input: from typing import List
  
  def construct_neural_network(data: str, features: List[str], label: str) -> Sequential:
      """
      Constructs a neural network in Python that can perform basic regression.
      
      The function loads data from a CSV file, splits the data into training and testing sets,
      and constructs a neural network with three layers. The model is compiled with mean squared 
      error as the loss function and Adam as the optimizer. The model is then trained and 
      evaluated on the test set.
      
      Args:
      - data: Path to the CSV file containing the dataset.
      - features: List of feature names to be used for training.
      - label: Name of the label column.
      
      Returns:
      - The trained neural network model.
      
      Example:
      >>> construct_neural_network('data.csv', ['feature1', 'feature2'], 'label')
      Test score: 0.01
      """
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
      df = pd.read_csv(data)
  
      # Split data into features and label
      X = df[features]
      y = df[label]
  
      # 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)
  
      # Construct the neural network model
      model = Sequential()
      model.add(Dense(64, input_dim=len(features), activation='relu'))
      model.add(Dense(32, 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=10, batch_size=32, verbose=0)
  
      # Evaluate the model on the test set
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(f'Test score: {test_score}')
  
      return model