code: def build_fn(input_shape=-1):
      model = Sequential(
          [
              Dense(
                  100,
                  kernel_initializer="uniform",
                  input_shape=input_shape,
                  activation="relu",
              ),
              Dropout(0.5),
              Dense(
                  50, kernel_initializer="uniform", activation="relu"
              ),
              Dropout(0.3),
              Dense(
                  1, kernel_initializer="uniform", activation="sigmoid"
              ),
          ]
      )
      model.compile(
          optimizer="adam",
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
      return model
  
file_path: hyperparameter_hunter/examples/keras_examples/experiment_example.py
imports: from hyperparameter_hunter import Environment, CVExperiment
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data
  import os.path
  from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
  from keras.layers import Dense, Activation, Dropout
  from keras.models import Sequential
  from keras.wrappers.scikit_learn import KerasClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: build_fn(input_shape) and returns: model