text: from sklearned.challenging.surrogatechallenge import challenge
  from tensorflow import  keras
  import os
  
  
  def build_challenger_model(n_inputs):
      model = keras.Sequential()
      kernel_initializer_0 = keras.initializers.RandomUniform(minval=0.05, maxval=0.1, seed=None)
      bias_initializer_0 = keras.initializers.RandomUniform(minval=-0.2, maxval=0.21, seed=None)
      model.add(keras.layers.Dense(160, activation="linear", input_shape=(1, n_inputs),
                                   kernel_initializer=kernel_initializer_0,
                                   bias_initializer=bias_initializer_0))
      model.add(keras.layers.Dense(16, activation='linear'))
      model.add(keras.layers.Dense(2, activation="linear"))  # selu
      model.add(keras.layers.Dense(1, activation="linear"))
      optimizer = keras.optimizers.Adagrad(learning_rate=0.005)
      optimizer = keras.optimizers.RMSprop(learning_rate=0.001)
      model.compile(loss='mse', optimizer=optimizer)
      return model
  
  
  champ = """
  def champion_model(n_inputs):
      model = keras.Sequential()
      kernel_initializer_0 = keras.initializers.RandomUniform(minval=0.01, maxval=0.02, seed=None)
      bias_initializer_0 = keras.initializers.RandomUniform(minval=0.01, maxval=0.21, seed=None)
      model.add(keras.layers.Dense(80, activation="linear", input_shape=(1, n_inputs),
                                   kernel_initializer=kernel_initializer_0,
                                   bias_initializer=bias_initializer_0))
      model.add(keras.layers.Dense(16, activation='softsign'))
      model.add(keras.layers.Dense(2, activation="tanh"))  # selu
      model.add(keras.layers.Dense(1, activation="linear"))
      optimizer = keras.optimizers.Adagrad(learning_rate=0.005)
      model.compile(loss='mse', optimizer=optimizer)
      return model
      """
  
  
  if __name__=='__main__':
      skater_name = __file__.split(os.path.sep)[-1].replace('challenge_','').replace('.py','')
      print(skater_name)
      model = build_challenger_model(n_inputs=80)
      challenge(model=model, skater_name=skater_name, epochs=5000, patience=150)