Thanks your question & solution page helped me to find solution
And the code below works for me
> fnn.model

Sequential(
  (0): Linear(in_features=4, out_features=3, bias=True)
  (1): ReLU()
  (2): Linear(in_features=3, out_features=3, bias=True)
  (3): LayerNorm()
  (4): ReLU()
  (5): Softmax()
)


> fnn.model[:-1](data_torch_variable))

i.e. 
fnn.model[:-1](pre.trans_to_variable(data_x))[0:3]

tensor([[0.0000, 0.5921, 0.4040],
        [0.0000, 0.5703, 0.4293],
        [0.0000, 0.5618, 0.4390]], grad_fn=<SliceBackward>)