input: 
  def construct_neural_network() -> None:
      """ Construct and train a neural network in Python that performs basic regression.
      >>> construct_neural_network()
      Test score: 0.0123456789
      """
  
output:     import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from sklearn.model_selection import train_test_split
      from sklearn.datasets import make_regression
      from sklearn.preprocessing import StandardScaler
  
      # Generate a synthetic dataset
      X, y = make_regression(n_samples=1000, n_features=20, noise=0.1)
  
      # 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
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(32, activation='relu'),
          Dense(1)
      ])
  
      # 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_loss = model.evaluate(X_test, y_test, verbose=0)
      print(f"Test score: {test_loss}")