code: def _create_train_job(version):
      return {
          "image_uri": _get_full_cpu_image_uri(version),
          "input_mode": "File",
          "input_config": [
              {
                  "ChannelName": "training",
                  "DataSource": {
                      "S3DataSource": {
                          "S3DataDistributionType": "FullyReplicated",
                          "S3DataType": "S3Prefix",
                      }
                  },
              }
          ],
          "role": ROLE,
          "job_name": JOB_NAME,
          "output_config": {
              "S3OutputPath": "s3://{}/".format(BUCKET_NAME)
          },
          "resource_config": {
              "InstanceType": "ml.c4.4xlarge",
              "InstanceCount": 1,
              "VolumeSizeInGB": 30,
          },
          "hyperparameters": {
              "sagemaker_program": json.dumps("dummy_script.py"),
              "sagemaker_container_log_level": str(logging.INFO),
              "sagemaker_job_name": json.dumps(JOB_NAME),
              "sagemaker_submit_directory": json.dumps(
                  "s3://{}/{}/source/sourcedir.tar.gz".format(
                      BUCKET_NAME, JOB_NAME
                  )
              ),
              "sagemaker_region": '"us-west-2"',
          },
          "stop_condition": {"MaxRuntimeInSeconds": 24 * 60 * 60},
          "retry_strategy": None,
          "metric_definitions": None,
          "tags": None,
          "vpc_config": None,
          "environment": None,
          "experiment_config": None,
          "debugger_hook_config": {
              "CollectionConfigurations": [],
              "S3OutputPath": "s3://{}/".format(BUCKET_NAME),
          },
          "profiler_config": {
              "DisableProfiler": False,
              "S3OutputPath": "s3://{}/".format(BUCKET_NAME),
          },
      }
  
imports: from __future__ import absolute_import
  import logging
  import json
  import os
  import pytest
  from mock import Mock
  from mock import patch
  from sagemaker.fw_utils import UploadedCode
  from sagemaker.session_settings import SessionSettings
  from sagemaker.sklearn import SKLearn, SKLearnModel, SKLearnPredictor
label_desc: Use image node_id 4 for calling a global function with example usage: _create_train_job(version) and returns: dict