@ptrblck Thank you so much for your kind help and extraordinary explanation. Yes, I need to make the validation Dataset larger artificially. Then it will serve my purpose.
transform = transforms.Compose([
                    transforms.Resize((28,28)),
                    transforms.Grayscale(num_output_channels=1),
                    transforms.ToTensor(),      
                    #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
                    transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.SVHN('SVHN', download=True, transform=transform, split='train')
valid_dataset = datasets.MNIST('MNIST', download=True, transform=transform, train=True)
class MyDataTrain(Dataset):
    def __init__(self, length):
        self.data = torch.randn(length, 1)
        self.target = torch.Tensor(length).uniform_(0, 10).long()
    def __getitem__(self, index):
        x = self.data[index]
        y = self.data[index]
        return x, y

    def __len__(self):
        return len(self.data)

class MyDataVal(Dataset):
    def __init__(self, length, fake_length):
        self.data = torch.randn(length, 1)
        self.target = torch.Tensor(length).uniform_(0, 10).long()
        self.fake_length = fake_length
        self.real_length = len(self.data)
    
    def __getitem__(self, index):
        index = index % self.real_length
        x = self.data[index]
        y = self.data[index]
        return x, y

    def __len__(self):
        return self.fake_length
train_dataset = MyDataTrain(length=60000)
val_dataset = MyDataVal(length=10000, fake_length=60000)
train_loader = DataLoader(train_dataset)
val_loader = DataLoader(val_dataset)

Gives me error.
NameError: name 'Dataset' is not defined