class_docstr: Holds the param-space coordinates (X) and target values (Y)
  
  Parameters
  ----------
  pbounds : dict
      Dictionary with parameters names and legal values.
  
  random_state : int, RandomState, or None
      optionally specify a seed for a random number generator, by default None.
class_docstr_tok: ["Holds","the","param-space","coordinates","(","X",")","and","target","values","(","Y",")","Parameters","--","--","--","--","--","pbounds",":","dict","Dictionary","with","parameters","names","and","legal","values",".","random_state",":","int",",","RandomState",",","or","None","optionally","specify","a","seed","for","a","random","number","generator",",","by","default","None","."]
docstr: Get all target values found and corresponding parameters.
  
  Returns
  -------
  list
      a list of target values and their corresponding parameters
docstr_tok: ["Get","all","target","values","found","and","corresponding","parameters",".","Returns","--","--","--","-","list","a","list","of","target","values","and","their","corresponding","parameters"]
code: def res(self):
      """
      Get all target values found and corresponding parameters.
  
      Returns
      -------
      list
          a list of target values and their corresponding parameters
      """
      params = [dict(zip(self.keys, p)) for p in self.params]
  
      return [
          {"target": target, "params": param}
          for target, param in zip(self.target, params)
      ]
  
file_path: nni/nni/algorithms/hpo/gp_tuner/target_space.py
imports: import numpy
  import nni.parameter_expressions
label_desc: Use image node_id 14 for calling the TargetSpace obj's underlying member method code with example usage: obj.res() and returns: unknown