code: def make_request(
      self, tid: int, uid: int, input_tokens: torch.Tensor, kwargs: Dict
  ) -> Request:
      kwargs["prompt_length"] = len(input_tokens)
      kwargs["max_length"] = kwargs.get("max_length", self.max_length)
      generate_params = GenerateParamsConfig(**kwargs)
  
      post_processing = []
  
      top_p = generate_params.top_p
      top_p_name = "_".join((TOP_P_NAME, str(top_p)))
      if top_p_name not in self._post_processors:
          self._post_processors[top_p_name] = TopPLogitProcessor(
              top_p=top_p
          )
      post_processing.append(top_p_name)
  
      top_k = generate_params.top_k
      if top_k is not None:
          top_k_name = "_".join((TOP_K_NAME, str(top_k)))
          if top_k_name not in self._post_processors:
              self._post_processors[top_k_name] = TopKLogitProcessor(
                  top_k=top_k
              )
          post_processing.append(top_k_name)
  
      temp = generate_params.temperature
      if temp is not None:
          temp_name = "_".join((TEMP_NAME, str(temp)))
          if temp_name not in self._post_processors:
              self._post_processors[
                  temp_name
              ] = TemperatureLogitProcessor(temperature=temp)
          post_processing.append(temp_name)
  
      do_sample = generate_params.do_sample
      if do_sample:
          sampler_name = "_".join((SAMPLER_NAME, "logits"))
          if sampler_name not in self._post_processors:
              self._post_processors[sampler_name] = LogitsSampler()
      else:
          sampler_name = "_".join((SAMPLER_NAME, "greedy"))
          if sampler_name not in self._post_processors:
              self._post_processors[sampler_name] = GreedySampler()
      post_processing.append(sampler_name)
  
      stop = generate_params.stop
      if stop != []:
          stop_name = "_".join([STOP_NAME] + stop)
          if stop_name not in self._post_processors:
              self._post_processors[stop_name] = TokenStopCriterion(
                  token=stop, tokenizer=self.tokenizer
              )
      else:
          stop_name = STOP_NAME
          if STOP_NAME not in self._post_processors:
              self._post_processors[
                  stop_name
              ] = EosGenerationStopCriterion(tokenizer=self.tokenizer)
      post_processing.append(stop_name)
  
      return Request(
          tid=tid,
          uid=uid,
          input_tokens=input_tokens,
          prompt_tokens=input_tokens,
          seq_length=0,
          last_in_prompt=True,
          post_processing=post_processing,
          generate_params=generate_params,
      )
  
imports: import copy
  import gc
  import os
  import queue
  import random
  import threading
  import time
  from collections import deque, defaultdict
  from functools import cached_property
  from typing import Dict, Tuple, List, Any, Union, DefaultDict
  import torch
  import ujson
  import zmq
  from deepspeed.accelerator import get_accelerator
  from deepspeed.utils.timer import SynchronizedWallClockTimer
  from mii.batching.constants import TOP_K_NAME, TOP_P_NAME, TEMP_NAME, SAMPLER_NAME, STOP_NAME
  from mii.batching.data_classes import Response, Request, RequestBatch
  from mii.batching.generation.logit_processors import TopPLogitProcessor, TopKLogitProcessor, TemperatureLogitProcessor
  from mii.batching.generation.samplers import LogitsSampler, GreedySampler
  from mii.batching.generation.stop_criterion import EosGenerationStopCriterion, TokenStopCriterion
  from mii.batching.postprocess import run_batch_logit_processing, run_batch_sampler, run_batch_stop_criterion
  from mii.batching.utils import sync_debug, profiler
  from mii.config import GenerateParamsConfig
  from mii.constants import GenerationFinishReason, ZMQ_RECV_TIMEOUT
  from mii.logging import logger
label_desc: Use image node_id 14 for calling the RaggedBatchBase obj's underlying member method code with example usage: obj.make_request(tid, uid, input_tokens, kwargs) and returns: Request