class_docstr: Mixed layer, in which fprop is decided by a weighted sum of several layers.
  Proposed in `DARTS: Differentiable Architecture Search <https://arxiv.org/abs/1806.09055>`__.
  
  The weight ``alpha`` is usually learnable, and optimized on validation dataset.
  
  Differentiable sampling layer requires all operators returning the same shape for one input,
  as all outputs will be weighted summed to get the final output.
  
  Parameters
  ----------
  paths : list[tuple[str, nn.Module]]
      Layers to choose from. Each is a tuple of name, and its module.
  alpha : Tensor
      Tensor that stores the "learnable" weights.
  softmax : nn.Module
      Customizable softmax function. Usually ``nn.Softmax(-1)``.
  label : str
      Name of the choice.
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docstr: Iterate over architecture parameters. Not recursive.
docstr_tok: ["Iterate","over","architecture","parameters",".","Not","recursive","."]
code: def arch_parameters(self):
      """Iterate over architecture parameters. Not recursive."""
      for name, p in self.named_parameters():
          if any(
              name == par_name
              for par_name in self._arch_parameter_names
          ):
              yield p
  
imports: from __future__ import annotations
  import functools
  import logging
  import warnings
  from typing import Any, Dict, Sequence, List, Tuple, cast
  import torch
  import torch.nn
  import torch.nn.functional
  from nni.mutable import MutableExpression, Mutable, Categorical
  from nni.nas.nn.pytorch import LayerChoice, InputChoice, Repeat
  from nni.nas.nn.pytorch.cell import preprocess_cell_inputs
  from .base import BaseSuperNetModule
  from .operation import MixedOperation, MixedOperationSamplingPolicy
  from .sampling import PathSamplingCell
  from ._expression_utils import traverse_all_options, weighted_sum
label_desc: Use image node_id 8 for calling the DifferentiableMixedLayer obj's underlying member method code with example usage: obj.arch_parameters() without return types