Thank you, @DAlolicorn.
I got the answer. I was looking for the index, that you mentioned by idx.
I think as you said that len(self.Test) determined the size of dataset, which is 10000 in this case.
I also would like to know if I want to have testing data and the shuffled version of it (instead of training data), this is correct?
class MyTestDataset():
        def __init__(self, transform_test=None):
            self.Test = datasets.CIFAR10(root='~/data', train=False,download=False,transform=transform_test)
            self.cifar_len = 10
         
            rand_idx = torch.randperm(len(self.Test))[:self.cifar_len]
            self.Test_shuffeled = self.Test[rand_idx]
           

        def __getitem__(self, index):    
            x1, y1 = self.Test_shuffeled[index]
            x2, y2 = self.Test[index]
            x = torch.cat((x1,x2),2)
            y = torch.stack((torch.tensor(y1), torch.tensor(y2)))
            
            return x, y, index
        
        def __len__(self):
            return len(self.Test)

 loader = torch.utils.data.DataLoader(dataset,batch_size=64,shuffle=False, num_workers=8)