def test_view_delitem(attr):
    adata = gen_adata((10, 10))
    getattr(adata, attr)["to_delete"] = np.ones((10, 10))
    # Shouldn’t be a subclass, should be an ndarray
    assert type(getattr(adata, attr)["to_delete"]) is np.ndarray
    view = adata[5:7, :][:, :5]
    adata_hash = joblib.hash(adata)
    view_hash = joblib.hash(view)

    getattr(view, attr).__delitem__("to_delete")

    assert not view.is_view
    assert "to_delete" not in getattr(view, attr)
    assert "to_delete" in getattr(adata, attr)
    assert adata_hash == joblib.hash(adata)
    assert view_hash != joblib.hash(view)
def nonlinear_1d(module, grad_input, grad_output):
    delta_out = module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2):]

    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # handles numerical instabilities where delta_in is very small by
    # just taking the gradient in those cases
    grads = [None for _ in grad_input]
    grads[0] = torch.where(torch.abs(delta_in.repeat(dup0)) < 1e-6, grad_input[0],
                           grad_output[0] * (delta_out / delta_in).repeat(dup0))
    return tuple(grads)