class_docstr: Multi-headed attention from 'Attention Is All You Need' paper
class_docstr_tok: ["Multi-headed","attention","from","'Attention","Is","All","You","Need","'","paper"]
code: def _clean_cache(self):
      self.kv_cache._clean_scores()
  
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 H2OLlamaAttention_streaming obj's underlying member method code with example usage: obj._clean_cache() without return types