using two hidden layerswith units eachnow with tanh nonlinearity mlp mlpclassifier(algorithm=' -bfgs'activation='tanh'random_state= hidden_layer_sizes=[ ]mlp fit(x_trainy_trainmglearn plots plot_ d_separator(mlpx_trainfill=truealpha mglearn discrete_scatter(x_train[: ]x_train[: ]y_trainplt xlabel("feature "plt ylabel("feature "figure - decision boundary learned using hidden layers with hidden units eachwith rect activation function supervised machine learning algorithms