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: Return a state dict that can be used to reset the state of the algorithm.
code: def state_dict(self):
      """Return a state dict that can be used to reset the state of the algorithm."""
      _state_dict = super().state_dict
      _state_dict["rng_state"] = self.rng.get_state()
      return _state_dict
  
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 3 for calling the Random obj's underlying member method code with example usage: obj.state_dict() and returns: _state_dict