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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def log_estimator_evaluation_result(self, eval_results):
 """Log the evaluation result for a estimator. The evaluate result is a directory 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() from a estimator. """

    if not isinstance(eval_results, dict):
      tf.logging.warning("eval_results should be directory for logging. Got %s",
                         type(eval_results))
      return
    global_step = eval_results[tf.GraphKeys.GLOBAL_STEP]
    for key in sorted(eval_results):
      if key != tf.GraphKeys.GLOBAL_STEP:
        self.log_metric(key, eval_results[key], global_step=global_step)