class_docstr: Classification network head for BERT modeling.
  
  This network implements a simple classifier head based on a dense layer.
  
  Attributes:
    input_width: The innermost dimension of the input tensor to this network.
    num_classes: The number of classes that this network should classify to.
    activation: The activation, if any, for the dense layer in this network.
    initializer: The intializer for the dense layer in this 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_dict
  
file_path: autodist/examples/benchmark/utils/modeling/networks/classification.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.python.keras.engine import network
label_desc: Use image node_id 2 for calling the Classification obj's underlying member method code with example usage: obj.get_config() and returns: self