docstr: Decoder covariates size.
  
  Returns:
      int: size of time-dependent covariates used by the decoder
docstr_tok: ["Decoder","covariates","size",".","Returns",":","int",":","size","of","time-dependent","covariates","used","by","the","decoder"]
code: def decoder_covariate_size(self) -> int:
      """Decoder covariates size.
  
      Returns:
          int: size of time-dependent covariates used by the decoder
      """
      return len(
          set(self.hparams.time_varying_reals_decoder)
          - set(self.target_names)
      ) + sum(
          self.embeddings.output_size[name]
          for name in self.hparams.time_varying_categoricals_decoder
      )
  
imports: from copy import copy
  from typing import Dict, List, Optional, Tuple, Union
  from matplotlib import pyplot
  import numpy
  import torch
  from torch import nn
  from pytorch_forecasting.data import TimeSeriesDataSet
  from pytorch_forecasting.data.encoders import NaNLabelEncoder
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, MultiLoss
  from pytorch_forecasting.models.base_model import BaseModelWithCovariates
  from pytorch_forecasting.models.nhits.sub_modules import NHiTS
  from pytorch_forecasting.models.nn.embeddings import MultiEmbedding
  from pytorch_forecasting.utils import create_mask, detach, to_list
label_desc: Use image node_id 2 for calling the NHiTS obj's underlying member method code with example usage: obj.decoder_covariate_size() and returns: unknown