class_docstr: Evaluates candidate ensemble performance.
docstr: Returns a fn which selects the best metric based on the objective.
docstr_tok: ["Returns","a","fn","which","selects","the","best","metric","based","on","the","objective","."]
code: def objective_fn(self):
      """Returns a fn which selects the best metric based on the objective."""
      return self._objective_fn
  
file_path: adanet/adanet/core/evaluator.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from absl import logging
  from adanet import tf_compat
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 5 for calling the Evaluator obj's underlying member method code with example usage: obj.objective_fn() and returns: self