def create_network(hp,batch_size,num_speakers,is_training):
    net = WaveNetModel(
        batch_size=batch_size,
        dilations=hp.dilations,
        filter_width=hp.filter_width,
        residual_channels=hp.residual_channels,
        dilation_channels=hp.dilation_channels,
        quantization_channels=hp.quantization_channels,
        out_channels =hp.out_channels,
        skip_channels=hp.skip_channels,
        use_biases=hp.use_biases,  #  True
        scalar_input=hp.scalar_input,
        global_condition_channels=hp.gc_channels,
        global_condition_cardinality=num_speakers,
        local_condition_channels=hp.num_mels,
        upsample_factor=hp.upsample_factor,
        legacy = hp.legacy,
        residual_legacy = hp.residual_legacy,
        drop_rate = hp.wavenet_dropout,
        train_mode=is_training)
    
    return net 

def setUp(self):
        self.net = WaveNetModel(batch_size=1,
                                dilations=[1, 2, 4, 8, 16, 32, 64, 128, 256],
                                filter_width=2,
                                residual_channels=16,
                                dilation_channels=16,
                                quantization_channels=128,
                                skip_channels=32) 

def setUp(self):
        self.net = WaveNetModel(batch_size=1,
                                dilations=[1, 2, 4, 8, 16, 32, 64, 128, 256],
                                filter_width=2,
                                use_biases=True,
                                residual_channels=16,
                                dilation_channels=16,
                                quantization_channels=128,
                                skip_channels=32)