code: def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
      if len(argv) > 1:
          raise app.UsageError("Too many command-line arguments.")
  
      # Normally path is a directory; if it's an archive file, download and
      # extract to the transformers cache.
      model_path = _MODEL_PATH.value
      if model_path.endswith(".tar.gz"):
          model_path = file_cache.cached_path(
              model_path, extract_compressed_file=True
          )
  
      # Load the model we defined above.
      models = {"sst": SimpleSentimentModel(model_path)}
      # Load SST-2 validation set from TFDS.
      datasets = {"sst_dev": glue.SST2Data("validation")}
  
      # Start the LIT server. See server_flags.py for server options.
      lit_demo = dev_server.Server(
          models, datasets, **server_flags.get_flags()
      )
      return lit_demo.serve()
  
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 tensorflow
  import transformers
label_desc: Use image node_id 3 for calling a global function with example usage: main(argv) and returns: lit_demo