class_docstr: LightningModule wrapper over WaveNet.
  
  Parameters
  ----------
  model_kwargs
      Keyword arguments to pass to WaveNet.
  lr, optional
      Learning rate, by default 1e-3
  weight_decay, optional
      Weight decay, by default 1e-8
class_docstr_tok: ["LightningModule","wrapper","over","WaveNet",".","Parameters","--","--","--","--","--","model_kwargs","Keyword","arguments","to","pass","to","WaveNet",".","lr",",","optional","Learning","rate",",","by","default","1e-3","weight_decay",",","optional","Weight","decay",",","by","default","1e-8"]
docstr: Returns the optimizer to use.
docstr_tok: ["Returns","the","optimizer","to","use","."]
code: def configure_optimizers(self):
      """
      Returns the optimizer to use.
      """
      return torch.optim.AdamW(
          self.model.parameters(),
          lr=self.lr,
          weight_decay=self.weight_decay,
      )
  
file_path: gluonts/src/gluonts/torch/model/wavenet/lightning_module.py
imports: import lightning.pytorch
  import torch
  from gluonts.core.component import validated
  from .module import WaveNet
label_desc: Use image node_id 5 for calling the WaveNetLightningModule obj's underlying member method code with example usage: obj.configure_optimizers() and returns: torch