class_docstr: Basic wrapper of generated model.
  Lightning modules used in NNI should inherit this class.
  
  It's a subclass of ``pytorch_lightning.LightningModule``.
  See https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html
  
  See :class:`SupervisedLearningModule` as an example.
class_docstr_tok: ["Basic","wrapper","of","generated","model",".","Lightning","modules","used","in","NNI","should","inherit","this","class",".","It","'s","a","subclass","of","``","pytorch_lightning.LightningModule","``",".","See","https",":","\/\/pytorch-lightning.readthedocs.io\/en\/stable\/common\/lightning_module.html","See",":","class",":","`","SupervisedLearningModule","`","as","an","example","."]
docstr: The inner model (architecture) to train / evaluate.
  
  It will be only available after calling :meth:`set_model`.
code: def model(self) -> nn.Module:
      """The inner model (architecture) to train / evaluate.
  
      It will be only available after calling :meth:`set_model`.
      """
      model = getattr(self, "_model", None)
      if model is None:
          raise RuntimeError(
              "Model is not set. Please call set_model() first."
          )
      return model
  
imports: import logging
  import os
  import warnings
  from pathlib import Path
  from typing import Any, Dict, Union, Optional, List, Type
  import pytorch_lightning
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchmetrics
  import torchmetrics.classification
  import torch.utils.data
  import nni
  from nni.common.serializer import is_traceable
  from nni.nas.evaluator import MutableEvaluator
label_desc: Use image node_id 1 for calling the LightningModule obj's underlying member method code with example usage: obj.model() and returns: model