PixelShuffle
class torch.nn.PixelShuffle(upscale_factor)
Rearranges elements in a tensor of shape (, C \times r^2, H, W) to
   a tensor of shape (, C, H \times r, W \times r), where r is an
   upscale factor.
This is useful for implementing efficient sub-pixel convolution
   with a stride of 1/r.
See the paper: Real-Time Single Image and Video Super-Resolution
   Using an Efficient Sub-Pixel Convolutional Neural Network by Shi
   et. al (2016) for more details.
Parameters:
      upscale_factor (int) -- factor to increase spatial
      resolution by
Shape:
      * Input: (*, C_{in}, H_{in}, W_{in}), where * is zero or more
        batch dimensions
  * Output: (*, C_{out}, H_{out}, W_{out}), where

  C_{out} = C_{in} \div \text{upscale\_factor}^2

  H_{out} = H_{in} \times \text{upscale\_factor}

  W_{out} = W_{in} \times \text{upscale\_factor}

Examples:
  >>> pixel_shuffle = nn.PixelShuffle(3)
  >>> input = torch.randn(1, 9, 4, 4)
