code: def test_categorical_tuple_match(env_digits):
      """Test that optimization of a `Categorical` space, whose values are tuples can be performed
      and that saved results from such a space are correctly identified as similar Experiments
      """
      model_extra_params = dict(
          batch_size=32, epochs=3, verbose=0, shuffle=True
      )
      exp_0 = CVExperiment(
          KerasClassifier, build_fn_digits_exp, model_extra_params
      )
  
      #################### First OptPro ####################
      opt_0 = BayesianOptPro(
          iterations=1, random_state=32, n_initial_points=1
      )
      opt_0.forge_experiment(
          KerasClassifier, build_fn_digits_opt, model_extra_params
      )
      opt_0.go()
      assert len(opt_0.similar_experiments) == 1  # Should match `exp_0`
  
      #################### Second OptPro ####################
      opt_1 = BayesianOptPro(
          iterations=1, random_state=32, n_initial_points=1
      )
      opt_1.forge_experiment(
          KerasClassifier, build_fn_digits_opt, model_extra_params
      )
      opt_1.go()
      assert len(opt_1.similar_experiments) == 2
  
imports: from hyperparameter_hunter import Environment, CVExperiment, Real, Integer, Categorical
  from hyperparameter_hunter import DummyOptPro, BayesianOptPro
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data, get_diabetes_data
  import pandas
  import pytest
  from keras.callbacks import ReduceLROnPlateau
  from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape
  from keras.models import Sequential
  from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
  from keras.initializers import glorot_normal, orthogonal, Orthogonal
  from sklearn.datasets import load_digits
label_desc: Use image node_id 10 for calling a global function with example usage: test_categorical_tuple_match(env_digits) without return types