text: Title: Support hierarchical hyperparameter combinations
  Body: /kind feature
  
  **Describe the solution you'd like**
  
  I'd like to be able to do hyperparameter tuning over a hierarchical hyperparameter space. Specifically, I'd like to be able to do something like this Optuna example:
  
  https://github.com/optuna/optuna-examples/blob/main/sklearn/sklearn_simple.py#L24-L32
  
  Where first a particular classifier is chosen, and then relevant hyperparameters for the chosen classifier are selected. This might even go on further, with particular parameters for SGD vs Adam. 
  
  **Anything else you would like to add:**
  
  Although Katib can use Optuna for hyperparameter suggestions, I didn't see a way get Katib to use Optuna features like the linked example.
  
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