input: Write a function to construct and train a basic regression neural network using Keras in Python. The function should load the data from a CSV file, split it into training and testing sets, create a neural network model, compile, train, and evaluate the model. The function should return the test score.
  assert construct_regression_nn('data.csv') == print('Test score:', score)
  assert type(construct_regression_nn('data.csv')) == print
  assert construct_regression_nn('nonexistent.csv') == FileNotFoundError
output: def construct_regression_nn(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      try:
          data = pd.read_csv(file_path)
      except FileNotFoundError:
          return FileNotFoundError
  
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      model = Sequential([
          Dense(64, input_shape=(X_train.shape[1],), activation='relu'),
          Dense(32, activation='relu'),
          Dense(1)
      ])
  
      model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')
  
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      test_score = model.evaluate(X_test, y_test, verbose=0)
  
      return test_score