class_docstr: Fetch results about a hyperparameter tuning job and make them accessible for analytics.
docstr: A (paginated) list of everything from ``ListTrainingJobsForTuningJob``.
  
  Args:
      force_refresh (bool): Set to True to fetch the latest data from
          SageMaker API.
  
  Returns:
      dict: The Amazon SageMaker response for
      ``ListTrainingJobsForTuningJob``.
docstr_tok: ["A","(","paginated",")","list","of","everything","from","``","ListTrainingJobsForTuningJob","``",".","Args",":","force_refresh","(","bool",")",":","Set","to","True","to","fetch","the","latest","data","from","SageMaker","API",".","Returns",":","dict",":","The","Amazon","SageMaker","response","for","``","ListTrainingJobsForTuningJob","``","."]
code: def training_job_summaries(self, force_refresh=False):
      """A (paginated) list of everything from ``ListTrainingJobsForTuningJob``.
  
      Args:
          force_refresh (bool): Set to True to fetch the latest data from
              SageMaker API.
  
      Returns:
          dict: The Amazon SageMaker response for
          ``ListTrainingJobsForTuningJob``.
      """
      if force_refresh:
          self.clear_cache()
      if self._training_job_summaries is not None:
          return self._training_job_summaries
      output = []
      next_args = {}
      for count in range(100):
          logger.debug(
              "Calling list_training_jobs_for_hyper_parameter_tuning_job %d",
              count,
          )
          raw_result = self._sage_client.list_training_jobs_for_hyper_parameter_tuning_job(
              HyperParameterTuningJobName=self.name,
              MaxResults=100,
              **next_args,
          )
          new_output = raw_result["TrainingJobSummaries"]
          output.extend(new_output)
          logger.debug(
              "Got %d more TrainingJobs. Total so far: %d",
              len(new_output),
              len(output),
          )
          if ("NextToken" in raw_result) and (len(new_output) > 0):
              next_args["NextToken"] = raw_result["NextToken"]
          else:
              break
      self._training_job_summaries = output
      return output
  
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 9 for calling the HyperparameterTuningJobAnalytics obj's underlying member method code with example usage: obj.training_job_summaries(force_refresh) and returns: output, self