'Create a new DeconvLayer parameter object.
This then is passed as an argument to all the convolution operations.
N: Number of images in mini-batch
C: Number of output feature maps
K: Number of input feature maps
M: Depth of input
P: Height of input
Q: Width of input
D: Depth  of output image
H: Height of output image
W: Width  of output image
T: Depth  of filter kernel
R: Height of filter kernel
S: Width  of filter kernel
padding: amount of zero-padding around the given edge
strides: factor to step the filters by in a given direction
dilation: dilation factor for each dimension
dtype: need to know dtype to setup proper kernels and params.'

def deconv_layer(self, dtype, N, C, K, M, P, Q, T=1, R=1, S=1, pad_d=0, pad_h=0, pad_w=0, str_d=1, str_h=1, str_w=1, dil_d=1, dil_h=1, dil_w=1):

    return DeconvLayer(self, dtype, N, C, K, M, P, Q, T, R, S, pad_d, pad_h, pad_w, str_d, str_h, str_w, dil_d, dil_h, dil_w)
