class_docstr: RNN model for text classification.
class_docstr_tok: ["RNN","model","for","text","classification","."]
code: def call(self, inputs, training=None, mask=None):
      input_x = inputs["input_x"]
      if self.use_dense_task:
          dense_input = inputs["input_dense"]
  
      # [batch_size]
      lens = self.compute_lens(input_x, self.max_len)
  
      # [batch_size, max_len, 1]
      mask = tf.expand_dims(
          tf.sequence_mask(lens, self.max_len, dtype=tf.float32),
          axis=-1,
      )
  
      # [batch_size, max_len, embed_len]
      out = self.embed(input_x)
      out = self.embed_d(out, training=training)
      # [batch_size, features]
      out = self.encoder(out, training=training, mask=mask)
      if self.use_dense_input:
          dense_out = self.dense_input_linear(dense_input)
          if self.only_dense_input:
              out = dense_out
          else:
              out = tf.keras.layers.Concatenate()([out, dense_out])
      # [batch_size, class_num]
      scores = self.final_dense(out)
      return scores
  
imports: import pickle
  import delta.compat
  from absl import logging
  from delta import layers
  from delta.models.text_cls_model import TextClassModel
  from delta.utils.register import registers
  from delta import utils
label_desc: Use image node_id 3 for calling the RnnAttentionModel obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: scores