code: def _create_transformer_inputs(self, data):
      # '_TimeSeriesSequentialDataset' stores time series in the
      # (batch_size, input_chunk_length, input_size) format. PyTorch's nn.Transformer
      # module needs it the (input_chunk_length, batch_size, input_size) format.
      # Therefore, the first two dimensions need to be swapped.
      src = data.permute(1, 0, 2)
      tgt = src[-1:, :, :]
  
      return src, tgt
  
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 2 for calling the _TransformerModule obj's underlying member method code with example usage: obj._create_transformer_inputs(data) and returns: src,  tgt