(self, layer, F, E, grad_I, alpha=1.0, repeat=1)
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.
Backward propagate the error through a convolutional network layer.
Backward propagate the error through a convolutional network layer .
def bprop_conv(self, layer, F, E, grad_I, alpha=1.0, repeat=1):
        """
        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.
        """
        raise NotImplementedError()
def bprop_conv ( self , layer , F , E , grad_I , alpha = 1.0 , repeat = 1 ) : raise NotImplementedError ( )