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 input indices.
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","input","indices","."]
code: def forward(self, input_tensors):
      if self._sampled is None:
          raise RuntimeError(
              "At least one path needs to be sampled before fprop."
          )
      if len(input_tensors) != self.n_candidates:
          raise ValueError(
              f"Expect {self.n_candidates} input tensors, found {len(input_tensors)}."
          )
      sampled = (
          [self._sampled]
          if not isinstance(self._sampled, list)
          else self._sampled
      )
      res = [input_tensors[samp] for samp in sampled]
      return self._reduction(res, sampled)
  
imports: from __future__ import annotations
  import copy
  import functools
  import random
  from typing import Any, List, Dict, Sequence, cast
  import torch
  import torch.nn
  from nni.mutable import MutableExpression, label_scope, Mutable, Categorical, CategoricalMultiple
  from nni.nas.nn.pytorch import LayerChoice, InputChoice, Repeat, Cell
  from nni.nas.nn.pytorch.cell import CellOpFactory, create_cell_op_candidates, preprocess_cell_inputs
  from .base import BaseSuperNetModule
  from ._expression_utils import weighted_sum
  from .operation import MixedOperationSamplingPolicy, MixedOperation
label_desc: Use image node_id 6 for calling the PathSamplingInput obj's underlying member method code with example usage: obj.forward(input_tensors) and returns: self