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 _vacate_kv_cache_before_prefill(self) -> torch.Tensor:
      assert self.batch_idx_to_req_ids.count(
          None
      ), "Expecting an empty slot in batch"
  
      new_batch_idx = self.batch_idx_to_req_ids.index(None)
  
      rearrangement_indices = torch.tensor(
          range(self.max_bsz), dtype=torch.long, device=self.device
      )
      rearrangement_indices[new_batch_idx], rearrangement_indices[0] = (
          rearrangement_indices[0],
          rearrangement_indices[new_batch_idx],
      )
      (
          self.batch_idx_to_req_ids[new_batch_idx],
          self.batch_idx_to_req_ids[0],
      ) = (
          self.batch_idx_to_req_ids[0],
          self.batch_idx_to_req_ids[new_batch_idx],
      )
  
      for l in self.model.layers:
          l.attention.cache_k = l.attention.cache_k[
              rearrangement_indices, ...
          ]
          l.attention.cache_v = l.attention.cache_v[
              rearrangement_indices, ...
          ]
  
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 12 for calling the LlamaHandler obj's underlying member method code with example usage: obj._vacate_kv_cache_before_prefill() without return types