def load(self, filename, create_if_missing=True):
        filename = Path(filename)
        if not filename.exists() and create_if_missing:
            self.load_pip_requirements()
            with filename.open("w") as f:
                f.write(yaml.dump(self.serialized, default_flow_style=False,
                                  encoding=None))
            self.filename = filename
            return self.save(filename)
        with filename.open() as f:
            for group, requirements in yaml.load(f.read()).items():
                for requirement in requirements:
                    self[group].add(Requirement.coerce(requirement))
        self.filename = filename
def make_data(cuda=False):
    train_x = torch.linspace(0, 1, 100)
    train_y = torch.sin(train_x * (2 * pi))
    train_y.add_(torch.randn_like(train_y), alpha=1e-2)
    test_x = torch.rand(51)
    test_y = torch.sin(test_x * (2 * pi))
    if cuda:
        train_x = train_x.cuda()
        train_y = train_y.cuda()
        test_x = test_x.cuda()
        test_y = test_y.cuda()
    return train_x, train_y, test_x, test_y