class_docstr: A metric in an experiment.
  
  A metric is a real-valued function of a model. Each metric is
  associated with a TensorBoard scalar summary, which logs the
  metric's value as the model trains.
class_docstr_tok: ["A","metric","in","an","experiment",".","A","metric","is","a","real-valued","function","of","a","model",".","Each","metric","is","associated","with","a","TensorBoard","scalar","summary",",","which","logs","the","metric","'s","value","as","the","model","trains","."]
code: def as_proto(self):
      return api_pb2.MetricInfo(
          name=api_pb2.MetricName(
              group=self._group,
              tag=self._tag,
          ),
          display_name=self._display_name,
          description=self._description,
          dataset_type=self._dataset_type,
      )
  
imports: import abc
  import hashlib
  import json
  import random
  import time
  import numpy
  from tensorboard.compat import tf2
  from tensorboard.compat.proto import summary_pb2
  from tensorboard.plugins.hparams import api_pb2
  from tensorboard.plugins.hparams import metadata
  from tensorboard.plugins.hparams import plugin_data_pb2
label_desc: Use image node_id 2 for calling the Metric obj's underlying member method code with example usage: obj.as_proto() and returns: api_pb2