class_docstr: Handles artifacts associated with a model version in the model registry via URIs of the form:
    - `models:/<model_name>/<model_version>`
    - `models:/<model_name>/<stage>`  (refers to the latest model version in the given stage)
    - `models:/<model_name>/latest` (refers to the latest of all model versions)
  It is a light wrapper that resolves the artifact path to an absolute URI then instantiates
  and uses the artifact repository for that URI.
docstr: Log the files in the specified local directory as artifacts, optionally taking
  an ``artifact_path`` to place them in within the run's artifacts.
  
  :param local_dir: Directory of local artifacts to log
  :param artifact_path: Directory within the run's artifact directory in which to log the
                        artifacts
docstr_tok: ["Log","the","files","in","the","specified","local","directory","as","artifacts",",","optionally","taking","an","``","artifact_path","``","to","place","them","in","within","the","run","'s","artifacts",".",":","param","local_dir",":","Directory","of","local","artifacts","to","log",":","param","artifact_path",":","Directory","within","the","run","'s","artifact","directory","in","which","to","log","the","artifacts"]
code: def log_artifacts(self, local_dir, artifact_path=None):
      """
      Log the files in the specified local directory as artifacts, optionally taking
      an ``artifact_path`` to place them in within the run's artifacts.
  
      :param local_dir: Directory of local artifacts to log
      :param artifact_path: Directory within the run's artifact directory in which to log the
                            artifacts
      """
      raise ValueError(
          "log_artifacts is not supported for models:/ URIs. Use register_model instead."
      )
  
imports: import logging
  import os
  import urllib.parse
  import mlflow
  from mlflow.exceptions import MlflowException
  from mlflow.store.artifact.artifact_repo import ArtifactRepository
  from mlflow.store.artifact.databricks_models_artifact_repo import DatabricksModelsArtifactRepository
  from mlflow.store.artifact.unity_catalog_models_artifact_repo import UnityCatalogModelsArtifactRepository
  from mlflow.store.artifact.utils.models import get_model_name_and_version, is_using_databricks_registry
  from mlflow.utils.file_utils import write_yaml
  from mlflow.utils.uri import add_databricks_profile_info_to_artifact_uri, get_databricks_profile_uri_from_artifact_uri, is_databricks_unity_catalog_uri
label_desc: Use image node_id 7 for calling the ModelsArtifactRepository obj's underlying member method code with example usage: obj.log_artifacts(local_dir, artifact_path) without return types