code: def test_training_step_with_jsonget_instance_type(pipeline_session):
      property_file = PropertyFile(
          name="my-property-file",
          output_name="TestOutputName",
          path="processing_output.json",
      )
      processor = Processor(
          image_uri=IMAGE_URI,
          role=ROLE,
          instance_count=1,
          instance_type="c4.4xlarge",
          sagemaker_session=pipeline_session,
      )
      process_arg = processor.run(
          outputs=[ProcessingOutput(output_name="TestOutputName")]
      )
      processing_step = ProcessingStep(
          name="inputProcessingStep",
          step_args=process_arg,
          property_files=[property_file],
      )
  
      json_get_function = JsonGet(
          step_name=processing_step.name,
          property_file=property_file.name,
          json_path="mse",
      )
  
      estimator = Estimator(
          image_uri=IMAGE_URI,
          role=ROLE,
          instance_count=1,
          instance_type=json_get_function,
          sagemaker_session=pipeline_session,
      )
  
      training_step = TrainingStep(
          name="MyTrainingStep",
          step_args=estimator.fit(
              inputs=TrainingInput(
                  s3_data=f"s3://{BUCKET}/train_manifest"
              )
          ),
      )
      pipeline = Pipeline(
          name="MyPipeline",
          steps=[processing_step, training_step],
          sagemaker_session=pipeline_session,
      )
  
      steps = json.loads(pipeline.definition())["Steps"]
      for step in steps:
          if step["Type"] == "Processing":
              continue
          assert step["Arguments"]["ResourceConfig"][
              "InstanceType"
          ] == {
              "Std:JsonGet": {
                  "Path": "mse",
                  "PropertyFile": {
                      "Get": "Steps.inputProcessingStep.PropertyFiles.my-property-file"
                  },
              }
          }
  
imports: from __future__ import absolute_import
  import os
  import json
  from mock import patch
  import pytest
  import warnings
  from copy import deepcopy
  from sagemaker import Processor, Model
  from sagemaker.parameter import IntegerParameter
  from sagemaker.processing import ProcessingOutput
  from sagemaker.transformer import Transformer
  from sagemaker.tuner import HyperparameterTuner
  from sagemaker.workflow.pipeline_context import _PipelineConfig
  from sagemaker.workflow.parameters import ParameterString, ParameterBoolean
  from sagemaker.workflow.properties import PropertyFile
  from sagemaker.workflow.steps import ProcessingStep, TrainingStep
  from sagemaker.workflow.pipeline import Pipeline, PipelineGraph
  from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
  from sagemaker.workflow.utilities import hash_files_or_dirs
  from sagemaker.workflow.functions import Join, JsonGet
  from sagemaker.estimator import Estimator
  from sagemaker.sklearn.estimator import SKLearn
  from sagemaker.pytorch.estimator import PyTorch
  from sagemaker.tensorflow.estimator import TensorFlow
  from sagemaker.huggingface.estimator import HuggingFace
  from sagemaker.xgboost.estimator import XGBoost
  from sagemaker.mxnet.estimator import MXNet
  from sagemaker.rl.estimator import RLEstimator, RLToolkit, RLFramework
  from sagemaker.chainer.estimator import Chainer
  from sagemaker.amazon.amazon_estimator import RecordSet
  from sagemaker.amazon.knn import KNN
  from sagemaker.amazon.kmeans import KMeans
  from sagemaker.amazon.linear_learner import LinearLearner
  from sagemaker.amazon.lda import LDA
  from sagemaker.amazon.pca import PCA
  from sagemaker.amazon.factorization_machines import FactorizationMachines
  from sagemaker.amazon.ipinsights import IPInsights
  from sagemaker.amazon.randomcutforest import RandomCutForest
  from sagemaker.amazon.ntm import NTM
  from sagemaker.amazon.object2vec import Object2Vec
  from tests.unit import DATA_DIR
  from sagemaker.inputs import TrainingInput
  from tests.unit.sagemaker.workflow.helpers import CustomStep, ordered, get_step_args_helper
  from sagemaker.workflow.condition_step import ConditionStep
  from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo
  from tests.unit.sagemaker.workflow.conftest import ROLE, BUCKET, IMAGE_URI, INSTANCE_TYPE
label_desc: Use image node_id 11 for calling a global function with example usage: test_training_step_with_jsonget_instance_type(pipeline_session) without return types