gives the output:

<a name='nnx.MultiSoftMax'/>
### MultiSoftMax ###
This Module takes 2D or 3D input and performs a softmax over the last dimension. 
It uses the existing [SoftMax](https://github.com/torch/nn/blob/master/doc/transfer.md#nn.SoftMax) 
CUDA/C code to do so such that the Module can be used on both GPU and CPU. 
This can be useful for [keypoint detection](https://github.com/nicholas-leonard/dp/blob/master/doc/facialkeypointstutorial.md#multisoftmax).

<a name='nnx.SpatialReSampling'/>
### SpatialReSampling ###
Applies a 2D re-sampling over an input image composed of
several input planes (or channels, colors). The input tensor in `forward(input)` is 
expected to be a 3D or 4D tensor of size : `[batchSize x] nInputPlane x width x height`. 
The number of output planes will be the same as the number of input
planes.

The re-sampling is done using [bilinear interpolation](http://en.wikipedia.org/wiki/Bilinear_interpolation). 
For a simple nearest-neihbor upsampling, use `nn.SpatialUpSampling()`,
and for a simple average-based down-sampling, use 
`nn.SpatialDownSampling()`.

If the input image is a 3D tensor of size `nInputPlane x height x width`,
the output image size will be `nInputPlane x oheight x owidth` where
`owidth` and `oheight` are given to the constructor.

Instead of `owidth` and `oheight`, one can provide `rwidth` and `rheight`, 
such that `owidth = iwidth*rwidth` and `oheight = iheight*rheight`.

As an example, we can run the following code on the famous Lenna image:
```lua
require 'image'                                                           
require 'nnx'
input = image.loadPNG('doc/image/Lenna.png')
l = nn.SpatialReSampling{owidth=150,oheight=150}
output = l:forward(input)
image.save('doc/image/Lenna-150x150-bilinear.png', output)