input: def construct_regression_neural_network() -> None:
      """ Constructs a neural network in Python that can perform basic regression.
      The network is trained on a dataset loaded from 'data.csv' and evaluated on a test set.
      The architecture consists of three layers with ReLU activation for the first two layers.
      The model is compiled using mean squared error as the loss function and the Adam optimizer.
      >>> construct_regression_neural_network()
      Test score: 0.03214567
      """
output:     import pandas as pd
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
  
      # Load the dataset
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # Split the dataset 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)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # 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))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # 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:.8f}")