class_docstr: Implementation of Repeat in a differentiable supernet.
  Result is a weighted sum of possible prefixes, sliced by possible depths.
  
  If the output is not a single tensor, it will be summed at every independant dimension.
  See :func:`weighted_sum` for details.
class_docstr_tok: ["Implementation","of","Repeat","in","a","differentiable","supernet",".","Result","is","a","weighted","sum","of","possible","prefixes",",","sliced","by","possible","depths",".","If","the","output","is","not","a","single","tensor",",","it","will","be","summed","at","every","independant","dimension",".","See",":","func",":","`","weighted_sum","`","for","details","."]
docstr: Choose argmax for each leaf value choice.
docstr_tok: ["Choose","argmax","for","each","leaf","value","choice","."]
code: def export(self, memo):
      """Choose argmax for each leaf value choice."""
      result = {}
      for name, spec in self.depth_choice.simplify().items():
          if name in memo:
              continue
          chosen_index = int(
              torch.argmax(self._arch_alpha[name]).item()
          )
          result[name] = cast(Categorical, spec).values[chosen_index]
      return result
  
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 DifferentiableMixedRepeat obj's underlying member method code with example usage: obj.export(memo) and returns: result