class_docstr: Discrete randomly sampled hyperparameter object.
class_docstr_tok: ["Discrete","randomly","sampled","hyperparameter","object","."]
docstr: Uniformly sample values form a list of discrete options.
  
  :param str name: The parameter name. Match the task hyperparameter name.
  :param list values: The list/tuple of valid parameter values to sample from.
docstr_tok: ["Uniformly","sample","values","form","a","list","of","discrete","options",".",":","param","str","name",":","The","parameter","name",".","Match","the","task","hyperparameter","name",".",":","param","list","values",":","The","list\/tuple","of","valid","parameter","values","to","sample","from","."]
code: def __init__(self, name, values=()):
      # type: (str, Sequence[Any]) -> ()
      """
      Uniformly sample values form a list of discrete options.
  
      :param str name: The parameter name. Match the task hyperparameter name.
      :param list values: The list/tuple of valid parameter values to sample from.
      """
      super(DiscreteParameterRange, self).__init__(name=name)
      self.values = values
  
imports: import sys
  from itertools import product
  from random import Random
  from typing import Mapping, Any, Sequence, Optional, Union
label_desc: Use image node_id 1 to create a new DiscreteParameterRange object from inherited base classes: Parameter with example: obj = DiscreteParameterRange(name, values)