input: Write a function to construct a neural network in Python that can perform basic regression. Load the data from a CSV file, split it into features and labels, and then train the neural network using the given data. The function should return the test score of the model.
  
  py
  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 train_regression_model(file_path):
      # Load the data
      data = pd.read_csv(file_path)
      
      # 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 round(train_regression_model('data.csv'), 2) == 2.35
  assert round(train_regression_model('another_data.csv'), 2) == 3.12
  assert round(train_regression_model('third_data.csv'), 2) == 1.78
  assert round(train_regression_model('empty_data.csv'), 2) == 0.0
  
output: 
  def train_regression_model(file_path):
      data = pd.read_csv(file_path)
      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
  