code: def log_artifacts(local_dir, run_id, artifact_path):
      """
      Log the files within a local directory 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_artifacts(local_dir, artifact_path)
      _logger.info(
          "Logged artifact from local dir %s to artifact_path=%s",
          local_dir,
          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 3 for calling a global function with example usage: log_artifacts(local_dir, run_id, artifact_path) without return types