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``.
code: def forward(self, *args, **kwargs):
      return self.model.forward(*args, **kwargs)
  
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 2 for calling the MQF2MultiHorizonLightningModule obj's underlying member method code with example usage: obj.forward() and returns: self