def test_view_delattr(attr, subset_func):
    base = gen_adata((10, 10))
    orig_hash = joblib.hash(base)
    subset = base[subset_func(base.obs_names), subset_func(base.var_names)]
    empty = ad.AnnData(obs=subset.obs[[]], var=subset.var[[]])

    delattr(subset, attr)

    assert not subset.is_view
    # Should now have same value as default
    assert_equal(getattr(subset, attr), getattr(empty, attr))
    assert orig_hash == joblib.hash(base)  # Original should not be modified
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