class_docstr: Uniform randomly sampled hyperparameter object.
class_docstr_tok: ["Uniform","randomly","sampled","hyperparameter","object","."]
docstr: Return uniformly sampled value based on object sampling definitions.
  
  :return: {self.name: random value [self.min_value, self.max_value)}
docstr_tok: ["Return","uniformly","sampled","value","based","on","object","sampling","definitions",".",":","return",":","{","self.name",":","random","value","[","self.min_value",",","self.max_value",")","}"]
code: def get_value(self):
      # type: () -> Mapping[str, Any]
      """
      Return uniformly sampled value based on object sampling definitions.
  
      :return: {self.name: random value [self.min_value, self.max_value)}
      """
      if not self.step_size:
          return {
              self.name: self._random.uniform(
                  self.min_value, self.max_value
              )
          }
      steps = (self.max_value - self.min_value) / self.step_size
      return {
          self.name: self.min_value
          + (
              self._random.randrange(start=0, stop=round(steps))
              * self.step_size
          )
      }
  
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 2 for calling the UniformParameterRange obj's underlying member method code with example usage: obj.get_value() and returns: dict, dict