code: def test_start_with_checkpoint_location(
      session,
      mock_stored_function,
      mock_runtime_manager,
      mock_script_upload,
      mock_user_workspace_upload,
      secret_token,
  ):
      job_settings = _JobSettings(
          image_uri=IMAGE,
          s3_root_uri=S3_URI,
          role=ROLE_ARN,
          include_local_workdir=True,
          instance_type="ml.m5.large",
          encrypt_inter_container_traffic=True,
      )
  
      input_checkpoint_location = CheckpointLocation(
          "s3://my-bucket/my-checkpoints/"
      )
  
      job = _Job.start(
          job_settings,
          job_function_with_checkpoint,
          func_args=(1,),
          func_kwargs={
              "b": 2,
              "checkpoint_2": input_checkpoint_location,
          },
      )
  
      assert job.job_name.startswith("job-function-with-checkpoint")
  
      mock_stored_function.assert_called_once_with(
          sagemaker_session=session(),
          s3_base_uri=f"{S3_URI}/{job.job_name}",
          hmac_key=HMAC_KEY,
          s3_kms_key=None,
      )
  
      mock_stored_function().save.assert_called_once_with(
          job_function_with_checkpoint,
          *(1,),
          **{"b": 2, "checkpoint_2": input_checkpoint_location},
      )
  
      mock_python_version = (
          mock_runtime_manager()._current_python_version()
      )
  
      session().sagemaker_client.create_training_job.assert_called_once_with(
          TrainingJobName=job.job_name,
          RoleArn=ROLE_ARN,
          StoppingCondition={"MaxRuntimeInSeconds": 86400},
          RetryStrategy={"MaximumRetryAttempts": 1},
          CheckpointConfig={
              "LocalPath": "/opt/ml/checkpoints/",
              "S3Uri": "s3://my-bucket/my-checkpoints/",
          },
          InputDataConfig=[
              dict(
                  ChannelName=RUNTIME_SCRIPTS_CHANNEL_NAME,
                  DataSource={
                      "S3DataSource": {
                          "S3Uri": mock_script_upload.return_value,
                          "S3DataType": "S3Prefix",
                      }
                  },
              ),
              dict(
                  ChannelName=REMOTE_FUNCTION_WORKSPACE,
                  DataSource={
                      "S3DataSource": {
                          "S3Uri": mock_user_workspace_upload.return_value,
                          "S3DataType": "S3Prefix",
                      }
                  },
              ),
          ],
          OutputDataConfig={"S3OutputPath": f"{S3_URI}/{job.job_name}"},
          AlgorithmSpecification=dict(
              TrainingImage=IMAGE,
              TrainingInputMode="File",
              ContainerEntrypoint=[
                  "/bin/bash",
                  "/opt/ml/input/data/sagemaker_remote_function_bootstrap/job_driver.sh",
              ],
              ContainerArguments=[
                  "--s3_base_uri",
                  f"{S3_URI}/{job.job_name}",
                  "--region",
                  TEST_REGION,
                  "--client_python_version",
                  mock_python_version,
                  "--dependency_settings",
                  '{"dependency_file": null}',
                  "--run_in_context",
                  '{"experiment_name": "my-exp-name", "run_name": "my-run-name"}',
              ],
          ),
          ResourceConfig=dict(
              VolumeSizeInGB=30,
              InstanceCount=1,
              InstanceType="ml.m5.large",
              KeepAlivePeriodInSeconds=0,
          ),
          EnableNetworkIsolation=False,
          EnableInterContainerTrafficEncryption=True,
          EnableManagedSpotTraining=False,
          Environment={
              "AWS_DEFAULT_REGION": "us-west-2",
              "REMOTE_FUNCTION_SECRET_KEY": HMAC_KEY,
          },
      )
  
imports: from __future__ import absolute_import
  import os
  import sys
  import pytest
  from mock import patch, Mock, ANY, mock_open
  from mock.mock import MagicMock
  from sagemaker.config import load_sagemaker_config
  from sagemaker.remote_function.checkpoint_location import CheckpointLocation
  from sagemaker.remote_function.core.stored_function import _SerializedData
  from sagemaker.session_settings import SessionSettings
  from sagemaker.remote_function.spark_config import SparkConfig
  from sagemaker.remote_function.custom_file_filter import CustomFileFilter
  from sagemaker.remote_function.core.pipeline_variables import Context
  from sagemaker.workflow.pipeline_context import _PipelineConfig
  from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
  from sagemaker.workflow.execution_variables import ExecutionVariables
  from sagemaker.utils import sagemaker_timestamp
  from tests.unit import DATA_DIR
  from sagemaker.remote_function.job import _JobSettings, _Job, _convert_run_to_json, _prepare_and_upload_runtime_scripts, _prepare_and_upload_workspace, _prepare_and_upload_spark_dependent_files, _upload_spark_submit_deps, _upload_serialized_spark_configuration, _extend_spark_config_to_request, _prepare_dependencies_and_pre_execution_scripts
label_desc: Use image node_id 16 for calling a global function with example usage: test_start_with_checkpoint_location(session, mock_stored_function, mock_runtime_manager, mock_script_upload, mock_user_workspace_upload, secret_token) without return types