code: from sklearn import neural_network
  
  import learners
  
  
  class ANNLearner(learners.BaseLearner):
      def __init__(self,
                   hidden_layer_sizes=(100,),
                   activation="relu",
                   solver='adam',
                   alpha=0.0001,
                   batch_size='auto',
                   learning_rate="constant",
                   learning_rate_init=0.001,
                   power_t=0.5,
                   max_iter=200,
                   shuffle=True,
                   random_state=None,
                   tol=1e-4,
                   verbose=False,
                   warm_start=False,
                   momentum=0.9,
                   nesterovs_momentum=True,
                   early_stopping=False,
                   validation_fraction=0.1,
                   beta_1=0.9,
                   beta_2=0.999,
                   epsilon=1e-8,
                   ):
          super().__init__(verbose)
          self._learner = neural_network.MLPClassifier(
              hidden_layer_sizes=hidden_layer_sizes,
              activation=activation,
              solver=solver,
              alpha=alpha,
              batch_size=batch_size,
              learning_rate=learning_rate,
              learning_rate_init=learning_rate_init,
              power_t=power_t,
              max_iter=max_iter,
              shuffle=shuffle,
              random_state=random_state,
              tol=tol,
              verbose=verbose,
              warm_start=warm_start,
              momentum=momentum,
              nesterovs_momentum=nesterovs_momentum,
              early_stopping=early_stopping,
              validation_fraction=validation_fraction,
              beta_1=beta_1,
              beta_2=beta_2,
              epsilon=epsilon
          )
  
      def learner(self):
          return self._learner
  
apis: sklearn.neural_network.MLPClassifier