class_docstr: Fetch results about a hyperparameter tuning job and make them accessible for analytics.
docstr: A dictionary describing the ranges of all tuned hyperparameters.
  
  The keys are the names of the hyperparameter, and the values are the ranges.
  
  The output can take one of two forms:
  
      * If the 'TrainingJobDefinition' field is present in the job description, the output
          is a dictionary constructed from 'ParameterRanges' in
          'HyperParameterTuningJobConfig' of the job description. The keys are the
          parameter names, while the values are the parameter ranges.
          Example:
          >>> {
          >>>     "eta": {"MaxValue": "1", "MinValue": "0", "Name": "eta"},
          >>>     "gamma": {"MaxValue": "10", "MinValue": "0", "Name": "gamma"},
          >>>     "iterations": {"MaxValue": "100", "MinValue": "50", "Name": "iterations"},
          >>>     "num_layers": {"MaxValue": "30", "MinValue": "5", "Name": "num_layers"},
          >>> }
      * If the 'TrainingJobDefinitions' field (list) is present in the job description,
          the output is a dictionary with keys as the 'DefinitionName' values from
          all items in 'TrainingJobDefinitions', and each value would be a dictionary
          constructed from 'HyperParameterRanges' in each item in 'TrainingJobDefinitions'
          in the same format as above
          Example:
          >>> {
          >>>     "estimator_1": {
          >>>         "eta": {"MaxValue": "1", "MinValue": "0", "Name": "eta"},
          >>>         "gamma": {"MaxValue": "10", "MinValue": "0", "Name": "gamma"},
          >>>     },
          >>>     "estimator_2": {
          >>>         "framework": {"Values": ["TF", "MXNet"], "Name": "framework"},
          >>>         "gamma": {"MaxValue": "1.0", "MinValue": "0.2", "Name": "gamma"}
          >>>     }
          >>> }
  
  For more details about the 'TrainingJobDefinition' and 'TrainingJobDefinitions' fields
  in job description, see
  https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_hyper_parameter_tuning_job
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code: def tuning_ranges(self):
      """A dictionary describing the ranges of all tuned hyperparameters.
  
      The keys are the names of the hyperparameter, and the values are the ranges.
  
      The output can take one of two forms:
  
          * If the 'TrainingJobDefinition' field is present in the job description, the output
              is a dictionary constructed from 'ParameterRanges' in
              'HyperParameterTuningJobConfig' of the job description. The keys are the
              parameter names, while the values are the parameter ranges.
              Example:
              >>> {
              >>>     "eta": {"MaxValue": "1", "MinValue": "0", "Name": "eta"},
              >>>     "gamma": {"MaxValue": "10", "MinValue": "0", "Name": "gamma"},
              >>>     "iterations": {"MaxValue": "100", "MinValue": "50", "Name": "iterations"},
              >>>     "num_layers": {"MaxValue": "30", "MinValue": "5", "Name": "num_layers"},
              >>> }
          * If the 'TrainingJobDefinitions' field (list) is present in the job description,
              the output is a dictionary with keys as the 'DefinitionName' values from
              all items in 'TrainingJobDefinitions', and each value would be a dictionary
              constructed from 'HyperParameterRanges' in each item in 'TrainingJobDefinitions'
              in the same format as above
              Example:
              >>> {
              >>>     "estimator_1": {
              >>>         "eta": {"MaxValue": "1", "MinValue": "0", "Name": "eta"},
              >>>         "gamma": {"MaxValue": "10", "MinValue": "0", "Name": "gamma"},
              >>>     },
              >>>     "estimator_2": {
              >>>         "framework": {"Values": ["TF", "MXNet"], "Name": "framework"},
              >>>         "gamma": {"MaxValue": "1.0", "MinValue": "0.2", "Name": "gamma"}
              >>>     }
              >>> }
  
      For more details about the 'TrainingJobDefinition' and 'TrainingJobDefinitions' fields
      in job description, see
      https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_hyper_parameter_tuning_job
      """
      description = self.description()
  
      if "TrainingJobDefinition" in description:
          return self._prepare_parameter_ranges(
              description["HyperParameterTuningJobConfig"][
                  "ParameterRanges"
              ]
          )
  
      return {
          training_job_definition[
              "DefinitionName"
          ]: self._prepare_parameter_ranges(
              training_job_definition["HyperParameterRanges"]
          )
          for training_job_definition in description[
              "TrainingJobDefinitions"
          ]
      }
  
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 6 for calling the HyperparameterTuningJobAnalytics obj's underlying member method code with example usage: obj.tuning_ranges() and returns: unknown, self