code: def encode_token(self, token: Token):
      # variable to store initial hidden states for decoder
      initial_hidden_for_decoder = []
      all_encoder_outputs = None
  
      # encode input characters by sending them through RNN
      if self.encode_characters:
          # note that we do not need to fill up with dummy symbols since we process each token seperately
          encoder_input_indices = self.words_to_char_indices(
              [token.text],
              start_symbol=self.start_symbol,
              end_symbol=self.end_symbol,
          )
          # embed character one-hots
          input_vector = self.encoder_character_embedding(
              encoder_input_indices
          )
          # send through encoder RNN (produces initial hidden for decoder)
          all_encoder_outputs, initial_hidden_states = self.encoder_rnn(
              input_vector
          )
  
          # since bidirectional rnn is only used in encoding we need to project outputs to hidden_size of decoder
          if (
              self.bi_encoding
              and self.bi_hidden_states_to_hidden_size is not None
          ):
              # project 2*hidden_size to hidden_size
              all_encoder_outputs = (
                  self.bi_hidden_states_to_hidden_size(
                      all_encoder_outputs
                  )
              )
  
              # concatenate the final hidden states of the encoder. These will be projected to hidden_size of decoder
              # later with self.emb_to_hidden
              conditions = torch.cat(
                  2 * [torch.eye(self.rnn_layers).bool()]
              )
              bi_states = [
                  initial_hidden_states[conditions[:, i], :, :]
                  for i in range(self.rnn_layers)
              ]
              initial_hidden_states = torch.stack(
                  [
                      torch.cat((b[0, :, :], b[1, :, :]), dim=1)
                      for b in bi_states
                  ]
              )
  
          initial_hidden_for_decoder.append(initial_hidden_states)
  
      # use token embedding as initial hidden state for decoder
      if self.encoder_embeddings:
          # create initial hidden state tensor for batch (num_layers, batch_size, hidden_size)
          token_embedding_hidden = torch.stack(
              self.rnn_layers * [token.get_embedding()]
          ).unsqueeze(1)
  
          initial_hidden_for_decoder.append(token_embedding_hidden)
  
      # concatenate everything together and project to appropriate size for decoder
      initial_hidden_for_decoder = self.emb_to_hidden(
          torch.cat(initial_hidden_for_decoder, dim=2)
      )
  
      return initial_hidden_for_decoder, all_encoder_outputs
  
imports: import logging
  from math import inf
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  import flair.embeddings
  import flair.nn
  from flair.data import Dictionary, Sentence, Token
  from flair.datasets import DataLoader, FlairDatapointDataset
  from flair.training_utils import Result, store_embeddings
label_desc: Use image node_id 9 for calling the Lemmatizer obj's underlying member method code with example usage: obj.encode_token(token) and returns: initial_hidden_for_decoder,  all_encoder_outputs