class_docstr: Encodes each time point of the support set time series via WaveNet
  
  Parameters
  ----------
  num_channels: The number of filters in each CNN layer.
  num_layers: The number of causal convolution blocks
  kernel_size: The kernel size of the filters in the causal convolution blocks
code: def forward(self, supps: SeriesBatch) -> torch.Tensor:
      output_padded = self.encoder(supps.sequences.transpose(1, 2))
      return SeriesBatch(
          output_padded.transpose(1, 2),
          supps.lengths,
          supps.split_sections,
      )
  
imports: from abc import ABC, abstractmethod
  import torch
  from meta.models.EcDc.components.tcn import CausalCNNEncoder
  from torch import nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from meta.data.batch import SeriesBatch
label_desc: Use image node_id 2 for calling the TcnSupportSetEncoder obj's underlying member method code with example usage: obj.forward(supps) and returns: SeriesBatch