class_docstr: Lightning Module of SupervisedLearning for Cross-Graph Optimization.
  Users who needs cross-graph optimization should use this module.
  
  Parameters
  ----------
  criterion : nn.Module
      Class for criterion module (not an instance). default: ``nn.CrossEntropyLoss``
  learning_rate : float
      Learning rate. default: 0.001
  weight_decay : float
      L2 weight decay. default: 0
  optimizer : Optimizer
      Class for optimizer (not an instance). default: ``Adam``
class_docstr_tok: ["Lightning","Module","of","SupervisedLearning","for","Cross-Graph","Optimization",".","Users","who","needs","cross-graph","optimization","should","use","this","module",".","Parameters","--","--","--","--","--","criterion",":","nn.Module","Class","for","criterion","module","(","not","an","instance",")",".","default",":","``","nn.CrossEntropyLoss","``","learning_rate",":","float","Learning","rate",".","default",":","0.001","weight_decay",":","float","L2","weight","decay",".","default",":","0","optimizer",":","Optimizer","Class","for","optimizer","(","not","an","instance",")",".","default",":","``","Adam","``"]
code: def __init__(
      self,
      criterion: Type[nn.Module],
      metrics: Dict[str, Type[torchmetrics.Metric]],
      learning_rate: float = 0.001,
      weight_decay: float = 0.0,
      optimizer: Type[optim.Optimizer] = optim.Adam,
  ):
      super().__init__(
          criterion,
          metrics,
          learning_rate=learning_rate,
          weight_decay=weight_decay,
          optimizer=optimizer,
      )
  
imports: import warnings
  from typing import Dict, List, Optional, Union, Type
  import torch.nn
  import torch.optim
  import torchmetrics
  from torch.utils.data import DataLoader
  import nni
  from ..lightning import LightningModule, _AccuracyWithLogits, Lightning
  from .trainer import Trainer
label_desc: Use image node_id 1 to create a new MultiModelSupervisedLearningModule object from inherited base classes: _MultiModelSupervisedLearningModule with example: obj = MultiModelSupervisedLearningModule(criterion, metrics, learning_rate, weight_decay, optimizer)