code: def train_step(
      self, input_seq, target_seq, enc_hidden  # type: ignore
  ):
      loss = 0
  
      with tf.GradientTape() as tape:
          enc_output, enc_hidden = self.encoder(input_seq, enc_hidden)
  
          dec_hidden = enc_hidden
  
          bos_token_id = self.encoder_processor.vocab2idx[
              self.encoder_processor.token_bos
          ]
          dec_input = tf.expand_dims(
              [bos_token_id] * target_seq.shape[0], 1
          )
  
          # Teacher forcing - feeding the target as the next input
          for t in range(1, target_seq.shape[1]):
              # pass enc_output to the decoder
              predictions, dec_hidden, _ = self.decoder(
                  dec_input, dec_hidden, enc_output
              )
              loss += self.loss_function(target_seq[:, t], predictions)
              # using teacher forcing
              dec_input = tf.expand_dims(target_seq[:, t], 1)
  
      batch_loss = loss / int(target_seq.shape[1])
      variables = (
          self.encoder.trainable_variables
          + self.decoder.trainable_variables
      )
      gradients = tape.gradient(loss, variables)
      self.optimizer.apply_gradients(zip(gradients, variables))
  
      return batch_loss
  
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 7 for calling the Seq2Seq obj's underlying member method code with example usage: obj.train_step(input_seq, target_seq, enc_hidden) and returns: batch_loss