def build(self, input_shape):
    self._type_embedding_layer = EmbeddingLayer(name='object_type', vocab_size=100, embedding_dim=self._hparams['hidden_size'])
    self._clickable_embedding_layer = EmbeddingLayer(name='object_clickable', vocab_size=2, embedding_dim=self._hparams['hidden_size'])
    self._pos_embedding_layers = [EmbeddingLayer(name='object_pos_0', vocab_size=self._hparams['max_pixel_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_pos_1', vocab_size=self._hparams['max_pixel_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_pos_2', vocab_size=self._hparams['max_pixel_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_pos_3', vocab_size=self._hparams['max_pixel_pos'], embedding_dim=self._hparams['hidden_size'])]
    self._dom_embedding_layers = [EmbeddingLayer(name='object_dom_pos_0', vocab_size=self._hparams['max_dom_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_dom_pos_1', vocab_size=self._hparams['max_dom_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_dom_pos_2', vocab_size=self._hparams['max_dom_pos'], embedding_dim=self._hparams['hidden_size'])]
    self._final_layer = tf.keras.layers.Dense(self._hparams['hidden_size'], activation=None)
    self._vh_final_layer = tf.keras.layers.Dense(self._hparams['hidden_size'], activation=tf.nn.tanh)
    self._pixel_layers = self._get_encoder3(initial_channel_size=1)
    self._transformer_encoder = nlp_transformer.EncoderStack(self._hparams)
def build(self, input_shape):
    self._type_embedding_layer = EmbeddingLayer(name='object_type', vocab_size=100, embedding_dim=self._hparams['hidden_size'])
    self._clickable_embedding_layer = EmbeddingLayer(name='object_clickable', vocab_size=2, embedding_dim=self._hparams['hidden_size'])
    self._pos_embedding_layers = [EmbeddingLayer(name='object_pos_0', vocab_size=self._hparams['max_pixel_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_pos_1', vocab_size=self._hparams['max_pixel_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_pos_2', vocab_size=self._hparams['max_pixel_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_pos_3', vocab_size=self._hparams['max_pixel_pos'], embedding_dim=self._hparams['hidden_size'])]
    self._dom_embedding_layers = [EmbeddingLayer(name='object_dom_pos_0', vocab_size=self._hparams['max_dom_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_dom_pos_1', vocab_size=self._hparams['max_dom_pos'], embedding_dim=self._hparams['hidden_size']), EmbeddingLayer(name='object_dom_pos_2', vocab_size=self._hparams['max_dom_pos'], embedding_dim=self._hparams['hidden_size'])]
    self._final_layer = tf.keras.layers.Dense(self._hparams['hidden_size'], activation=None)
    self._vh_final_layer = tf.keras.layers.Dense(self._hparams['hidden_size'], activation=tf.nn.tanh)
    self._pixel_layers = self._get_encoder3(initial_channel_size=1)
    self._transformer_encoder = nlp_transformer.EncoderStack(self._hparams)