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.
code: def __init__(self, artifact_uri):
      from mlflow.store.artifact.artifact_repository_registry import (
          get_artifact_repository,
      )
  
      super().__init__(artifact_uri)
      registry_uri = mlflow.get_registry_uri()
      if is_databricks_unity_catalog_uri(uri=registry_uri):
          self.repo = UnityCatalogModelsArtifactRepository(
              artifact_uri=artifact_uri, registry_uri=registry_uri
          )
          self.model_name = self.repo.model_name
          self.model_version = self.repo.model_version
      elif is_using_databricks_registry(artifact_uri):
          # Use the DatabricksModelsArtifactRepository if a databricks profile is being used.
          self.repo = DatabricksModelsArtifactRepository(artifact_uri)
          self.model_name = self.repo.model_name
          self.model_version = self.repo.model_version
      else:
          (
              self.model_name,
              self.model_version,
              underlying_uri,
          ) = ModelsArtifactRepository._get_model_uri_infos(
              artifact_uri
          )
          self.repo = get_artifact_repository(underlying_uri)
  
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 1 to create a new ModelsArtifactRepository object from inherited base classes: ArtifactRepository with example: obj = ModelsArtifactRepository(artifact_uri)