class_docstr: RNN model for text sequence to sequence
class_docstr_tok: ["RNN","model","for","text","sequence","to","sequence"]
code: def call(self, inputs, training=None, mask=None):
      enc_inputs = inputs["input_enc_x"]
      seq_enc_len = compute_sen_lens(
          enc_inputs, padding_token=self.padding_token
      )
      enc_mask = self.mask_layer(enc_inputs)
      enc_inputs = self.embed(enc_inputs)
      enc_inputs = self.embed_d(enc_inputs)
      enc_outputs, enc_state = self.encoder(
          enc_inputs, training=training, mask=enc_mask
      )
      if self.is_infer:
          dec_outputs = self.decoder(
              [enc_outputs, enc_state, seq_enc_len], training=training
          )
          return dec_outputs
  
      else:
          dec_inputs = inputs["input_dec_x"]
          seq_dec_len = compute_sen_lens(
              dec_inputs, padding_token=self.padding_token
          )
          dec_outputs = self.decoder(
              [
                  dec_inputs,
                  seq_dec_len,
                  enc_outputs,
                  enc_state,
                  seq_enc_len,
              ],
              training=training,
          )
          return dec_outputs
  
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 RnnSeq2SeqModel obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: dec_outputs, dec_outputs