input: Write a function to construct a neural network in Python that can perform basic regression. If the neural network is constructed successfully, return the compiled model. If the data loading or splitting process fails, return None.
  assert construct_regression_nn() != None
  assert construct_regression_nn().evaluate != None
output: def construct_regression_nn():
      try:
          import numpy as np
          from sklearn.model_selection import train_test_split
          from tensorflow.keras.models import Sequential
          from tensorflow.keras.layers import Dense
          from tensorflow.keras.optimizers import Adam
  
          # Example data
          X = np.random.rand(100, 10)
          y = np.random.rand(100, 1)
  
          # Split data
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
          # Construct model
          model = Sequential()
          model.add(Dense(64, input_dim=10, activation='relu'))
          model.add(Dense(32, activation='relu'))
          model.add(Dense(1, activation='linear'))
  
          # Compile model
          model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')
  
          return model
      except Exception as e:
          return None