def get_dir_from_zipfile(zip_file, fallback=None):
    """Return the name of the root folder in a zip file.

    :param zip_file: a :class:`zipfile.ZipFile` instance.
    :param fallback: if `None`, the name of the zip file is used. This is
        returned if the zip file contains more than one top-level directory,
        or none at all.
    """
    fallback = fallback or zip_file.filename
    directories = [name for name in zip_file.namelist() if name.endswith("/")
                   and len(PurePath(name).parts) == 1]
    return fallback if len(directories) > 1 else directories[0]
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