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","."]
docstr: Random choose one path / multiple paths if label is not found in memo.
  If one path is selected, only one integer will be in ``self._sampled``.
  If multiple paths are selected, a list will be in ``self._sampled``.
code: def resample(self, memo):
      """Random choose one path / multiple paths if label is not found in memo.
      If one path is selected, only one integer will be in ``self._sampled``.
      If multiple paths are selected, a list will be in ``self._sampled``.
      """
      if self.label in memo:
          self._sampled = memo[self.label]
      else:
          self._sampled = self.choice.random()
      return {self.label: self._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 2 for calling the PathSamplingInput obj's underlying member method code with example usage: obj.resample(memo) and returns: dict