class_docstr: Mutated module in super-net.
  Usually, the feed-forward of the module itself is undefined.
  It has to be resampled with ``resample()`` so that a specific path is selected.
  (Sometimes, this is not required. For example, differentiable super-net.)
  
  A super-net module usually corresponds to one sample. But two exceptions:
  
  * A module can have multiple parameter spec. For example, a convolution-2d can sample kernel size, channels at the same time.
  * Multiple modules can share one parameter spec. For example, multiple layer choices with the same label.
  
  For value choice compositions, the parameter spec are bounded to the underlying (original) value choices,
  rather than their compositions.
docstr: Export the probability / logits of every choice got chosen.
  
  Parameters
  ----------
  memo : dict[str, Any]
      Use memo to avoid the same label gets exported multiple times.
code: def export_probs(self, memo: dict[str, Any]) -> dict[str, Any]:
      """
      Export the probability / logits of every choice got chosen.
  
      Parameters
      ----------
      memo : dict[str, Any]
          Use memo to avoid the same label gets exported multiple times.
      """
      raise NotImplementedError()
  
file_path: nni/nni/nas/oneshot/pytorch/supermodule/base.py
imports: from __future__ import annotations
  from typing import Any
  import torch.nn
  from nni.nas.nn.pytorch import MutableModule
label_desc: Use image node_id 3 for calling the BaseSuperNetModule obj's underlying member method code with example usage: obj.export_probs(memo) without return types