(self, dtype,
                     N, C, K,
                     P, Q,
                     R=1, S=1,
                     pad_d=0, pad_h=0, pad_w=0,
                     str_d=1, str_h=1, str_w=1)
Create a new Deconvolution parameter object.
        This then is passed as an argument to all deconvolution kernels.

        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

            P (int): Height of output
            Q (int): Width of output

            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

        Leave spatial dimensions at 1 to allow feature map pooling in the fc layers.
Create a new Deconvolution parameter object.
        This then is passed as an argument to all deconvolution kernels.
Create a new Deconvolution parameter object . This then is passed as an argument to all deconvolution kernels .
def deconv_layer(self, dtype,
                     N, C, K,
                     P, Q,
                     R=1, S=1,
                     pad_d=0, pad_h=0, pad_w=0,
                     str_d=1, str_h=1, str_w=1):
        """
        Create a new Deconvolution parameter object.
        This then is passed as an argument to all deconvolution kernels.

        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

            P (int): Height of output
            Q (int): Width of output

            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

        Leave spatial dimensions at 1 to allow feature map pooling in the fc layers.
        """
        raise NotImplementedError()
def deconv_layer ( self , dtype , N , C , K , P , Q , R = 1 , S = 1 , pad_d = 0 , pad_h = 0 , pad_w = 0 , str_d = 1 , str_h = 1 , str_w = 1 ) : raise NotImplementedError ( )