input: 
  def neural_network_for_regression():
      """Constructs a neural network in Python that can perform basic regression.
      The function reads data from a CSV file, splits it into features and labels,
      splits the data into training and testing sets, creates a neural network with
      three fully connected layers, compiles and trains the model, and finally evaluates
      the model on the test set.
      >>> neural_network_for_regression()
      'Test score:'
      """
  
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load data from CSV file
      data = pd.read_csv('data.csv')
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split 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 a neural network model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
      # Evaluate the model on the test set
      test_loss = model.evaluate(X_test, y_test, verbose=0)
      return f'Test score: {test_loss}'
  