1 from torch.autograd import Variable
 2 import torch
 3 b = Variable(torch.FloatTensor([64, 100, 43]))
 4 print(b)
 5 ‘‘‘
 6 Variable containing:
 7   64
 8  100
 9   43
10 [torch.FloatTensor of size 3]
11 ‘‘‘

 1 from torch.autograd import Variable
 2 import torch
 3 
 4 a1 = Variable(torch.randn(64, 100, 43))
 5 # print(a)
 6 a2 = Variable(torch.randn(64, 100, 44))
 7 a3 = Variable(torch.randn(64, 100, 45))
 8 a = [a1, a2, a3]
 9 
10 # method 1
11 a = torch.cat((i.data for i in a), 2)
12 # method 2
13 a = torch.cat(a, 2)
14 print(a)
15 ‘‘‘
16 Variable containing:
17 ( 0 ,.,.) = 
18  -8.0339e-01 -1.0054e+00 -3.8750e-01  ...  -8.1924e-01 -5.0174e-01  1.2302e+00
19  -2.4266e+00  3.8993e-01  9.6254e-01  ...  -2.6002e-01  3.8668e-01 -1.0960e+00
20  -9.8618e-01  1.5732e-01 -7.6385e-01  ...  -9.6085e-01  4.7323e-01  1.0353e+00
21                  ...                   ?                   ...                
22 (63 ,.,.) =        
23  -8.9123e-01  8.9774e-01 -1.1528e+00  ...   2.8538e-01 -1.3932e+00 -3.7968e-01
24   1.0256e+00  2.1746e+00  6.9736e-01  ...   1.2657e+00 -1.0258e+00  5.7128e-01
25  -1.0966e+00  7.1878e-01  3.5268e-01  ...   3.5731e-01  1.9369e+00 -7.3994e-01
26 [torch.FloatTensor of size 64x100x132]
27 ‘‘‘

 1 >>> x = torch.randn(2, 3)
 2 >>> x
 3     
 4          0.5983 -0.0341  2.4918
 5          1.5981 -0.5265 -0.8735
 6         [torch.FloatTensor of size 2x3]
 7     
 8 >>> torch.cat((x, x, x), 0)
 9     
10          0.5983 -0.0341  2.4918
11          1.5981 -0.5265 -0.8735
12          0.5983 -0.0341  2.4918
13          1.5981 -0.5265 -0.8735
14          0.5983 -0.0341  2.4918
15          1.5981 -0.5265 -0.8735
16         [torch.FloatTensor of size 6x3]
17     
18 >>> torch.cat((x, x, x), 1)
19     
20          0.5983 -0.0341  2.4918  0.5983 -0.0341  2.4918  0.5983 -0.0341  2.4918
21          1.5981 -0.5265 -0.8735  1.5981 -0.5265 -0.8735  1.5981 -0.5265 -0.8735
22         [torch.FloatTensor of size 2x9]