Fix a bug in narrow docs
diff --git a/docs/tensor.md b/docs/tensor.md
index 7bda3aa..9cf4515 100644
--- a/docs/tensor.md
+++ b/docs/tensor.md
@@ -163,11 +163,11 @@
```python
>>> x = torch.Tensor(5).zero_()
>>> print(x)
-0
-0
-0
-0
-0
+ 0
+ 0
+ 0
+ 0
+ 0
[torch.FloatTensor of dimension 5]
>>> x.narrow(0, 1, 2).fill_(1)
>>> # narrow() returns a Tensor referencing the same Storage as x
@@ -175,7 +175,16 @@
0
1
1
- 1
+ 0
+ 0
+[torch.FloatTensor of dimension 5]
+>>> # same thing can be achieved with slice indexing
+>>> x[1:3] = 2
+>>> print(x)
+ 0
+ 2
+ 2
+ 0
0
[torch.FloatTensor of dimension 5]
```
@@ -241,7 +250,7 @@
### torch.Tensor(sizes) ###
Create a tensor of any number of dimensions. `sizes` gives the size in each dimension of
-the tensor and is of type `torch.Size`.
+the tensor and is of type `torch.Size`.
```python
Example, create a 4D 4x4x3x2 tensor:
@@ -368,7 +377,7 @@
When you create a `torch.cuda.*Tensor`, it is allocated on the current GPU.
However, you could allocate it on another GPU as well, using the `with torch.cuda.device(id)` context.
-All allocations within this context will be placed on the GPU `id`.
+All allocations within this context will be placed on the GPU `id`.
Once `Tensor`s are allocated, you can do operations on them from any GPU context, and the results
will be placed on the same device as where the source `Tensor` is located.
@@ -403,6 +412,6 @@
# even within a context, you can give a GPU id to the .cuda call
c = torch.randn(2).cuda(2)
# c.get_device() == 2
-
+
```
