### Question:
SymmetricTrainerContext implements TrainerContext { @Override public void init(Model model, Object... args) { }  @Override void init(Model model, Object... args); @Override Trainer create(String uuid, int threadId, Model model, int rootDevice, boolean useMDS, ParallelWrapper wrapper,
                    WorkspaceMode mode, int averagingFrequency); @Override void finalizeRound(Model originalModel, Model... models); @Override void finalizeTraining(Model originalModel, Model... models);  }### Answer:
@Test public void testEqualUuid1() { MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed) .l2(0.0005) .weightInit(WeightInit.XAVIER) .updater(new Nesterovs(0.01, 0.9)).list() .layer(0, new ConvolutionLayer.Builder(5, 5) .nIn(nChannels).stride(1, 1).nOut(20).activation(Activation.IDENTITY).build()) .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2) .stride(2, 2).build()) .layer(2, new ConvolutionLayer.Builder(5, 5) .stride(1, 1).nOut(50).activation(Activation.IDENTITY).build()) .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2) .stride(2, 2).build()) .layer(4, new DenseLayer.Builder().activation(Activation.RELU).nOut(500).build()) .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .nOut(outputNum).activation(Activation.SOFTMAX).build()) .setInputType(InputType.convolutionalFlat(28, 28, nChannels)); MultiLayerConfiguration conf = builder.build(); MultiLayerNetwork model = new MultiLayerNetwork(conf); model.init(); ParallelWrapper wrapper = new ParallelWrapper.Builder(model) .prefetchBuffer(24) .workers(2) .averagingFrequency(3) .reportScoreAfterAveraging(true) .build(); val trainer = new SymmetricTrainer(model, "alpha", 3, WorkspaceMode.NONE, wrapper, true); assertEquals("alpha_thread_3", trainer.getUuid()); }