class_docstr: Search of optimal performance on selected objective function
  over combinations of randomly selected hyperparameter values
  within specified bounds for specified number of selection iterations.
docstr: Generate the grid of hyperparameter value combinations
code: def generate_grid(self):
      """Generate the grid of hyperparameter value combinations"""
  
      options = dict(self.options)
      params = {}
  
      # Remove 'p' to hold as a constant in the paramater combinations
      p = options.pop("p")
      params["p"] = [p for _ in xrange(self.n_selection_iters)]
  
      # Assign generators based on parameter type
      param_generators = {
          "c1": np.random.uniform,
          "c2": np.random.uniform,
          "w": np.random.uniform,
          "k": np.random.randint,
      }
  
      # Generate random values for hyperparameters 'c1', 'c2', 'w', and 'k'
      for idx, bounds in options.items():
          params[idx] = param_generators[idx](
              *bounds, size=self.n_selection_iters
          )
  
      # Return list of dicts of hyperparameter combinations
      return [
          {
              "c1": params["c1"][i],
              "c2": params["c2"][i],
              "w": params["w"][i],
              "k": params["k"][i],
              "p": params["p"][i],
          }
          for i in xrange(self.n_selection_iters)
      ]
  
file_path: pyswarms/pyswarms/utils/search/random_search.py
imports: from __future__ import absolute_import, print_function, with_statement
  import numpy
  from past.builtins import xrange
  from pyswarms.utils.search.base_search import SearchBase
label_desc: Use image node_id 3 for calling the RandomSearch obj's underlying member method code with example usage: obj.generate_grid() and returns: unknown