code: def __init__(self, num_features=32, num_classes=3, lr=0.01):
      super().__init__()
  
      self.lr = lr
      for i in range(3):
          setattr(
              self, f"layer_{i}", nn.Linear(num_features, num_features)
          )
          setattr(self, f"layer_{i}a", torch.nn.ReLU())
      setattr(self, "layer_end", nn.Linear(num_features, 3))
  
      acc = (
          Accuracy(task="multiclass", num_classes=num_classes)
          if _TM_GE_0_11
          else Accuracy()
      )
      self.train_acc = acc.clone()
      self.valid_acc = acc.clone()
      self.test_acc = acc.clone()
  
imports: import operator
  import torch
  import torch.nn.functional
  from lightning.pytorch import LightningModule
  from lightning_utilities.core.imports import compare_version
  from torch import nn
  from torchmetrics import Accuracy, MeanSquaredError
label_desc: Use image node_id 1 to create a new ClassificationModel object from inherited base classes: LightningModule with example: obj = ClassificationModel(num_features, num_classes, lr)