code: def predict(self, x_data: TextSamplesVar) -> Tuple[List, np.ndarray]:
      results = []
      attentions = []
  
      bos_token_id = self.decoder_processor.vocab2idx[
          self.decoder_processor.token_bos
      ]
      eos_token_id = self.decoder_processor.vocab2idx[
          self.decoder_processor.token_eos
      ]
  
      for sample in x_data:
          input_seq = self.encoder_processor.transform(
              [sample], seq_length=self.encoder_seq_length
          )
          enc_hidden = tf.zeros((1, self.hidden_size))
          enc_output, enc_hidden = self.encoder(input_seq, enc_hidden)
          dec_hidden = enc_hidden
  
          attention_plot = np.zeros(
              (self.decoder_seq_length, self.encoder_seq_length)
          )
          token_out = []
  
          dec_input = tf.expand_dims([bos_token_id], 0)
  
          for t in range(self.decoder_seq_length):
              predictions, dec_hidden, att_weights = self.decoder(
                  dec_input, dec_hidden, enc_output
              )
              # storing the attention weights to plot later on
              attention_weights = tf.reshape(att_weights, (-1,))
              attention_plot[t] = attention_weights.numpy()
  
              next_tokens = tf.argmax(predictions[0]).numpy()
              token_out.append(next_tokens)
              if next_tokens == eos_token_id:
                  break
              dec_input = tf.expand_dims([next_tokens], 0)
          r = self.decoder_processor.inverse_transform([token_out])[0]
          results.append(r)
          attentions.append(attention_plot)
      return results, np.array(attentions)
  
imports: import json
  import os
  import pathlib
  from typing import Any, Tuple, List, Dict
  import numpy
  import tensorflow
  import tqdm
  import kashgari
  from kashgari.embeddings import BareEmbedding
  from kashgari.embeddings.abc_embedding import ABCEmbedding
  from kashgari.generators import CorpusGenerator, Seq2SeqDataSet
  from kashgari.logger import logger
  from kashgari.processors import SequenceProcessor
  from kashgari.tasks.seq2seq.decoder import AttGRUDecoder
  from kashgari.tasks.seq2seq.encoder import GRUEncoder
  from kashgari.types import TextSamplesVar
label_desc: Use image node_id 11 for calling the Seq2Seq obj's underlying member method code with example usage: obj.predict(x_data) and returns: results,  np