code: def _run_decode(self, ids):
      assert len(ids)
  
      encoded = self._prepare_model_inputs(ids)
  
      outputs = self.model.generate(
          **encoded,
          max_new_tokens=1,
          return_dict_in_generate=True,
          use_cache=True,
      )
  
      device = next(iter(self.model.parameters())).device
      dtype = torch.int64
      config = {"device": device, "dtype": dtype}
  
      results = {}
      for idx, req_id in enumerate(ids):
          self.context.cache[req_id]["encoded"][
              "past_key_values"
          ] = self._collect_kv_cache_of_idx_in_batch(idx)
          self.context.cache[req_id]["encoded"][
              "input_ids"
          ] = outputs.sequences[idx].unsqueeze(0)
          attention_mask = encoded["attention_mask"][idx].unsqueeze(0)
          attention_mask = torch.concat(
              (attention_mask, torch.ones((1, 1), **config)), dim=1
          )
          self.context.cache[req_id]["encoded"][
              "attention_mask"
          ] = attention_mask
          results[req_id] = {
              "text": self.tokenizer.decode(
                  outputs.sequences[idx][-1], skip_special_tokens=True
              ),
              "ids": [outputs.sequences[idx][-1].item()],
          }
      del self.context.kv_cache["past_key_values"]
      return results
  
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 6 for calling the StreamingHandler obj's underlying member method code with example usage: obj._run_decode(ids) and returns: results