code: def test_model_create_transformer(
      create_sagemaker_model, sagemaker_session
  ):
      model_name = "auto-generated-model"
      model = Model(
          MODEL_IMAGE,
          MODEL_DATA,
          name=model_name,
          sagemaker_session=sagemaker_session,
      )
  
      instance_type = "ml.m4.xlarge"
      transformer = model.transformer(
          instance_count=1, instance_type=instance_type
      )
  
      create_sagemaker_model.assert_called_with(
          instance_type, tags=None
      )
  
      assert isinstance(transformer, sagemaker.transformer.Transformer)
      assert transformer.model_name == model_name
      assert transformer.instance_type == instance_type
      assert transformer.instance_count == 1
      assert transformer.sagemaker_session == sagemaker_session
      assert transformer.base_transform_job_name == model_name
  
      assert transformer.strategy is None
      assert transformer.env is None
      assert transformer.output_path is None
      assert transformer.output_kms_key is None
      assert transformer.accept is None
      assert transformer.assemble_with is None
      assert transformer.volume_kms_key is None
      assert transformer.max_concurrent_transforms is None
      assert transformer.max_payload is None
      assert transformer.tags is None
  
imports: from __future__ import absolute_import
  import random
  from unittest.mock import MagicMock
  import pytest
  from mock import Mock, patch
  import sagemaker
  from sagemaker.async_inference import AsyncInferenceConfig
  from sagemaker.model import FrameworkModel, Model
  from sagemaker.huggingface.model import HuggingFaceModel
  from sagemaker.jumpstart.constants import JUMPSTART_GATED_AND_PUBLIC_BUCKET_NAME_SET, JUMPSTART_RESOURCE_BASE_NAME
  from sagemaker.jumpstart.enums import JumpStartTag
  from sagemaker.mxnet.model import MXNetModel
  from sagemaker.pytorch.model import PyTorchModel
  from sagemaker.session_settings import SessionSettings
  from sagemaker.sklearn.model import SKLearnModel
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.xgboost.model import XGBoostModel
  from sagemaker.enums import EndpointType
  from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
  from sagemaker.workflow.properties import Properties
  from tests.unit import _test_default_bucket_and_prefix_combinations, DEFAULT_S3_BUCKET_NAME, DEFAULT_S3_OBJECT_KEY_PREFIX_NAME, SAGEMAKER_CONFIG_EDGE_PACKAGING_JOB
label_desc: Use image node_id 20 for calling a global function with example usage: test_model_create_transformer(create_sagemaker_model, sagemaker_session) without return types