class_docstr: Span labeler model based on a BERT-style transformer-based encoder.
  
  This is an implementation of the network structure surrounding a transformer
  encoder as described in "BERT: Pre-training of Deep Bidirectional Transformers
  for Language Understanding" (https://arxiv.org/abs/1810.04805).
  
  The BertSpanLabeler allows a user to pass in a transformer stack, and
  instantiates a span labeling network based on a single dense layer.
  
  Attributes:
    network: A transformer network. This network should output a sequence output
      and a classification output. Furthermore, it should expose its embedding
      table via a "get_embedding_table" method.
    initializer: The initializer (if any) to use in the span labeling network.
      Defaults to a Glorot uniform initializer.
    output: The output style for this network. Can be either 'logits' or
      'predictions'.
code: def get_config(self):
      return self._config
  
file_path: autodist/examples/benchmark/utils/modeling/networks/bert_span_labeler.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from utils.modeling import networks
label_desc: Use image node_id 2 for calling the BertSpanLabeler obj's underlying member method code with example usage: obj.get_config() and returns: self