code: def test_invalid_feature_names():
      """Check invalid feature names raise errors"""
  
      for Symbolic in (SymbolicRegressor, SymbolicTransformer):
          # Check invalid length feature_names
          est = Symbolic(feature_names=["foo", "bar"])
          assert_raises(
              ValueError, est.fit, diabetes.data, diabetes.target
          )
  
          # Check invalid type feature_name
          feature_names = [str(n) for n in range(12)] + [0]
          est = Symbolic(feature_names=feature_names)
          assert_raises(
              ValueError, est.fit, diabetes.data, diabetes.target
          )
  
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 7 for calling a global function with example usage: test_invalid_feature_names() without return types