code: def decode(self, request: DecodeRequest) -> DecodeRequestResult:
      tokenizer = HuggingFaceTokenizers.get_tokenizer(request.tokenizer)
      cache_key = asdict(request)
  
      try:
  
          def do_it():
              return {
                  "text": tokenizer.decode(
                      request.tokens,
                      clean_up_tokenization_spaces=request.clean_up_tokenization_spaces,
                  )
              }
  
          result, cached = self.cache.get(
              cache_key, wrap_request_time(do_it)
          )
      except Exception as e:
          error: str = f"HuggingFace error: {e}"
          return DecodeRequestResult(
              success=False, cached=False, error=error, text=""
          )
  
      return DecodeRequestResult(
          success=True,
          cached=cached,
          text=result["text"],
          request_time=result["request_time"],
      )
  
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 5 for calling the HuggingFaceClient obj's underlying member method code with example usage: obj.decode(request) and returns: DecodeRequestResult, dict, DecodeRequestResult