code: def __init__(
      self,
      frame_dim,
      r,
      attn_type,
      use_attn_win,
      attn_norm,
      prenet_type,
      prenet_dropout,
      use_forward_attn,
      use_trans_agent,
      use_forward_attn_mask,
      use_location_attn,
      attn_K,
      separate_stopnet,
      speaker_emb_dim,
      enable_tflite,
      **kwargs,
  ):
      super(Decoder, self).__init__(**kwargs)
      self.frame_dim = frame_dim
      self.r_init = tf.constant(r, dtype=tf.int32)
      self.r = tf.constant(r, dtype=tf.int32)
      self.output_dim = r * self.frame_dim
      self.separate_stopnet = separate_stopnet
      self.enable_tflite = enable_tflite
  
      # layer constants
      self.max_decoder_steps = tf.constant(1000, dtype=tf.int32)
      self.stop_thresh = tf.constant(0.5, dtype=tf.float32)
  
      # model dimensions
      self.query_dim = 1024
      self.decoder_rnn_dim = 1024
      self.prenet_dim = 256
      self.attn_dim = 128
      self.p_attention_dropout = 0.1
      self.p_decoder_dropout = 0.1
  
      self.prenet = Prenet(
          prenet_type,
          prenet_dropout,
          [self.prenet_dim, self.prenet_dim],
          bias=False,
          name="prenet",
      )
      self.attention_rnn = keras.layers.LSTMCell(
          self.query_dim,
          use_bias=True,
          name="attention_rnn",
      )
      self.attention_rnn_dropout = keras.layers.Dropout(0.5)
  
      # TODO: implement other attn options
      self.attention = Attention(
          attn_dim=self.attn_dim,
          use_loc_attn=True,
          loc_attn_n_filters=32,
          loc_attn_kernel_size=31,
          use_windowing=False,
          norm=attn_norm,
          use_forward_attn=use_forward_attn,
          use_trans_agent=use_trans_agent,
          use_forward_attn_mask=use_forward_attn_mask,
          name="attention",
      )
      self.decoder_rnn = keras.layers.LSTMCell(
          self.decoder_rnn_dim, use_bias=True, name="decoder_rnn"
      )
      self.decoder_rnn_dropout = keras.layers.Dropout(0.5)
      self.linear_projection = keras.layers.Dense(
          self.frame_dim * r, name="linear_projection/linear_layer"
      )
      self.stopnet = keras.layers.Dense(1, name="stopnet/linear_layer")
  
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 1 to create a new Decoder object from inherited base classes: keras.layers with example: obj = Decoder(frame_dim, r, attn_type, use_attn_win, attn_norm, prenet_type, prenet_dropout, use_forward_attn, use_trans_agent, use_forward_attn_mask, use_location_attn, attn_K, separate_stopnet, speaker_emb_dim, enable_tflite)