code: def get_tokenizer(
      tokenizer_name: str,
      *args,
      tokenizer_mode: str = "auto",
      trust_remote_code: bool = False,
      tokenizer_revision: Optional[str] = None,
      **kwargs,
  ) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
      """Gets a tokenizer for the given model name via Huggingface."""
      if tokenizer_mode == "slow":
          if kwargs.get("use_fast", False):
              invalidInputError(
                  "Cannot use the fast tokenizer in slow tokenizer mode."
              )
          kwargs["use_fast"] = False
  
      if (
          "llama" in tokenizer_name.lower()
          and kwargs.get("use_fast", True)
          and tokenizer_name != _FAST_LLAMA_TOKENIZER
      ):
          logger.info(
              "For some LLaMA V1 models, initializing the fast tokenizer may "
              "take a long time. To reduce the initialization time, consider "
              f"using '{_FAST_LLAMA_TOKENIZER}' instead of the original "
              "tokenizer."
          )
      try:
          tokenizer = AutoTokenizer.from_pretrained(
              tokenizer_name,
              *args,
              trust_remote_code=trust_remote_code,
              tokenizer_revision=tokenizer_revision,
              **kwargs,
          )
      except TypeError as e:
          # The LLaMA tokenizer causes a protobuf error in some environments.
          err_msg = (
              "Failed to load the tokenizer. If you are using a LLaMA V1 model "
              f"consider using '{_FAST_LLAMA_TOKENIZER}' instead of the "
              "original tokenizer."
          )
          invalidInputError(err_msg)
      except ValueError as e:
          # If the error pertains to the tokenizer class not existing or not
          # currently being imported, suggest using the --trust-remote-code flag.
          if not trust_remote_code and (
              "does not exist or is not currently imported." in str(e)
              or "requires you to execute the tokenizer file" in str(e)
          ):
              err_msg = (
                  "Failed to load the tokenizer. If the tokenizer is a custom "
                  "tokenizer not yet available in the HuggingFace transformers "
                  "library, consider setting `trust_remote_code=True` in LLM "
                  "or using the `--trust-remote-code` flag in the CLI."
              )
              invalidInputError(err_msg)
          else:
              invalidInputError(e)
  
      if not isinstance(tokenizer, PreTrainedTokenizerFast):
          logger.warning(
              "Using a slow tokenizer. This might cause a significant "
              "slowdown. Consider using a fast tokenizer instead."
          )
      return tokenizer
  
imports: from typing import List, Optional, Tuple, Union
  from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
  from bigdl.llm.vllm.logger import init_logger
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 1 for calling a global function with example usage: get_tokenizer(tokenizer_name) and returns: tokenizer