code: def tokenize(
      self, request: TokenizationRequest
  ) -> TokenizationRequestResult:
      tokenizer = HuggingFaceTokenizers.get_tokenizer(request.tokenizer)
      cache_key = asdict(request)
  
      try:
  
          def do_it():
              if request.encode:
                  if request.truncation:
                      tokens = tokenizer.encode(
                          request.text,
                          truncation=request.truncation,
                          max_length=request.max_length,
                          add_special_tokens=False,
                      )
                  else:
                      tokens = tokenizer.encode(
                          request.text, add_special_tokens=False
                      )
              else:
                  if (
                      "gpt" in request.tokenizer
                      or request.tokenizer
                      in [
                          "bigscience/bloom",
                          "Writer/palmyra-base",
                          "facebook/opt-66b",
                      ]
                  ):
                      tokens = [
                          tokenizer.convert_tokens_to_string([i])
                          for i in tokenizer.tokenize(request.text)
                      ]
                  else:
                      tokens = tokenizer.tokenize(request.text)
                      # TODO(1522): Reenable this to revove "▁"
                      # tokens = [tokenizer.convert_tokens_to_string([i]) for i in tokenizer.tokenize(request.text)]
              return {"tokens": tokens}
  
          result, cached = self.cache.get(
              cache_key, wrap_request_time(do_it)
          )
      except Exception as e:
          error: str = f"HuggingFace error: {e}"
          return TokenizationRequestResult(
              success=False,
              cached=False,
              error=error,
              text="",
              tokens=[],
          )
  
      return TokenizationRequestResult(
          success=True,
          cached=cached,
          text=request.text,
          tokens=[
              TokenizationToken(value) for value in result["tokens"]
          ],
          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 4 for calling the HuggingFaceClient obj's underlying member method code with example usage: obj.tokenize(request) and returns: TokenizationRequestResult, dict, TokenizationRequestResult