docstr: Parameters
  ----------
  F: mx.symbol or mx.ndarray
      Gluon function space
  past_target: mx.nd.NDArray or mx.sym.Symbol
      past target
  feat_static_cat: mx.nd.NDArray or mx.sym.Symbol
      static categorical features
  past_feat_dynamic_real: mx.nd.NDArray or mx.sym.Symbol
      past dynamic real-valued features
  future_feat_dynamic_real: mx.nd.NDArray or mx.sym.Symbol
      future dynamic real-valued features
  
  Returns
  -------
  mx.nd.NDArray or mx.sym.Symbol
      the predicted sequence
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code: def hybrid_forward(
      self,
      F,
      past_target: Tensor,
      feat_static_cat: Tensor,
      past_feat_dynamic_real: Tensor,
      future_feat_dynamic_real: Tensor,
  ) -> Tuple[Tuple[Tensor, ...], Tensor, Tensor]:
      """
  
      Parameters
      ----------
      F: mx.symbol or mx.ndarray
          Gluon function space
      past_target: mx.nd.NDArray or mx.sym.Symbol
          past target
      feat_static_cat: mx.nd.NDArray or mx.sym.Symbol
          static categorical features
      past_feat_dynamic_real: mx.nd.NDArray or mx.sym.Symbol
          past dynamic real-valued features
      future_feat_dynamic_real: mx.nd.NDArray or mx.sym.Symbol
          future dynamic real-valued features
  
      Returns
      -------
      mx.nd.NDArray or mx.sym.Symbol
          the predicted sequence
      """
      scaled_decoder_output = self.compute_decoder_outputs(
          F,
          past_target=past_target,
          feat_static_cat=feat_static_cat,
          past_feat_dynamic_real=past_feat_dynamic_real,
          future_feat_dynamic_real=future_feat_dynamic_real,
      )
      predictions = self.quantile_proj(scaled_decoder_output)
  
      return (predictions,), None, None
  
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 1 for calling the Seq2SeqPredictionNetwork obj's underlying member method code with example usage: obj.hybrid_forward(F, past_target, feat_static_cat, past_feat_dynamic_real, future_feat_dynamic_real) and returns: predictions,  None,  None