code: def run_toy_SGLD(gpu_id=None):
      
      X, Y, X_test, Y_test = load_toy()
      minibatch_size = 1
      teacher_noise_precision = 1.0 / 9.0
      net = get_toy_sym(True, teacher_noise_precision)
      data_shape = (minibatch_size,) + X.shape[1::]
      data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
                     'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(gpu_id))}
      initializer = mx.init.Uniform(0.07)
      exe, params, _ = SGLD(sym=net,
                            data_inputs=data_inputs,
                            X=X,
                            Y=Y,
                            X_test=X_test,
                            Y_test=Y_test,
                            total_iter_num=50000,
                            initializer=initializer,
                            learning_rate=1E-4,
                            # lr_scheduler=mx.lr_scheduler.FactorScheduler(100000, 0.5),
                            prior_precision=0.1,
                            burn_in_iter_num=1000,
                            thin_interval=10,
                            task='regression',
                            minibatch_size=minibatch_size,
                            dev=dev(gpu_id))
docs: Run SGLD on toy dataset