class_docstr: Mixed layer, in which fprop is decided by exactly one inner layer or sum of multiple (sampled) layers.
  If multiple modules are selected, the result will be summed and returned.
  
  Attributes
  ----------
  _sampled : int or list of str
      Sampled module indices.
  label : str
      Name of the choice.
class_docstr_tok: ["Mixed","layer",",","in","which","fprop","is","decided","by","exactly","one","inner","layer","or","sum","of","multiple","(","sampled",")","layers",".","If","multiple","modules","are","selected",",","the","result","will","be","summed","and","returned",".","Attributes","--","--","--","--","--","_sampled",":","int","or","list","of","str","Sampled","module","indices",".","label",":","str","Name","of","the","choice","."]
code: def __init__(
      self, paths: dict[str, nn.Module] | list[nn.Module], label: str
  ):
      super().__init__(paths, label=label)
      self._sampled: list[str] | str | None = None
  
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 1 to create a new PathSamplingLayer object from inherited base classes: LayerChoice, BaseSuperNetModule with example: obj = PathSamplingLayer(paths, label)