class_docstr: The Tree-structured Parzen Estimator (TPE) is a sequential model-based optimization (SMBO) approach.
  
  Find the details in
  `Algorithms for Hyper-Parameter Optimization <https://papers.nips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf>`__.
  
  SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements,
  and then subsequently choose new hyperparameters to test based on this model.
  
  Parameters
  ----------
  *args
      Optional positional arguments passed to :class:`~nni.algorithms.hpo.tpe_tuner.TpeTuner`.
  **kwargs
      Optional keyboard arguments passed to :class:`~nni.algorithms.hpo.tpe_tuner.TpeTuner`.
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code: def __init__(self, *args, **kwargs):
      from nni.algorithms.hpo.tpe_tuner import TpeTuner
  
      super().__init__(TpeTuner(*args, **kwargs))
  
imports: from __future__ import annotations
  import logging
  import time
  import threading
  from typing import cast
  import nni
  from nni.nas.execution import ExecutionEngine
  from nni.nas.execution.event import FinalMetricEvent, TrainingEndEvent, ModelEventType
  from nni.nas.space import ExecutableModelSpace, ModelStatus
  from nni.tuner import Tuner
  from nni.typehint import SearchSpace
  from .base import Strategy
label_desc: Use image node_id 1 to create a new TPE object from inherited base classes: HPOTunerStrategy with example: obj = TPE()