class_docstr: Wrapper around the `Ax platform <https://ax.dev/>`_ for multi-objectives
  optimization and constraints.
  
  Parameters
  ----------
  space: `orion.algo.space.Space`
      Optimisation space with priors for each dimension.
  seed: None, int or sequence of int, optional
      random seed for reproducibility. Works only for Sobol quasi-random
      generator and for BoTorch-powered models. For the latter models, the
      trials generated from the same optimization setup with the same seed,
      will be mostly similar, but the exact parameter values may still vary
      and trials latter in the optimizations will diverge more and more. This
      is because a degree of randomness is essential for high performance of
      the Bayesian optimization models and is not controlled by the seed.
  
      .. note:: In multi-threaded environments, the random seed is
         thread-safe, but does not actually guarantee reproducibility. Whether
         the outcomes will be exactly the same for two same operations that
         use the random seed, depends on whether the threads modify the random
         state in the same order across the two operations.  Default: ``None``
  n_initial_trials: int, optional
      Specific number of initialization trials.
      Initialization trials are generated quasi-randomly using Sobol.
  extra_objectives: sequence of str, optional
      List of metrics' name which are also objectives to minimize.
      threshold: The bound in the objective's threshold constraint.
  
      .. note:: Orion expects the `extra_objectives` results to be stored in
         `orion.core.worker.Trial.statistics`
  constraints: sequence of str, optional
      Dict of list of string representation of metrics constraints of form
      ["metric_name >= bound"], like ["m1 <= 3"]
  
      .. note:: Orion expects the `constraints` results to be stored in
         `orion.core.worker.Trial.constraints`
      .. note:: See https://ax.dev/docs/core.html#optimization-config for more
         details about how Ax expects its outcome constraints
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docstr: Reset the state of the algorithm based on the given state_dict
  
  Parameters
  ----------
  state_dict: dict
      Dictionary representing state of an algorithm
docstr_tok: ["Reset","the","state","of","the","algorithm","based","on","the","given","state_dict","Parameters","--","--","--","--","--","state_dict",":","dict","Dictionary","representing","state","of","an","algorithm"]
code: def set_state(self, state_dict):
      """Reset the state of the algorithm based on the given state_dict
  
      Parameters
      ----------
      state_dict: dict
          Dictionary representing state of an algorithm
      """
      super().set_state(copy.deepcopy(state_dict))
      self._client_state = copy.deepcopy(
          state_dict.get("_client_state")
      )
      self._trials_map = copy.deepcopy(state_dict.get("_trials_map"))
  
imports: import contextlib
  import copy
  from typing import List, Optional
  from orion.algo.base import BaseAlgorithm
  from orion.algo.space import Fidelity, Space
  from orion.core.utils import format_trials
  from orion.core.utils.flatten import flatten
  from orion.core.utils.module_import import ImportOptional
  from orion.core.worker.transformer import TransformedDimension
label_desc: Use image node_id 4 for calling the AxOptimizer obj's underlying member method code with example usage: obj.set_state(state_dict) without return types