0
      for generation in est._programs:
          for program in generation:
              if program is None:
                  continue
              for element in program.program:
                  if isinstance(element, float):
                      float_count += 1
      assert float_count > 1
  
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 14 for calling a global function with example usage: test_none_const_range() without return types