code: def decode(self, memory, states, frames, memory_seq_length=None):
      B, _, _ = shape_list(memory)
      num_iter = shape_list(frames)[1] // self.r
      # init states
      frame_zero = tf.expand_dims(states[0], 1)
      frames = tf.concat([frame_zero, frames], axis=1)
      outputs = tf.TensorArray(dtype=tf.float32, size=num_iter)
      attentions = tf.TensorArray(dtype=tf.float32, size=num_iter)
      stop_tokens = tf.TensorArray(dtype=tf.float32, size=num_iter)
      # pre-computes
      self.attention.process_values(memory)
      prenet_output = self.prenet(frames, training=True)
      step_count = tf.constant(0, dtype=tf.int32)
  
      def _body(
          step,
          memory,
          prenet_output,
          states,
          outputs,
          stop_tokens,
          attentions,
      ):
          prenet_next = prenet_output[:, step]
          output, stop_token, states, attention = self.step(
              prenet_next, states, memory_seq_length
          )
          outputs = outputs.write(step, output)
          attentions = attentions.write(step, attention)
          stop_tokens = stop_tokens.write(step, stop_token)
          return (
              step + 1,
              memory,
              prenet_output,
              states,
              outputs,
              stop_tokens,
              attentions,
          )
  
      (
          _,
          memory,
          _,
          states,
          outputs,
          stop_tokens,
          attentions,
      ) = tf.while_loop(
          lambda *arg: True,
          _body,
          loop_vars=(
              step_count,
              memory,
              prenet_output,
              states,
              outputs,
              stop_tokens,
              attentions,
          ),
          parallel_iterations=32,
          swap_memory=True,
          maximum_iterations=num_iter,
      )
  
      outputs = outputs.stack()
      attentions = attentions.stack()
      stop_tokens = stop_tokens.stack()
      outputs = tf.transpose(outputs, [1, 0, 2])
      attentions = tf.transpose(attentions, [1, 0, 2])
      stop_tokens = tf.transpose(stop_tokens, [1, 0, 2])
      stop_tokens = tf.squeeze(stop_tokens, axis=2)
      outputs = tf.reshape(outputs, [B, -1, self.frame_dim])
      return outputs, stop_tokens, attentions
  
imports: import tensorflow
  from tensorflow import keras
  from TTS.tts.tf.utils.tf_utils import shape_list
  from TTS.tts.tf.layers.common_layers import Prenet, Attention
label_desc: Use image node_id 6 for calling the Decoder obj's underlying member method code with example usage: obj.decode(memory, states, frames, memory_seq_length) and returns: outputs,  stop_tokens,  attentions, unknown,  memory,  prenet_output,  states,  outputs,  stop_tokens,  attentions