chainerのサンプルコードのエラーをなくす

chainerのサンプルコーをGPUで動かそうとすると
No module named 'chainer.links'
というエラーがでてきます。
どういしたらエラーをなくすことができるでしょうか。
anaconda 3.5
chainer 1.31
cuda 8

#!/usr/bin/env python



from __future__ import print_function



import argparse



import chainer

import chainer.functions as F

import chainer.links as L

from chainer import training

from chainer.training import extensions





# Network definition

class MLP(chainer.Chain):



    def __init__(self, n_units, n_out):

        super(MLP, self).__init__()

        with self.init_scope():

            # the size of the inputs to each layer will be inferred

            self.l1 = L.Linear(None, n_units)  # n_in -> n_units

            self.l2 = L.Linear(None, n_units)  # n_units -> n_units

            self.l3 = L.Linear(None, n_out)  # n_units -> n_out



    def __call__(self, x):

        h1 = F.relu(self.l1(x))

        h2 = F.relu(self.l2(h1))

        return self.l3(h2)





def main():

    parser = argparse.ArgumentParser(description='Chainer example: MNIST')

    parser.add_argument('--batchsize', '-b', type=int, default=100,

                        help='Number of images in each mini-batch')

    parser.add_argument('--epoch', '-e', type=int, default=20,

                        help='Number of sweeps over the dataset to train')

    parser.add_argument('--frequency', '-f', type=int, default=-1,

                        help='Frequency of taking a snapshot')

    parser.add_argument('--gpu', '-g', type=int, default=-1,

                        help='GPU ID (negative value indicates CPU)')

    parser.add_argument('--out', '-o', default='result',

                        help='Directory to output the result')

    parser.add_argument('--resume', '-r', default='',

                        help='Resume the training from snapshot')

    parser.add_argument('--unit', '-u', type=int, default=1000,

                        help='Number of units')

    args = parser.parse_args()



    print('GPU: {}'.format(args.gpu))

    print('# unit: {}'.format(args.unit))

    print('# Minibatch-size: {}'.format(args.batchsize))

    print('# epoch: {}'.format(args.epoch))

    print('')



    # Set up a neural network to train

    # Classifier reports softmax cross entropy loss and accuracy at every

    # iteration, which will be used by the PrintReport extension below.

    model = L.Classifier(MLP(args.unit, 10))

    if args.gpu >= 0:

        # Make a specified GPU current

        chainer.cuda.get_device_from_id(args.gpu).use()

        model.to_gpu()  # Copy the model to the GPU



    # Setup an optimizer

    optimizer = chainer.optimizers.Adam()

    optimizer.setup(model)



    # Load the MNIST dataset

    train, test = chainer.datasets.get_mnist()



    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)

    test_iter = chainer.iterators.SerialIterator(test, args.batchsize,

                                                 repeat=False, shuffle=False)



    # Set up a trainer

    updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)

    trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)



    # Evaluate the model with the test dataset for each epoch

    trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))



    # Dump a computational graph from 'loss' variable at the first iteration

    # The "main" refers to the target link of the "main" optimizer.

    trainer.extend(extensions.dump_graph('main/loss'))



    # Take a snapshot for each specified epoch

    frequency = args.epoch if args.frequency == -1 else max(1, args.frequency)

    trainer.extend(extensions.snapshot(), trigger=(frequency, 'epoch'))



    # Write a log of evaluation statistics for each epoch

    trainer.extend(extensions.LogReport())



    # Save two plot images to the result dir

    if extensions.PlotReport.available():

        trainer.extend(

            extensions.PlotReport(['main/loss', 'validation/main/loss'],

                                  'epoch', file_name='loss.png'))

        trainer.extend(

            extensions.PlotReport(

                ['main/accuracy', 'validation/main/accuracy'],

                'epoch', file_name='accuracy.png'))



    # Print selected entries of the log to stdout

    # Here "main" refers to the target link of the "main" optimizer again, and

    # "validation" refers to the default name of the Evaluator extension.

    # Entries other than 'epoch' are reported by the Classifier link, called by

    # either the updater or the evaluator.

    trainer.extend(extensions.PrintReport(

        ['epoch', 'main/loss', 'validation/main/loss',

         'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))



    # Print a progress bar to stdout

    trainer.extend(extensions.ProgressBar())



    if args.resume:

        # Resume from a snapshot

        chainer.serializers.load_npz(args.resume, trainer)



    # Run the training

    trainer.run()





if __name__ == '__main__':

    main()