code: def deploy_transform_job(
      job_name,
      model_uri,
      input_data_type,
      input_uri,
      content_type,
      output_path,
      compression_type,
      split_type,
      accept,
      assemble_with,
      input_filter,
      output_filter,
      join_resource,
      execution_role_arn,
      bucket,
      image_url,
      region_name,
      instance_type,
      instance_count,
      vpc_config,
      flavor,
      archive,
      asynchronous,
      timeout,
  ):
      """
      Deploy model on Sagemaker as a batch transform job. Current active AWS account needs to have
      correct permissions setup.
  
      By default, unless the ``--async`` flag is specified, this command will block until
      either the batch transform job completes (definitively succeeds or fails) or the specified
      timeout elapses.
      """
      if vpc_config is not None:
          with open(vpc_config) as f:
              vpc_config = json.load(f)
  
      mlflow.sagemaker.deploy_transform_job(
          job_name=job_name,
          model_uri=model_uri,
          s3_input_data_type=input_data_type,
          s3_input_uri=input_uri,
          content_type=content_type,
          s3_output_path=output_path,
          compression_type=compression_type,
          split_type=split_type,
          accept=accept,
          assemble_with=assemble_with,
          input_filter=input_filter,
          output_filter=output_filter,
          join_resource=join_resource,
          execution_role_arn=execution_role_arn,
          bucket=bucket,
          image_url=image_url,
          region_name=region_name,
          instance_type=instance_type,
          instance_count=instance_count,
          vpc_config=vpc_config,
          flavor=flavor,
          archive=archive,
          synchronous=(not asynchronous),
          timeout_seconds=timeout,
      )
  
file_path: mlflow/mlflow/sagemaker/cli.py
imports: import json
  import os
  import click
  import mlflow
  import mlflow.models.docker_utils
  import mlflow.sagemaker
  from mlflow.sagemaker import DEFAULT_IMAGE_NAME
  from mlflow.utils import cli_args
  from mlflow.utils import env_manager
label_desc: Use image node_id 2 for calling a global function with example usage: deploy_transform_job(job_name, model_uri, input_data_type, input_uri, content_type, output_path, compression_type, split_type, accept, assemble_with, input_filter, output_filter, join_resource, execution_role_arn, bucket, image_url, region_name, instance_type, instance_count, vpc_config, flavor, archive, asynchronous, timeout) without return types