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 _prepare_model_inputs(self, ids):
      self._rearrange_kv_cache_for_decode(ids)
  
      prompt_lengths = [
          self.context.cache[req_id]["encoded"].size(-1)
          for req_id in ids
      ]
      old_padding = [
          self.context.cache[req_id]["padding"] for req_id in ids
      ]
  
      max_prompt_length = max(prompt_lengths)
      new_padding = [max_prompt_length - l for l in prompt_lengths]
  
      bsz = len(ids)
      pad_id = self.tokenizer.eos_id
      tokens = torch.full(
          (bsz, max_prompt_length),
          pad_id,
          dtype=torch.long,
          device=self.device,
      )
      for idx, req_id in enumerate(ids):
          tokens[idx, new_padding[idx] :] = self.context.cache[req_id][
              "encoded"
          ]
  
      for l in self.model.layers:
          for idx, (old_pad, new_pad) in enumerate(
              zip(old_padding, new_padding)
          ):
              seqlen = prompt_lengths[idx]
              l.attention.cache_k[
                  idx, new_pad : new_pad + seqlen
              ] = l.attention.cache_k[
                  idx, old_pad : old_pad + seqlen
              ].clone()
              l.attention.cache_v[
                  idx, new_pad : new_pad + seqlen
              ] = l.attention.cache_v[
                  idx, old_pad : old_pad + seqlen
              ].clone()
              l.attention.cache_k[idx, :new_pad] = 0
              l.attention.cache_v[idx, :new_pad] = 0
  
      for req_id, new_pad in zip(ids, new_padding):
          self.context.cache[req_id]["padding"] = new_pad
  
      return tokens, torch.tensor(
          new_padding, dtype=torch.long, device=self.device
      )
  
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 10 for calling the LlamaHandler obj's underlying member method code with example usage: obj._prepare_model_inputs(ids) and returns: tokens,  torch