code: def test_run_from_train_job_only(
      sagemaker_session,
      dev_sdk_tar,
      execution_role,
      sagemaker_client_config,
      sagemaker_metrics_config,
  ):
      # Notes:
      # 1. No Run created locally or specified in experiment config
      # 2. In training job, Run is initialized
      # which will create a Run according to the run_name passed in there
      # 3. Both metrics and parameters are logged in the Run created in job
      # 4. In a different training job, load the same Run and log more parameters there.
      exp_name = unique_name_from_base(_EXP_NAME_BASE_IN_SCRIPT)
      estimator = _generate_estimator(
          sdk_tar=dev_sdk_tar,
          sagemaker_session=sagemaker_session,
          exp_name=exp_name,
          execution_role=execution_role,
          sagemaker_client_config=sagemaker_client_config,
          sagemaker_metrics_config=sagemaker_metrics_config,
      )
      tc_name = Run._generate_trial_component_name(
          experiment_name=exp_name, run_name=_RUN_NAME_IN_SCRIPT
      )
  
      with cleanup_exp_resources(
          exp_names=[exp_name], sagemaker_session=sagemaker_session
      ):
          estimator.fit(
              job_name=f"train-job-{name()}",
              wait=True,  # wait the training job to finish
              logs="None",  # set to "All" to display logs fetched from the training job
          )
          _check_run_from_job_result(
              tc_name=tc_name,
              sagemaker_session=sagemaker_session,
          )
  
          estimator.environment["RUN_OPERATION"] = _RUN_LOAD
          estimator.fit(
              job_name=f"train-job-{name()}",
              wait=True,  # wait the training job to finish
              logs="None",  # set to "All" to display logs fetched from the training job
          )
          _check_run_from_job_result(
              tc_name=tc_name,
              sagemaker_session=sagemaker_session,
              is_init=False,
          )
  
imports: from __future__ import absolute_import
  import datetime
  import json
  import os
  import time
  import pytest
  from tests.conftest import CUSTOM_S3_OBJECT_KEY_PREFIX
  from tests.integ.sagemaker.experiments.conftest import TAGS
  from sagemaker.experiments._api_types import _TrialComponentStatusType
  from sagemaker.experiments._utils import is_run_trial_component
  from sagemaker.processing import FrameworkProcessor
  from sagemaker.pytorch import PyTorch
  from sagemaker.s3 import S3Uploader
  from sagemaker.xgboost import XGBoostModel
  from tests.integ import DATA_DIR
  from sagemaker.experiments._metrics import BATCH_SIZE
  from sagemaker.experiments.trial_component import _TrialComponent
  from sagemaker.sklearn import SKLearn
  from sagemaker.utils import retry_with_backoff, unique_name_from_base
  from tests.integ.sagemaker.experiments.helpers import name, cleanup_exp_resources
  from sagemaker.experiments.run import RUN_NAME_BASE, DELIMITER
  from sagemaker.experiments import Run, load_run, list_runs
  from sagemaker.experiments._helper import _DEFAULT_ARTIFACT_PREFIX
label_desc: Use image node_id 7 for calling a global function with example usage: test_run_from_train_job_only(sagemaker_session, dev_sdk_tar, execution_role, sagemaker_client_config, sagemaker_metrics_config) without return types