code: def test_tuning_ranges_multi_training_job_definitions():
      session = create_sagemaker_session(
          describe_tuning_result={
              "HyperParameterTuningJobConfig": {},
              "TrainingJobDefinitions": [
                  {
                      "DefinitionName": "estimator_1",
                      "HyperParameterRanges": {
                          "CategoricalParameterRanges": [],
                          "ContinuousParameterRanges": [
                              {
                                  "MaxValue": "1",
                                  "MinValue": "0",
                                  "Name": "eta",
                              },
                              {
                                  "MaxValue": "10",
                                  "MinValue": "0",
                                  "Name": "gamma",
                              },
                          ],
                          "IntegerParameterRanges": [
                              {
                                  "MaxValue": "30",
                                  "MinValue": "5",
                                  "Name": "num_layers",
                              },
                              {
                                  "MaxValue": "100",
                                  "MinValue": "50",
                                  "Name": "iterations",
                              },
                          ],
                      },
                      "AlgorithmSpecification": {
                          "TrainingImage": "training_image_1",
                          "TrainingInputMode": "File",
                      },
                  },
                  {
                      "DefinitionName": "estimator_2",
                      "HyperParameterRanges": {
                          "CategoricalParameterRanges": [
                              {
                                  "Values": ["TF", "MXNet"],
                                  "Name": "framework",
                              }
                          ],
                          "ContinuousParameterRanges": [
                              {
                                  "MaxValue": "1.0",
                                  "MinValue": "0.2",
                                  "Name": "gamma",
                              }
                          ],
                          "IntegerParameterRanges": [],
                      },
                      "AlgorithmSpecification": {
                          "TrainingImage": "training_image_2",
                          "TrainingInputMode": "File",
                      },
                  },
              ],
          }
      )
  
      expected_result = {
          "estimator_1": {
              "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",
              },
          },
          "estimator_2": {
              "framework": {
                  "Values": ["TF", "MXNet"],
                  "Name": "framework",
              },
              "gamma": {
                  "MaxValue": "1.0",
                  "MinValue": "0.2",
                  "Name": "gamma",
              },
          },
      }
  
      tuner = HyperparameterTuningJobAnalytics(
          "my-tuning-job", sagemaker_session=session
      )
  
      assert expected_result == tuner.tuning_ranges
  
imports: from __future__ import absolute_import
  import datetime
  import os
  import uuid
  import pytest
  from mock import Mock
  from sagemaker.analytics import AnalyticsMetricsBase, HyperparameterTuningJobAnalytics, TrainingJobAnalytics
  from sagemaker.session_settings import SessionSettings
label_desc: Use image node_id 10 for calling a global function with example usage: test_tuning_ranges_multi_training_job_definitions() without return types