(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,
                   relu=False, bsum=False)
Create a new ConvLayer parameter object.
        This is then passed as an argument to all the convolution operations.

        Arguments:
            dtype (data-type, optional): If present, specifies the underlying
                                         type to employ for each element.

            N (int): Number of images in mini-batch
            C (int): Number of input feature maps
            K (int): Number of output feature maps

            D (int, optional): Depth of input image.  Defaults to 1
            H (int, optional): Height of input image.  Defaults to 1
            W (int, optional): Width of input image.  Defaults to 1

            T (int, optional): Depth of filter kernel.  Defaults to 1
            R (int, optional): Height of filter kernel.  Defaults to 1
            S (int, optional): Width of filter kernel.  Defaults to 1

            pad_d (int, optional): amount of zero-padding around the depth edge
                                   Defaults to 0.
            pad_h (int, optional): amount of zero-padding around the height edge
                                   Defaults to 0.
            pad_w (int, optional): amount of zero-padding around the width edge
                                   Defaults to 0.

            str_d (int, optional): factor to step the filters by in the depth
                                   direction.  Defaults to 1
            str_h (int, optional): factor to step the filters by in the depth
                                   direction.  Defaults to 1
            str_w (int, optional): factor to step the filters by in the depth
                                   direction.  Defaults to 1

            relu (bool, optional): apply a relu transform to the output for
                                   fprop or bprop.  Defaults to False

            bsum (bool, optional): calculate the sum along the batchnorm axis
                                   for fprop or bprop.  Outputs an fp32 tensor
                                   of size Kx1.  Defaults to False.
Create a new ConvLayer parameter object.
        This is then passed as an argument to all the convolution operations.
Create a new ConvLayer parameter object . This is then 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,
                   relu=False, bsum=False):
        """
        Create a new ConvLayer parameter object.
        This is then passed as an argument to all the convolution operations.

        Arguments:
            dtype (data-type, optional): If present, specifies the underlying
                                         type to employ for each element.

            N (int): Number of images in mini-batch
            C (int): Number of input feature maps
            K (int): Number of output feature maps

            D (int, optional): Depth of input image.  Defaults to 1
            H (int, optional): Height of input image.  Defaults to 1
            W (int, optional): Width of input image.  Defaults to 1

            T (int, optional): Depth of filter kernel.  Defaults to 1
            R (int, optional): Height of filter kernel.  Defaults to 1
            S (int, optional): Width of filter kernel.  Defaults to 1

            pad_d (int, optional): amount of zero-padding around the depth edge
                                   Defaults to 0.
            pad_h (int, optional): amount of zero-padding around the height edge
                                   Defaults to 0.
            pad_w (int, optional): amount of zero-padding around the width edge
                                   Defaults to 0.

            str_d (int, optional): factor to step the filters by in the depth
                                   direction.  Defaults to 1
            str_h (int, optional): factor to step the filters by in the depth
                                   direction.  Defaults to 1
            str_w (int, optional): factor to step the filters by in the depth
                                   direction.  Defaults to 1

            relu (bool, optional): apply a relu transform to the output for
                                   fprop or bprop.  Defaults to False

            bsum (bool, optional): calculate the sum along the batchnorm axis
                                   for fprop or bprop.  Outputs an fp32 tensor
                                   of size Kx1.  Defaults to False.
        """
        raise NotImplementedError()
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 , relu = False , bsum = False ) : raise NotImplementedError ( )