def _cls_pooling(self, model_output: list) -> Any:
        """Use the CLS token as the pooling token."""
        if isinstance(model_output, dict):
            token_embeddings = model_output["last_hidden_state"]
        else:
            token_embeddings = model_output[0]
        return token_embeddings[:, 0]
Use the CLS token as the pooling token.
llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py