def forward(self, inputs):
        """
        Args:
            inputs (tuple[Tensor]): input features.
        Returns:
            tuple[Tensor]: CSPPAN features.
        """
        assert len(inputs) == len(self.in_channels)
        inputs = self.conv_t(inputs)

        # top-down path
        inner_outs = [inputs[-1]]
        for idx in range(len(self.in_channels) - 1, 0, -1):
            feat_heigh = inner_outs[0]
            feat_low = inputs[idx - 1]

            upsample_feat = self.upsample(feat_heigh)

            inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx](
                paddle.concat([upsample_feat, feat_low], 1))
            inner_outs.insert(0, inner_out)

        # bottom-up path
        outs = [inner_outs[0]]
        for idx in range(len(self.in_channels) - 1):
            feat_low = outs[-1]
            feat_height = inner_outs[idx + 1]
            downsample_feat = self.downsamples[idx](feat_low)
            out = self.bottom_up_blocks[idx](paddle.concat(
                [downsample_feat, feat_height], 1))
            outs.append(out)

        top_features = None
        if self.num_features == 4:
            top_features = self.first_top_conv(inputs[-1])
            top_features = top_features + self.second_top_conv(outs[-1])
            outs.append(top_features)

        return tuple(outs)

        Args:
            inputs (tuple[Tensor]): input features.
        Returns:
            tuple[Tensor]: CSPPAN features.
        
ppdet/modeling/necks/lc_pan.py