code: def run_local(
      target, name, model_uri, flavor=None, config=None
  ):  # pylint: disable=unused-argument
      """Deploys the specified model locally, for testing. This function should be defined
      within the plugin module. Also note that this function has a signature which is very
      similar to :py:meth:`BaseDeploymentClient.create_deployment` since both does logically
      similar operation.
  
      .. Note::
          This function is kept here only for documentation purpose and not implementing the
          actual feature. It should be implemented in the plugin's top level namescope and should
          be callable with ``plugin_module.run_local``
  
      Args:
          target: Which target to use. This information is used to call the appropriate plugin.
          name: Unique name to use for deployment. If another deployment exists with the same
              name, create_deployment will raise a
              :py:class:`mlflow.exceptions.MlflowException`.
          model_uri: URI of model to deploy.
          flavor: (optional) Model flavor to deploy. If unspecified, default flavor is chosen.
          config: (optional) Dict containing updated target-specific config for the deployment.
  
      Returns:
          None
      """
      raise NotImplementedError(
          "This function should be implemented in the deployment plugin. It is "
          "kept here only for documentation purpose and shouldn't be used in "
          "your application"
      )
  
file_path: mlflow/mlflow/deployments/base.py
imports: import abc
  from mlflow.exceptions import MlflowException
  from mlflow.utils.annotations import developer_stable
label_desc: Use image node_id 1 for calling a global function with example usage: run_local(target, name, model_uri, flavor, config) without return types