code: def test_mxnet(
      retrieve_image_uri,
      time,
      strftime,
      repack_model,
      create_tar_file,
      sagemaker_session,
      mxnet_training_version,
      mxnet_training_py_version,
  ):
      mx = MXNet(
          entry_point=SCRIPT_PATH,
          framework_version=mxnet_training_version,
          py_version=mxnet_training_py_version,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_count=INSTANCE_COUNT,
          instance_type=INSTANCE_TYPE,
          enable_sagemaker_metrics=False,
      )
      inputs = "s3://mybucket/train"
  
      mx.fit(inputs=inputs, experiment_config=EXPERIMENT_CONFIG)
  
      sagemaker_call_names = [
          c[0] for c in sagemaker_session.method_calls
      ]
      assert sagemaker_call_names == ["train", "logs_for_job"]
      boto_call_names = [
          c[0] for c in sagemaker_session.boto_session.method_calls
      ]
      assert boto_call_names == ["resource"]
  
      actual_train_args = sagemaker_session.method_calls[0][2]
      job_name = actual_train_args["job_name"]
      expected_train_args = _get_train_args(job_name)
      expected_train_args["input_config"][0]["DataSource"][
          "S3DataSource"
      ]["S3Uri"] = inputs
      expected_train_args["experiment_config"] = EXPERIMENT_CONFIG
      expected_train_args["enable_sagemaker_metrics"] = False
  
      assert actual_train_args == expected_train_args
  
      model = mx.create_model()
  
      actual_environment = model.prepare_container_def(GPU)
      submit_directory = actual_environment["Environment"][
          "SAGEMAKER_SUBMIT_DIRECTORY"
      ]
      model_url = actual_environment["ModelDataUrl"]
      expected_environment = _get_environment(
          submit_directory, model_url, IMAGE
      )
      assert actual_environment == expected_environment
  
      assert "cpu" in model.prepare_container_def(CPU)["Image"]
      predictor = mx.deploy(1, GPU)
      assert isinstance(predictor, MXNetPredictor)
      assert _is_mms_version(mxnet_training_version) ^ (
          create_tar_file.called and not repack_model.called
      )
  
imports: from __future__ import absolute_import
  import logging
  import json
  import os
  import pytest
  from mock import MagicMock, Mock, ANY
  from mock import patch
  from pkg_resources import parse_version
  from sagemaker.fw_utils import UploadedCode
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.model_metrics import FileSource, MetricsSource, ModelMetrics
  from sagemaker.mxnet import defaults
  from sagemaker.mxnet import MXNet
  from sagemaker.mxnet import MXNetPredictor, MXNetModel
  from sagemaker.session_settings import SessionSettings
label_desc: Use image node_id 12 for calling a global function with example usage: test_mxnet(retrieve_image_uri, time, strftime, repack_model, create_tar_file, sagemaker_session, mxnet_training_version, mxnet_training_py_version) without return types