class_docstr: Mixed input. Take a list of tensor as input, select some of them and return the sum.
  
  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","input",".","Take","a","list","of","tensor","as","input",",","select","some","of","them","and","return","the","sum",".","Attributes","--","--","--","--","--","sampled",":","int","or","list","of","int","Sampled","module","indices",".","mask",":","tensor","A","multi-hot","bool","1D-tensor","representing","the","sampled","mask","."]
code: def forward(self, input_tensors):
      if isinstance(self.sampled, list):
          return sum(
              [input_tensors[t] for t in self.sampled]
          )  # pylint: disable=not-an-iterable
      else:
          return input_tensors[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 2 for calling the PathSamplingInputChoice obj's underlying member method code with example usage: obj.forward(input_tensors) and returns: sum, input_tensors