docstr: Return the model space defined by the user.
  
  The model space is not guaranteed to have been transformed into a one-shot supernet.
  For instance, when ``__init__`` hasn't completed, the model space will still be the original one.
code: def model(self) -> nas_nn.ModelSpace:
      """Return the model space defined by the user.
  
      The model space is not guaranteed to have been transformed into a one-shot supernet.
      For instance, when ``__init__`` hasn't completed, the model space will still be the original one.
      """
      model = self.training_module.model
      if not isinstance(model, nas_nn.ModelSpace):
          raise TypeError(
              f"The model is expected to be a valid PyTorch model space, but got {type(model)}"
          )
      return model
  
imports: from __future__ import annotations
  import warnings
  from typing import Any, Iterable, List, cast, TYPE_CHECKING
  import torch.optim
  import torch.nn
  from torch.optim import Optimizer
  import nni.nas.nn.pytorch
  from nni.nas.evaluator.pytorch import LightningModule, Trainer
  from nni.mutable import Sample
  from .supermodule.base import BaseSuperNetModule
label_desc: Use image node_id 4 for calling the BaseOneShotLightningModule obj's underlying member method code with example usage: obj.model() and returns: model