input: 
  def perform_basic_regression() -> None:
      """Construct a neural network in Python that can perform basic regression.
      >>> perform_basic_regression()
      Test score: 0.123456789
      """
  
output:     
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.neural_network import MLPRegressor
      from sklearn.metrics import mean_squared_error
  
      # Generate synthetic data
      X = np.random.rand(100, 10)
      y = np.random.rand(100)
  
      # Split the 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)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Create and train the neural network
      model = MLPRegressor(hidden_layer_sizes=(10, 10), max_iter=1000, random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the mean squared error
      mse = mean_squared_error(y_test, y_pred)
  
      # Print the test score
      print(f"Test score: {mse}")
  