from sklearn.neural_network import MLPRegressor # Initialize the MLPRegressor model mlp = MLPRegressor(hidden_layer_sizes=(10,10), activation='relu', solver='adam', max_iter=1000, random_state=42) # Load in the data in smaller portions for i in range(0, len(X), 100): # Use the partial_fit method to fit the model on smaller data portions mlp.partial_fit(X[i:i+100], y[i:i+100])In this example, we first initialize an MLPRegressor model with 2 hidden layers of 10 neurons each, using the rectified linear unit (ReLU) activation function and the Adam solver. We then loop through the entire dataset in chunks of 100 and use the partial_fit method to incrementally update the model on each portion. Overall, the MLPRegressor partial_fit method is a useful tool for incremental or online learning with MLP neural networks, and it can be found in the scikit-learn (sklearn) package library.