How to use the text2vec.bert.modeling.BertConfig.from_json_file function in text2vec

def get_estimator(self):

        from tensorflow.python.estimator.estimator import Estimator
        from tensorflow.python.estimator.run_config import RunConfig

        bert_config = modeling.BertConfig.from_json_file(self.config_name)
        label_list = self.processor.get_labels()
        train_examples = self.processor.get_train_examples(self.data_dir)
        num_train_steps = int(
            len(train_examples) / self.batch_size * self.num_train_epochs)
        num_warmup_steps = int(num_train_steps * 0.1)

        if self.mode == tf.estimator.ModeKeys.TRAIN:
            init_checkpoint = self.ckpt_name
        else:
            init_checkpoint = self.output_dir

        model_fn = self.model_fn_builder(
            bert_config=bert_config,
            num_labels=len(label_list),
            init_checkpoint=init_checkpoint,
            learning_rate=self.learning_rate,

def train(self):
        if self.mode is None:
            raise ValueError("Please set the 'mode' parameter")

        bert_config = modeling.BertConfig.from_json_file(self.config_name)

        if self.max_seq_len > bert_config.max_position_embeddings:
            raise ValueError(
                "Cannot use sequence length %d because the BERT model "
                "was only trained up to sequence length %d" %
                (self.max_seq_len, bert_config.max_position_embeddings))

        tf.gfile.MakeDirs(self.output_dir)

        label_list = self.processor.get_labels()

        train_examples = self.processor.get_train_examples(self.data_dir)
        num_train_steps = int(len(train_examples) / self.batch_size * self.num_train_epochs)

        estimator = self.get_estimator()