def gradient(self, x, conditional_volume, lod):
        """
        return the gradient of specific lod
        :param x:
        :param lod:
        :return:
        """
        x.requires_grad_(True)
        output = self.sdf(x, conditional_volume, lod)
        y = output['sdf_pts_scale%d' % 0]

        d_output = torch.ones_like(y, requires_grad=False, device=y.device)

        gradients = torch.autograd.grad(
            outputs=y,
            inputs=x,
            grad_outputs=d_output,
            create_graph=True,
            retain_graph=True,
            only_inputs=True)[0]
        return gradients.unsqueeze(1)

        return the gradient of specific lod
        :param x:
        :param lod:
        :return:
        
mv-diffusion/One-2-3-45-by-view/reconstruction/models/sparse_sdf_network.py