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, _ = self.vectorizer(text_preprocessed)
      en_initial_states = self.encoder.init_states(1)
      en_outputs = self.encoder(
          tf.constant([en_inputs]), en_initial_states
      )
      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:],
      )
      de_input = tf.constant([de_start_index])
      de_state_h, de_state_c = en_outputs[1:]
      output = {
          "preprocessed_text": text_preprocessed,
          "logits": [],
          "alignment": [],
          "predicted_sequence": [],
      }
      for _ in range(self.max_prediction_len):
          de_output, de_state_h, de_state_c, alignment = self.decoder(
              de_input, (de_state_h, de_state_c), en_outputs[0]
          )
          de_input = tf.expand_dims(tf.argmax(de_output, -1), 0)
          pred_token_index = de_input.numpy()[0][0]
          if pred_token_index != 0:
              output["logits"].append(np.squeeze(de_output.numpy()))
              output["alignment"].append(np.squeeze(alignment.numpy()))
              output["predicted_sequence"].append(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, 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 5 for calling the AttentionSummarizer obj's underlying member method code with example usage: obj.predict_vectors(input_text, target_text) and returns: output