'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.'

@abc.abstractmethod
def update_conv(self, layer, I, E, grad_F, alpha=1.0, repeat=1):

    raise NotImplementedError()
