class_docstr: Causal convolution 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 (= number of dimensions
  of the multivariate time series), and `L` is the length of
  the input. Outputs a three-dimensional tensor (`B`, `C`, `L`).
  Parameters
  ----------
  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.
  final : Disables, if True, the last activation function.
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      out_causal = self.causal(x)
      if self.upordownsample is None:
          res = x
      else:
          res = self.upordownsample(x)
      if self.relu is None:
          return out_causal + res
      else:
          return self.relu(out_causal + res)
  
imports: import torch
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the CausalConvolutionBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown, self