code: def test_fit_predict(labeled_table_data):
      filename = "tl.jl"
      text, labels, tables = labeled_table_data
      tl = TableLabeler()
      tl.fit(text=text * 10, labels=labels * 10, tables=tables * 10)
      tl.save(filename)
      shed = Scheduler()
      preds = TableLabeler.predict_from_file(
          model_file_path=filename,
          text=text,
          tables=tables,
          scheduler=shed,
      )
      print([{**p, "confidence": None} for p in preds[0]])
      assert len(preds[0]) == len(labels[0])
      assert set(
          (p["start"], p["end"], p["label"]) for p in preds[0]
      ) == set((l["start"], l["end"], l["label"]) for l in labels[0])
  
file_path: finetune/tests/test_table_labeler.py
imports: import pytest
  from finetune.util.table_labeler import TableLabeler, TableETL
  from finetune.scheduler import Scheduler
label_desc: Use image node_id 5 for calling a global function with example usage: test_fit_predict(labeled_table_data) without return types