code: def new_train_step(
      self,
      loss_function: Callable[[tf.Tensor], tf.Tensor],
      batch_size: int,
      apply_gradients=True,
  ) -> Callable[[tf.Tensor, tf.Tensor], float]:
      train_step_signature = [
          tf.TensorSpec(shape=(batch_size, None), dtype=tf.int32),
          tf.TensorSpec(
              shape=(batch_size, self.vectorizer.max_output_len),
              dtype=tf.int32,
          ),
      ]
      encoder = self.encoder
      decoder = self.decoder
      optimizer = self.optimizer
  
      def train_step(source_seq, target_seq):
          loss = 0
          en_initial_states = encoder.init_states(batch_size)
          with tf.GradientTape() as tape:
              en_outputs = encoder(source_seq, en_initial_states)
              en_states = en_outputs[1:]
              de_state_h, de_state_c = en_states
              for i in range(target_seq.shape[1] - 1):
                  decoder_in = tf.expand_dims(target_seq[:, i], 1)
                  logit, de_state_h, de_state_c, _ = decoder(
                      decoder_in,
                      (de_state_h, de_state_c),
                      en_outputs[0],
                  )
                  loss += loss_function(target_seq[:, i + 1], logit)
          if apply_gradients is True:
              variables = (
                  encoder.trainable_variables
                  + decoder.trainable_variables
              )
              gradients = tape.gradient(loss, variables)
              optimizer.apply_gradients(zip(gradients, variables))
          return loss / (target_seq.shape[1] - 1)
  
      if self.vectorizer.max_output_len is not None:
          return tf.function(
              train_step, input_signature=train_step_signature
          )
      else:
          return train_step
  
imports: import os
  import pickle
  from typing import Callable, Dict, Union
  import numpy
  import tensorflow
  from headliner.model.attention_model import Encoder, Decoder
  from headliner.model.summarizer import Summarizer
  from headliner.preprocessing.preprocessor import Preprocessor
  from headliner.preprocessing.vectorizer import Vectorizer
label_desc: Use image node_id 6 for calling the AttentionSummarizer obj's underlying member method code with example usage: obj.new_train_step(loss_function, batch_size, apply_gradients) and returns: unknown, tf, train_step