class_docstr: The base class for BERT related TFEstimators.
  Common arguments:
  bert_config_file, init_checkpoint, use_one_hot_embeddings, optimizer, model_dir.
  
  For its subclass:
  - One can add additional arguments and access them via `params`.
  - One can utilize `_bert_model` to create model_fn and `bert_input_fn` to create input_fn.
class_docstr_tok: ["The","base","class","for","BERT","related","TFEstimators",".","Common","arguments",":","bert_config_file",",","init_checkpoint",",","use_one_hot_embeddings",",","optimizer",",","model_dir",".","For","its","subclass",":","-","One","can","add","additional","arguments","and","access","them","via","`","params","`",".","-","One","can","utilize","`","_bert_model","`","to","create","model_fn","and","`","bert_input_fn","`","to","create","input_fn","."]
code: def __init__(
      self,
      model_fn,
      bert_config_file,
      init_checkpoint=None,
      use_one_hot_embeddings=False,
      model_dir=None,
      **kwargs,
  ):
      import tensorflow as tf
  
      params = {
          "bert_config_file": bert_config_file,
          "init_checkpoint": init_checkpoint,
          "use_one_hot_embeddings": use_one_hot_embeddings,
      }
      for k, v in kwargs.items():
          params[k] = v
      estimator = tf.estimator.Estimator(
          model_fn, model_dir=model_dir, params=params
      )
      super(BERTBaseEstimator, self).__init__(estimator)
  
imports: from zoo.tfpark.estimator import *
  from bert import modeling
label_desc: Use image node_id 1 to create a new BERTBaseEstimator object from inherited base classes: TFEstimator with example: obj = BERTBaseEstimator(model_fn, bert_config_file, init_checkpoint, use_one_hot_embeddings, model_dir)