code: def transform_args(
      pipeline_session,
      sagemaker_session,
      pipeline_name,
      step_model_create,
  ):
      transform_input = pipeline_session.upload_data(
          path=os.path.join(DATA_DIR, "xgboost_abalone", "abalone"),
          key_prefix="integ-test-data/xgboost_abalone/abalone",
      )
  
      transform_output = f"s3://{sagemaker_session.default_bucket()}/{pipeline_name}Transform"
      transformer = Transformer(
          model_name=step_model_create.properties.ModelName,
          strategy="SingleRecord",
          instance_type="ml.m5.xlarge",
          instance_count=1,
          output_path=transform_output,
          sagemaker_session=pipeline_session,
      )
      transform_args = transformer.transform(
          data=transform_input,
          content_type="text/libsvm",
      )
      return transform_args
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import pytest
  from tests.integ.sagemaker.workflow.helpers import wait_pipeline_execution
  from sagemaker.clarify import BiasConfig, DataConfig
  from sagemaker.s3 import S3Uploader, S3Downloader
  from sagemaker.workflow.clarify_check_step import DataBiasCheckConfig
  from sagemaker.workflow.monitor_batch_transform_step import MonitorBatchTransformStep
  from sagemaker.workflow.model_step import ModelStep
  from sagemaker.workflow.parameters import ParameterString
  from tests.integ import DATA_DIR
  from sagemaker.model_monitor import DatasetFormat, Statistics, Constraints
  from sagemaker.xgboost import XGBoostModel
  from sagemaker.transformer import Transformer
  from sagemaker import get_execution_role, utils
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.check_job_config import CheckJobConfig
  from tests.integ.retry import retries
  from sagemaker.workflow.quality_check_step import DataQualityCheckConfig, ModelQualityCheckConfig
label_desc: Use image node_id 18 for calling a global function with example usage: transform_args(pipeline_session, sagemaker_session, pipeline_name, step_model_create) and returns: transform_args