class_docstr: Abstract class that defines API interfaces for storing Model Registry metadata.
class_docstr_tok: ["Abstract","class","that","defines","API","interfaces","for","storing","Model","Registry","metadata","."]
docstr: Update metadata associated with a model version in backend.
  
  :param name: Registered model name.
  :param version: Registered model version.
  :param description: New model description.
  :return: A single :py:class:`mlflow.entities.model_registry.ModelVersion` object.
docstr_tok: ["Update","metadata","associated","with","a","model","version","in","backend",".",":","param","name",":","Registered","model","name",".",":","param","version",":","Registered","model","version",".",":","param","description",":","New","model","description",".",":","return",":","A","single",":","py",":","class",":","`","mlflow.entities.model_registry.ModelVersion","`","object","."]
code: def update_model_version(self, name, version, description):
      """
      Update metadata associated with a model version in backend.
  
      :param name: Registered model name.
      :param version: Registered model version.
      :param description: New model description.
      :return: A single :py:class:`mlflow.entities.model_registry.ModelVersion` object.
      """
      pass
  
imports: import logging
  from abc import ABCMeta, abstractmethod
  from time import sleep, time
  from mlflow.entities.model_registry import ModelVersionTag
  from mlflow.entities.model_registry.model_version_status import ModelVersionStatus
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import RESOURCE_ALREADY_EXISTS, ErrorCode
  from mlflow.utils.annotations import developer_stable
label_desc: Use image node_id 12 for calling the AbstractStore obj's underlying member method code with example usage: obj.update_model_version(name, version, description) without return types