code: def push_model_to_sagemaker(
      model_name,
      model_uri,
      execution_role_arn,
      bucket,
      image_url,
      region_name,
      vpc_config,
      flavor,
  ):
      """
      Push an MLflow model to Sagemaker model registry. Current active AWS account needs to have
      correct permissions setup.
      """
      if vpc_config is not None:
          with open(vpc_config) as f:
              vpc_config = json.load(f)
  
      mlflow.sagemaker.push_model_to_sagemaker(
          model_name=model_name,
          model_uri=model_uri,
          execution_role_arn=execution_role_arn,
          bucket=bucket,
          image_url=image_url,
          region_name=region_name,
          vpc_config=vpc_config,
          flavor=flavor,
      )
  
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 4 for calling a global function with example usage: push_model_to_sagemaker(model_name, model_uri, execution_role_arn, bucket, image_url, region_name, vpc_config, flavor) without return types