class_docstr: Domain of a parameter with continuous values within a range of [low, high).
  
  Parameters
  ----------------
  name: str, required
      Name of the parameter.
      
  low: float, default: 0.0
      Lower bound of the searched values (included).
  
  high: float, default: 1.0
      Upper bound of the searched values (excluded).
          
class_docstr_tok: ["Domain","of","a","parameter","with","continuous","values","within","a","range","of","[","low",",","high",")",".","Parameters","--","--","--","--","--","--","--","--","name",":","str",",","required","Name","of","the","parameter",".","low",":","float",",","default",":","0.0","Lower","bound","of","the","searched","values","(","included",")",".","high",":","float",",","default",":","1.0","Upper","bound","of","the","searched","values","(","excluded",")","."]
docstr: Sample a value of parameter used for RandomSearch
docstr_tok: ["Sample","a","value","of","parameter","used","for","RandomSearch"]
code: def _sample(self, rng):
      """Sample a value of parameter used for RandomSearch"""
      return rng.uniform(low=self.low, high=self.high)
  
imports: import numpy
  from itertools import product
  from .models import Recommender
  from .metrics import RatingMetric, RankingMetric
  from .eval_methods import rating_eval, ranking_eval
  from .utils import get_rng
label_desc: Use image node_id 2 for calling the Continuous obj's underlying member method code with example usage: obj._sample(rng) and returns: rng