code: def __init__(
      self,
      context_length: int,
      prediction_length: int,
      d_hidden: int,
      d_var: int,
      n_head: int,
      dropout: float = 0.0,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.context_length = context_length
      self.prediction_length = prediction_length
  
      with self.name_scope():
          self.enrich = GatedResidualNetwork(
              d_hidden=d_hidden,
              d_static=d_var,
              dropout=dropout,
          )
          self.attention = SelfAttention(
              context_length=context_length,
              prediction_length=prediction_length,
              d_hidden=d_hidden,
              n_head=n_head,
              share_values=True,
              dropout=dropout,
          )
          self.att_net = nn.HybridSequential(prefix="attention_")
          self.att_net.add(nn.Dropout(dropout))
          self.att_net.add(
              nn.Dense(
                  units=d_hidden * 2,
                  in_units=d_hidden,
                  flatten=False,
                  weight_initializer=init.Xavier(),
              )
          )
          self.att_net.add(
              GatedLinearUnit(
                  axis=-1,
                  nonlinear=False,
              )
          )
          self.att_lnorm = nn.LayerNorm(
              axis=-1,
              in_channels=d_hidden,
          )
          self.ff_net = nn.HybridSequential()
          self.ff_net.add(
              GatedResidualNetwork(
                  d_hidden,
                  dropout=dropout,
              )
          )
          self.ff_net.add(
              nn.Dense(
                  units=d_hidden * 2,
                  in_units=d_hidden,
                  flatten=False,
                  weight_initializer=init.Xavier(),
              )
          )
          self.ff_net.add(
              GatedLinearUnit(
                  axis=-1,
                  nonlinear=False,
              )
          )
          self.ff_lnorm = nn.LayerNorm(axis=-1, in_channels=d_hidden)
  
imports: import math
  from typing import List, Optional, Tuple
  from mxnet import init
  from mxnet.gluon import HybridBlock, nn, rnn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 1 to create a new TemporalFusionDecoder object from inherited base classes: HybridBlock with example: obj = TemporalFusionDecoder(context_length, prediction_length, d_hidden, d_var, n_head, dropout)