class_docstr: Mixed module, in which fprop is decided by exactly one or multiple (sampled) module.
  If multiple module is selected, the result will be sumed and returned.
  
  Attributes
  ----------
  sampled : int or list of int
      Sampled module indices.
  mask : tensor
      A multi-hot bool 1D-tensor representing the sampled mask.
class_docstr_tok: ["Mixed","module",",","in","which","fprop","is","decided","by","exactly","one","or","multiple","(","sampled",")","module",".","If","multiple","module","is","selected",",","the","result","will","be","sumed","and","returned",".","Attributes","--","--","--","--","--","sampled",":","int","or","list","of","int","Sampled","module","indices",".","mask",":","tensor","A","multi-hot","bool","1D-tensor","representing","the","sampled","mask","."]
code: def sampled_choices(self):
      if self.sampled is None:
          return []
      elif isinstance(self.sampled, list):
          return [
              getattr(self, self.op_names[i]) for i in self.sampled
          ]  # pylint: disable=not-an-iterable
      else:
          return [getattr(self, self.op_names[self.sampled])]
  
imports: import logging
  from collections import OrderedDict
  import time
  import numpy
  import torch
  import nni.retiarii.nn.pytorch
  from nni.nas.pytorch.mutables import Mutable, InputChoice, LayerChoice
label_desc: Use image node_id 3 for calling the PathSamplingLayerChoice obj's underlying member method code with example usage: obj.sampled_choices() and returns: list, unknown, list