unflattened
  * **unflattened_size** (*Union**[**torch.Size**, **Tuple**,
    **List**, **NamedShape**]*) -- New shape of the unflattened
    dimension

-[ Examples ]-



input = torch.randn(2, 50)
With tuple of ints
m = nn.Sequential(
    nn.Linear(50, 50),
    nn.Unflatten(1, (2, 5, 5))
)
output = m(input)
output.size()
   torch.Size([2, 2, 5, 5])
With torch.Size
m = nn.Sequential(
    nn.Linear(50, 50),
    nn.Unflatten(1, torch.Size([2, 5, 5]))
)
output = m(input)
output.size()
   torch.Size([2, 2, 5, 5])
With namedshape (tuple of tuples)
input = torch.randn(2, 50, names=('N', 'features'))
unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5)))
output = unflatten(input)
output.size()
   torch.Size([2, 2, 5, 5])


