code: def predict_from_file(
      cls,
      text: t.List[str],
      tables: t.List[DocumentTables],
      model_file_path: str,
      scheduler: Scheduler,
      return_negative_confidence=False,
      config_overrides=None,
      cache_key=None,
      table_model_config_overrides=None,
  ):
      etl = scheduler.load_etl(model_file_path, cache_key)
      model_inputs = etl.get_table_text_chunks_and_context(
          text=text, tables=tables
      )
      table_model = scheduler.get_model(
          model_file_path,
          key="table",
          config_overrides=table_model_config_overrides,
      )
      if etl.chunk_tables:
          table_chunker = TableChunker.from_table_model(table_model)
          model_inputs = table_chunker.chunk(model_inputs)
      table_preds = scheduler.predict(
          model_file_path,
          model_inputs["table_text"],
          context=model_inputs["table_context"],
          key="table",
          return_negative_confidence=return_negative_confidence,
          cache_key=cache_key,
          config_overrides=table_model_config_overrides,
      )
      text_preds = scheduler.predict(
          model_file_path,
          model_inputs["doc_text"],
          key="text",
          config_overrides=config_overrides,
          return_negative_confidence=return_negative_confidence,
          cache_key=cache_key,
      )
  
      return etl.resolve_preds(
          table_preds=table_preds,
          text_preds=text_preds,
          table_chunks=model_inputs["table_chunks"],
          document_text=model_inputs["doc_text"],
          table_doc_i=model_inputs["table_doc_i"],
          tables=tables,
      )
  
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 5 for calling the TableLabeler obj's underlying member method code with example usage: obj.predict_from_file(cls, text, tables, model_file_path, scheduler, return_negative_confidence, config_overrides, cache_key, table_model_config_overrides) and returns: etl