Hi
Thanks for replying.!!
Using slicing x[:,-1,:] I got below matrix.

tensor([[-0.7032,  0.9809, -0.0366, -0.8463,  0.8746, -0.9249,  0.9830,  0.9964],
[ 0.5889,  0.9578,  0.9545,  0.9815, -0.9984,  0.9589,  0.8505,  0.9543],
[-0.3179,  0.9790,  0.9406,  0.9769, -0.6671, -0.7646,  0.9281, -0.9901],
[ 0.5905,  0.8615,  0.7148, -0.7225,  0.2396,  0.4143, -0.1313, -0.9152],
[-0.9196, -0.9925, -0.8548, -0.9785,  0.3584,  0.9860, -0.4431, -0.9985],
[-0.9984,  0.4017, -0.9045,  0.4047, -0.6658, -0.4878, -0.9790,  0.7657],
[ 0.8931,  0.6347, -0.7855, -0.9594, -0.9998,  0.8930,  0.3539, -0.0567],
[-0.9782, -0.9661, -0.8166, -0.9830, -0.6294,  0.9168,  0.4004, -0.9968],
[-0.8964, -0.8088,  0.9799,  0.8978, -0.2576,  0.8573, -0.7513, -0.9112],
[-0.9389, -0.9517, -0.6023, -0.9468,  0.3026,  0.9553, -0.1915, -0.9959]],
grad_fn=)

Don’t we have to take the last values of all rows (that’s what my slicing do) ?? or we need to apply Linear transformation ?? What if I don’t want to apply linear transformation.
Basically my question is we have 8 hidden units out for each input features which hidden output we need to select.
If I need to tell below one is +ve or -ve review which value we need to consider.
(-0.7032, 0.9809, -0.0366, -0.8463, 0.8746, -0.9249, 0.9830, 0.9964).