code: def forward(self, x):
      residual = x
  
      # first step
      left_padding = (self.dilation_base**self.nr_blocks_below) * (
          self.kernel_size - 1
      )
      x = F.pad(x, (left_padding, 0))
      x = self.dropout_fn(F.relu(self.conv1(x)))
  
      # second step
      x = F.pad(x, (left_padding, 0))
      x = self.conv2(x)
      if self.nr_blocks_below < self.num_layers - 1:
          x = F.relu(x)
      x = self.dropout_fn(x)
  
      # add residual
      if self.conv1.in_channels != self.conv2.out_channels:
          residual = self.conv3(residual)
      x = x + residual
  
      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 _ResidualBlock obj's underlying member method code with example usage: obj.forward(x) and returns: x