class_docstr: Transformers handler class for sequence, token classification and question answering.
class_docstr_tok: ["Transformers","handler","class","for","sequence",",","token","classification","and","question","answering","."]
code: def _compact_kv_cache(self):
      i = 0
      j = self.max_bsz - 1
  
      while i < j:
          if self.batch_idx_to_req_ids[j] is None:
              j -= 1
              continue
          elif self.batch_idx_to_req_ids[i] is None:
              self._swap_kv_cache(i, j)
              (
                  self.batch_idx_to_req_ids[i],
                  self.batch_idx_to_req_ids[j],
              ) = (
                  self.batch_idx_to_req_ids[j],
                  self.batch_idx_to_req_ids[i],
              )
              i += 1
              j -= 1
          else:
              i += 1
  
imports: import json
  import logging
  import os
  import sys
  import time
  from abc import ABC
  from typing import List
  import packaging.version
  import requests
  import torch
  import transformers
  from ts.torch_handler.base_handler import BaseHandler
  from generate import sample_top_p
  from llama2 import Llama
label_desc: Use image node_id 14 for calling the LlamaHandler obj's underlying member method code with example usage: obj._compact_kv_cache() without return types