code: def fixture_training_job_with_latest_inference_version(
      sagemaker_session,
      pytorch_inference_latest_version,
      pytorch_inference_latest_py_version,
      cpu_instance_type,
  ):
      with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          pytorch = _get_pytorch_estimator(
              sagemaker_session,
              pytorch_inference_latest_version,
              pytorch_inference_latest_py_version,
              cpu_instance_type,
          )
          pytorch.fit({"training": _upload_training_data(pytorch)})
          return pytorch.latest_training_job.name
  
imports: from __future__ import absolute_import
  import numpy
  import os
  import pytest
  from sagemaker.pytorch.estimator import PyTorch
  from sagemaker.pytorch.model import PyTorchModel
  from sagemaker.pytorch.processing import PyTorchProcessor
  from sagemaker.serverless import ServerlessInferenceConfig
  from sagemaker.utils import unique_name_from_base
  from tests.integ import test_region, DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, EI_SUPPORTED_REGIONS
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
label_desc: Use image node_id 3 for calling a global function with example usage: fixture_training_job_with_latest_inference_version(sagemaker_session, pytorch_inference_latest_version, pytorch_inference_latest_py_version, cpu_instance_type) and returns: pytorch