(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
        if self.actual_bsz is None and self.actual_seq_len is None:
            self.be.compound_dot(A=self.W, B=self.inputs, C=self.outputs, beta=beta,
                                 bsum=self.batch_sum)
        else:
            bsz = self.be.bsz if self.actual_bsz is None else self.actual_bsz
            steps = self.nsteps if self.actual_seq_len is None else self.actual_seq_len
            self.be.compound_dot(A=self.W,
                                 B=self.inputs[:, :bsz * steps],
                                 C=self.outputs[:, :bsz * steps],
                                 beta=beta,
                                 bsum=self.batch_sum)

        return self.outputs
def fprop ( self , inputs , inference = False , beta = 0.0 ) : self . inputs = inputs if self . actual_bsz is None and self . actual_seq_len is None : self . be . compound_dot ( A = self . W , B = self . inputs , C = self . outputs , beta = beta , bsum = self . batch_sum ) else : bsz = self . be . bsz if self . actual_bsz is None else self . actual_bsz steps = self . nsteps if self . actual_seq_len is None else self . actual_seq_len self . be . compound_dot ( A = self . W , B = self . inputs [ : , : bsz * steps ] , C = self . outputs [ : , : bsz * steps ] , beta = beta , bsum = self . batch_sum ) return self . outputs