How to update the weight about BlockLSTM ?

Start with the provided Scala example and the BlockLSTM.create call, then inspect the Java API for BlockLSTMGrad or other gradient operations. Run the example in the TensorFlow Java environment and determine whether the exposed APIs support updating the displayed weight tensors. Done means the supported weight-update workflow is documented or the API limitation is clearly stated.

HI :

now I use BlockLSTM for build lstm layer ,but I don't know how to update the lstm weight parameters , if need use blockLSTMGrad or something to do ,the coda is paste here:

object LstmExample {

  def initializeTruncatedNormalTensor(shape: Operand[TInt32], scope: Scope): TFloat32 = {
    TruncatedNormal.seed(1000L)
    //        TruncatedNormal<TFloat32> truncatedNormal = TruncatedNormal.create(scope, shape, TFloat32.DTYPE);
    //        DataType<TFloat32> DTYPE = DataType.create("FLOAT", 1, 4, TFloat32Impl::mapTensor);
    //        DataType DTYPE = DataType.valueOf("FLOAT");
    val truncatedNormal: TruncatedNormal[TFloat32] = TruncatedNormal.create(scope, shape, classOf[TFloat32])
    return truncatedNormal.asTensor
  }
  private def getWeightMatrix(shape: Operand[TInt32], scope: Scope) = { //        Tensor<TFloat32> tensorWeight = TensorValues.initializeTruncatedNormalTensor(shape, scope);
    val tensorWeight = TensorValues.initializeTruncatedNormalTensor(shape, scope)
    Constant.create(scope, tensorWeight)
  }

  def printTensor(tensor: Operand[TFloat32],name:String): Unit ={
    val data: Array[Float] = TensorResources.extractFloats(tensor.asTensor())
    println(s"data:${name},  ${data.mkString(" | ")}")
  }
  def main(args: Array[String]): Unit = {
    val libraryPath = System.getProperty("java.library.path")
    System.out.println(libraryPath)
    implicit val session = TestSession.createTestSession(TestSession.Mode.EAGER) // EagerSession.create()
    implicit val tf = session.getTF // Ops.create(session).withName("test")
    implicit val scope = tf.scope()
    //    val session = EagerSession.create
    //    val tf = Ops.create(session)
    //        Scope scope = new Scope(session);
    //    val scope = session.baseScope()
    val rawInputSequence = Array(Array(Array(0.1f, 0.2f)), Array(Array(0.3f, 0.4f))) //shape (timelen, batch_size, num_inputs).
    val inputSequence = tf.constant(rawInputSequence)
    val inputSize = 2
    val cellSize =  5
    val maximumTimeLength = 2
    val cellShape = Array(1, cellSize)
    val cellDims = Constant.vectorOf(scope, cellShape)
    val seqLenMax = tf.array(maximumTimeLength)
    //        Operand<TFloat32> initialCellState = Zeros.create(scope, cellDims, TFloat32.DTYPE);
    //        Operand<TFloat32> initialHiddenState = Zeros.create(scope, cellDims, TFloat32.DTYPE);
    val initialCellState = Zeros.create(scope, cellDims, classOf[TFloat32])
    val initialHiddenState = Zeros.create(scope, cellDims, classOf[TFloat32])
    val weightShape = Array(inputSize + cellSize, cellSize * 4)
    val weightMatrixDims = Constant.vectorOf(scope, weightShape)
    val weightMatrix = getWeightMatrix(weightMatrixDims, scope)
    //    session.print(weightMatrix)
    val weightGatesShape = Array(cellSize)
    val weightGatesDims = Constant.vectorOf(scope, weightGatesShape)
    val weightInputGate = getWeightMatrix(weightGatesDims, scope)
    printTensor(weightInputGate,"weightInputGate")
    val weightForgetGate = getWeightMatrix(weightGatesDims, scope)
    printTensor(weightForgetGate,"weightForgetGate")
    val weightOutputGate = getWeightMatrix(weightGatesDims, scope)
    printTensor(weightOutputGate ,"weightOutputGate")
    val biasShape = Array(cellSize * 4)
    val biasDim = Constant.vectorOf(scope, biasShape)
    //classOf[TFloat32]
    //        Operand<TFloat32> bias = Zeros.create(scope, biasDim, TFloat32.DTYPE);
    val bias = Zeros.create(scope, biasDim, classOf[TFloat32])
    val blockLSTM = BlockLSTM.create(scope, tf.dtypes.cast(seqLenMax, classOf[TInt64]), inputSequence, initialCellState, initialHiddenState, weightMatrix, weightInputGate, weightForgetGate, weightOutputGate, bias)

//    session.print(blockLSTM.i)
//    println("&&&cs")
//    session.print(blockLSTM.cs)
//    println("&&&f")
//    session.print(blockLSTM.f)
//    println("&&&o")
//    session.print(blockLSTM.o)
//    println("&&&ci")
//    session.print(blockLSTM.ci)
//    println("&&&co")
//    session.print(blockLSTM.co)
//    println("&&&h")
//    session.print(blockLSTM.h)

thanks for your help