input: 
  def construct_regression_neural_network():
      """Construct a neural network in Python that can perform basic regression.
      Example:
      for construct_regression_neural_network()
      should print 'Test score: [value]' where [value] is the mean squared error of the test set.
      """
  
output:     
      import tensorflow as tf
      from tensorflow.keras import layers, models
  
      model = models.Sequential()
      model.add(layers.Dense(64, activation='relu', input_shape=(10,)))
      model.add(layers.Dense(64, activation='relu'))
      model.add(layers.Dense(1))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Example data
      import numpy as np
      X_train = np.random.rand(1000, 10)
      y_train = np.random.rand(1000, 1)
      X_test = np.random.rand(200, 10)
      y_test = np.random.rand(200, 1)
  
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
  
      test_loss = model.evaluate(X_test, y_test)
      print(f'Test score: {test_loss}')
  