def get(url, session=None, silent=not debug, **kwargs):
    """Retrieve a given URL and log response.

    :param session: a :class:`requests.Session` object.
    :param silent: if **True**, response status and URL will not be printed.
    """
    session = session or requests
    kwargs["verify"] = kwargs.get("verify", True)
    r = session.get(url, **kwargs)
    if not silent:
        status_code = click.style(
            str(r.status_code),
            fg="green" if r.status_code in (200, 304) else "red")
        click.echo(status_code + " " + url)
        if r.status_code == 404:
            raise PackageNotFoundError
    return r
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