[1.5000, 1.7500, 2.2500, 2.5000],
                [2.5000, 2.7500, 3.2500, 3.5000],
                [3.0000, 3.2500, 3.7500, 4.0000]]]])
  >>> m = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
  >>> m(input)
  tensor([[[[1.0000, 1.3333, 1.6667, 2.0000],
            [1.6667, 2.0000, 2.3333, 2.6667],
            [2.3333, 2.6667, 3.0000, 3.3333],
            [3.0000, 3.3333, 3.6667, 4.0000]]]])

  >>> # Try scaling the same data in a larger tensor
  >>> input_3x3 = torch.zeros(3, 3).view(1, 1, 3, 3)
  >>> input_3x3[:, :, :2, :2].copy_(input)
  tensor([[[[1., 2.],
            [3., 4.]]]])
  >>> input_3x3
  tensor([[[[1., 2., 0.],
            [3., 4., 0.],
            [0., 0., 0.]]]])

  >>> m = nn.Upsample(scale_factor=2, mode='bilinear')  # align_corners=False
  >>> # Notice that values in top left corner are the same with the small input (except at boundary)
  >>> m(input_3x3)
