(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)
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.
Create a new ConvLayer parameter object.
        This then is passed as an argument to all the convolution operations.
Create a new ConvLayer parameter object . This then is passed as an argument to all the convolution operations .
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):
        """
        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.
        """
        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)
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 )