from torch.autograd import Variable

 import torch

 b = Variable(torch.FloatTensor([64, 100, 43]))

 print(b)

 '''

 Variable containing:

   64

  100

   43

 [torch.FloatTensor of size 3]

 '''

 from torch.autograd import Variable

 import torch

 a1 = Variable(torch.randn(64, 100, 43))

 # print(a)

 a2 = Variable(torch.randn(64, 100, 44))

 a3 = Variable(torch.randn(64, 100, 45))

 a = [a1, a2, a3]

 # method 1

 a = torch.cat((i.data for i in a), 2)

 # method 2

 a = torch.cat(a, 2)

 print(a)

 '''

 Variable containing:

 ( 0 ,.,.) =

  -8.0339e-01 -1.0054e+00 -3.8750e-01  ...  -8.1924e-01 -5.0174e-01  1.2302e+00

  -2.4266e+00  3.8993e-01  9.6254e-01  ...  -2.6002e-01  3.8668e-01 -1.0960e+00

  -9.8618e-01  1.5732e-01 -7.6385e-01  ...  -9.6085e-01  4.7323e-01  1.0353e+00

                  ...                   ⋱                   ...

 (63 ,.,.) =

  -8.9123e-01  8.9774e-01 -1.1528e+00  ...   2.8538e-01 -1.3932e+00 -3.7968e-01

   1.0256e+00  2.1746e+00  6.9736e-01  ...   1.2657e+00 -1.0258e+00  5.7128e-01

  -1.0966e+00  7.1878e-01  3.5268e-01  ...   3.5731e-01  1.9369e+00 -7.3994e-01

 [torch.FloatTensor of size 64x100x132]

 '''

 >>> x = torch.randn(2, 3)

 >>> x

          0.5983 -0.0341  2.4918

          1.5981 -0.5265 -0.8735

         [torch.FloatTensor of size 2x3]

 >>> torch.cat((x, x, x), 0)

          0.5983 -0.0341  2.4918

          1.5981 -0.5265 -0.8735

          0.5983 -0.0341  2.4918

          1.5981 -0.5265 -0.8735

          0.5983 -0.0341  2.4918

          1.5981 -0.5265 -0.8735

         [torch.FloatTensor of size 6x3]

 >>> torch.cat((x, x, x), 1)

          0.5983 -0.0341  2.4918  0.5983 -0.0341  2.4918  0.5983 -0.0341  2.4918

          1.5981 -0.5265 -0.8735  1.5981 -0.5265 -0.8735  1.5981 -0.5265 -0.8735

         [torch.FloatTensor of size 2x9]