code: def _prepare_tensors(
      self, sentences: List[Sentence]
  ) -> Tuple[Optional[torch.Tensor], ...]:
      # get all tokens
      tokens = [token for sentence in sentences for token in sentence]
  
      # encode input characters by sending them through RNN
      if self.encode_characters:
          # get one-hots for characters and add special symbols / padding
          encoder_input_indices = self.words_to_char_indices(
              [token.text for token in tokens],
              start_symbol=self.start_symbol,
              end_symbol=self.end_symbol,
              padding_in_front=False,
          )
  
          # determine length of each token
          extra = 0
          if self.start_symbol:
              extra += 1
          if self.end_symbol:
              extra += 1
          lengths = torch.tensor(
              [len(token.text) + extra for token in tokens],
              device=flair.device,
          )
      else:
          encoder_input_indices = None
          lengths = None
  
      if self.encoder_embeddings:
          # embed sentences
          self.encoder_embeddings.embed(sentences)
  
          # create initial hidden state tensor for batch (num_layers, batch_size, hidden_size)
          token_embedding_hidden = torch.stack(
              self.rnn_layers
              * [
                  torch.stack(
                      [token.get_embedding() for token in tokens]
                  )
              ]
          )
      else:
          token_embedding_hidden = None
  
      return encoder_input_indices, lengths, token_embedding_hidden
  
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 6 for calling the Lemmatizer obj's underlying member method code with example usage: obj._prepare_tensors(sentences) and returns: encoder_input_indices,  lengths,  token_embedding_hidden