input: def construct_neural_network() -> Sequential:
      """Constructs a neural network in Python that can perform basic regression.
      The network is created using the Keras Sequential API and consists of three 
      Dense layers. The first Dense layer has 12 units, the second Dense layer has 
      12 units, and the third Dense layer has 1 unit. The activation function for 
      the first and second layers is ReLU, and the model is compiled using the 
      Adam optimizer and mean squared error loss function.
  
      >>> model = construct_neural_network()
      >>> 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)
      >>> score = model.evaluate(X_test, y_test, verbose=0)
      >>> print('Test score:', score)
      Test score: 0.0283
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      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')
      return model