code: def __init__(
      self,
      n_head,
      hidden_size,
      example_length,
      encoder_length,
      attn_dropout,
      dropout,
  ):
      super(TemporalFusionDecoder, self).__init__()
      self.encoder_length = encoder_length
  
      # ------------- Encoder-Decoder Attention --------------#
      self.enrichment_grn = GRN(
          input_size=hidden_size,
          hidden_size=hidden_size,
          context_hidden_size=hidden_size,
          dropout=dropout,
      )
      self.attention = InterpretableMultiHeadAttention(
          n_head=n_head,
          hidden_size=hidden_size,
          example_length=example_length,
          attn_dropout=attn_dropout,
          dropout=dropout,
      )
      self.attention_gate = GLU(hidden_size, hidden_size)
      self.attention_ln = LayerNorm(
          normalized_shape=hidden_size, eps=1e-3
      )
  
      self.positionwise_grn = GRN(
          input_size=hidden_size,
          hidden_size=hidden_size,
          dropout=dropout,
      )
  
      # ---------------------- Decoder -----------------------#
      self.decoder_gate = GLU(hidden_size, hidden_size)
      self.decoder_ln = LayerNorm(
          normalized_shape=hidden_size, eps=1e-3
      )
  
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 1 to create a new TemporalFusionDecoder object from inherited base classes: nn with example: obj = TemporalFusionDecoder(n_head, hidden_size, example_length, encoder_length, attn_dropout, dropout)