class_docstr: Same as the GA optimiser, but the candidates to mutate from are picked randomly.
  This is used in the RAND baseline.
class_docstr_tok: ["Same","as","the","GA","optimiser",",","but","the","candidates","to","mutate","from","are","picked","randomly",".","This","is","used","in","the","RAND","baseline","."]
docstr: Returns a random list of points from the evaluations to mutate from. 
docstr_tok: ["Returns","a","random","list","of","points","from","the","evaluations","to","mutate","from","."]
code: def _get_candidates_to_mutate_from(
      self, num_mutations, num_candidates_to_mutate_from
  ):
      """Returns a random list of points from the evaluations to mutate from."""
      all_prev_eval_points = (
          self.prev_eval_points + self.history.query_points
      )
      candidates_to_mutate_from = choice(
          all_prev_eval_points,
          self.num_candidates_to_mutate_from,
          replace=False,
      )
      return candidates_to_mutate_from, num_mutations
  
imports: from argparse import Namespace
  from numpy.random import choice
  from .blackbox_optimiser import BlackboxOptimiser, blackbox_opt_args
  from ..utils.general_utils import sample_according_to_exp_probs
  from ..utils.option_handler import get_option_specs, load_options
label_desc: Use image node_id 2 for calling the GARandOptimiser obj's underlying member method code with example usage: obj._get_candidates_to_mutate_from(num_mutations, num_candidates_to_mutate_from) and returns: candidates_to_mutate_from,  num_mutations