class_docstr: A dummy model which fits the torchvision FakeData dataset.
class_docstr_tok: ["A","dummy","model","which","fits","the","torchvision","FakeData","dataset","."]
code: def configure_optimizers(self):
      return optim.SGD(
          self.parameters(),
          lr=self.hparams.model.lr,
          momentum=self.hparams.model.momentum,
          weight_decay=self.hparams.model.weight_decay,
      )
  
imports: from typing import Union
  import pytorch_lightning
  from omegaconf import DictConfig, ListConfig
  from torch import nn, optim
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.datasets import FakeData
  from anomalib.utils.callbacks import ImageVisualizerCallback
  from anomalib.utils.metrics import AnomalyScoreDistribution, AnomalyScoreThreshold, MinMax
  from tests.helpers.dummy import DummyModel
label_desc: Use image node_id 4 for calling the DummyLightningModule obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optim