class_docstr: Fetch results about a hyperparameter tuning job and make them accessible for analytics.
docstr: Call ``DescribeHyperParameterTuningJob`` for the hyperparameter tuning job.
  
  Args:
      force_refresh (bool): Set to True to fetch the latest data from
          SageMaker API.
  
  Returns:
      dict: The Amazon SageMaker response for
      ``DescribeHyperParameterTuningJob``.
docstr_tok: ["Call","``","DescribeHyperParameterTuningJob","``","for","the","hyperparameter","tuning","job",".","Args",":","force_refresh","(","bool",")",":","Set","to","True","to","fetch","the","latest","data","from","SageMaker","API",".","Returns",":","dict",":","The","Amazon","SageMaker","response","for","``","DescribeHyperParameterTuningJob","``","."]
code: def description(self, force_refresh=False):
      """Call ``DescribeHyperParameterTuningJob`` for the hyperparameter tuning job.
  
      Args:
          force_refresh (bool): Set to True to fetch the latest data from
              SageMaker API.
  
      Returns:
          dict: The Amazon SageMaker response for
          ``DescribeHyperParameterTuningJob``.
      """
      if force_refresh:
          self.clear_cache()
      if not self._tuning_job_describe_result:
          self._tuning_job_describe_result = self._sage_client.describe_hyper_parameter_tuning_job(  # noqa: E501 # pylint: disable=line-too-long
              HyperParameterTuningJobName=self.name
          )
      return self._tuning_job_describe_result
  
imports: from __future__ import print_function, absolute_import
  from abc import ABCMeta, abstractmethod
  from collections import defaultdict, OrderedDict
  import datetime
  import logging
  from six import with_metaclass
  from sagemaker.session import Session
  from sagemaker.utils import DeferredError
  from sagemaker.lineage import artifact
label_desc: Use image node_id 8 for calling the HyperparameterTuningJobAnalytics obj's underlying member method code with example usage: obj.description(force_refresh) and returns: self