code: def predict_vectors(
      self, input_text: str, target_text: str
  ) -> Dict[str, Union[str, np.array]]:
      text_preprocessed = self.preprocessor((input_text, target_text))
      en_inputs, sent_ids, _ = self.vectorizer(text_preprocessed)
      en_inputs = tf.expand_dims(en_inputs, 0)
      sent_ids = tf.expand_dims(sent_ids, 0)
      start_end_seq = self.vectorizer.encode_output(
          " ".join(
              [
                  self.preprocessor.start_token,
                  self.preprocessor.end_token,
              ]
          )
      )
      de_start_index, de_end_index = (
          start_end_seq[:1],
          start_end_seq[-1:],
      )
      decoder_output = tf.expand_dims(de_start_index, 0)
      output = {
          "preprocessed_text": text_preprocessed,
          "logits": [],
          "attention_weights": [],
          "predicted_sequence": [],
      }
      for _ in range(self.max_prediction_len):
          (
              enc_padding_mask,
              combined_mask,
              dec_padding_mask,
          ) = create_masks(en_inputs, decoder_output)
          predictions, attention_weights = self.transformer(
              en_inputs,
              sent_ids,
              decoder_output,
              False,
              enc_padding_mask,
              combined_mask,
              dec_padding_mask,
          )
  
          predictions = predictions[:, -1:, :]
          pred_token_index = tf.cast(
              tf.argmax(predictions, axis=-1), tf.int32
          )
          decoder_output = tf.concat(
              [decoder_output, pred_token_index], axis=-1
          )
          if pred_token_index != 0:
              output["logits"].append(np.squeeze(predictions.numpy()))
              output["attention_weights"] = attention_weights
              output["predicted_sequence"].append(int(pred_token_index))
              if pred_token_index == de_end_index:
                  break
      output["predicted_text"] = self.vectorizer.decode_output(
          output["predicted_sequence"]
      )
      return output
  
imports: import os
  import pickle
  from typing import Callable
  from typing import Dict, Union
  import numpy
  import tensorflow
  from headliner.model.bert_model import Transformer, create_masks
  from headliner.model.summarizer import Summarizer
  from headliner.preprocessing.bert_vectorizer import BertVectorizer
  from headliner.preprocessing.preprocessor import Preprocessor
  from headliner.utils.logger import get_logger
label_desc: Use image node_id 6 for calling the BertSummarizer obj's underlying member method code with example usage: obj.predict_vectors(input_text, target_text) and returns: output