code: def evict_for_space(self, past_key_values, num_coming):
      if past_key_values is None:
          return None
      seq_len = past_key_values[0][0].size(self.k_seq_dim)
      if seq_len + num_coming <= self.cache_size:
          return past_key_values
  
      # hh-selection
      bsz, num_heads, _, head_dim = past_key_values[0].shape
  
      select_hh_scores = self.hh_score[
          :, : seq_len - self.recent_size + num_coming
      ]
      _, keep_topk = torch.topk(select_hh_scores, self.hh_size, dim=-1)
      keep_topk = keep_topk.sort().values
  
      # keep_recent = torch.arange(seq_len - self.recent_size, seq_len).expand(keep_topk.shape[0], 1).to(keep_topk.device)
      keep_recent = torch.arange(
          seq_len - self.recent_size + num_coming,
          seq_len,
          device=keep_topk.device,
      ).repeat(keep_topk.shape[0], 1)
      keep_idx = torch.cat([keep_topk, keep_recent], dim=-1)
  
      mask = torch.zeros(self.hh_score.shape, dtype=torch.bool).to(
          past_key_values[0].device
      )
      mask = mask.scatter(-1, keep_idx, 1)
  
      k_hh_recent = (
          past_key_values[0]
          .squeeze()[mask]
          .view(bsz, num_heads, -1, head_dim)
      )
      v_hh_recent = (
          past_key_values[1]
          .squeeze()[mask]
          .view(bsz, num_heads, -1, head_dim)
      )
  
      self.hh_score = self.hh_score[mask].view(
          num_heads, self.cache_size
      )
  
      return (k_hh_recent, v_hh_recent)
  
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 3 for calling the H2OKVCache_LayerWise obj's underlying member method code with example usage: obj.evict_for_space(past_key_values, num_coming) and returns: k_hh_recent,  v_hh_recent, None, past_key_values