def sample(
        self,
        t0: torch.Tensor,
        t1: torch.Tensor,
        n_samples: int,
        epsilon: float = 1e-3,
    ) -> torch.Tensor:
        """
        Args:
            t0: start time has shape [batch_size, *shape, 1]
            t1: finish time has shape [batch_size, *shape, 1]
            n_samples: number of ts to sample
        Return:
            sampled ts of shape [batch_size, *shape, n_samples, 1]
        """
        ones = [1] * (len(t0.shape) - 1)
        ts = torch.linspace(0, 1, n_samples).view(*ones, n_samples).to(t0.dtype).to(t0.device)

        if self.depth_mode == "linear":
            ts = t0 * (1.0 - ts) + t1 * ts
        elif self.depth_mode == "geometric":
            ts = (t0.clamp(epsilon).log() * (1.0 - ts) + t1.clamp(epsilon).log() * ts).exp()
        elif self.depth_mode == "harmonic":
            # The original NeRF recommends this interpolation scheme for
            # spherical scenes, but there could be some weird edge cases when
            # the observer crosses from the inner to outer volume.
            ts = 1.0 / (1.0 / t0.clamp(epsilon) * (1.0 - ts) + 1.0 / t1.clamp(epsilon) * ts)

        mids = 0.5 * (ts[..., 1:] + ts[..., :-1])
        upper = torch.cat([mids, t1], dim=-1)
        lower = torch.cat([t0, mids], dim=-1)
        # yiyi notes: add a random seed here for testing, don't forget to remove
        torch.manual_seed(0)
        t_rand = torch.rand_like(ts)

        ts = lower + (upper - lower) * t_rand
        return ts.unsqueeze(-1)

        Args:
            t0: start time has shape [batch_size, *shape, 1]
            t1: finish time has shape [batch_size, *shape, 1]
            n_samples: number of ts to sample
        Return:
            sampled ts of shape [batch_size, *shape, n_samples, 1]
        
src/diffusers/pipelines/shap_e/renderer.py