code: def prep_data(n_class=2):
      input_data, target_data = load_digits(
          n_class=n_class, return_X_y=True
      )
      train_df = pd.DataFrame(
          data=input_data,
          columns=[
              "c_{:02d}".format(_) for _ in range(input_data.shape[1])
          ],
      )
      train_df["target"] = target_data
      return train_df
  
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 1 for calling a global function with example usage: prep_data(n_class) and returns: train_df