input: Write a function to construct and train a neural network in Python that can perform basic regression. The function should load data from a CSV file, split the data into features and labels, and then train a neural network on the training data. Finally, it should evaluate the model on the test data and print the test score.
  assert construct_regression_nn('data.csv')==None
  assert construct_regression_nn('another_data.csv')==None
output: def construct_regression_nn(csv_file):
      import pandas as pd
      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
  
      data = pd.read_csv(csv_file)
      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 = MLPRegressor(hidden_layer_sizes=(100,), max_iter=1000, random_state=42)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      test_score = mean_squared_error(y_test, y_pred)
      print(test_score)