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: Return a state dict that can be used to reset the state of the algorithm.
docstr_tok: ["Return","a","state","dict","that","can","be","used","to","reset","the","state","of","the","algorithm","."]
code: def state_dict(self) -> dict:
      """Return a state dict that can be used to reset the state of the algorithm."""
      state_dict = super().state_dict
      state_dict["grid"] = self.grid
      state_dict["index"] = self.index
      return state_dict
  
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 3 for calling the GridSearch obj's underlying member method code with example usage: obj.state_dict() and returns: state_dict