class_docstr: Multilayer perceptron decoder for sequence-to-sequence models.
  
  See [WTN+17]_ for details.
  
  Parameters
  ----------
  dec_len
      length of the decoder (usually the number of forecasted time steps).
  
  final_dim
      dimensionality of the output per time step (number of predicted
      quantiles).
  
  hidden_dimension_sequence
      number of hidden units for each MLP layer.
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docstr: ForkingMLPDecoder forward call.
  
  Parameters
  ----------
  F
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet.
  static_input
      not used in this decoder.
  dynamic_input
      dynamic_features, shape (batch_size, sequence_length, num_features)
      or (N, T, C) where sequence_length is equal to the encoder length,
      and num_features is equal to channels_seq[-1] + 1 +
      decoder_length * num_feat_dynamic for the MQ-CNN for example.
  
  Returns
  -------
  Tensor
      mlp output, shape (batch_size, sequence_length, decoder_length,
      decoder_mlp_dim_seq[0]).
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code: def hybrid_forward(
      self, F, static_input: Tensor, dynamic_input: Tensor
  ) -> Tensor:
      """
      ForkingMLPDecoder forward call.
  
      Parameters
      ----------
      F
          A module that can either refer to the Symbol API or the NDArray
          API in MXNet.
      static_input
          not used in this decoder.
      dynamic_input
          dynamic_features, shape (batch_size, sequence_length, num_features)
          or (N, T, C) where sequence_length is equal to the encoder length,
          and num_features is equal to channels_seq[-1] + 1 +
          decoder_length * num_feat_dynamic for the MQ-CNN for example.
  
      Returns
      -------
      Tensor
          mlp output, shape (batch_size, sequence_length, decoder_length,
          decoder_mlp_dim_seq[0]).
      """
      mlp_output = self.model(dynamic_input)
      mlp_output = mlp_output.reshape(
          shape=(0, 0, self.dec_len, self.final_dims)
      )
      return mlp_output
  
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 ForkingMLPDecoder obj's underlying member method code with example usage: obj.hybrid_forward(F, static_input, dynamic_input) and returns: mlp_output