class_docstr: GLUE benchmark model, using Keras/TF2 and Huggingface Transformers.
  
  This is a general-purpose classification or regression model. It works for
  one- or two-segment input, and predicts either a multiclass label or
  a regression score. See GlueModelConfig for available options.
  
  This implements the LIT API for inference (e.g. input_spec(), output_spec(),
  and predict()), but also provides a train() method to run fine-tuning.
  
  This is a full-featured implementation, which includes embeddings, attention,
  gradients, as well as support for the different input and output types above.
  For a more minimal example, see ../simple_tf2_demo.py.
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docstr: Load model. Can be overridden for testing.
docstr_tok: ["Load","model",".","Can","be","overridden","for","testing","."]
code: def _load_model(self, model_name_or_path):
      """Load model. Can be overridden for testing."""
      # Normally path is a directory; if it's an archive file, download and
      # extract to the transformers cache.
      if model_name_or_path.endswith(".tar.gz"):
          model_name_or_path = file_cache.cached_path(
              model_name_or_path, extract_compressed_file=True
          )
  
      self.tokenizer = transformers.AutoTokenizer.from_pretrained(
          model_name_or_path
      )
      self.vocab = self.tokenizer.convert_ids_to_tokens(
          range(len(self.tokenizer))
      )
      model_config = transformers.AutoConfig.from_pretrained(
          model_name_or_path,
          num_labels=1
          if self.is_regression
          else len(self.config.labels),
          return_dict=False,  # default for training; overridden for predict
          output_attentions=self.config.output_attention,
      )
      self.model = model_utils.load_pretrained(
          transformers.TFAutoModelForSequenceClassification,
          model_name_or_path,
          config=model_config,
      )
  
imports: import os
  from collections.abc import Iterable, Sequence
  import re
  import threading
  from typing import Any, Optional
  import attr
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.examples.models import model_utils
  from lit_nlp.lib import file_cache
  from lit_nlp.lib import utils
  import numpy
  import tensorflow
  import transformers
label_desc: Use image node_id 4 for calling the GlueModel obj's underlying member method code with example usage: obj._load_model(model_name_or_path) without return types