code: def runTest(self):
      document_assembler = (
          MultiDocumentAssembler()
          .setInputCols("table_json", "questions")
          .setOutputCols("document_questions", "document_table")
      )
  
      sentence_detector = (
          SentenceDetector()
          .setInputCols(["document_questions"])
          .setOutputCol("questions")
      )
  
      table_assembler = (
          TableAssembler()
          .setInputCols(["document_table"])
          .setOutputCol("table")
      )
  
      tapas = (
          TapasForQuestionAnswering()
          .pretrained()
          .setMaxSentenceLength(512)
          .setInputCols(["questions", "table"])
          .setOutputCol("answers")
      )
  
      pipeline = Pipeline(
          stages=[
              document_assembler,
              sentence_detector,
              table_assembler,
              tapas,
          ]
      )
  
      model = pipeline.fit(self.data)
  
      light_pipeline = LightPipeline(model)
      annotations_result = light_pipeline.fullAnnotate(
          self.question1, self.table
      )
  
      self.assertEqual(len(annotations_result), 1)
      self.assertAnnotations(annotations_result)
  
      questions = [self.question1, self.question2]
      annotations_result = light_pipeline.fullAnnotate(
          questions, [self.table, self.table]
      )
  
      self.assertEqual(len(annotations_result), len(questions))
      self.assertAnnotations(annotations_result)
  
imports: import os
  import unittest
  import pytest
  from pyspark.sql.functions import col
  from sparknlp.pretrained import PretrainedPipeline
  from test.util import SparkSessionForTest, SparkContextForTest
  from sparknlp.annotator import *
  from sparknlp.base import *
label_desc: Use image node_id 2 for calling the LightPipelineTapasInputTest obj's underlying member method code with example usage: obj.runTest() without return types