def grad(self, inp, grads):
        x, seed = inp
        gz, = grads
        gz = gpu_contiguous(gz)
        maxout = self(x)
        return [MaxPoolGrad(self.ds, self.stride, self.start)(x, maxout, gz), zeros_like(seed)]

    # Make sure the cuda_convnet library is compiled and up-to-date
Manipulate an image file according to the method, returning a
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/sandbox/cuda_convnet/stochastic_pool.py/StochasticMaxPool.grad--crossover with--dataset/ETHPy150Open beetbox/beets/beets/util/artresizer.py/ArtResizer.resize