input: from typing import List
  
  def construct_regression_neural_network() -> None:
      """ Constructs and trains a neural network in Python for basic regression. This function
      loads data from 'data.csv', splits it into training and testing sets, constructs a 
      Sequential model with three Dense layers, compiles and trains the model, and finally, 
      evaluates the model and prints the test score.
      >>> construct_regression_neural_network()  # Assuming 'data.csv' exists and is formatted correctly
      'Test score: {some_value}'
      """
output:     import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load data
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Construct the model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, 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=50, batch_size=32, verbose=0)
  
      # Evaluate the model
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f'Test score: {test_score}')