code: def build_fn_digits_exp(input_shape=-1):
      model = Sequential(
          [
              Reshape((8, 8, -1), input_shape=(64,)),
              Conv2D(filters=32, kernel_size=(5, 5), activation="relu"),
              MaxPooling2D(pool_size=(2, 2)),
              Dropout(0.5),
              Flatten(),
              Dense(1, activation="sigmoid"),
          ]
      )
      model.compile(
          optimizer="adam",
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
      return model
  
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 8 for calling a global function with example usage: build_fn_digits_exp(input_shape) and returns: model