code: def make_request(self, request: Request) -> RequestResult:
      # Embedding not supported for this model
      if request.embedding:
          return EMBEDDING_UNAVAILABLE_REQUEST_RESULT
  
      # Only a single stop sequence is supported as we can only pass in a single value for `eos_token_id`
      if len(request.stop_sequences) > 1:
          raise ValueError(
              "More than one stop sequence is not supported."
          )
  
      raw_request = {
          "engine": request.model_engine,
          "prompt": request.prompt,
          "temperature": 1e-7
          if request.temperature == 0
          else request.temperature,
          "num_return_sequences": request.num_completions,
          "max_new_tokens": request.max_tokens,
          "top_p": request.top_p,
          "echo_prompt": request.echo_prompt,
          "top_k_per_token": request.top_k_per_token,
          "stop_sequences": request.stop_sequences,
      }
  
      # Get cached model server instance if possible (to save on model and tokenizer
      # loading times).
      model_server_instance: HuggingFaceServer = (
          self.get_model_server_instance(request.model)
      )
  
      try:
  
          def do_it():
              return model_server_instance.serve_request(raw_request)
  
          cache_key = Client.make_cache_key(raw_request, request)
          response, cached = self.cache.get(
              cache_key, wrap_request_time(do_it)
          )
      except Exception as e:  # Do something if error is encountered.
          error: str = f"HuggingFace error: {e}"
          return RequestResult(
              success=False,
              cached=False,
              error=error,
              completions=[],
              embedding=[],
          )
  
      completions = []
      for raw_completion in response["completions"]:
          sequence_logprob: float = 0
          tokens: List[Token] = []
  
          if request.echo_prompt:
              # Add prompt to list of generated tokens.
              generated_tokens = raw_completion["tokens"][
                  response["input_length"] :
              ]
              for token_text in raw_completion["tokens"][
                  : response["input_length"]
              ]:
                  tokens.append(
                      Token(
                          text=token_text, logprob=0.0, top_logprobs={}
                      )
                  )
          else:
              generated_tokens = raw_completion["tokens"]
  
          # Compute logprob for the entire sequence.
          for token_text, logprob, top_logprobs_dict in zip(
              generated_tokens,
              raw_completion["logprobs"],
              raw_completion["top_logprobs_dicts"],
          ):
              tokens.append(
                  Token(
                      text=token_text,
                      logprob=logprob,
                      top_logprobs=top_logprobs_dict,
                  )
              )
              sequence_logprob += logprob
  
          completion = Sequence(
              text=raw_completion["text"],
              logprob=sequence_logprob,
              tokens=tokens,
          )
          completion = truncate_sequence(completion, request)
          completions.append(completion)
  
      return RequestResult(
          success=True,
          cached=cached,
          request_time=response["request_time"],
          request_datetime=response.get("request_datetime"),
          completions=completions,
          embedding=[],
      )
  
imports: from copy import deepcopy
  import torch
  from dataclasses import asdict
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from typing import Any, Dict, List
  from helm.common.cache import Cache, CacheConfig
  from helm.common.hierarchical_logger import htrack_block, hlog
  from helm.common.request import EMBEDDING_UNAVAILABLE_REQUEST_RESULT, Request, RequestResult, Sequence, Token
  from helm.common.tokenization_request import TokenizationRequest, TokenizationRequestResult, DecodeRequest, DecodeRequestResult, TokenizationToken
  from .client import Client, wrap_request_time, truncate_sequence
  from .huggingface_tokenizer import HuggingFaceTokenizers
  from helm.proxy.clients.huggingface_model_registry import HuggingFaceModelConfig, get_huggingface_model_config
label_desc: Use image node_id 3 for calling the HuggingFaceClient obj's underlying member method code with example usage: obj.make_request(request) and returns: RequestResult, EMBEDDING_UNAVAILABLE_REQUEST_RESULT, model_server_instance, RequestResult