class_docstr: Transformer model for text sequence to sequence
class_docstr_tok: ["Transformer","model","for","text","sequence","to","sequence"]
code: def call(self, inputs, training=None, mask=None):
      input_enc_x = inputs["input_enc_x"]
      enc_mask = create_padding_mask(input_enc_x)
      enc_emb = self.embed(input_enc_x)
      enc_pos_emb = self.pos_embed(input_enc_x)
      enc_emb += enc_pos_emb
      enc_emb = self.embed_d(enc_emb, training=training)
      enc_out = self.encoder(enc_emb, training=training, mask=enc_mask)
  
      if not self.is_infer:
          input_dec_x = inputs["input_dec_x"]
          dec_inputs = [input_dec_x, enc_out]
      else:
          dec_inputs = enc_out
      dec_out = self.decoder(
          dec_inputs, training=training, mask=enc_mask
      )
      return dec_out
  
file_path: delta/delta/models/text_seq2seq_model.py
imports: import pickle
  import delta.compat
  from absl import logging
  from delta import layers
  from delta import utils
  from delta.models.base_model import Model
  from delta.utils import registers
  from delta.layers.utils import compute_sen_lens
  from delta.layers.utils_tf import create_padding_mask
label_desc: Use image node_id 2 for calling the TransformerSeq2SeqModel obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: dec_out