code: def _update_hh_score(self, attn_score_cache):
      num_new_tokens = attn_score_cache.shape[2]
  
      if self.hh_score is None:
          self.hh_score = attn_score_cache.sum(0).sum(1)
      else:
          attn_score_cache = attn_score_cache.sum(0).sum(1)
          attn_score_cache[:, :-num_new_tokens] += self.hh_score
          self.hh_score = attn_score_cache
  
imports: import math
  from typing import Optional, Tuple
  import pdb
  import torch
  from torch import nn
  import torch.utils.checkpoint
  import torch.nn.functional
  from transformers.models.llama.modeling_llama import LlamaAttention, rotate_half, apply_rotary_pos_emb, LlamaRotaryEmbedding, apply_rotary_pos_emb, LlamaForCausalLM
  import types
  from transformers.configuration_utils import PretrainedConfig
label_desc: Use image node_id 4 for calling the H2OKVCache_LayerWise obj's underlying member method code with example usage: obj._update_hh_score(attn_score_cache) without return types