class_docstr: A Wrapper to shield user from Optuna specific configurations and API      Later may support other HPO search engines.
class_docstr_tok: ["A","Wrapper","to","shield","user","from","Optuna","specific","configurations","and","API","Later","may","support","other","HPO","search","engines","."]
docstr: Create a pruner by type.
docstr_tok: ["Create","a","pruner","by","type","."]
code: def create_pruner(pruner_type, kwargs):
      """Create a pruner by type."""
      pruner_class = OptunaBackend.pruner_map.get(pruner_type)
      return pruner_class(**kwargs)
  
file_path: BigDL/python/nano/src/bigdl/nano/deps/automl/optuna_backend.py
imports: from bigdl.nano.automl.hpo.backend import PrunerType, SamplerType
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.automl.hpo.space import SimpleSpace, NestedSpace, AutoObject
  from bigdl.nano.automl.hpo.space import AutoObject, Space, SingleParam, _get_hp_prefix
  import optuna
label_desc: Use image node_id 9 for calling the OptunaBackend obj's underlying member method code with example usage: obj.create_pruner(pruner_type, kwargs) and returns: pruner_class