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 mutate(cls, module, name, memo, mutate_kwargs):
      if type(module) is InputChoice:
          if module.reduction not in ["sum", "mean", "concat"]:
              raise ValueError(
                  "Only input choice of sum/mean/concat reduction is supported."
              )
          if module.n_chosen is None:
              raise ValueError("n_chosen is None is not supported yet.")
          return cls(
              module.n_candidates,
              module.n_chosen,
              module.reduction,
              module.label,
          )
  
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 4 for calling the PathSamplingInput obj's underlying member method code with example usage: obj.mutate(cls, module, name, memo, mutate_kwargs) and returns: cls