0])     x = keras.layers.Conv0D(00, (0, 0), activation=tf.nn.relu, padding='SAME')(x)    x = keras.layers.UpSampling0D((0,0))(x)    x = keras.layers.BatchNormalization()(x)    x = keras.layers.Dropout(0.0)(x)     x = keras.layers.Concatenate()([x,a])    x = keras.layers.Conv0D(00, (0, 0), activation=tf.nn.relu, padding='SAME')(x)    x = keras.layers.UpSampling0D((0,0))(x)    x = keras.layers.BatchNormalization()(x)    x = keras.layers.Dropout(0.0)(x)     decoded = keras.layers.Conv0D(0, (0, 0), activation=tf.nn.relu, padding='SAME')(x)     final_net= keras.models.Model(inputs=[input_0,input_0,input_0],outputs=decoded)    return final_net