class_docstr: Grid Search algorithm
  
  Parameters
  ----------
  n_values: int or dict
      Number of trials for each dimensions, or dictionary specifying number of trials for each
      dimension independently (name, n_values). For categorical dimensions, n_values will not be
      used, and all categories will be used to build the grid.
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: dict) -> None:
      """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(state_dict)
      self.grid = state_dict["grid"]
      self.index = state_dict["index"]
  
imports: from __future__ import annotations
  import itertools
  import logging
  import numpy
  from orion.algo.base import BaseAlgorithm
  from orion.algo.space import Categorical, Dimension, Fidelity, Integer, Real, Space
  from orion.core.utils import format_trials
label_desc: Use image node_id 4 for calling the GridSearch obj's underlying member method code with example usage: obj.set_state(state_dict) without return types