def freeze(h, output_yaml, resolve, groups):
    """Output installed packages."""
    if not groups:
        groups = filter(lambda group: not group.lower().startswith("bower"),
                        h.requirements.keys())
    else:
        groups = [text_type.strip(group) for group in groups.split(",")]

    if output_yaml:
        for requirements in h.requirements.values():
            for requirement in requirements:
                if resolve and not requirement.version:
                    requirement.load_installed_version()
        click.echo(h.requirements.yaml)
    else:
        for group in groups:
            if not h.requirements[group]:
                continue
            click.echo("# {}".format(group))
            for requirement in h.requirements[group]:
                if resolve and not requirement.version:
                    requirement.load_installed_version()
                click.echo(str(requirement))
def gradient(self, idx, inputs):
        self.model.zero_grad()
        X = [x.requires_grad_() for x in inputs]
        outputs = self.model(*X)
        selected = [val for val in outputs[:, idx]]
        grads = []
        if self.interim:
            interim_inputs = self.layer.target_input
            for idx, input in enumerate(interim_inputs):
                grad = torch.autograd.grad(selected, input,
                                           retain_graph=True if idx + 1 < len(interim_inputs) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            del self.layer.target_input
            return grads, [i.detach().cpu().numpy() for i in interim_inputs]
        else:
            for idx, x in enumerate(X):
                grad = torch.autograd.grad(selected, x,
                                           retain_graph=True if idx + 1 < len(X) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            return grads