'Create a new PoolLayer parameter object.
This then is passed as an argument to all pooling kernels.
N: Number of images in mini-batch
C: Number of input feature maps
H: Height of input image
W: Width  of input image
J: Size of feature map pooling window (maxout n_pieces)
padding: amount of zero-padding around the given image or feature map edge
strides: factor to step the window by in a given direction (overlap allowed)
Leave spatial dimensions at 1 to allow feature map pooling in the fc layers.'

def lrn_layer(self, dtype, N, C, D=1, H=1, W=1, J=1):

    assert ((J % 2) == 1), 'Only    support    odd    LRN    window    size'
    pad_c = (J // 2)
    op = 'lrn'
    lrn_opts = dict(T=1, R=1, S=1, pad_c=pad_c, pad_d=0, pad_h=0, pad_w=0, str_c=1, str_d=1, str_h=1, str_w=1)
    return PoolLayer(lib=self, dtype=dtype, op=op, N=N, C=C, D=D, H=H, W=W, J=J, **lrn_opts)
