code: def decode(
      self,
      decoder_input_indices,
      initial_hidden_states,
      all_encoder_outputs,
  ):
      # take decoder input and initial hidden and pass through RNN
      input_tensor = self.decoder_character_embedding(
          decoder_input_indices
      )
      output, hidden = self.decoder_rnn(
          input_tensor, initial_hidden_states
      )
  
      # if all encoder outputs are provided, use attention
      if self.use_attention:
          attention_coeff = torch.softmax(
              torch.matmul(
                  all_encoder_outputs, torch.transpose(output, 1, 2)
              ),
              dim=1,
          )
  
          # take convex combinations of encoder hidden states as new output using the computed attention coefficients
          attention_output = torch.transpose(
              torch.matmul(
                  torch.transpose(all_encoder_outputs, 1, 2),
                  attention_coeff,
              ),
              1,
              2,
          )
  
          output = torch.cat((output, attention_output), dim=2)
  
      # transform output to vectors of size len(char_dict) -> (batch_size, max_sequence_length, alphabet_size)
      output_vectors = self.character_decoder(output)
      return output_vectors, 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 5 for calling the Lemmatizer obj's underlying member method code with example usage: obj.decode(decoder_input_indices, initial_hidden_states, all_encoder_outputs) and returns: output_vectors,  hidden