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``
code: def __init__(
      self, space: Space, seed: int | Sequence[int] | None = None
  ):
      super().__init__(space)
      self.seed = seed
      self.seed_rng(seed)
  
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 1 to create a new Random object from inherited base classes: BaseAlgorithm with example: obj = Random(space, seed)