code: def decode_inference(self, memory, states):
      B, _, _ = shape_list(memory)
      # init states
      outputs = tf.TensorArray(
          dtype=tf.float32,
          size=0,
          clear_after_read=False,
          dynamic_size=True,
      )
      attentions = tf.TensorArray(
          dtype=tf.float32,
          size=0,
          clear_after_read=False,
          dynamic_size=True,
      )
      stop_tokens = tf.TensorArray(
          dtype=tf.float32,
          size=0,
          clear_after_read=False,
          dynamic_size=True,
      )
  
      # pre-computes
      self.attention.process_values(memory)
  
      # iter vars
      stop_flag = tf.constant(False, dtype=tf.bool)
      step_count = tf.constant(0, dtype=tf.int32)
  
      def _body(
          step,
          memory,
          states,
          outputs,
          stop_tokens,
          attentions,
          stop_flag,
      ):
          frame_next = states[0]
          prenet_next = self.prenet(frame_next, training=False)
          output, stop_token, states, attention = self.step(
              prenet_next, states, None, training=False
          )
          stop_token = tf.math.sigmoid(stop_token)
          outputs = outputs.write(step, output)
          attentions = attentions.write(step, attention)
          stop_tokens = stop_tokens.write(step, stop_token)
          stop_flag = tf.greater(stop_token, self.stop_thresh)
          stop_flag = tf.reduce_all(stop_flag)
          return (
              step + 1,
              memory,
              states,
              outputs,
              stop_tokens,
              attentions,
              stop_flag,
          )
  
      cond = lambda step, m, s, o, st, a, stop_flag: tf.equal(
          stop_flag, tf.constant(False, dtype=tf.bool)
      )
      (
          _,
          memory,
          states,
          outputs,
          stop_tokens,
          attentions,
          stop_flag,
      ) = tf.while_loop(
          cond,
          _body,
          loop_vars=(
              step_count,
              memory,
              states,
              outputs,
              stop_tokens,
              attentions,
              stop_flag,
          ),
          parallel_iterations=32,
          swap_memory=True,
          maximum_iterations=self.max_decoder_steps,
      )
  
      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 7 for calling the Decoder obj's underlying member method code with example usage: obj.decode_inference(memory, states) and returns: outputs,  stop_tokens,  attentions, unknown,  memory,  states,  outputs,  stop_tokens,  attentions,  stop_flag