code: def fit(
      self,
      text: t.List[str],
      tables: t.List[DocumentTables],
      labels: t.List[DocumentSpans],
  ):
      model_inputs = self.etl.get_table_text_chunks_and_context(
          text=text, tables=tables, labels=labels
      )
      if self.etl.chunk_tables:
          model_inputs = self.table_chunker.chunk(model_inputs)
      self.table_model.fit(
          model_inputs["table_text"],
          model_inputs["table_labels"],
          context=model_inputs["table_context"],
      )
      self.text_model.fit(
          model_inputs["doc_text"], model_inputs["doc_labels"]
      )
  
imports: import copy
  import functools
  import os
  import tempfile
  import typing
  import sys
  from finetune.base_models import TableRoBERTa
  from finetune.util.metrics import sequences_overlap
  from finetune.scheduler import Scheduler
  from finetune.encoding.input_encoder import BaseEncoder
  from finetune import SequenceLabeler
label_desc: Use image node_id 2 for calling the TableLabeler obj's underlying member method code with example usage: obj.fit(text, tables, labels) without return types