class_docstr: A ``pl.LightningModule`` class that can be used to train a
  ``PatchTSTModel`` with PyTorch Lightning.
  
  This is a thin layer around a (wrapped) ``PatchTSTModel`` object,
  that exposes the methods to evaluate training and validation loss.
  
  Parameters
  ----------
  model_kwargs
      Keyword arguments to construct the ``PatchTSTModel`` to be trained.
  loss
      Loss function to be used for training.
  lr
      Learning rate.
  weight_decay
      Weight decay regularization parameter.
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code: def __init__(
      self,
      model_kwargs: dict,
      lr: float = 1e-3,
      weight_decay: float = 1e-8,
  ):
      super().__init__()
      self.save_hyperparameters()
      self.model = PatchTSTModel(**model_kwargs)
      self.lr = lr
      self.weight_decay = weight_decay
      self.inputs = self.model.describe_inputs()
  
file_path: gluonts/src/gluonts/torch/model/patch_tst/lightning_module.py
imports: import lightning.pytorch
  import torch
  from gluonts.core.component import validated
  from gluonts.itertools import select
  from .module import PatchTSTModel
label_desc: Use image node_id 1 to create a new PatchTSTLightningModule object from inherited base classes: pl with example: obj = PatchTSTLightningModule(model_kwargs, lr, weight_decay)