'Forward propagate pooling layer.
Arguments:
layer (PoolLayer): The pool layer object, different backends have
different pool layers.
I (Tensor): Input tensor.
O (Tensor): output tensor.
argmax (Tensor): tensor to store location of the maximum'

def fprop_pool(self, layer, I, O, argmax=None, beta=0.0):

    assert (layer.sizeI == I.size)
    assert (layer.sizeO == O.size)
    if (layer.op == 'max'):
        assert (layer.sizeO == argmax.size)
    op = layer.op
    (J, T, R, S) = layer.JTRS
    (C, D, H, W, N) = layer.dimI
    (K, M, P, Q, N) = layer.dimO
    (pad_c, pad_d, pad_h, pad_w) = layer.padding
    (str_c, str_d, str_h, str_w) = layer.strides
    array_I = I._tensor.reshape(layer.dimI)
    array_O = O._tensor.reshape(layer.dimO)
    if (op == 'max'):
        array_argmax = argmax._tensor.reshape(layer.dimO)
    for k in range(K):
        (sliceC, _) = layer.kSlice[k]
        for m in range(M):
            (sliceD, _) = layer.mSlice[m]
            for p in range(P):
                (sliceH, _) = layer.pSlice[p]
                for q in range(Q):
                    (sliceW, _) = layer.qSlice[q]
                    sliceI = array_I[sliceC, sliceD, sliceH, sliceW, :].reshape((-1), N)
                    if (op == 'max'):
                        array_argmax[k, m, p, q, :] = np.argmax(sliceI, axis=0)
                        array_O[k, m, p, q, :] = ((array_O[k, m, p, q, :] * beta) + np.max(sliceI, axis=0))
                    elif (op == 'avg'):
                        array_O[k, m, p, q, :] = ((array_O[k, m, p, q, :] * beta) + np.mean(sliceI, axis=0))
                    elif (op == 'l2'):
                        array_O[k, m, p, q, :] = ((array_O[k, m, p, q, :] * beta) + np.sqrt(np.sum(np.square(sliceI), axis=0)))
