code: def call(
      self,
      inp,
      tar,
      training,
      enc_padding_mask,
      look_ahead_mask,
      dec_padding_mask,
  ):
      enc_output = self.encoder(inp, training, enc_padding_mask)
  
      dec_output, attention_weights = self.decoder(
          tar, enc_output, training, look_ahead_mask, dec_padding_mask
      )
      final_output = self.final_layer(
          dec_output
      )  # (batch_size, tar_seq_len, target_vocab_size)
      return final_output, attention_weights
  
file_path: headliner/headliner/model/transformer_model.py
imports: from typing import Tuple
  from headliner.model.transformer_util import *
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.call(inp, tar, training, enc_padding_mask, look_ahead_mask, dec_padding_mask) and returns: final_output,  attention_weights