code: def _random_shift(dm, random_state=None):
      """Random shifting of a vector.
  
      Randomization of the quasi-MC samples can be achieved in the easiest manner
      by random shift (or the Cranley-Patterson rotation).
  
      References
      -----------
      .. [1] C. Lemieux, "Monte Carlo and Quasi-Monte Carlo Sampling," Springer
         Series in Statistics 692, Springer Science+Business Media, New York,
         2009
  
      Parameters
      ----------
      dm : array, shape(n, d)
          Input matrix.
      random_state : int, RandomState instance, or None (default)
          Set random state to something other than None for reproducible
          results.
  
      Returns
      -------
      dm :  array, shape(n, d)
          Randomized Sobol' design matrix.
  
      """
      rng = check_random_state(random_state)
      # Generate random shift matrix from uniform distribution
      shift = np.repeat(rng.rand(1, dm.shape[1]), dm.shape[0], axis=0)
      # Return the shifted Sobol' design
      return (dm + shift) % 1
  
file_path: scikit-optimize/skopt/sampler/sobol.py
imports: from __future__ import division
  import warnings
  import numpy
  from .base import InitialPointGenerator
  from ..space import Space
  from sklearn.utils import check_random_state
label_desc: Use image node_id 3 for calling a global function with example usage: _random_shift(dm, random_state) and returns: unknown