if I have a tensor like
torch.tensor([6., 4., 9., 8.], requires_grad=True)

and I want to represent each of these numbers by n number of parameters, how do I use nn.Embedding, for this?
if I do
x = nn.Embedding(4, 10)

then x(input)
then input needs to be of type LongTensor, how do I pass input as a floating tensor and embedding would represent index so, 0th row would be for 6., 1st row would be for 4. and so on?