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 training and testing sets, create a neural network with three layers, compile the model, train it, and finally evaluate it on the test set, printing the test score.
  
  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
  
  def build_regression_model(data_file):
      # Load the data
      data = pd.read_csv(data_file)
      
      # 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)
      return score
  
  assert build_regression_model('data_file.csv') is not None
output: 
  def build_regression_model(data_file):
      data = pd.read_csv(data_file)
      X = data.drop(['label'], axis=1).values
      y = data['label'].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(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'))
      model.compile(loss='mean_squared_error', optimizer='adam')
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      score = model.evaluate(X_test, y_test, verbose=0)
      return score
  