class_docstr: MLP on the decoder.
  
  MLP that predicts output only based on information available in the decoder.
code: def from_dataset(cls, dataset: TimeSeriesDataSet, **kwargs):
      new_kwargs = cls.deduce_default_output_parameters(
          dataset, kwargs, QuantileLoss()
      )
      kwargs.update(new_kwargs)
      return super().from_dataset(dataset, **kwargs)
  
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 4 for calling the DecoderMLP obj's underlying member method code with example usage: obj.from_dataset(cls, dataset) and returns: super