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, None), dtype=tf.int32),
      ]
      encoder = self.encoder
      decoder = self.decoder
      optimizer = self.optimizer
  
      @tf.function(input_signature=train_step_signature)
      def train_step(
          source_seq: tf.Tensor, target_seq: tf.Tensor
      ) -> float:
          en_initial_states = self.encoder.init_states(
              source_seq.get_shape()[0]
          )
          with tf.GradientTape() as tape:
              en_outputs = encoder(source_seq, en_initial_states)
              en_states = en_outputs[1:]
              de_states = en_states
              de_outputs = decoder(target_seq[:, :-1], de_states)
              logits = de_outputs[0]
              loss = loss_function(target_seq[:, 1:], logits)
          if apply_gradients is True:
              variables = (
                  encoder.trainable_variables
                  + decoder.trainable_variables
              )
              gradients = tape.gradient(loss, variables)
              optimizer.apply_gradients(zip(gradients, variables))
          return float(loss)
  
      return train_step
  
imports: import os
  import pickle
  from typing import Callable, Dict, Union
  import numpy
  import tensorflow
  from headliner.model.basic_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 BasicSummarizer obj's underlying member method code with example usage: obj.new_train_step(loss_function, batch_size, apply_gradients) and returns: train_step, float