(self, layer, I, F, O, alpha=1.0, relu=False, repeat=1)
Forward propagate the inputs of a convolutional network layer to
        produce output.

        Arguments:
            layer: the conv layer as a parameter object
            I (Tensor): inputs
            F (Tensor): the weights (filters)
            O (Tensor): outputs
            alpha (float, optional): linear scaling.  Defaults to 1.0
            relu (bool, optional): apply ReLu before output.  Default not to.
            repeat (int, optional): Repeat this operation the specified number
                                    of times.  Defaults to 1.
Forward propagate the inputs of a convolutional network layer to
        produce output.
Forward propagate the inputs of a convolutional network layer to produce output .
def fprop_conv(self, layer, I, F, O, alpha=1.0, relu=False, repeat=1):
        """
        Forward propagate the inputs of a convolutional network layer to
        produce output.

        Arguments:
            layer: the conv layer as a parameter object
            I (Tensor): inputs
            F (Tensor): the weights (filters)
            O (Tensor): outputs
            alpha (float, optional): linear scaling.  Defaults to 1.0
            relu (bool, optional): apply ReLu before output.  Default not to.
            repeat (int, optional): Repeat this operation the specified number
                                    of times.  Defaults to 1.
        """
        raise NotImplementedError()
def fprop_conv ( self , layer , I , F , O , alpha = 1.0 , relu = False , repeat = 1 ) : raise NotImplementedError ( )