input: def create_neural_network() -> None:
      """ Constructs a neural network in Python that can perform basic regression, using the Keras library.
      >>> create_neural_network()
      Test score: # A float value representing the mean squared error loss on the test set
      """
output:     from keras.models import Sequential
      from keras.layers import Dense
      import numpy as np
  
      # Generate dummy data
      np.random.seed(0)
      X_train = np.random.random((1000, 20))
      y_train = np.random.random((1000, 1))
      X_test = np.random.random((100, 20))
      y_test = np.random.random((100, 1))
  
      # Create the model
      model = Sequential()
      model.add(Dense(64, input_dim=20, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Evaluate the model
      test_loss = model.evaluate(X_test, y_test, verbose=0)
      print(f"Test score: {test_loss}")