input: 
  def neural_network_regression():
      """Constructs a neural network in Python that can perform basic regression.
      The neural network is built using Keras with a Sequential model and includes:
      1. Two hidden layers with 12 neurons each.
      2. An output layer with a single neuron.
      The model is trained on a dataset from 'data.csv' and evaluated on a test set.
      
      >>> neural_network_regression()
      Test score: 0.0012  # Example output
      """
  
output:     
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
      from sklearn.model_selection import train_test_split
      import pandas as pd
  
      # Load the dataset
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # Split the dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Build the model
      model = Sequential()
      model.add(Dense(12, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(12, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=10, verbose=0)
  
      # Evaluate the model
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f"Test score: {test_score:.4f}")
  