class_docstr: A shell used to wrap choices as nn.Module for non-one-shot space definition
  You can use ``map_nn`` function
  
  Parameters
  ----------
  name : anything
      the name of module, can be any type
code: def __init__(self, name):
      super().__init__()
      self.str = name
  
imports: from abc import abstractmethod
  import torch.nn
  from nni.nas.pytorch import mutables
  from nni.nas.pytorch.fixed import FixedArchitecture
  import json
  from copy import deepcopy
  import torch
  from ...model import BaseAutoModel
  from ....utils import get_logger
  from ..utils import get_hardware_aware_metric
label_desc: Use image node_id 1 to create a new StrModule object from inherited base classes: nn with example: obj = StrModule(name)