class_docstr: Raw BERT Transformer for encoding sequences. Please see
  :class:`~texar.tf.modules.PretrainedBERTMixin` for a brief description
  of BERT.
  
  This module basically stacks
  :class:`~texar.tf.modules.WordEmbedder`,
  :class:`~texar.tf.modules.PositionEmbedder`,
  :class:`~texar.tf.modules.TransformerEncoder` and a dense pooler.
  
  Args:
      pretrained_model_name (optional): a `str`, the name
          of pre-trained model (e.g., ``bert-base-uncased``). Please refer to
          :class:`~texar.tf.modules.PretrainedBERTMixin` for
          all supported models.
          If `None`, the model name in :attr:`hparams` is used.
      cache_dir (optional): the path to a folder in which the
          pre-trained models will be cached. If `None` (default),
          a default directory (``texar_data`` folder under user's home
          directory) will be used.
      hparams (dict or HParams, optional): Hyperparameters. Missing
          hyperparameter will be set to default values. See
          :meth:`default_hparams` for the hyperparameter structure
          and default values.
  
  .. document private functions
  .. automethod:: _build
code: def reset_parameters(self):
      with tf.variable_scope(self.variable_scope):
          if self._hparams.initializer:
              tf.get_variable_scope().set_initializer(
                  get_initializer(self._hparams.initializer)
              )
  
file_path: texar/texar/tf/modules/encoders/bert_encoder.py
imports: import tensorflow
  from texar.tf.core.layers import get_initializer, get_layer
  from texar.tf.modules.encoders.transformer_encoders import TransformerEncoder
  from texar.tf.modules.embedders.embedders import WordEmbedder
  from texar.tf.modules.embedders.position_embedders import PositionEmbedder
  from texar.tf.modules.encoders.encoder_base import EncoderBase
  from texar.tf.modules.pretrained.bert import PretrainedBERTMixin
label_desc: Use image node_id 2 for calling the BERTEncoder obj's underlying member method code with example usage: obj.reset_parameters() without return types