input: def construct_neural_network() -> Sequential:
      """Create a neural network in Python that can perform basic regression.
      The network will be constructed using TensorFlow's Keras API and will be trained
      on a dataset loaded from a csv file named 'data.csv'. The dataset is assumed to have
      a 'label' column that contains the target values for regression. The neural network
      consists of three dense layers: two hidden layers with 12 neurons each and a ReLU
      activation function, and an output layer with a linear activation function. The
      model is compiled with mean squared error as the loss function and Adam as the optimizer.
      The model is then trained for 100 epochs with a batch size of 20. Finally, the model
      is evaluated on the test set, and the test score is printed.
      
      >>> model = construct_neural_network()
      Test score: 0.123
      >>> model.evaluate(X_test, y_test, verbose=0)
      0.123
      """
output:     import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      import pandas as pd
  
      # Load the dataset
      data = pd.read_csv('data.csv')
      X = data.drop('label', axis=1)
      y = data['label']
  
      # Split the dataset into training and testing sets
      from sklearn.model_selection import train_test_split
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Construct the neural network
      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=20, verbose=0)
  
      # Evaluate the model
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f'Test score: {test_score}')
  
      return model