code: def forward(self, x_in: Tuple):
      x, _ = x_in
      # data is of size (batch_size, input_chunk_length, input_size)
      batch_size = x.size(0)
      x = x.transpose(1, 2)
  
      for res_block in self.res_blocks_list:
          x = res_block(x)
  
      x = x.transpose(1, 2)
      x = x.view(
          batch_size,
          self.input_chunk_length,
          self.target_size,
          self.nr_params,
      )
  
      return x
  
imports: import math
  from typing import Optional, Sequence, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from darts.logging import get_logger, raise_if_not
  from darts.models.forecasting.pl_forecasting_module import PLPastCovariatesModule, io_processor
  from darts.models.forecasting.torch_forecasting_model import PastCovariatesTorchModel
  from darts.timeseries import TimeSeries
  from darts.utils.data import PastCovariatesShiftedDataset
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 2 for calling the _TCNModule obj's underlying member method code with example usage: obj.forward(x_in) and returns: x