class_docstr: Implementation of Cell under differentiable context.
  
  Similar to PathSamplingCell, this cell only handles cells of specific kinds (e.g., with loose end).
  
  An architecture parameter is created on each edge of the full-connected graph.
class_docstr_tok: ["Implementation","of","Cell","under","differentiable","context",".","Similar","to","PathSamplingCell",",","this","cell","only","handles","cells","of","specific","kinds","(","e.g.",",","with","loose","end",")",".","An","architecture","parameter","is","created","on","each","edge","of","the","full-connected","graph","."]
docstr: When export probability, we follow the structure in arch alpha.
docstr_tok: ["When","export","probability",",","we","follow","the","structure","in","arch","alpha","."]
code: def export_probs(self, memo):
      """When export probability, we follow the structure in arch alpha."""
      ret = {}
      for name, parameter in self._arch_alpha.items():
          if name in memo:
              continue
          weights = self._softmax(parameter).cpu().tolist()
          ret.update({name: dict(zip(self.op_names, weights))})
      return ret
  
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 3 for calling the DifferentiableMixedCell obj's underlying member method code with example usage: obj.export_probs(memo) and returns: ret