class_docstr: LTSF-DLinear Forecaster.
  
  Implementation of the Long-Term Short-Term Feature (LTSF) decomposition linear
  forecaster, aka LTSF-DLinear, by Zeng et al [1]_.
  
  Core logic is directly copied from the cure-lab LTSF-Linear implementation [2]_,
  which is unfortunately not available as a package.
  
  Parameters
  ----------
  seq_len : int
      length of input sequence
  pred_len : int
      length of prediction (forecast horizon)
  in_channels : int, default=None
      number of input channels passed to network
  individual : bool, default=False
      boolean flag that controls whether the network treats each channel individually"
      "or applies a single linear layer across all channels. If individual=True, the"
      "a separate linear layer is created for each input channel. If"
      "individual=False, a single shared linear layer is used for all channels."
  
  References
  ----------
  .. [1] Zeng A, Chen M, Zhang L, Xu Q. 2023.
  Are transformers effective for time series forecasting?
  Proceedings of the AAAI conference on artificial intelligence 2023
  (Vol. 37, No. 9, pp. 11121-11128).
  .. [2] https://github.com/cure-lab/LTSF-Linear
code: def _build(self):
      return self._LTSFDLinearNetwork(
          self.seq_len, self.pred_len, self.in_channels, self.individual
      )
  
file_path: sktime/sktime/networks/ltsf/_ltsf.py
imports: from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 2 for calling the LTSFDLinearNetwork obj's underlying member method code with example usage: obj._build() and returns: self