input: 
  def construct_neural_network(data_path: str) -> Sequential:
      """ Construct a neural network that can perform basic regression.
      The function reads data from a CSV file, splits it into features and labels,
      and then trains a neural network model for regression.
      >>> model = construct_neural_network('data.csv')
      >>> print('Test score:', model.evaluate(X_test, y_test, verbose=0))
      """
  
  
  Note: The `Sequential`, `Dense`, `X_test`, and `y_test` objects are assumed to be imported from appropriate libraries (`keras` or `tensorflow`).
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_path)
      
      # Assuming the last column is the label and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # 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)
      
      # Construct the 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)
      
      return model