class_docstr: Implements the path sampling in mixed operation.
  
  One mixed operation can have multiple value choices in its arguments.
  Each value choice can be further decomposed into "leaf value choices".
  We sample the leaf nodes, and composits them into the values on arguments.
class_docstr_tok: ["Implements","the","path","sampling","in","mixed","operation",".","One","mixed","operation","can","have","multiple","value","choices","in","its","arguments",".","Each","value","choice","can","be","further","decomposed","into","``","leaf","value","choices","''",".","We","sample","the","leaf","nodes",",","and","composits","them","into","the","values","on","arguments","."]
code: def forward_argument(
      self, operation: MixedOperation, name: str
  ) -> Any:
      # NOTE: we don't support sampling a list here.
      if self._sampled is None:
          raise ValueError(
              "Need to call resample() before running forward"
          )
      if name in operation.mutable_arguments:
          return self._sampled[name]
      return operation.init_arguments[name]
  
imports: from __future__ import annotations
  import copy
  import functools
  import random
  from typing import Any, List, Dict, Sequence, cast
  import torch
  import torch.nn
  from nni.mutable import MutableExpression, label_scope, Mutable, Categorical, CategoricalMultiple
  from nni.nas.nn.pytorch import LayerChoice, InputChoice, Repeat, Cell
  from nni.nas.nn.pytorch.cell import CellOpFactory, create_cell_op_candidates, preprocess_cell_inputs
  from .base import BaseSuperNetModule
  from ._expression_utils import weighted_sum
  from .operation import MixedOperationSamplingPolicy, MixedOperation
label_desc: Use image node_id 4 for calling the MixedOpPathSamplingPolicy obj's underlying member method code with example usage: obj.forward_argument(operation, name) and returns: operation, self