class_docstr: TapasForQuestionAnswering is an implementation of TaPas - a BERT-based model specifically designed for
  answering questions about tabular data. It takes TABLE and DOCUMENT annotations as input and tries to answer
  the questions in the document by using the data from the table. The model is based in BertForQuestionAnswering
  and shares all its parameters with it.
  
  Pretrained models can be loaded with :meth:`.pretrained` of the companion
  object:
  
  >>> tapas = TapasForQuestionAnswering.pretrained() \
  ...     .setInputCols(["table", "document"]) \
  ...     .setOutputCol("answer")
  
  The default model is ``"table_qa_tapas_base_finetuned_wtq"``, if no name
  is provided.
  
  For available pretrained models please see the `Models Hub
  <https://sparknlp.org/models?task=Question+Answering+Tapas>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``DOCUMENT, TABLE``    ``CHUNK``
  ====================== ======================
  
  Parameters
  ----------
  batchSize
      Batch size. Large values allows faster processing but requires more
      memory, by default 2
  caseSensitive
      Whether to ignore case in tokens for embeddings matching, by default
      False
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  maxSentenceLength
      Max sentence length to process, by default 512
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>>
  >>> document_assembler = MultiDocumentAssembler()\
  ...     .setInputCols("table_json", "questions")\
  ...     .setOutputCols("document_table", "document_questions")
  >>>
  >>> sentence_detector = SentenceDetector()\
  ...     .setInputCols(["document_questions"])\
  ...     .setOutputCol("questions")
  >>>
  >>> table_assembler = TableAssembler()\
  ...     .setInputCols(["document_table"])\
  ...     .setOutputCol("table")
  >>>
  >>> tapas = TapasForQuestionAnswering\
  ...     .pretrained()\
  ...     .setInputCols(["questions", "table"])\
  ...     .setOutputCol("answers")
  >>>
  >>> pipeline = Pipeline(stages=[
  ...     document_assembler,
  ...     sentence_detector,
  ...     table_assembler,
  ...     tapas])
  >>>
  >>> json_data = """
  ... {
  ...     "header": ["name", "money", "age"],
  ...     "rows": [
  ...     ["Donald Trump", "$100,000,000", "75"],
  ...     ["Elon Musk", "$20,000,000,000,000", "55"]
  ...     ]
  ...  }
  ...  """
  >>> model = pipeline.fit(data)
  >>> model\
  ...     .transform(data)\
  ...     .selectExpr("explode(answers) AS answer")\
  ...     .select("answer.metadata.question", "answer.result")\
  ...     .show(truncate=False)
  +-----------------------+----------------------------------------+
  |question               |result                                  |
  +-----------------------+----------------------------------------+
  |Who earns 100,000,000? |Donald Trump                            |
  |Who has more money?    |Elon Musk                               |
  |How much they all earn?|COUNT($100,000,000, $20,000,000,000,000)|
  |How old are they?      |AVERAGE(75, 55)                         |
  +-----------------------+----------------------------------------+
code: def __init__(
      self,
      classname="com.johnsnowlabs.nlp.annotators.classifier.dl.TapasForQuestionAnswering",
      java_model=None,
  ):
      super(TapasForQuestionAnswering, self).__init__(
          classname=classname, java_model=java_model
      )
      self._setDefault(
          batchSize=2, maxSentenceLength=512, caseSensitive=False
      )
  
imports: from sparknlp.common import *
  from sparknlp.annotator.classifier_dl import BertForQuestionAnswering
label_desc: Use image node_id 1 to create a new TapasForQuestionAnswering object from inherited base classes: BertForQuestionAnswering with example: obj = TapasForQuestionAnswering(classname, java_model)