sure, here they are:
tensor([[-0.4108,  2.3717, -1.2828,  ...,  0.1713,  1.2362, -1.3406],
        [-0.2165,  1.2525, -0.5553,  ...,  0.5631,  0.7271, -0.7836],
        [-0.3744,  1.2202, -0.3709,  ...,  0.3243,  0.6756, -0.9807],
        ...,
        [-0.5486,  1.7626, -0.7162,  ...,  0.2115,  0.9654, -1.0366],
        [-0.7164,  1.5161, -0.2281,  ...,  0.5199,  0.7857, -1.1157],
        [-0.3701,  1.3838, -0.5642,  ...,  0.3960,  0.9117, -0.7888]],
       device='cuda:0', grad_fn=<AddmmBackward>)

torch.Size([64, 100])
tensor([86, 67, 17, 30, 90, 60, 51, 84, 59, 30, 50, 21, 34, 45, 38, 80, 37, 81,
         5, 39, 41, 20, 21,  5, 23, 49,  1, 23, 70, 42, 26, 86, 28, 14, 65, 38,
        53, 80, 98, 79, 29, 41,  6, 77, 23, 16, 85, 59, 46, 14, 73, 43, 43, 39,
        72, 18, 61, 63,  1, 38, 67, 86, 84, 51], device='cuda:0')
torch.Size([64])

actually now it gives an error:
-> 2606     return torch._C._nn.multilabel_margin_loss(input, target, reduction_enum)
   2607 
   2608 

RuntimeError: inconsistent target size: [64] for input of size: [64, 100]

It did not give that error when I used one hot encoded labels, but reading the implementation of the loss one hot labels do not make sense.
Thank you for your help