'Backward propagate the error through a convolutional network layer.
Arguments:
layer: the conv layer as a parameter object
F (Tensor): the weights (filters)
E (Tensor): errors
grad_I (Tensor): gradient to inputs (output delta)
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 bprop_conv(self, layer, F, E, grad_I, alpha=1.0, repeat=1):

    raise NotImplementedError()
