code: def __init__(
      self,
      d_input: int,
      d_hidden: int,
  ):
      super().__init__()
  
      self.encoder_lstm = nn.LSTM(
          input_size=d_input, hidden_size=d_hidden, batch_first=True
      )
      self.decoder_lstm = nn.LSTM(
          input_size=d_input, hidden_size=d_hidden, batch_first=True
      )
  
      self.gate = nn.Sequential(
          nn.Linear(in_features=d_hidden, out_features=d_hidden * 2),
          GatedLinearUnit(nonlinear=False),
      )
      if d_input != d_hidden:
          self.skip_proj = nn.Linear(
              in_features=d_input, out_features=d_hidden
          )
          self.add_skip = True
      else:
          self.add_skip = False
  
      self.lnorm = nn.LayerNorm(d_hidden)
  
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 TemporalFusionEncoder object from inherited base classes: nn with example: obj = TemporalFusionEncoder(d_input, d_hidden)