code: def __init__(
      self,
      context_length: int,
      prediction_length: int,
      d_hidden: int,
      d_var: int,
      n_head: int,
      dropout: float = 0.0,
  ):
      super().__init__()
      self.context_length = context_length
      self.prediction_length = prediction_length
  
      self.enrich = GatedResidualNetwork(
          d_hidden=d_hidden,
          d_static=d_var,
          dropout=dropout,
      )
  
      self.attention = nn.MultiheadAttention(
          embed_dim=d_hidden, num_heads=n_head, dropout=dropout
      )
  
      self.att_net = nn.Sequential(
          nn.Linear(in_features=d_hidden, out_features=d_hidden * 2),
          GatedLinearUnit(nonlinear=False),
      )
      self.att_lnorm = nn.LayerNorm(d_hidden)
  
      self.ff_net = nn.Sequential(
          GatedResidualNetwork(d_hidden=d_hidden, dropout=dropout),
          nn.Linear(in_features=d_hidden, out_features=d_hidden * 2),
          GatedLinearUnit(nonlinear=False),
      )
      self.ff_lnorm = nn.LayerNorm(d_hidden)
  
      self.register_buffer(
          "attn_mask",
          self._generate_subsequent_mask(
              prediction_length, prediction_length + context_length
          ),
      )
  
imports: from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from pts.modules import FeatureEmbedder
label_desc: Use image node_id 1 to create a new TemporalFusionDecoder object from inherited base classes: nn with example: obj = TemporalFusionDecoder(context_length, prediction_length, d_hidden, d_var, n_head, dropout)