code: def __init__(
      self,
      context_length: int,
      prediction_length: int,
      d_input: int,
      d_hidden: int,
      **kwargs,
  ) -> None:
      super().__init__(**kwargs)
      self.context_length = context_length
      self.prediction_length = prediction_length
      with self.name_scope():
          self.encoder_lstm = rnn.HybridSequentialRNNCell(
              prefix="encoder_"
          )
          self.encoder_lstm.add(
              rnn.LSTMCell(
                  hidden_size=d_hidden,
                  input_size=d_input,
              )
          )
          self.decoder_lstm = rnn.HybridSequentialRNNCell(
              prefix="decoder_"
          )
          self.decoder_lstm.add(
              rnn.LSTMCell(
                  hidden_size=d_hidden,
                  input_size=d_input,
              )
          )
          self.gate = nn.HybridSequential()
          self.gate.add(
              nn.Dense(
                  units=d_hidden * 2, in_units=d_hidden, flatten=False
              )
          )
          self.gate.add(GatedLinearUnit(axis=-1, nonlinear=False))
          if d_input != d_hidden:
              self.skip_proj = nn.Dense(
                  units=d_hidden, in_units=d_input, flatten=False
              )
              self.add_skip = True
          else:
              self.add_skip = False
          self.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 TemporalFusionEncoder object from inherited base classes: HybridBlock with example: obj = TemporalFusionEncoder(context_length, prediction_length, d_input, d_hidden)