diff --git a/learning-pytorch/tensor_tutorial.py b/learning-pytorch/tensor_tutorial.py
index 95c46de..92f9277 100755
--- a/learning-pytorch/tensor_tutorial.py
+++ b/learning-pytorch/tensor_tutorial.py
@@ -3,48 +3,60 @@
 from __future__ import print_function
 import torch
 
-# x = torch.empty(5, 3)
-# x = torch.rand(5, 3)
-# x = torch.zeros(5, 3, dtype=torch.long)
-# x = torch.tensor([5.5, 3])
-# x = x.new_ones(5, 3, dtype=torch.double)
-# print(x)
-# x = torch.randn_like(x, dtype=torch.float)
-# print(x.size())
+x = torch.empty(5, 3)
+x = torch.rand(5, 3)
+x = torch.zeros(5, 3, dtype=torch.long)
+x = torch.tensor([5.5, 3])
+x = x.new_ones(5, 3, dtype=torch.double)
+print(x)
+x = torch.randn_like(x, dtype=torch.float)
+print(x.size())
 
-# y = torch.rand(5, 3)
-# print(x+y)
-# print(torch.add(x, y))
-# result = torch.empty(5, 3)
-# torch.add(x, y, out=result)
-# print(result)
+y = torch.rand(5, 3)
+print(x+y)
+print(torch.add(x, y))
+result = torch.empty(5, 3)
+torch.add(x, y, out=result)
+print(result)
 
-# print(x[:, 1])
+print(x[:, 1])
 
-# x = torch.randn(4, 4)
-# y = x.view(16)
-# z = x.view(-1, 8) # the size -1 is inferred from other dimensions
-# print(x.size(), y.size(), z.size())
+x = torch.randn(4, 4)
+y = x.view(16)
+z = x.view(-1, 8) # the size -1 is inferred from other dimensions
+print(x.size(), y.size(), z.size())
 
-# x = torch.randn(1)
-# print(x)
-# print(x.item())
+x = torch.randn(1)
+print(x)
+print(x.item())
 
-# a = torch.ones(5)
-# print(a)
+a = torch.ones(5)
+print(a)
 
-# b = a.numpy()
-# print(b)
+b = a.numpy()
+print(b)
 
-# a.add_(1)
-# print(a)
-# print(b)
+a.add_(1)
+print(a)
+print(b)
 
-# import numpy as np
-# a = np.ones(5)
-# b = torch.from_numpy(a)
-# np.add(a, 1, out=a)
-# print(a)
-# print(b)
+import numpy as np
+a = np.ones(5)
+b = torch.from_numpy(a)
+np.add(a, 1, out=a)
+print(a)
+print(b)
 
 print(torch.cuda.is_available())
+
+# let us run this cell only if CUDA is available
+# We will use ``torch.device`` objects to move tensors in and out of GPU
+if torch.cuda.is_available():
+    device = torch.device("cuda")          # a CUDA device object
+    y = torch.ones_like(x, device=device)  # directly create a tensor on GPU
+    x = x.to(device)                       # or just use strings ``.to("cuda")``
+    z = x + y
+    print(z)
+    print(z.to("cpu", torch.double))       # ``.to`` can also change dtype together!
+else:
+    print("you don't have cuda")