input: Write a function to construct a neural network in Python that can perform basic regression using the Keras library. The function should load data from a CSV file, split it into features and labels, and then train the neural network using the features to predict the labels. Finally, the function should evaluate the model and print the test score.
  
  def construct_regression_model():
      import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from keras.models import Sequential
      from keras.layers import Dense
   
      # Load the data
      data = pd.read_csv('data.csv')
   
      # Split the data into features and labels
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
   
      # 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)
   
      # Create the neural network
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
   
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
   
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
   
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      print('Test score:', score)
  
  assert construct_regression_model() is None
output: def construct_regression_model():
      import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from keras.models import Sequential
      from keras.layers import Dense
   
      # Load the data
      data = pd.read_csv('data.csv')
   
      # Split the data into features and labels
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
   
      # 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)
   
      # Create the neural network
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
   
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
   
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
   
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      print('Test score:', score)