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 _run_decode(self, ids):
      assert len(ids)
  
      encoded, padding = self._prepare_model_inputs(ids)
  
      logits = self.model.forward(
          encoded[:, -1:], encoded.size(-1) - 1, padding
      )
  
      if self.temperature > 0:
          probs = torch.softmax(
              logits[:, -1] / self.temperature, dim=-1
          )
          next_token = sample_top_p(probs, self.top_p)
      else:
          next_token = torch.argmax(logits[:, -1], dim=-1)
      results = {}
      for idx, req_id in enumerate(ids):
          self.context.cache[req_id]["encoded"] = torch.concat(
              (
                  self.context.cache[req_id]["encoded"],
                  next_token[idx].view(1),
              ),
              dim=-1,
          )
  
          current_text = self.tokenizer.decode(
              self.context.cache[req_id]["encoded"].view(-1).tolist()
          )
          prev_text_len = len(self.context.cache[req_id]["text"])
          new_text = current_text[prev_text_len:]
          self.context.cache[req_id]["text"] = current_text
  
          results[req_id] = {
              "text": new_text,
              "ids": next_token[idx].view(1).tolist(),
          }
  
      return results
  
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 9 for calling the LlamaHandler obj's underlying member method code with example usage: obj._run_decode(ids) and returns: results