class_docstr: A pre-built TFEstimator that that takes the hidden state of the final encoder layer of BERT
  to perform training and prediction on SQuAD dataset.
  The Stanford Question Answering Dataset (SQuAD) is a popular question answering
  benchmark dataset.
  
  :param bert_config_file: The path to the json file for BERT configurations.
  :param init_checkpoint: The path to the initial checkpoint of the pre-trained BERT model if any.
                          Default is None.
  :param use_one_hot_embeddings: Boolean. Whether to use one-hot for word embeddings.
                                 Default is False.
  :param optimizer: The optimizer used to train the estimator. It should be an instance of
                    tf.train.Optimizer.
                    Default is None if no training is involved.
  :param model_dir: The output directory for model checkpoints to be written if any.
                    Default is None.
class_docstr_tok: ["A","pre-built","TFEstimator","that","that","takes","the","hidden","state","of","the","final","encoder","layer","of","BERT","to","perform","training","and","prediction","on","SQuAD","dataset",".","The","Stanford","Question","Answering","Dataset","(","SQuAD",")","is","a","popular","question","answering","benchmark","dataset",".",":","param","bert_config_file",":","The","path","to","the","json","file","for","BERT","configurations",".",":","param","init_checkpoint",":","The","path","to","the","initial","checkpoint","of","the","pre-trained","BERT","model","if","any",".","Default","is","None",".",":","param","use_one_hot_embeddings",":","Boolean",".","Whether","to","use","one-hot","for","word","embeddings",".","Default","is","False",".",":","param","optimizer",":","The","optimizer","used","to","train","the","estimator",".","It","should","be","an","instance","of","tf.train.Optimizer",".","Default","is","None","if","no","training","is","involved",".",":","param","model_dir",":","The","output","directory","for","model","checkpoints","to","be","written","if","any",".","Default","is","None","."]
code: def __init__(
      self,
      bert_config_file,
      init_checkpoint=None,
      use_one_hot_embeddings=False,
      optimizer=None,
      model_dir=None,
  ):
      super(BERTSQuAD, self).__init__(
          model_fn=make_bert_squad_model_fn(optimizer),
          bert_config_file=bert_config_file,
          init_checkpoint=init_checkpoint,
          use_one_hot_embeddings=use_one_hot_embeddings,
          model_dir=model_dir,
      )
  
imports: from bigdl.orca.tfpark.text.estimator import *
  from bigdl.dllib.utils.log4Error import invalidInputError
label_desc: Use image node_id 1 to create a new BERTSQuAD object from inherited base classes: BERTBaseEstimator with example: obj = BERTSQuAD(bert_config_file, init_checkpoint, use_one_hot_embeddings, optimizer, model_dir)