def METHOD_NAME(self, split_count):
    all_scales = [[] for _ in range(split_count)]
    for dense_scale, qkv_scale, m4hh_scale, mh4h_scale in \
        zip(self.dense_scales, self.qkv_scales, self.mlp4hh_scales, self.mlph4h_scales):
        dense_scale = torch.split(dense_scale, dense_scale.numel() // split_count)
        qkv_scale = torch.split(qkv_scale, qkv_scale.numel() // split_count)
        m4hh_scale = torch.split(m4hh_scale, m4hh_scale.numel() // split_count)
        mh4h_scale = torch.split(mh4h_scale, mh4h_scale.numel() // split_count)
        for s in range(split_count):
            all_scales[s].append(
                torch.cat([
                    torch.cat((qkv_scale[s], torch.zeros_like(qkv_scale[s])), dim=1),
                    torch.cat((dense_scale[s], torch.zeros_like(dense_scale[s])), dim=1), mh4h_scale[s],
                    m4hh_scale[s]
                ]).unsqueeze(0))
        for scales_a in all_scales:
            torch.cat(scales_a)
    return all_scales