def two_scale_forward_high(self, x_1x):
        """
        Do we supervised both aux outputs, lo and high scale?
        Should attention be used to combine the aux output?
        Normally we only supervise the combined 1x output

        If we use attention to combine the aux outputs, then
        we can use normal weighting for aux vs. cls outputs
        """
        x_hi = nn.functional.interpolate(
            x_1x,
            scale_factor=1.5,
            align_corners=self.align_corners,
            mode='bilinear')

        lo_outs = self.single_scale_forward(x_1x)
        pred_10x = lo_outs['cls_out']
        p_lo = pred_10x
        aux_lo = lo_outs['aux_out']
        logit_attn = lo_outs['logit_attn']

        hi_outs = self.single_scale_forward(x_hi)
        pred_15x = hi_outs['cls_out']
        p_hi = pred_15x
        aux_hi = hi_outs['aux_out']

        p_lo = p_lo * logit_attn
        aux_lo = aux_lo * logit_attn
        p_hi = scale_as(p_hi, p_lo)
        aux_hi = scale_as(aux_hi, aux_lo)

        # combine lo and hi predictions with attention
        joint_pred = p_lo + p_hi * (1 - logit_attn)
        joint_aux = aux_lo + aux_hi * (1 - logit_attn)

        output = [joint_pred, joint_aux]

        # Optionally, apply supervision to the multi-scale predictions
        # directly.
        scaled_pred_15x = scale_as(pred_15x, p_lo)
        output.extend(output)
        return output

        Do we supervised both aux outputs, lo and high scale?
        Should attention be used to combine the aux output?
        Normally we only supervise the combined 1x output

        If we use attention to combine the aux outputs, then
        we can use normal weighting for aux vs. cls outputs
        
contrib/AutoNUE/models/mscale_ocrnet.py