code: def loss_function(
      self, real: tf.Tensor, pred: tf.Tensor
  ) -> tf.Tensor:
      mask = tf.math.logical_not(tf.math.equal(real, 0))
      loss_ = self.loss_object(real, pred)
  
      mask = tf.cast(mask, dtype=loss_.dtype)
      loss_ *= mask
  
      return tf.reduce_mean(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 3 for calling the Seq2Seq obj's underlying member method code with example usage: obj.loss_function(real, pred) and returns: tf