(self, layer, I, E, grad_F, alpha=1.0, repeat=1)
Compute the updated gradient for a convolutional network layer.

        Arguments:
            layer: the conv layer as a parameter object
            I (Tensor): the inputs
            E (Tensor): the errors
            grad_F (Tensor): filter gradients (weights) to update.
            alpha (float, optional): linear scaling.  Defaults to 1.0
            repeat (int, optional): Repeat this operation the specified number
                                    of times.  Defaults to 1.
Compute the updated gradient for a convolutional network layer.
Compute the updated gradient for a convolutional network layer .
def update_conv(self, layer, I, E, grad_F, alpha=1.0, repeat=1):
        """
        Compute the updated gradient for a convolutional network layer.

        Arguments:
            layer: the conv layer as a parameter object
            I (Tensor): the inputs
            E (Tensor): the errors
            grad_F (Tensor): filter gradients (weights) to update.
            alpha (float, optional): linear scaling.  Defaults to 1.0
            repeat (int, optional): Repeat this operation the specified number
                                    of times.  Defaults to 1.
        """
        raise NotImplementedError()
def update_conv ( self , layer , I , E , grad_F , alpha = 1.0 , repeat = 1 ) : raise NotImplementedError ( )