class_docstr: Client of an MLflow Model Registry Server that creates and manages registered
  models and model versions.
docstr: :param registry_uri: Address of local or remote model registry server.
  :param tracking_uri: Address of local or remote tracking server.
docstr_tok: [":","param","registry_uri",":","Address","of","local","or","remote","model","registry","server",".",":","param","tracking_uri",":","Address","of","local","or","remote","tracking","server","."]
code: def __init__(self, registry_uri, tracking_uri):
      """
      :param registry_uri: Address of local or remote model registry server.
      :param tracking_uri: Address of local or remote tracking server.
      """
      self.registry_uri = registry_uri
      self.tracking_uri = tracking_uri
      # NB: Fetch the tracking store (`self.store`) upon client initialization to ensure that
      # the tracking URI is valid and the store can be properly resolved. We define `store` as a
      # property method to ensure that the client is serializable, even if the store is not
      self.store
  
imports: import logging
  from mlflow.entities.model_registry import ModelVersionTag, RegisteredModelTag
  from mlflow.exceptions import MlflowException
  from mlflow.store.model_registry import SEARCH_MODEL_VERSION_MAX_RESULTS_DEFAULT, SEARCH_REGISTERED_MODEL_MAX_RESULTS_DEFAULT
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS, utils
  from mlflow.utils.arguments_utils import _get_arg_names
label_desc: Use image node_id 1 to create a new ModelRegistryClient object  with example: obj = ModelRegistryClient(registry_uri, tracking_uri)