code: def _run_prefill(self, req_id):
      assert (
          self.context.cache[req_id]["encoded"]["past_key_values"]
          is None
      ), "There should be no cached values"
      self.context.cache[req_id]["encoded"]
      output = self.model.generate(
          **self.context.cache[req_id]["encoded"],
          max_new_tokens=1,
          return_dict_in_generate=True,
          use_cache=True,
      )
      # Save extracted kv cache values and adjust attention mask for next call
      self.context.cache[req_id]["encoded"][
          "past_key_values"
      ] = self.context.kv_cache["past_key_values"]
      del self.context.kv_cache["past_key_values"]
      self.context.cache[req_id]["encoded"][
          "input_ids"
      ] = output.sequences
  
      device = next(iter(self.model.parameters())).device
      dtype = torch.int64
      config = {"device": device, "dtype": dtype}
      attention_mask = self.context.cache[req_id]["encoded"][
          "attention_mask"
      ]
      attention_mask = torch.concat(
          (attention_mask, torch.ones((1, 1), **config)), dim=1
      )
      self.context.cache[req_id]["encoded"][
          "attention_mask"
      ] = attention_mask
  
      result = {
          "text": self.tokenizer.decode(
              output.sequences[0], skip_special_tokens=True
          ),
          "ids": output.sequences[0].tolist(),
      }
      return result
  
file_path: serve/test/pytest/test_data/streaming/stream_handler.py
imports: import json
  import logging
  import types
  import torch
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from ts.torch_handler.base_handler import BaseHandler
label_desc: Use image node_id 5 for calling the StreamingHandler obj's underlying member method code with example usage: obj._run_prefill(req_id) and returns: result