code: def test_mxnet_model_registration_with_custom_inference(
      pipeline_session,
      role,
      pipeline_name,
  ):
      base_dir = os.path.join(DATA_DIR, "mxnet_mnist")
      source_dir = os.path.join(base_dir, "code")
      entry_point = os.path.join(source_dir, "inference.py")
      mx_mnist_model_data = os.path.join(base_dir, "model.tar.gz")
  
      instance_count = ParameterInteger(
          name="InstanceCount", default_value=1
      )
      instance_type = ParameterString(
          name="InstanceType", default_value="ml.m5.xlarge"
      )
  
      # No runtime repack needed as the model_data is not a PipelineVariable
      model = MXNetModel(
          entry_point=entry_point,
          source_dir=source_dir,
          role=role,
          model_data=mx_mnist_model_data,
          framework_version="1.7.0",
          py_version="py3",
          sagemaker_session=pipeline_session,
      )
  
      step_args = model.register(
          content_types=["*"],
          response_types=["application/json"],
          model_package_group_name=f"{pipeline_name}TestModelPackageGroup",
          inference_instances=["ml.m5.xlarge"],
          transform_instances=["ml.m5.xlarge"],
          description="test-description",
      )
      step_model_regis = ModelStep(
          name="mxnet-register-model",
          step_args=step_args,
      )
  
      pipeline = Pipeline(
          name=pipeline_name,
          parameters=[instance_count, instance_type],
          steps=[step_model_regis],
          sagemaker_session=pipeline_session,
      )
  
      try:
          pipeline.create(role)
  
          for _ in retries(
              max_retry_count=5,
              exception_message_prefix="Waiting for a successful execution of pipeline",
              seconds_to_sleep=10,
          ):
              execution = pipeline.start()
              wait_pipeline_execution(execution=execution)
              execution_steps = execution.list_steps()
  
              assert len(execution_steps) == 1
              failure_reason = execution_steps[0].get(
                  "FailureReason", ""
              )
              if failure_reason != "":
                  logging.error(
                      f"Pipeline execution failed with error: {failure_reason}."
                      " Retrying.."
                  )
                  continue
              assert execution_steps[0]["StepStatus"] == "Succeeded"
              if (
                  _REGISTER_MODEL_NAME_BASE
                  in execution_steps[0]["StepName"]
              ):
                  assert execution_steps[0]["Metadata"][
                      _REGISTER_MODEL_TYPE
                  ]
              break
  
      finally:
          try:
              pipeline.delete()
          except Exception as error:
              logging.error(error)
  
imports: from __future__ import absolute_import
  import logging
  import os
  import pytest
  from tests.integ.sagemaker.workflow.helpers import wait_pipeline_execution
  from sagemaker.workflow.fail_step import FailStep
  from sagemaker.workflow.functions import Join
  from tests.integ.timeout import timeout_and_delete_endpoint_by_name
  from sagemaker.tensorflow import TensorFlow, TensorFlowModel, TensorFlowPredictor
  from sagemaker.utils import unique_name_from_base
  from sagemaker.workflow.model_step import ModelStep, _REGISTER_MODEL_NAME_BASE, _CREATE_MODEL_NAME_BASE
  from tests.integ.retry import retries
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker import PipelineModel, TrainingInput, Model, ModelMetrics, MetricsSource, get_execution_role, ModelPackage
  from sagemaker import FileSource, utils
  from sagemaker.pytorch import PyTorch
  from sagemaker.s3 import S3Uploader
  from sagemaker.mxnet.model import MXNetModel
  from sagemaker.workflow.parameters import ParameterInteger, ParameterString
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.steps import TrainingStep
  from sagemaker.xgboost import XGBoost, XGBoostModel, XGBoostPredictor
  from tests.integ.kms_utils import get_or_create_kms_key
  from tests.integ import DATA_DIR
label_desc: Use image node_id 5 for calling a global function with example usage: test_mxnet_model_registration_with_custom_inference(pipeline_session, role, pipeline_name) without return types