input: from typing import List
  
  def construct_neural_network_for_regression(data_filepath: str, test_size: float, random_state: int, epochs: int, batch_size: int) -> None:
      """Construct and train a neural network for basic regression using Keras.
      The function will load data from a CSV file, split it into training and testing sets,
      construct a neural network with dense layers, compile it, train it, and evaluate it.
      Finally, it will print the test score.
  
      >>> construct_neural_network_for_regression('data.csv', 0.2, 42, 100, 20)
      Test score: 15.34
      """
output:     import pandas as pd
      from sklearn.model_selection import train_test_split
      from keras.models import Sequential
      from keras.layers import Dense
      from keras.optimizers import Adam
  
      # Load data
      data = pd.read_csv(data_filepath)
      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=test_size, random_state=random_state)
  
      # Construct the neural network
      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=epochs, batch_size=batch_size, verbose=0)
  
      # Evaluate the model
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f"Test score: {test_score:.2f}")