code: def __init__(self, model_config: HuggingFaceModelConfig):
      if torch.cuda.is_available():
          hlog("CUDA is available, initializing with a GPU...")
          self.device: str = "cuda:0"
      else:
          self.device = "cpu"
      model_kwargs = {}
      if model_config.revision:
          model_kwargs["revision"] = model_config.revision
      with htrack_block(
          f"Loading Hugging Face model for config {model_config}"
      ):
          self.model = AutoModelForCausalLM.from_pretrained(
              model_config.model_id,
              trust_remote_code=True,
              **model_kwargs,
          ).to(self.device)
      with htrack_block(
          f"Loading Hugging Face tokenizer model for config {model_config}"
      ):
          self.tokenizer = AutoTokenizer.from_pretrained(
              model_config.model_id, **model_kwargs
          )
  
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 1 to create a new HuggingFaceServer object  with example: obj = HuggingFaceServer(model_config)