class_docstr: Proxyless version of differentiable input choice.
  See :class:`ProxylessMixedLayer` for implementation details.
class_docstr_tok: ["Proxyless","version","of","differentiable","input","choice",".","See",":","class",":","`","ProxylessMixedLayer","`","for","implementation","details","."]
docstr: Choose one single input.
code: def forward(self, inputs):
      """Choose one single input."""
      if self._sampled is None:
          raise RuntimeError(
              "resample() needs to be called before fprop."
          )
      result = self.forward_path(self._sampled[0], *inputs)
      return _ProxylessFunction.apply(
          self.forward_path,
          self._sampled[0],
          len(inputs),
          self._softmax,
          self._arch_alpha,
          *inputs,
          result,
      )
  
imports: from __future__ import annotations
  from typing import Any, cast
  import torch
  import torch.nn
  from nni.mutable import Categorical, Mutable, Sample, SampleValidationError, MutableExpression, label_scope, auto_label
  from nni.nas.nn.pytorch import Repeat, recursive_freeze
  from .base import BaseSuperNetModule
  from .differentiable import DifferentiableMixedLayer, DifferentiableMixedInput
  from ._expression_utils import traverse_all_options
label_desc: Use image node_id 2 for calling the ProxylessMixedInput obj's underlying member method code with example usage: obj.forward(inputs) and returns: _ProxylessFunction