code: def run(
      self,
      project_uri,
      entry_point,
      params,
      version,
      backend_config,
      tracking_uri,
      experiment_id,
  ):
      """
      Submit an entrypoint. It must return a SubmittedRun object to track the execution
  
      :param project_uri: URI of the project to execute, e.g. a local filesystem path
             or a Git repository URI like https://github.com/mlflow/mlflow-example
      :param entry_point: Entry point to run within the project.
      :param params: Dict of parameters to pass to the entry point
      :param version: For git-based projects, either a commit hash or a branch name.
      :param backend_config: A dictionary, or a path to a JSON file (must end in '.json'), which
                             will be passed as config to the backend. The exact content which
                             should be provided is different for each execution backend and is
                             documented at https://www.mlflow.org/docs/latest/projects.html.
      :param tracking_uri: URI of tracking server against which to log run information related
                           to project execution.
      :param experiment_id: ID of experiment under which to launch the run.
  
      :return: A :py:class:`mlflow.projects.SubmittedRun`. This function is expected to run
               the project asynchronously, i.e. it should trigger project execution and then
               immediately return a `SubmittedRun` to track execution status.
      """
      raise NotImplementedError()
  
imports: from abc import ABCMeta, abstractmethod
  from mlflow.utils.annotations import developer_stable
label_desc: Use image node_id 1 for calling the AbstractBackend obj's underlying member method code with example usage: obj.run(project_uri, entry_point, params, version, backend_config, tracking_uri, experiment_id) without return types