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.
class_docstr_tok: ["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","."]
code: def __init__(
      self,
      dec_len: int,
      final_dim: int,
      hidden_dimension_sequence: List[int] = [],
      **kwargs,
  ) -> None:
      super().__init__(**kwargs)
  
      self.dec_len = dec_len
      self.final_dims = final_dim
  
      with self.name_scope():
          self.model = nn.HybridSequential()
  
          for layer_no, layer_dim in enumerate(
              hidden_dimension_sequence
          ):
              layer = nn.Dense(
                  dec_len * layer_dim,
                  flatten=False,
                  activation="relu",
                  prefix=f"mlp_{layer_no:#02d}'_",
              )
              self.model.add(layer)
  
          layer = nn.Dense(
              dec_len * final_dim,
              flatten=False,
              activation="softrelu",
              prefix=f"mlp_{len(hidden_dimension_sequence):#02d}'_",
          )
          self.model.add(layer)
  
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 1 to create a new ForkingMLPDecoder object from inherited base classes: Seq2SeqDecoder with example: obj = ForkingMLPDecoder(dec_len, final_dim, hidden_dimension_sequence)