code: def forward(self, temporal_features, ce):
      # ------------- Encoder-Decoder Attention --------------#
      # Static enrichment
      enriched = self.enrichment_grn(temporal_features, c=ce)
  
      # Temporal self attention
      x, _ = self.attention(enriched, mask_future_timesteps=True)
  
      # Don't compute historical quantiles
      x = x[:, self.encoder_length :, :]
      temporal_features = temporal_features[:, self.encoder_length :, :]
      enriched = enriched[:, self.encoder_length :, :]
  
      x = self.attention_gate(x)
      x = x + enriched
      x = self.attention_ln(x)
  
      # Position-wise feed-forward
      x = self.positionwise_grn(x)
  
      # ---------------------- Decoder ----------------------#
      # Final skip connection
      x = self.decoder_gate(x)
      x = x + temporal_features
      x = self.decoder_ln(x)
  
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import LayerNorm
  import logging
  import warnings
  from typing import Tuple, Optional
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 2 for calling the TemporalFusionDecoder obj's underlying member method code with example usage: obj.forward(temporal_features, ce) and returns: x