you might use

a.view(1,5)
Out: 

 1  2  3  4  5
[torch.FloatTensor of size 1x5]

Although both torch.view and torch.reshape are used to reshape tensors, here are the differences between them.

1. As the name suggests, torch.view merely creates a view of the original tensor. The new tensor will always share its data with the original tensor. This means that if you change the original tensor, the reshaped tensor will change and vice versa.

>>> z = torch.zeros(3, 2)
>>> x = z.view(2, 3)
>>> z.fill_(1)
>>> x
tensor([[1., 1., 1.],
        [1., 1., 1.]])

2. To ensure that the new tensor always shares its data with the original, torch.view imposes some contiguity constraints on the shapes of the two tensors [docs]. More often than not this is not a concern, but sometimes torch.view throws an error even if the shapes of the two tensors are compatible. Here's a famous counter-example.

>>> z = torch.zeros(3, 2)
>>> y = z.t()
>>> y.size()
torch.Size([2, 3])
>>> y.view(6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: invalid argument 2: view size is not compatible with input tensor's
size and stride (at least one dimension spans across two contiguous subspaces).
Call .contiguous() before .view().

3. torch.reshape doesn't impose any contiguity constraints, but also doesn't guarantee data sharing. The new tensor may be a view of the original tensor, or it may be a new tensor altogether.

>>> z = torch.zeros(3, 2)
>>> y = z.reshape(6)
>>> x = z.t().reshape(6)
>>> z.fill_(1)
tensor([[1., 1.],
        [1., 1.],
        [1., 1.]])
>>> y
tensor([1., 1., 1., 1., 1., 1.])
>>> x
tensor([0., 0., 0., 0., 0., 0.])

TL;DR:
If you just want to reshape tensors, use torch.reshape. If you're also concerned about memory usage and want to ensure that the two tensors share the same data, use torch.view.