code: def test_deploy_creates_sagemaker_transform_job_and_s3_resources_with_expected_names_from_s3(
      pretrained_model, sagemaker_client
  ):
      local_model_path = _download_artifact_from_uri(
          pretrained_model.model_uri
      )
      artifact_path = "model"
      region_name = sagemaker_client.meta.region_name
      default_bucket = mfs._get_default_s3_bucket(region_name)
      s3_artifact_repo = S3ArtifactRepository(f"s3://{default_bucket}")
      s3_artifact_repo.log_artifacts(
          local_model_path, artifact_path=artifact_path
      )
      model_s3_uri = (
          f"s3://{default_bucket}/{pretrained_model.model_path}"
      )
  
      job_name = "test-job"
      mfs.deploy_transform_job(
          job_name=job_name,
          model_uri=model_s3_uri,
          s3_input_data_type="Some Data Type",
          s3_input_uri="Some Input Uri",
          content_type="Some Content Type",
          s3_output_path="Some Output Path",
          archive=True,
      )
  
      transform_job_description = (
          sagemaker_client.describe_transform_job(
              TransformJobName=job_name
          )
      )
      model_name = transform_job_description["ModelName"]
      assert model_name in [
          model["ModelName"]
          for model in sagemaker_client.list_models()["Models"]
      ]
  
      s3_client = boto3.client("s3", region_name=region_name)
      object_names = [
          entry["Key"]
          for entry in s3_client.list_objects(Bucket=default_bucket)[
              "Contents"
          ]
      ]
      assert any(
          model_name in object_name for object_name in object_names
      )
      assert job_name in [
          transform_job["TransformJobName"]
          for transform_job in sagemaker_client.list_transform_jobs()[
              "TransformJobSummaries"
          ]
      ]
  
imports: import os
  import time
  from collections import namedtuple
  from functools import wraps
  from unittest import mock
  import boto3
  import botocore
  import numpy
  import pytest
  from click.testing import CliRunner
  from moto.core import DEFAULT_ACCOUNT_ID
  from sklearn.linear_model import LogisticRegression
  import mlflow
  import mlflow.pyfunc
  import mlflow.sagemaker
  import mlflow.sagemaker.cli
  import mlflow.sklearn
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model
  from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, INVALID_PARAMETER_VALUE, RESOURCE_DOES_NOT_EXIST, ErrorCode
  from mlflow.store.artifact.s3_artifact_repo import S3ArtifactRepository
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from tests.helper_functions import set_boto_credentials
  from tests.sagemaker.mock import TransformJob, TransformJobOperation, mock_sagemaker
label_desc: Use image node_id 11 for calling a global function with example usage: test_deploy_creates_sagemaker_transform_job_and_s3_resources_with_expected_names_from_s3(pretrained_model, sagemaker_client) without return types