class_docstr: Defines a set of plots to produce.
  
  This will create plots for the cartesian product of the parameters in this class.
  See :class:`BenchmarkSuite` if you want a union of parameters.
class_docstr_tok: ["Defines","a","set","of","plots","to","produce",".","This","will","create","plots","for","the","cartesian","product","of","the","parameters","in","this","class",".","See",":","class",":","`","BenchmarkSuite","`","if","you","want","a","union","of","parameters","."]
docstr: Compute ``BenchmarkTask`` objects for the cartesian product of the parameters of this
  object.
docstr_tok: ["Compute","``","BenchmarkTask","``","objects","for","the","cartesian","product","of","the","parameters","of","this","object","."]
code: def get_tasks(self) -> Collection[BenchmarkTask]:
      """
      Compute ``BenchmarkTask`` objects for the cartesian product of the parameters of this
      object.
      """
      result: List[BenchmarkTask] = []
      for dataset in self.datasets:
          dataset_name = dataset.name
          for model in self.models:
              if not model.dataset_req.satisfied(dataset.tags):
                  continue
  
              model_name = model.name
              for do_compile in self.do_compile:
                  for do_optimise in self.do_optimise:
                      result.append(
                          BenchmarkTask(
                              dataset_name,
                              model_name,
                              do_compile,
                              do_optimise,
                              self.do_predict,
                              self.do_posterior,
                              self.repetitions,
                          )
                      )
      return result
  
imports: from dataclasses import dataclass
  from typing import Any, Collection, Dict, List, Optional, Sequence, Set, Tuple
  import pandas
  from benchmark.dataset_api import DatasetFactory
  from benchmark.grouping import GroupingKey, GroupingSpec
  from benchmark.model_api import ModelFactory
  from benchmark.plotter_api import Plotter
  from benchmark.registry import Named, Registry
label_desc: Use image node_id 3 for calling the BenchmarkSet obj's underlying member method code with example usage: obj.get_tasks() and returns: result