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 tunable elements of this algorithm in a dictionary form
  appropriate for saving.
docstr_tok: ["Return","tunable","elements","of","this","algorithm","in","a","dictionary","form","appropriate","for","saving","."]
code: def configuration(self):
      """Return tunable elements of this algorithm in a dictionary form
      appropriate for saving.
  
      """
      # NOTE: Override parent method to ignore `seed`
      return {
          self.__class__.__name__.lower(): {"n_values": self.n_values}
      }
  
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 7 for calling the GridSearch obj's underlying member method code with example usage: obj.configuration() and returns: dict