class_docstr: LightningModule class for the model MQF2 proposed in the paper
  ``Multivariate Quantile Function Forecaster``
  by Kan, Aubet, Januschowski, Park, Benidis, Ruthotto, Gasthaus
  
  This is the multi-horizon (multivariate in time step) variant of MQF2
  
  This class is based on
  gluonts.torch.model.deepar.lightning_module.DeepARLightningModule
  
  Parameters
  ----------
  model_kwargs
      Keyword arguments to construct the ``MQF2MultiHorizonModel`` to be trained.
  loss
      Distribution loss.
  lr
      Learning rate.
  weight_decay
      Weight decay during training.
  patience
      Patience parameter for learning rate scheduler, default: ``10``.
docstr: Returns the optimizer to use.
docstr_tok: ["Returns","the","optimizer","to","use","."]
code: def configure_optimizers(self):
      """
      Returns the optimizer to use.
      """
      optimizer = torch.optim.Adam(
          self.model.parameters(),
          lr=self.lr,
          weight_decay=self.weight_decay,
      )
  
      return {
          "optimizer": optimizer,
          "lr_scheduler": {
              "scheduler": ReduceLROnPlateau(
                  optimizer=optimizer,
                  mode="min",
                  factor=0.5,
                  patience=self.patience,
              ),
              "monitor": "train_loss",
          },
      }
  
file_path: gluonts/src/gluonts/torch/model/mqf2/lightning_module.py
imports: from typing import Dict
  import lightning.pytorch
  import torch
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from gluonts.core.component import validated
  from gluonts.itertools import select
  from .module import MQF2MultiHorizonModel
label_desc: Use image node_id 6 for calling the MQF2MultiHorizonLightningModule obj's underlying member method code with example usage: obj.configure_optimizers() and returns: dict