class_docstr: Base class for support set encoders.
docstr: Encodes each time point of each support set time series with a vector of fixed size.
  
  Parameters
  ----------
  supps:
      A SeriesBatch containing sequences of size [batch, sequence length, n_features].
  
  Returns
  -------
  SeriesBatch:
      The transformed batch containing sequences with the encoded support set series
      of size [batch, sequence length, encoding size].
docstr_tok: ["Encodes","each","time","point","of","each","support","set","time","series","with","a","vector","of","fixed","size",".","Parameters","--","--","--","--","--","supps",":","A","SeriesBatch","containing","sequences","of","size","[","batch",",","sequence","length",",","n_features","]",".","Returns","--","--","--","-","SeriesBatch",":","The","transformed","batch","containing","sequences","with","the","encoded","support","set","series","of","size","[","batch",",","sequence","length",",","encoding","size","]","."]
code: def forward(self, supps: SeriesBatch) -> SeriesBatch:
      """
      Encodes each time point of each support set time series with a vector of fixed size.
  
      Parameters
      ----------
      supps:
          A SeriesBatch containing sequences of size [batch, sequence length, n_features].
  
      Returns
      -------
      SeriesBatch:
          The transformed batch containing sequences with the encoded support set series
          of size [batch, sequence length, encoding size].
      """
  
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 1 for calling the SupportSetEncoder obj's underlying member method code with example usage: obj.forward(supps) without return types