(self, nin, inputs, error, error_t, dW, pad_idx, alpha=1.0, beta=0)
Backward propagate lookup table layer.

        Arguments:
            nin (int): Number of input word_ids.
            inputs (Tensor): Input tensor.
            error (Tensor): Error tensor.
            error_t (Tensor): Transposed error tensor.
            dW (Tensor): Gradient tensor (delta).
            pad_idx (int):
            alpha (float):
            beta (float):
Backward propagate lookup table layer.
Backward propagate lookup table layer .
def compound_bprop_lut(self, nin, inputs, error, error_t, dW, pad_idx, alpha=1.0, beta=0):
        """
        Backward propagate lookup table layer.

        Arguments:
            nin (int): Number of input word_ids.
            inputs (Tensor): Input tensor.
            error (Tensor): Error tensor.
            error_t (Tensor): Transposed error tensor.
            dW (Tensor): Gradient tensor (delta).
            pad_idx (int):
            alpha (float):
            beta (float):
        """
        raise NotImplementedError()
def compound_bprop_lut ( self , nin , inputs , error , error_t , dW , pad_idx , alpha = 1.0 , beta = 0 ) : raise NotImplementedError ( )