code: def _from_pretrained(cls, *args, **kw):
      """Load a transformers model in PyTorch, with fallback to TF2/Keras weights."""
      try:
          return cls.from_pretrained(*args, **kw)
      except OSError as e:
          logging.warning("Caught OSError loading model: %s", e)
          logging.warning(
              "Re-trying to convert from TensorFlow checkpoint (from_tf=True)"
          )
          return cls.from_pretrained(*args, from_tf=True, **kw)
  
imports: from collections.abc import Sequence
  import sys
  from typing import Optional
  from absl import app
  from absl import flags
  from absl import logging
  from lit_nlp import dev_server
  from lit_nlp import server_flags
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.examples.datasets import glue
  from lit_nlp.lib import file_cache
  from lit_nlp.lib import utils
  import torch
  import transformers
label_desc: Use image node_id 1 for calling a global function with example usage: _from_pretrained(cls) and returns: cls, cls