code: def inference(self, input_data):
      tokenizer = self.tokenizer
      period_id = tokenizer.encode(".")[0]
  
      def call_me(x):
          nonlocal period_id, tokenizer
          text = self.tokenizer.decode([period_id] + x.tolist())[1:]
          send_intermediate_predict_response(
              [text],
              self.context.request_ids,
              "Intermediate Prediction success",
              200,
              self.context,
          )
  
      y, metrics = self.generate(
          input_data["encoded"],
          input_data["max_new_tokens"],
          callback=call_me
          if self.local_rank == 0 and self.stream
          else lambda x: x,
          temperature=0.8,
          top_k=1,
      )
      logger.info(f"Num tokens = {y.size(0) - self.prompt_length}")
  
      if self.is_speculative:
          counts_aggregated = [
              sum(i) for i in zip(*[metrics["accept_counts"]])
          ]
          acceptance_probs = [
              i / sum(counts_aggregated) for i in counts_aggregated
          ]
          logger.info(f"Acceptance probs: {acceptance_probs}")
          logger.info(
              f"Mean Accepted: {sum([idx * i for idx, i in enumerate(counts_aggregated)])/sum(counts_aggregated)}"
          )
  
      return y
  
file_path: serve/examples/large_models/gpt_fast/handler.py
imports: import json
  import logging
  import time
  from pathlib import Path
  import torch
  from generate import Transformer, _load_model, decode_one_token, encode_tokens, logits_to_probs, maybe_init_dist, model_forward, multinomial_sample_one_no_sync, prefill
  from sentencepiece import SentencePieceProcessor
  from ts.handler_utils.timer import timed
  from ts.handler_utils.utils import send_intermediate_predict_response
  from ts.torch_handler.base_handler import BaseHandler
label_desc: Use image node_id 4 for calling the GptHandler obj's underlying member method code with example usage: obj.inference(input_data) and returns: y