code: @Override
      public StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest request) {
          request = beforeClientExecution(request);
          return executeStartNotebookInstance(request);
      }
docs: <p>
  Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
  After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to
  <code>InService</code>. A notebook instance's status must be <code>InService</code> before you can connect to
  your Jupyter notebook.
  </p>
  
  @param startNotebookInstanceRequest
  @return Result of the StartNotebookInstance operation returned by the service.
  @throws ResourceLimitExceededException
  You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
  created.
  @sample AmazonSageMaker.StartNotebookInstance
  @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StartNotebookInstance"
  target="_top">AWS API Documentation</a>