input: 
  def construct_neural_network() -> Sequential:
      """Construct a basic neural network for regression using Keras.
      The network will have 3 layers, including an input layer, a hidden layer, and an output layer.
  
      >>> model = construct_neural_network()
      >>> score = model.evaluate(X_test, y_test, verbose=0)
      Test score: <some_value>
      """
      data = pd.read_csv('data.csv')
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
      model.compile(loss='mean_squared_error', optimizer='adam')
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      
      return model
  
output:     return model