# 如果此时有图像编码
      if image_encoders is not None:
            vision_proj = self.vision_proj(image_encoders)
            vision_proj = vision_proj.unsqueeze(0) if len(vision_proj.shape) == 3 else vision_proj
            if image_indices is not None:
                # 创建一个新的张量来存储拼接后的结果
                new_h = []
                for i in range(h.size(0)):
                    # i即为current_batch_idx索引
                    img_idx = 0
                    for batch_idx, start_idx, end_idx in image_indices:
                        if batch_idx == i:
                            # 插入vision_proj特征
                            before = h[i][:start_idx, :]
                            after = h[i][end_idx + 1:, :]
                            # 拼接 before, vision_proj, after
                            h[i] = torch.cat((before, vision_proj[i][img_idx], after), dim=0)[:seqlen]
                            img_idx += 1
                    new_h.append(h[i])
                # 将所有拼接后的结果重新堆叠起来
                new_h = torch.stack(new_h, dim=0)  # torch.Size([32, 511, 512])
                return new_h