'Create a new ConvLayer parameter object.
This then is passed as an argument to all the convolution operations.
N: Number of images in mini-batch
C: Number of input feature maps
K: Number of output feature maps
D: Depth  of input image
H: Height of input image
W: Width  of input 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.
bsum: calculate the sum along the batchnorm axis for fprop or bprop
outputs an fp32 tensor of size Kx1'

def conv_layer(self, dtype, N, C, K, D=1, H=1, W=1, 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 ConvLayer(self, dtype, N, C, K, D, H, W, T, R, S, pad_d, pad_h, pad_w, str_d, str_h, str_w, dil_d, dil_h, dil_w)
