class_docstr: MLP on the decoder.
  
  MLP that predicts output only based on information available in the decoder.
code: def decoder_reals_positions(self) -> List[int]:
      return [
          self.hparams.x_reals.index(name)
          for name in self.reals
          if name in self.decoder_variables + self.static_variables
      ]
  
file_path: pytorch-forecasting/pytorch_forecasting/models/mlp/__init__.py
imports: from typing import Dict, List, Tuple, Union
  import numpy
  import torch
  from torch import nn
  from pytorch_forecasting.data import TimeSeriesDataSet
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, QuantileLoss
  from pytorch_forecasting.models.base_model import BaseModelWithCovariates
  from pytorch_forecasting.models.mlp.submodules import FullyConnectedModule
  from pytorch_forecasting.models.nn.embeddings import MultiEmbedding
label_desc: Use image node_id 2 for calling the DecoderMLP obj's underlying member method code with example usage: obj.decoder_reals_positions() and returns: unknown