Here is main.py
    w1 = Parameter(torch.Tensor(np.random.rand(NF.shape[1]*2, NF.shape[1])))
    w2 = Parameter(torch.Tensor(np.random.rand(2,1)))
    w4 = Parameter(torch.Tensor(np.random.rand(NF.shape[1] * 2, NF.shape[1])))
    w5 = Parameter(torch.Tensor(np.random.rand(2, 1)))


    ## regression weight
    tmp = NF.shape[1] + EF.shape[1]

    w3 = Parameter(torch.Tensor(np.random.rand(tmp, 1)))
    bias = Parameter(torch.Tensor(np.random.rand(1, 1)))

    opt = optimizer.Adam([w1, w2, w3, bias, w4, w5], lr=0.01)

    torch.nn.init.xavier_uniform_(w1)
    torch.nn.init.xavier_uniform_(w2)
    torch.nn.init.xavier_uniform_(w3)
    torch.nn.init.xavier_uniform_(bias)
    torch.nn.init.xavier_uniform_(w4)
    torch.nn.init.xavier_uniform_(w5)

    mymodel = model(target, hop_num, A, NF, EF, w1, w2, w3, w4, w5, bias)

    ### train
    for epoch in range(3) :
        print('=================== epoch  {} start ======================='.format(epoch+1))
        var = mymodel.hop_info()
        y = torch.FloatTensor([[labels[target]]])

        cost = Func.mse_loss(var, y) # ( x.clone(), y )

        opt.zero_grad()
        cost.backward()
        opt.step()

and model.py
    def hop_info(self):

        with torch.autograd.set_detect_anomaly(True):

            # concat NF
            self.ANF = torch.matmul(self.A, self.NF)
            temp_NF = torch.cat((self.NF, self.ANF), dim=1)
            self.NF = Func.relu(normalize(torch.matmul(temp_NF, self.w1)))
            # self.NF = normalize(torch.matmul(temp_NF, self.w1))
            print(self.w1)

            # concat EF
            self.AEF = torch.matmul(self.A, self.EF)
            temp_EF = torch.cat((self.EF, self.AEF), dim=1)
            self.EF = Func.relu(normalize(torch.matmul(temp_EF, self.w2)))
            print(self.w2)

            # concat NF
            self.ANF = torch.matmul(self.A, self.NF)
            temp_NF = torch.cat((self.NF, self.ANF), dim=1)
            self.NF = Func.relu(normalize(torch.matmul(temp_NF, self.w4)))
            print(self.w4)

            # concat EF
            self.AEF = torch.matmul(self.A, self.EF)
            temp_EF = torch.cat((self.EF, self.AEF), dim=1)
            self.EF = Func.relu(normalize(torch.matmul(temp_EF, self.w5)))
            print(self.w5)


            self.allF = torch.cat((torch.Tensor(self.NF), torch.Tensor(self.EF)), dim=-1)

            self.targetF = self.allF[self.target]
            self.prediction = torch.matmul(self.targetF, self.w3) + self.bias
            return self.prediction

when I run the code, epoch 1 works.
but in epoch 2, opt.step() has a problem with the Error below.
the Error is
“RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [2, 1]] is at version 3; expected version 2 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!”
How can I fix it ?! Help please