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

        Arguments:
            inputs (Tensor): input data
            inference (bool): is inference only
            beta (int):  (Default value = 0.0)

        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):
        """
        Apply the forward pass transformation to the input data.

        Arguments:
            inputs (Tensor): input data
            inference (bool): is inference only
            beta (int):  (Default value = 0.0)

        Returns:
            Tensor: output data
        """
        self.init_buffers(inputs)
        self.outputs.fill(0)
        for x in self.xs:
            self.outputs[:] = self.outputs + self.sumscale * x
        return self.outputs
def fprop ( self , inputs , inference = False ) : self . init_buffers ( inputs ) self . outputs . fill ( 0 ) for x in self . xs : self . outputs [ : ] = self . outputs + self . sumscale * x return self . outputs