import torch
from torch.autograd import Variable
from torch.nn._functions.loss import CosineEmbeddingLoss

input1 = Variable(torch.rand(5,10))
input1 = input1.cuda()

input2 = Variable(torch.rand(5,10))
input2 = input2.cuda()

y = Variable(torch.FloatTensor([1.0] * input1.size()[0]))

loss = CosineEmbeddingLoss()
loss(input1, input2, y)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/_functions/loss.py", line 41, in forward
    torch.eq(y, -1, out=_idx)
TypeError: torch.eq received an invalid combination of arguments - got (torch.FloatTensor, int, out=torch.cuda.ByteTensor), but expected one of:
 * (torch.FloatTensor tensor, float value, *, torch.FloatTensor out)
      didn't match because some of the arguments have invalid types: (torch.FloatTensor, int, out=torch.cuda.ByteTensor)
 * (torch.FloatTensor tensor, torch.FloatTensor other, *, torch.FloatTensor out)
      didn't match because some of the arguments have invalid types: (torch.FloatTensor, int, out=torch.cuda.ByteTensor)
 * (torch.FloatTensor tensor, float value, *, torch.ByteTensor out)
      didn't match because some of the arguments have invalid types: (torch.FloatTensor, int, out=torch.cuda.ByteTensor)
 * (torch.FloatTensor tensor, torch.FloatTensor other, *, torch.ByteTensor out)
      didn't match because some of the arguments have invalid types: (torch.FloatTensor, int, out=torch.cuda.ByteTensor)