class_docstr: Proxyless version of differentiable mixed layer.
  It resamples a single-path every time, rather than compute the weighted sum.
  
  Currently the input and output of the candidate layers can only be tensors or tuple of tensors.
  They can't be dict, list or any complex types, or non-tensors (including none).
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code: def forward_path(self, index, *args, **kwargs):
      return self[self.names[index]](*args, **kwargs)
  
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 3 for calling the ProxylessMixedLayer obj's underlying member method code with example usage: obj.forward_path(index) and returns: self