input: Below is an instruction that describes a task. Write a response that appropriately completes the request.
  
  ### Instruction:
  Construct a neural network in Python that can perform basic regression.
  
  ### Input:
  Not applicable
  
  ### Output:
  
  def build_and_train_regression_model(data_path: str, epochs: int = 100, batch_size: int = 20) -> float:
      """ Construct a neural network using Keras that performs basic regression on the provided dataset.
      The dataset is expected to be in CSV format with a 'label' column that contains the target values.
      
      >>> build_and_train_regression_model('data.csv')
      Test score: 0.0123456789
      """
      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_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=epochs, batch_size=batch_size, verbose=0)
      
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      print('Test score:', score)
      return score
  
output:     # Save the model
      model.save('regression_model.h5')