(self, inputs, inference=False, beta=0.0)
return self.outputs
Apply the forward pass transformation to the input data.

        Arguments:
            inputs (Tensor): input data
            inference (bool): is inference only
            beta (float, optional): scale to apply to the outputs

        Returns:
            Tensor: output data
Apply the forward pass transformation to the input data.
Apply the forward pass transformation to the input data .
def fprop(self, inputs, inference=False, beta=0.0):
        """
        Apply the forward pass transformation to the input data.

        Arguments:
            inputs (Tensor): input data
            inference (bool): is inference only
            beta (float, optional): scale to apply to the outputs

        Returns:
            Tensor: output data
        """
        self.inputs = inputs
        self.be.fprop_conv(self.nglayer, inputs, self.W, self.outputs, beta=beta,
                           bias=self.weight_bias, bsum=self.batch_sum, layer_op=self)
        return self.outputs
def fprop ( self , inputs , inference = False , beta = 0.0 ) : self . inputs = inputs self . be . fprop_conv ( self . nglayer , inputs , self . W , self . outputs , beta = beta , bias = self . weight_bias , bsum = self . batch_sum , layer_op = self ) return self . outputs