class_docstr: Base network for the :class:`Seq2SeqEstimator`.
  
  Parameters
  ----------
  scaler : Scaler
      scale of the target time series, both as input or in the output
      distributions
  encoder : encoder
      see encoder.py for possible choices
  enc2dec : encoder to decoder
      see enc2dec.py for possible choices
  decoder : decoder
      see decoder.py for possible choices
  quantile_output : QuantileOutput
      quantile regression output
  kwargs : dict
      a dict of parameters to be passed to the parent initializer
class_docstr_tok: ["Base","network","for","the",":","class",":","`","Seq2SeqEstimator","`",".","Parameters","--","--","--","--","--","scaler",":","Scaler","scale","of","the","target","time","series",",","both","as","input","or","in","the","output","distributions","encoder",":","encoder","see","encoder.py","for","possible","choices","enc2dec",":","encoder","to","decoder","see","enc2dec.py","for","possible","choices","decoder",":","decoder","see","decoder.py","for","possible","choices","quantile_output",":","QuantileOutput","quantile","regression","output","kwargs",":","dict","a","dict","of","parameters","to","be","passed","to","the","parent","initializer"]
code: def compute_decoder_outputs(
      self,
      F,
      past_target: Tensor,
      feat_static_cat: Tensor,
      past_feat_dynamic_real: Tensor,
      future_feat_dynamic_real: Tensor,
  ) -> Tensor:
      scaled_target, scale = self.scaler(
          past_target, F.ones_like(past_target)
      )
  
      embedded_cat = self.embedder(
          feat_static_cat
      )  # (batch_size, num_features * embedding_size)
  
      encoder_output_static, encoder_output_dynamic = self.encoder(
          scaled_target, embedded_cat, past_feat_dynamic_real
      )
      decoder_input_static, decoder_input_dynamic = self.enc2dec(
          encoder_output_static,
          encoder_output_dynamic,
          future_feat_dynamic_real,
      )
      decoder_output = self.decoder(
          decoder_input_static, decoder_input_dynamic
      )
      scaled_decoder_output = F.broadcast_mul(
          decoder_output, scale.expand_dims(-1).expand_dims(-1)
      )
      return scaled_decoder_output
  
imports: from typing import Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.decoder import Seq2SeqDecoder
  from gluonts.mx.block.enc2dec import Seq2SeqEnc2Dec
  from gluonts.mx.block.encoder import Seq2SeqEncoder
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.quantile_output import QuantileOutput
  from gluonts.mx.block.scaler import Scaler
label_desc: Use image node_id 2 for calling the Seq2SeqNetworkBase obj's underlying member method code with example usage: obj.compute_decoder_outputs(F, past_target, feat_static_cat, past_feat_dynamic_real, future_feat_dynamic_real) and returns: scaled_decoder_output