code: def forward(self, x_in: Tuple):
      data, _ = x_in
      # Here we create 'src' and 'tgt', the inputs for the encoder and decoder
      # side of the Transformer architecture
      src, tgt = self._create_transformer_inputs(data)
  
      # "math.sqrt(self.input_size)" is a normalization factor
      # see section 3.2.1 in 'Attention is All you Need' by Vaswani et al. (2017)
      src = self.encoder(src) * math.sqrt(self.input_size)
      src = self.positional_encoding(src)
  
      tgt = self.encoder(tgt) * math.sqrt(self.input_size)
      tgt = self.positional_encoding(tgt)
  
      x = self.transformer(src=src, tgt=tgt)
      out = self.decoder(x)
  
      # Here we change the data format
      # from (1, batch_size, output_chunk_length * output_size)
      # to (batch_size, output_chunk_length, output_size, nr_params)
      predictions = out[0, :, :]
      predictions = predictions.view(
          -1, self.target_length, self.target_size, self.nr_params
      )
  
      return predictions
  
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 3 for calling the _TransformerModule obj's underlying member method code with example usage: obj.forward(x_in) and returns: predictions