class_docstr: Class to log the benchmark information to STDOUT.
class_docstr_tok: ["Class","to","log","the","benchmark","information","to","STDOUT","."]
docstr: Log the evaluation result.
  
  The evaluate result is a dictionary that contains metrics defined in
  model_fn. It also contains a entry for global_step which contains the value
  of the global step when evaluation was performed.
  
  Args:
    eval_results: dict, the result of evaluate.
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code: def log_evaluation_result(self, eval_results):
      """Log the evaluation result.
  
      The evaluate result is a dictionary that contains metrics defined in
      model_fn. It also contains a entry for global_step which contains the value
      of the global step when evaluation was performed.
  
      Args:
        eval_results: dict, the result of evaluate.
      """
      if not isinstance(eval_results, dict):
          tf.compat.v1.logging.warning(
              "eval_results should be dictionary for logging. Got %s",
              type(eval_results),
          )
          return
      global_step = eval_results[tf.compat.v1.GraphKeys.GLOBAL_STEP]
      for key in sorted(eval_results):
          if key != tf.compat.v1.GraphKeys.GLOBAL_STEP:
              self.log_metric(
                  key, eval_results[key], global_step=global_step
              )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import contextlib
  import datetime
  import json
  import multiprocessing
  import numbers
  import os
  import threading
  import uuid
  from six.moves import _thread
  from absl import flags
  import tensorflow
  from tensorflow.python.client import device_lib
  from utils.logs import cloud_lib
label_desc: Use image node_id 1 for calling the BaseBenchmarkLogger obj's underlying member method code with example usage: obj.log_evaluation_result(eval_results) without return types