class_docstr: Exhaustive search of optimal performance on selected objective function
  over all combinations of specified hyperparameter values.
docstr: Generate the grid of all hyperparameter value combinations
docstr_tok: ["Generate","the","grid","of","all","hyperparameter","value","combinations"]
code: def generate_grid(self):
      """Generate the grid of all hyperparameter value combinations"""
  
      # Extract keys and values from options dictionary
      params = self.options.keys()
      items = [
          x if type(x) == list else [x]
          for x in list(zip(*self.options.items()))[1]
      ]
  
      # Create list of cartesian products of hyperparameter values
      # from options
      list_of_products = list(itertools.product(*items))
  
      # Return list of dicts for all hyperparameter value combinations
      return [dict(zip(*[params, list(x)])) for x in list_of_products]
  
imports: from __future__ import absolute_import, print_function, with_statement
  import itertools
  from pyswarms.utils.search.base_search import SearchBase
label_desc: Use image node_id 2 for calling the GridSearch obj's underlying member method code with example usage: obj.generate_grid() and returns: unknown