class_docstr: Wrapper around an MLflow project run (e.g. a subprocess running an entry point
  command or a Databricks job run) and exposing methods for waiting on and cancelling the run.
  This class defines the interface that the MLflow project runner uses to manage the lifecycle
  of runs launched in different environments (e.g. runs launched locally or on Databricks).
  
  ``SubmittedRun`` is not thread-safe. That is, concurrent calls to wait() / cancel()
  from multiple threads may inadvertently kill resources (e.g. local processes) unrelated to the
  run.
  
  NOTE:
  
      Subclasses of ``SubmittedRun`` must expose a ``run_id`` member containing the
      run's MLflow run ID.
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docstr: Wait for the run to finish, returning True if the run succeeded and false otherwise. Note
  that in some cases (e.g. remote execution on Databricks), we may wait until the remote job
  completes rather than until the MLflow run completes.
docstr_tok: ["Wait","for","the","run","to","finish",",","returning","True","if","the","run","succeeded","and","false","otherwise",".","Note","that","in","some","cases","(","e.g",".","remote","execution","on","Databricks",")",",","we","may","wait","until","the","remote","job","completes","rather","than","until","the","MLflow","run","completes","."]
code: def wait(self):
      """
      Wait for the run to finish, returning True if the run succeeded and false otherwise. Note
      that in some cases (e.g. remote execution on Databricks), we may wait until the remote job
      completes rather than until the MLflow run completes.
      """
      pass
  
file_path: mlflow/mlflow/projects/submitted_run.py
imports: import logging
  import os
  import signal
  from abc import abstractmethod
  from mlflow.entities import RunStatus
  from mlflow.utils.annotations import developer_stable
label_desc: Use image node_id 1 for calling the SubmittedRun obj's underlying member method code with example usage: obj.wait() without return types