input: def construct_regression_neural_network() -> Sequential:
      """
      Construct a neural network in Python that can perform basic regression.
      
      >>> import numpy as np
      >>> import pandas as pd
      >>> from sklearn.model_selection import train_test_split
      >>> from keras.models import Sequential
      >>> from keras.layers import Dense
      >>> data = pd.DataFrame({'feature1': [1.0, 2.0, 3.0], 'feature2': [4.0, 5.0, 6.0], 'label': [7.0, 8.0, 9.0]})
      >>> X = data.drop(['label'], axis=1).values
      >>> y = data['label'].values
      >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      >>> model = construct_regression_neural_network()
      >>> model.compile(loss='mean_squared_error', optimizer='adam')
      >>> model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      >>> score = model.evaluate(X_test, y_test, verbose=0)
      >>> print('Test score:', score)
      Test score: 0.0
      """
      
output:     model = Sequential()
      model.add(Dense(10, input_dim=X.shape[1], activation='relu'))
      model.add(Dense(1))
      return model