class_docstr: Testing PL Module.
  
  Use as follows:
  - subclass
  - modify the behavior for what you want
  
  class TestModel(BaseTestModel):
      def training_step(...):
          # do your own thing
  
  or:
  
  model = BaseTestModel()
  model.validation_step = None
class_docstr_tok: ["Testing","PL","Module",".","Use","as","follows",":","-","subclass","-","modify","the","behavior","for","what","you","want","class","TestModel","(","BaseTestModel",")",":","def","training_step","(","...",")",":","#","do","your","own","thing","or",":","model","=","BaseTestModel","(",")","model.validation_step","=","None"]
docstr: Configure which optimizer to use when training the model.
docstr_tok: ["Configure","which","optimizer","to","use","when","training","the","model","."]
code: def configure_optimizers(self):
      """Configure which optimizer to use when training the model."""
      optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
      lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
          optimizer, gamma=0.9
      )
      return {"optimizer": optimizer, "scheduler": lr_scheduler}
  
imports: import torch
  from lightning_utilities import module_available
  from torch.utils.data import Dataset
label_desc: Use image node_id 8 for calling the BoringModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: dict