code: def _execute():
      env = Environment(
          train_dataset=prep_data(),
          results_path="HyperparameterHunterAssets",
          metrics=["roc_auc_score"],
          cv_type="StratifiedKFold",
          cv_params=dict(n_splits=3, shuffle=True, random_state=True),
      )
  
      experiment = CVExperiment(
          model_initializer=KerasClassifier,
          model_init_params=build_fn,
          model_extra_params=dict(
              batch_size=32, epochs=3, verbose=0, shuffle=True
          ),
      )
  
file_path: hyperparameter_hunter/examples/keras_examples/image_classification_example.py
imports: from hyperparameter_hunter import Environment, CVExperiment
  import pandas
  from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape
  from keras.models import Sequential
  from keras.wrappers.scikit_learn import KerasClassifier
  from sklearn.datasets import load_digits
label_desc: Use image node_id 3 for calling a global function with example usage: _execute() without return types