def METHOD_NAME(self):
    paddle.disable_static()
    net = paddle.vision.models.LeNet()
    optimizer = paddle.optimizer.Adam(
        learning_rate=0.001, parameters=net.parameters())
    inputs = [Input([None, 1, 28, 28], 'float32', name='image')]
    labels = [Input([None, 1], 'int64', name='label')]
    model = paddle.Model(net, inputs, labels)
    model.prepare(
        optimizer,
        paddle.nn.CrossEntropyLoss(),
        paddle.metric.Accuracy(topk=(1, 5)))
    model.fit(self.train_dataset, epochs=1, batch_size=128, verbose=1)
    result = model.evaluate(self.val_dataset, batch_size=128, verbose=1)
    pruner = None
    if self._pruner == 'l1norm':
        pruner = L1NormFilterPruner(net, [1, 1, 28, 28])
    elif self._pruner == 'fpgm':
        pruner = FPGMFilterPruner(net, [1, 1, 28, 28])
    def eval_fn():
        result = model.evaluate(self.val_dataset, batch_size=128)
        return result['acc_top1']
    sen = pruner.sensitive(
        eval_func=eval_fn,
        sen_file="_".join(["./dygraph_sen_", str(time.time())]),
        #sen_file="sen.pickle",
        target_vars=self._param_names)
    params = {}
    for param in net.parameters():
        params[param.name] = np.array(param.value().get_tensor())
    print(f'dygraph sen: {sen}')
    return sen, params