class_docstr: Exhaustive search of optimal performance on selected objective function
  over all combinations of specified hyperparameter values.
docstr: Initialize the Search
docstr_tok: ["Initialize","the","Search"]
code: def __init__(
      self,
      optimizer,
      n_particles,
      dimensions,
      options,
      objective_func,
      iters,
      bounds=None,
      velocity_clamp=(0, 1),
  ):
      """Initialize the Search"""
  
      # Assign attributes
      super(GridSearch, self).__init__(
          optimizer,
          n_particles,
          dimensions,
          options,
          objective_func,
          iters,
          bounds=bounds,
          velocity_clamp=velocity_clamp,
      )
      # invoke assertions
      self.assertions()
  
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 1 to create a new GridSearch object from inherited base classes: SearchBase with example: obj = GridSearch(optimizer, n_particles, dimensions, options, objective_func, iters, bounds, velocity_clamp)