That method directly resizes the associated tensor instance; having a View module would enable one to directly add it to the sequence of modules in a network rather than having to explicitly call it. For example:
class MyNetwork(torch.nn.Module):
    def __init__(self):
        super(MyNetwork, self).__init__()
        modules = [nn.Conv2d(1, 10, 3),
                   nn.MaxPool2d(2, 2),
                   ...,
                   View(100),
                   nn.Linear(100, 2)]
        for i, m in enumerate(modules):
            self.add_module(str(i), m)
    def forward(self, x):
        for child in self.children():
            x = child(x)
        return x