class_docstr: RNN model for text sequence to sequence
class_docstr_tok: ["RNN","model","for","text","sequence","to","sequence"]
code: def __init__(self, config, **kwargs):
      super().__init__(config, **kwargs)
      logging.info("Initialize RnnSeq2SeqModel...")
      model_config = config["model"]["net"]["structure"]
      self.is_infer = config["model"]["is_infer"]
      self.dropout_rate = model_config["dropout_rate"]
      self.padding_token = utils.PAD_IDX
      self.embed_d = tf.keras.layers.Dropout(self.dropout_rate)
      self.encoder = layers.RnnEncoder(config, name="encoder")
      self.decoder = layers.RnnDecoder(
          config,
          self.decoder_embed,
          self.decode_vocab_size,
          name="decoder",
      )
      self.mask_layer = tf.keras.layers.Lambda(
          lambda inputs: tf.cast(
              tf.not_equal(inputs, self.padding_token), tf.int32
          )
      )
  
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 1 to create a new RnnSeq2SeqModel object from inherited base classes: Seq2SeqModel with example: obj = RnnSeq2SeqModel(config)