class_docstr: Temporal Convolutional Network block.
  
  Composed sequentially of two causal convolutions (with leaky ReLU activation functions),
  and a parallel residual connection.
  
  Takes as input a three-dimensional tensor (`B`, `C`, `L`) where `B` is the
  batch size, `C` is the number of input channels, and `L` is the length of
  the input. Outputs a three-dimensional tensor (`B`, `C`, `L`).
  
  Args:
      in_channels : Number of input channels.
      out_channels : Number of output channels.
      kernel_size : Kernel size of the applied non-residual convolutions.
      dilation : Dilation parameter of non-residual convolutions.
      bias : If True, adds a learnable bias to the convolutions.
      fwd_time : If True, the network "causal" direction is from past to future (forward),
          if False, the relation is from future to past (backward).
      final : If True, the last activation function is disabled.
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code: def forward(self, x):
      out_causal = self.causal(x)
      res = x if self.upordownsample is None else self.upordownsample(x)
      if self.activation is None:
          return out_causal + res
      else:
          return self.activation(out_causal + res)
  
file_path: gluonts/src/gluonts/nursery/spliced_binned_pareto/tcn.py
imports: import torch
label_desc: Use image node_id 2 for calling the TCNBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown, self