def forward(self, x: torch.Tensor, t: torch.Tensor, *, low_res: torch.Tensor):
        """
        :param x: an [N x C1 x T] tensor.
        :param t: an [N] tensor.
        :param low_res: an [N x C2 x T'] tensor of conditioning points.
        :return: an [N x C3 x T] tensor.
        """
        assert x.shape[-1] == self.n_ctx
        t_embed = self.time_embed(timestep_embedding(t, self.backbone.width))
        low_res_embed = self._embed_low_res(low_res)
        cond = [(t_embed, self.time_token_cond), (low_res_embed, True)]
        return self._forward_with_cond(x, cond)

        :param x: an [N x C1 x T] tensor.
        :param t: an [N] tensor.
        :param low_res: an [N x C2 x T'] tensor of conditioning points.
        :return: an [N x C3 x T] tensor.
        
shap_e/models/generation/transformer.py