code: def test_fit_predict(self):
      model = SequenceLabeler(base_model=TableRoBERTa)
      model.fit(
          self.text * 20, self.labels * 20, context=self.context * 20
      )
      preds = model.predict(self.text, context=self.context)[0]
      assert len(preds) == 3
      assert set(p["text"] for p in preds) == set(
          l["text"] for l in self.labels[0]
      )
  
imports: import os
  import unittest
  from finetune import SequenceLabeler
  from finetune.base_models import TableRoBERTa
  from finetune.base_models.bert.table_utils import batch_packing, get_gather_indices, slice_by_table_indices, gather_col_vals, scatter_feats, get_summary_values, chunk_ragged_tensor
  import tensorflow
label_desc: Use image node_id 2 for calling the TestTableModel obj's underlying member method code with example usage: obj.test_fit_predict() without return types