code: def _generate_coder(
      d_model,
      dim_ff,
      dropout,
      nhead,
      num_layers,
      norm_layer,
      coder_cls,
      layer_cls,
      ffn_cls,
  ):
      """Generates an Encoder or Decoder with one of Darts' Feed-forward Network variants.
      Parameters
      ----------
      coder_cls
          Either `torch.nn.TransformerEncoder` or `...TransformerDecoder`
      layer_cls
          Either `darts.models.components.transformer.CustomFeedForwardEncoderLayer`,
          `...CustomFeedForwardDecoderLayer`, `nn.TransformerEncoderLayer`, or `nn.TransformerDecoderLayer`.
      ffn_cls
          One of Darts' Position-wise Feed-Forward Network variants `from darts.models.components.glu_variants`
      """
  
      ffn = (
          dict(
              ffn=ffn_cls(d_model=d_model, d_ff=dim_ff, dropout=dropout)
          )
          if ffn_cls
          else dict()
      )
      layer = layer_cls(
          **ffn,
          dropout=dropout,
          d_model=d_model,
          nhead=nhead,
          dim_feedforward=dim_ff,
      )
      return coder_cls(
          layer,
          num_layers=num_layers,
          norm=norm_layer(d_model),
      )
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
  from darts.models.components import glu_variants, layer_norm_variants
  from darts.models.components.glu_variants import GLU_FFN
  from darts.models.components.transformer import CustomFeedForwardDecoderLayer, CustomFeedForwardEncoderLayer
  from darts.models.forecasting.pl_forecasting_module import PLPastCovariatesModule, io_processor
  from darts.models.forecasting.torch_forecasting_model import PastCovariatesTorchModel
label_desc: Use image node_id 1 for calling a global function with example usage: _generate_coder(d_model, dim_ff, dropout, nhead, num_layers, norm_layer, coder_cls, layer_cls, ffn_cls) and returns: coder_cls