def METHOD_NAME(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_lo = nn.functional.interpolate(
        x_1x,
        scale_factor=0.5,
        align_corners=self.align_corners,
        mode='bilinear')
    lo_outs = self.single_scale_forward(x_lo)
    pred_05x = lo_outs['cls_out']
    p_lo = pred_05x
    aux_lo = lo_outs['aux_out']
    logit_attn = lo_outs['logit_attn']
    hi_outs = self.single_scale_forward(x_1x)
    pred_10x = hi_outs['cls_out']
    p_1x = pred_10x
    aux_1x = hi_outs['aux_out']
    p_lo = p_lo * logit_attn
    aux_lo = aux_lo * logit_attn
    p_lo = scale_as(p_lo, p_1x)
    aux_lo = scale_as(aux_lo, p_1x)
    logit_attn = scale_as(logit_attn, p_1x)
    # combine lo and hi predictions with attention
    joint_pred = p_lo + p_1x * (1 - logit_attn)
    joint_aux = aux_lo + aux_1x * (1 - logit_attn)
    output = [joint_pred, joint_aux]
    # Optionally, apply supervision to the multi-scale predictions
    # directly.
    scaled_pred_05x = scale_as(pred_05x, p_1x)
    output.extend([scaled_pred_05x, pred_10x])
    output.extend(output)
    return output