class_docstr: Client for a remote model registry server accessed via REST API calls
  
  :param get_host_creds: Method to be invoked prior to every REST request to get the
    :py:class:`mlflow.rest_utils.MlflowHostCreds` for the request. Note that this
    is a function so that we can obtain fresh credentials in the case of expiry.
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docstr: Create a new model version from given source and run ID.
  
  :param name: Registered model name.
  :param source: URI indicating the location of the model artifacts.
  :param run_id: Run ID from MLflow tracking server that generated the model.
  :param tags: A list of :py:class:`mlflow.entities.model_registry.ModelVersionTag`
               instances associated with this model version.
  :param run_link: Link to the run from an MLflow tracking server that generated this model.
  :param description: Description of the version.
  :return: A single object of :py:class:`mlflow.entities.model_registry.ModelVersion`
           created in the backend.
docstr_tok: ["Create","a","new","model","version","from","given","source","and","run","ID",".",":","param","name",":","Registered","model","name",".",":","param","source",":","URI","indicating","the","location","of","the","model","artifacts",".",":","param","run_id",":","Run","ID","from","MLflow","tracking","server","that","generated","the","model",".",":","param","tags",":","A","list","of",":","py",":","class",":","`","mlflow.entities.model_registry.ModelVersionTag","`","instances","associated","with","this","model","version",".",":","param","run_link",":","Link","to","the","run","from","an","MLflow","tracking","server","that","generated","this","model",".",":","param","description",":","Description","of","the","version",".",":","return",":","A","single","object","of",":","py",":","class",":","`","mlflow.entities.model_registry.ModelVersion","`","created","in","the","backend","."]
code: def create_model_version(
      self,
      name,
      source,
      run_id=None,
      tags=None,
      run_link=None,
      description=None,
      local_model_path=None,
  ):
      """
      Create a new model version from given source and run ID.
  
      :param name: Registered model name.
      :param source: URI indicating the location of the model artifacts.
      :param run_id: Run ID from MLflow tracking server that generated the model.
      :param tags: A list of :py:class:`mlflow.entities.model_registry.ModelVersionTag`
                   instances associated with this model version.
      :param run_link: Link to the run from an MLflow tracking server that generated this model.
      :param description: Description of the version.
      :return: A single object of :py:class:`mlflow.entities.model_registry.ModelVersion`
               created in the backend.
      """
      proto_tags = [tag.to_proto() for tag in tags or []]
      req_body = message_to_json(
          CreateModelVersion(
              name=name,
              source=source,
              run_id=run_id,
              run_link=run_link,
              tags=proto_tags,
              description=description,
          )
      )
      response_proto = self._call_endpoint(CreateModelVersion, req_body)
      return ModelVersion.from_proto(response_proto.model_version)
  
imports: import logging
  from mlflow.entities.model_registry import ModelVersion, RegisteredModel
  from mlflow.protos.model_registry_pb2 import CreateModelVersion, CreateRegisteredModel, DeleteModelVersion, DeleteModelVersionTag, DeleteRegisteredModel, DeleteRegisteredModelAlias, DeleteRegisteredModelTag, GetLatestVersions, GetModelVersion, GetModelVersionByAlias, GetModelVersionDownloadUri, GetRegisteredModel, ModelRegistryService, RenameRegisteredModel, SearchModelVersions, SearchRegisteredModels, SetModelVersionTag, SetRegisteredModelAlias, SetRegisteredModelTag, TransitionModelVersionStage, UpdateModelVersion, UpdateRegisteredModel
  from mlflow.store.entities.paged_list import PagedList
  from mlflow.store.model_registry.base_rest_store import BaseRestStore
  from mlflow.utils.proto_json_utils import message_to_json
  from mlflow.utils.rest_utils import _REST_API_PATH_PREFIX, extract_all_api_info_for_service, extract_api_info_for_service
label_desc: Use image node_id 13 for calling the RestStore obj's underlying member method code with example usage: obj.create_model_version(name, source, run_id, tags, run_link, description, local_model_path) and returns: ModelVersion