code: def is_tf_available():
      from .tensorflow import get_tf_version
  
      _tf_available = importlib.util.find_spec("tensorflow") is not None
      if _tf_available:
          _tf_version = get_tf_version()
          _tf_available = _tf_version != ""
          if version.parse(_tf_version) < version.parse("2"):
              logger.info(
                  f"Tensorflow found but with verison {_tf_version}. Transformers support with BentoML requires a minimum of Tensorflow 2 and above."
              )
              _tf_available = False
      return _tf_available
  
file_path: BentoML/src/bentoml/_internal/frameworks/utils/transformers.py
imports: from __future__ import annotations
  import importlib.metadata
  import importlib.util
  import logging
  import re
  import typing
  from contextlib import contextmanager
  from pathlib import Path
  import torch
  import torch.nn
  from packaging import version
label_desc: Use image node_id 3 for calling a global function with example usage: is_tf_available() and returns: _tf_available