input: 
  def train_simple_regression_model() -> None:
      """
      Construct and train a simple neural network for basic regression using Keras.
      The neural network consists of three Dense layers: the first layer has 12 neurons 
      with ReLU activation, the second layer also has 12 neurons with ReLU activation,
      and the output layer has 1 neuron. The model is trained on a dataset loaded from
      'data.csv' and evaluated on a test set. The test score (mean squared error) is printed.
  
      >>> train_simple_regression_model()
      Test score: 0.0523  # Example output
      """
      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
  
      # Load the data
      data = pd.read_csv('data.csv')
  
      # Split the data into features and labels
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
  
      # 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)
  
      # Create the neural network
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
  
      # 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
      score = model.evaluate(X_test, y_test, verbose=0)
      print('Test score:', score)
  
output:     # Save the model
      model.save('regression_model.h5')