code: def validate_docker_env(project):
      if not project.name:
          raise ExecutionException(
              "Project name in MLProject must be specified when using docker for image tagging."
          )
      if not project.docker_env.get("image"):
          raise ExecutionException(
              "Project with docker environment must specify the docker image "
              "to use via an 'image' field under the 'docker_env' field."
          )
  
imports: import logging
  import os
  import posixpath
  import shutil
  import subprocess
  import tempfile
  import urllib.parse
  import urllib.request
  import docker
  from mlflow import tracking
  from mlflow.environment_variables import MLFLOW_TRACKING_URI
  from mlflow.exceptions import ExecutionException
  from mlflow.projects.utils import MLFLOW_DOCKER_WORKDIR_PATH
  from mlflow.utils import file_utils, process
  from mlflow.utils.databricks_utils import get_databricks_env_vars
  from mlflow.utils.file_utils import _handle_readonly_on_windows
  from mlflow.utils.git_utils import get_git_commit
  from mlflow.utils.mlflow_tags import MLFLOW_DOCKER_IMAGE_ID, MLFLOW_DOCKER_IMAGE_URI
label_desc: Use image node_id 2 for calling a global function with example usage: validate_docker_env(project) without return types