input: def construct_neural_network_for_regression():
      """Construct a neural network in Python that can perform basic regression.
      The neural network is built using Keras with TensorFlow backend.
      The network consists of three fully connected (Dense) layers:
      - The first layer has 12 neurons, uses ReLU activation and the kernel initializer is set to 'normal'.
      - The second layer also has 12 neurons with the same settings as the first layer.
      - The third layer is the output layer with a single neuron without activation.
      The model is compiled using the mean squared error loss function and the Adam optimizer.
      The model is trained for 100 epochs with a batch size of 20 and evaluated on the test set.
      >>> construct_neural_network_for_regression()
      'Test score: <some_value>'
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Construct the neural network
      model = Sequential()
      model.add(Dense(12, input_dim=1, activation='relu', kernel_initializer='normal'))
      model.add(Dense(12, activation='relu', kernel_initializer='normal'))
      model.add(Dense(1, kernel_initializer='normal'))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
  
      # Evaluate the model on the test set
      test_score = model.evaluate(X_test, y_test, verbose=0)
  
      return f'Test score: {test_score}'