class_docstr: A data-driven algorithm generator. It optionally takes the following inputs:
  
      - training dataset properties (such as data statistics from ``monai.auto3dseg.analyzer``),
      - previous algorithm's scores measuring the model quality,
      - computational budgets,
  
  and generates ``Algo`` instances. The generated algos are to be trained with the training datasets::
  
                                scores
                      +------------------------+
                      |   +---------+          |
      +-----------+   +-->|         |    +-----+----+
      | Dataset,  |       | AlgoGen |--->|   Algo   |
      | summaries |------>|         |    +----------+
      +-----+-----+       +---------+          ^
            |                                  |
            +----------------------------------+
  
  This class also maintains a history of previously generated Algo and their corresponding validation scores.
  The Algo generation process may be stochastic (using ``Randomizable.R`` as the source random state).
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docstr: Launch the Algos. This is useful for light-weight Algos where there's no need to distribute the training jobs.
  
  If the generated Algos require significant scheduling of parallel executions, a job scheduler/controller
  implemented separately is preferred to run them. In this case the controller should also report back the
  scores and the algo history, so that the future ``AlgoGen.generate`` can leverage the information.
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code: def run_algo(self, *args, **kwargs):
      """
      Launch the Algos. This is useful for light-weight Algos where there's no need to distribute the training jobs.
  
      If the generated Algos require significant scheduling of parallel executions, a job scheduler/controller
      implemented separately is preferred to run them. In this case the controller should also report back the
      scores and the algo history, so that the future ``AlgoGen.generate`` can leverage the information.
      """
      pass
  
file_path: MONAI/monai/auto3dseg/algo_gen.py
imports: from __future__ import annotations
  from monai.config import PathLike
  from monai.transforms import Randomizable
label_desc: Use image node_id 8 for calling the AlgoGen obj's underlying member method code with example usage: obj.run_algo() without return types