class_docstr: Encoder of a time series using a causal CNN: the computed representation is
  the output of a fully connected layer applied to the output of an adaptive
  max pooling layer applied on top of the causal CNN, which reduces the
  length of the time series to a fixed size.
  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`).
  Parameters
  ----------
  in_channels : Number of input channels.
  channels : Number of channels manipulated in the causal CNN.
  depth : Depth of the causal CNN.
  reduced_size : Fixed length to which the output time series of the
         causal CNN is reduced.
  out_channels : Number of output channels.
  kernel_size : Kernel size of the applied non-residual convolutions.
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      u = self.network(x)
      return u
  
imports: import torch
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the CausalCNNEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: u