dev(narugo): use combined feature, ci skip

        # 7. Fuse features: average the cross-attended tag outputs, and combine with original features

        fused_feature = cross_attn.mean(dim=1)  # [B, embedding_dim]

        combined = torch.cat([feats, fused_feature], dim=1)  # [B, embedding_dim*2]

        combined_feature = torch.cat([feats, fused_feature], dim=1)  # [B, embedding_dim*2]

        # 8. Refined tag prediction

        refined_logits = self.refined_classifier(combined)  # [B, total_tags]

        refined_logits = self.refined_classifier(combined_feature)  # [B, total_tags]

        refined_preds = torch.clamp(refined_logits / self.temperature, min=-15.0, max=15.0)

        return feats, initial_preds, fused_feature, refined_preds

        return feats, initial_preds, combined_feature, refined_preds

def create_refined_model():