class_docstr: Search space for numeric hyperparameter that takes continuous values.
  
  Parameters
  ----------
  lower : float
      The lower bound of the search space (minimum possible value of hyperparameter)
  upper : float
      The upper bound of the search space (maximum possible value of hyperparameter)
  default : float (optional)
      Default value tried first during hyperparameter optimization
  log : (True/False)
      Whether to search the values on a logarithmic rather than linear scale.
      This is useful for numeric hyperparameters (such as learning rates) whose search space spans many orders of magnitude.
  
  Examples
  --------
  >>> learning_rate = Real(0.01, 0.1, log=True)
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code: def convert_to_sklearn(self):
      from scipy.stats import loguniform, uniform
  
      if self.log:
          sampler = loguniform(self.lower, self.upper)
      else:
          sampler = uniform(self.lower, self.upper - self.lower)
      return sampler
  
label_desc: Use image node_id 2 for calling the Real obj's underlying member method code with example usage: obj.convert_to_sklearn() and returns: sampler