class_docstr: Abstract class for the Decoder block in sequence-to-sequence models.
class_docstr_tok: ["Abstract","class","for","the","Decoder","block","in","sequence-to-sequence","models","."]
docstr: Abstract function definition of the hybrid_forward.
  
  Parameters
  ----------
  static_input
      static features, shape (batch_size, channels_seq[-1] + 1) or (N, C)
  
  dynamic_input
      dynamic_features, shape (batch_size, sequence_length,
      channels_seq[-1] + 1 + decoder_length * num_feat_dynamic)
      or (N, T, C)
docstr_tok: ["Abstract","function","definition","of","the","hybrid_forward",".","Parameters","--","--","--","--","--","static_input","static","features",",","shape","(","batch_size",",","channels_seq","[","-1","]","+","1",")","or","(","N",",","C",")","dynamic_input","dynamic_features",",","shape","(","batch_size",",","sequence_length",",","channels_seq","[","-1","]","+","1","+","decoder_length","*","num_feat_dynamic",")","or","(","N",",","T",",","C",")"]
code: def hybrid_forward(
      self, F, static_input: Tensor, dynamic_input: Tensor
  ) -> Tensor:
      """
      Abstract function definition of the hybrid_forward.
  
      Parameters
      ----------
      static_input
          static features, shape (batch_size, channels_seq[-1] + 1) or (N, C)
  
      dynamic_input
          dynamic_features, shape (batch_size, sequence_length,
          channels_seq[-1] + 1 + decoder_length * num_feat_dynamic)
          or (N, T, C)
      """
      raise NotImplementedError
  
file_path: gluonts/src/gluonts/mx/block/decoder.py
imports: from typing import List
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.mlp import MLP
label_desc: Use image node_id 2 for calling the Seq2SeqDecoder obj's underlying member method code with example usage: obj.hybrid_forward(F, static_input, dynamic_input) without return types