input: Write a function to construct 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, create a neural network using Keras, compile the model, train the model, and finally evaluate the model by printing the test score.
  assert construct_regression_network('data.csv')==None
  assert type(construct_regression_network('data.csv'))==None
  assert 'Test score:' in construct_regression_network('data.csv')
  assert 'Test score:' in construct_regression_network('data_with_label.csv')
  assert 'Test score:' in construct_regression_network('data_with_label.csv')==True
output: import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def construct_regression_network(file_path):
      data = pd.read_csv(file_path)
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1))
      
      model.compile(optimizer='adam', 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)
      print(f'Test score: {test_score}')