class_docstr: An algorithm that samples randomly from the problem's space.
  
  Parameters
  ----------
  space: `orion.algo.space.Space`
      Optimisation space with priors for each dimension.
  seed: None, int or sequence of int
      Seed for the random number generator used to sample new trials.
      Default: ``None``
docstr: Suggest a `num` of new sets of parameters.
  
  Randomly draw samples from the search space and return them.
  
  Parameters
  ----------
  num: int
      Number of trials to suggest.
  
  Returns
  -------
  List of unique trials suggested.
code: def suggest(self, num):
      """Suggest a `num` of new sets of parameters.
  
      Randomly draw samples from the search space and return them.
  
      Parameters
      ----------
      num: int
          Number of trials to suggest.
  
      Returns
      -------
      List of unique trials suggested.
      """
      trials = []
      while len(trials) < num and not self.is_done:
          # NOTE: space.sample() uses (and modifies) the random state here.
          seed = tuple(self.rng.randint(0, 1000000, size=3))
          new_trial = self.space.sample(1, seed=seed)[0]
          if not self.has_suggested(new_trial):
              self.register(new_trial)
              trials.append(new_trial)
  
      return trials
  
file_path: orion/src/orion/algo/random/random.py
imports: from __future__ import annotations
  from typing import Sequence
  import numpy
  from orion.algo.base import BaseAlgorithm
  from orion.algo.space import Space
label_desc: Use image node_id 5 for calling the Random obj's underlying member method code with example usage: obj.suggest(num) and returns: trials