code: def log_artifact(local_file, run_id, artifact_path):
      """
      Log a local file as an artifact of a run, optionally within a run-specific
      artifact path. Run artifacts can be organized into directories, so you can
      place the artifact in a directory this way.
      """
      store = _get_store()
      artifact_uri = store.get_run(run_id).info.artifact_uri
      artifact_repo = get_artifact_repository(artifact_uri)
      artifact_repo.log_artifact(local_file, artifact_path)
      _logger.info(
          "Logged artifact from local file %s to artifact_path=%s",
          local_file,
          artifact_path,
      )
  
file_path: mlflow/mlflow/store/artifact/cli.py
imports: import logging
  import click
  from mlflow.artifacts import download_artifacts
  from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository
  from mlflow.tracking import _get_store
  from mlflow.utils.proto_json_utils import message_to_json
label_desc: Use image node_id 2 for calling a global function with example usage: log_artifact(local_file, run_id, artifact_path) without return types