def hybrid_forward(self, F, inputs, **kwargs):<EOL>
current_input = inputs<EOL>for layer in self.hnet:<EOL><INDENT>projected_input = layer(current_input)<EOL>linear_transform = current_input<EOL>nonlinear_transform, transform_gate = projected_input.split(num_outputs=<NUM_LIT:2>, axis=-<NUM_LIT:1>)<EOL>nonlinear_transform = self._activation(nonlinear_transform)<EOL>transform_gate = transform_gate.sigmoid()<EOL>current_input = (<NUM_LIT:1> - transform_gate) * linear_transform +transform_gate * nonlinear_transform<EOL><DEDENT>return current_input<EOL>
r"""
        Forward computation for highway layer

        Parameters
        ----------
        inputs: NDArray
            The input tensor is of shape `(..., input_size)`.

        Returns
        ----------
        outputs: NDArray
            The output tensor is of the same shape with input tensor `(..., input_size)`.