code: def opt_regressor():
      optimizer = DummyOptPro(iterations=1)
      optimizer.forge_experiment(
          model_initializer=KerasRegressor,
          model_init_params=_build_fn_regressor,
          model_extra_params=dict(
              callbacks=[ReduceLROnPlateau(patience=Integer(5, 10))],
              batch_size=Categorical([32, 64], transform="onehot"),
              epochs=10,
              verbose=0,
          ),
      )
      optimizer.go()
  
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 6 for calling a global function with example usage: opt_regressor() without return types